Task4-Cascade Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import networkx as nx\n",
    "import ndlib.models.ModelConfig as mc\n",
    "import ndlib.models.epidemics as ep\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nodes of the graph\n",
    "path = \"./ED_70_nodes.csv\"\n",
    "dfnodes = pd.read_csv(path, sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Edges with different nodes\n",
    "path = \"./ED_70_Edges.csv\"\n",
    "dfedges = pd.read_csv(path, sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodesList = list(dfnodes.Id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Source', 'Target'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfedges.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "g=nx.Graph()\n",
    "\n",
    "# Adding nodes\n",
    "for nodes in nodesList:\n",
    "    g.add_node(nodes)\n",
    "\n",
    "# Adding edges\n",
    "for source,target in zip(list(dfedges.Source),list(dfedges.Target)):\n",
    "#     print(source,target)\n",
    "    g.add_edge(source,target)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting to undirected graph\n",
    "G = g.to_undirected()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QMgDj+oiwSIe4BNf4ybAjZ9460ppaMifa0YwfPx6jhj7YqmXfHAcM7hZAix1IA7ft0xWLLc6/ckYeMom9S2ixyspKtG/fHtXV1XV/bsUOYUqpBDPjw+78ROI2qKXbStY+/8pZHcu7glUHcp1u6tg333wDHx+fhsAFri/73rcOyhYsrqGzy8itKHRbyVrnXzm78moDPvoxAzEL92L6phNOsSHOwIED8dRTT932935+fqg8uRPvtHBxDSE3otBtJTFGt12ZWag7f+2pNUexKTnf3uU06o8//oBcLsfhw4dve2zIkCEoKysDx3GtWlxDSD2OWVhUHh0dzU6cOGHDcpxPmVaP+z/eR/26zcBzwIsPdMGbIyLsXUqDjz76CG+99Vajj73zzjuYO3duo4/RDmHEEo7jTjLGoht7jD4bt1Jrt450JwIDVv16HhyAN+wcvIIgoFu3bsjJaXyfiB07dmDUqFFNvp52CCMtRZ+LRTArPgxKqfOO1tva6oPn7drVkJKSAplM1mjg8jyP7Oxsi4FLSGtQ6IqgpVtHuiuBAe8lnsPB7FKb33v69Ono27cvhEbm3arValRVVd10oCQhYqPuBZHUD5rM35UBnakZR7q4OTMDnl3/G4Z3D8TMQWHoFWLdaVU6nQ4hISEoKytr9PEePXogNTUVvJtvRk+sj/6HiejW0W2a1WCZwIA954oxfm2yVbsbtm/fDg8PjyYDd/z48Th79iwFLrEJaumK7NatIzccyUfRNdrcvCkMdUuG5+9KByD+UT8PP/ww9uzZ0+TjixYtwmuvvSbqPQmxhELXSupHt2M7+2H82mSX2IvAmmqNAubvykBUsEaUFVylpaUICQmBXt/4ijiO47Bnzx4MGzas1fci5G7Q5ykro0G25tOZzKIc9ZOQkIC2bds2GbhyuRy5ubkUuMQuqKVrAzTI1jyMAfszS1Gu1bdogcGd5t4CQGBgIHJzc+Hl5dWaUglpMWp+2cidlpCSOi09xPH06dOQSqUWAzcmJgaXLl2iwCV2RS1dG2rqfDazwJCUZfs5q46oJYc4Pv300/j6668tPmfq1KlYt25da0ojRBQUunbQ2BLSTcn5+L/EcxCo6wHnss8D6H3H51VWViIoKAg1NTUWn7d06VLMnj1bpOoIaR36jOsgJsV2woePRUJC/yJIST4MjuMwevRoGI2Nn7X2+eefw8fHx2Lg8jyPffv2UeASh0ItXQcyKbYTooI1WLg7HUfPX7F3OXYhGPUwlOYDAHbu3Am5XA6NRoNDhw4hMjISer0eERERyMvLs3gdDw8PpKeno2PHjjaompDmo9B1MFHBGnz9QhzKtXos+ikT358uhN7kRn0OHIfqs3tv+quKigr06NGj2Zfo0KEDMjMzacCMOCT6MOug/LwV+OgvUcicOxJvPtINvBscNswEAbW5xyHUVrb4GgMHDsSFCxcocInDotB1AjMGhWHbzPvRPUhlletLOEDqCKluNkCfktjil0+fPh0HDx6ERELbbBLHRaHrJKKCNdj11wfx5dQYdAv0Bs+h1a1fngPiurTB9zPvx/uPdrfrqjnBoMOVX9ah6sI5AIBCcfeLIyorK2HpJBRCHAEd1+Okbpzrm1Z0DTkl1TA3M3CC1Ar8T3QIJsd1umnl16bkfJuvmmOCAGY24Oov66A9vVuUa/r4+ODs2bMICQkR5XqE3C1Lx/VQ6LqIM4UVWJGUg/2ZpeBQt8ignlLKgwEY3C0AM+PDLG4oY+k6d4sD0MnPE5eu6W67lmDUAxyH2tzjqEreAn1RdovvY8nMmTORkJAAuVxulesT0hgKXTci1oGJjV3Hx1OKC+U1OJRTBrPAmlzIwXNA/L0B+N9h9yIqWNNkTd2VlXh11os4efKk1bsFfH19sWzZMjz++OPw9va26r0IodAloqoP0dSCCmQVV0FvEqCQ8rg3UIVeIZq7CniDwYAPPvgACxYssHLVf4qLi8O0adPw6KOPom3btja7L3EfFLrEIZ06dQpjx45Ffn6+Xe4vk8kQGxuLMWPG4IknnkDnzp3tUgdxPRS6xKEYDAbMnTsXCxcuhNnc+ObuHMfZbCaCQqGASqVChw4dGgI4KioKHOcA0+iIU7IUurQijdjU6dOnMX78eGRmZjb5HKlUCpPJBIlEAsYYeJ7H5s2b8dVXX2H79u1N7sfQUnq9Hnq9HmVlZfD29sYXX3wBAA0BPGDAAJr7S0RD83SJTRgMBrz33nuIiYmxGLgSiQQmkwkKhQJmsxmenp7IysrC2LFjsXXrVhgMBvz2228YMGCAVeo8fPgw8vLyUF1dDalUitmzZyMoKAjPP/88du7cCZ2OzrsjrUPdC8TqUlNTMWHCBGRmZkIQLE9B4zgO3t7eqKqqQufOnXHq1Cn4+Pg0+fytW7fizTfftLh5eWvNmDEDXbt2xQ8//IDU1FQ89NBDGDNmDEaNGmWxNuK+LHUvgDHW5Fffvn0ZIS1lMBjYe++9xzw8PBjqDv5t9EuhUDAAjOd55uvrywCwwYMHM4PB0Ox7GY1GtmjRIqZWqy3eqzVf/v7+bPPmzexf//oXGz16NFOpVOyhhx5iK1euZJcuXbLid5I4GwAnWBO5SqFLrCI1NZX16NGDyeVyi0Hm5eXFADCpVMpUKhUDwGbPns0EQWjxvSsqKtiUKVOYRCKxWgAPHz6cnTx5kn377bfs6aefZhqNhsXGxrKPP/6YZWZmividJM6IQpfYjMFgYB9++CHz9va+Y3DVB7JCoWAKhYJxHMfWrl0raj3Z2dmsd+/eVgtfhULBZsyYwQoKCthPP/3EZsyYwYKCglj37t3ZnDlz2PHjx1v1C4Q4J0uhSwNpRDRnz55Fv379sHDhQmi12iafp1AowHEcDAYD1Go1jEYjBEHAgQMH8Pzzz4taU1hYGE6dOgXGGL7++mtoNE0vgW4JvV6PVatWISQkBFOmTEH79u2RmZmJdevWwWQyYeLEibjnnnswe/Zs7Nu3DyaTSdT7EyfUVBozaumSZjIYDGzu3LkN3QOWvnx9fRnP80wqlbL27dszAEytVrPc3Fyb1Ws0Gtn48eOt2v3QvXt3tmrVKmYwGNi5c+fY/PnzWXR0NPPz82OTJ09m33//PauurrbZeya2BepeINZy5swZ1rt374YBsKa+OI5jGo2GAWBKpZJ17tyZAWChoaHsypUrdqv/6NGjDbVY40sikbABAwawbdu2MUEQ2MWLF9myZcvYkCFDmFqtZmPGjGEbN25k5eXldvseEPFR6BLRGY1GNm/ePObj43PH4JHJZA0zFJRKJevQoQMDwIYOHcr0er293wpjjLHa2lr2yiuvNNRpjS+FQsFGjhzJjhw5whhjrLy8nG3cuJE98cQTTK1WsyFDhrClS5eyixcv2vm7QVqLQpeI6uzZs6xPnz6sbdu2dwwaPz8/JpPJGMdxzNvbm2k0GsZxHPvrX//qsANM+/btYz179rRa8NZ3qUycOJFlZWUxxhirrq5m27ZtY88++yzz8/Njffv2ZfPmzWPnzp1z2O8TaRqFLhGF0Whk8+fPZxqNhslksjt2JwQHBzOJRMKkUilr164dk8vljOd50WcoWEtFRQWbOXMm8/T0FD18ZTJZQ5dMYGAge+WVV1hxcTFjrO77vG/fPjZ79mwWEhLCunbtyl5//XV25MgRZjab7fxdIc1BoUta7ffff2d9+/ZtGPy6Uz9mQEAA4ziOeXp6ssjISMZxHJPJZCwpKcneb6VFvvvuOxYZGdnwC0XMAO7SpUvDJ4AuXbqwefPmMa1WyxhjTBAEduLECTZnzhwWGRnJgoKC2IwZM9iPP/7oMF0z5HYUuqTFjEYjW7BgAfP19W3W3Fu1Wt2wAk2lUrGYmBgGgPn4+DR8lHZmly5dYs8884xV+n59fX1ZbGws8/DwYDzPs6ioKLZ27VpmNBob7p+VlcU+/vhjFhcXx3x9fdnTTz/NvvnmG1ZVVWXH7wq5laXQpb0XSJPS0tLw3HPPobi4GBcvXrT4XI7j0LlzZ/zxxx/Q6/Xw8vJC165dcfr0aYSGhuLYsWPw8/OzUeXWJwgC1q9fj4ULFyI3NxcSiaTJbSpbYuTIkaioqMDx48fBGENsbCxef/11jB49umHLyaKiIiQmJuL777/HkSNH8OCDD+KhR/8CQ3AfFGoFVOpMUCulCG+nxpN97+7kENI6tJ8uuSsmkwmffvopPvnkEwDA1atXLT5fIpEgNDQUeXl5YIxBrVbD29sbBQUFGDp0KHbs2NGi032dxfnz5/Hqq69i586dEAThz4+RIujQoQMmTpyIPXv24MyZM5DL5Rg2bBjeeecd9O/fH2VaPTYezUfiqUL8UVEDk1C3DzHH/7nuqf6MvPhuAZg5KAy9QsRdIEJuR6FLmi0tLQ1TpkxBWVlZQ4ha4uHhAZVKhbKyMnh6eqJ9+/a4fPkytFotXn75ZSxZssRtNgMXBAEJCQlYvHgxLl26BJlMBpPJJFoAz5w5E4GBgfjyyy9RUM3Db9jzkLWPAJr5/eUAKGQ83hkZgUmxnUSpiTSOQpfckclkwj//+U98/PHHkMvlKC4utvh8juPQsWNHlJSUoKamBm3atEGPHj1w9OhRCIKAlStX4oUXXrBR9Y7n1KlT+Mc//oH9+/c3/NLheV6UDdjvGTIRkpjxdQeDtuAXGscEdFBJ0De0HSKCfKjrwQoodIlFGRkZeO6551BRUYGcnJw79k1KJBL07t0baWlpqK2thUajwQMPPIAdO3ZAJpNh165dGDp0qI2qd2x6vR5z587FqlWrUF5eDqlUCo1Gg7KyshZdz7v3CLR56KWbug9ag2MCwPHw85IhsoMP4rr4UwiLgEKXNMpsNmPx4sX46KOPoFKpcOHChTueTSaXy9GtWzdkZ2fDZDLBy8sLcXFx+PHHH6HRaHDkyBFERETY8F04jwMHDuD111/H8ePHwfM8lEolgoODLZ6kcSN5u65oN/lTcLw1jw5i4MDB31uO7u19ENfFT7QQLtPqseVkITIuV7r8IB+FLrlNRkYGpkyZgsrKSmRnZ8NoNFoMXI7j0K5dOwiC0NB/q1KpEBQUhJSUFISGhuLw4cN0pHkzVFVV4e2338aGDRug1WohkUgQGxuLvLw8XLp0qcnXtXs2AfJ2YTbtI+c5QMrzGBze8kG41IIKJPyShYPZZWCM1XWLXKeQ1rXYXW2Qj0KXNDCbzUhISMDChQvh7++PrKws8Dxv8RgdqVSK++67D2lpaaiurkbbtm3RsWNHFBUV4dKlSxgyZAgSExPh4eFhw3fiGhITE/HWW28hLS0NPM8jODgYo0aNwpo1a27q5uE9fRA8e5MdByUZZBIe743u3uggXFOt2KJrtfgy+cJNQdsYDoBSJsGckeF4pEeQ07eIKXQJACAzMxNTpkyBVqtFdnY2dDodFAoF9Hp9k6+RyWQYOHAgjh8/Dq1Wi3bt2iEuLg579uyBTqfDrFmzkJCQAF6kPkZ3VVxcjNdeew3ffPMNDAYDlEolpk2bhrKyMmzevBmawVOhjnnC/jNBzCbESC/g9bED0LdvX5wpvIblSTk4kFUKANCbLJ+B11w8h5uC2tmmvVHoujmz2YwlS5Zg4cKFCA4ORmpqasMPr6XuBI1Gg44dOzYEtI+PD8aOHYv169eD4zgsXboUL730ki3fistjjGHjxo147733cPHiRfA8j379+qH24XdxzWDv6urwTIBk/xLovQIhi3kKAieFOJPi7ozjAKW0rkXsyNPeKHTdWFZWFqZMmYLq6mrk5uaiuroaKpUKlZWVTb5GKpUiIiICJSUluHr1KhQKBeRyOcaMGYN///vfUCgUSExMxPDhw234TtxPXl4eZs2ahT179qD9/24GL1Pau6QGnf08camiFnqzreL2Zh4yHq8M7QqAc8huCApdN2Q2m/HZZ59hwYIF6Nq1K44dOwbGGJRKJXQ6XZOvk8vlGD58OA4cOIDq6mq0b98eHh4eiIyMRGJiIjQaDX799Vf06NHDhu/GvQmCgK5zdsLsSKdrMdaiOcJik0s4GG4IfkfphrAUug70r0jEkpWVhUGDBmHTpk1gjOHYsWPw9fUFgCYDl+M4eHp6Yvjw4Th06BC0Wi06duyI8PBweHt7IzExEbHAtEsAABOZSURBVGFhYTh37hwFro3xPA8vpdzeZdzEPu3b2xluaWnrTAL0JgF70ooxfm0yNiXn26cwC6T2LoCIx2w2Y+nSpZg/fz6ioqJw5MgR8DyPdu3aoaioqMnXSSQSdOrUCWq1GocOHUJVVRXat2+P4cOHIzExEWVlZRgyZAh++OEHeHl52fAdkXo92vvgyPlye5fRwO4DenfAGFBrNOP97Wn45mQh/L0VDtP9QN0LLiI7OxtTpkyBTqdDUVERioqK4OPjg+rqaotLT5VKJeLj45GSkoLKykpIpVLI5XLMmjULixYtgtlsxosvvojPPvsMEok1J+UTS7KKq/DQkl/tXYbTs1X3A3UvuDBBELBkyRLExcXBx8cHp06dQnFxMXr27ImKioo7Bu7YsWNx5MgRlJaWIiAgAN7e3pg9ezbmz58PQRCwePFifP755xS4dnZvoAq+njJ7l+H0HKH7gboXnFhOTk5D61aj0WD37t3w8PCAj48Pzpw50+TrJBIJ/Pz8EBsbi59++gmVlZUIDw+HQqHAAw88gA8//BAKhQJbt27FyJEjbfiOiCVzRkTgta1N/7uS5qvvfpi/Kx0AbDr9jLoXnJAgCFi2bBnmzp2LQYMGITExEYwxxMTE4NixYxZXlymVSkRHR6OqqgqFhYW4cuUKunbtisjISOj1euzevRsajQb79u1D7969bfiuSHPM/joF28803T9P7h4HoI2XDFIJDz8vBYZFtMWzcZ1a1e9LU8ZcSG5uLqZOnQqdTgedToezZ89CoVAgJiYGv/7adJ8fx3FQKpV46qmnsHPnTtTU1AAAfHx8MHnyZGzbtg3Z2dno3LkzkpKS0KFDB1u9JXKXKHitj+eAmE5t8NaIiBb1+1Kfrguob932798fwcHBOH36NH7//Xfcd999UCgUFgOX53moVCo888wz2LZtG65cuYI2bdpAqVRizpw5WL16NXJzczFo0CCkpKRQ4Dq4ZRP64J9PRiFQ5Rz7EDgjgQHJeVcwbvUR0ft9qaXrBHJzczFt2jTU1NRAKpXi2LFjkEqlePrpp7FhwwaLr/Xw8EBoaChCQ0ORnJyM4uJi9OrVC1evXsU777yDl19+GRzH4bnnnsPnn38OqZS6+Z1JTnEVXvpPCnJKtA4zd9bVyCRckxv9NIVauk7qxtZtaGgozp07h2PHjiEsLAy9evWyGLgcx8Hb2xtjxowBABw7dgwlJSXo06cPPD09MXv2bEyfPh08z+Pjjz/GypUrKXCdUFigCj//bRB+mHU/HokMhELKQ8o79hxaZ2M0M8zdmY4zhRWiXI9aug7q/PnzmDp1asPJDHv37oVEIsFLL72E1atXQ6/XN7klI8/z8PDwwMyZM7F+/XoYjUYYDAbcc889iIuLg1KpxOrVq6FUKvH111/j8ccft8M7JNZQfv2gys/359xxO0Vydx6JDMSqSY02Xm9DLV0nIggCli9fjpiYGERERCAzMxN79+5FYGAgZsyYgaVLlzZsxdhY4NafRjB16lSsW7cO165dg0qlgo+PD55//nnk5eVhzZo1DXsoUOC6Fj9vBV4d3g3DIgIdYWsEl7IvoxTl2qa3QW0uCl0HkpeXh6FDh+KLL75AfHw81q5di+rqakydOhWenp5YtmxZk8svOY6DWq1GfHw8oqKisG3bNly9ehW9evWCXq/H0qVL8fnnn+Pw4cPo3LkzTp8+jejo5v3WJs5nVnwYlFJa0CImgTFsSSls9XUodB2AIAhYsWIF+vXrh6ioKOTn5+P777+HSqXC8uXL8e9//xu5ubmQSCSN7n/L8zw8PT3x4osvIj8/H2fOnEFBQQEefPBBXLt2DevXr8fUqVNRXFyMgQMHIiUlBffcc48d3imxlV4hGswZGQ4PGf2Ii8UkMGQUVbX6OjRyYmf5+fmYOnUqqqurMWrUKKxYsQIcx+Gxxx6Dt7c3pk+f3vDcxk7plcvlUKlU+Nvf/oYlS5ZAEATU1NQgJiYGcrkcH3zwAcaMGQO5XI5JkyZhxYoVkMloOak7qB9tn78rAzqTGRaGb0gzVeqaXlbfXPRr0E4EQcDKlSsRHR2N++67D8XFxdi0aRPkcjk2bNiAAwcOYNOmTU3OKOA4Dj4+PujduzcmTZqEpUuXoqqqqqFPNy4uDoMHD8bEiRMhl8vx/vvvY82aNRS4bmZSbCdsfjEWD3evm9mgkFBHb2uola3/+aGWrh3k5+dj2rRp0Gq1GD9+PJYtWwbGGAYMGIApU6Zg4sSJAOpOcDCZTLe9vn6xw4QJE5Cfn48dO3agtLQU/fr1Q35+Pt58803s378fS5cuhaenJzZs2IBx48bZ+m0SBxEVrMGqSdEo1+rx9y2pSMostXdJTknKcwgPUrX6OtTStSHGGFatWoXo6Gj07dsXVVVVWLlyJXiex9KlS8HzPKZNm9YwWNZY4MpkMnh5eeHdd9/Fzz//jJycHOTm5mLEiBG4ePEi/vOf/2DlypX473//2zDVjAKXAHUzGzQe9EmnpXiOw7g+wa2+DrV0beTChQuYNm0arl27hueffx4JCQkQBAHdu3fHunXr8OCDD0Kv1zd5nE797ISAgAC89NJLWLBgAaRSKa5evYpHHnkEhYWF2L59O0aPHo2Kigp07NgRe/fuRefOne3wbomjqtTd/oucNM+Q8ABRNj+n0LUyxhgSVv4Li7YcxL39J+Ja6VWsSyuCV/QYTH+oF1QyoH///gDqWrGNBS7P8/D19UV8fDzat2+PxYsXo6amBmq1Gr169YKHhweWLVuGBx54ABKJBP3798e2bdsajughpJ5aST/yLaGQ8pgZHybKtehfwIq+PfQ73v72JIzKdlD0/x9cAAcEtIVXQDfIJcDX1UZU5xyHR3A4agszGt1wXCqVwtPTE6+++ip++eUXpKeno6ioCH379kVJSQlGjBiBrl27YvDgwfDy8sITTzyBtWvXQi53rDO1iGMIb6eGQnoZelPT23+Sm/Ec8O6oCEQFi3PKBPXpWkFSZgki5yTitR35MHr6ATyPul07/2QwA5DI4Nk1Fv5PzYN37xG3XUetVsPf3x8JCQlYs2YNSkpKkJ6ejjFjxiAvLw+LFi2C0WjE5MmT4eXlhTfffBMbNmygwCVNGte39X2S7uaNh7uJusk5tXRFlFpQgb9vPoGcMh0AvlmH93E8D45XwnfINACA9vRu8DwPf39/hIeHY9y4cXj99dfh6emJjIwMTJgwAQcPHsTu3bvxwQcfYNeuXfDy8sLq1asxYcIEK79D4uz8vRUYdG8Afk4vpnm7zeDrIcP0QeJ0K9Sj0G2BMq0eW04WIuNyJSp1JqiVUtQYTNiTdhmMcS06KZWXK+E7dBrMpeehqC7Gs88+C61Wi8WLF0Ov14PjOAwfPhx5eXlISkrCqFGjkJOTAx8fHyQmJmLgwIFWeKfEFc2KD8PB7DLUGm9fbENuZo2DKyl070JqQQUW783CoexSMODmXZwYAzi+VZuMcBI5fO8fj3mPdMS6detQU1ODgoIC9OrVC4IgoG3btkhISEC/fv2g0+kQEhKCPXv2ICxM3N/ExLXVLxGevysdtUbq222KlAPiQv3Ev67oV3RijbVgw9up8WTfYKw9eB5rDp5vers8EbZ04ngeHqHReHfeawhqo8Lp06fx5JNP4tChQ/j73/+O3r17IyoqCh4eHrjvvvvwww8/wM9P/P8UxPXduESYWryNk0h4Uebl3opCF3Ut2OVJOUjKLIWZMZhvSFYOl/DRjxk2q8VoNEDW7QGc/nkDZsyYgS1btmDjxo3Iy8vD8OHDodFoMGLECKxbtw4KBR3XQlpuUmwnRAVrsCIpBz+dK6aTJ27AccDgbuLMy72V289e2JScj/9ZcxR70ophMAs3BS4Am/9H5KQK6JV+mDRpEn7++WccOHAAiYmJePnll6FWqzF79mx8+eWXFLhEFPVLhE/MGYYu/p72LsdhKKUS0ebl3sqtW7qbkvPxwY40GM2O9TveN7ADCnNOIjk5GWPHjsXhw4ehUqnw2WefYfLkyfYuj7ggP28F9v19MHq+/yOq9O7d3eAh4zFnZLho83Jv5bKha6l/1s9bgdSCCocMXADw9ao7RqdPnz4oLi6Gj48Ptm7divj4eHuXRlzce6Mj8drWM/Yuwy44AEqZBHNGhos6L/dWLhe69f2zB7LqdlK6ceWNUnoZCXuzEN8tAIVXax0ycKWcgOExkejSpQs4jkNgYCB++uknhIeH27s04gbGRYfgQHYptp8psncpNiXlOQyLaIuZ8WFWa+E23MuqV7exTcn5Fjds1l0P4D0OPWjAYcHzj0KtUCA0NBTbt29HQECAvYsibmTZhD4AUtwieP295RjdMwizh3S1yqBZY1widMu0erz9/Rn8kl6C5jReHTdwGSozjkLjIUV8fDw2bNgADw8PexdF3NCyCX0Q360An/yYieKq1h/G6Gg4AG8+0k301WbN4dShW9+VsD+zxCG7Cu6WYNSDS9+DadOmYd68eeB5t59cQuzoL31C8Jc+IcgprsL83enIKdGivNqAWoO5VQ0XT7kEMp6DlOdhYgKMZgbGGLzkUoADKmuN4Dmu4ZMpACilPBiAPvfUffRPuVgBDrjpOc0l5Tm8/2h3q/bbWry/Xe4qglUHcvDpniyYmlyt4FwEkx66I//BwtdmYNq0afYuh5AGYYEqrH8upuHPZworsCIpB/szS5sVfPWBObhbQLP6TMu1emxJKURGURUqdUaolTKEB6kwrk9wQxfArc8xCwzny7QovFrb5AImngP6d26Dt0aIt2NYS3CNnS5bLzo6mp04ccKG5dxZakEF3vjuDDIut/5UTkfAGAMEAbrDX+CrD2Zg2LBh9i6JkGZpLBzvaVPXHXbxSm2TgWntmr44mo9fMkpQpjUAAPy85BgW0RaT4zrZrN+W47iTjLHoRh9zhtCtn/61+/cipBZes3c5omJMANu3FLvXL0FkZKS9yyGEiMBS6Dp098KN079MZqFZg2TOhDEGRUkGfk38Eu3atbN3OYQQG3DY0K2f/uXKm3FwAJ4cNYwClxA34pDD45uS8zF3R5pLBy4AgOOwOaUIZwor7F0JIcRGHC50Uwsq8F7iOehdrS+hCSaBYUVSjr3LIITYiMOF7vRNJ1yu7/ZO9meWolzrehPQCSG3c6jQfS/xLC5Xul/4cAC2pBTauwxCiA04TOimFlRg49GL9i7DLnQmARlFrjHvmBBimUOEbmpBBaZuPG7vMuyqUme0dwmEEBuw+5SxTcn5mLczvUVrqF2JWimzdwmEEBuwa0u3bi4uBa5SyiM8SGXvMgghNmDTlu6NpzkUXq3FqYtX3W6mQmMYYJVTRwkhjscmoWvpNAd3Z81TRwkhjsfqoXun0xzcnTVPHSWEOB6rhm59n22tkVq2jbH2qaOEEMdjtdBNLai4vmENBW5jPGxw6ighxPFYLXSXJ+VAZ3LxDWtaQMIDwyMCbXLqKCHE8VgldMu0ehzIKqU+3Os8pDwGhPkjpnMbm+2gTwhxTFYJ3S0naR+Begopj0NvDKGgJYQAsNLiiIzLlTQtDDQdjBByO6uEblpRpTUu63RoOhgh5Faih+6m5HzklmjFvqzToelghJDGiBq69dPE3H1pr4QD5oyMoOlghJDbiBq6NE2szjOxHSlwCSGNEi10aZpYHZ4DZg/pau8yCCEOSrTQpWlidcLaetNsBUJIk0QLXZomVufdkd3tXQIhxIGJFrqVOpNYl3Ja3YNUeODeAHuXQQhxYKKFrlpp95N/7ErKAR+NjbJ3GYQQByda6Ia3U0MhdYhzLm2OA/D+Y5E0J5cQckeipeS4vu573MyMQV1oihghpFlEC11/bwUG3RsAjhPrio6PA/DSg13wxiMR9i6FEOIkRO0PmBUfBqVUIuYlHZaEA+Y+Hok3RlDgEkKaT9TQ7RWiwZyR4fCQOX7fbhtPGfy95ZBLOCjvoi+a54C4Lm3w/cz7qUuBEHLXRJ9yUB9E729Pg0lwvOVpSimHd0Z1b6izXKvHlpRCZBRVoVJnhFopwz1tPFBjMOPo+XKUaQ0AAD8vOYZFtMXkuE60+IEQ0mJWmec1KbYTfk4vaThy3VHwHG4KXADw81Zg+oOh9iuKEOJWrDa5VsKLM6KmkPIwmgWAAa1Z78ZzwKJxUfhLnxBR6iKEkJawWuerWIslRvYIwvG3h+GNEeHoHezTomtIeQ4fPhZJgUsIsTurha4YiyWUUh7hQaqGLoBtswZi3uORkN5FK1om4fD+o91p0IsQ4hCsFrpiLJZgAMb1ufk6k2I74buXBiCuSxtYyt76WQZbZwygwCWEOAyr9enWL5b4Ob24RXvsWjrUMSpYg69fiEO5Vo8vjuZjb3oJyqsN1+8rx9BwmmVACHFMVt2lZlZ8GA5ml6HWePenSTTnUEc/bwX+Nrwb/ja8W0tLJIQQm7LqKoaWLpagQx0JIa7K6vsx1venzt+VAZ3JbLGrgePqWrhzRoZTPywhxCXZZBPcSbGdEBWswYqkHOzPLAUHQHfDKRNKKQ+Guj7cmfFh1MIlhLgsm+08HhWswapJ0Y0uuw0PUmFcn2Aa+CKEuDybH/dAy24JIe7M8bcDI4QQF0KhSwghNkShSwghNsQxC3O4OI4rBXDBduUQQohL6MgYC2jsAYuhSwghRFzUvUAIITZEoUsIITZEoUsIITZEoUsIITZEoUsIITb0/3M8T5bjFVlCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "nx.draw_networkx(G,with_labels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dumping ED_70_graph\n",
    "import pickle \n",
    "pickle.dump( G, open( \"ED_70_graph.p\", \"wb\" ) ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200/200 [00:00<00:00, 287.62it/s]\n",
      "100%|██████████| 200/200 [00:00<00:00, 279.14it/s]\n",
      "100%|██████████| 200/200 [00:00<00:00, 894.49it/s]\n",
      "100%|██████████| 200/200 [00:00<00:00, 329.22it/s]\n"
     ]
    }
   ],
   "source": [
    "# Using FractionInfercted and Threshold to understand cascade network.\n",
    "FractionInfected = [0.1,0.2]\n",
    "Threshold = [0.25,0.75]\n",
    "d = {}\n",
    "for fraction_infected in FractionInfected:\n",
    "    for threshold in Threshold:\n",
    "        model = ep.ThresholdModel(G)\n",
    "        # Model Configuration\n",
    "        config = mc.Configuration()\n",
    "        # fraction_infected (0.1,0.2)\n",
    "        config.add_model_parameter('fraction_infected', fraction_infected)\n",
    "\n",
    "        # Setting node parameters\n",
    "        for i in G.nodes():\n",
    "            config.add_node_configuration(\"threshold\", i, threshold)\n",
    "\n",
    "        model.set_initial_status(config)\n",
    "\n",
    "        # Simulation execution\n",
    "        iterations = model.iteration_bunch(200)\n",
    "        key = \"Fraction=\" + str(fraction_infected)+ \" Threshold=\"+str(threshold) + \" 200thIteration\"\n",
    "        d[key] = iterations[-1]\n",
    "#         print(\"Fraction:{} , Threshold:{}, Iterations_200:{}\".format(fraction_infected,threshold,iterations[-1]))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Fraction=0.1 Threshold=0.25 200thIteration': {'iteration': 199,\n",
       "  'status': {},\n",
       "  'node_count': {0: 351, 1: 38},\n",
       "  'status_delta': {0: 0, 1: 0}},\n",
       " 'Fraction=0.1 Threshold=0.75 200thIteration': {'iteration': 199,\n",
       "  'status': {},\n",
       "  'node_count': {0: 351, 1: 38},\n",
       "  'status_delta': {0: 0, 1: 0}},\n",
       " 'Fraction=0.2 Threshold=0.25 200thIteration': {'iteration': 199,\n",
       "  'status': {},\n",
       "  'node_count': {0: 87, 1: 302},\n",
       "  'status_delta': {0: 0, 1: 0}},\n",
       " 'Fraction=0.2 Threshold=0.75 200thIteration': {'iteration': 199,\n",
       "  'status': {},\n",
       "  'node_count': {0: 312, 1: 77},\n",
       "  'status_delta': {0: 0, 1: 0}}}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (e.g. 0.1, 0.25, 0.5, 0.75)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "{'iteration': 0, 'status': {'JudgeDread-BigSeven': 0, 'GramParsons-CryOneMoreTime': 1, \"HankWilliams-MySweetLoveAin'tAround\": 0, \"TheKing'sSingers-Can'tBuyMeLove\": 0, 'CharlieRich-LifeHasItsLittleUpsAndDowns': 0, 'RosanneCash-TheWheel': 1, 'SteveEarle-FearlessHeart': 0, 'JohnGorka-WhereTheBottlesBreak': 0, \"MerleHaggard-It'sNotLove(ButIt'sNotBad)\": 1, 'TheHouseOfLove-Ray': 0, 'BlindLemonJefferson-RamblerBlues': 0, 'MethodMan-PerfectWorld': 0, 'NovembersDoom-Awaken': 0, \"Nonchalant-5O'Clock\": 0, \"Ol'DirtyBastard-Gettin'High\": 0, 'TheDivineComedy-ThereIsALightThatNeverGoesOut': 0, 'Everlast-SoLong': 0, \"ProdigyOfMobbDeep-Don'tBeAFollower\": 0, 'RayPrice-HeartachesByTheNumber': 1, 'JohnCale-GhostStory': 0, 'TheLangleySchoolsMusicProject-GodOnlyKnows': 0, 'Hooverphonic-TheLastSupper': 1, 'DaynaKurtz-FredAstaire': 1, 'UncleTupelo-WhiskeyBottle': 1, 'MississippiFredMcDowell-61Highway': 0, 'GillianWelch-OneMonkey': 0, 'HowardShore-CirithUngol': 0, 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      "{'iteration': 46, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 47, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 48, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 49, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 50, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 51, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 52, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 53, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 54, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 55, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 56, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 57, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 58, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 59, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 60, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 61, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 62, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 63, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 64, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 65, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 66, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 67, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 68, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 69, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 70, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 71, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 72, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 73, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 74, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 75, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 76, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 77, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 78, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 79, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 80, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 81, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 82, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 83, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 84, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 85, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 86, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 87, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 88, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 89, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 90, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 91, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 92, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 93, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 94, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 95, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 96, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 97, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 98, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 99, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 100, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 101, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 102, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 103, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 104, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 105, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 106, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 107, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 108, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 109, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 110, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 111, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 112, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 113, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 114, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 115, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 116, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 117, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 118, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 119, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 120, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 121, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 122, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 123, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 124, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 125, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 126, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 127, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 128, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 129, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 130, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 131, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 132, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 133, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 134, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 135, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 136, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 137, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 138, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 139, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 140, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 141, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 142, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 143, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 144, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 145, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 146, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 147, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 148, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 149, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 150, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 151, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 152, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 153, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 154, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 155, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 156, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 157, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 158, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 159, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 160, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 161, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 162, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 163, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 164, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 165, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 166, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 167, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 168, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 169, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 170, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 171, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 172, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 173, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 174, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 175, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 176, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 177, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 178, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 179, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 180, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 181, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 182, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 183, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 184, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 185, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 186, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 187, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 188, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 189, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 190, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 191, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 192, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 193, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 194, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 195, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 196, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 197, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 198, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n",
      "{'iteration': 199, 'status': {}, 'node_count': {0: 244, 1: 145}, 'status_delta': {0: 0, 1: 0}}\n"
     ]
    }
   ],
   "source": [
    "print(type(iterations))\n",
    "for i in range(0,len(iterations)):\n",
    "    print(iterations[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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