BVM_2020/EIPH_Statistics_Contributors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from glob import glob\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "from math import pow, sqrt, ceil\n",
    "from collections import OrderedDict\n",
    "\n",
    "from sklearn.metrics import accuracy_score, cohen_kappa_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbols = {1: \"o\", \n",
    "           2: \"D\",\n",
    "           3: '*',           \n",
    "           4: \"<\",    \n",
    "           \"DL\": \"X\",\n",
    "        }\n",
    "\n",
    "color = {1: \"c\",  \n",
    "         2: \"r\",\n",
    "         3: \"m\",\n",
    "         4: \"b\",\n",
    "         \"DL\": \"g\",\n",
    "        }\n",
    "\n",
    "offset = {1: 0.15, \n",
    "           2: 0.05,\n",
    "           3: -0.05,\n",
    "           4: -0.15,        \n",
    "          \"DL\": 0\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "plt.rcParams[\"font.size\"] = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from convert_LabelBox_results import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Path(\"Results\")\n",
    "statistics = EIPH_Statistics(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    .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>Initials</th>\n",
       "      <th>Skill</th>\n",
       "      <th>File</th>\n",
       "      <th>Type</th>\n",
       "      <th>Order</th>\n",
       "      <th>Time</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Grade GT</th>\n",
       "      <th>ImageType</th>\n",
       "      <th>Acc</th>\n",
       "      <th>gt_num_cells</th>\n",
       "      <th>num_cells</th>\n",
       "      <th>unchanged_boxes</th>\n",
       "      <th>changed_grade</th>\n",
       "      <th>changed_boxes</th>\n",
       "      <th>unchanged_boxes_ration</th>\n",
       "      <th>changed_grade_ration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>30_EIPH_588355 Berliner Blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>360.255</td>\n",
       "      <td>1.076923</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>04_EIPH_567017 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>321.544</td>\n",
       "      <td>1.230769</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>14_EIPH_568381 berliner blau-001.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>533.430</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>17_EIPH_575796 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>116.749</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>23_EIPH_563476 Berliner Blau-001.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>155.112</td>\n",
       "      <td>1.714286</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>25_EIPH_568150 Berliner Blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>98.940</td>\n",
       "      <td>1.888889</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>28_EIPH_569948 L berliner blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>94.321</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>01_EIPH_563479 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>113.190</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>08_EIPH_574999 Berliner Blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>101.599</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>15_EIPH_568320 berliner blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>51.651</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>19_EIPH_566933 L Berliner Blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>25.400</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>23_EIPH_563476 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>142.280</td>\n",
       "      <td>1.733333</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>25_EIPH_568150 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>75.088</td>\n",
       "      <td>1.545455</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>30_EIPH_568355 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>75.685</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>02_EIPH_574162 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>122.359</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>13_EIPH_570370 Berliner Blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>366.928</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>15_EIPH_568320 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>113.241</td>\n",
       "      <td>1.642857</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>19_EIPH_566933 L Tunrbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>82.352</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>24_EIPH_576255 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>136.117</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>1.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>27_EIPH_571557 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>58.052</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>30_EIPH_588355 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>216.363</td>\n",
       "      <td>1.230769</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>04_EIPH_567017 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>132.819</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>14_EIPH_568381 berliner blau-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>257.089</td>\n",
       "      <td>2.111111</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>17_EIPH_575796 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>171.467</td>\n",
       "      <td>1.636364</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>23_EIPH_563476 Berliner Blau-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>142.989</td>\n",
       "      <td>1.642857</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>25_EIPH_568150 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>113.452</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>28_EIPH_569948 L berliner blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>61.143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>01_EIPH_563479 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>106.618</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>08_EIPH_574999 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>124.143</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>15_EIPH_568320 berliner blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>78.334</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</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",
       "      <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>810</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>19_EIPH_566933 L Berliner Blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>67.907</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>23_EIPH_563476 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>114.252</td>\n",
       "      <td>2.076923</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>25_EIPH_568150 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>186.153</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>30_EIPH_568355 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>35.581</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>02_EIPH_574162 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>20.965</td>\n",
       "      <td>1.888889</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>13_EIPH_570370 Berliner Blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>38.119</td>\n",
       "      <td>2.181818</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>15_EIPH_568320 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>35.834</td>\n",
       "      <td>1.714286</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>19_EIPH_566933 L Tunrbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>25.152</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>24_EIPH_576255 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>91.177</td>\n",
       "      <td>1.727273</td>\n",
       "      <td>1.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>27_EIPH_571557 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>26.933</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>30_EIPH_588355 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>212.099</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>04_EIPH_567017 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>121.412</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>14_EIPH_568381 berliner blau-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>186.285</td>\n",
       "      <td>2.444444</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>17_EIPH_575796 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>142.601</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>23_EIPH_563476 Berliner Blau-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>117.076</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>25_EIPH_568150 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>137.557</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>28_EIPH_569948 L berliner blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>68.571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>01_EIPH_563479 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>117.070</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>08_EIPH_574999 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>111.148</td>\n",
       "      <td>2.642857</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>15_EIPH_568320 berliner blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>61.535</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>19_EIPH_566933 L Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>78.373</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>23_EIPH_563476 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>61.587</td>\n",
       "      <td>2.083333</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>25_EIPH_568150 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>68.545</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>30_EIPH_568355 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>130.860</td>\n",
       "      <td>1.933333</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>02_EIPH_574162 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>95.419</td>\n",
       "      <td>1.363636</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>13_EIPH_570370 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>81.163</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>15_EIPH_568320 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>71.300</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>19_EIPH_566933 L Tunrbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>89.476</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>24_EIPH_576255 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>67.450</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>27_EIPH_571557 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>52.304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Initials Skill                                  File  \\\n",
       "0         LJ     3      30_EIPH_588355 Berliner Blau.png   \n",
       "1         LJ     3  04_EIPH_567017 Turnbull blue-001.png   \n",
       "2         LJ     3  14_EIPH_568381 berliner blau-001.png   \n",
       "3         LJ     3      17_EIPH_575796 Turnbull blue.png   \n",
       "4         LJ     3  23_EIPH_563476 Berliner Blau-001.png   \n",
       "5         LJ     3      25_EIPH_568150 Berliner Blau.png   \n",
       "6         LJ     3    28_EIPH_569948 L berliner blau.png   \n",
       "7         LJ     3      01_EIPH_563479 Turnbull blue.png   \n",
       "8         LJ     3      08_EIPH_574999 Berliner Blau.png   \n",
       "9         LJ     3      15_EIPH_568320 berliner blau.png   \n",
       "10        LJ     3    19_EIPH_566933 L Berliner Blau.png   \n",
       "11        LJ     3      23_EIPH_563476 Turnbull blue.png   \n",
       "12        LJ     3      25_EIPH_568150 Turnbull blue.png   \n",
       "13        LJ     3      30_EIPH_568355 Turnbull blue.png   \n",
       "14        LJ     3  02_EIPH_574162 Turnbull blue-001.png   \n",
       "15        LJ     3      13_EIPH_570370 Berliner Blau.png   \n",
       "16        LJ     3      15_EIPH_568320 Turnbull blue.png   \n",
       "17        LJ     3    19_EIPH_566933 L Tunrbull blue.png   \n",
       "18        LJ     3      24_EIPH_576255 Turnbull blue.png   \n",
       "19        LJ     3      27_EIPH_571557 Turnbull blue.png   \n",
       "20        LJ     3      30_EIPH_588355 Berliner Blau.png   \n",
       "21        LJ     3  04_EIPH_567017 Turnbull blue-001.png   \n",
       "22        LJ     3  14_EIPH_568381 berliner blau-001.png   \n",
       "23        LJ     3      17_EIPH_575796 Turnbull blue.png   \n",
       "24        LJ     3  23_EIPH_563476 Berliner Blau-001.png   \n",
       "25        LJ     3      25_EIPH_568150 Berliner Blau.png   \n",
       "26        LJ     3    28_EIPH_569948 L berliner blau.png   \n",
       "27        LJ     3      01_EIPH_563479 Turnbull blue.png   \n",
       "28        LJ     3      08_EIPH_574999 Berliner Blau.png   \n",
       "29        LJ     3      15_EIPH_568320 berliner blau.png   \n",
       "..       ...   ...                                   ...   \n",
       "810       HJ     2    19_EIPH_566933 L Berliner Blau.png   \n",
       "811       HJ     2      23_EIPH_563476 Turnbull blue.png   \n",
       "812       HJ     2      25_EIPH_568150 Turnbull blue.png   \n",
       "813       HJ     2      30_EIPH_568355 Turnbull blue.png   \n",
       "814       HJ     2  02_EIPH_574162 Turnbull blue-001.png   \n",
       "815       HJ     2      13_EIPH_570370 Berliner Blau.png   \n",
       "816       HJ     2      15_EIPH_568320 Turnbull blue.png   \n",
       "817       HJ     2    19_EIPH_566933 L Tunrbull blue.png   \n",
       "818       HJ     2      24_EIPH_576255 Turnbull blue.png   \n",
       "819       HJ     2      27_EIPH_571557 Turnbull blue.png   \n",
       "820       HJ     2      30_EIPH_588355 Berliner Blau.png   \n",
       "821       HJ     2  04_EIPH_567017 Turnbull blue-001.png   \n",
       "822       HJ     2  14_EIPH_568381 berliner blau-001.png   \n",
       "823       HJ     2      17_EIPH_575796 Turnbull blue.png   \n",
       "824       HJ     2  23_EIPH_563476 Berliner Blau-001.png   \n",
       "825       HJ     2      25_EIPH_568150 Berliner Blau.png   \n",
       "826       HJ     2    28_EIPH_569948 L berliner blau.png   \n",
       "827       HJ     2      01_EIPH_563479 Turnbull blue.png   \n",
       "828       HJ     2      08_EIPH_574999 Berliner Blau.png   \n",
       "829       HJ     2      15_EIPH_568320 berliner blau.png   \n",
       "830       HJ     2    19_EIPH_566933 L Berliner Blau.png   \n",
       "831       HJ     2      23_EIPH_563476 Turnbull blue.png   \n",
       "832       HJ     2      25_EIPH_568150 Turnbull blue.png   \n",
       "833       HJ     2      30_EIPH_568355 Turnbull blue.png   \n",
       "834       HJ     2  02_EIPH_574162 Turnbull blue-001.png   \n",
       "835       HJ     2      13_EIPH_570370 Berliner Blau.png   \n",
       "836       HJ     2      15_EIPH_568320 Turnbull blue.png   \n",
       "837       HJ     2    19_EIPH_566933 L Tunrbull blue.png   \n",
       "838       HJ     2      24_EIPH_576255 Turnbull blue.png   \n",
       "839       HJ     2      27_EIPH_571557 Turnbull blue.png   \n",
       "\n",
       "                           Type          Order     Time     Grade  Grade GT  \\\n",
       "0    ProjectType.CrowdAlgorithm  LabelOrder.AS  360.255  1.076923  1.500000   \n",
       "1    ProjectType.CrowdAlgorithm  LabelOrder.AS  321.544  1.230769  1.333333   \n",
       "2    ProjectType.CrowdAlgorithm  LabelOrder.AS  533.430  2.000000  1.666667   \n",
       "3    ProjectType.CrowdAlgorithm  LabelOrder.AS  116.749  1.666667  1.692308   \n",
       "4    ProjectType.CrowdAlgorithm  LabelOrder.AS  155.112  1.714286  0.916667   \n",
       "5    ProjectType.CrowdAlgorithm  LabelOrder.AS   98.940  1.888889  1.666667   \n",
       "6    ProjectType.CrowdAlgorithm  LabelOrder.AS   94.321  0.111111  0.142857   \n",
       "7    ProjectType.CrowdAlgorithm  LabelOrder.AS  113.190  2.000000  1.700000   \n",
       "8    ProjectType.CrowdAlgorithm  LabelOrder.AS  101.599  2.571429  2.000000   \n",
       "9    ProjectType.CrowdAlgorithm  LabelOrder.AS   51.651  1.666667  1.583333   \n",
       "10   ProjectType.CrowdAlgorithm  LabelOrder.AS   25.400  0.636364  0.636364   \n",
       "11   ProjectType.CrowdAlgorithm  LabelOrder.AS  142.280  1.733333  1.785714   \n",
       "12   ProjectType.CrowdAlgorithm  LabelOrder.AS   75.088  1.545455  1.583333   \n",
       "13   ProjectType.CrowdAlgorithm  LabelOrder.AS   75.685  1.250000  1.466667   \n",
       "14   ProjectType.CrowdAlgorithm  LabelOrder.AS  122.359  1.555556  1.333333   \n",
       "15   ProjectType.CrowdAlgorithm  LabelOrder.AS  366.928  1.454545  1.100000   \n",
       "16   ProjectType.CrowdAlgorithm  LabelOrder.AS  113.241  1.642857  1.785714   \n",
       "17   ProjectType.CrowdAlgorithm  LabelOrder.AS   82.352  0.846154  1.000000   \n",
       "18   ProjectType.CrowdAlgorithm  LabelOrder.AS  136.117  1.800000  1.636364   \n",
       "19   ProjectType.CrowdAlgorithm  LabelOrder.AS   58.052  0.583333  0.909091   \n",
       "20       ProjectType.Annotation  LabelOrder.AS  216.363  1.230769  1.500000   \n",
       "21       ProjectType.Annotation  LabelOrder.AS  132.819  1.166667  1.333333   \n",
       "22       ProjectType.Annotation  LabelOrder.AS  257.089  2.111111  1.666667   \n",
       "23       ProjectType.Annotation  LabelOrder.AS  171.467  1.636364  1.692308   \n",
       "24       ProjectType.Annotation  LabelOrder.AS  142.989  1.642857  0.916667   \n",
       "25       ProjectType.Annotation  LabelOrder.AS  113.452  2.142857  1.666667   \n",
       "26       ProjectType.Annotation  LabelOrder.AS   61.143  0.000000  0.142857   \n",
       "27       ProjectType.Annotation  LabelOrder.AS  106.618  1.700000  1.700000   \n",
       "28       ProjectType.Annotation  LabelOrder.AS  124.143  2.600000  2.000000   \n",
       "29       ProjectType.Annotation  LabelOrder.AS   78.334  1.454545  1.583333   \n",
       "..                          ...            ...      ...       ...       ...   \n",
       "810  ProjectType.CrowdAlgorithm  LabelOrder.SA   67.907  0.636364  0.636364   \n",
       "811  ProjectType.CrowdAlgorithm  LabelOrder.SA  114.252  2.076923  1.785714   \n",
       "812  ProjectType.CrowdAlgorithm  LabelOrder.SA  186.153  1.583333  1.583333   \n",
       "813  ProjectType.CrowdAlgorithm  LabelOrder.SA   35.581  1.571429  1.466667   \n",
       "814  ProjectType.CrowdAlgorithm  LabelOrder.SA   20.965  1.888889  1.333333   \n",
       "815  ProjectType.CrowdAlgorithm  LabelOrder.SA   38.119  2.181818  1.100000   \n",
       "816  ProjectType.CrowdAlgorithm  LabelOrder.SA   35.834  1.714286  1.785714   \n",
       "817  ProjectType.CrowdAlgorithm  LabelOrder.SA   25.152  0.846154  1.000000   \n",
       "818  ProjectType.CrowdAlgorithm  LabelOrder.SA   91.177  1.727273  1.636364   \n",
       "819  ProjectType.CrowdAlgorithm  LabelOrder.SA   26.933  0.750000  0.909091   \n",
       "820      ProjectType.Annotation  LabelOrder.SA  212.099  1.692308  1.500000   \n",
       "821      ProjectType.Annotation  LabelOrder.SA  121.412  2.000000  1.333333   \n",
       "822      ProjectType.Annotation  LabelOrder.SA  186.285  2.444444  1.666667   \n",
       "823      ProjectType.Annotation  LabelOrder.SA  142.601  2.300000  1.692308   \n",
       "824      ProjectType.Annotation  LabelOrder.SA  117.076  2.400000  0.916667   \n",
       "825      ProjectType.Annotation  LabelOrder.SA  137.557  1.666667  1.666667   \n",
       "826      ProjectType.Annotation  LabelOrder.SA   68.571  0.000000  0.142857   \n",
       "827      ProjectType.Annotation  LabelOrder.SA  117.070  2.300000  1.700000   \n",
       "828      ProjectType.Annotation  LabelOrder.SA  111.148  2.642857  2.000000   \n",
       "829      ProjectType.Annotation  LabelOrder.SA   61.535  1.600000  1.583333   \n",
       "830      ProjectType.Annotation  LabelOrder.SA   78.373  0.818182  0.636364   \n",
       "831      ProjectType.Annotation  LabelOrder.SA   61.587  2.083333  1.785714   \n",
       "832      ProjectType.Annotation  LabelOrder.SA   68.545  1.600000  1.583333   \n",
       "833      ProjectType.Annotation  LabelOrder.SA  130.860  1.933333  1.466667   \n",
       "834      ProjectType.Annotation  LabelOrder.SA   95.419  1.363636  1.333333   \n",
       "835      ProjectType.Annotation  LabelOrder.SA   81.163  1.454545  1.100000   \n",
       "836      ProjectType.Annotation  LabelOrder.SA   71.300  2.000000  1.785714   \n",
       "837      ProjectType.Annotation  LabelOrder.SA   89.476  0.928571  1.000000   \n",
       "838      ProjectType.Annotation  LabelOrder.SA   67.450  2.300000  1.636364   \n",
       "839      ProjectType.Annotation  LabelOrder.SA   52.304  1.000000  0.909091   \n",
       "\n",
       "                    ImageType       Acc  gt_num_cells  num_cells  \\\n",
       "0         ImageType.NoChanges  0.666667            12         13   \n",
       "1    ImageType.IncreasedGrade  1.000000            12         13   \n",
       "2      ImageType.RemovedRects  1.000000             9         10   \n",
       "3      ImageType.RemovedRects  0.583333            13         12   \n",
       "4         ImageType.NoChanges  0.727273            12         14   \n",
       "5         ImageType.NoChanges  0.777778             9          9   \n",
       "6      ImageType.RemovedRects  1.000000             7          9   \n",
       "7    ImageType.IncreasedGrade  0.500000            10         10   \n",
       "8    ImageType.IncreasedGrade  0.428571            14         14   \n",
       "9         ImageType.NoChanges  0.833333            12         12   \n",
       "10        ImageType.NoChanges  1.000000            11         11   \n",
       "11     ImageType.RemovedRects  0.785714            14         15   \n",
       "12        ImageType.NoChanges  0.909091            12         11   \n",
       "13     ImageType.RemovedRects  0.866667            15         16   \n",
       "14   ImageType.IncreasedGrade  0.555556             9          9   \n",
       "15   ImageType.IncreasedGrade  0.900000            10         11   \n",
       "16        ImageType.NoChanges  0.642857            14         14   \n",
       "17        ImageType.NoChanges  0.846154            13         13   \n",
       "18        ImageType.NoChanges  0.400000            11         10   \n",
       "19        ImageType.NoChanges  0.727273            11         12   \n",
       "20        ImageType.NoChanges  0.833333            12         13   \n",
       "21   ImageType.IncreasedGrade  0.833333            12         12   \n",
       "22     ImageType.RemovedRects  0.571429             9          9   \n",
       "23     ImageType.RemovedRects  0.700000            13         11   \n",
       "24        ImageType.NoChanges  0.750000            12         14   \n",
       "25        ImageType.NoChanges  0.857143             9          7   \n",
       "26     ImageType.RemovedRects  0.666667             7          3   \n",
       "27   ImageType.IncreasedGrade  0.800000            10         10   \n",
       "28   ImageType.IncreasedGrade  0.384615            14         15   \n",
       "29        ImageType.NoChanges  0.636364            12         11   \n",
       "..                        ...       ...           ...        ...   \n",
       "810       ImageType.NoChanges  1.000000            11         11   \n",
       "811    ImageType.RemovedRects  0.846154            14         13   \n",
       "812       ImageType.NoChanges  0.916667            12         12   \n",
       "813    ImageType.RemovedRects  1.000000            15         14   \n",
       "814  ImageType.IncreasedGrade  0.444444             9          9   \n",
       "815  ImageType.IncreasedGrade  0.100000            10         11   \n",
       "816       ImageType.NoChanges  0.857143            14         14   \n",
       "817       ImageType.NoChanges  0.846154            13         13   \n",
       "818       ImageType.NoChanges  0.909091            11         11   \n",
       "819       ImageType.NoChanges  0.909091            11         12   \n",
       "820       ImageType.NoChanges  0.750000            12         13   \n",
       "821  ImageType.IncreasedGrade  0.400000            12         11   \n",
       "822    ImageType.RemovedRects  0.428571             9          9   \n",
       "823    ImageType.RemovedRects  0.666667            13         10   \n",
       "824       ImageType.NoChanges  0.400000            12          5   \n",
       "825       ImageType.NoChanges  0.500000             9          6   \n",
       "826    ImageType.RemovedRects  0.500000             7          2   \n",
       "827  ImageType.IncreasedGrade  0.400000            10         10   \n",
       "828  ImageType.IncreasedGrade  0.461538            14         14   \n",
       "829       ImageType.NoChanges  0.700000            12         10   \n",
       "830       ImageType.NoChanges  0.818182            11         11   \n",
       "831    ImageType.RemovedRects  0.545455            14         12   \n",
       "832       ImageType.NoChanges  0.900000            12         10   \n",
       "833    ImageType.RemovedRects  0.461538            15         15   \n",
       "834  ImageType.IncreasedGrade  0.555556             9         11   \n",
       "835  ImageType.IncreasedGrade  0.900000            10         11   \n",
       "836       ImageType.NoChanges  0.714286            14         16   \n",
       "837       ImageType.NoChanges  0.846154            13         14   \n",
       "838       ImageType.NoChanges  0.666667            11         10   \n",
       "839       ImageType.NoChanges  0.900000            11         11   \n",
       "\n",
       "     unchanged_boxes  changed_grade  changed_boxes  unchanged_boxes_ration  \\\n",
       "0                  7              4              2                0.538462   \n",
       "1                  1              0             12                0.076923   \n",
       "2                  6              0              4                0.600000   \n",
       "3                  5              3              4                0.416667   \n",
       "4                  6              0              8                0.428571   \n",
       "5                  7              0              2                0.777778   \n",
       "6                  1              0              8                0.111111   \n",
       "7                  2              3              5                0.200000   \n",
       "8                  1              8              5                0.071429   \n",
       "9                 11              1              0                0.916667   \n",
       "10                11              0              0                1.000000   \n",
       "11                 7              0              8                0.466667   \n",
       "12                 8              0              3                0.727273   \n",
       "13                11              2              3                0.687500   \n",
       "14                 4              3              2                0.444444   \n",
       "15                 9              1              1                0.818182   \n",
       "16                 8              3              3                0.571429   \n",
       "17                11              2              0                0.846154   \n",
       "18                 4              2              4                0.400000   \n",
       "19                 8              3              1                0.666667   \n",
       "20                 0              0             13                0.000000   \n",
       "21                 0              0             12                0.000000   \n",
       "22                 0              0              9                0.000000   \n",
       "23                 0              0             11                0.000000   \n",
       "24                 0              0             14                0.000000   \n",
       "25                 0              0              7                0.000000   \n",
       "26                 0              0              3                0.000000   \n",
       "27                 0              0             10                0.000000   \n",
       "28                 0              0             15                0.000000   \n",
       "29                 0              0             11                0.000000   \n",
       "..               ...            ...            ...                     ...   \n",
       "810               11              0              0                1.000000   \n",
       "811               11              1              1                0.846154   \n",
       "812               10              0              2                0.833333   \n",
       "813               12              0              2                0.857143   \n",
       "814                4              5              0                0.444444   \n",
       "815                1              9              1                0.090909   \n",
       "816               13              1              0                0.928571   \n",
       "817               11              2              0                0.846154   \n",
       "818                9              1              1                0.818182   \n",
       "819               10              1              1                0.833333   \n",
       "820                0              0             13                0.000000   \n",
       "821                0              0             11                0.000000   \n",
       "822                0              0              9                0.000000   \n",
       "823                0              0             10                0.000000   \n",
       "824                0              0              5                0.000000   \n",
       "825                0              0              6                0.000000   \n",
       "826                0              0              2                0.000000   \n",
       "827                0              0             10                0.000000   \n",
       "828                0              0             14                0.000000   \n",
       "829                0              0             10                0.000000   \n",
       "830                0              0             11                0.000000   \n",
       "831                0              0             12                0.000000   \n",
       "832                0              0             10                0.000000   \n",
       "833                0              0             15                0.000000   \n",
       "834                0              0             11                0.000000   \n",
       "835                0              0             11                0.000000   \n",
       "836                0              0             16                0.000000   \n",
       "837                0              0             14                0.000000   \n",
       "838                0              0             10                0.000000   \n",
       "839                0              0             11                0.000000   \n",
       "\n",
       "     changed_grade_ration  \n",
       "0                0.307692  \n",
       "1                0.000000  \n",
       "2                0.000000  \n",
       "3                0.250000  \n",
       "4                0.000000  \n",
       "5                0.000000  \n",
       "6                0.000000  \n",
       "7                0.300000  \n",
       "8                0.571429  \n",
       "9                0.083333  \n",
       "10               0.000000  \n",
       "11               0.000000  \n",
       "12               0.000000  \n",
       "13               0.125000  \n",
       "14               0.333333  \n",
       "15               0.090909  \n",
       "16               0.214286  \n",
       "17               0.153846  \n",
       "18               0.200000  \n",
       "19               0.250000  \n",
       "20               0.000000  \n",
       "21               0.000000  \n",
       "22               0.000000  \n",
       "23               0.000000  \n",
       "24               0.000000  \n",
       "25               0.000000  \n",
       "26               0.000000  \n",
       "27               0.000000  \n",
       "28               0.000000  \n",
       "29               0.000000  \n",
       "..                    ...  \n",
       "810              0.000000  \n",
       "811              0.076923  \n",
       "812              0.000000  \n",
       "813              0.000000  \n",
       "814              0.555556  \n",
       "815              0.818182  \n",
       "816              0.071429  \n",
       "817              0.153846  \n",
       "818              0.090909  \n",
       "819              0.083333  \n",
       "820              0.000000  \n",
       "821              0.000000  \n",
       "822              0.000000  \n",
       "823              0.000000  \n",
       "824              0.000000  \n",
       "825              0.000000  \n",
       "826              0.000000  \n",
       "827              0.000000  \n",
       "828              0.000000  \n",
       "829              0.000000  \n",
       "830              0.000000  \n",
       "831              0.000000  \n",
       "832              0.000000  \n",
       "833              0.000000  \n",
       "834              0.000000  \n",
       "835              0.000000  \n",
       "836              0.000000  \n",
       "837              0.000000  \n",
       "838              0.000000  \n",
       "839              0.000000  \n",
       "\n",
       "[800 rows x 17 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_statistics = statistics.calc_statistics_image()\n",
    "image_statistics['unchanged_boxes_ration'] = image_statistics['unchanged_boxes'] / image_statistics['num_cells']\n",
    "image_statistics['changed_grade_ration'] = image_statistics['changed_grade'] / image_statistics['num_cells']\n",
    "image_statistics['unchanged_boxes_ration'] = image_statistics['unchanged_boxes'] / image_statistics['num_cells']\n",
    "\n",
    "dl_image_statistics = image_statistics[image_statistics['Initials'] == \"DL\"]\n",
    "image_statistics = image_statistics[image_statistics['Initials'] != \"DL\"]\n",
    "\n",
    "image_statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Initials</th>\n",
       "      <th>Skill</th>\n",
       "      <th>File</th>\n",
       "      <th>Type</th>\n",
       "      <th>Order</th>\n",
       "      <th>Time</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Grade GT</th>\n",
       "      <th>ImageType</th>\n",
       "      <th>Acc</th>\n",
       "      <th>gt_num_cells</th>\n",
       "      <th>num_cells</th>\n",
       "      <th>unchanged_boxes</th>\n",
       "      <th>changed_grade</th>\n",
       "      <th>changed_boxes</th>\n",
       "      <th>unchanged_boxes_ration</th>\n",
       "      <th>changed_grade_ration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>30_EIPH_588355 Berliner Blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>7.908</td>\n",
       "      <td>1.076923</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>04_EIPH_567017 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>2.601</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>14_EIPH_568381 berliner blau-001.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>3.431</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>17_EIPH_575796 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>7.306</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>23_EIPH_563476 Berliner Blau-001.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>2.018</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>25_EIPH_568150 Berliner Blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>2.634</td>\n",
       "      <td>1.444444</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>28_EIPH_569948 L berliner blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>2.617</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>01_EIPH_563479 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>2.326</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>08_EIPH_574999 Berliner Blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.690</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>15_EIPH_568320 berliner blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>2.619</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>19_EIPH_566933 L Berliner Blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>3.471</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>23_EIPH_563476 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.993</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>25_EIPH_568150 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.622</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>30_EIPH_568355 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>2.421</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>02_EIPH_574162 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.675</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>13_EIPH_570370 Berliner Blau.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>3.146</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>15_EIPH_568320 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.449</td>\n",
       "      <td>1.714286</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>19_EIPH_566933 L Tunrbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.395</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>24_EIPH_576255 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.545455</td>\n",
       "      <td>1.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>27_EIPH_571557 Turnbull blue.png</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.482</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>30_EIPH_588355 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>7.908</td>\n",
       "      <td>1.076923</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>04_EIPH_567017 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>2.601</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>14_EIPH_568381 berliner blau-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>3.431</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>17_EIPH_575796 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>7.306</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>23_EIPH_563476 Berliner Blau-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>2.018</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>25_EIPH_568150 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>2.634</td>\n",
       "      <td>1.444444</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>28_EIPH_569948 L berliner blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>2.617</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>01_EIPH_563479 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>2.326</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>08_EIPH_574999 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.690</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>15_EIPH_568320 berliner blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>2.619</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>19_EIPH_566933 L Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>3.471</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>23_EIPH_563476 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.993</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>25_EIPH_568150 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.622</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>30_EIPH_568355 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>2.421</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>02_EIPH_574162 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.675</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>13_EIPH_570370 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>3.146</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>15_EIPH_568320 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.449</td>\n",
       "      <td>1.714286</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>19_EIPH_566933 L Tunrbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.395</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>24_EIPH_576255 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.545455</td>\n",
       "      <td>1.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>27_EIPH_571557 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>1.482</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Initials Skill                                  File  \\\n",
       "40       DL    DL      30_EIPH_588355 Berliner Blau.png   \n",
       "41       DL    DL  04_EIPH_567017 Turnbull blue-001.png   \n",
       "42       DL    DL  14_EIPH_568381 berliner blau-001.png   \n",
       "43       DL    DL      17_EIPH_575796 Turnbull blue.png   \n",
       "44       DL    DL  23_EIPH_563476 Berliner Blau-001.png   \n",
       "45       DL    DL      25_EIPH_568150 Berliner Blau.png   \n",
       "46       DL    DL    28_EIPH_569948 L berliner blau.png   \n",
       "47       DL    DL      01_EIPH_563479 Turnbull blue.png   \n",
       "48       DL    DL      08_EIPH_574999 Berliner Blau.png   \n",
       "49       DL    DL      15_EIPH_568320 berliner blau.png   \n",
       "50       DL    DL    19_EIPH_566933 L Berliner Blau.png   \n",
       "51       DL    DL      23_EIPH_563476 Turnbull blue.png   \n",
       "52       DL    DL      25_EIPH_568150 Turnbull blue.png   \n",
       "53       DL    DL      30_EIPH_568355 Turnbull blue.png   \n",
       "54       DL    DL  02_EIPH_574162 Turnbull blue-001.png   \n",
       "55       DL    DL      13_EIPH_570370 Berliner Blau.png   \n",
       "56       DL    DL      15_EIPH_568320 Turnbull blue.png   \n",
       "57       DL    DL    19_EIPH_566933 L Tunrbull blue.png   \n",
       "58       DL    DL      24_EIPH_576255 Turnbull blue.png   \n",
       "59       DL    DL      27_EIPH_571557 Turnbull blue.png   \n",
       "60       DL    DL      30_EIPH_588355 Berliner Blau.png   \n",
       "61       DL    DL  04_EIPH_567017 Turnbull blue-001.png   \n",
       "62       DL    DL  14_EIPH_568381 berliner blau-001.png   \n",
       "63       DL    DL      17_EIPH_575796 Turnbull blue.png   \n",
       "64       DL    DL  23_EIPH_563476 Berliner Blau-001.png   \n",
       "65       DL    DL      25_EIPH_568150 Berliner Blau.png   \n",
       "66       DL    DL    28_EIPH_569948 L berliner blau.png   \n",
       "67       DL    DL      01_EIPH_563479 Turnbull blue.png   \n",
       "68       DL    DL      08_EIPH_574999 Berliner Blau.png   \n",
       "69       DL    DL      15_EIPH_568320 berliner blau.png   \n",
       "70       DL    DL    19_EIPH_566933 L Berliner Blau.png   \n",
       "71       DL    DL      23_EIPH_563476 Turnbull blue.png   \n",
       "72       DL    DL      25_EIPH_568150 Turnbull blue.png   \n",
       "73       DL    DL      30_EIPH_568355 Turnbull blue.png   \n",
       "74       DL    DL  02_EIPH_574162 Turnbull blue-001.png   \n",
       "75       DL    DL      13_EIPH_570370 Berliner Blau.png   \n",
       "76       DL    DL      15_EIPH_568320 Turnbull blue.png   \n",
       "77       DL    DL    19_EIPH_566933 L Tunrbull blue.png   \n",
       "78       DL    DL      24_EIPH_576255 Turnbull blue.png   \n",
       "79       DL    DL      27_EIPH_571557 Turnbull blue.png   \n",
       "\n",
       "                          Type          Order   Time     Grade  Grade GT  \\\n",
       "40  ProjectType.CrowdAlgorithm  LabelOrder.AS  7.908  1.076923  1.500000   \n",
       "41  ProjectType.CrowdAlgorithm  LabelOrder.AS  2.601  1.000000  1.333333   \n",
       "42  ProjectType.CrowdAlgorithm  LabelOrder.AS  3.431  1.666667  1.666667   \n",
       "43  ProjectType.CrowdAlgorithm  LabelOrder.AS  7.306  1.500000  1.692308   \n",
       "44  ProjectType.CrowdAlgorithm  LabelOrder.AS  2.018  1.083333  0.916667   \n",
       "45  ProjectType.CrowdAlgorithm  LabelOrder.AS  2.634  1.444444  1.666667   \n",
       "46  ProjectType.CrowdAlgorithm  LabelOrder.AS  2.617  0.111111  0.142857   \n",
       "47  ProjectType.CrowdAlgorithm  LabelOrder.AS  2.326  1.333333  1.700000   \n",
       "48  ProjectType.CrowdAlgorithm  LabelOrder.AS  1.690  1.857143  2.000000   \n",
       "49  ProjectType.CrowdAlgorithm  LabelOrder.AS  2.619  1.583333  1.583333   \n",
       "50  ProjectType.CrowdAlgorithm  LabelOrder.AS  3.471  0.583333  0.636364   \n",
       "51  ProjectType.CrowdAlgorithm  LabelOrder.AS  1.993  1.571429  1.785714   \n",
       "52  ProjectType.CrowdAlgorithm  LabelOrder.AS  1.622  1.583333  1.583333   \n",
       "53  ProjectType.CrowdAlgorithm  LabelOrder.AS  2.421  1.400000  1.466667   \n",
       "54  ProjectType.CrowdAlgorithm  LabelOrder.AS  1.675  0.888889  1.333333   \n",
       "55  ProjectType.CrowdAlgorithm  LabelOrder.AS  3.146  1.454545  1.100000   \n",
       "56  ProjectType.CrowdAlgorithm  LabelOrder.AS  1.449  1.714286  1.785714   \n",
       "57  ProjectType.CrowdAlgorithm  LabelOrder.AS  1.395  0.923077  1.000000   \n",
       "58  ProjectType.CrowdAlgorithm  LabelOrder.AS  1.000  1.545455  1.636364   \n",
       "59  ProjectType.CrowdAlgorithm  LabelOrder.AS  1.482  0.833333  0.909091   \n",
       "60      ProjectType.Annotation  LabelOrder.AS  7.908  1.076923  1.500000   \n",
       "61      ProjectType.Annotation  LabelOrder.AS  2.601  1.000000  1.333333   \n",
       "62      ProjectType.Annotation  LabelOrder.AS  3.431  1.666667  1.666667   \n",
       "63      ProjectType.Annotation  LabelOrder.AS  7.306  1.500000  1.692308   \n",
       "64      ProjectType.Annotation  LabelOrder.AS  2.018  1.083333  0.916667   \n",
       "65      ProjectType.Annotation  LabelOrder.AS  2.634  1.444444  1.666667   \n",
       "66      ProjectType.Annotation  LabelOrder.AS  2.617  0.111111  0.142857   \n",
       "67      ProjectType.Annotation  LabelOrder.AS  2.326  1.333333  1.700000   \n",
       "68      ProjectType.Annotation  LabelOrder.AS  1.690  1.857143  2.000000   \n",
       "69      ProjectType.Annotation  LabelOrder.AS  2.619  1.583333  1.583333   \n",
       "70      ProjectType.Annotation  LabelOrder.AS  3.471  0.583333  0.636364   \n",
       "71      ProjectType.Annotation  LabelOrder.AS  1.993  1.571429  1.785714   \n",
       "72      ProjectType.Annotation  LabelOrder.AS  1.622  1.583333  1.583333   \n",
       "73      ProjectType.Annotation  LabelOrder.AS  2.421  1.400000  1.466667   \n",
       "74      ProjectType.Annotation  LabelOrder.AS  1.675  0.888889  1.333333   \n",
       "75      ProjectType.Annotation  LabelOrder.AS  3.146  1.454545  1.100000   \n",
       "76      ProjectType.Annotation  LabelOrder.AS  1.449  1.714286  1.785714   \n",
       "77      ProjectType.Annotation  LabelOrder.AS  1.395  0.923077  1.000000   \n",
       "78      ProjectType.Annotation  LabelOrder.AS  1.000  1.545455  1.636364   \n",
       "79      ProjectType.Annotation  LabelOrder.AS  1.482  0.833333  0.909091   \n",
       "\n",
       "                   ImageType       Acc  gt_num_cells  num_cells  \\\n",
       "40       ImageType.NoChanges  0.500000            12         13   \n",
       "41  ImageType.IncreasedGrade  0.750000            12         13   \n",
       "42    ImageType.RemovedRects  0.777778             9          9   \n",
       "43    ImageType.RemovedRects  0.750000            13         12   \n",
       "44       ImageType.NoChanges  0.818182            12         12   \n",
       "45       ImageType.NoChanges  0.777778             9          9   \n",
       "46    ImageType.RemovedRects  1.000000             7          9   \n",
       "47  ImageType.IncreasedGrade  0.666667            10          9   \n",
       "48  ImageType.IncreasedGrade  0.857143            14         14   \n",
       "49       ImageType.NoChanges  0.750000            12         12   \n",
       "50       ImageType.NoChanges  0.909091            11         12   \n",
       "51    ImageType.RemovedRects  0.785714            14         14   \n",
       "52       ImageType.NoChanges  0.916667            12         12   \n",
       "53    ImageType.RemovedRects  0.933333            15         15   \n",
       "54  ImageType.IncreasedGrade  0.555556             9          9   \n",
       "55  ImageType.IncreasedGrade  0.900000            10         11   \n",
       "56       ImageType.NoChanges  0.857143            14         14   \n",
       "57       ImageType.NoChanges  0.923077            13         13   \n",
       "58       ImageType.NoChanges  0.909091            11         11   \n",
       "59       ImageType.NoChanges  1.000000            11         12   \n",
       "60       ImageType.NoChanges  0.500000            12         13   \n",
       "61  ImageType.IncreasedGrade  0.750000            12         13   \n",
       "62    ImageType.RemovedRects  0.777778             9          9   \n",
       "63    ImageType.RemovedRects  0.750000            13         12   \n",
       "64       ImageType.NoChanges  0.818182            12         12   \n",
       "65       ImageType.NoChanges  0.777778             9          9   \n",
       "66    ImageType.RemovedRects  1.000000             7          9   \n",
       "67  ImageType.IncreasedGrade  0.666667            10          9   \n",
       "68  ImageType.IncreasedGrade  0.857143            14         14   \n",
       "69       ImageType.NoChanges  0.750000            12         12   \n",
       "70       ImageType.NoChanges  0.909091            11         12   \n",
       "71    ImageType.RemovedRects  0.785714            14         14   \n",
       "72       ImageType.NoChanges  0.916667            12         12   \n",
       "73    ImageType.RemovedRects  0.933333            15         15   \n",
       "74  ImageType.IncreasedGrade  0.555556             9          9   \n",
       "75  ImageType.IncreasedGrade  0.900000            10         11   \n",
       "76       ImageType.NoChanges  0.857143            14         14   \n",
       "77       ImageType.NoChanges  0.923077            13         13   \n",
       "78       ImageType.NoChanges  0.909091            11         11   \n",
       "79       ImageType.NoChanges  1.000000            11         12   \n",
       "\n",
       "    unchanged_boxes  changed_grade  changed_boxes  unchanged_boxes_ration  \\\n",
       "40                6              5              2                0.461538   \n",
       "41                9              3              1                0.692308   \n",
       "42                7              2              0                0.777778   \n",
       "43                9              3              0                0.750000   \n",
       "44                9              2              1                0.750000   \n",
       "45                7              2              0                0.777778   \n",
       "46                7              0              2                0.777778   \n",
       "47                6              3              0                0.666667   \n",
       "48               11              2              1                0.785714   \n",
       "49               10              2              0                0.833333   \n",
       "50               10              1              1                0.833333   \n",
       "51               10              3              1                0.714286   \n",
       "52               11              0              1                0.916667   \n",
       "53               14              1              0                0.933333   \n",
       "54                5              4              0                0.555556   \n",
       "55                9              1              1                0.818182   \n",
       "56               13              1              0                0.928571   \n",
       "57               12              1              0                0.923077   \n",
       "58                9              1              1                0.818182   \n",
       "59               11              0              1                0.916667   \n",
       "60                6              5              2                0.461538   \n",
       "61                9              3              1                0.692308   \n",
       "62                7              2              0                0.777778   \n",
       "63                9              3              0                0.750000   \n",
       "64                9              2              1                0.750000   \n",
       "65                7              2              0                0.777778   \n",
       "66                7              0              2                0.777778   \n",
       "67                6              3              0                0.666667   \n",
       "68               11              2              1                0.785714   \n",
       "69               10              2              0                0.833333   \n",
       "70               10              1              1                0.833333   \n",
       "71               10              3              1                0.714286   \n",
       "72               11              0              1                0.916667   \n",
       "73               14              1              0                0.933333   \n",
       "74                5              4              0                0.555556   \n",
       "75                9              1              1                0.818182   \n",
       "76               13              1              0                0.928571   \n",
       "77               12              1              0                0.923077   \n",
       "78                9              1              1                0.818182   \n",
       "79               11              0              1                0.916667   \n",
       "\n",
       "    changed_grade_ration  \n",
       "40              0.384615  \n",
       "41              0.230769  \n",
       "42              0.222222  \n",
       "43              0.250000  \n",
       "44              0.166667  \n",
       "45              0.222222  \n",
       "46              0.000000  \n",
       "47              0.333333  \n",
       "48              0.142857  \n",
       "49              0.166667  \n",
       "50              0.083333  \n",
       "51              0.214286  \n",
       "52              0.000000  \n",
       "53              0.066667  \n",
       "54              0.444444  \n",
       "55              0.090909  \n",
       "56              0.071429  \n",
       "57              0.076923  \n",
       "58              0.090909  \n",
       "59              0.000000  \n",
       "60              0.384615  \n",
       "61              0.230769  \n",
       "62              0.222222  \n",
       "63              0.250000  \n",
       "64              0.166667  \n",
       "65              0.222222  \n",
       "66              0.000000  \n",
       "67              0.333333  \n",
       "68              0.142857  \n",
       "69              0.166667  \n",
       "70              0.083333  \n",
       "71              0.214286  \n",
       "72              0.000000  \n",
       "73              0.066667  \n",
       "74              0.444444  \n",
       "75              0.090909  \n",
       "76              0.071429  \n",
       "77              0.076923  \n",
       "78              0.090909  \n",
       "79              0.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl_image_statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BT',\n",
       " 'CB',\n",
       " 'DSS',\n",
       " 'HJ',\n",
       " 'JA',\n",
       " 'JM',\n",
       " 'JS',\n",
       " 'KJ',\n",
       " 'LJ',\n",
       " 'LM',\n",
       " 'MB',\n",
       " 'MH',\n",
       " 'MS',\n",
       " 'PA',\n",
       " 'PM',\n",
       " 'RK',\n",
       " 'SC',\n",
       " 'SE',\n",
       " 'SG',\n",
       " 'SO'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(image_statistics['Initials'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ImageType.NoChanges unchanged_boxes_ration: Min: 0.00 Max: 1.00  Mean: 0.67  STD: 0.29\n",
      "ImageType.IncreasedGrade unchanged_boxes_ration: Min: 0.00 Max: 0.93  Mean: 0.20  STD: 0.23\n",
      "ImageType.RemovedRects unchanged_boxes_ration: Min: 0.00 Max: 1.00  Mean: 0.64  STD: 0.29\n",
      "\n",
      "\n",
      "ImageType.NoChanges unchanged_boxes: Min: 0.00 Max: 13.00  Mean: 8.00  STD: 3.53\n",
      "ImageType.IncreasedGrade unchanged_boxes: Min: 0.00 Max: 13.00  Mean: 2.18  STD: 2.53\n",
      "ImageType.RemovedRects unchanged_boxes: Min: 0.00 Max: 15.00  Mean: 7.45  STD: 4.01\n",
      "\n",
      "\n",
      "ImageType.NoChanges changed_grade: Min: 0.00 Max: 5.00  Mean: 0.92  STD: 1.12\n",
      "ImageType.IncreasedGrade changed_grade: Min: 0.00 Max: 12.00  Mean: 5.36  STD: 3.99\n",
      "ImageType.RemovedRects changed_grade: Min: 0.00 Max: 3.00  Mean: 0.63  STD: 1.02\n",
      "\n",
      "\n",
      "ImageType.NoChanges changed_boxes: Min: 0.00 Max: 14.00  Mean: 3.18  STD: 3.72\n",
      "ImageType.IncreasedGrade changed_boxes: Min: 0.00 Max: 16.00  Mean: 4.14  STD: 4.25\n",
      "ImageType.RemovedRects changed_boxes: Min: 0.00 Max: 17.00  Mean: 3.57  STD: 3.80\n",
      "\n",
      "\n",
      "ImageType.NoChanges Time: Min: 2.81 Max: 4596.55  Mean: 104.63  STD: 325.23\n",
      "ImageType.IncreasedGrade Time: Min: 10.26 Max: 898.61  Mean: 117.71  STD: 124.65\n",
      "ImageType.RemovedRects Time: Min: 4.08 Max: 936.83  Mean: 105.09  STD: 121.33\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Annotation  -> ImageType.NoChanges\n",
    "annotation = image_statistics[image_statistics['Type'] == ProjectType.CrowdAlgorithm]\n",
    "\n",
    "\n",
    "for column in ['unchanged_boxes_ration', 'unchanged_boxes', 'changed_grade', 'changed_boxes', 'Time']:\n",
    "    for type_name in [ImageType.NoChanges, ImageType.IncreasedGrade, ImageType.RemovedRects]:\n",
    "    \n",
    "        image_type = annotation[annotation['ImageType'] == type_name]\n",
    "    \n",
    "        \n",
    "        df = image_type[image_type['Initials'] != \"DL\"][column] \n",
    "        \n",
    "        print( str(type_name) + \" \" + column + \": Min: {0:.2f} Max: {1:.2f}  Mean: {2:.2f}  STD: {3:.2f}\".format(np.min(df), np.max(df), np.mean(df), np.std(df)))\n",
    "        \n",
    "    print(\"\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9524"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_statistics['num_cells'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Num contributors: 20'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Num contributors: {}\".format(len(set(image_statistics['Initials'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num contributors: 5 per skill level: 1\n",
      "Num contributors: 5 per skill level: 2\n",
      "Num contributors: 5 per skill level: 3\n",
      "Num contributors: 5 per skill level: 4\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,5):\n",
    "    print(\"Num contributors: {0} per skill level: {1}\".format(len(set(image_statistics[image_statistics['Skill'] == i]['Initials'])), i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44c17a56d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc_statistics = image_statistics[image_statistics['Type'] == ProjectType.Annotation]\n",
    "\n",
    "\n",
    "f, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "columns = ['Acc']\n",
    "temp_data = pd.melt(acc_statistics[columns])\n",
    "temp_data['Order'] = len(columns) * [str(type).split('.')[-1] for type in list(acc_statistics['Order'].values)]\n",
    "ax = sns.boxplot(x=\"variable\", y=\"value\", data=temp_data, width=.3,  showfliers=False, hue='Order') #\n",
    "#ax.set(ylim=(0.55, 1))\n",
    "\n",
    "\n",
    "for i,box in enumerate(ax.artists):\n",
    "    box.set_edgecolor('black')\n",
    "    #box.set_facecolor('white')\n",
    "\n",
    "#ax.xaxis.grid(True)\n",
    "#ax.yaxis.grid(True)\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "by_label = OrderedDict(zip(labels, handles))\n",
    "\n",
    "ax.legend(by_label.values(), by_label.keys(), bbox_to_anchor=(1., 0.9), prop={'size': 25}, frameon=False)\n",
    "\n",
    "ax.set(ylabel=\"Acc\", xlabel=\"\", title=\"Trainings effect\")\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44c17b0a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_statistics = image_statistics[image_statistics['Type'] == ProjectType.CrowdAlgorithm]\n",
    "\n",
    "\n",
    "f, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "columns = ['changed_grade', 'changed_boxes']\n",
    "temp_data = pd.melt(time_statistics[columns])\n",
    "temp_data['ImageType'] = len(columns) * [str(type).split('.')[-1] for type in list(time_statistics['ImageType'].values)]\n",
    "ax = sns.boxplot(x=\"variable\", y=\"value\", data=temp_data, width=.3,  showfliers=False, hue='ImageType') #\n",
    "#ax.set(ylim=(0.55, 1))\n",
    "\n",
    "\n",
    "for i,box in enumerate(ax.artists):\n",
    "    box.set_edgecolor('black')\n",
    "    #box.set_facecolor('white')\n",
    "\n",
    "#ax.xaxis.grid(True)\n",
    "#ax.yaxis.grid(True)\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "by_label = OrderedDict(zip(labels, handles))\n",
    "\n",
    "ax.legend(by_label.values(), by_label.keys(), bbox_to_anchor=(1., 0.9), prop={'size': 25}, frameon=False)\n",
    "\n",
    "ax.set(ylabel=\"Interactions\", xlabel=\"\", title=\"Interactions\")\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44c16d3550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_statistics = image_statistics[image_statistics['Type'] == ProjectType.CrowdAlgorithm]\n",
    "\n",
    "\n",
    "f, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "columns = ['Time']\n",
    "temp_data = pd.melt(time_statistics[columns])\n",
    "temp_data['ImageType'] = len(columns) * [str(type).split('.')[-1] for type in list(time_statistics['ImageType'].values)]\n",
    "ax = sns.boxplot(x=\"variable\", y=\"value\", data=temp_data, width=.3,  showfliers=False, hue='ImageType') #\n",
    "#ax.set(ylim=(0.55, 1))\n",
    "\n",
    "\n",
    "for i,box in enumerate(ax.artists):\n",
    "    box.set_edgecolor('black')\n",
    "    #box.set_facecolor('white')\n",
    "\n",
    "#ax.xaxis.grid(True)\n",
    "#ax.yaxis.grid(True)\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "by_label = OrderedDict(zip(labels, handles))\n",
    "\n",
    "ax.legend(by_label.values(), by_label.keys(), bbox_to_anchor=(1., 0.9), prop={'size': 25}, frameon=False)\n",
    "\n",
    "ax.set(ylabel=\"Seconds\", xlabel=\"\", title=\"Interaction Time\")\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Initials</th>\n",
       "      <th>Skill</th>\n",
       "      <th>Order</th>\n",
       "      <th>Grade-Error</th>\n",
       "      <th>Kappa</th>\n",
       "      <th>mAP</th>\n",
       "      <th>mAP-IG</th>\n",
       "      <th>Acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>0.102888</td>\n",
       "      <td>0.707213</td>\n",
       "      <td>0.638740</td>\n",
       "      <td>0.873534</td>\n",
       "      <td>0.772727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>-0.144957</td>\n",
       "      <td>0.775178</td>\n",
       "      <td>0.653328</td>\n",
       "      <td>0.852950</td>\n",
       "      <td>0.826484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MB</td>\n",
       "      <td>3</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>0.102864</td>\n",
       "      <td>0.635367</td>\n",
       "      <td>0.473234</td>\n",
       "      <td>0.794824</td>\n",
       "      <td>0.732057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DSS</td>\n",
       "      <td>3</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>-0.272886</td>\n",
       "      <td>0.437763</td>\n",
       "      <td>0.360375</td>\n",
       "      <td>0.788714</td>\n",
       "      <td>0.567961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PA</td>\n",
       "      <td>4</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>-0.191163</td>\n",
       "      <td>0.546219</td>\n",
       "      <td>0.466292</td>\n",
       "      <td>0.850343</td>\n",
       "      <td>0.651376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LM</td>\n",
       "      <td>3</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>-0.168487</td>\n",
       "      <td>0.579377</td>\n",
       "      <td>0.492164</td>\n",
       "      <td>0.843734</td>\n",
       "      <td>0.669811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SE</td>\n",
       "      <td>2</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>0.133080</td>\n",
       "      <td>0.565379</td>\n",
       "      <td>0.392915</td>\n",
       "      <td>0.746324</td>\n",
       "      <td>0.669643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RK</td>\n",
       "      <td>4</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>-0.116392</td>\n",
       "      <td>0.587849</td>\n",
       "      <td>0.406171</td>\n",
       "      <td>0.795050</td>\n",
       "      <td>0.676768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CB</td>\n",
       "      <td>4</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>0.027955</td>\n",
       "      <td>0.811753</td>\n",
       "      <td>0.748701</td>\n",
       "      <td>0.915706</td>\n",
       "      <td>0.860987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MH</td>\n",
       "      <td>3</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>0.009284</td>\n",
       "      <td>0.831000</td>\n",
       "      <td>0.730057</td>\n",
       "      <td>0.848606</td>\n",
       "      <td>0.868778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>JA</td>\n",
       "      <td>1</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>0.300214</td>\n",
       "      <td>0.444019</td>\n",
       "      <td>0.326740</td>\n",
       "      <td>0.774868</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SO</td>\n",
       "      <td>1</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>0.180045</td>\n",
       "      <td>0.531604</td>\n",
       "      <td>0.263823</td>\n",
       "      <td>0.395398</td>\n",
       "      <td>0.638060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MS</td>\n",
       "      <td>4</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>-0.267904</td>\n",
       "      <td>0.679316</td>\n",
       "      <td>0.447949</td>\n",
       "      <td>0.702276</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SC</td>\n",
       "      <td>1</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>-0.273107</td>\n",
       "      <td>0.505211</td>\n",
       "      <td>0.361470</td>\n",
       "      <td>0.758877</td>\n",
       "      <td>0.607527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PM</td>\n",
       "      <td>2</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>-0.134309</td>\n",
       "      <td>0.561772</td>\n",
       "      <td>0.318144</td>\n",
       "      <td>0.619121</td>\n",
       "      <td>0.664948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>JM</td>\n",
       "      <td>1</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>-0.093136</td>\n",
       "      <td>0.516034</td>\n",
       "      <td>0.424160</td>\n",
       "      <td>0.817492</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BT</td>\n",
       "      <td>4</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>-0.044898</td>\n",
       "      <td>0.431095</td>\n",
       "      <td>0.358308</td>\n",
       "      <td>0.764198</td>\n",
       "      <td>0.560847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SG</td>\n",
       "      <td>2</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>0.093178</td>\n",
       "      <td>0.649570</td>\n",
       "      <td>0.478638</td>\n",
       "      <td>0.893418</td>\n",
       "      <td>0.725225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>-0.096829</td>\n",
       "      <td>0.598026</td>\n",
       "      <td>0.476946</td>\n",
       "      <td>0.798931</td>\n",
       "      <td>0.695238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>-0.145533</td>\n",
       "      <td>0.532470</td>\n",
       "      <td>0.392298</td>\n",
       "      <td>0.777146</td>\n",
       "      <td>0.641026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Initials Skill          Order  Grade-Error     Kappa       mAP    mAP-IG  \\\n",
       "0        LJ     3  LabelOrder.AS     0.102888  0.707213  0.638740  0.873534   \n",
       "2        JS     1  LabelOrder.AS    -0.144957  0.775178  0.653328  0.852950   \n",
       "3        MB     3  LabelOrder.SA     0.102864  0.635367  0.473234  0.794824   \n",
       "4       DSS     3  LabelOrder.AS    -0.272886  0.437763  0.360375  0.788714   \n",
       "5        PA     4  LabelOrder.SA    -0.191163  0.546219  0.466292  0.850343   \n",
       "6        LM     3  LabelOrder.SA    -0.168487  0.579377  0.492164  0.843734   \n",
       "7        SE     2  LabelOrder.SA     0.133080  0.565379  0.392915  0.746324   \n",
       "8        RK     4  LabelOrder.SA    -0.116392  0.587849  0.406171  0.795050   \n",
       "9        CB     4  LabelOrder.AS     0.027955  0.811753  0.748701  0.915706   \n",
       "10       MH     3  LabelOrder.AS     0.009284  0.831000  0.730057  0.848606   \n",
       "11       JA     1  LabelOrder.SA     0.300214  0.444019  0.326740  0.774868   \n",
       "12       SO     1  LabelOrder.AS     0.180045  0.531604  0.263823  0.395398   \n",
       "13       MS     4  LabelOrder.AS    -0.267904  0.679316  0.447949  0.702276   \n",
       "14       SC     1  LabelOrder.SA    -0.273107  0.505211  0.361470  0.758877   \n",
       "15       PM     2  LabelOrder.AS    -0.134309  0.561772  0.318144  0.619121   \n",
       "16       JM     1  LabelOrder.AS    -0.093136  0.516034  0.424160  0.817492   \n",
       "17       BT     4  LabelOrder.AS    -0.044898  0.431095  0.358308  0.764198   \n",
       "18       SG     2  LabelOrder.SA     0.093178  0.649570  0.478638  0.893418   \n",
       "19       KJ     2  LabelOrder.AS    -0.096829  0.598026  0.476946  0.798931   \n",
       "20       HJ     2  LabelOrder.SA    -0.145533  0.532470  0.392298  0.777146   \n",
       "\n",
       "         Acc  \n",
       "0   0.772727  \n",
       "2   0.826484  \n",
       "3   0.732057  \n",
       "4   0.567961  \n",
       "5   0.651376  \n",
       "6   0.669811  \n",
       "7   0.669643  \n",
       "8   0.676768  \n",
       "9   0.860987  \n",
       "10  0.868778  \n",
       "11  0.571429  \n",
       "12  0.638060  \n",
       "13  0.750000  \n",
       "14  0.607527  \n",
       "15  0.664948  \n",
       "16  0.625000  \n",
       "17  0.560847  \n",
       "18  0.725225  \n",
       "19  0.695238  \n",
       "20  0.641026  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics_intra_observer = statistics.calc_statistics_intra_observer()\n",
    "\n",
    "dl_statistics_intra_observer = statistics_intra_observer[statistics_intra_observer['Initials'] == \"DL\"]\n",
    "statistics_intra_observer = statistics_intra_observer[statistics_intra_observer['Initials'] != \"DL\"]\n",
    "\n",
    "statistics_intra_observer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: Min: 0.5608465608465608 Max: 0.8687782805429864  Mean: 0.6887945915988166  STD: 0.08892430034801012\n",
      "Kappa: Min: 0.4310945093203743 Max: 0.830999657200116  Mean: 0.5963107321640465  STD: 0.11416919418397228\n",
      "mAP: Min: 0.2638226660981201 Max: 0.7487008327366522  Mean: 0.460522618680354  STD: 0.1317637379930873\n",
      "Grade-Error: Min: -0.27310703185703145 Max: 0.3002140732074945  Mean: -0.05000459274706413  STD: 0.15820318726139843\n"
     ]
    }
   ],
   "source": [
    "for column in ['Acc', 'Kappa', 'mAP', 'Grade-Error']:\n",
    "    df = statistics_intra_observer[statistics_intra_observer['Initials'] != \"DL\"][column] \n",
    "    print(column + \": Min: {} Max: {}  Mean: {}  STD: {}\".format(np.min(df), np.max(df), np.mean(df), np.std(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3kY0iuomsKylk3arPz+dLfUZVK6ZiUrmxbKnKxvP9JknbmPhJKvoQA0nfJ4CPluuCFxGVrtMZiVsZaGk6olLBMg4lG+QCstaEFRXKlm2FLCh2ad3hi1zerXJJRLyVLN4jyYbifxHZsVsAXB4R81NKnyxT1+KU0k+HEUu13UjWdXEP4PUR8f5C6+RQ3Txh4DX0kA1Q0jM+YdZc8W/11ZTSaWVL1laxy2S5rthFxR8yKnW3rEsRMQM4O3/6V7Lz6K4yZcer6/BgxVuAzCxbqrLi++0Fo+wuKklDcnAXSUVH549/Ibu1Qrmkbxe2H5RlOymlBSmlKDcNKn5jYf6YQQOqDMebCvM3DXGt2N7DqK9YpuxgOCmlLSmlW1JKn0gpHUd2fdfZZKPtAfzzoAE0ii2R+1MD+eAb/QOV7Ek+aEx+Teff5cvvTCn9pkwV/a+hmVG0zk4iNf9bDVPxnpf7DKN8sUxVBnqaYM9n4IepS8olfblKPQ6q6QkGErf9RlnHZHm/SZrkTPykyaXYJWk0XdiG8uT88aEhRqY7mip9fuQjEd6cP20GPjLcbfORIpcUFl00xCYviIihuka9uDD/6+HGklLamFL6NHBVvmgnBm6EDlBs4XsNtXN5Yb6/le9EBq5dupzy6uU1jKuU0moGroc7PCIGX+tZL35VmD+mUsH82rijC4uG/d6uI08uzP9+iLIvHc9A+uU/NPW30O0eEc+vVL6MKXFeSao9Ez9pcil2CRpO166R6r8G7hlRZhjDiJgGfLjK+z2XgaT2HcMY1Y6IaCNLUvqPw3UppVsrbAJZK+WScivzQWn6f3G/LU8ARmplYb7Ynf52snt1QTYqaMUv6uMlpfS/DNyy4oT8OPYngL1kNxcv5ztk94sDeM8oWmcnk6X54zTgk5UK1tDPye4JCfDKiFhYoexJDNyqY1lKaahBVupR8RrdZ5YrFBGHkd2Tc6IUu0Z/IiJG+t3qfxjoov6yfIAtSao6Ez9pcikO23/oONTf3wowh2zUvO3kA6l8hWykw6rJr2n5WP9ugCsj4ow8ydxBROxNNnz9C/JFq4E3D3N3F+RfDAfX+Uzga4VFnx20/pCI+GhEPJkyImI22RdsyLp83tO/Lm8Z+FCh+BURUbFVIiKeFRGXVCozSv1fVGcC7yC7PhGyrrJlk92U0iMM3DNwd+D6/G9RUmSOiohzqhDzRLuYgYGHlkTEBfn7v6SIaI+IMyfyS3s+GumF+dMW4Hul3p8RcQjZrTz6fWoCwhsPtxfm3xoRO3TnzJPf7zGx32++w8CPOi8CvhYR00sVjIjmiHh5cVneu6L42fDdiDiaCiJi/4j44hhiljQFObiLNImklNZHxJ3AIcCLI+JLZN0kNxTK/GQMu7iYgS5jn4uIxcD1ZIOm7EN2Pd0+wC35YzXv4fcxshHy3kv2JfYLwBkRcTXZMOmbyLp6vZhsBMr+AV0eAV6eUvrLDjXu6Mdkr++XEbGUrMWkl6xL5j8yMDjD1SmlqwZtu2se47kR8UuyQWkeIDv2u5Hd7/ANDFz7+O2U0qpiBSmlH0XEx8hGJpwF/CQifk42curDZIOm9N9D7kV5nb3A24fx2kbi22SD90T+2P8luVI3z34fAhaRXR94EPDbiPgB8DOyBLyF7O90MNmx3ovsPfqJKsY/7lJKG/KW51vIRsB8H3BKRHyXLKHfSPZ+eQbZPfFeTDbC4usnONRPA8eTDTR0ANnf4zLgLrL/8S8kO2/7uzhfMsbPiJpJKT0cET8EXkl2/tyTfwbeR/Yefj7wRrJuy5fn8xMRV09EnEj2mTCLrFfBMRHxHeBuslFS55D9WPd3ZJ8ZPx5Ux/cj4pNkvSl2B26MiJ8CPyH7bOjNl/d/NhxAdpuJd477C5TUMEz8pMnnHLKbik8D3ppPRaO+9i9PTP6VgV+fX5lPRb8ETqHK1wjlLWLvyxPbfyVLKp8JvL/cJsB3gTNTSn8qU2awX5N1ZfwqcGo+DfZjspvXD9bfFXUa8Lf5VM6VwOmlVqSUzo2IR8haFNvJvpi/sFTZ3KMV1o1KSmlV/qVyMQMJwUbgmrIbDWzbnbdYfJYsIW0hu0bwxAqbTeR9+qompXRHRDyX7D1zMNl78j0VNtnK0PeEq6o86XgpWavTy8l+OCh1ziSyH1PeN4HhjYdTyZLx/cnOn8H3veslS55+zQQlfgAppfsj4giya3z3J/vB471lipe8WXxK6ZyIWAV8huzHhhcx0BpfStU/GyQ1NhM/aZJJKV2XDyBwBtmv/HswMNJdNer/cET8DPgn4LlkLV1rgd+RfQH+Rv5ls1q7HLz/b0XE98gSiVcAf0M2YuZ0spbHVWQ3Sv9uSunuUdZ/N9nxewnZF7ROshaSy1JK/1lmu5/l3cheQta6cxBZItCWb78KWA4sTSn9bIgYvpq/xreQDUJxAAP30FtP1pL4P2S/9i8b6WscpsvJEr9+308pDedecKSUuoB3RcQXyFpKX0zW8jWL7BrAP5O9X34BXJtSureKcU+olNLv8q6SrwROIDvnnkx2bekGsms67wb+G/hhSumJGsS4AXhFRBxHluwcmcfYQzZ65y3Al1NKd5avZXJIKa3Jk/F3k3Wr7h9d9k9kg6R8KaX066G6So5TbCsi4iCyH8ZeS9aTYC5Za+Qasu6gNwIlP2PyOr4cEVeSnVf91xz3fzasY/vPhlrcEkbSJBaVRz6XJEmSJE12Du4iSZIkSQ3OxE+SJEmSGpyJnyRJkiQ1OBM/SZIkSWpwjupZPY6SI0mSpIkwPkNrq6HZ4idJkiRJDc7ET5IkSZIanImfJEmSJDU4Ez9JkiRJanAmfpIkSZLU4Ez8JEmSJKnBmfhJkiRJUoMz8ZMkSZKkBmfiJ0mSJEkNzsRPkiRJkhqciZ8kSZIkNTgTP0mSJElqcCZ+kiRJktTgTPwkSZIkqcGZ+EmSJElSgzPxkyRJkqQGZ+InSZIkSQ3OxE+SJEmSGpyJnyRJkiQ1OBM/SZIkSWpwJn6SJEmS1OBM/CRJkiSpwZn4SZIkSVKDM/GTJEmSpAZn4idJkiRJDc7ET5IkSZIanImfJEmSJDW4uk38IqItIo6LiI9ExNUR8XBEpHw6r0r7eHJEfDYiVkTE5ohYFxE/j4hTIyKqsQ9JkiRJqrXmWgdQweHAj8er8oj4G+B6YPd80UZgF+AF+XRSRLwypbR1vGJQbXV2d9LW0jbs5ZKqw3NPqg3PPWlqq9sWv9x64GbgM8DrgdXVqDQidgWuJUv67gcOSyntAswA/gnoBo4FPl+N/an+XHL7Jez6b7ty7QPXbrf82geuZdd/25VLbr+kRpFJjc1zT6oNzz1J9Zz4/TyltFtK6eiU0gdSSt8BqtX6dhawB7AZeHlK6XaAlFJXSumLwLl5udMjYt8q7VN14pLbL+GsG86ip6+Hk7978rZ/gtc+cC0nf/dkevp6OOuGs/wnKFWZ555UG557kgAipVTrGIYtIlYCTwPOTymdN4Z6HgbmA19PKb2lxPqZwJ+AmcDHUkrnDi5TwuQ5kFNY/z+/zu7ObcumN0/nzOedyYXLL2Rzz+Zty9ta2rjg2At4+3PeXotQpYbiuSfVhudew3IsCo3YlEv8ImIhWfdOgJNTSt8tU+7HwHHA8pTSEcOoevIcyCmqs7uTXf9tV3r6enZYt3Pzzmzp2bLD8uamZv76wb967YM0Bp57Um147jU0Ez+NWD139RwvBxTm76tQrn/ds8cxFk2gtpY2vn/K95nePH2HdaX++U1vns73T/m+//ykMfLck2rDc09S0VRM/PYqzP+xQrn+de151081gOP3PZ4rT7qy5D/BounN07nypCs5ft/jJygyqbF57km14bknqd9UTPx2Kcx3li21/bpdShWIiNMj4vaIuP3SSy+tSnAaf8fvezxnPu9Mdm7eueT6nZt35sznnek/P6nKPPek2vDckwRTM/GrmpTSpSml56SUnnP66afXOhwN07UPXMuFyy8s2c0Fsu4vFy6/cIchryWNjeeeVBuee5JgaiZ+GwrzlTqxF9dtKFtKk0r/0NXFUcxK2dyzebshryWNjeeeVBuee5L6TcXE77HC/FMqlOtf15FS2jiO8WiCdHZ38porXlPyn1+p7i+bezbzmites90Q2JJGznNPqg3PPUlFUzHxK47keUDZUgPrfjuOsWgCtbW0cdFxF+0wWtn05um853nv2eHC93LlJY2M555UG557koqmXOKXUloBrMqfvqxUmYiYAbwwf3rDRMSlifH257ydC469YNs/tf5RzD551Ce3G/XMm9hK1eW5J9WG556kflMu8ct9M398XUQsKLH+ncBMoBf49gTFpAnS/0+wual5u6Gr+4e8bm5q9p+fNA4896Ta8NyTBBAppVrHUFZEzAKmFRb9LzAP+Azw6cLyLcXr8CLiPODc/OnTU0orB9W7K3A/sAdZV843pZTuiIhW4B+BC4FW4JKU0juGGW79HkiV1NndWbI7S7nlkqrDc0+qDc+9hhK1DkCTT70nfiuBpw2j6NKU0psL251HhcQvL/M3wPXA7vmiDcDOQEv+/AbglSmlrcMMt34PpCRJkhqJiZ9GbKp29SSldAewP/B54P/IEr5NwC+A04DjRpD0SZIkSVLdqusWv0nGAylJkqSJYIufRmzKtvhJkiRJ0lRh4idJkiRJDc7ET5IkSZIanImfJEmSJDU4Ez9JkiRJanAmfpIkSZLU4Ez8JEmSJKnBmfhJkiRJUoMz8ZMkSZKkBmfiJ0mSJEkNzsRPkiRJkhqciZ8kSZIkNTgTP0mSJElqcCZ+kiRJktTgTPwkSZIkqcGZ+EmSJElSgzPxkyRJkqQGZ+InSZIkSQ3OxE+SJEmSGpyJnyRJkiQ1OBM/SZIkSWpwJn6SJEmS1OBM/CRJkiSpwZn4SZIkSVKDM/GTJEmSpAZn4idJkiRJDc7ET5IkSZIanImfJEmSJDU4Ez9JkiRJanAmfpIkSZLU4Ez8JEmSJKnBmfhJkiRJUoMz8ZMkSZKkBmfiJ0mSJEkNzsRPkiRJkhqciZ8kSZIkNTgTP0mSJElqcCZ+kiRJktTgTPwkSZIkqcGZ+EmSJElSgzPxkyRJkqQGZ+InSZIkSQ3OxE+SJEmSGpyJnyRJkiQNQ0S0RcRxEfGRiLg6Ih6OiJRP59U6vkqaax2AJEmSJE0ShwM/rnUQo2HiJ0mSJEnDtx7438L0eWCPmkY0DCZ+kiRJkjQ8P08p7VZcEBH/VqtgRsLET5IkSdK4i2XL2oF2oCMtXtxR63hGI6XUW+sYRsvET5IkSdK4iGXLWoDXAmcD+wPdQEssW/Yb4FPAVWnx4u4ahjhlOKqnJEmSpKqLZctmAbcBXwEWAS1AW/64KF9+W15O48zET5IkSVJV5S19NwIHAjPLFJuZr78xL69xZOInSZIkqdpeCywEWoco15qXO2HcI5riTPwkSZIkVdvZlG/pG2xmXl7jyMRPkiRJUtXko3fuP8LNDsi30zgx8ZMkSZJUTe1ko3eORHe+ncaJiZ8kSZKkauogG7lzJFry7TROTPwkSZIkVU1+c/bfjHCz+ybrTd0nCxM/SZIkSdX2KWDjMMtuzMtrHJn4SZIkSaq2q4AVQNcQ5bryclePe0RVEhGzImJ2/8RATtVWXB4Rwx3VdEJESqnWMTQKD6QkSZImQtQ6gOGIZctmkd3EfSGlb+2wkSzpOyYtXrx+ImMbi4hYCTxtGEWXppTePL7RDJ8tfpIkSZKqLk/mjgBOBe4kG7mzM3+8M19+xGRK+iazum/xi4hdgPcBrwWeDvQCDwDfAS5OKQ3VfFyp7lcApwPPBXYD1gF3AJemlH4wwurq+0BKkiSpUUyKFr/B8vv0tQMdDuQy8eo68YuIpwHLgAX5ok5gGrBT/vxO4KiU0oh+JYiIacBlwJJ8UQKeAHYBmvNlXwNOTcM/QPV7ICVJktRIJmXip9qq266eeXL2I7Kk70/AMSmlGUAb8DpgA3AI8O1RVH8+A0nfF4C5KaXdgCcBZ5I1P78F+NAYXoIkSZIk1YW6bfGLiH8Evpo/PTKldNug9a8H/jN/enRK6eZh1rs78CiwM3BNSuk1JcqcB5xL1sL49JTSX4ZRdX0eSEmSJDUaW/w0YnWYn0NXAAAgAElEQVTb4sdAi9wtg5O+3HeAh/L5N42g3qPJkj6Az5QpcwHQR9a6ePII6pYkSZKkulOXiV9EtAHPz59eV6pMfu3dT/Knx46g+uLQq78tU/dGslZBgJeNoG5JkiRJqjt1mfgBz2IgtvsqlOtft0dE7DaK/UwbxroDR1GvJEmSJNWNek389irM/7FCueK6vcqW2t7KwvwBpQpExKxCfWXrjYjTI+L2iLj90ksvHebuJUmSJGliNQ9dpCZ2Kcx3VihXXLdL2VLbuxnYQnad3znAT0uU+TADF802R8T0lNLmwYVSSpcC/Rmfg7tIkiRJqkv12uI3blJKjwMX5k+PiYhvRcSzIqIlIuZFxL+Q3TC+u7BZ34QHKkmSJElVUq8tfhsK820VyhXXbShbakcfBZ4K/APw9/lU9ADw/4D3AJtTSltHULckSZIk1ZV6bfF7rDD/lArliuseK1tqkJRST0rpjcBLye4F+FtgFbCcrJvnIcCMvPgDw61XkiRJkupRvbb4/Y6se2UT2QAsJW/pwMDgLKtTSutGupOU0g3ADaXWRcTf5rO/HGm9kiRJklRP6rLFL6XUyUDCVfI+ehERZC12UCZ5G62IeD6wX/50aTXrliRJkqSJVpeJX64/4XpxRDy3xPqTgGfk89+s1k4jYhfgi/nTG1JKv6pW3ZIkSZJUC/We+N1LdluFqyLiKICIaIqIk4Cv5OWuSyndXNwwIs6LiJRPCwZXHBHPjYgPR8SzI6IlX7ZTRLwCuBU4GPgzcOo4vTZJkiRJmjD1eo0fKaWeiHglcAuwALgpIjrJktWd82J3suOInMOxJ/CJfOqLiL8C7cC0fP0K4JUppUdG/wokSZIkqT7Uc4sfKaWVwEHAx4D7yG6S3g3cAZwFPC+ltH4UVd8BfJpsFM81wEzgceC/gXcAB6WUHM1TkiRJUkOIlFKtY2gUHkhJkiRNhKh1AFNVROwOvBI4CjgUeBpZL8o1wO3A0pTS92sXYXkmftXjgZQkSdJEMPGrkYjoZvvL5bYAvQzcAxyyW9GdmN+poG7UdVdPSZIkSQ0iYjciPkfEbrUOZQyagV+RXR72zJTS9JTSTODpwGV5meOAL9covrJs8aseD6QkSZImwuRr8cuSvVvJEqSHgCNJaV1tgxq5iHhxSumWCuu/BLw1fzq/ngaLtMVPkiRJ0vjZPulrzR9vnYwtf5WSvtxlhfnnjGcsI2XiJ0mSJGl87Jj0wSRP/oawpTA/rWypGjDxkyRJklR9pZO+fo2a/C0uzN9bqyBK8Rq/6vFAShLQ19VHU6u/K0rSOKr/a/wqJ31FXUzia/6KIuJJwG+BPYGfp5T+tsYhbcf/zJKkqula08Wte9xK15quWociSaqV4Sd90CAtfxHRBFxOlvRtBd5V24h2ZOInSaqatdespWd9D2t/sLbWoUiSamFkSV+/Rkj+vgAcn8+/I6V0dy2DKcXET5JUNau/vnq7R0nSFDK6pK/fpE3+IuIC4J/yp+9JKX2tlvGUY+InSaqK7vXdbLhjAwAbbt9A9xPdNY5IkjTBPsLokr5+/cnfR6oW0TiLiE8D78ufvj+ldGEt46nEwV2qxwMpaUpZcfoK1nxvzbZPv9STSCnRt6mPphlNRATRnI8/EDDnpDks/PLC2gUsSY2jPgd3GVuLH0yygV4i4jPAWfnTD6SUPlPLeIZi4lc9HkhJk15nJ7S1DV3uoosuYvWvV3PEtUfQtqGNab3lb1XUO62Xzl06ue3429g4a+O25Y8++igAT33qU8cU8957780ZZ5wxpjokaZKpz8QPxpL8Tbak7wIGWvrqPukDu3pKkoAHH4RTToH2dlg9zMvzNs7ayM1vuJmVz15JT3NPyTI9zT08tP9D3PSGm7ZL+gA2b97M5s2bxxq6JKmeZEnbkWRJ3HCHeJ7MSd9ZkyHpA1v8qskDKWnSefBBOOcc+OEPoacHWlrgt7+FBQtGVs/Kj6/kkU8/Qu/G3m3LmmY2Mf8D81nw0dKV9bfSXXTRRaOMXpKmrPpt8evXoPfxi4hPAR/In743pfT5WsYzErb4SdIU1N/Cd+CBcPXVsGVLlvg1jfK/QsetHQNJX3P20Lexj47lHdUJWJI0uQyv5W+yJX3zGUj6+oCzI2J1hemsCtVNuOZaByBJmjiDW/h6SvfQHJHezl7W37IegKbpTez+d7vz+I8ep29zH+tvXk/v5l6mTS9/DaAkqUGltI6IIynd8jepkr5c06D5Jw9RfuY4xjJiJn6SNAWMR8LXb93160hbE617tnLADw6g/bB2On7dwX2vuo+uP3Wx7vp1zHn1nOrtUJI0eZRO/iZj0kdKaSWToZttGXb1lKQGVq5LZzVtWbWFOSfP4fAVh9N+WDsA7Ye1c/j9hzPn5DlseXhLdXcoSZpcduz2OemSvkZgi58kNaitW2HRIti8Gfr6xm8/8949D9694/Lm9mb2v2L/8duxJGnyGGj5+wjwLyZ9E88WP0lqUDvtBHfdBSeeCDvvDM3+1CdJqqWU1pHSe036asPET5Ia2N57wxVXwL33wgknmABKkjRVmfhJ0hRgAihJ0tRm4idJU4gJoCRJU5OJnyRNQeUSwPEcBEaSJNWOiZ8kTWGDE8CuriwJlCRJjcUOPpKkbQlgZye0tdU6GkmSVG22+EmStjHpkySpMZn4SZIkSVKDM/GTJEmSpAZn4idJkiRJDc7ET5IkSZIanImfJEmSJDU4Ez9JkiRJanAmfpIkSZLU4Ez8JEmSJKnBNdc6AGm89HX10dTqbxuSJEmqjog4FPg74G+AfYE5QDvQAdwP/Bi4JKW0rmZBluG3YjWkrjVd3LrHrXSt6ap1KJIkSWocbwHOI0v+FgJtwGZgN+BI4F+AFRFxRK0CLMfETw1p7TVr6Vnfw9ofrK11KJIkSWocvwLeDxwBzEopTU8ptQO7AG8G1gCzgWsiYteaRVmCiZ8a0uqvr97uUZJK6ejp4dEtW+jo6al1KJKkSSCl9M2U0gUppeUppScKyzemlJYC/5AvmgscX5Mgy/AaPzWc7vXdbLhjAwAbbt9A9xPdtDyppcZRSaoX3X19XLVmDZ9atYr7OjtpjaArJQ5oa+Ps+fN57Zw5tDT5u6gkjYdlsax1cVrcyNfiLC/MP7VmUZRg4qdJb8XpK1jzvTWQsuepJxEtQerKHpfPW040R7YyYM5Jc1j45YW1C1hSzazv7ubou+/mgc5ONvb1AdCTsg+PuzZt4rQVK/jMI49w08EHM6vFH4wkqZqWxbI5wIplsWzh4rR4Ta3jGScvLMz/vmZRlGDip0ntoosuYvXm1RyRjqBtQxvTeqdtt75vU9+2+d5pvXTu0smPO3/MxjM2blv+6KOPAvDUp47+R5m9996bM844Y9TbSxp/3X19HH333dy3aRNdebI32Ma+Pu7btImj776b5YceasufJFXXq4FZwKuAr9Y4lqqJiJ2APcm6dn4sX/wg8KOaBVWC/9E06W2ctZGb33AzK5+9kp7m0tfp9DT38ND+D3HTG25i46yN263bvHkzmzdvnohQJdXQVWvWsKKzs2zS168rJVZ0dnL1WgeHkqQqe/Ogx0ktIrZERAK2AA8BF5Mltr8Ejkopba1lfIPZ4qdJbXAr28qPr+SRTz9C78bebcuaZjax9wf25uiPHl2xjosuumj8ApVUc59atYpNfX1DFwQ29fXxqVWrOGXu3HGOSpKmhmWxbBbwnPzpYcti2ZMWp8VPVNpmElgN7AzMBGbky24BPpBSWlWzqMow8VND6bi1YyDpawZ6oG9jHx3LO2oal6Ta6ujp4b7OzhFtc9+mTXT09NDe7L9KSRqpZbHsy8BJhUUtQBfQmj8+siyWdRfWX7k4LX7bBIY4ZimlBf3zETEXeCNwDvCriPiXlNI/1yq2UuzqqYbR29nL+lvWA9A0vYk5J8yhaXr2Fl9/83p6N/dW2lxSA+vo6aE1YkTbtER4mwdJGr3PAn8hu8H5LLJWsZn5uv75Wfn6vwCfq0GMVZNS+ktK6bPAy8iGHPxoRNTV7RxM/NQw1l2/jrQ10bpnK4t+uoj9r9ifRT9dROueraStiXXXr6t1iJJqpL25echr+wbrTsnWPkkapcVp8QPAwcBXgHJdLjqBS4GD8vKTXkrpV8Av8qen1zKWwfyPpoaxZdUW5pw8h4VfXUjzLtlbu/2wdg6//3BWnLaCLQ9vqXGEkmqlvbmZA9rauGvTpmFvc8CMGSZ+kjQGi9PircC7lsWyvwAfYKDFD2Aj8OnFafHHaxLc+Ppj/rh3TaMYxP9oahjz3j0P3r3j8ub2Zva/Yv+JD0hSXTl7/nxOXbFiWAO8zGhq4uz58ycgKkmaEo5gIOnrIctBZgLPq1lE4+sZ+eOGmkYxiF09JUlTwmvnzGFhW9uQ1/q1RrBfWxsnzJ49QZFJUuNaFsvagBfnTzcDV+ePAC9ZFsum1ySwUYiIaRGV/4lExFHA4fnTZeMe1AiY+KkujXDwPUkaUktTEzcdfDAHzJjBjDI3Zp/R1MSBM2Zw48EHe/N2SaqOl5Ld8uBPwIsWp8WnAC/Kn++cr58s5gF3RsRbI+IZxSQwIuZFxAeBHwABrAM+X6M4S/K/murKgw/CKadAezusXl3raCQ1mlktLSw/9FAu228/Dpk5k5YI2pqaaIngkJkzuWy//bjt0EOZ1dJS61AlqVHMB64EFi5Oi38NkD/uly9/Wg1jG42DgS8Bvwe2RMSaiNgIrAL+lex+fg8BR6eU6urbrNf4qS48+CCccw788IfQ0wOtrbDFsVgkjYOWpiZOmTuXU+bOpaOnZ9u9+hzIRZKqb3Fa/AXgCyWWdwCnTHxEY/IYcDKwGHgusCcwG+glS/zuJmvx+8+U0uYyddSM/+VUU4MTvv5bZu20U23jkjQ1mPBJkoYrpdQFfDefJh3/26kmyiV8kiRJkqrPxE8TyoRPkiRJmngmfpoQJnySJElS7Zj4adxt3QqLFsHmzTCM+yZLkiRJqjJv56Bxt9NOcNddcOKJsPPO4DgKkiRJ0sSq+8QvInaJiPMi4t6I2BgRf42IX0fE+yKidYx1nxgRP4qIxyKiKyI2RcSKiPhKRCyq1msQ7L03XHEF3HsvnHCCCaAkSZI0keo68YuIpwH3AOcCBwAB7AQ8B7gAWB4Rs0ZR704R8UOyoViPJ7sHx1ayrq/7AqcCd0TEe6rxOjTABFCSJEmaeHWb+EXENOBHwALgT8AxKaUZQBvwOmADcAjw7VFU/2Hg7/L5/wCemlLaBZhOllT+guzYfDYinjOGl6EyTAAlSZKkiVO3iR/wZuDAfP61KaWbAFJKfSmlK4C35uuOi4ijRlj3m/LHn6aU3plS+mOh7jvIWgE3krUwvnYMr0FDKJcAOgiMJEmSVD31nPgtyR9vSSndVmL9d4CH8vk3lVhfyZ754+2lVqaU/go8kD+dOcK6NQqDE8CuriwJlCRJkjR2dZn4RUQb8Pz86XWlyqSUEvCT/OmxI9zFH/LHvymz/13JrvWDMsmhxkd/AtjRAXvsUetoJEmSpMZQl4kf8CwGYruvQrn+dXtExG4jqP+S/HFxRHwxIp4CEJlDgWvJWvqWM7prCDVGbW21jkCSJElqHPWa+O1VmP9jhXLFdXuVLbWjLwKfBvqAdwCPRsQGYAtwB7A38G/AS1JKPeUqiYjTI+L2iLj90ksvHcHuJUmSJGni1Os4irsU5jsrlCuu26VsqUFSSn0R8SHgt8C/k7XuFa/l2xnYFZgBbK5Qz6VAf8aXhrt/SZIkSZpI9driN64iYjZwM/AN4DbgBcCTyAZ9OQFYA7wd+J/+bqCSJEmSNFnVa+K3oTBf6Wqv4roNZUvtaCmwGPgp8NKU0i9TSn9NKa1OKX2fLBFcCzyDrMunJEmSJE1a9Zr4PVaYr9TiVlz3WNlSBRHxLODl+dPP5qODbiel9Bfgm/nTEyIihlO3JEmSJNWjek38fkc28ArAARXK9a9bnVJaN8y6n12Y/32Fcv+XP7YBc4dZtyRJkqQpJCI+GBGpf6p1POXUZeKXUuoEfpk/fVmpMnkr3EvzpzeMoPq+wvzTKpR7cmF+4wjqlyRJkjQFRMRC4NxaxzEcdZn45Zbmjy+OiOeWWH8S2TV4MNAtczj+tzD/9lIFImIG8Kb86T0ppU0jqF+SJElSg4uIJuAysjsC3FbjcIZU74nfvUAAV0XEUZAd4Ig4CfhKXu66lNLNxQ0j4rxCc+uC4rqU0sPAj/KnfxcRl0fEM/Obt7dExJHAMgaSys+Ow2uTJEmSNLm9C3g+8G1G1gOxJuo28ctvnP5KYCXZIC43RcQmYBNwJdAO3An8/SiqfwvZjdoB/gF4kKw7Z38X0+fk6y5IKY2kNVGSJEnSCEVUHMm/7kTE04FPAI8D76lxOMNSt4kfQEppJXAQ8DHgPrKbpHeTJW1nAc9LKa0fRb1rgecBpwLXA38GWoAe4A/At4AXppTeP/ZXIUmSJKmUCPaO4DtARwR71DqeEfgKMAN4b0ppTa2DGY7mWgcwlJTSBrILJod90WRK6TzgvCHK9JD1yb1sDOFJkiRJGqEI9gb+BXgVWU7SRXatXN2LiNOAo4CbJlPvwLpP/CRJkiQ1hhIJX38+srVmQY1ARDwF+AywGXhrjcMZERM/qQ509PTQ0dNDe3Mz7c2elpIkqbFUSPgmmy8DuwJnp5T+UOtgRmKyHnBp0uvu6+OqNWv41KpV3NfZSWsEXSlxQFsbZ8+fz2vnzKGlqa4vw5UkSaqogRI+IuIfgFcAdwGfq3E4IzZpD7w0ma3v7ubou+/mgc5ONvb1AdCTEgB3bdrEaStW8JlHHuGmgw9mVktLLUOVRqyvq4+mVn+0kKSprJESPoCImAtcCPQCp+XjhUwq/meWJlh3Xx9H3303923atC3pG2xjXx/3bdrE0XffTXeZMlI96lrTxa173ErXmq5ahyJJqpEIdiK77dpJZAO2TOqkL/cpYHfgUuD+iJhZnIDW/oKF5a3lKqsFEz9pgl21Zg0rOjvpylv4yulKiRWdnVy9du0ERSaN3dpr1tKzvoe1P/B9K0lTVUpsBQ4BvgtsIbtl2mT39Pzx7cCGEtOHCmX7l316IgMciomfNME+tWoVm4bZirepr49PrVo1zhFJ1bP666u3e5QkTU0p8WBKvA44ELiKxkkAJy0TP2kCdfT0cF9n54i2uW/TJjp6/JxU/ete382GOzYAsOH2DXQ/0V3jiCRJtdYoCWBKaXFKKcpNwPmFsv3Lz6xhyDtohP620qTR0dNDa8S2gVyGoyVi260epHqy4vQVrPneGsjfzqknES1B6soel89bTjRHtjJgzklzWPjlhbULWJJUMynxIPC6Rhv0ZTKxxU+aQO3NzUNe2zdYd0omfapL886aR8vcFno7e+l5oofejb30bcq6Mfdt6qN3Y768s5eWuS3Me9+8GkcsSaq1Ci2A5iXjzAMsTaD25mYOaGsb0TYHzJhh4qe61LZvG4fdfRh7nrYnTW2l/500TW9ir9P34rB7DqNt35G99yVJjatEAthKlgRqnJj4SRPs7PnzmTHMG7PPaGri7PnzxzkiafSadmpi33/fl/kfnM+0mdO2Xzezifkfms8+F+/jff0kSSUVEsD2lJi0I4OllM4rXO9Xl/xPLE2w186Zw8K2Nlqj8udCawT7tbVxwuzZExSZNHodt3bQu7E3e5I3UPdt7KNjeUftgpIkTRopMbLR7zRiJn7SBGtpauKmgw/mgBkzyrb8zWhq4sAZM7jx4INpGWbroFQrvZ29rL9lPZB17Zxzwhyapmfv2/U3r6d3c28tw5MkSZj4STUxq6WF5YceymX77cchM2fSEkFbUxMtERwycyaX7bcftx16KLNaWmodqjSkddevI21NtO7ZyqKfLmL/K/Zn0U8X0bpnK2lrYt3162odoiRJU54jRkg10tLUxClz53LK3Ll09PRsu2WDA7lostmyagtzTp7Dwq8upHmX7P3bflg7h99/OCtOW8GWh71WX5KkWvMbplQHTPg0mc179zx4947Lm9ub2f+K/Sc+IEmStAO7ekqSJElSgzPxkyRJkqQGZ+InSZIkSQ3OxE+SJEmSGpyJnyRpm05vnytJUkMy8ZMk8eCDcMop0N4Oq1fXOhppavKHF0njycRPkqaw/oTvwAPh6quhtRW2eNs9aUL5w4ukieCNwyRpCnrwQTjnHPjhD6GnJ5sAdtqptnFJU8ng89AfXiSNJ1v8JGkKGdzCt2XLQNInaWKUOw+bJuhb2dq1a3nXu97F448/PjE7lFQXTPwkaQow4ZNqr17Ow6VLl3LPPfewdOnSid+5pJox8ZOkBlYvXzSlqayezsO1a9dy3XXXkVLiuuuus9VPmkJM/CSpQW3dCosWwfe+Z8In1UI9JXz9li5dSkoJgL6+Plv9pCnExE+SGtROO8Fdd8GJJ8LOO0Ozw3lJE6Zef3i58cYb6e7uBqC7u5sbbrihxhFJmigmfpLUwPbeG664Au69F044wQRQmij1+sPLMcccQ0tLCwAtLS0ce+yxNY5Imlwi4ryISIOmvojoiIhHI+LWiPhiRJwYEa0V6llQ2P7NExG7iZ8kTQEmgNLEq8fzbsmSJUQEAE1NTSxZsqS2AUmT25/z6S9AAvYCjgDeAXwXeCwi3h79J12NmfhJ0hRSj19EpUZXT+fd7NmzOe6444gIjjvuOHbffffaBCI1gJTSHoVpV6AFOAh4H/AQsDvwH8C36iH5M/GTpCmo3BfRvr5aRyY1rnpJAJcsWcJBBx1ka59UZSml3pTSvSmlzwEHAN/JV70B+GDtIsuY+EnSFDb4i2hXV/ZlVNL4qfUPL7Nnz+biiy+2tU8TJs6PtpEsbwQppU5gCXBnvuiDEbFbDUMy8ZMkDXwR7eiAPfYYW119XTYbSsPhDy+aCuL8eBvwRJwfxw9afny+/G21iWz8pZS6gE/mT9uBV9cwHBM/SdKAtjH+9tq1potb97iVrjVd1QlImgKq+cOLVE/ypO6zZNe+Xdmf/OWPV+bLP9vIyR/wE6A3n39RLQMx8ZMkVc3aa9bSs76HtT9YW+tQpElnrD+82NquelJI+vrf2dPJkr9PkCV90/PlbTRw8pdS2gj8IX/6zFrG4lhukjTFnHnmmdx1113jUvdb7nwL85nPTe+7ia9962sVy/blFzQtXrx4yHp36e7mjQ8/zOVPexob8nuQVcuiRYu48MILq1qnNNG61nTxq4W/4vAVh9M6p+ytw6QJkV+7dxFZi17RdOC9wOBOzW3ARXF+fDOdmzonIMSJti5/9Bo/SdLEueuuu8Yl8du5e2f22rAXAHtt2Iude6pzsdIu3d188c47ec1jj/HFO+9kl+7uqtQL43cspIlma7vqSZ68nQBsLrG61D+HzcAJDZr0AdT8Vg5gi58kTUmLFi1i2bJlY6pjxekrWPO9Ndkta4HUk0htib5NfbS2tXLO3ecQzfn/uoA5J81h4ZcXjmwn69bBEUdAby+kxPzeXn60di3cdhvsNvYfTofT2ihNBqu/vnrb416n7lWx7Nq1azn//PM577zzHNlT4yadm66N8+Nktu/WWcpm4OR0brp2YiKriVn54+O1DMIWP0nSqMw7ax4tc1vo7eyl54keejf20rcp677Zt6mP3o358s5eWua2MO9980a2g/6kb+XKbLhDyB5XrsyWr1tXaWtpyuhe382GOzYAsOH2DXQ/UblVfOnSpdxzzz0sXbp0IsLTFJYnc58HtpQpsgX4fCMnfRExE3hG/vT3tYzFxE+SNCpt+7Zx2N2Hsedpe9LUVvrfSdP0JvY6fS8Ou+cw2vYdwcgVpZK+fiZ/muJWnL6CX+z2C34xK5uWz19OtGSt69ESLJ+3fNu6X+z2C1a8dcW2bdeuXct1111HSonrrruOxx+vaQOEGlw+eud7KN29k3z5ewbf6qHBvAyYls8vq2EcJn6SpNFr2qmJff99X+Z/cD7TZk7bft3MJuZ/aD77XLwPTa0j+HdTKenrZ/KnKWwsre1Lly4lpax/dl9fn61+GjeFWzZU6uYJA6N9NlzyFxGtwIfzp38FrqlhOCZ+kqSx67i1g96N+W2K8qvH+zb20bG8Y2QVDSfp62fypylqLK3tN954I935AEnd3d3ccMMNExKzppZ8VM+rKZ30ler2OR24Ot+uIUTEdOAbwCH5on9NKT1Ru4hM/CRJY9Tb2cv6W9YD2ZfNOSfMoWl69u9l/c3r6d3cW2nzASNJ+vqZ/GmKGm1r+zHHHENLfkuUlpYWjj322AmLWVNHPjrnGcDgUTo3A59jx9E+O4EzJvuonhHRFBEHRMR7gd8Ar89XXQ58usKmMyNi9hDTtArbD4uJnyRpTNZdv460NdG6ZyuLfrqI/a/Yn0U/XUTrnq2krYl11w8jIRtN0tfP5E9T2Ehb25csWUJEdj1gU1MTS5YsmYgwNQWlc9OXgPcxkPz1j955DnAyA8lfJ/C+vPykEhGrC9N6oBu4l+zG9U8H1gJvSym9KfX3sS7tYmDNENOBY43XxE+SNCZbVm1hzslzOHzF4bQf1g5A+2HtHH7/4cw5eQ5bHi43mFvBxz8ODz008qSvX1dXtv3HPz667aVJaDSt7bNnz+a4444jIjjuuOO8nYPGVSH566Zwy4b88eR8+aRM+nJPzqe5ZD+9rAaWA5cAJwJPSSl9uXbhbc/7+EmSxmTeu+fBu3dc3tzezP5X7D+8Sj76Ufjxj0fX4gfQ2goLFmT1SFNEsbX9gB8cQPth7XT8uoP7XnUfXX/qYt3165jz6jk7bLdkyRJWrlxpa58mRDo3fSnOj28O7saZ3+fvSZOte2dK6TzgvCrUs5IJvrG7LX6SpNrbbbfspuwLFmRJ3Ej0J31Vuqm7NFmMtrV99uzZXHzxxbb2acKUS+4mW9I32dniJ0mqD/3J30iu9TPp0xRWldZ2SVOGLX6SpNu61I8AACAASURBVPoxkpY/kz5JkobNxE+SVF+Gk/yZ9EmSNCImfpKk+lMp+TPpkyRpxEz8JEn1qVTyZ9InSdKomPipYfV19dU6BEljVUz+WlpM+iRJGiUTPzWkrjVd3LrHrXStGeXNoCXVj/7k753vNOmTJGmUTPzUkNZes5ae9T2s/cHaWociqRp22w0+/3mTPkmSRsnETw1p9ddXb/coSZIkTWUmfmo43eu72XDHBgA23L6B7v/P3t3Hx3nWd77//MYzI2nkqNixFNuJhE0c28Q2lt3YsUvLCa+4DWVb6HHimC0LtIWE7oZjyCZgOFnTnHh7iunpJmtIuwQChVMohtgLSRsIcZqH3dhuHhwJqwERFRslIc5KkcxIM5Lm6do/7nuSsayHmdGMZkb+vl+ved0zc1/3dV/KK7LmO9fTmWSFWyQiIiIiUlnBSjdgOmZ2AXALcC2wHEgDPwO+DXzBOVfQJC4zWwacLOCSv3XO/XEh95DZ1X1jN3339YHzXruUw0KGS3jHY63HsKB5Jw2adzSz6kurKtdgEREREZFZVtXBz8zeDDwGLPPfigN1wBX+4/1mdrVzbrCAatPAq9OUqQd+zX/+dAF1SwW03trKmSfOMHpyFJdwZ53LxN5Y2dPCRv3yelpvaZ3tJoqIiIiIVFTVDvU0s3nAA3ih7xXgt51zjUAEeB8wBGwAvllIvc65F51zi6d6AP+/X3wE+FaJfiQpk8jKCJs6N7HkhiUEIhP/Lx1oCLD0xqVs+vEmIisjs9xCEZmxgQG4+WbvKCIiIgWr2uAH/BGwzn9+rXPuMIBzLuOcOwB81D/3u2Z2daluamb1wPv9lwedc2dKVbeUT6AuwMovrqTt023Mmz/v7HPzA7R9po3LvnAZgXA1/y8vIhMaGICtW+Huu72jwp+IiEjBqvlT8If846POuaMTnP82b8zV+2AJ77sdWOA//0oJ65VZED0SJT2c9l74A5kzwxmix6KVa5SIFC8b+k6dgmTSOyr8iYiIFKwqg5+ZRYC3+y9/MFEZ55wDfui//J0S3v7D/vEF59zjJaxXyiwdTzP4qDfdM9AQoHl7M4EG73/xwUcGSY+kK9k8ESlUbuhL+Ot4JRIKfyIiIkWoyuAHvJU32tY1RbnsucVmNuNdfc3sLcA7/Zf3zrQ+mV0DDw3gxhzhJWHaH29nzYE1tD/eTnhJGDfmGHhIHxJFasZEoS9L4U9ERKRg1Rr8luY8f3mKcrnnlk5aKn9/AhiQAr4+XWEzu9HMnjGzZ+65554S3F5mYrR3lObrm9ncvZmmTU0ANG1qYvNPN9N8fTOjvxitcAtFJC9Thb4shT8REZGCVOt2DhfkPI9PUS733AWTlsqDv4roH/kv/9E5d3q6a5xz9wDZxOemKivl1/rxVvj4ue8Hm4KsObBm9hskIoXLJ/Rl5Ya/o0dh4YwHfoiIiMxZ1drjVwnvAi72n2tRFxGR2VZI6MtSz5+IiEheqjX4DeU8n2rTtdxzQ5OWys9H/OPLTLKgjIiIlEkxoS9L4U9ERGRa1Rr8fpnz/OJJS5197peTlpqGmbUA/8Z/+bfOOS3/KCIym/buhZMnCw99WYmEd/3evaVtl4iIyBxRrcHvJ0DGf752inLZc6edczP5mvdDQAhvnt5XZ1CPiIgUY88eWL4cwuHirg+Hvev37Cltu0REROaIqgx+zrk48KT/8l0TlTEzA67xX/5ohrfM7t33qHPu5zOsS0RECrVwobdAy7JlhYe/cNi7Tgu8iIiITKoqg58vu53CO83sygnO7wDe4j//RrE3MbPfBFb5L7Woi4hIpRQT/hT6RERE8lLtwe8E3r56B83sagAzC5jZDuDLfrkfOOceyb3QzG43M+c/lk1zn+yiLgPAoVI1XkREilBI+FPoExERyVvVBj/nXAp4D3AKbxGXw2YWA2LAd4Am4Dng/cXew8wuwOs5BPg759zYTNosIiIlkE/4U+gTEREpSNUGPwDn3CngbcAdQBfe4itJ4FngVmCLc25wBrf4t7yxJYSGeVaReLzSLRCRipoq/Cn0iYiIFKyqgx+Ac27IOfdnzrl1zrn5zrkm59wVzrm/cs5NuO63c+5255z5j1NT1H1PTrkTZfshJG89PbBzJzQ1wenTlW6NiFTUROFPoU9ERKQoVR/85PyQDXzr1sGhQ95nu9HRSrdK5PxTdb3tueEvFFLoExERKZKCn1TU+MA3OgqpFAT0f6bIrKrq3vZs+LvpJoU+ERGRIgUr3QA5P/X0wG23wf33e0Evlap0i0TOT+N/F6u2t33hQrjzzkq3QkREpGapX0Vm1WQ9fCIyu0ZGLq6t3vaBAbj5Zu8oIiIiBVOPn8wK9fCJVIeeHnj++c/y2mtv5/jxGvldHBiArVvh5El48EEN9xQRESlCtX63K3PI2Bi0t8N996mHT6RScnvb+/vfQSZTV7bfxUwiU7rKsqHv1ClIJr3j1q3q+RMRESmQgp+UXV0ddHTAdddBfT0E1c8sMmsmGl7tXPl+CRN9CY4sPkKib8LddgqTG/oSfn2JhMKfiIhIERT8ZFasWAEHDsCJE7B9uwKgyGyoRG97//f6SQ2m6P9+/8wqmij0ZSn8iYiIFEzBT2aVAqDI7KlEb/vpr50+61iUqUJflsKfiIhIQRT8pCIUAEVmx0S/a2bl6fpLDiYZenYIgKFnhkieSRZeST6hL0vhT0REJG/6qC0Vlf1QOn7Vz0wJ14YQkbN/137jN57gtdfeTiAwswVeum/spu++PnDea5dyWMhwCe94rPUYFjTvpEHzjmZWfWnV5BUWEvqycsOfVvsUERGZ1IyDn5kFgF8H3gxEnHPfmHGr5LwzPgAePOj1TIhIaa1YAatX387IyMX86lef5Oc/X0cmY68v+DI2Nsbtt/8FTU3T96DNH5nPVreVyFCEeel5Z53LxN749iY9L038gjgPxh9keNfwhHVFRke5+bvfZWE0SqjQb34SCZI9PQxceil37thBvIB/PDL6lklERM4TMxrqaWb/F/AKcAw4AHxt3PkFZtZlZj81M30NK9PKBsBoFBYvrnRrROauhoaXede7/pY//MO/YMWKHzNvXhKzFM5Z3nUMLxjmkT98hFOXnyIVnLjrMBVMcXLNSQ7/4WGGF0wc+gCueeopLiwm9PlCmQwXRqNc89RTRV0vIiIy1xXd42dmXwT+PWBAFJjvP3+dc27QzJ4F/h3we4B6AyUvkUilWyAydwUC3nd++/fvf/293N72z33u9oK/eDm19xQvfv5F0sPpN+4zP8CKT61g255t01eQu0l7soi5gaEQweXLeec//RPvLGC451VXXVX4vURERGpQUT1+ZnYN8B+AYeD/dM69CeibpPi38ALhe4pqoYiIlN1Me9ujR6JvhD7/K8XMcIbosWh+FSxcCA8+WPiNcz34oOb4iYiITKLYoZ5/ijed/7POue9PU/aof2wv8l4iIjJLiultT8fTDD46CECgIUDz9mYCDd6fl8FHBkmPpKe63DMwAO9+d+E3z/Xud2t1TxERkUkUG/y2+MevTlfQORfFGwq6pMh7iYhIFRt4aAA35ggvCdP+eDtrDqyh/fF2wkvCuDHHwEN5hLG9e4sf5gnedSdPevWIiIjIOYoNfguBXznnhvIsnwHmTVtKRERqzmjvKM3XN7O5ezNNm5oAaNrUxOafbqb5+mZGfzE6fSV79sDy5RAOF9eIcNi7fs+e4q4XERGZ44pd3CUKLDCzkHNuyq9nzWwR8Cbgl0XeS0REqljrx1vh4+e+H2wKsubAmvwqWbjQ24ev0H38wAt9y5ZpHz+RGhNNpYimUjQFgzQFtbW0SLkV2+P3L3gLtmzKo+wH/OOzRd5LpHoMDMDNN2sekUg5ZMPfsmX59/wp9InUlGQmw7dffZUNTz/NhU8+yaqnnuLCJ59kw9NP8+1XXyWpvTVFyqbY4HcIL/jd7m/gPiEz+w3gDryFYL5T5L1EqkN2ufm77/aOCn8yB8XjFW5AIeFPoU+kpgwmk2w5fpwburvpiMVIOUc8kyHlHB2xGDd0d7Pl+HEGi53rKyJTKrZf/UvAx4CrgR+a2X/BD5H+0M63Ae8DPgSEgA7g72fcWplTPvGJT9DR0VHpZpDxv12caj+vC5JJ7n7uORaPjhJ2jsQLL3D6kku4acMGhkKhkrSjvb2du+66qyR1iRQqdx+/l14qbkuHksln2KdCn0hNSWYybOvspCsWI+HchGWGMxm6YjG2dXZybONGQoFi+ydEZCJF/UY558aAfwP0AtuAfwQW+adfBR4GPowX+v4Vb68/9d3LWTo6Oqoi+E1nfOgDCDvH4tFR7n7uOS4owTeTtfLfQuaenh7YuRPWrYNDh7w8NZrHWixlN1XPn0KfSM052NdHdzw+aejLSjhHdzzOof7+WWqZyPmj6Jm0zrkXzKwd+E/AH+Ot9Jkrirfdw17n3GDxTZS5rL29nccee6zSzZhcdnhnOg3j/liFnaMtneaB/v4ZfwCdqrdRpBxGRi5m5064/35IpbwHQF1dZdt1lol6/hT6RGrSvt5eYnnO34tlMuzr7WVnS0uZWyVyfpnREkrOuV8BnwQ+aWaXA0vxtm04DXQ55/LYtVekSmVD31QrDCYS3vmtW/VBVGpCTw88//xnee21t3P8+BuBr2rlhr+TJxX65Ly3f/9+enp6ZlTHSy+9BMAll1wyo3pWrFjBrl27pi0XTaXoKnACcVcs9vqKnyJSGkUN9TSzNv9Rn33POfe8c+6wc+4h51ynQp/UtHxCX1Zu+NOCL1Klcod09ve/g0ymrvpDX1Y2/N10k0KfSAmMjIwwMjIya/eLplKEzQq6JmRGtGb+kRKpDcV+jXIKb1P2NrQ/n8w1hYS+LPX8SZXKLtpy9pBOfYMuUqvy6WHLt479+/fPuK58NAWD087tGy/pnHr7REqs2OWShoFfOecU+mRuKSb0ZannT6rM2Bi0t8N993kLttTsl+faSkWkpjUFg6yNRAq6Zm1jo4KfSIkVG/xOAREzm1fCtohU3t693jyiQkNfViLhXb93b2nbJVKEujro6IDrroP6eqjJz1C5X8Ykk/pyRaRG7W5rozHP7RkaAwF2t7WVuUUi559ig9/3gDDelg4ic8eePbB8+fQbR08mHPau37OntO0SKdKKFXDgAJw4Adu3ewHQrEa6/ibqgVfPukhNura5mVWRyLRz/cJmrI5E2L5o0ZTlRKRwxQa/fUAP8Ddm9rYStkeksqbaO2w6WmZeqlhuAFy06AkCgbHq7gGcath1CcNfqq6OsaYmLSIhc0ImUb1bJocCAQ6vX8/axsZJe/4aAwHWNTby8Pr12rxdpAyK/bN/LfAl4HbgGTP7IfAk8L+ASVfzdM59o8j7icyeifYOm45Cn9SIFSvg8svvYGTkYpYt++Y5+/hVhTJvpZLMZDjY18e+3l46P/UpLJ3mwiefZG0kwu62Nq5tbtaHTqk5ib4ET616is3dmwk3FzlqpcwWhEIc27iRQ/397OvtpSsWI2RG0jnWNjayu62N7YsW6fdPpEyKDX5/C2SXZzK8IZ/TDft0gIKf1IZCwp9Cn9SghoaXOXDg3FU/89xfuXyK3Uolz9+/wWSSbZ2d/CweZziTgXnzcPPmkXGOjliMG7q7+csXX+Tw+vUsCIVK8iOJzIb+7/WTGkzR//1+ln5kaaWbM6lQIMDOlhZ2trQQTaVe36tPC7mIlF+xX6n05jx+Me71ZI8XZ9pYkULMeMhLPsM+Ffqkxo2fA5hIePMAK2KmW6lMM+wzmcmwrbOTrljMC30TGM5k6IrF2NbZSbLiKVgkf6e/dvqsYy1oCga5pL5eoU9klhQV/Jxzy5xzywt9lLrxIpNJ9CU4svgIib4iV+fMmir8KfTJHJINgNEoLF5cgQbMwlYqB/v66I7Hp91PLOEc3fE4h/r7C2uHSIUkB5MMPTsEwNAzQyTPJCvcIhGpRvqKReakkg55mWjYp0KfzFEFbrVVOtmtVJJFfmDN3UrlzjsnLLKvt5dYnr14sUyGfb297GxpKa49ImXUfWM3fff1vT7pxqUcFjJcwjseaz2GBf3VMw2adzSz6kurKtdgEakKmj0rc1LJh7zk9vyFQgp9IqVW5q1UoqkUXfF4QVV2xWJa7VOqUuutrYRaQqTjaVJnUqSH02Ri3pcamViG9LD/fjxNqCVE6y2tFW6xiFSDkgQ/M1tjZh8ys0+a2a3+88tLUbdIoco25CUb/m66SaFPpNTKvJVKNJWadv+w8UJmCn5SlSIrI2zq3MSSG5YQiEz8US7QEGDpjUvZ9ONNRFZWqitfRKrJjIZ6mtk1wOeBtZOcPwF8yjn3o5ncR2QqszrkZeHCSYeRicgMlXErlaZgcNq5feMlndOiE1K1AnUBVn5xJeGLwrz4+RdJD7+xm1ZgfoC2T7WxbM+yyjVQRKpO0X/RzOxjwH/NvsTbvy87E/5Cv+63AT8ws13Oubtn0lCRybTe2sqZJ84wenIUlzj7g1126AuAhY365fUa8iICdHR0cNVVV1W0DRl/vl1g3J5dFyxaxN0vvshiM8JThLWEGafnzeOmRYsY2r592vvVffSjpJYsybt9axsbFfyk6kWPRN8IfUEgBZnhDNFj0Yq2S0SqT1FDPc1sPXAXXuB7Cng3MN85t8Q5twS4wH/vqF/mLjN7W2maLHI2DXkRKUx7ezvt7e2VbsakhkIhbtqwgdP19SQmGZ6ZMON0fT03bdjAUJ777bU9+SSBsbG8yjYGAuxua8u7zSKVkI6nGXx0EPD+zjVvbybQ4P0dHHxkkPRIeqrLReQ8U+xXmf8RLzQ+AGx3zp31L4tzbgz4oZk9DBwCfh+4GfjjGbRVZFIa8iKSv7vuuqvSTQBg165dAOzfv3/iApNt8RAOE162jLajR3mggLm2yUyGLceP0xWLTTnsM2zG6kiE7YsW5V23SCUMPDSAG3OEl4RZ+/21NG1qIvp0lK73dpF4JcHAQwM0/0FzpZspIlWi2MVd/g+8GVUfHx/6cvnnPuG/fGeR9xLJ2zlDXtCQF5GaNdGCLzPYSiUUCHB4/XrWNDZSN0lPYp0ZaxsbeXj9ekIBLXwt1W20d5Tm65vZ3L2Zpk1NADRtamLzTzfTfH0zo78YrXALRaSaFNvjdxHwK+fcqekKOudOmtkZ/xqRshk/5OXC37+Q1x54jcxI5vUhL/Ma5lW4lSJSkNwFX06eLM1WKtMs8uIKXARGpFJaP94KHz/3/WBTkDUH1sx+g0SkqhX7deYIEDGzaYOjXybiXyNSNrlDXtofb2fNgTW0P95OeEkYN+YYeGig0k0UkWKUaCuVZCbDts5O/iUeZ2yScDfmHP8Sj7Ots5Nknpu9i4iI1IJig99PgBBwXR5ldwBh/xqRstGQF5E5LLuVygx6+g729dEdj0+7rUPCObrjcQ71909ZTkREpJYUO9Tzu8BW4K/NrN85d3iiQma2DfhrvPmA3ynyXiJ50ZAXEZnKvt5eYnn24sUyGfb19rKzpaXMrRIREZkdxQa/vwE+DKwBHjKzo8Bh4GW8kNcKXI0XDg3o8q8RERGZddFUiq54vKBrumIxoqmU9vITEZE5oai/Zs65MTO7Bm+rhs3Ab+CFvFzZJdP+GbjWOZdARESkAqKpFGEzUgUs3BIyU/ATEZE5o+i1qp1zv8QLfO8D/jvwEpDwHy/57+0E3u6XFRERqYimYHDauX3jJZ1T6BMRkTljRn/RnHMZvLl7mr8nIiJVqykYZG0kQkcslvc1axsbFfxERGTO0O60IiJyXtjd1kZjnpuyNwYC7G5rK3OLREREZk/Rwc/Mmsxsfh7l5ptZU7H3ERERKYVrm5tZFYkQNpuyXNiM1ZEI2xctmqWWiYiIlF9Rwc/MtgODwD15FP87YNDM3lPMvUREREohFAhweP161jY2Ttrz1xgIsK6xkYfXryeUZ++giIhILSj2r9oO/3hvHmW/jLfC5/VF3ktERKQkFoRCHNu4kXtXr2bD/PmEzIgEAoTM2DB/PveuXs3RjRtZEApVuqkiIiIlVeys9Q3+8dk8yj7pHzcWeS8REZGSCQUC7GxpYWdLCy+NjvLK2BhL6uq4pL6+0k0TEREpm2KD38XAkHPuzHQFnXNnzGzIv0ZERKSikpkMB/v62NfbS1c8TtiMhHOsjUTY3dbGtc3NGuYpIiJzTrHBzwGFjIMJ+teIiIhUzGAyybbOTn4WjzOcyQC8vql7RyzGDd3d/OWLL3J4/XoN9xQRkTml2K80XwTqzWzddAXNbD3QALxc5L1ERERmLJnJsK2zk65Y7PXQN95wJkNXLMa2zk6Sk5QRERGpRcUGv8fwFmz5f/Ioezteb9+jRd5LRERkxg729dEdj5NwUw9ASThHdzzOof7+WWqZiIhI+RUb/L4AZID3mtnfmdlF4wuY2UVm9i3gvX7Z/cXcyMwuMLPbzeyEmQ2b2a/M7Gkzu8XMwkW2P7f+xWa218yeNbMBMxsxs1+Y2Q/N7NNmprE+8oaBAbj5Zu8oIjVlX28vsTx78WKZDPt6e8vcIhERkdlT1Bw/59xPzew24C+AfwtcZ2bPAr/A691bBlyRU/9/cs49X+h9zOzNeL2Ly/y34kCdX/cVwPvN7Grn3GAxP4eZ7cTbizC7wXwCGAHa/Mc1wH8Dpl3ERs4DAwOwdSucPAkPPghHj8LChZVulYjkIZpK0RWPF3RNVyxGNJWiKVjsdHgREZHqUfSyZc65fcBNwDAQBrYCO4H3+c9DwBDwp37ZgpjZPOABvND3CvDbzrlGIOLfYwhvW4lvFtN+M9sBfAsv9B0ANjjn6pxzbwIuAH4LuBNIFlO/zDHZ0HfqFCST3nHrVvX8idSIaCpF2Kyga0JmRFOpMrVIRERkds3oa0zn3N+Y2d8D1wG/ASz2T70CHAG+65yLFln9HwHZxWOudc4d9e+ZAQ6YWQAvuP2u3+v3SL4Vm9kS4Et4wfdO59x/HPdzDQP/03/I+S439CUS3nuJxBvhTz1/IlWvKRicdm7feEnn1NsnIiJzxoz/ovl7+X3Ff5TSh/zjo9nQN863gT8HlgMfBPIOfsAuYAHwEvDpmTRS5riJQl+Wwp9IzWgKBlkbidARi+V9zdrGRgU/ERGZM0q6Q62Zhc3sPWb2STPbZWa/WWQ9EeDt/ssfTFTGOeeAH/ovf6fAW3zQP/6dcy4xZUk5f00V+rJyw5+GfYpUtd1tbTTmuTF7YyDA7ra2MrdIRERk9uT1F9BfWfOD/qNukjKbgBeA/w58Dm9+3ONmdsTMFk90zRTemtO2rinKZc8tNrO8ulvMbDmw1H/5uJltMLMDZnbazMbM7EUz+7aZbS2wzTKX5BP6shT+RGrCtc3NrIpEpp3rFzZjdSTC9kWLZqllIiIi5Zdvj9/VwN8Cn3DOjY0/aWYtwD8Cl+Dt75f7uBK4v8B2Lc15PtXG77nnlk5a6mwrc55vBv4ZuB74NbwVPS/BW6TmSTP7zFQVmdmNZvaMmT1zzz335Hl7qXqFhL4shT+RqhcKBDi8fj1rGxsn7flrDARY19jIw+vXE8qzd1BERKQW5PtX7bf847cmOb8byH41+nW8YZrr8Xr9DPh1M7uugHZdkPN8qvW3c89dMGmpsy3Ief5nwKvAu4BGf0XPt+LNFzTg/zWzP5isIufcPc65K5xzV9x444153l6qWjGhL0vhT6TqLQiFOLZxI/euXs2G+fMJmREJBAiZsWH+fO5dvZqjGzeyIKQtXEVEZG7Jd9b6Zrz9+X44yfn3++cfcM79cc77t/hDMD8EXAvcV2xDSygw7vkO59yx7Bv+HoXvBX6G14t4O/C9WW2hVM7evd4+fckid/FIJLzr9+6FO+8sbdtEpCRCgQA7W1rY2dJCNJV6fa8+LeQiIiJzWb49fkuAFHDOJuxmtgZo8V/un+Da/+ofNxTQrqGc55EpyuWeG5q01OR1/8/c0JflnIsBf+2/XG9mF+VZt5RIgfssl86ePbB8OYTDxV0fDnvX79lT2naJSFk0BYNcUl+v0CciInNevsHvIiDq76E33mb/mGDife+68HoD852DB/DLnOcXT1Eu99wvJy11ttx5gT+ZolzuuTfnWbfMUE8P7NwJTU1w+nQFGrBwobc1w7JlhYe/cNi7Tls7iIiIiEiVyTf4zQOaJjn36/7xJxNtjeCcSwGDQEMB7foJkA2Za6colz132jmX76Sq54F0tnlTlMtd9q2wXX+lYNnAt24dHDrkZajR0Qo1ppjwp9AnIiIiIlUs3+D3v4CgmV06wbmteMHo6Smunw/kvWuucy4OPOm/fNdEZczMgGv8lz8qoO5R4An/5eVTFH1r9hLgVL71S2FGRi4+K/CNjkIqBRVfTK+Q8KfQJyIiIiJVLt+P18f941lLV5rZZUC7//LxiS40szcDYeClAtv2df/4TjO7coLzO4C3+M+/UWDdX/OPvznRfn3+BvL/3n/5z865vgLrl2n09MDzz3+WZ5756lmBr6rkE/4U+kRERESkBuQb/P4eb+jjzWb2STNbZWZXA9/1348BD0xy7Tv841QbsU/k68AJv/6D/v0ws4CZ7QC+7Jf7gXPukdwLzex2M3P+Y9kEdX8TeMp/fsDMrjGzgH/tarx9B5fiDTe9rcB2yxRyh3T297+DTKau+gJfrqnCn0KfiIiIiNSIvIKfc+67eMMjg8Dn8ObJ/QhYhzcU8r845yZbVXOnX2aihV+mumcKeA/eMMuLgcNmFsMLmd/Bm3P4HN5WEgXxF6l5r/9ztOJtUzFsZmfw5hdeDSSBP3XO/VOh9cu5xs/hGx0F52pkFb2Jwp9Cn4iIiIjUkEJmUr0X+Ae8HrjsA+ArwB0TXeAPBc3O0Xuw0MY5504Bb/Prz64OmgSeBW4FtjjnBgut16/7NLDRr+dpvFVJG/CC5leBjc65L09ageRtbAza2+G++2Z3SGcmMdEitEXKv+gFcAAAIABJREFUDX+hkEKfiIiIiNSUvIOfc+5Xzrn3ACuB6/3HW5xzH51kmwfwQtp7gd91zv28mAY654acc3/mnFvnnJvvnGtyzl3hnPuriVYR9a+53Tln/uPUFHWP+fVsds69yTlX55xb7pz7sHOu0KGpMom6OujogOuug/p6mI3tshJ9CY4sPkKib8L/RYqTDX833aTQJyIiIiI1peC1E51zPc65+/zHL6Ype8o594/OubxX3ZS5acUKOHAATpyA7du9AGhWvq6//u/1kxpM0f/9/tJWvHAh3HmnQp+IiIiI1JRKL5ov55ncALho0RMEAmNl6QE8/bXTZx1FRERERM5nCn5SEStWwOWX38EVV/zJ6z2ApQqAycEkQ896aw0NPTNE8kyyNBWLiIiIiNSoGllWUeaqhoaXOXDAW/Xzttvg/vu9xV8yBazL0n1jN3339XlL/wAu5bCQ4RLe8VjrMSzor0Vk0LyjmVVfWlX6H0ZEREREpEqpx0+qwvg5gImE1wuYj9ZbWwm1hEjH06TOpEgPp8nEvOSYiWVID/vvx9OEWkK03tJaxp9ERGpJNJXipdFRolW9oaiIiMjMqcdPqko2AMbjEInkd01kZYRNnZvouaWH0187TSZ+bndhoCHAkg8v4dK/upRAWN93iJzPkpkMB/v62NfbS1c8TtiMhHOsjUTY3dbGtc3NhAL6d0JEROYW/WWTqpRv6MsK1AVY+cWVtH26jXnz5519bn6Ats+0cdkXLlPoEznPDSaTbDl+nBu6u+mIxUg5RzyTIeUcHbEYN3R3s+X4cQaTmhssIiJziz4Fy5wSPRIlPZz2Xvj92ZnhDNFj0dLcYGAAbr7ZO4rIhDKJAibpzqJkJsO2zk66YjGGJ5lIPJzJ0BWLsa2zk2Qhk41FRESqnIKfzBnpeJrBRwcBb2hn8/ZmAg3e/+KDjwySHknP7AYDA7B1K9x9t3dU+BM5R6IvwZHFR0j0JSrdlHMc7OujOx4n4dyU5RLO0R2Pc6i/xPuAioiIVJDm+EnFZPxv03ft2lWS+pb+61KuHLuS0cgox37vGIMXDbLg9xew5R+2UB+v5/PXfZ5XLn2lqLojo6Pc/N3vsjAaJZTJkOzpYeDSS7lzxw7i+a5CM4WMehZkjuj/Xj+pwRT93+9n6UeWVro5Z9nX20ssz9+1WCbDvt5edra0lLlVIiIis0M9fjJnNAw18NKKl3j4Aw8zeJHX8zd40SAPf+BhXlrxEpGhAicO+saHPoBQJsPCaJSbv/tdIqOjJfsZRGrd6a+dPutYLaKpFF3xeEHXdMViWu1TRETmDPX4ScUE/FXz9u/fX9J6P8AHSldZdnhnPH7O5oKhTIaL4nE+9/jjcPQoLFxY9G2uuuqqGTZUpPKSg0mGnh0CYOiZIZJnkoTeFKpwqzzRVIqwGalphnnmCpkRTaVoCupPpYiI1D79NROZTDb0nTrlbSw4kUTCO79164zDn0g1KGQrle4bu+m7rw/8LOVSDgsZLuEdj7Uew4LmnTRo3tHMqi+tKk/Dp9EUDE47t2+8pHMKfSIiMmdoqKfIRPIJfVm54U8LvkiN6umBnTuhqQlO5zlKs/XWVkItIdLxNKkzKdLDaTIxr2c8E8uQHvbfj6cJtYRovaW1jD/B1JqCQdYWuE/M2sZGBT8REZkzFPxExisk9GUp/EmNyga+devg0CEIhyHfaauRlRE2dW5iyQ1LCEQm/nMSaAiw9MalbPrxJiIri5tnWyq729pozHNj9sZAgN1tbWVukYiIyOxR8BPJVUzoy1L4kxoyPvCNjkIqBXnmotcF6gKs/OJK2j7dxrz5884+Nz9A22fauOwLlxEIV/7PzbXNzayKRAibTVkubMbqSITtixbNUstERETKr/J/iUWqyd69cPJk4aEvK5Hwrt+7t7TtEimRyQLfTEWPREkP+3tl+qMjM8MZoseiM6+8REKBAIfXr2dtY+OkPX+NgQDrGht5eP16QoWmYBERkSqmv2oiufbsgTe/eWZ1vPnNXj0iVaRcgQ8gHU8z+Ki3hUqgIUDz9mYCDd6fl8FHBkmPpEtzoxJYEApxbONG7l29mg3z5xMyIxIIEDJjw/z53Lt6NUc3bmRBqDpWIxURESkVzVoXGa/Alf9Kfr1ICfX0wG23wf33e0GvVGFv//799PT0ALD0X5dy5diVjEZGOfZ7xxi8aJAFv7+ALf+whfp4PZ+/7vO8cukr59TxwgsvALBr164ZtWXFihUF1REKBNjZ0sLOlhaiqdTrWzZoIRcREZnL9FdOJNfevdDbO7M6enu9eu68szRtEinS2Bi0t8PIyDnbUJZUw1ADL614ieeufo5U2EuWgxcN8vAHHmbDIxuIDE28qEtDQ0P5GpUnBT4RETlf6K+dSK49e+DBB4tb3AW8JRGXLdNQT6kKdXXQ0VGeHr+Jetg+wAdKU7mIiIiUnOb4ieRauNDbiH3ZMi/EFSIb+rSRu1SRFSvgwAE4cQK2b4f6elAHl4iIyPlHwU9kvGLCn0KfVDkFQBERkfObgp/IRAoJfwp9UkMUAEVERM5PCn5SleLxSreA/MKfQp/UqMkCYDkXgREREZHKUfCTqpLda6ypCU6frnRrmDr8KfTJHDA+ACYSXggUERGRuUXBT6rC+M2lw2Fvg+mqMFH4U+iTOSYbAKNRWLy40q0RERGRUlPwk4oaGbn4rMA3OuotNx+otv8zc8NfKKTQJ3NWZOIt90RERKTGaUq/VERPDzz//Gd57bW3c/x46fYWK6ts+Nu719unT6FPRERERGpEtfWryByXO6Szv/8dZDJ1tRH6shYuhDvvVOgTERERkZqiHj+ZFT09cNttcP/9Xu+eF/b0v5+IiIiIyGzQJ28pu7ExaG+HkREtFS8iIiIiUgka6illV1cHHR1w3XXaLFpEREREpBIU/GRWTLRZtFktTe7zDQzAzTd7RxERERGRGqHgJ7MqNwAuWvQEgcBY7fQADgzA1q1w993eUeFPRERERGqEgp9UxIoVcPnld3DFFX/yeg9gVQfAbOg7dQqSSe+o8CciIiIiNULBTyqqoeHlc4aABoNVtghMbuhLJLz3EomShr9UXR1jTU1Ea2pvCxERERGpFQp+UhXGzwFMJLwQWHEThb6sGYa/ZCbDt199lQ1PP82RT32Kpz72MS588kk2PP003371VZJVlX5FREREpJYp+ElVyQbAaBQWL65wY6YKfVlFhr/BZJItx49zQ3c3HbEYbt48MuEwKefoiMW4obubLcePM5hMluRHEREREZHzm4KfVKVIpMINyCf0ZRUY/pKZDNs6O+mKxRiepFdvOJOhKxZjW2enev5EREREZMYU/ETGKyT0ZRUQ/g729dEdj5NwbuoqnaM7HudQf39+bRARERERmYSCn0iuYkJfVp7hb19vL7E8e/FimQz7ensLa4eIiIiIyDgKfiK59u6FkycLD31ZiYR3/d69E56OplJ0xeMFVdkVi2m1TxERERGZEQU/kVx79sDy5RAOF3d9OOxdv2fPhKejqRRhs4KqDJkp+ImIiIjIjCj4ieRauBCOHoVlywoPf+Gwd93Ro149E2gKBqed2zde0jmaqnp3exERERGpdvo0KRXV0dHBVVddVdE2ZPz5doHAG9+DXLBoEXe/+CKLzQjnEdQSZpyeN4+bFi1iaPv2KcvWffSjpJYsybt9axsbFfxEREREZEbU4ycV097eTnt7e6WbMaGhUIibNmzgdH09iWmGZibMOF1fz00bNjAUCk1bd9uTTxIYG8urHY2BALvb2vIqKyIiIiIyGXUjSMXcddddlW4CALt27QJg//79556cbpXPcJjwsmW0HT3KA5MM7xwvmcmw5fhxumKxKYd9hs1YHYmwfdGivOoVEREREZmMevxEpjLVnL885vRNJBQIcHj9etY2NtIYmPhXsDEQYF1jIw+vX09okjIiIiIiIvnSJ0qR6UwU/ooMfVkLQiH+R3s7H16yhIZxQ0kbzPjwkiU80d7OgjyGjoqIiIiITEfBTyQfueEvFJpR6AMYTCb5rY4OvvrKK4yMG+454hxffeUVfqujg8FkcuZtFxEREZHznoKfSL6y4e+mm2YU+pKZDNs6O+mKxRj2VxQdbziToSsWY1tnJ8lJyoiIiIiI5EvBT6QQCxfCnXcWHfoADvb10R2PT7ufX8I5uuNxDvX3F30vERERERFQ8BOZdft6e4nl2YsXy2TY19tb5haJiIiIyFyn4Ccyi6KpFF3xeEHXdMViRFOpMrVIRERERM4HCn4isyiaShGeZkP48UJmCn4iIiIiMiMKfiKzqCkYnHZu33hJ52gKBsvUIhERERE5Hyj4icyipmCQtZFIQdesbWxU8BMRERGRGVHwE5llu9vaiOQ53DNixu62tjK3SERERETmOgU/kVn2ngsvxOU7z8+M35/B1hEiIiIiIlADwc/MLjCz283shJkNm9mvzOxpM7vFzMJF1nm7mbk8HitK/fOI3P/aa3n/4hnwwMBAOZsjIiIiIueBqp44ZGZvBh4DlvlvxYE64Ar/8X4zu9o5N1jkLZLAVJ+qtZSilFwx+/jtbGkpc6tEREREZC6r2h4/M5sHPIAX+l4Bfts51whEgPcBQ8AG4JszuM0R59ziKR6nZvZTiJxN+/iJiIiISCVUbfAD/ghY5z+/1jl3GMA5l3HOHQA+6p/7XTO7ugLtEymY9vETERERkUqo5uD3If/4qHPu6ATnvw2c9J9/cHaaJDIz2sdPRERERCqhKoOfmUWAt/svfzBRGeecA37ov/yd2WiXyExpHz8RERERqYSqDH7AW3mjbV1TlMueW2xmxax5v8bMusxsxF8xtNvMvmxmG4qoSyQvu9vaaAzk96vXGAhoHz8RERERmbFqDX5Lc56/PEW53HNLJy01uUV4ITO7WuhK4CPAs2b2n4uoT2Ra1zY3syoSmXauX9iM1ZEI2xctmqWWiYhILcsk8lsxWkTOT9Ua/C7IeT7VEoi55y6YtNS5XgA+BawC6p1zFwKNwDXAs3jbp91mZrdMVYmZ3Whmz5jZM/fcc08Bt5fzWSgQ4PD69axtbJy0568xEGBdYyMPr19PKM/eQREROX8l+hIcWXyERF+i0k0RkSp1Xk4ccs6dswWEcy4B/MjMngCeADYBt5vZV5xzv5qknnuAbOIrbMUOOa8tCIU4tnEjh/r72dfbS1csRsiMpHOsbWxkd1sb2xctUugTEZG89H+vn9Rgiv7v97P0I8UMghKRua5ag99QzvOpVsLIPTc0aakCOOdGzez/Bh4G5gNXA4dKUbdIrlAgwM6WFna2tBBNpYimUjQFg1rIRURECnb6a6dfPyr4ichEqrU74Zc5zy+eolzuuV9OWqpwudtHvKWE9YpMqCkY5JL6eoU+EREpWHIwydCz3vffQ88MkTyTrHCLRKQaVeunzJ8AGbxgupZJtnTwzwGcds4NzEbDRERERCqp+8Zu+u7re32SiUs5LGS4hHc81noMC/oLiBk072hm1ZdWVa7BIlIVqrLHzzkXB570X75rojJmZniLsQD8qMRN2JLz/OSkpURERERmWeutrYRaQqTjaVJnUqSH02Ri3oqemViG9LD/fjxNqCVE6y2tFW6xiFSDqgx+vq/7x3ea2ZUTnN/BG8Mwv5FvpX5gnOp8HfDn/ssY8Ei+dYuIiIiUW2RlhE2dm1hywxICkYk/ygUaAiy9cSmbfryJyMqplksQkfNFtQe/E3hbKxw0s6sBzCxgZjuAL/vlfuCcOyucmdntZub8x7Jx9b7DzA6b2b8zs0tyrgn59/gfQDZo3uGcO1Pyn0xERERkBgJ1AVZ+cSVtn25j3vx5Z5+bH6DtM21c9oXLCIRn+FFvYABuvtk7ikhNq9Y5fjjnUmb2HuBRYBlw2MzieGG13i/2HPD+Aqs2vJU6s0FyBK9n79eAkF8mA3zOOff5mfwMIiIiIuUUPRIlPZz2XgSBFGSGM0SPRWde+cAAbN0KJ0/Cgw/C0aOwcOHM6xWRiqjmHj+cc6eAtwF3AF1405iTeJus3wpscc4NFljtCf/ag8DPgBHgTf6xE/gi0O6cu60EP4KIiIhIWaTjaQYf9T4GBRoCNG9vJtDgfbQbfGSQ9Ei6+MpzQ18y6R23blXPn0gNq9oevyzn3BDwZ/4j32tuB26f5NxrwF+Vom0iIiIilTLw0ABuzBFeEmbt99fStKmJ6NNRut7bReKVBAMPDdD8B81FVDwu9MHZ4U89fyI1qap7/ERERERkYqO9ozRf38zm7s00bWoCoGlTE5t/upnm65sZ/cVo4ZVOFPqy1PMnUtOqvsdPRERERM7V+vFW+Pi57webgqw5sKbwCqcKfVnq+ROpWQp+IiIiIrPkE5/4BB0dHRVtQybj7fl31VVXvf7eBckkdz/3HItHRghPV0EySeJnP+P0JZdw04YNDIVC010xqfb2du66666irxeR/Gmop4iIiMgs6ejoKFvwm5eZN32hCRQU+nxhYPHICHc/9xwXTNY7OI1y/rcQkXOpx09ERERkFrW3t/PYY4+VtM5EX4KnVj3F5u7NhJvzjW+8MbwzlSr4nmGgLZXigf7+ooZ95vY4ikj5qcdPREREpMb1f6+f1GCK/u/3F3bh3r3w859PPqdvOsmkd/3evcVdLyKzRsFPREREpMad/trps455+9jHwGxmNzfz6hGRqqbgJyIiIlLDkoNJhp4dAmDomSGSZwrovfviF0vTiFLVIyJlozl+IiIiIlUgHodIZPpy3Td203dfHzjvtUs5LGS4hHc81noMC/q9eAbNO5pZ9aVVE1e2Zw88+ODUWzhMJRSC5cu9ekSkqqnHT0RERKSCenpg505oaoLTeYzUbL21lVBLiHQ8TepMivRwmkzM26IhE8uQHvbfj6cJtYRovaV18soWLvQWZlm+3AtxhciGPu3nJ1ITFPxEREREKiAb+Natg0OHIByG0dHpr4usjLCpcxNLblhCIDLxR7lAQ4ClNy5l0483EVk5TTdiMeFPoU+k5ij4iYiIiMyikZGLzwp8o6PebgqBAj6VBeoCrPziSto+3ca8+Wfv3xeYH6DtM21c9oXLCITzrLSQ8KfQJ1KTNMdPREREZBb09MDzz3+W1157O8ePF7V13jmiR6Kkh9PeiyCQgsxwhuixaOGVZcPf1q2Tz/lT6BOpWerxExERESmj3CGd/f3vIJOpK0noS8fTDD46CHhDO5u3NxNo8D7aDT4ySHokXXilU/X8KfSJ1DQFPxEREZEyGD+Hb3QUnCvdYKuBhwZwY47wkjDtj7ez5sAa2h9vJ7wkjBtzDDw0UFzFE4U/hT6RmqehniIiIiIlNjYG7e0wMgKZTHnuMdo7SvP1zaz6yiqCF3gf6Zo2NbH5p5vpvqGb0V/ksVLMZMYP+1ToE6l56vETERERKbG6OujogOuug/p6CJbhq/bWj7ey5sCa10NfVrApyJoDa2j9+BTbOOQjG/5uukmhT2QOUPATERERKYMVK+DAAThxArZv9wKgWQkm982mhQvhzjsV+kTmAAU/ERERkTLKDYCLFj1BIDBWlh5AEZGp6J8dERERkVmwYgVcfvkdjIxczLJl3+T++70tHUqxwqeIyHTU4yciIiIyixoaXj5nCGgwWL5FYEREQMFPREREpCLGzwFMJLwQKCJSDhrqKSIiIlJB2QAYj0MkUunWiMhcpR4/ERERERGROU7BT0RERKSCenpg505oaoLTpyvdGhGZqzTUU0RERKQCenrgttt4fXXPcBhGRyvdKhGZqxT8RKpANJUimkrRFAzSpM2dRETmtJGRi9m5k3O2c6irq2y7RGRu0ydMkQpJZjIc7OtjX28vXfE4YTMSzrE2EmF3WxvXNjcTCmg0tojIXNHTA88//1lee+3tHD+u/ftEZHbpU6VIBQwmk2w5fpwburvpiMVIOUc8kyHlHB2xGDd0d7Pl+HEGk8lKN1VERGYoO4dv3Tro738HmUydQp+IzDoFP5FZlsxk2NbZSVcsxvAku/UOZzJ0xWJs6+wkqR19RURqUm7gO3TIm7/nnAZbiUhl6F8fkVl2sK+P7nichHNTlks4R3c8zqH+fna2tMxS60REpBTGxqC9HUZGQN/fiUg1UI+fzFmZRHX+pd3X20ssz08BsUyGfb29ZW6RiIiUWl0ddHTAdddBfT1o3S4RqTQFP5mTEn0Jjiw+QqIvUemmnCWaStEVjxd0TVcsRlSTQUREas6KFXDgAJw4Adu3ewHQTP+ei0hlKPjJnNT/vX5Sgyn6v99f6aacJZpKETYr6JqQmYKfiEgNyw2AixY9QSAwph5AEZl1Cn4yJ53+2umzjtWiKRicdm7feEnntLefiMgcsGIFXH75HVxxxZ+83gOof95FZLYo+MmckxxMMvTsEABDzwyRPFM9WyI0BYOsjUQKumZtY6OCn4jIHNLQ8PI5Q0CDQS0CIyLlpU+TUpXiccg3H3Xf2E3ffX3gd6S5lMNChkt4x2Otx7CgP7zSoHlHM6u+tKo8Dc/D7rY2PtLdndcCL42BALvb2mahVSIiMtuyQ0B7euC22+DgQS8EioiUg3r8pKpk9zxqaoLTeY7SbL21lVBLiHQ8TepMivRwmkzMC1WZWIb0sP9+PE2oJUTrLa1l/Ammd21zM6sikWnn+oXNWB2JsH3RollqmYiIVEI2AEajsHhxpVsjInOVevykKmS/7bz/fkilIBz2NrrNR2RlhE2dm+i5pYfTXztNJn5uT1qgIcCSDy/h0r+6lEC4st93hAIBDq9fz7bOTrrj8Ql7/hoDAVZHIjy8fj2hgL6fERGZKzL+v/m7du2qcEsqL6OxrSKzSp8opaKyPXzr1sGhQ17YS6Wg0KwTqAuw8osraft0G/Pmzzv73PwAbZ9p47IvXFbx0Je1IBTi2MaN3Lt6NRvmzydkRiQQIGTGhvnzuXf1ao5u3MiCUKjSTRURERGROUA9flIR43v4SrVbQfRIlPRw2nsRBFKQGc4QPRYtzQ1KKBQIsLOlhZ0tLURTKaKpFE3BoBZyERGZwwL+N5v79++vcEsq76qrrqp0E0TOK9XR/SHnjcl6+EohHU8z+Ogg4A3tbN7eTKDB+1988JFB0iPp0tyoDJqCQS6pr1foExGR80aqro6xpibtVSsyS8wVuKeYTEr/IadQaA9fYyN0dcGyZVOX279/Pz09PQAs/delXPnglYxGRjn2e8cYvGiQBa8uYMs/bKE+Xs+xdx/jlUtfOaeOF154AYDLLrusmB8NgBUrVmi+hoiITCvby/XYY49VtB2VksxkONjXx77eXjqjUSydJlBXx9pIhN1tbVzb3Ky57fmZeoU4kQmoe0HKbmwM2tthZKS8exQ1DDXw0oqXeO7q50iFvWQ5eNEgD3/gYTY8soHI0MT7QzQ0NJSvUSIiIgLAYDLJts5OfhaPM5zJwLx5uHnzyDhHRyzGDd3d/OWLL3J4/XrNcRcpA/X4lY7+Q06hXD1+IiIiteR87fFLZjJsOX6crliMxBSfPcNmrG1s5NjGjer5m5p6/KRg+o2SWZHdo+jECdi+3dugVtPZREREzg8H+/rojsenDH0ACefojsc51N8/Sy0TOX8o+MmsUgAUERE5/+zr7Z1w39qJxDIZ9vX2lrlFIucfBT+pCAVAERGR80M0laIrHi/omq5YTKt9ipSYgp9U1GQBsJyLwIiIiMjsiaZShK2wKWkhMwU/kRJT8JOqMD4AJhJeCBQREZHa1hQMTju3b7ykc9rbVqTEFPykqmQDYDQKixdXujUiIiIyU03BIGsjE2+pNJm1jY0KfiIlpuAnVanAvw8iIiJSxXa3tdGY5/YMjYEAu9vaytwikfOPgp+IiIiIlNW1zc2sikSmnesXNmN1JML2RYtmqWUi5w8FPxEREREpq1AgwOH161nb2Dhpz19jIMC6xkYeXr9em7eLlIF+q0RERESk7BaEQhzbuJF7V69mw/z5WDpNIJEgZMaG+fO5d/Vqjm7cyIJQqNJNFZmTNGtWRERERGZFKBBgZ0sLO1ta+M1rriFdV8dDhw5pIReRWaAePxEREZEqUOAe5zUvODZGXTSq0CcySxT8RERERCqopwd27oSmJjh9utKtEZG5Sl+xiIiIiFRATw/cdhvcfz+kUhAOw+hopVslInOVgp+IiIjILBoZuZidO98IfKmU935dXWXbJSJzW9UP9TSzC8zsdjM7YWbDZvYrM3vazG4xs3CJ7/XfzMz5j1OlrFtERETObz098Pzzn+WZZ77KoUNe71429ImIlFtVBz8zezPwY+DPgLWAAXXAFcD/BxwzswUlutdVwI2lqEtEREQkKzuHb9066O9/B5lMnQKfiMy6qg1+ZjYPeABYBrwC/LZzrhGIAO8DhoANwDdLcK8I8BUgBTwz0/pEREREcgNftofPOc2yEZHKqOZ/ff4IWOc/v9Y5dxTAOZcBDphZAPgW8LtmdrVz7pEZ3OvPgUv94yV4PYoiIiIiRRkbg/Z2GBmBTKbSrRERqeIeP+BD/vHRbOgb59vASf/5B4u9iZltAXYBPwP+c7H1iIiIiGTV1UFHB1x3HdTXQ81uVTcwADff7B1FpKZVZfDzh16+3X/5g4nKOOcc8EP/5e8UeZ864Kt4cwc/6pzTIsoiIiJSEitWwIEDcOIEbN/uBUCzGprcNzAAV14J+/d7R4U/kZpWlcEPeCtvtK1rinLZc4vNbGER9/msf697nXOPFXG9iIiIyJRyA+CiRU8QCIxVfw9gNvT19HhjVXt6FP5Ealy1Br+lOc9fnqJc7rmlk5aagJltAD4FvOofRURERMpmxQq4/PI7uOKKP3m9B7AqA2Bu6Mul8CdS06o1+F2Q8zw+RbnccxdMWmocMwviDfEMArucc4OFNe/1em40s2fM7Jl77rmnmCpERETkPNPQ8PI5Q0CDwSpZBGay0Jel8CdSs6o1+JXbp4F24B+cc98pthI+GfFiAAAcv0lEQVTn3D3OuSucc1fceKO2ABQREZH8jZ8DmEh4IbBipgt9WQp/IjWpWoPfUM7zyBTlcs8NTVoqh5ldDuwBhoH/UHjTREREREonGwCjUVi8uEKNyDf0ZSn8idScag1+v8x5fvEU5XLP/XLSUme7Gwjj7dk3aGbzcx+8sbeh5bwfyrvlIiIiIkWITPVVdzkVGvqyFP5Eakq1Br+fANmR7munKJc9d9o5l++/Osv941/g9RKOf7zfP9+W895NedYtIiIiUjuKDX1ZCn8iNaMqg59zLg486b9810RlzMyAa/yXP5qNdomIiIjMKbfdVnzoy+rp8eoRkapWlcHP93X/+E4zu3KC8zuAt/jPv5Fvpc65Zc45m+yRc99f5Lx/V/E/hoiIiIiISGVVe/A7ARhw0MyuBjCzgJntAL7sl/uBc+6R3AvN7HYzc/5j2Sy2WURERKR2/Pmfw6WXzqyOSy/16hGRqla1wc85lwLeA5zCW8TlsJnFgBjwHaAJeI435uSJiIiISCEWLoSnnio+/F16qXf9woWlbZeIlFzVBj8A59wp4G3AHUAX4ID/3d79h8lV1Xkef3873Z2kO2kNpBEU2zCCBAk2ZEVh0B3dySj+WMYJKjiOoLiO7rIbRR0yq6isrDNmXcVB3fXHsjPDPLqiAorOCIKIP0JwBCRDHI1GfjT+QBIS6KQ7TXelzv5xb5Gi6equ6q7uqq5+v56nnqq69557T/WTm6rPPfecMwbcDrwbOGW6k69LkiSJ6Yc/Q580rzR18ANIKe1NKX0gpXRCSmlZSqknnzT9oyml0QplLi7rn3dvjcd7Y15uVT3qL0mS1PRqDX+GPmneafrgJ0mSpDlQbfgz9EnzksFPkiRJmanCn6FPmrcMfpIkSTqoUvgz9EnzmsFPkiRJj1ce/iIMfVILMPhJkiTpiUrh7+1vN/RJLaC90RWQJElSkzrkELj00kbXQlId2OInSZIkSS3O4CdJkiRJLc7gJ0mSJEktzuAnSZIkSS3O4CdJkiRJLc7gJ0mSJEktzuAnSZKkOVdYvJhHe3oYLBQaXRVpQXAeP0mSpBZQHC3S1tnc1/THikWu2rmTTQMDbL3wQuLAAQ7dvJk1XV1s7OvjzN5eOtqa+zNI85VnliRJ0jw3unOUWw6/hdGdo42uSkV7xsY45Y47eMv27dw5NERatIhiZyeFlLhzaIi3bN/OKXfcwZ6xsUZXVWpJBj9JkqQmMDw8/bK7vrqLwp4Cu762q34VqqOxYpF1W7eybWiIfcXihNvsKxbZNjTEuq1bGauwjaTpM/hJkiQ10I4dcNZZ0NMDDzwwvX088LcPPO652Vy1cyfbh4cZTWnS7UZTYvvwMFfvas4AK81nBj9JkqQGKAW+E06Aq6+Gzk4YGal9P2N7xth7+14A9t62l7GHm+9WyU0DAwxV2Yo3VCyyaWBglmskLTwO7iJJkjSHbr/9EQ477CYeeug0UlpEStnPsWJxP2ef/SaWLJm81e6V21/J8buOf+x9W2qDBItZzEhhhJtW3kQxDoasn/T+hG886xuPvS/mAaxtjgZRKSxezNYLL4RFi6ous21oiMFCgZ52f6pK9eLZJEmSNAd27IDf/OZjDA8fz9DQwcBXqy1P38KqR1bx5JEn0z5uH4uLix97XYgCDy95mC1HbplRvWfqwOLFxIEDpBqCX0eEwU+qs0hT3GutqvmHlCRJT7BjB7z3vXDttVAoZI+JdHfDtm2watXU+yw+WmTHu3bwwN8+QHH4ibdQti1t44g3H8EzP/rMJ0zxsGHDBgAuu+yyWj/KtAwWChy6eTOFGn5zdkSw67TTDH6VRaMroPnHPn6SJEmzYHwfvpGRyqGvVm2L23jWJ59F31/2sWjZ41vS2pa10fdf+zjmE8c0xbx+Pe3trOnqqqnMmu5uQ59UZ43/30CSJKnFPPoonHgifOUr9Q184w3eMsiBfQeyN3lOKu4rMnjr4OwccJo29vXRXWWfwu62Njb29c1yjaSFx+AnSZJUZ4sXw513wqtfDUuWwGw0Xh0YPsCe7+wBsls7e9f30rY0+2m359t7OLD/QP0POk1n9vZybFcXnTH5HYqdEazu6mL9ypVzVDNp4TD4SZIkzYKjj4Yrr4S77oL16+sfAHdfv5v0aKLziE5O/O6JHH/l8Zz43RPpPKKT9Ghi9/W763ewGepoa+PG/n7WdHdXbPnrbmvjhO5ubujvp2OORhyVFhLPKkmSpFk0WwFwZGCE3tf28rztz6Pn5B4Aek7u4Xk/ex69r+1l5L5pTAo4i1Z0dHDr2rVcvno1Jy1bRkcEXW1tdERw0rJlXL56NVvWrmVFR0ejqyq1JEf1rB//kJIkaUqVRvmsZVTPmZjrUT0rGSwUHpuywYFcauaonqqZLX6SJElzqFILYPGJszK0tJ72do5cssTQJ80Rg58kSVIDjA+Ao6NZCJSk2eAlFkmSpAYqBcDhYahxujtJqpotfpIkSU3A0CdpNhn8JEmSJKnFGfwkSZIkqcUZ/CRJkiSpxRn8JEmSJKnFGfwkSZIkqcUZ/CRJkiSpxRn8JEmSJKnFGfwkSZIkqcUZ/CRJkjTnBgsFfjUywmCh0OiqSAtCe6MrIEmSpIVhrFjkqp072TQwwLbhYTojGE2JNV1dbOzr48zeXjrabJeQZoPBT5IkSbNuz9gY67Zu5efDw+wrFgEopATAnUNDvGX7dj5y//3c2N/Pio6ORlZVakleUpEkSdKsGisWWbd1K9uGhh4LfePtKxbZNjTEuq1bGauwjaTpM/hJkiRpVl21cyfbh4cZzVv4KhlNie3Dw1y9a9cc1UxaOAx+kiRJmtju3XDBBdnzDGwaGGCoyla8oWKRTQMDMzqepCcy+EmSJOmJdu+GU0+FT30qe55m+BssFNg2PFxTmW1DQ472KdWZwU+SJEmPVwp9994LY2PZ8zTD32ChQGdETWU6Igx+Up0Z/CRJknRQeegbHc2WjY5OO/z1tLdP2bdvvLGU6Gl38Hmpngx+kiRJykwU+kqmGf562ttZ09VVUzXWdHcb/KQ6M/hJkiRp8tBXMs3wt7Gvj+4qJ2bvbmtjY19f1fuWVB2DnyRJ0kJXTegrmUb4O7O3l2O7uqbs69cZwequLtavXFldvSVVzeAnSZK0kNUS+kpqDH8dbW3c2N/Pmu7uii1/3W1tnNDdzQ39/XRU2TooqXqeVZIkSQvVdEJfSY3hb0VHB7euXcvlq1dz0rJldETQ1dZGRwQnLVvG5atXs2XtWlZ0dEzro0ianL1mJUmSFqpLLoF77smmbJiO0dGs/CWXwKWXTrl5R1sbZx12GGcddhiDhQKDhQI97e0O5CLNAVv8JEmSFqr3vQ+OOgo6O6dXvrMzK/++99VctKe9nSOXLDH0SXPE4CdJkrRQHXIIbNkCq1bVHv46O7NyW7Zk+5HU1Ax+kiRJC9l0wp+hT5p3DH6SJEkLXS3hz9AnzUsGP0mSJFUX/gx90rxl8JMkSVJmsvBn6JPmtaYPfhGxPCIujoi7ImJfRDwSET+KiHdFxLSGoIqIP4iID0XE9RHxi4jYExFjEfFgRHwnIjZExNJ6fxZJkqSmN1H4M/RJ815Tj58bEc8AbgZW5YuGgcXAc/PH6yPiD1NKe2rc9V8Aryh7PwQ8CvQCL8of74iI01NKP59m9SVJkuanUvg79dRsnj5DnzTvNW2LX0QsAr5OFvp+C/xRSqkb6ALOBvYCJwGfn8bubwQ2AGuBnpTSspTSMmBlvnw/cBRwTUQ07d9IkiRp1pTC3/nnG/qkFtDMLX5vBE7IX5+ZUtoCkFIqAlfmgewLwMvyVr9vV7vjlNLHKyx/CPhERDwKfAZ4NnAqsHnan0KSJGm+OuQQuPTSRtdCUh00c2vWufnzd0qhb5wvAvfkr8+p87FvLXt9ZJ33LUmSJElzqimDX0R0Aaflb7850TYppQRcl799SZ2r8MKy17+s874lSZIkaU41662ex3EwlG6bZLvSusMj4pCU0u7pHjAfxfNI4DXA+/PF30sp3TbdfUqSJElSM2jW4PfUste/nmS78nVPBWoKfhFxONnAMRP5Olk/Q0mSJEma15ryVk9gednr4Um2K1+3vOJWlR0Afpc/RsqWfxm4cKoWxIj484i4LSJu++xnPzuNw0uSJEnS7GvWFr85kVLaCRwOEBEBPA14G/Au4FUR8Z9TShUTXb6utD7NcnUlSZIkaVqatcVvb9nrrkm2K1+3t+JWVUiZX6WULgJeD3QA/zsi+meyX0mSJElqtGYNfr8pe/20SbYrX/ebilvVKKV0NXAf2d/nzfXaryRJkiQ1QrMGv58Cxfz1mkm2K617YCYjelZQCpJH13m/kiRJkjSnmjL4pZSGgc3529Mn2ibvk/fS/O236nn8fN9H5W9ndAupJEmSJDVaUwa/3N/nzy+OiOdPsP41wO/lr6+odqcRUc2ANm8iH/QFuLnafUuSJElSM2r24HcXEMBVEfGHABHRFhGvAT6Xb/fNlNK3ywtGxMURkfLHqnH7fUFEfC8i3hARR44rd0xEfBj4TL7ol8Df1fNDSZIkSdJca9rpHFJKhYg4A/gOsAq4MSKGycLqknyzH5ONwFmrF+YPImIE2Ad0A0vLttkKvCqltH9aH0CSJEmSmkQzt/iRUroXeA7wQWAb2Vx5Y8DtwLuBU1JKe2rc7e3AOcD/JQt3jwBPJhtM5pdkk7efDfyb/PiSJEmSNK9FSs47Xif+ISVJUtPbsGEDAJdddlmDa6IZiEZXQPNPU7f4SZIkSZJmzuAnSZIkSS3O4CdJkiRJLc7gJ0mSJEktzuAnSZIkSS3O4CdJkiRJLc7gJ0mSJEktzuAnSZIkSS3O4CdJkiRJLc7gJ0mSJEktzuAnSZKkOTdYKPCrkREGC4VGV0VaENobXQFJkiQtDGPFIlft3MmmgQG2DQ/TGcFoSqzp6mJjXx9n9vbS0Wa7hDQbDH6SJEmadXvGxli3dSs/Hx5mX7EIQCElAO4cGuIt27fzkfvv58b+flZ0dDSyqlJL8pKKJEmSZtVYsci6rVvZNjT0WOgbb1+xyLahIdZt3cpYhW0kTZ/BT5IkSbPqqp072T48zGjewlfJaEpsHx7m6l275qhm0sJh8JMkSdKs2jQwwFCVrXhDxSKbBgZmuUbSwhNpiisvqpp/SEmSNKsuu+wyduzYMaN9/OIXvwDgmGOOmdF+jj76aDZs2DDldoOFAodu3vxYf75qdESw67TT6Gl3OIoKotEV0Pxji58kSdICsnTpUpYuXTpnxxssFOiM2nJKR4TTPEh1Zotf/fiHlCRJGscWv1lhi59qZoufJEmSZk1PeztrurpqKrOmu9vQJ9WZwU+SJEmzamNfH91VTsze3dbGxr6+Wa6RtPAY/CRJkjSrzuzt5diurin7+nVGsLqri/UrV85RzaSFw+AnSZKkWdXR1saN/f2s6e6u2PLX3dbGCd3d3NDfT0eVrYOSqufgLvXjH1KSJGkSY8UiV+/axaaBAbYNDdERwVhKrOnuZmNfH+tXrjT0VcfBXVQzg1/9+IeUJEmq0mChwGChQE97uwO51M7gp5oZ/OrHP6QkSZLmgsFPNbMtXZIkSZJanMFPkiRJklqcwU+SJEmSWpzBT5IkSZJanMFPkiRJklqcwU+SJEmSWpzBT5IkSZJanMFPkiRJklqcwU+SJEmSWpzBT5IkSZJanMFPkiRJklqcwU+SJEmSWpzBT5IkSZJanMFPkiRJklqcwU+SJEmSWpzBT5IkSZJanMFPkiRJklqcwU+SJEmSWpzBT5IkSZJanMFPkiRJklqcwU+SJEmSWpzBT5IkSZJanMFPkiRJklqcwU+SJEmSWpzBT5IkSZJanMFPkiRJklpcpJQaXYeWEBHXASsbXQ9N20pgV6MrIS1AnntSY3juzW+7UkqnN7oSml8MfhIQEbellJ7b6HpIC43nntQYnnvSwuOtnpIkSZLU4gx+kiRJktTiDH5S5rONroC0QHnuSY3huSctMPbxkyRJkqQWZ4ufJEmSJLU4g58kSZIktTiDnyRJkiS1OIOf5lxEXBwRKSIm7WAaEW+KiEK+7fci4klzVUdJ1YmIFRExUjqnI+KYRtdJUvUi4ktl5+9/r7JMmuBRjIiHI+KOiPgfEdE323WXVBuDn5pSRFwAXA4sAv4ReGlK6ZHG1krSBF4PLC57f16jKiKpNhFxKHBG2aI3RsSiGnYxBPwufzwEPAk4CfgL4CcR8fJ61VXSzBn81HQi4oPAx4AAvgC8KqW0v7G1klTBm/PnT+TP59b4w1FS4/wZ2YWbfwJ+CTwNeGkN5f9nSunw/NEL9ABvA/YCy4ArI+Ipda6zpGky+KlpROaTwPvyRZ8E/iylVGhgtSRVEBFrgROBh4ELgbuBI4CXNbJekqpWunBzBfAP+etpt9qnlPamlD4DXJAvWga8cdq1k1RXBj81hYhoJ/vSOT9fdElK6b+kCSaajIi1EfH+vN/ffXn/oocj4taI2BgRyyY5Tqkvwosi4vCI+GRE3JPv44GI+HxErK5QdlVZ+VURcUxE/F1E/CoiHo2IgYj4dEQ8bZLjT7vuUhMq/Wi8MqU0wsEfjm+usP1jIuLQ/Fz4YUTszs+FeyPi+oh4W6U+vRFxXER8KiL+NSL2RsS+iNgeEV+MiDMjwu81zWsRcXP+PXNxRCyKiAsi4sf5v/UHI+KrEdFftn1XRFwUEdsiYigiHoqIKyPimVMc52TgBOAR4Gtk4S8BZ0RE7ww/xueBYv765BnuS1KdOIG75lxEXAx8ACClFBGxBPgy8EqyL513pJQum6R8+T/aIjAIPLls2b8CL04pPThJ2fOAvwIOB/YDB8iuTAKMAH+SUrpuXNlVwD3527OBzwHLgX1kfRGX5ut2A3+UUrqjnnWXmkl+3v6W7N/vaSmlWyLi94AdZOfTkSml31Uo+xLgi8CKfFGB7DwqPxf+JKX01XHlNpKdt6VwNwKMkZ2HJStSSg/P5LNJjRQRNwN/QPZv/XnAOmCU7N96d77ZPuDFZN9JN5D1qxsh+w4tfRc9CJycUhqocJxPA28FPpdS+vN82XeBfwu8K6X0sUnqWPou+28ppYsrbPMg0AvckFJ6yVSfW9Ls88qoGioieoDryEJfATh3stCXu5EsuD0DWJxSWgF0AeuB7cCzgU9PsY9Lyb5IXwJ0p5SWA88H7gKWkPVLOHKS8p8h+8J9fl62m6xfxABwCHBNRCyfoFw96i41gzPJgtqOlNItACmlu4EfAO3AGyYqFBEnkbUurAB+Arwc6MrPhW6y1oGPkvURKi/3H4EPk31vXQuclFJamlLqAQ4lO5ev5GArgzTf/SeyQPcasguTy8mC4N35+78huwC5guz7pztfvg7YCRxGFh6fICK6gNflb68oW/X3+fOMBmmKiG5gZf5290z2Jal+bPHTnCtv8QPuANaSXak8K6V07Qz3/TSyDuqdwKrxVzrLrlKOAiemlH46bv1hwE/Jwtv/SimdX7ZuFQdb/B4Cnj2+ZS4ijgPuzI9/YUrpI/Wqu9RMIuImshaH96eULilb/h/Ifoz+LKV03ATlvg+8APgFWWvElKP1RsQK4D6yH75fBP50otvApVZQ1uIH8MKU0g/Grf93wLfzt/uB56SUdozb5jyykbH3A09KKY2NW38OWcj7ZUrp6LLly8lG6FwKnJJS+mGFOk7a4hcR7wZK338XpJQ+PtlnljQ3bPFTo63Nny+faegDSCn9GthKNiLo70+y6ZfHh768/IMcbHE7a5Lyn57odsx8n1/J355dVaUPlq227lJD5bd0vojstrJ/GLf6S2Q/NldHxO+PK3cMWegDeE8NU7S8miz0jQHvNPRpgfjB+NCX+y7waP76K+NDX+76/HkpMNHcmqV+uI87f1NKe4Frxm1Tlbw/4tER8QGgNB/gbg62IkpqMIOfGu2W/Pn8iNhQTYGIaIuIP42Ia/MBVfaXDbqSyG6FAZjsVs2bqlh3aEQcNYPyz4mIjlmou9Ro55FdoPh+Sune8hUppUHgq2XblSsFwQPAN2s4Xqnc7Sml39ZWVWne+ueJFqaUDgC78rc/qlC2vH/tivIVEXE0WT++iS7cwMGgdnZ+S+hkPlD2/VUga8m/mGyKiJ3AH6eU9kyxD0lzxOCnRjsd2Jy//puIeMdkG+dfQjeSjRj274Gnk/073s3BSWRLt7R0T7SP3K+rXHfYDMq3k90yCtS17lLD5KNmnpu/vaLCZqUfjmeNG6n28Px5V0ppqIbDlsrdV0MZab7bO8m6wmTbjJsGqWPc6tIFmc15v9zxbiT7HltO1r9wMuUTuP+WLPhdTza9y3EVWiwlNYjBTw2V31ZyOvD9fNGlEfHOSYq8l6xf0X6yeYKeASxJKR1amkQWKPVJiMkOPbOaT6t8veouNdJLOdgi/X/KW6zLrvyXRsRdBrx2gn1M9/zzFk9pBiJiEQcv3Lygwvl7gGwid5j6ds/yCdyfmlJ6Vkrp9JTSR1JKD83W55A0PQY/NVxKaR/ZhM/fyxd9NO8YPpFSv7kPppQ+nlIamKC/z+HjC01gslspy+fhqzStQjXlCzx+NLN61V1qpJr6/fD42z1Lt2n25qP+VatUblWNx5b0eC8DnlrD9i/M++ZKagEGPzWF/LavlwM354s+EhEXTrDp0/PnH0+0n3zkzaMnWjfOi6tYtzuldM8U20y27l/GjaRWr7pLDZFP6nxG/rY04EqlR6m/6mkRsTp/XerTu4jsB2i1SuWeGxFHTK/2kjh44eYaJj9/l5ONug0znNpBUvMw+Klp5OHvFRwcHGVTRPzluM1KowD2V9jNh6s83Gsi4tjxCyNiJdmEtpDNCVbJ2/Jtx5c/luwH8UTl61V3qVHeQNZf6BHg6ymlfZM8fgT8LC93HkA++mCpZf+v8nk8q/FlYJCs3+ylEeGt0FKNIuIpZHPmAlw5xfm7j+y8Azg3v0VU0jxn8FNTSSkNk30xleYo+uuIeE/ZJqW+QxdFxPqIaAeIiKMi4gtk/YmqGUFsBLguItaVfkRGxMlkndpXknWYnyyIdQA35GWIzDqyTu2Lgft54kTs9aq71CilK/9fSymNVrF96YfjOaV/78Dbyc6/Y4DNEXF6afTbiOiKiOdHxKfz8wmAfNqH0h0AZwHXRMSJpfURsSIiXhERX6shTEoLzTlkF0/2A9+oYvsv5c9HUFsLvaQmZfBT00kp7Scb9fKGfNGHIuKi/PVFZKOHLQeuAvZHxMPA3cDryAZQ+ZcqDvNOYEl+jH0RsZds6Ox+svmRXjfFBOpvBZ4J/HNedl++r2cADwPr82Hty9Wr7tKci4hTgOPzt1+ebNsype2eQtaaT0rpTuCPyVoN15BN6zAUEbvJRgi8lez8Kh8NlJTSZ4D3AMW8/I8jYjgiBsn60n6D7DZUv9ekiZUu3PxTNaPq5iN+lm73rLVvr6Qm5BekmlIe/s7g4CS0l0TE+1NK9wHPBS4HfpOvGyH70ffSlNJfV3mIu4GTgE+RzTXUSTaQy/8DTkop/eMU5X+Y1+MKsh+w7WTDX38OOCGldNsEn6ledZcaofTD7xHgW9UUSCndBfx0XHlSSt8ia/H7EFmf1/1kE03fS3bOv5UJ5srMz5F+svOsNGl1ANvJzt31ZLeESioTEacBpb62X5ps23FK274yv1VU0jwWTxxUUGpd+VDVAC9OKd1cY9lVQGmwl6PGT1wtSZIkNStb/CRJkiSpxRn8JEmSJKnFGfwkSZIkqcUZ/CRJkiSpxTm4iyRJkiS1OFv8JEmSJKnFGfwkSZIkqcUZ/CRJkiSpxRn8JEmSJKnFGfwkSZIkqcX9f6XrjaXH2YtHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44c15c0400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "temp_data = pd.melt(statistics_intra_observer[['Kappa', 'Acc', 'mAP']]) #, 'mAP-IG'\n",
    "ax = sns.boxplot(x=\"variable\", y=\"value\", data=temp_data, width=.3,  showfliers=True) #\n",
    "#ax.set(ylim=(0.55, 1))\n",
    "\n",
    "\n",
    "for i,box in enumerate(ax.artists):\n",
    "    box.set_edgecolor('black')\n",
    "    box.set_facecolor('white')\n",
    "\n",
    "for index, row in enumerate(statistics_intra_observer.sort_values(by=['Skill']).values.tolist()):\n",
    "    name = row[1]\n",
    "    kappa = row[4]\n",
    "    mAP = row[5]\n",
    "    mAP_IG = row[6]\n",
    "    acc = row[7]\n",
    "\n",
    "    plt.scatter(x=0 + offset[name], y=kappa, marker=symbols[name], s=150, c=color[name], zorder=100)\n",
    "    plt.scatter(x=1 + offset[name], y=acc, marker=symbols[name], s=150, c=color[name], zorder=100)\n",
    "    \n",
    "    plt.scatter(x=2 + offset[name], y=mAP, marker=symbols[name], label=name, s=150, c=color[name], zorder=100)\n",
    "    #plt.scatter(x=3 + offset[name], y=mAP_IG, marker=symbols[name], s=150, c=color[name], zorder=100)\n",
    "    \n",
    "# DL results:\n",
    "for index, row in enumerate(dl_statistics_intra_observer.values.tolist()):\n",
    "    name = row[1]\n",
    "    kappa = 1#row[4]\n",
    "    mAP = 1#row[5]\n",
    "    mAP_IG = 1#row[6]\n",
    "    acc = 1#row[7]\n",
    "\n",
    "    plt.scatter(x=0 + offset[name], y=kappa, marker=symbols[name], s=150, c=color[name], zorder=100)\n",
    "    plt.scatter(x=1 + offset[name], y=acc, marker=symbols[name], s=150, c=color[name], zorder=100)\n",
    "    \n",
    "    plt.scatter(x=2 + offset[name], y=mAP, marker=symbols[name], label=name, s=150, c=color[name], zorder=100)\n",
    "    \n",
    "\n",
    "#ax.xaxis.grid(True)\n",
    "#ax.yaxis.grid(True)\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "by_label = OrderedDict(zip(labels, handles))\n",
    "\n",
    "ax.legend(by_label.values(), by_label.keys(), bbox_to_anchor=(1., 0.9), prop={'size': 25}, frameon=False)\n",
    "\n",
    "ax.set(ylabel=\"Score\", xlabel=\"\", title=\"Intra-Observer Performance\")\n",
    "sns.despine(left=True)\n",
    "\n",
    "plt.savefig(\"IntraObserverPerformance.pdf\", transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vu4rrHYKqVv2Hs9Fu5lKKiHmMd8f6CPl3dGedsjN1yGSi6lPz5tct1Vj19+15Le4mr8tGYnuO04r7X5NPeaoXzvvw2MZdj5FSWppSinq3CcW/WTX8ogkNs5rxxqrhFZMcyzy0iflVl6nbqC6ltD2ldFNK6X0ppZeRjz9eSG5dCfCeCQ1xqrfsn0EHFI14Kg2eDqBofFa0OfgfxfN3pPoXHqm8hl5a2NuxG+n4Z9Wk6j4DntZE+eoybWkwOstOZnwD4iP1wrnQaA9eO/2G8YA9osV5zOj3zYCePdW7Ymbi2tNPLO7vn6Ql4mm06XMvWp5+q3jYS+5JpylFy+Bzqp76x0kmeV5ETLZL6AVVw01f0i+ltDWl9LfANcVTcxjv8AOgeot54hWWZtNnq4YrW82vY/zY2mepryyvYUal3DFM5XjtCRExsS1CWfxX1fCL6pbi0WO3p1U9tTtervKJVcM/naTsS2ayIhXFBkFli/cJxXUFpmpGf1cG9Oyp3hXSzC6tqaoco31K1Gm2GhF78diLDLTDexn/8/EnTbRiJCIGyGFSeR+uTyl9t8EkkLf6z6k3smjcVvkHe0tq3INXPWurhqsP/9xGPtcRcivwhivUmZJSup3xU8lOL97HSlDvJHeiUc+XyOfbAlzQwt6O3clVxf1e5B7kyug/yefUA7wqIg5vUPZMxk+hW5lSmqyxVhlVtyF5ar1CEXE8uU+D2VJ9SOh9ETHVTPwe44fmXlo01G0bA3r2VJ9Oc9wMzL/yr3oxNbrwLBpkfYzcsrVtimMul1QWQ+6i78+LPwO7iIhDyaeVPK946iHgTU0u7gPFD3jiPJ9K7iKx4u8njD82Ii6KiCdSR0QsIq8IoegNqTKu+Kf9zqriyyOi4b/8iDgyIj7SqEyLKiuU+eSeiZ5fPF7R6E9JSulnjJ9z/QTgG8VnUVNkp0bEu9pQ59l2BeMNGM+JiA8U3/+aImJBRJzf7pVrI0Xr88uLh33AV2p9PyPiWPIpdhWXzUL1ZsJtVcNviYhddmMXf1K+wuzm0pcY//P9fOCTETG3VsGI6I2I6mt9U+ytrF43fDkiTqOBiHhGRHy4mcrZSGyWpJQ2RcQdwLHACyLi/5F3D2+pKnPDNBZxBeO7yv4hIk4h94n7MPn41RuL+5uK+3aeA30JuUXkX5BXNh8C/jwirmX8ql9PJO+CfhnjDcN+Brw8NXct6H8jv76bI+Iq8hZIdV/clUYe16aUrpkw7eOKOr43Im4mN267j/zeLySfL/67jB+b/3xKaV31DFJKX4+IS8gtUfcFboiI/yS3lH+A3Piqcg7u84t57mS8r/F2+Ty5EWAU95WVWaPd2xXvJPcpfyq5pezdEfEv5O4JHyJ/dk8ElpHf6wPJ39H3tbH+My6ltKXYk3MTucXz24CzI+LL5D9elf7Xn0I+p/gF5Ba1vzPLVf1bcv/OJ5H76r87Ij5B7tmrl3yO7RsZb+37kWmuIzompfRARHwNeBX59/PDYh24mvwdPpncwcze5O/y79ebV5vrNRoRr2O8L+5zyO1pvkTuPXAbeaPnOHJbjy3kdVH1PL4aEe8n7518Avka9/9B7hf8AfJ64AmMrxuOIp/+dV4zFezaG1Pr6vPiSeY1aVlyONW9msuEshdXjTulydfz/nrzLm7fKb5sa4vHa9v8fv4e430bN7qNkXtHatgd4MT3tJh/oytJ/Ss1rmZFvujAZHWq3JbTuD/zPyS3Qm1mXjXf36rxK1t8n2+asJwtwLwmp+0n/5lr5qpCCbhqOr+Ldv8mpzivI8lh18zr3A6cVmMeTfWr3eo05D8Q/9rE7+WDNHk1q3bWkRqXdpzkt1Ep+3s1xi8mB3K91zlK/hN5WtVzu3RHPMU6NVWWfBGLRnWr3Hbp+rZqHm9h/FKrk93WNPM5uQU9i1JK1xcNEf6c/K95f8ZbNrZj/n8VEd8mX1XlOeQtxw3kHpe+SL6E4WgTPSu1uvzPRcRXyA2XXkHu6H8/8mt8mHy+4wpyBxOrWpz/KvL790LyFt4QeSX8iZTSF+pM9+1i99kLyVtLzyTvQRgopl9H7inuqpTStyepw8eL1/i/yI1ZjmL8HORN5C3z75H/Pa+c6mts0mfJIVnx1ZRSM+fSknJvbH8WER8i73l4AXlLcl/yMepfkb8v3yF3QXlXG+s9q1JK9xS7iF9Fvs7vSeQ9BPPIf2rWkreS/h34Wpq8m8aZqOMW4BUR8TLyVuNzizqOkltr3wR8NFV1TLO7Simtj4jnAG8lH06qnE3wS3Jjq/+XUvr+ZLuIZ6hu90bEM4GzyRe/OJ687uohtxX4EfmslZrrmGIeH42Iq8m/q0qbmMq6YSOPXTc0dapmFMkvSZJKxEZikiSVkAEtSVIJGdCSJJWQAS1JUgnZirvcbMEnSVM3M6eqzDK3oCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQS6sqAjoh9IuLiiLgrIrZGxCMR8f2IeFtE9Lc4z+dHxPsi4hsR8ZOI2BQRIxHx64i4KSL+PCLmtvu1SGrd5tFRfr59O5tHRztdFWkXkVLqdB1mVUQ8GVgJLC2eGgL2AuYUj+8ATk0pbZrifK8DXlH11GBxP6/qufuBl6aU7mtytt314UizYGRsjGvWr+eydetYPTREfwTDKXHUwAAXLlnCGYsX09fTldsue5LodAXaoasCOiL2Igfw0cAvgTemlFZERA9wJvAxYB/g+pTSy6c47/OBncB3gDUppS3F808Afhe4DJgL3A0cnVIaa2K23fPhSLNg08gIp61axX1DQ2wd2/UnOL+nh8MGBlixbBn79vV1oIZqEwN6dxMRfwB8vHj43JTSLRPG/w7wheLhaSmlb7Vx2ecCHy0ePi+ldHMTk3XPhyPNsJGxMU68/XZWDw4y3GC91x/BUfPmcetxx7klvfvaIwK627595xT3N00M58KXyLuhAd7Y5mXfWjX8pDbPW9Ikrlm/nnuHhhqGM8BwStw7NMS1GzbMUs2k2romoCNiADi5eHh9rTIp7064oXj44jZX4beqhn/a5nlLmsRl69YxWGO3di2DY2Nctm7dDNdIaqy30xWYRUcy/odkdYNylXH7R8TClNLGVhdYtNp+Evn49nuKp7+dUrqt1XlKjVxxxRWsWbNmRpfx4IMPAnDQQQfN6HIADj30UP7sz/5s2vPZPDrK6qGhKU2zenCQzaOjLOjtptWkyqRrtqCBA6uGH2xQrnrcgXVL1RER+0dEiohEbiF+H/A+civxrwOvnWT6cyPitoi47corr5zq4qUZt23bNrZt29bpakzJ5tFR+mNqhyX7Ijz9Sh3VTX8N96kabvRXunrcPnVL1bcT+FUx/Dhg72L4y8B7JtsiTyldCVSS2UZimpJ2bG1O5q1vfSsAH/rQh2Z8We2yoLd30mPPE42k5NazOqqbtqBnRUppfUpp/5TS/sAAcDB5C/p/AD8sWnNLmkULens5amBgStMcNW+eAa2O6qaA3lI13OiXWj1uS91STUjZz1NK7wbeAPQBH4mIZdOZr6Spu3DJEuY1edrUvJ4eLlyyZIZrJDXWTQH9i6rhRq1bqsf9om6pKUopXQs8QH7P/6Bd85XUnDMWL+bwgYFJj0X3R3DEwACnL1o0SzWTauumgL4HqJxjcVSDcpVxD02nBXcdlcA/tM3zlTSJvp4eVixbxlHz5tXdkp7X08PR8+bxzWXL7KREHdc138CU0hBQ6b3rpbXKREQALyke3tjO5RfzPqR4OK1d55Jas29fH7cedxyfOOIIjp0/n74IBnp66Ivg2Pnz+cQRR3DLccfZzadKodtaQFxF7jDkBRHxnJTS9yaMPxN4SjH8mWZnGhG9KaXJzsf4n8D+xfDKZuctqb36eno4e7/9OHu//dg8Ovrouc42CFPZdM0WdOEq4C5yP63XRMSpABHRExGVi2VAvljGY/rhLi5PmYrb0gnzfV5EfDsifj8injRhuqdFxN8w3g/3T4FPt/NFSWrNgt5enrT33oazSqmrvpUppdGIeBVwE/lykysiYoj8R6VyvvId5BbXU/VbxY2I2A5sJV9qsvoa0KuA16SUdq9eHiRJs67btqBJKa0FnglcQu7WMwEjwA+AtwMnTvVa0MW0bwQ+SQ7hR4DHkxul/ZTcScnrgWcVy5ckqaGuutzkbsgPR6WzO/Ykpq7j5SYlSdLMMKAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKqGuDOiI2CciLo6IuyJia0Q8EhHfj4i3RUR/i/M8KCL+JCK+HBFrImJbcbs/Ir4YES9s9+uQJO25ejtdgdkWEU8GVgJLi6eGgDnAs4vbGyLi1JTSpinM82DgASCqnh4qHi8tbq+PiE8C56aUdk7rRUiS9nhdtQUdEXsBXycH5i+BF6WU5gEDwOuBLcCxwOenOOu9yGH8LeAc4KBivvOBZwD/UpT7X8DF03oRkqSu0FUBDbwJOLoYPiOltAIgpTSWUloOvKUY97KIOHUK890EPCuldFpK6TMppV9Uzfdu4LXADUXZ8yNi7+m+EEnSnq3bAvqc4v6mlNItNcZ/Cbi/GH5jszNNKT2SUrq9wfgEfLJ4OB84stl5S5K6U9cEdEQMACcXD6+vVaYI0sqW7ovbXIXtVcN7tXnekqQ9TNcENHmrtfJ6VzcoVxm3f0QsbOPyTynuh4H72jhfSdIeqJsC+sCq4QcblKsed2DdUlMQEYcAf1Q8XJ5S2tyO+UqS9lzdFND7VA0PNShXPW6fuqWaFBFzgS+TW4o/DLxzkvLnRsRtEXHblVdeOd3FS5J2U113HvRsiohe4AvAs4AR4HdTSo223kkpXQlUkjnNbA0lSWXVTVvQW6qGBxqUqx63pW6pSRTnXH8OeA0wSg7nG1udnySpu3RTQP+iavigBuWqx/2ibqkGqsL5bGAn8Hsppa+0Mi9JUnfqpoC+Bxgrho9qUK4y7qGU0sapLqQI58+TeyarhPPyqc5HktTduiagU0pDwM3Fw5fWKhMRAbykeDjl3dFV4Vy95fylqddWktTtuiagC1cV9y+IiOfUGH8m8JRi+DNTmXERzl8gh/Mo8AbDWZLUqm4M6LvIF7a4ptLfdkT0RMSZwMeKctenlL5VPWFxecpU3JZOGLcX8FngLMYbhLlbW5LUsq46zSqlNBoRrwJuIl/RakVEDJH/qFQuYHEH8IYpzvpk4HcqiwGuiIgrGpR/qwEuSWqkqwIaIKW0NiKeCbwdOB04hHyO8o+ALwJXpJSGpzjb6j0RfcATJyk/d4rzlyR1ma4LaICU0hbgvcWt2Wkups61nFNKK8m7zSVJaotuOwYtSdJuwYCWJKmEDGhJXWvz6Cg/376dzaOjna6KtIuuPAYtqXuNjI1xzfr1XLZuHauHhuiPYDgljhoY4MKLVj6ZAAAgAElEQVQlSzhj8WL6etx2UecZ0JK6xqaREU5btYr7hobYOpZ7/h1N+aJxdw4O8uZ77+XvfvYzVixbxr59fZ2squQubkndYWRsjNNWrWL14OCj4TzR1rExVg8OctqqVYzUKSPNFgNaUle4Zv167h0aYjg1vsz6cErcOzTEtRs2zFLNpNoMaEld4bJ16xhscqt4cGyMy9atm+EaSY0Z0JL2eJtHR1k9NDSlaVYPDtq6Wx1lQEva420eHaU/ptbZX1+EAa2OMqAl7fEW9PZOeux5opGUWNDriS7qHANa0h5vQW8vRw0MTGmao+bNM6DVUQa0pK5w4ZIlzGuyA5J5PT1cuGTJDNdIasyAltQVzli8mMMHBiY9Ft0fwREDA5y+aNEs1UyqzYCW1BX6enpYsWwZR82bV3dLel5PD0fPm8c3ly2zu091nN9ASV1j374+bj3uOD5xxBEcO38+fREM9PTQF8Gx8+fziSOO4JbjjrObT5WCLSAkdZW+nh7O3m8/zt5vPzaPjrJ5dJQFvb02CFPp+I2U1LUMZpWZu7glSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlzb6NG+GCC/K9pJoMaEmza+NGOOkk+PCH870hLdVkQEuaPZVwXrsWRkbyvSEt1WRAS5od1eE8PJyfGx42pKU6DGhJM69WOFcY0lJNBrSkmdUonCsMaWkXBrSkmdNMOFcY0tJjGNCSZsZUwrnCkJYeZUBLar9WwrnCkJYAA1rSTLj0Urj//qmHc8XwcJ7+0kvbWy9pN2JAS2q/iy6CQw6B/v7Wpu/vz9NfdFF76yXtRgxoSe23cCHccgssXTr1kO7vz9Pdckuej9SlDGhJM6OVkDacpUcZ0JJmzlRC2nCWHsOAljSzmglpw1nahQEtaeY1CmnDWarJgJY0O2qFtOEs1WVAS5o91SHd12c4Sw0Y0JJmVyWkzzvPcJYa6O10BSR1oYUL4YMf7HQtpFJzC1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1rqQqNbRztdBUmTMKClLjN4zyDfWfAdBu8Z7HRVJDXQlQEdEftExMURcVdEbI2IRyLi+xHxtojob3Gej4+IV0fEJRFxXUT8MiJScXtTm1+C1LIH/s8DkGDd36zrdFUkNdB114OOiCcDK4GlxVNDwBzg2cXtDRFxakpp0xRn/RrgU22qpjRjHv7qwwBs+OoGuKrDlZFUV1dtQUfEXsDXyeH8S+BFKaV5wADwemALcCzw+RYX8RBwPfA+4Izp1ldqt233b2Pn1p0A7Nyyk20PbOtwjSTV021b0G8Cji6Gz0gp3QKQUhoDlkdED/AF4GXFVvS3pjDvz6WUPl39RERMv8bSNHzvyO+x7d5tkOqMX/q98QcBA4cPcMI9J8xO5SQ11FVb0MA5xf1NlXCe4EvA/cXwG6cy45SSzWJVOodefijR29wfxegNnnr5U2e4RpKa1TUBHREDwMnFw+trlUkpJeCG4uGLZ6Ne0kx6wkuewMkbTmbgqIGG5eYdPY+TN57ME17yhFmqmaTJdE1AA0cy/npXNyhXGbd/RCyc2SpJM693QS8n3HUCj3/h42uOf/wLH8/xPzye3vmzeMRr40a44IJ8L6mmbgroA6uGH2xQrnrcgXVLSbuZrT/cWvP5wbtm+XzojRvhpJPgwx/O94a0VFM3BfQ+VcNDDcpVj9unbqkZEhHnRsRtEXHblVdeOduL1x5qeMMwoxvGm0n0Hzx+uv/I+hGGNw7PTkUq4bx2LYyM5HtDWqqpmwJ6t5BSujKl9OyU0rPPPffcTldHe4if/d3P8kAPHP7pw3nuuudy+KcPf3QN8Oj4mVQdzsPFH4LhYUNaqqObAnpL1XCjFjPV47bULSXtRobuG2LOk+Zw4s9O5IBzDgDggHMO4MQHTmTOk+Yw9ONGO5XaoFY4VxjSUk3ddB70L6qGDwJ+WKfcQXWmkabl/PPP58477+xcBZ4K/G6d5zcBp0xtdqec0twE+4yM8OE77mD/7dvpT3VOyB4eZvgnP+GhJz2J8449li19fVOrzDQcc8wxXH755bO2PKlZ3bQFfQ8wVgwf1aBcZdxDKSX/zqtt7rzzzs4GdAc0Fc6F/pTYf/t2PnzHHewzMjIr9evGz0S7j67Zgk4pDUXEzcBvAS8F/m5imchdf72keHjjLFZPXeKYY45h5cqVna7G7Kjs1t65EyYJ54r+lFiycydf37ABbrkFFs7smY7N7gWQOqGbtqBh/NIAL4iI59QYfybwlGL4M7NTJWkP1OiY82Q8Ji0B3RnQdwEBXBMRpwJERE9EnAl8rCh3/cR+uIvLU1YuH7m01swjYlH1rWrU/AnjGnfrJO3uLr0U7r9/6uFcMTycp7/00vbWS9qNdFVAF/1lvwpYS24MtiIiBoFB4GpgAXAH8IYWF7F+wq3iignPv6PF+Uu7h4sugkMOgf6WLq+epzvkkDwfqUt1VUADpJTWAs8ELiF365mAEeAHwNuBE1u4FrSkagsX5mPIS5dOPaT7+/N0s3AMWiqzrmkkVi2ltAV4b3FrdpqLgYsnKeP1JaWKSkhP5Vi04Sw9quu2oCXNoqlsSRvO0mMY0JJmVjMhbThLuzCgJc28RiFtOEs1GdCSZketkDacpboMaEmzpzqk+/oMZ6kBA1rS7KqE9HnnGc5SA115mpWkDlu4ED74wU7XQio1t6AlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKqHe6c4gInqAZwFPBgZSSp+Zdq0kSepy09qCjog/A34J3AosBz41Yfy+EbE6In4cEQunsyxJkrpJywEdEf8XuBxYDGwB0sQyKaVNwA+ApwGvbHVZkiR1m5YCOiJeAvwJsBV4bUrp8cD6OsW/AATwqpZqKElSF2p1C/qPyFvM70kp/cskZW8p7o9pcVmSJHWdVgP6xOL+k5MVTCltBjYDB7S4LEmSuk6rAb0QeCSltKXJ8mPAXi0uS5KkrtNqQG8GFkRE32QFI2IR8HhgQ4vLkiSp67Qa0D8iN/w6vomyv1/c/6DFZUmS1HVaDehryQF9cdFRSU0R8VzgEnKDsqtbXJa0xxsa6nQNJJVNqwH9UWANcCpwQ0S8tDKviFgUES+MiCuBm4B5wCrgi22or7RHWbMGzj4bFiyAhx7qdG0klUlLXX2mlHZExCuAG4HTyEFd8auq4QB+Sj5XeqzlWkp7mDVr4F3vgq99DUZHob8ftm/vdK0klUnLPYmllH5CPrf574FN5DCuvm0h9zR2QkrpgelXVdr9bdt2EGefDUcfDddem0N5dBR6vGyNpAmmdbGMlNIjwF8CfxkRTwcOJJ9O9RCwOqW0c/pVlHZ/a9bA3Xe/h4cfPpnbb8+hLEmNtBTQEbGkGPx1Smk7QErpbuDudlVM2hNU78reseO3SamXMQ/2SGpCq1vQa8mdjywBftG22kh7iInHmPMW87Sv7iqpi7S6xtgKjKSUDGdpgh074JhjYNs23FqW1LJWm6asBQYiwu47pQnmzIE774TXvQ723ht63XCW1IJWA/qfgX7gFW2si7THOPRQWL4c7roLTj89B3WELcMkNa/VgL6M3FHJRyLimW2sj7RHqQ7qRYu+TU/PDreoJTWl1VXFGeTexC4GbouIG4CbgV8DdU+tSil9psXlSbu1Qw+Fpz/9ErZtO4ilSz8/ofGYJO2q1YD+NLl/bcidkryCyXd3J8CAVlebO/dBli/ftZW3jckkTdRqQK9jPKAlTVFl13clqK+5Jh+nlqSKVvviXtrmekhdqRLUQ0MwMNDp2kgqE3sAlkrAcJY0kQEtSVIJteWEj4h4BvBsYD/ysen1wPeL/rklSdIUTSugI+IlwN8CR9UZfxfwjpTSjdNZjiRJ3ablXdwR8afAv5HDOcgXz/g14+dCB/BM4PqIOG/6VZXULmPDntcllV1LAR0Ry4DLySH8X8DLgfkppQNSSgcA+xTP3VKUudwex6RyGF4/zHf3/y7D64c7XRVJDbS6Bf0XxbRfB56XUrohpbSjMjKltCOldAPw20WZvYALpltZSdO34Z83MLpplA3/sqHTVZHUQKsB/XxyY7C3ppQade25Ezi/ePiCFpclqY0e+tRDj7mXVE6tBvQTgUdSSmsnK5hSuh/4TTGNpA4a2TTClh9sAWDLbVsY+c1Ih2skqZ5WW3FvI18Pujel1LC7/4joBQaAoRaXJalF9557L+u/sv7RjnnTaCL6gjSc7289+FaiN/LIgMVnLubwjx7euQpLelSrW9D3AH3A65ooeyb52tH3tLgsSS06+O0H07dfHzuHdjL6m1F2bt3J2GBuwT02OMbOrcXzQzvp26+Pg992cIdrLKmi1YD+Mrl19j9FxGn1ChXj/on8//3qFpclqUUDhw1w/KrjOeDNB9AzUPvn3jO3hwPPPZDjf3g8A4fZ56hUFq3u4v4I8AfAM4BvRMQtwArgQXIYHwycCpxEDvLVxTSSZlnPnB4O+7+H0f/Efn72tz9j59bxdp0983tY8o4lLL1oaecqKKmmVq9mtaPoRexa4ATgueQwrlYc2OJ7wBkppdKcdBkR+wBvA84ADiF3rHIf8CXgiunUNSKeCLwDeCWwhHy8/kfAVcAnUkpeplMdsfm7m8fDuRcYhbGtY2y+dXNT0w+NDDHQt+sWdr3nJU1Pyz2JpZR+QQ7m1wNfBX4ODBe3nxfPnQ2cXJQthYh4MvBD4L2M94I2h9yX+AeAWyNi3xbn/SxyGP8FcBgwSu605XnAx4AbImLOdF+DNFU7h3ay6aZNQN6lvfj0xfTMzT//Td/axM5tdc+WBOAjt32Ex/3N47juvuse8/x1913H4/7mcXzkNneQSe02ratZpZTGUkpXp5TOSCk9OaU0t7g9uXjuyyml0vQpGBF7kTtOWQr8EnhRSmkeuZX564EtwLHA51uY9+OA64AnAD8Gjk8p7QPMA/4UGAFeDHxw2i9EmqKN39hI2pHoP6CfY/7jGJ6x/Bkc8x/H0H9AP2lHYuM3Ntad9iO3fYS33/h2RsdGOevLZz0a0tfddx1nffksRsdGefuNbzekpTbrtstNvgk4uhg+I6W0Ah79o7EceEsx7mURceoU5/12YH/yLu2Xp5RuK+Y9nFL6MHmLHeDciDhsGq9BmrLt67az+KzFnHDvCSw4fgEAC45fwAk/PoHFZy1m+wPba05XCeehkXyW5LbRbZz15bP4q2/9FWd9+Sy2jW4D8m5uQ1pqr2j1kGhELADGUkpbJyk3H+hJKTV3oGsGRcS3gd8CbkopvbDG+AB+Sj4u/ZmU0jlTmPcD5GPOn0op/a8a4+eTt9rnA5eklN47sUwNHq/eg5xyyikArFy5sqP1aNbQyBCP+5vHMTq2a1cHe/fuzfbRXUO9t6eXR/6/R3abY9K722eipsXkRcqv1YtlnA5sAq5sovjngE0R8apWltUuETEAnFw8vL5WmaIB1w3FwxdPYd6Hk8O50by3Av851XlLnTLQN8BXz/4qc3vn7jKuVjjP7Z3LV8/+6m4TzlLZtbqL+8zi/hNNlP0Y+d/MWS0uq12OZPz1rm5QrjJu/4hY2OS8q6+H3cy8n97kfKWOeuVhr+TqM6+uGdLV5vbO5eozr+aVh71ylmom7flaDehji/sfNFH25uL+uBaX1S4HVg0/2KBc9bgD65aa3rwXFLu8dxER50bEbRFx25VXNrODQppZrzzslZx/4vns3bt3zfF79+7N+SeebzhLbdZqRyUHAVtSSr+ZrGBK6TcRsaWYppP2qRpu1C949bh96pZqz7x3OX6fUrqS8UMHHoNWx11333VcfuvlNXdrQ97dffmtl/Pcg59rSEtt1OoWdCL3xd2sXvI1oSXtRiqnUlVaa9dTad098TxpSa1rNaB/BuwdEUdPVjAilgFzabzrdzZsqRpu1IqletyWuqVmb95SRwyNDPHa5a+tGc61dndvG93Ga5e/9tFTsiRNT6sBvZLc8Ouvmyh7MXmL+6YWl9Uu1b2ZNdrdXj2u2R7QpjrvzZOdniZ12kDfAP/4sn/cpVX23N65XHDiBbs0HKtXXlJrWg3oK4Ax4NUR8bmi/+nHiIgnRsQXgFcXZf+x9Wq2xT1FPeCxra4nqox7KKVUv3ulx6puud3MvO9ucr5SR/3xs/+YD7z4A4+GbqW19vtPff9jWncP9A3wgRd/gD9+9h93srrSHqWlgE4p/Rh4F3kr+neAByLi5oj4QkR8PiJuBtaR++IGeHdKqaOhlFIaYrxF+UtrlSk6KnlJ8fDGKcz7XvLrbTTveeROUqY0b6nTKiHd29P7mFOpKqdg9fb0Gs7SDGi5JzGAiPhj4G8Yb8VcmVmlF5fNwDuKlskdFxF/AHycXM+TUkrfmzD+LGB58fC0lNK3pjDvS4F3k1tqPyOltHbC+HcAl5GvnPX0lNJ9TczWVtx7kN2916o98WpWu/tnorq6tyexipTSR4AnA+cCnwa+Udw+BbwZWFKWcC5cBdxF/vCuqfS3HRE9EXEmuVMVgOsnhnNEXBwRqbgtrTHvDwAPkRuC/WtxZSsior/4I3NpUe7KJsNZKpV6Iby7hrNUdq2eB/2o4lzojxe3UkspjRZdjt5EvqLViogYIv9RqTRLvQN4QwvzfiQiXkn+g/J04Lbi/O+9GT8l7Ubggmm9CElSV5h2QFeLiH7yMdjDgR3A7Sml77RzGdOVUlobEc8kX33qdPKFMUbI13H+InBFSmm4xXn/ICKeAVwIvBI4GBgkNyK7CvhkmS6/KUkqr6aOQUfEPsBri4fLU0o7apQ5HvgK8KQJo74HnJ5Semiade1GHoPeg3i8s3z8TPZYXXUM+lTyMebz64TzfsC/ksM5JtyeA3ytHZWVJKlbNBvQldODvlBn/IXAomL4KvJlHZcBHySH9LMi4nWtVlKSpG7T7DHoE8i7W2+oM/4Nxfivp5T+Z9Xzbysu2XgOcAZ5F7gkSZpEs1vQBwCj1OgBq2gUtV/xsFZvYR8q7o+tMU6SJNXQbEA/kdx/dK0WyCcU98NArRbbq8lb181eW1mSpK7XbEDvBSyoM+5Zxf09tU5PSimNApvIV7SSJElNaDagfw30RsRTa4w7ibyF/P0G088nnw8sSZKa0GxA317cn1v9ZEQ8DTimePgftSaMiCcD/cDPW6mgJEndqNmA/iL5dKkLIuIvI+Lwoh/rLxfPDwJfrzPtbxf3q+uMlyRJEzQV0CmlLwPfJp+W9Tfk1tw3AkeTd2//Q0ppS53Jzy7KlKrLT0mSymwqV7N6NXAdj+0lDPJFMi6pNUGxC7xyfeR/a7GOkiR1naYvlpFSegR4VUQcyvhx5++nlB5oMNkIOdhHUkr/3Xo1JUnqLlO+mlVKaQ2wpsmya4G1U12GJEndbiq7uCVJ0iwxoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWpKkEjKgJUkqIQNakqQSMqAlSSohA1qSpBIyoCVJKiEDWiqBoaFO10BS2RjQUgetWQNnnw0LFsBDD3W6NpLKpLfTFZC60Zo18K53wde+BqOj0N8P27d3ulaSysSAlmbRtm0HcfbZ48E8OpqfnzOns/WSVD4GtDQL1qyBu+9+Dw8/fDK33z4ezJJUj8egpRlUOcZ89NGwYcNvMzY2x3CW1BS3oKUZMPEYcw5lf26SmucaQ2qzHTvgmGNg2zYYG+t0bSTtrtzFLbXZnDlw553wutfB3ntDr3+DJbXAgJZmwKGHwvLlcNddcPrpOagjPPgsqXkGtDSDqoN60aJv09Ozwy1qSU1xVSHNgkMPhac//RK2bTuIpUs/v8t50JI0kVvQ0iyaO/fBXXZ99/bamEzSrgxoqQMmHqMeHs5hLUkV7uKWOqgS1ENDMDDQ6dpIKhO3oKUSMJwlTWRAS5JUQga0JEklZEBLklRCBrQkSSVkQEuSVEIGtCRJJWRAS5JUQga0JEklZEBLklRCBrQkSSVkQEuSVEIGtCRJJWRAS5JUQga0JEklZEBLklRCBrQkSSVkQEuSVEIGtCRJJWRAS5JUQga0JEklZEBLklRCBrQkSSVkQEuSVEIGtCRJJWRAS5JUQga0JEklZEBLklRCBrQkSSVkQEuSVEIGtCRJJWRAS5JUQga0JEklZEBLklRCBrSkrjU6Zw47Fixg8+hop6si7cKAljT7Nm6ECy7I97NsZGyML/3qVxz7/e/z3Xe8g//60z/lCTffzLHf/z5f+tWvGBkbm/U6SbVESqnTdVB9fjh7kFNOOQWAlStXdrQeHbdxI5x0Etx/PxxyCNxyCyxcOCuL3jQywmmrVnHf0BBbawTx/J4eDhsYYMWyZezb1zcrddKMiE5XoB3cgpY0eyrhvHYtjIzk+5NOmpUt6ZGxMU5btYrVg4M1wxlg69gYqwcHOW3VKrek1XEGtKTZUR3Ow8P5ueHhWQvpa9av596hIYYn2Ws4nBL3Dg1x7YYNM1ofaTIGtKSZVyucK2YppC9bt47BJreKB8fGuGzduhmri9SMrgvoiNgnIi6OiLsiYmtEPBIR34+It0VE/zTm+/iIeHVEXBIR10XELyMiFbc3tfElSLuXRuFcMcMhvXl0lNVDQ1OaZvXgoK271VG9na7AbIqIJwMrgaXFU0PAHODZxe0NEXFqSmlTC7N/DfCpNlRT2nM0E84V1SHd5oZjm0dH6Y9gdAqNYvsi2Dw6yoLerlpNqkS65psXEXsBXyeH8y+BN6aUVkRED3Am8DHgWODzwMtbXMxDwB3A7cXtmmlWW3ugt771rZ2uwqwY2L6d86++moWbN9PXbIOr4WFG1qxh41OfyuVnncXQ3nu3pS47envZ/opXQE/zOw1HUjKc1VHdtIv7TcDRxfAZKaUVACmlsZTScuAtxbiXRcSpLcz/cymlA1JKL08pvTuldO30qyztnloK50Lf2BgLN2/m/KuvZmD79rbUZ87oKAs3b57SNEfNm2dAq6O66dt3TnF/U0rplhrjvwS8DzgEeCPwranMPKXkwSo15UMf+lCnqzDzLrgABgehxVOV+sbGeOLgIP9nYAA++MG2VOmkX/2KP7z33qYais3r6eHCJUvaslypVV2xBR0RA8DJxcPra5VJuceWG4qHL56Nekl7rIsuyp2Q9LfY7rK/P09/0UVtq9IZixdz+MAA/dG4D4v+CI4YGOD0RYvatmypFV0R0MCRjL/W1Q3KVcbtHxGz07WRtCdauDA39Fq6dOoh3d+fp2tzQ7G+nh5WLFvGUfPmMa/Oseh5PT0cPW8e31y2jL4pHK+WZkK3fAMPrBp+sEG56nEH1i0laXKthPQMhXPFvn193HrccXziiCM4dv58YudOeoaH6Yvg2Pnz+cQRR3DLccfZzadKoVuOQe9TNdzoZMjqcfvULTWDIuJc4FyAj370o5x77rmdqIbUHpWQbuZUqxkO54q+nh7O3m8/zt5vP573kpewc84cvnHttTYIU+mU9htZdO4xnfOKX5ZSumHyYuWSUroSuLLysJN1kdqimZCepXCeqHfHDnp37DCcVUrdsot7S9XwQINy1eO21C0laWoa7e7uUDhLZVfmv41fBK6bxvSPVA3/omr4IOCHdaY5qM40kqar1pa04SzVVdqATintAHa0aXb3AGPkPQZHUedUq2IcwEMppdm/kry0p6sO6fvvN5ylBrpiF3dKaQi4uXj40lplIiKAlxQPb5yNekldqRLS551nOEsNdEVAF64q7l8QEc+pMf5M4CnF8Gdmp0pSl1q4MPcQZjhLdXVbQN8FBHBNpb/tiOiJiMrFMgCuTynt0s1ncYnKyuUjl9ZaQEQsqr5VjZo/YVyjhmqSJHVPQBd9Zb8KWEtuDLYiIgaBQeBqYAH5SlRvmMZi1k+4VVwx4fl3TGMZkqQu0DUBDZBSWgs8E7iE3K1nAkaAHwBvB05s8VrQ0rQMNeo+R1JX6qqABkgpbUkpvTeldHRKaX5KaUFK6dkppb9PKdXt5iildHFKKYrb2jplosnbxTP1+rR7WbMGzj4bFiyAhx7qdG0klUlpT7OS9mRr1sC73gVf+xqMjubTgdt06WNJewgDWppF27YdxNlnjwfzaHEV8TlzOlsvSeVjQEuzYM0auPvu9/Dwwydz++3jwSxJ9XTdMWhpNlWOMR99NGzY8NuMjc0xnCU1xS1oaQZMPMacQ9mfm6TmucaQ2mzHDjjmGNi2DcbGOl0bSbsrd3FLbTZnDtx5J7zudbD33uClhiW1woCWZsChh8Ly5XDXXXD66TmoIzz4LKl5BrQ0g6qDetGib9PTs8MtaklNcVUhzYJDD4WnP/0Stm07iKVLP7/LedCSNJFb0NIsmjv3wV12fff22phM0q4MaKkDJh6jHh7OYS1JFe7iljqoEtRDQzDgVcIlVXELWioBw1nSRAa0pNm3cSNccEG+l1STAS1pdm3cCCedBB/+cL43pKWaDGhJs6cSzmvXwshIvjekpZoMaHeVdhcAABpPSURBVEmzozqch4fzc8PDhrRUhwEtaebVCucKQ1qqyYCWNLMahXOFIS3twoCWNHOaCecKQ1p6DANa0syYSjhXGNLSowxoSe3XSjhXGNISYEBLmgmXXgr33z/1cK4YHs7TX3ppe+sl7UYMaEntd9FFcMgh0N/f2vT9/Xn6iy5qb72k3YgBLan9Fi6EW26BpUunHtL9/Xm6W27J85G6lAEtaWa0EtKGs/QoA1rSzJlKSHcgnEfnzGHHggVsHh2dleVJUxEppU7XQfX54exBTjnlFABWrlzZ0Xp0RKVV9/335z64J+rry8ecZyGcR8bGuGb9ei5bt45VmzcTO3fSM2cORw0McOGSJZyxeDF9PW677Oai0xVoBwO63Pxw9iBdHdAAP/0pHHlk/YC+5x546lNntAqbRkY4bdUq7hsaYuvY2C7j5/f0cNjAACuWLWPfvr4ZrYtm1B4R0P5NlDTzNm6El7+8cZmXv3xGz3seGRvjtFWrWD04WDOcAbaOjbF6cJDTVq1ipE4ZabYY0JJm1sRLTNYyC5eevGb9eu4dGmJ4kr2Gwylx79AQ127YMCP1kJplQEuaOSXqi/uydesYbHKreHBsjMvWrWt7HaSpMKAlzYwS9cW9eXSU1UNDU5pm9eCgrbvVUQa0pPYrWV/cm0dH6Y+ptRvqizCg1VEGtKT2K1lf3At6eyc99jzRSEos6O1ty/KlVhjQktqvZH1xL+jt5aiBgSlNc9S8eQa0OsqAltR+JeyL+8IlS5jXZAck83p6uHDJkrYtW2qFAS1pZpSsL+4zFi/m8IGBSY9F90dwxMAApy9a1NblS1NlQEuaOSXqi7uvp4cVy5Zx1Lx5dbek5/X0cPS8eXxz2TK7+1THeYBFmkV33nnno11+dpN9Fi3iwz/7GftH0F+jsdZwBA/ttRfnLVrEltNPn9G6zOvpYcmRR/Kz5z2PrYsX0zM2xl5z5nDUvHlcuGQJpy9aZDirFOyLu9z8cPYg559/PnfeeWenq9Ex+4yM8OE77mD/7dsfE9LDETy0996cd+yxbJnl/q9H58zh8GOP5YP/+3/bIGzPskf0xW1Al5sfjkrnrW99KwAf+tCHpj7xxPOjvf6zZsYeEdDux5E0e6qPSff1Gc5SAwa0pNlVCenzzjOcpQY86CJp9i1cCB/8YKdrIZWaW9CSJJWQAS1JUgkZ0JIklZABLUlSCRnQkiSVkAEtdaGx4bFOV0HSJAxoqcsMrx/mu/t/l+H1w52uiqQGDGipy2z45w2Mbhplw79s6HRVJDVgQEtd5qFPPfSYe0nlZEBLXWRk0whbfrAFgC23bWHkNyMdrpGkeuzqU9qD3Xvuvaz/yvpHr4uWRhPRF6ThfH/rwbcSvcWFfwIWn7mYwz96eOcqLOlRbkFLe7CD334wffv1sXNoJ6O/GWXn1p2MDeYW3GODY+zcWjw/tJO+/fo4+G0Hd7jGkioMaGkPNnDYAMevOp4D3nwAPQO1f+49c3s48NwDOf6HxzNw2MAs11BSPQa0/v/27j1IrrO88/j3GUkja0YaY+MBSbZlmZUxRAaLxRYUJgvE5lYECuQbjiEYCCaVimMW2BiKS7SBbGKyLNmYLEGkuDkYVGAChipssJCdxIkKBL5ykVdeCxsbBQkNHkmj28w8+8c5Y7WH7p779NH091N1qs/p8573vKdbml+/56o5rmNhB0//+NNZ8Z4VzFs874nzFnew4r0rOOO6M+jo9M+BVCX+j5TaRP+/9TO0b6iYKM8+Gd43TP+W/tY1SlJDBrTUBoYGhujb3AcUu7R71/XSsaj479+3qY+hA0OtbJ6kOgxoqQ3suWUPeSjpXNbJmtvXsHrjatbcvobOZZ3koWTPLXta3URJoxjQUhs4+NBBei/pZe22tfSc2wNAz7k9rP3pWnov6eXgzw62uIWSRvM6aKkNnHr1qXD1b74/v2c+qzeunv0GSRqTPWhJkirIgJYkqYIMaEmSKsiAliSpggxoSZIqyICWJKmCDGhJkirIgJYkqYIMaEmSKsiAlipgYKDVLZBUNQa01ELbt8Oll0JPD+zc2erWSKoS78UttcD27fC+98FNN8HgIHR2wkGfVyGphgEtzaLRwTw4WLy/cGFr2yWpegxoaRY0CmZJasSAlmaQwSxpsgxoaQYYzJKmyoCWptmhQ7BmDRw4AMPDrW6NpGNV211mFRFLImJ9RNwbEfsi4rGI+H5EvCsiOqdQ78kR8UcR8eWI2B4RB8rhwYj4YkT8znRuh6pr4UK46y646CI47jiY789gSZPQVn86IuI04DZgZfnWALAQOKccLo+I8zOzb4L1ngr8DIiatwfK6ZXl8PqI+DRwZWYOTXojdExYtQo2bnRXt6TJa5sedETMA75BEZa/AF6amd1AF/B6YC/wHOALk6h+HkUYbwLeBJxc1r0YWA18vSz3FmD9pDdCx5yRoL73Xli3zh61pPFrm4AGrgCeVY5fmJm3AmTmcGZuBN5ezntlRJw/wbr7gOdm5gWZ+fnMfLSm7h8DrwNuLsu+IyKOm8qG6NhjUEuaqHYK6DeVr5sz89/rzP8S8GA5/vsTqTgzH8vMHzaZn8Cny8nFwDMnUr/mjkZB7clkkkZri4COiC7gvHLyW/XKlCE60st92Qw0o/ZGjvNmoH4dQ0YH9eHDRVhL0oh22cn2TI7+GLmvSbmReUsj4sTM3DONbXhx+XoYuH8a69UxbCSoBwagq6vVrZFUJW3RgwaW14w/0qRc7bzlDUtNUEScDvxhObkxM/unq27NDYazpNHaJaCX1Iw3e/Ju7bwlDUtNQEQsAr5Mcbb4r4D3jlH+yojYGhFbN2zYMB1NkCQdgyq7izsirgA+M4UqXpmZN49dbOZExHzgBuC5wBHg9zKzWQ+ezNwAjCRzzmwLJUlV1S496L014812JtbO29uw1DiU113/I/BaYJAinL89lTolSe2jsj1o4IvAN6ew/GM144/WjJ8M3NNgmZMbLDMhNeF8KTAEvCEzvzLZ+iRJ7aeyAZ2Zh4BD01TdT4Bhij0GZ9HgUqtyHsDOyZ7BXYbzF3hiOG+cTF2SpPbVFru4M3MAuKOcfEW9MhERwMvLyUntim4Qzl+aTF2SpPbWFgFd+lz5+pKIeF6d+RcDTyvHPz/RystwvoEinAeByw1nSdJktVtA30vxUIsbR+63HREdEXEx8Kmy3Lcyc9PohctHVGY5rBw1bx5wPXAJR08Ic7e2JGnSKnsMerpl5mBEvAbYTPFEq1sjYoDiR8rITRbvBC6fRPXnAZeNrAq4LiKua1L+agNcktRM2wQ0QGbuiIhnA+8G1gGnU1yf/COKs8avy8zDk6i6dk/EAuCpY5RfNIl1SJLaSFsFNEBm7gX+rBwmstx6GjzLOTNvo9h1LknStGinY9CSJB0zDGhJkirIgJYkqYIMaEmSKsiAliSpggxoSZIqyICWJKmCDGhJkirIgJYkqYIMaEmSKsiAliSpggxoSZIqyICWJKmCDGhJkirIgJYkqYIMaEmSKsiAliSpggxoSZIqyICWJKmCDGhJkirIgJYkqYIMaEmSKsiAliSpggxoSZIqyICWJKmCDGhJkirIgJYkqYIMaEmSKsiAliSpggxoSZIqyICWJKmCDGhJkirIgJYkqYIMaEmSKsiAliSpggxoSZIqyICWJKmCDGhJkirIgJYkqYIMaEmSKsiAliSpggxoSZIqyICWJKmCDGhJkirIgJYkqYIMaEmSKsiAliSpggxoSZIqyICWJKmCDGhJkirIgJYkqYIMaKkCBgZa3QJJVWNASy20fTtcein09MDOna1ujaQqmd/qBkjtaPt2eN/74KabYHAQOjvh4MFWt0pSlRjQ0iwaHcyDg8X7Cxe2tl2SqseAlmZBo2CWpEYMaGkGVTWYhw8P09HpKSj9g4P0Dw7SM38+PfP9c6hq8V+kNAOqGswAh3cd5ntnfo+129bS2dvZ6ubMuiPDw9y4axfXPvQQ9w0M0BnB4UzO6urimhUruLC3lwUd/nhR6xnQ0jQ7dAjWrIEDB2B4uNWt+U27v7abwb5Bdn99N8v/YHmrmzOr+o4c4YK77+b+gQH2lV/OYCYAd+3fz9u2beOvH36YW88+mxMWLGhlUyUvs5Km28KFcNddcNFFcNxxULU9pzs/s/MJr+3iyPAwF9x9N/ft3/94OI+2b3iY+/bv54K77+ZIFX9dqa0Y0NIMWLUKNm6Ee++FdeuqE9RH+o6w9wd7Adi7dS9Hfn2kxS2aPTfu2sW2gQEOlz3mRg5nsm1ggK/u3j1LLZPqq8CfDGnuGgnqVh2T3nblNnZ9ZReUmZSDSSwI8nDxuuXULcT8KGYG9F7cy5mfPHN2GjfLrn3oIfaPs1e8f3iYax96iEuf8pQZbpXUWOQYvybVUn45c0yjoO7uhvvug5Urp1b/ddddx/bt2x+fXty3mOd/4/l07e1i3tC8hssNzRtiYMkAW169hX0n7BtjG4r6V61aNbXGjsOqVau46qqrplxP/+AgT77jjsePN4/Hggh2n3eeZ3cfm6LVDZgO7uKWZlGjXd8zdbhz3wn7+O7vfZcdq3cwOL9+t31w/iA7Vu9g0+WbxgxngEWLFrFo0aLpbuqM6h8cpDMm9jd7QQT9VTr9Xm3HHnS1+eXMcSM96htvhJ//HJYunbl17fjQDh7+yMMM7Rt6/L2OxR2s+NMVrPzAyplbcQXYg2479qAlTc1Ij7q/f2bDGaD/3/qPhnOZOcP7hunf0j+zK66AnvnzOaura0LLnNXdbTirpQxoqQImmB0TNjQwRN/mPgA6FnXQu66XjkXFf/++TX0MHRhqtviccM2KFXSP8wYk3R0dXLNixQy3SGrOgJbawJ5b9pCHks5lnay5fQ2rN65mze1r6FzWSR5K9tyyp9VNnHEX9vZyZlfXmMeiOyN4RlcX6046aZZaJtVnQEtt4OBDB+m9pJe129bSc24PAD3n9rD2p2vpvaSXgz+b+8+6XNDRwa1nn81Z3d0Ne9LdHR08q7ub75x9trf7VMt5kli1+eVI0+zI8DBf3b27uBf3/v0siOBIJmd1d3PNihWsO+kkw/nYNydOEjOgq80vR5pBPs1qzjKgNeP8ciRp4uZEQLsfR5KkCjKgJUmqIANakqQKMqAlSaogA1qSpAoyoCVJqiADWpKkCjKgJUmqIANakqQKMqAlSaogA1qSpApqu4COiCURsT4i7o2IfRHxWER8PyLeFRGdU6j3RRHxFxFxS0T834joi4gjEfHLiNgcEX8SEYumc1skSXNXWz0sIyJOA24DVpZvDQDzgIXl9J3A+ZnZN4m6vwm8quat/eVrd817DwKvyMz7x1lt+3w5kjR9fFjGsSQi5gHfoAjnXwAvzcxuoAt4PbAXeA7whUmu4lbgT4D/DPRk5uLMXAycVL5/ADgd+KeIaJvPXZI0OW3Tg46ItwL/UE6+IDP/fdT8y4AbyskLMnPTNK//SuCT5eQLM/OOcSzWHl+OJE0ve9DHmDeVr5tHh3PpSxS7oAF+fwbWv6Vm/JQZqF+SNIe0RUBHRBdwXjn5rXplstiVcHM5+bIZaMZv14w/MAP1S5LmkPmtbsAseSZHf4zc16TcyLylEXFiZu6ZykrLs7ZPAS4GPli+/c+ZuXUq9UqS5r626EEDy2vGH2lSrnbe8oalmoiIpRGREZEUZ4nfD/wFxZni3wBeN8byV0bE1ojYumHDhsk0QZI0B7RLD3pJzfhAk3K185Y0LNXcEPAf5fjxwHHl+JeBD47VK8/MDcBIMnuSmCS1qcr2oCPiipGe6CSHV7Si3Zm5KzOXZuZSiku4TqXoQb8auKc8m1uSpKYqG9DTbG/NeFeTcrXz9jYsNU5Z+Hlmvh+4HFgAfCIizp5q3ZKkua3Ku7i/CHxzCss/VjP+aM34ycA9DZY5ucEyU5aZX42InwGnAW+luHmJJEl1VTagM/MQcGiaqvsJMEyxx+AsGlxqVc4D2DnVM7gbeJQioFfNQN2SpDmkLXZxZ+YAMHLnrrrHpiMigJeXk9+e7jaU9Z9eTk5597kkaW5ri4Aufa58fUlEPK/O/IuBp5Xjn59IxRExnj0RbwaWluO3TaR+SVL7abeAvpfiHq03RsT5ABHREREXA58qy32r3n24y0dUjpwhvnLU7BdGxD9HxBsj4pRRy50REX/F0ftwPwB8dro2SpI0N1X2GPR0y8zBiHgNsJniiVa3RsQAxY+UkWuV76Q423oyfrsciIiDwD6KR03WPgP6buC1mXlgPBVGxC0UT8OSquYkYHerGyE1cHNmtuRS2+nUNgENkJk7IuLZwLuBdRTHhI8AP6I4a/y6zDw8iap/QPGAjRcDz6XYlf1kipPcHgB+CNwIfCUzhybQ3mP+H5jmpojYmpnntLod0lzWNo+blDR9DGhp5rXTMWhJko4ZBrSkyfBJLtIMcxe3JEkVZA9akqQKMqAlSaogA1pqIxFxW3mznfWtbouk5gxoaRzKO869LiI+HRE/johfRcSRiOiLiPsi4vqIuDwielrd1qqKiJUTfKb7Fa1us9RKbXWjEmkyynu3fw44s+btIYpHmnYDq8vhDUB/RKzPzI/NekOPLf3AWHfUG9cd96S5yoCWmoiI1wIbgU7gV8DfAP8E/DjLSyAi4ikUt3l9I/Bq4FLAgG7u6sz8bKsbIVWZAS01EBHPAK6nCOd7gFdm5qOjy2XmLylu5XpjRKwG/mBWGyppTvIYtNTYh4HFwH7gdfXCebTM/FFm/tfa92qehHZbOX1hRHw7In4ZEcO1J2xFxJkR8d8i4taIeCAiDkREf0TcGREfjoimD0+JiHkR8ccR8cOI2B8Re8oTwy4a70ZHxHPKY+0PRMRAROyLiLvHs/6ZNN7PcdSx7pUR8Z8iYkNEPBgRhyJiR526XxwRX46IR8oyuyNiU0S8OSLmTaU90mTZg5bqiIhlFA9UAbg+M//fNNX7UeCdQAK/BoZHFbkFOK0cT4rj3McDa8rhiog4PzO31al7IfB14OXlW8PAYeC/AC+KiGvH0b7/DnyA4rGsAAPAAuDZ5fCWiHhVZt45rg2eIeP4HEe8gOJRr4sptuVInbr+FzDyo2rkM38S8Dvl8IaIeG1m7p2G9kjjZg9aqu8lHA2pm6apzudS/BH/CPDUzDyR4iSzz9SU2QJcBawCjsvMEygeh3oB8D3gZOCGBvX/JUU4J/B+4IRy+aXAJ4BrKEK+roh4B/BBikelvhdYlpndQBdwDvBdYBlwU0QsnujGT6PxfI4jPknxtLpzM7M7MxcDLxuZGRF/zNFw3gAsLz+z48v3BylC+lM0NpH2SOOXmQ4ODqMGit3bWQ7LpljX+pq6PjqFehYDO8t6Xjhq3nKK3mECf95g+Rtq2rF+1LyTKHblDwPnN1h+PrC1XP4dk2j/ypr1P1ZuS8Nhsp/jqPXsABY3KLeI4sS/BG5oUOaqmrrOmYnv1cGh0WAPWqrvyTXje+oViIhVEbGzwfCCOosMA2PuZm4kM/cBt5eTLxw1+yKKAD0A/M8GVaxvUv3lFD3lrZm5qcH6Bymemw5Hd6NPVg/w1DGGRibyOX68/NzqeSlwYjm+vkGZ/wP8ohy/bBraI42bx6ClyZtP4yDprPPe9izO+G4qIn6X4pKtc8v6u+oUO2XU9MizmbdmZn+9ejPz/oh4hGI3+WgjgX9WROxs0rxF5etpTcqMx5tz8pdZjetzLN3RZN7IZ/ZwZt5fr0BmDkXEdyl+wDR6/vVE2iONmwEt1fermvETOdqLelxm/pSjx6mJiJXAg03qbPpHPCI6gH/kiT21QaCP4mQvKI6NHkdxjLPWU8rXR5qtA/g59QN6efm6iKMh3MzjPxoi4t3AuxuUOzczHx5HfRMxkTBsVnYin1lt+am0Rxo3d3FL9f24ZrzhiVUTNDTG/LdShPMQ8OfAGcDCzDwxM5dm5lLgK2XZaFDHZJ8fO3Ip0d9nZoxjWFmz7GIa76aue4nSFI31OU607Hg/s0blJtIeadwMaKm+zRz9g/yaWVrn68vXf8jMP8vM7Zk5+nKdpQ2WHenFjd71PVq93jMUJ2YBPGuM5X9DZq5vEuQ7JlrfLBr5zE4do9zIZ7prBtsi/QYDWqojM38BfLWcfGNEnD4Lqx0JirrXGJeXNj2vwbJby9dzImJJg+XPoHGAjxyrfX5ETPX48rFi5DM7JSKeXq9AeZOSl5ST35+VVkklA1pq7P0Ulx51A1+LiOVjlJ+qx8rXsxvM/wBQN3wpbjU6SHH8+F0NynywybqvpzgDfB7wd43ungWPP9nrSU3qOlZ8h6PnGqxvUObtHD0+/8UGZaQZYUBLDZQngb2B4gStZwP3RMT7I2J1RNSeHNYTEa8ArpviKm8uX98WEVdGRGdZ/9KI+Bjwpzzx5LXatj5CcUkQwAci4r0jPemI6I2Ij5fb8liD5XcC7yknXwV8JyLOGwnqKDwjIt4J3Af87hS3teUy8wBHg/myiPj7iHgqQER0RcRVFA9HAdiYmT9oQTPVxjyLW2oiM78WES8CPkvxuMkPlcNQRPya4jaYtc+A3ktxR6ktk1jdRymuZ34GxR2wPhER/RRnbkf53nHAmxosfw3wWxR3HfsfwIfK5Z9ULn8t8HzgRQ229W/L24X+JcVu3X8FDkfE3nIbF9QWn8T21frfEfFXY5TZmJlXT3E9TWXmxyPiaRR3DXs7cGX5vS7h6N/HzcDbZrIdUj32oKUxZOYWiuC7kCKof0rxPOPjKW5S8RPgCxTBuSwzP5yZByexnl9T3Dv6byjugDVEsdv6NuCyzPzDMZY/CLwSuBq4i6LnH8C/AJdk5nuaLD5Sx19T/ED4GMUTvA5SBPw+imOwHynb2Oh2o+M1nhuVHD/FdYxLZr6T4naeNwL/QXFW+l6KYH4L8NJsch9uaaZE5lR/CEuSpOlmD1qSpAoyoCVJqiADWpKkCjKgJUmqIANakqQKMqAlSaogA1qSpAoyoCVJqiADWpKkCjKgJUmqoP8P+mkVtMuVRqQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44c15ff278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "f, ax = plt.subplots(figsize=(6, 12))\n",
    "\n",
    "temp_data = pd.melt(statistics_intra_observer[['Grade-Error']])\n",
    "ax = sns.boxplot(x=\"variable\", y=\"value\", data=temp_data, width=.3,  showfliers=False, whis=[0, 100]) #\n",
    "#ax.set(ylim=(0.55, 1))\n",
    "\n",
    "\n",
    "for i,box in enumerate(ax.artists):\n",
    "    box.set_edgecolor('black')\n",
    "    box.set_facecolor('white')\n",
    "    \n",
    "for index, row in enumerate(statistics_intra_observer.values.tolist()):\n",
    "    name = row[1]\n",
    "    grade = row[3]\n",
    "\n",
    "    plt.scatter(x=0 + offset[name], y=grade, marker=symbols[name], s=150, c=color[name], zorder=100)\n",
    "    \n",
    "for index, row in enumerate(dl_statistics_intra_observer.values.tolist()):\n",
    "    name = row[1]\n",
    "    grade = row[3]\n",
    "\n",
    "    plt.scatter(x=0 + offset[name], y=grade, marker=symbols[name], s=150, c=color[name], zorder=100)\n",
    "    \n",
    "#ax.xaxis.grid(True)\n",
    "#ax.yaxis.grid(True)\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "by_label = OrderedDict(zip(labels, handles))\n",
    "\n",
    "ax.legend(by_label.values(), by_label.keys(), bbox_to_anchor=(1., 0.9), prop={'size': 25}, frameon=False)\n",
    "\n",
    "ax.set(ylabel=\"Score\", xlabel=\"\", title=\"Intra-Observer Performance\")\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Initials</th>\n",
       "      <th>Skill</th>\n",
       "      <th>mAP</th>\n",
       "      <th>mAP-IG</th>\n",
       "      <th>Acc</th>\n",
       "      <th>ratio_decreased_cells</th>\n",
       "      <th>ratio_removed_boxes</th>\n",
       "      <th>seconds</th>\n",
       "      <th>grade</th>\n",
       "      <th>num_cells</th>\n",
       "      <th>num_interactionen</th>\n",
       "      <th>Order</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>0.589968</td>\n",
       "      <td>0.909977</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>47.272727</td>\n",
       "      <td>100.0</td>\n",
       "      <td>113.2155</td>\n",
       "      <td>1.448717</td>\n",
       "      <td>238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>0.498623</td>\n",
       "      <td>0.857983</td>\n",
       "      <td>0.674528</td>\n",
       "      <td>40.740741</td>\n",
       "      <td>80.0</td>\n",
       "      <td>118.7975</td>\n",
       "      <td>1.345829</td>\n",
       "      <td>232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>0.658129</td>\n",
       "      <td>0.927085</td>\n",
       "      <td>0.825112</td>\n",
       "      <td>55.769231</td>\n",
       "      <td>100.0</td>\n",
       "      <td>93.2930</td>\n",
       "      <td>1.449369</td>\n",
       "      <td>235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>0.509945</td>\n",
       "      <td>0.800145</td>\n",
       "      <td>0.710900</td>\n",
       "      <td>45.283019</td>\n",
       "      <td>80.0</td>\n",
       "      <td>134.3665</td>\n",
       "      <td>1.594326</td>\n",
       "      <td>243</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MB</td>\n",
       "      <td>3</td>\n",
       "      <td>0.583423</td>\n",
       "      <td>0.925835</td>\n",
       "      <td>0.776824</td>\n",
       "      <td>32.727273</td>\n",
       "      <td>60.0</td>\n",
       "      <td>79.5860</td>\n",
       "      <td>1.529922</td>\n",
       "      <td>241</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MB</td>\n",
       "      <td>3</td>\n",
       "      <td>0.590276</td>\n",
       "      <td>0.813106</td>\n",
       "      <td>0.796020</td>\n",
       "      <td>56.603774</td>\n",
       "      <td>100.0</td>\n",
       "      <td>93.3775</td>\n",
       "      <td>1.427058</td>\n",
       "      <td>218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DSS</td>\n",
       "      <td>3</td>\n",
       "      <td>0.501903</td>\n",
       "      <td>0.944191</td>\n",
       "      <td>0.709251</td>\n",
       "      <td>27.272727</td>\n",
       "      <td>80.0</td>\n",
       "      <td>24.0370</td>\n",
       "      <td>1.544626</td>\n",
       "      <td>238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DSS</td>\n",
       "      <td>3</td>\n",
       "      <td>0.346627</td>\n",
       "      <td>0.781040</td>\n",
       "      <td>0.530303</td>\n",
       "      <td>41.176471</td>\n",
       "      <td>100.0</td>\n",
       "      <td>76.3840</td>\n",
       "      <td>1.817512</td>\n",
       "      <td>219</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PA</td>\n",
       "      <td>4</td>\n",
       "      <td>0.577649</td>\n",
       "      <td>0.917297</td>\n",
       "      <td>0.743243</td>\n",
       "      <td>32.727273</td>\n",
       "      <td>60.0</td>\n",
       "      <td>117.2520</td>\n",
       "      <td>1.542089</td>\n",
       "      <td>232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PA</td>\n",
       "      <td>4</td>\n",
       "      <td>0.497812</td>\n",
       "      <td>0.855991</td>\n",
       "      <td>0.623318</td>\n",
       "      <td>41.176471</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120.5505</td>\n",
       "      <td>1.733252</td>\n",
       "      <td>242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LM</td>\n",
       "      <td>3</td>\n",
       "      <td>0.468365</td>\n",
       "      <td>0.930199</td>\n",
       "      <td>0.678571</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>80.0</td>\n",
       "      <td>67.7755</td>\n",
       "      <td>1.647687</td>\n",
       "      <td>236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LM</td>\n",
       "      <td>3</td>\n",
       "      <td>0.439452</td>\n",
       "      <td>0.878653</td>\n",
       "      <td>0.585714</td>\n",
       "      <td>37.254902</td>\n",
       "      <td>100.0</td>\n",
       "      <td>103.7285</td>\n",
       "      <td>1.816173</td>\n",
       "      <td>224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SE</td>\n",
       "      <td>2</td>\n",
       "      <td>0.518407</td>\n",
       "      <td>0.921627</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>23.636364</td>\n",
       "      <td>100.0</td>\n",
       "      <td>81.9200</td>\n",
       "      <td>1.532446</td>\n",
       "      <td>242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SE</td>\n",
       "      <td>2</td>\n",
       "      <td>0.482341</td>\n",
       "      <td>0.748993</td>\n",
       "      <td>0.724771</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>121.7275</td>\n",
       "      <td>1.399366</td>\n",
       "      <td>279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RK</td>\n",
       "      <td>4</td>\n",
       "      <td>0.543241</td>\n",
       "      <td>0.938519</td>\n",
       "      <td>0.747748</td>\n",
       "      <td>38.181818</td>\n",
       "      <td>60.0</td>\n",
       "      <td>53.4530</td>\n",
       "      <td>1.539760</td>\n",
       "      <td>229</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RK</td>\n",
       "      <td>4</td>\n",
       "      <td>0.496651</td>\n",
       "      <td>0.829570</td>\n",
       "      <td>0.683417</td>\n",
       "      <td>47.169811</td>\n",
       "      <td>80.0</td>\n",
       "      <td>66.7320</td>\n",
       "      <td>1.656151</td>\n",
       "      <td>211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CB</td>\n",
       "      <td>4</td>\n",
       "      <td>0.664171</td>\n",
       "      <td>0.934547</td>\n",
       "      <td>0.840708</td>\n",
       "      <td>54.545455</td>\n",
       "      <td>80.0</td>\n",
       "      <td>138.3255</td>\n",
       "      <td>1.337494</td>\n",
       "      <td>237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CB</td>\n",
       "      <td>4</td>\n",
       "      <td>0.681731</td>\n",
       "      <td>0.912400</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>56.363636</td>\n",
       "      <td>100.0</td>\n",
       "      <td>79.2870</td>\n",
       "      <td>1.309538</td>\n",
       "      <td>231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MH</td>\n",
       "      <td>3</td>\n",
       "      <td>0.578463</td>\n",
       "      <td>0.914132</td>\n",
       "      <td>0.726457</td>\n",
       "      <td>28.301887</td>\n",
       "      <td>60.0</td>\n",
       "      <td>119.7415</td>\n",
       "      <td>1.602292</td>\n",
       "      <td>235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MH</td>\n",
       "      <td>3</td>\n",
       "      <td>0.562563</td>\n",
       "      <td>0.832154</td>\n",
       "      <td>0.734884</td>\n",
       "      <td>49.056604</td>\n",
       "      <td>100.0</td>\n",
       "      <td>139.0595</td>\n",
       "      <td>1.593008</td>\n",
       "      <td>245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>JA</td>\n",
       "      <td>1</td>\n",
       "      <td>0.497024</td>\n",
       "      <td>0.916462</td>\n",
       "      <td>0.718750</td>\n",
       "      <td>29.090909</td>\n",
       "      <td>80.0</td>\n",
       "      <td>91.3600</td>\n",
       "      <td>1.507703</td>\n",
       "      <td>242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>JA</td>\n",
       "      <td>1</td>\n",
       "      <td>0.453712</td>\n",
       "      <td>0.781514</td>\n",
       "      <td>0.671362</td>\n",
       "      <td>37.735849</td>\n",
       "      <td>80.0</td>\n",
       "      <td>106.2130</td>\n",
       "      <td>1.207489</td>\n",
       "      <td>254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SO</td>\n",
       "      <td>1</td>\n",
       "      <td>0.495597</td>\n",
       "      <td>0.784843</td>\n",
       "      <td>0.690566</td>\n",
       "      <td>36.363636</td>\n",
       "      <td>60.0</td>\n",
       "      <td>107.3405</td>\n",
       "      <td>1.563039</td>\n",
       "      <td>284</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SO</td>\n",
       "      <td>1</td>\n",
       "      <td>0.288426</td>\n",
       "      <td>0.474567</td>\n",
       "      <td>0.655814</td>\n",
       "      <td>48.148148</td>\n",
       "      <td>100.0</td>\n",
       "      <td>237.9325</td>\n",
       "      <td>1.382994</td>\n",
       "      <td>449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MS</td>\n",
       "      <td>4</td>\n",
       "      <td>0.595748</td>\n",
       "      <td>0.904853</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>26.923077</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60.0140</td>\n",
       "      <td>1.444311</td>\n",
       "      <td>233</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MS</td>\n",
       "      <td>4</td>\n",
       "      <td>0.341398</td>\n",
       "      <td>0.699028</td>\n",
       "      <td>0.615854</td>\n",
       "      <td>37.777778</td>\n",
       "      <td>80.0</td>\n",
       "      <td>48.0025</td>\n",
       "      <td>1.712215</td>\n",
       "      <td>175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SC</td>\n",
       "      <td>1</td>\n",
       "      <td>0.486234</td>\n",
       "      <td>0.925993</td>\n",
       "      <td>0.713004</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>71.4495</td>\n",
       "      <td>1.579943</td>\n",
       "      <td>234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SC</td>\n",
       "      <td>1</td>\n",
       "      <td>0.448927</td>\n",
       "      <td>0.726014</td>\n",
       "      <td>0.685083</td>\n",
       "      <td>47.169811</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.7005</td>\n",
       "      <td>1.853050</td>\n",
       "      <td>194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PM</td>\n",
       "      <td>2</td>\n",
       "      <td>0.516787</td>\n",
       "      <td>0.934243</td>\n",
       "      <td>0.711712</td>\n",
       "      <td>21.818182</td>\n",
       "      <td>60.0</td>\n",
       "      <td>27.4140</td>\n",
       "      <td>1.596753</td>\n",
       "      <td>230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>PM</td>\n",
       "      <td>2</td>\n",
       "      <td>0.345975</td>\n",
       "      <td>0.588206</td>\n",
       "      <td>0.668478</td>\n",
       "      <td>40.816327</td>\n",
       "      <td>80.0</td>\n",
       "      <td>141.1445</td>\n",
       "      <td>1.731062</td>\n",
       "      <td>254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>JM</td>\n",
       "      <td>1</td>\n",
       "      <td>0.493416</td>\n",
       "      <td>0.934039</td>\n",
       "      <td>0.687783</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>53.0520</td>\n",
       "      <td>1.657312</td>\n",
       "      <td>228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>JM</td>\n",
       "      <td>1</td>\n",
       "      <td>0.482279</td>\n",
       "      <td>0.829724</td>\n",
       "      <td>0.606796</td>\n",
       "      <td>40.740741</td>\n",
       "      <td>100.0</td>\n",
       "      <td>76.7405</td>\n",
       "      <td>1.750448</td>\n",
       "      <td>227</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>BT</td>\n",
       "      <td>4</td>\n",
       "      <td>0.489444</td>\n",
       "      <td>0.930311</td>\n",
       "      <td>0.698198</td>\n",
       "      <td>27.272727</td>\n",
       "      <td>60.0</td>\n",
       "      <td>11.0590</td>\n",
       "      <td>1.603669</td>\n",
       "      <td>231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>BT</td>\n",
       "      <td>4</td>\n",
       "      <td>0.497823</td>\n",
       "      <td>0.778254</td>\n",
       "      <td>0.687831</td>\n",
       "      <td>40.425532</td>\n",
       "      <td>40.0</td>\n",
       "      <td>194.3495</td>\n",
       "      <td>1.648567</td>\n",
       "      <td>204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>SG</td>\n",
       "      <td>2</td>\n",
       "      <td>0.472438</td>\n",
       "      <td>0.870832</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>33.962264</td>\n",
       "      <td>80.0</td>\n",
       "      <td>71.9770</td>\n",
       "      <td>1.657725</td>\n",
       "      <td>240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>SG</td>\n",
       "      <td>2</td>\n",
       "      <td>0.446144</td>\n",
       "      <td>0.845195</td>\n",
       "      <td>0.641509</td>\n",
       "      <td>45.098039</td>\n",
       "      <td>60.0</td>\n",
       "      <td>48.9205</td>\n",
       "      <td>1.564547</td>\n",
       "      <td>231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>0.775794</td>\n",
       "      <td>0.956102</td>\n",
       "      <td>0.890351</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.8490</td>\n",
       "      <td>1.322166</td>\n",
       "      <td>237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>0.465913</td>\n",
       "      <td>0.798627</td>\n",
       "      <td>0.687805</td>\n",
       "      <td>30.612245</td>\n",
       "      <td>60.0</td>\n",
       "      <td>53.9700</td>\n",
       "      <td>1.418994</td>\n",
       "      <td>230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>0.526252</td>\n",
       "      <td>0.938519</td>\n",
       "      <td>0.725225</td>\n",
       "      <td>23.636364</td>\n",
       "      <td>60.0</td>\n",
       "      <td>65.6290</td>\n",
       "      <td>1.580861</td>\n",
       "      <td>229</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>0.454237</td>\n",
       "      <td>0.791673</td>\n",
       "      <td>0.644330</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>92.4475</td>\n",
       "      <td>1.726394</td>\n",
       "      <td>211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Initials Skill       mAP    mAP-IG       Acc  ratio_decreased_cells  \\\n",
       "0        LJ     3  0.589968  0.909977  0.750000              47.272727   \n",
       "1        LJ     3  0.498623  0.857983  0.674528              40.740741   \n",
       "4        JS     1  0.658129  0.927085  0.825112              55.769231   \n",
       "5        JS     1  0.509945  0.800145  0.710900              45.283019   \n",
       "6        MB     3  0.583423  0.925835  0.776824              32.727273   \n",
       "7        MB     3  0.590276  0.813106  0.796020              56.603774   \n",
       "8       DSS     3  0.501903  0.944191  0.709251              27.272727   \n",
       "9       DSS     3  0.346627  0.781040  0.530303              41.176471   \n",
       "10       PA     4  0.577649  0.917297  0.743243              32.727273   \n",
       "11       PA     4  0.497812  0.855991  0.623318              41.176471   \n",
       "12       LM     3  0.468365  0.930199  0.678571              18.181818   \n",
       "13       LM     3  0.439452  0.878653  0.585714              37.254902   \n",
       "14       SE     2  0.518407  0.921627  0.733333              23.636364   \n",
       "15       SE     2  0.482341  0.748993  0.724771              38.000000   \n",
       "16       RK     4  0.543241  0.938519  0.747748              38.181818   \n",
       "17       RK     4  0.496651  0.829570  0.683417              47.169811   \n",
       "18       CB     4  0.664171  0.934547  0.840708              54.545455   \n",
       "19       CB     4  0.681731  0.912400  0.863636              56.363636   \n",
       "20       MH     3  0.578463  0.914132  0.726457              28.301887   \n",
       "21       MH     3  0.562563  0.832154  0.734884              49.056604   \n",
       "22       JA     1  0.497024  0.916462  0.718750              29.090909   \n",
       "23       JA     1  0.453712  0.781514  0.671362              37.735849   \n",
       "24       SO     1  0.495597  0.784843  0.690566              36.363636   \n",
       "25       SO     1  0.288426  0.474567  0.655814              48.148148   \n",
       "26       MS     4  0.595748  0.904853  0.772727              26.923077   \n",
       "27       MS     4  0.341398  0.699028  0.615854              37.777778   \n",
       "28       SC     1  0.486234  0.925993  0.713004              20.000000   \n",
       "29       SC     1  0.448927  0.726014  0.685083              47.169811   \n",
       "30       PM     2  0.516787  0.934243  0.711712              21.818182   \n",
       "31       PM     2  0.345975  0.588206  0.668478              40.816327   \n",
       "32       JM     1  0.493416  0.934039  0.687783              20.000000   \n",
       "33       JM     1  0.482279  0.829724  0.606796              40.740741   \n",
       "34       BT     4  0.489444  0.930311  0.698198              27.272727   \n",
       "35       BT     4  0.497823  0.778254  0.687831              40.425532   \n",
       "36       SG     2  0.472438  0.870832  0.666667              33.962264   \n",
       "37       SG     2  0.446144  0.845195  0.641509              45.098039   \n",
       "38       KJ     2  0.775794  0.956102  0.890351              60.000000   \n",
       "39       KJ     2  0.465913  0.798627  0.687805              30.612245   \n",
       "40       HJ     2  0.526252  0.938519  0.725225              23.636364   \n",
       "41       HJ     2  0.454237  0.791673  0.644330              44.000000   \n",
       "\n",
       "    ratio_removed_boxes   seconds     grade  num_cells  num_interactionen  \\\n",
       "0                 100.0  113.2155  1.448717        238                NaN   \n",
       "1                  80.0  118.7975  1.345829        232                NaN   \n",
       "4                 100.0   93.2930  1.449369        235                NaN   \n",
       "5                  80.0  134.3665  1.594326        243                NaN   \n",
       "6                  60.0   79.5860  1.529922        241                NaN   \n",
       "7                 100.0   93.3775  1.427058        218                NaN   \n",
       "8                  80.0   24.0370  1.544626        238                NaN   \n",
       "9                 100.0   76.3840  1.817512        219                NaN   \n",
       "10                 60.0  117.2520  1.542089        232                NaN   \n",
       "11                100.0  120.5505  1.733252        242                NaN   \n",
       "12                 80.0   67.7755  1.647687        236                NaN   \n",
       "13                100.0  103.7285  1.816173        224                NaN   \n",
       "14                100.0   81.9200  1.532446        242                NaN   \n",
       "15                100.0  121.7275  1.399366        279                NaN   \n",
       "16                 60.0   53.4530  1.539760        229                NaN   \n",
       "17                 80.0   66.7320  1.656151        211                NaN   \n",
       "18                 80.0  138.3255  1.337494        237                NaN   \n",
       "19                100.0   79.2870  1.309538        231                NaN   \n",
       "20                 60.0  119.7415  1.602292        235                NaN   \n",
       "21                100.0  139.0595  1.593008        245                NaN   \n",
       "22                 80.0   91.3600  1.507703        242                NaN   \n",
       "23                 80.0  106.2130  1.207489        254                NaN   \n",
       "24                 60.0  107.3405  1.563039        284                NaN   \n",
       "25                100.0  237.9325  1.382994        449                NaN   \n",
       "26                 80.0   60.0140  1.444311        233                NaN   \n",
       "27                 80.0   48.0025  1.712215        175                NaN   \n",
       "28                 60.0   71.4495  1.579943        234                NaN   \n",
       "29                 60.0   70.7005  1.853050        194                NaN   \n",
       "30                 60.0   27.4140  1.596753        230                NaN   \n",
       "31                 80.0  141.1445  1.731062        254                NaN   \n",
       "32                 60.0   53.0520  1.657312        228                NaN   \n",
       "33                100.0   76.7405  1.750448        227                NaN   \n",
       "34                 60.0   11.0590  1.603669        231                NaN   \n",
       "35                 40.0  194.3495  1.648567        204                NaN   \n",
       "36                 80.0   71.9770  1.657725        240                NaN   \n",
       "37                 60.0   48.9205  1.564547        231                NaN   \n",
       "38                100.0   38.8490  1.322166        237                NaN   \n",
       "39                 60.0   53.9700  1.418994        230                NaN   \n",
       "40                 60.0   65.6290  1.580861        229                NaN   \n",
       "41                 80.0   92.4475  1.726394        211                NaN   \n",
       "\n",
       "            Order                        Type  \n",
       "0   LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "1   LabelOrder.AS      ProjectType.Annotation  \n",
       "4   LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "5   LabelOrder.AS      ProjectType.Annotation  \n",
       "6   LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "7   LabelOrder.SA      ProjectType.Annotation  \n",
       "8   LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "9   LabelOrder.AS      ProjectType.Annotation  \n",
       "10  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "11  LabelOrder.SA      ProjectType.Annotation  \n",
       "12  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "13  LabelOrder.SA      ProjectType.Annotation  \n",
       "14  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "15  LabelOrder.SA      ProjectType.Annotation  \n",
       "16  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "17  LabelOrder.SA      ProjectType.Annotation  \n",
       "18  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "19  LabelOrder.AS      ProjectType.Annotation  \n",
       "20  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "21  LabelOrder.AS      ProjectType.Annotation  \n",
       "22  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "23  LabelOrder.SA      ProjectType.Annotation  \n",
       "24  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "25  LabelOrder.AS      ProjectType.Annotation  \n",
       "26  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "27  LabelOrder.AS      ProjectType.Annotation  \n",
       "28  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "29  LabelOrder.SA      ProjectType.Annotation  \n",
       "30  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "31  LabelOrder.AS      ProjectType.Annotation  \n",
       "32  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "33  LabelOrder.AS      ProjectType.Annotation  \n",
       "34  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "35  LabelOrder.AS      ProjectType.Annotation  \n",
       "36  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "37  LabelOrder.SA      ProjectType.Annotation  \n",
       "38  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "39  LabelOrder.AS      ProjectType.Annotation  \n",
       "40  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "41  LabelOrder.SA      ProjectType.Annotation  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics_initials = statistics.calc_statistics_initials()\n",
    "\n",
    "dl_statistics_initials = statistics_initials[statistics_initials['Initials'] == \"DL\"]\n",
    "statistics_initials = statistics_initials[statistics_initials['Initials'] != \"DL\"]\n",
    "\n",
    "statistics_initials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: Min: 0.53 Max: 0.86  Mean: 0.67  STD: 0.07\n",
      "mAP: Min: 0.29 Max: 0.68  Mean: 0.47  STD: 0.09\n",
      "mAP-IG: Min: 0.47 Max: 0.91  Mean: 0.78  STD: 0.10\n",
      "seconds: Min: 48.00 Max: 237.93  Mean: 106.22  STD: 46.84\n",
      "grade: Min: 1.21 Max: 1.85  Mean: 1.58  STD: 0.19\n"
     ]
    }
   ],
   "source": [
    "# Annotation\n",
    "annotation = statistics_initials[statistics_initials['Type'] == ProjectType.Annotation]\n",
    "\n",
    "for column in ['Acc', 'mAP', 'mAP-IG', 'seconds', 'grade']:\n",
    "    df = annotation[annotation['Initials'] != \"DL\"][column] \n",
    "    print(column + \": Min: {0:.2f} Max: {1:.2f}  Mean: {2:.2f}  STD: {3:.2f}\".format(np.min(df), np.max(df), np.mean(df), np.std(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: Min: 0.67 Max: 0.89  Mean: 0.74  STD: 0.06\n",
      "mAP: Min: 0.47 Max: 0.78  Mean: 0.55  STD: 0.08\n",
      "mAP-IG: Min: 0.78 Max: 0.96  Mean: 0.92  STD: 0.04\n",
      "seconds: Min: 11.06 Max: 138.33  Mean: 74.34  STD: 33.58\n",
      "grade: Min: 1.32 Max: 1.66  Mean: 1.53  STD: 0.09\n"
     ]
    }
   ],
   "source": [
    "# Supervision\n",
    "\n",
    "supervision = statistics_initials[statistics_initials['Type'] == ProjectType.CrowdAlgorithm]\n",
    "for column in ['Acc', 'mAP', 'mAP-IG', 'seconds', 'grade']:\n",
    "    df = supervision[supervision['Initials'] != \"DL\"][column] \n",
    "    \n",
    "    print(column + \": Min: {0:.2f} Max: {1:.2f}  Mean: {2:.2f}  STD: {3:.2f}\".format(np.min(df), np.max(df), np.mean(df), np.std(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1203.7248333333334"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(statistics_initials['seconds'].sum() * 20) / 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Initials</th>\n",
       "      <th>Skill</th>\n",
       "      <th>File</th>\n",
       "      <th>Type</th>\n",
       "      <th>Order</th>\n",
       "      <th>Time</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Grade GT</th>\n",
       "      <th>ImageType</th>\n",
       "      <th>Acc</th>\n",
       "      <th>gt_num_cells</th>\n",
       "      <th>num_cells</th>\n",
       "      <th>unchanged_boxes</th>\n",
       "      <th>changed_grade</th>\n",
       "      <th>changed_boxes</th>\n",
       "      <th>unchanged_boxes_ration</th>\n",
       "      <th>changed_grade_ration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>30_EIPH_588355 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>216.363</td>\n",
       "      <td>1.230769</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>04_EIPH_567017 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>132.819</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>14_EIPH_568381 berliner blau-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>257.089</td>\n",
       "      <td>2.111111</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>17_EIPH_575796 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>171.467</td>\n",
       "      <td>1.636364</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>23_EIPH_563476 Berliner Blau-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>142.989</td>\n",
       "      <td>1.642857</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>25_EIPH_568150 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>113.452</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>28_EIPH_569948 L berliner blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>61.143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>01_EIPH_563479 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>106.618</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>08_EIPH_574999 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>124.143</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>15_EIPH_568320 berliner blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>78.334</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>19_EIPH_566933 L Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>104.459</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>23_EIPH_563476 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>146.190</td>\n",
       "      <td>1.823529</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>25_EIPH_568150 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>124.955</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>30_EIPH_568355 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>151.570</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>02_EIPH_574162 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>104.763</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>13_EIPH_570370 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>100.550</td>\n",
       "      <td>1.181818</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>15_EIPH_568320 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>140.614</td>\n",
       "      <td>1.312500</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>19_EIPH_566933 L Tunrbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>104.469</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>24_EIPH_576255 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>99.517</td>\n",
       "      <td>1.444444</td>\n",
       "      <td>1.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>27_EIPH_571557 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>84.914</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>30_EIPH_588355 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>381.055</td>\n",
       "      <td>1.384615</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>04_EIPH_567017 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>390.923</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>14_EIPH_568381 berliner blau-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>451.679</td>\n",
       "      <td>1.777778</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>17_EIPH_575796 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>327.268</td>\n",
       "      <td>1.714286</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>23_EIPH_563476 Berliner Blau-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>225.421</td>\n",
       "      <td>1.923077</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>25_EIPH_568150 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>134.954</td>\n",
       "      <td>2.428571</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>28_EIPH_569948 L berliner blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>51.402</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>01_EIPH_563479 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>167.892</td>\n",
       "      <td>2.090909</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>08_EIPH_574999 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>205.369</td>\n",
       "      <td>2.538462</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>15_EIPH_568320 berliner blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>96.979</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <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>790</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>19_EIPH_566933 L Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>28.222</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>23_EIPH_563476 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>39.402</td>\n",
       "      <td>1.384615</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>25_EIPH_568150 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>39.977</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>30_EIPH_568355 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>63.976</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>02_EIPH_574162 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>31.856</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>13_EIPH_570370 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>48.611</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>15_EIPH_568320 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>52.713</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>19_EIPH_566933 L Tunrbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>120.690</td>\n",
       "      <td>1.363636</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>24_EIPH_576255 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>38.808</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>1.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>27_EIPH_571557 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>55.227</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>30_EIPH_588355 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>212.099</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>04_EIPH_567017 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>121.412</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>14_EIPH_568381 berliner blau-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>186.285</td>\n",
       "      <td>2.444444</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>17_EIPH_575796 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>142.601</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>23_EIPH_563476 Berliner Blau-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>117.076</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>25_EIPH_568150 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>137.557</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>28_EIPH_569948 L berliner blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>68.571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>01_EIPH_563479 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>117.070</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>08_EIPH_574999 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>111.148</td>\n",
       "      <td>2.642857</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>15_EIPH_568320 berliner blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>61.535</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>19_EIPH_566933 L Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>78.373</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>23_EIPH_563476 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>61.587</td>\n",
       "      <td>2.083333</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>25_EIPH_568150 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>68.545</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>30_EIPH_568355 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>130.860</td>\n",
       "      <td>1.933333</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>ImageType.RemovedRects</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>02_EIPH_574162 Turnbull blue-001.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>95.419</td>\n",
       "      <td>1.363636</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>13_EIPH_570370 Berliner Blau.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>81.163</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>ImageType.IncreasedGrade</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>15_EIPH_568320 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>71.300</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>19_EIPH_566933 L Tunrbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>89.476</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>24_EIPH_576255 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>67.450</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.636364</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>27_EIPH_571557 Turnbull blue.png</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>52.304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>ImageType.NoChanges</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Initials Skill                                  File  \\\n",
       "20        LJ     3      30_EIPH_588355 Berliner Blau.png   \n",
       "21        LJ     3  04_EIPH_567017 Turnbull blue-001.png   \n",
       "22        LJ     3  14_EIPH_568381 berliner blau-001.png   \n",
       "23        LJ     3      17_EIPH_575796 Turnbull blue.png   \n",
       "24        LJ     3  23_EIPH_563476 Berliner Blau-001.png   \n",
       "25        LJ     3      25_EIPH_568150 Berliner Blau.png   \n",
       "26        LJ     3    28_EIPH_569948 L berliner blau.png   \n",
       "27        LJ     3      01_EIPH_563479 Turnbull blue.png   \n",
       "28        LJ     3      08_EIPH_574999 Berliner Blau.png   \n",
       "29        LJ     3      15_EIPH_568320 berliner blau.png   \n",
       "30        LJ     3    19_EIPH_566933 L Berliner Blau.png   \n",
       "31        LJ     3      23_EIPH_563476 Turnbull blue.png   \n",
       "32        LJ     3      25_EIPH_568150 Turnbull blue.png   \n",
       "33        LJ     3      30_EIPH_568355 Turnbull blue.png   \n",
       "34        LJ     3  02_EIPH_574162 Turnbull blue-001.png   \n",
       "35        LJ     3      13_EIPH_570370 Berliner Blau.png   \n",
       "36        LJ     3      15_EIPH_568320 Turnbull blue.png   \n",
       "37        LJ     3    19_EIPH_566933 L Tunrbull blue.png   \n",
       "38        LJ     3      24_EIPH_576255 Turnbull blue.png   \n",
       "39        LJ     3      27_EIPH_571557 Turnbull blue.png   \n",
       "100       JS     1      30_EIPH_588355 Berliner Blau.png   \n",
       "101       JS     1  04_EIPH_567017 Turnbull blue-001.png   \n",
       "102       JS     1  14_EIPH_568381 berliner blau-001.png   \n",
       "103       JS     1      17_EIPH_575796 Turnbull blue.png   \n",
       "104       JS     1  23_EIPH_563476 Berliner Blau-001.png   \n",
       "105       JS     1      25_EIPH_568150 Berliner Blau.png   \n",
       "106       JS     1    28_EIPH_569948 L berliner blau.png   \n",
       "107       JS     1      01_EIPH_563479 Turnbull blue.png   \n",
       "108       JS     1      08_EIPH_574999 Berliner Blau.png   \n",
       "109       JS     1      15_EIPH_568320 berliner blau.png   \n",
       "..       ...   ...                                   ...   \n",
       "790       KJ     2    19_EIPH_566933 L Berliner Blau.png   \n",
       "791       KJ     2      23_EIPH_563476 Turnbull blue.png   \n",
       "792       KJ     2      25_EIPH_568150 Turnbull blue.png   \n",
       "793       KJ     2      30_EIPH_568355 Turnbull blue.png   \n",
       "794       KJ     2  02_EIPH_574162 Turnbull blue-001.png   \n",
       "795       KJ     2      13_EIPH_570370 Berliner Blau.png   \n",
       "796       KJ     2      15_EIPH_568320 Turnbull blue.png   \n",
       "797       KJ     2    19_EIPH_566933 L Tunrbull blue.png   \n",
       "798       KJ     2      24_EIPH_576255 Turnbull blue.png   \n",
       "799       KJ     2      27_EIPH_571557 Turnbull blue.png   \n",
       "820       HJ     2      30_EIPH_588355 Berliner Blau.png   \n",
       "821       HJ     2  04_EIPH_567017 Turnbull blue-001.png   \n",
       "822       HJ     2  14_EIPH_568381 berliner blau-001.png   \n",
       "823       HJ     2      17_EIPH_575796 Turnbull blue.png   \n",
       "824       HJ     2  23_EIPH_563476 Berliner Blau-001.png   \n",
       "825       HJ     2      25_EIPH_568150 Berliner Blau.png   \n",
       "826       HJ     2    28_EIPH_569948 L berliner blau.png   \n",
       "827       HJ     2      01_EIPH_563479 Turnbull blue.png   \n",
       "828       HJ     2      08_EIPH_574999 Berliner Blau.png   \n",
       "829       HJ     2      15_EIPH_568320 berliner blau.png   \n",
       "830       HJ     2    19_EIPH_566933 L Berliner Blau.png   \n",
       "831       HJ     2      23_EIPH_563476 Turnbull blue.png   \n",
       "832       HJ     2      25_EIPH_568150 Turnbull blue.png   \n",
       "833       HJ     2      30_EIPH_568355 Turnbull blue.png   \n",
       "834       HJ     2  02_EIPH_574162 Turnbull blue-001.png   \n",
       "835       HJ     2      13_EIPH_570370 Berliner Blau.png   \n",
       "836       HJ     2      15_EIPH_568320 Turnbull blue.png   \n",
       "837       HJ     2    19_EIPH_566933 L Tunrbull blue.png   \n",
       "838       HJ     2      24_EIPH_576255 Turnbull blue.png   \n",
       "839       HJ     2      27_EIPH_571557 Turnbull blue.png   \n",
       "\n",
       "                       Type          Order     Time     Grade  Grade GT  \\\n",
       "20   ProjectType.Annotation  LabelOrder.AS  216.363  1.230769  1.500000   \n",
       "21   ProjectType.Annotation  LabelOrder.AS  132.819  1.166667  1.333333   \n",
       "22   ProjectType.Annotation  LabelOrder.AS  257.089  2.111111  1.666667   \n",
       "23   ProjectType.Annotation  LabelOrder.AS  171.467  1.636364  1.692308   \n",
       "24   ProjectType.Annotation  LabelOrder.AS  142.989  1.642857  0.916667   \n",
       "25   ProjectType.Annotation  LabelOrder.AS  113.452  2.142857  1.666667   \n",
       "26   ProjectType.Annotation  LabelOrder.AS   61.143  0.000000  0.142857   \n",
       "27   ProjectType.Annotation  LabelOrder.AS  106.618  1.700000  1.700000   \n",
       "28   ProjectType.Annotation  LabelOrder.AS  124.143  2.600000  2.000000   \n",
       "29   ProjectType.Annotation  LabelOrder.AS   78.334  1.454545  1.583333   \n",
       "30   ProjectType.Annotation  LabelOrder.AS  104.459  0.636364  0.636364   \n",
       "31   ProjectType.Annotation  LabelOrder.AS  146.190  1.823529  1.785714   \n",
       "32   ProjectType.Annotation  LabelOrder.AS  124.955  1.800000  1.583333   \n",
       "33   ProjectType.Annotation  LabelOrder.AS  151.570  0.944444  1.466667   \n",
       "34   ProjectType.Annotation  LabelOrder.AS  104.763  0.900000  1.333333   \n",
       "35   ProjectType.Annotation  LabelOrder.AS  100.550  1.181818  1.100000   \n",
       "36   ProjectType.Annotation  LabelOrder.AS  140.614  1.312500  1.785714   \n",
       "37   ProjectType.Annotation  LabelOrder.AS  104.469  0.642857  1.000000   \n",
       "38   ProjectType.Annotation  LabelOrder.AS   99.517  1.444444  1.636364   \n",
       "39   ProjectType.Annotation  LabelOrder.AS   84.914  0.545455  0.909091   \n",
       "100  ProjectType.Annotation  LabelOrder.AS  381.055  1.384615  1.500000   \n",
       "101  ProjectType.Annotation  LabelOrder.AS  390.923  1.285714  1.333333   \n",
       "102  ProjectType.Annotation  LabelOrder.AS  451.679  1.777778  1.666667   \n",
       "103  ProjectType.Annotation  LabelOrder.AS  327.268  1.714286  1.692308   \n",
       "104  ProjectType.Annotation  LabelOrder.AS  225.421  1.923077  0.916667   \n",
       "105  ProjectType.Annotation  LabelOrder.AS  134.954  2.428571  1.666667   \n",
       "106  ProjectType.Annotation  LabelOrder.AS   51.402  0.000000  0.142857   \n",
       "107  ProjectType.Annotation  LabelOrder.AS  167.892  2.090909  1.700000   \n",
       "108  ProjectType.Annotation  LabelOrder.AS  205.369  2.538462  2.000000   \n",
       "109  ProjectType.Annotation  LabelOrder.AS   96.979  1.833333  1.583333   \n",
       "..                      ...            ...      ...       ...       ...   \n",
       "790  ProjectType.Annotation  LabelOrder.AS   28.222  0.727273  0.636364   \n",
       "791  ProjectType.Annotation  LabelOrder.AS   39.402  1.384615  1.785714   \n",
       "792  ProjectType.Annotation  LabelOrder.AS   39.977  1.800000  1.583333   \n",
       "793  ProjectType.Annotation  LabelOrder.AS   63.976  1.142857  1.466667   \n",
       "794  ProjectType.Annotation  LabelOrder.AS   31.856  1.250000  1.333333   \n",
       "795  ProjectType.Annotation  LabelOrder.AS   48.611  1.000000  1.100000   \n",
       "796  ProjectType.Annotation  LabelOrder.AS   52.713  1.333333  1.785714   \n",
       "797  ProjectType.Annotation  LabelOrder.AS  120.690  1.363636  1.000000   \n",
       "798  ProjectType.Annotation  LabelOrder.AS   38.808  2.333333  1.636364   \n",
       "799  ProjectType.Annotation  LabelOrder.AS   55.227  0.714286  0.909091   \n",
       "820  ProjectType.Annotation  LabelOrder.SA  212.099  1.692308  1.500000   \n",
       "821  ProjectType.Annotation  LabelOrder.SA  121.412  2.000000  1.333333   \n",
       "822  ProjectType.Annotation  LabelOrder.SA  186.285  2.444444  1.666667   \n",
       "823  ProjectType.Annotation  LabelOrder.SA  142.601  2.300000  1.692308   \n",
       "824  ProjectType.Annotation  LabelOrder.SA  117.076  2.400000  0.916667   \n",
       "825  ProjectType.Annotation  LabelOrder.SA  137.557  1.666667  1.666667   \n",
       "826  ProjectType.Annotation  LabelOrder.SA   68.571  0.000000  0.142857   \n",
       "827  ProjectType.Annotation  LabelOrder.SA  117.070  2.300000  1.700000   \n",
       "828  ProjectType.Annotation  LabelOrder.SA  111.148  2.642857  2.000000   \n",
       "829  ProjectType.Annotation  LabelOrder.SA   61.535  1.600000  1.583333   \n",
       "830  ProjectType.Annotation  LabelOrder.SA   78.373  0.818182  0.636364   \n",
       "831  ProjectType.Annotation  LabelOrder.SA   61.587  2.083333  1.785714   \n",
       "832  ProjectType.Annotation  LabelOrder.SA   68.545  1.600000  1.583333   \n",
       "833  ProjectType.Annotation  LabelOrder.SA  130.860  1.933333  1.466667   \n",
       "834  ProjectType.Annotation  LabelOrder.SA   95.419  1.363636  1.333333   \n",
       "835  ProjectType.Annotation  LabelOrder.SA   81.163  1.454545  1.100000   \n",
       "836  ProjectType.Annotation  LabelOrder.SA   71.300  2.000000  1.785714   \n",
       "837  ProjectType.Annotation  LabelOrder.SA   89.476  0.928571  1.000000   \n",
       "838  ProjectType.Annotation  LabelOrder.SA   67.450  2.300000  1.636364   \n",
       "839  ProjectType.Annotation  LabelOrder.SA   52.304  1.000000  0.909091   \n",
       "\n",
       "                    ImageType       Acc  gt_num_cells  num_cells  \\\n",
       "20        ImageType.NoChanges  0.833333            12         13   \n",
       "21   ImageType.IncreasedGrade  0.833333            12         12   \n",
       "22     ImageType.RemovedRects  0.571429             9          9   \n",
       "23     ImageType.RemovedRects  0.700000            13         11   \n",
       "24        ImageType.NoChanges  0.750000            12         14   \n",
       "25        ImageType.NoChanges  0.857143             9          7   \n",
       "26     ImageType.RemovedRects  0.666667             7          3   \n",
       "27   ImageType.IncreasedGrade  0.800000            10         10   \n",
       "28   ImageType.IncreasedGrade  0.384615            14         15   \n",
       "29        ImageType.NoChanges  0.636364            12         11   \n",
       "30        ImageType.NoChanges  1.000000            11         11   \n",
       "31     ImageType.RemovedRects  0.428571            14         17   \n",
       "32        ImageType.NoChanges  0.900000            12         10   \n",
       "33     ImageType.RemovedRects  0.666667            15         18   \n",
       "34   ImageType.IncreasedGrade  0.444444             9         10   \n",
       "35   ImageType.IncreasedGrade  0.800000            10         11   \n",
       "36        ImageType.NoChanges  0.714286            14         16   \n",
       "37        ImageType.NoChanges  0.692308            13         14   \n",
       "38        ImageType.NoChanges  0.222222            11          9   \n",
       "39        ImageType.NoChanges  0.600000            11         11   \n",
       "100       ImageType.NoChanges  0.727273            12         13   \n",
       "101  ImageType.IncreasedGrade  0.916667            12         14   \n",
       "102    ImageType.RemovedRects  0.555556             9         18   \n",
       "103    ImageType.RemovedRects  0.750000            13         14   \n",
       "104       ImageType.NoChanges  0.636364            12         13   \n",
       "105       ImageType.NoChanges  0.714286             9          7   \n",
       "106    ImageType.RemovedRects  1.000000             7          2   \n",
       "107  ImageType.IncreasedGrade  0.500000            10         11   \n",
       "108  ImageType.IncreasedGrade  0.416667            14         13   \n",
       "109       ImageType.NoChanges  0.833333            12         12   \n",
       "..                        ...       ...           ...        ...   \n",
       "790       ImageType.NoChanges  0.909091            11         11   \n",
       "791    ImageType.RemovedRects  0.500000            14         13   \n",
       "792       ImageType.NoChanges  0.900000            12         10   \n",
       "793    ImageType.RemovedRects  0.384615            15         14   \n",
       "794  ImageType.IncreasedGrade  0.666667             9         12   \n",
       "795  ImageType.IncreasedGrade  0.800000            10         11   \n",
       "796       ImageType.NoChanges  0.727273            14         12   \n",
       "797       ImageType.NoChanges  0.636364            13         11   \n",
       "798       ImageType.NoChanges  0.666667            11          9   \n",
       "799       ImageType.NoChanges  1.000000            11         14   \n",
       "820       ImageType.NoChanges  0.750000            12         13   \n",
       "821  ImageType.IncreasedGrade  0.400000            12         11   \n",
       "822    ImageType.RemovedRects  0.428571             9          9   \n",
       "823    ImageType.RemovedRects  0.666667            13         10   \n",
       "824       ImageType.NoChanges  0.400000            12          5   \n",
       "825       ImageType.NoChanges  0.500000             9          6   \n",
       "826    ImageType.RemovedRects  0.500000             7          2   \n",
       "827  ImageType.IncreasedGrade  0.400000            10         10   \n",
       "828  ImageType.IncreasedGrade  0.461538            14         14   \n",
       "829       ImageType.NoChanges  0.700000            12         10   \n",
       "830       ImageType.NoChanges  0.818182            11         11   \n",
       "831    ImageType.RemovedRects  0.545455            14         12   \n",
       "832       ImageType.NoChanges  0.900000            12         10   \n",
       "833    ImageType.RemovedRects  0.461538            15         15   \n",
       "834  ImageType.IncreasedGrade  0.555556             9         11   \n",
       "835  ImageType.IncreasedGrade  0.900000            10         11   \n",
       "836       ImageType.NoChanges  0.714286            14         16   \n",
       "837       ImageType.NoChanges  0.846154            13         14   \n",
       "838       ImageType.NoChanges  0.666667            11         10   \n",
       "839       ImageType.NoChanges  0.900000            11         11   \n",
       "\n",
       "     unchanged_boxes  changed_grade  changed_boxes  unchanged_boxes_ration  \\\n",
       "20                 0              0             13                     0.0   \n",
       "21                 0              0             12                     0.0   \n",
       "22                 0              0              9                     0.0   \n",
       "23                 0              0             11                     0.0   \n",
       "24                 0              0             14                     0.0   \n",
       "25                 0              0              7                     0.0   \n",
       "26                 0              0              3                     0.0   \n",
       "27                 0              0             10                     0.0   \n",
       "28                 0              0             15                     0.0   \n",
       "29                 0              0             11                     0.0   \n",
       "30                 0              0             11                     0.0   \n",
       "31                 0              0             17                     0.0   \n",
       "32                 0              0             10                     0.0   \n",
       "33                 0              0             18                     0.0   \n",
       "34                 0              0             10                     0.0   \n",
       "35                 0              0             11                     0.0   \n",
       "36                 0              0             16                     0.0   \n",
       "37                 0              0             14                     0.0   \n",
       "38                 0              0              9                     0.0   \n",
       "39                 0              0             11                     0.0   \n",
       "100                0              0             13                     0.0   \n",
       "101                0              0             14                     0.0   \n",
       "102                0              0             18                     0.0   \n",
       "103                0              0             14                     0.0   \n",
       "104                0              0             13                     0.0   \n",
       "105                0              0              7                     0.0   \n",
       "106                0              0              2                     0.0   \n",
       "107                0              0             11                     0.0   \n",
       "108                0              0             13                     0.0   \n",
       "109                0              0             12                     0.0   \n",
       "..               ...            ...            ...                     ...   \n",
       "790                0              0             11                     0.0   \n",
       "791                0              0             13                     0.0   \n",
       "792                0              0             10                     0.0   \n",
       "793                0              0             14                     0.0   \n",
       "794                0              0             12                     0.0   \n",
       "795                0              0             11                     0.0   \n",
       "796                0              0             12                     0.0   \n",
       "797                0              0             11                     0.0   \n",
       "798                0              0              9                     0.0   \n",
       "799                0              0             14                     0.0   \n",
       "820                0              0             13                     0.0   \n",
       "821                0              0             11                     0.0   \n",
       "822                0              0              9                     0.0   \n",
       "823                0              0             10                     0.0   \n",
       "824                0              0              5                     0.0   \n",
       "825                0              0              6                     0.0   \n",
       "826                0              0              2                     0.0   \n",
       "827                0              0             10                     0.0   \n",
       "828                0              0             14                     0.0   \n",
       "829                0              0             10                     0.0   \n",
       "830                0              0             11                     0.0   \n",
       "831                0              0             12                     0.0   \n",
       "832                0              0             10                     0.0   \n",
       "833                0              0             15                     0.0   \n",
       "834                0              0             11                     0.0   \n",
       "835                0              0             11                     0.0   \n",
       "836                0              0             16                     0.0   \n",
       "837                0              0             14                     0.0   \n",
       "838                0              0             10                     0.0   \n",
       "839                0              0             11                     0.0   \n",
       "\n",
       "     changed_grade_ration  \n",
       "20                    0.0  \n",
       "21                    0.0  \n",
       "22                    0.0  \n",
       "23                    0.0  \n",
       "24                    0.0  \n",
       "25                    0.0  \n",
       "26                    0.0  \n",
       "27                    0.0  \n",
       "28                    0.0  \n",
       "29                    0.0  \n",
       "30                    0.0  \n",
       "31                    0.0  \n",
       "32                    0.0  \n",
       "33                    0.0  \n",
       "34                    0.0  \n",
       "35                    0.0  \n",
       "36                    0.0  \n",
       "37                    0.0  \n",
       "38                    0.0  \n",
       "39                    0.0  \n",
       "100                   0.0  \n",
       "101                   0.0  \n",
       "102                   0.0  \n",
       "103                   0.0  \n",
       "104                   0.0  \n",
       "105                   0.0  \n",
       "106                   0.0  \n",
       "107                   0.0  \n",
       "108                   0.0  \n",
       "109                   0.0  \n",
       "..                    ...  \n",
       "790                   0.0  \n",
       "791                   0.0  \n",
       "792                   0.0  \n",
       "793                   0.0  \n",
       "794                   0.0  \n",
       "795                   0.0  \n",
       "796                   0.0  \n",
       "797                   0.0  \n",
       "798                   0.0  \n",
       "799                   0.0  \n",
       "820                   0.0  \n",
       "821                   0.0  \n",
       "822                   0.0  \n",
       "823                   0.0  \n",
       "824                   0.0  \n",
       "825                   0.0  \n",
       "826                   0.0  \n",
       "827                   0.0  \n",
       "828                   0.0  \n",
       "829                   0.0  \n",
       "830                   0.0  \n",
       "831                   0.0  \n",
       "832                   0.0  \n",
       "833                   0.0  \n",
       "834                   0.0  \n",
       "835                   0.0  \n",
       "836                   0.0  \n",
       "837                   0.0  \n",
       "838                   0.0  \n",
       "839                   0.0  \n",
       "\n",
       "[400 rows x 17 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: Min: 0.59 Max: 0.80  Mean: 0.67  STD: 0.06\n",
      "mAP: Min: 0.44 Max: 0.59  Mean: 0.48  STD: 0.04\n",
      "seconds: Min: 48.92 Max: 121.73  Mean: 91.60  STD: 23.54\n",
      "grade: Min: 1.21 Max: 1.85  Mean: 1.60  STD: 0.20\n"
     ]
    }
   ],
   "source": [
    "# Annotation -> LabelOrder.SA\n",
    "annotation = statistics_initials[statistics_initials['Type'] == ProjectType.Annotation]\n",
    "sa_mode =  annotation[annotation['Order'] == LabelOrder.SA] \n",
    "\n",
    "for column in ['Acc', 'mAP', 'seconds', 'grade']:\n",
    "    df = sa_mode[sa_mode['Initials'] != \"DL\"][column] \n",
    "    print(column + \": Min: {0:.2f} Max: {1:.2f}  Mean: {2:.2f}  STD: {3:.2f}\".format(np.min(df), np.max(df), np.mean(df), np.std(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: Min: 0.53 Max: 0.86  Mean: 0.68  STD: 0.08\n",
      "mAP: Min: 0.29 Max: 0.68  Mean: 0.46  STD: 0.11\n",
      "seconds: Min: 48.00 Max: 237.93  Mean: 118.18  STD: 56.72\n",
      "grade: Min: 1.31 Max: 1.82  Mean: 1.57  STD: 0.17\n"
     ]
    }
   ],
   "source": [
    "# Annotation -> LabelOrder.AS\n",
    "annotation = statistics_initials[statistics_initials['Type'] == ProjectType.Annotation]\n",
    "as_mode =  annotation[annotation['Order'] == LabelOrder.AS] \n",
    "\n",
    "for column in ['Acc', 'mAP', 'seconds', 'grade']:\n",
    "    df = as_mode[as_mode['Initials'] != \"DL\"][column] \n",
    "    print(column + \": Min: {0:.2f} Max: {1:.2f}  Mean: {2:.2f}  STD: {3:.2f}\".format(np.min(df), np.max(df), np.mean(df), np.std(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Initials</th>\n",
       "      <th>Skill</th>\n",
       "      <th>mAP</th>\n",
       "      <th>mAP-IG</th>\n",
       "      <th>Acc</th>\n",
       "      <th>ratio_decreased_cells</th>\n",
       "      <th>ratio_removed_boxes</th>\n",
       "      <th>seconds</th>\n",
       "      <th>grade</th>\n",
       "      <th>num_cells</th>\n",
       "      <th>num_interactionen</th>\n",
       "      <th>Order</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>0.589968</td>\n",
       "      <td>0.909977</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>47.272727</td>\n",
       "      <td>100.0</td>\n",
       "      <td>113.2155</td>\n",
       "      <td>1.448717</td>\n",
       "      <td>238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>0.658129</td>\n",
       "      <td>0.927085</td>\n",
       "      <td>0.825112</td>\n",
       "      <td>55.769231</td>\n",
       "      <td>100.0</td>\n",
       "      <td>93.2930</td>\n",
       "      <td>1.449369</td>\n",
       "      <td>235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MB</td>\n",
       "      <td>3</td>\n",
       "      <td>0.583423</td>\n",
       "      <td>0.925835</td>\n",
       "      <td>0.776824</td>\n",
       "      <td>32.727273</td>\n",
       "      <td>60.0</td>\n",
       "      <td>79.5860</td>\n",
       "      <td>1.529922</td>\n",
       "      <td>241</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DSS</td>\n",
       "      <td>3</td>\n",
       "      <td>0.501903</td>\n",
       "      <td>0.944191</td>\n",
       "      <td>0.709251</td>\n",
       "      <td>27.272727</td>\n",
       "      <td>80.0</td>\n",
       "      <td>24.0370</td>\n",
       "      <td>1.544626</td>\n",
       "      <td>238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PA</td>\n",
       "      <td>4</td>\n",
       "      <td>0.577649</td>\n",
       "      <td>0.917297</td>\n",
       "      <td>0.743243</td>\n",
       "      <td>32.727273</td>\n",
       "      <td>60.0</td>\n",
       "      <td>117.2520</td>\n",
       "      <td>1.542089</td>\n",
       "      <td>232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LM</td>\n",
       "      <td>3</td>\n",
       "      <td>0.468365</td>\n",
       "      <td>0.930199</td>\n",
       "      <td>0.678571</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>80.0</td>\n",
       "      <td>67.7755</td>\n",
       "      <td>1.647687</td>\n",
       "      <td>236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SE</td>\n",
       "      <td>2</td>\n",
       "      <td>0.518407</td>\n",
       "      <td>0.921627</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>23.636364</td>\n",
       "      <td>100.0</td>\n",
       "      <td>81.9200</td>\n",
       "      <td>1.532446</td>\n",
       "      <td>242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RK</td>\n",
       "      <td>4</td>\n",
       "      <td>0.543241</td>\n",
       "      <td>0.938519</td>\n",
       "      <td>0.747748</td>\n",
       "      <td>38.181818</td>\n",
       "      <td>60.0</td>\n",
       "      <td>53.4530</td>\n",
       "      <td>1.539760</td>\n",
       "      <td>229</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CB</td>\n",
       "      <td>4</td>\n",
       "      <td>0.664171</td>\n",
       "      <td>0.934547</td>\n",
       "      <td>0.840708</td>\n",
       "      <td>54.545455</td>\n",
       "      <td>80.0</td>\n",
       "      <td>138.3255</td>\n",
       "      <td>1.337494</td>\n",
       "      <td>237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MH</td>\n",
       "      <td>3</td>\n",
       "      <td>0.578463</td>\n",
       "      <td>0.914132</td>\n",
       "      <td>0.726457</td>\n",
       "      <td>28.301887</td>\n",
       "      <td>60.0</td>\n",
       "      <td>119.7415</td>\n",
       "      <td>1.602292</td>\n",
       "      <td>235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>JA</td>\n",
       "      <td>1</td>\n",
       "      <td>0.497024</td>\n",
       "      <td>0.916462</td>\n",
       "      <td>0.718750</td>\n",
       "      <td>29.090909</td>\n",
       "      <td>80.0</td>\n",
       "      <td>91.3600</td>\n",
       "      <td>1.507703</td>\n",
       "      <td>242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SO</td>\n",
       "      <td>1</td>\n",
       "      <td>0.495597</td>\n",
       "      <td>0.784843</td>\n",
       "      <td>0.690566</td>\n",
       "      <td>36.363636</td>\n",
       "      <td>60.0</td>\n",
       "      <td>107.3405</td>\n",
       "      <td>1.563039</td>\n",
       "      <td>284</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MS</td>\n",
       "      <td>4</td>\n",
       "      <td>0.595748</td>\n",
       "      <td>0.904853</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>26.923077</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60.0140</td>\n",
       "      <td>1.444311</td>\n",
       "      <td>233</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SC</td>\n",
       "      <td>1</td>\n",
       "      <td>0.486234</td>\n",
       "      <td>0.925993</td>\n",
       "      <td>0.713004</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>71.4495</td>\n",
       "      <td>1.579943</td>\n",
       "      <td>234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PM</td>\n",
       "      <td>2</td>\n",
       "      <td>0.516787</td>\n",
       "      <td>0.934243</td>\n",
       "      <td>0.711712</td>\n",
       "      <td>21.818182</td>\n",
       "      <td>60.0</td>\n",
       "      <td>27.4140</td>\n",
       "      <td>1.596753</td>\n",
       "      <td>230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>JM</td>\n",
       "      <td>1</td>\n",
       "      <td>0.493416</td>\n",
       "      <td>0.934039</td>\n",
       "      <td>0.687783</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>53.0520</td>\n",
       "      <td>1.657312</td>\n",
       "      <td>228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>BT</td>\n",
       "      <td>4</td>\n",
       "      <td>0.489444</td>\n",
       "      <td>0.930311</td>\n",
       "      <td>0.698198</td>\n",
       "      <td>27.272727</td>\n",
       "      <td>60.0</td>\n",
       "      <td>11.0590</td>\n",
       "      <td>1.603669</td>\n",
       "      <td>231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>SG</td>\n",
       "      <td>2</td>\n",
       "      <td>0.472438</td>\n",
       "      <td>0.870832</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>33.962264</td>\n",
       "      <td>80.0</td>\n",
       "      <td>71.9770</td>\n",
       "      <td>1.657725</td>\n",
       "      <td>240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>0.775794</td>\n",
       "      <td>0.956102</td>\n",
       "      <td>0.890351</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.8490</td>\n",
       "      <td>1.322166</td>\n",
       "      <td>237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>0.526252</td>\n",
       "      <td>0.938519</td>\n",
       "      <td>0.725225</td>\n",
       "      <td>23.636364</td>\n",
       "      <td>60.0</td>\n",
       "      <td>65.6290</td>\n",
       "      <td>1.580861</td>\n",
       "      <td>229</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Initials Skill       mAP    mAP-IG       Acc  ratio_decreased_cells  \\\n",
       "0        LJ     3  0.589968  0.909977  0.750000              47.272727   \n",
       "4        JS     1  0.658129  0.927085  0.825112              55.769231   \n",
       "6        MB     3  0.583423  0.925835  0.776824              32.727273   \n",
       "8       DSS     3  0.501903  0.944191  0.709251              27.272727   \n",
       "10       PA     4  0.577649  0.917297  0.743243              32.727273   \n",
       "12       LM     3  0.468365  0.930199  0.678571              18.181818   \n",
       "14       SE     2  0.518407  0.921627  0.733333              23.636364   \n",
       "16       RK     4  0.543241  0.938519  0.747748              38.181818   \n",
       "18       CB     4  0.664171  0.934547  0.840708              54.545455   \n",
       "20       MH     3  0.578463  0.914132  0.726457              28.301887   \n",
       "22       JA     1  0.497024  0.916462  0.718750              29.090909   \n",
       "24       SO     1  0.495597  0.784843  0.690566              36.363636   \n",
       "26       MS     4  0.595748  0.904853  0.772727              26.923077   \n",
       "28       SC     1  0.486234  0.925993  0.713004              20.000000   \n",
       "30       PM     2  0.516787  0.934243  0.711712              21.818182   \n",
       "32       JM     1  0.493416  0.934039  0.687783              20.000000   \n",
       "34       BT     4  0.489444  0.930311  0.698198              27.272727   \n",
       "36       SG     2  0.472438  0.870832  0.666667              33.962264   \n",
       "38       KJ     2  0.775794  0.956102  0.890351              60.000000   \n",
       "40       HJ     2  0.526252  0.938519  0.725225              23.636364   \n",
       "\n",
       "    ratio_removed_boxes   seconds     grade  num_cells  num_interactionen  \\\n",
       "0                 100.0  113.2155  1.448717        238                NaN   \n",
       "4                 100.0   93.2930  1.449369        235                NaN   \n",
       "6                  60.0   79.5860  1.529922        241                NaN   \n",
       "8                  80.0   24.0370  1.544626        238                NaN   \n",
       "10                 60.0  117.2520  1.542089        232                NaN   \n",
       "12                 80.0   67.7755  1.647687        236                NaN   \n",
       "14                100.0   81.9200  1.532446        242                NaN   \n",
       "16                 60.0   53.4530  1.539760        229                NaN   \n",
       "18                 80.0  138.3255  1.337494        237                NaN   \n",
       "20                 60.0  119.7415  1.602292        235                NaN   \n",
       "22                 80.0   91.3600  1.507703        242                NaN   \n",
       "24                 60.0  107.3405  1.563039        284                NaN   \n",
       "26                 80.0   60.0140  1.444311        233                NaN   \n",
       "28                 60.0   71.4495  1.579943        234                NaN   \n",
       "30                 60.0   27.4140  1.596753        230                NaN   \n",
       "32                 60.0   53.0520  1.657312        228                NaN   \n",
       "34                 60.0   11.0590  1.603669        231                NaN   \n",
       "36                 80.0   71.9770  1.657725        240                NaN   \n",
       "38                100.0   38.8490  1.322166        237                NaN   \n",
       "40                 60.0   65.6290  1.580861        229                NaN   \n",
       "\n",
       "            Order                        Type  \n",
       "0   LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "4   LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "6   LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "8   LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "10  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "12  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "14  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "16  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "18  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "20  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "22  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "24  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "26  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "28  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "30  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "32  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "34  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "36  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "38  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "40  LabelOrder.SA  ProjectType.CrowdAlgorithm  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics_initials_supervision = statistics_initials[statistics_initials['Type'] == ProjectType.CrowdAlgorithm]\n",
    "\n",
    "dl_statistics_initials_supervision = statistics_initials_supervision[statistics_initials_supervision['Initials'] == \"DL\"]\n",
    "statistics_initials_supervision = statistics_initials_supervision[statistics_initials_supervision['Initials'] != \"DL\"]\n",
    "\n",
    "statistics_initials_supervision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44bb1989b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc_statistics = statistics_initials[statistics_initials['Type'] == ProjectType.Annotation]\n",
    "acc_statistics = acc_statistics[acc_statistics['Initials'] != \"DL\"]\n",
    "\n",
    "f, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "columns = ['Acc', 'mAP']\n",
    "temp_data = pd.melt(acc_statistics[columns])\n",
    "temp_data['Order'] = len(columns) * [str(type).split('.')[-1] for type in list(acc_statistics['Order'].values)]\n",
    "ax = sns.boxplot(x=\"variable\", y=\"value\", data=temp_data.sort_values(by=['Order'], ascending=False),  showfliers=False, hue='Order') #width=.3,\n",
    "\n",
    "ax.set(ylim=(0.2, 0.9))\n",
    "\n",
    "#ax.set(ylim=(0.55, 1))\n",
    "\n",
    "\n",
    "for i,box in enumerate(ax.artists):\n",
    "    box.set_edgecolor('black')\n",
    "    #box.set_facecolor('white')\n",
    "\n",
    "for index, row in enumerate(acc_statistics.sort_values(by=['Skill']).values.tolist()):\n",
    "    name = row[1]\n",
    "    \n",
    "    type = str(row[-2])\n",
    "    acc = row[4]\n",
    "    mAP = row[2]\n",
    "\n",
    "    #type_offset = -0.075\n",
    "    type_offset = -0.2\n",
    "    if \"AS\" in type:\n",
    "        type_offset = type_offset * -1\n",
    "        \n",
    "    plt.scatter(x=1 + offset[name] + type_offset, y=acc, marker=symbols[name], label=name, s=150, c=color[name], zorder=100)    \n",
    "    plt.scatter(x=0 + offset[name] + type_offset, y=mAP, marker=symbols[name], label=name, s=150, c=color[name], zorder=100)    \n",
    "\n",
    "    \n",
    "#ax.xaxis.grid(True)\n",
    "#ax.yaxis.grid(True)\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "by_label = OrderedDict(zip(labels, handles))\n",
    "\n",
    "ax.legend(by_label.values(), by_label.keys(), bbox_to_anchor=(1., 0.9), prop={'size': 25}, frameon=False)\n",
    "\n",
    "ax.set(ylabel=\"Acc\", xlabel=\"\", title=\"Training effect\")\n",
    "sns.despine(left=True)\n",
    "\n",
    "plt.savefig(\"TrainingsEffect.pdf\", transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_autopct(values):\n",
    "    def my_autopct(pct):\n",
    "        total = sum(values)\n",
    "        val = int(round(pct*total/100.0))\n",
    "        return '{v:d}%'.format(v=val)\n",
    "    return my_autopct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44c1485f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "found = np.mean(statistics_initials_supervision[\"ratio_removed_boxes\"].values)\n",
    "not_found = 100 - found\n",
    "\n",
    "labels = ['Found', 'Overlooked']\n",
    "sizes = [found, not_found]\n",
    "explode = (0.1, 0.1)  \n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_title('Artificially removed cells')\n",
    "#ax1.set_xlabel('Grade with cell count')\n",
    "\n",
    "ax1.pie(sizes, explode=explode, labels=labels, #, colors = ['b', 'g', 'r', 'c', 'm']\n",
    "        shadow=True, startangle=90, autopct=make_autopct(sizes))\n",
    "# Equal aspect ratio ensures that pie is drawn as a circle\n",
    "ax1.axis('equal') \n",
    "\n",
    "plt.tight_layout(pad=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rg5beSj3TvyTjXNmNTXne5fVLbGLsESfpPgbcmvJyaoOxxbTXCHxY7G0xe+qNlOfZbj5yVi/WA4AxZjvtB1rH5tCyenZv19lJn3yHxpgd2JbtAMfkcPCf7bN7xfk7BjugicpR1iQrIqOwDSDAXtO5wRgTyvbAXldI6ouz2dQqiz6toukDqS1nb+7lTiWT5HWVMSLS+RaDqW7tYlq+JLs5BLCjP81y/n/MGLPThXh640HswQHAjSIyvh/W+QDt10q/LvY+xz2Reu0t41mfiEykb36T61Oe77v0ZIyJ035nriDtIw71RGojmy+LSNrWtc7Z1dd7sZ5UyTG5i4GMY22LyKF03SK3J3L9Di8ifbedVMnPrgj4UhdlHUH2rjapB8s/7kb3zCEvlzPZT9LebPzhbjRSup/2vm+fcFrM9UZq0/fjellWnzLGLMQ2TgDb8vexTEeOYvXkDOnNZBHYhk3pyv0h+RtKsStP0t73NPXHPNCqijHGbKI97pHAkyJyYKb5ReR0EelVFaWzzmTfw5HA0yKSsfrO+a7PSnPtfRXt1xQvEpGZnaYnu6jMZf97QXee74/OzjfTdB8dG7ct6TTLj7EjOQF8V+wg/hn3NyIyWkS+LyIdEocxZgm2wR/Yrki/77yDdxLsT2kfkrK3fkv7gdYPRGS/a5DO7/t++mgYxxRvpjz/poikaz1ejR35Kpvf0t6S/mZxbmrQqayx2IO8bEnzXymxnQb8vauDfREpEZHrROTjOcQ5qOXS8CnXVsUdGGN2iMgz2PFdR2Kr3v7VvfA6lFcnIoux3ULOEJG7sC2ZG1PmeSrT8v3gOmzV1kHY66fvi8gDtI9dPBx75HkB9lpKd6u8f48dGccLfEVEjgEewQ5ZNwn4BPazWYltxXh8L99PzowxcRG5G3uZIGkd7TvHgeY/sf0zT8SON71aRB7Edv7fg01Qh2O37enY7zxjFX6Ovuus6yzsdrJS7F2OXsF+x35sVeLR2PGCD8Bu//vGjTbGRETkD8A3nPlfcRqmvYlNGsdhf8/DsF3tPtVFPJ8DPiciyfFzlzvvvQx7HfLjtHcle49Ov21jzGZnB/s49kzqf4DPi8jD2IOBMPa6fRU2OZ6K3bZfShPLDdg+oJXY6/4zRSQ5dvE47LZfjf39TaR74wnvxxjzrojcjh0RrhR4VkTuB16g49jFY4GHgCucRftiQJ3Xse/1eGxXqHedfd1qJ5YzsW0eBNut6hNdvI/3ReQW7OhsxcB8Zzt+1nkfR2E/z1HYs96MlyqMMQmxY1G/ju05chX2UsCDTrx7sTVZk5zYz8E2BL2lJx/CoNLVcFDYH2VyKK3NZBn6MM3yH09Zfl6naS8lp3WjvPPoOPxXh0eneeekTJuTobxQyjyzu1jv7JT5Ql3MN5b9x9BN90j0ZB3YnU28i3JXYhswdPnZ5vjZrKcbw9Nhd2zRlHK/151tJUvZU1LKvTfDPDlvT7nMi91BPJTDd2noNLZxrttLmnUWYc9oM27jnR5/SVNGCTYZdLXcXdhEmfEzJbe7wBjsGeyULt7TSbQPA5nt0UiGMXux1wGz3YXnwO5ut1m+j19lifdX2APq5P9fz1BOcvpLOa73ENLfqSf5aMUecM9JeS3t79gp78dZvs87ct1msd31nuuirNRHDNsLok/2AwP1ka26OPUs9h+m+/0P59LeAOIjItLbI8z52G4E/8CeKRVU/1pjzHZjzGzsGc592I7qzdjkswPbwCWEvZNKT8q/E/v+H8Ke3STLXYA9eznB2GED+50xZivtgzfEsLflG7CMMU3GmCuwn/cfsWcSjdj3tht7RP8z4FhjG3/1xTojxpgbsdWiP8GOxbvTWWcYu80/ib3GeZQx5ro0ZbRir6990YmxEVtluAF784iPGGO+QPazrnHYblj/DzsQfh32AK8Fm8gexXbPO85kuAOME89C7Pb+SWf967DtK2LYM+NFzjquAsYZY5ZlKGcBttbgJ9i+4S3Ys6e3gG9jb1LQJ99Dyjq/hj1zfBjbHzyCbTn9GHCuM31kyiJ9MuCK8xs+Fvhv7G+qFfuZrcZWAR9njMmlujhZ3vewVbwP0fF9PA581BjzlW6U9YEx5mxsUv4D9k5Be7HbRgP2QP9B7AnBJGPM3bmWPViJc3SiVK+ISBW22hBgrjHGjWvDSvUrEfk59gAXbPJb7GY8qvDke0QgNXSktsL8Q8a5lBoknO49yZGZdtF+Yw6l9tEkq3rNuQzweeffWtr7zSo1IInIOKeLTqbpw7DVr8k+6feY3o3UpQap7t6FRynAdl3BtiY8EFtdlhxS8jaj1yDUwHcItnV2DbYh2XvY9hVBbIPQq2m/mcFa0nSrUwr0mqzqIRFZz/7Duum1WDUoiMgp5HYLzmXABcaYDVnnVEOSnsmq3mrFdtH4C7YrgFKDwSLsDePPxfbBHY1tSZzAtvhehO2n/oCxI1wplZaeySqllFJ5og2flFJKqTzRJKuUUkrliSZZpZRSKk80ySqllFJ5oklWKaWUyhNNskoppVSeaJJVSiml8kSTrFJKKZUnmmSVUkqpPNEkq5RSSuWJjl2shpQpN80rBsYBw7B3Uen8twzwOo/l639yvt4bVynVY5pk1aAz5aZ5o4HpwEHAwc7f5OMAQHIs6nH0BvRKqV7QJKsGtCk3zTsAe3/P44Djnb8TXQ1KKaUcmmTVgDLlpnmHAmc5jw9hq36VUqogaZJVBW3KTfPGYRPq2c7fSe5GpJRSudMkqwrOlJvmHQhcCVwBzHQ5HKWU6jFNsqogOIn1CmPMFSJS7XY8SinVFzTJKtdMuWleKXC1MeazwCyx3A5LKaX6jCZZ1e+m3DRvujHmC8B1IhLUxKqUGqw0yap+MeWmeX7gMmPMDSJymiZWpdRQoElW5ZVTJfw5YxLfEfEcoMlVKTWUaJJVeTHlpnnlJpH4Ephvicc7UmSID5MdCh4AHAXEnUcbsBeoA+oI1be4GJ1SKk80yao+NeWmeUGTiH8N5Ovi8QTdjqeAnAX8NePUULCNZMKF3cAGYC2wzvm7FthCqD6R90iVUn1Gk6zqE1NumldsEvGvg3xPPN4Kt+MpBBdO8wtwJlDxzZOLjj1tcpc/t+SNC5IjWJ2SZp42QsH1wFLgLWAR8Bah+r19FrRSqk9pklW9MuWmeWJikWsQ+R/x+nWIw45KgE8BkY31iao+KK8YmOY8rtj3aij4Pu1J9xVgEaH6eB+sTynVS5pkVY9N/tbcE00ifrfHX3yU27EUsDiwpcQnI/O4jqnO40rn/72Egi8AzwDPEqpfm8d1K6W6oElWdduUm+aNSLQ13ylFgSs8Xp82Fy48w4BLnQeEgmuxCfcx4HlC9TH3QlNqaNEkq7pl0o1/u85TXH6Hp7is0u1YVM4OBr7gPHYRCj4MPAi8rA2plMovTbIqJxO/fN948Xgf9JYNP9XtWFSvjAL+w3l8QCj4L+B+QvWvuxuWUoPTEO+8qHIx8Yv33ugtKV/jDQQ1wQ4u44EbgQWEgssIBW8gFCx3OyilBhM9k1UZTbzhnvHiL3nUVzla74oz+B0B/B74CaHgfcDvCNWvcjkmpQY8PZNVaY2f8+vLPYHgam8gqAl2aKkEvgSsJBR8gVDwYkJBbdymVA/pmazqIDjrSm/ZjNPvKRp78LUiHt25Dm1nOI+lhIL/BTxMqN64HJNSA4qeyap9xlx2y0EVx52/vGj0lE9pglUpjgIeApYQCl6hZ7ZK5U6TrAJg3DU/vbJk8tFLfRWjDnM7FlWwjgT+CSwjFLxSk61S2WmSHeICVdXecdf89KfFE6bf7ykq1ZalKheHY/vZvkEomG6MZaWUQ5PsEBaoqq6oPOmKf5dMOuLb4vHqtqC66wTgVULBhwgFp7gdjFKFSHesQ1TFCRdMGHbadQtKJkw/1+1Y1IB3ObY18q2EgiVuB6NUIdEkOwQNP/26Y4MzL60pGj35CLdjUYNGKXAbsIJQUA/clHJokh1iRpz1ufPLjzn3BV/lmAlux6IGpYOB+YSC/49QUO8rrIY8TbJDRKCqWoafft2csiPPfsBbWjnM7XjUoHc9sJxQ8Cy3A1HKTZpkh4BAVbW3aNwhX6847vzfekvKtQWx6i8HAs8SCv6OULDM7WCUcoMm2UEuUFXtLRp/6DcrT7j4h57iMt3Rqf4mwBexo0ad7HYwSvU3TbKDWKCq2ld0wLSbKk+46FZPcUATrHLTwcDLhIJfdzsQpfqTJtlBKlBV7S8ae8hNlcdfeLOnqDTgdjxKYcdK/wWh4IN6Sz01VGiSHYQCVdV+X3DcNypPuPA7nqLSUrfjUaqTK4E3CQWnux2IUvmmSXaQCVRVez2llV8KnnTFtzzayEkVrsOwwzJe5XYgSuWTJtl+IiJTRMQ4jyn5WEegqlrEV3RN8OSPf9tbPnxkPtahVB8qBx5wbqOn1KCUlyQrIqGUhNLlIx/rH7JEzg/OuirkHzZuvNuhKNUN3ycU/DOhoN7fWg06/XEmuz3LQ/WBQFX1qZUnXvLTojEHHeR2LEr1wBxgnjaIUoNN3pOsMWZcV498r38oCFRVHxWYdsrtJZOOmOF2LEr1woex3Xx0v6AGDb0mO8AFqqoPKhpzcKhsxumz3I5FqT5wHPA6oeA0twNRqi8UXJIVkaCI3Coib4tIg4i0iEitiNwpIgdnWCanRkUist6ZZ05Xy4vIWBH5tYisE5FWEdkuIg+IyGFZYp8gIn8QkU0i0iYim0XkzyJySA8+iqwCVdXDPKWV366ceelZ4vHq9Sw1WEzBntFqFx814BVUkhWRw4Hl2FtmHQv4gShwCPAFYKWIXJbnMA4HlgJfAcY4r40BrgJqROTodAuJyHHAMuDzwEQgDgSx15reAmb2ZZCBqmo/4vlCcNZV53uKA5V9WbZSBWAs8IKe0aqBrmCSrIhUAE9gE9QW4HygzBhTCRwDLASKgb9nSnR95D6gFjjRGFOG7WZwDvABUAn8JkPsjwLDgY3Ya0tlxpgKYBawCfhDXwUYqKoW4JKK4z52tX/4+El9Va5SBWYc8CKh4KFuB6JUT+U9yYrIti4eh6fM+kXgIOyZ67nGmCeNMQkAY8wSbOJaj020t+cx5O3AOcaYRc66Y8aY54D/cKafKiITOy1zA/aOIxEn9meNMcZZfiFwNtCX3ZVOKJl89OdLJh99VB+WqVQhGo9NtHm55KJUvvXHmezYLh7+lPmSI7/8yxizvHMhxphG4GfOv+eJSDBP8f7cGNOS5vX52CQKcGSnaR93/j5kjFnVeUFjzDbgrr4ILlBVPdFTUvHl8qM/Ui0ifVGkUoXuAGyinep2IEp1V3904ZEuHu8AiEgRkDwre66L4p51/nqwrRDzoSbdi8aYGLDT+XdE8nUn9mTSfaGLcrualpNAVXUJcGNl9WWzPP4S7U+ohpKJwHOEgmPdDkSp7iiUa7IjAK/zfEsX821OeT4m41y909jFtJjzN/UMfAT27iKQe+w9dWmgatbMolEHVvVBWUoNNFOAJwgF9a5SasAolCSbqqtrlybD80KRt5gCVdUzvOUjLy6bMbs6X+tQagA4Efg7oWAh7ruU2k+hbKh7sF1eALpqLZs6bWfK81jK85Iuls/HddzU2Ds3iEo1oacrCFRVlwOfD1ZffqL4/HrrOjXUXQz8wu0glMpFQSRZY0wE2zcV4KwuZj3b+ZsA3k55vS7ledokLSKHAsN6GmMmnWI/o4tZz+xJ+U53nasD00871jds7JSelKHUIPRVQsGvuB2EUtkURJJ1POD8vVxEjug8UUTKgW87/z5pjKlPTjPGNAPvO/9mGqzi5r4KNI0Hnb9XiMh+nedFZAx2MI2eONZTUnFW2aEnn9Dj6JQanH5JKHi+20Eo1ZVCSrJ3AuuwjYrmi8h5IuIBEJEjgaex/WgjwPfTLH+/8/czIvJFESl1lp0kIndjuwiF8xj7Zmwf3qdE5Cxx+teIyExsi+luf9aBquoK4LOVJ140XXxF2thDqY48wH2EgpPdDkSpTAomyTr9YC/EttCdCDwJNItIPbY69mSgDbjGGZyis58CK7FJ+ndAk4jUYUdg+hR2eMOdaZbri9gbgEuAvdgWkM8562/EdgmaQvtgFt1xSdG4qon+0QfpoBOfno9EAAAgAElEQVRKpTcceIhQsMjtQJRKp2CSLIAzCMXhQAh4B9ugqRhbFXwXcLgx5l8Zlm0CTsE2iFjnLBsFHgZmGWMeSLdcH8a+CNvX927sgYIPqAf+gu3T+0Z3ygtUVU8Fzqw45rzjdNAJpbp0IvBzt4NQKp283LnFGBPCJsqeLFuPvUHAbT1Ytg74T+eRbvqUDK+vB7JmskzLp0zfBHyui1lyypaBqmov8KnA9NMO8JYN66rFslLK+jKh4CuE6h9yOxClUhXUmazaZ5b4ig4OVJ10ktuBKDWA3E0oqAO1qIKiSbbAOH1iry4/8pwJHn9JhdvxKDWAVAIPEgrqvZVVwdAkW3jOFX9JecmBR+rITkp137HATW4HoVSSJtkCEqiqHg6cW370RyZplx2leuwWQsH9+tor5QZNsoXlI1Ic8JdMmKHXYpXquSLgHkJBb9Y5lcozTbIFIlBVPQo4p+Loc6fo+MRK9dqJZOhloFR/0iRbOM7zFJd5iw+YptdileobtxEK7jfMqVL9SZNsAQhUVY8Bzig74swJ4vUXux2PUoNECXZwGKVco0m2MJyHSLz4gMP0JgBK9a1TCAU/4XYQaujS/mQuC1RVDwNOCxx6coWnqLTPbsXXtm0NLWveILJ9DdE9W0mE60lEwniKAvhHTqT04BMoP/ajeEv374rbunEZLesXE/mgllj9NhLhBhLRVjwl5fhHHUigahblR38ET4aT7nhTHXWv/JWWtW+SaGnCFxxD2RFnEjzpCsSTvi1KPFzP1rtvQLw+Drj+LjzF2rha9ZmfEQrOJVTf7HYgaujRJOu+DwGUTD7mxL4stHnZszS+PW/f/+IrQnxFJFobaduyirYtq2hYNJcxl91C8YTpHZZteOMRWt5/s31Zfwni9ZMI19O2cRltG5fRuGguY678If4RHe9FH29tYtvfv0Vs77Z9y8bqtlL/6t+I7ljH6Iu/mzbeuhf/RKKlgdGXfE8TrOprE4Dvkv7uXUrllSZZFwWqqouA84rGHoKvYuSUviy7aPyhDJs9lpKJM/CPnIinpByARKSF8OoF1L10D4lwPTseuZ0Jn/8DnuKyfcuWTD6GkoOOo2TiDHzDxu9LevGWBppXvszel+4lVr+dnY/czvjP/hbnjoQANL75GLG92/CPOYgxl92Cr3IMrRuWsuOR/yK8+jVaNy6l5MCONxVq2bCE5uUvUFp1EoFDT+7Lj0GppG8QCt5FqH6z24GooUWvybrraKAsMO1Dx/Z1weVHnEWw+lKKJxy2L8ECeIpKKT/yLEZ9zPZuSIT3El7T8QZBlSdeROXxF1A0dmqHs0pvaSWVx1/A8LPsPRCiuzfStuXdDsu2rF8MwLDTPoWvcgwAJZOPovyoD9vp6xZ3mN/EIux5+ndIUSkjzu7pfe2VyqoU+JHbQaihR5OsSwJV1QJ8zFNc1uIfOfHI/l5/8QGH7Xseb9zdzWXbe0XEG3d1mJZoaQDAP2xch9eT1cqJcH2H1/cueJBY3VaGnXotvspR3YpDqW66llDwaLeDUEOLJln3TAUmBw49+QDxePu92r5184p9z32dEmI2bR2WHd9hmqekEoCoc002KVq31U4PBPe9Ftm1kYaahykaV0XF8R/rVgxK9YAH+IHbQaihRZOse2YDbUXjDz0q24x9xcSixOq30/DWE+z+t73HtW/4eAKHZB//IhFtI7pnC/Wv/5M9L/wJgOJJR1A8vuOdxUqnHAPA3lfuI9awE4DWTctpWvK0M93WjBtj2PPUb8EkGHnujR2u6yqVRxcTCh7udhBq6NCGTy4IVFUHgGpfcGyzt3zElHyvb8P/XgLx6H6vF0+YwagLv4n4/GmXizfVsfl316adVnrITEZ+9Ov7vV5x4sU0rXiR6I61bLnz04i/BBNttcscOouSyfaYomnJU7RtWUnlzEspGntwT9+aUt0lwM2A9p1V/UKTrDtmAL7SqpNmiIjke2XesuGYeAQTad2X8IoPPIrhsz+9r3FSWh4PnjLbdde0hTGxCACBaacw7NRr0vax9ZZWMO6T/8PeV/5Ky9q3SLQ24Rs2nrLDzyA460rAJu+9L92LNziW4Cl2X9eybjH1r91PZPv74PVROvlohp1+3X5dhJTqA1cSCt5KqH6N24GowU+TrDtOB5qKxhzUL1XFE2+4Z9/zePNemle8QP3r/2TbX79B8OSrGHbqJ9Mu5w0EmfTlvwG2ejfeuJvGd+bT+OajhGsXMuKcL1BxzLn7LeerGMmo8/c/y03a8/wfSbQ1M+aCb+HxlxCurWHno7cjPj+lVdUkWpsIv7eA1k3LGT/nV10fCCjVfV5sv9nPuh2IGvz0Qlg/c0Z4Orxo7CFeb2nl2P5ev7dsGJUzL2XMFbeBCPULHtivC086IoKvchTDT7uWUR/7JiRi7Hnm90R2rO3W+lveX0T43VcJTD+N0qknYBJx9jx3F5gEYy4PMfrCbzP2yh9SOesqEi0N1L38l56+VaW6ci2h4IFuB6EGP02y/e9ogJLJRx2WbcZ8Kj5gGsUTZwD2+mh3BKadjLdyDJgETUufzXm5RLSV3c/eiae4jBFOX9vItjXEG3ZSNO4QSg5s78kUnHkJILSseQNjEt2KT6kc+IFvuB2EGvw0yfYjp2/smUC9f8SEqmzz55u3fCQAsboPur2sr3wE0N41Jxf1//cP4vXbGTb703jLhtt1O1199u8KVI4nUImJtOzXt1apPnIdoaDeu1nllSbZ/jUSmOQJDIt4AsNcb9Gzb3zhou7tZ4wxxOq3A+Apym2c4cj2tTQsmkvxxBmUH/2R/cuMtaV5LeI8y3vbMDU0DQOucjsINbhpku1fVQClU46dms9WxSYRxxjT5Twt698h8sF7AB2qaU0inrX85mXPEm+u22/ZjPGYBLuf/g2IMOIjXyb1rScHwohse7/DuiM7N2AiLUhRKZ5AZdZ1KNVD/+F2AGpw0yTbv04AwkVjpuS1qjjeuIsP7v0Kje/MJ7p3W4eEG2vYSf3Ch9j5yI8Ag6ekgsoTLt43vW3zCrb9/Ts0LX+BWEPHIROje7ZQ99K97H76d4Ct4i074qys8TS+9W8iH9QSrL6colEd25oUjTsEb/lI4k272fvKfZh4lHhrE3ue+wMApVNn6kAVKp9O0qEWVT5pF55+4txx50hguzc4dmq+1xfdsY49TjLE68NTFMDEIvv6yQL4gmMZfcn38JYP77Bs2+YV+4ZOFF+RM6BEW4cqXf+Ygxhz6fcz3lM2Kdawi72v3odvxASCs/avmROPl+Fnf55dj/2Ehpp/0bBoLiTiYBJ4SioYfvqnevoRKJWr/wC+6HYQanDSJNt/DgK8xROmj/H4isqyzt0L3vIRjLroJlo3LiPywWriTXuItzQg4sVbOZqiMQdReshJlM04fb8kWTT2EEae/w277PY1xJvrSLQ0Il4/vmHjKRo7lcC0kwlM+1DGG7Cn2vPcXZhICyM//KWMI0uVTfsQnst/QP2CB4jsWAd+PyWTj2L46XPwBfu9l5Maej5JKPhtQvVNbgeiBh9Nsv3nCMAUjT1kcr5XJF4/ZYedQtlhp3R7WU9xgPIjzqT8iDP7JJYxl+Z2n+zSqSdQOvWEPlmnUt1UAVwM/M3tQNTgoxe7+oHTdWcmsMcXHDPR7XiUUvu5wu0A1OCkSbZ/VAKjgbC3fIQmWaUKz4cJBfcfjFupXtIk2z8mAsZbObrcU1QazDq3Uqq/lQAXuB2EGnw0yfaPKQDF46fpWaxShetytwNQg48m2f5xBNDoHzFBk6xShes8QsFyt4NQg4sm2TwLVFV7galAo7dilOtDKSqlMioBznc7CDW4aJLNv3HY+1fGPaUVo90ORinVpY+6HYAaXDTJ5t9EQDyllSX5HoRCKdVrZ7gdgBpcNMnm3yQg7h914Ei3A1FKZTWJUND121CqwUOTbP5NBsK+4NhRbgeilMpJ3wx3phSaZPvDBCDsKx+pZ7JKDQzZby2lVI40yeZRoKq6GHtj6DZPIKhnskoNDLMJBfN2v2c1tGiSza+RQALAU1KuZ7JKDQyjsbelVKrXNMnm1yhAADz+Yh0XVamBo9rtANTgoEk2v2ySFY/g9Ze6HYxSKmfHuh2AGhw0yebXGCDqrRhZJqKXeJQaQDTJqj6hSTa/RgARb9lwHYRCqYHlSEJB3T+qXtONKL+GAVFvaaUmWaUGljLgULeDUAOfJtn8GgZEPKUVmmSVGniOcTsANfBpks2vIBD1FJcF3A5EKdVtel1W9Zom2TwJVFUXAX4gLr6iYrfjUUp122FuB6AGPk2y+VMGGADx+Hwux6KU6r4pbgegBj5NsvlTjJNk8Xg1ySo18Ex2OwA18GmSzZ99iVU0ySo1EAUJBYNuB6EGNk2y+dOeWHUkCqUGKj2bVb2iSTZ/UpOsfs4D0063A1Cu0ySrekV3/vmT8tnqmexAkmhtagQ+AXzO7ViU66a4HYAa2PRaYf60J1aTiLsYh8qRiUXCLRuWvN607NmN4Xdfu9/teFRBOMDtANTApkk2f/adyZp4LOpmIKprJhGLtm1dXdO4eP46Ewk3AJpgVZI2fFK9okk2f8y+J/FoxM1AVHrGJBLRnesXNy5+cnW8aU8z8DjwQri2Jux2bKpgVLodgBrYNMnmz77EauJRPZMtMNG6D1Y1vjN/aWzP5jDwLDA/XFuz1+24VMHRJKt6RZNs/uxLrCamZ7KFIta0Z0PzsucWtW19twl4HZgbrq3Z5nZcqmBpdbHqFU2y+dOeWOMRPZN1WbylcUf43VcXtqxdtBdYATwUrq1Z53ZcquDpmazqFU2y+bMvySZiET2TdUki0lLfsnbR680rX9qBMZuwjZpWhmtrTLZllUKTrOolTbL5E8XpxmPamltcjmXIMbFoS+umZQsblzy9mXh0Nza5vhWurdHuVKo7yt0OQA1smmTzJ4LTjSfWuKfB5ViGDJOIxyLbat9sXPzkWmdQiYeA/wvX1mhtguoJr9sBqIFNk2z+RIE4ILH67Zpk88wYY6K7Ni5pXPzkqnjjzmZgHvBcuLamye3YlFJDlybZPAnX1phAVfVeoNhEwq0mHm0Vr7/E7bgGo9je7e81LnlqSXTXhmbgReDf4dqaPW7HBeAV5Lpj/Meec7BvttuxKKX6nybZ/NoFjAVaE9HWBq8m2T4Vb67b1LT8hTfbNq9oAhYBD4dra7a6HVfSfZeUnh+Jm4tHl3mGuR2L6rGE2wGogU2TbH7tBA4EMJHWBkoqxrgcz6CQaG3e1fzea6+31C6sA94DHgTeL5QWw/FbK2dF4twRLJETUoewVgOSdr9TvaJJNr+2AcUAibZwo8uxDHiJaGtj67q3X29a/sI2TGIrtsXwskJJroSCh4Wj5lcBv3ykVO9vNVhogznVK5pk86sO51Qm0VJfENcIByITj7a1bl65sGnJUxtNtK0OeAB4M1xbE3M7NgBCwQPCUfOTEh/XBPx67+BBRs9kVa9oks2vBpwbBcTqd+xwOZYBxyQS8cj29xc1Lp63JtHS0AQ8ArwSrq1pdTs2AELBypaouaXIy40BvxS7HY7Ki3q3A1ADmybZ/KrHSbKRXRs1yebIGGNiezYvb1w8f0WsflsYmA88Ha6tKYwq91CwqDVmvuYVbi71i44INLjtcjsANbBpks2vnSQHpNizea+JxyLi9RW5HFNBizXsfL9pydNvR3asbQZeBR4P19YUxo4uFJRo3Fxr4CclPhnvdjiqX+x2OwA1sGmSzaNwbU1roKp6N1AKtCRam3Z4y4ZNdDuuQhQP129tXvnSG60bljQA7wD/CtfWbHI7rqTYrZXnxRL8ssQn09yORfWrwjjAUwOWJtn8WwfMAFri4b2aZDtJtIXrwrULF4RX/98u7Gf1APBeobQYjt9aeUIkzh2lfpnl0yZNQ5EmWdUrmmTzbw1wPEC8cfd2Rk9xN5oCkYhFmlvXL369adlzW0nEdwD/AJaEa2sKo/N/KDg1HDW/LPXxsVK/aGfXoUuri1WvaJLNv303BI/u3vRB6cHHuxmL60w8Fmnb+u4bje/MX2ciLQ3YgSQWhmtrCqOrRCg4Jhw1Py7xMSfgFx0cXumZrOoVTbL5tz35pG3Lqq3m+Ati4vEOuc/dmEQiumPd4sbFT66ON9c1A3OBF8K1NYVxG8BQsLwlar7r9/KNgF90+EuVtMXtANTANuR29i7YhR3/1Gvi0Xi8ee9mX8XIKS7H1G+MMcTqtq5sfGf+sljd1jDwDDA/XFtTGP0PQ0F/W8x80SP8oNQvw90ORxWcWrcDUAObJtk8C9fWxANV1auBSUBdrH77hqGSZGONu9c3LXt2UeSD95qBBcDccG3N9mzL9QvbHefKhOF/i32ijdFUOvWE6ne6HYQa2DTJ9o93sC2M66I7128smTjD7XjyKt7SuL151SsLW9e9VQ8sBx4K19asdzmsfeK3Vp4RTXBHiU+OcDsWVdD0LFb1mibZ/rEeZ+Sn1s0rNpUfc25CxDPoOoQkIi31Le+/uaB51cs7MWYDtjvOqkLpjkMoeFQ4au4I+OV076D79FUeaJJVvaZJtn8kB1XwmEhLNBFu+MBbNmyCqxH1IROLtLRuXLagcekzW4hHd2HvjrM4XFsTdzs2AELByeGo+d9SH5cFtDuOyp0mWdVrmmT7Qbi2pi1QVb0WGAXUR+u2vj8YkqxJxGNtH7z3RtPiJ9cm2pobgH8Br4Vrawrj9mCh4Mhw1Pyw2MvnAn7xux2OGnDWuB2AGvg0yfafJcAlQH3b5hXvlkyccZrbAfWUMcZEd214p/Htee/Gm3Y3A/8GngvX1jS7HRsAoWBpS9R8y+/l2wG/lLkdjhqwVrodgBr4NMn2n/eST9q2rPogEWlt8BSVDLg7uET3bnu36Z35S6O7N4WBF4B/h2tr6tyOC4BQ0NsWM9eL8KNSv4xyOxw1oLUBS90OQg18mmT7zzrsDaD9QDRWt+XdorFTZ7ocU87iTXWbmpY//2bblpWNwJvAI+Hamg/cjispekvlpU53nIPcjkUNCu8Qqi+MUcjUgKZJtp+Ea2uigarqRdhxjLe3bV29eiAk2URr067m1a+93rKmpg54F/hnuLbmfbfjSorfWvkhZwD/49yORQ0qb7gdgBocNMn2r0XAyQAt6xevLz/qnFbx+gtyCL9EtLWxZe1bC5pXvLgDk9iMbTG8vIC640wPR82vA345p1S743RLQ5vhzjcjzF0do3ZPgoY2w+iAUDXSw+mTvXztpGKGlbQ3wpbbGnIue/YULy9e1/EyeGOb4dYX23h4VZRtTYbxFcIVM/zcNruYsqL0jb3bYoaj7mpmS0OClV8q58Bgv3/JmmRVn9Ak27/ew/aX9ZCIJ2J7t9f6R0480u2gUpl4tLV104qFTUue2mRikTpscl0Urq2JuR0bAKHghHDU/LTEx9UBv2h67aYX18W4+uEWtjfbYyWfB8qLYEujYUtjnJfWx7n4MD/HjGu/N8LYsq57PUUTsKfFlnfiAR3vqRCJG86+r5k3ttibK5X5YWO94eevR3h9c5yXrgvg9+5f/o9fbeO93Ql+8eFiNxIsaJJVfUSTbD8K19aEA1XVK4CDgN2tm1csLZQkaxKJeGT7mkWNi+fVJloam7HdcV4N19a0uR0bAKFgsCVqQkVevhjwS5Hb4QxEr22Mcf4/wrTE4OyDvYROL2bWJC8eEVqihpU7Ezz6bpRgccekt+2bFV2W+/MFbXzzWbuZfPbYjj2l/rY0yhtbEhwYFOZ9IsARY7y8sy3O+f8Is2BTnL8vizLnmI5f57u74vzktQjHjffwlWpXvuo6tI+s6iOaZPvfAuBIYHfL+2+8Xzbj9AaP371WxsYYE929aVnj4idXxBt2hIH5wDPh2ppGt2LqIBQsbo2Zr3mFm0v90vXeXmUUjho+9VgLLTG4bLqPf15RiidlXI5Sv3D8AV6OP6D7d/f702LbPuiUA71MG9Vx+WfX2gqQb59czBFj7LRjxnn5zoeK+OpTbTzzfqxDkjXG8PknWokn4I8fK8XrcWXskNcJ1RfGZRE14GmS7X+rSFYZG5OI7li/pHjCYae6EUisfseaxqXPvB3dsbYFeBl4PFxbUxg3qQ4FPZG4uQ747xKfjHU7nIHuviVR1tYZSn1w18dKOiTY3liwKcaqXbYq+Ppj9x/vY1fY5qqpIzpW+VY5/+8Md8xlf1oc5dWNcb5+UlGPEn4fecatFavBR5NsPwvX1tQHqqrfxt4wYEe4duHiogOmnSr9ONpfPLx3S/OKl95o3bi0EXgbeDhcW7O53wLIInZr5fmxBL8o8cmhbscyWPx1qT3bvOgwH6MCfXeN809v23Iri+GKw/dPsiNL7Xb9/p5Eh9fX7LHJdXSgfbvf0Zzg28+2cmBQ+OEZxX0WYw885ebK1eCiSdYdL2K78hDdvbEu3ly3wVc+YnK+V5poa94Trl24ILz6tT3YIeMeBGoLpcVw/NbKmU53nGqfNmnqM20xw6Ktdhjp0yf7WFuX4PZX2nj6/Rg7w4bhJUL1RC9fON7PeVW5jz7ZFDH8c6VNsp84wk/Av/+B4jkH+3hwRYyfLWhj9hQvh4/xsmx7nJ8taNs3PelrT7VS1wp/vaSE8gytjvvBOkL1q91auRp8NMm6YzXQBJQArW1bVi32TftQ3pJsItrW1Lp+8etNy5//gER8G7bF8NJwbU0i27L9IhSsCkfNLwN+OV+74/S99XsTRJxbNWxuSHDUnU00R6HICwE/bG82PL46xuOrY3zh+Bh3fqw0p3IfWB6lyRml+vrj0jdQ+uRRfu56K8KirQmOuLOZ8iL2LVM9wcs1R9mk/vSaGPcvj3H5DB8fO9TVYaafdnPlavDRJOuCcG1NLFBV/RxwIbAp/N6ClYFDZp7b131mTTwWaduyqqbxnfnrTbR1L/BPYGEBdccZG46a/y7x8amAX1y7ADfY1bW2V1T89/9FqCyG+y8s5bLpPvxeYVN9gm8/18oDy2Pc9VaUw0Z5+OpJ2atr73aqio8e68l4/bTYJzx3bdm+frLbmw0TK4XLp/v54RnFFHlty+Yb5rUQLIY7zrU/gbe2xvneC60s2BTHGDh5kpcfn1XCCfm/TqtVxapPaZJ1Tw1wMYCJtEQj299fVHzAYaf0RcHGJBKR7Wvfalz8ZG0ivLcJmAu8GK6taemL8nstFKxoiZrvFXn5WsAvBTkYx2CSMB2f33V+KVcd0X62OCno4e+XlrJ6VzOLtyX40asRvjSzCF8XLXtX7IhTs8WeHmc6i00Klgi/Pq+EX5+X/qsOvdTGur2GO88vYXyFh0Vb45z252Yicbhwmg8RmPtujNP+3Mwrny7LW6I1xkRF5Pm8FK6GLE2yLgnX1mwLVFWvAcYAdU0rXqwpGnfoLPF4erwHMcYQq9u6ovGd+ctidVvD2FaST4Vra+r7Ku5eCQX9rTHzZa9wa6lfhrkdzlBRkXJ9c1KldEiwSR4R/nNWMZ98tIVdYcNbW+NUT8y8e0iexZb44Joje169u2x7nF8sjDBropf/ON6W89WnWmmJwb0XlXCd073n7rcjfO6JVr76VCuvfSY/N1YSkZcJ1TflpXA1ZGmSddeTwFeAunjDzqbo7k1LikZP7tEYvLHGXeualj77VmRbbTPwGjA3XFuzoy+D7bFQUCJxc7Ux/KzEJwP+ProDzYTK9gvdh43KfNF7+uj2aRvqDdUT088XiRv+tswm2cum+xle2rNGSglj+Py/WxHgjxeUICLsaE6wYFOc0QHh2qPbk/enj/Hz3efbWLApzs7mBKPL8nLx/h/5KFQNbZpk3bUM2A2UA03Nq15Z4B/1yeO6050n3tKwrXnlKzWt69+ux96a61/h2poN+Qm3+xI/qDwrEueOEp/McDuWoWpEqTChQtjSaOhq0zIp1cpdbYFz343t6/96/XE9P4u9880oCzfH+d4pRfsGqlhbZ9viHTRcOvTl9XqEg4YJu8KGtXV9n2SNMa0i8nCfFqoUmmRd5TSAmgt8BmiK7ly3O1a/fbV/2Lhp2ZZNRFr2tqx5Y0Hzqpd3AeuBB4B3C6g7zjFtce4I+OXUEt3KXPfhqT7+/E6UVTsTGGNIdyC3ald83/ODhmdOYncvts2DDxlhbyjQE1sbE3zvhVamDhduOX3/RlYtaW4y1+I018tHn3IRmUeoPvc7ISiVI939ue9N4OM43Xlaahe+5j/x4oxJ1sQi4ZYNS19vWvbMVuKxHdjuOIsLqDvOQeGo+Xmpj4sD/n4cYUN16dPH+PnzO1E2NRgeXBHj452uyyaM4Rev2+Q5oUI4bnz6JLuxPsFza20y/swx/h4nvBvnt9LQBg9fWUqJr72Mg4bZ9dbuSVDXYvZVRe8OJ1jjDGgxZVheNiutKlZ5ob0SXRaurWkF5mEbQNG6cemmWOOudZ3nM4l4tHXzytd2PXXHI03vPLmaeOwe4Hvh2pq3CiLBhoKjwjdX3pkw5r2AXy6R/hzCSmV16mQfl8+wx9Q3zGvhweVRonFb6bGpPsE1j7SweJvdjG4/szjjsIv3LI6QMPbuPXOO6VlV8ROrozyyKsa1R/k5++COx/ljyz1UT/DSGrOJOBw1hKOGG+e30hqDWRO9jOn7quK92N+gUn1OjCmI2sUhLVBVXQn8HNgBRIsnHj4hWH3Z9WC740R3bnincfGT78abdoeBx4Hnw7U1YRdDbhcKBlpj5js+D9/0eSTgdjgqs+aI4aP/CPPKBnsmWuwMRlHX2j7PracVcdsZ6bvaJIzh4F83saHecOE0H3M/3v2vuylimPG7JsJRePfLZWmHeHx9U4wz/hKmLW6TuWBvp1fig5fnlDFzQp934fkTofrr+7pQpUCriwtCuLamIVBV/QxwLrCpbfOKLdFDT14F0LTkqSXR3ZtageeAJ8O1NXVuxrpPKOiLxM3ngR+W+GSk2+Go7MqKhBevC3DP4ij3LY2yfEeCxhm0aOkAABheSURBVDbDhArh1MlebpxZxMmTMu8SnlsbZ4Nzc5p0NwPIxfdfaGNTg+HPF5VkHEN51iQfL88J8P0X23h9kz0gOG2yHYwiDwkW4M/5KFQp0DPZghGoqg4C/wPsAiJ4fGNJxEqxg1Y8Gq6t2eZqgCmit1RenjD8b7FP8j7eslL5lDBmsee2hh51m1MqF3omWyCcu/M8AVwNREnEVgH/DNfWrHU5tH3it1aeGonzm1K/HO12LEr1BY/IHW7HoAY3TbKF5QVsA6gaYEWhdMchFDw8HDW/DvjlLB3AXw0WCWN2e0TudzsONbhpdbHKLBScGI6a/yn1cZW2FlaDjTHmx3Jbw81ux6EGNz2TVfsLBYeFo+a2Yi83BPzi6n3HlMoHY0xMRH7vdhxq8NMkq9qFgiWtMfMNn4fvBvxS7nY4SuWLgUclVL/F7TjU4KdJVkEo6InEzWeA20t8MsbtcJTKJ2OM8Yj8yO041NCgSXaIi91aeUEswS9KfHKI27Eo1R9iCeb6/6t+qdtxqKFBk+wQFb+1strpjnOiT1sMqyHCGGP8XtHGTqrfaJIdakLBQ8NR86uAX87T7jhqqInEeaT4R/Ur3Y5DDR26mx0qQsFx4Zsr700YszLgl/PcDkep/maMSRT75Htux1FIRGSOiBgRWe92LKmcmIyIzHY7ls5EZL0T25xc5tckOwSs+UrFrHjCrAv45TqPSF4Gf1Wq0EXiPEio/r3uLCMiJSLyHyLyhIhsFJEWEakXkVUi8kcROSNf8arBQauLB7ELp/lLgNkCl/zhgpLmceWS/vYqSg1yCWOixb7uXYsVkXOAe4CJKS83AMXAYc7jcyIyH7jWGLO7r+JVg4eeyQ5SF07zC3AL8HEDex5ZFfu32zEp5ZbWGL8hVL/ffZozEZErgSexCXYLcD0wwhgTNMaUANOBXwEx4DxgoYh2f1P70yQ7SD2+OmqADUAEaHtqTWz9urrEapfDUqrftcXMnoBffpDr/CJyGPYM1gcsA441xvzJGLPvNpPGmHeNMV8HLsL+xg4B/tG3kavBQJPs4PYo4MW5LPC7NyPzo3ETdTckpfpXJM63CNU3dWOR24EyoA24whizM9OMxpgngeTAFmeJyPkAIvINp3HMdhHJeFlOrA3OvN9PM93rNE562ikrIiI7nf8/nmlM8dTGOSJSLiI/FJFlItLovD4l1w9DRKaKyJ0iUutck24QkbdF5FYRqcyybNCZ721nuRannDtF5OBcY+hUpsdZ3ohIWEQuSjNPiYh8RUReFpFdzue2TUQeE5Fzs5RfKiLfF5GVTrw7RORJETmrJ/Fqkh3EHl8d3Ymt8joA4L3difqXN8RfdDcqpfpPY5t5q+K/G+7JdX4RGQ9c7Px7vzEml9qfXwKNzvMvOX//DsSxd9X6SBfLngYcCBjgb51iGQu8hr2p/IedssLAKOf/+4HHRKSoi/JHAm9hLx0diq3ezplTbb4C+AL2bD0KFAHHArcBy0VkeoZlDweWO/MdC/id5Q9xylspIpd1M54S4F/O8nXAOcaYuZ3mqQKWAr/Gfr4jsJ/bWGzNw/xM41aLyAjsZ/5f2EsCPifu84BnReSG7sQLmmSHgqewO4BygN+/GVm4K5womBvAK5Uv8YRJeIRPdXOx2bTvFx/OZQFjTBPwjPPvqSLiM8ZsT3nt2i4WT0571RizPvmikzifAKqBt4HzgTJjzDDsb/k6YAdwIfDTLsoPAZXApUC5MWY4MMlZtksichw28RdjE8/RxphKIOCs9wOnrCdEOo51LiIVTvzJa9rJ+CuBY4CFTrl/F8nt/tQiMgz7mV4CbAZOMca8lmGeKuytQ08DSp3PbRjwDaAJuEFEvppmNXdjDwjasIm8wvnMpgCPYRP36FziTdIkO8g9vjraDNyLPQqWWAJzz+LoEwm9x6Ea5Pa2mjvLftzQ3YEnDk95vrgby73j/C0HJjvP/+r8vUhEgp0XcM7KLu80b9LngBOxZ5GzjTFPGmPCAMaYZmPMX4GPYs+Av9hFo6tS4KPGmEeNsZeKjDGbk2VlcTv2LG4N8GFjzFJn+YQx5gls4owBU7EJKdUXgYOwZ67nOvEnnOWXYM/E12MT7e3ZAhGRicCrwKnASmCWMSbdd3szNiG+AHzEGPOqMabNWW+9MeaXsO/A6/upVfkiMpP/396dR0lV3Qkc/95X9XqhW1qWFmRTQSlEUMeFccG44ZaESkadSJwYl8lykow5cXKi8Uxy9KgzDjGa8Rgng8eMGo9xi46WGjGKCsqioqCCUAhCC7Q0dGN39UJXvXrvzh/3Fl22VU03dHX18vucU+dV9VvqV0XTv3fvu/f3TAIH+LHWeoHWut3uWwP8I+Ze38P2FW82SbJDw2pgJTAW4M1P/drVO4K3ixuSEIWTSOpPRw1z/nU/dh2V9bwnU3LqcxzjWcyUn+xkmi0KVAHtmC7QbN+zy//WWjeTg9b6XUwSLgHyzdddqLXuyckCsLdFmOnmviNXUrbHfdq+/Han1ZfZ5V+01mty7NsM/Ma+vCjXSUhWLNOBZcAMTIt6ttZ6W47tFHCNfXmn1jpf1/gzmH+X0cCJWT+fZ5dbMV30nWP2Md3IPSJJdgiwI43/DCjMmSO/W558tSWlE0UNTIgC8AMd1Lfpy7i5KdWHb/ulAUha6z10JM9cXcaZnz2rtW7aeyDT1XqsfXmrHbCT8wFE7HaHkdvSPD/flxPo+EyvdLHdy3Z5rFLm3tO2qzsTf3f2dez75XIapgU7EXPScl72KO9OpmOuvwI82MV39hn28hlf/N5OssvXdf6eviX08Lq2JNkhIhb36oHHgEMBmpKkHlydelq6jcVgU9Ok7518d/OK/dw9u/U6Ku9WX5avBZzpBv6KUmrvH3SlVDVwYadtMsbS8bd5JGbATr6Ha7fL14W5z2uveWR3P3d1391MizJMR4IbiZnV0N19O79ftn+3x/sAuMSeuOQzLut5NV1/b5nvN/t7y8SQN2bbfdyjoiOSZIeWJcBm7IX7v23ya1Zs898obkhC9J76tmBjbXPwswM4RPZ1vnytq1z+zi5bMPPTM5bY1wr4TtbP52ESU/YAqYzs0qenaK1VNx4354nL78FnOFC5Tti7OonXeZ5ne8SuO5Z9d9Vmf29ju/m9PdjDmHtMkuwQEot7acwk+zJst/Gdy1KLd7QEW4samBC9IOVrb2er/ofZ/9saHMBhXgMy+3dreokdWXuefflG9rVA2+2YmZqT3WWcef5ojmuHdVnPZ3YnhgLIbgFPyLtVx7o0ZkoNwG46kvvELvbNXpdvLvL9wA8wie9GpdRv8mwHkD1rYn++t8xnzvt5lVKl9KyHQ5LsUBOLe1sx/+nHAcoLCH67LPVUyjcj8IQYqGoag1un39vypUE2PaG1/gxz7Q9gnlIq0tX21nXAQfZ5rvmXme7giFLqZHvMkzuty47hczpa1PM6r+8j79FxstFVEYY5dvl+1ujlFKZ7t7v7Bvb9ctJa3w/8s93uF0qpu/JsugYzoAn273tbaZdn5ivygZkS1KOa/5Jkh6bFmF+ovUUqnvoo/VxxQxJi/21pDBYfdU9Lj0d+5vFrYA+mt+dJpdTofBsqpS4CMpWaXgNe6LyN1noDZuoHmOkjmVbsmi5G/t5nl+cqpbpMGLaAQq/SWjcCL9mXv1BKfemar53fmmntP9pp9WN2ealSakaOfSuB6+3Lv2YP/MoTzwPAVZhEe51S6u4c22R66gCuVErN7uqYOb63x+1yEmYecuftHTr+rbtNkuwQFIt7AfAQpgrKcIBH13hrP6zz855NCtFf7WwNdiypSUd763ha67WYKTQ+pttxlVLqGjutBQCl1FTboophptB8AlzexajUh+1yHh3XZh/Osy3A/9CRmB9WSt2mlNrbvaqUGqaUOksp9XtgU88+Ybf9Gx0Vml5SSs207+0opb6KqSYXtu+/oNO+f8CM/3AxFZYuskkKe5yXMPNoU3QzcWmtH8acoPjAT5VS9+Zocd5q4wkDC5Upb7m3eIQt83ihUuohzKjl7OO/hfn3BPiDUur7tnsYpdQkTBI+FfN3s9uUDC4duqIRdxrwS8wov3R5mNDdF5VdObbS6eo6ihD9Rpun25/fkD7rW0+2vbXvrXvG1rj9I18ctdqEaeFm3zbyb8B3uqpxrJQaBdRiEjKYFtkkrXXekay2Bf04cE7WjxN23yo6ptiktdZup323YKanXJ1ncE9mu6swc0JrtNaH51h/GaZLOxN3wj7PfP6tmKIP63LsOwNTcW68/VE7Jqlm6h0nMd9b5znCKKUyielsrfXrndZ9CzMgKoxJ7j/KPrlRSh2BqdueXUmqEdOozK61vFFrfVSnY48CFmXt6wGtmGpRGvgXTAt8n99thrRkh7BY3FuPuf40EWBPGv+WxcnHW1Jdd90I0R8EWuuln/o/L0SCBdBaL8S04n4MvIiZ2lGG+cO7AZOA52itL+gqwdpjNWBafhmLukqwdp96zHXLb2Dm227FJPhyG8uLmD/6h/f0s3WX1vpxTBWsBZgWYilmkNNq4CZgRq4Ea/ddY/e92W6ftvtvwrTUj8mVYLsR0xOYHgEP+CFwX3aLVmu9GTPn9bvA85h5sRWYk4PNmAR8DaZV2vnYDZi5uTcB6zEnNGnMycJ5WuucNY+7Ii3ZIS4acV3gZ5han7UAs8aHxtxwesk1bqjLwuNCFNXb2/1HbluSvMIWWxGiX5KW7BAXi3se5qyyEVNmjLe3+3V//tB7Wk7ARH8Vr/ffu21J8mpJsKK/kyQriMW9ZuC/MNc4KgGeWpeOL67xFxU1MCFy+OTzYMuCd73z7QmiEP2aJFkBQCzu1QL3YFqzJQB3LU+9KSOORX+yPRHsWrAy9bW7lid7VNpOiGKRJCv2isW9tZhCFROwvxu/fi35/IYG/4Am+AvRG+rbgqb730vNm7802dPb1wlRNJJkRWeL7OMwQAUafeMryf/b0hhsKHJcYghrTuq2B1Z5P7zp9eSrxY5FiJ6QJCu+wA4keQQzEf4wAC8guOHl9ie2JYLNRQ1ODEl7PJ360/upG9/41H+i2LEI0VOSZMWX2BsJ3I+Z2zYJzBzaX77S/qjcTED0pTZPty94N3XLS5v838tIYjEQSZIVOcXiXgoztSeOLVaRSOLd+ErykV2tQW1RgxNDQktK7/nd8tQdr27259tSoEIMOFKMQnQpGnErgJ9jBkPVAlSVUnL7nLJ5E4Y7RxQ1ODFoJZK69c5lybtX7QhuicU9uUOUGLAkyYp9ikbcgzD1Og/BlCijPEzoP84tu2TKSOfoogYnBp3Gdt1yx9Lkbz/cGdxue1SEGLAkyYpuiUbcKkz5xUmYGqqEHdQtZ5d+fcYhoROKGpwYNBragsQdy1LzP9oV3CHFJsRgIElWdJvtOv4JMA2TaDXAr75SMmfW+PDpxYxNDHw1jUHd/KXJ27cl9L128J0QA54kWdEj0YhbCnwfOBnYgk20184qOfXcyaHznC/f31GIfXq31t80f2ny9vY0D8binl/seIToLZJkRY9FI24Ycxups4AazE2UmTs1POW7x7mXloZVWRe7C7GX1pq/fpxeteBd7z+Bv8goYjHYSJIV+yUacR3gUuDrmHtbJgGOqXZGXn966bwR5aq6mPGJ/i8d6PQDq7w3n9uQvgl4Q+bBisFIkqzYb9GIq4AzgauA3UACYEQZJTefVXbxESOcSBHDE/1YIqmb716RevmdWv9XsbiX86bfQgwGkmTFAYtG3KnAT4EQUAeggOtPLznrtImhM+Uyrci2vt7fPP/N1HMNe/R8e/cnIQYtSbKiV0Qj7mjgWkx1qL0jj+dODU/5p2Pdbw5zVWUx4xPF5wfaf2Z9euVD73vPAvfG4l6i2DEJUWiSZEWviUbcMsyAqNmYROsBHFqpht0wu3Tu5BHOtGLGJ4qnsV1/ftfy5NLVO4KHgWekyIQYKiTJil5lB0TNAb4NNGOu1QLwvRPcEy46MnyhG1JuseITfe/DOv/j+UuTixNJ7onFvQ+KHY8QfUmSrCiIaMQ9HPgRUA1sAwIwo4+vO7Xk4kMqnPFFDE/0gdaUbn7ofe/thRvTi4D7YnGvodgxCdHXJMmKgolG3HLMNJ85wC6gBcB1cK79+5LTZk8KnRl2VLiYMYrep7Vm1Y7gg7uWJ9cmkjwJPCcVnMRQJUlWFFw04h4L/AAowd7JB2B6tTPiJyeXfG1ilTOlaMGJXtXYrhsWrEy9tXSrvx64X6bniKFOkqzoE9GIezBwJXAiZppPa2bd5TPdY+ZODV9QUaIOKlZ84sD4gfaXbvVX3fNWal3S5wUgFot7e4odlxDFJklW9BlbvOIETLIdhmnVBgDDS3GvnVVy5knjQqeEHBUqYpiihzbuDuL3vp1cs+lzHQf+GIt7nxQ7JiH6C0myos9FI24l8E3gXEyLtj6zbtpo5+Crj3fPjox2ZsrNBvq3Xa1B7Z/e995ZXOM3AE8DL8vUHCG+SJKsKJpoxD0CuAKYgulCbsusO/FQp/qK40rOkbm1/U8iqXc/s95b/tRH6QYN7wOPSeUmIXKTJCuKKhpxQ8ApwDygEpNs2zPrz5gUGjdvhnvuxCpncpFCFFZTu25YtDm98pEPvDovYAfwMLBWCvsLkZ8kWdEv2GpRZwAXA6XADmBv1+P5U0KHzZ3qzp5UpY6UXuS+Vd8WfPbix+kVT61LNwWaVuBxYEUs7nnFjk2I/k6SrOhXohG3AjgHmIu54cBnwN45lsePdUZfOt09ZXq1c5zMsS2s7YlgSyyefuvFjek9mBOeZ4HXZNSwEN0nSVb0S9GIWwWcD1wAOJhu5GRm/dhKVX75TPekWeNDs+TmA70nHWhvQ0OwNhZPr1m21fcxBUSeA5bG4l5LkcMTYsCRJCv6tWjEHQGcDnwVKMfUQm7OrC8LE7rkaPfoUyeGjp8wXE2WEcn7p74t+GzFNv+9J9Z6NY3tVAANwDPAO7G4176P3YUQeUiSFQNCNOKWYgpZRIGxdEz92fsLfFiVqvzGNHfmCYc6x40sd8YUJ9KBI+Xr5LpdwYcvfJxetWKbH2CuhX+GmY6zWkohCnHgJMmKAcXe5WcapmU7HZNkd5NVQQpg1vjQmAumhI+bXu0cU1Gihvd9pP1Te1q3bdwdxN/e7q9buDFd355mOOADy4AlwCexuBcUN0ohBg9JsmLAikbcQzCt23OBUZgBUjux97HNmDU+NGb2pNBR00Y7Uw+pUBOGWpdyS0o3bWgI1i/bml636BN/h68ZDYSB7cBCTKtVrrcKUQCSZMWAZ1u3h2Pm254BlGHm2u6mU8IdU6HKz5sSPvK4Mc5Rk6qcyeWuqujreAstmdZ7apv1px/vDrasrPW3rNjmNwEjMQPI9mBarcuAGpnjKkRhSZIVg0o04pYARwMnY1q5JXbV59hb7WWbXu2MOGlcaOKRI50JE4ariSPL1ZiB1tJtT+u27Qld8/HuoGZlrb/lne1+nTaFPUYACmgC3gRWA1ticc8vZrxCDCWSZMWgFY24YWAScAymlTsOcw23HUiQVVkqY3gp7qkTwuOnjXbGjalUo0eVq1EjytXosrAa1pex5+IH2m9s17t2tuqdtc26btPnQd2HdX5dTZNuASqAgzFzi8EU81gGfABsk+usQhSHJFkxZEQj7ijgKGAGJvFWYZKuxkwLasbeFaizMRWq/OhqZ9QRBzujx1aqkcNLVWVFiaqocKksd1VFWZiKAy2O4fk61eqRaE7qRCKpE43tuqlhj07sbNWJrU1B45qdwW4vIMBcT63AtFYd+6jF1BFej+kGbjqQWIQQvUOSrBiS7G33qoCJwBHATLsE08WauX7ZZpf77GIdUUbJoQc5FZUlqqQsTLgsTNh1VCjk4DgKFVI4XoDf5ulUm4fXktKp5qROJZKkEknt2QSazcHMDa6wy8DGlgS2ABuBDZgu4GaEEP2OJFkhrGjEdYFqzEjlQ4DDMN3Nh2JajxqT5DLXbD37SNll0OmR/Z9LYbpyOz8cwMXMUYWOlrSDGS29C/gE2ITpAq4DGmXAkhADgyRZIfYhq9U7AtOqrACGY0bsjrCPKkyidO0jTEeLWGOSZ7t9JDGt43a7bMImT/s8YR9tkkyFGNgkyQpRQDZBK0BLwhRi6JEkK4QQQhSIU+wAhBBCiMFKkqwQQghRIJJkhRBCiAKRJCuEEEIUiCRZIYQQokAkyQohhBAFIklWCCGEKBBJskIIIUSBSJIVQgghCkSSrBBCCFEgkmSFEEKIApEkK4QQQhSIJFkhhBCiQCTJCiGEEAUiSVYIIYQoEEmyQgghRIFIkhVCCCEKRJKsEEIIUSD/D64PkJWsKfybAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44c1485cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "found = np.mean(statistics_initials_supervision[\"ratio_decreased_cells\"].values)\n",
    "not_found = 100 - found\n",
    "\n",
    "labels = ['Found', 'Overlooked']\n",
    "sizes = [found, not_found]\n",
    "explode = (0.1, 0.1)  \n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_title('Artificially increased grade')\n",
    "#ax1.set_xlabel('Grade with cell count')\n",
    "\n",
    "ax1.pie(sizes, explode=explode, labels=labels, #, colors = ['b', 'g', 'r', 'c', 'm']\n",
    "        shadow=True, startangle=90, autopct=make_autopct(sizes))\n",
    "# Equal aspect ratio ensures that pie is drawn as a circle\n",
    "ax1.axis('equal') \n",
    "\n",
    "plt.tight_layout(pad=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['Acc', 'mAP'] #, 'mAP-IG'\n",
    "statistics_initials_melt = pd.melt(statistics_initials[ columns ])\n",
    "statistics_initials_melt['Type'] = len(columns) * [str(type).split('.')[-1] for type in list(statistics_initials['Type'].values)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Initials</th>\n",
       "      <th>Skill</th>\n",
       "      <th>mAP</th>\n",
       "      <th>mAP-IG</th>\n",
       "      <th>Acc</th>\n",
       "      <th>ratio_decreased_cells</th>\n",
       "      <th>ratio_removed_boxes</th>\n",
       "      <th>seconds</th>\n",
       "      <th>grade</th>\n",
       "      <th>num_cells</th>\n",
       "      <th>num_interactionen</th>\n",
       "      <th>Order</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>JA</td>\n",
       "      <td>1</td>\n",
       "      <td>0.453712</td>\n",
       "      <td>0.781514</td>\n",
       "      <td>0.671362</td>\n",
       "      <td>37.735849</td>\n",
       "      <td>80.0</td>\n",
       "      <td>106.2130</td>\n",
       "      <td>1.207489</td>\n",
       "      <td>254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>0.658129</td>\n",
       "      <td>0.927085</td>\n",
       "      <td>0.825112</td>\n",
       "      <td>55.769231</td>\n",
       "      <td>100.0</td>\n",
       "      <td>93.2930</td>\n",
       "      <td>1.449369</td>\n",
       "      <td>235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JS</td>\n",
       "      <td>1</td>\n",
       "      <td>0.509945</td>\n",
       "      <td>0.800145</td>\n",
       "      <td>0.710900</td>\n",
       "      <td>45.283019</td>\n",
       "      <td>80.0</td>\n",
       "      <td>134.3665</td>\n",
       "      <td>1.594326</td>\n",
       "      <td>243</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SO</td>\n",
       "      <td>1</td>\n",
       "      <td>0.495597</td>\n",
       "      <td>0.784843</td>\n",
       "      <td>0.690566</td>\n",
       "      <td>36.363636</td>\n",
       "      <td>60.0</td>\n",
       "      <td>107.3405</td>\n",
       "      <td>1.563039</td>\n",
       "      <td>284</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SO</td>\n",
       "      <td>1</td>\n",
       "      <td>0.288426</td>\n",
       "      <td>0.474567</td>\n",
       "      <td>0.655814</td>\n",
       "      <td>48.148148</td>\n",
       "      <td>100.0</td>\n",
       "      <td>237.9325</td>\n",
       "      <td>1.382994</td>\n",
       "      <td>449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SC</td>\n",
       "      <td>1</td>\n",
       "      <td>0.486234</td>\n",
       "      <td>0.925993</td>\n",
       "      <td>0.713004</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>71.4495</td>\n",
       "      <td>1.579943</td>\n",
       "      <td>234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>JM</td>\n",
       "      <td>1</td>\n",
       "      <td>0.482279</td>\n",
       "      <td>0.829724</td>\n",
       "      <td>0.606796</td>\n",
       "      <td>40.740741</td>\n",
       "      <td>100.0</td>\n",
       "      <td>76.7405</td>\n",
       "      <td>1.750448</td>\n",
       "      <td>227</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>JM</td>\n",
       "      <td>1</td>\n",
       "      <td>0.493416</td>\n",
       "      <td>0.934039</td>\n",
       "      <td>0.687783</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>53.0520</td>\n",
       "      <td>1.657312</td>\n",
       "      <td>228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>JA</td>\n",
       "      <td>1</td>\n",
       "      <td>0.497024</td>\n",
       "      <td>0.916462</td>\n",
       "      <td>0.718750</td>\n",
       "      <td>29.090909</td>\n",
       "      <td>80.0</td>\n",
       "      <td>91.3600</td>\n",
       "      <td>1.507703</td>\n",
       "      <td>242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SC</td>\n",
       "      <td>1</td>\n",
       "      <td>0.448927</td>\n",
       "      <td>0.726014</td>\n",
       "      <td>0.685083</td>\n",
       "      <td>47.169811</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.7005</td>\n",
       "      <td>1.853050</td>\n",
       "      <td>194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>0.454237</td>\n",
       "      <td>0.791673</td>\n",
       "      <td>0.644330</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>92.4475</td>\n",
       "      <td>1.726394</td>\n",
       "      <td>211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SE</td>\n",
       "      <td>2</td>\n",
       "      <td>0.482341</td>\n",
       "      <td>0.748993</td>\n",
       "      <td>0.724771</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>121.7275</td>\n",
       "      <td>1.399366</td>\n",
       "      <td>279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>PM</td>\n",
       "      <td>2</td>\n",
       "      <td>0.345975</td>\n",
       "      <td>0.588206</td>\n",
       "      <td>0.668478</td>\n",
       "      <td>40.816327</td>\n",
       "      <td>80.0</td>\n",
       "      <td>141.1445</td>\n",
       "      <td>1.731062</td>\n",
       "      <td>254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PM</td>\n",
       "      <td>2</td>\n",
       "      <td>0.516787</td>\n",
       "      <td>0.934243</td>\n",
       "      <td>0.711712</td>\n",
       "      <td>21.818182</td>\n",
       "      <td>60.0</td>\n",
       "      <td>27.4140</td>\n",
       "      <td>1.596753</td>\n",
       "      <td>230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>SG</td>\n",
       "      <td>2</td>\n",
       "      <td>0.472438</td>\n",
       "      <td>0.870832</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>33.962264</td>\n",
       "      <td>80.0</td>\n",
       "      <td>71.9770</td>\n",
       "      <td>1.657725</td>\n",
       "      <td>240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>SG</td>\n",
       "      <td>2</td>\n",
       "      <td>0.446144</td>\n",
       "      <td>0.845195</td>\n",
       "      <td>0.641509</td>\n",
       "      <td>45.098039</td>\n",
       "      <td>60.0</td>\n",
       "      <td>48.9205</td>\n",
       "      <td>1.564547</td>\n",
       "      <td>231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>0.775794</td>\n",
       "      <td>0.956102</td>\n",
       "      <td>0.890351</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.8490</td>\n",
       "      <td>1.322166</td>\n",
       "      <td>237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>KJ</td>\n",
       "      <td>2</td>\n",
       "      <td>0.465913</td>\n",
       "      <td>0.798627</td>\n",
       "      <td>0.687805</td>\n",
       "      <td>30.612245</td>\n",
       "      <td>60.0</td>\n",
       "      <td>53.9700</td>\n",
       "      <td>1.418994</td>\n",
       "      <td>230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SE</td>\n",
       "      <td>2</td>\n",
       "      <td>0.518407</td>\n",
       "      <td>0.921627</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>23.636364</td>\n",
       "      <td>100.0</td>\n",
       "      <td>81.9200</td>\n",
       "      <td>1.532446</td>\n",
       "      <td>242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>HJ</td>\n",
       "      <td>2</td>\n",
       "      <td>0.526252</td>\n",
       "      <td>0.938519</td>\n",
       "      <td>0.725225</td>\n",
       "      <td>23.636364</td>\n",
       "      <td>60.0</td>\n",
       "      <td>65.6290</td>\n",
       "      <td>1.580861</td>\n",
       "      <td>229</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>0.589968</td>\n",
       "      <td>0.909977</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>47.272727</td>\n",
       "      <td>100.0</td>\n",
       "      <td>113.2155</td>\n",
       "      <td>1.448717</td>\n",
       "      <td>238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MH</td>\n",
       "      <td>3</td>\n",
       "      <td>0.562563</td>\n",
       "      <td>0.832154</td>\n",
       "      <td>0.734884</td>\n",
       "      <td>49.056604</td>\n",
       "      <td>100.0</td>\n",
       "      <td>139.0595</td>\n",
       "      <td>1.593008</td>\n",
       "      <td>245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LJ</td>\n",
       "      <td>3</td>\n",
       "      <td>0.498623</td>\n",
       "      <td>0.857983</td>\n",
       "      <td>0.674528</td>\n",
       "      <td>40.740741</td>\n",
       "      <td>80.0</td>\n",
       "      <td>118.7975</td>\n",
       "      <td>1.345829</td>\n",
       "      <td>232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MB</td>\n",
       "      <td>3</td>\n",
       "      <td>0.583423</td>\n",
       "      <td>0.925835</td>\n",
       "      <td>0.776824</td>\n",
       "      <td>32.727273</td>\n",
       "      <td>60.0</td>\n",
       "      <td>79.5860</td>\n",
       "      <td>1.529922</td>\n",
       "      <td>241</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MB</td>\n",
       "      <td>3</td>\n",
       "      <td>0.590276</td>\n",
       "      <td>0.813106</td>\n",
       "      <td>0.796020</td>\n",
       "      <td>56.603774</td>\n",
       "      <td>100.0</td>\n",
       "      <td>93.3775</td>\n",
       "      <td>1.427058</td>\n",
       "      <td>218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DSS</td>\n",
       "      <td>3</td>\n",
       "      <td>0.501903</td>\n",
       "      <td>0.944191</td>\n",
       "      <td>0.709251</td>\n",
       "      <td>27.272727</td>\n",
       "      <td>80.0</td>\n",
       "      <td>24.0370</td>\n",
       "      <td>1.544626</td>\n",
       "      <td>238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LM</td>\n",
       "      <td>3</td>\n",
       "      <td>0.439452</td>\n",
       "      <td>0.878653</td>\n",
       "      <td>0.585714</td>\n",
       "      <td>37.254902</td>\n",
       "      <td>100.0</td>\n",
       "      <td>103.7285</td>\n",
       "      <td>1.816173</td>\n",
       "      <td>224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LM</td>\n",
       "      <td>3</td>\n",
       "      <td>0.468365</td>\n",
       "      <td>0.930199</td>\n",
       "      <td>0.678571</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>80.0</td>\n",
       "      <td>67.7755</td>\n",
       "      <td>1.647687</td>\n",
       "      <td>236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MH</td>\n",
       "      <td>3</td>\n",
       "      <td>0.578463</td>\n",
       "      <td>0.914132</td>\n",
       "      <td>0.726457</td>\n",
       "      <td>28.301887</td>\n",
       "      <td>60.0</td>\n",
       "      <td>119.7415</td>\n",
       "      <td>1.602292</td>\n",
       "      <td>235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DSS</td>\n",
       "      <td>3</td>\n",
       "      <td>0.346627</td>\n",
       "      <td>0.781040</td>\n",
       "      <td>0.530303</td>\n",
       "      <td>41.176471</td>\n",
       "      <td>100.0</td>\n",
       "      <td>76.3840</td>\n",
       "      <td>1.817512</td>\n",
       "      <td>219</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>BT</td>\n",
       "      <td>4</td>\n",
       "      <td>0.497823</td>\n",
       "      <td>0.778254</td>\n",
       "      <td>0.687831</td>\n",
       "      <td>40.425532</td>\n",
       "      <td>40.0</td>\n",
       "      <td>194.3495</td>\n",
       "      <td>1.648567</td>\n",
       "      <td>204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>BT</td>\n",
       "      <td>4</td>\n",
       "      <td>0.489444</td>\n",
       "      <td>0.930311</td>\n",
       "      <td>0.698198</td>\n",
       "      <td>27.272727</td>\n",
       "      <td>60.0</td>\n",
       "      <td>11.0590</td>\n",
       "      <td>1.603669</td>\n",
       "      <td>231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CB</td>\n",
       "      <td>4</td>\n",
       "      <td>0.681731</td>\n",
       "      <td>0.912400</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>56.363636</td>\n",
       "      <td>100.0</td>\n",
       "      <td>79.2870</td>\n",
       "      <td>1.309538</td>\n",
       "      <td>231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RK</td>\n",
       "      <td>4</td>\n",
       "      <td>0.543241</td>\n",
       "      <td>0.938519</td>\n",
       "      <td>0.747748</td>\n",
       "      <td>38.181818</td>\n",
       "      <td>60.0</td>\n",
       "      <td>53.4530</td>\n",
       "      <td>1.539760</td>\n",
       "      <td>229</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MS</td>\n",
       "      <td>4</td>\n",
       "      <td>0.341398</td>\n",
       "      <td>0.699028</td>\n",
       "      <td>0.615854</td>\n",
       "      <td>37.777778</td>\n",
       "      <td>80.0</td>\n",
       "      <td>48.0025</td>\n",
       "      <td>1.712215</td>\n",
       "      <td>175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MS</td>\n",
       "      <td>4</td>\n",
       "      <td>0.595748</td>\n",
       "      <td>0.904853</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>26.923077</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60.0140</td>\n",
       "      <td>1.444311</td>\n",
       "      <td>233</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RK</td>\n",
       "      <td>4</td>\n",
       "      <td>0.496651</td>\n",
       "      <td>0.829570</td>\n",
       "      <td>0.683417</td>\n",
       "      <td>47.169811</td>\n",
       "      <td>80.0</td>\n",
       "      <td>66.7320</td>\n",
       "      <td>1.656151</td>\n",
       "      <td>211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CB</td>\n",
       "      <td>4</td>\n",
       "      <td>0.664171</td>\n",
       "      <td>0.934547</td>\n",
       "      <td>0.840708</td>\n",
       "      <td>54.545455</td>\n",
       "      <td>80.0</td>\n",
       "      <td>138.3255</td>\n",
       "      <td>1.337494</td>\n",
       "      <td>237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.AS</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PA</td>\n",
       "      <td>4</td>\n",
       "      <td>0.497812</td>\n",
       "      <td>0.855991</td>\n",
       "      <td>0.623318</td>\n",
       "      <td>41.176471</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120.5505</td>\n",
       "      <td>1.733252</td>\n",
       "      <td>242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.Annotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PA</td>\n",
       "      <td>4</td>\n",
       "      <td>0.577649</td>\n",
       "      <td>0.917297</td>\n",
       "      <td>0.743243</td>\n",
       "      <td>32.727273</td>\n",
       "      <td>60.0</td>\n",
       "      <td>117.2520</td>\n",
       "      <td>1.542089</td>\n",
       "      <td>232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LabelOrder.SA</td>\n",
       "      <td>ProjectType.CrowdAlgorithm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Initials Skill       mAP    mAP-IG       Acc  ratio_decreased_cells  \\\n",
       "23       JA     1  0.453712  0.781514  0.671362              37.735849   \n",
       "4        JS     1  0.658129  0.927085  0.825112              55.769231   \n",
       "5        JS     1  0.509945  0.800145  0.710900              45.283019   \n",
       "24       SO     1  0.495597  0.784843  0.690566              36.363636   \n",
       "25       SO     1  0.288426  0.474567  0.655814              48.148148   \n",
       "28       SC     1  0.486234  0.925993  0.713004              20.000000   \n",
       "33       JM     1  0.482279  0.829724  0.606796              40.740741   \n",
       "32       JM     1  0.493416  0.934039  0.687783              20.000000   \n",
       "22       JA     1  0.497024  0.916462  0.718750              29.090909   \n",
       "29       SC     1  0.448927  0.726014  0.685083              47.169811   \n",
       "41       HJ     2  0.454237  0.791673  0.644330              44.000000   \n",
       "15       SE     2  0.482341  0.748993  0.724771              38.000000   \n",
       "31       PM     2  0.345975  0.588206  0.668478              40.816327   \n",
       "30       PM     2  0.516787  0.934243  0.711712              21.818182   \n",
       "36       SG     2  0.472438  0.870832  0.666667              33.962264   \n",
       "37       SG     2  0.446144  0.845195  0.641509              45.098039   \n",
       "38       KJ     2  0.775794  0.956102  0.890351              60.000000   \n",
       "39       KJ     2  0.465913  0.798627  0.687805              30.612245   \n",
       "14       SE     2  0.518407  0.921627  0.733333              23.636364   \n",
       "40       HJ     2  0.526252  0.938519  0.725225              23.636364   \n",
       "0        LJ     3  0.589968  0.909977  0.750000              47.272727   \n",
       "21       MH     3  0.562563  0.832154  0.734884              49.056604   \n",
       "1        LJ     3  0.498623  0.857983  0.674528              40.740741   \n",
       "6        MB     3  0.583423  0.925835  0.776824              32.727273   \n",
       "7        MB     3  0.590276  0.813106  0.796020              56.603774   \n",
       "8       DSS     3  0.501903  0.944191  0.709251              27.272727   \n",
       "13       LM     3  0.439452  0.878653  0.585714              37.254902   \n",
       "12       LM     3  0.468365  0.930199  0.678571              18.181818   \n",
       "20       MH     3  0.578463  0.914132  0.726457              28.301887   \n",
       "9       DSS     3  0.346627  0.781040  0.530303              41.176471   \n",
       "35       BT     4  0.497823  0.778254  0.687831              40.425532   \n",
       "34       BT     4  0.489444  0.930311  0.698198              27.272727   \n",
       "19       CB     4  0.681731  0.912400  0.863636              56.363636   \n",
       "16       RK     4  0.543241  0.938519  0.747748              38.181818   \n",
       "27       MS     4  0.341398  0.699028  0.615854              37.777778   \n",
       "26       MS     4  0.595748  0.904853  0.772727              26.923077   \n",
       "17       RK     4  0.496651  0.829570  0.683417              47.169811   \n",
       "18       CB     4  0.664171  0.934547  0.840708              54.545455   \n",
       "11       PA     4  0.497812  0.855991  0.623318              41.176471   \n",
       "10       PA     4  0.577649  0.917297  0.743243              32.727273   \n",
       "\n",
       "    ratio_removed_boxes   seconds     grade  num_cells  num_interactionen  \\\n",
       "23                 80.0  106.2130  1.207489        254                NaN   \n",
       "4                 100.0   93.2930  1.449369        235                NaN   \n",
       "5                  80.0  134.3665  1.594326        243                NaN   \n",
       "24                 60.0  107.3405  1.563039        284                NaN   \n",
       "25                100.0  237.9325  1.382994        449                NaN   \n",
       "28                 60.0   71.4495  1.579943        234                NaN   \n",
       "33                100.0   76.7405  1.750448        227                NaN   \n",
       "32                 60.0   53.0520  1.657312        228                NaN   \n",
       "22                 80.0   91.3600  1.507703        242                NaN   \n",
       "29                 60.0   70.7005  1.853050        194                NaN   \n",
       "41                 80.0   92.4475  1.726394        211                NaN   \n",
       "15                100.0  121.7275  1.399366        279                NaN   \n",
       "31                 80.0  141.1445  1.731062        254                NaN   \n",
       "30                 60.0   27.4140  1.596753        230                NaN   \n",
       "36                 80.0   71.9770  1.657725        240                NaN   \n",
       "37                 60.0   48.9205  1.564547        231                NaN   \n",
       "38                100.0   38.8490  1.322166        237                NaN   \n",
       "39                 60.0   53.9700  1.418994        230                NaN   \n",
       "14                100.0   81.9200  1.532446        242                NaN   \n",
       "40                 60.0   65.6290  1.580861        229                NaN   \n",
       "0                 100.0  113.2155  1.448717        238                NaN   \n",
       "21                100.0  139.0595  1.593008        245                NaN   \n",
       "1                  80.0  118.7975  1.345829        232                NaN   \n",
       "6                  60.0   79.5860  1.529922        241                NaN   \n",
       "7                 100.0   93.3775  1.427058        218                NaN   \n",
       "8                  80.0   24.0370  1.544626        238                NaN   \n",
       "13                100.0  103.7285  1.816173        224                NaN   \n",
       "12                 80.0   67.7755  1.647687        236                NaN   \n",
       "20                 60.0  119.7415  1.602292        235                NaN   \n",
       "9                 100.0   76.3840  1.817512        219                NaN   \n",
       "35                 40.0  194.3495  1.648567        204                NaN   \n",
       "34                 60.0   11.0590  1.603669        231                NaN   \n",
       "19                100.0   79.2870  1.309538        231                NaN   \n",
       "16                 60.0   53.4530  1.539760        229                NaN   \n",
       "27                 80.0   48.0025  1.712215        175                NaN   \n",
       "26                 80.0   60.0140  1.444311        233                NaN   \n",
       "17                 80.0   66.7320  1.656151        211                NaN   \n",
       "18                 80.0  138.3255  1.337494        237                NaN   \n",
       "11                100.0  120.5505  1.733252        242                NaN   \n",
       "10                 60.0  117.2520  1.542089        232                NaN   \n",
       "\n",
       "            Order                        Type  \n",
       "23  LabelOrder.SA      ProjectType.Annotation  \n",
       "4   LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "5   LabelOrder.AS      ProjectType.Annotation  \n",
       "24  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "25  LabelOrder.AS      ProjectType.Annotation  \n",
       "28  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "33  LabelOrder.AS      ProjectType.Annotation  \n",
       "32  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "22  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "29  LabelOrder.SA      ProjectType.Annotation  \n",
       "41  LabelOrder.SA      ProjectType.Annotation  \n",
       "15  LabelOrder.SA      ProjectType.Annotation  \n",
       "31  LabelOrder.AS      ProjectType.Annotation  \n",
       "30  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "36  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "37  LabelOrder.SA      ProjectType.Annotation  \n",
       "38  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "39  LabelOrder.AS      ProjectType.Annotation  \n",
       "14  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "40  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "0   LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "21  LabelOrder.AS      ProjectType.Annotation  \n",
       "1   LabelOrder.AS      ProjectType.Annotation  \n",
       "6   LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "7   LabelOrder.SA      ProjectType.Annotation  \n",
       "8   LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "13  LabelOrder.SA      ProjectType.Annotation  \n",
       "12  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "20  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "9   LabelOrder.AS      ProjectType.Annotation  \n",
       "35  LabelOrder.AS      ProjectType.Annotation  \n",
       "34  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "19  LabelOrder.AS      ProjectType.Annotation  \n",
       "16  LabelOrder.SA  ProjectType.CrowdAlgorithm  \n",
       "27  LabelOrder.AS      ProjectType.Annotation  \n",
       "26  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "17  LabelOrder.SA      ProjectType.Annotation  \n",
       "18  LabelOrder.AS  ProjectType.CrowdAlgorithm  \n",
       "11  LabelOrder.SA      ProjectType.Annotation  \n",
       "10  LabelOrder.SA  ProjectType.CrowdAlgorithm  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics_initials.sort_values(by=['Skill'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44c175db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "ax = sns.boxplot(x=\"variable\", y=\"value\", data=statistics_initials_melt,\n",
    "                 showfliers=False, hue='Type', whis=[0, 100], dodge=True) # width=.3,  \n",
    "ax.set(ylim=(0.2, 0.95))\n",
    "\n",
    "\n",
    "for i,box in enumerate(ax.artists):\n",
    "    box.set_edgecolor('black')\n",
    "    #box.set_facecolor('white')\n",
    "\n",
    "for index, row in enumerate(statistics_initials.sort_values(by=['Skill']).values.tolist()):\n",
    "    name = row[1]\n",
    "    \n",
    "    mAP = row[2]\n",
    "    mAP_IG = row[3]\n",
    "    acc = row[4]\n",
    "    type = str(row[-1])\n",
    "\n",
    "    type_offset = 0.2\n",
    "    if \"CrowdAlgorithm\" in type:\n",
    "        type_offset = type_offset * -1\n",
    "        \n",
    "    plt.scatter(x=0 + offset[name] + type_offset, y=acc, marker=symbols[name], s=150, c=color[name], zorder=100)    \n",
    "    plt.scatter(x=1 + offset[name] + type_offset, y=mAP, marker=symbols[name], label=name, s=150, c=color[name], zorder=100)\n",
    "    #plt.scatter(x=2 + offset[name] + type_offset, y=mAP_IG, marker=symbols[name], s=150, c=color[name], zorder=100)\n",
    "\n",
    "for index, row in enumerate(dl_statistics_initials.values.tolist()):\n",
    "    name = row[1]\n",
    "    \n",
    "    mAP = row[2]\n",
    "    mAP_IG = row[3]\n",
    "    acc = row[4]\n",
    "    type = str(row[-1])\n",
    "\n",
    "    type_offset = 0.2\n",
    "    if \"CrowdAlgorithm\" in type:\n",
    "        type_offset = type_offset * -1\n",
    "        \n",
    "    plt.scatter(x=0 + offset[name] + type_offset, y=acc, marker=symbols[name], s=150, c=color[name], zorder=100)    \n",
    "    plt.scatter(x=1 + offset[name] + type_offset, y=mAP, marker=symbols[name], label=name, s=150, c=color[name], zorder=100)\n",
    "    \n",
    "\n",
    "#ax.xaxis.grid(True)\n",
    "#ax.yaxis.grid(True)\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "by_label = OrderedDict(zip(labels, handles))\n",
    "\n",
    "ax.legend(by_label.values(), by_label.keys(), bbox_to_anchor=(1., 0.9), prop={'size': 25}, frameon=False)\n",
    "\n",
    "ax.set(ylabel=\"Score\", xlabel=\"\", title=\"Crowd-Algorithm versus Annotation mode\")\n",
    "sns.despine(left=True)\n",
    "\n",
    "plt.savefig(\"SupervisionAccuracy.pdf\", transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['seconds']\n",
    "statistics_initials_melt = pd.melt(statistics_initials[ columns ])\n",
    "statistics_initials_melt['Type'] = len(columns) * [str(type).split('.')[-1] for type in list(statistics_initials['Type'].values)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "offset = {1: 0.05, \n",
    "           2: 0.025,\n",
    "           3: -0.025,\n",
    "           4: -0.05,  \n",
    "          \"DL\": 0\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44c16a6240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "ax = sns.boxplot(x=\"variable\", y=\"value\", data=statistics_initials_melt,\n",
    "                 showfliers=False, hue='Type', whis=[0, 100],  width=.3, dodge=True) # width=.3,  \n",
    "#ax.set(ylim=(0.55, 1))\n",
    "\n",
    "\n",
    "for i,box in enumerate(ax.artists):\n",
    "    box.set_edgecolor('black')\n",
    "    #box.set_facecolor('white')\n",
    "\n",
    "for index, row in enumerate(statistics_initials.sort_values(by=['Skill']).values.tolist()):\n",
    "    name = row[1]\n",
    "    \n",
    "    type = str(row[-1])\n",
    "    seconds = row[7]\n",
    "\n",
    "    type_offset = 0.075\n",
    "    if \"CrowdAlgorithm\" in type:\n",
    "        type_offset = type_offset * -1\n",
    "        \n",
    "    plt.scatter(x=0 + offset[name] + type_offset, y=seconds, marker=symbols[name], label=name, s=150, c=color[name], zorder=100)    \n",
    "\n",
    "for index, row in enumerate(dl_statistics_initials.values.tolist()):\n",
    "    name = row[1]\n",
    "    \n",
    "    type = str(row[-1])\n",
    "    seconds = row[7]\n",
    "\n",
    "    type_offset = 0.075\n",
    "    if \"CrowdAlgorithm\" in type:\n",
    "        type_offset = type_offset * -1\n",
    "        \n",
    "    plt.scatter(x=0 + offset[name] + type_offset, y=seconds, marker=symbols[name], label=name, s=150, c=color[name], zorder=100)    \n",
    "\n",
    "    \n",
    "    \n",
    "#ax.xaxis.grid(True)\n",
    "#ax.yaxis.grid(True)\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "by_label = OrderedDict(zip(labels, handles))\n",
    "\n",
    "ax.legend(by_label.values(), by_label.keys(), prop={'size': 25}, frameon=False) #, bbox_to_anchor=(1., 0.9)\n",
    "\n",
    "ax.set(ylabel=\"Mean seconds per image\", xlabel=\"\", title=\"CrowdAlgorithm versus Annotation mode\")\n",
    "sns.despine(left=True)\n",
    "\n",
    "plt.savefig(\"AnnotationTime.pdf\", transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['grade']\n",
    "statistics_initials_melt = pd.melt(statistics_initials[ columns ])\n",
    "statistics_initials_melt['Type'] = len(columns) * [str(type).split('.')[-1] for type in list(statistics_initials['Type'].values)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44bbb929b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "ax = sns.boxplot(x=\"variable\", y=\"value\", data=statistics_initials_melt,\n",
    "                 showfliers=False, hue='Type', whis=[0, 100],  width=.3, dodge=True) # width=.3,  \n",
    "#ax.set(ylim=(0.55, 1))\n",
    "\n",
    "\n",
    "for i,box in enumerate(ax.artists):\n",
    "    box.set_edgecolor('black')\n",
    "    #box.set_facecolor('white')\n",
    "\n",
    "for index, row in enumerate(statistics_initials.values.tolist()):\n",
    "    name = row[1]\n",
    "    \n",
    "    type = str(row[-1])\n",
    "    seconds = row[8]\n",
    "\n",
    "    type_offset = 0.075\n",
    "    if \"CrowdAlgorithm\" in type:\n",
    "        type_offset = type_offset * -1\n",
    "        \n",
    "    plt.scatter(x=0 + offset[name] + type_offset, y=seconds, marker=symbols[name], label=name, s=150, c=color[name], zorder=100)    \n",
    "\n",
    "\n",
    "for index, row in enumerate(dl_statistics_initials.values.tolist()):\n",
    "    name = row[1]\n",
    "    \n",
    "    type = str(row[-1])\n",
    "    seconds = row[8]\n",
    "\n",
    "    type_offset = 0.075\n",
    "    if \"CrowdAlgorithm\" in type:\n",
    "        type_offset = type_offset * -1\n",
    "        \n",
    "    plt.scatter(x=0 + offset[name] + type_offset, y=seconds, marker=symbols[name], label=name, s=150, c=color[name], zorder=100)    \n",
    "\n",
    "    \n",
    "#ax.xaxis.grid(True)\n",
    "#ax.yaxis.grid(True)\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "by_label = OrderedDict(zip(labels, handles))\n",
    "\n",
    "ax.legend(by_label.values(), by_label.keys(), prop={'size': 25}, frameon=False) #, bbox_to_anchor=(1., 0.9)\n",
    "\n",
    "ax.set(ylabel=\"Score\", xlabel=\"\", title=\"Supervision versus Annotation mode\")\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 920,  167,   16,    2,    1],\n",
       "       [  91, 1302,  308,   24,    3],\n",
       "       [   1,   55,  731,  284,    4],\n",
       "       [   0,    0,  103,  295,  117],\n",
       "       [   0,    0,    0,   38,  280]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_annotation = statistics.calc_confusion_matrix(ProjectType.Annotation)\n",
    "cm_supervision = statistics.calc_confusion_matrix(ProjectType.CrowdAlgorithm)\n",
    "\n",
    "cm_supervision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44bbb0ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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z7CFW4zPX8q0m9LTItwfZzs9au0VONzIpf6AI1H3KfZ1HbkBZba1dG+T53fMYjAxWSWNMbYW/DsMdHMZZa8eGu8mZX2O/b5tLitjFyW2ya3lEiPqXkzPHQiiTXMs3hRlWdrTy/u8E/L15wN21pqhdfuBMJjjHd/tJRZ+7w839Ox4drJBvmOtRQbaTnHl7cls1h/mGuw5mVIh1khO6//Ytn2SMCRmWARQvggeAXEMl5XalOEbSbGNMr1AbGGO6G2OmypkL4lAvvPxaeScEvSWNM8YU+htljBkjZ3hPyRnh6b2CZVxm+H42UuALy1Xgsa7K634xI0C5XG8pb8Sjy40xwwLUM0HShwrx7auve8mZvrt7JX0e4jn9rLXb5czeLDnzsJwVonggkyWt9i2fZoy5vWAB38ngSwrTTcZau0jOzOWS08XlzUBByBgzRHmTBqb49n04rHYtdzlMz1nqWWt/t9b28t36HcDcHW4TJW31LQ82xgwvWMB3nL0oZ2Z0yRlcId+Qub4Rr3KvL6kp6T33vDuufd0o6cJQFfK1xNzpeugjY0zIOXGMMW2NMeHmswFQBF6MXgOgFLLWbjfGnCjpS0mt5FxY+qsxZo6ceTXWyOm3XV3O9RWnKe8i7OJ4/hzfyenPci5yHyXpeGPMe3ICUW05JxW5F4jvlzNLdkaI3U5X3jf2Ma7HCpoh55oD92zggcrl1nW3MeYGSR/J6b7zpjHmfElfyLkGpLWkK+UEnv9T8C4dl8q5tkSSJllrizr6j+QMuZsbOC7XAbQgWGv3G2OuljN3QrSkx4wxp8vpwrXNV+8hco6BTyWd79s02IhL10iaJylR0sWSuhhj3pIz9GlVSWcrL2BJ0vW+kccOh7/knPzWknSpMWabnPk5ck+k0621M4NtjINnrU0xxlwu53MRLek5Y8y5co6pgseZ5ATSy4Ls7nY5f3MayBlla7Ex5g05x1g1OZMOniwnaO5RiEEyrLVfGmMekHSvb9tvjTE/yQnQayVlyfk7117ONV9HyZnYMVzrH4AwCB4A/Ky1y4wxPSU9IufEuZycybwCTdKXK0nSg8o/o/LBPv98X/j5TM5Y/l0U+FvqnZIGW2tnhNllwfWrfJPYFXzeHcaYv5R3AiSFCB6+bT42xtSUc31MjJwT6zMLFPtMzrerwYKHu5tVkWY4d/lSzglWFTmtFnUP5GTeWvujL+i9IWc+iON8N7dZkq5TXvAIOAqVtXaDMaaPnBPM1nKC68MBiqbJCR2hWqmKlbU2yxhzj5zZwGMl3VagyFrln9sBxchaO8UYc56cVsIqcro89g9QdJ2kc621SwOsk7V2p+9vw3dyfl/NJD1UoNgGOZ+15xSGtfY+Y8x6SU/JaZXsq/yTCxZ0IBfWAwiCrlYA8rHW7rbW/ldOq8Ytkr6SMyFXspxvAndImi9pvKQBkhpaa1+y1mYV0/P/Kqd7z0g5F4Juk9O6sUPSr5LuktTcWvtd0J3k7Wub8rpvSaHDhHvdyqKM4GOtfUlOMJoop9vXPjkzS/8gJxidr7xrMfIxxnRVXotRkoo+w3nuc2co7yLzaAX/pjjUPj6Q1EHSK3K+Kc6U04VstqRr5VwY7/6CameIfS2T83pyW1KS5Lz2XXJaQx6R1NJaeyBDshYLa+14Od+Wfy7nBLIo842gmFhrJ8v5e3KvnGtGdsg5NrbKmTNkhKQ24WY39x1jHeT8DVgg5/qdFEmL5Xz50dlau/AA6jVBUmM5ratT5Yyelem7JckJ3uPkfA7Cje4GoAiM090RAIDCjDFnyWnJkKSR1tpnIlkfAEDpRYsHACCUG13LMyJVCQBA6UfwAIAjlDGm4DUd7nVRxpjHlDe/wu/W2vmHp2YAgLKIrlYAcIQyxmTJubbjWzmjP+2Uc6F5W0kXKG8o3X2SehE8AACHguABAEcoX/CIDlNsp6QLC86tAADAgWI4XQA4cp0gZ06E/nKGL64hZwjlnZL+kdMS8qq1NjlSFQQAlB1HTItHTIUqNrZq7UhXA6Vci1oJka4CyoDYaC6vw6Ezka4AAPjMm/fndmttYrhyR0yLR2zV2mpx5UuRrgZKucnDe0e6CigDaleJi3QVUAZERxE9AJQM8bFmbVHK8bUbAAAAAM8RPAAAAAB4juABAAAAwHMEDwAAAACeI3gAAAAA8BzBAwAAAIDnCB4AAAAAPEfwAAAAAOA5ggcAAAAAzxE8AAAAAHiO4AEAAADAcwQPAAAAAJ4jeAAAAADwHMEDAAAAgOcIHgAAAAA8R/AAAAAA4DmCBwAAAADPETwAAAAAeI7gAQAAAMBzBA8AAAAAniN4AAAAAPAcwQMAAACA5wgeAAAAADxH8AAAAADgOYIHAAAAAM8RPAAAAAB4juABAAAAwHMEDwAAAACeI3gAAAAA8BzBAwAAAIDnCB4AAAAAPEfwAAAAAOA5ggcAAAAAzxE8AAAAAHiO4AEAAADAcwQPAAAAAJ4jeAAAAADwHMEDAAAAgOcIHgAAAAA8R/AAAAAA4DmCBwAAAADPETwAAAAAeI7gAQAAAMBzBA8AAAAAniN4AAAAAPAcwQMAAACA5wgeAAAAADxH8AAAAADgOYIHAAAAAM8RPAAAAAB4juABAAAAwHMEDwAAAACeI3gAAAAA8BzBAwAAAIDnCB4AAAAAPEfwAAAAAOA5ggcAAAAAzxE8AAAAAHiO4AEAAADAcwQPAAAAAJ6LiXQFcGg6Naqiwb0aqnOjqqpesZxSM7O0fEuqvpi/WV8s2BxwmyrxsTq+TU31bF5dbetWUt2q8YqJMtq1d5/+3pSsyfM3a9qSbWGfu0K5aA3t3Vgnt6+l+lXjlG2ltdv36pu/tuj9OeuVlW2L++WimKWnpWnOrz9p8cL5+nvRAi1eNF+bNqyXJA2/dYxG3HZ3kfazbUuS3nnzVc344TttWLdWGRnpqlEzUc1btlav3v105fUjFBsb6y+/Yd1aHdetbZHred7Fl+qJ58cf2ItDiZGWlqbZs2Zq/vw/tWD+fC2Y/6fWr1snSbrz7nt11z1ji7SfLUlJevWV/2nqt99o7ZrVSk9PV2KtWmrduq36Hnecht88Kt9xhiPLjh079PWXX2j6tB+1YME8rVu7VllZWaqZmKguXbvp0iFDNeCccyNdTZRwaWlp+mnWTM2f96fmz5+n+fPy/l7ddc99uvvesZGtYClH8CjFbjqxua7p39R/Pzl9vxLKx6hns+rq2ay6Tj+qtoa/v1D7CwSA6bf3VWx0XmNXxv5sZWXnqHaVONWuEqcT2tbST8u2a+SHi5SxPyfgc9etEqc3ruyqBtXiJUlp+7JVLkrq0KCKOjSoojM71tHVb85TckaWB68cxWXh/Lm6ctCh/SP+6vNPddeoG5WakixJKleunMrHxWvThvXatGG9fpr+gwYPvUqxVar6t4mKjlbNxFoh95uZmamU5D2SpKM7dT2kOiKy5v7xuwYOOPOQ9vHpJx9p+H+vVXJy3nEWHx+v9evWaf26dfrh++905dXXqWrVqmH2hLKqSYM6ysrK+58TFxen2NhYbdq4UZs2btRXX0zWqaedrvc/+lQVKlSIYE1Rks3943edc9YZka5GmUXwKKUGdq3nDx1TFiXp6e+Wa0typmKijU5tX1t3n9VGfVrV1JgzW+v+L5bm2zY2OkqL1u/R5Pmb9MuKndqwK12SVK9qnK45rqnO61ZffVvV1L1nt9WYz/4u9NxRRnrx0o5qUC1eW5Mzdddnf+u3VTtljHRK+9oaO6Ct2tWrrMcu6KD/vrPA+zcDh6RK1Wpqf1QntT/auT18z23atnVLkbad8sUk3XLdMOXk5OjMc87XdTeNUrujOkqS9qamasniRfru68mKicn/LXS9+g005+81Ifc99s6Reuf1VxQXH6+zz7vooF4bSo5q1aqpY6cu6tS5szp27qI7Ro/UlqSkIm076bNPdMVllygnJ0fnXXCRRo2+XUd37CRJSk1N1aKFC/TF55No7TjCZWVlqVv3Hhpy2TCdfMqpatqsmSRp7Zo1euyRhzTxzdf13bff6Mbrr9Ubb70T4dqiJKtWrZo6de7iv91+6y1KKuLfK4RW4oOHMaaSpFGSzpPUVFK2pGWSPpT0grV2XwSrFxFRRrrxxOaSpH82Juv2Txb712VlW329KEkx0UYPDWyvc7vW1/tz1mv5lr3+Mle88af+WL2r0H437c7Q2MlLlJ1jdWGPBjqrU1099/0KbUnOzFduQOd6alWnkiRp5IeLtHC98620tdJ3i7coykhPXHiU+raqqZ7NqmnOqsLPhZKhe6/emrdsY77Hxj10T5G23bpls+6+9Sbl5OToimtv0l0PPp5vfcWEBHXrday69Tr2gOuVmZGhLz77SJJ02pnnqHIVvsUuzXr36av1STvyPXbf3XcWadukzZs14obrlJOToxuH36zHxj2db31CQoKO7d1Hx/buU2z1Ren07ffTdFz/4ws93rhJE708foJiYmI04bVX9cH77+r+hx5Rw4YNI1BLlHS9+/TVpq078z12z113RKg2ZU+JvrjcGNNY0iJJ90nqIMlIKi+pm6QnJf1mjKkWuRpGRvv6lZVYqbwk6a1f1gYsM3n+Zm1PyVR0lNHZnerlWxcodLhNmrcp33MVNKBzXUnSnFU7/aHD7Zu/tmjDzjRJ0lmd6oZ8LkRWdHT0QW/71msva8/uXapTr75G3/NgMdZK+u7rydqz2zlOL7x0WLHuG4ffoRxnL//vee3atUv1GzTQAw8/Voy1QlkTKHS4Db38Sv/yvD/nel0dlFKH8vcK4ZXY4GGMiZb0paQmkjZLOtlaW1FSBUkXS0qR1FnSe5GqY6TUqxrnX165dW/Qcmu2Oyf/vVvWOKD978vK9i9HR5l86+Jio9SpkfPt8+xl+b/BdJu93Fl3bIsDe26UHv/3sfPRO+f8QSpXrlyx7vvj99+SJDVp1kI9j+1brPtG6fL+e06XmIsHXVLsxxmOLHFxef87s7OzQ5QE4JUSGzwkDZN0lG/5PGvtD5Jkrc2x1n4k6VrfutONMSdGoH4lQsFg4BblW9ekZgXFRAcvV1D3JtX9y8u3pOZb1yyxov85V2zNv85thS8QJVYqr8rxJb5HHw7Q+rVrtCXJGTWtx7F99PdfC3TT1UPUs30TtW1QVb07tdTway7TvD/mHPC+161Zrd9mz5QkXTh4aLHWG6XLmtWrtXmT0wLbp+9xWrhgvi675GI1a1RX1SvFqXXzRhp66SDN+e3XCNcUpcGsmTP8yx06HBW8IADPlOTgkXvGMd1aG+i/yoeSVvuWLzs8VSoZNu7K8C+3qJUQsEx0lFGTms6oHbHRUapeoWjfFFaKi9GV/ZpIkv5cs8vfapIrt4uXJG0tcO2Hm3tdLdc2KBtWr1zuX140b67OO+04TZn8mVJSklU+Ll5Jmzbq688/1YX/OUEvPzfugPb9yQdvyVqrmJgYDbzokuKuOkqRFcuX+Zfnzv1d/fv00qRPP1bynj2Kj4/Xxg0b9NknH+mk/n305BOPRrCmKOl2796tcb5jpHefvmrVunWEawQcmUpk8DDGVJDU23f3m0BlrLVW0re+u6ccjnqVFP9sSta2FOfE/oq+jQO2elzQrb6qV8wLGxXjwvdZNEZ65Lz2qlW5vDL3Z+uRr/8tVKZi+bz9pO8P3lSd4VpXsTwtHmXNnj27/cvPP/mIaiTW0psfTtbiNdu1YMVmTf15vo7te7ystXry4fs0dcoXRdpvdna2Jn34riSp/0mnKbF2HU/qj9Jh1+6869EefegB1apdW59/+Y227krVxq279OfCf9T/+BNlrdXYe+7Sl5M/j2BtUVLl5OToymFDlLR5s8qXL6+nn30h0lUCjlglMnhIaqu8ui0OUS53XR1jTPUQ5cqUHCu9PG2VJKl5rQT979JOalevkmKijWpULKchxzTSrae11P6svDk4bBHm8rvjjNbq3yZRkvTQV/9qWVLwrlQ4stmcvGMrJydHL054T/1OOFlRUc7HtnnL1nr17Y9Vu44zuMDz4x4u0n5nTZuqpM1O15qLuKj8iFfwOHvn/Y910imn+o+z1m3a6KPPPlfdes4AGo88dH9E6omSbdQtIzTl668kSc+98JKO7tgxwjUCjlxqgc1sAAAgAElEQVQlNXi4h2HaGLRU/nX1gpYqgz6Zu1ETZjk9zXq3rKGPru+p+WNP1Iw7+um2M1opJSNLr81a4y+fnL4/5P5GndpSg3s5Qws+PuVffe4a2cptb2ZeS0Z8bPBWlDjXur2ZTCJY1lRMyOvi163nsercrUehMhUqVtQll18jSVry91/aXoS5QT56d6IkqU7dejruxFOLp7IotRISKvmXj+ndRz169ipUpmLFirr62uslSX8tWqgtW4o2Bw2ODHfcdqteeelFSdITTz6joZdfEeEaAUe2kho8KrmW04KWyr+uUsGVxphrjDFzjTFzs/cWHva1tHvu+5Ua/Orv+mzuRi1LStHm3Rn6e2Oyxs9crXNf/E37sp1vC/ek7dfOvcGDxy2ntNCwPo0lSU9+u0zv/ro+aNncLl6SVKty8Gs33Ou2pgS/FgSlU+26eTm/eavgfaVbtMxbt3HDupD73L5tq2b84PSeHHjxpQxpCNWrX9+/3Lp1m6DlWrdp619evy7wEOM48oy54zY998xTkqRHHh+nm0bcHOEaASjTne+tteMljZek+HqtitDZqPT5a0Oy/tqQHHBdt8bOsLeB5trINfLUFrq8TxNJ0lPfLtdbP4c+OVy1ba+yc6yio4xa1ErwD5tbUItaFSU5QSU5nRaPsqZlq7aKjo5Wdna2jIKPmObu4mdM6JHVJn30nvbv3y9jjC4YxGhWkNq0bZd3nIU4fqzrQAt3nOHIcOfto/Xs009Kkh5+7AndMvLWCNcIgFRyWzxSXMsVQpRzr0sJWuoIVKdKefVq7lz28sWCwN2mRp3aMl/omPhz+G8KM/bnaME658LiUPOD5K77ZUXwuT5QepWPi1P3Y5yZolcsWxq03IrlzjpjjOo3bBxyn5/45u7o1ec4NWrStJhqitIsLi5Ovfv2kyQtXbokaLl/feuMMWrUuMnhqBpKsDtuuzVf6Bg5anSEawQgV0kNHu4z5fpBS+VfF/js+ggUE2V034C2iomO0vItqfrxn22Fyow6taW/e9W4b5YVKXTkmjzfN39D02o6qkHhmc1P7VBLDas7mfDLBZsP5iWgFDj/4iGSpLlzfgk4X0d6Wpren/iaJKlTl+6qUTMx6L7m/vaLVq1whk698JJhxV9ZlFpDLhsmSfr159kB5+tIS0vThPGvSJK69+ipxMTgxxnKvjtuu9XfverRJ54kdAAlTEkNHksk5Q5n0iFEudx1Sdband5WqWRpUC1eN53YXG3rVlK5GOfXGGWkbk2qasLlXdSnZU3tzczSmM/+VlZO/l5m7ms6npiyTG//Erp7VUFfLNisZUkpiooyeubio9WzWTVJznC8p7SvpfsGtJMk/bRsu+as2hVqVygB9uzepZ07tvtvOb6RhNLT0/M9vjc1/yhnA86/WB27dJMkDb9miGZN+96/7crl/+qaIRdoS9JmRUVFaeSYsSHr8NF7EyVJVatV16lnDijW14eSYdeuXdq+fbv/5j/O0tLyPZ5a4Di7aNAl6tbdGbxg6KUX64ep3/m3/XfpUl04cIA2b9qkqKgo3Xv/Q4f3RaFEuevO2/2h4/FxT+vmW0ZFuEYorYL9vUoL8/cK4RlblHFWI8AYM0tSX0nTrLWFZiY3TkfeFZKaSXrbWhuyU3h8vVa2xZUveVLXSGhdJ0Gf3uCM8JKTY5WSkaUK5aMVG+2EkC3JGRr14V+Fru+oU6W8vr+1ryQpO8dq1959IZ9n4s9rA173Ua9qnF6/oqsaVIuXJKXty1aUyRvN6p9Nybr6zXlKzihb13dMHt47fKFSpl/XNtq4Pnz4HHjRpRr3wvh8j23bkqQh55+p5f86XV3i4uMVG1tOKcnOcRcbG6uxjz2ji4cEH0kmNTVFvTo0VXpamoZe/V/d+/CTh/BqSofaVeIiXYXDrl2rplq3NnzL6iVDhurVCW/me2xLUpLOPO0kLV3yjyQpPj5e5cqV0549ecfZ08+9qMuvvLr4K16CBZrD6Ui1bt06tW7ufKEWFRUVtuVrxMhbue4DQbVu0aRIf68uHTJUr70x0fsKlQLxseZPa223cOVK8sXlb8kJHscbY3paawv25bhATuiQpLcPa81KgE27M/Ty9FXq3qSaGtaIV7UK5ZSakaW1O9I0bck2ffj7BqXvKzzBX5TrwsvoKKOaYWYVr1Au8CGyaXeGznvxNw3r01gntaul+lXjlJUj/b01WVMWJen9OeuVlV0yQy2KT2LtOpr8wy965/VX9PXkT7V65QplZqSrQaPGOqbPcbr82pvUum37kPv4ctLHSk9zBqi7iG5WCKB2nTr6ec6fevWlF/XZpx9rxfJlSk9PV+PGTXTc8cfrhuG3qH37UI3jKOsKzvkSbljlgi24AA6PktziESNpnqSj5MzXMdRa+6MxJkrSeZImSKos6Rtr7Rnh9lfWWjwQGWWxxQOH35HY4oHiR4sHgJKi1Ld4WGuzjDFnS5ouqYmkH4wxaXKuS8n9rz1f0iWRqSEAAACAoiqpF5dLkqy1ayQdLekBSYslWUn7Jf0p6VZJvay1XL0MAAAAlHAltsUjl7U2RdJ9vhsAAACAUqhEt3gAAAAAKBsIHgAAAAA8R/AAAAAA4DmCBwAAAADPETwAAAAAeI7gAQAAAMBzBA8AAAAAniN4AAAAAPAcwQMAAACA5wgeAAAAADxH8AAAAADgOYIHAAAAAM8RPAAAAAB4juABAAAAwHMEDwAAAACeI3gAAAAA8BzBAwAAAIDnCB4AAAAAPEfwAAAAAOA5ggcAAAAAzxE8AAAAAHiO4AEAAADAcwQPAAAAAJ4jeAAAAADwHMEDAAAAgOcIHgAAAAA8R/AAAAAA4DmCBwAAAADPETwAAAAAeI7gAQAAAMBzBA8AAAAAniN4AAAAAPAcwQMAAACA5wgeAAAAADxH8AAAAADgOYIHAAAAAM8RPAAAAAB4juABAAAAwHMEDwAAAACeI3gAAAAA8BzBAwAAAIDnCB4AAAAAPEfwAAAAAOA5ggcAAAAAzxE8AAAAAHiO4AEAAADAcwQPAAAAAJ4jeAAAAADwHMEDAAAAgOcIHgAAAAA8R/AAAAAA4DmCBwAAAADPETwAAAAAeI7gAQAAAMBzBA8AAAAAniN4AAAAAPAcwQMAAACA5wgeAAAAADxH8AAAAADgOYIHAAAAAM8RPAAAAAB4juABAAAAwHMxka7A4dK6TiVNve24SFcDpVzjfrdEugooA2Z8+lCkq4AyoH2DypGuAsqAmGi+g8bhw9EGAAAAwHMEDwAAAACeI3gAAAAA8BzBAwAAAIDnCB4AAAAAPEfwAAAAAOA5ggcAAAAAzxE8AAAAAHiO4AEAAADAcwQPAAAAAJ4jeAAAAADwHMEDAAAAgOcIHgAAAAA8R/AAAAAA4DmCBwAAAADPETwAAAAAeI7gAQAAAMBzBA8AAAAAniN4AAAAAPAcwQMAAACA5wgeAAAAADxH8AAAAADgOYIHAAAAAM8RPAAAAAB4juABAAAAwHMEDwAAAACeI3gAAAAA8BzBAwAAAIDnCB4AAAAAPEfwAAAAAOA5ggcAAAAAzxE8AAAAAHiO4AEAAADAcwQPAAAAAJ4jeAAAAADwHMEDAAAAgOcIHgAAAAA8R/AAAAAA4DmCBwAAAADPETwAAAAAeI7gAQAAAMBzBA8AAAAAniN4AAAAAPAcwQMAAACA5wgeAAAAADxH8AAAAADgOYIHAAAAAM8RPAAAAAB4juABAAAAwHMEDwAAAACeI3gAAAAA8BzBAwAAAIDnCB4AAAAAPEfwAAAAAOA5ggcAAAAAzxE8AAAAAHiO4AEAAADAcwQPAAAAAJ4jeAAAAADwHMEDAAAAgOcIHgAAAAA8FxPpCsBbv//2iya88j/9MedXbd+2VZUqVVab9h100aBLdeHgITLGFNomLS1Nv86epYUL5umvhfO1aMF8bVi/TpJ06x13a/SYew/3y8BBio+LVd+uLdW5bUN1btNQnds1UqO61SVJD70yRQ+/OiXotn26ttBJvdqqS7tGatqghmpUTVBCfHntSknTkpWb9cX0hXpj0i/KyNwfsg5NG9TUyKEn6aRj2qhOzSpK3puhhUs36I1JP+vzHxcE3a5Dy3o6o99R6tOlhdq3qKua1RKUuS9L6zbv1Mw/lunlD2dpxbqtB/fGoFgtXbxQs6d9q6WLF2j9mpXatXO79qamqGJCJTVu1lLH9j9ZAwdfqSpVqwXdx47tW/Xu+Of18/TvtGXTBpWPi1PTlm10xrmDdPaFgf9W5crKytKUSR/o+68nacWSxUpO3q3y5eNUt35DdT2mny687Fo1aNzUi5eOw2jHjh2a8tUXmjl9mhYsmK/169YqKytLNRMT1blLVw2+9DKdPeDcA9rnU+Me19h7xvjvp2RkF3e1UcrMnzdPU77+UvPn/anly5dp+7ZtSk5OVuXKldWqdRuddvoZuvra61W9evVIV7VUMtbaSNfhsOjUpaudOvO3SFfjsHr0gXv17JOP+e9XqVpV6Wlp2rdvnyTphJNO1cQPPlX58uXzbffzTzM18MyTA+7zSA8ejfvdEukqHJC+XVtq6oQRAdeFCx6fPXedzujXwX8/NS1TkpRQIe94Wb1hu86+4aWgAeDUPu303hNXqmK8s82elHQlVCiv6GinsfWtz3/Vdfe/V2i7i0/vpjcfGZbvsd0paUqIL6+YmGhJUua+/Rr1xKd6/bOfg76GkmrGpw9FugrF6smxo/XpuxP898uXj1N0TKzS9qb4H6tarYbGvfq+jurSo9D2Sxcv0IjLz9OeXTslSRUqJigzM0PZWVmSpJ59TtC4V99XuQJ/qyQpec9u3XLFBfp74Vz/YxUqVlJmZrp/+3Llyuu+J1/RiWecUzwvuIRo36BypKtwWFVLKK8s3+9UkuLi4hQdHa29e/f6Hzvl1NP0zgefqEKFCmH3t2zZv+rdo4syMjL8jx2JwSMmms4vbjcPv1Gvvvw///24uDjFxsYqJSXv71nNmjX1yaQv1OuYYyJRxRIpPtb8aa3tFq4cR1sZ9e5bb/hDxznnXaj5S1Zp2bqtWrlpp/732kQlVKqkaT98pzGjbw64fdWq1dS3/wm6YcRIvfLGO6pVu87hrD6K0c49ezVtzlI9PfF7XXbHm9q8bU+Rtps+Z6lGPv6Jel38mBJ7j/Lf6ve/XSMf/0Rp6fvUtEFNffT01QG/jW5cr4befdwJHb/MX6mjBjygOv1Gq3bfW/2BZ+g5x2jk0JMKbRsTE62MzP16/6vfdc5NL6lWn1tVt99tqn7MSJ1+7fNavHyTypeL1fNjLtLxPVsf2huEQ9auY1fddMcDmvDJVH0/b41m/r1Z0xau07SF63XPEy+pWvWa2r1rh267/lKlpuQ//lJT9mjU1Rdrz66daty8ld78v2matnC9ZizaqFvve0IxsbGaM3uann14TMDnfvahO/2h46rhd+i7P1Zq2sJ1mvV3kl567ys1a9lG+/Zl6oHb/qutSZs8fy/gnaysLHXr3kPPPP+iFv2zXNt271XSjmQtXrpSlw27QpI09btvNeKG68LuKycnRzdce7UyMjLUoxcnj8jTvXsPPfL4OM346Vdt3rZLu1LStXVnsrbtStFrr09UYmKitm/frovOP0d79hTt/ynylNgWD2NMBUnHSeoqqYvvZyPf6vuttWMPZH9HUotHdna2OrVpqq1bknR0p86aOvO3QieGH773tkZcf5WioqL0489/qF37o/JtHx0dna98tw4ttX7dWlo8SlmLR1SUUU5O/s/40q/vV+N6NcK2eIRzxcDe+t89gyRJJwx7Wr8uXJVv/esPXqbB/+mhzdv2qPPAh7QnNT3f+hfuulhXnd9He1LS1ebMe7U7JW99y8a1lJqWGTQkVUmI1/xJd6tuYhVNm7NUZ1734kG/jkgoay0e4cz5aZpGXH6eJGnsU6/qtAEX+te9+szDevN/T6p8XLw++OZX1WvYON+2b738tF5+6kFFR0frg29/U6OmLfzr9mVm6qTOjbVvX6bOGDhI9z7xUqHn3rB2tc4/sYsk6fYHn9a5gy734iVGxJHW4jFrxnT163980PUjbrxeb0wYL0lasnyNGjRsGLTsSy8+r9tvvUUXXTxYzZq30KMPPyCJFg+E98P3U3XWGadKkt54610NGnxJhGtUMpSFFo8ekqZIelDSucoLHQhjwbw/tXVLkiTpuhtvDvht9EWDhyixVm3l5OTo4/ffzbeuYOhA6VUwdBSn3/9a7V+uX7tqvnUV4srpnBM7SZJe+/SnQqFDksa9MVWSVKVSvM46vmO+dcvXbg3ZMrMnNV2Tpy2UJHVt1zhoOZQMHTrl/S8q2Orwzf99KEk6+T8DC4UOSbrgsmtUoWKCsrOz9d3kT/KtS0nerX37nC6AbTt0CvjcDRo3VWXftSVpri45KH1ChQ5J/lYPSZo3b27QcmtWr9YD992t6jVq6NFxTxdb/XBk6NGzl39548YNEaxJ6VSSg4ck7ZL0o6RxkgZJSopsdUqHDevX+pdbt2kbsIwxRi1atpIkTfvhu8NSL5QtvTvnffO8asP2fOuO7dxcFeLLSZKmzv4n4PbrNu/UklWbJUknHdPmgJ8/c59zUXs039aVeAvm/upfbtAo7yLvtauWK2mT84/7mH6Fu9xJzvUeHbs5/+jnzJ6eb131mrUUX6GiJGnJ4sADFWxYu1rJu3dJktoe1fkgXwFKg7i4OP9yTnbwloub/nut9u7dq0cff1KJiYmHo2ooQ36e/ZN/uVmz5hGsSelUkv9j/2StrW6tPclae5u19kNJmZGuVGmTnZ0TYp3zh3nl8mX+C86BUOLKx6p5o0SNvuIUPTbSGT3mpz+Xa94/6/KVa9+irn/5H1+4COSfFc66ts3qBi0TTN+uLSVJf6+g335JtC8zU5s2rNMnb4/X/bc6fe4bNG6mPiec5i+zctkS/3KzVoG/JHGvW71iab7HjTE65+JhkqQpkz7QhOcf91+gnp2drXlzftZt1w2WJJ1w2gB16dn70F8YSqyfZs30L7frcFTAMm++/ppmTP9Rx59wogZfetnhqhpKuczMTK1ds0Yv/+9FXTlsiCSpeYsWOvM/Z0W4ZqVPiR1O11p75HW0LCYNGzXxLy/9Z7E6du5SqExWVpZWLl/mX96xfZvq1qt/uKqIUqR2jUpa88OjAdd9NfMvXXPvO4Uer5tYRZJzYXt6RvDhdjdt3Z2vfFGdf0oXdWnn9L58Y1LpG9WqLOvXro6/+5Pb0V176oFnXss3MtX2rXmN2LVqBw+ftWrXkyTtTU1R2t5UVaiY4F933ai7tWfXDk35vw814fnHNOH5x1QxoZIyMpxRreo3bKIbbhurwVfeWBwvDyXU7t279dQ4Z0CVY3v3VatWhQed2LRxo+4Zc7vi4+P13IuvHO4qohSqmhCnzMzCf8+OOba3Jr7zfqFRQRFeSW7xwEHq2LmLfxSqF599Mt/wg7nefuM17diR1z0mJSX5sNUPpUt2jlXS9mQlbU9WekZey9hnU+fprmc/167ktELbJFRwujy4yweS5gsllSoW/Y93i0a19PxdF0uSfp63Qu98MafI28J71RNr5esCJUlde/XVLXc/qjr18l/sm5aaNzxl+fjgw5+Wj4vP22Zvav515eM05pHnddMdDygmNlaSE1Byh9LNyEhX8u5d2h8gDKFsyMnJ0dVXXKakzZtVvnx5PfnMcwHLDb/xOu3Zs0d33n2fmjZrdphridKodp06ql27tipWzPt7dlz/4zXuqWfVqBGXHh8MgkcZFB0drdF33iNJWvbvUl1y/gAtnD9P+/bt09atW/TKi89p7F23Kdb3T1qSoqI4FBDY9l2panryGDU9eYyqHzNSLU69W4+99q3O6NdBf3x8p64YePi6r9SuUUn/98J1qla5gjZt3a2hd05USR2Z70j1+cxFmvLbv5q+aIOmzFmmm+54UMuX/KUrBp6o8c8+UuzPt2n9Wg09p79eeOxeHX/qWZr4+XRNW7hek2f9pXueeEnGGL0z/jldN+jMQqEFZcNto27Wt1O+liQ9/dyLOurojoXKfPj+u/rumyk6umMn3TSidI1OiMj5d8UardmQpO27U7V24xY9+sSTWrRwgfoe20MPjD1yR/g8FJxtllGXXXG1bho5WpI0Y9r3OuW4XmpYM0FHtWio+8aMVuUqVTVi1O3+8qFmFAbcNm7drftf+kqX3/WWysXG6PkxF+moVvm76aWmORNyxceVC7mvCnFO+E3ZG/7b6MRqCZry6nC1aFRLSduTdfq1L2ijr6sWSqbqNRJ1yVU36pk3PpUxRm+8OE6zp33rX18hoZJ/OTO9cMuZf11G3qho7m5W2dnZGn3dYK389x+dce7FevDZ19WmQydVqJig2vUa6MyBg/TC25+rXLnyWrp4gd4ZH/ibcJReY+4Y7Z/s7bFxT+cb2SrXtq1bdfvokYqOjtYLL72qmJgS28scJVitWrV08y2jNPmrb2WM0aMPP6gpX38V6WqVOmU6eBhjrjHGzDXGzN2xfXv4DcqYu8c+rCk/ztbgyy5X2/YdVL9BQ3Xs3EUjbr1dM+fM9/e1rlq1mmrWZGQPHJjJ0xZq7aYdio6O0rBz8k/AlTsUbvUqFRUfFxtoc0lSvVpV85UPJrFagr4ZP1ztmtfVlh3JOv3a57VszZZDfAU4XNp37KqOXZ2RqT7/8C3/4zVr5U1MunVL8EEItm5xBhComFApX/CYM3uaVv7rjJo2+KrA13A0bdFaxx5/iiRp+rdfHuQrQEl095jb9cKzznC4Dz36hG64aUTAcvfcdYd27tihy6+8Wq1at1Fqamq+m3twlUCPAW7de/TQsb37SJJ/3hgUXZmO/dba8ZLGS84EghGuTkR07d5DXbv3CLjut59nS5K69egVcK4PIJzN2/aocb0aatYwf3D9e0XeSWS7ZnX1Z4FRr/zrfKNfLQkx8lVitQR9+9qIvNBxzfNauoqRtUubxDrO73rD2rz5X5q7RrJatWyJmrYIPAv9Kt/oV01b5B92efXyf/3L7mF6C2rY2OnPv3nD2qBlULrcdedtev6ZpyRJDz7yuEbcMipo2bVr1kiSJox/RRPGh76ovG5NZ6CL/944XI8/+UzxVBZlTr36Tiv/ypUrIlyT0qdMt3gguA3r12nm9B8kSRcOvjTCtUFp1bheDUlS6t6MfI//Mn+l0tKdbwxP7t0u4LaN6lbzD6P7w69LA5apVb1SodCxhNBRKm1c55z0V0jIa7Fo3Kyl6tRrIEn6bdaPAbdLT9urhXN/kyT17JN/Ajn3tWmbN64P+tw7d2xzntvVWoLSa8wdo/OFjptH3hrhGuFIs3rVKklSgqu7KIqG4HEE2r9/v24d/l9lZ2erTbv2OuOscyJdJZQwRZmU77IBvfzD4M76c3m+dWkZ+/T5j86Ebtdc0FeVE+IKbT9y2MmSpOTUdH05fWGh9e7uVUnbk3UaoaNEys7ODnuB/x+/zNQ/i/6UpEJzaZx+rjNC2fdfT9KmDYVbxj59d4LS9qYqOjpapw64IN+61u3zLiKe9P4bAZ97x7YtmjnV6YfdoXP3MK8GJd2YO0b7u1c9/Ni4IoWOb76fppSM7KC3O+/Ku0g49zFaO45MRfl7Nn3aj5r7x++SpH7H9T8MtSpbCB5l1JrVq/ToA/dq0YL5yshwvo3Ozs7WL7Nn6bz/nKLpP05VxYQEvfjqG/lGt8q1e9cu7dix3X/LyXEmIkxPT8v3+N5URokp6apWileNqhX9tyhft7oKcbH5Hq8Yn3ch+LGdmuv712/WoDO7q77vOoxczRsl6sHhZ+vFuwZJklau2xZwSNsHXv5aqWmZqptYRZ89d52aN0r0PW853XnNabr6fKeP7OMTvtPulPR829Z0hY7N2/botGueo3tVCbVl80ZddnY//d8Hb2rjujX5/mlv2bRBb7/yjG677hJZa1W5ajUNuvy/+ba/5KobVSOxtjLS0zTqqou01DcD+f59+/TZe69r/DPOSFgDLhqqRk1b5Nu2U/dj1LJtB0nSp++8pmcfvkvbfNeKZGZm6NeZP+i6QWcqNSVZxhgNuuIGz94HeO+eu+7wh45Hn3hKw28eGeEaoazZsH69enXrrAnjX9XqVavy/T1bv369xj3xmC4YOEDWWlWvXp0R0g6CKU1DURpj1khqLOl+a+3YA9m2U5eudurM37yoVom0eNECndjHubbDGKMqVaoqNTXFP6dHnbr1NOHtD9W9Z6+A23fr0FLr14XvD33R4CF6/pXXi6/iJVzjfqXvj8zSr+/3d4kK5Z0vftM1970ryZkVfOqEvAs10zP2KTU9UxXjyquCK6As/HeDLrxlvNZt3hlwn6f2aaf3nrhSFeOdgQx2p6QpIb68YmKiJUlvT/5N1459t9B2d15zmu69/j+SpNS0TKWmhR71qu+lT2jDltIzwtWMTx+KdBWKzaYN6zSwf17LQ2xsOVVMqKTMzAylp+31P16vYWM9+uLbat3+6EL7WLp4gUZcfp5/1vEKFStp374MZe135nnp2ecEjXv1/XyTD+basHa1RgwbqI3r1/gfq1AxQRnpaf4vTKKjozV8zMO6aOi1xfKaS4r2DSpHugqHzfp169SulXMdT1RUlGomhh4QZfjNo0Je9+H2yIP369GHH5DktHgcaWKK0MJ9pFi7Zo3atMy7XqxcuXKqXLmy0tPTtXdv3t+zJk2b6oOPPlOnzp0jUc0SKT7W/Gmt7RauXJm+uPxI1rBRE426/S79MnuWVq9aqZ07tqty5Spq1qKlTv/P2br8qutUMYH+zghs/pJ1uuLut9SvW0t1bttItWtUVo0qFZW5P0sr123TgqXr9fmPCzTph/nKyQn+5cV3s/9R9wsf1ahhJ+vEXq1VN7GKdqeka8HS9Xr9s5/93bEKijJ5/wgTKpRXQoXQEwwyD03kJNaqo4eff1Pz5szW3xiBMtUAACAASURBVAv/1I5tW7R75w5FRUerTr0GatGmg/qddIZOOft8xbkmAnRr06GT3v/mV73z6nP6efp32rp5o+LjK6hZx7Y649xBOuuCS4P+jhs0bqp3v56tyR+9rVk/TNGqZUuUkrJH5crHqXa9BurS41gNvORKtWzTwcu3AR7LDZG5y1u3hB7VjtZ4HIy69erp3Q8+1k8zZ+iP3+doc9Jm7di+XdHR0WrYqJGOPrqj/nPWAF00aLDi4wP/PUNoJbrFwxhTTVK066F5khpKGifpCdfjGdbakH9ljrQWD3ijNLZ4oOQpSy0eiJwjqcUD3qHFA8WhqC0eJf1omy9pm+vW0Pf46AKPvxiR2gEAAAAokpIePAAAAACUASX6Gg9rbZNI1wEAAADAoaPFAwAAAIDnCB4AAAAAPEfwAAAAAOA5ggcAAAAAzxE8AAAAAHiO4AEAAADAcwQPAAAAAJ4jeAAAAADwHMEDAAAAgOcIHgAAAAA8R/AAAAAA4DmCBwAAAADPETwAAAAAeI7gAQAAAMBzBA8AAAAAniN4AAAAAPAcwQMAAACA5wgeAAAAADxH8AAAAADgOYIHAAAAAM8RPAAAAAB4juABAAAAwHMEDwAAAACeI3gAAAAA8BzBAwAAAIDnCB4AAAAAPEfwAAAAAOA5ggcAAAAAzxE8AAAAAHiO4AEAAADAcwQPAAAAAJ4jeAAAAADwHMEDAAAAgOcIHgAAAAA8R/AAAAAA4DmCBwAAAADPETwAAAAAeI7gAQAAAMBzBA8AAAAAniN4AAAAAPAcwQMAAACA5wgeAAAAADxH8AAAAADgOYIHAAAAAM8RPAAAAAB4juABAAAAwHMEDwAAAACeI3gAAADg/9m77/Aoqv2P4+9JKKH3KioIYq+ggh1s15+9oSjYu9eu1wbqtcC1iw3FjmIXUa+iV7Ahikq1YSeAQOgthJqc3x8blsQUCGRI4f16nn2YzJyz+10Zl3x2zjkjxc7gIUmSJCl2Bg9JkiRJsTN4SJIkSYqdwUOSJElS7AwekiRJkmJn8JAkSZIUO4OHJEmSpNgZPCRJkiTFzuAhSZIkKXYGD0mSJEmxM3hIkiRJip3BQ5IkSVLsDB6SJEmSYmfwkCRJkhS7KmVdwMaSGkXUrVG1rMtQBTdjZL+yLkGVwIWvf1fWJagSuPaAtmVdgiqBHVrVLesStAnxiockSZKk2Bk8JEmSJMXO4CFJkiQpdgYPSZIkSbEzeEiSJEmKncFDkiRJUuwMHpIkSZJiZ/CQJEmSFDuDhyRJkqTYGTwkSZIkxc7gIUmSJCl2Bg9JkiRJsTN4SJIkSYqdwUOSJElS7AwekiRJkmJn8JAkSZIUO4OHJEmSpNgZPCRJkiTFzuAhSZIkKXYGD0mSJEmxM3hIkiRJip3BQ5IkSVLsDB6SJEmSYmfwkCRJkhQ7g4ckSZKk2Bk8JEmSJMXO4CFJkiQpdgYPSZIkSbEzeEiSJEmKncFDkiRJUuwMHpIkSZJiZ/CQJEmSFDuDhyRJkqTYGTwkSZIkxc7gIUmSJCl2Bg9JkiRJsTN4SJIkSYpdlaIORFF0emm9SAhhYGk9lyRJkqSKp8jgATwHhFJ4jQAYPCRJkqRNWHHBYwqlEzwkSZIkbeKKDB4hhNYbsQ5JkiRJlZiTyyVJkiTFzuAhSZIkKXYGD0mSJEmxK25yebGiKGoF7A20AmoBUVFtQwi3re/rSJIkSar4Shw8oihqDDwOHEsxYWN1cxIrYxk8JEmSpE1YiYJHFEW1gE+B7YAVwARgz9ztb4DmQLvc5vOA70urUEmSJEkVV0nneFwCbA/8AmwVQuiUu39eCGH/EEJ7oA3wGlAf+CCE0KXUqpUkSZJUIZU0eBxHYujUDSGEGYU1CCFMDiGcQiJ89Imi6KANrFGSJElSBVfS4LFt7p8f/G1/1ULa3kRijselJS1KkiRJUuVS0uCRBiwIISzPs28ZUPvvDUMIk4CFJOaASJIkSdqElTR4zATqRlGUt99soFru8rpJURSlklhmt9GGlShJkiSpoitp8Jic26dlnn3jc/887m9tjyaxatas9StNkiRJUmVR0uAxPPfPrnn2vUpiLkffKIqujaLokCiKrgGeJTERfeiGlylJkiSpIitp8HiXRMg4Lc++l0nc26Mm8B8SE8/vAuqSGJp164YWKUmSJKliK1HwCCGMCyGkhBAOy7MvAEcAfYFJwCpgLvAi0CmEML0U65UkSZJUAZXozuVFCSEsJbF87k2l8XySJEmSKpeSDrWSJEmSpBIzeEiSJEmKXYmGWkVRtP/6vEgI4fP16SdJkiSpcijpHI9PSSyRWxJhPV5HkiRJUiWyPoEgirm9NoKsrCxGfP4Z48aOYdy4sYwbO4apU6YAcFPvW+h1861lW6DKhZdeeJ5LLjxnre3eevcDDux6cL59F59/Ni8PGrjWvrMXLqNKFb+bqKheOWO3dW7744zF3P6/3/Pt265ZbXZuWYetGtWkaZ1q1KlehbSqqSxZvoq/Fizj2ykLGf7bHFZmF/6dV720KmzXrDZtGtWkTaMatGlYkzppifPptg9+46eZmev/5rRRLZg/j8+Hvc+3Iz/j5x8nMGPaVLKzV9GgYWO222k3jjyhO10OO6rY5xg14mOGvPI8P4wfw7w5s4iiiMZNm7HT7nty3Cln0qHTviWq6bn+9/PI3f9O/jx60sL1em+q2O65+z/cfNMNyZ+Xrizpd/BarUT/2ocQip0TEkVRXWAv4EZgJ+C4EMKI9S9PcRn97Tcce9T/lXUZqiBSUlJo3LhJkcerVa9e5LG0tDTq1q1X5PEo8ruJimzB0pXFHk+NomQQ+GNuVoHjR+3QlN03X3N+LFuZzarsHOrVqEq9GlXZoUUdDt++Cf8Z9gczFi0v0P+QbRpz4q4tNvBdqDw4bM+tyV61Kvlz9eppVKlSlVkZ05mVMZ3PPnqPvQ88hLsfG0hajZr5+oYQ6NvrSga/9Gy+/kQR06ZOZtrUyXzw9uuces4lXNWrzzrVk/7HbzzZ767SeXOqsH795Rf63P7vtTfUOinVrxlDCIuAj6IoGgYMAd6Jomj3EMKk0nwdlY4GDRqw6267Jx/XXXMlGRkZZV2WyqHNWm3OdxP/WK++x53QjccGPFPKFam8uPC1H4o9fsT2Tem5x2YAfPLb3ALHv5+xmAnTF/HLrCVkLFrOslU5ANSunso+bRpwaofNaFanOld1acO/3v65wFjfAMzJXMGkeVlMmruU+UtXcsHeW5TGW9NGlr1qFTvs0oGjTjyVTvsfRKst2gAw/a/JPP3wPbz92gt8+elH3HnjFdz+wIB8fd99Y1AydBx0+DFccu0tbNGmLZAIEA/fdQufffQeLz39KLvt0XmtV05ycnK4/fp/snz5MnbefU++G/tNDO9Y5V1OTg4Xnn8Oy5YtY69Onfl61FdlXVKFF8v4hhBCiKLoX8BEoDdwdkmfI4qiRsDRwEHA7sCWJOqdDYwGng8hvFVqRW9i9tl3P6bPmpdvX++bri+jaiRVVl22bgjAzzMzC71iMXTi7EL7ZS7P5sOf57AqO3De3luwef0abN2kFr/OXpKv3eDvMnhzwpovTJrUqlaK1Wtjevyld+nYueAaNi1bbUnvux4htUoVBr/0LEOHvMol195M85atkm3eG/wKAJu33oo7H3om3/DN1m235q5Hn+eEgzsybUo6H7331lqDx6vPP8GE0aM4/JhutGq9lcFjE/XYIw/z1ZcjOaX7abRt187gUQpiW043hPALsAg4ZD2fIgN4BjgN2I5ErSuBzYBjgMFRFL0fRVHNop9CRUlNTS3rEiRVcu2b1KJV/RoAfFzI1Y518ducNUGjUa2qBY4Hh1pXGoWFjryO6dYzuT3x+3H5js2ZlQifW2+7Y6FzxqpUrUr77XcCYOmSJQWO5zVtajqP3Xs79Ro05KrefdepdlU+6ZMmcevNN9GoUSPuvu+Bsi6n0ogteERRVBWoARQ9MLx4VYBvgIuBtiGEGiGE2kAb4OncNocDT2xorZKk0tdl60YAZK3IZlT6/PV6jm2b1k5uz1xc8IqJNh3Vq6clt7Ozs/Md22yL1gD89vMPrMozT2S1VStX8utP3wOw3c7FL4hw5w2XsTRrCVfe1IcGjRpvYNWqqC6+8DyWLFnCXffcT5Mm6/urrP4uzhsIHgtUBWatZ/+uIYS9Qgj9Qwh/rt4ZQkgPIZzLmsDRI4qizTewVknFmDtnNgfusyetmtajRaPa7LrD1px/9ul88fmna+372acf03GX7WjesBZbNG/A3nvsyg3XXsUfv/8Wf+EqM9WrpNCpdX0ARk6ax4oiVqUqTNXUiOZ1qnPsTs3okTs/5KeMxfw5d2kstapiGD3qi+R2u212yHfsxNMSq+9NTf+Tmy47m6npa+akpf/xG9f/80ymTUmn1ZZtOPXsi4t8jbdefo5vRn7GnvscyJEndC/ld6CK4pmnnuSTj4fT9aCDOa3n6WVdTqVSqnM8oiiqBmwOnEBiZasADF2f5wohfLKWJk8DF+RudwSmrs/rSFq7rKwsJowfS/0GDVi5ZAmT0ycxOX0Sr7/6Eqf1PJMHH3m8yCVxp0/7i9TUVOrUrcviRYuY+NMPTPzpB5556nH63H0/55x34UZ+N9oY9m7TgBpVE0M6P/517cOs6qVV4YmTdyr02OipC+n/xeRSrU8Vy+JFC3iu//0A7LbH3rRuu3W+4/sffDhX9e7Lw3fdwvChbzN86NtUT0sM81u+bCl16tbjxB7ncNHVvahdp26hrzErYzr9+t5M9bQa3NjnwXjfkMqtadOmceP111KjRg0eecxBNaWtRFc8oijKLu4BLAV+BfoCdYHpQFxrkC3Ls+2EBSkGzVu04LobezNi1Fgy5i1h0l+zmT5nMR8M/5wDuxwEwKAXnuPG664u0HeXXXfj7vsfYsLEP5g5P4tJf81mSsZ8Br70Om22asuKFSu45op/8vZbb27st6WNoGvuMKv0eVlMmrf2KxU5IbE074KlK1mRu7IVwFfp83lp9DSWrMguprcqs5ycHG6+8gLmzMqgWrXqXHvr3YW2O/Xsi7mn/4s0bJQYFrN82VKWL0uceytWrGBJZiaZixcV+Tp9brqCzMULOf/y65MramnTc+nFF7Bw4UJuuvlW2my1VVmXU+mU9IrHui64vxR4E7ghhDC9hK+xrg7Ms/19TK8hbdK6HnwoXQ8+NN++1NRU9uq0N2++M5Se3U/k/f++w9MD+nPBRf+kbbs130JecPGlBZ6vZs2aHHXMceyz7/502W8vpkxOp/eN/+LoY4/3fh6VSKv6aWzdpBZQ+BK6hVm8fFW+pXkb1qzKwds05sjtm7LH5vV49uu/GL6eE9RVsd377+sY8fEHAFx3+33JSeJ5LVuaxb+vvZiP3nuL7XfejdseGMC2O+4KIfDzjxN47N7bGDrkVb76fBj9X3yHrbfbMV//9996lS8+/pD22+/Eaef+c6O8L5U/Lw96kaHvv8cuu+zK5VdcVdblVEolnePRZS2P/YAdgXohhNNDCNNKsdakKIrqA6tvITkidwUtSRtRSkoKt/dJfPOYk5PDB+//d537NmzUiKv/lfhfeOqUyXw3ftxaeqgiWX21Y8WqHL74c/0mlc/LWslr42bw8Ih0qqSmcE6nzdmiQY3SLFMVwIN33sRrAxP37Liqd998K1vl1a9Pbz567y22aNOOAa8OpdN+XanfoCH1Gzai035dGfDqULZo044F8+Zy183X5Os7b85s7rv9elJTU+nV96Eih42qcps1axbXXn0FqampPPr4k54HMSlR8AghfLaWx8gQwk8hhIJLSpSSKIpSgBeAFsByoODXqmvanh9F0egoikbPnlP4WvGS1t9WbdvRqHFi1Zf09JLdJ3SPPTslt0vaV+VXakrEvls1AODryQs2eIjUt1MWMjtzOSkpUfKeINo09OvbmxefegSAy2+4vchJ4UsyFzP4lecA6Hb6+aSlFQyoaWk16Hb6+QCMH/0V8/L8TvDwXbewcP48jut+Jq3btidrSWa+x6qVK5JtV+9buWJFgddQxdbrhuuYO3cu55x7Pttsuy2ZmZn5Hivy/J0Xtk/rpiLGuX7AkbnbF4cQJhTVMIQwABgA0KFDR1d7l6SYddy8HnXTEvfbWNdhVmszP2slTWpXp3md6qXyfCr/+vXpxQtPPgzAZdffRs/zLyuy7ZRJv5Odu4Ruqy2Lnpux+k7mANOnTqZh48RckGlTEwsXvPHi07zx4tOF9l1t/x0Tq6x1P+sirr75P+vwTlRRrP4CbMAT/RnwRP9i2zZpUAeASy69nHvvdyGCkijp5PKcKIrWefhUFEWToigqtasfURTdC6wefHllCOGZ0npuSSU36c8/mDtnDgBbbtm6RH1Hf/t1cnvL1iXrq/Jr9TCrGYuW8dPMzFJ5zia1E4Fj2cqctbRUZfDgnTflCx2nX3B5se2jlDW/ymRMm1Jku3lz1qzuX7N27SLbSYrP+lzxKOkM0FKZMRpF0d3A6qVzrg0hGDGlGIUQip3wHULg5puuAxLzPQ47/Ih17jt/3jzuvyfxbWHLzVqx8y7F39BLFUOjWlXZqUXim8BPf5u31vYpUWI1q+Ic2K4hDWomrqCUVpBR+fXgnTclh1ddceMd9DivyNHUSa3btqd6Wg2WL1vKkFcHcuwpZxYYn5+dnc3gl58DoG69+my51ZqFMAa88l6xz//Eg315sl/i82r0pIUleTuqQP43/NNij99x263ceXtiodalKx1Es77ivIEgQDVgg7+iiqLoHuDa3B//FUK4d0OfUzB//nzmzJmTfOTkJP6qsrKy8u3PzPQf+03R1CmTOWj/Tjz79ADSJ/1JCIkP2pycHL79ZhQnHXsE/31nCABnnnM+W7ffJtn31ZcH0bP7ibwzZDCzZ635lnHp0qW89+7bHNJlH9InJe4Lenufu0lJifujSBtDl3aNSEmJWJUT+OyPtQ+z2rZpbW75x9bst1UDGuaGi9Wa16lO991bcm7nLQDIWLScT38v+JwRUKd6avJRq/qa1dVrVEvNd6xKiiunlWcP/efmZOi4slefdQodkJi/cezJiZu8/fzDBK4692R+//lHcnJyyMnJ4beJP3D5WSfy3ZjEVdbuZ19Maqqr8EtlIbY5HrkrTzUFFmzg89zLmisd/woh3LOhtSmh0x67MWVywZtyPXDfPTxw35r/zD16nsGTzzy3EStTeTF2zGjGjhkNQPXq1aldpw6ZixezfPnyZJvTep7JXffmvwCZk53Nf98ZkgwmtWrVonpaGgsXLCA7Ozv5fHf8516OP7HbRno3ilMEHNAuMfl7/F8LWbB03UbZbtesNts1Swx7WbEqh2WrsqleJZXqVdaE0fR5Wdz38SRWFnL388a1qvHwiTsU2A9wbdf8a/D3/2Iyn/2x9isx2vgypk1l4BP9gMQV1IGPP8jAx4se2NDjvEvzzfu49Pp/MzX9D778bFjyUa1aYojeihVrPq8OO/pEzr7kmgLPJ2njKDZ4RFG0M7Dr33bXiKKouPvHR0B94EQSV1TGrm9xfwsd14QQ7lvf55JUMk2aNuOu+/rx7dej+OH7CcyZPZsFC+aTlpbGlq3bsOdenTnt9DPp1HmfAn33PeBAet16O99+PYpff/mZefPmsmjhQurUrctWW7VlvwO6cNY557Nla2/SVVns1LJOci7Gx+s4qfzPuVk8OiKd7ZvXoU2jGtSvUZXa1auwKjuHjEXLmTQvi28mL2DU5AUERzZUajlhzeCInJwc5uaZj1GYrKwl+X5OS6tBv2ffYPjQtxk65DV+/mE88+bOJiKiWctW7LDz7hx9Ug/27XpYLPVLWjdRKObTPIqiW4Cb8+4C1vXjf3Xb/wshfFjiwqLoLuBfuT9eFUJ4oKTPkVeHDh3DyK9Hb8hTSCzz7skqBRe+/l1Zl6BK4NoD2q69kbQWO7SqW9YlqBKoUTUaE0LouLZ2axtqtQDIu0TEliTmbPxVTJ8cYBHwAzAghDBibUX8XRRFW7AmdOQA10VRdF0xXe513ockSZJUfhUbPEII/UjcNwNILKcLzA4hxD0+IuVv283W0t518SRJkqRyrKSTy/8NxL7EUQghnVJahleSJElS2StR8Agh/DuuQiRJkiRVXiUKHlEU1QG6AItDCJ+spW1XEkOgPg4heCMISZIkaRNW0rt2nQq8BRy+Dm1Pym17ckmLkiRJklS5lDR4HJf75yvr0PY5EvM0Tijha0iSJEmqZEoaPLbJ/fOndWi7eqH6bYptJUmSJKnSK2nwaA4sCCEsW1vDEMJSYH5uH0mSJEmbsJIGjyygdhRFqWtrGEVRFRKTy1esT2GSJEmSKo+SBo/fSKyEddA6tD0IqAr8UdKiJEmSJFUuJQ0e75OYMH5P7tK6hYqiqDZwDxBy+0iSJEnahJU0eDxCYt7GjsC3URQdF0VRjdUHoyiqEUXR8cDo3DYLgX6lVawkSZKkiqmkdy6fF0VRd2AI0B54A8iOomgOiasbTYBUEldFlgHdQghzS7dkSZIkSRVNSa94EEL4H7APMJJEwKhCYuWqFrnbEfA50DmEMKz0SpUkSZJUUZXoisdqIYRxwH5RFLUD9mbNkrkzgC9DCE4olyRJkpS0XsFjtRDC78DvhR2LoigFOAI4J4Rw7Ia8jiRJkqSKbYOCR2GiKGoPnA2cDjQr7eeXJEmSVPGUSvCIoqgm0I1E4Nhn9e7cPyeWxmtIkiRJqrg2KHhEUdSJRNg4mcRdyiEROH4GXgdeDyH8sEEVSpIkSarwShw8oihqAvQEzgG2Xb07988A7BFCGFM65UmSJEmqDNYpeERRFAGHkwgbR7Jm2dylJO7p8TzwQW5zh1ZJkiRJyqfY4BFFUVsSQ6nOIHGfjojEVY0vgIHAayGExblt461UkiRJUoW1tisev5EIGhHwJ/ACMDCEMCnuwiRJkiRVHus6x+Mh4F8hhBVxFiNJkiSpckpZy/EVJK52XApMj6Lo0dyVrCRJkiRpna0teDQHLgO+AxoCFwEjoyj6JYqiG6Mo2iLuAiVJkiRVfMUGjxDCghDCIyGE3YAOQH9gIbA1cDvwZxRFH0dRdFb8pUqSJEmqqNZ2xSMphDAuhHAJidWtegKfkRiGdSDwVJ6mh0ZRVCp3RJckSZJUOaxz8FgthLA8hDAohNAVaAf0AablHo6AN4FZURQ9G0XR/xlCJEmSJJU4eOQVQpgUQugFbAn8HzAYWAXUB04H3gVmbmiRkiRJkiq2DQoeq4WED0IIJwKbAdcAP5G4AlK/NF5DkiRJUsVVKsEjrxDCnBDC/SGEnYC9gadL+zUkSZIkVSyxzr8IIYwCRsX5GpIkSZLKv1K/4iFJkiRJf2fwkCRJkhQ7g4ckSZKk2Bk8JEmSJMXO4CFJkiQpdgYPSZIkSbEzeEiSJEmKncFDkiRJUuwMHpIkSZJiZ/CQJEmSFDuDhyRJkqTYGTwkSZIkxc7gIUmSJCl2Bg9JkiRJsTN4SJIkSYqdwUOSJElS7AwekiRJkmJn8JAkSZIUO4OHJEmSpNgZPCRJkiTFzuAhSZIkKXYGD0mSJEmxM3hIkiRJip3BQ5IkSVLsDB6SJEmSYmfwkCRJkhQ7g4ckSZKk2Bk8JEmSJMXO4CFJkiQpdgYPSZIkSbEzeEiSJEmKXZWyLkCqSNKqpZZ1CaoEHjx2h7IuQZXA1LlLy7oEVQJzM1eUdQnahHjFQ5IkSVLsDB6SJEmSYmfwkCRJkhQ7g4ckSZKk2Bk8JEmSJMXO4CFJkiQpdgYPSZIkSbEzeEiSJEmKncFDkiRJUuwMHpIkSZJiZ/CQJEmSFDuDhyRJkqTYGTwkSZIkxc7gIUmSJCl2Bg9JkiRJsTN4SJIkSYqdwUOSJElS7AwekiRJkmJn8JAkSZIUO4OHJEmSpNgZPCRJkiTFzuAhSZIkKXYGD0mSJEmxM3hIkiRJip3BQ5IkSVLsDB6SJEmSYmfwkCRJkhQ7g4ckSZKk2Bk8JEmSJMXO4CFJkiQpdgYPSZIkSbEzeEiSJEmKncFDkiRJUuwMHpIkSZJiZ/CQJEmSFDuDhyRJkqTYGTwkSZIkxc7gIUmSJCl2Bg9JkiRJsTN4SJIkSYqdwUOSJElS7AwekiRJkmJn8JAkSZIUO4OHJEmSpNgZPCRJkiTFzuAhSZIkKXYGD0mSJEmxM3hIkiRJip3BQ5IkSVLsDB6SJEmSYmfwkCRJkhQ7g4ckSZKk2Bk8JEmSJMXO4CFJkiQpdgYPSZIkSbEzeEiSJEmKncFDkiRJUuwMHpIkSZJiZ/CQJEmSFDuDhyRJkqTYGTwkSZIkxa5KWRegsrV48WL6PXAfQwa/SXr6JFJTU2m3dXtOOvkULr7kUqpVq1bWJaoC8DzatGVlZfHVyM/5bvw4vp8wju/Gj2PaX1MAuPq6XlxzQ++1PsfsWTN5tN99DPvwfab9NZW0tBq03257unXvwak9zyKKokL7/W/of/lq5Ijka86dO4fly5bRsFFjtt9xJ44+9kROPOU0qlTxn7vybsH8eYwY/j7ffvk5v/w4gRnTppKdvYoGDRuz7Y67csTx3ely2FHFPseE0aN4beAAJoz5mvnzZlOrdh3att+eI47vzhHHdy/yPPrvG4O47bpL1lrjIwOHsOc+B67P29NGsjT38+j7CYnPo+/zfB5deV0vrr6+6M+jhQsXMCr3s+yH3P6zZmYAcP+jT9Lt1NOL7PvlF5/R7ahD17nOq67vzVXX9Vrn9pWFn8SbsMmTJ3PYwQcyOT0dgJo1a7J8+XLGjhnN2DGjefWlQbz/v+E0aNCgbAtVueZ5pPFjvqXHScesd/8J48fS/fgjmT9vLgC1atcmM3Mx33w1km++Gsm7Q97k+ZcHU7169QJ9+9zWm18m/pT8uXadOqSmpjIzYwYzM2bwybD/8cyT/Rn0+ts0adpsvWtU/A7v1J7sOBEm1AAAIABJREFUVauSP1evnkaVKlWZlTGdWRnT+XzY++x9wCH859HnSatRs0D//vfdwbOP3Zv8uU7deizJXMyYUSMYM2oEH/13MPc8PohqhZxHq6WkpFC/YeMij1f1S5Ryb9zYbzm92/p9Hn343jtcdcl569W3WtVqa/2MycpawpLMTAB22a3Der1ORedQq01UdnY2Jx57FJPT02neogXvffARcxcuYd6iLAYOeoU6deowfvw4zjr9tLIuVeWY55FWq1+/Afsd0IWLL7uK/k+/QNNmzdep36KFCzn95OOYP28u7dpvw9BPvuT3v+byx/T59LnnQapWrcpnHw/jlhuvKbT/kUcfzz39HuPTUeP5c8YCfps6h0kZCxk3cRLX3nAzKSkpfD9hHJdddE5pvl3FIHvVKnbYpQP/uu0+3vpkPCN+yuCz76cx5LMJHN2tJwBffvYRfXtdUaDvkFcHJkPHoUeewLtf/MjwcZP5dMJf3Hb/AGrVrsNXnw/j3n//q9gamrXYjA++/rXIx2577F36b1ylrl79Bux7QBcuvPQqHn1q3T+PAJo2a06Xgw/jsquvZ8DAV9e5X8e9OjPulynFPjrvsz8AzVtuxoEHrfvVkcqk3F7xiKJod+AooAPQHmgC1AUWAT8D7wP9QwjzyqzICuyF55/jhx++B+DlV9+kU+fOQOLbnpO6nUxOTg5n9jyVDz8YyicfD6dL14PKslyVU55HAthr732ZmJ6Rb9+dt67bEIL+Dz/ArJkZpNWowaDX3maL1m0AqFatGmeddxGLFy+m7229efG5pznvoktp2659vv5FDeNq3qIlV113E8uWL+Ph++/m0+EfMX3aX7TcrNV6vENtDI+9+A4dO+9fYH/LVlvSq+/DpKZW4a2Xn2XokNe4+OqbadYy8XeZnZ3NEw/cCcC2O+zC7Q8+lRxSVbVaNf5xTDdWrVzJbdddwjuvv0C3M86n3TY7bLw3po1qr8778uOk/J9Hff+9bp9Hx3c7tdjhVBsiY8Z0Phn2IQAnn3o6qampsbxOeVeer3icDdxKInxsA9QElgINgb2BO4BfoijqXFYFVmQvvvA8AAcc2CX5y2Je3U4+hdZtEr8ADHph4EatTRWH55GADfoH9PVXBgFw7PHdkqEjr3POv5hatWuTnZ3N4NdeKfHzd+i4Z3I7Y8b09a5T8SssdOR1TO5VD4Cfvh+X3J74/Tjmzp4JwKnn/rPQeRxHnHAqDRs3JScnh/fefLmUKlZ5tCGfR3HOBXv9pRfIzs4miiJO7nFGbK9T3pXn4PENcC3QGWgQQqgRQqgL1AHOBGYDjYEhURTVK7MqK6CsrCy++nIkAIf+4/BC20RRxKGH/gOA4cP+t9FqU8XheaQN9ftvvyQnfXY95LBC29SqXZu9Ou8DwGefDCvxa3z91cjkdmHBRhVH3rkZOTk5ye0Z06Ymt7dqt02hfaMoYsut2gHw1efDY6pQKlwIgVcGPQfAvgd0YYstN93PonI71CqEUOjXoyGETOD5KIpmAB8CTYEjgUEbsbwK7eeJE5Mf2jvssGOR7bbPPZaRkcG8efNo2LDhRqlPFYPnkTbUzz+tmRS+zXbbF9lu2+124OOPPuTXXyau0/Muycxk6pTJvP7Kizz+yIMAnHRKDxo3brJhBatMjRn1RXK73TaFny/Z2TmF7gfIyT02edJvrFyxotCJ4vPnzeX0ow9g8qTfycnOplHTZuy8+14c060nHTrtt4HvQJuqL7/4jMmT/gSge8+zy7iaslVug8c6GJVn20G7JTAjz3CDli03K7Jdy83WHJsxfbq/MCofzyNtqJkZa86hFi2KPoeat2gJwOJFi1iSmUmt2rULtBnz7dcceUjBoTqpqal0696TO+95sBQqVllZvGgBzz/+AAC77tGZLbfaOnmsZastktt//PoT2+20a4H+q1atYvKk34DEJPb58+bQtHnLAu2WLc3i5x8nULdefZauWsn0qZOZPnUyH7z9GkedeBo33NnPpZlVYq+88BwADRo24rAjji7bYspYeR5qtTZ5v3r4o8yqqIAWL16c3K5Zs+CShIUdy9tHAs8jbbjM3GUlAWoUcw7lPZaZWfg5VDV3KcsmTZtRtWrV5P6eZ53LVdfdRI0aNUqhYpWFnJwcbrn6AubMyqBatepcc8vd+Y5vu+OuNGqSWMb0hQH9WJVnSd7V3nr5WRbkLtcMsORv51HjZi0497LrGPTeF3zx00yGjU3n8x9m8NRrHybv2/HuG4N44I4bSvndqbJbuHAB77/7FgDHd+te6LLgm5IKFTyiKKoeRVHrKIr+CbyQu/t34N0yLEuSVMZ23nU3vvt1Ct/9OoX0mYv4cuyPnHnuBQx85kkO7LwbH77vPxMV1X23XccXHydWA/rXbffSfrud8h1PTU3lvMuuB2DS779w5bndmPj9eFauWMHcObN46ZlH6denF1XyBNKUlPy//nTaryvnX34DW2+7Y3IuSWpqKjt32IuHnhvM/gf/HwBvDnqaKZP8rlPr7q3XXmb5smWAw6ygggSPKIqWRVEUgGXAJOBhoAEwEjgohLC8LOuraOrUqZPczsrKKrJd3mN5+0jgeaQNVzvPkKmlxZxDeY/Vrr32cyglJYU2W7Wj770P0fu2vizJzOSS889kZsaMDStYG12/Pr14/YUnAbiyVx+OPqlnoe2OP/UszrjwSgC+HvExZxx7IPts15TD92rPg3feRO269TjzoquS7evUrb/ONaSkpHD5DXcAiasvIz4eur5vR5ugV158DoDdOu7Jttu7jHOFCB5ABjATWJJn3yfAFSGEKUV1iqLo/CiKRkdRNHr2nNlx11hhtGixZlzr9OnTimw3fdqaYy1aFhwLq02b55E2VLM8Y+xnzCj6HFq9DG6dunULnd9RnDPPvZDq1auzJDOTt95Y95uBqew99J+bGfT0IwBcdv3tdD/r4mLbX3LtLTzz5jCO7taTdttsT7MWrdh2x10586KreWXoV1SrlriSUbdefRo0Kvru5IXZvPVW1G/YCIDpUyevx7vRpuj7CeP44bvxAHTveVYZV1M+VIgZUiGE1qu3oyhqCvQEbgK+iaLojhDCzUX0GwAMAOjQoWPYCKVWCNtutx0pKSnk5OTw448/cFgRS6H+9OMPADRv3twJwSrA80gbatvt16xM9MvEn2i/zXaFtvt54o8ARR4vTlpaGvUbNGRmxgzSHSJTYTzUtzcvPvUwAJdedxs9zrt0nfrtuGtHdty1Y6HHxn3zJQA77bZnoff6kErbKy88C0DNWrU45vhuZVxN+VBRrngkhRBmhRDuA/4BBKB3FEVHlnFZFUrNmjXpvHdiXfyPPvyg0DYhBD76KDGm9qCDD91otani8DzShmq39TZslrsi0SdF3Ocla8mS5L04DuhycIlfI3PxYubmXvFel2FaKnv9+vTKFzp6nn/ZBj9nxvSpfDPyEwD+7/juJe7/1+RJycnpeVfRkoqydOlShuReZT3quJNKfLW2sqpwwWO1EMI3wOpFvc8vy1oqoh49E3fN/OzTT/jm668LHH/zjdeZ9GdizenTep6+UWtTxeF5pA110imnATBk8GtMnZxe4PizT/VnSWYmqampHN/tlHzHClu96O8ee+j+ZLvO+xZ/Z2yVvX59eiWHV11+w+2lEjpWrVxJnxuvIDs7m7btt6fLofm/qwyh+AERIQQe+k9vIDHfY9+u/9jgmlT5vf/OWyxcuACAUx1mlVRhg0eu1YOC25VpFRVQj9PPYMcddyKEQPeTT+CTjxN3cs3JyeHNN17nkgvPA+CwfxxOl64HlWWpKsc8j7TaggXzmTt3TvKRExI3a1u6NCvf/iV5ltAFuOjSK2narDlLs7LocfKxTBg/FoAVK1bw/NNPcPed/wagx5nn0LZd+3x9B7/2MmeccjzvvTOEObNnJffn5OTw0w/fc83lF/HAPX0A2KPT3nQ9uPC7o6t8ePiuW5Kh44qb7uS0c9dteBXAtCnp9L/vDn7+YTzLlydWEMrOzmbs119wUY+jGTViODVr1ebWe/vnW90KYMa0KZx5XFcGv/Qs06akJ4NITk4O34/7lsvPOpFP//dfAI7rfla+e4iofFqwYD7z5s5JPlbf7HZpVla+/X//PALyHZ83d05y/5LMzHz7i1sQA+Dl3GFW7bfdjg57dirFd1exRWtL+uVZFEWjgL2Ab0IIexXXtkOHjmHk16M3TmEVxOT0dA47pAuT09OBxNCZnJwcluUu+7brrrvx/v+G06BBgzKsUuWd51HJLViyoqxLKHV77NSev9Zh0m237j3p1/+pfPsmjB9L9+OPZH7uUJbadeqwfNkyVq5cCcABXQ/m+ZcHF1j//tVBA7nikvOSP9esVYu0tBpkLl7EihVr/hvvu/+BDHj+ZRo0qFxzjKbOXVrWJZSajOlTOXq/xDK5KSkp1G9Y/OTvHudemm/ex68/fUePoxJXtKIook7deixZkkl27tWuJs1a0PeR59l59z0LPNf0vyZz7AG7JH+uVq06NWvXJiszkxUr1iyaWVlvINiyYeW7x02nndft8+ik7j154LH8n0etGqzbfTauvK4XV1/fu9Bjk/78nf077kgIgVvuvJvzLr58nZ6zImvVoPqYEELhE6zyKJf/90RRlArkhGJSURRFBwGrP0E+3Rh1VTZbtm7Nt2O/48H77+XttwaTnj6JqlWrsv32O3DSKd25+JJLqVatWlmXqXLO80gbapddd+ezUeN45MF7Gfbh+0yf9hc1a9Zim+13oFv3HnTvcWaB+y4AHHTY4dzT7zG+/OJzfvrhO2bPmsXCBfNJq1GDLVq3YdfdOnLsCSdx0KGFL3yg8mP1N9Krt+fNmVVMa8jKyv9NdYtWW3Dupf9izNdfJOZjzJ9L7Tp12aJ1Ow445AhO7HEONWsVPsa+YeOmXHPL3Xw/7lt+nfg9C+bOYdGiBVSvnkbLzbdkp9335OgTe7BLR7+11rp59cXnCSFQrVo1Tjj5tLIup1wpl1c8oihqDQwB+gMfAZNWh5AoijYHTgN6AbWAecAOIYSM4p7TKx6SyovKeMVDG19luuKhslMZr3ho46vQVzxy7QI8nru9IoqiRUANEmFjtUnACWsLHZIkSZLKVnkNHtOBbsCBJOZwtAAaA9nAFGAC8DbwUgjBr3wkSZKkcq5cBo8Qwgrg9dyHJEmSpAquoi+nK0mSJKkCMHhIkiRJip3BQ5IkSVLsDB6SJEmSYmfwkCRJkhQ7g4ckSZKk2Bk8JEmSJMXO4CFJkiQpdgYPSZIkSbEzeEiSJEmKncFDkiRJUuwMHpIkSZJiZ/CQJEmSFDuDhyRJkqTYGTwkSZIkxc7gIUmSJCl2Bg9JkiRJsTN4SJIkSYqdwUOSJElS7AwekiRJkmJn8JAkSZIUO4OHJEmSpNgZPCRJkiTFzuAhSZIkKXYGD0mSJEmxM3hIkiRJip3BQ5IkSVLsDB6SJEmSYmfwkCRJkhQ7g4ckSZKk2Bk8JEmSJMXO4CFJkiQpdgYPSZIkSbEzeEiSJEmKncFDkiRJUuwMHpIkSZJiZ/CQJEmSFDuDhyRJkqTYGTwkSZIkxc7gIUmSJCl2Bg9JkiRJsTN4SJIkSYqdwUOSJElS7AwekiRJkmJn8JAkSZIUO4OHJEmSpNgZPCRJkiTFzuAhSZIkKXYGD0mSJEmxM3hIkiRJip3BQ5IkSVLsDB6SJEmSYmfwkCRJkhQ7g4ckSZKk2Bk8JEmSJMXO4CFJkiQpdgYPSZIkSbEzeEiSJEmKncFDkiRJUuwMHpIkSZJiZ/CQJEmSFDuDhyRJkqTYVSnrAiRpU1O/VrWyLkGVQM3q/hOuDfdbRmZZl6BNiFc8JEmSJMXO4CFJkiQpdgYPSZIkSbEzeEiSJEmKncFDkiRJUuwMHpIkSZJiZ/CQJEmSFDuDhyRJkqTYGTwkSZIkxc7gIUmSJCl2Bg9JkiRJsTN4SJIkSYqdwUOSJElS7AwekiRJkmJn8JAkSZIUO4OHJEmSpNgZPCRJkiTFzuAhSZIkKXYGD0mSJEmxM3hIkiRJip3BQ5IkSVLsDB6SJEmSYmfwkCRJkhQ7g4ckSZKk2Bk8JEmSJMXO4CFJkiQpdgYPSZIkSbEzeEiSJEmKncFDkiRJUuwMHpIkSZJiZ/CQJEmSFDuDhyRJkqTYGTwkSZIkxc7gIUmSJCl2Bg9JkiRJsTN4SJIkSYqdwUOSJElS7AwekiRJkmJn8JAkSZIUO4OHJEmSpNgZPCRJkiTFzuAhSZIkKXYGD0mSJEmxM3hIkiRJip3BQ5IkSVLsDB6SJEmSYmfwkCRJkhQ7g4ckSZKk2Bk8JEmSJMXO4CFJkiQpdgYPSZIkSbEzeEiSJEmKncFDkiRJUuwMHpIkSZJiZ/CQJEmSFDuDhyRJkqTYGTwkSZIkxc7gIUmSJCl2Bg9JkiRJsTN4SJIkSYqdwUOSJElS7KqUdQEqW4sXL6bfA/cxZPCbpKdPIjU1lXZbt+ekk0/h4ksupVq1amVdoioAzyOVBs8jFWf8uLF88P5/GT9uDL//9htz5sxm8aJF1Klbl/btt+WQfxzOOeddSMOGDYt8jiGD3+DlQQMZP3Ysc+fOoWrVqrTcrBV777sf511wMTvvsutGfEeKw4L58/h82Pt8O/Izfv5xAjOmTSU7exUNGjZmu51248gTutPlsKOKfY5RIz5myCvP88P4McybM4soimjctBk77b4nx51yJh067Vts/yWZi3nxqUf4+IN3mD51MimpqWzZpi2HHnkCJ59xAVU34c+yKIRQ1jVsFB06dAwjvx5d1mWUK5MnT+awgw9kcno6ADVr1iQ7O5vly5cDsOuuu/H+/4bToEGDMqxS5Z3nkUqD51HJrViVU9YlbFTXXHEpTz7xWPLntLQ0qlatyuLFi5P7GjVuzCuvD2HPTp3z9V2+fDlnnNaNoe/9N7mvdu3arFixghUrVgCQkpLCHX3v4ZLLroj5nZQvv2VklnUJpWqvrRuRvWpV8ufq1dNISU1ladaS5L69DzyEux8bSFqNmvn6hhDo2+tKBr/0bL7+RBHLly1N7jv1nEu4qlefQl9/xl9TuKD7EUz/awoAaTVqkpOdzYoVic+ybXbYmf6D3qFuvcr1WdaxTb0xIYSOa2vnUKtNVHZ2NiceexST09Np3qIF733wEXMXLmHeoiwGDnqFOnXqMH78OM46/bSyLlXlmOeRSoPnkdZFhz324PY+dzPs05FMnjGXmfOX8NesBUybvZD+Tz5D4yZNmDtnDqeefDwLFy7M1/e+u/smQ8e5F1zExN+nMG32QmbOX8KnI7+h8977kpOTw03XX8PYMX5JWZFlr1rFDrt04Prb72PIZ+MZ+fNMRvw4nXdGfMcx3XoC8OWnH3HnjQUD5rtvDEqGjoMOP4bBH49l5M8zGTkxgzeGjeaAQ44A4KWnH+WTD98t+NrZ2Vx57ilM/2sKjZs259EXhvDFTzP4YmIGfR5+hlq16/DLj9/R64rzYvwvUL5VqCseURRdD/Rd/XMIIVrXvl7xyO+5Z57mogvOBeCTz7+kU+f83w69+srLnNnzVADe/3AYXboetNFrVPnneaTS4Hm0fja1Kx5rM3zY/zj+qMMBePKZgXTrviao7rRtW6ZMTmff/Q7gvf99XKDvwoUL2b7dFmRmZnLlNddx6+2Ff5tdGVW2Kx6jv/qcjp33L/J4n5uuSIaL/478keYtWyWPXdD9SMaMGsHmrbfi9Y++pUqV/DMSVq1cyQkHd2TalHQOPeoE+jz0TL7jQ14dyB3XXwrAM29+xM6775nv+AfvvEGvy88B4LEX32bPfQ5c7/dZ3lS6Kx5RFG0D3FLWdVQWL77wPAAHHNilwD/yAN1OPoXWbdoAMOiFgRu1NlUcnkcqDZ5HKg177NkpuT1t2rR8x2ZmzABg1907FNq3Xr16tN26PQBLMivXL+KbmuJCB5C86gEw8ftx+Y7NmZUBwNbb7lggdABUqVqV9tvvBMDSJUsKHH/vzZdza9ivQOgAOOyoE9hs8y0TbQe/UmydlVWFCB5RFKUATwNpwFdlXE6Fl5WVxVdfjgTg0H8cXmibKIo49NB/AIlvkaS/8zxSafA8Umn5auSI5HabrbbKd6x1m8TPE8aNLbTvwoUL+eO3XwHYrUPh4USVQ/Xqacnt7OzsfMc226I1AL/9/AOr8swTWW3VypX8+tP3AGy38275ji1bmsWEMaMA2PuAQwp97SiK6HzAwUBiAvumqEIED+BSYB9gEOC/Ohvo54kTyclJXKLfYYcdi2y3fe6xjIwM5s2bt1FqU8XheaTS4HmkDbF8+XImT05nQP9HOf+cMwDYqm07Dj8i/6pF55x3IQAjPv+Uq6/4J9Nzr4iEEBg/biwnH380mZmZ7LFnJ7qd4lyiymz0qC+S2+222SHfsRNPSwyDmpr+JzdddjZT0/9IHkv/4zeu/+eZTJuSTqst23Dq2Rfn6zvp91+Sn2Vtt9m+yNdv2347AObOnsnCBZveZ1m5X043iqI2wJ3AXOBK4JKyrajimzFjenK7ZcvNimzXcrM1x2ZMn17sEoXa9HgeqTR4Hml9NK1fM7niWV6dOu/DU8+9SPXq1fPtP+/Ci5k27S8efvA+nnqiP0890T/fqlbNmjfnymuu47obexc6xEaVw+JFC3iu//0A7LbH3rRuu3W+4/sffDhX9e7Lw3fdwvChbzN86NtUT6sBwPJlS6lTtx4n9jiHi67uRe06dfP1nT0zI7ndtFmLImto0qxlvj716m9an2UV4YrHk0At4KoQwuyyLqYyyLv0YM2aNYtsl/dY3j4SeB6pdHgeaX00a9acps2aUatWreS+/Q7oQt977mfzLbYo0D4lJYVbb+/Do088Re3atQHIzMxMLqW7bNkyFi1aSFYh4/ZVOeTk5HDzlRcwZ1YG1apV59pb7y603alnX8w9/V+kYaMmQCJwrF5Kd8WKFSzJzCRz8aIC/bKWrJkblFajRpF15D2Wt8+molwHjyiKzgMOAoaFEJxRKEmS+P6XP/ktfTrT5yzi98kzuKPvPXz/3Xi67teJO28ruA7N3DlzOPrwQ7jovLPZY69OfDj8c6ZkzOPXSdN48ZU3aNy4CU8PeJyu+3dODsNS5XLvv69jxMcfAHDd7fclJ4nntWxpFjf880yuOKcbzTdrxSMD32LY2EkMG/Mnjwx8i7btt2XokFc549iu/Dbxh439FiqFchs8oijaDLgHWApcUMblVCp16tRJbmdlZRXZLu+xvH0k8DxS6fA80oZq0rQpl15xFYPffp8oiri77x188P5/87W58LwzGfH5p+y73wG89e4HdNp7H+rVq0ez5s056pjj+HD45zRq3Jj0SX9ya+8byuidKC4P3nkTrw0cAMBVvfvmW9kqr359evPRe2+xRZt2DHh1KJ3260r9Bg2p37ARnfbryoBXh7JFm3YsmDeXu26+Jl/fmrVqJ7eXLV3696cu9FjePpuKchs8gCeAesCtIYQ/1+cJoig6P4qi0VEUjZ49x1Faq7VosWZ84fTpRX+zk/dbnxYtWxbZTpsmzyOVBs8jlZYOe+xJ5733BeC5Z55M7v/l54n874OhAPzz8iuJooK3AGvStCndT038Mvru229Rke5xpuL169ubF596BIDLb7i9wKTw1ZZkLmbwK88B0O3080lLKzhcKi2tBt1OPx+A8aO/Yl6e3y2bNGue3J41c0aR9cyeuWZeW94+m4pyGTyiKOoBHAGMB+5f3+cJIQwIIXQMIXRs0rhJqdVX0W273XakpCT+6n/8sehLhT/lHmvevLkTOVWA55FKg+eRStPqUPrnH2tWI/p54k/J7TZbtS2y71bt2gGJq2uzZ82KqUJtTP369OKFAQ8BcNn1t9Hz/MuKbDtl0u9k5y6h22rLNkW226LNmnNo+tTJye027bZJfpb98ctPBfqt9sevEwFo1KTZJjexHMph8IiiqCnwIJANnBdCKLiQsjZIzZo16bz3PgB89OEHhbYJIfDRRx8CcNDBh2602lRxeB6pNHgeqTSlT5oEQO3aa4bjrf5lEGDqlMkF+qyWN2zUqr3pDYGpbB688yZeePJhIBE6Tr/g8mLbR3nOk4xpU4psN2/OmvOkZp7zJK1GTXbpkLiJ5VefDyu0bwiBrz4fDkCn/bqu5R1UTuUueAB3AY2AAcDPURTVzvsAqq1umGd/taKeTIXr0TOx3vlnn37CN19/XeD4m2+8zqQ/EyPcTut5+katTRWH55FKg+eR1iY7O3utw58+/WQ4Y0Z/A8C++x+Q3L/Lrrsnt59+8vFC+y5ZsoSXB70AwI477ZxvtSxVPA/eeVNyeNUVN96x1tAB0Lpt++TSuUNeHVjoDQSzs7MZ/PJzANStV58tt8q/HO8RJ3QHYPRXI/hh3OgC/Ye99xbTpqQn2h5/yjq/n8qkPAaP1de3LgIWF/LIO+tr9b7C10RTkXqcfgY77rgTIQS6n3wCn3ycSOA5OTm8+cbrXHLheQAc9o/D6dL1oLIsVeWY55FKg+eR1uavv6ayX6cOPPPUE0ya9Ge+EPLX1Kncf89dnPr/7d15uBxVmcfx75tAIAESguzgBHABh0wERATBkT0sGUQHZBlEREBWCesTURl0UEQQcVd4VBZ3fVgHEAVEBJF1WOIIMkDCErYEgQQIN8s7f5y6pLm5W8KtdHfu9/M8/VS6+5yqU30r3fWrqlNn7w+TmYxeZRWOOmbi6+//05gx7Lr7BACuueq/OezgA3nkkYfJTObMmcNtt/6Z3XfenimPlnB79LHHLdmV04D65ldOfT10HPe5L3PAocf0q97yyw9nz33KgY0HJt/L8Yfsw/898Ffmz5/P/Pnzeehvkzn2E3tx313l4Mh+Bx/J0KFD3zCPCf++P2/fcGMyk5OP/Bi333IjUL7LrrvqUk4/pQSg92+7E1tsve0ArG37iVbrQBURNwIf7Ks4/iYpAAASoUlEQVRcF9/IzIm9FXjPezbPW25bOH0OZlOnTGH8TtsxdcoUoFzyMH/+fGbPng3AJptsytW/u57Ro0c3sZVqdW5HGghuR4uuY+78ZjdhiZk6dQrjNlpwbf2wYcNYaeRIZr/6Ki83jL0xZr31ufjnv+bdm2z6hvozpk/nIx/ajXvuvuv110aMGEFHR8cbjmwfM/EETj9jcB3LfOjppWcsiaeffJwJ24wFyiV2o1dZtdfyBxx6zBv6fcye/SonH34Af/7jgkulhg0rg1F2dCwYsHL8HnvxxXPOWyh4AEx7YiqH7zeBaU+Uy7WWHz6CnD+f114r32UbbjyO7/30CkaOWrq+yzZff9Rdmbl5X+VaLnj0JSJOA/4TIDMXvjVFDwwe3Zs5cybnnnM2l196CVOmPMqQIUN4xzveyd777seRRx3DsGFexaa+uR1pILgdLZrBFDw6Ojq46srLuflPf+TOO27nmaeeYsaM6QwdOpRVV12NsePGsduEPdh7n/0Z3sPgbXPnzuVnP7mQyy75Dfffdy//eP55hg4dypprrc37ttyKT3zyMLbaepslvGbNtzQFj2lPTGWPD4zrd/lDj53Epya+8fbJmcn111zONZf9igcm38PzM54jCEavuhobj9uMPfY+gG22H9/rfF+eNZOfnP8tbrj2SqY9PpUhQ4bwT+u/jfH/thf7fPxTLLsUfpcZPLoweEiSliaDKXioPktT8FDz9Dd4tGIfD0mSJElLGYOHJEmSpNq1XfDIzNMyMxblMitJkiRJzdV2wUOSJElS+zF4SJIkSaqdwUOSJElS7QwekiRJkmpn8JAkSZJUO4OHJEmSpNoZPCRJkiTVzuAhSZIkqXYGD0mSJEm1M3hIkiRJqp3BQ5IkSVLtDB6SJEmSamfwkCRJklQ7g4ckSZKk2hk8JEmSJNXO4CFJkiSpdgYPSZIkSbUzeEiSJEmqncFDkiRJUu0MHpIkSZJqZ/CQJEmSVDuDhyRJkqTaGTwkSZIk1c7gIUmSJKl2Bg9JkiRJtTN4SJIkSaqdwUOSJElS7QwekiRJkmpn8JAkSZJUO4OHJEmSpNoZPCRJkiTVzuAhSZIkqXYGD0mSJEm1M3hIkiRJqp3BQ5IkSVLtDB6SJEmSamfwkCRJklQ7g4ckSZKk2hk8JEmSJNXO4CFJkiSpdgYPSZIkSbUzeEiSJEmqncFDkiRJUu0MHpIkSZJqZ/CQJEmSVDuDhyRJkqTaGTwkSZIk1c7gIUmSJKl2Bg9JkiRJtTN4SJIkSaqdwUOSJElS7QwekiRJkmpn8JAkSZJUO4OHJEmSpNoZPCRJkiTVzuAhSZIkqXYGD0mSJEm1M3hIkiRJqp3BQ5IkSVLtDB6SJEmSamfwkCRJklQ7g4ckSZKk2kVmNrsNS0REPAdMbXY7WtyqwPRmN0Jtz+1IA8HtSAPB7UgDwe2ob2Myc7W+Cg2a4KG+RcSdmbl5s9uh9uZ2pIHgdqSB4HakgeB2NHC81EqSJElS7QwekiRJkmpn8FCj85rdAC0V3I40ENyONBDcjjQQ3I4GiH08JEmSJNXOMx6SJEmSamfwkCRJklQ7g4ckSZKk2hk8JEmSJNVumWY3QO0jIlYG1gZWql6aCUzLzBea1ypJkqTWERHLAmcCmZknNLs9rcS7WqlXEbEdcCCwM7BmD8WeBn4HXJSZf1hSbZM0uETEOsDmwFDg/sx8qB91jgdWzMwv1t0+tb6IWAmYAKwLPAxcmZlzGt7fDTgMeDvl4NofgHMz89kmNFdtKiJWoGw/mZlDm92eVmLwULciYhTwU2DXzpf6qNK5IV0NHJCZL9bVNg0O1Rf3tyhf3J9sdnvUPBGxPPAD4IAub90EHJOZk3up+xSwuj/+iogtgUuB1RtefgjYITOfjIgvAJ/rLF5NE5gB7JiZ9y2xxqqtGTx6ZvDQQqpThH8BNqF8+T4K/BaYDDwJvFIVHQGsA4wFxgMbUL6k7wa2ysy5S7blWppExFuA5/CLe9CLiKuAXej+AMhs4IjMvLCHugYPERGjgQeBVbt5+0/AccAdlN+wmym/dRsAW1C2u4eBsZn52hJpsFpORMwbgNlkZg7qbg6DeuXVo2OATYGXgMMz8xf9qRQRH6WM7rkZcDRwbm0tlDQoRMSelDOvSTnr8UNK2NgOmASsBfwoIkZm5rea1lC1uiMooeNZ4GOUg2v/ClwEbAN8FXgB2CUz7+isFBE7AJdRQsh+wAVLtNVqJX1d+aF+8IyHFhIRtwPvAfbNzF8vYt29gV8Cd2bmFnW0T4ODZzwEEBGXAnsAP8zMw7q8Nwr4CbA7JZicmJlf71LGMx4iIm4GtgIOysyLG14/GvgmPWw/VZlJwJeBKzJzzyXUZLWYiJhP2U5uBX7fR/FhwGeq8m/oX5aZX6ilgW3C4KGFRMQLwLDMHLEYdQN4GejIzJUHvHFqKxFx6puoPgI4GYPHoBYRj1Puprd+Zj7WzftB2XE8ivIjf1JmntPwvsFDRMR0YDSwUma+0vD6GMrlxAmskZnTu6m7LvAYMDUz119CTVaLqQLoqcBylAMex2fmjB7K2sejBwYPLSQiZgJk5kp9le2h/ixgfmaOHNCGqe00HCFa7FngF/egFhGzKQcyev0+iYizgePpEj4MHgKIiA7gla4HxCJiGOXSvVm9bWPV72Jk5or1tlStLCI2pFzu+X7KGfnjM/On3ZQzePTAAQTVnUeBEdWtdBdJRGxPOVL96IC3Su3sGcoRw0V5PNGUlqrVzKXcPrdXmXkicDYlrJ4VERPrbpjaykvAsl1fzMyO6p+z+qj/Kv3YDrV0y8wHgQ9QbkYwArgoIn4bEes1s13txOCh7lxC+fG+OCL63U8jIt4LXEg54nhJTW1Te+m8NGZiZq6/KA9KPyNpKrB8f37YM/Nk4GuU76+vRcSx9TZNbeQ5ynbU3VmNDspZj96MBBa6DEuDTxbfAMZRxnnZGZgcESdEhPvVffADUnfOpvzYrw3cGhHXVf+hxkfE2IjYoHqMrV47ISJ+T+lwtQ4wpZqHdFc13Wwx6nodqKDcnhvKj3ufMvMk4BxK+DgHWK2mdqm9/L2abtT1jcxcPjM36KliRKxP6Sz8VE1tUxvKzEczc0fgU8Acyp3Rbo+ITZvbstZm8NBCMnMWsC1l3I6g3Lbyq5TBAe+lDLj0UPXvq6v3dqBsT/cD22fmy0u84WpFd1O2ocUJHhLA9ZRt6KD+Vqguu+oMH/7OCcpBkKDcOndR7VRNbxu45mhpkZnnAxsD11B+624Dzmxqo1qYX8jqVmZOpQwgeChlMKWkfGl390jKAEyHAJtVdSV4c2c85lHdSWbgmqM2dAVlW3hfRHywv5Wq8HFWba1Su7kS+A5lHI9FdUQ1vXHAWqOlSmZOy8wJwIGU/kRH9FFl0PKuVuqXiBgOvIty+VXn3a5mAtOAv2Xmq81qm1pXtd109hO6Kf3C0RIWEW8FhnhARIujumb/rdXTaZk5p5ntUeuLiNUoV4KsB5CZi3yjnqWZwUOSJElS7bzUSpIkSVLtDB6SJEmSamfwkCQNGhFxY0RkRJzW7LYsqog4qGr7lGa3RZIWh8FDkpogIk6rdiIbH/Mj4qWIeCIi/hwR34mIvSJiWI3tmFi1ZZO6liFJEsAyzW6AJIlnGv49nHL3uHWArYAjgRkR8Xng+zXcGWwiMIYy8Oc9AzzvVvQY8CCOQi1JS5zBQ5KaLDPXbHweEUOBf6YMXHY0sD7wXWCbiDjA2xIvvsw8sNltkKTBykutJKnFZOa8zLw/M88BxgK/qN7aH5jUvJZJkrT4DB6S1MIy8xXg48D/VC9NiohVupaLiKFV5+NrI+KZiOiIiOeq5/tGRHQpf1pEJOUyK4Afd+1z0l17ImLbiPh5RDwWEbMj4sWIuD0iTo6IFXqoc0E1zwuiOCQibo6IGdXrBzXM+/VlR8S4alnTIuLViPhbRJwYEcs0zHvriLgsIp6q2jM5Io7qur4N5XvsXB4RUzrbExHDIuKkiLg3Il6u1vOGiNilu/l2mc+mEfGjiHg4Il6JiFnVfE6PiFX7qLtltT7Tq3V+MCK+FBEr9rVcSWp1Bg9JanGZ2QF8uXo6Etiz8f2IWAO4BfgxsDOwOvAKsGr1/OfAZV06qc+i9C2ZXz1/qXre+GhcxjIRcT7wB2BfymjOc4AVgPcCZwJ3RcQYehbAr4DzKf1XomH5bywYsStwW7WsEcBywEbAWcDFVZlDgD8Ce1D6xiwHbAx8Gzijl3b0ZUXgJsrow++q2jgS2A64OiIO7nEFI74A3AV8AtgASGBZYBzwWeC+iNi0h7oHU/6OHwLeArxGGf34FOAOYPSbWCdJajqDhyS1h98C86p/f7DzxSpMXAm8D7gb2B1YITNXpuxAfxx4lrJzfmZnvcw8u+pb8nj10rGZuWbjo8vyzwYOoQSSI4G3ZOZKlB3+7ShnZDYELomInn5bPkIJTScCozNzFWAUcG03ZX8GXA6MqdZlFAvCxL4RMYnS7+W7wJpVmVWAC6oyJ0XEO3toR1++CKxbtXWFaj03Av5CCUvfiIhRXStFxETgVEqo+wywVmauQAlOmwM3AGsBV3Q9gxERmwE/oPwu3wi8q+FvuB+wZjVvSWpbBg9JagOZOQt4pHr6toa3DqWccfgrsG1mXl1dnkVmvpyZFwG7UY68HxkRqy/qsiNiLPBpylmUnTLze5n5fLWMOZl5IyUMPQFsRgk53VkROD4zv5aZL3WuV2Y+1U3ZO4D9MvOxqtzMzDwF+FP1/hnAhZn56cx8tirzD0o4mkL5ffvooq5rZQSwY2Zenplzqnk/WK3X7Go9JjRWqC6h+hLlc/5wZn4lM5+u6s7LzLuA8ZSzIetW7Wx0OuWGL38HdsvMB6q6czLzF5QzPysv5vpIUksweEhS+3i+mjb28ejcgf1uZs7srlK10/tXYBjl7MSi+iTlSP9VmXl/D8uYCVxWPR3fw3z+QTmq3x9n9nD3rsazIwtdTpWZ84Drqqfj+rmsrn7TuePfZd7PAbf2MO//oASWOzPz+u5mmplzKZe9QcNnFBErNzw/KzNf7abutQ3LlqS25O10Jal9dO0gvhILdoD/KyJ6uxSnM6z01gejJ9tU010j4uleynVePtTTMu6o+qv0x+09vN7Z9+T5zHykjzKL2yfitl7em1ZNu3bw7/yMxvbxGQ2vpo2f0WYsOBB4Qy91b6D0jZGktmTwkKT20bkjPaOarsmCHdaF7nTVgxGLsdy1q+mKLAgXi7OMZ/u7wJ7O3gBzq2lP7zeWWba/y+ticebd+RkNZ0G46E3jZ9R4+duTvdR5oh/zlaSWZfCQpDZQdUbeoHr6cDUd2lBky8zs7Uj9m9G5nEmZeWavJXs3r+8ibavzM/p+Zh7R1JZIUouyj4cktYddWLBze2M1bbzl7b/UuOzOS4fqXEa7ezOfUeOZoHV6Kdfbe5LU8gwektTiqlvmnlI9fZGqE3d1F6f/rV7fdzFn3zmORrcD7lVuqaa7O5Bdjzo/oy37GMukO3ez4O/QW+f/7Re5VZLUQgwektTCImI4ZWyKzkHnzsjMFxqKnFdNd4iIXsNHdyOeUwYOhN5v1Xo+5TaxK1MG8OttGcsO0nByMfAq5azUdyJiaE8FI2JIdScrAKq/5++qpydGxPLd1NkReP/ANlmSliyDhyS1mGrHdGxEHE+5De5+1VsXU0bTbvR9FtyF6eKIOD0i3towrxERsW1EfJsFfUMaTa6me0VEt3eBysx7gHOrp4dHxK8jYpOIiGoZQyPi3RHx+WoZmyzaGre/asyOSdXT3YHfR8TWnQEkio2qv+lkuowDAnye0gdmI+CqiNiwqrdMRHyUMuL7C0hSG7NzuSQ1WZfbry4HjOSNB4amA5/LzIXGwMjM1yJiAvBLyqU4nwU+GxEvUS7fGcWCy6jmdq1POWOyP+Vo+nMR8SzQUc17vYZyJ1XzmQjsVT1mR8TL1TIaf0+6G39jqZeZ34yI5Sjji2wH3Ax0RMRMyt+08U5Y2aXunRFxJCVIbg88EBEvAstTtokHKH+rc2pfEUmqiWc8JKn51qgeq1N24J8G/gJ8j7KDv053oaNTZk4HdgQ+BPwGeJyyszqccnvWa4CjgfW6qXsT5Qj9dZT+I2tQxpgY06XcvMw8jjLmxHnAg5Qj9KMoAwPeApwGbJKZtzBIZeZZlLMWXwfuo4x0vjIwizIa+1cpIe9n3dQ9D9gauJIyWORywFRKkNmC8jlLUtuK7geGlSRJkqSB4xkPSZIkSbUzeEiSJEmqncFDkiRJUu0MHpIkSZJqZ/CQJEmSVDuDhyRJkqTaGTwkSZIk1c7gIUmSJKl2Bg9JkiRJtTN4SJIkSaqdwUOSJElS7f4f9lF71IiOY04AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44bbb149b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for title, cm in [(\"Annotation Mode\", cm_annotation), (\"CrowdAlgorithm Mode\", cm_supervision)]:\n",
    "    cmap = plt.cm.Blues\n",
    "    fig, ax = plt.subplots(figsize=(12,12))\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "\n",
    "\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "                   yticks=np.arange(cm.shape[0]),\n",
    "                   # ... and label them with the respective list entries\n",
    "                   xticklabels=[0,1,2,3,4], yticklabels=[0,1,2,3,4],\n",
    "                   title=title,\n",
    "                   ylabel=\"Actual\",\n",
    "                   xlabel=\"Determined\")\n",
    "\n",
    "    plt.setp(ax.get_xticklabels(), rotation=90, ha=\"right\",rotation_mode=\"anchor\")\n",
    "\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = 'd'#'.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                            ha=\"center\", va=\"center\",\n",
    "                            color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    fig.tight_layout()\n",
    "    \n",
    "    plt.savefig(\"ConfusionMatrix_{0}.pdf\".format(title), transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kappa "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7103063394009218"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics.calc_fleiss_kappa(ProjectType.CrowdAlgorithm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5157005191268501"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics.calc_fleiss_kappa(ProjectType.Annotation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining human results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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