FINAL WORK-Dolphin(2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 717,
   "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": 718,
   "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": 719,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gateway graph nodes: [14, 0, 3, 15, 19, 1, 20, 8, 18, 24, 27, 7, 28, 29, 10, 30, 33, 21, 36, 23, 37, 40, 43, 44, 2, 45, 47, 50, 42, 51, 52, 54, 57, 39, 59, 61]\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))\n",
    "gatewayGraph=nx.Graph()\n",
    "gatewayGraph.add_edges_from(gateEdges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gate graph\n",
    "# This is the gate way graph\n",
    "# gate\n",
    "tot_g=[]\n",
    "tot_graphs=[]\n",
    "for i in range(0,500):\n",
    "    g = G\n",
    "    gatew = gatewayGraph\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.subgraph(gatew))\n",
    "    tot_graphs.append(g)\n",
    "\n",
    "# for k in range(0,500):\n",
    "#     print(len(tot_g[k]))\n",
    "#     tot_g[k].add_nodes_from(gateNodesList)\n",
    "#     print(len(tot_g[k]))\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))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 721,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(15, 0.2773109243697496), (40, 0.2319327731092446), (20, 0.15798319327731122), (8, 0.15756302521008356), (36, 0.1512605042016807), (45, 0.1478991596638665), (37, 0.14453781512605016), (0, 0.1277310924369742), (50, 0.1243697478991588), (14, 0.12268907563025293), (28, 0.11890756302521086), (29, 0.08907563025210134), (1, 0.07226890756302508), (30, 0.06890756302520983), (61, 0.0642857142857145), (7, 0.05210084033613418), (19, 0.04705882352941154), (42, 0.04705882352941154), (59, 0.04705882352941154), (2, 0.0462184873949583), (21, 0.04159663865546223), (47, 0.040756302521007995), (39, 0.040336134453781224), (18, 0.03151260504201704), (54, 0.0302521008403359), (33, 0.020168067226890612), (51, 0.01512605042016795), (44, 0.01218487394957975), (10, 0.010084033613445306), (57, 0.008403361344537806), (3, 0.0), (24, 0.0), (27, 0.0), (23, 0.0), (43, 0.0), (52, 0.0)]\n"
     ]
    }
   ],
   "source": [
    "#BCS Score\n",
    "def BCS(gg,nodes,nodeList):\n",
    "    BCS_={}\n",
    "    for node in nodeList:\n",
    "        BCS_[node]=0\n",
    "    for i in gg:\n",
    "        x=(nx.betweenness_centrality(i,weight='weight'))\n",
    "        for j in x:\n",
    "            BCS_[j]+=(1/500)*x[j]\n",
    "    return BCS_\n",
    "BCS_value=BCS(tot_g,len(gateNodesList),gateNodesList)\n",
    "BCS_value=(sorted(BCS_value.items(), key=lambda x: x[1],reverse=True))\n",
    "print(BCS_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 722,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200/200 [00:00<00:00, 2777.93it/s]\n"
     ]
    }
   ],
   "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": 723,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 21, 2: 32, 3: 23, 4: 27, 5: 29, 6: 43, 7: 35, 8: 14, 9: 30, 10: 24, 11: 72, 12: 62, 13: 38, 14: 12, 15: 35, 16: 17, 17: 40, 18: 28, 19: 51, 20: 2, 21: 35, 22: 50, 23: 34, 24: 22, 25: 43, 26: 28, 27: 46, 28: 11, 29: 41, 30: 21, 31: 87, 32: 60, 33: 19, 34: 14, 35: 88, 36: 0, 37: 8, 38: 9, 39: 29, 40: 35, 41: 44, 42: 23, 43: 31, 44: 22, 45: 14, 46: 54, 47: 5, 48: 34, 49: 45, 50: 18, 51: 21, 52: 18, 53: 53, 54: 22, 55: 61, 56: 59, 57: 26, 58: 12, 59: 51, 60: 64, 61: 36, 0: 39}\n"
     ]
    }
   ],
   "source": [
    "print(time_of_diffusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15, 40, 20]\n",
      "[14, 0, 3, 15, 19, 1, 20, 8, 18, 24, 27, 7, 28, 29, 10, 30, 33, 21, 36, 23, 37, 40, 43, 44, 2, 45, 47, 50, 42, 51, 52, 54, 57, 39, 59, 61]\n",
      "96\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": 725,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[33, 33]\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": 727,
   "metadata": {},
   "outputs": [
    {
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    }
   ],
   "source": [
    "for i in timeOfDiffusions:\n",
    "    print(i)\n",
    "print(len(gateNodesList))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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G2FWa3Ymjz1Ccg8dFd3lPmpEVg15aYAoZkvDoz/PzZYcQ077O2LteIGnYZGss4UaZyya4+7JixhR8OmP7tKzjyfd+jydlJP9v4Rs7Yjb4xX3gO7d/OndfVozbHvc/apvuXr0elpfMmIJ0CjLc3fq4cU0ImrcuR0pJOPzp6eLa2lqEEDz66KPs27ePiy66iHnz5rF3716efPJJbO7uv7lw9ymM6mEVgSRwcGukPFovBh7N7kIfNJ7KpEFUhRwgEucacDoXDsvl+imD2v7d2bKOvpAZs339EuKdcP2UQdx92Qjcdj1hlj3NoL/bHuvNV19k9OjRrF/+TzQSs9JDR0jTxFFVQm5aEn379iUYDFJcXMx5551HRkYGr776KkII5s6dy+zZs3E4HEgpkVJiM4PdPh5Dim6dRXaUNAyumDJS1QS1iCRH+zONXSnr6A/HZl+/hAh8EAl+z98yhRH58b9cJ4D6955BGuGu3+kbIYqyk3C73ZSW7seQCfOW6TwjiPfDFxkxYgRNTU2MGTOGo0ePsnHjRrxeL7feeisej4c1a9YwYcIE6uvrGTZsGElJSfRP0ZGfLSTdBWYogE1EM+BI5o7uR1LuALDF/x5jLNh6pJ4ab+Q91tWyjlLCsu0V3LD4A77//EYeXrW37bGtLK5PdX7W5kP1LHhsXfzXpzNN+lR+SOPB7XgnfK1LicgyHMT+xq/wjJhOY+EcTE0dcDkdM+in4d3HCWxbTm5uLqNHj2bz5s3YbDYOHz7Mb37zG1auXMno0aN54oknsNvtlJeXk5WVRTgcJjkrH/uX7we9e15nGQ7SvPsDPCNmdMvjdZTLpvHCt6byxxW7WbmzMipjUNoTgMOmMXN4DmluOy9uOEx3tSeNlZOfcZ3H16o1J+WJtfvjP+gBaBq7q1qofedlstKKSRo6pVMHG6RpMsjh5f/eeodrHvsAs5teu1gsKH2mMUvTRBpB6lYuJrdxF1d+5zu89dZb3HvvvdTV1TF37lxcLhe/+MUvCAaD7Nu3D5vNRkVFBQB1dXVIKUlOTiYrWEGNux+RS1TnCSS+/RsIle+GoZOjMOOSXDgshzEF6TjUErllSCJpCv/eVtHtj+0/dhJ02fYKVpdUc/dlxe32E60irgPf6XJS4p1r8HlkXn4HRlM1wgyD1vGLnjSCfPj0QubuKsFdOAm6eCpQAJLYC3pAJMHcMJHSbLdfpkkDwzDw7V1PcPO/8O7bghd46KGHaGlpYerUqWiahmEYNDc3A5CZmcmkSZN49dVX8Xg8OJ1O6urq+NGPfsT999/fbSsTRihAw/svEGqoJOOCr3bpsTpHtO0nlXz4DsIzsn3VFSVuHX/yE7Bc8Ivbpc7I8dydpyzNlCjMUCBSRUTTOnSqzQz6qVu5GP+edfT99mJEF2YLUe2g0B1J96ZBilEPQR8thkYwECDYVIf0NRCqPoB/xyr89VUn/dYJEyaQn5/P4cOHmTJlCo888gjTpk2jubmZG2+8kVdeeYV3332XPn36cOTIp10Rnlpbyi9e2YzZyUBhhvw0rvo703IMDh48SP78n7PXn4Ts4iyyo2YX5/K1wT6+fO3XSLrmD116HymxyW3Xef6WKe3SJ6ItLk8qdCQnJd5pdidCtx1LoZLH6h6emjRNZDiA970n8W56k6wJl3X5gIyIZr3KLj6vPFY2q8meRVNyAUZqX0R6X5wDRoHdRcv+zW1BLzc3l9TU1Lbvtdls/Pa3v2XNmjVceumlCCHaDrXk5ubSp08f1qxZg81m47LLLmv7Pp/Px7/++BMaV/0dM+Q/4++s3Y+LRIb8hD58nqnZYQ4ePMiVV17J+icWIsO93227quwAF198MdVHSvHt29Chn8Vq4nSO0OOsePIz7gJfZ3NS4p3QtGPBRyKNME5b+4AgwwF0TGzl26hf+guaN7+FpmmEU/KjehQ+2oQ4sSmrZnei2RwkFU0m77r7SJ/wOTRNo7Kysu3i6HA4EELw8ssvc+ONN5KXl8fGjRsZN24cdrud9957j+985ztkZWWRlJTEgQMHAHjqqadIS0tjyZIl1HzwChXP3EnL7rWY4SBm+NSpDjZx7CBLyTrS1j/OP//fj9i6dSu7d+/mkUcewe2r5Lpz3Lh6seO2GQqw+p/PEQwG+cpXvsLff3wNmL0ffLvDSI+fwJ4PMEOBmA7e0SAlvLOrylKnPfVf/vKXv4z2ILrTz//5CSWVTdEehmUJoSGNEP5ty+njsZHhkMweW8T0AW4OvLIIuyuZUE4xfad/Ca3/GGw5g9GT0qI9bGsSAqHbcBSMwq2ZjOqbysGDB9F1HdM00XWdjz76iCVLlrB//36efvppKioqGDduHGVlZbjdbpqbm0lNTaW0tJSGhgZefPHFtiAIYHhradn5H5o3vcXgfnnkpSUxpE82h/fvxmiuY9qIgUwanM1gvZaVv/06toMf8clHa/jnP//JkiVLKCgoYMuWLWRmZvL284s52gIio1/vzMClScNbf+bD9//D9773PQ7u2sprS5/HzC5qKy5tdULA1P5u/v2z+dRvWYl//8e403MQafmxuVcdJXZNkJZkZ8LAzGgPBYizGV9Xc1IShdDthOwePvrTd/j4j7ew49WHWL5pH/WTv03DgOk4hk+nJaMQ1/AZ2DP7RXu4lqc5XDgmLyCjaCzz5s3j4MGDaJpGMBjEMAz+8pe/UFpaSnNzM+eddx41NTUAuN1uvF4v5eXlJCcns3TpUrZv305OTs4JzyH9TdSvXUr/wyt56Xuz+Erafuqe/QlP3zyF8wJbeOT7V1GQk47dbmf48OG8/vrrZGVlccUVV5CVlYXL5eLDOie2QeN6py2TaeKq3cvPfnwH48aNA+BPf/oTt88dQ93KxRAOnnnmJE1kONQ9+aidZAYDrFv8K3w+HwC2xjJ+NDWdwJ51URlPrPKHTXYetc6EJDZuu87S0g2Hoz2EmCA0DXfhRDR3Kvrw89ne9xJE0IHQBHzmMIXVmpFalm5nr6OQH39hAqmpqW3BJTs7m4ULFwKgJaWxKZBD1rhZ5E6wI8wQ6X220cdXysvPPcWIESPQ9cjrrWka5rHAkJSUhMfj4eDBg/TrF7kR8Xg85Ofn8/nPf55ly5Zxww03sHr1asLhMIFAgD//+c9MnjwZh8PBnXfeyfPL15E95/vIbsoPPOPLIUzktrf477+8DsC+ffv4z3/+w+DBg/FuepNg+W4ypi/AM3QyAOHjike3dqzw7V1Pw9oXAMj/3A+Qmf17dZZlF5Lvzh7CnuC5/Pnj1QC0tLTw4x//GNd5V+AYPD6htwE6qtFvnWXumD3VebJ+USUVXrYfbYz20GKCDAdo2bOe5KETIQGq9vcGMxykaNvf6ZPhYcmSJZimic1mQ2QNxDP5KpIKJ0Qu3McFHxkKIHQd356PaFy3BIe3nIyMDAKBAOXlkb5ySUlJnHPOOezcuRO3201FRQXnn38+BQUFvPDCC4wePZqysjKmTp1KYWEhw4YN4+GHH2b//v2kpqYydOhQBt2wkHdLarucknJWjCChD57n6V9+i/POO4+nnnqK+++/n4qKCqSUbQEdQHOnknruJTjzhiCcSYSaGwlWlRLatRp/QzW33norixYtwul08tuXPuSRdUdB0xA9WHdTiEj3grsvK+YcZx0TJ07EMNqnljhSs8i/5TF1SrUDrhzbj0VXj432MIAYDHyny83TBJgx9dNElzTCMbPXEgvMUIDg5tcJNDehZw/gvIlT2VF6FC17AEJoZ0yAxwhRu/Kv3DBlEK+8tYLm3FEMGDON6gYv+ZlpHNy6joaNb7Hh/VWMHz8ewzBwuVzYbDbefPNNPv74Y1auXMkDDzzAzJkzOXDgAB6Ph1XrPuZLT2xrN6vqCdI0wQzRsncDOgbY3YRbGpF1R2jY+BZ62Iemafj9kTqyuq7z3nvvMXHiRIqLiwEoLCxk2bJlCCH482N/Ry86v+3mVjcCfLRmNWWNAdyDxkYSM7ox8LRWHbloeA63zSzC3nSUmTNnUltbi2EYkV6Kmobb7cbj8cD0b+IqmqSKX58Fl03jjouH8a0LCqM9FCDGAp/KzetesVhBxeqkaSCNcKeXwMxQgFDtYRxZ/U/atBYh8O/bQMP7L2BW7yczMxOv1wtE9gybmprweDzU1ta2fV/f2V9FH/uFLi3LSSkxAy1oNscJSfyRpUkNw9eI7k496ec5tnTZuHYJwfLdp30uR/7QY417J5zyNfCVbsKoL8eenIpweAj7GhE2J+7CCR36OaVpEKo+iGg4gll7mPDuNeBvwjTNtmo6J6PrOnrOEPKvux+hljvPyGnTeP8nsyzTvT1mAt+nuXnqKLES3862PJq2+RWSyz7Gbrezc+dO+vfvz8GDB4FIGsb48eNZt24dQ7/6G/z5Y7o8rtzm/VyS6+XBNzbQp3g8NU0+PA6NvIKBHDZSELrjtKXxji/r5t34JgCXX345O3bsoLS0lCFDhtDcZxyuqdcij+WenpJpghnmnFAJ9tK1vP7664TDYTxj55Ex+6YzjgVpogvJ92YU8NP5Mzhw4EBbkNu+fTuXXnopwWAQIQSGYZCamkpjY/ttFIfDQc78X2ErGNWr7ZZizam6vUdTTMzRVW5e94uR+52EdKZZuNA0NLsL55Rr+Npv/srixYv5/e9/z8GDBwmFQtx111243W7GjRuH0+mktsnXLeMaes65LPjiFTSvf5V9//glzg8ep273Bo6YqWh21xnrwbaOO2PWTWRN/iIAK1euZPLkyXg8HvwFE3FOWYCpnSHoQWSv0uZgq17EigNBwuEw6enpeDe9ScUzd5ITKMNp0yLl+o4jwwHsumDG4DS8r9zLRQU2DMMgKSmJnJwclixZwkUXXdQW9IQQ/P73vz/h6YuLi5l480IV9M7Cybq9R1tMBL6u9ItSTk4tcca+QFjy+MYGmuwZPPvss/Tp0wcpJc888wxer5d169ZFTocGmrvl+Va8+S9Gjx5NKBQiJSWFoVPmkDH7ZoStY8tXmt1F8vTrcfWNpF14PB5CqX3RJlyF7OBBK83hImP2TaQOGsW0adNwOBwEy/fwzr3X8v5PZjHUv4tzkpuZUZSNJ1hHmhakX7qb/Q1hCq66my/9vzfIueAa1m/dycSJE7n99tux2yOHj2644QZSU1O55557TpjtXXjTPRz2DFdB7wzcdu2Ebu9WYPnAp3LzTkW9IN0tFmfBvlCYa+59nE2bNuF0OtsOj0ydOjWSTJ+cjislE2l27cbRDAUIVOxj9OjRAPzhD3+gOnc8YbNzF35hc5A+7Ss899xzrFmzhuSJX4JOtrzSdAe5M69n48aNuFwunE4nt99+O1keJ0M8Bl5fkLV7q2mypdGopVBa08LhOh81YTvhvqPRzruSG5Ye5MCAS3D2GUZeXh633XYbS5Ysoba2lmAwyOjRo3nooYeAyB7kWxVJvXNCNkYJEanRefdlIyxXoBpiYI/v4VV7WbS8JKE6K5yWlIS9Nfj3bSB55Mz421jvjqLSnX1q0wBEp1o4RZMmDV7++jmMLBzAgAEDKC8v5+KrbyI4dBalgSRM0+xyvpkZDuJZfh/lB/bg9XpJye5L2lf/hCm61uuxavG3CQQCFNz2eJdSA3RMSv/vemZMOo+WlhbKysr40k8W8UaZK3Ka9SzeU62nUoMfPEeBbz/r169H0zQu//IC9KLz+bDkMCFsOHIHo6dkx9z7pLdoAi4ZmcdtM4ssN9NrZfnf3M7yRhX0jicEenIGyaNmI8PBLt/JW42UJkShmLIZ8tOw7iWkcep6mFblsNt5as1uxo4di9/vJ3PyFyjpfzn7gymg27sc9KRp4mnYT8XBvbS0tADw/T8917Yk2OnHlRLb0OmkjJnT5dl2KBRizi33kJ6eTllZGdO++hNePaATRjvrGymhaQibE+fka9gZzsHVdxg5V/6UzQOvYkMwH/vQ80kaOhlbWq4Keqdx6Tn5PHz9BMsGPYiBwNfoD5/5ixKM0HSEbkNzp7RVVrH4xP2sSSNM854PI50EeuFninSsMAhWHkCEWqhf/QxmyN/jz9ud/GGTpcvXsnDhQvrPupbUC74GtjOcauwII8ScvpKPP/6YgoICzj33XB5b8nqXb0g1uxNPv6F86Rvf6yDQiQ4AACAASURBVHJw1uxOCkZN5tlnn8Uz8Bw+DPXr/GqIzUHGnFvIufZ+nIWTEDaHqtByllw2zbJd149n+cCX6lIJ1mcjXg6raHYnMhyg8h93EqjYFwlMZwiArV/TkeDf+vWt3Rdc/YaTOuN6Mi76Oqa/GTMcW1X4hww/h36jp9E4ZE6HD4icji4NhjV/wp9++UMGDhxIRUUF1113HeeM7Z6j6dLhptbb0i2P9e+Vq0lNTaWh3xRkJ/cLWwndhtadNw8JQgJXjSuI9jDOyPK/1eL8VGxafFzUlbOjOZNJDddR/vf/IvDW7xENZZEl3c8EIjMUQIaDBCv2IaTRoeB/sh6BkYa9OronE6E7kMEWZDgUSZo+jhVn12W1TXzpnoe7r8O5NHHbdeYP1RF7/wNAc3Mzzc3NTJgwgSEFfbrlaTKSnDTWVHbLY3kcGhdccjnOweO6HLDi5Uayt503IN0ySeqnY+np1OZD9SzdcIiwqkOWUMxAM411dYwcOZLyQ1up2/FdNHcqntGzkWn90FzJmP5mglWlhKv2k/2lu08ort0VbRc9ZzKYYXx71iOQ2LL6Y0vNRXO6u+25ukvIk4/uzu7ySUMzFMBms1GUFOD335gBtQf516IyADZv3ozdbqe8vJyXHn8V+7gru1YNJhzkyPaPOLId0qZf16XHctk07vjG1TQ3f44HV5cSXzvfsWPjgXqeXldqyZOcx7Ns4Ht6XSn3/msHASN2lpuUrtNkmGBlKaFQiB07diClZMCAAezZs4dp06axce1S7HZ7W73H7Ct/iuihItutBaWThk4BInuBVt3rMSRIodOVeYo0DEKHP6Fv+XsMKBrI6lcqqK6uZvfu3cybN4/169cTDAaZP38+enI6/cZ/qcvjDuxYRdgwSL/ghq49TjDITxfMQpswn+RzLuryuJTOCRgmC9/YAWDp4GfJwNdankwFvd4nTSOqrYikhPTaHXiFaFtSbGxsZNKkSWzevJnXXnsNl8vFvHnzkE5PpONBD+/DiGOtgqxe0Lury3NC1wl5G/jovZVsWKPx5JNPEg5HeuEtX76ccDhy0EzTNMyWBlr2ridp6JROvf7SNPHtXU/frFT279+Pf98G3J0s+CxNk+bdH1BfcZgcl6fD3690L1/I5FevbWdMQbplT3Zabo9PlSeLHjMUwLd3A96tKwlVH+r1VInIxfAjWuoqyc3NBcBms7XN+Pr27cuCBQv42c9+Rp8+fZh09fcsud8Wy1ypGei6zvnnn8///d//sW3bNgYNGsQ999yDruukpaVht9vJz89HfvIWmuzce0QaQTLL13PkyBEAWta/jBnuXCqJNII0ro20gQr7rNPsNJGFTMktT62P9jBOyXKB78F39+ALqRX6qBCCmjf+SM3rf6D8H3dCbwc+I0jTBy8yduxYvvzlLzNy5EjC4TDbtm0jEAhw66238vbbbzN16lS8Xi+7q5otu/QYqy46fzLnnXce9913H7feeivDhw8nEAjwyCOPYBgGDQ0NjBo1irlz51K/bzPVyx9FdjD9Q4YC1K1YzK61ywgGI8HOe2Ab2qaXkZ85SHRGRoi6FYsJlu8BwKw9fMJhJCU6yhsD3PLUR9EexklZKvBVewO8s6t7TngpHdM620p1Rt4SrUtZvXWk3wz50Te/ir+shCuuuIKqqipKSkrQNA2n00lxcTEvv/wy06dP54EHHmDixImcN/n8XhlbopBSsmZfLQcHX8G1D6+m/1fuIfP8q6mob+bo0aNAZJlzz549PPvss9hsNpo3vYXv/X8gQ2eR/iFNNDNMy5qn8W56s92nHA4HE9L9pO1fcXaP1UpouIechyN/KBdffDGOIx+rE5kWsmx7JS9+fCjawziBpUqWPbxqL//z752qmWwUCDNE2ZM/artz1pLSSJt2NSnjLu/RPb/WMlG1K/5Ky+Z/R/aPTBNd15k2bRper5cNGza0u5hpmoZhGGRd8QM8o2b12NgS0mdKxsljy496xU7Klv8dW+MRHn30UX79619TW1tLVVUVQ4cOZfrnr+XNAybOweNOKJEW6dd3rB/fB0tJNxqoqKg44altNhv9+/cne9g46vpOIpg9PPLeO0Mgk6aJhkHt8sdo2vgmo2/7M/XJA6yTgxfFMnxWkJfi5IOfzon2MNqx1G795kN1KuhFQzhAzbHlouQBI3GP/2JbE9AeP+giBA3vL8W3ZRlZWVnU1NRgmiamabJjxw6qq6vJzMxESsmTTz5JU1MT69evZ+PGjWz3ViLDgQ53B1BO47O5jcfqZxp9RpF37X1McZXhdrspKSlB13WSk5N58MEH+cIXvoDP54uknYyZgz17EJorGZdm0nhwJ01b3sb0RTocVACZmZnU1dXhdDrp378/paWl9OvXj2AwSLhyHzMuuJw1PjuB8JkvCELTkGikzfwGpinZ8+qD5F93f9RTGmQ4GDkQleAd2iuaAuypaKIoLyXaQ2ljqRnf7D+8y96q7mmhopxZW2PQFYvxbnrz7Jt4ducYpARp4vvPE1S9/9Jpv1YIgcfjIT09nczMTNLz+3Pg3Ju7L2lbOSNdhqlbuZi6j15D13VM0zzhgJGu6xhGJOw4nU6CwWDb19x+++387//+L3/84x954oknuO6667jvvvuQUpKcnExBQQF/fPIlbl1a0qm9fjPk5wrXHp56YxUZF92E5nB1/YfuDNMgpfkwtfUN2Pt3vQlwrLtoeA6Pf31StIfRxlJXDFWMuvdIKQlVlVL37wfxl+1qC3qavXcvFEIIEDquKdeSHgzR+PHr2GyRt2XrwYdBgwaRN7CIklAmjrzBhB1JNGgSv68KKkuQucXWWdaKc4aw4bngazQf2tG2LH48IQRZWVl4vV4mT55MdXU1SUlJ7N69m9raWg4dOsRdd93FY489hs1m45NPPsHj8fCb3/yGF154galTp/L1/30WCs6FTmQlCt3Bku0NkQ7vEjJn3wy6vfffH5pObloyRQP6srGud5/aivZUeqM9hHYsFficNnXx6i1CCOxZ/TBNA0f+0KgEveNpDhcpF36NwNES+idLysrKCAaDJPcfSWDSfCoHjyPVNOBYsroEfOEACA2hehP2KqE7SJ06n5pX7sfpdLYVE4DIDVVVVRUej4e0tDS2bt3KD37wA6SU3H333bz11luEQiF++MMf8tOf/pSMjAxSU1OZO3cu//3f/83TS17hmf95B7OTqfhC00gqnIjmTsXY9S6m2YAccQn6gHPRdBuySyn+HVPS7MTRVI9Izu7V57WiRl/vd1w5HUtFmmEWWgNOCJqd1KnzSZ06v8eqn3SE0B0kT/oSJSUleL1ePGPnkfmVX2MfND7S9+0zYxQ2J0K3IxEqn68XCU3DXTgRPSkNXdeZNGkSuq4zYMAAxLHCA+PHj2fnzp187Wtf4/nnn+eBBx4AwO1285WvfIXXXnuN+vp6wuEwzc3N9O/fn8LCQn799DKMcNc6skgpyRg/j4yMDNKMBszVj3D4ga/RsOpJROPRXun6AaC7UzCScxI+6AEELVaMxFKB79yCdPUW6UWtd8e9Uf2kI+PR3KmRoHdsFnqmsQlNa7vgxlJHhVim6Xayr/4NoZS+bakOhw4dIiMjA5fLxcGDBykvL6e6uppFixbx8MMPo2kan/vc53juuef47ne/y/Tp01mxYgWpqanU1NQwfPhwli5f16WGtBDp8EF6P8rKyti+fTtNTU2YvkbMkA8zOTumTljGyw1dIGxS47VOfqWlDrdUewNMXLhcLVz1ImmEkYBmkXJcZihA06Z/kzL2kk4tvUrTQIizbz6qdIGUyHCQ+nf+RuPHr7d9OC0tjaFDh5Ka24/atGG0ODNoaA5g+Jsxag5iO7QeX10lPp+v7RCM2+3G5/OR8+WfkTR0cpeH1lfWkF/yCu+99x4tLS2EU/uRf/39MXcCWEiT8wszEZqN/+ypjtlro0PX+OElw/jWBYXRHgpgsT0+iFyvrBOK45/QbZaaZWt2J8nDpnR+6bX1DaQCX88TAmF3kjbzRkzTxLd1GYZhEEzpQ/O466jLKkIIQciE1lsYGQrgmPlVrijK4pbpA3ngVz/imWeewefzAZHOHN1h9/bNfPD6q7hcLm6++WaWlGdYYjm/I1w2jXsuP4fR/dJ58N09gDz21o6993bQMNl51Drl5CwV+JZuOIwmULl8CU5Pye700mtkttf1MbQ2qVXOTHO4yJrzTQLBGm69+36e2NJEsxQIKfjsFEXYnYRMWFlSzYptZdRuq233eZszues3LkaIEflpyGnTWLNmDW+9swb7pT+LnZshaWITcNelxVTX1HDVPz8hZEjQtJj5EU6m0W+dAy6WCnw7yxtRGQ2KFf64VdDrGKnb0S+6nb9taoikD5zx5YvMFjNm3QTAEPMIjXljMQeP64Y3gGBseoBxN93E2rVraR40Az2KHUfOljBCkdPK+9Yjtr/Nd/+dTcasmxB2J8RBM24rndq3zkiAam/nqrNbmTRNzHCo1zsdnC1dAEbXTtF1J2maCV/pIhYJIbClZIJu79D3aQ4XGbNvYq/MwTj3SoStY99/wjiAyf2Taag4zB133EFSUhLOokmWvZGRUiLCATL9RxklS6la/G3Ef/7KhRdeSPqsb0SCXpxosFBKg6WuMHXN1jn102VSghkmsPcDml76JU3vLLZk8NM1DbvdQhN/i16glLPRydw73UH6Rd/oln6HZjjAqofuJiUlhQsuuICQ5sJwWLdHnxACU3dQrWeyecNHhL111NbW8l5tUsztSZ6JlU51WuiKR1zlu5imQdPaF6j/zz8QQqAd+ATH0Gm4BoyK9tDamVWcgynh7e0VUT8xpvbVek60GwyfjtA0dHdq13/34WCk2Pmu9Ty440OcTieu86449r7qnrH2BHHskJCceDVuw07ok7dwDBwLFkgx6k71PuusLFkq8GUlx88djqbb0NPygcgF3TAMwo3WarkkpeSlJx7BXr4N+6U/gi7mTynWZIYC+Patxz1kfK/WYe2IrgY9KSUtpRsjpcqOCQQCeLIHWjbgf5bQdNKmXoW/7zAkIo6mAdZjqb8Aj8tScbjLNFdyu3+Hqkot1SRTCIF77DxE9kAaVz2OGexYQ9GeGI/S/YQQhN9/iprn70E7uhUhDaSMr1NkQghsyZnt/j158mTcqVlRHFXHCSFwDRjdLcu+VmOliY2lAp8vaL09sK7w9C8m64ofkDblywwcdg7h3WvQbdZ6QwubE238VST5a/C9/wyEg6Cqn8QNKSVGcz3moEkEW7x4m5qPpQvF302Glpze9v9SSj744AMCTbFXIdqKM/LuMGdEbrSH0MZSV2G3Pb5+4aYrLdIo1QghpSS/qoSBHsn+Fmsl6QvdQaDoQmpeuZ/A0RJc47+Ia8h4kLJdQ1El9gghsKXlkn7h10mfeWPbx+LRyRYHA5X7cA2bqt7HUaYJ+OrUQdEeRhtLBb6AYaFo0J10OwIw8keyr9EAEQYL9ZBrrZFZ60qh6cA2mg5sQ3Onkjx6Do6cSENRPSULR86guFyCSQTxOos4ntnSAHzaGim3eCJDv/QtNlbEX5pUrDmnbypZHuvcfFjqKmalBMceITTQtUiummla6mIkEaScezFJB96noaGBlpZG5PZlNIfD+P1+3Ok55Nz8SLSHqSgnJaXEWb0LXdd56KGHcI6aw6//uZWN5QFL/Z0p1mCpd0S87fGditA0y/0xajY7M679HmVlZfzhD3+ILJHZbPziF79gyJAh9M/NYEhWUrSHqSgnJZA8v/B2duzYAUUzuPdf2wijW+7vLFFtK2tUeXynEm97fLFm65EGLv/539m47QB5X/kl5084j3989C7S6eFgY4BQfQBEbBwNVxKHlBL/ga1MPvfzOPKHknfdfVFtqqycyJTw5NpS7rh4eLSHAlgs8MXtHl+MkAg+CWTiGHY+AOtrwOx/PmLADLJ8jRhSWDoRWElQhkH9u48DWKapsnKi5TsqVeA7mdQ4y+OLNUKIE2Z0rafhdE9m3J4GVGKXlJJQzQGC5XvQktIs01RZOVFNs3UOGVnqHVKcnxr/B1xilAp6ihUJIbBn9Udzp5J67iVx07Fc6VmWijJXjS/AVG9cRVE6QNME/7N0NcVTZqt8PQvL9lhnCdpSgS/b4yQjyTovjqIo1ic1O/c//CSlR8qjPRTlNGYXW6dyi6UCX7U3QF2LddaBFUWJDZrTQ9jXFO1hKKfxuTF9oz2ENpYKfEs3HEZTe0mnppaBFeWkDJ+XYOV+SxWBVz4lgBW7rNOdxlKBb2d5I4GwKpD8WWYogBkOEvbWQpxV1VeUrjJDAYJVpXi3rlCHsCxKAjuPWmdGbqnA1+i3TqNCq5BS0rjhXxz92/fQ3SmRsmcxRp20U3qSEIJiZz3JycmkiIBaGbGoRn8o2kNoY6nEOZXHd3KOrP7IoZMinaSjPZjOkBKV+a6cTqRLesffI9I08R/ewf7sKWR+/bs0C6HeaxaV6rJHewhtLBVpInl85Wq58zhCCBw5AzAD3tg8qh0OEtj/Mc7CiTHTCVuJIdLAWVCM0Oygaai5njW5bBrFfVKiPYw2llo3u2p8QbSHYEnC7sSdba3X5kzLl9I0kaEANcsfQ9v2BiIGl2iV3tOp2Z4RQkrQbE5VrcXiJHDVOOtcwyz1bsn2OLlwWE60h2E5ZtCPdKef+Qs7qLN7b9I0Mbw1kQa74fan6FoP4vj2fEDDy78itGMl/S68ulvGKs3E6N6R6M7qpiocBBnpKqJY30XDc1Q/vtO5fWYRK3ZUoOpVHyMlmCZ6avffEHT2BJzbaSNz25v4a8qoSBpEMCmHtJw+hJobaDiwneCu9/DXVwHgGTuPyrTiLp+2E0IgpUCaBtI0ELpdneCLY9I0kUao3fK+DAcQQsO/bz3CkYyz/6gojlA5W7oQ3DazKNrDaMdyge/c/ukU5nooqfBGeyjWIAR6eh700P6YNAyEfvaPbQb9HHlrMbs2vXlseP9BSkmNEOjHHmfQoEHsa6whf/pV6JOu6ba9vdblLCGE2suJY0IITNMgcGgbtow+CLsLGQoQqjlI0+onMXyN9PnWX9XyZowoyk1mTEH3r1h1heUCH8DIPqkq8B1H03vu1xSs2IM9ZyBCd5z2QhK5Aw9St3Ix3mNBT0tKwzN6NvacQWjOZMxAM6GqUvZtWY4ttxB90tU9sxQltNg83aqcNaHpuAaOQRz33tc9Gbi/+gdCdWVRHJl1tS0RS9NSB8lcduuMpZUlA5863dk7zFCA5p1rCCx7iNSp83EXTgQp2y0vmaEAQgh8e9fTuG4Jbl8VhZNmYQ6fg5FXjDzJ16fNuB7T16T6oimdJoSAz9zwtb7P7NkDYv6wlJQSpEmo5jDC5sCe0afLjxmqOUzNy78lb/wcwlmF2PqPtkQA3FbWyJbD9Zaa9Qlpweziam+AafevIKg2+nqUDAc5/ODXMX2NAGjuVJJHz8GRMwjNlYwI+whVHsC3/R1SHAJN0/AXTCR15tfPPEPsZF6WokSblGa7wNqd72UzFAAhsFXsxPvhi/iO7EI75xLSp1+H6EK6khkK0LjmH4S2vMmiRYv4d8tANpQHLVHwQgBzz8nj4esnRHsobSw548v2OBmYlczuSrXc2VOkadKy96O2oAdg+hpp+vClE75W0zRqgNxpXyZ96jVwFjM5FfSUWGSGA/j3b8YMeLGl5uLsV9xuubXTpImrfj9VW9dQv/Gtdn93+icrSZ9xfZceXug2AuV78be0cNsPfkK/W/+GsFljxUUC7+yqosYbsMzJzujfDpxC33R3tIfQo6I90ZZGkGlpXn7/+99z7bXX4nK52n1e0zQKCgrIy8tDSokttxD75KvPKugpSqzSNZ2krS8yIbSNFIfolhmTGfTTsOIxSh79PrXvvwABL6NHj2bOnDkMHDgQ6WukZe96pNmFrR2hkfPle0gbfwUZ4y613FKwlJKlHx+O9jDaWHLGB6Br8T1jiBzPj9JyoGngXf0Eq3e/xxtPe3G5XCQnJ+P3+yOVYhwOTNPkyJEjbWNMnTpf7dkpcU2aJi27P6SqZBuHDh+m4LZvde3k6LHc02DVAewDx5LVZzjByn0EdqyisrKS5uZmNE2jb9++VK5dgnvIOITmOvPjnoQQAmF3kT7rZoQMI3vwQFxnBA1pqSLV1np1jpMIdTujtRwopcS/5wP8DQ3k5ORgmia1tbVt4wkGIz0R+/btS25uLmGbm4bCCer4uBLXpBGkYe0LaJpG/tQvdLk2rgQQ4Oo3vO1jycOnYZt1I8NTwvRv2sFjv7uHOXPmkFleTsW6Z3FOubZLe33oNqRFL+vVXuu0jLLmK4Q62dmTbDYbU+96mqN7d+BrqKXl6G70DW8yd+b5jBw5kocffphBgwbxX//1X1RUVPCfGhdNqg6iEsfMoJ+Gd/+GrDlATvEE7CMvhi7WxhVCgPjMJdbmICzhk3qdT4zhJI2Zy7Jlb6LrOoaxBU9zC5mzbwabI+72ya3UZNyyge+q8QUsWl4S7WHEJUPC3togZBRCRiHOfmPIn3gVaUVZXHvZGBoaGnjppZdobGzkrrvu4vvPb2TbJpU7pcQhaWKGg5gbljJnkIuyc39CWfYEpKb3aK6o0DTQnGRefAvC7qLpo5cBaN78FmZTJTlX/QLiLFvVSvkDlg18rXU7395RYakXLB615ketKW3ko0ffp9A5kO985zssWrSItLQ0GjVVGkqJcZ9tjRUOotts5JnVNG94iZLdJawr/C72nEG9OtMSup2MWd/AXTiB+nf/Tl9XiNrsQZjhUGx2YzmNzGTrnBGwZB5fq82H6lnw2Dp8IVWcuDeZQT+Nq/6O+8h6ampqGHLdr/Dnj4n2sBSlw6RhgDQwGyoI1pcjTRPT30ywqpTmrctJd9voe+HVNBVegtRtUd13l+EATaueIG/U1Lj8e7tybD8WXT022sMALDzjg0jdzrsvK2bhGzvwhdReX2/RHC4yZt2E5wOT6veXcXTHetKyhsfdHagS/0J1R6h45s52eXPHCw6fR8OQ2Wg2e1QXFltPZabN/iZhEX/XOg1UP76OuH7KIO6+bARuu64aK/ciKXRq+0zkmmuuwX5oQ5fvhM9mYcHCiw9KjAoe3XPKoOfIH0rG7JvQ7J1LIegRQiNs7flI5wjVj6/Drp8yiOdvmcLckXk4bRouW0wMO7ZpGkmFE3lm6avUlB3oUoKtNE2MloZIS6GTBDcpJVJKzEAL0gh3LZFXUY4xQwGM2oNApCCDzdY+oKjc1N6Tn+qyTNUWsPhS5/HGFKTz8PUTqPEGWPrxYXYebWLV7ipqm61zRDbeBAIBMidcRnDzG3g/WNrpBFtpBKle+iuMxiqSxl5G8ojp2NLyIiWVpGzLD9RdyZjhYCS53zSRZhjtuLJLZiiA0G2WKLyrWJ/NZsO//V0ATNPEPO6GSktKI0nlpvaaS0bmRXsI7cRM4GuV5XHyrQsKAfj5q5/w5LoDUR5R/NLsTmRqX5qampCNjdStWEzGrJvQHGcf/DQzTH75Wmobywg0N2M212FLy/20kexnllBbA500TRAC/5GdmC0NmP5mwtUHsLmT8Uy8EnTVeVs5NWmaZIcqSB3Uj61ba9sFPZvNRvr4eWgqN7VX2DTBd2cNjfYw2om5wHe8Rn8o2kOIe7rbQ2pqKoZhRPrwCciYddNZ9e/DCFG14q/s3/QmdrudnKlfwj3t7KrQC01DoOHIGUT9O3+jaeMbQOROPXnCF+Msw6lnJHKHDGkE2fLc/xIs3wNARkYGLpeLyspKbDYbIr0AqcX05S8mCGDOiFxLLXNCzAe+cLSHEFXSCIOm9+jFzZmeS00IjBYvaWlp+LevpOLo7tP270MI/HvX0/TBiwxMgW8vWkS9nsbjB9MQto79AWgOF+mzvoHDe5TmQzsI+BrxH9mJa8DohL2od4Q0zZhezovM/OlY0WUjiH3ra4Qr9yGlxG6309DQQF1dHUII/H4/KY74LoJvFTZdcNvMomgP4wQxHfgSoZ7nyYS9dfj3b8Se3R9nn55dQhAZBfS97XGMuqMYTdU4jDBmoJkMo4H9f/su7qLJOHIGYUtKwQw0E6zcT9Pmt3GJMEYoxK6yEHfccQfZV/6UpKFTOjcG3YE+5jKCezeTfO6lOPsVd/NPGaeONTuNkTNsJyXNMA2rnyZtxrVntcogjSB1Kxbj3fQmmqbhcrkIBAKkpKTgcrnaatKageZe/CkSl5TSck1oIcYDX6LW87x4VD9uubaQKxf9u8efS+g2BCCyB2DPGdj28YARpm/xXIyWBoIVe/Ed3Ip3y/K2o+P+pDQ84y4lLXcwjtRsbP1GdnrmITQN1+DxeCZ8kbQZ17Y78KKcmtA0Qkf3oGcPsNaR/bMkTRP/3vU0fvgS/oNbTrnKQDiI0DSKkvzccck5zPuff7Fv3z7+9re/8dJLL3H06FFcLheNjY0MGzaMo0ePEqoqxQwFVG5qDwubsPCNnYwpSLdU8LN05ZYzqfYGmHr/CkIJ1KldhgP4P1xK1XvPkXnFD/Ccc1G0hwSAGQ4hkATLdqHrOnpeEfKzF6guPX4QhEBTh1o6xDi4maSG/fiKLz3jjMlqzJCf2hd+hqulEr/fT3NzM/kDi8gYfxmVQRuaM7mtCovr6CZchKisrGTIkCFcfPHFXHTRRUyfPp3a2loeffRRHnnkEbxeL06nk5DuouC2xy3TrDWeCQFzR1qrA3tMBL5qb4ClGw6zs7yRRn+YVJeN4vxU5o8vYN6f3qOyyTrtLnqaDAdpee6HpNihaeilJI291FLH+1vfTj2x/5bIhzU6K71uF/pHz7DtaBOpU+bjKpxw4ozJgsygn7qVizF2vYumaYTDYUKhULvTmV3VuvweSzcDscpp03j/J7Msc8jF0oFv86F6Hnx3D6tKqgDaLWm6bBqmhJBhJsyRZCklSElw/3qC1YfwjL8C9PhrX6J0DyklvpK1FkRWMQAAIABJREFUhN99KJKTmZlJWU0jyaPn4MgZhCNvMPas/ggLNS2VUiKNEPUr/kp69VYaGxv5zW9+wxe+8AU0TaO6upprrrmGkpIS0tPTaWhooF+/fjQ3N+N0Ojly5AiapqHrOqHQ6U99O/KHknfdfTG5DBxrXDaNOy4e1paKFm2WDXxPrytl4Rs78YcN1Z3hM1pz3FTAU85EGiFqlz+Kd2MkpeSzwcAzdh4Zs8+cntJrpERIg9rlj9G08Q369u3LkSNHKCoq4uKLL2bq1KlMmjSJuXPncvToUUzTRErJkCFD2LNnD2lpaZimSWNjI7quc+ONN/LjH/8Yt9tNWVkZ27dv5+233+aFF14gHA5Hfv4O5qYqnWOlItUWeKefKBL0duALqaB3MkLTVNCLIaZhRFJPolCKTeh2MmffgiO/6KQzIO+mN6l45k5adq+N9KULtd826PX7YiGQmo2sOd9k9JW3UlFRQf/+/SkrK0MIwV//+lcmTJjAgQMHCIfDhMNhDMNg3759zJgxg+bmZux2O88++yzz58/n6aefZuzYsXzzm9/k4MGDeL1ePvzwQ5KSksjOzmZMUgN1KxdjhvyqVF4Ps1LeteVmfKoVkRJvpGlQ8fzPSR13eVT22KSU+A9sofK5u9t9vPXmqfUSoLlTGXn5Nyj3a4hjB0fc/Yahp/eFKMwGNTNMYek/ee/VZ9ry7/r06UNRURGmafLNb36T1157jRdffLHte0aNGsWMGTNYsmQJN998MwsWLOAHP/gBa9euJRgM4nA4uO6662hpaWHp0qWMGDGCLVu2YM8rJGP6ApyDxp1wKEvtLXcPK834LBf4bnlqvWo+q8SVcHMdR/58AxAJLq17bK6BY7ClZvfKGKSU+Ne/TO26lwg317d9XNd19OT0/9/efcdXWd0PHP884+4kZAeSQAJhJGFKEIKIIEjZOEAc4PiBonXV1opWWsXW4qhW1KJYilhxVFFRaxFBGSp7yR4BwgjZITu583l+f1wTQVb2vUnO+/Xy5esVyJNzLzfP9znfc873izlpCJZ2ndEUE5qjHFfeMcp2fYMxpC2Rtz5b68IDDTJmTcN9bBt5S+fg8XiIj4/n1KlTuFwuBgwYQHh4OOvXr8dms9G9e3e+++47HA4HZrMZk8lEcXExkiQhyzIpKSkUFBRgMBiIiYnhm2++wePxcP/992MwGJg7dy5vvvkmAeHt2Fag8PnarZTY3RhNZtToRPDB629JxBrfReSXORj0/KpWdy5PaNmc+SfJ+tevz/l6xMQ/Ye0yoMnGoXncqLKEtSwDi6eM3HIdLTgG2RLkXVs7Y5NLVQWeyiNbaWuTsccNrNuxoV92Pq8lkyrzw8yh/Osfc3nhhRcIDg7m2LFjgLfjQlRUFO3bt6dPnz6MHj2aW2+9FYPBgN1uR5ZlevTowd69ewkJCaGwsBCLxcLp06ex2Wx4PB7atWuH0Wjk8ssv57XXXiM42HvWzOVyERgYiNFoJGjSX1Cj/K/6SHNiUCQ2Pj7cb3Z1+tUa38fbMnw9BEFocLrLfv4/cFU26ThkRUWTFEoDOpAX3B0ppgeKLQRJVs7Z2SkbTMiqEWuXVIrb9sXlqdvSQ32fqiXg0x2ZTJgwgW7dunH8+HHCwsIYMGAAy5YtY9iwYezbt49//vOfTJ48GYDS0lJkWWbMmDFkZmYyd+5cOnbsiK7rhIWF8eCDDxISEoLdbicjI4Pg4GB2795N+/btueOOO9i6dWv1zPCRNz5DDY+v56sQAk2q3wQ98LPAdyC7RMz2hBZH9dhJSkoiMPDsDtSOnKPnbCZpCrVZr5JkGVk1out1m7XVd23M7tZ4fv5irrzySsaPH09kZCSyLBMeHs7rr7/O3LlzefTRRwEICgpC13X69OmDrut8+umnOBwOHnjgATZu3MjQoUMpLCwkLCyMhQsXEh0dTXJyMlu2bMHhcNC5c2c++OADBg0aRFRUFOWmcN7dWwF+dNyjuSqsdFFQ5j/nrf0q8LX2otNCyxRmM3Lo0KGzdkh26NAB+/61zWbThC/HaQ0Op2PHjrz44ouUlZUxefJktmzZwpYtW4iJieHw4cMkJyfz6aefcujQIX71q19hs9kwmUwUFBTQqVMnevbsycqVKykuLmbu3LlMnjyZ7OxsIiMjUVWVQ4cOkZ6ezsKFC1mxYgXjxo1DT/4VzgapCuU3q0k+o+vwzoZjvh5GNb8KfK216LTQgmlucg7t4Oqrr6aioqL6yzk5OThLCqg8uk1so7+EEUOuZPv27dx000306NGDJUuWkJubS25uLmFhYbRp04ZRo0bx/fff0759e0aMGEFERARxcXEYjUYOHz6MxWJh3bp1pKamEh4e7i1UrWmsXLkSp9OJpmmUlZUxY8YMHnnkEW68bRrWzpc3yNlG3eNGdztb/b/ztwdyfT2Ean4V+LxFp/1qSEIrpbkc6JqH+j6t65pGD2sZq1atIi4uDqvVCni72wMUr/8I3eOs73BbLAUPwVIFn3zyCYsWLSIjI4MnnniC48ePM336dAoLC5k3bx7vvvsub731FqNHj2b69Ok8++yzHDhwgFOnThEdHc2ePXsYNGgQPXr0YMiQIRQVFZ31cyRJQlEU7HY75phuzPjPXvQGuD3qHje2g8uRvn0Zx5HN6Jqn6c9G+on8Mv/5nItdnYLwC5rLgTntGxz716INfRA5NLZuqT5NoyJtI3lL55zzRwEBAZSVlREcHIw7fiChw++qUYPe1kZ3O8maPx3ZVYHZbGbt2rX07t27+s8XLVrE448/TnFxMQ6HA6vVSseOHbnhhhu47rrruOyyy8jMzOTKK6+kd+/eLF++vPqho4okSZhMJm655RYG3jaTv68+1mDniO0ZByj86Al69erF9u3bsUUnEDb17+itsJVy2yAzG/8w3NfDAPxsxhceYGJI14j67H4WGpgfPRc1HUni5v7xZB8/TMFXryBrdVt71jxOijd8hKqqyLKMqv6cym/Xrh0Gg4GioiLse1ZStPotUT3kF3RNo/LIViyyh5SUFHr06MG4ceNITk7mySefZPfu3YSHh/98AF+W+fOf/8w///lP7HY7N910E3FxcTz33HPMnj2bTZs28dRTTxEUFFR9bAG8n/EFCxawuzKYv361v0GLZ2iVxTidzuqdorGBKp6CE63y9yrM5j+dMPxqxgeicos/0DUPlcd+RNI0bLHd0EyB6Ph2g0NT0TWNirQN5C99tvprA6b8nuzoK0Cp+S9uVXcB575vcTqddO3alfz8fCorK7Hb7d7qILKMpmm8++679OzZk6E33I7t6hnIYR2AlvN+17Xyiay5KVn6NIVHdqJpGmPHjuXGG2/k5MmTrFmzhu+//x6Px8PQoUNJSEjg7bffRpIk3nvvPQYPHozRaCQ9PZ1ly5bxxRdfcPDgQex2Ow899BBpaWl8/fXXhIeHExAQQIUlAvVXv8fdwHOBst2rKF35D5xOJ/Hx8cQMuYmTEQNaZTuk3wzrzG9HdPP1MAA/DHxwZq1O8fTb1Kpu/Kc/f56IgTcQMPj2Wt0Mmnt5J93jJnvx70luG8DBgweru3frCYNoc/W0WncBB4iKiqKwsBBN0wgJCcHhcFBS4m3YGxoayhtvvMGkSZOIGjQR25W3Qwu7KeoeN7rmrlUXBN3loPS7t+mm5JGenk5OTg5ms5mIiAgsFgvp6enExcUREBBATk4OBQUFuFyu6s9fcHAwbrcbh8OB0+nEYDBgMBjQNA273X5WubawsDBCxs/EGZnUoIW6NZeDou/fI1k6xeTJk1m98yj72o3AI7XOTXzbZl3jN2f5/CrVWWVqajyzxiRhMSgi7dnEdI+Tkg1LUCMTMKbeXOsnYEmSvK1lmmnKTi/Owpl9mB9//JH4+HiSk5MpLS2lZPsyct57HMfRzejnKeasuRzeXolpG8h57/HqoAdUl9Hq168f48ePJyAgAACTycSCBQv405/+xI8nCgm46o6WF/R0HWfecQq/rVkhaF3T0F0O2mWvp4PzBPHx8RQVFZGYmIjL5SIjI4Ps7GzWrl1LWloaO3bsIDMzE7vdzpdffonR6H3/iouLueWWW6pnhaWlpWRlZXHixAkWLVpESEgIiYmJ3ooulW6c4V0avjuFJFG++xs2bdrE448/zpaKUNy6X95yG12w1eA3QQ/8dMZXZVdGEa+vOcyKfTlofjvKlkNz2tG2f8ypNf+pV5NOXdPwlJ9GsQSdW/DX4wLZ+8Rbm5mh7nF7r6U2bgf2yIpjbH3tQeSfXndiYiL79u2jS5cuHDt2DJfLhWwJwtpjOKbIeCSTja4d2xPoLmbFm39BqyypvpaiKHg8HmRZxmQy4XA4UFW1emby7LPP8thjjzFs2DCMwx/gYJkRpJZ3Y9TdTk5/uwBnVhpBA2/EmnA5wFnpPs3lQFYUnOnbMR1ZQ0XGAcLDw8nLy6O0tJTAwEAKCgq8tUUVBVVV+fjjj+nVqxeKohAZGQlAx44dmTZtGn/729+QJImIiAhcsome195NUIckDNYgbEaZsoxDLH1pJgP6dCc/4jKKYgdBA362fpkyl61tWnXH96FdI3j7//r7ehjV/DrwgXen5xXPrcLpaZ4ziOZA13V0twNl5+cUbvmCkHYd0MY9Xa9fUs3tJOutB7F0HoAxIh7Z7K3278w7hivnCMHDpmGM7ARcIgDqGoqkYz2ympK4qxr1xqG5HOi7/kvGyreZOXMm+fn5LF68mP79+3PkyBGcTidlZWU4nWdvy66a5QJ069YNs9nMzp07CQ8PJyIigoMHDxIaGkpxcfFZrYHat2/PqFGjUKxtWGa4okXfFGXNxemPn6L46K7qQt0hHbtjCw6nIPskjpx0osrTObp/F263m8TEREJDQ9m4cSOaptGhQwcuu+wyfvjhBwoKCjCZTDidTlRVRVEUevTogcvlIj09HaPRiMfjocIcQdDAG7F0Sjn3AayqFunRrcgGE+aOKQ38et3EHPiI28cN5cknnyRw6DSccakt8sHmUlRZ4tGR3fymQDWA3yebP96WIdKdjUwCzOU5mFyFHC8pwdK3Nwa9nhuudR1rlwHo+1ZSsdtNSkoKR48epSAzE1mWcX3xZ3qNncJeYzeU0FjvOGSl+tsV3YOiqiRYXWx6688cP7aHkAnWOs9Ca0JRFPK2f4UlJBJ7x8FsKTpI5+kvcTQnkwp3FBV7VmFU1XMC35m7Cj0eDzk5OQwcOJANGzbwzjvvkJeXxx133FE9i6xSUlJCnz59+CKtwrs21Sivyj94UDD1GU/bilyCg4M5sPlTSjd/SmBgIE6nE5fLRbGmoaoqAQEBHDp0CE3TkGWZtm3bcvLkScrLy5k2bRrZ2dl88MEHGAyG6u+NiorimWee4YsvvmDbtm0Mv/dpXv3uJC7tp/6VvxhP1dERS+dUGrqyiuayU/LdOxzb+l82fbWE0P7X4ugwAKkVBj0ARZaY1DfW18M4i9/P+B7+cAef/Zjp62G0eGduyjDFJhHQY1i9r+k8+D1ZS58nMDAQTdMoLy8HYOHChTzwwANUVlYSFhbGnJde5bNd2ezJKKRbjz7s3bEFuTSb7PWfEWozUlpaSs+ePcl0GJBH/K5WmyRqToecQ5QXFWBN6Od92FLOTsVJkoTrxE6K1n9IZcYB2rRpg67rlJSUVM9aQ0ND+cMf/sCcOXOw2+0YjUYOHDhAbGwsbvfPxyKuvfZaNm/ezKlTp5j00pdsK2j5N0Xd7aTw7fsxS266dOnC+vXrkWUZo9FIZGQk2dnZmM1mrFard23Q6aSwsBBVVdE0DUXxPhh16dKFm266idmzZ591LOAPf/gDd9xxB7+67y9YB01p+s1xuo7ZqHBDPCx59iEyMzO5868L+SrLgib7/RyjUUgSjEyOYv7Ufr4eylmU2bNnz/b1IC7mo20ZpOeX+3oYLZ4kSUiKirlDL5SAEGSTtd7X1E02zB16Isf2xBgWg8lRjL2shK++8m78MBqNnD59mti2kSinj3NkzSd8/+7L2AoPU3p0J7+5/9fk5+eTkZFBVlYWpbkZeCpKMcf1OqebQH1J6EjWEAxh7b3XPmP2CSApKpKsILdphzV5CKOuvpIbhvZjxYoVJCQkUFhYiK7rBIS3Y12uzJDpf+R0cDek6B6898UK4kItZJ08DnjP8G3evJl58+YRFxfH/BU/ooZEN+jr8Ue6x429tJCOgTpjx46ltLSU7OxsXC5X9Q5Mh8NBYEQ0jy/8Ckf7y6HTQIzxl3HVuEl0jgri2OGDZGVlsWbNmurryrKMruv88MMPHCpwkN9tPG6UCw+kMV7bT5t4TJsW8e85jxAdHc3n3+8gI+5XLW7DUm1YDAovTOxFVFBjPKzWnZjxNXf17Hd2/ks2/JGEM9dUitcvwZVzGKPRSFJSEseOHeOqq66iS5cuuFwulixZQl5eHm63G6PRiKZpuN1uLBYLRqORCb/7G2tLwkAxNEza86cyUrUJpgZJ5/S3/6J0xzJ69uzJiXIJqfsoLJ1SvGm1X27ckGUqjmyheP0SpMIT/OMf/+Cll14iLS2NsHG/w5I0pP6voxko27OKgi//TmBgIMOGDaOwsJBdu3ZRVFSEqV1XAlMnYU3od+6anNuJoqgMT26L5eh3vPXiUxgMBoqLi9F1naCgIMrLywmZ8FijpsMvRHM7OTXvTrTKEmRZpkuXLhT2uBFrl9RWW6LaYpCZNSaJqanxvh7KOfx+xnesoIItx07jEds6z6VptLVolHsa/pe8oQNf1YxJDYnB1uNqPBWlSIUncLvdSJJERkYGiqLQvn17Jk6cSP/+/fn+++9xuVy0adOGnj17kpOTg6IobF3xCZVHtyFbgzAEt0X3uM8KWhKArl/60L2ueXeZApJSux19GhKG2O4EV2bhCO+GctVdGMI6XHC2yBmvXXZV8tH8FwkJCfHuVGzTFlNscoPPYmtD17UmOX/pLs7FmbYel8tFfn5+9aHyuOFTsY64H2N4nPez8ov3QpIVdEniaG4px+RIpk25iZ2r/8tjjz2G1Wplz549KLZgwkbej6Q07WwPTcOZvpWy3d9gMBiYMGECB46dwjL4znM+C63J3Vd25L6ru/h6GOfl9wsLk1L8a1HUnwQobjKOHkJv4GfKxrwBSrKMbDATMnw6iePuoqKigvXr15OTk8PSpUvZvXs3Tz/9NAEBAbhcruqn+uPHj2O32ykpKfGmlbIPk7/0WfIW3ktfQxautHX0CIFxPSJJch4k/+PZOI9swiAD7rM3o1SduQu3Z5ISF1rngCMpRuy9JuLuNQFJNV1yllH12i1XTOWmWf8gLS0NXddxp/2AoraONSDNXl691pmXl4eqqnQZPQ2tz3XIBvOlZ2qyjMOt897+SgbePpN//etfxMfHs2PHDkJTRqPrTV/xSUenb5SRhx9+2LsO7HIx841Pm3wc/kQC0gv8d4nK7wOfqN95YWWaitIuEZrhfkDZYKai22guHzmRlStXUlBQwMCBA1m1ahVZWVk88sgj2Gw2PB4PmqaRm+ttaVJ1QDk1NZX58+cjOcr44vkHeXpUJ+5N1vnvE5MIztqK4/hOuuZ+R+G/H6D4h/cp27MKPWMXYaVH0Hd+wZxUhdz/vcqe7HLq+v5JsowhsiOSWruDubLRzLrKKALjugMQEWjmmqS2Pv6MN/4P11wOnHnHAPB4PFgsFrqkXkNl0ujab1hSjOyUEgjokMz8+fMZMmQIHXpdUet/iwYhSey39ULuNoTPPvuM5cuX88rbS1r08ZRL0YHVB/P8qvnsmfw+8AHcP7QzZrX1pgwuSJKbfC2jIdldHtoMnMycOXPo1KkTO3fuxG634/F419wcDgddunQhOvrnjR+yLBMaGsqTTz7Jtm3bSEhIoG/fvtxzzz3cdNNNjBgxgj179uB2u9m8eTPxbcMo3/Y5w60ZuL59jTXP/h/WExtI6d6V6CtvOOtcXV3UNVxIihFz32ux2Wzk5ORwVYQdk+K7f8umSHNKkoTzwFpiYmKIjIwkKSmJougByLWogXomTVLwdLuGvXv34pJNZFY2zPsnoYNe8x2hkiSjySqfHNG47el/EhMTg7OJN9f4Iwn4eHuGr4dxXs3irtm7fTCzxiRiMTSL4Qo1pAO7CzQ8qoXKykokSao+6+bxeIiOjubUqVNkZWXx6quvkpSUhN1ux+Vycffdd5ORkcG4cePYv38/RqMRu93O4sWL2b9/P1arlSeeeAKn00l4eDjp6enMmTOHwMBAbDYb5eXldLl8CHp9t5nXMWBIsow5oR+du/ehTZs2PHDLeIrXLEKqxQ23WdE0ugW6aRcayMmTJ1m0aBG6MQC1fS+o48ObJMtUhiYw+E8fEHHXm6ihMQ0y1NgQ6zlnLmtCV4wEDL6NEjUYzeG/ab6mYndrHMgq9fUwzqvZRJLa1O+UJO822vG92olg6eckYNR9s6sPLlfdcKKiotA0jcTEREaOHEl6ejohISG0a9eOsrIyTp06xcqVK3nppZe49tprWbJkCampqQwaNAi321vebNOmTezZs4d7770XgNtvvx0Am81GXl4e6zZv99XLBkDSdcxJQ8nJycHhcFC+czmR5pYZ+AyKxBPX9as+8zhmzBhmPLfwrFZNdSEpBiwdU5BU41kFEOrjZGEFWh1rzeqyAeNl43HlHUNz+0/j1UtprPl+ib1+GZXG0qyiwtTUeD6ckcrI5ChMqoz5F93azaqMSZUZmRzFhzNSee2Wvswak9QMV8BaD7tb47/fb8Nut+N2u9E0jaFDh+J2u/nLX/5CdnY2UVFRfPTRR8iyTHZ2dvX3RkZGMmDAAJ577jnGjRvH3LlzWb16NUFBQaSlpbFy5UpUVeXFF19kzpw5yLKM2+0mNzeXCRMmUFKQ48NX7q0ecrigki5dujBu3DgUReHo9h98OqbGoLvsODa+z+3jvJ3Pq/4ND2SX4tbr/9vZ8Ol+qc6lxSRZxppwOW1j42q9U9hXFKmha9f8LMjsn+9Bswp8AL1ig5k/tR/rHxvGb0d05fo+MQxPjOT6PjH8dkRX1j82jPlT+9Er1ttocmpqPAM7hfp41MLFGG3B3HbbbdVHF0pLS9E0jWeeeYaCggIWL15M27Zt2b59O23btsVgMGCz2UhISGDkyJH069ePl19+mVGjRmG1WpEkiTvvvBNZlrnzzjsJDw9nypQpTJs2jZiYmOpyWNFWbx1BX1ItgVit3mIBGRkZJEcHndP5oSk15LFeXdPQXHZOf7uQ05u/4KmnnkKSJLp168att97K8czcBvtZ/kTTdBydr/b/9ly6TlSQCaWRfgfMqkxiu8BGuXZ9+f0B9trIL3Pw8bYMDmSXUGJ3E2RWSWwbRIXTzT9WHxYdHvxU+Z5VlKzwNutMTEykqKiInJwcdF3HYDDw61//moULF2I2m5k3bx7r1q1j3bp1VFZWkpKSwtixY5k6dSrgbfXz8ssvc88993Dbbbfxv//9j/fff7+6+aiqqtU1Hud8toV5P5zy6Wu35Owm7Z0/YjKZKC4u5rPl3/L77yrBB7MF3e0CWa53ylD7qViB89h2HhnVgz/cMwWPx4OiKAQGBrJx40a++uorXt1SjNbBv0pZtTYGRcLlaZwbo0mVWf/YML9qR1SlRQS+nSeLmLfmMGsP5QHgcP+cnzerMpqu4/I09Gk3oSFUNess3ew999S2bVtuuOEGXn/9deDn3YY33ngjb7zxBqGhoezcuZPhw4czcOBAMjMzOXDgAFdccQXffPMNAOHh4ei6TmlpKbGxsZw6dYo2bdqwYMEC3nnnHZYuXUpAh+5E3jIHVz1SbfWucON2Ur7pI0o3L8XhcPDuu++yadMmdrVJ5ZgrsMkr+UuaB03X6pSi0zUPjoz9uItzcecfx75/DVplCfPnz8dsNjNjxgwcDgdut5vbb7+dt99+mze/O8JLKw4i+k23PP5ao7NKsw983m7tB7C7PTTvV9I66bqOs+AkRSv/iZR7ELfbjdVqpbi4uPrv9OvXjw0bNpy1EaJXr16kp6djtVoxGo3k5eXh8XiqD0ebzWbsdjsAjz76KM888wwLFizg73//O1lZWSTNeJkCU3SddxRWjb0+gU93O8l+8y5uv+kGFi1ahMfjwWq18shfX+X93MgmbVqqaxqquwLNGFCnB8Qz+8/JskxcXBwnTpwgIiKCjIwM4uLimDBhAm+88QaSJBEUFMTEKXeyOnAYmiS2/rc0FoPChzNSq5ec/E2zW+M7kzfo7afSJYJecyVJEsaw9kTe/BcSH/43g8bfelbQUxSFIUOGnLP7b9SoUZSVlfHHP/6R3r17YzKZzup+YLfbkSSJ8ePHs3z5cpYtW8YzzzxD27ZtGXzNGAot9Qt6oGN2nIY67v6TJOgRKmGRPaxZswaDwTvL8ng8LF26FGdpIU35obYYVTSjtc5ZEUmWsSRcjmwJwmg0VvfKy87OpmfPnjgcDhYsWFBdULqiooK3Xn8FKXs/jbe1QvAFb43ORL8NetCMA9/Ok0X8ddmBpm89IjQ4SZKQJIkCzcLRTtcR0Gc0AIGBgURHR3P8+HG++uortm/fTmZmJmvWrGHRokUoisKBAwfYu3cvSUlJ5732l19+icViYfLkyTz00EMUFBQQ3HdU/YOKrtEtWKqu9VlbRlliztQhdOvWjZycHPbt24ckSRiShlHS/y5kW2iDFx+/EFWGq7pGYKxv2TRdx9bzGux2O1lZWciyjKIopKWlMW7cOAwGAzExMURFRVU/pGR8vdCnm3mEhlN1jMxfC1OfqdmmOmcs3srK/Tl1v381QleDplDdsLTJxq7T1CXRNJeTwlUL6BtYzoABA8jJySEjv5hTxlhK5CDckoqiOXHmHqN4x3IssoeysrKzrmEymbj66qu59957mTx5MhaLBV3Xeeutt1i4z82+ioB6j7NvqIvjW1eT335IdWPTmlDRKP9hMfN+ezOdO3cmJSWFt956i98u1zmsAAAXyklEQVTP/xzzwFtrda2GYFAkrkgIY+2h/Hpfq2z3KgpXLySg53AMEfHIJhuaoxx3/nHKd3+Lu7wIm82G0WisnvmFDrgWw+U3IRv9q3WNUHOqLHFNUiT3De3s1zO9Ks0y8OWXORj0/KqzNrHUliyBKsvIkvcsWbPSREFbdztwFZzCEBZ7TpuYxqa57MwaYCU1dWD1xiVN087aCKFr3oLEnrLT2A9vwrHtM9zlRdjtdiIjI9F1nfz8fMxmM4qiUFZWRocOHTBe8xCuyMR6jzGo7Dg5Hz1NRXQKYSPuBkm9aPpUkkDRNUKPr2beb29h4sSJTJs2je+++44jhW4Y/hukRmmyewm6Bo4yMAfV+1LustMo5oDzthWSJAnt1B5uS4lk1r1T6d+/PzfffDMffPABJ4xxhAyfjqQYL3ouT9c1JKRm+dDakplViT+OTfb7mV6VZpnq/Hhb/eu/GRWZXw/pxJCuEQ0woibWFEFP16lM24Tzi6cpfudBHFs/oWz3Kuwn9qB73Je+QD1JipF/73Nx84KNrNyfg8OtnbP7T5IVb6ujoAhsl40lbPqbjH3uc1577ws8Hg9XXHEFo0ePZs+ePdhsNu644w5OnjxJYW7D9HfMTD+C3W6ncvcKshbPxHlsG3jc56TuqrpBRDqz+eCu/ngOrmXv3r1s3ryZFStWYDAYqOg4GKmONSvrTZLRjbYGuZRiC0FSjec8JEmq0ds/MbY372aF03vSA9x///288sorTJo0ifKdyzm95EnM+QfR3c5zOmpUFafoYnWC1vQdGISLs7t1/rrsALsyinw9lBppljO+hmpOO7RrBBuOFtRr5tiSaS47pz/6E1r+MSorK6u/HtBnNCHDpjd6aqouuyYlwGxQSCjdyfLXZrFq1SrWrVvHRx99RFFRESdPnsTW7zraXHlrvWawvzyGIcsyvXv35tCJTOSEQXToOYCkPimoHhfLPljAn6aM4Ntln+N2u3nyySeZOHEiu3btIiQkhLvu/y3fhYzwaTV/3eNGB+Qm6gmouez8ekAUkSUHeeihh7Db7SQkJFBcXMygYSP5/pQLJawD5oAQeiQmMH5wXyb1jSXUZqTvb+ZTaImt5+YkoaH5+xGGMzXLwDft31tYdaD+VR8CTAplDvH0eCG6puE8uhn9+wUEBQVx/PhxnE7vk3jAZaOJHvsATrd/no/UXQ6uDinkk+cepry8nJQrhnDK1J6eg0ex9/AJlJjkeh3U1txOsudPx1VWCHh3n44cOZJly5ahKAr9+vWjtLSUzMxMioqKiIiIoF27dhQVFVFUVETHjh3RdZ1HH32U1dkqK7PPnSU1NRloykdAi0HhH9d3ZlT/JEwmE+3atWPVqlVMmTKluoLMjh07cLvdLFy4kJSUFF555RWWfLsZ24RZTb4WKlyaPx9aP1OzfGQKMjfMU6kIehcnyTKB3QYy4cZbOX78OAaDgdTUVAwGA+GF+1lyzxWEVpxEQcOk+tdHSTKYWF0YQlCvEUTcMIu8K36Dpf8kDjraoLbv6e3oXcdnPl3TqDyyBXe5N60TGhpKu3btWLZsGSaTieTkZDZu3MjevXt5//33AVi/fj2LFi3i9ddf58orr2Tfvn3s2bOHO++8ky++2+rzoAcQFmBs0qUzu8vDnM+3MXjwYDweD1lZWbRv357Vq1dzzTXXkJaWxo033ojD4WD69On06tULSZL4cdXnhJ9ci+ay1+rnNcNn/GbHn1sRnalZtn1ObBuESc0WKcom4LDb+faod3dldnY2W7ZswePxUFFRQXH6bk59OJtl337HHbPfwBWRhCco2m9qFEoGE+qVd6IigSzzy8ecuo5TxkPppk8ICAigrKyMiIgIDh06BEBKSgojRoxg586dpKenM3fuXAAmTJhAVlZW9RlFq9WK3W5HlmWCI9rhDzXsk9sF8f3B7Pq3aqohHThSYWJM737YbDa+/PJLDh06RNeuXfnd736Hy+XihRdeQJZlwsPDUVWVDz74wLsZ6MgRbv5jb1YVSciq6aJZB13T4KcjM432Wn4Kqv7y2fcVf25FdCb/ekyvoUkpsb4eQqshGUwEtO/G119/jSRJeDze8JGbm8vEiRO5++67mTR+FJU7/ou74KTf/eJ7N8A03MfcIOt0KNqNuUMvjEPuptP//Y2S5OsJ7H89siWI9evX8/zzzzN27FgeeOABfvjB221BVVXuv/9+Nm3ahNvtJj8/n88//xxJkhpss0196G4HRzcsJ/T4GsxN2MpL1yHT3IHLL78ci8XCxIkTGT16NEFBQaxdu5Z3332XTp064fF4mDJlCpWVlezfvx+73c7W/7zMNezBVnT4vN1aNI/bu/NXa4LNWI0cWJsTf21FdKZmOeMLDzAxpGtE/c7xCTXWd8AgLBZLddCrqqJit9uZN28emqZx11138Z9MU/N8kqoJTQPNTVnBSY6F9SBoUHdkgwk3YIwEQ6fLCR48lYojWynf8ik9uscxZswYZs+ejdFopHv37uTk5PDYY49x+PBhcnNzq4tlu/KOoWueBusnVzcSh1Ys5ui+nSzdnVejMoD1rlUKSKqBXcVGBgabUFWVPXv2sHfvXsxmMzNnzmT8+PEYg8J4eO4HLNzvIeS6J3BVlODKTWf/7m8J2fQNAU4nV48aT/ywm3lp8X+pNIcjW9sg4X3wwafva+vjr62IzqTMnj17tq8HURcdQq189mMmbtFyodHFh9m47rIYZs2ahdPp5LnnnmPlypU4HA5sNhuffPIJ+fn5nNJDqDD6/+HVi/nlzVxzOdB1jTZUoBmtYAlGUlSkX+x+lBQVSVYwhMZgTR7CoT0/8uW/51FZWUlAQABGo5GRI0cyfvx4EhMTWbVqFQ6Hw9uAl0rU7r9qhL5yNaNrGhWHN3F66/8wmUzcf8t4hnSNYPWeDEqdFy6Y0FAzHM0UyIaMCspP7EMrL0TXdTRNI7xrCktPGljwYynu0I4YIjuhhkRjjOyIKTaZwP7Xkec2U3DiIBtX/pejp+2URPVCsQQgSbLP3s/WzKzKjO8dTb84/24F1yx3dVb5uVZnzdf6FAkaqQtHi6TrOpKuk+xKY93Cpxk5ciQVFRX873//Q5Ik0tLSiIiIICEhgXvmfsx7u4qabI2o0eg6lek7wF6CWpaDR9MxpVxfq12EustBfOF2vnvrr/Tu3ZvLLruMyZMn8+CDD3L06FFUVWXGjBns2LGDmTNn8tSqHIos0T45mF11bKX85H5UVaVjx44kTX+BnYVqk41H13UkzU359+9Q9uNXqIlXEz5iBqiGi886NQ1Jc1N2cD3WrqnIvigAIFRrLrs6m3Xgg5p3Z5AkMKsKHUItHMwpu/BfFM5P13HlHcP+3VucPrwDg8GA2+1m8ODB2Gw28vPzyS4sR7lhTrMPfJrLAdkHcJYVgy0UU2xSndKQustBzvuP07t9CJs3b0aWZTRNY+rUqbz66qsoikJMTAxZWVlsO5bP7Yt3NXm6U3M5Kfx2AeU7l2MwGHC5XAQPuYPAARN9tGalYXMVU4qlVkGsIdKuLYUEhNiMGBWZMJuRHjFBfP5jZqNXqGpO5/ia9x0Kb4f1XrHBvL7mMKsP5iFxdgkysyqjA1d3i+C+oZ15a126CHx1IUmoEfEE3PA0wSWnuHdsKv/43RSOHj1a3TQ2KCgI5fAWLJ0HNOs0k2wwQUwPzPUMQpJiIDB1Eju+fBFJkrBYLBgMBhISElAUhRUrVnDllVcSEBBA6bE1FG/6lDapk5rmBq7r6OhU/Pgl5TuXVwflgLjuBPa/3odBRKZcDUaubeECEfR+JkH/+JCzAlDPmDa1zo7VlllVuG9o50a7fkNq9jO+MxWUOfh4ewYHskopsbsIMhtIbBfIpL6x1VPv+WuP8PI3h8RRiHpSJW/dxJKDG9H3LGfD//7D5s2bmfb7p4ma8qxIOf1EdzvJmj8dxV1Jfn4+OTk5PPXUU6xYsYIOHTowZcoURo4cyWWXXYaqqpiH/ZqA5CFNlmI0KZC/cgGnN38OQPydL6BFJYlA0swZFZl7h3TixOkKSuxugswqFU4P3x3Kw+HRLp4do/aNorytiPy/K0OVFhX4aqIhClwLP9M1DUmGjiEW9vzwNc7co6iKSuCgW6GJyl/5M83lICp3C1sXP0dBQQFBQUF8/fXXBAYGMmLECEJCQigrK6NNmzZkZ2cTGxtLZdeRWPqOA0lukgCkOe0UrlpIxaH1xN7/73M27gjN0y/3M5hVGY+uE2I1UljhRJGkC2bHOobZeHvD8RovIc0ak9hsgh60wsAHDdDSSLgg3e3ybh//6YZdrw7lLWTd5vo+0bz5f4Po1asX7733Hp07d8ZgMCDLcnW3eUVR8Hg8ZGZmct1117HzZCHBw+7CGJ3obQzVyO+D5rJTtvNrAvuO8/GxCqGxSRKYFJkhXSOwGtULZsd2ZRTVeAmpObQiOlOrDHw7TxZx84KNVLpEyTJ/pf/U2bw5rxVWGZ4YyY5X7iE8PByr1crXX3+N3f5zua1Bgwaxbt06YmNjCQ8P59ChQ1h6/grb4NtANjTRe6DjLs5HbdMMu5UIdVLT9GRNlpCam1YZ+KBuRyEsBplrkqJYsS9HpEobma65kZr57tAq1/eJ4ch7T3Lbbbfxr3/9i40bN1JSUgJAXFwcGRkZBAQEcPr0ae677z4+3JZJ0JA7m7wIszdt3fwfNISasxgUPpyR2uxmbPXVaj/lU1PjmTUmCYtBueQ+AknyfkBmjUnitVv68qexP31f0wy11dF1HYvaMoKegofEdoHExMSQm5vLM888Ux30AE6fPo3VauXhhx+mtLSUrzbtI2TYdJ90HpBl8YlubexuD6+vOezrYTS5Vhv4wBv8PpyRysjkqPPW+qtqfjkyOYoPZ6RWpwSqv6/7+b+visUg0ys2CFXcUGpFkiTsLWRC7fZoXN8nmpiYGA4fPsyQIUPO+vPS0lLcbje33HILb7/9NhUdB+Px0a+ljoRY+G5ddB1WH8yjoMxx6b/cgrTaVOcv1TWPXZPv6z9nJbmlzgteQ2ihdA017xATUpNJy6vgeGYOuaeOY88+QunOlWiVP8/8Zs6cyZ7Dx9nT6WYk1Ze1DnUQuYxWxazK/HZEV+65KsHXQ2kyIvA1sp0ni7j+9XVN2uBT8A+65kGW8B4WP2O9UtbduD0alUe2UrJhCf06RfDQ0y/x+49+RAmLRQQeoald3yeGl2/q4+thNJmWsZDix+atOSyCXivkPYoho0vSOXFMk1RkFWxdU7F2SiEpOYrHV2SjhPo46Om6T2qFCr7XHFoJNSQR+BpRfpmDtYfyfD0MwQdqdO5OkpEMJr5JKwLFD1q5iKDXajWHVkINqVVvbmlsH2/L8PUQBEEQLsqsyiS2C/T1MJqUCHyN6EB2iTjvJwiCX9OBSX1jfT2MJiVSnfWUX+bg420ZHMguqS4Gm9g2iBtTYimxu309PEEQWrggs0qpw12nkyiS5C071lwrsNSV2NVZRztPFjFvzeHqNTzHeerYhdqMZBXbL3AFQRCE+lEViSn9O/DR1ow6lWBsrZVbxIyvDi7V/LaqmKsIeoIgNCZFknhoWBe6RAbUqQTjrDGJrS7ogQh8tVaXGp+CIAgN7cw0ZVVVqYs9kJ/5fc2xlVBDEqnOWhBdHQRB8BfnS1O25FZCDUkEvloQffwEQfAHl2op1BJbCTUkEfhqSHRuFwTB10SasmGINb4aEofRBUFoLLLkrZdZ7nSLNGUTEIGvhhrqMHobs0qxON8nCMJPzKrMH8f+nLYUacrGJwJfDTXUYfR+8aFcHh/CiysO4dZEllkQWqOLzeDCAkytqkWQL4jAV0NB5oZ5qzyazvYTRUiSN71xvthXVSpYhEXhQrzdH0RR6eZGluCqLhEMTAgTMzgfEoGvhhLbBmFSs+uV7lRliR8O5+PR9YvuDNXxPhHGh9vYn1UibnDCOcRnovmRJfjzhO5iU4ofEEWqa2hSSv2LuLo1Hbd28aBXxe7WSMsp9d8bnC52twpCbdxzVScR9PyECHw1FB5gYkjXiCZtWebx51ynJD46glATsgS/vqoTj41K8vVQhJ+Iu1ct3D+0M2ZV8fUw/ICY7QlCTZhVmT9P6M5jo0XQ8yfiAHstiVqdgiBcjDhz5//E5pZaqm0xWEWS0HXdv9OWtaBr3oAvySJZIAhVotuYSWoXJM7cNRNixldHNS0GW+nysPZQvs/G2dB0zY0ki+clQTiTSZVZ/9gwEeyaCRH46ulSVRam/XsLqw7k+nqYDUPXCLYYKLKL7hSCcCazKvPbEV3FwfNmQjy619Olqiw01MF3RfKDXZ6SLIKeIJyH3a1xIKvU18MQakgs1DQy78H3+r3NZlXmmqQoLAalSY9TCIJQcyV2l6+HINSQCHyNrCEOvuvAnOt78uGMVEYmR2FSZcz1DKaCIDSsILPB10MQakikOhtZ1cH3ujawlSTvJpmwABNhASbmT+131rpiRmEF208U4RFLtYLgM2ZVJrFdoK+HIdSQCHxN4P6hnfk+LZ9KV+3Xx8yqwn1DO5/1tV+uK4qzhYLgWzowqW/9sztC0xD5sibQu30ws8YkYjHU7u22GGRmjUm85AHYqanxzBqTVO+1REEQau/MrIzQPIg7ZROpCk412aAiSWAxKMwak1TjorZTU+NZcs9AkkW6RRCa1PmyMoJ/E+f4mlhND77Xp9TRm2sP8zfR6FYQGp03K1PzB1TBP4jA5yOXOvheXxcLsIIgXJzExRtBS5J3pjdrTKIIes2QCHwt3JkBdv3RfHJKHL4ekiD4nTOzLSOSo1i5L6dRszKCb4nA14rMX3uEl785VK8u8oLQVIyKhKbTqCn7yAATgzqHnzfb0thZGcF3ROBrRfLLHAx6fpUIfEKz0Se2DQdzShvlqI4swWOjEkV9zVZI7OpsRXzRRV4Q6iMswFTj3dC1pciSOHvXSonA18qILvJCcxJkNjA1Nb7By/VJwPDESJGybKVEqrMVEpVehObgfK1+zi3XV1inriUWg8KHM1LF5pRWSgS+Vsob/C7dRb4pSXjTT+L8oQA1a+5al4c4cfZOEKnOVupS6SNZ8v4X3cZMasdQzLUst1YX917ViWGJkS16DVKu5Wur7d9vKWpaBqyxKyIJLZOY8Qk12rbdmDNEWYJ7rurEY6OS2HmyiJsXbKxTQW9FkujbIZisEjsZhZUNO8gGYDEo3Dkwjrc3HL/k+1h1QLpLVAC7MoqbbpAN7FIHwS+ktqnIpqiIJLQcIvAJNVbTm0vHcBtvr7/0zb3q+/449uwn8Pqmr/zxvOKZ46vNTXr9kQJeXHGw2aZ/u0QGkFFY0WSpSHH2TqgJEfiEWqvJzaW+T+A1nWGer3SUP51XvFhpq5q8j/llDq54bhVOj+9fS1188/BVbEwvqPO/pSA0BhH4hEZVnyfw+gTPGYu31rn5b0NoyPTajMVbWbEvp+EGdwGK5E1LhtmMdI9uw5Zjpyl31j7lXCUq0MSmJ64BRCpS8C8i8Al+ry7Bsz5rhXWhyhI9ooMICzA1eHpt58kiJv9zQ6PMYPt2CCbEajzvmD/eepLff7Krztd+6cZeTOzb/qyviVSk4A9E4BNarMY+r9iUM5V3Nx7j6S/34arLobULGN+rHa/d0veif+fBD7bz311ZjXJtQfAVEfiEFq2hdqOqsoSON30XE2whNsTa5DOVhgx+tQlMtQ1+IugJ/k4EPqHFu9j6kiyBpv/8/yq/XO8amBDmF+m4XRlFPPvVfjaln6YuGz2jAk3MHNXtnBTkpXyy/SQvLD9ITumF21rV9dqC0NRE4BNajQutLw3vFsm3B3Ob1bpTQZmDdzYc49sDueSXOQFvkL4mKZLBncOZt/YIh3PLqHB6sBoVOkcGMGt0Ep2jAuv1cw/nlPLXr/Y3yrUFoamIwCcIgiC0KqJkmSAIgtCqiMAnCIIgtCoi8AmCIAitigh8giAIQqsiAp8gCILQqojAJwiCILQqIvAJgiAIrYoIfIIgCEKrIgKfIAiC0KqIwCcIgiC0KiLwCYIgCK2KCHyCIAhCqyICnyAIgtCqiMAnCIIgtCoi8AmCIAitigh8giAIQqsiAp8gCILQqojAJwiCILQqIvAJgiAIrYoIfIIgCEKrIgKfIAiC0Kr8PyD292Kjth8lAAAAAElFTkSuQmCC\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\", 0.07)\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",
    "#         print(\"WWWW \",time_of_diffusion[17])\n",
    "        \n",
    "#         print(\"--->> \",sensor_nodes)\n",
    "#         print(\"***** \")\n",
    "#         for s in sensor_nodes:\n",
    "#             print(time_of_diffusion[s])\n",
    "#         print(\"***** \")\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": 729,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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51: -1, 52: 25, 53: -1, 54: 169, 55: -1, 56: -1, 57: 23, 58: -1, 59: -1, 60: -1, 61: -1, 0: -1}, {1: -1, 2: -1, 3: 0, 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: 0, 21: -1, 22: -1, 23: -1, 24: -1, 25: -1, 26: -1, 27: -1, 28: -1, 29: -1, 30: -1, 31: -1, 32: -1, 33: -1, 34: -1, 35: -1, 36: -1, 37: -1, 38: -1, 39: -1, 40: -1, 41: -1, 42: -1, 43: -1, 44: -1, 45: -1, 46: -1, 47: -1, 48: -1, 49: -1, 50: -1, 51: -1, 52: -1, 53: -1, 54: -1, 55: -1, 56: -1, 57: -1, 58: -1, 59: -1, 60: -1, 61: -1, 0: -1}]\n"
     ]
    }
   ],
   "source": [
    "print(allDiffusionTime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 730,
   "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 = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 36\n"
     ]
    }
   ],
   "source": [
    "print(len(toTakeArgmax[0]),len(toTakeArgmax))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 732,
   "metadata": {},
   "outputs": [],
   "source": [
    "finArg = np.argmax(toTakeArgmax,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 733,
   "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": 734,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39\n"
     ]
    }
   ],
   "source": [
    "print(clusterNode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 735,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "partition[clusterNode]\n",
    "color=partition[clusterNode]\n",
    "print(partition[clusterNode])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(partition[36])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 737,
   "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": 738,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{3, 36, 39, 8, 59, 28}\n",
      "list [3, 36, 39, 8, 59, 28]\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": 739,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\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": 740,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n"
     ]
    },
    {
     "data": {
      "image/png": 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\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_graphs[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_graphs[7].subgraph(my))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 741,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "nodes_in_cg [3, 36, 39, 8, 59, 28]\n",
      "6\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",
    "print(len(tot_cg[0]))\n",
    "BCS_value_cg=BCS(tot_cg,len(tot_cg)+2,nodes_in_cg)\n",
    "BCS_value_cg=(sorted(BCS_value_cg.items(), key=lambda x: x[1],reverse=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 742,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200/200 [00:00<00:00, 6668.16it/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": 743,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: -1, 2: -1, 3: 34, 4: -1, 5: -1, 6: -1, 7: -1, 8: 35, 9: -1, 10: -1, 11: -1, 12: -1, 13: -1, 14: -1, 15: -1, 16: -1, 17: -1, 18: -1, 19: -1, 20: -1, 21: -1, 22: -1, 23: -1, 24: -1, 25: -1, 26: -1, 27: -1, 28: 65, 29: -1, 30: -1, 31: -1, 32: -1, 33: -1, 34: -1, 35: -1, 36: 0, 37: -1, 38: -1, 39: 50, 40: -1, 41: -1, 42: -1, 43: -1, 44: -1, 45: -1, 46: -1, 47: -1, 48: -1, 49: -1, 50: -1, 51: -1, 52: -1, 53: -1, 54: -1, 55: -1, 56: -1, 57: -1, 58: -1, 59: 13, 60: -1, 61: -1}\n"
     ]
    }
   ],
   "source": [
    "print(time_of_diffusion_cg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 744,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[59, 36, 8]\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": 745,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13, 35]\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": 746,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
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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": 747,
   "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": 748,
   "metadata": {},
   "outputs": [],
   "source": [
    "finArg_cg = np.argmax(toTakeArgmax_cg,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 749,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\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": 751,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(partition[source_node])\n",
    "print(partition[36])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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