PlotlyCufflinks And GeographicalPlotting Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* PLOTLY AND CUFFLINKS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotly import __version__\n",
    "#Plotly is used to create 3D models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.4.1\n"
     ]
    }
   ],
   "source": [
    "print(__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cufflinks as cf \n",
    "#Connects plotly to Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot\n",
    "#To use plotly enterprise software offline as localhost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "init_notebook_mode(connected = True) #Connects notebook to Javascript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf.go_offline() #Use cufflinks offline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(100,4),columns='A B C D'.split()) #Create 100rows x 4cols with random numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.416594</td>\n",
       "      <td>-1.784099</td>\n",
       "      <td>-0.596712</td>\n",
       "      <td>0.607925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.147384</td>\n",
       "      <td>2.171245</td>\n",
       "      <td>0.853239</td>\n",
       "      <td>-0.767309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-1.784480</td>\n",
       "      <td>0.681435</td>\n",
       "      <td>1.667209</td>\n",
       "      <td>2.791800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.091694</td>\n",
       "      <td>1.691505</td>\n",
       "      <td>-0.003760</td>\n",
       "      <td>1.531391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.869031</td>\n",
       "      <td>0.184816</td>\n",
       "      <td>-0.907183</td>\n",
       "      <td>-1.434545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  1.416594 -1.784099 -0.596712  0.607925\n",
       "1 -0.147384  2.171245  0.853239 -0.767309\n",
       "2 -1.784480  0.681435  1.667209  2.791800\n",
       "3  1.091694  1.691505 -0.003760  1.531391\n",
       "4  0.869031  0.184816 -0.907183 -1.434545"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({'Category':['A','B','C'],'Values':[32,43,50]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Category</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Category  Values\n",
       "0        A      32\n",
       "1        B      43\n",
       "2        C      50"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2562e628148>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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z137uBcZmZK3cK6UUUsobpJT7on/fWNeLg548cqdbYGBMTSFtm7C9ukK2fAs32JzgWS9RbiVrpfPDB2l+81v4Z870Pd6rwlcSec2KFLnwOL64FpEfgYnLFJlviMh7VPU67RVp20lmzGaq8lwuR61W23ySjLNVCpOAOG97RUpJrVYjF+2i81KAlDLqtdL1yEU+D7r+0t6AubfP0CoT/NcOfo2PP/7xCzigLkZb2RkRg2zVkE4VkYuIPMklnx/ISQ3CACdwcILNIYVectlKWSvx1ne9m1JDP5GvtFaWomCnOJu1snwUrniLUp8bslZ6xrJOIu8NfoWOg1Ysrv/91sCuXbuYnZ1lfn6T01R9F1pnoCjBbUOwAOXGxipgVyCXy7Fr165NHORoEYufXo9cCIFWKr20g52xrQKreuR/f/TveXjuYd5/w/vRxIWtbh0xkUfEYDchLPdlrUDUb+Wyy/peYgfqNZumyHuac20lRR4TeGitIHJvOJEvOzGRexxbGuL3e5ZKPxy7VP186vH1D6p3ObnOzJXeVdVmKvJMJsNlK66NTcGBb8OXfw7+zXegsaCqX9/7Nbj8Ted8SH9xkdbXvsbYz/7sZo1ypIjPY2/WCoCWyxHaW8ee3HT0EfkgV7S9Nm2vzfHmcS6tXHoBBzbqpllx58NQqZ0kayXut3LwsYGXWFHmhBtuEpH3KvItRORx3rW0+scUBzvHzfEBIu8NhB5fqrMqlo+r/8f3QiWyVtZrT/g2ZCJFvU5FHrReHCJ/0RBPUMUpuOptkC3Bk186r0PWb7+dU//x/8U7dWoTBjh6dPfr7K8q1nK5vi0bX3Jweu4pf9Aj70Tkvr+2/0KNKMHoiVwzkIEi8nvO3MfzS893G2fd9WcDL7GiJc2mWStbVJGfzVrZUdox4JH3qvVTjSZ+sErVZpyxMr4XKpeolML1+sC+rcgfoLO+YGevIg8dl9Pt07z/jvcPjH3LoNND5Jk8XPNPYf/t/fGBDSJYVrELf25uM0Y4csQTsrZCkYt8fuB6fUlhhbUipeT3v/ksB88oO6njK/54ZvHs3UnWi/U6DyMmchvKOxIiv/vM/Xzn6HfQq1XQwF8e9NusQBG5t9E+IUMQblEiDyNlE67IpGm4DQpGYVVF3vG7v4e4nKqvclPFOeTjl3ZJeb0BT98BswxmpUt4Z/sc7e45lI7NE/NPcP/J+zm0fGh977kKnEOHmP3VX3txeta3F0A3lRIHReR2HU4/dc6HjIncWxG4vljRVeTZvsc101TB7ZcqYiLPlsHrMN9y+It7DnHHM2qCbntKtGyWIr/90O28+nOv5jNPf+asQf3REnngQmUnYUTkng51t47QNIy8xG/5A42dYmtlo4q85fh88aHjA19IbGFopdKWIvL4hlh5YzTdJuVsmUq2siaRow3JXFk6olLrStuUIocNELmtVGph4pyCndJxkovdOo+CivYDD9D89rdxXzhyzscYik5NqfE4uDke+fD14+d8yKCuCMA/c+H6B72YCJ2YyF+airzt+PzKrY9yprnis8RZK+Xt4NksdxQ3WZ4qIEuIfHH/eWdTeaHHnz/65xjC4GMPfYxfu/vX1lzFjl6RV3Yio0I6NwN1pw6ehWF6+LY2sIRPPPINBju/9tgJ/v2XnuDpkysaTUWdD/WxsS1F5KEbFyn1E17TbVIxK5Sz5YET64RtkDoAQrgcXy2XfOmICnQK0aPI15m54ttgmFCYWj+Rr7BWWp4i9nhldS6Ij+mfeRGsivZClHoYYWy3+v88iDxMiPwlpsjNFYo8l9tStRjniv2nGtz++EkeOLzCPrTrqvaiMAFeh6W2+h5sLyCUIZZvMZmbpO7UOdneQFrvKvjbQ3/LyfZJ/uhNf8Rv/thv8r3Z7/FzX/+5oc8fvUde2k4YqGG4ekTk9RMYuRDf0gcII1ZyGyXyY5E6ffZ0v4qNL0p9fHxLEXkcNFqZBdBwG5Qz5YTIe2d+V7bRwzEAdMNbPQVx6ajyx0GpcqFtTJEbOUV068xaCXoVueskRG6vEixaL8JWTOQvAjF2FpQij5EbU0vp5fNQ5MsvNSKPslZWWCsil7u4WjEMQdNRtQTLnRUc4zQgV1WrUs9iKVbkbpAIzJu33wycn73ihz63PHEL101ex+sveT3vue49fPptn8ZfoyPn6Ik8W0DqqrzXzQjqbh3qx9GzIaEnBok8tlbCjV0wsxGpPXd6hSKPLjx9bAxpWcgtsnluHOxczVqpZJUi90M/SccE8GWbDIrIJ0piUJFLqTzy8Sg1SjegtH1jHrlhKiI/h2CntG3abmStnEfToa4ifxGIsb2gVhwxhFCq/HyslTjY+ZIh8siOXCX98KWgyFu2IsyYqBPY9YjIC+BbCdF33CCxVW6cvhFd6DxTO/eA5zde+AazrVnef8P7k8Z3+2b28eV3fnnoa0ZH5IGv9n80ckijAiiPvOE0oD6L0KXyzocQ+UYVeVyyvlKRxwEzfUwR4Fa5ELvBziEeuVlJfgelcKXwyQnVz32qLAY9cmtJqYpYkQNUdmzIWmkfsZD5CaVc1+EDhq3VrZXzU+TqGN6LkQUSe+S9qO4+P0UeWSveJlpBbccfWel/rLoHFbn5kvDIW5EiX2yv4Bi7rgL9Rq5PkdtekKQejufGuWLsCvYvnpsiD8KATzzxCa4ev5o3735z39+qZnXo60ZC5Esdt5uHaZj4usoQSDzyxgk0QyL9QSI/14Kg40uKoAesFaefyLeKvSKTgqD+8fR65DC4yURRV0Q+Xup+5gS9qYcx4lzydcCeczj2ySdoH7XV+VtHH5Kw3QZD1Z31BTs3RZGvHTz0Tpzg+be8Bff4OknYs8Bt9XvkECnyc9sYQ3reuse7Xsw1bF71n+/gngOjCZ52PfKVijz/kshaiRX5gLViN7qK3OsqcssLaPvqHBeNItdOXMsztWfOaaL99pFvc6RxhPff+P4NbVAyEiJf7njd5jO6yZKuCoFKxXHqbp1w+Zjy2wKBbK+uyAMZDHhGMgg489/++0CaV9vxWWy7TJWyzDcd5lsW//bv/y13HL2j65GPqdluqxB5HOyUPYo8CAOaXqTIM5Eij3LHY0IvZxQJVQuS+aaD7fX0zI6JfKyn6qxyybqJ3G8qBRL6kRJbR8AzbLUwxsfVZ+nxyM+PyNUxzmZV2M89h3/yFO7hw+qB+/4Enrl9+At6i4F6Ud2t1Jjdb8sdmGvytcfWXs3EatzYto2w0RhIJz0XPHhkEdsLk7jPhUYsfno3lgAQ+RyhbZ+VwIIw2NKNxGKPfHVrpdLjkUdE7nYVeSFT4NrJa1m0F5m3Nj7R3nbwNvZW9vKTe35yQ68bCZEHoexT5DWphnH5zLWEMqRVP4ZWngApkM3+L6PTowJXqnLn4CFq//N/0rrr7r7HZyNl+pZr1F6Ktz/3PX5w6gc8cOqBvmAnbB0i7wY7u0Qek2A5M2itxIp8LKtIqJRXN0pfF8TeHPIYlZ3gNgdIajUEVkTkREpsHQHPsN1Gn5yMPsvmKPIgVrhnsVaCReXjJ/bUDz8Bj986/AVxbnxhBZEPyVz56384ym/f9uTaY4iI3HzFK9SYN6E3zGPHlOceWwAXGnGwU1uZtWLmIAwHOnauxAfv/CAf+YePvGjjO1+0hwU7E4+831rp9FgrxUyR6yavA/oDnkuf/zydRx8963s3nAZ7K3s33KtlJETuh7JbKWfkWIpK9K/e/iMA1Jsn0SqRiqv3X/hWD5F7Yf8FEyu0oNFPSrFX/FPXbgPgjqPfBGDRXkwuSmOLWSthEuyMCO/EIzRbpwEoZ8uJtVKPyoaXo2KFCVNt2lEwVdC2L3Nl6YiyDcwyQSix3GD9ueRSUouu6xfiLovrCHgG7RbGRHQunZ6slWATslZqtTVb4/q1iMjj79CzoL0Gkcarv+KKHZOq0Y5WK3zyluPTcQMlTIZggMg3IeD52HH1/bdHRuRDCoLyamW9lr1yePkw9528j6ONo0OfM2oMDXY6DZXFFAU76231Oe2eYGchU+Dq8asRiL4KzzN//N9Y/sIXz/relm+RMzbeKXOEijwm8iz16GbcO6Y6xDXac4hKpOLq/arPsrvl5CuLgmK1E9T7Nx+Oszd+dM8440XBc837AahZtR5rZWsReaLIO5b6rj71VpqP/CVAn0ceK/L5qBf5dGEagSCXVZZKX+ZKT+rh//3Fx7n+d7/Fb3xLfWfff/jxtZe7vkPLV5fLmU5U4bYea6XdRiuVEZmMslbcTQh2ttsqmyQME7JeDbEiT/p/eNbaq4jOEGtliCJvRjd82x1OqHHqoXnVVcD5E7kXhDx5Qh2z7Yxmq7lwSNMskVPb4q0M0PfitufVBmLnc/5fbMQrnaXeYGfgq/iJWUm2/2tHXGF5QVKeXzAKFDIF9lb3JopcSknY6QwIzNXQ8TvkjeHbCw7DSIg8lBLXiQjGyNGIVHE1o4JidXy0qrJBZHNFQdBaRB7dJOGAIrfIZ3SmSll27jiCj0rcX7QXu1krsbWyRVrZJrsW2TZYyxC4NKOAW5x+CF0ijzeVmMhVyRk5NN3DNLT+zJW4GAiVV7+9kkOrqqKg2+99iBcW1tghybcJXXW5NKwoYLyOMv2w1UYrlRCmSehskkfeapGJ2sKuRYz+Uo8il1L1lVmLyOO/rQx2FmdAz6pNmXufHt3wsYJbDXHqYazIvbnzI/LnTjdxfLXaWs1akVLSsDenfcUwSHewjS2AFrWhjlNnV8ILPL5++OvA+a3IXmzEHnnT8fHifkVxQ7o42AnYneha9rqKvBg1lbt24tokc0V6Hvj+gMBcDbZvXzyKHKAV5xcbJh3HJtQkcXJNXdMQ48oGCZv9Xfysng5/K/utJIp8uf81s0sddo3nEUIQFB5B+iV+cs9PKkXuuKBpaGXlOa+pyJ+5Hf781QNtAzYbMgyTlYK0LLW1GtDoKBIoZ8tktAx5I58Q+WJkd0wVq+SNPE7gsGs837VWwkApykiRtx2f6y+p8l//z7cBsINF6tYan6uHyFudBojBYq3VELbbaMUiIpdTeeTRBX+uikxKSdhuk71clc6vVd0Z1GJFHu3sIkMVDxi2u0tnAbSMull7oWlQ3QX12b6HYyW+llcdWyvZ3bsQudx5K/LYVimZxqrWyt3PzXHz73+JE8svXhqtHJp+uLYi/97s91i0F5nMTW5tRd4zEcZl+EmflVxVpR8CrhVdy73BTkOR/HWT13G6fVqJxYjrwvrZFbnlWxePIocukXvCwHYsQh0qUbl8XdPQxncAELb6Sdl2u5WCQ62VlYp8yWLXeJ6W2+K0/whe4wayjNH0mviOhTBNtKI6AWsS+dO3wfyz6y6GOVf0tda17eQiakbEGavx3jL9JbuODEwqOZOcnsPyLXZPFLrWSvO02v1mTPm9lhdQzOpgmHi5SbaL2tpLdd8GV8UyOu16VBS0NpHH27xppSJaNotndzeOPldFLjsdkBJzb0zk61Dklt2/EcCwlURcnr9a2ld1sCgoVuLNtRR5fTkSCmWMmZlNIfKJYpYrZ0qrTiB3Hbsb87KP8kc/+MR5vc9akK6LyGQQWj99JIp8SE/y2w7exkx+hjfsesN5K3IncAZiZBtF58EH8RcH7+WW4yeXQBLwTIi8kijyjLQxDY1OpMhzeg5dUy0ybpq5CYBvvvDNpDblbNaKH/p4oXdxEXk7IvKTfouMJ0GXVF31geu6hjapfEnpOOB2ydXyu8v/lT3JVwt2SimZXeywe6LAXcfvIpAeXuNGbFstgaxOA5HNohXUyQnabb722InBFrBSwlHlrccK+cVCb6ZKaFnKWgEarrqYKtlK8n+syOtOAxnkKecy5Iwctm+ze7zQtVbiPTYj/7ftBOSzysrySzvYIRbXzoLwnYTIPauFV5g4a9ZKTLp6ZK14Vu95PDcijzNWMpfuAU1bs6NgsKhsOGmvIPJh416tGCjGKkVB8fe1piJfXkavVlUjuJnp8ybyx48vs2/32FBFPm+pgPgdc5/g4499/EVJ85OuM6DGoUeRr9KTfK49x70n7uWdV76TYqZ43or8F7/5i/zpI396zq+vffKTHH3Pe1n81KcG/tZ2ArZXlOpOAp591or6nDlcdo7lCUJJy21TiC83jhQAACAASURBVAge4JXTr+RV217Fp578FHZT3b9nI/L4O7moiLwTKd+jTo2MD7ouyVhLFIXBspHFKUbBzkD0bfRr+TbFqIx+ZfrhasHOhuXTdHx2jxf4xuFvsKO4E2nvod5S6sG2mohsRl2YmsbJU4v86ucf4/vPr7jZFw9DK1rG2/2rhM1G0iM9k4mslUiRhx4CkfhwlWyFptPAeuwxGk4dGeYp5wxF5IHNtopJw/ZVLnncczyvYgGW6ytFDsjyTraLxbWzIHwb4ajLJeNJZotjZ12ZxKQbWyt+pNQE4pwVWZyxopcrGFNT+EM8ZyklQU2tGELbVv54jGFEvrJhVi/GdkPrdF9f8vj7aju+St/8zDth4WDfy4J6XbVlBjLnqcibtsfB+Rb7do9RNPVVV1DNaIVmdH6M//H4/+BjD31s08k8dN2BQCf0Zq0MTtK3H7qdUIa868p3JULjfHCofuicWiFLKTnzJ3/Cmf/6MWD1dNCm7bFrXJFpUt0Z3/NmRaUfooh8R1X93HDbia0S4wM3foAz1hnufk5lyUnLWrP1cixuRkbkQohPCSHOCCHW3bTZivylo/YCWR8MHejUqKLRMEv8xQMqHS5cUd1pBS7VYJDIpZTdYGePFxVbC9WSzQ9O/YB/evk7uHSiyJlltbuJY7XQsqbac7BQoNNQ1s1cY8WFdvS+nsG/uIo8Tt/Sx6qRtRIpck2jZBSSHNNytkzl4BxH3v3zjB89gwzylEyDnK5ulKKpFLfl9hB5bgwpJR0voBARuahcwg6xuGb2BZ6N5ihFnvXhuJk7q7USk65WLKFlswR2t4z5XBV52DM5GNu2DSXGsN1O8pmlba3TWplfW5FD4pOHoaTtKiJt2b6y3F64B57sTzELe4jcmJ7Bm58/Z2J9YraOlHDj7jGKprHqSqDlN5BBjqWj7+Kn9/4cf/XMX/HXz/z1Ob3fMEjHXV2RRzt8rfTI/SDkM09+gR+dfhV7KnvI6Tl86Z+zNWL7NpZvsWRvbGNsKSVzv/d71D7+F1R/9mcwr70Wf3Fp4DmtSPjBatZKN9iZFw47xxTpttx2IrBivHr7q9k3vY9v7f9q8ljcCXPY5wJGGuz8S+BtG3mBE93UR60zFEIDw9AUkQch9WyOM9H3J32tn8ilRzVS5E5PTnlYryM9D61a7buJY2thIXiCQAa8de9buXp7mePzisQ8q51clFqhgBvtAr7QWrE8PHq/6hQIq1orfugn/u9a+Oz+z/LOr75zzZs5XpoaY+NI20Z21MXW1DUqelcJlbNlxLKatPRmUxF5ziBv5PuIvOX4fYrc9kKkhEL0d2N8F2Oijd3pb1/Q/wFt9Mhayfhw1DDOmrXSS7rCNAmibIbJ/LkHu+KqTq1UXNNzDnq8zwGPfFgueac2WAwUY0UKYu+k14wVOcAL3+sfx3I9SW01tm1DdjpDNygOQsnfP3166LURBzr37YqslVUmXstvQVAANG7Iv5ebZm7iiwe+uKHJww9CzqwUMj2QrjvQwhZ6FPmKrJVvHXicun+aS83XA12ics5x16VlR30Pi/bGYlWte+5h6a/+mvH3vIcd/+k/YUxNESz1E7nlBYQSdk8osk6sFXuItRIp8pY3SORCCD5w4wfo9GTerWWvxCmMI1PkUsrvAev+VjUhsKPl17HOacrSRGQN6CxS8V3qukFDKpIJA0FnqXuzWjKkGiol5DoNDs+3+NwDxxKv1LzySgCCpiKluKrTkmfQhMYVY1dw9fYKxxciInesPiL3ovTDhdaKJdDR+2D3q9XP0ezc8TrccfQOfut7v8UbPv8GPnjnB8/62Z9eeJoX6i8w1xmebRHfCEkjr4aayJqaRlnoyfPK2XISnJWuhQzzFLPKWrECi2LkgXfcfmslJoDYWjHGVAqi1hy+p6S0O+ieIvKcLzimSXXMcHiAtJd0hWkmE9R0fnpzFPnM9NDqTr/WnfzDlYp8NWvFd5QPejZFHvnkvWq4ZfvdyX32QXC7cZxgeTlp/2DMqJTaYZPP95+f531//TCPHFtdaT52fJnLp4pUCxmKQzxyK2iS1UpMlbI88MIS//zKf86RxhGeWuhfLJ9ctnjNR+9MtinrxecfPM5P/OHdq29Mgopbaat65Ksr8ucWVNrmmKFSRmOi2qi9FgcmYwJfcjamyL1Z1U5h6v3vQwiBPj7WN+FDN4A9XTYxDW1QkZtliMafx2VHpMjbXpt8ZpCAX7vztVxl7k5+D9ZQ5CO3VtYDIcT7hBAPCSEeEjLEjYm8dZKSzKJlM9A4QdW1qAtoiC6R3/fUAaJfsAWMCWWLuG6Lv3noOL/zlSdpnlBBHvOKK4DuF3Z8qUPZNJi3T7KjuIOMluGa7WXCMENeLxBEWSugiDwmivleRb58XOUQX/0O9bu1TMfr8NYvv5UPffdD3H/yforZIs8vPX/W72HBUiTy3OJzQ5/Tba0b9X9pLkFxhqamUZHdjIpKttKtovNsMhTQNZF4kAVTEXXbjRS5bkImTyfyVuNgpyipVE+tM7zqMah3b5oKOY6HjkrnWyNe0Eu6Ws5Mqmin8lM4gbOuFczAMSM1q5dKZLZtI6jXV+2BHSstkc+rgqCzEXm86htG5JVLANFV5L1E7njdYFjowdF/6I6jXkeLrZUZVTE6jMjjlMHVtuiTUvJYFOgElX7oBRLH759IXdkmK4q8+vJJfnC4xk/t+SlM3eT2Q/09Zp493eB0w+aZU4MK8dhiB9cP+fg9q3vQoesM7A4Eqo0tDPbQP1ZXNmkG1dDNjFaVG5nMW/fex/OvfR32s8+yHE2alm/1tew4G+LYWWJ1jU/gr1DkcQ55OWcwXsgm/VSSzoeanijyvHDYVok/S4ei0a/IQanyn5z+iZ4xrGGtRBNbTt/CeeRSyluklDdLKW/OZgx818IRcLIzRyE0FJme2U81DKnj04w2mwh9wYHDR5hr2Hit0/hCUI2yNhy3ScNSX/zcC8q7jBV57EUdX+ywa6LA8dZxdpWUIrh6u0rfy+lVAsdJdgPXCoUkVWih2UMOx6Ib8/I3ql3k7WVeaLzAsrPMv7/533P3z93N2y97uyr5P8sSNm6k89zScCKPlWuiyFtLUJikYWQpB90bt5wtk3XV+xm+T1aUos+liLwUWScdJ1Lk+XEQgo7Xr8jjknTdGm6V9F6ApTDL0XhbuTUyV3pJV2TNZIKayiuyPBd7pTeAakxHCneVgFWstDI7d/YHO7UhllB7SJ+VGEYWyjt6FHn3PLScoDuhaQa88F2g2/mwN9gJw4l8LiLwvmsvwsm6zXzT4caIyONztzLg6ckWpl7iNZdPcqpus9TSefPuN/PNI9/sq7uYj96jttJCBGrRavRLD81yerVJxV3dI4+JXK7IWjnZPoWUAi1U9+25WCuLn/40AO6RIyw6XRW9EXslWK6r4rSMut/1iQkVgOxpZBZP0MWswVghw2K7J2slri+IPPKJbJCsei2/M2CtxLjS3JX83KoNX4nHm+aspuzPhpFkreiawHNsZg0DicQMNBUoCRyqYUgjsGl7AV7GJCRHVTb40zufpxM1farmVWaB67WTKrbF42rWN18RWSuRFzW7ZLF7PM9sc5ZdZfWF7p0sktU1dFlBug5apC5EsYCI1ESttzz36H1qNt72I+pk2ssca6jl4mt2vgZDM5jMTeKFXt9O9qshVuQHlg4MfU7XWomqTZuqWU9D1yn3XPyVbIVcnOASgKmrCyn2yONgZuKRRxkr7USRR0ReUgSTtYcHL8NGtDmCBrlQ56S7jAdrBjyDFR65iCoCYyIfUGT1WYgi/EPHEQdQS6XEqrjltgd4dIUdEZfuZ3buVJNzrMgrl6zukQ8rz+9FzwYTvdWcrdgj1wzY/Ro4fA/QvQYTj3xaTZjDUiZPR770gK1Ht1FWrMjj+MdKeyUQbfJamR+/XKnfHxyu8dNX/DR1p873T3w/eV5M5AM9t4FGq8nPlx7hl8QXqf3lv4Rb3gzHHkj+Lp3Vs1bIZEDTBhR5zZ5H+mWi079ha8U5/ALt+1Sygb9Q6wtybiTgqWyuseR3PeoB1OuTx+e1FCny5ZWKHBJFPpYJKEREbgedvvTDXvS2or7r6eHdNy8Ka6UXuiYIPYujWTUzZ33VyxigGoT4MsTyOwSZLFLk2TcZ8PkHj3PylGpHWi1uBxSRx8UYnVNz6ubepv4W1Osqh3zJYvu4xqK9mBC5rgm2VU1EUEK6Xp9HbjiRIu9VKkfvhz2vUcuq/BhYyxxpHAFgT1kV2ExGk0vNGk5sXuAlgZq1rJWVijxsNyA/RlNA2eleFJVshZynFHnGh4LRVTz9HrmvMm2S1ENF5DEZxCl3pjNc3QQNNUEtlzVMXxAiOXmWgGcv6Qozi3B9TN1MCpoGbuTv/xH8zS/AGrs0he026DrCNBMif+DBZ/n20/1KJ1hcRBQKai/Wnjzypez21VcRccOswhSH64dXPz89RUGxR57RhaoEjDvjXf5GOP0kdBaT8ny9qs6jViyilUpD+5LPNdR5Hwi0o/aR1DXBtTvUOS71BrIjSCmRwqKYKXPFtPLJf3B4kdfufC0TuQm+fujryXPjyaK2CpG/duHL/J7/MX7FuI3S4lPI00/As93XKkWeGXidEELtErRCkTe9BaQ3lmxSHFsH67VWlj77WaWiNQ1/Yb6PvDemyPuJPG6v3Ju5ElsrJdNgorjCWkkUeUzkPvmsBkicwB5IP4whOx01yQGHjj/Gw3MPr/q8+H7I66NLP7wV+AfgaiHErBDi36z1fEMTSM/mZKSEdS9E5JWajDNS7KBJkDWR0mRPziIIJafPHFHPqajggeNZNKKycv/MGYyZGfSqutCDeoOFlovlBZSKatkbWysA2ys5fLeEcP0ukecLZFwHIVRprheE0JqHhQNw6WvVC3NjYNc51jjG9uL2ZJk4mTs7kdcixTuTn+FY89jQC7mryNWFI9stPLOCRUjFbiQ785SzZcxI5WR9KBqxZZTDD32yGfW8thv0K/Io2JnPRIpcz9DUyhS8tYhc2SSNsk7GU+foaMZYU5H3kq5m5hCeTzFTTL4zy1vx+U89oapPreEqS1WKlhBCMBdNCJNWg+aK/iL+0iLG+HhELF0i39+pDvHIu4r8Yw9+jN+9/3cHnzO2G+onIAwTJTxTzikydRpKsV32RkDCC99L7KjYWgGVuTIsQDuXKPJBIj/dsJkpm2QNdcuupshbbgtESClTQQiR+OS60HnHZe/gntl7km6ZiSJfRf3vdA6yZExz9P3P8yb3vzGXvwLmup38pOMMbPMWQ+TzA4rcZpHQryZEbhpRDcc6rLWg1aL+la9Qecfb0ScnCGq1viDnhom851zoE2rVEix1jxEr8nJOWSt9Jfq5SJFrOh4GVd0jl9FBeEjCodZK2OmgF4uIUpFtQYmP/MNHVt0YZ+SKXEr581LKHVLKjJRyl5Tyk2s9X9cE0nepGRl0oSMcF62g/N1KJlJrYYvQzBHKDKarTpwdtXEtje1BlxLX6yTWira0iDE9jV6JiXw56cWdMdXrd5e70eNtlRyWk0fzA4jURWDmMH2HPVHqUa3ldv3xS1+n/o+slaONo1xa7vb1nsipi2KtC2s+Cia+9pLXEspwaEFD6KxQ5J02LVNdJGXfTYiul8gzvupTDl0P0jB6ClZ6iHxAkQNNfZyiN5xA402UmxUDLbohj5+NyHtIV5gmuhtQypQS5dKnyMMA5p5WP6+l8ttttGIBKSX/8c6juJrBJUF7oEw+qC2iT0wkmx3EHvnxcAq8dl+1MKDIXeiQG2PZWV59U4DqbhXMbJ1OlPD2ak69d6zYLrkJsiVF5FHPnz4VuKK6U/p+UgAWE/n8KuQ617CZqXSDYMVVFPlCVN9QjmJIr7l8kp9v/xWnn7mXd17xTrzQ49tHvq3eI/bI24OTxm7/GLXCFVy2Y5p/dsNOftDaTni6m/WiSvQHPXJQ+3j2bobSdjykvoz0KliuEgCxIl/ZYmM11G/7CmGnw/gv/ALG5BT+Qo1le5lLSqr98vko8rhRXm/mSqtHkY8XsixbHmEo+xU5YGFSMnzyGR2hqc8x1Fppd9RqvzrGq0vX80L9BT755CBFxkR+0TTNMjSBiceiblA1q6pSLCLyakH5iFK0kWYOGRoY0QzsRkSYz02SlRLXt5JgZ6GxhDEzjchkVPZJo5FsdRboimxiawVgRzVHq50nE4CrK+VqGSZ53+GabWosCy2HU3/4P1h4bgx27FMvzI+BVVdEXukSeWKt2DXs/ft5/g1vxDt9uu9zx/7463aqSWGYvRIvTZOOjJZFI6tm6UoYJkUp5Wy5xyOXyWYT3RndQwjoJESuLuJYkcceOkAnM0E5GF7oFDYV8bUqWXBdipkix7L5riWx2mtaraSHjTCzaKGkrBVWX1rXDnYDkmuq/BZ6scRtj5zg+wdryMkpdvrN1RX5xASaqYjct9uEUnDUH4/eY8Vk0VmAwgRoGk23yZK9NBi4Huv2JU+IvJKj7fo8YS/wllydp5cOqEn/hXu61spYlwBWVnee+PVf5+gv/mtsL0hyllcLdp5pOGwrd1VwKVHk3WDnqShfeTza2/HHLy3zK8ZXqT/0Ba6ZuIYrx65M7JU4K2ultWLZLpdzgmZFxZo++OYreNLfhdaeS871sMpOiBR5TxbRwYV5hOYS+mNYUZA9vj7PZq3IMGTps58ld+MN5F/5SlXJW6uxaC+yo7iDvJHfGJHX6/2TaqTIezNX4vNaNJUiD0KpJureYCdgyywlzVMeuaa+w7UUuVYsoFUrTLom//jSf8ynn/70wPMs30IgkqyejWBEHrlGVngs6hoTpip6EUX1JY2VlMctdAtMkzDU0KxFQOI56kIq5MYjIrdp2B55Q2PMquONqROjjVUJ6o0kD7YdzqlddSKlAkqRe26RjA+OppRCR89gyJBrpxT5LLQcGo8eo3lyTGUtQKTY6jTcBnsqe5LjjZvjCAQ1q0bn0Ufxz5yh8+BDfZ87Vnn7ZvZRMApDM1cG8sh9aEYKqBzIZLPkilnps1bGohs4yQoIbQoZHdvuqP01VyjyXiK3sxNU5XAiD1odPEPiFTJI22FPaTdHc/m1SbfTRi+qSTFeile1wuo38qknuj+vlQnTbhPk8/ynv3uGV106ztjunUxYjUFFvriUKHI8D6fdwibLMbuw+nu0F5KMlYbbwAu9pFNjgqS6UxG5oQkmilma7jK/odWYJ1CbCVz2BqgdJJhTAfE+a2WmW93ZvOtumnd8B/fQIc5E/vhEMctCyxmYROaaNtv6FHmctdL93GfaipDG8+r9riirv1mNGkII3rz7zTy58CS2byeTxcpg5/Kp58kJD3dc9U+/ZnuFpfJV0RuoFdOwgiBQmSu9G5g/Mx91jPSryXUXX59nC3a277sP9+hRJn7hFwAwJieVR+4sMZ4bZyI3se5gp/R9wkaD/f7xJO1Vq1RA15OePKCIPKMLTENjvKA+41Lb6Qt2On5AR2YpChfT0BJFvlr6ISgiF4UCeqVK0Ghw7cS1WL41YK/EnQ/jvTo30pJ4tIpcE0xmxyAM0SIir0bkKPQO5HKEHojQY1x3CH21VM3nFZHbvo3rh/zYdAYz9FmOZky9UiWo15ldspgoZjndOcGu8q6+zUx3VPNIv0QmAFuLSq2FOnHXjSu1s3hmidCRODW/uxNNrsoRqS7UvZW9yfF0TWc8N86ivYh3QhGtvb9/J+2aVUMgmMxPctX4VUMzV0LbUR3zIhIMA0FDV/ZPryIvZUp91spEPgp2RopX5ZIbA31WYhUXR9wBHHOCCVkfmj4ZtC0cUyDNDIQhe4u7Oa5razYQC1ottGIRpERE3m6VfPdG7vVIT/cQ+RrWStBqsySzLHc8PvzT12Nsm6HSWe4j8rjPij4xngTRnVYTiywnPPWdrkrkUcZKvPnFQMFJNVrRLR+j7fgUTYOiKXAn/jc1ochhobOgAp5AcOwpdR5LpeQQxvQMeB7+6dPMffSj6nn1OnNL6j2v31nB8cM+y8T2ApY7XpKzDKsHOxfa6lxMRudZxCmRkeVy/eT1BDLgqflnaTo+uYzWjQVFcE5G9tbMtd2PfemNAIm9Ip3Vm2aBKgrqbfp2aFFdq5O5ma5Hrq/PI69//evo4+NU3vpWAPSpSYKFGsvWEuPmOOPm+LoVeZxB9I3Fe3liXl1rqihofMAjL5kGQqhJGqDeWFI1ExG/LHc8LLLkNQ9NE5hZdRMOSxsMO8pa0auKyGPlHvfmj7GyF/kdz8zxY//5O4kNthZGlrVi4rEsBFNCkY+oTMDM9VT2quR5oVuIXB7pK2LZk7MIA3Uy8maVrAQ7SsX7sbK6EOcy6obRKxWCRoOjtfZA6mGM7VWT0C+R8aGjqRPRQN0cV5ajZetxdRFKP8Q9Gm1NlR/jWBSB7lXkoHzymlXDO6FSIe39z/T9fd6aZzw3TkbLKCJfPIA7OztAntJxELkcWiHqJhcImrpSYGVEosg1oVGIdu3JehqVKI83CSb6FiXTQMa9YaIbvOP6mIaGrnUnNj8/RVW0cYZsChC2HawcyGjzj0vNnZwUIZ67erm5ek1Hkdi3/wPiO78DQOXA98h96d8m40tw+gmYUXsdrmnXtNs4UbbT7ok8mZkZyq0lmpbb9xzpeRgTk0nZuNdUirxGtCpb1VqZxAmcpKvmgNozS+pmbpyk5agb/jnnq+jF5/nt5Q7jIqtWXTPXQ2GS4MTzSefDGHGmzdxHfw9vdpby21Vni/njyoa7fqcii94UxPhGXs0jb/d55Gq804XIPojOuxE10rp2UpHzQ6cUIV+1TcVUlnr2pgzn9iMBbdsrkseuv+oVzMsKjaOPAUqRrxbsfPTMo/gZrW+rt7gYaE9lB1YUJI9XZGfzyJ3nDpC74ZXJpGFMTiFdF6exrBR5fmL9RB7ZXM08PFPr3pfG+Hhf1krL8Snl1Hc7VlD3ebsevUdE5EsdF4csOanGn80o/hhqrVgWWr6geKleT563csW3shf5s9EmIk+fPHuTvtEQudUmi8eykEzqEZHn8vBL92Ne/y8wtRxC76Dl84TRyd9ttiFUHzyXyZNFJET+Cl1dOMeEWjbr1SrB8jKPHV/mhl0VTrRO9GWsAGyv5sEvYITQFupELKNO3LZMQD6j4x7vti11notskFyVIxkDXWgDx5zMT1Kza3gn1cXrPLO/j6TnrfnES7964mrMhQaH/slbad97X99xQsdGM81ugYUvaEakW87PqMyJCHk/7kioJxdgkqcb5ZLr9koiD/psFYAwzu1eWj2jIui4isijJfUecxu+gFPu8Lz5MFbk88+ileKKxBnypx5Pxqc+oFQpe5fcBGb1LCmNLdyIyPNZHWNmhozr4Ld6yuKj4JU+MZE0cvI7LWyZpSYjIl+ZSx4p8mbP51l12V6YAmuRlu2TKR7iofrf4C/v42eXa0wZBUXkmgY3vJvg9BH0Ur9KM7YpIm/ecQeVd7yDytvfDkD95BwgKY8p8dCbuXKmqb6nmR6PPKNrZA2NVk+/laVIgc+UojhAdN6zUfHWjuIOqmaVpxYUkV0dEXmvvWLUDvDZwgy/9vB7k6X/qy+b4LlwN/7Jp1SK45Bg5wfv/CAvOCf6FPlcew6kYCw7iR1ZK5kHP4WGWNMjl76Pe/hwUuAHYEype6faloznNqjIIyJv5Uh27gF1jfTmkTdtn5KpeCC2VjqNmMjVtbPU9rCkiYk6R2Y28tXXsFaUIq8Q1usYqGvybEQe1xWs1kZhJUZC5HJ5CUO4NDTJRFSNGJMWQCFTjog8RxhdqFfrp4ia75E38phCw4kutGq0h+TBUB1Dq1awl5bpuAHX7VabNK9U5DNlk2wQqZrohCyGitw0x2aylEU/1d0g1n42JvIxjhkGO3NTZPT+XNrJ3GSkyE8gslmCeh3/ZHdT45pVYzqvgrlXjV/FdB0IQ7zT/T1OpK0UuchkwNAJfUEDNaFVKjv6NkrOJdaKliy3E2slsClmDXSnn8jbrt9nqwDIqLrTrg8hcsulkxMQEfmujCKkY+HwEmm1X2dR7XU4ptoAlHe/gfzYXqBHkTdOKq99+w1QnDyrR25nc+iaIKtrGDPquGZjUWUX0CVyY2I8UeShZWGTpU2OQMv2v0fgKdIrTicbdcCQjIhoQ4226+PlHyavF8mc/icIYDpT7qafvv5DhF4GXfarqbi6UysUmPmt38RYUHGU1tw8+cph/uLAr6PljvcFPOP88l6PHAZ3Cao7dWSoMxUlDsSKvChb2F6AEIJrJ67lUP1ZoFvh3JuCWKg/z2OZMRrucvL5d43nOZ69nErzYBK/Eaap4hp//hpo12h7bZpuk0XZ7ks/XHTmMahSNM2kolj8w5+Ri6zRPviuKj7a/7e4x44hPQ/zyu7KwJhSYmOsrWJSsSJfT0OwhMjzom93e31ifEXWikc5uo9iIrea/Yp8ueNikSUbKXLDUN/f0KyViMi1ahXpedz5iBIRZ1r9tqTt231EHlf6Hphbu8gQRtWP3PcJdMVA45qaxXp7NxT0MugWRiGPdNTzruIYnUiV5o08WTTcqA1mIfpCnnai0ttqFRk1zdo+qYhmJZFndI3tuajyMSLyWqB+DzsdpkomufmTCE1i7prqKvL8GEczGfbkBvtWT+QmaDVrBLUaxdepzJRen3zemk+qGl8x/grKlroA454kMaRjJ02JtKyhrBUZYAiDfHkXNLpbjplx1oqnUY4Uea+1UjR1sl5EJj3BzjhYFkMrKSJ36/2ZNjECy6eT7xL5joz6/MfC4T5nosidFoGpLvKizJK7SqlQK8pp5vST6v/tNyjFO0SRx9u8WZkchYyOECKxKiaseqJO/V5FHq9qLAsLExDY2RWbYsR95otTiT8OQ5oyFSagU6PlBEijzpS5kwrqfafMajdtsThFYMyghzU40S0AMWZmMHbuYPo3PkRm2zaMU98FwD6zI5qmPQAAIABJREFUQKWixq1llvoUeZyWuJLIV/Ykb7gNZFignIsERqTIq7ST/i3XTl7LKesFwOea7UphLsSKPAyoto9wOron4+I1IQTh9HVkpUN4Wt0HIpuFh/8S5vfDwoEktXZRtvqslVawQEGbIJfRVfqh24b6cXJhgL0yUH7iYTj5CJx4GOd51dc9UeRSogs1ycaKPK6mHghKr4I4FbSZh0PLhxJbZ2W/lbbTvTfKOQNNgBfFHjBja0V55EYUrDUy6vvLPH+c1n39q2voUeQV9frJ574LwA+PnOx73jBF/vxWVeT4AXZGXYBVGaWn5bpEntOVIjcKeULbQUrYGxzBFoKMMDA0g6zQcEJ1A5n1RTwzz4FGSBBK9EoV3XO5YTrHsqfU7u7SblZiZ0GdsAZKQcyvIPLC4hkyxQDz8l3YB1RgUpoVjmYM9maqA8ebzE9SrKmJo/TmN4GmYT/T3Ul7wVpIiLyYKbJHRumFrf4LMXTchIC0jK6sldBTbWvHdikFGxVOZSPrKeOLZEnYG0wsmMYAkbfd7u5AMfSKIsSgOaS/t+XTyonEr6ySpyB0jrF6VF2GoSqEKJXAbWLn1fkthhn0a95BNpTYC1GwNybybdergOMQjzzeccgyTHJx58aoEdWk1UiKw7qKfAItr24M6TjYUn0/HWOsf7JI3v9H1mGtTEJniZbtEYhlJnMzVFHnfDo3yYK1kCjEwDfR8xm48/9LXi4yGa68804m/tW/AkB3VWaLtnCKXF6dJy3T6Msln2s4ZHTBeKF/BVjM9vckb3mNpJUxkCjysrA4uag+13UT1xFIH808w1VRmu1iy6F9//3YD9xFRjosR0G7mMgBJi7/UQDmn3sk+hwG7I/Kze16MoFZRogflaQHocRlibHsNIWsrjY4WVCN5XKhxO4RJEC3BbDTxDn4PAiBecXl6rHZhzC+9vMAVNtKNI3n1PW8cuV0x9E7Bsg9VuROMYsv/aTBnT4+nrTAhtgjj3ovaYLxQhY/2ti81yO3yaJHRK7r6lx1/uenmfvP/6XvfWUYInusFYCbl9TE/viRI33PtXyrL9gZT+AH51pnXXWMxloJfBxNXYBVERFWT/DEFKWIyAsQBIDBJe5hLKGRjz6oqRl4MvLclhcJxidx/ZCTyxZhSS0ZX7/dZLY1iyY0tkdpjb3YHhF5XaoL70zUbztsd5guZyk1FskUAnJX7MU/dYqgXmdBhFiaxh59cBk1mZtkuq6+cPPKK8leflmiyOtOHT/0E2sFYG84Eb3fCkVu28nEJjIaYaDRCGxV2l65RG0k3FGEYcS+o9/NZOj1yItZnbwXbZZsqu+ld3egGJlKtNl1a7AQRvo+oStp5bqtSqXjcoVR4QcZbdUuhmFH7VyvFUvgtLCjz5MPVT+SHGDVom6Rpx+HicuVB1mYHKrIg2jCa+u5xOOPrYopu5u5EgevlEcefY+OTaDnyGd06tpYv0d+8lHVa377K9dB5EqRt50Al0VmCjOURUTkhWn80E8IMGg00C6/CQ5/V/2LkGRPuW00+xRCk5SWT6BlFCHl8+1+j7xhM1PO9WVdwaC10vGbiLCAaUTntiejaH5BTdBxwLNSnWOqZKIJ5ZGf+vBHOPPf/0x9v1GqbS+RX/UjNxNIQe3QowBozWPd79CuJzUSboakIGi+6SCMOtOFbeQzOpYXIBcOsHSwwGueltitFW2TEyJv4Bw8SGbXrmQLRpqn0LMhUkjG2pIxc6xbhPfoZ9TuTJ7NkfoRPvTdD/F3h/+u79DB8jKBBtdfejPQDXgm/VaiKtxmlLUSY6yQIbR6NpVAWSueyKFF1qCmuyANZL0x0N0wTsXUioWkWPFaV33uk2eO4Prde6dXkbt+yELLZbps0nT8VTti9mI0ijyU2NHNX5bRjWZ2Z6KsVkLoFtl4Q+TsJCV/CUsT5OONT4WBhyIxUVtIlNkLC22OeepC/rEJndnmbNK+diV25NWNsRwFUU9F906syCutBkbRx7xK5dHazz3HkUjd7mXweJP5SeV7A5lLLiF37XXYz6gLJlYssSIH2OErReQ2+r0yFeyMFbkglFkaXqNL5AD1WUX40USdDehaKz0eeSFrkA8bSedDUMvHlcHOQmkMR2YQqzSUinu7N3I9G+w6NpfXtnEoa3D30TsHXtPbwha3hRURaiE0QDfI6yZ245iq6Dz9pLJVIFLkC0kbgtWO2TaySXsBrVgk2LaDq5aOJ0Qe1GqIfB4t+gcgXI/QyDFZyrIkKv2q/+SjMH0NZItJ07PJ3OQQa2USfAvPXcSnw/bidiqocU0V1Ybh89Y8v/xXP1SdD1/x41DZBd/5yOBnWjyMEKDnAsrNBTxNkaFptvtSzlQOeX+WSOi6XHf6AK+/+/O88C9+hrnf+32soIVOj8Do2cmqXlPndXd5Nzo5zOKpRHHWWg7+/Dz2Cyq472TUdVLvaVF8+Y4pjomdhKcia+XMY6pJGChFHlkrriEQno8MQ1UMpDvsKu8gn9UJQkl45jmWni/ymqd07N4J2+3A7A/Vz04T9+DBvkAnTgOhgZeTkUfeQ+QP/LnanenRv06qpU+1+ycJb2mRZg5eOX0DlWxFBTwfuAXj+S8B4H/y/4C/+w3lkee6RD5eyCJiCzAKdi62PWQmD54iV01z0aRJ0GgkPZ6S8xTtFxB75AB5R/09K2v84HDPpjk9RB6r8ddfqfjibPbKyPbsdB1F5CUZK/JuFFyniNA6XSLPqFmzLjJkNfVBTT2DR4ihCcLaPIXtSlEeqbV5OhJV11cFs63ZgeySGDM59fGXZZu241GLN7PodJjKSIqOTbYYYF6r0uKc5w5wLNoQYk84uNN6rMiloWNMT5O77jr8uTn8xcVViXzSVZ+5sdwfYJS20+2RboCUBk23qYi8GhF540Rf+81eRR73sojTD4tBExnZKqB2QVkZ7CzmMsxTRVtFDcctgRu5nu28bJvq4jZ2eR63PHHLwNIv2VSikIPApRalT+ajgHI+W8YKXDh4Jywdge2v5NFjS3zvhFRl8E6DlYiP2dTM/olo3028cuEwzY4iv7iqE7pBdOG5hHqeyZLJfFjpqkkpFZHvVNZBrMgvrVw63FoBMlLZApeUt1OOrLnpqGx8obPA84cUkWgTU/DaX1be7+Lh/mNFvxtmyJhdpxMq1axnmis8cqfPHw8aDQ6+4Y38/N/8Aa9/9A78hQWWbr0VYTXI0s1Z71XkjSX1eTWhkfn/mXvTaEuvu7zzt9/5zNMd6w41ayipNNuSZQYDZnRoRkMAAwtMkyzijM0i/tAh6QBrZXXodpIVIG3TrA7JYoXJYGwDdrAxNpYEliXZkkpSlVTzrbrjuWd652H3h73Pe+6tKttrJXQ77xepqu4595x3ePazn//zf/75GjjK+dStOYz6I2QYko9CbkZzSF2/OsjIhRD066doTZQBQGw+N8vnjwbshrs4hkO9oc6PjCJe2VELw4nOEZVJAhQ758lTm2piEKWByjICuPZXaqdpWEh/SHz5ymEg1xN6wlaFri9xXvkw3T31Wfpzp2D1zfCX7+OSlky2g8MSYdTfYVJRZOve3r2c230Z/uTnMAfKU5739+Bzv04j7d/CyB2MeASWB5ZLtr/P4rOfRtgV1WQnJRgxSFex8Tyn8A9MLpsycu0jBxCxhZCSpjXm4+dmNakoj0oSNgXyrzmtgfwrFDy/akCe6lWpqh9sccC1YlFDGDmiqk6otJV1bSgcHGMqrTikFDQ9i2x7h9qRRWqOycUdn8/3FTPzouCOHvLpMe8qMI6Ngkv9bSJTAWsR+CzpVdiu5lhHjmJ2OsTnX+PK6Cq2lCynt2vDU0aezCnvsHev2sZG514p3Qzz1Zm00tQ4PB4cZsEyjkrmK8yCIldA3nSait0BjG4o+UIfdi5LbdQ2bCzDKodLtJggvVlrsh9n9KLhoQiBumOxJ5tY0e1APm2mGHlgaiBPwoh+4vDu4Yhz+6/y9M2nD71mmkVuOOoW+8SGuhE9nTPvVbpEhgmf/tfqBcsP8p+evsIfvKYB7A7OlSkjHwl7FsELOI+9iWYakOgC2bSrE1TLOICRZki7wlzNYTOrqziAxFeefH/nEJBbwmKptvSlXStAzVKL79HWEZpCM3Ld+bkdbpNoeWdTOrD6JvXa7cN9Bewp9mhWoJYEpFI9vNIc3VbsPAjk6Y0b5IMBz33TO3nP3/7fWfk370MmCQ9cGOEYByxw4QD0TjQYz5ifjI4QGdfIi5xe3SHdnZ3rK/sLFFoqOgjkAGLxfhYy9T4iG8EDP6Dz+YdlIb/TUhJmEcdcGqhi3t29tXIHJXdeJY8FlUgQCTGbhXvp0xSGxU+uH+dju9uQpmUkNVAu7JNuk64v4E9+js4f/AwA+w+8E972XhhtcPHyJ9U5u2UCV7K/p4Dc63Gme4YLgwukSMyv/7sA5A/+HQDeYrx8KIOoU7Wx0nEpqww/+EG+849+FSs3QebK8WTEyNwpZZViNNvJTBm5qFQoqurabJsnqUpJrxrxX89tlW6rMA3LpqJpofPMkSa9msOFrf9BGXkWgZubCO00OOhaMQr1hROd3ldor/lYWFhC/ZxtOCTAgpkhowh7YZHj8zXO3RjxXF9JLsHe9qH42luPOUcBeWrBxf1NCsNEOg5FENCb6DzrWo6otHDvvpvotfNcGV1hrRCYd5iM0/W6zA8lwZz6/N699wAQnTt3R0ZuaX9wODxc3CvipGS+hpFT5MaMkdfm1KSf4XWkLipNPHDyAtucXc6KWSnth20xIXNnQB4mOV/7ofdz/Wdmo+lqrsmebOLcAbxyPcx6WAFTA+NoMMGXHv/T2KdhtPjAFz9w+Dto0DX1Ob6ppUA3084jp05Ybc+200tneXFjSL9s2Lm94DldHIaGQ8WePWyNxxVQGl9Uhbisv1dqn9Pai5nlGE6FXt1hI9Fg5+8oNg6HgLzhNL50+7cGcs9Wn2+tuUxTqAV1rnUcgOujbbxIfdaXxxIW7gHEoQRBAPpvQG0B2ajhxqpgtlxbJmXA7lj9OUxyxlHG/AEPeXk9Tt3HXmFReeghzMUFnng1wTMbs/ePBmU+TKwtdFJK/MkSBQmXhpfo1VzyA2Px9vZrZLpz+VYgnzv5MFLvRA3Xg1NvL0PkdsIdqmaHZkPtjIfDba6N1K7keGeFimNgkmPuXkbmEi/M1UJ+5Sn15pf+An/lET4nYj6sJyUdllbGYNcY1gSt0IRoiCsMalaFfhHByW+Elce4qK2FtzLybDBgXBF0vS5nemdIi4w3HBtrRU0Uy2STwm3zVuPl0n4IasfiZuOyPX8aQTy1LpOFFMQ4sQ2a3B3UyWfSSo1LV14BISmqR6lhULFDtkYxX9wYkhc5SZGUEbbTgR5LTY/Ti3XOb/8PysjzCLzcItBhTMYB1wqFklRCSz39haG2i4FhYmogdy2XVMBqrgDDmp/n+Fydv77cZ1+fjH29tftSQN7VvzK1BNeG+sJX1JSgpmbJv3i0xXuf/t9w776L+MIFrg4usy6cO7amO6bD4lAw1G9stlrYKytEr5xjN9ylYlUOdX/lAwUUmX/4Ih0qdhoZMhczRi4ENI8cklamQH7wODiAuY1P6uhIXCnxk4zm7ibRuXPldB3LNBiIFl7S58Y/fS/jP//z2efUDGPigalb3kfDCSEuDnA8eoxnt57l+e3nZ6+ZMnK9GI9t3YGa67mfpkc4DSGqLRC4c7yxM2FPaiD6Coz8oLTSOX6Um9Uu3jnVaJT397G6CnBLRp7nGE6VXt09kLeyp4DcsJRjhhmQd7wOQRbc3n2ogdy01LVbrC3Ss0Jio0rFqVOza1wfbdLQ6Yqf3y/AqUHnWJlVUh79S9A9QVZvYEcFSMlDCw+RSJ8wi/DjrGwGOiSt6JFlVrulAtCEwHn7N/DQRUk3O/AchQP1e4EiGJQT4uOJmtH6yt7LdGsOYn8G5NlQkH4JIF+5+zG0vwBx/HGVy63z+XeDXd7YNPjiTfX7L229yk6gmoHmq/NUbIs1sU0R6uJ8UpAarmLk0RBuPI+/phbkxV1JIcA6fmz2y3WM7H61oD4pkD/2R/Duj9OtzKl4aCGQX/dzXNK5SbcCOcPxIWkF4BXHwVxVPvV8f4B/5C281XyJ+gFrbrvqUJc+xRTI9aLnTqXVNKQQERV/9prpQguUMotRrTJ6+WOYtqRWW6QmLAoRYhqCj7+8OcsiP6CRe7ZBq2JzeqHxFZ0rXzUgJzJwcqcE8oNpajLXA01167zU28VAGAhdHHVMl0TAcqpv6oUFjmsmnLgeCMFoV23t1uprahv94u8dKjh19LlPTbihtTqzWqXwAyr9HaSAP5t3+eilj/LZygYyDEmuXuWYUb3jrMoiimj5kr327LR6Z84Qn3uF3WD3EBuHmbfVClNuHqjgF3E8K3YaCXkqifO4TDekuQLDjVJaCarebUDuWZ7ykTsmLTEhthRoxllBUUg8vQvwn55JIiOzg7UzYvihDzH68GyQQKGlFd8D01MgOBn5+LpDTWys0nE7vP+L75+9Zlrs1DXhKZDb2mRRsSpEOtGRpbO8cnNEIWEoNLjfQaufLg596RwCctcyeHn+JO3zLyKLgrzfLxm5cBy1+OUC063SqzlsF9PFYgduvKByRfSW9iCQwx2cKxUl2WCPqVktXNOlY0ZMohoXnngLX3ulyqa/TUPPkvyr3Uw5Exbvu52R770BvZME9TZGAdUYHppXKZvCUjr5rBnoICPXsl+7RSFVzSP/hsexc3jk0gGZLpoBeTUfMwhSdicJRTKHLWzOfexn+abh72HrYrvbTqkNQ6RuPptml08Ps3OUSDfdibu+Uf2l1yqllTiucb6vSNfV3dfZT3awRRPbUFLYSXGDPJnVlozUUtHFr34UZMHkiMp0eWgzZ6sNv3vlQ7NfrvPetysJVlZQ9B6A3slDO6etlQcIDIOVXHVNHuwJMMY+Yw3ka401asLmZddBdNcxWi3yfp/+4pOsil3m0lnndKdq0xAhic76L4E8nwJ5QC4jasHsmc/vIK0YtSqt658idwycHOqmR5hFPH68y8fPbd2WRb45inmwGHDj5/4pd/eUc2Uqt9zp+OoAuWlgRAZ27hBMpkA+YxxFph8qSwF5oVvvYwOEVDq2Y3kkQrCkpQBrfp4TGsgfWO9iNpsEfbUqrzZW4eU/hN9/N2zNcpUdTS9SC7Y1A7TqNWXg39kkqZqEpsFSbYn/GH8KgOWthHWndcgRMD3SGwqMbzZnTRremXtJrlxhuH/zkPVQZllZRKwk8Nz2c7N/i6JZsZOEXHvFF6tq20prRTNyXVTx7sLOs0MrdjmA2YamCIlstQgESU4r8TFTtXX3P/tU+Rrf7hBu6NFV005WZsDhe2BV1LXwxwGhXlStJObrl7+Lv9z4y/LhL6cD6Uz08dTko6U0z/IIZQ4Pvwse/CFevK5e96b7FEPKxre7Z6bsZl9aZfEMVCHuwpG7cYIJ4fPPI5OkLHYKIVSQUyaw3RpzdZc9WkggGG8cKnSCAvK6U6frqtffDuRtJILM8um66nq2Rch4UCEfDvmej/TZH2+XjHxHeGoM3cIZJaVMh2nEE5hsQvcEezq24VjaLPN7ZkB+OyOf3jeOjjmexBnj00vsNOHBVy7ri5aqjtr6Irnh0hQ+G4NQu2FMjtkLvGLC2y69j7+VPgNCUFuMWRgNMLRmeysjxzDYN9U9KI7P8vnjaKCakdIGMQrwbuxeJMj3qJuKvFRsk1PiBnk8gxyVrCHh078Mlsekp6Sp4zuSYLXL+z7/Pq6NdUxGNEJ6DTYddT4yretPg+oALumpXU8ECsCnrLwIQ4wkI6iaNOwGhjC4x2rwilcBt4nV6ZAP9rnZexyApb1nys84b044I67g148BkOyo3+voyVykERkRtfAAkN9BWhFmwcnoJZJqowzOmhQJ33pPl9e3J7y2rRaIqY98axjxDdefZ/ThD3NahwV+OZ38qwLkwjRxQoGdu0S+ruoecK1kqd6+C8VGpPaap0JSFAoRHKtCIQTzoQbyA4z8yZM9jFaLZNCfxdfqoRTcEpAPkJkG/WiPVsVWjDwISG/cJGyoVfcX3/qLdO95kELA0W3JMa93R2llmnp4vTbbjru64OlcvHmIkU8LiKJSoRpTyhJSSjWBxXMhjZS0olPjSiBvrsD4pmqQAcZO7dD3AQ3keURLW+MCQz1gfpwxpxcho9HAf+qpcgEIrC7jDV3MvHy5lG7ywT6YktQS2FpaCScBkXYQVYiZTBTwTYu6JSM3FXCbVZPUhJvbevHS0g/f9SvwwDt5cWPEXN3lmx84RiBd9rYPd709c/MZNrYugGUxLIzb7JNX11U9YvSnanCC2T3QeevYFLnA8ar06g57ssG/b7f45nP/njTs3wbkTaf5pRm5YZI6LWIrYq6iPOwtIyDydT7HXsybPn1FAblhEDkun31jDxbPqAS9Hb1ATh0svZNseOp9jif1crE3rBE742QG5I2D0soIbJtqU4cvxTmjZMwz9wiOvn5J3VvTHWOlTeE2y+7Oqa3xHqfLq67DxpG381DyOqabIxar2EXOcl/VcgaxkmP2/8tvl3NGn26qcyVqelfjtdlN1O+SWROvrj7Tle1L5MaAjl7sKrZi5JGc1WrsIFP1nv4bsP4EkyLByiTmyOC+h96GKUx+6RndYBOPCJ0Gu9WZxRRU8XIK5BeH6pw+oX3s04LntBlINuulF/9MYXLetshkjmi3eO3is/yrq/+eDdmjszkjN3dd+x1ckXL5hGpGKhl5NgXykLSIqIezncZ0BwvKOAGwe/lz2GTIzqIKznKb+Ibg7asSN0v4q0uKBE4Z+c1RyPE91Sy2Lr5yq/5XB8gtk0oAZuYR+YEaB2bPfNlZpm7aof4ChakufmbkFLn6OVd/4WagPMNmvca9y01+9Imj/MBja2UCYhlfO/UNbx0EcnVT50adYbLPXN1BVCsKyDc2GDXVxbqnew+//C3/lu05i6PbsF5dVg/KLZrVFMgvVmcrp6eti+0r/cNArtuCndUV7By+eOM5/ZkUGAvXg2iAYUpEWiCkZLE2BfIjUGQUA/VwDbW3Xh4I9K+YCigbUn2WiQbyMM2Z10DefMd3KP/whQvKf5u6RH0H7+7jUBTEF5SVKx/uIxzdSWp7CMchmgR4VfWeJ1pwSZsEpg9VMZmAZZFl6kZutywSU3BjW/3uEsj18dLGkLMrTR492qFPg9HeYR/wv3vu3/GFy89gVKvkktuAPJtbYL89z/hjCsit7sxuKW0HmQncSp25ukvsjPiNdpORzJQt8iCQp0paaWuXT/8Oc0xjp4Nvp+XCWicgnphg22w+vMY7PjPi+HgTs9Xi/tUOT72+qxIRYeZcmQJ59wTnLeU/Xw+t8h4RlnKubI9jXMugWZkV4PLhELPVoqY7ef04YzcY8NS9BmaeM/7EJ2c7Rq+NqHRoCZ8bg7B0w5y1qviGwbOPv5dXgzUsNydYVq6bo9uStcYa42TM5IXn2PwX/4L+b6hBCOelApfyefVa7Og41iJr8I43KVa9O9jAsAcsVpWLpeIYnDI2CJg9A16Qkq8+qv5w/OuYpBOW+yCkoHvyJD949w/yzM1nCNIAohF9t8KwpgAz21XPc9frMogGFLLg0vASDbvOGV04njLyKZAbB3Lh741jIgF/cf0veD55g2Rvh6v+KzyV30ftxtOqvyGNWH7tP/Hn+YPcsNeReY7U9Ql7Okk6DUhlSP2A6jGVTGHGyIM3/gJfunhHjlIMh9S8Nr5hML/7Gr/zxz9P8lklcXqWh5SSrWHE4uYlAGrRhO5XcK58dTo7TYNGAEbukQTRbdNGosRESIuh9ucWlUWKf/wy0sjINZBbGshrk2E5ndzJA35h+z2sha9itlqYk5CFqmI7pW94a1ZwmoJmTI1Jtq863ao18tGIbHub3bakkRu03BZzlTmOPPQkZ4dNFmpLarZkokBKSkmU5iTXr1OYBlecManOgbEW5jHabea3okPWw+nNZa+oQuz1rQtqe6pzKgzPhXCAYU1b8Zl9F90UVOyrXUbfmrWhT49SIy/UKj4UdX7thV/j+mizBPL2930fAC989P/hez70PbRvKF/u3Pd9nboOr6pwpWIwRDq6icFwEJ66bvWGejDO9Axe21D/XgK572PWaqSBZhFWQWobbO2ND30+UM6MC9tj7l9psdD0GBttktFhaWUQD8j9MUJntN8aMdD0bC4eubucvjO1HwIUtsqr8ao1ujUbd+mPyDQz27PdWXwuWiO3G6W0MrjDzmtoNfHNgiO6W7guffIJ2EeWufmT34ZZwBM3X8ZstXjy1BwvXBvg19cV+5zef3095q97gs9LdV2Xxhkdr4MlrEPSymLT45PXPsnP/NnPqKx1DeQHM8m3gz5vLEPUm2f0p38y2zFW2pjVDm0RcENLK6YhOKM5iG/ssh81mCzfwyfu/4dkQpRADrD3R3+ozssnPqF2i3phHk67eb0Wu7pQJ7MmR4+o8+ZkGcKMWWsu6+ttcErcIChmYFqLIF57s/rD8a9nkkxY29Wd0Ue6PLL4iMpP330J4hH7tsNQewUOSiuZzBgnYy4NL3G8dYIFDWslkJc1hdnifkbbMf/Rn/8j9ryUudghlTGfKe7DiPZVrPJLv4cV7vLr+XcwCBLywQChozFsnQGVxGNymVGP1OcWlUq52wbd2SkErd2neFacpbUwRz4aUa/MMxEG4ef+GqfI6H1BFeorVoX9IKU17uNO1Ptk/T6nF+pc+DLOla8OkBsGrQCMvEYWhLdlG4dJgSVq9LWfVUYhcU1tlbNMMwFDsVDXnwE5/TeU5nn1GcxWE9dPyu6vsni2dbu0EuY1omLEXMPFqFZJrl6FouB6SzKfziSfuUffgrc9ZOMDnyKZmPz0Bz7Bw//y49z1v/4J9/yzP+Uzn32JdL6FNAR9LfkIIWBpjt54NqAZZozcXlWNz4iRAAAgAElEQVRA7iWSL2x/QQ2VQBd/oyHCUjfIvGjOAnWaynVQaGdNXzcRFPHt0kpFZ7hfzEN+9Qu/yqdvfJz5cIC0bLz77sM5doz8r59DIlm59AZ2PaN+dw+jXid+9VUubI3Z3dyZAbnpIFyHNAypNxRrPdU28IY5X/diwX44BXIVmJUE6venIiW3DMYjn0GQULEqZDIjLVJe2VSFzvtXtLOm2sMI9w5p/sN4iAhiCp1mWLEPM/KGZ/HKgQxtszMD8tyykLmgUq3z+d1PYdVe55sjBYJ7vWOgG6jSIlVNVE6dptvEFOYdveRXLbWYrDYUSFULHznJcVZWaJw4zUffpBYJs9XirSfnyArJX18ZwfzdsH2O57efJ9i9APVFMqvGqyKnAOaHMYYw6FV6eJ5/AMhdPnn1k3xm4zOEWaiAvFYpYxb8OKMfDkEIJm95G/5nnyLf0dKU10ZU2vSsoNTIezWHuUiBQi7GdOIRw85Rzouj3Oi0Obqt6kqikIQf/wSiWiW9dk3t0DR5eX08ff8WO9r2KrMGNS33TAeenOoq0lFL+zRFQJhWyg7jegjhgz8I3/TP4cgj+KnP2o4EIXEW6zw4/yACoWTHaMS+ZTOqgDQE2Z56ng/OylVAfhzPrtMU1m3SitvTOJGnHBtsMm94nOmd4e0Pfi8VPwMpeQp9D138FDz9q8iFM3y2uJ++n5a7AAArVM9poGWlelQgLQt7aelwsdNXcdy9dJNJ+y7Mdltp5NV5AkMQnlNkaf2q2ulUrSqbw4i79mcR2nl/n7sWG//fM3IhxLcJIV4TQrwuhHjvV/p5aUIjgCo10ugOjDzNsanTL/SWLYxK9pakuvtSOyac8aSMtyxZ92QLo9nCC/IZkPu7Sgnxd2CiiyAa+BKapOwzX1dAPvWDnu9CM66UgNL54R9m7u/9PSYvXOTiHy/w4J/+No8tVfiprz3BW0/1MLZuUiyqm+UgAKS9Fr2RvDMjX1U3ej01eX77+VLuOSitACxbM2AqGflI2a762pI5fS0oe1+URVQyBaQb2kZ3w7/OXDiE+QWEYVB78kmaL1+nHkjWrlynsRIhwj3lm3/1Nd7/6Yvs3NyjcGeM3HA98jCi22oAgvWG5Bsuv8Z7PlJQPKs75Xwfo14nC0fE0iIlAsfCyVM++/reobmdL22oG/+sBnK3tUCjGLKh/cR5kTNOxlQSSHSg0W0RA27Bsyuzc35QWilMgyIT4Nn8H8/+MsQrfHeoFtXdzixMbepyaDiqINZyW3ds07+mgX+1uQRSUikmWH6GdeQI89V5Pvikwahex1pc5LFjHRzL4LOv78LifQRbL/MTf/oT/IfBF6B7kp1JjHSGTCrQHOjMlso8tjNmd5ywPYpZaHhl6/letEe+v4u59xwLN1Q0wiTO2AsHyNyFR94MWUZ0IK2TSpu2CLg5jNidxMzVXTo6CCouRrTjCaNKkz0/YWOhxfq2ZLW+ypmrEvb2WfjH/1jdZx//U4Qukl/wr5fvv2OZGBjIvEatoYDcuQXIq0P1+ZPYxlpcRApBLZLElTZ87T8Bw2CSTljbBaeeYRQRLbfFyfZJnt/6PGQh+6aJNARGu01+QFoBuDq6yk64w/HWcXDrLAi7ZOTTdMNaT0uTky1MJH9017v5re/4LVoL6xh5QTWG0K3B/L3w9K/A9suIt/w9Gq7NlT2f3/8zxZpTw8TUOryvaxG1OEfWG5jNZlmMBj3mreJhUlDtKcmXPKcpq+RCELyh5Nj1wT6NQOJZHlujiNOD60jTVFJiv8/pxTrjA7k6tx7/3UAuhDCBXwG+HTgD/JAQ4syXe01hqF/czdRYqFsZeZDkuEadYTpSjoMwLIE8TtQDnAvFTq2xPwPyabuvv0NWd6lF0HV1Zf/VXS784RFlf9KsfMrIo3QFwx7jesNZSA9wpW1QiWuMdIaH4TjM//33cPID/5Lm0ZC3vf48/+DSn/FPv+0efvwtx1gI9gk66rPsRbPVO+hU6I055FrJSo1cMfJ73HWe235uFgHqOOz3dzA0I182DwB5tQemgxzvIyoVQqE7YG+RVqIswk7UA7upz99WeJ35cICxqG7q2lufxE5y3vmXBVZewBEDJtt4d99N/OqrbA4DqklI7s4YOa4LScxCswJOnYqMOaYv4fKHVMW/mPgqByUa4+ORFAHCtanKnM9c2DmU0Pji9SHdmkOjkvH6/uu0esv0GPHcVZ0hnU6QSCqxJNQ2xoOdnb/58m/y6egfcmPto2y3BZENhXegeG4ayFzwWzufYjvcphP9IORqN7Fbm+2SpkA+ne36pZqCNgz1u5eqi5BFiDTDiVLsIyvMV+aJXMFv/vQPsPTPfx7PNnl0vcMnX9vmM6NFhuEuucz5r/kI2T3O5jDCcPoMa1AdzsK3Dkor8w27LOT1oz7Ffh/TzqgPVXqkH+cqizyv4J5WjDJ+45K+EdrgtWnIiZJWJjHzDZfKZBsXwWS4g5en7Lt1+n7M1mKDuTGsyQ5fc05SVFza3/99VB54gPEn/gw7V/fBGxPNGL0Wu6ZJ3agDBo2WqpssmOq5O6J3LfZAdd2mkU4crFWoR4fHvU2SCeu7AreVlZ2cDy88zBd2vkgO7Ot64nQI8/QaAXx+SyUKnmidAKfBojRLIA/2FDOvz6nPMs3zr3dPYhpmueg3A6hWcjWqz9+B2gKcfSftms0Hn9/gvz6l2LO5fhQ5VveKr/PrG3FGXmuU84KnRxEE5Lp5rbWwXiYgNmIDUUji60PkKT0f9bqkYlXYHEWcHlzDPHkaa2GBbL/P6YUDjV53OP4mGPmbgdellBellAnwX4Dv+nIvmFow5zKXIrp9IneY5nhGg0E8UMNco5Awneqp6iFKiwp2KjHDZCatHGDkUdXGlNBDzYwcvhqRx5D6ZqlTyjhGmiZZqKIyfXHhEJDvNcFN2ofapQHs1aMceXzAy0dPYH3kD0hv3uThpSq9aMSOnvtYDhgAxm2HegTdA4FG+f4A4bqYPQUkZyrHeWn3JRJd5f78VsC//cjnEJqRLxizaj+GAY1liskIvAqJeTuQVyzV2SnCAYUU7GiJZTfaYD4YYC0pfbf65jeTC/iW5yRBxWbYqyL9Hdx776EIArLrG1STiOyAtJLbDnaeKW+zU4VkwrGK+vfFl24SvfqqHipRp4jG+LJCnAdI12HBE3zmwu6hhMYXN4bcv9LiP577j7zrT95Fe26Jikh48aJ64KaWxkoCvv4cU0Y+Tsb862f/NR17jeDyT2OernC9dzg0KTcFRS746PbTvH397Sy797BftGjkBXve7JqMUv1QOuqh6XidOwL5TS0NLFqqPT0NNLlYWKTr6klLcwJLX9uvOT3HxR2fD7zmMdIyxHXb4GOBy9YoRth9RlWBPY6hKJivzFMYI670A/wkp1bzSyLTD/vkozGmU+AGis35cVZmkdeOLGK0WsRX9ffXjLxS+GyPFCufb7iIyQ5twyXeVnWWPbtO30/YXlaMunO5z+OvScaP34NRqVB/+zdRvHKBpX3Vd3E10ENXvBY7pkldqNc1Wuq/KzqvfkE7e4zd8/jSRQYJZruFbNaUtJLPYib8cMTCfoHTzFQnJwrIJ5nP67bNPgWWYWHPzZca+a1Arhh5gwU508iD3S0iG7o64XM683YqUU7rKc0APDeFE29T//7m/xkslx968zp/+01rvPcJ7VK69y7yofp8gV7863FKVmuUA5anRxEEZHqOwuLK0bLgWo8ER/pAKpl/1w8Tmwb3XtVAPgg5PbhO/YH71QSj/j6nFw9k6Nzh+JsA8hXg2oE/X9d/d+gQQvy0EOJZIcSzod7m93IbmUSHclZAFb/qdo8tf0tlFByQVsJYPTQJFdo6/dWav1Va2VbTbFDBVHLSZ7KhGFpuzs2AXM8eLOIlZO6ynb5aAnnarpFZAjPpHprWAiiWA7x+9hQg2f3VX6OlW6AvGeoGPsjI95p6TNtgpmHngwFmp6PyuoHTzipxHnNpR636/VSojrKpRm7csiI3V1SIlOeR6K6bWzXyMAuRQZ+xqDHK1OcbJzv0oiHuEcVOAhcurIAp4dW7O/SNFvl4G+8eZedrXH0dL0sPMfLUtHHzlIWmq7oW04CuSBl7BrFrsPd//8ahoRITPMI8AMema8PGIOTGvrJUDqIJF7YnnF1pwlOf592/NyLQQ6Qv6jmpB4F8bKnXTTXyqyOlLb6p893k4XGcBwf8sx8zuab/HiAzBHku2I0H3Dd3H3N1l2fzu+gJi11mOvw0MKuuZ7+23fZt0srwIx8l3fNp5AXVNIRoROqrhTSdWyRJHWRhgTV7mH/ircf4wI89xvv+wY8wOjC/81c3b/Dxc5sYdp+4bpNHAiabzFXnSBmzM9bFdHuWidOf7FBEKYZTYI8VkE/ijHEyQuZVGp6De/oU8bUdsKtK//faCCQ1GbAzjlmqSEjGdK1qqftuWlX2JgnbR3S95bf+gHoEN55QLeyNb/omAB69IElNwU1fn1+vza5p0ihUJG6t4iBsmzO1k/z4mR+fTdHaPc8VsYLtjzDbbWjUqN3CyGW/jyFVvtE0JOuhBdUg9YLnsi8zum4Xe36OXAP51F10bu8ctmGzUl1m569D1vZTdsNd0iIl2t8pm4GA2YStKZDrekojlFScDE59M3zHL8MTKsvlZ952in/1fQ/QTSZg2zjrR8lHI6SEYHrPxClpta7GTN4irWSGuseWVo+XwyXqoeTUDfX3zUcf4dJyj3uvKWnFv3SFRhpSPXu2nGA0V3f5tvtuj+KeHn8TQH57DCAHno7pX0j5finlY1LKxwxtm2pnEpEk5TQcgCwvSPKCrrOoIkVdhyKaSSt+ZFAUkrA4AOSlRq4LmpNtxp76CK3YInj60xSp+qp5ZW0mraTqdzumRR4e44r/IoZOXBx1bWpFQZJ2Dg3CBRTLAXrzNp0f+EEGH/xg2SH5+biBZ3plsRNgq67AJ9+atQ3n+/uY7XY5YX3dVLuKNzZVVkSIRUv4bOlzM8etQH6EIggoDjLyWzRygDjsMxF1/Fzf+L7EkgWVFXUTXxtf44vH1Ll54W6XXdkCf1dt0Q2DUzeVBTE5AOSJaeMUKQsNT4UmJT5eHNKvOjz9WJPRH/8x6daWGvOWThjgkRZq59W1JCvtCr/xaQVCr233yQvJ2ZUWvReu8DXnJPuBkrL2dm4QJBlDXVCqxDDUTWJTaeWybgJZqa9zhD3Ww31yU3Bt/43ZuRaSVG8DjzaP0qs7fDh5mLnlR9jVC274wgtM9GI8ZeS3SisyTbnx3vfy6F/vsJhnKg8mGqpdHhD2FtmbpMisQSZmD3PVsfjmM4v0FtcY6SjUelGw1djjg89tYLp9RLtOFhmwf6VkscJSbC+UM0/9WNsyTadADK9TdUz8OCPIxsi8QsOzcE+dIr45RE7zdXQUQkuHe6256n3bTpNCD+G4XHiM44xJq2BSM4ieeoaJB1fuVe/hnjhBeKRLNVEa8XZ0TdWONCNvFFY5fV5UKixbPX72TT9bfm52z3PVWMMJJpitFkazST2SxNkBkrSnu7RrZimtrNZXmXOaPO+57BcxHa+D2VPSipQS27BpOk0ymXG0eZT0Cy+y+xc7nH4hRiLV6MXBfhmYBSggt6slITsorThOBqal2Lh7mAVneypV0+y0VcphZuLrDt56EpNU6kojH4+RuXrmiyAgExkFArOxWEorlTDnxKZEWhJnucP143Mc3wI7zDBfV/WNytn7D00w+g8/+ihf6vibAPLrwMHxO6vAjS/xs4Bq7AFopClGkhxi5KFufum5CmhS10QeYORF7jBJMqLMpT1R73ObtOLvMNTVlmZsMPnzT5XvXzhLqikjTyniGOE4LLZc8uA4N4IrBDobZLtRcDJJmVC9TVqZBuisejFzf/fvIGyb7f/zfQCcp0HL6Rxi5BsV9dnTzVkim2LkbcVagWoqWG+sc0lPzZkIk5YIuKHb2DsHc6YBWivIKCZzXFLNyG/VyAGisI9vNAmLPovVRXqaKE4Z+bXxNT75oOCZJzs8eyJjVzYRwQ6G5yHWj/LgrtI2E32JHMMhMizcPFXdho7KG3fikMC2+chjQuVeBAFmrY6ZTNjTxVjDdTHShP/wrkcZBApYX9lUC8x9R1oYQwUwo6t60ZFDvnBtqBwrUuIlMNSDtqcxvFdHVxEI1hprnDGusJDnOIXk2vAwkBfZASCvuQyClK5uJkm3trj8Qz+M8WcqiW+qkXe8DsNYBRoBJNevQ5axuBOxmOUQ7EOsgDwXAr/eVsXLrEFU3G5bRAhGbVUT+baJT+BsUa1MMO19rLkeRWpQbL9RFsWFZvWDbIOu11XdgH3Fzk1HwvA6NcfETzLCXAF5zbVwT5+mCDOyQkc6aOLR1JOMjpjD8vsZffU7XtVjEqUZsX1E3Wsv3FdhkM+cEtcfVs9kYtqkMmIr2CJzavRNg3pmlCPmDNctZ3sCSiYZbbBhruCEE0Vgmk3q0WFpxdhXn8VqVUogF0LwcG2dF1yX/Tyk7bWxej1kHJdNZ1N55XjreDlqrXNdkYGtYItiMCoDs9QNdl0ZBrREZnYOALl9C2k7cGR7u1i9ntpRAHleJcjVOW3EMVGlpoBaSgqd4V+EAZKUsdkB0y6HS7hhxsmbknQuQ0xusn33HIaE7ac/R/PqBTLTxj11qhwOLYvbh7ccPP4mgPxzwGkhxHEhhAP8beCPvtwLUlFQCEk1ibHylOJAM9AUyBcqCmgSS7XYlhe8cBgGKWEqSkZu3upakTl9oU5kNcgZP/15KnO6+cfsqtzjvdeRSYpwXZaaHlmgGhkux4rxXK3FHE8VkO/dAuQ5BiNZZcGJsObn6b7rR9QqbJrseU1s0Zpp5GnElYnq2swODFkuGbkG8mLic2/vXnb2FVMNMOmZAbs1BeTOARskoKSVFGIhSkZe3KKRA0TRPoHVIKHPm5feTG+sFip7eQbk/abgxk+/g32xz65sYMZDyBLi9ROs69FvsS5jOKZDKCzlfa05WiMPsKIA33G5VvVpvkPlVBu1GmbmMzB1iJhXQcYxZ1db/MzXKenmd5+7SLtqs9qp4OiAqOiqWvB6jLnWDxjGQ9xE3az7lnpwqgcY+ZH6EbrVKmfEFQxgNcsOSSu5UZSFmfXGOr26Opc1q81uuEv8+usgJakOMSs1creDRJat6okezbW8l3EkyWeMPDDZrzSYZJKdcUyRNfCzOyQnAiM9G/V7crUy/uA37CBNn8qSas3Pr50vm4IMW4HZVniFE60TdL0uobYVmr15yEJW3IBxlBEXE0xZxbGMWcFzrO2qmnlOGfm83i10KvNYgwlSGAx1d3BOyL52D736yNwhH/0r96nzkhpOee77MkMKQSuR5UCGqRxaHnuKDPRZwpASs93GbLeoh4elFXtfLRpWq1Zq5AAPV5bYsC0u+jfpul2sOW1F1oFvB4F8GjnhbEqMQrIdbCNGY/yKoD3doYxulLIKoIaPeB7NQGJZdx5dCJDv7mEeAvIKfhpiFJJaFhO6tVIDn+rkuR9gmimx3mVNM8mdYcCxLQjmchheZ3RXh8yAm595msWblxgcOYpwHLVbyPND3aJ3Ov67gVxKmQHvAT4GvAL8jpTy5S/3mpyCsALVKMTJU1JrBlKhHl22VFUye2gVSlrRxU4pbYZhShQL2hOJFJS5Gvi7aqsP7Aj98H3hi6Sbe7SOhWAa5NPg/a2XkUmCcByWWhVqxVFc0+W1SOmyVxspJ9IUo9I6ND8RVDLZUNbomQpUuu9+N0athr20hOc55GmttB+On/kVrpgZUUWSvvSZ2TkYDLA6HYRpqhvf9znVPsVEx+f60qQtAsZtxY7Ov3FLM0DzCEUuiGVeun7kQY1cSytBPGTfrlOIiNOd0yyM1Lm2dbHz2vgaXa/LsdYxcplxczrCLthluHK8fL9YS1Wu6eJLk0qRYRhCM3IfMwwILBdJgfWu7wchMHtd7DxgYqvPYnoehXYKfeeDx9T1zSLOrrTIigzP1yFplxTr7IoRoyhllIyo6K82sAKgKLNWroyusN5Yp+HZnDHUtVtPU64dLHYKiZkLlr1FPMtjTgO5K9pM0gnBRQU0WeAjEOU09ClATOWV5LJ6f6uA4zuFBnKlkW9XO0yijJ1xjMyaDJI7zx0dVlsYUnJ/6zjHmsf4zJZquGkuH1Wf4cbFsvFLWGOqjsHl8UVOtlVAVLKrGfkJlRR41OozTnwKchydEloC+UA7ezQjX9Y5JV2pG8LqS1TGCWmjSSEUFKSFz5Unj9L54R9mcGblUN7KSwsJfsMmM3TC4fASO7H6nu00L4Hc8DyKaMa0p3M6J1ItUGarjd1qK408nf2cq+2mZrtZauQAD+vBMuPML6UVmLXpT6/TSWOR6MUXcZZbkAmObqmCpzUOSeoupnYbKSA/XMYzux2aAZjWl2Hk/T5Wr4elGXyeOQR5RE2vRYE3G7A8da5kE5+KlSC0e0dUq2BZiC++hpPDcKGA4XXshuCNRYvk2Wc53r9GdPwu/bm6+nffmRhMj78RH7mU8o+llHdJKU9KKX/pK/18JguSisQNfJwiIzFmXXpTRt6tNGm5LXwzOyStUDiMwpQgFnR8yKsmwrJUu7y/rTItgG3UDTj88EcAqB+JMBtNisxRsaVbLyHjGOE6/PhbjvLe77ifs3Nn+by8DMDVecGJJKPW7HKtP5v4AXBlL2BElbZuWLI6HZZ/8ReY+7t/hwdWW4x9j71oj9Tf4X955dcJTQOn6ZC+8ldw4wVknqumDr2yG7Uahe9zun0aW2c4TKRJU/j0awp0XrjQV8Nrp0fzCEUmSGTG4pzarh3SyKfSSjxiy1H/v1RbYnFUIbJEyRyujq6y3lgvwWNTs3v8HTbnZopZqBm5bdiMpYmrO1dx6pD6iMAn0LuAyVqX47//e7S/93tx8gDf0t24brW0V05zl8+uenz3QyvsRXs09Wm2Lt1Emg49MWYcZQzjIT0dbRw4hQY4EyklV0dXOdo8SsOzOCMuM6mts5plXA+3VSeilEihztuJqvo+vboO+5I65/4NpUnmYUDdqWNoUJsW0qYFz+TyZeUYApa3xCGNfLPaZRwrILdki1Eyuj0CFxg5VRpFgdE9yTeuf2PZtDK3rLK3880NOm4HAwNhjZlvJ4yTMcdbx+l63bKQZt6rum/XjD1GWoao6Cxyq9PBrELcn3Zfqu+xXlUg1czV9+k2Vmn7kLdn3ZZREZCeWmPp5/8ZrUrnEJDvxLu8/I57eP74Y5h4XBpeYlc32vWSuJRWhOchowPfffc8CJNERxSb7RZ2u4spIZnMANsbRSR1F6PeOsTI78aloqWFttcuzQ3ZLV7yY+dHICXz36UWufs2DLYmm9h+QqEblcgzGG8eYuQAdrdLIwBh3H7N/smn/gl/64PvINnZRnZaM0aeKiBvRuqe8J1aqYFPr5MMQ6pWhKf99EIIzGaT7DnlSd9dNGB4jUIkvLbq0b70KtUsRtyjMpqmhdi8f2diMD2+Kp2duSzIKxJnMsbNUxJzJq0EmpF7jslKfYWxkVBEMyCXhcMwTPFjQXsCaU1/hXikJBPdbr1dDMhMQd7v4621sds1jFaTfDyBubtLRm7YDo8d6/Ijjx/l0cVH+Uv7Ml/8v97Da2uCE2nK0ZVlXtw4PIfvWj9gKGvU5Ew/bH77t9N55zt5ZL3D/thlP9rnl/74J3natfj5+36a5t1vJosc+O0fJd+6ClJi6pZhs1ajmEw41TmFoz3/Y2nQxOeGnmKUhxEfemEWr0lzhSITpEXM6oK6sW7VyN/ySoH8PaMcs7ZUW6I3Mug3jTI86Nr4GmuNtdLjvjsdTjHZ4VJ7drOHU43cdBgXBk6ugdyuQuJTTCaE2u3Rj/p4Z85geB5uobRzALtSLT/jdKH5/jct8n2PrrIX7NIMIDPAu9lH2nMsmgrIR8mIOakexNAB2x1gmwb9qM84HXOsdYwmPuvGDjfbj7GWZoSFci1EaQE6o/q4o75PTy+OIlfAEl26qM9xQNNpsjOOuTkM6biHg7OSy5cp7jlOZENrz4Fgj2KyTxYabFTnFCOfxDQ0g5wOJD54jEyLpgSWH+Qb17+x/PvFVcXAst1tTMOkW+khrBH1htqhTRm5nOZb3/21AKyIPSbpFMhnxTmvlRHvaKqoGfmKG+OYBm60C9Ue7UqPli/J27PXBdm4dO203FYJ5FIqmWLvO5/gU098Fx7LipFPB6akcRkZcDsjPw+dYzQydc+Y7TZuWwOUzi7Ji5zGOCNt11QN6sCoPzsZc79Ozey63dLWOe3uXKwtYhkWjRcuYtTrNJ44i13LeOCGxWBvA0MCTW0WmGypyT6tW4x1rTbNQNwRyM/tnWO4dxMjy/n167/Df95Q8bpZauHnCT2dDTV2KqV0UoyGCl+ylLodUZubzUQwWy1kFDHxoL9Yh+F1gizgjaPN8mdqZ88Cs0Js1r/DtKoDx1cFyAsk0iswRyPsIiMSs+aOSAN51TZZra8yECHyQEMQ0mIYpgSRoO1LpsNeSsfK4v0A9NMxSU0BSP0uNVnHbKggLZbuh62XKRJV7Jwejyw+QiELfnv4CRwMVnLJvWuLDMOUa/3ZjXml7zMWNZz0dt3qkfUOeVojlzm/H1zm3c4K3/PY38deXSdL6zDZJP/dvw+gqt/MGPlqfZVKrs7FqDCpS59NU5Kbgo6R8+zlA9ur2jwyEwhiVhf1zXNAWqlYFY5vSox9C7GrPvtidZHuqGC3WRDnMVGmClZrzbUyAGpgTUM4drhChVFFPQCBBwIDy7AYFAbWFMidGjIKkFFE7qqfLbta0wCDgsjSI+gq9VJamWr40+u6t3cdq4DzKyAkxGGTeXPCOEoZxkO6OqM+dAWup8771bFOh2us0xypIvGlxiOsZeqhvza+xjBMKXQI+lFXyUlTRp6lCrCKK8pXLMOIul3nZ3/3C7znt56/g7RymclSh8sLUIvC33YAACAASURBVOlbEOyRbW0Bgu1KFz/O2BlHdLSXfDqQ+OAxygOa82fg0Z/g7NxZ5ipz1O06neVj6jPtDyFPWajO4zgTHE/VKE62FJAb2tFjLh4Fu8qS3CHQ3bs1SwNBnuE0IuLNiSqS2VUwbE41M958vIvwt6G2QMfr0PYh66iHyDRy4jwuawRtt80oHiGlqhOkhQoKa3o2Vr54CMgXk+CARu4hD2rkuxdg7i6aOtrXbLVxOwqMpxKEn/m0J5K829RAfkBKjMc8pEcEdryOKk4aRukl/6F7foj/9G2/Sfz0X1F7yxOIapvKXMKpqymTHSWxWe0D+jjcJq0UrTYtH+QdgHyUjHjnwreoly0f5d+cfz/SEOSJgV8k9HT9amRVMHQxMx+NZvM6LYmhM2eAsuB55YiF7zVgeJ0wDbl5okOBIDJt5u67W/1sd8rI/3+QVv5bDrMiEMN93DwlFLcz8opjstJYYZ+g7OxUuq/BMEwZR9CeQFwCuX5oeifAdNnPfLKaWikbx4QC8mZT5SAs3gejDWQUHmpGemj+IUxhcmH/AsfMKqbX5OyqugG+cH22xbyyF5A5TUR0O5A/vN5GZupB+BY/5B98668BYC0ukY8nxF/7z8kvqO7HKSM36nVyf4JpmCyabaSAIC+oSZ8tmZA7FssuvLJ5IIxHGBS5gWfFHFu+AyM3PTyN682rI6QUaqbiKGavCRvjDTYmuj24sV4W2CY6Pxx/h81hxM7COsI2iWyBgU2SFYxygZnnymLl1Mj1Qyur6vuUQB6rHUusO9ucSg2yDJlluKaLQJTFrv1tBcrnT2pJaFihJ0altNIp9EBpzcgBLg8vA3CseQx3R5VlzlceZE1LUNfG1xiECb4O/li1lXzU9Cwc0yCKqio2dUuPQYsUiD13dZ+LO5NDCYiF75NtbbE953FlUWDugfT3SLfUlne/0WMSZ+xOEuar6lzeiZGP4zFNrwumhSEMfuTeH+HtR9+OWashXO0l719ivjLPYjdhfWlCw24wV5mjV+lRC0F4Wk5srTJX7BDoKVlTtw3RELeVIZNMJXIKAV6Lh+bgP//U4yqior5A22nT9iHtqEWyXdcD0Z2Zjz6TGZN0UjbXzFfmFWAnC2wFW1wdXaWDSUv6B1wrHsXUtVLkqtg5d7octmF22iVzlSMF2H7i0/ZBdtvgNg5p5EQjHteRHEfqRxCmidntklxU3asNp8HpSZ30xg1qTz4JTp3qfEJ9nFF9XQG329WGiJHe1d4irRSn72V+XLD+xuHUzUIW+KlPVw+O+NG3vgfXqhDXHPLYIJAZHe34GdozRp4PhrMscktC40BxVcsvG6sVfKcKg2tq8HKnxevtFc6311juqWtQAvn+/4CMHNRAFun7mLIgOPAxphp51VGMPLIKijBUuSF2BdMQDMOUSZDT9iGsaB1wCuS1BfL6IvuF0sWs5WXc2gRq8xitJsVwVI71ksHoECOv2lXu7Spt6oRwwW1y12IDxzJ4cWPmC77WDxCVzh0zyXt1l8edBX5ub8Avnfh+jK4qGNrLig2+//I9XIwVGzArin0rRq4u+pzZIrUEMpmQAbt5iHRt5m3J+a0JmZ4EJJMEJNTNiOMLDYRt36aRe5o0L10eIrMGaZTT9AN2G6pIOG2mWWusYZs2Ha9DbgVqBNdki61RzMaDT1K/b5kQE6TJziQm1lKYjCJwaqVH36zdMoxBd73Flvp3V7N7GccIIQ4lIE50/nhycpWwYhL1BR1UsXOYDGll6jolbgXTUdfi6vgqlrBYri/D5ovs0eJm3mbFrGKggTxImbhqcTpi6fFvQtCrO/iBx+IAhJbNRJxgUmUcZewHKWkmaDgN9qN9FaQG3OgILs7bkErSzR3SbfVd/e58qZEv19SCMWWrB49RMppNegJ+6uxP8Qtv/QVAFe2zyITf+Fbm/D0yhoyLDU60TyCEoGuqtnZZ0+SjtUov2yLWCZfl+0YDvJa6+NMoYirt2f062YL6Iq3cwckgbtkIAe2aOk/TBWHq8hjEg1LLX6gu0PAs0lgB4+c2P8ec4dLAnxU7DzLywRUlec7dRS3ylZ+62cRsafKhgXyUjGhPQPQ64GlpZSpnxiMet9r84Xf9IffPqR136zu/k/HHPsb4U59S51+7Vdwn3sKvf26H6v/L3ptHS5Ke5Z2/L/bIyO1m5t3q3lq6tt7UXS21kFpSL2pJSBhJILAByxghG8MY42EbG4wBezxgYMZgMGMfM9jGPgfw2GMDY1sYI4EQQmLTrpZ6U1VXde237p5rLBkR88f3RWTkcm/dqkLqRmfec/pIde/NzMiIL554vud93vedlyzm+Gflw9Rtqs6hezDywdvewXpF583/4+yYjNqP+iRpQrUnf1ZeWuVNR97Eth0R+dBPh9RC+b13NAfNthGOQ9xus6usnZqRQGVUzJN99xtHqzJ/1LnGIOrT9Cr82Gvey794/XsomwJ+98fQfvFRtLL3pUl23k647uijexSSnZlGrqSVwBQQx/h+j5JRouZK10rS7mEkMChNAvk8O+UmKbD+V9/CoZ/4x4i+1ATz8lklv6T93hiQAzy8KE33xxMNnCqWoXHvcpXPFhn5Vh/Tq0PUh+F0lvvrKpf4lnYb+7V/M/+ZsSgv5NlnzvOfe48CoD/zy4Bk5Nlg4aZWIdBTtGiNTV0nJgXHYc5ICIcJL2xI9pU97ctGn7taHsK2p+yHmdNj+WqfUrdM58p1NGSl6cXOxXz6ypGKtL4tuAuYVoeO0STtrHGj47Pzpq9m9T2vJNAMksRgre3nOY0kCMAskURSbzfLVbTEKzByeZOGhsAQBqaaLlSUVzJGPtiQQGG3Fri6ZBKsBdSSESOvqq6XsdUENS/zxfaLrFZWMTQDrn+Wc/pxOkGM6dZYFjYXOxfZ6UfsOBKgWoXq2GbZYrufcKotj0nYNloQEYajHdrVnUHe7zqzHp6vhpxvKm39yi7hZhcEhHMttrohu4OIlco8utBnSythe8ScJ0JfWCSefy0s3MvCCx9la7DJFzaf4URdVlc2gh6eD7FqTEVtlVq4RpRmfmolHwx2sGryOwfPKyB36qMe+oqRlztqelNZY65kUSnJe69Y2Qqysjb7LhLITfy+fCiu9ddoaCWqFKQVW/ZQAnLHCq3TlPwuPdORjFolWIWqXu3vbmAPVXGfXQHSnAjgt8Gp5ucBYP57vwf77ru59vd/mOH6Or0//EPMw4dZr87zX55uY1WHDEsmZ86pXUYz67NyBQwX3FFTNYBuovMfX9ti9VKXzvs/kP88q/atdOW5MRoNvubE17DrJGwOQvppTNWX639bOcWynf+lq/IhohkpVKalla1jDbq6DqQMoh5N12NQb9JacOBXvh7+4Kdh4zn0imyctV+8ZEBeLo3Au5vOYuQGq5VV1K6FaNDFNVxqrsl2P8RuK5ueq5wcWcOsUpMttcW3vuJVeI88IlvYevPyBHc6pN4CWBXS0B+bTAQFII9isOVie3ClxueutEmSlN1BxE4/wq2qKrEZszsfsK7RSV0uxqMm+uaS1KD7l67QCeRWVn/uP8DVT6F5pby4oUaJ0IRYXGTNkIxdd11qyJvu6avyKZ8q/a1u9qi7JsK2p+yHdgiJnqKlcP8Fjf5l1buk5nKxfZGLnYtUzAo19T3nS/NoZocdo8Vw5wpRnLJYsWHoE2g6caxzeXtAUCxAsso5IzdrFdJ4BOSJapUa6XK7rqnCrzzhqTv50NlQZeXtZovzC+Bf6+DEfQaDHu2gTSVSPXbMJomuqhHbFzhaPSofpuvPctE8QcePwKmxisHlzmXag4gtR64RLRx1j2t6NpvdgONteUz26dNoQUTPH63LKzs+dbvOVrCVA/lZr8eLtSZoAv+6z3Crj1G1KJVsLmzKa7hQdWk6zSlpJU1T2sHeQG40mgy7Ibz3N2md+WZSATtRl+NVuatrdLcoD1LCzIFRO4IXbWHqXdJUo+aon/vb6GaKudiUHnkAt872n1zl2j/4ERgOoLwAiuVtujHHWx4L9XFpJVsX2/72lLTS7dZzd8+cUaYsfKrqVtJcZ9T8TRW40TqF6/foWCWiOMnBTFMze/trUtKw5hfzgrtcJw/axGaFX/rIeYKhupa2zcrP/DRJr8fVv/dD9P/4j/Fe/3p6QUwPByEgPNGilMmL84qBt69IWUWMF6R3giG/d3qe6wsW6z/7s6Qqz9JWTbFKnUhaaufmeGT5EcKyQ7cX0iOl4gsCy6Gnlpdeq5Hs7nLpimplbWn50G6A8uOPUX3nO0nm66i6OPygjdtd43uaf8q/Dv4OvPhH8JVqp1bSXr7SSrU00sU76SjZmWvkps6yt4zatTDs93B0h6prcmV7wJwCia470nRx6mBYbDtyITazkWzJELwWWrUCUSQXWfUQaTScYuSPrT7Gjz7yozwZRHKLBzywWqMbDDm/2cutiJW6AukZ8sqR+EXOpiv8wdlC/2Ll224OdqmFPWLdQKs24X3fn7tWAKqpTWjAUL/CmnKbGCUPNx1i6RrPXJMLK0uk2EYEg22EbY1r5MLADVM6CwkDCx58MWBwRQK5X1/gYucilzuXOVw9nDtYFkuLYLTZ0pokbXljLdUcGAZEuk6aGnziwhZRxsh9H6wSsWLkdq1KPBwBud+XD7lQT/BMr+B3l8fpGm4urcSqGMdtLfGFVkQaDgm7OnpwnWE6pBRqYBjEaZOh2CJJEy61L0kg33gO4pBr7ik6/hCcOofjNNfIb6iGXjm4IBn5RjdkZUvQrZjorSZGFLPd0VmoyOO8sj3IG2eFFy5gLC1xLVknZA5toU6wrRNu+1iNEmXbyIF8vmLTKrWmpJXBcMAwHY5JK8UwWk3pxBCC+RNvzX9+XJ2vufZ12TGworz+NemEKOnbpHGJqqvWshocYt11LJdWBhs61z/YZuc//TphVwdvIbfvrbsR/+6vv4Z3PSzBZpa0cmNwg4bTwNRNKo5JHOsc8iQ4VnX195q8lsJxC4z8eSi1oNTA7ndpWyUGUYxwHCJDoHdVV1MF5Gcjl25WxZzp5H6bs22N/+19T/Oh50bn1D55koUf/AF6H/0oSb+P94bX0w+HdFNJlMyTUk5JBDTmlWtkohgoi64/JMHhv765SnjhAju/9uvy52r6kd320efmEIaBrunMLR5BDIZc18DzU0K3nJNQrVYl3m1z7bq8D7RKI7etApSfeIKVf/J/4FllumpAx2A4oPT8B/iO7Z+h7jnwbe+HN3w3lJfQreHLU1oRCMqFGYTtuMDIs+G8poapmzhl5dns93FNycgvbQ+YU0/rjpfKQbO9dfBUL3BbLoQ5qzoa8ebNj8z67TZUl0miIcIa77xoaAbfePc3YvkdtcWDB1fl6566vMuLmxLI5xpKc5sxhNnZOcsV8yi///xo0Wm2TeBVafm7HNaH9BwP8bYfh6ufRPOvkYYhaRhiDwVDQ2NorrOm3B6WVwHf59RimaczIO+PMuK0r6JZttTI/5/3wE+sYP/kCk4IPReePiJ46PIuvgJyWodzRp5NggHJyBPR4bqYw+hdB1JZhj8cEGkapDp/cn6LyCgy8pFG7tRrshhqIBddoDzCfSGZbXaus+EZRY1c7LSJdUGlPs95dWqDHRN9KG/wUihtmsQNEhHwhe0v4Me+BPLrTwGwXj5N24/ArXM4iiQAdXe4poqZitWGrbLNZi9gYXPIWlMjsU3sCNZ3Nd587wK6JnJpZdvfJjh/gWsNQTvaZNg7gXFsGX/bJOqmmM0KZduQVkcUkLutsQ6YMGJ3e0orjSbxlizHzqdBASeeez8A9a0XKQ+gl8mSCsgdvU0au7n9LyMXzunThC+8QLy7y5X/dA5d9cvpXHagvJDb9645A8q2QaCSpsVkJ0hp5Ub/Rn5MmYSyWpZFTGU9qxxV1kjHhjgmjSIprcxLB4bV79CxPPwwRghB4BoYPV/WA5yVMt/PfbLDL39KgZa6x9OgzVMb8txmRCaLuXe/m/KTTyJME++1r6UXSkYOUDspd8FdB5rZLIAZxUAAvXBImth87FSK+8pXsvHP/znJYJBLK9ZOH6M5aiV9dPUVVPrQFlDqJ4TeCMj1qmyctbEhr4M+Nz/1eQCe6dFLQpLv/CMCTcN5+L3wXR9D+9sfg0OyWRiL96Nr3ZentGIAZmEq+E482uYMohjX1HOWWFESRjLo59LKVi+koZ7Wux5yaklvIwfyTQU0jVSMtPNSs2DWb0N1hXSYTjHyPIJ2vsU7OV/GMTU+c3mHi4qRt+ZVS8xJaaW3ieitoy3eyx+e3SAcjnokbHl1jiddVvWALaNEfOptAGhJVs7bk95TxyGwt7mh61iaiVmqkPgD7l2u8sw1leVXbTQzIBe2LQdNPP1f4NijiDf+fdxIsFNv8tljguVOn+Qzn6JrOFSqR7jWu8bV7tVcHwfVL12knBcl9HhAhUHOyIeaDqnJs9c7uGU1I9T3lbQir5U3VyWNy3mfmXAgz82N+Don6icQSsbKkrIZI0/TFL3dJ6q6zDkNLrcAXcPfNnE1uYDtIEHzPIahBI0/viadPzmQGy6DyjHFyGscVq6J893P0baEOt6RhXR1zsWPEqprfS7NJQxNHSuCQWDx0OE6S1WHKzvSS77tb9E99xyftK/z6rmvZdh+FfbJuxgOdIZ9HXNhbgSijIB8UlrJujjuKa00m6CKxTIXkSt0lp7/Hdj4Avr6c3g+eUM46vIhrBtdUA2zgJxc2PfcTxpFXP6uv020NWD19dvYdx2ic8mV04k2NkgEXNMl68z1YGU/zAZs7AQ7rPfXp4B80ZVrx1EdP6uqBYBQA7oT35eMvCUrTc1eh45ZygHP90yMTsB7fulP+exnpQPlnvtO88HzqjIs2IUkRoRdLg/kPT0J5EIIVn72n3LXr/8aeq1GPxjSRxKGpZU6oS6BvOE0pINmD0be8YeQ2AziAfPf+70M19fpvP/9+TnRd7t5RSlAc+kYViwnITn9iGGpklun9ZrMxW1vyOutzS3OvN5ls0wv6uE3jslrXT8G86fBLHSDXbwfI91muLU1loSdjJcEyHVAc0sIV17wneEIyPthPDb9pVZT2yPfV0AuF9Gc3yEwoW8JBeTroHqBb2kCLU2phYPRiDelkYM061NZJo1ThDk++xGQCaGgk0srhq5x/6EaT13e5eJWj6ZnUco18glGvi67Fy6deIheGPPxFyUQhcOES3qF5bBDYzhg1yxxrqODO4cWywue9KQfW3dK9K0O1w2DxdKi1Bz7Esg3ugHrnYDra2qgrJFC+4rUyDcvgtDgnT8Pb/xB3BC2jCGfvUsB7ac/xnqpzmJplZSUOI3HGHl2o76IvGmWNNk+k6FPpGnoyiaaTYFJghDMErFi5NVmnXTo0QnbDJMhYb/NrqaxO9zhVP3UtEauhl/0oh5eLyapetSdOkNDkBxexN8xecCQw4otP5aj43y5O/qjq7LbZA7ki/dTdu1cWjkykDf85eBThErFSwrVhm+5dxEvHOB2fC7VYwZGLF0+icMDK3VW6m4urbi9IUYvoHbyXl5b/VYA3Puy2SkCc7FF2Rmto6Zn59Pds4ZbUGDke0grumJ8az/2YwR/78f5X38l5u0vzqPpNnzk50huXEBPYUe5cKgcIkUQmF2SqDECcn8HDBdbVQf2P/5xWu94FaV5n+qZZQabFlFfEG9sElQctlV3yU4kQcszMl+5TtWq5q6VrGisqmyGTUvuCAxd3gsVlXTV1Di+ZPOqrH5tyWIno9vJpRWAyDPRuwF/8IUNHnBjhhr8o7/8OH2hCpSCTl4YNDQrvOXexZzIFENznLwtQTcYkqLRTV1sQs4dMdmpG7KdbvfG7GIg9TqR2oRJiPnwGfRmk+4ffCS/ZmJ7Ny9EAvLqzvIArH5EXC7Tj2JJSqpV4t1dhl11PhrLU58HipFHPfpD+eDKiuTGYukBdCuC4TCXX2fFS8PIUwGGnfdI2Yq1/GkziOK8jwbAXE1qy2GvkzNygEbQoVvWCTUh3SO9dZnAAbaIqScJem+j4GZpoRWkldRbglQgxIwmOWFPXvDCDffASo3PX23zwnqPI81SXi2XFyJlcUMC+d0PvgZTF7m88tSVXW44VSrdLUqDLm3L46nLuzB3DE2VTCe9LkkQYDhlfGPI047LoreE5pZIfJ97lyVTeuZam7V1+RphCCWtWKQ71+H4k1CRDMAOYVPrc6UJ624FkSZsuHWWSyPwngXkl5Dn/7TbxdQ1GAaEmsA1JaOuqGRbGmT2QwG6Rq1WJo29vNHUcNDheUter5NzJ3PPflLQyP2hz8Zgg8oghXotr6b0Tx6m16nyevPjGMMUY3NXzhEdyHPwibVP4OgOC05LDspdeoCKY9INhiROndWBXPQb8WfzhHmRkR+qu7ylIln7tQbsagFWBDolTi2WWZlzuaKklWW1q33Xm76TXpggBFTPvCp/L/PQMmXVmnmuZGIZGi23RZzGYyXuN5NWnHvuQSuX6f3xnzC8cIHjN+ANT8Xw0Lvh079KrAb8bmUd+gyLHW+BnhmSBIv5MTDYBreOdfw4mCbumTO0vvEt8todkw/1zkc+wXBzk6heYiuQbK8bdimb5VFPEqS8sjHYYMvfyovGsgfG6crreM9976GmS6AuqdGMwlYP7DU1bq51mjSK0AYy2Zk50yLPwe0P0TXB8jCkXRYszXk88aBM7u5ub3JDFf3ce2yVhw7XuLjVlwntPSLLsXVxGA7a/JdvOc6v/RW1xvewHgJ0/AhLzQEexD7lR99A7yMfoat2/snmdv6gBfJ+K0u9GLsXkparxElKFKdycEa/T109GLXm9OeBBPKUNM8p5TN5i7F4P7otd/X7ySsvCZBXAPTRdBxf6Pk8usEEI2/NyZMQ9seBfM5v06sYBAK5lRxs5dLKdhrRiGP5BM408lJrJK20O6SKXWhJoQIti6w82BndcA+u1hhEMZ+8uM2RRkl+lrcAVz4x/tr158Cq4LWO8OqjDX5fJWc+fmGLdbeO3mmjba7Td8vSm14/ihbIv0l6PVLfx3QkWL1oCBa9RTklaTDgvmV5PM9ca7Optm2i2pTSSjqQtr4zfxmAdDjEGqb4JmhC51NKp1x362PgPQvI15A3w0lHsZ+hTwh4astXrStGnvnIhxq6Y1HzLNKhZFPb/jaJ3+FpUy7OU/VTuUY+mexcH6xT7Utr15wjb5D2kSZ0Q3afm+ef/qsYnjlL6ZFHGAQOOhZ+7HOkegTt8p9KeevYo1QVwARGBS9NadhzhGIdoU1r5ABvqag5pk3BVtrFiuF4vYGpa6zU5cit1yw+wje4bwDAO36KbjDEswzMlbswSnLNmiurObi1VNVo1oq2KK9kPVH2AnL7xAnu/vjHOP2HH+X4f/tvlB5/jCObAh75LiAlDuXtum6OHkgvqKk3SbAwkncGO+DU0RyHo//u37L6C/8SoTz+tnYZq57S+cDvMNzcIJmrMkyG9KIe7bCd6+NZ1OwaZ9WYtpG0onIkcYm/+xV/l67qoeKolrc5I7+eWQ9P5d0Ai0A+LDu4fkzDszC3u3Qr8vjf/ZgsT//TZ1/ktz8hXS+vuecY96r1/+z1vafJ91SOrZc6BL1dHrr7CR44/Zi6AKoYqDLNkLd6IZ5quNeLeniPPU68s4P23AVqqUPa72MUpJWMkf9fL25idAPSsjwHgyjOqztXfbn2xNwqsyL7vGyNzATy5ikMlRMZbr7MgLyRMMbIQ81kty+fsoMoHpvHuNCQQGNHcutRZOSDikUohCw6gJG0MuzTiBPZRKu3Lm2EhoVekSc7ae+SqDJqoRI8Y5Fly+1xIAeI4pSjjZK0Lx17FC58ZFS4ALD+rEzuCMEb757n2esdru/6fOzCFprS1ZNeD7sxJ4cOzx1Dj9Qw6G6XJPAxMz8w0kkiXOnLrZcslmsOz1xrs70pj1FvLElpZXCDNNHhnrfL91I+84EFrdI8n1wYAXmrNEfVquLozthA6Dl7DoHGJvJaHLHUeYh8QgEVxbTqtaywJ5SVnaFAc03qJYs0lotz298mDTo8Z9lUzCoLpYXc6jlpP9wcbFLpg9OYzy1vG6sSUF7x0SGBCYtfW6L6N95Lkgoqhjzmo9Wj8Llfk77g01+Vg2lfdQE8rFqHHo6HCFMf7/8BvCLdJRaCtTqsqzzFA6q3xaG6S5ykxMMyb+IeMAzMlRV6wVACpl3FmYuBFHPlcD7Rfl45XjKNu5jwvJm0MhmNe88QX7lK4q3C6b9AooB8TR9tsV/w1C4zXChIK7v5jrH08MOSPWY7yI0vUD1dov+JTxCev4Cmvu92sJ0z8mLU7XpeOJatlexzOmqW7cZQzc/NZIjMnXTjHBgO1A7nw8bblpdLK3HZxRskND0La7dPrypfd3hJfs7zF6/w4adkX/n51nwO5JM6eTH6QcbIXaJBm+97+Pv4odf+kPxlTw12KRTnZLHRDampQRK9qIf3hteDENQ+fY7lQDL1rH0ujIA87ekwHCLUbt+P4txUccjfVh7y2ZN9snO9L5AbFnrW4ngfC+JLYz9MUzCcfKsS6tIbDnJr5BaklaWmzIzbQyYYeYdB3Z4AcuVaCXdlorN7Q3nI5QXQFJDHu21SW1X5JTOAPGfko65wd7XK+c16pKn8uscehc5V2Hph9Nobz8CC7LX9xN3yeD703A0+dmGbxRMj9ltbmufzV9sk9aNomvzuSa9HGoRohk1dTRhZKC2gqT7eaRznCc/Ojtq2NQ7B9nlE/xqpXpZtZdV7AfiWHBL82cW72S7P8WzjKJ5lcLR6lNXKau4FBqmJlvQ6kd6jnXqs6Mo9oBh5VSWxGg25ANPAB8MlGQo0x6Dumjkj3/K3IOhy1jRlolOIfIBIMsHIN7o38HwotZZkIZPhcvGIC29+K//88Xv44b9u03DPIX77h+W5U6X2R8uHZXL39FvBLudMsacKfw7bcn0diYZoljne/wOw/5k9xwAAIABJREFUrl1mp9ok1gVXVf/wBxuqwZQqW7+yMyC8cAHr8GGEYUhGbuvyJr/HoH68j6g0KavPzoHcUf1WChbEdtiWjq0JsNwr7BOyI2Lwwnl4+08Tv/q7AbhhDIhUr5sLtomZphDWx5OdBTIAjP4ddqg8uARJQtJu59O1tv1tOlFnardQs2ukauDXpLSSSRxbocEQPU+yair3lay/CM2ToOk5kHcKGjkVj1IIC66Ou+vj15RGrOnEZhkn6ROrhDlOleWaJHL7AXmRkSeDCeaeyaAFT3cWm92AmjMCcmNuDueBB1j47BWWAlXoM0MjD7uqOlvt9vthnBc7zfsdhJGMlecX40CMHDAOSxK2X+Osl8hHnoBhYTTkiQl1gx3FyP0JRt6oyW2QHUHJKFF1Tcw4ohINCGsuIQK2L8g/zoB8sEVDt5W0MrIlCl1Hq1SkRq7LkyiiGdu0GYxc1wT3q4b7RxrK53pMbdkufET+b29DPjjmZZLp7sUKS1WHX/roeXYHEcfvO56/38LKAoMo5ppYkCW8QNztSidIGnAylOdjqbSEVpIXOFU6+dn1bu5aEY3DsH0BjZBUGy2EjJH7lux6GFeqfPe7/hGfnj+FZ+t896u+m+9/+PunvnrZkF33rqVzzKcZkAcEpCx4ZQxNcNeKBMjED0DTSIYmuq1TsnQMCo2zog4vWhp3N6SGOpJW5IMrsx/url9GA7x5yVzqdp0tutT+8U/xO/fXsaw6vOY7sJ76VQ6LNRq2BJSjUSCv7yv+IjBKwnVRA4CNivq7IcI2R/0/VATnL8iJ6KlgPZWA8VBLXuOVurx5r2ZAfuyYfO8gziWM6v1Nll+zC041/9m8klay+ZCT0krmBDlI2KckkIfnzkJtlbisEnrOqLXuBRFxLIpo0cMr2g/dCSAv/Ns+fhjzqGR59oI85zvBjmTkE9JKlrOAESP3LAMhpPcaoBPGdIWXO7jypPbmpdyxkrV17ZgjaSVRSfNlLcDtRoR1L/8s3alybwPuqijQt2sIIbhvucrTMxKeWfQDSQR7uKThRHKwtyHJWaHbahabvZCGK797Pxvf9uijzJ/f4fC2vF5jyU7VUyXsyHNuqH8PwjgnjKVeb19GPgnk2QyBydDvehCQLY73ipeUkRsLkln5uj3GyIsaue5mE3JGjDzzkId1TzLy7REjj+KITtShYZRlT4nepixIyN5Plc+mkQRKMZzxdA9GLKAYDyogP9pUQN46JXXyDMjV4GTmJSMXQvDE6XmeX5ML6syrTuXvdfgu+ZR+qj+HpsbLJb0eSRCgJX1OqONb9BZHTNb3uXdZJlX0MCC2bERd9Tl2HJKCjbMorSx5S3i2wWZPlcZbBo8sP8Jjq49NffW61UIYbdbSOepqzidDn4iUhlfiM//wrTx0QgJpNs4rHupolkAIQdWuAYItf4vtuENfl/o4UJBW5OuyLP3Omty6Z1Jb3a6z7W9TcQyENsAWZXj0+0g1g+/U/ytNR94YR689Lfuhn5LFMxlT3FUFJUvKT3w0itBsa4yRp2lKeOECS/ffTRp7+OpheldZ3uSH6nLdXd7sEb744gjI/WjkUMmYnTNiwxkjL5klSkZpHMj3Kc+fFdbhw2CaBGelvJCBYdcZNSY7N+xwIow4Zm7JxDTsz8gBUVmk+lZ5zkoLkiht+9t0wk5uPcwiaxxmamYO6pomKFsG7QzI/SE9rZwDeb5ed2/kjpV4O5NWSnlffVFVElhvDS2FeK7w2XaF1x4y+LtPLOf/Brh3ucpz19vEyWwrXi8cslxz6AsXLZoA8v7GGBZkEcUJO/2IZkne372h3M16jz2KlsKZT8nvVQRyYZponpsDeQbsgyhmVzFrbRijmWJsZ1+MgzJy7egrEXpCfPXszN/DSwbkCRg2ta97F9Wf+TnatsfuQGnk4bhrJVsUdgHIG6qqM5yrEGjjjDxb4HN2TbK1gi0RyBtnparfhwhnzFacwcgB3vO6Y/zI2+/NK/+mdHLlWMmkFRjJK/MVm6PLjXw+4MrRJVxT52NbHtlcjaQrk51a0uNMEMip4OUVtMyXO/BzndAZhtK+qaxUYuFU/p2gKK0IFkuLahCDOk2FB+VkNOwWmtFhLZ2jHCpZYBgQIgfderYxJZEkkUBN/2LOtTEos+1vc0n5ik/NSSCf5VoB2L0h28hm52bOkQMNPMtAM/oYwoPqMtunv4m/pH+YM85hTtZOcOrsh+Hur5Yd2BgB+Y4aQnEi8RCJwYNBiLCtUbUhMLxxg3QwoH73SUp6PbcoauocliyDuZLJzsUrpEGQA3kviPHUvNC8X4ddydlwBuTAVFHQZMOsm4UwTexjRwnOKSBv75JaJpEp2Bps0Y/6XA22ORFFHLfUOo6HEHamGbldkdZUgPIi1Xe+E2Hb1O87A4yklUnZJ8tZLJQW8toOkOe6kwN5hK9Vphn5kBGQ59LKSCNPVIJwcVsWAyWNAuA5VfSwmydQM1J173IFP0ryKtrJ6Icxnm2QmB7mcOJvehtjWJDFtiI4i56SRxQjdx94gL6rcfg5ufspSisAeq2SSyt2Yw6DIeGgywv+6P7SbHOqHUAW2bnO1ohrzgZyFl+BYScMr1+e/XteSkau2+jlMgtf9ZUAbPdGyc4iIxe6Tmzq2FE6YuQKyONmXUoru5dBM8Gp5UDedJpyEkh/c+zi6dWa7LeiblgtnKE7zXCtABxplvgbjx0fW9BjOvn6s2BVxuxNbzjZQtcErznWQAiRl+pbzQb3HarymWt9xNwqwtTkYk9TjKTLazoG//kd75MDcpULIB30Odb0cEwNOw7RSyU49EqoHUFbfXCsRH9SWikWrLj7AHnLnUcYfa6IGtZgXRZRDAcEaYKtZm8KXQfTzKfAJJHIdxX1komelNnyt7istP+s2ZGYGEmX9yTflA2zikC+7W+jaQLd8NHVUImL9347AG+98mF+476/Rbm/Da/4+vzYM418SwH5vB9z4vlv5VQUoTn2mP0wPH9BXodjx1guL8y0KK7MuXmPlZG0MhydS68ldwS6yb3LFf7qI0d4/PQoedxyW2z4t8/IAawTJwnOSSYW7+7mWuymv8n59nlSUsnIdfXAyArUJhm5KDBDbwHn9Gnu/uQnmHvFKzE1k61gi27YnTq+rLoz85BnUXHMXCPv+EMCo5zXVGT1IclQjEsrhkHfsHOLYKikjMaGlAy01sjeh12R96Hflve22r3dLOHZDYYSP+wKVjw+2UtiwXSV5YYa5bhYUYw8UoVNhsHTx01EKhvbaYWW1yC7GKaqKr0WX+Z3rb/Dg+97B2fbhYHS7my5BMBT+awsj7KXtEJ5Ab2kE29ON2HLP2fP33xRI80vjKlrVGyDnYE8mYOJZCdAqsqnHcOhbBs0VbVV0qpLH3kaywskRA7kDW9RWhKz36nQKxXi9m7OCkWwOe46Abl4hCZv0ptFUSdffy53rGRRc01+9pse4nveoqrbFqUsodfruTc9rR9BM0fjnKx0l8vpIkdVfiC/MXwfXRPcs1SllISYXgkax+H7nkLUl2WZfzqSaQB8UwJ5NnXe0ASWvvdlz4uCdA+RxtC5RpImDEmw9FEVrGZZuUSShCm6Gavva5HGEshfNGLqsZGzOqFpst1uwbUCYLSzPtUKyO3RiDGh9yFRhWP2Er8WP87Suf8If/ILEphOjKbs5Iw81MBwOX/pChVdSWiOO1YQFF6QlYTWsWOcbi3LLpuMetgAHKq5RDfkzWMszJOmKR0/GmnRj3wXfN0vAGAbOj/+rgdy+yEwVd25X8OsvcI+cYLo0mUS3yfZ3c1boG75W7ywI5PsK0OdFS0DcsXMJxk5jMBd1VsIXVZQz9lzXOteI07jKY08A/JiywAYZ+Rdf0hkVkeMPHtgx0ImO5GMXK/VsEw9Z+QDWwJZWTFNveAKyYdLBLLzYXZPnVqUeZq9gLwfDvFsA92pYBGNdScddtb5vUsxvWA49prNnlwXK+rcZgU6aZry8btU18PmdIJUL4zIO/7h78AWEV73Asc///P0lVVX80ozjxMOLq0A6LUy8c7eSd47AnIhxDcIIT4vhEiEEK++pRcbowVfK5ns9COSJFX2w/FqS+G62EO5FRFCsDTskgpBWq9KjRxG1sMMyMuFTHFplrSibvA0lAUUxQjaais6e0s0FkWdvOBYKcbXnDnE6UU1T3F5CUwTrVzmgRXpTe84K+hGnPtE3XiXyyyia/LzcxeAApm3P7DMqiPQSqNFkrPdMHPAKI3czjRy+XB0LX18RzERy2X5oFk31fXZvoCqwB8DcuE4JH5AEgSkMWiGXPD1kkmiGmddMGEp9sbeX7bbVXM71cKtKOKUAXndlkORozgCbUAay+/phzH/Mv4aRBLDud+Fe945to4cU8fSNTr+kMSucm1tjTeeqKhzOD5+LDx/AeE4GIuLHCow8qLXfGXOxVcTbPRKhXPrXdr+kNOLCuxaJ+Hed+55LqeA/BalFQD75AlIEsLz54l3djHrc5JB+1uc2zmHIQwaxjyvsi5C2B/1/plk5DAC9/I4KNedej5taZb9EPYA8iBj5BFDawTkOfFwFnIXVbyzg16v45p6XsreVQ8N56oEcrNV+IxsuETQGZM4bUPnxHx5ZoUnyGRnydIxS+o1WcIzSdAGW3x+15ryoW/mjLyMLvSckfuxzyfvksRIb01LMtk8TbQU/7538JXBP+HskW/iDZv/icSR94rmVaZel4WlWRiakbcBmFnZqcJoNhl2p6cXZXGnjPxzwNcDH77lVxZuwLmSxU4/JFB9SSYZuePV+Ir6GR6cl9nbJxcMqNWxLJdACGmOyhwrGZDXjo3eYFJaaRc0cj0dVXxl4bfzFrY3jUwn/8JvjzlW9orGt3wLh37iJxBC5NvEa9oimh4x3JDsz6XNNW1UtJBrjkrj/fbHj3N33cwBHhj1Mcl0a8XIW41Vmk4zZ5HexENyMlZUVaiv5lqydV7KV8iFlx+TbZMGQV42rGnypp4rmYSBy+Zgg/OWwWI6fh6F44y5VgCqg5TYtdBU35usKGitv0YqQuJIfs9+GHMxXaR3+uvkmxVklSwkU4zYTj3KaZevulvtBhx3LNkZXDiPdewYQtNouk0CdVrGpJW6i67+rXke739aSkBvuW9274zJaLktOmEHfygbQ92etCJlqeDsOeJ2G71Wo+E0JJDvnuNo9ShLr/4GVtufhp9/CD7+b+QL92Xk48c/58xxqS116snjy65FZj3MQkorQ+IkpRfGJHYtf4iIYRdISWon87/Pho2XrBEj76pWANZ2m74NpXLhmB01gFn1Ii/GfYeqeTvnyeiFsmDLVolLv6ukJn8HjZittMr13XH30oYCyPmyQ8ks5UDeCTvslAW9+47mDqJi6E2JOXqljP81v0iHEn9y4n9mLW1QN9VQicreOCKEyFm5ozv7upn0xRXiYO/f3xGQp2n6TJqmz93WiwtAXi+ZbPcj+soDWprQcDXXZUmfy7/oEQY4S4s5sAxhDMgNzaBcG3m2x4G8KgGoo4oXtBQ64+Od8u3cQePYoyNtcn6akRfDPn6c2jvfAUCrLI9/3VxGM1NiVY6s6Qk3jBGQ5wynPwKZpN8fA/LJPiaZRv7r3/TfpT9cAfjkuZ2MI6olQlBSf7d9gUAx+ClGHvgkHdVUSJfgXC9ZRJFHJ+oSCcGSNr4llZLMeLKz2oe0NmIuGQt8sS3dSFEkv1s/a7b0xI/Cm/8hHH/j1PFXHIONbsClvsXRUsghtWnJ2hxkUbQUNt3mqB9LkZHXXUrDgFTTEY7D+z+/xoOrNZZre2+Bi5EXBfmbsoVtMrx1ID92DHSd4NxZCYYFIH9h5wWO14/Dm34Y/tpvSZnt078qX7gXI9fMqd/N2XN5n5VJaaXltvjJx36Srz35tWM/z6SVbiZTOFWIA4h8xNnfQegpaeVo/veZtOKaOgPVJbI3NOirJbXjMe6YsSsQ9eRueWIXc+9yhettP09SFqMfyGSnW1ES1LaSnJSHfDOtcG13vDBssxdiaIKqa+T9T2DURGzjp/42Sz/6o1Oflbms9OZifn89uy34oeivYxlKuq3MuA6FyHZA+8kqAMah41Kq2iO+ZBq5EOI7hBAfF0J8XB7ZaBtRL1nsDqL8ST3JyDXXHdMuh+vrGK1WDiyBEGPSSsNpICoFBlHUyFWyaJiDZjoq3c3Cb08tnn3jrsdH/3+GtLJXVFVx05q2hGakxGrslabDulWY8Zdr5KNzkPb7ub8cRh7tbABz0u8jSiWE6oOcOVVK9v5AvlRukCY6iZvIPMH2eaJZQG7bpH5AnDUGUkNra66ZV3cCLJrjTE7Y9lj3Q5DSijY3WvAZC7zQvgBAoKb2ZFtyu7ECj32/PFETUXFMPvjsDTZjl6NeJPvwAFrJy3c0aRgSXb6CddcxQIJVLq1MJDvdYUDqutzoBHz60g5vPSAbh3Ev+a1WdWahWRbW0aOE586NAfnV7lUudy+PpuYcfb0E82/+NXj8B3K3yFjM3ysHj2vjt312voEp+yHAO46/Y+xvYJTszBKeInPw+Lvw3G+imYJEH71XJq04pp77yLu+Rlct4e3ySDMGRvdf+8qUfW+vhGeappKR2zplxYR3d6VsGnVkVecW04x8sxvQLFuSIRte7lrJu0F6czLBPxFZUZBeq2GrcYafvrTDh5JXEjckIdJq09p6MXJGvo+sAqCvntr39zcFciHE7wghPjfjv6+92WuLkabpL6Zp+uo0TaWWXgCFupr6k13gSVdF1mtEvQ/R1asY8/PjQJ41zPK3pGPFK+hthUouraKAfF0CudCA9iQj3701Rt48KberE46Vm4Vj6tiGxmUW0MwkT7oKPWXXGfVnEBPSCki9XMySVsKRtFLU0EtKWinN6vZYCMvQEWkJxxnKc7h1Ps9DmNqokEJKK/5IWhHy2OqlUXWnlqYs2+N9JoRtj/UjB6gMUqzG6BpNMnI/kECeuR1K+8hDFccgilNwanhJF9QoOVHyckYeXr4McYytGHnLaRVcK6NzfKjuUop8ItvlA0pWeev9s4s7ZkXGyMeA/BYZOciEp//5p0kHgxzIz+2cI0kTTtRG488QAk69RTJ0bcat/cQPwN/44NSPi0U/FXNvTbcY2XnOHB96KWsidwPO/q6UsgrJ5UxacS2dQaSSpH3piQfY8SYqXpVvnM61GYxc/vvpCSAPhglJKtdHrSbZclsB+ZoqptlMq1xrTwJ5SFPNQZ3FyGc93KAA5NUqmiZwzNHgF/207M+jLe9P7DIgvxkj11sL+/5+/7saSNP0LTf7m9uKwhNoriTncPbC2YxcuC7JptwiBc8+S7y5SenVryYjl5EQo4ZZ/rbsPWyVJLBq+lglV87I1fuJSlPaB4vht2+qdY8foIAHvxE6awdLkBai5ppcjapo1ujGiwyTwCo8fPJk5ziQa+4IqCen7yT9/ljGvGwfjJEDHK41WZ0DtpZg+3wurWT2Q8iklZCkq6QVrQ9JQt0d9Vs5Eg1xG+NMLtPWocDIB+A0Rws1Y38ZkPcGNmma0o+GWIaWJ4FnReZcObpyCHH90wVGXiYdyN7n4XnlWLlLdtk7VD7EnNci1dbHznHTsygnAYHl8IGn1zjaLHFq4WDl9TDeb6VmSYZ4O0BunTxB5wNyjqRel0Celc0frx/f76XjIcTM9VkvSC17gdZkZA3Kru5IgmV66j2eeR+EXTTvZJ7UTnxftmeu1XB1PZdQ2wONniOAlB1vQtbJiFSajEBdRats0yrbfGFtvOAnc6N4tk69LtdQry11+421K6wCaak1rZH3QppK5iyZpbwgKAPySbkpixzIlXvFNXW2o4jlmoNrLtIHtPL+5/OgQJ7JOHvFSzbqbdy1YpGmsKaelFMauTNyHHQ++EEQgvIbn5iQVkYaeb4NLC9MFQBkPcmH6zKxKOqL08nOW9XIAd764/AX/9WtvQYJ5Lv+EL0y+rwts5kzaFBAbhjEmyMHhATyIiOfAPJeD6002qoeVCMHaLp1elFHNuAfbM+UVjTbJvV94oyRGylE/TFGfiqKMNzx8ygKQJ4x8lofrMboOmV2xQzI46FDP4xnWlMn4/BciZW6y12rK3KbH/ZBaIhS1no3mPKGl8wSv/dNH0IvlcaSnUII6mlEW7P5w3MbvPW+xX0dP5PRcBoIxB1JKzDquQJy/TZcpc0KnWPVY7f8fpNRlE32Aq3JyDz7IyBX7/GZfw9mCVFt5MnlrBgol1aURr7VTXNpZae8ByOHmfdiq2zllcpZ9ILRjq2igLzfk7mr9uZ1AO45fmymtJLZRj1zWlrZc6KTcllpqqozu8dOL1byltnFXfGsOLC08sUEciHE1wkhLgOvA35TCPHbB35xMdmptOJralE41iQjd/JF0f29D+GeOYPRbObJznCGRi7f+PCU1JG1mMwcImJuZVxaSdNb18jvIGqu3I1o1RErWjOaY/KBMAyce+5h8NTn5CGGIQyHN9XIi4w8sx/uJ0tkUbWrEnhUj4gwsx9q4xq5THYqIDdHQJ4oID8dhrl7YOx14aggyIpS7Gh8oZqaScWscLUrH7BpXKLjD6daHM+KH/iqe/it730M3ZsDUrnVN0t5dWzq+wTnz6M3m/lDPT8215lqdVtNQ9aGGlGc3pKsAnJs4Jwzd+fSysmRfKIpaQVkC+Liw/V2I5NWDGHsXZQyEZWckat2CxW1g9y5CCfehFaY29n7iGxhYR1epWTpeYn+dg96ubQyqZEX1s2Me7HmmrQH433Js4ZZnqUj1GtCBeT+7ho9Siw3a6y1/bESfymtyPM4Jq1EB5VW5LE6poTTu5cqecn+zYD8oMnO3Oq4R9ypa+U30jRdTdPUTtN0MU3Ttx34xUVpxVNAvrsHI3dLJIMB0doN/M99jvKTTwKjrX6oGHk/6jMYDkZA/jX/p/yvENkJjjONvH5oPNkZ9WUR0a0y8tuMHMjro4TsFW1+Kk/gnjnD4KmnSOM4T/wWNXJtUiPv98cWUWY73K88P4uKVVFALhOus10rNmkQkvQyIE8g7FIvWZCUeHv/Ud7d7mB7k4zcynVoUzOpq6n1+tx4dr/u1ElJ0dAgsen4Ef2JhmqzwjI02TwrS5B1roPhILIe2b5PeH7kWCmG5own1QG8YUDfcGh6Fq86Mjf1mptFy5VDmG/Wi3y/sI4dyzVvvVbP13ee6LzDyBh5xaoceMcxychL1QLQ3PN2OdXK94m7PW78s3+G+9BDlF73OulaCWOSJGW7m+Yaebuij4PZTRh5dt8UI3e92UZezBerSVH0NuibcyzXHIZJyqayHPbDIYMopqkYeckYtx9amjUmKRbDaDYpvfrVlB6WQ0aytXl6sZJLuPsVBMHBpRXNK+G+6lV7/37fV38xoyitqOnfV9SimHKtOLIfd/dDHwKg/OQbAeT4JhRjLLXyWZEjRn4E5o6OvZeet7LdRZgmorYiq+HCbE6gKhb4UjPywly/i2J+6mHmPnSGtN8nOHs2B5uZBUF7SCuZj3yy2GpWVK1JRj5LWlE3aqeDMA1pIAl7eJaOoQkO7VaoJSmON87INdsZayXQUlnGbOJKFhlLLBlVQNA+ICPPI9N9O9cVI1dAPlDdDO86NvUSzXHGpBUAJwoYGHY+kPlWI+u3krWwPagGPXlc5mGZNNbrNZnMB47XbkEf3yeyc31QWQUKjFxZ+bzMnSE0OPU2hC3v2c1/86+J1zdY/Hs/iBBCJTtjdgcRcSLou/J6BjV3/CFSBO89GPkkkGfSimfpYFhEmMR+h51+SCnaJnEbLCnraEYas2KgTCPPpJU0TemEnX3PiTBNjv7KL+O97nXACLfuXqxQevhhKl/1Vdin7973PB4UyIUQHPv3v7rn7186INeLBUHjjHySdQnXgSii84EPYK6u5vP5sidl8Nr/CUwn34qvlPd2jgjTRCgAFLY9GsSaecmzhll7dCz7s45qBuTzo2M+y8JMRg4w+PRncj95Mdk52ZBqUlrJAPAgjLxqVemGXRJl4ZxpP3QckiAg6fbQPLUIw77UlUsmUV+ex/KEj7YorQC8sSJZhj4J5DlLlDdx25d1BjfTyPPICmLaV8F0c+fP8MY68eZm7lgZOzbXnZJWzKBP33B46323JqtkkVV3ZtN3DtrCdjIynVyvVlmtrFK1qnzF0lfc1ntNRuYSupWHTFFa0TWB65bkgI/Dj4DXRHMdhtevs/VL/5bq29+O+5CcCp/ZD7Oy+LV5m74N/vwEWN8BI89IS6iXEGGXZ693aIoOemWBZdXzPMOarBioVQDyYTokTEI64XR/9v3CVe19Ty6UMebnWf25n0Uve/u+5qBAfrN4WTDyekmexGt7MnL5JXt/9EeUn3wyf3Ln0spp2ZLzckeW+q5WZo9WyiLTRoVljYA8S3huqlaRsyrjvghRc+WcybQ5Oubn06WpCkzz8GH0uTkGn/kMySBzYkxr5FnV5N6M/GBAnpLSceXDbJa0oimJJOl2R8ehyqHrJQs76ROnAm8iay9sa4yR/5VlWeI+qQFm4JLdSB1/yCBKDrSjAEYP4sEWmE6+hoLnZKvhzLFSjGJSHaTVVfcHfMV9K7zx7ulmSweJpttkY7DBbrB7W7JKFs4r7kerVNDKZWp2jY+++6O87tDrbvv9imHqMidxUOshjKSVjW4g2w0LAW/4Hnj87wCykjZLci78L6O+966pE8YJa225Bp65p8y3fY+OXp34bLMEQq3VGVXWNddkEMWEqhocioxcrpHY9DDjPp+5tENDtPHmFllSQH5d7SRyRu6N2g+DbJzViabb+u4XcyWT4y3vQPdYFsXKzjuJlwWQZ1N/1jrKljZVEKS+ZBxTedOT+c8zX3MYy4txuXsZQxhT5cSTkQO5bY+md7Svyk5/H/xxKckcffQ2v9itRfbdBwUJ6EWxOCUhCCGkTv6Zz+Seem0PjTxN0ylGPleyEGK0hdwvMmdFW/VH7NxOAAAZwklEQVTJyKWVsWSnQxqGxJ02uqceGCrbX3dNPHx6uFTc8Sb+mmWPebXjbdUidEIjzxh5BugdP2IQDikdlJEXqxfNUr6G/Kdlq+FZGnkxqQ6y6Io05ZX3rmLs02hsv2g5LaIk4kr3yh0BefPbvo27fuM38gKvP+tolVpjNsSbRbGbZj6Z6MkfgpNvBkBz5P3deO97MQ+NituydX15W02ON11iXUw/RIQYsfJZjFzt4ousfKSRy89ITI8yAz78/A0aooNTW6RRsrB0LfeSZzuDorQCCshn9GffL/7+V9/Lv/7WW9sl5cnOvVrYHjAOSG++CFEAcl0TVB3ZqN7StambJkvqaeUypYcfzn+eSyuxvBiXO5c5VD40NgV8VowYuQlVVQrfuQqf+mW48Xn4hn8H5p09IQ8aGZD3FHAJHYbCnPlUdx86Q/dDHyJak8UpYg/7YRoEEMdjjHy+YvP//q035MUU+0XOgjUBuj1TIxfqRo03t/KJKIQySVQvmZQZ0MOhOnktHWdMWhlub4GmTTlIMgBvqF1Bxx9ODR3ZN4rSmOHkk939Z58FTZNDGyZCc1yi4kMmq1r1Dq4dT0bmJX9h9wXua9532++j2TbW6sGLzW41fvKxn7wlRq5rgrJtqLa+0xN3rOMnMI8cofnt3z72cycHcpUkVQA25ljJwq7K/JU9fVzZfbM7iPIe8N0JRi7sCh4+z164gmnG4LUQmmCxZucWxI0JRp4dRz/q0wk7LHvTg5r3isXqrWPGl4G0Mv6l55T9ZxaAZdvi8uOPSTlERQYsOSPvXL6prAIj36dmWXKR2FVYf16y8SOvg/vedRtf6PYiW5CdLGdgZjbBGUCudPL+n/wpMDvZmQRh3mdl0vp05nAdy7j5Jc9YSDvqQGUpB/Ji9l5TwDjc3JwC8ppr4QmfgZjO2AvbguGQdCjZU7y9jV6rTZVAZ4y84dbRNaEYeTxlTd0z7Mpoa15g5MG5c5irq2PrKP9OrpvvdmDUeEzz9tc594tsPNqt6q1f6ri/eT9Hqkdu6TUZE88ZeSEaf/WbOfE/fmtKI85225e3B2gCygrIZ84xzZj4HslOmGbkQoxsgLpbxRMDKonqCKksystVdyzZ6Vl6jjueMc7IDzpf9Xbjz7+0MmHpybzks5JZmQab2Q6zmALy7mVWyzcH8hEjV8dQWYbP/gc5Teht//iWqzPvJLItYlvI/01LWZXY9M3hPPAACEHvj/8YGJdWMmBKg2AE5LcJQBngtIM2VA8RqhFGxRL97MEx3NzMfbSTjDzQplmGNtFuN97emVnskDHyml2Tu7WBtIkdWFopDlIwHYQiA0TRTMcKKB95sQ1CZq28ScJqv8j6rcDtWQ9fzpEBeHUGkAMzZaDs/r601afhWXkhzEx3SPYwtqbPfwbkRS95Nr0py6FZpSplfJooA4NqZ71Uc3JGvtkLcushjGvkswZt/FnHn29GLsRUL4gs4TmLiZZe+1oWfvAHqbxt3KZelFY6YYedYOdAjDxLrOSsrHpIlgKfeTesPLzPK//sI3uA7SggTxTTnXUe9HIZ++RJoouyd/QYkAuBsCzSMBgxyZsUI+wVWWVlZkEMlc1zLNmppBWiCC1zphQ1cuET6jMYuTXurom3t6f0cRgx8qpVpeKYtP1oanrUTSMHcndsUot9bDrRKb+TOwHkd87IM2kFbq+q8+UcWcKzqJffLFzViuLy9oCmZ4+AfBbztat7zgXYi5EX14fpVqmIAU2hgDxj5ArI0zSVxUCFvFEGrDvBDn7s35Zd9FbiaPUoTx5+kocX7wx3XiJGPv2xdcVMnVmM3LZp/rX35v2qs8gZeRJypSuLeg4krRRdKwBzx2SW/M3/4MDf4M8q8gUZJNL+Zuz9QAOpk2ch3HGglNWWQT5U4o4ZediGk19J2LwLXegYWqHa1B4Bo1atSOtZ5lrxLDx8Qn368zNtPc2BfGvKQw6w7C2jCY2V8kremjZND+aDzyNzHhnu2PHuxcg118n7sQB5QzC9fPvb64pZyZPEX66MPAP0g0R2f691fJplK5cUZmrk5YWp3ulZzALynprXmYWwK5SFT1Oo2hBvxMjDOGGrF7LRDXJ9vHgca32Zh/piA7ljOPz8m37+lmWtyXjpGPlEzO3DyPeKvEQ/DkfWwwNJK2rYgHJ68KYfgW//vZEV8UsY1cIWUfM8YjM7D7MBK9PJgal5gLKPSXjHjNw1XAyhJpe88psJT711qhQ8A2RQQGd5I2nFNSmLAbExfXNm0kq822b9X/wLgvMXMJamE0pL3hLve9f7eGL1CSqOkW+FXfMWlmzmwjDd0Q4CsPZg5MJxIU2nhnPcCSMXQuSs/MsPyE31vwd/uGbrOk2hWbb3l1be/A/h3f/3zPepzmLkwTBvRQGAXcbF53hJ5T1KI0YOcL3ts9kLcw85FIC8t7b3cb0M46VxrcwoisiesLfiwRRCYGrmOJAfRFqpTTByrzVzuvaXIrJWtruDCH0MyPdg5ArIhW1PJQizoQ13qpELIWS/FVVWHibhmD4OjA2i1cpl6Jby6th6SdoPkxksK5NWLn7rtxLv7FD96r9A6zv/5szjOFyVzpKqY/L5K/JYDtIrJo+CtCIMQ84L3UcjL1Z/ao4zagh2B4wcpLxytXf1y1BakdeifAtAXsyBNT0LXd9HWinPy/9mhKlreJY+wciH4+vDKqOR8pUrIVwt5060rLrz6o7PVi8cm7NaMiT5ud6XTbb+vDx8XyIgn8XI90527he2bhPEAZe7l6nZtQNthTJpRbPsm/zllyZqrsluP0Kr1YjS/YHcOnECrVxGGNOXLhvaMGKSt8fIodBvBbnjmWLkRWmlXJG9LTJpxbXwGJDMYDN5o6FWk5Wf+1m8Rx65+bE4Jh3VovRWHvS5tKKcEcJ1wTQxFmb3ds76sWQ+9zuVqLLIEp5/XkDhoHE70krx/m6VLQb7aeQ3icnqzn4Y582vALDlex4TN8ZmEmSM/LnrbeIkHdPITd3E1MyckX+xpZU/q3iJfOSzNPK97Yf7haVbREkkrYcHkFVgorLzZRDZglz60R/hjz67Bk8Fe54HoWm4Dz5AeOHF6d/Z9r72w1uJvN8KEsgnGwcVpQqt7MkcQ5bstBIsEc/0/5Ze+xqO/sov4545gzAPBgDFrfttJTuVI0CzbfTVlT0bQ2UtD7Iy/aTbBcPIHTq3G5m0kvUk/3KJqgLwvVwrs8Ip9N1vlW3WFZB7M5wpN/38CSDvBkMONwprPgPh7fNjU8JaZRtdE3xO7fKKrhWQ8sr1nmTkt+KtfynjZcPIs2TnLd2oSCDPGPk9jYONWXu5Arn7wMOsrznw1PP7Sgjz3/u9RNevT/08K3+/U2kFJJDvBrIF6CxpJetdAqoRWUEjX7j+e/JvWtPjqYQQlF796ls7lgJQ3NKOzRln5Mb8PPZ9ew8M0XJGLjXVpNdD87xb6kE+K+ZdCSJfroz8llwrRWmlbNON1XCR2wDMKUYexOO9hDIisXsZFkbFWLomWKzYfO6qXN8tbxwHPNPLzRP/v0a+X8zQyDNGPsu1sl/Yuo0/9LnSvcJbjhxsmJFWLNF/GUTNNfMChYNMwXEffBD3wQenfq5ZarJ9rwe6fkcPqqpV5VJHTlYP4mBaWrEmNHKrDP0tSBLsj/4Mw7kTvOYvvOe2P78Yxa37nUgrh//VL45JQpORPZwyC2LS7Y7aD9xBvKL1Cpa8JVqllyYP88WKO5VWmmWL693xispbiZprcnGrn/97SiNX0gppkic6s1isOXzq4o46jnEcyLzk8Ofn4fsycq3cHiM3NZNLnUsMk+GBEp0w6kkurIMvwC9mFJlFP7hFr3QhZGfBQPVZuTMmWbWr+YSUKI72l1a8shytF3bhuf8Oa5/DeOMPYB5QOrnpsbhFaeX2kp0g+0fv141uNFJPMfJ+744TnQCPrT7GB/7SB+646OPlFvcsVZmv2BxrHVzCM3QNS7VtaHk2hyuH8UxvzG9/0CjeN2ma0g/jcddKkU1740OQM50cpvsPZdWdutD/3Fyzlx0jv6UbFcnIX9h9ATiYYwWkVlp+4xspvfKVt/RZX6woan39MJ7qfHjQELZNuhFODV6+rWNSGnmapgeQVgr2w9//36FxHF7xl+7o84tRZHy3ppGPfOQHCW1iyHXc7d5xovPLOe5drvKxH771kb6OqRHGCc2yxeHGE/z+N/3+nsMb9ouaa7LTl/dNMEyIk3TMRz6Wo5lg5EtVlQAXI+tzFtnu4FYGbbzU8dIA+R79hX/q6x/g8dO31i7U0i0GQ8mgDprsBDj8C//ylj7nixlZK9thnDCIhrec8P3/2rvbGLnKKoDj/zPvs9vdbattqd2WF23Rxpa3DSCKVWgCCFKVF2s0VNQQjUY0GAFJNMYQxHcTldAAhg+oGCSBYGIE5INfIIJ8EAUCokJppa1lt3R32e3sHj/ce2fuzM5uZ+bevS97zy8hdKaTnWdv75w9e57nOY8n56uRBw3kA6UBZnSGidoE0zPTczKTpuWH/f1Q7HeOVRvfDzt+Afnwbi3/ZGdXpbfhEdi6E4Y72zXnbeP3+r3PHhmvr7Ix4amWnFa2faU8ItJTEIfmVrYT080Ns4CWjLw5kHsZ+cq+0pwypldaWew+K2GKJ5DPUyvceWb3u5u82m1e8hzX31vz/7jV+0Z02+GvhXg18onxwJmkv9/K9Mx0vfdJXaEAuRxSqThLIb1VBytOgK1XBnrvVr1n5EPwsds7fnmufhxcY7KzuG7xOg5mVbWYp5jPBc52/a1svXNAm+6Psj+QNyeIXl/ydm2d/Rl5WsTXxjYk3k/ztf1rm7aQp4l/u/FEByfFz8c7fWd2PHhGXu9JPn24/TpyEaRSaUwGltz3O/c6yIc799DzqpUueeUi9S0/DNIwy7RXKeYph/Dv6P/ceIcpN5VW/BOofe1r5P7t+R4vkKdlohMCBnIR+T7wYWAa+CdwtaqOhjGwTnnb9DutjyeR/4acnJ5p2jLcDf/yw+Lazvsot+Pvt9KuRg5OeaU+GbjxAmeZ19adgd63HS8jLxdy5Ho4N7NT3g+/+qqV8fFQVq2YZluHh9r2MO9Wu236TRl5LtfYqNZSWlkoI/d2d2YpI38YuFFVayJyK3AjcH3wYXXOyxRTHcj7/Bl5jb5Sb9l0rtxYfhjGZCc4gXxqZqptHVMqlUYv8jWb4eIfBnrP+Xg18l5LTp2SUglE0Dcn0dlZdx15euqkafG9y0859os64G9lW8g7P+D7W9e0lwecQN5Szl09UEGEpu35Hi8jT8sacggYyFX1j76HjwPhLVXoUD2QdzHRmTTLW0srQWrk09P1jSxB1A+XmDrM0Zmjc0or4PR2OdbhsmGoFPOU8rmuVzR1S0TqBzDXD7i2jDyx/L/JVuY7kKW0zNl13JIclQo5bvnoFs44fm7nzazXyD8D3DvfX4rINcA1ABs2BGvZ6OdliqnOyFsCeScn3bfjbXCaGR0Nt0Y+T2mlePwGSuvD+7dccDzVQs8/4LrhHcDcOFQiPVlZ1vg/N7Nu6+E5S3fLy7peXLEkA7mIPAK0Ww5yk6o+4L7mJqAG3DPf11HV3cBugJGREe1ptG14ASbNgdzfynZyeqa7nts+3gHMznmdwQL5suIyBKmXVtpl5Otvuy2y05QGKsVFnej05CrOAcxhtLA1i8sfyL25kzmlleoK6HIRhLf8cElNdqrqgiv+RWQXcAlwvnod+SNUz8hTXFrxWtn+78h0fX1tL6R1bXcAOckxUBpgbGqM2mytfY08v/iB1TNQKXTdvqEXUnVOCZo9EvyYN7O4ivkcfW4r27J7Fm3Tzk6AC26B2VpXX3dJZuQLEZELcSY3t6nqxLFevxjOHT6XidpE/XiytBqqFnntsLNaIsg6ck/QjBycjOTQm4cA2mbkUfrCtrcv2H8mLLlKhdnJiXpGbqtWkm25uyu6v1xwjmkttHx2VnfWSM9vRdk9+Lsy9yzZpApaI/8ZUAYedhf3P66q7U8JWCRnrDkj8Hl3SeA0znIm2Hqe7Cw3gm0YJYHB8iAHJw8CtK2RR+miLcGWU3ZKqk5pJaxDJczi8tpbLK8W6SvmQ1meumnFJm7ffjtnrT0rhBFGI+iqlXeENZCsG6oWeXXUCeS9ZuTN2+aDZ+QDpQH2HtkL0PM26rTJVfuYGRtr1MgtkCea1zhrfLpGXxftdBciIpyz7pxQvlZUYjp82bTyl1aqxd6bZnlyfSFk5KVGRh53aSUqzmTnJLNHbLIzDYaqRQ5PHmW8tRd5xlggT4ihahF3l/HcCZsONdXIQ8jIB0uD9YZkcZdWoiLVSstkp2XkSeZl5BOtvcgzxgJ5QnhLECFIacVXIw9jstN3WHBmSiuVqruOfByKRXIJOUXKtFcvrUzNdHVS0VJjgTwhhnyBPJTSShiTnb51tJkprVS9deThnA5kFtdQtcjE9Axjk0fp6/E32aXAAnlCDIWQkTfXyMMprXi85mRLnVTcdeQhtDkwi8/rU7RvbLLnA1mWAgvkCRFKIF+EdeSeLGXk1GrURketPp4C3ufm9Ymji95ULckskCdEU2klYI1cymXnsIeAshnInVOCZg4ctIw8BfxzS3O252eIBfKE8H5FhO7PLfV4pZUwsnFonuzMSiD3jnurHTxo2/NTIIzfZJcCC+QJ4bWyLRdyPW9FrwfykDLJbGbkzoEDtUOHLCNPgSHLyAEL5Inh3ZBBsgpxl8qFlpFncrLTCeTUauStRp54lpE7LJAnxGA9kPeeVYgIUiqFlkn6T0jJTkZebfzZTgdKPMvIHRbIE8JrZRv08AQpl0PLyAu5Qr2lZ1Yy8pyXkWPb89PAa2ULbQ6VyBAL5AkyVC0G/vVQyuVQA5BXXslKRu5NdoJtz0+LelnSNgSZJBiqBj8Fp7BqFYXj1oQ0ouwFcm+yE8LpV2MWnxfIs5yRZ/c7T6Dtm9cE7hex4a47m9rZBjVYHiQnOQpdHpeVVv4auU12psNgCAsF0i4bn86UuP7C7k8zaVVYMfdU8CAGS4OZaZgFzlFvHquRp0M9I7fJTmPaGywNZqaFLbRMdlpGngqN0opl5Ma0dcWmK9i6amvcw4iM2KqV1GlMdmY3nGX3Ozcd2bJqC1tWbYl7GJGRXA4pl9GpKcvIU2LTmmWsHijTF3ChQJoFCuQi8h1gBzAL7Ac+rap7wxiYMXHJVSrMTE1ZRp4SV46s57LTh0M5eDmtgtbIv6+qW1X1VOAh4JshjMmYWHkTnhbI00FEKOSzPd0X6LtX1cO+h/2ABhuOMfHLVauIHfNmUiRwjVxEbgauAsaADwYekTExk2rF6uMmVY6ZkYvIIyLyTJv/dgCo6k2quh64B/jSAl/nGhF5UkSePHDgQHjfgTEhy1WqFshNqhwzI1fV7R1+rV8Bvwe+Nc/X2Q3sBhgZGbESjEmsXKVi9XGTKkFXrWxU1Rfch5cCzwUfkjHxWrnrKmYnJuIehjEdC1oj/66InIyz/PA/wOeDD8mYeC3bti3uIRjTlUCBXFUvC2sgxhhjepPtxZfGGLMEWCA3xpiUs0BujDEpZ4HcGGNSzgK5McaknAVyY4xJOQvkxhiTcqIa/W55EXkDeD7yN06utwIH4x5EQti1aGbXo8GuBRyvqqtan4zrhKDnVXUkpvdOHBF50q6Hw65FM7seDXYt5melFWOMSTkL5MYYk3JxBfLdMb1vUtn1aLBr0cyuR4Ndi3nEMtlpjDEmPFZaMcaYlLNAbowxKRdpIBeRC0XkeRF5UURuiPK9k0BE1ovIYyLyrIj8XUSudZ9fKSIPi8gL7v9XxD3WqIhIXkSeFpGH3McnisgT7rW4V0Qyc5S9iCwXkftE5Dn3HnlPxu+Nr7qfk2dE5NciUsny/bGQyAK5iOSBnwMXAZuBT4jI5qjePyFqwHWq+i7gbOCL7jW4AXhUVTcCj7qPs+Ja4Fnf41uBH7vX4nXgs7GMKh4/Bf6gqu8ETsG5Lpm8N0RkHfBlYERV3w3kgZ1k+/6YV5QZ+ZnAi6r6kqpOA78BdkT4/rFT1X2q+lf3z2/gfFDX4VyHu92X3Q18JJ4RRktEhoGLgTvcxwKcB9znviRL12IQeD9wJ4CqTqvqKBm9N1wFoCoiBaAP2EdG749jiTKQrwNe8T3e4z6XSSJyAnAa8ASwRlX3gRPsgdXxjSxSPwG+jnPmK8BbgFFVrbmPs3SPnAQcAH7plpruEJF+MnpvqOqrwA+Al3EC+BjwFNm9PxYUZSCXNs9lcu2jiCwDfgd8RVUPxz2eOIjIJcB+VX3K/3Sbl2blHikApwO3qeppwDgZKaO0484F7ABOBN4G9OOUZVtl5f5YUJSBfA+w3vd4GNgb4fsngogUcYL4Pap6v/v0ayKy1v37tcD+uMYXofcCl4rIv3HKbOfhZOjL3V+lIVv3yB5gj6o+4T6+DyewZ/HeANgO/EtVD6jqUeB+4Byye38sKMpA/hdgozvrXMKZuHgwwvePnVsDvhN4VlV/5PurB4Fd7p93AQ9EPbaoqeqNqjqsqifg3At/UtVPAo8Bl7svy8S1AFDV/wKviMjJ7lPnA/8gg/eG62XgbBHpcz833vXI5P1xLJHu7BSRD+FkXXngLlW9ObI3TwAReR/wZ+BvNOrC38Cpk/8W2IBzA1+hqodiGWQMROQDwNdU9RIROQknQ18JPA18SlWn4hxfVETkVJyJ3xLwEnA1TrKVyXtDRL4NfBxntdfTwOdwauKZvD8WYlv0jTEm5WxnpzHGpJwFcmOMSTkL5MYYk3IWyI0xJuUskBtjTMpZIDfGmJSzQG6MMSn3f3tz2ws3WyiUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "line": {
          "color": "rgba(255, 153, 51, 1.0)",
          "dash": "solid",
          "shape": "linear",
          "width": 1.3
         },
         "mode": "lines",
         "name": "A",
         "text": "",
         "type": "scatter",
         "x": [
          0,
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         ],
         "y": [
          1.416593517660878,
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          -1.7844796810424377,
          1.0916939562052876,
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          0.999815987739978,
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       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('392196da-b311-4da2-a017-34bd946becc1');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TO GET AN INTERACTIVE PLOT( I- PLOT)!\n",
    "df.iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "line": {
          "color": "rgba(255, 153, 51, 1.0)",
          "dash": "solid",
          "shape": "linear",
          "width": 1.3
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    "df.iplot(kind = 'scatter', x = 'A',y = 'B') #Funny,but of no use"
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   "source": [
    "# Bar i plot\n",
    "df2.iplot(kind = 'bar',x = 'Category',y = 'Values')"
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   "source": [
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    "df.iplot(kind = 'bar')"
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   "source": [
    "#Spread plot used in finances to find the spread btw 2 stocks\n",
    "df[['A','B']].iplot(kind = 'spread')"
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   ],
   "source": [
    "#Bubble plot - Similar to scatter plots but size based on a variable\n",
    "#World GDP in comparison to population , Happiness factor are some of its uses\n",
    "df.iplot(kind = 'bubble', x = 'A', y = 'B', size = 'C')"
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       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('03b011dd-34bf-43a9-9b53-665e37a6c44f');\n",
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       "            Plotly.purge(gd);\n",
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       "\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "        </div>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter matrix plots similar to Seaborn PairPlots\n",
    "#Columns must be numerical\n",
    "df.scatter_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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