1_Ego_Networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Networkx\n",
    "This notebook walks through the basics of how to manually create a small graph and plot it using the networkx package.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In each of the following notebooks focused on learning the basics of NetworkX we will use the first Python cell to import the networkx package (and any other packages we end up needing). We are also including the matplotlib package so that our visualizations appear in the notebook below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graphs\n",
    "The basic object we want from the networkx package is the Graph class, which will give us a way to organize nodes and edges to construct our networks. These Graph objects also allow annotations to be added to the components and will be the inputs to graph algorithms and plotting functions. In later notebooks we will look at ways to import and interact with larger graphs but here we will start by adding individual nodes an edges to an empty graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_ego_graph = nx.Graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nodes and Edges\n",
    "Nodes and edges can be added to the graph individually or as a collection. The labels for the nodes (their names) can be any type of Pyton type, although as we will see the default tends to be integers starting at 0. Edges are represented as tuples and if you attempt to add an edge that contains a node label not already in the graph, the node will be added automatically. The cells below show examples of this process, as well as how to get access to the sets of nodes or edges of the current graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_ego_graph.add_node('Daryl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_ego_graph.add_nodes_from(['Katie','Izzy','Skye'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_ego_graph.add_edge('Daryl','Katie')\n",
    "my_ego_graph.add_edge('Daryl','Skye')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_ego_graph.add_edge('Daryl','Parents')\n",
    "my_ego_graph.add_edge('Daryl','Siblings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_ego_graph.add_edges_from([('Skye','Katie'),('Daryl', 'Izzy'),('Izzy','Daryl'),('Parents','Siblings'),('Daryl','Siblings')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Daryl', 'Katie', 'Izzy', 'Skye', 'Parents', 'Siblings']\n"
     ]
    }
   ],
   "source": [
    "print(my_ego_graph.nodes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Daryl', 'Katie'), ('Daryl', 'Skye'), ('Daryl', 'Parents'), ('Daryl', 'Siblings'), ('Daryl', 'Izzy'), ('Katie', 'Skye'), ('Parents', 'Siblings')]\n"
     ]
    }
   ],
   "source": [
    "print(my_ego_graph.edges())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting\n",
    "We've now got a reasonable starting point for a graph to play with but looking at the list of edges doesn't give us much intuition for the properties of the network. Since this is a small graph, we can use some of the visualization tools to try to get a better understanding. The main function for plotting networks is the draw function, which takes a long list of parameters that control the positioning, size, shape, and color (among other things) of the nodes. The main documentation for the draw function can be found here: https://networkx.org/documentation/stable//reference/drawing.html \n",
    "\n",
    "The examples below show some basic ideas of how the draw parameters can change the look of a graph, and we will see some more complex examples in later notebooks as our networks get more complex. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1jh8/juDgYJw5cwZTpkzB9evX0bBhQ9HR9BLv8RHRCxUWFsLW1hbHjx9H27ZtRceRnPLycvz4448IDg7Gw4cPMXv2bLz//vu85FxDPOMjohfatGkT3nzzTZZeLSssLER0dDTCwsLQqFEj+Pn5YciQITA2rt6DoumfWHxE9FwqlQphYWFYu3at6CiSkZ2djcjISKxcuRIuLi6Ijo5Gz549OeFew7jelYiea+/evbC2toarq6voKAbv2Rl4t2/fRnx8PPbs2YP/+q//YulpAYuPiJ4rJCQEs2fP5g9eLTpz5gx8fX2fOwOPtIeXOonoP5w+fRq3bt3CsGHDREcxOCqVCrGxsQgODkZmZiZmzpyJDRs2PHcGHmkHi4+I/kNoaChmzpxZ4Xw2qpri4mJs3boVISEhMDMzezoDz8TERHQ0yeF2BiL6h/T0dHTr1g3p6emwtLQUHUfv5eTkYNWqVQgPD0eHDh0wd+5c9OnTh5eQBeKvc0T0D0uXLsWECRNYejV08+ZNLF26FNHR0Rg4cCD279+Pjh07io5FYPER0TMePXqEzZs34/Lly6Kj6K2kpCQEBwcjNjYWH374oUZn4JFmcFUnET21evVqDBo0CE2bNhUdRa+o1WocOnQIXl5e8Pb2RocOHZCWlobQ0FCWng7iPT4iAgCUlJTA1taWl+Sq4MkMvJCQEJSUlDydgWdmZiY6GlWAlzqJCADw/fffw8nJiaVXCf8+A2/BggWcgadHWHxEBLVajZCQEISGhoqOotPu3r2LiIgIrFmzhjPw9Bh/PSEiHDx4EDKZDH379hUdRSclJydjwoQJaN++PXJzc3H69Gls376dpaeneMZHRAgNDeXjyf6NWq3GiRMnEBwcjNOnT2PKlCm4du0aZ+AZAC5uIZK4pKQkeHt7Iz09HaampqLjCFdeXo7du3cjODgYDx484Aw8A8QzPiKJCwsLw7Rp0yRfekVFRU9n4DVs2BCff/45Z+AZKJ7xEUnY7du30aFDB9y4cQP169cXHUeI+/fvIzIyEitWrECPHj3w+eefcwaegePiFiIJi4iIwHvvvSfJ0rtx4wamTJmC1q1b49atWzh27Bh++uknzsCTAF7qJJKovLw8REVF4ezZs6Kj1KozZ84gODgYR48exccff4w//vgDTZo0ER2LahGLj0ii1q1bhz59+sDOzk50FK1TqVTYv38/Fi9ejIyMDMycORPr16/ng7glivf4iCSorKwM9vb2+OGHH9CjRw/RcbSmuLgY3333HUJCQmBqasoZeASAZ3xEkrRz5040a9bMYEsvJycHq1evRnh4OJycnLBs2TLOwKOnuLiFSGLUajVCQ0MxZ84c0VE07tatW5g9ezZatWqF33//HbGxsYiLi4OnpydLj55i8RFJzPHjx5GTk4NBgwaJjqIxSUlJeO+9954+YPvixYvYvHkzH7hNz8XiI5KY0NBQzJo1S+8nCajVahw+fBj9+vWDt7c3nJycOAOPKoWLW4gk5OrVq3B1dUVGRgbMzc1Fx6mW0tJS7NixA8HBwZyBR9XCxS1EEhIWFoZJkybpZenl5+dj7dq1WLJkCWfgUY2w+Igk4q+//sL27dtx9epV0VGq5N69ewgPD8eaNWvQu3dv7NixA927dxcdi/QYf1UikogVK1bA19cXr776qugolZKSkoIJEyagXbt2ePz4MU6fPs3SI43gGR+RBBQVFWHlypWIj48XHaVCarUaJ0+eRHBwMBITEzF58mRcv36dM/BIo1h8RBKwadMmdO/eHQ4ODqKjPFd5eTn27NmD4OBgZGdnY/bs2fj+++/18l4k6T6u6iQycCqVCu3atcOaNWvQq1cv0XH+oaioCBs3bkRoaCgaNGiAuXPn4u233+YMPNIqnvERGbiff/4ZlpaWcHNzEx3lqfv372PFihWIjIxEjx49sH79eo4DolrDxS1EBi4kJARz5szRiVJJS0vD1KlT0aZNG9y8efPpDDxXV1edyEfSwOIjMmBnzpzBzZs3MXz4cKE5fvvtN7z77rvo3r07rKyscOXKFaxduxbt2rUTmoukiZc6iQxYaGgoPvvsM8jltf+t/mQGXnBwMNLT0zFz5kysW7eOM/BIOC5uITJQGRkZ6NKlCzIyMmq1bJ6dgWdiYoK5c+fi3Xff5Qw80hk84yMyUEuXLsX48eNrrfSenYHXvn17LF26lOOASCfpTfHdzy9GzLkspNzLRa6yDFYKORwaW8G3iw0aWPDhtETPevToETZt2oRLly5p/b1u3bqFpUuXYsOGDfD29sa+ffvQqVMnrb8vUXXpfPEl3cpB5LFUxF/LBgAUl6mefkwhv4clh6/BvW0jTO5lj47NrAWlJNIta9aswcCBA2FjY6O197h06RKCg4Oxb98+fPDBB7h48SKaN2+utfcj0hSdvse3JTEDC2NToCwrR0UpZTJAITdGoLcDxrrY1lo+Il1UUlICOzs7rZx5qdVq/PLLLwgODsalS5cwffp0fPrpp7C2ttbo+xBpk86e8f1deskoKlW99HPVaqCotBwLY5MBgOVHkrZt2zY4OjpqtPTKysqezsBTKpWYM2cO9uzZwxl4pJd08owv6VYORkYloqi0vMqvrWNijB8+doGzjbXmgxHpOLVajY4dO2Lx4sXo379/jY+Xn5+PdevWYcmSJWjRogXmzp0Lb29vzsAjvaaT/3sjj6VCWVb10gMAZVk5VhxL1XAiIv1w+PBhqNVq9OvXr0bHuXfvHgIDA2FnZ4cTJ07ghx9+QHx8PHx8fFh6pPd07n/w/fxixF/LrvCeXkXUauDo1Ww8yC/WbDAiPRASEoJZs2ZVewtBSkoKJk6ciHbt2iEnJweJiYnYsWMHevTooeGkROLoXPHFnMuq8TFkAGLO1/w4RPrk0qVLuHz5MkaPHl2l16nVapw4cQJDhgyBm5sbmjZtimvXriEyMhKtWrXSUloicXRucUvKvdx/bFmoDmWZCt/tO4Z7R7fA3NwcdevWRd26dZ/++Xl/V7duXSgUCl7GIZ33oj2tCRuXY+rUqZVecPLvM/BmzZrFGXgkCTpXfLnKMs0cyNQc5eXl+PPPP1FQUICCggIUFhb+x5+f/bvi4mLUqVPnhcVYUXFW5jXm5uYsVqq2iva0mhnfhbLBAJg1fg1Jt3Iq3NP6ZAZeWFgY6tevj7lz52Lo0KGcgUeSoXPFZ6XQTKTOTu3wPyNGVek1KpUKhYWFzy3IF5VldnY2MjIyKvWaoqIiKBSKKpVlVQrW3NycP7wM1Mv2tBaXqyGTm+JY6iMkZiQ+d0/rgwcPEBkZicjISHTv3h1r167lOCCSJJ0rPofGVjCT36vR5U6F3AgOTar+fEIjIyNYWFjAwsKi2u9dEZVKhaKioucW6IvK8sGDB7h582alXlNYWAhTU9NqnY1W9jUs1tpX0z2taWlpCAsLw9atW/HOO+/g6NGjcHR01HZsIp2lc/v47ucXo+e3v9So+MzkRvjVz0Nyz/BUq9VQKpWVPlutzt/J5fIaXe592d/xCf7/VJM9rWbGgH3mPpzevx0TJ07E9OnT0aRJEy2kJNIvOnfG19DCDL3aNMKh5D+rtaVBJgN6t20kudIDAJlMhjp16qBOnTpo2LChxo+vVqtRXFxc6bPVgoICPH78GHfv3q3UawoKCmBsbKy1s1Vzc3OYmppq/N9Fm2q0p7VUBWVLN6Snf8sZeETP0LniA4Ap7vY4fv1+tX7LVciNMdndXgupSCaTQaFQQKFQoEGDBho/vlqtRklJyUvPPP/9z3/99VelXlNQUACZTKa1s9W6devC1NRUY/fMarqnVWZkhCxVPZTI9KvsibRNJ4uvYzNrBHo7VPq+xhN1TIwQ6O3Ax5XpKZlMBjMzM5iZmeGVV17RynuUlJRU+tLukz/fv3+/0q9RqVQ1Xv375O/ibqlQ0zsRT/a0fuLG/XhET+hk8QH//6BpTmcgTTI1NYWpqanWpgmUlpZW+d7pw4cPn/vxv1oNQMnrnWqUR1mmQsrdPM18cUQGQmeLD/i7/JxtrLHiWCqOXs2GDH9/Iz9hJpdBqSxGr7aNMLu/E8/0SDgTExPUq1cP9erVq/GxPtr4G35J+avGx8lVltb4GESGRKeLDwCcbayxamxXPMgvRsz5LKTczUOushRWChM4NLHEmR8iYHOrHpxt/kt0VCKN0tSeVisFV8oSPUvni++JBhZmz71P4drwY3h4eGDevHmcDUYGxaGxJUyMgCrc5v4P1d3TSmTI9P75WY6OjnB2dsb27dtFRyHSCJVKhd27d2Pd/I9RWlqzy5RqAMM722gmGJGB0PviA4Dp06dj2bJlNV4BRyRSSUkJoqOj0b59eyxYsAD+M6agr1NTVHd3hJT3tBJVxCCKz9vb++nsMCJ9U1BQgGXLlsHe3h5btmxBREQEfvvtNwwbNgxTe7eGQl69x8SZGsm4p5XoOQyi+IyMjDB16lSEh4eLjkJUaQ8fPsT//M//wM7ODgkJCdi5cycOHz4MT0/Pp5vgn+xprWNStW9VEyM18hI2ojArRRvRifSaQRQfAHz44YeIi4vD7du3RUchqlBWVhZmzZoFe3t7ZGZmPi29bt26Pffzx7rYItC7HeqYGL/0sqdMBtQxMcZXg5ywLuADDB48GEePHtXCV0Gkvwym+OrVq4fRo0dj1apVoqMQPdfVq1cxfvx4ODs7AwCSkpKwbt06ODg4vPS1Y11s8cPHLujn+BrM5EZQyP/5rauQG8FMboR+jq/hh49dMNbFFgMGDMD27dsxYsQIxMbGauVrItJHOjedoSZSUlLQq1cvZGZmQqFQiI5DBAA4e/YsgoKCkJCQgClTpmDq1Kk1etbpi/a0Du9s89yFLKdPn8aQIUMQEREBX1/fmnwpRAbBoIoPAPr3749Ro0bh/fffFx2FJEytVuOXX37BN998g2vXrmH27NmYMGEC6tatKyRPUlISBgwYgEWLFuGDDz4QkoFIVxhc8cXGxmL+/Pk4d+4cJ0tTrVOpVPjxxx8RFBSE/Px8+Pn5YfTo0ToxDunq1avo27cv/Pz8MGXKFNFxiIQxuOJTqVRo27YtoqOj0bNnT9FxSCJKSkqwZcsWLF68GPXq1UNAQAAGDx4MIyPduo2ekZEBT09PTJgwAf7+/qLjEAlhcMUHAOHh4Thx4gSf5kJal5+fj6ioKISFhaFdu3YICAiAu7u7Tl9tuHPnDvr27YuhQ4fi66+/1umsRNpgkMWXm5sLW1tbJCUloVmzZqLjkAG6f/8+IiIisGLFCvTu3Rt+fn7o0qWL6FiVdv/+fXh5ecHV1RVLlizRuTNTIm0yyP/tVlZWGDt2LFauXCk6ChmYmzdvYsaMGWjTpg3u3LmDkydPYvv27XpVegDQsGFD/PLLLzh79iwmTpyI8vJy0ZGIao1BFh8ATJ06FVFRUSgqKhIdhQxAcnIyPvzwQ3Tq1AlyuRyXL19GVFQU2rRpIzpatVlbWyMuLg6ZmZkYM2ZMjR+ITaQvDLb42rRpg27duuH7778XHYX02JkzZzB06FC4u7ujVatWSE1NRUhICJo2bSo6mkZYWFjg559/RmFhIYYNGwalUik6EpHWGWzxAX9PbQgPD+fUBqoStVqNgwcPwsPDA76+vvDw8EB6ejrmz5+PV155RXQ8jVMoFNi5cyfMzc3h4+OD/Px80ZGItMqgi8/LywtFRUU4fvy46CikB8rLy7Fjxw507doVM2fOxIcffojU1FRMmzYN5ubmouNplYmJCbZu3QpbW1t4eXkhJydHdCQirTHIVZ3PWr58OY4dO4aYmBjRUUhHFRcXY/PmzVi8eDEaNGiAgIAA+Pj4SHKlo0qlwsyZM3H8+HHExcWhUaNGoiMRaZzBF19eXh5atGiBixcvonnz5qLjkA7Jy8vD6tWrsWTJEjg7O8Pf3x9ubm6S39emVqvxxRdfYNeuXTh8+DBef/110ZGINMrgf6W1tLTEuHHjsGLFCtFRSEdkZ2fjiy++gJ2dHc6ePYt9+/Zh//796NWrl+RLDwBkMhkWLFiA999/H66ursjIyBAdiUijDL74gL+3Nqxbtw6FhYWio5BAmZmZmDZtGtq2bYvs7GwkJiZi27Zt6NSpk+hoOsnPzw+zZs2Cm5sbUlI40JYMhySKz97eHi4uLvjuu+9ERyEBrly5gnHjxqFz584wNzfHlStXsGrVKtjb24uOpvOmTJmCr7/+Gh4eHkhKShIdh0gjJFF8ALc2SNGpU6cwZMgQ9OnTB+3atcONGzfw7bffokmTJqKj6ZX3338fy5Ytg5eXF06fPi06DlGNSab4PD09UVZWhvj4eNFRSIvUajUOHDiAXr16YfTo0ejXrx/S09MREBAAa2tr0fH0lq+vLzZs2IBBgwbh2LFjouMQ1YjBr+p81sqVK3Ho0CHs2rVLdBTSsLKyMsTExCAoKAjl5eXw9/fHiBEjIJfLRUczKEePHsWIESMQHR0Nb29v0XGIqkVSxZefn48WLVrg3LlzsLW1FR2HNECpVGLjxo0IDg5G48aNERAQAG9vb67O1KLExEQMGTIEkZGRGD58uOg4RFUmmUudwN/PJfzggw+4tcEA5ObmYvHixWjZsiX27t2L6OhonDhxAgMHDmTpaZmLiwsOHjyI6dOnY+PGjaLjEFWZpM74ACAtLQ3du3dHZmYm6tatKzoOVdGff/6JZcuWYc2aNejXrx/8/Pzg7OwsOpYkpaSkwMvLC/7+/pg8ebLoOESVJqkzPgBo2bIlevbsia1bt4qOQlWQnp6OKVOmoF27dnj8+DHOnDmDrVu3svQEcnBwQHx8PEJDQ7F48WLRcYgqTXLFB3Brgz65fPkyxowZg65du6JevXpITk5GZGQkWrZsKToaAbCzs0NCQgI2bNiAL774gt9TpBckWXweHh5Qq9U4evSo6Cj0AidOnICPjw+8vLzg7OyMtLQ0LFq0CK+99proaPRvmjZtioSEBPz888+YOXMmy490nuTu8T2xevVq7N+/H7t37xYdhf6PWq1GbGwsgoKCcOfOHXz++ed4//33oVAoREejSsjJyYG3tzccHR2xevVqGBsbi45E9FySLb6CggK0aNECZ86c4WUzwcrKyrB9+3YEBQXByMgI/v7+GD58OPfg6aH8/HwMGTIEr776KjZt2gQTExPRkYj+g2SLDwDmzp0LlUqF0NBQ0VEkqaioCBs2bEBwcDCaN28Of39/9O/fn9sR9JxSqYSvry+MjIzwww8/8IyddI6kiy8jIwNdunRBZmYmLCwsRMeRjJycHKxcuRLLli1D9+7d4e/vj7feekt0LNKg0tJSjB07Fg8ePMCePXu4dYh0iiQXtzxha2sLNzc3bN68WXQUSbh37x78/f3RqlUr/PHHHzh8+DB++uknlp4BMjExwXfffYcWLVrAy8sLOTk5oiMRPSXp4gO4taE23LhxA59++ikcHR1RUFCAc+fOYfPmzXBychIdjbTI2NgYUVFR6Nq1Kzw8PHD//n3RkYgAsPjg7u4OuVyOw4cPi45icC5evIhRo0ahR48eaNiwIVJSUhAREcHnpEqIkZERli5digEDBqBXr164c+eO6EhELD6ZTPb0rI9qTq1WIyEhAd7e3hg4cCC6dOmCtLQ0LFiwAK+++qroeCSATCbDwoULMXbsWLi5uSEjI0N0JJI4SS9ueaKwsBDNmzdHYmIip3JXk0qlwr59+/DNN98gOzsbn3/+OcaNGwczMzPR0UiHLF++HIsXL8ahQ4fQtm1b0XFIolh8/8fPzw8lJSVYsmSJ6Ch6pbS0FNu2bcO3334LU1NT+Pv7Y9iwYdy8TC+0YcMGBAYG4sCBA3zWKgnB4vs/mZmZeOONN5CZmQlLS0vRcXReYWEh1q9fj5CQELRs2RL+/v7o27cv9+BRpezYsQNTp07F3r170b17d9FxSGIkf4/viRYtWqB3797YtGmT6Cg67dGjR1iwYAHs7Oxw5MgRbNu2Db/88gu8vLxYelRpvr6+WL9+PXx8fBAfHy86DkkMi+8Zn332GSIiIqBSqURH0Tl37tzB3LlzYW9vj9TUVBw9ehQ//vgjXFxcREcjPTVw4EBs27YNvr6+2L9/v+g4JCEsvme4urpCoVDg0KFDoqPojOvXr+Pjjz+Gk5MTSktLceHCBURHR8PR0VF0NDIAHh4e2LNnDz744APs3LlTdBySCBbfM7i14f+dP38e7777Lt566y00adIE165dw9KlS9G8eXPR0cjAvPnmm4iLi8O0adN4q4FqBRe3/JuioiK0aNECJ0+eROvWrUXHqVVqtRrHjh1DUFAQrly5glmzZmHixIlc7EO1IiUlBX379sW8efMwadIk0XHIgLH4nmPevHkoKCjAsmXLREepFSqVCj/99BOCgoLw6NEj+Pn5YcyYMdyDR7UuLS0Nnp6emDRpEubOnSs6DhkoFt9z3Lp1Cx07dkRGRgasrKxEx9Ga0tJSfPfdd/j2229hbm6OgIAAvP3229yDR0Ldvn0bnp6e8PX1xX//939ztTBpHO/xPUezZs3g6emJjRs3io6iFU/OZlu1aoXNmzcjPDwcv/32Gzeek05o2rQp4uPjsXfvXsyePZsPkCeN4xnfC5w4cQIfffQRUlJSYGRkGL8fPHz4EMuXL8fy5cvh5uYGPz8/dOvWTXQsoud69OgRvL294eTkhFWrVvGXMtIYw/iJrgU9e/aEhYUF4uLiREepsaysLMyePRv29vbIzMxEQkICYmJiWHqk0+rXr49Dhw7hxo0beO+991BaWio6EhkIFt8LGMLWhqtXr2L8+PFwdnaGWq3GpUuXsG7dOjg4OIiORlQpFhYW2LdvH3Jzc+Hr6wulUik6EhkAFl8FRo4cifPnz+Pq1auio1TJ2bNnMXz4cLi6uqJFixa4fv06wsLCYGNjIzoaUZXVqVMHu3btgqmpKQYNGoSCggLRkUjPsfgqoFAoMHHiRCxfvlx0lJdSq9U4cuQIPD098c4778DV1RXp6en48ssv0aBBA9HxiGrE1NQU33//PWxsbNCvXz88fvxYdCTSY1zc8hK3b99Ghw4dkJ6ejnr16omO8x9UKhV2796NoKAg5OXlwc/PD6NHj4apqanoaEQap1Kp8Nlnn+HUqVM4cOAAGjZsKDoS6SEWXyWMHDkSLi4umDFjhugoT5WUlGDLli1YvHgx6tWrh4CAAAwePNhgVqASvYharUZgYCD27NmDw4cPo0mTJqIjkZ5h8VXCr7/+ivfeew/Xrl0TvqQ6Pz8fUVFRCAsLg6OjI/z9/eHu7s5NviQ5ixYtwvr163HkyBG0aNFCdBzSIzw9qIQ333wT9evXFzo65f79+/jXv/6Fli1b4tSpU9i9ezfi4uLQu3dvlh5J0rx58zB9+nS4ubnh2rVrouOQHmHxVYLIrQ03b97EjBkz0KZNG9y+fRsnTpzA9u3b0aVLl1rPQqRrpk+fjq+++gq9e/fGpUuXRMchPcHiq6QRI0bg0qVL+OOPP2rl/ZKTk/Hhhx/ijTfegImJCS5fvoyoqCi0adOmVt6fSF989NFHCAsLg5eXF86cOSM6DukBFl8lmZmZ4eOPP9b61oYzZ85g6NChcHd3R6tWrZCamorg4GA0bdpUq+9LpM9GjBiBtWvXwsfHBwkJCaLjkI7j4pYquHPnDtq3b4/09HRYW1tr7LhqtRqHDh1CUFAQbty4gTlz5mD8+PEwNzfX2HsQScGRI0cwatQobNq0Cf379xcdh3QUi6+KRo8eDcfOPdCwmw9S7uUiV1kGK4UcDo2t4NvFBg0sKj/Drry8HLt27UJQUBCUSiX8/f0xcuRImJiYaPErIDJsv/76K4YOHYqVK1finXfeER2HdBCLrwqSbuVgwY9n8FtWIczMzFBcpnr6MYXcCGoA7m0bYXIve3RsZv3C4xQXF2Pz5s1YvHgxGjRogICAAPj4+HAPHpGGXLhwAd7e3li8eDHee+890XFIx7D4KmlLYgYWxqZAWVaOiv7FZDJAITdGoLcDxrrY/uNjeXl5WL16NZYsWQJnZ2cEBATA1dWV2xGItOCPP/5Av379EBgYiE8//VR0HNIhctEB9MHfpZeMolLVSz9XrQaKSsuxMDYZADDWxRbZ2dkIDw/HqlWr4OnpiX379qFTp05aTk0kbY6OjoiPj4enpyfy8/MxZ84c0ZFIR/CM7yWSbuVgZFQiikrLq/xaM7kM3XNPYt/mlXj33XcxZ84c2NvbayElEb1IVlYWPD09MWLECPzrX//iFRbiGd/LRB5LhbKs6qUHAMqScqTVaY0rV67weYJEgtjY2CAhIQFeXl7Iy8tDaGgoy0/iuJqiAvfzixF/LbvCe3oVkRkZ4b7JazC1fEWzwYioSl599VUcPXoUv/76Kz755BOUl1fvl1kyDCy+CsScy6rxMWQAYs7X/DhEVDP169fHoUOHcP36dYwbNw6lpaWiI5EgLL4KpNzL/ceWhepQlqmQcjdPQ4mIqCYsLS0RGxuLnJwc+Pr6ori4WHQkEoDFV4FcZZmGjsPfLIl0RZ06dfDjjz/CxMQEgwYNQkFBgehIVMtYfBWwUmhm7Y+Vgk9iIdIlpqam+P777/H666+jf//+ePz4sehIVItYfBVwaGwFM3nN/okUciM4NLHUUCIi0hS5XI7169ejY8eO6NOnDx48eCA6EtUSFl8FhnexqfEx1ACGd675cYhI84yMjBAREQFPT0/06tULd+/eFR2JagGLrwINLczQq00jVHfLj0wG9G7bqEoPriai2iWTyRAUFITRo0fDzc0NmZmZoiORlrH4XmKKuz0UcuNqvVYhN8Zkdz6phUgfzJs3D1OnToWbmxuuX78uOg5pEYvvJTo2s0agtwPqmFTtn6qOiRECvR3gbGOtnWBEpHGfffYZvvzyS7i7u+Py5cui45CW8JFllfBkykJNpzMQke4bP3486tati759+2Lv3r3o1q2b6EikYXxIdRVcysrBimOpOHo1GzL8vTn9iSfz+Hq3bYTJ7vY80yPSc3v37sX48eMRExMDNzc30XFIg1h81fAgvxgx57OQcjcPucpSWClM4NDEEsM7V20COxHptiNHjmDUqFHYvHkz+vXrJzoOaQiLj4ioAidPnsTQoUOxevVqDB06VHQc0gDe4yMiqkDPnj1x4MABDBw4EAUFBRg7dqzoSFRDLD4iopfo3Lkzjhw5Ai8vLxQUFOCTTz4RHYlqgMVHRFQJjo6OiI+Ph6enJ/Lz8zF79mzRkaiaWHxERJXUqlUrJCQkwNPTE3l5efjqq684zV0PcXELEVEV/fnnn/Dy8oKnpydCQkJYfnqGxUdEVA0PHz7EgAED8MYbb2DFihUwMuKDsPQFi4+IqJry8vIwaNAg2NjYIDo6GnI57x7pA/6KQkRUTZaWloiNjcXDhw/h6+uL4uJi0ZGoElh8REQ1YG5ujt27d8PY2BiDBw9GYWGh6Ej0Eiw+IqIaMjU1xbZt29C4cWP0798fubm5oiNRBVh8REQaIJfLsWHDBjg5OaFPnz548OCB6Ej0Aiw+IiINMTIyQmRkJDw8PODu7o579+6JjkTPwSVIREQaJJPJEBQUBEtLS7i5ueHw4cNo3ry56Fj0DBYfEZGGyWQyzJ8/HxYWFnBzc8OhQ4fQunVr0bHo/7D4iIi0ZMaMGbCwsIC7uzvi4uLg5OQkOhKBxUdEpFUTJkxA3bp14enpiZ9//hldu3YVHUnyWHxERFo2atQomJubw9vbGzt37oSrq6voSJLGR5YREdWSQ4cOYfTo0di6dSu8vLxEx5EsFh8RUS06efIkhg4dijVr1uDtt9/+x8fu5xcj5lwWUu7lIldZBiuFHA6NreDbxQYNLMzEBDZALD4iolp27tw5DBw4EKGhoRgzZgySbuUg8lgq4q9lAwCKy1RPP1chN4IagHvbRpjcyx4dm1mLCW1AWHxERAJcuXIF/fr1g8+MIMTnNoSyrBwV/TSWyQCF3BiB3g4Y62JbazkNEYuPiEiQJXt/w7KEW4C88pcx65gYIdC7HcuvBvjIMiIiAZJu5WDNbw+qVHoAUFSqwsLYFFzKytFOMAlg8RERCRB5LBXKsvJqvVZZVo4Vx1I1nEg6WHxERLXsfn4x4q9lV3hPryJqNXD0ajYe5HPwbXWw+IiIalnMuawaH0MGIOZ8zY8jRSw+IqJalnIv9x9bFqpDWaZCyt08DSWSFhYfEVEty1WWaeg4pRo5jtSw+IiIapmVQjOPSbZSmGjkOFLD4iMiqmUOja1gJq/Zj1+F3AgOTSw1lEhaWHxERLVseBebGh9DDWB455ofR4pYfEREtayhhRl6tWkEmax6r5fJgN5tG/HB1dXE4iMiEmCKuz0UcuNqvVYhN8Zkd3sNJ5IOFh8RkQAdm1kj0NsBdUyq9mP472d1OsDZxlo7wSSAE9iJiAR58qDphbEpnM5QizidgYhIsEtZOVhxLBVHr2ZDhr83pz/xZB5f77aNMNndnmd6GsDiIyLSEQ/yixFzPgspd/OQqyyFlcIEDk0sMbwzJ7BrEouPiIgkhYtbiIhIUlh8REQkKSw+IiKSFBYfERFJCouPiIgkhcVHRESSwuIjIiJJYfEREZGksPiIiEhSWHxERCQpLD4iIpIUFh8REUkKi4+IiCSFxUdERJLC4iMiIklh8RERkaSw+IiISFJYfEREJCksPiIikhQWHxERSQqLj4iIJOV/AU+D+tGnw0M/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(my_ego_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(my_ego_graph,with_labels=True, node_size=2500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bNw/z58/HmTNnmn0z+YkTJzB69Gi888476NKlC7Kysmy1bNy4EWPHjsXbb7+Nbt263bHwZs+ePQgKCsI777yDN998844RntD/jin4iCC8+uqrCA0Nxdy5c2mxC/FYt3c18vLywksvvQQAyMzMxLJly5CWloYbN27c97lRUVHw9vZG586dERER0ezeuP3790fPnj0hFosxePBg28KU4uJidO/eHQMGDIBYLEZ4eDg6dOhgex7DMLh69SquX78OlmWhUCju+zqEiIKPCIJIJMI333yDX3/9FStXruS7HEJ4cXdg+Pn5YcqUKVi8eDFeeeUVXL9+HVqt9r7P9fX1tf27c+fOzb5V6PYwY1nWdg/h9evX0alTJ9vPRCLRHV+PHz8eAPDtt99i1apVKCgoeODrEBq6xkcEo3379khPT0d4eDhCQ0MRExPDd0mEOBXDMA8MDT8/P4SGhuLo0aMICgq65+dGoxHdu3e3/btjx45tqqVjx453NJngOA7Xrl274+eTJ08GcGtRzrp166BQKGxTtEIm7OqIx1EoFEhJScGsWbNc/kZeQlrq9sC4ePEiDh06ZNvk1Wg0oqioCD179rzvcw8dOoT6+noYjUbk5eVh4MCBbaqlb9++uHDhAk6fPg2LxYJffvnljmnWkydP2mpr3749RCKRrX6hBx+N+IjgjBs3Dm+++Sbi4+Nx4MABeHl58V0SIU7h4+NjC5N27dqhvLwcubm5uHnzJry8vPDEE09g/PjxOH369D3PffLJJ7F69Wo0NDQgNDQUYWFhba5l2rRp0Gg02LJlCwYPHgy5XA6J5FZsVFRUQKvV4ubNm+jQoQPi4uLw2GOP2Z4rZCKOVhIQAeI4DjNmzICPjw/+8Y9/QCQS8V0SIQ63cePG+4aaEFitVixfvhwJCQno3bv3Qx87YMAA260NQiTs8SjxWCKRCN9//z0OHz6Mb775hu9yCHEKhUJhG1EJQUlJCerr69HY2IgDBw4AwAOnWptIJJI7VngKkXB+w4TcxcfHBxkZGYiKikJISAieeuopvksixKHkcjnEYrFg9uIrKyvD5s2bYbFY4OfnhxkzZjyyl6hYLIZMJnNSha1DwUcELTg4GP/85z8xffp0HD58WPB/UIS0hb+/v6B2NoiJiWnx6mqz2Qx/f38HVWQfNNVJBG/ixImYP38+pk6d6rL7lBHyMCaTCZmZmVCr1dDr9XyX0yaBgYHN2mGCTxR8xCUsWbIEfn5+eOONN/guhRC74DgOeXl5WLRoEQICAvDJJ59g7NixeOWVVyCVSvkur1WkUimioqL4LuORaKqTuASGYbBu3TqMGDEC//znP/Hiiy/yXRIhrVJaWoqUlBSkpKSAYRgkJSXhl19+sa2UtFqtyM7OdsnZDZZlbY20hYyCj7iMTp06ISMjA6NHj8bgwYMxbNgwvksipFkuX76MjRs3Ijk5GSUlJZgxYwbS0tIwbNiwe27VYRgGERER2Ldvn2AWuTSHRCJBZGSkS9x6RFOdxKX0798fq1evRkJCAi5cuMB3OYQ80M2bN7Fp0yb8/ve/R1BQEPbv348lS5agoqICn3/+OYYPH/7AkAgLC3OJALmdSCSCUqnku4xmoRvYiUtasmQJDh06hJ9//llQ9z0Rz2a1WnHw4EEkJydj8+bNUCqVSEpKQnx8/B0NnpujoKAAWq1WUKs8H4RlWcTFxbW5W4yzUPARl2SxWDBp0iT0798fy5cv57sc4uFOnz6NlJQUpKamomPHjkhKSsKsWbMeebP3w3Ach7Vr16K8vFzQux0wDIOePXti9uzZLjNKpalO4pLEYjHS0tKQmZmJtLQ0vsshHqi6uhorV67EsGHDMHbsWDQ0NGDr1q04fvw43n777TaFHnBr6jA+Ph5isdhOFTuGWCxGfHy8y4QeQCM+4uKOHz+OsWPHYvfu3QgJCeG7HOLmamtrsXXrViQnJyM3NxfPPvssEhMTERsb67CA0ul00Gg0gpzyZFkWKpXKZa7tNaHgIy5vw4YNWLJkCQ4fPowuXbrwXQ5xMxaLBXv27EFycjIyMzMRERGBpKQkPPvss07bhSArKwv5+fmCCj+WZREREYHY2Fi+S2kxCj7iFhYvXoyTJ0/iX//6l+CnhohrOHbsGFJSUpCWlgaZTIbExETMmDGDl3ZcHMdBo9GgsLBQEOHHsixCQ0OhUqlcaoqzCQUfcQuNjY2YMGECwsPD8b//+798l0NcVHl5OdLS0pCSkgKj0YjExESo1WoMGDCA79LAcRyys7N5H/k1jfRiYmJcMvQACj7iRi5evIjhw4dj+fLliI+P57sc4iKuXbuG9PR0JCcnQ6fTISEhAUlJSRg5cqQgdxJvuuZnsVicutqTYRiIxWKXvKZ3Nwo+4laOHj0KlUqFvXv3CuJTOhEms9mMXbt2ISUlBTt27MCYMWOQlJSESZMmwcvLi+/yHsloNCI9PR0Gg8Epoz+WZSGTyRAfHw9fX1+Hn8/RKPiI21m7di2WLl2KX375xS3+SIl9cByHI0eOICUlBRs2bEBQUBASExMxffp0dOvWje/yWozjOOh0Omi1WnAc55D2ZhKJBCKRCHFxcVAqlS47tXk3Cj7ilhYtWoTy8nJkZGQIcrqKOM/Zs2eRmpqKlJQUNDY2IikpCYmJiQgKCuK7NLuoq6tDQUEB8vLyYDab7dLcWiqVgmVZREZGQqlUwtvb2w6VCgcFH3FLJpMJsbGxePrpp/GXv/yF73KIk125cgU//fQTkpOTcebMGUyfPh1JSUkIDw93m1HL3TiOQ0lJCXJyclBWVgaWZWGxWJo1EhSJRDCbzZBKpQgMDMTIkSMRFBTktr8rCj7itqqqqjBs2DCsXr0av/vd7/guhzhYQ0MDduzYgZSUFOzevRsTJkxAYmIi4uLiXHZ/u9YymUyorq6GwWCAXq9HRUUFamtrYbVaYbVawTAMGIaBj48PAgIC4Ofnh/nz50On07nd6O5+KPiIW2vqrpGTk4O+ffvyXQ6xM47jcOjQISQnJ2PTpk0YNGgQkpKSkJCQgM6dO/NdnksJCQnBt99+ixEjRvBdisNRW3vi1iIjI/Ff//VfmDJlCvLy8tChQwe+SyJ28Ntvv9k2c/Xy8kJSUhIKCgrQq1cvvktzWdHR0di/f79HBB9d9Sdub/78+QgPD8dLL70EmuBwXRcvXsQXX3yB8PBwjBo1CtevX8emTZtw8uRJ/OlPf6LQa6NRo0Zh//79fJfhFDTVSTzCzZs3MWrUKEydOhVvv/023+WQZqqvr0dmZiZSUlJw4MABTJo0CYmJiRg3bhztw2hnBoMBAwcOxKVLl9x+JTT9n0M8gpeXFzZv3ozw8HAolUqMHz+e75LIA1itVuzbtw/JycnIyMjA8OHDkZSUhPXr19NUtQPJZDJ069YNRUVFGDJkCN/lOJR7xzohtwkMDMT69euRlJSEc+fO8V0OuUtRURHeffddKBQKvPnmmxg4cCBOnjyJXbt2ISkpiULPCUaNGoUDBw7wXYbDUfARjzJ69Gi8++67iI+PR319Pd/leDyDwYC///3vUCqVUKlUAACNRgOdTofFixdDLpfzXKFn8ZTrfHSNj3gcjuOQmJgIhmGwbt06t71JV6hu3LiBjIwMJCcn4/Dhw5gyZQqSkpIwevRot7+2JHTnzp1DZGQkKisr3frvgoKPeKS6ujo89dRTeOmll/D666/zXY7ba2xsRFZWFpKTk7F9+3ZER0cjMTERkydPRvv27fkuj/wHx3Ho1asXsrOz3fq+V1rcQjySt7c3MjIyEBkZidDQUIwaNYrvktxOUxPllJQUrF+/Hr169UJSUhJWrFgBPz8/vssj9yESiWzTnRR8hLih3r17Y926dZgxYwZ++eUX9OzZs9nPbWoJVVlZCb1ej8rKyge2hJLL5VAoFJDL5fD39wfLsg58Vfw7f/68rSl0fX09EhMTsW/fPjzxxBN8l0aaoSn45syZw3cpDkNTncTjffzxx9iyZQv27duHdu3aPfBxVqsVpaWlrWoCLJFIIBaLYTabERgYiKioKAQHB7vNdRSj0YhNmzYhOTkZJ06cwLRp05CUlISnnnrKbV6jpzh9+jQmTpyIs2fP8l2Kw1DwEY/HcRymTZuGLl26YM2aNff83JHbvkRERCAsLMwlGwObTCbs3LkTycnJ2LlzJ8aNG4fExERMnDjxoR8giLBxHIfu3bujoKAAgYGBfJfjEBR8hAC4fv06IiIi8Ic//AHz5s0DQBt93g/HccjPz0dKSgo2btyIJ598EklJSZg6dSq6dOnCd3nEThISEpCQkIBZs2bxXYpDUPAR8h+//fYbRo4ciczMTPTv3x/p6ekwGAwwm80OPzfLspDJZIiPjxfkrvGlpaW2ptAMwyApKQlqtRq9e/fmuzTiACtXrsSZM2fwzTff8F2KQ1DwEXKb7du3Y/ny5Rg7dqxtoYqzMAwDsVgMlUoFpVLptPM+yOXLl7Fx40YkJyejpKQEM2bMQFJSEoYNGyb4kSlpm4KCAiQmJuLUqVN8l+IQFHyE/AfHccjOzsaBAwd4fWNnWRbh4eGIjY11eh03b97E9u3bkZKSgr1790KlUiEpKQnjx493+9Wo5P9YLBZ06dIFxcXF6N69O9/l2B3dzkAIboWeRqNBYWEh76MZs9mM/Px8NDQ0QKVSObweq9WKgwcPIjk5Genp6QgNDUVSUhLWrVuHTp06OfTcRJjEYjGioqJw8OBBxMfH812O3VHwEQIgOzsbhYWFTrme1xxmsxmFhYXw8vJCbGysQ85x+vRppKSkIDU1FR07dkRSUhKOHTvWovsZiftqup+Pgo8QN6TT6ZCfny+Y0GtiNpuRl5eHxx57zG7X/Kqrq7FhwwYkJyfDYDBg1qxZ2Lp1K0JCQuxyfOI+Ro0ahddee43vMhyCrvERj2Y0GrFq1SrBhd7tWJbFokWLWr3as66uDlu2bEFKSgpyc3MxefJkJCUlISYmBmKx2M7VEndhMpnQtWtXlJeXC3KlcVtQK3TisTiOQ3p6OiwWC9+lPJTFYkF6ejpa8hnVYrFg9+7deOGFFxAQEICUlBQkJiaivLwcP/zwA8aNG0ehRx5KKpVi2LBhOHToEN+l2B0FH/FYOp0OBoPBqbcstIbVaoXBYIBOp3vkY48dO4a33noLvXr1wrvvvouwsDCcOXMGO3bswKxZs+Dj4+OEiom7cNf9+Sj4iEeqq6uDVqsV9BTn7cxmM7RaLerq6u75WXl5OZYtW4YhQ4Zg8uTJkEql2L17N44cOYI//OEP6NGjBw8VE3fgrsFHi1uIRyooKGjR1KEQNLVQi4qKwrVr15Ceno6UlBQUFBQgISEBX375JUaOHEmbuRK7iYiIQGFhIerr691q30Ra3EI8jtVqxfLly1FbW8t3KS0mkUhQVFQEjUaDMWPGIDExEZMmTYKXlxffpRE3FRkZiaVLl2LMmDF8l2I3NOIjHqe0tNRlpjjvdvPmTYSHh+PLL79E165d+S6HeICm6U53Cj6aEyEeJycnxy5bC/FBIpGgc+fOFHrEadzxOh8FH/EoJpMJZWVlfJfRJmVlZS47YiWuJyoqCvn5+S77YfF+KPiIR6murhZks+UPP/wQly9fbtZjWZZFVVWVgysi5JbOnTsjKCgIBQUFfJdiN3SNj3iUysrKZt2wvmLFCtTW1oJhGIhEIvj5+SEkJARDhw7lfdWkxWKBwWBw292xifA0TXdGRETwXYpd0IiPeBS9Xt/sndRnzpyJ9957D3/84x8xcuRI5OTkIDMzs8XntHdnmMbGRuj1ersek5CHcbfrfDTiIx6lsrKyxc/x8vJCv3790KFDB3z33XeIjIyE0WhEdnY2ampq4OXlBaVSiZiYGADAlStX8Nlnn2Hy5MnYu3cvOnfujHbt2iE4OBjh4eG243711VeIiYlB//79W1xTRUVFi59DSGtFR0dj7ty5sFgsbtHqjoKPeJS23LvXs2dPdOrUCefPn0e3bt0wZcoU+Pn54cKFC0hOToa/v/8dIXbu3Dm8+uqrEIlE+PXXX5Gbm2sLvqqqKly/fh19+/Z1+usgpKV69OiBHj16oKioyC128qDgIx6lrX05O3bsiPr6evTu3dv2PX9/fwwaNAh6vf6O4BszZgykUikAoF+/fti+fTsuX76Mrl274tixYxg4cCAkktb9CQq9vyhxP6NHj0ZWVhZu3rwJvV6PyspK1NbWwmq1wmq1gmEYMAwDHx8fyOVyKBQKyOVy+Pv7C25BGQUf8ShtDYzr16+jffv2KC8vx+7du3HhwgVYLBY0NjZi4MCBdzz29q1cJBIJBg4ciOPHj2P06NEoKirC9OnTW10HBR9xBqvVitLSUuTk5EAul+Pq1avYvXv3fa+TNwWg0WiE0WhEcXExxGIxzGYzAgMDERUVheDgYIhEIh5eyZ0o+IhHYRim1aFRUVGBa9euoVevXtiwYQNGjBgBtVoNlmWh0Wju20D6dqGhoUhPT0evXr3AsmybVmXyvbKUuLe6ujoUFBQgLy8PZrPZdg+fWCxu9uKwxsZG22P1ej0MBgNYlkVERATCwsLg7e3tsPofhYKPeJTWBF/T1I5Wq8WQIUPQo0cPNDQ0oH379mBZFuXl5Thx4gSCgoIeepzAwECIRCLs3LkTQ4YMacvLgNVqxdatW/H444/j8ccfd7uNQgk/mhqha7VacBzX7JBrDpPJBJPJhH379mH//v2Ii4uDUqnkZQRIwUc8io+PD4xGY7Meu379+jvu44uMjMSwYcMAAL/73e+wa9cu7NixAwqFAgMHDsTNmzcfecyQkBDs2bMHM2bMaNPrsFgs+O6773Du3DmcPXsWLMvaQrB37962fzf916lTpzadj7g/o9GI9PR0GAwGh3YGagpTrVaLY8eOIT4+3ukf3Gh3BuJRNm7ciNOnT/N2/sLCQhw9ehRz5sxp03EGDBiAadOmAbj1Kb2mpsYWgufOnbvnP6lUek8YUjCSJjqdDhqNBhaLxanXjxmGgVgshkqlglKpdNp5acRHPIpCoUBxcbFdp3Cay2Qy4fDhwxg+fHibjiORSKBQKGxfi0QidO3aFV27dsXQoUPveTzHcbh8+fIdQfjbb79h165dFIwejuM4ZGdnIz8/n5f+r00LYjQaDWpqahAbG+uUqU8KPuJR5HJ5iy7Q20tJSQl+/PFH9OnTB4MHD27TscRiMWQyWbMfLxKJ0K1bN3Tr1s02VXu7RwXj2bNn4eXl9dBg7NixY5teE3E+juOg0WhQWFjIe9Nzs9mM/Px8NDQ0QKVSOTz8aKqTeJSzZ89i3bp1fJfRJgzD4N1333XavVEcx+HSpUv3nUJt+o+C0fVkZWXxNtJ7kKZVn7GxsQ49DwUfcXs1NTXYtGkT0tLScOLECcybN8+ldyxXKBSYPXs232XYtCUYe/fuDYVCQcHoZE3X9IQUek1YlnX4NT8KPuKW6uvrsX37dqSmpmLPnj14+umnMWvWLEycOBHnz5/Hpk2bXHJ/MalUiqlTp7a61RkfHhSMty/E8fb2fuBokYLRvoxGI1atWiXI0GvCsiwWLVrksNWeFHzEbVgsFmRnZyMtLQ1btmzB0KFDoVar71kubbVasXz5cpfsd+nj44PFixcLovuFvXAch4sXLz50xPiwYHz88cfRoUMHvl+GS+A4DmvXrkV5ebmgu/8wDIOePXti9uzZDvl/nYKPuDSO43D06FGkpqZiw4YNCAgIwKxZszBjxgzI5fIHPu/gwYPYt28fL6s7W0sikWDMmDGIioriuxSnamkw3n0fo0KhoGD8j4KCAmi1WkGP9pqwLIu4uDiEhYXZ/di0qpO4pJKSEqSmpiItLQ2NjY1Qq9XYs2cP+vXr16znh4WFudz+YiKRyKn3OgmFSCRC9+7d0b17d4wYMeKen98ejE3Tp0VFRdi+fTvOnTsHvV4PHx+fh06lekIw1tXVuUzoAbdWemq1WvTr18/u7c1oxEdcRnV1NX788Uekpqbi3LlzmD59OtRqNcLDw1s1HUKffj0Dx3G4cOHCQ0eMHTp0eOhUqo+PD98vo81oluP/UPARQbt+/ToyMjKQmpqK/Px8PPPMM1Cr1Rg3blyrt/RpQtc7CNC8YOzYseNDR4xCD0a6rn0nCj4iOCaTCTt37kRqaio0Gg1GjRqFWbNmYfLkyXZ/g6EVbuRROI5DdXX1A0NRr9cLPhiLi4tpJfNtKPiIIFitVuTk5CA1NRWbNm1Cv379oFarMW3aNHTr1s2h5/b0e5pI21it1oeOGB8WjE33MTp6i561a9dCr9c79ByOZO97V2lxC+FVUVGRbZFKhw4doFarceTIETz++ONOq0GpVKKmpkawXSwo9ISNYRj4+/vD398fERER9/z8fsF4/PhxbN26FefOncP58+fRqVOnh44Y2xKMJpMJZWVlbXmJvCsrK4PZbLZbtyIKPuJ058+fx/r165GWloaamhrMnDkTmZmZGDJkCG/XsGJjY9HQ0CCIvoXArdALDQ1FTEwM36WQNmpOMN49lVpYWIgtW7bYRoy+vr6tDsbq6mqwLIuGhgasWLECtbW1EIlEkEql6Nu3L1QqFdq1a+fIX8EdPvzwQ7z22mvo2rVrs5/DsiyqqqratHnz7Sj4iFM0tQ1LTU1FUVEREhIS8Nlnn2HUqFGC2E1cJBLZ3gD4Hvk1jfRiYmJoMYsHYBgGMpkMMpkMkZGR9/y8pcF4932Mly5dgsVisR1v5syZCAoKwrVr15CcnIz9+/dj/PjxzaqV4zhwHOf0v1mLxQKDwWC34KNrfMRh6uvrsW3bNqSmpmLv3r14+umnoVarnf4Js6V0Oh127NiBhoYGiMVip52Xr73JiGu7PRjvtx9jWFgY+vfvDwBYsWIFJk+ejKCgIADArl27cPHiRQBARUUFrFYrAgMDMWnSJNtiqn/+85/o1asXzp07B4PBgIULF9q2EqqsrISPjw9iYmIwaNAgAEBGRgakUimuXr0KvV4PPz8/JCQkoEuXLvj+++9x/vx525Tls88+i969e2PLli04f/687Z7N2bNn3xOut+9B2VY04iN21djYiD179iA1NRVbt27FsGHDoFarsW7dOpdZlahUKrFy5Ur06tULLMs6ZfTHsixkMhkvu1ET1/aoEeOKFStw7dq1e75vNBpRXFxsGyFOmzYNHMdh69at2LFjB2bOnGl77LFjx5CYmIiuXbvCbDbjq6++QkxMDNRqNaqrq5GcnGxrMgAAJ06cQGJiImQyGbZs2YKsrCxMmzYNL730Ej788EMsWLDANtW5e/dudOrUCW+//TYAoLy8/L4zHRUVFXb5fQEUfMQOOI7DkSNHkJqaih9//BEBAQFQq9VYunRpi/aNEwqtVosDBw7g+PHj+O2336DVasFxnENu/JVIJBCJRIiLi4NSqaSpTWJ3dXV1d3y9YcMGMAwDLy8v9O3bF+PHj79j0Uh0dDR++OGHO54TGhpqC7XTp0+jc+fOtlkJuVyOAQMG4NSpU7bH9O/fHz179gQADB48GDt37nxgfQzD4MaNG7h69Sq6du16xybLt7PnPYgUfKTViouLkZaWhrS0NFgsFqjVauzduxdPPvkk36W1Wm1tLRYuXIhvvvkGHTp0QFhYGPr164eCggLk5eXBbDbb5V4oqVQKlmURGRkJpVLp8OXsxHPd3ZxhxowZtqlO4Naqz23btqGkpAT19fW271mtVtt04+2zEFevXkV5eTmWLl16xzlCQkJsX9/eAo5l2Yf+zURFRWHv3r1ITk4GAAwdOhTR0dGPfB1tQcFHWqSqqsrWNkyv1+O5557DunXrMGLECLcYrXzwwQeIiorChAkTbN/z9vbGyJEjERUVhZKSEuTk5KCsrAwsy8JisTRrJCiRSCAWi2E2mxEYGIiRI0ciKCjILX5nRNgeFRi5ubm4dOkSXn75ZXTs2BEGgwGrV6/Gg5Z/NC2kef755+1SX7t27TBhwgRMmDABFy5cwNq1axEQEIA+ffq06HW0BAUfeaS724ZNnjwZ//3f/42xY8e2uW2YkBw9ehTJyckoKiq6789FIhH69u2Lvn37wmQyobq6GgaDAXq9HhUVFaitrYXVarV9UmYYBj4+PggICIBCoYBMJoO/v7/Tdk4nBLg1lfiw0GhoaADLsvDy8kJdXR327dv30OM98cQT2L17N44dO2Zb0FJVVQWpVAo/P79H1uPj44MrV67YrvH9+uuv6NatG7p06YJ27dqBYZj7fiC050pS93nXInZlMpmg1WqRmpoKrVaLUaNG4cUXX0RGRoZbTss1NjZi7ty5WLZsWbP+eKVSKQIDAxEYGHjfHQMIEYpHBV9ERAQ2b96MZcuWoWPHjnjqqadw5syZBz6+Xbt2SEpKws6dO7Fz505wHIcePXrcMUvyMGPGjMGWLVtgNpvxzDPP4Pr169ixYwfq6urg5eWF4cOHo3fv3vd9HfZCtzMQm7vbhvXv39/WNqwlN5u6ok8//RRarRY///wzTT8St7Jy5UoYjUa+y2gzX19fvPHGG3Y5Fo34CE6cOIHU1FSsX78eHTt25KVtGJ/Onj2Ljz/+GHl5eRR6xO3I5XK3CL6AgAC7HYuCz0M1tQ1LTU3FlStXMGvWLGzbtg2DBw/2qDd/juOwYMECvPXWWwgODua7HELsTqFQoLi42KX24bubRCJ54G0OrTqe3Y5EBK+mpgY//fQTUlNTcfLkSUydOhVffPEFoqOjBdE2jA9paWmorq7Gm2++yXcphDiEXC6HWCx26eATi8V2vSeYgs/N1dXVYfv27ba2YRMmTMDixYsRFxcn6LZhznDp0iUsXrwY27Zto5WWxG35+/sLovF6W5jNZvj7+9vteBR8bqixsRHZ2dlITU1FZmYmhg8fjlmzZiE5ORmdOnXiuzzBWLx4MWbMmIHhw4fzXQohDsOyLAIDA116P77AwEC7fjil4HMTd7cN69mzJ9RqNT7++GOXbBvmaLt378bevXtx8uRJvkshxOGioqJgMBhcdgf2qKgoux5TsMHXdINwZWUl9Ho9KisrH3iDsFwuh0KhgFwu97gbhIuLi20buXIcB7VajX379uGJJ57guzTBqqurw/z58/HVV1/d0VqJEHcVFBT0yNZhQsWyrN0XngnqPj6r1YrS0lK7tISKiopCcHCwW65QvL1t2Pnz5/Hcc89BrVZj+PDhbvl67e3dd9/FuXPnsGHDBr5LIcRpDh48iH379rnUIheJRIIxY8bYfcQniOCrq6tzWBPgiIgIhIWFuXy3kWvXrtnahh0+fBiTJ0+GWq1GbGysW7UNc7TCwkI8/fTTOHHiBHr06MF3OYQ4TV1dHVauXOlSC11YlsUbb7xh9/dvXoOP4zjodDra9uUBTCYTNBoN0tLSoNVqMXr0aKjVajzzzDMuH+R8sFgsiIiIwIIFCzBnzhy+yyHE6QoKCqDVal0i/FiWRVxcHMLCwux+bN6Cz2g0Ij09HQaDgTb6vI3VasXBgweRmpqKzZs3Y8CAAVCr1Zg6darbtw1ztJUrV2Lr1q3Izs52qQ9AhNgLx3FYu3YtysvL7brbgb0xDIOePXti9uzZDvlb5SX4dDodNBoNLBaLU3/5DMNALBZDpVLZNlEUiuPHjyMtLQ3r169Hp06doFarMXPmTLt2K/Bker0eQ4cOxaFDh2jhD/FoRqMRq1atEvSoj2VZLFq0yGGDFKcGH8dxyM7ORn5+Pq+/dJZlER4ejtjYWF4/+Z8/fx5paWlITU2F0WjErFmzoFarMXjwYN5qckccx2HSpEl46qmnsGTJEr7LIYR3TYMPIYYfy7IOH5w4bVUEx3HQaDQoLCzk/ZdtNpuRn5+PhoYGqFQqp4bf5cuXsWnTJqSmpuLUqVNISEjAqlWrMHLkSI9tG+ZoGzduxPnz55GRkcF3KYQIglKpRE1NDe+DkLs1LUh09Iyc00Z8WVlZgv0lx8bGOvQ8dXV12LZtG1JTU7Fv3z7ExcVBrVYjLi4OUqnUoef2dDU1NRg4cCDS09MRGRnJdzmECIaQBiPArffj0NBQpwxGnBJ8njisbmxsRFZWFtLS0mxtw9RqNaZMmUJtw5zo5ZdfhpeXF7788ku+SyFEcIR0+SkiIgIxMTFOmYFz+FSn0WgUbOgBt6Y9NRoN+vTp0+YLqRzH4fDhw7a2Yb169YJarcbf/vY3uzZYJc2zd+9e7Ny5k9qSEfIAIpEIY8eORZcuXTxqwaFDR3yesnT2t99+sy1SAQC1Wo1Zs2bR6kEe1dfXIyQkBJ988gmeffZZvsshRPA86RYzhwafO98sWVVVhQ0bNiA1NRVlZWWYMWMG1Go1hg0bRveICcCf//xnnDlzBps2beK7FEJchqc0FXFY8Llje5xr164hPT0daWlp1DZMwE6cOIHY2FgcO3YMcrmc73IIcTmObCMZGRkJpVLJa/cphwWfuzREbWoblpqaip07d2LMmDG2tmHt27fnsVpyPxaLBVFRUXjxxRcxf/58vsshxKVxHIeSkhK7bBwwcuRIBAUFCWJGzCHDFKvViry8PJcKPeDWSszc3FxERETg4MGDSEtLw+bNmzFw4EDMmjULX3/9NbUNE7ivv/4aLMti7ty5fJdCiMsTiUTo27cv+vbta9sqzmAwQK/Xo6Ki4oFbxQUEBEChUEAmkwlyqziHjPiKi4uxadMml9z7ieM47Nq1Czdu3LC1DevVqxffZZFmKCsrg1KpxIEDB9C/f3++yyGECJRDgm/t2rUuvc19t27dsGjRIr7LIC3AcRx+//vfIywsDB988AHf5RBCBMzuU50mkwllZWX2PqxT1dTUwGw2C254Th4sPT0dxcXF2LhxI9+lEEIEzu7NIaurq+0SGDqdDv/4xz/sUFHLsSyLqqoqXs5NWu7q1at4/fXXsWbNGrRr147vcgghAmf3EV9lZSUsFkuzH6/X6/Hzzz/j4sWLEIlE8PPzQ1xcnL3LahGLxQKDwYDAwEBe6yDN8+677+KZZ57ByJEj+S6FEOIC7B58er2+2as5b968ibS0NEyaNAkDBw6ExWKBXq+HWCy2d1kt0tjYCL1ejxEjRvBaB3m0AwcOYNu2bdSWjBDSbA4Z8TXX5cuXAcC2/xzDMAgODgYAGAyGOx67a9culJWVYejQocjPz7/jHq1Dhw5Br9dj5syZtubQJ0+ehMViQb9+/RAXF9fi6deKiooWPZ44X0NDA+bNm4fPP/8cnTt35rscQoiLsPs1vtra2mY/tmvXrmAYBhkZGSguLkZ9ff09j7FarcjMzER1dTWSkpIwaNAgXLlyBRcvXrQ95vjx4wgJCQEA/Pzzz7h8+TIWLFiA119/HdevX8e+ffsc+joIP5YuXYonn3wS8fHxfJdCCHEhdg++ljSj9vLywksvvQQAyMzMxLJly5CWloYbN27YjrV582bU19dj5syZkEqlkEgkGDRoEI4fPw4AuHDhAq5evYonnngCHMehoKAAcXFx8Pb2Rrt27RAdHY2ioiKHvg7ifKdOncKXX36JL7/8UhCdIAghrsPuU50tDQw/Pz9MmTIFAHDx4kWkp6dDq9UiKCgINTU1qKqqwty5c+/ohRkSEoLNmzfb+jEOHDgQEokEN27cgNlsxurVq9tUU2ufQ5zDarVi3rx5+Otf/4qePXvyXQ4hxMXYPfgYhml1aPj5+SE0NBRHjx5FUFAQunXrhhEjRiA1NRUvvPACunXrBgAIDAyEWCyGXq/HiRMnkJCQAADw9vaGRCLBokWL2rzZK8PYfTBM7GTNmjWwWCxYsGAB36UQQlyQ3d/dWxIYFy9exKFDh2A0GgHc2g+qqKjojk/xgwcPxtixY7Fu3TrU1NTYvh8SEoIdO3aAYRgoFArbuYcOHQqtVmubLr127RpKSkoc+jqI81RWVuL999/Ht99+y/vqX0KIa7L7iM/Hx8cWZI/Srl07lJeXIzc3Fzdv3oSXlxeeeOIJjB8/HqdPn7Y9LjQ0FBaLBT/88ANmz56Nxx57DEOGDEF2djZGjx59xzHHjRuHffv24bvvvkNdXR06deqEYcOG2VaLtuR1EOF57bXXsGDBAgwaNIjvUgghLsruvTo3btx4R2g5itlsxieffIL58+c7ZMeEAQMGYNq0aXY/Lmm9LVu24J133sGxY8fg5eXFdzmEEBdl9/k8hULhlE1ZDx8+DLlc7pDQk0gktulTIgzXrl3Da6+9hjVr1lDoEULaxO4JJZfLIRaLHboX34oVKwAAM2bMcMjxxWIxZDKZQ45NWue9997DhAkT7pnaJoSQlrJ78Pn7+8NsNtv7sHf44x//6NDjm81m+Pv7O/QcpPlyc3ORnp5ObckIIXZh96lOlmVdvrlzYGAgbUkkECaTCXPnzsWKFSvw2GOP8V0OIcQNOGTNflRUFKRSqSMO7XBSqRRRUVF8l0H+Y9myZXj88ccxffp0vkshhLgJh6xCCQoKAsuyMJlMjji8Q7Es2+JbH4hj/Prrr1i5ciUKCgqoLRkhxG4cMuJjGAYRERFOWd1pTxKJBJGRkfQmKwBNbcnef/999OrVi+9yCCFuxGHtScLCwlwuQEQiEZRKJd9lEADff/896uvr8eqrr/JdCiHEzTgs+Ly9vVu1Dx6fxo0bB29vb77L8HhVVVV47733qC0ZIcQhHNqQUqlUQiaTCb7vpUgkwrVr1zBnzhwcOnSI73I83h/+8AfMmTPHtsciIYTYk0MTSSQSIT4+XvCf2iUSCd5//328//77mDp1KhYsWICrV6/yXZZH2r59O44ePYq//OUvfJdCCHFTDh+K+fr6QqVSCXbKk2VZqFQqdO7cGdOnT8epU6fAMAwGDBiAH3/8EXZuZUoe4vr161i0aBFWr16N9u3b810OIcRN2b1J9YNkZWUhPz/f4V1dWoJlWURERCA2Nvaen+Xm5mLevHno2bMnvvrqK/Tu3ZuHCj3LG2+8gatXr2Lt2rV8l0IIcWNOu/gWGxuL0NBQwYz8WJZFaGgoYmJi7vvzyMhIFBQUYPTo0Rg+fDiWLVsmqNB2N7/88gs2bNiAv//973yXQghxc04b8QEAx3HIzs7mfeTXNNKLiYlp1i0X//73v7Fw4UJUVVVhzZo1CA8Pd0KVnsNsNmPYsGF4++23oVar+S6HEOLmnBp8TXQ6HTQaDSwWC6xWq9POyzAMxGIxVCpVi+/X4zgOGzZswJtvvomEhAR89NFH8PX1dVClnuXjjz/G3r17odFoXO7eT0KI6+El+ADAaDQiPT0dBoPBKaM/lmUhk8kQHx/fpsCqqanBu+++ix07dmDlypVISEigN+s2KCkpQUREBA4fPkzXUQkhTsFb8AG3RlE6nQ5arRYcxzlkDz+JRAKRSIS4uDgolUq7hdSBAwcwf/58BAUF4csvv6SNa1uB4ziMHz8eKpUKixcv5rscQoiH4DX4mtTV1aGgoAB5eXkwm812aW4tlUrBsiwiIyOhVCod0pHFZDJh2bJlWLlyJd577z28/vrrLteflE9r167FF198gfz8fPq9EUKcRhDB14TjOJSUlCAnJwdlZWVgWRYWi6VZI0GJRAKxWAyz2YzAwECMHDkSQUFBTpmGLC4uxoIFC3DlyhWsWbMGw4YNc/g5Xd2FCxcwaNAgaLVahIWF8V0OIcSDCCr4bmcymVBdXQ2DwQC9Xo+KigrU1tbCarXCarWCYRgwDAMfHx8EBARAoVBAJpPB39+fl1smOI5DSkoK3nrrLTz33HP4n//5H3Ts2NHpdbgKtVoNmUyGTz/9lO9SCCEeRrDB56ouX76Mt956Cz///DO++OIL/P73v+e7JMHRarV45ZVXcOLECfj4+PBdDiHEw1DwOcjevXuxYMEC9OvXD1988QUCAwP5LkkQamtrMWjQIHzzzTeYMGEC3+UQQjyQsLdNcGFjxozBsWPHoFQqoVQq8dlnn8FisfBdFu8++OADREVFUegRQnhDIz4nOHPmDBYsWIAbN25gzZo1HruY4+jRo5g4cSKKiorg5+fHdzmEEA9FIz4n6NevH/bs2YNXX33Vds/ajRs3+C7LqRobGzF37lwsW7aMQo8QwisKPicRiUSYPXs2ioqKcOnSJQwcOBDbt2/nuyynWblyJbp06YLnn3+e71IIIR6Opjp5kpWVhQULFiAkJASff/455HI53yU5zNmzZzF8+HDk5eUhODiY73IIIR6ORnw8GTt2LE6cOIEBAwYgJCQEq1atcsvFLxzHYcGCBXjrrbco9AghgkAjPgE4deoU5s+fD7PZjNWrVyMkJISXOpqaBlRWVkKv16OysvKBTQPkcjkUCgXkcvlDmwakpqbik08+weHDhwWzFyMhxLNR8AmE1WrF999/j/feew+zZ8/GBx984JSbu61WK0pLS+3SJi4qKgrBwcG2NnGXLl3CoEGDsG3bNgwfPtzRL4UQQpqFgk9gqqur8eabb+LQoUP46quvoFKpHHIeRzYGj4iIQFhYGBYuXIjHHnsMK1eubHvBhBBiJxR8ArVr1y4sXLgQw4cPx4oVKyCTyexyXGdtBWW1WrFv3z5kZGRQz1JCiKDQ4haBevrpp1FUVIQ+ffpgyJAh+Oabb9q8W73RaMTatWuh1WphNpsdEnrArXv2rFYrRo8ejU2bNsFoNDrkPIQQ0ho04nMBJ06cwPz58wEAa9aswaBBg1p8DJ1OB41GA4vF0uYAbQmGYSAWi6FSqaBUKp12XkIIeRAa8bmAwYMH4+DBg3j++ecRExOD9957D/X19c16LsdxyMrKgkajgdlsdmroAbcWz5jNZmg0GmRlZYE+ZxFC+EbB5yIYhsGCBQtw/PhxlJaWYtCgQdi1a9dDn8NxHDQaDfLz82E2m51U6f2ZzWbk5+dDo9FQ+BFCeEVTnS5qx44dWLRoEZ566iksX74cPXr0uOcxWVlZggi92zWt+oyNjeW7FEKIh6IRn4tq2uUgICAAgwcPxnfffXfHNKZOpxNc6AG3Rn55eXnQ6XR8l0II8VA04nMDx44dw7x58yCVSrF69WoEBARg1apVggu927Esi0WLFsHX15fvUgghHoZGfG4gJCQEhw4dwowZMzB69GgsX75c8H0/LRYL0tPT6XofIcTpaMTnZrKysrBv3z6IxWK+S3kklmURFxfnsRvzEkL4QSM+N1JXV4f8/HyXCD3g1vU+rVaLuro6vkshhHgQCj43UlBQ4HJTh00t1AghxFko+NyE1WpFXl6ew9qQOUpjYyNyc3NdLrAJIa6Lgs9NlJaWCnoV58OYzWaUlJTwXQYhxENQ8LmJnJwcu2wtxAeTyYScnBy+yyCEeAgKPjdgMplQVlbGdxltUlZW5rIjVkKIa6HgcwPV1dVgWdap57x69So++ugjuzW9ZlkWVVVVdjkWIYQ8DAWfG6isrGz2DesrVqxAaWmp7esTJ07g448/xrlz51r0vM6dO2PJkiVgGPv8L2SxWGAwGOxyLEIIeRgKPjeg1+tbtZqzsLAQO3bswKxZs/D444/bv7AWaGxshF6v57UGQohnkPBdAGm7ysrKFj/nyJEjyMrKQmJiIgICAlBTU4PMzExUV1cDAIKDgzFx4kS0b98e6enpMBqNWL9+PUQiEUaPHo2BAwfis88+w/vvvw+xWIybN29i586dKC4uhkgkQmhoKGJiYlo0IqyoqGjx6yCEkJai4HMDtbW1LXr8kSNHcP78ebzwwgvw9/cHcOtG8ujoaCgUCjQ0NODHH3/E3r17oVKpEB8fD71ej8mTJyMoKAgAcOXKlTuOmZGRgQ4dOuD111+HyWRCWloafH19MWzYMIe9DkIIaQ2a6nQDLV1gUlpaip49e6J79+6273Xt2hVBQUGQSCTw8fFBZGRks6ceb9y4gZKSEsTFxUEqlaJDhw6IjIxEUVGRQ18HIYS0Bo343EBLA2PSpEnYv38/MjMz8eyzz0IkEuHGjRvQaDQ4f/48GhoawHEc2rdv36zjXb16FRaLBZ9++qntexzHtXjLIQo+QogzUPC5AYZhWhQaPj4+eP7557F27Vr861//wqRJk5CVlQWRSISFCxfC29sbp0+fxo4dO2zPEYlEDzyer68vJBIJ3n777TY1yLbXClFCCHkYeqdxA60JjE6dOuGFF15ASUkJtFotGhoaIJVK4eXlhWvXruHQoUN3PN7Hx+ee63pNOnbsiKCgIOzatQs3b96E1WpFTU3NI2+RsMfrIISQlqJ3Gjfg4+PTquf5+vrihRdewKlTp2C1WmEwGLB06VKkpqaif//+dzw2Ojoa+/fvx9KlS+/bXmzKlCmwWCxYtWoV/va3v2Hjxo24fv26U14HIYS0BG1E6wY2btyI06dP811Gmw0YMADTpk3juwxCiJujEZ8bUCgUkEhc+3KtRCKBQqHguwxCiAeg4HMDcrncZXZdfxCxWAyZTMZ3GYQQD0DB5wb8/f1dfmcDs9lsu5meEEIciYLPDbAsi8DAQL7LaJPAwECn7zBBCPFMFHxuIioqClKplO8yWkUqlSIqKorvMgghHoKCz00EBQW57IiJZVkEBwfzXQYhxENQ8LkJhmEQERHhcqs7JRIJIiMjH9oZhhBC7ImCz42EhYW5XICIRCIolUq+yyCEeBAKPjfi7e2NuLg4l5nyZFkWcXFx8Pb25rsUQogHoeBzM0qlEjKZTPB9LxmGgUwmo9EeIcTphP3uSFpMJBIhPj5e8De0i8VixMfHu9zULCHE9VHwuSFfX1+oVCrBTnmyLAuVStXi/foIIcQeKPjclFKpRHh4uODCj2VZRERE0BQnIYQ3FHxuLDY2FqGhoYIJP5ZlERoaipiYGL5LIYR4MNqWyM1xHIfs7Gzk5+fz2s+zaaQXExND1/UIIbyi4PMQOp0OGo0GFosFVqvVaedlGAZisRgqlYqmNwkhgkDB50GMRiPS09NhMBicMvpjWRYymQzx8fG0kIUQIhgUfB6G4zjodDpotVpwHIfGxka7n0MikUAkEiEuLg5KpZKmNgkhgkLB56Hq6upQUFCAvLw8mM1mmEymNh9TKpWCZVlERkZCqVRSRxZCiCBR8Hk4juNQUlKCnJwclJWVgWVZWCyWZo0EJRIJxGIxzGYzAgMDMXLkSAQFBdEIjxAiaBR8xMZkMqG6uhoGgwF6vR4VFRWora2F1WqF1WoFwzBgGAY+Pj4ICAiAQqGATCaDv7+/YG6ZIISQR6HgI4QQ4lHoBnZCCCEehYKPEEKIR6HgI4QQ4lEo+AghhHgUCj5CCCEe5f8DtmHzlU8WvIIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(my_ego_graph,with_labels=True, node_size=2500,node_color = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(my_ego_graph,with_labels=True, node_size=2500,node_color = ['red','blue','green','hotpink','orange','yellow'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(my_ego_graph,with_labels=True, node_size=2500,node_shape='s',node_color = ['red','blue','green','hotpink','orange','yellow'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_dictionary = {'Daryl':'red','Katie':'blue','Izzy':'green','Skye':'hotpink','Parents':'orange','Siblings':'yellow'}\n",
    "nx.draw(my_ego_graph,with_labels=True, node_size=2500,node_shape='s',node_color = [color_dictionary[name] for name in my_ego_graph.nodes()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Assignment:** Create and draw your own ego graph with at least 6 nodes, following the examples given above. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the nice features of networkx is that it lets us attach arbitrary annotations to the nodes and edges of our graphs, which we can use in other calculations. It is also frequently convenient to iterate over the nodes or edges of the graph, in order to set or check properties of the individual components. An example of this is given above, where we accessed the individual colors in the dictionary by evaluating each node individually. Some more complex examples are presented below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AtlasView({'Katie': {}, 'Skye': {}, 'Parents': {}, 'Siblings': {}, 'Izzy': {}})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_ego_graph['Daryl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AtlasView({'Daryl': {}, 'Siblings': {}})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_ego_graph['Parents']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_ego_graph.nodes['Daryl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_ego_graph.nodes['Daryl']['Age']=32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Age': 32}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_ego_graph.nodes['Daryl']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_ego_graph.nodes['Daryl']['Color']='red'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Age': 32, 'Color': 'red'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_ego_graph.nodes['Daryl']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_ego_graph.nodes['Katie']['Age']=31\n",
    "my_ego_graph.nodes['Izzy']['Age']=13\n",
    "my_ego_graph.nodes['Skye']['Age']=10\n",
    "my_ego_graph.nodes['Parents']['Age']=54\n",
    "my_ego_graph.nodes['Siblings']['Age']=22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_ego_graph['Daryl']['Katie']['Length'] = 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AtlasView({'Katie': {'Length': 13}, 'Skye': {}, 'Parents': {}, 'Siblings': {}, 'Izzy': {}})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_ego_graph['Daryl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for edge in my_ego_graph.edges():\n",
    "    my_ego_graph[edge[0]][edge[1]]['Age Difference'] = abs(my_ego_graph.nodes[edge[0]]['Age']-my_ego_graph.nodes[edge[1]]['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AtlasView({'Katie': {'Length': 13, 'Age Difference': 1}, 'Skye': {'Age Difference': 22}, 'Parents': {'Age Difference': 22}, 'Siblings': {'Age Difference': 10}, 'Izzy': {'Age Difference': 19}})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_ego_graph['Daryl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(my_ego_graph,with_labels=True, node_size=[100*my_ego_graph.nodes[name]['Age'] for name in my_ego_graph.nodes()],node_shape='s',node_color = [color_dictionary[name] for name in my_ego_graph.nodes()], width = [my_ego_graph[edge[0]][edge[1]]['Age Difference']/4 for edge in my_ego_graph.edges()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Assignment:** Add attributes containing age and favorite color to your ego network and use those in a visualization. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to export your plot as a .png or other static image type you can right click on the image and select ``Save Image As`` Alternatively, you can use the `plt.savefig()` command, in which case the figure will be saved in your current directory, as in the cell below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(my_ego_graph,with_labels=True, node_size=[100*my_ego_graph.nodes[name]['Age'] for name in my_ego_graph.nodes()],node_shape='s',node_color = [color_dictionary[name] for name in my_ego_graph.nodes()], width = [my_ego_graph[edge[0]][edge[1]]['Age Difference']/4 for edge in my_ego_graph.edges()])\n",
    "plt.savefig(\"./my_first_network-image.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want a little more control over the layout, you can set the notebook to allow for interactive plots with `%matplotlib notebook` (this can be a little sensitive to OS and setup and luckily won't be necessary for what we are trying to do today). We'll discuss some other ways to specify the layout in the next notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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6dMjUkhx553FBX7604iPf1wAUlywU3sKQDq18vZNYCABAchAQv6cAAECBAgQIECAAAECBAgQIEAgE1h0APK730VstVXECy+8FmqJJSLe/e7uz//7fxFz53a3+f3vI55+emFUAYgbrZiAAKSYm14E6i8gAKl/je2QAAECBAgQIECAAAECBAgQINAEgf4DkHS2xxprRPz3fy+YIoUdRx0VccABEW97W/9TP/tsxLRpESn0uOGGiDlzun/tCZAmlKrjhhCAdFzJbZhATgEBSE4ozQgQIECAAAECBAgQIECAAAECnS3QfwCSDhj/xjcWyCy/fEQ6g2PMmPxazz8fcfbZER/5SMSHPpS/36JatuoZIIPfmRH6FxCAuDMIEOhfQADiziBAgAABAgQIECBAgAABAgQIEMgh0H8Akl5v9dhjC7pfdFHE3nvnGK7EJgKQEnFbcmgBSEuWxaIItICAAKQFimAJBAgQIECAAAECBAgQIECAAIHWF1g4AHnmmYh3vGPBykeMiJg5M+L1r692NwKQav2HfnYByNCbm5FAewgIQNqjTlZJgAABAgQIECBAgAABAgQIEKhYYOEA5J57Ij7wgQXLevvb+z/cfKgXPlAAMm9exO23R/zXf3Wvd6mlIlZYIWLzzSOWW66x1U6YEHHhhQv6PP54xKqrNjZG0dYpbPpV9r3/P/2p+3D5ZZaJeOc7u18jNnJk0VG7+/3v/0b88pcRTz7ZPXZ6tdmKK0Z8+MMRSy89uLF7es+eHXHffREPPdQ9RzpPJoVnb3xjxEorRaSni97znohhwwaaTwAykJA/J9CpAgKQTq28fRMgQIAAAQIECBAgQIAAAQIEGhJYOAC5446ITTZZMEj6xvhLL0UsuWRDA+dq3EjQsKgAJH3D/ZRTIs45pzs06HuldW+7bcSpp0asuWauZUUj63riiYjVVlsw7v77R1xwwaLn2WKLiFtuWfDn8+d3//qppyKOPTbiiiu6g4q+Vwp0DjooIp3PkkKRRq7nnos45piISy/tf+wUTuy+e8QJJ3SHRQOFTf3N/Yc/RPzbv0VcdVX3E0OLu970pojkMHFixMc+tqiWApBGaqwtgU4SEIB0UrXtlQABAgQIECBAgAABAgQIECBQWGDhACQ97bD66q8dcMqUiLFjC0+yyI6NBA39fVP+4IMjtt8+4ne/G3ht6VVe3/lOROoz0NXIupoRgFx/fcR++0X8z/8MtLKINdaImDat+2mKPFcKWz7+8YgXXxy4dXpiJgUwKVTacssF7VPo8vWvL7r/xRdHfOpTEXPmDDxH7xabbRbx63QL9nsJQBrT1JpA5wgIQDqn1nZKgAABAgQIECBAgAABAgQIEBiEQP+HoKfXXj377IJhV1kl4oYbIsaMGcRU/XRtJGjoG4AcfXR3EPCb33QPvMQSERts0P2qqrlzI6ZPj3jkkYUn/f73u5+kWNzVyLoGG4CkAGCrrRaEB299a8T739/9lEcKLdITOX2DkU037Q4O0p4Xd6VXaaXg6u9/f22rdMbLuutGvPnNEX/+c8Sdd0bMmtXdJj0BcvrpEfvss6DP4gKQNMdHPhLR8yRLT6/0mqv0uqv0tMcrr3Q/RfTYYxHpSZH0urJ0CUCa+/fJaAQ6RUAA0imVtk8CBAgQIECAAAECBAgQIECAwKAE+g9Ajjii+5VRva/XvS7iE5+I2HPP7tcXveENg5q4q3MjQUPfACQFBH/9a/caPvnJ7tdgpScYel933RXx2c9GpHNNeq60jxSarLXWotffyLoGG4C85S0Rf/lLxDrrRJx2WsTWW792Xf/4R8SZZ0Ycfnh3kNBz/fCHEfvuu+g9pNdQrb12xB//uKBNelVXGis9NdP7DI70xMdZZ0V8+cvdZ3b0rKmn5+ICkBTGpLNXeq6eV2n1fYqo589T0HLzzRE/+UnEM890/7r/yxMgg/8bZgQC9RQQgNSzrnZFgAABAgQIECBAgAABAgQIEGiyQP8BSPqG/HrrdZ9L0d+VztVIAcJGG0VsuGH3mSHpm+0DPZHQd6xGgoa+AUjPWIcdtnBY03uedJ7Gdtt1Hyzec6UDxXt/PZh1DTYASXOn101NmhQxatSiy3vGGRGHHJJ/D+kJmZNOWtA+PY2RDkB/29sWPUf68/TESApEel+LCkDS2SLpiZWeKwVjP/tZngPOu3ukJ1MWfbC7AKTJf9kNR6A2AgKQ2pTSRggQIECAAAECBAgQIECAAAECZQr0H4CkGe+9N2KHHRYdgvRdVXoiI73KKb06ady4iHTmxkDXYAOQ9Mqr9PqmgYKX//7v7td39bzmKa0rPRWSXjXV39XIugYbgKTXUD38cMTyyy9eKz0Jks796Hk12fDh3a+V6i9ASAHGiisueHVWetrj1ltfe7j9omY7/vjuJ0F6X4sKQNITNikE67m++92If/7ngaqe988FIHmltCPQaQICkE6ruP0SIECAAAECBAgQIECAAAECBAoJLDoAScM9/3zEscdGXHBBRPoGfN7rXe+KOPnkiJ13XnyPRoKG/p4ASU9N7LhjvlWlJ0W+9a0FbT//+e5D0fu7GlnXYAOQo46KOPHEfHuYOLG7Fj1XOh+kdwDR8/vXXhvxsY8taLfNNt3npeS50hMz6SmR9AqtnitvAPLVr0Z84xt5ZsnTRgCSR0kbAp0oIADpxKrbMwECBAgQIECAAAECBAgQIECgYYHFByA9w6VzJC6+OOKKKyJ+97sFh1gPNF0KGdKrmxb1hEYjQUN/Z4CkVzClJyHyXOmJjw98YEHL9ddfcIB63/6NrGuwAUh67dSHP5xnB92hUgpMeq5FBUB9X391zjkRn/50vjlSq732irjkkgXtFxWApEPa03khPWeTpF+nV4utsUb+uRbdUgDSDEVjEKijgACkjlW1JwIECBAgQIAAAQIECBAgQIBA0wXyBSC9p50xI+K227pfIfXb30akpxDSK6YWdaXXKX3zm/3/aSNBQ98AJB0WfuON+UHSEyxvfOOC8y3SOSbpaYd0KHrfq5F1DTYASeetjB6dbx8pyPjMZxa0veiiiL33XrhvOuR8ypQFv5/qlM50yXudfnrEoYcuaL24Q9DHj4+47roFbZdaqntN6TD0zTePSF8XuwQgxdz0IlB/AQFI/WtshwQIECBAgAABAgQIECBAgACBJgg0HoD0N+ljj0VcdllEOgPiz39+bYv09Mf990esuebCPRsJGvoGIP/0TxHf+15jBOkckN//fkGfp5+OePvbB7euwQYg6emJgc4w6Vlhev1Veg1Wz3X++RHJsO+VDqa/++4Fv/vCCxHpjJa815VXRuy224LWiwtA/vCH7rNFUpDT93r967ufukl/np5ySZ9ll827CgFIXintCHSagACk0ypuvwQIECBAgAABAgQIECBAgACBQgLNCUB6pk6HjH/qUxGXX/7axaTXL6WnF/pegwlA0mueTjihsU2nb8SnJ1Z6rgceiFhrrcGta7AByPz5+feQNwB597sjUijVc738cv5XhaU+6cmabbdd0H9xAUhq9V//1f2KrZ//fPF7SU/dbLllxMEHR+yyS0Q6nH3RlwAk/52hJYHOEhCAdFa97ZYAAQIECBAgQIAAAQIECBAgUFCguQFIWkR61VQKGtIrsnqulVeOePLJhZc4mADkmGMijj++sW33DUAefHDwT6a0YgCy9toRaW8919/+FrH00vmtJk9+7eHyAwUgPSPffntEei1XOnD90UcXP1+qxaWXRqyyyqLaCUDyV0xLAp0lIADprHrbLQECBAgQIECAAAECBAgQIECgoEDzA5C0kB/9KGK//V67pHTexsiRr/29wQQgdXkFVhlPgKRXTf06lfbVK53RssIK+W+RvvXLG4D0niEdUJ8CkbSO9GRICsT67nX11bt//81v7m9tApD8FdOSQGcJCEA6q952S4AAAQIECBAgQIAAAQIECBAoKFBOAJLO/Fhnndcu6amnIt7xjuYFIM04BD09GZHOqeh7NRLMtOITIPvsE3HxxQt2dcMNEdttl/8WOfLIiFNOWdC+SADSd7ZU///4j4hTT42YM2fBn375yxHf/GZ/axOA5K+YlgQ6S0AA0ln1tlsCBAgQIECAAAECBAgQIECAQEGBcgKQhx5a+GyNdD7IqFHNC0DSod7pKYPhw/NtPT1pkA7k7rnWXz/iN7/pv2+7ByBnnhnx+c8v2FsKNE46KZ9TatX3EPVmBCA9s//kJxG7775gLWuu+drXdS34EwFI/oppSaCzBAQgnVVvuyVAgAABAgQIECBAgAABAgQIFBQoJwC54oqIT3xiwZLe9KaIF19ceImNBA2/+EX3Adq9r0mTXntWxeIQDjss4lvfWtAiBQTf+U7/PRpZVys+AfL730eMGbNgb29/e/dB5UstNfBt8tvfRrz//a9t18wAJL0GK4VXM2Z0z5Fei5Zej7bwJQAZuFpaEOhMAQFIZ9bdrgkQIECAAAECBAgQIECAAAECDQosHIDMnBnx5z9HvOtdDQ7Vq/m220bceOOC39h554irrlp4vEaChv4CkA02iLjzzoglllj8WtMZGCkQSE+h9Fx33x2R+vd3NbKuVgxA0p622CLillsW7C7PofHpAPvNN4+47bbXqjQzAEkjL7tsxF//2j1HOv+j59evnVUAUvxvoJ4E6i0gAKl3fe2OAAECBAgQIECAAAECBAgQINAkgYUDkPQN/RR+7LVXxFFHLfwqq4Em/vrXI/71X1/bKr32aLfdFu7ZSNDQXwCSRjz88NeeV9F3lr//PWLs2Ihfpe+nv3ptttlrDwnv26eRdbVqAHLzzRHpnJTeVzp/49BDI4YNW7gW6TyUtO/09E7fa1EBSKrJK69EbLXVQHfFgj+/7rqI8eMXfL3pphG33tpffwFIflUtCXSWgACks+pttwQIECBAgAABAgQIECBAgACBggL9ByCrrbZguHRuxr77dn+TO53X0N83z9M3wdM3w48/PuJnP3vtUj7yke4/6+9qJGjoG4Ck1yj1PDmQwpqTT45YYYXXznLXXRGf/WxEOv+j53rd67q/XnvtRZM1sq5WDUDS7g4+OOKss167z002iZg4sfs1V+nVZOkclfSkyPe/H/GnP3W33XPPiEsvXdBvUQHIGWdEHHJI99M16VyPj30sYp11IpZccmHbdPD5hRd2B1bpKaOeK8170EH91UIAUvAvtW4Eai8gAKl9iW2QAAECBAgQIECAAAECBAgQINAMgYEDkN6zpG+Yp292v+Ut3a8uSk9XPPtsxP33R7z00sLrSYFJehLhbW/rf62NBA19A5Cjj46YMiXid7/rHju9Bisd3r3KKhFz50ZMnx7x8MMLz7vob7gvaNvIulo5AEkO6SyWa6/Nf6/st193QNL7vJX0VE8KQfpePQFI799feunucOmtb+0OWF5+OSK9giydLdL3rI8UjqXArP9XmAlA8ldNSwKdJSAA6ax62y0BAgQIECBAgAABAgQIECBAoKDAwgFIeiIgBQl//GPBIV/ttvfeEekb5CksWdTVSNDQNwBJ35BPTw6k11ulAGaga8SI7kPP01MRA12NrKuVA5C0zxRA/Mu/RJx2WvevF3WlpzbSq8uOPbY7WBo3bkHL1De9Oqvv1V8AMpBtz59//OMRF10UMWrUonoIQPJaakeg0wQEIJ1WcfslQIAAAQIECBAgQIAAAQIECBQSWDgA6Rkm/cR+Oq8hnZ1xxx39P+HRd8oUdqSzPg44oDtEGehqJGjoLwBJTyakpwpOOiniBz+IeOqphWdM39jfZpvuACA9kZLnamRdrR6A9Oz3sccifvSjiBtuiHjyyYgXX4xYbrmIlVaK2H77iPTkx6qrdre+5JLuM2B6rnPPjfjUpxaWmzEjYvLkiOuvj/jlLxe8QmtRxsOHd59L8oUvdM+5+EsAMpCQPyfQqQICkE6tvH0TIECAAAECBAgQIECAAAECBBoSWHQA0nuYefMi0jf6H320+8mQ9LqrFDyk1x39v//X/YqrdPZDev1UVZehLkYAACAASURBVFda4223RfzXf0U8/XTEUkt1nwmy+eYRyy9f1arac96vfjXim99csPYUhO2ww8B7eeaZ7lePpXvlhRe6X5E2cmT369Le+96Iddftvl/yXQKQfE5aEeg8AQFI59XcjgkQIECAAAECBAgQIECAAAECBQTyBSAFBtaljQXS2RzpiY6eKwUbizrHpbxtCkDKszUygfYWEIC0d/2sngABAgQIECBAgAABAgQIECAwRAICkCGCbptpHnooYq21Fiw3vRbr8cerWL4ApAp1cxJoBwEBSDtUyRoJECBAgAABAgQIECBAgAABApULCEAqL0ELLeAf/4hIT3/ceuuCRaUD1NPh6EN/CUCG3tyMBNpDQADSHnWySgIECBAgQIAAAQIECBAgQIBAxQICkIoLUOr0f/pTxFFHRXzlKwMfAP/88xH77BMxbdqCJaXzO9Lh6e94R6nLXMTgApAq1M1JoB0EBCDtUCVrJECAAAECBAgQIECAAAECBAhULiAAqbwEJS4gHUa+2mrdE2y0UcT48REbbNB9OHw6wH7GjIg//CHi5psjLrqo+2D73td//EfEwQeXuMDFDi0AqUrevARaXUAA0uoVsj4CBAgQIECAAAECBAgQIECAQEsICEBaogwlLaJ3ANLoFEccEXHyyY32amZ7AUgzNY1FoE4CApA6VdNeCBAgQIAAAQIECBAgQIAAAQKlCQhASqNtgYGfeSbiPe+JmDUr/2LSEyPHHRfxyU/m71NOSwFIOa5GJdD+AgKQ9q+hHRAgQIAAAQIECBAgQIAAAQIEhkBAADIEyJVOMWdO9yuufpXlCffcE/H44xHPPdf9uqvhwyOWWSbibW+L2HTTiK22ithpp+7fr/4SgFRfAysg0JoCApDWrItVESBAgAABAgQIECBAgAABAgRaTEAA0mIFsZz/ExCAuBkIEOhfQADiziBAgAABAgQIECBAgAABAgQIEMghIADJgaRJJQICkErYTUqgDQQEIG1QJEskQIAAAQIECBAgQIAAAQIECFQvIACpvgZW0L+AAMSdQYBA/wICEHcGAQIECBAgQIAAAQIECBAgQIBADgEBSA4kTSoREIBUwm5SAm0gIABpgyJZIgECBAgQIECAAAECBAgQIECgegEBSPU1sIL+BQQg7gwCBPoXEIC4MwgQIECAAAECBAgQIECAAAECBHIICEByIGlSiYAApBJ2kxJoAwEBSBsUyRIJECBAgAABAgQIECBAgAABAtULCECqr4EV9C8gAHFnECDQv4AAxJ1BgAABAgQIECBAgAABAgQIECCQQ0AAkgNJk0oEBCCVsJuUQBsICEDaoEiWSIAAAQIECBAgQIAAAQIECBCoXkAAUn0NrKB/AQGIO4MAgf4FBCDuDAIECBAgQIAAAQIECBAgQIAAgRwCApAcSJpUIiAAqYTdpATaQEAA0gZFskQCBAgQIECAAAECBAgQIECAQPUCApDqa2AF/QsIQNwZBAj0LyAAcWcQIECAAAECBAgQIECAAAECBAjkEBCA5EDSpBIBAUgl7CYl0AYCApA2KJIlEiBAgAABAgQIECBAgAABAgSqFxCAVF8DK+hfQADiziBAoH8BAYg7gwABAgQIECBAgAABAgQIECBAIIeAACQHkiaVCAhAKmE3KYE2EBCAtEGRBrPEl1+OuPfeiNmzBzOKvgTaX2CppSLWXTdixIj234sdECBAgAABAgQIECBAoBoBAUg17mYdWEAAMrCRFgQ6U0AAUuO6p/Bj440jfvvbGm/S1gg0ILD++hF33CEEaYBMUwIECBAgQIAAAQIECPQSEIC4HVpVQADSqpWxLgJVCwhAqq5AifPffXfEhhuWOIGhCbShwF13RXzgA224cEsmQIAAAQIECBAgQIBA5QICkMpLYAGLEBCAuDUIEOhfQABS4zvj19l/l3z4wzXeoK0RKCDwq+y/iT70oQIddSFAgAABAgQIECBAgEDHCwhAOv4WaFkAAUjLlsbCCFQsIACpuABlTi8AKVPX2O0qIABp18pZNwECBAgQIECAAAEC1QsIQKqvgRX0LyAAcWcQINC/gACkxneGAKTGxbW1wgICkMJ0OhIgQIAAAQIECBAg0PECApCOvwVaFkAA0rKlsTACFQsIQCouQJnTC0DK1DV2uwoIQNq1ctZNgAABAgQIECBAgED1AgKQ6mtgBf0LCEDcGQQI9C8gAKnxnSEAqXFxba2wgACkMJ2OBAgQIECAAAECBAh0vIAApONvgZYFEIC0bGksjEDFAgKQigtQ5vQCkDJ1jd2uAgKQdq2cdRMgQIAAAQIECBAgUL2AAKT6GlhB/wICEHcGAQL9CwhAanxnCEBqXFxbKywgAClMpyMBAgQIECBAgAABAh0vIADp+FugZQEEIC1bGgsjULGAAKTiApQ5vQCkTF1jt6uAAKRdK2fdBAgQIECAAAECBAhULyAAqb4GVtC/gADEnUGAQP8CApAa3xkCkBoX19YKCwhACtPpSIAAAQIECBAgQIBAxwsIQDr+FmhZAAFIy5bGwghULCAAqbgAZU4vAClT19jtKiAAadfKWTcBAgQIECBAgAABAtULCECqr4EV9C8gAHFnECDQv4AApMZ3hgCkxsW1tcICApDCdDoSIECAAAECBAgQINDxAgKQjr8FWhZAANKypbEwAhULCEAqLkCZ0wtAytQ1drsKCEDatXLWTYAAAQIECBAgQIBA9QICkOprYAX9CwhA3BkECPQvIACp8Z0hAKlxcW2tsIAApDCdjgQIECBAgAABAgQIdLyAAKTjb4GWBRCAtGxpLIxAxQICkIoLUOb0ApAydY3drgICkHatnHUTIECAAAECBAgQIFC9gACk+hpYQf8CAhB3BgEC/QsIQGp8ZwhAalxcWyssIAApTKcjAQIECBAgQIAAAQIdLyAA6fhboGUBBCAtWxoLI1CxgACk4gKUOb0ApExdY7ergACkXStn3QQIECBAgAABAgQIVC8gAKm+BlbQv4AAxJ1BgED/AgKQGt8ZApAaF9fWCgsIQArT6UiAAAECBAgQIECAQMcLCEA6/hZoWQABSMuWxsIIVCwgAKm4AGVOLwApU9fY7SogAGnXylk3AQIECBAgQIAAAQLVCwhAqq+BFfQvIABxZxAg0L+AAKTGd4YApMbFtbXCAgKQwnQ6EiBAgAABAgQIECDQ8QICkI6/BVoWQADSsqWxMAIVCwhAKi5AmdMLQMrUNXa7CghA2rVy1k2AAAECBAgQIECAQPUCApDqa2AF/QsIQNwZBAj0LyAAqfGdIQCpcXFtrbCAAKQwnY4ECBAgQIAAAQIECHS8gACk42+BlgUQgLRsaSyMQMUCApCKC1Dm9AKQMnWN3a4CApB2rZx1EyBAgAABAgQIECBQvYAApPoaWEH/AgIQdwYBAv0LCEBqfGcIQGpcXFsrLCAAKUynIwECBAgQIECAAAECHS8gAOn4W6BlAQQgLVsaCyNQsYAApOIClDm9AKRMXWO3q4AApF0rZ90ECBAgQIAAAQIECFQvIACpvgZW0L+AAMSdQYBA/wICkBrfGQKQGhfX1goLCEAK0+lIgAABAgQIECBAgEDHCwhAOv4WaFkAAUjLlsbCCFQsIACpuABlTi8AKVPX2O0qIABp18pZNwECBAgQIECAAAEC1QsIQKqvgRX0LyAAcWcQINC/gACkxneGAKTGxbW1wgICkMJ0OhIgQIAAAQIECBAg0PECApCOvwVaFkAA0rKlsTACFQsIQCouQJnTC0DK1DV2uwoIQNq1ctZNgAABAgQIECBAgED1AgKQ6mtgBf0LCEDcGQQI9C8gAKnxnSEAqXFxba2wgACkMJ2OBAgQIECAAAECBAh0vIAApONvgZYFEIC0bGksjEDFAgKQigtQ5vQCkDJ1jd2uAgKQdq2cdRMgQIAAAQIECBAgUL2AAKT6GlhB/wICEHcGAQL9CwhAanxnCEBqXFxbKywgAClMpyMBAgQIECBAgAABAh0vIADp+FugZQEEIC1bGgsjULGAAKTiApQ5vQCkTF1jt6uAAKRdK2fdBAgQIECAAAECBAhULyAAqb4GVtC/gADEnUGAQP8CApAa3xkCkBoX19YKCwhACtPpSIAAAQIECBAgQIBAhwtcf/2xMW7cCR2uYPutKSAAac26WBWB6gUEINXXoLQVCEBKozVwGwsIQNq4eJZOgAABAgQIECBAgEAlAvPnz4+TTz45Jk06OtL3GlwEWk9AANJ6NbEiAq0hIABpjTqUsgoBSCmsBm1zAQFImxfQ8gkQIECAAAECBAgQGFKBefPmxeGHHx6nn356bLZZCECGVN9k+QUEIPmttCTQWQICkBrXWwBS4+LaWmEBAUhhOh0JECBAgAABAgQIEOgwgblz58YBBxwQF198cdfOBSAddgO01XYFIG1VLoslMIQCApAhxB7qqQQgQy1uvnYQEIC0Q5WskQABAgQIECBAgACBqgVmzZoVu+22W0ydOvX/liIAqboq5l+0gADE3UGAQP8CApAa3xkCkBoX19YKCwhACtPpSIAAAQIECBAgQIBAhwj85S9/iR122CHuvPPO1+xYANIhN0BbblMA0pZls2gCQyAgABkC5KqmEIBUJW/eVhYQgLRydayNAAECBAgQIECAAIGqBZ588skYO3ZsPPzwwwstRQBSdXXMv2gBAYi7gwCB/gUEIDW+MwQgNS6urRUWEIAUptORAAECBAgQIECAAIGaCzz44INd4cdTTz3V704FIDW/Adp6ewKQti6fxRMoUUAAUiJu1UMLQKqugPlbUUAA0opVsSYCBAgQIECAAAECBKoW+M///M/Ycccd48UXX1zkUgQgVVfJ/IsWEIC4OwgQ6F9AAFLjO0MAUuPi2lphAQFIYTodCRAgQIAAAQIECBCoqcCkSZNi9913j9mzZy92hwKQmt4AtdiWAKQWZbQJAiUICEBKQG2VIQUgrVIJ62glAQFIK1XDWggQIECAAAECBAgQqFrg/PPPjwMPPDBeeeWVAZciABmQSIPKBAQgldGbmECLCwhAWrxAg1meAGQwevrWVUAAUtfK2hcBAgQIECBAgAABAo0IzJ8/P04++eQ4+uijc3cTgOSm0nDIBQQgQ05uQgJtIiAAaZNCFVmmAKSImj51FxCA1L3C9keAAAECBAgQIECAwEAC8+bNi8MPPzxOP/30gZr+358vueSScd11R8d22x2Xu4+GBIZOQAAydNZmItBeAgKQ9qpXQ6sVgDTEpXGHCAhAOqTQtkmAAAECBAgQIECAQL8Cc+fOjQMOOCAuvvji3EIjR46MK664IsaNWz7rs2HufhoSGDqBu7KpPjB005mJAIG2ERCAtE2pGl+oAKRxMz3qLyAAqX+N7ZAAAQIECBAgQIAAgf4FZs2aFbvttltMnTo1N9EyyywTkydPjk033TTr83L22Tj7/DZ3fw0JlC+wfjbFHdlnRPlTmYEAgbYTEIC0XcnyL1gAkt9Ky84REIB0Tq3tlAABAgQIECBAgACBBQJ/+ctfYocddog777wzN8uKK67YFZasueaavfqkEOTe7DM79zgatofAL37xi/jKV75a6WJ33HGHhs6liVgqW++62Uf4UWnhTE6ghQUEIC1cnMEuTQAyWEH96yggAKljVe2JAAECBAgQIECAAIHFCTz55JMxduzYePjhh3NDjRkzpiv8WGmllXL30bC9BebPnx8777xzXHPNNZVu5Oc//3lsscUWla7B5AQI1EdAAFKfWi60EwFIjYtra4UFBCCF6XQkQIAAAQIECBAgQKANBR544IHs4PLt4qmnnsq9+k022SQ78Py6GD16dO4+GtZDIN0nKfyaOXNmZRt697vfHffee2+ks2dcBAgQGKyAAGSwgi3cXwDSwsWxtMoEBCCV0ZuYAAECBAgQIECAAIEhFvh19o2B8ePHx4svvph75nHjxsXll18eo0aNyt1Hw3oJ/Pu//3t87nOfq3RTxx57bBx33HGVrsHkBAjUQ0AAUo869rsLAUiNi2trhQUEIIXpdCRAgAABAgQIECBAoI0EJk2aFLvvvnvMnp3/rI799tsvzj333BgxwnkKbVTqpi913rx58eEPfzhuvfXWpo+dd8Dhw4fHPffcE+uss07eLtoRIECgXwEBSI1vDAFIjYtra4UFBCCF6XQkQIAAAQIECBAgQKBNBM4///w48MAD45VXXsm94iOOOCJOOumkGDZsWO4+GtZX4KGHHor11lsvXn45HXpfzbXhhhvGbbfdFksuuWQ1CzArAQK1EBCA1KKM/W9CAFLj4tpaYQEBSGE6HQkQIECAAAECBAgQaHGBdIh1CjGOOeaYhlZ6yimnxOGHH95QH43rL/C1r30tvvGNb1S60TPOOCO++MUvVroGkxMg0N4CApD2rt9iVy8AqXFxba2wgACkMJ2OBAgQIECAAAECBAi0sEB6bdFhhx0W6RvGea/0mqHzzjsv9t1337xdtOsggTlz5nQ9BfL73/++sl2ns2gefPDBWGWVVSpbg4kJEGhvAQFIe9dPAFLj+tlaOQICkHJcjUqAAAECBAgQIECAQHUCc+fOjYkTJ8aPf/zj3IsYOXJkXHnllbH99tvn7qNh5wn8Ovvp2nQeSJXXuHHj4rrrrvN6tiqLYG4CbSwgAGnj4g20dE+ADCTkzztRQADSiVW3ZwIECBAgQIAAAQL1FZg1a1bstttuMXXq1NybXHbZZWPy5MmxySab5O6jYecKHHzwwXHWWWdVCnDJJZfEnnvuWekaTE6AQHsKCEDas265Vi0AycWkUYcJCEA6rOC2S4AAAQIECBAgQKDGAn/5y19ihx12iDvvvDP3LldcccWYNm1ajBkzJncfDTtbYMaMGV33yzPPPFMZxPLLLx/Tp0+PFN65CBAg0IiAAKQRrTZrKwBps4JZ7pAICECGhNkkBAgQIECAAAECBAiULPDkk0/G2LFj4+GHH849U/omdnpSZKWVVsrdR0MCSeCqq66KXXfdtVKM9Jq3dGaNiwABAo0ICEAa0WqztgKQNiuY5Q6JgABkSJhNQoAAAQIECBAgQIBAiQIPPPBAV/jx9NNP555l00037TpHwU/Q5ybTsI/AzjvvHFdffXWlLjfffHN89KMfrXQNJidAoL0EBCDtVa+GVisAaYhL4w4REIB0SKFtkwABAgQIECBAgEBNBdKh1OPHj48XX3wx9w7TIdI/+clPYumll87dR0MCfQWeeuqpWHPNNeOll16qDOed73xn3H///TFy5MjK1mBiAgTaS0AA0l71ami1ApCGuDTuEAEBSIcU2jYJECBAgAABAgQI1FBg0qRJsfvuu8fs2bNz726//faLc889N0aMGJG7j4YEFiXwH//xH/FP//RPlQIdffTRccIJJ1S6BpMTINA+AgKQ9qlVwysVgDRMpkMHCAhAOqDItkiAAAECBAgQIECghgLnn39+HHjggfHKK6/k3t0RRxwRJ510UgwbNix3Hw0JLE5g3rx5sfnmm8d//ud/Vga15JJLxj333BPrrrtuZWswMQEC7SMgAGmfWjW8UgFIw2Q6dICAAKQDimyLBAgQIECAAAECBGokMH/+/K4Q45hjjmloV6eeemocdthhDfXRmEAegYceeijWW2+9ePnll/M0L6XNhhtuGLfddlukMMRFgACBxQkIQGp8fwhAalxcWyssIAApTKcjAQIECBAgQIAAAQJDLJB+2j6FGGeccUbumYcPHx7nnXde7Lvvvrn7aEigUYGvf/3r8a//+q+Ndmtq+9NPPz2+9KUvNXVMgxEgUD8BAUj9avp/OxKA1Li4tlZYQABSmE5HAgQIECBAgAABAgSGUGDu3LkxceLE+PGPf5x71nTI+RVXXBHbb7997j4aEigiMGfOnFh//fVj+vTpRbo3pc+oUaPiwQcfjFVWWaUp4xmEAIF6CghA6lnXrl0JQGpcXFsrLCAAKUynIwECBAgQIECAAAECQyQwa9as2G233WLq1Km5Z1x22WVj8uTJsckmm+TuoyGBwQj8OvvG04c//OHBDDHovinsS/e9c24GTWkAArUVEIDUtrQCkBqX1tYGISAAGQSergQIECBAgAABAgQIlC7w/PPPxw477BB33XVX7rlWWmmlrrBkzJgxuftoSKAZAp/97Gfj+9//fjOGKjxGekrqk5/8ZOH+OhIgUG8BAUiN6+sJkBoX19YKCwhACtPpSIAAAQIECBAgQIBAyQJPPvlkbLvttvHII4/knmnNNdeMKVOmRApBXASGWmDGjBmR7sGnn356qKf+v/mWW265rldxjR49urI1mJgAgdYVEIC0bm0GvTIByKAJDVBDAQFIDYtqSwQIECBAgAABAgRqIPDAAw/E2LFjG/pG8qabbhrXXXddpNdfuQhUJfDTn/40dtlll6qm75p3woQJcf7551e6BpMTINCaAgKQ1qxLU1YlAGkKo0FqJiAAqVlBbYcAAQIECBAgQIBADQTSWQrjx4+PF198Mfdu0muyLr/88kgHn7sIVC2QApAUhFR53XTTTbHVVltVuQRzEyDQggICkBYsSrOWJABplqRx6iQgAKlTNe2FAAECBAgQIECAQPsLXHvttbHHHnvE7Nmzc29m//33j3POOSdGjBiRu4+GBMoUSK/ASmfQvPTSS2VOs9ix3/nOd8b9998fI0eOrGwNJiZAoPUEBCCtV5OmrUgA0jRKA9VIQABSo2LaCgECBAgQIECAAIE2FzjvvPPiM5/5TLzyyiu5d3LkkUfGiSeeGMOGDcvdR0MCQyGQDkNPh6JXeR111FFdfz9cBAgQ6BEQgNT4XhCA1Li4tlZYQABSmE5HAgQIECBAgAABAgSaJDB//vyub9Iee+yxDY146qmnxmGHHdZQH40JDJXAvHnz4iMf+UikV7pVdS255JJx9913x3rrrVfVEsxLgECLCQhAWqwgzVyOAKSZmsaqi4AApC6VtA8CBAgQIECAAAEC7SmQvkl86KGHxre//e3cGxg+fHjXAc/77LNP7j4aEqhCYPr06V3hw9y5c6uYvmvOD3zgA3H77bdHCkNcBAgQEIDU+B4QgNS4uLZWWEAAUphORwIECBAgQIAAAQIEBimQvik8ceLE+PGPf5x7pHTI+RVXXBHbb7997j4aEqhS4Bvf+EZ87Wtfq3IJ8a1vfSsOOeSQStdgcgIEWkNAANIadShlFQKQUlgN2uYCApA2L6DlEyBAgAABAgQIEGhTgVmzZsWuu+4a06ZNy72DZZddNiZPnhybbLJJ7j4aEqhaIAV966+/fjz00EOVLSUFhw8++GCsuuqqla3BxAQItIaAAKQ16lDKKgQgpbBWPOiEbP4Le63h8ezX/se8kaIIQBrR0pYAAQIECBAgQIAAgWYIPP/887HDDjvEXXfdlXu4lVZaKaZOnRpjxozJ3UdDAq0icOutt8aHPvShSOfdVHVtt912cf3118ewYcOqWoJ5CRBoAQEBSAsUoawl1CcASd/gf7IX08+zX2/RANs/srYTss/FffpMzL4+J/u00zsh0z4EIA0Uf6GmApDB6OlLgAABAgQIECBAgECjAk8++WRsu+228cgjj+Tuuuaaa8aUKVMihSAuAu0q8LnPfS7+/d//vdLlX3zxxbHXXntVugaTEyBQrYAApFr/UmcXgCTev2ef3bPPdX2sD8u+PiX7DPVPAfSe7yPZ/L9o8B6YkLUXgDSI9prmApDB6OlLgAABAgQIECBAgEAjAvfff3+kn0J/+umnc3f74Ac/GJMmTYr0+isXgXYWeOmllyKFeU899VRl21huueUiHcw+evToytZgYgIEqhUQgFTrX+rsApCXMt/x2eeXfZy/mX395VLtFz24AKQi+P+bVgBSdQXMT4AAAQIECBAgQKAzBH6d/Z/y8ePHx4svvph7w+k1WZdffnmk8wtcBOogcM0118THP/7xSrey//77xwUXXFDpGkxOgEB1AgKQ6uxLn7mzA5DnM9/tss9vejkvkf36e9nn4NLtFz2BAKRC/K6pBSBVV8D8BAgQIECAAAECBOovcO2118Yee+wRs2fPzr3Z9E3ac845J0aMGJG7j4YE2kFgt912iyuvvLLSpd54442x9dZbV7oGkxMgUI2AAKQa9yGZtXMDkD9lvttkn4d7Oaf/gPxR9tljSOwXPclgA5CKl1+D6QUgNSiiLRAgQIAAAQIECBBoYYHzzjsvDjzwwJg3b17uVR555JFx4oknOqw5t5iG7STwzDPPxJgxY2LGjBmVLXv11VeP9Eo6T1dVVgITE6hMQABSGX35E3dmAJJCjxR+pBCk50qPDqefNEhPhFR9CUCqroAApOoKmJ8AAQIECBAgQIBAPQXmz5/fFWIce+yxDW3wtNNOi0MPPbShPhoTaDeBs88+Ow466KBKl52CxpNOOqnSNZicAIGhFxCADL35kM3YeQFIet1VCjnS6696rjdnv5icfT44ZO6Ln0gAUnUhBCBVV8D8BAgQIECAAAECBOonkJ72SCHGt7/97dybGz58eJx//vmxzz775O6jIYF2FUh/R7bYYovstdS/qmwLSy65ZNx1112x/vrrV7YGExMgMPQCApChNx+yGTsrAEkHnacDz9PB5z3X27JfTM0+6xQwn5n1eSj7PJJ9/pJ9/pZ93ph9ls0+a7865pIFxm2VACTt7f7sk8KiZJb29fbs86HsM7rAvtqniwCkfWplpQQIECBAgAABAgTaQWDu3LkxYcKEuOSSS3IvN72G54orrojtt98+dx8NCbS7wO9///tYd911I/2dqeraYIMN4vbbb48UQLoIEOgMAQFIjevcOQHIdVkVP5F9eh8ut1r29Y3Z550NVPiBrO1l2SeFJulpklcW0zeFIemndA7PPqsPMMcW2Z/f0sA6epp+LfvF1/v0m5B9fWGv33s8+/WqOcf+n6zdKdnnouzz1CL6pIPi09Myae56Hg4mAMl5u2hGgAABZEvxMwAAIABJREFUAgQIECBAgMCAArNmzYpdd901pk2bNmDbngbLLrtsXH/99bHxxhvn7qMhgboI/Nu//Vv8y7/8S6Xb8dq5SvlNTmDIBQQgQ04+dBN2RgBycQY6Ifv8oxdsekIjhRjvaAA7BR4bNNC+p+nI7BdnZ5/FPbK8RfbnVQcgP8zW8Pns0/sJmYG2m/b0g+zzuoEattWfC0DaqlwWS4AAAQIECBAgQKBlBZ5//vnYYYcdul6pk/daaaWVYurUqV0HQrsIdKJAevrj/e9/fzz44IOVbT89gfXAAw/EaqulH551ESBQdwEBSI0rXP8A5HtZ9dI39ef3quIm2a+vzz7LNFjZu7P2G/bqk15Vlf6H8N3Z5/9lnxHZ56/ZJ/0P9B/7Gfsn2e/ttog5t8h+v8oAJP1kxb/1WVva33tf3V96miXtLRn0Pj8lddkq+0zJPvV5NFQA0uBfDc0JECBAgAABAgQIEFhI4IknnoixY8fGI4+k1ybnu9Zcc82u8GPFFVfM10ErAjUVuO2222KzzTaL+fN7fz9naDeb/v7ecMMNMWxY71eVD+0azEaAwNAICECGxrmSWeodgKRDs/o+Mrlt9ntXZZ9RBbzTN/83zz57Zp+PZZ/0jf83LGKc9Kqsb2SfFHr0XClEeCz7LN9Pn2ez3+t5PVfvny5Ijztfupi1pgPc06f3NSH7opFXYKW2qU/PlV5x9bnsk17dtXKfsdN/eFyTfb6YfXqHPEdnX5+wmHW21x8JQNqrXlZLgAABAgQIECBAoNUE7r///thuu+3i6aefzr20D37wgzFp0qRIr79yESAQ8c///M/xve+lH2yt7vrRj34U++yzuDd6VLc2MxMg0DwBAUjzLFtupPoGIB/OrFMA0vtKT1+k12EVfV1TejVUOvOjkSdHvp61/9dei+jvzI6+t8VgD0GfkA2YNwB5MmubHqv++6uLeH32z6uzz3YD3KvPZX++WfZJgU660mHvj2afejwaKgBpuX9VWRABAgQIECBAgACBthH4VfZ/KMaPHx8zZszIveb0mqzLL7880mt3XAQIdAu89NJLkZ6KeuqpRZ1RWr7UW97ylpg+fXqkf7oIEKivgACkvrWN+gYgfYt2YPYb388+6emGobzmZZOtn33ue3XSd2X/TEHB4q6hDED+OVtI75+m+Pfs68/mBErvsN2oV9v0qrHv5Ozb2s0EIK1dH6sjQIAAAQIECBAg0KoC1157beyxxx4xe3bPE/4Dr3TChAlx9tlnx4gR6bXKLgIEegukv1Mf+1h6C0d117777hs//GE6N9VFgEBdBQQgda1stq/OCUA+ne02HURexXsbv57N2/spkPT0xHKLuauGKgD526vr6Hn6Y/Xs6xTONBISpVeC9Txps0r26ydq8bdFAFKLMtoEAQIECBAgQIAAgSEV+MEPfhCf+cxnYt689INw+a4jjzwyTjzxRGcM5OPSqkMFPvGJT8QVV1xR6e7T2Tzbbpteq+4iQKCOAgKQOlb11T3VNwB5R7bDvu9aPSD7vXOyTyPf4M9b/DlZw5nZJ4UKfQ/ouiz7vXRGRs+VDjtPwcGirqEKQKZmC+j9qqsjs69PyrvhV9ulV3qls056rvRKrb7nhjQ4ZAs0F4C0QBEsgQABAgQIECBAgECbCKRDmlOIceyxxza04tNOOy0OPfTQhvpoTKATBZ555pkYM2ZMQ6+Va7bTaqutFulsn1Gjipwp2+zVGI8AgWYLCECaLdpC49U3AJmcKZ+SfX7RR3ti9vW52WewIUg65DwFG+nphwezz18aqGo6Y2Nxj28OVQDy1Wwd3+y17jMGWFd/W0xnqnyl1x/8PPv1Fg1YtGZTAUhr1sWqCBAgQIAAAQIECLSaQHra45BDDonvfCf/64CHDx8e559/voOVW62Y1tPSAuecc07XE1ZVXocffnicckr6XpOLAIG6CQhA6lbRXvupbwCSvhGfzqcYn31+1qeC+2dfn5d9ioQg6QmHL2WfFGIUvdIB5fstpvNQBSATsjX0Piy96H5697sy+2KXZgxU6RgCkEr5TU6AAAECBAgQIECgLQTmzp0b6fyOSy65JPd60yHnV155ZWy3Xe+n8XN315BAxwqksHHLLbeMX/7yl5UZLLHEEnHXXXfF+9///srWYGICBMoREICU49oSo9Y7ANkiM07nW+yUfW7q471v9vUF2aeREGR61n6r7PPMIGt3ftZ/wmLGGKoAJIVD1w1yL327J9MUMLX3JQBp7/pZPQECBAgQIECAAIGyBWbOnBm77rpr3HjjjbmnGj16dEyePDk23njj3H00JEBggcDDDz8c6667bsyZk15DXs21/vrrx5133hnpSS4XAQL1ERCA1KeWC+2k/gFI2vLs7JNeOTWtz/73zr5OT0AsmaPC/8jabJB97uvVdqns15/IPikUWTv7rJB93ph90u/3HvOC7Ov06q2eq1UCkLH9mOSgWGyTgfY22PGHpr8AZGiczUKAAAECBAgQIECgHQWef/75GDduXNx99925l7/SSivFtGnTYo011sjdR0MCBBYW+OY3vxlf/Wp6pXd1V3oNVnodlosAgfoICEDqU8uFdtIZAUjadgpBPp590sHfva9PZl/8KPsMFIJcnrXZo1fHd7061uo57o70Ltgv9mo3UEgwVE+ApADox73WdWv2601z7Kf+TQQg9a+xHRIgQIAAAQIECBAoIvDEE0/E2LFj45FHHsndfc0114ypU6fGiiuumLuPhgQI9C+QXj23wQYbxAMPpLNZq7lGjhzZNf/qq+f5nlA1azQrAQKNCQhAGvNqq9adE4CksqRHJNP5FNf3qVEKNi7KPot7fLFvWJDOFdkyZ62Pztqd1KttqwQgh2ZrOr3Xui7Ift3+r6/KWZTFNhOANEPRGAQIECBAgAABAgTqJXD//fd3hR/PPJP/tcgf/OAHY9KkSbHsssvWC8NuCFQocPvtt0f6uzV//vzKVrHNNtt0BZvDhvX+IdbKlmNiAgQGKSAAGSRgK3fvrAAkVSKFILtmn8l9ypJeZZWehlhUCJIOVL/r1T6jsn/OzD55/0du86ztr3rN1yoByBXZmtK+e669sl9c3Mq365CtTQAyZNQmIkCAAAECBAgQINAWAr/K/k/C+PHjY8aMGbnXu+OOO8Zll10W6eBzFwECzRX4/Oc/H2eeeWZzB21wtB/+8Iex777pjFkXAQLtLiAAafcKLmb9nReAJIy52Sd94//aPjIpGLk0+/QXgrwn+/1HX23/9uyfT+e8Kx7L2r03+8zr1X6gACSdIdJzoNcm2a9vyzlXT7MJ2S/S2SY91+PZL1btZ4y/Zr+3XPZ55dU/e0P2zz9kn+UbnK9+zQUg9aupHREgQIAAAQIECBAoKnDNNdfEnnvuGbNnp1cr57smTJgQZ599dowYMSJfB60IEGhIYObMmZFeL/ff//3fDfVrZuPRo0fH9OnTY7nl0vdWXAQItLOAAKSdqzfA2jszAEkoL2efFIJc00covSIrhSB9/yN14+z37ny1bTov5Pnss0yOOyON99M+7QYKQN6Wtf/zq31Wyf75RI55ejeZkH2RJwBJfdIrr37Yq3P6yYXeXzc4dU2aC0BqUkjbIECAAAECBAgQIDBIgR/84Afxmc98JubN6/1DbYsf9KijjooTTjjBq3EGaa87gYEE0uvldtppp4Galfrn++yzT/zoR+lsWRcBAu0sIABp5+oNsPbODUASTApB9sw+V/VRSoelp0PPe4cgn8m+PqdXu2OyXx8/gO7Xsj//Rj9tBgpA0tkiv+jV7/fZr9NTJHmvCVnDvAFIejpkTPbpeeIkzXHkq3sb6GD43ut5KPviL9knve6r/S8BSPvX0A4IECBAgAABAgQIDEYgnS1w4oknxrHHHtvQMN/61rfikEMOaaiPxgQIFBfYfffd4yc/+UnxAZrQc8qUKV3nA7kIEGhfAQFI+9ZuwJV3dgCSeP6RfVIIcmUfq/QTBOl/QF/36u/fnP1z6z5tvpx9nQ44T6+O6n09nH2RApKeJz/So5DpiZGea6AAJIUmKTzpuVL4keZZL/u8Kfv0PnvkzdnX6dP7mpB9kTcASf3SEx99Dz9Pr95K/6G/ffZZ1LkoT2R/ls5SSXY/f3XNX++zlvb8UgDSnnWzagIECBAgQIAAAQLNEEhPe6QQ4zvf+U7u4YYPHx7nn39+pJ8GdxEgMHQCzz77bIwZMyZefPHFoZu0z0yrrrpqPPDAAzFqVDoz1kWAQDsKCEDasWo51ywASVApBEkHgPf9iYEds99L39zvCUHGZb++oY9sCj/SAenpXJBZ2eeR7DO9V5v0dMXB2eeLvX5voAAknS+yRvZJB60PdKWg5Ot9Gk3Ivm4kAEndT84+KbTp+1h3Oqxv/ezz1uwz8tU1pSc90hMfff/jor+1DLT+1vxzAUhr1sWqCBAgQIAAAQIECJQtMHfu3Nh///3j0kvTq5HzXemQ8yuvvDK22267fB20IkCgqQLnnntuHHjggU0ds9HBDjvssDj11FMb7aY9AQItIiAAaZFClLEMAUiPajoIfO/sc1kf5h2yr1MI8vrsk77hv032uTtnKdITG9dlnxuzz8RefQYKQFLTFLSkUGagn2BoVgCS5pzy6jqfzbm/vs3OyH6jd9BTcJgW6CYAaYEiWAIBAgQIECBAgACBIRZIhyrvuuuuceON6f/D5bvSIciTJ0+OjTdO50a6CBCoQiC9sm7LLbeMW265pYrpu+ZcYokl4s4774wNNtigsjWYmACB4gICkOJ2Ld9TANK7RCkESYeAX9Knbuk1UOl1VikESWdlpLM/zsw+LyyivumVVQdknxQGpD4XZJ9GA5A09HPZ56Lsk16/1XPGxv9mv+79lEYzA5A05+zsc172Seed3Jt95i/mHk5npGyYfbbNPimsefdi2rbXHwlA2qteVkuAAAECBAgQIEBgsALPP/98jBs3Lu6+O+8PvEWstNJKMW3atFhjjfQEv4sAgSoFHnnkkVhnnXVizpzeZ5wO7YrWX3/9rhAkvRLPRYBAewkIQNqrXg2ttj4BSEPbbkLj9D+od2SfB7PPX7NPehXW27JPeuXV+5owfisMkQKe27PPM9kn/TodGp/2uXz2eU/2Sf+Rn16RVb9LAFK/mtoRAQIECBAgQIAAgUUJPPHEE7HtttvGo48+mhtprbXWinTw8Yorrpi7j4YECJQrcNxxx8VXvvKVcicZYPSTTz45jjjiiErXYHICBBoXEIA0btY2PQQgbVMqCx1CAQHIEGKbigABAgQIECBAgECFAvfdd1/X2R3PPJN+8Cvf9cEPfjAmTZoUyy67bL4OWhEgMCQC6Qyf9AqqdCB5VdfIkSO75l999dWrWoJ5CRAoICAAKYDWLl0EIO1SKescSgEByFBqm4sAAQIECBAgQIBANQK/yv7Df/z48TFjxozcC9hxxx3jsssui3TwuYsAgdYTuOOOO2LTTTeNdC5IVdfWW2/d9Xq8YcOGVbUE8xIg0KCAAKRBsHZqLgBpp2pZ61AJCECGSto8BAgQIECAAAECBKoRuOaaa2KPPfZo6LyAiRMnxtlnn+39/tWUzKwEcgt84QtfiO9+97u525fR8MILL4z99tuvjKGNSYBACQICkBJQW2VIAUirVMI6WklAANJK1bAWAgQIECBAgAABAs0VOPfcc+Oggw6KefPm5R74qKOOihNOOMFPdOcW05BAdQIzZ86MdE7Pn/70p8oWMXr06Jg+fXost9xyla3BxAQI5BcQgOS3aruWApC2K5kFD4GAAGQIkE1BgAABAgQIECBAYIgF0itxUojx5S9/uaGZv/Wtb8UhhxzSUB+NCRCoVuC6667resVdldfee+8dF110UZVLMDcBAjkFBCA5odqxmQCkHatmzWULCEDKFjY+AQIECBAgQIAAgaEVSE97fOlLX2rotTjDhw+PCy64INI3MV0ECLSfwJ577tl1Zk+V15QpU2Ls2LFVLsHcBAjkEBCA5EBq1yYCkHatnHWXKSAAKVPX2AQIECBAgAABAgSGVmDu3Lmx//77x6WXXpp74nTI+ZVXXhnbbbdd7j4aEiDQWgJ//vOfY8yYMfHXv/61soWtuuqq8cADD8SoUaMqW4OJCRAYWEAAMrBR27YQgLRt6Sy8RAEBSIm4hiZAgAABAgQIECAwhALpLIBddtklbrrpptyzpnf3T548OTbeeOPcfTQkQKA1Bc4777z41Kc+VeniDj300DjttNMqXYPJCRBYvIAApMZ3iACkxsW1tcICApDCdDoSIECAAAECBAgQaBmB559/PsaNGxd333137jWtvPLKMXXq1FhjjTVy99GQAIHWFUhn/2y11Vbx85//vLJFLrHEEnHHHXfEBz7wgcrWYGICBAQgHXsPCEA6tvQ2vhgBAYjbgwABAgQIECBAgEB7CzzxxBOx7bbbxqOPPpp7I2uttVak9/WvuOKKuftoSIBA6wukfw+8733vizlz5lS22PXWWy/uvPPOGDFiRGVrMDEBAosW8ARIje8OAUiNi2trhQUEIIXpdCRAgAABAgQIECBQucB9993XdXbHM888k3stm222WUyaNCmWWWaZ3H00JECgfQROOOGEOPbYYytd8EknnRRHHnlkpWswOQEC/QsIQGp8ZwhAalxcWyssIAApTKcjAQIECBAgQIAAgUoFfpX9x/z48eNjxowZudex4447xmWXXRbp4HMXAQL1FHj55Ze7XkGVAtKqrqWWWqrrQPR3vvOdVS3BvAQILEJAAFLjW0MAUuPi2lphAQFIYTodCRAgQIAAAQIECFQmcPXVV8eee+7Z0GtuJk6cGGeffXYMHz68snWbmACBoRFIr6DaZJNNIp0LUtWVziO58cYbY9iwYVUtwbwECPQjIACp8W0hAKlxcW2tsIAApDCdjgQIECBAgAABAgQqETj33HPjoIMOinnz5uWe/+ijj47jjz/eNyJzi2lIoP0FvvSlL8W3v/3tSjdywQUXxP7771/pGkxOgMBrBQQgNb4jBCA1Lq6tFRYQgBSm05EAAQIECBAgQIDAkAqkn+RO7/b/8pe/3NC8p59+eqRvhLoIEOgsgVmzZsVaa60Vf/zjHyvb+LLLLhvTp0+P5ZdfvrI1mJgAAQFIx9wDApCOKbWNNiAgAGkAS1MCBAgQIECAAAECFQmkpz1SiPHd73439wrSq67ST1/vvffeuftoSIBAvQSuv/762GGHHSrd1F577RUXX3xxpWswOQECCwQ8AVLju0EAUuPi2lphAQFIYTodCRAgQIAAAQIECAyJwNy5c7teIXPppZfmnm/UqFFx5ZVXxtixY3P30ZAAgXoKfPKTn2zo3x9lKKQgZvvtty9jaGMSINCggACkQbB2ai4AaadqWetQCQhAhkraPAQIECBAgAABAgQaF5g5c2bssssucdNNN+XuPHr06EjfbNxoo41y99GQAIH6Cvz5z3+OMWPGxF//+tfKNrnKKqvEAw88EG94wxsqW4OJCRDoFhCA1PhOEIDUuLi2VlhAAFKYTkcCBAgQIECAAAECpQo899xzXa+uufvuu3PPs/LKK8fUqVNjjTXWyN1HQwIE6i9w/vnnxwEHHFDpRtNr/NKZRC4CBKoVEIBU61/q7AKQUnkN3qYCApA2LZxlEyBAgAABAgQI1Frg8ccf73p91aOPPpp7n2uvvXZMmTIlVlhhhdx9NCRAoDME5s+fH1tvvXX87Gc/q2zDSyyxRNx+++2x4YYbVrYGExMg4AmQWt8DApBal9fmCgoIQArC6UaAAAECBAgQIECgJIH77rsvtttuu3jmmWdyz7DZZpvFpEmTYplllsndR0MCBDpL4LHHHov3ve99MXv27Mo2vs4663Q91TZixIjK1mBiAp0u4AmQGt8BApAaF9fWCgsIQArT6UiAAAECBAgQIECg6QK//OUvY6eddooZM2bkHnv8+PFdBxwvvfTSuftoSIBAZwqceOKJccwxx1S6+RNOOCGOPvroStdgcgKdLCAAqXH1BSA1Lq6tFRYQgBSm05EAAQIECBAgQIBAUwWuvvrq2HPPPWPOnDm5x03v9D/rrLNi+PDhuftoSIBA5wq8/PLLXa+guvfeeytDWGqppeL++++Pd73rXZWtwcQEOllAAFLj6gtAalxcWyssIAApTKcjAQIECBAgQIAAgaYJnHvuuXHQQQfFvHnzco+ZfoL6+OOPj2HDhuXuoyEBAgTuuuuu2GSTTRr6902z1T760Y/GTTfd5N9fzYY1HoEcAgKQHEjt2kQA0q6Vs+4yBQQgZeoamwABAgQIECBAgMDiBdLBxCnE+MpXvtIQ1emnnx5f+tKXGuqjMQECBHoEDjnkkDjjjDMqBTnvvPNi4sSJla7B5AQ6UUAAUuOqC0BqXFxbKywgAClMpyMBAgQIECBAgACBQQmkpz2++MUvxplnnpl7nPSqqwsvvDD22muv3H00JECAQF+BWbNmxdprrx1PPvlkZTjLLLNMTJ8+Pd761rdWtgYTE+hEAQFIjasuAKlxcW2tsIAApDCdjgQIECBAgAABAgQKC6RzPvbff/+47LLLco8xatSouPLKK2Ps2LG5+2hIgACBRQnccMMNMW7cuEqB0rlHl1xySaVrMDmBThMQgNS44gKQGhfX1goLCEAK0+lIgAABAgQIECBAoJDAzJkzY5dddul6/33ea/To0XH99dfHRhttlLeLdgQIEBhQID1NVnUAMXny5MqDmAGhNCBQIwEBSI2K2XcrApAaF9fWCgsIQArT6UiAAAECBAgQIECgYYHnnnuu6xt999xzT+6+K6+8ckybNi3e+9735u6jIQECBPIIpH8njRkzJl544YU8zUtpk/4d9+CDD8Yb3vCGUsY3KAECrxUQgNT4jhCA1Li4tlZYQABSmE5HAgQIECBAgAABAg0JPP74412vr3r00Udz90vv6J8yZUqssMIKuftoSIAAgUYELrjggsoPI0/nIVV9KHsjZtoSaGcBAUg7V2+AtQtAalxcWyssIAApTKcjAQIECBAgQIAAgdwC9913X1f48eyzz+bus9lmm8WkSZMiHRTsIkCAQFkC8+fPj2222SZuvvnmsqYYcNxhw4bF7bff7jV/A0ppQGDwAgKQwRu27AgCkJYtjYVVKCAAqRDf1AQIECBAgAABAh0h8Mtf/jJ22mmnmDFjRu79jh8/vuuA9JEjR+buoyEBAgSKCjz22GPxvve9L2bPnl10iEH3S/On1wOOGDFi0GMZgACBRQsIQGp8dwhAalxcWyssIAApTKcjAQIECBAgQIAAgQEFrr766thzzz1jzpw5A7btaXDAAQfEWWedFcOHD8/dR0MCBAgMVuCkk06Ko48+erDDDKr/8ccfH8ccc8ygxtCZAIHFCwhAanyHCEBqXFxbKyxw2WVPxe67e59wYUAdCRAgQIAAAQIECCxC4Nxzz42DDjoo5s2bl9sofePvuOOOi/Q6GBcBAgSGUuDll1+ODTfcMO69996hnPY1c73+9a+P+++/P9797ndXtgYTE6i7gACkxhUWgNS4uLZWWGC11faLTTd9peunPNLjpi4CBAgQIECAAAECBAYnkN6nn36K+Stf+UpDA6UDgNNBwC4CBAhUJXD33XfHxhtv3FBw2+y1brnlll3nkQiCmy1rPALdAgKQGt8JApAaF9fWCgvccsu8+J//uSZOPPHEeOtb39r1qOmmm25aeDwdCRAgQIAAAQIECHSyQHraI4UYZ555Zm6G9KqrCy+8MPbaa6/cfTQkQIBAWQKHHnponH766WUNn2vcH/zgB5FeB+giQKD5AgKQ5pu2zIgCkJYphYW0kEDPGSDpp9TST1iccMIJXT/pkYKQbbbZxk9ctFCtLIUAAQIECBAgQKC1BdI5H/vvv3/X4eV5r1GjRsVVV10V2267bd4u2hEgQKBUgVmzZsXaa68dTz75ZKnzLG7wZZZZJqZPn971g5ouAgSaKyAAaa5nS40mAGmpclhMiwj0dwj6HXfc0RWEPPXUU12vxtp5551jiSWWaJEVWwYBAgQIECBAgACB1hOYOXNm7LLLLnHTTTflXtzo0aPj+uuvj4022ih3Hw0JECAwFAJTpkyJ7bfffiimWuQce+yxR1x66aWVrsHkBOooIACpY1Vf3ZMApMbFtbXCAv0FID2DPfjgg3HSSSfFb3/720iPwO69997xute9rvBcOhIgQIAAAQIECBCoo8Bzzz0X48aNi3vuuSf39lZeeeWYNm1avPe9783dR0MCBAgMpUD6HsCPf/zjoZxyobmuu+662GGHHSpdg8kJ1E1AAFK3ivbajwCkxsW1tcICiwtAegZ9/PHH49RTT+36abbPfe5z8elPfzqWXnrpwnPqSIAAAQIECBAgQKAuAum/ldPrqx577LHcW0qvlkk/Xb3CCivk7qMhAQIEhloghbtjxoyJF154Yain/r/5VlpppUg/nPnGN76xsjWYmEDdBAQgdauoAKTGFbW1ZgjkCUB65nn22WfjjDPOiCuuuCImTpzYFYa8+c1vbsYyjEGAAAECBAgQIECg7QTuvffe2G677SL9d3Le60Mf+lBce+21kd5v7yJAgECrC1x44YUxYcKESpf5hS98Ib797W9XugaTE6iTgACkTtXssxdPgNS4uLZWWKCRAKRnkhdffDG+973vxfnnnx+77bZbHHLIIQ4mK1wBHQkQIECAAAECBNpR4JZbbomddtopXnrppdzLT+3T++xHjhyZu4+GBAgQqFJg/vz5XU+5NXK+UbPXO2zYsLjtttti4403bvbQxiPQkQICkBqXXQBS4+LaWmGBIgFIz2T/+7//G+eee25XGLL11lvHEUccEauuumrhtehIgAABAgQIECBAoB0EfvrTn8YnP/nJmDNnTu7lHnDAAXHWWWfF8OHDc/fRkAABAq0g8Ic//CHe9773xd///vfKlpPmT+csjRgxorJQ7dzvAAAgAElEQVQ1mJhAXQQEIHWpZD/7EIDUuLi2VlhgMAFIz6Rz586Niy++OE477bR4//vfH0cddVSstdZahdekIwECBAgQIECAAIFWFTjnnHPi4IMPjnnz5uVe4jHHHBPHHXdcpJ9idhEgQKAdBU455ZQ48sgjK116+vfoscceW+kaTE6gDgICkDpUcRF7EIDUuLi2VligGQFIz+SvvPJKXH311XHiiSd2HeiY/o+eR1QLl0ZHAgQIECBAgACBFhJIr4FJ33z76le/2tCq0hl6X/ziFxvqozEBAgRaTeAf//hHbLTRRvHb3/62sqW9/vWvj/vuuy/e8573VLYGExOog4AApA5VFIDUuIq21myBZgYgPWtL/+fwxhtvjOOPPz6WXHLJrp/Q+OhHP+on3ppdPOMRIECAAAECBAgMiUB62iMdwpte/Zr3Sq+6SocH77XXXnm7aEeAAIGWFkivoEohSCNPwDV7Q1tssUX87Gc/8/2FZsMar6MEBCA1LrcnQGpcXFsrLFBGANJ7MbfeemuccMIJ8dxzz3U9EZIOflxiiSUKr1dHAgQIECBAgAABAkMpkM752G+//eLyyy/PPe2oUaPiqquu6jo42EWAAIE6CRx++OFdr7+u8kpnkX7qU5+qcgnmJtDWAgKQti7f4hcvAKlxcW2tsEDZAUjPwu6///6uV2Olf6b/YEqHRjq8rHDZdCRAgAABAgQIEBgCgZkzZ8bOO+8cN998c+7Z3vKWt8TkyZO7fkraRYAAgboJ/O1vf4u11147nnjiicq29uY3vzmmT58eb3vb2ypbg4kJtLOAAKSdqzfA2gUgNS6urRUWGKoApGeBf/jDHyIdnvaLX/wiPv/5z8cBBxwQI0eOLLx+HQkQIECAAAECBAiUIZCeYN5+++3jN7/5Te7hV1lllZg6dWq8973vzd1HQwIECLSbwLRp02Ls2LGVLnv33XePyy67rNI1mJxAuwoIQNq1cjnWLQDJgaRJxwkMdQDSA/z000/H6aefHj/96U/j05/+dHz2s5+NN73pTR3nb8MECBAgQIAAAQKtJ/D44493vb7qsccey7249BPRU6ZMiRVWWCF3Hw0JECDQrgL77rtvXHTRRZUuf9KkSbHjjjtWugaTE2hHAQFIO1Yt55oFIDmhNOsogaoCkB7kF154Ic4888yuAyL32GOP+P/s3Qd4VUXCxvE3kFCkS1FABFzARRZRhEUUFBUpFooNabJYEJAmLIRgw0aC0juIi3SVJlgoroCr4vqhFHVXXaoCShGBgPTAd+ZiJMQknOSee+855/7neXgUMzNn5jdXSO57Z6ZXr14qVapUVK0Bk0UAAQQQQAABBBBwj8D69evVpEkT7dy50/ag6tWrp0WLFqlYsWK221ARAQQQ8LLAnj17VLVqVe3duzdi07jkkkv03//+V4UKFYrYGHgwAl4UIADx4qrZHDMBiE0oqkWVQKQDkFTsQ4cO6ZVXXtH48eMDW2nNPSHmCAEKAggggAACCCCAAALhEvjwww/VrFkzJScn236kqf/6669zrKttMSoigIBfBKZPn64HHnggotMxR2uPGjUqomPg4Qh4TYAAxGsrlo3xEoBkA4uqUSPglgAkFfzYsWOBbbTDhg1T7dq1FR8fH/hUCQUBBBBAAAEEEEAAgVAKmKNZW7duLfP9qN1i7rObOHGiYmNj7TahHgIIIBA6gVMnpH3rpZSjoXtGmp5P67R69+6t1as/D8vzMnpITIw0YcIEVbuiWsTGwIMRcIVA7nxSsRpSrrjzDocA5LxE3q1AAOLdtWPkoRNwWwCSOtOUlBTNmzdPgwcPVoUKFZSQkKBatWqFDoKeEUAAAQQQQAABBKJWwOxE7ty5s06dOmXbYMCAAXrhhRcUY959oyCAAAKRFjDhx9I6VgCyNtIj4fkIIBApgWJXS40/O28IQgASqQUKw3MJQMKAzCM8J+DWACQV8vTp04HLJBMTE5UvX75AENKgQQN+0PTcK40BI4AAAggggAAC7hMw32u++OKLeuqpp7I1uJEjR6pHjx7ZakNlBBBAIKQCe61dGEtrh/QRdI4AAh4QaLxaKp71B4gJQDywjjkdIgFITuVo52cBtwcgae0/tv4nNkHIvn371L9/f91xxx3KlSuXn5eHuSGAAAIIIIAAAgiESMDsOO7Zs6fGjh1r+wlxcXGaOnVq4KgsCgIIIOAqgd0fS/+s76ohMRgEEIiAQMOPpFL1snwwAUgE1iVcjyQACZc0z/GSgJcCkFTXdevWKSkpSd9++6369u2rVq1ace6yl150jBUBBBBAAAEEEIiwgLnnw1zc++abb9oeSYECBTR//nw1atTIdhsqIoAAAmETIAAJGzUPQsDVAgQgrl6ekA+OACTkxDzAgwJeDEBSmTds2KCXXnpJZmeI+fTe3/72t8AxWRQEEEAAAQQQQAABBDITOHjwoFq2bKkPPvjANlKJEiX03nvvqXZtjpexjUZFBBAIrwABSHi9eRoCbhUgAHHryoRnXAQg4XHmKd4S8HIAkiq9fft2DRs2TG+//bY6deoUuMCyUKFC3loIRosAAggggAACCCAQcoHdu3eradOmWrNmje1nlS9fXkuXLtXll19uuw0VEUAAgbALEICEnZwHIuBKAQIQVy5L2AZFABI2ah7kIQE/BCCp3Hv37tXo0aM1ffp0tWnTJrArxHxaj4IAAggggAACCCCAwJYtWwLHV23cuNE2RvXq1bVkyRKVKVPGdhsqIoAAAhERIACJCDsPRcB1AgQgrluSsA6IACSs3DzMIwJ+CkBSyc2xBpMmTdKECRN0++23q0+fPipXrpxHVoRhIoAAAggggAACCDgtsH79ejVp0kQ7d+603XW9evUCO4yLFi1quw0VEUAAgYgJEIBEjJ4HI+AqAQIQVy1H2AdDABJ2ch7oAQE/BiCp7OZyy6lTp2rEiBGqW7eu4uPjVaVKFQ+sCkNEAAEEEEAAAQQQcErgww8/VLNmzZScnGy7S1P/9ddfV/78+W23oSICCCAQUQECkIjy83AEXCNAAOKapYjIQAhAIsLOQ10u4OcAJJX+5MmTmjNnTuDC9EqVKikhIUE1a9Z0+cowPAQQQAABBBBAAIFgBebPnx84GtV8MMZueeihhwI7iWNjY+02oR4CCCAQeQECkMivASNAwA0CBCBuWIXIjYEAJHL2PNm9AtEQgKTqnz59Wu+++64SExMDl6QPGDBA9evXV0xMjHsXiJEhgAACCCCAAAII5EjAHInapUsXnTp1ynZ78/3hCy+8wPeHtsWoiAACrhEgAHHNUjAQBCIqQAASUf6IP5wAJOJLwABcKBBNAUjaIOQja+KDBg3SoUOHAjtCbrvtNn7QdeHrkyEhgAACCCCAAALZFTAfejEhxtNPP52tpiNHjlSPHj2y1YbKCCCAgGsECEBcsxQMBIGIChCARJQ/4g8nAIn4EjAAFwpEYwCSdhnWrFmjpKQkbdiwQf369dO9997LcQcufJ0yJAQQQAABBBBAwI5ASkqKevbsqbFjx9qpHqgTFxcXuDeudevWtttQEQEEEHCdAAGI65aEASEQEQECkIiwu+ahBCCuWQoG4iKBaA9AUpfiu+++C9wR8umnn6pXr17q0KGD8ubN66KVYigIIIAAAggggAACWQmYez4eeOABvfnmm7ahChQoIHNPSKNGjWy3oSICCCDgSgECEFcuC4NCIOwCBCBhJ3fVAwlAXLUcDMYlAgQg5y7Etm3bNHTo0MBdIZ07d9ajjz6qggULumS1GAYCCCCAAAIIIIBARgLJyclq2bKlli9fbhuoRIkSeu+991S7dm3bbaiIAAIIuFaAAMS1S8PAEAirAAFIWLld9zACENctCQNygQABSMaLsGfPHo0aNUqzZs1S+/bt1b17dxUvXtwFK8YQEEAAAQQQQAABBNIK7Nq1K3Cfmzna1G4pX768li1bpipVqthtQj0EEEDA3QIEIO5eH0aHQLgECEDCJe3O5xCAuHNdGFVkBQhAsvY3nyacMGGCJk2apObNm6t3794qW7ZsZBeNpyOAAAIIIIAAAggEBDZv3qzGjRtr48aNtkWqV6+uJUuWqEyZMrbbUBEBBBBwvQABiOuXiAEiEBYBApCwMLv2IQQgrl0aBhZBAQIQe/hHjx7VlClTNHLkSN1www2BC9MrVapkrzG1EEAAAQQQQAABBBwXWL9+vZo0aaKdO3fa7rt+/fpatGiRihYtarsNFRFAAAFPCBCAeGKZGCQCIRcgAAk5sasfQADi6uVhcBESIADJHvzJkyf1xhtvBC5Mr1q1qhISElSjRo3sdUJtBBBAAAEEEEAAgaAEPvzwQzVr1kxmt67dYnbzzp49W/nz57fbhHoIIICAdwQIQLyzVowUgVAKEICEUtf9fROAuH+NGGH4BQhAcmZ+6tQpvfPOO0pMTNSFF14YCELq1auXs85ohQACCCCAAAIIIGBbYP78+WrTpo2OHTtmu81DDz0UONY0NjbWdhsqIoAAAp4SIADx1HIxWARCJkAAEjJaT3RMAOKJZWKQYRYgAAkO/PTp01q5cmUgCDHHZJkgxBzFEBMTE1zHtEYAAQQQQAABBBD4g4C5l61Lly4yH0axW5544gk9//zzfH9mF4x6CCDgTQECEG+uG6NGwGkBAhCnRb3VHwGIt9aL0YZHgADEOefVq1crKSlJW7duVXx8vO6++27lzp3buQfQEwIIIIAAAgggEKUC5kMnL7zwgp5++ulsCYwaNUrdu3fPVhsqI4AAAp4UIADx5LIxaAQcFyAAcZzUUx0SgHhquRhsmAQIQJyH/uabbzR48GCZQKR3795q37698uTJ4/yD6BEBBBBAAAEEEIgCgZSUFPXs2VNjx461Pdu4uDhNmzZN999/v+02VEQAAQQ8LUAA4unlY/AIOCZAAOIYpSc7IgDx5LIx6BALEICEDvj777/XkCFDtHTpUnXt2lWPPPKIChQoELoH0jMCCCCAAAIIIOAzAXPPh/kwyZw5c2zPzHy/tWDBAt16662221ARAQQQ8LwAAYjnl5AJIOCIAAGII4ye7YQAxLNLx8BDKEAAEkLc37revXu3RowYoTfffFMdOnRQt27dVKxYsdA/mCcggAACCCCAAAIeFkhOTlbLli21fPly27MoUaKEFi9erFq1atluQ0UEEEDAFwIEIL5YRiaBQNACBCBBE3q6AwIQTy8fgw+RAAFIiGAz6PbAgQMaP368Jk+eHPhh3hyPVbp06fANgCchgAACCCCAAAIeEdi1a5duu+02rVmzxvaIy5cvr2XLlqlKlSq221ARAQQQ8I0AAYhvlpKJIBCUAAFIUHyeb0wA4vklZAIhECAACQHqebo8cuSI/vGPf8hcynnTTTepX79+uuyyy8I/EJ6IAAIIIIAAAgi4UGDz5s1q1KiRNm3aZHt01atX15IlS1SmTBnbbaiIAAII+EqAAMRXy8lkEMixAAFIjul80ZAAxBfLyCQcFiAAcRg0G92dOHFCs2fP1ssvv6wrr7xS/fv3l/nhnYIAAggggAACCESrwLp169S0aVPt3LnTNkH9+vW1aNEiFS1a1HYbKiKAAAK+EyAA8d2SMiEEciRAAJIjNt80IgDxzVIyEQcFCEAcxMxhV6dOndLChQuVlJSkiy66SAkJCapbt24Oe6MZAggggAACCCDgTYGVK1eqefPmMnd/2C2mvvlASf78+e02oR4CCCDgTwECEH+uK7NCILsCBCDZFfNXfQIQf60ns3FGgADEGUcnejl9+rQ++OADJSYmyoQiJgi59dZbFRMT40T39IEAAggggAACCLhWYP78+WrdurWOHz9ue4wPP/xw4H612NhY222oiAACCPhWgADEt0vLxBDIlgABSLa4fFeZAMR3S8qEHBAgAHEAMQRdfPbZZ4EgZMeOHYGjscyl6bly5QrBk+gSAQQQQAABBBCIrMDEiRPVtWvXwAdA7JYnnnhCzz//PB8UsQtGPQQQ8L8AAYj/15gZImBHgADEjpJ/63z+uVS7tn/nx8wQyInA6tVSrVo5aUmbcAj85z//CRyNtXbtWvXp00dt27ZVnjx5wvFonoEAAggggAACCIRUwOx+NSHGM888Y/s5ZmfsyJEj1b17d9ttqIgAAghEhQABSFQsM5NE4LwCBCDnJfJ1Beu+YdWpI+uNRF9Pk8khYFvg6qsla6OB4uJsN6FihAS2bNkSuCzdHJH12GOPyRz5cMEFF0RoNDwWAQQQQAABBBAITiAlJUU9evTQuHHjbHcUZ33TOm3aNN1///2221ARAQQQiBoBApCoWWomikCWAgQgvEBMCLJ+vXT0aHRbmE9btWjRQr/8sjdoiBo1rtKYMWOC7ocOwiuQL59UowbhR3jVg3/azp07NWLECM2dO1cdO3YMhCFFixYNvmN6QAABBBBAAAEEwiRw7NgxtW/fXnPmzLH9xAIFCmjBggWB+9EoCCCAAAIZCBCA8LJAAAEjQADC6wCBMwI//PCDypcv7whHv379NHjwYEf6ohMEELAnsH//fo0dO1ZTpkzRPffco8cff1wXXXSRvcbUQgABBBBAAAEEIiSQnJwcuNts+fLltkdQokQJLV682Dq2lXNbbaNREQEEok+AACT61pwZI5CRAAEIrwsEzgiYT1vdd999jnDMmzdPd911lyN90QkCCGRP4PDhw5o8eXIgDGnYsKH69u2rChUqZK8TaiOAAAIIIIAAAmEQ2LVrl5o2bRq428xuMR/aWrZsmapUqWK3CfUQQACB6BQgAInOdWfWCKQXIADhNYHAGQFzmfKwYcMc4dixY4fKlCnjSF90ggACORM4fvy4Zs6cqaFDh6pmzZqKj49XtWrVctYZrRBAAAEEEEAAAYcFNm/erEaNGmnTpk22e65evbqWLFnCzxq2xaiIAAJRLUAAEtXLz+QR+F2AAIQXAwJnBOrVq6dPPvkkaI5LLrlE27ZtC7ofOkAAAWcEzIWib731lpKSklS2bFklJCSoTp06znROLwgggAACCCCAQA4E1q1bpyZNmsjsALFb6tevr0WLFnHXmV0w6iGAAAIEILwGEEDACBCA8DpAQDph3QRfuHBh6yL44G+Cv/vuuwOXMVMQQMBdAqdPn9b777+vQYMGKXfu3IEg5JZbblFMTIy7BspoEEAAAQQQQMDXAitXrlTz5s1l7v6wW1q0aKFZs2Ypf/78dptQDwEEEECAAITXAAIIEIDwGkDgjMAXX3zh2AWCL7/8sv7+979DiwACLhZYtWqVEhMTtXv3bvXv3z/wJkSuXLlcPGKGhgACCCCAAAJ+EDB3BbZp00bmqE675eGHH9b48eMVGxtrtwn1EEAAAQSMAAEIrwMEEDAC7ADhdYCANG7cOD322GOOUPzrX/+S2Z5OQQAB9wt89dVXgaOxzD9NcNm6dWvFxcW5f+CMEAEEEEAAAQQ8JzBx4kR16dJFZleq3fLkk0/queeeY8eqXTDqIYAAAmkFCEB4PSCAAAEIrwEEzgh06NBB06ZNC5rDHKtjtrJfcMEFQfdFBwggED4Bc/mo2b1ljqTo3r27HnzwQY6YCB8/T0IAAQQQQMDXAibweP755/XMM8/Ynqc5onPkyJGB70soCCCAAAI5FCAAySEczRDwmQA7QHy2oEwnRwKXX365/ve//+WobdpGV199tdasWRN0P3SAAAKREfjxxx81fPhwLViwQOa4CfMpzSJFikRmMDwVAQQQQAABBDwvkJKSoh49egR2nNstZjeq+XDW/fffb7cJ9RBAAAEEMhIgAOF1gQACRoAAhNdBtAv88ssvKl68uCMMnTt3DpzPS0EAAW8LmD8XxowZo6lTp6pVq1bq1auXSpUq5e1JMXoEEEAAAQQQCKvAsWPH1K5dO82dO9f2cwsWLBj4IEbDhg1tt6EiAggggEAmAgQgvDQQQIAAhNcAAtKSJUvUtGlTRyhee+21wHFaFAQQ8IfAoUOH9MorrwSCzcaNGwfuCSlfvrw/JscsEEAAAQQQQCBkAuZY3JYtW2r58uW2n1GyZEm99957qlWrlu02VEQAAQQQyEKAAISXBwIIEIDwGkBAevbZZzVw4EBHKL755hv9+c9/dqQvOkEAAfcImE9wzpgxQ8OGDVPt2rUVHx+vqlWrumeAjAQBBBBAAAEEXCOwa9euwAes1q5da3tMFSpU0NKlS1WlShXbbaiIAAIIIHAeAQIQXiIIIEAAwmsAAem2227T4sWLg6Yw9wSYY3Ny5coVdF90gAAC7hQw53jPmzdPgwcPlnmjIiEhgU9punOpGBUCCCCAAAIREdi8ebMaNWqkTZs22X7+lVdeGfh5pEyZMrbbUBEBBBBAwIYAAYgNJKogEAUC3AESBYvMFDMVOH36tEqUKBEILoIt5gcd86ktCgII+F/A/Nlhjs9LTExUvnz5AkFIgwYNFBMT4//JM0MEEEAAAQQQyFBg3bp1atKkicwOELvlhhtu0MKFC1W0aFG7TaiHAAIIIGBXgADErhT1EPC3AAGIv9eX2WUtsGHDBse2mT/11FN67rnnIEcAgSgT+PjjjwNByL59+9S/f3/dcccd7ASLstcA00UAAQQQQGDFihVq3ry5Dh48aBujRYsWmjVrlvLnz2+7DRURQAABBLIhQACSDSyqIuBjAQIQHy8uUzuvwPTp0/XAAw+ct56dCu+++27gOC0KAghEp4D51GdSUpK+/fZb9e3bV61atVJsbGx0YjBrBBBAAAEEokjAHI/Zpk0bHT9+3PasH3nkEY0bN47vFWyLUREBBBDIgQABSA7QaIKADwUIQHy4qEzJtkC3bt00duxY2/Wzqrhnz57AcVoUBBCIbgGzs+yll16S2RnSo0cPdezYMXBMFgUBBBBAAAEE/CcwYcIEde3aVeZ4TLvlySefDOwc5+hMu2LUQwABBHIoQACSQziaIeAzAQIQny0o08mWQO3atfX5559nq01Glf/0pz9p48aNQfdDBwgg4B+B7du3a9iwYXr77bdlPuXZuXNnFS5c2D8TZCYIIIAAAghEsYAJPEyIMXDgQNsKJvAYOXKkunfvbrsNFRFAAAEEghAgAAkCj6YI+EiAAMRHi8lUsiVw5MiRwJuRJ0+ezFa7jCq3bdtWM2bMCLofOkAAAf8J7N27V6NHj5Y5cs8cj9GzZ092i/lvmZkRAggggEAUCaSkpARCjPHjx9uedVxcXOB7AXNEJgUBBBBAIEwCBCBhguYxCLhcgADE5QvE8EImsGrVKl1//fWO9D9q1Cg+yeWIJJ0g4F8BcynqpEmTZI7KuP3229WnTx+VK1fOvxNmZggggAACCPhQ4NixY2rXrp3mzp1re3YFCxbUggUL1LBhQ9ttqIgAAggg4IAAAYgDiHSBgA8ECEB8sIhMIUcCw4cPV+/evXPUNn2jzz77TH/9618d6YtOEEDA3wLmjZOpU6dqxIgRqlu3ruLj41WlShV/T5rZIYAAAggg4AOB5ORktWjRQitWrLA9m5IlS+q9995TrVq1bLehIgIIIICAQwIEIA5B0g0CHhcgAPH4AjL8HAuY7edvvvlmjtunNsybN6/MD0N58uQJui86QACB6BEwx+/NmTMncGF6pUqVlJCQoJo1a0YPADNFAAEEEEDAQwK7du1S06ZNtXbtWtujrlChgpYtW6bKlSvbbkNFBBBAAAEHBQhAHMSkKwQ8LEAA4uHFY+hBCZgfSL7//vug+jCNr732Wn366adB90MHCCAQnQLmEtV3331XiYmJKlSokAYMGKD69evLXJRKQQABBBBAAIHIC2zatEmNGzeW+afdcuWVV2rx4sUqU6aM3SbUQwABBBBwWoAAxGlR+kPAmwIEIN5cN0YdnMDOnTtVunTp4Dr5rXWvXr1kjtOiIIAAAsEImCDko48+0qBBg3To0CH1798/cFcIQUgwqrRFAAEEEEAgOAGz48Ps/DA7QOyWG264QQsXLlTRokXtNqEeAggggEAoBAhAQqFKnwh4T4AAxHtrxoiDFzA/kJjze50os2fP1v333+9EV/SBAAIIBATWrFmjpKQkbdiwQf369dO9996r2NhYdBBAAAEEEEAgjALmro/mzZvr4MGDtp9qfsYwPx/ky5fPdhsqIoAAAgiESIAAJESwdIuAxwQIQDy2YAzXEQFzxIw5bsaJsnnzZlWsWNGJrugDAQQQOEfgu+++C9wRYo7ZM7vNOnToIHPvEAUBBBBAAAEEQiswd+5ctW3bVsePH7f9oEceeUTjxo3jQwu2xaiIAAIIhFiAACTEwHSPgEcECEA8slAM01GBm2++WeYTXcGWUqVKyRynxRE1wUrSHgEEshLYtm2bhg4dGrgrpHPnznr00UdVsGBB0BBAAAEEEEAgBAITJkxQ165dZY6ntFueeuopPfvss/xcYBeMeggggEA4BAhAwqHMMxBwvwABiPvXiBE6K5CSkhI4j9ecsR9sufPOO7Vo0aJgu6E9AgggYEtgz549GjVqlGbNmqX27dure/fuKl68uK22VEIAAQQQQACBrAVM4PHcc89p4MCBtqnMB6HM383dunWz3YaKCCCAAAJhEiAACRM0j0HA5QIEIC5fIIbnuMBXX32lK6+80pF+X3zxRZnjtCgIIIBAOAWSk5NlPp06adIkNWvWTH369FHZsmXDOQSehQACCCCAgK8EzIekzAcLxo8fb3tecXFxmj59ulq1amW7DRURQAABBMIoQAASRmwehYCLBQhAXLw4DC0kApMnT5Y5n9eJ8s9//lO33HKLE13RBwIIIJBtgaNHj2rKlCkaOXKk6tevr/j4eFWqVCnb/dAAAQQQQACBaBY4duyY2rVrJ3Pvh91ijqJcsGCBGjZsaLcJ9RBAAAEEwi1AABJucZ6HgDsFCEDcuS6MKnQCJvwwIUiwxWx3379/vwoXLhxsV7RHAAEEghI4efKk3njjjcCF6VWrVlVCQoJq1KgRVJ80Rm2j5XIAACAASURBVAABBBBAIBoEzK7KFi1aZOt+wJIlS2rx4sW65pprooGIOSKAAALeFSAA8e7aMXIEnBQgAHFSk768IFC9enV9/fXXQQ+1WrVqjvQT9EDoAAEEEPhN4NSpU3rnnXeUmJioCy+8MBCE1KtXDx8EEEAAAQQQyEBg586datq0qdatW2fbp0KFClq2bJkqV65suw0VEUAAAQQiJEAAEiF4HouAywQIQFy2IAwnpAIHDx5UkSJFZC44DLY8+OCDevXVV4PthvYIIICA4wLmz7iVK1dq0KBBMsd6mCCkSZMmMjvXKAgggAACCCAgbdq0SY0aNdLmzZttc5h7BJcsWaLSpUvbbkNFBBBAAIEIChCARBCfRyPgIgECEBctBkMJucCKFSt08803O/KciRMnqlOnTo70RScIIIBAqARWr16tpKQkbd26NXBHyN13363cuXOH6nH0iwACCCCAgOsF1q5dG9j5sWvXLttjveGGG7Ro0aLAh6koCCCAAAIeESAA8chCMUwEQixAABJiYLp3lYA5FmbAgAGOjGn9+vUynwKjIIAAAl4Q+OabbzR48GCZQKR3795q37698uTJ44WhM0YEEEAAAQQcEzAfiGrevLnMznC7pWXLlpo1a5by5ctntwn1EEAAAQTcIEAA4oZVYAwIRF6AACTya8AIwidgLjhcuHBh0A8sUKCADhw4wKeog5akAwQQCLfA999/ryFDhmjp0qXq2rWrHnnkEZk/0ygIIIAAAgj4XWDu3Llq27atjh8/bnuqZsf3uHHj+L7fthgVEUAAARcJEIC4aDEYCgIRFCAAiSA+jw6rgDkT35zXm52t7pkNsEGDBjKfHqMggAACXhXYvXu3RowYoTfffFMdOnRQt27dVKxYMa9Oh3EjgAACCCCQpcCECRMCwX927gJ86qmn9Oyzz3KHFq8tBBBAwKsCBCBeXTnGjYCzAgQgznrSm3sFzKeeK1So4MgAzTn65kx9CgIIIOB1AbObbfz48Zo8ebLMER/meCwud/X6qjJ+BBBAAIFUARN4mBDD/LJbYmJiNGrUqMCHAygIIIAAAh4WIADx8OIxdAQcFCAAcRCTrlwtYD7l3KpVK0fGOH/+/MAbhRQEEEDALwJHjhzRP/7xj8AbPjfddJP69eunyy67zC/TYx4IIIAAAlEokJKSEggxzO4PuyUuLk4zZszQfffdZ7cJ9RBAAAEE3CpAAOLWlWFcCIRXgAAkvN48LXIC5lPNw4cPd2QAO3bsUJkyZRzpi04QQAABNwmcOHFCs2fP1ssvv6wrr7xS/fv3V/Xq1d00RMaCAAIIIIDAeQWOHj2qdu3aad68eeetm1qhYMGCWrBggRo2bGi7DRURQAABBFwsQADi4sVhaAiEUYAAJIzYPCqiAtdff71WrVoV9BjKlSunH374Ieh+6AABBBBws8CpU6e0cOHCwHF/pUqV0oABA1S3bl03D5mxIYAAAgggEBBITk5W8+bNtXLlStsiJUuW1OLFi3XNNdfYbkNFBBBAAAGXCxCAuHyBGB4CYRIgAAkTNI+JqMDx48dVuHBhHTt2LOhx3HPPPZozZ07Q/dABAggg4AUBc3b6Bx98oMTERJmjREwQcuutt3IhrBcWjzEigAACUSiwc+dONW3aVOvWrbM9e3NP4LJly1S5cmXbbaiIAAIIIOABAQIQDywSQ0QgDAIEIGFA5hERF/j8889Vu3ZtR8YxZMgQ9enTx5G+6AQBBBDwksBnn30WCELMMYDmaCxzF1KuXLm8NAXGigACCCDgY4FNmzapUaNG2rx5s+1ZmuMelyxZotKlS9tuQ0UEEEAAAY8IEIB4ZKEYJgIhFiAACTEw3btCYOzYsYELEJ0oH330kerVq+dEV/SBAAIIeFLg66+/1uDBg7V27dpAINy2bVvlyZPHk3Nh0AgggAAC/hAwfyc1adJEu3fvtj2hG2+8MXDcY5EiRWy3oSICCCCAgIcECEA8tFgMFYEQChCAhBCXrl0j0L59e82YMSPo8cTGxurAgQO64IILgu6LDhBAAAGvC2zZsiVwWbo5Iuuxxx7Tww8/zJ+PXl9Uxo8AAgh4UGDFihWBOz8OHjxoe/RmF+OsWbOUL18+222oiAACCCDgMQECEI8tGMNFIEQCBCAhgqVbVwlUqVJFGzZsCHpMNWvW1BdffBF0P3SAAAII+EnAnLc+YsQIzZ07Vx07dgyEIUWLFvXTFJkLAggggIBLBczfPWYnornzz27p1KmTxo0bp9y5c9ttQj0EEEAAAS8KEIB4cdUYMwLOCxCAOG9Kj+4S2Lt3r0qUKOHIoLp06RL4YYmCAAIIIPBHgf3798scOThlyhTdc8896tWrly6++GKoEEAAAQQQCInA+PHjA6H76dOnbff/1FNP6dlnn1VMTIztNlREAAEEEPCoAAGIRxeOYSPgsAABiMOgdOc6gcWLF+u2225zZFxTp07VAw884EhfdIIAAgj4VeDw4cOaPHlyIAxp2LCh+vbtqwoVKvh1uswLAQQQQCDMAibwMCGG+WW3mMBj9OjRgcCEggACCCAQJQIEIFGy0EwTgfMIEIDwEvG7wMCBA7P1w1FWHt9++60uv/xyv5MxPwQQQMARAXMcycyZMzV06FCZIwTj4+NVrVo1R/qmEwQQQACB6BRISUlRt27dNGHCBNsAcXFxgfsA77vvPtttqIgAAggg4AMBAhAfLCJTQMABAQIQBxDpwtUCTZo00dKlS4MeY7FixfTzzz8rV65cQfdFBwgggEA0CZg3qxYsWKDBgwerbNmySkhIUJ06daKJgLkigAACCDggcPToUbVr107z5s2z3VvBggX11ltv6ZZbbrHdhooIIIAAAj4RIADxyUIyDQSCFCAACRKQ5q4WMNvjixcvrn379gU9zsaNG2vJkiVB90MHCCCAQLQKmD+T33//fQ0aNChw8awJQswbUpzDHq2vCOaNAAII2Bc4cOCAWrRooZUrV9puVLJkSZnjcK+55hrbbaiIAAIIIOAjAQIQHy0mU0EgCAECkCDwaOp6gf/973+OHVn19NNPO3aUluvhGCACCCAQYoFVq1YpMTFRu3fvVv/+/dW8eXN22IXYnO4RQAABrwrs3LlTTZs21bp162xPoWLFioFd4JUrV7bdhooIIIAAAj4TIADx2YIyHQRyKEAAkkM4mnlCYNq0aerQoYMjY33vvfcCP3hREEAAAQScE/jqq6+UlJQk88+///3vat26tcxZ7RQEEEAAAQSMwKZNm9SoUSNt3rzZNkiNGjUCOz9Kly5tuw0VEUAAAQR8KEAA4sNFZUoI5ECAACQHaDTxjMBjjz2mcePGOTJec/+HOU6LggACCCDgvIB5g+vll1/WihUr1KNHDz344IPKnz+/8w+iRwQQQAABzwisXbtW5j4/s1vQbrnxxhu1cOFCFSlSxG4T6iGAAAII+FWAAMSvK8u8EMieAAFI9ryo7S2BWrVq6Ysvvgh60JUqVdKGDRuC7ocOEEAAAQSyFvjxxx81fPjwwKXpDz/8sLp06cKbWLxoEEAAgSgUWL58eeDOj4MHD9qe/V133aWZM2cqX758tttQEQEEEEDAxwIEID5eXKaGQDYECECygUVVTwkcOXJEhQsX1smTJ4Med7t27TR9+vSg+6EDBBBAAAF7Ar/88ovGjBmjqVOnqlWrVurVq5dKlSplrzG1EEAAAQQ8LTB37ly1bdtWx48ftz2PTp06BXZ+586d23YbKiKAAAII+FyAAMTnC8z0ELApQABiE4pqnhP45JNPVK9ePUfGPXr0aHXr1s2RvugEAQQQQMC+wKFDh/TKK69o/Pjxaty4ceCekPLly9vvgJoIIIAAAp4SMH/em2NsT58+bXvcTz/9tAYOHKiYmBjbbaiIAAIIIBAFAgQgUbDITBEBGwIEIDaQqOJJgaFDhwbeKHOirF69WuY4LQoCCCCAQGQEjh07phkzZmjYsGGqXbu24uPjVbVq1cgMhqcigAACCDguYAIPE2I899xztvs2gYf5oJIJTCgIIIAAAgj8QYAAhBcFAggYAQIQXgd+Fbjvvvs0Z86coKeXN29eJScnK0+ePEH3RQcIIIAAAsEJpKSkaN68eRo8eLAqVKighIQEAurgSGmNAAIIRFzA/NluQoyJEyfaHktcXFwgGDff81MQQAABBBDIUIAAhBcGAggQgPAa8LOAOSLlhx9+CHqKdevW1apVq4Luhw4QQAABBJwTMJ8UXrJkiRITEwOX3ZogpEGDBhx/4hwxPSGAAAJhETh69Gjgvo/58+fbfl7BggX11ltv6ZZbbrHdhooIIIAAAlEoQAAShYvOlBHIQIAdILws/Cjw008/qUyZMo5M7fHHHw8cuUJBAAEEEHCnwMcffxwIQvbt26f+/fvrjjvuUK5cudw5WEaFAAIIIPC7wIEDB9SiRQutXLnStkqpUqW0ePFi1axZ03YbKiKAAAIIRKkAAUiULjzTRiCdAAEILwk/CphPhLVs2dKRqb3++utq1aqVI33RCQIIIIBA6ATWrVunpKQkffvtt+rbt2/gz+7Y2NjQPZCeEUAAAQRyLLBz5041bdpU5s9uu6VixYpatmyZKlWqZLcJ9RBAAAEEolmAACSaV5+5I3BWgACEV4MfBcwngM358E6UrVu3yhynRUEAAQQQ8IbAhg0b9NJLL8nsDOnRo4c6duwYOCaLggACCCDgDoGNGzeqcePG2rx5s+0B1ahRI7Dzo3Tp0rbbUBEBBBBAIMoFCECi/AXA9BH4TYAAhJeCHwVuuummbG2lz8zgoosukjlOKyYmxo9MzAkBBBDwtcD27dsDRxi+/fbbeuSRR9S5c2cVLlzY13NmcggggIDbBdasWRPY+bF7927bQ73xxhu1cOFCFSlSxHYbKiKAAAIIICACEF4ECCBgBAhAeB34TSAlJSXww9Gvv/4a9NSaNWsW+GGLggACCCDgXYG9e/dq9OjRmj59utq0aRPYFVKyZEnvToiRI4AAAh4VWL58eeDOj4MHD9qewV133aWZM2eyk8+2GBURQAABBH4XIADhxYAAAgQgvAb8KPDll1/KbJF3ogwaNEgJCQlOdEUfCCCAAAIRFjBvuE2aNEkTJkzQ7bffrj59+qhcuXIRHhWPRwABBKJDYM6cOWrXrp2OHz9ue8KdOnXSuHHjlDt3btttqIgAAggggAABCK8BBBA4R4AdILwg/CbwyiuvyPyw5ET54IMPdPPNNzvRFX0ggAACCLhE4OjRo5o2bZqGDx+u6667TvHx8apSpYpLRscwEEAAAf8JmBCjW7duOn36tO3JPf300xo4cCBH0doWoyICCCCAwB8E2AHCiwIBBIwAAQivA78JPPzww3r11VeDnpa592P//v2cFx+0JB0ggAAC7hQ4efKkzCeSzYXplSpVCuz4q1mzpjsHy6gQQAABDwqYwMOEGM8995zt0ZvvwceMGaOuXbvabkNFBBBAAAEEMhQgAOGFgQACBCC8Bvwo8Je//EX/+c9/gp6a6eerr74Kuh86QAABBBBwt4B5g+7dd99VYmKiChUqpAEDBqh+/fp86tjdy8boEEDA5QLmXr7HHntMEydOtD3SPHnyaMaMGbr33nttt6EiAggggAACmQoQgPDiQAABAhBeA34TSE5OVtGiRbO1vT4zg4ceekiTJ0/2GxHzQQABBBDIRMAEIR999JHM/U+HDh1S//79A3eFmE8jUxBAAAEE7AuYowbbtm2r+fPn225kAui33nqL42dti1ERAQQQQOC8AgQg5yWiAgJRIcARWFGxzFEzSXNnR8OGDR2Zr7lLxBynRUEAAQQQiD6BNWvWKCkpSRs2bFC/fv0Cn0aOjY2NPghmjAACCGRT4MCBA2rRooVWrlxpu2WpUqW0ePFijiG0LUZFBBBAAAFbAgQgtpiohIDvBQhAfL/EUTVB86ndJ554wpE5f/nll6pevbojfdEJAggggIA3Bb777rvAHSGffvqpevXqpQ4dOihv3rzenAyjRgABBEIs8NNPP6lp06Zav3697SdddtllWrp0aeAuJgoCCCCAAAKOChCAOMpJZwh4VoAAxLNLx8AzEGjevLkWLVoUtE3BggUDF6Dnzp076L7oAAEEEEDA+wLbtm3T0KFDA3eFdO7cWY8++qjM3xUUBBBAAIEzAhs3blSjRo20ZcsW2yQ1atTQkiVLdPHFF9tuQ0UEEEAAAQRsCxCA2KaiIgK+FiAA8fXyRtXkzNnt5oen3bt3Bz3vm266ScuXLw+6HzpAAAEEEPCXwJ49ezRq1CjNmjVL7du3V/fu3VW8eHF/TZLZIIAAAtkUMMcGmp0f2fk+vEGDBoE7P4oUKZLNp1EdAQQQQAABmwIEIDahqIaAzwUIQHy+wFE0va1bt6pixYqOzNhcfJuYmOhIX3SCAAIIIOA/geTkZE2YMEGTJk1Ss2bN1KdPH5UtW9Z/E2VGCCCAwHkEzB185s6PQ4cO2ba66667NHPmTOXLl892GyoigAACCCCQbQECkGyT0QABXwoQgPhyWaNyUq+//rpat27tyNzNp9HMcVoUBBBAAAEEshI4evSopkyZopEjR6p+/fqBC9MrV64MGgIIIBAVAnPmzFG7du10/Phx2/M1RwiOHTuWo2Zti1ERAQQQQCDHAgQgOaajIQK+EiAA8dVyRvVkHn/8cY0YMcIRA3OBI2cRO0JJJwgggEBUCJw8eVJvvPFG4ML0qlWryuwkvOqqq6Ji7kwSAQSiU2DcuHHq1q2bzDG0dsszzzwj8ysmJsZuE+ohgAACCCCQcwGfByAVekrf/5w1Ty7rr9wiF0hFrV9VrQ3rtS+T7q4tVb8056y0RMBzAgQgnlsyBpyJwHXXXadPP/00aJ9LL71U33//fdD90AECCCCAQPQJnDp1Sm+//baSkpJ04YUXKiEhQfXq1Ys+CC/P+MQJaf16ydrdQ0EAgd8EzFFV1oXliosLBB4mxHj++edt85jAY8yYMeratavtNlREAAEEEEAgaAECkEwJb/izNOHBM6EIJfICW/dIFXudHUeH+tJrnSM/Lt+MgADEN0sZ1RMx2+4LFy6sY8eOBe1w3333BT7FS0EAAQQQQCCnAuYNwpUrV2rQoEGBv5tMENKkSRM+9ZxT0HC1M+FHnTrS2rXheiLPQcA7AldfrZRVq9S1Z8/A/Ud2S548eTRjxgzde++9dptQDwEEEEAAAWcECECydMwXJ83uJrWo5Qw3veRcgAAk53a2WhKA2GKikssFVq9erb/+9a+OjHLo0KHq3bu3I33RCQIIIIAAAubvKLMjZOvWrYqPj9fdd9/N2fdufVl8/rl1LoB1JgAFAQQyFOh30016ecUK2zqFChWSuVvv5ptvtt2GiggggAACCDgmEGUBiAkzrq10rl7KKWnvIWnNFmnqR9K/N577dROCfPS0VMs6GosSOQECkBDbE4CEGJjuwyJgttR3797dkWd9/PHHuv766x3pi04QQAABBBBIFfjmm280ePBgmUDEBO3t27eX+WQ0xUUC1vcA1m32LhoQQ0HAXQLmQL9PbA6pVKlSWrx4sWrWrGmzBdUQQAABBBBwWCDKApAVT0gNrsjacMwyqcc0WUdanq1X7/IzIQglcgIEICG2JwAJMTDdh0WgXbt2mjlzZtDPio2NVXJysvLnzx90X3SAAAIIIIBARgLmnqkhQ4Zo6dKl6tKlizp16qQCBQqA5QYBAhA3rAJjcLGA3QDksssuC/wZV6lSuo+hunhuDA0BBBBAwIcCBCAZLurfrbfPhr537pfWvChdXcGHrwGPTIkAJMQLRQASYmC6D4tA5cqVtXFjun18OXjyNddco8/N8RcUBBBAAAEEQiywe/dujRgxInDvVIcOHdStW7fAxemUCAoQgEQQn0d7QcBOAFLDuix9yZIluvjii70wJcaIAAIIIOBnAQKQDFf3F+tIrIu7SidSzn456X4p/k4/vxjcPTcCkBCvDwFIiIHpPuQCP//8s0qWLOnIcx577DGZ47QoCCCAAAIIhEvgwIEDGjdunF599VW1bNkycDxW6dKlw/V4npNWgACE1wMCWQqcLwBp0KBB4M6PIkWKIIkAAggggEDkBQhAMl2D6wZKn244++UHrFNgp3bOuPq2vdI3O6TNu6XkI5K5V6SYtYG9lPXXfZ0/SWVD8BmuEyelVdb4fvhZ2nlAOmUd2VWtrHSHjZM1/7td+mqbtOfgmfFeaI21dFHJHPVVvJCzL8ujx6WP/yd9b41ztzXOwtaBMuVLSDf82fr3C+w/y+kA5KQVbn1tGXxtWfxsORw6KsXFSgXznlmvP5WSrrhEyp3L/hg9XZMAxNPLx+Atgffee0+33367IxbTpk0LnMlOQQABBBBAINwCR44c0T/+8Q+NGjVKN1kXDffr10/mGBlKGAUIQMKIzaO8KJBVAHLXXXcFjqTNly+fF6fGmBFAAAEE/ChAAJLpqrYaJb352dkv33aV9G7fM78/boUPS7+U5v2ftPy/kglAsipVrM9u9WwsPXijlM/mFYcD50nPzj/ba+r9JWZ3ylNzpNf/LZl/T1tqXCqtS8x4JHutN/lffleaYV3pt2NfxnVyxUjXVZGeuUtq+Bd7L/i/TThzeXxq2TJCqmB9Bnv/r9Y455553v7Df+wrNrfU5jpp0H1ZB0QVep4JT7JbpnSS/mZ5py+7rBDmxbekWaukven80te9wApD6lkeretm3Fd2x+Tq+gQgrl4eBmdD4JlnntFzzz1no+b5q3z33XeqUsX6v5+CAAIIIIBAhAROnDih2bNn6+WXX9aVV16p/v37q3r16hEaTZQ9lgAkyhac6WZXILMA5NFHH9XYsWOVO7f10z4FAQQQQAABtwgQgGS6EvdZAcicTAKQHlOl0dZl6dktJqCY/7h0mbW74HwlowAkzvo2otXozAOMzAKQaVZA0d0as9ntYbe0u1561QoR8li7IrIqGQUgu5Ml42cnuDA7T5bES1daNhkVJwOQ5f+R7rICmgMZBDJZzbFsMWm73w/DIQCx+78G9dwq0LhxYy1bloM/mdNNqFixYtq7d69iYqxImIIAAggggECEBU6dOqWFCxcqKSlJpUqV0oABA1S3rvXxHEroBAhAQmdLz74QyCgAMR9GMr/4HtoXS8wkEEAAAX8JEIBkup5ZHYHV7TVp7Ptnm5qdAlXLnDlGyhzxZHaImGOp1n8vHbSOVkpbLi0urR1kHTtVMOuXUvoA5NVHpL6zz+76KGA9s7a1Gf5i65nm+KYNO63dJXF/3AHytLUL4/kF5z7LvK13ubUrpbJ1HVkha2PqPmu3xudbrCOxrOAibbml2plwwuzWyKykD0CWWvVbjz07TnMUWK2KUgnraK1fj515zo/pdqCYQOhLa+dKgQw2yToVgBifGgnSEetIrrTFHMdl1s6M0xQTEm3Zc8Yz9Q4YApAzNjGnreKvPwGZjV8EzJtDxYsX1/79+4OeUpMmTbR48eKg+6EDBBBAAAEEnBQw34Z98MEHSkxMVEpKihISEtSoUSPebHQSObUvApBQqNKnjwTSBiAm8DC7Prp06eKjGTIVBBBAAAFfCRCAZLicJhAwl6CbICO1JLaS+jc78zsTgCyxjsB6wPqL//arpavLS7kyuCvC3NPx1hdWu9fP3A+SWlpdax1h1T3rV1L6AMQEFSZMMW/Um2OjOlrHO+W1Ao+0ZYv1jIppdpdM/Zd1dNPEszXMEVeP3Sr93Tol/1Lrjf+0xbyzvdAaa89p1r0iaY706m9d/J5oXQCfWUkfgJigw9ypYYKFIW2klrXPvUfDPMccP9XpVemwFYiklufusY7MavnHp2y3xnLSulNl+y9S/TSH29xt9TukbebjMuMomCZQaW3t4Hj907P1b7bCnWFW+xrW2mVUjp2QPvxGWvC5tNL65zcvZ71env8qO0A8v4RRPYFvv/1WVatWdcRg4MCBgU+vURBAAAEEEHCrwGeffRYIQnbs2KH4+PjApekcOePgahGAOIhJV34USA1A8uTJE7jv4557rJ/mKQgggAACCLhVgAAkw5WJt3ZavPTOuV/6/AXpGmsngynm4vFLrIuyMwo9MurQBCo3v2jtzrB2hJhigogNw7I+Cit9AGLamV0jHz4p/aXc+V9Q31u7GKr2O7vjwYQlb1nHbzWpkXVbc1H59c9KG3edqWcuAd8w9NxgJW0P6QMQ87Vq1uXh5s6SkoUzf9ZbVrDQcvjZr5udMVtHWrsMMjl0JphL0E3oUuihMztQTDF3spgdJ+kDpMxGa3aN5Ld5d8v5V8alNQhAXLowDMuWwNSpU/W3v/3NVt3zVTK7P8wuEAoCCCCAAAJuF/j66681ePBgrV27Vn369FHbtm1l3pCkBClAABIkIM39LmACkC8LFdJbb72lm2++2e/TZX4IIIAAAl4XIAD5wwpO+KfU9TUp7Vk/11WWPhkY3GJ/YR39VMsKL1LLC/dKT7TIvM+MApBpnaX29e2NI/0xXeM6Sl0a2mu7epP016fP1u3eSBrVIeO26QMQc1yWCReqlj3/s2o/ZR2Jtflsvf9ZQYs5liujEkwAYo72KpVmQ26f27LePXL+kfuwBgGIDxc1iqbUtWtXjR8/3pEZm/s/LrzQirgpCCCAAAIIeERgy5YtgcvSzRFZjz32mB5++GFdcMEFHhm9C4dJAOLCRWFIbhK4s2hRPbd8ua6+2joPg4IAAggggIDbBaIsAJndTbq20rmLkmIdr2R2aKzZKpnLwj/537lfN7sEzK6LOuna5WRp095ncdtV0rt9M+8lfQBSzuyQsC7wtrPr5FfrqKyS1hv+qfddmDs2zC4OO21TR3SDddzUR9+d+Z05zsrszsiopA9A7BzvldrPs/OkgfPP9vqGdSzYfdbxYBkVJwOQ9tYnVqZxQum5zAQgOflfmjZuEahZs2bg06/BlipVqui77377ky/YzmiPAAIIIIBAjqGqNAAAIABJREFUmAV27typESNGaO7cuerYsWMgDClqvVFJyaYAAUg2wagebQLbZ8/WJfdncVB2tIEwXwQQQAABdwtEWQCS3cUw4cdM6y6Qu/9qr6XZNWKOWTpoXaR9LM39Iamt7x11dsdDxZLWvSBWoJFZSR+AmLs7xvzN3jiWWveTNBl8tm6/O6TBre21Ta31jHV5+nNpLk//3gpA0t8bYuqmD0Cys0vlzX9LrUafHddoa5dJN2u3SUYlmADErIvZAWLuJjHFHGdlLmuv/+fsmfi6NgGIr5fX15M7fPiwChcuHLgQNtjSvn17TZtm3YREQQABBBBAwMMC+/fvD1xKPGXKlMDZ/L169dLFF2eyz9rD8wzZ0AlAQkZLxz4R+Mj66Gg9cxAWBQEEEEAAAQ8IEIBkukjm2KuJ1r0RWd23YS5JX2aFDfOt+yzWWEdcffuTFXxYl2fbKUWtTen7Xsm8ZvoAZPIj0kMN7PRsXSY+R3rhrbN1R7SXml9jr21qrZmfSE9a/aQWc6dHgyv+2Ef6ACTtXSnne+L7X0mNks7WyupYsGACEPOE7lOtAGnZ2WeZu03usYKt++tKDf9y7oXp5xu3L79OAOLLZY2KSX1svUlRv77NwwHPIzJmzJjAp2UpCCCAAAII+EHAfEhg8uTJMn+/NWzYUH379lXFir/dauiHCYZqDgQgoZKlX78IEID4ZSWZBwIIIBAdAgQggQvJC+WXTCDx5zLW3Rd/ku6qJV1VIeuXwEIr9Hh8hrTFumw8J8Vc9n3Kap9ZSR+ALOoj3VnT3pMyupjcXsvMa83rZbnU/uPX0z9rk3WxuTlyy05Z+V/pJuty+NTyzF3WkVh3Z9wy2ABkr7X7o84z0qbfLndP+xQThlxVXqprhV71qkg3WDtDShezMwMf1SEA8dFiRtlUhgwZEnhDx4ny+eef65prshkXO/Fg+kAAAQQQQCCEAsePH9fMmTM1dOjQwJn9/fv3V7Vq1UL4RI93TQDi8QVk+CEXIAAJOTEPQAABBBBwUCDKApDMdjFkV3TE4jPhR7Dl9MzMe0gfgGRn7HcOkd4J/jT8cwb32qNShxv+ON70AcgW61ivCtbxXnZKOAMQMx5zGXqXf0jzVmc9OhNOmbtizI6bB6yNvXGxdmbj8ToEIB5fwCge/r333hs46zzYki9fPiUnJysuzjr8kIIAAggggIAPBcxxkQsWLFBSUpIuueQSJSQkqE6dOj6caZBTIgAJEpDmvhcgAPH9EjNBBBBAwFcCBCDZXs7PNkrXDbR2b1j3SqQW84Z/m+us/27tIDC7Hy62rhrMb72FZu4QMW+mp5YGL1gXqn9z9vehCkAaW8dKLbOOl3KyTOlk3fdx4x979FIAkjr6r7dJ0z+WFq+Xvt4umTtCMiuXl5bM5ew1rB0ivi4EIL5eXl9Prly5ctq+3fo/Ochy/fXXyxynRUEAAQQQQMDvAqet737ff/99DRo0SLlz5w4EIbfccov1g0uan1z8jpDV/AhAonn1mbsdAQIQO0rUQQABBBBwiwABSLZX4raXzrxxnlo63SyN/ZsUm/v8XdW07tFYu/VsvVAFIG3HSrNWnX3OqoFnjncKRfFiAJLWYf+v0mebpE/+J5kdKZ9aAdfJdFcpFy8orX5eqmjzaK9QOIe8TwKQkBPzgBAI/PjjjypbtqwjPffu3TtwNAgFAQQQQACBaBJYtWqVEhMTtXv37sDRWM2bN1euXNYBsdFcCECiefWZux0BAhA7StRBAAEEEHCLAAFItlbi16PShdZRUObyc1PMbo/vrOOm7IQfpn5p62rdnfvPPjJUAUhv63iu4dYxXakls+OrsjX5TCp7PQBJP61fDkn/+PDMJfIHDp/9atvrpRldnRBzaR8EIC5dGIaVpYA5xuOuu6zbgxwob7zxhu677z4HeqILBBBAAAEEvCfw1VdfBY7GMv/8+9//rtatW0fvsZAEIN57ATPi8AoQgITXm6chgAACCAQnQACSLb//WoesVIs/26RrQ2v3R0d7XWzeLf3p8XPrhioAmfuZdO+os88yx3PNtMKXUJRwBSDfW5fNV7AuYk8tHepLr3UOxYzO9GmOOqs78OzxWIXySftfkfWBuNA9M6I9E4BElJ+H51AgPj5eL71k7ctzoHz//fe69NJLHeiJLhBAAAEEEPCuwKZNm/Tyyy9rxYoV6tGjhx588EHlz5/fuxPKycgJQHKiRptoEiAAiabVZq4IIICA9wUIQLK1hqusY5Kuf/Zsk4Rm0qBW9rp42rqi9/kF59YNVQCyzzrWqaQVDqScOvO8gtab95uGSaWK2BtrdmqFKwAxO2fMDprUcn9daXa37Iw0+3Wv7C99Zd0Xklp2jQuNYfZHFoIWBCAhQKXLkAs0aNBAH35o7dkKslx88cUyx2lx9nmQkDRHAAEEEPCNgPl7cfjw4YFL0x9++GF16dJFRYqE4KcJN4oRgLhxVRiTmwQIQNy0GowFAQQQQOB8AgQg5xM65+vf7JCu6Hf2P7WsJc1Pt6sjow7N7oUaA849UsnUC1UAYvruMEGa9tHZ0bSvZ/2+S7ama6tyuAKQo8eti+XT7La5sap1Z8eTtoaY40rp72wxO0CKXJDj7tzdkADE3evD6P4ocPLkycAbMYcPpzmsLodQ5rzzt96yDr6jIIAAAggggMA5Ar/88ovGjBmjqVOnqlWrVurVq5dKlfLzzXjW9AlA+L8AgawFCEB4hSCAAAIIuFDg9OnT2rVrl7Zu3Rr4ZU76MP8s8OtaDWlinZfk01Khp/T9z2cnt8K6hLzBFTmfrLn7o7h1B8gh6y4QU+Ksi8/XvCj9pVzmff58UGqcZNXb+sc6oQxAtlhHblW1wppjJ84+t98dZ3as5M7GMU7m2C8zhxuswCGjEq4AxDy7onUE1lYrTDLlgrzSjtFS0QLnX891lv0Wq12za+zP/csfpKut0OrU6TP9ly4q/WhdLu/bQgDi26X17cTWr1+vq666ypH5mctfzcWvFAQQQAABBBDIWODQoUN65ZVXNH78eDVu3DhwT0j58uX9yUUA4s91ZVbOCRCAOGdJTwgggAACtgVOnTp1TsCRPugwgcfRo7+9a5+m1+urWJ9vecb2YzxX0ekAxAC0t45BmvHJWYoyxaRXH5Ga1DiXxxw/9dbn0t9nnXnTPibGukDderN+r3XJdmoJZQBinmF2gJidIGnLtZWkAc2lptZ4M7u83Yz33bXSvNXSiv9Kz1hXDA+8O+PlD2cA8uAkaUqaw27q/El6vKl1L8slUgErEDHGqaVEoTNHf5li1qHlcKl8Cem+OlILa+dO7cusACv2j3M6mXKmfvdp515Y3/9OKfF+z/0vYH/ABCD2rajpDoFJkybp0UetSNqBsnz5ct10000O9EQXCCCAAAII+Fvg2LFjmjFjhoYNG6batWvL3MdVtWomH5XyKgUBiFdXjnGHS4AAJFzSPAcBBBCIKgETcPz000/n7N5IDTnMP3/44QeZ70WzWwhAsitm3aWxS7rK2hmQugsktQfz5vrVFaydCXnO7JhYvVkyd3Gklies0OFj6w6RD785+99CHYCYJ730tpTwxtmdDKlPNzsorrY+s3WRdZJvfmvMB4+cGfd/rWO+9qc7UMYtAYjZyVH7acmEFOcrUzpJf7vxTK3UACRtmzxW+HFFWamsFWCZXSQmsDL3jKz9/o9Hlf25jPT581bI8lugcr5ne/LrBCCeXLaoHrS5lHXKlClBG+TKlUv79+9XoUJWbEpBAAEEEEAAAVsCKSkpmjdvngYPHqwKFSooISFBtWpZHzPyQyEA8cMqModQChCAhFKXvhFAAAHfCpjvH1MDjvS7N1IDjuPHrUsQHC4EIDkDXfqldM/IP4YgmfXW93YriGhjHb/1QvgDEDOmJeuljtbuCfMGf07KiPZSzyYZtwznDhAzArMDpKv1lufRNEd7ZTSy8wUgdh3M/yPzraO3QnGBvN0xhKUeAUhYmHmIgwLVqlXTf/9r7VELslSvXl1ffmn9qU5BAAEEEEAAgWwLmLOWlyxZInOcZN68eTVgwAA1aNDA2pqdZm92tnuNcAMCkAgvAI93vQABiOuXiAEigAACkRAwAcePP/74+x0caXdvmOOpzA6OEyfO845uCAZOAJJz1O9+lOJflxatsS4z/+2eiLS9meOlbqkmmXs3brb+aUqkAhDzbHOJ+D+s8OCVFdJ6636LjMacOn5zt4k5IqpRdanN9VLlizN3CncAYkZiLpWf9rH0r2+lb611MDttDlvzSzuntAHIYWtzlAmt3ltnXZxu7cDZaO3iyaqYH9fqWcFH51uk1tede7RWzl8xLm9JAOLyBWJ45wgcOHBAxYoVs/6nz+BP32xaPfzww4EzzSkIIIAAAgggEJzAx1ZwYIIQc3G62RFyxx13yOy09FwhAPHckjHgMAsQgIQZnMchgAAC7hA4efKkduzYkekRVdu2bZOp47bi9wAkHN67DlhHW30n/bBXOmK9CV/SOkTFXJh9bWXJ3EPhxvKLdQ/JvzdKP1k7Qsy/n7COlCpoHYlldjlUscIOc+STOSLLr2WvddTXf6zL3c3F6OZOFhOQ5I2TCueXKl1kHXFmHQ1W3KVrF7I1IQAJGS0dh0Dgn//8p2699VZHep48ebIeeughR/qiEwQQQAABBBCQ1q1bp6SkJH377bfq27evWrVqpdjYDG7fcysWAYhbV4ZxuUWAAMQtK8E4EEAAAUcFTHixffv2c3ZwmJ0bqTs5TMBhdnl4rRCAeG3FGC8CIRIgAAkRLN2GRODFF1/Uk08+6UjfX331lf7yl7840hedIIAAAggggMBZgQ0bNuill16S2RnSo0cPdezYUfnyeeBWPQIQXsYIZC1AAMIrBAEEEPCkgDl+yoQYaUONtMdUmd0dXgw4zrcYBCDnE+LrCESJAAFIlCy0T6Z555136p133gl6Nubi83379il3buvgPwoCCCCAAAIIhETAfJJw2LBhevvtt/XII4+oc+fOKly4cEie5UinBCCOMNKJjwUIQHy8uEwNAQS8LGAuEDcBR0YXjJv/ZgKOU6dOeXmKORo7AUiO2GiEgP8ECED8t6Z+nZG59+Oiiy7Snj3WIXZBlptvvlkffPBBkL3QHAEEEEAAAQTsCOzdu1ejR4/W9OnT1aZNm8CukJIlS9ppGt46BCDh9eZp3hMgAPHemjFiBBDwhcCxY8fOCTjSBx0m4HDirlRfYKWZBAGI31aU+SCQQwECkBzC0SzsAlu2bNFll13myHPNBa2DBg1ypC86QQABBBBAAAF7AgcPHtSkSZM0YcIE3X777erTp4/KlStnr3E4ahGAhEOZZ3hZgADEy6vH2BFAwMUCR48e1Q8//JDpEVU//fQTAUcO1o8AJAdoNEHAjwIEIH5cVX/Oafbs2YFPjTpRFi5cqGbNmjnRFX0ggAACCCCAQDYFzA/506ZN0/Dhw3XdddcpPj5eVapUyWYvIahOABICVLr0lQABiK+Wk8kggED4BI4cORIIODI7osoEHBTnBQhAnDelRwQ8KUAA4slli8pB9+rVSyNHjnRk7jt37gwcp0VBAAEEEEAAgcgJnDx5UnPmzAlcmF6pUiWZHZo1a9aM3IAIQCJnz5O9IUAA4o11YpQIIBB2ARNwZHbBuPnv5j0ISvgFCEDCb84TEXClAAGIK5eFQWUgcO211+qzzz4L2qZ8+fKBT11QEEAAAQQQQMAdAubM6nfffVeJiYkqVKiQBgwYoPr16ysmJia8AyQACa83T/OeAAGI99aMESOAgCMCv/766+8BR0ZBx+7dux15Dp04K0AA4qwnvSHgWQECEM8uXVQN3Fz4VbhwYR0/fjzoebdq1Uqvv/560P3QAQIIIIAAAgg4K2CCkI+sN1jNPV2HDh1S//79A3eFhC0IIQBxdkHpzX8CBCD+W1NmhAACAQHzfUf6YCPt7/fs2YOUBwUIQDy4aAwZgVAIEICEQpU+nRb4v//7P9WpU8eRbocNG6bHH3/ckb7oBAEEEEAAAQRCI7BmzZrAjpCNGzeqX79+uvfeexUbGxuah6X2SgASWl96974AAYj315AZIBClAgcPHszyiKqff/45SmX8PW0CEH+vL7NDwLYAAYhtKipGUGDUqFHq2bOnIyNYtWqV6tat60hfdIIAAggggAACoRX47rvvNHjwYH366aeBDzB06NBBefPmDc1DCUBC40qv/hEgAPHPWjITBHwmkJyc/PsF4+bI6/S7OX755RefzZjp2BEgALGjRB0EokCAACQKFtkHU2zbtq1mzZoV9Ezi4uJkvjHKly9f0H3RAQIIIIAAAgiET2Dbtm0aOnRo4K6Qzp0769FHH1XBggWdHQABiLOe9OY/AQIQ/60pM0LAIwIHDhw4J+AwIUfaoGPfvn0emQnDDKcAAUg4tXkWAi4WIABx8eIwtN8FKlWqpE2bNgUtUqtWLa1evTrofugAAQQQQAABBCIjYM7gNjtDzQcj2rdvr+7du6t48eLODIYAxBlHevGvAAGIf9eWmSEQQQFzB9j+/fvPCTRSA47Uf5oAhIJAdgUIQLIrRn0EfCpAAOLThfXRtMwbHaVKlXJkRt26ddPo0aMd6YtOEEAAAQQQQCByAmZH54QJEzRp0iQ1a9ZMffr0UdmyZYMbEAFIcH609r8AAYj/15gZIhACARNwmB0aaUON9EdUmb/XKQg4IXDRRRepQoUKgV83XhGrLpVmOtEtfSCAgJcFCEC8vHrRMXZz1MUdd9zhyGSnT5+udu3aOdIXnSCAAAIIIIBA5AWOHj2qKVOmaOTIkapfv37gwvTKlSvnbGAEIDlzo1X0CBCARM9aM1MEsiFgAg5zx0b6XRtpj6gyl5BTEHBCoHTp0oFwo3z58r8HHamBx6WXXqr8+fOffczuj6V/1nfisfSBAAJeFiAA8fLqRcfYn3rqKb3wwguOTHbDhg0yx2lREEAAAQQQQMBfAidPntQbb7yhl156SVWrVlX//v111VVXZW+SBCDZ86J29AkQgETfmjNjBCwBE3D8/PPPvwcc6XdvmKDj119/xQoBRwTKlClzTrCRNugwAUe27nQlAHFkTegEAc8LEIB4fgl9P4FGjRrp/fffD3qe5nxwc5xWTExM0H3RAQIIIIAAAgi4U+DUqVN6++23lZSUpAsvvFAJCQmqV6+evcESgNhzolb0ChCARO/aM3NfC5iAw/ysnH4HR9qg4/Dhw742YHLhETDvx5gjSzPavWH+mwk48ubN69xgCECcs6QnBLwsQADi5dXz/9jNmxjmzQsnLjxr2rSp3nvvPf+jMUMEEEAAAQQQCHxadeXKlRo0aJCOHTsWCEKaNGmS9QchCEB45SCQtQABCK8QBDwpYP5O3LVrlzLauZF6TNWRI0c8OTcG7S4BE3Bccsklv+/gSB90lCtXTnny5AnfoAlAwmfNkxBwswABiJtXh7F98803uuKKKxyBePbZZ/X000870hedIIAAAggggIB3BFavXh3YEWLe5ImPj9fdd9+t3Llz/3ECBCDeWVRGGhkBApDIuPNUBM4jYD44aAKO1B0c6YMO83tzZxYFgWAFcuXKdU7AkXr3RmrQYcKPsAYc55sQAcj5hPg6AtEhQAASHevs1Vm+9tpr6tixoyPDX7JkiRo3buxIX3SCAAIIIIAAAt4TMB+sGDx4sEwg0rt3b7Vv3/7cH9IJQLy3qIw4vAIEIOH15mkI/CZgAo6dO3dmekSVCTjMbkcKAsEKmA+ImF0aGR1RZcIOc3xVXFxcsI8JX3sCkPBZ8yQE3CxAAOLm1WFsnTt31sSJEx2B+OWXX1SsWDFH+qITBBBAAAEEEPCugHmjaMiQIVq6dKm6dOmiTp06qUCBAhIBiHcXlZGHR4AAJDzOPCXqBFJSUvTTTz8FAo6Mjqn64YcfdPz48ahzYcLOC5iAw9yzkX7nRurvTcARGxvr/IMj1SMBSKTkeS4C7hIgAHHXejCacwWuvvpqrVu3LmiWyy+/XN9++23Q/dABAggggAACCPhHwBwXMnLkSL3xxhvq0KGDetWqpcK33+6fCTITBJwWIABxWpT+okTABBw//vjjOTs40gYdJuA4ceJElGgwzVAKmPAibcCRPugoU6aMvwKO82ESgJxPiK8jEB0CBCDRsc5enOWvv/6qIkWKyHyzGGx54IEHNHXq1GC7oT0CCCCAAAII+FDgwIEDGjdunNaNGaM3rDeoKAggkIkAAQgvDQQyFDh58uQfAo6093GYgMPUoSAQrIA5fir1eKqMjqkqXbp0xvecBftgr7YnAPHqyjFuBJwVIABx1pPenBP417/+pRtvvNGRDs2bGuaICwoCCCCAAAIIIJCZwLEPPlDehg0BQgCBzAQIQHhtRKmACS+2b9+e6RFV27Ztc+SDe1HKy7TTCJgLxNMHG2l/bwIOcxE5xaYAAYhNKKoh4HMBAhCfL7CHp/fyyy+rX79+jszgiy++UM2aNR3pi04QQAABBBBAwKcC3AHi04VlWo4JEIA4RklH7hIwx0+lBhxpd26k/rv5mhMnE7hr1owmEgJ58+YN3L+R0e4N898uvvhiAg4nF4YAxElN+kLAuwIEIN5dO7+P/J577tG8efOCnma+fPmUnJwss1WUggACCCCAAAIIZCpAAMKLA4GsBQhAeIV4VMBcIG52aWR0wbgJOXbs2KFTp055dHYM200C5v2H1Hs3Mgo6SpUqRcARzgUjAAmnNs9CwL0CBCDuXZtoH9kll1wS+EY02FKvXj19ZH5YoyCAAAIIIIAAAlkJEIDw+kCAAITXgCcFjh07Fgg4Mtq9kRpwnD592pNzY9DuEsifP/85AUf6S8ZNwBETE+OuQUfzaAhAonn1mTsCZwUIQHg1uFHAbDEuV66cI0Pr06ePhgwZ4khfdIIAAggggAACPhYgAPHx4jI1RwTYAeIII51kX8AEHOYi8dSAI33Q8eOPP4qAI/uutPijQIECBX4PODI6pqpEiRIEHF564RCAeGm1GCsCoRMgAAmdLT3nXMAcfWWOwHKizJkzx7G+nBgPfSCAAAIIIICASwUIQFy6MAzLNQIEIK5ZCr8N5OjRo4HjqTI7ouqnn37y25SZT4QEChYs+IcdHGmDjuLFixNwRGhtQvJYApCQsNIpAp4TIADx3JJFxYDN5efmEnQnivmkkFO7SZwYD30ggAACCCCAgEsFCEBcujAMyzUCBCCuWQqvDeTIkSPnhBvpg46dO3d6bUqM16UChQoVyvCIqtT7OC688EICDpeuXUiGRQASElY6RcBzAgQgnluyqBjwjTfeqH/9619Bz7V06dKBe0Q4gzNoSjpAAAEEEEDA/wIEIP5fY2YYnAABSHB+Pm59+PDhTHdvmLBj165dPp49UwunQOHChVWxYsVAyJHREVVFixbl5/9wLojbn0UA4vYVYnwIhEeAACQ8zjzFvsDJkydVpEgRmW+igy0tW7bU/Pnzg+2G9ggggAACCCAQDQIEINGwyswxGAECkGD0PN320KFDvwccGR1TtWfPHk/Pj8G7R8AEGKkXi6e/YNz83nydgoBtAQIQ21RURMDXAgQgvl5eT05u3bp1uvrqqx0Ze1JSkuLj4x3pi04QQAABBBBAwOcCBCA+X2CmF7QAAUjQhG7t4ODBg3/YwZE26Pj555/dOnTG5TEBcwRVZrs3zI4O82FICgKOCRCAOEZJRwh4WoAAxNPL58vBT5gwQV26dHFkbitWrFCDBg0c6YtOEEAAAQQQQMDnAgQgPl9gphe0AAFI0ISR6iA5OTnLI6r27t0bqaHxXJ8JmEvE0+7gSHtMlfl3c4QVBYGwCRCAhI2aByHgagECEFcvT1QOrmPHjnrttdeCnnuuXLl04MABFSxYMOi+6AABBBBAAAEEokCAACQKFpkpBiVAABIUXygbm597tm7dGviV0RFV+/btC+Xj6TuKBEqUKJHhJeMm3DC/zCXkFARcI0AA4pqlYCAIRFSAACSi/Dw8A4ErrrhC33zzTdA2V155pdavXx90P3SAAAIIIIAAAlEiQAASJQvNNHMsQACSY7pgG+7fv//3gCM16EgbdpivUxBwQqBUqVJZHlFVoEABJx5DHwiER4AAJDzOPAUBtwsQgLh9haJrfOYb92LFijky6U6dOmnixImO9EUnCCCAAAIIIBAFAgQgUbDITDEoAQKQoPgya3z69GmZHRqZ7d4w/90cYUVBwAmBiy66KMsjqi644AInHkMfCLhDgADEHevAKBCItAABSKRXgOenFXj//ffVqFEjR1BeffVVPfjgg470RScIIIAAAgggEAUCBCBRsMhMMSgBApAc8ZmA45dffjlnB0f6Y6rMJeQUBJwQKF26dOAoqrT3cKT++6WXXqr8+fM78Rj6QMAbAgQg3lgnRolAqAUIQEItTP/ZEXjhhRf01FNPZadJpnW//vprVatWzZG+6AQBBBBAAAEEokCAACQKFpkpBiVAAJIhnwk4zCXiaY+mSn8fx6FDh4KipzECqQJlypT5PdxIH3SYgCNfvnxgIYBAqgABCK8FBBAwAgQgvA7cJHDHHXfo3XffDXpIhQsXDmwjNxehUxBAAAEEEEAAAVsCBCC2mKgUxQJRGoCYgGPPnj1ZXjJ++PDhKH5hMHWnBGJiYpQ24EjduZEadJQrV46Awyls+okOAQKQ6FhnZonA+QQIQM4nxNfDJWB+sDAXrv38889BP/KWW27RP//5z6D7oQMEEEAAAQQQiCIBApAoWmymmiMBnwYg5ueQ3bt3/2EHR9pjqo4cOZIjMhohkFbABByXXHJJpkdUmYAjT548oCGAgFMCBCBOSdIPAt4WIADx9vr5afSbNm1SpUqVHJnSE088IXOcFgUBBBBAAAEEELAtQABim4qKUSrg0QDk1KlT2rVrl9Lfu5H2mKqjR49G6aIybScFzAkEJuBIv3Mj9ffmawQcTorTFwLnESA/aX8lAAAgAElEQVQA4SWCAAJGgACE14FbBGbNmqW2bds6MpxFixbpzjvvdKQvOkEAAQQQQACBKBEgAImShWaaORZwaQBiAo6dO3dmekSVCT6OHTuW42nTEIFUgdy5c58TcKS9aNwcU2UCjri4OMAQQMAtAgQgblkJxoFAZAUIQCLrz9PPCvTs2VOjRo1yhMR8wsscp0VBAAEEEEAAAQRsCxCA2KaiYpQKRCgAMQHHTz/9lOkRVSbgOH78eJQuCtN2UsAEHOYicRNspL9g3Py3smXLKjY21slH0hcCCIRSgAAklLr0jYB3BAhAvLNWfh9pnTp19H//939BT7NixYravHlz0P3QAQIIIIAAAghEmQABSJQtONPNtkCIApCUlBT9+OOPgYAjo2OqfvjhB504cSLbw6UBAukFTHiRGnBkdEyVuYCcgIPXDQI+EiAA8dFiMhUEghAgAAkCj6aOCZgt6YULF3bkk1v333+/Zs+e7djY6AgBBBBAAAEEokSAACRKFppp5lgghwGICTh27Nhxzg6OtEGHCThOnjyZ42HREIFUAXP8VPqAI23QYQIOs8uDggACUSJAABIlC800ETiPAAEILxE3CPz73/9W3bp1HRnK8OHD1atXL0f6ohMEEEAAAQQQiCIBApAoWmymmiOBTAIQE16kDzjSXjC+bds2Ao4cgdMovYC5QDz1aKqMjqi6+OKLCTh42SCAwFkBAhBeDQggYAQIQHgduEFg5MiRjoUWn376qa699lo3TIsxIIAAAggggICXBAhAvLRajDUCAmus79m/tHZtp4Ybqf/cvn27zC4PCgLBCuTNmzfDuzdSww4TcOTKlSvYx9AeAQSiRYAAJFpWmnkikLUAAQivEDcItGnTxpFjq8yW5+TkZOXLl88N02IMCCCAAAIIIOAlAQIQL60WY42AQD3rmZ9E4Lk80j8C5ue0zC4YN/+9VKlSBBz+WW5mgkDkBQhAIr8GjAABNwgQgLhhFRjDZZddpi1btgQN8de//lWfffZZ0P3QAQIIIIAAAghEoQABSBQuOlPOjgABSHa0orNu/vz5AwFHRheMpwYcMTEx0YnDrBFAIPwCBCDhN+eJCLhRgADEjasSXWPas2dP4JM+TpTu3btr1KhRTnRFHwgggAACCCAQbQIEING24iGbbwOr5w/T9H46ZE8Kb8cEIOH1duPTLrjggnMCjvRBR8mSJUXA4caVY0wIRKkAAUiULjzTRiCdAAEIL4lIC7zzzju68847HRnGjBkz1LZtW0f6ohMEEEAAAQQQiDIBHwQgFawl+/48y5bX+rr5Vdz6dZH160/Wr2rWr7rWL3OLGgeJBv+6b2B1QQASvCM9hF+gQIECqlixYqbHVBUvXpyAI/zLwhMRQCCnAgQgOZWjHQL+EiAA8dd6enE2Tz75pF588UVHhr5x40b96U/mx3gKAggggAACCCCQTYEoCUCyUilofdF8LOX/2bsX8Kiqc//jbwIJSEiAQAAJ4aKg2NqIHBUFL6GItqDYKkVbRUGtqdoLKrWKqK2o1BaK9t+iYiuIt6JSWxA9tRGwCgK2jUiPBQGBEC4GwiUBQiCX/1qRGSeTmWRm3/fMd/fZD5OZvd619mfNeY7wy9rrNnXq3/bnMCZQoJoRgBizo5W9ApmZmU1WcAQ2GNerObKzswk47J0CqiOAgJMCBCBOatMXAt4VIADx7twky8hGjBghRUVFpm+3S5cuUlZWxn+wm5akAAIIIIAAAkkqQADSaOKHq59+p84BSfp1MHPbBCBm9GhrRiArKyviI6oCG4936tSJvy+ZAaYtAgj4S4AAxF/zxWgRsEuAAMQuWerGIlBXVyf6P8IrKipiubzZa0aOHCmLFy82XYcCCCCAAAIIIJCkAgkYgLysplI/1ir0OKZ+2H/81I/LWqXO5er8b4Rpb6fem6POsUn6lTB62wWqIStAjOrRrjmBjh07Rt1gXIcc+nMOBBBAAIHjAgQgfBUQQEALEIDwPXBT4JNPPpGvflU/ddr88dBDD8n9999vvhAVEEAAAQQQQCA5BRIwAFmqZrIgxtlcoa77jToXhF2fqn5+UZ3XxFiHy74wJwDhm2BEQP9yWGBj8fANxvWjqgg4jKjSBgEEklaAACRpp54bR6CRAAEIXwg3BebMmSM33nijJUP429/+JpdccokltSiCAAIIIIAAAkkokOQBSGDGX1IvCtV5MOQrcIJ6vVqdpyfh18LILReoRgQgRuQSv43eRDzwOKpIQYd+hBUHAggggIBFAgQgFkFSBgGfCxCA+HwCfT78wsJCmT17tiV3sW/fPn4jyhJJiiCAAAIIIJCkAgQgwYnXu7Ndqs66kK/ChWH/qJ+k35KYbrsgzKo+plbev+h8NUT9uDSO6AJ6X8LQYCN0g3H9Wm9CzoEAAggg4JAAAYhD0HSDgMcFCEA8PkEJPryBAwfKmjVrTN/lgAED5L//jfTkatOlKYAAAggggAACySJAANJopn+ufvpF2NzH80itTartOnXqfUb0bm8p6uykzh7qHKzOHBu+V1Wq5vvq3K7Oz9XZSp3nqFOHN04eBaozVoA4Ke5cXzk5ORE3Gdfhhj7bt2/v3GDoCQEEEECgeQECEL4hCCCgBQhA+B64JXDw4EHp0KGD6I3QzR433HCDzJ0712wZ2iOAAAIIIIBAMgsQgDSa/Ur1U546D4S8q/cB0RurRzoOqTffUOef1blMnWUtfJcGqc/vVKeuqYOKWI7x6qLnQi7crF73Uec2dU5W51/UGfroLn3pFcff/4r6M/DrMnpfk43q7BtLpyHXlKvXueqsPv7eierPEnW2DqtToH4mAIkT1yOXd+vWLeojqnr16iUZGRkeGSnDQAABBBBoUYAApEUiLkAgKQQIQJJimj15k++++64UFOi/Hpo/nnzySfnBD35gvhAVEEAAAQQQQCB5BQhAmsz9Heqdx0Pe7aBe71Fn+D/460tGq3ORgW/P11WbP6kzlhUh49V14QHIR+q9CercH6XvQADyO/X5j0KuuUe9nhbneKer638a0uZ+9fqhCDUK1HsEIHHiOnR59+7doz6iSgcc7dq1c2gkdIMAAgggYLsAAYjtxHSAgC8ECEB8MU0JOchf/epX8rOf/cySe/v3v/8tZ555piW1KIIAAggggAACSSpAANJk4t9U74wKe7dY/TwwwlfkMvXe4pD39VbOetVFV3XqXQ+OqLNUnR+rUz+qKvTQq0FWqLNNC1+98erz0ABEByc3qDOwIqOjen22OruoUwcinxwfq14Zoh/DpVdvBFaI6HHp8aS10GfgY72Px6nq3HD8Db1qRa9A0atkwo8C9QYBSIywFl/Wo0ePhkdRRdpgXAccJ5xwgsU9Ug4BBBBAwLMCBCCenRoGhoCjAgQgjnLTWYjAlVdeKa+//rppE/2XmIqKCmndOtLvIpouTwEEEEAAAQQQSBYBApAmM71XvaPDhNBNvOeqn3XoEH7oAEQHCtepc6Q6dfgR6Tis3nxJnfepM/QxWfrXYn7ZwndtvPo8NADRwYp+VFdPderVGWPUGfo4LT1uvQdJn+N1b1N/PhnSx3z1emwLfQY+1hvDjwi5Vq94+WuUtgXqfQKQGGHjuCwlJUV0wBEIN8KDjry8PGnbtm0cFbkUAQQQQCChBQhAEnp6uTkEYhYgAImZigstFsjNzZUdO3aYrnrBBRfIP/7xD9N1KIAAAggggAACSS5AABLxC9BNvRsaVOhHPulHP4UfW9QbfeL4Cum9M85Xp96/Qx96xYh+rf+MdoxXH4QGIPq6k9SpwwYdgrR0/Edd8LWQi4ap10taanT8cx2uLAi5Vq+O+WaUtgXHxxT4ODRAirE7T16m52u5jSPTAYf+O0Kk1Rv6PR1wtGnT0johGwdIaQQQQAABfwkQgPhrvhgtAnYJEIDYJUvd5gRKS0sb/gJjxTFp0iT59a9/bUUpaiCAAAIIIIBAMgsQgESc/QHq3fUhn+h9NH5r0fdEBwo6WAgcL6gX1zZTe7z6LDwA0b8Gc0Ec47lIXRv6qzPr1M/60VbNHbvUh/q/XGuOX6Q3T9ebqOvN1CMdBepNVoA0lUlNTZWePXtGfESVDjj0Z+np6XHMJpcigAACCCDQjAABCF8PBBDQAgQgfA/cEHjttdfkO9/5jiVd61pXXXWVJbUoggACCCCAAAJJLEAAEnHyz1Pvrgz55Gb1+hmLviZHVR39GCv9pz70I6p+30zt8eqz0ABkqPr5/TjH8oq6/uqQNnqj99+0UONh9Xnoqhe9ebreRD3aUaA+SMYARAcc+pecoj2iSgccaWmx7roS58RyOQIIIIAAAuECBCB8JxBAQAsQgPA9cEPgpz/9qUyfrp/UbP7Ytm1bw2+LcSCAAAIIIIAAAqYECEAi8g1W764O+eT76vXsOKDr1LWH1Kn36ggEHaHNdcCiV1joo6VHUo1X14QGIHoN8KQ4xqIvPabO3urcebxdtvpzuzqj7Ryhx69XfOhHdulDr0/Qj+rSm6hHOwrUB4kYgFykAo4StZF4+COq9M96Pw79+CoCjji/kFyOAAIIIGCfAAGIfbZURsBPAgQgfpqtxBmr3rfjff2PDCYPvQni9u36r6wcCCCAAAIIIICASQECkIiA+vFQn4Z88mP1+olmqHXY8YY69QbhH6lzgzoDj45qaYYGqguKm7lovPosNADRG5MPb6lohM9/rt77Rcj789TrcVHq6Hu5POSza9Trl1vos0B9nogBSM3SpdK6QN8dBwIIIIAAAj4QIADxwSQxRAQcECAAcQCZLhoJHDt2TDp06CBVVVWmZa688kpZsCB0O0rTJSmAAAIIIIAAAskqQAASceb1SofdIZ/ox0HdF+U78gf1/uSw6+P5OumVFp8102C8+iw0APlY/Ry6qXmsfe1QF+pVIIFgZoh6HW1z78vUZ4tDCutg48IWOipQnydiACLvvad2rtdboXMggAACCCDgAwECEB9MEkNEwAEBAhAHkOmikUBxcbEMGjTIEpXHHntM7r77bktqUQQBBBBAAAEEklyAAKTJF2CPeicn7N1oqyX0XhqPm/wK6VBiSzM1xqvPQgOQzernPgb71LvRvRbSdq16fXpYLf3YKx3K6Mdg6eMr6vy/GPorUNcQgMQAxSUIIIAAAgjYKUAAYqcutRHwjwABiH/mKlFG+uSTT8ptt+ktLs0f7777rlx4YUu/g2e+HyoggAACCCCAQBIIEIA0meTwxz/pC9aoMz/sylfVz2PD3vuq+lk/LuocdfZRZzd16n022oRdpz/bevw9JwOQZapPvedI4Lhdvfhd2NimqJ8fCXnvt+r1j2L4P4UCdQ0BSAxQXIIAAggggICdAgQgdupSGwH/CBCA+GeuEmWk48ePl+eeC/3dPWN31qpVKzlw4IBkZGQYK0ArBBBAAAEEEEAgVIAApMn34SfqHf2P/oGjk3qhH4fVKuxKvTLivyHvTVWv9WOyUmL4hulNyPcdv87JAER3qVd8BFZ0ZKnX+tFYgf+y1I/H6qXOwGbp7Y5/3iGGeypQ1xCAxADFJQgggAACCNgpQABipy61EfCPAAGIf+YqUUZ62mmnybp160zfzhlnnCEffaS31uRAAAEEEEAAAQQsECAAaYRYoX7KU6f+M3Bcq168EEatNzk/JeQ9vTY39B//m5sZvSOcDhzqj1/kdADye9XvD0MG+Ix6ffPxn/XjsfRjsgLHTeqF3uMklqMgzCBwf7G09fQ17AHi6elhcAgggAACYQIEIHwlEEBACxCA8D1wUmDfvn2Sna1/z8/8UVhYKE899ZT5QlRAAAEEEEAAgaQWOHjwoHz22Wey/4035ML7om3v7Q+iPmqYgcdJ6REvVWeBwaE/qNo9FNY20gbgb6prRoVc9yv1+qcx9rlEXTc85FqnA5BK1XeuOvWf+jhLnR8ef32x+vOdkLHp9/XnsRzanBUgsUhxDQIIIIAAAjYKEIDYiEtpBHwkQADio8lKgKG+/fbbcumll1pyJ88++6xMmDDBkloUQQABBBBAAIHEFairq5MdO3Y0hByBc9OmTcHXZWVlDTc/VJ3v+5yhjxq/FQFIkaqj/4stsPm3ZilQpw5Uwo+X1Bt6ZUjgeFq9uCVGx+vVdc+HXOt0AKK71nt/zAoZwz/Va/2YK72qJbByIzQYieXWtBUBSCxSXIMAAggggICNAgQgNuJSGgEfCRCA+GiyEmCoDz30kDz4oP59QvPHJ598IvpxWhwIIIAAAggggMDhw4cbBRw66AiEHJs3b5bq6uoWkQhAviB6WZ06wDgYIqb3v9DBQKT/8npLvT8y5No71OvftKj9Rb0h6jwWcq0bAcgnqn+9YXvg0I/A0gHIjJD39KOv9COwYj0K1IUEILFqcR0CCCCAAAI2CRCA2ARLWQR8JkAA4rMJ8/lwR40aJW++qR+UYO7o0KGD7N27V1JTU80VojUCCCCAAAII+EKgvr5edu3aFQw1wldy6M/MHskegHygAHVwofe+CD30hud6lcfYKMB643D9GKnAoR92qjcW797MhHymPtOPvtoSdo0bAYgewjB1Ljs+Fr0nSVt1lh//WYch+h51CBTrUaAuJACJVYvrEEAAAQQQsEmAAMQmWMoi4DMBAhCfTZiPh6v/4SInJ0fKywN/nTR+MxdffLH8/e9/N16AlggggAACCCDgOYGqqirZsmVLxJBDr+LQn9t5JGIAoldznBuGVqN+PqDO/erUj8tapU796C+9EiL80GHAXHWOaQH+guM1ApcNUC/mROhbr8PRj7yarM7d6tRBQ2t1BlabuBWAvKrGEC3g+ZH67LdxfvEK1PUEIHGicTkCCCCAAAJWCxCAWC1KPQT8KUAA4s958+OoN27cKP3797dk6FOmTJGpU6daUosiCCCAAAIIIOCMgP5lCL3fRqR9OPTjqvQ+HW4eiRiAmPHUm4D/Tp2nxlBkhbrmInXqcCX00G1PV2e6Oj9X52p1hj5a6xn188PqDOxb4lYAoset+470DdSrWb4Sg0HoJQXqBwKQONG4HAEEEEAAAasFCECsFqUeAv4UIADx57z5cdQvvviiXHfddZYMfdGiRXLZZZdZUosiCCCAAAIIIGCdgN5rQ6/iiBRy6PcOHTpkXWcWVyIAEWmvTC9X523qPD9O37nqer13SOieHtFK6Mdq6cdt/VidfdTpdgCix/kLdf48bMAXqp9Dg4xYSQrC2gU2U4+1vWeve+899cWI95vh2bthYAgggAACiS5AAJLoM8z9IRCbAAFIbE5cZV7gRz/6kfzud/r3CM0f+rdH9eO0OBBAAAEEEEDAWQG9imPPnj2NNhwPbDauA47S0lLR1/jxSJYAJE1Njn70lN6rQ+/TcbI69Sbg5x0/9WdGD/04rXvUuSxKAV17lDr1I7AGHb+mj/rTCwHITjUOvQokNMDRjxC7xgBGgWrDChADcDRBAAEEEEDASgECECs1qYWAfwUIQPw7d34b+TnnnCMffvih6WGfdNJJDc8G50AAAQQQQAABewSOHj0qW7dujRpyVFZW2tOxy1UTIQBxmTDYfYl6tVyd+pFSOlDoqs4e6tTGmV4ZZNg49L4oeoyHj7+vx7xNnfrxXRzHBVgBwlcBAQQQQMBPAgQgfpotxoqAfQIEIPbZUvlLgSNHjkhWVpYcOxbLQxGal/vud78rL730ErwIIIAAAgggYFBAr9DYt29fxM3G9SqObdu2SV1dncHq/m1GAOLfubNi5LNUkdtDCumVLNOsKJxINQhAEmk2uRcEEEAg8QUIQBJ/jrlDBGIRIACJRYlrzAp88MEHMmTIELNlGto/8cQT8uMf6ydGcyCAAAIIIIBANAH9Swc6yAh9PJUONwI/Hzigf9+dI1SAACS5vw9fU7f/n+MEqerPjersm9wkTe+eAIRvBAIIIICAnwQIQPw0W4wVAfsECEDss6XylwKPP/643HHHHZaQrFy5UgYPHmxJLYoggAACCCDgZ4H9+/c3PKYqUshRUlIitbW1fr49x8dOAOI4uWc6fEuNZGTIaEar13/1zOg8NBACEA9NBkNBAAEEEGhRgACkRSIuQCApBAhAkmKaXb9J/diqP/3pT6bHkZ6eLhUVFdKmTRvTtSiAAAIIIICA1wVqamoaNhWPFnLox1hxWCdAAGKdpZ8qVanBnqXOT0IGvUy9vshPN+HUWAlAnJKmHwQQQAABKwQIQKxQpAYC/hcgAPH/HPrhDvr27StbtmwxPVS98kOvAOFAAAEEEEAgUQR0sK8Djkghh/7/nToE4XBGgADEGWe3ewn8F6ne7FyHHnqfj3+HDGqEev2224P0av8EIF6dGcaFAAIIIBBJgACE7wUCCGgBAhC+B3YLfP7559K9e3dLutF7f+g9QDgQQAABBBDwi4B+DNX27dujhhx79uzxy60k/DgJQBJ+ihtuMKWZ2zxBfbZGnf2TgyL+uyQAid+MFggggAAC7gkQgLhnT88IeEmAAMRLs5GYY1m4cKFcccUVltzcSy+9JPpxWhwIIIAAAgh4SeDgwYPBgCN8JYdexXH06FEvDZexRBEgAEmOr0a0AKStuv356tT7f3BEESAA4auBAAIIIOAnAQIQP80WY0XAPgECEPtsqfyFwH333SePPvqoJRx6k9eTTjrJkloUQQABBBBAIFaBuro62blzZ6PNxkM3Hi8rK4u1FNd5WIAAxMOTY+HQQgMQvatcnjovVued6mTlRwvQBCAWfhMphQACCCBguwABiO3EdICALwQIQHwxTb4e5MUXXyzvvPOO6Xvo0qWL6H9gSklp7sEFpruhAAIIIIBAkgocPnxYNm/eHDHk0O9XV1cnqUzy3DYBSPLMNXdqUIAAxCAczRBAAAEEXBEgAHGFnU4R8JwAAYjnpiShBqSfe96pUyeprKw0fV+XXXaZLFq0yHQdCiCAAAIIJKdAfX297Nq1K+Jm43o1h/6MI7kFCECSe/65+xgECEBiQOISBBBAAAHPCBCAeGYqGAgCrgoQgLjKn/Cd/9///Z+cfvrpltzn1KlTZcqUKZbUoggCCCCAQGIKVFVVid5zI3wfDv2zPvXnHAhEExjepo0UsdKHLwgC0QUIQPh2IIAAAgj4SYAAxE+zxVgRsE+AAMQ+WyqL/PGPf5Sbb77ZEoq3335bRowYYUktiiCAAAII+FNAr+LQj0MMBBqh+3Do1zt27PDnjTFqxwR69OjRsJ+YPk8++eRGr7t++qmkXHihY2OhIwR8J0AA4rspY8AIIIBAUgsQgCT19HPzCAQFCED4MtgpcMstt8gzzzxjugu978e+ffukQ4cOpmtRAAEEEEDA2wJ6r43AKo5IKzkOHTrk7RtgdK4KtG3bNhhqhIccffv2lRNOOCH6+N5/X+SCC1wdP50j4GkBAhBPTw+DQwABBBAIEyAA4SuBAAJagACE74GdAmeccYZ8/PHHprs47bTT5JNPPjFdhwIIIIAAAu4L6FUc5eXlETcb14FHaWmp6Gs4EIgm0L1796ghh/4sNTXVGB4BiDE3WiWPAAFI8sw1d4oAAggkggABSCLMIveAgHkBAhDzhlSILHDw4MGGFRt1dXWmiSZMmCDPPvus6ToUQAABBBBwRuDo0aNSUlISNeSorKx0ZiD04kuBNmovDr1aI/CoqtCVHPr9jIwMe+6LAMQeV6omjgABSOLMJXeCAAIIJIMAAUgyzDL3iEDLAgQgLRtxhTGBZcuWybBhw4w1Dmv11FNPSWFhoSW1KIIAAgggYF5Ar9DQjyYMf0RVYE+Obdu2WRKAmx8pFbwqkJOT02gPjtCQQ+/TYXgVh5kbJgAxo0fbZBAgAEmGWeYeEUAAgcQRIABJnLnkThAwI0AAYkaPts0JPPbYY3LPPfdYglRcXCwDBw60pBZFEEAAAQRiEzh27JjoICNayHHgwIHYCnFVUgqkpaVJnz59IoYcehVHZmam91wIQLw3J4zIWwIEIN6aD0aDAAIIINC8AAEI3xAEENACBCB8D+wS+Pa3vy1/+ctfTJdv166d6H9ka926telaFEAAAQQQaCywf//+hoAjUsihH2FVW1sLGQJRBTp37hx8TNXJJ5/c6HVubq60atXKX3oEIP6aL0brvAABiPPm9IgAAgggYFyAAMS4HS0RSCQBApBEmk3v3It+NIp+fMWuXbtMD+rCCy+Ud99913QdCiCAAALJKFBTU9OwqXikkEO/t3fv3mRk4Z5jFNC/fNC7d++GYCM04AjszaH3+kqogwAkoaaTm7FBgADEBlRKIoAAAgjYJkAAYhsthRHwlQABiK+myzeD1b81rP/BxIrj7rvvFv04LQ4EEEAAgcgCFRUVwYAjfCXHli1bRIcgHAhEE+jUqVPEVRw65MjLy0uuFZgEIPwfCgLNCxCA8A1BAAEEEPCTAAGIn2aLsSJgnwABiH22ESvX1omUVogcU38m8LFk6VJ54IEHLLnDRx55WC668CJLapkqkpYq0jNLpJX6kwMBBBBwUEA/hmr79u1RQ449e/Y4OBq68puAfgxVr169ooYcOgDhOC5AAMJXAYHmBQhA+IYggAACCPhJgADET7PFWBGwT4AAxD7bJpV1+PGr5SLbVADC4U+BPBWA3D2UEMSfs8eoEfC0wMGDBxsFHKErOfQqjqNHj3p6/AzOXYGsrKxGj6gKfVyVDj/0huQcMQgQgMSAxCVJLUAAktTTz80jgAACvhMgAPHdlDFgBGwRIACxhTVy0a37RR5TAQiHvwV+pgKQ3h39fQ+MHgEEHBeoq6uTnTt3RtxsXIcdZWVljo+JDv0jkJKS0vA4qvB9OAI/Z2dni76Gw6QAAYhJQJonvAABSMJPMTeIAAIIJJQAAUhCTSc3g4BhAQIQw3TxN9yoNpr9zQfxt6OFtwTuPE+kX7a3xsRoEEDAEwKHDx+WzZs3Rww59PvV1dWeGCeD8KZA+/btI242rkMOva9Wenq6NweeSKMiAEmk2RwG8voAACAASURBVORe7BAgALFDlZoIIIAAAnYJEIDYJUtdBPwlQADi4HwRgDiIbWNXBCA24lIaAW8L1NfXy65du4KPqtq0aVOj1/ozDgSiCegVGrm5uVFDji5durCKw+2vDwGI2zNA/14XIADx+gwxPgQQQACBUAECEL4PCCCgBQhAHPweEIA4iG1jVwQgNuJSGgH3BaqqqkTvuaEfSxW6D0fgZ/05BwLRBNq1axd1s/E+ffpI27ZtwfOyAAGIl2eHsXlBgADEC7PAGBBAAAEEYhUgAIlViusQSGwBAhAH55cAxEFsG7siALERl9II2C+gV3Ho/TYCgUZ4yLF9+3b7B0EPvhbo0aNHxJBDP6qqa9eurOLw8+wSgPh59hi7EwIEIE4o0wcCCCCAgFUCBCBWSVIHAX8LEIA4OH8EIA5i29gVAYiNuJRGwBoBvddG6CqO8JDj0KFD1nRElYQU0Ks0TjrppIghh17FoVd5cCSoAAFIgk4st2WZAAGIZZQUQgABBBBwQIAAxAFkukDABwIEIA5OEgGIg9g2dkUAYiMupRGITUCv4igvL5fQPThCQ47S0lLR13AgEE2gW7duoldsBIKO0Nfdu3eX1NRU8JJRgAAkGWede45HgAAkHi2uRQABBBBwW4AAxO0ZoH8EvCFAAOLgPBCAOIhtY1cEIDbiUhqBLwWOHj0qJSUlUUOOyspKuBCIKpCeni59+/aNGHLo9zMyMtBDoKkAAQjfCgSaFyAA4RuCAAIIIOAnAQIQP80WY0XAPgECEPtsm1QmAHEQ28auCEBsxKV0MgnoFRr79u2LuNG4Xtmxbds2qaurSyYS7jVOgZycnIYVHJFWcuh9OljFEScol4sQgPAtQIAAhO8AAggggEDiCBCAJM5ccicImBEgADGjF2dbApA4wTx6OQGIRyeGYXlR4NixYw1BRvgeHIGfDxw44MVhMyaPCKSlpYnecyM85Ag8tiozM9MjI2UYCSNAAJIwU8mN2CTAChCbYCmLAAIIIGCLAAGILawURcB3AgQgDk4ZAYiD2DZ2RQBiIy6l/Siwf//+hoAjUsixdetWqa2t9eNtMWaHBDp37hxxHw69qiM3N1datWrl0EjoBgElQADC1wCB5gUIQPiGIIAAAgj4SYAAxE+zxVgRsE+AAMQ+2yaVCUAcxLaxKwIQG3Ep7UUBHWAEVnFECjn27t3rxWEzJo8ItG7dWnr37h0x5NArOTp06OCRkTIMBAhA+A4g0KIAAUiLRFyAAAIIIOAhgfJ/ivztbA8NiKEggIArApd+KNL5rGa7TlHPaa93ZXCJ1ikBSGLMKAFIYswjd9FIoKKiIriKIzzk2LJli9TU1CCGQFSBjh07RtyHQwcceXl5okMQDgR8IcAKEF9ME4N0UYAAxEV8ukYAAQQQiFug7pgKQAaL7CuOuykNEEAgQQQ6nSly6SqR1DQCEEemlADEEWbbOyEAsZ2YDqwX0Ks4duzYIXpz8cDjqkKDjj179ljfKRUTRkBvJt6rV6+oIUenTp0S5l65kSQXIABJ8i8At9+iAAFIi0RcgAACCCDgMQEdguxbI1J7xGMDYzgIIGC7QKu2Ip3OaDH80ONgBYhVs0EAYpWku3UIQNz1p/eoAgcPHpTNmzdHDDn0Ko6jR4+ih0BUAb2huN53Q5+BTcYDr3X4oTck50Ag4QUIQBJ+irlBkwIEICYBaY4AAggggAACCCDgRQECEKtmhQDEKkl36xCAuOufxL3X1dXJzp07m2w2HljVUVZWlsQ63HpLAikpKQ2Po9LhRqSQIzs7W/Q1HAgktQABSFJPPzcfgwABSAxIXIIAAggggAACCCDgNwECEKtmjADEKkl36xCAuOuf4L0fPny4YRVHpM3G9ftHjrBsN8G/AqZur3379hE3G9eBh17F0aZNG1P1aYxAwgsQgCT8FHODJgUIQEwC0hwBBBBAAAEEEEDAiwIEIFbNCgGIVZLu1iEAcdff573X19fLrl27gvtwhO7JoV/rzzgQaE6gZ8+eUUOOLl26sIqDrw8CZgQIQMzo0TYZBAhAkmGWuUcEEEAAAQQQQCDpBAhArJpyAhCrJN2tQwDirr8PeterNAKrOCKt5KiqqvLBXTBEtwTatWsXDDjCH1fVp08fadtWbeLFgQAC9ggQgNjjStXEESAASZy55E4QQAABBBBAAAEEggIEIFZ9GQhArJJ0tw4BiLv+Huhdr+LYvXt3o83GQ1dybN++3QOjZAheFujRo0fUkKNbt26s4vDy5DG2xBYgAEns+eXuzAsQgJg3pAICCCCAAAIIIICA5wQIQKyaEgIQqyTdrUMA4q6/Q71XV1fL1q1bo4Ychw4dcmgkdONHAb1KQ6/eCD0DG4/rVRx6lQcHAgh4UIAAxIOTwpA8JUAA4qnpYDAIIIAAAggggAAC1ggQgFjjKEIAYpWku3UIQNz1t6h3vYqjvLy8yWbjgZUcpaWloq/hQCCagF6pEQg1AkFH4Ofu3btLamoqeAgg4DcBAhC/zRjjdVqAAMRpcfpDAAEEEEAAAQQQcECAAMQqZAIQqyTdrUMA4q5/HL0fPXpUSkpKmoQcgX05Kisr46jGpckmkJ6eLn379o0Ycuj3MzIyko2E+0Ug8QUIQBJ/jrlDcwIEIOb8aI0AAggggAACCCDgSQECEKumhQDEKkl36xCAuOsf1vvevXsbAo7wzcb1So5t27ZJXV2dp8bLYLwlkJOT0/CYqkgrOfQ+Hazi8NZ8MRoEbBcgALGdmA58LkAA4vMJZPgIIIAAAggggAACkQQIQKz6XhCAWCXpbh0CEEf9a2pqgqs4wkMO/fP+/fsdHQ+d+UsgLS1N9J4bkUIO/V5mZqa/bojRIoCAvQIEIPb6Ut3/AgQg/p9D7gABBBBAAAEEEECgiQABiFVfCgIQqyTdrUMAYrm/DjECqzjCQw69EXltba3lfVIwcQQ6d+4c3Gw8fCVHz549pVWrVolzs9wJAgjYK0AAYq8v1f0vQADi/znkDhBAAAEEEEAAAQQIQGz7DhCA2EbraGECkLi5dYChNxUPbDAeHnLox1hxIBBNoHXr1tK7d+9gyBG+mqNDhw7gIYAAAtYIEIBY40iVxBUgAEncueXOEEAAAQQQQACBJBZgBYhVk08AYpWku3UIQCL6V1RUyObNmyOGHHoVx7Fjx9ydN3r3tEDHjh0j7sOhw468vDzRIQgHAgggYLsAAYjtxHTgcwECEJ9PIMNHAAEEEEAAAQQQiCRAAGLV94IAxCpJd+skaQCiNxPfvn17xM3G9YqOPXv2uDsv9O5pAb2ZeK9evaKGHJ06dfL0+BkcAggkiQABSJJMNLdpWIAAxDAdDRFAAAEEEEAAAQS8K0AAYtXcEIBYJelunQQOQA4ePNiwiiP8EVX60VVbtmyRo0ePumtP754W0BuK6z04wvfh0D/r8ENvSM6BAAIIeFqAAMTT08PgPCBAAOKBSWAICCCAAAIIIIAAAlYLEIBYJUoAYpWku3V8HIDoVRw7d+4MbjgevidHWVmZu7b07mmBlJSUhsdRhe/BEQg8srOzRV/DgQACCPhWgADEt1PHwB0SIABxCJpuEEAAAQQQQAABBJwUIACxSpsAxCpJd+t4PAA5fPhwcBVH+EoOvbrjyJEj7vrRu6cFMjIygis4wldy6I3I27Rp4+nxMzgEEEDAlAABiCk+GieBAAFIEkwyt4gAAggggAACCCSfAAGIVXNOAGKVpLt1XA5A6uvrZdeuXcFVHOEhh17hwYFAcwI9e/ZsWMURvpJD/5yTk8MqDr4+CCCQvAIEIMk799x5bAIEILE5cRUCCCCAAAIIIICArwQIQKyaLgIQqyTdreNAAKJXaQT24tABR3jIUVVV5a4BvXtaoF27dsGAIzzk6NOnj7Rt29bT42dwCCCAgGsCBCCu0dOxTwQIQHwyUQwTAQQQQAABBBBAIB4BApB4tJq7lgDEKkl361gQgOhVHLt375bQPThCQ47t27e7e4/07nmBE088sdFm46GPq+rWrRurODw/gwwQAQQ8KUAA4slpYVAeEiAA8dBkMBQEEEAAAQQQQAABqwQIQKyStCAAOXTsiPx790bZWLFT9lcfkkM1R6Rtq3TJTDtBerbvIn0yu8opHXIlrVXrmEZd8Nd75N0d/wleW3/rGzG1S+qLYgxAqqurZevWrVFDjkOHDiU1IzffvIBepdG3b9+IIYdexaFXeXAggAACCFgsQABiMSjlEk6AACThppQbQgABBBBAAAEEEBAhALHqW2AwAKmrr5P5G9+TZz75m7y78z+if27uaNMqTfI795ELTzxdLs0bJBf2OF30e5EOAhADk3s8ANGrOMrLy5s8niqwkqO0tFT0NRwIRBPQKzXCH1EVWMnRvXt3SU1NBQ8BBBBAwEkBAhAntenLjwIEIH6cNcaMAAIIIIAAAggg0IIAAYhVXxEDAch/922T8UtmyuqyTw2P4vVv3Cff6nseAYhhwcYN79u7WN5c/0FD8FFRUWFRVcokokB6enrDKo5IIYd+PyMjIxFvm3tCAAEE/CtAAOLfuWPkzggQgDjjTC8IIIAAAggggAACjgoQgFjFHWcA8tGez2T4wvtkb3VloxGkpqRK/w491HmiZKW3k6O1NQ3XrNtfKjsO7W0yWgIQqybwizrnv363LN/1ibVFqeZbgZycnOCG46H7cOjXPXr0YBWHb2eWgSOAQFIKEIAk5bRz03EIEIDEgcWlCCCAAAIIIIAAAn4RIACxaqbiCED0Xh8DXv6BlB7aE+xdhx0/GzhGbjxthHRv1yniqHYd3idvb/u3vL75A3mr5F9SXXtMCECsmkACEGsl/VEtLS1N9J4behVH+EoOvYojKyvLHzfCKBFAAAEEWhYgAGnZiCuSW4AAJLnnn7tHAAEEEEAAAQQSVIAAxKqJjSMAeXD1i/LQv14O9tz1hI6y7IppclqnvJhHs7vqgMz+5H/lIrUHyPknfjViO/YAiZkzeCErQOI383qLzp07BwOO8JCjZ8+e0qpVK6/fAuNDAAEEELBCgADECkVqJLIAAUgizy73hgACCCCAAAIIJK0AAYhVUx9HANL/pe/LxgM7gz2/MPwuufaUYVaNJFiHACR+UgKQ+M3cbqEDjN69e0v4I6p02KFXcXTs2NHtIdI/AggggIAXBAhAvDALjMHLAgQgXp4dxoYAAggggAACCCBgUIAAxCBck2YxBiA71T4ePeZdH2yeltpaKm9+Vdq0SrNqJAQgJiQJQEzg2dhUhxihAUfoSo68vDxp3bq1jb1TGgEEEEAgIQQIQBJiGrkJGwUIQGzEpTQCCCCAAAIIIICAWwIEIFbJxxiA/Gv3RjnrtYnBXk9sly07bphn1Sga1UnEFSA1dbWyuuxT+axil+jHgOl9UHJO6CB9MrvK0O5fkbat001ZEoCY4jPcODU1VXr16tVkH45A6NGpU+R9cQx3SEMEEEAAgeQTIABJvjnnjuMTIACJz4urEUAAAQQQQAABBHwhQABi1TTFGICs+ny9nPvnu4K9tmvdRipuekVapVq/D4HRAOT/rV0kdyx/Rmrr6xrGqVepPHXhbbL7SIXcs3JucOw/P+t78uDZ34tbcMzfHpUFn60Itntj5IMyqvfZzdb5775t8vC//iRvbP1QKo4ejnjtCcpydJ9z5KGzr5NTOubGPS7dgADEEFtMjTIzM4OrOMIfV6XDD70hOQcCCCCAAAK2CRCA2EZL4QQRIABJkInkNhBAAAEEEEAAAQRCBQhArPo+xBiAbFYrF0568eZGvf7vqF/Ipb3+x6qRBOvEG4DUqtUVP1k+W37/n8XBGp3atJfXLrlXvt7zDNmjVlz0fH58w6oLffTM6CJbrvtjXOHNrsP7JE/V0Cs59NFbrdz47No/SGpKasT712Oa9MGz8lsVytQdD2RagmqtwqTp590oP8m/oqVLm3xOABI3WbBBSkqK6MdR6cdThW82rgOP7Oxs0ddwIIAAAggg4IoAAYgr7HTqIwECEB9NFkNFAAEEEEAAAQQQiFWAACRWqZauizEA0WVOfG6c6CAgcOgQ4C0VgpzWKa+lXuL6PJ4ARK+quPrtx+R/t/0r2MfJWSfKYrU649ROPYPv3fDOb2Tep0uCP//lG1Pkir7nxjwuvYrj/tUvBK9/5JzrZfL/jI3YvqqmWr71vw/L29uKG32uV6QM7NK3IYDRe6doS/1YrMPq+tDjwbO+Kz8/+9qYx6YvJABpnisjIyPiZuM68NAbkbdp0yYuby5GAAEEEEDAMQECEMeo6cinAgQgPp04ho0AAggggAACCCDQnAABiFXfjzgCkJ+ueFamr/lzo57T1T/qf+fk8+WafhdKQe7XpH3aCaZHFmsAsrWyTC578xfyn71bg32er/bTeP0b90kXtb9G6LFaPcJrcMgjvL6pVq68qcKbWA69gqPvCzdJycHdDZfrIGPbuDnSrV3k/R1uXPq4zFlXFCzdIT1D9GO3bjpthGSmt2vUpQ5LZqmVK1NUuHKk9mjDZynqfzrA+Wbvs2IZXsM1BCBqZU/PnsFVHOErOXJycljFEfO3iQsRQAABBDwlQADiqelgMB4UIADx4KQwJAQQQAABBBBAAAGzAgQgZgUD7eMIQPSjpAa++mPZfqg8Yu+t1OOgvprdS87peoqcrc5zu50qp2f3jvqYqGi3EEsAovckueKtqfJ51f5gmetOGSZ/KPhxw+qKSIfexF1v5q4P/eiqTd97RvpkdWtR8o0tq+Xytx4KXjf25Atk/iU/i9ju1U3vy9i3fxn8TK+SWTZ6Wov9rNj1Xxm+8L5gCNKvw4my/rtPx2yXDAFIu3btGgUcoSFHnz59pG3bti3OJRcggAACCCDgOwECEN9NGQN2WIAAxGFwukMAAQQQQAABBBBwQoAAxCrlOAIQ3eWaPZ/JKLXqIloIEj4svRfH8NwzRIcTI3udJWmtWrc48pYCkPkb/yHjlzweDAt0wVg2Np+z7u9y49Ingv3fe+Z35NFzb2hxPHqVyWK1iXngWDr6UbXaJb9Ju/r6esl/5YfBFSl6T49/jXlc8jv3bbEPfcGvixfI3SvnBK9dcOlkufKkITG1TZQA5MQTT2z0qKrQkKNbt26s4ojp28BFCCCAAAIJJUAAklDTyc3YIEAAYgMqJRFAAAEEEEAAAQTcFiAAsWoG4gxAdLe71UqQyauek7nr3wluCh7LcPSqhl+dO0G+3cI/6jcXgOi9OB5Y/aLUq//pQ6/2mDNsony3/0UtDuFIzVHJnXeD7K2ubLi22wkd1aOs5jYbypSox2z1VZu/BzYyH9Cxp/z3u09F7GtJ6RoZvui+4Gc3nDpc5n79jhbHFbjg8LEj0nnO94LBTjzt/RKA6FUaffv2jRhy6FUcepUHBwIIIIAAAgiECBCA8HVAoHkBAhC+IQgggAACCCCAAAIJKEAAYtWkGghAAl3rcODFDcvktU3L5aPyzcGQoKWh/ehrl8vjQ78f9fFOkQKQo7XH5OZlv5XnP10aLJ/TtoP85ZtTZEj301rqMvh5+D4mr1xyT8MeJtGOKauel0f+PT/4sR73T/KviHj5vSvnyi+LXwt+9ubIn8e1j4dueNFf7pF/7PxPQ42TsrrLpmv/ENO9eSkA0Ss1wvfgCPzcvXt3SU1NjemeuAgBBBBAAAEElAABCF8DBJoXIADhG4IAAggggAACCCCQgAIEIFZNqokAJHQIB6oPyQefr2vYY6N4zyZZ9fmnUnpoT9RR3jfoanl48LiIn4cHIHsmvCTfeutheX/XJ8HrT+uU17BReF8VEsRzfFaxS/q/dEswrPm6epTVO+qRVpGOmrpa6fX8BNl5eG/Dxye0biM7rn9OOqrHekU6Lnj97kZjXHnljIZVJvEcP3r/KXnj+OO29D4lxwr/EtM+IE4GIOnp6Q2rOCKFHPq9jIyMeG6ZaxFAAAEEEECgOQECEL4fCDQvQADCNwQBBBBAAAEEEEAgAQUIQKyaVIsCkEjD2Xhgh8zf+J78v7WLGm1Wrq/V/7i/duzv5Ctq0/TwIzwAOTnrRNlUsTN42cU9B8prl9wrHdoY+4f2kYsflLdK/tVQL0X9b516pNUpHXObjOM1taH5d0I2NJ8w4GJ5Vj1uK9rR54UbZataFWPlUT7hZclum9liSasDkJycnOCG4yeffHKjzcdzc3NZxdHijHABAggggAACFgkQgFgESZmEFSAASdip5cYQQAABBBBAAIFkFiAAsWr2bQxAAkM8eKxKblr6W3ll03uNRn3zaZfIMwU/bnIn4QFI6AV6Y/HVV/5Gzsw52bCA3tBcb2weOO4649syfchNTepdvPA+eWf7muD7q9SKjnO6nRq138w/fEf0vVp5bLnuWemd2bXFkvEGIGlpaaL33NArNgJnIOjQqzuysrJa7JMLEEAAAQQQQMABAQIQB5DpwtcCBCC+nj4GjwACCCCAAAIIIBBZgADEqm+GAwGIHqp+nNS5f76r4RFZgaNX+xzZOm5OkzsJD0Au732OLNq6OnhdJ/UIKr2/xrndBxhS0Bua93vx+7K58vOG9p3bZsl29WgrvaF64NCrV055qTC42fqgLifLv77zRLP9tXn6W3K0rsbQmKI12nztH6VPVrcWa0YKQLKzsyNuNq4Dj549e0qrVq1arMsFCCCAAAIIIOCyAAGIyxNA954XIADx/BQxQAQQQAABBBBAAIH4BQhA4jeL3MKhAER3/vz6JXL9kt80Gsfh7y9o2Fsj9AgPQI4V/lUmLH1cXgjZAD2jdVt5/Rv3yYi8Mw1J/Lp4gdy98svw5fnhd8l1pwwL1pq04o8yY83rwZ9nX/RD+f5XvtFsX7nzrpcdh77YL6Rtq3TR95aSkmJofPE2+nXdcqk/qWNwXw69iqNjx/j2H4m3T65HAAEEEEAAAQcECEAcQKYLXwsQgPh6+hg8AggggAACCCCAQGQBAhCrvhkOBiBry7dI/is/bDRyvfKiR0bnRu+FByD1t74h9fX18pPlsxv2Ewkc6amt5cWLJ8mYk8+PW6P8SIX0nDdejtQebWg7tPtX5P1v/6rhdXXtMcmdd4Poa/SRld5ObX4+TzLS2jbbz6BXf9KwAXzgiPXxVXEPPlKDO88T6ZdtSSmKIIAAAggggICHBAhAPDQZDMWTAgQgnpwWBoUAAggggAACCCBgToAAxJzfl60dDEA+2VsiX51/W6ORH7z5tSbBQqQAJNDo5x++KL/458vBGnoz9acvvF1u/sqlcYuMXzJTnlv/TrDdf67+vXw1u3fDSpNx78wIvn/76aPkdxfc2mL9H773pPz+P4uD18WyaqTForFeQAASqxTXIYAAAggg4C8BAhB/zRejdV6AAMR5c3pEAAEEEEAAAQQQsF2AAMQqYgcDkNc2vS/fefuXwZF3SM+Q/TfNb3InzQUg+uLffrxQJi5/Jrg/h37vsXPHy91njolL5cOyT+WcBXcG2/zw9Mvk/13wAwnfT2Pt2N/J6Z37tFh70ZZVMvqtqcHrzul6iqy6qvEjv1osYvQCAhCjcrRDAAEEEEDA2wIEIN6eH0bnvgABiPtzwAgQQAABBBBAAAEELBcgALGKNMYApPLoYfm8ar/069DDcM+XLLpf/l5aHGz/7b7nyZ/VPh7hR0sBiL5e7ydy47InGjZXDxw/UwHIL1UQEs9xzoI75MOyDQ1NdCDz9mUPyWC1WXvgOF89Guu944/GaqmuHsuAl38gmyp2Bi/9Y8GP5cbTLmmpqfnPCUDMG1IBAQQQQAABLwoQgHhxVhiTlwQIQLw0G4wFAQQQQAABBBBAwCIBAhCLICXGAGRLxefS76Xvy/f6XyQ6aNCPiornCH90lW776iX3RNy/I5YARLfXKy7Gvv1YcB8P/d4taqPyJy+8TfSjsWI5nlv3joxfOjN4aa/2OVJycHfw5xeHT5LvnVIQS6mGa176dJlc+8704PV6n5KXR9wtV540JOYa+sKi0o/kpKzuDWdMBwFITExchAACCCCAgO8ECEB8N2UM2GEBAhCHwekOAQQQQAABBBBAwAkBAhCrlOMIQPq+eFOw17Ny+su4U4bJ8J5nyFc69ZKUlJQmI6pVKyKW7Vgrj/77FVmy/eNGn1/U43RZdsWXj8MK/TDWAES3eVfV14+dqlArVALH2JMvkBeG3yVprVq3qHSk5qj0fH58cMPz0AZd2mZJqdqkvU2rtBbrhF5w09In5Nl1f2/URgdHd57xbfmfnH4Ra2mrj9Um8QtVqDN/03vy333bZOnoR6UgNz+2vglAYnPiKgQQQAABBPwmQADitxljvE4LEIA4LU5/CCCAAAIIIIAAAg4IEIBYhWwwAAntXj866rROPUUHBh3btJeqmmrZdXi/rN27pVEwEWijA5N3Rj8i3dt1ingX8QQgusC/dm+Ub77xoOw+ciBY79K8QfLnSydLu7S2LUrd/cGz8uuP/tzkursHXiWPnTehxfbhFxyrrZGr//6YvL75gyZtc9p2kDO69JXObTMlVf2v4thh2XFob0PgcaT2aKPrCUDipqcBAggggAACiSdAAJJ4c8odWStAAGKtJ9UQQAABBBBAAAEEPCFAAGLVNMQYgJSpQONstV9G6OOhjAzh2v4F8vjQ70uXEzpEbR5vAKILrd9XKiPeuF+2hTy+akj302TxyAcbQpnmjs0Vu9TjvW6Ruvq64GUpkiIbr30m9kdQhXVQX1+vQpUF8uCHLzUJNmJxS1OPzlqu9h45W22kHtPBCpCYmLgIAQQQQAAB3wn8858iZ5/tu2EzYAQcE/jwQ5GzznKsOzpCAAEEEEAAAQQQQMAJAQIQq5RjDEAC3RXv3iRvbF0t7+38RFaVrY+4wiN8aHplyJiThqrNwEfE9A/6RgIQ3acOP0aojdbX7y8NDiG/cx/522VTo642CVx42Zu/kMVb1V+ejh96Bcn/qg3RzR67Du+TJz7+q7y88R+ytbKs/4yF/wAAIABJREFU2XKZaSfIBSd+VUb1Pluu7neBWiWSFXv3BCCxW3ElAggggAACfhI4dkxk8GCR4mI/jZqxIuCMwJlniqxaJZIW3yNrnRkcvSCAAAIIIIAAAgggYFyAAMS4XeOWcQYgoY31iokt6h/1N+zf3rAyRO/DcVg9/qpd6zaSld6uIXTI79xXemd2tWq0ttW5tujX8tKGd4P1X//GffKtvudZ2p9eafJvFSDpR3Xtqz7YsFG7Dj16ZGTLgI49pX+HHtIqtZWxPglAjLnRCgEEEEAAAT8I6BBkzRqRI0f8MFrGqAQ+/fRTufGmL/fPM4Ly7W99S+666y4jTZOjTVv1qNszziD8SI7Z5i4RQAABBBBAAIGkEyAAsWrKTQQgVg3B7Tp7j1RKj3nXS3Wt+scFdfTM6CJbrvuj8TDCjRsiAHFDnT4RQAABBBBAAIGIAtXV1dK+fXupqakxLDRkyBBZvny54fY0RAABBBBAAAEEEEAAAf8KEIBYNXcEIDJDbYA+SW2EHjh+cfa18sBZ37VK2Jk6BCDOONMLAggggAACCCAQo0B+fr6sXbs2xqubXqYDlAMHDkhqaqrhGjREAAEEEEAAAQQQQAABfwoQgFg1b0kegBxVqz70BuiBzdPT1ebjJePmSDf1+C5fHQQgvpouBosAAggggAACiS8wbtw4eeGFF0zd6IYNG6Rfv36matAYAQQQQAABBBBAAAEE/CdAAGLVnCV5APKLD1+Sn//zpaDm+FMvljlfn2iVrnN1CECcs6YnBBBAAAEEEEAgBoHp06fLT3/60xiujH7Ja6+9JldddZWpGjRGAAEEEEAAAQQQQAAB/wkQgFg1Z0kUgOypOiAHjx2ROqmX0oN71Kbny2T2J39TP9U3aOrVH+u++5T0zepula5zdQhAnLOmJwQQQAABBBBAIAaBoqIiGTFiRAxXRr9kypQpMnXqVFM1aIwAAggggAACCCCAAAL+EyAAsWrOkigAGb9kpjy3/p2ocg+fM07u+5+rrZJ1tg4BiLPe9IYAAggggAACCLQgsHv3bunataspp8svv1wWLlxoqgaNEUAAAQQQQAABBBBAwH8CBCBWzRkBSIPkdacMk+e+foekpvh0k0kCEKv+L4I6CCCAAAIIIICAZQK5ubmyY8cOw/Xy8vKkpKTEcHsaIoAAAggggAACCCCAgD8FCECsmrckDUB00NG5Taac1bWf3DTgErnq5KFWibpThwDEHXd6RQABBBBAAAEEmhEYOXKkvPXWW6aMysvLJTs721QNGiOAAAIIIIAAAggggIC/BAhArJqvJApArCLzZB0CEE9OC4NCAAEEEEAAgeQWmDx5skybNs0UwtKlS6WgoMBUDRojgAACCCCAAAIIIICAvwQIQKyaLwIQqyTdrUMA4q4/vSOAAAIIIIAAAhEE5s+fL9dcc40pm5kzZ8rEiRNN1aAxAggggAACCCCAAAII+EuAAMSq+SIAsUrS3ToEIO760zsCCCCAAAIIIBBBYN26dXLaaaeZshk/frzMmTPHVA0aI4AAAggggAACCCCAgL8ECECsmi8CEKsk3a1DAOKuP70jgAACCCCAAAIRBGprayUzM1OqqqoM+wwcOFCKi4sNt6chAggggAACCCCAAAII+E+AAMSqOSMAsUrS3ToEIO760zsCCCCAAAIIIBBFYPDgwbJ69WrDPunp6XLw4EFJS0szXIOGCCCAAAIIIIAAAggg4C8BAhCr5osAxCpJd+sQgLjrT+8IIIAAAggggEAUgcLCQpk9e7Ypn48//li+9rWvmapBYwQQQAABBBBAAAEEEPCPAAGIVXNFAGKVpLt1CEDc9ad3BBBAAAEEEEAgisCsWbPk9ttvN+Uzb948GTdunKkaNEYAAQQQQAABBBBAAAH/CBCAWDVXBCBWSbpbhwDEXX96RwABBBBAAAEEoggsX75czj//fFM+d911l0yfPt1UDRojgAACCCCAAAIIIICAfwQIQKyaKwIQqyTdrUMA4q4/vSOAAAIIIIAAAlEEKisrJSsry5TP8OHDpaioyFQNGiOAAAIIIIAAAggggIB/BAhArJorAhCrJN2tQwDirj+9I4AAAggggAACzQj069dPNm3aZNioS5cuUlZWJikpKYZr0BABBBBAAAEEEEAAAQT8I0AAYtVcEYBYJeluHQIQd/3pHQEEEEAAAQQQaEZgzJgxsmDBAlNG27dvlx49epiqQWMEEEAAAQQQQAABBBDwhwABiFXzRABilaS7dQhA3PWndwQQQAABBBBAoBmBqVOnygMPPGDKaPHixTJy5EhTNWiMAAIIIIAAAggggAAC/hAgALFqnghArJJ0tw4BiLv+9I4AAggggAACCDQjsHDhQrniiitMGT366KNy7733mqpBYwQQQAABBBBAAAEEEPCHAAGIVfNEAGKVpLt1CEDc9ad3BBBAAAEEEECgGYGSkhLp3bu3KaOxY8fK/PnzTdWgMQIIIIAAAggggAACCPhDgADEqnkiALFK0t06BCDu+tM7AggggAACCCDQjEB9fb1kZ2fL/v37DTudeuqpsm7dOsPtaYgAAggggAACCCCAAAL+ESAAsWquCECsknS3DgGIu/70jgACCCCAAAIItCAwbNgwWbZsmWGnlJQUqayslIyMDMM1aIgAAggggAACCCCAAAL+ECAAsWqeCECsknS3DgGIu/70jgACCCCAAAIItCAwceJEeeKJJ0w5rVy5UgYPHmyqBo0RQAABBBBAAAEEEEDA+wIEIFbNEQGIVZLu1iEAcdef3hFAAAEEEEAAgRYE5s6dKxMmTDDl9PTTT8stt9xiqgaNEUAAAQQQQAABBBBAwPsCBCBWzREBiFWS7tYhAHHXn94RQAABBBBAAIEWBIqLi2XQoEGmnG699VaZNWuWqRo0RgABBBBAAAEEEEAAAe8LEIBYNUcEIFZJuluHAMRdf3pHAAEEEEAAAQRaEKiurpb27dtLTU2NYashQ4bI8uXLDbenIQIIIIAAAggggAACCPhDgADEqnkiALFK0t06BCDu+tM7AggggAACCCAQg0B+fr6sXbs2hisjX6I3QK+oqJDU1FTDNWiIAAIIIIAAAggggAAC3hcgALFqjghArJJ0tw4BiLv+9I4AAggggAACCMQgMG7cOHnhhRdiuDL6JRs2bJB+/fqZqkFjBBBAAAEEEEAAAQQQ8LYAAYhV80MAYpWku3UIQNz1p3cEEEAAAQQQQCAGgRkzZsikSZNiuDL6Ja+99ppcddVVpmrQGAEEEEAAAQQQQAABBLwtQABi1fwQgFgl6W4dAhB3/ekdAQQQQAABBBCIQaCoqEhGjBgRw5XRL5kyZYpMnTrVVA0aI4AAAggggAACCCCAgLcFCECsmh8CEKsk3a1DAOKuP70jgAACCCCAAAIxCOzevVu6du0aw5XRL7n88stl4cKFpmrQGAEEEEAAAQQQQAABBLwtQABi1fwQgFgl6W4dAhB3/ekdAQQQQAABBBCIUSA3N1d27NgR49VNL8vLy5OSkhLD7WmIAAIIIIAAAggggAAC3hcgALFqjghArJJ0tw4BiLv+9I4AAggggAACCMQoMHLkSHnrrbdivDryZeXl5ZKdnW2qBo0RQAABBBBAAAEEEEDAuwIEIFbNDQGIVZLu1iEAcdef3hFAAAEEEEAAgRgFJk+eLNOmTYvx6siXLV26VAoKCkzVoDECCCCAAAIIIIAAAgh4V4AAxKq5IQCxStLdOgQg7vrTOwIIIIAAAgggEKPA/Pnz5Zprronx6siXzZw5UyZOnGiqBo0RQAABBBBAAAEEEEDAuwIEIFbNDQGIVZLu1iEAcdef3hFAAAEEEEAAgRgF1q9fLwMGDIjx6siXjR8/XubMmWOqBo0RQAABBBBAAAEEEEDAuwIEIFbNDQGIVZLu1iEAcdef3hFAAAEEEEAAgRgFamtrJTMzU6qqqmJs0fSygQMHSnFxseH2NEQAAQQQQAABBBBAAAFvCxCAWDU/BCBWSbpbhwDEXX96RwABBBBAAAEE4hAYPHiwrF69Oo4WjS9NT0+XyspK0X9yIIAAAggggAACCCCAQOIJEIBYNacEIFZJuluHAMRdf3pHAAEEEEAAAQTiECgsLJTZs2fH0aLppWvWrJH8/HxTNWiMAAIIIIAAAggggAAC3hQgALFqXghArJJ0tw4BiLv+9I4AAggggAACCMQhMGvWLLn99tvjaNH00nnz5sm4ceNM1aAxAggggAACCCCAAAIIeFOAAMSqeSEAsUrS3ToEIO760zsCCCCAAAIIIBCHwIoVK2To0KFxtGh66V133SXTp083VYPGCCCAAAIIIIAAAggg4E0BAhCr5mXrfpHHlltVjTpuCfxM/QW6d0e3eqdfBBBAAAEEEEAAgTgE9P4dWVlZcbRoeunw4cOlqKjIVA0aI4AAAggggAACCCCAgDcFCECsmpfaOpFfqQBkW4VVFanjtECe+svz3SoAaZXqdM/0hwACCCCAAAIIIGBQoF+/frJp0yaDrUW6dOkiZWVlkpKSYrgGDRFAAAEEEEAAAQQQQMCbAgQgVs6LDkFKVQByTP3J4S+BNBV69FQBCOGHv+aN0SKAAAIIIIBA0guMGTNGFixYYMqhtLRUcnNzTdWgMQIIIIAAAggggAACCHhPgADEe3PCiBBAAAEEEEAAAQQQQCBGgalTp8oDDzwQ49WRL1u8eLGMHDnSVA0aI4AAAggggAACCCCAgPcECEC8NyeMCAEEEEAAAQQQQAABBGIUWLRokYwePTrGqyNf9uijj8q9995rqgaNEUAAAQQQQAABBBBAwHsCBCDemxNGhAACCCCAAAIIIIAAAjEKlJSUSO/evWO8OvJlY8eOlfnz55uqQWMEEEAAAQQQQAABBBDwngABiPfmhBEhgAACCCCAAAIIIIBAjAL19fWSnZ0t+/fvj7FF08tOPfVUWbduneH2NEQAAQQQQAABBBBAAAFvChCAeHNeGBUCCCCAAAIIIIAAAgjEKDBs2DBZtmxZjFc3vSwlJUUqKyslIyPDcA0aIoAAAggggAACCCCAgPcECEC8NyeMCAEEEEAAAQQQQAABBOIQmDhxojzxxBNxtGh66cqVK2Xw4MGmatAYAQQQQAABBBBAAAEEvCVAAOKt+WA0CCCAAAIIIIAAAgggEKfA3LlzZcKECXG2anz5008/LbfccoupGjRGAAEEEEAAAQQQQAABbwkQgHhrPhgNAggggAACCCCAAAIIxClQXFwsgwYNirNV48tvvfVWmTVrlqkaNEYAAQQQQAABBBBAAAFvCRCAeGs+GA0CCCCAAAIIIIAAAgjEKVBdXS3t27eXmpqaOFt+efmQIUNk+fLlhtvTEAEEEEAAAQQQQAABBLwnQADivTlhRAgggAACCCCAAAIIIBCnQH5+vqxduzbOVl9erjdAr6iokNTUVMM1aIgAAggggAACCCCAAALeEiAA8dZ8MBoEEEAAAQQQQAABBBAwIDBu3Dh54YUXDLT8ssmGDRukX79+pmrQGAEEEEAAAQQQQAABBLwjQADinblgJAgggAACCCCAAAIIIGBQYMaMGTJp0iSDrb9o9uqrr8qYMWNM1aAxAggggAACCCCAAAIIeEeAAMQ7c8FIEEAAAQQQQAABBBBAwKBAUVGRjBgxwmDrL5pNmTJFpk6daqoGjRFAAAEEEEAAAQQQQMA7AgQg3pkLRoIAAggggAACCCCAAAIGBXbv3i1du3Y12PqLZpdffrksXLjQVA0aI4AAAggggAACCCCAgHcECEC8MxeMBAEEEEAAAQQQQAABBEwI5Obmyo4dOwxXyMvLk5KSEsPtaYgAAggggAACCCCAAALeEiAA8dZ8MBoEEEAAAQQQQAABBBAwKDBy5Eh56623DLb+oll5eblkZ2ebqkFjBBBAAAEEEEAAAQQQ8IYAAYg35oFRIIAAAggggAACCCCAgEmByZMny7Rp00xVWbJkiQwbNsxUDRojgAACCCCAAAIIIICANwQIQLwxD4wCAQQQQAABBBBAAAEETArMnz9frrnmGlNVZs6cKRMnTjRVg8YIIIAAAggggAACCCDgDQECEG/MA6NAAAEEEEAAAQQQQAABkwLr16+XAQMGmKoyfvx4mTNnjqkaNEYAAQQQQAABBBBAAAFvCBCAeGMeGAUCCCCAAAIIIIAAAgiYFKitrZXMzEypqqoyXGngwIFSXFxsuD0NEUAAAQQQQAABBBBAwDsCBCDemQtGggACCCCAAAIIIIAAAiYFBg8eLKtXrzZcJT09XSorK0X/yYEAAggggAACCCCAAAL+FiAA8ff8MXoEEEAAAQQQQAABBBAIESgsLJTZs2ebMlmzZo3k5+ebqkFjBBBAAAEEEEAAAQQQcF+AAMT9OWAECCCAAAIIIIAAAgggYJHArFmz5PbbbzdVbd68eTJu3DhTNWiMAAIIIIAAAggggAAC7gsQgLg/B4wAAQQQQAABBBBAAAEELBJYsWKFDB061FS1u+66S6ZPn26qBo0RQAABBBBAAAEEEEDAfQECEPfngBEggAACCCCAAAIIIICARQJ6/46srCxT1YYPHy5FRUWmatAYAQQQQAABBBBAAAEE3BcgAHF/DhgBAggggAACCCCAAAIIWCjQv39/2bhxo+GKXbp0kbKyMklJSTFcg4YIIIAAAggggAACCCDgvgABiPtzwAgQQAABBBBAAAEEEEDAQoExY8bIggULTFUsLS2V3NxcUzVojAACCCCAAAIIIIAAAu4KEIC460/vCCCAAAIIIIAAAgggYLHA1KlT5YEHHjBVdfHixTJy5EhTNWiMAAIIIIAAAggggAAC7goQgLjrT+8IIIAAAggggAACCCBgscCiRYtk9OjRpqo++uijcu+995qqQWMEEEAAAQQQQAABBBBwV4AAxF1/ekcAAQQQQAABBBBAAAGLBUpKSqR3796mqo4dO1bmz59vqgaNEUAAAQQQQAABBBBAwF0BAhB3/ekdAQQQQAABBBBAAAEELBaor6+Xzp07y759+wxXPvXUU2XdunWG29MQAQQQQAABBBBAAAEE3BcgAHF/DhgBAggggAACCCCAAAIIWCwwbNgwWbZsmeGqKSkpUllZKRkZGYZr0BABBBBAAAEEEEAAAQTcFSAAcdef3hFAAAEEEEAAAQQQQMAGgYkTJ8oTTzxhqvLKlStl8ODBpmrQGAEEEEAAAQQQQAABBNwTIABxz56eEUAAAQQQQAABBBBAwCaBuXPnyoQJE0xVf+qpp6SwsNBUDRojgAACCCCAAAIIIICAewIEIO7Z0zMCCCCAAAIIIIAAAgjYJFBcXCyDBg0yVf3WW2+VWbNmmapBYwQQQAABBBBAAAEEEHBPgADEPXt6RgABBBBAAAEEEEAAAZsEqqurpX379lJTU2O4hyFDhsjy5csNt6chAggggAACCCCAAAIIuCtAAOKuP70jgAACCCCAAAIIIICATQL5+fmydu1aw9X1BugVFRWSmppquAYNEUAAAQQQQAABBBBAwD0BAhD37OkZAQQQQAABBBBAAAEEbBS4/vrr5fnnnzfVw4YNG6Rfv36matAYAQQQQAABBBBAAAEE3BEgAHHHnV4RQAABBBBAAAEEEEDAZoEZM2bIpEmTTPXy6quvypgxY0zVoDECCCCAAAIIIIAAAgi4I0AA4o47vSKAAAIIIIAAAggggIDNAkVFRTJixAhTvUyZMkWmTp1qqgaNEUAAAQQQQAABBBBAwB0BAhB33OkVAQQQQAABBBBAAAEEbBbYvXu3dO3a1VQvl19+uSxcuNBUDRojgAACCCCAAAIIIICAOwIEIO640ysCCCCAAAIIIIAAAgg4IJCbmys7duww3FNeXp6UlJQYbk9DBBBAAAEEEEAAAQQQcE+AAMQ9e3pGAAEEEEAAAQQQQAABmwVGjRolb775pqleysvLJTs721QNGiOAAAIIIIAAAggggIDzAgQgzpvTIwIIIIAAAggggAACCDgkMHnyZJk2bZqp3pYsWSLDhg0zVYPGCCCAAAIIIIAAAggg4LwAAYjz5vSIAAIIIIAAAggggAACDgnMnz9frrnmGlO9zZw5UyZOnGiqBo0RQAABBBBAAAEEEEDAeQECEOfN6REBBBBAAAEEEEAAAQQcEli/fr0MGDDAVG/jx4+XOXPmmKpBYwQQQAABBBBAAAEEEHBegADEeXN6RAABBBBAAAEEEEAAAYcEamtrJTMzU6qqqgz3OHDgQCkuLjbcnoYIIIAAAggggAACCCDgjgABiDvu9IoAAggggAACCCCAAAIOCZx77rmyatUqw72lp6dLZWWl6D85EEAAAQQQQAABBBBAwD8CBCD+mStGigACCCCAAAIIIIAAAgYECgsLZfbs2QZaftlkzZo1kp+fb6oGjRFAAAEEEEAAAQQQQMBZAQIQZ73pDQEEEEAAAQQQQAABBBwWmDVrltx+++2mep03b56MGzfOVA0aI4AAAggggAACCCCAgLMCBCDOetMbAggggAACCCCAAAIIOCywYsUKGTp0qKle77zzTpkxY4apGjRGAAEEEEAAAQQQQAABZwUIQJz1pjcEEEAAAQQQQAABBBBwWEDv35GVlWWq1+HDh0tRUZGpGjRGAAEEEEAAAQQQQAABZwUIQJz1pjcEEEAAAQQQQAABBBBwQaB///6yceNGwz136dJFysrKJCUlxXANGiKAAAIIIIAAAggggICzAgQgznrTGwIIIIAAAggggAACCLggMGbMGFmwYIGpnktLSyU3N9dUDRojgAACCCCAAAIIIICAcwIEIM5Z0xMCCCCAAAIIIIAAAgi4JPDwww/L/fffb6r3xYsXy8iRI03VoDECCCCAAAIIIIAAAgg4J0AA4pw1PSGAAAIIIIAAAggggIBLAosWLZLRo0eb6v2RRx6RyZMnm6pBYwQQQAABBBBAAAEEEHBOgADEOWt6QgABBBBAAAEEEEAAAZcESkpKpHfv3qZ6Hzt2rMyfP99UDRojgAACCCCAAAIIIICAcwIEIM5Z0xMCCCCAAAIIIIAAAgi4JFBfXy+dO3eWffv2GR7BqaeeKuvWrTPcnoYIIIAAAggggAACCCDgrAABiLPe9IYAAggggAACCCCAAAIuCQwbNkyWLVtmuPeUlBSprKyUjIwMwzVoiAACCCCAAAIIIIAAAs4JEIA4Z01PCCCAAAIIIIAAAggg4KLAHXfcIY8//ripEaxcuVIGDx5sqgaNEUAAAQQQQAABBBBAwBkBAhBnnOkFAQQQQAABBBBAAAEEXBaYO3euTJgwwdQonnrqKSksLDRVg8YIIIAAAggggAACCCDgjAABiDPO9IIAAggggAACCCCAAAIuCxQXF8ugQYNMjeLWW2+VWbNmmapBYwQQQAABBBBAAAEEEHBGgADEGWd6QQABBBBAAAEEEEAAAZcFqqurpX379lJTU2N4JEOGDJHly5cbbk9DBBBAAAEEEEAAAQQQcE6AAMQ5a3pCAAEEEEAAAQQQQAABlwXy8/Nl7dq1hkehN0CvqKiQ1NRUwzVoiAACCCCAAAIIIIAAAs4IEIA440wvCCCAAAIIIIAAAggg4AGB66+/Xp5//nlTI9mwYYP069fPVA0aI4AAAggggAACCCCAgP0CBCD2G9MDAggggAACCCCAAAIIeERgxowZMmnSJFOjefXVV2XMmDGmatAYAQQQQAABBBBAAAEE7BcgALHfmB4QQAABBBBAAAEEEEDAIwJFRUUyYsQIU6OZMmWKTJ061VQNGiOAAAIIIIAAAggggID9AgQg9hvTAwIIIIAAAggggAACCHhEYPfu3dK1a1dTo7nssstk0aJFpmrQGAEEEEAAAQQQQAABBOwXIACx35geEEAAAQQQQAABBBBAwEMCubm5smPHDsMjysvLk5KSEsPtaYgAAggggAACCCCAAALOCBCAOONMLwgggAACCCCAAAIIIOARgVGjRsmbb75pajTl5eWSnZ1tqgaNEUAAAQQQQAABBBBAwF4BAhB7famOAAIIIIAAAggggAACHhOYPHmyTJs2zdSolixZIsOGDTNVg8YIIIAAAggggAACCCBgrwABiL2+VEcAAQQQQAABBBBAAAGPCbzyyity9dVXmxrVzJkzZeLEiaZq0BgBBBBAAAEEEEAAAQTsFSAAsdeX6ggggAACCCCAAAIIIOAxgfXr18uAAQNMjeqGG26QuXPnmqpBYwQQQAABBBBAAAEEELBXgADEXl+qI4AAAggggAACCCCAgMcEamtrJTMzU6qqqgyPbODAgVJcXGy4PQ0RQAABBBBAAAEEEEDAfgECEPuN6QEBBBBAAAEEEEAAAQQ8JnDuuefKqlWrDI8qPT1dKisrRf/JgQACCCCAAAIIIIAAAt4UIADx5rwwKgQQQAABBBBAAAEEELBRoLCwUGbPnm2qhzVr1kh+fr6pGjRGAAEEEEAAAQQQQAAB+wQIQOyzpTICCCCAAAIIIIAAAgh4VODJJ5+U2267zdTo5s2bJ+PGjTNVg8YIIIAAAggggAACCCBgnwABiH22VEYAAQQQQAABBBBAAAGPCqxYsUKGDh1qanR33nmnzJgxw1QNGiOAAAIIIIAAAggggIB9AgQg9tlSGQEEEEAAAQQQQAABBDwqoPfvyMrKMjW64cOHS1FRkakaNEYAAQQQQAABBBBAAAH7BAhA7LOlMgIIIIAAAggggAACCHhYoH///rJx40bDI+zSpYuUlZVJSkqK4Ro0RAABBBBAAAEEEEAAAfsECEDss6UyAggggAACCCCAAAIIeFhgzJgxsmDBAlMjLC0tldzcXFM1aIwAAggggAACCCCAAAL2CBCA2ONKVQQQQAABBBBAAAEEEPC4wMMPPyz333+/qVEuXrxYRo4caaoGjRFAAAEEEEAAAQQQQMAeAQIQe1ypigACCCCAAAIIIIAAAh4XWLRokYwePdrUKB9iDwbfAAAgAElEQVR55BGZPHmyqRo0RgABBBBAAAEEEEAAAXsECEDscaUqAggggAACCCCAAAIIeFxg27Zt0qtXL1OjHDt2rMyfP99UDRojgAACCCCAAAIIIICAPQIEIPa4UhUBBBBAAAEEEEAAAQQ8LlBfXy+dO3eWffv2GR7pKaecIuvXrzfcnoYIIIAAAggggAACCCBgnwABiH22VEYAAQQQQAABBBBAAAGPCwwbNkyWLVtmeJQpKSlSWVkpGRkZhmvQEAEEEEAAAQQQQAABBOwRIACxx5WqCCCAAAIIIIAAAggg4AOBO+64Qx5//HFTI125cqUMHjzYVA0aI4AAAggggAACCCCAgPUCBCDWm1IRAQQQQAABBBBAAAEEfCIwd+5cmTBhgqnRPvXUU1JYWGiqBo0RQAABBBBAAAEEEEDAegECEOtNqYgAAggggAACCCCAAAI+Efjoo4/kzDPPNDXaW2+9VWbNmmWqBo0RQAABBBBAAAEEEEDAegECEOtNqYgAAggggAACCCCAAAI+Eaiurpb27dtLTU2N4RGfd955smLFCsPtaYgAAggggAACCCCAAAL2CBCA2ONKVQQQQAABBBBAAAEEEPCJQH5+vqxdu9bwaPUG6BUVFZKammq4Bg0RQAABBBBAAAEEEEDAegECEOtNqYgAAggggAACCCCAAAI+Erj++uvl+eefNzXiDRs2SL9+/UzVoDECCCCAAAIIIIAAAghYK0AAYq0n1RBAAAEEEEAAAQQQQMBnAjNmzJBJkyaZGvWrr74qY8aMMVWDxggggAACCCCAAAIIIGCtAAGItZ5UQwABBBBAAAEEEEAAAZ8JvPPOO3LxxRebGvWUKVNk6tSppmrQGAEEEEAAAQQQQAABBKwVIACx1pNqCCCAAAIIIIAAAggg4DOBPXv2SE5OjqlRX3bZZbJo0SJTNWiMAAIIIIAAAggggAAC1goQgFjrSTUEEEAAAQQQQAABBBDwoUBubq7s2LHD8Mjz8vKkpKTEcHsaIoAAAggggAACCCCAgPUCBCDWm1IRAQQQQAABBBBAAAEEfCYwatQoefPNN02Nury8XLKzs03VoDECCCCAAAIIIIAAAghYJ0AAYp0llRBAAAEEEEAAAQQQQMCnApMnT5Zp06aZGv2SJUtk2LBhpmrQGAEEEEAAAQQQQAABBKwTIACxzpJKCCCAAAIIIIAAAggg4FOBV155Ra6++mpTo585c6ZMnDjRVA0aI4AAAggggAACCCCAgHUCBCDWWVIJAQQQQAABBBBAAAEEfCqwfv16GTBggKnR33DDDTJ37lxTNWiMAAIIIIAAAggggAAC1gkQgFhnSSUEEEAAAQQQQAABBBDwqUBtba1kZWXJ4cOHDd/BwIEDpbi42HB7GiKAAAIIIIAAAggggIC1AgQg1npSDQEEEEAAAQQQQAABBHwqcO6558qqVasMjz4tLU0OHjwo6enphmvQEAEEEEAAAQQQQAABBKwTIACxzpJKCCCAAAIIIIAAAggg4GOBwsJCmT17tqk7WLNmjeTn55uqQWMEEEAAAQQQQAABBBCwRoAAxBpHqiCAAAIIIIAAAggggIDPBZ588km57bbbTN3FvHnzZNy4caZq0BgBBBBAAAEEEEAAAQSsESAAscaRKggggAACCCCAAAIIIOBzgRUrVsjQoUNN3cWdd94pM2bMMFWDxggggAACCCCAAAIIIGCNAAGINY5UQQABBBBAAAEEEEAAAZ8LVFZWSocOHaS+vt7wnQwfPlyKiooMt6chAggggAACCCCAAAIIWCdAAGKdJZUQQAABBBBAAAEEEEDA5wL9+/eXjRs3Gr6Lzp07y+7duyUlJcVwDRoigAACCCCAAAIIIICANQIEINY4UgUBBBBAAAEEEEAAAQQSQGDMmDGyYMECU3dSWloqubm5pmrQGAEEEEAAAQQQQAABBMwLEICYN6QCAv+/vfsNrirN88P+iDV4szC0jdS1zraE/+wKpsqbFlRmPBKzGxeiq7ZqBGOXjejEm5UYZ12IrpR3JPYVol+NxL5CdOKqBnbXNqIdp0bCTraAdiVpianyBtGZTbUg8QtEO65qiYk3/OksovE0NKOcM9OdUfd0C3Sec6/OOfdzq25xgfP7nef5/J5337r3ECBAgAABAgQIEKiIwMjISHj11VejdnPp0qXwjW98I6qHYgIECBAgQIAAAQIE4gUEIPGGOhAgQIAAAQIECBAgUBGBCxcuhG9+85tRuxkdHQ1Hjx6N6qGYAAECBAgQIECAAIF4AQFIvKEOBAgQIECAAAECBAhURGB+fj5s3bo1ajcHDhwI3/3ud6N6KCZAgAABAgQIECBAIF5AABJvqAMBAgQIECBAgAABAhURWFpaCumDzN9///3MO9q2bVu4ceNG5nqFBAgQIECAAAECBAjkIyAAycdRFwIECBAgQIAAAQIEKiKwe/fu8L3vfS/zbpqamsLi4mLYuHFj5h4KCRAgQIAAAQIECBCIFxCAxBvqQIAAAQIECBAgQIBAhQQGBwfDa6+9FrWjq1evhq997WtRPRQTIECAAAECBAgQIBAnIACJ81NNgAABAgQIECBAgEDFBM6ePRu+9a1vRe3q9OnT4dChQ1E9FBMgQIAAAQIECBAgECcgAInzU02AAAECBAgQIECAQMUEZmdnw86dO6N2dfjw4fD6669H9VBMgAABAgQIECBAgECcgAAkzk81AQIECBAgQIAAAQIVE/jwww/Dpk2bwkcffZR5Z11dXeHKlSuZ6xUSIECAAAECBAgQIBAvIACJN9SBAAECBAgQIECAAIGKCXR0dITr169n3lX6APT79++HdevWZe6hkAABAgQIECBAgACBOAEBSJyfagIECBAgQIAAAQIEKijQ19cX3njjjaidzc3Nhfb29qgeigkQIECAAAECBAgQyC4gAMlup5IAAQIECBAgQIAAgYoKnDhxIvzu7/5u1O4mJyfD/v37o3ooJkCAAAECBAgQIEAgu4AAJLudSgIECBAgQIAAAQIEKiowNTUVXnrppajdHTt2LHznO9+J6qGYAAECBAgQIECAAIHsAgKQ7HYqCRAgQIAAAQIECBCoqMCdO3fC888/H7W7vXv3hgsXLkT1UEyAAAECBAgQIECAQHYBAUh2O5UECBAgQIAAAQIECFRYoLW1Ndy6dSvzDtva2sJ7772XuV4hAQIECBAgQIAAAQJxAgKQOD/VBAgQIECAAAECBAhUVKCnpye8+eabUbu7e/du2LJlS1QPxQQIECBAgAABAgQIZBMQgGRzU0WAAAECBAgQIECAQMUFjh49Gn7v934vapfT09Nh9+7dUT0UEyBAgAABAgQIECCQTUAAks1NFQECBAgQIECAAAECFReYmJgIL7/8ctQuT548Gb797W9H9VBMgAABAgQIECBAgEA2AQFINjdVBAgQIECAAAECBAhUXODGjRvhy1/+ctQu+/v7w9mzZ6N6KCZAgAABAgQIECBAIJuAACSbmyoCBAgQIECAAAECBCou8OTJk7B58+bw8OHDzDvdsWNHeOeddzLXKyRAgAABAgQIECBAILuAACS7nUoCBAgQIECAAAECBCou0NnZGd5+++3Mu1y/fn148OBB2LBhQ+YeCgkQIECAAAECBAgQyCYgAMnmpooAAQIECBAgQIAAgQYQGBgYCGfOnIna6bVr18KLL74Y1UMxAQIECBAgQIAAAQKrFxCArN5MBQECBAgQIECAAAECDSJw6tSp8Morr0Tt9ty5c+G3fuu3onooJkCAAAECBAgQIEBg9QICkNWbqSBAgAABAgQIECBAoEEErly5Er7+9a9H7XZoaCicOHEiqodiAgQIECBAgAABAgRWLyAAWb2ZCgIECBAgQIAAAQIEGkRgcXExPPfcc2FpaSnzjvfs2RPeeuutzPUKCRAgQIAAAQIECBDIJiAAyeamigABAgQIECBAgACBBhFob28P7777bubdNjc3h9u3b4empqbMPRQSIECAAAECBAgQILB6AQHI6s1UECBAgAABAgQIECDQQAK9vb3h/PnzUTteWFgIL7zwQlQPxQQIECBAgAABAgQIrE5AALI6L1cTIECAAAECBAgQINBgAiMjI+HVV1+N2vXFixdDT09PVA/FBAgQIECAAAECBAisTkAAsjovVxMgQIAAAQIECBAg0GACFy5cCN/85jejdj06OhqOHj0a1UMxAQIECBAgQIAAAQKrExCArM7L1QQIECBAgAABAgQINJjA/Px82Lp1a9SuDxw4EL773e9G9VBMgAABAgQIECBAgMDqBAQgq/NyNQECBAgQIECAAAECDSawtLQU0geZv//++5l3vm3btnDjxo3M9QoJECBAgAABAgQIEFi9gABk9WYqCBAgQIAAAQIECBBoMIHu7u5w+fLlzLtuamoKi4uLYePGjZl7KCRAgAABAgQIECBAYHUCApDVebmaAAECBAgQIECAAIEGFBgcHAyvvfZa1M5nZmZCZ2dnVA/FBAgQIECAAAECBAg8u4AA5NmtXEmAAAECBAgQIECAQIMKnD17NnzrW9+K2v3p06fDoUOHonooJkCAAAECBAgQIEDg2QUEIM9u5UoCBAgQIECAAAECBBpUYHZ2NuzcuTNq94cPHw6vv/56VA/FBAgQIECAAAECBAg8u4AA5NmtXEmAAAECBAgQIECAQIMKfPjhh2HTpk3ho48+yizQ1dUVrly5krleIQECBAgQIECAAAECqxMQgKzOy9UECBAgQIAAAQIECDSoQEdHR7h+/Xrm3acPQL9//35Yt25d5h4KCRAgQIAAAQIECBB4dgEByLNbuZIAAQIECBAgQIAAgQYW6OvrC2+88UaUwNzcXGhvb4/qoZgAAQIECBAgQIAAgWcTEIA8m5OrCBAgQIAAAQIECBBocIGxsbFw5MiRKIXJycmwf//+qB6KCRAgQIAAAQIECBB4NgEByLM5uYoAAQIECBAgQIAAgQYXmJqaCi+99FKUwrFjx8J3vvOdqB6KCRAgQIAAAQIECBB4NgEByLM5uYoAAQIECBAgQIAAgQYXuHPnTnj++eejFPbu3RsuXLgQ1UMxAQIECBAgQIAAAQLPJiAAeTYnVxEgQIAAAQIECBAgQCC0traGW7duZZZoa2sL7733XuZ6hQQIECBAgAABAgQIPLuAAOTZrVxJgAABAgQIECBAgECDC/T09IQ333wzSuHu3bthy5YtUT0UEyBAgAABAgQIECDwdAEByNONXEGAAAECBAgQIECAAIEfCwwPD4fjx49HaUxPT4fdu3dH9VBMgAABAgQIECBAgMDTBQQgTzdyBQECBAgQIECAAAECBH4sMDExEV5++eUojbGxsTA4OBjVQzEBAgQIECBAgAABAk8XEIA83cgVBAgQIECAAAECBAgQ+LHAjRs3wpe//OUojf7+/nD27NmoHooJECBAgAABAgQIEHi6gADk6UauIECAAAECBAgQIECAwI8Fnjx5EjZv3hwePnyYWWTHjh3hnXfeyVyvkAABAgQIECBAgACBZxMQgDybk6sIECBAgAABAgQIECDwY4HOzs7w9ttvZ9ZYv359ePDgQdiwYUPmHgoJECBAgAABAgQIEHi6gADk6UauIECAAAECBAgQIECAwP8vMDAwEM6cORMlcu3atfDiiy9G9VBMgAABAgQIECBAgMDKAgIQJ4QAAQIECBAgQIAAAQKrEDh16lR45ZVXVlHxs5eOj4+Hvr6+qB6KCRAgQIAAAQIECBAQgDgDBAgQIECAAAECBAgQyE3gypUr4etf/3pUv6GhoXDixImoHooJECBAgAABAgQIEBCAOAMECBAgQIAAAQIECBDITWBxcTE899xzYWlpKXPPPXv2hLfeeitzvUICBAgQIECAAAECBJ4u4Cewnm7kCgIECBAgQIAAAQIECHxKoL29Pbz77ruZVZqbm8Pt27dDU1NT5h4KCRAgQIAAAQIECBBYWUAA4oQQIECAAAECBAgQIEBglQK9vb3h/Pnzq6z69OULCwvhhRdeiOqhmAABAgQIECBAgACBLxYQgDgdBAgQIECAAAECBAgQWKXAyMhIePXVV1dZ9enLL168GHp6eqJ6KCZAgAABAgQIECBAQADiDBAgQIAAAQIECBAgQCA3gTS82LdvX1S/0dHRcPTo0ageigkQIECAAAECBAgQEIA4AwQIECBAgAABAgQIEMhNYH5+PmzdujWq34EDB8J3v/vdqB6KCRAgQIAAAQIECBAQgDgDBAgQIECAAAECBAgQyE1gaWkppA8yf//99zP33LZtW7hx40bmeoUECBAgQIAAAQIECKws4BkgTggBAgQIECBAgAABAgQyCHR3d4fLly9nqPxJSVNTU1hcXAwbN27M3EMhAQIECBAgQIAAAQJfLCAAcToIECBAgAABAgQIECCQQWBwcDC89tprGSp/WjIzMxM6OzujeigmQIAAAQIECBAgQODzBQQgTgYBAgQIECBAgAABAgQyCIyPj4eDBw9mqPxpyenTp8OhQ4eieigmQIAAAQIECBAgQEAA4gwQIECAAAECBAgQIEAgN4HZ2dmwc+fOqH6HDx8Or7/+elQPxQQIECBAgAABAgQICECcAQIECBAgQIAAAQIECOQm8OGHH4ZNmzaFjz76KHPPrq6ucOXKlcz1CgkQIECAAAECBAgQ+GIBP4HldBAgQIAAAQIECBAgQCCjQEdHR7h+/XrG6vDjB6Dfv38/rFu3LnMPhQQIECBAgAABAgQIfL6AAMTJIECAAAECBAgQIECAQEaBvr6+8MYbb2Ss/knZ3NxcaG9vj+qhmAABAgQIECBAgACBnxUQgDgVBAgQIECAAAECBAgQyCgwNjYWjhw5krH6J2WTk5Nh//79UT0UEyBAgAABAgQIECAgAHEGCBAgQIAAAQIECBAgkJvA1NRUeOmll6L6DQ8Ph5GRkageigkQIECAAAECBAgQEIA4AwQIECBAgAABAgQIEMhN4M6dO+H555+P6rd3795w4cKFqB6KCRAgQIAAAQIECBAQgDgDBAgQIECAAAECBAgQyFWgtbU13Lp1K3PPtra28N5772WuV0iAAAECBAgQIECAwOcLeAaIk0GAAAECBAgQIECAAIEIgZ6envDmm29GdAjh7t27YcuWLVE9FBMgQIAAAQIECBAg8GkBAYgTQYAAAQIECBAgQIAAgQiB9Bkex48fj+gQwvT0dNi9e3dUD8UECBAgQIAAAQIECAhAnAECBAgQIECAAAECBAjkJjAxMRFefvnlqH5jY2NhcHAwqodiAgQIECBAgAABAgQEIM4AAQIECBAgQIAAAQIEchOYm5sL27dvj+rX398fzp49G9VDMQECBAgQIECAAAECAhBngAABAgQIECBAgAABArkJPHnyJGzevDk8fPgwc88dO3aEd955J3O9QgIECBAgQIAAAQIEflbAM0CcCgIECBAgQIAAAQIECEQKdHZ2hrfffjtzl/Xr14cHDx6EDRs2ZO6hkAABAgQIECBAgACBTwsIQJwIAgQIECBAgAABAgQIRAoMDAyEM2fORHW5du1aePHFF6N6KCZAgAABAgQIECBA4KcCAhCngQABAgQIECBAgAABApECp06dCq+88kpUl/Hx8dDX1xfVQzEBAgQIECBAgAABAgIQZ4AAAQIECBAgQIAAAQK5CczMzIRdu3ZF9RsaGgonTpyI6qGYAAECBAgQIECAAAEBiDNAgAABAgQIECBAgACB3ATS53ekD0JfWlrK3HPPnj3hrbfeylyvkAABAgQIECBAgACBTwv4CSwnggABAgQIECBAgAABAjkItLe3h3fffTdzp+bm5nD79u3Q1NSUuYdCAgQIECBAgAABAgR+KiAAcRoIECBAgAABAgQIECCQg0Bvb284f/58VKeFhYXwwgsvRPVQTIAAAQIECBAgQIDATwQEIE4CAQIECBAgQIAAAQIEchAYGRkJr776alSnixcvhp6enqgeigkQIECAAAECBAgQEIA4AwQIECBAgAABAgQIEMhNIA0v9u3bF9VvdHQ0HD16NKqHYgIECBAgQIAAAQIEBCDOAAECBAgQIECAAAECBHITmJ+fD1u3bo3ql/6M1sTERFQPxQQIECBAgAABAgQICECcAQIECBAgQIAAAQIECOQmsLS0FFpaWsK9e/cy99y2bVu4ceNG5nqFBAgQIECAAAECBAj8VMAzQJwGAgQIECBAgAABAgQI5CTQ3d0dLl++nLlbU1NTWFxcDBs3bszcQyEBAgQIECBAgAABAj8REIA4CQQIECBAgAABAgQIEMhJYHBwMLz22mtR3WZmZkJnZ2dUD8UECBAgQIAAAQIECAhAnAECBAgQIECAAAECBAjkJjA+Ph4OHjwY1e/06dPh0KFDUT0UEyBAgAABAgQIECAgAHEGCBAgQIAAAQIECBAgkJvA7Oxs2LlzZ1S/gYGBcOrUqageigkQIECAAAECBAgQEIA4AwQIECBAgAABAgQIEMhN4NGjR2HTpk3h8ePHmXt2dXWFK1euZK5XSIAAAQIECBAgQIDATwQ8A8RJIECAAAECBAgQIECAQI4CHR0d4fr165k7pg9Av3//fli3bl3mHgoJECBAgAABAgQIEBCAOAMECBAgQIAAAQIECBDIVaCvry+88cYbUT3n5uZCe3t7VA/FBAgQIECAAAECBBpdwDdAGv0E2D8BAgQIECBAgAABArkKjI2NhSNHjkT1nJycDPv374/qoZgAAQIECBAgQIBAowsIQBr9BNg/AQIECBAgQIAAAQK5CkxNTYWXXnopqufw8HAYGRmJ6qGYAAECBAgQIECAQKMLCEAa/QTYPwECBAgQIECAAAECuQrcuXMnPP/881E99+7dGy5cuBDVQzEBAgQIECBAgACBRhcQgDT6CbB/AgQIECBAgAABAgRyF2htbQ23bt3K3LetrS289957mesVEiBAgAABAgQIECDgIejOAAECBAgQIECAAAECBHIX6OnpCW+++WZU37t374YtW7ZE9VBMgAABAgQIECBAoJEFfAOkkadv7wQIECBAgAABAgQI1EQgfYbH8ePHo3pPT0+H3bt3R/VQTIAAAQIECBAgQKCRBQQgjTx9eydAgAABAgQIECBAoCYCExMT4eWXX47qPTY2FgYHB6N6KCZAgAABAgQIECDQyAICkEaevr0TIECAAAECBAgQIFATgbm5ubB9+/ao3v39/eHs2bNRPRQTIECAAAECBAgQaGQBAUgjT9/eCRAgQIAAAQIECBCoicCTJ0/C5s2bw8OHDzP37+joCLOzs5nrFRIgQIAAAQIECBBodAEBSKOfAPsnQIAAAQIECBAgQKAmAl1dXeHq1auZe69fvz48ePAgbNiwIXMPhQQIECBAgAABAgQaWUAA0sjTt3cCBAgQIECAAAECBGomMDAwEM6cORPV/9q1a+HFF1+M6qGYAAECBAgQIECAQKMKCEAadfL2TYAAAQIECBAgQIBATQVOnToVXnnllah7jI+Ph76+vqgeigkQIECAAAECBAg0qoAApFEnb98ECBAgQIAAAQIECNRUYGZmJuzatSvqHkNDQ+HEiRNRPRQTIECAAAECBAgQaFQBAUijTt6+CRAgQIAAAQIECBCoqUD6/I70QehLS0uZ79Pd3R2mpqYy1yskQIAAAQIECBAg0MgCApBGnr69EyBAgAABAgQIECBQU4Ft27aFmzdvZr5Hc3NzuH37dmhqasrcQyEBAgQIECBAgACBRhUQgDTq5O2bAAECBAgQIECAAIGaC/T29obz589H3WdhYSG88MILUT0UEyBAgAABAgQIEGhEAQFII07dngkQIECAAAECBAgQqIvAyMhIePXVV6PudfHixdDT0xPVQzEBAgQIECBAgACBRhQQgDTi1O2ZAAECBAgQIECAAIG6CKThxb59+6LuNTo6Go4ePRrVQzEBAgQIECBAgACBRhQQgDTi1O2ZAAECBAgQIECAAIG6CMzPz4etW7dG3Sv9Ga2JiYmoHooJECBAgAABAgQINKKAAKQRp27PBAgQIECAAAECBAjURWBpaSm0tLSEe/fuZb5f+iD1GzduZK5XSIAAAQIECBAgQKBRBQQgjTp5+yZAgAABAgQIECBAoC4C3d3d4fLly5nv1dTUFBYXF8PGjRsz91BIgAABAgQIECBAoBEFBCCNOHV7JkCAAAECBAgQIECgbgJDQ0Ph5MmTUfebmZkJnZ2dUT0UEyBAgAABAgQIEGg0AQFIo03cfgkQIECAAAECBAgQqKvA+Ph4OHjwYNQ9T58+HQ4dOhTVQzEBAgQIECBAgACBRhMQgDTaxO2XAAECBAgQIECAAIG6CszOzoadO3dG3XNgYCCcOnUqqodiAgQIECBAgAABAo0mIABptInbLwECBAgQIECAAAECdRV49OhR2LRpU3j8+HHm+3Z1dYUrV65krldIgAABAgQIECBAoBEFBCCNOHV7JkCAAAECBAgQIECgrgIdHR3h+vXrme+ZPgD9/v37Yd26dZl7KCRAgAABAgQIECDQaAICkEabuP0SIECAAAECBAgQIFB3gf7+/nDu3Lmo+87NzYX29vaoHooJECBAgAABAgQINJKAAKSRpm2vBAgQIECAAAECBAisicDY2Fg4cuRI1L0nJyfD/v37o3ooJkCAAAECBAgQINBIAgKQRpq2vRIgQIAAAQIECBAgsCYCU1NT4aWXXoq69/DwcBgZGYnqoZgAAQIECBAgQIBAIwkIQBpp2vZKgAABAgQIECBAgMCaCNy5cyc8//zzUffeu3dvuHDhQlQPxQQIECBAgAABAgQaSUAA0kjTtlcCBAgQIECAAAECBNZMoLW1Ndy6dSvz/dP6+fn5zPUKCRAgQIAAAQIECDSagACk0SZuvwQIECBAgAABAgQIrIlA+g2OS5cuRd377t27YcuWLVE9FBMgQIAAAQIECBBoFAEBSP+gZPgAACAASURBVKNM2j4JECBAgAABAgQIEFhTgfQZHsePH49aw/T0dNi9e3dUD8UECBAgQIAAAQIEGkVAANIok7ZPAgQIECBAgAABAgTWVGBiYiK8/PLLUWsYGxsLg4ODUT0UEyBAgAABAgQIEGgUAQFIo0zaPgkQIECAAAECBAgQWFOBubm5sH379qg19Pf3h7Nnz0b1UEyAAAECBAgQIECgUQQEII0yafskQIAAAQIECBAgQGBNBZ48eRI2b94cHj58mHkdHR0dYXZ2NnO9QgIECBAgQIAAAQKNJCAAaaRp2ysBAgQIECBAgAABAmsq0NXVFa5evZp5DevXrw8PHjwIGzZsyNxDIQECBAgQIECAAIFGERCANMqk7ZMAAQIECBAgQIAAgTUXGBgYCGfOnIlax7Vr18KLL74Y1UMxAQIECBAgQIAAgUYQEIA0wpTtkQABAgQIECBAgACBQgicPn06HD58OGot4+Pjoa+vL6qHYgIECBAgQIAAAQKNICAAaYQp2yMBAgQIECBAgAABAoUQmJmZCbt27Ypay9DQUDhx4kRUD8UECBAgQIAAAQIEGkFAANIIU7ZHAgQIECBAgAABAgQKIZA+vyN9EPrS0lLm9XR3d4epqanM9QoJECBAgAABAgQINIqAAKRRJm2fBAgQIECAAAECBAgUQmDbtm3h5s2bmdfS3Nwcbt++HZqamjL3UEiAAAECBAgQIECgEQQEII0wZXskQIAAAQIECBAgQKAwAr29veH8+fNR65mfnw+tra1RPRQTIECAAAECBAgQqLqAAKTqE7Y/AgQIECBAgAABAgQKJTA6OhqOHTsWtaaLFy+Gnp6eqB6KCRAgQIAAAQIECFRdQABS9QnbHwECBAgQIECAAAEChRJIw4t9+/ZFrSkNUY4ePRrVQzEBAgQIECBAgACBqgsIQKo+YfsjQIAAAQIECBAgQKBQAunPV23dujVqTenPaE1MTET1UEyAAAECBAgQIECg6gICkKpP2P4IECBAgAABAgQIECiUwNLSUmhpaQn37t3LvK70Qeo3btzIXK+QAAECBAgQIECAQCMICEAaYcr2SIAAAQIECBAgQIBAoQS6u7vD5cuXM6+pqakpLC4uho0bN2buoZAAAQIECBAgQIBA1QUEIFWfsP0RIECAAAECBAgQIFA4gaGhoXDy5Mmodc3MzITOzs6oHooJECBAgAABAgQIVFlAAFLl6dobAQIECBAgQIAAAQKFFBgfHw8HDx6MWtvp06fDoUOHonooJkCAAAECBAgQIFBlAQFIladrbwQIECBAgAABAgQIFFJgdnY27Ny5M2ptAwMD4dSpU1E9FBMgQIAAAQIECBCosoAApMrTtTcCBAgQIECAAAECBAop8OjRo7Bp06bw+PHjzOvr6uoKV65cyVyvkAABAgQIECBAgEDVBQQgVZ+w/REgQIAAAQIECBAgUEiBjo6OcP369cxrSx+Afv/+/bBu3brMPRQSIECAAAECBAgQqLKAAKTK07U3AgQIECBAgAABAgQKK9Df3x/OnTsXtb65ubnQ3t4e1UMxAQIECBAgQIAAgaoKCECqOln7IkCAAAECBAgQIECg0AJjY2PhyJEjUWucnJwM+/fvj+qhmAABAgQIECBAgEBVBQQgVZ2sfREgQIAAAQIECBAgUGiB6enpsGfPnqg1Dg8Ph5GRkageigkQIECAAAECBAhUVUAAUtXJ2hcBAgQIECBAgAABAoUWuHv3bmhpaYla4969e8OFCxeieigmQIAAAQIECBAgUFUBAUhVJ2tfBAgQIECAAAECBAgUXqC1tTXcunUr8zrT+vn5+cz1CgkQIECAAAECBAhUWUAAUuXp2hsBAgQIECBAgAABAoUWSL/BcenSpag13rlzJzQ3N0f1UEyAAAECBAgQIECgigICkCpO1Z4IECBAgAABAgQIECiFQPoMj+PHj0etdWpqKnR3d0f1UEyAAAECBAgQIECgigICkCpO1Z4IECBAgAABAgQIECiFwOTkZDhw4EDUWsfGxsLg4GBUD8UECBAgQIAAAQIEqiggAKniVO2JAAECBAgQIECAAIFSCMzNzYXt27dHrbW/vz+cPXs2qodiAgQIECBAgAABAlUUEIBUcar2RIAAAQIECBAgQIBAKQSePHkSNm/eHB4+fJh5vR0dHWF2djZzvUICBAgQIECAAAECVRUQgFR1svZFgAABAgQIECBAgEApBLq6usLVq1czr3X9+vXhwYMHYcOGDZl7KCRAgAABAgQIECBQRQEBSBWnak8ECBAgQIAAAQIECJRGYGBgIJw5cyZqvek3QNJvgngRIECAAAECBAgQIPBTAQGI00CAAAECBAgQIECAAIE1FDh9+nQ4fPhw1ArGx8dDX19fVA/FBAgQIECAAAECBKomIACp2kTthwABAgQIECBAgACBUgnMzMyEXbt2Ra15aGgonDhxIqqHYgIECBAgQIAAAQJVExCAVG2i9kOAAAECBAgQIECAQKkE0ud3pA9CX1payrzu7u7uMDU1lbleIQECBAgQIECAAIEqCghAqjhVeyJAgAABAgQIECBAoFQC27ZtCzdv3sy85ubm5nD79u3Q1NSUuYdCAgQIECBAgAABAlUTEIBUbaL2Q4AAAQIECBAgQIBA6QR6e3vD+fPno9Y9Pz8fWltbo3ooJkCAAAECBAgQIFAlAQFIlaZpLwQIECBAgAABAgQIlFJgdHQ0HDt2LGrtFy9eDD09PVE9FBMgQIAAAQIECBCokoAApErTtBcCBAgQIECAAAECBEopkIYX+/bti1p7GqIcPXo0qodiAgQIECBAgAABAlUSEIBUaZr2QoAAAQIECBAgQIBAKQUWFhZCW1tb1NrTn9GamJiI6qGYAAECBAgQIECAQJUEBCBVmqa9ECBAgAABAgQIECBQSoGlpaXQ0tIS7t27l3n96YPUb9y4kbleIQECBAgQIECAAIGqCQhAqjZR+yFAgAABAgQIECBAoJQC3d3d4fLly5nX3tTUFBYXF8PGjRsz91BIgAABAgQIECBAoEoCApAqTdNeCBAgQIAAAQIECBAorcDQ0FA4efJk1PpnZmZCZ2dnVA/FBAgQIECAAAECBKoiIACpyiTtgwABAgQIECBAgACBUguMj4+HgwcPRu3h1KlTYWBgIKqHYgIECBAgQIAAAQJVERCAVGWS9kGAAAECBAgQIECAQKkFrl27Fnbs2BG1hzT8SEMQLwIECBAgQIAAAQIEQhCAOAUECBAgQIAAAQIECBAogMCjR4/Cpk2bwuPHjzOvpqurK1y5ciVzvUICBAgQIECAAAECVRIQgFRpmvZCgAABAgQIECBAgECpBTo6OsL169cz7yF9APr9+/fDunXrMvdQSIAAAQIECBAgQKAqAgKQqkzSPggQIECAAAECBAgQKL1Af39/OHfuXNQ+5ubmQnt7e1QPxQQIECBAgAABAgSqICAAqcIU7YEAAQIECBAgQIAAgUoIjI2NhSNHjkTtZWJiIvT29kb1UEyAAAECBAgQIECgCgICkCpM0R4IECBAgAABAgQIEKiEwPT0dNizZ0/UXoaHh8PIyEhUD8UECBAgQIAAAQIEqiAgAKnCFO2BAAECBAgQIECAAIFKCNy9eze0tLRE7WXv3r3hwoULUT0UEyBAgAABAgQIEKiCgACkClO0BwIECBAgQIAAAQIEKiPQ1tYWFhYWMu+ntbU1zM/PZ65XSIAAAQIECBAgQKAqAgKQqkzSPggQIECAAAECBAgQqIRA+g2OS5cuRe3lzp07obm5OaqHYgIECBAgQIAAAQJlFxCAlH2C1k+AAAECBAgQIECAQKUE0md4HD9+PGpPU1NTobu7O6qHYgIECBAgQIAAAQJlFxCAlH2C1k+AAAECBAgQIECAQKUEJicnw4EDB6L2NDY2FgYHB6N6KCZAgAABAgQIECBQdgEBSNknaP0ECBAgQIAAAQIECFRKYG5uLmzfvj1qT/39/eHs2bNRPRQTIECAAAECBAgQKLuAAKTsE7R+AgQIECBAgAABAgQqJfDkyZPw3HPPhQ8++CDzvjo6OsLs7GzmeoUECBAgQIAAAQIEqiAgAKnCFO2BAAECBAgQIECAAIFKCXR1dYWrV69m3tP69evDgwcPwoYNGzL3UEiAAAECBAgQIECg7AICkLJP0PoJECBAgAABAgQIEKicwMDAQDhz5kzUvtJvgKTfBPEiQIAAAQIECBAg0KgCApBGnbx9EyBAgAABAgQIECBQWIHTp0+Hw4cPR61vfHw89PX1RfVQTIAAAQIECBAgQKDMAgKQMk/P2gkQIECAAAECBAgQqKTAzMxM2LVrV9TeBgcHw9jYWFQPxQQIECBAgAABAgTKLCAAKfP0rJ0AAQIECBAgQIAAgUoKpM/v2Lx5c1haWsq8v+7u7jA1NZW5XiEBAgQIECBAgACBsgsIQMo+QesnQIAAAQIECBAgQKCSAtu2bQs3b97MvLfm5uZw+/bt0NTUlLmHQgIECBAgQIAAAQJlFhCAlHl61k6AAAECBAgQIECAQGUFent7w/nz56P2Nz8/H1pbW6N6KCZAgAABAgQIECBQVgEBSFknZ90ECBAgQIAAAQIECFRaYHR0NBw7dixqjxcvXgw9PT1RPRQTIECAAAECBAgQKKuAAKSsk7NuAgQIECBAgAABAgQqLZCGF/v27Yva48jISBgeHo7qoZgAAQIECBAgQIBAWQUEIGWdnHUTIECAAAECBAgQIFBpgYWFhdDW1ha1x/RntCYmJqJ6KCZAgAABAgQIECBQVgEBSFknZ90ECBAgQIAAAQIECFRaYGlpKbS0tIR79+5l3mf6IPUbN25krldIgAABAgQIECBAoMwCApAyT8/aCRAgQIAAAQIECBCotMCePXvC9PR05j02NTWFxcXFsHHjxsw9FBIgQIAAAQIECBAoq4AApKyTs24CBAgQIECAAAECBCovMDQ0FE6ePBm1z5mZmdDZ2RnVQzEBAgQIECBAgACBMgoIQMo4NWsmQIAAAQIECBAgQKAhBMbHx8PBgwej9nrq1KkwMDAQ1UMxAQIECBAgQIAAgTIKCEDKODVrJkCAAAECBAgQIECgIQSuXbsWduzYEbXXNPxIQxAvAgQIECBAgAABAo0mIABptInbLwECBAgQIECAAAECpRF49OhR2LRpU3j8+HHmNXd1dYUrV65krldIgAABAgQIECBAoKwCApCyTs66CRAgQIAAAQIECBBoCIH0GyDpN0GyvtIHoN+/fz+sW7cuawt1BAgQIECAAAECBEopIAAp5dgsmgABAgQIECBAgACBRhHo7+8P586di9ru3NxcaG9vj+qhmAABAgQIECBAgEDZBAQgZZuY9RIgQIAAAQIECBAg0FACY2Nj4ciRI1F7npiYCL29vVE9FBMgQIAAAQIECBAom4AApGwTs14CBAgQIECAAAECBBpKYHp6OuzZsydqz8PDw2FkZCSqh2ICBAgQIECAAAECZRMQgJRtYtZLgAABAgQIECBAgEBDCdy9eze0tLRE7Xnv3r3hwoULUT0UEyBAgAABAgQIECibgACkbBOzXgIECBAgQIAAAQIEGk6gra0tLCwsZN53a2trmJ+fz1yvkAABAgQIECBAgEAZBQQgZZyaNRMgQIAAAQIECBAg0FAC6Tc4Ll26FLXnO3fuhObm5qgeigkQIECAAAECBAiUSUAAUqZpWSsBAgQIECBAgAABAg0pcOzYsTA6Ohq196mpqdDd3R3VQzEBAgQIECBAgACBMgkIQMo0LWslQIAAAQIECBAgQKAhBSYnJ8OBAwei9j42NhYGBwejeigmQIAAAQIECBAgUCYBAUiZpmWtBAgQIECAAAECBAg0pMDc3FzYvn171N77+vrC+Ph4VA/FBAgQIECAAAECBMokIAAp07SslQABAgQIECBAgACBhhR48uRJeO6558IHH3yQef8dHR1hdnY2c71CAgQIECBAgAABAmUTEICUbWLWS4AAAQIECBAgQIBAQwp0dXWFq1evZt77+vXrw4MHD8KGDRsy91BIgAABAgQIECBAoEwCApAyTctaCRAgQIAAAQIECBBoWIHDhw+H06dPR+0//QZI+k0QLwIECBAgQIAAAQKNICAAaYQp2yMBAgQIECBAgAABAqUXSMOPNASJeaXPAEmfBeJFgAABAgQIECBAoBEEBCCNMGV7JECAAAECBAgQIECg9AIzMzNh165dUfsYHBwMY2NjUT0UEyBAgAABAgQIECiLgACkLJOyTgIECBAgQIAAAQIEGlogfX7H5s2bw9LSUmaH7u7uMDU1lbleIQECBAgQIECAAIEyCQhAyjQtayVAgAABAgQIECBAoKEFtm3bFm7evJnZoLm5Ody+fTs0NTVl7qGQAAECBAgQIECAQFkEBCBlmZR1EiBAgAABAgQIECDQ8AIHDhwIk5OTUQ7z8/OhtbU1qodiAgQIECBAgAABAmUQEICUYUrWSIAAAQIECBAgQIAAgURgdHQ0HDt2LMri4sWLoaenJ6qHYgIECBAgQIAAAQJlEBCAlGFK1kiAAAECBAgQIECAAIFEIA0v9u3bF2UxMjIShoeHo3ooJkCAAAECBAgQIFAGAQFIGaZkjQQIECBAgAABAgQIEEgEFhYWQltbW5RFb29vmJiYiOqhmAABAgQIECBAgEAZBAQgZZiSNRIgQIAAAQIECBAgQCARWFpaCi0tLeHevXuZPdIHqd+4cSNzvUICBAgQIECAAAECZREQgJRlUtZJgAABAgQIECBAgACBRGDPnj1heno6s0VTU1NYXFwMGzduzNxDIQECBAgQIECAAIEyCAhAyjAlayRAgAABAgQIECBAgMDHAkNDQ+HkyZNRHjMzM6GzszOqh2ICBAgQIECAAAECRRcQgBR9QtZHgAABAgQIECBAgACBZQLnzp0L/f39USanTp0KAwMDUT0UEyBAgAABAgQIECi6gACk6BOyPgIECBAgQIAAAQIECCwTuHbtWtixY0eUSRp+pCGIFwECBAgQIECAAIEqCwhAqjxdeyNAgAABAgQIECBAoHICjx49Cps2bQqPHz/OvLf056/Sn8HyIkCAAAECBAgQIFBlAQFIladrbwQIECBAgAABAgQIVFIg/QZI+k2QrK/0Aej3798P69aty9pCHQECBAgQIECAAIHCCwhACj8iCyRAgAABAgQIECBAgMCnBdJngKTPAol5zc3Nhfb29pgWagkQIECAAAECBAgUWkAAUujxWBwBAgQIECBAgAABAgR+VuDkyZNhaGgoimZiYiL09vZG9VBMgAABAgQIECBAoMgCApAiT8faCBAgQIAAAQIECBAg8DkC09PTYc+ePVE2w8PDYWRkJKqHYgIECBAgQIAAAQJFFhCAFHk61kaAAAECBAgQIECAAIHPEbh7925oaWmJsunp6QkXL16M6qGYAAECBAgQIECAQJEFBCBFno61ESBAgAABAgQIECBA4AsE2trawsLCQmaf1tbWMD8/n7leIQECBAgQIECAAIGiCwhAij4h6yNAgAABAgQIECBAgMDnCOzduzdcunQpyubOnTuhubk5qodiAgQIECBAgAABAkUVEIAUdTLWRYAAAQIECBAgQIAAgRUEjh07FkZHR6OMpqamQnd3d1QPxQQIECBAgAABAgSKKiAAKepkrIsAAQIECBAgQIAAAQIrCExOToYDBw5EGY2NjYXBwcGoHooJECBAgAABAgQIFFVAAFLUyVgXAQIECBAgQIAAAQIEVhCYm5sL27dvjzLq6+sL4+PjUT0UEyBAgAABAgQIECiqgACkqJOxLgIECBAgQIAAAQIECKwg8OTJk/Dcc8+FDz74ILNTR0dHmJ2dzVyvkAABAgQIECBAgECRBQQgRZ6OtREgQIAAAQIECBAgQGAFga6urnD16tXMRuvXrw8PHjwIGzZsyNxDIQECBAgQIECAAIGiCghAijoZ6yJAgAABAgQIECBAgMBTBA4fPhxOnz4d5ZR+AyT9JogXAQIECBAgQIAAgaoJCECqNlH7IUCAAAECBAgQIECgYQTS8CMNQWJe6TNA0meBeBEgQIAAAQIECBComoAApGoTtR8CBAgQIECAAAECBBpGIP35q/RnsGJeg4ODYWxsLKaFWgIECBAgQIAAAQKFFBCAFHIsFkWAAAECBAgQIECAAIGnC6QPQP/Sl74UlpaWnn7xF1zR3d0dpqamMtcrJECAAAECBAgQIFBUAQFIUSdjXQQIECBAgAABAgQIEHgGgW3btoWbN28+w5Wff8mWLVvCnTt3QlNTU+YeCgkQIECAAAECBAgUUUAAUsSpWBMBAgQIECBAgAABAgSeUeDAgQNhcnLyGa/+/Mvm5+dDa2trVA/FBAgQIECAAAECBIomIAAp2kSshwABAgQIECBAgAABAqsQGB0dDceOHVtFxc9eevHixdDT0xPVQzEBAgQIECBAgACBogkIQIo2EeshQIAAAQIECBAgQIDAKgQuXboU9u7du4qKn710ZGQkDA8PR/VQTIAAAQIECBAgQKBoAgKQok3EeggQIECAAAECBAgQILAKgYWFhdDW1raKip+9tLe3N0xMTET1UEyAAAECBAgQIECgaAICkKJNxHoIECBAgAABAgQIECCwCoGlpaXQ0tIS7t27t4qqT1/a3t4e5ubmMtcrJECAAAECBAgQIFBEAQFIEadiTQQIECBAgAABAgQIEFiFwJ49e8L09PQqKj59aVNTU1hcXAwbN27M3EMhAQIECBAgQIAAgaIJCECKNhHrIUCAAAECBAgQIECAwCoFhoaGwsmTJ1dZ9enLZ2ZmQmdnZ1QPxQQIECBAgAABAgSKJCAAKdI0rIUAAQIECBAgQIAAAQIZBM6dOxf6+/szVP605NSpU2FgYCCqh2ICBAgQIECAAAECRRIQgBRpGtZCgAABAgQIECBAgACBDALXrl0LO3bsyFD505I0/EhDEC8CBAgQIECAAAECVREQgFRlkvZBgAABAgQIECBAgEDDCjx69Chs2rQpPH78OLNB+vNX6c9geREgQIAAAQIECBCoioAApCqTtA8CBAgQIECAAAECBBpaIP0GSPpNkKyv9AHo9+/fD+vWrcvaQh0BAgQIECBAgACBQgkIQAo1DoshQIAAAQIECBAgQIBANoH0GSDps0BiXnNzc6G9vT2mhVoCBAgQIECAAAEChREQgBRmFBZCgAABAgQIECBAgACB7AInT54MQ0ND2RsklRMTE6G3tzeqh2ICBAgQIECAAAECRREQgBRlEtZBgAABAgQIECBAgACBCIHp6emwZ8+eiA4hDA8Ph5GRkageigkQIECAAAECBAgURUAAUpRJWAcBAgQIECBAgAABAgQiBO7evRtaWloiOoTQ09MTLl68GNVDMQECBAgQIECAAIGiCAhAijIJ6yBAgAABAgQIECBAgECkQFtbW1hYWMjcpbW1NczPz2euV0iAAAECBAgQIECgSAICkCJNw1oIECBAgAABAgQIECAQIbB3795w6dKliA4h3LlzJzQ3N0f1UEyAAAECBAgQIECgCAICkCJMwRoIECBAgAABAgQIECCQg8CxY8fC6OhoVKepqanQ3d0d1UMxAQIECBAgQIAAgSIICECKMAVrIECAAAECBAgQIECAQA4Ck5OT4cCBA1GdxsbGwuDgYFQPxQQIECBAgAABAgSKICAAKcIUrIEAAQIECBAgQIAAAQI5CNy8eTNs27YtqlNfX18YHx+P6qGYAAECBAgQIECAQBEEBCBFmII1ECBAgAABAgQIECBQaIHHTx6Ha396Lfzwox8Wep0/+tGPwm/8xm+EH/4w+zp/+Vd+OZz9p2cLvc96Le7n/9zPh45f7Ajrf259vW7pPgQIECBAgAABAjkKCEByxNSKAAECBAgQIECAAIHqCaThx9f+8GvhnX//TvU2Z0dPFdj5l3aGt3/7bSHIU6VcQIAAAQIECBAonoAApHgzsSICBAgQIECAAAECBAok8Cc/+JPw1T/4aoFWZCn1Fvj+P/h++MovfaXet3U/AgQIECBAgACBSAEBSCSgcgIECBAgQIAAAQIEqi3wx+/9cfj1f/rr1d6k3a0o8K+/9a/Dr239NUoECBAgQIAAAQIlExCAlGxglkuAAAECBAgQIECAQH0FBCD19S7i3QQgRZyKNREgQIAAAQIEni4gAHm6kSsIECBAgAABAgQIEGhgAQFIAw//460LQJwBAgQIECBAgEA5BQQg5ZybVRMgQIAAAQIECBAgUCcBAUidoAt8GwFIgYdjaQQIECBAgACBFQQEII4HAQIECBAgQIAAAQIEVhAQgDgeAhBngAABAgQIECBQTgEBSDnnZtUECBAgQIAAAQIECNRJQABSJ+gC30YAUuDhWBoBAgQIECBAYAUBAYjjQYAAAQIECBAgQIAAgRUEBCCOhwDEGSBAgAABAgQIlFNAAFLOuVk1AQIECBAgQIAAAQJ1EhCA1Am6wLcRgBR4OJZGgAABAgQIEFhBQADieBAgQIAAAQIECBAgQGAFAQGI4yEAcQYIECBAgAABAuUUEICUc25WTYAAAQIECBAgQIBAnQQEIHWCLvBtBCAFHo6lESBAgAABAgRWEBCAOB4ECBAgQIAAAQIECBBYQUAA4ngIQJwBAgQIECBAgEA5BQQg5ZybVRMgQIAAAQIECBAgUCcBAUidoAt8GwFIgYdjaQQIECBAgACBFQQEII4HAQIECBAgQIAAAQIEVhAQgDgeAhBngAABAgQIECBQTgEBSDnnZtUECBAgQIAAAQIECNRJQABSJ+gC30YAUuDhWBoBAgQIECBAYAUBAYjjQYAAAQIECBAgQIAAgRUEBCCOhwDEGSBAgAABAgQIlFNAAFLOuVk1AQIECBAgQIAAAQJ1EhCA1Am6wLcRgBR4OJZGgAABAgQIEFhBQADieBAgQIAAAQIECBAgQGAFAQGItbmj1QAAHFZJREFU4yEAcQYIECBAgAABAuUUEICUc25WTYAAAQIECBAgQIBAnQQEIHWCLvBtBCAFHo6lESBAgAABAgRWEBCAOB4ECBAgQIAAAQIECBBYQUAA4ngIQJwBAgQIECBAgEA5BQQg5ZybVRMgQIAAAQIECBAgUCcBAUidoAt8GwFIgYdjaQQIECBAgACBFQQEII4HAQIECBAgQIAAAQIEVhAQgDgeAhBngAABAgQIECBQTgEBSDnnZtUECBAgQIAAAQIECNRJQABSJ+gC30YAUuDhWBoBAgQIECBAYAUBAYjjQYAAAQIECBAgQIAAgRUEBCCOhwDEGSBAgAABAgQIlFNAAFLOuVk1AQIECBAgQIAAAQJ1EhCA1Am6wLcRgBR4OJZGgAABAgQIEFhBQADieBAgQIAAAQIECBAgQGAFAQGI4yEAcQYIECBAgAABAuUUEICUc25WTYAAAQIECBAgQIBAnQQEIHWCLvBtBCAFHo6lESBAgAABAgRWEBCAOB4ECBAgQIAAAQIECBBYQUAA4ngIQJwBAgQIECBAgEA5BQQg5ZybVRMgQIAAAQIECBAgUCcBAUidoAt8GwFIgYdjaQQIECBAgACBFQQEII4HAQIECBAgQIAAAQIEVhAQgDgeAhBngAABAgQIECBQTgEBSDnnZtUECBAgQIAAAQIECNRJQABSJ+gC30YAUuDhWBoBAgQIECBAYAUBAYjjQYAAAQIECBAgQIAAgRUEBCCOhwDEGSBAgAABAgQIlFNAAFLOuVk1AQIECBAgQIAAAQJ1EhCA1Am6wLcRgBR4OJZGgAABAgQIEFhBQADieBAgQIAAAQIECBAgQGAFAQGI4yEAcQYIECBAgAABAuUUEICUc25WTYAAAQIECBAgQIBAnQQEIHWCLvBtBCAFHo6lESBAgAABAgRWEBCAOB4ECBAgQIAAAQIECBBYQUAA4ngIQJwBAgQIECBAgEA5BQQg5ZybVRMgQIAAAQIECBAgUCcBAUidoAt8GwFIgYdjaQQIECBAgACBFQQEII4HAQIECBAgQIAAAQIEVhAQgDgeAhBngAABAgQIECBQTgEBSDnnZtUECBAgQIAAAQIECNRJQABSJ+gC30YAUuDhWBoBAgQIECBAYAUBAYjjQYAAAQIECBAgQIAAgRUEBCCOhwDEGSBAgAABAgQIlFNAAFLOuVk1AQIECBAgQIAAAQJ1EhCA1Am6wLcRgBR4OJZGgAABAgQIEFhBQADieBAgQIAAAQIECBAgQGAFAQGI4yEAcQYIECBAgAABAuUUEICUc25WTYAAAQIECBAgQIBAnQQEIHWCLvBtBCAFHo6lESBAgAABAgRWEBCAOB4ECBAgQIAAAQIECBBYQUAA4ngIQJwBAgQIECBAgEA5BQQg5ZybVRMgQIAAAQIECBAgUCeBugcgJ5ON/dmyzfUnn/9qnTbrNp8rIABxMAgQIECAAAEC5RQQgJRzblZNgAABAgQIECBAgECdBAQgdYIu8G0EIAUejqURIECAAAECBFYQEIA4HgQIECBAgAABAgQIEFhBQADieAhAnAECBAgQIECAQDkFBCDlnJtVEyBAgAABAgQIECBQJwEBSJ2gC3wbAUiBh2NpBAgQIECAAIEVBAQgjgcBAgQIECBAgAABAgRWEBCAOB4CEGeAAAECBAgQIFBOAQFIOedm1QQIECBAgAABAgQI1ElAAFIn6ALfRgBS4OFYGgECBAgQIEBgBQEBiONBgAABAgQIECBAgACBFQQEII6HAMQZIECAAAECBAiUU0AAUs65WTUBAgQIECBAgAABAnUSEIDUCbrAtxGAFHg4lkaAAAECBAgQWEFAAOJ4ECBAgAABAgQIECBAYAWBQgYg7yQL/qMcxva3kh47l/X5H5LP13Lo+ztJj7/4cZ9/lvz57rKeB5PPf2WV93iSXH8yeT/4uO7nkj+PJO9fWGWfjJcLQDLCKSNAgAABAgQIrLGAAGSNB+D2BAgQIECAAAECBAgUW0AAkmE+ywOQG0n9f7+sx68mn/evsue/Sa6fXFbzYvL576yyR8TlApAIPKUECBAgQIAAgTUUEICsIb5bEyBAgAABAgQIECBQfAEBSIYZLQ9AfpTU/7fJ+//9uE/67Y2h5L1xFX3Hk2v/3bLr/37yeesq6iMvFYBEAionQIAAAQIECKyRgABkjeDdlgABAgQIECBAgACBcggUMgD5MLF7uEq/W8n1/zJ5p4HEJ6//MvnwK8v+/kHy+dEq+34/uf7KspoNyedvJ+/lP0/1x8nf31p2zUvJ5197xvvcTa77R8uu/cXk8+FnrM3pMgFITpDaECBAgAABAgTqLCAAqTO42xEgQIAAAQIECBAgUC6BQgYgqyW8nRT8k+T9H5YV7k4+/83VNvrM9f9H8vd/sezf0m93/Gby/mufuS4Na8aS90cf/3v6fJB/mLybnuH+/1Nyzcyy63qSz199hrocLxGA5IipFQECBAgQIECgjgICkDpiuxUBAgQIECBAgAABAuUTKH0A8meJ+T9O3veX2f+N5PM3ImeRPtj8nyfvT75RkoYZ6bM9/voX9P3sA9Y/++2TzytLA5MTyfuT4Cb9dkn68PM/H7n2VZYLQFYJ5nICBAgQIECAQEEEBCAFGYRlECBAgAABAgQIECBQTIFSByDpNy/Sb37cWWabPoT87ybvZ/n2xReNZCH5j/S5HI+XXfC0b2akNX+47PovJ5//86fM/Fry/2lw8snrP00+7Kv/ORGA1N/cHQkQIECAAAECeQgIQPJQ1IMAAQIECBAgQIAAgcoKlDYASZ/lkYYU6bM/Pnn9cvLh7yXv9Keqsr5ifk7r95Ob/uDjG69L/hxM3l9aYSHpN1fml/3/oeTzf5x14dnrBCDZ7VQSIECAAAECBNZSQACylvruTYAAAQIECBAgQIBA4QVKGYA8SVjTn6f6t8t4X0g+9yfv9Geksr5if07rneTGf7Ts5is9h+RPk+tOLbu2Nfn821kXHlcnAInzU02AAAECBAgQWCsBAchaybsvAQIECBAgQIAAAQKlEChdAJI+k+NfJu//cxlvS/L57yfvX4ggz+PntNKfzEofhv7JMz02J5+/nbzTb4N89nUp+YfvL/vHv5183hGx/ohSAUgEnlICBAgQIECAwBoKCEDWEN+tCRAgQIAAAQIECBAovkDpApDPBgdpyPBfJe/nIqzz/Dmt/zlZx5Vla/kvks/bP7O29H7pw88//Pjf/6Pkz6HkvT5iDxGlApAIPKUECBAgQIAAgTUUEICsIb5bEyBAgAABAgQIECBQfIFSBSDfSzzT9yevNDhIv/nxfIRz3j+ndS9Zyz9K3ksfr6k9+fM3P7O+/z35+4Vl/9aVfP6NiD1ElgpAIgGVEyBAgAABAgTWSEAAskbwbkuAAAECBAgQIECAQDkEShOApD8XlX7745NX+qyPvuSdPjsj6ysNKf5F8s7757T+u6TnzY8X1ZT8+TvJ+y8sW+SZ5PP/vezv/3XyOf0ZrzV6CUDWCN5tCRAgQIAAAQKRAgKQSEDlBAgQIECAAAECBAhUW6AUAci/SWZwPnl/8q2Kn0s+/73k/cuRs3kzqf/flvXI4+e00nZzyTt9SPsnr19PPuz5+C8/SP78/WX/91eTz+nD29fwJQBZQ3y3JkCAAAECBAhECAhAIvCUEiBAgAABAgQIECBQfYHCByD/NplBGiakP1WVvtJvVPzd5P2rkbP5XlKfvj955fFzWp/0Sh/Unv4M1vsf/8Om5M/B5J0GN3+UvN9Zdt/e5PNfj9xLZLkAJBJQOQECBAgQIEBgjQQEIGsE77YECBAgQIAAAQIECJRDoNAByK3EcDx5pw8N/+T1jeTD34i0/ezPaaUPH0+/hRHzc1qfXdL/mvzD/7LsHw8kn/9a8k4ffv74439fHoxEbimmXAASo6eWAAECBAgQILB2AgKQtbN3ZwIECBAgQIAAAQIESiBQ2ADkToL3T5L3w2WIfzP5vDsS9bM/p7Uu6Zf+nNavRPb9bHm67rHk/dHH/5GGH9uT979aduF/lnzuzvm+GdoJQDKgKSFAgAABAgQIFEBAAFKAIVgCAQIECBAgQIAAAQLFFShkAHI/8frHyfvPlrl9Jfm8N9Lx/0rq0weUf/JzWmm79Oe0/pPIvl9U/j8m/zG77D/TZ4yke0tf6U95fTt5P1eje6+irQBkFVguJUCAAAECBAgUSEAAUqBhWAoBAgQIECBAgAABAsUTKGQA8oeJ08Iyq7+cfP5bH4cGqyH8heTiP/9xQfqNjNeS9/Kf09qV/P2rq2n48bVpkJE+z+Npr/QnvP7gCy7alvx7+s2TArwEIAUYgiUQIECAAAECBDIICEAyoCkhQIAAAQIECBAgQKBxBAoZgJxM/Jd/+yPrONLQZOfHxekDyf+brI0+U/c7yd//4jP2+v3kuh98zrW/mfxb+zP2qPFlApAaA2tPgAABAgQIEKiRgACkRrDaEiBAgAABAgQIECBQDQEBSIY5riYASX8CK/0prOWvv5D85R8m7/T5IwV4CUAKMARLIECAAAECBAhkEBCAZEBTQoAAAQIECBAgQIBA4wgIQDLMejUByOOkf/ow9P+w7D57ks+/nuG+NSoRgNQIVlsCBAgQIECAQI0FBCA1BtaeAAECBAgQIECAAIFyC9Q9ACk31+pX/6OkJH32yCcPP0+fHTKYvDetvlWtKgQgtZLVlwABAgQIECBQWwEBSG19dSdAgAABAgQIECBAoOQCApAaD3Au6f/Pl93jV5PP+2t8z1W2F4CsEszlBAgQIECAAIGCCAhACjIIyyBAgAABAgQIECBAoJgCApAazyUNP9IQ5JPXweTDX6nxPVfZXgCySjCXEyBAgAABAgQKIiAAKcggLIMAAQIECBAgQIAAgWIKCEBqOJf/J+n9+rL+v5h8PlzD+2VsLQDJCKeMAAECBAgQILDGAgKQNR6A2xMgQIAAAQIECBAgUGwBAUiN5pM+++ON5P3vlvX/28nnHTW6X0RbAUgEnlICBAgQIECAwBoKCEDWEN+tCRAgQIAAAQIECBAovoAAJKcZ/VnSJw09niTve8l7JnkvDz9akr+/krzX5XS/HNsIQHLE1IoAAQIECBAgUEcBAUgdsd2KAAECBAgQIECAAIHyCQhAcprZyaRPGoJ83qsp+ceDyfsv53SvnNsIQHIG1Y4AAQIECBAgUCcBAUidoN2GAAECBAgQIECAAIFyCghAcprbFwUgafjxjeT91ZzuU4M2ApAaoGpJgAABAgQIEKiDgACkDshuQYAAAQIECBAgQIBAeQUEIDnNbnkAkv7M1ZeSd/qNj87k/Us53aNGbQQgNYLVlgABAgQIECBQYwEBSI2BtSdAgAABAgQIECBAoNwCApByzy+P1QtA8lDUgwABAgQIECBQfwEBSP3N3ZEAAQIECBAgQIAAgRIJCEBKNKwaLVUAUiNYbQkQIECAAAECNRYQgNQYWHsCBAgQIECAAAECBMotIAAp9/zyWL0AJA9FPQgQIECAAAEC9RcQgNTf3B0JECBAgAABAgQIECiRgACkRMOq0VIFIDWC1ZYAAQIECBAgUGMBAUiNgbUnQIAAAQIECBAgQKDcAgKQcs8vj9ULQPJQ1IMAAQIECBAgUH8BAUj9zd2RAAECBAgQIECAAIESCQhASjSsGi1VAFIjWG0JECBAgAABAjUWEIDUGFh7AgQIECBAgAABAgTKLSAAKff88li9ACQPRT0IECBAgAABAvUXEIDU39wdCRAgQIAAAQIECBAokYAApETDqtFSBSA1gtWWAAECBAgQIFBjAQFIjYG1J0CAAAECBAgQIECg3AICkHLPL4/VC0DyUNSDAAECBAgQIFB/AQFI/c3dkQABAgQIECBAgACBEgkIQEo0rBotVQBSI1htCRAgQIAAAQI1FhCA1BhYewIECBAgQIAAAQIEyi0gACn3/PJYvQAkD0U9CBAgQIAAAQL1FxCA1N/cHQkQIECAAAECBAgQKJGAAKREw6rRUgUgNYLVlgABAgQIECBQYwEBSI2BtSdAgAABAgQIECBAoNwCApByzy+P1QtA8lDUgwABAgQIECBQfwEBSP3N3ZEAAQIECBAgQIAAgRIJCEBKNKwaLVUAUiNYbQkQIECAAAECNRYQgNQYWHsCBAgQIECAAAECBMotIAAp9/zyWL0AJA9FPQgQIECAAAEC9RcQgNTf3B0JECBAgAABAgQIECiRgACkRMOq0VIFIDWC1ZYAAQIECBAgUGMBAUiNgbUnQIAAAQIECBAgQKDcAgKQcs8vj9ULQPJQ1IMAAQIECBAgUH8BAUj9zd2RAAECBAgQIECAAIESCQhASjSsGi1VAFIjWG0JECBAgAABAjUWEIDUGFh7AgQIECBAgAABAgTKLSAAKff88li9ACQPRT0IECBAgAABAvUXEIDU39wdCRAgQIAAAQIECBAokYAApETDqtFSBSA1gtWWAAECBAgQIFBjAQFIjYG1J0CAAAECBAgQIECg3AICkHLPL4/VC0DyUNSDAAECBAgQIFB/AQFI/c3dkQABAgQIECBAgACBEgkIQEo0rBotVQBSI1htCRAgQIAAAQI1FhCA1BhYewIECBAgQIAAAQIEyi0gACn3/PJYvQAkD0U9CBAgQIAAAQL1FxCA1N/cHQkQIECAAAECBAgQKJGAAKREw6rRUgUgNYLVlgABAgQIECBQYwEBSI2BtSdAgAABAgQIECBAoNwCApByzy+P1QtA8lDUgwABAgQIECBQfwEBSP3N3ZEAAQIECBAgQIAAgRIJCEBKNKwaLVUAUiNYbQkQIECAAAECNRYQgNQYWHsCBAgQIECAAAECBMotIAAp9/zyWL0AJA9FPQgQIECAAAEC9RcQgNTf3B0JECBAgAABAgQIECiRgACkRMOq0VIFIDWC1ZYAAQIECBAgUGMBAUiNgbUnQIAAAQIECBAgQKDcAgKQcs8vj9ULQPJQ1IMAAQIECBAgUH8BAUj9zd2RAAECBAgQIECAAIESCQhASjSsGi1VAFIjWG0JECBAgAABAjUWEIDUGFh7AgQIECBAgAABAgTKLSAAKff88li9ACQPRT0IECBAgAABAvUXEIDU39wdCRAgQIAAAQIECBAokYAApETDqtFSBSA1gtWWAAECBAgQIFBjAQFIjYG1J0CAAAECBAgQIECg3AICkHLPL4/VC0DyUNSDAAECBAgQIFB/AQFI/c3dkQABAgQIECBAgACBEgkIQEo0rBotVQBSI1htCRAgQIAAAQI1FhCA1BhYewIECBAgQIAAAQIEyi0gACn3/PJYvQAkD0U9CBAgQIAAAQL1FxCA1N/cHQkQIECAAAECBAgQKJGAAKREw6rRUgUgNYLVlgABAgQIECBQYwEBSI2BtSdAgAABAgQIECBAoNwCApByzy+P1QtA8lDUgwABAgQIECBQfwEBSP3N3ZEAAQIECBAgQIAAgRIJCEBKNKwaLVUAUiNYbQkQIECAAAECNRYQgNQYWHsCBAgQIECAAAECBMotIAAp9/zyWL0AJA9FPQgQIECAAAEC9RcQgNTf3B0JECBAgAABAgQIECiRgACkRMOq0VIFIDWC1ZYAAQIECBAgUGMBAUiNgbUnQIAAAQIECBAgQKDcAgKQcs8vj9ULQPJQ1IMAAQIECBAgUH8BAUj9zd2RAAECBAgQIECAAIESCQhASjSsGi1VAFIjWG0JECBAgAABAjUWEIDUGFh7AgQIECBAgAABAgTKLfAnP/iT8NU/+Gq5N2H1UQLf/wffD1/5pa9E9VBMgAABAgQIECBQfwEBSP3N3ZEAAQIECBAgQIAAgRIJPH7yOHztD78W3vn375Ro1Zaal8DOv7QzvP3bb4f1P7c+r5b6ECBAgAABAgQI1ElAAFInaLchQIAAAQIECBAgQKC8AmkIcu1Pr4UffvTD8m7Cylct8PN/7udDxy92CD9WLaeAAAECBAgQIFAMAQFIMeZgFQQIECBAgAABAgQIECBAgAABAgQIECBAgECOAgKQHDG1IkCAAAECBAgQIECAAAECBAgQIECAAAECBIohIAApxhysggABAgQIECBAgAABAgQIECBAgAABAgQIEMhRQACSI6ZWBAgQIECAAAECBAgQIECAAAECBAgQIECAQDEEBCDFmINVECBAgAABAgQIECBAgAABAgQIECBAgAABAjkKCEByxNSKAAECBAgQIECAAAECBAgQIECAAAECBAgQKIaAAKQYc7AKAgQIECBAgAABAgQIECBAgAABAgQIECBAIEcBAUiOmFoRIECAAAECBAgQIECAAAECBAgQIECAAAECxRAQgBRjDlZBgAABAgQIECBAgAABAgQIECBAgAABAgQI5CggAMkRUysCBAgQIECAAAECBAgQIECAAAECBAgQIECgGAICkGLMwSoIECBAgAABAgQIECBAgAABAgQIECBAgACBHAUEIDliakWAAAECBAgQIECAAAECBAgQIECAAAECBAgUQ0AAUow5WAUBAgQIECBAgAABAgQIECBAgAABAgQIECCQo4AAJEdMrQgQIECAAAECBAgQIECAAAECBAgQIECAAIFiCAhAijEHqyBAgAABAgQIECBAgAABAgQIECBAgAABAgRyFBCA5IipFQECBAgQIECAAAECBAgQIECAAAECBAgQIFAMAQFIMeZgFQQIECBAgAABAgQIECBAgAABAgQIECBAgECOAgKQHDG1IkCAAAECBAgQIECAAAECBAgQIECAAAECBIohIAApxhysggABAgQIECBAgAABAgQIECBAgAABAgQIEMhRQACSI6ZWBAgQIECAAAECBAgQIECAAAECBAgQIECAQDEEBCDFmINVECBAgAABAgQIECBAgAABAgQIECBAgAABAjkKCEByxNSKAAECBAgQIECAAAECBAgQIECAAAECBAgQKIaAAKQYc7AKAgQIECBAgAABAgQIECBAgAABAgQIECBAIEcBAUiOmFoRIECAAAECBAgQIECAAAECBAgQIECAAAECxRAQgBRjDlZBgAABAgQIECBAgAABAgQIECBAgAABAgQI5CggAMkRUysCBAgQIECAAAECBAgQIECAAAECBAgQIECgGAICkGLMwSoIECBAgAABAgQIECBAgAABAgQIECBAgACBHAUEIDliakWAAAECBAgQIECAAAECBAgQIECAAAECBAgUQ0AAUow5WAUBAgQIECBAgAABAgQIECBAgAABAgQIECCQo4AAJEdMrQgQIECAAAECBAgQIECAAAECBAgQIECAAIFiCAhAijEHqyBAgAABAgQIECBAgAABAgQIECBAgAABAgRyFBCA5IipFQECBAgQIECAAAECBAgQIECAAAECBAgQIFAMAQFIMeZgFQQIECBAgAABAgQIECBAgAABAgQIECBAgECOAgKQHDG1IkCAAAECBAgQIECAAAECBAgQIECAAAECBIohIAApxhysggABAgQIECBAgAABAgQIECBAgAABAgQIEMhRQACSI6ZWBAgQIECAAAECBAgQIECAAAECBAgQIECAQDEEBCDFmINVECBAgAABAgQIECBAgAABAgQIECBAgAABAjkKCEByxNSKAAECBAgQIECAAAECBAgQIECAAAECBAgQKIaAAKQYc7AKAgQIECBAgAABAgQIECBAgAABAgQIECBAIEeB/w/FIKn6VDU7QwAAAABJRU5ErkJggg==\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "nx.draw(my_ego_graph,with_labels=True, node_size=[100*my_ego_graph.nodes[name]['Age'] for name in my_ego_graph.nodes()],node_shape='s',node_color = [color_dictionary[name] for name in my_ego_graph.nodes()], width = [my_ego_graph[edge[0]][edge[1]]['Age Difference']/4 for edge in my_ego_graph.edges()])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}