diff --git a/Experiment100PadiWeb.ipynb b/Experiment100PadiWeb.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:10.843088Z",
+     "start_time": "2018-07-16T13:19:09.478873Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import networkx as nx\n",
+    "import bqplot.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:10.860729Z",
+     "start_time": "2018-07-16T13:19:10.845346Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data_bilan=pd.read_csv(\"is_bilan.csv\",sep=\";\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:10.866942Z",
+     "start_time": "2018-07-16T13:19:10.862464Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data_bilan[\"IS_BILAN\"]=data_bilan[\"IS_BILAN\"].apply(lambda x: \"BILAN\" if x ==1 else \"EPIDEMIE\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyse de la structure des STRs avec un cas d'étude : Bilan/Récapitulatif d'une épidémie\n",
+    "\n",
+    "**La spatialité s'exprime-t-elle de la même manière dans certaines classes ou types de document ?** Dans le domaine de surveillance d'épidémies animales utilisant Google News, les chercheurs ont besoin de différencier un récapitulatif/bilan de la situation concernant une épidémie et la déclaration de celle-ci. Dans cette expérimentation, nous allons essayer de voir si ces deux classes de documents possèdent des caractéristiques spécifiques au travers de la STR.\n",
+    "\n",
+    "\n",
+    "## Définition des deux classes\n",
+    "\n",
+    "À l'aide du corpus de PadiWeb, on selectionne un échantillon de 100 documents que l'on divise en deux classes:\n",
+    "\n",
+    "  * **Bilan**. Un récapitulatif d'un événement terminé ou en cours.\n",
+    "  * **Épidémie**. Son but est d'annoncer le déclenchment d'une épidémie (le point de départ).\n",
+    "\n",
+    "L'effectif de chacune des classes est indiqué ci-dessous."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:10.998510Z",
+     "start_time": "2018-07-16T13:19:10.868556Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x109c1af98>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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CdZB80LTJF36HilukmPQE7nAdIl9U3oDnB6cAR7vOISI5d5LnB99zHSIfSn7axPOD7sB8wqUmRaT4LAGGpJLxT10HySWNvOFKVNwixawGuMF1iFwr6ZG35wd7APOAKtdZRCSv6oChqWT8XddBcqXUR963ouIWKQVlwHWuQ+RSyY68PT84BJjiOoeIdKp9U8n4dNchcqGUR95Xug4gIp1uvOsAuVKSI2/PD0YBb7rOISJOHJ5Kxl90HaKjSnXkPdZ1ABFxpij+6i65kbfnB3sTHmFiXGcREWeGpJLxd1yH6IhSHHlfjopbpNSd7zpAR5XUyNvzg+2AFegiCyKl7lOgJpWMf+46SHuV2sj7JFTcIgLbA6Ndh+iIUivvM1wHEJGCcZ7rAB1RMtMmnh/sCqTQfLeIfGFQKhlf4DpEe5TSyHs0Km4R2Vpkl4tttbyNMb/vhByd4WTXAUSk4BRveRNezDPSPD/oTRF8HyKSc/t7fvAV1yHaozyLfboZY75GC1MO1tqZuY2UF4ehKRMR+bIYcCxwv+sgbZVNedcAN9F8+VnCYix0UcgoIm4cSZGW90JrbdTL73DXAUSkYO3rOkB7FP3RJplDBPdwnUNECtYAzw92ch2irbIp7/9t6QFjTBRKcZTrACJS8CI3+s6mvG81xmx1mJ0xposx5jrg+fzEyqm9XQcQkYIXuUFeNuX9HeAcY8zfjTEDjTHfA+YQXvvxa3lNlxuDXQcQkYIXuZF3q29YWmvfB44yxlwGvAssB75rrZ2X73A5ovIWkdbs5TpAW2VzhmW5MeYK4KeEa+BOB243xgzKd7gciUpOEXFnZ9cB2iqbaZNZhMd6j7TW3metPR64BfizMaagL+bp+UFfYDvXOUSk4G3n+UEP1yHaIpvyPttaO8Zam264w1o7iXC+uz5vyXIjcof/iIgzkRp9Z1Peaxs+McZUNXxurV0PPJePUDm0vesAIhIZRVfejzb6fFqTx+7KYZZ8UHmLSLb6uQ7QFtmUt2nh8+ZuFxqVt4hkq5vrAG2RTXnbFj5v7nahUXmLSLayWeupYGQTtr8x5nbCUXbD52Ru1+QtWW7oYsPyJcfGXps+Ivb+Otc5pLCsttUbIe46RtayKe/LGn0+vcljTW8Xms2uA0hh+U7szVm3V9y5jzHRGmVJp/gD3Os6Q9ay+QEeZK0dm/ck+bHBdQApHLuaFR/fU3HrripuacEW1wHaIps57yPzniJ/1ra+i5SCrmxc/3ylvzZm7I6us0jBitRgL5sRSJkxpictXwZtTW4j5dQnrgNIYZhUOXZWN7Pxm65zSEFb7jpAW2RT3oOBGbR8GbTdc5oot2pdBxD3bii/7+U9YssOdp1DCt5S1wHaIpvyfttaG4WlX5sTqd+kknvHxl6bfnLZSwe6ziGRsMx1gLYo9sugLQF0SFiJ2t0sXXxbxZ0DjSn6n3PpuA0k0pH6Sz2bH+rbsnkhY8wdHcySc6lk3AILXOeQzted9Z//pfKKzTHDDq6zSCREatQNWZS3tfb3Wb7WAR2Lkjcq75Jj7V8r/bldzOaBrpNIZMx3HaCtSuHPycj9T5GOua3izqm7xlZ9w3UOiZQZrgO0lcpbispJZS+9cVxs2kGuc0jklHR5F+oKg2+4DiCdY7D58INfl9832JiC/VmUwlXS5Z3VG5udLZWMv0d41IkUse1Ym3628ipjjFaSlDZbTSL9oesQbZVVeRtjzjLGzDTGrM1s040xZzbepw1vbLowxXUAyR9Dff3fqn65oNJs2c11FomkSP51ns3V488EfgFcQniZoBrgcuCipgVewFTeRezeilum9jNr9nWdQyLrL64DtEc2I+/zge9ba6dYa9PW2k+stS8CJ2Yei4KXXAeQ/DizbPK075TNOMR1Dom0Qr8Wb7OyKe/trbWppndm7ovE/GIqGf8AWOQ6h+TWcPP+e9eUPzjcdQ6JtLeiON8N2ZX3+nY+Vmgecx1Acqeazz95qjJRZQzdXWeRSIvkqBuyW5hqb2PMW83cbyjsFQWbegjwXYeQjotRX/dC1WXvV5i6ka6zSORFtryNtdu+hrAxZsC2HrfWLs5pojzy/GAmENUVEiVjYsX4lw8qm6MlXqWjFgJ7kUgX+oXUm9XqyDtK5ZyFh1F5R9q5ZZNeVXFLjtwX1eKG7A4V/MwY82kz22fGmE87I2QO/QGocx1C2meUmf/OFeWPft11DikKG4EHXIfoiFanTYqN5wfPAMe7ziFtsyPp1a9XXbCx3NTXuM4iReEPJNKnuQ7REaWwMFVTSdcBpG3KqNvyQtVlH6u4JYfudR2go0quvFPJ+OvAy65zSPb+UHndaz3N5/u4ziFF4y0S6ch3QMmVd4ZG3xFxUdlT/9wvNl9LvEouXe06QC6U3Jx3A88PZgEazRWwb8bmznuk4vqBxlDlOosUjRkk0qNch8iFUh15A4x3HUBa1oc1Kx+qGL+jilty7H9dB8iVUi7vJ4BprkPIl1WwZdPfqi5fUWZsX9dZpKj8i0Q6kisINqdkyztzZfmfA6U5b1TAnqxMvF5t1g1znUOKzpWuA+RSyZY3QCoZn07ED9QvNleUP/rKiNgHB7rOIUXnMRLpF12HyKWSLu+MsUDUzhQtSofE/v3WuWWTdNV3ybU04QVlikrJl3cqGV8BXOs6R6mrYdWy+ytu7GcMFa6zSNG5gkR6uesQuVby5Z1xK/Cm6xClqopNGyZX/bK2zNjerrNI0fkXRXA2ZXNU3kAqGd8CjAbWuc5Sip6tvGpGD7NhiOscUnQ2Az8lka53HSQfVN4ZqWR8AXCp6xyl5pryB6YOin18gOscUpSuJJFu7kIyRUHl3UgqGb8bCFznKBXfjb0x68yyv3/TdQ4pSn8BJrgOkU8q7y/7EbDKdYhiN8As//juitsGGJPVpfhE2mIJcFaUL7SQDZV3E5mjT85AF23Im65sXPfXSn9tzNherrNI0akDTiORXu06SL6pvJuRSsYnA5e5zlGsgsqx/+5mNg1ynUOK0jUk0lNdh+gMKu8WpJLxW4D7XecoNr8uv+el3WPLNM8t+fAUcJ3rEJ1F5b1t5wOvuA5RLL4Xe3X6SWVTtTa35MN04Ixin+durGTX886W5we9gTcAz3GUSNvDLFn8QuVlOxhDtessUnQWA/uTSC9zHaQzaeTdilQyvgo4BljjOktUdWf9Z0Hl2C0qbsmDNcCRpVbcoPLOSioZnwccDXzuOkv0WPt85S/f7mI27+E6iRSddcBxJNLvug7igso7S5kLFx8HbHCdJUrurLhj6i6x1f/lOocUnXXAMSTSr7oO4orKuw1SyfgU4Hhgo+ssUXBy2ZQ34rF/6Q1KybWG4p7iOohLesOyHTw/OAp4BnR9xZbsbRa//5fKK75iDNu5ziJFRcWdofJuJ88PvkN4XGkP11kKzXasTc+oOq+20mzxXGeRorIOiJNIv+Q6SCHQtEk7pZLxvwGHAitdZykkhvr6v1ddvkDFLTn2CXCUivsLKu8OyFwD8wDgA9dZCsX/q7hpal9Tu6/rHFJUUsA3S+W092ypvDsolYwvBL4JzHSdxbWzyp6f9u2yWYe4ziFFZTrwDRLpd1wHKTQq7xzIrER4MPA311lcGWEWLkiUTxzuOocUlWeBg0mkV7gOUohU3jmSSsY/JzyR5ybXWTrbDnxW+2TlNV2NobvrLFI07gS+TyKtSxO2QEeb5IHnBycBv6MEjkSJUV/3RtX5s3cyn37ddRYpCuuA80mkH3QdpNBp5J0HqWT8CWA/YL7rLPn2UMX4f6q4JUfmA/+l4s6OyjtPUsn4O8C+wNOus+TLz8qeffWAsnkHu84hReGPwCgS6bmug0SFpk06gecHvwDGA11cZ8mVfc277zxeOW43Y4rnexInNgEXk0jf5TpI1Ki8O4nnB3sDE4FRrrN01E58supfVWM2l5v6nV1nkUh7CzibRHqW6yBRpGmTTpKZRtkfuAbY4jhOu5WzZfMLVZctVXFLB2whvFzZKBV3+2nk7YDnB6MIR+F7u87SVk9WJqaOii3QSoHSXnMJR9szXAeJOo28HcicVv914NfAZsdxsnZx+ROvqLilneqA64GRKu7c0MjbMc8PhhCekHCo6yzb8q3YnDkPVYwfZAyVrrNI5PwTuJBE+t+ugxQTlXeB8PzgFMKzMwtuLrkva1a8WnUhZcb2cZ1FImUpcDmJ9COugxQjTZsUiFQy/kdgEDCBAppKqWDLpr9VXbZKxS1tsIlwSnCQijt/NPIuQJmplCRwrOssz1WOfWVYLHWg6xwSGX8FfkEivcB1kGKn8i5gnh/sT/gmzyEuvv6V5Q9P/Un5X/QGpWTjVeBKEumXXQcpFSrvCMhccu1XdOIJPofFZs6+v2LCEGOo6KyvKZE0A7iKRPp510FKjco7Qjw/OBG4ljwfH97frFo6tfIXFTFje+fz60ikzQP+h0T6GddBSpXesIyQVDL+FDAUOBF4PR9fowsb10+u/GVaxS0tmAGcCgxXcbulkXeEeX5wIHA5EAdMLl5zcuXlrw6KfXxALl5LioYlfCNyAon0FNdhJKTyLgKZRa8uBUZD+0+iua78/pdHl/9DS7xKg43Aw8DNJNJvuw4jW1N5FxHPD/oB5wI/AnZpy3OPjr0+8zcVt40whrK8hJMoWQb8FviNrh9ZuFTeRcjzgxhwJGGRx4Hybe5vln30YuUlPWKGnp2RTwqSBf4O3As8SyId2ZUvS4XKu8h5frAzcA7wY8Br+ng3NqydUfWzJV3Npr06O5sUhA+BB4Dfk0inHGeRNlB5lwjPDwzwbeA04ARge4AplRdP2y22Yn+X2aTTfQL8GXgE+AeJdL3jPNIOKu8S5PlBFRA/v+zP37q84rGfAt1cZ5K8+4ywsB8HJpNIb3KcRzpI5V3qEtXdgKOBkwjnx7u7DSQ5tBZ4DngMeJ5EekNHXswYUwfMaXTXH621SWPMS0A/YAPwOfDf1tr5mfsvtdZON8akCH+BAJQRXpj7WmvtRmOMB7xDePX4BjdbaydmnveRtfb/1tcxxvwbKLfWDjXGHJL5GscYY84GbgSWNHqd06y1RXmkzDbfyJISkEivA54EniRRXQUcRFjmRwOaB4+eucDkzDaVRHpjDl97vbV2nxYeOz1T0ucSFuhxzexzqLV2tTGmB3BfZjsr89j723jt7Ywxu1hrPzLGtHZ28WPW2jGtfSPFQOUtXwj/of89s11MonoPwhI/inBxrK7uwkkLagn/f4WFnUgvaWX/fJsK/GJbO1hrPzfG/Az4yBjTK4vXfBz4IeFyyacCfwDO6GjQqFN5S8sS6feBO4A7SFR3BQ4AvpX5+A2gh8N0paoOeBN4nrCw3ySRruukr901M2XRYLy19rEm+xzL1lMrzbLWfmqMWQTsCawA9mjy2hdaa1/JfP4k8HvC8j4WOJ2Wy/uHxphvNbq9v7V2fWt5okjlLdlJpNcDL2Q2SFSXASMIi7yh1GtcxStiS4FZmW0m8BKJdK2jLNuaNnnEGLMeSAEXZvl6jZd02Na0yRqg1hhzCuHc+LptvKamTUS2KRztzcxsd4T3VQ9g6zIfihY/y5YF3ueLkg4LO5Fe6TRV9k631k7PdmdjzHaE5x0sAKqzeMpjwG+As9sTrhipvCV3EunFwGLg0fB2dVdgIOEbn3sRXuat4fMd3YQsCFuAt2lc0jCbRPpTp6k6SeYNy7uAP1lra40x2ZT3M4RHtEymAK/z6oLKW/InnGqZQ3NzoInqXnxR5I2LfSDFcdx5mvCQtSWEUx9LgEWERT03x0eBdJamc97PW2v9Njx/ijHGEP419gzh2vQNms55/85ae3vDDWvtZ8ANAOFLtKjpnPf51trX2pAxMnSctxSWRLUB+vNFqe8M9AR2aLQ1vt3ZRb+ZcOGmpsW89e1Eem0n55ISo/KWaEtUV7J1sTct955AF6CecF65uY/1hMufrm+0bWhy+zPCYl5JIq1/NOKcyltEJIJ0JICISASpvEVEIkjlLSISQSpvEZEIUnmLiESQyltEJIJU3iIiEaRl1mDCAAAAh0lEQVTyFhGJIJW3iEgEqbxFRCJI5S0iEkEqbxGRCFJ5i4hEkMpbRCSCVN4iIhGk8hYRiSCVt4hIBKm8RUQiSOUtIhJBKm8RkQhSeYuIRJDKW0QkglTeIiIRpPIWEYkglbeISASpvEVEIkjlLSISQSpvEZEIUnmLiESQyltEJIJU3iIiEfT/Ac18soLaKubeAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10214ef60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_bilan.groupby(\"IS_BILAN\").count().plot.pie(\"ID_TEXT\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:11.006615Z",
+     "start_time": "2018-07-16T13:19:11.000324Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "def number_of_edges(x,color=None):\n",
+    "    \"\"\"\n",
+    "    Dedicated function to count edges based on their color\n",
+    "    \"\"\"\n",
+    "    if not color:\n",
+    "        return len(x.number_of_edges())\n",
+    "    edges=list(x.edges(data=True))\n",
+    "    cp=0\n",
+    "    for ed in edges:\n",
+    "        if ed[-1][\"color\"] == color:\n",
+    "            cp+=1\n",
+    "    return cp\n",
+    "\n",
+    "\n",
+    "def flattern(A):\n",
+    "    rt = []\n",
+    "    for i in A:\n",
+    "        if isinstance(i, list):\n",
+    "            rt.extend(flattern(i))\n",
+    "        elif isinstance(i, np.ndarray):\n",
+    "            rt.extend(flattern(i.tolist()))\n",
+    "        else:\n",
+    "            rt.append(i)\n",
+    "    return rt\n",
+    "def most_common(lst):\n",
+    "    if not lst:\n",
+    "        return \"P-PPL\"\n",
+    "    if len(list(set(lst))) >1 and \"P-PPL\" in set(lst):\n",
+    "        lst=[x for x in lst if x != \"PPL\"]\n",
+    "    return max(set(lst), key=lst.count)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:11.103841Z",
+     "start_time": "2018-07-16T13:19:11.008238Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import nxpd\n",
+    "nxpd.nxpdParams[\"show\"]=\"ipynb\"\n",
+    "from strpython.helpers.gazeteer_helpers import get_data\n",
+    "def class_graph(g):\n",
+    "    mapping={}\n",
+    "    g2=g.copy()\n",
+    "    for n in g2:\n",
+    "        c=get_data(n)[\"class\"]\n",
+    "        g2.nodes[n][\"label\"]=most_common(c)\n",
+    "    return g2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pour faire une comparaison entre les STRs générées dans chaque classe de document, on utilise plusieurs indicateurs :\n",
+    "\n",
+    " * **Granularité** La granularité est définie le niveau dans l'échelle spatiale ($village$ < $ville < pays$) d'une entité. Ici, elle nous indique à quel niveau la spatialité est utilisé pour décrire la situation.\n",
+    " * **Densité** La densité est définie par le nombre d'arêtes moyen pour un noeud dans un graphe. Un graphe d'une STR avec une forte densité, indique une forte cohésion entre les entités spatiales.\n",
+    " * **Ratio $Relation_i/Relation_j$** Dans la STR, chaque entité peut-être reliée à une autre par deux type de relations : inclusion et adjacence. Avec ce ratio, on souhaite savoir combien il existe de $relation_j$ pour une $relation_i$. Par exemple, pour une relation d'inclusion, combien de relations d'adjacence ?\n",
+    " * **Nombre de noeuds(entités spatiales)** Indique si des textes sont fortement spatialisés.\n",
+    "\n",
+    "\n",
+    "### Calcul de la granularité d'une STR\n",
+    "\n",
+    "On récupére les **classes associées** aux différentes **entités de la STR**, puis on récupére **la classe la plus fréquente**. Par exemple:\n",
+    "\n",
+    "$STR_1$ --> France, Montpellier, Clapiers, Caen --> [A-PCLI], [P-PPL, A-ADM4], [A-ADM4], [A-ADM4]\n",
+    "\n",
+    "On a donc pour granularité : **A-ADM4**\n",
+    "\n",
+    "### Calcul de la densité d'une STR\n",
+    "Le calcul de la densité d'une STR (ici son graphe) se calcule à l'aide de la formule suivante : $$\\frac{2\\times|E|}{|V|\\times(|V|-1)}$$\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:12.360291Z",
+     "start_time": "2018-07-16T13:19:11.105857Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data_bilan[\"GRAPH\"]=data_bilan[\"ID_TEXT\"].apply(lambda x:nx.read_gexf(\"str_PADI100/{0}.gexf\".format(x)))\n",
+    "data_bilan[\"GRAPH_C\"]=data_bilan[\"GRAPH\"].apply(lambda x:class_graph(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:12.378025Z",
+     "start_time": "2018-07-16T13:19:12.362254Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data_bilan[\"DENSITY\"]=data_bilan[\"GRAPH\"].apply(lambda x: (2*x.number_of_edges())/(x.number_of_nodes()*(x.number_of_nodes()-1)) if len(x) >1 else 0)\n",
+    "data_bilan[\"NB_NODE\"]=data_bilan[\"GRAPH\"].apply(lambda x: len(x))\n",
+    "data_bilan[\"NB_ED_ADJ\"]=data_bilan[\"GRAPH\"].apply(lambda x: number_of_edges(x,color=\"green\"))\n",
+    "data_bilan[\"NB_ED_INC\"]=data_bilan[\"GRAPH\"].apply(lambda x: number_of_edges(x,color=\"red\"))\n",
+    "data_bilan[\"R_ADJ_INC\"]=((data_bilan[\"NB_ED_ADJ\"]/2)/data_bilan[\"NB_ED_INC\"]).replace([np.inf, -np.inf], np.nan).fillna(0)\n",
+    "data_bilan[\"R_INC_ADJ\"]=(data_bilan[\"NB_ED_INC\"]/(data_bilan[\"NB_ED_ADJ\"]/2)).replace([np.inf, -np.inf], np.nan).fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:13.124807Z",
+     "start_time": "2018-07-16T13:19:12.380975Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data_bilan[\"CLASS\"]=data_bilan[\"GRAPH\"].apply(lambda x: flattern([get_data(n)[\"class\"] for n in list(x.nodes())]))\n",
+    "data_bilan[\"MEAN_LVL\"]=data_bilan[\"CLASS\"].apply(lambda x: most_common(x) if len(x)>0 else \"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:13.155512Z",
+     "start_time": "2018-07-16T13:19:13.127069Z"
+    },
+    "scrolled": true
+   },
+   "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>ID_TEXT</th>\n",
+       "      <th>IS_BILAN</th>\n",
+       "      <th>MIXED</th>\n",
+       "      <th>GRAPH</th>\n",
+       "      <th>GRAPH_C</th>\n",
+       "      <th>DENSITY</th>\n",
+       "      <th>NB_NODE</th>\n",
+       "      <th>NB_ED_ADJ</th>\n",
+       "      <th>NB_ED_INC</th>\n",
+       "      <th>R_ADJ_INC</th>\n",
+       "      <th>R_INC_ADJ</th>\n",
+       "      <th>CLASS</th>\n",
+       "      <th>MEAN_LVL</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>BILAN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(GD4103071, GD4468122, GD95073, GD791183)</td>\n",
+       "      <td>(GD4103071, GD4468122, GD95073, GD791183)</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[P-PPLA, P-PPL, P-PPLA, A-ADM1, P-PPLA]</td>\n",
+       "      <td>P-PPLA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>EPIDEMIE</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(GD1685421)</td>\n",
+       "      <td>(GD1685421)</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[P-PPL]</td>\n",
+       "      <td>P-PPL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>BILAN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>(GD2032795)</td>\n",
+       "      <td>(GD2032795)</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[A-PCLI]</td>\n",
+       "      <td>A-PCLI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>BILAN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(GD1626932, GD3274230)</td>\n",
+       "      <td>(GD1626932, GD3274230)</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[A-PCLI, P-PPL]</td>\n",
+       "      <td>P-PPL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>EPIDEMIE</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(GD639917, GD3789919, GD1316637, GD2055944)</td>\n",
+       "      <td>(GD639917, GD3789919, GD1316637, GD2055944)</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[A-PCLI, A-ADM1, P-PPLA, P-PPLC, A-PCLI, A-PCLI]</td>\n",
+       "      <td>A-PCLI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>EPIDEMIE</td>\n",
+       "      <td>1</td>\n",
+       "      <td>(GD639917, GD3995806, GD3789919, GD1316637, GD...</td>\n",
+       "      <td>(GD639917, GD3995806, GD3789919, GD1316637, GD...</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[A-PCLI, P-PPL, A-ADM1, P-PPLA, P-PPLC, A-PCLI...</td>\n",
+       "      <td>A-PCLI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>EPIDEMIE</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(GD639917, GD3789919, GD2055944)</td>\n",
+       "      <td>(GD639917, GD3789919, GD2055944)</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[A-PCLI, A-ADM1, P-PPLA, P-PPLC, A-PCLI]</td>\n",
+       "      <td>A-PCLI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>BILAN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(GD5526704, GD976842, GD1316637, GD2055944)</td>\n",
+       "      <td>(GD5526704, GD976842, GD1316637, GD2055944)</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[A-PCLI, A-PCLI, A-PCLI, A-PCLI]</td>\n",
+       "      <td>A-PCLI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>BILAN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(GD2908705, GD1404948, GD9642903, GD3995806, G...</td>\n",
+       "      <td>(GD2908705, GD1404948, GD9642903, GD3995806, G...</td>\n",
+       "      <td>0.285714</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[A-ADM1, P-PPL, P-PPL, P-PPL, A-ADM1, A-ADM1, ...</td>\n",
+       "      <td>A-ADM1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ID_TEXT  IS_BILAN  MIXED  \\\n",
+       "0        0     BILAN      0   \n",
+       "1        1  EPIDEMIE      0   \n",
+       "2        2     BILAN      1   \n",
+       "3        3     BILAN      0   \n",
+       "4        4  EPIDEMIE      0   \n",
+       "5        5  EPIDEMIE      1   \n",
+       "6        6  EPIDEMIE      0   \n",
+       "7        7     BILAN      0   \n",
+       "8        8     BILAN      0   \n",
+       "\n",
+       "                                               GRAPH  \\\n",
+       "0          (GD4103071, GD4468122, GD95073, GD791183)   \n",
+       "1                                        (GD1685421)   \n",
+       "2                                        (GD2032795)   \n",
+       "3                             (GD1626932, GD3274230)   \n",
+       "4        (GD639917, GD3789919, GD1316637, GD2055944)   \n",
+       "5  (GD639917, GD3995806, GD3789919, GD1316637, GD...   \n",
+       "6                   (GD639917, GD3789919, GD2055944)   \n",
+       "7        (GD5526704, GD976842, GD1316637, GD2055944)   \n",
+       "8  (GD2908705, GD1404948, GD9642903, GD3995806, G...   \n",
+       "\n",
+       "                                             GRAPH_C   DENSITY  NB_NODE  \\\n",
+       "0          (GD4103071, GD4468122, GD95073, GD791183)  0.000000        4   \n",
+       "1                                        (GD1685421)  0.000000        1   \n",
+       "2                                        (GD2032795)  0.000000        1   \n",
+       "3                             (GD1626932, GD3274230)  0.000000        2   \n",
+       "4        (GD639917, GD3789919, GD1316637, GD2055944)  0.166667        4   \n",
+       "5  (GD639917, GD3995806, GD3789919, GD1316637, GD...  0.200000        5   \n",
+       "6                   (GD639917, GD3789919, GD2055944)  0.000000        3   \n",
+       "7        (GD5526704, GD976842, GD1316637, GD2055944)  0.333333        4   \n",
+       "8  (GD2908705, GD1404948, GD9642903, GD3995806, G...  0.285714        7   \n",
+       "\n",
+       "   NB_ED_ADJ  NB_ED_INC  R_ADJ_INC  R_INC_ADJ  \\\n",
+       "0          0          0        0.0        0.0   \n",
+       "1          0          0        0.0        0.0   \n",
+       "2          0          0        0.0        0.0   \n",
+       "3          0          0        0.0        0.0   \n",
+       "4          0          1        0.0        0.0   \n",
+       "5          0          2        0.0        0.0   \n",
+       "6          0          0        0.0        0.0   \n",
+       "7          2          0        0.0        0.0   \n",
+       "8          4          2        1.0        1.0   \n",
+       "\n",
+       "                                               CLASS MEAN_LVL  \n",
+       "0            [P-PPLA, P-PPL, P-PPLA, A-ADM1, P-PPLA]   P-PPLA  \n",
+       "1                                            [P-PPL]    P-PPL  \n",
+       "2                                           [A-PCLI]   A-PCLI  \n",
+       "3                                    [A-PCLI, P-PPL]    P-PPL  \n",
+       "4   [A-PCLI, A-ADM1, P-PPLA, P-PPLC, A-PCLI, A-PCLI]   A-PCLI  \n",
+       "5  [A-PCLI, P-PPL, A-ADM1, P-PPLA, P-PPLC, A-PCLI...   A-PCLI  \n",
+       "6           [A-PCLI, A-ADM1, P-PPLA, P-PPLC, A-PCLI]   A-PCLI  \n",
+       "7                   [A-PCLI, A-PCLI, A-PCLI, A-PCLI]   A-PCLI  \n",
+       "8  [A-ADM1, P-PPL, P-PPL, P-PPL, A-ADM1, A-ADM1, ...   A-ADM1  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_bilan.head(9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Résultats\n",
+    "\n",
+    "### Granularité sur les documents de classe **BILAN**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:13.367470Z",
+     "start_time": "2018-07-16T13:19:13.158508Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x10a1db828>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x104a11ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_bilan[data_bilan[\"IS_BILAN\"] == \"BILAN\"].groupby(\"MEAN_LVL\").count().plot.pie(\"ID_TEXT\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Granularité sur les documents de classe **EPIDEMIE**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:13.528700Z",
+     "start_time": "2018-07-16T13:19:13.369629Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x10a2c9ba8>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10a2d4fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_bilan[data_bilan[\"IS_BILAN\"] == \"EPIDEMIE\"].groupby(\"MEAN_LVL\").count().plot.pie(\"ID_TEXT\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Valeurs moyennes obtenues pour chaque indicateur"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:13.545917Z",
+     "start_time": "2018-07-16T13:19:13.530312Z"
+    }
+   },
+   "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>ID_TEXT</th>\n",
+       "      <th>MIXED</th>\n",
+       "      <th>DENSITY</th>\n",
+       "      <th>NB_NODE</th>\n",
+       "      <th>NB_ED_ADJ</th>\n",
+       "      <th>NB_ED_INC</th>\n",
+       "      <th>R_ADJ_INC</th>\n",
+       "      <th>R_INC_ADJ</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IS_BILAN</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>BILAN</th>\n",
+       "      <td>51.588235</td>\n",
+       "      <td>0.014706</td>\n",
+       "      <td>0.293545</td>\n",
+       "      <td>6.455882</td>\n",
+       "      <td>4.117647</td>\n",
+       "      <td>2.705882</td>\n",
+       "      <td>0.559194</td>\n",
+       "      <td>1.049048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EPIDEMIE</th>\n",
+       "      <td>46.727273</td>\n",
+       "      <td>0.030303</td>\n",
+       "      <td>0.379501</td>\n",
+       "      <td>4.636364</td>\n",
+       "      <td>2.969697</td>\n",
+       "      <td>1.303030</td>\n",
+       "      <td>0.398990</td>\n",
+       "      <td>0.240657</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            ID_TEXT     MIXED   DENSITY   NB_NODE  NB_ED_ADJ  NB_ED_INC  \\\n",
+       "IS_BILAN                                                                  \n",
+       "BILAN     51.588235  0.014706  0.293545  6.455882   4.117647   2.705882   \n",
+       "EPIDEMIE  46.727273  0.030303  0.379501  4.636364   2.969697   1.303030   \n",
+       "\n",
+       "          R_ADJ_INC  R_INC_ADJ  \n",
+       "IS_BILAN                        \n",
+       "BILAN      0.559194   1.049048  \n",
+       "EPIDEMIE   0.398990   0.240657  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_bilan.groupby(\"IS_BILAN\").mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analyse des résultats\n",
+    "\n",
+    "\n",
+    "### Granularité\n",
+    "\n",
+    "En regardant les deux camemberts ci-dessus, on remarque que la granularité observé dans les STRs différe selon le type de texte. Les textes de classe **épidémie** sont généralement plus \"haut\" dans la hiérarchie spatiale, de part la forte présence de classe telles que: A-PCLI ($\\approx$Pays), A-ADM1(premier découpage administratif d'un pays *equiv* région en France, état aux Etats-Unis, *etc.*). Ceux de la classe **BILAN**, ont une granularité un peu plus fine avec un spectre de classe plus étendue : T-ISL (ile), S-BLDG (batiment).\n",
+    "\n",
+    "En se basant sur la classification proposé, on conclue que les documents de type **bilan** sont plus \"fin\" spatialement que ceux de la classe **épidémie**. \n",
+    "\n",
+    "### Densité/ Nombre de noeuds/ Nombre d'arrêtes\n",
+    "\n",
+    "Malheuresement la densité moyenne ne permet de faire aucune conclusion.\n",
+    "\n",
+    "On observe que le nombre de noeuds dans les documents de classes Bilan est plus élevé. Ce qui indique que le nombre d'entités spatiales dans ces documents est plus élevés. Ce qui semble tout à fait normal car contrairement à une déclaration d'épidémie, le bilan fait un récapitulatif de la propagation d'une maladie sur un laps de temps et une spatialité (souvent) plus importante.\n",
+    "\n",
+    "Pour le nombre de relations d'ajacence et d'inclusion, on observe un même rapport de \"force\" : Il y a plus d'arêtes d'inclusion que d'arêtes d'adjacence.\n",
+    "\n",
+    "### Ratio Adjacence/Inclusion VS Inclusion/Adjacence\n",
+    "\n",
+    "| CLASSE   | ADJ/INC  | INC/ADJ  |\n",
+    "|----------|----------|----------|\n",
+    "| BILAN    | 0.559194 | 1.04905  |\n",
+    "| EPIDEMIE | 0.39899  | 0.240657 |\n",
+    "\n",
+    "On reprend les résultats concernat les rapports ADJ/INC (combien de relations d'inclusion pour une relation d'adjacence ?) et INC/ADJ (le contraire de ADJ/INC). A partir de ces résultats, on observe que les rapports sont inversés ! Pour les documents de classe EPIDEMIE, on va favoriser plus les relations d'inclusion, contrairement aux documents de classe BILAN qui favorisent les relations d'adjacences.\n",
+    "\n",
+    "Est-ce que parce que les relations d'inclusions sont favorisés (ratio ADJ/INC élevé), on se retrouve sur des zones limitées, donc plus local ? Ca rentre bien dans le cadre de la classe épidémie.\n",
+    "\n",
+    "Est-ce qu'un ratio élevé INC/ADJ traduit une information concernant la dispertion d'une maladie ? \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:32:14.236322Z",
+     "start_time": "2018-07-16T13:32:14.233834Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
+    "import ipywidgets as widgets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:13.645466Z",
+     "start_time": "2018-07-16T13:19:13.554626Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "479f1072f38a4a5991a18cb3f381a035",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='x'), Output()), _dom_classes=('widget-interact',))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from nxpd import draw\n",
+    "\n",
+    "def f(x):\n",
+    "    global data_bilan\n",
+    "    return draw(data_bilan[data_bilan[\"IS_BILAN\"]==\"BILAN\"].iloc[x][\"GRAPH_C\"],show=\"ipynb\")\n",
+    "interact(f, x=widgets.IntSlider(min=0,max=100,step=1));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:19:33.768901Z",
+     "start_time": "2018-07-16T13:19:33.762369Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dd=data_bilan.groupby(\"IS_BILAN\").mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-07-16T13:27:13.438513Z",
+     "start_time": "2018-07-16T13:27:13.433856Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|:---------|---------:|---------:|\n",
+      "| BILAN    | 0.559194 | 1.04905  |\n",
+      "| EPIDEMIE | 0.39899  | 0.240657 |\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tabulate import tabulate\n",
+    "print(tabulate(dd[[\"R_ADJ_INC\",  \"R_INC_ADJ\"]],tablefmt=\"pipe\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": "block",
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/dispersion_bilan_outbreak.pdf b/dispersion_bilan_outbreak.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..fdeef7f62c8be67c400c1367f92aac08a3dad137
Binary files /dev/null and b/dispersion_bilan_outbreak.pdf differ
diff --git a/dispersion_bilan_outbreak.png b/dispersion_bilan_outbreak.png
new file mode 100644
index 0000000000000000000000000000000000000000..0f44713661474d643fc521d9a40422baa286c723
Binary files /dev/null and b/dispersion_bilan_outbreak.png differ
diff --git a/is_bilan.csv b/is_bilan.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b7ac7f6cbffcca8ddd3137eae4d771f322aa7656
--- /dev/null
+++ b/is_bilan.csv
@@ -0,0 +1,102 @@
+ID_TEXT;IS_BILAN;MIXED
+0;1;0
+1;0;0
+2;1;1
+3;1;0
+4;0;0
+5;0;1
+6;0;0
+7;1;0
+8;1;0
+9;0;0
+10;0;0
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+13;1;0
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+15;1;0
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+17;0;0
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+19;1;0
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+21;1;0
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+24;1;0
+25;0;0
+26;0;0
+27;1;0
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+39;1;0
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+42;0;0
+43;1;0
+44;1;0
+45;1;0
+46;1;0
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+48;1;0
+49;0;0
+50;1;0
+51;1;0
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+95;1;0
+96;0;0
+97;0;0
+98;1;0
+99;0;0
+100;0;0
\ No newline at end of file
diff --git a/is_bilan.xlsx b/is_bilan.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..41ef835a35c62e86af66119458c1eec12d4281ac
Binary files /dev/null and b/is_bilan.xlsx differ
diff --git a/notebook.tex b/notebook.tex
new file mode 100644
index 0000000000000000000000000000000000000000..c430aa1ccf82bd8b66cb38e6ff2fd2cf7490bde4
--- /dev/null
+++ b/notebook.tex
@@ -0,0 +1,682 @@
+
+% Default to the notebook output style
+
+    
+
+
+% Inherit from the specified cell style.
+
+
+
+
+    
+\documentclass[11pt]{article}
+
+    
+    
+    \usepackage[T1]{fontenc}
+    % Nicer default font (+ math font) than Computer Modern for most use cases
+    \usepackage{mathpazo}
+
+    % Basic figure setup, for now with no caption control since it's done
+    % automatically by Pandoc (which extracts ![](path) syntax from Markdown).
+    \usepackage{graphicx}
+    % We will generate all images so they have a width \maxwidth. This means
+    % that they will get their normal width if they fit onto the page, but
+    % are scaled down if they would overflow the margins.
+    \makeatletter
+    \def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
+    \else\Gin@nat@width\fi}
+    \makeatother
+    \let\Oldincludegraphics\includegraphics
+    % Set max figure width to be 80% of text width, for now hardcoded.
+    \renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}}
+    % Ensure that by default, figures have no caption (until we provide a
+    % proper Figure object with a Caption API and a way to capture that
+    % in the conversion process - todo).
+    \usepackage{caption}
+    \DeclareCaptionLabelFormat{nolabel}{}
+    \captionsetup{labelformat=nolabel}
+
+    \usepackage{adjustbox} % Used to constrain images to a maximum size 
+    \usepackage{xcolor} % Allow colors to be defined
+    \usepackage{enumerate} % Needed for markdown enumerations to work
+    \usepackage{geometry} % Used to adjust the document margins
+    \usepackage{amsmath} % Equations
+    \usepackage{amssymb} % Equations
+    \usepackage{textcomp} % defines textquotesingle
+    % Hack from http://tex.stackexchange.com/a/47451/13684:
+    \AtBeginDocument{%
+        \def\PYZsq{\textquotesingle}% Upright quotes in Pygmentized code
+    }
+    \usepackage{upquote} % Upright quotes for verbatim code
+    \usepackage{eurosym} % defines \euro
+    \usepackage[mathletters]{ucs} % Extended unicode (utf-8) support
+    \usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document
+    \usepackage{fancyvrb} % verbatim replacement that allows latex
+    \usepackage{grffile} % extends the file name processing of package graphics 
+                         % to support a larger range 
+    % The hyperref package gives us a pdf with properly built
+    % internal navigation ('pdf bookmarks' for the table of contents,
+    % internal cross-reference links, web links for URLs, etc.)
+    \usepackage{hyperref}
+    \usepackage{longtable} % longtable support required by pandoc >1.10
+    \usepackage{booktabs}  % table support for pandoc > 1.12.2
+    \usepackage[inline]{enumitem} % IRkernel/repr support (it uses the enumerate* environment)
+    \usepackage[normalem]{ulem} % ulem is needed to support strikethroughs (\sout)
+                                % normalem makes italics be italics, not underlines
+    
+
+    
+    
+    % Colors for the hyperref package
+    \definecolor{urlcolor}{rgb}{0,.145,.698}
+    \definecolor{linkcolor}{rgb}{.71,0.21,0.01}
+    \definecolor{citecolor}{rgb}{.12,.54,.11}
+
+    % ANSI colors
+    \definecolor{ansi-black}{HTML}{3E424D}
+    \definecolor{ansi-black-intense}{HTML}{282C36}
+    \definecolor{ansi-red}{HTML}{E75C58}
+    \definecolor{ansi-red-intense}{HTML}{B22B31}
+    \definecolor{ansi-green}{HTML}{00A250}
+    \definecolor{ansi-green-intense}{HTML}{007427}
+    \definecolor{ansi-yellow}{HTML}{DDB62B}
+    \definecolor{ansi-yellow-intense}{HTML}{B27D12}
+    \definecolor{ansi-blue}{HTML}{208FFB}
+    \definecolor{ansi-blue-intense}{HTML}{0065CA}
+    \definecolor{ansi-magenta}{HTML}{D160C4}
+    \definecolor{ansi-magenta-intense}{HTML}{A03196}
+    \definecolor{ansi-cyan}{HTML}{60C6C8}
+    \definecolor{ansi-cyan-intense}{HTML}{258F8F}
+    \definecolor{ansi-white}{HTML}{C5C1B4}
+    \definecolor{ansi-white-intense}{HTML}{A1A6B2}
+
+    % commands and environments needed by pandoc snippets
+    % extracted from the output of `pandoc -s`
+    \providecommand{\tightlist}{%
+      \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}
+    \DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
+    % Add ',fontsize=\small' for more characters per line
+    \newenvironment{Shaded}{}{}
+    \newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
+    \newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}}
+    \newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+    \newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}}
+    \newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}}
+    \newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
+    \newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}}
+    \newcommand{\RegionMarkerTok}[1]{{#1}}
+    \newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
+    \newcommand{\NormalTok}[1]{{#1}}
+    
+    % Additional commands for more recent versions of Pandoc
+    \newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.53,0.00,0.00}{{#1}}}
+    \newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+    \newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.73,0.40,0.53}{{#1}}}
+    \newcommand{\ImportTok}[1]{{#1}}
+    \newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.73,0.13,0.13}{\textit{{#1}}}}
+    \newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    \newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    \newcommand{\VariableTok}[1]{\textcolor[rgb]{0.10,0.09,0.49}{{#1}}}
+    \newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
+    \newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.40,0.40,0.40}{{#1}}}
+    \newcommand{\BuiltInTok}[1]{{#1}}
+    \newcommand{\ExtensionTok}[1]{{#1}}
+    \newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.74,0.48,0.00}{{#1}}}
+    \newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.49,0.56,0.16}{{#1}}}
+    \newcommand{\InformationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    \newcommand{\WarningTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
+    
+    
+    % Define a nice break command that doesn't care if a line doesn't already
+    % exist.
+    \def\br{\hspace*{\fill} \\* }
+    % Math Jax compatability definitions
+    \def\gt{>}
+    \def\lt{<}
+    % Document parameters
+    \title{Experiment100PadiWeb}
+    
+    
+    
+
+    % Pygments definitions
+    
+\makeatletter
+\def\PY@reset{\let\PY@it=\relax \let\PY@bf=\relax%
+    \let\PY@ul=\relax \let\PY@tc=\relax%
+    \let\PY@bc=\relax \let\PY@ff=\relax}
+\def\PY@tok#1{\csname PY@tok@#1\endcsname}
+\def\PY@toks#1+{\ifx\relax#1\empty\else%
+    \PY@tok{#1}\expandafter\PY@toks\fi}
+\def\PY@do#1{\PY@bc{\PY@tc{\PY@ul{%
+    \PY@it{\PY@bf{\PY@ff{#1}}}}}}}
+\def\PY#1#2{\PY@reset\PY@toks#1+\relax+\PY@do{#2}}
+
+\expandafter\def\csname PY@tok@w\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.73,0.73}{##1}}}
+\expandafter\def\csname PY@tok@c\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cp\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.74,0.48,0.00}{##1}}}
+\expandafter\def\csname PY@tok@k\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kp\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kt\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.69,0.00,0.25}{##1}}}
+\expandafter\def\csname PY@tok@o\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@ow\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.67,0.13,1.00}{##1}}}
+\expandafter\def\csname PY@tok@nb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@nf\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
+\expandafter\def\csname PY@tok@nc\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
+\expandafter\def\csname PY@tok@nn\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
+\expandafter\def\csname PY@tok@ne\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.82,0.25,0.23}{##1}}}
+\expandafter\def\csname PY@tok@nv\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@no\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.53,0.00,0.00}{##1}}}
+\expandafter\def\csname PY@tok@nl\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.63,0.63,0.00}{##1}}}
+\expandafter\def\csname PY@tok@ni\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.60,0.60,0.60}{##1}}}
+\expandafter\def\csname PY@tok@na\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.49,0.56,0.16}{##1}}}
+\expandafter\def\csname PY@tok@nt\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@nd\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.67,0.13,1.00}{##1}}}
+\expandafter\def\csname PY@tok@s\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sd\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@si\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.53}{##1}}}
+\expandafter\def\csname PY@tok@se\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sr\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.53}{##1}}}
+\expandafter\def\csname PY@tok@ss\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@sx\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@m\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@gh\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,0.50}{##1}}}
+\expandafter\def\csname PY@tok@gu\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.50,0.00,0.50}{##1}}}
+\expandafter\def\csname PY@tok@gd\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.63,0.00,0.00}{##1}}}
+\expandafter\def\csname PY@tok@gi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.63,0.00}{##1}}}
+\expandafter\def\csname PY@tok@gr\endcsname{\def\PY@tc##1{\textcolor[rgb]{1.00,0.00,0.00}{##1}}}
+\expandafter\def\csname PY@tok@ge\endcsname{\let\PY@it=\textit}
+\expandafter\def\csname PY@tok@gs\endcsname{\let\PY@bf=\textbf}
+\expandafter\def\csname PY@tok@gp\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,0.50}{##1}}}
+\expandafter\def\csname PY@tok@go\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.53,0.53,0.53}{##1}}}
+\expandafter\def\csname PY@tok@gt\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.27,0.87}{##1}}}
+\expandafter\def\csname PY@tok@err\endcsname{\def\PY@bc##1{\setlength{\fboxsep}{0pt}\fcolorbox[rgb]{1.00,0.00,0.00}{1,1,1}{\strut ##1}}}
+\expandafter\def\csname PY@tok@kc\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kd\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kn\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@kr\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@bp\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
+\expandafter\def\csname PY@tok@fm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
+\expandafter\def\csname PY@tok@vc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vg\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@vm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
+\expandafter\def\csname PY@tok@sa\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@dl\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@s2\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@sh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@s1\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
+\expandafter\def\csname PY@tok@mb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mf\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@il\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@mo\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
+\expandafter\def\csname PY@tok@ch\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cm\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cpf\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@c1\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+\expandafter\def\csname PY@tok@cs\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
+
+\def\PYZbs{\char`\\}
+\def\PYZus{\char`\_}
+\def\PYZob{\char`\{}
+\def\PYZcb{\char`\}}
+\def\PYZca{\char`\^}
+\def\PYZam{\char`\&}
+\def\PYZlt{\char`\<}
+\def\PYZgt{\char`\>}
+\def\PYZsh{\char`\#}
+\def\PYZpc{\char`\%}
+\def\PYZdl{\char`\$}
+\def\PYZhy{\char`\-}
+\def\PYZsq{\char`\'}
+\def\PYZdq{\char`\"}
+\def\PYZti{\char`\~}
+% for compatibility with earlier versions
+\def\PYZat{@}
+\def\PYZlb{[}
+\def\PYZrb{]}
+\makeatother
+
+
+    % Exact colors from NB
+    \definecolor{incolor}{rgb}{0.0, 0.0, 0.5}
+    \definecolor{outcolor}{rgb}{0.545, 0.0, 0.0}
+
+
+
+    
+    % Prevent overflowing lines due to hard-to-break entities
+    \sloppy 
+    % Setup hyperref package
+    \hypersetup{
+      breaklinks=true,  % so long urls are correctly broken across lines
+      colorlinks=true,
+      urlcolor=urlcolor,
+      linkcolor=linkcolor,
+      citecolor=citecolor,
+      }
+    % Slightly bigger margins than the latex defaults
+    
+    \geometry{verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in}
+    
+    
+
+    \begin{document}
+    
+    
+    \maketitle
+    
+    
+
+    
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}1}]:} \PY{k+kn}{import} \PY{n+nn}{pandas} \PY{k}{as} \PY{n+nn}{pd}
+        \PY{k+kn}{import} \PY{n+nn}{networkx} \PY{k}{as} \PY{n+nn}{nx}
+        \PY{k+kn}{import} \PY{n+nn}{bqplot}\PY{n+nn}{.}\PY{n+nn}{pyplot} \PY{k}{as} \PY{n+nn}{plt}
+        \PY{o}{\PYZpc{}}\PY{k}{matplotlib} inline
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}2}]:} \PY{n}{data\PYZus{}bilan}\PY{o}{=}\PY{n}{pd}\PY{o}{.}\PY{n}{read\PYZus{}csv}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{is\PYZus{}bilan.csv}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{n}{sep}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{;}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}3}]:} \PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{IS\PYZus{}BILAN}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{=}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{IS\PYZus{}BILAN}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{k}{lambda} \PY{n}{x}\PY{p}{:} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{BILAN}\PY{l+s+s2}{\PYZdq{}} \PY{k}{if} \PY{n}{x} \PY{o}{==}\PY{l+m+mi}{1} \PY{k}{else} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{EPIDEMIE}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
+\end{Verbatim}
+
+
+    \section{Analyse de la structure des STRs avec un cas d'étude :
+Bilan/Récapitulatif d'une
+épidémie}\label{analyse-de-la-structure-des-strs-avec-un-cas-duxe9tude-bilanruxe9capitulatif-dune-uxe9piduxe9mie}
+
+\textbf{La spatialité s'exprime-t-elle de la même manière dans certaines
+classes ou types de document ?} Dans la surveillance d'épidémies
+animales utilisant Google News, les chercheurs ont besoin de
+différencier un récapitulatif/bilan de la situation concernant une
+épidémie et la déclaration de celle-ci. Dans cette expérimentation, nous
+allons essayer de voir si ces deux classes de documents possèdent des
+caractéristiques spécifiques au travers de la STR.
+
+\subsection{Définition des deux
+classes}\label{duxe9finition-des-deux-classes}
+
+A l'aide du corpus de PadiWeb, on selectionne un échantillon de 100
+documents que l'on divise en deux classes:
+
+\begin{itemize}
+\tightlist
+\item
+  \textbf{Bilan}. Un récapitulatif d'un événement terminé ou en cours.
+\item
+  \textbf{Épidémie}. Son but est d'annoncer le déclenchment d'une
+  épidémie (le point de départ).
+\end{itemize}
+
+L'effectif de chacune des classes est indiqué ci-dessous.
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}4}]:} \PY{n}{data\PYZus{}bilan}\PY{o}{.}\PY{n}{groupby}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{IS\PYZus{}BILAN}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{o}{.}\PY{n}{count}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{plot}\PY{o}{.}\PY{n}{pie}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{ID\PYZus{}TEXT}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
+\end{Verbatim}
+
+
+\begin{Verbatim}[commandchars=\\\{\}]
+{\color{outcolor}Out[{\color{outcolor}4}]:} <matplotlib.axes.\_subplots.AxesSubplot at 0x109c1af98>
+\end{Verbatim}
+            
+    \begin{center}
+    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_4_1.png}
+    \end{center}
+    { \hspace*{\fill} \\}
+    
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}5}]:} \PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
+        \PY{k}{def} \PY{n+nf}{number\PYZus{}of\PYZus{}edges}\PY{p}{(}\PY{n}{x}\PY{p}{,}\PY{n}{color}\PY{o}{=}\PY{k+kc}{None}\PY{p}{)}\PY{p}{:}
+            \PY{l+s+sd}{\PYZdq{}\PYZdq{}\PYZdq{}}
+        \PY{l+s+sd}{    Dedicated function to count edges based on their color}
+        \PY{l+s+sd}{    \PYZdq{}\PYZdq{}\PYZdq{}}
+            \PY{k}{if} \PY{o+ow}{not} \PY{n}{color}\PY{p}{:}
+                \PY{k}{return} \PY{n+nb}{len}\PY{p}{(}\PY{n}{x}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}edges}\PY{p}{(}\PY{p}{)}\PY{p}{)}
+            \PY{n}{edges}\PY{o}{=}\PY{n+nb}{list}\PY{p}{(}\PY{n}{x}\PY{o}{.}\PY{n}{edges}\PY{p}{(}\PY{n}{data}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}\PY{p}{)}
+            \PY{n}{cp}\PY{o}{=}\PY{l+m+mi}{0}
+            \PY{k}{for} \PY{n}{ed} \PY{o+ow}{in} \PY{n}{edges}\PY{p}{:}
+                \PY{k}{if} \PY{n}{ed}\PY{p}{[}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{color}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{o}{==} \PY{n}{color}\PY{p}{:}
+                    \PY{n}{cp}\PY{o}{+}\PY{o}{=}\PY{l+m+mi}{1}
+            \PY{k}{return} \PY{n}{cp}
+        
+        
+        \PY{k}{def} \PY{n+nf}{flattern}\PY{p}{(}\PY{n}{A}\PY{p}{)}\PY{p}{:}
+            \PY{n}{rt} \PY{o}{=} \PY{p}{[}\PY{p}{]}
+            \PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n}{A}\PY{p}{:}
+                \PY{k}{if} \PY{n+nb}{isinstance}\PY{p}{(}\PY{n}{i}\PY{p}{,} \PY{n+nb}{list}\PY{p}{)}\PY{p}{:}
+                    \PY{n}{rt}\PY{o}{.}\PY{n}{extend}\PY{p}{(}\PY{n}{flattern}\PY{p}{(}\PY{n}{i}\PY{p}{)}\PY{p}{)}
+                \PY{k}{elif} \PY{n+nb}{isinstance}\PY{p}{(}\PY{n}{i}\PY{p}{,} \PY{n}{np}\PY{o}{.}\PY{n}{ndarray}\PY{p}{)}\PY{p}{:}
+                    \PY{n}{rt}\PY{o}{.}\PY{n}{extend}\PY{p}{(}\PY{n}{flattern}\PY{p}{(}\PY{n}{i}\PY{o}{.}\PY{n}{tolist}\PY{p}{(}\PY{p}{)}\PY{p}{)}\PY{p}{)}
+                \PY{k}{else}\PY{p}{:}
+                    \PY{n}{rt}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{i}\PY{p}{)}
+            \PY{k}{return} \PY{n}{rt}
+        \PY{k}{def} \PY{n+nf}{most\PYZus{}common}\PY{p}{(}\PY{n}{lst}\PY{p}{)}\PY{p}{:}
+            \PY{k}{if} \PY{o+ow}{not} \PY{n}{lst}\PY{p}{:}
+                \PY{k}{return} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{P\PYZhy{}PPL}\PY{l+s+s2}{\PYZdq{}}
+            \PY{k}{if} \PY{n+nb}{len}\PY{p}{(}\PY{n+nb}{list}\PY{p}{(}\PY{n+nb}{set}\PY{p}{(}\PY{n}{lst}\PY{p}{)}\PY{p}{)}\PY{p}{)} \PY{o}{\PYZgt{}}\PY{l+m+mi}{1} \PY{o+ow}{and} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{P\PYZhy{}PPL}\PY{l+s+s2}{\PYZdq{}} \PY{o+ow}{in} \PY{n+nb}{set}\PY{p}{(}\PY{n}{lst}\PY{p}{)}\PY{p}{:}
+                \PY{n}{lst}\PY{o}{=}\PY{p}{[}\PY{n}{x} \PY{k}{for} \PY{n}{x} \PY{o+ow}{in} \PY{n}{lst} \PY{k}{if} \PY{n}{x} \PY{o}{!=} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{PPL}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}
+            \PY{k}{return} \PY{n+nb}{max}\PY{p}{(}\PY{n+nb}{set}\PY{p}{(}\PY{n}{lst}\PY{p}{)}\PY{p}{,} \PY{n}{key}\PY{o}{=}\PY{n}{lst}\PY{o}{.}\PY{n}{count}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}6}]:} \PY{k+kn}{import} \PY{n+nn}{nxpd}
+        \PY{n}{nxpd}\PY{o}{.}\PY{n}{nxpdParams}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{show}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{ipynb}\PY{l+s+s2}{\PYZdq{}}
+        \PY{k+kn}{from} \PY{n+nn}{strpython}\PY{n+nn}{.}\PY{n+nn}{helpers}\PY{n+nn}{.}\PY{n+nn}{gazeteer\PYZus{}helpers} \PY{k}{import} \PY{n}{get\PYZus{}data}
+        \PY{k}{def} \PY{n+nf}{class\PYZus{}graph}\PY{p}{(}\PY{n}{g}\PY{p}{)}\PY{p}{:}
+            \PY{n}{mapping}\PY{o}{=}\PY{p}{\PYZob{}}\PY{p}{\PYZcb{}}
+            \PY{n}{g2}\PY{o}{=}\PY{n}{g}\PY{o}{.}\PY{n}{copy}\PY{p}{(}\PY{p}{)}
+            \PY{k}{for} \PY{n}{n} \PY{o+ow}{in} \PY{n}{g2}\PY{p}{:}
+                \PY{n}{c}\PY{o}{=}\PY{n}{get\PYZus{}data}\PY{p}{(}\PY{n}{n}\PY{p}{)}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{class}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}
+                \PY{n}{g2}\PY{o}{.}\PY{n}{nodes}\PY{p}{[}\PY{n}{n}\PY{p}{]}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{label}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{=}\PY{n}{most\PYZus{}common}\PY{p}{(}\PY{n}{c}\PY{p}{)}
+            \PY{k}{return} \PY{n}{g2}
+\end{Verbatim}
+
+
+    Pour faire une comparaison entre les STRs générées dans chaque classe de
+document, on utilise plusieurs indicateurs :
+
+\begin{itemize}
+\tightlist
+\item
+  \textbf{Granularité} La granularité est définie le niveau dans
+  l'échelle spatiale (\(village\) \textless{} \(ville < pays\)) d'une
+  entité. Ici, elle nous indique à quel niveau la spatialité est utilisé
+  pour décrire la situation.
+\item
+  \textbf{Densité} La densité est définie par le nombre d'arrête pour un
+  noeud dans un graphe. Un graphe d'une STR avec une forte densité,
+  indique une forte cohésion entre les entités spatiales.
+\item
+  \textbf{Ratio \(Relation_i/Relation_j\)} Dans la STR, chaque entité
+  peut-être relié à une autre par deux type de relations : inclusion et
+  adjacence. Avec ce ratio, on souhaite savoir combien il existe de
+  \(relation_j\) pour une \(relation_i\). Par exemple, pour une relation
+  d'inclusion, combien de relations d'adjacence ?
+\item
+  \textbf{Nombre de noeuds(entités spatiales)} Indique si des textes
+  sont fortement spatialisés.
+\end{itemize}
+
+\subsubsection{Calcul de la granularité d'une
+STR}\label{calcul-de-la-granularituxe9-dune-str}
+
+On récupére les \textbf{classes associées} aux différentes
+\textbf{entités de la STR}, puis on récupére \textbf{la classe la plus
+fréquente}. Par exemple:
+
+\(STR_1\) -\/-\textgreater{} France, Montpellier, Clapiers, Caen
+-\/-\textgreater{} {[}A-PCLI{]}, {[}P-PPL, A-ADM4{]}, {[}A-ADM4{]},
+{[}A-ADM4{]}
+
+On a donc pour granularité : \textbf{A-ADM4}
+
+\subsubsection{Calcul de la densité d'une
+STR}\label{calcul-de-la-densituxe9-dune-str}
+
+Le calcul de la densité d'une STR (ici son graphe) se calcule à l'aide
+de la formule suivante : \[\frac{2\times|E|}{|V|\times(|V|-1)}\]
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}7}]:} \PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{GRAPH}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{=}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{ID\PYZus{}TEXT}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{k}{lambda} \PY{n}{x}\PY{p}{:}\PY{n}{nx}\PY{o}{.}\PY{n}{read\PYZus{}gexf}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{str\PYZus{}PADI100/}\PY{l+s+si}{\PYZob{}0\PYZcb{}}\PY{l+s+s2}{.gexf}\PY{l+s+s2}{\PYZdq{}}\PY{o}{.}\PY{n}{format}\PY{p}{(}\PY{n}{x}\PY{p}{)}\PY{p}{)}\PY{p}{)}
+        \PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{GRAPH\PYZus{}C}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{=}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{GRAPH}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{k}{lambda} \PY{n}{x}\PY{p}{:}\PY{n}{class\PYZus{}graph}\PY{p}{(}\PY{n}{x}\PY{p}{)}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}8}]:} \PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{DENSITY}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{=}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{GRAPH}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{k}{lambda} \PY{n}{x}\PY{p}{:} \PY{p}{(}\PY{l+m+mi}{2}\PY{o}{*}\PY{n}{x}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}edges}\PY{p}{(}\PY{p}{)}\PY{p}{)}\PY{o}{/}\PY{p}{(}\PY{n}{x}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}nodes}\PY{p}{(}\PY{p}{)}\PY{o}{*}\PY{p}{(}\PY{n}{x}\PY{o}{.}\PY{n}{number\PYZus{}of\PYZus{}nodes}\PY{p}{(}\PY{p}{)}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{)}\PY{p}{)} \PY{k}{if} \PY{n+nb}{len}\PY{p}{(}\PY{n}{x}\PY{p}{)} \PY{o}{\PYZgt{}}\PY{l+m+mi}{1} \PY{k}{else} \PY{l+m+mi}{0}\PY{p}{)}
+        \PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{NB\PYZus{}NODE}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{=}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{GRAPH}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{k}{lambda} \PY{n}{x}\PY{p}{:} \PY{n+nb}{len}\PY{p}{(}\PY{n}{x}\PY{p}{)}\PY{p}{)}
+        \PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{NB\PYZus{}ED\PYZus{}ADJ}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{=}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{GRAPH}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{k}{lambda} \PY{n}{x}\PY{p}{:} \PY{n}{number\PYZus{}of\PYZus{}edges}\PY{p}{(}\PY{n}{x}\PY{p}{,}\PY{n}{color}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{green}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{p}{)}
+        \PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{NB\PYZus{}ED\PYZus{}INC}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{=}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{GRAPH}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{k}{lambda} \PY{n}{x}\PY{p}{:} \PY{n}{number\PYZus{}of\PYZus{}edges}\PY{p}{(}\PY{n}{x}\PY{p}{,}\PY{n}{color}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{red}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{p}{)}
+        \PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{R\PYZus{}ADJ\PYZus{}INC}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{=}\PY{p}{(}\PY{p}{(}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{NB\PYZus{}ED\PYZus{}ADJ}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{/}\PY{l+m+mi}{2}\PY{p}{)}\PY{o}{/}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{NB\PYZus{}ED\PYZus{}INC}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{p}{)}\PY{o}{.}\PY{n}{replace}\PY{p}{(}\PY{p}{[}\PY{n}{np}\PY{o}{.}\PY{n}{inf}\PY{p}{,} \PY{o}{\PYZhy{}}\PY{n}{np}\PY{o}{.}\PY{n}{inf}\PY{p}{]}\PY{p}{,} \PY{n}{np}\PY{o}{.}\PY{n}{nan}\PY{p}{)}\PY{o}{.}\PY{n}{fillna}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{)}
+        \PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{R\PYZus{}INC\PYZus{}ADJ}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{=}\PY{p}{(}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{NB\PYZus{}ED\PYZus{}INC}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{/}\PY{p}{(}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{NB\PYZus{}ED\PYZus{}ADJ}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{/}\PY{l+m+mi}{2}\PY{p}{)}\PY{p}{)}\PY{o}{.}\PY{n}{replace}\PY{p}{(}\PY{p}{[}\PY{n}{np}\PY{o}{.}\PY{n}{inf}\PY{p}{,} \PY{o}{\PYZhy{}}\PY{n}{np}\PY{o}{.}\PY{n}{inf}\PY{p}{]}\PY{p}{,} \PY{n}{np}\PY{o}{.}\PY{n}{nan}\PY{p}{)}\PY{o}{.}\PY{n}{fillna}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}9}]:} \PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{CLASS}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{=}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{GRAPH}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{k}{lambda} \PY{n}{x}\PY{p}{:} \PY{n}{flattern}\PY{p}{(}\PY{p}{[}\PY{n}{get\PYZus{}data}\PY{p}{(}\PY{n}{n}\PY{p}{)}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{class}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{k}{for} \PY{n}{n} \PY{o+ow}{in} \PY{n+nb}{list}\PY{p}{(}\PY{n}{x}\PY{o}{.}\PY{n}{nodes}\PY{p}{(}\PY{p}{)}\PY{p}{)}\PY{p}{]}\PY{p}{)}\PY{p}{)}
+        \PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{MEAN\PYZus{}LVL}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{=}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{CLASS}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{k}{lambda} \PY{n}{x}\PY{p}{:} \PY{n}{most\PYZus{}common}\PY{p}{(}\PY{n}{x}\PY{p}{)} \PY{k}{if} \PY{n+nb}{len}\PY{p}{(}\PY{n}{x}\PY{p}{)}\PY{o}{\PYZgt{}}\PY{l+m+mi}{0} \PY{k}{else} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}10}]:} \PY{n}{data\PYZus{}bilan}\PY{o}{.}\PY{n}{head}\PY{p}{(}\PY{l+m+mi}{9}\PY{p}{)}
+\end{Verbatim}
+
+
+\begin{Verbatim}[commandchars=\\\{\}]
+{\color{outcolor}Out[{\color{outcolor}10}]:}    ID\_TEXT  IS\_BILAN  MIXED  \textbackslash{}
+         0        0     BILAN      0   
+         1        1  EPIDEMIE      0   
+         2        2     BILAN      1   
+         3        3     BILAN      0   
+         4        4  EPIDEMIE      0   
+         5        5  EPIDEMIE      1   
+         6        6  EPIDEMIE      0   
+         7        7     BILAN      0   
+         8        8     BILAN      0   
+         
+                                                        GRAPH  \textbackslash{}
+         0          (GD4103071, GD4468122, GD95073, GD791183)   
+         1                                        (GD1685421)   
+         2                                        (GD2032795)   
+         3                             (GD1626932, GD3274230)   
+         4        (GD639917, GD3789919, GD1316637, GD2055944)   
+         5  (GD639917, GD3995806, GD3789919, GD1316637, GD{\ldots}   
+         6                   (GD639917, GD3789919, GD2055944)   
+         7        (GD5526704, GD976842, GD1316637, GD2055944)   
+         8  (GD2908705, GD1404948, GD9642903, GD3995806, G{\ldots}   
+         
+                                                      GRAPH\_C   DENSITY  NB\_NODE  \textbackslash{}
+         0          (GD4103071, GD4468122, GD95073, GD791183)  0.000000        4   
+         1                                        (GD1685421)  0.000000        1   
+         2                                        (GD2032795)  0.000000        1   
+         3                             (GD1626932, GD3274230)  0.000000        2   
+         4        (GD639917, GD3789919, GD1316637, GD2055944)  0.166667        4   
+         5  (GD639917, GD3995806, GD3789919, GD1316637, GD{\ldots}  0.200000        5   
+         6                   (GD639917, GD3789919, GD2055944)  0.000000        3   
+         7        (GD5526704, GD976842, GD1316637, GD2055944)  0.333333        4   
+         8  (GD2908705, GD1404948, GD9642903, GD3995806, G{\ldots}  0.285714        7   
+         
+            NB\_ED\_ADJ  NB\_ED\_INC  R\_ADJ\_INC  R\_INC\_ADJ  \textbackslash{}
+         0          0          0        0.0        0.0   
+         1          0          0        0.0        0.0   
+         2          0          0        0.0        0.0   
+         3          0          0        0.0        0.0   
+         4          0          1        0.0        0.0   
+         5          0          2        0.0        0.0   
+         6          0          0        0.0        0.0   
+         7          2          0        0.0        0.0   
+         8          4          2        1.0        1.0   
+         
+                                                        CLASS MEAN\_LVL  
+         0            [P-PPLA, P-PPL, P-PPLA, A-ADM1, P-PPLA]   P-PPLA  
+         1                                            [P-PPL]    P-PPL  
+         2                                           [A-PCLI]   A-PCLI  
+         3                                    [A-PCLI, P-PPL]    P-PPL  
+         4   [A-PCLI, A-ADM1, P-PPLA, P-PPLC, A-PCLI, A-PCLI]   A-PCLI  
+         5  [A-PCLI, P-PPL, A-ADM1, P-PPLA, P-PPLC, A-PCLI{\ldots}   A-PCLI  
+         6           [A-PCLI, A-ADM1, P-PPLA, P-PPLC, A-PCLI]   A-PCLI  
+         7                   [A-PCLI, A-PCLI, A-PCLI, A-PCLI]   A-PCLI  
+         8  [A-ADM1, P-PPL, P-PPL, P-PPL, A-ADM1, A-ADM1, {\ldots}   A-ADM1  
+\end{Verbatim}
+            
+    \section{Résultats}\label{ruxe9sultats}
+
+\subsubsection{\texorpdfstring{~Granularité sur les documents de classe
+\textbf{BILAN}}{~Granularité sur les documents de classe BILAN}}\label{granularituxe9-sur-les-documents-de-classe-bilan}
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}11}]:} \PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{IS\PYZus{}BILAN}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{o}{==} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{BILAN}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{groupby}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{MEAN\PYZus{}LVL}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{o}{.}\PY{n}{count}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{plot}\PY{o}{.}\PY{n}{pie}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{ID\PYZus{}TEXT}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
+\end{Verbatim}
+
+
+\begin{Verbatim}[commandchars=\\\{\}]
+{\color{outcolor}Out[{\color{outcolor}11}]:} <matplotlib.axes.\_subplots.AxesSubplot at 0x10a1db828>
+\end{Verbatim}
+            
+    \begin{center}
+    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_13_1.png}
+    \end{center}
+    { \hspace*{\fill} \\}
+    
+    \subsubsection{\texorpdfstring{Granularité sur les documents de classe
+\textbf{EPIDEMIE}}{Granularité sur les documents de classe EPIDEMIE}}\label{granularituxe9-sur-les-documents-de-classe-epidemie}
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}12}]:} \PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{IS\PYZus{}BILAN}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{o}{==} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{EPIDEMIE}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{groupby}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{MEAN\PYZus{}LVL}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{o}{.}\PY{n}{count}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{plot}\PY{o}{.}\PY{n}{pie}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{ID\PYZus{}TEXT}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
+\end{Verbatim}
+
+
+\begin{Verbatim}[commandchars=\\\{\}]
+{\color{outcolor}Out[{\color{outcolor}12}]:} <matplotlib.axes.\_subplots.AxesSubplot at 0x10a2c9ba8>
+\end{Verbatim}
+            
+    \begin{center}
+    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_15_1.png}
+    \end{center}
+    { \hspace*{\fill} \\}
+    
+    \subsubsection{Valeurs moyennes obtenues pour chaque
+indicateur}\label{valeurs-moyennes-obtenues-pour-chaque-indicateur}
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}13}]:} \PY{n}{data\PYZus{}bilan}\PY{o}{.}\PY{n}{groupby}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{IS\PYZus{}BILAN}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{o}{.}\PY{n}{mean}\PY{p}{(}\PY{p}{)}
+\end{Verbatim}
+
+
+\begin{Verbatim}[commandchars=\\\{\}]
+{\color{outcolor}Out[{\color{outcolor}13}]:}             ID\_TEXT     MIXED   DENSITY   NB\_NODE  NB\_ED\_ADJ  NB\_ED\_INC  \textbackslash{}
+         IS\_BILAN                                                                  
+         BILAN     51.588235  0.014706  0.293545  6.455882   4.117647   2.705882   
+         EPIDEMIE  46.727273  0.030303  0.379501  4.636364   2.969697   1.303030   
+         
+                   R\_ADJ\_INC  R\_INC\_ADJ  
+         IS\_BILAN                        
+         BILAN      0.559194   1.049048  
+         EPIDEMIE   0.398990   0.240657  
+\end{Verbatim}
+            
+    \subsection{Analyse des résultats}\label{analyse-des-ruxe9sultats}
+
+\subsubsection{Granularité}\label{granularituxe9}
+
+En regardant les deux camemberts ci-dessus, on remarque que la
+granularité observé dans les STRs différe selon le type de texte. Les
+textes de classe \textbf{épidémie} sont généralement plus "haut" dans la
+hiérarchie spatiale, de part la forte présence de classe telles que:
+A-PCLI (\(\approx\)Pays), A-ADM1(premier découpage administratif d'un
+pays \emph{equiv} région en France, état aux Etats-Unis, \emph{etc.}).
+Ceux de la classe \textbf{BILAN}, ont une granularité un peu plus fine
+avec un spectre de classe plus étendue : T-ISL (ile), S-BLDG (batiment).
+
+En se basant sur la classification proposé, on conclue que les documents
+de type \textbf{bilan} sont plus "fin" spatialement que ceux de la
+classe \textbf{épidémie}.
+
+\subsubsection{Densité/ Nombre de noeuds/ Nombre
+d'arrêtes}\label{densituxe9-nombre-de-noeuds-nombre-darruxeates}
+
+Malheuresement la densité moyenne ne permet de faire aucune conclusion.
+
+On observe que le nombre de noeuds dans les documents de classes Bilan
+est plus élevé. Ce qui indique que le nombre d'entités spatiales dans
+ces documents est plus élevés. Ce qui semble tout à fait normal car
+contrairement à une déclaration d'épidémie, le bilan fait un
+récapitulatif de la propagation d'une maladie sur un laps de temps et
+une spatialité (souvent) plus importante.
+
+Pour le nombre de relations d'ajacence et d'inclusion, on observe un
+même rapport de "force" : Il y a plus d'arêtes d'inclusion que d'arêtes
+d'adjacence.
+
+\subsubsection{Ratio Adjacence/Inclusion VS
+Inclusion/Adjacence}\label{ratio-adjacenceinclusion-vs-inclusionadjacence}
+
+\begin{longtable}[]{@{}lll@{}}
+\toprule
+CLASSE & ADJ/INC & INC/ADJ\tabularnewline
+\midrule
+\endhead
+BILAN & 0.559194 & 1.04905\tabularnewline
+EPIDEMIE & 0.39899 & 0.240657\tabularnewline
+\bottomrule
+\end{longtable}
+
+On reprend les résultats concernat les rapports ADJ/INC (combien de
+relations d'inclusion pour une relation d'adjacence ?) et INC/ADJ (le
+contraire de ADJ/INC). A partir de ces résultats, on observe que les
+rapports sont inversés ! Pour les documents de classe EPIDEMIE, on va
+favoriser plus les relations d'inclusion, contrairement aux documents de
+classe BILAN qui favorisent les relations d'adjacences.
+
+Est-ce que parce que les relations d'inclusions sont favorisés (ratio
+ADJ/INC élevé), on se retrouve sur des zones limitées, donc plus local ?
+Ca rentre bien dans le cadre de la classe épidémie.
+
+Est-ce qu'un ratio élevé INC/ADJ traduit une information concernant la
+dispertion d'une maladie ?
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}29}]:} \PY{k+kn}{from} \PY{n+nn}{ipywidgets} \PY{k}{import} \PY{n}{interact}\PY{p}{,} \PY{n}{interactive}\PY{p}{,} \PY{n}{fixed}\PY{p}{,} \PY{n}{interact\PYZus{}manual}
+         \PY{k+kn}{import} \PY{n+nn}{ipywidgets} \PY{k}{as} \PY{n+nn}{widgets}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}15}]:} \PY{k+kn}{from} \PY{n+nn}{nxpd} \PY{k}{import} \PY{n}{draw}
+         
+         \PY{k}{def} \PY{n+nf}{f}\PY{p}{(}\PY{n}{x}\PY{p}{)}\PY{p}{:}
+             \PY{k}{global} \PY{n}{data\PYZus{}bilan}
+             \PY{k}{return} \PY{n}{draw}\PY{p}{(}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{n}{data\PYZus{}bilan}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{IS\PYZus{}BILAN}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{==}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{BILAN}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{iloc}\PY{p}{[}\PY{n}{x}\PY{p}{]}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{GRAPH\PYZus{}C}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{p}{,}\PY{n}{show}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{ipynb}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
+         \PY{n}{interact}\PY{p}{(}\PY{n}{f}\PY{p}{,} \PY{n}{x}\PY{o}{=}\PY{n}{widgets}\PY{o}{.}\PY{n}{IntSlider}\PY{p}{(}\PY{n+nb}{min}\PY{o}{=}\PY{l+m+mi}{0}\PY{p}{,}\PY{n+nb}{max}\PY{o}{=}\PY{l+m+mi}{100}\PY{p}{,}\PY{n}{step}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}\PY{p}{)}\PY{p}{;}
+\end{Verbatim}
+
+
+    
+    \begin{verbatim}
+interactive(children=(IntSlider(value=0, description='x'), Output()), _dom_classes=('widget-interact',))
+    \end{verbatim}
+
+    
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}16}]:} \PY{n}{dd}\PY{o}{=}\PY{n}{data\PYZus{}bilan}\PY{o}{.}\PY{n}{groupby}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{IS\PYZus{}BILAN}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{o}{.}\PY{n}{mean}\PY{p}{(}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+{\color{incolor}In [{\color{incolor}28}]:} \PY{k+kn}{from} \PY{n+nn}{tabulate} \PY{k}{import} \PY{n}{tabulate}
+         \PY{n+nb}{print}\PY{p}{(}\PY{n}{tabulate}\PY{p}{(}\PY{n}{dd}\PY{p}{[}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{R\PYZus{}ADJ\PYZus{}INC}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}  \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{R\PYZus{}INC\PYZus{}ADJ}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{p}{]}\PY{p}{,}\PY{n}{tablefmt}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{pipe}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{p}{)}
+\end{Verbatim}
+
+
+    \begin{Verbatim}[commandchars=\\\{\}]
+|:---------|---------:|---------:|
+| BILAN    | 0.559194 | 1.04905  |
+| EPIDEMIE | 0.39899  | 0.240657 |
+
+    \end{Verbatim}
+
+
+    % Add a bibliography block to the postdoc
+    
+    
+    
+    \end{document}
diff --git a/output_13_1.png b/output_13_1.png
new file mode 100644
index 0000000000000000000000000000000000000000..6de78df9cbc3fc135767c0cea29ee7e86d86cbc4
Binary files /dev/null and b/output_13_1.png differ
diff --git a/output_15_1.png b/output_15_1.png
new file mode 100644
index 0000000000000000000000000000000000000000..d06a3b9c48a801376c2cc66d8b24df5fb1f01b7c
Binary files /dev/null and b/output_15_1.png differ
diff --git a/output_4_1.png b/output_4_1.png
new file mode 100644
index 0000000000000000000000000000000000000000..e94839d9747748a709f6d65021ee0c7f220d6f09
Binary files /dev/null and b/output_4_1.png differ
diff --git a/str_PADI100/0.gexf b/str_PADI100/0.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..599e78deefe422837a8d6050196b16a79e71a4e7
--- /dev/null
+++ b/str_PADI100/0.gexf
@@ -0,0 +1,11 @@
+<gexf version="1.1" xmlns="http://www.gexf.net/1.1draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
+  <graph defaultedgetype="directed" mode="static">
+    <nodes>
+      <node id="GD4103071" label="Kano" />
+      <node id="GD4468122" label="Lagos" />
+      <node id="GD95073" label="Plateau State" />
+      <node id="GD791183" label="Port Harcourt" />
+    </nodes>
+    <edges />
+  </graph>
+</gexf>
diff --git a/str_PADI100/1.gexf b/str_PADI100/1.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..ae8bd87d230f8f9e84cb4c6268600e80768f6746
--- /dev/null
+++ b/str_PADI100/1.gexf
@@ -0,0 +1,8 @@
+<gexf version="1.1" xmlns="http://www.gexf.net/1.1draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
+  <graph defaultedgetype="directed" mode="static">
+    <nodes>
+      <node id="GD1685421" label="Abeokuta" />
+    </nodes>
+    <edges />
+  </graph>
+</gexf>
diff --git a/str_PADI100/10.gexf b/str_PADI100/10.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..cd1e705ea4d673f11259991636d06da77e5ee6c2
--- /dev/null
+++ b/str_PADI100/10.gexf
@@ -0,0 +1,22 @@
+<gexf version="1.1" xmlns="http://www.gexf.net/1.1draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
+  <graph defaultedgetype="directed" mode="static">
+    <attributes class="edge" mode="static">
+      <attribute id="0" title="color" type="string" />
+      <attribute id="1" title="networkx_key" type="long" />
+    </attributes>
+    <nodes>
+      <node id="GD5400765" label="Paris" />
+      <node id="GD3789919" label="Seoul" />
+      <node id="GD1316637" label="South Korea" />
+      <node id="GD3949715" label="Taiwan" />
+    </nodes>
+    <edges>
+      <edge id="0" source="GD3789919" target="GD1316637">
+        <attvalues>
+          <attvalue for="0" value="red" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+    </edges>
+  </graph>
+</gexf>
diff --git a/str_PADI100/100.gexf b/str_PADI100/100.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..54911507768364a64ae04af266c6184c5062ed3a
--- /dev/null
+++ b/str_PADI100/100.gexf
@@ -0,0 +1,26 @@
+<gexf version="1.1" xmlns="http://www.gexf.net/1.1draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
+  <graph defaultedgetype="directed" mode="static">
+    <attributes class="edge" mode="static">
+      <attribute id="0" title="color" type="string" />
+      <attribute id="1" title="networkx_key" type="long" />
+    </attributes>
+    <nodes>
+      <node id="GD2880488" label="Angola" />
+      <node id="GD1335306" label="Namibia" />
+    </nodes>
+    <edges>
+      <edge id="0" source="GD2880488" target="GD1335306">
+        <attvalues>
+          <attvalue for="0" value="green" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="1" source="GD1335306" target="GD2880488">
+        <attvalues>
+          <attvalue for="0" value="green" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+    </edges>
+  </graph>
+</gexf>
diff --git a/str_PADI100/11.gexf b/str_PADI100/11.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..2998e0e15143d35ac67eb9027759c22aa76cfe60
--- /dev/null
+++ b/str_PADI100/11.gexf
@@ -0,0 +1,8 @@
+<gexf version="1.1" xmlns="http://www.gexf.net/1.1draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
+  <graph defaultedgetype="directed" mode="static">
+    <nodes>
+      <node id="GD1316637" label="South Korea" />
+    </nodes>
+    <edges />
+  </graph>
+</gexf>
diff --git a/str_PADI100/12.gexf b/str_PADI100/12.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..334c7d0fb285071ef25abd1365cbeb5d35fc325c
--- /dev/null
+++ b/str_PADI100/12.gexf
@@ -0,0 +1,66 @@
+<gexf version="1.1" xmlns="http://www.gexf.net/1.1draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
+  <graph defaultedgetype="directed" mode="static">
+    <attributes class="edge" mode="static">
+      <attribute id="0" title="color" type="string" />
+      <attribute id="1" title="networkx_key" type="long" />
+    </attributes>
+    <nodes>
+      <node id="GD1955440" label="Alberta" />
+      <node id="GD5063243" label="Australia" />
+      <node id="GD3364804" label="Canada" />
+      <node id="GD3995806" label="Korea" />
+      <node id="GD934193" label="New Zealand" />
+      <node id="GD2055944" label="U.S." />
+    </nodes>
+    <edges>
+      <edge id="0" source="GD1955440" target="GD2055944">
+        <attvalues>
+          <attvalue for="0" value="green" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="1" source="GD1955440" target="GD3364804">
+        <attvalues>
+          <attvalue for="0" value="red" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="2" source="GD5063243" target="GD934193">
+        <attvalues>
+          <attvalue for="0" value="green" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="3" source="GD3364804" target="GD2055944">
+        <attvalues>
+          <attvalue for="0" value="green" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="4" source="GD3995806" target="GD2055944">
+        <attvalues>
+          <attvalue for="0" value="red" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="5" source="GD934193" target="GD5063243">
+        <attvalues>
+          <attvalue for="0" value="green" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="6" source="GD2055944" target="GD1955440">
+        <attvalues>
+          <attvalue for="0" value="green" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="7" source="GD2055944" target="GD3364804">
+        <attvalues>
+          <attvalue for="0" value="green" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+    </edges>
+  </graph>
+</gexf>
diff --git a/str_PADI100/13.gexf b/str_PADI100/13.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..5fd687016c36919a82254d9a176eb506de0da48f
--- /dev/null
+++ b/str_PADI100/13.gexf
@@ -0,0 +1,47 @@
+<gexf version="1.1" xmlns="http://www.gexf.net/1.1draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
+  <graph defaultedgetype="directed" mode="static">
+    <attributes class="edge" mode="static">
+      <attribute id="0" title="color" type="string" />
+      <attribute id="1" title="networkx_key" type="long" />
+    </attributes>
+    <nodes>
+      <node id="GD5573894" label="Bauchi" />
+      <node id="GD509348" label="Bauchi State" />
+      <node id="GD4243745" label="Katagum" />
+      <node id="GD1607251" label="Nigeria" />
+      <node id="GD2878541" label="Toro" />
+    </nodes>
+    <edges>
+      <edge id="0" source="GD5573894" target="GD509348">
+        <attvalues>
+          <attvalue for="0" value="red" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="1" source="GD5573894" target="GD1607251">
+        <attvalues>
+          <attvalue for="0" value="red" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="2" source="GD509348" target="GD1607251">
+        <attvalues>
+          <attvalue for="0" value="red" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="3" source="GD4243745" target="GD509348">
+        <attvalues>
+          <attvalue for="0" value="red" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="4" source="GD4243745" target="GD1607251">
+        <attvalues>
+          <attvalue for="0" value="red" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+    </edges>
+  </graph>
+</gexf>
diff --git a/str_PADI100/14.gexf b/str_PADI100/14.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..97e920e049b94ef05b48d9888f7d7493c56268f4
--- /dev/null
+++ b/str_PADI100/14.gexf
@@ -0,0 +1,48 @@
+<gexf version="1.1" xmlns="http://www.gexf.net/1.1draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
+  <graph defaultedgetype="directed" mode="static">
+    <attributes class="edge" mode="static">
+      <attribute id="0" title="color" type="string" />
+      <attribute id="1" title="networkx_key" type="long" />
+    </attributes>
+    <nodes>
+      <node id="GD639917" label="Germany" />
+      <node id="GD3649667" label="North Chungcheong Province" />
+      <node id="GD3789919" label="Seoul" />
+      <node id="GD753475" label="South Chungcheong Province" />
+      <node id="GD1316637" label="South Korea" />
+      <node id="GD2055944" label="United States" />
+    </nodes>
+    <edges>
+      <edge id="0" source="GD3649667" target="GD753475">
+        <attvalues>
+          <attvalue for="0" value="green" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="1" source="GD3649667" target="GD1316637">
+        <attvalues>
+          <attvalue for="0" value="red" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="2" source="GD3789919" target="GD1316637">
+        <attvalues>
+          <attvalue for="0" value="red" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="3" source="GD753475" target="GD3649667">
+        <attvalues>
+          <attvalue for="0" value="green" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="4" source="GD753475" target="GD1316637">
+        <attvalues>
+          <attvalue for="0" value="red" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+    </edges>
+  </graph>
+</gexf>
diff --git a/str_PADI100/15.gexf b/str_PADI100/15.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..e897e0eaf00f8033dfcbbc8a0a1e2d2e5e02f342
--- /dev/null
+++ b/str_PADI100/15.gexf
@@ -0,0 +1,8 @@
+<gexf version="1.1" xmlns="http://www.gexf.net/1.1draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
+  <graph defaultedgetype="directed" mode="static">
+    <nodes>
+      <node id="GD1184772" label="China" />
+    </nodes>
+    <edges />
+  </graph>
+</gexf>
diff --git a/str_PADI100/16.gexf b/str_PADI100/16.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..7b66ac00c9d0bfca21a3fc4778c78ca2b97c09fe
--- /dev/null
+++ b/str_PADI100/16.gexf
@@ -0,0 +1,6 @@
+<gexf version="1.1" xmlns="http://www.gexf.net/1.1draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
+  <graph defaultedgetype="directed" mode="static">
+    <nodes />
+    <edges />
+  </graph>
+</gexf>
diff --git a/str_PADI100/17.gexf b/str_PADI100/17.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..29b51ad4ab693c655fcdb898e29c033f19c060d1
--- /dev/null
+++ b/str_PADI100/17.gexf
@@ -0,0 +1,9 @@
+<gexf version="1.1" xmlns="http://www.gexf.net/1.1draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
+  <graph defaultedgetype="directed" mode="static">
+    <nodes>
+      <node id="GD5622144" label="Asia" />
+      <node id="GD1184772" label="China" />
+    </nodes>
+    <edges />
+  </graph>
+</gexf>
diff --git a/str_PADI100/18.gexf b/str_PADI100/18.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..4ccafb67511576ccf60b9b99bdf24705ddcac901
--- /dev/null
+++ b/str_PADI100/18.gexf
@@ -0,0 +1,9 @@
+<gexf version="1.1" xmlns="http://www.gexf.net/1.1draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
+  <graph defaultedgetype="directed" mode="static">
+    <nodes>
+      <node id="GD1184772" label="China" />
+      <node id="GD6493741" label="Sinovac" />
+    </nodes>
+    <edges />
+  </graph>
+</gexf>
diff --git a/str_PADI100/19.gexf b/str_PADI100/19.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..1e760791300b2573eaa2951b2ce6d1c0f58eb616
--- /dev/null
+++ b/str_PADI100/19.gexf
@@ -0,0 +1,115 @@
+<gexf version="1.1" xmlns="http://www.gexf.net/1.1draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
+  <graph defaultedgetype="directed" mode="static">
+    <attributes class="edge" mode="static">
+      <attribute id="0" title="color" type="string" />
+      <attribute id="1" title="networkx_key" type="long" />
+    </attributes>
+    <nodes>
+      <node id="GD5622144" label="Asia" />
+      <node id="GD1184772" label="China" />
+      <node id="GD14300647" label="Diaoyu" />
+      <node id="GD2281224" label="East China Sea" />
+      <node id="GD5754084" label="Japan" />
+      <node id="GD3970383" label="Middle East" />
+      <node id="GD4001885" label="Okinawa" />
+      <node id="GD3916933" label="Pacific Ocean" />
+      <node id="GD3949715" label="Taiwan" />
+      <node id="GD1316637" label="South Korea" />
+      <node id="GD91392" label="Syria" />
+      <node id="GD2055944" label="US" />
+      <node id="GD3317465" label="Vietnam" />
+    </nodes>
+    <edges>
+      <edge id="0" source="GD1184772" target="GD5754084">
+        <attvalues>
+          <attvalue for="0" value="red" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="1" source="GD2281224" target="GD5754084">
+        <attvalues>
+          <attvalue for="0" value="green" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="2" source="GD2281224" target="GD3949715">
+        <attvalues>
+          <attvalue for="0" value="red" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="3" source="GD5754084" target="GD2281224">
+        <attvalues>
+          <attvalue for="0" value="green" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="4" source="GD5754084" target="GD3949715">
+        <attvalues>
+          <attvalue for="0" value="green" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
+      <edge id="5" source="GD5754084" target="GD1316637">
+        <attvalues>
+          <attvalue for="0" value="green" />
+          <attvalue for="1" value="0" />
+        </attvalues>
+      </edge>
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diff --git a/str_PADI100/2.gexf b/str_PADI100/2.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..17d8db46b8dd431b52be1b8363435690bdb94f76
--- /dev/null
+++ b/str_PADI100/2.gexf
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+  <graph defaultedgetype="directed" mode="static">
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diff --git a/str_PADI100/20.gexf b/str_PADI100/20.gexf
new file mode 100644
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diff --git a/str_PADI100/21.gexf b/str_PADI100/21.gexf
new file mode 100644
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+      <node id="GD1184772" label="China" />
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+      <node id="GD3850521" label="Shanghai" />
+      <node id="GD2589931" label="UK" />
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diff --git a/str_PADI100/22.gexf b/str_PADI100/22.gexf
new file mode 100644
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+      <node id="GD4316386" label="Hong Kong" />
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diff --git a/str_PADI100/23.gexf b/str_PADI100/23.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..1986e32bad6de970689cf24109311d84300c17b0
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+++ b/str_PADI100/23.gexf
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+      <attribute id="1" title="networkx_key" type="long" />
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+      <node id="GD1184772" label="China" />
+      <node id="GD4486468" label="Emington" />
+      <node id="GD3086749" label="Europe" />
+      <node id="GD1256319" label="Ill." />
+      <node id="GD934193" label="New Zealand" />
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diff --git a/str_PADI100/24.gexf b/str_PADI100/24.gexf
new file mode 100644
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diff --git a/str_PADI100/25.gexf b/str_PADI100/25.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..7b66ac00c9d0bfca21a3fc4778c78ca2b97c09fe
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+++ b/str_PADI100/25.gexf
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+  <graph defaultedgetype="directed" mode="static">
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diff --git a/str_PADI100/26.gexf b/str_PADI100/26.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..2a4aaf534337047e884397709470e963c186b820
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+++ b/str_PADI100/26.gexf
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+  <graph defaultedgetype="directed" mode="static">
+    <nodes>
+      <node id="GD1184772" label="China" />
+      <node id="GD3086749" label="Europe" />
+      <node id="GD2055944" label="United States" />
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diff --git a/str_PADI100/27.gexf b/str_PADI100/27.gexf
new file mode 100644
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+++ b/str_PADI100/27.gexf
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+      <node id="GD1184772" label="China" />
+      <node id="GD6025860" label="Fengcheng" />
+      <node id="GD505602" label="Jiangxi" />
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diff --git a/str_PADI100/28.gexf b/str_PADI100/28.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..171e3ad1c688a5f8f15478148446e3b5bc1fe0f9
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+++ b/str_PADI100/28.gexf
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+      <node id="GD4193576" label="Qinghai" />
+      <node id="GD5321881" label="Shandong" />
+      <node id="GD2055944" label="US" />
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diff --git a/str_PADI100/29.gexf b/str_PADI100/29.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..58d36998a5a426a8921bd1a49447b8e684808a0d
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+++ b/str_PADI100/29.gexf
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diff --git a/str_PADI100/3.gexf b/str_PADI100/3.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..3c856bbb0aec22b82a703b6084fbe5055eb1cecb
--- /dev/null
+++ b/str_PADI100/3.gexf
@@ -0,0 +1,9 @@
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diff --git a/str_PADI100/30.gexf b/str_PADI100/30.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..97f2cfe1b28811553e2b02d57d4d98141155a2eb
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+      <node id="GD976842" label="Brazil" />
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+      <node id="GD1496067" label="Santiago" />
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new file mode 100644
index 0000000000000000000000000000000000000000..b9c088d1e7299abf2ea49f763702b3683f96e7d5
--- /dev/null
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diff --git a/str_PADI100/39.gexf b/str_PADI100/39.gexf
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index 0000000000000000000000000000000000000000..f38952f3ad103567c67335bb793e5dd1688a3867
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diff --git a/str_PADI100/41.gexf b/str_PADI100/41.gexf
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new file mode 100644
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diff --git a/str_PADI100/45.gexf b/str_PADI100/45.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..dfe1d97b282c65e8e3bb87b7964216bce0098e33
--- /dev/null
+++ b/str_PADI100/45.gexf
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new file mode 100644
index 0000000000000000000000000000000000000000..baf44b0754bf05b5600371aeab3332237c9584e5
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diff --git a/str_PADI100/47.gexf b/str_PADI100/47.gexf
new file mode 100644
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diff --git a/str_PADI100/48.gexf b/str_PADI100/48.gexf
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diff --git a/str_PADI100/51.gexf b/str_PADI100/51.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..9a129a8759bc05c9f88f9e04bf855642c2d14716
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diff --git a/str_PADI100/52.gexf b/str_PADI100/52.gexf
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new file mode 100644
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diff --git a/str_PADI100/59.gexf b/str_PADI100/59.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..d0e85d7c81e5e73cf60387e42bb50383c468b6a9
--- /dev/null
+++ b/str_PADI100/59.gexf
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diff --git a/str_PADI100/61.gexf b/str_PADI100/61.gexf
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index 0000000000000000000000000000000000000000..43a68fe5732a3eff5238f80407a9435c68ea51ff
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new file mode 100644
index 0000000000000000000000000000000000000000..7397ec185dcd73c218880d58532525d120b9b426
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diff --git a/str_PADI100/63.gexf b/str_PADI100/63.gexf
new file mode 100644
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diff --git a/str_PADI100/66.gexf b/str_PADI100/66.gexf
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diff --git a/str_PADI100/67.gexf b/str_PADI100/67.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..490a7a7f992a238f484f9e8da511def580181a8a
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diff --git a/str_PADI100/68.gexf b/str_PADI100/68.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..dc346589913b4c340a94c7876c96fdab5e1ae9bc
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new file mode 100644
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diff --git a/str_PADI100/89.gexf b/str_PADI100/89.gexf
new file mode 100644
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diff --git a/str_PADI100/9.gexf b/str_PADI100/9.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..0587d449d4295e00b466d3b704abbd12968540e6
--- /dev/null
+++ b/str_PADI100/9.gexf
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diff --git a/str_PADI100/90.gexf b/str_PADI100/90.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..522bfca5482ba76de4d6f5a546fd1b571fd2f53c
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diff --git a/str_PADI100/91.gexf b/str_PADI100/91.gexf
new file mode 100644
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diff --git a/str_PADI100/92.gexf b/str_PADI100/92.gexf
new file mode 100644
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diff --git a/str_PADI100/93.gexf b/str_PADI100/93.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..63dc11266b3f788388a724b1cb2e48150063670d
--- /dev/null
+++ b/str_PADI100/93.gexf
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diff --git a/str_PADI100/94.gexf b/str_PADI100/94.gexf
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diff --git a/str_PADI100/95.gexf b/str_PADI100/95.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..cab15f6cde47af96e1ee2d07922400490561bf0f
--- /dev/null
+++ b/str_PADI100/95.gexf
@@ -0,0 +1,26 @@
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diff --git a/str_PADI100/96.gexf b/str_PADI100/96.gexf
new file mode 100644
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diff --git a/str_PADI100/97.gexf b/str_PADI100/97.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..643a9c00378c237bf804214a7390d63b4ee282ea
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diff --git a/str_PADI100/98.gexf b/str_PADI100/98.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..1940c401390b2033ec8651e72f11f79bfdcd5394
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+++ b/str_PADI100/98.gexf
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diff --git a/str_PADI100/99.gexf b/str_PADI100/99.gexf
new file mode 100644
index 0000000000000000000000000000000000000000..271a1cf39fda49cf30046ed224fdf5a04899e09a
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