{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "becoming-preservation",
   "metadata": {},
   "source": [
    "## Extract features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "increasing-rebecca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import movekit as mkit\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import animation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "important-buying",
   "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>1</td>\n",
       "      <td>312</td>\n",
       "      <td>405.29</td>\n",
       "      <td>417.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>511</td>\n",
       "      <td>369.99</td>\n",
       "      <td>428.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>607</td>\n",
       "      <td>390.33</td>\n",
       "      <td>405.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>811</td>\n",
       "      <td>445.15</td>\n",
       "      <td>411.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>905</td>\n",
       "      <td>366.06</td>\n",
       "      <td>451.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time  animal_id       x       y\n",
       "0     1        312  405.29  417.76\n",
       "1     1        511  369.99  428.78\n",
       "2     1        607  390.33  405.89\n",
       "3     1        811  445.15  411.94\n",
       "4     1        905  366.06  451.76"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Enter path to CSV file\n",
    "path = \"./datasets/fish-5-cleaned.csv\"\n",
    "\n",
    "data = mkit.read_data(path)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e5d902b-de0a-4518-8006-356820424c9f",
   "metadata": {},
   "source": [
    "#### Extract features from preprocessed data for each animal: Distance, Average Speed, Average Acceleration, Direction, Stopped, Turning angle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eight-encounter",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Extracting all absolute features: 100%|██████████| 100.0/100 [00:01<00:00, 52.82it/s]\n"
     ]
    },
    {
     "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",
       "      <th>distance</th>\n",
       "      <th>direction</th>\n",
       "      <th>turning</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>average_acceleration</th>\n",
       "      <th>stopped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>312</td>\n",
       "      <td>405.29</td>\n",
       "      <td>417.76</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.180171</td>\n",
       "      <td>0.027515</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>511</td>\n",
       "      <td>369.99</td>\n",
       "      <td>428.78</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019907</td>\n",
       "      <td>0.001813</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>607</td>\n",
       "      <td>390.33</td>\n",
       "      <td>405.89</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040104</td>\n",
       "      <td>0.013106</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>811</td>\n",
       "      <td>445.15</td>\n",
       "      <td>411.94</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.228980</td>\n",
       "      <td>0.064587</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>905</td>\n",
       "      <td>366.06</td>\n",
       "      <td>451.76</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.093333</td>\n",
       "      <td>0.016944</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>312</td>\n",
       "      <td>405.31</td>\n",
       "      <td>417.37</td>\n",
       "      <td>0.390512</td>\n",
       "      <td>(0.02, -0.39)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.265210</td>\n",
       "      <td>0.014511</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>511</td>\n",
       "      <td>370.01</td>\n",
       "      <td>428.82</td>\n",
       "      <td>0.044721</td>\n",
       "      <td>(0.02, 0.04)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026574</td>\n",
       "      <td>0.000505</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>607</td>\n",
       "      <td>390.25</td>\n",
       "      <td>405.89</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>(-0.08, 0.0)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.070208</td>\n",
       "      <td>0.017008</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>811</td>\n",
       "      <td>445.48</td>\n",
       "      <td>412.26</td>\n",
       "      <td>0.459674</td>\n",
       "      <td>(0.33, 0.32)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.382856</td>\n",
       "      <td>0.078737</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>905</td>\n",
       "      <td>365.86</td>\n",
       "      <td>451.76</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>(-0.2, 0.0)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.141667</td>\n",
       "      <td>0.012778</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>312</td>\n",
       "      <td>405.31</td>\n",
       "      <td>417.07</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>(0.0, -0.3)</td>\n",
       "      <td>0.998688</td>\n",
       "      <td>0.260220</td>\n",
       "      <td>-0.021231</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>511</td>\n",
       "      <td>370.01</td>\n",
       "      <td>428.85</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>(0.0, 0.03)</td>\n",
       "      <td>0.894427</td>\n",
       "      <td>0.024120</td>\n",
       "      <td>-0.001373</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>607</td>\n",
       "      <td>390.17</td>\n",
       "      <td>405.88</td>\n",
       "      <td>0.080623</td>\n",
       "      <td>(-0.08, -0.01)</td>\n",
       "      <td>0.992278</td>\n",
       "      <td>0.088633</td>\n",
       "      <td>0.013083</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>811</td>\n",
       "      <td>445.77</td>\n",
       "      <td>412.61</td>\n",
       "      <td>0.454533</td>\n",
       "      <td>(0.29, 0.35)</td>\n",
       "      <td>0.994081</td>\n",
       "      <td>0.462626</td>\n",
       "      <td>0.055248</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>905</td>\n",
       "      <td>365.70</td>\n",
       "      <td>451.76</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>(-0.16, 0.0)</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.146667</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>312</td>\n",
       "      <td>405.30</td>\n",
       "      <td>416.86</td>\n",
       "      <td>0.210238</td>\n",
       "      <td>(-0.01, -0.21)</td>\n",
       "      <td>0.998868</td>\n",
       "      <td>0.187187</td>\n",
       "      <td>-0.050651</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>511</td>\n",
       "      <td>370.01</td>\n",
       "      <td>428.86</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>(0.0, 0.01)</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018727</td>\n",
       "      <td>-0.001601</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4</td>\n",
       "      <td>607</td>\n",
       "      <td>390.07</td>\n",
       "      <td>405.88</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>(-0.1, 0.0)</td>\n",
       "      <td>0.992278</td>\n",
       "      <td>0.093622</td>\n",
       "      <td>0.004977</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4</td>\n",
       "      <td>811</td>\n",
       "      <td>446.03</td>\n",
       "      <td>413.00</td>\n",
       "      <td>0.468722</td>\n",
       "      <td>(0.26, 0.39)</td>\n",
       "      <td>0.994605</td>\n",
       "      <td>0.467753</td>\n",
       "      <td>0.019630</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>905</td>\n",
       "      <td>365.57</td>\n",
       "      <td>451.76</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>(-0.13, 0.0)</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>-0.023333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    time  animal_id       x       y  distance       direction   turning  \\\n",
       "0      1        312  405.29  417.76  0.000000      (0.0, 0.0)  0.000000   \n",
       "1      1        511  369.99  428.78  0.000000      (0.0, 0.0)  0.000000   \n",
       "2      1        607  390.33  405.89  0.000000      (0.0, 0.0)  0.000000   \n",
       "3      1        811  445.15  411.94  0.000000      (0.0, 0.0)  0.000000   \n",
       "4      1        905  366.06  451.76  0.000000      (0.0, 0.0)  0.000000   \n",
       "5      2        312  405.31  417.37  0.390512   (0.02, -0.39)  0.000000   \n",
       "6      2        511  370.01  428.82  0.044721    (0.02, 0.04)  0.000000   \n",
       "7      2        607  390.25  405.89  0.080000    (-0.08, 0.0)  0.000000   \n",
       "8      2        811  445.48  412.26  0.459674    (0.33, 0.32)  0.000000   \n",
       "9      2        905  365.86  451.76  0.200000     (-0.2, 0.0)  0.000000   \n",
       "10     3        312  405.31  417.07  0.300000     (0.0, -0.3)  0.998688   \n",
       "11     3        511  370.01  428.85  0.030000     (0.0, 0.03)  0.894427   \n",
       "12     3        607  390.17  405.88  0.080623  (-0.08, -0.01)  0.992278   \n",
       "13     3        811  445.77  412.61  0.454533    (0.29, 0.35)  0.994081   \n",
       "14     3        905  365.70  451.76  0.160000    (-0.16, 0.0)  1.000000   \n",
       "15     4        312  405.30  416.86  0.210238  (-0.01, -0.21)  0.998868   \n",
       "16     4        511  370.01  428.86  0.010000     (0.0, 0.01)  1.000000   \n",
       "17     4        607  390.07  405.88  0.100000     (-0.1, 0.0)  0.992278   \n",
       "18     4        811  446.03  413.00  0.468722    (0.26, 0.39)  0.994605   \n",
       "19     4        905  365.57  451.76  0.130000    (-0.13, 0.0)  1.000000   \n",
       "\n",
       "    average_speed  average_acceleration  stopped  \n",
       "0        0.180171              0.027515        1  \n",
       "1        0.019907              0.001813        1  \n",
       "2        0.040104              0.013106        1  \n",
       "3        0.228980              0.064587        0  \n",
       "4        0.093333              0.016944        1  \n",
       "5        0.265210              0.014511        0  \n",
       "6        0.026574              0.000505        1  \n",
       "7        0.070208              0.017008        1  \n",
       "8        0.382856              0.078737        0  \n",
       "9        0.141667              0.012778        1  \n",
       "10       0.260220             -0.021231        0  \n",
       "11       0.024120             -0.001373        1  \n",
       "12       0.088633              0.013083        1  \n",
       "13       0.462626              0.055248        0  \n",
       "14       0.146667             -0.005000        1  \n",
       "15       0.187187             -0.050651        1  \n",
       "16       0.018727             -0.001601        1  \n",
       "17       0.093622              0.004977        1  \n",
       "18       0.467753              0.019630        0  \n",
       "19       0.116667             -0.023333        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract additional features from the preprocessed movement data: Distance traveled between two timestamps, average speed between two timestamps, average acceleration between two timestamps, direction of the mover, whether the mover stopped at time stamp, and the turning angle of the mover at time stamp.\n",
    "\n",
    "#Parameters:\n",
    "# data: pandas DataFrame with all records of movements.\n",
    "# fps: window size for average_speed and average_acceleration calculations. For integer-time type of fps is integer, otherwise type of fps is string.\n",
    "# stop_threshold: integer to specify threshold for average speed, such that mover is considered to make a \"stop\".\n",
    "\n",
    "data_features = mkit.extract_features(data, fps = 3, stop_threshold = 0.2)\n",
    "data_features.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "320e4898-c36b-4b42-bc54-de2bc6f6d079",
   "metadata": {},
   "source": [
    "For two-dimensional data one can also compute the direction and the turning angle of a mover."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef8adfd3-70aa-4eca-ade5-6fea9947af08",
   "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",
       "      <th>distance</th>\n",
       "      <th>direction</th>\n",
       "      <th>turning</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>average_acceleration</th>\n",
       "      <th>stopped</th>\n",
       "      <th>direction_angle</th>\n",
       "      <th>turning_angle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>312</td>\n",
       "      <td>405.29</td>\n",
       "      <td>417.76</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.180171</td>\n",
       "      <td>0.027515</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>511</td>\n",
       "      <td>369.99</td>\n",
       "      <td>428.78</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.019907</td>\n",
       "      <td>0.001813</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>607</td>\n",
       "      <td>390.33</td>\n",
       "      <td>405.89</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040104</td>\n",
       "      <td>0.013106</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>811</td>\n",
       "      <td>445.15</td>\n",
       "      <td>411.94</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.228980</td>\n",
       "      <td>0.064587</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>905</td>\n",
       "      <td>366.06</td>\n",
       "      <td>451.76</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.093333</td>\n",
       "      <td>0.016944</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>312</td>\n",
       "      <td>405.31</td>\n",
       "      <td>417.37</td>\n",
       "      <td>0.390512</td>\n",
       "      <td>(0.02, -0.39)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.265210</td>\n",
       "      <td>0.014511</td>\n",
       "      <td>0</td>\n",
       "      <td>272.935673</td>\n",
       "      <td>87.064327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>511</td>\n",
       "      <td>370.01</td>\n",
       "      <td>428.82</td>\n",
       "      <td>0.044721</td>\n",
       "      <td>(0.02, 0.04)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.026574</td>\n",
       "      <td>0.000505</td>\n",
       "      <td>1</td>\n",
       "      <td>63.434949</td>\n",
       "      <td>63.434949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>607</td>\n",
       "      <td>390.25</td>\n",
       "      <td>405.89</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>(-0.08, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.070208</td>\n",
       "      <td>0.017008</td>\n",
       "      <td>1</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>811</td>\n",
       "      <td>445.48</td>\n",
       "      <td>412.26</td>\n",
       "      <td>0.459674</td>\n",
       "      <td>(0.33, 0.32)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.382856</td>\n",
       "      <td>0.078737</td>\n",
       "      <td>0</td>\n",
       "      <td>44.118596</td>\n",
       "      <td>44.118596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>905</td>\n",
       "      <td>365.86</td>\n",
       "      <td>451.76</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>(-0.2, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.141667</td>\n",
       "      <td>0.012778</td>\n",
       "      <td>1</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time  animal_id       x       y  distance      direction  turning  \\\n",
       "0     1        312  405.29  417.76  0.000000     (0.0, 0.0)      0.0   \n",
       "1     1        511  369.99  428.78  0.000000     (0.0, 0.0)      0.0   \n",
       "2     1        607  390.33  405.89  0.000000     (0.0, 0.0)      0.0   \n",
       "3     1        811  445.15  411.94  0.000000     (0.0, 0.0)      0.0   \n",
       "4     1        905  366.06  451.76  0.000000     (0.0, 0.0)      0.0   \n",
       "5     2        312  405.31  417.37  0.390512  (0.02, -0.39)      0.0   \n",
       "6     2        511  370.01  428.82  0.044721   (0.02, 0.04)      0.0   \n",
       "7     2        607  390.25  405.89  0.080000   (-0.08, 0.0)      0.0   \n",
       "8     2        811  445.48  412.26  0.459674   (0.33, 0.32)      0.0   \n",
       "9     2        905  365.86  451.76  0.200000    (-0.2, 0.0)      0.0   \n",
       "\n",
       "   average_speed  average_acceleration  stopped  direction_angle  \\\n",
       "0       0.180171              0.027515        1         0.000000   \n",
       "1       0.019907              0.001813        1         0.000000   \n",
       "2       0.040104              0.013106        1         0.000000   \n",
       "3       0.228980              0.064587        0         0.000000   \n",
       "4       0.093333              0.016944        1         0.000000   \n",
       "5       0.265210              0.014511        0       272.935673   \n",
       "6       0.026574              0.000505        1        63.434949   \n",
       "7       0.070208              0.017008        1       180.000000   \n",
       "8       0.382856              0.078737        0        44.118596   \n",
       "9       0.141667              0.012778        1       180.000000   \n",
       "\n",
       "   turning_angle  \n",
       "0            NaN  \n",
       "1            NaN  \n",
       "2            NaN  \n",
       "3            NaN  \n",
       "4            NaN  \n",
       "5      87.064327  \n",
       "6      63.434949  \n",
       "7     180.000000  \n",
       "8      44.118596  \n",
       "9     180.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "angle_data = mkit.compute_direction_angle(data_features).head(10)\n",
    "mkit.compute_turning_angle(angle_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f3fab5a-41a9-46cf-b614-0db8164f3363",
   "metadata": {},
   "source": [
    "#### Computing distances between movers and between timestamps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48bdf059-471f-4401-afbc-ae0536677bb9",
   "metadata": {
    "tags": []
   },
   "source": [
    "Calculate the euclidean distance between different movers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb513e8c-acb6-4aca-8061-972a685dfcc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing euclidean distance: 100%|██████████| 1000/1000 [00:02<00:00, 421.32it/s]\n"
     ]
    },
    {
     "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",
       "      <th>312</th>\n",
       "      <th>511</th>\n",
       "      <th>607</th>\n",
       "      <th>811</th>\n",
       "      <th>905</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>312</td>\n",
       "      <td>405.29</td>\n",
       "      <td>417.76</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.980135</td>\n",
       "      <td>19.097081</td>\n",
       "      <td>40.282651</td>\n",
       "      <td>51.913321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>511</td>\n",
       "      <td>369.99</td>\n",
       "      <td>428.78</td>\n",
       "      <td>36.980135</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.621360</td>\n",
       "      <td>77.023446</td>\n",
       "      <td>23.313629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>607</td>\n",
       "      <td>390.33</td>\n",
       "      <td>405.89</td>\n",
       "      <td>19.097081</td>\n",
       "      <td>30.621360</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.152832</td>\n",
       "      <td>51.894988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>811</td>\n",
       "      <td>445.15</td>\n",
       "      <td>411.94</td>\n",
       "      <td>40.282651</td>\n",
       "      <td>77.023446</td>\n",
       "      <td>55.152832</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>88.548634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>905</td>\n",
       "      <td>366.06</td>\n",
       "      <td>451.76</td>\n",
       "      <td>51.913321</td>\n",
       "      <td>23.313629</td>\n",
       "      <td>51.894988</td>\n",
       "      <td>88.548634</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>1000</td>\n",
       "      <td>312</td>\n",
       "      <td>720.96</td>\n",
       "      <td>244.60</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.509642</td>\n",
       "      <td>51.770954</td>\n",
       "      <td>75.437726</td>\n",
       "      <td>73.481324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>1000</td>\n",
       "      <td>511</td>\n",
       "      <td>662.56</td>\n",
       "      <td>225.29</td>\n",
       "      <td>61.509642</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>93.117875</td>\n",
       "      <td>129.431823</td>\n",
       "      <td>43.068319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>1000</td>\n",
       "      <td>607</td>\n",
       "      <td>722.75</td>\n",
       "      <td>296.34</td>\n",
       "      <td>51.770954</td>\n",
       "      <td>93.117875</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.259047</td>\n",
       "      <td>119.983706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>1000</td>\n",
       "      <td>811</td>\n",
       "      <td>762.44</td>\n",
       "      <td>307.61</td>\n",
       "      <td>75.437726</td>\n",
       "      <td>129.431823</td>\n",
       "      <td>41.259047</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>148.883426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>1000</td>\n",
       "      <td>905</td>\n",
       "      <td>677.91</td>\n",
       "      <td>185.05</td>\n",
       "      <td>73.481324</td>\n",
       "      <td>43.068319</td>\n",
       "      <td>119.983706</td>\n",
       "      <td>148.883426</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      time  animal_id       x       y        312         511         607  \\\n",
       "0        1        312  405.29  417.76   0.000000   36.980135   19.097081   \n",
       "1        1        511  369.99  428.78  36.980135    0.000000   30.621360   \n",
       "2        1        607  390.33  405.89  19.097081   30.621360    0.000000   \n",
       "3        1        811  445.15  411.94  40.282651   77.023446   55.152832   \n",
       "4        1        905  366.06  451.76  51.913321   23.313629   51.894988   \n",
       "...    ...        ...     ...     ...        ...         ...         ...   \n",
       "4995  1000        312  720.96  244.60   0.000000   61.509642   51.770954   \n",
       "4996  1000        511  662.56  225.29  61.509642    0.000000   93.117875   \n",
       "4997  1000        607  722.75  296.34  51.770954   93.117875    0.000000   \n",
       "4998  1000        811  762.44  307.61  75.437726  129.431823   41.259047   \n",
       "4999  1000        905  677.91  185.05  73.481324   43.068319  119.983706   \n",
       "\n",
       "             811         905  \n",
       "0      40.282651   51.913321  \n",
       "1      77.023446   23.313629  \n",
       "2      55.152832   51.894988  \n",
       "3       0.000000   88.548634  \n",
       "4      88.548634    0.000000  \n",
       "...          ...         ...  \n",
       "4995   75.437726   73.481324  \n",
       "4996  129.431823   43.068319  \n",
       "4997   41.259047  119.983706  \n",
       "4998    0.000000  148.883426  \n",
       "4999  148.883426    0.000000  \n",
       "\n",
       "[5000 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkit.euclidean_dist(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44b01cb7-fca6-4dce-b8b7-975b1c7a2cd2",
   "metadata": {},
   "source": [
    "Compute the euclidean distance between positions for a particular time window for all movers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dbf5af87-fa8d-4089-a527-41a243fdb95e",
   "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>animal_id</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>312</td>\n",
       "      <td>0.660571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>511</td>\n",
       "      <td>0.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>607</td>\n",
       "      <td>0.271176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>811</td>\n",
       "      <td>1.392829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>905</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   animal_id  distance\n",
       "0        312  0.660571\n",
       "1        511  0.060000\n",
       "2        607  0.271176\n",
       "3        811  1.392829\n",
       "4        905  0.390000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkit.distance_by_time(data, 3, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5026444e-b7a8-4268-a9fe-829b437aa5ee",
   "metadata": {},
   "source": [
    "Additionally one can obtain a matrix of the trajectory similarities, based on the Hausdorff distance of trajectories of the animals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86724b28-c085-43d9-a5a1-84b4fe5e4be2",
   "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>312</th>\n",
       "      <th>511</th>\n",
       "      <th>607</th>\n",
       "      <th>811</th>\n",
       "      <th>905</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.836554</td>\n",
       "      <td>54.412682</td>\n",
       "      <td>75.437726</td>\n",
       "      <td>73.481324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>61.836554</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>93.117875</td>\n",
       "      <td>129.431823</td>\n",
       "      <td>43.068319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>54.412682</td>\n",
       "      <td>93.117875</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>83.522165</td>\n",
       "      <td>119.983706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>75.437726</td>\n",
       "      <td>129.431823</td>\n",
       "      <td>83.522165</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>148.883426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>73.481324</td>\n",
       "      <td>43.068319</td>\n",
       "      <td>119.983706</td>\n",
       "      <td>148.883426</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           312         511         607         811         905\n",
       "312   0.000000   61.836554   54.412682   75.437726   73.481324\n",
       "511  61.836554    0.000000   93.117875  129.431823   43.068319\n",
       "607  54.412682   93.117875    0.000000   83.522165  119.983706\n",
       "811  75.437726  129.431823   83.522165    0.000000  148.883426\n",
       "905  73.481324   43.068319  119.983706  148.883426    0.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkit.hausdorff_distance(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "533ffd71-1051-4001-8ab7-81db3eabad26",
   "metadata": {},
   "source": [
    "#### Computing centroids and medoids for each time stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "55b26aa9-a77a-466a-82d5-23f1ef0d87ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating centroid distances: 100%|██████████| 1000/1000 [00:05<00:00, 184.80it/s]\n"
     ]
    },
    {
     "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",
       "      <th>x_centroid</th>\n",
       "      <th>y_centroid</th>\n",
       "      <th>medoid</th>\n",
       "      <th>distance_to_centroid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>312</td>\n",
       "      <td>405.29</td>\n",
       "      <td>417.76</td>\n",
       "      <td>395.364</td>\n",
       "      <td>423.226</td>\n",
       "      <td>312</td>\n",
       "      <td>11.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>511</td>\n",
       "      <td>369.99</td>\n",
       "      <td>428.78</td>\n",
       "      <td>395.364</td>\n",
       "      <td>423.226</td>\n",
       "      <td>312</td>\n",
       "      <td>25.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>607</td>\n",
       "      <td>390.33</td>\n",
       "      <td>405.89</td>\n",
       "      <td>395.364</td>\n",
       "      <td>423.226</td>\n",
       "      <td>312</td>\n",
       "      <td>18.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>811</td>\n",
       "      <td>445.15</td>\n",
       "      <td>411.94</td>\n",
       "      <td>395.364</td>\n",
       "      <td>423.226</td>\n",
       "      <td>312</td>\n",
       "      <td>51.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>905</td>\n",
       "      <td>366.06</td>\n",
       "      <td>451.76</td>\n",
       "      <td>395.364</td>\n",
       "      <td>423.226</td>\n",
       "      <td>312</td>\n",
       "      <td>40.901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>1000</td>\n",
       "      <td>312</td>\n",
       "      <td>720.96</td>\n",
       "      <td>244.60</td>\n",
       "      <td>709.324</td>\n",
       "      <td>251.778</td>\n",
       "      <td>312</td>\n",
       "      <td>13.672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>1000</td>\n",
       "      <td>511</td>\n",
       "      <td>662.56</td>\n",
       "      <td>225.29</td>\n",
       "      <td>709.324</td>\n",
       "      <td>251.778</td>\n",
       "      <td>312</td>\n",
       "      <td>53.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>1000</td>\n",
       "      <td>607</td>\n",
       "      <td>722.75</td>\n",
       "      <td>296.34</td>\n",
       "      <td>709.324</td>\n",
       "      <td>251.778</td>\n",
       "      <td>312</td>\n",
       "      <td>46.541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>1000</td>\n",
       "      <td>811</td>\n",
       "      <td>762.44</td>\n",
       "      <td>307.61</td>\n",
       "      <td>709.324</td>\n",
       "      <td>251.778</td>\n",
       "      <td>312</td>\n",
       "      <td>77.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>1000</td>\n",
       "      <td>905</td>\n",
       "      <td>677.91</td>\n",
       "      <td>185.05</td>\n",
       "      <td>709.324</td>\n",
       "      <td>251.778</td>\n",
       "      <td>312</td>\n",
       "      <td>73.753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      time  animal_id       x       y  x_centroid  y_centroid  medoid  \\\n",
       "0        1        312  405.29  417.76     395.364     423.226     312   \n",
       "1        1        511  369.99  428.78     395.364     423.226     312   \n",
       "2        1        607  390.33  405.89     395.364     423.226     312   \n",
       "3        1        811  445.15  411.94     395.364     423.226     312   \n",
       "4        1        905  366.06  451.76     395.364     423.226     312   \n",
       "...    ...        ...     ...     ...         ...         ...     ...   \n",
       "4995  1000        312  720.96  244.60     709.324     251.778     312   \n",
       "4996  1000        511  662.56  225.29     709.324     251.778     312   \n",
       "4997  1000        607  722.75  296.34     709.324     251.778     312   \n",
       "4998  1000        811  762.44  307.61     709.324     251.778     312   \n",
       "4999  1000        905  677.91  185.05     709.324     251.778     312   \n",
       "\n",
       "      distance_to_centroid  \n",
       "0                   11.331  \n",
       "1                   25.975  \n",
       "2                   18.052  \n",
       "3                   51.049  \n",
       "4                   40.901  \n",
       "...                    ...  \n",
       "4995                13.672  \n",
       "4996                53.745  \n",
       "4997                46.541  \n",
       "4998                77.062  \n",
       "4999                73.753  \n",
       "\n",
       "[5000 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculates the data point (animal_id) closest to center/centroid/medoid for each time step.\n",
    "mkit.centroid_medoid_computation(data, object_output = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "phantom-edmonton",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Exploring the geospatial features and plotting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d4d0bc-f249-4caf-a3e8-494b28d47f51",
   "metadata": {},
   "source": [
    "One possibility is to plot the movement from all movers/animals in the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "raised-retirement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 416.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot movement from all movers/animals in a specified time period\n",
    "mkit.plot_movement(data, frm=0, to=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef3382b8-7d9d-4ad0-ae77-df56a172c4ed",
   "metadata": {},
   "source": [
    "Another option is to plot each individual mover/animal individually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "68cc0185-10b8-432e-ad98-1d5612bf3ef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mkit.plot_animal(data, 312)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46130db4-2fd5-410f-94ac-5b04661daf7c",
   "metadata": {},
   "source": [
    "One can also plot either the average acceleration or the average speed for each individual mover/animal over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "sustained-circulation",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 224x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mkit.plot_pace(data_features, \"speed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6cd945-327b-4508-98d1-55b30cf40e47",
   "metadata": {},
   "source": [
    "One can additionally check the geospatial distribution of the different movers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "complex-works",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating covered areas:  83%|████████▎ | 83.33333333333334/100 [00:00<00:00, 438.79it/s] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Area (polygon) covered by animal ID = 312 is = 139873.97 sq. units\n",
      "\n",
      "\n",
      "Area (polygon) covered by animal ID = 511 is = 151556.80 sq. units\n",
      "\n",
      "\n",
      "Area (polygon) covered by animal ID = 607 is = 171971.54 sq. units\n",
      "\n",
      "\n",
      "Area (polygon) covered by animal ID = 811 is = 196394.24 sq. units\n",
      "\n",
      "\n",
      "Area (polygon) covered by animal ID = 905 is = 151805.74 sq. units\n",
      "\n",
      "\n",
      "Area (polygon) covered by animals collectively is =  214704.49029999992 sq. units\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD5CAYAAADcDXXiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACNNklEQVR4nOydd3hURReH37ubTe+9NzqE3nuH0AUR7IgoiqiggiJFpPMhSkepglgoIkVK6L13COmQhBTSe93s7v3+2BASNyS0hAD3fZ48bGbmzp0bkt+ePXPmHEEURSQkJCQkXixkz3oBEhISEhJPH0ncJSQkJF5AJHGXkJCQeAGRxF1CQkLiBUQSdwkJCYkXEEncJSQkJF5A9B5mkCAIEUAmoAZUoig2EwTBGtgEeAIRwBBRFFMFQRCARUBvIAd4TxTFy2XNb2trK3p6ej7mI0hISEi8nFy6dClJFEW70voeStwL6SyKYlKx7ycAh0RRnCsIwoTC778BegE1Cr9aAj8X/vtAPD09uXjx4iMsRUJCQkJCEITIB/U9iVtmALC+8PV64JVi7b+JWs4CloIgOD3BfSQkJCQkHpGHFXcR2C8IwiVBEEYWtjmIoni38HUc4FD42gWIKnZtdGGbhISEhEQl8bBumXaiKMYIgmAPHBAEIah4pyiKoiAIj5THoPBNYiSAu7v7o1wqISEhIVEOD2W5i6IYU/hvArANaAHE33O3FP6bUDg8BnArdrlrYdt/51wpimIzURSb2dmVuh8gISEhIfGYlCvugiCYCIJgdu810APwB3YCwwqHDQN2FL7eCbwraGkFpBdz30hISEhIVAIP45ZxALZpIxzRA/4URdFPEIQLwGZBEEYAkcCQwvF70IZBhqENhRz+1FctIVFJiKJIdHQ04eHhmJubU79+feRy+bNeloREuZQr7qIo3gYaltKeDHQtpV0ERj+V1UlIVBKiKBIXF8fNmze5efMm/v7+Ra8zMjKKxllbW9O7d28GDx5Mr1690NfXf4arlpB4MEJVyOferFkzUYpzl6gskpKSSoj3vdcpKSmPNI+rqysTJkzgww8/lERe4pkgCMIlURSbldonibvEi0paWpqOgPv7+5OQkFD+xY+Ap6cn06ZN46233pJcNhKViiTuEi80WVlZBAQElBDwmzdvEhOjE6RVodStW5eZM2fyyiuvULhHJSFRoZQl7o+SfkBC4pmSm5tLYGCgjiUeGfnAE9hPBRMTE2rUqEFISAg5OTkPHBcQEMCgQYNo3rw5s2fPplu3bhW6LgmJspDEXaLKkZ+fT0hIiI4lfuvWLSryk6a+TEZtNzfqt2qFT6NG+Pj4UK9ePTw8PJDJZOTl5XHw4EHWrFnD9u3bHzjPhQsX6N69O126dGHWrFm0atWqwtYsIfEgJLeMxDOjoKCAsLAwHUs8NDQUtVpdYfdVyOV4mZhQTRSpYWJCg9ZtaDHsXer06YOe3sPZO1euXGHy5Mns2bOn3LEDBgxg5syZ+Pj4POnSJSRKIPncJZ4parWa8PBwHUs8ODgYpVJZYfeVy+XUqFGDevXqUcveHo/ERFxuBuCmVmNaswaWQ4ZiMaA/cnPzx77HyZMnmThxIidOnChznCAIvPXWW0ybNg1vb+/Hvp+ERHEkcZeoFDQaDXfu3NGJEw8ICCAvL6/C7isIAt7e3tSrV6/IleLj40MNNzfyDx0idfMW8q5fRzAwwNzXF8uhQzFq3OipbXqKosi+ffuYOHEiV65cKXOsnp4eH374IZMnT8bZ2fmp3F/i5UUSd4mniiiKxMbG6sSKBwQEkJWVVaH3dnd3LyHg9erVo06dOhgbGxeNyQsOJm3TJtJ3/osmKwv96tWwumelW1hU2No0Gg1bt25l8uTJhISElDnWyMiIzz77jG+++QZra+sKW5PEi40k7hKPhSiKJCQk6Fji/v7+pKenV+i9nZ2ddSzxunXrYmZmVup4TW4uGXv2krZ5M7nXriHo62PeyxfLIUMwatKkUkMTVSoV69evZ9q0aURFRZU51tzcnPHjxzN27FhMTU0raYUSLwqSuEuUS0pKSqmnNpOSksq/+Amws7PTscTr1auHlZVV2Rdq1JAWSd7FY6Tt2Ef62VA02Tnoe3tjNXQIFgMGILe0rNC1l0deXh6//PILs2bNKvfnaGdnx6RJk/j4448xMDCopBVKPO9I4i5RREZGRqmnNuPi4ir0vlZWVjqWeL169Sg33XNeBiSHQlIYJIVAUgiauBAyrsaQFmJAbrI+gkzEzD0fqzffxejtqQgyGaIokpGUS3x4BhlJucjkMuw8zHCtaYUgq9wDRpmZmSxcuJD58+eXyFNTGu7u7kydOpV33333oSN3JF5eJHF/CcnOziYgIEBHyMtzEzwpZmZmRdZ3cUvcycnpwa4RjQYyogvF+76IkxwGmfezRedn6JMa5Uh6qIgmT42+kzWW/XpgMXAAqmPziQ+OJt5yAPGG7Um4k0tedoHOrWo0s6fr8LrI5U9SYfLxSE5O5n//+x9Lliwpd4O5du3azJgxg0GDBiGTVf5aJZ4PJHF/gcnLyyMoKEjHEo+IiKjQAz9GRkbUrVu3hID7+Pjg5ub2YBFXZmsFOym08CvkvlWuyr0/zsAC7GqCTQ005l5khuSSevgauTcCERQKjLv7oukykHQjVxIiM4gPzyA98d71GqwN4nGo64VDXQ8cvMyxcjBBpdJw40g053bexsPHhp4jfVDoP5s8MDExMcycOZPVq1ejUqnKHNukSRNmz55Njx49pJQGEjpI4v4CoFQqCQ0N1YkVDwsLQ6PRVNh99fX1qVOnjo4l7uXlVbpFKYpaazsp5D8iHgbpxT81CGDlATY1wLYm2NYo/KoJJnbk375N6qZNpO3YSbZSnxyvZuT6dCBd35GkuFw0Ku3vrYmFPg5eFth7mmn/5Sb6/46AvDToNQ+avAvFRPHmiRiO/hmMUzUL+oxuiIHRs3N9hIWF8f333/Pnn3+W+0bcoUMH5syZQ5s2bSppdRLPA5K4P0eoVCpu3bqlY4mHhISUa+U9CXp6etSsWVPHEq9WrVrpvt+CPEi5VYoVHgrKYuGQ+qZgU71QwIuJuHU1UBiWmFKTn0/iv/uI/Pc0ifEqMiy8yLSqRgHadLp6BnLs3c1w8DLHwdMcBy9zTK1KzgFAViL88wHcPgoNXoe+P4G+SVF36MV4Dq4NwNrFhP6fN8LI7Nmm671+/TpTpkxh586d5Y7t06cPs2bNomFDnRILEi8hkrhXQTQaDRERETqWeFBQEPn5+RV2X5lMRvXq1XX84jVr1tTNSS6KkJ1YihUeCqmRQLHfHXPX+5Z3cSvczKmE5VwcVYGapKgsYi6GE3M2lKRUyDWwAUBAxMrRCMdqVjh4WWjdK04myB52M1SjhuPz4egc7TqGrAf7OkXdkf7J+K24gam1If3HNMLMupQ3iUrmzJkzTJw4kaNHj5Y79o033mDatGnUqFGj4hcmUWWRxP0ZIooiUVFROpZ4YGBgmRkGnwZeXl46lnitWrUwMjIqOVClhNTw++KdFFpohYdAXrF4dj2jQiv8PyJuU72EZfwg8rILiAlOJSY0jbhbaSRHZaIRtWJtkJ+KtVEuzo3dcW3vg72nOfqGT8FlcvsobP1A6+/v8yM0erOoKzYsjd1Lr6FvrMeAMY2xdDB+8DyVhCiKHDx4kIkTJ1Le34RcLmfEiBFMmTIFV1fXSlqhRFVCEvdK4F6Ztv9a4jdv3iQzM7NC7+3m5qZjidepU0f3UExOSjEBLybiKeEgFkvUZepYuhVu7gqPELmhUWuIC88gKiCFOzeTSbiTCSLIBTXmGRGYp4RhpZ+Fe4+mOA3tj56t7VP6ifyHzDitwEecgMZvQ68fQF8r5Il3Mvl3yVUA+n3eCDu30g9JVTaiKLJt2zYmT55MYGBgmWMNDAz49NNPmTBhArYV9TOUqJJI4v6USUxMLPXUZmpqaoXe19HRsdRTmxbFj9SrVZAWWVLEkwvDC3OS74+T62v93jpWeA0wfPxEWhlJudwJSCEqIIXooBSUeWoEAWwt1VjF38DU/xDmudFYdO6E5dAhmLRujVAZoX5qFRybq3XV2NfVumlstS6N1Lhsdi66ijJPTZ/RDXCublnx63lI1Go1v//+O1OnTi03b72ZmRlfffUVX3755QNP8kq8WEji/pikpqaWWjD5aZdp+y82Njalntq0sbG5Pygv/T8x4YU+8eRboCkW321sq2uB29YASw+QPXkoYEG+mpiQ1CJBT4vXuppMrQ1wddPHMu4qhgf/QpZ8F4WrK5avvYbloIHolXd4qaIIOwj/jARVPvRbBPUHA5CZksfORVfJSsnD9+P6eNSzKWeiyiU/P5+VK1cyc+bMcn//bG1t+fbbbxk1apSuC07ihUIS93LIzMwsUabtnpjHxsZW6H0tLCxKPbVpb2+vjWnWaLThg6VZ4Vnx9ycS5GDtXSjcxSJTbKqD8dNNSiWKIskx2dwJSCYqIIXYsDQ0KhE9hQznmla41TLHJi0Y9Z5N5J49C3I5Zl26YDl0KCZtKslKL4/0GPj7fYg6C80/gJ5zQE+fnAwl/y65SkpsNt2G16VGM4dnvVIdsrOzWbRoEfPmzSs3v4+LiwtTp07lvffeQ6FQVNIKJSoTSdwLycnJISgoSMcvXhll2v7rE/fx8cHZ2Vkr4vlZxQ73FLfCw0BV7CSjoQXY1ipphdvUACtP0Ku4cL7cLCVRgSlE3UzhTmAKOenaHOzWzia417XGva4NNoYZZG3bSto/21AnJ6NwdsZyyGtYDBqEwt6+wtb22KgL4NA0OL0EXFvAkN/A3In8XBW7l13j7q10Or1Zi3rtXZ71SkslNTWVefPmsWjRInJzc8scW6NGDaZPn86QIUOk064vGC+luKemprJv3z5u3LhRJOS3b9+u0FObhoaG1KlTR8cSd3d3RyYIkBGrG42SFAoZxQo5CzKty+S/rhSbGmBi+8CwwqeJWq0h/nZGkXV+byPUwEQPtzrWuNe1xq2ODSYmApmHj5C2eRPZp8+AXI5p505YDR2KSZs2CPJncwL0kbi5DbaPBgNTeG09eLSmQKnGb4U/d24m03pQNZr08HjWq3wgd+/eZdasWaxcuZKCAt10C8Vp2LAhs2bNonfv3tJp1xeEl07cN2zYwMcff1xhoYYKhYLatWvr+MS9vb2Ra5Rav7eOiIdBQfb9SfTNiol3MQG39tY53FMZlLoRKhNw8DTHvZ7WOrfzMEMmE1BGRZG2eQtp27ahTkpCz9kJy8GDsXz1VRQOVc+VUS4JgbDxLe1GdM850OJD1GqRg+sCCLuYQJOeHrR6xbtKC2J4eDjff/89GzZsKNeAadu2LbNnz6ZDhw6VtDqJiuKlEvf9+/fTs2fPpzKXXC7Hw9uDWnVrUbtOber51MOnng+1a9bG2NAYvdwMhOgLcPcqxF6FhJuQFkWJwz0W7sX84DXuH7c3c6wUK/xBlLUR6l7XBve61rjWtsLAWOurFQsKCq30zWSfOgUyGaadOmE1dAgm7do9H1Z6WeSmwbaPIMQPGr4BfRegkRty/K9gbp6IpV4HFzq+XrPSM0o+Kjdv3mTKlCls27at3LG+vr7MmjWLJk2aVMLKJCqCl0bcNRoNPj4+5cYF6yCAvr0+Bs4GGLoaYuBigKGLIfqO+sgUD/ZRCqKIQgR9RPSRoSdXoC83QF/PEH2FCQp9UxRyA/Tl+tovmT4KmQKFXFHie325vrat2Pf68mJ9svvj731/b3xp4/Rkuod/tBuhWdy5mcKdgBTu3iq5Eepe1xr3etZYOhiXsFCV0TGkbdlC2j9bUScmoefkhOXgV7VWuqPjo/2cqzoaDRz/QXuq1bE+DP0d0dKds9tvc3lfJDWaO9D1vTrPJKPko3L+/HkmTZrEwYMHyx372muvMWPGDGrVqlUJK5N4mrw04r5v3z58fX3LHOPh4UHdenWpWacmNWvXpHqd6nhU80BuIKdAU4BSo0SpVlKgLrj/fXYSypC9FESfp0CZiVKmh9LCDaWVGwXmzhQYWaIElBql9prC64t/r1RrXxf/XqlRotKoUKqVqIsfInpCZIIMhUyBmdoSl/RauKTWwD7FG8N87aGmbLMUMh3uku2QQIFDOgp9Pe0bRuGbg4Goh+u1u7gfDcbmehQIAulNqpHaszn5zeuh0Dd44BtL8TcihVxR4nv5Uwi9rBRC9sHWD7WhooPXQLUuXN4XyZltt/Cob4Pvhz7oPaOMko/K4cOHmThxIufOnStznEwm47333mPq1Km4u7tX0uoknpSXRtwHDx7M1q1bS7R5eXkxefJk6tWrV2aZtlIRRbi2EfwmQH4m1OwJjd6Cal2KTjg+LdQadelvDupibziaghLf3+u/d01+gZKCWDmqO4aIUSYISUYICGj0C8h1TCLbMZ50+1hyDTJLvd4kOYcWFzJoezkXyyyRZDM43FDgcEMZyeZP7o6QC/KiTxb33hyKPonIFBjIDTBRmGCsMMZM3wxThSmm+qaY65sXvTZTmGFuYI6ZvlnRGJlQAZZ08i3Y9DYkBkHX76DtWPxPxHLsr2Ccq1vS+5MGzzSj5KMgiiI7d+5k0qRJ3Lx5s8yx+vr6jBo1iokTJ2JfFaOcJErwUoh7Wloajo6OOkm3tm/fzoABAx5jwijYNVZ76MWtFfRfos0xXsW4txF652YyMcGpRRuhjl7muNUtuRFaGqJKRdaxY6Ru2kT2iZMAmHbogOXQIZh26IBaxv03nP/8+983h6K2e29Cxb5/mE8veeo8cgtyySzIJFuZTWZBJrmqssP8BAQsDCxwMnGiiUMTfD19aWjX8OlsfuZnwc7P4OY/UKc/vLKc0Os5HPw1ABtXU/p91vCZZ5R8FNRqNX/99Rffffcd4eHhZY41MTHhiy++YNy4cSVPQEtUKV4Kcf/jjz94++23S7TZ29sTExPzaOXKNBq4uAYOfq+13LtNheYfPlJOlYqk+EbonZvJpCdoxa9oI7SeNa617m+EPnCe2FjS/t5K2tatqOLj0bO31/rSBw9G4excGY/yUBRoCoqEPkuZRaYyk0xlJhnKjKKvtLw0ojKjuBR/CaVGibOJMz29etLLsxe1rWtToBbJzleRla8iW6kiO19FZp6K7Hz1/fZ8FVmFfdn5arLyVQjAe609aJO4CQ58pz0U9vofRNy1wm+lP+Y2hvT7vGpklHwUlEola9asYcaMGdy9e7fMsdbW1kyYMIHRo0djbPzsE6tJlOSlEPd33nmH33//vUTb6NGjWbp06cNPkhSmtdTunAbvztrj6VbPNsb5cTdCS51LpSLr+AnSNm0i68QJEEVM2rfDauhQTDt2RKgiNTs1GrFQhNVFwltcnLPy1WTlqUoIc7ZSRXp+Fgmqi6TKzpOvFwSCBo3SjoL0BhRkNEJUlp3yQF9PhqmBHiYGckz09UjJVpKQmU/X2vZMb5iCy4FR2sNPA38hVt6W3cuqVkbJRyUnJ4elS5cyd+7ccvMiOTk58d133zFixAjptGsV4oUXd1EUcXV11UkX4Ofn93BhkWoVnFmqjZLQM4Ces7W+9WcUqqhWaYgKTCH8aiIR/slFJ0JtXExwKwxTdKpugZ7i4Tb1CuLiSNvyt9ZKj4tDbmeL5auvYjn4NfRdn/wEpiiK5Ks09y3g/FKs4nttSu3rEuL8HyHPUT7c5rJMABMDvUJB1it8rRVmff1c0mWXuas6Q3xBACDiYOhNE+vOtHbsiru5C6YGCkwM5EXXK/4TBZNXoObXUxEsPxJGToGaUY30GZs8A734q9BhPInen/LvshtA1coo+aikpaXx448/smDBArKzs8sc6+3tzfTp03n99deRP+/hry8AL7y4R0RE4OXlVaJNoVCQlpZW/kfJ9GjY9A7EXobafbU5v80qP8RPoxGJCUkl9Hw8t68mkp+jQt9QjltdGzx8tCdCTa0MHno+Ua0m68QJ0jZtJuvYMa2V3rYtlkOHYNapE2qZXCuoyv9YwPlay/hBbaWLsxq15uF+jwwVsiIx1f1XK8wlBVuuM+5em5FC/lC+9YScBPZH7Gdv+F6uJ10HoKFdQ3p59aKnZ09sjcpOk5uclc/Cg6H8ef4Olgo1vztvoc7dHVC9O6ntl7Dzl9so89T0Hd0ApyqUUfJRiY+PZ86cOfz8888olcoyx/r4+DBr1iz69etXpQ93vei88OK+ZcsWhgwZUqKtZcuWnD17tuwL42/C74O1ZeH6LYJ6AyvVWhdFkcQ7mYScjyf0Yjw56UoUBnK8G9lRvZk9bnWskevp+voL1Bri0vNIyMwjPbeAzLz7QlwQn4DDiX14nDuIaXoyWSYWXK7bjhM12xJlaFkkyHkFD1d3VU8mFBNW+f3X+nqYGpbeXnx8cavaRF+O3jOOEY/OjMYvwo+94XsJSQ1BJsho7tAcXy9funt0x8LgwZuHYQmZzNkTxKGgeD4xPcE49RoESxeyev7Gzr9yq2xGyUclMjKS6dOns27dunLr87Zq1YrZs2fTuXPnSlqdRHGeirgLgiAHLgIxoij2FQTBC9gI2ACXgHdEUVQKgmAA/AY0BZKBoaIoRpQ195OK+5QpU5g5c2aJts8++4zFixc/+KK0KFjZSZvX/O2/waHeY9//UclMySPkfBzBZ+NIjctBJhfw8LGhRnMHvBrYFsVQi6JIdGouFyNTuBaVTnBcJhHJ2cRn5FHcUJaJGpokBNMr/Cwt4wORixr8nWpzpm57btVogpGxwX9cF8XFWa7bXkywDfRkL6xldivtVpHQR2ZEoifo0calDb6evnRx74KJovTqUqfCkpi5OxDDuEusNlqMlZBNXo/l/HvQjZTYbLq/X4/qTZ//MMKgoCCmTJnC33//Xe7Y7t27M2vWLJo3b14JK5O4x9MS9y+BZoB5obhvBv4RRXGjIAi/ANdEUfxZEIRPgAaiKH4sCMLrwEBRFIeWNfeTivvAgQPZvn17ibZVq1bxwQcflH5BQR6s6aatA/rBoUoJcVTmqgi7nEDIuThiQtIAcKpuQa2WjlRrYo+hiYICtYabsRlcjEjhUmQqlyJTScjUhnYa68up6WCGt50JrlbGuFoa4VCQifXxfej5/Qtxd5FZW2M5aBBWQ4eg7+ZW4c/0oiCKIoEpgewN34tfhB9x2XEYyA3o4NqBXl69aO/SHkO9khExao3I1svR/Op3lu+V82kpCyKp3iccj3iVuNsZdHqrNnXbVZ2ooyfh0qVLTJo0iX379pU7dtCgQcyYMYO6detWwsoknljcBUFwBdYDs4AvgX5AIuAoiqJKEITWwPeiKPYUBGFf4eszgiDoAXGAnVjGjZ5U3OvXr4+/v3+JtlOnTtGmTZvSL9jzNZxfAW9sglpln2h9EjRqDXcCUgg5F8fta0moCzRY2BtRq6UjtVo6ojGWc/lOKhcjtEJ+LTqtyF3iYmlEM08rmnlY0dTDmlqOZshlAqJGQ/apU6Ru2kTWkaOgVmPcuhVWQ4di1qULwn+LXEs8EhpRw7XEa+y5vYf9kftJyUvBRGFCF7cu+Hr50tq5NQrZ/WiRHKWK1UdDsDw5nXdle7lt0pxrslnEhmbTZlB1Gvd4cU57Hjt2jIkTJ3L69Okyx8lkMt555x2+//57PD09K2dxLylPQ9z/BuYAZsA44D3grCiK1Qv73YC9oij6CILgD/iKohhd2HcLaCmKYtKD5n8ScRdFEQsLC506pXfv3sWxtNwnwX7w11BoOQp6zX2se5a3nqSoLILPxhFyMZ7cDCUGJnrUaOqAeR1LQlX5XIlK42JEKqEJWQDIZQL1nM1p6mFFMw9rmnpY4WhR0lJUJSaStvUf0rZsoSAmBrm1NZaDBmL52mvoe1TdlLTPMyqNigtxF9gbvpeDdw6SqczEwsCC7h7d6eXZi6YOTYtSKsRn5HF48xIGRv2PVCzxky8jJ1ZBE18PWg2o2hklHwVRFNmzZw8TJ07k+vXrZY5VKBR89NFHTJo0qfS/RYkn5onEXRCEvkBvURQ/EQShE09J3AVBGAmMBHB3d2/6uAUz0tLSsLKyKtFmYGBATk6ObmGCrARY3grMnOHDQ9qwx6dEVmoeIefjCT4XR0psNjI9Actq5qTZ63O1II9L0WkkZWkjEMwN9Wjicd8qb+hmgbF+Kcm+NBqyT58hbdMmMo8cAZUK41atsBryGqbduiGTrPRKQ6lWcjr2NHvC93A06ii5qlzsjOzo6dkTXy9fGtg2QBAEbl8/jemO9zArSGVFzo8oslyp18GZjq/XqvIZJR8FjUbDpk2b+O677wgLCytzrLGxMWPGjGH8+PE6f6sST8aTivsc4B1ABRgC5sA2oCdVwC1z7do1GjVqVKKtevXqhIaG6g7eNwnO/gyjToN97ce6X3GUeSpuX0kk6GwcMSGpIILKWsEtY5EjOdlkFkYaeNgYF1nlzTytqG5n+sB0AACqpCTS/tmmtdKjopBbWWExcCCWrw3G4D8hnxKVT05BDsdjjrP39l5OxJygQFOAi6kLvp6+9PLqRQ19G1J/H4b13ZOsyZhEfm4z7OpZ8+onDZ6LjJKPQkFBAb/++ivTp08nJiamzLGWlpZ8/fXXfP7555iYlL5ZLfFoPLVQyHuWe+GG6hZga7EN1euiKC4XBGE0UL/YhuogURSHlDHtE4n7zp07dXLHdO3aVTfVaU4KLPCB2n3g1VWPdS/Q+tGjglK4dCyauzdTQS2SpYBr8gICFGqy9cHHxaLIKm/iYYm9WfnH00WNhpyzZ0ndtJnMQ4e0VnqLFtq49O7dJSu9ipKpzOTwncPsDd/L2btnUYtqvC288fXsSc+7t/A+t5o9WR8SntWbbBsFb4xtjJud6bNe9lMnNzeX5cuXM2fOHJKTk8sc6+DgwOTJkxk5ciT60u/1E1FR4u6NNhTSGrgCvC2KYr4gCIbABqAxkAK8Lori7bLmfRJxX7p0KZ999lmJtuHDh7N27dqSA4/+D47OhlFnwOHRdvLzCtScvRzHzdOxFNzOQlEgkiuIBCvU3DEHt+qWNPW0oamHFQ1cLTB8yJOjAKrkZNL++Ye0LX9TcOcOcgsLrZU+ZAgG3pKV/jyRkpfCwciD7Anfw+X4y4iI1DF2xvduKF5xHbiZ8g6xeiLWvi583L0GpgZVI93D0yQjI4OffvqJH3/8kaysrDLHenp68v333/P2229Lp10fkxf6ENP48eOZP39+ibbvv/+eqVOn3m9QZmutdreW8ObGcufUaEQOBSVwPiCBBP8UrBILsFPLUCMSZyKgqGZK3SYONPW2oZqdySNvlokaDTnnz5O6aROZBw9BQQHGzZphOXQoZj26IzN4ensBEs+GuOy4olOx/snaSK6ud+tTPeJ94uVqDtvLGe1bkyHNXJ/5wa6KIDExkblz57Js2TKdTK3/pU6dOsycOZOBAwe+MBvPlcULLe5Dhw5l8+bNJdrWrl3L8OHD7zdcWg//fg7v7wP3VmXOl5qRz+yVlyAiG3eVDBkCaisFTg1tadfFHSf7x/cVqlJSSN+2jdTNmymILLTSX3kFyyGvYVCt2mPPK1G1icqIwi9sO3v8f0OZ4k2P4OFkG2SxxSYEG7PGTO7VlE61nv9DT6URFRXFjBkzWLt2LWp12TmDmjVrxuzZs+nWrZsk8g/JCy3urVq10qkyc+jQIbp06XK/4Y/XtMWqP79SanoBjUYkJiiVoLN3CbwYj1wDgqkejdq7ULeV0xNl/BNFkZxz50nbvJnMAwcQCwowatoUq6FDMOvZU7LSXyY0GkIPT8Hv/DnUMV+Qp5fDrtorSFZbU824HTN6DKGx64sZMhgSEsLUqVPZuLH8T86dO3dm9uzZtGpVtiEm8YKLu7Ozs05O6rCwMKrds4RFEX6oBrV6wYBlJcYlxxTGo5+PIztdiaAv4ypK2nfz4O1+NZ/IelClppK+bTtpmzejjIhAZm6OxSsDsHrtNQxq1HjseSVeAEIPkvDndLYnfEW+noI9ddYTbRSAqFHgrN+Ej5u+Sp8aXTCQv3hv/FevXmXy5Mns3r273LH9+/dn5syZ1K9fvxJW9nzywop7fn4+hoa6kSh5eXkY3LOI02NgQV3oPR9afEh2ej6hF+IJOhtHcnQWMpmAu48NtVo68vXpEDIL1Oz/osNjCbsoiuRcuEDaps1k7t+vtdKbNMFyyGuY+/oiK2WtEi8pKeGkrv+CnaFvoJRb4TLUmJXR2wjLPoWgl41CMKaHZ1f6VutNS6eWJU7FvgicPHmSiRMncuLEiTLHCYLAm2++ybRp0+4bbBJFvLDiHhYWRo3/WMFWdlakJKTcbwjeS8Gf7xLeajvBoSZEBaYgimDvaU6tlo7UaGaPkZk+CZl5tJh1iPE9azG6c/VHWocqNZX0HTtI27wF5e3byMzMsBgwAMshr2FYs+qV5pOoIihzyNw8gZ1nmpMlOtDrw9oUuFgw0W8711KPoDC/CbI8rAys6O7RHV8vX5o6NK2YmrHPAFEU2bdvHxMnTuTKlStljtXT0+ODDz5gypQpOFehSmHPmhdW3A8fPkzXrl1LtFVvUJ3Qa9oDTKIocmn5b1z2t6VANMLM2pBarRyp2cIBK8eSG6N7b9xl1B+X+eeTNjRxL/8UnSiK5F66pI1L37cPUanEqFEjLIcOxdy3JzIjo0d+HomXEFEk5+gq/t0qJ0XtRvch9lTv3Ihzt5OZuec6gWkXsHEMRGXgj1KTh72RfVEJQR9bnxdi41Gj0bB161amTJlCcHBwmWONjIz47LPP+Prrr7Gxeb5TKz8NXlhx//XXX3n//fdLtLXwbcG5vdoN1uBzcRz8NQBvsxs0HPkuTtUsH3gEfOauAH47G4n/9z3RLyWH+j3UaWmk79hB6uYtKG/dQmZqikX//lgOHYphLclKl3g88oNOsPuXQOLyvOnUVUnd13qj0YjsvBbLPL8gYjMyaFw7Fmv7m1xOOoNKo8LV1JVeXr3w9fKlptXz/7unUqn47bff+P7774mKiipzrLm5OePHj2fs2LGYmr54h8IelrLE/bn+fHfnzh2dNivH+1Z32MV4zPWT8G1yHucaVmXm9rh0J5WGrhalCrsoiuRcvkzsN98Q2rET8XPmIjM1wWnWLGocP4bjd1MkYZd4Igxqt6ffpF64mYdz5JAhV5atRoaGVxq7cHhcJ8b3qE/o7eocO9GXXma/8E2zqbiZubHGfw2v7nyVgTsGsuLaCu5k6P5NPC/o6enx/vvvExISwsKFC7Gze3DN24yMDKZMmYK3tzeLFi0iLy+vElf6fPBci3tpycYsHS0BbZqAmNA03BRXEGzKPumZV6DGPyadJh4l3THq9HRSfttAeP/+RL75FpkHD2ExaCBe27fhtWkTlq8OQiZVhJd4Sijs3Og9/W2qO8Vw+oY3Z2fNR8xOxlAhZ3Tn6hwZ14mhzd3YeC6J/20xo5nhN/gNOsiklpMw1zdn6dWl9NnWh9d3vc76m+uJy4571o/0WBgaGjJmzBhu3brFjBkzMDc3f+DYxMRExo4dS82aNVm7di0qlaoSV1q1ea7FvTTL3dxB+4uQEJlJQZ4aV8VVMHMqcx7/mHQK1CJN3K20VvqVK8RO+JbQDh2Jnz0bwdAIp5kzqHHiOE5Tp2JY+8mTjklIlIbc0IjuU96mbu0sLsU05/iMpYixVwGwMzNg1sD6+I3tQFMPK2bvCWLI8huYKjuwzncdBwYfYFyzcYiIzL84n+5/d2fY3mFsDNpIcm7Z+V6qImZmZkyePJnbt28zfvz4UiPj7hEVFcWIESPw8fFhy5Yt5ZYHfBl4rsW9NMvd3F4r7tHBqQC46N8ATdkn4y7fSUUQNdQLOk/4KwOJfONNMvfvx2LgK3j9sxWvLZuxHDxYstIlKgWZTKDTmH40bq2Pf1p7Ds7fifry/cM/NR3MWDe8BRtGtMBEX49P/7zCqz+fJibJgGH1hrGp7yZ2DdzFp40+JT0/nVnnZtF1S1c+OvAR20K3kaHMeIZP9+jY2Ngwb948bt26xccff4ye3oNz8gQHBzNkyBCaN2+On58fVWFP8Vnx3G6oajQajIyMdKq0j9s7jh98f2DHwivkZip5Xd0DOk+Cjl+XOo8oivwwdTWNDmzCLTUG/erVsH73XSz69EEmpSWVeMZc2nmTs3vi8TS4QM9uqej1ngHy+zHvao3I35eimL8/hMTMfPo2cOIb39q4WWsNEVEUCU0LxS9cWys2OisahUxBO5d29PLqRUfXjhgrni+j5datW0ydOpU///yzXPHu0KEDs2fPpm3btpW0usrlhYyWiYuLw8mppLtFYaRgyJYhrOuxntVfnsCngwvtIvtAta4w8GedObLPnCFh4ULyrl0nzdqBut+Ow7x3LwQpQ51EFcL/6B2ObQzFWXGTPg0Oov/6KjBzKDEmO1/FiuO3WXn8FhoNDG/rySedq2NhdP+NQBRF/JP82Ruxl33h+0jITcBIz4hOrp3w9fKlnUs79OXPTwreGzduMHnyZHbu3Fnu2D59+jBz5kyd2g/POy+kuJ87d04n94S9tz3VZ1ZnU+N/2bHwKn1GN8DzxmhICIQx14ryyuRcuULiwkXknDuHYO/AAucOtB71Du91lNICSFRNQi7EcejXm9jIw+nnuhyjN38BtxY64+6m5zJ/Xwj/XInG0kjB2G41ebOlO4r/ZJ7UiBouxV/CL9yP/ZH7SctPw0xhRlePrvTy7EULpxboyZ6PlMRnzpxh4sSJHD16tNyxr7/+OtOnT9c5/Pi88kKGQpa2merk6kSGMoOI29ooAUcvC6jRHdIiITGYvMBAoj4eReQbb5IfFobDxG+JWLCefZ4taeT94LArCYlnTc3mjvQa1ZAU0ZttMePIWv02XFijzZ1UDCcLI34c0pB/P21HbUdzpu68Sc+FxzkQEF/ChSETZDR3bM6U1lM4POQwP3f7mc7unTkYeZCPDn5E1y1dmXl2JpfiL6ERq/bmZOvWrTl8+DD79++nWbNSda6IjRs3UqdOHUaOHEl0dHQlrfDZ8NyKe2mbqTW8tO/GSYJW3OMjMqB2X/Iz9Yke8xnhAweRc/kydl98QfX9+7B+910ux+egryejrtODw60kJKoCnvVt6T+mMVmCM/+kzSNtxw+w41Mo0I3x9nGx4M8PW7L6Xa3YffjbRd5cdQ7/mHSdsfd88LPazeLo0KMs7LSQ5o7N2RG2g/f83qPH3z2Yf2E+N5NuVtkNSkEQ6N69O+fPn2fr1q3UqVPngWPVajWrVq2ievXqfPXVVyQmJlbiSiuP59Yt8/nnn7NkyZISbTNnz2Sr61beqvE2jnvboMoroLOwn+wdfyPIRayHf4DNhyORF4ubHfLLGQo0GrZ98mJuuEi8eCREZvDvkmsIqmz6m3yDrbslDP0dLN1KHV+g1vDX+TssPBhKao6SQY1dGd+zFo4WZSeyyy7I5mjUUfzC/TgZexKVRoW7mTu+Xr708uxFdatHy8FUmajVan7//XemTp1aqiFYHFNTU7766iu+/PLLMmPqqyIvpFumtP8wb09v6tvWJzj8DA1VZ8lKy+dKoBzrgd2p3jce+2ZiCWEHyMgrwM70xUutKvHiYu9hzqBxTZAbmbE96yfuxspgZUe4fazU8Qq5jHdbe3J0fCdGdvDm32uxdJp/hJ/2B5Od/+BDPyYKE/p492FJ1yUcHXKUaW2m4WzqzOobqxm4cyCDdg5i1fVVRGWUnSrgWSCXyxk2bBjBwcEsWbIEBweHB47Nyspi2rRpeHt78+OPP5Kbm1uJK604nlvLvXHjxly9erVE27G9e8k+uB4nv6sYICeyx9fcynFl4FdNcL78KYQd1hbsKBZp0GvRCSyNFPw1UioMIPF8kZGcy85FV8lOzaOX82rclXug2zRo81mpRWnuEZWSw//8gth1/S52ZgZ81b0mrzVzQ15Geo7iJOUmsT9iP34RflxJ0GZzrG9bH19PX3p69sTB5MFC+qzIzs5m8eLFzJs3j7S0tDLHuri48N133zF8+HAUiqqdavmFjJaxtrYmNTW1RNtRn/rYqVQcrweNv5lDkwZ92TjjHIJM4PVPbNFb2RoavQH977tz5u8LZumRMP76sBWtq0lZ5iSeL3IylOxcfJXUu9l0r32I6slLoN5A6L8UDMpOqHX5TiozdwVw+U4atR3NmNi7Dh1qPlpgQWxWLPsi9rE3fC+BKYEICDR1aEovr1508+iGtaH1kzzeUyc1NZUffviBRYsWkZOTU+bY6tWrM336dIYOHYpMVjWdHC+cuGdmZur4xvSAsE9GYz76QzpdfJeRDUYyutFoooNS2LHwKo27u9PGZC2cXQ4fnwBHbXWXXKWaHguPoZDL2DumPQZ6Uoy7xPNFfk4Bu5ddJ+52Op1aRlE3cizY1db64W3KLnAhiiJ7bsQx1y+QqJRcOta0Y1KfOtR0MHvkdYSnh+MXoT0sFZ4ejlyQ08qpFb28etHFvQtm+o8+Z0URFxfHrFmzWLFiBQUFBWWObdiwIbNmzaJ3795VLsXyCyfugYGB1K1bt0SbmYkJaRkZyGQyhu4aionChLU91wJw9I8gAk7G8uqYmjjsaAdODeGd7UUfXY8GJ/Derxf4sntNPu/6YsS/SrxcFOSr8Vt5gzs3U2jTCRpHvg8aDQxaCbV8y70+X6Vm/ekIlhwOIztfxest3PmiW03szB59P0oURUJSQ9gbvhe/CD9ismLQl+nT3rU9vl6+dHTtiJFe1ah3EB4ezrRp09iwYUO5+Wjatm3L7Nmz6dChQyWtrnxeuA1VfX3dU3SZ2dmsXasV8yb2TbieeJ0CtfYduc2g6phYGnB4cwzqdt/C7aMQeqDo2k617OnTwImlR8IIT8qulGeQkHiaKAzk9B7VgGpN7Dl9FM56/oNo6QF/DYUjc7RCXwYGenJGdqjGsfGdebe1J5svRNF5/lGWHQkjr6Ds3Ez/RRAEalnXYmzTsewdtJffe//OkFpDuJZ4jfHHxtNxU0e+Of4NR6OOFv2NPiu8vLxYt24dN27cYNCgQWWOPXXqFB07dsTX15fLly9X0gofn+fSchdFER8fHwICAkq0W1hYEBgYSIAygLFHx7Kh1wYa2TcCINI/mV1Lr9Gslxst77wOggxGnS7K05GQkUfXH4/R0M2SDSNaVLmPXxISD4NGI3LsjyACTt2lfnsH2hstQbj+F9T0hYErwMjyoea5nZjFnL1BHAiIx9nCkPG+tRjQ0AXZQ266loZao+ZS/CX2RuzlQOQB0vPTMdM305YQ9PSluWPzZ34q9sKFC0yaNIkDBw6UO3bw4MHMmDGD2s8wS+wLZ7kLgsCCBQt02tPT0/nss89o7NAYgIvx998wPHxsqN3Kkcv7oklsOAOSQuDSuqJ+e3PtL/DJsCR2Xout8GeQkKgIZDKBTm/XpnF3d26ciOdg2meofedD2EFY1RniA8qfBPC2M2XVu83468NWWJvq88Wma7yy/BTnw1PKv/gByGVyWji1YGrrqRx57QjLui6jk2sn/ML9GHlgJF23dGX2udlcSbjyzE7FNm/enP3793P48GFatmxZ5ti///6bevXqMWLEiFJPzD9rnkvL/R7Dhg3jt99+02n/559/WCesw8XUheXdlhe152UX8Ne0cxhb6DPYaSbyJH9taKShBaDNsDfo59PEpOZw6MtOWBhX7TAoCYkHIYoil/dFcnb7bTwb2NKzZzZ624ZBfhYMWAo+ZbsgiqPRiGy/GsMP+4K5m55Hz3oOTOhVBy/bp5M1NU+Vx4mYE+wN38vx6OPkq/NxMnHC19MXXy9f6ljXeSafpEVR5N9//2XSpEn4+/uXOVZfX59Ro0YxceJE7O3tK2mFL+CG6j2Sk5OpU6eOzvFhJycnhq0bxtGko5x8/SRy2f0ImNtXE9n7yw1adjaiWWAvaPs5dJ9e1H8zNp3+S08xpJkbcwbVf/yHkpCoAvgfi+bYxhCcq1vS5x179P99H6LOaWPhu34P8od3g+Qq1aw5eZvlR2+hVGl4p7UHY7rWwNL46WWSzC7I5vCdw/hF+HE65jQqUYWnuWfRqVhvS++ndq+HRa1Ws3HjRr777jtu375d5lgTExO++OILxo0bh4WFRYWv7YUVd9AmAnrjjTd02nu+3pMY3xi29NtCbeuSPrF9q/25fTWRoa38sA5fC59eACvPov6ZuwJYfTKcraNa09SjasXpSkg8KtqMkoHYupnSd1RdjE59BxdWg1cHGPwrmNg+0nwJmXksOBDCpgtRmBro8XnXGrzT2uOphxGn5aVx8M5B/ML9OB93HhGRmlY1tUXBPX1xNXN9qvcrD6VSydq1a5k+fTp3794tc6yVlRUTJkzg008/xbgCi/y80OIuiiL9+vVj9+7dOn1eE7yY8e4M3qrzVon23Ewlf047h4W1nEGawchq9YDX1hX1Z+er6P7TMcwMFez6vJ1OulQJieeNiBtJ+K30x9zGkP5jGmMa8Tfs+gJM7WHIb+DS5JHnDI7LZNaeQI6HJOJubcyEXrXp5eNYIS6UxJxE9kfuZ2/4Xq4lXgOggV0Denn2oodnD+yNK88VkpOTw9KlS5k7d67OQcr/4uTkxJQpUxgxYkSpUX5Pygst7qCtn1i3bl2ysrJKtBs7GTPi1xEs7rlY55rQC/HsX3OTto0iaRQ3Ft7fD+73N1D234xj5IZLTOhVm487ln0QRELieSAmJJXdy69jaKyg/9hGWKqCYdM7kJUAfX+Cxm8/1rxHgxOYvSeQkPgsmnlYMalPHRq7W5V/4WMSkxWDX7gffhF+BKUEISDQ3LE5vl6+dHfvjqWhZYXduzjp6enMnz+fBQsWkJ1ddgi1t7c306ZN44033kD+FIsBvfDiDrBs2TI+/fRTnXa3AW5EbovUsSZEUWTvLze4E5DM605TsLQzgA8OlsjJ8eFvFzkRmsiBLzoWlS2TkHieKcooKRPo/3kjbK3y4O/3IfwYNHsffP8Heo9uYarUGjZfjOanA8EkZSnp39CZr31r4WpVsX83t9NuF52KjciIQE/Qo7Vza3p59aKzW2dM9ctOwfA0SEhIYPbs2fz88886ZT//i4+PDzNnzqR///5P5RPOSyHuGo2G9u3bc/r06RLtggx2HdtN73a9da7JTsvnr+nnsLHI5hXNGwiDV0P9wUX9sWm5dPvpGK28bVgzrJkU+y7xQpAal83ORVcpyFfT99OGOHqYwKFpcHoxuLbQumnMncqfqBSy8lX8cvQWq07cRgTeb+vFJ52rYW5YsZFnoigSlBLE3oi9+IX7cTf7LgZyAzq4dsDX05cOrh0w1Cs7xfGTcufOHaZNm8a6devKPe3asmVLZs+eTZcuXZ7oni+FuAMEBATQuHFjnXfP6nW8CLweUmrV9MDTdzn8WyAdXHdR32S/dnNVcf9o9OoTt5m5O5Cf32pCr/qP9wsvIVHVyEjOZefCq2Sn59P74wa41bWGm9tg+2jQN4Eh68GjzWPPH5uWy/x9wfxzJQYbE33Gdq/JG83d0KuE/SuNqOF64nX2hu9lX8Q+kvOSMdYzprN7Z3p79aa1U2sU8op7swkKCuK7775jy5Yt5Y7t1q0bs2bNokUL3ZKJD8NLI+4A06dPZ+rUqTrtP/zwA+PGjdNpF0WRXUuuERuawhsWIzHv+Qm0/7KoX6XW0H/pKZKz8zn4ZUfMKtgCkZCoLIpnlOwxoh7Vmthr6w1vfEtbmrLnbGgxssz0weVxIzqdmbsDOBeeQnV7Uyb2rk3nWvaV9ilYrVFzIf4CfuF+HIg8QIYyA3N9c7p7dKeXVy+aOTQrESr9NLl06RKTJ0/Gz8+vzHGCILBixQo+/PDDR77HSyXuSqWSJk2acPPmzRLtRgoF/kFBeHvrxslmpuTx17RzOBpH0s90IsKYK2B6P/Xp1ag0Bi4/xbDWnnzfv95TWaeERFUgP6eAXUuvEx+eTqe3a1O3rTPkpsG2jyDEDxq8Dn0XgP7j+85FUeRAQDxz9gYRnpRN2+o2TOpdl7rOlVv1qEBdwOnY0+yN2MvhO4fJVeVia2RLT8+e+Hr60tCuYYW86Rw/fpxvv/1Wx2VcHFNTU1JSUh45f/wTibsgCIbAccAAbWbdv0VRnCoIghewEbABLgHviKKoFATBAPgNaAokA0NFUYwo6x5PU9wBzp49S5s2bXTqPXZu3pxD586V+h/ofzyGY38G09lyOXXbuWl/oYsxZbs/v5+LZMfotjRwtXxqa5WQeNYU5KvxW3GDOwEptB1cnUbd3LWJxo7/AEfngKOPNn1wsbMgj4NSpeGPc5EsOhRKem4Bg5u4Mq5nLRzMK9YXXhq5qlyORx/HL9yP49HHUWqUOJs4aw9LefWillWtpyr0oiiyZ88eJk2axLVr13T6BUEgKSkJa+tHO1fzpOIuACaiKGYJgqAATgJjgC+Bf0RR3CgIwi/ANVEUfxYE4ROggSiKHwuC8DowUBTFoWXd42mLO8CYMWNYvFg3BHL1wkWMGPO5TruoEdmx8AqJtxN5w/oTTD/dBfb3i+xm5BXQ9cdjOJgbsP2TtpXiO5SQqCzUKg0H1gZw63ICzXp70qKfl1bcQvbB1g9BJoNX10D1rk98r/ScApYeCWX96UjkMoGRHbz5qKM3xvrPJmlYljKLw1GH2Ru+lzOxZ1CLarwsvOjl2QtfL1+8LLye2r3Cw8OpWbMmKlXJ8oadO3fm8OHDjzzfU3PLCIJgjFbcRwG7AUdRFFWCILQGvhdFsacgCPsKX58RBEEPiAPsxDJuVBHinpWVRb169XQS+pgr9LkZEoyrp6fONemJOWycfg5XvSv0bnIK4Z2tJfp3XY/l0z+v8F3furzf7un9h0tIVAU0GpGjfwQReOou9Tu50n5IDQSZAMm3YNPbkBgEXaZAuy+eyA9/jzvJ2nJ/u2/cxd7MgHE9avFqU9eHLvdXEaTmpXIg8gB+EX5cjLuIiEht69pFp2KdTZ2faP5XXnmFHTt2lGiTyWRcvnyZhg0bPvJ8T5wVUhAEuSAIV4EE4ABwC0gTRfHe20804FL42gWIAijsT0fruqlUTE1N+eWXX3TaMwqUDPP11XHZAFjYGdPqlepE5DYi1D9fm0mvGH3qO9Gxph0/7g/mbvqLUURXQuIeMplA57dr06i7OzeORnNwfQBqtUZbzWnEAaj7ijZkcvM7kJ/5xPdztzFm2VtN+Pvj1jhbGvH11uv0XXKSk6FJT/4wj4mVoRVDag1hbc+1HHztIF83/xp9mT4LLi2g59aevL3nbf4I/IOk3Edf465du3SEHeDzzz9/LGEvF1EUH/oLsASOAO2AsGLtboB/4Wt/wLVY3y3AtpS5RgIXgYvu7u5iRfHWW2+JgM7X0q++KnW8Wq0R/557Xlz1yU4xe0FXUVQVlOiPTMoWa07aI37028UKW7OExLNEo9GIF/aEi0s/OiTuWnZNLFCq7nWI4qnFovi9pSguaS6KiSFP9Z47rsaIbeYcEj2+2SUO//W8GBqf8dTmf1LuZNwRV11fJQ7aMUj0WecjNljfQByxb4R4N+vuQ12fnZ0tenp66uiQk5OTmJ6e/tjrAi6KD9DrR3Ici6KYVijurQHLQrcLgCsQU/g6plDsKey3QLux+t+5Voqi2EwUxWZ2do9WlPdRWLx4MY6Ojjrt3yxYQOiZMzrtMplA53frUiAaczyiI1zZUKLf3caYz7vWwO9mHIcC4yts3RISzwpBEGjWy5OOb9Qk4kYSu5ZcQ5mn0rpi2nymLVGZkwQrO0Pgrqd2z/4NnTn0VUcm9KrNhfAUei48weTtN0jKyn8q93gS3Mzc+KD+B2ztv5XtA7bTwLYB5+6eIyEn4aGunzNnDhERETrtP/30k0496KdFueIuCIKdIAiWha+NgO5AIFqRv3eccxhw7/PGzsLvKew/XPgO80ywtrZm1apVOu3ZGg3vDRiAKlfXvWLtZEKLftW4ldeWW//uhbyMEv0ftvemhr0p3+24SY5SpXO9hMSLgE9HV7oPr0tsWDo7FlwhN6vwcKB3Rxh5DGyrw6a34NAM0DxaKb4HYaiQ83HHahwd34m3Wrrz1/koOv1wlJ+P3nrkcn8VhZm+GcGpwXR260wDuwbljg8JCWHevHk67V27dmXo0DJjTZ6Ih7HcnYAjgiBcBy4AB0RR3AV8A3wpCEIYWp/6msLxawCbwvYvgQlPf9mPRt++fXnvvfd02k8nJjJ/8GDdC4BGPdyxc5RzLHEoeYeWlOjT15Mxe1B9YtJyWXQwtCKWLCFRJajZwpHeH9cnOTabbT9eISu10Iq2dIPhftpkYyfmw59DIOfxqzT9FxtTA6YP8GHf2A608rbmf35BdP3xGDuuxpS6X1aZLLy0EJVGxfhm48sdK4oio0eP1jk1r1AoWLZsWcUe5nqQv6Yyv5o2bfrYPqeHJS0tTXR1ddXxeRkJgnh15cpSr0mMyhCXf3xAPDB2qiimRur0f73lmuj97W4xIPbxfWYSEs8D0cEp4ooxR8X1E0+JqfHZ9zs0GlG8sEYUp9mI4sIGonj3eoXc/1Roothr4XHR45tdYv+lJ8UL4ckVcp/yuJpwVfRZ5yMuuLjgocZv2rSp1D2/iRMnPpX1UIbP/YU7oVoW+/fvp2fPnrr3NzHl+OVLGNWsqdN3bstVLh5KoU+jo3h+PL1EX2q2kq4/HcPDxpitH7d5ouLBEhJVnYTIDP5dfA1BXphR0rVYxsWo87D5Xe3p1v5LoMFrT/3+ao3IP5ejmb8/mPiMfHr5ODKhV208bJ5Oub/y0Iga3tr9FvE58fw78F9MFGXfNzMzk9q1axMbW7Ims4eHBwEBAU+liMcLVyD7cenRowcjR47Uab+YncWsVwaiKSUnc7OBDbC2yObodR/yQ8+V6LMy0WdS7zpcuZPGXxeqXoFcCYmnib2HOQPHNUEmE9j+02Xibqff73RrofXDOzeGfz4Av29BXfBU7y+XCbzWzI0j4zrxRbeaHA1OpNtPx5ixK4D0nKd7r9LYeWsn/sn+fNH0i3KFHeD777/XEXaAJUuWVGh1pnu8VJY7aN9NGzRooLNzbSAI7B/2Hu3XrtHxg8WHxLH1J3/q2Fyh84wvoViiIVEUeXPVOfxj0zn0VUfszSr/KLWERGWSkZTLzkX/ySh5D3UB7J8M534Bj3bw2q/aak8VQEJGHj/uD2HzpSjMDRXacn+tPNDXe/o2a5Yyi77b+uJi5sKGXhuQCWXf4/r16zRp0gS1uuQmcL9+/di5c+dTW5dkuRfDzMyMtWvX6rTniyKfb/yL5D//1OlzqOlIo8b5BCQ3JerfTSX6BEFg5kAf8gs0zNwVWGHrlpCoKpjbGjFwXBMs7IzZtfwat64UCweUK6DX/2DgSoi5CCs6QnTFGG725ob8b3ADdn/WnvouFszYFUCPBcfw84976puuK6+vJDkvmQnNJ5Qr7BqNhlGjRukIu5GREYsWLXqq6yqLl07cQZvH4bPPPtNpv5aXx9yvxpF7w1+nr8V7PbE0TOHIATnK1MQSfdXsTBnVqRo7r8VyPCRR51oJiRcNEwsDXvmyMfbu5uxb6U/g6f+4HxoOhRH7Qa4Hv/aCS+sqbC11nc3ZMKIFv77XHD25jI9/v8TQFWe5Hp32VOaPzIhkQ+AGBlQbQH27+uWOX79+fakZICdPnoyXV+WlLXnp3DL3yM7OplGjRoSFhZVoVwgCO5q3oMf+fcgtLEr03b1wlX/WJFHfM4IOEz4o0ZdXoKbXohNoRJF9YztgqKiYHNESElWJgnw1e1fcIKp4Rsni5KTA1hFw6zA0eRd6zwc9gwpbj0qtYeOFKBYcCCE5W8krjZwZ71sbF0uj8i9+AJ8e+pQLcRfYPWg3tka2ZY5NSUmhVq1aJCWVTE9Qq1Ytrl27hoHB0312yS1TCiYmJqxbt07Hv14gioy/fp3I8V8j/qdUllPzRjTwjuZGhDexZ86X6DNUyJn1ig+RyTksO1LyDUNC4kVFYSCnz6gGVGtix6m/wzi383ZJl4ixNbz1N7T7Ei7/prXi06MrbD16chlvt/Lg6PhOfNKpGnv84+gy/yjz/ILIzHv0TdeTMSc5Fn2Mjxp+VK6wA0ycOFFH2EFb4/lpC3t5vLTiDtC2bVu+/PJLnfabebks/OcfUkrxzbf6qD/meokc3hiJ6j+/LG2q2zKwsQu/HLtFWMKTJ1aSkHgekCtk9PjAhzptnbi4J4ITm0MRNcUEXiaHblNhyAZIDNb64cNPVOiazAwVfO1bmyPjOtHLx5HlR2/Ref5R/jgXiUpddn3TexSoC/jf+f/hbubO23XeLnf8uXPnWLlypU7766+/TteuT54q+VF5qcUdYMaMGdSuXVun/efUFI7NmUPOhQsl2hUW1nTqpUd6vg3nf92rc92kPnUw1tdj0jb/Z36STkKisijKKNnNjRtHimWULE7d/vDhYTCygt8GwJllUMF/Iy6WRix8vTE7RrfF29aUSdv86bXoBEeCE8r9+/wr6C8iMiK0mSHl+mWOVavVfPLJJzpzmpmZ8eOPPz7xczwOL724GxkZsX79emSykj8KlUbDpIREbo8diyqx5CapW+9B1LW7ytVrxsQHlfyIaWtqwIRetTkXnsLflyru46eERFVDEATavFqdlgO8CTkXj98Kf1T/zQdjV0sr8DV9Yd9E+OdDUOqeL3naNHSzZNNHrfjl7SYo1RqG/3qBd9eeJ/BuRqnjk3OT+fnaz7R1aUsH1w7lzv/LL79w+fJlnfYZM2bg7PxkOeAfl5de3AFatGjBhAm6KXCCsjJZFnaLmHHjEYuHNQkCbd7vgokshcNrLqEuKGmhDG3mRjMPK2bvCSQlW4mExMvCvYySHV6vScT1JHYtLcwoWRxDc23Zvi5T4MbfsKYHpNyulLX5+jhx4IuOTOlbl+vR6fRZfIJv/r5OQkZeibFLriwhT5XH182/Ljf/S1xcHJMmTdJpb9iwIaNHj36qz/AoSOJeyHfffUf9+rphTquSEjl39CiJS0omDzPwakTHZuGkZJpxcUvJzVWZTGDWwPpk5qmYs0eKfZd4+ajfyZVuw+sSG6rNKJmX9Z/NTJkMOozTbramR8PKThB6oFLWpq8nY0Q7L46N78Twtl78cyWaTvOPsuhgKDlKFTeTb/JP6D+8UecNvC28y51v/PjxpKen67QvX74cPb1nUzoQJHEvwsDAgPXr1+v8Z6g1GibnZBO7/Geyjh0r0ef5xkhqmZ7m8olMEu+U/HhXy9GMD9p7s+VSNGdv66Szl5B44anV0pFeH9cnOSabf368fD+jZHFqdIORR8HCHf54DY79oC3OXQlYGuszpW9dDnzRkQ417FhwMIRO848w/vB0LA0s+bjhx+XOcfToUX7//Xed9hEjRtCmTZuKWPZDI4l7MRo3bszkyZN12kOTklguaoj9+hsKiueKMLKi3SAPDIQMDq88o7OBNKZrDVytjJi07Qb5qqqRi1pCojLxamBLv88akpWaxz/zL5GemKM7yNpLe+Cp/mA4MlNbrzVP1xKuKDxtTfjlnaZs/qg1Ztb+ROUEIKT2xj+qbJeqUqks1e1ibW3N3LlzK2q5D40k7v9h4sSJNGnSRKd99a1bXEpPJ3rsF4jFcjMbtnqLTp4HSUpScGVPydzuRvpyZrziw63EbFYeq3ifooREVcSllhWvfNGYgjw1//xwmeSYLN1B+sYwaBX4zoUQP1jVBRKCKnWdPq6GYLMLJ6NqFKQ3481V5/hg/QXCEkpZL7Bw4UICAgJ02ufOnYutbfkx8RWNJO7/QaFQsH79evT1S4Y+iaLIlKxMUq9eJX7eD/c7ZDK83/qA6oanuLA3ipTYkjv/nWvZ06e+E0uOhBGRVPFRARISVRF7D3MGftUEQSaw7cf/ZJS8hyBAq1EwbKfWcl/dFQJ0C0pXFGv915KQk8Dcjt9x+KvOfO1bi7O3U+i58Djf7fAnuVi5vzt37jBt2jSdOVq2bMmIESMqbc1lIYl7Kfj4+JT6H3crJoZf7GxJ/f13MvYWi3F3aUr7tunoi1kcXnsZjaZkrOt3/epiIJcxZYcU+y7x8mLtbMKgcU0wMFGwY9FVogIfULnJs502fbBdbW2O+ANTn1oZvwcRkxXDupvr6OXViyYOTTBUyPmkU3WOju/EGy3c+OPcHTr9cJQVx7Tl/saOHUtOTkkXk0wm4+eff9YJq35WVI1VVEHGjRtHy5YtddpXnT7NNWdn7k6aTP7t8KJ2497f0t7mT+KjC7jsF1HiGgdzQ8b1rMWJ0CR2XtPN7ywh8bJgbmvEoHFNsLA1Ytey/2SULI6FCwzfA03fg1ML4fdBT7WM33/58eKPyAQZXzYteWLd1tSAma/Ux29Me5p7WTNnbxBNRs5j27ZtOnN8+umnNG7cuMLW+KhI4v4A9PT0WLduHYaGuvnZJ94KI0dPj5gxY9DcK7BtYkON/j2obniC8//e1vnY+XYrDxq4WjBjVyDpuRVfWEBCoqpyP6OkWWFGybulD9QzgH6LoN9iiDytTVsQe/Wpr+f83fMciDzA+z7v42jiWOqYGg5mrH2vOWveakDEv0t1+h0dHZk+fXopVz47JHEvg9q1azNr1iyd9vA7d1jm7Ex+WBhx06YXuVqEZsPpVP0kpvIUDqzxR5l7//CGXCYwe2B9UrLzmedXuRtFEhJVDUMTBf3HNMa1jjWHfwvk2qGoBw9uOkxbjFtUw9qecPWvp7YOlUbF3AtzcTF14b1675U7/tiWVeQm6376/vHHH7H4TxbZZ40k7uUwZswY2rVrp9O+Zsd2/Lt0IX37dtK3btU2yuQY9J9Jd/MfyEzJ49hfwSWu8XGx4L02Xvx5/g6X76RWxvIlJKosRRklG9txckso5/69/eA9KdemWj+8SzPY/jHsHgeqJz/9/XfI34SmhvJVs68w1Cu7ilpoaCizSwlx7Ny5M2+88cYTr+VpI4l7OcjlctatW1dqzcOv/t2JullT4mbMJC+w8CSqe0ucmjeluelmQs7HE3y25EfOL3vUxNHckIn/3KDgIbPTSUi8qGgzStajThsnLu4uJaNkcUzt4N0d0Go0XFgFv/WHzLjHvnd6fjpLry6lhWMLurl3K3OsKIoMGfYh6oKSLlWFQsGyZcvKTVHwLJDE/SGoVq0a8+bN02mPioriR0BuYUH0mLGoMwvT/HafRlOrvTibx3DsrxDSEu7vqpsa6DG1Xz2C4jL59VS4zpwSEi8bMrmMzu/UpmFhRslD6wPRPMjwkeuB72x4dY3W/76iI9w5V/rYclh6ZSmZysyHyh8zc+mvXD1zTKf9q6++ok6dOo91/4pGEveHZNSoUXTp0kWnfd0ff3Dj1UEUxMRwd+Ik7cdKU3tkXb6lm8FUZKg4sOYmatX9X9ae9RzoVseBBQdCiU4t5cSehMRLhiAItH21Oi37exN8Lg6/laVklCxO/cHwwUFQGMK6PnBh9SOlDw5JDWFzyGZeq/kataxrlTn2ZMAdpk/6Wqfd3d291BPtVQVJ3B8SmUzGmjVrMDU11en79H//Q/HRR2QeOEDK+vXaxuYfYubkQGebtSREZnL+3/snVAVBYNqAeggCTN1xU4p9l5CgMKNkb21GyfBrSexael03o2RxHH20eWm8O8Hur2DHaCjILfc+oigy7/w8TBWmfNro0zLH+sekM/CDL1Fl6uaHWrx4MSYmJuXe71khifsj4OnpyU8//aTTfvfuXb4/fw7Tbl1JmP8jOZevaD8+9p5PNc0u6nrHc3n/HaKC7sfpulga8UW3mhwKSmDfzcf3G0pIvGjczyiZVnpGyeIYWcGbm6DD13D1D1jrC2l3ypz/0J1DnIs7x6eNP8XS0PKB44LjMhk8exNJ53Rj2vv27Uv//v0f9pGeCZK4PyIffPABvr6+Ou1//PEH51u3RuHkRMwXX6BKSQHPtlD/NdrljsfKVsHBXwPIzby/wz+8rSd1nMz5fmcAWfllWCgSEi8ZD5VR8h4yOXSZBK//pc0Lv7ITRF8qdWi+Op/5F+dT3bI6r9V87YFT3krM4s1VZ4jZvUQnS6WhoSGLFy+ukpuoxZHE/RERBIFVq1aVGtM66osvMJj6HerUVGLHf60t8NF9BgqFSA+3v8jLLuDwhqAiN4yeXMbsgT7EZ+bx4/5gnfkkJF5mijJKpuSx7ccHZJQsTu3e8N5uyEmGsNJzw6+/uZ6YrBgmtJiAnqz0XOuRydm8ueosKVcPkBXpr9M/adIkvLy8Hvl5KhtJ3B8DV1dXFi9erNOemJjIV4sX4zB5EtmnTpH0yy9g7gQdv8E29g/atFMRcT2JG0djiq5p7G7FWy3dWX86Av+YyktzKiHxPOBSy4pXvmyMMreMjJLFuedzd9QtvBOXHcfqG6vp5t6Nlk66qUUAYtJyeXPVObIz08k4tk6nv0aNGowfP/5RH+OZIIn7Y/LOO+/Qr18/nfYtW7awT6PBYkB/kpYuI+vUKWj5MdjWpEHcODzqWnJ6axhJ0fd/Scf3rI2NqQETt91A/aAYXwmJl5SijJIC2oyS4WUYQXevav911s3xsuDSAtQaNV81+6rUS+Mz8nhz1Vky8grwid1LSnKSzphly5ZhYGDwOI9R6Uji/pgIgsDKlSuxtrbW6Rs9ejR89BEG1asRO248BUkp0Hs+QloEXWocRN9Yj/1rblKg1IZ6WRgpimo6bjgTUclPIiFR9bF2NmHQ+KbajJILy8goGXsFTB3AzKlE85WEK+wJ38OwesNwNXPVuSwpK583V50lKTOfr5vqsXnDWp0xQ4YMoXv37k/leSoDSdyfAEdHR5YtW6bTnpKSwqgvvsB54ULE/HxivvgS0a0N1B+C8cUf6DbAhNS72Zz6O6zomn4NnGhfw5b5+0OIS8/TmVNC4mXnfkZJQ3Ytu8btK4m6g2KvgFMjbW74QjSihrnn52JvbM8H9T/QuSQ1W8nbq88Rk5bLqneasGjaNzrhyaampqVGylVlJHF/QoYOHcrgwYN12nfu3MnmU6dwnDGd3CtXSPhpAfjOAQNz3G9+QaOurtw8HlP0CyoIAjNf8aFArWH6rpuV/RgSEs8F2oySTbB3N8Nv5Y2SGSXzsyApRMclsz1sOwHJAXzZ9EuMFSXTiKTnFvDu2vPcTspm9bvNuXZwK5cu6UbaTJ8+HRcXlwp5popCEvcnRBAEli9fjp2dnU7f559/TlajRli9+SYpv/5Kxukr0OdHiL1CK9t/sXM34/CGQDKStZtAHjYmfNalOntuxHE4KL6yH0VC4rmgKKNkbauSGSXjboCoAedGRWMzlZksuryIxvaN6e3Vu8Q8Wfkq3vv1PEFxGax4uyk1zNV8++23OverX78+n332WUU+UoVQrrgLguAmCMIRQRACBEG4KQjCmMJ2a0EQDgiCEFr4r1VhuyAIwmJBEMIEQbguCIJuQdIXDDs7O1asWKHTnp6ezgcffIDdN19jWL8+d7+diNKsCdQbiPzEHHq+okDUiOxffT89wcgO1ahub8qU7TfJUUqx7xISpaEwkNPnk4Z4F2aUPP/vbcR7ud6dGhWNW3FtBal5qXzT4psScem5SjXvr7vA9eh0lrzRhM617fn6669JT9fdrF2+fDl6eqWHTVZlHsZyVwFfiaJYF2gFjBYEoS4wATgkimIN4FDh9wC9gBqFXyOBn5/6qqsgAwcO5M0339Rp9/Pz49cNG3BduADkcqLHjEXTZRYYWWFx5CO6vOlNfHgGZ7bdAkBfT8bsgfWJSctl0aFQnfkkJCS0yBUyen5Qj9ptnLiwO4ITRwREU2dt+DEQnh7OH4F/MLDGQOrZ1Cu6Lq9AzcgNF7kYkcKCoY3w9XHk+PHj/Pbbbzr3GD58eKkpv58HyhV3URTviqJ4ufB1JhAIuAADgMJEKqwHXil8PQD4TdRyFrAUBKHk1vULypIlS3By0n3UL7/8kliVCuf/zSU/MJD4hStg0ApIDKJa/Hzqd3bl2qEobl/V+t9beFkzpJkra06EExSXUdmPISHx3CCTy+hyL6PknZocyhhTlFFy3oV5GOoZ8nnjz4vGK1UaPvnjMidCk5g3uCH9GzpTUFDAJ598ojO3lZUV//vf/yrtWZ42j+RzFwTBE2gMnAMcRFG8t5sRBzgUvnYBipdViS5se+GxtrZm1apVOu2ZmZm8//77mHTogM3IkaRt2ULajQxoOxYuraNtnQDsPcw4tD6QjCSt//3bXnUwN1Iw8Z8bOgW3JSQk7iMIAm37OtDS9E+CE2rit9KfoxHHOBlzko8bfoyNkQ0AKrWGMRuvcDgogVkDfRjcVBsSuWjRIm7e1A1imDNnTql7ac8LDy3ugiCYAluBsaIoljAnRW3c0CMpkCAIIwVBuCgIwsXExFJCmp5T+vTpw/Dhw3XaDx8+zC+//ILd559h3Lw5cd9PI89tKLg0Rb7nc3oO1cbL71vlj7pAg5WJPhN71+HynTQ2XiijBJmEhARC3A2amW6hfWc14deSOL4ynGrGNXizttZVqtaIfLXlGnv94/iub13eaukBaGsyfP/99zrztWjRgg8+0A2bfJ54KHEXBEGBVtj/EEXxn8Lm+HvulsJ/75UxjwHcil3uWthWAlEUV4qi2EwUxWbP87tjaSxYsAA3Nzed9vHjx3M7MhKXn35EZmZKzBfjUPtqExOZH/mErm/XJCEyk1P/aOPfX23iQitva+buDSQxs4zESRISLzuFJ1Mb9GmIQfckrFJc6B/4Gepc0GhEJmy9zo6rsXztW4v3293PC/PFF1+QnZ1dYiqZTMby5cuRy+WV+QRPnYeJlhGANUCgKIrFo/h3AsMKXw8DdhRrf7cwaqYVkF7MffNSYGFhwZo1a3Tac3JyGD58ODIbG1zm/4gyMpK4BWsQ+y6AqLN4p6+hYRdtNZqwSwmFse/1yS1QM3N3wDN4EgmJ54TYK2DuQpJcxur8n4huew5lgsC2Hy/z/eZrbLkUzZiuNfikU/WiS/z8/Nh6r/5xMT755BOaNm1amauvEB7Gcm8LvAN0EQThauFXb2Au0F0QhFCgW+H3AHuA20AYsArQ3al4CejevTsfffSRTvuJEydYtGgRJi1bYDd2LBl79pJ6QwmN3oLjP9C6yV3sPc05siGQ9MQcqtub8kmn6uy4GsuR4IRS7iQhIXHvZOriy4vJV+czqv/b9P20AckJORgcT+LjZu6M7VajaHheXh6ffqpbqMPBwYEZM2ZU5sorjIeJljkpiqIgimIDURQbFX7tEUUxWRTFrqIo1hBFsZsoiimF40VRFEeLolhNFMX6oiherPjHqJr88MMPeHp66rRPnDiR4OBgbD4YgWmnTsTP/R+5rsPAphryHR/R821XBJlQVGrsk87VqGZnwqR/bpCZV0bhAgmJl5G8DEgO46aNG9vDtvN2nbfxtPDkr8gEfjfKxVwux+FCOimx990v//vf/7h165bOVPPnz8fS0rISF19xSCdUKxAzMzN+/fVXnfa8vDyGDRuGWqPBee4cFHZ2xHw9EXWPxZCThPmJL+k6rA5JUVmc+jsMAz058wY35G5GHrP3BD2DJ5GQqMLcvYYIzMkKwNrQmo8afMSSQ6EsO3KLzm3ceHNCcwTuZ5S8desWc+bM0ZmmY8eOvPXWW5W+/IpCEvcKplOnTnz++ec67efOnePHH39EbmmJy6KFqBITif1pA2KX7yF4N17522nU3R3/YzGEXoynqYcVIzt489f5O1JqAgmJ4ty9yi4TY65lRjCmyRj+PBvPjwdCGNTEhVmv+GDjYsqg8U3RN9Jjx4IrfDJqNPn5JQMU9PT0WL58eZWvrvQoSOJeCcyZM4fq1avrtH/33Xf4+/tjVL8+9hO+IevYMZL99aBGD9g3iVatcnH0NufI70GkxefwRbea1HEy56vN16TMkRISheTEXGShrQ31bOqRntCQ2XuC6NPAiXmvNkAm04q1vpEeKqWakOTz7D+wT2eOL7/8krp161b20isUSdwrAWNjY9atW6djFSiVSoYNG0ZBQQFWb76Jee/eJC5aTLbrSDCyRL5tBD2GVUMmF/Bb5Y+eCEveaEy+SsNnf12mQK15wB0lJF4eVqdcIUEm0Mx0ON//G0j3ug4sHNoIPfl9eTv1dyhpaZlsPKJbQc3NzY0pU6ZU5pIrBUncK4m2bdvy1Ve6FWAuX77MnDlzEAQBx+nT0ffwIGbSdAo6/QBJIZidmkC39+qSHJ3FiS2hVLc3Zc6g+lyISGXW7sBn8CQSElWHqMQA1utraCdzY9k+FR1r2rH0zcYoigl7VGAKQWfiuJy0ndi7OkduWLRoEaamppW57EpBEvdKZMaMGdSpU6fU9itXriA3NcF18SI02dnELtqK2P4buL4Rz9xtNOnpTsCJWELOxzGgkQvvt/Vi3ekI/jx35xk8iYRE1WD+uVnIEUkLaUprbxtWvNMUA737h48K8tUc/SOITO6yaZducEPv3r155ZVXKnHFlYck7pWIoaEh69ev1zn5plKpGDZsGPn5+RjUqIHT91PJuXCBxCv6UNMX/L6lZZNknKpbcOSPYFLjspnYuzadatkxZYc/R4Kk+HeJl48zsWc4nHydkWkZGDq1ZfWwZhgqSv5tnfv3NumJuey4vByVqmQKbQMDAxYvXvxCbaIWRxL3SqZ58+ZMmDBBp/3GjRtFhycsBgzAcsgQklevJtPmHbBwRbblXXq8ZoWeQobfSn9EtcjSN5tQ29GMUX9c4mLEA2pKSki8gKg0KqaenI11gZyemUYsfr8rxvolc67HR2Rw/VAUsbKLnL1wWmeOiRMnUq1atcpacqUjifsz4LvvvqNBgwY67XPnzuXChQsAOEyaiEHdOsR+NxNlp0WgysX036F0f9ONlNhsTmwMwdRAj/Xvt8DZwojhv17genRaJT+JhMSzYe6ptdzNjeCz1Dwca7TEzFBRol+t0nBkQyDo57PmH93ap9WrV+frr7+urOU+EyRxfwbo6+uzfv16neouarWad955h5ycHGQGBrguXAgaDTEzlqJ5dQOkRuJ+ZSRNezgTePouQWfvYmtqwO8ftMTcSMHbq89JAi/xwnMk9DYbQ1djo3Th1ey7KNx088Bc2R9Jckw2Z+P/JiFB1225bNkyDA0NK2O5zwxJ3J8RjRo1KjX8Kjg4mLFjxwKg7+6O85zZ5Pn7k/DXcXh1FURfoEXKWJyrmXLsz2CSY7JwtjRi48hWmBspeGv1Oc6HSy4aiReTa1FpfLZ3LjJZHquSryHY1YbG75QYkxqXzYU9EYi2ify+WXcTdfDgwfTo0aOylvzMkMT9GfLtt9+Wmn1u1apVrF27FgCzbt2wHj6c1D//JD1cDwb/iiz2Ij0Mv0NfX2DvLzfIyyrAzdqYzR+1xs7MgHfWnGPPjZcqEafES8DN2HTe2bAdwfwMb2RmUsO6FgzfCyY2RWNEjciR34OQ68G6vfPRlpq4j4mJCQsWLKjspT8TJHF/higUCn777bdSPx6OGjWKs2fPAmD/5RcYNWnC3SnfkW9YH97YiEl2AL4mU8lMzmHPL9dRKdU4Wxqx5aPW1HM255M/LrPkUKjOL7eExPNISHwmb685h73NWiw1aj4xqw3v7gRj6xLj/I/HcDcsnVj9C1y6rJuzcNq0abi6ulbWsp8pkrg/Y+rWrcvChQt12pVKJQMGDOD27dsICgUuC35CZmhI9JgxaFzawMencPIwopvZj9wNS2f/qmto1BpsTA3488NWvNLImR8PhPDJH5elTJISzzXhSdm8tfocnY1WEW+YymcKVyze+gcMzUuMy0zJ48y2W5i5iixePVdnHh8fn1LzPL2oSOJeBRg5ciTDhg3TaU9ISMDX15eEhAQUDg64zP8B5a3bxE2bhmjhCu/tokavjrQ3X034jTQO/3IKUSNiqJCzYGgjJvauzf6AePouOcmVO6nP4MkkJJ6MqJQc3lx5hpHqddy09KemzJhXX98JCqMS40RR5NhfwYiiyP6b60hN1f19X758OQqFQqf9RUUS9yqAIAj8/PPPNG/eXKcvNDQUX19f0tLSMGnTBttPR5O+YydpW7aATA6dvqHBp5/TwmY3wTcKOPrTFkS1GkEQGNmhGn992AqVWmTwL2eYuzeIXKX6GTyhhMSjE5uWy1srT/GV8mdU5seIVegxodsS5ApdN2boxXgibySj753MHxt/1+kfNmwY7du3r4xlVxkkca8iGBkZsX379lJrr165coWePXuSnp6O7ahRmLRtS/zMWeTeq9ju3opmkybTxOMGAWG2HJ++GDE9FoAWXtbsGdOeV5u48MuxW3T76Ri7rsdKvniJKk1CRh7DVp3k29yfaCs/wlprG3p49KC5UwudsblZSk5sCsXazZif1nyv029pacm8efMqYdVVC0ncqxDOzs7s2bOn1Eow58+fp0uXLiSnpOD8wzzk1tbEjP0CdUYGAIKxFa2++YzGDTLxj2/I8Zk/IwbtBcDCSMG8wQ3ZOLIVZoZ6fPrnFfovPcWBgHg0GknkJaoWyVn5vL/qOFOyZtJLOM2Cuu0RZXp81Uw38R7AqS1hKHNUhCmP4O/vr9M/e/Zs7O3tK3rZVQ5J3KsYPj4+7N69G2NjY52+y5cv06FDB+JycnBZ8BMFd+8SO3FikRUuyGS0HtWfxu1M8c/szPEVhxF3jYOCXABaeduw+/P2/DC4AWm5Sj787SI9Fx7n70vRKFVS+mCJZ09ajpKRq48wNWMK7YVrXOoynr1ZtxnuMxxnU2ed8ZE3kwk+F4drM33m/TRbp79Zs2aMHDmyMpZe5ZDEvQrSpk0bdu7cWWqIZGBgIO3atSPG1BSH8ePIOniIlF/XFfULgkDrt5rTuJsL/rm9OH5IQFzZBRK06YHlMoHXmrlx5KtOLHq9EXKZwLgt1+j4wxFWn7gtRdZIPDMy8gr4bPUBpqVOoKksDM2rq5ibfg1HE0fe93lfZ7wyT8XRP4KwcjTmtz0LycrKKtF/by/rv4n6XhYkca+idO3alZ07d2JkZKTTFxkZSbt27Yhs0ACzHj1I+PFHci5dKuoXBIHWr9akcXd3rcBHdkNc0RkurIFCK19PLmNAIxf2jmnPr8Ob425tzMzdgbSZc5hZuwOITs2ptGeVkMjOV/HV6j1MTR5HHb1YZG/8xTYDgaCUIL5q+hVGerp/B+d23CYrNR/BI5a/t/6t0z9q1CiaNWtWGcuvkghVYWOtWbNm4sWLugcOJODkyZP06dOHjELfenHMzc3ZtnEj7gsXIubm4bXtH/Rsip3WE0XObLvFlf138HG8Tgfxe4TavaHfIjC105nvalQaq0/cZq9/HAC+Po580M6Lxu5WFfeAEi89eQVqvl21na/iv8ZBkYfinS1kOPnQ95++eFl4sc5Xt4pZ3O10tv5wiZqtbPlk5quEhYWV6LezsyM4OBgrqxf7d1cQhEuiKJb6DiZZ7lWcdu3acfToUezsdMU4IyODPoMGcblfP9Tp6cSOH4+ovh/qKAgCrQdWo3EPd/zjGnDcYjVi6EFY3gqubymy4u/RyM2SpW824fjXnRnRzovjwYkMXH6aV38+zZ4bd1FJZf0knjL5KjUz1mxhYvwX2BmoUby/Gzza8PPVn0nLT2NCiwk6wq4u0HB4QxCmlgacur1NR9gB5s+f/8ILe3lI4v4c0LhxY06ePIm7u7tOX15eHm+MGcO/zZuRffoMScuWl+gvIfAh1hx32YFo6QH/fAB/vAZpupWcXCyNmNi7DmcmdmVqv7okZObxyR+X6TT/KGtOhkt+eYmnQoFaw49rfufru19gYmSIwYf7wbkRt9NuszFoI6/WfJU6NrqVyy7tiyT1bjZeHQz43zzdk6jt27fnnXfe0Wl/2ZDcMs8RUVFR9OzZk8DA0munftyiJZ+mp+OxahWm7duV6CvhomnvTAfvowiHp2s7u06BFiO1h6JKQa0RORAQz5qTt7kQkYqZgR5Dm7vxXltPXK10o3okJMpDpdbw85rVjIiZjMrEAfORu8HSHVEU+fjgx9xIvMGuQbuwNiyZOyY5NovNsy7g3diOxdu+Yc+ePSX69fT0uHLlCj4+PpX5OM8MyS3zguDm5saJEydo0UL3IAfAL+fPMSYtlZAvvqDgbsmskCUs+BOxHAvvjDjqDHi0Ab8JsLIjRJwqdV65TMDXx5EtH7dh++i2dKptz6+nI+gw7wij/7zMZSm1gcQjoNaI/LZ2KSNjviXXzB3zTw6BpfZT6bHoY5yOPc0njT7REXaNRuTIhiD0DfVIMwnUEXaAsWPHvjTCXh6S5f4ckp2dzZAhQ0r95QaobmjIqi5d6bjtHwR9/RJ9oihydvstLu+7Q70OLnQcWgMhcDvsnwIZ0VBvIHSfAZa6J2WLE5uWy/rTEfx5/g6ZeSqauFsyop03Pes5oCeXbAaJ0tFoRLau/R+DouaSYFEfp1E7wUjrG1eqlbyy4xUUMgV/9/8bhaxkHphrh6M4uTmUNq97MuDdTkRFRZXod3FxISgoCFNT00p7nmeNZLm/YJiYmLBjxw4++OCDUvvD8vIY4LeXraNG6fQJgkCrV6rRpKc7N4/HcGxTKGLdgfDpBej0LQT7wdJmcGQOKB8cDulsacS3vetw9tuufN+vLsnZSkb/eZmOPxyV4uUlSkUURfatmcpr0XOIsmyB06d+RcIOsCFgA1GZUXzT/BsdYc9IzuXsjtu417Nh494VOsIOsGjRopdK2MtDstyfY0RRZO7cuUycOLHUfjkw++NRjF++TCfiQMeCf70mgkyAtCg4OBX8t4K5C3SfDj6vQjkV4tUakYOB8aw5Ec75iBRM7/nl23jiZi355V92RI2Gk6vH0T52DUFWnaj1ySaEYgnAEnIS6LetHy2cWrCky5KS14oiu5ZcI/ZWOo2HmtG2Y0tUKlWJMb6+vuzZs0fn9/xFpyzLHVEUn/lX06ZNRYnHZ9u2baKpqakIlPr13pAhYl5ens51Go1GPP1PqLj0o0Pikd8DRY1ac78z4pQo/txOFKeai+LqHqIYc+Wh13MtKlX8/K/LYrVvd4teE3aJn/x+SbwYkfIUnlTiuUStFi/9MlIUp5qLlxa9LmpUSp0h44+NFxv/1li8k35Hpy/o7F1x6UeHxKuHIsVOnTrp/H4bGBiIoaGhlfEkVQ7govgAXZXcMi8Ar7zyCqdPn8bLy6vU/nWbN9OlUyfi4+NLtN930Xhw80Qsh38PQnMvlt2jDYw8Cv0WQ3IYrOwEOz+DrMRy19PA1ZJFrzfmxDedGdmhGidCE3n159MMXH6K3delePmXCrWKwBXv0uTuRk7YvEaj0b8jyEu6XI5HH2dv+F4+rP8hbuYl93pyM5Wc3ByKo7c512OOc/ToUZ1bTJgwgerVq1fkUzyXSG6ZF4ikpCSGDBnCkSNHSu13c3Nj+/btNGnSpES7KIpc2BXOhd0ReNa3ofuIeugb6t0fkJcOx+bBuV9AYQwdv4YWH4GePg9Ddr6Kvy9F8+upcCKSc3CxNGJ4W0+GNHfD3PDlKZ7w0qHKJ2LF63gmHmav7XB6jPoJ+X8229Py0hi0cxAWBhZs6rsJfXnJ36n9a25y63ICPT+tQZvOzXQMlGrVqnHjxo1S03S8DEgbqi8Jtra27Nu3j9GjR5faHxUVRbt27di0aVOJdkEQaNHPmw6v1yTSP5mt8y6RllBsM9XQAnrOgk/Ogntr2D9Ze8o1ZN9DrcvEQI9hbTw59FUnVr7TFBcrI2buDqT17EN8v/MmEUnZj/3MElWU/Cxifx6AZ+JhNtt9SvdShF0URaafnU5qfipz2s/REfaIG0mEXoinaS9PFv48T0fYAZYsWfLSCnt5SJb7C8rKlSsZPXq0zsbTPSZNmsT06dORyUr+wd0JSGb/mpuIapFOb9emRjMH3YtDD4Dft5AcCtW7Q8/ZYFfzkdZ3IzqdX0+F8+/1WFQaka617RnWxpN21W1fuk2xF47cVJJWDMAq9Trr7cbz9sffoq+na0duDNrIrHOz+KLpFzpZH5W5Kv6afg59Iz2q+8pp1bolGk1Jd96gQYPYunVrhT5KVacsy10S9xeY48eP8+qrr5KUlFRqf//+/dmwYQPm5iULDWck57J/9U3iwzOo1dKR9kNrYGD8H/eJugDOr4Sj/4OCbO0J1w7jdarRl0dCRh6/n43kj3N3SM5W4m1rwhst3BnUxAUbU4NHmkvi2aPOiCN1RR/MsiL42XYiH388FkOF7snna4nXeM/vPVo7tWZp16XIhJLif+yvYPyPxzDwq8YMersX586dK9FvYmJCYGBgqZXLXiaeSNwFQVgL9AUSRFH0KWyzBjYBnkAEMEQUxVRBa3ItAnoDOcB7oiheLm+BkrhXHJGRkfTv35/r16+X2l+rVi22bt1KvXr1SrSr1Rou7ong0t5IjEwVtOzvTa3WjjofrclKhCMz4fJvYGAOHcZB8w+hlDqXZZGvUrPnxl3+OHuHi5GpKOQCXWrbM6iJK51r2Zdq+UlULYKD/DHf8hrmqmTWuM5i5HvvlyrscdlxvLH7DQzlhmzsuxELA4sS/bFhaWybf5kGXVw5eWtrqaG+8+bNY/z48RX2LM8LTyruHYAs4Ldi4j4PSBFFca4gCBMAK1EUvxEEoTfwGVpxbwksEkWxZXkLlMS9YsnOzmbYsGEP/AhraGjI7Nmz+eyzz9DT0yvRlxCZwfGNIcSHZ2BqZUCdNk7Ubu2Eue1//JzxN7WnXG8dAnNX6DQBGr4B8pLzPQwh8ZlsuhDFjqsxJGUpsTRW0LeBEwMbu9DE3Upy21QxwhIy2bz3EMNvf4GJkM+Ftivo0q1vqf9PKXkpDPcbTnxOPL/3+p3qViWjXFQFajbPuoBKqSHW7CTfTvxGZ466dety9epVFAppM/6J3TKCIHgCu4qJezDQSRTFu4IgOAFHRVGsJQjCisLXf/13XFnzS+Je8Wg0GmbOnMnUqVMfOKZRo0YsX76c1q1bl2gXRZFI/2SuH4kmKjAFRHD0Nqd6Uwe8G9thZl3MSr99DA5Ng5hLYFsTOnytTWnwGCKvUms4EZrEtisx7A+II69Ag4eNMa80cmFgYxc8bU0eeU6Jp4Moipy9ncLaU+HEBZ1hveJ/6OsrEN/ehplHo1KvScxJ5KODH3En4w4/d/uZ5o7Ndcac23mb87tvcz3/H1av/7nUeY4dO0aHDh2e5uM8t1SEuKeJomhZ+FoAUkVRtBQEYRcwVxTFk4V9h4BvRFEsU7klca88tm3bxjvvvEN29oMjVIYPH86sWbNwcnLS6ctMySPkfByhFxJIjtGWNbP3NMezvg3udW2w8zBDJgBBu+DwTEgMAksPaPs5NHoLFI8X2ZCZV4Cffxzbr8Zw+lYyogiN3S0Z1NiFPg2csTZ5uLBMiScjKSuf7Vdi2HQhitCELLoahbJcNg89E2vkw3aATbVSrwtJDeHTQ5+Slp/Gos6LaO3cWmdMUnQWf804zfarizh8vvS8SePGjeOHH354qs/0PFOh4l74faooilaPIu6CIIwERgK4u7s3jYyMfOQHk3g8AgICGDx48ANTBwMYGxvz5ZdfMn78eJ0N13ukxedw60oCt68mkRCZASIYmOjhWssK19rWuNa0wCL1GMLJnyDmIpjYQfMPoOlwMCslCuchuZuey46rsWy7HENwfCZ6MoFOtewZ1MSFLrXtS/XzSjw+uUo1h4Li2X4lhqPBiag0Ig3dLBnnGUG7K18iWLrDO9vBwkXn2gJ1ARsCN/Dz1Z8x0zdjSdcl1LOppzNOlZ3DhskHmf/3NAKiS9+me/3119mwYYOO6/BlRnLLSOiQk5PDN998w7Jlyyjrd8DGxobJkyczatQoDAweHL2Sm6kkKiiFqJspRAenkpWaD4CplQGuta1wtU3ENWEtJpHbQabQumpafgQuTcvNW/MgRFEk8G4m265Es+NqLAmZ+ZgZ6tGnvhOvNHahhac1Mpnkn38clCoNp8KS2Hktlv0348hWqrE3M2BgYxdebepKzYR9sO0jcPCBt7eCiW2J60VR5Fj0MeZfnE9kRiRd3LowpfUUbI3ujxMLCsg+c4aM3bs5eTGNcdePEpUUUup6Ro8ezaJFi17aYtcPoiLE/QcgudiGqrUoil8LgtAH+JT7G6qLRVEsPfl4MSRxf3acO3eOUaNGceXKlTLHeXh4MH36dN56661y/8BEUSQ9IZfooBSig1KJDkklP1sbb29lr8DVJAzXzG24yC5g4OgFjd6EBkPA1P6xn0OtETl9K4ltl2PwuxlHjlKNo7khveo74lvPkWae1sgloS+VvAI1USk5RCTncCsxi8uRqZy5lUxmvgpzQz1613eif0NnWnrbaH+GF9fCri+1KSre2AiGJT/ZBacE88PFHzh39xzeFt6MazaO9q7tAW0CsdwrV0jftYtMv32oU1MJNbHjg7BbJGbElrq+mTNnMnHiRGkjvRSeNFrmL6ATYAvEA1OB7cBmwB2IRBsKmVLof18K+KINhRxenr8dJHF/1qjVan755RcmT55MWlpamWN9fHyYO3cuvXv3fug/No1GJCkqUyv0wancDU1DVaBBEETsjGJxFc7gZuCPY1039JoMhZq+D53aoDRylCoOBiaw82osx0MTUao0mBvq0a6GLe2q29Guui3uNi9XpspcpZo7KTmEJ2UTmZxNRHIOkcnZRCbnEJueW6Kcrpu1EW2r2dKjngPtqtvdD0MtyIW938Dl9VCjBwz5rcQeSnJuMsuuLmNr6FbM9M34pOEnvFbrNfQEPfKDg8nYvZv03btRxd5FMDTErEtnbtWsSf/xk0jPStFZs0wmY+XKlYwYMaKifzzPLdIhJomHIjk5mVmzZrFs2TKUSmWZY9u3b8///vc/nciah0FdoCEuPF0r9kGpxEekI2pALihxUgRqLXsfZ+w69kbm0uix3TYAWfkqjgYncDwkkeMhScRl5AFaAWtX3Za21W1pU832hdiQzc5XEVko2hHJOUQkZRNRKOD3nvseVsYKPG1N8LQxwcPGuOhfb1tTLP57YA0gIRD+HgEJN6Hdl9B5IhQmAFOqlfwZ+Ccrrq8gT5XH67Vf5+OGH2OUkKEV9F27UIbdArkck3ZtsejbF9POXThy9gwD+r9CTq7u5r6hoSGbN2+mX79+FfKzelGQxF3ikYiMjOS7775jw4YNZfrjAfr178fcOXOpW7fuY99PmasiNjSN6MBkom9Ek5ykFXN9IRsXs0itz75jB6y8XZ/oo7koitxKzOZUWBInw5I4W+h6AKjnbF4k9s09rTHSr5q+3cy8AiKTc4pE+56ARyTnkJiZX2Ksrak+HoWi7WVjgoetCZ42xnhYm5Qu4KWhLoCTC+HY/7Q5hgaugBrdAO3P8/Cdw/x46UeiMqPo4NqBL72GY3UykPTdu8i7pj04Z9SsKRZ9+2LWsyd6VtriHBs3buTdd9+loEC3qIuVlRW7du2iTZs2j/+DekmQxF3isbhx4wYTJ05k165dZY4TZAJ9h/Rl8dzFeHp4PvF9czKUxNyIJvrcdaIj1GQotYJgrJ+Nq4eAa31XXJvWwszmyRJGqdQarsekcypUK/aX76RSoBbRl8to6mFFuxpasa/vYlGp/vr03AIik7MLXSj3hTwyOZukrJKfqOzNDO5b38UscQ8bY8yeNONm7FXY+SnE3dAWbOk1r2jj9ErCFRZeWsjlhMvU0/fkq5x2OJwKJvvsOdBoMKhTB4u+fTDv1QuFs3OJaRctWsTYsWNLvaWbmxv79u2jTp06T7b2lwRJ3CWeiBMnTvDNN99w5syZMscJCoFWg1sx7utx9KjbA1P9p1PyLD3In+jjJ4gOzSImuzq5Gu1xdQvDNFwds3Ctpo9rPWcMXappxUfv8XLS5ChVXIhI1Vr2oUkE3M0AwNxQj9bVbIosey9bkyf+BJGaU1Dk8/6vHzw1p6Q162RhWMx1Umh9F4q4iUEFhAWmRsCR2XB9szZ8te8CqNMXURQ5c/cMa2+s5XLUWTreMWXoHScsL91CLChA4e6uFfQ+fTCophvvfufOHSZOnMgff/xR6m3r1auHn58frq6uT/+ZXlAkcZd4YkRRZOfOnUycOJGAgIAyx8oMZdh2s6X9m+3pUKsDzRyb0ciuEcaKJ9zE1GgQ4wNIvnGd6MAEomMMiM10p0A0AjTY6YXjanAdV+NQnCwTUZiagJE1GNtoE5oZWWv/NbbR1u4s3qZvquPbT8rK58ytZE6FJXEiNImYtFxAay3Xd7GguoMp3rYmqDWgVKmp62xBtlJFdr6KrDwVmXkqMvIKSMspIDVHSVJWPgkZ+cRl5JGjVBfdRxDA2cKomPVtXCjiJrhbG1eeiyg+AM4uh2sbQSaHlh8jth1LWH4SR6OOsitkB2Y3wukabEDzYDXyXCVyO1ssevfGvE8fDOvXL/VNLzAwkB9++IENGzY8MEtpu3bt2LlzJ1ZWVqX2S5SOJO4STw21Ws1vv/3Gd999R3R0dJlj5QZyLNtZYtXVClMXU+ra1qWZQzOaOjSliX2Tp2LZqwtUJATcJvp6NNG38ohL0EejkSET1DiaxeNqEoqrwXXsxWvI85PLWKz+faEvehO4/1o0siZRbcLlRBmXEuFSgoB/qoBSXbYFLwhgYaTA2lgfG1N97M0NcTAzxNnSEE8bEzxtjXG1Mn52B68KciF4D1zeALePgJ4ROQ2HcL5GR06kBnAi+jhmIXdpd1ND+xA5JlkqZGZmmPXojkXfvhi3aIFQSmisRqNh//79LFmyhD17Sj9teo8BAwbw119/SXnZHwNJ3CWeOrm5uSxbtozZs2eTmppa7viaLWvi1MOJtGppqFEjE2TUtq5NM4dmNHNoRhOHJjrZAR+Hgnw1d8PSisIuE6MyQQSFgRznGha4ehvg6qbGxjwTITcVcpIhNwVyUu7/W/x1bgpoSrc2RUGGxsCCbLkFGYIZRhb2YGyFYGyDnokN+uY26JvZITOxKfnG8QRhnk+F/ExtDqDgvRC4E/IziLJ04Xi11pxQwIXEqzjE5dM5UE7HIBlmKXlgoI9Z5y5Y9O2DSYcOyPRLf4aUlBTWrVvHzz//TFhYWLlLGTlyJMuWLZNOnT4mkrhLVBhpaWnMmzePhQsXkpubW+54D08P+r7VF9curgQpg7iReAOlRomAQA2rGlqxd9Ra99aGj5YbvjTysgqICdGGXEYFpZCeoF2jkZkCl1pWuNaywq2OtW6Wy3uIIuRnFBP80t4QknX7VWX8LPTNwNiqdJdRKZ8aMLbRljd8HD+/KEJaJNy9DnHXIeocRJ6hQFPAZTNrjjvX4rhcRURuPHZpIn1vWdD2pgbzmDRt6GLbNlj06YNp127ITR+cqO3ChQssX76cjRs3kpeX98Bx97CxsWHu3LmMGDFCOpz0BEjiLlHhxMXFMWfOHFasWEF+fn654w0MDHjttdd4/8P3Ma1hyqWES1yMv8i1hGvkqbXiUM2iWpHQN3Nohp2x3ROvMzMlr9Cq156ezUnXRp+Y2xoW5cRxqWWFsfkTWtcFuWWLv86bQyrkpz94PrnBg8XfwFwr2inhUP9V7bypkdqN0bRIUGoTvMXqKThr78UJC2vOFKSQrc7DJkePITEuNL+eh2lIDABGTZti3qc35r6+6Fk/+A02PT2djRs3snLlSi5fLrdsAwDW1tZ8/vnnjBkzBktLy4f9aUo8AEncJSqNmJgYFixYwIoVK8jKynqoa2rXrs17773HW2+9hYOTAzeTb3Ix/iIX4y9yJf4KOSptPVcPc48in31zx+Y4mjg+0VpFUSQ1LqfwMFUKMSFpKHO1LhgbFxNca1njWtsK55qWJQuGVxRqFZTqKirrzSEVRHWJaTQKY2Ks3Ag2tyXIwIBgmYaAglQS8rXuM3eZLUPi3Gl4JQODqyHa0MXatTHv0xuL3r1RuOgmALuHSqXi8OHDbNiwga1btz7UpzUAT09Pxo4dy4gRIzA1fTpRVBKSuEs8A9LT01m/fj1Lly4lNDT0oa4RBIF27drx6quv0q9fP7y9vVFpVASlBHExTiv2l+Mvk1mQCYCLqUuRVd/MsRmupk92yEmj1pB4J6vIqr8blo5apUGQCTh4mmsPU9W2wtHLArmialSGylPmEJZ8k6D4qwSnhxGcHUNIWhjZBdpTnzJBhqe5Jz5mNWkTaUC187Fw+hKiUonCzU0r6H36YFCjxgPvkZyczKFDh9i3bx+7du0iISHhodfXvXt3Pv30U/r06SMl/aoAJHGXeGZoNBoOHDhQFDXxKL9vderUoXfv3vTq1Yt27dphYGCAWqMm9P/tnW1sVFd6x3/PeIzfxsZvAYFJDAY8BuUFG3uAZOVigo1C0H5aKdlEWiJlVSVtpF3th9WuKlXqx/ZD1VaqSqskVVv6simb7lYgwC4GkqLAYGMTjF8x2GAKOHjwG2B77Dn9cI8ntrGJbbB9Mzw/6WrOPff63v+dY/997nOee25vW9Tsa+/U0jvcC8DK5JWTwjhr09Y+kdmPhse43d4XHZzt7ujHGPDGe1i1MT0ar89e40MW4SGnuw/v0hJqoTnUTMu9FlpCLXT0dxAxzoujU+JT8Gf4yc/IpyCzgIK0jaxq6WHoaCUDVVVE7t8nLjubtDfeYPm+N0l8+eVpv5/BwUG+/PJLqqurqa6upq6ubk7tlpGRwXvvvccHH3xAfv7cXpyuzA01d8UVtLe3c+DAAT799FNCoUcninocKSkpvP7661RUVLBnzx7Wr1+PiBAxEdp726NGX3O7hp4hJ+UxOyk7mnZZuKKQjRkb8XrmH14ZfhDmZmsvXS3OAO29W07vOCHZS/bzPtJXJJOWnURqViKpmYn4MhJJXr5sztMOj0ZG6ezvdIz8XjOtoVaaQ83R6wJYlbIKf6afgswC/Bl+/Jl+cnw5CMLD+nr6Dx+h/9gxxnp6nNTF8nKW73vTSV2ckply+/ZtgsEgZ86c4fTp09TU1DA2NjZV1ndSUlLChx9+yFtvvUVy8rM1MdtSoeauuIqhoSEOHTrEJ598wqlTp+Z1jHXr1lFRUUFFRQW7du2KDs4ZY+jo73Bi9rZ33/3ACSMkeZPwZ/jZlLWJzVmb2Zy1mbzlefM2/Pt9w3Q13+P/rvTS0zVIb/eD6NTG43g8QnL6sqjZ+zISop+pmYmIb5TOkau09rbSEnJ64229bQyPOYPSXo+XDekb8GdYI890euZT00aHWlsdQz9yhPDNm0hCAr6dO0nb9ya+0lI8di7+wcFBamtrCQaD0eX69evzun5weunvvvsu77//Plu2bJn3cZT5oeauuJarV69y8OBBDh48OOvY/FQ8Hg/btm2Lmn0gEIjmTRtjuHX/FnXddTTcbaCxp5GmUBMPbapiQlzCI4a/Pn098Z75zcsy/HCUwdAQA6EhBu8NM2g/B0JD9IXu86A3PHX8k7BnhPvLehlKHCA+DdIyk1i5IosXVq9m45pc0rN9kwZ0jTGYiGHoehd9R4/Td7yKoWudRLzLSCwqJvkPykgKbCPs8dLYeJkL9bXUXazlYsMF2tpbiEQi87q2cRISEti7dy/vvPMO+/btIzEx8bt/SFkQ1NwV12OMoaGhgc8//5wjR45w/vz5eR8rLS0tGsKpqKggLy9v0vaxyBidA52O0fc0RQ1/fBAy3hNPfkY+m7M2R01/Y/pGlsXNLj0yPBamva/diY2HWqLx8f6RfjBCctjHhvhN5MX5WU0umWMrSHiYymg/DN4b5kHfMDP9WYoHzDTebIyhZ+A2nd3NdH7TTGd3M9fvthEe/e601NmQn59PeXk55eXl7N69m5QUfTm5G1BzV7533Llzh2PHjnH06FEqKytn9RTsTKxfv549e/ZQUVFBWVnZtO+EjZgINwZu0NjT+K3phxoZGHEyc7weLxvTN7I5azMFmQWs9q3mev91IiZCji+HrsEuWu85sfGrvVcZNU54JjEukfyMfPyZ/mhsPD8j/7Hz7IyNRXjQN2J7/0N01t+htbaHFyJXkK52JDLGQ18CHUnQNtJLU2czl1suEup9zPQKcyQ3N5ddu3ZRVlZGWVmZTublUtTcle81o6OjnD9/nqqqKiorKzl79uy8BvwA4uLi2LFjR7RXX1xcPGOKnjGGroEuGkOTDb9vhoeNViStID8zf9Ig5wupLxDnmXsKYGR4mMHTp+k/fIS71dU0DQxwOSGBplQfF3t6aH+KL5SPi4vjpZdeIhAI8Nprr1FaWsratWuf2vGVhUPNXYkp+vr6qK6uprKykuPHj3Pt2rV5Hys9PZ3du3dHzT43N/ex+xtj6H7QzZ0Hd6jrrsMjHrau3MrqlNWkJ6bPWweAGR1l4KuvuPDP/8JXVVV83dfLpXCYlocPGX3COPlE1q1bRyAQYNu2bQQCAQoLCzW75XuKmrsS07S3t1NZWUllZSUnTpxgYGBg3sfKz8+PGv3OnTtJTU19ikofpauri//97DPO/O731Hx9kUsDA9x/ikaemZlJIBCImnlJSQnPPffk0zgo7kDNXXlmCIfDBIPBqNkHg8F5Z4d4vV62b9/Ozp07KS0t5dVXX32igcT+/n5qamoIBoOcra4mGAxyq+8x88nMkYSEBIqKiib1yvPy8nRirhhGzV15ZgmFQpNCOE+S0+31etm6dSvbt2+nuLiYoqIi/H7/tDH7kZERLl26NCmfvKmpaU5Pej4OEWHTpk2TeuUvvvgiy2aYileJTdTcFQUnXt7W1hbt1Z88eXLWk5vNRGJi4qymuH1ScnJyokYeCAQoLi6eNutHebZQc1eUaRgZGeHs2bNRs6+pqXlqPesnITU1lZKSkklmnvOYmRqVZxc1d0WZBRNnP6yqquLGjRsLfk6v18srr7wyycgLCgrweNwx66TibtTcFWWOGGO4cuUKp0+f5osvvuDUqVNPxew3bNgwycgLCwv18X1l3qi5K8pToKOjg3PnzlFbW0ttbS319fWPnd0yOzs7mrUSCAQoKSkhKytrERUrsY6au6IsAMYYuru7OXz4MB9//DGDg4OEw2F8Ph8fffQR+/fv1zREZUFRc1cURYlBHmfuOmqjKIoSg6i5K4qixCBq7oqiKDGImruiKEoMouauKIoSg6i5K4qixCCuSIUUkW+Ap/dqmScjG7i71CJmiWpdGFTrwqBanz65xphpJ+h3hbm7CRGpmSlv1G2o1oVBtS4MqnVx0bCMoihKDKLmriiKEoOouT/KPyy1gDmgWhcG1bowqNZFRGPuiqIoMYj23BVFUWKQZ87cReRTEekWkYYJdZkiUiUibfYzw9aLiPyNiFwRka9FpGiRtT4vIidFpFFELovIz9yqV0QSRSQoIhet1j+z9etE5JzV9BsRWWbrE+z6Fbt97WJpteePE5E6ETnscp0dInJJROpFpMbWua797fnTReSQiDSLSJOI7HCjVhHx2+9zfOkXkZ+7UesTYYx5phagFCgCGibU/QXwK1v+FfDntrwXOAoIsB04t8haVwFFtpwKtAKb3ajXntNny/HAOavhM+BtW38A+NCW/wg4YMtvA79Z5O/2F8C/AYftult1dgDZU+pc1/72/P8E/NSWlwHpbtU6QXMccBvIdbvWOV/bUgtYogZdO8XcW4BVtrwKaLHlvwd+PN1+S6T790C52/UCycAFYBvOgyBeW78DOG7Lx4Edtuy1+8ki6VsDnAB2AYftH63rdNpzTmfurmt/YDlwbep340atU/RVAGe+D1rnujxzYZkZWGmMuWXLt4GVtpwDTHxxZpetW3RsOKAQp0fsSr021FEPdANVQDvQa4wZnUZPVKvd3gcs1jvo/gr4JRCx61ku1QlggEoRqRWRP7R1bmz/dcA3wD/acNfHIpLiUq0TeRv4d1t2u9Y5oeY+BeP8a3ZVCpGI+IDfAj83xvRP3OYmvcaYMWPMFpyecQAoWFpFjyIi+4BuY0ztUmuZJT8wxhQBbwB/LCKlEze6qP29OOHOvzPGFAL3cUIbUVykFQA7rvJD4D+nbnOb1vmg5u5wR0RWAdjPblt/E3h+wn5rbN2iISLxOMb+r8aYz221a/UCGGN6gZM44Y10EfFOoyeq1W5fDvQsgrzXgB+KSAfwHzihmb92oU4AjDE37Wc38F84/zTd2P5dQJcx5pxdP4Rj9m7UOs4bwAVjzB277matc0bN3eG/gf22vB8ntj1e/xM7Wr4d6Jtw27bgiIgAnwBNxpi/dLNeEXlORNJtOQlnbKAJx+R/NIPW8Wv4EVBte0sLijHm18aYNcaYtTi35NXGmHfdphNARFJEJHW8jBMfbsCF7W+MuQ3cEBG/rXodaHSj1gn8mG9DMuOa3Kp17ix10H+xF5zGvAWEcXob7+PEUE8AbcD/AJl2XwH+Fid2fAkoXmStP8C5NfwaqLfLXjfqBV4G6qzWBuBPbX0eEASu4Nz+Jtj6RLt+xW7PW4LfhZ18my3jOp1W00W7XAb+xNa7rv3t+bcANfZ34HdAhou1puDcgS2fUOdKrfNd9AlVRVGUGETDMoqiKDGImruiKEoMouauKIoSg6i5K4qixCBq7oqiKDGImruiKEoMouauKIoSg6i5K4qixCD/D3K65BY8PNsrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating covered areas: 100%|██████████| 100.00000000000001/100 [00:00<00:00, 179.69it/s]\n"
     ]
    }
   ],
   "source": [
    "#Show exploration of environment space by each animal. Gives singular descriptions of polygon area covered by each animal and combined.\n",
    "#Additionally a plot of the respective areas is provided.\n",
    "# mkit.explore_features_geospatial(data)\n",
    "mkit.explore_features_geospatial(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f729e4-d3be-4698-9b5a-1dbd7b9b03ba",
   "metadata": {},
   "source": [
    "The data can also be animated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e4538b1-15e2-4de5-a629-841cd5c4b8af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAPtklEQVR4nO3dbWyd5X3H8e9/CU8DRgjxoiwJNaUIxEQHWcSCUlUIxAYpgrygLQxKVGWNtPECBFpHR7UWCTTYJGjRJjo2uoWW8djyMMbGsgCahkaQQ0J4CAzThhIPSAoENihtgf9enMvpievEdnx8Hi5/P9KRr/u6b9s/Wyc/3+c69zmJzESSVJdf6XQASVLrWe6SVCHLXZIqZLlLUoUsd0mq0MxOBwCYM2dO9vf3dzqGJPWU9evX/zgz+0bb1xXl3t/fz8DAQKdjSFJPiYiXd7fPZRlJqpDlLkkVstwlqUKWuyRVyHKXpAp1xdUyUg3u3TDEXz70Av+z4yf8xqwD+OPfO5rlJ8zvdCxNU5a71AL3bhjisrue4sOPGu+yOrTjJ1x211MAFrw6wmUZqQWuuOfpncU+7MOPkivuebpDiTTdWe5SC7z7sw8nNC9NNctdkipkuUtT7N4NQ52OoGnIJ1RVjf7L//mX5rZc85kOJNnVlf/0rE+qqu08c1cVRiv2Pc2301vv/bzTETQNWe7qeZ/82r/ucX87Cn7WAftM+feQJsJyV89756edvyLl62f9ZqcjSLuw3KUWGGtN/av3er272styl9rgu4//qNMRNM1Y7up5v7bfjE5HkLqO5a6et+nK0/dY8N1wOSS4NKP28jp3VWHTlad3OgIXLDl8j8sv3338R1y1/Lg2JtJ05pm71CIWt7qJ5S61kP+g1C28L0otdN3nj+90BAlwzV0V6uR7zCw/YT6X3LGxLd9L2hPP3FWVbn6PGfAdItU+lrumjW4oeM/q1S6Wu9RmY73RmdQKlrvUZt3wRmeqn0+oSiOc/7f/xWMvvblze+mRs7n1SyeN+/MDyDGO+eq9T3tdvKaUZ+6qyv4zYlKfP7LYAR576c0Jrdf/cBxX5vhGYppqlruq8vzVy3Zb8OO5HHJksTc75ooHx52jW97PRtOXyzKqzvNXL5uSr/v+h2MttkjdwzN3aQJOu+7RcR971K8fOHVBpDFY7tIEvLjt3Sk5Vmo1l2U0Le3uLQrGc6WL1As8c9e0s6e3KBjPlS6tMPfgfdvyfTR9jfvMPSJmAAPAUGaeGRFHALcDhwHrgS9k5s8iYj/gFuC3gTeAz2fmlpYnl8Zw2nWPdnRpZOmRs0e9+mZmwLorTutAIk0nEzlzvxjY3LR9LXB9Zn4CeAtYWeZXAm+V+evLcVJbTabYW3UZ461fOomlR87eZW7pkbMZ/HMvk9TUG9eZe0QsAD4DXA1cGhEBnAL8fjlkNfB14Ebg7DIGuBv4q4iIzHQpU20z2TP23f2XeRcsOXxCX2cir2yVWmm8Z+7fAL4MfFS2DwN2ZOYHZXsrML+M5wOvAJT9b5fjpZ5x1fLjuGDJ4cyIxguiZkRwwZLDfcsA9Ywxz9wj4kxgW2auj4iTW/WNI2IVsArg8MMndjYktcNVy4+zzNWzxnPmvhQ4KyK20HgC9RTgm8CsiBj+47AAGP5fCIaAhQBl/yE0nljdRWbelJmLM3NxX1/fpH4IaaS9eQHRRJdcpG42Zrln5lcyc0Fm9gPnAg9n5vnAI8A55bAVwH1lfH/Zpux/2PV2tduaS0+eUMG75KLaTOZFTH8C3B4RVwEbgJvL/M3AdyJiEHiTxh8Eqe3WXHpypyNIHTOhcs/MR4FHy/gHwImjHPM+8NkWZJMk7SVfoSpJFbLcJalClrskVchyl6QKWe6SVCHLXZIqZLlLUoUsd0mqkOUuSRWy3CWpQpa7JFXIcpekClnuklQhy12SKmS5S1KFLHdJqpDlLkkVstwlqUKWuyRVyHKXpApZ7pJUIctdkipkuUtShSx3SaqQ5S5JFbLcJalClrskVchyl6QKWe6SVCHLXZIqZLlLUoUsd0mqkOUuSRWy3CWpQpa7JFXIcpekClnuklQhy12SKmS5S1KFLHdJqtCY5R4R+0fEExHxVEQ8GxFXlvkjImJdRAxGxB0RsW+Z369sD5b9/VP8M0iSRhjPmftPgVMy87eA44HTI2IJcC1wfWZ+AngLWFmOXwm8VeavL8dJktpozHLPhv8rm/uUWwKnAHeX+dXA8jI+u2xT9p8aEdGqwJKksY1rzT0iZkTERmAbsAZ4CdiRmR+UQ7YC88t4PvAKQNn/NnDYKF9zVUQMRMTA9u3bJ/VDSJJ2Na5yz8wPM/N4YAFwInDMZL9xZt6UmYszc3FfX99kv5wkqcmErpbJzB3AI8BJwKyImFl2LQCGyngIWAhQ9h8CvNGKsJKk8RnP1TJ9ETGrjA8ATgM20yj5c8phK4D7yvj+sk3Z/3BmZgszS5LGMHPsQ5gHrI6IGTT+GNyZmQ9ExHPA7RFxFbABuLkcfzPwnYgYBN4Ezp2C3JKkPRiz3DNzE3DCKPM/oLH+PnL+feCzLUknSdorvkJVkipkuUtShSx3SaqQ5S5JFbLcJalClrskVchyl6QKWe6SVCHLXZIqZLlLUoUsd0mqkOUuSRWy3CWpQpa7JFXIcpekClnuklQhy12SKmS5S1KFLHdJqpDlLkkVstwlqUKWuyRVyHKXpApZ7pJUIctdkipkuUtShSx3SaqQ5S5JFbLcJalClrskVchyl6QKWe6SVCHLXZIqZLlLUoUsd0mqkOUuSRWy3CWpQpa7JFXIcpekCo1Z7hGxMCIeiYjnIuLZiLi4zM+OiDUR8WL5eGiZj4i4ISIGI2JTRCya6h9CkrSr8Zy5fwBclpnHAkuAiyLiWOByYG1mHgWsLdsAZwBHldsq4MaWp5Yk7dGY5Z6Zr2bmk2X8v8BmYD5wNrC6HLYaWF7GZwO3ZMPjwKyImNfq4JKk3ZvQmntE9AMnAOuAuZn5atn1GjC3jOcDrzR92tYyN/JrrYqIgYgY2L59+0RzS5L2YNzlHhEHAd8DLsnMd5r3ZWYCOZFvnJk3ZebizFzc19c3kU+VJI1hXOUeEfvQKPZbM/P7Zfr14eWW8nFbmR8CFjZ9+oIyJ0lqk/FcLRPAzcDmzLyuadf9wIoyXgHc1zR/YblqZgnwdtPyjSSpDWaO45ilwBeApyNiY5n7U+Aa4M6IWAm8DHyu7HsQWAYMAu8BX2xlYEnS2MYs98z8TyB2s/vUUY5P4KJJ5pIkTYKvUJWkClnuklQhy12SKmS5S1KFLHdJqpDlLkkVstwlqUKWuyRVyHKXpApZ7pJUIctdkipkuUtShSx3SaqQ5S5JFbLcJalClrskVchyl6QKWe6SVCHLXZIqZLlLUoUsd0mqkOUuSRWy3CWpQpa7JFXIcpekClnuklQhy12SKmS5S1KFLHdJqpDlLkkVstwlqUKWuyRVyHKXpApZ7pJUIctdkipkuUtShSx3SaqQ5S5JFbLcJalCY5Z7RHw7IrZFxDNNc7MjYk1EvFg+HlrmIyJuiIjBiNgUEYumMrwkaXTjOXP/B+D0EXOXA2sz8yhgbdkGOAM4qtxWATe2JqYkaSLGLPfM/A/gzRHTZwOry3g1sLxp/pZseByYFRHzWpRVkjROe7vmPjczXy3j14C5ZTwfeKXpuK1l7pdExKqIGIiIge3bt+9lDEnSaCb9hGpmJpB78Xk3ZebizFzc19c32RiSpCZ7W+6vDy+3lI/byvwQsLDpuAVlTpLURntb7vcDK8p4BXBf0/yF5aqZJcDbTcs3kqQ2mTnWARFxG3AyMCcitgJfA64B7oyIlcDLwOfK4Q8Cy4BB4D3gi1OQWZI0hjHLPTPP282uU0c5NoGLJhtKkjQ5vkJVkipkuUtShSx3SaqQ5S5JFbLcJalClrskVchyl6QKWe6SVCHLXZIqZLlLUoUsd0mqkOUuSRWy3CWpQpa7JFXIcpekClnuklQhy12SKmS5S1KFLHdJqpDlLkkVstwlqUKWuyRVyHKXpApZ7pJUIctdkipkuUtShSIzO52BiNgOvDwFX3oO8OMp+Lrt0MvZobfzm70zzD5xH8vMvtF2dEW5T5WIGMjMxZ3OsTd6OTv0dn6zd4bZW8tlGUmqkOUuSRWqvdxv6nSASejl7NDb+c3eGWZvoarX3CVpuqr9zF2SpiXLXZIq1NPlHhHfjohtEfFM09zsiFgTES+Wj4eW+YiIGyJiMCI2RcSiziWHiFgYEY9ExHMR8WxEXNwr+SNi/4h4IiKeKtmvLPNHRMS6kvGOiNi3zO9XtgfL/v5OZR8WETMiYkNEPFC2eyJ7RGyJiKcjYmNEDJS5rr/PDIuIWRFxd0Q8HxGbI+KkXsgfEUeX3/nw7Z2IuKSrs2dmz96ATwOLgGea5v4CuLyMLweuLeNlwL8AASwB1nU4+zxgURkfDPw3cGwv5C8ZDirjfYB1JdOdwLll/lvAH5bxHwHfKuNzgTu64L5zKfCPwANluyeyA1uAOSPmuv4+05R1NfAHZbwvMKuX8pdcM4DXgI91c/aO/6Ja8IvuH1HuLwDzynge8EIZ/w1w3mjHdcMNuA84rdfyA78KPAn8Do1X6M0s8ycBD5XxQ8BJZTyzHBcdzLwAWAucAjxQ/gH2SvbRyr0n7jPAIcAPR/7+eiV/U47fBR7r9uw9vSyzG3Mz89Uyfg2YW8bzgVeajtta5jquPNQ/gcYZcE/kL8saG4FtwBrgJWBHZn5QDmnOtzN72f82cFhbA+/qG8CXgY/K9mH0TvYE/i0i1kfEqjLXE/cZ4AhgO/D3ZUns7yLiQHon/7BzgdvKuGuz11juO2XjT2ZXX+sZEQcB3wMuycx3mvd1c/7M/DAzj6dxFnwicExnE41PRJwJbMvM9Z3Ospc+lZmLgDOAiyLi0807u/k+Q+ORzyLgxsw8AXiXxlLGTl2en/JczFnAXSP3dVv2Gsv99YiYB1A+bivzQ8DCpuMWlLmOiYh9aBT7rZn5/TLdM/kBMnMH8AiNpYxZETGz7GrOtzN72X8I8EZ7k+60FDgrIrYAt9NYmvkmvZGdzBwqH7cB99D4w9or95mtwNbMXFe276ZR9r2SHxp/VJ/MzNfLdtdmr7Hc7wdWlPEKGmvZw/MXlmexlwBvNz2caruICOBmYHNmXte0q+vzR0RfRMwq4wNoPFewmUbJn1MOG5l9+Gc6B3i4nOW0XWZ+JTMXZGY/jYfXD2fm+fRA9og4MCIOHh7TWPt9hh64zwBk5mvAKxFxdJk6FXiOHslfnMcvlmSgm7N3+smJST6xcRvwKvBzGmcFK2msh64FXgT+HZhdjg3gr2msDT8NLO5w9k/ReAi3CdhYbst6IT/wSWBDyf4M8Gdl/uPAE8AgjYet+5X5/cv2YNn/8U7fd0quk/nF1TJdn71kfKrcngWuKPNdf59p+hmOBwbKfede4NBeyQ8cSONR2yFNc12b3bcfkKQK1bgsI0nTnuUuSRWy3CWpQpa7JFXIcpekClnuklQhy12SKvT/OvqIKaKtt6MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "# animate the data\n",
    "anim = mkit.animate_movement(data, viewsize = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e38dcfa-a045-4e1a-abd5-dd37764a0496",
   "metadata": {},
   "outputs": [],
   "source": [
    "# one can save the created animation as gif and mp4 \n",
    "# mkit.save_animation_plot(anim, 'movers_animation')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf1ccde-c80b-4902-a0b9-53a3cc01d384",
   "metadata": {},
   "source": [
    "#### Splitting the trajectory of each animal in stopping and moving phases and analyze their respective duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "14309759-e545-47e1-9c27-aae9d3a6a0d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " For animal 312 the trajectory was split in 20 phases of moving and stopping.\n",
      " For animal 511 the trajectory was split in 14 phases of moving and stopping.\n",
      " For animal 607 the trajectory was split in 24 phases of moving and stopping.\n",
      " For animal 811 the trajectory was split in 14 phases of moving and stopping.\n",
      " For animal 905 the trajectory was split in 22 phases of moving and stopping.\n"
     ]
    }
   ],
   "source": [
    "phases_dict = mkit.split_movement_trajectory(data_features, stop_threshold = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4532a9ab-09e2-4077-828b-47a773a8db71",
   "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",
       "      <th>distance</th>\n",
       "      <th>direction</th>\n",
       "      <th>turning</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>average_acceleration</th>\n",
       "      <th>stopped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>312</td>\n",
       "      <td>405.31</td>\n",
       "      <td>417.37</td>\n",
       "      <td>0.390512</td>\n",
       "      <td>(0.02, -0.39)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.26521</td>\n",
       "      <td>0.014511</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>312</td>\n",
       "      <td>405.31</td>\n",
       "      <td>417.07</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>(0.0, -0.3)</td>\n",
       "      <td>0.998688</td>\n",
       "      <td>0.26022</td>\n",
       "      <td>-0.021231</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time  animal_id       x       y  distance      direction   turning  \\\n",
       "0     2        312  405.31  417.37  0.390512  (0.02, -0.39)  0.000000   \n",
       "1     3        312  405.31  417.07  0.300000    (0.0, -0.3)  0.998688   \n",
       "\n",
       "   average_speed  average_acceleration  stopped  \n",
       "0        0.26521              0.014511        0  \n",
       "1        0.26022             -0.021231        0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phases_dict[312][1]  # dataframe of first moving phase of animal 312"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2249ef3f-d20d-42a3-8827-148365f759f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " For animal 312 the trajectory was split in 20 phases of moving and stopping.\n",
      " For animal 511 the trajectory was split in 14 phases of moving and stopping.\n",
      " For animal 607 the trajectory was split in 24 phases of moving and stopping.\n",
      " For animal 811 the trajectory was split in 14 phases of moving and stopping.\n",
      " For animal 905 the trajectory was split in 22 phases of moving and stopping.\n"
     ]
    },
    {
     "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>animal_id</th>\n",
       "      <th>Duration of phase 1 (stopping)</th>\n",
       "      <th>Duration of phase 2 (moving)</th>\n",
       "      <th>Duration of phase 3 (stopping)</th>\n",
       "      <th>Duration of phase 4 (moving)</th>\n",
       "      <th>Duration of phase 5 (stopping)</th>\n",
       "      <th>Duration of phase 6 (moving)</th>\n",
       "      <th>Duration of phase 7 (stopping)</th>\n",
       "      <th>Duration of phase 8 (moving)</th>\n",
       "      <th>Duration of phase 9 (stopping)</th>\n",
       "      <th>...</th>\n",
       "      <th>Duration of phase 11 (stopping)</th>\n",
       "      <th>Duration of phase 12 (moving)</th>\n",
       "      <th>Duration of phase 13 (stopping)</th>\n",
       "      <th>Duration of phase 14 (moving)</th>\n",
       "      <th>Duration of phase 15 (stopping)</th>\n",
       "      <th>Duration of phase 16 (moving)</th>\n",
       "      <th>Duration of phase 17 (stopping)</th>\n",
       "      <th>Duration of phase 18 (moving)</th>\n",
       "      <th>Duration of phase 19 (stopping)</th>\n",
       "      <th>Duration of phase 20 (moving)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>312</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>190</td>\n",
       "      <td>147</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>52</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   animal_id  Duration of phase 1 (stopping)  Duration of phase 2 (moving)  \\\n",
       "0        312                               1                             2   \n",
       "\n",
       "   Duration of phase 3 (stopping)  Duration of phase 4 (moving)  \\\n",
       "0                              43                             2   \n",
       "\n",
       "   Duration of phase 5 (stopping)  Duration of phase 6 (moving)  \\\n",
       "0                              52                             1   \n",
       "\n",
       "   Duration of phase 7 (stopping)  Duration of phase 8 (moving)  \\\n",
       "0                             190                           147   \n",
       "\n",
       "   Duration of phase 9 (stopping)  ...  Duration of phase 11 (stopping)  \\\n",
       "0                               6  ...                               26   \n",
       "\n",
       "   Duration of phase 12 (moving)  Duration of phase 13 (stopping)  \\\n",
       "0                             52                               24   \n",
       "\n",
       "   Duration of phase 14 (moving)  Duration of phase 15 (stopping)  \\\n",
       "0                              1                                5   \n",
       "\n",
       "   Duration of phase 16 (moving)  Duration of phase 17 (stopping)  \\\n",
       "0                              2                                5   \n",
       "\n",
       "   Duration of phase 18 (moving)  Duration of phase 19 (stopping)  \\\n",
       "0                              1                               21   \n",
       "\n",
       "   Duration of phase 20 (moving)  \n",
       "0                            365  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration_dict = mkit.movement_stopping_durations(data_features, stop_threshold = 0.5)\n",
    "duration_dict[312]  # duration of phases for animal 312"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adverse-morning",
   "metadata": {},
   "source": [
    "## Time series analysis\n",
    "\n",
    "Possible parameters to extract time series features are: \n",
    "\n",
    "- *absolute_sum_of_changes(x)*\tReturns the sum over the absolute value of consecutive changes in the series x\n",
    "- *agg_autocorrelation(x, param)*\tCalculates the value of an aggregation function f_{agg} \n",
    "- *agg_linear_trend(x, param)*\tCalculates a linear least-squares regression for values of the time series that were aggregated over chunks versus the sequence from 0 up to the number of chunks minus one.\n",
    "- *approximate_entropy(x, m, r)*\tImplements a vectorized Approximate entropy algorithm.\n",
    "- *ar_coefficient(x, param)*\tThis feature calculator fits the unconditional maximum likelihood of an autoregressive AR(k) process.\n",
    "- *augmented_dickey_fuller(x, param)*\tThe Augmented Dickey-Fuller test is a hypothesis test which checks whether a unit root is present in a time series sample.\n",
    "- *autocorrelation(x, lag)*\tCalculates the autocorrelation of the specified lag, according to the formula [1]\n",
    "- *has_duplicate(x)*\tChecks if any value in x occurs more than once\n",
    "- *kurtosis(x)*\tReturns the kurtosis of x (calculated with the adjusted Fisher-Pearson standardized moment coefficient G2).\n",
    "- *large_standard_deviation(x, r)*\tBoolean variable denoting if the standard dev of x is higher than ‘r’ times the range = difference between max and min of x.\n",
    "- *last_location_of_maximum(x)*\tReturns the relative last location of the maximum value of x.\n",
    "- *length(x)*\tReturns the length of x\n",
    "- *linear_trend(x, param)*\tCalculate a linear least-squares regression for the values of the time series versus the sequence from 0 to length of the time series minus one.\n",
    "- *maximum(x)*\tCalculates the highest value of the time series x.\n",
    "- *mean(x)*\tReturns the mean of x\n",
    "- *median(x)*\tReturns the median of x\n",
    "- *minimum(x)*\tCalculates the lowest value of the time series x.\n",
    "- *number_crossing_m(x, m)*\tCalculates the number of crossings of x on m.\n",
    "- *number_cwt_peaks(x, n)*\tThis feature calculator searches for different peaks in x.\n",
    "- *number_peaks(x, n)*\tCalculates the number of peaks of at least support n in the time series x.\n",
    "- *partial_autocorrelation(x, param)*\tCalculates the value of the partial autocorrelation function at the given lag.\n",
    "- *quantile(x, q)*\tCalculates the q quantile of x.\n",
    "- *range_count(x, min, max)*\tCount observed values within the interval [min, max].\n",
    "- *sample_entropy(x)*\tCalculate and return sample entropy of x.\n",
    "- *set_property(key, value)*\tThis method returns a decorator that sets the property key of the function to value\n",
    "- *skewness(x)*\tReturns the sample skewness of x (calculated with the adjusted Fisher-Pearson standardized moment coefficient G1).\n",
    "- *standard_deviation(x)*\tReturns the standard deviation of x\n",
    "- *sum_of_reoccurring_data_points(x)*\tReturns the sum of all data points, that are present in the time series more than once.\n",
    "- *sum_of_reoccurring_values(x)*\tReturns the sum of all values, that are present in the time series more than once.\n",
    "- *sum_values(x)*\tCalculates the sum over the time series values\n",
    "- *value_count(x, value)*\tCount occurrences of value in time series x.\n",
    "- *variance(x)*\tReturns the variance of x\n",
    "\n",
    "For all possible features to extract please refer to https://tsfresh.readthedocs.io/en/latest/text/list_of_features.html."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "grave-royalty",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████| 10/10 [00:07<00:00,  1.42it/s]\n"
     ]
    },
    {
     "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>variable</th>\n",
       "      <th>average_acceleration__autocorrelation__lag_0</th>\n",
       "      <th>average_acceleration__autocorrelation__lag_1</th>\n",
       "      <th>average_acceleration__autocorrelation__lag_2</th>\n",
       "      <th>average_acceleration__autocorrelation__lag_3</th>\n",
       "      <th>average_acceleration__autocorrelation__lag_4</th>\n",
       "      <th>average_acceleration__autocorrelation__lag_5</th>\n",
       "      <th>average_acceleration__autocorrelation__lag_6</th>\n",
       "      <th>average_acceleration__autocorrelation__lag_7</th>\n",
       "      <th>average_acceleration__autocorrelation__lag_8</th>\n",
       "      <th>average_acceleration__autocorrelation__lag_9</th>\n",
       "      <th>...</th>\n",
       "      <th>y__autocorrelation__lag_0</th>\n",
       "      <th>y__autocorrelation__lag_1</th>\n",
       "      <th>y__autocorrelation__lag_2</th>\n",
       "      <th>y__autocorrelation__lag_3</th>\n",
       "      <th>y__autocorrelation__lag_4</th>\n",
       "      <th>y__autocorrelation__lag_5</th>\n",
       "      <th>y__autocorrelation__lag_6</th>\n",
       "      <th>y__autocorrelation__lag_7</th>\n",
       "      <th>y__autocorrelation__lag_8</th>\n",
       "      <th>y__autocorrelation__lag_9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.952556</td>\n",
       "      <td>0.832297</td>\n",
       "      <td>0.671888</td>\n",
       "      <td>0.495713</td>\n",
       "      <td>0.314675</td>\n",
       "      <td>0.138799</td>\n",
       "      <td>-0.020842</td>\n",
       "      <td>-0.154753</td>\n",
       "      <td>-0.256890</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000433</td>\n",
       "      <td>1.000799</td>\n",
       "      <td>1.001097</td>\n",
       "      <td>1.001328</td>\n",
       "      <td>1.001492</td>\n",
       "      <td>1.001589</td>\n",
       "      <td>1.001619</td>\n",
       "      <td>1.001585</td>\n",
       "      <td>1.001487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.962266</td>\n",
       "      <td>0.859110</td>\n",
       "      <td>0.712870</td>\n",
       "      <td>0.545754</td>\n",
       "      <td>0.373388</td>\n",
       "      <td>0.206931</td>\n",
       "      <td>0.054882</td>\n",
       "      <td>-0.076210</td>\n",
       "      <td>-0.181685</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000432</td>\n",
       "      <td>1.000795</td>\n",
       "      <td>1.001090</td>\n",
       "      <td>1.001316</td>\n",
       "      <td>1.001472</td>\n",
       "      <td>1.001556</td>\n",
       "      <td>1.001568</td>\n",
       "      <td>1.001507</td>\n",
       "      <td>1.001375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.944128</td>\n",
       "      <td>0.797021</td>\n",
       "      <td>0.601672</td>\n",
       "      <td>0.397333</td>\n",
       "      <td>0.205985</td>\n",
       "      <td>0.038301</td>\n",
       "      <td>-0.098281</td>\n",
       "      <td>-0.195330</td>\n",
       "      <td>-0.245064</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000653</td>\n",
       "      <td>1.001235</td>\n",
       "      <td>1.001747</td>\n",
       "      <td>1.002191</td>\n",
       "      <td>1.002566</td>\n",
       "      <td>1.002874</td>\n",
       "      <td>1.003116</td>\n",
       "      <td>1.003293</td>\n",
       "      <td>1.003406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.933647</td>\n",
       "      <td>0.760026</td>\n",
       "      <td>0.540593</td>\n",
       "      <td>0.331180</td>\n",
       "      <td>0.158069</td>\n",
       "      <td>0.023213</td>\n",
       "      <td>-0.078022</td>\n",
       "      <td>-0.145525</td>\n",
       "      <td>-0.174610</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000724</td>\n",
       "      <td>1.001366</td>\n",
       "      <td>1.001928</td>\n",
       "      <td>1.002409</td>\n",
       "      <td>1.002810</td>\n",
       "      <td>1.003132</td>\n",
       "      <td>1.003375</td>\n",
       "      <td>1.003540</td>\n",
       "      <td>1.003628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.937338</td>\n",
       "      <td>0.775279</td>\n",
       "      <td>0.557322</td>\n",
       "      <td>0.322768</td>\n",
       "      <td>0.096927</td>\n",
       "      <td>-0.101056</td>\n",
       "      <td>-0.258491</td>\n",
       "      <td>-0.369088</td>\n",
       "      <td>-0.430305</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000210</td>\n",
       "      <td>1.000370</td>\n",
       "      <td>1.000482</td>\n",
       "      <td>1.000546</td>\n",
       "      <td>1.000563</td>\n",
       "      <td>1.000537</td>\n",
       "      <td>1.000469</td>\n",
       "      <td>1.000361</td>\n",
       "      <td>1.000215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "variable  average_acceleration__autocorrelation__lag_0  \\\n",
       "id                                                       \n",
       "312                                                1.0   \n",
       "511                                                1.0   \n",
       "607                                                1.0   \n",
       "811                                                1.0   \n",
       "905                                                1.0   \n",
       "\n",
       "variable  average_acceleration__autocorrelation__lag_1  \\\n",
       "id                                                       \n",
       "312                                           0.952556   \n",
       "511                                           0.962266   \n",
       "607                                           0.944128   \n",
       "811                                           0.933647   \n",
       "905                                           0.937338   \n",
       "\n",
       "variable  average_acceleration__autocorrelation__lag_2  \\\n",
       "id                                                       \n",
       "312                                           0.832297   \n",
       "511                                           0.859110   \n",
       "607                                           0.797021   \n",
       "811                                           0.760026   \n",
       "905                                           0.775279   \n",
       "\n",
       "variable  average_acceleration__autocorrelation__lag_3  \\\n",
       "id                                                       \n",
       "312                                           0.671888   \n",
       "511                                           0.712870   \n",
       "607                                           0.601672   \n",
       "811                                           0.540593   \n",
       "905                                           0.557322   \n",
       "\n",
       "variable  average_acceleration__autocorrelation__lag_4  \\\n",
       "id                                                       \n",
       "312                                           0.495713   \n",
       "511                                           0.545754   \n",
       "607                                           0.397333   \n",
       "811                                           0.331180   \n",
       "905                                           0.322768   \n",
       "\n",
       "variable  average_acceleration__autocorrelation__lag_5  \\\n",
       "id                                                       \n",
       "312                                           0.314675   \n",
       "511                                           0.373388   \n",
       "607                                           0.205985   \n",
       "811                                           0.158069   \n",
       "905                                           0.096927   \n",
       "\n",
       "variable  average_acceleration__autocorrelation__lag_6  \\\n",
       "id                                                       \n",
       "312                                           0.138799   \n",
       "511                                           0.206931   \n",
       "607                                           0.038301   \n",
       "811                                           0.023213   \n",
       "905                                          -0.101056   \n",
       "\n",
       "variable  average_acceleration__autocorrelation__lag_7  \\\n",
       "id                                                       \n",
       "312                                          -0.020842   \n",
       "511                                           0.054882   \n",
       "607                                          -0.098281   \n",
       "811                                          -0.078022   \n",
       "905                                          -0.258491   \n",
       "\n",
       "variable  average_acceleration__autocorrelation__lag_8  \\\n",
       "id                                                       \n",
       "312                                          -0.154753   \n",
       "511                                          -0.076210   \n",
       "607                                          -0.195330   \n",
       "811                                          -0.145525   \n",
       "905                                          -0.369088   \n",
       "\n",
       "variable  average_acceleration__autocorrelation__lag_9  ...  \\\n",
       "id                                                      ...   \n",
       "312                                          -0.256890  ...   \n",
       "511                                          -0.181685  ...   \n",
       "607                                          -0.245064  ...   \n",
       "811                                          -0.174610  ...   \n",
       "905                                          -0.430305  ...   \n",
       "\n",
       "variable  y__autocorrelation__lag_0  y__autocorrelation__lag_1  \\\n",
       "id                                                               \n",
       "312                             1.0                   1.000433   \n",
       "511                             1.0                   1.000432   \n",
       "607                             1.0                   1.000653   \n",
       "811                             1.0                   1.000724   \n",
       "905                             1.0                   1.000210   \n",
       "\n",
       "variable  y__autocorrelation__lag_2  y__autocorrelation__lag_3  \\\n",
       "id                                                               \n",
       "312                        1.000799                   1.001097   \n",
       "511                        1.000795                   1.001090   \n",
       "607                        1.001235                   1.001747   \n",
       "811                        1.001366                   1.001928   \n",
       "905                        1.000370                   1.000482   \n",
       "\n",
       "variable  y__autocorrelation__lag_4  y__autocorrelation__lag_5  \\\n",
       "id                                                               \n",
       "312                        1.001328                   1.001492   \n",
       "511                        1.001316                   1.001472   \n",
       "607                        1.002191                   1.002566   \n",
       "811                        1.002409                   1.002810   \n",
       "905                        1.000546                   1.000563   \n",
       "\n",
       "variable  y__autocorrelation__lag_6  y__autocorrelation__lag_7  \\\n",
       "id                                                               \n",
       "312                        1.001589                   1.001619   \n",
       "511                        1.001556                   1.001568   \n",
       "607                        1.002874                   1.003116   \n",
       "811                        1.003132                   1.003375   \n",
       "905                        1.000537                   1.000469   \n",
       "\n",
       "variable  y__autocorrelation__lag_8  y__autocorrelation__lag_9  \n",
       "id                                                              \n",
       "312                        1.001585                   1.001487  \n",
       "511                        1.001507                   1.001375  \n",
       "607                        1.003293                   1.003406  \n",
       "811                        1.003540                   1.003628  \n",
       "905                        1.000361                   1.000215  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To extract a specific feature from the time series just put the feature name in the function call. The additional parameters needed for the specific feature are inserted automatically by the function.\n",
    "#For example autocorrelation\n",
    "#mkit.ts_feature(data, feature)\n",
    "\n",
    "auto_corr = mkit.ts_feature(data_features, 'autocorrelation')\n",
    "auto_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "increased-lottery",
   "metadata": {},
   "source": [
    "__Extract all possible time series features__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "national-israeli",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████| 10/10 [00:31<00:00,  3.16s/it]\n"
     ]
    },
    {
     "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>turning__variance_larger_than_standard_deviation</th>\n",
       "      <th>turning__has_duplicate_max</th>\n",
       "      <th>turning__has_duplicate_min</th>\n",
       "      <th>turning__has_duplicate</th>\n",
       "      <th>turning__sum_values</th>\n",
       "      <th>turning__abs_energy</th>\n",
       "      <th>turning__mean_abs_change</th>\n",
       "      <th>turning__mean_change</th>\n",
       "      <th>turning__mean_second_derivative_central</th>\n",
       "      <th>turning__median</th>\n",
       "      <th>...</th>\n",
       "      <th>distance__permutation_entropy__dimension_6__tau_1</th>\n",
       "      <th>distance__permutation_entropy__dimension_7__tau_1</th>\n",
       "      <th>distance__query_similarity_count__query_None__threshold_0.0</th>\n",
       "      <th>distance__matrix_profile__feature_\"min\"__threshold_0.98</th>\n",
       "      <th>distance__matrix_profile__feature_\"max\"__threshold_0.98</th>\n",
       "      <th>distance__matrix_profile__feature_\"mean\"__threshold_0.98</th>\n",
       "      <th>distance__matrix_profile__feature_\"median\"__threshold_0.98</th>\n",
       "      <th>distance__matrix_profile__feature_\"25\"__threshold_0.98</th>\n",
       "      <th>distance__matrix_profile__feature_\"75\"__threshold_0.98</th>\n",
       "      <th>distance__mean_n_absolute_max__number_of_maxima_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>905.550165</td>\n",
       "      <td>912.787630</td>\n",
       "      <td>0.097776</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>-1.482374e-08</td>\n",
       "      <td>0.999061</td>\n",
       "      <td>...</td>\n",
       "      <td>4.552744</td>\n",
       "      <td>5.153861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.424424</td>\n",
       "      <td>10.368385</td>\n",
       "      <td>6.428906</td>\n",
       "      <td>6.061785</td>\n",
       "      <td>4.893105</td>\n",
       "      <td>8.329089</td>\n",
       "      <td>5.141784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>914.901319</td>\n",
       "      <td>927.126123</td>\n",
       "      <td>0.102501</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>-4.343774e-08</td>\n",
       "      <td>0.999013</td>\n",
       "      <td>...</td>\n",
       "      <td>4.550446</td>\n",
       "      <td>5.160369</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.608737</td>\n",
       "      <td>9.560575</td>\n",
       "      <td>4.402903</td>\n",
       "      <td>3.702528</td>\n",
       "      <td>2.593091</td>\n",
       "      <td>6.365235</td>\n",
       "      <td>5.600553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>967.790964</td>\n",
       "      <td>959.704184</td>\n",
       "      <td>0.040628</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>1.363993e-07</td>\n",
       "      <td>0.999321</td>\n",
       "      <td>...</td>\n",
       "      <td>4.250439</td>\n",
       "      <td>4.918822</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.803152</td>\n",
       "      <td>8.261652</td>\n",
       "      <td>3.685940</td>\n",
       "      <td>3.680112</td>\n",
       "      <td>2.786459</td>\n",
       "      <td>4.417432</td>\n",
       "      <td>5.216763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>963.611859</td>\n",
       "      <td>966.760355</td>\n",
       "      <td>0.038524</td>\n",
       "      <td>0.000993</td>\n",
       "      <td>-3.868799e-06</td>\n",
       "      <td>0.999117</td>\n",
       "      <td>...</td>\n",
       "      <td>4.871424</td>\n",
       "      <td>5.586094</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.525412</td>\n",
       "      <td>7.275725</td>\n",
       "      <td>3.918884</td>\n",
       "      <td>3.886348</td>\n",
       "      <td>3.085988</td>\n",
       "      <td>4.900854</td>\n",
       "      <td>5.052743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>940.638854</td>\n",
       "      <td>934.911722</td>\n",
       "      <td>0.076747</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>-3.821096e-08</td>\n",
       "      <td>0.998712</td>\n",
       "      <td>...</td>\n",
       "      <td>4.235639</td>\n",
       "      <td>4.863876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.529394</td>\n",
       "      <td>8.000601</td>\n",
       "      <td>4.259319</td>\n",
       "      <td>4.081086</td>\n",
       "      <td>3.478502</td>\n",
       "      <td>4.963320</td>\n",
       "      <td>4.796128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 4734 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     turning__variance_larger_than_standard_deviation  \\\n",
       "312                                               0.0   \n",
       "511                                               0.0   \n",
       "607                                               0.0   \n",
       "811                                               0.0   \n",
       "905                                               0.0   \n",
       "\n",
       "     turning__has_duplicate_max  turning__has_duplicate_min  \\\n",
       "312                         1.0                         1.0   \n",
       "511                         1.0                         1.0   \n",
       "607                         0.0                         0.0   \n",
       "811                         0.0                         0.0   \n",
       "905                         1.0                         0.0   \n",
       "\n",
       "     turning__has_duplicate  turning__sum_values  turning__abs_energy  \\\n",
       "312                     1.0           905.550165           912.787630   \n",
       "511                     1.0           914.901319           927.126123   \n",
       "607                     1.0           967.790964           959.704184   \n",
       "811                     1.0           963.611859           966.760355   \n",
       "905                     1.0           940.638854           934.911722   \n",
       "\n",
       "     turning__mean_abs_change  turning__mean_change  \\\n",
       "312                  0.097776              0.001001   \n",
       "511                  0.102501              0.001001   \n",
       "607                  0.040628              0.001001   \n",
       "811                  0.038524              0.000993   \n",
       "905                  0.076747              0.001001   \n",
       "\n",
       "     turning__mean_second_derivative_central  turning__median  ...  \\\n",
       "312                            -1.482374e-08         0.999061  ...   \n",
       "511                            -4.343774e-08         0.999013  ...   \n",
       "607                             1.363993e-07         0.999321  ...   \n",
       "811                            -3.868799e-06         0.999117  ...   \n",
       "905                            -3.821096e-08         0.998712  ...   \n",
       "\n",
       "     distance__permutation_entropy__dimension_6__tau_1  \\\n",
       "312                                           4.552744   \n",
       "511                                           4.550446   \n",
       "607                                           4.250439   \n",
       "811                                           4.871424   \n",
       "905                                           4.235639   \n",
       "\n",
       "     distance__permutation_entropy__dimension_7__tau_1  \\\n",
       "312                                           5.153861   \n",
       "511                                           5.160369   \n",
       "607                                           4.918822   \n",
       "811                                           5.586094   \n",
       "905                                           4.863876   \n",
       "\n",
       "     distance__query_similarity_count__query_None__threshold_0.0  \\\n",
       "312                                                0.0             \n",
       "511                                                0.0             \n",
       "607                                                0.0             \n",
       "811                                                0.0             \n",
       "905                                                0.0             \n",
       "\n",
       "     distance__matrix_profile__feature_\"min\"__threshold_0.98  \\\n",
       "312                                           2.424424         \n",
       "511                                           1.608737         \n",
       "607                                           1.803152         \n",
       "811                                           1.525412         \n",
       "905                                           1.529394         \n",
       "\n",
       "     distance__matrix_profile__feature_\"max\"__threshold_0.98  \\\n",
       "312                                          10.368385         \n",
       "511                                           9.560575         \n",
       "607                                           8.261652         \n",
       "811                                           7.275725         \n",
       "905                                           8.000601         \n",
       "\n",
       "     distance__matrix_profile__feature_\"mean\"__threshold_0.98  \\\n",
       "312                                           6.428906          \n",
       "511                                           4.402903          \n",
       "607                                           3.685940          \n",
       "811                                           3.918884          \n",
       "905                                           4.259319          \n",
       "\n",
       "     distance__matrix_profile__feature_\"median\"__threshold_0.98  \\\n",
       "312                                           6.061785            \n",
       "511                                           3.702528            \n",
       "607                                           3.680112            \n",
       "811                                           3.886348            \n",
       "905                                           4.081086            \n",
       "\n",
       "     distance__matrix_profile__feature_\"25\"__threshold_0.98  \\\n",
       "312                                           4.893105        \n",
       "511                                           2.593091        \n",
       "607                                           2.786459        \n",
       "811                                           3.085988        \n",
       "905                                           3.478502        \n",
       "\n",
       "     distance__matrix_profile__feature_\"75\"__threshold_0.98  \\\n",
       "312                                           8.329089        \n",
       "511                                           6.365235        \n",
       "607                                           4.417432        \n",
       "811                                           4.900854        \n",
       "905                                           4.963320        \n",
       "\n",
       "     distance__mean_n_absolute_max__number_of_maxima_7  \n",
       "312                                           5.141784  \n",
       "511                                           5.600553  \n",
       "607                                           5.216763  \n",
       "811                                           5.052743  \n",
       "905                                           4.796128  \n",
       "\n",
       "[5 rows x 4734 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mkit.ts_all_features(data)\n",
    "#Note that function columnwise replaces all NaNs and infs from the time series results with average/extreme values from the same columns. For more information see https://tsfresh.readthedocs.io/en/v0.5.0/api/tsfresh.utilities.html .\n",
    "\n",
    "ts_features = mkit.ts_all_features(data_features)\n",
    "ts_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82ed30bb-fd17-44ae-9d08-0286e671ba80",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mkit",
   "language": "python",
   "name": "mkit"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}