demo/demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of using ST-DBSCAN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from st_dbscan import ST_DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>animal_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>201.623002</td>\n",
       "      <td>186.148565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>247.658670</td>\n",
       "      <td>177.650135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>127.457230</td>\n",
       "      <td>20.572136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>231.180722</td>\n",
       "      <td>172.861323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>217.739517</td>\n",
       "      <td>189.203565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time  animal_id           x           y\n",
       "0     0          0  201.623002  186.148565\n",
       "1     0          1  247.658670  177.650135\n",
       "2     0          2  127.457230   20.572136\n",
       "3     0          3  231.180722  172.861323\n",
       "4     0          4  217.739517  189.203565"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('test-data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize the data \n",
    "df['x'] = (df['x'] - df['x'].min()) / (df['x'].max() - df['x'].min())\n",
    "df['y'] = (df['y'] - df['y'].min()) / (df['y'].max() - df['y'].min())\n",
    "# transform to numpy array\n",
    "data = df.loc[:, ['time','x','y']].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The input data format is: ```['temporal_index','x','y', <optional attributes>]```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_dbscan = ST_DBSCAN(eps1 = 0.05, eps2 = 10, min_samples = 5) \n",
    "# for list of all parameter description see - src code comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/erencakmak/opt/anaconda3/envs/movekit_env/lib/python3.8/site-packages/sklearn/neighbors/_base.py:175: EfficiencyWarning: Precomputed sparse input was not sorted by data.\n",
      "  warnings.warn('Precomputed sparse input was not sorted by data.',\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<st_dbscan.st_dbscan.ST_DBSCAN at 0x7fc4a055ecd0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_dbscan.fit(data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(data, labels):\n",
    "    colors = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a']\n",
    "    \n",
    "    for i in range(-1, len(set(labels))):\n",
    "        if i == -1:\n",
    "            col = [0, 0, 0, 1]\n",
    "        else:\n",
    "            col = colors[i % len(colors)]\n",
    "        \n",
    "        clust = data[np.where(labels==i)]\n",
    "        plt.scatter(clust[:,0], clust[:,1], c=[col], s=1)\n",
    "    plt.show()\n",
    "\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uvGEv0UTEeL3MUsbq2jV8dvPn+PtTzxtFyoC0kgmZ6ZeZ2xXXyKwRXio1w6dLRItzftAPMlkeIN/XrgS9ADGLuRKU8Zgj+O8cf5xILMxodNSYPAWwCitxGU97nwWLUbrAbXXjdlQwEOxnUUULdy39EM5UL1W9/6o5B15NoqaTmY+tr6JsrnDQWGFnTWO6uJkFHygp8S+kKF0JegHyQttzfOfYY0rMp4E5iu/0XWZp1TJ+cu5F1tSu5ZL3ErFE9IbHaHA24ov6iCYi7Fi4kzpHHW/1vsnuJXfRXLHA6Jv6jfd/Cyj++u0RLc75AT8IxgnwVN57qNNDny9Mo9tOsgyhYDCQ/Jw3L6qmpdrJoc4RwrEETRV2LnlChuC3XR0zBFAX+5ZqJ73ecNEJfSGNXwl6ATFZX1LF9Mn0xJ965/u83v0qiUQCBAyFBo197RZHmkWT6clDsqwwEvoCvSyuWEKVo5LTw6f46NqPGyUQiu070yNLgI16ZDkNcfeFY+zvGMYXSX5W7jIrNisEInGW1LoYi2iGwAuuVZ51l1lw2a3sWFJHlbPMGEeF3Yo/GmdjcyVrmiqKLoovhFWkStALCD0yn++eea4xlwrIXMXqsDiImMQdkh58LBEDwGVzE0w19Ybkoii/5mdxxRJ6/F3c3LiJcls5D2/+HCcGjhe00JsjdIC2q8mMokVVTra0VF832jRH6C6bIBqXaBly4bRZCGsJ47lNQFxeE/bmCgeVThueYJRwLE4glty30V1GvcvO2cEA6xrdOMtsBSfsE80ZmD+TfNkvaul/ATDXxbbmO+aKkpl1YPYsv4e/fPvPOHzlkCHOY9ExzqT6tVqFBQCLsJCQCfyan2pHjVFV8r1UNk6Xr5OrgSu82rWP9wZPGl2ZCqndnsNmNSLhlmonSBgMRo1FM32+MFo8gc1qoaXaSedI0Ijgzw/66fOFcdstBKKJCY9vFnNgnOD3+yP0+6/dPPUbwGAgRiCanP/oHg0RiCW44gtT6yrDJkRBTD7qcwZaIoHNYjGEfefSWs4P+guygJcS9DlC5UDnj4nKBf/ZB76dJrid3kv85aE/Y239Om5ZcCt/feTbfGbTH/Kz9n8kFo8ZK1oBNtTfRIW9ki5fJwAjoWGjWJn+HQMF832bS8KuaapAu+oD4KbmSpoqHGiJBO/0eY2FNABaPIEnlLRZ4omkSltT0TeAFUifjp4aEdMNIJiK1gOxBBV2a5r4a4kEnrBGpd2Ks8w6bf8/G+iTu1o8kVZS12GzYrNYeKfPi81qyfskqRkl6DlGReaFSWZNmqXVy/n+rz5jPL998fsB+PCKPUaEf/TKYU4MHOOOxR/ggXUP8tQ73+dQ31s8+v4/4+amTRMWLpvL73uyibuWaicD/ggt1U46hoOcHUzaSVdcYbREguFgjHVNFaysdxNPSPr9EXp8YfyROC6bhWBKhOOm6HsmYg6Td3ezWqDMKojFJc0VDoaDMQYDUfpT2/vHIlgsgvctqGIwEJ0Te8ZhsxoTunonI/NnXIhldpWHnmOUZ146mCdh9V9chfC96iITjmmcHQzQ6LYzGIjiKrNQXmbFImAwEKO5woHbbqV/LEJTpQO/aUKz0W0nISWhqIazzMZIKEaZRSDlNd/cZgFtYucla1iBGlcZ8YRkNJz8hWARkPqRgMNqIRJPTGkOIBeY0xeBvKQyKg89j2QuP1cUL+aoXv8+d7Xu5oW25/LqlRv54ZWO1CtJ9QvGEoatYROkedndnqAh1AIIxTT8KZ88Gk9ODscS6cFersUckpH/cDCW9lpCJsdoFRCJJ3CVWejzhRn0R4wxmlMjcynwug3TUu2k0xNkY3NlQfUgVYKeI8zRXL4jOEX20cVd/wUG+fPKzSLT6w3TUu3k4rCf9qEgWkrwMicrzc8lGGIOYLGIdH+lAJBcG3MsNbZYQlJmgU5PgF5viHpXmWEnrah3zTj//nroNsyZ/jHaro6xqMpJREsUTPqlEvQcoSZB5wfmX2D5quaoiwxAlbMMgC0ttSDh7GCAelcZWiKBNzw15ztaYGKeSSwhjZz3WAJj8hZgXaObK2Nh/JEY7cNBAGyW7E9cmqsxxuJxBgMxwpqW/NyvQ65/RShBzxHKapkfmG2YQojWzWxYUGXkd58f9OO9OnbjNxUJE91yxiIaY6Eo4QQMpV4rtwr8UY0TvaOARItLvGGNeredDamMlZlgrsbY6w0B0DMaZkPz9SdIc92EekqTokKIPcATJOcs/lZK+RcZ25cAzwM1qX3+REr58+sdsxQnRVW97flN5vdfSJU0zQWmAjGNSyOhvI2lUHCXWWitLWdDc9WsomXzatrrrSDVF3lpMpnXPlMraFaTokIIK/A94ENAD3BECLFXSnnatNuXgR9LKZ8UQmwAfg4sm/ZIi5hSa1KhmD6ZqZB7239mVHQ8PdSW15o9DpuVmxcmMzMiWhx3mQ1NJtAS4A3FGIvECGca7SWIlWR0nyCZ/352IEC3J8zty+pmnA5Z5Szj7jWNHLg8Qp8vzPlBv/FZwzWbRUskaOtP+u59vkBOrCDLFPbZAbRLKTuklFHgReC+jH0kUJV6XA30ZW+IhU9m5cRStlki8SDt3sNE4sF8D6XguXfV/Xxq08PsWLiTA7372dv+MyD57+WFtucYDXvyMi6HzcrNi6rZ0lLLLa213LminmV1LmrLbVhFXoY0Z8SBzGSdQCzOvvZB3unzsr9jiIg2/Sx7h81Ko8sOwKA/mnYMw2aRyYJmW1qq2byoOifZMVPx0FuAbtPzHuDWjH2+Dvy7EOI/AW7g7okOJIT4NPBpgCVLlkx3rAWH/pP65MAJDl85NC8qJ3b72zjjeZ3hcDebG+7BYS2clK1CQ1+hOlFjjWdOPklIC41bwZoPOoaDnB0IUOWwEZfJKG8OMhQLCj1DczAQ45/eu4KFZC/RpXXuKU9krmmqYDAYpX8skhalZ2YhOWy5W106lQh9KjwI/FBKuRj4CPB3Qohxx5ZSPi2l3C6l3N7Y2JilU+cHPSp/5uST80bMAVorNtJUvoKBUAfd/ra0bXr0PhYdVlG8Cd2KKdR/GyvqXSyqcuKLaFiFIIFRy4vCWP849ySAtzo9vPROD//73EAqeh8mosWJaHHO9I+Ni+Qzo3RfOMaZ/uRE9PrmSnq9Yd7p89IxnLv/L6YSofcCrabni1OvmfkksAdASnlQCOEEGoCBbAyyENH7gO5YsJNNzVvyPuk1VzisLjY33EO3v43m8pW0ew/TWrERh9WVFr0PhDpUFD8JD6x7cFyJgHyiZ2zoVQSrHDYsAkbD2oyX+JcKCQljqSJig4Eoe9uuYLdZCMYSDPgj41armqP019oHCcQSaIkENy+sTivBkCumIuhHgNVCiOUkhfzjwG9n7NMF3AX8UAixHnACg5QgE9VmmQ9CbsZhdbGqegft3sOc8bwOwKrqHbRWbASg1t5CIDbKQKiDd4ZeVqKeQebkaSFgTsNbUe/i9FUfo2ENl00QnAeTpVNFk6DFEtgsgj5fmH5fmDjQPRpg18pGI0rvH4sYpYL1HMteb9gofparWuo3FHQppSaE+BzwbyR/gT0rpTwlhHgUOCql3At8EXhGCPGF1PAfkvkqEpNj1IKha7RWbERLxNASUSLxoCH0Zz1vEtBGcNlqDGtmVfWOfA9XcQPMC5T0HHa9pO6VsRDDQe0GR5gflFmulUXQf8EMBzX+5fRVti+uSWbL1LnwhmNYhGBhlZP3+ryE43Eq7Fb6fGE6hoM58dGntLAolVP+84zXvmp6fBq4I7tDK0zUgqFrOKwubJYyznhexxvtN0XiSQe20bGMgM1DJB40BF9RHJjF/eZF1azRKni3z0unJ2QsudcDUHPxrPlAbJIZ42hc8lZnMnPJ3L3p7S6P0fEJoMphy5ntkq1J0ZLGnGZW6BNcc81Ek6TLq7awuvp2AvERhsKddPiOjJtAVRQXDpuVW5bU8es3LWBRlZNYAirsVtY1ublrVSMVjqR9YFeKAqSvZPVFNOpdZdS7yii3CXwRjYvDgUnfOxvUxz8FdJtFzyNWXEOfJF1fu8vw0PXIfSjcRZ1jMQ3OJdTaW1TmSwmge+2Lqpz4o3GcNhuDgSj+SJzmCge17mTFx+YKB9XO+ZojM55gVGM4GCOUmo8Y9Edu8I6ZoWq5TAFls1wfh9VFa8VGuv1tRsaLLu5aIsoF70Es4hADoQ4A5acXOZkTqDpaImFUINzSUk2nJ4jFG8QTis+4w1GpENJkmg2TK5SgT4FCzEooNCZacLSqegdj0WE8kT7KrVWsqNyeNoGqKF7MHjtcq0eu997sGA7SdnUsVaM9Tq2rjKFgjHKboMJRRkuVg/eujuEsEwSi88OAN19lNEedjpSgK7JCa8VGI//cnKrYH7rIULiTIToNr91msasovQQxi/xENdp7vWEjii+zWohLaK1xMRyIEo0niGoJonFZaKXYc8JYJFmo6+ZF1TfeeRooQVdkBd1Lf2fo5bRURT21ESQt7vXUO1sNO0ZRukxUo73KWWZE8S3VTqNB9WAgxqIqJ95wmHVNFfSMhvBH47jKLEbHpVJEy0FmtxJ0RdaYbBXputo7iMSDdPvbqLW38M7Qy2yo3U2lvT7fQ1bMMZlCnynwK+pdbGiuNBoxH+4aYTAQo7a8DEgQiSVAQrAEwnibyH4lNCXoiqySuYpU99R1j91lqyGojZKQcW5b8LF8D1eRgX7j1Se3c81EkTxca/5w54oGY/JVr1q4cUElg/5oWo/UYqTebc/6MZWgK3KC2VPXBQJgLDpMUBvFbVN5/IVGJB40LDMojGykiXz5FfUu1jTC+UE//WPhVARvYzSk5TyLJJuc6PWyqLo8q8dUeeiKnKDbL6urb0dLRIGkQJTbUmXzJbzd/4+MRYfzOEqFmW5/GwOhDprKVxTkPIcu7g6b1WjYceeKBjYvquaDqxpZ21QBgNMKrjILteWFHa9uXJCnpf8KxUwwlwbQM1uWV20BoDdw2rBeGsuXzdlPfMV4dJuluXwlQFF9F2n1Z5orcdqsafZMmUUYdVfmIg98OlwYCrC0zp3VYypBV+SUiQp42SxlBLVRyizluG21aRUbFXOPPr8Bxf0dTGTPNLrtHOwcwR+Ns6rBjS+sMRKMGiKfT+I5GIOyXBQ5RRfwC96DRj0Xvf5LLBEioHlYXX17Qf7EL3Ui8SBnPQeIaAFWV99WUt+BLu4NFQ4+vLaJzYuquXlhFQurnMQSEmsqw6TMkr+eezIHvxdUhK7IORNF6Zsb7uH44L8wFO4EJC3xdXOaXaGAS77jXPAeBGB97a6S/dyvt+AprMU5O+A3qkfOpS2Ti3uJEnRFzpmszG6toyW5ijTcxWnPawWVXVGqmNMS9TLHDc6lJRWdX4+J8uCdNmvaitbDXR4GA9Gci7s5TTNbKEFXzAmZaYzmCVJ9FWm1fYGq9ZJDMtMSl1dtwWYpm9e/iibKg79zRb2xsOnc4Bgdw8Gc1HsfHItm/ZhK0BVzgnkVqbnMrr6K9JLvBJ5ID0PhroxmGYrZYI7IM9MS9UVginTMIu+0WUnIZBqkw2YlosUIxrJzHn8s+/UnlaAr8k63v40L3rcAcFmTbesu+U6wrnZeNMHKKeYMFv1GOp8j8ukQ0eIM+pNR9Io6NzcvquaV9kGCseysUG1WK0UVxcxk6XHmAl5xqdHhO8JwuIuznqQtoMRnepjzyrVEzMhgURH59Dg/4KffH6HRbQeRFPhKu5X+LB2/1qU8dEURM1G2C4y3XhqcSxgKdzES6QFQkfo0MdemHwh1lHQGS05JZaGEtDhtV8ewWSw4y9LrlzusgsgMC4VFc1BgTOWhK+aMiXLSzVyzXgR1jsUARLWAKhEwRSLxIO3ewzSXr2R97S421O5Oaw2omB5rGitornAk2+tVJitBLq110ei2Y0uJ/UzFHKBnNPvtGFWErphTzD7uRNv0qHJF5S3YLHbGtGFGIj34oyO0VKxnedVWFW2ayJz0zLS0VInimeOwWaktt9Hvj1DrtOGwWTl91cdgIEq104I3PLta7dEclHpXgq6YUybqP2repmfCaImoIeyReJCANsIF70GGw91YhIWNdXfPS7HKLG872aSnYvZEtDiesAaAzZo0M4ZTKS6ByOzVWE2KKkqC69UO0SfuIvEgNoud1oqNrKrZkZbWCHDa8xobancbf0tV3M0TnP2hi0bTbcDoCAWoSc8ccH7AT/9YBLtVsLDSSUSLoy810iRphb9mgupYpCgJphJJmoW929/G8qotLGcL7aOH8cX6DTEfCHUYFRt10Sv2tLyJbBTdilpdfXuaL65EPIekfPJoXPLuFR8Lq5wMBmI0uu0Eo3ECsbhRMqBQUIKumHPMYq23qZtMgDMtBYfNxdaaX8dhdbGhdjcAFWX1nPG8zmDoMkPhTiJaEKulDJBF47lP5oXrwt1cvtLox1oM11MKLK110THkJ6hJKp1Wow6MP6oxGEjmp89GzKsc2ZdfJeiKvDGVsq3maD5z/0p7Pbc2/6aRAhmJBxkKd+KL9RvWzGDoMjaLnY11dxWULTNVL9wcgRfS+OcDF1NiDskVoxEtwZWxML5QdpaK+qNaVo5jRgm6Im9Mx3q53v7miN9hddFcvpK2kX0MhTsZjV4BoG1kX0E00tCFXHnhhY8+IVpht7KmsYIDl0ay2sdU9RRVlBTXy3iZbH9d6PRFSGZbxbx9a+N/4JLvBNF4kIDmMRppaIko62rfn+tLG0emkCsvvLDxhWMkEpJ6V1lypShQ60qmMFqAbNjmG5qrsnCUdJSgK/LKTLvlmOu/6O3tzOirT3XOeg4AEE9oqce58dczs1IyLRWzkCsvvHA50etlMBDFYbUwHAzgi8RZ3eCmzxvBYRUMptIXZ1NiN6IlcNisN95xGkxJ0IUQe4AnACvwt1LKv5hgn48BXyd5fSellL+dxXEqSpSZ5k6b67/o7830pc3opWKTEXLyRpCtqo7Xy0qBiS0VRWGzpaUaf0TDF9Goctjo84WN51ZTY4rZJB4e7hrh7jXNsx6rmRsKuhDCCnwP+BDQAxwRQuyVUp427bMa+K/AHVJKjxCiKaujVJQs07VdzO/LrPGii6mWiBl1vvXXWys2Gj47CDyRHgZCHRwf/BdqHS3TKgKWaffcKCtFH6+yVIqHKmcZd66o50Svl5uaK1OrQ20c6x2lsszGUDCCSMDsKppnv2XRVCL0HUC7lLIDQAjxInAfcNq0z6eA70kpPQBSyoFsD1RRumSrSbEunloiahwPSBNbc0673uxhKNxp3ADM9doneqzbJ2a7R2WllB4RLc7R7lH6/RHiCckdy+s4P+jHH4njj2SnjvnmRdVZOY6ZqQh6C9Btet4D3JqxzxoAIcQBkrbM16WUv8g8kBDi08CnAZYsWTKT8SpKkMmqME6XiVaZms+ReePYULubhIxTVdZIrb2FN6/8T4LaKIOhS9Q6FhuiDaS9L9PuUdF36dExHDQyWvr9Ec4P+I3cc3eZhUAWVhN1e0M0VDhmfRwz2ZoUtQGrgd3AYuANIcTNUspR805SyqeBpwG2b98+V71YFQWOuefoRBOcMzme+RiTpT32hy4yFO6ksXwZ7b5DBLVRAIbCXdQ6WlhdfTtaIkqLe33a+yayexSlRbLHaAgtEcdmsaJJSb8/QpXDxsYFlRzp9sx6hagnW62PTExF0HuBVtPzxanXzPQAb0spY8AlIcR5kgJ/JCujVJQ82YrSr0em0JsFvrl8Jf7YCEFtlAbnEpZXbeWS70QqV1ywvGrLtH1+RfHS6QkaETnEaa5w0FzhoN8f4WCnJyvNo7e31mThKOlMRdCPAKuFEMtJCvnHgcwMlp8BDwLPCSEaSFowHVkcp6LEyXaUPtVz6udxWF28f+F/zBDt5P+2nkgP+OCC9y2Gw91sqN1Nb+AsxVRaQDE9tMS18Lu50sGapgoYTHYwkoBFkJPG0bPlhoIupdSEEJ8D/o2kP/6slPKUEOJR4KiUcm9q24eFEKeBOPDHUkrVkUAxLeYiSr8emRH88qqteKP9DIQ6qHW00FS+wkhF1P96In1sbfwPStRLDC3VuKLeVcYdy+pw2KwsrXXR5w3jDcUQzH5x0YleL7tWNsx6rGam5KFLKX8O/Dzjta+aHkvgkdR/CsWMyEeUfqPx6PXZzdkuzeUrScg4Q+FOhsKdtI8exq8Nl3QZ3/mGN5z0ty0i2egiosU50etlRK/jkoXofEtLfrJcFIo5o9CaNEw2waqXFgCJJ9LLULjLyJgZjV6lxrGQVdU7VORepNS77QwGYoAgosXpGA7S5wtn1Wqpcqom0QpFQWDOdBmLDnPa8xoVZfV0+JJ5ACORHqNQWKk34ShFNjRX4QvH6fOF6RgO0lLt5Ez/GJF4ARU/nwAl6IqCIplZ8hZaIlY0qYHmMr5IaUTorRUbjcVLgdgom+r30O47pMS9wNEj8i0t1TRVJJtDnx/wE4knZt2lKNcoQVcUGDLjb/HgsLq4qf6Daa9tqN1NIDZKQBvh5PAvCGgj+KMjLHCvxirKplVyQDE3dAwHeafPy4A/ws6ltThsVjSZjMyz1aEo+4v+kyhBVxQUy6u2jlvlWcxU2uu5Y+GDdPvbqLW3GKKuWzPDqUYcNfYFrKq5VYl7AbCi3sWAP2LYLeubK41tesOL2ZKrcEUJuqKgmGmxrkLGPLF6x8IHueQ7QVzG8EUHGAp3AinP3eYuuWsvVqocVuKVDlqqk82hPcHsdheyZPVo11CCrig4itFHnyrmydRIPEj76NtJz92+gObylYbnDrMrVKaYOecH/JwdDADQ6w0DyXouteU2PKHsCLvdlhvTRQm6ogApXh99OmR67u3ewwyEOmhwLiUSD3LWc0B57PkgpbXNlQ6jMTTA+cGxrJ2iucKZtWOZUYKuKDhKzUefKubyv3q/UcAo7auEfW5Y01iBzWIxxLxjOMiKehfdo0GC2ZgRBXyR7BfmAiXoigJkvpajNZf/TSKIyxgXPG/lrRfqfEfPeNHiCepcdjzBWFb6iUa13OSz58qbVyhmRSQepN172CRu84ekz/5+1tXegVXoqwkFY9Fh3u7/R8aiqkxSLtFFXF9QtKjKiSYTXBgKZEXMATbloLkFqAhdUaBkq4tRsaP3QjUvUtISUWwWu1qglCN0q2VFvctY8l/lrMBuFUTjEpsF4onZzfCcGxxjaZ07OwM2oQRdUZA0l69kONxNc/nKfA8lr5jtpw21u4Gkx65nwpiLhymPPTs4bFYj91wXdy2eIBqXhqjPllCWvPhMlKArCpLewFkGQh1U2xewzl5aqYszRS8xoNeO2VC7m0u+41zwHlQee47QxT2ixbFZLfijGu1DAewWiM5Ck5srs9t6Tkd56IoCZX6kLs4EXdiTdouezyzm9bxDrtGF3WlNSuZsy7m47bmJpVWErihI5mvq4nQxe+x6tB7RAsaqU2XDZJeFVU66RkO4y6xc8UewkuzoM13WNFZke2iAEnRFgVKKJQByQXqKZzJa98UGGRo7SqfvJAvcq1Vd9ixyqn8MX0TDF0muGJ2JmOeqMBcoy0VRwOiZLt3+tnwPpShYXrWF9bW72Fh3F25bHcH4KB2+I6lGHIpssLph9pkpkmR5gVygBF1RsDSXr6SpfMW8z3SZKnq0Xmmv55am+3HZalJb1DxEtrgwFMjOgXIUpitBVxQs/aGLDIQ66A9dzPdQio5Kez3vX/gfWV+7i+VVW/M9nJJhS0s1rrKkbFY7Z+ZYC5SHrpiHtFZsREvE0BJRIvGg8oGnyXwtoZBLqpxlrKhz09Y/hs0yszDbVZbMmskFStAVBYvD6sJmKeOM53VsFrsSJ0VBsKapApvVwqA/Aky/yFZc5m5aVFkuioJG+eiKQkPPSR/wR9JeF0zNGtfikog2k/yYG6MEXVHQKB9dUagsqkrWNC8T4LQJrGJq08+aTBYAywXKclEUNPrCIrXASFEoRLQ4HcNBylOTozEJmiannEskgJbq3DS4UBG6oqAxLzBSS9oVhYBeXtdmtbC8rtyIzMssYkqWiwQ6R3Lzb1kJuqLgUQuMFIXEinoXmxdVs7TWxXAgRlxClcNGncs+5Shdk7lZG6AsF0XBo2wXRSGhT4qeSZUBqHLYuHNFPVEtwb4Lg1NsgqEEXTFPUXVdFIWIuRGGw2blvT7vlDsa2URuzBFluSiKAt12eWfoZeWlKwoCPVI3FglNI718YZWaFFXMY1orNtJUvoKBUIfy0hUFydJaF3br1FT9VP9YTsYwJUEXQuwRQpwTQrQLIf7kOvv9phBCCiG2Z2+ICkXSdtnccA/ra3cpL11RkPR6w1NuT3dTqsVdtrmhoAshrMD3gHuADcCDQogNE+xXCXweeDvbg1Qo4FptEuWhKwqRlmonbvvUTI8rY+GcjGEqZ98BtEspO6SUUeBF4L4J9vtvwF8CuRmpQgGqzZqiIIlocY52jxKYaqPRHFU0noqgtwDdpuc9qdcMhBBbgVYp5b9e70BCiE8LIY4KIY4ODg5Oe7AKxSXfCc54XldNGxQFxfkBP/2p2i5TKcJYsJOiQggL8BjwxRvtK6V8Wkq5XUq5vbGxcbanVsxLVPNoRQGSEvEKh3VKDaRzNSk6lTz0XqDV9Hxx6jWdSmAj8JoQAmABsFcIca+U8mi2BqpQwLXm0c3lK2n3HlZ56YqCYE1jBTaLhUa3ndcuDhG7jqpbyOOkKHAEWC2EWC6EsAMfB/bqG6WUXillg5RymZRyGXAIUGKuyAn6xGh/6KIqB6AoGPSc9MFA9LpiDpAgd5OiN4zQpZSaEOJzwL8BVuBZKeUpIcSjwFEp5d7rH0GhyD6qHICiEFlR76JjOIAvol1/xxw5hkLmqEjMjdi+fbs8elQF8YqZE4kHVTkARcHhC8c4cGmY0fDEot5c4eCO5XUzbkMnhDgmpZxwrY9aKaooWlQ5AEUhUuUsw2Wf3Px438KqnPUUVYKuKFpUOQBFIRLR4lQ5rZOKa64yXEBVW1QUMXo5AN12USgKgY7hIGcHArhsFoLa+IVGqxvcOTu3itAVRY3qaKQoNFbUu1hU5ZxQzAEuDAVydm4l6IqiR3U0UhQSDpuVLS3VVDkmNkBylYMOynJRlAAqhVFRaPR6w/giGhbBuJWjV3xhGiocOTmvitAVRY++2AhQhbsUBUFLtRN3mWXCMgCanGpfo+mjBF1RMqjCXYpCodcbJhCbTLin0dpomijLRVFCqMJdisJgRb2LsKbR7QkTiMXTN+ZwMaeK0BWTIsNhEidPIsPFUeJ+edVW1tfuYnnV1nwPRTHPSU6M1rJ7VQPltvSI3GbNnewWpaCPBKI89cZFRgLRfA+lpJHnzpE4/Dby3LmiEHeVwqgoNDo9QULatYjcKpK9R3NFUVouPznWzZ+/fBaAz3xgZZ5HU7qItWuxpP7q4m4BxKZN+R7apOgpjIAxUapQ5I0MdyUuk/56lbMsJ6crSkH/6LZWgtE4wajGSCBKndue7yGVJMLpvCbeZnEPh0mcagMJlo0bEc7cdF+ZCSqFUVFILK1zcaZ/DN1Fr3HaWFGvIvRxvNszyqvnBnHZbSpKnwPM4p44eRJ5/DgAsqysoCJ2s+2iqjAq8k2vN4x5StRhs+asMBcUoaCPBKL80YvHebN9mPevauCj21pv/CZFVhFr1yK0GMjkY0hOoMpz55Lb8hyxK9tFUSisqHdx5qqXSCqDcV1TRU7PV3SC/vzBy7zZPgzAtqU1ym7JA8LpxLotvRyz2WMn5bnnS9yV7aIoFBw2K8sb3JwdSNZvabvqY1F1ec7OV4RZLslZhluX1xGKJnj8l+dUtksBINauxbLj1rQJVHnuHHAt/TExOjonmTIq20VRSGxorkI3WfzhWE7PVXQR+iduWw7AsU4PT+/vSL0qSAq94BO3LVNRex6YbAIVrkXv4kofsrt7TqJ43XYZDnezueEe5aUr8obDZsViEcQTkkQOV4lCEQp6nduOy24zPPRtS2sAyRP72o19vnD3mryNT5Eh7lxLf2TpUsTCRWlRvLjSh2X3B7Mu6q0VGxkOdxvNL5SXrsgnty+r42DnCC1VTiJaPGcTo0Un6HAtbTEUjeMJRGnr87Go2kmfN8zRyyNcHPTzyzP93L2+mV+e6eej21pV1J5H0gS+pib5d+1aI2KX584hNm0yJlZZuhQ6O2cVvavmF4pCosJhw261cMkTwm23cfOi6pycpygFPSnO0mS5JFnZ6ObAxWG++a+nefXcIIc6hnn13CCQvAn85Fi3EvcCQTidWHZ/0LBdYLw1Q28PoqnZyHWfbiaNSmFUFAoner34o8kExlxWWyxKQU+S7kW5ygRjYY0NCyoY9kfYuqSa1loXn79rFXevb+aLP3mHV88Nsv/CINuW1nLvphYVveeZ61kzQDJ67+01ct0ny6QBxj3Wo/zO5lE6fEeoOHOF5s2/mveUSsX8ZEtLNd5QLFWoS1VbHMcnblsGSA51DPP2JQ/BmCQYizAwFjH2Od7lZWWjG08gxqvnBllW7+LN9mHebB/mX969wsXBZCqRit4LA7PAy90fNFaj6kI9WSkCYNxjPcqvurmJxeEyGi70I8vPGTcCs60DZM3qUSgmwmGz4LJbkoKew2qLRSvodW47X7h7LSOBKD94/SJvXhjgbL9/XEH5i4MBBsaSaXJVThutteU0Vjo43jXKB9c28tFtrTx/8BJP7GsnGNX4wt1r83A1ikwmynW/XiZN5mN9ArZ2VSvBwFkSNWVYMyZjjYwbGPea7ukbN5VVq5TYK2ZMx3CQwUAyZTGX1RaLVtB16tx2/vQj63nqDTunUwW7MhkLxxHAu70+ILnatLW2nE/ctoyfHOvGk/qgPYEYT71xkVuW1fHdVy7w5V/bwMrG3K7sUsyMcXbNRI9ranAAK9y3Q1Nq4wQZN8CEr8lz54wSBwwNzlnKpaL0WFHvQkskCGtxBv1RfOFYTgp0CZnD8P96bN++XR49ejRrxzve5eG3nzlEeJJO2xPhtFkIawmaKx30j0XYuqSG412jtNaW0+0JsXVJDdXlZUrY5ymTRehGlN/ampOUS0Xp8svzAwwGojS67dy9punGb5gAIcQxKeX2ibYVfYSu891XLhDWElhTCfxTQRf//pTvfu5qMoKvdCRzRK96wxzvGuVkz1v87e/dwpHLI8pnn0eMs30mSblUEbti6uS2q1bJCPqXf20DcJqhsTDv9Y3N6BiBaFLgz6fqLlQ4rVi8MBKI8fkXT9DtCfHK2QF2rqhTWTLzmMyUy0KqY6MobDYvquHtLg+bF9Xk5PglY7noXBz088nnj3B5OLs1PNwOK4HItUKY+kKm1tpy7tm4gM/uWqWEfZ5izo837JitWxG2MiXsCoOIFufApRH6/RE2NlfOeHHR9SyXkhN0SE56Pn/wMp5AhLY+Hye7R4nn+DKry2386oYFLKxx8onblitxn6fo4i5jMeSJ44itqf6mKlOmoIiPjBB86ce4HvgY1rq6OTnnmf4x3unzArCuyc2WltoZHWfWHroQYg/wBGAF/lZK+RcZ2x8B/gDQgEHg/5ZSds5otFkgmdKYrOfy1BsXOd41Om4fvZxXtvCGNH58rAeAl450s7TezX/Zs0757vMMPftGhsPIsjJD2AEjU0ZoMRW95xn/cz9k7LHHSQSDVH/xkTk554p6F73ea+mLueCGgi6EsALfAz4E9ABHhBB7pZSnTbudALZLKYNCiIeBbwMP5GLA0+Wj21p54/wgBy4ma6hbLYK1TW4icWksLMo2V30Rrvoi/MaTbwEw7I9yqs/LysYKat1lRgQ/EoiqBU0lilnYE6noQaxahVi4CBmLpfVnTYyOIg8dROy8DYs+8aooORw2K82VTgYDMWyW3OSiTyVC3wG0Syk7AIQQLwL3AYagSylfNe1/CPidbA5yNtS57Xz3wa08f/Ayekz+xL52bl1emzNBz+SFg5cIa9K4qey/MMSdqxuMsQSjcVx2qyomVoJMlCmjR+9Gvvuhg8naNQB77knz5AE1yZpl4iMjAFQ+8gUqfv+hOT33msYKbBZLzvqKTkXQW4Bu0/Me4Nbr7P9J4OWJNgghPg18GmDJkiVTHOLsMVswI4EoLruNu9c3G0W8HFZBJC6xALkomxPW0s2d412jHO8aZU2Tmyqnjb8/eJnhYIzvvXYBXyg58fqZD6w05gJC0Tjldovy5kuEcYuidt6W9neisga6TaPKE8ye4Es/Zuyxx6n+8pfmzD/XcdisrG+uzNnxs5q2KIT4HWA7sGui7VLKp4GnITkpms1zT5U6t91oKv3XH93MT451G5HxxYExfnysF5sFprE+acbo6ZE6vlAcC7D//CDff62dhgpH2q8Il92m6s6UIJaaGthzj/FcTFDWQLdpMssTKKaP64GPpf0tJW6Y5SKEuA34upTyV1PP/yuAlPLPM/a7G/gusEtKOXCjE+cyy2WmjASiRlXGRdVOBv0RYrlOj5ki72upwma1cNUbps8b5v2r6vnGvRuVRTNPmKxWfCE15y5k8pHVkitmlbYohLAB54G7gF7gCPDbUspTpn22AD8F9kgpL0xlUIUo6EDaROUPXr/I0/s7xuWg5wOrYFzqZYXDij8SZ8PCSoLROF//9Zs41z+mBH4ekTh5MmnPmPq5KnEfz9iTP8D7zW9R/eUvUfnwZ/M9nFkx6zx0IcRHgL8hmbb4rJTyW0KIR4GjUsq9QohfAjcDV1Jv6ZJS3nu9YxaqoJvRxf3u9c28dKSLl9uu0u0JGduznfo4W2wWgZaQrGx081e/tUmlTM4DJlrUpMR9PCpCzzHFIOiZjASi/NGLx3mzfZildS42t1bzzyev3PiNeaCq3IovFKe1tpyF1U7+/DfepwqMlThqxerElJKYwzwpzjUX1LntfOfjWw1LBmBZQwWhaJzRYJRfnLqKL6zleZRJ9GyZbk+Ibk+IB58+yPJGN2ubq6h127l30yLlv5cYE9WLNyZT5+FiJl3IE8EgY489DlD0dsuNUBF6FtHTDK+MhjhwcYjRYNQo+FVo6CWCl9W7+PCGBXx2VzLzx5z1o8S++JmoFIEu7FDaOe7ev36Msccex/3wZ7HV16sIXTE9zPnukCwU9rW9p6h32zjYMcLQWDQnee4zQZ8LuDwc5On9HTx74BJ3r2/iF6f6+dHhLqO4mTlNEjAWaKmc+OJgolIEk+W4l6qwW8rLSz4y11ER+hxycdDPH//0Hc5d8aeaxRYuNotASkm1q4yRQIxKhxWX3WbUjr91eR2bFldTbrel+rui8uOLgIlWoU4UvZeCsM/GOy9k311NihYYI4Eof/HyGV4+dQUtLgnFCiVunxnL6l00Vzl4+5KHz9+1mmTuj0gTet3GUXZO4ZFpy1h23IqliBctZUOMCznNUVkuBUad2863f2sT3/6tTfzZz8/w9P6OfA9pVlweDhoWzT8d76bbk2zK/eLhLpY1uHj7koen3rjISCDGoY5hXj03aNSv0a0cs62jIv25JdOW0aP3YiX40o/xfvNbwMwnQZ0f/hCRgwdxfvhD2RxazlGCnmf0ycijnSP0joTo90fG7VNo+e7XQxdzSLb20y2akUCM8jILv7GlhVhccqhjiLcveXjl7AD9vnBaQ5I/TzX7/ui21jTPHpTY55LMGjPFSjaW9gd/9s+E971C2aZNc1ZeNxsoQc8zdW47f/qR9cC1hUxrmyv5k396F18ohhAUbKbMdAnFEvynF99Je+3tS8nKdxYBr5y5ytl+P3tuaubfTl3hhYOX6R29doN4t8fLq+cGAVRNG8U4zFZLodkkc4Xy0Aucx395jif2tY973W23lIzQ3wi33YLbUcbAWISWGifbl9bxyrl+xsJxNiyoIBhL8NjHNrN1ycw6wChKAz1NsfKRL8w6qi7WSVEVoRc4n7hteVpkClDptDIWvpYlk6uyv4VCIJogEE1aN72jYXpH+4xtp6/6Afjk80d49N6b+OreU2xpraFjKMAXP7SGl452s2FhNZ/dtVJF8oopUchifiNUhF4EZNZF37Wmic+/eCKtrozdKoimqnfphbzKLIJYoljc95lhBfRbW+ZcQ5lVGNUyty6p4c7VDdy7qYW9J/sIRTVAqDrzJUS2hLiQM1xARehFT+aCJYDf2NrCE/va2dJajdtRxoWrY/T7I2lVGc1ibhOglaC2m7P5My/PXPpYbyry5GvtRDOWAJjrzJvTKkFNwhYD2fbOizXDBZSgFy2fuG25IUR1bjvHuzz88U9P8pVf28ArZwf4n293ppXb1cW81O2ZG5Ep5jYB/3i8mx8euMwVX9joP3uoY5j3La425i/0piiKwiMbaYpmwv/+vwnvewXHbbdR9nBxfe/KcilRjnd5eOTH77BpcTVXfWGW1rs5fGkkLT1QMZ6qcivltuSK2D03NfP2pRE+sLqBZQ1uZc0UKNn0vOMjI/if+yEAFb//UEF66MpymYdsXVLLa//vB9Neu+bFJ/1jkJzsGeXtSx6aKx30j0WMv5BMJSxxC34cvlAcX8rI+cWpfgCjRPLRyx6+++BWQFkxhUK2JzDz2W80GyhBn0dM5MWbm3joS/P3nuwFBFdGg/z4WG/ahOt85sDFYR546gDdnjBhLUEwqvGFu4t7VWWx43/uh4w99jiJYDArC4CKvd+oEvR5jrlptt4AQxepkUCUlU2V3L2+meffusy+s/1UO8voHg0yFo5T6ypjeYOb412jLKp2EtXiDAViuMogGMvbJeWUC4PXLKtzV8e4/S/2oSUkC6ocbG6todbt4BO3LVORexFSzOmKOkrQFZNiFvtH79vIo/dtBJJVI7/5r6f58q9tADAe7z3ZxxP7LnDz4jrevjTC1iU1hKNxLg8H0At2BYu8EJk5NfK1c4OEteT1DIxFeLfXB0AoqlFf4VCWzBzguv8+YidP4rr/vlkfK9uTq/lACbpi2qxsrOC5h3YYz/XHn7htGS67dVxFxafeuMifv3yWT9+5nNNXfCyqdtI5EsQTiHB+IMDa5gouDQWIxmVa7rjbbqWxwsEVb4imSgfdpjIAduv4jJVs4LAKIiZ7yW23EItLw3JqrLATjMXxR+IsqSvHG9JISIk3FDXGc7LHy9uXRpQlMwdkMyOl2O0WUIKuyCIT2TeAkdOdGbHq/v1Ht7VyeTjAH//0JNuW1PDjY70ABKJxFugCK4Txvtbacm5bUUdb3xg9owHWN1fx9mUPGxZUMuSPMjBBgTOrRRCfYIZXX3xV5bRx/+ZFnOv38/alETYscHPFF+Xxj23mK//cRrcnhM0iGPBHuWNlPQcuDnN+IMB/vWcdkCwotrTOxa/etABI1qgJRRM89cZF47r1SWnVICR7ZEuES8FuASXoijnALPSTvV7ntrPvkd2MBKIsrHERimqU223sWtPId1+5QGttOS8c6mJpnYvOkSDdKdEH6PMmI/fty+r45ZlkZkpzpYP7NrcAcPqKlzfbh9POvbDKyRVfmFhCsrLRzdO/u51fnunnhUNdfHBtI+9bXMMT+y5wrn+MJz6+xcjxP9c/ljZxrN+sgDThrq+wE4xqRuXIz3xgJT851s0T+y6k9hZG+WAl7DMjWyIcHxnB85+/QHjfK0Dx2i2gBF1RYEyUifPcQzsYCURpqXWlxDS5dD8cS3Bx0M/KxgpeONRJx1CAPm/YEGj9V4K5dAJAud1ilAAwR8u1rqSw6iJtFtx9j+wGYPfaJoBxVor5hqXfqEYCUWPxl37cYDRO0oWXaWKvmD7Z8ryDL/2Y8L5XcN71K0Vtt4BaWKQoATJTL4sh6jXbTYU+1kIkmwuAis1uUQuLFCXNZN59ITOZDaWYGnr+eeUjX5hXYn4jlKArFFlEjxwToRCW8vKCXT6uSFIKqYpmlKArFFlEXzquI0MhtPPnqf7aV7HU1qbZBPr+pRIdziUVv/8QFpdr1p53KaQqmlGCrlBkEdcDHyMRDBoRevTIESL73wTAcdttaWIfPXKUyP79JIJBXPffh/cbj6YJvwyFEKkoP+Hx4P3Go7h//yECz/2Qys//EdHDRwwhmk83hlKzSbKJEnSFIotY6+rSaorELl5ME+pE8FrpgMj+/cZj7zceNdLmMoXf4nIROXiQ8L5XiL7zDonhEbTOTrT2i0QOHqRs0ybGHnucyMGDVH/tqwR+9CKxtjZqvvVNAEa/9BWsK1dgratL+2Xg/PCHCP/7/86aMM6V0GbTJlGWi0KhmDJlK1fS8MLzxnNd7OMjI8ZregQOpAm/HqG7HviY0WzBHKGPPfEdozO9865fMW4I+l/vNx4FUjeO1M3D4nIlt33zWwRefBGt/SLh19/Afst2XPffR/Bn/2yMSRdls1BD+s3AfFPQxVG/aem/UszHnewcOhOdS79B6Nv0zyIbi4kSwSCVj3xBWS4KhWLmZEby1rq6CYV/ou3lH0yWRbb9zeMTCq1tzRpibW1Uf+2rAMhozIjQ9X31iN+2aiWR/fuJ7N9P7ORJ42ZgcblwfvhDeL/xKNY1awg8+QMSwaCxj/5+/aag/zoASASDab8wzMc1P44eOUrZxpsQ5eU4d+9i7InvTHiuRDCIxeVCGx4m8OQP0IaHsdXXz/o7KPZSuROhBF2hKFKsdXVpNoH+uObLX0rbr/HFfxj33trUzcD54Q+lRc9lmzYlHz/wMWP1pCMao/rLXyIRDBoLcHTx1m8Kej2Vyoc/a/z6MEfoxnFTj/X5A912Cu3di9Z+ccJzQfIXhePOOwHQ2k4RSL1vNjZJqU2IwhQXFgkh9gBPkOzJ+7dSyr/I2O4AXgC2AcPAA1LKy9c7plpYpFAUNmb/v2zlynEeudkCma4Xr6d36raSHqFPdC6Y3OYxj2O65y/WidXrLSxCSnnd/0iK+EVgBWAHTgIbMvb5Q+AHqccfB1660XG3bdsmFQqFYrb4vv+k7F60WPq+/6TUhoeNv5mYt5nfU2wAR+UkujoVy2UH0C6l7EjdHV4E7gNOm/a5D/h66vFPgf8uhBCpkysUCkXOMFsnmROzcG3y1ZzRUop2C0zNQ28Buk3Pe4BbJ9tHSqkJIbxAPTBk3kkI8Wng0wBLliyZ4ZAVCoXiGua5BF2gzROzFpeLyoc/mybimfMPpcKcTopKKZ8Gnoakhz6X51YoFKWPLtTmtFBdyEtVxM1MRdB7gVbT88Wp1ybap0cIYQOqSU6OKhQKxZyTmRY6X7BMYZ8jwGohxHIhhJ3kpOfejH32Ap9IPf4t4BXlnysUCsXccsMIPeWJfw74N5IZL89KKU8JIR4lOdu6F/gfwN8JIdqBEZKir1AoFIo5ZEoeupTy58DPM177qulxGPhodoemUCgUiukwFctFoVAoFEWAEnSFQqEoEZSgKxQKRYmgBF2hUChKhCkV58rJiYUYBDpn+PYGMlahzgPUNc8P1DXPD2ZzzUullI0TbciboM8GIcRROVm1sRJFXfP8QF3z/CBX16wsF4VCoSgRlKArFApFiVCsgv50vgeQB9Q1zw/UNc8PcnLNRemhKxQKhWI8xRqhKxQKhSIDJegKhUJRIhS0oAsh9gghzgkh2oUQfzLBdocQ4qXU9reFEMvyMMysMoVrfkQIcVoI8a4QYp8QYmk+xplNbnTNpv1+UwghhRBFn+I2lWsWQnws9V2fEkL8w1yPMdtM4d/2EiHEq0KIE6l/3x/JxzizhRDiWSHEgBCibZLtQgjxndTn8a4QYuusTzpZs9F8/0eOmlMX8n9TvOYPAq7U44fnwzWn9qsE3gAOAdvzPe45+J5XAyeA2tTzpnyPew6u+Wng4dTjDcDlfI97ltf8AWAr0DbJ9o8ALwMC2Am8PdtzFnKEbjSnllJGAb05tZn7gOdTj38K3CWEEHM4xmxzw2uWUr4qpdQ74B4i2UGqmJnK9wzw34C/BMJzObgcMZVr/hTwPSmlB0BKOTDHY8w2U7lmCVSlHlcDfXM4vqwjpXyDZH+IybgPeEEmOQTUCCEWzuachSzoEzWnbplsHymlBujNqYuVqVyzmU+SvMMXMze85tRP0VYp5b/O5cByyFS+5zXAGiHEASHEISHEnjkbXW6YyjV/HfgdIUQPyf4L/2luhpY3pvv/+w2Z0ybRiuwhhPgdYDuwK99jySVCCAvwGPBQnocy19hI2i67Sf4Ke0MIcbOUcjSfg8oxDwI/lFL+tRDiNpJd0DZKKRP5HlixUMgR+nSaU1Mizamncs0IIe4GvgTcK6WMzNHYcsWNrrkS2Ai8JoS4TNJr3FvkE6NT+Z57gL1SypiU8hJwnqTAFytTueZPAj8GkFIeBJwki1iVKlP6/306FLKgz8fm1De8ZiHEFuApkmJe7L4q3OCapZReKWWDlHKZlHIZyXmDe6WUR/Mz3KwwlX/bPyMZnSOEaCBpwXTM4RizzVSuuQu4C0AIsZ6koA/O6Sjnlr3A76WyXXYCXinllVkdMd8zwTeYJf4IycjkIvCl1GuPkvwfGpJf+E+AduAwsCLfY56Da/4l0A+8k/pvb77HnOtrztj3NYo8y2WK37MgaTWdBt4DPp7vMc/BNW8ADpDMgHkH+HC+xzzL6/0RcAWIkfzF9Ungs8BnTd/x91Kfx3vZ+Hetlv4rFApFiVDIlotCoVAopoESdIVCoSgRlKArFApFiaAEXaFQKEoEJegKhUJRIihBVygUihJBCbpCoVCUCP8HQPVYNQkd8b8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data[:,1:], st_dbscan.labels) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}