nunl-counts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>parent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3ccc13b3-d1b5-409d-bc7c-e233ae3524a4</td>\n",
       "      <td>Popeye_xl</td>\n",
       "      <td>20200404</td>\n",
       "      <td>Onzin, dus mondkapjes werken wel in de ziekenh...</td>\n",
       "      <td>3dd2894c-f443-4097-8605-9573748dea90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>f29d5e9f-29c9-4c55-9e96-0e742a5021da</td>\n",
       "      <td>Milanevic</td>\n",
       "      <td>20200404</td>\n",
       "      <td>Dat het niet beschermd dat je word besmet klop...</td>\n",
       "      <td>1d18b602-7ee6-4dff-b92d-c0549bb28947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>07c65bf4-c1e1-4907-8b14-1fa8865e6d03</td>\n",
       "      <td>ilovejb</td>\n",
       "      <td>20200404</td>\n",
       "      <td>Ik vermoed omdat er een groot tekort aan mondk...</td>\n",
       "      <td>446eb98f-2e98-4b78-a6ab-6ece7a4a158f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5e45326d-9e79-4110-9a68-1004946a72a1</td>\n",
       "      <td>Suuuzzy</td>\n",
       "      <td>20200404</td>\n",
       "      <td>Dat ik heb de overheid een paar keer horen zeg...</td>\n",
       "      <td>51398b4f-41c6-400c-bf29-3c8c3ca71720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ab98306d-f5cd-4925-af52-386ba0aaebbf</td>\n",
       "      <td>IkBenThomas</td>\n",
       "      <td>20200404</td>\n",
       "      <td>Er is gewoon wereldwijd een tekort aan mondkap...</td>\n",
       "      <td>01500981-3b4c-4134-99c7-e17967e9662f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>eeb324f8-72a9-43fa-b90e-ac16797b2a20</td>\n",
       "      <td>Welkenaamisdanweloke</td>\n",
       "      <td>20200403</td>\n",
       "      <td>Zoals eerder vermeld. Dat beleid is gericht op...</td>\n",
       "      <td>1e307ca4-704c-4cf4-be67-c3cdcd73ebec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>029f7035-99b2-46e8-9385-830cc28be1ef</td>\n",
       "      <td>RatuAdil</td>\n",
       "      <td>20200403</td>\n",
       "      <td>Nee dat beleid is niet enkel daar op gericht.</td>\n",
       "      <td>b867aefe-5fbf-4e35-9937-80578f956b14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15cde194-cf6e-4259-94f8-8de97ff56cdf</td>\n",
       "      <td>RatuAdil</td>\n",
       "      <td>20200403</td>\n",
       "      <td>Het is zelfs zo dat onjuist gebruik van dergel...</td>\n",
       "      <td>59e137d1-8e4d-4168-b3d1-ff4e6bc6bb36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>b48b40d1-0e3d-4f12-92c4-e3f787b168e3</td>\n",
       "      <td>Welkenaamisdanweloke</td>\n",
       "      <td>20200403</td>\n",
       "      <td>Mag u mij eens uitleggen watvoor magisch mondk...</td>\n",
       "      <td>8bc9d554-7f38-4b39-a288-22eab22cb95f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1caf6462-0ae7-469b-94db-78c1f200978a</td>\n",
       "      <td>Kees_Lees</td>\n",
       "      <td>20200403</td>\n",
       "      <td>Precies dit. Ik vind het oprecht zorgwekkend d...</td>\n",
       "      <td>e65fed9a-37f9-4c62-92a7-8abc05f0851d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>745 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      name      date  \\\n",
       "id                                                                     \n",
       "3ccc13b3-d1b5-409d-bc7c-e233ae3524a4             Popeye_xl  20200404   \n",
       "f29d5e9f-29c9-4c55-9e96-0e742a5021da             Milanevic  20200404   \n",
       "07c65bf4-c1e1-4907-8b14-1fa8865e6d03               ilovejb  20200404   \n",
       "5e45326d-9e79-4110-9a68-1004946a72a1               Suuuzzy  20200404   \n",
       "ab98306d-f5cd-4925-af52-386ba0aaebbf           IkBenThomas  20200404   \n",
       "...                                                    ...       ...   \n",
       "eeb324f8-72a9-43fa-b90e-ac16797b2a20  Welkenaamisdanweloke  20200403   \n",
       "029f7035-99b2-46e8-9385-830cc28be1ef              RatuAdil  20200403   \n",
       "15cde194-cf6e-4259-94f8-8de97ff56cdf              RatuAdil  20200403   \n",
       "b48b40d1-0e3d-4f12-92c4-e3f787b168e3  Welkenaamisdanweloke  20200403   \n",
       "1caf6462-0ae7-469b-94db-78c1f200978a             Kees_Lees  20200403   \n",
       "\n",
       "                                                                                   text  \\\n",
       "id                                                                                        \n",
       "3ccc13b3-d1b5-409d-bc7c-e233ae3524a4  Onzin, dus mondkapjes werken wel in de ziekenh...   \n",
       "f29d5e9f-29c9-4c55-9e96-0e742a5021da  Dat het niet beschermd dat je word besmet klop...   \n",
       "07c65bf4-c1e1-4907-8b14-1fa8865e6d03  Ik vermoed omdat er een groot tekort aan mondk...   \n",
       "5e45326d-9e79-4110-9a68-1004946a72a1  Dat ik heb de overheid een paar keer horen zeg...   \n",
       "ab98306d-f5cd-4925-af52-386ba0aaebbf  Er is gewoon wereldwijd een tekort aan mondkap...   \n",
       "...                                                                                 ...   \n",
       "eeb324f8-72a9-43fa-b90e-ac16797b2a20  Zoals eerder vermeld. Dat beleid is gericht op...   \n",
       "029f7035-99b2-46e8-9385-830cc28be1ef      Nee dat beleid is niet enkel daar op gericht.   \n",
       "15cde194-cf6e-4259-94f8-8de97ff56cdf  Het is zelfs zo dat onjuist gebruik van dergel...   \n",
       "b48b40d1-0e3d-4f12-92c4-e3f787b168e3  Mag u mij eens uitleggen watvoor magisch mondk...   \n",
       "1caf6462-0ae7-469b-94db-78c1f200978a  Precies dit. Ik vind het oprecht zorgwekkend d...   \n",
       "\n",
       "                                                                    parent  \n",
       "id                                                                          \n",
       "3ccc13b3-d1b5-409d-bc7c-e233ae3524a4  3dd2894c-f443-4097-8605-9573748dea90  \n",
       "f29d5e9f-29c9-4c55-9e96-0e742a5021da  1d18b602-7ee6-4dff-b92d-c0549bb28947  \n",
       "07c65bf4-c1e1-4907-8b14-1fa8865e6d03  446eb98f-2e98-4b78-a6ab-6ece7a4a158f  \n",
       "5e45326d-9e79-4110-9a68-1004946a72a1  51398b4f-41c6-400c-bf29-3c8c3ca71720  \n",
       "ab98306d-f5cd-4925-af52-386ba0aaebbf  01500981-3b4c-4134-99c7-e17967e9662f  \n",
       "...                                                                    ...  \n",
       "eeb324f8-72a9-43fa-b90e-ac16797b2a20  1e307ca4-704c-4cf4-be67-c3cdcd73ebec  \n",
       "029f7035-99b2-46e8-9385-830cc28be1ef  b867aefe-5fbf-4e35-9937-80578f956b14  \n",
       "15cde194-cf6e-4259-94f8-8de97ff56cdf  59e137d1-8e4d-4168-b3d1-ff4e6bc6bb36  \n",
       "b48b40d1-0e3d-4f12-92c4-e3f787b168e3  8bc9d554-7f38-4b39-a288-22eab22cb95f  \n",
       "1caf6462-0ae7-469b-94db-78c1f200978a  e65fed9a-37f9-4c62-92a7-8abc05f0851d  \n",
       "\n",
       "[745 rows x 4 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "DATAFILE = \"nunl-comments3.csv\"\n",
    "DATE = \"date\"\n",
    "\n",
    "df = pd.read_csv(DATAFILE,index_col=\"id\")\n",
    "for i in df.index.values:\n",
    "    date = re.sub(r\",.*$\",\"\",df.loc[i][DATE])\n",
    "    month,day,year = date.split(\"/\")\n",
    "    month = month.zfill(2)\n",
    "    day = day.zfill(2)\n",
    "    df.loc[i][DATE] = year+month+day\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "annotation time: 235 minutes (3.2 tweets per minute)\n"
     ]
    }
   ],
   "source": [
    "ANNOTATIONFILE = \"human-labels.txt\"\n",
    "\n",
    "inFile = open(ANNOTATIONFILE,\"r\")\n",
    "annotations = {}\n",
    "minutes = {}\n",
    "for line in inFile:\n",
    "    (userName,date,tweetId,nbr,label) = line.strip().split()\n",
    "    annotations[tweetId] = label\n",
    "    minutes[date[0:12]] = True\n",
    "inFile.close()\n",
    "\n",
    "print(\"annotation time: {0:d} minutes ({1:0.1f} tweets per minute)\".\\\n",
    "      format(len(minutes),round(len(annotations)/len(minutes),1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'20200404': {'REJECTS': 16, 'NEUTRAL': 6, 'SUPPORTS': 15},\n",
       " '20200403': {'REJECTS': 265, 'NEUTRAL': 47, 'SUPPORTS': 168}}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IRRELEVANT = \"IRRELEVANT\"\n",
    "NEUTRAL = \"NEUTRAL\"\n",
    "SUPPORTS = \"SUPPORTS\"\n",
    "REJECTS = \"REJECTS\"\n",
    "ID = \"id\"\n",
    "\n",
    "labelsPerDate = {}\n",
    "totals = {}\n",
    "for textId in df.index.values:\n",
    "    if textId in annotations and annotations[textId] != IRRELEVANT:\n",
    "        date = df.loc[textId][DATE]\n",
    "        label = annotations[textId]\n",
    "        if not date in labelsPerDate: labelsPerDate[date] = {}\n",
    "        labelsPerDate[date][label] = labelsPerDate[date][label]+1 if label in labelsPerDate[date] else 1\n",
    "        totals[label] = totals[label]+1 if label in totals else 1\n",
    "labelsPerDate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rfJb/O36EqY5mtg04lZimTIky3yb5FnyFsREQ2wY6+hiJnM//GxJV2DSAl/DNIi/kUL+SNvg3J08557LySeIEfJOHJWb2Cr4fy7su+c7K+Z6z5ocbPh741MU0iQmOsYX4J+YJMbMG+JtaB/x1J7YvShXgh4Ku18zOwJ/fZ+NHNYtuh59MU7lEz/mp+DdPk8zsVXxF+W3n3GoKQRL5WR5/PfkyzrkfrVXwu4mFDy1fBqgaNK3YFDX985DrcX73n2L45hVrorbpIvwb3mYEbyKilok+9xYEcTxtZp3wbxjfds4ty2PbngQuwDdlvDHJa1SyEj1PI/l9tpmdHBLPfvK/F0T72Dm3M3pCcM68h7+enYovj4BvLt0d388n0nTvEvx98bn8VlTA87QV/mFov5B7X+R8PZwyRkHj/zBkGpDUMVlQ78aeO865nWb2Cf7cPp5DTb4L6p2QaZF7VGh/0nwkUzYOVdChHR/Ft9W8J15nlCNke8i0yM06V4fLBEXG81+bQNgK+Ce4YdZFhYkVlu4DcaZHtiesA1u8eOLNi42rEv6pQ12i+mWEKJfgug837wFwzmWZWQf8BfBCDg3buMbMHnUJ9FUA3wkUf1Nqhr9YjcM/ITgQTOuOfysQK9e2BWmCnNuW6LFyOPkcLXIsrY8zP94xF68gmkXyH9e8wzk3wvxQsifgR5HriX9KeHcS8UzCP6n5k5kNcL6zeF6du2NNxD/M6It/M7QfeDmJ9cfGdT7+qdI/gY34vMkAriTnMRLZ5xtD4om3X14BRuDfLka2LdKG+KX8Euecm2Bm+/FPPgfg2wJnmdlM/JOovApY0RI5ZyOdGMO2D/zT1YSY72j9QRDnHPw+34F/2hjpZxDJ26TXG/RZ+08Q3yxgGf5JrONQ/4dEJXTOO+dWBP0VMvFtmvsEaVkMDHap68+US5L5mcx9rHLw+4p8wpUDoisYh3v/wczuAIbj9+8MfMUj8vDkXqLOPefcj0He34evNFwUxLEU/7T7nyHrbIPPn6lHuHKRzHkaye9bUrTqeOdk5HoU/Y2imfiHT33N7L6gkHs1Ps8TuX4W5PpQGV+2PFJljILGH3q9TuaYPAzJ7LNUruNw4k/mmhKqQBWM4MR/CH8THYC/UYcGzWMd6cQvCBWmSKe6sDcGsbbjn5qFqREV5mgVSdv7zrnWRZqSGMGTshvN7CZ8n5ZO+AvySDPb7nJ2FIynO74i8axzLsdoEmZ2VzD/cCR6rKQqnyPx1Igzv9COOecHHFhqZhfj210PMrN/O+cWJ7j8nuAp3w1AD/Pfx+iNLyBOyHNhv/yu4Alyb/zFf1rM09WEmFlLfOViJr7P0sGoeb3wFYxokWtUNaKewgZC94tzbr2Z/QfoFHQS3oAvHC1zzr2fSDqdcxOBicETxDb4wu0VQGMza+IS7xCan0hHx2px5se73oXpj3/S2Du28Gdmrcj5FK0g6x2Mf4J/tnPuvegZFjJKXKo45z4D/hA8wGiJH1zgZmCKmTVzuQdZSJVk8jNynCZ6HwM4zzk387BTmSDz3635C77A0jT6/DU/EmKuAqNzbiVwRfCAoxm+4++twMtmts45NzdmkQvxD5YmmlmPJLcvci0IK7OEFtASPE8j+X1Sio6V/Mog2eWqoLXE8/iHd781s3XAmcDLzrnYAQXCFOQ83Q7sd87FGzXwcBU0/lytIQpyTBZQovss7jFoIR+3DVlHbIuYXMdEEpIpG4dK9mlmtKfxG3MH/jVtmK34zl45mP/E+uE2q0mV/wt+/y6BsJ/gR8xoFjLvnKgwRyXn3E/4yuDJdpjDr+Uh8kSrQG81nLfE+dGeIsO5Rg//mlf8JwS/p4TMO7sg6YmR0LGSwnz+Gl+YPtPChxpsF/wOa55wRATNewbi39Ak+02cyNP8vvhCWjXgNZfHiEoxxuGbb1Qiv9Er4oscI9NDnnCGHSORvG0TMi9sWsRL+Ovrpfg3cpUpwBsX57/lMN05dyW+aVoDkmiylEj8+L4hDc2P6JXN/OP8ZCrIoeefmZXGN4U73PWegO9DFFu5qBG17iPGObfPOfeuc+5u/KhiafgOpxEHgvQc1hvdKMnk5w58H4wTg/trXj4Ifhf2Q6aq+LetC0MeDuR5fXbOHXDOfeScewDfNBv8W41YK/Dt3tcDb1gwHHyCtga/c5VZ8INJ5JW+vM7TVOd382CErWzBOfMbfAH185jwz+OPzauDH0igeRQU+Dz9AKiRwHFYUKmMv8DHZJLOtpj2XME+bIYvyK8KJkcK9Ukfg/gRsmJF7lEFKSMkUzYOVeAKRtAmPBO/c+6IE+z/gAyL+kZE8BTo8bDA5r8J0dhixgY+kpxzH+CHsfutmV0WkqboHT0++P1Q9E0kGMrrf/C1xMlHMLmpMBr/2vOvwY0qBzNrYn5o1IKKtNPMc8zxmHVmmFlY84ZI7Tu6/0Fe8Uf6IeS4MJj/7kTYzShZU/BPOvpY+Ljf0cfKYedz0IxoAj4fbo9Z/kp85/j5LsUfx8mPc24WfiSsc80s4Yuwc+4jfGfcc/FDBkJizaMiy7+LfwvVEz9STkHEO0Zac6jgEu1f+Jvz7dE32OAadWse65mE7xjZmySaRwVxt40tpJofgrRq8G/cr6cX0Cv4itvgmOmXAmFtxuPJlbfBTfUhwp/gJbve74DKwfU2En8p/AAb+X4ToSDM7HTz/bpiJXttKohk83Ms/snn0xbzHRYzq2CHvrfwBsHDweC4JyZsmeANSaptwOfX6Rb1bYPgXHowJB0nx7lGhuV9tqAPSodgfZPMLNECUqRAdYXl/L7TWYQMg5/Eefp3fNO2hyzqWxVRy6SbWX6Fx2iVOHT9jLgK3//iTRcz5Klzbg3+etkT/3ZlGb4pcaKSPU9HB7+fN7NcD5KD+31GEus/kvEndUwehkYcahIcMQi/L1+K9M9wzm3HP1hsY77/VSQ9x+DP+7z8Oeocj75H7cUPeJCUJMvGoQraByPiRfzTzHhP1B7H137eDJpHRMYq3sahzmHReuJPxhfJ+8u/qXYZvlPUP8ysL4favTbDt+WLnPzj8SNmdQU+MbM38QfIJfinWVcFB8jRbCz+SUdvoK2ZzcW35a+F7xzWHN8ROOG21zEW4kc+6m/+w04bAZxzw/JYphn+2xDv45/Crcf3X+iB77g5OirsPIIRp8ysCb5St8059xS+Q+Z3wF3BvK/wza264At8PQu4TQTbsMfM/oS/WP/HzKbjO6dXxnc8WxWkGVKXz3fi3449YP4bHIs5NO75ZqDf4WzTYbgPPzb9ffghcBP1Av67Jq3xgxaEdUyLyzkX9nYqGe/jO/tdYv7Df/+HHwygG74CeWHM+paa2YP4J9afmdlr+LdnvYJlu8ZJ5w4zmwz8CX8T/iCJvhOjgZpm9i6+45/hC0zN8E3D8usQn6yH8Ns9MCjofIB/AtsN39ehM4de3eflGfxNdKKZTcCfm23x30+Yj3+yfDjrfQp//3g3iD8L35SyJP4JXUE6MubnCuAaM3sHP3LiT/iKfRf8tSb6xj0Xf3943cxm4AsunzrnphZw3cnm59P4c7E7vjnjVPw9tz7+LVobfOf2veY7tc4A3jOz2fiBNkrg+yGdg7+OJ/P0P19Bc52x+GbVHwfXz8r4UfUW4K9r0X4HDDez/8WPYLUF/+2GC/BliLhP4Z1zy83365uP/+ZUN+fcW/mkb1FwznUEFprZ2/h+Pd3x95bY+0dC56lzboOZ9cY/LPo8KDd8gy9b1MPvxxfx/VoT8Q5wa1A5/JBD94Mt+LwN8yw+39LwoyklM3hKUuepc+5N803oBwPfmNksfGftaviyYmt85WRlEmnIlsr4C3BMFtRsYKyZdcVXIFrgr2Ur8A/qoz2G75y/MLjfFMOfv/9H3lbgBxx4Hd+UtBc+T24LKpkFkWjZOJxLfGi0XENPBvN7BPMdMcPUBvP/iH9quRdfqXgSP2rLSuIPKxg7LFgmyQ0flxEWTz7beSz+Ar0SX6jdEGTsFTHhSuKfHnyBv4Fsw59k54TEOY44w5mFbX9eaSdkeDKXcxi0sLzPK9964wvrW4N9812wHf2IGgIvn20IjR/fxv0D/M3Nkc/QjUAdfHObRRx6orAC36TktJDwVwbH1J4g/pVR807AVyY24gsD7+AvgEkfJ3nkeSN8ZXNtcKz8gL9Zdw0Jm1A+55M/1fGFq++C9a0L1h9vKNpcx1Uwb35++yLkvIo79Cv+gucixz75XCuCMFU4NETz4MNZf0HCBuFrBMf12uAY/Qh/Q2pP/HPpBnyFdW9wbP45ONbyOn7O59C18eZ80h59XPbCF1y/DdK3BV+QuJmYYTfD9jcFO2cjebIZ3y/mXXxhKfJdnlxDc8fZnt/iP1b1UxDX6/hviYSmKdn14tu4fxzky7pg2Rphx3acvI0cowmd8/gHB8/gHyRsC9L4Jb5TaPWYZUsAj+AfNOzPaz1J7K9k87M4/tryf0Fad+ArD48BFWPC1sVfV77FH9db8c1rRpPzOwJx8ywsj/PaLnzBZwj+KfqeYN2ZwfTYvD8J/zBicbDtu4Pl/krMtS+P/DgB/7ZmN3BuAvuiCr6wvzk4xhbiKzphx1LC52kQ/uQgnd/jr+Obgm17GGicQNqy9wO+kjsT3x9hO/7hSKM8li3OocEs8v3+UsjySV8f8IXi6cF27sP3YVuAfxtfNb9rUgLH12HHn+wxmd/5GhJ3+yBsZvD3O0H+bcGPihi6L/D3m6XBdq3C96EpGSc984PpZfDn+Wr8+fw5MeXXvPI0LO5gekJl47CfyNi7IiIiOQRP7n+D/6ZHon1lfrbrFfklCpogL8f3PeuWX/gk4tV5moegKfU8/KhnmUWbmsJ3OJ28RUTkFyCs35uZXYJvVjP3SBUeimq9Ir8y/fHlvb8WZGGdp1IQh9sHQ0REfv5mm9mP+L4Me/D9GX6Lb2ITbxCPn/N6RX7RzA9r2g/ftOp/8M3mZhQwOp2nkjRVMEREZBy+H8ql+D5ym/GdUu93zn3xC1yvyC9dJXwH7d34ZjrXuoK3iR+HzlNJUqH0wYhqhxbrR+dcxahwlfBfCe+B77CyEP9FzBzjOgfDft6P7+FeEf/tibucc28fkQ0QEREREZGEFPYbjFvIOdRW5DPwkfG9p+Jf592MH9FiMDDP/NdSV0ct9zx+eMg78B2XbgRmmdlZzrmj9kN3IiIiIiK/dIX9BuNc59ycOGG64z8A1NE5Ny+Ylo4fEvIl59wtwbSm+DcWVznn/h5MK4EfNvbrREZIqFq1qsvIyDjczRIRERERydNHH320yTlXrajTUZiOpj4Y3YC1kcoF+M/UBx8L6o5/+xEJtx/f/i8SLsvM/gUMMrM057+AHFdGRgYffvhhyjdARERERCSama0q6jQUtsIepvZlMztgZpvN7J9mVjdqXhP8x4xifQHUjfoEehNghXNuV0i4UvgvTIqIiIiISBEorDcYP+K/MLgA/9XJ5sDd+E+hN3fObcB/nn1lyLJbgt+V8EOiVcb3z4gXrnJYAszsWuBagLp164YFERERERGRw1QoFQzn3MfAx1GTFpjZ28AH+KZPfymENPwN+BtAixYt9PlyEREREZEjoMi+5O2cWwwsBVoGk7bi31LEqhw1P5FwW0LmiYiIiIhIISiyCkaUyNuEL/D9K2KdDHwX9Sn6L4B6ZlY2JNw+YNkRSaWIiIiIiOSryCoYZtYCaIRvJgUwBTjWzM6JClMBuCCYFzEVKAlcFBWuBNALmJ3fCFIiIiIiInLkFEofDDN7Gf89i8XANnwn78HAGuDJINgU/Je7XzKzOzj0oT0Dhkfics59bGYTgJFmVjKItx9QD+hdGNsjIiIiBbdnzx42btzInj17yMrKyn8BkaNQyZIlqV69OhUqVCjqpBx1CmsUqSXAn/Bf6C4LrAMmAvc65zYBOOcOmtnvgRHAGKA0vsLRwTn3fUx8fYEHgGFAReBToEvQr0NERESOUj/++CPr16+nWrVq1KxZkxIlSmBmRZ0skaQ459i9ezdr1qwBUCUjRqF8yfto06JFC6cP7YmIiBS+b7/9llq1alG2bGxXSpGfn127drF27VoaNIj/GTYz+8g516IQk1XkjoZO3iIiIvIrsW/fPsqUKYX+dvkAACAASURBVFPUyRBJiTJlyrB///6iTsZRRxUMERERKVRqEiW/FDqWw6mCISIiIiIiKaMKhoiIiIiIpExhjSIlIiIikqeMQdOLdP0rH+5apOs/Gm3bto2RI0fSrVs3Tj/99JTF+/jjjzNu3Dg+/fRTzIyffvqJq6++msWLF/PDDz9QsmRJGjZsyC233MJll12WY9mMjAxWrVqVK85JkybRo0eP7P8fe+wxnnjiCfbv389VV13FAw88QLFih56tv//++3Tq1InPP/+cjIyMHHH16NGD2rVrM2bMmJRt86+JKhgiIiIiEmrbtm0MHTqUOnXqpKyCsW3bNh544AGeeeaZ7D4M+/bto0SJEgwePJiMjAz27t3LhAkTuPzyy9m4cSMDBgzIEUfnzp3JzMzMMa1Ro0bZf8+dO5dBgwbx9NNPc8wxx3DdddfRqFEj+vTpA8CBAwfo168fd999d67KBcC9997LmWeeSf/+/WnYsGFKtvvXRBUMEREREcll7969RyTe559/nlKlStGzZ8/saVWqVOGf//xnjnDnn38+S5cu5YUXXshVwahatSqtW7eOu44ZM2Zw7rnncu211wLw9ttvM2PGjOwKxtixY9m9ezcDBw4MXb558+Y0b96ckSNH6i1GAagPhoiIiMhhWLp0KT179qR69eqULl2aunXrctFFF2V/pXzcuHGYGStXrsyxXGZmZq5RiMyMP//5zzzwwAPUqVOHMmXK0K5dOz755JMc4dq3b0+bNm2YPHkyp5xyCmlpaTRu3JhXX301V/pmzpzJWWedRZkyZUhPT6dHjx58/fXXofFNnTqV5s2bk5aWxpgxY6hXrx4A11xzDWaGmTFu3DgAZs2axW9+8xvS09MpX748jRo14r777ss3v5577jkuvvhiihcvnm/YKlWqUKJE8s/DY4dDLlu2LHv27AFg/fr13HPPPYwZM4aSJUvGjeOSSy7h5ZdfZvfu3Umv/9dOFQwRERGRw9C1a1fWrFnD2LFjmTVrFg8//DBpaWkcPHiwQPGNHz+eN998k6eeeopx48axfv16fvvb37Jly5Yc4ZYtW8Ytt9zC7bffzsSJE2nQoAGXXHIJ8+bNyw4zc+ZMunbtSvny5ZkwYQJjx45lyZIltGnTJvsr1BFLly7llltu4eabb2bWrFl07NiRiRMnAjB48GAWLlzIwoUL6dq1K8uXL6dbt27Uq1ePCRMmMGXKFG677TZ27tyZ57atWrWKr776irZt24bOd86RlZXF5s2b+dvf/sasWbNyvb0AmDp1KmXLliUtLY3WrVvzxhtv5JjfqlUr5syZw+LFi1m2bBmvvfZa9huPgQMH0rVrVzp06JBnWtu1a8f27dtZuHBhnuEkNzWREhERESmgTZs2sWzZMiZPnky3bt2yp1966aUFjnP37t3Mnj2bcuXKAb6wfOKJJ/LEE09w//33Z4dbv349CxcuzC44d+nShSZNmjBkyBDeeecdAP7yl79Qv359ZsyYkf0m4KyzzqJhw4Y89thjPP744zm2Zfbs2TRr1ix7Wnp6OgD169fP0SRpwYIF7Nu3j7Fjx1KhQgUAOnbsmO+2LVq0CICmTZuGzn/66ae5+eabAShZsiSjRo3iiiuuyBHmggsuoGXLltSrV4/169fz1FNP0bNnT/7xj39kdwjv1asXb7zxBmeccQYAHTp04JZbbmHBggVMmzaNr776Kt+0Nm3alGLFirFo0aKEtk0OUQWjkBX1CBki8vOjkW1Ejl5VqlShfv36DBo0iPXr19O+fXtOPPHEw4rz/PPPz65cgB81qXXr1rmepB933HE5Cv3FixfnoosuYvjw4Rw8eJDdu3ezePFi7r777hzNjOrVq8fZZ5/NggULcsSXkZGRo3KRl2bNmlGyZEkuueQSrrrqKtq1a0f16tXzXW7t2rUAVKtWLXR+r169aN26NZs2bWLKlCncfPPNFC9enOuuuy47zOjRo3Ms07NnT1q3bs3gwYOzKxjFixfn1VdfZe3atWRlZVG3bl3279/PjTfeyLBhw6hRowajRo1i1KhR7Nixgz/84Q888cQTOZpVlSxZkvT09Ow0S+LUREpERESkgMyMt956ixYtWjB48GAaNmxI/fr1GTt2bIHjrFGjRui02CZN8cLt27ePjRs3snXrVpxz1KpVK1e4mjVr5mpyFRYungYNGjBr1iwOHjzI5ZdfTs2aNWndunWuSkusSD+ItLS00PnVqlWjRYsWdOnShTFjxnD55ZczcOBA9u/fHzfOSMVq9erV/PDDDznm1a5dm7p16wIwcuRISpcuTb9+/Xjrrbe45557mDhxIl988QUffPABDz74YK64y5Qpoz4YBaAKhoiIiMhhqF+/PuPHj2fjxo18/PHHdOzYkRtuuIEZM2YAULp0acB3PI62efPm0PjWr18fOu3YY49NKFypUqWoVq0alSpVwsxYt25drnDr1q2jcuXKOabFdjjPT4cOHZg5cybbtm1jzpw5lChRgq5du7Jp06a4y1SpUgWArVu3JrSOFi1asGPHjtBtDRNvG1avXs2wYcMYO3YsxYoVY+bMmZx77rk0a9aMatWq0bdvX2bOnJlruS1btlC1atWE1i2HqIIhIiIikgJmRrNmzbL7NSxZsgSA448/Psf/AFlZWcyePTs0njfffDNHZ+mVK1eyaNEizjrrrBzhvv/+++w+DeC/7fDaa69x5plnUqxYMcqVK8cZZ5zBa6+9xoEDB7LDrVq1ivfee4/27dvnu02RNw15PcVPS0ujY8eO3HnnnezcuZMVK1bEDdu4cWMAli9fnu+6wff1KF++fJ7Nr7KyspgwYQJ169alZs2aoWH69+9P7969admyZfa06DzesWMHzrkcy6xbt449e/bk+L6GJEZ9MEREREQK6LPPPuPWW2+lV69eNGjQgAMHDjBu3DhKlCiR3TG4ZcuWnHDCCdxxxx0cPHgwewjYeN+ZKFOmDL/73e+444472Lt3L/feey8VKlTINZpSjRo16NWrF0OHDqVatWqMHTuWpUuX5miedf/999O1a1d+//vfc8MNN7Bjxw7uvfde0tPTuf322/Pdvho1alClShX+9a9/cdppp1GuXDnq1avHa6+9xttvv83555/Pcccdx6ZNm3jooYeoXbs2p5xyStz4zjzzTNLS0vjggw9o06ZN9vRnnnmGRYsW0alTJ+rUqcPmzZt59dVX+fe//83DDz9MqVKlAHjllVeYPHly9nrXr1/P008/zeLFi3nllVdC1zlr1izeeeedHEPzdurUiVGjRjFmzBhq167N6NGjs7+REfH+++8DfjQpSY4qGCIiInJU+DkOaFCzZk3q1q3L448/zurVqyldujSnnnoq06ZNyx7BqESJEkyePJkbb7yRPn36ULlyZfr370+rVq0YOnRorjivuOIKypUrx0033cSmTZto2bIl//rXv3I1aWrQoAF33nknd999N9988w0ZGRm88sorOYZf7dKlC9OnT2fo0KFcfPHFlCpVivbt2zN8+HBq166d7/YVK1aM5557jrvvvptOnTqRlZXF3//+d5o2bcqMGTMYPHgwGzZsoHLlyrRp04aXX345R0fpWKVLl6Z79+5MnTqV2267LXv6qaeeyuTJkxk4cGB2s6STTjqJadOm0bXroeOiXr16bNiwgTvuuIMtW7ZQrlw5WrRowcyZM+ncuXOu9e3du5ebbrqJRx99lIoVK2ZPP++883jwwQd58MEH2bVrFz169OAvf/lLjmUj+7BBgwb55pPkZLGvg34NWrRo4T788MMiWbdGkRKRZP0cC10i8Xz55ZecdNJJRZ2Mo1bkQ3vDhg3LM1z79u3Jysrif//3fwspZakzf/58OnbsyMqVK7M7YB9t9uzZQ61atRgxYgRXX311nmHzO6bN7CPnXItUp/Fopj4YIiIiIlJo2rdvz29/+1uGDx9e1EmJ65lnnqF69epceeWVRZ2UnyVVMERERESkUI0ePZo6derk6lh9tEhLS8vuSyPJU66JiIiIHCUSLXDPnz//yCbkCGvcuDGDBg0q6mTEdf311xd1En7W9AZDRERERERSRhUMERERERFJGVUwREREREQkZVTBEBERERGRlFEFQ0REREREUkYVDBERERERSRlVMEREREREJGX0HQwRERE5OmSmF/H6fyza9Sdp5cqVjBs3jiuuuIL69eunNO758+fToUMH5s2bR/v27fMN361bN+rWrctTTz0FwE8//cTQoUP58MMPWbx4MT/99FPcuA4ePMgjjzzCM888w7p162jUqBFDhgzhwgsvzBHuscce44knnmD//v1cddVVPPDAAxQrduhZ+fvvv0+nTp34/PPPycjIyLFsjx49qF27NmPGjEk6LyR5eoMhIiIi8jO0cuVKhg4dyvLly4s0HW+//TazZ89m8ODB2dM2b97MCy+8QIkSJTj33HPzXP6ee+4hMzOTm266iRkzZtC6dWsuuugi3nzzzewwc+fOZdCgQQwZMoSRI0fy9NNPM378+Oz5Bw4coF+/ftx99925KhcA9957L88++yxLly49/A2WfKmCISIiIvIL55xj3759RyTuRx99lAsuuIBjjz02e9rxxx/Pli1bmDNnDv369Yu77IYNGxgxYgSDBg1i4MCBdOjQgWeeeYYOHTrk+NL3jBkzOPfcc7n22mv505/+RO/evZkxY0b2/LFjx7J7924GDhwYup7mzZvTvHlzRo4cmYItlvyogiEiIiJyGDIzMzEzvvnmG7p27Ur58uU5/vjjue+++zh48GCOsBs3buT666/n2GOPJS0tjcaNG/O3v/0tNL5Yffr0yX46H2nCBHDuuediZpgZ8+fPByAjI4PLLruMF154gcaNG1OqVCmmT58O+Kf5p59+OhUqVKBq1ap07NiRRYsWFWjb165dy4wZM7j00ktzTA9Lf5hZs2axb98+LrvsshzTL7vsMj7//HNWrFgBwL59+yhTpkz2/LJly7Jnzx4A1q9fzz333MOYMWMoWbJk3HVdcsklvPzyy+zevTuhtEnBqYIhIiIikgI9e/akY8eOvPHGG/To0YN7772XF198MXv+9u3badOmDW+++SaZmZlMnz6dCy64gH79+jF69Oik1nX66afz9NNPA/Dkk0+ycOFCFi5cyOmnn54dZt68eTz++OPce++9zJw5k9NOOw2ANWvWMGDAACZPnsy4ceOoXr067dq14/PPP096m9966y0OHDhA27Ztk14W4IsvviAtLY0GDRrkmN6kSRMA/vvf/wLQqlUr5syZw+LFi1m2bBmvvfYarVu3BmDgwIF07do1u8IVT7t27di+fTsLFy4sUFolcerkLSIiIpICt99+O3379gWgU6dOzJ07l1deeSV72qhRo1i1ahWff/45J554Yna4bdu2MXToUPr160eJEokVzSpUqMDJJ58MwEknnZRd2I62detWPvroI2rWrJlj+nPPPZf994EDB+jSpQtNmjThueeeY9SoUUlt86JFi6hduzZVq1ZNarmILVu2ULFixVxvPCpXrpw9H6BXr1688cYbnHHGGQB06NCBW265hQULFjBt2jS++uqrfNfVtGlTihUrxqJFi+jYsWOB0iuJ0RsMERERkRTo2rVrjv9POeUUvvvuu+z/Z86cSatWrahXrx5ZWVnZP507d2bz5s3ZT+tTpXXr1rkqFwBz5syhQ4cOVKlShRIlSlCyZEmWLl3K119/nfQ61q5dS7Vq1VKR3DwVL16cV199lTVr1rBq1Srmzp1LqVKluPHGGxk2bBg1atRg1KhR1K9fn+rVq3P99dfnagpVsmRJ0tPTWbt27RFP76+d3mCIiIiIpEDkqXtEWlpadj8B8B2aly1bFrefwObNm1Oanlq1auWatnjxYs4//3w6d+7M888/T61atShevDj/8z//kyOtidqzZw9paWkFTmOlSpXYtm0bzrkcbzEiby5i87R27drZf48cOZLSpUvTr18/3nrrLe655x7efvttjj32WDp37syDDz7I/fffn2P5MmXKqA9GIVAFQ0RERKQQVKlSherVq8dthtSoUSMASpcuDfiOzaVKlcqen2wFJKyj9euvv06JEiWYOHFijorO1q1bqVixYlLxg9+mSEfsgmjSpAl79+7l22+/zdEPI/I2J9IMLNbq1asZNmwYc+bMoVixYsycOZNzzz2XZs2aAdC3b1/Gjx+fq4KxZcuWAjfnksSpiZSIiIhIIejSpQtfffUVdevWpUWLFrl+jjnmGMAP8QqwZMmS7GW3bdvGe++9lyO+yJuDZJ7I79q1i+LFi+eofMydOzdHU65kNG7cmO+//56srKwCLd+lSxdKlizJyy+/nGP6Sy+9xCmnnEK9evVCl+vfvz+9e/emZcuW2dN27tyZ/feOHTtwzuVYZt26dezZsye7IidHjt5giIiIiBSCAQMGMGHCBNq2bcuAAQNo1KgRO3fu5KuvvuKdd95h8uTJAJx33nmkp6dzzTXXMHToUPbu3cvw4cMpX758jvgaNmxIiRIleOGFF6hcuTJpaWk0atQou6ISpkuXLowcOZI+ffrQt29fli5dyv3335/jGxbJaNeuHffeey+fffZZjhGswH+7YufOndmjUy1YsIBNmzZRrlw5zjvvPACqV6/ObbfdxkMPPcQxxxzD6aefzoQJE5g7dy5TpkwJXeesWbN45513cvQZ6dSpE6NGjWLMmDHUrl2b0aNH06dPnxzLvf/++9lpliNLFQwRERE5OmT+WNQpOKLS09N57733uO+++3jkkUdYs2YNFStWpFGjRlx44YXZ4SpWrMi0adMYMGAAF198MXXq1GHIkCHMmTMn+zsX4JsnPfXUUzzyyCOcc845HDhwgHnz5tG+ffu4aejcuTNPPvkkjz/+OK+//jqnnHIK48ePZ9iwYQXaprZt21K7dm2mTp2aq4LRr18/Vq1alf1/ZmYm4N/QrFy5Mnv6Aw88QPny5Rk1ahTr1q2jUaNGvPrqq/z+97/Ptb69e/dy00038eijj+Zo0nXeeefx4IMP8uCDD7Jr1y569OjBX/7ylxzLTps2jTPOOCPXkLiSehb7+ujXoEWLFu7DDz8sknVnDJpeJOsVkZ+vlQ93zT+QyM/El19+yUknnVTUyZAUyszM5OWXX2bp0qUJf2CvsO3Zs4datWoxYsQIrr766pTGnd8xbWYfOedapHSlRzn1wRARERGRAhswYADbtm3j9ddfL+qkxPXMM89QvXp1rrzyyqJOyq+CKhgiIiIiUmDp6en84x//YN++fUWdlLjS0tIYN25cwh8ylMOjXBYRERGRw9KlS5eiTkKerr/++qJOwq+K3mCIiIiIiEjKqIIhIiIiIiIpowqGiIiIiIikjCoYIiIiIiKSMqpgiIiIiIhIyqiCISIiIiIiKaMKhoiIiIiIpIwqGCIiInJ0MCvanyKUkZFBnz59jkjcI0eOZOLEiSmN86OPPqJs2bKsWbMme1qfPn0ws1w//fv3jxvPtm3bqFWrFmbGnDlzcsz7+OOPOfPMMznmmGPo1KkTq1atyjE/KyuLpk2b8uijj+aK94033qBGjRrs2LHjMLdUCkIVDBEREZEiNmnSJO65554jEveRqGDccccdXHXVVRx77LE5plerVo2FCxfm+BkwYEDceO66667Q6VlZWfzxj3/khBNOYOLEiRw4cIArr7wyR5jRo0dz4MCB0Pi7d+9OrVq1QisfcuTpS94iIiIiKbZ3717S0tISDt+8efMjmJrU+uijj5g3bx6jR4/ONa9UqVK0bt06oXjeffddXnrpJUaPHs3VV1+dY97XX3/N8uXLWbhwIdWrV6dChQqcddZZ7Nq1i7Jly7J27VoyMzOZOnUqJUrkLs6aGddeey333HMPgwcPpnTp0gXbWCkQvcEQEREROQyZmZmYGUuWLKFz586UL1+eiy++OHv+xIkTad26NWXLlqVixYpcdNFFfPfddzniCGsitWLFCnr37k21atVIS0ujWbNmTJo0Kdf6P/30U3r27EmVKlUoU6YMjRo14qGHHsqOd9WqVbz88svZTZYi61m6dCk9e/akevXqlC5dmrp163LRRReRlZWV5/Y+99xznHbaaTRp0qQAueXt37+f6667jkGDBlG/fv1c8/ft2wdAmTJlAChbtizOOfbu3QvAbbfdRs+ePWnXrl3cdVx88cVs27Yt5W9vJH+qYIiIiIikQPfu3TnnnHOYMmVKdrOdv/71r1x44YWcfPLJ/Pvf/+aZZ55hyZIlnHPOOfz0009x4/r+++9p1aoVn376KU888QRTpkzh9NNP58ILL2TKlCnZ4T744APOOussvv32W5544gmmT5/ObbfdxurVqwHf9KpmzZp07tw5u8lSpClW165dWbNmDWPHjmXWrFk8/PDDpKWlcfDgwTy3c+bMmbRt2zZ03oYNG6hatSolSpSgYcOGPPLIIxw4cCBXuOHDh7Nv3z7uvPPO0HgaNWpExYoVGTlyJFu3bmXUqFE0bNiQSpUq8Z///IfZs2fn2/ypatWqnHTSScycOTPPcJJ6aiIlIiIikgK33HILt956a/b/O3bs4K677qJv37688MIL2dPPPPNMGjVqxPPPPx+3A3RmZibOORYsWECVKlUA6Ny5M99//z1DhgyhW7duAAwcOJAqVaqwaNEiypYtC0DHjh2z42nevDlpaWlUrVo1R9OlTZs2sWzZMiZPnpwdF8Cll16a5zauX7+elStX0rRp01zzmjVrxhlnnEGTJk3Ys2cPkyZNYvDgwXzzzTc899xz2eGWLVvGsGHDmDp1atxmZGXLluWvf/0rffv2ZciQIVSpUoVJkyaxb98+brzxRh588EGqVauWZ1oj279o0aJ8w0lq6Q2GiIiISAr07Nkzx/8LFy5k+/bt9O7dm6ysrOyf4447jsaNG/P222/HjWvmzJmcf/75pKen51i2c+fOfPrpp2zfvp1du3bx7rvv0rt37+zKRaKqVKlC/fr1GTRoEM8++yzffPNNQsutXbsWILRw379/f26++WY6duzI+eefz7PPPsutt97K888/nyP+fv360b17dzp16pTnunr16sWmTZv48ssvWbt2LW3btmXEiBGkp6dz7bXX8sknn9CmTRsqVarE2WefzWeffZYrjmrVqmWnWQqPKhgiIiIiKVCrVq0c/2/YsAGATp06UbJkyRw/n3/+OZs3b44b14YNGxg/fnyu5e644w4ANm/ezNatWzl48CB16tRJOq1mxltvvUWLFi0YPHgwDRs2pH79+owdOzbP5fbs2QOQcAf2P/3pTwB8+OGHALz66qu89957DBkyhG3btrFt27bsoWR37tzJjz/+mGP5smXL0rhxY0qVKsWqVat4+OGHGTt2LAcOHKBnz5507tyZtWvX8rvf/Y6ePXuyf//+HMuXKVMmO81SeNRESkRERCQFLOZbGpGmTePGjQvtEH3MMcfEjatKlSq0bds27jCutWvX5sCBAxQrVizHtyiSUb9+fcaPH49zjk8//ZSnnnqKG264gYyMDM4777y46QLYunVrUuuK5M1///tfdu3aFZofPXr0ID09nW3btoXGceutt3LllVdy+umn8/nnn7Ny5Ur69+9PmTJluO2228jMzGTp0qU54t6yZUt2mqXwqIIhIiIicgT85je/4ZhjjmHZsmW5vuGQny5durBw4UKaNGmSPZJSmDZt2vDSSy8xZMiQuOHS0tLYvXt33DjMjGbNmvH444/z/PPPs2TJkrgVjIyMDEqXLs3y5csT2o7I6FUtW7YE/Mf42rdvnyPMJ598woABAxgxYgStWrUKjWf69Om8//77vPjiizmm79y5k2OOOSb7LYhzLsf8FStW0KhRo4TSKqmjCoaIiIjIEVChQgUeffRRbrzxRjZu3Mh5551Heno6a9asYcGCBbRv3z5up+r77ruPM888k3bt2nHTTTeRkZHB1q1bWbJkCcuXL8/uND5ixAjOOecczjrrLG6//Xbq1KnD8uXL+eSTT7K/U3HyySfzzjvvMG3aNGrWrEnVqlXZvn07t956K7169aJBgwYcOHCAcePGUaJEiRydxGOVKlWKVq1a8cEHH+SYvmrVKi6//HIuueQSGjRowN69e5k0aRLjxo3juuuu44QTTgB8BSUjIyM07qZNm9KmTZtc03fv3s3NN9/MY489Rnp6OuBHmTruuOO4+eabue666xg7dizHH398jsqEc44PPviAG264Ie72yJGhCoaIiIgcHWKePv8SXHfddRx33HE8+uij/POf/yQrK4tjjz2Wtm3b0qxZsxxho5tY1a1blw8//JDMzEzuvvtuNm7cSJUqVTjllFNyvA1p2bIl7777LkOGDOHmm29m7969HH/88fTt2zc7zEMPPcQ111zDxRdfzO7du7nyyisZPnw4devW5fHHH2f16tWULl2aU089lWnTpnHGGWfkuU29evXijjvuYOfOnZQrVw7wzb0qV67MI488wvr16ylWrBiNGzfmySefPOwC/kMPPUS9evVyVMZKlSrF66+/zo033kj37t1p0qQJr7/+OiVLlswO895777F161YuueSSw1q/JM9iXyX9GrRo0cJFOhsVtoxB04tkvSLy87Xy4a5FnQSRlPnyyy856aSTijoZR53KlStz1VVXMWLEiKJOSr62b99OnTp1GDNmDJdddllRJyeufv36sWTJEt55550jup78jmkz+8g51+KIJuIoozcYIiIiIkXkv//9L2+++SZbt27N8Z2Ko1mFChW46667GD58OL17987Vuf1osG7dOl588UV9ZK+IqIIhIiIiUkT+/Oc/s3DhQgYMGMAf/vCHok5Owm677TYOHDjADz/8QO3atYs6ObmsXLmSxx57jHbt2hV1Un6VVMEQERERKSKTJk0q6iQUSJkyZRgyZEhRJyOu1q1b/2zeCP0S6UN7IiIiIiKSMqpgiIiIiIhIyqiCvWb1+QAAGxZJREFUISIiIoXq1ziCpfwy6VgOpwqGiIiIFJpSpUrl+VVpkZ+T3bt35/j2hniqYIiIiEihqVq1KqtXr2bLli3s379fT4DlZ8k5x65du1izZg3Vq1cv6uQcdTSKlIiIiBSa9PR00tLS2LhxI5s3byYrK6uokyRSICVLlqRGjRpUqFChqJNy1FEFQ0RERApV6dKlOe6444o6GSJyhBRZEykzm2lmzsyGxUyvZGbPmdkmM9tpZnPM7NSQ5Uub2aNm9oOZ7TazhWamr6mIiIiIiBShIqlgmNmfgKYh0w2YCnQBbgYuBEoC88ysTkzw54FrgCHA74EfgFlm1uwIJl1ERERERPJQ6BUMM6sEPAHcFjK7G3A2cLlz7hXn3MxgWjHgzqg4mgKXAgOcc8865/4DXAx8B9x3hDdBRERERETiKIo3GI8AS5xzr4TM6wasdc7Ni0xwzv2If6vRPSbcfmBCVLgs4F9AZzNLOxIJFxERERGRvBVqBcPM2gBXADfGCdIEWBIy/QugrpmVjwq3wjm3KyRcKaBBCpIrIiIiIiJJKrQKhpmVAp4BRjjnvo4TrDKwNWT6luB3pQTDVS5oOkVEREREpOAK8w3GnUAZ4IFCXGc2M7vWzD40sw83btxYFEkQEREREfnFK5QKhpnVBf4M3AOkmVlFM6sYzI78Xxz/VqJSSBSRNxJbo37nFW5L7Azn3N+ccy2ccy2qVatW0E0REREREZE8FNYbjPpAaeAlfOUg8gMwMPj7VHwfiiYhy58MfOec2xH8/wVQz8zKhoTbByxLaepFRERERCQhhVXB+AToEPIDvtLRAV8pmAIca2bnRBY0swrABcG8iKn472NcFBWuBNALmO2c23vEtkREREREROIqURgrcc5tA+bHTvff1WOVc25+8P8UYCHwkpndgX+zMRgwYHhUfB+b2QRgpJmVBFYA/YB6QO8juS0iIiIiIhJfkXzJOx7n3EH8V7nfAsYAk4ADQAfn3PcxwfsCfweGAdOB44AuzrnFhZdiERERERGJVihvMOJxzlnItC3AVcFPXsvuxn8NPOyL4CIiIiIiUgSOqjcYIiIiIiLy86YKhoiIiIiIpIwqGCIiIiIikjKqYIiIiIiISMqogiEiIiIiIimjCoaIiIiIiKSMKhgiIiIiIpIyqmCIiIiIiEjKqIIhIiIiIiIpowqGiIiIiIikjCoYIiIiIiKSMqpgiIiIiIhIyqiCISIiIiIiKaMKhoiIiIiIpIwqGCIiIiIikjKqYIiIiIiISMqogiEiIiIiIimjCoaIiIiIiKSMKhgiIiIiIpIyqmCIiIiIiEjKqIIhIiIiIiIpowqGiIiIiIikjCoYIiIiIiKSMqpgiIiIiIhIyqiCISIiIiIiKaMKhoiIiIiIpIwqGCIiIiIikjKqYIiIiIiISMqogiEiIiIiIimjCoaIiIiIiKSMKhgiIiIiIpIyqmCIiIiIiEjKqIIhIiIiIiIpowqGiIiIiIikjCoYIiIiIiKSMqpgiIiIiIhIyqiCISIiIiIiKaMKhoiIiIiIpIwqGCIiIiIikjKqYIiIiIiISMqogiEiIiIiIimjCoaIiIiIiKSMKhgiIiIiIpIyqmCIiIiIiEjKqIIhIiIiIiIpowqGiIiIiIikjCoYIiIiIiKSMqpgiIiIiIhIyqiCISIiIiIiKaMKhoiIiIiIpIwqGCIiIiIikjKqYIiIiIiISMqogiEiIiIiIimjCoaIiIiIiKSMKhgiIiIiIpIyqmCIiIiIiEjKqIIhIiIiIiIpowqGiIiIiIikjCoYIiIiIiKSMqpgiIiIiIhIyqiCISIiIiIiKaMKhoiIiIiIpIwqGCIiIiIikjKqYIiIiIiISMqogiEiIiIiIimjCoaIiIiIiKSMKhgiIiIiIpIyqmCIiIiIiEjKqIIhIiIiIiIpU6KoE/Brs7L0pUWdBBH52fmxqBMgIiKSML3BEBERERGRlFEFQ0REREREUkYVDBERERERSZlCqWCYWWczm2tm68xsr5mtNrNXzezkmHDHmdm/zexHM9tuZhPNrG5IfJXM7Dkz22RmO81sjpmd+v/t3X+0ZWV5H/DvIyCIhMCsToxiDCBWCjUxzbRh1aygxAoaHSYLTYjGRbBCtI3mhxolmMSoiT9Qa5NqImiIqxi12iAQrREFscswJgNWZTQQzCBYsQ4yQhCFoG//2PvGw+HMzJ2Zd86Zy3w+a92179n72Xu/54+7z/2e9333nsd7AQAAtm5ePRirklyV5FeSPCnJWUmOTbK+qn44SarqwCSXJTk6yWlJnp3kUUkur6oHLx2oqirJJUlOSvKCJKck2W+se/ic3g8AADDDXO4i1Vp7d5J3T66rqr9J8ndJnp7kjUnOSHJkkke31q4faz6b5O+T/HKSN427rk3yuCQntNYuH+uuTLIpyW8meeHufj8AAMBsi5yD8fVxec+4XJtk/VK4SJLW2qYkn0xy8sR+a5N8ZSlcjHW3ZejVmKwDAADmbK4Bo6r2qaoHVtWjkrwtyVfzvZ6NY5NcM2O3jUkm52psq+4RVXVQxyYDAAA7YN49GJ9KcleS65L8SIZhTl8bt61KsmXGPrcmOXTi9bbqMlULAADM0bwDxrOTHJfkmUluT3JpVR0+jxNX1ZlVtaGqNmzevHkepwQAgL3OXANGa+0LrbVPjZO+fzrJQUleNm7ektm9D9M9FtuqS2b3bqS1dm5rbU1rbc3q1at3qv0AAMC2LWySd2vtG0muT3LUuGpjhvkV045J8vmJ19uqu7G1dkfPdgIAAMu3sIBRVQ/J8MyLL46rLk5yXFUdOVFzeIZb0l48sevFSQ6rquMn6g5O8rSpOgAAYM7m8hyMqrowydVJPpth7sW/TPLrGW5R+8ax7LwMD+K7qKpenqQleVWSmzLccWrJxUmuTHJBVb0kw5Cos5JUktfv9jcDAABs1bx6MNYnWZfknUk+mOQ3klyR5LGtteuSpLX2zSQnZLjD1H9P8q4MD887YXLYU2vtu0memuTSJG9NcmGS7yR5Qmvtpjm9HwAAYIZ5Pcn7dUlet4y6G5Ocsoy6W5M8Z/wBAAD2EIt8kjcAAHA/I2AAAADdCBgAAEA3AgYAANCNgAEAAHQjYAAAAN0IGAAAQDcCBgAA0I2AAQAAdCNgAAAA3QgYAABANwIGAADQjYABAAB0I2AAAADdCBgAAEA3AgYAANCNgAEAAHQjYAAAAN0IGAAAQDcCBgAA0I2AAQAAdCNgAAAA3QgYAABAN/suugF7nd+7fdEtAFaaVyy6AQCwfHowAACAbgQMAACgGwEDAADoRsAAAAC6ETAAAIBuBAwAAKAbAQMAAOhGwAAAALoRMAAAgG4EDAAAoBsBAwAA6EbAAAAAuhEwAACAbgQMAACgGwEDAADoRsAAAAC6ETAAAIBuBAwAAKAbAQMAAOhGwAAAALoRMAAAgG4EDAAAoBsBAwAA6EbAAAAAuhEwAACAbgQMAACgGwEDAADoRsAAAAC6ETAAAIBuBAwAAKAbAQMAAOhGwAAAALoRMAAAgG4EDAAAoBsBAwAA6EbAAAAAuhEwAACAbgQMAACgGwEDAADoRsAAAAC6ETAAAIBuBAwAAKAbAQMAAOhGwAAAALoRMAAAgG4EDAAAoBsBAwAA6EbAAAAAuhEwAACAbgQMAACgm7kEjKp6elX9z6r6UlV9q6qurarXVNX3TdUdWlVvr6pbquqbVfXRqnrMjOMdUFXnVNXN4/GurKqfmsd7AQAAtm5ePRgvTvKdJL+V5KQkf5zk+UkuraoHJElVVZJLxu0vSHJKkv2SXF5VD5863juSnJHkd5I8NcnNSf6qqh67+98KAACwNfvO6TxPa61tnnh9RVXdmuSdSR6f5LIka5M8LskJrbXLk6SqrkyyKclvJnnhuO5HkzwzyXNaa+eP665IsjHJK8fjAAAACzCXHoypcLHkb8flYeNybZKvLIWLcb/bMvRqnDyx39ok/5TkvRN19yR5T5ITq2r/jk0HAAB2wCIneR8/Lr8wLo9Ncs2Muo1JHlFVB03UbWqt3Tmj7oFJjurdUAAAYHkWEjCq6rAMw5k+2lrbMK5elWTLjPJbx+Why6xbtZVznllVG6pqw+bNszpUAACAXTX3gDH2RFyU5J4kp8/rvK21c1tra1pra1avXj2v0wIAwF5lrgGjqh6UYU7FkUlObK19eWLzlnyvl2LSqonty6m7dcY2AABgDuYWMKpqvyTvT7ImyVNaa5+bKtmYYX7FtGOS3Nhau2Oi7oiqOnBG3d1Jru/XagAAYEfM60F7D0jyriQnJFnXWls/o+ziJIdV1fET+x2c5GnjtiWXZHg+xjMm6vZN8vNJPtJau6v/OwAAAJZjXs/BeEuGQPD7Sb5ZVcdNbPvyOFTq4iRXJrmgql6SYSjUWUkqyeuXiltrn66q9yZ589grsinDQ/uOSPKsebwZAABgtnkNkXryuDw7Q4iY/HlukrTWvpvhqdyXJnlrkgszPP37Ca21m6aOd3qS85O8OskHk/xQkpNaa1fv3rcBAABsS7XWFt2GuVuzZk3bsGHD9gt3h6rFnBdYufbC6zTA/UVVXdVaW7PodszTIh+0BwAA3M8IGAAAQDcCBgAA0I2AAQAAdCNgAAAA3QgYAABANwIGAADQjYABAAB0I2AAAADdCBgAAEA3AgYAANCNgAEAAHQjYAAAAN0IGAAAQDcCBgAA0I2AAQAAdCNgAAAA3QgYAABANwIGAADQjYABAAB0I2AAAADdCBgAAEA3AgYAANCNgAEAAHQjYAAAAN0IGAAAQDcCBgAA0I2AAQAAdCNgAAAA3QgYAABANwIGAADQjYABAAB0I2AAAADdCBgAAEA3AgYAANCNgAEAAHQjYAAAAN0IGAAAQDcCBgAA0I2AAQAAdCNgAAAA3QgYAABANwIGAADQjYABAAB0I2AAAADdCBgAAEA3AgYAANCNgAEAAHQjYAAAAN0IGAAAQDcCBgAA0I2AAQAAdCNgAAAA3QgYAABANwIGAADQjYABAAB0I2AAAADdCBgAAEA3AgYAANCNgAEAAHQjYAAAAN0IGAAAQDcCBgAA0I2AAQAAdCNgAAAA3QgYAABANwIGAADQjYABAAB0I2AAAADdCBgAAEA3AgYAANCNgAEAAHQjYAAAAN3MLWBU1cOr6o+q6sqqurOqWlUdPqPugKo6p6purqpvjfU/NaPuAVV1VlXdUFXfrqrPVNUp83gvAADAbPPswTgqyc8l2ZLkf2+j7h1JzkjyO0memuTmJH9VVY+dqntVklck+W9JnpxkfZL3VdVT+jYbAABYrn3neK5PtNYekiRV9dwkT5ouqKofTfLMJM9prZ0/rrsiycYkr0yydlz3A0lenOS1rbU3jLtfXlVHJXltkg/t5vcCAADMMLcejNbad5dRtjbJPyV578R+9yR5T5ITq2r/cfWJSR6Y5IKp/S9I8piqOmLXWwwAAOyoPW2S97FJNrXW7pxavzFDoDhqou6uJNfPqEuSY3ZbCwEAgK3a0wLGqgxzNKbdOrF9afmN1lrbTh0AADBHe1rA2G2q6syq2lBVGzZv3rzo5gAAwP3SnhYwtiQ5dMb6pR6JWyfqDqmq2k7dP2utndtaW9NaW7N69eoujQUAAO5tTwsYG5McUVUHTq0/Jsnd+d6ci41J9k/yyBl1SfL53dZCAABgq/a0gHFJkv2SPGNpRVXtm+Tnk3yktXbXuPrDGe429ayp/X8xyTWttU1zaCsAADBlns/BSFU9ffz1x8flk6tqc5LNrbUrWmufrqr3JnlzVe2XZFOS5yc5IhNhorX2tap6U5Kzquofk1ydIYSckPFZGQAAwPzNNWAked/U67eOyyuSPH78/fQkv5/k1UkOSfKZJCe11q6e2vfsJHck+dUkP5jk2iQ/11r7y/7NBgAAlqPue6fX+781a9a0DRs2LObk95mXDrAde+F1GuD+oqquaq2tWXQ75mlPm4MBAACsYAIGAADQjYABAAB0I2AAAADdCBgAAEA3AgYAANCNgAEAAHQjYAAAAN0IGAAAQDcCBgAA0I2AAQAAdCNgAAAA3QgYAABANwIGAADQjYABAAB0I2AAAADdCBgAAEA3AgYAANCNgAEAAHQjYAAAAN0IGAAAQDcCBgAA0I2AAQAAdCNgAAAA3QgYAABANwIGAADQjYABAAB0I2AAAADdCBgAAEA3AgYAANCNgAEAAHQjYAAAAN0IGAAAQDcCBgAA0I2AAQAAdCNgAAAA3QgYAABANwIGAADQjYABAAB0I2AAAADdCBgAAEA3AgYAANCNgAEAAHQjYAAAAN0IGAAAQDcCBgAA0I2AAQAAdLPvohsAADvi8Jd9cNFNAFagG177M4tuwl5DDwYAANCNgAEAAHQjYAAAAN0IGAAAQDcCBgAA0I2AAQAAdCNgAAAA3QgYAABANwIGAADQjYABAAB0I2AAAADdCBgAAEA3+y66AQCwI2444JmLbgKwIt226AbsNfRgAAAA3QgYAABANwIGAADQjYABAAB0Y5I3ACvL792+6BYAK9ErFt2AvYceDAAAoBsBAwAA6EbAAAAAuhEwAACAbgQMAACgGwEDAADoRsAAAAC6WbEBo6p+qKreX1W3VdXtVfUXVfWIRbcLAAD2ZisyYFTVgUkuS3J0ktOSPDvJo5JcXlUPXmTbAABgb7ZSn+R9RpIjkzy6tXZ9klTVZ5P8fZJfTvKmBbYNAAD2WiuyByPJ2iTrl8JFkrTWNiX5ZJKTF9YqAADYy63UgHFskmtmrN+Y5Jg5twUAABit1ICxKsmWGetvTXLonNsCAACMVuocjB1WVWcmOXN8eUdVXbvI9sAM/yLJLYtuBHugqkW3AFYK11G2bnHX0h9e1IkXZaUGjC2Z3VOxtZ6NtNbOTXLu7mwU7Iqq2tBaW7PodgCsVK6jsGdYqUOkNmaYhzHtmCSfn3NbAACA0UoNGBcnOa6qjlxaUVWHJ3ncuA0AAFiAlRowzktyQ5KLqurkqlqb5KIkNyV52yIbBrvAED6AXeM6CnuAaq0tug07paoekeS/JPkPSSrJx5L8WmvthkW2CwAA9mYrNmAAAAB7npU6RArmrqp+qaraVn6euIz9X11V9yyj7pyq+mxVfaOq7qyqL1TVy6vqQVupf3lV/d1Wtl06tu8V232DALtJVa2rqk9U1deq6ltV9aWq+kBVnbSTx/qNnWzHqeM18cvbqPmTqvrojPX7VdXnxv2fuzPnh73FSr1NLSzSM5JMfzgt5+5lf5LkkmXUfV+SP01ybZK7k/xkkt9O8mNJTplRvy7JB6ZXVtWzk/zrZZwPYLepqhcm+a8ZrmvnJPlmkkcm+ZkkJyT58A4ecl2SJyZ50w6245Akb07y1W3UVJK1Sf5gxuYXZ3jOBrAdAgbsuP/TWrt+ucVVtX9r7a7W2pdz32ByH621502t+lhVHZTkRVV1SGvtGxPHfniSH0/yK1PnXJXkjUl+NcmfL7etALvBi5N8oLX2HyfWXZbkvKqa50iK1yf5TJKbMwSUWX4iyUMz9aXNeNfKlyc5I8m7dmMb4X7BECnoqKqeOHafr6uqP62qW5L833HbsoZIbcXXx+X0/usyfFh+amr9G5JcneR9O3k+gF5WZSu9Bq217y79XlWrq+ptVXXdODz0pqr686o6bKLmz5KcluSwiSGqN2yvAVX1uCS/mOQ/b6d0XZKrxi+EJv1xkvck+evtnQvQgwE7Y5+qmvzbaa2170zVvCXJB5M8K8kBO3OS8RwHJPn3SX4tyXmttTumytYlubhN3K2hqh6f5BeSPGZnzgvQ2d8kOa2q/iHJRa2167ZStyrJt5OclWRzkocleVGST1bV0a21byd5VZLVSf5thqFMSXLXtk5eVftluH3tOa2164dRUFu1LskFU/s/K8maDNfzg7a1MzAQMGDHTU+o/mSGeRKT/rq1dubOnqCqHpvk0xOrzk/yn6ZqDklyfIbeiqV1+2eY6/G68YPU3ziwaM9L8v4MQ5ReX1VfT3JpkvNbax9ZKmqtXZthWGeSpKr2yXB9vTHJk5Nc2Fr7YlVtTnJ3a239Ms//0iT7J3nNtoqq6ugkj87E8KiqOjTDXI+XttZuGYerAtvhnw/YcT+be8+l+McZNRfu4jmuzfAN3UEZnlD/siT7ZBgasOSpSe7MMJZ5yVkZ/q5fu4vnB+iitXZdVf1YhmvZk5Icl+E6empV/XZr7dVLtVX1/AyB5JFJHjxxmEfvzLmr6qgkZyf52bEHZFvWJbm+tXbNxLpzknwxyTt25vywtxIwYMdds4xJ3jfvyglaa99KsmF8+fGq+n8ZJkT+UWttaf26JP+rtXZ3klTVERkCxmlJDqiqAzKEkoyvD0ly++SYZ4B5GIeRfmL8SVU9LMPdo363qt7SWttSVS9I8ocZegxekmRLhrmi67OTQ03H412WZP14DUySBw5NqEOS3DVeb5PhmnrR0o5V9RNJTs9wp6vvH4dWHTxuftC4/22TQ1SBgUnesHv0/sBZChVHJckYHk7Mve908sgMH5zvzvDBvCXJLeO2l46vj+ncLoAd1lr7SpK3Z/ii81Hj6lOTfKy19qLW2kdaa3+b5Gu7eKpjkjwl37smbskwR+1h4++vSZKqemiSf5d7X1P/VYb/kz4+se9nxm1/OL7+/l1sH9wv6cGAleH4cfnFcfnEDGHiQxM1VyV5wtR++yT5aJJ3JvmzJJt2XxMB7quqHtpam9Wre/S4XLrD1IFJbp+qOX3Gfnclmfng0RlOzX17P16W4fbek880OjnDxPLJu0R9OPe9pv5ghi9x3pDhRh7TN94AImDAHqWq/k2GBzy9P0MY2D/J45O8MMlfjt/oJUNX/uWttX/+MG6tbcnwTdvk8Zb+xm9ord1rG8CcXDM+GftDGa5rB2foVXhekv/RWrtxrPtwkpdW1W9luPPUCUmePuN4n0+yapyvsSHJt1trn5t14lkTwavqlzIMjfr4xOp1SS6ZHELaWvtqpm6vW1WHj79e65oKWydgwJ7l5gzd7mdn+Kbszgy9Fr+ecZLh+GCqpyX53QW1EWBHnJ0hULwyyUOSfCfJdRl6Et48UffKJIdkuN4dkOSKDENB/2HqeG/PMFH8D8b6LyU5fGcbV1UHZ+ipOGVnjwHcW5mbBCtLVf1khomSh21l2AEAy1RVpyY5L8nqZdxpClgGAQMAAOjGXaQAAIBuBAwAAKAbAQMAAOhGwAAAALoRMAAAgG4EDAAAoBsBAwAA6EbAAAAAuvn/kDykxPoZEKoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "REJECTS = \"REJECTS\"\n",
    "SUPPORTS = \"SUPPORTS\"\n",
    "NEUTRAL = \"NEUTRAL\"\n",
    "\n",
    "labels = {\"REJECTS\":\"rejects\",\"SUPPORTS\":\"supports\",\"NEUTRAL\":\"neutral\"}\n",
    "percentages = {key:round(100*totals[key]/sum(totals.values())) for key in totals}\n",
    "font = {\"size\":16}\n",
    "matplotlib.rc(\"font\",**font)\n",
    "\n",
    "for date in labelsPerDate:\n",
    "    for label in [REJECTS,NEUTRAL,SUPPORTS]:\n",
    "        if not label in labelsPerDate[date]:\n",
    "            labelsPerDate[date][label] = 0\n",
    "\n",
    "negative = [labelsPerDate[date][REJECTS] for date in sorted(labelsPerDate.keys())]\n",
    "neutral = [labelsPerDate[date][NEUTRAL] for date in sorted(labelsPerDate.keys())]\n",
    "positive = [labelsPerDate[date][SUPPORTS] for date in sorted(labelsPerDate.keys())]\n",
    "negplusneu = [negative[i]+neutral[i] for i in range(0,len(neutral))]\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.bar(list(sorted(labelsPerDate.keys())),positive,label=labels[SUPPORTS]+\" (\"+str(percentages[SUPPORTS])+\"%)\",bottom=negplusneu)\n",
    "plt.bar(list(sorted(labelsPerDate.keys())),neutral,label=labels[NEUTRAL]+\" (\"+str(percentages[NEUTRAL])+\"%)\",bottom=negative)\n",
    "plt.bar(list(sorted(labelsPerDate.keys())),negative,label=labels[REJECTS]+\" (\"+str(percentages[REJECTS])+\"%)\",color=\"r\")\n",
    "plt.title(\"Nu.nl comment stance on RIVM advising against facemask use by general public\")\n",
    "plt.xticks(list(sorted(labelsPerDate.keys())),[\"Fri 3/4\",\"Sat 4/4\"])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('HarryDavidson', 27),\n",
       " ('Jasper_Hillebrand', 24),\n",
       " ('moje', 22),\n",
       " ('Vlierbes', 20),\n",
       " ('Huup', 16),\n",
       " ('jannne', 16),\n",
       " ('LFL_L', 15),\n",
       " ('Astrid', 11),\n",
       " ('RatuAdil', 11),\n",
       " ('Miep_Kraak_dfdcdf', 10),\n",
       " ('Brousant', 9),\n",
       " ('Mmroos', 9),\n",
       " ('Arnold_Leen', 8),\n",
       " ('DeBioloog', 8),\n",
       " ('Droog', 8),\n",
       " ('Marc2', 8),\n",
       " ('NiMa', 8),\n",
       " ('PSV_BER', 8),\n",
       " ('Vrouwvan29', 8),\n",
       " ('arjan_petersen', 8),\n",
       " ('ligfries', 8),\n",
       " ('Dragonheart', 7),\n",
       " ('Moccona', 7),\n",
       " ('Ramana', 7),\n",
       " ('Bram_Ovic', 6),\n",
       " ('Erwin1129', 6),\n",
       " ('Grisso', 6),\n",
       " ('Hans_NL', 6),\n",
       " ('IkBenThomas', 6),\n",
       " ('Ma_Di', 6),\n",
       " ('Ahaa', 5),\n",
       " ('Behave', 5),\n",
       " ('Ekmekvarme', 5),\n",
       " ('Met_stomheid_geslagen', 5),\n",
       " ('Michiel_de_B', 5),\n",
       " ('R_Synthus', 5),\n",
       " ('Roaringlion', 5),\n",
       " ('Suuuzzy', 5),\n",
       " ('nuik12345', 5),\n",
       " ('radioman', 5),\n",
       " ('Be_Pa', 4),\n",
       " ('Bertie_P', 4),\n",
       " ('Cor83', 4),\n",
       " ('Dick_Pelle', 4),\n",
       " ('GeenRedactie', 4),\n",
       " ('J_Vlerken', 4),\n",
       " ('JayLoo', 4),\n",
       " ('Kees78', 4),\n",
       " ('Mariska_B_cbccad', 4),\n",
       " ('Milanevic', 4),\n",
       " ('Uhh_Duhh', 4),\n",
       " ('Welkenaamisdanweloke', 4),\n",
       " ('michielvdm', 4),\n",
       " ('pannekoek_koek', 4),\n",
       " ('Aufklarung', 3),\n",
       " ('Auto_Wagen', 3),\n",
       " ('Ch_Lin', 3),\n",
       " ('Dave222222', 3),\n",
       " ('DaveKoch', 3),\n",
       " ('Displee', 3),\n",
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       " ('Frea', 3),\n",
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       " ('Hammerhead', 3),\n",
       " ('HankMoody', 3),\n",
       " ('Janus_Pam', 3),\n",
       " ('John_Wals', 3),\n",
       " ('LezeresVanBoeken', 3),\n",
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       " ('RichardM', 3),\n",
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       " ('RobbieB', 3),\n",
       " ('Robertwo22', 3),\n",
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       " ('cowboycot', 3),\n",
       " ('joke_adema', 3),\n",
       " ('redbiker', 3),\n",
       " ('rusman_peters', 3),\n",
       " ('w_v_abbeae', 3),\n",
       " ('wiv', 3),\n",
       " ('24lepels', 2),\n",
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       " ('Cella', 2),\n",
       " ('Daniel_W', 2),\n",
       " ('DonDon', 2),\n",
       " ('Ed_pelleboer', 2),\n",
       " ('Flying2U', 2),\n",
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       " ('Gaby_Jane', 2),\n",
       " ('Hansie_V', 2),\n",
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       " ('James_Robertson', 2),\n",
       " ('JvN', 2),\n",
       " ('Klakske', 2),\n",
       " ('Kritische_Kip', 2),\n",
       " ('Kum', 2),\n",
       " ('Leo_Meer', 2),\n",
       " ('MariellaCara', 2),\n",
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       " ('Michelangelo', 2),\n",
       " ('MoedDoetGoed', 2),\n",
       " ('Nietsiswathetlijkt', 2),\n",
       " ('Ongehoorzaam', 2),\n",
       " ('Patrick1981', 2),\n",
       " ('Paulavdw', 2),\n",
       " ('Rechtdoorzee', 2),\n",
       " ('RootsP', 2),\n",
       " ('Roster', 2),\n",
       " ('SGNL', 2),\n",
       " ('TGM_Beuningen', 2),\n",
       " ('T_N', 2),\n",
       " ('Tezz', 2),\n",
       " ('VanDijk', 2),\n",
       " ('Vegan_George', 2),\n",
       " ('Vill_Vill', 2),\n",
       " ('Welly75', 2),\n",
       " ('Zeggiktoch', 2),\n",
       " ('Zwerver', 2),\n",
       " ('Zwijgende_meerderheid', 2),\n",
       " ('_B_V_', 2),\n",
       " ('anna_H', 2),\n",
       " ('ikvraagmeaf', 2),\n",
       " ('pietsky', 2),\n",
       " ('ssssss_dddddd', 2),\n",
       " ('struikrover3', 2),\n",
       " ('65432', 1),\n",
       " ('Amstel_Amstel', 1),\n",
       " ('Animiles', 1),\n",
       " ('Blat0r', 1),\n",
       " ('BoZ_BoZ', 1),\n",
       " ('Brabant76', 1),\n",
       " ('Charlie2507', 1),\n",
       " ('Conjou', 1),\n",
       " ('ConnectedEurope', 1),\n",
       " ('Cookie_Monster', 1),\n",
       " ('Cum_grano_salis', 1),\n",
       " ('DH_Achterdeduinen', 1),\n",
       " ('DaStef', 1),\n",
       " ('Daan_Wit', 1),\n",
       " ('Danil', 1),\n",
       " ('Darkzod_Zoddark', 1),\n",
       " ('Datbenik', 1),\n",
       " ('Daveyh', 1),\n",
       " ('Dennis_999', 1),\n",
       " ('DjallaDjalla', 1),\n",
       " ('DonnieBarkie', 1),\n",
       " ('Drift_Moon', 1),\n",
       " ('Een_persoon', 1),\n",
       " ('Elf', 1),\n",
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       " ('Erikzonderz', 1),\n",
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       " ('EvW1985', 1),\n",
       " ('Fam_Tovar', 1),\n",
       " ('FeedBack', 1),\n",
       " ('Funman', 1),\n",
       " ('Ge_Rijn', 1),\n",
       " ('George_Alwis', 1),\n",
       " ('GidiG', 1),\n",
       " ('Gijsmans', 1),\n",
       " ('Greenfever', 1),\n",
       " ('H_L', 1),\n",
       " ('HenkC', 1),\n",
       " ('Hokkie_Stick', 1),\n",
       " ('IMarks', 1),\n",
       " ('Jahjoh', 1),\n",
       " ('Jakobus', 1),\n",
       " ('Jamal_Mahassine_ebcedb', 1),\n",
       " ('Jan_Adema', 1),\n",
       " ('Jan_Meijers', 1),\n",
       " ('Jasmijn_Jaspers', 1),\n",
       " ('Jason_Postma', 1),\n",
       " ('Jo_Pe', 1),\n",
       " ('Joey_S', 1),\n",
       " ('John_De_Jong_dcdfec', 1),\n",
       " ('Jon_Jonson', 1),\n",
       " ('Joop_Autosloop', 1),\n",
       " ('Kareltje_Zuurpruim', 1),\n",
       " ('Kees_Lees', 1),\n",
       " ('Klaverstein', 1),\n",
       " ('LI', 1),\n",
       " ('LaDecima', 1),\n",
       " ('Leon_Cox', 1),\n",
       " ('MBZ0000', 1),\n",
       " ('MK_95', 1),\n",
       " ('Marc_M', 1),\n",
       " ('MarianneGM', 1),\n",
       " ('Martin_Mortel', 1),\n",
       " ('Masks4All', 1),\n",
       " ('MaxMidlife', 1),\n",
       " ('Mechel', 1),\n",
       " ('Mitch', 1),\n",
       " ('Multatuli', 1),\n",
       " ('MvE_Culture', 1),\n",
       " ('Naomi0701', 1),\n",
       " ('Neus', 1),\n",
       " ('NickyG', 1),\n",
       " ('Nicole_Oude_Wolbers', 1),\n",
       " ('NietJij', 1),\n",
       " ('Nonius', 1),\n",
       " ('Ot_Ensien', 1),\n",
       " ('Outmail', 1),\n",
       " ('Ovb', 1),\n",
       " ('PVWbD', 1),\n",
       " ('Peter_Davenport', 1),\n",
       " ('Peterderix', 1),\n",
       " ('Peterindelft', 1),\n",
       " ('Phil_L', 1),\n",
       " ('Popeye_xl', 1),\n",
       " ('Qin', 1),\n",
       " ('Ramp61', 1),\n",
       " ('Re_Des_acbeda', 1),\n",
       " ('Rene_Otter', 1),\n",
       " ('Rickert79', 1),\n",
       " ('Rob_', 1),\n",
       " ('RotterdammerT1908', 1),\n",
       " ('RubberenRonnie', 1),\n",
       " ('Ruud_van_de_Kerkhof', 1),\n",
       " ('Sam_H', 1),\n",
       " ('SangN', 1),\n",
       " ('SaskiaSI', 1),\n",
       " ('Sietske59', 1),\n",
       " ('Snappertje', 1),\n",
       " ('Suicul', 1),\n",
       " ('Supreme_One', 1),\n",
       " ('TINEKE_VAN_VELLEP', 1),\n",
       " ('TheKeyIsNuance', 1),\n",
       " ('Tom_de_Boer', 1),\n",
       " ('Vervelendzeg', 1),\n",
       " ('Victory_X', 1),\n",
       " ('Walter_Fraans', 1),\n",
       " ('WillNeverForget', 1),\n",
       " ('WillemHeerema', 1),\n",
       " ('b_no_more', 1),\n",
       " ('davy_T', 1),\n",
       " ('en_niets_dan', 1),\n",
       " ('gerries', 1),\n",
       " ('gilbert_koens', 1),\n",
       " ('hollandmix', 1),\n",
       " ('hs5', 1),\n",
       " ('ilovejb', 1),\n",
       " ('j1', 1),\n",
       " ('j55Lezer', 1),\n",
       " ('john_leur', 1),\n",
       " ('lida_vries', 1),\n",
       " ('mandyrs', 1),\n",
       " ('masakai', 1),\n",
       " ('nizen', 1),\n",
       " ('oeioeioei', 1),\n",
       " ('patrick_k_', 1),\n",
       " ('pawi', 1),\n",
       " ('radio_hoofd', 1),\n",
       " ('toines', 1),\n",
       " ('vip121', 1)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "users,counts = np.unique(df[\"name\"],return_counts=True)\n",
    "combined = [(users[i],counts[i]) for i in range(0,len(users))]\n",
    "sorted(combined,key=lambda x:x[1],reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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