forecast_lost_objects.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecast lost object on train stations \n",
    "\n",
    "The aim of this project is to forecast lost object from Paris SNCF stations using time series prediction, the study was done in 2018.\n",
    "\n",
    "Technical report could be found [here](https://docs.google.com/presentation/d/1H58gBLj_xadYaVtUGKRI2wh9h7VlnW5jYEs1VSigMCs)\n",
    "\n",
    "Note : This notebook needs some serious rework."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'ggplot2' is in use and will not be installed\"Warning message:\n",
      "\"package 'dplyr' is in use and will not be installed\"Warning message:\n",
      "\"package 'lubridate' is in use and will not be installed\"Warning message:\n",
      "\"package 'tstools' is in use and will not be installed\"also installing the dependencies 'RcppParallel', 'lamW', 'pracma', 'statmod', 'TTR', 'curl', 'greybox', 'nloptr', 'quadprog', 'quantmod', 'codetools', 'iterators', 'smooth', 'fracdiff', 'lmtest', 'nnet', 'timeDate', 'tseries', 'urca', 'RcppArmadillo', 'foreach', 'shape', 'Deriv', 'MAPA', 'forecast', 'glmnet', 'neuralnet', 'plotrix', 'tsutils', 'uroot'\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  There is a binary version available but the source version is later:\n",
      "             binary source needs_compilation\n",
      "RcppParallel  5.0.0  5.0.1              TRUE\n",
      "\n",
      "  Binaries will be installed\n",
      "package 'RcppParallel' successfully unpacked and MD5 sums checked\n",
      "package 'lamW' successfully unpacked and MD5 sums checked\n",
      "package 'pracma' successfully unpacked and MD5 sums checked\n",
      "package 'statmod' successfully unpacked and MD5 sums checked\n",
      "package 'TTR' successfully unpacked and MD5 sums checked\n",
      "package 'curl' successfully unpacked and MD5 sums checked\n",
      "package 'greybox' successfully unpacked and MD5 sums checked\n",
      "package 'nloptr' successfully unpacked and MD5 sums checked\n",
      "package 'quadprog' successfully unpacked and MD5 sums checked\n",
      "package 'quantmod' successfully unpacked and MD5 sums checked\n",
      "package 'codetools' successfully unpacked and MD5 sums checked\n",
      "package 'iterators' successfully unpacked and MD5 sums checked\n",
      "package 'smooth' successfully unpacked and MD5 sums checked\n",
      "package 'fracdiff' successfully unpacked and MD5 sums checked\n",
      "package 'lmtest' successfully unpacked and MD5 sums checked\n",
      "package 'nnet' successfully unpacked and MD5 sums checked\n",
      "package 'timeDate' successfully unpacked and MD5 sums checked\n",
      "package 'tseries' successfully unpacked and MD5 sums checked\n",
      "package 'urca' successfully unpacked and MD5 sums checked\n",
      "package 'RcppArmadillo' successfully unpacked and MD5 sums checked\n",
      "package 'foreach' successfully unpacked and MD5 sums checked\n",
      "package 'shape' successfully unpacked and MD5 sums checked\n",
      "package 'Deriv' successfully unpacked and MD5 sums checked\n",
      "package 'MAPA' successfully unpacked and MD5 sums checked\n",
      "package 'forecast' successfully unpacked and MD5 sums checked\n",
      "package 'glmnet' successfully unpacked and MD5 sums checked\n",
      "package 'neuralnet' successfully unpacked and MD5 sums checked\n",
      "package 'plotrix' successfully unpacked and MD5 sums checked\n",
      "package 'tsutils' successfully unpacked and MD5 sums checked\n",
      "package 'uroot' successfully unpacked and MD5 sums checked\n",
      "package 'nnfor' successfully unpacked and MD5 sums checked\n",
      "\n",
      "The downloaded binary packages are in\n",
      "\tC:\\Users\\Zeryab\\AppData\\Local\\Temp\\RtmpmGIzto\\downloaded_packages\n"
     ]
    }
   ],
   "source": [
    "install.packages('ggplot2')\n",
    "install.packages('dplyr')\n",
    "install.packages('lubridate')\n",
    "install.packages('tstools')\n",
    "install.packages('nnfor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load dependencies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: forecast\n",
      "Registered S3 method overwritten by 'quantmod':\n",
      "  method            from\n",
      "  as.zoo.data.frame zoo \n"
     ]
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "library(dplyr)\n",
    "library(lubridate)\n",
    "library(tstools)\n",
    "library(nnfor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing and visualising data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t664870 obs. of  7 variables:\n",
      " $ Date                        : chr  \"2014-03-09T14:25:29+01:00\" \"2018-01-23T15:07:32+01:00\" \"2018-02-06T15:35:49+01:00\" \"2014-03-10T09:07:15+01:00\" ...\n",
      " $ Date.et.heure.de.restitution: chr  \"\" \"\" \"\" \"\" ...\n",
      " $ Gare                        : chr  \"Paris Montparnasse\" \"Saint-Étienne Châteaucreux\" \"Rennes\" \"Toulouse Matabiau\" ...\n",
      " $ Code.UIC                    : int  87391003 87726000 87471003 87611004 87223263 87113001 87741009 87722025 87391003 87212027 ...\n",
      " $ Nature.d.objets             : chr  \"Manteau, veste, blazer, parka, blouson, cape\" \"Montre\" \"Clés, porte-clés\" \"Sac de voyage, sac de sport, sac à bandoulière\" ...\n",
      " $ Type.d.objets               : chr  \"Vêtements, chaussures\" \"Bijoux, montres\" \"Clés, porte-clés, badge magnétique\" \"Bagagerie: sacs, valises, cartables\" ...\n",
      " $ Type.d.enregistrement       : chr  \"Objet trouvé\" \"Objet trouvé\" \"Objet trouvé\" \"Objet trouvé\" ...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "     Date           Date.et.heure.de.restitution     Gare          \n",
       " Length:664870      Length:664870                Length:664870     \n",
       " Class :character   Class :character             Class :character  \n",
       " Mode  :character   Mode  :character             Mode  :character  \n",
       "                                                                   \n",
       "                                                                   \n",
       "                                                                   \n",
       "                                                                   \n",
       "    Code.UIC        Nature.d.objets    Type.d.objets      Type.d.enregistrement\n",
       " Min.   :87111849   Length:664870      Length:664870      Length:664870        \n",
       " 1st Qu.:87286542   Class :character   Class :character   Class :character     \n",
       " Median :87474098   Mode  :character   Mode  :character   Mode  :character     \n",
       " Mean   :87482464                                                              \n",
       " 3rd Qu.:87686006                                                              \n",
       " Max.   :87785006                                                              \n",
       " NA's   :291                                                                   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(stringsAsFactors = FALSE)\n",
    "# Downloading and reading a large file : This may take time \n",
    "objets_trouvees <- read.csv2(\"https://www.data.gouv.fr/fr/datasets/r/49f5bbda-6e2e-42bd-b8fc-a71417fe10fc\"\n",
    "                ,fileEncoding=\"UTF-8\"\n",
    "                ,header = TRUE)\n",
    "\n",
    "str(objets_trouvees)\n",
    "summary(objets_trouvees)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "objets_trouvees$Date = as.Date(objets_trouvees$Date, \"%Y-%m-%d\")\n",
    "objets_trouvees_table = table(objets_trouvees$Date,objets_trouvees$Type.d.objets,objets_trouvees$Gare)\n",
    "objets_trouvees_table = data.frame(objets_trouvees_table,stringsAsFactors = FALSE)\n",
    "names(objets_trouvees_table) = c(\"Date\", \"Type\", \"Gare\",\"Objets\") # TODO : to DNN\n",
    "objets_trouvees_table$Date = as.Date(objets_trouvees_table$Date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in `==.default`(objets_trouvees_table$Gare, gare):\n",
      "\"la taille d'un objet plus long n'est pas multiple de la taille d'un objet plus court\"Warning message in is.na(e1) | is.na(e2):\n",
      "\"la taille d'un objet plus long n'est pas multiple de la taille d'un objet plus court\""
     ]
    },
    {
     "data": {
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skCJMmWzdnyTRYgSbZszpZvsgBJsmVz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\nEFRBAAmCKgggQVAF/R8+nCidjnHSZwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Filtred value\n",
    "selectedGare = order(table(objets_trouvees$Gare), decreasing = TRUE ) [2:7]\n",
    "gare=data.frame(table(objets_trouvees$Gare) , stringsAsFactors = FALSE)[selectedGare,]$Var1 \n",
    "type=\"Bagagerie: sacs, valises, cartables\"\n",
    "\n",
    "objets_trouvees_table_selected = objets_trouvees_table[which(objets_trouvees_table$Date >= \"2017-06-01\" \n",
    "                                                             & objets_trouvees_table$Date <= \"2017-09-01\"\n",
    "                                                             & objets_trouvees_table$Gare == gare \n",
    "                                                             & objets_trouvees_table$Type == type\n",
    "                                                             )\n",
    "                                                              , ]\n",
    "\n",
    "ggplot(objets_trouvees_table_selected, aes(x = Date, y = Objets, color=Gare)) +\n",
    "  geom_line() + \n",
    "  scale_x_date(date_labels = \"%d-%b\") + \n",
    "  xlab(\"\") + \n",
    "  ylab(\"Number of Luggage\") + \n",
    "  #facet_grid(Gare~Type)\n",
    "  ggtitle(\"Luggage reported to SNCF on Summer 2017 on Biggest Stations\") +\n",
    "  theme(plot.title = element_text(hjust = 0.5)) +\n",
    "  theme(legend.position=\"bottom\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JYZxh8DkHRNQBLLDOOPAUi6JiCJZYbx\nxwAkXROQxDLD+GMAkq4JSGKZYfwxAEnXBCSxzDD+GICkawKSWGYYfwxA0jUBSSwzjD8GIOma\ngCSWGcYfA5B0TUASywzjjwFIuiYgiWWG8ccAJF0TkMQyw/hjAJKuCUhimWH8MQBJ1/QSkFyh\nC0heBUjVxwFJLDN0NYbtrYBUfRyQxDJDV2PY3gpI1ccBSSwzdDWG7a2AVH0ckMQyQ1dj2N4K\nSNXHAUksM3Q1hu2tgFR9HJDEMkNXY9jeCkjVxwFJLDN0NYbtrYBUfRyQxDJDV2PY3gpI1ccB\nSSwzdDWG7a2AVH0ckMQyQ1dj2N4KSNXHAUksM3Q1hu2tgFR9HJDEMkNXY9jeCkjVxwFJLDN0\nNYbtrYBUfRyQxDJDV2PY3gpI1ccBSSwzdDWG7a2AVH0ckMQyQ1dj2N4KSNXHAUksM3Q1hu2t\ngFR9HJDEMkNXY9jeCkjVxwFJLDN0NYbtrYBUfRyQxDJDV2PY3gpI1ccBSSwzdDWG7a2AVH3c\nK0Byy9UApOJWQKo+DkhimaGnMSi2AlL1cUASyww9jUGxFZCqjwOSWGboaQyKrYBUfRyQxDJD\nT2NQbAWk6uOAJJYZehqDYisgVR8HJLHM0NMYFFsBqfo4IIllhp7GoNgKSNXHAUksM/Q0BsVW\nQKo+DkhimaGnMSi2AlL1cUASyww9jUGxFZCqjwOSWGboaQyKrYBUfRyQxDJDT2NQbAWk6uOA\nJJYZehqDYisgVR8HJLHM0NMYFFsBqfo4IIllhp7GoNgKSNXHAUksM/Q0BsVWQKo+DkhimaGn\nMSi2AlL1cUASyww9jUGxFZCqjwOSWGboaQyKrYBUfRyQxDJDT2NQbAWk6uOAJJYZehqDYisg\nVR8HJLHM0NMYFFsBqfo4IIllhp7GoNgKSNXHAUksM/Q0BsVWQKo+DkhimaGnMSi2AlL1cUAS\nyww9jUGxFZCqjwOSWGboaQyKrYBUfRyQxDJDT2NQbDU6JJbpctwn9et5o+MZSd3EM5JXeuDV\nsGsMbt1WubuqBKSKJiB5JSABqfMrCEjarYBUfRyQxDJDT2NQbAWk6uOAJJYZehqDYisgVR8H\nJLHM0NMYFFt1DOnf785d3/8HJCBtlIBUgPTnm/tYV+d+AQlI5RKQCpD+dj8+FF1/uncgAalc\nAlIB0gei+T8gZUpAAtJSAlJFE5C8EpAKkO4v7X64v4EEpHIJSAVIfy7ua11+A+mZkOarAUjF\nrbqFdL3+8825bz/+FB0BCUhA2oCkWvrzA2nv7rcrAUiqrYBUfRyQxDJDR2PQbNUtJDevy9+F\nT5P0538yJHf8hOvSsTHkiQBpR2kYSB8rL0l/fiDFFSA1KXUO6T/3/sHn97v7WXwLXH9+IMUV\nIDUpdQ7pm7u9X+e+Ff9SVn9+IMUVIDUpdQ5pwrPx1Q368wMprgCpSalzSO/TS7v366/PZyUg\nJUpAAtJSSkP6vXxlg3P/AilZAhKQllIa0vXP/JUN7kfWEZCABKQyJN3Snx9IcQVITUpAAtLW\nCYGk3qpfSD+mv40FEpDKJSAVIM2OgASkjRKQCpAu7n/v7vef97N88xMglSpAahA0Denjmegf\n99/1z1m++QmQShUgNQiahfTf598f8dKuUAISkJZSGtJ39/O3+3b9BaRCCUhAWkppSJ+C3j/f\nazjJNz8BUqkCpAZB05Cu/337/FZCpa9qANKhMTggNSn1Dkm39OcHUlQBUpsSkIC0dUIgqbfq\nFhLfs0FRAhKQltIWJL5nA5DKJSAVIPE9GxQlIAFpKaUh8T0bFCUgAWkppSHxPRsUJSABaSml\nIfE9GxQlIAFpKaUh8T0bFCUgAWkppSGd7Hs23H7QUFdXEJDalHqHpFv68wMpqgCpTQlIQNo6\nIZDUW3UL6WRf2dAjpOmOBtKh0jCQTvGVDUAqVoDUIGga0sm+sgFIxQqQGgRNQzrZVzYAqVgB\nUoOgaUgn+8oGIBUrQGoQNA3pZF/ZAKRiBUgNgqYhnewrG4BUrACpQdA0JL6yQVECEpCWUgaS\nbunPD6SoAqQ2JSABaeuEbv4FSBtbdQtp+dtYIAGpXAISkIB0qAlIXikN6bZ+v/9TdAQkS0jO\n+xVI5a06h3T948qS9OcHUlQBUpvSGJDO8tMogFSsAKlB0CKkn+4CJCCVS0AqQJrfayh/F339\n+YEUVYDUpjQGpMtJfhoFkIoVIDUImoakXPrzAymqAKlNCUhA2jghkDSlQSDxrl2hBCQgLSUg\nVQc9MgZXIAKkHSUgAWnjhEDSlIAEpI0TAklTAhKQNk4IJE0JSEDaOCGQNKWeIQXfHRJIQCqX\neoUUSAJSrglIcjTD4SYgeaUEJP3Snx9IYQVIjUpAAtLGCYGkKQEJSBsnBJKmBCQgbZwQSJoS\nkIC0cUIgaUo9Q/qmJqU/P5DCCpAalXqG5FzqL2Jv/+j88rGABCSvBKRdkG5+ll+ABCQgeaUE\npPfEX8herkCKS0AC0lJKQJp+pkv4lQ1AiktAAtJSSkC6Jr9YNYb018daNXW6XM1bkqbLbb9L\n6rxfWRXrWaPbOC/PSHHpyBh4RmpU6vwZ6frnx/SDxoAEpGIJSAVI3o++BBKQiiUgFSD9Pf0w\n5r+BBKRyCUgFSNObDbxrVygBCUhLaR8kvrLBKwEJSEspDSnx0i619OcHUlgBUqNS55ASbzYA\nKS4BCUhLKQ0p8fY3kOISkIC0lDKQdEt/fiCFFSA1KgEJSBsnBJKmBCQgbZwQSJoSkB4Iyd3+\n6+kKUkO6fwBScSsgVR8HJDma4XATkLwSkKqDAglISykN6b38F7FAWkcAUuMIp4B00T096c8P\npKByT1Ta3XmtQNrYqltI/3v/Uf6aBiABCUh+KQ3pXD+NAkjlCpAaBAVSddCDkLJEgLSj1Dkk\n5dKfH0hBBUitSkACEpAalLqH9O/3j5d17/8DEpA2SkAqQPrz7evzI+d+AQlI5RKQCpD+dj8+\n/5n5T/cOJCCVS0AqQPp8t276D0iZEpCAtJSAVB0USEBaSmlI95d2P87xzU+AVK4AqUHQNKQ/\np/rmJ0AqV4DUIGga0vX6z4m++QmQyhUgNQiag6Ra+vMDKagAqVUJSEACUoNS75Bu39fuH17a\nFUpAAtJSSkN68ndaddtNJ4Xk5tIjIDlN01bpFSC5UlMJ0vv0vb+/AwlI5RKQCpDufxH750l/\nIQskIJ0D0nd3++yIZ6RCCUhAWkppSNfvt5d2ZUdAAhKQ8pCcv4AEpHIJSEACkv4wIJWPS0DS\nL31IIAUVILUqAQlIQGpQAhKQgNSg1DmkP38/93Mkt9l0VkhuKgHpDJC+P/nNBiAB6RSQnPtZ\nFASkVQQgVUU4Wuoc0rfn/jSKl4Xk/YzOB0ByQNIFddWQfm/+41ggAQlIXikN6frzmZ8jOQES\nkE4B6blvNgBJgHQOSM99s+H0kJz3K5AOlTqH9P2pbzYASYDUFSTFlwikIV2///3EH30JJAHS\nOSA996u/gSRAAlJ9bCABaW+pc0jKpQ8JJL+yB9LtNzaQppMAqdwEpGk/IKUqQFIG5aXdtB+Q\nUhUgKYMCadoPSKkKkJRBj760+/3+T9ERkIAEJAWk6x9XlqQPCSS/ooDkvFphKyBJPkPx6xEe\nCulJP/oSSEDSlsaA9NNdgASkcglIBUjzew0/ngNpDv5ykKagQDoTpEvZEZCABKQiJOXSh3wm\npOmyBVJUAZIyKJCm7QRIiQqQlEErIT39e38DCUjaEpCABKQGpZ4hzesfdyn/i3N9SCD5FSA1\nKw0A6fe3rx8kC6THQfLeXgTSWSD969y/ZUZAAtLRCIdLvUP6/b75dASkwSEtYwZSsclJNSTN\n0xGQgHQ0wvFS15A+no6+ab6JEJCABKQ8pJ+XjX89ASQgLSUg5SDx90i6EpCAtJQ6hHS/nMpN\nQJJjGYCk3v3Amw3KpQ8JJL/ivA9AOlQCEpCA1KAEJCABqUEJSEACUoMSkMqQ8nfx2SHdgwIJ\nSEDa2h1IzUrngGSz3PxLw/36uXlLlFUk75a7VbdFjI6GYrBa3bqdg+IZaWfQ6jFMD288Ix0v\nPegZadmLl3ZAiipAUu4OJFn2A9KqAiTl7kCSZT8grSpAUu4OJFn2A9KqAiTl7kCSZT8grSpA\nUu4OJFn2A9KqAiTl7kCSZT8grSpAUu4OJFn2A9KqAiTl7kCSZb8RIPlBgQQkIG3sDiT97rqt\ngJTLDaSwCqTiVkDK5QZSWDaA5F0eQCo1+dcjkIAUV4Ck3B1I9wWkZMUbM5BKTUC6LyAlK0BS\n7g6k+8pCStk6NyR/KyApd19BcqmuXiFl7+LTQxIHJH0JSEACUoMSkHqA5LqF5N9XQNrcCkip\nEpCApC4BKVuaLqfycUCSigxAqtgdSOLvBCQBUtXuQBJ/JyAJkKp2B5L4OyXftYtHAqRsdCDN\nJSBFC0hAUuwOJPF3AtLnVg5Iu3cHkvg7AUmAVLU7kMTfCUgCpKrdgST+TukvEVpJOjWkr0cO\nIO3cPYa0evwFEpCAtL07kMTfaQ3p86ICkjY6kKYSkKIOIAFJszuQxN+pH0jzOSNIsy8g6UtA\nehgk//oMlrtNpTookF4I0rwZkIAEpKrdB4e0+gwiPO7EkD5/AyQg1ccG0vwbIAGpPvYzICWH\noiuNDcm/PIBUaAo+1QASkARIVbsDyd9HgCRAqtodSP4+AiQBUtXuQPL3ESAJkKp2B5K/j2Qh\nRVMBUqYCpKkEpKgDSEBS7A4kfx8BkgCpancg+fsIkKaNgbRvdyD5+wiQBEhVuwPJ30fykMKx\nAClTGRCSN0Ug5Y4DkuzPAKSK3Z8Pya0qmluSu8yi44Ak+zM8EFLFyyqn6gqWK48BSPPhp4YU\nEQHS7gxAypeABCR1hheBVBxmP5AqxY8HyUnPkNwgkJzE5oEEJCAVS6eAlLmAXgDSNXrx4PKb\nA0nyGYC0RAeSAClfAlK+BCQgqTO8CKTiDekIUvlefxQkb1iPg5T7TPTxkNY/GgRIQCpWXgPS\n3osYSIXrBUipCpBaQXIbYwDSHN0Qkrc9kLajA2kOCqRgAWn+AKRCUw+QvDeiNLGfBqlKvCWk\n8I6ygOS8Wg+QXHx19gIpGJQACUhA2rm7ACkoASk4g3sIpPI7tmlIsSQgAalYARKQgOSVKiF5\ndwKQ5mjRZHQZXgfScnpN7OdBqhEPpJeCNP0GSEAC0q7dBUhBCUjBGYCk3V2AFJSAFJwBSNrd\npSdI8+k1sZ8IqUI8kNpBcvFkdBmAVCgBCUjaDEAqlIAEJG0GIBVK4XX2mpAESKoMQCqUgHQO\nSKX3b4C0H9L9Q/eQ9osfDtL9ka1DSG41GV2GZ0AKnh+844AUtgEpsxGQ/A9ACnYHEpCKJTtI\nl8+luveCe0p/x/gJgTT/HyAlSsv4BoQU/KkUEkhAUoSauoG0eb1k7qreIO1+DdoYkvO7XgvS\nNIYXghQ6AhKQgFQHafkU6a+PNR86La/VrX6jWskt9i+3/m282+rP69tQm6F4UCLa6phwgwNz\nWG/jwj0b7Z04l37n2iyb/S1uXHwdu7BaedbL/MvXiiE7j+hzn5HcUtI+I913d6uu/c9I2c+3\ntM9Iigy1z0jRQyrPSKWS2TPSoglIQAKSd5w9pMwV2yukcEYOSFulw5DmCC8Eqeal3ZMhhTnC\n3YEEpGLJFJL3zl18fiAFZyrfMfJMSFsvvIF0r6wun0Yv7YIvbAASkIBUBylc8fnTkIog4lJ4\nGRtCyl9BwaUOpK0SkNpD8q+gp0JyjSC5UnYgNYHk5hKQ4pM9H9LyHh2QolSPghS8PilsBSQg\nZcfg4kpy9/NCCs6hhhTdQ8ch6V9eZpu6glQCEZc6gBQcAqStEpCABCQgFUtAagPJFTNkx+Ck\nLaRkhrNA8j+fBFJ0MiAlUwEpUQFSGVJ01Q4Fae40gBTdPiDVQ/KiA2lVAhKQlBmAVLolPUFy\n5QyDQfIfHfqBFDQNACn6B8bLcUBK7P5CkIIMhQhAut+oa7SzxGMA0rITkIB0r3QGKf5j/pZo\nAZpC8l8Inw5S9M2rgVQoAQlIq72AtGv3e+VJkJYr6KmQnLSANGl6CKTVHRP2nRVSeKUDKTwZ\nkIAkQDKBFH9znuwt6QOSLBcbkDZKQOoRUvxEdjZI6ydqIEWHqTMAqXBLmkGadwLScq4lwyMg\nTScB0mtDctPvgFQsHYHkwhKQwpOlIMWFziHNGIC0VQISkIAEpGIJSE+GlN29E0jR5XFaSMuN\nLm+VbfITAXz9YmkAAA6wSURBVGn1u/lPQAJSqdQjpJUkDaQ1v0dB8o4HUrF0AJKLSkCKggMJ\nSAKkFpASaoC0vjkvAylN5BAk51V2QXJBZf5pk0Ba79UC0tIHpGJpdEjLRyABCUgngzRdQU+F\n5PwjM5DCaZZSZd85zI1hZtwMUioDkLxKA0jpRysgASmuAClx4IiQ4hqQngRJ3MMhuY4gueDO\n8S8PIAGpL0guOKMeUjgfIAEJSN4ZgQSkr84OICXmsBeSWypAktTcU49WMaTUT4I8OaRreG/G\ne3UGaesKGhLSMuQ0pPWDB5DC5EBKQ1pt9TKQkjcQSI+DlP2XtUCSUob1XkCaS0DyjgOSlDKs\n9wLSXAKSd9yLQVrPAUhepQmk1KMVkICUSHWvACmdYRRIUfHUkNLX56tCcukb+ABI044hreU2\nJ66J4HxAAlIq1a0CpPQJgZSIAKRUqlsFSOkTdgXpHhJIqb0OQEpPFEgtISUereJrBkjx6YHk\n1YF0a+oPUvqFyDakjVviHTcipPWXRQIpLO2AFAgBUil3ugIkvwlIksmQapsKQFqOezlIsaQj\nkPytgOTtHl9tLv7T60Lyb298/QApExRI4dHLn8aFtLptQPKbgCSZDKm2qTA6pO05ASmdoS0k\niQdhDMlJP5DmqyL50A6kOMF85BiQtp+rh4R0+00Okq6UywCksBR2ASmdIbw8vSYgSSZDqk22\nIMVXG5AaQlonKENaXlIOAWl5eamIkCoBCUhAEiAFuwMpStAjpMQV+zqQgnMCCUheF5CAtGze\nHNJXyGdCmkd3Nkip8akhOb8CpOzcV3SCP7nVTvPmLwopnm8pFZCKJSA9FVL6xw4VIRVGB6Tc\nkR1AWgYSbvUMSG7+sFYzEKRAxbkhJa7PfiBtxToIKbrLdkBKdOUyKCFFr+Hd/BFIQPKagCTp\nDFHfaSG5JKT1JXv1b7s/EyBJIcN6LyAF+2chBbXoi1qAdK8AKdUDpGD3riAlQQQnd8kvYTs1\nJJfYHUjR7mpILqgAaXXo+jggSaoLSF4FSKtD18cBSVJdQPIqLwvJrUImct+PWy7M229fF1Li\nMgOSpDNEjXpIQXogjQUpeYcmSsNBWu61dfbkgweQguRZSN5VZg/JuxdvFSCleqwhuUz2Z0Dy\nkFzXdyyQgAQkIHlHAimXAUhR6QUgiUtceuH96SSlJvV3tFf/EP9izEJy4ZHdQHJ+j1cEkr97\nB5DmcuKhEEhfFSAVMigjrEtAei6kdVMGkt8NpKgyDqTgnoj+vOwOpPh8mQkAKd4bSP7uLwop\nvVxhK7ecKW7KHuSW/6nI6MLfuvz/0q6dg8lETe+SHkWjJLpDm93vqR3d8ifVQNRh6hpd8LvM\nHtlrzeWupspoy0o/cmw+I/mf9QQPHflnpNWjRJjB9Bkp943802PIPZ7xjBQd1+0z0urVz3JQ\nZy/tgORVgeTvDqQgeh6Sd3EDSV4BkltfxE+D5IL66m4FUo+QJHrvBEjRcQ+C5A/+ZJBcHCSX\nW2ZI3lPyoJCCVxh++TGQ4qs4TAak8LD5wokhJSMAaThIaw9AknQGCTuBFBwiQIoqQJJ0Bgk7\nx4SU/uLT+ezTRRzeECAVM0Sbvzqk8CIGUjakf9zpIcX7nwiS9+2iogjpBw8gBecrQpqv9OCG\npHLLNPJgAGVILjwSSJkDg2RAig5z6ftrcEjRT+EGklcBkqQzeAmWCpDWmx+D5L3+ywVNHhd0\nAQlIS9ejIS0qgCTnhBTfr3sglYL6pSpIwSlz9+r9GCD5R/YDKfdSEkjR7kDyz1eElLwlmQfs\nxACmViDlMygODKIBKT4sfX8BKT4hkIJoQIoPewVIz3xpFxw3BCR3X/ndNyCFl0virgCSGSTt\nI5x3U4qQUiUgST5DcILMVn6HISRvLxdUng6psLuuCUhAijqAlCkNByl3ne2GtLqzzgUpun0P\ngOTOAyn1GcJGqdyUvhqAlD3huSGFQeMmIOVLQKqF5IKgOyBlz2cNqfi+TyLoqskF2aMvB3ga\npD1jAFLxlvQPyb+IgbTK/jBIX80vAmnfdS1B7rANSNH+QJogZU5YLG00Ja+GU0CK7oZGkOZ2\nBSTvfGeA5ARI+dILQXLBgbUnBJIAKVF6GUjhl7v2Cim+8F4C0nYTkPSJXJAHSKUM4UZACioK\nSDue9vVNbikBSXFCIAmQUqWzQnJxW3NIydH5R/YFqfiGnKLJSQxpPeMnQFo1AcmLs++WDAUp\nfwOBlG0C0mkhLZfVK0HyXs76ruYuIPUCKb44VLcESJLNEG6/uftLQNoz5B1Nbi4BSXFCIAmQ\nkqVzQnKrttNBireyhRTcP0DK7A0kIG00AWmrqSdIxdEBKSoBKdW0CWk140aQbkqDCM+AFN9G\nIOUzpLcaCVKQHUgvDin1ZO4f2RUkp7qF+yCt7q/HQ1pZBlJTSG7d1gekwieBXUPybzaQ0iUg\n7TkhkARI6VI/kNZTUd2SQ5DyL7VKkLxfXwrS/Hlh8Epv7hoBkuZrd+sgzTMGkuKEdZDWp3sJ\nSPmbo4FUGlYlpI9+IB2EJANA2veq4ySQFE1AehIkl2oD0vbuOUjRZ5RASpZ6geTKXUCKSkCq\ngKT667RKSNP4gKQ4IZAESLlSF5DCdwOGgPT1YRek+MqToMUQkltVchmSTRlI8aX+cEiJSwZI\nQAJSpglIuyDV3pIVpPiuaQnpdoAGUpDCAtK8mzkk5zXcSsNCqjWia3JAAtI9Q6oJSK8JKd3W\nEJJ/sBLS5o+dHAPS/Q2WzHsnxpDWbHqD1MVLu+rjlJDCyoMh6S5iIKWb2kFSVoAEJCABqfK4\nKkirV/lAkhwkF3TIsyDFPwdBgNT2ljwP0vySu3dITp8h0ZSHFI3v0ZBSn1YDqfq4k0AS90KQ\nvOxAAhKQgJQoAUlzwteD5MKOoASkROm1IN3vBSCFff1CCgYPJCBlK27t6AUgFR48gNQqka7p\n4ZCW++/1IEXXM5ASJSBpTvhykNz6j0AqloCkOWFHkKZTAMk7DkjVx1VCut0NxyBJ/C9Q4u33\n7G4Hyekz7IQUPitaQnK5UnQckKqPA5LkMgR9QFJXgASk1pDiCpA2S0DSnBBIQNooAUlzQiA9\nBZJbl4DU+JbUQgqvCSB9dXYOKbi/gNT2lgBJchmCPiCpK0DaC8ltNsWl8EsnTw9p/TdL4Xf9\nLUEqGFFB8tUA6UgiXROQJJch6AOSugKkfXu5CkjR9e5SV9BXmy6Cv5UVJLeq5DPshRRsbgfJ\nrWq5H74EpOrjgCSZDKm2jQxAOtIEJCDdW6OSWzcBaasEJNUJgdQC0nwckID0tUVDSLsncwJI\nxQcPIDVKpGuqh+SjGBFS8HbcmSGFb5Teu4DU+JYASTIZgq6aDC7RBKStEpBUJwQSkMqlnZAu\nH+slIYUlt47QGSSXOFCZIQUpfkfcy/BQSKsjB4V0mX8BEpCA5JVeEFLVu3Zh6cyQXLLp8ZAS\nqYAEpFwFSHHTq0H662MpjuhxVbytUnHIkzaefkxgxSGlI9vk1IRL56i4Wc9ar/OMdLgp+VBs\nOYbsVlYZGMPeVECqaOIK8kqMAUhcQYeaGINXAlJFE1eQV2IMQOIKOtTEGLzSPkin+MqG401c\nQV6JMdRAClfj2EDaUervCmocYdQxAEnfxBXklRgDkLiCDjUxBq8EpIomriCvxBiAxBV0qIkx\neCUgVTRxBXklxgAkrqBDTYzBKwGpookryCsxBiBxBR1qYgxeCUgVTVxBXokxAIkr6FATY/BK\nQKpo4grySowBSFxBh5oYg1cCUkUTV5BXYgxA4go61MQYvBKQKpq4grwSYwASV9ChJsbglYBU\n0cQV5JUYA5C4gg41MQavBKSKJq4gr8QYgMQVdKiJMXglIFU0cQV5JcYAJK6gQ02MwSsBqaKJ\nK8grMQYgcQUdamIMXglIFU1cQV6JMQCJK+hQE2PwSocg3ZbmB/fpfrifqivVVH1gXVN1BuMx\ntMvAGPRNcReQ1E1cQfkuxgAkdRNXUL6LMQBJ3cQVlO9iDCP8wGgWq/sFJBarwQISi9VgAYnF\narCAxGI1WEBisRqsnT/VfPnh5t6POb8om1IdiTNdUi2qCNtdigj5ng7G0CgDY7geHkO0NJAu\nyy+FE242fdXL21xuN2/dktp9vclmhsvtDKUIS8+lJoPtGFLbM4Z0BOMxxGsXpEtJ7mbTPLr8\nNssmuXvPO7YwutwZlrEUHgZzm6gy2I4htT1jSEcwHkO89j0jeZtdlvr9keOy0bQ8BvmZpqfO\nr4/3m+Y/6cYtn9vcP14uiQjFDMtjUDbC/BAYRNiXwW4Ml6XVj7AzA2NoOoavc7WCdLksaPeM\n7hK0zaPzn2zvN+Xqz21qCSJck5PZHF0YYXktcblWZLAdw1Kfo3g3Wp2BMRwfQ7RTw2ekutGF\nbXlIV+/e8+IHEa51kKKu/BWkyWA7huAKiv68IwNjOD6GMEsPkPwn5eTo7uXbc7oJpOB1QXJ0\n6gy2Y7iV4pcxra4gxrBjDFPn1FsBaT64FDvRFI1ufn3ptUWjm1+YzjdnulXT3RhEuEaTWWdI\nNoQR4tHtzGA8hsT2m1cQYzAbw/U6H/3sZ6RL0JZ+DJpuincTppYgwjWazDpD9atidQbbMay2\nD260OgNjOD6G5djbx6OQLprYl/Lo4idz764KWi73m3aZBxFGuOYm4+2QGd3Wk7k6g+0Y/Bs5\nR9h/VzCG42O4XryPl4Nf2bA8oV5UTcsTY7D31Da3TPPzTj+t2+2fnuqjCNsZpu0LEa6pCPoM\ntmO4zhGucYQ9GRjD8TF4Hz93qv5au8t2i7Kp+sy63TvIYBuhhwwdRHhyBiA9IMOpr6BeIowJ\n6aIJpGqqOPee3TvIYBuhhwwdRHh6Bv4ZBYvVYAGJxWqwgMRiNVhAYrEaLCCxWA0WkFisBgtI\nLFaDBSQWq8ECEovVYAGJxWqwgMRiNVhAYrEaLCCxWA0WkFisBgtILFaDBSQWq8ECEovVYAGJ\nxWqwgMRiNVhAYrEaLCCxWA0WkFisBgtILFaDBSQWq8ECEovVYAGJxWqwgMRiNVj/B2UtFZXg\n1CIVAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ggplot(objets_trouvees_table_selected, aes(x = Date, y = Objets)) +\n",
    "  geom_line() + \n",
    "  scale_x_date(date_labels = \"%d-%b\") + \n",
    "  xlab(\"\") + \n",
    "  ylab(\"Number of Luggage\") + \n",
    "  facet_grid(~Gare)\n",
    "  ggtitle(\"Luggage reported to SNCF on Summer 2017 on Biggest Stations\") +\n",
    "  theme(plot.title = element_text(hjust = 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in `==.default`(objets_trouvees_table$Type, type):\n",
      "\"la taille d'un objet plus long n'est pas multiple de la taille d'un objet plus court\"Warning message in is.na(e1) | is.na(e2):\n",
      "\"la taille d'un objet plus long n'est pas multiple de la taille d'un objet plus court\""
     ]
    },
    {
     "data": {
      "image/png": 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abhvgDJ15cRApBqNQ33BUi+vowQgFSr\nabgvQPL1ZYQApFpNw30Bkq8vIwQg1Woa7tsqP0N9AZIQApBqNQ33BUi+vowQgFSrabBvq70I\n9AVIQghAqtU02BcgefsyQgBSrabBvgDJ25cRApBqNQ32BUjevowQgFSrabAvQPL2ZYQApFpN\ng31rByk/SQDpyKbBvgDJ25cRApBqNQ32BUjevowQgFSrabAvQPL2ZYQApFpNg30BkrcvIwQg\n1Woa7AuQvH0ZIQCpVtNgX4Dk7csIAUi1mgb7pgKpzT6kAVI+ASRSCUHKPKajf5EqUADpyKbB\nvgDJVwDpyKbBvgDJVwDpyKbBvgDJVwDpyKbBvgDJVwDpyKbBvq3hVYgvQBJDAFKtpsG+AMlX\nAOnIpsG+AMlXAOnIpsG+AMlXAOnIpsG+AMlXAOnIpsG+AMlXAOnIpsG+AMlXAOnIpsG+AMlX\nAOnIpqG+rfFlgC9AEkMAUq2mob4AyVsA6cimob4AyVsA6cimob4AyVsA6cimob71g5SdJIB0\nZNNQX4DkLYB0ZNNQX4DkLYB0ZNNQX4DkLYB0ZNNQ31QgtTEZgtTO7QVI2wsgUQJI3gJIRzYN\n9QVI3gJIRzYN9U0KUtYxDZAyCiBRAkjeAkhHNg31bS2vfX0BkhQCkGo1DfUFSN4CSEc2DfUF\nSN4CSEc2DfUFSN4CSEc2DfUFSN4CSEc2DfUFSN4CSEc2DfUFSN4CSEc2DfUFSN4CSEc2DfUF\nSN4CSEc2DfUFSN4CSEc2DfUFSN4CSPuYZjvOAIksksQYIO1jCpC2EUDKKIBECSB5CyDtY1o0\nSK31jacvQJJCAFJ6U4C0jQBSRgEkQvWC1K7tZbsCpFABJEIAyV8AaR/TU4DURmUIEUDKqSJA\nyjW6ABJZJkn9ANIupm220RUNUmBNAZIeApCSmwKkbQSQcgogEUoMUkaSAkBKdCgA0i6mAGkb\nAaScAkiEAJK/ANIupgBpGwGknAJIhACSvwDSLqYAaRsBpJwCSIQAkr+ygASpau//K1et411I\nnnxtbY0viSL5DwVmpFSmmJG20cFnpDj/5AJIhACSvwDSLqYAaRsBpJwqAqRcowsgkUUAUqAA\nEqGTgZSkfgBpF1OAtI0AUk4BJELVgtT2ACmjABIhgOQvgLSHadsDpC0EkLIKIBECSP4CSHuY\nAqRtBJCyCiC5pdYsqKY3oRxAGkMAUmpTgLSNRJCYrqkOBUDawxQgbSOAlFUAyS2AFCCAtIfp\nyUDKRhJAyiqA5BZAChBA2sO0Xf7J68sTQAoQQNrDFCBtI4CUVQDJrdOBlKJ+AGkPU4C0jQBS\nVgEktwBSgADSHqYAaRsBpKwCSG4BpAABpD1MAdI2AkhZtTtIrfBvTl+mAFKAANIOpgBpIwGk\nrAJIblULUtsDpJwCSG4BpAABpB1MAdI2Akh5BZDcAkgBAkg7mLbSj3y+TGn1CqkoQNJDAFJi\nU4C0jQBSXgEkt84EUrKzbIC0g+k5QBJKAaRnCEBKbAqQtlEwSAkqCJB2MAVI2wgg5RVAcusg\nILFcAVKEAJJTerUAEi2AtIPp6UDKRBJAyiuA5BRAChFA2sEUIG0jgJRXe4PUKj9z+TIFkEIE\nkPKbAqSNBJDyCiA5BZBCBJDym7baizy+TAGkEAGk/KYAaSMBpLwCSE5VC1I7+nqZAqQIASSn\nAFKIAFJ+U4C0jQBSZgEkp84IUnwFAVJ+U4C0jQBSZgEkpwy1CqgoQNJDAFJaU4C0jQJASnco\nDgkS0SslgjRk8GXKApJnDQGSHlIbSC1AMvoyBZBCBJAyiQJpK5J2A0lKA5D6+kBqqW4BSE4B\npBAdD6S2B0hmX6YAUogAUiaR10gbkZQKJF/SAZIeUhVI7fJPPk+OVlOhcutLgBQvgJRSrfBv\nLk+OANLmAkgJ1Uo/8niyRIK0EUknBonhCpAsYj3xUSBIQ188SJ71k0HKQhJASieAZPflCiCF\n6Fgg8fqlUJC2Icm7saauOz5I0RU8FEjGq4+NPbkCSJurYpA+X5vmdv0BSLQA0uaqFqS/l+au\nW9N8lwFSa3m9pSdbAGlrjRY1gvTWvN8puv1rrkWAxF5vLRWkTUhKBJL38jxA0kNsIN0hWv4r\nDSRXxwAklwBSkI4DUut8u42nhwDS1qoXpOnU7r15KwAktSNqBGkLkgASXYYX61bUYsOleery\nC5BIVQ1S3wIkQnHL3x8vTfPy/kdwlAMkrR+qAGl5DZDiFQAS834JR0e5Iav3g71nygVpA5IA\nElmEF0voICAZugEgKb5MAaQgxa3azbq8OS+T4vwZstxE3NTTU4up8XYXQIrXEUC6y0VSnD9D\nhwEpPUmJQOoBklsxIH011zs+v9fmH7EEHudPy9wJ1q4pD6R1jB4GJP5tvVSqF6SXZlyva16I\nm7Jx/qQsfQCQJF+urCD51a02kGJrGPtkw/wTIBFigpScJIBEFmEGuxUD0nU+tbvevh+z0k4g\n2XoAIEm+XAGkIMWA9Ls+2dA0n3uBZO0AgCT5cgWQghR1H+lvebKheXdwtBNI1j1Fg5SaJIBE\nFmEGu1X9DVlH+wFSD5AYRZjBbiUH6TL+exdAMpoCpI1UMUjv891YgaPLQtNKUpy/U67m1wlS\nYpIKASkDSfWCtHC0br/c8oLkbH0FIE3vSgdpqACkyeBm2EaVYcUSigHp0vxcm9+/q/THTwoC\nybYXILkEkIIUeUP2o/m6/Ul//EQB6b+Hblupjdq9j1rDu0HcJL3xyZVGS85u3TY8N3pUTK9a\nWFUj+4IybS2vt5YO0tfj/pH0VEPWGYn4FKlzRvL45Gd8iobPSN26bbcZycOy4hnptfn327zc\nvgESQ7WAtKYESD6KAelB0PWx1iA++V0SSJb9AImVUgTpuRkgORS1/P318vhTQvJTDQDJLA+Q\n+MOH+v72HiBxyrBiCW1zQzYTSF73CRJ5hmo2Nd6oBEgWbQuS6TMtVFU/2XAUkNThUihIXiQl\nAcnnawIrBmn3v9lwVJDYIzYTSEPAow0ASQ+hQdrpbzbQTTdGHAakltEDwSB1K0n7geS/8FIj\nSLv/zQaABJBUgxpB2v1vNhwWJOb4aRldAJDIIrxgQpGPCC0/ARIhgBSgk4C0+99sYDTdFFIS\nSJZ15epBalVfgFTw32w4Lki8AQSQdIMaQdr9bzYAJICkGlQJEldx/lZxWg6QOImU+jwFkLwE\nkDLJEyTWCGoN+Wy+XJUEktdf7q8YpL2fbGC13BAEkIicT60gDT1AIlXzkw0AyZDP5ssVQApT\nzU82HBokzhACSHr+GkHa+8kGgGTIZ/PlaiuQQsZpXpDiSKr5yQZew/UogETkfAogeanmJxtq\nBEmrjRUkxhjaGKSZJIDEUM1PNhwFJMtg4YFE9UJKkHwu/YsAiTAtBaSdn2xgtvu4ILXmjEZf\ntuZ0XS+B5DslpQKJ/zuO088qQeIqzt8mgGTOaPRl60QgmS5Xg3V8kPS4SkAix1AmkIZ1O0Cy\nKs0NWYBEygbSfQtAMmvwsQRIwQJIlowmX7YAUpjiT+1+rx8ERwCpDwKJGkStLaXBlytx0Q4g\neSjBNdJfQ5EU528Tu91qIEAicwIkX6VYbCj81A4geQggBSoBSP+ai3E7QDKYeoFEjCKAZEhf\nI0jLWoPzbixAEk0BEl9nA+lCcbQNSPxmAyS+AFKg6r0h69FsJbQekNzDqFV+uny5SgRSq/sC\nJICUQgWAxOkxCaTnP4Ow49ggRZFU76odQAJIhvQAyVcA6eAgDcK/HMdRAMlXPq2WY3cEyVDp\n1jVUUoLE+DIlgBQsgJRJ6UFqtRd232eYD0jd8q8IksewTgUS03LJDpB8dRSQnA0BSAAJIBlM\nfUFyDaPW8Mrm+4wCSESNdgFJ+uuQO4Dk1WiA9Ixi9BlACtQ5QJKjzwlSy+ozK0h+qw0GkLyH\n6UlA8lGcv1kAKQ9Ig7QHINkEkDIJIJUGkumWXrgAUiYBJIAEkBJoS5BcKW5CSFUgDcpPynDS\nzbLdUYQTTOokIEnhAIlOCpA8FQzSiwdVcf5mAaQMIN1/ACSeIpa/6RuxAEk1BUhs7QBSDEkA\nKZMAEkAygnTd94asd5OFAjWB5BhHXiC1tJOUCSB5Khik+TtdABJPm4LkyAGQyCKsaEoxq3Z7\nntodCKQh8NxuC5CWiBmkQd4FkGyqdfn7OCANgRdJrfWNwdcXpPlbL1WQuCQlBIlnWTdIf+/z\nF40BJEo3pRKLMoJE91oikFpTJwMkG0jCV1+WD5JQ4oQgtVSYkgcg+SoGpLf5y5jfABIpgOR3\nyAbDK7fjqBpBmhcbqli1A0jOMCVPbSBZDy5AIgSQWsc71RcgOctwoilVemoX0mLb7J9HAAkg\n2UDacbEBIHmB1FJRahqA5KtKl7/PA5J1HGUBaeiUXQDJokpvyAKk7CCN+wCSRQApkwBSYSAZ\n94STdCKQlkJHBMmaBCCRRVjhlABSJu0JUksFaVm2AMnvoAEkgGSUA6SwZTuAZEwNkLwEkA4O\n0mB86TKcVCNIV+pGbGEgzaXOCxLZbwApVDEgXXb7xb7A9h4JJLWMA6SWjlL2L/iUABLHs26Q\nfq7v1DMNAGmWDaTBvFkOMOhZRh3mZt9wkAbdgfssdlKQ2KekdYK0399sqBQky4SUBCRbkiOA\nRH+zkxUkuylA6s8FkmXoAiRz6hpBYivO3yCAdCaQGN+QBpAAkmW7HKGLD1JLB6n7iwKppZZj\n+vpB+ny9n9ZdfwASLYAEkGwg/b08r4+a5hsgkdoGJHrZLhIkgaSdQGrJGwR97SC9Ne+PXzP/\n11wrAWkqdxiQOsaUtAdIbR8LkjghHR+kx2rd/F9WkIKbC5CcTiWC1NK3rKXdcSCFDy2AlEm7\ngSRtZg7IckBq+xOANJ3avWf/4ydHA8ndoP1AGnqAxFXUYsNef/wEIIWCRHRdcSC105tjg3S7\nfezzx08A0h4gLaOaVDKQVnZ59Tb41gESU3H+us4Fknns6iCZswAkugwnnBJAyqS9QGqdby3B\nhYDUrm+Sg2TZvg9I49+1+8CpHUOJQXqW2AokYdFuT5Ba4d2hQdrtL60eCCTpo9cigHRwkK7z\n3/5+BUiktgGJXm1IDRL391XjQWrFd4cGaboR+8f+LtlEancomUIm98G6R40x5Oqe/3fmV7e5\nnaa9c9LhJhm01trwTPi9P8jRA7PePrui6xgjBZjXZrw6yj0jBX9qTEX3mpGsl0iBM1LX0zNS\ny/7Va2GvsNaQckbiHzhleYFcbah8Rrq9jqd2FEcAqc8CkilNNEghd2RTgNTKb48LUiMKIJEC\nSAAJICUQQPICqZXfHhgkH8X5awJIbJBatbGVgCRHZgQpeHABpEyKAMk0dgGSJTFA4ut4IPlP\nSaEgsQbk7iApgUcH6e8N10hs5QBJT1MnSK3iEQOS78XQHiC97rPYEMERQKoWJGfhykFqmn8E\nQQBp0UYguc/tWo9fKxB27gySyhEJkrirRpBe9vk2CoDUZwFpEN+sezcHqdUsjg7SL+OXYwHS\npLQgPeO3AkmakAJAsnZyKEjURVLtIN3+4RqJLYDEPHKtweLgIFW42GA969hc6UEaR7hztQEg\nVQFShYsNpYEk/bUcq0JBMjU2AiQuSVEgtSaHg4P0Wt9iA0DyBsl/SooBqTU6pAfJmm2Xa6TX\ntz2++hIgnRIkR+naQdrn6e8ojnxA8jQiwuW/wa0OVqKwNrK2AUla/d4FJItDCSARzQZIqYwA\nkhQGkPQQG0hsxfkrAkg9EyTjgGaDtHieCyTrHoCklS4ApE4ByfciaQXJQZJ5QNMX7XuDpBsU\nAZK73Ti1S2VEfPU2QKobpIFoN0BKZQSQ3L5uazHidCCN+r1+EBzVCRLjS+l9whWQ1NG6AUjt\n5GsoatHWIJE9ajMgfo/4GCDd/hqKpDh/RQCpB0j2tBWDlPmrL48AUqeNVoDkMigCJHfDE4D0\nr7nUA9Kj+DFAmsd3p+2RXgEksghrz5YgLWsN7xlBiuToGCAJE1J6kNR1kAJAIlYbDgLSheII\nIPWRIKnHkANSO/tqFbUKINn3ZDi1oxXnL6tckNzxIkjrXy0pFyTBsZP3nxKkwVIxu68hBCDx\n8gIkISwjSNL26kDa629/Hwqk9ciEg2RbttsMJJKkSJBM6Z2rDQCJ5y8LIPVOkJZMAMllWg5I\niz6aC/Ub53H+sgoFqe0LA2lOyBxYwh6AZKvAliD9vjy/SPZ4IJFkeIbfZI7Gf4Uj4/YqAyTe\nRVJr8XVaE+nLAMnV8kiQPoCIzn0AACAASURBVJvmk8IIID0EkJzWRPqMIJl3bQvS75UxHaUF\nKZYjgASQiIrkB4k3HZ0HJGc8QHJaE+n3BmmQfpgUDtJ9Onrh/RGhokDq9S9osBqVDNI6uo0g\nLekCQRINiwHJUr5ukP5dyN+eAEiCVpCEp7bZICmH0AWSUhWAlBAkR9Nru48EkKZggGTOCpB4\nAkhT8GFBMmY/MEg+ciWn/SUBpClYwMd0kQSQnKYACSD1yqKdEaQ1mzdI2qJdCSA5l+0AEs9f\nUqEgKUtlBi0giU9tlwWSMCHJfn6Pfx8ZJHvbAZLLh+3WSj9M0kB6vNgEpMfuKkFyZd8XpMHw\nSlFlIMVzBJAqBsmcACDx/EUdCyTxqCQFSUhmaKzNanuQ3K0ESACJD5K4bK3RYVdmkOTx4vd7\nFIcGydp2gOQyAkiyAUACSEKKGkHSTgMBkjlnPEiGnQDJmOMIIInwqCBJf88fIAEkg+oFSVtr\n2BIk4XVtIFlyA6RTgeQooE1I6UAyP203CSDFgzRY37h89ZCTgtQBJDX/7iCZEgAkpr+gFBxt\nAVKrvbCFZAFJVFKQnJcxcgxA0kNOCNK8LsY0DAKp7w4JUmv1dViTuQESQJL21AGSMljqBslQ\nxt3XAImZZEeQ5PFfC0hezwgdHSRLDQGSzajzuEjKC9JM+ZrIUc4XJMOZXQkgOZbtABLTXxBA\nmiMBkiUnQOKoeJDsBWJBEo5fLEg2qxpAIqnYAKTB+dbuq4UAJEa49soYApCoggAJIMmvTCHy\niJdB4p/bHRkka+pyQDLX8YQgcbLkAcmrWhJISlEHSVuA5CQJINlUDkhpOEoPUmt8aYgBSFTB\nEkBSdwOk8DQAyZwfIBne23y1kPOBNF6FsO/IckFSxvvQaREOZQRJGyk5QCIuvwoCyVhJgGSL\nAEiG/ADJuMHsq4WcEqTH2OGe27WW1+LmWw+QUoCkp9gaJL1KAImbBiBZ8gMk8xaAZI04H0jt\nkmo7kByNDAVJeQ+QGKocJHW4+4EkDaXMIC0GAMm2CSBZIgCSyWBrkOyJAVI6kBJxBJDMVgDJ\nvBsghecpGqS211PZSfIEyXg/1uthu41Asq5/AySu/6JUIN3IRH4gtdY3wuakIGncJAXJMEwK\nAklNAZC4/ouqAMl2AXLTB7sCEnfZDiC5MnJAog6qtN9YJX0jQDIbjSOHR1IwSL3XlHRskMjE\nAAkg9ScCaU4NkPQQgESGW98tWwGS3ZmVOCNIUgBAsnvuApKBo2pBcgz4c4CkbwVIZiOAZM4P\nkCxbAZLZqECQ5oMHkFwZOU9ynBykVBwBpGJBcq0GSg/t2jMCJFKVgGQZpQDJ7rzsKBAkS5Vy\ngXR5CCAJ2/ShnhIkO0n+IBmHDvthu5OApG3fCqQTzUicwwSQXM7LDoB0NJDmcdMdEyRLHXuA\npAXsC5LMUXEgUalKB8lADUBy+FYM0nKJ9N9D3GJ3tUG7mOp4qdo1sqNtBzXAVKBdvdeCYo3o\nWo0FpNoJMmxyyVzHMc1gKtAJQbYYa2bG7pZIK+6TczCGBecI2QJsVXJVlZTfjBS22GD/tIif\nkbrZ051KOHdiXCQNrBmp0zth6D2npEGtnaBEM5Jl0W77Gakl0vbWKWmnGUndseHyN0AStgEk\nu/G6PQAkNSFAMjSCtd1DeUAylQBIDuN1e3kg2WuUBaSIUztrM1NMSN3kmRCkQQ/YCqTx2G0N\nknnopALJeXSdHJ0WJGHlzpXc1QrOZg8BJKcTQDJE7AzSTXqwwXf529zOqkHqDA2tEiTyHOw0\nICm7SnzWbn+Q1mFJ3kgaWq2veSAN43aioF7EDJKVJIB0XpDMDS0WJK2z6wSpnbKwQLIPsApA\nYoykg4Bk/cCME0ByOVUEkpgEILn8NwGp6zcA6XGJRIPUtdWAZBs53NWG84Ak7ysTJOuBjhEX\nJGmkUqsNJpD0ElaQ5PF/XpCoKy9lbzaQmDe2LL6K9gDJ0NgUII3D4eZO5g0SPSWlBcnIjB9I\ntv6NBmnJC5A0lQFSikskgOSwAkiGkPpBCliypLQnSIaGAiRtK0DaAKSA1hPaD6T7hMQCiUXS\ncUFyc1QeSNLe84DULf8kBGnoc4NkWWtIA5KdowJAMt9IAkikf+t4FyAPkMRB6V7/Hke22t8A\naWOQhCwAifSvAyTTEABIZYHkP5SIVuoVKhkkucE1gaQWyQGSjSSApAWdDiSpxWlAev6bG6Rn\nCoBkM162FgQSaxlR2g+QDK4AyZ4eIJlrVDRIjv7yVrf+cIOkjFT3st1g7nE+SN43kg4Jkvew\nbbUXTt/Tg+ToMF9tAtJg6fFqQbKPHIDkrtFJQXLkywTSsOy0lrP4WYjJC5JtjGUCaUkDkJj+\n1g7z1X4gjRkKA0m3IkCa8wMkc5VKB2ludU6Q5CHpWm2wgSQVSQjS85psX5CIc7sIkDxGbSaQ\nyBoBpNQguaYkgOT0XTYCpE1BmpodC1InvEgJkuVMByCxH+tftuUCiTWUDgjS2KYiQVonJAdI\nXc8HiXGRtC1IDmuA5KwTQNLt6gTJsh0gqVGnBenZqNJBUjpdBunxDiDZjHuAlA+kVGsN1YPU\n2kd7PEiuS6QCQDKRBJD4/o9W5QNJG6mOG0kpQPK8kXRIkFpXRkEAyZGc9r+rTXZmlwmkdft0\niQSQrMbFgsTgaA0CSKoVH6ShPxBI1CUSD6Q1K0DSVABIkad2gzhEOgokdThZL5IkkGzndgCJ\nMJ43MWbDjCA9oixNNKsSkOJkAMnanWWD5Fi0OzBIXUEg2UoDJEUAiUgPkIw6A0hDP2wFUiu9\nk9LMhfsyQVKtSJDmvflB6mZvKaQ1Z9sYJGvhc4AkjpCiQSKP8v4gOZcGAJJDAMkcK5/ZGQ+z\nHaQ1OBlI5j0eIA0+IFEnPulA6vpyQLKXBUiKEoE07wFIhO+yyQZSt3rnBcmLI4CkCiBR6fcA\nafxXB0lPZvZtLa/tAkiaBuWWfecCqdXHo+2OLAekbnltBclvteGIILkuumbXIkBylQRIskwD\n1TIlASRX0kQgdeLPnUFyFjwFSPKfa9sKJNMpvDdI1GEuACTXwAdIrhCABJD6rUFyc2QA6Vks\nJ0jucscHaXgeBXHgpgFp0LLoIAn3MNOA1A7bgURwtC9IlikJIHn4R2p/kJ4vE4HkGu7HA6lT\nXqYAiceRDhJR7rQgWTrGD6RW3yRk2gQkx2g/HEid+jonSH4cASRFAIlOfwqQDNeQTp0BpK6X\nD4EwvDXtDpL7iDlBMu7bACTHuRgDJL2BtnSd9m4/kMhShwdp0D/MsoEkrDWUB5I2UiiQlClp\nc5A6/e1uINGFAJIsNkj6WkPlILnWA5X0AEkXQJJkHqgmkkwgSQeaDZLPRdIRQbJcchme1NJv\nJGUCiVEGIEkCSJz0AEkXQJKUCyRpRBwHJCFlLEimM+y9QOIUOTpIw3JI1F9c2Bqkvp1T+YHk\nPGwFgUT8moEnSDRHmUEa1jhWCYAkCSCx0tumJIDkDDkaSLfnljiQjBwp53bbgGR+KM2UqNd9\n7T6tubg5/eYgmY/CPiDxCgAkSQDJoXwgmWvS7QISM/68IBl7qHiQ1MEkKQKkdqgPpN705QoA\naRM9er4TXo+eeUCSr81zgGSqfI0gWSqig2ToqtQgcRf5jg9SJ7yePB0gWT4LuSAZn1jwBMlx\n7Fp9MEmKAoniqACQ1HM7gOTjHyOAtAlIz7KbgWSryB4gcTkCSJJsq8v6uR0HJOkzzQES/9wu\nKUiSz34gjW/tzx5KFcgG0jAGsjk6OEjiJdJ6sBKANNgyiHerqgKJvkRSz+0AEu0rhxwOpN62\n/g2QnNoOJBZH+UHicwSQJMWDtB5qgMSw7TWQ3E9uhIK0mnIvkcyL63YdHqROejd5lg2S/QAC\nJGcqhy9AitK2IBn7eXOQPO/IbgPSI34TkNyVUB/r2BYkH46ODZJ8ZrccriwgdT1Aom17gLQR\nSPTR9JA/SNZbgXKw/cyuSJAsJQQb15c8G9OHgjQMQztIGt/qDsYK5D218xFj4AIk7Y6sA6TZ\npFsCDCAp4yEZSIbu2w4kQ1oGSJblb93BWAFX252+niDRPoq6E89Ihj4tHqROL+zMNIFkLlIf\nSIw6AqS5Rr5NcEh9EHMByTYlFQKS9WiHgTRYiuwAkuHaUnzn83CFRfuA1BV3jZQSJHVCmvvH\nCpL990+ViyQaJMGABok9JR0QJK8JqVSQuvIWGwCSPXM4SOYyMSCZVxsAkksASQVpmMOttpWB\nNHgvfwMkQZ3dVxRAKgWksX6+IA3WMovN4Gi6JbsJJLHa24BERwAksVZpBJD6GSRiSsoFkuGU\n2GutoUyQOoevqFpB0jmaOigRSLZDIvi2a5wXSJbcU/287simBIm6I3sYkPw5AkiirKvfyvq3\nGyRh1b1dwwBSD5COB5Jwm0fQgUEyFooBqSe+9nsjkMjvnskOUuf0FZUZpGQknREkPdNtMFtK\nLplAMl1bCm84NSgOpLk+xwVpveZXt8aC5F5reOyf81QC0uC6PLRlB0hPASSABJCsLpygp5bq\nHBskPZUIktqprsEkLttRIK1HmwsSk6QJ9CCQTKVWkFj3Y7cAyfMSqTSQ1toApFWHBGnQXugm\nNYFERQEkU+1iVAJIj5ijgvT8K8fmfA8ZDuxgCPM8s4sGaSuO8oDkqS5FksGSaRA8WnlX63Du\nhOBhDrcHrylNYYOjxFpQV9tZijsSDYZXqsnweMXpdSmm1TLae0SqgBgmvGYd9iFwdLTKT8KE\np+CBWtOMZF5reG6/zbvUGcllvK42kBOSYGuekfRPvAwzkqFYu+wImpG0jGebkcSqHPbUbleQ\nliOxCUhed2QPBxIRlxEkqSKHBsmUqBKQjOkDQBoEXxtJZwBpNjoVSElIOiBIy2LIFiCx+jwK\npMEUtr5mHnTq3C4fSHI9jgqS7czusWdrkLp+P5DUTGlBIta/TwWSUg2AtOroINlu/ASCpK9/\nAySnANIzXnieZoy2h0qDZleQBrdvO2+tCSR3ZAKQQjg6L0iPnXKvugfTckd2E5BYy3ZHAUmI\n811rKAYkrQ5FghRPkp2j5WxHm5JSgdT1KUAy5F9mzFCQzBc1mUAaTHHeExJAsiXnVdNX5YA0\nhwEkU1ytIOlVAEiLnJdI60USC6Q1CiDp3pEgOWOzgGSoAEBa5AeS8xKpFJAGtYOVknEgaYs1\n4huApIfkBymapGODxL4jWxBIgzFwecU+4CWAZPI/JkiDM8m6NwAk3pldRSAN4wuAJFk4dTKQ\nrDlygtQHg6Q7JABJKQmQLA5OGe3PC5J2IykpSFqc81GdhCBJmSiQHh4VguSK3h4kszlAWnQ8\nkIZ+Y5D0+W0RQNJDdgAp6LiuigLJ0pEAqY8AaTAHzq/4H5y7g2Txrhwkc5h7rWH+wzoKSPNY\nsn0tV7eucY3hlipJdRBM1epZyokVUjckAEm/SPICyXlu5w+S/4S0PUhhHNUNUmcJCwNpmUpc\nU5K2iKDXSarEaqpWz1ZurZD2fo3hrTZwQBrfE6e1xuxaFbOARJHkBmmzCal2kMxxVF+7QBp6\nW19WB9Kg+BpKAiRjervsn85k5rJBMgayQRI7thVupJqHuggSdYmUACRtUUyZa6gKmEFShzRA\n0rPb5e8rhuwBEqdrO1tcESApSdKC5DjgRYJkWZXwX2sASL7+XJAMgcQlUn9bI1SQ5n40Lqxx\nQLLOGkWDxF5rODpIwRwBpEXyFx0bevQgICl3dKJAGuT7B+KbbUHyv1YBSFZ1ys9F4SCt/WgB\nib/WoOZIDJL9kCsXaQRI05kdQBJyW+Wq5gFA0kLpWw0WkMR+1Pu0EpCUO1kASbRygxTBUbkg\n0Z1rA4makB6eM2t2kPRe5YBkH+sBIKmXIIlAEotmAkmprAqSD0ehII1eAMkZoBxdqrgNJLkf\nAZIr+ZTpUCDFcFQxSJ3ldShIndqPOkmeIEkZUoNEXiQNmq+p5HzGUxtI/k8YkCBFcQSQZqkT\nUq/3bNduCBJ9kZQMJGlKmv4NBEl5WlEQB6SQSySA5O1P1LyzvWGD1HcSSFqcDtK65SAgDVlA\n0qoqP0HqxdFmIMVxVC9Ine0tydHTc5qSnCCpfSuAxOJISnAzbXSV1VzUGtqO/KYguc7tqgYp\nkiOANMv4kIzcu9lBkm3SgSTcd64WJFspgORZd3Wvch+SCdLSs0P5IDmSGxLNe/OBJPdJZSDF\nclQtSNrOOJAGy2ObUv/6gySUjwZJr6AzkR9I/LG8O0jEuV0YSNEclQySs/r6Pvn2SSKQpB4+\nDkjL3iwg6fWUQPLkCCB5+/uBJA8gCqRn3ALSYB1LXiC5Fv7ygTRmygSSeJUp7akKpHiOagXJ\ntEsEydl0I0i2EhJJyysuSGvxDUByZTL4GkrOHNQIkqUcQPKqv3GX8EnMA6kX/qCJtYTQydlB\nUp4FdFRNzUSANO0/G0jbcXQokIQR5AXSfL1kFkDSkotpDgBSCo6KBsneBMuOCJAcQ2nt5/V7\nZNkgGa5ViPs/ktwgOZ6RKAgkQyVb4V9fjgCSv78vSP3ymxA0SNP5XDv1o2soLR1NgORcQAdI\ngpKAZC7pD1ISjg4G0vIItwdIYwk/kPgT0k4gmVYLDUWH+Ro8HKTe8uFSDUhpOKoTJHvjOsaZ\nnTdIyxpXCEhz4U1AMudigzT0AOkUILmuhZwlPECa+tE9lKYTofwg+T0MuCYqCCRTFQWQvDlK\nDFIijo4G0jDu44A03jyab80nAMmSIRVIzhNHNREN0iMkBCS5HtWDlIqjk4N0PxVkgTRdUVQD\nkrD5wCAZywIkfjvc6wKdO6KXQeI+ODmeCc1RPiBNx2obkCwkASSzp3bcknEEkLKDZD12mUHq\nhwQgiSQBJCpkP5DMLSFWqm1f9qJ6Pj6S2SCNw+5YIA2zgcevI3mDZKzgCpI/RylBSsdRjSCR\nd3wsXz+meQ4Df0Yax50LJNeFWx8M0jpM3ZdgciZfkHxGzO4guUnyASkhRwCJfWv/CZL0kBi3\nWkyQ3FOSF0jCa0cH7wxS4JkdQHL6W9pkAWmwyV7K7OkBUr+C5HVmd3KQ7I1KAZJFxmKtWsV5\ntHhlsakUkOxPuxg2EZ8kZIPEZF4PmzlAciUY+u1AMuUbhFjqAHuDZF22M4LUDabcqUBStw+T\nr60nWzm8D62BQYWDZGhmUpAWNAASW34g2VsVeokEkNz+3Cmpi+VoE5Cc5TODNIipyAPc+oIk\n1XNZQVc7ZfTtBnOztgBpvENx06ooeLZKeGgNDKoOpPgJyQQStzvHOB0kovwQDtJsZh/sWsJy\nQOr6YkGakxwPJOtzY+qGWI7OANKaawOQxIoyQDK1awOQxq3Dckpp8tRBSsVRSSCRC1DjhmiO\nZJD8rnktIFHFE4BELApqG+b4LUASKuMEqVPqIpkmBmlQfho9WzU8GUfFg6Q2NcGElB4kxk2o\nDUHSMpYC0nIWbgUpbBwHg6RxdFCQOFNSggnp6CDJH7abgLTWhgWSXvnkIC3bblZGDCCl46gs\nkExdpM1I8UoNEutmbnaQphLbgLRUZ7mpq4PUaXWRTJOCtG66OTxbJTwhR6cHaVxtYI+kzjRm\nUoHkJOlwIAVfIgEkhj9FUpKmx4Lkz9G6mrQFSErK5d2zyEYgzfWxgyRypFW/jZiQzHfOFl8r\nvStIG3AEkKJB4hVdQHJd4x0JpE6uWEkgpV9p6IsDydBJ0u0/gGRMbnnzKLMVSCJJDJCU+qcF\nSdxw622kqCAl5ag4kPReEu/+pWm7DhI/7yOyVTdwpC7P2pLrGoep02ewvO54IIV1a7fYGUDq\nboa6SKbhINkuCp+SfBXPVgxPyxFAigSJXfLIIPXaPN31xYK0yYldgSA5SBoSNb4ykHr65NMG\n0jygXQof0OvZmwWkQQ9fTb1+v12W5ZrwqZu0TfYUQUrMUYEgaUNtXWlN1PiEIPELzjcKNwFJ\nyKrk7+gDHD6il4U5FaSuN4Ak2iQESTG5yVslz7bfjCOA9EwaCpJHhc4E0rzIUSBIG53YFQmS\npf+HZK1XQPI6t9kLJMLJChKjihEjuvMCSfBJB5Lqoa6Oip7tdhNSVSClan0ykHwqNN9x3wak\nNW0BIC33r8oDabMJqUiQjCSlm5B0kHz+hrzwML5XfTggOZbtgkG6kbWMGNF9ZwBpzHYzNXUx\naqNcB+PLfvaV94ie23FUJkiG5Z6hLwakoAkpBiTOwpp1UfBGFo0Z0vPT+C0PpMUpFUi6hf4r\n14LnZid2VYGUrPkaSD6Zu7AJaRlYG4E0590fpG72LRAkZUtKlQmSejMk6YSUCCTP6uwHElW2\nDXpCaNZYdgVpzmUGad6dCCSDw00Pmz2HsCPHU6EgKffEh/FHIp0NJKJwHEjj0yZskBbwUoBk\nMjD9yvVMEv3UYrhqAGk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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "  \n",
    "  # fixed one station\n",
    "  \n",
    "  #FIltred value\n",
    "\n",
    "  \n",
    "  selectedType = order(table(objets_trouvees$Type.d.objets), decreasing = TRUE ) [1:3]\n",
    "  type=data.frame(table(objets_trouvees$Type.d.objets) , stringsAsFactors = FALSE)[selectedType,]$Var1 \n",
    "  gare=\"Paris Gare de Lyon\"\n",
    "\n",
    "  objets_trouvees_table_selected2 = objets_trouvees_table[which(objets_trouvees_table$Date >= \"2017-06-01\" \n",
    "                                                               & objets_trouvees_table$Date <= \"2017-09-01\"\n",
    "                                                               & objets_trouvees_table$Gare == gare \n",
    "                                                               & objets_trouvees_table$Type == type\n",
    "  )\n",
    "  , ]\n",
    "  \n",
    "  ggplot(objets_trouvees_table_selected2, aes(x = Date, y = Objets, color=Type)) +\n",
    "    geom_line() + \n",
    "    scale_x_date(date_labels = \"%d-%b\") + \n",
    "    xlab(\"\") + \n",
    "    ylab(\"Number of Luggage\") + \n",
    "    #facet_grid(Gare~Type)\n",
    "    ggtitle(\"Lost Object reported to SNCF on Summer 2017 on Paris Lyon Station\") +\n",
    "    theme(plot.title = element_text(hjust = 0.5)) +\n",
    "    theme(legend.position=\"bottom\")\n",
    "  \n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YiESFUS/BrFm9afvlSauojkIJQsXvtvnxGvzjVbHriq/6EJTu1ye4+ESA5Kf43C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\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " # for all date\n",
    "  \n",
    "  objets_trouvees_table2 = table(objets_trouvees$Type.d.objets,objets_trouvees$Gare)\n",
    "  objets_trouvees_table2 = data.frame(objets_trouvees_table2,stringsAsFactors = FALSE)\n",
    "  names(objets_trouvees_table2) = c(\"Type\", \"Gare\",\"Objets\") # TODO : to DNN\n",
    "   \n",
    "  #FIltred value\n",
    "  selectedType = order(table(objets_trouvees$Type.d.objets), decreasing = TRUE ) [1:5]\n",
    "  type=data.frame(table(objets_trouvees$Type.d.objets) , stringsAsFactors = FALSE)[selectedType,]$Var1 \n",
    "  \n",
    "  \n",
    "  objets_trouvees_table_selected2 = objets_trouvees_table2[which(objets_trouvees_table2$Gare == \"Paris Gare de Lyon\"\n",
    "                                                                 | objets_trouvees_table2$Gare == \"Paris Gare du Nord\"\n",
    "                                                                | objets_trouvees_table2$Gare == \"Paris Montparnasse\"\n",
    "                                                           | objets_trouvees_table2$Gare == \"Strasbourg\"), ]\n",
    "\n",
    "  \n",
    "  ggplot(objets_trouvees_table_selected2, aes(x = Gare, y = Objets, fill=Type)) +\n",
    "    geom_bar(stat=\"identity\") + \n",
    "    #scale_x_date(date_labels = \"%d-%b\") + \n",
    "    xlab(\"\") + \n",
    "    ylab(\"Number of Luggage\") + \n",
    "    #facet_grid(Gare~Type)\n",
    "    ggtitle(\"Lost Object reported on biggest stations\") +\n",
    "    theme(plot.title = element_text(hjust = 0.5)) +\n",
    "    theme(legend.position=\"right\")\n",
    "  \n",
    "  \n",
    "  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "frequentation <- read.csv2(\"https://www.data.gouv.fr/fr/datasets/r/d4cf16d5-dd91-453c-887e-6084639caac8\"\n",
    "                                 ,fileEncoding=\"UTF-8\"\n",
    "                                 ,header = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Nom.de.la.gare     Code.UIC.complet    Code.postal    Segmentation.DRG.2018\n",
       " Length:3015        Min.   :87000028   Min.   : 1000   Length:3015          \n",
       " Class :character   1st Qu.:87313118   1st Qu.:31795   Class :character     \n",
       " Mode  :character   Median :87486423   Median :58320   Mode  :character     \n",
       "                    Mean   :87488091   Mean   :53011                        \n",
       "                    3rd Qu.:87683590   3rd Qu.:74385                        \n",
       "                    Max.   :87988717   Max.   :98000                        \n",
       " Total.Voyageurs.2018 Total.Voyageurs...Non.voyageurs.2018 Total.Voyageurs.2017\n",
       " Min.   :        0    Min.   :        0                    Min.   :        0   \n",
       " 1st Qu.:     6769    1st Qu.:     6769                    1st Qu.:     7496   \n",
       " Median :    40228    Median :    40228                    Median :    43305   \n",
       " Mean   :   899957    Mean   :   996567                    Mean   :   915421   \n",
       " 3rd Qu.:   219762    3rd Qu.:   266110                    3rd Qu.:   233358   \n",
       " Max.   :249978244    Max.   :277300314                    Max.   :251397192   \n",
       " Total.Voyageurs...Non.voyageurs.2017 Total.Voyageurs.2016\n",
       " Min.   :        0                    Min.   :        0   \n",
       " 1st Qu.:     7496                    1st Qu.:     7626   \n",
       " Median :    43305                    Median :    41396   \n",
       " Mean   :  1017108                    Mean   :   889358   \n",
       " 3rd Qu.:   284949                    3rd Qu.:   222246   \n",
       " Max.   :279352628                    Max.   :242958012   \n",
       " Total.Voyageurs...Non.voyageurs.2016 Total.Voyageurs.2015\n",
       " Length:3015                          Min.   :        0   \n",
       " Class :character                     1st Qu.:     7770   \n",
       " Mode  :character                     Median :    43147   \n",
       "                                      Mean   :   879421   \n",
       "                                      3rd Qu.:   227379   \n",
       "                                      Max.   :237432718   \n",
       " Total.Voyageurs...Non.voyageurs.2015\n",
       " Min.   :        0                   \n",
       " 1st Qu.:     7770                   \n",
       " Median :    43147                   \n",
       " Mean   :   976012                   \n",
       " 3rd Qu.:   276654                   \n",
       " Max.   :262843175                   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(frequentation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PJbQAISkNYWkExeYQHJHgKS3wISkIC0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\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# FIXME : this code regressed after update of the dataset\n",
    "\n",
    "#   ggplot(frequentation, aes(x = Gare, y = Objets, fill=Type)) +\n",
    "#     geom_bar(stat=\"identity\") + \n",
    "#     #scale_x_date(date_labels = \"%d-%b\") + \n",
    "#     xlab(\"\") + \n",
    "#     ylab(\"Number of Luggage\") + \n",
    "#     #facet_grid(Gare~Type)\n",
    "#     ggtitle(\"Lost Object reported on biggest stations\") +\n",
    "#     theme(plot.title = element_text(hjust = 0.5)) +\n",
    "#     theme(legend.position=\"right\") \n",
    "    \n",
    "objets_par_gare=data.frame(table(objets_trouvees$Gare))\n",
    "names(objets_par_gare)=c('Gare',\"Objets perdues\")\n",
    "frequentation_2016_par_gare=frequentation[,c(\"Nom.de.la.gare\",\"Total.Voyageurs.2016\")]\n",
    "names(frequentation_2016_par_gare)=c('Gare','Voyageurs 2016')\n",
    "merge_freq_objets=merge(frequentation_2016_par_gare,objets_par_gare,by=\"Gare\")\n",
    "merge_freq_objets$Rate = 1000 * merge_freq_objets$`Objets perdues` / merge_freq_objets$`Voyageurs 2016`\n",
    "\n",
    "\n",
    "type3=\"Bagagerie: sacs, valises, cartables\" \n",
    "gare3=\"Paris Gare de Lyon\"\n",
    "\n",
    "objets_trouvees_table_selected3 = objets_trouvees_table[which(objets_trouvees_table$Date >= \"2017-01-01\" \n",
    "                                                              & objets_trouvees_table$Date <= \"2018-02-01\"\n",
    "                                                              & objets_trouvees_table$Gare == gare3 \n",
    "                                                              & objets_trouvees_table$Type == type3\n",
    ")\n",
    ", ]\n",
    "\n",
    "objets_trouvees_table_selected3=objets_trouvees_table_selected3[,c('Date','Objets')]\n",
    "\n",
    "ggplot(objets_trouvees_table_selected3, aes(x = Date, y = Objets)) +\n",
    "  geom_line() + \n",
    "  scale_x_date(date_labels = \"%d-%b\") + \n",
    "  xlab(\"\") + \n",
    "  ylab(\"Number of Luggage\") + \n",
    "  #facet_grid(Gare~Type)\n",
    "  ggtitle(\"Luggage reported to SNCF on Summer 2017 on Paris Lyon Station\") +\n",
    "  theme(plot.title = element_text(hjust = 0.5)) +\n",
    "  theme(legend.position=\"bottom\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict lost object "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsSNCF=objets_trouvees_table_selected3\n",
    "startTs=min(tsSNCF$Date)\n",
    "tsSNCFts = ts(tsSNCF$Objets, start=c(2017,1), frequency = 365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in preprocess(y, m, lags, keep, difforder, sel.lag, allow.det.season, :\n",
      "\"No inputs left in the network after pre-selection, forcing AR(1).\""
     ]
    }
   ],
   "source": [
    "tsSNCF.fit=mlp(tsSNCFts)\n",
    "# tsSNCF.fit=mlp(tsSNCFts,hd.auto.type=\"valid\",hd.max=20)\n",
    "plot(tsSNCF.fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QYiZUlApIgyiDRYfJYu64tApKgyiDRY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\nJAAFEAlAAUQCUACRABRAJAAF/g8naegcLUkveQAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"Forecasts from MLP\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsSNCF.frc = forecast(tsSNCF.fit,h=5)\n",
    "plot(tsSNCF.frc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
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