Sketches around bipartite graphs

hypergraphs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Notes and Fragments on Graphs and Hypergraphs\n",
    "\n",
    "Start off with a few simple worked examples based on JJ's *Hypernetworks in the Science of Complex Systems*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Use pandas and networkx\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Simple Bipartite Graphs\n",
    "\n",
    "Example from p.33."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Create graph\n",
    "B = nx.Graph() \n",
    "B.add_edges_from([('a1','b1'),('a1','b2'), ('a1','b3'),\n",
    "                  ('a2','b2'),('a2','b3'), ('a2','b4'), ('a2','b5'), ('a2','b6'),\n",
    "                  ('a3','b5'),('a3','b6'), ('a3','b7'), ('a3','b8')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['b2', 'a2', 'b7', 'a1', 'b8', 'a3', 'b3', 'b5', 'b4', 'b6', 'b1'],\n",
       " 1,\n",
       " [('b2', {}),\n",
       "  ('a2', {}),\n",
       "  ('b7', {}),\n",
       "  ('a1', {}),\n",
       "  ('b8', {}),\n",
       "  ('a3', {}),\n",
       "  ('b3', {}),\n",
       "  ('b5', {}),\n",
       "  ('b4', {}),\n",
       "  ('b6', {}),\n",
       "  ('b1', {})])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can see the nodes in the gaph, and get index of a particular node in that list\n",
    "B.nodes(), B.nodes().index('a2'), B.nodes(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'b1', 'b2', 'b3', 'b4', 'b5', 'b6', 'b7', 'b8'}, {'a1', 'a2', 'a3'})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bipartite graph has two non-intersecting sets of nodes\n",
    "# Edges go from one set to another\n",
    "X, Y = nx.bipartite.sets(B)\n",
    "\n",
    "X, Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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3799H//79YWZmhh07dkBVVRU+Pj5ISUmBoqIijIyMsGLFCrx580bi8RkaGuKvv/6Cjo4O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\nA8QHB4QoxvtmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1110e0c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simple chart of nodes\n",
    "\n",
    "def bipartite_plot(B, X=None, Y=None):\n",
    "    if X is None and Y is None:\n",
    "        X, Y = nx.bipartite.sets(B)\n",
    "    #http://stackoverflow.com/a/27085151/454773\n",
    "    c = nx.bipartite.color(B)\n",
    "    nx.set_node_attributes(B, 'group', c)\n",
    "\n",
    "    pos = dict()\n",
    "    pos.update( (n, (1, i)) for i, n in enumerate(X) ) # put nodes from X at x=1\n",
    "    pos.update( (n, (2, i)) for i, n in enumerate(Y) ) # put nodes from Y at x=2\n",
    "\n",
    "    colors=[]\n",
    "    for i in B:\n",
    "        if B.node[i]['group']:\n",
    "            colors.append('lightblue')\n",
    "        else: \n",
    "            colors.append('pink')\n",
    "\n",
    "    nx.draw(B, pos=pos,with_labels = True,node_color=colors)\n",
    "    \n",
    "bipartite_plot(B, X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['b2', 'b3', 'b1'], ['a2', 'a1'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Neighbours / hyperedges\n",
    "B.neighbors('a1'), B.neighbors('b3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Is there a path connecting two nodes?\n",
    "nx.has_path(B,'a1','a3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['a1', 'b2', 'a2', 'b5', 'a3'], 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.shortest_path(B,'a1','a3'), nx.shortest_path_length(B,'a1','a3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a1', 'b2', 'a2', 'b5', 'a3']\n",
      "['a1', 'b2', 'a2', 'b6', 'a3']\n",
      "['a1', 'b3', 'a2', 'b5', 'a3']\n",
      "['a1', 'b3', 'a2', 'b6', 'a3']\n"
     ]
    }
   ],
   "source": [
    "for p in nx.all_simple_paths(B,'a1','a3'):\n",
    "    print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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z584lJCSEEiVK6Ly/nj174uLiQq9evbTedkpKCvb29nh4ePDZZ59pvf3sGjp0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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1110d74a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Projections onto one set:\n",
    "P1 = nx.bipartite.projected_graph(B, X)\n",
    "nx.draw(P1,with_labels = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8yOVyaXtxsWpXr1bY1q0aUF+vzmp9Mtbe0FDZ4uPVPStLMWlpLF8A8HMUsCE2\nq1WVFRU6Y7erc7du6hMVxd3OABBAKGAAAAzgPWAAAAyggAEAMIACBgDAAAoYAAADKGAAAAyggAEA\nMIACBgDAAAoYAAADKGAAAAyggAEAMIACBgDAAAoYAAADKGAAAAyggAEAMIACBgDAAAoYAAADKGAA\nAAyggAEAMIACBgDAAAoYAAADKGAAAAyggAEAMIACBgDAAAoYAAADKGAAAAyggAEAMIACBgDAAAoY\nAAADKGAAAAyggAEAMIACBgDAAAoYAAADKGAAAAyggAEAMIACBgDAAAoYAAADKGAAAAyggAEAMIAC\nBgDAAAoYAAADKGAAAAyggAEAMIACBgDAAAoYAAADKGAAAAyggAEAMIACBgDAAAoYAAADKGAAAAyg\ngAEAMIACBgDAAAoYAAADKGAAAAz4f2v1nDBdONURAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111226208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "P2 = nx.bipartite.projected_graph(B, Y, multigraph=True)\n",
    "#Simple plotter doesn't reflect multi-edges though...\n",
    "nx.draw(P2,with_labels = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'b2': {'a2': {}},\n",
       "  'b3': {'a2': {}},\n",
       "  'b4': {'a2': {}},\n",
       "  'b5': {'a2': {}, 'a3': {}},\n",
       "  'b7': {'a3': {}},\n",
       "  'b8': {'a3': {}}},\n",
       " [('b2', ['a2']),\n",
       "  ('b3', ['a2']),\n",
       "  ('b7', ['a3']),\n",
       "  ('b8', ['a3']),\n",
       "  ('b5', ['a2', 'a3']),\n",
       "  ('b4', ['a2'])])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also project onto a multigraph to see the separate means by which nodes are connected\n",
    "P1= nx.bipartite.projected_graph(B, X, multigraph=True)\n",
    "PN = P1 if 'b1' in P1 else P2\n",
    "PN['b6'], [(k, list(PN['b6'][k].keys())) for k in PN['b6'].keys() ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'b2': {'a2': {}},\n",
       "  'b3': {'a2': {}},\n",
       "  'b4': {'a2': {}},\n",
       "  'b5': {'a2': {}, 'a3': {}},\n",
       "  'b7': {'a3': {}},\n",
       "  'b8': {'a3': {}}},\n",
       " ['b2', 'b3', 'b7', 'b8', 'b5', 'b4'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find nodes connected to a particular node via one or more in the other set\n",
    "PN['b6'], list(PN['b6'].keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Displaying Adjacency Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#SparseDataFrame map from http://stackoverflow.com/a/17819427/454773\n",
    "import numpy as np\n",
    "\n",
    "def pretty_matrix(B, X=None, Y=None):\n",
    "    if X is None and Y is None:\n",
    "        X, Y = nx.bipartite.sets(B)\n",
    "\n",
    "    m=nx.adjacency_matrix(B)\n",
    "\n",
    "    df=pd.SparseDataFrame([ pd.SparseSeries(m[i].toarray().ravel()) \n",
    "                                  for i in np.arange(m.shape[0]) ])\n",
    "    dfa=df[[B.nodes().index(x) for x in X ]]\n",
    "    dfa.columns=X\n",
    "    dfa=dfa.iloc[[B.nodes().index(y) for y in Y ]]\n",
    "    return dfa[sorted(dfa.columns)].rename({B.nodes().index(y):y for y in Y}).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b1</th>\n",
       "      <th>b2</th>\n",
       "      <th>b3</th>\n",
       "      <th>b4</th>\n",
       "      <th>b5</th>\n",
       "      <th>b6</th>\n",
       "      <th>b7</th>\n",
       "      <th>b8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    b1  b2  b3  b4  b5  b6  b7  b8\n",
       "a1   1   1   1   0   0   0   0   0\n",
       "a2   0   1   1   1   1   1   0   0\n",
       "a3   0   0   0   0   1   1   1   1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretty_matrix(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def pretty_bipartite_matrix(B, X=None, Y=None):\n",
    "    if X is None and Y is None:\n",
    "        X, Y = nx.bipartite.sets(B)\n",
    "    #There is a nx.bipartite.biadjacency_matrix(B, Y, X) function, but I don't see how to match index and label?\n",
    "    m=nx.bipartite.biadjacency_matrix(B, Y, X)\n",
    "    m2=pd.SparseDataFrame([ pd.SparseSeries(m[i].toarray().ravel()) \n",
    "                                  for i in np.arange(m.shape[0]) ])\n",
    "    #Is this guaranteed to work correctly with the orderings?\n",
    "    m2.columns=X\n",
    "    return m2[sorted(m2.columns)].rename({[c for c in Y].index(y):y for y in Y}).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b1</th>\n",
       "      <th>b2</th>\n",
       "      <th>b3</th>\n",
       "      <th>b4</th>\n",
       "      <th>b5</th>\n",
       "      <th>b6</th>\n",
       "      <th>b7</th>\n",
       "      <th>b8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    b1  b2  b3  b4  b5  b6  b7  b8\n",
       "a1   1   1   1   0   0   0   0   0\n",
       "a2   0   1   1   1   1   1   0   0\n",
       "a3   0   0   0   0   1   1   1   1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretty_bipartite_matrix(B, X, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Creating Graphs From Sets\n",
    "\n",
    "For example, p.34-36"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Rsets= {'mouse':{'tiny','brown','quadruped','vegetarian'},\n",
    "    'hare':{'small','brown','quadruped','vegetarian'},\n",
    "    'deer':{'large','brown','quadruped','vegetarian','hooves','antlers'},\n",
    "    'camel':{'large','brown','quadruped','vegetarian','hooves','hump'},\n",
    "    'tiger':{'large','quadruped'},\n",
    "    'falcon':{'small'},\n",
    "    'chimpanzee':{'large','vegetarian'}\n",
    "   }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def bipartite_graphBuilder(R,B=None,undirected=True):\n",
    "    if B is None:\n",
    "        if undirected: B=nx.Graph()\n",
    "        else: B=nx.DiGraph()\n",
    "    for key in R:\n",
    "        for descriptor in R[key]:\n",
    "            B.add_edge(key,descriptor)\n",
    "    return B "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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QEICrq6tOHqe6kMvl3L17l6ioKKKiojh58iT379+nQYMGyOVymjVrRk5uLjKZ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\nC99Qa7mWT8inAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c4710f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "B2=bipartite_graphBuilder(Rsets)\n",
    "bipartite_plot(B2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'antlers': {},\n",
       " 'brown': {},\n",
       " 'hooves': {},\n",
       " 'large': {},\n",
       " 'quadruped': {},\n",
       " 'vegetarian': {}}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#If we ask about an item in one set, we get as dict keys the things it's connnected to in the other\n",
    "B2['deer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brown', 'quadruped', 'vegetarian'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This means we can easily find intersecting connected properties of items in one set connected from the other\n",
    "def R(B, keys):\n",
    "    setlist=[set(B[key].keys()) for key in keys]\n",
    "    return set.intersection(*setlist)\n",
    "\n",
    "R(B2, ['hare','deer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'camel', 'deer', 'hare', 'mouse'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R(B2, R(B2, ['hare','deer']) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'camel', 'deer', 'hare', 'mouse'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R(B2, R(B2, {'hare','deer'}) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Galois Pairs\n",
    "\n",
    "p. 36"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Galoir pairs\n",
    "def GaloisPair(B,vals):\n",
    "    return (vals, R(B, vals))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'brown', 'quadruped', 'vegetarian'}, {'camel', 'deer', 'hare', 'mouse'})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GaloisPair(B2,{'brown','quadruped','vegetarian'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'camel', 'deer', 'hare', 'mouse'}, {'brown', 'quadruped', 'vegetarian'})"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GaloisPair(B2,{'camel', 'deer', 'hare', 'mouse'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>camel</th>\n",
       "      <th>chimpanzee</th>\n",
       "      <th>deer</th>\n",
       "      <th>falcon</th>\n",
       "      <th>hare</th>\n",
       "      <th>mouse</th>\n",
       "      <th>tiger</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>antlers</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brown</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hooves</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hump</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>large</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quadruped</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tiny</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vegetarian</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            camel  chimpanzee  deer  falcon  hare  mouse  tiger\n",
       "antlers         0           0     1       0     0      0      0\n",
       "brown           1           0     1       0     1      1      0\n",
       "hooves          1           0     1       0     0      0      0\n",
       "hump            1           0     0       0     0      0      0\n",
       "large           1           1     1       0     0      0      1\n",
       "quadruped       1           0     1       0     1      1      1\n",
       "small           0           0     0       1     1      0      0\n",
       "tiny            0           0     0       0     0      1      0\n",
       "vegetarian      1           1     1       0     1      1      0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretty_bipartite_matrix(B2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Q-Analysis\n",
    "\n",
    "pp. 51-52"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Qsets={\n",
    "    'Pete':{'gaming','pubs','cars','sport'},\n",
    "    'Sam':{'pubs','cars','sport','fashion'},\n",
    "    'Sue':{'fashion','painting','history','literature'},\n",
    "    'Jane':{'history','literature','gardening','cooking'},\n",
    "    'Tim':{'gardening','cooking','nature','science'}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2pvA8uEeMGIH4+HhMnz4dbm5umDx5MsaMGYPU1FSYmZlhyJAhGDp0KKKioj6Z\np111dXWxYMECxMXF4fjx41BRUYGTkxM6duwIb29v3Lp1S+bnbNu2Lby9vXH37l3MmzcP27dvh66u\nLubPn4+UlBSZn48xVvc4XF/RsmVLLF++HKampti/fz+Cg4Ph4+ODkSNHwsbGBitXrqwIJE1lBaAK\nD6iYWtviUuQxbFm1tOK1gsePcD/9bsViCMD/5mKNjIzw448/YtCgQejZsyeSkpJktoC8nJwcJkyY\ngKSkJIwcORIjRoyAq6srhg8fjtTUVIwaNeqTKON5k7ou7ZGXl8eoUaMQERGBc+fOQSAQoHfv3hVX\nuG/8QMYY+yDw08IviIqKgpeXFxISEqq0fXZ2NoIiTqOjlW21z3Uz8jDG2tti48aN+OOPP15awvDO\nnTsIDg7GsmXLAADh4eFYu3Ytzp8/X+3zVKawsBCbNm3CunXrMHToUHh7e0NHRwf79u3DDz/88EmV\n8bxJXZf2FBcXIywsDGKxGHfu3MG0adMwbdq0Om3FxxirOb5yrYFWrVrBoq0mslKqd+swLS4WI/r0\nQseOHeHn5/fa3OfDhw+RmZkJExMTmJmZwdfXF0FBQbXyHlRUVDB//nwkJydDV1cXlpaWmDNnDmxs\nbBAbG4tNmzYhNDQUBgYG+PHHH1FQ8O4FMz42cnJy6N+/PwIDA5GVlYU5c+YgMjIS7du3x/Dhw7Fz\n506ZPv3bqFEjjB8/HtHR0Thy5Ahyc3NhamqKUaNG4dixY5/UnQTGPmR85SoDh46dwO2CMuiZmL1z\nOyJCSswZ2HftgB4W5q99Pz8/H/7+/ti4cWO9LWGYk5OD1atXIzg4GJ6enpg/fz7U1dVx5coVrFmz\nBpGRkR91N56qqsuuPU+fPsXOnTshFotRUFAAT09PuLq6okWLFjI9D2NMdjhcZeT6zQREXYpDgUAR\nhpbWkHvhqePiwkKkXT4PDUUBhtv3g07bNu88VnFxMbZv345169ahefPm+Oabb+Do6Fjjp4arIz09\nHStWrMD+/fsxd+5cfPnll2jcuDFSUlKwfv36125lf8rqqmsPESE2NhZisRj79u3D8OHDIRKJYGNj\nw4tTMNbAcLjKWF5eHg4cO4ESCUEiLYe8UA5NVZTg6DAISkpK1TpWeXl5vc993rp1C8uWLcPp06ex\naNEieHh4QElJCf/88w98fX0REBDwSXTjqao3de1xdnaGpaWlTAPw0aNH+O233+Dn5wdFRUWIRCJM\nnDgRamokld0eAAAgAElEQVRqMjsHY+z9cbjKUH5+PsKPRCC7oAilEEIKgpxACEUqh35LdTg6vN8t\nQyLCX3/9hR9++KHeljCMi4vDkiVLcPPmTXh7e2PixImQk5NrELeyG6q66Nrz738bfn5+OH78OMaO\nHQuRSARz89enHRhjdYfDVQYkEgkCQnbhiUABBhbWkFdQeG2bomfPcDfuAvTVVeE8esR7n6u+5z7P\nnDmDRYsW4eHDh1i5ciVGjx4NgUDQIG5lN1REhIsXLyI0NBS///47WrduDWdnZ4wbN06mTwFnZWUh\nKCgImzdvhra2NkQiEZycnKCsrCyzczDGqobDtYZKS0uxVhwIg76DoaRceS1q3oMcFN6Kx3TXiTW6\nuqvPuU8iwtGjR7F48WIIhUL4+Phg4MCBEAgEDeJWdkNWF6U95eXlOHz4MPz8/BATE4OJEydCJBLV\nSZ9bxthzHK41QERYJw6Ant1gKFQjPPIfPQTdSYCr89gaj6E+5z6lUinCwsKwdOlSaGlpwcfHB717\n9wbQMG5lN3R10bUnLS0NAQEBCAoKgrGxMby8vDBixAj+sMNYLeNwrYEz584jqVQezbWrf2sv5XIM\nXAfaQFNTUyZjqc+5T4lEguDgYHh7e6Nbt274/vvvXwr4+r6V/SGo7dKe0tJShIeHQywW49atW5g6\ndSrc3d2hp6cng9Ezxl7FE2I1EHc7tdJg9epvheRrca+93t68Bw4cOymzsaipqWH+/Pn1soShvLw8\n3NzccPv2bQwYMACDBw+Gi4sLkpOTAQAWFhbYtWsXzp8//8l146mq2u7ao6ioCCcnJ5w6dQqRkZEo\nKCiAhYUFPv/8cxw6dKhWOgIx9injcH1POTk5KFZs/N77C4VCZBeWyvyXWqNGjeDu7o7ExER8/fXX\nWLVqFbp06YKtW7fWSu/SV889e/ZspKSkwNjYGL169YKnp2fFmryGhoZvXJHqU+nGU1WVde25dOlS\njRordO7cGb6+vsjIyMCoUaPw3XffwcDAAD4+PsjOzpbhO2HsE0asyk6dOkWWlpY0evRo0tVrR0bd\nLMj3cBR9NmosTf5mOYUlZVJYUuZLX7dso0OfjRpLBl1MSdeoE81Y9SOFJWVSyJUUMrPpS8bGxmRp\naUkeHh61MmapVEonT56kgQMHUtu2bWnDhg2Un59fK+d61cOHD2nBggWkrq5Oc+fOpdzc3Je+n5eX\nR2vXriVtbW0aMmQInTp1iqRSaZ2M7UN08+ZNWrJkCRkYGJCRkREtX76ckpKSZHLsS5cu0bRp06hZ\ns2Y0duxY+uuvv/jfgrEa4CvXarp69SrmzJmDVevWw370OPy84MtK5zWVlFWwNuwolm0JxY4ffXDv\n72TEnDgCiUSCLVu3IjY2FgCQmpoq8/EKBALY29vj2LFj2L9/P2JiYtC+fXssXbq0ohtPbdHQ0MCa\nNWtw48YNFBcXV7Rxy8/PB1C/t7I/RLXZtcfS0hIBAQFIS0uDnZ0dZsyYAWNjY/j6+uLx48cyfBeM\nfRo4XKvJxMTk+bJ2QiHsRzshLfEGCp68+5fPIKeJAAD1lq1gZtsP186fQWdLK2Sm/Y1ZM2bghx9+\nwJw5c9C+fftaHXt9zX22bt0amzZtwsWLF5GamgojIyNs2LABRUVFAOr3VvaHSCAQoGfPnti4cSPu\n3buHtWvXIikpCaampujXrx/8/f3x8OHD9zp2s2bNMHPmTNy4cQObN2+u+DDm5uaGixer1r+YMcbh\nWm3y/10zuLl6UzzNewwQQVVdA3jhl46krOylfYQvrC9LRJCXV0DLNm3hsXw1VFRUcP78efTr1w9h\nYWF18h7qa+6zffv2CA4OxsmTJxEdHQ0jIyNs3rwZZf/9ecnJyWHMmDHcjacaaqtrj0AggJ2dHXbu\n3Ilbt26hY8eOcHJyQvfu3REYGIhnz57Vwrth7CNS3/elPySnTp0iBQUFio+PJ4lEQsPHTSDj7tY0\n0n0G2Qx1pLCkTNp6/jq10G790pzriKleFJaUSX6RsdRMsyX9EnGWPLx/IB0DQ1JSUiINDQ2Sl5cn\nFRUV+s9//kO+vr50+fJlKisrq5P3VV9znzExMdS/f38yNDSknTt3Unl5+WvbXL58mcaOHUstWrSg\nJUuWUHZ2dq2P62OQn59P27dvpyFDhlDTpk1p3LhxdODAASopKXnvY5aXl9ORI0fI0dGRNDQ0aObM\nmXTjxg0ZjpqxjwfXuVZDVFQUxo8fjx49euDvv/9GmVSKL3/aAnkFefz09UzkPXqAlm100FSjBfQ6\nGcPR1RNeA6zRvd8AJF6JRbmkHGNnzEWvwcPwMDsLO5bNQ072P5BKpZBKpSgrK8OTJ0+gpaWFZ8+e\nIS8vD1ZWVrC1tYWtrS2srKxktrjAm9TXEoYnT57EokWLUFxcjFWrVmHYsGGvzWNzN573VxtdezIy\nMhAQEIDAwEAYGhpCJBJhzJgx1W5OwdjHisO1GqKiouDl5YWEhAQAQHZ2NoIiTqOjlW21j3Uz8jAW\neUx57ZdbSkoKDh8+jEOHDuHs2bMwMDBA8+bNkZeXh1u3bqFTp04VYWtjYwNtbW2ZvLcX1ccShkSE\nAwcOYPHixVBTU4OPjw/69ev32nbcjadmZN21p6ysDAcOHICfnx+uXbuGKVOmwNPTs9afH2CsoeNw\nrYZXwxUAIiJPIaUI0Das+rqtaXGxGNjVCKZdjN+53dOnTxEZGYlDhw7h8OHDkJeXh6WlJdTU1JCd\nnY0LFy6gWbNmL4Vtp06dZHalSfWwhGF5eTlCQ0OxbNkyGBkZYdWqVejevftr23E3npqTddee27dv\nw9/fH7/99hu6d+8OLy8vDBs2rOI5BcY+JRyuMnDo2AncLiiDnonZO7cjIqTEnIF91w7oYVG9lmBE\nhOvXr1cEbXx8PPr06QNLS0soKysjMTER0dHRePLkCWxsbCrCtnv37jK5VVfXSxiWlpZiy5Yt+P77\n72FtbY3vv/8enTt3fm077sZTc/Tfrj07d+7Erl27aty1p6ioCLt374afnx8yMjLg7u6OadOmoXXr\n1rUwesYaJg5XGbl+MwFRl+JQIFCEoaU15F74tF5cWIi0y+ehoSjAcPt+0Gnbpsbne/ToESIiInDo\n0CEcPXoU2traGDZsGKysrFBSUoILFy4gOjoaSUlJMDc3rwjc3r17Q0ND473PW9dzn4WFhdi0aRPW\nrVuHoUOHwtvbG+3atXttO+7GIxuy7toTHx8PPz8//P7777C3t4eXlxfs7e35ww/76HG4ylheXh4O\nHDuBEglBIi2HvFAOTVWU4OgwqNYe9igvL0dsbGzFVe2dO3cwaNAgDBs2DLa2tvj7779x9uxZREdH\nIyYmBrq6uhVha2tri3bt2lX7dmpdz33m5eVhw4YN2LRpE1xcXLB48WJoaWm9tl193Mr+WMmya09B\nQQFCQkIgFotRVFQET09PTJkyRWZt9hhraDhcZSg/Px/hRyKQXVCEUgghBUFOIIQilUO/pTocHWTT\n4aQymZmZOHz4MA4fPoyTJ0+ic+fOGDZsGIYOHYquXbvi+vXrFWEbHR0NgUDwUtiamppWeZ6sruc+\nc3JysHr1agQHB8PT0xPz58+Hurr6G7flbjyyI6uuPUSECxcuQCwW48CBA3B0dIRIJEKvXr14vpx9\nVDhcZUAikSAgZBeeCBRgYGENeQWF17YpevYMd+MuQF9dFc6jR9TZ2EpKShAdHY1Dhw7h0KFDyM/P\nx9ChQzF06FAMHDgQqqqquHPnTkXQRkdHIyMjo9olQHU995meno4VK1Zg//79mDt3Lr788ks0bvzm\nRgpcxiNbsirtefjwIbZt2wY/Pz+oqKjAy8sL48eP57sM7KPA4VpDpaWlWCsOhEHfwVBSVql0+7wH\nOSi8FY/prhPr5ZP6i6U+586dQ8+ePTF06FAMGzYMHTt2hEAgwKNHj3Du3LmKsL169WqVS4Dqeu7z\n1q1bWLZsGU6fPo1FixbBw8PjrbffuYxH9mRR2iOVShEZGQk/Pz9ERkbCyckJIpGI/23YB43DtQaI\nCOvEAdCzGwyFaoRH/qOHoDsJcHUeW4ujq9yrpT6KiooVQduvXz80atQIwPOr0suXL1eE7blz5yot\nAarruc+4uDgsWbIEN2/ehLe3NyZOnPjWqygu46kdsijtyczMRGBgIAICAqCjowORSIQvvvgCysrK\ntThyxmSPw7UGzpw7j6RS+Uobpr9JyuUYuA60gaamZi2MrPreVOrTt2/firlaXV3dim2lUimSkpIQ\nHR1dMXf7rhKgupz7PHPmDBYtWoSHDx9i5cqVGD169FtDk8t4aocsSnskEgkOHToEPz8/XLp0CZMm\nTYJIJIKRkVEtj54x2eBwrYGft4WgbS/7N37vP53bYOv5G1Bt9vLDNl+PHoQVwWFopNIYeVfOYOr4\ncXUx1Gp7W6nPsGHD0KtXr9ceeMrMzMTZs2crwvZNJUCPHj2qk7lPIsLRo0exePFiCIVC+Pj4YODA\ngW8NWS7jqT2vlvZ07doVLi4u1SrtSU1NxebNm7F161Z07doVXl5ecHR0hMIbnm1grKHgcH1POTk5\n2HbiHAwtrd74/S+M2yLo3PXXwvVFSaeP4xu38e+9vmtdeVepj4ODwxuvvgsKChATE/PGEiATExMk\nJCRg9+7dtTr3KZVKERYWhqVLl0JLSws+Pj7o3bv3W7fnMp7aVdPSnpKSEuzduxdisRgpKSmYNm0a\n3N3doaOjUwejZ6yaarEpwAdpy5Yt1KVLF+rWrRv179+fMjIyyN/fn0xMTMjMzIwGDx5Mt2/fpl17\n99FvsYnUx3EM6Rp1Ir2OxjRy2nTanXCPwpIySSAQ0LYLNynwzFXSNepE7st8Xnp95uqfyMTKhgYN\nGkQmJiZkaWlJN2/eJCKilJQU6tOnD3Xt2pUGDhxIAwYMoN9++62efzL/c//+fQoICKBRo0aRmpoa\nWVlZ0YoVK+jSpUtv7GxDRFRWVkaXLl0iX19f+uKLL0hbW5u0tLTI1NSU1NTUyMbGhk6ePFkr3XjK\nyspoy5YtpKOjQ8OHD6erV69Wug9346ldNe3ac+PGDZo5cyZpaGiQo6MjHT58+K3/7TFWHzhcXxAf\nH0+ampp0//59IiLy9fWlDh06kJGRET18+JCIiLZt20bGxsa0/Y891G/EFzR8sjuFJWXSrut3ycy2\nH038enFFiG7Yd5x0jDrSVxt+pbCkTApLyiShUFgRriqqanTw0CEiIpo1axZNmTKFiIh69epF/v7+\nRESUmJhIjRs3blDh+qLi4mI6ceIEffXVV9ShQwfS0tIiNzc32rNnD+Xl5b11P6lUSqmpqRQcHExu\nbm6kra1NAoGAmjZtSk5OTnTs2DEqKCiQ6ViLiorop59+olatWpGzszPdvn270n2Sk5PJ09OT1NXV\nacaMGZSamirTMTGinJwc+vXXX8nW1pY0NDTI3d2d/vrrL5JIJJXu+/TpUwoICCALCwvS19en1atX\n8wch1iBwuL7gxx9/pIkTJ7702oIFC2jJkiUvvdasWTPy/dWPmjZvQZuOnasIzgW/bKEuPXtXhKtG\nK20y6mZR8f1Xw7WTRU8aOmw4LVy4kCZOnEhWVlYUHR1NcnJyL/1icXR0bLDh+qrk5GTy9fWlQYMG\nUZMmTcje3p7Wr19PiYmJlV6V5uTk0MKFC0lLS4uUlZVJUVGRzM3Nafbs2bR7927KzMyUyRgLCgpo\n5cqV1Lx5c/Lw8KCMjIxK98nKyqKFCxdS8+bNycXFpUpXv6z67t69S2vWrCEzMzNq3bo1zZ07ly5e\nvFilOxqxsbHk5uZGzZo1o3HjxlFUVFSd9CVm7E34scgXyMvLv/TQS0lJCVJSUl7bjoig2kQFUqn0\n5delhHJJWcXXou/WQCgQ4MBW/zeej6TlsLO1QZMmTZCRkYG0tDRMmzYN5eXlaNasGUxMTDB06FBc\nu3YNBw8exI4dOxAVFYW0tDSUlpbK6F3LlqGhIb788ktEREQgKysLs2fPxu3btzFw4EAYGhpi1qxZ\nOHr0KIqLi1/bV1NTE6tXr0ZmZib+/PNP2NnZ4f79+0hJScGWLVtgYmICAwMDTJ48GQEBAUhISHjt\n36AqmjRpgiVLluD27dto1qwZTE1NMW/ePDx48OCt+2hpaWH16tVITU2FmZkZhgwZgqFDhyIqKgrE\njy3IjK6uLhYsWIC4uDgcP34cKioqGDduHDp27Ahvb2/cunXrrfv26NEDW7ZsQWpqKnr16gVPT0+Y\nmJjg//7v/5CXl1eH74Ix8Jzri65fv06tW7emf/75h4iINm7cSCoqKmRkZES5ublERBQUFETt27cn\niURC3ax60fDJHhSWlEm/X0sjM9t+5DTr65fmVv/vyBlq0rQZ/fTnqdfmXFvr6VfMWY4aNYrs7Oyo\nvLyc+vbtSxs3bqT4+HgKCAigRo0a0fDhw8nFxYVsbW1JV1eXFBQUqHXr1mRtbU1jx46lr7/+mn7+\n+WcKDw+ny5cvU25uboP61C6VSik+Pp58fHzI1taWVFVVafjw4SQWi+nu3btv3e/Fuc/FixfT6dOn\nyd/fnyZNmkTt27cnDQ0N+vzzz2nNmjUUHR1NxcXF1R7b/fv3afr06aShoUHLly9/5+3sfxUVFdHm\nzZvJyMiIrK2tKTw8nOf8aolUKqWYmBiaPXs2aWlpkYWFBa1bt67SOw5SqZROnTpFTk5O1KxZM5o6\ndSpdunSpjkbNPnX8tPArdu7cibVr10IgEEBbWxtBQUEIDw+HWCwGEUFTUxObNm1C586d8ZNfAML+\nPIz020mQSMpgbvcZJi9YBjl5+ZeeFj64bTOiDuzBD7sOwbmbPoLOXcfZw/sRH3EAp6NOITo6GmvX\nrsXZs2ehqqqKPn36IDExEcDzT/L379/HkiVLMGbMmIpxSiQSZGVlIT09HRkZGUhPT3/tT3FxMXR1\ndV/6o6Oj89Lf/10ooq79W+pz+PBhHD16FFpaWu8s9XnbEoZVKQGqaheg1NRUeHt7IyIiAgsWLMD0\n6dMrXbyAy3jq1vuW9mRnZyMoKAj+/v7Q1NSEl5cXxo0bBxWVyldVY+x9cLjWQHZ2NoIiTqOjlW21\n970ZeRiLPKa8VoaTkpKCOXPm4MmTJ4iPj4eFhQWuX7+OHTt2YMiQIdVaRaigoAAZGRlvDd979+6h\nWbNmbwzef/+0bNmy1hdVqE6pT2VLGL6rBKiqXYBu3LiBpUuX4uLFi1i2bBlcXV0rrakkLuOpc+9T\n2lNeXo6IiAiIxWKcO3cOEyZMgKenJ4yNjet49Oxjx+FaQxGRp5BSBGgbVn2Jt7S4WAzsagTTLm/+\nH3rPnj34/vvvATxvtaajo4O0tLS3Lk/4vqRSKXJyct4YvP/+yc/PR9u2bd8YvP8GcnXbj1XmXV19\nzM3NIRQKq7yEoUQiQXx8/Ht1AYqNjcWiRYtw9+5drFixAk5OTlX6oMHdeOre+3TtuXv3LgICArBl\nyxZ07NgRIpEIo0eP5rsOTCY4XGXg0LETuF1QBj0Ts3duR0RIiTkD+64d0MPCvFrnoGosTyhLRUVF\nuHfv3huD998rYmVl5beGr66uLrS1td97oYzKuvooKipWawlDIqp2F6CTJ09i0aJFKC4uxqpVqzBs\n2LAq3UHgbjz1o7pde0pLS7F//36IxWIkJCTAzc0NHh4eaNeuXd0Pnn00OFxl5PrNBERdikOBQBGG\nltaQe+FKqLiwEGmXz0NDUYDh9v2g07ZNjc9X3eUJawsR4eHDh28N3vT0dDx48ABaWlpvDV9dXV00\nbdq0Sud7W1cfBwcHJCYmYs2aNdWe+6xKFyAtLS0cOHAAixcvhpqaGnx8fNCvX78qjZm78dSf6nbt\nSUpKgr+/P7Zv3w4rKyuIRCIMHTq0wa+ixhoeDlcZy8vLw4FjJ1AiIZSVS6AgJ4+mKkpwdBj01lZo\nNfU+yxPWpdLSUty/f/+t4Xv37l0IBIK33nbW1dVFmzZtXgvKN3X1GTJkCNq2bYsTJ07g1q1b7zX3\n+a4uQL1798aTJ0/g7+8PIyMjrFq1Ct27d6/ScbkbT/2qTteeoqIi7Nq1C35+fsjKyoK7uzumTZsG\nLS2tehg5+xBxuMpQfn4+wo9EILugCKUQQgqCnEAIRSqHfkt1ODq8ff5HlqoyZ9mQEBHy8vLeGr7p\n6enIysqCpqbmW8NXR0fnpfcdHx8PMzMzFBYWIi0tDdOnT3/vuc83dQF6/Pgx2rZti7t378Lc3By+\nvr5Vvhrlbjz1i6rZtScuLg5+fn74448/MGDAAHh5eeGzzz7jD0XsnThcZUAikSAgZBeeCBRgYGEN\n+Tc8WVr07Bnuxl2AvroqnEePqLOxVTZnqaamVmdjqYnqlh61atUKxcXFuH//Pq5fvw45OTmUlJRg\nyJAhWLNmTY1bl/1bAnTq1Cns378f9+/fR8uWLTF69GgMGzasSiVAXMZT/6pT2pOXl4eQkBCIxWKU\nlpZCJBJh8uTJVS71Yp8WDtcaKi0txVpxIAz6DoaScuU1c3kPclB4Kx7TXSfWyyfft81ZDhs2DB07\ndvygP42/rfTo7t27SE5ORlZWFoDnVy7KysowMzODg4MDjI2Na1x6dO/ePSxcuBB79+5F8+bN8fjx\nY7Rr165KJUBcxtMwVLW0h4hw7tw5iMVi/Pnnnxg5ciS8vLzQs2fPD/r/HyZbHK41QERYJw6Ant1g\nKFTjaiP/0UPQnQS4Oo+txdFV7k1zlrIs9Wlo/i09OnPmDH788UdcunQJ5eXlaNy4MVRVVVFUVITC\nwkLo6Oi8d+lRTk4OVq9ejeDgYIwYMQIdOnTAlStXqlwCxGU8DUNVS3sePHiArVu3wt/fH6qqqvDy\n8oKLi4vMy9PYh4fDtQbOnDuPpFJ5NNd+fZ6mMimXY+A60KbeHzb6V32V+tSn4uJiBAUFwcfHB1Kp\nFAKBABKJBH369IGxsTFatWqF3Nzc9yo9un//PlasWIH9+/dj7ty5mDVrFnJzc6tcAsRlPA1HVUp7\npFIpTpw4AbFYjKioKDg7O0MkEqFr1671PHpWXzhca+DnbSFo28seBY8fwbV3V+xJvF/lfaVSKfKu\nnMHU8eMAABYWFjh16lSDmQNtKKU+deHFuc9Hjx7BysoKubm5uHDhwmu3zQFUq/SoWbNmSElJQWZm\nJpydneHu7g5DQ0M0bdq0SiVAAoGAy3gakKqU9ty7dw+BgYEICAiAvr4+RCIR/vOf/3x0d4LYu3G4\nvqecnBxsO3EOhpZWyH/8EFNtumF3wr1qHSPp9HF84za+wdfQNfRSH1l5de5z+vTp0NfXx19//VWt\n2+ZvKj2Ki4vD6dOn8eTJEwiFQigoKEBPT++lK14tLS0UFhYiIyMDN27cwIULFypKgCwtLZGeno6Q\nkBCYm5tzGU8DUFlpj0QiwcGDB+Hn54e4uDhMnjwZnp6eMDQ0rOeRs7rwyYbrDz/8gKCgIKipqcHO\nzg779u3DsWPHMGPGDDx79gyZmZkwMzPDrl27oKioiEaNGmHEiBG4du0aQkJCELLrD+zasxeNVFRg\n0MUUJ3aHVITrybBQROz87XlrumbqmLZ0FVrrG+CXb+dAuYkq0m8n4cE/mWjeUgu/B22GiYkJhEIh\nHjx4gIMHDyI8PBxCoRDJyclQUlJCcHAwjI2N8ffff8PNzQ2PHz+GlpYWiAgTJ07EpEmT6vRn96GV\n+ryPV+c+Z86ciezs7BrfNj9z5gwWLVqE3NxceHh4wMjI6I0PYWVlZaFFixbQ1NSEgoICioqKkJ2d\njZKSEmhra+Phw4fQ1tbG8uXLMWbMmI/iZ/6hqkppT0pKCjZv3oxt27bBzMwMXl5e+Pzzzz+qO0Ds\nFbXSa6eBO3r0KHXu3Jny8/OJiGjq1Kmkr69PCxYsoJCQECIiKisrI1NTU9q7dy8REQkEgorvZWdn\nk0rjxvTz4dMUlpRJk+YvJaFQSGFJmbQiOIyMu1tTaHwqhSVl0rItodTWsAOFJWXSZ6PGUmfLnvTH\njXT640Y66XboRJ4iEd29e5eEQiHl5ubStm3bSF1dvaIx+KxZs2jKlClERNSrVy/y9/cnIqLExERq\n3LhxvTdRLy4uphMnTtBXX31FHTp0IC0tLXJzc6M9e/ZUqXVbQ5ecnEyenp6krq5OM2bMoNTUVCIi\nevjwIe3cuZMmTJhALVq0IBMTE/rmm2/o9OnTVFZW9s5jSqVSOnz4MJmbm5OlpSVFRES81h6wrKyM\n0tPTKTo6mkJDQ2nNmjU0Y8YMGjhwIOnp6ZGioiIJBAICQEKhkNq2bUtOTk70888/04kTJ+j27dtU\nVFRUaz8X9mYSiYROnDhBU6dOJXV1derTpw/5+fnRgwcPiOh5q8IdO3aQjY0NtWnThpYvX15p6zz2\nYfokw3X27Nm0bNmyiq+vXLlC+vr6REQUERFBa9eupWnTppGmpiYFBwcT0fNw/bfvaFhYGHUyNqaw\npEwKS8qkHZduV4TryGnTSaOVNukbm5B+5y6k37kLabTUouDYRPps1FhynvNNxX7Wg4ZRixYtqE2b\nNgSABAIBNWrUiJSUlMjExIRsbGzI1NSU2rRpQ66uriQQCGjlypX0yy+/0Pbt26lnz5707bff0tWr\nVyktLY0ePXpEEomk7n+gL0hOTiZfX18aNGgQNWnShOzt7Wn9+vWUmJjYoPrLVldWVhYtXLiQmjdv\nTi4uLnT16tWK70kkEjp37hwtXryYzM3NSV1dnZycnCg4OJhycnLeeszy8nL6448/qEOHDtS3b186\ne/ZstcZUUFBAsbGxNHXqVGrRogXJycmRUCgkFRUVUlVVJTk5OWrevDlZWlrS6NGjac6cObRhwwba\nvXs3xcTEUFZWFvegrUXFxcUUHh5OY8eOJTU1NRo2bBiFhIRQQUEBERFdu3aNpk+fTurq6jRy5Eg6\nevQo/3t8RD7J28Jff/01VFRUsGLFCgDAtWvXMGLECFhZWaG8vBxjx46FiYkJ5syZg/Hjx2PSpEkV\nt4PqgvoAACAASURBVG01NDQQHh6OZcuXY37gLjRp2gwlRYWYYNkBuxPu4bc130FOXgET5i2qON+D\nrPtood0Gv3w7B7odOsPR1RMA8J2bExRKCjF79mxMnjwZ//zzD/bs2YO9e/diw4YNyMvLw969e3H6\n9GlMmDAB8+fPx7x581BQUIC8vDxERkZCTU0NSkpKyMvLQ15eHp4+fQoVFRU0bdr0tT9qampvfP1N\nf2Rxu+pjLPWpyhKG1b1tLpFIEBwcDG9vb3Tr1g2rVq2Cqalptcd25coVrF69GidOnEDPnj2hpKSE\n2NhYSKVSGBoaolWrVlBWVkZhYWFFM4Z/ux69reVgbXQ9+hS9WNpz9uxZDB06tKK0p6SkBKGhoRCL\nxcjPz4enpydcXV0/mmcZPlWfZLj+9ddfmDlzJs6fPw81NTXMnj0bBw8exJMnTxAVFYWuXbsiISEB\nvXv3xsaNG+Hq6vpSuD548AAdO3bE+Jlz0c/FDUd3bsOW75dgd8I9XI0+Bb9lC7B6159Q12yJY7t2\n4M/fNuPnw6dfC9cVE0dBv402FBQUsHv3bmhpaUFHRwdFRUX47bffYGpqipCQEISFheHAgQPo168f\nXFxc4PH/7J13WFTX1offGUB6EURAUUARFQHFXqMSsUSjib0EE+zdG2M0sV0bRuONUa+JLSbGxJJi\nLLlGxXJtwRYUIiAqhmIFRellmJn9/ZGPuYCUmWGQ4rzPc55Hh3Pm7DMwZ5299vqt36RJxMbG0qpV\nKzZt2kRAQIDq2pRKpSr4pqamkpaWpvq3ultaWhomJiZaB+b8raAeUNQwqY+6LQw16ZCVk5PD1q1b\n+eSTT/Dz82PZsmVadZIqKOMZNWoUI0aMID4+vlgJULt27ahXr16pFdAV6Xr0KlKStKdr166Ehoay\nefNmDh48yIABA5gyZYqqalxP9eKVDK4An3/+OTt27MDMzIwWLVrwxx9/MH36dNauXauSUZiZmeHo\n6EhQUBAGBgY8efJE1ers+PHjjJswEVMbWzzbduTIru2qgqZje7/l+J5vkRpIMbWwZMryT3Fu1IQv\nFrxPgybNGBg4mYzUFL5b9A/69e3DnDlzMDAw4I8//mDz5s0cP34cCwsL7t27R4MGDZDL5Xz55Zc4\nOTkxa9YskpOTqV+/Pg8ePGDRokUMGTJEp5+NEIKMjAytAnPBzcjIqMTAbGxsTFJSEvHx8dy6dQtb\nW1s6deqEn58fXbt2xc7ODmtr6yo/u9W0haE6HbIyMjJYv34969evZ8iQISxevLjYnrdlke/Gs23b\nNvr27auS8agjAXJycgJevuvRq0ZJ0h43Nzd27drFli1bqFWrFlOmTCEgIKDKSPX0lM0rGVxDQ0MJ\nCQlh5syZwN+B9sqVK+zdu1ej90lMTOTr4+do2qGrxmOIPP0bCya9V+oTf9Gb4JUrV/Dw8KBXr160\nbt2aRYsWERwcTLNmzTQ+f0UjhCA7O1utIPz8+XPi4+OJj48nKSmJ3NxcjIyMkMvlSKVSjWfMRWfb\nZmZmFf7kL7RoYZiZmcmpU6dKTJtnZWWxZs0atm/fTmBgIB9//DF16tTReGypqals3bqV9evXF5vK\nLs0FKH9r2rRpiRXJFeV69KpRnLRn5MiRPHz4kC1btnDixAmGDRvG1KlT8fXVzA9az8vnlQyu6enp\njB8/nps3byKRSHBxcWHbtm2qp3VNOH76DDHZ4OT+om1VScRev4K/dxN8WnhqdK49e/bwz3/+k6ys\nLNLT01EoFDg6OhaacTRr1qzayzKKrlm6u7urtJ6Ojo6F0t7FpbSLviaXy7VKaxc8xsLCQu0ArU0L\nw9LS5m3atGHnzp3s27ePmTNnMmfOHK1mMOqmsgu6AOU7AaWkpKhaN3bp0oW2bduqbaEodOB61LBh\nQ+zs7F6J9KgoQdrTs2dPjh07xrZt23B0dGTq1KmMGDECU1PTyh6ynmJ4JYOrrjkSfJLb6Xm4eLUq\ndT8hBDGXz+Pn7UG71uV/8izOCq08N8GqiC5cfWQymUbrzcW9npubi6WlpUZBOT09nV9++YXg4GCG\nDx/O/Pnzady4sVrXnd8h67fffuPYsWM4OjrSpUsX4uLiuH79OvPmzWPatGla3Vi1cePJdwHK/zuL\njo7G19dX9bemjgtQaWjqelRc8G3QoEGVX0bQlOJce0aOHImVlRV79uzh8uXLBAQEMGXKlGJ9afVU\nHvrgqiNuREZx9o/rpEtq4d6mIwYFqm1zsrKIDb2IbS0JA/x60MC5foWNo6JvgpVNZbn65OXllbkG\nXdLPnz17RnJyMnl5eRgaGmJnZ4e9vb3aFd0WFhbExcUREhLC6dOnuXv3LpaWlmRlZbFw4UJmzZqF\nUTE2h2WhTSo7n/T0dC5fvqz6O7t8+TINGzZUywVIWzIyMkoMvAkJCdy/fx8bG5sSg295XI+qAsW5\n9vTq1Yv79+/z/fff4+npydSpUxk0aNArn2KvCuiDq45JTU3lcPBJcuUCuVKBodQAazNjBvbtXSkz\nyMq4Cb4sqpvU5/nz52zcuJEvv/ySJk2aMHz4cFxdXUtNcxfdMjIyMDExoVatWshkMrKyspBKpdSr\nV482bdrg7u6OjY2NxlKroqnsWbNmaSQFkcvlhIeHq/7O1HUB0iX5rkclBd+aJD0qKu3p06cPrq6u\nXL58mdu3bzN+/HgmTpyIi4uLzs6ZHyqq472iMtAHVx2SlpbGgaPHSUzPRoYUJQIDiZRaQoFb3doM\n7Nun0p8oq8JNsCKoTlIfddc+i6Oo1Orp06d8//337N+/n8zMTIyMjHB3d6devXpYW1uTlZWlkdRK\nIpFw69Yt7ty5Q5s2bRg4cKDKaKA0qVVRhBDExcWp7QL0ssjOzlZpfGuK9KiotMfPzw+lUsmZM2fo\n3LkzU6ZMoW/fvlqNOSIsjFtXQjHMzEUqlIAEhQTkZiZ4dmqPp4/e9ack9MFVB8jlcrbv/oEUiRGN\nW3fEsJgUXXZmJvHXL+FW25JRgwdVwiiLp6reBMtLcWuWVc3VR5u1z5IQQnD48GE+/PBD8vLyqFOn\nDtHR0cWmzdWRWj148IDz588TERGBvb09jo6OKBQKtaVWxW0SiYS4uDiio6MJDw8nMjKyRAlQZVHd\npUcFpT2PHz/Gx8eH+/fvk5mZyaRJkxg/fjwODg5lvs/tqCjCgv9LS3snmjoX/2AalRBHxLNE2vTz\np7GHh64vpdqjD67lRCaT8enmr2jcvQ/GpmZl7p/6NImsW+FMCwyosukVTXSQ1YGq7upTnrXPoigU\nCvbu3cuSJUto1KgR/fr14/bt21qnzUuS8QBqS61KWp9OSUlBCKF6mMiXYNnb2+Ps7Ezjxo1xdXUt\nMc39MqVWBaku0qOC0h6ZTEadOnW4e/cuffv2ZerUqXTv3r3Yzy3sylWeX4ugZ3P1ZqUnI8Nx6OiL\nd+vWur6Eao0+uJYDIQRrN2/HpVsfjDT4oqQ9S0bERRE4angFjk53lKWDrG4SoKrs6qONjKc4ZDIZ\nO3bsYOXKlXTs2JEVK1Ygl8u1TpuXJ5Vd1vsW1DtHRERw9epV/vzzT27fvk1WVhZOTk7Y2tpibm6O\ngYHBC2vUL1tqVRZFpUfFFWG9TOlRQWnP3r17qVWrFnl5eVhaWjJjxgzGjh1L7dq1Afjrzh3+OnGW\nXi1KVz4U5diNUJq/4Y9Lo0blHm9NQR9cy8H5kItEywyxc9K8e05M6GUC/btU+qxJG2qSBEgXUp+K\noGALw9GjR/PBBx/g5uam8ftkZWWxadMm1q5dS//+/Vm6dCmurq5ap811mcpWB3Wq3y0sLCpFalV0\ns7S0VPthI196VFr1c0VIj/KlPXv27OHHH3/E1NSUzMxM3nrrLf7xj38Qd/U6w1poNwP9+WYYQ6dM\n0OrYmog+uJaDjTt349zJT+395w7uzfJd+zGzsESpVJJ67Tzjx4yswBG+PGqKBKiypD4lkd/CcPv2\n7fTp00fVwlBTUlNT+eyzz/jiiy8YPXo0CxcuxNHREdAuba7LVLYmVFT1e3mkVvlbVlYWFhYWWgfn\n/J/lFx5VtPQoX9rzzTffcOTIEZRKJa6OTjx4+oRGTvUxNTZGCEGOTMblzd9gZV563cXpiDBajngL\nOy26iNVE9MFVS5KSkth5MgT3Nh20fo/ocyeYP25Mlao81BU1QQJUlaQ+6rjxqENSUhKffPIJu3bt\nYvLkyXz44YeqlGA+mqbNyyvjKQ9VqfpdoVCUGIDV7dNdmqtV0cCc/yCTnZ1NRkYGKSkpPH36lMTE\nRI1dj9LT01m3dAVL3hxG49FvsX/5p/g20awphUKh4NC92wx+N6DsnV8B9MG1GFavXs3XX3+NlZUV\n3bp14+DBgwQHBzN9+nQyMzN5+PAh9nUdeH/bXoxNTBjVshED3p3IH2dOkpOZQcCHiwg59h8Sbt/E\ntq4jH2/5FmMTU4Y2r883FyP4478nuHzyKLlZWeQkJ2JpacmuXbvw9PTk7t27jBs3jufPn+Po6IgQ\ngoCAAMaOHVvZH0u5qEo3QW2oKlIfXa19JiQksHz5cg4dOsScOXOYNWsW5ubmL+ynSdpcV6ns8lDd\nq9+LSq00CczFSa2srKwwMTHB0NAQiUSCQqFAJpOpAvLz588xNjambt26DO/Wk08Cp+A2chD7l6+h\ntcffPculPdvz9NAJfg05z/5zp8nOzSXu8SMaOjgw/a1hbDrwE3fuJ/D+sNG4+3gxcPL4Sv4Uqwg6\n8oWtMRw7dkw0b95cpKWlCSGEGD9+vHBzcxPz5s0Tu3fvFkIIkZeXJxq4uIh5/94h9kc/FBKJRExY\nHCT2Rz8UAXMXCjNLK/HV+TCxP/qhaNzCR7z/2Zdif/RDIZVKxc5LkWLGJ+uFhbWNWPvLcXHx8mUx\nc+ZM8d577wkhhOjUqZPYunWrEEKImzdvCnNzc/Htt99WzodRgSiVSvHXX3+JXbt2iUmTJglPT09h\naWkpevXqJZYuXSpOnjypMpWuiiQnJ4s9e/aId955R9SpU0d4eXmJ+fPni3Pnzom8vLwKP79cLhc/\n//yzaNu2rWjWrJn4+uuvRW5ursbvEx0dLYYPHy4cHR3Fxo0bRU5OTqn737lzR2zYsEH07t1bWFhY\nCD8/P/Gvf/1L3Lx5UyiVSiHE/4zlbW1tXzCWrwySk5PFr7/+KubPny+6dOkizM3NRZs2bcTs2bPF\nTz/9JB4+fFip49M1SqVSpKWlifv374vIyEgREhIijh49Kvbt2ye2bt0qPv30U7Fw4UIxY8YMERAQ\nIPr27SvatGkjFo6dIMSZq8LVsZ4I3fadEGeuCnHmqpBKpSL58Emx86N/itqWVuLBz78JceaqaOHa\nSAzv2UuIM1dF+I49wtTYWBz6cltlX36VQR9cizB79myxZMkS1f+vXbsm3NzchBBCHD9+XHz66adi\nwoQJwsrKSsxcs1EVXL86d13sj34o5m7YLpq3aS/2Rz8U+6Mfik59BoiJS1ap9ssPrj6du4nNJy+L\nN/oPEG+88Ybw8fER+/btEwYGBiI5OVl1/oEDB9bI4Foc1fUmKJfLRUhIiFi4cKHw9fUVtWvXFiNG\njBC7du0SSUlJFXpupVIpTp06Jfz9/YWzs7P47LPPVA+GmnDt2jXxxhtvCBcXF/HNN98IuVxe5jEZ\nGRni0KFDYtKkScLZ2Vk0atRIzJgxQxw9elRkZ2eLlJQUsWbNGuHk5CT69esnzpw5owrAlUl2dra4\ncOGCWL16tRgwYICwtbUVjRo1EmPHjhXbtm0TUVFRQqFQVPYwXzqHv9xeZnDt3a6j6vUBnbqKrz5c\nJMSZqyLj6DkhlUrFTxu+qOzLqDJUD+3ES8TQ0FDV5gvAwMAAIQQjR45k+/btuLq6MmfOHJp4eJCT\nlfm/4wpUSxoYFt/nteD6WC1jE549vs8b/fpiYmJCamoqO3bsQKFQ0LBhQ6ysrPDy8uLy5cvs3LmT\nVatW8f3333P27FliY2ORyWQVcPWVi62tLQMGDGD16tVcuHCBp0+fsmHDBpycnPj222/x8vKicePG\nvPvuu2zfvp2oqCiUSmVlDxsDAwM6derEypUruXbtGhEREfTq1YsDBw7g7u6uksKEhobqfLwSiQQ/\nPz+Cg4M5dOgQly9fplGjRixevJgnT56o/T6+vr4cOXKE7777jh07duDt7c3+/fsLfReKYm5uzsCB\nA9m6dSsJCQkcOHCAevXqERQURN26dXnnnXewsrLizJkzvP3220ycOJHOnTtz8ODBSv29mZiY0KVL\nF+bPn8+vv/7KkydP+PXXX+nSpQvnz59nwIAB2NvbM3DgQD799FN+//13cnNzK228LwuFtOz1e+Mi\nDXKMiizjyKtuCcVLp2oucFUi/fv3Z8aMGcydOxcrKyt27NiBRCIhODiYs2fP4u3tTVRUFDF37uAS\nF6PRexe9UZnkZjJ9+nQsLCzIy8vj8OHD9OjRg1GjRjFixAhCQkIYNmwYDRo0ID09naNHj75SFl35\nN8H8G2FRK7TVq1dXSQlQvXr1mDBhAhMmTCi0Zjl69OgKlfq0bt2aH374QbX22bRpU43XPrt168a5\nc+c4duwYCxcu5JNPPmHVqlX4+/uX+rckkUjw8fHBx8eHjz/+uJDUZ/HixTg6OvLWW29haWnJypUr\n+fjjjytcxqMuUqkUT09PPD09mTRpElC4+n327NnVtvpdE8zrOZKakfHC66U9YBXdz7pBxZmSVDf0\nwbUIPXv2ZMKECXTu3BkzMzNatGiBubk58+bN46233lK1PRsyZAiPnj5FqVSWHsAK/KzgfnK5HI96\nL7Yh+/bbbxk/fjxbtmyhfv36eHh4MHDgQIYMGVJov+Isum7dusWJEydqrEVXdbwJGhsb8/rrr/P6\n66+zbt06ldRn27ZtvPfeexUi9XF3d2fLli0sXbqUDRs20LZtW/r27au2jEcikdCvXz/69OnD/v37\nmTlzJk5OTqxatYrOnTurNQZbW1tGjRrFqFGjVFKf3377jQMHDhAbG0urVq1Yt24dixYt4oMPPngp\nMh5NqFevHsOGDWPYsGFA4er3DRs2MHr06GpX/V4WPfv14T//3krRSyjpmoq+LgG69Xq9gkZX/dBX\nCxchNDSUkJAQZs6cCcDnn3/OlStX2Lt37wv7JiYm8vXxczTt0FXj80Se/o0Fk957QYazatUqhg4d\nioeHB2lpabRs2ZKjR4/SrFkzjc+Rnp7OvXv3StTKFdXJFVeqX90suqqTBCgzM5NTp05VuNSnpBaG\n6n4GcrmcXbt2sXTpUlq2bElQUBA+Pj5aj+fhw4ccPXqUI0eOEBwcjLGxMTk5OYwaNYqgoCC1et9W\nNtW9+r0kDuz6nj4Orphp+LeXkZXFqWcPGDRmVAWNrPqhD65FSE9PZ/z48dy8eROJRIKLiwvbtm0r\nsZfu8dNniMkGJ3f1NWGx16/g790EnxaeL/zs559/ZuXKlUilUhQKBdOnT1fN0nSNJhZdJbmEVHWL\nrupyExQvQepTXhlPTk4OW7du5ZNPPsHPz49ly5bRpEmTco0pP22+e/dufvnlF9LS0mjWrBkzZsxQ\nrdlWB0Q1lwDlI5fL+e7zjQS07qz2dyJPLmf39UuMnTOrWj2IVzT64KoDjgSf5HZ6Hi5epffjFEIQ\nc/k8ft4etGvt+5JGVz5qmkVXdbkJVqSrT3lbGGZkZLB+/XrWr1/PkCFDWLx4Mc7OmrcALY6LFy+y\nZMkSzp49ixACX19fRowYUWkdsspDdTXAyM7OZt+mLQz1ao2l2Yva54KkZmRw4FY4o6ZPqfRah6qG\nPrjqiBuRUZz94zrpklq4t+mIQYGbX05WFrGhF7GtJWGAXw8aONecRX9RzS26oOrfBCvK1UeUs4Xh\ns2fPWLNmDdu3bycwMJCPP/6YOjpqfZeamsq///1v1q1bh4WFBTk5OVhYWKgeMF52hyxdUJYBRteu\nXWnatGmVmP0pFAqO/XKQ7IeJtLSvR5P6hR+eou/FE/k8CXNnJ3oPKr+BQ01EH1x1TGpqKoeDT5Ir\nF+Qp5BgZGGJtZszAvr1f2Se76mLRlU9VdwGqCFef8rQwfPjwIUFBQezbt4+ZM2cyZ84cnaVzC6ay\nTU1N8fHxITY2lj///LNSOmTpkqLV77///nuVrH4PDw0lLiIKFH8XbwqplMYtvfFqpXmP61cJfXDV\nIWlpaRw4epzE9GxkSFEiMJBIqSUUuNWtzcC+fSpddlAVEUUsuoqz6apM6VFVdgHStatPeVoY/vXX\nXyxdupTjx48zb948pk2bhqmpqTaX9QJFU9nTpk3DxsaG4OBgnafNK5OqZoARERbGrSuhGGbmIhVK\nQIJCAnIzEzw7tcfTRz3P11cRfXDVAXK5nO27fyBFYkTj1h0xNHqxiUR2Zibx1y/hVtuSUYMHVcIo\nqzfFSY9ehkVXSVS1m2A+unL1yXfj2bZtm0YyHoCIiAgWL17M1atXWbJkCYGBgRgV853QhuJS2ePG\njePmzZuq69ZV2rwqUFnV77ejoggL/i8t7Z1o6lx8ViAqIY6IZ4m06edPYw8PnZ6/JqAPruVEJpPx\n6eavaNy9D8amZmXun/o0iaxb4UwLDKhWxRnVgcqUHlVFCZAupD7lkfFcuXKFBQsWEB8fz/Llyxkx\nYoROU+klpbILSn10lTavKryM6vewK1d5fi2Cns3Vm5WejAzHoaMv3q2184GtqeiDazkQQrB283Zc\nuvXBSIN0b9qzZERcFIGjhlfg6PQU5WVKj6qaBKi8Up/yyHhOnTrFggULyMnJISgoiP79++v0IaO0\nVLau0+ZVDV1Xv/915w5/nThLrxalKx+KcuxGKM3f8MelUSNtLqNGog+u5eB8yEWiZYbYOWkuQ4gJ\nvUygf5dqnbKqiVSU9KiqSYC0lfpoK+MRQnD48GEWLlyIlZUVq1atokePHjq9JnVS2bpKm1dlylP9\n/vPmbQz11G4G+vPNMIZOmVCeodco9MG1HGzcuRvnTn5aHatUKkm9dp7xY0ayYsUKWrVqxZtvvqnj\nEerRNbqUHlUVCZA2Uh9tZTwKhYK9e/eyZMkSmjRpQlBQEG3bttXp9aibyn5ZHbIqm7Kq3y0tLdm2\nbRtJiYlkp6TSrKEra6fMwtNVs1no6YgwWo54CzsdybGqO/rgqiVJSUnsPBmCe5sOWr9H9LkTzB83\nhl69ejFz5kwGDx6swxHqqSy0lR45OjqSlZXFvXv3iIiI4NKlS5UiAdJU6qONjEcmk7Fjxw5WrlxJ\nx44dWblyJc2bN9fpdWiSyn4ZHbKqCgWr38+ePcu+ffv+1hC/1oPdHyxiz8ljLPxqM7H7Dmk0k1co\nFBy6d5vB7wZU4OirD/rgqiaZmZkEBgYSExODVCrFxtaWrqMn8P2/gqhTz5nHCXEYm5gy/ZN1ODdq\nQlZGOtuXLyDuZiQSqRTfbj0YM2cBUqmUkT5utHu9NzE3whk15G127NhB3bp1WbduHYMG6SuJazrq\nSo/q1KmDvb09RkZGZGdnk5iYSG5uLq1bt6ZHjx74+/vTrl27CpUAabJmqY2MJysri02bNrF27Vr6\n9+/P0qVLcXV11ek1aJPKrsgOWVWJlJQU6taty48//kji9Qgm+/UF4D8h5+nTvhNzN6/nys0o0rMy\nEQi++nARnVr4ELh6GabGxlyNjiLx+TOG9Xgde+vafHvqGHkS+Oqrr3Se9q926Nogtqby3XffiX79\n+gkhhFAoFKJnL38xdeW/hNTAQKz4/oDYH/1QTF66RjT2ain2Rz8UPQYNEwPenSj2Rz8UP9yIF626\n9hABcxeqTNP/sXaT2H72mti9d6/o2LGj2LVr1ytp0KynePLy8kRCQoK4cOGC2Lt3r1izZo2YPn26\n8Pf3Fy4uLsLY2FhIJBIhkUiEjY2N8PHxEaNHjxYbN24UJ0+eFLdv3xbZ2dk6H9edO3fEhg0bRO/e\nvYWFhYXw8/MT//rXv8TNmzdVRuiPHj0SH330kbC1tRWjR48WYWFhZb5vSkqKWLx4sbC1tRUzZswQ\njx490vnYtTWWl8vlIiQkRCxatEj4+vqK2rVrixEjRohdu3aJpKQknY/zZfP5558LMzMz4VjHXgT0\nfkN8PX+xyDp+Xlz84msxvGcvlTn66kkzxMAurwlx5qp4r+8A0amFt1Ccviwe/3JMSCQS8cU/5olD\nX24TGzZsEH369Knsy6p09DNXNYmLi6N79+40atQIf39/jMwsyLFz5utVi/ns4EkA5Hl5jPZtzI4L\n4czu351Vew/j2NAVgMsnj3Jk1w6W7/qZoc3rs/nUZYRSMKNvFxRyeSVemR49evTAwM6v0dzFlYMX\nziKRSLiyZSePkp9y+tof3H14nzNhoViZm3Nq3WYCVy/Do0FDPh4TCIBlv+6EfbWbqLRkpA0cWbZs\nGVeuXKnkK6pcakZu4yXg6upKTEwMZ86c4fTp02xbt47RcxdjYPC/j1AIJUIIDAwNEUploeOFUqCQ\n56n+b2JmTmLcXR49fMjw4cOZOXMmgwYNIi0tjdTU1EJbca+VtGVkZGBmZoa1tfULm5WVVbGvF7fV\nlLTXq0ZBCdD58+c5f/48SqUSd3d3HBwcMDU1JSsrS1URnS89Kkn3q670SJSyZunn58fZs2c1kvEk\nJCSwfPlyDh06xJw5c5g1axbm5qU3kdeG8nSkgqon9RFC8PTp01KbrTx58gQbGxtMTEzIyckhPT0d\npVKJo709qyfPZNXE6XgFjuTA+TOs2LWDuSPe4a2uPWjW0JXdJ4+pzmVsVDitbmRoiFwC+h50f6Of\nuarJli1bOH/+PLt37wbgvffe49zFy9yL/Ys1P/+Ga1NPfvtuBxePH2HF97+w4cMZWNnWIfDjpeTJ\nclk9LZCmvm0ZPn0OQ5vX55uLESRHhjJnXAD+/v5MmDCBESNGlHucSqWS9PR0rQJzwWNMTEy0Dsz5\nm77VY+UjypAAtWvXjnr16pVaAa2N9Ki4Nct+/fphaWnJoUOHyMzMVGvt89atWyxZsoRz586xEGL+\nUAAAIABJREFUYMECJk2aVCFrzOXpSFWQipb6ZGdnFwqaRQPovXv3MDU1pWHDhtSpUwdjY2MUCgVp\naWk8fvyYBw8e4OLigo+PD97e3tjZ2bFgwQIOHTpEXnom7aXmpGZm0G3WRLzcGtOsoQufTXufXJmM\nof+cT1pWJmc3bCNw9TK8G7kzZ/gY4O+Z6/mN23nubEeWQq6fuaIPrmqTlZXF+PHjCQ8Px8LCgoYN\nG+LQwIV9e/fg7t2KxwnxWNvZMW3lOurWdyY95Tk7Vi4i/tZN5PI8fLv15N15SzAwNGSYpzP/PnaB\nhop0BvTtzcaNG1m7di2rVq0iIKDyK+2EEGRkZGgVmAtuRkZGWgfm/K2myCGqEppIgIQOpEf169fn\n1q1bqqATGxtLq1atePr0Kc+ePeODDz4oU8Zz/fp1Fi1aRGRkJEuXLiUgIKBCbAzLayxfEE2lPkql\nksTExFIL3QpmGvI3W1tb8vLySElJ4eHDh9y6dYuIiAisrKzw8vLC29tbtTVv3vyF8549e5YlS5Zw\n79495Nk5NKhjz9L3JuHi4MioFYsQQlDb0pJBXbrzrx++J+HH/7wQXK3e6MGnc+Yxadkijh49qg+u\n6INruThw4ACTZ8xky+mrGh8befo3Fkx6r8r4nOoaIQTZ2dlaB+b8wA5oHZzzjzMzM6sRzQEqivJa\noWkqPbKzsyMzM1MlOTIxMUEmkzFq1CiCgoJwcHAocaznz59nwYIFJCcns2LFCgYPHlwhv9vyGssX\nRQjBpUuX+PHHHzlx4gR3797F2dlZZTaRlJTEgwcPsLa2LvEhxd7enidPnhAZGcmNGze4ceMGERER\nZGZm4u3tXSiQenl5adXb+sCu7+nj4IqZhg+1GVlZnHr2gEFjRml8zpqKPriWg7NnzzJ27LvM+3In\nTu5N1T4u9voV/L2b4NPCswJHVzPIycnRKigX3ORyuVYBuuAxFhYWr0yA1rUVmihFehQXF0dMTAxP\nnjxR7WtpaUn79u3p3bs3Hh4eL7geCSE4duwYCxcuRCqVsmrVKvz9/Svk96OujKegsURJs/zc3FzV\nOraDgwM5OTk8ePCAqKgoHBwcePPNNxk0aBDt27cnLi6OiIgIVRC9ceMG9+7dw8PD44VA2qBBA51d\nu1wu57vPNxLQurPadRd5cjm7r19i7JxZ1bpvs67RB1cdcCT4JLfT83DxKr0fpxCCmMvn8fP2oF1r\n35c0Oj0ymaxcwTk1NZXc3FwsLS01DsoFN0tLy2p786loF6D84PTbb7+xadMmoqKiEEJgbW2Nqakp\nmZmZyGQyXFxcVMHW2dmZpKQkfv31V+rVq8fq1avp2bOnDq/6b4QQpKSk8PPPP/Pll18SFxeHr68v\ndnZ2qoCamJioskQsaX3a1ta2UBAUQvDo0SPCwsI4evQo586d486dO2RnZ2Nubo6Hhwfdu3enQ4cO\neHt74+HhoTN3odLIzs5m36YtDPVqjaVZ6UVkqRkZHLgVzqjpUyrdd7aqoQ+uOuJGZBRn/7hOuqQW\n7m06YlDgqS8nK4vY0IvY1pIwwK8HDZzrV95A9WhFXl5emWvQZf08KysLCwsLrQJzwZ9XhaWEinYB\nSk1N5d///jfr1q3DwsKCnJwczM3N6dy5M56entSuXVsV2OLi4oiKiuLp06fUqlULd3d3mjVrVmyg\nK871SCaTcf/+/RKraxMSEpBIJKrAbmpqyu3bt4mLi+Ptt99m1qxZ+Pj4lBr40tLSVDPRgjNSqVRa\naE00P517/vz5SnX1USgUHPvlINkPE2lpX48m9Qv3T4++F0/k8yTMnZ3oPUj7dHlNRh9cdUxqaiqH\ng0+SKxfkKeQYGRhibWbMwL699U92rzj5VZs1UWpVUS5ABdc+TU1N8fHxITY2lj///POF9oRZWVl8\n9tlnrF+/nubNm9OtWzeysrKIiYkhNjaWBw8eqB5wjIyMEEKQk5NDdnY2dnZ2NGjQgMaNG9O4ceMX\ngrK1tfULYytOxpNfvFVwTfTGjRs8efIET0/PFwKpg4NDqQ8gVUHqEx4aSlxEFCjE32l5qYTGLb3x\naqV5NfWrhD646pC0tDQOHD1OYno2MqQoERhIpNQSCtzq1mZg3z56eYqeclFdpFZlSYA0dQEquvY5\nbdo0bGxsOHr0KMeOHcPa2prmzZvj6OiITCbj4sWLxMXFIZFIsLS0xNXVlYYNG1KvXj0sLS2pVauW\nKrg+f/68kA+wOtIjqVRKfHw8N27cICQkhEOHDnH79m2EEDRo0IB27doVCqRubm46yTi8bFefiLAw\nbl0JxTAzF6lQAhIUEpCbmeDZqT2ePup5vr6K6IOrDpDL5Wzf/QMpEiMat+6IYTHpoezMTOKvX8Kt\ntiWjBuv7B+upPCpLaiWRSIiLiyM6Oprw8HAiIyNLdQEqTpqSkJDAtWvXCA8PJzMzE4lEQoMGDbC1\ntUUul5OUlERaWhrt2rWje/fuPH78mJ9++onAwEA+/vhj6pTh2FKc9OjWrVtERUURFxdHUlIS2dnZ\nABgZGalmvM2aNcPT05MHDx7w008/4evrWy4ZjzpUpKvP7agowoL/S0t7J5o6F29aEJUQR8SzRNr0\n86exh4fW56qp6INrOZHJZHy6+Ssad++DsalZmfunPk0i61Y40wIDXpnqUz01D11IrVJSUlAqlRga\nGiKEIC8vD6lUqkod5+XlYWxsjJ2dHfb29jg5OdGgQQNcXFxwd3dHoVDw008/ce7cuUJuPA8fPuTo\n0aOqNcvGjRtjaGjI7du3mT17Nh988EGx6dTs7GyioqIKVegWJ3Vp1qwZtra2pKWlFSs9iouLQy6X\nI4TAzMyM9u3b061bN1xdXVUz4vr16+s0i1VahyxNXX3Crlzl+bUIejZXb1Z6MjIch46+eLfWzge2\npqIPruVACMHazdtx6dYHIw2+KGnPkhFxUQSOGl6Bo9Ojp/JQV5ri7OyMo6MjdevWpXbt2uTl5fH0\n6VMePHhAfHw82dnZODk5YWtri7m5OQYGBoXS4qmpqeTl5WFoaEheXh62trY0atQIR0dHlYQqNTWV\ne/fuERUVRVpaGhKJhA4dOuDv78/z58+JiYnh5s2b3L9/XydSl3zpUWxsLD/++CN79uwhMzMTd3d3\nDAwMuH//Po8ePVJVFxdtN1lUeqQN2rr6/HXnDn+dOEuvFqUrH4py7EYozd/wx6WRZh6wNRl9cC0H\n50MuEi0zxM7JueydixATeplA/y5l+l7q0VPVyJemlFZdq400pTjUkQDlB9CYmBi2bdvG/v37adWq\nFa+//joSiYSYmBji4+N59OgRjx8/Jj09XfX+EolENQZLS0tsbGx0LrUSxRjLBwYGkpGRUepnmJOT\nU2rwbdCggVqpX4VCwZUrV1RrtXFxcfTu3Zv+/fvTt2/fQvegnzdvY6indjPQn2+GMXTKBK2OrYno\ngyt/N4OYMWMGQ4YMwd3dnXfeeYcVK1bQqlUr3nzzzRKP27hzN86d/NQ+z/Lxo3j/sy+xtKmNUqkk\n9dp5xo8ZqYtL0KNHZ2gqTSkugNavX79CNJklSYDatWtHgwYNMDY2Ji4ujlOnThEXF4ehoSEtWrSg\nW7du+Pj44OXlRYsWLbC0tOTs2bPMmjVLtZ+lpSWvvfYa7du3x8PD44W0ty6kVhkZGVy8eJE7d+7Q\nr18/xowZg4uLS7FSq7KC7/3797GxsSkx+JYkPSqYNj9x4gQZGRk4OTlhZmaGNDcPcxMTZg0ZQWC/\ngSX+Hv6IjmLHb4fZPOcj1WunI8JoOeIt7MpY135V0Fuf/D8SiYSlS5eq/n/69GlatGhR4v5JSUnk\n1NLMpePPkHOqf0ulUhKzZCgUiiqhW9TzaqCOa8rTp0+pV69eoZt0q1atGDhwYKnSlJeBsbExDg4O\nNGnShJycHGrVqsW1a9fYvXu3Sg9rZGREq1atCAgIQKFQ8OOPP3L16lX8/Pxo3769Kth0796d8PBw\nTp06xYIFC0hJSUEikbBv375CUp9BgwaptWZZktSqaDvP9u3bU69ePS5evMgvv/yClZUVxsbGZGZm\nliq1sra2pm7dujRp0kQ1U4a/14ozMjJISUnh1q1bnDt3Ti3Xo6CgIJYsWUKXLl0YOXIkUZevcjRo\nHY+Sn+IVOJJ2TT3xauRe7LVGxN7lwdOkQq91b+7NoSNHGfxu5fdHrwroZ678b+batm1bvLy8MDU1\nZf78+dStW5d169bxxhtvMH/+fM6dO4dCocDX15cevXpj5tuN6f6daNLSl/jb0Yx5/yMMDAzZv3Uj\nCrmc1OSn9HhrGCNnfcgXC97nvwd+pKFHcxZu3cWiMW8TMHcRU998ncaNG+Pm5sb+/fuxs7OjW7du\nNG/enPj4eM6ePcvdu3f56KOPyMrKQiqV8s9//pP+/ftX9sempwqiiWtKSSnH4lxuXjZCCJXUpWBx\nUUxMDC4uLoVkLgWlLsVJgBISEnBzcyM5ORlDQ0M++ugjxo8fX6igSAjB4cOHWbhwIVZWVnz00Udk\nZmZqvGapKUXdeObOnUujRo201kEXlVpZWVlhYmKCoaEhEokEhUKBTCZTBeRnz54hl8tp1KgRw7v1\n5JPAKQB0mPoe80eNJSUjgy8P/oxAYGdlzabZH2JmbEKXmRNIy8xk8Gs92TFvMb+GnCPou294mpmG\nU8OGrF27lo4dO+rkM6qu6IMr/wuu7dq1w8vLizlz5tCzZ09mzZrF22+/zYoVK8jIyGDNmjUALFy4\nkCuh15i64Wumvt6B14eNZuiU2QAsfW84U5Z/imNDV54nJTLZrx07LoRjaVOboc3rs/NSJBbWNkx9\nvQOTlq6muYWUrp07061bN/bv34+9vT1ubm5cuHCBzp07k5KSQseOHQkODqZhw4Y8evSIDh06EBIS\ngrOz5mu9eqov2rqmFAye6vqzvkySk5NfCKKauLqURb4LUH7Xo5s3b2JgYEDHjh2ZOHEivXr1UkmA\nFAoFe/fuZcmSJTRp0oSgoCB8fX3VXrPUltRU3bnxqCu1yl83P3LkCM2aNWNg6/asGj+Ni5F/8ubH\nc/hlxacs/noLxz/9NybGxpz44zL/2PQZkTt/5Ntj/2H/udMcXrWOmPv3GLxkHmc3bOX8vbu4d+9C\nr169uHv3LqampuX+bKor+rRwKeQ/d/znP/8hNTWV4OBg4G+JAJL/rWM0b9NB9e+PvtxJ6JkTnPv1\nF+7fvQNAbnYWlja1C70ngEIuZ+rkWSiVCjIyMmjXrh2mpqYIIZg0aRI2Njbk5uby119/0aZNG6RS\nKQYGBshkMtavX0/37t2rbHs8PZqTnp5ebKo2/7XiXFNcXFzo1q1bIeeUqtqKTh2pS8uWLXnnnXe0\ndnUpDltbWwYMGMCAAQNYs2YNOTk57Nmzhw0bNjBhwgSkUikODg50795dpbmNiorim2++YdCgQXTs\n2JGVK1eyYsUKVqxYoVqzPHDgADNmzNBJe0Jra2vmzZvHrFmz+O6775g4cWKpbjxCCLKystSaxZb2\nc/j7gSImJoavHz3m1wvnsLepze5FKzhy6XfuPnhA5xnjVfetlIwMUgoUhAGcCL3M42fJvD5nGinZ\nWVhvs8PQ0JCYmBi8vV/dJhP64KoGCoWCDRs20KdPH+Bv8fbR4GAyUlMAMDX7W9+am53F3Ld706F3\nPzzbdOD1ISO5eup4oYCqehKVSEh9msiqVUGkpKSwatUqWrZsSVJSEnfv3iUuLk5VjGBqakqnTp1U\n6Z3s7Gzi4+PZsmVLtWmP96qjqWtKfrD08/Mr1Ki+Ovjb5t+sy3J1mT17ts5dXdTBxMSEcePGMW7c\nOGJiYli7di379u0jISGB3377jdWrV6tcgKZOncrDhw957bXX6N+/P0uXLsXV1ZXx48czfvz4Qu0J\nR48erVZ7wvyZZWlBb8iQIYSFhTF+/HhkMhn169fH2NhYJUNKS0vD0NCwzO+xm5tbqd//xMREvL29\nSUtL4+DWHbzV7H8tDU/8cZmA3v34ZNIM1WsJiY+xKeK7q1Aoeb11O/YuCeJAdDhvTx5PQkICDRo0\nqLhfYjVAfwctgXzdHECfPn3YtGkTPXv2xNDQkMmTJ2NmZoZru8JPcI/iY8nOymD07PkYGBpy9vB+\n5HkylEolAAaGhsj//z2t7eqQdDuKL3Z9w6VLl1i8eDGbNm3Czs4OLy8v1Q33xo0bTJ48WVW8cPPm\nTcLCwjAwMKBu3bo0bNgQT0/PQlWadnZ2WFtbI5VKX9AE5m+PHj2q1PZ4NQltpSnNmjWjd+/eGklT\nqhL5ri5Fm9FHR0fj4OCgSuUOGzaM5cuXvzRXF01wd3dn69atLFu2rNDa53vvvUdKSgq///47V65c\nISsri9OnT/PDDz/QvXt35s2bh729PampqeTk5NC2bVuaNGnC3bt3CQ8P5/333+fRo0eq746RkRE5\nOTmkpaUV+/0qbuvXrx8jRowgISGBw4cPc//+fcaNG8fEiRN12oQi/+HfvJ4jqRkZWP//skHvdh2Z\n9K9VzB4yEke7Omz79Rc+/2kvN3f9hKGBAXlyOQB+rdvyz53buBR5A5tGzhw/fpzRo0dz//59fVpY\nDy/c1N58803mzp2LTCZjyZIlfPDBB/j6+iKEoFWrVqxbt469B3+FAse5NPWkbY9ezHrjNWrXdaCZ\nbzsatfDhcXwsDs4Nad+rLwvHvMVHX3zDkMmz+P6TxbRu3Zo2bdrQtm3bQmOxsbHBxsYGHx8fnJ2d\nmT9/Pjk5OQghOHDgAP3791fNhPJv6rdu3eLEiRNl6uTatm1bqk5O3TWbmJgYnbbHK26rCjM1baUp\n+Z1xKlKa8rJQx9Wla9euTJkyRSV1qWqUVs2bmpqKpaUlAQEBXLp0iYEDB2JpaUndunURQmBjY0Ny\ncjI5OTkEBwdz/PhxDAwMqF27Nk5OTqq+xTY2NvTo0YNBgwZhYmJCbGwsN27c4MqVKxgbGzN06FAG\nDRpEr169NPrbXrJkCdeuXWPNmjW0b9++UEeq8pJ/7+vZrw//+fdW3mr99zJX73YdmT96LP5zZ2Ag\nlWJlbsGBFWsB6NTCm0U7tjBkyTz2L/+UbR8sYFTQEmzq1sHQ0JBff/31lQ6soC9oKheJiYl8ffwc\nTTt01fjYyNO/sWDSexW6Ppq/hldSUCiqkytO7F+cTk4ddNEeL39NSNvgnH+cmZlZiTNCbaUp6rim\nVEdkMplOXV10RV5entbVs/lbdna2ypO3rL8pU1NTrl27xsGDB7G1tWX69OkMHToUa2trDAwM+Ouv\nv5gzZw4nT57Ezc2NJ0+eYGBgUKILkC7bExbnxuPm5qaTz/nAru/p4+CKmYYPtRlZWZx69oBBY0bp\nZBw1AX1wLSfHT58hJhuc3JuqfUzs9Sv4ezfBp4VnBY6sbJRKJUlJSSXOxgpWn5bUZaeiq09zcnK0\nCsoFN7lcjqmpKcbGxqqbnVwuJzc3l6ysLIyMjLC1taVOnTo4OTmpqm0bNWpE06ZNVbrC6pSyLQtt\npS7akJubWy5pSWpqKjKZ7IWAqOlDl4WFhcYPikXdeObNm8eYMWNUKdlbt26xZMkSzp07x+TJk2nY\nsCGXL18u0wVI2/aEBSkq45k3bx4tW5bPBk4ul/Pd5xsJaN1Z7bqLPLmc3dcvMXbOrCpbTFcZ6IOr\nDjgSfJLb6Xm4eJXej1MIQczl8/h5e9Cute9LGl35yM7OVgnSSyrEUceiqyJm6CW5phScgaalpVG/\nfn2cnJyoW7cudnZ2WFlZYWFhoQq2BQN4STf+3Nxc1axH3Rlz0a2k9ngVjbZSl3xLNm2qUAtuCoVC\n48+q6GZubl6pDzfFtTCcOHGiKv19/fp1Fi1aRGRkJEuXLiUgIIDU1FRCQkJUetuwsLBiXYA0aU9Y\nHKmpupPxwN/f+X2btjDUqzWWZqU3yknNyODArXBGTZ+i96sugj646ogbkVGc/eM66ZJauLfpiEGB\np76crCxiQy9iW0vCAL8eNHCuX3kD1THFWXQVDXBPnz7F0dGxxOBbUlpVG2lK0U1X0pS8vLwyA4ou\n2uOVFWxKk1oVlLr8+eefhIeHq6QujRs3VjXJt7W1xdLSUq1rkkgkWj9MFEyx1qRZf/7a5+nTp19Y\n+zx//jwLFiwgOTmZFStWMHjwYNW15+TkEBoaqgq2ISEh2NjYqIJt165dadq0KY8fPy7k6qOu1Keg\nsXxpMh51UCgUHPvlINkPE2lpX48m9Qtr6qPvxRP5PAlzZyd6D9LuHDUdfXDVMampqRwOPkmuXCBX\nKjCUGmBtZszAvr1f2Sc7mUzGgwcPCgXI+Ph4YmJiiIuL49GjRwghVK4nSqWSzMxMlEqlKii7u7vj\n5uZWKHBWF2lKPiUV1BR9LSUlheTkZJ49e8bz589JTU0lPT2djIwMVXs/IyMjJBIJSqUShUKBXC5H\noVAglUpVr0ulUszNzbGyssLKygobGxtV+rt27dpqBcnq9Pm+bEpa+xRCcOzYMRYuXIhUKmXVqlX4\n+/u/8IChVCqJjo5WBdvff/9dJQHKn9l6e3tz9epVjhw5wpEjR9SS+pSVytaU8NBQ4iKiQKFEIpEg\npFIat/TGq1X5UtA1HX1w1SFpaWkcOHqcxPRsZEhRIjCQSKklFLjVrc3Avn1eCXmKptIUZ2dnHBwc\nMDc3x9DQEIVCQXZ2Nk+ePFHtX9EWXbpCqVSWKH9Sd8ZbUKqR/5kolUpyc3NV721ubk7dunVxcHDA\n3t5eZdmmUCjIy8sjJyenRC2lXmqlW0pa+1Qqlezfv59Fixbh5OTEqlWr6Ny5c6nvVZYLkKOjIyEh\nIRw5coSQkBDat2+vMkhv2rRpob//slLZ6hARFsatK6EYZuYiFUpAgkICcjMTPDu1x9Pn1W0SURb6\n4KoD5HI523f/QIrEiMatO2JYjOQiOzOT+OuXcKttyajBgyphlLqjMlxTCjZhKOm85bXoKkuqoU6Q\nzMjIUM0WNQlUBgYGPH78mPj4eO7evatK7xaUuuRX6JZX6qKu1KqsraZIrXRFSWufCoWCXbt2sXTp\nUlq2bElQUBA+Pj5qvWdJLkBdunShXbt2CCG4evUqR48epVatWqpA26NHj0KfbWmp7OK4HRVFWPB/\naWnvRFPn4iuZoxLiiHiWSJt+/jT28NDsw3oF0AfXciKTyfh081c07t4HY1OzMvdPfZpE1q1wpgUG\nVPosqziqqzQlLy+PBw8eEB0dzd27d4mNjSUhIYGHDx+SmJhIcnIyaWlp1KpVCxMTE4yMjJBKpSiV\nSvLy8sjOzlYVLdnY2GgdMCwtLUst3qqqUhdNqS5Sq8qgpLVPmUzG1q1b+eSTT/Dz82PZsmU0adJE\no/eWy+WEh4ergu2FCxeQSCR06dIFNzc3MjMzCQsLK+TqU1Dqo46MJ+zKVZ5fi6Bnc/VmpScjw3Ho\n6It3a+18YGsq+uBaDoQQrN28HZdufTDSIEWW9iwZERdF4KjhFTi64tHWNaVg8NR19e/LkmrkV+vm\nu4Kkp6eTkpLC06dPefz4MQ8ePNCZ9Kio1CU/iFaE1KU6oyuplTYBuuAxFhYWOg/QJa195vcGX79+\nPUOGDGHx4sVam3AU5wJ07949WrduTZ06dUhJSSEsLAwnJ6dCUp+nT58Wm8r+684d/jpxll4tSlc+\nFOXYjVCav+GPS6NGWl1HTUQfXMvB+ZCLRMsMsXPS/IsRE3qZQP8uOumwko+60hRduabURKmGNtIj\nOzs7hBCkp6eTmJhIbGwskZGROnN10VM6MpmsXME5NbVipVYlrX3m5eWxZs0atm/fTmBgIB9//DF1\ndGA0nu8CVFAC1KBBA6ysrEhKSuL58+f07duX/v3707lzZ/bv369KZffv0IXpPfpodd6fb4YxdMqE\nco+/pqAPruVg487dOHfy0+pYpVJJ6rXzjB8zUu1jdClNUcdVQ50A+SpJNbKzs4mMjOTixYtcvnyZ\niIgIYmNjycnJUaWD8xtTODg44OrqqpH0SE/l8bKkVhkZGVy8eJE7d+7Qr18/xowZg6mpKbt27eLI\nkSPMmDGDuXPnFlsFrC1FJUAXLlzAyMgIExMTnjx5goeHB2+++SZBQUHsWricAP9+Wp3ndEQYLUe8\nhZ0OHhBqAvrgqiVJSUnsPBmCewG7OU2JPneC+ePGYGBgUKprSn4AzXdNcXZ2xsnJCXt7e2xtbbGy\nssLMzAwjI6MX1sFKuiGo66pRVoCsibMwdV1d8mejRV1dipMeFXwoio+PRyKRlJg9yC/40lfjVi9K\nK4gr+vr9+/cJDw/n0aNHWFlZqRxvMjIykEgkWFpaUq9evRckU+pmckrrrlRQAnT27Fn++9//8vz5\nc3Jycnh66AR21jZaX/+he7cZ/G6Ath9hjUIfXLVk6oyZHDl+AgNDAyxr2zI9aB2Hv9nKnT+vk5OV\niRCCaSv+RVPftmz6+B9kpKaQeC+BNj160bZnL3auXkZOViZJ9+KpVatWoadeMzMzVfcgiUSCQqFA\nJpOppBXqumqUFSBf9Zu3uq4u+YFUV64uQghSU1NLDL7VSXqkp3wUlfHMnTuXnJwcli1bRlhYGGPH\njqVr165kZmZqlPrWVGqVl5fHyJEjSToQzIrvvuLKzSjSszIRCL76cBGdWvgQuHoZVubm3PjrLveS\nEmnW0IUf/vkJZiYmRMfHMXvTZ/yV9BhL29rMmjWL9957r7I/3kpFH1y14M8//6Rrt26s+/UMtg6O\nHNn1FdfOncbM0pIPPt8KwIHtm7h1/Q8++nInmz7+B88SH7Pk630ALA0cjv/wd2jeuj1B7w5GJpPR\npUuXYoX9xQXIfAsrPeqjjquLrqQuuuBlSI/0VB1SU1+U8ZiYmLBw4ULi4+NZvnw5I0aMUKsTkrZS\nq7Nnz3Lh31+x8Zd9/PDPTwBYs+dbQiL/5FDQZwSuXkbMg/v89/PNAHSYFsiswSN4x78fLceP5vuF\ny0nIy6LHmBF06tSJb775hvbt21fo51aV0VvOacGpU6do2coXWwdHAPqPnUD/sRN4GHtkMJjQAAAP\nGUlEQVSX4/t28TghjsgrFzGz+N8Nulmb//2Rdek3iK+WL6BZ6/YEjhvH+//4R6XfzGsK6kpdBg0a\nVKWlLoaGhqoCs5LIyMh4IfCeOXOmRNej4iRT2roe6dEt1tbWzJs3j1mzZvHdd98xceJElYzH3Nyc\nRYsWsXr1aoKCgujfv3+pf7P5aWVLS0vq11e/1apUKqW5iysrxk1hy6H93H14nzNhoViZ/6+/cN/2\n/zMV8HZrzLP0NG7fT+DuwweM+3QFKdlZWG/7kpycHK5fv64Prno0w9DQEBPjWmSkpmBhbUOeLJfj\n+77j6O5vGBQ4hQ69+lK/kTvnfz2gOsakQANs/+FjaNvTn+C9Owm7fh1vb29u3LihD7AaoK7UZdy4\ncTVW6mJhYUHz5s1p3rx5sT8vyfXowoULxVaPFyc/qmjXIz2FMTExYeLEiYwbN46DBw8SFBRERkYG\nH374IVZWVnz00UesWrWKVatW0aNHj3KdKywsDENDQ7y8vFSvXbgRxgdfbmDuiHd4q2sPmjV0ZffJ\nY6qfmxZo4SqRSBBCoFAoqG1pybXt33MgOpy3J4/n8ePH1K5du1zjq+7og6sW9OzZk9WrVxN9/iRt\nBwzl+L7viLj8O+169qb3yADyZLn8sm0TSoWi2OMXjBrIkCmz6dCmNROHLadhw4Y8f/5cH1xLQB1X\nl759+/Lhhx/qpS4FkEqlODo64ujoWOIMojjp0aVLl/jxxx8r3fXoVcbAwIAhQ4YwePDgQjKe2bNn\nY21tzbhx42jSpAlBQUG0bdtWq3P85z//4fbt2+zatYvQ0FBsrK05duUSA7t0Y/LAweTKZKzesxOF\nsvj7WD5NG7piUqsWWw7tp2nvHty/f582bdpw8OBBOnXqpNXYagL64KoFXl5erF27lvkfL2DvV1up\nXdeB4dPeZ/vyBcwd3BsLKxva+fXm8Ddbij1+7IeL2b58AQbyXPZu3sjSpUs1MkuuqRR0dSkYSDMz\nM1XroS1btuSdd97By8sLW1vbyh5ytcfU1JQmTZqU2CmoJNej0NDQcrse6SkbiUSCn58ffn5+hVoY\nTpw4ERsbGwYNGkTHjh1ZuXJliRmMkpgxYwZjxozBy8sLhULBzm+/5d7FP/j6l59oPfEdaltaMqhL\nd/71w/cljg3AyNCQQ0GfMSpoCQanfkMul7Ny5cpXOrCCvqCpXCQmJvL18XM07dBV42MjT//Ggknv\nvZJP/OWVuuipWuilRy+Xgi0Mhw0bRu3atdmxYwf9+/dn6dKluLq6av3eB3Z9Tx8HV8w0zP5kZGVx\n6tkDBo0ZpfW5axr64FpOjp8+Q0w2OLk3VfuY2OtX8Pdugk8LzwocWeVTWVIXPVULvfSoYigo4/Hz\n88PW1paff/6Z0aNHs3DhQhwdHTV+T7lcznefbySgdedStbIFyZPL2X39EmPnzNIXxxVAH1x1wJHg\nk9xOz8PFq/R+nEIIYi6fx8/bg3atfV/S6F4O1U3qoqdqoZceaU9BGU/z5s2xtbXl9OnTTJ48mQ8/\n/FDjwqLs7Gz2bdrCUK/WWBYoxCz23BkZHLgVzqjpU15Zv+qS0AdXHXEjMoqzf1wnXVIL9zYdMSjw\n1JeTlUVs6EVsa0kY4NeDBs7ql8dXNWqKq4ue6kdx0qOC26suPSroxmNhYYGdnR1hYWHMmTOHWbNm\nYW5eeqAsiEKh4NgvB8l+mEhL+3o0qV+4f3r0vXginydh7uxE70EDa+xnWh70wVXHpKamcjj4JLly\ngVypwFBqgLWZMQP79q5WT3Z6Vxc91Y2SpEcFt1dBelTQjSc5OZk6depw7949FixYwKRJkzS+D4WH\nhhIXEQUK5d/yG6mUxi298WrVsoKuoGagD646JC0tjQNHj5OYno0MKUoEBhIptYQCt7q1Gdi3T5Us\n2lBH6qJ3ddFTE9DG9ai6So8KuvGEh4dja2tLVlYWy5YtIyAgQK1riAgL49aVUAwzc5EKJSBBIQG5\nmQmendrj6aOe5+uriD646gC5XM723T+QIjGiceuOGBZTlJOdmUn89Uu41bZk1OBBlTBK9aQuBVO6\neqmLnleNkqRHBdPR1VF6lC/jOX78OFZWVpiamrJq1SoGDx5c7LLN7agowoL/S0t7J5o6Fy8TjEqI\nI+JZIm36+dPYw6OiL6HaoQ+u5UQmk/Hp5q9o3L0PxqZmZe6f+jSJrFvhTAsMqLC1SL3URY+eiqM6\nS49iYmJYu3Yte/bswcTEBEdHRz777DP8/f1V94CwK1d5fi2Cns3Vm5WejAzHoaMv3q1bV+TQqx36\n4FoOhBCs3bwdl259MNLgi5L2LBkRF0XgqOHlPr9e6qJHT9WiqPSouCKsypYePX78mPXr1/PFF18g\nkUjw8PBg48aNONrb89eJs/RqUbryoSjHboTS/A1/XBo1qpDxVkf0wbUcnA+5SLTMEDsn57J3LkJM\n6GUC/btgb2+v1v56qYsePTWHfOlRadXPL0N6lJqaypdffsmaNWvIzc1l7BtvsnXWPK3e6+ebYQyd\nMqFc46lJ6INrMUydOpXg4GBGjx7NihUrStxv487dOHfyU/0/8spFvlqxkM9/Pf3Cvvs2rsXJtRHd\nBw4B/q5sTL12nvFjRhbaTy910aNHD7xc6VFOTg5z3n+f88dPYCCVolAqaejgyNops/B0VW82ejoi\njJYj3sKuTp3yXnqNQB9ci8HAwIB79+5Rr169EvdJSkpi58kQ3Nt0UL0WeeUiX61cxOeHT6l1nj9P\nHqFV/TpERUXppS569OjRCF1Kj2QyGfZ2dTj7+WZaNfm729zuE0dZ+NVmYvcdUusBXqFQcOjebQa/\nG1DRl14t0AfXIrz22mv/1979xlR1HnAc/13+SMU2WjZlUEVW0bEKYiWptqBjZoGl6LWr0+k2AUWl\nruJw/zpt1hnnmlRGp4tNnS6VP5LObCojgNNsFLFUi0Wl9oIGVrF2IFdWUbjIgHvPXjhxODbmfFBs\nvp9XnJtzTp5zX/DNOeee56iiokIRERFasGCBDhw4oO7ubjmdTiUlJWnjxo1yuVz6Sny8mi9flZeX\nlx6dPEXPbdwsR+VRbVuXoYlR09R4rl7dXV1a9bNMhU97QtvWZShk0hdlX5qmmvfeVV7mJrVfuayr\nLU4lJCTIbreroaFBx48fl6+vr+rq6uTn56fc3Fw99tine5pEAIPjf330KDg4WDU1NTq85deKnXLz\nfmvRO0fkN2yYvvfaL3V6128lSYdPVWn11sze5Zd379K+8rfksTzyG+Gv/QdK/q+pFz9teCvOLcrL\ny+Xt7a3S0lItXLhQubm5mjBhgpqamhQSEqKMjAyVlJSo49o1/WL/IXk8Hu3Y8GNdvHBekvSJ86Ls\nS59TWGSUinJ2as+2LP30jT29+29rvaysjJVa93quAsYEakTjWa1JT1dWVpYsy9KWLVvkcDgUFBSk\nNWvWKDMzU7t27bpXXweA+9jtvPUo88WXlPCjdAUFfFZPRUzRlx+P1qLZ8aqsdfzbmeuN5dyDxTr9\nYb0qt2fLy8tLq3f8SqmpqSouLh70YxvqiGs/LMuSzWZTYWGhioqKlJ+fr9raWkmSy+VSbGys1q5d\nq5eSvq6op2ZpTvIKfW7ceP2tqVGB48YrLPL6zCWh4ZNVum9Pn33XvX9SQeM/r7DIKDk/vqAlS5bI\n3dPT500Wt16Ozs7OHtTjBYDvzl8k5/5DOlx9QuXVJ7T5zTxtfjNPr6St/o/bFB97W8fP1Ch65fVL\nwa3XOuTrP/xuDXlII679sNlscrlcmj59uubPn6+ZM2dq2bJlKigokGVZCg0NVU5+vsrOXlD96VPa\nkLJQy3/ycz006mF5+/j22c+tV90tj6f3s6stzWpqbFRqaqrsdrt8fX21d+9eFRYWSpJycnL6LAPA\nYKioqNDOV7dqxPDhenpGjJ6eEaOXVzyviKWLVP2Xuj7/x7p6enr/drs9emFxstLsz0qSfueoUuzX\n7Hd9/EMRsy33w7IsXbp0Se3t7dq0aZMSExNVVlamrq4uud1ubd++XbuzszXqAV99+/vrNTX2S/qo\n7syNjf/rvidFTVNjw4eqP10tH1ernE6njhw5ori4uME/MADox5gxY7T3YIkOHHun97OPLznV0dmp\nZ2Lj9JHzolpaW2VZlgreLutdJ+GJGfpNcYHaOlxqaW1VQflbSklJufsHMARx5tqPG7OrJCYmKjw8\nXMHBwYqJiVF0dLTq6+uVnJyssrIybduwXv4Br2p08FglJi1XQ61DGuBXdQ89HKAfbNmhHRtekNvV\nprxRI5Wdna2wsDBVVFTcpSMEgJsmTpyoPxQWKn1Fmp7f+or8/R7QyAcf1M4fvqjIR8OUNvdZRact\nUfBnRmvOk7G92y1PfEaNLS2a8Z2lau/s1OTHp3Ib65/4tfAdaG5u1hsHy/WF6bEDr3wLR2mJ1q9M\n4fEaAEPG/tzdSggMlf9tTk7R3tGhP3/yV8371uJBGtn9h8vCdyAwMFDTxo5WU/3Z29ru3MlKzZv1\nJGEFMKTM/eYi7Xm/Uj3/cl91IN09Pfq944TmLv7GII7s/kNc71DC7DiF+vbo/AenBlzXsizVHSvX\nrEkhmjKZZ1cBDC0+Pj5atHqV8k4cVVuHa8D1r7S3K7/6XS1OX8UL02/BZWFDTjtqdPi9k2qzDVNY\n9Ax5+9y8nd3Z0aFzVUcVMMymObPjNG7sI/duoAAwALfbrT/uK9C1xmZFjQ7WxEf6zp9+5sJ5OS47\nNWJskOLn2QlrP4irYVeuXFHhoT/p7z2Wejxu+Xh5a6S/n+xfjZefn9+9Hh4A3Jbqqio1fFAjm+f6\n8/8em00ToiIVMTXqXg9tSCOuAAAYxrk8AACGEVcAAAwjrgAAGEZcAQAwjLgCAGAYcQUAwDDiCgCA\nYcQVAADDiCsAAIYRVwAADCOuAAAYRlwBADCMuAIAYBhxBQDAMOIKAIBhxBUAAMOIKwAAhhFXAAAM\nI64AABhGXAEAMIy4AgBgGHEFAMAw4goAgGHEFQAAw4grAACGEVcAAAwjrgAAGEZcAQAwjLgCAGAY\ncQUAwDDiCgCAYcQVAADDiCsAAIYRVwAADCOuAAAYRlwBADCMuAIAYBhxBQDAMOIKAIBhxBUAAMOI\nKwAAhhFXAAAMI64AABhGXAEAMIy4AgBgGHEFAMAw4goAgGHEFQAAw4grAACG/QOmp2V0tuw+QgAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111686cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "QG=bipartite_graphBuilder(Qsets)\n",
    "bipartite_plot(QG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cars', 'gaming', 'sport', 'pubs']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QG.neighbors('Pete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pete', 'cars', 'Sam']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.shortest_path(QG,'Pete','Sam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Pete', 'cars', 'Sam']\n",
      "['Pete', 'sport', 'Sam']\n",
      "['Pete', 'pubs', 'Sam']\n"
     ]
    }
   ],
   "source": [
    "for p in nx.all_shortest_paths(QG,'Pete','Sam'):\n",
    "    print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Construct a function based on a literal interpretation of the graph\n",
    "def Qnear(B,x,y):\n",
    "    #Treat returning -1 as an error code?\n",
    "    return len([e for e in nx.all_shortest_paths(QG,x,y) if len(e)==3])-1\n",
    "\n",
    "Qnear(QG,'Pete','Sam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1, 1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qnear(QG,'Sam','Sue'), Qnear(QG,'Sue','Jane'), Qnear(QG,'Jane','Tim')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#We can then look for Q-connected items - other than the singletons\n",
    "import itertools\n",
    "\n",
    "def componentFinder(setval):\n",
    "    #Make a graph from connected pairs then find components across the graph\n",
    "    T=nx.Graph()\n",
    "    sets=[]\n",
    "    for (x,y) in setval:\n",
    "        T.add_edge(x,y)\n",
    "\n",
    "    return [c for c in nx.connected_components(T)]\n",
    "        \n",
    "#This is not the same as in the example - the singletons (connected to themselves) are ignored\n",
    "def Qconnected(B,N,X=None,anchor=None):\n",
    "    ''' q-connected for q==N '''\n",
    "    \n",
    "    def allcombos(B,X,N):\n",
    "        qconnectedN=[]\n",
    "        for (x,y) in itertools.combinations(X,2):\n",
    "            if Qnear(B,x,y)>=N:\n",
    "                qconnectedN.append((x,y))\n",
    "        return qconnectedN\n",
    "    \n",
    "    setval=None\n",
    "    \n",
    "    if X is None:\n",
    "        X, Y = nx.bipartite.sets(B)\n",
    "\n",
    "    if anchor is not None and anchor in Y:\n",
    "        setval=allcombos(B,Y,N)\n",
    "    else:\n",
    "        setval=allcombos(B,X,N)\n",
    "        \n",
    "    return componentFinder(setval) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Jane', 'Sue', 'Tim'}, {'Pete', 'Sam'}]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qconnected(QG,1,anchor=\"Sam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Jane', 'Pete', 'Sam', 'Sue', 'Tim'}]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qconnected(QG,0,anchor=\"Sam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Pete', 'Sam'}]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qconnected(QG,2,anchor=\"Sam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qconnected(QG,3,anchor=\"Sam\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### An Improved Model\n",
    "\n",
    "We could hack the above to also return as single set items members in the superset that aren't already returned, but that's a fudge. So how can we do it properly?\n",
    "\n",
    "p. 60-61 suggests a matrix calculation route.\n",
    "\n",
    "We can get the same (I think?) if we use the bipartite \"biadjency\" matrix that describes adjacent (connected) nodes in the projected bipartite graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def nodematrix(B, anchor=None, X=None, Y=None):\n",
    "    if X is None and Y is None:\n",
    "        X, Y = nx.bipartite.sets(B)\n",
    "    if anchor is not None and anchor in X:\n",
    "        X,Y=Y,X\n",
    "    mxb=nx.bipartite.biadjacency_matrix(B, Y, X)\n",
    "    mx=mxb * mxb.T\n",
    "    m2=pd.SparseDataFrame([ pd.SparseSeries(mx[i].toarray().ravel()) \n",
    "                                      for i in np.arange(mx.shape[0]) ])-1\n",
    "    m2.columns=Y\n",
    "    m2=m2[sorted(m2.columns)].rename({[c for c in Y].index(y):y for y in Y}).sort_index()\n",
    "    return m2.replace(-1, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jane</th>\n",
       "      <th>Pete</th>\n",
       "      <th>Sam</th>\n",
       "      <th>Sue</th>\n",
       "      <th>Tim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jane</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pete</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sam</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sue</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tim</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Jane  Pete  Sam  Sue  Tim\n",
       "Jane   3.0   NaN  NaN  1.0  1.0\n",
       "Pete   NaN   3.0  2.0  NaN  NaN\n",
       "Sam    NaN   2.0  3.0  0.0  NaN\n",
       "Sue    1.0   NaN  0.0  3.0  NaN\n",
       "Tim    1.0   NaN  NaN  NaN  3.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This could be used to give us the singletons?\n",
    "NM=nodematrix(QG,anchor=\"Sam\")\n",
    "NM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cars</th>\n",
       "      <th>cooking</th>\n",
       "      <th>fashion</th>\n",
       "      <th>gaming</th>\n",
       "      <th>gardening</th>\n",
       "      <th>history</th>\n",
       "      <th>literature</th>\n",
       "      <th>nature</th>\n",
       "      <th>painting</th>\n",
       "      <th>pubs</th>\n",
       "      <th>science</th>\n",
       "      <th>sport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cars</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cooking</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fashion</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaming</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gardening</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>history</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>literature</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nature</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>painting</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pubs</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>science</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sport</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cars  cooking  fashion  gaming  gardening  history  literature  \\\n",
       "cars         1.0      NaN      0.0     0.0        NaN      NaN         NaN   \n",
       "cooking      NaN      1.0      NaN     NaN        1.0      0.0         0.0   \n",
       "fashion      0.0      NaN      1.0     NaN        NaN      0.0         0.0   \n",
       "gaming       0.0      NaN      NaN     0.0        NaN      NaN         NaN   \n",
       "gardening    NaN      1.0      NaN     NaN        1.0      0.0         0.0   \n",
       "history      NaN      0.0      0.0     NaN        0.0      1.0         1.0   \n",
       "literature   NaN      0.0      0.0     NaN        0.0      1.0         1.0   \n",
       "nature       NaN      0.0      NaN     NaN        0.0      NaN         NaN   \n",
       "painting     NaN      NaN      0.0     NaN        NaN      0.0         0.0   \n",
       "pubs         1.0      NaN      0.0     0.0        NaN      NaN         NaN   \n",
       "science      NaN      0.0      NaN     NaN        0.0      NaN         NaN   \n",
       "sport        1.0      NaN      0.0     0.0        NaN      NaN         NaN   \n",
       "\n",
       "            nature  painting  pubs  science  sport  \n",
       "cars           NaN       NaN   1.0      NaN    1.0  \n",
       "cooking        0.0       NaN   NaN      0.0    NaN  \n",
       "fashion        NaN       0.0   0.0      NaN    0.0  \n",
       "gaming         NaN       NaN   0.0      NaN    0.0  \n",
       "gardening      0.0       NaN   NaN      0.0    NaN  \n",
       "history        NaN       0.0   NaN      NaN    NaN  \n",
       "literature     NaN       0.0   NaN      NaN    NaN  \n",
       "nature         0.0       NaN   NaN      0.0    NaN  \n",
       "painting       NaN       0.0   NaN      NaN    NaN  \n",
       "pubs           NaN       NaN   1.0      NaN    1.0  \n",
       "science        0.0       NaN   NaN      0.0    NaN  \n",
       "sport          NaN       NaN   1.0      NaN    1.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodematrix(QG,anchor=\"cars\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def anotherQnear(NM,x,y):\n",
    "    return NM.ix[x][y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQnear(NM,'Sam','Pete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQnear(NM,'Sam','Sam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def anotherQconnected(NM,N,X=None,anchor=None):\n",
    "    ''' q-connected for q==N '''\n",
    "    \n",
    "    def allcombosDiag(NM,X,N):\n",
    "        qconnectedN=[]\n",
    "        combos=[x for x in itertools.combinations(X,2)]+[(x,x) for x in X]\n",
    "        for (x,y) in combos:\n",
    "            if anotherQnear(NM,x,y)>=N:\n",
    "                qconnectedN.append((x,y))\n",
    "        return qconnectedN\n",
    "\n",
    "    \n",
    "    setval=None\n",
    "    \n",
    "    if X is None:\n",
    "        X = list(NM.columns)\n",
    "\n",
    "    if anchor is not None and anchor in Y:\n",
    "        setval=allcombosDiag(NM,Y,N)\n",
    "    else:\n",
    "        setval=allcombosDiag(NM,X,N)\n",
    "        \n",
    "    return componentFinder(setval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Sue'}, {'Pete', 'Sam'}, {'Jane'}, {'Tim'}]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(NM,2,anchor='Sam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Sue'}, {'Jane'}, {'Pete'}, {'Sam'}, {'Tim'}]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(NM,3,anchor='Sam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Jane', 'Sue', 'Tim'}, {'Pete', 'Sam'}]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(NM,1,anchor='Sam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Jane', 'Pete', 'Sam', 'Sue', 'Tim'}]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(NM,0,anchor='Sam')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Directed Graphs\n",
    "\n",
    "Trading network - directed graph, p. 52-53\n",
    "\n",
    "I'm not sure about the sense of these, in that the information flows one way, so can something be connected to another thing going one way, but not the other? e.g. can we say *x* is `qnear` *y* but *y* is not `qnear` *x*? I need to go back and read the definitions again..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Arg', 'Bar', 'Bol', 'Bra', 'Chi', 'Col', 'Ecu', 'Tri', 'Par', 'Per', 'Uru', 'Ven']\n"
     ]
    }
   ],
   "source": [
    "countries=['Argentina','Barbados','Bolivia','Brazil','Chile','Colombia','Ecuador',\n",
    "           'Trinidad-Tobago','Paraguay', 'Peru','Uruguay','Venezuela']\n",
    "countrycodes=[x[:3] for x in countries]\n",
    "print(countrycodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Argentina': ['Arg', 'Bol', 'Bra', 'Chi', 'Par', 'Uru'],\n",
       " 'Barbados': ['Bar', 'Tri'],\n",
       " 'Bolivia': ['Bol'],\n",
       " 'Brazil': ['Arg',\n",
       "  'Bar',\n",
       "  'Bol',\n",
       "  'Bra',\n",
       "  'Chi',\n",
       "  'Col',\n",
       "  'Ecu',\n",
       "  'Tri',\n",
       "  'Par',\n",
       "  'Per',\n",
       "  'Uru',\n",
       "  'Ven'],\n",
       " 'Chile': ['Arg', 'Bol', 'Chi', 'Par', 'Per', 'Uru'],\n",
       " 'Colombia': ['Bol', 'Chi', 'Col', 'Ecu', 'Per', 'Ven'],\n",
       " 'Ecuador': ['Col', 'Ecu', 'Per'],\n",
       " 'Paraguay': ['Par'],\n",
       " 'Peru': ['Bol', 'Per'],\n",
       " 'Trinidad-Tobago': ['Bar', 'Tri'],\n",
       " 'Uruguay': ['Uru'],\n",
       " 'Venezuela': ['Bol', 'Chi', 'Col', 'Ecu', 'Tri', 'Par', 'Per', 'Ven']}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tlist={\n",
    "    'Argentina': [1,0,1,1,1,0,0,0,1,0,1,0],\n",
    "    'Barbados':  [0,1,0,0,0,0,0,1,0,0,0,0],\n",
    "    'Bolivia':   [0,0,1,0,0,0,0,0,0,0,0,0],\n",
    "    'Brazil':    [1,1,1,1,1,1,1,1,1,1,1,1],\n",
    "    'Chile':     [1,0,1,0,1,0,0,0,1,1,1,0],\n",
    "    'Colombia':  [0,0,1,0,1,1,1,0,0,1,0,1],\n",
    "    'Ecuador':   [0,0,0,0,0,1,1,0,0,1,0,0],\n",
    "    'Trinidad-Tobago': [0,1,0,0,0,0,0,1,0,0,0,0],\n",
    "    'Paraguay':  [0,0,0,0,0,0,0,0,1,0,0,0],\n",
    "    'Peru':      [0,0,1,0,0,0,0,0,0,1,0,0],\n",
    "    'Uruguay':   [0,0,0,0,0,0,0,0,0,0,1,0],\n",
    "    'Venezuela': [0,0,1,0,1,1,1,1,1,1,0,1]\n",
    "}\n",
    "\n",
    "Tset={k:[c[1] for c in zip(Tlist[k],countrycodes) if c[0]] for k in Tlist}\n",
    "Tset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YMmSIcDRcV1eHU3sC8JG1A1SUxK9ZAwCnuhrB6YnwWuT7RnxebxJUXKVEUkoq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11189dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BD=bipartite_graphBuilder(Tset,undirected=False)\n",
    "\n",
    "bipartite_plot(BD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Arg</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Bol</th>\n",
       "      <th>Bra</th>\n",
       "      <th>Chi</th>\n",
       "      <th>Col</th>\n",
       "      <th>Ecu</th>\n",
       "      <th>Par</th>\n",
       "      <th>Per</th>\n",
       "      <th>Tri</th>\n",
       "      <th>Uru</th>\n",
       "      <th>Ven</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbados</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ecuador</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paraguay</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peru</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad-Tobago</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Arg  Bar  Bol  Bra  Chi  Col  Ecu  Par  Per  Tri  Uru  Ven\n",
       "Argentina          1    0    1    1    1    0    0    1    0    0    1    0\n",
       "Barbados           0    1    0    0    0    0    0    0    0    1    0    0\n",
       "Bolivia            0    0    1    0    0    0    0    0    0    0    0    0\n",
       "Brazil             1    1    1    1    1    1    1    1    1    1    1    1\n",
       "Chile              1    0    1    0    1    0    0    1    1    0    1    0\n",
       "Colombia           0    0    1    0    1    1    1    0    1    0    0    1\n",
       "Ecuador            0    0    0    0    0    1    1    0    1    0    0    0\n",
       "Paraguay           0    0    0    0    0    0    0    1    0    0    0    0\n",
       "Peru               0    0    1    0    0    0    0    0    1    0    0    0\n",
       "Trinidad-Tobago    0    1    0    0    0    0    0    0    0    1    0    0\n",
       "Uruguay            0    0    0    0    0    0    0    0    0    0    1    0\n",
       "Venezuela          0    0    1    0    1    1    1    1    1    1    0    1"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretty_matrix(BD, Y=countries, X=countrycodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Barbados</th>\n",
       "      <th>Bolivia</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>Chile</th>\n",
       "      <th>Colombia</th>\n",
       "      <th>Ecuador</th>\n",
       "      <th>Paraguay</th>\n",
       "      <th>Peru</th>\n",
       "      <th>Trinidad-Tobago</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Venezuela</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbados</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ecuador</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paraguay</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peru</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad-Tobago</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Argentina  Barbados  Bolivia  Brazil  Chile  Colombia  \\\n",
       "Argentina              5.0       NaN      0.0       5    4.0       1.0   \n",
       "Barbados               NaN       1.0      NaN       1    NaN       NaN   \n",
       "Bolivia                0.0       NaN      0.0       0    0.0       0.0   \n",
       "Brazil                 5.0       1.0      0.0      11    5.0       5.0   \n",
       "Chile                  4.0       NaN      0.0       5    5.0       2.0   \n",
       "Colombia               1.0       NaN      0.0       5    2.0       5.0   \n",
       "Ecuador                NaN       NaN      NaN       2    0.0       2.0   \n",
       "Paraguay               0.0       NaN      NaN       0    0.0       NaN   \n",
       "Peru                   0.0       NaN      0.0       1    1.0       1.0   \n",
       "Trinidad-Tobago        NaN       1.0      NaN       1    NaN       NaN   \n",
       "Uruguay                0.0       NaN      NaN       0    0.0       NaN   \n",
       "Venezuela              2.0       0.0      0.0       7    3.0       5.0   \n",
       "\n",
       "                 Ecuador  Paraguay  Peru  Trinidad-Tobago  Uruguay  Venezuela  \n",
       "Argentina            NaN       0.0   0.0              NaN      0.0        2.0  \n",
       "Barbados             NaN       NaN   NaN              1.0      NaN        0.0  \n",
       "Bolivia              NaN       NaN   0.0              NaN      NaN        0.0  \n",
       "Brazil               2.0       0.0   1.0              1.0      0.0        7.0  \n",
       "Chile                0.0       0.0   1.0              NaN      0.0        3.0  \n",
       "Colombia             2.0       NaN   1.0              NaN      NaN        5.0  \n",
       "Ecuador              2.0       NaN   0.0              NaN      NaN        2.0  \n",
       "Paraguay             NaN       0.0   NaN              NaN      NaN        0.0  \n",
       "Peru                 0.0       NaN   1.0              NaN      NaN        1.0  \n",
       "Trinidad-Tobago      NaN       NaN   NaN              1.0      NaN        0.0  \n",
       "Uruguay              NaN       NaN   NaN              NaN      0.0        NaN  \n",
       "Venezuela            2.0       0.0   1.0              0.0      NaN        7.0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ND=nodematrix(BD,anchor=\"Peru\")\n",
    "ND"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([{'Brazil', 'Venezuela'}], [{'Brazil', 'Venezuela'}])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(ND,7,anchor='Brazil'), anotherQconnected(ND,6,anchor='Brazil')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([{'Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'}],\n",
       " [{'Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'}])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(ND,5,anchor='Brazil'), anotherQconnected(ND,3,anchor='Brazil')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Argentina', 'Brazil', 'Chile', 'Colombia', 'Ecuador', 'Venezuela'}]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(ND,2,anchor='Brazil')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Argentina',\n",
       "  'Barbados',\n",
       "  'Brazil',\n",
       "  'Chile',\n",
       "  'Colombia',\n",
       "  'Ecuador',\n",
       "  'Peru',\n",
       "  'Trinidad-Tobago',\n",
       "  'Venezuela'}]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(ND,1,anchor='Brazil')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Argentina',\n",
       "  'Barbados',\n",
       "  'Bolivia',\n",
       "  'Brazil',\n",
       "  'Chile',\n",
       "  'Colombia',\n",
       "  'Ecuador',\n",
       "  'Paraguay',\n",
       "  'Peru',\n",
       "  'Trinidad-Tobago',\n",
       "  'Uruguay',\n",
       "  'Venezuela'}]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(ND,0,anchor='Brazil')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove a  Node - Brazil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Barbados</th>\n",
       "      <th>Bolivia</th>\n",
       "      <th>Chile</th>\n",
       "      <th>Colombia</th>\n",
       "      <th>Ecuador</th>\n",
       "      <th>Paraguay</th>\n",
       "      <th>Peru</th>\n",
       "      <th>Trinidad-Tobago</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Venezuela</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ecuador</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paraguay</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peru</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad-Tobago</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Argentina  Barbados  Bolivia  Chile  Colombia  Ecuador  \\\n",
       "Argentina              5.0       NaN      0.0    4.0       1.0      NaN   \n",
       "Barbados               NaN       1.0      NaN    NaN       NaN      NaN   \n",
       "Bolivia                0.0       NaN      0.0    0.0       0.0      NaN   \n",
       "Chile                  4.0       NaN      0.0    5.0       2.0      0.0   \n",
       "Colombia               1.0       NaN      0.0    2.0       5.0      2.0   \n",
       "Ecuador                NaN       NaN      NaN    0.0       2.0      2.0   \n",
       "Paraguay               0.0       NaN      NaN    0.0       NaN      NaN   \n",
       "Peru                   0.0       NaN      0.0    1.0       1.0      0.0   \n",
       "Trinidad-Tobago        NaN       1.0      NaN    NaN       NaN      NaN   \n",
       "Uruguay                0.0       NaN      NaN    0.0       NaN      NaN   \n",
       "Venezuela              2.0       0.0      0.0    3.0       5.0      2.0   \n",
       "\n",
       "                 Paraguay  Peru  Trinidad-Tobago  Uruguay  Venezuela  \n",
       "Argentina             0.0   0.0              NaN      0.0        2.0  \n",
       "Barbados              NaN   NaN              1.0      NaN        0.0  \n",
       "Bolivia               NaN   0.0              NaN      NaN        0.0  \n",
       "Chile                 0.0   1.0              NaN      0.0        3.0  \n",
       "Colombia              NaN   1.0              NaN      NaN        5.0  \n",
       "Ecuador               NaN   0.0              NaN      NaN        2.0  \n",
       "Paraguay              0.0   NaN              NaN      NaN        0.0  \n",
       "Peru                  NaN   1.0              NaN      NaN        1.0  \n",
       "Trinidad-Tobago       NaN   NaN              1.0      NaN        0.0  \n",
       "Uruguay               NaN   NaN              NaN      0.0        NaN  \n",
       "Venezuela             0.0   1.0              0.0      NaN        7.0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BD.remove_node('Brazil')\n",
    "NDx=nodematrix(BD,anchor=\"Peru\")\n",
    "NDx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([{'Venezuela'}], [{'Venezuela'}])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(NDx,7,anchor='Chile'), anotherQconnected(NDx,6,anchor='Chile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Chile'}, {'Argentina'}, {'Colombia', 'Venezuela'}]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(NDx,5,anchor='Chile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Argentina', 'Chile'}, {'Colombia', 'Venezuela'}]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(NDx,4,anchor='Chile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Argentina', 'Chile', 'Colombia', 'Venezuela'}]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(NDx,3,anchor='Chile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Argentina', 'Chile', 'Colombia', 'Ecuador', 'Venezuela'}]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(NDx,2,anchor='Chile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Barbados', 'Trinidad-Tobago'},\n",
       " {'Argentina', 'Chile', 'Colombia', 'Ecuador', 'Peru', 'Venezuela'}]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(NDx,1,anchor='Chile') #This is different to JJ's?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Argentina',\n",
       "  'Barbados',\n",
       "  'Bolivia',\n",
       "  'Chile',\n",
       "  'Colombia',\n",
       "  'Ecuador',\n",
       "  'Paraguay',\n",
       "  'Peru',\n",
       "  'Trinidad-Tobago',\n",
       "  'Uruguay',\n",
       "  'Venezuela'}]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(NDx,0,anchor='Chile')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How about importing countries?\n",
    "What happens if we reverse the edges and rerun the analysis? Is that the sense of this (p54)?\n",
    "\n",
    "Sense of reading the arrows is now more along lines of \"gives money to, for exports\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Arg': ['Brazil', 'Chile', 'Argentina'],\n",
       " 'Bar': ['Brazil', 'Barbados', 'Trinidad-Tobago'],\n",
       " 'Bol': ['Bolivia',\n",
       "  'Venezuela',\n",
       "  'Brazil',\n",
       "  'Colombia',\n",
       "  'Chile',\n",
       "  'Argentina',\n",
       "  'Peru'],\n",
       " 'Bra': ['Brazil', 'Argentina'],\n",
       " 'Chi': ['Venezuela', 'Brazil', 'Colombia', 'Chile', 'Argentina'],\n",
       " 'Col': ['Ecuador', 'Venezuela', 'Brazil', 'Colombia'],\n",
       " 'Ecu': ['Ecuador', 'Venezuela', 'Brazil', 'Colombia'],\n",
       " 'Par': ['Venezuela', 'Brazil', 'Chile', 'Argentina', 'Paraguay'],\n",
       " 'Per': ['Ecuador', 'Venezuela', 'Brazil', 'Colombia', 'Chile', 'Peru'],\n",
       " 'Tri': ['Venezuela', 'Brazil', 'Barbados', 'Trinidad-Tobago'],\n",
       " 'Uru': ['Brazil', 'Uruguay', 'Chile', 'Argentina'],\n",
       " 'Ven': ['Venezuela', 'Brazil', 'Colombia']}"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tset2={c:[] for c in countrycodes}\n",
    "for c in Tset:\n",
    "    for c2 in Tset[c]:\n",
    "        Tset2[c2].append(c)\n",
    "Tset2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EiF80KtRxsVgshIWF8bqAP3z4UGu3mapdZaoKqqB2lWkrghgzJkUlsOupA8km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\nV65cwbJly1BcXIy1a9di7NixOHjwoMrdaTpLZ0Zi2ht1URQ96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u6Apb0DVrw/GTr6BshI\nvouSggJoaGhAIBDAyMgIL730ktwqW4qmEPL843A4MDU1hampKV566SWFx9TU1DR79ZuVlYXMzExU\nVVWpfA5O1jb4+1YcPtm6AXd++hUAcDkhVvZ5QUkx5nwfgoLSYjwuLoKdmQV+/yYExgYGuHI7Hp9t\n24hyUTXORV2RW4G9e/du/PDDD+Dz+TAzM8P27dvRu3dvzJo1C8XFxXj48CHGjRuHkJAQlc+9O6Li\nqiKRWAyOguHVlPibePwoA1wuF9r6+gj+9RQAYNfKJTj780/4IHANAEDXwABrDx2FJOUmxoweTdEU\nQkiLNDQ04OjoCEdHR4W3MwyDkpISWcGdOHEijI2NIRaLIRKJIBKJWtzoPym9LqrT+A18/ee/XojA\nUFc3fPXuDADA60sW4OD5M/jkzXfwzsql+GX5OjzR4EJk0hN79+4FAFy4cAEbN25EdHQ0DA0NsX//\nfkyYMAFJSUkA6hZY3blzp30/mG6KiquKeE8La021sK7lHMNAIpZAz9AICzbugMfwkbB2dMKZQ/vw\n+FEGkq7/A+eB/74j7TdoCMS1tfAeMkSu3yIhhKiCw+HA0NAQhoaG8PCo2+c8JSVFrktVffRo48aN\nSEhIQEBAgGzNhoexRYuP/9lb7+Lq7QRs/uMwUrMfISn9IbxcXHHnYRrU1dQwcuAgnLp3C5MnT5Y9\nZ3h4OKZMmSLLzM+cORMLFixAZmYmgLqG6s8rKq4qMuqpj7zysiZzrvXO/bIfkX/8jIBpszHijUnQ\n0TdAYU6W7PYeWtp4IsiHpZtXZ542IeQF0ni9qpqaGuzs7NCnTx88efIES5cuBVA35Lz32+/rtjds\ncB9Rg4jR4l0/4GbKXcweOx6+A19CrVgMhqnbErF+GFj89KK3PqqnaIMOqVQqu4J+npMJtGxURa++\n8gry7t1uttXTrajL8H1zCnzfehcWdr1w8+J5SCXyf2j8yrrFS4QQ0plGjRqFyMhI5OfnAwBCQ0Nx\n6Mwp9FDXwKOCxxCUloJhGBy/ekl2n4gb0VgweSqmjR4LY30DnL95HRKpFAN61Q1T/3bhPAzsrHHm\nzBkIBAIAwJgxY/Dbb7/JPv/pp59gbGyM3r17d+433AXoylVFPB4PRj34zS4wmjB7LkJXfI1LJ45A\n16AnXn7NH3GX/wegbvimuqoSfZxsOvOUCSEvEA6Hg1GjRsmuIuuvMoODg+Hv748NGzZgzJgx4HA4\nsLCwwB9/HsGN349jzhuTMGjODFgamWCc97/DtitmfohFO7ci+NB/YdqzJ94e+SrScrLA5/NxbM0G\nTFkbBP1Txhg4cKCsschrr72GhQsXwtfXFwzDwMTEBKdPn5ad3/OMcq7tkJ+fj33hf8N5SNvnDZIu\nnMGyj/+PdjoihHQbxw4cwhgze2i1MdJXUVWF/xXnYMK0qR10Zs8eGhZuBzMzM3hamyAvLaVN90uP\nv44JI7ypsBJCupU33nsXv92+3qbtHGvFYhxJisMbU6d04Jk9e6i4ttMY35GwVxMjMzGh1WMZhkFq\n9N8Y0ccWbv1dOv7kCCGkDfh8Pt79ZB4Oxl1DeVVlq8eXVVTg51sxmPrpPNr5rREaFmbJnaS7uHwz\nHuUcdfQe5AUe/9/p7OqqKqTHXoOhOgfjfEfCxtqq606UEEJaIZFIcO7ocQhz8+FuYgknK/n905Oz\nMpFUUgBtawv4TWi+e9eLjIory8rKynAyIhI1YgZiqQR8Lg/6WhoY7+8HDQ2Nrj49Qghpk1uxschI\nvAuO9GnshsOBo/sAuHo03aGO/IuKKyGEEMIyupYnhBBCWEbFlRBCCGEZFVdCCCGEZVRcCSGEEJZR\ncSWEEEJYRsWVEEIIYRkVV0IIIYRlVFwJIYQQllFxJYQQQlhGxZUQQghhGRVXQgghhGVUXAkhhBCW\nUXElhBBCWEbFlRBCCGEZFVdCCCGEZVRcCSGEEJZRcSWEEEJYRsWVEEIIYRkVV0IIIYRlVFwJIYQQ\nllFxJYQQQlhGxZUQQghhGRVXQgghhGVUXAkhhBCWUXElhBBCWEbFlRBCCGEZFVdCCCGEZVRcCSGE\nEJZRcSWEEEJYRsWVEEIIYRkVV0IIIYRlVFwJIYQQllFxJYQQQlhGxZUQQghhGRVXQgghhGVUXAkh\nhBCWUXElhBBCWEbFlRBCCGEZFVdCCCGEZVRcCSGEEJZRcSWEEEJYRsWVEEIIYRkVV0IIIYRlVFwJ\nIYQQllFxJYQQQlhGxZUQQghhGRVXQgghhGVUXAkhhBCWUXElhBBCWPb/SwUxXd0JLcUAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1114b2400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BD2=bipartite_graphBuilder(Tset2,undirected=False)\n",
    "\n",
    "bipartite_plot(BD2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Arg</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Bol</th>\n",
       "      <th>Bra</th>\n",
       "      <th>Chi</th>\n",
       "      <th>Col</th>\n",
       "      <th>Ecu</th>\n",
       "      <th>Par</th>\n",
       "      <th>Per</th>\n",
       "      <th>Tri</th>\n",
       "      <th>Uru</th>\n",
       "      <th>Ven</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arg</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bar</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bol</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bra</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chi</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ecu</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Par</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Per</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tri</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uru</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ven</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Arg  Bar  Bol  Bra  Chi  Col  Ecu  Par  Per  Tri  Uru  Ven\n",
       "Arg    2    0    2    1    2    0    0    2    1    0    2    0\n",
       "Bar    0    2    0    0    0    0    0    0    0    2    0    0\n",
       "Bol    2    0    6    1    4    2    2    3    4    1    2    2\n",
       "Bra    1    0    1    1    1    0    0    1    0    0    1    0\n",
       "Chi    2    0    4    1    4    2    2    3    3    1    2    2\n",
       "Col    0    0    2    0    2    3    3    1    3    1    0    2\n",
       "Ecu    0    0    2    0    2    3    3    1    3    1    0    2\n",
       "Par    2    0    3    1    3    1    1    4    2    1    2    1\n",
       "Per    1    0    4    0    3    3    3    2    5    1    1    2\n",
       "Tri    0    2    1    0    1    1    1    1    1    3    0    1\n",
       "Uru    2    0    2    1    2    0    0    2    1    0    3    0\n",
       "Ven    0    0    2    0    2    2    2    1    2    1    0    2"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ND2=nodematrix(BD2,anchor=\"Per\")\n",
    "ND2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Bol'}]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(ND2,6,anchor='Bra')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Bol'}, {'Per'}]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(ND2,5,anchor='Bra')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Par'}, {'Bol', 'Chi', 'Per'}]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(ND2,4,anchor='Bra')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Bol', 'Chi', 'Col', 'Ecu', 'Par', 'Per'}, {'Uru'}, {'Tri'}]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(ND2,3,anchor='Bra') # Differs from JJ's?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Arg', 'Bol', 'Chi', 'Col', 'Ecu', 'Par', 'Per', 'Uru', 'Ven'},\n",
       " {'Bar', 'Tri'}]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(ND2,2,anchor='Bra') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Arg',\n",
       "  'Bar',\n",
       "  'Bol',\n",
       "  'Bra',\n",
       "  'Chi',\n",
       "  'Col',\n",
       "  'Ecu',\n",
       "  'Par',\n",
       "  'Per',\n",
       "  'Tri',\n",
       "  'Uru',\n",
       "  'Ven'}]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherQconnected(ND2,1,anchor='Bra') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'Bol', 'Chi', 'Per'}, {'Brazil', 'Chile', 'Colombia', 'Venezuela'})"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GaloisPair(BD2,{'Bol','Chi','Per'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eccentricity\n",
    "\n",
    "p. 59"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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YCutRRgZy4lOEWjB9QFdDnLseh9aysmjfoUOdztsYseDKMEyDk5+fj8jISISE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\nIiJiMIWriIiIwRSuIiIiBvs/Wqg2YGleGTYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1114a52e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "glasses={'g1':{'ThinStem','Small','TulipShape','Narrow','Curved'},\n",
    "         'g2':{'ThinStem','Tall','CupShape','Wide','Curved','Logo'},\n",
    "         'g3':{'ThinStem','Tall','TulipShape','Narrow','Curved'},\n",
    "         'g4':{'FatStem','Tall','VeeShape','Narrow','Straight'},\n",
    "         'g5':{'FatStem','Small','VeeShape','Narrow','Straight'},\n",
    "         'g6':{'ThinStem','Small','TubeShape','Narrow','Straight'}\n",
    "        }\n",
    "\n",
    "GL=bipartite_graphBuilder(glasses)\n",
    "bipartite_plot(GL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def _ecc(B,x,y):\n",
    "     return  len(set(B[x].keys())-set(B[y].keys())) / len(B[x].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.2, 0.5, 0.2, 0.2, 0.2, 0.4)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_ecc(GL,'g1','g3'), _ecc(GL,'g2','g3'), _ecc(GL,'g3','g1'), _ecc(GL,'g4','g5'), _ecc(GL,'g5','g4'), _ecc(GL,'g6','g1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def _eccg(B,x):\n",
    "    X, Y = nx.bipartite.sets(B)\n",
    "    if x in Y: X,Y=Y,X\n",
    "    return min([_ecc(B,x,z) for z in X if z!=x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.2, 0.5, 0.2, 0.2, 0.2, 0.4)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_eccg(GL,'g1'), _eccg(GL,'g2'), _eccg(GL,'g3'), _eccg(GL,'g4'), _eccg(GL,'g5'), _eccg(GL,'g6')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ecc(B,x,y=None):\n",
    "    if y is None: return _eccg(B,x)\n",
    "    return _ecc(B,x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.2, 0.5)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecc(GL,'g1'), ecc(GL,'g2','g3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def qAnalysis(B,anchor):\n",
    "    X, Y = nx.bipartite.sets(B)\n",
    "    if anchor is not None and anchor in X:\n",
    "        X,Y=Y,X\n",
    "    N=nodematrix(B,anchor=anchor, X=X, Y=Y)\n",
    "    S=len(Y)\n",
    "    for i in range(S-1,-1, -1):\n",
    "        print('{}: {}'.format(i, anotherQconnected(N,i,anchor=anchor)))\n",
    "    for y in sorted(list(Y)):\n",
    "        print('Eccentricity of {}: {}'.format(y, ecc(B,y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5: [{'g2'}]\n",
      "4: [{'g2'}, {'g5'}, {'g4'}, {'g6'}, {'g3'}, {'g1'}]\n",
      "3: [{'g2'}, {'g5', 'g4'}, {'g6'}, {'g3', 'g1'}]\n",
      "2: [{'g2', 'g5', 'g4', 'g6', 'g3', 'g1'}]\n",
      "1: [{'g2', 'g5', 'g4', 'g6', 'g3', 'g1'}]\n",
      "0: [{'g2', 'g5', 'g4', 'g6', 'g3', 'g1'}]\n",
      "Eccentricity of g1: 0.2\n",
      "Eccentricity of g2: 0.5\n",
      "Eccentricity of g3: 0.2\n",
      "Eccentricity of g4: 0.2\n",
      "Eccentricity of g5: 0.2\n",
      "Eccentricity of g6: 0.4\n"
     ]
    }
   ],
   "source": [
    "qAnalysis(GL,'g1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}