telemed_survey.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "import numpy as np\n",
    "sns.set() # Default seaborn styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Use_of_Telemedicine_During_COVID-19.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remap Indicator Values to Easier to Read Acronyms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "indicator_remap = {'Provider offers telemedicine': 'TM', 'Provider offered telemedicine prior to pandemic': 'PTM', 'Scheduled one or more telemedicine appointments': 'STM' }\n",
    "df['Indicator'] = df['Indicator'].map(indicator_remap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get %  of people answering \"Yes\" one of 3 Telemedicine Questions, Grouped by Age, Chronic Conditions, Etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "all_yes = df.groupby(['Round','Group', 'Subgroup', 'Indicator']).nth(1).drop(['Sample Size', 'Suppression', 'Significant'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot % of People with access to Telemedicine Before and After Pandemic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pivot so columns are % telehealth before, % scheduled telehealth, % telehealth after pandemic\n",
    "indicator_by_sgroup = pd.pivot(all_yes.loc[1].reset_index(), index='Subgroup', columns='Indicator', values='Percent')\n",
    "# Add a column calculating % change between telehealth now and telehealth before pandemic\n",
    "indicator_by_sgroup['telemed before and after pandemic % change'] = (100 * (indicator_by_sgroup['TM'] - indicator_by_sgroup['PTM']) / indicator_by_sgroup['PTM']).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r1y7759jJvS5ut1umaWr27NkqUKCAJOn8+fPy9vbW33//LS8vryTjeHl5JTuna/759zp79qzcbneS8Im8K7f0gGvv+YsXL3p2nMTExGjLli3697//rSJFimR4Ha+v+X/65zmW1/vqq680d+5cPfnkkwoJCVHhwoWTXLDk2s4a6WptX1vHlNb1Zj3sn69zarVtGEaSPmCapvbt28ehsPnEzz//rF9//VXdunVTUFCQgoKC9Morr6h169bauHHjDe+P2NhYHTt2TFLS99X171u3263XX3/dsyM0KipKcXFxOn78+A0X0rrWA65/vHQ1jP5Tatu0OXPmZOjzwfVj3+xzgcvl0t13362vv/7ac9upU6dUtGhRLV26NM39LaUe88/XILVTadq1a6eHH35YV65c0dy5c/Xmm2/esEy5cuW0cuVKRUREaPPmzeratauGDx8uPz+/NLwi+QtXhQVusUaNGmnZsmWeK6vOmjVLTz/9dJJlKlSokOTD1YkTJ9S6dWvt2rUrzc/ToEEDbdy4UX/++ackad26dWrTpo1iY2P1wAMPaN68eXK73bp48aJWrVqV5nHXrl2rMmXKqGjRomrUqJHmzZunK1euSJImTZqk11577YbHPPDAA5o/f77n3JOZM2eqbt26yZ5T2KhRI3322WcyTVPx8fHq0aOHvvjiizTPT7oaRnfu3Km+ffsqODhYJ0+e1F9//eU5ByUlKb0u/v7+qlOnjj799FPP+B07dkzX63a9Bg0a6JtvvlF8fLzcbreGDRumZcuWZWgs5D65pQds3LhR586d0w8//KDVq1dr9erVWr9+vUqUKKE5c+bcsLxhGJ4dRmlZx4zYsGGD2rZtq/bt26tChQpavXq1XC5Xqo8JDAzUxo0bPd8uLFy40HNfWntYWtSuXVt//vmn5yqgq1atuun5o8g7ihYtqunTp2vbtm2e286cOaMrV66ocuXKqlevnjZt2uSpidmzZ2vs2LGpjtmoUSN9+eWXnm3FkCFD9O677yogIECmaWrdunWSrr7XroW3okWL6vjx4zp37pxM00x225LaNi21ekmLtNRUnTp1dOTIEW3dulXS1avIt2jRIl0Xv0mtxzzwwAOeHnX8+HFFRESkOE6RIkUUFBSkyZMnyzAM1alT54ZlvvrqKw0cOFCNGjVSv3791KhRI+3evTvJMtf3v/yMbyyBW6xRo0Z67rnn9Mwzz8hms8nf319Tp05NsifT6XTqvffe06hRo/Txxx8rMTFRffr00b333ptqg7xexYoVNXz4cL3yyisyTVMOh0PTp0+Xn5+fevXqpTfeeEMtW7ZU0aJFUz05f9u2bQoNDZXNZlNiYqIKFy6sadOmyW63q3379jp16pQef/xx2Ww23XHHHXr77bdvGOOxxx7TiRMn1L59e7ndbpUvX17jxo1L9vkGDRqkUaNGKSQkRAkJCWrYsKG6deuWpnW+5rbbblP37t3Vtm1b+fr6qlSpUvq///s/HTlyJMmFh/4ptddl3LhxGjFihEJCQhQfH6/WrVurTZs2GfoplSeeeELHjh1TWFiYTNPU/fffn+ol2pG35JYeMGvWLD3++OMqWLCg5zaHw6Hnn39ekydP1rPPPptk+fr166tv374aMWKEhgwZctN1zIhnnnlGQ4cO9RwqX6dOHe3fvz/VxwQEBKhfv356+umn5efnp1q1annuS2sPS4vixYtr3Lhx6t+/v1wul/z9/TVhwoQMjYXcp0KFCpo2bZomTJigkydPytvbWwULFtTo0aN11113SZL69evn2Z6VKFFCo0eP1uHDh1Mc88UXX9SYMWPUtm1buVwuVa1aVQMGDJCXl5emTZumYcOG6d1331XVqlU9h29WrFhRTzzxhNq1a6cSJUrooYce0s6dO28YO6Vt2rV5JlcvaZGWmipatKgmT56sd955R3FxcTJNU++8847Kli17wyHmKUmtj77xxhsaOHCgWrZsqdtvv/2mRw106tRJjz/+eIpXeH700Ue1ZcsWtWrVSgUKFNAdd9yh8PBw7d2717NMnTp1NG3aNPXs2VNTp05N0zrkRTbzn9+lAwAAAACQDhwKCwAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhKvCIlc4c+Zydk8BQDJKlCh484XSiXoHcibqHcg/MlLvfGMJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALDEkd0TAHBzhmGXw7BJbrcK+PnIy+lQQnyiYqJiJbtdiS5TLpc7u6cJAMgh2G4A1lBD6UewBHIwu90mp8MmmaZWfrVOOzbu1pG9kYq5EqsC/j4qX6WsagVWU/OOD8rLsCs+0ZTbbWb3tAEA2YTtBmANNZRxNtM0eSWQ4505czm7p3DLOZ2GvJ2G5k9bpm/+s1LuVPaKGQ67HunaXO1eekRx8S7Fx7tu4UyRn5UoUTDTx8yP9Q5khqzeblDvyOv47PU/Gal3giVyhfy24XE6DRkyNTz8XR3ZF5nmx5UPKKuhM1+RS7Y81+CQM/FBE8gZbsV2g3pHXsZnr6QyUu9cvAfIYex2m7ydRrobmyQd2Rep4eHvyttpyG63ZdEMAQA5CdsNwBpqKHMQLIEcxumwaf60ZelubNcc2RepBe8tu3p+AAAgz2O7AVhDDWUOgiWQgxiGXTJNffOflZbGWfrJSsn873gAgDyL7QZgDTWUefLvmkuKjIxUjRo1FBoaqtDQULVo0UIDBw7U2bNnJUk7d+7UoEGDsnmWNxcZGakmTZrcdLmAgABJ0qxZszRr1qxUl23SpIkiI9O+12bHjh0aO3ZsmpdH8hyGTStn/ZjqyeJp4Xa5tXLWuquXyQYA5FlsNwBrqKHMk6+DpSSVLFlSixcv1uLFi/X999+rePHi6t27tySpZs2aGjVqVDbPMPN17NhRHTt2zNQx//jjD507dy5Tx8yX3G7t2Lg7U4ba+dMeyc3vKwFAnsZ2A7CGGso0/I7ldWw2m3r16qXAwEDt3btXFy9e1NSpUzVz5kxt2bJFEyZMUGxsrC5duqSBAweqWbNmOnnypPr27auLFy+qcuXK2rp1q3788UdNmTJFp06d0pEjR3Ts2DG1b99ePXr0kNvt1ujRo7Vp0ybZbDa1adNG3bt394wTHR0tu92uwYMHq06dOtqxY4feeustxcbGqkiRInrzzTdVrlw57d692/NtapUqVZJdn8jISPXr10/R0dGqXbu25/YpU6ZIknr16qUvvvhCixcvVkxMjLy8vDR+/HjdddddkqSpU6dq79698vb21ptvvqkqVaro7NmzGjp0qE6ePCmbzaZXX31VNWrU0OTJkxUdHa3p06ere/fueuedd7Rlyxa5XC6FhYWpS5cuKa4j/qeAn4+O7M3Y8f3/dHjPURXw81HcxZhMGQ8AkPOw3QCsoYYyD8HyH5xOp8qXL6+DBw+qWLFintu/+OILjRw5Unfffbc2bdqk0aNHq1mzZho1apRatmypJ598UitXrtQ333zjecy+ffv05Zdf6vLly2rWrJmefPJJLV26VCdOnNCSJUsUHx+v8PBwVa5cWbt27dJDDz2kbt266ccff9TPP/+satWqafDgwXr//fdVunRprV+/XkOGDNFnn32m/v37a8CAAQoMDNS0adMUERFxw7qMGDFCYWFhat++vRYtWqQ5c+Ykuf/KlSv64YcfNHPmTPn4+GjSpEn68ssvNWTIEElS+fLl9fbbb2vdunUaMGCAFi1apFGjRqldu3Zq2rSpTp8+rU6dOmnRokXq3bu3tmzZoh49engOs124cKHi4+P17LPPqkaNGtq8efMN65jWYFmkiK8cDiO9f85cKeZKbOaMExUrh5eRJZeHB7JSfqp3IDPk5u0G9Y6cIDfXUE5CsEyGzWaTj49PktvGjh2rNWvW6Pvvv9dvv/2mqKgoSdLGjRv11ltvSZKaN2+u2267zfOYevXqyel0qlixYipcuLAuX76siIgItW3bVoZhqECBAgoJCdGmTZsUHBysXr16ac+ePWrcuLGeeuopHT58WEePHlWPHj08Y165ckXnz5/X6dOnFRgYKEkKCwvT/Pnzb1iPLVu2aPz48ZKkNm3aaPDgwUnu9/f31/jx47Vs2TIdPnxY69evV9WqVT33t2/fXpLUuHFj9evXT5cuXdJPP/2kgwcPavLkyZKkxMREHT16NMm4mzZt0p49e7R582ZJUnR0tPbt26cGDRrcsI5p9fff0WleNjcrXKiACvj7KPqy9T1dBfx8lJjg0oV8utcMt0ZWbDzzS70DmeFWbjeod+RFfPZKXkbqnWD5D/Hx8Tp06JAqVqyoEydOeG7v1KmT6tWrp3r16qlBgwbq27evJMkwDJmmmexY3t7env+22WwyTVPufxx3bZqmXC6X7r33Xi1btkxr167Vt99+q4ULF6p///4qW7asFi9eLElyuVw6e/asZ6xrDCPlPX3XlrPZbLLbk55Se+LECYWHh+upp57Sgw8+qOLFi2vPnj3JjmuaphwOh9xut2bMmKHChQtLkk6fPq1ixYoleZzL5VK/fv0UHBwsSTp//rz8/Pzk7e19wzp++umnKc49P4qJilX5KmW1Z+sBy2P9q2o5xURlzh44AEDOxHYDsIYayjz5/uI913O73ZoyZYpq166tO++803P7hQsXdPjwYfXp00cPPvigVq1aJZfLJUlq0KCBli5dKklat26dLl26lOpz1K9fX4sWLZLL5VJMTIyWLl2qevXq6Z133tGSJUvUtm1bDR06VLt379Zdd92lixcvatu2bZKk+fPnq2/fvipSpIhKly6ttWvXSlKSw2+v17BhQy1ZskSStGLFCsXFxSW5f+fOnSpfvry6dOmimjVr6ocffvCslyTPeq1cuVJ33323fH19Vb9+fX311VeSrl6wJyQkRDExMTIMQ4mJiZ51nDt3rhISEhQVFaVOnTpp+/btya4j/sFuV63AapkyVM2GVSU7JQ4AeRrbDcAaaijT5PtvLE+fPq3Q0FBJV4Nl1apV9e677yZZpnDhwnrsscf0yCOPyOFwqH79+oqNjVV0dLQGDRqk/v37a+7cuapSpUqSQ2GT06FDBx0+fFihoaFKSEhQSEiImjdvrho1aujVV1/VggULZBiGxowZI6fTqUmTJmnUqFGKi4uTv7+/xowZI+nqobkDBw7UxIkTUzxPcejQoerXr5/mzJmjGjVqyM/PL8n9gYGBmjVrllq1aiXTNFW3bl0dOPC/vTXX5unn56e3335bkjR48GANHTpUISEhkqR33nlH/v7+qlWrlqZOnapx48apT58+OnLkiNq2bavExESFhYWpXr16uvPOO29YRySV6DLVvOODmjd1qVyJGb+qmOGwq3nHxkpwJf9tOgAgb2C7AVhDDWUem5nScZxIk88//1wNGzZUxYoV9fvvv2vIkCFasGBBdk8rzzlz5nJ2T+GW8XHateSj5Vry0fIMjxHavYVCurVQbHz+veQ1bo2sOOcqP9U7kBlu1XaDekdexWevG3GOZTYoX768XnnlFdntdnl7e2vEiBHZPSXkcvGJptq99Ih++/F3HdmX/stfl69SVmEvPqKo6IQsmB0AIKdhuwFYQw1lDr6xRK6Q3/ZoOp2GDJkaHv5uuhpc+SplNfTzV+SSTfHxrps/ALCIbzCAnOFWbDeod+RlfPZKKiP1TrBErpAfNzxOpyFvp6EF7y3T0k9Wyu1K+dAKw2FX62eaK+zFRxQX78pTjQ05Gx80gZwjq7cb1DvyOj57/Q/BEnlWft3w2O02OR02yZRWzlqnnT/t0eE9RxUTFasCfj76V9Vyqtmwqpp3bCzZrh7K4XZT0rh1+KAJ5CxZud2g3pEf8NnrKoIl8qz8vuExDLschk1yu1XAz0cOL0OJCa6rv5VktyvRZcqVyl41IKvwQRPImbJiu0G9Iz/J75+9uHgPkEe5XG5d+4nRuIsx/7z3ls8HAJCzsd0ArKGG0i///oInAAAAACBTECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWOLJ7AkBeYBh2eRk2yTRVwNdbDqdDifGJiomOk2w2JbhMuVzu7J4mAORK9Fjg5qgTZDeCJWCB3W6Tt8MumabWzv5Rv2/co6P7jin2Sqx8/H1ULqCMqgdW1UMdHpBpGIpPdMvtNrN72gCQK9BjgZujTpBT2EzT5J2FHO/MmcvZPYUbOJ2GfJyGlrz3nVZ8tkruVPYCGg67mj/dVG1ebKnYeJfi4123cKZA1ilRomCmj5kT6x23Hj0256Hecx7qBFklI/VOsESukNM2PE6nIYekd7pMVOS+Y2l+XNmAMnrts5eVKNHQkSfwQRNZgR6bM1HvOQt1gqyUkXrn4j1AOtntNvk4jXQ3ckmK3HdM73SZKB+nIbvdlkUzBIDcix4L3Bx1gpyIYAmkk7fDriXvfZfuRn5N5L5jWjr9OzkdlB8A/BM9Frg56gQ5Ee8mIB0M4+rJ8Ss+W2VpnOWfrpLNNK+OBwCQRI8F0oI6QU7FOymdIiMjFRAQoI0bNya5vUmTJoqMjMyUsYcOHZrk9j179iggIEALFixI9fGTJ0/Wtm3bLM3hZgYMGKAFCxbo1KlTeu655yRJa9as0aeffpqlz5tTeBk2rZ2zPtWT49PC7XJr7Zz1Vy8LDgCQRI8F0oI6QU5FsMwALy8vDRkyRFeuXMn0sQsXLqz169fL5frfydTffvutihYtetPHbt26NcnjslKpUqX00UcfSZJ27dqVJa9FjmSa+n3jnkwZavdPeyWunQUA/0OPBW6OOkEOxe9YZkDJkiXVsGFDjRkzRiNGjEhy3/vvv68lS5bIMAwFBgaqX79+OnHihHr27KlKlSppz549KlasmCZNmqTChQvfMLafn5+qVKmirVu3qn79+pKkjRs3qmHDhp5lfvzxR02ePFmJiYkqW7asRowYoXXr1mnXrl0aPHiwpk6dqpEjR6pQoUI6cOCAJk6cqJMnT2rixIlyu90qV66chg8fruLFi6tJkyZ6+OGH9dNPP0mSRo8erWrVqunQoUMaOnSoLly4IF9fXw0aNEi1atXyzCEyMlKdO3fWhx9+qNmzZ0uSSpcurUaNGun111/X5cuXdfr0abVt21Z9+vTRggULtH79el28eFFHjx5VYGCghg0blsl/maxXwNdbRzN4PsM//bU3UgX8vBV7ISZTxgOA3I4eC9wcdYKcimCZQQMGDFBISIg2btyowMBASVcD3+rVqzV//nx5eXmpV69emj17tho3bqy9e/d6QluvXr20dOlShYeHJzt2y5YttXz5ctWvX187duxQQECArv0qzPnz5zV+/Hh9/vnnKlSokGbPnq1x48Zp1KhRmj9/vnr27KmAgABJUkBAgKZOnapz586pe/fumjVrlsqWLauPP/5Yw4cP1+TJkyVJvr6+WrRokVavXq3+/ftr6dKl6tevn7p3767g4GBt375dffr00fLly2+Ya8WKFfXEE09Iktq1a6dPPvlErVu3Vtu2bXX58mU1btzYs56//vqrvvnmGxmGoYcfflgdO3b0zPVmihTxlcNhpOMvlHVir8RmzjhRsTIcRpZcvh3IzXJSvePWo8fmL9R7xlAnyIkIlhnk7++vESNGaMiQIVqyZIkkafPmzXrkkUdUoEABSVeD1qJFi9S4cWMVK1ZM1apVkyRVqlRJFy9eTHHsJk2aeL5d/O6779SyZUt9++23kqTffvtNJ06cUOfOnSVJbrdbhQoVSnaca98w7tixQ7Vq1VLZsmUlSR06dNCHH37oWe7xxx/3PO+AAQN08uRJ/fXXXwoODpYk1alTR4UKFdLBgwdv+ro8++yz2rx5sz755BMdOHBACQkJiom5uhfsnnvukb+/vySpXLlyqb4G//T339FpXjYrFSlUQD7+Poq5bH3Pno+fj1yJLv3NXkLkYlnxYSSn1DtuPXpszka95wzUCW4FfsfyFmvUqJHnkFjpasj7p8TEREmSt7e35zabzSbTNLVq1SqFhoYqNDRUkyZN8tx/7XDYn3/+WZs3b05yGKzL5dL//d//afHixVq8eLHmzZvn+ebxn3x8fJKdl2mannlJksPxv/0Lbrc72fM0TdNM0/mbb7/9tmbOnKnSpUurR48eKlKkiOfb1uReg9wmJjpO5QLKZMpYd1Ypq5iouEwZCwDyAnoscHPUCXIqgqVFAwYM0IYNG3T69GnVr19fy5YtU2xsrBITEzV//nzPeZLJadq0qScg9unTJ8l9LVu21Pjx41WjRo0kwa927dravn27Dh06JEl677339M4770iSDMNINvzVrl1bv/32m+eqtXPmzFG9evU89y9btkyStHLlSt19990qU6aMypYtqxUrVkiStm/frrNnz6pSpUrJrodhGJ6gunHjRj377LNq2bKlDh06pFOnTiUbuHMtm03VA6tmylDVGlaRbFyJDQA86LHAzVEnyKE4FNaia4fEPvvss3rooYd06dIltWvXTomJiWrUqJGeeuopnTx5Mt3jBgUFadCgQTcEzhIlSmj06NF6+eWX5Xa7VapUKY0dO1aS9MADD+iNN97wfIN6TfHixTV8+HD17NlTCQkJKl26tEaNGuW5/5dfftG8efNUoEABvf3225KksWPHatiwYZoyZYq8vLw0ZcoUOZ3OZOdat25d9e/fX8WLF9fzzz+v1157TT4+Prr99ttVo0YNyz/DkpMkuEw91OEBLZm2TK7EjAdmw2HXQx0eULwr931rCwBZhR4L3Bx1gpzKZubG4xGRaZo0aaLPP//cc/5lTnXmzOXsnoJHAaeh7z5eoe8/WZnhMVp2a64WzwYrNv7W/DwMkFWy4pyrnFTvuPXosTkX9Z5zUCfIapxjCdwCcYlutXmxpcpm8PyGclXKKqRHS8Vb2MsIAHkVPRa4OeoEORHBMp9bvXp1jv+2Mqdxu03Fxrv02mcvp7uhl6tSVv0+7aPYeJfcbg4WAIB/oscCN0edICfiUFjkCjnxUBmn05CP09DS6d9p+aer5HalvNfPcNgV3KWpQnq0VGy8S/EcdoI8gkPjkFXosTkP9Z7zUCfIKhmpd4IlcoWcuuGx221yOuyymabWzlmv3T/t1V97IxUbFSsfPx/dWaWsqjWsooc6PCDTZlN8opu9g8hT+KCJrESPzVmo95yJOkFWIFgiz8rpGx7DsMvLsEmmqQJ+3jIchlyJrqu/DWWzKcFlypXKXkQgt+KDJm4FemzOQL3nbNQJMlNG6p2fGwEygcvl1rWfEI29EJO9kwGAPIYeC9wcdYLsxsV7AAAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWOLJ7AkB+Zhh2eRk22UxTPn7ecng5lJiQqNioOJk2mxJcplwud3ZPEwCQDHo4kHbUS95HsASygd1uk7fDLptpasOc9dq3aZ+O7zum2Cux8vH3UemAMqrSIECBjzeSaRiKS3TL7Taze9oAANHDgfSgXvIPm2ma/OWQ4505czm7p5BpnE5DPk6Hlr//ndbMWCN3Knvn7A67gjoHqcULLRUbn6j4eNctnClwcyVKFMz0MfNSvSPvyc89nHpHeuXnesntMlLvBEvkCnllw+N0GvKSNOWZyTq+/3iaH1e6cmn1+k9vJUg0WuQofNBEfpLfezj1jvTI7/WS22Wk3rl4D3CL2O02+Tgd6W6wknR8/3FNeWayfJwO2e22LJohACAl9HAg7aiX/IlgCdwi3g67lr//Xbob7DXH9x/Xig++l7eDsgWAW40eDqQd9ZI/8dcCbgHDuHrS+poZayyNs/qz1bLJlGFQugBwq9DDgbSjXvKvXPOXioyMVI0aNRQaGqrQ0FC1aNFCAwcO1NmzZyVJO3fu1KBBg7J5ljcXGRmpJk2a3HD7gAEDtGDBgls2j4EDB+rYsWOZNl5uef2zi5dh04a5G1I9aT0t3C63Ns7ZIC+DQ0MA4FahhwNpR73kX7kmWEpSyZIltXjxYi1evFjff/+9ihcvrt69e0uSatasqVGjRmXzDHOPiIgIZeZ1m3j9U2czTe3btC9Txtq3aZ9sXHMLAG4ZejiQdtRL/pVrf8fSZrOpV69eCgwM1N69e3Xx4kVNnTpVM2fO1JYtWzRhwgTFxsbq0qVLGjhwoJo1a6aTJ0+qb9++unjxoipXrqytW7fqxx9/1JQpU3Tq1CkdOXJEx44dU/v27dWjRw+53W6NHj1amzZtks1mU5s2bdS9e3fPONHR0bLb7Ro8eLDq1KmjHTt26K233lJsbKyKFCmiN998U+XKldPu3bs93+ZVqVIlxXVau3atvvrqK507d04vvPCC2rdvr2bNmumTTz5RhQoVFB0drZYtW2rFihV66KGH1Lx5c/3666/y8/PTuHHjVLZs2RTnEB4erkKFCunAgQNq166dTp8+re7du+vLL7/U0aNHU3xMzZo19fPPP+v8+fMaPHiwGjdurKVLl+rjjz+WYRgqW7asxo4dq+3bt3te/0OHDmno0KG6cOGCfH19NWjQINWqVUsDBgyQv7+/fv/9d506dUovvfSS2rVrd6veMtnKx89bx/dlzjfEx/Ydk4+ft2IuxGTKeACA1NHDgbSjXvKvXBssJcnpdKp8+fI6ePCgihUr5rn9iy++0MiRI3X33Xdr06ZNGj16tJo1a6ZRo0apZcuWevLJJ7Vy5Up98803nsfs27dPX375pS5fvqxmzZrpySef1NKlS3XixAktWbJE8fHxCg8PV+XKlbVr1y499NBD6tatm3788Uf9/PPPqlatmgYPHqz3339fpUuX1vr16zVkyBB99tln6t+/vwYMGKDAwEBNmzZNERERya5PfHy8vv76ax04cECdO3dWhw4d9Oijj2rJkiXq06ePJ1B6e3vr/PnzuueeezR8+HDNnDlTI0eO1OTJk1OcgyQFBARo6tSpkqTZs2frww8/lJ+fX6qPSUhI0Jw5c7R69WpNmjRJjRs31sSJEzV37lwVK1ZMY8aM0cGDB5OsR79+/dS9e3cFBwdr+/bt6tOnj5YvXy5JOnnypL766ivt379fnTt3TnOwLFLEVw6HkaZlc6rYK7GZM05UrAyHkSWXfQdygrxQ78h76OFZg3rPm6iX/ClXB0vp6jeXPj4+SW4bO3as1qxZo++//16//faboqKiJEkbN27UW2+9JUlq3ry5brvtNs9j6tWrJ6fTqWLFiqlw4cK6fPmyIiIi1LZtWxmGoQIFCigkJESbNm1ScHCwevXqpT179qhx48Z66qmndPjwYR09elQ9evTwjHnlyhWdP39ep0+fVmBgoCQpLCxM8+fPT3ZdmjZtKpvNpkqVKunvv//2LN+1a1f16dNHCxcu1CuvvCJJ8vb21qOPPipJatu2rd59990U53BNrVq1bnjOmz3mgQcekCRVqlRJFy5ckCQFBQWpY8eOatasmVq0aKGqVat6wnJUVJT++usvBQcHS5Lq1KmjQoUKecJnYGCgbDabKleu7BkvLf7+OzrNy+ZERQoXkI+/j2IuW9/j5uPnI1eiS3+z9w45QFZs7HN7vSPvoYdfRb0jLaiXvCEj9Z6rg2V8fLwOHTqkihUr6sSJE57bO3XqpHr16qlevXpq0KCB+vbtK0kyDCPF8wq9vb09/22z2WSaptzupCcdm6Ypl8ule++9V8uWLdPatWv17bffauHCherfv7/Kli2rxYsXS5JcLpfOnj3rGesaw0h5r9y1+2y2/52kXLZsWZUuXVorVqzQuXPnVLt2bUmS3W73LOd2u2UYhtxud7JzuOafAfzaY1N7zLXX5fo5DR48WHv37tW6devUr18/9ezZU7fffrvnNfqna69bSuPlB7FRcSodUEZ/bvvD8lhlAsooNiouE2YFAEgLejiQdtRL/pWrLt5zPbfbrSlTpqh27dq68847PbdfuHBBhw8fVp8+ffTggw9q1apVnlDToEEDLV26VJK0bt06Xbp0KdXnqF+/vhYtWiSXy6WYmBgtXbpU9erV0zvvvKMlS5aobdu2Gjp0qHbv3q277rpLFy9e1LZt2yRJ8+fPV9++fVWkSBGVLl1aa9eulaQkh9+mVbt27TRy5Ei1adPGc1tMTIxWr14tSVqwYIEefPDBFOeQHMMw5HK50vUYSUpMTFRwcLCKFCmi559/XqGhodqzZ4/nfn9/f5UtW1YrVqyQJG3fvl1nz55VpUqV0r3eeYlps6lKg4BMGSugQYDMfBbMASA70cOBtKNe8q9c9Y3l6dOnFRoaKulqsKxatarefffdJMsULlxYjz32mB555BE5HA7Vr19fsbGxio6O1qBBg9S/f3/NnTtXVapUSXIobHI6dOigw4cPKzQ0VAkJCQoJCVHz5s1Vo0YNvfrqq1qwYIEMw9CYMWPkdDo1adIkjRo1SnFxcfL399eYMWMkXT00d+DAgZo4caLq1KmT7vUODg7WkCFDPOt+zffff68JEyaoZMmSN53DPz300EPq3r27Pv744zQ/RpIcDod69+6tZ555Rt7e3ipWrJjefvtt/fHH//ZKjR07VsOGDdOUKVPk5eWlKVOmyOl0pnu985IEl6nAxxvpu+nfyZ2Y8ctv2x12BXZopDgXV0gDgFuFHg6kHfWSf9nMzPzNiRzu888/V8OGDVWxYkX9/vvvGjJkyC397ciMME1TP/74o2bNmqX333/fc3tAQID27cucSznnBmfOXM7uKVhWwGlo9X9WatV/VmV4jGbPNlNQ12aKiXdl4syAjMuKc67yQr0j76GHU+9IO+ol98t351imV/ny5fXKK6/IbrfL29tbI0aMyO4p3dTo0aO1Zs0affTRR9k9FVgUl+hWixdaas+GPTq+/3i6H18moIyCn39YV6Ljs2B2AIDU0MOBtKNe8qd89Y0lcq+8skfT6TTkJWnKM5PT1WjLBJRRz096KUFSPHvukIPwDQbyk/zew6l3pEd+r5fcLiP1TrBErpCXNjxOpyEfp0MrPvheqz9bLbcr5fMP7A67mjzdRMHPP6zY+EQaLHIcPmgiv8nPPZx6R3rl53rJ7QiWyLPy2obHbrfJ22GXTaY2ztmgfZv26di+Y4qNipWPn4/KBJRRQIMABXZoJFM2xSW65XZTqsh5+KCJ/Ci/9nDqHRmRX+sltyNYIs/Kqxsew7DLy7DJZpry8fOW4TDkSnQpNipOps2mBJcpVyp794DsxgdN5Gf5rYdT77Aiv9VLbsfFe4BcxuVy678/s6qYCzHZOxkAQLrQw4G0o17yPnt2TwAAAAAAkLsRLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJYQLAEAAAAAlhAsAQAAAACWECwBAAAAAJY4snsCQH5nGHZ5GTbZTFM+ft5yeDmUmJCo2Kg4mTabElymXC53dk8TAJAC+jhwI+oi/yFYAtnEbrfJx2GXTFNb5mzUH5v36eT+Y4q7Eidvf2/dXrmMKjYI0P3tG8o0DMUluuV2m9k9bQDAf9HHgRtRF/mXzTRN/pLI8c6cuZzdU8hUTqehAk6H1nywXBs+Xyt3Knvs7A67GoU/pKDnWygmPlHx8a5bOFMgdSVKFMz0MfNavSNvyo99nHrHzeTHusirMlLvBEvkCnlpw+N0GvKySZ88O00n9x9P8+Nur1xaz37ykhJM0XyRY/BBE/lRfu3j1DtSk1/rIq/KSL1z8R7gFrLbbSrgdKS76UrSyf3H9cmz01TA6ZDdbsuiGQIAUkMfB25EXUAiWAK3lI/DrjUfLE93073m5P7jWvPhCnk7KF0AyA70ceBG1AUkgiVwyxjG1RPZN3y+1tI4G2askf3aeACAW4Y+DtyIusA1/OXSYPXq1QoLC1PLli01cuRIz+0DBw5UcHCwQkNDFRoaqpUrV2bjLDNPRESEwsPDs3saeY6XYdOWuT+leiJ7WrhdbkXM3Sgvg8NFAOBWoo8DN6IucA3B8iaOHj2qN954Q++9956WLFmi3bt3a926dZKkXbt26YsvvtDixYu1ePFiNW/ePJtni5zMZpr6Y/O+TBnrz837ZOO6WwBwS9HHgRtRF7iG37G8iZUrV6pVq1a6/fbbJUkTJkyQt7e3YmJidPz4cb3++us6deqUmjdvrp49e8pu/19W//rrr7V582aNHz9ekjRlyhR5e3vrySef1PDhw3XgwAG5XC4999xzat26ta5cueIZ7/Tp02rQoIFGjRqlLVu2aOzYsXK73apUqZIeffRRjR07VpJUqFAhjR8/XkWLFk0y7wkTJmjTpk26ePGiSpYsqQkTJqh48eJq1KiRWrRooZ9//lmGYWjixIkqV66cNmzYoLfeekve3t6qUKFCsq/FoUOHNHToUF24cEG+vr4aNGiQypUrp9atW2vt2rXy8vLS/v371bdvXy1ZskSLFi3SjBkz5Ha7Vb16db3xxhvy9vZW/fr1VaNGDZ05c0bz5s2Tl5dXVvzpchwfP2+d3H8sU8Y6se+YfPy8FXMhJlPGAwDcHH0cuBF1gWsIljdx5MgReXl56YUXXtCJEyf00EMP6eWXX1ZkZKTq16+vN954QwULFtTzzz+vefPm6fHHH/c8tlWrVpowYYKuXLkif39/ffPNN/r88881ffp0Va9eXWPGjNGVK1f0xBNPqHbt2vrtt99UtWpVTZ48WfHx8XrkkUf0+++/S5IOHz6sNWvWqGDBggoPD9ewYcNUq1YtffTRR9q9e7caNWqUZM4HDx7U7NmzZbfb9dprr2nJkiV65plndObMGTVo0EBDhgzR22+/rS+//FKvvPKKBgwYoBkzZujuu+/WoEGDkn0t+vXrp+7duys4OFjbt29Xnz59tHz5ctWqVUsbNmxQUFCQli1bpjZt2ujAgQOaO3euZs+eLW9vb40fP16ffPKJXnzxRf3999967rnnVK9evTT/HYoU8ZXDYWTwr5hzxF2Jy5xxouJkOIwsufQ7kN3ySr0jb6KPZy7qPW+gLiARLG/K5XJp27Ztmjlzpnx9fdWjRw8tXLhQYWFhmjZtmme58PBwLVq0KEmw9PPzU+PGjbVy5UqVK1dO5cqVU6lSpfTTTz8pNjZW8+fPlyRFR0frwIEDat26tXbs2KHPPvtMBw8e1IULFxQdHS1JqlChggoWvFpkTZs2Vc+ePdWsWTM1bdpUgYGBSeZcvnx59e/fX19//bUOHTqk7du368477/Tc/8ADD0iSKlWqpG3btmnfvn0qWbKk7r77bklS27ZtNWnSpCRjRkVF6a+//lJwcLAkqU6dOipUqJAOHjyoNm3aaNmyZQoKCtJ3332nmTNnauXKlTpy5Ijn9UhISFC1atU849WuXTtdf4e//45O1/I5UZHCBeTt763Yy7GWx/L285Yr0aW/2aOHbJYVG/+8UO/Im/J7H6fekZz8Xhd5VUbqnWB5E8WLF1eDBg08h5o2a9ZMO3bsUPXq1XX48GG1aNFCkmSaphyOG1/Odu3aafr06SpbtqzCwsIkSW63W2PHjlX16tUlSWfPnlWhQoU0c+ZMLV++XI8//rgaNmyo/fv3y/zvceY+Pj6eMbt06aKgoCCtWbNGY8eO1Y4dO9SjRw/P/bt27dKrr76qLl26qEWLFrLb7Z5xJMnb21uSZLPZZJqm5/+vMYwb9xyayRzvbpqmXC6XmjZtqrfffltbt27VHXfcoVKlSsnlcqlly5YaPHiwpKvB1OX634/eXr8++UVsVJxur1xGh3/+0/JYdwSUUWxU5uwdBACkDX0cuBF1gWu4eM9NBAUFacOGDbp06ZJcLpfWr1+v6tWryzRNjR49WhcvXlRCQoLmzJmT7MV77rvvPp08eVIRERFq1qyZJKl+/fqaNWuWJOn06dNq06aNTpw4oY0bN6pDhw5q06aN4uLitHfvXrndN15hq3379oqKilKXLl3UpUsX7d69O8n9W7du1f3336+OHTvqX//6l9auXZsk1P1TQECAzp49q71790qSli1bdsMy/v7+Klu2rFasWCFJ2r59u86ePatKlSrJ6XTqgQce0OjRo9WmTRtJUr169bRy5UqdO3dOpmlq2LBhmjFjRlpe8jzLtNlUsUFApox1d/0AmTaumgYAtxJ9HLgRdYFr+MbyJmrXrq1u3bqpU6dOSkhIUGBgoNq1aye73a7u3burY8eOSkxMVHBwsFq3bp3sGM2bN9eFCxfkdDolST179tSwYcPUunVruVwu9evXT3feeaeefvppDRs2TB9++KH8/f11zz33KDIyMslhrJI850Q6HA75+vom+QkU6eq5nT179lRISIgkqUaNGoqMjExxHb28vPTuu++qX79+cjgcSQ5Zvd7YsWM1bNgwTZkyRV5eXpoyZYpnnUJDQ7VkyRLPN7hVqlRRz5499fTTT8vtdqtq1arq3r17Gl7xvCvBZer+9g21+v3v5U7M+CW57Q676j0eqFgXV00DgFuJPg7ciLrANTYzuWMckSlM01RCQoK6du2q119/3XPoK9LvzJnL2T2FTOHrNLTh01X68dPVGR7jwWeaKrBLE8XEp/wtNHCrZMU5V3ml3pE35ec+Tr0jJfm5LvKqjNQ7h8JmoTNnzigwMFC1a9cmVEKSFJvoVtDzLXR75dIZevwdAaUV1D1YcRb2CAIAMo4+DtyIuoDEN5bIJfLSHk2n05CXTfrk2Wk6uf94mh93R0BpPfPxS0owpXj25iGH4BsM5Ef5tY9T70hNfq2LvCoj9U6wRK6Q1zY8TqehAk6H1ny4QhtmrJHblfIeOrvDrkadgxTUPVgx8Yk0XeQofNBEfpUf+zj1jpvJj3WRVxEskWflxQ2P3W6Tt8Muu6SIuRv15+Z9OrHvmOKi4uTt5607Asro7voBqvd4oNyS4hLdcrspV+QsfNBEfpbf+jj1jrTIb3WRVxEskWfl5Q2PYdjlZdhkM035+HnLcBhyJboUGxUn02ZTgsuUK5U9fkB24oMmkH/6OPWO9MgvdZFXZaTe+bkRIJu5XG5d+5nRmAsx2TsZAEC60ceBG1EX+Q9XhQUAAAAAWEKwBAAAAABYQrAEAAAAAFhCsAQAAAAAWEKwBAAAAABYQrAEAAAAAFhCsAQAAAAAWEKwBAAAAABYQrAEAAAAAFhCsAQAAAAAWEKwBAAAAABYQrAEAAAAAFhCsAQAAAAAWEKwBAAAAABYQrAEAAAAAFhCsAQAAAAAWEKwBAAAAABYQrAEAAAAAFhCsAQAAAAAWEKwBAAAAABYQrAEAAAAAFhCsAQAAAAAWEKwBAAAAABYQrAEAAAAAFhCsAQAAAAAWEKwBAAAAABYQrAEAAAAAFhCsAQAAAAAWEKwBAAAAABYQrAEAAAAAFhCsAQAAAAAWOLI7gkAkAzDLqdhk8005e3nLYeXQ4kJiYqLipNpsyneZcrlcmf3NAEA/0XfBlJGfeRPBEsgG9ntNvk47LKZprZ/vVGHNu/X6f3HFBcVJ28/b5WsXEYV6ldWnccayjQMxSa65Xab2T1tAMi36NtAyqiP/M1mmiZ/TeR4Z85czu4pZDqn01ABp0MbP1qhLTPXykxlz53dYVfdpx5S4HPBiolPVHy869ZNFEhFiRIFM33MvFjvyBvye9+m3pGa/F4feU1G6p1giVwhr214nE5DTps067n3dPrA8TQ/rmSl0ur40YuKN0UTRo7AB03kF/Rt6h0poz7ynozUOxfvAW4xu92mAk5HupuvJJ0+cFyznntPBZwO2e22LJohAOB69G0gZdQHriFYAreYj8OujR+tSHfzveb0geP66eMV8nFQvgBwK9C3gZRRH7iGvyBwCxnG1RPat8xca2mciM/Xyv7f8QAAWYe+DaSM+sD1bvrXi4qK0ptvvqnmzZurTZs26tSpkzZt2nQr5pajNWnSRJGRkZk+7qBBg7Rz585MHzetFixYoAEDBkiSnnvuOZ06dUpHjx7V66+/LknauXOnBg0alG3zy+2chk3b5/2U6gntaWG63Pr1641yGhw2AgBZib4NpIz6wPVSDZamaeqFF16Ql5eXli1bpiVLlmjw4MHq16+fIiIibtUc85VRo0apZs2a2T0NSdJHH32kUqVK6fjx4zp69KgkqWbNmho1alQ2zyz3spmmDm3enyljHY7YLxvX3gKALEXfBlJGfeB6qf6O5ZYtW3T8+HF9/vnnstmu7kGoVq2aevTooffee0/16tVTeHi4atasqZ9//lnnz5/X4MGD1bhxY509e1ZDhw7VyZMnZbPZ9Oqrr6phw4ZJxo+JidHgwYO1b98+2Ww2Pfvss3r00Ue1YMECLVy4UBcuXFBQUJBeeeUVz2MGDBigAgUKaPfu3bp06ZJeeeUVLV68WHv37lWzZs00YMAAud1ujR49Wps2bZLNZlObNm3UvXt3RUREaOzYsXK73apUqZKGDh2q4cOH68CBA3K5XHruuefUunXrJHOMi4vTm2++qZ9//lleXl568cUX1apVK0nStGnTtGfPHsXExOidd95R7dq1FR4erkKFCunAgQOaOHGiTp48qYkTJ8rtdqtcuXIaPny4ihcvriZNmqhNmzbasGGDYmJiNGbMGNWoUUPh4eHq2bOn7r//fo0bN04//PCDDMNQhw4d9PTTTyeZ2549ezR06FDFxsaqUKFCGjdunG6//Xa9//77WrJkiQzDUGBgoPr166cTJ06oZ8+eqlSpkvbs2aNixYpp0qRJKly4sBYtWqTp06fL399fZcqUka+vr6Sr38p+/vnnGjlypCIjI/Xmm2/q4Ycf1tSpUzVz5kwdOnRIQ4cO1YULF+Tr66tBgwapVq1aGjBggPz9/fX777/r1KlTeumll9SuXTtt2rRJY8eOlSQVKlRI48ePV9GiRTPyvs21vP28dXr/sUwZ69S+Y/L281b0hZhMGQ8AcCP6NpAy6gPXSzVY7ty5UzVq1PCEymvq1q2r8ePHe/6dkJCgOXPmaPXq1Zo0aZIaN26sUaNGqV27dmratKlOnz6tTp06adGiRfL39/c8bsqUKSpSpIi++eYbnT9/Xu3bt1eVKlUkSadOndK3334rh+PGKZ4+fVpz5szRwoULNXDgQC1fvlze3t568MEH9dJLL2nJkiU6ceKElixZovj4eIWHh6ty5coqUKCADh8+rDVr1qhgwYIaN26cqlevrjFjxujKlSt64oknVLt2bZUrV87zXDNnzlR0dLS+++47nTt3Tl26dFGzZs0kSRUrVtRbb72lL774Qp988okmT54sSQoICNDUqVN17tw5de/eXbNmzVLZsmX18ccfa/jw4Z7lChcurHnz5mnmzJn64IMPNGXKFM/zfv/99/rll1+0dOlSJSQkqFOnTmrVqpVKlCjhWaZv377q27evgoKC9NVXX2nGjBmqX7++Vq9erfnz58vLy0u9evXS7Nmz1bhxY+3du1ejR49WtWrV1KtXLy1dulTBwcEaN26cFi1apMKFC+v555/3BMtrBg8erKlTp+qNN95I8k11v3791L17dwUHB2v79u3q06ePli9fLkk6efKkvvrqK+3fv1+dO3dWu3bt9N5772nYsGGqVauWPvroI+3evVuNGjVK7S3oUaSIrxwOI03L5nRxUXGZMk58dJwMh5Ell38HslNeqnfkDfTtrEO9537UB65JNVjabDa5XDf+pkxCQkKSsPnAAw9IkipVqqQLFy5Ikn766ScdPHjQE6ISExN19OhRVa1a1fO4zZs3a/To0ZKkokWLqmnTptqyZYv8/f1VrVq1ZEOlJD344IOSpNKlS6tSpUoqVqyYpKtB7eLFi4qIiFDbtm1lGIYKFCigkJAQbdq0SU2aNFGFChVUsGBBzxxjY2M1f/58SVJ0dLQOHDiQJFhu3bpVjz/+uOx2u0qUKKFly5Z57rs+YF4LVJJUq1YtSdKOHTtUq1YtlS1bVpLUoUMHffjhh8m+bitWrEiyjlu3blXLli3ldDrldDq1ePHiJPefP39eZ86cUVBQkCSpU6dOkqQxY8bokUceUYECBSRJ7dq106JFi9S4cWMVK1ZM1apV8zznxYsX9euvv+qee+5R8eLFJUkhISHavHlzsq/79aKiovTXX38pODhYklSnTh0VKlRIBw8elCQFBgbKZrOpcuXKnvdE06ZN1bNnTzVr1kxNmzZVYGDgTZ/nmr//jk7zsjlZkcIF5O3nrbgrsZbHcvp6y5Xo0t/s2UM2yooPAHml3pE30Lf/h3rHP1EfeVdG6j3VYFm7dm3NnDlTCQkJ8vLy8ty+fft21ahRw/Nvb29vSUoSNt1ut2bMmKHChQtLuvot47UAeI35j+OoTdP0BFkfH58U53X9XJILn2530hOIUxrX7XZr7Nixql69uiTp7NmzKlSoUJLHOhyOJOt15MgR3XHHHZIkw7i6h+2f3+hee47k5pGYmOj5d3KvW0rPGxkZqaJFi3q+TfTy8kpyf1xcnE6fPn3Dc0ryPOe157v2nKZpev7/+udNi3/+7a7ddu11Tm7dunTpoqCgIK1Zs0Zjx47Vjh071KNHjzQ9X14RFxWnkpXL6Ogvf1oeq1RAmUzbSwgASB59G0gZ9YHrpXrxnvvuu08VK1bU6NGjlZCQIEnatWuXpk+frhdffDHVgevXr6+vvvpKkvTHH38oJCREMTExNywzb948SVe/gVu1apXuv//+DK/M9eMuWrRILpdLMTExWrp0qerVq5fscrNmzZJ0Nfi2adNGJ06cSLJM3bp19e2338o0TZ07d05PPfWU4uPj0zSP2rVr67fffvNcPXbOnDnJziM5devW1YoVK5SQkKCYmBh169ZNp06d8txfsGBBlSpVShs2bJAkLV68WJMmTVL9+vW1bNkyxcbGKjExUfPnz1f9+vVTfJ57771X27dv16lTp+R2u/Xtt9/esIxhGEkCsST5+/urbNmynm9at2/frrNnz6pSpUopPlf79u0VFRWlLl26qEuXLtq9e3eaXou8xLTZVKF+5UwZ61/1KstMZqcEACDz0LeBlFEfuN5Nv56aOnWqJkyYoNatW8swDBUqVEhjx469aUAaPHiwhg4dqpCQEEnSO++8k+T8Skl66aWXNGzYMIWEhMjlcumFF15Q9erVtW/fPgurdPWQ08OHDys0NFQJCQkKCQlR8+bNb7iSbc+ePTVs2DC1bt1aLpdL/fr105133plkmU6dOmnkyJFq06aNJGnIkCE3rEdKihcvruHDh6tnz55KSEhQ6dKl03xF1ebNm2vXrl0KCwuT2+1W586dVaFChSTLjB07VsOGDdPYsWNVpEgRvfPOOypZsqT27Nmjdu3aKTExUY0aNdJTTz2lkydPpjjHwYMHq0uXLipQoIAqVqx4wzJ33323Ll++rH79+umxxx674fmnTJkiLy8vTZkyRU6nM8V1euWVVzRgwAA5HA75+vpq5MiRaXot8pJ4l6k6jzXUhg+Xy52Y8Utz2x123dM+UDEurp4GAFmJvg2kjPrA9Wxmcsc0AjnMmTOXs3sKmcbXaWjLjNWK+Gx1hseo37WJ6nZuouj4G8+BBm6lrDjnKi/VO/IG+vZV1DuSQ33kTRmp91QPhQWQ+WIT3Qp8LlglK5XO0ONLVi6tht2CFWthzyAAIO3o20DKqA9cQ7AEbjG321RMfKI6fvRiuptwycql1fHDFxUTnyi3m4MNAOBWoG8DKaM+cA2HwiJXyIuHyjidhgo4Hfrp4xWK+HytTFfKe+rsDrvuD39IDbsFKyY+UfEcKoIcgkPjkJ/k975NvSM1+b0+8pqM1DvBErlCXt3w2O02+Tjsskv69euNOhyxX6f2HVN8dJycvt4qFVBG/6pXWfe0D5RbVw83YY8echI+aCK/yc99m3rHzeTn+shrCJbIs/L6hscw7HIaNtlMU95+3jIchlyJLsVFxcm02RTvMuVKZc8fkF34oIn8Kj/2beodaZUf6yOvyUi93/TnRgBkPZfLrZj/HgUSfSEm9YUBANmOvg2kjPrIn7h4DwAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEkd2TwDIywzDLqdhk9005e3nLcPLIVdCouKi4uS22RTvMuVyubN7mgCQa9FngbShVpDVCJZAFrDbbSrgsMsmU7vmbdRfEft1dv8xxUfFyennreKVy+jOepVV47GGMg1DMYluud1mdk8bAHIN+iyQNtQKbhWbaZq8c5DjnTlzObunkGZOpyFfp0NbPlqhX79cKzOVvX92w646Tz6k+58LVnR8ouLjXbdwpoB1JUoUzPQxc1O9I3vQZ7MH9Z77UCvIqIzUO8ESuUJu2fA4nYa8bdLC59/T2QPH0/y44pVKq+0HLyrOFI0cuQofNHGr0WezD/Weu1ArsCIj9c7Fe4BMYrfb5Ot0pLuBS9LZA8e18Pn35Ot0yG63ZdEMASB3o88CaUOtIDsQLIFMUsBh15aPVqS7gV9z9sBxbf14hQo4KEsASA59FkgbagXZgXcLkAkM4+pJ8b9+udbSOL98sVa2/44HAPgf+iyQNtQKsgvvFEldu3bVDz/84Pn3mDFjdM899yg+Pt5zW6NGjRQZGakmTZooMjLyhjEGDRqknTt36vLly3rppZduybxvJjw8XBEREUluGzBggBYsWCBJCg0NvSXzuPba5GVOw6Zd835K9aT4tDBdbu2at1FOg0NPAOB69FkgbagVZBeCpaT69evr559/9vz7p59+Up06dTy3HTlyRL6+vipbtmyKY4waNUo1a9bUxYsXtWfPniyfc2ZYvHjxLXmea69NXmY3Tf0VsT9Txjq6Zb/sXFMLAJKgzwJpQ60guxAsJTVo0EC//vqrJOnUqVNyOp1q0aKFNmzYIEnatm2bAgMDPctPmzZNjz76qFq0aKHffvtN0v++HRw5cqROnz7t+dZy0aJFatu2rUJDQ/X6668rLi7uhudv1KiRRowYoUcffVTt2rXT0aNHJUnbt29X+/bt1aZNGz399NM6cuSI57neeecddejQQc2bN9e6desytN4BAQGSpE2bNiksLExhYWHq2rWrzp8/r8jISIWEhKhPnz565JFH9Nxzz+nChQuSpC+++ELt27dX69at1bZtWx08eFCS1KRJE02cOFGPPfaYHnnkEe3atSvJa2OapsaOHasWLVqoVatWmjFjRobmnRN5+3nr7P5jmTLWmX3H5O3nnSljAUBeQZ8F0oZaQXZxZPcEcoLq1avrr7/+UlxcnDZs2KDAwEAFBgaqZ8+e6tevn7Zt26amTZt6lq9YsaLeeustffHFF/rkk080efJkz32DBw9W586dNW3aNB04cEBz587V7Nmz5e3trfHjx+uTTz7Riy++mOT5z5w5owYNGmjIkCF6++239eWXX+qVV17RK6+8ookTJ6pWrVr67rvv9Morr2j+/PmSpISEBM2ZM0erV6/WpEmT1Lhx42TXbfDgwfL19fX8+8SJE7r//vuTLPPee+9p2LBhqlWrlj766CPt3r1b//rXv7R//34NHjxY9erV09tvv62pU6fq5Zdf1g8//KCZM2fKx8dHkyZN0pdffqkhQ4ZIkgoXLqx58+Zp5syZ+uCDDzRlyhTP83z//ff65ZdftHTpUiUkJKhTp05q1aqVSpQocdO/UZEivnI4jJsul53io27caZARCdFxsjuMLLmsO5Ab5IZ6R/agz+Y91HvWoFaQHQiWkgzDUO3atbVz505t2LBBTz75pMqVK6fY2FhdvHhRv/76qwYNGuRZvlmzZpKuBszly5enOG5ERISOHDmixx9/XNLVMFitWrVkl33ggQckSZUqVdK2bdt0+PBh3XbbbapVq5YkqWXLlho6dKguX758w/LXvklMzsiRI1WvXj3PvwcMGHDDMk2bNlXPnj3VrFkzNW3aVIGBgYqMjNS//vUvz2MfffRR9e3bV/7+/ho/fryWLVumw4cPa/369apatWqy67FixYokz7N161a1bNlSTqdTTqczXYfi/v13dJqXzQ5FCxeQ089b8VdiLY/l5estd6JL5y/EZMLMgKyVFR82cnq9I3vQZ7Mf9Z47UCvIDBmpd4Llf9WvX1+//PKLduzYobFjx0q6eojsqlWrVKRIEfn7+3uWNYyre9ZsttRPZna5XGrZsqUGDx4sSYqKipLLlfwPzXp7e3vGNE1TbveNJ1ybpul5/PXLX3P9xXjSE9q6dOmioKAgrVmzRmPHjtWOHTsUEhIih+N/bw/TNGUYhk6cOKHw8HA99dRTevDBB1W8ePEk55QmN69rHA5HktsjIyNVtGjRJN+o5lZxUXEqXrmMjv/yp+WxSgSUUVwm7WkEgLyCPgukDbWC7MI5lv/VoEEDLV68WJUrV/YEqsDAQH366adJzq+8GYfDocTERElSvXr1tHLlSp07d06maWrYsGFpPq/wrrvu0oULF7Rjxw5J0rfffqvSpUurcOHCKT5m8eLFnv+lR/v27RUVFaUuXbqoS5cu2r17tyTp0KFDntA4f/58Pfjgg9q5c6fKly+vLl26qGbNmvrhhx9SDMv/VLduXa1YsUIJCQmKiYlRt27ddOrUqXTNNady22y6s17lTBmr3P2V5b7JTgsAyG/os0DaUCvILnxj+V+VK1fWhQsX1KlTJ89t9evX18svv6yGDRumeZxixYqpdOnSCg8P18yZM9WzZ089/fTTcrvdqlq1qrp3756mcZxOpyZMmKARI0YoJiZGhQoV0oQJE9K9XmnxyiuvaMCAAXI4HPL19dXIkSMlSYUKFdLkyZP1119/KSAgQCNHjpRpmpo1a5ZatWol0zRVt25dHThwIE3P07x5c+3atUthYWFyu93q3LmzKlSokCXrdKvFu0zVeKyhtny4XG4Ll/e2G3bVeCxQMS6uwAYA16PPAmlDrSC72EyTawjjRpGRkercubNWr16d3VORJJ05czm7p3BTfk5Dv85YrV8+z/hrdu/TTVSncxNFxaftW2Agu2XFOVe5od6RPeiz2Yt6zz2oFViVkXrnUFggk8QkunX/c8EqXql0hh5fvHJp1e0WrJhEaz9oDAB5FX0WSBtqBdmBYIlklS1bNsd8W5lbuN2mouMT1faDF9PdyItXLq2277+o6PhEud0cRAAAyaHPAmlDrSA7cCgscoXcdKiM02nI1+nQ1o9X6Jcv1spM5fwGu2HXPU89pLrdghUdn6h4DjdBLsOhccgO9NnsQb3nPtQKMioj9U6wRK6Q2zY8drtNBRx22STtmrdRR7fs15l9x5QQHScvX2+VCCijcvdXVo3HAmXq6iEr7BVEbsQHTWQX+uytR73nTtQKMoJgiTwrt254DMMup2GT3TTl7ectu8OQO9GluKg4uW02xbtMuSxcsQ3IbnzQRHajz9461HvuRq0gPTJS7/zcCJCFXC63Yv57JEnUhZjsnQwA5EH0WSBtqBVkNS7eAwAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALCEYAkAAAAAsIRgCQAAAACwhGAJAAAAALDEkd0TAPILw7DLadhkN015+3nL8HLIlZCouKg4uW02xbtMuVzu7J4mAOQ59F8gbagVWEGwBLKY3W5TAS+77KapvQs26viWffr7wDHFR8XJ6eetIpXKqPT9AaoS1lBuw1BMoltut5nd0waAXI/+C6QNtYLMYDNNk3cFcrwzZy5n9xQyxOk05Ot06Lf/LNeur9bKTGUvn82wq0anh1T7mRaKjk9UfLzrFs4UyJgSJQpm+pi5td6Rs9B/Mx/1njdRK0hORuqdYIlcITdueJxOQz426bsXp+nvP46n+XFFKpZWy/deUqwpGjZyPD5oIiei/2YN6j3voVaQkozUOxfvAbKA3W6Tr9OR7kYtSX//cVzfvThNvk6H7HZbFs0QAPIm+i+QNtQKMhvBEsgCBbzs+u0/y9PdqK/5+4/j+u0/K1TAQYkCQHrQf4G0oVaQ2Xgn5AJjxozRgAEDPP8eOHCggoODFRoaqtDQUK1cuTLZx61evVphYWFq2bKlRo4cecP9X3zxhcLDw7Ns3vmVYVw9+X3XV2stjbPrqzWy/3c8AMDN0X+BtKFWkBV4F+RwmzZt0sKFC5PctmvXLn3xxRdavHixFi9erObNm9/wuKNHj+qNN97Qe++9pyVLlmj37t1at26d5/4//vhDH374YZbPPz9yGjbtXfBTqie/p4Xpcmvvgo1yGhxiAgBpQf8F0oZaQVYgWOZgFy5c0IQJE/TCCy94bouJidHx48f1+uuvKyQkRJMnT5bbfWNTWLlypVq1aqXbb79dXl5emjBhgmrXri1Jio+P19ChQ9W7d+9kn/frr7/Wq6++6vn3lClT9OGHHyoqKkr9+/dXWFiYQkND9c0330iSrly5ot69e6tDhw4KCgrS66+/LtM0FRERoccee0xhYWHq37+/Nm3apLCwMIWFhalr1646f/58Zr5cOYbdNHV8y75MGevE1n2yc30tAEgT+i+QNtQKsgLBMgcbOnSo/v3vf+u2227z3Hb27FnVr19fo0eP1ty5c7Vt2zbNmzfvhsceOXJELpdLL7zwgkJDQ/XVV1+pUKFCkqTx48erXbt2KleuXLLP26pVK23atElXrlyRJH3zzTcKDQ3V9OnTVb16dS1YsEBffvml3n//fR09elRr165V1apVNWfOHC1fvlxbt27V77//Lkk6fPiwZsyYoTFjxui9997TsGHDtGDBAjVs2FC7d+/O7JcsR/D289bfB45lyljn9x+Tt593powFAHkd/RdIG2oFWcGR3RNA8r7++mvdcccdatCggRYsWOC5vVy5cpo2bZrn3+Hh4Vq0aJEef/zxJI93uVzatm2bZs6cKV9fX/Xo0UMLFy5UqVKldOLECQ0cOFARERHJPrefn58aN26slStXqly5cipXrpxKlSqln376SbGxsZo/f74kKTo6WgcOHFDr1q21Y8cOffbZZzp48KAuXLig6OhoSVKFChVUsODVyxU3bdpUPXv2VLNmzdS0aVMFBgam+fUoUsRXDoeR5uWzW3xUXKaMkxAdJ7vDyJJLvAM5VW6rd+Qs9N/chXrPPtQKMhvBMof69ttvdebMGYWGhurixYuKjo7W6NGj1a5dOx0+fFgtWrSQJJmmKYfDoVWrVmny5MmSpCZNmqh48eJq0KCBihYtKklq1qyZduzYobi4OB04cEChoaGKjo7W2bNn9fLLL2vixIlJnr9du3aaPn26ypYtq7CwMEmS2+3W2LFjVb16dUlXvz0tVKiQZs6cqeXLl+vxxx9Xw4YNtX//fl37eVQfHx/PmF26dFFQUJDWrFmjsWPHaseOHerRo0eaXo+//47O+It5ixUtXEBOP2/FX4m1PJaXr7fciS6dvxCTCTMDMl9WfJDITfWOnIX+m7Wo97yDWsHN8DuWecinn36qb775RosXL1bv3r3VpEkTz7mLo0eP1sWLF5WQkKA5c+aoefPmatq0qediPn369FFQUJA2bNigS5cuyeVyaf369apevbreeustfffdd1q8eLFGjhypGjVq3BAqJem+++7TyZMnFRERoWbNmkmS6tevr1mzZkmSTp8+rTZt2ujEiRPauHGjOnTooDZt2iguLk579+5N9rzP9u3bKyoqSl26dFGXLl3y7KGwcVFxKlKpTKaMVbRyGcVl0h5FAMjr6L9A2lAryAp8Y5nLVKlSRd27d1fHjh2VmJio4OBgtW7d+oblateurW7duqlTp05KSEhQYGCg2rVrl67nat68uS5cuCCn0ylJ6tmzp4YNG6bWrVvL5XKpX79+uvPOO/X0009r2LBh+vDDD+Xv76977rlHkZGRuvPOO5OM98orr2jAgAFyOBzy9fVN9idQ8gK3zabS9wfo1K9/Wh7rjroBctu40hoApAX9F0gbagVZwWaaXMYJSZmmqYSEBHXt2lWvv/6659DX7HTmzOXsnkKaGYZdvoY0u9VQS5fxthl2PfHtCEW7TLksXg4cyCpZcWhcbqp35Cz036xFvecd1ApuhkNhkSnOnDmjwMBA1a5dO0eEytzG5XLLbbOpRqeHLI1To1OQ3P8dDwBwc/RfIG2oFWQFgiVuULJkSW3dulWvvfZadk8l14pJcKv2My1UpGLpDD2+aKXSqv1MsGISadQAkB70XyBtqBVkNoIlkAXcblPR8Ylq+d5L6W7YRSuV1sPTXlJ0fKLcbo5UB4D0oP8CaUOtILNxjiVyhdx6DobTacjX6dBv/1mhXV+tSfU8BpthV41OQar9TLCi4xMVH++6hTMFMoZzrpBT0X8zH/WeN1ErSE5G6p1giVwhN2947HabCjjsskvau2CjTmzdp/P7jykhOk5evt4qWrmM7qgboCphgXJLikl0s/cPuQYfNJGT0X8zF/Wed1Er+CeCJfKsvLDhMQy7nIZNdtOUt5+37A5D7kSX4qLi5LbZFM8V1ZAL8UETuQH9N3NQ73kftYJrMlLv/I4lcIu4XG7F/PeIkagLMdk7GQDIR+i/QNpQK7CCi/cAAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACxxZPcEgPzGMOxyGjYZMuX09Zbh5ZArIVHx0XFyyaZ4lymXy53d0wSAPIO+C6SOGkFmIFgCt4jdbpOvl11209SfCzfo1Na9uvjHcSVExcrLz0eFKpZWqfur6O5HA+V2GIpOcMvtNrN72gCQa9F3gdRRI8hMNtM0eXcgxztz5nJ2T8ESp9OQr9OhPZ9+r/2z18hMZa+fzbCr8hNBqtr1YUXHJyo+3nULZwqkT4kSBTN9zNxe78gZ6LuZj3rPW6gRpCYj9U6wRK6Qmzc8TqchH5u0rtcUXfzjeJofV6hiaTWe0kuxpmjgyLH4oImciL6bNaj3vIMawc1kpN65eA+Qhex2m3ydjnQ3bkm6+Mdxres1Rb5Oh+x2WxbNEADyFvoukDpqBFmFYAlkIV8vu/Z8+n26G/c1F/84rj2fLZevF6UKAGlB3wVSR40gq/COyGciIyNVo0YNhYaGJvnfiRMnsuS5mjRpkunj5haGcfVk+P2z11gaZ/+s1bLLlGFQrgCQGvoukDpqBFmJq8LmQyVLltTixYuzexp5ntOw6c+FG1I9GT4tTJdbfy7cqDsfbaQYTmcAgBTRd4HUUSPISgRLSJLOnj2roUOH6uTJk7LZbHr11VfVsGFDTZkyRcePH9fhw4d1/vx59ejRQ5s2bdJvv/2mKlWqaMKECXK5XBo2bJgOHDigs2fPKiAgQO+++26axs/LDJk6tXVvpox1eus+VQgNzJSxACCvou8CqaNGkJUIlvnQ6dOnFRoa6vl3SEiIfv/9d7Vr105NmzbV6dOn1alTJy1atEiStH//fs2ZM0e//PKLnn76aS1dulT/+te/1KpVK+3bt0+XL1+Wl5eX5syZI7fbraefflrr1q1T9erVPc8xatSoZMf39/e/1at/yzh9vTN8/sI/XThwTE4/b+lCTKaMBwB5EX0XSB01gqxEsMyHkjsUtl69ejp48KAmT54sSUpMTNTRo0clSYGBgXI4HCpdurRKlCihihUrSpJKlSqlixcvql69eipcuLC+/PJLHTx4UIcPH1Z0dHSS8X/66adkx69atWqa5lykiK8cDsPSemeHhKjYzBknOlZ2h5Ell3oHcprcWu/IGei7uQv1futRI8gqBEtIktxut2bMmKHChQtLuvqtZrFixfTDDz/Iy8vLs5zDceNbZtWqVZo8ebI6d+6ssLAw/f333/rnz6OmNH5a/f139M0XymGKFi4gLz8fJVyxvifPy9dH7kSXzrNXEDlMVnygyI31jpyBvpu1qPfcjxpBWvE7lsiw+vXr66uvvpIk/fHHHwoJCVFMTNoaxaZNm9SyZUu1a9dOt912myIiIuRyJT2T28r4uVV8dJwKVSydKWMVrlRG8VFxmTIWAORV9F0gddQIshLfWEKSNHjwYA0dOlQhISGSpHfeeSfN5z+2b99effv21bJly+Tl5aX/+7//U2RkZKaNn1u5ZFOp+6vo7PY/LY9Vsm6AXDZ+iBgAUkPfBVJHjSAr2cx/HrMI5EBnzlzO7imkm2HY5WdIS0MGW7qst82wK+SbkYpKlFwWLw8OZLasODQuN9Y7cgb6btai3nM/agRpxaGwQA7icrnlttlU+YkgS+NU7thEbtlo3ABwE/RdIHXUCLISwRLIQtEJblXt+nCGz2coVKmMqnZpoegEGjcApAV9F0gdNYKsQrAEspDbbSo6PlGNp/RKdwMvVKmMGk/uqej4RLndHLEOAGlB3wVSR40gq3COJXKF3H4OhtNpyNfp0J7Plmv/rNWpntdgM+yq3LHJ1b2B8YmKj3eluCyQ3TjnCjkVfTfzUe95CzWC1GSk3gmWyBXywobHbrfJ18suu0z9uXCjTm/dpwsHjikhOlZevj4qXKmMStYN0N1tA+WWTdEJbvYGIsfjgyZyMvpu5qLe8x5qBCkhWCLPyksbHsOwy2nYZJimnH7esjsMuRNdio+Kk8tmU7zL5GR45Bp80ERuQN/NHNR73kWN4J8yUu/8jiVwi7lcbsVcO4LkQky2zgUA8gP6LpA6agSZgYv3AAAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAsIVgCAAAAACwhWAIAAAAALCFYAgAAAAAscWT3BIC8zjDscho2OWTKy9dbhpdDroREJUTHKVE2xbtMuVzu7J4mAOQ49E8g/agbZBeCJZBF7HabfL3sMkxTfy3+Uee27tWlP48pMSpWDj8f3XZ3GRWrW0V3hj4gl8NQdIJbbreZ3dMGgGxH/wTSj7pBdrOZpsk7CjnemTOXs3sK6eJ0GvJzGvpjxnc6PGe1zFT2DNoMu/7VoYkqPt1SUfEuxce7buFMAWtKlCiY6WPmtnpH5qJ/5lzUe85F3SCzZaTeCZbIFXLThsfpNFTALm3pPUmX/zyW5scVvLuM7p/cRzFu0eSRa/BBE5mJ/pmzUe85E3WDrJCReufiPUAmsttt8nMa6W7uknT5z2Pa0nuS/JyG7HZbFs0QAHIm+ieQftQNchKCJZCJfL3s+mPGd+lu7tdc/vOY/vj8e/l6UZoA8hf6J5B+1A1yEt5FQCYxjKsnzB+es9rSOIdnr5IhU4ZBeQLIH+ifQPpRN8hpsuQdFBkZqSZNmtxwe0BAgCRp1apVmjRpUqpjhIeHKyIiItPmlFnjLViwQAMGDMiEGaUsIiJC4eHhGXrsjh07NHbs2EyeUVK34jXIjZyGTX8tXp/qCfNpYbrc+mvRejkNDksBkD/QP4H0o26Q02TLrommTZuqT58+2fHUed4ff/yhc+fOZfc08iWHTJ3bujdTxjq3bZ8c4rpaAPIH+ieQftQNcppsCZbXf+MVERGhkJAQPfrooxo2bFiSb+rmzZuntm3bqmnTplq9+sav+ZcuXarQ0FCFhYWpd+/eiouLk2maGjt2rFq0aKFWrVppxowZqY539uxZPf/88woJCVHbtm31448/SpJiYmL06quvqnXr1goJCdGiRYtSXaeU1iM8PFw9e/ZUixYttGfPHn3xxRdq3769WrdurbZt2+rgwYOSpA0bNuiRRx5RWFiY5s6d6xn3+m9ar/8meP/+/QoPD1e7du0UFBSkWbNm6dKlS5o8ebJWr16t6dOny+Vy6a233lLbtm3Vpk0bffbZZ8nO/f3331erVq0UEhKit99+Wy6XS5GRkXr44YfVsWNHde3aNcX13rFjhzp27Ki2bdvqmWee0dGjRyVJn376qdq0aaNHH31UQ4cOlSTt3btXjz/+uMLCwtSxY0cdPnw41dc0t/Hy9dalDJ7j8E+X/oiUl693powFADkd/RNIP+oGOY0jqwY+ffq0QkNDU10mISFBr732mj744ANVqVJFI0eOTHJ/wYIFtXDhQq1Zs0ZTp0694fDaiRMnau7cuSpWrJjGjBmjgwcP6vDhw/rll1+0dOlSJSQkqFOnTmrVqlWK440YMUL169dX165ddfToUXXs2FGLFi3Sf/7zHxUpUkTffPONzp8/r/bt26tKlSoZWo+AgABNnTpVV65c0ZgxYzRz5kz5+Pho0qRJ+vLLL9W/f38NGDBAM2bM0N13361Bgwbd9PX9+uuv9eKLL6pBgwY6evSo2rRpo44dO6p3797asmWLevTooVmzZkmSFi5cqPj4eD377LOqUaOG7rvvPs8469at0+rVqzV//nx5eXmpV69emj17tho3bqxDhw7p448/VtmyZZOdQ3x8vAYPHqz3339fpUuX1vr16zVkyBB98skn+uCDD7R+/XoZhqFBgwbp1KlTmjFjhrp27aqWLVtq4cKF2r59u/71r3/ddF0lqUgRXzkcRpqWzU6JUbGZM050rOwOI0su7Q7kdLml3pG56J/5E/VuDXWDnCTLgmXJkiW1ePHiJLddO8fymv3796tYsWKewPbYY49p1KhRnvubNWsmSapYsaL+/vvvG54jKChIHTt2VLNmzdSiRQtVrVpVX3/9tVq2bCmn0ymn05lkDsmNt3nzZk8QLFeunGrXrq3ffvtNmzdv1ujRoyVJRYsWVdOmTbVlyxb5+/vfMI+brUetWrUkSf7+/ho/fryWLVumw4cPa/369apatar27dunkiVL6u6775YktW3b9qbnoA4YMEDr16/XBx98oP379ys6OvqGZTZt2qQ9e/Zo8+bNkqTo6Gjt27cvSbDcvHmzHnnkERUoUECS1K5dOy1atEiNGzdWsWLFUgyVknT48GEdPXpUPXr08Nx25coVGYahe+65R4899piaNm2qrl27qlSpUmrcuLGGDx+u9evXq0mTJgoKCkp1Ha/39983rl9OU7RwATn8fJR4JcbyWA5fH7kTXTp/wfpYQFbKig8huaHekbnon7kD9Z6zUDfIShmp9ywLlmlhGIbc7pRPODaMq3uwbLbkTyYePHiw9u7dq3Xr1qlfv37q2bOnHA5HkuUjIyNVtGjRFMczzaTHk5umKZfLleLtGVkPHx8fSdKJEycUHh6up556Sg8++KCKFy+uPXv2yGazJXm+a/P85xwTExM9t7388su67bbbFBQUpFatWumbb7654XldLpf69eun4OBgSdL58+fl5+eXZJnk5n3tea7NOyVut1tly5b1hHeXy6WzZ89Kkt577z1t375dP/74o7p166Zx48bp4Ycf1j333KM1a9bos88+09q1a2/4djc3S4iO0213l9H53/6wPNZtFcsqITouE2YFADkf/RNIP+oGOU22Xlf4rrvu0qVLl7Rv3z5JV8+ZTKvExEQFBwerSJEiev755xUaGqo9e/aobt26WrFihRISEhQTE6Nu3brp1KlTKY5Tv359zZs3T5J09OhR/fLLL6pTp06S28+fP69Vq1bp/vvvt7QeO3fuVPny5dWlSxfVrFlTP/zwg1wulwICAnT27Fnt3Xv1BOxly5Z5HlOkSBH98cfVhvHDDz94bt+4caN69+6tZs2aec4LdblcMgzDEwzr16+vuXPnKiEhQVFRUerUqZO2b99+w/ovW7ZMsbGxSkxM1Pz581W/fv0UX69/rvfFixe1bds2SdL8+fPVt29fnT9/Xq1atVLlypXVp08fBQYGat++fXr55Ze1c+dOPfHEE+rTp492796dpufJLRJlU7G6yR8unV7F7gtQorg6G4D8gf4JpB91g5wmW7+xdDqdeuedd9S/f3/Z7XZVqFDhpt+SXeNwONS7d28988wz8vb2VrFixfT222+rWLFi2rVrl8LCwuR2u9W5c2dVqFAhxXEGDRqkoUOHasGCBZKkkSNHqmTJknrppZc0bNgwhYSEyOVy6YUXXlD16tU94TEj6xEYGKhZs2apVatWMk1TdevW1YEDB+Tl5aV3331X/fr1k8PhULVq1TyP6datmwYMGKD58+eradOmntt79eqlTp06ydvbW1WqVFGZMmUUGRmpWrVqaerUqRo3bpz69OmjI0eOqG3btkpMTFRYWJjq1auXZE5BQUHas2eP2rVrp8TERDVq1EhPPfWUTp48edO/gdPp1KRJkzRq1CjFxcXJ399fY8aMUdGiRdWhQwc99thjKlCggCpUqKB27dqpbt26GjRokKZNmyYvLy8NGzbsps+Rm8S7TN0Z+oD++PRbS5f+thl23fnoA7qSyNXZAOQP9E8g/agb5DQ285/HfN5Cbrdb48aNU8+ePeXr66tPP/1Up06dynW/kZhX1iMnO3PmcnZPIU38vQ0d+WqFDn31w80XTkGFJ5urfMfmuhKX/KHXQE6SFedc5ZZ6R+aif+Z81HvOQ90gq+S6cyztdrsKFy6sxx57TF5eXipTpkySi97kFnllPWBddIJbFZ9uqbMRe3Q5A5cAL1ixjCp2flgXoxOyYHYAkHPRP4H0o26Qk2TrN5ZAWuWmPZpOp6ECdmlL70npavIFK5bR/ZP6KMYtxcez1xC5A99gIDPRP3M26j1nom6QFTJS7wRL5Aq5bcPjdBrycxr64/PvdXj2qlTPfbAZdv3riaaq2PlhRcW7aO7IVfigicxG/8y5qPeci7pBZiNYIs/KjRseu90mXy+7DJn6a9F6ndu2T5f+iFRidKwcvj66rWJZFbsvQHc++oBcsik6wS23m3JE7sIHTWQF+mfORL3nbNQNMhPBEnlWbt7wGIZdTsMmh0x5+XrL7jDkTnQpITpOibIp3mXKZeFqbkB24oMmshL9M2eh3nMH6gaZIdddvAfID1wut2KuHWUSH5OtcwGA3IT+CaQfdYPsYs/uCQAAAAAAcjeCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBKCJQAAAADAEoIlAAAAAMASgiUAAAAAwBJHdk8AyE8Mwy5vh00O0y0vXx/ZvRxyJyQqITpWiTa74hJNuVzu7J4mAORo9FIg7agX3CoES+AWsNtt8vOyy266dXLJOl3YtkdX/oyUKzpWhq+P/O8uq8L3VdXtIQ/K7TAUleCW221m97QBIEehlwJpR73gVrOZpsk7CDnemTOXs3sKGeZ0GvLzNnT082WKnPuD5E55r6DNsKtM+2Yq1/kRRcW5FB/vuoUzBdKvRImCmT5mbq53ZB16afaj3nMP6gVWZaTeCZbIFXLrhsfpNORrN7Xz3+8q6uCxND/O764yqjnhFUW7bTR45Gh80MStQC/NGaj33IF6QWbISL1z8R4gi9jtNvl5G+lu7JIUdfCYdv77Xfl5G7LbbVk0QwDI+eilQNpRL8hOBEsgi/h52XX082XpbuzXRB08pqMzv5WfF2UKIP+ilwJpR70gO/GuAbKAYVw9WT5y7g+Wxomcs1KGTBkGpQog/6GXAmlHvSC78Y7JRFFRUXrzzTfVvHlztWnTRp06ddKmTZuy9DkjIiIUHh4uSQoPD1dERESyy4WFhemFF17I0rngf7wdNp1c+mOqJ8unidutE0vWydvBISkA8h96KZB21AuyG8Eyk5imqRdeeEFeXl5atmyZlixZosGDB6tfv34phr1bZe/evXI6ndq7d69OnDiRrXPJLxymWxe27cmUsS78vFeGye9LAch/6KVA2lEvyG4Ey0yyZcsWHT9+XAMHDpTT6ZQkVatWTT169NB7772nvXv3KiQkxLP86tWr1aNHD0nShx9+qLZt26pNmzZ65513ZJqmIiMj9fDDD6tjx47q2rWrrly5ot69e6tDhw4KCgrS66+/rrRe0HfBggUKDAxU06b/3959hzdZ7/8ff9250zQtLRtRGQ5QpiAOlgMZooVCWQLql+FRXPB1cEQQEXAhQxzAUQ9Hfw6OR0RlaAERQRTZqIgoAoqMIqOMQmeSJvfvD770iAzb3kmTtM/HdXldNuT+3J+M1zt53yvtNWvWLEkK6XwmT56sjh07qk+fPhoyZIhmz54tSZo7d666d++ulJQUjRw5Uh6Px+azHrli4t3K+jUtKGNl/bJbrnh3UMYCgGhCLQUKj7wg3JzhnkBp8cMPP6hx48YyjJMPG7j66qs1efJk1a9fX4ZhaOvWrbr00ks1f/58de3aVV999ZU2bdqkDz/8UIZhaNiwYfr444915ZVX6rffftPrr7+umjVrKjU1VQ0aNNCUKVPk9XrVuXNn/fjjj385L5/Pp08++UQzZsxQRkaGHn74YQ0ePDhk8zlw4IC++eYbpaamKjc3V927d1e7du20bds2zZo1SzNnzlRsbKwmT56sN954Q/fff3+hnt9KleLldJrFem3CxZ+TF5xxcvNkOM2QXOYdiETRmHeEDrW0dCPvwUVeEE40lkFiGIb8/lN/88fn8xU0m127dtX8+fNVu3ZtrVu3TuPGjdNLL72kjRs3qkePHpKkvLw8nX/++bryyitVpUoV1axZU5KUnJysjRs36q233tL27duVkZGhnJycv5zXsmXLVK1aNdWtW1eWZcnhcOiLL74oOA802PNZuXKlkpKS5HK55HK51KFDB0nHzwXduXOnevfuXfC8NGzYsNDP75Ejf/1YI0mVinEy493yZ+faHsuMc8vK9+tghv2xgGALxZeOaMs7QodaGlnIe2QjLwim4uSdxjJImjZtqhkzZsjn8ykmJqbg9g0bNqhx48aSpC5dumjAgAGqX7++rr32WsXGxsrv92vAgAG64447JEnHjh2TaZo6cuSI3O7/HoIwY8YMLVq0SL1791br1q21devWQh0K+9FHH2nv3r1q166dJCkrK0szZ87UjTfeGJL5OBwOBU5z0rjf71dSUpJGjRol6fiFjk7XiJcWvpw8JdSpqaMbt9keK6FuLXmDtAUSAKIJtRQoPPKCcOMcyyC56qqrVLduXY0bN04+n0+StGnTJr366qsFh3tWr15d5513nqZPn66uXbtKklq2bKl58+YpOztb+fn5Gjx4sBYtWnTK+CtWrFCfPn3UtWtXeTwe/fzzz6dt4P7o4MGDWrlypVJTU7V06VItXbpUc+fO1erVq7V79+6QzKd169b67LPP5PV6lZWVpWXLlskwDLVo0UKLFy/WoUOHZFmWxo4dq7ffftvWcx7J8g2HKl7VIChjVbyyvvwGUQVQ9lBLgcIjLwg33jFBNG3aNLlcLiUnJ6tTp0569tlnNWnSJLVo0aLgPikpKTp8+LCaN28uSWrXrp06duyo3r17Kzk5WfXr11f37t1PGXvAgAGaNm2aunTponHjxqlZs2ZKSzv7Cdrz5s1TmzZtVL169YLbatWqpXbt2un9998PyXxuuOEGXXXVVerevbvuvvtunXPOOYqNjVX9+vU1ZMgQDRgwQJ07d1YgENDdd99d9Cc5SnjyLZ3b5XoZNn8DyjAdOq9rG3nyC3ehJgAoTailQOGRF4SbYRX20qJAIXz33XfasWOHunfvLp/Ppz59+mjcuHGqX7++rXHT0zODNMOSkxhrau/MT5U287Nij1Hz1pt0Xp+blOkpvYcNI7qF4pyraMw7QodaGjnIe+QjLwiW4uSdPZYIqosuukipqanq2rWrevTooc6dO9tuKqNVti+gWv07q9zFNYq1fLk6NVWrXydl+/gdKQBlF7UUKDzygnBijyWiQrRu0XS5TMU7LP3w8AvK3r6n0MuVq1NTl73wsHIChrxethgicrEHAyWBWhoZyHt0IC8IhuLkncYSUSGaP3hcLlPlYk3tnrFAae8vls5y0SXDdKhG7xuPby30+CnsiHh80URJoZaGH3mPHuQFdtFYotSK9g8eh8NQuRiHTFna+/GXyvjmZ2X9slv+3DyZcW4l1K2lilfW13ld28gvQ9m+gAIBoonIxxdNlCRqaXiR9+hCXmAHjSVKrdLywWOaDsU6DZlWQK54twynKSvfL29OnvyGQ558S34/5zUgevBFE+FALQ0P8h6dyAuKozh5d4ZgHgDOwO8PKOfEESbe3D//a0lPBwCiErUUKDzygpLCVWEBAAAAALbQWAIAAAAAbKGxBAAAAADYQmMJAAAAALCFxhIAAAAAYAuNJQAAAADAFhpLAAAAAIAtNJYAAAAAAFtoLAEAAAAAttBYAgAAAABsobEEAAAAANhCYwkAAAAAsIXGEgAAAABgC40lAAAAAMAWGksAAAAAgC00lgAAAAAAW2gsAQAAAAC20FgCAAAAAGyhsQQAAAAA2EJjCQAAAACwhcYSAAAAAGALjSUAAAAAwBYaSwAAAACALTSWAAAAAABbaCwBAAAAALbQWAIAAAAAbKGxBAAAAADYQmMJAAAAALCFxhIAAAAAYAuNJQAAAADAFhpLAAAAAIAtznBPACgrTNOhWKchpxVQTLxbjhinAr58+XLylG845Mm35PcHwj1NAIgo1E7AHjKEkkJjCYSYw2GoXIwhU5bSU79Q5rc/KXf7bvlz8mTGuxV3cS2Vv6KRqia3kd/pULbPUiBghXvaABBW1E7AHjKEkmZYlsU7CBEvPT0z3FMoFpfLVEKsqb3vfqL9H34mBc6yRdA0Vb3njTrv9i7K8vjl9fpLbqJAMVWrlhj0MaM17wgeamdkIu/RgwzBruLkncYSUSEaP3hcLlPxDkvbHp2k3O1phV4u7uKaumTiMOUEDIo7Ih5fNBFs1M7IRd6jAxlCMBQn71y8BwgBh8NQQqxZ5KIuSbnb07Tt0UlKiDXlcBghmiEARB5qJ2APGUI40VgCIVAuxtDedz8pclE/IXd7mva+m6pyMRR2AGUHtROwhwwhnGgsgSAzTYdMWcfPabBh/4eLZP7feABQ2lE7AXvIEMKNd0yYfPrpp+rRo4e6du2qLl266PXXXw/3lM6oX79+WrNmjdasWaN+/fqFezoRL9ZpKD112dlPlC+MQEAHU5cp1slWQwClH7UTsIcMIdxoLMNg//79mjBhgt544w19/PHHmjlzphYsWKAlS5aEe2oIAqcVUOa3PwVlrGPf/SSnxW9LASj9qJ2APWQI4cbvWIbBkSNH5PP5lJeXJ0kqV66cxo8fr9jYWEnShg0b9Oyzz8rj8ahSpUp66qmndMEFF6hfv35q2LChvvnmG3k8Hj3yyCN655139Ouvv2rgwIEaOHCgsrOz9dRTT2nbtm3y+/0aNGiQkpOTT1q/x+PRk08+qW+++UYxMTG6//771alTpzOu93R27typsWPHKiMjQ263W0888YQaNmyoffv26ZFHHtHRo0d16aWXat26dfrqq68KNa/SIiberdztu4MyVu6vuxQT75a8uUEZDwAiFbUTsIcMIdxoLMOgfv36at++vTp06KAGDRqoRYsW6tKliy644AJ5vV4NHTpUL730kpo0aaKFCxdq6NCh+uijjyRJlmXpww8/1LRp0/TMM8/o448/1uHDh9WtWzcNHDhQr776qho1aqQJEyYoKytLffv2VdOmTVWrVq2C9c+YMUM5OTlauHChDh06pIEDB6pDhw5nXe+fDR8+XKNHj1bDhg31yy+/aPDgwVq0aJGeffZZJSUl6fbbb9fixYuVmpoqSYWa19lUqhQvp9O0+cyXHH9OXnDGyc2T4TRDcol3IFJFW94RPNTOsoe8BxcZQjjRWIbJk08+qfvvv19ff/21vv76a/Xu3VvPP/+8LrzwQpUvX15NmjSRJCUlJWn06NHKzDz+O0/XX3+9JOn8889X06ZNFRcXpxo1aujYsWOSpJUrVyovL6+gIczJydG2bdtOauDWrVun3r17y+FwqFq1apo/f762bt161vX+UXZ2tjZt2qTHHnus4LacnBwdOXJEK1as0HPPPSdJuvHGG1W+fPlCz+tsjhzJKeQzG35VKsbJjHfLn21/K58Z55aV79fBDLYYIjKF4ktHNOUdwUPtjHzkPbKRIQRTcfJOYxkGy5YtU05Ojjp16qSePXuqZ8+emjVrlj788EMNHTr0lPtbliW///gP1cbExBTc7nSe+vIFAgFNmjRJjRo1kiQdPHhQFSpUOOk+TqdThvHfE7J37typwGlO9P7jev+8DpfLpXnz5hXctm/fPlWsWFGmacqyrGLNq7Tw5eQp7uJayvphq+2x4urUli9IWx8BIJJROwF7yBDCjYv3hIHb7dbkyZOVlnb8N4Ysy9LmzZvVoEEDXXzxxcrIyNDGjRslSQsWLND555+vihUrFmrsli1b6r333pMkHThwQF27dtXevXtPus/VV1+tBQsWyLIsHTp0SP/zP/+jGjVqFHq9iYmJuvDCCwsayxUrVuj222+XJLVq1UqffPKJJOnLL78s2JNamHmVFvmGQ+WvaBSUsco3a6h8g5gCKP2onYA9ZAjhxh7LMGjZsqWGDBmie++9Vz6fT5J03XXXafDgwXK5XHrxxRf19NNPKzc3VxUqVNCLL75Y6LGHDBmisWPHKjk5WX6/X8OGDVPt2rVPus9tt92mZ555Rl27dpUkPfHEE0pMTCzSeidNmqSxY8fq9ddfV0xMjF588UUZhqHHH39cw4cP16xZs1S/fv2CQ2ELM6/SwpNvqWpyG/3+74+l0+zxLTTTVNXkG5SZf+oeYAAobaidgD1kCOFmWKc7bhEopnfeeUetW7dW3bp19eOPP+qJJ57Q7NmzbY+bnn7quZ6RLDHWoYMfLNT+WZ8We4zqvZNU9Zablenhct+IXKE45yra8o7goXZGNvIe+cgQgoVzLBF2F1xwgYYOHSqHw6HY2Fg9/fTT4Z5SWGT7LJ13excdW79JudvTirx83MW1dN7tycrI9oVgdgAQmaidgD1kCOHEHktEhWjcoulymYp3WNr26KQiFfe4i2vpkomPKCdgyOu1cSgLUALYg4Fgo3ZGLvIeHcgQgqE4eaexRFSI1g8el8tUQqypve+mav+Hi6TTXH23gGmqes+OOu/2ZGV5/BR1RAW+aCIUqJ2RibxHDzIEu2gsUWpF8wePw2GoXIwhU9LB1GU69t1Pyv11l/y5eTLj3IqrU1vlmzVU1eQb5Nfxw1gCAWKJ6MAXTYQKtTPykPfoQoZgB40lSq3S8MFjmg7FOg05rYBi4t0ynKasfL98OXnKNxzy5Fvy+zlRHtGFL5oINWpn5CDv0YkMoTi4eA8Qwfz+gHJOHF3izf3zv5b0dAAgKlA7AXvIEEoKv3wKAAAAALCFxhIAAAAAYAuNJQAAAADAFhpLAAAAAIAtNJYAAAAAAFtoLAEAAAAAttBYAgAAAABsobEEAAAAANhCYwkAAAAAsIXGEgAAAABgC40lAAAAAMAWGksAAAAAgC00lgAAAAAAW2gsAQAAAAC20FgCAAAAAGyhsQQAAAAA2EJjCQAAAACwhcYSAAAAAGALjSUAAAAAwBYaSwAAAACALTSWAAAAAABbaCwBAAAAALbQWAIAAAAAbKGxBAAAAADYQmMJAAAAALCFxhIAAAAAYAuNJQAAAADAFhpLAAAAAIAtNJYAAAAAAFtoLAEAAAAAttBYAgAAAABscYZ7AkBZZ5oOxToNxSggZ5xbjhinAr585efmySeHPPmW/P5AuKcJAFGJGgucilwgFGgsgTBxOAwluAyZlqUjny5Rzvc/Km/HbgVycuWIj5P7wlqKv7yxKt3UVv4Yh7K8lgIBK9zTBoCoQI0FTkUuEEqGZVm8WxDx0tMzwz2FoHK5TCXEmjo4a54Oz/1UCpxlq6BpqnLKTaraO0VZHr+8Xn/JTRT4C9WqJQZ9zNKWd5Q8amxokPfoRi5QFMXJO40lokJp+uBxuUyVMy3tGjVenh27C71c7IW1VPuZEcr2GxR4RAy+aCLSUGNDh7xHL3KBoipO3rl4D1CCHA5DCbFmkQu7JHl27NauUeOVEGvK4TBCNEMAiF7UWOBU5AIlhcYSKEEJLkMHZ80rcmE/wbNjtw7O+lgJLoo7APwZNRY4FblASaGxBEqIaTpkWtbx8xpsODx3oUxZMk3iCwAnUGOBU5ELlKQSe3esWbNGzZo1U0pKirp27aqkpCS9/fbbxR6rX79+RVqmXr16Rbp/u3bttGbNmiItI0lTp07V1KlTi7xcJOvXr1+xngucLNZp6MinS89+snxhBAI68ukXinWy5RAATqDGAqciFyhJJbrZoXHjxpo3b54+/vhjffDBB/p//+//6ZdffinJKRRabGys4uLiwj0NlCIxCijn+x+DMlbO9z8qRvy+FACcQI0FTkUuUJLC9juWHo9HpmkqMfH4FYcWLlyoN998U3l5efJ6vRo3bpyuuOIKbd68WaNHj1ZeXp4qVKig559/XpJ0+PBhDRo0SLt27dJFF12kKVOmyOVyae7cuXr77bcVCATUqFEjjRkzRrGxsQXrzc3N1ahRo7RlyxYZhqE777xT3bp10+zZszVnzhxlZGSobdu2uuKKK3TxxRdr1apVmjRpkiSpQoUKmjx5sipXrnzSY3n99dc1a9YsVapUSeXLl1eTJk0kSV999ZWmTJmi/Px81axZU08//bQqVaqkNWvW6JlnnpFpmrr88sv166+/asaMGerXr58qVKigbdu26aWXXlJ6evppl9+4caOee+455eXlqVKlSnryySdVq1atk+b022+/afTo0crIyFB8fLwef/xxNWnSRCNGjFBGRoZ27typYcOGqV27dgXLnOk1kKRZs2bpueeekyQ99thjatGixRmfy+7du+vpp59W48aN5ff71bZtW82ZM0d79uz5y3mXZs44t/KKeX7Dn+X9tkvOOLfkyQ3KeAAQ7aixwKnIBUpSiTaWmzZtUkpKigKBgHbt2qWkpCSdc845CgQCmjlzpl577TVVrlxZH374oaZPn67XXntNjzzyiB555BG1bdtW//nPf/T222/rhhtu0O+//67XXntNNWrUUO/evbVy5UrVqFFDs2bN0syZMxUbG6vJkyfrjTfe0P33318wh6lTp6pSpUpKTU3V4cOHdcstt6h+/fqSpP3792vBggVyOv/7tLzyyisaO3asmjRpon/961/66aefdO211xb8+w8//KCPPvpIc+bMkWEY6tOnj5o0aaLDhw9r8uTJeuedd1ShQgXNnDlTzz//vMaOHatHH31U//znP1W/fn0988wzJz1H9erV07Rp03T48GGNGDHilOXHjBmjUaNG6bXXXtP555+v5cuX64knntBbb7110jjDhg3T3XffrY4dO2rDhg168MEHtWjRIklSxYoV9dprr510/7O9BpIUHx+vuXPn6ueff9bdd9+tzz///IzPZUpKiubPn6/GjRtr9erVql+/vhITEws17zOpVCleTqdZqPtGskBOcIpxIDdXhtMMyaXfgXArLXlHyaPGRh/yHnrkAiWlRBvLxo0ba8aMGZKkrKws3XXXXZo+fbruuece/eMf/9DSpUv122+/ae3atXI4HDp8+LDS09PVtm1bSdJtt90m6fg5lvXr1y/Y21WnTh0dOXJEaWlp2rlzp3r37i1J8vl8atiw4UlzWL16tcaNGydJqly5stq3b6+1a9cqISFBDRs2PKmplKT27dtryJAh6tChg9q3b69rrrnmpH9fu3at2rRpo3LlykmSbr75ZgUCAX3//ffau3ev+vfvL+l441ahQgVt3bpVVapUKWhme/XqpWeffbZgvBN7O8+0/I4dO7R7927dd999BctkZWWdNKfs7Gzt2rVLHTt2lCRdfvnlqlChgrZv337SOv7I4XCc9jU4oVevXpKk+vXrq0qVKtq+ffsZn8vOnTurT58+evTRR5WamqquXbsWat5nc+RITqHvG6mqVIyTIz5OgWz7j8URFycr36+DGWw1RHiF4gtGacg7Sh41NvTIe/QhFyiu4uQ9bIfCJiQkKCkpSStXrlR2drZ69eqlrl276uqrr1a9evX07rvvKiYmRobx35OEPR6PDhw4cHzif2gADcOQZVny+/1KSkrSqFGjJB1vsPz+k3/M1bKsU/4+cR+3233KPAcOHKi2bdvqiy++0KRJk7Rx48aTmqMT6z7B6XTK6/XK7/friiuuKNjj5/F4lJ2drQMHDihwlhOoT8zhbMvXrFlT8+bNK7jfwYMHz/oYC/M4z/QanGCa/92aGAgE5HQ6z/hcVqtWTRdddJHWrFmjVatWafTo0dq5c+dfzru0y8/Nk/vCWsr5cYvtsdwX1VZ+bl4QZgUApQM1FjgVuUBJCts1g/1+v9auXauGDRtqx44dMgxD9957r1q0aKHFixfL7/crMTFR1atX19dffy1Jmjdvnl5++eUzjnli2UOHDsmyLI0dO/aUK8+2bNlSH374oaTj52kuWbJEzZs3P+OYt9xyi7KzszVw4EANHDhQP/3000n/3qpVK33xxRfKzMyUx+PR4sWLJUlNmzbVhg0b9Ntvv0k6fkjtxIkTdfHFF+vYsWPasuV4wD/55JPTrvdsyx89elTr16+XJH300Ud65JFHTlo2ISFBNWvW1GeffSZJ2rBhgw4ePKhLLrnkjI/zTK/BCSfm+cMPPyg7O1sXXHDBWZ/LlJQUTZgwQS1atFBcXFyh5l3a+eRQ/OWNgzJWfNNG8vFrQQBQgBoLnIpcoCSF5RxLwzCUn5+vevXqadCgQYqNjVWDBg2UlJQkwzB07bXX6ptvvpEkTZo0SWPHjtWkSZNUqVIlTZw4saDZ+rP69etryJAhGjBggAKBgBo0aKC77777pPsMHjxYY8eOVZcuXeT3+3XvvfeqUaNGBY3enw0dOlQjRoyQ0+lUfHz8KedENmjQQAMGDFCvXr1Uvnx5nX/++ZKkatWqady4cXrooYcUCARUvXp1TZo0SS6XSxMnTtTw4cPlcDh00UUXnXYP4tmWf/nll/Xss8/K4/EoISFBEyZMOGX5E8/b1KlTFRMTo6lTp8rlcp3xtalfv/4ZXwNJysnJUbdu3eRwODR58mTFxMSc8bmUpBtvvFFjxowpaB4LO+/SzJNvqdJNbXVw5lzpT3vSi8Q0VenmtjrmO3XPNACUVdRY4FTkAiXJsE533CRCJhAI6Pnnn9eQIUMUHx+vN998U/v379eIESPCPbWIlp6eGe4pBEV5t0MZc+br8OwFxR6jco/Oqti9k47lcclvhF8ozrkqLXlHyaPGhhZ5j07kAsVRnLyzP7uEORwOVaxYUb169VJKSorWrVune+65J9zTQgnJ8lqq2jtFsRcW72dWYi+qraq9uyrLy/YgAPgzaixwKnKBksIeS0SF0rRF0+UyVc60tGvUeHmK8NtSsRfVVu2nhyvbb8jrtXE4CxBE7MFApKHGhg55j17kAkVVnLzTWCIqlLYPHpfLVEKsqYOzPtbhuQuls1wpWKapyik3H99a6PFT2BFR+KKJSESNDQ3yHt3IBYqCxhKlVmn84HE4DCW4DJmydOTTL5Tz/Y/K+22XArm5csTFyX1RbcU3baRKN7eVX4ayvJYCAeKKyMIXTUQqamzwkffoRy5QWDSWKLVK8wePaToU6zQUo4CccW4ZTlNWvl/5uXnyySFPviW/n5PlEZn4oolIR40NHvJeepAL/JXi5L1Ef24EwKn8/oByThxh4sn987+W9HQAoFShxgKnIhcIBa4KCwAAAACwhcYSAAAAAGALjSUAAAAAwBYaSwAAAACALTSWAAAAAABbaCwBAAAAALbQWAIAAAAAbKGxBAAAAADYQmMJAAAAALCFxhIAAAAAYAuNJQAAAADAFhpLAAAAAIAtNJYAAAAAAFtoLAEAAAAAttBYAgAAAABsobEEAAAAANhCYwkAAAAAsIXGEgAAAABgC40lAAAAAMAWGksAAAAAgC00lgAAAAAAW2gsAQAAAAC20FgCAAAAAGyhsQQAAAAA2EJjCQAAAACwhcYSAAAAAGALjSUAAAAAwBYaSwAAAACALTSWAAAAAABbaCwBAAAAALbQWAIAAAAAbHGGewJAWWSaDrmdhpwKyBnnliPGqYAvX/m5ecqXQ3n5lvz+QLinCQARiRoKFA2ZQUmgsQRKkMNhKMFlyJSlzM8/V8YPm+TdtUuB3Fw54uLkql1bcU0aq3z79vLHOJTltRQIWOGeNgBEBGooUDRkBiXJsCyLdw8iXnp6ZrinYJvLZSoh1lTGnLk6On+BFDjLlkHTVIVOSarYvZuyPH55vf6SmyhQBNWqJQZ9zNKQdwQfNTT8yHt0ITOwozh5p7FEVIj2Dx6Xy1Q509K+Z8bJu2tX4ZerXVvnjhqpbL9BkUdE4osmSgI1NDKQ9+hBZmBXcfLOxXuAEHM4DCXEmkUu7pLk3bVL+54Zp4RYUw6HEaIZAkDkooYCRUNmEC40lkCIJbgMZcyZW+TifoJ31y5lzJmrBBcFHkDZQw0FiobMIFxoLIEQMk2HTFnHz22w4ej8BTINS6ZJZAGUHdRQoGjIDMKJd0uESUtLU7169TR69OiTbt+8ebPq1aun2bNnn3HZKVOmaP369SGd34gRIzR79mzt379fgwYNkiR98cUXevPNN0O63mjldhrK/HzJ2U+YL4xAQJmfL5HbydZDAGUHNRQoGjKDcKKxjEAVK1bU8uXL5ff/96TpBQsWqHLlymddbt26dSctE0rVq1fXv/71L0nSpk2blJWVVSLrjTZOBZT7w6agjJX7wyY5xW9MASg7qKFA0ZAZhBO/YxmBypUrp/r162vdunVq2bKlJGnFihVq3bq1JOmrr77SlClTlJ+fr5o1a+rpp5/Wl19+qU2bNmnUqFGaNm2annnmGVWoUEHbtm3TSy+9pH379umll15SIBBQrVq19NRTT6lq1apq166dbr75Zq1cuVKSNG7cODVs2FC//fabRo8erYyMDMXHx+vxxx9XkyZNCuaYlpam/v37a/r06Zo5c6Yk6fzzz9e1116rkSNHKjMzUwcOHFD37t314IMPavbs2Vq+fLmOHj2q3bt365prrtHYsWNL9okNA2ecu9jnOPyZd+cuOePckic3KOMBQKSjhgJFQ2YQTjSWESopKUmLFi1Sy5YttXHjRtWrV0+WZenw4cN6++239c4776hChQqaOXOmnn/+eT377LP66KOPNGTIENWrV0+SVK9ePU2bNk2HDh3S3Xffrffee081a9bU66+/rqeeekpTpkyRJMXHx2vu3LlaunSphg8frk8++UTDhg3T3XffrY4dO2rDhg168MEHtWjRolPmWbduXfXt21eS1LNnT73xxhtKTk5W9+7dlZmZqTZt2qhfv36SpO+++06pqakyTVM333yzbr311oK5/pVKleLldJrBeGpLXCA3OAU5kJsrw2mG5HLvQCSJ5rwj+KihpRt5Dz4yg3ChsYxQ7dq1K9jDuHDhQiUlJWnBggVyu93au3ev+vfvL0kKBAKqUKHCacc4sYdx48aNatKkiWrWrClJ6tOnj6ZPn15wv969exesc8SIEdq3b5927dqljh07SpIuv/xyVahQQdu3b//Led95551avXq13njjDW3btk0+n0+5/1fgmjVrpoSEBElSrVq1dPTo0UI/H0eO5BT6vpGkSsU4OeLiFMixP39HXJysfL8OZrDlEJEjFF84ojXvCD5qaGQh75GPzCBYipN3GssIdeJw2G+++UarV6/W3//+dy1YsEB+v19XXHGFXnvtNUmSx+NRdnb2acdwu92Sjjeff2RZlvLz8wv+djr/+zYIBAKnPU/TsqxCnb85fvx47d69W8nJyerQoYNWrlwpy7IkSbGxsQX3Mwyj4PbSLD83T67atZX388+2x3JdUFv5uXlBmBUARAdqKFA0ZAbhxMV7IlhSUpImT56sxo0bFzR/Ho9HGzZs0G+//SZJeuWVVzRx4kRJkmmap23+mjZtqu+//15paWmSpPfff18tWrQo+Pf58+dLkhYvXqw6deqoRo0aqlmzpj777DNJ0oYNG3Tw4EFdcsklp52naZoFjeqKFSt05513KikpSb/99pv2799/SmNbluTLobgmjYMyVtxljZVPZAGUIdRQoGjIDMKJPZYRrG3btnr88cf14IMPFtxWtWpVjRs3Tg899JACgYCqV6+uSZMmSZKuu+46jRkzRhMmTDhpnKpVq+qpp57SkCFD5PP5dP755+vZZ58t+Pdvv/1WH374oeLi4jR+/HhJ0qRJkzR27FhNnTpVMTExmjp1qlwu12nnefXVV2v48OGqWrWq7rnnHj366KNyu90699xz1bhx44KGtizKy7dUvn17HflojmTnir2mqcQO7XXMW/r38gLACdRQoGjIDMLJsMrC8Yg4o3bt2umdd94pOP8yUqWnZ4Z7CsVW3u1QVmqqjn6SWuwxKnRJVkJyso7lld29v4hMoTjnKprzjuCjhkYO8h4dyAyCoTh5Z/82EGJZXksVu3eTq3btYi3vuqC2Knbvpiy2GgIog6ihQNGQGYQLjWUZt3Tp0ojfWxntAgFLWR6/zh01sshF3nVBbZ37+EhlefwKBCjwAMoeaihQNGQG4cKhsIgKpeFQGZfLVEKsqYw5c3V0/gLpbBc1Mk1V6JR0fIuhxy+v18Z5EkAIcWgcSgo1NPzIe3QhM7CjOHmnsURUKC0fPA6HoQSXIdOwlPn5EuX+sEnenbsUyM2VIy5OrgtqK+6yxkrs0F5+y1CW12KLISIaXzRRkqih4UXeow+ZQXHRWKLUKm0fPKbpkNtpyKmAnHFuGU5TVr5f+bl5ypdDefmW/H5OmEfk44smwoEaGh7kPXqRGRRVcfLOz40AYeD3B5R94igTT+6f/7WkpwMAUYUaChQNmUFJ4OI9AAAAAABbaCwBAAAAALbQWAIAAAAAbKGxBAAAAADYQmMJAAAAALCFxhIAAAAAYAuNJQAAAADAFhpLAAAAAIAtNJYAAAAAAFtoLAEAAAAAttBYAgAAAABsobEEAAAAANhCYwkAAAAAsIXGEgAAAABgC40lAAAAAMAWGksAAAAAgC00lgAAAAAAW2gsAQAAAAC20FgCAAAAAGyhsQQAAAAA2EJjCQAAAACwhcYSAAAAAGALjSUAAAAAwBYaSwAAAACALTSWAAAAAABbaCwBAAAAALbQWAIAAAAAbKGxBAAAAADYQmMJAAAAALCFxhIAAAAAYAuNJQAAAADAFme4JwCg8EzTIXeMIacCcsa55XA6FcjPV35unvLlUJ7Pkt8fCPc0AaBMoTYDoUXGogONJRAFHA5DCbGGnLKU9eXnytq8Sd7du2Tl5cpwx8lVq7ZiGzRWhTbtlS+HsjyWAgEr3NMGgFKN2gyEFhmLLoZlWTz7iHjp6ZnhnkLYuFymEt2mjqXOVebiBVLgLFvkTFOJHZJUPrmbMvP88nr9JTdRlEnVqiUGfcyynHdEj7JYm8k7SlJZzFgkKU7eaSwRFcrqB4/LZSrBaSl98jj50nYVermYmrVV7e8jlZVvUFwRUnzRRFlUVmszeUdJKasZiyTFyTsX7wEilMNhKNFtFrmoSpIvbZfSJ49TotuUw2GEaIYAUPZQm4HQImPRi8YSiFAJsYaOpc4tclE9wZe2S8fmz1VCLIUVAIKF2gyEFhmLXjSWQAQyTYecso6fU2BD5mcL5JQl0yTqAGAXtRkILTIW3Xi2QyArK0tPPvmkkpOTlZKSon79+unHH38s0Tns3r1bI0eOtD3OrFmzlJqaKkkaMWKEZs+ebXtM/DV3jKGsL5ec/UT1wggElPXlErlj2GoHAHZRm4HQImPRjcYyyAKBgAYNGqQKFSpo7ty5mjdvngYPHqxBgwbpyJEjJTaP33//Xbt377Y9zrfffiuv1xuEGaEonArIs3lTUMbybN4kp/htJwCwi9oMhBYZi278jmWQrVmzRnv37tUDDzwgh+N4396yZUs999xzCgQCWrNmjaZNm6YZM2ZIOr4XsHnz5mrevLnuuusuVapUSW63W126dNGcOXOUkZGhtm3bqn///ho9erT27dsnwzD097//Xa1bt9bUqVO1f/9+7dy5U3v27NEtt9yi++67T88884zS0tL05JNPasyYMQXzy8/P19ixY7Vt2zYdPHhQ9erV0wsvvKD8/HwNHTpUBw8elCQNHjxYcXFxWrp0qVavXq1q1apJkpYtW6b//Oc/OnTokO6991716dNHU6dO1e+//64dO3bo8OHDuu+++7Rq1Sp9//33ql+/vl588UX5/f7TrtftdpfwKxQdnHFueXcX79yCP/Pu3iVnnFvKyw3KeABQVlGbgdAiY9GNxjLIfvrpJ9WvX7+gqTyhTZs2kqRffvnljMv+9ttvev3111WzZk3Nnj1b+/fv14IFC+R0OvXwww+rZ8+eat++vQ4cOKDbbrtNc+fOlSRt2bJF7777rjIzM9WhQwfdfvvtGjVqlKZNm3ZSUylJ3333nWJiYvT+++8rEAhowIAB+vLLL5WTk6MaNWpo+vTp2rx5sz7++GMNHz5c7dq1U/PmzXXddddp/vz58nq9+uCDD7Rt2zb1799fffr0kSRt3bpV77//vr799lsNGDBAn3zyiS688EJ16tRJW7ZsUWZm5mnXe9NNNxXqea1UKV5Op1nYl6FUsIJUCK28XBmmGZLLxAOhUBbzjuhBbQ4u8o4/I2PRi8YyyBwOh2JjY4u1bJUqVVSzZs2Cvxs2bCin8/hLtHLlSm3fvl1TpkyRdHzP44lDXVu0aCGXy6UqVaqoYsWKysw8829CXX311apYsaLeffddbd++XTt27FBOTo6aNWumF154Qfv379cNN9ygwYMHn3b59u3byzAMXXLJJScd2nvNNdfI6XTq/PPPV7Vq1VS3bl1JUvXq1XX06FG1aNHitOstrCNHCn/f0qBKpTgZ7jhZufYft+GOk+X36+ARttgh+ELxgV3W8o7oUdZrM3lHqJX1jEUSfscyAjRu3Fg//fSTLMs66fYXXnhBq1evlmEYJ/2bz+cr+P8/Hxb6x78DgYDefvttzZs3T/PmzdOsWbN06aWXStJJjeyfx/+zJUuW6JFHHpHb7VaPHj109dVXy7IsXXjhhVq4cKG6dOmi9evXq1evXgqc5sRp0zQL1vNHMTExBf9/ohkuzHpxevm5eXLVqh2UsVy1ais/Ny8oYwFAWUZtBkKLjEU3Gssgu+qqq1SlShVNmzZNfr9fkrR8+XLNnj1bdevWVaVKlbR79255PB5lZGTom2++KdS4LVu21H/+8x9Jxw+n7dKli3Jzz7wFxjRN5efnn3L7qlWrlJSUpJ49e6p8+fJas2aN/H6//v3vf2vq1KlKSkrSmDFjdPjwYWVlZck0zYLHYceZ1ovTy5dDsQ0aB2Ws2AaNlU/UAcA2ajMQWmQsunEobJAZhqFXXnlFzz33nJKTk+V0OlWpUiVNnz5dVatWVdWqVdWmTRt17txZNWrU0JVXXlmocUeNGqXRo0erS5cukqSJEycqISHhjPevU6eOMjMzNWzYME2aNKng9ltuuUWPPPKI5s+fr5iYGF1xxRVKS0vToEGDNHToUHXp0kWmaWrYsGEqX768WrdurRdeeEGJifYOfznTenF6eT5LFdq017HUOZKdBtw0ldCmvY562TsMAHZRm4HQImPRzbA4HhFRID39zOeNllbl4xzKXZSqzEWpxR4j8eZkxXVM1rFcLreN0AjFOVdlMe+IHmW5NpN3lISynLFIwjmWQCmS5bFUPrmbYmoW71yDmJq1Vb5zN2V52HYEAMFCbQZCi4xFLxpLIEIFApYy8/yq9veRRS6uMTVrq9rfRyozz69AgMIKAMFCbQZCi4xFLw6FRVQoy4fKuFymEt2mjs2fq8zPFkinuVpvAdNU4o1JKt+5mzLz/PJ6uUASQotD41BWlcXaTN5RkspixiJJcfJOY4moUNY/eBwOQwmxhpyylPXlEnk2b5J3967jP/7rjpOrVm3FNmishDbtlS9DWR6LLXUoEXzRRFlW1mozeUdJK2sZiyQ0lii1+OA5zjQdcscYciogZ5xbhmnK8vuVn5unfDmU57Pk93OiOkoOXzSBslObyTvCpaxkLJIUJ+/83AgQRfz+gLJPHN2R9+ffMeWwDwAIB2ozEFpkLDpw8R4AAAAAgC00lgAAAAAAW2gsAQAAAAC20FgCAAAAAGyhsQQAAAAA2EJjCQAAAACwhcYSAAAAAGALjSUAAAAAwBYaSwAAAACALTSWAAAAAABbaCwBAAAAALbQWAIAAAAAbKGxBAAAAADYQmMJAAAAALCFxhIAAAAAYAuNJQAAAADAFhpLAAAAAIAtNJYAAAAAAFtoLAEAAAAAttBYAgAAAABsobEEAAAAANhCYwkAAAAAsIXGEgAAAABgC40lAAAAAMAWGksAAAAAgC00lgAAAAAAW2gsAQAAAAC20FgCAAAAAGyhsQQAAAAA2EJjCQAAAACwhcYSAAAAAGCLM9wTAMoK03TIHWPIaQTkdLvlcDoVyM9Xfl6e8i2H8nyW/P5AuKcJACWO+ggUD9lBJKGxBELM4TCUGGvINCzlrFyinK0/yvf7blmeXBmxcYo5v5Zi6zVWhVZtlW85lOWxFAhY4Z42AIQc9REoHrKDSGRYlsW7DBEvPT0z3FMoFpfLVKLbVObiecr+8lMpcJathg5T5drcpMQbU5SZ55fX6y+5iQLFVK1aYtDHjNa8o2ioj9GHvEcGsoOSUJy801giKkTjB4/LZSohxtKhV8Yrf+/uQi/nPK+Wqtw/Qlk+gw8ARDy+aKI4qI/RibyHH9lBSSlO3rl4DxACDoehRLdZ5MIvSfl7d+vQK+OV6DblcBghmiEAhAf1ESgesoNIR2MJhEBirKHMxfOKXPhPyN+7W5mff6yEWIo/gNKF+ggUD9lBpKOxBILMNB0yDev4eQ82ZC9bKKdhyTSJKYDSgfoIFA/ZQTTgXVWKpaWlqV27dqfcXq9ePS1ZskQvv/xyyOewf/9+DRo0KOTriSTuGEM5K5ee/WT6wggElLPqC7lj2LIIoHSgPgLFQ3YQDWgsy6j27dvrwQcfDPl6qlevrn/9618hX08kcRoBebb+GJSxPFt/lNPg96cAlA7UR6B4yA6iAb9jWUbNnj1ba9eu1fjx4zVhwgStWLFCDodDHTp00JAhQzR16lT9/vvv+vXXX3XkyBH16dNHd911l7KysjRy5Ejt379fBw4cUKtWrfTss89q7dq1+uc//ym3261ff/1V9erV0/PPP68DBw6of//+Wrp0qfbs2aPHHntMhw8fltvt1jPPPKP69euH+6kIOqfbLd/vxTv/4c98e3bJ6XZLublBGQ8Awon6CBQP2UE0oLEs5Q4cOKCUlJQz/vuePXv01Vdfaf78+crNzdVjjz0mj8cjSdq0aZNmzpypQCCgHj16qFWrVvrtt9/UoEEDTZkyRV6vV507d9aPPx7fgvbdd99p4cKFOuecc9S7d299/fXXuvTSSwvW9eSTT+qmm27S7bffri+//FKvvvpqoQ/HrVQpXk6naeOZKFmWJzjF2vLkyjDNkFziHYhU0ZZ3FA31EX9E3guP7CDS0ViWcuecc47mzZt30m316tUr+P/q1asrNjZWffv2Vdu2bfXII48oNjZWkpScnKxy5cpJktq1a6fVq1frzjvv1MaNG/XWW29p+/btysjIUE5OjiTpkksu0bnnnitJqlOnjo4ePXrSetetW6cXXnhBktSmTRu1adOm0I/jyJGcIj7y8KlSKU5GbJysPPtzNmLjZPn9OniErYqITKH4YhJNeUfRUB+jG3kPH7KDksbvWKLInE6nPvjgAz344IPKyMhQ37599dtvv0mSTPO/WxADgYBM09SMGTM0ceJEVa5cWf/zP/+jOnXqyLIsSSpoSCXJMIyC2/+4rhMsy9Ivv/wSyocWNvl5eYo5v1ZQxoqpUVv5eXlBGQsAwo36CBQP2UE0oLEs43766Sf9z//8j66++moNHz5cderUKWgsP//8c3m9Xh09elRffPGFrr32Wq1YsUJ9+vRR165d5fF49PPPPytQyCuUXXXVVZo/f74kaeXKlXriiSdC9rjCKd9yKLZe46CMFXtpI+VbxBRA6UB9BIqH7CAa8K4q4xo2bKjLL79cycnJ6t69u+rWravrr79e0vE9kLfddpv69Omje+65R3Xr1tWAAQM0bdo0denSRePGjVOzZs2UlpZWqHWNHj1an332mVJSUjR16lQ9/fTToXxoYZPnsxTfqq3ksHnOiMNUfKu2yvNZf31fAIgC1EegeMgOooFh/fl4RUDS1KlTJUn/+7//G+aZHJeenhnuKRRJhTiHcpfNV/YXC4o9Rrl2neVu00nHcrkkOCJXKM65ira8o2ioj9GLvIcX2UFJ4hxLIEJkeiwl3pgi53nFOx/CeX5tJXboqiwP230AlC7UR6B4yA4iHXssERWicYumy2UqIcbSoVfGK39v4X97ynl+bVW5b7iyfIa8Xn8IZwjYxx4MFAf1MTqR9/AjOygpxck7jSWiQrR+8LhcphLdpjI//1jZyxZKZ7vQkcNUuRtuVmKHrsrM81P4ERX4oonioj5GH/IeGcgOSgKNJUqtaP7gcTgMJcQachqWclZ9Ic/WH+Xbs+v4DxTHximmRm3FXtpI8a3aKt8ylOWxFAgQS0QHvmjCDupjdCHvkYPsINRoLFFqlYYPHtN0yB1jyGkE5HS7ZZimLL9f+Xl5yrccyvNZ8vs5mR7RhS+aCAbqY3Qg75GH7CBUipN351/fBUAw+P0BZZ84AiU398//WtLTAYCIQX0EiofsIJJwVVgAAAAAgC00lgAAAAAAW2gsAQAAAAC20FgCAAAAAGyhsQQAAAAA2EJjCQAAAACwhcYSAAAAAGALjSUAAAAAwBYaSwAAAACALTSWAAAAAABbaCwBAAAAALbQWAIAAAAAbKGxBAAAAADYQmMJAAAAALCFxhIAAAAAYAuNJQAAAADAFhpLAAAAAIAtNJYAAAAAAFtoLAEAAAAAttBYAgAAAABsobEEAAAAANhCYwkAAAAAsIXGEgAAAABgC40lAAAAAMAWGksAAAAAgC00lgAAAAAAW2gsAQAAAAC20FgCAAAAAGyhsQQAAAAA2EJjCQAAAACwhcYSAAAAAGCLM9wTAMoK03TIHWMoxhGQGeuWw+lUID9ffk+efAGH8nyW/P5AuKcJACWO+ggUH/lBpKCxBELM4TCU6DZkGpZy132hY7/+pPz9u2V58mTEuuWsXkuxdRqpwtVtlG85lJVnKRCwwj1tAAg56iNQfOQHkcawLIt3GCJeenpmuKdQLC6XqcQ4U1nLPlHOqs+kwFm2GDpMxbe6UQk3dFFmrl9er7/kJgoUU7VqiUEfM1rzjqKhPkYf8h45yA9CrTh5p7FEVIjGDx6Xy1SCy9KRNycpf39aoZdzVq+pSncMU5bXoPgj4vFFE8VBfYxO5D0ykB+UhOLknYv3ACHgcBhKjDOLXPQlKX9/mo68OUmJcaYcDiNEMwSA8KA+AsVHfhDJaCyBEEh0G8pa9kmRi/4J+fvTlPVlqhLcFH4ApQv1ESg+8oNIRmMJBJlpOmQa1vFzHmzIWblITuP4eABQGlAfgeIjP4h0vKPwl9LS0lSvXj2NHj36pNs3b96sevXqafbs2Wdctl27dkpLK95WtWjljjGUu27Z2U+kL4xAQLnrlskdw1ZFAKUD9REoPvKDSEdjiUKpWLGili9fLr//vyd7L1iwQJUrVw7jrCJTjCMgz68/BWUs7/afFGPw21MASgfqI1B85AeRjsYShVKuXDk1aNBA69atK7htxYoVat26tSTp3//+t2655RYlJyere/fu2r59+0nL+/1+Pffcc+revbu6du2qt956qySnX6LMWLfy9+8Oyli+fbtkut1BGQsAwo36CBQf+UGkc4Z7AogeSUlJWrRokVq2bKmNGzeqXr16sixLWVlZWrp0qWbMmCG3262XX35Z7777rp544omCZWfNmiVJmjNnjrxer+688041btxYV111VaHWXalSvJxOMySPKxQsT17QxjFMMySXeAciVbTlHUVDfcQfkfeiIT+IZDSWKLR27drppZdeUiAQ0MKFC5WUlKQFCxYoISFBkydP1vz587Vjxw4tX75cDRo0OGnZVatWafPmzVq9erUkKScnR1u2bCl0Y3nkSE7QH0+oVKkUJyPWLSsv1/ZYRqxblt+vg0fsjwWEQii+lERT3lE01MfoRt7Di/ygJPE7lgipcuXKqX79+vrmm2+0evXqgsNg9+7dqz59+igzM1PXX3+9unfvLsuyTlrW7/dr2LBhmjdvnubNm6f3339fvXr1CsfDCDm/J0/O6rWCMlbMubXlzwvO1kkACDfqI1B85AeRjsYSRZKUlKTJkyercePGcjqP7/COj4/XBRdcoIEDB+qyyy7T559/ftJFfiSpZcuWmjVrlnw+n7Kzs3Xbbbdpw4YNYXgEoecLOBRbp1FQxnJd3FA+i5gCKB2oj0DxkR9EOt5RKJK2bdtq8+bN6tSpU8FtMTExCgQC6tSpk7p3766LLrrolJ8Y6du3ry688EJ1795dPXv2VI8ePdSiRYuSnn6JyPNZiru6jeSwec6Iw1Tc1Tcoz2f99X0BIApQH4HiIz+IdIb152MWgQiUnp4Z7ikUSYV4h/JWLFTOik+LPUb8tUmKbX2zjuVwOXBErlCccxVteUfRUB+jF3kPP/KDksI5lkCEyMyzlHBDFzmr1yzW8s5zaymhTbKy8tjuA6B0oT4CxUd+EMloLIEQCAQsZeb6VemOYUUu/s5za6nSwEeUmetXIEDhB1C6UB+B4iM/iGQcCouoEK2HyrhcphLjTGV9maqclYukwFkOO3GYim/dUQltkpWZ65fX6z/zfYEIwaFxKC7qY/Qh75GD/CDUipN3GktEhWj+4HE4DCW4DTkNKXfdMnm3/yTfvl3Hf5w41q2Yc2vLdXFDxV19g/ItKSvPYksiogZfNGEH9TG6kPfIQn4QSjSWKLVKwwePaTrkjjEUYwRkut0yTFOW3y9/Xp58lkN5Pkt+PyfSI7rwRRPBQH2MDuQ9MpEfhEJx8u4MwTwAnIbfH1D2iaNPcnP//K8lPR0AiBjUR6D4yA8iBRfvAQAAAADYQmMJAAAAALCFxhIAAAAAYAuNJQAAAADAFhpLAAAAAIAtNJYAAAAAAFtoLAEAAAAAttBYAgAAAABsobEEAAAAANhCYwkAAAAAsIXGEgAAAABgC40lAAAAAMAWGksAAAAAgC00lgAAAAAAW2gsAQAAAAC20FgCAAAAAGyhsQQAAAAA2EJjCQAAAACwhcYSAAAAAGALjSUAAAAAwBYaSwAAAACALTSWAAAAAABbaCwBAAAAALbQWAIAAAAAbKGxBAAAAADYQmMJAAAAALCFxhIAAAAAYAuNJQAAAADAFhpLAAAAAIAtNJYAAAAAAFtoLAEAAAAAttBYAgAAAABscYZ7AkBZYZoOuV2GYgxLpjtWDtOpgD9f/jyPfJahPK8lvz8Q7mkCgC3UOsAeMoRoRWMJhJjDYSjR7ZDpsJS34Stl7dys/PQ9sjx5MmLdclarIdeFDVSh6XXKD5jKygsoELDCPW0AKBJqHWAPGUK0MyzL4h2JiJeenhnuKRSLy2UqMc5U9qqFyl2/RLLOsoXR4VDcle1VrlWSMnP98nr9JTdRoJiqVUsM+pjRmveyjFpXNpD30CFDiDTFyTuNJaJCNH7wuFymElzS0VkvKT99T6GXc1aroQq9H1KWV3xYIOLxRRPUurKDvIcGGUIkKk7euXgPEAIOh6HEOLPIHxKSlJ++R0dnvaTEOFMOhxGiGQKAfdQ6wB4yhNKExhIIgUS3Q9mrFhb5Q+KE/PQ9ylm9UAluIgogclHrAHvIEEoT3oURLisrS8nJyUpLSyu47euvv1bXrl2VnJysRx99VF6v96xjTJgwQSNGjDjl9n//+9/q169f0Odc1pnm8RPvc9cvsTVOzrolcjosmSYxBRB5qHWAPWQIpQ3vwAj2/fff69Zbb9WOHTtOuv3xxx/Xiy++qNTUVOXl5WnevHlnHGPVqlWaM2fOKbf/8ssvmj59erCnDElul6G8DcvPfuJ9YVgB5X2/XG4Xh7cAiDzUOsAeMoTShsYygs2aNUtjxozROeecc9Ltfr9fWVlZ8vv98ng8io2NPe3yGRkZevHFF3XvvfeedLvX69Xo0aP1wAMPnHa5Dz74QH//+98L/p46daqmT5+u7OxsDR8+XD169FBKSopSU1MlHd+r+sADD6hPnz5q27atRo4cKcuytGbNGvXq1Us9evTQ8OHDtWrVKvXo0UM9evTQHXfcocOHD9t5eiJWjGHJu3NzUMby7vxZMQbX1wIQeah1gD1kCKUNv2MZwZ599tnT3j527Fj169dPCQkJqlmzpm6++ebT3m/06NF6+OGHtXfv3pNunzx5snr27KmaNWuedrlOnTrpxRdfVFZWlhISEpSamqp33nlHr776qho1aqQJEyYoKytLffv2VdOmTfX999+rQYMGmjJlirxerzp37qwff/xRkrRjxw598cUXSkxMVL9+/TR27Fg1adJE//rXv/TTTz/p2muvtfEMRSbTHVvscyX+LP9Amkx3rJSTG5TxACBYqHWAPWQIpQ2NZZRJT0/X888/r9TUVNWsWVPPPfecnnvuOY0ZM+ak+33wwQc677zz1KpVK82ePbvg9hUrVmjv3r167LHHtGbNmtOuo1y5cmrTpo0WL16sWrVqqVatWqpevbpWrlypvLw8ffTRR5KknJwcbdu2TcnJydq4caPeeustbd++XRkZGcrJyZEkXXTRRUpMPH654vbt22vIkCHq0KGD2rdvr2uuuabQj7tSpXg5nWaRnqtwsjx5wRnHmyfDYYbkEu9ApIq2vJdl1DrYVdbzToZQmtBYRpn169fr0ksvVe3atSVJvXv31kMPPaQlS5ZoypQpkqR27dppw4YNSk9PV0pKio4ePaqcnByNGzdOmZmZ2rZtm1JSUpSTk6ODBw/qoYce0ksvvXTSenr27KlXX31VNWvWVI8ePSRJgUBAkyZNUqNGjSRJBw8eVIUKFTRjxgwtWrRIvXv3VuvWrbV161ad+HlUt9tdMObAgQPVtm1bffHFF5o0aZI2btyo++67r1CP+8iRHFvPW0mqUjlORqxblsf+VkPD5ZYV8OvgYbZAIjKF4ktMNOW9LKPWlT3kPbjIECJZcfJOYxllLr30Uk2YMEEHDx5U1apVtWTJEl122WVq37692rdvf9plZs+erbVr12rkyJEn3b5mzRpNmzbtlKZSkq666irt27dPe/bs0eOPPy5Jatmypd577z0988wzOnDggLp166aZM2dqxYoV6tOnj7p06aIffvhBP//8swKBgByOk0/hveWWW/Tkk09q4MCBqlixopYssXcVtEjlz/PIWa2GfGm/2B7LeU5N+fM8QZgVAAQXtQ6whwyhtKGxjDJ16tTRgw8+qP79+8s0TV1wwQV66qmnQrKuG2+8URkZGXK5XJKkIUOGaOzYsUpOTpbf79ewYcNUu3ZtDRgwQGPHjtX06dOVkJCgZs2aKS0trWCv6glDhw7ViBEj5HQ6FR8fr2eeeSYk8w43n2XIdWGDoHxQuC6oL5/FVd4ARB5qHWAPGUJpY1gnjlkE/o9lWfL5fLrjjjs0cuTIgkNfwyk9PTPcUyg003SoQpx06NURUsDGJcQdDlW5b7yO5kp+v81LkQMhEopD46Ip72UZta7sIe/BRYYQyYqTd35uBKdIT0/XNddco6ZNm0ZEUxlt/P6A/AFDcVee/tDkwoq/qr3yAwYfEgAiErUOsIcMobThUFic4pxzztG6devCPY2olpkXUKVWSfLt+KlYlxJ3Vqup+JZJOpLpC8HsACA4qHWAPWQIpQl7LIEQCAQsZeb6VaH3Q3JWq1GkZZ3VaqpC7weVmetXIMCR6gAiF7UOsIcMoTThHEtEhWg9B8PlMpUYZypn9ULlrFsiWWc5TMXhUPxV7RXfMkmZuX55vf6SmyhQTJxzBYlaV1aQ99AhQ4g0xck7jSWiQjR/8DgchhLcDjkdlvK+Xy7vzp+VfyDt+I8Zu9xynlNTrgvqy930OuUHDGXlBdjyiKjBF02cQK0r/ch7aJEhRBIaS5RapeGDxzQdcrsMxRiWTHesDIcpK+CXP88jn2Uoz2tx4j2iDl808WfUutKLvJcMMoRIUJy8c/EeoIT4/QFl5/7fHzm5Z70vAEQrah1gDxlCtOLiPQAAAAAAW2gsAQAAAAC20FgCAAAAAGyhsQQAAAAA2EJjCQAAAACwhcYSAAAAAGALjSUAAAAAwBYaSwAAAACALTSWAAAAAABbaCwBAAAAALbQWAIAAAAAbKGxBAAAAADYQmMJAAAAALCFxhIAAAAAYAuNJQAAAADAFhpLAAAAAIAtNJYAAAAAAFtoLAEAAAAAttBYAgAAAABsobEEAAAAANhCYwkAAAAAsIXGEgAAAABgC40lAAAAAMAWGksAAAAAgC00lgAAAAAAW2gsAQAAAAC20FgCAAAAAGyhsQQAAAAA2EJjCQAAAACwhcYSAAAAAGALjSUAAAAAwBZnuCcAoHBM0yG3y1CMw5IzNlaG6ZTlz1e+xyNfwFCe15LfHwj3NAEgrKiVgH3kCMVBYwlEOIfDUGKcQ6bDUt6PK5S9Z6v8h36X5fXIcMXKrHK+YmrUU4VGreQPmMrMDSgQsMI9bQAoUdRKwD5yBDsMy7J4NyDipadnhnsKYeFymUqMdyrnm8+Ut/FLyTrL1kGHQ+7L2ij+yo7KzMmX1+svuYmizKpWLTHoY5bVvKP4qJUlg7yXbuQIf1ScvNNYIiqUxQ8el8tUQqx07JNX5D+8t9DLmZXPU/ku9yvLIwo9Qo4vmgg3amXJIe+lFznCnxUn71y8B4hADoehxHhnkQu8JPkP79WxT15RYrxTDocRohkCQPhRKwH7yBGChcYSiECJcQ7lfPNZkQv8Cf7De5Xz7WIlxhFxAKUXtRKwjxwhWHgH/J81a9aoWbNmSklJUdeuXZWUlKS333674N/btWuntLS0Io/br18/rVmzJphTLbR69eqdctuJx/HDDz/o8ccfL5F5pKSklMh6SgvT/L+T5jd+aWucvO+XyXQcHw8AShtqJWAfOUIwcVXYP2jcuLFmzJghScrKylLnzp11zTXXqG7dumGeWfBddtlluuyyy0pkXfPmzSuR9ZQWbpehvB9XnP2k+cKwAsr7caXc9VorOzc4cwOASEGtBOwjRwgmGssz8Hg8Mk1TiYknn7ialZWlkSNHav/+/Tpw4IBatWqlZ599VpL0/PPP6/PPP5dpmurTp48GDBhQsNyhQ4c0YMAAPfTQQ+rQoUPB7WvWrNE///lPud1u/frrr6pXr56ef/55uVwuffTRR3rzzTdlGIYaNWqkJ554QuXKldO1116rm266Sd98841M09RLL72kWrVqFenxrVmzRtOmTdOMGTP05ptvas6cOXI4HGrSpImeeuopzZ49W8uWLdOhQ4eUnp6utm3basSIEfL7/Ro7dqy2bdumgwcPql69enrhhRd08OBBDRkyRJdccok2b96sKlWq6OWXX1bFihVVr149bdmyRRkZGXr88ce1fft2uVwujRgxQq1atbLxKpVOMQ5L2Xu2BmWs/D1bFd+A5xhA6UOtBOwjRwgm9lf/waZNm5SSkqIuXbqoXbt2at68uc4555yT7rNs2TI1aNBA77//vhYtWqR169bpxx9/1Keffqpvv/1Wn3zyiT744APNnj1b6enpkqTMzEzdfffdGjJkyElN5QnfffedRo8erYULF+r333/X119/rS1btui1117TjBkz9MknnyguLk7Tpk2TJKWnp6tVq1aaO3eurr76ar377rtnfEwpKSkn/XfgwIGT/t3v9+uf//ynPvroI82ePVs+n0/79++XJH3zzTd6+eWXlZqaqu+//16LFy/Wd999p5iYGL3//vtavHixMjMz9eWXxw+f+Pnnn3XHHXcoNTVV5cuX1yeffHLSul5++WXVrl1bCxcu1MSJE/XSSy8V7QUqI5yxsfIf+j0oY+Uf2iNnbGxQxgKASEKtBOwjRwgm9lj+wZ8Phb3rrrs0ffp03XPPPQX3SU5O1saNG/XWW29p+/btysjIUE5OjtatW6ekpCS5XC65XK6TDv8cM2aMqlatqo4dO552vZdcconOPfdcSVKdOnV09OhR/f7772rbtq0qVaokSerTp48ee+yxgmWuu+66gmXXr19/xsf058NQ27Vrd9LfpmmqWbNm6tWrl9q3b6877rhD1atXlyS1b99eVatWlSR16tRJq1ev1ujRo1WxYkW9++672r59u3bs2KGcnBxJUpUqVdSwYcOCeR09evSkda1bt07PP/+8pOPnf77//vtnnPefVaoUL6fTLPT9o53l9QRnHJ9HcpghuUQ8ECplLe8oPmpl9CPv4UeOECw0lmeQkJCgpKQkrVy58qTbZ8yYoUWLFql3795q3bq1tm7dKsuy5HQ6ZRj/vcxyWlqaKleuLEkaNGiQvvzyS7333nu6/fbbT1lX7B+27hiGIcuyFAicfKy7ZVnKz88/ZZkT9//hhx80atQoSccb5BOH5xbGK6+8og0bNuirr77SXXfdVdD8meZ/C30gEJBpmlqyZImmTJmi/v37q0ePHjpy5IhO/BTq6R7HH/35Ofr111910UUXyeH46x3nR47kFPrxRLuqleNkuGJlefNsj2XExEoBv9IPc8IDQiMUXyDKUt5RfNTKkkfeSx9yhDPhdyyDyO/3a+3atQV74E5YsWKF+vTpo65du8rj8ejnn39WIBDQ1Vdfrc8++0w+n0+5ubm66667Cg4pbdCggcaMGaNp06YV3PZXmjdvrqVLlyojI0OSNGvWLLVo0eKM97/ssss0b948zZs3r0hN5eHDh9WpUyddeumlevDBB3XNNddoy5YtkqTly5crMzNTHo9H8+fP1/XXX69Vq1YpKSlJPXv2VPny5bVmzRr5/YX7QdyrrrpK8+fPl3S8qRw0aNBJjSaOy/d4ZFY5PyhjOavUUL4nOFsiASCSUCsB+8gRgok9ln9w4hxLwzCUn5+vevXqadCgQSfdZ8CAARo7dqymT5+uhIQENWvWTGlpabrlllu0adMm9ejRQ4FAQP3799dFF11UsNyFF16o22+/XU899ZT+8Y9//OVc6tevr3vuuUf9+vWTz+dTo0aN9OSTTwb9MVeuXFl9+vRRr169FBcXp4suukg9e/bUp59+qsqVK2vQoEE6cuSIunbtquuuu07nnHOOHnnkEc2fP18xMTG64oorCv0zLA888IBGjRqlrl27yul0auLEiTSWp+ELGIqpUU/5e7fbHstZ41L5AjzHAEofaiVgHzlCMBnWn49XBCTNnj1ba9eu1fjx48M9FUlSenpmuKdQYkzToQrx0pEZY6WAjct/Oxyq1O9JHc2x5PfbvIw4cAahODSuLOUdxUetLHnkvfQhRzgTDoUFSgG/PyB/wJD7sja2xnE3uUH+gCjwAEolaiVgHzlCMNFY4rR69OgRMXsry6LM3IDir+wos/J5xVrerHK+4q+4UZm5FHgApRe1ErCPHCFYaCyBCBQIWMrMyVf5LvcXudCbVc5X+eT7lJmTr0CAI90BlF7USsA+coRg4RxLRIWyeg6Gy2UqMd6pnG8XK+/7ZZJ1lq2BDofcTW44vtUwJ19eb+Gu1gvYwTlXiATUypJB3ks3coQ/Kk7eaSwRFcryB4/DYSgxziHTIeX9uFL5e7Yq/9AeWT6PjJhYOavUkLPGpXI3ai1/4PghLWw1REnhiyYiBbUy9Mh76UeOcAKNJUotPniOX7nN7TIU47DkjI2VHKYU8Cvf45EvYCjPy5XYUPL4oolIQ60MHfJedpAjFCfv/I4lECX8/oCyc//vj4L/AQD8EbUSsI8coTi4eA8AAAAAwBYaSwAAAACALTSWAAAAAABbaCwBAAAAALbQWAIAAAAAbKGxBAAAAADYQmMJAAAAALCFxhIAAAAAYAuNJQAAAADAFhpLAAAAAIAtNJYAAAAAAFtoLAEAAAAAttBYAgAAAABsobEEAAAAANhCYwkAAAAAsIXGEgAAAABgC40lAAAAAMAWGksAAAAAgC00lgAAAAAAW2gsAQAAAAC20FgCAAAAAGyhsQQAAAAA2EJjCQAAAACwhcYSAAAAAGALjSUAAAAAwBYaSwAAAACALTSWAAAAAABbaCwBAAAAALbQWAIAAAAAbKGxBAAAAADYQmMJAAAAALDFGe4JAPhrpumQ22UoxrTkdMXKMJ2y/PnK93rk8xvK81ry+wPhniYA2EKtA05FLhAtaCyBCOZwGEqMN2Ualjzb1ihn3y/yH9kry+eRERMrs9J5ijm3ripc0kL+gKnM3IACASvc0waAIqHWAaciF4g2hmVZvAMR8dLTM8M9hRLncplKjHcq94elyvv5a8k6y9ZIwyF3/WsVd1k7Zebky+v1l9xEUaZVq5YY9DHLYt7LMmpd9CDvJYdcINyKk3caS0SFsvbB43KZSnAbyvz8X/Jn7Cv0cmbFc5XYYZCy8iw+WFAi+KIJO6h10YW8lwxygUhQnLxz8R4gwhw/9MVZ5A8USfJn7FPm5/9SYrxTDocRohkCgH3UOuBU5ALRjMYSiDCJ8aZyf1ha5A+UE/wZ+5S7aakS44g3gMhFrQNORS4QzXjXARHENB0yDev4+RQ25G3+WqZ5fDwAiDTUOuBU5ALRjnfc/8nOztaTTz6pG2+8UV27dtVtt92mVatWFfz7rFmzlJqaKkkaMWKEZs+eHa6pFsrs2bM1YsSIk25bs2aN+vXrJ0l6+eWXtWTJkpDPY8mSJXr55ZdDvp7Swu0y5Nm25uwn6ReGFZBn6xq5XRwKAyDyUOuAU5ELRDsaS0mWZenee+9VTEyM5s+fr48//lijRo3SsGHDtGbNGknSt99+K6/XG+aZBs+DDz6o9u3bh3w97du314MPPhjy9ZQWMaYl375fgjKWb98vijGDMhQABBW1DjgVuUC043csJa1du1a///673nnnHRnG8a07DRs21H333adXXnlFfr9fS5cu1erVq1WtWjVJ0rJly/Sf//xHhw4d0r333qs+ffooOztbTz31lLZt2ya/369BgwYpOTlZs2fP1pw5c5SRkaG2bdtq6NChBeseMWKEEhIS9OOPP2r//v0aPHiwevbsqdzcXI0aNUpbtmyRYRi688471a1bN82ePVvLly/X0aNHtXv3bl1zzTUaO3ZskR/ziBEj1Lx5c3Xs2FFDhw7VwYMHJUmDBw9W+/bt1a9fP9WvX1/r16+Xx+PRyJEjde2112rr1q16+umnlZOTo8OHD+vuu+/WrbfeqqlTp2r//v3auXOn9uzZo1tuuUX33XefZs+erbVr12r8+PFauXKlxo8fL8uydP7552vy5MlKSEiw/wKWIk5XrPxH9gZlLP+RvXK6XJJygzIeAAQLtQ44FblAtKOxlPTDDz+ocePGBU3lCVdffbUmT56s1q1bq127dmrevLmuu+46zZ8/X16vVx988IG2bdum/v37q0+fPnr11VfVqFEjTZgwQVlZWerbt6+aNm0qSdq/f78WLFggp/PUp3zfvn36z3/+o61bt6p///7q2bOnpk6dqkqVKik1NVWHDx/WLbfcovr160uSvvvuO6Wmpso0Td1888269dZbVa9evVPGXbp0qVJSUgr+zsnJ0bnnnnvSfRYvXqwaNWpo+vTp2rx5sz7++OOCPZlZWVmaM2eONm/erEGDBmnp0qX64IMPdP/996tVq1bavXu3unbtqltvvVWStGXLFr377rvKzMxUhw4ddPvttxesx+v16pFHHtEbb7yhBg0aaPLkyZozZ07Bobl/pVKleDmdZWPTm+XzBGecfI/kMENyeXgglMpS3ssyah0k8v5n5ALRjMZSkmEY8vtP/b0fn893SrN5Qvv27WUYhi655BIdOXJEkrRy5Url5eXpo48+knS8kdu2bZuk43tAT9dUStI111wjwzB06aWXKiMjQ5K0evVqjRs3TpJUuXJltW/fXmvXrlVCQoKaNWtWsKevVq1aOnr06GnHbdeuncaPH1/w95o1azRt2rST7tOsWTO98MIL2r9/v2644QYNHjy44N969+4tSWrQoIGqVaumLVu2aMSIEVq+fLn++c9/auvWrcrJySm4f4sWLeRyuVSlShVVrFhRmZn//W2qLVu2qHr16mrQoIEk6e9///tp53wmR47k/PWdSoGqleNkxMTK8uXZHstwxkoBv9IPs7USoROKLy1lJe9lGbUuOpH30CIXiCT8jmUxNW3aVJs2bZLP5zvp9g0bNqhx48anXcY0j29d+2PjGQgENGnSJM2bN0/z5s3TrFmzdN1110mS3G73GdcfGxt7yliWZZ10H8uyCprfE/c/sYxlWXr55ZeVkpKilJSUIl2U58ILL9TChQvVpUsXrV+/Xr169VIgEDjpMZ54bE6nUw899JAWL16sOnXq6KGHHjrt4/jjvE6IiYk56fFlZmZq377iXUq7NMv3emRWOi8oY5mVzlN+KTovGEDpQa0DTkUuEO1oLCVdddVVqlu3rsaNG1fQXG7atEmvvvqq7r//fknHm6zT7dX8o5YtW+q9996TJB04cEBdu3bV3r3FO1a+ZcuW+vDDDyVJhw8f1pIlS9S8efMz3v/BBx8saGiLclGef//735o6daqSkpI0ZswYHT58WFlZWZKkBQsWSDp+qPCxY8d06aWXasWKFXrggQfUoUMHffXVV5L0l8+LJF100UU6dOiQfvnl+Enpr7/+esFzhf/y+Q3FnFs3KGPFnFtXvr9+aQCgxFHrgFORC0Q7DoX9P9OmTdOLL76o5ORkmaapChUqaNKkSWrRooUkqXXr1nrhhReUmHjm3cJDhgzR2LFjlZycLL/fr2HDhql27dpav359keczePBgjR07Vl26dJHf79e9996rRo0aacuWLcV+jKfTrVs3DR06VF26dJFpmho2bJjKly8vSdq9e7e6d+8uSXrxxRdlmqb+93//V7fddptiY2NVv3591ahRQ2lpaX+5ntjYWE2aNEmPPvqofD6fateurYkTJwb1sZQGeV5LFS5podwflti73LjhUOylLXQ02/rr+wJACaPWAaciF4h2hvXnYy4BSf369dOQIUMKGutwS0/P/Os7lRIVEpzybf5SeZu/KvYY7obXK6be9TqazeZKhFYozrkqS3kvy6h10Ye8hx65QKTgHEugFMjM8SvusnYyK57713c+DbPSeYpr3E6ZuTZ/YBkAQohaB5yKXCCasccSUaGsbdF0uUwluA1lfv4v+TMKf5Ejs9J5Smx/l7LyLHm9bKlE6LEHA3ZQ66ILeS8Z5AKRoDh5p7FEVCiLHzwul6nEeKdyNy1V3uavz36+heGQu8G1x7dS5uTzgYISwxdN2EWtix7kveSQC4QbjSVKrbL6weNwGEqMc8g0Jc/WNfLt+0X+I3tl5XtkOGNlVjpPMefWVeylLeT3S5m5AQUCRBolhy+aCAZqXXQg7yWLXCCcaCxRapX1Dx7TdMjtMhRjSk6XS3KYUsCvfK9XPv/xK8n5/ZxPgZLHF00EE7UuspH38CAXCIfi5J2fGwGigN8fUHbuib9yz3ZXAIha1DrgVOQC0YKrwgIAAAAAbKGxBAAAAADYQmMJAAAAALCFxhIAAAAAYAuNJQAAAADAFhpLAAAAAIAtNJYAAAAAAFtoLAEAAAAAttBYAgAAAABsobEEAAAAANhCYwkAAAAAsIXGEgAAAABgC40lAAAAAMAWGksAAAAAgC2GZVlWuCcBAAAAAIhe7LEEAAAAANhCYwkAAAAAsIXGEgAAAABgC40lAAAAAMAWGksAAAAAgC00lgAAAAAAW2gsAQAAAAC20FgCAAAAAGyhsQQAAAAA2EJjCQAAAACwhcYSEe2TTz5Rp06d1LFjR7377rvhno6ysrKUnJystLQ0SdLKlSvVpUsXdezYUS+++GLY5jVt2jR17txZnTt31sSJEyNqbi+//LI6deqkzp07680334youUnShAkTNGLEiIiZV79+/dS5c2elpKQoJSVF33//fUTMS5KWLl2qHj16KCkpSc8884yk4D5nkZZ3KTIzT96LL9LyLkVu5kOddynyMk/ei4a8F12pz7sFRKh9+/ZZbdu2tY4cOWJlZ2dbXbp0sbZt2xa2+WzYsMFKTk62GjVqZO3evdvKzc212rRpY+3atcvy+XzW3/72N2vZsmUlPq8VK1ZYffr0sTwej+X1eq3+/ftbn3zySUTMbc2aNVbfvn0tn89n5ebmWm3btrU2b94cEXOzLMtauXKl1aJFC2v48OER8XoGAgHr2muvtXw+X8FtkTAvy7KsXbt2Wddee621d+9ey+v1Wrfeequ1bNmyoM0t0vJuWZGZefJefJGWd8uK3MyHOu+WFXmZJ+9FQ96LrizknT2WiFgrV65Uy5YtVbFiRcXHx+umm27Sp59+Grb5zJo1S2PGjNE555wjSdq4caMuuOAC1apVS06nU126dAnL/KpVq6YRI0bI5XIpJiZGderU0Y4dOyJibs2bN9c777wjp9OpQ4cOye/369ixYxExt4yMDL344ou69957JUXG67l9+3ZJ0t/+9jd17dpV//73vyNiXpK0ePFiderUSeeee65iYmL04osvKi4uLmhzi7S8S5GZefJePJGYdylyMx/qvEuRl3nyXjTkvejKQt5pLBGxDhw4oGrVqhX8fc4552j//v1hm8+zzz6rq666quDvSJnfJZdcossvv1yStGPHDi1cuFCGYUTE3CQpJiZGU6ZMUefOndWqVauIed5Gjx6thx9+WOXLl5cUGa/nsWPH1KpVK/3jH//QW2+9pZkzZ+r3338P+7wkaefOnfL7/br33nuVkpKi//znP0F9ziLh+f+zSMw8eS+eSMy7FLmZD3Xepch5DU4g70VH3oumLOSdxhIRKxAIyDCMgr8tyzrp73CLtPlt27ZNf/vb3/Too4+qVq1aETW3Bx54QKtWrdLevXu1Y8eOsM/tgw8+0HnnnadWrVoV3BYJr2ezZs00ceJEJSYmqnLlyurVq5emTJkS9nlJkt/v16pVqzRu3Di9//772rhxo3bv3h20uUXC8/9XImmO5L3wIjXvUuRmPtR5lyLnNTiTSJofeS888l50wcy7M5QTBew499xztX79+oK/09PTCw5RiQTnnnuu0tPTC/4O5/y++eYbPfDAAxo5cqQ6d+6stWvXRsTcfv31V3m9XjVo0EBxcXHq2LGjPv30U5mmGda5LViwQOnp6UpJSdHRo0eVk5OjPXv2hH1e69evl8/nK/hAtCxLNWrUiIjXsmrVqmrVqpUqV64sSerQoUNQX8tIz7sUOZkn70UTqXmXIjfzoc67FPmZJ+9nR96LrizknT2WiFitW7fWqlWrdPjwYeXm5uqzzz7T9ddfH+5pFWjatKl+++23gkMIUlNTwzK/vXv3avDgwXr++efVuXPniJpbWlqaRo0aJa/XK6/XqyVLlqhv375hn9ubb76p1NRUzZs3Tw888IDatWun119/PezzyszM1MSJE+XxeJSVlaU5c+Zo6NChYZ+XJLVt21Zff/21jh07Jr/fr+XLl+vmm28O2twiPe9SZOSKvBddpOZditzMhzrvUuRnPhJyRd6LjrwXXTDzzh5LRKzq1avr4YcfVv/+/eXz+dSrVy81adIk3NMqEBsbq/Hjx+t///d/5fF41KZNG918880lPo833nhDHo9H48ePL7itb9++ETG3Nm3aaOPGjerWrZtM01THjh3VuXNnVa5cOexz+7NIeD3btm2r77//Xt26dVMgENBtt92mZs2ahX1e0vEvM3fddZduu+02+Xw+XXPNNbr11lt18cUXB2VukZ53KTLeI+Q9OCLhtZQiN/OhzrsU+ZmPhPcIeQ+OSHgtpbKRd8OyLKsE5gwAAAAAKKU4FBYAAAAAYAuNJQAAAADAFhpLAAAAAIAtNJYAAAAAAFtoLAEAAAAAttBYAlHkscce04033qj77rtPPp9PknT06FH16dNHXq/3jMu1a9dON910k1JSUtStWzd16tRJycnJ+uqrr0pq6kWWnJysNWvWaP/+/erbt29Qxnz99dfVvn17ZWVlnXT7Qw89pEceeSQo6wCChbzbQ94RTci7PeQ9MtBYAlHi559/1oEDB7R48WJVq1ZNX3/9tSTppZde0r333iuXy3XW5Z9//nnNmzdPc+fO1YIFCzRkyBCNHDmyJKZuS/Xq1TVz5sygjHXnnXeqdu3aeu655wpumzdvnjZv3qwnn3wyKOsAgoG820feES3Iu33kPTI4wz0BAIXjcrnk8Xjk9XqVnZ2tmJgY/fzzz9q3b5/atm1bpLEsy1JaWpoqVKhQcNsHH3yg9957T4FAQBUrVtQTTzyhOnXqaP369Ro/frwCgYAk6Z577tFNN92kESNGKDY2Vj///LMOHTqka665RqNGjVJMTIzWr1+viRMnKjc3VzExMXrooYd0/fXXa/bs2Vq8eLEcDod27twpt9utCRMmqE6dOvrll180cuRI5ebm6uKLL1ZOTo4kKS0tTV26dNF3332nqVOnas+ePUpPT9eePXtUvXp1TZo0Seecc442btyosWPHyufzqXbt2vr99981YsQItWjRouAxGoahCRMmKCUlRcuWLVODBg00adIkvf766ypXrpyWLl2qV199VT6fT263W8OHD1ezZs3066+/6vHHH5fX65VlWerVq5duv/32ILyqwOmRd/KOsoO8k/dSwwIQNV544QUrKSnJeuKJJyy/328NGDDA2rFjx18u17ZtW6tjx45Wly5drOuuu8667rrrrMcee8zatWuXZVmWtWbNGuu2226zcnJyLMuyrOXLl1s333yzZVmW1b9/fys1NdWyLMvavHmzNXbsWMuyLGv48OFWt27drKysLMvj8Vi33367NWPGDOvw4cNWq1atrA0bNliWZVlbt261mjdvbu3atcv66KOPrCuvvNLau3evZVmW9dRTT1mPPvqoZVmWlZKSYs2aNcuyLMtav369Va9ePWv16tXW7t27rcsvv9yyLMuaMmWK1b59eyszM9OyLMu65557rJdfftny+XzW9ddfby1btsyyLMtatWpVwfKns2TJEqtNmzbWwIEDrffee8+yLMv67bffrOTkZOvw4cMF877mmmus7Oxs67HHHrP++c9/WpZlWQcOHLAeeughy+/3F+IVA4qPvJN3lB3knbyXBuyxBKLIww8/rIcffliSNHfuXDVt2lQJCQl6+OGHlZmZqTvuuEPXXHPNaZd9/vnnddlll2n37t2644471KBBA9WqVUuStGzZMu3cufOkcx2OHTumjIwMJSUl6amnntLSpUvVunVrDR06tOA+3bt3V7ly5SRJKSkpWrJkiWrVqqXatWuradOmkqRLLrlEV1xxhdauXSvDMNSoUSOde+65kqSGDRtq8eLFOnLkiLZs2aJu3bpJkq688kpdcsklp30czZs3V0JCQsHyR48e1datWyVJbdq0kSS1bNnyjMtLx89J+fTTT3X48OGCx7xixQodOHBAAwcOLLifYRjatWuXbrzxRg0fPlwbN25Uq1atNGrUKDkcnEmA0CLv5B1lB3kn76UBjSUQhbKysvTuu+/q7bff1muvvaY2bdropptuUq9evTR//vyzLlurVi1NnDhR/fv3V9OmTdWkSRMFAgGlpKRo2LBhkqRAIKADBw6oQoUK6tu3r9q2basVK1Zo+fLlmjZtmj799FNJkmmaBeNaliWHwyG/3y/DME5ap2VZys/PV0xMjNxud8HthmHIsqyT7neC03n68nS65U3TPGnZP8/tTM/DiQ+wE4+5VatWeumllwpu27t3r8455xzVr19fixYt0sqVK7Vq1Sr94x//0OzZsws+QIFQIu8nL0/eUZqR95OXJ+/RhZYciELTpk3THXfcofj4eHm9XjmdTjkcDuXm5hZq+SuuuELdunXT2LFjFQgEdO2112r+/Pk6cOCAJOm9997TgAEDJEl9+/bV5s2b1aNHDz399NM6duyY0tPTJUkLFy6U1+uVx+PRnDlz1LZtW11++eXavn27Nm7cKEnatm2b1q1bp+bNm59xPpUqVVKjRo30wQcfSJJ+/PHHgq2UhVGnTh25XK6Cq+Bt3LhRW7duPeUD8GxatWqlFStW6Ndff5Ukffnll+ratavy8vL097//XQsWLFDnzp01ZswYJSQkaNeuXYUeG7CDvJ+MvKM0I+8nI+/RhT2WQJT59ddftXXrVo0YMUKS1KdPHz344IOaMmWK7rvvvkKPM3ToUCUlJWnWrFnq27evBg0apL/97W8yDEMJCQmaNm2aDMPQI488onHjxumll16SYRgaMmSIatasKen41sXbbrtNx44d00033aSePXvK4XDo5Zdf1tNPP628vDwZhqHnnntOF110kb777rszzueFF17QY489ppkzZ6p27dq6+OKLC/1YnE6npk6dqjFjxuiFF17QhRdeqKpVq5609fOv1K1bV0899ZSGDh0qy7LkdDr16quvqly5crr//vv1+OOP6/3335dpmurQoYOuvvrqQo8NFBd5PxV5R2lF3k9F3qOLYf15/zIAFMKIESN0ySWX6M477wz3VCRJEyZM0J133qmqVatq7969SklJ0eeff67y5cuHe2pA1CPvQNlB3lFc7LEEUCrUqFFDAwcOlNPplGVZeuaZZ/jQAUop8g6UHeQ9erDHEgAAAABgCxfvAQAAAADYQmMJAAAAALCFxhIAAAAAYAuNJQAAAADAFhpLAAAAAIAt/x/pRJ1mHzKf1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 810x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reorder columns\n",
    "indicator_by_sgroup = indicator_by_sgroup.reindex(columns=['PTM', 'TM', \"STM\", 'telemed before and after pandemic % change'])\n",
    "# Rename the 'Total' subgroup to be more clear\n",
    "indicator_by_sgroup.rename(index={'Total': \"Overall Average\"}, inplace=True)\n",
    "\n",
    "# Make the PairGrid\n",
    "g = sns.PairGrid(indicator_by_sgroup.sort_values('telemed before and after pandemic % change', ascending=False).reset_index(),\n",
    "                 x_vars=indicator_by_sgroup.columns[:-1], y_vars='Subgroup', height=15, aspect=.25)\n",
    "\n",
    "# Draw a dot plot using the stripplot function\n",
    "g.map(sns.stripplot, size=20, orient=\"h\", jitter=False, palette=\"flare_r\", linewidth=1, edgecolor=\"w\")\n",
    "\n",
    "# Use the same x axis limits on all columns and add better labels\n",
    "g.set(xlim=(0, 60), xlabel=\"% Responding Yes\", ylabel=\"\")\n",
    "\n",
    "# Use semantically meaningful titles for the columns\n",
    "titles = [\"Telemed Before Pandemic\", \"Telemed After Pandemic\", \"Scheduled Telemed Visit\"]\n",
    "\n",
    "for ax, title in zip(g.axes.flat, titles):\n",
    "\n",
    "    # Set a different title for each axes\n",
    "    ax.set(title=title)\n",
    "\n",
    "    # Make the grid horizontal instead of vertical\n",
    "    ax.xaxis.grid(False)\n",
    "    ax.yaxis.grid(True)\n",
    "\n",
    "sns.despine(left=True, bottom=True)\n",
    "\n",
    "g.savefig('yes_by_indicator.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2bbfbf47df0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns_data = indicator_by_sgroup.sort_values('telemed before and after pandemic % change', ascending=False).reset_index()\n",
    "# sns.stripplot(sns_data, x=indicator_by_sgroup.columns[:-1], y=\"Subgroup\")\n",
    "\n",
    "# Make the PairGrid\n",
    "g2 = sns.PairGrid(indicator_by_sgroup.sort_values('telemed before and after pandemic % change', ascending=False).reset_index(),\n",
    "                 x_vars=indicator_by_sgroup.columns[:-1], y_vars='Subgroup', height=15, aspect=.25)\n",
    "\n",
    "# Draw a dot plot using the stripplot function\n",
    "g2.map(sns.stripplot, size=20, orient=\"h\", jitter=False, linewidth=1, edgecolor=\"w\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table % of People with access to Telehealth Before and After Pandemic and % Ppl that schedule telehealth appointments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "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>Indicator</th>\n",
       "      <th>PTM</th>\n",
       "      <th>TM</th>\n",
       "      <th>STM</th>\n",
       "      <th>telemed before and after pandemic % change</th>\n",
       "      <th>%scheduled of available telemed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subgroup</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Diagnosed diabetes</th>\n",
       "      <td>17.8</td>\n",
       "      <td>55.7</td>\n",
       "      <td>44.4</td>\n",
       "      <td>212.92</td>\n",
       "      <td>79.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Metropolitan</th>\n",
       "      <td>11.6</td>\n",
       "      <td>35.7</td>\n",
       "      <td>22.6</td>\n",
       "      <td>207.76</td>\n",
       "      <td>63.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diagnosed hypertension</th>\n",
       "      <td>15.1</td>\n",
       "      <td>45.6</td>\n",
       "      <td>32.9</td>\n",
       "      <td>201.99</td>\n",
       "      <td>72.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65 years and over</th>\n",
       "      <td>15.3</td>\n",
       "      <td>45.9</td>\n",
       "      <td>32.0</td>\n",
       "      <td>200.00</td>\n",
       "      <td>69.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>One or more chronic conditions</th>\n",
       "      <td>15.6</td>\n",
       "      <td>45.3</td>\n",
       "      <td>31.6</td>\n",
       "      <td>190.38</td>\n",
       "      <td>69.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White non-Hispanic</th>\n",
       "      <td>13.0</td>\n",
       "      <td>37.4</td>\n",
       "      <td>24.7</td>\n",
       "      <td>187.69</td>\n",
       "      <td>66.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-64 years</th>\n",
       "      <td>13.9</td>\n",
       "      <td>39.8</td>\n",
       "      <td>26.9</td>\n",
       "      <td>186.33</td>\n",
       "      <td>67.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>14.4</td>\n",
       "      <td>39.7</td>\n",
       "      <td>26.4</td>\n",
       "      <td>175.69</td>\n",
       "      <td>66.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High school graduate or less</th>\n",
       "      <td>13.2</td>\n",
       "      <td>35.0</td>\n",
       "      <td>23.1</td>\n",
       "      <td>165.15</td>\n",
       "      <td>66.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall Average</th>\n",
       "      <td>14.1</td>\n",
       "      <td>36.6</td>\n",
       "      <td>24.2</td>\n",
       "      <td>159.57</td>\n",
       "      <td>66.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Some college</th>\n",
       "      <td>13.8</td>\n",
       "      <td>35.4</td>\n",
       "      <td>24.2</td>\n",
       "      <td>156.52</td>\n",
       "      <td>68.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bachelor's degree or above</th>\n",
       "      <td>15.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>154.84</td>\n",
       "      <td>64.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metropolitan</th>\n",
       "      <td>14.5</td>\n",
       "      <td>36.7</td>\n",
       "      <td>24.4</td>\n",
       "      <td>153.10</td>\n",
       "      <td>66.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Current asthma</th>\n",
       "      <td>18.3</td>\n",
       "      <td>46.0</td>\n",
       "      <td>36.9</td>\n",
       "      <td>151.37</td>\n",
       "      <td>80.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>13.9</td>\n",
       "      <td>33.4</td>\n",
       "      <td>21.9</td>\n",
       "      <td>140.29</td>\n",
       "      <td>65.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>13.9</td>\n",
       "      <td>33.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>140.29</td>\n",
       "      <td>65.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-44 years</th>\n",
       "      <td>13.7</td>\n",
       "      <td>30.2</td>\n",
       "      <td>18.8</td>\n",
       "      <td>120.44</td>\n",
       "      <td>62.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black non-Hispanic</th>\n",
       "      <td>18.0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>27.4</td>\n",
       "      <td>113.89</td>\n",
       "      <td>71.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other non-Hispanic</th>\n",
       "      <td>17.7</td>\n",
       "      <td>34.1</td>\n",
       "      <td>19.9</td>\n",
       "      <td>92.66</td>\n",
       "      <td>58.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Indicator                        PTM    TM   STM  \\\n",
       "Subgroup                                           \n",
       "Diagnosed diabetes              17.8  55.7  44.4   \n",
       "Non-Metropolitan                11.6  35.7  22.6   \n",
       "Diagnosed hypertension          15.1  45.6  32.9   \n",
       "65 years and over               15.3  45.9  32.0   \n",
       "One or more chronic conditions  15.6  45.3  31.6   \n",
       "White non-Hispanic              13.0  37.4  24.7   \n",
       "45-64 years                     13.9  39.8  26.9   \n",
       "Female                          14.4  39.7  26.4   \n",
       "High school graduate or less    13.2  35.0  23.1   \n",
       "Overall Average                 14.1  36.6  24.2   \n",
       "Some college                    13.8  35.4  24.2   \n",
       "Bachelor's degree or above      15.5  39.5  25.5   \n",
       "Metropolitan                    14.5  36.7  24.4   \n",
       "Current asthma                  18.3  46.0  36.9   \n",
       "Hispanic                        13.9  33.4  21.9   \n",
       "Male                            13.9  33.4  22.0   \n",
       "18-44 years                     13.7  30.2  18.8   \n",
       "Black non-Hispanic              18.0  38.5  27.4   \n",
       "Other non-Hispanic              17.7  34.1  19.9   \n",
       "\n",
       "Indicator                       telemed before and after pandemic % change  \\\n",
       "Subgroup                                                                     \n",
       "Diagnosed diabetes                                                  212.92   \n",
       "Non-Metropolitan                                                    207.76   \n",
       "Diagnosed hypertension                                              201.99   \n",
       "65 years and over                                                   200.00   \n",
       "One or more chronic conditions                                      190.38   \n",
       "White non-Hispanic                                                  187.69   \n",
       "45-64 years                                                         186.33   \n",
       "Female                                                              175.69   \n",
       "High school graduate or less                                        165.15   \n",
       "Overall Average                                                     159.57   \n",
       "Some college                                                        156.52   \n",
       "Bachelor's degree or above                                          154.84   \n",
       "Metropolitan                                                        153.10   \n",
       "Current asthma                                                      151.37   \n",
       "Hispanic                                                            140.29   \n",
       "Male                                                                140.29   \n",
       "18-44 years                                                         120.44   \n",
       "Black non-Hispanic                                                  113.89   \n",
       "Other non-Hispanic                                                   92.66   \n",
       "\n",
       "Indicator                       %scheduled of available telemed  \n",
       "Subgroup                                                         \n",
       "Diagnosed diabetes                                        79.71  \n",
       "Non-Metropolitan                                          63.31  \n",
       "Diagnosed hypertension                                    72.15  \n",
       "65 years and over                                         69.72  \n",
       "One or more chronic conditions                            69.76  \n",
       "White non-Hispanic                                        66.04  \n",
       "45-64 years                                               67.59  \n",
       "Female                                                    66.50  \n",
       "High school graduate or less                              66.00  \n",
       "Overall Average                                           66.12  \n",
       "Some college                                              68.36  \n",
       "Bachelor's degree or above                                64.56  \n",
       "Metropolitan                                              66.49  \n",
       "Current asthma                                            80.22  \n",
       "Hispanic                                                  65.57  \n",
       "Male                                                      65.87  \n",
       "18-44 years                                               62.25  \n",
       "Black non-Hispanic                                        71.17  \n",
       "Other non-Hispanic                                        58.36  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a column calculating percentage of ppl with access to telemed actually scheduled \n",
    "indicator_by_sgroup['%scheduled of available telemed'] = (100 * indicator_by_sgroup['STM'] / indicator_by_sgroup['TM']).round(2)\n",
    "indicator_by_sgroup.sort_values('telemed before and after pandemic % change', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_summary = indicator_by_sgroup.iloc[:, 0:3].rename({'PTM': '% Patients offered Telemedicine Before Covid', 'TM': '% Patients Offered Telemedicine During Covid', 'STM': '% Patients Scheduled Telemedicine Visit'}, axis=1)\n",
    "survey_summary.to_csv('survey_summary.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create weights "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "discarded_sgroups = ['Current asthma', 'Diagnosed diabetes', 'Diagnosed hypertension', 'Metropolitan', 'Non-Metropolitan', 'Overall Average', 'One or more chronic conditions']\n",
    "\n",
    "# Remove pre-existing condition subgroups, metropolitan/non metro. subgroups, and the overall average\n",
    "pre_weight_sgroups = indicator_by_sgroup.drop(discarded_sgroups, axis = 0).iloc[:, 3:]\n",
    "\n",
    "# Calculate TM score metric\n",
    "pre_weight_sgroups[\"TM_score\"] = pre_weight_sgroups[\"telemed before and after pandemic % change\"] / pre_weight_sgroups[\"%scheduled of available telemed\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "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>telemed before and after pandemic % change</th>\n",
       "      <th>%scheduled of available telemed</th>\n",
       "      <th>TM_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subgroup</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18-44 years</th>\n",
       "      <td>0.258804</td>\n",
       "      <td>0.303669</td>\n",
       "      <td>0.270944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-64 years</th>\n",
       "      <td>0.872648</td>\n",
       "      <td>0.720531</td>\n",
       "      <td>0.912679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65 years and over</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.886807</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bachelor's degree or above</th>\n",
       "      <td>0.579281</td>\n",
       "      <td>0.483997</td>\n",
       "      <td>0.632888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black non-Hispanic</th>\n",
       "      <td>0.197783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>0.773523</td>\n",
       "      <td>0.635441</td>\n",
       "      <td>0.823042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High school graduate or less</th>\n",
       "      <td>0.675331</td>\n",
       "      <td>0.596409</td>\n",
       "      <td>0.713991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>0.443730</td>\n",
       "      <td>0.562842</td>\n",
       "      <td>0.430807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.443730</td>\n",
       "      <td>0.586261</td>\n",
       "      <td>0.423199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other non-Hispanic</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Some college</th>\n",
       "      <td>0.594932</td>\n",
       "      <td>0.780640</td>\n",
       "      <td>0.547989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White non-Hispanic</th>\n",
       "      <td>0.885318</td>\n",
       "      <td>0.599532</td>\n",
       "      <td>0.979271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              telemed before and after pandemic % change  \\\n",
       "Subgroup                                                                   \n",
       "18-44 years                                                     0.258804   \n",
       "45-64 years                                                     0.872648   \n",
       "65 years and over                                               1.000000   \n",
       "Bachelor's degree or above                                      0.579281   \n",
       "Black non-Hispanic                                              0.197783   \n",
       "Female                                                          0.773523   \n",
       "High school graduate or less                                    0.675331   \n",
       "Hispanic                                                        0.443730   \n",
       "Male                                                            0.443730   \n",
       "Other non-Hispanic                                              0.000000   \n",
       "Some college                                                    0.594932   \n",
       "White non-Hispanic                                              0.885318   \n",
       "\n",
       "                              %scheduled of available telemed  TM_score  \n",
       "Subgroup                                                                 \n",
       "18-44 years                                          0.303669  0.270944  \n",
       "45-64 years                                          0.720531  0.912679  \n",
       "65 years and over                                    0.886807  1.000000  \n",
       "Bachelor's degree or above                           0.483997  0.632888  \n",
       "Black non-Hispanic                                   1.000000  0.009776  \n",
       "Female                                               0.635441  0.823042  \n",
       "High school graduate or less                         0.596409  0.713991  \n",
       "Hispanic                                             0.562842  0.430807  \n",
       "Male                                                 0.586261  0.423199  \n",
       "Other non-Hispanic                                   0.000000  0.000000  \n",
       "Some college                                         0.780640  0.547989  \n",
       "White non-Hispanic                                   0.599532  0.979271  "
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = preprocessing.MinMaxScaler()\n",
    "\n",
    "# Fit the scaler and rename columns, indices to original values\n",
    "weights = pd.DataFrame(scaler.fit_transform(pre_weight_sgroups)).set_index(pre_weight_sgroups.index).rename({0: 'telemed before and after pandemic % change', 1: '%scheduled of available telemed', 2: 'TM_score'}, axis=1)\n",
    "weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Put weights into a dictionary for easy mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keys are demographic subgroups. Values are a dictionary with the name of the score component and value is the weight itself\n",
    "# {[subgroup] : {[score_component] : float, ...}, ...}\n",
    "weights_dict = weights.to_dict(orient='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Score Census Tracks Using Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights_detail = pd.DataFrame(weights.iloc[:, -1].rename('TM Score % Increase in Telemed Usage / % Scheduling Telemed Visit'))\n",
    "weights_detail.to_csv('weights_detail.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_order = ['Hispanic', 'Black non-Hispanic', 'Other non-Hispanic',\n",
    "       'White non-Hispanic', 'Male', 'Female', '18-44 years', '45-64 years', '65 years and over',\n",
    "       'Bachelor\\'s degree or above', 'High school graduate or less',\n",
    "       'Some college', 'tract_x', 'tract_y', 'latitude', 'longitude',\n",
    "       'google_loc']\n",
    "\n",
    "# Demographics % before scoring\n",
    "tracks_raw = pd.read_csv('ready2weigh.csv')\n",
    "# Assert column order\n",
    "tracks_raw = tracks_raw[column_order]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Apply TM_score weighting for each demographic\n",
    "tracks = tracks_raw.iloc[:, :-5].apply(lambda series: series * weights_dict.get(series.name)['TM_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add up the weighted demographic components of the LOT score\n",
    "tracks['LOT_raw'] = tracks.iloc[:, :-5].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\whou1\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LOT_raw', ylabel='Density'>"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(tracks['LOT_raw'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Segment data into 5 LOT bins\n",
    "tracks['LOT'] = pd.cut(tracks['LOT_raw'], bins=5, labels=[1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LOT', ylabel='Count'>"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(tracks['LOT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Geograph. back data to df\n",
    "tracks['Lat'] = tracks_raw['latitude']\n",
    "tracks['Long'] = tracks_raw['longitude']\n",
    "tracks.iloc[:-1, -3:].to_csv('track_LOT.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}