Stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a5a669f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import rasterio as rio\n",
    "from itertools import product\n",
    "from rasterio import windows\n",
    "import scienceplots\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "import seaborn as sns\n",
    "\n",
    "from osgeo import gdal\n",
    "from osgeo.gdalconst import GDT_Int16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fbaa7c53",
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_2d_array(x: np.ndarray)->np.ndarray: #misha grol function\n",
    "    return x.reshape(x.shape[0], x.shape[1] * x.shape[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c936c6f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(['science', 'no-latex'])\n",
    "\n",
    "sns.set_style(\"whitegrid\", {'axes.grid' : False})\n",
    "plt.rcParams.update({\n",
    "    \"font.family\": \"serif\",  \n",
    "    \"font.serif\": 'Palatino',  \n",
    "    \"font.size\":9,\n",
    "    \"axes.labelsize\":9,\n",
    "    \"xtick.labelsize\" : 9,\n",
    "    \"ytick.labelsize\" : 9})    \n",
    "\n",
    "dict_normal_names_rus = {#0: 'Нет данных',\n",
    "    1: 'Деревья',\n",
    "    2: 'Заболоченные участки',\n",
    "    3: 'Вода',\n",
    "    4: 'Поселения, н-земли',\n",
    "    5: 'Пустоши',\n",
    "    6: 'С/Х и травы',\n",
    "    7: 'Кустарники',\n",
    "    8: 'Породы',\n",
    "    9: 'Редкая растительность'}\n",
    "\n",
    "dict_normal_names_eng = {#0: 'No data',\n",
    "    1: 'Trees',\n",
    "    2: 'Flooded area',\n",
    "    3: 'Open water',\n",
    "    4: 'Settlements and damaged lands',\n",
    "    5: 'Barrens',\n",
    "    6: 'Agriculture and grassland',\n",
    "    7: 'Shrubs',\n",
    "    8: 'Rocks',\n",
    "    9: 'Sparse vegetation'}\n",
    "\n",
    "colors =[\n",
    "    '#117733', '#9e2a90', '#88ccee', '#ca7878', '#dbd73e', '#c7c7c9', '#60d52a','#418c84' #, '#92462d'\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c87f02ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pictures(fname, output_name='', data_return = False, plt_show = True, save_pics = False):\n",
    "    with rio.open(fname, 'r+') as src:\n",
    "        x = src.read()\n",
    "        col_names = ['class']\n",
    "        params = x[:len(x), ...]\n",
    "        params = to_2d_array(x[:len(x), ...]) \n",
    "        raw_data = pd.DataFrame(params.T, columns=col_names)\n",
    "\n",
    "    data = raw_data.groupby('class')['class'].agg({'count'}).reset_index()\n",
    "    data = data[data['class']!=0]\n",
    "    data['class'] = data['class'].apply(lambda x: 5 if x==8 else x) #helper!\n",
    "    data['percent'] = data['count'].apply(lambda x: x/sum(data['count'])*100)\n",
    "    data['names_rus'] = data['class'].apply(lambda x: dict_normal_names_rus[x])\n",
    "    data['names_eng'] = data['class'].apply(lambda x: dict_normal_names_eng[x])\n",
    "\n",
    "    data['area_ha'] = data['count']*0.01 #from pixel area equals to 10x10 m2\n",
    "\n",
    "    plt.figure(figsize=(7,4))\n",
    "    sns.barplot(data=data, x='names_eng', y='percent', palette=colors)\n",
    "    plt.xlabel('')\n",
    "    #plt.ylabel('Area, ha')\n",
    "    plt.ylabel('Percent of area')\n",
    "    #plt.title('Number of pixels per class')\n",
    "    plt.xticks(rotation=45)\n",
    "    if save_pics == True:\n",
    "        plt.savefig(output_name+'.png', dpi = 300, bbox_inches=\"tight\")\n",
    "    \n",
    "    if plt_show == True:\n",
    "        plt.show()\n",
    "    \n",
    "    if data_return == True:\n",
    "        return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "01d0afb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iIiIiGdlTg1Xv3r3Zt29fim0rV6587hcxGAz88ccfTJgwIcX2smXLUqpUKUqUKEGJEiVYsWLFcz+3iIiISHrx1K7Ar7/+mrNnz2JlZQWA0WjEysqKtm3bPvMLnD9/nkWLFmEwGDh48GCKn3Xq1Ilhw4aZbtvZ2T1P7SIiIiLpylODVevWrenTp48pWAGsWrXquV6gaNGijBgxghUrVjwRrFxdXbW4s4iIiGQaT+0K7Nu3Lzt37mT//v0AxMfHm+a4MoeDBw/i4+NDxYoV+eijj4iOjjbbc4uIiIiktacGq6+//ppx48axbt064OFUC99//73ZXrx9+/b4+/uzceNGjh8/zuzZs8323CIiIiJp7anBymAwsHnzZipUqAA8HAN148YNs714mzZtAMidOze+vr6cOnXKbM8tIiIiktaeGqxef/11ANMYq/j4eI4fP26WF37w4EGK20ajES8vL7M8t4jIizAaM+f0L5n1fYmkR08dvB4VFYWfnx/nz58nKSkJf39/ypYt+0IvZDQaU9z28/OjW7du2NnZYTAYOHr0KCNHjnyh5xYRMQcrK2tCT0/lwf3Lli7FbLI4FaD4ax9augyRV8ZTg1XHjh35/vvvOXr0KHv37qVWrVp89NFHz/0i27ZtY/v27Vy9epXly5fTqFEjqlatSq9evShevDguLi4MHjyY3Llzv/AbERExhwf3L3M/5rylyxCRDOo/l7TJlSsXS5YsMW3bvXs3NWvWfK4XqV+/PvXr10+xrXz58ixYsOC5nkdEREQkPXvqGKt169bh4+OTYltgYGCqFiQiIiKSUT01WJUtWzbF5KAAFy5cSM16RERERDKspwarfPnycfToUeDhVXw//vijJvEUERER+RdPHWPVqVMnevfuzalTp4iPjydHjhzMmjUrrWoTERERyVCeGqxCQ0OZNGkSSUlJPHjwAE9PT+zt7dOqNhEREZEM5aldgf379+fixYsULlyYkiVLYm9vz507d9KoNBEREZGM5anBavz48dy8eZPExETTtq1bt6Z6USIiIiIZ0VO7AqdMmcLZs2cZPnw48HD2dCsrqyemYBARERGR/whWrVq1ok+fPimmXPD390/1okREREQyoqd2Bfbt25eYmBgiIiJM29q1a5fqRYmIiIhkRE8NVitXrqR69eqMGzcOeLgo89KlS9OkMBEREZGM5qldgTt37mTbtm3s3r0bABcXF06fPp0mhYmIiIhkNE9tsapduza5c+dOse3UqVOpWpCIiIhIRvXUFqt79+4RGhrKgwcPuHTpEn5+fpogVERERORfPDVYtW7dmsGDBxMcHAxAqVKlmDZtWpoUJiIiIpLR/GOwCgsLIyEhgWLFirFw4ULi4uKIiYnB3d09resTERERyTCeGGP1448/0qhRI1q0aMHgwYNJSEjAwcFBoUpERETkPzwRrNavX89vv/1GUFAQNWrU0ISgIiIiIs/oiWBVp04dKleujIuLC++88w6xsbGWqEtEREQkw3kiWD1+1V+FChVS3N60aVOqFiQiIiKSUT0xeH3lypWcOXMGa+v/y1y//vqr6d+nTp2iSZMmaVOdiIiISAbyRLDKmTMnJUqU+NcHREZGpmpBIiIiIhnVE8FqwIAB1KtX718fUKlSpVQtSERERCSjemKM1dNCFUCNGjVSrRgRERGRjOypawWKiIiIyLNTsBIRERExEwUrERERETNRsBIRERExEwUrERERETNRsBIRERExEwWrV4AhyWDpElJFZn1fIiKScT0xQahkPtY21mz6cjWRF25ZuhSzcS/iQZMvW1u6DBERkRQUrF4RkRducfP0dUuXISIikqmpK1BERETETBSsRERERMxEwUpERETETBSsRERERMwkTQavGwwGtm/fTlBQEEOHDjVt37ZtG2fOnCEyMhKDwcCIESOwsrJKi5JEREREzC7Vg9X58+dZtGgRBoOBgwcPmrZfv36d1atXM336dAAGDRrE+vXradGiRWqXJCIiIpIqUr0rsGjRoowYMYLSpUun2B4QEEDZsmVNt2vUqEFQUFBqlyMiIiKSaiw2xurixYu4ubmZbufNm5cbN25YqhwRERGRl2axYHX//n1sbGxMtx0dHYmPj7dUOSIiIiIvzWLByt7enpiYGNPtxMREXFxcLFWOiIiIyEuzWLDKly8fERERptsREREUL17cUuWIiIiIvLQ0C1ZGozHF7caNG7Nnzx7T7cOHD9O0adO0KkdERETE7NJkHqtt27axfft2rl69yvLly2nUqBEFCxakdevWjBw5Ei8vLypVqkSxYsXSohwRERGRVJEmwap+/frUr1//ie2dO3dOi5cXERERSRNa0kZERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMxEwUpERETETBSsRERERMzEosEqISEBg8FgyRJEREREzMaiwaps2bKUKlWKEiVKUKJECVasWGHJckREREReiq0lX7xTp04MGzbMdNvOzs6C1YiIiIi8HIsGK1dXVxwcHCxZgoiIiIjZWLQr8ODBg/j4+FCxYkU++ugjoqOjLVmOiIiIyEuxaLBq3749/v7+bNy4kePHjzN79mxLliMiIiLyUiwarNq0aQNA7ty58fX15dSpU5YsR0REROSlWCxYPXjwIMVto9GIl5eXhaoREREReXkWC1Z+fn4kJCQAYDAYOHr0KN27d7dUOSIiIiIvzWJXBVatWpVevXpRvHhxXFxcGDx4MLlz57ZUOSIiIiIvzWLBqnz58ixYsMBSLy8iIiJidlorUERERMRMFKxEREREzETBSkRERMRMFKxEREREzETBSkRERMRMFKxEREREzETBSkRERMRMFKxEREREzETBSkRERMRMFKxEREREzETBSkRERMRMFKxEREREzETBSkRERMRMFKxEREREzETBSkRERMRMFKxEREREzETBSkRERMRMFKxEREREzETBSkRERMRMFKxEREREzETBSkRERMRMFKxEREREzETBSkRERJ6LwWCwdAmp5mXfm62Z6hAREZFXhLW1Nd+uWsalWzctXYpZFfTIyZA2HV7qORSsRERE5LldunWTs9euWrqMdEddgSIiIiJmomAlIiIiYiYKViIiIiJmomAlIiIiYiYKViIiIiJmomAlIiIiYiYKViIiIiJmomAlIiIiYiYKViIiIiJmomAlIiIiYiYKViIiIs/AYEyydAmpIrO+L0vRWoEiIiLPwNrKhl9OjuXa/YuWLsVs8jgVpmepUZYuI1OxaLDatm0bZ86cITIyEoPBwIgRI7CysrJkSSIiIv/q2v2LXIoOtXQZko5ZrCvw+vXrrF69mn79+jF8+HCuXbvG+vXrLVWOiIiIyEuzWItVQEAAZcuWNd2uUaMGQUFBtGjRwizP75WniFmeJ7142ffjXsTDPIWkEy/zfrJnsTNjJenDy7wnJ4/M9bfxsu8ni1MBM1WSPmS292NpeZwKW7oEs3qZ91PQI6cZK0kfzPGerIxGo9EMtTy3r7/+Gi8vL3x9fQH4448/WLJkCbNnz36mxx8/fhwHB4fULFFEREQEgLi4OMqUKfOf97NYi9X9+/exsbEx3XZ0dCQ+Pv6ZH/8sb05EREQkLVlsjJW9vT0xMTGm24mJibi4uFiqHBEREZGXZrFglS9fPiIiIky3IyIiKF68uKXKEREREXlpFgtWjRs3Zs+ePabbhw8fpmnTppYqR0REROSlWWzwOoCfnx8nTpzAy8sLDw8PWrZsaalSRERERF6aRYOViIiISGaitQJFREREzETBSkRERMRMFKxEREREzETBSkRE5AXExsYCoKHKkpyClUgGoAO3SPpy9epVZs+ezaVLl7CystJnVEwUrCTdenSgSn7AehUPXkajESsrK0uXIc/AYDCY9flexb/3jMBoNLJ06VL++OMPfvrpJ4UrSUHBStKl5GHi8uXLJCUlkZSU9ModvAwGg+n3MGzYMKZNm/ZKvf+Mxtr64SH16NGjZnm+R/s++SoVkj507NiRzz77jMTERH766SeuXr36yh2fMqukpKR/3P6s+1bBKhUcOXKEvXv3WrqMDO3RCcXf35+BAwcybNgwVqxYQUJCwit18Hp0ot66dSvFihWjdOnSJCQkWLgqeSQxMfGJbQEBASxevJgLFy6Y5TX279/P9OnTOX36tFmeT8wjd+7cVK5cmaioKA4cOMAPP/zA5cuX0/T4dPToUf7+++80ea1XhdFoxMbGBoCJEycyYcIEfvnlF+DheelZWqUVrMzsxIkTBAcHc//+/RSLTJu7i+BVcOTIEUJCQpg9eza2trYEBQWxcuVKgFeqa2zVqlV888037Nq1i4YNG2Jvb/+v36gk7Vy8eJGgoKAU4erPP/8ke/bsjBo1itjYWLZs2cKhQ4de+DWCg4O5desWgwYNwsXFhXPnzilgpQOPjj/Lli0jX7589OjRg5iYGGbPnm1quUptJ06cYOfOndy/f5/o6GjTdp1rXlzyHoKZM2dy6NAhkpKS2LBhAxMmTAD+78vu02jmdTM6efIkQUFBdOvWjQsXLnD8+HGOHz/Op59+aunSMoTk3X9Go5EDBw7g7OyMh4cHAQEBWFtbs3v3bgoWLEjr1q3x8vKycMWp4/ExVXfv3uXOnTsMGjSIatWqMXLkSAtWJ/BwH33wwQfs3LmTyZMnU6dOHezs7Jg/fz6enp7s2bOHO3fucPDgQZo2bcq7776Lu7v7c7/Opk2bKFy4MMePH+fEiRPs37+fChUq0Lt3bwoXLpwK70ye1cmTJwkODqZr164A7NixgyVLlpAvXz6qVKlC9erVcXV1TZXXDgkJYffu3fTs2ZMrV67w999/c+rUKT744INnOvHL023dupWwsDB69epFfHw8AQEB/Pbbb1SpUgUfHx/y5s1ratX6J7ZpWGum5+7uzs2bN01/3NbW1oSFheHn50fnzp0tXV669yhM3L17FxsbG7y9vYmKiiIoKIhatWpRsGBBDh06xMWLF3FycrJwtakjeaiaP38+N27coEiRIvj6+jJ16lRGjBjBhAkT8PHxIXfu3GTNmtXCFb+ajEYjQ4YMoWLFikycOBGAevXq4eXlxaxZsyhUqBBdunThk08+Yf369cTExPxnsPqnixQSExP5+uuviYqKonfv3rRu3ZqwsDCcnZ1T7b3Js7O1fXgKTUxM5I033mDPnj0EBgYSERFBw4YNzfY6BoMhRWDKli0bSUlJfPzxxwDExcVhbW2Nn5+fKejJi9mwYQNLly4lLi6Otm3b4u7uTp06dTAajUyYMIHbt28zevTopz6HgpUZbN++nYSEBBITE6lQoQIXLlygW7duVKhQgVu3bqnp/jns2LGDRYsWERUVha+vL61ateL333/nrbfeonz58pQtW5YWLVqQLVs2S5eaKh6dWLdv3w5A0aJFWbhwITVq1MDT05Np06bRtGlToqKiGDNmjCVLfaVZW1tTsGBBunfvzpo1axg3bhxJSUk0bNiQihUr4uTkxN27d4mMjCRnzpwUKFDgP58zeffS6dOnKVq0KB06dKBWrVrY2dnh7OzM7du3OXbs2CvVFZ4eJCUlPdFCERUVxbRp0yhTpgzly5cHoESJEpQrV44mTZqYxlq96L6KjIzE3t4eZ2dnU6gKDg4GIEuWLKbWsHfeeYdSpUpx584dzp8//6Jv8ZX1+D6qVKkSxYsXZ8GCBcyZM4e+ffvi7u7Om2++ibu7O1WrVv3PfaquwJd0+PBhNmzYwN27d7G2tmbgwIEULFgQgIMHD3L58mVq1KhBzpw5LVxp+nf48GEuX75MqVKl2Lx5M05OTnTv3p2NGzfy+eef4+7uzuzZsylatKilS001BoOBW7duERwcTLNmzdixYwcGg4E333wTg8HA/fv3WbduHR06dDB9WxbLWbRoEefOncPKyorAwECGDh1KvXr1uHXrFuPGjaNq1ap07tz5qSfZ5NvXr1/PDz/8QOnSpbly5QolS5bk/fffx2Aw8NNPP5EvXz5atWqFm5tbGr9TgYfjbiIjI2nZsiXe3t788MMPzJo1i5YtW3Lt2jVcXFyYOnUq1tbWT7QyPa/Bgwdz8eJFFi5ciKurKz///DM//fQTRqORmjVr0q5dO+rUqQPA2bNnOX78ONWrVydXrlzmeruZXvLP3rx587h//z53797lk08+ITQ0lBUrVmBvb0+9evXw9vY2HXP/KWgnpyPzSwgPD+fSpUuMGDGCgIAAbt26RYECBQgPD2f37t2EhobSu3dvhap/kfyPOiEhgVu3btGoUSOuXr1KwYIFadasGQBNmzalYsWKWFtbZ8rf5ZIlSzAYDLzzzjucPn2ayMhI1q9fj7OzM87OzlSsWBF42JpXpkwZOnbsCPz3h1tS18GDBwFMY95y5MjB119/jYODAyVLluS9996jePHipr/xf/uW+2j7yZMnOXv2LOvWrQNgzZo1bNmyhTlz5phaJapXr65QlYaSh6Px48dz7NgxXFxc+OCDD/j8888ZMGAAhQoV4sSJE7i5ufHxxx9jbW2N0Wh8qVAVExNDgwYN+P333+nRowcTJ04kIiKCdevWERMTww8//MC+ffsoX748v//+Ow8ePKBr166Z8viYWpKffwIDA0lKSiJfvnz88ccftG3bllWrVtGxY0dGjhxJbGwsVapUMT32v467arF6Do9/4wwJCWHx4sU0atSI+Ph4U5N9eHg4NjY2ZM2aVWMh/kXy3+Vvv/2GjY0NBw4c4MKFCwwYMID69esDsGfPHqKjo806XiE9MRgMBAQEsGDBAjw8PHjjjTdo164db7/9tmkSQgA/Pz9iYmLo06ePuoHSiU2bNmFtbU2jRo1MJ+D+/fvz559/0r17d4YNGwY8OT7mnxw5coRZs2YRHR3N0KFDKV++PEajkXXr1vHbb7+ZWkJ0PLGMAwcOcPHiRdq1awfAhAkT2LFjB0OHDqVEiRLkzp3bdF9zf+Hx9fXl7t27DBo0iBYtWgAP5/YbNmwYkydPxsrKiixZsuDi4mK218ys4uLiOHjwINWrVzcdR3fv3k14eDgdOnQw3a979+6ULl2avn37smXLFtq3b/9c+1SXDzyHRzvixIkTXL9+HYPBQEJCAqtWraJy5crY2dmxfft204FQB8F/9+h3eenSJU6cOEG+fPkYOHAgWbNmZdWqVdy8eZNVq1axcuVKypUrZ+FqU4+1tTVvvvkmJUqU4Ny5c6ZxE2PGjOHu3bvUq1ePrl27EhQURK9evV6pObzSk3+a3sLW1pZ58+Zx8uRJU3Dq1KkTX3zxBUOGDDHd779CVVJSEtmzZ+fjjz/mtddeIzAwkJCQEKysrGjRogV9+vThyy+/1PHEQvz9/fnggw84evSoaSqDoUOH8sYbbzBgwACWL1+e4v4vG6oe/3wvXbqUnDlzMn36dO7fvw9AgQIF6Ny5Mx4eHuTKlUuh6hnNnz+fAwcOEB8fb9r2aPhFfHy8af8OHz6cokWL4ubmhq+vLzY2Ns81xY1arJ5TcHAwv/zyC7a2thQqVAhHR0cOHTpETEwM1apV4/Dhw4wdO9Y0zkr+3Y4dOxg/fjze3t4MGTIEFxcXgoKCTN/CrKysmDBhAp6enpYu1eweb8XYvXs3Z86c4ejRozRu3JgGDRoAmMaa1axZExsbm5cetyHPL3nr6rJly7CysqJWrVrkzp2bL774gtOnT9OkSRNOnDiBg4OD6aKCxMTE/xwHt2XLFnbs2EFkZCRjxozh6tWrrFmzBldXV8qVK0e1atVwdHRM9fco/+fxFqeIiAiOHTvG6NGj+eSTT0xDFOBh6GnXrl2qjHfcvn07t2/fpk2bNgB06NABg8HAb7/9xu7du9mzZw8ffvihQtVziImJISEhATc3N3bt2kX16tX5+++/mTNnDp06daJSpUo4OzsTGBjIrVu38PX1faELEBSs/kPyD9nZs2c5deoUTZs25aeffiJv3rw0btyYffv2ceDAAWxsbGjWrBnFihWzcNXp06M/0Ef/P3nyJMePH2fWrFmMGjXK1P0HcOfOHWxtbTPlt/Tk4ejkyZMULlwYJycnIiIiWLt2LUePHqVVq1YkJiZSsmRJ8ufPD2hMlSUk31dfffUVf/31F3ny5OHatWssWLAAGxsbfvvtN/7++2/c3d355ptvsLOze6bnPnjwIBcvXiR//vwsW7aMatWq0b59e44ePcqsWbOAh11OmfUK2PQo+Ul05cqVODk5Ua1aNdzc3Ni8eTPffvstn3zyCa+99lqKL3zm/mxevXqVn3/+mW3bttGlSxd69eoFQLt27bh+/Tq5c+dmypQpFClSxGyvmZkZjUbu3LlD9uzZAfj5559Zvnw5PXr0wMfHh7Fjx7J37148PT3x9PTE3d2drl27vvCwCwWrfxEWFkbBggVNv1iDwcAff/xBdHQ0FStW5Pjx49SrVw97e3tu3LihKzH+Q/ID1tmzZ8mXLx/29vbY2NiwcuVKZs6cyejRoylevDiOjo6vxMnks88+IzQ0lPj4eIYPH06VKlW4ceMGGzZs4Pfff8fDw4PffvtNLVTpwB9//MHNmzfx9fXlypUrTJ8+naNHj5rGxiX3by1VyT8DBoOBjRs30rx5c06cOMHZs2dp2rQptra2xMXFsXr1amrUqKGW7zRw586dJy4ImDlzJgcPHiRbtmxcvnyZH3/8EXd3d7Zv306/fv3o06ePaQ6p1HLt2jV27drF7Nmz6dixI7179wYejkmtW7eu/jaeQ0BAAFevXqVDhw4EBweTJUsWAgMDOXfuHI0bN6Zt27Zs2rSJK1euEB8fT9++fbGxsXnh6TIUrP7B999/z927d6lfvz5Vq1ZlyZIl7Nq1i/bt2zN37lyqV6/O+++/D8DOnTsJCwvj7bff1uXvz2DFihXMmTOHMmXKULBgQQYOHIi9vT2bN2/m448/pn379gwZMiRTtlQlFxgYyMWLF+nRowd9+vQBoH///lSqVAl4GD6LFCnyUh9uMY9FixaxaNEiWrRoQd++fUlKSuLmzZt89913rF69mnHjxtG+fXvgnyf5fHz7qlWrcHV15ZdffsHT05NmzZpRvXp1AIKCgrC2tub1119/5pYveXHh4eEsWbKEhg0bUrZsWQAOHTrE/v376du3L2vWrGHr1q1cu3aNefPm4ejoyJIlS8x6vE/+t7F9+3YCAwNNE1DevXuXrVu3MmvWLOrVq0e3bt0oVKiQWV73VbJixQr++usvbG1t8fDwYMiQIcTGxjJz5kzOnDlDkyZNqFixYorf7csMu9BX4cfMnTsXeHiSq1q1KocPHyYxMZEaNWqQO3duChYsyPHjx/n111/x9/dn5cqV1K1bV6HqGTxalmPevHkUKFCAc+fOmbo8GjduTMeOHXn33XczZah6fP0ua2trKlWqZBqUXqFCBb799lumTp1KWFgYxYoVMw2YVKhKW48PUvX09OSdd95h06ZNBAQEYGNjQ548efjf//7H4MGDad26tem+/zWlQnh4OIsXLyZ79uy8++67BAcHExISAjwcb7Vq1SoKFy6sUJVG1q1bR0BAAGvWrOH48eMAFClShNq1a3Pq1CmcnJyYMWMGbm5uvPHGG/z555907twZW1vbf1yA+0U8+ts4fvw4oaGhnDp1yjR7erZs2ahVqxbVqlVj165dGgrwgtq1a4eHhweHDx/mzTffBMDR0ZGBAwdSqlQppkyZwpo1a1I85mV6CtRilcyYMWP466+/mDlzJl5eXvz999+MHj2aqKgofv31V/Lly8ehQ4f466+/2L17Nx4eHgwaNIjixYtbuvR055/S/qlTp7CysiJ79uwEBgaSL18+li5dipOTE127dsXLyyvTD9SdN28eb775JoUKFcJgMLB06VLTckeDBw8mS5YsjB8/Xt1/FvJ464GXlxfZsmXD2dkZf39/1qxZQ48ePahduzb29vamxz1L99+2bdtYsGAB1apVo1evXsTGxrJ69WomT55MuXLliI6OZurUqRo3k4ZiYmI4c+YMv/76K+7u7rRt25YyZcoAD0PXzZs36dGjBzt27OD48eP06dMnVVqqHl0B3bVrVwoVKsQXX3yBo6Mj8+bNY9++fZw7d47mzZun2tqDmdGjcW+PzkVXrlxh5cqVHDt2jG7dulGlShVsbW0JCQlh9erVfPLJJ2YLrgpW/5+fnx9///03V69epWzZsrRp04b8+fMTExPD2LFjiY6O5pdffsHKysrU+pCQkICDg4OFK09fIiMjTWuiPX6yiYiIwNramsOHD+Pq6kqlSpWYMmUKFy9eZMiQIc+07EdGk/zgeejQIUaPHs3IkSOpUqUK8fHxtG3blmbNmuHh4UG+fPmoXbu26W9M4cpyJkyYwP79+/Hw8KBixYr4+PiQPXt21q1bx9ixY+nfvz/du3d/5uc7deoUZ8+eZf/+/ezfv5+ff/7ZNPfR9evXuX//Pi4uLk+M15LUk/yzGRQUxO+//06OHDlo27YtZcuWZcGCBRw6dIjExERKly7NgAEDAPMPVA8ODubEiRPkyZOHRo0aAQ/nSBwyZAi3b9/GycmJH3/8MVOvOGFuyfft4cOHsbe3p3Tp0gDMmDGDo0eP0qtXLw4cOIC3tzc1atQAzLdvFayA2bNnExUVxZAhQzhy5AgTJkygZMmS+Pj4ULJkSWJiYhg+fDjx8fF88sknZMuWTTPc/osBAwYQERFhmtjy8XBlNBoZMWIEefPmpUyZMoSFheHj45Mpu/+Si42NxdHRkQsXLvDXX3/RvHlz3NzcWLduHXPnzsXFxYX58+drSoU0duzYMS5dukTTpk1N2/bs2cPp06fp1q0bX3zxBQ8ePKBEiRK88847XLp0iXXr1vHhhx8+9QD8T60R/fv3p0aNGowYMYJz584xe/ZsjEYjWbNmTdH6JWnr6tWr5M2bl0OHDplarrp27YqzszPz5883TYXxsi1V/zT+7sKFC4wfP55r167RtWtX2rRpg62tLQaDgcjISLZv306VKlU0ruoFffPNNxw9epQHDx5QrVo1hg4dCsD06dM5cOAASUlJ/Prrr2bvYlWw4uEl73///bdpqZD9+/czbdo0SpYsSYcOHShZsiQA7du3x2Aw8Msvv5gu25T/c/v2bVauXMnatWtxdHRk0aJFwJPhaseOHXzxxRdkzZqVadOmZfrpKUJCQli+fDmdO3emSJEi/PXXX1SpUgV7e3sSEhJ48OCBaaFVhaq0NXz4cFauXMnIkSNN41qCg4NxdnYmKSmJa9euYWVlhZ+fH46OjvTo0YPKlSsDz/bt9lFrRN68ealbt64pQI0dO5atW7fy7rvv0rlz50zfBZ5erVy5kps3b1KjRg3KlStHcHAwv/32G7ly5aJGjRq8/vrrphb4F23NSP6Zjo+PTxGiDQYDVlZWTJs2jXPnztG7d2/Kly+v48BLeBRg//jjD0JDQ+nZsycLFixg+fLl1K5dm+HDhwNw5coV8ubNmyrHXQWr/y8uLi5Ft15wcDDfffcdpUqVokuXLri7u7NlyxaqVKlC4cKFLVhpxlCtWjW8vb2ZPXs28H/h6tEf/fXr17G3t8+UAfXxA/CJEyf48ssviY6OxsfHh5CQEOrWrUuTJk1SfJh1ME17d+7c4eTJk/Ts2ZPhw4fz7rvvEhcXh52dHStWrKBGjRrkz5+fb7/9FgcHB957771nPrk+3hrRtm1b02Nv377NRx99xOeff54pJ8DNCNauXUvu3LmpWrUqly5d4saNGxQtWpS7d+8yYMAAKlasyLhx417q4pGkpCSmT59ObGwsQ4cONX2+165dy927d8mbN69p/r7x48dz69Yt6tSpQ6FChahQoYKOB89hzZo1tGrVCng4VUV8fDxxcXHcvXuXhIQEcuXKxXvvvUeuXLn47LPPTGOjU+O4q2D1mOTNtfv37zc117/11lt06NCBLFmyWLjC9O/RIN3z589ToEABfv/9d+DhmLRHLVevwpVufn5+NGjQgNy5c7Nr1y4uXrxIYmIiy5YtI2fOnEyaNMn0bVjS1qOD6aP/BwQEMGjQIIYNG0a3bt1ISkqic+fOeHl5UahQIbJly4avr+9Tx7893tWTmJiIjY3NE60Rj646fNRSKWnj8f22ZMkSihYtSlBQEOfPnycsLIwCBQowfPhwdu/eTcuWLV96upMzZ86wa9cuDh06RLZs2fjqq68ICAhg5MiRlC1blhs3btCxY8cUF7CEhYXx008/abjJc0hKSqJdu3a4u7vTrFkzsmbNSo0aNXB1dWXp0qW89tprVKpUiaVLl7Jv3z4mTpyYqldYKlj9h8WLFzN//nx+/PFHtVQ9gyNHjrBt2zY6depk6jpxdHQ0hatXxc8//8y3335L586d6dSpE7du3cLLy4scOXKYZljv1q3bKxEw05MHDx488eXo0Qk3MDCQ9957j6FDh9K9e3dCQkIYOXIkOXLk4Pvvv0/R4prc49vWrFljWhj30dJEyVsjChQoYOpOlLSRfB9t3ryZevXqsWjRIrZv346NjQ1du3alZMmS7N69mxYtWphOui87mHn58uXcu3eP7NmzExAQQLZs2Shfvjzt2rUjJiaGNWvWsHLlSjp16oSPjw/BwcHkzZvXtNqC/Lfk+7ZBgwY4Ojqybt06AO7fv8+UKVNwdnYmJiYGb29v05JEqbmShYLVv3i0s0JDQ3F1dU2xerk86dHva/PmzRQpUoQSJUoAcOPGDVq0aIGLiws9e/bknXfeeSWaty9dukRSUhJTpkzB1dWVsLAwqlSpQu/evVOc2DX5Z9oJCwtj48aN+Pj4PNFSmLzlavDgwYwaNYoWLVpgb2+Pg4MDVlZW/3ggfvDgAY6OjqZ9uGnTJhYsWEClSpXYs2cPb7/9Nj4+PsD/tUbMmTNHKzVYyM6dO9m5cye+vr54eXlx+/Zt03CEEydOcOTIEd5++22zfCY3btzIhQsXqFixIvBwaocVK1Zw69YtFi5ciJ2dHdevX2fjxo3MmTOHZs2aMWTIEF1p/hwefW4fzSn26EKRIkWKMG7cOODh5KA7duzg3r17zJkzJ03mnFSwkhf2T6Hg119/5caNGwwYMMDUzTF//nx2797Np59+ipeXlyVKTXOPTsL37t3j9OnTLF++nL/++otVq1ap+89Cvv/+e6ZPn26a7f7fwtXOnTvp06cP3bt3Z9iwYSl+ltykSZMIDg6mYsWKtGrVigIFCrBw4UJ69uzJ3r17OXDgAIcPH8bHx4eWLVuqNcLCgoODiYuLo1atWgQHB3Py5Enc3d2Ji4vj8uXL5M2blw4dOqRYz/RFbdiwgdjYWNq1a2faFhUVxd69e1m+fDnZs2fn66+/xsrKyrTwdqNGjTSlwgv6+++/SUhIoHLlyty+fZvBgweTP39+vvnmG06dOkXWrFnJnz//v35BMjcFK3lpQUFBhIaGUrNmTUJDQ1m3bh21atWiYcOGHDx4kCNHjvDee+9lym9i//QhfXyx6UdWrFhBmzZtXokWu/TowYMH3Llzh969e/PWW2/Ro0cPcuTIkeI+BoOBhIQE5s+fT69evf712+2qVatMi+GuXbuWJk2a4OPjw969eylZsiSbNm2iY8eOzJkzBz8/P+rXr8/QoUMz5WcgvUoeho1GI6tWrSIsLIxr165hMBgICwujRo0aNG/enOvXr1O9enWzfTZ/+OEHqlWrZmqtOnv2LFOnTuX27dvUq1ePEydO4OjoyHvvvUfevHm1wPpLWLFiBUePHmXNmjX07duXfv36cf36dd577z0uX75M9erV+e6778wSmJ+VgpU8s4SEhCeW2jh69CgbNmzg2rVr3Lhxg2+//ZbFixcTFBREREQERqORmTNnmqasyEySH7hPnDhBrly5cHNze2IszuMHTV39l/aS749PPvmErVu34uPjw8CBA5/agvhPM6qPHTuW2NhYxo4dCzy8ZH/dunXMmTOH0NBQChYsyHfffceoUaPYvXs3N27cwNvbW2M001Dyz9ijeapiYmIYN24c2bJlo0OHDhQrVoylS5dSsmRJypcv/8TjXsbixYu5d+8e5cuXJyYmhk8//ZTatWtja2tLYGAgo0aNYuHCheTIkYPp06eTNWvWl37NV1FAQAB2dnbUrVuXZcuWkSVLFpo3bw48nDtw5cqV+Pj4pPmScwpW8kzi4+PZsWMH3t7e5MiRA6PRyIULF9i3bx8dO3bk0qVLBAQE0KNHD+DhmmiRkZHkzJkzU45Pe/xEffDgQfLmzUuzZs3o0KEDDg4OClDp0KJFi7h58ybVqlXj888/54033uD9998nW7Zsz/T4rVu38uWXX1KjRg06depExYoViYyMZMiQIXh6erJx40bTl4j8+fNz8+ZNxo8f/8zPL+Y1duxYDh8+TIkSJfD19eX1118HMK3PePnyZerXr2/2VoxLly4xdepUQkJCcHR0pFWrVnTr1g2AadOm4ejoSGxsLC1atMj08/iZ0+PH1M2bN+Pl5cXVq1exsrKiVq1aAFy8eDHFF5l/W3IqtShYyTOZM2cOP//8M4MGDaJp06bkyJGD69evs3XrVgoVKkRMTAxNmjTBysqKkydPUqpUKUuXnGqSh6oNGzZw6NAhBg0axKxZs7h8+TI1a9Z8ZQbpZyRXr141DVyGh90zPXv2pFmzZlSqVImqVav+51pscXFx3Lhxg6+//pps2bLRtGlTjh49iqenJ15eXuzfv58NGzZQokQJEhMT6dKlC6+99lpavD15zJo1awgPD6d+/fpMmTKFnDlz0qBBA9544w1Gjx5Nzpw5GTBgQKp1Ed27d49bt26RmJjIa6+9ZgoFGzZsIFeuXLoy9DklD1UrVqzg9ddfZ/ny5axdu5avv/6aOnXqAODv7w9gumjEEhSs5JmcP3+e4OBg1q1bR8OGDWnWrBnR0dEMHDiQcuXKMXLkSJydndm9ezc7d+6kX79+uLm5WbrsVLVo0SLOnz9P27ZtKVWqFDExMcydO5czZ86QNWtWunXrlqkDZnr3+LfbAwcOsGzZMsaPH2/62cKFCxk3bhzNmzdn0qRJz3xyvXDhAuPGjePu3bu0bNnSNGv7pUuX+Omnnxg1apSWqUljj4ejLVu2UKdOHUJCQrh48SKXL1/m0qVLlC9fnnr16pE7d+40+fLzaD4zKysrjh49SlBQEK1atdI8Vc8h+b6dOnUqf/75J6tWrQKgW7duXLt2DV9fX86fP4+VlRWjR4+26BdbfaWWZ1K0aFF8fHzImTMnCxcuZO3atRQqVIihQ4eyYcMGPvnkEz799FNmzpxJp06dMmWoerT4NjxcDuHatWuEhoaya9cubty4QdasWenXrx8FChTAyspKLRUW9ujAevHiRRISEihevDi3b99m0aJFpp/lzZuXefPmMXHiRFPLxbMoUqQIn3/+OW5ubpw4cYL9+/cDDwfI29nZ8eDBg9R5U/KPHi0NA/Dpp5/yxx9/mLrYTp8+TZs2bWjatCkxMTGcPHnSFKqSf6ZTS2xsLEuXLmXhwoX88ssvtGzZUqHqOSVfd/Pq1avcv3+fqVOnAjBv3jxq1apFWFgYtra2fPHFF2m2b/+1XrVYybPatWsXK1euJFeuXOzbt49WrVrRvXt3/v77b/bv309CQgJNmjTJ9JcM79ixgyxZslC+fHn279/PypUrqVmzJm+99Rbu7u6mGeafNku3pI3FixezefNmXF1dadmyJWFhYWzZsgUHBwecnZ2JjY1l7ty5L3wZ9sWLFxk7dix58+bF09OT+/fv0759+0w5rjAj2L17N3/88QeVK1emXr163L59my5duvDdd99x/vx5bG1tady4cZpeIQYPWzIDAwN54403Mv3xMbXs2LGD69ev4+PjQ0JCAosXLza1FD/y6Hhr6assFazkPxmNRi5fvszatWtNrVH+/v4sXrwYX19fKleuTMGCBV+Jro/NmzezZcsWEhISKFiwIEOGDGHt2rXs3r0bOzs7WrVqZRo7ock/LWvPnj1YW1vj6enJpEmT6NKlC4ULFyYkJIRt27bh7OxM//79sbOze6l9deXKFfr3709SUhJz584lX758Zn4n8m+S7zc/Pz9+/PFHGjZsyGeffWa6z9ixY1mzZg2lSpXil19+eellasxRq/y3x7+U7t27l+PHj9O8eXNu3rzJjRs3qF+/Pr/88gthYWF069aNokWLWjxUgYKVPKMLFy6wf/9+fHx8TH/wY8eOZcmSJdSpU+eVuPLp4MGDJCQkUK1aNebPn0+BAgVMS5ZMmzaN8+fPM2XKFLVQWcjjB+JffvmFevXqce/ePW7fvk3dunWBJ6e/MMeB+MCBA3h4eGhKhTSUfL+Fh4dz+/Ztrl27xhdffMHQoUNp2bKl6b4hISG89tprKdaHlIzhxIkTuLq6Eh4ezqJFi7CxsSFbtmx89tlnfPvttxw5coRChQpx4MABVq9enS7mikvbyR0kw0pISOCHH36gQIEC1KhRA4AWLVrg4eFBixYtMmWoevwAfObMGWxtbTly5AheXl7Url0beLhUSvv27cmXL58O3BaS/CS7ZcsWXn/9de7du8eMGTNo0qQJDRs2BGDp0qUUKVKEqlWrmh5rjm+3lSpVeunnkGdnNBpN+2327Nk4OTnx7rvvUqZMGYxGo2mR3apVq+Lm5maaAiM9tGbIs/Pz8+Pw4cPs2LGDqVOn8sEHHxAXF8fkyZMZMGAAUVFR/Prrr9jY2PDLL7+km14TBSv5V8lXZS9fvjy9e/dmzJgxtGzZkqSkJA4dOsSkSZNMa21lJskPwFFRUWTNmhVPT0/Gjh1Ls2bN6Nu3LwC///47zs7OtGrVCnh4wFeoSlvJT7JTpkzh5s2bNGrUiBIlShAUFMSRI0fInz8/gYGBPHjwgA4dOli4YnkZybvUvv32W1auXEnFihV59913gYcL8To5OdGzZ0/69+/Phx9+aHqsQlXGERgYiKenJ507d2bChAnExsZSpEgRADp06MCsWbOYNGkSNjY2HDlyhHv37pkWWbd0l6u6AuUfHTt2jMDAQLJnz8727dsxGAzUq1cPBwcHli9fjpubGx9++GGmnFE9ueHDh3P69Gly5cpF+fLlSUhI4I8//qBOnTrcuXMHGxsbRo0apQN2OrB48WJ+//13SpUqxUcffUSePHn49ddf2bNnD7dv3yZ37txMmTIFW1tbtSpmAgEBAdy9e5f69eszbtw43n77bdP4xqtXr+Lv78/AgQPTfNZteTGPtyauWrWKkiVLcv36dezt7U09JdHR0WTNmpWpU6dSr149EhISuHz5MnXq1HliiSpLUbCSJ1y4cIENGzbw7rvv4uzszLlz51i/fj329vb069eP2NhYrK2t002zqzkl/3DPnj2b/fv3884777B7924iIiJM3X/79+8nZ86cfPTRRzpRpwN79uzh3r17vPXWW8yaNYvSpUvTuHFj4OE+jY2NxcnJKc0WYZXUlZiYyPz582ndujU5c+Zk+/btVKxYERcXF86cOYOTk5PpIoK0nnVbXs7ixYtp0qQJ06ZN4/Lly3Tr1s103F20aBFZsmShTZs2zJ49mxMnTuDs7MwHH3yQrq7E1V+b/OOiwVZWVjg7O2MwGPD09KRGjRps2rSJ+Ph4HB0dLVxx6nl0wv3rr7+oVKkS/fv3B6BcuXIsWrQIDw8P3nrrrRSz+upEbVlJSUkcO3YMT09P7O3teeONN0zfXNetW4e7uzsFCxYka9asGAwG7asM5N8WOb9//z6nTp1i2bJluLq6ki1bNlxcXPjqq68ICQnhwoULzJs3jxIlSihUZSA7d+5k+fLlNGrUiD59+uDr68uyZcu4evUq58+fJz4+nmHDhgHQv39/oqOjsbW1TXfnJH3FFlOYSkhIAMDNzY3ff/+defPmmVph8uTJkyknvIyPj+fixYscPnyYU6dOAQ+boD/66COmT58OPBzEnjt3bipWrEhERMQTz6ETddpKSkoy/fvR+Kr8+fPz0UcfMWHCBNasWUORIkX45ptv+PHHH1mwYAEHDhwAUKtiBnL27Fm+++4703HpESsrK1xdXWnUqBH29vbs27cPR0dHvv/+e0JDQ5kzZw59+/blyJEjFqpcnlXyz7LBYKBs2bL079+fyMhI8uXLx8KFC0lISDB15w8bNgxbW1sSExMBcHZ2TnehCtRiJf/f9u3bWbNmDQDe3t58++239O/fnxMnTlC8eHEMBgMdOnTIVN1/sbGxjBw5EisrK06fPs25c+fo2LEjVatWZfPmzbRs2ZKJEyfy6aefAnD9+nU8PDwsXPWrLflA9Xnz5hEbG0uTJk1o1qwZuXLlIjw8nLi4OL7++mtOnjzJ2rVr+fPPPzl27JiFK5fntW3bNubOncvZs2eZOXOmab8/all/dKXn3r17mThxItHR0WzZsgV4uFJEfHy8xWqXZ/Non86aNQtvb29q1qxJsWLFWLduHb6+vhQtWpTvvvsuxXknKSkp3bdCaozVKyYmJoasWbOm2Hbs2DGOHj1K0aJFOXPmDLNnz6Zly5a0adOGpUuX4uzsTPv27SlUqJCFqja/+Ph45s+fj4eHB+3atePBgwcEBwczZcoUChcuzNixY4mJiaFly5Y0a9aMHDlyEBERwYgRIzJVuMyofv75Z44ePUr27NlZtWoVy5Yto1ixYoSGhvLtt98SFhbGunXrsLW15eTJkxw7dox27dqpdTEDiY2NxdHRkYYNG1KhQgXGjRv3j5+9+Ph41q9fT3R0NF27duXkyZMcOnSI+vXrp6txN/LP/P39mThxIi4uLnz00UeUK1cOKysrcuTIgbOzc4ru4IwyyWr6jn1iVnFxcaxYsYLq1atTvHhxAG7cuEFAQADdu3fHzc2NatWqUbJkSX7++Wfy5cvHqFGjMsQf8vNISEhgzpw5lCpVivr16wNgb29PnTp1yJ49O19++SULFy5kwIABrFu3jo4dOxITE8O+ffsAjamytLNnz5KQkMDUqVPZt28fsbGxdOjQgTVr1lC8eHE+++wzpk6dys2bN7l//z6HDx+mfv362mcZiNFoNHXxvPXWWyxYsIDY2Fi+++67J1or7O3tTasg2NvbEx4eTvfu3TPlNDCZUYMGDfDx8WHBggXs37+f6dOn4+zsTJMmTejXr1+Kz21GORdpwMErZNu2bUyfPp2lS5cSGhoKgLu7O0lJSdy7d8+0kGmpUqVMUwtklD/k53Hp0iWcnZ1NoepR95LRaKRs2bJ88sknbNiwgcjISHLlyoW/vz/wsLk6Ojo6xbgASX2PL6ZarFgxatSowblz54iMjGT8+PE0aNCAhg0bMm/ePHLmzEmBAgWYNGkSy5Yto3HjxuTKlctC1cuLSL5MjbOzM+vXr+f48eO8//77KRbKfvTvypUr87///Q93d3c6duyoUJVBGAwG07569913GTVqFNOmTaNkyZLY2dlZuLoXp67AV8SaNWvImjUrxYoVY8yYMRQrVgwfHx+KFi3KoEGDcHBw4L333qN48eJcvnyZP//8k5YtW2bKGdX37t3Ln3/+ybBhw56YJuHRoMjhw4czevRoHBwcsLGxITo6msqVK1O7dm2mTp2Ks7Ozpcp/pSTfP2FhYWTNmtV0xV9gYCBbt25l/PjxhISEcPToUVN336VLl0hISMDZ2VmhKoO6ceMG27dvN12BGx8fT4MGDahVqxZNmjTB29sbV1fXDNM99KpLvp8e/zeQYtH65ENWMuL+VVfgK+Dnn3/m0qVLlC1bliJFijB48GAmT57MkiVL6NevH+PHj+edd95hxIgRFCxYEHd3d/r3758pQxVAjhw5CAoK4vjx45QpU8a03Wg0mroZSpYsiZOTE4DpBD18+HBq166tUJWGHoWqoUOHEhQUhKenJ61bt6ZVq1Y4OTlx48YN3n77bcqXL8/IkSOBh2GsYMGClixbzODatWvs3LkTHx8fjEYj9vb2vP/++3z++edcv36dN954A8g43UOvsuThaN68eVy/fp08efLQvXv3FPvv0ec9I4cqULDK9JYsWUKWLFn48ssvTdtef/11Pv74YyZPnsz333/PoEGDWLlyJTt37iQmJobKlStn6qvfChYsSIkSJVi7di2Ojo4UK1YsRcvI2rVriY2NNd3/UZN0586d0/3VKJlF8nFsO3bsIEeOHEyePJm1a9eydu1a7O3tadKkCVevXuXChQsMGjTI9FhNqZDx/NO4RS8vL6ytrVm+fDlt27bFysqKrFmzsn79eooUKfLE3HuSfj3aR9u3b8fOzo6yZcvy008/UbduXYoWLfqfj8to1BWYiX311VecPHmSRYsWAXD37l2WLFlCu3bt8PDw4MiRI0yaNImyZctSt25dSpYsmWlbqR536tQpPv/8c/LkyUObNm2oWbMmDg4OzJ8/n+joaAYOHKgTtIUkP1lu2bKF8PBwateujZeXF2fPnmX16tWcPHmSMmXK4OPjQ/78+QHNsJ1RJd/fkyZNAiB37tx07dqVOXPm8NdffxEbG4uHhwf37t1jwYIFmkE/A7p+/ToHDhygWbNm7NixA6PRSN26dTNlOFawyqT27NnDTz/9hI2NDTVq1KBnz54AdO/eHS8vLz7++GOyZMlCaGgo7733Hvnz52fKlCmv1KDP0NBQZs2aRWBgILlz56ZYsWJkyZLFtLBnZvzAp3fJT5ZjxowhMDAQOzs7smTJwqJFi3BycuLcuXMsXLiQu3fvMmnSJAXgDCx5S/H06dPZvXs3pUuX5tChQzRr1ow+ffqwf/9+9u7di9FoNK39p89m+pd8HyUkJHDx4kXGjx9Ply5dcHd35/XXXwcejpUsU6ZMppoaQ8Eqk4qPjyc2NpbVq1ezY8cOSpcubVrIMn/+/KYBvVZWVixevJjq1aubVg5/lcTHx3Pu3Dlu3rxJzpw5ee2117C2ttbafxa2a9curly5wttvv825c+eYOnUqt2/f5pdffsHOzo7bt2/j5uaWYsCrZFwBAQGEhYXRs2dPYmNj2bJlCwsXLqRhw4a0bt06xUlXLVXpX/JQtXr1ah48eEDHjh3p0aMHt27dYu3atQD89ttvxMTE0Ldv30wVlBWsMrnbt2+zcuVKNm7ciKenJxMmTABSXomhA1VKOlFblp+fH1OmTOHNN99k8uTJJCYmcvXqVSZPnkxQUBDjx4+nbt26QMYd3Cr/Z926dSxbtoykpCS+//57XFxcuHv3Ln/++ScTJ06kYcOGfPHFF9rPGdCePXtITEzE3d2dkiVLEhoayvjx4zlz5gyenp5kz56d7777LtP1EChYvQLu3r3L8uXL2bNnD1WqVKFv376WLknE5PFgHxYWxvnz5+nfvz9Dhw6le/fuAKxfv56NGzcybdo0fRHIwB7f31euXOHGjRssW7YMNzc3+vfvj4uLC9HR0Rw8eJBatWppf2cQyfftxYsXWbVqFR988AGbN29m//79/PXXXyxYsIAzZ84AULVqVWxsbDLdl1kFq1fE7du3WbFiBYcOHcLV1ZX69etTr169TPMNQTKm5N9SAwMDKVKkCDly5CBbtmwEBAQwePBgRo4cyZtvvomjoyPu7u5qZc3Aku9vPz8/smfPTrly5ShYsCBBQUFs2rQJJycn6tevT/ny5U37WPs7/UsejmbMmEG3bt2YMmUKW7dupWrVqlSqVMk0zULnzp1Nj8uM+1aX0LwismfPTpcuXbhw4QLBwcEMHDhQoUos7tHf4OTJk9m9ezcFCxYkV65c9O/fnwYNGjBnzhx69+5NWFgYw4cPB1IuxCwZy6P9PWXKFM6ePcvdu3cpWrSoafFzBwcHRo8ezYMHD/D29jY9Tvs7fTMajaZQFRAQwM6dOylVqhTvv/8+5cqVo1GjRri4uHD27FkuXbqU4rGZcd+qxeoVYjQa2bBhA6+//joFChSwdDnyCjp9+jSRkZFUr17dtG3Pnj2EhITQvXt3Jk+ezI0bN8iRIwfvvfceDg4O/PLLL/Tq1UtTKWQSISEhHD58mI4dO/L777+zd+9e8ubNyzvvvEO2bNn4888/adWqVaY84WZ2fn5+rF27lt69e9OgQQPgYYvU+fPnCQwMxNPTk/r162f6L/UKVq+YzNaXLRnLoEGD2Lp1K9OnT6dRo0YAHDt2jKxZsxIbG8vly5fJkSMHP/74I/fv32fIkCGUL18e0DxVmUV4eDghISHkzZuXmzdvUrZsWT766CPu37+Pr6+vaQmbzNhFlNkkP5/cu3ePzZs3c/jwYc6dO8e0adPIlSsXMTExnD17loMHD/Luu+9ibW2dqQaq/xMFKxFJM+Hh4QQHBzNs2DBmzpxJvXr1iIqKIkuWLKxduxYvLy/KlSvH7NmziYuL4/3339fJNZNavXo11tbWtGzZkhUrVhAZGUmPHj20vzOI5KHq9u3bpjkQIyMjmTNnDhcvXmTcuHFky5YtxT59Fb7cK1iJSKp7/Btqx44dOXXqFBMmTKBRo0bEx8fzwQcfkCdPHlxdXcmZMyddunQB1HKRUf3TCTT5tl9//ZVZs2ZRp04dihYtynvvvacLEzKg0aNHc/bsWUqVKkWfPn3w8PDgxo0b+Pn5sW7dOgYOHEj79u0tXWaaytyxUUTSheShauHChXh7e9OjRw8+/PBDNmzYgL29PYMGDeLUqVOcOXOGjh07AhqonpE9ClBBQUGcP3+e6OhoUzcQPAzXffv2xd7env79+5vW/tP+zjjWrFmDm5sbI0aMIDAwkPnz5xMREUGuXLnw9vamatWqtGnTxtJlpjm1WIlImjl69CgnTpzg7bffBmDp0qV89dVXzJo1i0qVKmFlZYWTk5NaLjKJtWvXsnHjRqysrKhSpQpdunT513Fy2t/pX/KW56ioKNavX0/Dhg25cOECYWFhbNiwAS8vL7y9valQoQI5c+Z8JT/LarESkTQTHh5u+ndCQgK+vr68+eab9OvXjylTppA1a1a1XGRgyb+nBwUFkSdPHmbNmkXx4sXx9PRMEaoe/06v/Z2+JSUlpWh5dnFxoVGjRtjb23Pu3Dnatm3LwIEDCQgI4M8//zSFqlfxs6wWKxExq4iICLJnz/6PA1T37dvHwoUL6devH+XKlQNg586dREVF0ahRI131l4Elb80wGAwsWrSI/Pnzkz17dqKioqhduzaAaV1OyZgmTJjAtWvXKFGiBA0aNCBHjhw0b94cPz8/wsPDMRgM1KpV65W4+u/f6CgmImZz8+ZNJk2aRPPmzalZs+YTQSlv3rxkzZqVWbNm8frrr3Pp0iUSExOZOHEioCkVMqrkg9JHjhyJk5MTJUqUYO7cubRo0cI0Zm7RokW4u7vTuHFjS5YrzyH5vh07dizHjh2jQoUK/PXXX9y4cYNBgwZRv359OnfuTMmSJZk7d+4rv5C9jmAiYjaHDx/mxIkTxMbGYmNjQ/Xq1VMssFqoUCE6d+7Mjh07CA4OJnv27IwfP970eIWqjOnRCXTDhg0UL16cokWL4urqioeHB4GBgRgMBsLCwkhMTMTX19fC1cp/MRgMJCUlYWdnZ9q3f//9N1WqVGHUqFHAw4l9V6xYQVxcHKNGjaJ169Z4e3ubWqpe1VAF6goUETN69C31s88+49atW3Tt2pVq1ar950KraqnK+NavX8+MGTNwcnJi2bJlWFtbs2fPHoKCgjh58iS5cuXi888/x9bW9pVuzUjv4uLimDt3LjY2NnTp0gVnZ2f8/PyYO3cub7/9Nv379ychIQE7OztmzpxJuXLlqFu3runxr9pA9X+iYCUiZpF8PMX48eNZv349r732Gr169aJatWopTqjJT6yv6jiMjC75fktISCAqKgqDwcBHH31Ezpw5mTJlyj/eVyfe9Cs+Ph5/f3+yZ89O3rx5cXd3J3/+/Bw7doxTp04xa9YsvvnmG2rWrAnAihUrKFeuHMWLF7dw5emLgpWImJW/vz+RkZF07NiRcePG8eDBAzp37pxifUDJPNasWUNiYiLNmzfHwcGB69evM3z4cDw8POjWrRsFChQgW7ZsgEJ0emYwGPDz86NYsWKm4AQPZ1V3dHQkS5YsrFy5khkzZvD5559z5swZrl+/ztChQ9Xa/Bj9NkTEbOLj4zEajfTr1w94ONj1f//7HzNmzGDfvn00b94cLy8vC1cpLyN5ODp16hSHDh1i3bp1JCUl0bJlS3Lnzs306dNp3LgxRqPRdGECoFCVjiUlJXH16lWqVatm2rZu3Tp++uknU0tk27ZtSUhI4H//+x8FCxZk9erVwKuxTM3z0G9CRF7Y4w3eV65cYc2aNTx48AAAe3t73n//fe7evcvly5fx9PS0RJliJslDVWRkJCVKlOCLL77gww8/ZOrUqaxfv56kpCSsra0ZMmQI33zzjcJUBmFnZ4e7uzs7d+5k48aNzJkzh+HDh9O8eXPKly/P8OHDuXr1Kr6+vowePZqoqChOnDgBoFD1GHUFisgLST5WJjo6GmdnZwA+/PBDrK2t+eqrr3B2dmb58uV4eHhQp06dV/4y7IwqKSmJpKQk7O3tAVi1ahXHjh2jS5cuFClSBID58+fz/fff07BhQ959911KlChheqzGVGUMFy5c4Pvvvyc0NBRnZ2c++OADKleuDDycv+qNN96gRo0aAGzatIlPPvmEHj16MGjQINPfhqgrUERe0KOT5ahRo7h58ybOzs5MnjyZ3r17M3HiROrVq8drr71GYmIifn5+ClUZ2ObNm3F3d6d69eoEBgYC8Ndff3H37l0GDBiAp6cn3bt3JyQkhOvXr6cYzKxQlXEUKVKEL7/8kgcPHmA0GvHw8DC1UiYfKwdQtWpVWrZsSZs2bRSqHqMWKxF5LsnDkb+/Pxs3buTtt99m3LhxlC1blu+//x54OD7D2traNKO6QlXGknyG9G+++YawsDCKFCmCh4cHPXv2JDw8nIEDB/Laa6/Rp08f/vjjD8qVK0f16tWxsrLS/s7gYmNjsbe3x9ramq1btxIZGYmPj0+KfXr//n2cnJwsWGX6pGAlIi9k8eLF2NjY4OPjA8DVq1fp2rUrxYsX57333qNkyZKmq4XUHZSx3Lx5kylTptCgQQPq168PwMCBAwkJCWHZsmW4u7sDEBYWxoABAwBwcnIy/U3o6r+M786dO6xYsYLw8HCSkpL47LPPXullap6HgpWIPLedO3cye/Zszpw5w7JlyyhYsCDwMFy1adOG119/ndmzZ6vFIoNavnw5q1atwsPDg2bNmtGwYUNOnDjBTz/9xL179xg3bhx58uQBICoqigsXLlC6dOn/nAhWMpbw8HAOHDhAs2bNFJifg4KViPynx0+W8fHx2Nvb8/XXXxMcHMyPP/5o6jaKiIjAzc1NLVQZWHx8PNevX2fx4sWEhYXRoUMH0+zaH330EXfu3GHy5Mls27aN6tWrm4K1WiYzL+3bZ6dgJSJPlfxb6oQJE3B1dSVnzpx06NABeDjL+oEDB+jfvz9Vq1bF1dUV0IE4o0q+v0+fPs3KlSu5cuUKLVu2pGHDhsDDKz9DQ0Px8PBg3rx5aqESSUbBSkSeyY8//siFCxfIly8fR44coVatWnTv3h2ATp064eLiwuzZs9VVkEk8Wr8xJCSE1atXc/nyZVq3bk2DBg2AhwtulytXTt1/Io9RsBKR/xQREUFgYCA+Pj6sXLmSa9euce7cOapUqcKbb77JmTNnTIstaxxGxjdv3jx2797N7NmzsbGxISQkhHXr1nH16lWKFy+Or6+vaQC7WiZFUtJXDBH5T3Z2duTJk4erV69iZWXFgAEDyJs3L/Pnz+e3336jZs2a2NjYkJSUpFCVARkMBtO/o6Ki8PDw4N69e7Ro0YKEhARKlixJ69atiYiI4OTJk7i5uZnur1AlkpJarETkmR04cICAgACGDh3K+vXryZIlC3Xr1tXJNZO4dOmSaSD67du3GTx4MHfu3GHt2rUkJiYSFBSklkmR/6AWKxExSf49KyEh4YltMTExLF68mHfffZcdO3bw1ltvmVqqJONJ3lLl7+9Pr1692Lp1KwBubm5MmjSJ6OhoypYty9atW9UyKfIM1GIlIkDKq8HWr19PXFwczZo1w9HRMcXPAgMDuXjxIl27dsXW1lYtFxlU8v22f/9+IiMjuXr1Kt988w0zZ840DVJfvXo1x48f59NPPzVN+Coi/06fEhEBMJ1k9+zZQ5YsWShZsiTW1tbExcXh4OBgmruqXr16psdo4HLG8yhQJZ9CIzw8nObNm9O9e3cSExN5//33GT9+PKdOncLNzY0RI0YA/3eloIj8O7VYibzirly5Qv78+QEIDQ1l8+bNvP/++/z555/s2bOH3bt38+OPP5IvXz4LVyov459C0cqVK7l58yZ9+/ZNsX3u3LksXryYggUL8tNPPylMiTwHBSuRV9j27dvZv38/77zzDseOHaNChQrMmzePTZs2UalSJUqWLInBYCBLliy8++67li5XXlBiYiL/+9//aNWqlamLD2Dp0qU0a9YMZ2dnYmJiyJo1q+lnkZGRZM+eHSsrK7VMijwHfQ0ReYVlz56da9euMW7cOAoUKECjRo3w8fGhaNGiNG/eHGdnZ0JDQwkPD7d0qfIS4uPjad++PcWLF08xmeeff/7JgQMHmDBhgilUbdiwgQIFClC+fHng4QB3hSqRZ6cWK5FX3OLFi5k3bx69evWiQ4cOWFtbk5iYyKVLl9i/fz/Zs2enfv36mlk7kxg5ciQFChRgwIABhIaGMnToUAoUKECfPn0IDAzk/v37DB06VPtb5AWpxUrkFfN4t067du3InTs38+bNIykpibZt2+Lo6MjJkycBTKFKV/9lTMn326lTp4iKisLPzw97e3t69erF0KFDGTt2LFOmTMHFxYXvvvtO+1vkJShYibxCjEajKVRt3LgROzs76tSpw1tvvYXBYGDevHkAXLx4kZIlS+Lr6wugteAyqOQh+tNPP8Xb25vPP/+cAwcO8OWXX2JjY0P37t1Zu3YtcXFx2Nvba0yVyEtSV6DIKyJ5OPrss8/Yt28frq6uWFlZ8euvv+Lk5ERAQADr168nKiqK2bNn62qwTCIoKIhDhw5Rvnx5qlatisFgYOvWrXzzzTdUrFiRQYMGUaxYMQC1VIm8JAUrkVfM9u3buXz5Mm+//Tbnzp1j4sSJREVFsWjRIqytrYmJicHJyUktFxlY8nC0ZMkSvvnmG6pVq8bs2bNT3Gfs2LGcOnWKBQsWqEVSxEz0SRJ5hfj5+TF16lScnZ2xtbWlSJEijBw5EhcXF7y9vQkICCBr1qxYWVml6DaUjCP5cjOhoaFUqlQJf39/9u3bx88//2y6n8FgoEOHDqZQlXx5GxF5cWrnF8nEknf/xcXF4enpyVtvvcWCBQsoXLgwFSpUoHDhwgwfPpwlS5bw5ptvmh6r7qCMJ3kYnjVrFra2tvTr1w94GKo7deqEjY0NTZs2JUuWLJQqVQrQGDoRc1JXoEgmlbw7aNWqVYSFhdG8eXMKFSrEggUL2LFjB0OGDKFMmTIpQpS6/zKm5Pt7+vTpLF26lCJFivDbb7+Z7nPhwgWaNGlC165dGTlypKVKFcnU9BVFJJN6dJLdvn07N2/e5ObNm4waNYro6Gg6dOhAo0aN6N+/PytWrEjxOIWqjCn5ItnFihVj165d5M2bl8DAQOBhYC5cuDBDhw5l6NChlixVJFNTV6BIJnbs2DGio6Pp06cPwcHBREREkD17dgDKli1Lo0aNaNOmjWWLFLM6e/YsNWvWBMDX1xdPT08ADh06RM6cOenRowegBZVFUou6AkUykcfHygQHB/P3339TsmRJjEYjtWvXBmDPnj2UKVMGV1dXQN1/GY3RaCQpKQlbW1vTPjcYDCQmJjJjxgwiIiIoVKgQ2bJl45133mHMmDH8/fffXLx4kVmzZlG1alVLvwWRTEtdgSKZRFJSkilU3b9/H4CcOXMye/Zs1q9fbwpVixYt4uTJk7i4uJgeq1CVcRgMBtavX8+SJUuIjIw07XNra2vs7e2pX78+1apV48qVK9ja2vLDDz9w+vRpli1bxmeffUZwcLCF34FI5qYWK5FM5vPPP+f48ePUqVOHvn37cuzYMXr37o23tzfW1ta4ubkxadIkhakMymAwMGbMGDZs2ICjoyPdunXD09MzxRWdAKdPn2bYsGHcvXuXbdu2AQ9bMMPDw2nVqpUFKhd5NShYiWRwybvxlixZwr59+yhfvjxLly7lzTffpE+fPty7d4/9+/cD0KZNmxRdSJLx3Lt3j6NHj3Lz5k0uXbrEokWLqFevHmXLluXtt982zUO2d+9e9u/fz+DBgwkJCeHQoUPUq1eP3LlzW/otiGRaGrkokoEln7fo5MmTuLq6MnHiRGxtbSlQoAA///wz8+fPp0qVKjRv3pwsWbIAGlOVkRmNRhITEzl8+DA9e/YkS5YsREdHc+vWLQICAliwYAFly5alRYsW5MuXj127dmFvb8/du3fp27ev6eIFEUkdClYiGVTyFqdhw4axY8cOkpKSOHr0KP/73/9o0KABAN988w137tyhVq1apscqVGVcVlZWuLu788YbbxAYGEhYWBje3t40adIEgK5du3Ljxg1q1qyJra0tH374IXZ2dhQsWFChSiQNqCtQJIP7888/uXTpEq1atWLp0qWcPHmSEiVKmGbcPnbsGKVKlcLGxkYL7GYikZGR/Pzzz5QrV84UqoxGIwcOHKBixYpYW1urZVLEAjTAQiQDmz9/Pl988QVZsmQhW7ZstG/fnooVK3Lq1Ck+/vhjQkJCKFu2LDY2NinWkJOMz93dnXz58hEfH2/aZmVlReXKlU3TLyhUiaQ9BSuRDCT5QrmxsbEUL16cRo0aMWXKFA4cOIC7uztt27YlS5YsxMXF8dprr5nur5Ns5vGoo6FFixbY2tpy/PjxJ+6jCxNELENjrEQyCKPRaDpZbtmyhbt379KkSRNq1aqFg4MDw4cPZ9KkSZQvX55u3brh5eVlarnQSTZzedTyaGdnx4wZM6hZsyZlypSxcFUiAhpjJZKuxcfHY29vn2Lbtm3b2L9/P5cuXTLNtB0bG8vChQuZMWMGY8aMwcfHB3hyJnbJXBITE1m+fDnt2rXDzs7O0uWICGqxEkm3zpw5w7lz56hbty4ODg4AHDx4EBsbG4YNG0ZAQIApeNnb21OzZk3Cw8Np27at6TkUqjI3W1tbfH19sbKy0kB1kXRCR12RdGru3Ll8/PHHbNy4kbi4OABu3rxJZGQkhw8fxsHBgWbNmgFw5MgRihcvzrhx47C1tSUxMdGSpUsaetQtqFAlkj4oWImkU0OGDGH06NHMnDmTDRs2AA/X/vvqq6/YsWMHderUAWDx4sUcO3YMZ2dn02NtbdUYLSJiCTr6iqRDsbGx5MiRg3bt2mE0GpkxYwZJSUl06NCBIUOGMG7cOC5fvkxMTAyurq6MHTvW0iWLiAgKViLpjr+/PxcuXGDIkCFs3ryZ9u3bk5CQwPfff4+1tTU+Pj54enpy4MAB7O3t6dmzJzY2NhqoLiKSDihYiaQz1apVY+7cuYSEhPDmm28C0LFjR4xGIz/88AO2trZUrlyZatWqmYKUBi6LiKQP+norkk4kJCQAUKhQIfr168fBgweJjY0FHk6b8M4779CvXz8+/fRTlixZkqJ1SqFKRCR90DxWIunAqlWr8PPzw9/fnx07duDq6oqTkxMdOnRg4MCB9O/fH3i4PtyyZcvo2bOnBqiLiKRDClYi6UTHjh25ceMGnTp1onfv3gCEhITg4+PD4MGDsbKyonjx4tStWxd4ODmkwpWISPqirkARCzIajSQlJQHw/vvv4+DgkGLdt5IlS+Lv78+aNWvYvXs3tWvXNv1MoUpEJP1Ri5WIhRiNRtPkjgkJCVhbWxMdHc3//vc/nJ2dmTZtmum+MTExODk5YWVlpav/RETSMQUrEQtIHqrmzp3LyZMncXV15YsvviAiIoJPPvkEFxcXvLy8KFasGM2bNwe09p+ISHqnI7RIGjEYDKYr/x6Fqh07dpAnTx569uzJoUOHOHbsGDly5GDKlCkkJiby999/07hxY9NzKFSJiKRvarESSQNxcXHMnTsXGxsbunTpgrOzM1FRUWzatIkOHToQHBxMXFwctWvXTtGa9ejfmqdKRCRj0OhXkVQWHx/PsmXLKFq0KHnz5iUiIsIUrLZu3YqNjQ1FixalSpUqAKxbt47SpUtTrFgxrKysMBqNClUiIhmE+hVEUpHBYGDJkiUULVqUZs2a4e3tTeHChblz5w758uUjT548TJw4EVdXV+Li4vj111+5ePEiRYsWNT3Ho9YrERFJ/9RiJZKKkpKSuHr1KtWqVTNtW7duHT/++CP29vYMHjyYO3fu0K1bN/Lnz0+OHDmYMWMG1tbWKboERUQkY9AYK5FUNnfuXKysrMiXLx+XLl1ixowZDBo0iPPnz7Nr1y42bNjA9evXMRgMFCtWTAsqi4hkYApWIqnswoULfP/994SGhuLs7MwHH3xA5cqVAfj6669p0KABVatWNd1foUpEJONSsBJJA/fv3+fBgwcYjUY8PDxM3Xx+fn54e3tTunRpS5coIiJmoDFWImnAyckJJycnYmNjTS1SW7duxdbWlpIlS1q6PBERMRMFK5E0FBsby++//054eDhJSUl89tlnGqguIpKJqCtQJI2Fh4dz4MABmjVrho2NjUKViEgmomAlYkGaUV1EJHNRsBIRERExE13TLSIiImImClYiIiIiZqJgJSIiImImClYiIiIiZvL/AEbkhtQ9nojNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "      <th>names_rus</th>\n",
       "      <th>names_eng</th>\n",
       "      <th>area_ha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>96142</td>\n",
       "      <td>31.122981</td>\n",
       "      <td>Деревья</td>\n",
       "      <td>Trees</td>\n",
       "      <td>961.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>34264</td>\n",
       "      <td>11.091904</td>\n",
       "      <td>Заболоченные участки</td>\n",
       "      <td>Flooded area</td>\n",
       "      <td>342.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>56670</td>\n",
       "      <td>18.345149</td>\n",
       "      <td>Вода</td>\n",
       "      <td>Open water</td>\n",
       "      <td>566.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>68726</td>\n",
       "      <td>22.247904</td>\n",
       "      <td>Поселения, н-земли</td>\n",
       "      <td>Settlements and damaged lands</td>\n",
       "      <td>687.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>45439</td>\n",
       "      <td>14.709462</td>\n",
       "      <td>Пустоши</td>\n",
       "      <td>Barrens</td>\n",
       "      <td>454.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>75</td>\n",
       "      <td>0.024279</td>\n",
       "      <td>С/Х и травы</td>\n",
       "      <td>Agriculture and grassland</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1788</td>\n",
       "      <td>0.578809</td>\n",
       "      <td>Кустарники</td>\n",
       "      <td>Shrubs</td>\n",
       "      <td>17.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>5806</td>\n",
       "      <td>1.879512</td>\n",
       "      <td>Редкая растительность</td>\n",
       "      <td>Sparse vegetation</td>\n",
       "      <td>58.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class  count    percent              names_rus  \\\n",
       "1      1  96142  31.122981                Деревья   \n",
       "2      2  34264  11.091904   Заболоченные участки   \n",
       "3      3  56670  18.345149                   Вода   \n",
       "4      4  68726  22.247904     Поселения, н-земли   \n",
       "5      5  45439  14.709462                Пустоши   \n",
       "6      6     75   0.024279            С/Х и травы   \n",
       "7      7   1788   0.578809             Кустарники   \n",
       "8      9   5806   1.879512  Редкая растительность   \n",
       "\n",
       "                       names_eng  area_ha  \n",
       "1                          Trees   961.42  \n",
       "2                   Flooded area   342.64  \n",
       "3                     Open water   566.70  \n",
       "4  Settlements and damaged lands   687.26  \n",
       "5                        Barrens   454.39  \n",
       "6      Agriculture and grassland     0.75  \n",
       "7                         Shrubs    17.88  \n",
       "8              Sparse vegetation    58.06  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_pictures(fname='data/RF_best_model_2021.tif',\n",
    "            save_pics=False, data_return=True)"
   ]
  }
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