capstone_eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f49c1d48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b707d1fe",
   "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>image_id</th>\n",
       "      <th>class_name</th>\n",
       "      <th>class_id</th>\n",
       "      <th>rad_id</th>\n",
       "      <th>x_min</th>\n",
       "      <th>y_min</th>\n",
       "      <th>x_max</th>\n",
       "      <th>y_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50a418190bc3fb1ef1633bf9678929b3</td>\n",
       "      <td>No finding</td>\n",
       "      <td>14</td>\n",
       "      <td>R11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21a10246a5ec7af151081d0cd6d65dc9</td>\n",
       "      <td>No finding</td>\n",
       "      <td>14</td>\n",
       "      <td>R7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9a5094b2563a1ef3ff50dc5c7ff71345</td>\n",
       "      <td>Cardiomegaly</td>\n",
       "      <td>3</td>\n",
       "      <td>R10</td>\n",
       "      <td>691.0</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>1653.0</td>\n",
       "      <td>1831.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>051132a778e61a86eb147c7c6f564dfe</td>\n",
       "      <td>Aortic enlargement</td>\n",
       "      <td>0</td>\n",
       "      <td>R10</td>\n",
       "      <td>1264.0</td>\n",
       "      <td>743.0</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>1019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>063319de25ce7edb9b1c6b8881290140</td>\n",
       "      <td>No finding</td>\n",
       "      <td>14</td>\n",
       "      <td>R10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           image_id          class_name  class_id rad_id  \\\n",
       "0  50a418190bc3fb1ef1633bf9678929b3          No finding        14    R11   \n",
       "1  21a10246a5ec7af151081d0cd6d65dc9          No finding        14     R7   \n",
       "2  9a5094b2563a1ef3ff50dc5c7ff71345        Cardiomegaly         3    R10   \n",
       "3  051132a778e61a86eb147c7c6f564dfe  Aortic enlargement         0    R10   \n",
       "4  063319de25ce7edb9b1c6b8881290140          No finding        14    R10   \n",
       "\n",
       "    x_min   y_min   x_max   y_max  \n",
       "0     NaN     NaN     NaN     NaN  \n",
       "1     NaN     NaN     NaN     NaN  \n",
       "2   691.0  1375.0  1653.0  1831.0  \n",
       "3  1264.0   743.0  1611.0  1019.0  \n",
       "4     NaN     NaN     NaN     NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c82bf192",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 67914 entries, 0 to 67913\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   image_id       67914 non-null  object \n",
      " 1   class_name     67914 non-null  object \n",
      " 2   class_id       67914 non-null  int64  \n",
      " 3   rad_id         67914 non-null  object \n",
      " 4   x_min          36096 non-null  float64\n",
      " 5   y_min          36096 non-null  float64\n",
      " 6   x_max          36096 non-null  float64\n",
      " 7   y_max          36096 non-null  float64\n",
      " 8   bounding_size  36096 non-null  float64\n",
      "dtypes: float64(5), int64(1), object(3)\n",
      "memory usage: 4.7+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# basic info\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "95ec2581",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate records: 0\n"
     ]
    }
   ],
   "source": [
    "# check for duplicates\n",
    "dupe_num = df.duplicated().sum()\n",
    "print(f'Number of duplicate records: {dupe_num}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e44b362b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create feature for bounding box size\n",
    "df['bounding_size'] = (df['x_max'] - df['x_min']) * (df['y_max'] - df['y_min'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c7721d3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bnTp1NHLkyAxdz72YOQMAAACQY1SsWFHh4eGaMGGCjhw5onfeeUd37txJ17l79uxR+/bt5evrq8WLF2vp0qWqU6eOevfurX379mWojl69eun//u//NGTIEH300UcaPXq0Dh06pH79+mX6sVrMnAEAAADIMUqUKKESJUooICBAXl5e6tixozZv3qzXXnvtoecuXrxYRYoU0dKlS+Xo6Cjp7mxX69atNXny5Pveq/Zvv/32m3bu3KmJEyfqrbfekiRVr15dXl5e6tGjh3744Qe98MILGb42Zs4AAAAAGFpUVJQ+/fRTRUVFWbWnPFc4MjIyXeOcP39e5cuXtwQzSbKzs1O1atV04sSJdNfz999/S5KqVKli1R4QECBJ+vPPP9M91r0IZwAAAAAM7fbt2xo+fLjWrVtn1b5jxw5JD7/XLEWpUqX0xx9/WO3waDabtX//fhUrVizd9ZQqVUqSUi2F/O233yQpQ2Pdi2WNAAAAAAzNy8tLr7/+uubPny87Ozv5+/vr0KFDWrhwoerUqaN69eqla5ygoCC1adNGXbt2VYcOHWRvb68NGzbowIEDCgkJSXc9fn5+eumllzR16lTduHFDFStW1IkTJzR37lyVK1dOjRs3ztR1Es4AAAAAGN6ECRNUsmRJbdiwQXPnzpWHh4fat2+voKAgmUymdI3h7++v8PBwhYSEaPDgwXJwcFDZsmX14Ycfqnr16hmqZ8aMGVq4cKFWr16tOXPm6Nlnn9Ubb7yh4OBgOTg4ZOYSZTJndisR3NfBgwcl/bMGFgAAAMDTKSPZgHvOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAOQI5mSzrUt4qEep0T4L6wAAAACAbGOyM+nG5iNKvHrb1qWkyb6Aq/K+5Jf587OwFgAAAADIVolXbyvx8i1bl5EtWNYIAAAAAAZAOAMAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAIQzAAAAADAAwhkAAAAAGADhDAAAAAAMgHAGAAAAAAZAOAMAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAIQzAAAAADAAwhkAAAAAGADhDAAAAAAMgHAGAAAAAAZg83AWFRWlIUOGqGbNmqpcubK6d++uEydOWI4fPXpUgYGBqlSpkho0aKCwsDCr85OTkzVnzhzVrVtXFStWVOfOnXXmzBmrPlkxBgAAAABkJ5uHs169euncuXNaunSp1q9fL2dnZ3Xs2FGxsbG6du2aOnXqpJIlS2rDhg3q06ePQkJCtGHDBsv5CxYs0OrVqzVx4kStWbNGJpNJ3bp1U3x8vCRlyRgAAAAAkN1sGs6uXbumYsWKacKECfL395ePj4+CgoJ0+fJl/fnnn1q7dq0cHR01btw4+fj4qFWrVurYsaOWLl0qSYqPj9eyZcvUp08f1a9fX76+vpo1a5YuXryoLVu2SFKWjAEAAAAA2c2m4Sx//vyaOXOmnnvuOUnSlStXFBYWJk9PT5UuXVr79u1TQECA7O3tLefUrFlTp06dUlRUlCIiIhQTE6OaNWtajufJk0d+fn7au3evJGXJGAAAAACQ3ewf3uXxGDNmjGWWa+HChXJ1dVVkZKTKlClj1a9w4cKSpL///luRkZGSpGeeeSZVnwsXLkhSloyRGWazWbdv3870+QAAAAD+YTKZ5OLiYusy0iU2NlZms1nS3VxgMpnSdZ5hwlmHDh3UunVrffLJJwoODtbHH3+suLg4OTo6WvVzcnKSJN25c0exsbGSlGafGzduSFKWjJEZCQkJOnr0aKbPBwAAAPAPFxcX+fn52bqMdDl16pQlZ0ips8b9GCaclS5dWpI0YcIEHThwQOHh4XJ2dk61KcedO3ckSa6urnJ2dpZ0976xlL+n9ElJ1VkxRmY4ODhYrgkAAADAo0nv7JMReHt7W2bO7t2J/mFsGs6ioqK0e/duvfLKK8qVK5ckyc7OTj4+Prp06ZI8PT116dIlq3NSvi5SpIgSExMtbcWLF7fq4+vrK0lZMkZmmEwmubq6Zvp8AAAAADnTvZM8GQmVNt0Q5NKlSxo0aJB++eUXS1tCQoKOHDkiHx8fBQQE6Ndff1VSUpLl+O7du+Xt7a2CBQvK19dXbm5u2rNnj+V4dHS0jhw5omrVqklSlowBAAAAANnNpuHM19dXderU0fjx47Vv3z4dP35cw4YNU3R0tDp27KhWrVrp1q1bGjVqlE6cOKGNGzdq5cqV6tGjh6S7azcDAwM1Y8YMbdu2TRERERowYIA8PT3VuHFjScqSMQAAAAAgu9l0WaPJZNLs2bP1wQcfqH///rp586aqVaumVatW6dlnn5UkhYaGatKkSWrZsqU8PDw0dOhQtWzZ0jJG3759lZiYqNGjRysuLk4BAQEKCwuz3HRXsGDBRx4DAAAAALKbyZxypxqyzMGDByVJ/v7+Nq4EAAAAeLJEfbJPiZdv2bqMNNl7uKngu9a3RmUkG9h0WSMAAAAA4C7CGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMACbh7Pr169r7NixqlevnqpUqaJ3331X+/btsxwfMWKEypYta/WnXr16luPJycmaM2eO6tatq4oVK6pz5846c+aM1fc4evSoAgMDValSJTVo0EBhYWFWx9MzBgAAAABkJ5uHs4EDB+r333/XzJkztX79epUrV05dunTRyZMnJUnHjh1Tz549tXPnTsufTZs2Wc5fsGCBVq9erYkTJ2rNmjUymUzq1q2b4uPjJUnXrl1Tp06dVLJkSW3YsEF9+vRRSEiINmzYkO4xAAAAACC72TScnTlzRrt27dJ7772natWqqVSpUho1apSKFCmiL7/8UklJSTpx4oT8/f3l4eFh+VOgQAFJUnx8vJYtW6Y+ffqofv368vX11axZs3Tx4kVt2bJFkrR27Vo5Ojpq3Lhx8vHxUatWrdSxY0ctXbo03WMAAAAAQHazaTjLnz+/lixZovLly1vaTCaTzGazbty4odOnT+vOnTvy8fFJ8/yIiAjFxMSoZs2alrY8efLIz89Pe/fulSTt27dPAQEBsre3t/SpWbOmTp06paioqHSNAQAAAADZzf7hXbJPnjx5VL9+fau2b775RmfPnlWdOnV0/PhxmUwmrVy5Uj/++KPs7OxUv3599e/fX+7u7oqMjJQkPfPMM1ZjFC5cWBcuXJAkRUZGqkyZMqmOS9Lff/+drjEAAAAAILvZNJz926+//qqRI0fqxRdfVMOGDTVnzhzZ2dmpaNGiWrRokc6cOaP3339fx48f18qVKxUbGytJcnR0tBrHyclJN27ckCTFxcWleVyS7ty5k64xMsNsNuv27duZPh8AAADAP0wmk1xcXGxdRrrExsbKbDZLupsLTCZTus4zTDjbunWrBg8erIoVK2rmzJmSpD59+qhjx47KkyePJKlMmTLy8PBQ69atdfDgQTk7O0u6e99Yyt+lu6Er5Qfn7OycamOPO3fuSJJcXV3TNUZmJCQk6OjRo5k+HwAAAMA/XFxc5OfnZ+sy0uXUqVOWSSAp9UTQ/RginIWHh2vSpElq3LixZsyYYSneZDJZglmKlCWKkZGRlqWIly5dUvHixS19Ll26JF9fX0mSp6enLl26ZDVGytdFihRRYmLiQ8fIDAcHB5UuXTrT5wMAAAD4R3pnn4zA29vbMnN24sSJdJ9n83D28ccfa8KECWrXrp1GjhwpO7t/9igZNGiQrl+/bvVcsoMHD0qSSpcuLS8vL7m5uWnPnj2WYBUdHa0jR44oMDBQkhQQEKDVq1crKSlJuXLlkiTt3r1b3t7eKliwoNzd3R86RmaYTCa5urpm+nwAAAAAOdO9K/AyEiptulvjqVOnNHnyZDVu3Fg9evRQVFSULl++rMuXL+vmzZtq3ry5du3apYULF+rs2bPavn27Ro4cqebNm8vHx0eOjo4KDAzUjBkztG3bNkVERGjAgAHy9PRU48aNJUmtWrXSrVu3NGrUKJ04cUIbN27UypUr1aNHD0lK1xgAAAAAkN1sOnO2efNmJSQkaMuWLameKdayZUtNnTpVISEhWrRokRYtWiR3d3e9+uqr6t+/v6Vf3759lZiYqNGjRysuLk4BAQEKCwuzLI0sWLCgQkNDNWnSJLVs2VIeHh4aOnSoWrZsme4xAAAAACC7mcwpiyGRZVKWXvr7+9u4EgAAAODJEvXJPiVevmXrMtJk7+Gmgu9Ws2rLSDaw6bJGAAAAAMBdhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAANg9n169f19ixY1WvXj1VqVJF7777rvbt22c5fvToUQUGBqpSpUpq0KCBwsLCrM5PTk7WnDlzVLduXVWsWFGdO3fWmTNnrPpkxRgAAAAAkJ1sHs4GDhyo33//XTNnztT69etVrlw5denSRSdPntS1a9fUqVMnlSxZUhs2bFCfPn0UEhKiDRs2WM5fsGCBVq9erYkTJ2rNmjUymUzq1q2b4uPjJSlLxgAAAACA7GZvy29+5swZ7dq1S5988omqVKkiSRo1apR+/PFHffnll3J2dpajo6PGjRsne3t7+fj46MyZM1q6dKlatWql+Ph4LVu2TEOGDFH9+vUlSbNmzVLdunW1ZcsWNWvWTGvXrn3kMQAAAAAgu9l05ix//vxasmSJypcvb2kzmUwym826ceOG9u3bp4CAANnb/5Mha9asqVOnTikqKkoRERGKiYlRzZo1Lcfz5MkjPz8/7d27V5KyZAwAAAAAyG42DWd58uRR/fr15ejoaGn75ptvdPbsWdWpU0eRkZHy9PS0Oqdw4cKSpL///luRkZGSpGeeeSZVnwsXLkhSlowBAAAAANnNpssa/+3XX3/VyJEj9eKLL6phw4aaMmWKVXCTJCcnJ0nSnTt3FBsbK0lp9rlx44YkKS4u7pHHyAyz2azbt29n+nwAAAAA/zCZTHJxcbF1GekSGxsrs9ks6W4uMJlM6TrPMOFs69atGjx4sCpWrKiZM2dKkpydnVNtynHnzh1Jkqurq5ydnSVJ8fHxlr+n9En5wWXFGJmRkJCgo0ePZvp8AAAAAP9wcXGRn5+frctIl1OnTlkmgaTUE0H3Y4hwFh4erkmTJqlx48aaMWOGpXhPT09dunTJqm/K10WKFFFiYqKlrXjx4lZ9fH19s2yMzHBwcFDp0qUzfT4AAACAf6R39skIvL29LTNnJ06cSPd5Ng9nH3/8sSZMmKB27dpp5MiRsrP75za4gIAArV69WklJScqVK5ckaffu3fL29lbBggXl7u4uNzc37dmzxxKsoqOjdeTIEQUGBmbZGJlhMpnk6uqa6fMBAAAA5Ez3rsDLSKi06YYgp06d0uTJk9W4cWP16NFDUVFRunz5si5fvqybN2+qVatWunXrlkaNGqUTJ05o48aNWrlypXr06CHp7vRgYGCgZsyYoW3btikiIkIDBgyQp6enGjduLElZMgYAAAAAZDebzpxt3rxZCQkJ2rJli7Zs2WJ1rGXLlpo6dapCQ0M1adIktWzZUh4eHho6dKhatmxp6de3b18lJiZq9OjRiouLU0BAgMLCwixLIwsWLPjIYwAAAABAdjOZUxZDIsscPHhQkuTv72/jSgAAAIAnS9Qn+5R4+Zaty0iTvYebCr5bzaotI9nApssaAQAAAAB3Ec4AAAAAwAAIZwAAAABgAIQzAAAAADAAwhkAAAAAGADhDAAAAAAMgHAGAAAAAAZAOAMAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAIQzAAAAADAAwhkAAAAAGADhDAAAAAAMgHAGAAAAAAZAOAMAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAAAABgAJkKZ3v37lVMTEyax6Kjo/XVV189UlEAAAAA8LTJVDhr3769Tp48meaxI0eOaMSIEY9UFAAAAAA8bezT23HYsGG6cOGCJMlsNmvcuHFyc3NL1e/06dMqVKhQ1lUIAAAAAE+BdM+cvfTSSzKbzTKbzZa2lK9T/tjZ2alSpUqaMmVKthQLAAAAAE+qdM+cNWzYUA0bNpQktWvXTuPGjZOPj0+2FQYAAAAAT5N0h7N7ffTRR1ldBwAAAAA81TIVzmJjY7Vo0SJ9//33io2NVXJystVxk8mkrVu3ZkmBAAAAAPA0yFQ4mzRpkjZs2KDq1avrP//5j+zseFwaAAAAADyKTIWzb7/9VgMGDFD37t2zuh4AAAAAeCplasorMTFRFSpUyOpaAAAAAOCplalwVqdOHf34449ZXQsAAAAAPLUytayxadOmeu+993T16lVVrFhRLi4uqfq8/vrrj1obAAAAADw1MhXO+vfvL0natGmTNm3alOq4yWQinAEAAABABmQqnG3bti2r6wAAAACAp1qmwlnRokWzug4AAAAAeKplKpzNmzfvoX169+6dmaEBAAAA4KmU5eHMzc1NhQsXJpwBAAAAQAZkKpxFRESkart9+7Z+/fVXjRs3TmPGjHnkwgAAAADgaZKp55ylxdXVVXXr1lVwcLCmTZuWVcMCAAAAwFMhy8JZimeeeUYnT57M6mEBAAAA4ImWqWWNaTGbzbpw4YKWLl3Kbo4AAAAAkEGZCme+vr4ymUxpHjObzSxrBAAAAIAMylQ4Cw4OTjOcubm5qUGDBipZsuSj1gUAAAAAT5VMhbM+ffpkdR0AAAAA8FTL9D1n8fHx2rhxo/bs2aPo6Gjlz59f1apVU8uWLeXk5JSVNQIAAADAEy9T4Sw6Olrt27dXRESEnn32WXl4eOjUqVP68ssvtWrVKn388cdyd3fP6loBAAAA4ImVqa30P/jgA0VGRio8PFzfffed1qxZo++++07h4eGKiopSSEhIVtcJAAAAAE+0TIWzbdu2qX///qpWrZpVe7Vq1dS3b199++23WVIcAAAAADwtMhXOYmJi5OXlleYxLy8vXb9+/VFqAgAAAICnTqbCWalSpfT999+neWzbtm0qUaLEIxUFAAAAAE+bTG0I0qVLFw0cOFDx8fF69dVXVahQIV25ckVffPGF1q1bp3HjxmVxmQAAAADwZMtUOGvatKlOnz6tRYsWad26dZZ2BwcHBQcHq3Xr1llWIAAAAAA8DTIVzm7fvq2goCAFBgbqwIEDunHjhi5cuKDWrVsrb968WV0jAAAAADzxMnTP2dGjR/X6669rxYoVkqQ8efKoXr16qlevnmbPnq02bdro5MmT2VEnAAAAADzR0h3Ozp07p44dO+rGjRsqXbq01TFHR0eNHDlSMTExatOmjSIjI7O8UAAAAAB4kqU7nC1ZskT58+fXp59+qiZNmlgdc3FxUWBgoDZs2CBXV1ctWrQoywsFAAAAgCdZusPZ7t271bVrV+XLl+++fQoWLKhOnTpp9+7dWVEbAAAAADw10h3OLl++nK7nl5UpU4ZljQAAAACQQekOZwUKFNClS5ce2u/q1asPnF17kAULFqhdu3ZWbSNGjFDZsmWt/tSrV89yPDk5WXPmzFHdunVVsWJFde7cWWfOnLEa4+jRowoMDFSlSpXUoEEDhYWFWR1PzxgAAAAAkJ3SHc4CAgK0cePGh/bbtGmT/vOf/2S4kBUrVmjOnDmp2o8dO6aePXtq586dlj+bNm2yHF+wYIFWr16tiRMnas2aNTKZTOrWrZvi4+MlSdeuXVOnTp1UsmRJbdiwQX369FFISIg2bNiQ7jEAAAAAILulO5y1a9dOe/bs0dSpU3Xnzp1Ux+Pj4/X+++9rx44datu2bboLuHjxorp27aqQkBB5e3tbHUtKStKJEyfk7+8vDw8Py58CBQpYvueyZcvUp08f1a9fX76+vpo1a5YuXryoLVu2SJLWrl0rR0dHjRs3Tj4+PmrVqpU6duyopUuXpnsMAAAAAMhu6X4Itb+/v0aMGKHJkyfrs88+U61atVSsWDElJSXp77//1p49e3Tt2jX169dPdevWTXcBhw8fVt68efX5559r/vz5On/+vOXY6dOndefOHfn4+KR5bkREhGJiYlSzZk1LW548eeTn56e9e/eqWbNm2rdvnwICAmRv/8+l1qxZU4sXL1ZUVJTOnz//0DEAAAAAILulO5xJUtu2beXr66uwsDBt27bNMoOWO3du1alTR507d1bFihUzVEDDhg3VsGHDNI8dP35cJpNJK1eu1I8//ig7OzvVr19f/fv3l7u7u2XjkWeeecbqvMKFC+vChQuSpMjISJUpUybVcUn6+++/0zUGAAAAAGS3DIUzSapataqqVq0q6e79XHZ2dsqbN2+WFyZJf/75p+zs7FS0aFEtWrRIZ86c0fvvv6/jx49r5cqVio2NlXT3Idj3cnJy0o0bNyRJcXFxaR6XpDt37qRrjMwwm826fft2ps8HAAAA8A+TySQXFxdbl5EusbGxMpvNku7mApPJlK7zMhzO7pU/f/5HOf2h+vTpo44dOypPnjyS7m7T7+HhodatW+vgwYNydnaWdPe+sZS/S3dDV8oPztnZOdXGHikzfq6urukaIzMSEhJ09OjRTJ8PAAAA4B8uLi7y8/OzdRnpcurUKcskkJR6Iuh+HimcZTeTyWQJZilSlihGRkZaliJeunRJxYsXt/S5dOmSfH19JUmenp6pHgGQ8nWRIkWUmJj40DEyw8HBQaVLl870+QAAAAD+kd7ZJyPw9va2zJydOHEi3ecZOpwNGjRI169ft3ou2cGDByVJpUuXlpeXl9zc3LRnzx5LsIqOjtaRI0cUGBgo6e4jAFavXq2kpCTlypVLkrR79255e3urYMGCcnd3f+gYmWEymeTq6prp8wEAAADkTPeuwMtIqEz3Vvq20Lx5c+3atUsLFy7U2bNntX37do0cOVLNmzeXj4+PHB0dFRgYqBkzZmjbtm2KiIjQgAED5OnpqcaNG0uSWrVqpVu3bmnUqFE6ceKENm7cqJUrV6pHjx6SlK4xAAAAACC7GXrm7IUXXlBISIgWLVqkRYsWyd3dXa+++qr69+9v6dO3b18lJiZq9OjRiouLU0BAgMLCwizrOgsWLKjQ0FBNmjRJLVu2lIeHh4YOHaqWLVumewwAAAAAyG4mc8piSGSZlKWX/v7+Nq4EAAAAeLJEfbJPiZdv2bqMNNl7uKngu9Ws2jKSDQy9rBEAAAAAnhaEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADIBwBgAAAAAGQDgDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAEOFswULFqhdu3ZWbUePHlVgYKAqVaqkBg0aKCwszOp4cnKy5syZo7p166pixYrq3Lmzzpw5k+VjAAAAAEB2Mkw4W7FihebMmWPVdu3aNXXq1EklS5bUhg0b1KdPH4WEhGjDhg2WPgsWLNDq1as1ceJErVmzRiaTSd26dVN8fHyWjQEAAAAA2c3m4ezixYvq2rWrQkJC5O3tbXVs7dq1cnR01Lhx4+Tj46NWrVqpY8eOWrp0qSQpPj5ey5YtU58+fVS/fn35+vpq1qxZunjxorZs2ZJlYwAAAABAdrN5ODt8+LDy5s2rzz//XBUrVrQ6tm/fPgUEBMje3t7SVrNmTZ06dUpRUVGKiIhQTEyMatasaTmeJ08e+fn5ae/evVk2BgAAAABkN/uHd8leDRs2VMOGDdM8FhkZqTJlyli1FS5cWJL0999/KzIyUpL0zDPPpOpz4cKFLBsjM8xms27fvp3p8wEAAAD8w2QyycXFxdZlpEtsbKzMZrOku7nAZDKl6zybh7MHiYuLk6Ojo1Wbk5OTJOnOnTuKjY2VpDT73LhxI8vGyIyEhAQdPXo00+cDAAAA+IeLi4v8/PxsXUa6nDp1ypIzpNRZ434MHc6cnZ1Tbcpx584dSZKrq6ucnZ0l3b1vLOXvKX1SUnVWjJEZDg4OKl26dKbPBwAAAPCP9M4+GYG3t7dl5uzEiRPpPs/Q4czT01OXLl2yakv5ukiRIkpMTLS0FS9e3KqPr69vlo2RGSaTSa6urpk+HwAAAEDOdO8kT0ZCpc03BHmQgIAA/frrr0pKSrK07d69W97e3ipYsKB8fX3l5uamPXv2WI5HR0fryJEjqlatWpaNAQAAAADZzdDhrFWrVrp165ZGjRqlEydOaOPGjVq5cqV69Ogh6e7azcDAQM2YMUPbtm1TRESEBgwYIE9PTzVu3DjLxgAAAACA7GboZY0FCxZUaGioJk2apJYtW8rDw0NDhw5Vy5YtLX369u2rxMREjR49WnFxcQoICFBYWJjlprusGAMAAAAAspvJnHKnGrLMwYMHJUn+/v42rgQAAAB4skR9sk+Jl2/Zuow02Xu4qeC71rdGZSQbGHpZIwAAAAA8LQhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCmY3s2bNHZcuWve+fefPmWfVPSEjQW2+9pblz56YaKz4+XjNnzlSDBg1UoUIFvfbaa/r666+t+ty6dUvTpk1T48aNValSJTVv3lyrVq1ScnJytl4nAAAAgPSxt3UBT6ty5cppzZo1qdpnz56tgwcPqlmzZpa2uLg4DRkyRH/88Yfq1auX6pzBgwdr586dGjx4sEqWLKnPPvtMAwcOlJubm6X/oEGDdODAAfXt21elSpXSzz//rEmTJun69esKDg7OvgsFAAAAkC6EMxtxc3NTpUqVrNq2bt2q3bt3KyQkRN7e3pKkffv2afz48bp06VKa4+zdu1ebN2/WkiVLVL9+fUlSrVq1dObMGf3444+qV6+eDh8+rB9++EGzZ8/WK6+8YukTHR2t0NBQBQUFyWQyZd/FAgAAAHgoljUaRFxcnCZOnKgGDRro5ZdftrT36tVLRYsW1caNG9M8b/PmzfLy8rIEM0kymUxavXq1Ro8ebWlr3bq1atWqZXVuyZIldfv2bUVFRWXx1QAAAADIKGbODGLFihW6dOmSVq5cadUeHh6usmXL3ve8iIgIlSlTRl988YUWLFigM2fOqHjx4howYIBeeuklSXeXUP73v/9Nde6WLVtUsGBBFShQIGsvBgAAAECGMXNmAPHx8froo4/UtGlTlShRwurYg4KZJF29elV//PGHZsyYoR49emjp0qXy8fFRv379tH379vuet3z5cu3du1c9evSQnR0vAwAAAMDWmDkzgP/7v//TlStX1LVr1wyfm5CQoMuXL2vjxo0qV66cJKlmzZpq0aKFFixYYLXcMcXKlSv1/vvvq3nz5mrfvv0j1w8AAADg0TFlYgCbN2/Wc889J19f3wyfmzt3bnl4eFiCmSTlypVLtWrV0tGjR636Jicna+rUqZo8ebKaN2+u999/n41AAAAAAIMgnNlYQkKCdu3aZbUJSEaUKFFCCQkJMpvNVu2JiYlydna2fB0fH6++fftq+fLl6tChg6ZPny57eyZOAQAAAKMgnNnY8ePHFRsbq6pVq2bq/Pr16+v69evatWuXpS0+Pl47duywGnP48OHaunWrRowYoZEjRzJjBgAAABgMUyc2dvz4cUmSj49Pps5/9dVXFR4ersGDB2vQoEEqUqSIPvzwQ0VGRiokJETS3eenffXVV2rYsKEqVaqkAwcOWI3h5+cnR0fHR7oOAAAAAI+GcGZjV65ckSTlzZs3U+c7ODho+fLlmjlzpmbNmqWYmBj5+flpxYoVlvvQvv32W0nSd999p++++y7VGNu2bVOxYsUyeQUAAAAAsoLJ/O+blfDIDh48KEny9/e3cSUAAADAkyXqk31KvHzL1mWkyd7DTQXfrWbVlpFswD1nAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEMAAAAAAyAcAYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnj5E5OdnWJaRLTqkTAAAAeJLY27qAp4nJzk7RW3cq6Vq0rUu5r1z58yhPozq2LgMAAAB46hDOHrOka9FKvHLV1mUAAAAAMBjCGfAUiI2NVZUqVZT8ryWrjo6OOnjwYKr+K1eu1OTJk7Vt2zYVK1bM0n7u3Dl98MEH2rdvn2JjY/Xcc8+pX79+qlWrliRp7ty5mjdv3n3r+Oijj1S9evUsuioAAIAnC+EMeAocO3ZMycnJmjlzpooWLWppt7NLfdvp6dOnNXPmzFTt169fV2BgoPLkyaORI0fKzc1N69evV+fOnbVy5UpVr15db731lurWrWt1Xnx8vAYOHCgPDw9VqFAh6y8OAADgCUE4A54CR48elYODg5o0aSIHB4f79ktKStLw4cOVL18+RUZGWh379NNPFRUVpbVr16pIkSKSpDp16qhFixYKCwtT9erV5enpKU9PT6vzJk+erJiYGH3yySdydnbO+osDAAB4QrBbI/AUOHr0qEqXLv3AYCZJYWFhunLlirp3757qWJEiRdSxY0dLMJPuzrwVL15cZ8+eTXO8iIgIffTRR+rdu7e8vLwe7SIAAACecMycAU+BiIgI2dnZqVOnTtq/f78cHR318ssva+jQoXJzc5Mk/fnnn5o3b55CQ0P1119/pRqjadOmatq0qVXb9evX9csvv1juOfu3adOmycvLSx06dMj6iwIAAHjCMHMGPOGSk5N1/PhxnT59Wo0bN9bSpUvVs2dPffnll+revbuSk5OVmJioYcOG6a233kr3hh1JSUkaNWqUbt++rW7duqU6HhERoV27dqlbt26yt+dzIAAAgIfhHRPwhDObzVq8eLEKFSokHx8fSVJAQIAKFSqkIUOGaMeOHTp48KCio6M1aNCgdI2ZkJCgYcOGaevWrRo3bpz8/f1T9QkPD1fBggXVokWLLL0eAACAJxXhDHjC5cqVSzVq1EjV3qBBA0l3Z7gWLVqkpUuXytHRUYmJiZYt95OTk5WUlKRcuXJZzrtx44Z69+6tvXv3auzYsXr33XdTjZ2UlKStW7eqadOmcnR0zJ4LAwAAeMKwrBF4wl28eFFr165NtftiXFycJGnVqlVKSEhQx44dVa5cOZUrV06jRo2SJDVu3FgdO3a0nHPhwgW98847OnDggGbOnKm2bdum+T0PHDiga9eu6ZVXXsmeiwIA5Bi9e/dWw4YNrdr27dunNm3aqEqVKmrQoIEmTpyoW7du3XeMQ4cOqVy5ctq4caNV+61btzRt2jQ1btxYlSpVUvPmzbVq1apUz/UEcgpmzoAnXHx8vMaMGaOgoCD169fP0v7111/Lzs5OM2bMkIuLi9U5P/zwg+bNm6eFCxeqZMmSku7+AuzYsaOuXLmiZcuWKSAg4L7f848//pC9vT3PNQOAp9xnn32mLVu2WD1j8/jx4+rUqZOqVq2q2bNn6+LFi5o+fbr++usvLVq0KNUY8fHxGj58uBITE1MdGzRokA4cOKC+ffuqVKlS+vnnnzVp0iRdv35dwcHB2XptQHYgnAFPOC8vL7Vo0cKybLFSpUr69ddftWjRIrVp0ybNDUD+/PNPSVKZMmVUrFgxSdKcOXN0+vRp9enTRw4ODjpw4IClv6Ojo/z8/CxfHz9+XMWKFZOTk1P2XhwAwLAuXryoSZMmpXr+5ZdffimTyaT58+crd+7ckqTExESNGzdO58+ftwpykjR79mzdvHkz1fiHDx/WDz/8oNmzZ1tWatSqVUvR0dEKDQ1VUFCQTCZTNl0dkD0IZ8BTYMKECSpRooQ2bdqkBQsWqEiRIurbt6+6dOmS7jG+/fZbSdLcuXM1d+5cq2NFixbVd999Z/n6ypUryps3b9YUDwDIkUaPHq3atWvLyclJv/zyi6U9Pj5e9vb2Vqs28ufPL+nuI1ruDWf79+9XeHi4Zs2apaCgoFTfo3Xr1qke51KyZEndvn1bUVFRKlSoUFZf1hOnd+/eOnLkiNXv8YsXL2ratGnauXOnEhISVKFCBQ0dOtTqg9gZM2Zo6dKlqcYbNGiQ5XmpP/74Y5o7OtepU0dhYWHZcDU5H+EMeAo4OTkpODg43Us83njjDb3xxhtWbT/88EO6v19a/1gDAJ4e69at0+HDh/Xll19q2rRpVsfefPNNrVu3TlOmTFFQUJCuXLmi+fPnq0yZMvL19bX0i4uL0/Dhw9WjRw+VLVs21fcoV66c/vvf/6Zq37JliwoWLKgCBQpk/YU9YdJadnrr1i21bdtWDg4OGj9+vJycnLRgwQJ16tRJX3zxhQoXLixJOnr0qGrVqqX+/ftbjfnMM89Y/h4REaG8efNqyZIlVn3c3d2z76JyOMIZAAAAssz58+c1ZcoUTZkyJc2AVLp0aQ0aNEgTJkzQhx9+KOnuCoxVq1ZZ7Q48Y8YMubq6qkePHqk2tbqf5cuXa+/evRo5cqTs7Nj37kHut+x0xYoVunbtmr755htLECtfvrzeeOMN/fLLL2revLmku8Hr3XffVaVKle77PY4ePaqyZcs+sA+sEc4AAACQJcxms0aOHKn69evrpZdeSrPP4sWLLTv+Nm7cWNeuXdOCBQvUsWNHrVq1SoUKFdKePXu0Zs0arVu3Tvb26Xu7unLlSr3//vtq3ry52rdvn5WX9US637LTb7/9Vi+99JIlmEmSh4eHduzYYfn6ypUrunLlitVMZ1qOHj2qevXqZX3xTzA+UgAAAECWWLVqlY4dO6aRI0cqMTFRiYmJMpvNku5u+hEfH6+FCxfq1Vdf1dixY1WrVi01bdpUK1as0KVLlxQWFqaYmBiNGDFC3bp1U+nSpVM9f/PfuzYmJydr6tSpmjx5spo3b67333+fjUAeImXZ6ZgxY6zaExISdPLkSZUqVUqzZ89WnTp1VK5cOQUGBurYsWOWfkeOHJEkbdu2TS+88ILKlSun119/Xdu3b7f0iY2N1ZkzZ3T27Fm99tprKl++vF544QWFhYVZXhNIjZkzAAAAZInNmzfr2rVrqlOnTqpj5cqVU+vWrRUbG6sqVapYHStUqJC8vb31559/6tChQzp//rzmz5+v+fPnW/UbNWqURo0aZQkK8fHxGjhwoLZs2aIOHTpoxIgRBLOHeNCy0+joaCUmJmrFihXy8vLSxIkTFR8frzlz5qh9+/b6/PPPVaRIEUVEREiSrl69aukTHh6unj17asmSJapbt66OHTum5ORknTlzRv369VPevHm1bds2TZ8+XdHR0RowYIAtLt/wCGcAAADIEuPHj1dMTIxV2/z583Xo0CEtXLhQhQsX1ubNm/Xrr7+qTZs2lj5Xr17V6dOnVaFCBZUrV07r16+3GuPy5cvq1auXevfurQYNGljahw8frq1bt2rEiBHq2LFjdl7aE+Fhy04TEhIsfw8NDbU86qB8+fJq0qSJwsPDNWjQIDVr1kxly5ZV3bp1Lff21alTRy1atNCcOXNUt25dlSpVSkuXLpW/v79lN85atWopLi5OYWFh6tq1KxuDpIFwBgAAgCxRqlSpVG358uWTo6Oj/P39JUl9+vTRhAkTlDt3br3yyiu6du2aFi9erFy5cqlz585yc3Oz9E3x119/Sbq7cUjKsa1bt+qrr75Sw4YNValSJavnb0qSn5+fHB0ds+Eqc66UZadffPGFZXnovctOU8JYjRo1LH+XpGeffVY+Pj46evSopLs/h38/j87BwUG1a9fWmjVrJEl58uRJ836zBg0aaN26dTp58iQbhaSBcAYAAIDHJjAwUO7u7lq+fLk2btyo/Pnzq1q1apo/f76KFSuW7nFSnr/53XffWT2jK8W2bdsyNN7T4GHLTnv37q2CBQsqPj4+1fHExEQ5OztLuvt4nfj4eDVp0sSqz507d5QvXz5J0qFDh3Tw4EG98847VktN4+LiJP3zbDtYI5wBAAAg20ydOjVVW4sWLdSiRYt0j1GsWDGrDSkkadq0aameoYYHS8+y0/Pnz2vLli26evWq5Z60//3vfzp16pTeeustSdLXX3+tbdu2qUaNGsqbN68k6fbt2/rhhx9UvXp1SXe32h83bpxKlixp9aDwr7/+Ws8++yzB+T4IZ8ATyJycJJNdrod3tLGcUicAAE+C9Cw7DQ4O1tatW9WlSxcFBwcrISFBs2bNkqenp958801JUteuXbV582Z1795d3bt3V1JSkpYuXarbt2+rb9++kqSmTZsqLCxMQ4cOVf/+/eXh4aEvvvhC3333nWbNmmX1TDv8I0eEs/Pnz6thw4ap2idOnKi33npLR48e1aRJk3To0CHly5dP7dq1U5cuXSz9kpOTNW/ePK1bt07R0dGqWrWq3nvvPZUoUcLS52FjADmJyS6X9mx/XzdvnLN1KfflntdLNeoPs3UZAADgHl5eXlq9erVmzJihIUOGKFeuXHr++ec1cuRIubm5SZLKlCmj8PBwzZ49WyNHjlR8fLwCAgI0adIkFS9eXJLk6uqqlStXatasWQoJCdG1a9f03HPPad68eWrUqJEtL9HQckQ4O3bsmJycnLR161arNavu7u66du2aOnXqpEaNGmn8+PE6cOCAxo8fr3z58qlVq1aSpAULFmj16tWaMmWKihQpounTp6tbt2768ssv5ejomK4xgJzm5o1zuh51wtZlAAAAA0tr2Wnp0qW1aNGiB57n7++vsLCwB/YpXLiwpkyZ8kj1PW1yRDg7fvy4vL29rZ5UnmLlypVydHTUuHHjZG9vLx8fH505c0ZLly5Vq1atFB8fr2XLlmnIkCGqX7++JGnWrFmqW7eutmzZombNmmnt2rUPHAMAAAAAspudrQtIj2PHjql06dJpHtu3b58CAgJkb/9PzqxZs6ZOnTqlqKgoRUREKCYmRjVr1rQcz5Mnj/z8/LR37950jQEAAAAA2S3HzJx5eHioTZs2On36tEqUKKGgoCDVrVtXkZGRKlOmjFX/lBm2v//+W5GRkZKkZ555JlWfCxcuSNJDxyhYsGCGazabzbp9+7bla5PJJBcXlwyPYyuxsbGW514gZ+G1BgAAnkQ56T3Ove9vzGaz1a1ZD2L4cBYfH6/Tp0/LxcVFQ4cOlaurqz7//HN169ZNy5cvV1xcXKoHDDo5OUm6+6yF2NhYSUqzz40bNyTpoWNkRkJCguVBfZLk4uIiPz+/TI1lC6dOnbL8t0POwmsNAAA8iXLSe5x/v79J7wPRDR/OHB0dtXfvXtnb21suqnz58jp58qTCwsLk7Oyc6kF5KYHK1dXV8rC8+Ph4y99T+qQk74eNkRkODg5WSzHTm5aNwtvbm9mMHIrXGgDgccspv3v4fZOz5ZTXmWT9/ubEifRv0Gb4cCalHZDKlCmjnTt3ytPTU5cuXbI6lvJ1kSJFlJiYaGlL2doz5WtfX19JeugYmWEymTId7Iwgp0wZI+fjtQYAOV9Glm3ZSk6oEU+Oe9/fZOR1Z/hwFhERoXfffVdLly5VtWrVLO2HDh1S6dKl9Z///EerV69WUlKS5WF2u3fvlre3twoWLCh3d3e5ublpz549lnAWHR2tI0eOKDAwUJIUEBDwwDEAAABwfyaTSXv33NTNm4m2LiVN7u72CqjhbusygIcyfDgrU6aMnnvuOY0fP17vvfee8ufPr7Vr1+rAgQNav369ChUqpNDQUI0aNUpdu3bVH3/8oZUrV2r8+PGS7i6LDAwM1IwZM1SgQAEVLVpU06dPl6enpxo3bixJatWq1QPHAAAAwIPdvJmo69eTbF0GkKMZPpzZ2dlp0aJFmjFjhvr376/o6Gj5+flp+fLlKlu2rCQpNDRUkyZNUsuWLeXh4aGhQ4eqZcuWljH69u2rxMREjR49WnFxcQoICFBYWJjlHraCBQs+dAwAAAAgpzEnm2WyM/ZyzpxQ4+Ni+HAmSQUKFNDkyZPve7xChQpas2bNfY/nypVLQ4YM0ZAhQzI9BgAAAJDTmOxMuv5VpBKjEmxdSprsCzooXzNPW5dhGDkinAEAAADInMSoBCVeytzjofB42dm6AAAAAAAA4QwAAAAADIFwBgAAAAAGQDgDAAAAAAMgnAEA8BQwm81as2aNXn31VVWuXFkvvviiJk2apFu3bln6vP322ypbtmyqPwcOHLD0OX/+vPr166datWqpRo0aCgoK0tmzZ1N9v+XLl6tRo0by9/dXixYttHXr1sdxmQCQo7FbIwAAT4HQ0FDNmjVLXbp0Ua1atXTmzBmFhITozz//1PLly2U2m3X8+HF16dJFTZo0sTr3ueeekyTFxsaqc+fOSkxM1JgxY+To6KiQkBC1a9dOX3zxhfLkyWP5XjNnzlRwcLDKly+vDRs2qG/fvlq5cqUCAgIe+7UDQE5BOAMA4D7MZrPWrl2r8PBw/fXXXypQoIAaNmyofv36yc3NLVX/lStXavLkydq2bZuKFStmaT9//rymTZumX375RcnJyapataqGDx+u4sWLW/pcvXpVM2fO1A8//KCYmBhVqFBBgwYNUoUKFR75OpKTk7VkyRK1bt1agwYNkiQ9//zzypcvn/r3769Dhw7J1dVVsbGxatCggSpVqpTmOL/++qtOnz6tFStWqFatWpIkb29vNW3aVNu2bVPLli0VFxenxYsXq2PHjgoODpYk1atXT++8847mz5+vFStWPPL1PImy4rXWrl07/fLLL/f9HseOHbP8fePGjVq+fLlOnz4tDw8PvfHGG+rVq5dy5cqV9RcHIN0IZwAA3MfDZptMJpOl7+nTpzVz5sxUY6Rntik5OVlBQUE6f/68Bg8erIIFC2rFihXq0KGDPv30U5UsWfKRruPWrVt67bXX1LRpU6t2b29vSdK5c+dkNpslSb6+vvcdJz4+XpKUO3duS1v+/PklSdevX5ck/f7774qOjraafTOZTGrcuLFmzpypuLg4OTs7P9L1PImy4rX23nvvWS1TlaSzZ89q2LBhevvtty1tq1at0n//+1917txZI0eO1IEDBzR//nzFx8dr4MCB2XeRAB6KcAYAQBrSM9vk7+8vSUpKStLw4cOVL18+RUZGWo2TntmmPXv2aP/+/VqyZInq168vSapWrZpq1qypDRs2WL5/ZuXJk0djxoxJ1f7tt99Kurts8bPPPpO7u7smT56s77//Xrdv31bNmjU1YsQIlSpVSpJUu3ZtlSlTRtOnT9fkyZPl7OysyZMny9XVVY0aNZIknTx5UpJSBcoSJUooKSlJZ8+eVZkyZR7pep40WfVaK126tNXXiYmJmjBhgnx9fTVq1ChJ0u3bt/XBBx+oS5cuGjp0qCSpVq1aio6O1k8//UQ4A2yMDUEAAEhDymxT8+bNrdrvnW1KERYWpitXrqh79+6pxknPbFPFihX1ySefqHbt2pY+Dg4OMplMlvOz2m+//aalS5eqUaNGeu6553T06FHdvHlT+fPn1/z58zVx4kSdOXNGbdu21cWLFyVJTk5O+u9//6vjx4+rUaNGqlOnjrZu3ap58+bJy8tLknTz5k1JSrUUL+X6/z2zk1np2eBkz549CgwMVEBAgGrXrq3evXvrzJkzVuPcunVLY8eOVe3atVWpUiV16tRJJ06csOpz9epVjR49WnXq1FHlypXVoUMH/fHHH1lyHSk1ZMVr7d9Wr16tI0eOaPz48XJ0dJQk7dq1SzExMQoMDLTqO2zYMK1fv/5RLwXAIyKcAQCQhpTZpqpVq1q13zvbJEl//vmn5s2bp8mTJ8vFxSXVOPfONp07d06XL1/WhAkTrGabXF1dVaVKFdnb2ysxMVGnTp3SsGHDZDab9cYbb2T5te3bt0/du3dX8eLFNWnSJEnS4MGD9fHHH2vYsGGqVq2aWrRoobCwMN28eVMffvihpLthp3379vL19dXixYu1dOlS1alTR71799a+ffsk3Z0FSkvKskk7u6x56xEaGqrx48erQYMGmj9/vrp27aovvvhCvXv3ltls1v79+9W5c2flz59fM2bM0JgxY3Tu3Dm1adNGV69etYwzaNAgbdmyRYMGDdK0adMUFRWlDh06WIJzypLT7du3a/DgwZozZ47s7e3VoUMHnT59OkuuJatea/eKiYnR3Llz1aJFC6v7Fo8ePSp3d3ddvXpVbdu2Vfny5VW7dm3Nmzfvvj87AI8PyxoBAEinf882JSYmatiwYXrrrbdUvXp1/fXXX6nOSZlt6tmzpyWMOTo6atGiRZbZpnuNGzdO69atkyQFBwdn+RLAr776SsOHD5e3t7fCwsKUL18+SdJ//vOfVH29vLzk4+OjiIgISdLixYtVpEgRLV261DITU6dOHbVu3VqTJ0/Wxo0bLTs2xsTEKG/evJaxbt++LUlyd3d/5GtIzzLAxYsXq1SpUgoJCbEEwipVqqhBgwb69NNP1aVLF+3fv18//PBDquWkL774oj7++GMFBQVl+5LT+8nMa+1e69evV3R0tHr06GHVfvXqVSUlJal79+7q0KGD+vTpo127dmn+/PmKjY3VkCFDsuV6AKQPM2cAAKRDWrNNixYtUnR09APfoKdntuleb7/9tj766CMFBwdryZIlGjt2bJZdQ2hoqAYNGqRKlSpp1apV8vDwkCQlJCRo48aNVs8zSxEXF2dZhnn+/HmVL1/eEsykuzNh1apVsywFTFmK9+/lg2fOnJGjo2OagTSj0rMMsEKFCurQoYPVTF3hwoXl5uZmeS7bzp075erqarWctECBAgoICNCPP/4oyTZLTjP7WrvXxx9/rIYNG1r+m6RISEjQ7du31a1bN/Xo0UM1a9bUoEGD9NZbb2nlypVZtuwUQOYQzgAAeIivvvpKnTp10rPPPqsVK1YoX758OnLkiBYtWqQJEybI0dFRiYmJlmVhycnJSkpKkmQ929SgQQPVq1dP8+fPV+nSpTV58uRU36tChQqqXr26+vbtq27dumndunWpNn7IjNWrV2v69Ol6+eWXFRYWZjWD5eDgoLlz52r69OlW5xw+fFhnz55VjRo1JEmlSpXSH3/8YRVKUpYQpmznXrlyZbm6umrz5s1WfbZs2aLq1atbBbvMSs8ywKCgIL355ptWx3/++WfduHHDMht58uRJFStWTPb21guJihcvrlOnTkl6/EtOH+W1liIiIkKnT5/Wq6++mmr8lHv/GjRoYNVer149JSQkWDZ0AWAbLGsEAOABQkNDNWPGDAUEBGjBggWWULNt2zYlJCSoY8eOqc5p3Lixqlevro8++uiBs02rVq2SdPdeoj/++EOtWrWyGsff319ms1mRkZHy9PTM9DVcvnxZU6ZMUdGiRRUYGKgjR45YHS9evLiCg4M1atQoDR8+XK+++qrOnz+vOXPmqGzZsmrZsqUkKSgoSG3atFHXrl3VoUMH2dvba8OGDTpw4IBCQkIkSS4uLurcubPmz58vBwcHVa5cWRs2bNDhw4e1cuXKTF/Dw/x7GeC/Xb16VWPGjJGnp6def/11SXc3L0nrGWK5c+dWTExMqvbsXnL6qK+1FN9//71cXFxSBTDp7q6ZklLN+iUkJEi6uwwXgO0QzgAAuI+U2aZXXnlF06ZNswpYb7/9dqo3vz/88IPmzZunhQsXWraSv3e2KeX8f882/f777xo1apS8vb1VpUoVy3g7duyQo6OjZSv7zNq+fbvi4uJ0/vx5tW3bNtXxKVOm6M0335SLi4vCwsIUHBwsFxcXNW7cWAMHDrTMLPn7+ys8PFwhISEaPHiwHBwcVLZsWX344YeqXr26ZbzevXsrV65cWrt2rZYtW6bSpUtrwYIFqWa6ssq+ffvUs2dPq2WA97p48aK6du2qqKgorVixwjJ7lJycbPX8sHul1f7222/rtdde088//6wlS5ZYNnfJClnxWkvx+++/y8/PL83nydWrV08mk0lfffWVypYta2n/7rvvlC9fPvn4+GTJ9QDIHMIZAABpSM9sU8qzp1L8+eefkqQyZcpYgld6ZpuaNm2qZcuWadCgQerbt688PDy0bds2ffLJJxowYIBlk43MevPNN1Mt8UtLs2bN1KxZswf2qVixopYtW/bAPiaTSUFBQQoKCspQnZlxvw1OUhw7dkw9evTQ7du3FRoaarVzobu7u6KiolKNGRMTk+bGJSnnVq9eXWazWQsXLlRwcPAjzWpKWfdaS3H8+HHVqVMnze/l5eWlwMBAhYaGyt7eXgEBAfr+++/1+eefa8yYMXJwcHikawHwaAhnAACkIT2zTem55yg9s02urq5auXKlZs2apVmzZunatWvy8fHR1KlTLUvwkNr9lgGm2L17t4KDg+Xu7q7w8PBUyxC9vb21c+dOJScnW20ccvbsWcsMUnYvOZWy7rWWIioq6oGBfuTIkfL09NSaNWu0ZMkSFStWTBMnTtRbb72VqfoBZB3CGQAAaUjvbNO93njjjTTfRKdntsnDwyPNDUKQtgctA5SkI0eOqFevXipWrJjCwsJUpEiRVGPUqVNHixYt0o4dOyzb5F+9elV79+5Vz549JWX/klMpa19r0t2aH8TOzk5du3ZV165dM/Q9AWQ/whkAAMhR0rMMcNSoUUpMTFTv3r114cIFXbhwwXK8QIECKl68uAICAlS9enUNGTJEQ4YMUb58+TR37ly5u7vrnXfekZT9S04B4F6EMwAAkKM8bBngpEmTLIGtX79+qY63bNlSU6dOlSTNmzdPU6dO1bRp05ScnKwqVapo9uzZlgdos+QUwONEOAMAADlKepYBpneZYN68eTVlyhRNmTLlvn1YcgrgceEh1AAAAABgAIQzAEC2uHDhgqpVq6Y9e/ZYte/Zs0eBgYEKCAhQ7dq11bt3b505c+a+4xw6dEjlypXTxo0bLW1z585V2bJl7/vnl19+ybbrAgAgu7CsEQCQ5c6fP68uXbro5s2bVu379+9X586d1bBhQ82YMUOxsbFauHCh2rRpoy+++EIFChSw6h8fH6/hw4crMTHRqv2tt95S3bp1U/UdOHCgPDw8rJ5lBQBATkE4AwBkmeTkZH366aeaNm1amscXL16sUqVKKSQkxPJcqSpVqqhBgwb69NNP1aVLF6v+s2fPThXwJMnT0zPVs6UmT56smJgYffLJJ3J2ds6iKwIA4PFhWSMAGMj9lgL+73//U/fu3VW1alXVqFFDI0eOVHR0tFWfW7duaezYsapdu7YqVaqkTp066cSJE6m+x/Lly9WoUSP5+/urRYsW2rp1a5bVf+zYMY0bN06vv/56mgGtQoUK6tChg9UDfwsXLiw3NzedPXvWqu/+/fsVHh6usWPHPvT7RkRE6KOPPlLv3r3l5eX16BfyhEg2m21dwkPlhBoB4HFh5gxIp+TkZC1fvlyrV69WZGSkihYtqnfffVft27eXyWSSJF28eFHTpk3Tzp07lZCQoAoVKmjo0KHy8/OzjHPr1i1NmzZN27ZtU0xMjCpXrqxRo0apdOnStro0GMT9lgJGR0erY8eOKly4sKZNm6aoqChNnz5dkZGRVg82HjRokP744w8NGTJEbm5umjdvnjp06KCvvvpK+fLlkySFhoZq5syZCg4OVvny5bVhwwb17dtXK1euVEBAwCNfwzPPPKMtW7bI09MzVcCUpKCgoFRtP//8s27cuKEyZcpY2uLi4jR8+HD16NFDZcuWfej3nTZtmry8vNShQ4dHqj/ZnCw7k7E/t8xIjXYmk2bs2au/0ph9NIJi7u4aXOPRX3c5UbLZLLv//7vDqHJCjcCThnAGpNPUqVO1cuVKvfPOO2rcuLHOnTunkJAQnT9/XiNHjtStW7fUtm1bOTg4aPz48XJyctKCBQvUqVMnffHFFypcuLCk9L2BxtPlYUsBP/nkE0VHR2vTpk2We7KKFCmi7t27a9++fapWrZr279+vH374QUuWLFH9+vUlSdWqVdOLL76ojz/+WEFBQYqLi9PixYvVsWNHBQcHS5Lq1aund955R/Pnz9eKFSse+Voy+hq+evWqxowZI09PT6tnRs2YMUOurq7q0aOHIiMjHzhGRESEdu3apYkTJ8re/tF+rdmZ7DT9l+917ub1Rxonu3i559OQ6i9k6Jy/bt7UyevXs6cgZJqdyaQP90Yp8maCrUtJk6e7g9oHFLR1GcBTh3AGpMPVq1cVHh6ut99+W+PHj7e0P/vss+rZs6dat26tb775RteuXdM333xjCWLly5fXG2+8oV9++UXNmzdP1xtoPH1SlgK2adNGzz//vLp37251fOfOnapatarVZhl169ZV7ty59eOPP6patWrauXOnXF1dVbt2bUufAgUKKCAgQD/++KOCgoL0+++/Kzo6Wk2aNLH0MZlMaty4sWbOnKm4uLjHeq/WxYsX1bVrV0VFRWnFihXKnTu3pLu7Oa5Zs0br1q1LV9gKDw9XwYIF1aJFiyyp69zN6zp5PSpLxgIeJPJmgv66YcxwBsA2jL12AzCI06dPKykpSS+8YP2JdUBAgJKTk7Vjxw59++23eumllyzBTLr74NIdO3aoefPmkvTQN9B4OqUsBRwxYkSa4ejkyZPy9va2arOzs1OxYsV0+vRpS59ixYqlCjPFixfXqVOnLH0kqWTJklZ9SpQooaSkpFT3fGWnY8eOqXXr1rp48aJCQ0MtuyvGxMRoxIgR6tatm0qXLq3ExEQlJydLujvD+O9dG5OSkrR161a9/PLLcnR0fGz1AwCQHQhnQDqkzFicP3/eqj3lzeypU6d08uRJlSpVSrNnz1adOnVUrlw5BQYG6tixY5b+6XkDjadPvnz5Uu08eK/o6GjLrNK9cufOrVu3bkmSbt68KTc3tzT7xMTEWPpIStUvZeyUsbLb7t279e6778psNis8PFxVqlSxHDt06JDOnz+v+fPnq1y5cipXrpwaN24sSRo1apTKlStnNdaBAwd07do1vfLKK4+ldthOTtk4JKfUCcCYWNYIpEPJkiVVpUoVzZs3T56enqpZs6bOnTunMWPGyNHRUfHx8UpMTNSKFSvk5eWliRMnKj4+XnPmzFH79u31+eefq0iRIul6A43MiY2NVZUqVSyzLCkcHR118OBBSdLbb7+t33//PdW5a9asUaVKlSTdfVbWvHnz9Pnnn+vq1asqWbKkevbsqaZNm2b7NTyIKY2b8s1ms6U9OTk5zT73nvvv/zb3jiPJagfF7HLkyBH16tVLxYoVU1hYmIoUKWJ1vFy5clq/fr1V2+XLl9WrVy/17t1bDRo0sDr2xx9/yN7enueaPQXsTCbN+uWY/rp529al3Fcxd1cNqP7wDWwA4H4IZ0A6zZ07V2PHjlXv3r0lSXny5NGQIUO0YMECq36hoaGWmYjy5curSZMmCg8P16BBg9L1BhqZc+zYMSUnJ2vmzJkqWrSopT0lcCQnJ+v48ePq0qWL1T1XkvTcc89Z/j548GDt3LlTgwcPVsmSJfXZZ59p4MCBcnNzU7169R7PxfyLm5tbmrNat2/ftsy4ubu7Kyoq9X1SMTExcnd3l3T3NZvSljdvXqtxUsbIbqNGjVJiYqJ69+6tCxcu6MKFC5ZjBQoUUPHixeXv7291zl9//SVJKlq0aKpjx48fV7FixeTk5JTttcP2/rp5W/+7zgdZAJ5chDMgnQoVKqQFCxYoOjpaly5dUvHixWVnZ6dx48ZZ3tTWqFHDavnZs88+Kx8fHx09elRS+t5AI3OOHj0qBwcHNWnSRA4ODqmOnzp1SrGxsWrQoIFlluzf9u7dq82bN1tt2FKrVi2dOXNGP/74o83Cmbe3d6r7wZKTk/XXX39Zgqa3t7d27typ5ORkqxmws2fPysfHx9JHks6cOWM103TmzBk5Ojpm+/PBzp07pyNHjkiS+vXrl+p4y5YtNXXq1AyNeeXKFaugCQBATkY4A9Lpq6++ko+Pj3x9fS0zEAcPHlRSUpJq1KihL7/8UvHx8anOS0xMtGzykJ430Mico0ePqnTp0mkGM+nuduuS5Ovre98xNm/eLC8vL0swk+7OaK5evTpri82g2rVrKywsTFevXrXc/7hjxw7FxMRYNpepU6eOFi1apB07dljqv3r1qvbu3auePXtKkipXrixXV1dt3rzZEs7MZrO2bNmi6tWrZ/mGGjVq1LC659LLy8vq6/QqVqzYfc9bunRppusDAMBo2BAE2cZsNmvNmjV69dVXVblyZb344ouaNGmSZXlW2bJl7/unXbt2lnFOnjypnj17qnLlyqpevbp69+6t//3vf4/9ehYuXKglS5ZYta1YsUJ58uRRjRo1VK9ePf3000+6evWq5fj//vc/nTp1StWqVZN09w10TEyMduzYYemT8ga6Tp06j+dCnlARERGys7NTp06dVKlSJVWvXl1jx461vN6OHj0qd3d3TZ48WTVq1JC/v7+6detm9VqKiIhQmTJl9MUXX+iVV16Rn5+fXn75ZW3evNlWlyVJatOmjZycnNSpUydt2bJF69at05AhQ1SvXj1VrlxZ0t2dQ6tXr64hQ4Zo3bp12rJlizp27Ch3d3e98847kiQXFxd17txZYWFhmj17trZv365+/frp8OHDPMYBAAADYOYM2SY0NFSzZs1Sly5dLEvDQkJC9Oeff2r58uVas2ZNqnO+/fZbhYWFWd5Mnjt3Tu+++67c3d01duxYFSxYUBs2bNA777yjDRs2ZPsyrHu1a9dO7733nkqXLq0qVaro66+/1pdffqlx48bJzc1NwcHB2rp1q7p06aLg4GAlJCRo1qxZ8vT01JtvvinJ+g30kCFDlC9fPs2dO9fqDTQyLuV+Mjs7Ow0ePFhBQUE6ePCg5s2bpxMnTig8PFxHjx7VzZs3lT9/fs2fP9+yI2Dbtm21adMmFSlSRFevXtXp06d1+PBhDRgwQB4eHvr444/Vr18/LV682GpG7XEqUKCAPvzwQ02ePFmDBw9W7ty59fLLL2vo0KFW/ebNm6epU6dq2rRpSk5OVpUqVTR79myrZX+9e/dWrly5tHbtWi1btkylS5fWggULVLVq1cd9WQCQY5nNZq1du1bh4eH666+/VKBAATVs2FD9+vWzbPy1Z88ezZ07V8eOHZOjo6MqV66sIUOGqESJEpZxjLoJFWyHcIZskZycrCVLlqh169YaNGiQJOn5559Xvnz51L9/fx06dCjVfT9///231q5dq7Zt26pZs2aSpJUrVyo2NtYqiNWtW1etW7fW7Nmz9cEHHzy2a2rdurXi4uIUHh6uJUuWyNvbWx988IHlGWZeXl5avXq1ZsyYoSFDhihXrlx6/vnnNXLkSKsdGtPzBhoZYzabtXjxYhUqVMiyPDQgIECFChXSkCFDtGPHDktoSwkh1apVU5UqVfTKK6/oww8/1JAhQ5SQkKDLly9r48aNli3ba9asqRYtWmjBggWPJZz9eylgijJlymjFihUPPDdv3ryaMmWKpkyZct8+JpNJQUFBzJQBwCN42AfQBw4cUOfOndWwYUPNmDFDsbGxWrhwodq0aaMvvvjCskTdiJtQwbYIZ8gWt27d0muvvZbqk5+UDQnOnTuXate1qVOnytnZWQMHDrS0/e9//9Nzzz1nNUNmMplUtWpVrVu3LhuvIG0dOnRQhw4d7nu8dOnSWrRo0QPHSM8baGRMrly5VKNGjVTtKduuHzt2LM1g5eXlJR8fH8v9aLlz55aHh4fVs7Ry5cqlWrVqpTnTCwB4+qTnA+jFixerVKlSCgkJsdxjXqVKFTVo0ECffvqpunTpYthNqGBbhDNkizx58mjMmDGp2r/99ltJ1luXS9Jvv/2mzZs3a8qUKVazTPnz59fx48eVkJBgtdHDuXPndPPmTV2/fl358uXLnotAjnHx4kVt375d9erVs3qYc1xcnKS7D3neuHGjSpUqlWrGNi4uTvnz55cklShRQhcuXLB6fphkvakLAODplp4PoCtUqKBGjRpZbf5VuHBhubm5WXbfNeomVLAtNgTBY/Pbb79p6dKlatSoUapwFhYWpqJFi+q1116zan/jjTd0+fJlDRs2TOfOndO1a9e0YsUKy4YasbGxj61+GFd8fLzGjBmTanbr66+/lp2dnapVq6a5c+dq+vTpVscPHz6ss2fPWmbd6tevr+vXr2vXrl1WY+/YsYN7sgAAkv75APrfvxfu/QA6KCjIcr95ip9//lk3btxQmTJlJBl3EyrYFuEMj8W+ffvUvXt3FS9eXJMmTbI6duHCBX333Xfq0KGD7O2tJ3Nr166t6dOn66efflKjRo1Us2ZN/fDDD+rRo4eku7vPAV5eXmrRooWWLl2qhQsXavfu3Zo3b55mzJihNm3aqFSpUgoODta+ffs0fPhw7dq1S2vXrlWPHj1UtmxZtWzZUpL06quvqly5cho8eLDWrVunH3/8UUFBQYqMjLQ8fBwAgH970AfQ0t2dmceMGSNPT0+9/vrrlrY//vhDM2bMUI8ePbR06VL5+PioX79+2r59+2O+AhgFyxqR7b766isNHz5c3t7eCgsLS7UM8dtvv5XJZLJsAvJvr732mpo3b66zZ8/KxcVFRYoU0Zw5c2RnZ8eDm2ExYcIElShRQps2bdKCBQtUpEgR9e3bV126dJEkvfnmm3JxcVFYWJiCg4Pl4uKixo0ba+DAgZYPBRwcHLR8+XLNnDlTs2bNUkxMjPz8/LRixQqr+9AAAEixb98+9ezZM80PoKW7S++7du2qqKgorVixQrlz55YkQ2xCBeMhnCFbhYaGasaMGQoICNCCBQvSDFM//PCDqlWrpkKFCqU6dvLkSR08eFCvv/66SpYsaWk/fPiwfH19lStXruwsHzmIk5OTgoODFRwcfN8+zZo1u++HACny5s2r8ePHa/z48Vld4hMp2ZwsO5PxF2HklDoB5CwP+wD62LFj6tGjh27fvq3Q0FBVqFDBcoxNqJAWwhmyzerVqzV9+nS98sormjZtmhwdHVP1MZvNOnjwoAIDA9Mc488//9SwYcPk7+9v2SL9xIkT2rlzJ1uBAwZgZ7LTtL2bdO7mFVuXcl9e7oU0NOB1W5cB4AnzsA+gd+/ereDgYLm7uys8PNxyr1kKNqFCWghnyBaXL1/WlClTVLRoUQUGBurIkSNWx4sXL64CBQro77//1s2bN1W6dOk0x6lfv76KFy+uwYMHq1+/foqJidG0adNUrFixB25pD+DxOXfzik7eiLR1GQDw2DzsA+gjR46oV69eKlasmMLCwlSkSJFUY9SvX1//93//p127dqlOnTqS2IQKhDNkk+3btysuLk7nz59X27ZtUx2fMmWK3njjDUVFRUm6u/NRWlxcXBQaGqrJkydr8ODBcnJyUr169SwPaARyomRzkuxMxl6SmxNqBABbSM8H0KNGjVJiYqJ69+6tCxcu6MKFC5bjBQoUUPHixfXqq68qPDxcgwcP1qBBg1SkSBF9+OGHioyMVEhIyOO+LBgE4QzZ4s0330y1hWxaKlSooGPHjj2wT4kSJbR48eKsKu2+zMnJMtkZ+56UnFAjHs7OlEuL976vCzfP2bqUND3j7qUeAcNsXQYAGNLDPoCeNGmSJbD169cv1fGWLVtq6tSpbEKFNBHOgP/PZGenP7+bodhrxnzD7JLfS881HGzrMpBFLtw8pzM3Tti6DABABqXnA+j0fEAtsQkVUiOcAfeIvXZOMVEnbV0GAAAAnkKsjwIAAAAAAyCcAQAAAIABEM4AAAAAwAAIZwAML9mcZOsSHion1AgAAIyNDUEAGJ6dKZc275qqqzeMuZNmgbxeeqn2cFuXAQAAcjjC2f+XnJysefPmad26dYqOjlbVqlX13nvvqUSJErYuDYCkqzfO6fI1tp4HAABPLpY1/n8LFizQ6tWrNXHiRK1Zs0Ymk0ndunVTfHy8rUszLHNysq1LeKicUCMAAAAgMXMmSYqPj9eyZcs0ZMgQ1a9fX5I0a9Ys1a1bV1u2bFGzZs1sXKExmezsdG1LuBKvXrR1KWmyL1BE+RsH2roMAACQg5iTzTLZmWxdxkPllDqRMYQzSREREYqJiVHNmjUtbXny5JGfn5/27t1LOHuAxKsXlXDlvK3LAAAAyBImO5NOf39DcdeNu9GTc75cKvlCXluXgWxgMpvNZlsXYWvffvut+vTpo99//13Ozs6W9n79+ikuLk6LFy/O0Hi//fabzGazHBwcrNpNJpOSY+MkIy+1s7OTnYuz0vOyuHs9t2RONuY/Xia7XLJzcUvXtUh3rych7obMSYnZXFnmmHLZy8E5b7p/NnfirivZoD8bSbKzyyUn53zpvp7YuOtKMuj15LLLJZd0Xot093pu3rmuRIPu8GhvyiV3p/T/bG7cuW3Ya5HuXk9eJ9cMXE+cEg3677S9nZ3yOqXv32gp5XruKDHZmL/q7e1MyuvklIGfTYKSDHotkpTLzqS8Tg7pvp5bd5KVZNC3YblMJrk52WXotXbnjllmg/58THYmOTmZ0v2zSYxLltmY/wxIkkx2kr1z+n4+JpNJybeTJIP+bGRnkp1rrgy91pJjE6Qkg/6ActnJzsX634GEhASZTCZVqVLloaczcyYpNjZWkuTo6GjV7uTkpBs3bmR4PJPJZPW/97JzcU7VZkRp1Z4WOxe3bK7k0aX3WiTJwdn4n0Kl93qcnPNlbyFZJL3X45IDricjrzV3p3zZV0gWSe/15HVyzeZKskb6r8f4/05n5LWW18kpGyvJGun/2Tg8vJMBpPd63JyMf+t/Rl5rTk4mScZeZpfe67F3Nv7PRsrA+zXXXNlcyaPLyGvNzsX4/xbcez0mkyn9r73sKignSZkti4+Pt5o5u3PnjlxcXDI8XuXKlbOsNgAAAABPh5zxsUA2e+aZZyRJly5dsmq/dOmSPD09bVESAAAAgKcM4UySr6+v3NzctGfPHktbdHS0jhw5omrVqtmwMgAAAABPC5Y16u69ZoGBgZoxY4YKFCigokWLavr06fL09FTjxo1tXR4AAACApwDh7P/r27evEhMTNXr0aMXFxSkgIEBhYWGpNgkBAAAAgOzAVvoAAAAAYADccwYAAAAABkA4AwAAAAADIJwBAAAAgAEQzgAAAADAAAhnAAAAAGAAhDMAAAAAMADCGQAAAAAYAOEsB0pOTtacOXNUt25dVaxYUZ07d9aZM2dsXVaWWLBggdq1a2frMh7J9evXNXbsWNWrV09VqlTRu+++q3379tm6rEyJiorSkCFDVLNmTVWuXFndu3fXiRMnbF1Wljh16pQqV66sjRs32rqUTDt//rzKli2b6s+6detsXVqmbNq0SU2bNpW/v7+aNWumb775xtYlZcqePXvS/LmULVtWL774oq3Ly5SEhATNmjVLDRo0UOXKldWmTRv99ttvti4rU2JiYjRhwgTVr19fVatWVVBQkM6ePWvrsjIlrd+ZR48eVWBgoCpVqqQGDRooLCzMRtVl3P3eA5w6dUqVKlXSX3/9ZYOqMieta/nuu+/UqlUrVa5cWQ0bNtT777+vuLg4G1WYMWldz1dffaVXX31VFSpUUKNGjbRkyRLllMcnP+z95ujRo9WwYcPHWNE/CGc50IIFC7R69WpNnDhRa9askclkUrdu3RQfH2/r0h7JihUrNGfOHFuX8cgGDhyo33//XTNnztT69etVrlw5denSRSdPnrR1aRnWq1cvnTt3TkuXLtX69evl7Oysjh07KjY21talPZKEhAQNHjxYt2/ftnUpj+TYsWNycnLSjh07tHPnTsufV1991dalZdhnn32mkSNHqnXr1vryyy/VtGlTDRw4UPv377d1aRlWuXJlq5/Hzp07tWzZMtnb26tnz562Li9TFi5cqA0bNmjixInatGmTSpUqpW7duunixYu2Li3D+vfvry1btmjcuHFau3atChYsqHfffVfXrl2zdWkZktbvzGvXrqlTp04qWbKkNmzYoD59+igkJEQbNmywUZXpd7/3AMeOHVOnTp1y1O+dtK5l37596t27t1566SVt2rRJ48aN0zfffKPx48fbqMr0S+t6tm/frqFDh+qdd97RV199paFDh2rhwoVauXKljapMv4e939y6datNP+QknOUw8fHxWrZsmfr06aP69evL19dXs2bN0sWLF7VlyxZbl5cpFy9eVNeuXRUSEiJvb29bl/NIzpw5o127dum9995TtWrVVKpUKY0aNUpFihTRl19+aevyMuTatWsqVqyYJkyYIH9/f/n4+CgoKEiXL1/Wn3/+aevyHsncuXOVO3duW5fxyI4fPy5vb28VLlxYHh4elj/Ozs62Li1DzGazQkJC1KFDB3Xo0EElSpRQcHCwnn/+ef3yyy+2Li/DHB0drX4e+fLl05QpU9SkSRO99dZbti4vU7Zt26bmzZurTp06KlGihIYPH65bt27pwIEDti4tQyIiIvTjjz9qwoQJeuGFF+Tj46Px48fLzc1NH3/8sa3LS5cH/c5cu3atHB0dNW7cOPn4+KhVq1bq2LGjli5daqNqH+5B17Nw4UK9/fbbKlCggI2qy5gHXcvq1atVs2ZNde/eXSVKlFC9evU0YMAAff7554b9cP1B13P58mV169ZNbdu2lZeXl5o0aaLnn39eP/30k42qfbj0vN+8dOmSxowZo+rVqz/m6v5BOMthIiIiFBMTo5o1a1ra8uTJIz8/P+3du9eGlWXe4cOHlTdvXn3++eeqWLGirct5JPnz59eSJUtUvnx5S5vJZJLZbNaNGzdsWFnG5c+fXzNnztRzzz0nSbpy5YrCwsLk6emp0qVL27i6zNu7d6/WrFmj999/39alPLJjx47l6J9Fiv/97386f/58qhm/sLAw9ejRw0ZVZZ1Vq1bpwoULGjFihK1LybR8+fLp+++/119//aWkpCStWbNGjo6O+s9//mPr0jLk1KlTkqRq1apZ2uzs7OTr65tjfoc+6Hfmvn37FBAQIHt7e0tbzZo1derUKUVFRT3uUtPlQdezY8cOTZ8+XcOGDbNRdRnzoGvp3Lmzhg4dmuqcxMRE3bp163GVmCEPup4333xT/fv3lyQlJSXpxx9/1C+//KLatWvboNL0edj7TbPZrOHDh6tFixY2DWf2D+8CI4mMjJQkPfPMM1bthQsX1oULF2xR0iNr2LChzdb1ZrU8efKofv36Vm3ffPONzp49qzp16tioqkc3ZswYyyeyCxculKurq61LypTo6GgNHTpUo0ePTvX/oZzo+PHj8vDwUJs2bXT69GmVKFFCQUFBqlu3rq1Ly5DTp09Lkm7fvq0uXbroyJEjKlasmHr16pXj/224c+eOFi1apA4dOqhw4cK2LifTRo0apQEDBujFF19Urly5ZGdnp5CQEBUvXtzWpWWIh4eHpLu/S318fCzt58+f1507d2xVVoY86HdmZGSkypQpY9WW8rr7+++/VbBgwWyvL6MedD0ps5l79ux5nCVl2oOuxc/Pz+rr+Ph4LV++XOXKlTPszGB63p/9/fffatSokZKSklSnTh29++67j6m6jHvY9axYsUKXL1/WokWLtHjx4sdYmTVmznKYlDXXjo6OVu1OTk455hfL0+TXX3/VyJEj9eKLL+boN5kdOnTQhg0b9Nprryk4OFiHDx+2dUmZMm7cOFWqVClH3pP1b/Hx8Tp9+rRu3bql/v37a8mSJfL391e3bt20e/duW5eXISmfGg8bNkzNmzfXsmXLVLt2bQUFBeW4a/m3zz77THfu3MnxGx2dPHlSefLk0fz587VmzRq98cYbGjZsmCIiImxdWoZUrFhRPj4+eu+993ThwgXFx8drxYoVOnr0qGGXlmVEXFxcmu8PJPEewUASExM1dOhQnThxQu+9956ty3kkefLk0fr16xUSEqJjx46lOTuYE0RERGjevHmaPn16qv8PPW7MnOUwKfeSxMfHW91XcufOHbm4uNiqLKRh69atGjx4sCpWrKiZM2faupxHkrJ0bsKECTpw4IDCw8M1ZcoUG1eVMZs2bdK+ffv0xRdf2LqULOHo6Ki9e/fK3t7e8oukfPnyOnnypMLCwlSrVi0bV5h+Dg4OkqQuXbqoZcuWkqT//Oc/OnLkiJYvX56jruXfNm3apCZNmih//vy2LiXTzp8/ryFDhmjFihWW5YD+/v46ceKE5s6dq/nz59u4wvRzcHDQ/PnzNXz4cDVo0ED29vZq0KCB3nzzTR06dMjW5T0yZ2fnVCEzJZTl1BUPT5qUD9T27NmjOXPm5PjbOdzc3OTn5yc/Pz8lJydrwIABGjJkiIoWLWrr0tLtzp07Gjx4sHr16iVfX19bl8PMWU6TshTr0qVLVu2XLl2Sp6enLUpCGsLDw9WnTx/Vq1dPS5cuzXEbNEh3t9H/8ssvlZSUZGmzs7OTj49PqtdfTrBhwwZFRUVZtgKvXLmyJOm9995Ts2bNbFxd5ri6uqb6hK9MmTI5bge9lH+7/r0cq3Tp0jlq6+x/u3r1qvbv36+mTZvaupRH8scffyghIUH+/v5W7RUrVrQsSc1JvL29tWbNGv3yyy/avXu35s+fr+vXr6tkyZK2Lu2ReXp6pvn+QJKKFClii5Jwj0uXLqlt27bav3+/li5dmqNX1Ozbt08HDx60aku5Rz2nvUf4/fff9eeff2revHmW9weLFy/W33//rcqVK+vzzz9/rPUQznIYX19fubm5Wa2/jo6O1pEjR6xucIbtfPzxx5owYYLatm2r2bNn23x6PLMuXbqkQYMGWe2Wl5CQoCNHjljdq5FTzJgxQ19//bU2bdpk+SNJffv21ZIlS2xbXCZERESocuXKqZ6hd+jQoRy3SYifn59y586t33//3ar9+PHjOe6epnv99ttvMplMNr2xPCukfCh47Ngxq/bjx4+rRIkStigp027duqXAwEAdOnRIefPmVZ48eXTz5k399NNPOe5ezbQEBATo119/tfpQbffu3fL29jbk/WZPkxs3bqhDhw66evWqPv74Y6uN3XKiZcuWpVpB8/vvv8ve3j7HfdBRoUIFffvtt/rss88s7w/eeecdFS5cWJs2bXrsIZpljTmMo6OjAgMDNWPGDBUoUEBFixbV9OnT5enpqcaNG9u6vKfeqVOnNHnyZDVu3Fg9evSw2h3L2dlZ7u7uNqwuY3x9fVWnTh2NHz9eEydOVJ48ebRo0SJFR0erY8eOti4vw+73qXHBggVz1PKLFGXKlNFzzz2n8ePH67333lP+/Pm1du1aHThwQOvXr7d1eRni7Oysrl27av78+SpSpIgqVKigr776Srt27dKKFStsXV6mRUREyMvLK8cvOa9QoYKqVaumYcOG6b333pOnp6c2bdqk3bt355jt51O4ubnJZDJp8uTJeu+992Q2mzVhwgQ9++yzat68ua3Le2StWrVSaGioRo0apa5du+qPP/7QypUrc8SztJ50U6ZM0blz5xQaGqoCBQro8uXLlmMFChRQrly5bFhdxnXu3Fnt27fXnDlz1KJFCx0+fFjTp09X+/btc9wybmdn51QfNOXNm1f29vY2+QCKcJYD9e3bV4mJiRo9erTi4uIUEBCgsLCwHDtD8yTZvHmzEhIStGXLllTPnWvZsqWmTp1qo8oyzmQyafbs2frggw/Uv39/3bx5U9WqVdOqVav07LPP2rq8p56dnZ0WLVqkGTNmqH///oqOjpafn5+WL1+usmXL2rq8DAsKCpKLi4vluY0+Pj6aO3euatSoYevSMu3KlSvKly+frct4ZHZ2dlqwYIFmz56tESNG6MaNGypTpoxWrFihSpUq2bq8DPvggw80ceJEBQYGys7OTi+88IKGDh1qtf18TlWwYEGFhoZq0qRJatmypTw8PDR06FDLvZywjeTkZH399ddKSEhQhw4dUh3ftm2bihUrZoPKMq9atWpavHixZs+erbCwMBUoUECdO3dWt27dbF1ajmcym81mWxcBAAAAAE877jkDAAAAAAMgnAEAAACAARDOAAAAAMAACGcAAAAAYACEMwAAAAAwAMIZAAAAABgA4QwAAAAADCDnP3ERAIB0OnjwoD788EPt3btXV69elYeHh2rVqqUePXrIy8vL0q9du3aSpI8++shWpUqS9uzZo/bt2+vDDz+0PJD73w8Zz5Url9zd3eXn56eOHTuqfv36tigVAJAFCGcAgKfCqlWrNHnyZNWoUUODBg1S4cKFdfbsWYWGhurbb7/V8uXLVa5cOVuXmS5vvvmm3nrrLUlSQkKCLl++rPXr16t79+4aM2aMAgMDbVwhACAzCGcAgCfer7/+qkmTJqlt27YaNWqUpb1GjRp68cUX9cYbb2jEiBH6/PPPbVhl+nl6eqpSpUpWba+88oqCg4M1depUNWjQQMWKFbNNcQCATOOeMwDAEy8sLEzu7u4aOHBgqmMFChTQ8OHD1aRJE926dSvN869evarx48frhRdeUPny5VW9enUFBwfrr7/+svQ5d+6cevXqpRo1aqhixYpq3bq1tm/fbjl+584djR8/XvXq1VP58uX18ssva9myZVl2jf+vnfsJiXpt4zB+zSFjrM1IY5obg/5YFlJQiCJJYRi5shYtQhe10JKSEJKIxIQWBZWFEEgYWYKLyHBRUEQYg2BkRhuDGCoKcyzJpCKz9F0Iw+mtc+A9Z47vHLk+2+f53cy9/DL3cwcCAerq6picnOTatWsJqytJmj3+cyZJmtOmp6eJRCJs2bKF1NTUX97Ztm3bn35fVVXFhw8fqKurIz09ncHBQc6dO0dDQwNtbW1MTU1RVVVFeno6p06dYt68ebS3t7N//35u3rxJdnY2J06cIBKJUF9fTzgc5v79+5w8eZJQKMSOHTsS0uuyZctYsmQJ/f39CaknSZpdhjNJ0pz2/v17JiYm/vKY38jICKmpqdTX17NhwwZgZhzy9evXdHZ2AjA6Oko0GqW6ujq+kCMvL4+WlhYmJiYAePDgAYWFhZSVlcVrLFiwgLS0tL/b4g/C4TDv3r1LaE1J0uwwnEmS5rTffpuZ4P/+/ftf+j4jI4P29nYAhoaGePnyJdFolEePHjE5OQnMBKLly5dz7Ngxent72bRpE0VFRRw5ciReJz8/n87OTmKxGJs3b6a4uJiampq/2d2vBQKBf6SuJOmfZTiTJM1poVCIhQsXMjQ09Id3Pn/+zNevXwmFQr887+7u5syZM7x584ZQKMSqVasIBoPx80AgQFtbGxcuXODOnTt0dXWRkpJCSUkJjY2NhEIhjh49SmZmJt3d3Rw/fhyA9evX09DQQG5ubsL6jcVirFixImH1JEmzx4UgkqQ5r6ioiL6+vviI4X+7fv06BQUFDAwM/HT28OFD6uvr2bp1Kz09PfT19XH58uWftiVmZGTQ2NhIJBLhxo0b7N27l9u3b3P27FkA5s+fz759+7h16xb37t2joaGBV69eUVdXl7A+o9EoIyMjbNy4MWE1JUmzx3AmSZrz9uzZw9jYWDwo/d7o6CgXL14kOzv7p8AFMDAwwNTUFAcPHiQzMxOYGZHs7e0FYGpqioGBAQoLC3ny5AmBQIDVq1dz6NAhVq5cyfDwMF++fKG0tDS+nTErK4vdu3dTVlbG8PBwwvo8f/48wWCQ8vLyhNWUJM0exxolSXPeunXrqK2tpbm5mWg0Snl5OWlpaTx79oy2tjY+ffpEa2vrL99q5eXlAdDU1MTOnTsZHx/n6tWrPH36FJgZiczNzSUYDHL48GEOHDhAOBymt7eXwcFBKisrCQaDrFmzhpaWFlJSUsjJyeH58+d0dXVRWlr6P/czPDzM48ePAfj27RuxWIyuri4ikQhNTU3xEClJ+ncJTE9PT/+/f4QkSbOhp6eHjo4OBgcHGRsbIzMzk4KCAqqrq8nKyorfq6ioAODKlSsAdHR0cOnSJWKxGOFwmPz8fEpKSqipqaG1tZXi4mJevHjB6dOn6e/vZ3x8nKVLl1JRUcGuXbsA+PjxI83Nzdy9e5e3b9+yaNEitm/fTm1t7Q/v136vr6+PyspK2tvbyc/PByAnJ+eHOykpKSxevJi1a9dSWVkZ3ygpSfr3MZxJkiRJUhLwzZkkSZIkJQHDmSRJkiQlAcOZJEmSJCUBw5kkSZIkJQHDmSRJkiQlAcOZJEmSJCUBw5kkSZIkJQHDmSRJkiQlAcOZJEmSJCUBw5kkSZIkJQHDmSRJkiQlAcOZJEmSJCWB/wCk5DXBexfuZgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot class distribution\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "ax = sns.countplot(x='class_id', data=df)\n",
    "for p in ax.patches:\n",
    "    ax.annotate(f'{p.get_height():.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
    "\n",
    "plt.title('Class Distribution')\n",
    "plt.xlabel('Class ID')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c3f5394d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bounding box dimensions\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='class_id', y='bounding_size', data=df)\n",
    "plt.title('Bounding Box Dimensions by Class')\n",
    "plt.xlabel('Class ID')\n",
    "plt.ylabel('bounding_size')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c71b388d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique image id's:  15000\n",
      "\n",
      "Image IDs with counts greater than 1:  03e6ecfa6f6fb33dfeac6ca4f9b459c9    57\n",
      "fa109c087e46fe1ea27e48ce6d154d2f    52\n",
      "e31be972e181987a8600a8700c1ebe88    48\n",
      "6d5acf3f8a973a26844d617fffe72998    46\n",
      "3a302fbbbf3364aa1a7731b59e6b98ec    46\n",
      "                                    ..\n",
      "be353c629cd9b269bbcb6623339283ae     3\n",
      "43b0f51d257ee4154014ef7724a82644     3\n",
      "9a4ed54cefaea64fc59bf8debdc79ec9     3\n",
      "3018e6640df6a1e3f9978cf55c8d0fd3     3\n",
      "839b2311ffad9bdd9aa98c74aad6e251     3\n",
      "Name: image_id, Length: 15000, dtype: int64\n",
      "\n",
      "Number of image_id's with more than 1 record: 15000\n",
      "\n",
      "Total number of records containing duplicate image_id's: 67914\n"
     ]
    }
   ],
   "source": [
    "# determine how many images have multiple records\n",
    "# num unique id's\n",
    "image_id_unique_value_counts = df['image_id'].nunique()\n",
    "print('unique image id\\'s: ', image_id_unique_value_counts)\n",
    "# >1 record per image\n",
    "image_id_value_counts = df['image_id'].value_counts()\n",
    "image_id_greater_than_1 = image_id_value_counts[image_id_value_counts > 1]\n",
    "print(\"\\nImage IDs with counts greater than 1: \", image_id_greater_than_1)\n",
    "# images with more than one record\n",
    "more_than_one_count = (image_id_value_counts > 1).sum()\n",
    "print(f\"\\nNumber of image_id's with more than 1 record: {more_than_one_count}\")\n",
    "# total number of records containing a image_id with more than one record\n",
    "total_duplicate_records = (image_id_value_counts[image_id_value_counts > 1]).sum()\n",
    "print(f\"\\nTotal number of records containing duplicate image_id's: {total_duplicate_records}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "296a7a14",
   "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>image_id</th>\n",
       "      <th>class_name</th>\n",
       "      <th>class_id</th>\n",
       "      <th>rad_id</th>\n",
       "      <th>x_min</th>\n",
       "      <th>y_min</th>\n",
       "      <th>x_max</th>\n",
       "      <th>y_max</th>\n",
       "      <th>bounding_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>562.0</td>\n",
       "      <td>2045.0</td>\n",
       "      <td>629.0</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>4489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R10</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>778.0</td>\n",
       "      <td>2308.0</td>\n",
       "      <td>1111.0</td>\n",
       "      <td>87246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2087</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R10</td>\n",
       "      <td>1766.0</td>\n",
       "      <td>634.0</td>\n",
       "      <td>2645.0</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>1699107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>2239.0</td>\n",
       "      <td>1592.0</td>\n",
       "      <td>2336.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>9506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2610</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>515.0</td>\n",
       "      <td>1732.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>1799.0</td>\n",
       "      <td>4958.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3318</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>2030.0</td>\n",
       "      <td>792.0</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>145960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5824</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>628.0</td>\n",
       "      <td>1186.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>1247.0</td>\n",
       "      <td>4148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6628</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1302.0</td>\n",
       "      <td>2088.0</td>\n",
       "      <td>1372.0</td>\n",
       "      <td>4900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9194</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>1775.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1852.0</td>\n",
       "      <td>831.0</td>\n",
       "      <td>6237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12042</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>424.0</td>\n",
       "      <td>1349.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>4761.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12234</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>2438.0</td>\n",
       "      <td>1108.0</td>\n",
       "      <td>2523.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>7990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13136</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>2432.0</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>2518.0</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>7568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14463</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>448.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>1053.0</td>\n",
       "      <td>2303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15346</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>2431.0</td>\n",
       "      <td>2364.0</td>\n",
       "      <td>2205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16073</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>374.0</td>\n",
       "      <td>2198.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>2308.0</td>\n",
       "      <td>8910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17059</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>368.0</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>2103.0</td>\n",
       "      <td>4977.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18169</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>537.0</td>\n",
       "      <td>1622.0</td>\n",
       "      <td>617.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>5440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18566</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>2476.0</td>\n",
       "      <td>2189.0</td>\n",
       "      <td>2546.0</td>\n",
       "      <td>2284.0</td>\n",
       "      <td>6650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20253</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>400.0</td>\n",
       "      <td>1919.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>6142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21867</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>2425.0</td>\n",
       "      <td>1306.0</td>\n",
       "      <td>2508.0</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>6557.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25027</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R10</td>\n",
       "      <td>244.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>2655.0</td>\n",
       "      <td>1879060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26314</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>823.0</td>\n",
       "      <td>1376.0</td>\n",
       "      <td>921.0</td>\n",
       "      <td>1493.0</td>\n",
       "      <td>11466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27714</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>757.0</td>\n",
       "      <td>759.0</td>\n",
       "      <td>853.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>9408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33358</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>397.0</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>1536.0</td>\n",
       "      <td>5508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34016</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>503.0</td>\n",
       "      <td>2329.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>2419.0</td>\n",
       "      <td>7200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35289</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>2213.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2298.0</td>\n",
       "      <td>1522.0</td>\n",
       "      <td>5100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36073</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>894.0</td>\n",
       "      <td>1512.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>2958.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37005</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>704.0</td>\n",
       "      <td>2133.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>2222.0</td>\n",
       "      <td>6853.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37684</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>556.0</td>\n",
       "      <td>2051.0</td>\n",
       "      <td>628.0</td>\n",
       "      <td>2108.0</td>\n",
       "      <td>4104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37877</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>682.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>1072.0</td>\n",
       "      <td>3575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38374</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>2442.0</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>2492.0</td>\n",
       "      <td>1437.0</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39813</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>2353.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>2442.0</td>\n",
       "      <td>1784.0</td>\n",
       "      <td>7921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39915</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>894.0</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>955.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3782.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41536</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>408.0</td>\n",
       "      <td>1921.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>5183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41550</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>761.0</td>\n",
       "      <td>2338.0</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>161525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42949</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>618.0</td>\n",
       "      <td>1061.0</td>\n",
       "      <td>693.0</td>\n",
       "      <td>1122.0</td>\n",
       "      <td>4575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43357</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>2448.0</td>\n",
       "      <td>1794.0</td>\n",
       "      <td>8910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48953</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>532.0</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>621.0</td>\n",
       "      <td>1228.0</td>\n",
       "      <td>8900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48977</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>537.0</td>\n",
       "      <td>1134.0</td>\n",
       "      <td>619.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>6806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49143</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>2269.0</td>\n",
       "      <td>2035.0</td>\n",
       "      <td>2365.0</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>8064.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49434</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>1601.0</td>\n",
       "      <td>2336.0</td>\n",
       "      <td>1686.0</td>\n",
       "      <td>8160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50453</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>536.0</td>\n",
       "      <td>1623.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>1697.0</td>\n",
       "      <td>6216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50893</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>ILD</td>\n",
       "      <td>5</td>\n",
       "      <td>R9</td>\n",
       "      <td>1751.0</td>\n",
       "      <td>617.0</td>\n",
       "      <td>2592.0</td>\n",
       "      <td>2431.0</td>\n",
       "      <td>1525574.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51363</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>949.0</td>\n",
       "      <td>1458.0</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>1527.0</td>\n",
       "      <td>4140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52787</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>691.0</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>754.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>3780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53293</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>2427.0</td>\n",
       "      <td>1536.0</td>\n",
       "      <td>2525.0</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>9996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55165</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>2553.0</td>\n",
       "      <td>2445.0</td>\n",
       "      <td>2609.0</td>\n",
       "      <td>2510.0</td>\n",
       "      <td>3640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56529</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>2071.0</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>2176.0</td>\n",
       "      <td>11445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59265</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>451.0</td>\n",
       "      <td>989.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>3294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59749</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>2272.0</td>\n",
       "      <td>1717.0</td>\n",
       "      <td>2351.0</td>\n",
       "      <td>1801.0</td>\n",
       "      <td>6636.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62236</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>606.0</td>\n",
       "      <td>1051.0</td>\n",
       "      <td>698.0</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>7176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63260</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>359.0</td>\n",
       "      <td>2201.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>2311.0</td>\n",
       "      <td>11660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65022</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R8</td>\n",
       "      <td>389.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>7504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66012</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>472.0</td>\n",
       "      <td>1686.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>1779.0</td>\n",
       "      <td>9207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66242</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>ILD</td>\n",
       "      <td>5</td>\n",
       "      <td>R9</td>\n",
       "      <td>273.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>1139.0</td>\n",
       "      <td>2550.0</td>\n",
       "      <td>1570924.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66294</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>410.0</td>\n",
       "      <td>1345.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>6750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66969</th>\n",
       "      <td>03e6ecfa6f6fb33dfeac6ca4f9b459c9</td>\n",
       "      <td>Nodule/Mass</td>\n",
       "      <td>8</td>\n",
       "      <td>R9</td>\n",
       "      <td>812.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>954.0</td>\n",
       "      <td>1493.0</td>\n",
       "      <td>20306.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               image_id   class_name  class_id rad_id   x_min  \\\n",
       "757    03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   562.0   \n",
       "1906   03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8    R10  2046.0   \n",
       "2087   03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8    R10  1766.0   \n",
       "2489   03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8  2239.0   \n",
       "2610   03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   515.0   \n",
       "3318   03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8  2030.0   \n",
       "5824   03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   628.0   \n",
       "6628   03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8  2018.0   \n",
       "9194   03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9  1775.0   \n",
       "12042  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   424.0   \n",
       "12234  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8  2438.0   \n",
       "13136  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9  2432.0   \n",
       "14463  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   448.0   \n",
       "15346  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9  2382.0   \n",
       "16073  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   374.0   \n",
       "17059  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   368.0   \n",
       "18169  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   537.0   \n",
       "18566  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8  2476.0   \n",
       "20253  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   400.0   \n",
       "21867  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8  2425.0   \n",
       "25027  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8    R10   244.0   \n",
       "26314  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   823.0   \n",
       "27714  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   757.0   \n",
       "33358  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   397.0   \n",
       "34016  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   503.0   \n",
       "35289  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8  2213.0   \n",
       "36073  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   894.0   \n",
       "37005  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   704.0   \n",
       "37684  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   556.0   \n",
       "37877  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   682.0   \n",
       "38374  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8  2442.0   \n",
       "39813  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9  2353.0   \n",
       "39915  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   894.0   \n",
       "41536  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   408.0   \n",
       "41550  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9  2013.0   \n",
       "42949  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   618.0   \n",
       "43357  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8  2358.0   \n",
       "48953  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   532.0   \n",
       "48977  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   537.0   \n",
       "49143  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8  2269.0   \n",
       "49434  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9  2240.0   \n",
       "50453  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   536.0   \n",
       "50893  03e6ecfa6f6fb33dfeac6ca4f9b459c9          ILD         5     R9  1751.0   \n",
       "51363  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   949.0   \n",
       "52787  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   691.0   \n",
       "53293  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8  2427.0   \n",
       "55165  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8  2553.0   \n",
       "56529  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9  1872.0   \n",
       "59265  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   451.0   \n",
       "59749  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8  2272.0   \n",
       "62236  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   606.0   \n",
       "63260  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   359.0   \n",
       "65022  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R8   389.0   \n",
       "66012  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   472.0   \n",
       "66242  03e6ecfa6f6fb33dfeac6ca4f9b459c9          ILD         5     R9   273.0   \n",
       "66294  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   410.0   \n",
       "66969  03e6ecfa6f6fb33dfeac6ca4f9b459c9  Nodule/Mass         8     R9   812.0   \n",
       "\n",
       "        y_min   x_max   y_max  bounding_size  \n",
       "757    2045.0   629.0  2112.0         4489.0  \n",
       "1906    778.0  2308.0  1111.0        87246.0  \n",
       "2087    634.0  2645.0  2567.0      1699107.0  \n",
       "2489   1592.0  2336.0  1690.0         9506.0  \n",
       "2610   1732.0   589.0  1799.0         4958.0  \n",
       "3318    792.0  2358.0  1237.0       145960.0  \n",
       "5824   1186.0   696.0  1247.0         4148.0  \n",
       "6628   1302.0  2088.0  1372.0         4900.0  \n",
       "9194    750.0  1852.0   831.0         6237.0  \n",
       "12042  1349.0   493.0  1418.0         4761.0  \n",
       "12234  1108.0  2523.0  1202.0         7990.0  \n",
       "13136  1102.0  2518.0  1190.0         7568.0  \n",
       "14463  1006.0   497.0  1053.0         2303.0  \n",
       "15346  2319.0  2431.0  2364.0         2205.0  \n",
       "16073  2198.0   455.0  2308.0         8910.0  \n",
       "17059  2024.0   431.0  2103.0         4977.0  \n",
       "18169  1622.0   617.0  1690.0         5440.0  \n",
       "18566  2189.0  2546.0  2284.0         6650.0  \n",
       "20253  1919.0   483.0  1993.0         6142.0  \n",
       "21867  1306.0  2508.0  1385.0         6557.0  \n",
       "25027   656.0  1184.0  2655.0      1879060.0  \n",
       "26314  1376.0   921.0  1493.0        11466.0  \n",
       "27714   759.0   853.0   857.0         9408.0  \n",
       "33358  1455.0   465.0  1536.0         5508.0  \n",
       "34016  2329.0   583.0  2419.0         7200.0  \n",
       "35289  1462.0  2298.0  1522.0         5100.0  \n",
       "36073  1512.0   952.0  1563.0         2958.0  \n",
       "37005  2133.0   781.0  2222.0         6853.0  \n",
       "37684  2051.0   628.0  2108.0         4104.0  \n",
       "37877  1007.0   737.0  1072.0         3575.0  \n",
       "38374  1385.0  2492.0  1437.0         2600.0  \n",
       "39813  1695.0  2442.0  1784.0         7921.0  \n",
       "39915  1401.0   955.0  1463.0         3782.0  \n",
       "41536  1921.0   479.0  1994.0         5183.0  \n",
       "41550   761.0  2338.0  1258.0       161525.0  \n",
       "42949  1061.0   693.0  1122.0         4575.0  \n",
       "43357  1695.0  2448.0  1794.0         8910.0  \n",
       "48953  1128.0   621.0  1228.0         8900.0  \n",
       "48977  1134.0   619.0  1217.0         6806.0  \n",
       "49143  2035.0  2365.0  2119.0         8064.0  \n",
       "49434  1601.0  2336.0  1686.0         8160.0  \n",
       "50453  1623.0   620.0  1697.0         6216.0  \n",
       "50893   617.0  2592.0  2431.0      1525574.0  \n",
       "51363  1458.0  1009.0  1527.0         4140.0  \n",
       "52787  1009.0   754.0  1069.0         3780.0  \n",
       "53293  1536.0  2525.0  1638.0         9996.0  \n",
       "55165  2445.0  2609.0  2510.0         3640.0  \n",
       "56529  2071.0  1981.0  2176.0        11445.0  \n",
       "59265   989.0   505.0  1050.0         3294.0  \n",
       "59749  1717.0  2351.0  1801.0         6636.0  \n",
       "62236  1051.0   698.0  1129.0         7176.0  \n",
       "63260  2201.0   465.0  2311.0        11660.0  \n",
       "65022  1470.0   456.0  1582.0         7504.0  \n",
       "66012  1686.0   571.0  1779.0         9207.0  \n",
       "66242   736.0  1139.0  2550.0      1570924.0  \n",
       "66294  1345.0   500.0  1420.0         6750.0  \n",
       "66969  1350.0   954.0  1493.0        20306.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show duplicate image_id records for 03e6ecfa6f6fb33dfeac6ca4f9b459c9, the most frequent image_id\n",
    "selected_image_id = '03e6ecfa6f6fb33dfeac6ca4f9b459c9'\n",
    "selected_records = df[df['image_id'] == selected_image_id]\n",
    "selected_records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d064441a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of image_id's classed as both finding and no finding:  0\n",
      "Number of image_id's with no findings:  10606\n",
      "Number of image_id's with findings:  4394\n"
     ]
    }
   ],
   "source": [
    "# determine if there are images with both finding and no finding\n",
    "# images with no finding\n",
    "no_finding_image_ids = df.loc[df['class_id'] == 14, 'image_id'].unique()\n",
    "# images with finding\n",
    "finding_image_ids = df.loc[df['class_id'] != 14, 'image_id'].unique()\n",
    "# Find common image_id's between the two sets\n",
    "common_image_ids = set(no_finding_image_ids) & set(finding_image_ids)\n",
    "\n",
    "print('Number of image_id\\'s classed as both finding and no finding: ', len(common_image_ids))\n",
    "print('Number of image_id\\'s with no findings: ', len(no_finding_image_ids))\n",
    "print('Number of image_id\\'s with findings: ', len(finding_image_ids))"
   ]
  }
 ],
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