FINAL WORK.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 645,
   "metadata": {},
   "outputs": [],
   "source": [
    "import community as community_louvain\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import ndlib.models.ModelConfig as mc\n",
    "import ndlib.models.epidemics as ep\n",
    "import matplotlib.pyplot as plt\n",
    "import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G=nx.Graph()\n",
    "gr=pd.read_csv('karate.csv')\n",
    "#gr=pd.read_csv('dolphin.csv')\n",
    "#gr=pd.read_csv('football.csv')\n",
    "edge=[(gr['Source'][i],gr['Destination'][i]) for i in range(0,len(gr['Source']))]\n",
    "G.add_edges_from(edge)\n",
    "node=max([max(x[0],x[1]) for x in edge])\n",
    "# compute the best partition\n",
    "# partition contains color of each node\n",
    "partition = community_louvain.best_partition(G)\n",
    "\n",
    "# visualization of clusters\n",
    "pos = nx.spring_layout(G)\n",
    "cmap = cm.get_cmap('viridis', max(partition.values()) + 1)\n",
    "nx.draw_networkx_nodes(G, pos, partition.keys(), node_size=200,cmap=cmap, node_color=list(partition.values()),)\n",
    "nx.draw_networkx_edges(G, pos, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gateway graph nodes: [5, 1, 6, 7, 9, 3, 10, 11, 28, 29, 30, 24, 31, 2, 32, 33, 34, 14, 20]\n"
     ]
    }
   ],
   "source": [
    "# Generating gateEdges\n",
    "# gateEdges contains edges--> [(a,b),(b,c)]\n",
    "gateEdges=[]\n",
    "for x in edge:\n",
    "    if(partition[x[0]]!=partition[x[1]]):\n",
    "        gateEdges.append(x)\n",
    "\n",
    "# Generating gate nodes \n",
    "gateNodesDict={}\n",
    "\n",
    "for tup in gateEdges:\n",
    "    gateNodesDict[tup[0]]=1\n",
    "    gateNodesDict[tup[1]]=1\n",
    "\n",
    "gateNodesList=list(gateNodesDict.keys())\n",
    "\n",
    "print(\"Gateway graph nodes:\",gateNodesList)\n",
    "for x in gateNodesList:\n",
    "    for y in gateNodesList:\n",
    "        if(x==y or ((x,y) in gateEdges) or ((y,x) in gateEdges)):\n",
    "            continue\n",
    "        else:\n",
    "            if(partition[x]==partition[y]):\n",
    "                if((x,y) in edge or (y,x) in edge):\n",
    "                    gateEdges.append((x,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 648,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gate graph\n",
    "# This is the gate way graph\n",
    "# gate\n",
    "tot_g=[]\n",
    "for i in range(0,500):\n",
    "    g = G\n",
    "    attr = {(u, v): {\"weight\": round(random.uniform(0,1),2)} for (u, v) in G.edges()}\n",
    "    nx.set_edge_attributes(g, attr)\n",
    "    tot_g.append(g)\n",
    "\n",
    "for k in range(0,500):\n",
    "    tot_g[k].add_nodes_from(gateNodesList)\n",
    "    for i in range(0,len(gateEdges)):\n",
    "        tot_g[k].add_edge(gateEdges[i][0],gateEdges[i][1],weight=round(random.uniform(0,1),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 29.947916666666455), (34, 27.62784090909075), (32, 19.81534090909079), (4, 11.079545454545407), (33, 7.623106060606124), (3, 5.800189393939438), (5, 5.681818181818216), (24, 5.681818181818216), (7, 2.9356060606060557), (26, 2.9356060606060557), (9, 1.0416666666666659), (31, 0.6155303030302995), (2, 0.28409090909090984), (25, 0.094696969696969), (6, 0.0), (8, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), (13, 0.0), (14, 0.0), (15, 0.0), (16, 0.0), (17, 0.0), (18, 0.0), (19, 0.0), (20, 0.0), (21, 0.0), (22, 0.0), (23, 0.0), (27, 0.0), (28, 0.0), (29, 0.0), (30, 0.0)]\n"
     ]
    }
   ],
   "source": [
    "#BCS Score\n",
    "def BCS(gg,nodes):\n",
    "    BCS_=[0]*(nodes+1)\n",
    "    for i in gg:\n",
    "        x=(nx.betweenness_centrality(i,weight='weight'))\n",
    "        #print(x)\n",
    "        for j in x:\n",
    "            BCS_[j]+=(1/10)*x[j]\n",
    "    #BCS_=[round(i,3) for i in BCS_]\n",
    "    return BCS_\n",
    "BCSSS=BCS(tot_g,max([max(a[0],a[1]) for a in gateEdges]))\n",
    "BCS_value={}\n",
    "for i in range(1,max([max(a[0],a[1]) for a in gateEdges])+1):\n",
    "    BCS_value[i]=BCSSS[i]\n",
    "BCS_value=(sorted(BCS_value.items(), key=lambda x: x[1],reverse=True))\n",
    "print(BCS_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# Model selection - diffusion time\n",
    "model = ep.SIModel(G)\n",
    "\n",
    "# Model Configuration\n",
    "cfg = mc.Configuration()\n",
    "cfg.add_model_parameter('beta', 0.03)\n",
    "cfg.add_model_parameter(\"fraction_infected\", 1/max([max(x[0],x[1]) for x in edge]))\n",
    "model.set_initial_status(cfg)\n",
    "\n",
    "# Simulation execution\n",
    "iterations = model.iteration_bunch(200)\n",
    "\n",
    "#Mapping diffusion_time_to_each_node\n",
    "time_of_diffusion={}\n",
    "for i in range(1,node+1):\n",
    "    time_of_diffusion[i]=-1\n",
    "for i in iterations:\n",
    "    for j in i['status']:\n",
    "        if(i['status'][j]==1):\n",
    "            time_of_diffusion[j]=i['iteration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 17, 2: 12, 3: 14, 4: 18, 5: 18, 6: 27, 7: 19, 8: 14, 9: 4, 10: 17, 11: 31, 12: 26, 13: 24, 14: 13, 15: 38, 16: 42, 17: 74, 18: 31, 19: 10, 20: 10, 21: 24, 22: 59, 23: 47, 24: 6, 25: 9, 26: 46, 27: 11, 28: 5, 29: 0, 30: 13, 31: 8, 32: 17, 33: 17, 34: 3}\n"
     ]
    }
   ],
   "source": [
    "print(time_of_diffusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 34, 32]\n",
      "[5, 1, 6, 7, 9, 3, 10, 11, 28, 29, 30, 24, 31, 2, 32, 33, 34, 14, 20]\n",
      "44\n"
     ]
    }
   ],
   "source": [
    "sensor_nodes=[BCS_value[i][0] for i in range(0,3)]\n",
    "print(sensor_nodes)\n",
    "print(gateNodesList)\n",
    "print(len(gateEdges))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14, 14]\n"
     ]
    }
   ],
   "source": [
    "delta_t=[]\n",
    "min_v=min([time_of_diffusion[i] for i in sensor_nodes])\n",
    "min_n=-1\n",
    "for i in sensor_nodes:\n",
    "    if(time_of_diffusion[i]==min_v):\n",
    "        min_n=i\n",
    "        break\n",
    "delta_t=[abs(time_of_diffusion[i]-min_v) for i in sensor_nodes if(i!=min_n)]\n",
    "print(delta_t)"
   ]
  },
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   ],
   "source": [
    "timeOfDiffusions=[]\n",
    "for i in tot_g:\n",
    "    print(len(i.edges()))\n",
    "    # Model selection - diffusion time\n",
    "    model = ep.SIModel(i)\n",
    "\n",
    "    # Model Configuration\n",
    "    cfg = mc.Configuration()\n",
    "    cfg.add_model_parameter('beta', 0.03)\n",
    "    cfg.add_model_parameter(\"fraction_infected\", 1/max([max(x[0],x[1]) for x in edge]))\n",
    "    model.set_initial_status(cfg)\n",
    "\n",
    "    # Simulation execution\n",
    "    iterations = model.iteration_bunch(200)\n",
    "\n",
    "    #Mapping diffusion_time_to_each_node\n",
    "    time_of_diffusion={}\n",
    "    for i in range(1,node+1):\n",
    "        time_of_diffusion[i]=-1\n",
    "    for i in iterations:\n",
    "        for j in i['status']:\n",
    "            if(i['status'][j]==1):\n",
    "                time_of_diffusion[j]=i['iteration']\n",
    "    timeOfDiffusions.append(len(time_of_diffusion))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {},
   "outputs": [
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     ]
    }
   ],
   "source": [
    "for i in timeOfDiffusions:\n",
    "    print(i)\n",
    "print(len(gateNodesList))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g__ = nx.Graph()\n",
    "g__.add_edges_from(gateEdges)\n",
    "print(len(gateEdges))\n",
    "allDiffusionTime = []\n",
    "mcaps=[tot_g[i] for i in random.sample(range(0, 500), 20)]\n",
    "toTakeArgmax=[]\n",
    "for node in gateNodesList:\n",
    "#     print(node)\n",
    "    bfs_tree = nx.bfs_tree(g__, source = node)\n",
    "    nx.draw(bfs_tree)\n",
    "    vecOfArgmax=[]\n",
    "    for mcap in mcaps:\n",
    "        model = ep.SIModel(bfs_tree)\n",
    "        # Model Configuration\n",
    "        cfg = mc.Configuration()\n",
    "        cfg.add_model_parameter('beta', 0.03)\n",
    "        cfg.add_model_parameter(\"fraction_infected\", 1/max([max(x[0],x[1]) for x in bfs_tree.edges()]))\n",
    "        model.set_initial_status(cfg)\n",
    "\n",
    "        # Simulation execution\n",
    "        iterations = model.iteration_bunch(200)\n",
    "\n",
    "        #Mapping diffusion_time_to_each_node\n",
    "        \n",
    "        time_of_diffusion={}\n",
    "        for i in range(1,node+1):\n",
    "            time_of_diffusion[i]=-1\n",
    "        for i in iterations:\n",
    "            for j in i['status']:\n",
    "                if(i['status'][j]==1):\n",
    "                    time_of_diffusion[j]=i['iteration']\n",
    "        allDiffusionTime.append(time_of_diffusion)\n",
    "        \n",
    "        for node in gateNodesList:\n",
    "            if node not in time_of_diffusion.keys(): \n",
    "                time_of_diffusion[node]=-1\n",
    "        \n",
    "        min_v=min([time_of_diffusion[i] for i in sensor_nodes])\n",
    "        min_n=-1\n",
    "        for i in sensor_nodes:\n",
    "            if(time_of_diffusion[i]==min_v):\n",
    "                min_n=i\n",
    "                break\n",
    "        mu=[abs(time_of_diffusion[i]+min_v)/2 for i in sensor_nodes if(i!=min_n)]\n",
    "#         print(len(delta_t),len(mu))\n",
    "        \n",
    "        arrivals=[]\n",
    "        \n",
    "        for sensor in sensor_nodes:\n",
    "            if(time_of_diffusion[sensor]==-1):\n",
    "                arrivals.append(200)\n",
    "            else:\n",
    "                arrivals.append(time_of_diffusion[sensor])\n",
    "                \n",
    "#         print(\"arrivals\",arrivals)\n",
    "                \n",
    "        new_delta_ts = []\n",
    "            \n",
    "        for i in range(0,len(arrivals)):\n",
    "            new_delta_t=[]\n",
    "            for j in range(0,len(arrivals)):\n",
    "                if(i!=j):\n",
    "                    new_delta_t.append(abs(arrivals[j]-arrivals[i]))\n",
    "            new_delta_ts.append(new_delta_t)\n",
    "#             print(\"new_delta_ts\",new_delta_ts)\n",
    "#         print(new_delta_ts)\n",
    "            \n",
    "        lambd = []\n",
    "        for i in range(0,len(new_delta_ts)-1):\n",
    "#             print(\"---> \",len(new_delta_ts[i]),len(new_delta_ts[i+1]))\n",
    "            val = np.correlate(new_delta_ts[i],new_delta_ts[i+1],\"same\")\n",
    "            for i in range(0,len(val)):\n",
    "                if (val[i]==0):\n",
    "                    val[i]=1\n",
    "            lambd.append(val)\n",
    "#         print(\"lambd\",lambd)\n",
    "#         print(\"delta_t\",delta_t,\"mu\",mu)    \n",
    "        sub = np.subtract(delta_t,mu)\n",
    "#         print(\"sub\",sub)\n",
    "        lambd[0][1]=0\n",
    "        powerOfe = np.dot(np.dot((sub), np.linalg.inv(lambd) ),np.transpose(sub))\n",
    "#         print(\"powerOfe\",powerOfe)\n",
    "        powerOfe=abs(powerOfe)\n",
    "        fun = (1/np.linalg.det(lambd))*np.exp(-(1/2)*powerOfe)\n",
    "        vecOfArgmax.append(fun)\n",
    "    toTakeArgmax.append(vecOfArgmax)\n",
    "print(toTakeArgmax)\n",
    "#     print(node)\n",
    "# nx.draw(nx.bfs_tree(g, source = 5))\n",
    "#     print(time_of_diffusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 657,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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-1, 32: -1, 33: -1, 34: -1}, {1: 113, 2: 67, 3: 160, 4: -1, 5: 124, 6: 119, 7: 136, 8: -1, 9: 47, 10: 62, 11: 152, 12: -1, 13: -1, 14: 32, 15: -1, 16: -1, 17: -1, 18: -1, 19: -1, 20: 0, 34: 28, 29: 37, 24: 39, 33: 39, 31: 43, 30: 72, 28: 79, 32: 161}, {1: -1, 2: -1, 3: -1, 4: -1, 5: -1, 6: -1, 7: -1, 8: -1, 9: -1, 10: -1, 11: -1, 12: -1, 13: -1, 14: -1, 15: -1, 16: -1, 17: -1, 18: -1, 19: -1, 20: -1, 29: 0, 28: -1, 30: -1, 24: -1, 31: -1, 32: -1, 33: -1, 34: -1}, {1: -1, 2: -1, 3: -1, 4: -1, 5: -1, 6: 0, 7: -1, 8: -1, 9: -1, 10: -1, 11: -1, 12: -1, 13: -1, 14: -1, 15: -1, 16: -1, 17: -1, 18: -1, 19: -1, 20: -1, 28: -1, 29: -1, 30: -1, 24: -1, 31: -1, 32: -1, 33: -1, 34: -1}, {1: -1, 2: -1, 3: -1, 4: -1, 5: -1, 6: -1, 7: -1, 8: -1, 9: -1, 10: -1, 11: -1, 12: -1, 13: -1, 14: -1, 15: -1, 16: -1, 17: -1, 18: -1, 19: -1, 20: -1, 32: 0, 28: -1, 29: -1, 30: -1, 24: -1, 31: -1, 33: -1, 34: -1}]\n"
     ]
    }
   ],
   "source": [
    "print(allDiffusionTime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(nx.bfs_tree(g, source = 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 19\n"
     ]
    }
   ],
   "source": [
    "print(len(toTakeArgmax[0]),len(toTakeArgmax))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 660,
   "metadata": {},
   "outputs": [],
   "source": [
    "finArg = np.argmax(toTakeArgmax,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "metadata": {},
   "outputs": [],
   "source": [
    "id=-1\n",
    "mx=-1\n",
    "for i in range(0,len(finArg)):\n",
    "    if(toTakeArgmax[i][finArg[i]]>mx):\n",
    "        mx=toTakeArgmax[i][finArg[i]]\n",
    "        id=i\n",
    "clusterNode=gateNodesList[id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    }
   ],
   "source": [
    "print(clusterNode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "partition[clusterNode]\n",
    "color=partition[clusterNode]\n",
    "print(partition[clusterNode])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 680,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(partition[29])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "candidateCluster=[]\n",
    "for e in G.edges():\n",
    "    if(partition[e[0]]==color and partition[e[1]]==color):\n",
    "        candidateCluster.append(e)\n",
    "candidateGraph = nx.Graph()\n",
    "candidateGraph.add_edges_from(candidateCluster)\n",
    "nx.draw(candidateGraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 666,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{32, 24, 25, 26, 28, 29}\n",
      "list [32, 24, 25, 26, 28, 29]\n"
     ]
    }
   ],
   "source": [
    "myset=set([])\n",
    "for e in candidateCluster:\n",
    "    myset.add(e[0])\n",
    "    myset.add(e[1])\n",
    "print(myset)\n",
    "my=[]\n",
    "for ele in myset:\n",
    "    my.append(ele)\n",
    "print(\"list\",my)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 667,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dkIgahMVHAIATJ05gyJAheP311/HKK68YP36s9BZW5Z9F3qkyyHDv4fT77p/HN7CrO+aE+zZqetMUer0eJSUl9YqwvLwcYWFh6NevH/r374/evXujVavGbbJNRNLA4iMcPHgQI0eOxEcffYSEhIQHvuZGpRYph0tRcqUC5dW10KiU8G/vjLjelnEC++XLl43rhIWFhTh9+jSCgoKMI8K+ffuidevWomMSkQVg8UlcXl4exo0bhzVr1mDkyJGi45jN7du3sW/fPmMRHjx4EL6+vsYRYb9+/eDl5SU6JhEJwOKTsK1bt2LGjBlITk5GeHi46DjNqqamBt9++2296VEnJ6d664TdunWDnR33dCCydSw+ifr73/+OhQsXYvv27ejTp4/oOC3OYDDg1KlT9Yrw5s2b9Z4n7NOnD+ztxU/jEpF5sfgkaOXKlVixYgUyMzPh7+8vOo7FuHLlSr3nCU+dOoXevXsbR4TPPPMMXFxa5gYeImo+LD4JMRgMWLJkCTZs2ICsrCw89thjoiNZtIqKinrrhAcOHMDjjz9uHBH269cPHTt2FB2TiBqJxScRer0eiYmJKCwsRHp6Ojw8PERHsjq1tbU4cuSIcURYWFgItVpdb50wICCA64REFo7FJwG1tbWYOnUqLly4gG+++Ya39ZuJwWDAmTNn6q0TXr9+Hc8884yxCIOCgqBSqURHJaJfYfHZuKqqKowbNw56vR7JyclwcHAQHcmm/fTTTygqKjKOCE+cOIFevXrVWyds27at6JhEksbis2Hl5eUYNWoUvLy88OWXX/LEAwEqKyuxf/9+44hw//796NSpU73p0ccee4zbrRG1IBafjSorK8OQIUMQGhqKv/zlL1x3shA6nQ5Hjx41FuHu3buhVCrrFeGTTz4JuVwuOiqRzWLx2aCLFy8iKioKY8aMwbJlyziasGAGgwHnzp2rt0549erVeuuEwcHBUKvVoqMS2QwWn405deoUoqKikJiYiNdee010HGqCa9euYc+ePcYR4XfffYeePXsaizAsLAyurq6iYxJZLRafDTl8+DCGDx+O999/H1OmTBEdh8zkzp07OHDggHFEuG/fPnh5edV7ntDHx4cje6IGYvHZiF27diEuLg6fffYZoqOjRcehZqTT6XD8+PF6zxPKZLJ664SBgYFcJyT6HSw+G7Bjxw5MmTIF69evR2RkpOg41MIMBgN++OGHeuuEly9fRt++fY1FGBISwkdZiH7B4rNy//jHPzB//nxs27YNISEhouOQhbh+/Tr27NljHBEeO3YMPXr0qLdO6O7uLjomkRAsPiuWlJSEDz74ABkZGejevbvoOGTB7t69i4MHDxpHhHv37kX79u3rTY927tyZ64QkCSw+K2QwGLB8+XKsXbsWWVlZ8PHxER2JrExdXR2+++4744hw9+7d0Ov19YqwZ8+eUCgUoqMSmR2Lz8ro9XrMnz8fubm5yMjIgKenp+hIZAMMBgMuXLhQb53w4sWLCA0NNRbh008/DUdHR9FRiUzG4rMiOp0OM2bMwOnTp7Fjxw6eDUfN6ubNm/XWCY8cOYKAgIB6o0Ke8kHWiMVnJaqrqzF+/HhUV1cjJSWF33lTi6uqqsKhQ4eMI8I9e/bAw8OjXhH6+vpynZAsHovPClRUVGD06NFwd3fH3//+d7Rq1Up0JCLo9Xr861//Mq4R7t69GzU1NcYS7N+/P5566qkWXSe8XqlFSnEpSq6Wo7xaB41KAX9PDcb08Yark32L5SDLxuKzcDdu3MDQoUPRu3dvJCUl8aFksmgXL16st054/vx5hISEGMswNDQUTk5OZv+8Ry/dQlL+WRScLgMAaHV646+pFHYwAAjv6o45A3zRsyOXCKSOxWfBLl++jKioKIwaNQrvv/8+p5DI6vz888/Yu3evcZ3w22+/hb+/v3FEGBYWZvINWuv2ncfytBJU6+rwsH/NZDJApZBj8TB/JIT6mPQ5ybqx+CzUmTNnEBUVhTlz5uD1118XHYfILKqrq1FcXGwcERYVFcHV1bXeOqGfn1+Dv8m7V3onUVWrf/SLf6FW2mHxsG4sPwlj8Vmgo0ePYtiwYVi6dCmmT58uOg5Rs9Hr9Th58qRxRFhYWIi7d+8iLCzMuAl3r169HniI8tFLtxD/t32oqq1r9OdVK+XYODMUgd6c9pQiFp+FKSoqQkxMDJKSkhAXFyc6DlGLu3TpEoqKioxFeO7cOQQHB9dbJ9RoNJj51SFknfzpodObv0cmAwZ3b4dPE4LM/wWQxWPxWZD09HRMnjwZ69atQ1RUlOg4RBbh1q1b2Lt3r7EIi4uL4ftkL5SHvw69rOk3e9kr7LBnYQTv9pQgFp+F2LhxI+bNm4ctW7agb9++ouMQWSytVoulyUXYeOIO6mDX5OuoFHZ4bZAfZj37hBnTkTXgRnwW4LPPPsOyZcuQnZ2NHj16iI5DZDF0Oh3u3LmDyspKVFZWGv//TFmVSaUHANU6PUquVJgpKVkTFp9ABoMBH3zwAVavXo1du3bhiSf4nSdZJ71ej7t37/6moH7v5w19jU6ng6OjI5ycnIz/OTo64uaTY4HWj5ucu7y61gxfPVkbFp+ZNXTnCIPBgIULF2Lnzp3YvXs3OnToIDA1SYXBYEB1dbVJZfSg11RVVUGtVv+moH7v597e3o98jZOTE+zt7R/4aEPixm+x5ciPJv95aFS/vVuUbB+Lz0wevnPEVfxP9mnjzhFPdnDGrFmz8N1336GgoABt27YVFZssWE1NjVlHT/c/1qpVq98tmv/8mLu7O3x8fB76GicnJ6jVatjZmTb12Bj+nhrYK67We581lkphB//2zmZMRdaCN7eYQWN2jrBX2MGjtBD2lw4gNTW1WbZvopZ1fx3qUeXT2MIyGAxwdnZu0Miooa9xcHCwiTP2rldqEfZhrknFx7s6pcv63wGCNWbnCIMBqK7Vo9Q9GO9MncLSa2F6vR5VVVVmHT1VVlaipqamUWXk5ubWoMLiZuS/z83JHgP83E16jm9gV3eWnkSx+Exw9NItLE8radR2SQCgt1Pi/2WeRR8fN+4c8QAGgwFardbkgvrPn99fh2ro6KlDhw4NGmGpVCruoyrAy+G+2H3mepN2blEp5JgT7tsMqcgasPhMkJR/FtW6xr/pAKBaV4dV+WetfueI2tpas07v3f+5QqFo8FSem5ubcR3qYQXl4ODQoutQ1Lx6dnTB4mH+jd6rE3U18Ks9hx5eg5svHFk0Fl8TXa/UouB0WZOmWYB70555p8pwo1LbItMtdXV1xoIx5918er2+wQWl0WjQoUOHR04JOjo62sQ6FDW/+xtNN+Z0htei/PHp/PewePFNLF++nKN1CeK/Lk2UUlxq8jVkAFIOl9bbOcJgMBifhzLn3XxarfY35fKw8nFzc2vQmhXXoUi0hFAfBHq7YFX+WeSdKoMM9x5Ov+/+eXwDu7pjTrgvAr1dEJOZiYEDB0KtVuOdd94Rlp3E4F2dTWSu54iUl7+FYc+XxsK6e/cuVCpVo+7Ue9RrHB0doVar+Z0t2bwblVqkHC5FyZUKlFfXQqNSwr+9M+J6//YE9p9++gkDBgzA9OnTsWDBAkGJSQSO+JqovFpnluv0DOqLD/4YbywoBwcHnrJO1ESuTvYN3nuzXbt2yM7OxoABA6BSqTB37txmTkeWgsXXRBqVef7oOrZzha8v7y4jEsHb2xs5OTnG8uP5l9LA4msi7hxBZBt8fHyQnZ2NgQMHQqVSISEhQXQkama8t7uJ4vp4m3wNA4C43qZfh4hM06VLF2RmZuL1119HSkqK6DjUzFh8TXR/54im3i/CnSOILEv37t2Rnp6Ol19+Gd98843oONSMWHwmeDncFypF025EsTPUYdaznc2ciIhM0bNnT2zfvh3Tpk1DZmam6DjUTFh8Jri/c4Ra2bg/RpXCDq3PZeOtWRNx8+bNZkpHRE0RHByM1NRUJCQkoKCgQHQcagYsPhMlhPpg8bBuUCvlj5z2lMkAtVKOt4d3w/5/rEC3bt0QHByMo0ePtkxYImqQsLAwbNy4EWPGjMHevXtFxyEzky9ZsmSJ6BDWLtDbBc92ccPPd2pw6ecqKO1k0On/vS+ASmEHuZ0Mz3XzwP+LDcSg7p6Qy+UYPHgwPDw8MH78eHh7e6NHjx4Cvwoi+rXHH38cPXr0QHx8PCIiInhYtA3hzi1m1pidI+47duwYYmJiMGLECPz3f/83lEqeCk1kKbZu3YpZs2YhMzMTgYGBouOQGbD4LMTPP/+MiRMn4s6dO0hOTka7du1ERyKiXyQnJyMxMRG5ubnw9/cXHYdMxDU+C9GmTRts374d4eHhCAoKwr59+0RHIqJfjB07Fh988AEGDRqEc+fOiY5DJuIanwWRyWQYOHAgunTpgvHjx0Oj0aBPnz7cXJrIAvTs2ROOjo6YMWMGYmJi0Lp1a9GRqIk41WmhTp8+jejoaPTt2xd//etfoVKpREciIgArV67EX//6VxQUFPCGFyvFqU4L5efnh/3796O8vBz9+/fHxYsXRUciIgCvvvoqpk2bhsjISFy7dk10HGoCFp8Fc3JywsaNGzF27Fg8/fTTyMvLEx2JiAAsWrQIY8eOxaBBg7gJhRXiVKeVyMnJwcSJE7FgwQLMnz+f635EghkMBixcuBC5ubnIycnhmp8VYfFZkQsXLiA2NhZPPPEE1qxZAycnJ9GRiCTNYDDg1VdfRXFxMTIyMvietBKc6rQinTp1QmFhIRwdHREaGoozZ86IjkQkaTKZDB9//DG6d++OkSNH4u7du6IjUQOw+KyMSqXCmjVrMHfuXISFhfH4FCLB7Ozs8Omnn8Lb2xvR0dHQarWiI9EjcKrTiu3btw9jxozB1KlT8e6778LOjt/HEImi0+kwYcIEaLVapKSkcOtBC8bis3JXr17F2LFj4ezsjHXr1qFNmzaiIxFJVm1tLWJjY6FSqfD1119DoVCIjkQPwCGClfP09EROTg66dOmC4OBgHD9+XHQkIslSKpVITk7G7du3MXXqVOj1etGR6AFYfDZAqVTi448/xpIlSxAREYENGzaIjkQkWSqVCqmpqbh06RJeeuklcFLN8nCq08YcOXIEMTExiI6OxocffsipFiJBKisrERUVhaCgIKxcuZLP3loQFp8NunnzpnGRfePGjfDw8BAdiUiSbt++jcjISERGRuKDDz5g+VkITnXaoLZt22LHjh0ICwtDUFAQDhw4IDoSkSS1bt0aGRkZ2LlzJ5YuXSo6Dv2CxWej5HI5/uu//guffPIJRowYgdWrV4uORCRJrq6uyMrKwsaNG/Hhhx+KjkPgVKcknDp1CtHR0ejXrx/+8pe/wN7eXnQkIsm5fPkyBgwYgHnz5mHevHmi40gaR3wS0LVrV+zfvx83b97Es88+i9LSUtGRiCTHy8sLOTk5+POf/4zPP/9cdBxJY/FJhLOzMzZt2oSYmBiEhIQgPz9fdCQiyenUqROys7Px3nvv4auvvhIdR7LkS5YsWSI6BLUMmUyGfv36ITAwEBMnToSdnR1CQ0N5pxlRC2rbti2GDh2KSZMmoWPHjggICBAdSXK4xidR58+fR2xsLPz8/LB69Wo4OjqKjkQkKceOHUNUVBQ+++wzPP/886LjSAqnOiXKx8cHhYWFsLe3R2hoKM6ePSs6EpGkBAYGYseOHZg5cyYyMjJEx5EUFp+EqdVqfPHFF5g9ezaeeeYZ7NixQ3QkIknp06cPUlNTMWnSJOTl5YmOIxmc6iQAwJ49ezB27FjMnDkTb7/9No84ImpB+fn5GDt2LFJTUxEWFiY6js1j8ZHRlStXMGbMGLRp0wZfffUVXFxcREcikozMzEwkJCQgLS0NQUFBouPYNH5bT0bt27dHbm4ufHx8EBISgu+++050JCLJiIqKwpo1azBixAgcPXpUdBybxscZqB65XI5hw4ahdevWmDBhAnx8fHi7NVEL6dq1Kzp16oSJEydi2LBhcHd3Fx3JJnGqk37Xt99+i5iYGMTFxeFPf/oTjzgiaiHr1q3DokWLkJ+fD19fX9FxbA6Ljx7qxo0bmDBhAnQ6HTZs2MDvQIlayOrVq/Hee++hoKAAPj4+ouPYFK7x0UO5uroiLS0NTz/9NIKCgjggzosAAAyaSURBVHDo0CHRkYgkYfr06Xj99dcRGRmJy5cvi45jU7jGR49kZ2eHyMhIdOrUCfHx8XBzc0OvXr1ExyKyeSEhIaipqUFiYiLGjBkDJycn0ZFsAqc6qVFOnjyJ6OhohIeHY+XKlTziiKgFLF26FCkpKcjLy4Obm5voOFaPU53UKN26dcOBAwdw7do1hIeHcwqGqAX88Y9/xPDhwxEVFYVbt26JjmP1WHzUaBqNBps3b8aoUaMQHByMgoIC0ZGIbJpMJsOf/vQn9O/fH0OHDkVFRYXoSFaNU51kkszMTEyaNAlvvfUW5s2bxyOOiJqRwWDASy+9hJKSEuzcuRMODg6iI1klFh+Z7IcffkBMTAy6d++Ov/3tb3wzEjUjvV6PKVOm4MqVK9i2bRtUKpXoSFaHU51ksscffxxFRUWQy+Xo27cvvv/+e9GRiGyWnZ0d1qxZgzZt2mDMmDGoqakRHcnqsPjILBwcHLB27VrMmDEDffv2xc6dO0VHIrJZCoUC69atg1wuN24wQQ3HqU4yu8LCQowbNw6zZ8/GW2+9xSOOiJqJVqvF6NGj4erqirVr10Iul4uOZBVYfNQsfvzxR4wZMwbu7u5Yu3YtWrduLToSkU2qqqrC8OHD0blzZ3z++ef8RrMB+CdEzaJDhw7Iy8uDl5cXQkJCcOLECdGRiGySWq3Gtm3bUFJSgnnz5oFjmUdj8VGzadWqFZKSkvDmm29iwIABSElJER2JyCY5OTlhx44d2L9/P9544w2W3yNwqpNaRHFxMWJjYzFu3DgsX76cRxwRNYObN28iIiICo0aNwrJly0THsVgsPmox169fR3x8PGQyGdavX889B4maQVlZGQYMGIBJkybhzTffFB3HInGqk1qMm5sb0tPT0adPHwQFBaG4uFh0JCKb4+7ujpycHPzf//0fPv74Y9FxLBKPJaIWZWdnh+eeew4dO3ZEfHw8PDw88NRTT4mORWRTnJ2dMXr0aLz00ktQqVQICgoSHcmicKqThDlx4gSio6MRGRmJjz/+GK1atRIdicimfP/99xgwYADee+89vPjii6LjWAxOdZIw3bt3x4EDB/Djjz8iPDwcP/74o+hIRDalc+fOyMrKwltvvYUNGzaIjmMxWHwkVOvWrfHPf/4Tw4cPR3BwMHbv3i06EpFN8ff3R0ZGBhITE5Gamio6jkXgGh8JJ5PJ8OyzzyIgIAATJkyASqVCSEgIjzgiMpN27dohIiIC48ePR0BAALp06SI6klBc4yOL8v333yMmJgaBgYH49NNPecQRkRnt27cPo0aNwvr16xEZGSk6jjCc6iSL0rlzZ+zZswd1dXUICwvDDz/8IDoSkc0IDQ1FSkoK4uPjJb2swOIji+Pg4IB169ZhypQpCA0NRUZGhuhIRDbj2Wefxddff43Y2FgcOHBAdBwhONVJFm3Xrl2Ij4/H3Llz8eabb3Ldj8hMtm/fjmnTpiEjI0Nyz9Ky+MjiXb58GXFxcWjfvj2+/PJLaDQa0ZGIbMLmzZsxd+5cZGdnIyAgQHScFsOpTrJ4Xl5eyM/PR7t27RASEoKTJ0+KjkRkE2JjY7FixQoMHjwYZ86cER2nxfBxBrIKCoUCI0aMgIODAxISEuDr64tu3bqJjkVk9Xr06AEXFxdMmzYNo0ePRps2bURHanac6iSrc+jQIcTGxmLixIl47733IJfLRUcisnpJSUlYsWIFdu3aBW9vb9FxmhWLj6xSWVkZ4uPjIZfLsX79eri6uoqORGT1VqxYgc8//xwFBQXw9PQUHafZcI2PrJK7u7vxbrSgoCAcPnxYdCQiqzd//nxMmjQJzz33HK5fvy46TrPhiI+s3qZNmzBnzhysWLECkydPFh2HyOotXrwYaWlpyM3Ntck1PxYf2YR//etfiI6ORlRUFP785z/ziCMiExgMBsyfPx9FRUXIysqyuUeIONVJNiEgIAAHDhzAxYsXERERgStXroiORGS1ZDIZVqxYgd69e2P48OG4c+eO6EhmxeIjm+Hi4oItW7Zg8ODBCA4ORlFRkehIRFZLJpMhKSkJvr6+eP7551FVVSU6ktlwqpNsUlpaGl588UW8++67mDNnDrc6I2qiuro6JCQkoLy8HKmpqTaxjMDiI5t19uxZxMTEoHfv3vjf//1fqNVq0ZGIrFJtbS3GjRsHANi4cSOUSqXgRKbhVCfZLF9fX+zduxdarRZhYWE4f/686EhEVkmpVGL9+vWorq7G5MmTUVdXJzqSSVh8ZNMcHR3x9ddfY9KkSQgNDUVWVpboSERWyd7eHps3b8a1a9cwffp06PV60ZGajFOdJBn5+fmYMGECXn31Vbzxxhtc9yNqgjt37mDIkCHo0aMHkpKS6r2PrldqkVJcipKr5Siv1kGjUsDfU4Mxfbzh6mQvMHV9LD6SlNLSUsTFxcHb2xtffPEFnJ2dRUcisjrl5eUYNGgQwsLCsGLFChwrvY2k/LMoOF0GANDq/j0aVCnsYAAQ3tUdcwb4omdHF0Gp/43FR5Kj1Woxb9487N69G6mpqejatavoSERW5+eff0ZERAR8h0zBd0o/VOvq8LA2kckAlUKOxcP8kRDq02I5H4THEpHkKBQKjBw5Evb29khISECXLl3g7+8vOhaRVVGr1TD49kPyGR10DbxdRKc3YO/3N+CiViLQW9zIjyM+krQDBw4gLi4OkydPxtKlS3nEEVEDHb10C/F/24eq2sbf4alWyrFxZqiw8uNdnSRpISEhOHToEIqKijB8+HDcvHlTdCQiq5CUfxbVuqY91lCtq8Oq/LNmTtRwLD6SPA8PD2RlZeHJJ59EUFAQjhw5IjoSkUW7XqlFwemyh67pPYzBAOSdKsONSq15gzUQi48I99b9PvroI7z//vsYNGgQ1q1bJzoSkcVKKS41+RoyACmHTb9OUyiEfFYiCxUfH4+AgABER0fj4MGD+Oijj6x+eyYic9LpdDh28Xq9RxaaolqnR8mVCjOlahze3EL0ALdu3TJuzJucnAxPT0/RkYiaTKfToaKiAuXl5fV+fNDHfu/H+/+v1WrRbuwStPLpbXKuSH8PrHkh2AxfYeNwxEf0AC4uLti2bRuWLVuG4OBgJCcno2/fvg98rbXsVkHWpba29jfl1JSiqqiogFarhbOzM5ydnaHRaH73R1dXV/j4+Dz0NQ4ODngt+Qi2HPnR5K9RoxIzm8LiI/oddnZ2WLJkCYKCgvD8889j2bJlmDVrlnGLpqOXbj1kt4qr+J/s0xa1WwU1v/tl1ZSR1X9+rKam5pFF5ezsDDc3N3Tu3Pmhr3FwcDDrFn3+nhrYK66aNN2pUtjBv72YnZM41UnUAGfOnEFMTAyCg4OxatUqpBy5iuVpJVa1WwU9WG1trVmKqqKiAjU1NY8sqoaUmUajgVqtttj9ZK9XahH2Ya5JxWevsMOehRFCZkVYfEQNVFlZienTp+N4tQt0PUZBq2v4W0ettMPiYd1YfmZSU1NjlqIqLy+HTqczS1E5OztbdFmZ28yvDiHr5E9NeqRBJgMGd2+HTxOCzB+sIZ+fxUfUcEcu/Yy4VYUN3qLp10TvViFaTU2NyWtV93/U6XRmGVlJrazMyZp3bmHxETWCNX+X2xRardZsIyu9Xm+WotJoNFCpVCwrC7Bu33ksTzuJqtqGT3lawuwHb24haiBz7lbRnOsaWq3WLEVVUVEBvV7foKJq3749/Pz8HvpalpXtuV9e1rbezeIjaiBz7lYx69knjB8zGAzGkZWpRVVeXg6DwQCNRvPIEVWHDh0eWWr29vYsK3qohFAfBHq7YFX+WeSdKoMM9x5Ov+/+eXwDu7pjTrivRUz1c6qTqIESN35rlmeXHH46DsWhr+sVFgCzTAE6OzuzrEiYG5VapBwuRcmVCpRX10KjUsK/vTPielvWM60sPqIGmrr2IHJLrpl8nV4eCrw7sF298rK3t5x/FIhsHac6iRpIozLP28WnQzs89dRTZrkWETUeT2cgaqB7u1WY9pYRuVsFEd3D4iNqoLg+3iZfwwAgrrfp1yGipmPxETWQm5M9Bvi5o6n3jchk9+5ss6RFfiIpYvERNcLL4b5QKeRN+r0qhRxzwn3NnIiIGovFR9QIPTu6YPEwf6iVjXvr3Nutwt8inmEikjre1UnUSNa6WwUR3cPn+Iia6FjpLavarYKI7mHxEZnIWnarIKJ7WHxERCQpvLmFiIgkhcVHRESSwuIjIiJJYfEREZGksPiIiEhSWHxERCQpLD4iIpIUFh8REUkKi4+IiCSFxUdERJLC4iMiIklh8RERkaSw+IiISFJYfEREJCn/H/a7QMQJmuCVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cG = G.subgraph(my)\n",
    "nx.draw(cG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 668,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n"
     ]
    },
    {
     "data": {
      "image/png": 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zc5GQkICRI0eKjkJkMqWu9srUr18fCxYswOTJk2EwGETHITKJbIpvx44dGDBgADw8PERHITKZ0osPAJ555hk4Ozvjs88+Ex2FyCSyubhlwIABeOGFFxAVFSU6CpFJDAYDGjdujJMnTyr+vPTx48fRv39/nDhxAk2aNBEdh6hKsljxXblyBUeOHMGwYcNERyEy2c8//4zmzZsrvvQAwM/PD3/5y18wY8YM0VGIqiWL4tuyZQvCw8Ph6uoqOgqRyZSyTZmp5syZg/j4eBw8eFB0FKIqyaL4yh5BRCQnaji/V56bmxsWL16MSZMmoaSkRHQcokrZ/Tm+9PR0hISE4PLly7J7cjWpV0lJCRo2bIhz586hYcOGouPYjCRJGDRoEIYMGYJXX31VdByiCtn9im/Tpk0YNWoUS49k5fvvv4ePj4+qSg8ANBoNoqOjMX/+fFy+fFl0HKIK2XXxSZKEjRs3csxJsqO2MWd57dq1wwsvvMAVH9ktuy6+o0ePQq/Xo3v37qKjENWImosPAN544w2kpqYiPj5edBSih9h18cXFxWHs2LGK2tWelK+oqAipqano1auX6CjC1KlTBx9//DGmTJmC4uJi0XGI7mO3xWc0Gu8VH5GcHD58GI8//jjc3NxERxFq+PDh6NChAxYtWiQ6CtF97Lb4Dh06BA8PD/j5+YmOQlQjah9zlvfRRx/hww8/xLlz50RHIbrHbouPT2IguWLx/aF169Z49dVXMW3aNNFRiO6xy/v4SkpK4OnpidTUVLRp00Z0HCKT3bp1C82bN0d2djZ3GvpdcXEx/P398f777yMsLEx0HCL7XPHFx8ejbdu2LD2SneTkZAQGBrL0ynF2dkZ0dDReeuklFBYWio5DZJ/FxzEnyRXHnBXr378/unfvjnnz5omOQmR/o87CwkJ4enri9OnTaNq0qeg4RDXStWtXLF26FCEhIaKj2J2srCw8/vjjOHjwIHx9fUXHIRWzuxXfF198gaCgIJYeyc5vv/2GM2fOIDAwUHQUu+Tp6YlZs2Zh6tSpsLPvt0ll7K74OOYkuUpKSkJISAicnJxER7FbU6dORXZ2NjZv3iw6CqmYXRXfzZs3ceDAAURGRoqOQlRjPL9XPZ1Oh5iYGPzjH/9Afn6+6DikUnZVfNu3b8fAgQPh7u4uOgpRjbH4TBMSEoInn3wSb731lugopFJ2dXFLaGgopk6dipEjR4qOQlQjV69eRceOHZGdnQ0HBwfRcexednY2OnXqhPj4ePj7+yOnoBjbjlxC2tV85OtL4e6ig28zdzzdtQUa1nUWHZcUxm6KLysrC507d0ZWVhZcXFxExyGqkbi4OGzZsgU7d+4UHUU2VqxYgc+2f4M//XkGks7kAACKS433ft5Fp4UEoG/7xpjcxwf+LesJSkpKYzdPd928eTPCw8NZeiRLCQkJ6Nevn+gYsuLiNxDZ6Y3xzalrAB5+Aov+9xL85uQ1/PfXHMwc6ovxwa1tG5IUyW7O8fFqTpIznt+rmQ0pmXj3qzTAwQkVlV55kgQUlRgwb+8pbEjJtEk+Uja7KL4zZ87g4sWL/I6ZZCkzMxMFBQXo1KmT6CiycPRiLubtTUNRibH6Ty6nqMSIeXvTcOxSrpWSkVrYfNRZ0UnsSydSET7qz9Dp7GbySmSyxMRE9OvXjw9MNlH0gXToSw21+rX6UgNiDqRj2fgnLJyK1MRmTXP0Yi6iD6Qj6ddsAPefxEZpEzi6tcDfN/zAk9gkO4mJiRxzmiinoBhJv2ajtpfUSRKQeDobNwqKebUn1ZpNRp0bUjIx5rMUxJ+6huJS4/2lBwA6J5QY757EHvNZCuf4JBuSJPH8Xg1sO3LJ7GNoAGz70fzjkHpZfcW3ISUT8/aeMmmeX/4kNgBewUV278yZM9BqtfD29hYdRRbSruY//I1vDelLjUi7cstCiUiNrFp85p7EfrxFPTzegmNPsl9lqz2e36vYzZs3kZ6ejoyMDGRkZCA5pyng0tzs4+brSyyQjtTKqsXHk9ikdAkJCRg+fLjoGMIYjUZcuXIFGRkZ9xVc2VtpaSm8vb3vvTVr2A43bpv/uu4ujuYfhFTLasXHk9ikdEajEYmJifjggw9ER7GqkpISZGZm3ldoZSV37tw5uLm5wcfH5165DR8+HN7e3vDx8UGjRo3uWw0vS8pA+v5fzRp3uui08G3uZokvjVTKasVnyZPYf+/N8yck3oO34hiKCuDWLRJ16jcRHc1sBQUFD63WygouKysLnp6e98rM29sbPXv2hLe3N7y8vODmZnoJRXVtgQ/3/2pWVglAVJcWZh2D1M1qxWepk9gns/IslIiodqq6FUfrNww93kuw+/0kJUlCTk5OpSPJ/Px8tGnT5t6qzc/PDxEREfD29karVq0s9ozBRnWd0addY8SfularaZBGA/Rr35hTIDKL1Tapnrj2eySkXTf7OEXpqfht13y4urqiTp06Jr+vyeeWf8+b6Km8u1clp0FfaqjyH2qNBnDROQjdT9JgMODSpUsPjSPL3nQ63X3n28qv4Jo3bw6t1jYbOR29mIsxn6WgqKTm5/9dHR2w+flgXvRGZrFa8b28+Sfs+jnL7ONEBnjivchOKCoqQmFhodXel/23g4NDrQqzNqXMx9fYt5rcilPG1VGLmUM7WK389Ho9zp07V+FI8vz582jYsOF959vKvzVo0MAqmWqjNr+3Lo5azLLi7y2ph9WWN77N3OGsu2qBk9jucHJygpOTEzw8PCyY8GGSJOHOnTu1Ks9r167V+Nc5OjpavWRdXV1ZsrUg8lac3NzcCs+3ZWRk4Nq1a3jsscfuW60NGDAA3t7eaNOmDerUqVOr17S1svIydTWN0hK0u5OO8cFDbJKPlM1qK76cgmKEvJdgVvE567T4bkaoIuf5ZSVryirU3PdFRUVwdHS06Ei4qpK11cjMmp5f/4NZ56EGd2xa6a04kiTh6tWrlZ5v0+v1lY4kW7Zsqahx/LFLuYg5kI7E09nQ4I9HEQF/PI+vX/vGGN+lKSZG9Mdrr72GiRMnCstLymDVB9Fa8x8PMp0kSSguLrbKaPjB93q9Hk5OThYdCVf23sXFxSola6lv2mJHe+FG1vmHCu7s2bN45JFHKhxH+vj4oEmTJqq7If5GQTG2/XgJaVduIV9fAncXR/g2d0NUlz+ewH7q1Cn07t0bu3fvRo8ePQQnJjmzavHxJLb6SJIEvV5v1fOxZe+Li4vh7Oxs8VXrV5ml2PTLLdwx1P6vhrGkGJoTe+B951yF59zc3d0t+LuuHnv27MFzzz2H//3vf2jZsqXoOCRTVi0+wD4vECBlMBqN0Ov1Fh8N57QdjtIWfzI7X2TAo/hwdIAFvlIqb8GCBdi2bRuSk5Ph6uoqOg7JkNWLD5DXJeFElroVp79vE6z6v0ALJKLyJEnCn//8ZwDAxo0bVTcWJvPZ5CqE8cGtsfn5YAzu2BTOOi1cdPe/rItOC2edFoM7NsXm54NZeiSUu4tlLh7hfpLWodFosHLlSpw+fRrvv/++6DgkQza7POzxFvWwbPwTJp3EJhLJcrficD9Ja6lTpw527dqFoKAgdO7cGcOGDRMdiWTEJqNOIjnhrTjy8d133yEiIgJJSUno0KGD6DgkE/K/4YrIwsr2k6ztqSPuJ2k7PXr0wIIFCxAeHo6bN2+KjkMyweIjqsCUvj5w0dVutxsXnQMm9/WxcCKqzMSJEzFkyBCMHTsWBkPtnv9J6sLiI6qAf8t6mDnUF66ONfsrcvdWHF/ef2pjH3zwAUpLSzFjxgzRUUgGWHxElRgf3Bozh3aAq6NDtWNPjebupgu8/1QMnU6HzZs3Y9euXVi3bp3oOGTneHELUTVM3U9ycl8frvQE++WXX9C3b1/s2bMH3bp1Ex2H7BSLj8hEvBVHHnbv3o0pU6YgNTUVnp6eouOQHWLxEZHizJ07F1988QWSkpLg4uIiOg7ZGRYfESmOJEkYPXo0XF1dsWbNGm5rRvfhxS1EpDgajQaff/45jh07hg8//FB0HLIzXPERkWKdP38ewcHBWLNmDQYPHiw6DtkJFh8RKVpycjKioqJw8OBBtG3bVnQcsgMcdRKRovXq1QvvvPMOwsLCkJeXJzoO2QGu+IhIFSZPnowLFy5g9+7dcHCo3XZ0pAxc8RGRKnz00UcoKCjArFmzREchwVh8RKQKjo6O2Lp1KzZt2oS4uDjRcUggjjqJSFWOHj2KAQMGYN++fejatavoOCQAV3xEpCr+/v5YtmwZIiMjcfXqVdFxSACu+IhIld566y3s378fCQkJcHbmXqtqwuIjIlUyGo2IiopC/fr1sXLlSm5rpiIcdRKRKmm1Wqxbtw6pqan4+OOPRcchG+KKj4hU7ezZs+jRowc2btyI/v37i45DNsAVHxGpmpeXF2JjYzFu3DhkZGSIjkM2wOIjItULDQ3F7NmzER4ejlu3bomOQ1bGUScREe4+w+/5559HdnY2duzYAa2W6wKl4v9ZIiLcfYZfdHQ0cnJy8NZbb4mOQ1bE4iMi+p2TkxO2b9+OdevWYevWraLjkJVw1ElE9IAff/wRgwcPRnx8PAICAkTHIQvjio+I6AFdunTBJ598goiICFy/fl10HLIwrviIiCoxc+ZMJCcnY//+/XBychIdhyyExUdEVAmj0YiIiAh4enpi2bJlouOQhXDUSURUCa1Wiw0bNiA5ORmffvqp6DhkIVzxERFVIz09HSEhIdiyZQv69OkjOg6ZiSs+IqJq+Pj4YMOGDRg9ejQyMzNFxyEzsfiIiEwwcOBAvPbaawgPD0dBQYHoOGQGjjqJiEwkSRImTpyIW7duYcuWLdzWTKb4f42IyEQajQbLli3D5cuXMXfuXNFxqJZ0ogMQEcmJs7MzduzYgW7dusHPzw+RkZGiI1ENcdRJRFQL33//PYYOHYqEhAT4+fmJjkM1wFEnEVEtBAYGYsmSJQgPD0dOTo7oOFQDXPEREZnhX//6F3744Qd8/fXXcHR0FB2HTMDiIyIyg8FgwIgRI+Dt7Y2PP/5YdBwyAUedRERmcHBwQGxsLOLj47Fy5UrRccgEXPEREVnA6dOn0atXL+zYsQM9e/YUHYeqwBUfEZEFtG/fHmvXrsWoUaNw4cIF0XGoCiw+IiILGTJkCF555RVERESgsLBQdByqBEedREQWJEkSnnnmGZSUlCAuLg4ajUZ0JHoAV3xERBak0WiwYsUKZGRk4N133xUdhyrAFR8RkRVcvnwZ3bp1w7JlyzBixAjRcagcFh8RkZWkpKRgxIgRSEpKQseOHUXHod9x1ElEZCXBwcFYuHAhwsLC8Ntvv4mOQ7/jio+IyMpeeeUVnDhxAl999RV0Oj4URzSu+IiIrGzhwoXQaDSYPn266CgEFh8RkdXpdDps2rQJX375JdasWSM6jupx1ElEZCOnTp1Cnz59sHv3bnTv3l10HNXiio+IyEY6dOiA1atXIyoqCpcvXxYdR7VYfERENjR8+HBMnToVERERKCoqEh1HlTjqJCKyMUmSMG7cODg4OGD9+vXc1szGuOIjIrIxjUaDVatW4dSpU1i0aJHoOKrDFR8RkSAXL15EUFAQVq1ahSFDhoiOoxosPiIigQ4dOoTIyEgkJyejffv2ouOoAkedREQChYSEYP78+QgLC0Nubq7oOKrAFR8RkR148cUXkZ6eji+//BIODg6i4ygaV3xERHZg8eLFKC4uxuuvvy46iuKx+IiI7ICjoyO2bt2Kbdu2YcOGDaLjKBpHnUREduT48eMIDQ3F3r17ERgYKDqOInHFR0RkR/z8/LBixQqMHDkSV65cER1HkVh8RER2JjIyEs899xxGjhwJvV4vOo7icNRJRGSHjEYjRo0aBTc3N6xevZrbmlkQV3xERHZIq9VizZo1+PHHH/HRRx+JjqMoXPEREdmxzMxMdO/eHevWrcPAgQNFx1EEFh8RkZ1LSkrCqFGjcOjQIfj4+IiOI3scdRIR2bk+ffpgzpw5CAsLQ35+vug4sscVHxGRTLzwwgu4fPkydu3axW3NzMAVHxGRTCxduhR5eXmYPXu26CiyxuIjIpIJJycnbNu2DbGxsdi0aZPoOLLFUScRkcz8/PPPGDhwIL7++mt06dJFdBzZ4QxxpaIAAAcYSURBVIqPiEhmAgICEBMTg8jISFy7dk10HNnhio+ISKZmz56NxMREfPvtt3B2dhYdRzZYfEREMmU0GjFy5Eg0btwYK1as4LZmJuKok4hIprRaLdavX4/Dhw8jOjpadBzZ4IqPiEjmzp49ix49eiA2NhahoaGi49g9rviIiGTOy8sLsbGxGDduHM6ePSs6jt1j8RERKUBoaChmzpyJ8PBw3Lp1S3Qcu8ZRJxGRQkiShOeeew43btzA9u3bodVybVMR/q4QESmERqNBdHQ0rl+/jrffflt0HLvF4iMiUhBnZ2fs2LEDn3/+ObZv3y46jl3iqJOISIGOHDmCJ598Evv374e/v7/oOHaFKz4iIgXq2rUrli5dioiICGRnZ4uOY1e44iMiUrDXX38dhw8fRnx8PBwdHUXHsQssPiIiBTMYDAgPD8djjz2GmJgY0XHsAkedREQK5uDggI0bNyIxMRHLly8XHccucMVHRKQCZ86cQc+ePbF161b07t1bdByhuOIjIlKBtm3bYv369Rg9ejTOnz8vOo5QLD4iIpUYNGgQpk+fjvDwcNy+fVt0HGE46iQiUhFJkvDXv/4VRUVF2Lx5syqf4ccVHxGRimg0GixfvhwXLlzAvHnzRMcRgis+IiIVysrKQrdu3RAdHY3w8HDRcWyKxUdEpFKpqakYNmwYEhMT0blzZ9FxbIajTiIilerWrRsWL16M8PBw3LhxQ3Qcm+GKj4hI5f75z3/ip59+wr59+1SxrRmLj4hI5QwGA4YNG4Z27dph6dKlouNYHUedREQq5+DggLi4OOzbtw+rVq0SHcfquOIjIiIAQFpaGnr16oVdu3YhJCREdByr4YqPiIgAAL6+vli7di2efvppXLx4UXQcq2HxERHRPUOHDsW0adMQERGBwsJC0XGsgqNOIiK6jyRJGD9+PIxGI2JjYxW3rRlXfEREdB+NRoOVK1ciPT0d7733nug4FscVHxERVejSpUsICgrC8uXLMXz4cNFxLIbFR0RElTp8+DDCw8ORlJSEDh06iI5jERx1EhFRpbp374733nsP4eHhuHnzpug4FsEVHxERVevll1/GqVOnsGfPHuh0OtFxzMIVHxERVWvRokUwGo2YMWOG6ChmY/EREVG1dDodNm/ejN27d2PdunWi45iFo04iIjLZL7/8gr59++LLL79EUFCQ6Di1whUfERGZrFOnTli1ahWeeuopZGVliY5TKyw+IiKqkbCwMEyaNAmRkZHQ6/Wi49QYR51ERFRjkiRhzJgxcHZ2xtq1a2W1rRlXfEREVGMajQarV6/G8ePHsXjxYtFxaoQrPiIiqrULFy4gKCgIa9asweDBg0XHMQmLj4iIzJKcnIynnnoKBw8eRLt27UTHqRZHnUREZJZevXph7ty5CAsLQ15enug41eKKj4iILGLKlCnIzMzEf/7zHzg4OIiOUymu+IiIyCKWLFmCwsJCzJw5U3SUKrH4iIjIIhwdHbF161Zs2bIFsbGxouNUiqNOIiKyqGPHjqF///746quv8MQTT4iO8xAWHxERWdyOHTswbdo0pKamonnz5gCAnIJibDtyCWlX85GvL4W7iw6+zdzxdNcWaFjX2WbZWHxERGQVc+bMwTfffIOPNuzCZ4cuIOnXbABAcanx3ue46LSQAPRt3xiT+/jAv2U9q+di8RERkVUYjUb0+dssXG4aBEmrQ1Vto9EALjoHzBzqi/HBra2aixe3EBGRVcSmXkB2i14waqouPQCQJKCoxIB5e09hQ0qmVXOx+IiIyOKOXszFvL1p0Jcba5qiqMSIeXvTcOxSrpWSsfiIiMgKog+kQ19qqNWv1ZcaEHMg3cKJ/sDiIyIii8opKEbSr9nVjjcrI0lA4uls3Cgotmyw37H4iIjIorYduWT2MTQAtv1o/nEqwuIjIiKLSruaf98tC7WhLzUi7cotCyW6H4uPiIgsKl9faqHjlFjkOA9i8RERkUW5u+gsdBxHixznQSw+IiKyKN9m7nDWmVcvLjotfJu7WSjR/Vh8RERkUVFdW5h9DAlAVBfzj1MRFh8REVlUo7rO6NOuMTSa2v16jQbo176x1TauZvEREZHFTenrAxdd7Z7C7qJzwOS+PhZO9AcWHxERWZx/y3qYOdQXro41qxlXRy1mDvXF4y2s95QGy1x6Q0RE9ICypyzc3bPTYDdPZ+BjiYiIyKqOXcpFzIF0JJ7Ohga4b+Pqsufx9WvfGJP7+lh1pVeGxUdERDZxo6AY2368hLQrt5CvL4G7iyN8m7shqgufwE5ERGQ1vLiFiIhUhcVHRESqwuIjIiJVYfEREZGqsPiIiEhVWHxERKQqLD4iIlIVFh8REakKi4+IiFSFxUdERKrC4iMiIlVh8RERkaqw+IiISFVYfEREpCosPiIiUhUWHxERqQqLj4iIVOX/AWp+jJ2Zuac3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tot_cg_set=set([])\n",
    "for i in range(0,500):\n",
    "    tot_cg_set.add(tot_g[i].subgraph(my))\n",
    "print(len(tot_cg_set))\n",
    "tot_cg=[]\n",
    "for g in tot_cg_set:\n",
    "    tot_cg.append(g)\n",
    "# print(len(tot_cg))\n",
    "nx.draw(tot_g[7].subgraph(my))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "nodes_in_cg [32, 24, 25, 26, 28, 29]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 19.99999999999975, 19.99999999999975, 0, 0.0, 0.0, 0, 0, 19.99999999999975, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[(25, 19.99999999999975), (26, 19.99999999999975), (32, 19.99999999999975), (1, 0), (2, 0), (3, 0), (4, 0), (5, 0), (6, 0), (7, 0), (8, 0), (9, 0), (10, 0), (11, 0), (12, 0), (13, 0), (14, 0), (15, 0), (16, 0), (17, 0), (18, 0), (19, 0), (20, 0), (21, 0), (22, 0), (23, 0), (24, 0.0), (27, 0), (28, 0.0), (29, 0.0), (30, 0), (31, 0)]\n"
     ]
    }
   ],
   "source": [
    "print(len(cG.edges()))\n",
    "nodes_in_cg=[]\n",
    "nodes_in_cg_set=set([])\n",
    "for ee in cG.edges():\n",
    "    nodes_in_cg_set.add(ee[0])\n",
    "    nodes_in_cg_set.add(ee[1])\n",
    "for ele in nodes_in_cg_set:\n",
    "    nodes_in_cg.append(ele)\n",
    "print(\"nodes_in_cg\",nodes_in_cg)\n",
    "BCSSS_cg=BCS(tot_cg,len(tot_cg)+2)\n",
    "print(BCSSS_cg)\n",
    "BCS_value_cg={}\n",
    "for i in range(1,max([max(a[0],a[1]) for a in cG.edges()])+1):\n",
    "    BCS_value_cg[i]=BCSSS_cg[i]\n",
    "BCS_value_cg=(sorted(BCS_value_cg.items(), key=lambda x: x[1],reverse=True))\n",
    "print(BCS_value_cg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200/200 [00:00<00:00, 6250.73it/s]\n"
     ]
    }
   ],
   "source": [
    "# Model selection - diffusion time\n",
    "model = ep.SIModel(cG)\n",
    "\n",
    "# Model Configuration\n",
    "cfg = mc.Configuration()\n",
    "cfg.add_model_parameter('beta', 0.03)\n",
    "cfg.add_model_parameter(\"fraction_infected\", 1/max([max(x[0],x[1]) for x in cG.edges()]))\n",
    "model.set_initial_status(cfg)\n",
    "\n",
    "# Simulation execution\n",
    "iterations = model.iteration_bunch(200)\n",
    "\n",
    "#Mapping diffusion_time_to_each_node\n",
    "time_of_diffusion_cg={}\n",
    "for i in range(1,node+1):\n",
    "    time_of_diffusion_cg[i]=-1\n",
    "for i in iterations:\n",
    "    for j in i['status']:\n",
    "        if(i['status'][j]==1):\n",
    "            time_of_diffusion_cg[j]=i['iteration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: -1, 2: -1, 3: -1, 4: -1, 5: -1, 6: -1, 7: -1, 8: -1, 9: -1, 10: -1, 11: -1, 12: -1, 13: -1, 14: -1, 15: -1, 16: -1, 17: -1, 18: -1, 19: -1, 20: -1, 24: 0, 26: 7, 25: 21, 28: 36, 32: 45, 29: 49}\n"
     ]
    }
   ],
   "source": [
    "print(time_of_diffusion_cg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 672,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[25, 26, 32]\n"
     ]
    }
   ],
   "source": [
    "sensor_nodes_cg=[BCS_value_cg[i][0] for i in range(0,3)]\n",
    "print(sensor_nodes_cg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 673,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14, 38]\n"
     ]
    }
   ],
   "source": [
    "delta_t_cg=[]\n",
    "min_v_cg=min([time_of_diffusion_cg[i] for i in sensor_nodes_cg])\n",
    "min_n_cg=-1\n",
    "for i in sensor_nodes_cg:\n",
    "    if(time_of_diffusion_cg[i]==min_v_cg):\n",
    "        min_n_cg=i\n",
    "        break\n",
    "delta_t_cg=[abs(time_of_diffusion_cg[i]-min_v_cg) for i in sensor_nodes_cg if(i!=min_n_cg)]\n",
    "print(delta_t_cg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 674,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.236284602273372e-10, 2.70904999922544e-07, 2.005008781961654e-37, 6.236284602273372e-10, 2.005008781961654e-37, 6.651056416240756e-45, 2.0567931839828568e-05, 2.005008781961654e-37, 2.005008781961654e-37, 2.005008781961654e-37, 7.431090219616691e-05, 2.005008781961654e-37, 2.005008781961654e-37, 2.005008781961654e-37, 2.005008781961654e-37, 2.005008781961654e-37, 2.1423024306199903e-08, 2.005008781961654e-37, 6.651056416240756e-45, 3.6303719067496965e-06], [2.005008781961654e-37, 2.005008781961654e-37, 2.005008781961654e-37, 6.651056416240756e-45, 2.005008781961654e-37, 0.00010627963050387914, 2.343064848915187e-06, 4.130230086152972e-06, 1.1654769721203018e-07, 5.353514168278474e-09, 5.012099041206115e-42, 2.892150249108451e-06, 2.005008781961654e-37, 5.012099041206115e-42, 2.005008781961654e-37, 1.2047781414288944e-09, 5.012099041206115e-42, 2.0745036396732652e-09, 2.005008781961654e-37, 5.012099041206115e-42], [2.005008781961654e-37, 2.005008781961654e-37, 2.005008781961654e-37, 2.005008781961654e-37, 1.0987321156811105e-06, 2.005008781961654e-37, 2.005008781961654e-37, 5.012099041206115e-42, 2.005008781961654e-37, 5.012099041206115e-42, 6.236284602273372e-10, 2.005008781961654e-37, 6.236284602273372e-10, 2.005008781961654e-37, 2.005008781961654e-37, 6.942478565037343e-08, 5.012099041206115e-42, 5.012099041206115e-42, 5.012099041206115e-42, 6.236284602273372e-10], [5.012099041206115e-42, 2.005008781961654e-37, 1.1332954307504629e-08, 4.984986286177748e-07, 2.005008781961654e-37, 6.230757425661327e-08, 1.2972802371750347e-06, 5.012099041206115e-42, 2.005008781961654e-37, 2.005008781961654e-37, 2.005008781961654e-37, 4.8885842786851345e-06, 2.005008781961654e-37, 2.005008781961654e-37, 2.005008781961654e-37, 6.651056416240756e-45, 6.651056416240756e-45, 6.651056416240756e-45, 6.651056416240756e-45, 2.005008781961654e-37], [2.005008781961654e-37, 8.339622264973287e-10, 1.6356080198344028e-09, 5.012099041206115e-42, 4.236752855036758e-10, 5.012099041206115e-42, 6.236284602273372e-10, 4.352022769895147e-09, 6.236284602273372e-10, 5.012099041206115e-42, 6.236284602273372e-10, 5.012099041206115e-42, 5.012099041206115e-42, 1.139440425933415e-09, 8.160665903500738e-10, 8.849113020025201e-07, 8.909169645937063e-10, 1.970762121240638e-08, 2.005008781961654e-37, 8.001779124257591e-10], [3.993017874268785e-19, 2.005008781961654e-37, 1.4485569107702976e-07, 2.005008781961654e-37, 1.5366832180606485e-12, 6.651056416240756e-45, 6.651056416240756e-45, 6.651056416240756e-45, 2.005008781961654e-37, 6.236284602273372e-10, 2.005008781961654e-37, 6.651056416240756e-45, 2.005008781961654e-37, 6.651056416240756e-45, 2.005008781961654e-37, 3.172516768401843e-07, 6.651056416240756e-45, 4.065739097645751e-08, 2.005008781961654e-37, 2.005008781961654e-37]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g___cg = nx.Graph()\n",
    "g___cg.add_edges_from(cG.edges())\n",
    "# allDiffusionTime = []\n",
    "mcaps_cg=[tot_cg[i] for i in random.sample(range(0, 500), 20)]\n",
    "toTakeArgmax_cg=[]\n",
    "for node in nodes_in_cg:\n",
    "#     print(node)\n",
    "    bfs_tree = nx.bfs_tree(g___cg, source = node)\n",
    "    nx.draw(bfs_tree)\n",
    "    vecOfArgmax_cg=[]\n",
    "    for mcap_cg in mcaps_cg:\n",
    "        model = ep.SIModel(bfs_tree)\n",
    "        # Model Configuration\n",
    "        cfg = mc.Configuration()\n",
    "        cfg.add_model_parameter('beta', 0.03)\n",
    "        cfg.add_model_parameter(\"fraction_infected\", 1/max([max(x[0],x[1]) for x in bfs_tree.edges()]))\n",
    "        model.set_initial_status(cfg)\n",
    "\n",
    "        # Simulation execution\n",
    "        iterations = model.iteration_bunch(200)\n",
    "\n",
    "        #Mapping diffusion_time_to_each_node\n",
    "        \n",
    "        time_of_diffusion_cg={}\n",
    "        for i in range(1,node+1):\n",
    "            time_of_diffusion_cg[i]=-1\n",
    "        for i in iterations:\n",
    "            for j in i['status']:\n",
    "                if(i['status'][j]==1):\n",
    "                    time_of_diffusion_cg[j]=i['iteration']\n",
    "        \n",
    "        for node in nodes_in_cg:\n",
    "            if node not in time_of_diffusion_cg.keys(): \n",
    "                time_of_diffusion_cg[node]=-1\n",
    "        \n",
    "        min_v_cg=min([time_of_diffusion_cg[i] for i in sensor_nodes_cg])\n",
    "        min_n_cg=-1\n",
    "        for i in sensor_nodes_cg:\n",
    "            if(time_of_diffusion_cg[i]==min_v_cg):\n",
    "                min_n_cg=i\n",
    "                break\n",
    "        mu_cg=[abs(time_of_diffusion_cg[i]+min_v_cg)/2 for i in sensor_nodes_cg if(i!=min_n_cg)]\n",
    "#         print(len(delta_t_cg),len(mu_cg))\n",
    "        \n",
    "        arrivals_cg=[]\n",
    "        \n",
    "        for sensor in sensor_nodes_cg:\n",
    "            if(time_of_diffusion_cg[sensor]==-1):\n",
    "                arrivals_cg.append(200)\n",
    "            else:\n",
    "                arrivals_cg.append(time_of_diffusion_cg[sensor])\n",
    "                \n",
    "#         print(\"arrivals_cg\",arrivals_cg)\n",
    "                \n",
    "        new_delta_ts_cg = []\n",
    "            \n",
    "        for i in range(0,len(arrivals_cg)):\n",
    "            new_delta_t_cg=[]\n",
    "            for j in range(0,len(arrivals_cg)):\n",
    "                if(i!=j):\n",
    "                    new_delta_t_cg.append(abs(arrivals_cg[j]-arrivals_cg[i]))\n",
    "            new_delta_ts_cg.append(new_delta_t_cg)\n",
    "#             print(\"new_delta_ts_cg\",new_delta_ts_cg)\n",
    "#         print(new_delta_ts_cg)\n",
    "            \n",
    "        lambd_cg = []\n",
    "        for i in range(0,len(new_delta_ts_cg)-1):\n",
    "#             print(\"---> \",len(new_delta_ts_cg[i]),len(new_delta_ts_cg[i+1]))\n",
    "            val_cg = np.correlate(new_delta_ts_cg[i],new_delta_ts_cg[i+1],\"same\")\n",
    "            for i in range(0,len(val_cg)):\n",
    "                if (val_cg[i]==0):\n",
    "                    val_cg[i]=1\n",
    "            lambd_cg.append(val_cg)\n",
    "#         print(\"lambd_cg\",lambd_cg)\n",
    "#         print(\"delta_t_cg\",delta_t_cg,\"mu_cg\",mu_cg)    \n",
    "        sub_cg = np.subtract(delta_t,mu_cg)\n",
    "#         print(\"sub_cg\",sub_cg)\n",
    "        lambd_cg[0][1]=0\n",
    "        powerOfe_cg = np.dot(np.dot((sub_cg), np.linalg.inv(lambd_cg) ),np.transpose(sub_cg))\n",
    "#         print(\"powerOfe_cg\",powerOfe_cg)\n",
    "        powerOfe_cg=abs(powerOfe_cg)\n",
    "        fun_cg = (1/np.linalg.det(lambd_cg))*np.exp(-(1/2)*powerOfe_cg)\n",
    "        vecOfArgmax_cg.append(fun_cg)\n",
    "    toTakeArgmax_cg.append(vecOfArgmax_cg)\n",
    "print(toTakeArgmax_cg)\n",
    "#     print(node)\n",
    "# nx.draw(nx.bfs_tree(g, source = 5))\n",
    "#     print(time_of_diffusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 675,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 6\n"
     ]
    }
   ],
   "source": [
    "print(len(toTakeArgmax_cg[0]),len(toTakeArgmax_cg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 676,
   "metadata": {},
   "outputs": [],
   "source": [
    "finArg_cg = np.argmax(toTakeArgmax_cg,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 677,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n"
     ]
    }
   ],
   "source": [
    "id_cg=-1\n",
    "mx_cg=-1\n",
    "for i in range(0,len(finArg_cg)):\n",
    "    if(toTakeArgmax_cg[i][finArg_cg[i]]>mx):\n",
    "        mx_cg=toTakeArgmax_cg[i][finArg_cg[i]]\n",
    "        id_cg=i\n",
    "source_node=nodes_in_cg[id_cg]\n",
    "print(source_node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 679,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "print(partition[source_node])\n",
    "print(partition[29])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}