Appendix_1_Getting_data_in.ipynb

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   "source": [
    "# Appendix 1 Getting Data into NetworkX"
   ]
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   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import json\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from src.helper import draw_spring"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69dacf62-8995-4795-882c-0ec68ac85154",
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   "source": [
    "The main tool used in this textbook is NetworkX. However, the purpose of the textbook is not to introduce this library, but to explore Network Analysis for law, so some details of how this package works are omitted from the main discussion. However it can be difficult to get data into NetworkX go get started, so here we will introduce some ways this can be done.\n",
    "\n",
    "## Starting from Excel\n",
    "\n",
    "Since Excel is what most people will be familiar with, here will start with this. \n",
    "Here is an Excel sheet recording the friendships between 6 individuals.\n",
    "\n",
    "<img src=\"images/ch5/Excel1.png\">"
   ]
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   "source": [
    "To be used in python it is best to save it as a \"csv\" file, pandas can also read an Excel sheet but this might be harder to do (at least you might need to install additional dependencies). \n",
    "\n",
    "<img src=\"images/ch5/Excel2.png\">"
   ]
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   "cell_type": "markdown",
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   "source": [
    "Then we can read the csv file into a pandas dataframe. A csv file literally means \"comma separated values\". But really it does not have to be a comma. The delimiter separating the values can be something else. Excel defaults into using a semicolon, so we need to tell pandas taht the separator (\"sep\") is \";\"."
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Andrew</th>\n",
       "      <th>Bart</th>\n",
       "      <th>Caroline</th>\n",
       "      <th>Deborah</th>\n",
       "      <th>Edward</th>\n",
       "      <th>Fred</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andrew</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bart</td>\n",
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       "      <th>2</th>\n",
       "      <td>Caroline</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Deborah</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Edward</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fred</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "      Names  Andrew  Bart  Caroline  Deborah  Edward  Fred\n",
       "0    Andrew       0     1         0        0       1     0\n",
       "1      Bart       1     0         1        0       1     0\n",
       "2  Caroline       0     1         0        1       0     0\n",
       "3   Deborah       0     0         1        0       1     0\n",
       "4    Edward       1     1         0        1       0     1\n",
       "5      Fred       0     0         0        0       1     0"
      ]
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     "execution_count": 2,
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   ],
   "source": [
    "df_friends = pd.read_csv('data/friendship_data.csv', sep=\";\")\n",
    "df_friends"
   ]
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   "source": [
    "We can directly see this with the \"shape\" attribute of the dataframe."
   ]
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      "text/plain": [
       "(6, 7)"
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     "execution_count": 3,
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   "source": [
    "df_friends.shape"
   ]
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    "You may note that this shows that there is a problem with our data, as an adjacency matrix must be $$ M \\times M $$ It must be a square matrix with the same number of rows and columns. \n",
    "What happens is that the \"Names\" column must become a row index and this can be done as shown below:"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Andrew</th>\n",
       "      <th>Bart</th>\n",
       "      <th>Caroline</th>\n",
       "      <th>Deborah</th>\n",
       "      <th>Edward</th>\n",
       "      <th>Fred</th>\n",
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       "      <th>Names</th>\n",
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       "      <th>Andrew</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>Bart</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>Caroline</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Deborah</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Edward</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fred</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "          Andrew  Bart  Caroline  Deborah  Edward  Fred\n",
       "Names                                                  \n",
       "Andrew         0     1         0        0       1     0\n",
       "Bart           1     0         1        0       1     0\n",
       "Caroline       0     1         0        1       0     0\n",
       "Deborah        0     0         1        0       1     0\n",
       "Edward         1     1         0        1       0     1\n",
       "Fred           0     0         0        0       1     0"
      ]
     },
     "execution_count": 4,
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   "source": [
    "df_friends.set_index(\"Names\", inplace=True)\n",
    "df_friends"
   ]
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   "source": [
    "Now the matrix is $ M \\times M $."
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "id": "88aa323f-21de-4d8a-ad43-ca836d117b66",
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    {
     "data": {
      "text/plain": [
       "(6, 6)"
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     "execution_count": 5,
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   "source": [
    "df_friends.shape"
   ]
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   "source": [
    "Then we are all set to just directly import the matrix into NetworkX using ```\n",
    "nx.from_pandas_adjacency\n",
    "```"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84bdf145-71e3-4795-b1c5-4b4b259c80e6",
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   "outputs": [],
   "source": [
    "g_friends = nx.from_pandas_adjacency(df_friends)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "id": "e51d128a-8348-4a37-8630-cd14fb31881a",
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2LACrVwOcAE4DR4H5QC8g+uon9cNemr8Da4EPgbDr9hSJl1cpXn31FMOHD8eyLIYOHcqxY8fYs2cPR44cYfTo0TdkW7p0KatXr+aPP/5gx44dhIeHO+qvQe5ChelBhg8fTtGiRQkODuZOtxOJeKIiRYrw119/MWfOHKZMmULevHnJmTMnw4YN4+OPPwbg999T3j0OyAo0BFoAS4EkYAkwEciJffQ5GFhw/V5ISOjH7t2Z8PHxoUyZMjRt2pSsWbNSoEABBg0axNq1a2/I1b9/f4oUKULevHlp1aoV27Ztc9RfgdyFJi7wIDabjfDwcOrWrcuMGTN46aWXTEcScRpHjx4lMTGRuLg4atasee15y7Kuzc8cGwuQB3jgui1LAMewjzqvXH18/WtHr3tcDLAf2gU4deoU/fv356effuLixYskJyeTJ0+eG3IVKlTo2p+zZ8+u0yoGaYTpYXLkyEFERATjxo3jhx9+MB1HxCls2rSJo0eP0rZtW3x8fNi1axfnzp3j3LlznD9/nlh7U5IjB8BZ4O/rtj4MFMF+TjMzcOim1x667rENgJROHDp0KDabjR07dnDhwgU++ugjHf1xYipMD1SqVCkWLlxIly5dOHTo0N03EHFTFy5c4IsvvqBz58507dqVqlWr0rNnT0JCQjh16hRgH3l+/fXXAJQqlbLlKOyjyZ+AL4COgDfwNDAcuIi9ON8But6wTx8fqFzZ/ueLFy+SI0cOcufOzdGjR5k0aZIjv125TypMD9WkSRNefvll2rZtS9y93lAm4uJatWpFzpw5KVasGBMmTGDQoEGEhdkvznnzzTcpU6YMtWvXJleuXDRp0oTt27czZ84cfvjhNeBB7IdliwDPArOBclc/+V3sh2tLAY8CzwDP37Bvy4IePex/HjVqFFu3bsXX15cWLVrQvn17B3/ncj80l6wHsyyLbt26kZiYyKJFi7DZbKYjiTiN5ORkfvjhB8LCwvj8889p3LgxQUFBfPDBk3z2mdddZ/i5FZsN2rWDZcvSP6+kH80lK/9is9l4//33+e2333QoSOSqP/74g9GjR1O6dGkGDRqEv78/+/fvZ9myZbRs2ZLhw73w8UnbZ/v42CdiF9ekwvRwPj4+rFixgqlTp7LafpOZiMf5+++/+fDDD3n88ccJDAzkr7/+Yvny5Wzbto0BAwaQP3/+a+8NCIDJkyF79nvbR/bs9u38/zVuEVeh20qEYsWKsWTJEjp06MC6det4+OGHTUcScTjLsli/fj1hYWEsX76cunXrEhwcTMuWLcmaNesdt+3b1/7fIUPs093d6fCszWbh42Nj8uR/thPXpMIUAOrXr8/YsWNp06YNGzduJFeuXKYjiTjE0aNH+fDDDwkPD8fLy4ugoCB27dpF4cKF7+lz+va1jzYnToQvv7Sfn7x+jlkfH7h8OR5//1PMnFlMI0s3oIt+5Aa9e/fm5MmTLF++HC8vHbEX93D58mU+++wzwsLCiIyMpGPHjgQFBVGrVq10udgtJgbCw2HnTvukBHny2G8dKVjwS2bNGquFD1zM7S76UWHKDa5cucLjjz9OkyZN/jWnpYgrsSyLLVu2EB4ezscff0y1atUICgqiXbt2ZL/XE5BplJSUxCOPPMLChQupXbt2huxT7t/tClOHZOUGWbJk4dNPPyUgIICqVavSrl0705FE7smpU6f46KOPCAsL4++//6ZHjx5s2bKFEiVK3H3jdObt7U3//v2ZMmUKS5YsyfD9S/rSCFNuadOmTTz55JP88MMPVKxY0XQckTtKSEjgyy+/JCwsjLVr19KmTRuCgoKoX7++8VMLFy9exM/Pj19++YXixYsbzSKpo/sw5Z4EBATw9ttv06ZNG/766y/TcURu6ddff2Xw4MEUK1aMyZMn07p1aw4fPkx4eDgNGzY0XpYAOXPmpHv37oSGhpqOIvdJI0y5o5CQEHbv3s2XX36Jt7e36TginD17lsWLFxMWFsbx48fp3r07PXr0cOrboQ4ePIi/vz8HDx4kh30Gd3FiGmFKmkyaNInExESGanoSMSgpKYnVq1fTuXNnSpYsyY8//sj48eM5dOgQEyZMcOqyBPDz86NRo0Za/NnFqTDljjJlysSSJUv49NNPWbRokek44mH27dvHsGHDKFGiBCNHjqRhw4b88ccffPzxxzRr1syljnoMHDiQadOmkZycbDqKpJEKU+4qf/78REREMGDAALZu3Wo6jri5ixcvMnfuXB599FEaNGjAlStXWL16NVFRUfTt2/dfCyy7inr16pE7d25WrVplOoqkkQpTUqVKlSrMnDmTdu3aXVsnUCS9pKwM0r17d4oXL84XX3zBK6+8wpEjR5g8eTKVKlUyHfG+2Ww2QkJCmDp1qukokkYqTEm1jh070rVrVzp27EhCQoLpOOIGDh48yNixYylTpgz9+/enWrVqREdHs2LFClq3bk3mzJlNR0xXTz31FNHR0Wzfvt10FEkDFabck7Fjx5IzZ05CQkJMRxEXFRcXx0cffUTjxo3x9/cnJiaGTz75hO3btxMSEkLBggVNR3SYLFmyEBwcrFGmi9JtJXLPzp8/T2BgIK+88gr//e9/TccRF2BZFhs3biQsLIxPP/2U2rVrExQUROvWre+6Moi7OXPmDGXKlGHv3r08+OCDpuPILWhqPEk3vr6+rFy5kgYNGlChQgXq1KljOpI4qWPHjrFgwQLCw8NJTk4mKCiInTt38tBDD5mOZky+fPno1KkTs2bN0nzNLkaHZCVNypUrx7x583jqqac4evSo6TjiROLj4/n0009p0aIFlSpVYv/+/cydO5e9e/fy2muveXRZphgwYACzZ8/m8uXLpqPIPVBhSpq1bNmSF198kfbt2+t/fA9nWRZbt26lX79+FC1alJkzZ9K5c2eOHDnCnDlzqFu3broso+UuypcvT/Xq1Vm8eLHpKHIPVJhyX4YNG0bx4sV58cUXudP5cHFPMTExTJ06lWrVqtGhQwfy589PVFQU3333Hc899xwPPPCA6YhOKyQkhClTpuj/GxeiwpT7YrPZCAsLY/PmzZpc2kMkJiby+eef0759ex5++GG2bt3K1KlTOXDgAKNGjaJkyZKmI7qEpk2bkpSUxHfffWc6iqSSLvqR+5YjRw4iIiKoU6cOlSpV4rHHHjMdSRxg9+7dhIWF8dFHH1GqVCmCgoIIDw8nV65cpqO5JJvNxsCBA5k6dSqNGzc2HUdSQSNMSRelSpVi4cKFdOnShYMHD5qOI+nk3LlzzJo1i1q1atG0aVMyZcrEDz/8wLp163jhhRdUlvepa9euREVFsW/fPtNRJBVUmJJumjRpwquvvkrbtm2Ji4szHUfSKCkpiW+++YYuXbrg5+fHDz/8wOjRozl8+DATJ06kbNmypiO6DR8fH3r16sX06dNNR5FU0MQFkq4sy6Jbt24kJCSwePFiXRnpQvbv3094eDgffvghBQsWJCgoiC5dupA3b17T0dza8ePHqVixIgcOHHDZieXdjdbDlAxhs9l4//33OXDgAG+99ZbpOHIXsbGxhIWF0aBBA+rVq0dcXByrVq1i8+bNBAcHqywzQOHChWnZsiVz5swxHUXuQiNMcYgjR45Qq1Yt5s6dS/PmzU3HketYlsWPP/5IeHg4ERERNGjQgKCgIJ588kmyZMliOp5H2rp1K23btuXAgQNuN+G8K9IIUzJUsWLFWLp0Kd27d9cFDU7i8OHDjBs3jjJlyhAcHEylSpXYu3cvK1eupG3btipLg2rUqEHJkiVZvny56ShyBypMcZhHH32UcePG0bZtWy5cuGA6jke6dOkSixYtomnTptSoUYMTJ06wZMkSdu7cyeDBgzX5txNJmchAnJcKUxyqd+/e1K9fn+eee47k5GTTcTyCZVlERkbSp08fihYtyocffsgLL7zAn3/+yYwZM/D399fFWE6oVatWxMTEsHHjRtNR5DZUmOJw7777LmfOnGHMmDGmo7i1EydOMGnSJCpWrEjXrl0pXrw427dvZ/Xq1XTq1Ils2bKZjih34O3tTf/+/TXKdGIqTHG4LFmy8OmnnxIWFqZzNOnsypUrLFu2jFatWlG+fHn27t3L+++/z759+xg2bBhFixY1HVHuwfPPP8+aNWs4fPiw6ShyCypMyRCFChVi+fLl9O7dm19//dV0HJe3bds2BgwYQNGiRQkNDaVjx478+eefzJ07l0cffVSHXF1Uzpw56d69u+ZldlIqTMkw/v7+vP3227Rt25a//vrLdByXc/r0aaZPn0716tVp06YNuXPnZuPGjXz//fd069ZNK4O4if79+zNv3jxiY2NNR5GbqDAlQ3Xr1o1WrVrRpUsXEhMTTcdxeomJiaxatYqnnnqKMmXKEBUVxeTJk/njjz8YM2YMpUqVMh1R0pmfnx+NGjUiPDzcdBS5iSYukAyXmJjIf/7zH6pXr86kSZNMx3FKe/bsITw8nAULFlCiRAmCgoLo1KkTvr6+pqNJBvj5558JCgoiOjoaLy+NazKaJi4Qp5EpUyaWLFnCsmXLWLhwoek4TuP8+fO899571K5dm8aNG2Oz2fj222/ZsGEDvXr1Ull6kHr16pE7d25WrVplOopcR4UpRuTLl4+IiAgGDhzIli1bTMcxJjk5mTVr1vDss89SokQJ1qxZw8iRIzl8+DBvvPEG5cuXNx1RDLh+rUxxHipMMaZKlSrMmjWL9u3bc+rUKdNxMtTvv//OyJEjKVmyJK+88gq1a9fmwIEDfPLJJzz55JNkyqS13T1dx44diY6OZvv27aajyFUqTDHqqaeeomvXrnTs2JGEhATTcRwqNjaW+fPn06hRI2rXrs2FCxf47LPP2Lp1K/369SNfvnymI4oTyZIlC8HBwRplOhFd9CPGJScn07p1a0qUKMGMGTNMx0lXlmXx888/ExYWxooVK3j00UcJCgqiZcuWmuxc7urMmTOUKVOGvXv3at7fDKSLfsRpeXl5sXDhQr799ls++OAD03HSxZEjR5gwYQKPPPIIffr0oUKFCuzZs4fPP/+c9u3bqywlVfLly0enTp2YNWuW6SiCRpjiRKKjo6lfvz4RERHUrVvXdJx7dvnyZSIiIggLC2Pz5s08/fTTBAUFERAQoJl3JM327NnDY489xsGDBzUfcAbRCFOcXtmyZQkLC6Njx44cPXrUdJxUsSyLTZs28eKLL/LQQw8xb948evTowZ9//smsWbMIDAxUWcp9KV++PNWrV2fx4sWmo3g8FaY4lRYtWvDiiy/Svn17Ll++bDrObZ08eZK3336bypUr06VLF4oUKcIvv/zCN998Q5cuXfDx8TEdUdxIylqZdzoiKI6nwhSnM2zYMIoXL07fvn2d6gfElStXWLFiBa1bt6ZcuXLs2rWLmTNn8ttvvzFixAiKFy9uOqK4qaZNm5KUlMR3331nOopHU2GK07HZbISFhbFlyxbeffdd03HYsWMHISEhFCtWjKlTp9K+fXuOHDnCvHnzaNCggQ65isNpIgPnoMIUp5QjRw4iIiKYMGEC33//fYbv/6+//iI0NJSaNWvSsmVLcuTIwfr161m7di09evQgR44cGZ5JPFvXrl2JjIxk3759pqN4LF0lK05tzZo1dO3alY0bN+Ln5wfAqVMQHg47dsD58+DrC1WqQFAQFCiQ9n0lJibyzTffEB4ezjfffMOTTz5JUFAQjz/+ON7e3uny/YjcjxEjRnDu3Dmtl+lgt7tKVoUpTm/KlCnMnz+f6dM3MHWqD199ZX/++muCfHzAsqB5cxg6FAICUv/50dHRhIWFsWDBAooWLUpQUBCdO3cmd+7c6fp9iNyv48ePU7FiRQ4cOECePHlMx3Fbuq1EXNbAgQPJmnUAjz/uRUSExeXLN5YlwKVL9uciIqBRI7jbfd4XLlxgzpw51K1bl0aNGpGcnMz//d//ERkZSZ8+fVSW4pQKFy5My5YtmTNnjukoHkmFKU7n4MGD2Gy2awtMz55tY+fOHiQlZcWy7nyBjWVBXBwMGfLv0kxOTua7777jueeeo3jx4qxevZphw4Zx5MgR3nrrLaKionj00Ucd9W2JpIuBAwcSGhrq9nMvOyMVpmQYPz8/fHx8yJEjx7Wvl1566Y7bbNpkL79Ll+7tStSU0ty8Gf744w9Gjx5N6dKlGTRoEP7+/uzfv59ly5bRsmVLrQwiLqVGjRqULFmS5cuXm47icfSTQjLU559/TpMmTVL9/okT7Ydb0+LSpWSaN/8Z6ECXLl1Yvnw51atXB7g2ehVxRSEhIbzxxht06tTJdBSPohGmGJeUlMSQIUPInz8/pUqVurbK/KlT8MUX32NZla97dxMg8LrHjwIRV//8BlAayAlUwLJWcv58PX755U9q1KjBSy+9REhICHnz5mX06NGcOXOG1q1bkytXLgIDAzlw4IDjv1mRdNCqVStiYmLYuHGj6SgeRSNMMW7OnDl88cUX/PLLLzzwwAN06NABgPnzwcurDrAfOA3kBn7F/nveRez/fLcA9a9+UmngJ6AQ8AnQFW/v/SxeXJgCBSAyMpLOnTtz6tQpEhISCAoKIlu2bBw/fpw//viDZs2aUbJkyYz81kXSxNvbm/79+zNlyhSWLFliOo7H0AhTMlTbtm3JnTv3ta85c+awdOlSBg4cSLFixcibNy9Dhw4FYOdOiI/PBvgDPwKbgSrYR5XrgI3Aw0DKwssdgSLY/1l3Ah7m8uUodu60v1qkSBH69etHpkyZyJIlC8uWLWPs2LE88MADVKpUie7du2fY34PI/Xr++edZs2YNhw8fNh3FY6gwJUNFRERw7ty5a189e/bk2LFjFCtW7Np7SpQoAdgnJbBrCPyAvTQbAo2AtVe/Gl736R8C1bCPRHNjH42e5uxZ+6vX7yMmJobExMRb7lfEFeTMmZPu3btrEoMMpMIU4woXLsyRI0euPU75jdnXN+WZmwuzIf8uzENATyAUOAOcAyoBFin3d18/52uBAgXIlCnTLfcr4ir69evHvHnziI2NNR3FI6gwxbinn36a6dOn8+eff3L27FneeOMNACpXBvt6uXWBaCAK+wU/FbEXZCTQ4Oqn/A3YgJS58cKAX8mc2f45N/P29qZ9+/aMHj2auLg4du/ezfz58x32PYo4QsmSJWnYsKH+7WYQFaZkqFatWt1wH2a7du3o2bMnzZo1o2rVqtSoUYP27dsD8M8pxQeAGtiLMsvV5+oAJYCCVx9XAAZfff5BYCdQD8uCHj1unSU0NJTY2FgKFSpEjx49CAoKSu9vV8ThQkJCmDZtGsnJyaajuD3NJStOrX17+3R3aVkW02aDdu1g2bJ0jyXiNCzLIjAwkJEjR9KqVSvTcdyC5pIVlzR0qH1i9bTw8bFvL+LOtFZmxlFhilMLCIDJkyF79nvbLnt2+3b+//odUcT9dOzYkejoaLZv3246iltTYYrT69v3n9K03XVK2eRrZdm3b0akEzEvS5YsBAcHa5TpYCpMcQl9+8LatfZzkpkzJ+HldeP6Xj4+kC2bhY/PaqZP366yFI/Tq1cvIiIiOHnypOkobkuFKS7D399+AU+/fpNo2PA7nnsOWraE556DMWPg8GEb06cf46OPBnKni9lE3FG+fPno1KkTs+62GKykma6SFZfTuHFjBg0aRIsWLf71WkJCAhUqVGD27Nk0btzYQDoRc/bs2cNjjz3GwYMHyWa/iVnSQFfJiltITEwkKiqKOnXq3PL1zJkzM2bMGIYPH65Rpnic8uXLU716dRYvXmw6iltSYYpL2b59O8WLFydv3ry3fU/nzp35+++/+eKLLzIwmYhzCAkJYcqUKfqF0QFUmOJS1q1bR7169e74Hi8vL8aNG8eIESM0+4l4nKZNm5KUlMT3339vOorbUWGKS1m3bh1169a96/vatGlD1qxZ+eSTTzIglYjzSJnIYMqUKaajuB0VpriU9evX33WECfYfGuPHj2fkyJEkJiZmQDIR59G1a1ciIyPZt2+f6ShuRYUpLuPw4cPEx8dTpkyZVL2/adOmFCpUiAULFjg4mYhz8fHxoVevXkyfPt10FLeiwhSXkXL+0nb36X4A+yhzwoQJjBkzhvj4eAenE3EuwcHBLFq0iLMpK6jLfVNhistIzQU/N3v00UcpX748H3zwgYNSiTinwoUL07JlS+bMmWM6ittQYYrLSEthAowfP54JEyYQFxfngFQizmvgwIGEhoaSkJBgOopbUGGKS7h48SK//fYbNWrUuOdta9asSZ06dZgxY4YDkok4rxo1alCyZEmWL19uOopbUGGKS9i4cSPVq1cna9asadp+7NixTJo0iQsXLqRzMhHnljKRgdw/Faa4hLQejk1RsWJFmjVrph8c4nFatWpFTEwMGzduNB3F5akwxSXcb2ECjB49mnfffZczZ86kUyoR5+ft7U3//v31y2I6UGGK00tKSiIyMvK2E66nVunSpenQoQNvvfVWOiUTcQ3PP/88a9as4fDhw6ajuDQVpji9nTt3UqRIEfLnz3/fn/X6668zZ84cjh8/ng7JRFxDzpw56d69O6GhoaajuDQVpji99Dgcm6Jo0aJ0796d//3vf+nyeSKuol+/fsybN4/Y2FjTUVyWClOcXnoWJsDQoUNZtGgRhw4dSrfPFHF2JUuWpGHDhsyfP990FJelwhSnl96FWbBgQfr06cPYsWPT7TNFXEFISAjTpk3TsndppMIUp/bnn38SFxfHI488kq6fO2TIEFauXKnVHMSj1KtXD19fX1atWmU6iktSYYpTS1n/MrUTrqdWnjx5CAkJYdSoUen6uSLOLGWtzKlTp5qO4pJUmOLU0vtw7PX69+/Pd999x44dOxzy+SLOqGPHjkRHR7N9+3bTUVyOClOcmiMLM2fOnLz66quMHDnSIZ8v4oyyZMlCcHCwRplpYLMs67Yv+vv7W5s3b87AOCL/iI2N5cEHH+TMmTNky5bNIfu4dOkSDz/8MMuXLycwMNAh+xBxNmfOnKFMmTLs3buXBx980HQcp2Oz2bZYluV/8/MaYYrTioqKomrVqg4rS7CvTD9ixAhGjBjhsH2IOJt8+fLRqVMnZs2aZTqKS1FhitNy5OHY6z3//PPs37+ftWvXOnxfIs5iwIABzJ49m8uXL5uO4jJUmOK0Mqows2TJwujRoxk+fDh3OkUh4k7Kly9P9erVWbx4sekoLkOFKU4pKSmJjRs3Urdu3QzZ37PPPstff/3F6tWrM2R/Is4gZa1M/aKYOipMcUq7du2iYMGCFCxYMEP25+3tzdixYxkxYoR+eIjHaNq0KUlJSXz//femo7gEFaY4pYw6HHu99u3bY1kWy5cvz9D9ipiSMpGB1spMHRWmOCUThenl5cX48eN5/fXXSUpKytB9i5jStWtXIiMjNU1kKqgwxSmZKEyA5s2bkydPHhYtWpTh+xYxwcfHh169ejF9+nTTUZyeClOczrFjx7hw4QJly5bN8H3bbDYmTJjA6NGjSUhIyPD9i5gQHBzMokWLOHv2rOkoTk2FKU5n3bp11KlTBy8vM/88GzVqRKlSpZg3b56R/YtktMKFC9OyZUvmzJljOopTU2GK01m/fr2Rw7HXmzBhAuPHj9dN3eIxBg4cSGhoqI6s3IEKU5yOqfOX1wsMDKRGjRqaOkw8Ro0aNShZsqSuEr8DFaY4lbi4OHbt2kVAQIDpKIwbN44333yT2NhY01FEMkTKRAZyaypMcSpRUVFUrlwZHx8f01GoUqUKjz32GNOmTTMdRSRDtGrVipiYGDZu3Gg6ilNSYYpTcYbDsdcbM2YMU6ZM0dWD4hG8vb3p37+/Rpm3ocIUp+JshfnII4/Qpk0bJk+ebDqKSIYICgpizZo1HD582HQUp6PCFKeRnJzMhg0bnKowAUaOHMns2bM5deqU6SgiDpcrVy66d+9OaGio6ShOR4UpTmP37t3ky5fP6VaAL1GiBM888wwTJ040HUUkQ/Tr14958+bpgrebqDDFaTjb4djrDR8+nPnz5/Pnn3+ajiLicCVLlqRhw4bMnz/fdBSnosIUp7F+/foMW//yXhUqVIiePXsybtw401FEMkRISAjTpk0jOTnZdBSnocIUp+HMI0yAV155hWXLlnHgwAHTUUQcrl69evj6+rJq1SrTUZyGClOcwsmTJzlz5gwVKlQwHeW28uXLR79+/RgzZozpKCIOl7JW5tSpU01HcRoqTHEKpidcT62QkBBWr17N7t27TUcRcbiOHTsSHR3N9u3bTUdxCs7900k8hrMfjk2RK1cuhgwZwsiRI01HEXG4LFmyEBwcrFHmVSpMcQquUpgAL730EuvXr2fr1q2mo4g4XK9evYiIiODkyZOmoxinwhTjLl26xM6dOwkMDDQdJVWyZ8/OsGHDGDFihOkoIg6XL18+OnXqpJV7UGGKE9i0aRMVK1Yke/bspqOkWs+ePdm9ezfr1q0zHUXE4QYMGMDs2bM9fn1YFaYY50qHY1NkzZqVkSNHMnz4cCzLMh1HxKHKly9P9erVWbx4sekoRqkwxbh169Y57YQFd9KtWzeOHz/OmjVrTEcRcbiUtTI9+RdEFaYY5awTrqdGpkyZGDNmjEaZ4hGaNm1KUlIS33//vekoxqgwxajo6Ghy5cpFkSJFTEdJk6effpr4+Hg+++wz01FEHCplIgNPXitThSlGueL5y+t5eXkxbtw4Xn/9dc25KW6va9euREZGsm/fPtNRjFBhilGuXpgArVq1wsfHhyVLlpiOIuJQPj4+9OrVi+nTp5uOYoQKU4xyh8K02WxMmDCBUaNGkZiYaDqOiEMFBwezaNEizp49azpKhlNhijGnTp3i1KlTVKxY0XSU+9a4cWMeeughrR8obq9w4cK0bNmSOXPmmI6S4VSYYsz69eupXbs23t7epqPct5RR5tixY4mPjzcdR8ShBg4cSGhoKAkJCaajZCgVphjjDodjr1e3bl0qVarE+++/bzqKiEPVqFGDkiVLsnz5ctNRMpQKU4xxt8IEGD9+PP/73//4+++/TUcRcaiQkBCPW8VEhSlGXL58me3bt7vMhOupVb16dR599FFCQ0NNRxFxqFatWnHq1Ck2btxoOkqGUWGKEVu2bKFcuXLkyJHDdJR0N3bsWN5++23Onz9vOoqIw3h7e9O/f3+PmshAhSlGuOPh2BTly5enefPmvPPOO6ajiDhUUFAQa9as4fDhw6ajZAgVphjhzoUJMGrUKEJDQzl9+rTpKCIOkytXLrp37+4xpyBUmJLhLMti/fr1bl2YpUqV4umnn+att94yHUXEofr168e8efOIjY01HcXhVJiS4fbt20f27NkpWrSo6SgONWLECObOncvx48dNRxFxmJIlS9KwYUOPmLRDhSkZzt0Px6Z46KGH6NGjBxMmTDAdRcShQkJCmDZtmtsvQKDClAznKYUJ8Nprr7F48WIOHjxoOoqIw9SrVw9fX19WrVplOopDqTAlw3lSYRYoUIAXX3yRsWPHmo4i4jApa2W6+0QGKkzJUKdPn+bYsWNUqlTJdJQMM3jwYD7//HOio6NNRxFxmI4dOxIdHc327dtNR3EYFaZkqA0bNlCrVi0yZcpkOkqGyZ07NyEhIYwaNcp0FBGHyZIlC8HBwW49ylRhSobypMOx1+vfvz8//PCDW//2LdKrVy8iIiI4efKk6SgOocKUDOWphZkjRw5ee+01Xn/9ddNRRBwmX758dOrUiVmzZpmO4hA2y7Ju+6K/v7+1efPmDIwj7iw+Pp58+fJx/PhxcubMaTpOhrt8+TIPP/wwn3zyCbVr1zYdR8Qh9uzZw2OPPcbBgwfJli2b6ThpYrPZtliW5X/z8xphSobZunUrDz/8sEeWJUC2bNl4/fXXGTFihOkoIg5Tvnx5qlevzuLFi01HSXcqTMkwnno49npBQUEcPHiQ77//3nQUEYcJCQlhypQp3OkIpitSYUqGUWFC5syZGT16NMOHD3e7HyYiKZo2bUpSUpLb/WKowpQMYVmWCvOqLl26cP78eb788kvTUUQcImUiA3dbK1OFKRli//79ZM2aleLFi5uOYpy3tzfjxo1jxIgRbj/3pniurl27EhkZyb59+0xHSTcqTMkQ69ato27duqZjOI127drh7e3NsmXLTEcRcQgfHx969erF9OnTTUdJNypMyRDuvv7lvbLZbIwfP56RI0eSlJRkOo6IQwQHB7No0SLOnj1rOkq6UGFKhtD5y39r1qwZ+fPn56OPPjIdRcQhChcuTMuWLZkzZ47pKOlChSkO99dff3H48GGqVq1qOopTsdlsTJgwgdGjR3PlyhXTcUQcYuDAgYSGhpKQkGA6yn1TYYrDbdiwgcDAQI+acD21GjRowCOPPMLcuXNNRxFxiBo1alCyZEmWL19uOsp9008wcTgdjr2z8ePH07ZtW3r06IGPj4/pOCLpbuDAgbz11ls89lgnwsNhxw44fx58faFKFQgKggIFTKe8OxWmONy6desYNmyY6RhOKyAggMDAQGbOnMngwYNNxxFJd4ULt2bHjuwUK5aMl5cXly//89ry5TBqFDRvDkOHQkCAuZx3o8nXxaGuXLlC3rx5OXr0KL6+vqbjOK1ff/2Vxo0bs3//fo+da1fc06xZMGQIXLqUjGXd/iygzQY+PjB5MvTtm4EBb5lFk6+LAb/88gulS5dWWd5FpUqVaNKkCdOmTTMdRSTdpJRlXBx3LEsAy7IRF7efIUOgfv0+jBs3LoNSpp4KUxxKExak3pgxY5g2bZrb3LMm7mHRokX4+/uTI0cOChcuTPPmzfn555/vut2mTf+U5b2Ii4OtW2fTvLnzrR2rwhSH0oQFqVemTBnatm3LpEmTTEcRAeCdd95h4MCBDBs2jJMnT3L48GFefPFFVq5ceddtJ06ES5dSHiXe034vXbJv73Qsy7rtV82aNS2RtEpOTrYKFSpk/f7776ajuIxDhw5ZefPmtU6cOGE6ini4c+fOWQ888IC1dOnSW74eGRlp1a5d2/L19bUKFSpkBQcHW/Hx8ZZlWdbJk5YFWBBqQRkL/CywLHjfgtIW5LGglQVHrz6f8v7frv65u+XtPdw6dcqyvv/+e+uhhx6yJk+ebBUoUMAqVKiQNW/evGs5Ll++bA0ePNgqVqyYVbBgQat3795WXFzcfX3vwGbrFp2oEaY4zB9//IHNZsPPz890FJdRvHhxunbtykSn/PVaPMmGDRu4fPky7dq1u+Xr3t7eTJkyhdOnT7Nhwwa+/fZbZs6cCUB4eMq7IoBIYDfwHTAUWAocB0oAnW+7f5vtn885ceIE58+f5+jRo8ydO5fg4OBrpy5effVV9u3bx7Zt29i/fz9Hjx5l7Nix9/W9344KUxwm5f5Lm81mOopLGTZsGAsWLODIkSOmo4gHO3PmDPnz57/thCM1a9akdu3aZMqUCT8/P3r37s3atWsB+32WdkOBvIAPsBB4HqgBZAUmAhuAg7f8/MRE2LnT/ufMmTMzcuRIMmfOzJNPPkmOHDmIjo7GsizmzJnDlClTyJs3Lzlz5mTYsGF8/PHH6fJ3cDPdhykOowkL0ubBBx+kV69ejBs3jvfff990HPFQ+fLl4/Tp0yQmJt6yNPft28egQYPYvHkzcXFxJCYmUrNmTcA+KYFdseu2OIa9LFPkAPIBRwG/W2ZIuf4tX758N2TInj07sbGxxMTEEBcXd22/YD/N6KgFDTTCFIdRYabdyy+/zPLly9m/f7/pKOKh6tSpQ7Zs2YiIiLjl63379qVcuXL89ttvXLhwgf/9739YV+/r/+cusuuPLhUBDl33+G/gDPDQbTPkyXPnjPnz58fHx4ddu3Zx7tw5zp07x/nz54mNjb3zhmmkwhSHOHfuHAcPHqRatWqmo7ikvHnz0r9/f0aPHm06ingoX19fxo4dS3BwMBEREcTFxZGQkMBXX33FK6+8wsWLF8mVKxc5cuRg7969zJo169q2+fMfvcUnPgOEAduAeGAYUIvbjS4zZYLKle+c0cvLi549exISEsKpU6cAOHr0KF9//fW9fruposIUh9iwYQP+/v5kzpzZdBSXNXDgQP7v//6PX3/91XQU8VCDBg3inXfeYfz48RQoUIBixYoRGhpK27ZtmTx5MosWLSJnzpz07NmTjh07cubMGRo2bMiSJU/e4tMaA+OADkBh4ABw+3ONlgU9etw945tvvkmZMmWoXbs2uXLlokmTJkRHR6fl270rTY0nDjFixAjAPrG4pN3kyZNZv369W6z0IO7p7NmzfPDBB8yYMYOHHnqI/v370759ezp1ykxExNUbRu6RzQbt2sGyZekeN5X719R4koE0w0/6CA4OJjIyEv3iKs5m9+7d9OnTh1KlSrFjxw4++eQT1q1bR6dOncicOTNDh9rnhk0LHx/7ROzORoUp6S4hIYHNmzdTp04d01Fcno+PD8OHD782YhcxKTk5mS+++IInnniCxx9/nEKFCrFnzx4WLFhAwE3LjAQE2CdSz5793vaRPbt9O/9/je/MU2FKutu+fTslSpQgz90ucZNUeeGFF4iOjuann34yHUU81IULF5g+fTply5Zl9OjRPPfccxw6dIjRo0dTqFCh227Xt+8/pXm327Fttn/K0vRqJbejwpR0p9tJ0leWLFkYNWoUw4cP507XHIikt99++40BAwbg5+fHunXrCA8PZ9OmTTz33HNkzZo1VZ/Rty+sXWs/J5kt278P0/r42J9v187+PmctS9DEBeIA69ato2XLlqZjuJWuXbvyxhtv8M0339CsWTPTccSNWZbFmjVrmDZtGlFRUbzwwgts376dYsWK3X3j2/D3t1/AExNjn+5u5077pAR58thvHenRAwoUSLdvwWF0laykK8uyKFq0KD/++COlS5c2HcetLF26lEmTJhEVFaXpBiXd/f333yxYsIDp06fj7e3NgAEDePbZZ/FJ65U7LkxXyUqGOHToEElJSZQqVcp0FLfz1FNPkZCQcNuZV0TS4tChQ7zyyiuUKFGC1atXM2PGDHbs2MELL7zgkWV5JypMSVeacN1xvLy8GD9+PK+//rrD5soUz2BZFmvXrqVDhw7UqFGDpKQkoqKiiIiI4LHHHtP/v7ehwpR0pQt+HKtFixbkzJnTYasxiHu7fPkyYWFhVK9end69e9O4cWMOHTrE22+/raNCqaDClHSlCQscy2azMWHCBEaNGkVCQoLpOOIijh07xogRIyhRogSffPIJb7zxBrt37+bFF18kR44cpuO5DBWmpJsLFy5w4MABatSocfc3S5o9/vjjlChRgvnz55uOIk5u48aNdOnShUqVKnHu3Dl+/PFHvvzyS/7zn//g5aUf//dKf2OSbjZu3EiNGjXIkiWL6Shub8KECYwdO5b4+HjTUcTJXLlyhYULF1KrVi2eeeYZAgMD+eOPPwgNDaVs2bKm47k0FaakG52/zDi1a9ematWqvPfee6ajiJM4deoU48aNw8/Pj3nz5jF8+HB+++03QkJC8P1ngUq5DypMSTcqzIw1btw4Jk6cyN9//206ihj0yy+/0KNHD8qWLcuRI0f4+uuv+fbbb2ndujXe3t6m47kVFaaki8TERKKionTBTwaqVq0aDRo04N133zUdRTJYYmIin376KfXr16d169aUK1eO/fv38/7771P5bqsuS5ppajxJFzt27KBo0aLkzZvXdBSPMmbMGOrXr0+fPn3InTu36TjiYH/99Rdz5sxhxowZlChRgv79+9OuXTsyZdKP8oygEaakCx2ONaNcuXK0bNmSd955x3QUcaBff/2V3r17U7p0aXbv3s2KFSv46aef6Nixo8oyA6kwJV2oMM0ZNWoUM2bMICYmxnQUSUdJSUl89tlnNGnShKZNm/LQQw+xd+9e5s+fT82aNU3H80gqTEkXKkxz/Pz86Ny5M2+++abpKJIOzp8/z9SpU3nkkUcYN24cPXr04NChQ4wcOZIHH3zQdDyPpsKU+3b48GHi4+MpU6aM6Sgea/jw4YSFhXHs2DHTUSSN9u3bR79+/ShZsiSRkZF89NFHREVF0bVrV93b7CRUmHLf1q9fT926dTVhs0FFihTh+eefZ/z48aajyD2wLIuvv/6aJ598kkcffZRcuXKxc+dOFi9eTJ06dfT/lJPR2WK5bzoc6xxeffVVypYty8svv0zJkiVNx5E7iI2N5cMPP+Tdd98lS5YsDBgwgGXLlmk5LSenEabcNxWmc8ifPz/BwcGMGTPGdBS5jT/++IPBgwdTokQJ1qxZw+zZs9m2bRvPP/+8ytIFqDDlvly8eJHo6GhdteckBg8ezKpVq9izZ4/pKHKVZVn88MMPtGvXjoCAALy8vNiyZQvLly+nYcOGOuzqQlSYcl8iIyOpXr06WbNmNR1FAF9fXwYPHsyoUaNMR/F4ly5dYu7cuVSrVo2+ffvyxBNPcOjQISZNmoSfn5/peJIGOocp90WHY51Pv379KFOmDL/88gvVq1c3Hcfj/Pnnn8ycOZMPPviAgIAAJk2aRNOmTTWSdAMaYcp9UWE6nwceeIChQ4fy+uuvm47iMSzLYv369XTq1IkqVaoQGxvLunXrWLVqFU888YTK0k2oMCXNkpKSiIyM1ITrTqh3797s2LGDDRs2mI7i1uLj41mwYAGBgYF069aNunXrcvDgQaZPn87DDz9sOp6kMx2SlTTbuXMnhQoVIn/+/KajyE2yZs3KyJEjGT58ON99953pOG7n5MmTzJ49m9mzZ1OpUiVGjRpF8+bNtZyWm9MIU9JMh2OdW/fu3Tly5Ajffvut6ShuY8uWLXTr1o1y5cpx7Ngx1qxZw//93//RsmVLlaUHUGFKmq1fv16F6cQyZ87MmDFjGD58OJZlmY7jshISEli6dCn16tWjffv2VKpUiQMHDvDee+9RsWJF0/EkA6kwJc00wnR+nTt35u+//+aLL74wHcXlnDlzhokTJ1KqVClCQ0MZNGgQBw4c4JVXXtG6rx5KhSlpcvToUWJjYylbtqzpKHIHXl5ejBs3jtdff53k5GTTcVzCzp076dmzJ2XKlGHfvn189tln/Pjjj3To0EFrT3o4Faakybp16zThuoto06YNWbJk4dNPPzUdxWklJSWxcuVKHn/8cf7zn/9QokQJoqOjCQsL072sco1+XZI00eFY12Gz2Rg/fjz9+/enffv2GiVd59y5c8ybN4/Q0FAKFizIgAED6NChg5bTklvSCFPSRIXpWpo2bcqDDz7IRx99ZDqKU4iOjiY4OJhSpUqxZcsWFi9ezMaNG+nSpYvKUm5LhSn3LDY2lj179uDv7286iqSSzWZjwoQJjBkzhitXrpiOY0RycjJfffUVzZs3p0GDBuTNm5dff/2VhQsXUqtWLdPxxAXo2Izcs6ioKKpWrUq2bNlMR5F78Oijj1KuXDk++OADXnzxRdNxMszFixevrT3p4+PDgAEDWLFihf79yj3TCFPuWcoFP+J6xo8fz4QJE4iLizMdxeF+//13Bg0ahJ+fH99//z1z5sxh69at9OjRQ2UpaaLClHumCQtcV82aNalduzYzZ840HcUhLMviu+++o02bNgQGBpI5c2a2bt3Kp59+Sv369XVVt9wX251mAPH397c2b96cgXHE2SUnJ5M3b16io6N58MEHTceRNNi1axePP/44v/32G7ly5TIdJ13ExcWxcOFCpk+fTnJyMv3796dr16488MADpqOJC7LZbFssy/rXRRoaYco92bVrFwUKFFBZurCKFSvyxBNPMHXqVNNR7tuRI0cYOnQofn5+fP7550yZMoVff/2V3r17qywl3akw5Z7odhL3MHr0aKZPn85ff/1lOso9syyLn3/+maeffpqqVaty6dIl1q9fz2effUaTJk102FUcRoUp90SF6R5Kly5Nhw4deOutt0xHSbX4+Hg+/PBD/P39CQoKon79+hw8eJCpU6dSpkwZ0/HEA+gcptyTUqVK8cUXX1ChQgXTUeQ+/fnnn1StWpVdu3ZRqFAh03Fu68SJE8yaNYv33nuPqlWrMmDAAP7zn//g5aXf98UxdA5T7tvx48c5d+4c5cqVMx1F0kHRokXp1q0b//vf/0xHuaVNmzbRtWtXypcvT0xMDN9//z1ff/01Tz75pMpSjNC/Okm1lPsv9cPKfQwdOpSFCxdy+PBh01EA+9qTH3/8MXXr1qVjx45Uq1aN33//nZkzZ1K+fHnT8cTDaaYfSTVNWOB+ChYsSO/evRk7diwffPCBsRwxMTHMmTOHmTNnUqZMGV5++WVat26Nt7e3sUwiN9NQQVJNF/y4p5dffpmIiAh+++23DN/39u3b+e9//8sjjzzCgQMHWLVqFT/88APt2rVTWYrTUWFKqsTFxbFr1y4CAgJMR5F0lidPHgYOHMioUaMyZH9JSUmsWLGCRo0a0aJFC0qXLs2+ffuYO3cuVatWzZAMImmhQ7KSKps2baJSpUpkz57ddBRxgAEDBlCmTBl27txJ5cqVHbKPs2fPMnfuXEJDQylSpAj9+/enQ4cOZM6c2SH7E0lvKkxJFR2OdW85c+bk1Vdf5fXXX+f99yMID4cdO+D8efD1hSpVICgIChS498/es2cP7777LosXL6ZFixZ88sknOlIhLkmFKamybt06nn/+edMxxIECAl5k+PDyFCuWjJeXF5cv//Pa8uUwahQ0bw5Dh8Ld+i5l7cnp06ezfft2evfuze7duylcuLBjvwkRB9LEBXJXycnJ5M+fn927dzv1De6SdrNmwZAhcOlSMpZ1+0sbbDbw8YHJk6Fv33+/fvHiRcLDw3n33XfJmTMnAwYMoFOnTmTNmtWB6UXS1+0mLtAIU+5qz5495MmTR2XpplLK0r5E5p2vA7Qs+/uGDLE/TinNAwcO8O6777JgwQIaN27MvHnzqFevnuZ1FbeiwpS70vlL9+Ln58fJkyfx9vbGyyszf/9dl+Tk2UCxVH+GvTQtYmIiGDWqPfny5aNnz5788ssvFC9e3HHhRQzSbSVyVypM9/P5558TGxvLY48dJzn5QaDfPX9GXNwVJk2KBeD3339n4sSJKktxaypMuSvN8OOeTp2Cb77JBjwF7L767CqgOpAL+4hz9HVbHARswFygONCU2NjhABQpUoQcOXKwYcOGDMkuYoIOycodnTx5ktOnT1OxYkXTUSSdhYeDZcUBS4DaV599APgQqAj8CjQFqgFtr9tyLbAH8CJr1pPEx5fk3LlzZMqkHyfi3vQvXO5o/fr11KlTRxOuu5m2bdty5UomEhJigYLA11dfaXTdu6oAXbAXZNvrnh+NvVghPt7BQUWciH4Kyh3p/KV7ioiIoFmzc0A8EAo0BE4AkcBjQAHAF5gNnL5p69RfHCTiTlSYckcqTPdx+fJlNm7cyMWLF5k4cSJr164EvIH2V//7M/AM0Bo4ApwH+gA336ttu82fRdybDsnKbV26dIkdO3YQGBhoOorco+TkZKKjo4mKiiIyMpKoqCj27NlDuXLluHLlCtWrV6datVrMmJFMfPznwFmgPHARyAtkA6KARcATt91PtmwFiI/34vfff+eRRx7JgO9MxBwVptzW5s2bqVChAg888IDpKHIXR48eJSoq6trX5s2bKVCgAIGBgQQGBtK1a1eqV6+Oj48Pfn5+zJgxAy+v2cTH24ASwHzsF/rMBAYDL2E/TPs0cO4Oe85OSMhw6tWrR0JCAqtXr6Z27dp3eL+I69LUeHJbb7zxBidOnGDq1Kmmo8h1Lly4wObNm28YPV65coXAwEBq1apFYGAgAQEB5MuX766f1b49RETYZ/C5VzYbtGsHy5bd+7YizkxT48k9W7duHd26dTMdw6NduXKFHTt23DB6PHz4MNWrVycwMJDOnTvzzjvv4Ofnl6Zp6IYOha+/TpkW7974+Ni3F/EUKky5peTkZNavX8/s2bNNR/EYlmWxf//+G8pxx44dlC5dmsDAQOrVq0dISAgVK1ZMt3seAwLsE6n/M5ds6mTPbt/O/1+/g4u4LxWm3FJ0dDQ5c+bkoYceMh3FbZ08eZJNmzZdO6y6adMmcuXKde28Y4cOHahRowY5cuRwaI6UCdTtq5Xc+fDs3VYrEXFnKky5pfXr1+t2knQUGxvL1q1bbxg9nj9//lo5vvTSSwQEBBhbEaZvX/toc+JE+PJLezFeuvTP6z4+9iJ98kn7YViNLMUTqTDllnT/ZdolJiaya9euG8px//79VK5cmVq1atGmTRsmTJhAmTJlnGr5K39/+wU8MTH2afN27oSzZyFPHqhcGXr0gAIFTKcUMUeFKbe0bt06BgwYYDqG07Msi0OHDl07rBoVFXVtiauU0WPv3r2pUqUKWbJkMR03VQoUgJdfNp1CxPmoMOVfYmJiOHHiBJUqVTIdxemcOXOGTZs23TB6zJQp07XbOcaMGUPNmjXx9fU1HVVE0pkKU/5l/fr11K5dG29vb9NRjLp06RLbtm274X7HmJgY/P39CQwM5L///S/vvfeeLowS8RAqTPkXTzx/mZSUxN69e28YOe7du5fy5csTGBjIE088wYgRIyhbtqzH/yIh4qlUmPIv69atY+zYsaZjOIxlWbecSu7BBx+8NltO9+7dqVatGtmyZTMdV0SchApTbnD58mW2bdtGrVq1TEdJN+fPn2fz5s03XJiTmJh47bzjq6++ir+/f6qmkhMRz6XClBts2bKFsmXLOvxmeUeJj4//11Ryf/7557Wp5J555hmmTZtG8eLFneqWDhFxfipMuYErnb9MTk6+YSq5yMhIfv31Vx5++GECAwOpX78+gwcPpkKFCuk2lZyIeC79FJEbrF+/ns6dO5uOcUsnTpy4YeS4adMmcufOfe1+x44dO1KjRg0tRyYiDqHC9HCnTtlnddmxA86ft/i//3uWChWa0rix2VldYmNj2bJlyw0FefHixWvlOGDAAAICAihYsKC5kCLiUbQepofatMk+b+hXX9kfX778z2sp84Y2b26fNzQgwLFZEhISrk0ll3Jhzu+//07VqlWvFWRgYCClS5fWeUcRcbjbrYepwvRAs2aZW5nCsiz++OOPG0aO27Zto0SJEjeUY+XKlV1mKjkRcS9aQFqAf8ryn7UPGwFdgRf+9V7Lsr9vyBD747SU5unTp/81lVzWrFmv3e84btw4atasSa5cudL2DYmIZBAVpgtr1KgR27dv58SJE2TNmvWu79+06d4XCoZ/SjMg4M7LOl26dIlffvnlhvsdT58+TUBAAIGBgfTs2ZP3339fU8mJiEtSYbqogwcP8tNPP+Hr68tnn31Gx44d77rNxIk3rnF4d4mk/BO5dMm+/bJl9leSkpLYs2fPDSPH6OhoKlSoQGBgIM2bN2fUqFE88sgjeHl53fP3JyLibPSTzEV9+OGH1K5dmx49ejB//vxrz/fo0YPg4GBatGhBzpw5qVWrFgcOHODUKfsFPpb1f0A5wBd4Cbj+JGY4UA8IAfICo4F4YAiWVZzlyx+kUiV/6tevT548eQgMDGT+/PlUrFiR3r17ExcXx5gxY5gxYwZFihShc+fOKksRcRv6aeaiPvzwQ5599lmeffZZvv76a06ePHnttcWLFzNq1CjOnj1LmTJlGD58OOHhYFmngQ7AeOA0UBpYd9MnRwKlgFPAcOBVYB+wDZttFydPZuahhx7i4MGDDBkyhCpVqtCvXz/OnDlDqVKlWLt2LQA//vgjDRs2dPDfgohIxlFhuqCff/6ZQ4cO8fTTT1OzZk1Kly7NokWLrr3evn17AgMDyZQpE88++yzbtm1jxw6Ij/8SqAA8BWQGBgKFbvr0IkA/7IdiswFzgClAXiwrP/7+7xAZGUnevHlp2LDhDQU5dOjQa4/Xrl2rwhQRt6LCdEHz58/niSeeIH/+/AA888wzNxyWLVTonxLMnj07sbGxnD8PcAwodt0n2W56zE2PY4A4oCaQG8jNmjX/ISYmBoA6deqwb98+Tp48ybZt2+jWrRtHjhzh9OnTREVF0aBBg/T5hkVEnIAu+nExly5dYunSpSQlJV0rxvj4eM6dO8f27dtvu52vL0Bh4Mh1z1o3PQZ7iabID/gAuwD7la1dusCHH9pfzZ49OzVr1mTatGlUqlSJLFmyULduXd555x1Kly59rdBFRNyBRpguJiIiAm9vb3bv3s22bdvYtm0be/bsoX79+nyY0mS3UKUKZM3aAnv5Lcd+Bex04MQd9uYF9MR+EdApfHygePGjfP3119fe0bBhQ0JDQ68dfm3UqNENj0VE3IUK08XMnz+foKAgihcvTqFCha59vfTSSyxcuJDExMRbbtejB9hs+YFPgNeAfMBv2K+KvZM3gTJAbS5dysXSpU2Ijo6+9mrDhg25ePHitcOvNz8WEXEXmhrPg7RvDxERd54O73ZsNmjX7p/7MEVE3NXtpsbTCNODDB1qnxs2LXx87NuLiHgqFaYHCQiwT6SePfu9bZc9u327O02LJyLi7nSVrIdJmUDd1GolIiKuSiNMD9S3L6xdaz8nmS3bvw/T+vjYn2/Xzv4+laWIiEaYHsvf334BT0wMhIfDzp1w9izkyQOVK9uvqi1QwHRKERHnocL0cAUKwMsvm04hIuL8dEhWREQkFVSYIiIiqaDCFBERSQUVpoiISCqoMEVERFJBhSkiIpIKKkwREZFUUGGKiIikggpTREQkFVSYIiIiqaDCFBERSQUVpoiISCqoMEVERFJBhSkiIpIKKkwREZFUUGGKiIikggpTREQkFVSYIiIiqaDCFBERSQUVpoiISCqoMEVERFJBhSkiIpIKKkwREZFUUGGKiIikggpTREQkFVSYIiIiqaDCFBERSQWbZVm3f9FmiwEOZVwcERER40pYllXg5ifvWJgiIiJip0OyIiIiqaDCFBERSQUVpoiISCqoMEVERFJBhSkiIpIK/w+6km66Yuf9pAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_spring(g_friends)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb0b96bb-2935-40b8-9267-33bbb18c8367",
   "metadata": {},
   "source": [
    "## Working with node-edge lists\n",
    "\n",
    "If you are working with node-edge lists, it is likely that the most reliable way to get data into networkX is through looping through the data structure. Lets see how this might look, going back do our dataframe of book reviews, which already has this format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "21d5ca99-d157-4b9b-b04b-4085661a6ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.305785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.351564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.401144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.858865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.566387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.303694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.142796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source  target    weight\n",
       "0       0       1  0.305785\n",
       "1       0       2  0.351564\n",
       "2       0       3  0.401144\n",
       "3       0       4  0.858865\n",
       "4       0       5  0.566387\n",
       "5       0       6  0.303694\n",
       "6       0       7  0.142796"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_docs = pd.read_csv(\"data/docsimilarity.csv\", index_col=0)\n",
    "df_docs.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4240bb6-9d82-498a-a16f-215c554fc832",
   "metadata": {},
   "source": [
    "first we use the ```nx.Graph()``` constructor to create an empty graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df2257e4-03ed-4e52-978c-6c5ca50e3237",
   "metadata": {},
   "outputs": [],
   "source": [
    "G_test = nx.Graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df82d253-5ddc-4e68-ae3c-57f1ab45af77",
   "metadata": {},
   "source": [
    "then we can use the graph method ```G_test.add_node()``` to add nodes. You can do this through looping: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6ba7ca5b-1fec-4098-ae15-1eed63acd5ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "for node in df_docs.source:\n",
    "    G_test.add_node(node)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e4789dd-3cd3-47c1-b946-9bf3bfdef185",
   "metadata": {},
   "source": [
    "Or you can use list comprehensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9fafc52e-e243-4d9d-84a1-bf6f91fdb617",
   "metadata": {},
   "outputs": [],
   "source": [
    "G_test = nx.Graph()\n",
    "[G_test.add_node(node) for node in df_docs.source];"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05646b44-900e-4cd2-8308-2ee844ae6ffe",
   "metadata": {},
   "source": [
    "Getting the edges in is tricky, as here you need to \"zip\" various lists into a \"tuple\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d06b7bc0-073e-45af-8205-ed9582661630",
   "metadata": {},
   "outputs": [],
   "source": [
    "for node, edge, weight in zip(df_docs.source, df_docs.target, df_docs.weight):\n",
    "    G_test.add_edge(node, edge, weight=weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e96d8e6f-1ecd-44e9-b51c-8ccce4ad3914",
   "metadata": {},
   "source": [
    "Here is the expected result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8208434e-03f6-4b76-9a65-7420e6aa7023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_spring(G_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ff4570-c825-47ce-9a8a-2ff025c2103d",
   "metadata": {},
   "source": [
    "## Working with JSON"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a48ba78-86b9-4887-a21c-de6fcbeb2cad",
   "metadata": {},
   "source": [
    "Sometimes node-edge lists can be hard to work with, especially if one wants to have a lot of metadata, especially metadata that is large in size (like the text of a case). JSON is a more flexible way of storing these type of data. Literally JSON means \"Javascript Objection Notation\", and stores information about objects using pairs. JSON is virtually identical to the python dictionary format. \n",
    "\n",
    "Python will load a JSON file into a dictionary for inspection or manipulation. For this you need to use the json library.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b522c27f-4fe8-444b-80e7-1da5ee72f708",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"data/g_docs.json\", \"r\")\n",
    "docs_dict = json.load(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08010b70-a588-41ae-aeab-a462e05d9f63",
   "metadata": {},
   "source": [
    "Here we can see that the loaded JSON file is a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1dfcfe56-1a1f-4888-a872-c5275d345a89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(docs_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea8ff9b-f662-4c64-a75b-6108d1852977",
   "metadata": {},
   "source": [
    "Lets view it in its totality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c3f34c84-0ebf-41f4-b0a5-f1fd0115945f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'directed': False,\n",
       " 'multigraph': False,\n",
       " 'graph': {},\n",
       " 'nodes': [{'id': 0},\n",
       "  {'id': 1},\n",
       "  {'id': 2},\n",
       "  {'id': 3},\n",
       "  {'id': 4},\n",
       "  {'id': 5},\n",
       "  {'id': 6},\n",
       "  {'id': 7},\n",
       "  {'id': 8}],\n",
       " 'links': [{'weight': 0.3057850860342909, 'source': 0, 'target': 1},\n",
       "  {'weight': 0.3515643806261561, 'source': 0, 'target': 2},\n",
       "  {'weight': 0.4011441744419861, 'source': 0, 'target': 3},\n",
       "  {'weight': 0.8588650335933432, 'source': 0, 'target': 4},\n",
       "  {'weight': 0.5663867925847853, 'source': 0, 'target': 5},\n",
       "  {'weight': 0.3036944274298017, 'source': 0, 'target': 6},\n",
       "  {'weight': 0.1427956434168483, 'source': 0, 'target': 7},\n",
       "  {'weight': 0.2663586423650602, 'source': 0, 'target': 8},\n",
       "  {'weight': 0.1887291756697731, 'source': 1, 'target': 2},\n",
       "  {'weight': 0.2301873550390033, 'source': 1, 'target': 3},\n",
       "  {'weight': 0.800124882934437, 'source': 1, 'target': 4},\n",
       "  {'weight': 0.4122354734424759, 'source': 1, 'target': 5},\n",
       "  {'weight': 0.5097321851655097, 'source': 1, 'target': 6},\n",
       "  {'weight': 0.3027866619120244, 'source': 1, 'target': 7},\n",
       "  {'weight': 0.1868838597034725, 'source': 1, 'target': 8},\n",
       "  {'weight': 0.2280158058874547, 'source': 2, 'target': 3},\n",
       "  {'weight': 0.7995610591992457, 'source': 2, 'target': 4},\n",
       "  {'weight': 0.366590703499365, 'source': 2, 'target': 5},\n",
       "  {'weight': 0.5195230803085847, 'source': 2, 'target': 6},\n",
       "  {'weight': 0.317271203011655, 'source': 2, 'target': 7},\n",
       "  {'weight': 0.1896235506799158, 'source': 2, 'target': 8},\n",
       "  {'weight': 0.7678964587257362, 'source': 3, 'target': 4},\n",
       "  {'weight': 0.3480240462634174, 'source': 3, 'target': 5},\n",
       "  {'weight': 0.5762447937744228, 'source': 3, 'target': 6},\n",
       "  {'weight': 0.3808681395940203, 'source': 3, 'target': 7},\n",
       "  {'weight': 0.2481169176073587, 'source': 3, 'target': 8},\n",
       "  {'weight': 0.6878902741436519, 'source': 4, 'target': 5},\n",
       "  {'weight': 0.9008941138337112, 'source': 4, 'target': 6},\n",
       "  {'weight': 0.8540864919561113, 'source': 4, 'target': 7},\n",
       "  {'weight': 0.8214267919513534, 'source': 4, 'target': 8},\n",
       "  {'weight': 0.6824646015295808, 'source': 5, 'target': 6},\n",
       "  {'weight': 0.5453013285723745, 'source': 5, 'target': 7},\n",
       "  {'weight': 0.4356888455303136, 'source': 5, 'target': 8},\n",
       "  {'weight': 0.3289244386028924, 'source': 6, 'target': 7},\n",
       "  {'weight': 0.4649224309916062, 'source': 6, 'target': 8},\n",
       "  {'weight': 0.2451832039590595, 'source': 7, 'target': 8}]}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66acc750-a8a7-4c63-90dd-39fae3a9bf7e",
   "metadata": {},
   "source": [
    "First explore how the dictionary is nested. It is a single dictionary for the whole graph object. \n",
    "The first things we see are properties of the graph:\n",
    "\n",
    "1. directed\n",
    "2. multigraph\n",
    "3. graph \n",
    "4. nodes\n",
    "5. links\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "24955c52-ef69-4779-8ca9-de08edb5dde0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['directed', 'multigraph', 'graph', 'nodes', 'links'])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bce53b4-1371-4e0c-95f0-f9eacb16b10c",
   "metadata": {},
   "source": [
    "Directed and multigraph take in a boolean value depending on whether the graph is directed or not, multigraph or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1e3ffe61-037d-424f-9dd1-26d77c65cc92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "print(docs_dict['directed'])\n",
    "print(docs_dict['multigraph'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3401e867-5ba0-493b-9ae2-287711c59f6b",
   "metadata": {},
   "source": [
    "The nodes key in turn contains a list of all the nodes in the network. Every node is itself a dictionary (or a JSON object) containing its own set of information. In practice this includes:\n",
    "\n",
    "1. node id\n",
    "2. Some nodes have \"type\" information.\n",
    "\n",
    "But other arbitrary information could be added, such as the text of the actual legal source.\n",
    "\n",
    "Notice the square brackets showing that we are dealing with a list when we query \"nodes\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a806ce35-b94a-4fff-a87f-fe0fd3c611d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': 0},\n",
       " {'id': 1},\n",
       " {'id': 2},\n",
       " {'id': 3},\n",
       " {'id': 4},\n",
       " {'id': 5},\n",
       " {'id': 6},\n",
       " {'id': 7},\n",
       " {'id': 8}]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_dict['nodes']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ace499-0bca-4597-8ca5-9ecdb7e5282f",
   "metadata": {},
   "source": [
    "Lets query the third node in our list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0b1f3907-f7e1-4460-b5f3-2f7ba76e7275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['id'])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_dict['nodes'][2].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b245c15-40c4-4195-a0e5-76fa3878301f",
   "metadata": {},
   "source": [
    "The same goes for edge data, which will show necessarily the nodes at either side on the edge, as well as the weight, plus any other information one might want to add here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a37cb1f5-c1b3-402b-8379-3a3a7fa40f08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'weight': 0.3057850860342909, 'source': 0, 'target': 1},\n",
       " {'weight': 0.3515643806261561, 'source': 0, 'target': 2},\n",
       " {'weight': 0.4011441744419861, 'source': 0, 'target': 3},\n",
       " {'weight': 0.8588650335933432, 'source': 0, 'target': 4},\n",
       " {'weight': 0.5663867925847853, 'source': 0, 'target': 5},\n",
       " {'weight': 0.3036944274298017, 'source': 0, 'target': 6},\n",
       " {'weight': 0.1427956434168483, 'source': 0, 'target': 7},\n",
       " {'weight': 0.2663586423650602, 'source': 0, 'target': 8},\n",
       " {'weight': 0.1887291756697731, 'source': 1, 'target': 2},\n",
       " {'weight': 0.2301873550390033, 'source': 1, 'target': 3},\n",
       " {'weight': 0.800124882934437, 'source': 1, 'target': 4},\n",
       " {'weight': 0.4122354734424759, 'source': 1, 'target': 5},\n",
       " {'weight': 0.5097321851655097, 'source': 1, 'target': 6},\n",
       " {'weight': 0.3027866619120244, 'source': 1, 'target': 7},\n",
       " {'weight': 0.1868838597034725, 'source': 1, 'target': 8},\n",
       " {'weight': 0.2280158058874547, 'source': 2, 'target': 3},\n",
       " {'weight': 0.7995610591992457, 'source': 2, 'target': 4},\n",
       " {'weight': 0.366590703499365, 'source': 2, 'target': 5},\n",
       " {'weight': 0.5195230803085847, 'source': 2, 'target': 6},\n",
       " {'weight': 0.317271203011655, 'source': 2, 'target': 7},\n",
       " {'weight': 0.1896235506799158, 'source': 2, 'target': 8},\n",
       " {'weight': 0.7678964587257362, 'source': 3, 'target': 4},\n",
       " {'weight': 0.3480240462634174, 'source': 3, 'target': 5},\n",
       " {'weight': 0.5762447937744228, 'source': 3, 'target': 6},\n",
       " {'weight': 0.3808681395940203, 'source': 3, 'target': 7},\n",
       " {'weight': 0.2481169176073587, 'source': 3, 'target': 8},\n",
       " {'weight': 0.6878902741436519, 'source': 4, 'target': 5},\n",
       " {'weight': 0.9008941138337112, 'source': 4, 'target': 6},\n",
       " {'weight': 0.8540864919561113, 'source': 4, 'target': 7},\n",
       " {'weight': 0.8214267919513534, 'source': 4, 'target': 8},\n",
       " {'weight': 0.6824646015295808, 'source': 5, 'target': 6},\n",
       " {'weight': 0.5453013285723745, 'source': 5, 'target': 7},\n",
       " {'weight': 0.4356888455303136, 'source': 5, 'target': 8},\n",
       " {'weight': 0.3289244386028924, 'source': 6, 'target': 7},\n",
       " {'weight': 0.4649224309916062, 'source': 6, 'target': 8},\n",
       " {'weight': 0.2451832039590595, 'source': 7, 'target': 8}]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_dict['links']"
   ]
  }
 ],
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