Notebook_Chapter_I_FoodWebs/Chapter_I_FoodWebs_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter I - Food Webs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the Adjacency Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "adjacency_matrix=[\n",
    "                  [0,1,0,1],\n",
    "                  [1,0,1,1],\n",
    "                  [0,1,0,0],\n",
    "                  [1,1,0,0]\n",
    "                  ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Browsing the rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 0, 1]\n",
      "[1, 0, 1, 1]\n",
      "[0, 1, 0, 0]\n",
      "[1, 1, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "for row in adjacency_matrix:\n",
    "    print row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Browsing the link information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 0 1 \r\n",
      "1 0 1 1 \r\n",
      "0 1 0 0 \r\n",
      "1 1 0 0 \r\n"
     ]
    }
   ],
   "source": [
    "for row in adjacency_matrix:\n",
    "    for a_ij in row:\n",
    "        print a_ij,\n",
    "    print \"\\r\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Directed Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#in the case of directed networks the adjacency matrix is not symetric, like for Food Wes\n",
    "#if a non zero element is present in row 2, column 3, this means there is an arc (directed edge)\n",
    "#from node 2 toward node 3\n",
    "adjacency_matrix_directed=[\n",
    "                  [0,1,0,1],\n",
    "                  [0,0,1,0],\n",
    "                  [0,0,0,1],\n",
    "                  [0,0,0,0]\n",
    "                  ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of species 4\n",
      "number of predations 4\n",
      "classes Basal, Top, Intermediate:  1 1 2\n",
      "connectance 0.25\n"
     ]
    }
   ],
   "source": [
    "#the number of species is the number of rows or columns of \n",
    "#the adjacency matrix\n",
    "num_species=len(adjacency_matrix_directed[0])\n",
    "\n",
    "#the number of links or predations is the non zero elements \n",
    "#of the adjacency matrix (this holds for directed graphs\n",
    "num_predations=0\n",
    "for i in range(num_species):\n",
    "    for j in range(num_species):\n",
    "        if adjacency_matrix_directed[i][j]!=0:\n",
    "            num_predations=num_predations+1\n",
    "\n",
    "#to check if a specie is a Basal (B), an Intermediate (I) or\n",
    "#a Top (T) one  we have to check the presence of 1s both in \n",
    "#the row and in the column of each specie\n",
    "row_count=[0,0,0,0]\n",
    "column_count=[0,0,0,0]\n",
    "for i in range(num_species):\n",
    "    for j in range(num_species):\n",
    "        row_count[i]=row_count[i]+adjacency_matrix_directed[i][j]\n",
    "        column_count[j]=column_count[j]+ \\\n",
    "        adjacency_matrix_directed[i][j]\n",
    "\n",
    "number_B=0\n",
    "number_I=0\n",
    "number_T=0\n",
    "\n",
    "for n in range(num_species):\n",
    "    if row_count[n]==0:\n",
    "        number_T+=1\n",
    "        continue\n",
    "    if column_count[n]==0:\n",
    "        number_B+=1\n",
    "        continue\n",
    "    else:\n",
    "        number_I+=1\n",
    "   \n",
    "print \"number of species\", num_species\n",
    "print \"number of predations\", num_predations\n",
    "print \"classes Basal, Top, Intermediate: \",number_B,number_T,number_I\n",
    "print \"connectance\", float(num_predations)/float(num_species**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "degree of node 2: 3\n",
      "out_degree node 3: 1\n",
      "in_degree node 4: 2\n"
     ]
    }
   ],
   "source": [
    "#for the undirected network\n",
    "degree_node_2=0\n",
    "for j in adjacency_matrix[1]:\n",
    "    degree_node_2=degree_node_2+j\n",
    "print \"degree of node 2:\",degree_node_2\n",
    "\n",
    "#and for the directed case we already calculated the sum over \n",
    "#the rows and columns for the adjacency_matrix_directed\n",
    "out_degree_node_3=row_count[2]\n",
    "in_degree_node_4=column_count[3]\n",
    "\n",
    "print \"out_degree node 3:\",out_degree_node_3\n",
    "print \"in_degree node 4:\",in_degree_node_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Degree in Networkx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "import networkx as nx\n",
    "\n",
    "#generate an empty graph\n",
    "G=nx.Graph()\n",
    "\n",
    "#define the nodes\n",
    "G.add_node(1)\n",
    "G.add_node(2)\n",
    "G.add_node(3)\n",
    "G.add_node(4)\n",
    "\n",
    "#link the nodes\n",
    "G.add_edge(1,2)\n",
    "G.add_edge(1,4)\n",
    "G.add_edge(2,3)\n",
    "G.add_edge(2,4)\n",
    "\n",
    "#degree of the node 2\n",
    "print G.degree(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Degree sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 1, 2]\n"
     ]
    }
   ],
   "source": [
    "degree_sequence=[]\n",
    "for row in range(len(adjacency_matrix)):\n",
    "    degree=0\n",
    "    for j in adjacency_matrix[row]:\n",
    "        degree=degree+j\n",
    "    degree_sequence.append(degree)\n",
    "\n",
    "print degree_sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# this command is to activate the graphic interface\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEACAYAAAB4ayemAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIkSZIkSZIkSZIkSZI0+/4NBq7U7OQqawMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e75ff0c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist([1,1,1,1,1,1,1,2,2,2,3,3,4,5],bins=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering Coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "[0, 2, 3]\n",
      "1.0\n",
      "0.333333333333\n"
     ]
    }
   ],
   "source": [
    "row=1 #stands for the node 2\n",
    "node_index_count=0\n",
    "node_index_list=[]\n",
    "for a_ij in adjacency_matrix[row]:\n",
    "    if a_ij==1:\n",
    "        node_index_list.append(node_index_count)\n",
    "    node_index_count=node_index_count+1    \n",
    "print \"\\r\"\n",
    "\n",
    "print node_index_list\n",
    "\n",
    "#then we will check for all the possible neighbours couples if a link actually exist:\n",
    "\n",
    "neighb_conn=0\n",
    "for n1 in node_index_list:\n",
    "    for n2 in node_index_list:\n",
    "        if adjacency_matrix[n1][n2]==1:\n",
    "            neighb_conn=neighb_conn+1\n",
    "   \n",
    "#we have indeed counted them twice...\n",
    "neighb_conn=neighb_conn/2.0\n",
    "\n",
    "print neighb_conn\n",
    "\n",
    "#Finally the clustering coefficient for node '2' is given by the expression:\n",
    "\n",
    "clustering_coefficient=neighb_conn/ \\\n",
    "(degree_node_2*(degree_node_2-1)/2.0) \n",
    "\n",
    "print clustering_coefficient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating the bowtie stucture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAFBCAYAAABXdIcpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e75e2b990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#loading the network\n",
    "file_name=\"./data/Ythan_Estuary.txt\"\n",
    "\n",
    "DG = nx.DiGraph()\n",
    "\n",
    "in_file=open(file_name,'r')\n",
    "while True:\n",
    "    next_line=in_file.readline()\n",
    "    if not next_line:\n",
    "        break\n",
    "    next_line_fields=next_line[:-2].split(' ')\n",
    "    node_a=next_line_fields[1] #there is a space in the beginning \n",
    "                               #of each edge\n",
    "    node_b=next_line_fields[2]\n",
    "    DG.add_edge(node_a, node_b)\n",
    "\n",
    "#deleting the environment\n",
    "DG.remove_node('0')\n",
    "\n",
    "#getting the biggest strongly connected component\n",
    "scc=[(len(c),c) for c in sorted( nx.strongly_connected_components \\\n",
    "                               (DG), key=len, reverse=True)][0][1]\n",
    "\n",
    "#preparing the IN and OUT component\n",
    "IN_component=[]\n",
    "for n in scc:\n",
    "    for s in DG.predecessors(n):\n",
    "        if s in scc: continue\n",
    "        if not s in IN_component:\n",
    "            IN_component.append(s)\n",
    "            \n",
    "OUT_component=[]\n",
    "for n in scc:\n",
    "    for s in DG.successors(n):\n",
    "        if s in scc: continue\n",
    "        if not s in OUT_component:\n",
    "            OUT_component.append(s)\n",
    "\n",
    "#generating the subgraph\n",
    "bowtie=scc+IN_component+OUT_component\n",
    "DG_bowtie = DG.subgraph(bowtie)\n",
    "\n",
    "#defining the proper layout\n",
    "pos={}\n",
    "in_y=100.\n",
    "pos['89']=(150.,in_y)\n",
    "\n",
    "in_step=700.\n",
    "for in_n in IN_component:\n",
    "    pos[in_n]=(100.,in_y)\n",
    "    in_y=in_y+in_step\n",
    "\n",
    "out_y=100.\n",
    "out_step=500.   \n",
    "for out_n in OUT_component:\n",
    "    pos[out_n]=(200,out_y)\n",
    "    out_y=out_y+out_step\n",
    "\n",
    "pos['90']=(150.,out_y)\n",
    "    \n",
    "#plot the bowtie structure\n",
    "nx.draw(DG_bowtie, pos, node_size=50)\n",
    "\n",
    "nx.draw_networkx_nodes(DG_bowtie, pos, IN_component, \\\n",
    "                       node_size=100, node_color='Black')\n",
    "nx.draw_networkx_nodes(DG_bowtie, pos, OUT_component, \\\n",
    "                       node_size=100, node_color='White')\n",
    "nx.draw_networkx_nodes(DG_bowtie, pos, scc, \\\n",
    "                       node_size=200, node_color='Grey')\n",
    "\n",
    "savefig('./data/bowtie.png',dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distance with Breadth First Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create the undirected graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['C', 'B']\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAFBCAYAAAA2bKVrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe786d78910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating the graph\n",
    "G=nx.Graph()\n",
    "G.add_edges_from([('A','B'),('A','C'),('C','D'),('C','E'),('D','F'),\n",
    "('D','H'),('D','G'),('E','H'),('E','I')])\n",
    "\n",
    "#printing the neighbors of the node 'A'\n",
    "print G.neighbors('A')\n",
    "\n",
    "nx.draw(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A 0\n",
      "C 1\n",
      "B 1\n",
      "E 2\n",
      "D 2\n",
      "G 3\n",
      "F 3\n",
      "I 3\n",
      "H 3\n"
     ]
    }
   ],
   "source": [
    "root_node='A'\n",
    "queue=[]\n",
    "queue.append('A')\n",
    "G.node['A'][\"distance\"]=0\n",
    "while len(queue):\n",
    "    working_node=queue.pop(0)\n",
    "    for n in G.neighbors(working_node):\n",
    "        if len(G.node[n])==0:\n",
    "            G.node[n][\"distance\"]=G.node[working_node][\"distance\"]+1\n",
    "            queue.append(n)\n",
    "for n in G.nodes():\n",
    "    print n,G.node[n][\"distance\"]\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the file with Food Web data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 11\n",
      "0 61\n",
      "0 80\n",
      "0 123\n",
      "0 124\n",
      "0 125\n",
      "0 126\n",
      "0 127\n",
      "0 128\n",
      "0 129\n",
      "0 130\n",
      "0 131\n",
      "0 132\n",
      "0 133\n",
      "0 134\n",
      "0 135\n",
      "0 136\n",
      "0 137\n",
      "0 138\n",
      "0 139\n",
      "0 140\n",
      "0 141\n",
      "0 142\n",
      "0 143\n",
      "0 144\n",
      "0 145\n",
      "0 146\n",
      "0 147\n",
      "0 148\n",
      "0 149\n",
      "0 150\n",
      "0 151\n",
      "0 152\n",
      "0 153\n",
      "0 154\n",
      "0 155\n",
      "0 156\n",
      "0 157\n",
      "0 158\n",
      "0 159\n",
      "0 160\n",
      "0 161\n",
      "0 162\n",
      "0 163\n",
      "0 164\n",
      "0 165\n",
      "0 166\n",
      "0 167\n",
      "0 168\n",
      "0 169\n",
      "0 170\n",
      "0 171\n",
      "0 172\n",
      "0 173\n",
      "0 174\n",
      "0 175\n",
      "0 176\n",
      "0 177\n",
      "0 178\n",
      "0 179\n",
      "0 180\n",
      "0 181\n",
      "0 182\n",
      "1 2\n",
      "1 4\n",
      "1 6\n",
      "1 8\n",
      "1 9\n",
      "1 13\n",
      "1 14\n",
      "2 4\n",
      "2 116\n",
      "3 2\n",
      "3 4\n",
      "3 6\n",
      "3 8\n",
      "3 9\n",
      "3 13\n",
      "3 14\n",
      "3 116\n",
      "4 4\n",
      "4 116\n",
      "5 2\n",
      "5 4\n",
      "5 6\n",
      "5 8\n",
      "5 9\n",
      "5 13\n",
      "5 14\n",
      "5 116\n",
      "6 4\n",
      "6 116\n",
      "7 2\n",
      "7 4\n",
      "7 6\n",
      "7 8\n",
      "7 9\n",
      "7 13\n",
      "7 14\n",
      "7 116\n",
      "8 4\n",
      "8 116\n",
      "9 2\n",
      "9 4\n",
      "9 6\n",
      "9 8\n",
      "9 9\n",
      "9 116\n",
      "10 2\n",
      "10 4\n",
      "10 6\n",
      "10 8\n",
      "10 9\n",
      "11 2\n",
      "11 6\n",
      "11 8\n",
      "11 9\n",
      "11 10\n",
      "12 1\n",
      "12 3\n",
      "12 5\n",
      "12 6\n",
      "12 7\n",
      "12 8\n",
      "12 10\n",
      "12 13\n",
      "12 14\n",
      "12 15\n",
      "12 16\n",
      "12 23\n",
      "12 24\n",
      "12 36\n",
      "12 41\n",
      "12 78\n",
      "13 1\n",
      "13 3\n",
      "13 5\n",
      "13 7\n",
      "13 8\n",
      "13 10\n",
      "13 13\n",
      "13 14\n",
      "13 16\n",
      "13 23\n",
      "13 24\n",
      "13 41\n",
      "13 78\n",
      "14 1\n",
      "14 2\n",
      "14 3\n",
      "14 5\n",
      "14 7\n",
      "14 8\n",
      "14 10\n",
      "14 13\n",
      "14 14\n",
      "14 16\n",
      "14 23\n",
      "14 24\n",
      "14 41\n",
      "14 78\n",
      "15 1\n",
      "15 2\n",
      "15 3\n",
      "15 5\n",
      "15 7\n",
      "15 8\n",
      "15 10\n",
      "15 13\n",
      "15 14\n",
      "15 15\n",
      "15 16\n",
      "15 23\n",
      "15 24\n",
      "15 41\n",
      "15 78\n",
      "16 1\n",
      "16 2\n",
      "16 3\n",
      "16 5\n",
      "16 6\n",
      "16 7\n",
      "16 8\n",
      "16 9\n",
      "16 10\n",
      "16 16\n",
      "16 23\n",
      "16 24\n",
      "16 78\n",
      "17 1\n",
      "17 2\n",
      "17 3\n",
      "17 5\n",
      "17 7\n",
      "17 8\n",
      "17 10\n",
      "17 13\n",
      "17 14\n",
      "17 15\n",
      "17 16\n",
      "17 23\n",
      "17 24\n",
      "17 36\n",
      "17 41\n",
      "17 60\n",
      "17 77\n",
      "18 1\n",
      "18 3\n",
      "18 5\n",
      "18 7\n",
      "18 8\n",
      "18 10\n",
      "18 13\n",
      "18 14\n",
      "18 15\n",
      "18 16\n",
      "18 23\n",
      "18 24\n",
      "18 36\n",
      "18 41\n",
      "18 60\n",
      "18 77\n",
      "19 1\n",
      "19 2\n",
      "19 3\n",
      "19 5\n",
      "19 6\n",
      "19 7\n",
      "19 8\n",
      "19 9\n",
      "19 10\n",
      "19 13\n",
      "19 14\n",
      "19 15\n",
      "19 16\n",
      "19 23\n",
      "19 24\n",
      "19 41\n",
      "19 60\n",
      "19 77\n",
      "19 78\n",
      "20 1\n",
      "20 2\n",
      "20 3\n",
      "20 5\n",
      "20 6\n",
      "20 7\n",
      "20 8\n",
      "20 9\n",
      "20 10\n",
      "20 13\n",
      "20 14\n",
      "20 15\n",
      "20 16\n",
      "20 23\n",
      "20 24\n",
      "20 41\n",
      "20 60\n",
      "20 77\n",
      "20 78\n",
      "21 1\n",
      "21 2\n",
      "21 3\n",
      "21 5\n",
      "21 6\n",
      "21 7\n",
      "21 8\n",
      "21 9\n",
      "21 10\n",
      "21 13\n",
      "21 14\n",
      "21 15\n",
      "21 16\n",
      "21 23\n",
      "21 24\n",
      "21 41\n",
      "21 60\n",
      "21 77\n",
      "21 78\n",
      "22 1\n",
      "22 2\n",
      "22 3\n",
      "22 5\n",
      "22 6\n",
      "22 7\n",
      "22 8\n",
      "22 9\n",
      "22 10\n",
      "22 15\n",
      "22 60\n",
      "22 77\n",
      "23 1\n",
      "23 2\n",
      "23 3\n",
      "23 5\n",
      "23 7\n",
      "23 8\n",
      "23 9\n",
      "23 10\n",
      "23 16\n",
      "23 60\n",
      "23 77\n",
      "24 1\n",
      "24 3\n",
      "24 5\n",
      "24 6\n",
      "24 7\n",
      "24 8\n",
      "24 9\n",
      "24 10\n",
      "24 16\n",
      "24 60\n",
      "24 77\n",
      "25 1\n",
      "25 3\n",
      "25 5\n",
      "25 7\n",
      "25 8\n",
      "25 9\n",
      "25 10\n",
      "26 1\n",
      "26 3\n",
      "26 5\n",
      "26 7\n",
      "26 8\n",
      "26 9\n",
      "26 10\n",
      "27 1\n",
      "27 3\n",
      "27 5\n",
      "27 7\n",
      "27 8\n",
      "27 9\n",
      "27 10\n",
      "28 1\n",
      "28 3\n",
      "28 5\n",
      "28 7\n",
      "28 8\n",
      "28 9\n",
      "28 10\n",
      "29 1\n",
      "29 3\n",
      "29 5\n",
      "29 7\n",
      "29 8\n",
      "29 9\n",
      "29 10\n",
      "29 36\n",
      "30 1\n",
      "30 3\n",
      "30 5\n",
      "30 7\n",
      "30 8\n",
      "30 9\n",
      "30 10\n",
      "30 36\n",
      "31 1\n",
      "31 3\n",
      "31 5\n",
      "31 7\n",
      "31 8\n",
      "31 9\n",
      "31 10\n",
      "31 36\n",
      "32 1\n",
      "32 3\n",
      "32 5\n",
      "32 7\n",
      "32 8\n",
      "32 9\n",
      "32 10\n",
      "32 36\n",
      "33 1\n",
      "33 3\n",
      "33 5\n",
      "33 7\n",
      "33 8\n",
      "33 9\n",
      "33 10\n",
      "33 13\n",
      "33 14\n",
      "33 15\n",
      "33 16\n",
      "33 23\n",
      "33 24\n",
      "33 41\n",
      "34 1\n",
      "34 3\n",
      "34 5\n",
      "34 7\n",
      "34 8\n",
      "34 9\n",
      "34 10\n",
      "34 13\n",
      "34 14\n",
      "34 15\n",
      "34 16\n",
      "34 23\n",
      "34 24\n",
      "34 41\n",
      "35 1\n",
      "35 3\n",
      "35 5\n",
      "35 7\n",
      "35 8\n",
      "35 9\n",
      "35 10\n",
      "35 13\n",
      "35 14\n",
      "35 15\n",
      "35 16\n",
      "35 23\n",
      "35 24\n",
      "35 36\n",
      "35 41\n",
      "36 1\n",
      "36 3\n",
      "36 5\n",
      "36 7\n",
      "36 8\n",
      "36 9\n",
      "36 10\n",
      "36 13\n",
      "36 14\n",
      "36 15\n",
      "36 16\n",
      "36 23\n",
      "36 24\n",
      "36 36\n",
      "36 41\n",
      "37 1\n",
      "37 3\n",
      "37 5\n",
      "37 7\n",
      "37 8\n",
      "37 9\n",
      "37 10\n",
      "37 13\n",
      "37 14\n",
      "37 15\n",
      "37 16\n",
      "37 23\n",
      "37 24\n",
      "37 36\n",
      "37 41\n",
      "38 1\n",
      "38 3\n",
      "38 5\n",
      "38 7\n",
      "38 8\n",
      "38 9\n",
      "38 10\n",
      "38 13\n",
      "38 14\n",
      "38 15\n",
      "38 16\n",
      "38 23\n",
      "38 24\n",
      "38 36\n",
      "38 41\n",
      "39 1\n",
      "39 3\n",
      "39 5\n",
      "39 7\n",
      "39 8\n",
      "39 9\n",
      "39 10\n",
      "39 13\n",
      "39 14\n",
      "39 15\n",
      "39 16\n",
      "39 23\n",
      "39 24\n",
      "39 36\n",
      "39 41\n",
      "40 1\n",
      "40 3\n",
      "40 5\n",
      "40 7\n",
      "40 8\n",
      "40 9\n",
      "40 10\n",
      "40 13\n",
      "40 14\n",
      "40 15\n",
      "40 16\n",
      "40 23\n",
      "40 24\n",
      "40 41\n",
      "41 1\n",
      "41 3\n",
      "41 5\n",
      "41 7\n",
      "41 8\n",
      "41 9\n",
      "41 10\n",
      "41 13\n",
      "41 14\n",
      "41 15\n",
      "41 16\n",
      "41 23\n",
      "41 24\n",
      "41 41\n",
      "42 1\n",
      "42 7\n",
      "42 8\n",
      "42 9\n",
      "42 10\n",
      "42 13\n",
      "42 14\n",
      "42 15\n",
      "42 16\n",
      "42 23\n",
      "42 24\n",
      "42 41\n",
      "42 54\n",
      "42 55\n",
      "42 56\n",
      "42 57\n",
      "42 64\n",
      "42 65\n",
      "42 67\n",
      "42 71\n",
      "42 72\n",
      "42 73\n",
      "42 74\n",
      "42 81\n",
      "42 85\n",
      "42 86\n",
      "42 87\n",
      "42 88\n",
      "42 89\n",
      "42 90\n",
      "42 91\n",
      "42 101\n",
      "42 105\n",
      "42 107\n",
      "43 1\n",
      "43 7\n",
      "43 8\n",
      "43 9\n",
      "43 10\n",
      "43 13\n",
      "43 14\n",
      "43 15\n",
      "43 16\n",
      "43 23\n",
      "43 24\n",
      "43 41\n",
      "43 54\n",
      "43 55\n",
      "43 56\n",
      "43 57\n",
      "43 64\n",
      "43 65\n",
      "43 67\n",
      "43 71\n",
      "43 72\n",
      "43 73\n",
      "43 74\n",
      "43 81\n",
      "43 85\n",
      "43 86\n",
      "43 87\n",
      "43 88\n",
      "43 89\n",
      "43 90\n",
      "43 91\n",
      "43 101\n",
      "43 105\n",
      "43 107\n",
      "44 1\n",
      "44 7\n",
      "44 9\n",
      "44 10\n",
      "44 13\n",
      "44 14\n",
      "44 15\n",
      "44 16\n",
      "44 23\n",
      "44 24\n",
      "44 41\n",
      "44 54\n",
      "44 55\n",
      "44 56\n",
      "44 57\n",
      "44 64\n",
      "44 65\n",
      "44 67\n",
      "44 71\n",
      "44 72\n",
      "44 73\n",
      "44 74\n",
      "44 81\n",
      "44 85\n",
      "44 86\n",
      "44 87\n",
      "44 88\n",
      "44 89\n",
      "44 90\n",
      "44 91\n",
      "44 101\n",
      "44 105\n",
      "44 107\n",
      "45 1\n",
      "45 7\n",
      "45 9\n",
      "45 10\n",
      "45 13\n",
      "45 14\n",
      "45 15\n",
      "45 16\n",
      "45 23\n",
      "45 24\n",
      "45 41\n",
      "45 54\n",
      "45 55\n",
      "45 56\n",
      "45 57\n",
      "45 64\n",
      "45 65\n",
      "45 67\n",
      "45 71\n",
      "45 72\n",
      "45 73\n",
      "45 74\n",
      "45 81\n",
      "45 85\n",
      "45 86\n",
      "45 87\n",
      "45 88\n",
      "45 89\n",
      "45 90\n",
      "45 91\n",
      "45 101\n",
      "45 105\n",
      "45 107\n",
      "46 1\n",
      "46 7\n",
      "46 9\n",
      "46 10\n",
      "46 13\n",
      "46 14\n",
      "46 15\n",
      "46 16\n",
      "46 23\n",
      "46 24\n",
      "46 41\n",
      "46 54\n",
      "46 55\n",
      "46 56\n",
      "46 57\n",
      "46 64\n",
      "46 65\n",
      "46 67\n",
      "46 71\n",
      "46 72\n",
      "46 73\n",
      "46 74\n",
      "46 81\n",
      "46 85\n",
      "46 86\n",
      "46 87\n",
      "46 88\n",
      "46 89\n",
      "46 90\n",
      "46 91\n",
      "46 101\n",
      "46 105\n",
      "46 107\n",
      "47 1\n",
      "47 7\n",
      "47 9\n",
      "47 10\n",
      "47 13\n",
      "47 14\n",
      "47 15\n",
      "47 16\n",
      "47 23\n",
      "47 24\n",
      "47 41\n",
      "47 54\n",
      "47 55\n",
      "47 56\n",
      "47 57\n",
      "47 64\n",
      "47 65\n",
      "47 67\n",
      "47 71\n",
      "47 72\n",
      "47 73\n",
      "47 74\n",
      "47 81\n",
      "47 85\n",
      "47 86\n",
      "47 87\n",
      "47 88\n",
      "47 89\n",
      "47 90\n",
      "47 91\n",
      "47 101\n",
      "47 105\n",
      "47 107\n",
      "48 1\n",
      "48 3\n",
      "48 7\n",
      "48 9\n",
      "48 10\n",
      "48 13\n",
      "48 14\n",
      "48 15\n",
      "48 16\n",
      "48 23\n",
      "48 24\n",
      "48 41\n",
      "48 54\n",
      "48 55\n",
      "48 56\n",
      "48 57\n",
      "48 64\n",
      "48 65\n",
      "48 67\n",
      "48 71\n",
      "48 72\n",
      "48 73\n",
      "48 74\n",
      "48 81\n",
      "48 83\n",
      "48 84\n",
      "48 85\n",
      "48 86\n",
      "48 87\n",
      "48 88\n",
      "48 89\n",
      "48 90\n",
      "48 91\n",
      "48 101\n",
      "48 105\n",
      "48 107\n",
      "49 1\n",
      "49 3\n",
      "49 7\n",
      "49 9\n",
      "49 10\n",
      "49 13\n",
      "49 14\n",
      "49 15\n",
      "49 16\n",
      "49 23\n",
      "49 24\n",
      "49 41\n",
      "49 54\n",
      "49 55\n",
      "49 56\n",
      "49 57\n",
      "49 65\n",
      "49 67\n",
      "49 71\n",
      "49 72\n",
      "49 73\n",
      "49 74\n",
      "49 81\n",
      "49 85\n",
      "49 86\n",
      "49 87\n",
      "49 88\n",
      "49 89\n",
      "49 90\n",
      "49 91\n",
      "49 101\n",
      "49 105\n",
      "49 107\n",
      "50 1\n",
      "50 2\n",
      "50 7\n",
      "50 8\n",
      "50 9\n",
      "50 10\n",
      "50 13\n",
      "50 14\n",
      "50 15\n",
      "50 16\n",
      "50 23\n",
      "50 24\n",
      "50 41\n",
      "50 65\n",
      "50 67\n",
      "50 71\n",
      "50 72\n",
      "50 73\n",
      "50 74\n",
      "50 101\n",
      "50 105\n",
      "50 107\n",
      "51 1\n",
      "51 2\n",
      "51 6\n",
      "51 7\n",
      "51 8\n",
      "51 9\n",
      "51 10\n",
      "51 13\n",
      "51 14\n",
      "51 15\n",
      "51 16\n",
      "51 23\n",
      "51 24\n",
      "51 41\n",
      "51 65\n",
      "51 67\n",
      "51 71\n",
      "51 72\n",
      "51 73\n",
      "51 74\n",
      "51 101\n",
      "51 105\n",
      "51 107\n",
      "52 1\n",
      "52 7\n",
      "52 8\n",
      "52 9\n",
      "52 10\n",
      "52 13\n",
      "52 14\n",
      "52 15\n",
      "52 16\n",
      "52 23\n",
      "52 24\n",
      "52 41\n",
      "52 65\n",
      "52 67\n",
      "52 71\n",
      "52 72\n",
      "52 73\n",
      "52 74\n",
      "52 83\n",
      "52 84\n",
      "52 101\n",
      "52 105\n",
      "52 107\n",
      "53 1\n",
      "53 7\n",
      "53 8\n",
      "53 9\n",
      "53 10\n",
      "53 13\n",
      "53 14\n",
      "53 15\n",
      "53 16\n",
      "53 23\n",
      "53 24\n",
      "53 41\n",
      "53 65\n",
      "53 67\n",
      "53 71\n",
      "53 72\n",
      "53 73\n",
      "53 74\n",
      "53 83\n",
      "53 84\n",
      "53 101\n",
      "53 105\n",
      "53 107\n",
      "54 1\n",
      "54 7\n",
      "54 8\n",
      "54 9\n",
      "54 10\n",
      "54 13\n",
      "54 14\n",
      "54 15\n",
      "54 16\n",
      "54 23\n",
      "54 24\n",
      "54 41\n",
      "54 71\n",
      "54 72\n",
      "54 73\n",
      "54 74\n",
      "54 101\n",
      "54 105\n",
      "54 107\n",
      "55 1\n",
      "55 7\n",
      "55 9\n",
      "55 10\n",
      "55 13\n",
      "55 14\n",
      "55 15\n",
      "55 16\n",
      "55 23\n",
      "55 24\n",
      "55 41\n",
      "55 71\n",
      "55 72\n",
      "55 73\n",
      "55 74\n",
      "55 83\n",
      "55 84\n",
      "55 101\n",
      "55 105\n",
      "55 107\n",
      "56 1\n",
      "56 7\n",
      "56 8\n",
      "56 9\n",
      "56 10\n",
      "56 13\n",
      "56 14\n",
      "56 15\n",
      "56 16\n",
      "56 23\n",
      "56 24\n",
      "56 41\n",
      "56 71\n",
      "56 72\n",
      "56 73\n",
      "56 74\n",
      "56 83\n",
      "56 84\n",
      "56 101\n",
      "56 105\n",
      "56 107\n",
      "57 1\n",
      "57 7\n",
      "57 9\n",
      "57 10\n",
      "57 13\n",
      "57 14\n",
      "57 15\n",
      "57 16\n",
      "57 23\n",
      "57 24\n",
      "57 41\n",
      "57 71\n",
      "57 72\n",
      "57 73\n",
      "57 74\n",
      "57 101\n",
      "57 105\n",
      "57 107\n",
      "58 1\n",
      "58 7\n",
      "58 9\n",
      "58 10\n",
      "58 71\n",
      "58 72\n",
      "58 73\n",
      "58 74\n",
      "58 101\n",
      "58 105\n",
      "58 107\n",
      "58 117\n",
      "59 1\n",
      "59 7\n",
      "59 9\n",
      "59 10\n",
      "59 117\n",
      "60 3\n",
      "60 4\n",
      "60 6\n",
      "60 60\n",
      "61 2\n",
      "61 76\n",
      "62 1\n",
      "62 2\n",
      "62 3\n",
      "62 5\n",
      "62 8\n",
      "62 9\n",
      "62 76\n",
      "63 1\n",
      "63 2\n",
      "63 3\n",
      "63 5\n",
      "63 6\n",
      "63 8\n",
      "63 9\n",
      "63 71\n",
      "63 72\n",
      "63 73\n",
      "63 76\n",
      "64 1\n",
      "64 3\n",
      "64 4\n",
      "64 5\n",
      "64 6\n",
      "64 8\n",
      "64 9\n",
      "64 76\n",
      "65 1\n",
      "65 3\n",
      "65 4\n",
      "65 5\n",
      "65 8\n",
      "65 9\n",
      "65 76\n",
      "66 2\n",
      "66 3\n",
      "66 6\n",
      "66 9\n",
      "66 76\n",
      "67 2\n",
      "67 3\n",
      "67 4\n",
      "67 6\n",
      "67 9\n",
      "67 76\n",
      "68 2\n",
      "68 3\n",
      "68 4\n",
      "68 6\n",
      "68 9\n",
      "68 76\n",
      "69 1\n",
      "69 3\n",
      "69 4\n",
      "69 5\n",
      "69 6\n",
      "69 7\n",
      "69 9\n",
      "69 76\n",
      "70 1\n",
      "70 2\n",
      "70 3\n",
      "70 4\n",
      "70 5\n",
      "70 6\n",
      "70 7\n",
      "70 8\n",
      "70 9\n",
      "70 76\n",
      "71 2\n",
      "71 4\n",
      "71 6\n",
      "71 76\n",
      "72 2\n",
      "72 4\n",
      "72 6\n",
      "72 76\n",
      "73 2\n",
      "73 4\n",
      "73 6\n",
      "73 76\n",
      "74 2\n",
      "74 3\n",
      "74 4\n",
      "74 5\n",
      "74 6\n",
      "74 7\n",
      "74 9\n",
      "74 76\n",
      "75 1\n",
      "75 6\n",
      "75 7\n",
      "75 9\n",
      "75 76\n",
      "76 1\n",
      "76 3\n",
      "76 5\n",
      "76 6\n",
      "76 8\n",
      "76 9\n",
      "76 76\n",
      "77 1\n",
      "77 3\n",
      "77 5\n",
      "77 6\n",
      "77 8\n",
      "77 9\n",
      "77 76\n",
      "78 1\n",
      "78 2\n",
      "78 3\n",
      "78 4\n",
      "78 5\n",
      "78 6\n",
      "78 8\n",
      "78 9\n",
      "78 76\n",
      "78 78\n",
      "79 1\n",
      "79 2\n",
      "79 3\n",
      "79 4\n",
      "79 5\n",
      "79 6\n",
      "79 8\n",
      "79 9\n",
      "79 76\n",
      "80 1\n",
      "80 3\n",
      "80 5\n",
      "80 6\n",
      "80 8\n",
      "80 9\n",
      "80 76\n",
      "80 122\n",
      "81 1\n",
      "81 2\n",
      "81 3\n",
      "81 4\n",
      "81 5\n",
      "81 6\n",
      "81 8\n",
      "81 9\n",
      "81 68\n",
      "81 69\n",
      "81 71\n",
      "81 72\n",
      "81 73\n",
      "81 76\n",
      "81 79\n",
      "81 85\n",
      "81 86\n",
      "81 87\n",
      "81 88\n",
      "81 89\n",
      "81 90\n",
      "81 91\n",
      "81 101\n",
      "81 105\n",
      "81 107\n",
      "82 1\n",
      "82 2\n",
      "82 3\n",
      "82 4\n",
      "82 5\n",
      "82 6\n",
      "82 8\n",
      "82 9\n",
      "82 76\n",
      "82 86\n",
      "82 90\n",
      "82 91\n",
      "82 101\n",
      "82 122\n",
      "83 1\n",
      "83 2\n",
      "83 3\n",
      "83 5\n",
      "83 6\n",
      "83 8\n",
      "83 9\n",
      "83 68\n",
      "83 69\n",
      "83 71\n",
      "83 72\n",
      "83 73\n",
      "83 74\n",
      "83 76\n",
      "83 79\n",
      "83 122\n",
      "84 1\n",
      "84 2\n",
      "84 3\n",
      "84 5\n",
      "84 6\n",
      "84 8\n",
      "84 9\n",
      "84 68\n",
      "84 69\n",
      "84 71\n",
      "84 72\n",
      "84 73\n",
      "84 74\n",
      "84 76\n",
      "84 79\n",
      "84 122\n",
      "85 1\n",
      "85 2\n",
      "85 3\n",
      "85 5\n",
      "85 6\n",
      "85 8\n",
      "85 9\n",
      "85 68\n",
      "85 69\n",
      "85 71\n",
      "85 72\n",
      "85 73\n",
      "85 74\n",
      "85 76\n",
      "85 79\n",
      "85 85\n",
      "85 86\n",
      "85 87\n",
      "85 88\n",
      "85 89\n",
      "85 90\n",
      "85 91\n",
      "85 122\n",
      "86 1\n",
      "86 2\n",
      "86 3\n",
      "86 5\n",
      "86 6\n",
      "86 7\n",
      "86 8\n",
      "86 9\n",
      "86 68\n",
      "86 69\n",
      "86 71\n",
      "86 72\n",
      "86 73\n",
      "86 74\n",
      "86 76\n",
      "86 79\n",
      "86 85\n",
      "86 86\n",
      "86 87\n",
      "86 88\n",
      "86 89\n",
      "86 90\n",
      "86 91\n",
      "86 122\n",
      "87 1\n",
      "87 2\n",
      "87 3\n",
      "87 5\n",
      "87 6\n",
      "87 7\n",
      "87 8\n",
      "87 9\n",
      "87 68\n",
      "87 69\n",
      "87 71\n",
      "87 72\n",
      "87 73\n",
      "87 74\n",
      "87 76\n",
      "87 79\n",
      "87 85\n",
      "87 86\n",
      "87 87\n",
      "87 88\n",
      "87 89\n",
      "87 90\n",
      "87 91\n",
      "87 122\n",
      "88 1\n",
      "88 2\n",
      "88 3\n",
      "88 5\n",
      "88 6\n",
      "88 7\n",
      "88 8\n",
      "88 9\n",
      "88 68\n",
      "88 69\n",
      "88 71\n",
      "88 72\n",
      "88 73\n",
      "88 74\n",
      "88 76\n",
      "88 79\n",
      "88 85\n",
      "88 86\n",
      "88 87\n",
      "88 88\n",
      "88 89\n",
      "88 90\n",
      "88 91\n",
      "88 122\n",
      "89 1\n",
      "89 2\n",
      "89 3\n",
      "89 5\n",
      "89 6\n",
      "89 7\n",
      "89 8\n",
      "89 9\n",
      "89 68\n",
      "89 69\n",
      "89 71\n",
      "89 72\n",
      "89 73\n",
      "89 74\n",
      "89 76\n",
      "89 79\n",
      "89 85\n",
      "89 86\n",
      "89 87\n",
      "89 88\n",
      "89 89\n",
      "89 90\n",
      "89 91\n",
      "89 122\n",
      "90 1\n",
      "90 2\n",
      "90 3\n",
      "90 5\n",
      "90 6\n",
      "90 7\n",
      "90 8\n",
      "90 9\n",
      "90 68\n",
      "90 69\n",
      "90 71\n",
      "90 72\n",
      "90 73\n",
      "90 74\n",
      "90 76\n",
      "90 79\n",
      "90 85\n",
      "90 86\n",
      "90 87\n",
      "90 88\n",
      "90 89\n",
      "90 90\n",
      "90 91\n",
      "90 122\n",
      "91 1\n",
      "91 2\n",
      "91 3\n",
      "91 5\n",
      "91 6\n",
      "91 7\n",
      "91 8\n",
      "91 9\n",
      "91 68\n",
      "91 69\n",
      "91 71\n",
      "91 72\n",
      "91 73\n",
      "91 74\n",
      "91 76\n",
      "91 79\n",
      "91 85\n",
      "91 86\n",
      "91 87\n",
      "91 88\n",
      "91 89\n",
      "91 90\n",
      "91 91\n",
      "91 122\n",
      "92 1\n",
      "92 2\n",
      "92 3\n",
      "92 5\n",
      "92 6\n",
      "92 7\n",
      "92 8\n",
      "92 9\n",
      "92 68\n",
      "92 69\n",
      "92 71\n",
      "92 72\n",
      "92 73\n",
      "92 74\n",
      "92 76\n",
      "92 79\n",
      "92 85\n",
      "92 86\n",
      "92 87\n",
      "92 88\n",
      "92 89\n",
      "92 90\n",
      "92 91\n",
      "92 122\n",
      "93 1\n",
      "93 2\n",
      "93 3\n",
      "93 5\n",
      "93 6\n",
      "93 7\n",
      "93 8\n",
      "93 9\n",
      "93 68\n",
      "93 69\n",
      "93 71\n",
      "93 72\n",
      "93 73\n",
      "93 74\n",
      "93 76\n",
      "93 79\n",
      "93 85\n",
      "93 86\n",
      "93 87\n",
      "93 88\n",
      "93 89\n",
      "93 90\n",
      "93 91\n",
      "93 122\n",
      "94 1\n",
      "94 2\n",
      "94 3\n",
      "94 5\n",
      "94 6\n",
      "94 7\n",
      "94 8\n",
      "94 9\n",
      "94 68\n",
      "94 69\n",
      "94 71\n",
      "94 72\n",
      "94 73\n",
      "94 74\n",
      "94 76\n",
      "94 79\n",
      "94 85\n",
      "94 86\n",
      "94 87\n",
      "94 88\n",
      "94 89\n",
      "94 90\n",
      "94 91\n",
      "94 122\n",
      "95 1\n",
      "95 2\n",
      "95 3\n",
      "95 5\n",
      "95 6\n",
      "95 7\n",
      "95 8\n",
      "95 9\n",
      "95 68\n",
      "95 69\n",
      "95 71\n",
      "95 72\n",
      "95 73\n",
      "95 74\n",
      "95 76\n",
      "95 79\n",
      "95 85\n",
      "95 86\n",
      "95 87\n",
      "95 88\n",
      "95 89\n",
      "95 90\n",
      "95 91\n",
      "95 122\n",
      "96 1\n",
      "96 2\n",
      "96 3\n",
      "96 5\n",
      "96 6\n",
      "96 7\n",
      "96 8\n",
      "96 9\n",
      "96 68\n",
      "96 69\n",
      "96 71\n",
      "96 72\n",
      "96 73\n",
      "96 74\n",
      "96 76\n",
      "96 79\n",
      "96 85\n",
      "96 86\n",
      "96 87\n",
      "96 88\n",
      "96 89\n",
      "96 90\n",
      "96 91\n",
      "96 122\n",
      "97 1\n",
      "97 2\n",
      "97 3\n",
      "97 5\n",
      "97 6\n",
      "97 7\n",
      "97 8\n",
      "97 9\n",
      "97 68\n",
      "97 69\n",
      "97 71\n",
      "97 72\n",
      "97 73\n",
      "97 74\n",
      "97 76\n",
      "97 79\n",
      "97 85\n",
      "97 86\n",
      "97 87\n",
      "97 88\n",
      "97 89\n",
      "97 90\n",
      "97 91\n",
      "97 122\n",
      "98 1\n",
      "98 7\n",
      "98 9\n",
      "98 68\n",
      "98 69\n",
      "98 71\n",
      "98 72\n",
      "98 73\n",
      "98 74\n",
      "98 76\n",
      "98 79\n",
      "98 81\n",
      "98 83\n",
      "98 84\n",
      "98 85\n",
      "98 86\n",
      "98 87\n",
      "98 88\n",
      "98 89\n",
      "98 90\n",
      "98 91\n",
      "98 101\n",
      "98 105\n",
      "98 107\n",
      "98 122\n",
      "99 2\n",
      "99 3\n",
      "99 5\n",
      "99 6\n",
      "99 7\n",
      "99 9\n",
      "99 76\n",
      "99 85\n",
      "99 86\n",
      "99 87\n",
      "99 88\n",
      "99 89\n",
      "99 90\n",
      "99 91\n",
      "100 1\n",
      "100 2\n",
      "100 3\n",
      "100 5\n",
      "100 6\n",
      "100 7\n",
      "100 9\n",
      "100 68\n",
      "100 69\n",
      "100 71\n",
      "100 72\n",
      "100 73\n",
      "100 74\n",
      "100 76\n",
      "100 79\n",
      "100 85\n",
      "100 86\n",
      "100 87\n",
      "100 88\n",
      "100 89\n",
      "100 90\n",
      "100 91\n",
      "100 122\n",
      "101 1\n",
      "101 2\n",
      "101 3\n",
      "101 5\n",
      "101 6\n",
      "101 7\n",
      "101 9\n",
      "101 68\n",
      "101 69\n",
      "101 71\n",
      "101 72\n",
      "101 73\n",
      "101 74\n",
      "101 76\n",
      "101 79\n",
      "101 85\n",
      "101 86\n",
      "101 87\n",
      "101 88\n",
      "101 89\n",
      "101 90\n",
      "101 91\n",
      "101 122\n",
      "102 1\n",
      "102 2\n",
      "102 3\n",
      "102 5\n",
      "102 6\n",
      "102 7\n",
      "102 9\n",
      "102 68\n",
      "102 69\n",
      "102 71\n",
      "102 72\n",
      "102 73\n",
      "102 74\n",
      "102 76\n",
      "102 79\n",
      "102 85\n",
      "102 86\n",
      "102 87\n",
      "102 88\n",
      "102 89\n",
      "102 90\n",
      "102 91\n",
      "102 122\n",
      "103 1\n",
      "103 2\n",
      "103 3\n",
      "103 5\n",
      "103 6\n",
      "103 7\n",
      "103 9\n",
      "103 68\n",
      "103 69\n",
      "103 71\n",
      "103 72\n",
      "103 73\n",
      "103 74\n",
      "103 76\n",
      "103 79\n",
      "103 85\n",
      "103 86\n",
      "103 87\n",
      "103 88\n",
      "103 89\n",
      "103 90\n",
      "103 91\n",
      "103 122\n",
      "104 1\n",
      "104 2\n",
      "104 3\n",
      "104 5\n",
      "104 6\n",
      "104 7\n",
      "104 9\n",
      "104 68\n",
      "104 69\n",
      "104 71\n",
      "104 72\n",
      "104 73\n",
      "104 74\n",
      "104 76\n",
      "104 79\n",
      "104 85\n",
      "104 86\n",
      "104 87\n",
      "104 88\n",
      "104 89\n",
      "104 90\n",
      "104 91\n",
      "104 122\n",
      "105 1\n",
      "105 2\n",
      "105 3\n",
      "105 5\n",
      "105 6\n",
      "105 7\n",
      "105 9\n",
      "105 68\n",
      "105 69\n",
      "105 71\n",
      "105 72\n",
      "105 73\n",
      "105 74\n",
      "105 76\n",
      "105 79\n",
      "105 85\n",
      "105 86\n",
      "105 87\n",
      "105 88\n",
      "105 89\n",
      "105 90\n",
      "105 91\n",
      "105 122\n",
      "106 1\n",
      "106 2\n",
      "106 3\n",
      "106 5\n",
      "106 6\n",
      "106 7\n",
      "106 9\n",
      "106 68\n",
      "106 69\n",
      "106 71\n",
      "106 72\n",
      "106 73\n",
      "106 74\n",
      "106 76\n",
      "106 79\n",
      "106 85\n",
      "106 86\n",
      "106 87\n",
      "106 88\n",
      "106 89\n",
      "106 90\n",
      "106 91\n",
      "106 122\n",
      "107 1\n",
      "107 2\n",
      "107 3\n",
      "107 5\n",
      "107 6\n",
      "107 7\n",
      "107 9\n",
      "107 68\n",
      "107 69\n",
      "107 71\n",
      "107 72\n",
      "107 73\n",
      "107 74\n",
      "107 76\n",
      "107 79\n",
      "107 85\n",
      "107 86\n",
      "107 87\n",
      "107 88\n",
      "107 89\n",
      "107 90\n",
      "107 91\n",
      "107 122\n",
      "108 1\n",
      "108 2\n",
      "108 3\n",
      "108 5\n",
      "108 6\n",
      "108 7\n",
      "108 9\n",
      "108 68\n",
      "108 69\n",
      "108 71\n",
      "108 72\n",
      "108 73\n",
      "108 74\n",
      "108 76\n",
      "108 79\n",
      "108 85\n",
      "108 86\n",
      "108 87\n",
      "108 88\n",
      "108 89\n",
      "108 90\n",
      "108 91\n",
      "108 122\n",
      "109 1\n",
      "109 2\n",
      "109 3\n",
      "109 5\n",
      "109 6\n",
      "109 7\n",
      "109 9\n",
      "109 68\n",
      "109 69\n",
      "109 71\n",
      "109 72\n",
      "109 73\n",
      "109 74\n",
      "109 76\n",
      "109 79\n",
      "109 85\n",
      "109 86\n",
      "109 87\n",
      "109 88\n",
      "109 89\n",
      "109 90\n",
      "109 91\n",
      "109 122\n",
      "110 1\n",
      "110 2\n",
      "110 3\n",
      "110 5\n",
      "110 6\n",
      "110 7\n",
      "110 9\n",
      "110 76\n",
      "110 85\n",
      "110 86\n",
      "110 87\n",
      "110 88\n",
      "110 89\n",
      "110 90\n",
      "110 91\n",
      "111 1\n",
      "111 2\n",
      "111 3\n",
      "111 5\n",
      "111 6\n",
      "111 7\n",
      "111 9\n",
      "111 68\n",
      "111 69\n",
      "111 71\n",
      "111 72\n",
      "111 73\n",
      "111 74\n",
      "111 76\n",
      "111 79\n",
      "111 85\n",
      "111 86\n",
      "111 87\n",
      "111 88\n",
      "111 89\n",
      "111 90\n",
      "111 91\n",
      "111 122\n",
      "113 1\n",
      "113 2\n",
      "113 3\n",
      "113 5\n",
      "113 6\n",
      "113 8\n",
      "113 9\n",
      "113 116\n",
      "114 1\n",
      "114 2\n",
      "114 3\n",
      "114 5\n",
      "114 6\n",
      "114 8\n",
      "114 9\n",
      "115 1\n",
      "115 3\n",
      "115 5\n",
      "115 7\n",
      "115 9\n",
      "115 67\n",
      "115 68\n",
      "115 69\n",
      "115 71\n",
      "115 72\n",
      "115 73\n",
      "115 74\n",
      "115 79\n",
      "115 81\n",
      "115 82\n",
      "115 85\n",
      "115 86\n",
      "115 87\n",
      "115 88\n",
      "115 89\n",
      "115 90\n",
      "115 91\n",
      "115 101\n",
      "115 116\n",
      "116 2\n",
      "116 3\n",
      "116 4\n",
      "116 5\n",
      "116 6\n",
      "116 8\n",
      "116 9\n",
      "117 2\n",
      "117 3\n",
      "117 9\n",
      "118 1\n",
      "118 2\n",
      "118 3\n",
      "118 5\n",
      "118 6\n",
      "118 8\n",
      "118 9\n",
      "118 71\n",
      "118 72\n",
      "118 73\n",
      "118 74\n",
      "119 75\n",
      "119 111\n",
      "120 75\n",
      "120 111\n",
      "121 75\n",
      "121 111\n",
      "122 2\n",
      "122 6\n",
      "123 53\n",
      "123 62\n",
      "123 118\n",
      "124 53\n",
      "124 62\n",
      "124 118\n",
      "125 53\n",
      "125 62\n",
      "125 118\n",
      "126 53\n",
      "126 62\n",
      "126 118\n",
      "127 53\n",
      "127 62\n",
      "127 118\n",
      "128 53\n",
      "128 62\n",
      "128 118\n",
      "129 53\n",
      "129 62\n",
      "129 118\n",
      "130 53\n",
      "130 62\n",
      "130 118\n",
      "131 53\n",
      "131 62\n",
      "131 118\n",
      "132 53\n",
      "132 62\n",
      "132 118\n",
      "133 53\n",
      "133 62\n",
      "133 118\n",
      "134 53\n",
      "134 62\n",
      "134 118\n",
      "135 53\n",
      "135 62\n",
      "135 118\n",
      "136 53\n",
      "136 62\n",
      "136 118\n",
      "137 53\n",
      "137 62\n",
      "137 118\n",
      "138 53\n",
      "138 62\n",
      "138 118\n",
      "139 53\n",
      "139 62\n",
      "139 118\n",
      "140 53\n",
      "140 62\n",
      "140 118\n",
      "141 53\n",
      "141 62\n",
      "141 118\n",
      "142 53\n",
      "142 62\n",
      "142 118\n",
      "143 53\n",
      "143 62\n",
      "143 118\n",
      "144 53\n",
      "144 62\n",
      "144 118\n",
      "145 53\n",
      "145 62\n",
      "145 118\n",
      "146 53\n",
      "146 62\n",
      "146 118\n",
      "147 53\n",
      "147 62\n",
      "147 118\n",
      "148 12\n",
      "148 15\n",
      "148 16\n",
      "148 17\n",
      "148 18\n",
      "148 19\n",
      "148 20\n",
      "148 21\n",
      "148 22\n",
      "148 25\n",
      "148 26\n",
      "148 27\n",
      "148 28\n",
      "148 29\n",
      "148 30\n",
      "148 31\n",
      "148 32\n",
      "148 33\n",
      "148 34\n",
      "148 35\n",
      "148 36\n",
      "148 37\n",
      "148 38\n",
      "148 39\n",
      "148 40\n",
      "148 41\n",
      "149 12\n",
      "149 15\n",
      "149 16\n",
      "150 12\n",
      "150 15\n",
      "150 16\n",
      "150 17\n",
      "150 18\n",
      "150 19\n",
      "150 20\n",
      "150 21\n",
      "150 22\n",
      "150 25\n",
      "150 26\n",
      "150 27\n",
      "150 28\n",
      "150 29\n",
      "150 30\n",
      "150 31\n",
      "150 32\n",
      "150 33\n",
      "150 34\n",
      "150 35\n",
      "150 36\n",
      "150 37\n",
      "150 38\n",
      "150 39\n",
      "150 40\n",
      "150 41\n",
      "151 12\n",
      "151 15\n",
      "151 16\n",
      "152 12\n",
      "152 15\n",
      "152 16\n",
      "152 17\n",
      "152 18\n",
      "152 19\n",
      "152 20\n",
      "152 21\n",
      "152 22\n",
      "152 25\n",
      "152 26\n",
      "152 27\n",
      "152 28\n",
      "152 29\n",
      "152 30\n",
      "152 31\n",
      "152 32\n",
      "152 33\n",
      "152 34\n",
      "152 35\n",
      "152 36\n",
      "152 37\n",
      "152 38\n",
      "152 39\n",
      "152 40\n",
      "152 41\n",
      "153 12\n",
      "153 15\n",
      "153 16\n",
      "154 12\n",
      "154 15\n",
      "154 16\n",
      "154 17\n",
      "154 18\n",
      "154 19\n",
      "154 20\n",
      "154 21\n",
      "154 22\n",
      "154 25\n",
      "154 26\n",
      "154 27\n",
      "154 28\n",
      "154 29\n",
      "154 30\n",
      "154 31\n",
      "154 32\n",
      "154 33\n",
      "154 34\n",
      "154 35\n",
      "154 36\n",
      "154 37\n",
      "154 38\n",
      "154 39\n",
      "154 40\n",
      "154 41\n",
      "155 12\n",
      "155 15\n",
      "155 16\n",
      "155 17\n",
      "155 18\n",
      "155 19\n",
      "155 20\n",
      "155 21\n",
      "155 22\n",
      "155 25\n",
      "155 26\n",
      "155 27\n",
      "155 28\n",
      "155 29\n",
      "155 30\n",
      "155 31\n",
      "155 32\n",
      "155 33\n",
      "155 34\n",
      "155 35\n",
      "155 36\n",
      "155 37\n",
      "155 38\n",
      "155 39\n",
      "155 40\n",
      "155 41\n",
      "156 17\n",
      "157 12\n",
      "157 15\n",
      "157 16\n",
      "157 19\n",
      "157 20\n",
      "157 21\n",
      "157 22\n",
      "158 12\n",
      "158 15\n",
      "158 16\n",
      "158 19\n",
      "158 20\n",
      "158 21\n",
      "158 22\n",
      "159 12\n",
      "159 15\n",
      "159 16\n",
      "159 17\n",
      "159 18\n",
      "159 19\n",
      "159 20\n",
      "159 21\n",
      "159 22\n",
      "159 25\n",
      "159 26\n",
      "159 27\n",
      "159 28\n",
      "159 29\n",
      "159 30\n",
      "159 31\n",
      "159 32\n",
      "159 33\n",
      "159 34\n",
      "159 35\n",
      "159 36\n",
      "159 37\n",
      "159 38\n",
      "159 39\n",
      "159 40\n",
      "159 41\n",
      "160 12\n",
      "160 15\n",
      "160 16\n",
      "160 17\n",
      "160 18\n",
      "160 19\n",
      "160 20\n",
      "160 21\n",
      "160 22\n",
      "160 25\n",
      "160 26\n",
      "160 27\n",
      "160 28\n",
      "160 29\n",
      "160 30\n",
      "160 31\n",
      "160 32\n",
      "160 33\n",
      "160 34\n",
      "160 35\n",
      "160 36\n",
      "160 37\n",
      "160 38\n",
      "160 39\n",
      "160 40\n",
      "160 41\n",
      "161 12\n",
      "161 15\n",
      "161 16\n",
      "161 17\n",
      "161 18\n",
      "161 19\n",
      "161 20\n",
      "161 21\n",
      "161 22\n",
      "161 25\n",
      "161 26\n",
      "161 27\n",
      "161 28\n",
      "161 29\n",
      "161 30\n",
      "161 31\n",
      "161 32\n",
      "161 33\n",
      "161 34\n",
      "161 35\n",
      "161 36\n",
      "161 37\n",
      "161 38\n",
      "161 39\n",
      "161 40\n",
      "161 41\n",
      "162 12\n",
      "162 15\n",
      "162 16\n",
      "162 19\n",
      "162 20\n",
      "162 21\n",
      "162 22\n",
      "163 12\n",
      "163 15\n",
      "163 16\n",
      "163 19\n",
      "163 20\n",
      "163 21\n",
      "163 22\n",
      "164 12\n",
      "164 15\n",
      "164 16\n",
      "164 17\n",
      "164 18\n",
      "164 19\n",
      "164 20\n",
      "164 21\n",
      "164 22\n",
      "164 25\n",
      "164 26\n",
      "164 27\n",
      "164 28\n",
      "164 29\n",
      "164 30\n",
      "164 31\n",
      "164 32\n",
      "164 33\n",
      "164 34\n",
      "164 35\n",
      "164 36\n",
      "164 37\n",
      "164 38\n",
      "164 39\n",
      "164 40\n",
      "164 41\n",
      "165 12\n",
      "165 15\n",
      "165 16\n",
      "165 19\n",
      "165 20\n",
      "165 21\n",
      "165 22\n",
      "166 12\n",
      "166 15\n",
      "166 16\n",
      "166 19\n",
      "166 20\n",
      "166 21\n",
      "166 22\n",
      "167 12\n",
      "167 15\n",
      "167 16\n",
      "167 17\n",
      "167 18\n",
      "167 19\n",
      "167 20\n",
      "167 21\n",
      "167 22\n",
      "167 25\n",
      "167 26\n",
      "167 27\n",
      "167 28\n",
      "167 29\n",
      "167 30\n",
      "167 31\n",
      "167 32\n",
      "167 33\n",
      "167 34\n",
      "167 35\n",
      "167 36\n",
      "167 37\n",
      "167 38\n",
      "167 39\n",
      "167 40\n",
      "167 41\n",
      "168 12\n",
      "168 15\n",
      "168 16\n",
      "168 17\n",
      "168 18\n",
      "168 19\n",
      "168 20\n",
      "168 21\n",
      "168 22\n",
      "168 25\n",
      "168 26\n",
      "168 27\n",
      "168 28\n",
      "168 29\n",
      "168 30\n",
      "168 31\n",
      "168 32\n",
      "168 33\n",
      "168 34\n",
      "168 35\n",
      "168 36\n",
      "168 37\n",
      "168 38\n",
      "168 39\n",
      "168 40\n",
      "168 41\n",
      "169 12\n",
      "169 15\n",
      "169 16\n",
      "169 19\n",
      "169 20\n",
      "169 21\n",
      "169 22\n",
      "170 12\n",
      "170 15\n",
      "170 16\n",
      "170 17\n",
      "170 18\n",
      "170 19\n",
      "170 20\n",
      "170 21\n",
      "170 22\n",
      "170 25\n",
      "170 26\n",
      "170 27\n",
      "170 28\n",
      "170 29\n",
      "170 30\n",
      "170 31\n",
      "170 32\n",
      "170 33\n",
      "170 34\n",
      "170 35\n",
      "170 36\n",
      "170 37\n",
      "170 38\n",
      "170 39\n",
      "170 40\n",
      "170 41\n",
      "171 12\n",
      "171 15\n",
      "171 16\n",
      "171 17\n",
      "171 18\n",
      "171 19\n",
      "171 20\n",
      "171 21\n",
      "171 22\n",
      "171 25\n",
      "171 26\n",
      "171 27\n",
      "171 28\n",
      "171 29\n",
      "171 30\n",
      "171 31\n",
      "171 32\n",
      "171 33\n",
      "171 34\n",
      "171 35\n",
      "171 36\n",
      "171 37\n",
      "171 38\n",
      "171 39\n",
      "171 40\n",
      "171 41\n",
      "172 12\n",
      "172 15\n",
      "172 16\n",
      "172 19\n",
      "172 20\n",
      "172 21\n",
      "172 22\n",
      "173 12\n",
      "173 15\n",
      "173 16\n",
      "173 19\n",
      "173 20\n",
      "173 21\n",
      "173 22\n",
      "174 12\n",
      "174 15\n",
      "174 16\n",
      "174 17\n",
      "174 18\n",
      "174 19\n",
      "174 20\n",
      "174 21\n",
      "174 22\n",
      "174 25\n",
      "174 26\n",
      "174 27\n",
      "174 28\n",
      "174 29\n",
      "174 30\n",
      "174 31\n",
      "174 32\n",
      "174 33\n",
      "174 34\n",
      "174 35\n",
      "174 36\n",
      "174 37\n",
      "174 38\n",
      "174 39\n",
      "174 40\n",
      "174 41\n",
      "175 12\n",
      "175 15\n",
      "175 16\n",
      "175 17\n",
      "175 18\n",
      "175 19\n",
      "175 20\n",
      "175 21\n",
      "175 22\n",
      "175 25\n",
      "175 26\n",
      "175 27\n",
      "175 28\n",
      "175 29\n",
      "175 30\n",
      "175 31\n",
      "175 32\n",
      "175 33\n",
      "175 34\n",
      "175 35\n",
      "175 36\n",
      "175 37\n",
      "175 38\n",
      "175 39\n",
      "175 40\n",
      "175 41\n",
      "176 12\n",
      "176 15\n",
      "176 16\n",
      "176 17\n",
      "176 18\n",
      "176 19\n",
      "176 20\n",
      "176 21\n",
      "176 22\n",
      "177 12\n",
      "177 15\n",
      "177 16\n",
      "178 12\n",
      "178 15\n",
      "178 16\n",
      "178 17\n",
      "178 18\n",
      "178 19\n",
      "178 20\n",
      "178 21\n",
      "178 22\n",
      "179 12\n",
      "179 15\n",
      "179 16\n",
      "179 42\n",
      "179 43\n",
      "179 44\n",
      "179 45\n",
      "179 46\n",
      "179 47\n",
      "179 48\n",
      "179 49\n",
      "179 51\n",
      "179 52\n",
      "179 53\n",
      "180 12\n",
      "180 15\n",
      "180 16\n",
      "181 12\n",
      "181 15\n",
      "181 16\n",
      "181 19\n",
      "181 20\n",
      "181 21\n",
      "181 22\n",
      "181 42\n",
      "181 43\n",
      "181 44\n",
      "181 45\n",
      "181 46\n",
      "181 47\n",
      "181 48\n",
      "181 49\n",
      "181 51\n",
      "181 52\n",
      "181 53\n",
      "182 42\n",
      "182 43\n",
      "182 44\n",
      "182 45\n",
      "182 46\n",
      "182 47\n",
      "182 48\n",
      "182 49\n",
      "182 50\n",
      "182 51\n",
      "182 52\n",
      "182 53\n",
      "182 58\n",
      "182 59\n",
      "182 62\n",
      "182 63\n",
      "182 66\n",
      "182 70\n",
      "182 82\n",
      "182 92\n",
      "182 93\n",
      "182 94\n",
      "182 95\n",
      "182 96\n",
      "182 97\n",
      "182 98\n",
      "182 99\n",
      "182 100\n",
      "182 102\n",
      "182 103\n",
      "182 104\n",
      "182 105\n",
      "182 106\n",
      "182 107\n",
      "182 108\n",
      "182 109\n",
      "182 110\n",
      "182 112\n",
      "182 113\n",
      "182 114\n",
      "182 115\n",
      "182 119\n",
      "182 120\n",
      "182 121\n"
     ]
    }
   ],
   "source": [
    "file_name=\"./data/Little_Rock_Lake.txt\"\n",
    "\n",
    "DG = nx.DiGraph()\n",
    "\n",
    "in_file=open(file_name,'r')\n",
    "while True:\n",
    "    next_line=in_file.readline()\n",
    "    if not next_line:\n",
    "        break\n",
    "    next_line_fields=next_line[:-2].split(' ')\n",
    "    node_a=next_line_fields[1] #there is a space in the beginning \n",
    "                               #of each edge\n",
    "    node_b=next_line_fields[2]\n",
    "    print node_a,node_b\n",
    "    DG.add_edge(node_a, node_b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trophic Species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the trophic pattern key\n",
    "this is a way to generate a unique key starting from the ordered lists of preys and predators attached to nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_node_key(node):\n",
    "    out_list=[]\n",
    "    for out_edge in DG.out_edges(node):\n",
    "        out_list.append(out_edge[1])\n",
    "    in_list=[]\n",
    "    for in_edge in DG.in_edges(node):\n",
    "        in_list.append(in_edge[0])\n",
    "    out_list.sort()\n",
    "    out_list.append('-')\n",
    "    in_list.sort()\n",
    "    out_list.extend(in_list)\n",
    "    return out_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping the Trophic Species and Regenerating the Trophic network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S: 93\n",
      "L: 1034\n",
      "L/S: 11.1182795699\n"
     ]
    }
   ],
   "source": [
    "def TrophicNetwork(DG):\n",
    "    trophic={}\n",
    "    for n in DG.nodes():\n",
    "        k=tuple(get_node_key(n))\n",
    "        if not trophic.has_key(k):\n",
    "            trophic[k]=[]\n",
    "        trophic[k].append(n)\n",
    "    for specie in trophic.keys():\n",
    "        if len(trophic[specie])>1:\n",
    "            for n in trophic[specie][1:]:\n",
    "                DG.remove_node(n)\n",
    "    return DG\n",
    "\n",
    "#deleting the environment\n",
    "DG.remove_node('0')\n",
    "\n",
    "TrophicDG=TrophicNetwork(DG)\n",
    "print \"S:\",TrophicDG.number_of_nodes()\n",
    "print \"L:\",TrophicDG.number_of_edges()\n",
    "print \"L/S:\",float(TrophicDG.number_of_edges())/ \\\n",
    "TrophicDG.number_of_nodes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classes in Food Webs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B: 0.129032258065\n",
      "I: 0.860215053763\n",
      "T: 0.010752688172\n"
     ]
    }
   ],
   "source": [
    "def compute_classes(DG):\n",
    "    basal_species=[]\n",
    "    top_species=[]\n",
    "    intermediate_species=[]\n",
    "    for n in DG.nodes():\n",
    "        if DG.in_degree(n)==0:\n",
    "            basal_species.append(n)\n",
    "        elif DG.out_degree(n)==0:\n",
    "            top_species.append(n)\n",
    "        else:\n",
    "            intermediate_species.append(n)\n",
    "    return (basal_species,intermediate_species,top_species)\n",
    "\n",
    "(B,I,T)=compute_classes(TrophicDG)\n",
    "print \"B:\",float(len(B))/(len(B)+len(T)+len(I))\n",
    "print \"I:\",float(len(I))/(len(B)+len(T)+len(I))\n",
    "print \"T:\",float(len(T))/(len(B)+len(T)+len(I))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Proportion of links among classes and ratio prey/predators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "links in BT: 0.000967117988395\n",
      "links in BI: 0.0909090909091\n",
      "links in II: 0.908123791103\n",
      "links in IT: 0.0\n",
      "P/R: 1.13580246914\n"
     ]
    }
   ],
   "source": [
    "def InterclassLinkProportion(DG,C1,C2):\n",
    "    count=0\n",
    "    for n1 in C1:\n",
    "        for n2 in C2:\n",
    "            if DG.has_edge(n1,n2):\n",
    "                count+=1\n",
    "    return float(count)/DG.number_of_edges()\n",
    "    \n",
    "print \"links in BT:\",InterclassLinkProportion(TrophicDG,B,T)\n",
    "print \"links in BI:\",InterclassLinkProportion(TrophicDG,B,I)\n",
    "print \"links in II:\",InterclassLinkProportion(TrophicDG,I,I)\n",
    "print \"links in IT:\",InterclassLinkProportion(TrophicDG,I,T)\n",
    "\n",
    "#Ratio prey/predators\n",
    "print \"P/R:\",float((len(B)+len(I)))/(len(I)+len(T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}