qfnb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QF TODO\n",
    "- Commit latest version, make it a tag or branch\n",
    "- Re-create code as modules\n",
    "- Py4Cytoscape - use Cytoscape for debugging?\n",
    "- Expand size of fields\n",
    "- Try starting everyone in a tighter center\n",
    "- Test the recursive collapse algorithm - is this already a thing?\n",
    "- Run clustering and color clusters on layout\n",
    "- Search for published algorithms - what is novel here?\n",
    "- implement causal flow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'igraph'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-de47b7e88c2f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0migraph\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnetworkx\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mrandom\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrandint\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'igraph'"
     ]
    }
   ],
   "source": [
    "import ndex2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math\n",
    "import json\n",
    "import igraph\n",
    "import networkx as nx\n",
    "from random import randint\n",
    "from cyjupyter import Cytoscape\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "NETWORK_UUID = \"cfd3528f-a7a4-11eb-9e72-0ac135e8bacf\"\n",
    "SERVER = \"https://ndexbio.org\"\n",
    "test_network = ndex2.create_nice_cx_from_server(SERVER, uuid=NETWORK_UUID)\n",
    "test_network.print_summary()\n",
    "#test_networkx = test_network.to_networkx()\n",
    "#test_pos = make_networkx_pos_from_cx_coordinates(test_network)\n",
    "#nx.draw(test_networkx, test_pos)\n",
    "#print(test_network.get_opaque_aspect(ndex2.constants.CARTESIAN_LAYOUT_ASPECT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def igraph_from_nice_cx(nice_cx):\n",
    "    g = igraph.Graph(len(nice_cx.get_nodes()))\n",
    "    # network attributes\n",
    "    for name in nice_cx.get_network_attribute_names():\n",
    "        value = nice_cx.get_network_attribute(name)[\"v\"]\n",
    "        #print(value)\n",
    "        g[name]=value      \n",
    "    # attribute declarations\n",
    "    # nodes and attributes and coordinates\n",
    "    # make a node_id list\n",
    "    node_id_list = []\n",
    "    n_id = 0\n",
    "    for node_id, node in nice_cx.get_nodes():\n",
    "        g.vs[n_id][\"cx_id\"] = node_id\n",
    "        atts = nice_cx.get_node_attributes(node_id)\n",
    "        for att in atts:\n",
    "            att_name = att[\"n\"]\n",
    "            att_value = att[\"v\"]\n",
    "            g.vs[n_id][att_name] = att_value\n",
    "        n_id += 1\n",
    "        \n",
    "    # edges and attributes\n",
    "    for edge_id, edge in nice_cx.get_edges():\n",
    "        cx_source_id = edge[\"s\"]\n",
    "        cx_target_id = edge[\"t\"]\n",
    "        g_source = g.vs.find(cx_id=cx_source_id)\n",
    "        g_target = g.vs.find(cx_id=cx_target_id)\n",
    "        g.add_edges([(g_source, g_target)])\n",
    "        e_id = g.get_eid(g_source, g_target)\n",
    "        g.es[e_id][\"cx_id\"] = edge_id\n",
    "        atts = nice_cx.get_edge_attributes(node_id)\n",
    "        for att in atts:\n",
    "            att_name = att[\"n\"]\n",
    "            att_value = att[\"v\"]\n",
    "            g.es[e_id][att_name] = att_value\n",
    "\n",
    "    # cartesian coordinates\n",
    "    g[\"cartesian_layout\"] = nice_cx.get_opaque_aspect(ndex2.constants.CARTESIAN_LAYOUT_ASPECT)\n",
    "    #visual styles\n",
    "    # bypasses\n",
    "    # rules\n",
    "    return g\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "igraph.Edge(<igraph.Graph object at 0x1212a57c0>, 1, {'cx_id': 1, 'citation': ['pubmed:16571352'], 'mechanism': 'transcriptional regulation', 'residue': '', 'sequence': '', 'cell_data': ['BTO:0002572'], 'tissue_data': [''], 'notes': '', 'annotator': 'lperfetto', 'sentence': 'Primary mouse embryonic fibroblasts responded to Shh stimulation with the induction of Hh target genes Gli1, Ptc1, and Hip1.These observations support the previously advanced notion of a functional redundancy or cooperativity between Gli2 and Gli1 in activation of target genes [18] and [43] and indicate a functional cooperation between Gli3 and Gli1.', 'direct': 'false'})\n"
     ]
    }
   ],
   "source": [
    "g = igraph_from_nice_cx(test_network)\n",
    "print(g.es[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def igraph_layout_from_cx_cartesian_layout(cx_layout):\n",
    "    igraph_layout = [(0,0)] * len(cx_layout)\n",
    "    for node in cx_layout:\n",
    "        igraph_node_id = node[\"node\"]\n",
    "        igraph_layout[igraph_node_id] = (node[\"x\"],node[\"y\"])\n",
    "    return igraph.Layout(igraph_layout)\n",
    "\n",
    "def draw_igraph_with_cx_layout(g, cx_layout):\n",
    "    l = igraph_layout_from_cx_cartesian_layout(cx_layout)\n",
    "    fig, ax = plt.subplots()\n",
    "    igraph.plot(g, layout=l, target=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Layout with 18 vertices and 2 dimensions>\n"
     ]
    },
    {
     "data": {
      "image/png": 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SEhAeHo7IyEi8ePECkZGRiI6ORkpKCiwsLGBpaQkrKytYWFjAxMQEOjo6yMrKEjyrfuhl9ePtjIwMaGlpFSoexsbGQvoffhZ3VbePWzMlbdF83JopSaumuK0ZmUyGGzduwNfXF/v370eVKlXg4+OD1q1b/7Sto58JuTD8j/D27VsEBATA398fp0+fho2NDRo3bgx9fX28fPkS58+fR1ZWliAEHh4esLKy+qpr5ObmCoPDUVFRePDgATZu3IjatWvj2rVrsLGxQVJSElJTU6GgoAAnJyeIRCLB26dIJIKioiL27dsHPT09LFiwQIijqalZRk/m60lPT8eTJ08KuIqIiIiAra0tatSoIYSqVasW2j2WV1h/LBwpKSl4+/YtXr16hZiYGOHz9evX0NTUFETiQ8H4cNvAwKBMB2pJIicnp8RdZXlxsrKyhLGZ4naXqaqq4ty5c9i6dStSU1MxcuRIDBgwAOXKlSuz5/ClZ3Q97C3OP3yFclqq6FjTFtZGOl8+8SdCLgw/KVKpFHfu3IGfnx/8/f3x7NkzNGnSBHZ2doKZYEREBBo1aiSME7i4uHy2UMnIyBA8luaFPBGIiopCfHw8zM3NYWtrCxsbG9y6dQtOTk5wcnLCpUuXsGvXLhgZGUFXVxempqYICgqCpaVlgeuIxWLMmTMH27Ztw/bt2wXzxh8dsViMJ0+eIDAwUAiPHj2CnZ0datasKYhF5cqVv3oGMEkkJiYKYvGxcOTty87OFsTC3t5eGDNxdnaGra3tD+fLqLDWTElaNQoKClBQUIBEIoGenh5sbGxgbGxcLOEpbmtmy9lQHA+MQrZYCkUFQEVZCUv71oGTuV7pP8DvhFwYfiISEhJw6tQp+Pv749SpUzAxMUGVKlWgpqaG58+f4+7du6hWrZogBDVr1sznyyY9PR3Pnz8vtNCPiopCenq6YNVjY2MjCEBeMDc3F+Ye+Pn5YfTo0Xj48CF+++03NGjQAEOHDhWu1aNHD7Rq1Qr9+/f/5P1cuHABffv2Rffu3bFgwYKf0gVGbm4uHj16lE8snjx5AicnJ0EoatasCVdX11Lpu8/IyBDE4tmzZ4LTu9DQUCQkJMDR0TGfWDg7O8PR0fF/wlVFXmsmLS0Nz58/x/bt23Hw4EHY2NjA09MT9vb2wop6RRGezMzMfJZmnxOWcuXKwdvbG6oa2uix4izEH07zB1DNToSFv9T+Tk+m9JELww8MSdy7dw9+fn7w8/PD48ePUbNmTRgZGSEpKQm3b9+GjY2NIAQNGzaEtrY2kpKSEBoaipCQEOEzJCQEiYmJKF++fIECP++7sbFxkbopsrKy4OrqirVr18LDwwMmJiZ49OgRzM3NhThbtmzBhQsXsHfv3s+mlZiYiF9//RUvX77E33//DScnpxI/t+9NdnY2goOD84lFeHg4KlWqJIhFrVq14OLiUqr95enp6QgLC8snFqGhoXjx4gUsLS0LCIazs/N3644pLXJycuDr64vVq1cjKSkJI0eOxK+//lqkhYk+15rJE5Z3797h5cuXiIqKwsKFC2Fg6QCfLVeRLZbmS8tETwO7fJqV1W1+c+TC8IMhk8lw8+ZNHDx4EAcPHoSysjIqVKgAqVSKx48fQ1VVVRgjcHNzQ1xcXD4BCA0NRWZmJlxcXODi4gJnZ2fh08bGplS6GmbPno2HDx/i0KFDOHfuHKZOnYpbt27lixMTE4MqVaogJibmi7VVktiwYQNmzpyJxYsXY8CAAf9zE54yMjIQFBQkCMWNGzeQmpqKxo0bo2nTpmjatCkqVqxYJvctFovx/PlzQSg+HDvR09NDrVq1ULt2bdSuXRs1atSAtrZ2qeehrMkze121ahXOnTsHHx8fjBo1Cnp6ekU6Pzs7G0+fPkVISAiCgoJw69YtPHnyBHFxcUL6Pj4+WLFyJXosP4u0bLFwrrKiAlpWtcKo1m5YtWoVbGxs0KpVq5+6lSYXhh8AmUyGa9eu4eDBgzhw4ACUlZVhamqKhIQEpKSkoE6dOihfvjx0dHTw9u1bPHnyBCEhIVBSUkKlSpXyFf4uLi4wNzcvs4I1PDwctWvXxv3792FlZYVRo0bB1NQUU6dOLRC3RYsWGDRoUJEntz169Ai9evWCi4sLNm7cWOSX+mfl5cuXuHDhghBycnLQpEkTQSgcHBzKVCDzFkO6deuWEIKDg2Fvby8IRe3ateHi4vLDjV18jqdPn2LBggU4ceIEhg8fjjFjxsDAwAAAkJqaKghkSEgIHj9+jODgYLx9+xaampqQyWTIzMyEjo6OYMnWpEkTdOvWDV26dIG2tjbuRsRjju9dKCkogCBEOupY0b8edDVV4evriw0bNiAoKAjt27dHr1690KxZs5/OPblcGMqQqPg0ZORI4GReDkofdRlIpVJcvXoV+/fvh6+vL5SUlKCqqorExETY2dlBR0cHaWlpiIqKgp6eXqEtgK+ZoFUakESbNm3QuHFjTJo0CSRhY2MDf3//Qmes7t27F3v37oWfn1+Rr5GVlYUJEybg5MmT2Lt3L+rVq1eat/BD8+LFC0Ekzp8/D0VFRUEkmjZtCltb2zLPQ25uLh48eJBPLGJjY1G9evV8YvFht+GPBknExcXh3LlzWLt2Le7evQszMzNkZ2cjJSUFxsbGUFFRQUZGBhITE2FlZQVjY2Okp6cjPDwcbm5uaN26NVq1aoVq1aoVKooZOWI8eJEIHQ0VVLI2gOJHAv769WscOHAA+/fvR3h4OLp27YoRI0b8NDO75cJQBmRkizH179t4EZcGpf+sFub3rgVHs3IICQnBrFmz4O/vLwym6erqQllZGSkpKXB2dkaNGjVQrVo1VKlSBZUqVfph+oGPHDmCadOmISgoCKqqqrh//z66d++Op0+fFlqzzczMhKWlZYHxh6Lw77//YvDgwRgxYgSmTp36U9VYSwOSePbsWb4WhaamZj6hKMziqyzIG8/6UCw0NTUFkahbty5q1679zWvFeUuW5nWhfhgAwNbWFrq6usjIyMCzZ8/w7t07GBgYoFmzZhCJREhISMC9e/eQmZkJLy8vtGrVCs2bNxdaF59i4cKFGDNmDDQ0NBAaGvrFGdovXrzA7t27sX79eri7u2PcuHHw9PT8obtL5cJQBqzxewT/oGhIpP//GRnpqmP3qGbo1asXDh48CACws7ND7dq1UblyZdjb28PU1BQ5OTlIT09Heno6srKykJ2dDYlEIngg/Xii1offP9yf5600b1/eDN48r6ZKSkr5gqKiYr6Zvh9vSyQSzJkzB7/++iuqVasGdXV1HDx4EGKxGOPGjYOGhgbU1dULfA4bNgyOjo6YOHHiVz/HV69ewdvbG1KpFHv27PnqeRf/S5BEaGioIBIXL16EoaEh2rdvj44dO6JOnTrfTDxJIjw8XBCJq1evIjw8HE2aNIGnpyc8PT1LtRtMLBYjPDw8XxdQaGgowsLCoKenB2dnZ1haWkJZWRnp6el4+fIlgoODIRKJBDNiY2NjhISEYO/evXj9+jXKly+PAQMGoG3btnB3dy9yXm/cuIHevXvj2bNnyMrKgoGBAe7duwc3N7cvnpuTk4N9+/Zh5cqVkEqlGDt2LPr06fNDjkXIhaEM6LPqHBLSsvPtU1VWxJZhjbFj41+YPHlyoefl2WgrKCgUKJg/xYe/A0nh+4efnwsKCgpQVVWFiopKvqCsrCx8KikpIT4+HmKxGPb29gDe19ZCQkJgZmYGdXV1iMViiMVi5OTkIDc3VxA1RUVFyGQyGBoaCjOS84Qjzwzww1nBH3/X1tbGgQMHsHv3bqxevRo9e/b8oWta3wqZTIb79+/j2LFjOHr0KN6+fYt27dqhY8eO8PDwKPYs6uISHx+Ps2fP4vTp0zh9+jRUVVXRsmVLeHp6olmzZkUaL8rMzCxgVRUSEoIXL17AwsJC6EI1NzeHRCJBXFwcHj58iMDAQCgrK6NmzZpCqFChAoKCghAQEICAgAAAQKtWreDl5QVzc3PMmzcPjx49woIFC9CjR48iW4f16NEDDRo0gI+PD0hCWVkZmzdvxsCBA4v8rEji/PnzWLFiBe7evYuhQ4di+PDhMDY2LnIaZY1cGMoAny1X8fRNSr59KkqK+Gd8c6S/S8KKFSsEvz05OTmCj5+MjAzhMy/kfZdKpYIfnzyfPkX5/qU4KioqIInc3Fykp6cjIyOjwGd4eDhWrlyJQYMGAXhvYfP27VucOnUK9erVE/L64XkymQzGxsYwMjJCWFgY6tatC3t7e8GPkJaWFtTU1IRabmZmZoEZwh/OGk5ISEBycjIUFBSgp6cHPT09QTz09fVhZGQEIyMj4Zofh/91b6YRERGCSAQFBaFFixbo0KED2rRp88WukdKGJEJCQgSRuHr1Ktzc3ITWRIUKFfDs2bMCXUBv3ryBg4NDvnE0CwsLpKam4sGDB7hz5w7u3LmDzMxMYX5I3qe5uTkePHggCMHdu3dRr149eHl5wcvLq1CLr4sXL+L333+HgoICli5diiZNmnz2vtLT02FhYYEXL14Iz1QkEqFr167YsGFDsZ5VaGgoVq1aBV9fX3Tp0gVjx479IcYh5MJQBtwNj8cc30DkSN5PglFTUYJnZUuMbOWK9u3bw8DAAD4+PqhevXqR05RIJAXEoqjfixJHUVGxUPHQ1NREUFAQrK2tUb16deH4/fv3kZCQgEGDBhUqOMrKysI1tmzZgpiYGHh5eSEuLq7QoK6uDmNjYyGYmJjk+25sbAxtbW0sXboUDx48wLJly2Bubo6UlBQkJSUhPj7+kyEhIQFaWlqFCsaHgmJiYgJLS0sYGhr+1K2ShIQEnDhxAkePHsX58+dRs2ZNdOzYER06dIC1tfU3yQNJxMbGIjQ0FMHBwbhw4QKCgoLw+vVrSCQS6Ovrw8nJCQ0bNkT9+vXh4uICY2NjPHz4UBCAO3fu4O3bt6hatarQEqhRowbKly8PBQUFJCUl4cyZM4IY6OjoCGMFjRs3LpKbFZlMhn/++QdTp06Fq6srVq5cCQcHh0Lj7tu3D7t3785nTOHm5gZdXV1cu3atRM8rPj4eGzduxNq1a9G4cWMsX74cFhYWJUqzJMiFoZSJjIyEra0tHkYn4cD1cKRlieFZ2RItq1rhaVgYqlevLvj2d3R0xLBhw9CzZ8/vajtOEmKxuFAxOXnyJPbt24fZs2cjJydHOLZlyxa4ubnBxMSkSAJEEvr6+tDW1i4gQJqamlBVVYWCgkI+J3d5XVKZmZlIT0/Hu3fvEBcXBzU1NWGSXcuWLeHk5ITy5cujfPnyMDc3L9DXLpPJ8O7du8+KR96CQa9evUJGRkYBJ3cfb5uamv4UA+KZmZk4c+YMjh49iuPHj8Pa2hodO3ZEx44dS2UVNZlMhqioqEIHgJWVlfPV/vOCsrIyTp06hQMHDuDKlSsAAFVVVWRkZMDd3T2fCFSsWFF4zlKpFHfv3oW/vz8CAgLw+PFjNG7cGK1atULLli2Fbs7ikJOTgz///BNLliyBj48PJk2aVKDvv1OnTujYsSP69esn7OvcuTOuX7+O2NjYYl/7QzIzM7FgwQJs2LABU6dOhY+Pz3cxdZULQymyYcMGLF26FM+ePSvQZ3nq1CmMHTsW9erVw4EDB5CVlSVMXouKikLv3r0xZMgQuLu7f7P8fonU1FQ4OzvD19cX9evXF/YnJSXBzs5OWGHtS5BEixYt0KNHD7Rs2bJErZv09HSkpqYiKSkJycnJkMnet8rynrdMJoOqqirU1dWhra0tLBFqaGgIExMTGBoafrF7TVFREWlpaUhOTkZSUhJiY2Px+vVrxMTECH6LEhISYGxsXKh4WFtbC8YEP1LLQyKR4Nq1a0KXk6KiIvr27Yt+/frBxsbms+fm5uYKk+Q+FIGnT5/C0NAwX8GfJwQikQjA+wI9NDQ0X0sgJCQEDg4OqFGjBszMzPD27VtcvnwZOTk5wnoM1atXR1xcHE6fPi04hzQ1NRXGCho0aFDqblSio6MxduxYPHjwAGvWrIGXlxeA9/9hQ0NDhISEwNTUVIg/Y8YMLFq0COnp6aWal6dPn2LkyJF48+YN1q1bJ7iw/1bIhaGU2LJlC/744w+cP38e5cuXL3B827ZtmDt3LpSUlLB48WKMHj0a+vr6CA0NhampKVq2bInTp0/DysoKAwYMQPfu3b/7BK9x48YhOTkZ27dvz7d/9+7dOHz4MI4cOVLktP7++2/s3LkTp06dKrX85ebmYubMmdizZw82bNiAWrVqITExEc+ePRPcaect1hMbG4v4+HgAgJ6entBy0dbWFsY6srOzCxUkiURSYM0GDQ0NYXnRPIux3NxcZGdnIz09HcnJycjJyYGxsTHMzMxgaWkJGxsb2NnZoUKFCnB0dBS8yn6P8Y+8pTZ37NghuLXu378/WrZsiejo6AIDwEliVTg27gZdPQPYaWSgjpOpsOCRjo5OvnTDw8PziUBQUBDMzc3ztQSqVq1aoKuHJO7fv49Vq1bhxIkTyMjIgIKCAho0aIAePXoIa4h8C/z8/ODj44Nq1aph5cqVkEgkqFevHl6/fp0v3tatWzF+/Hhcvny51Ct1JHHw4EGMGzcOzZo1w5IlS2BiYlKq1/gUcmEoBfz8/PDrr7/i6tWrn2zOkoSnp6fgn+XgwYMYPXq00LoIDQ2Fh4cHunTpglOnTuHs2bNo3bo1+vXrh+bNm3/zbouHDx/Cw8MDjx49KmAt0bVrV7Rt2/azDvI+JisrC/b29jhx4gSqVatWqnk9d+4c+vXrhz59+mDevHmfLGhJIikpCS9evEBERASeP3+Ohw8fIjg4GBEREXBwcIC7uzsqV64Md3d3uLu7w8zMDFKp9IvGAYV9z+v6yluAKDU1FRkZGcjOzoZYLBZEJc8yTENDQ2jl5FlklYaBQWH/nQ/9aT18+BCXL19GWFgYsrOzYWBggKpVq6JBgwaoVKkSqGeDPYGJyJXIQADqKkrwbuyIrnXtkZ6ejjNnzggicPfuXejo6OQTgbz1wj9FTEyMME5w7tw5lC9fHi1btoSDgwOePHmCQ4cOQVFRUWhJfI15aUnIysrCokWLsHbtWrRt2xbx8fE4efJkvjhnzpxBnz59BIu5siAtLQ1z587Fjh07MGfOHAwZMqTMywO5MJSQBw8eoEWLFjh27Bjq1q372biRkZGoUaMGGjRoAIlEgiNHjmD79u2YNm0avLy8cOjQISgoKGDQoEEYMWIETp8+jR07duDNmzfw9vZGv379ULFixTK9H+B9AdqoUSP07t0bw4YNy3csOzsbJiYmCA8PF7oKisqaNWsQEBCAEydOlGZ2AbwfvBs4cCBiY2Oxb9++Tw4gfors7GyEhIQgODhYCA8ePABJQSTyQqVKlUpsDiqVSvH69WuEh4fj2bNnCAsLw/Pnz/HixQtER0cjJycH5ubmMDc3h7GxMUQiEcqVKwcNDQ3k5OQUqQsuLygrKwsr0slkMojFYpCEtrY2ypUrB5FIBGNjY5ibm0NLSwvPnj3DvXv3oKmpicGDB+OJVk3EpuTky7+GqhIOTfDE8X//xZYtW/JZCH3J7DInJwdXr14VxODNmzfw9PSEl5cXWrZsWaBWnOdMMm+NaDU1tXxrRJe1SDx79gxeXl7IzMzE+fPn801oe/LkCerVq4eRI0di7ty5ZZqPx48fY/jw4cjOzsbBgwfLtPUkF4YS8Pr1a9SpUwdLly5Fjx49inTOhg0bsG3bNujp6cHW1hYbN25EWFgYevfuLXhGTUtLQ3p6OiZMmIDRo0cjPDwcO3fuxJ49e2Bra4t+/fqhR48exVpxrSjs3LkTa9aswc2bNwvUTE6ePIklS5bg0qVLX51uTk4OKlSogP37939RRIsDSaxduxZz5szBihUr4O3tXeL03r59m08sgoODERYWBhsbm3xiUblyZVhbW5daIZWSkoKnT58W6NKJjo6Gra1tvj79ChUqQF1dvUAXUGhoKNTV1eHk5AQHBwfY2trC0tISZmZm0NTURGZm5idbPOnp6XBxcUFwcDDCjZpDSS1/t4+SogIO/N4CWmpFGxiNiIgQhODSpUtwcXERxgqqV69e5BowSdy5c0cQCW1tbXTv3h3e3t4lGnz+ElOmTMHjx49x/fp1TJkyBWPGjIGSkhIyMjKgp6eH9u3b49ChQ2V2/TxIYtmyZVi5ciUOHTpUJu8RUHxh+OLEqR8hVK9enWWFRCJh/fr1OXfu3K86TyqVslmzZpw7dy6rVavGOXPmkCRzc3O5cOFCGhoaskWLFhSJRKxfvz4tLS25detWSiQSisVinjhxgl27dqWuri579OhBf39/SiSSUruvpKQkmpqa8vbt24Ue/+2337hixYpip79582Y2a9as2OcXhQcPHtDZ2Zl9+vRhSkpKqaefk5PD4OBg7tmzhxMnTqSXlxdNTU1pZmbGbt26cdWqVQwMDKRYLC7V62ZnZ/Pu3btctmwZu3XrRldXV+rp6VFBQYEKCgpUV1enjY0NPTw8OGHCBJ44cYIJCQklvu70vTfoOfc4PeeeeB/mHGef5f6UyWSfPCcjI4N+fn708fGho6MjTU1N2b9/f+7fv79U8kS+f5du3LjB0aNHUyQSsUWLFjx48CBzc3NLJf0PGTVqFFeuXMnw8HA2atSI9evX57Nnz0iS5cqVo6OjY6lf83OcOHGCRkZG3LlzZ5mkDyCQxShzv3uhX5RQlsKwcOFCNmnShFKp9KvPjYiIoKGhIS9evEg7Oztu3rxZOPb06VM2adKETk5ONDExYb9+/digQQO6urryxIkTwsuYmJjItWvXsmbNmjQ3N+fEiRP5+PHjEt/X8OHDOWTIkEKPSSQSmpiYMDw8vNjp5+bm0t7enufOnSt2GkUhIyODQ4YMYfny5Xnz5s0yvRZJymQyRkREcNeuXRw8eDBdXFyoo6PD5s2bc/bs2Tx79izT0tKKlFZqaipv377NnTt3cvLkyWzfvj0dHR2ppqZGJycndurUiVOnTuXu3bsZGBjItLQ0SiQShoeH88SJE1yyZAkHDBjAOnXqUFdXl5aWlmzbti2nT5/OgwcP8vnz51/1v41PyaL36nPssMif7Rf6sfXco7SvXIfNmjWjn58fZTIZZTIZQ0NDuWLFCnp6elJbW5uNGzfmwoULef/+/WK9J19DVlYW9+7dy4YNG9LMzIzTpk1jZGRkqaU/aNAgbtiwgeR7QVq1ahUNDQ25evVquru7U01NjTk5OaV2vaLw+PFj2tvbc8KECaVaOSTlwlAs7t+/T5FIVKI/3vbt2+nk5MS7d+/S0tKSmzZtEo7JZDJu2bKFIpGI5ubm7NatGw8ePEhnZ2c2adKkQG3+0aNHnDBhAs3MzFilShUuXLiQERERX52nwMBAmpiYMDExsdDj165do5ub21en+zG7d+9mvXr1PlvjLC0OHTpEY2NjLliwoNRfni+RkJDA48ePc9KkSaxfvz41NTVZo0YNjhkzhgcOHOCjR4946dIlbtiwgaNHj6anpyetrKyoqanJKlWqsFevXpw7dy4PHjzIx48fF6vgkclkDA8P56FDhzhjxgy2a9eOVlZW1NXVZcOGDenj48OtW7fy7t27zM7O/mQ6EqmMwVGJDAyPY45YwpycHG7atIm2trbU19enoaEhLS0tOXjwYB4+fJjv3r0ryaMrEY8fP+bo0aNpYGDA1q1b89ixYyVuvU2aNIkLFizIty8sLIx16tShSCSiiYkJHz16VKJrFIeEhAQ2bdqUbdu2ZVZWVqml+92EAYAVgAsAQgA8BjD6v/0GAM4AePbfp/5/+xUArAbwHEAwgGpfukZZCENubi7d3Ny4Y8eOEqc1ZMgQdu7cmU+fPqW1tTXXrFmT7/ibN2/YuXNnamtr093dnXFxcdy0aRPNzc3ZvXt3Pn/+PF98iUTCc+fOcfDgwRSJRKxZsyaXLVvG6OjoL+ZFKpWyVq1a3LZt2yfjTJw4kdOnTy/ezX6UTxcXF/r5+ZU4raIQHR3NRo0asWnTpoyJifkm18xDJpMxOjqap06d4pIlS9i+fXva2tpSRUWFAKimpkYHBwd27dqV69evZ3h4eJnXrsn3Bcq5c+e4fPlyent7083Njerq6nRzc6O3tzeXL1/Oc+fO5askyGQyBgUFcdGiRWzSpAm1tbXZvHlzDh48mPXr16e5uTkXLlzIpKSkMs9/UcjIyOCOHTtYt25dWlpacvbs2cX+/VevXs3hw4cX2C+RSFi7dm0qKytz8uTJJc1yscjNzWWPHj3Ypk2bUmu1fE9hMMsr3AHoAHgKwAXAEgCT/9s/GcDi/7ZbA/D/TyDqALj1pWuUhTBs3LiRzZo1K5XabnZ2NmvWrMklS5YwIiKCdnZ2hfbfHzlyhNra2ixXrhxDQkKYnp7OP/74g4aGhvTx8WFcXFyBc3JzcxkQEMCBAwfSwMCA9evX5+rVq/n69etP3le9evU+WyhVqFCBgYGBxb/hDzh48CCrVav2TVoN5PsXeN68eTQxMeHRo0dLPX2xWMywsDAePXqUCxcuZN++fVmjRg1qa2vT1NSUTZs25fDhw7lmzRqeO3eOr1+/pkQi4cOHD7lhwwb+8ssvtLW1pUgkYo8ePbh7927Gx8eXej4/R1ZWFgMDA7llyxaOHDmSDRo0oLa2Ng0NDWljY0NdXV1aWFhw6NChPHnyJNPT0/OdHxQURG9vb+rr63PMmDGl2pVTUh48eMDhw4dTX1+fHTp0oJ+f31e1IA8fPsz27dsXemzx4sV0dnamjo4Of//99zIZ4/gSubm57NixIzt16lQq1/9hupIAHAPQAkAYADP+f/EI+297I4BeH8QX4n0qlLYwZGZm0sLC4pMDs8UhKiqKJiYmPHfuHKOioujg4MBFixYViJeSksKaNWtSWVmZW7dupUwmY1xcHH18fGhoaMh58+YVeFHzyMnJ4YkTJ+jt7U09PT02adKE69evFwQlPj6exsbGDAoK+mQ+Q0NDaWlpWWoFuVQqZdWqVXn48OFSSa+oXLt2jba2thw+fDgzMzO/+vysrCw+ePCA+/bt48yZM4VBYHV1ddrZ2bF169YcP348t2zZwuvXr3917Tk6OpqbN29mx44dqaury7p163L+/PkMCgr6JiIqlUp5+/Ztzp07l3Xr1qWOjg6bNm3Kfv360dvbmzVr1qSmpiarVq3KoUOHcseOHQwNDc1XoXj58iUnTJhAAwMD9uzZs9QqE6VBWloaN2/ezBo1atDW1pbz58/nmzdvvnje06dPaWFhUehvsG/fPtaqVYvt27dnmzZtWLt2bb548aIMcv95srOz2apVK3p7e5f4v/JDCAMAWwDRAHQBvPtgv0LedwAnADT44Ng5ADU+l25pC8OSJUvYpUuXUk2TJM+ePUtTU1NGR0czJiaGTk5OhVo7yWQy9unTh+rq6vTy8uLLly9Jks+ePWP37t1pbm7OTZs2fbY/NSsri4cPH2aPHj2oq6vLFi1asEGDBp8ccM5j0aJFHDFiRMlu9CNOnDjBSpUqffO+/+TkZPbo0YOurq58+PBhoXFSUlJ48+ZNbt++nRMnTmTbtm1pb29PNTU1Ojs7s0uXLpw+fTr37t3Le/fuMSMjo9TzmZ2dzVOnTnHUqFEsX748LS0tOWTIEP7777+ler23b99y165d7N27N0UiEStVqsTx48fzzJkzhfZbZ2Vl8caNG1y5ciV79uxJW1tb6unp0dPTkzNmzOCJEycYHx/Pd+/ecdmyZbS0tGTTpk158uTJb9ZCLAqBgYEcNGgQ9fX1OXjw4AJdsx8ik8lobW3N0NDQAseuXbtGV1dXuri4UCqVctmyZTQyMvrmlR7yfeW1atWqXL16dYnS+e7CAEAbwF0Anf/7/u6j48n8CmEAMBhAIIBAa2vrEj2cD8nJyaGxsTFDQkJKLc0PWbRoEWvXrs3s7Gy+efOGLi4unD59eoEXSSaTcciQIbS2tqZIJOKaNWuEgvXWrVts3LgxnZ2deezYsS++hOnp6Zw3bx7V1dWpo6PDVq1acdOmTYV2N9WpU4enT58uvRv+714aNmxY4j9xca+9detWGhoacvTo0Vy7di19fHzYvHlzmpubU0tLi9WqVWOfPn34xx9/8PDhwwwNDf0u3QR5+Q0NDeWyZcuE/v1WrVpxzZo1X107FYvFvHLlCqdNm8bq1auzXLly7Ny5Mzdt2sSoqKhi5S82Npb//vsvp06dSg8PD+rq6tLe3p59+vThypUrOXfuXLq5ubFWrVq8ePFisa5RVsTHx3PGjBk0NDRk7969GRwcXGi8gQMH8q+//iqwPzo6mqamplRXVxf+Hzdv3qStrS1Hjx5d6mbLXyI8PJzGxsa8du1asdP4rsIAQAXAKQDjPtj3Q3YlHTlyhA0bNiy19D5GJpOxU6dOHDZsGEkyLi6O7u7unDBhQoECXiqVslu3buzatSsbNWpENzc3BgQECOnk1cQbNGjAGzdufPKaYrGYVapU4d69e5mamsq///6bPXr0oJ6eHmvXrs0FCxbw0aNHfPXqFfX09MrEHC8sLIwikUiwCS8LpFIpIyMj6e/vz+XLl/O3335j/fr1aWBgQF1dXWppadHGxoZz586ln58fIyMjv8kAcEl49+4dfX192bdvXxoZGbFSpUqcOHEiL126VGhB9PLlS27ZsoVdu3alvr4+q1atyilTpvDSpUtlInYSiYSPHj3i1q1bOXjwYLq5uVFbW5tubm7U19dnvXr1eO/evVK/bklISUnh4sWLaWpqynbt2hV4dw4dOlRoGSAWi6miokIHB4d8Fcfk5GR6eXmxefPm33xA/vjx47S0tCz2db/n4LMCgF0AVn20f+lHg89L/ttu89Hg8+0vXaM0haFjx47cunVrqaVXGO/evWOFChUEi6eEhARWq1aNo0ePLiAOqampdHJy4vbt23nkyBE6OjrS09OTDx48IPn+xdy2bRstLS3ZpUsXhoWFFbje6tWr2bRp0wJp5+Tk8PTp0xw5ciStrKxoZGREJycnXrx4sUxqP6tWrWKDBg1KXBjn5uYyNDSUhw8f5vz589mnTx9Wq1aNWlpaNDc3p4eHB0eOHMl169bxwoULjI2NpUwmY3Z2NsePH09LS0ueP3++lO7q2yGRSHjz5k1Onz6dVatWpb6+Pvv06cPFixdz/PjxdHV1Ffr7d+7cWaQ+9bIgMTGRR48epY+PDy0sLKigoEAzMzOOGzeOFy5cKNaYT1mQmZnJtWvX0sbGhk2bNuWZM2cok8mYm5tLCwuLQsfirK2t2aJFCx44cCDffrFYzLFjx9LR0ZFPnjz5VrdAkhw6dChHjhxZrHO/pzA0AMD/TE+D/gutARj+1030DMBZAAb8/0KyFkA4gIdfGl9gKQpDQkICy5UrVyazaD/m0aNHFIlEvH//Psn3tY7atWtz2LBhBQrOvLgPHjxgbm4u//rrL5qYmHDgwIGCWV5GRoYwo3rYsGGMjY0l+d4UViQSfXFSnEwmY4MGDdi1a1dWrVqVhoaG9Pb25sGDB4s8YetLSKVSNmzYkCtXrixS/IyMDN67d4979+7l9OnT2aVLF7q4uFBNTY329vZs27YtJ0yYwG3btvHmzZtFtqkPCAigmZkZp06d+t26jEpCREQE161bx+bNm1NNTY2amprU0dFhnz59frjaOUlGRkaya9euVFdXp7m5OTU1NdmgQQNOmzaNp0+fLrX/V3HJzc3lrl276OzszJo1a/Lw4cOcN28ef/311wJx69evzz59+gieDD5m69atNDIyElr234LExEQaGxsLZcnX8N3HGMoylJYwHD16lF5eXqWSVlHYt28fy5cvLzQDU1JSWL9+ff76668FBmp37NjBypUrC/vfvXvHyZMn08DAgDNmzGBqairJ9/2oY8aMoYGBAWfPns2ePXty4sSJX8xLamoqdXR0BFGMjo7mmjVr6OnpKYxLbNiwga9evSrRPT9//pwikShfyyY5OZnXr1/n1q1bOX78eLZu3Zp2dnZUV1dnpUqV2K1bN86cOZP79u1jUFBQqdQ4Y2Nj6eXlxdq1a5dohve3IDMzk/7+/hw1ahQrVKggzJTft2+f4Hbi8ePHnDJlCq2srOjm5sYlS5Z887kcXyI6Opr9+/enkZERhw0bxsmTJ7NBgwbU0tJi7dq1OXHiRJ48efKbVMwKQyqV8vDhw6xRowYrVKhALS0tPn36NF+cXr16cejQoezRo8cn07ly5QpNTU25atWqbzYIv3HjRjZo0OCrrycXhiIwZcoUzpo1q1TSKipjxoxh69athQI/LS2NjRs3pre3dz5xkMlkbNy4MdeuXZvv/KioKHp7e9PMzIwbN24UuoAiIiLo4eFBRUVFrly58os14+PHj7Nly5aFHnv37h3379/P3r17U19fnzVr1uSMGTN49erVInc5yWQyvn79mufOnWO3bt0Em39TU1Nqa2uzRo0a9Pb25oIFC3j06FGGhYWV+WCeVCrlihUrKBKJuHfv3jK91teQNwC9cuVKtmzZktra2mzUqBEXLFjAe/fufbYrTiqV8uLFi/z111+pr69PDw8P7tixQ6g4/Ag8ePCArVq1Yvny5bl//36mp6fz/PnznDVrFps2bUptbW02aNCAc+fO5a1bt765NZtMJuPp06dpZ2dHDQ0Nrlu3TrDamjRpEkeMGEFXV9fPpvHixQu6ubnxt99++yYuNCQSCe3t7Xn9+vWvOk8uDEUgzyfMtyQ3N5ceHh787bffBLXPyMhg8+bN2a1bt3y14+DgYBoZGRU60S0wMJBNmjShs7MzT5w4wZycHFaqVImLFy9ms2bNWKFCBR46dOiTNYqUlJQitQZyc3N57tw5Tpw4kVWqVKGenh47derE9evXMyIiglKplBERETx58iSXLl3KgQMHsm7dutTT06OhoSEbNmzIQYMG0cHBgb/99hujoqK+u2njvXv36OTkxL59+363AjQ1NZVHjx7l0KFDaWtrS0tLSw4aNIiHDh0qttuJzMxM+vr6sl27dixXrhx79epFPz+/b2498ynOnTvH6tWrs0aNGrx06ZKwPyMjgwEBARw3bhwrVapEAwMDdu/enVu2bCnS7P7SIjs7m5aWlqxVqxYtLCy4fft2rl69mgMHDqS6uvoXn2Nqairbt2/PRo0aFfrOljYrVqxgr169vuocuTAUAZFI9F0G7NLS0li7dm2OHz9eKCSzsrLYs2dP1qpVK59Z6ahRo+jj41NoOjKZjMePH2fFihXp4ODAunXrCo7P/P396e7uzrp16/Lu3bvCOS/epvJEYCTvR8RT+hUFdE5ODh8/fszNmzezS5cutLW1pbKyMhUUFKitrc0qVapw6NCh3LBhAy9dulTgxYiIiKBIJCozs+CvJT09nb/99hsdHBx4586dMr+eTCbjgwcPuHjxYqGW3Lx5cy5btoyPHz8udbGMi4vjmjVrWLt2bZqYmHD06NG8c+fOdxdlqVTKv//+W/C/VJgIvnz5ktu2bWOPHj1oaGhIZ2dnjhkzhn5+fmUyr+RD/P39aWtry4CAANavX5+2trasWbMm7e3tizTILJVKOWXKFNrZ2X3SPLa0SE5Opp6e3ie9HhSGXBi+gEwmo5KS0jf3nJhHYmIi3dzc8k14k8lknDdvHi0tLYVZpa9evaK+vv5na5EvXryglpYWRSIR+/btK7gskEgk3Llzp+DldfOZELZd4Me2C/zYfqE/x26/xhxx/mZ7eno6AwMDuXv3bk6dOpWdOnWik5MT1dTU6OjoyPbt23PSpEncsWMHb9y4wStXrnDRokVs1qyZ4Hlz/vz5DAwMLNAFsn79etaqVeuHqcGSpK+vL42MjLhkyZJSN2VNSkqir68vBwwYQHNzc5YvX54jRozgiRMnPjmbvSx4+vQpZ86cyfLly9PV1ZWbN2/+7pZC796946BBg2hlZcWTJ09+Mp5EIuHt27f5xx9/sGHDhtTW1qaHhweXLFlSZrPGx4wZwzZt2lAikXD58uVUVVWlkZFRkY0oSHLPnj0UiURlPijdp08fbty4scjx5cLwBdLT06murl7idErCmzdv6OjoyFWrVuXbf+jQIYpEIv7zzz8kyR49ehSI8yHdunUTBqSnT59OAwMD9uvXL1/tPDo+jW0X+P1/3/tzT7DdQn8G3H/fVB8yZAhtbGwEh2vdu3fn7Nmz+c8//zA4OLhIHh7T09N58uRJjho1ihUrVqRIJGKvXr24fft2vnz5kjKZjM2bN+cff/xRnMdVZkRGRrJ+/fps0aLFV9W+PkYqlfLOnTucO3cu69WrRx0dHbZu3Zp//fVXmc7nKCoymYxnzpxhmzZtaGxszOnTp5fofkuDs2fP0s7Ojt7e3kVazyElJYVHjx7l8OHDaW9vT1NTU/bt25e+vr6l1i2Ym5vL+vXrc968eUxMTKSuri6bN29OLS0t/vrrr0U2yLh69SqNjIz477//lkq+CmPHjh3s1q1bkePLheELvH79msbGxiVOp6RERkbS2tq6gPfT+/fv08bGhjNnzuTly5fp6OhYaO3o1KlTtLOzy1cDTE5O5h9//EFjY2N26dKFgYGBPB30ku0X+ucTBs+5J7jw8Htzx6CgID579qxUB/4iIyO5efNmdu3alYaGhixfvjy7detGPT09bt68+bt3a3yIWCzmrFmzaGpqyhMnThT5vLi4OO7evZt9+vShkZERXVxcOG7cOJ4+fbpU3SWXNk+ePOGIESOor6/PX3755bv6PUpLS+OoUaNoZmbGgwcPftW5z58/59q1a9myZUvq6OjQy8uL69atK7GF1qtXr2hubs59+/ZRS0uLGzZsYJcuXThx4kQaGBhw1qxZRTK7vXXrFo2NjXno0KES5edTvHz5koaGhkVu7cqF4Qu8e/eO2traJU6nNAgLC6OZmVmBSTSxsbGsV6+e0Kf/cZ9ldnY2HR0defz48ULTTU9P559//klLS0t6dv6Fbf7ILwptF/jR9/qn/ciUJlKplI8ePeLatWvp4eFBBQUFmpiY8JdffuGmTZsYFhb2QwjF5cuXaW1tzVGjRhVasIvFYl69epXTp09njRo1WK5cOXbq1IkbN278obyOFpWkpCQuXbqU1tbWbNCgAQ8ePPjNrYLyuHr1Kp2cnNilS5dijf2lpKTQ19eXffr0ob6+PqtXr865c+fywYMHxfpvPXjwgCYmJrS0tOT+/ftZuXJlku+7bnv37k0zMzNu3rz5i8/r7t27NDEx4f79+786D0XBxsamyC1SuTB8AZlMRk1NzR/GrC8oKIjGxsb09/fPtz87O5v9+/enoaEhZ86cme/YH3/88UmXwR+Sk5PDLVu2sv6gRWwx8/D7bqQFfvzlz7NMz/o+E772799Pc3NzLlmyhH369KGlpSXNzMzYo0cPrlu3rkwGZItKUlISu3btysqVKzMkJIQxMTHcunUru3XrRn19fVapUoWTJ0/mxYsXf8oJc4UhFot54MABYcB1+fLl32VRnqysLE6ePJlGRkbctWtXsf8Dubm5PH/+PEePHk1bW1va2tpy1KhRPHfu3Ff9ZoGBgVRVVeXUqVOpoaGRTwTu3LnDRo0asVKlSsKKd58iODiYZmZm3L17d7Hu53M0atSIFy5cKFJcuTAUAXt7+0JdSnwvrl+/TiMjI16+fDnffplMxl9++YVqamqC3XJERAQNDAy+ytFaTq6Yi7YcZIN+M1in0xDu3LOPKSkp3LJly3fxITR37lzWrFmTGRkZwhKa27dvZ//+/WlnZ0eRSMTOnTvzzz//ZFBQ0Deryebk5PDcuXNs2bIllZSUqKmpye7du3PHjh3fvU/+W3Dr1i1hDouPj893GR8JDAyku7s7W7duXWKTVZlMxuDgYM6bN481a9akvr4+e/fuzf379xdpcl379u2pra1NkUhUYAKcTCbjsWPH6OTkxObNm392NvLjx49pbm5e6i54evXqVWTBkQtDEWjevPknu2G+F2fOnKGRkVGBPt/Xr19TW1ubRkZG3LJlC9u1a1fsQVyZTEY/Pz/Wr1+f9vb2tLe3Z+/evb957TfP3XjXrl0LFabo6Gju2bOHgwYNopOTk7CGwOTJk3nkyJFSLaRfvHjB9evXs3379tTV1WWtWrU4a9Ys7tu3j5UrV2bnzp0/uTTq/yoxMTGcOnUqjYyM2K5duyLXSkuLnJwczpkzhyKRiEeOHCm1dGNiYrh+/Xq2atWK2trabNGiBTdt2vTJwe+5c+dy8ODB1NTUZIcOHQqtoOTm5nLdunU0MTHhqFGjPtkTERYWRisrK2Gd6dJg7NixXLJkSZHiyoWhCMybN4/jxo0rlbRKk6NHj9LU1DSfvyOZTEY1NTXeu3eP1tbW1NHRKZWVwC5fvswWLVoI6xHk+Vz6VmRlZbFevXqcNm3aF+MmJibS39+fs2fPppeXFw0MDGhtbc1u3bpx+fLlvHr1apHNMDMzMxkQEMDRo0fTycmJxsbG7Nu3L//+++8CzzUrK4ujR4+mlZVVvolZ/1fIyMjgpk2b6ODgQA8PD968efObXv/WrVu0srLi1KlTS73VmJqaSl9fX3br1o26urr08vLi9u3bmZycLMTZvn07f/nlF44cOVJYtOnDbraYxHSeefCSIS+TGB8fz/79+9Pa2vqTRgzPnz+njY1Nqbml9/Hx+azV4ofIhaEIXL9+XRhQ+tHYvXs3LSws8g0429ra8tGjR7SxsaGXlxcdHR2L5UirMG7evEkbGxsqKSmxf//+pZZuUYiLi6OdnR137dr1VefJZDI+ffqUu3bt4ogRI1ijRg1qaGiwevXqHD58OHfu3CkMastkMj558oSrVq2il5cXdXR02LBhQ86fP593794tUlfayZMnaWpqyhkzZvxQczG+Fbm5udy0aRMtLS3ZsWPHTy6GVBa8ffuWTZo0oaenZ5HMWotDWloa9+3bJ6yy165dO+7Zs4fHjx9n48aNuX37dvbq1YvDhw+ns7Mznz17xt2Xwth2gR87LPJn+4X+nLznJsUSKc+cOcPy5cuzZ8+ehVa2IiMjWb58eS5durTE+R44cKAwV+lLyIWhCOTm5lJXV/e7uSv+Evv27aORkZHgMrpu3brs16+fYLe8d+9eikQirl27tlQGaqVSKQcPHkxjY2OamZmxVq1a3LZtW5nPNiXf978aGRnxypUrJUonMzOT165d4/Lly9mpUycaGRlRTU2NGhoawsp269evz1cj/Bpev37NFi1asF69et9lmccfgaysLC5fvpzGxsb09vb+Zk4JxWIxx48fTzs7uzL3Kvvu3Tvu2rWLbdq0oZaWFjU1NTl//nyhIrlu3TpaOFSi19zj+Sz92i/05+mg9yswZmRkcMKECTQyMuK2bdsKvKMvX76ko6Mj58+fX6K89ujRg3///XeR4sqFoYj8+uuvP9yEqw85f/48jY2NuW/fPlarVo3lypUTlv4k389qrVKlCrt27Vrswu5DZDIZFy9eTBsbG27YsIFt27algYEBR44cWeY1xFOnTtHU1LTYg515g4xLliwRZmJ7eHhw5syZXL16NSdPnsxWrVrR1NSU+vr6bNq0KceOHcudO3cKLs6LglQq5dKlS2lkZCRMQvy/SEpKCmfPnk1DQ0MOHz78mw3M79+/nyKR6KtbmMXl1atXVFZWZtOmTQmA3bp14+HDh7n+0EXByu/DsPhIftG6e/cuq1atSg8PjwLLjL5+/ZoVK1bkrFmzil25q169epFXdZMLQxEJCgqipaXlD901EBwcTCsrK6qqqnLMmDEFjmdlZXHEiBG0s7Pj1atXS+WaW7dupampKe/cucOoqCjOmDGD5ubmrF+/Pnfv3l1mk7e2bNlCS0vLQtfgLYzk5GQeOHCAv/76Ky0sLAS3E8ePH//sBKQ3b94wICCAixYtYs+ePVmxYkVqaGiwWrVqHDhwIFevXs3Lly9/1mTzzp07dHBw4MCBA7+pi4sfjfj4eI4fP54GBgacNGkS4+PjmZmZydTUVGZkZDAnJ6fUrd4ePnxIR0dHjhw58pu4tTE2Nubr169paWnJefPmsWnTprR0rknP2Ufyzw2af5L7rxas2IjFYi5dupSGhoZcvHhxvvImNjaWrq6unDJlyleLQ25uLjU0NIr8/yuuMCi8P/fHpkaNGgwMDCy19Bo1aoRRo0aha9eupZZmabNhwwYMGzYM/fr1w7Zt26CoqFggzrFjxzBs2DD07t0b8+bNg4aGRomueezYMfz222/Yt28fmjdvDrFYjBMnTmDjxo24e/cuvL29MWTIEDg5OZXoOh+zc+dOTJkyBQEBAXB3d893TCaT4f79+/D390dAQACCg4PRsGFDeHl5wcvLCw4ODlBQUCjWdTMyMvDo0SMEBQUJ4eHDhzAxMUGVKlWE4OrqChsbGygqKiItLQ0+Pj64ceMG9u3bh2rVqpXGI/imkER6ejri4+MLDcnJycjKykJ2djays7OF7Y/3ZWZmIiMjAzKZDMrKylBVVYVMJoNUKoVEIgFJKCsrQ1lZGUpKSp/cVlNTg66uLvT09FCuXDkhfPhdT08PioqKWLp0KTIyMrB7925UqFABSkpKZfKMatSogbVr12LOnDkYNmwY2rVrhzdv3uD3HdcQn60EBWVVyCS5kGRnoHdF4rf+vxT6P4yIiMCQIUOQkJCALVu2oHr16gCAhIQEtGjRAq1atcKCBQuKnK8HDx6gZ8+eCA0NLVJ8BQWFuyRrFPkCeef9XxSG48ePY9KkSXjw4AFUVFRKLd3SIj09Hc7OzoiLi0OtWrVgZmaGXbt2QV1dvUDchIQEjBgxAsHBwdixYwdq165domtfvnwZXbt2xZo1a9C9e3dhf0REBDZv3ozt27fD2dkZQ4cORadOnaCqqlqi6+Xh6+sLHx8fnDhxAra2tjh9+jQCAgJw6tQpiEQiQQgaNmxYYgH8HFKpFM+fP0dQUBDu37+PoKAgPH78GElJSahQoQJcXFzg7OyMxMRE7Ny5E5MnT8bvv/9eqHB/a0giKSkJERERiIiIwIsXLxAbG4v4+HgkJCTkK/yVlJRgZGRUaNDX14empibU1dWhoaGR77OwfdHR0Zg9ezYuXLiAKVOmYMiQIVBTU8snEh9/fridk5ODlJQUIbx79+6T2+/evUN0dDRSU1OhoKAAAwMDWFhYwMLCAubm5oVuGxoafvXv07lzZ/Tq1Qu3bt2CkZERJk2aBKlUCkIBFx69xoWgcMRGPEbA9mVISXwLMzMzbN++HQ0bNiz0d9m9ezcmTJgAb29vzJkzB1paWkhMTES9evUwatQojBgxokj5+uuvv3D37l3s2LGjSPHlwvAVkESrVq3QsmVLjB07ttTSLS0mTpyIkJAQPH/+HA8ePEDfvn0RGxuLo0ePQl9fv9Bz8grWgQMHYvbs2VBTUyv29R88eIDWrVtj+vTpGDZsWL5jubm5OHr0KDZu3Ijg4GB06tQJ3bt3R5MmTaCsrFys60kkEty+fRurV6/GoUOHoKamhhYtWsDLywstW7aEra1tse+ltEhLS8OTJ08QGhqK0NBQhISEIDg4GFFRUdDU1ETDhg1RtWpVODs7w9nZGRUrVoS2tnap5yM3NxfR0dEIDw8XBODDAAD29vYoX7487OzsYGpqWmjhr6mpWep5e/DgAaZNm4bHjx/jr7/+Qtu2bUv9Gnn4+/ujf//+mDt3LmrWrIlXr17h9evXePXqVYHt9PR0mJmZ5RMMS0tLODo6wtHREfb29gXelzFjxsDa2hp6enq4ePEimjVrBl9fX/j5+eWLJ5VKcfbsWcyYMQOBgYGwsbHBhAkTMGjQoAKVzri4OIwdOxY3btzAxo0b0aJFC7x48QINGjTAmjVr0KlTpy/ed8OGDTFp0qQiP9viCsN3Hz8oSijNMYY8njx58t3WZ/gcees/L1myhH379iX5fvBz7NixdHFxYVRU1CfPjY2NZceOHenq6ppvTYbiEB4eTnt7e86ePfuT/aAvXrzg0qVLWbNmTRoZGXHIkCE8f/58kWzPX716xW3btrF79+7U19dn5cqVOWnSJC5btowikYhnzpwpUf6/FWlpaRw0aBD19fXp7e3Nnj17snLlytTQ0KC1tTVbtmzJMWPGcMOGDTxz5gzDw8OLNL6VlZXFwMBAbt26lT4+PmzatCltbGyoqqpKOzs7enh4cNCgQVy4cCH/+ecf3rlzh4mJiT+E/6mzZ8/S0dGRXbt2LfFSsZ8jODiY5ubm3L59+2fjZWVlMTw8nJcvX+b+/fu5fPlywdV2hQoVqKamRjs7O3p6enLkyJFcvXo1f/vtN/bv359Xr16lo6MjjY2NvzgO9vz5c7Zr144qKipUVVVlly5dCjUD9/Pzo6WlpeCfKzAwkEZGRl8cUH758iUNDAy+aowF8sHnr2fy5Mns0KHDD/Eykf9/ec/Vq1dzwIABBZb5XLFiBS0tLRkUFPTZNPbs2UMjIyOOHz++RL6hYmNjWaVKFQ4fPvyLhX14eDgXLVrEatWq0cTEhMOHD+fFixeF83JycnjhwgVOmjSJ7u7u1NfXZ/fu3bl9+/YChcelS5doZGT0VV5Pvzfnz5+npaUlx40bx+zsbEokEoaHh/P48eNcsmQJf/3113yFe/ny5dm8eXMOHjyYM2bM4IwZMzh69Gh2796drq6ugjt0b29vLlu2jKdOnWJ4ePhP46spKyuLM2bMoEgk4po1a8rMvcmTJ09oZWVV4F35GnJzc/n06VOePHmSK1eu5LBhw+jm5kYNDQ2qqKgQAOvWrcvff/+dGzdu5KVLlz5rEZiVlcX58+dTJBJRRUWF1tbWXLZsWb7FrBITE9mlSxe6ubnx0aNH9Pf3p4mJyWfFZ+nSpRwwYMBX3ZtcGIpBdnY2a9WqVSqTTkqDPXv2sEqVKszIyKBIJCrUXnz//v00MjLi2bNnP5vW27dv2a9fP1paWvLgwYPFFr93796xSZMm7N69O7Ozs4t0zrNnz7hgwQI6OztTV1eXdnZ21NbWZq1atThz5kxev379i7XmPPfFH3ug/ZFJSEhghw4dWLVq1U+u/hUTE8O///6bQ4cOZc2aNVmuXDmqqqrS0NCQhoaGVFNTo66uLqtVq8bu3btzypQp3Lx5M8+dO8cXL1780NZ0hfH48WM2aNCAtWvX/myFpiRERETQzs6uVN/jmzdvsnLlyrS1taWuri7XrVvHRYsWCUvZamtr087Ojp06deKcOXP477//Mjo6Ot97lueKpnr16tTQ0KCamhpbtWrF48ePUyKRUCaTccuWLYJ4btu2jba2toWaAefm5tLKyoq3b9/+qvuQC0MxiYyMpImJyXd3ffDu3TuamZnx+vXrPHz4MBs1avTJuBcuXKCxsXGRViG7dOkSXVxc6OXlVez5AllZWezUqRObN2/+2RZIVlYWAwICOGbMGFasWJHGxsbs0KEDu3XrRhcXF5qbm3P06NG8du1akcwZ79+/T1NT029mv14ayGQyrl+/niKRiFu2bOGzZ8+4bds29u/fn+XLl6ehoSHbtWvHWbNm8ciRI4yIiChQmMTFxfHmzZvcu3cv582bxwEDBrBRo0a0sLCgiooKLSwsWLt2bXbu3JmjRo3ikiVLuHfvXl66dInh4eE/3LoQUqmUmzdvppGRESdMmFAmpr4vX76kk5MT58yZUyo9AM+fP6eysjKnT59OT0/PAq1XiUTCsLAw7t+/n5MnT6aXlxdNTExoaGhIDw8Pjh8/nnv27OGjR48oFov58OFD/vLLL9TU1KSRkRFNTU05Z84cxsTEMCwsjNWrV2fbtm05efJkoXL4IaGhoezRo8dX30dxheH/5ODzxwQEBODXX3/F9evXYWNjU2bX+RyjR49GRkYGtmzZgo4dO6J9+/YYOHDgJ+NHRUWhR48eEIlE2LFjB0Qi0SfjisVirFq1CosXL4aPjw8mTZpUqIXT55BKpRg2bBju378PPz8/GBkZgSSePXuGgIAABAQE4OrVq6hcubJgQVS1atV81iChoaE4cOAAfH19kZKSgs6dO8PLywuNGzf+5GBoSEgIPD09MXr0aPz+++/FNk39FpBEaGgoLl++jOPHj+P06dNQVlZG69at0bx5czRq1AjOzs4lsmASi8V48+YNXr16hZiYGMTExBTYfv36NXR1dYVBVktLywLbZmZm0NPT+6bP8+3btxg3bhyuX7+OdevWoVWrVqWefp4J6KJFi4p9bzKZDD179sShQ4eQnp6OadOmwczMDBMmTPjiubGxsYI1W97nq1evUKlSJVSpUgX29vaIiIjAoUOHoKCggPT0dDRq1Ag+Pj64cuUKdu/ejV69emHixIkwMTFBrkQKAFBVLp5ZrtwqqYSsXr0af/31Fy5fvgwzM7MyvdbHBAUFwdPTEyEhIUhNTUXNmjXx4sUL6Orqfva83NxcTJ06Fb6+vvj777/RoEGDz8aPjo7GmDFj8PDhQyxduhQdOnT4qpeHJCZNmoS9e/fCw8MD165dQ05OjiAEzZs3h56eXpHSevz4MY4ePYrTp0/j3r17qF27Njw9PeHp6Ql3d/d8hWd0dDS6dOkCOzs7bNu2rUysfYoDSQQHB+PixYu4dOkSrly5Ah0dHTRq1AiNGjVC7dq1sX79epw8eRJ79+5FvXr1vkm+ZDIZ4uPjPykeMTExiI2NRVZWFgwNDfNZKxkbG3/SjNXAwKBUzHJPnz6NYcOGoWbNmli1ahVMTU1L4a7fk5iYCC8vL9SpUwd//vlnsfI7depUXLp0Ca9evcLZs2dx8eJFXL16tcgmoh+TlpaG4OBgBAUF4e7du7h58yZevnwJe3t7kERYWBiUlJSgoaGBHj16wMfHB7b2jlh5/AGuhsaCAGo5GGNCh8rQUv8683q5MJQCCxYswN9//42LFy9+tgZemshkMjRo0AD9+/fH4MGD8csvv6BChQqYOXNmkdM4ceIEfvvtN4wZMwYTJ0784ssQEBCACRMmQE9PD0uXLkWdOnU+GZckHj9+jICAAPj7++P27dswMzNDXFwcduzY8dXiUhhpaWm4ePEiTp8+jdOnTyMlJQUtWrSAp6cnWrRoAVNTU2RnZ2PEiBG4efMmjhw5ggoVKpTomsUlOzsbFy5cwPHjx3H8+HGoqakJrYGGDRvCysqqwDn//vsvBg8ejOHDh2PatGllNinra8nJySkwv+HjEBcXJ2ynpaVBX18/n3gYGhqiXLly0NXVzTc57eN9urq6+cyZs7KyMG/ePGzZsgXz5s3DoEGDSm0uSEpKCtq0aQMnJyds2rTpq573tm3bsGDBAty4cQNdu3bFrFmzoK6ujjFjxuD27dulkj8ASEpKwq1bt3Djxg1cu3YNN2/eBAA4ODjgwYMHWHUiGOcevkKuRAYAUFZSQB1HE8zoVv2rriMXhlJi6tSpOHXqFE6fPg1DQ8Myv962bduwceNG3LhxAw8fPkTLli3x7Nkz6OjofFU6L1++RK9evaCjo4Ndu3bByMjos/GlUil27tyJmTNnol69eliwYAEcHBwAAO/evcO5c+eE2cYqKipo1aoVWrVqhaZNm0JbWxt79+7FuHHjcOTIkVKvCb948QJnzpzB6dOncf78eVhZWaFly5Zo0aIFnj59ijlz5mDLli1o3759qV73U8TFxeHkyZM4fvw4zp07B3d3d7Rr1w7t2rVDxYoViySMr169Qt++fSEWi7Fnzx5YW1t/g5yXLmKxGImJifmEIzExEampqfkmqKWkpBTYl5aWBnV19QICQhIPHjyAkpISOnXqBDMzswIT6IoyyU5DQwPKysrCb5GRkYH27dvDxMQEu3btKtIcm3PnzqF37964fPkynJyc4O3tjebNm6NDhw6wsrISJtUVFZLCZD6JRCLMGE9LS0NSUhKSkpKQnJwshMjISHTu3BkdO3ZEh0UByBZL86WnpKiA41NaQUmx6HmQC0MpQRJTpkzBkSNH4O/vj/Lly5fZtZKSkuDs7Aw/Pz9Uq1YNrVu3hpeXF0aPHl2s9MRiMWbOnIk9e/Zg7969aNSo0RfPyczMxPLly7F8+XK4uLhAJpMhJCQEDRo0ELqIHB0dC30hAgIC4O3tjZ07d6J169bFyvOXkEgkCAwMxOnTp3Hq1CkEBwejUqVKCAsLQ/fu3bF27dpiT6z7FCQREhKCf//9F8ePH0dISAhatGiBdu3aoXXr1sVuTUqlUixduhQrV67EunXr0KVLl1LN948MSWRkZBQqHMnJyfDz88O5c+fg5eUFGxubQl1xfOlTJpNBQ0MDKioqUFJSgoKCAlJTU6GiogJ9fX0oKipCSUkJioqKQsj7npubi4iICJQvXx7lypWDkpISXr58CQAwNjbGw4cPYWlpCeD9eyYWiwvM4pbJZPlCHh++O3nlbd6181yD5MXp1asXNmzYgM5LTiEjR5LvGaooKeLfKV5Q/ApxkgtDKbNu3TrMmzcPR48eLbGbiU8xdOhQKCkpYe3atdixYwdWrFiBO3fulGjWMvC+wB4wYABGjBiBKVOmFNqUTkhIyOd2oly5ctDU1ERERATGjBmDcePGFWm84ObNm+jYsSOWLl0Kb2/vEuW7KLx79w4XLlzA0aNH4evrC4lEInTj1K5dG7Vq1SpWS48kbt26hf379+Pff/+FTCYTWgWNGzcu8W/yIbdv30avXr3g4eGBVatWlcks5J+RO3fuoGfPnmjVqhWWLVv21QYSYrEYycnJSEpKQlpaGtLT09/7N/r9dzRp0gTVqlVDWloaMjIykJGRIWwnJyfj5s2bMDExgbq6OjIzM5GZmYm0tDSIxWJoampCLBZDX18fhoaG0NLSgra2thB0dHSEoKurK7SKdHV1oaOjky9eWloaQkJCcP/+fdy7dw/37t1DUlISzMzMkJ2djRo1auDw4cPYcf4JDt9+gRzxe4FRU1ZE88qWGNXa7aueiVwYyoDjx49j4MCB2LhxIzp37lyqad+5cwft27dHSEgIUlJSULNmTZw9exaVK1culfRfvXqF3r17Q0VFBXv37oVIJMLt27cFC6InT56gadOmgtsJOzs7AMCzZ88wf/58nDhxAsOHD8eYMWNgYGDw2WuFhITAy8sLY8eO/aYuRsRiMXx8fHD06FG0adMGkZGRCAwMhLGxMWrXri2EypUrf7Jgf/HiBfbs2YPdu3dDQUEBffr0QceOHeHm5lamFjupqakYMWIE7t69i3379pXa7/6zQBK5ublCAZ6eno60tDTExsZiyZIleP36NQYMGABNTc0CcT61nZGRATU1NaEQziuQlZSUcPv2bTRq1AhOTk75CmpVVVUsX74cNWvWxLBhw/Kde+3aNWzatAmnTp3C6NGjYW1tjfHjxxfp/jIzMxEWFoaQkJB8QiCRSFC9enVUqFABmZmZePz4MZ48eYLWrVujRYsWuHz5MkaNGgX3ylXw95VnOBEYBSkJrypW6NfUCSpKXzcOIxeGj5CRuBH2FoHh8bAw0ELLKlbQ0fh6h3mBgYHo2rUr2rVrh6VLl351LaYwpFIpateujVGjRuGXX35Bs2bN0KpVK0yaNKnEaX/Iy5cvMWLECJw9exbKysqws7ODl5cXWrVqhXr16n3WAV54eDgWLlyII0eOYMiQIRg3btxnu1Cio6PRsmVLdOzYEQsWLPimZpD79++Hj48PVq5ciV69euHJkye4deuWEJ49ewY3NzdBKJydnREYGIjdu3cjNDQUPXr0gLe3N2rVqvXNzWF3796NcePGYcaMGfDx8flhzXHFYnGRCue87aLEU1RULFCjztt+/fo1goOD0bx5c9SsWfOT8T7czhOBwrhy5Qq6dOmCixcvwsXFBcB7w48ePXpAVVUVe/bsKfDsHz16hO7duyMkJASbNm3CrVu3sHXr1nxxUlJSBN9ZH36+efMGjo6Ogu+sqlWrwtzcHDdu3MDBgwfx8OFDtGnTBt26dUOLFi1w9OhRjBs3Dt7e3pg3b16plDOAXBgKsPDwPdx8GodssRSqyorQUlPBhiENoaf19V0C7969w5AhQxASEoL9+/ejUqVKX53Gh6xfvx779u3DpUuXMH36dFy9ehXnz58vsbWKWCzG9evXhUHj6OhoNG/eHNbW1ti7dy86duyIhQsXFtmkFAAiIyOxaNEi+Pr64tdff8Xvv/8OExOTQuMmJCSgTZs2cHV1xcaNG0u97/9zPHz4EJ06dUKTJk2wfPlylCtXTjiWnp6OW7duYc+ePThz5gxev34NFRUVuLm5oXXr1qhevTqcnZ1Rvnz5b5rnPJ4/f47evXvD2NgY27dv/6LhwJeQSqXIyMgociFelG2JRFKkwrmo21paWl/0zHv//n10794dHh4eWLlyZYm96u7atQuzZ88WPKZOmTIFV69exZkzZwotiFNSUmBhYYG0tDScOHECkyZNwqhRo/KJQEpKCipWrCh43c37zPsvRUVF4ciRIzh48CBCQkLQrl07QQzU1NQQExODYcOGITIyEtu2bUPNmjVLdI8fIxeGD4iKT8PILVcFUy8AUFFSQLe69ujX1Om9+1xSGKAqCiSxY8cOTJw4UfDRXpzaXVxcHFxdXXHu3DkEBgbijz/+wM2bN4tdGERFRQndQxcuXICjoyNatWoFLy8v1KpVSyjo3r17hylTpuDff//FypUr0a1bt6/K/8uXL7FkyRLs2bMHnTp1go+PD6pWrVogXnp6Orp06QINDQ3s27evTF1kf0xqaiomTZqEEydOYP369WjTpg3u3buHXbt2Yf/+/XBwcEDfvn3RrVs3ZGVlCS2KR48eCbU8e3t7VKxYUajpOTs7w8nJqczGAUgiMzMTiYmJmD9/Po4cOYLJkyfD2dm5WAV4eno6srOz8/WDl0aBrqam9l1aM6mpqUKlzNfXt8RrgUybNg2XLl1Cnz59sGLFCty4cUNoCYvFYsTExOD58+dC4b9lyxaUK1cOEokEGRkZ6NevHypVqiSIgJWVVT4z29zcXFy7dg3+/v7w8/PD27dv0a5dO3Tt2hXNmzcXxFAqlWLLli2YPn06Ro4ciSlTppSaC/sPkQvDB1wNfYPl/wYjMzf/qH4tByPM61ULvXr1gq+vr7DAiIqKivCZt62hoVFoU1UikeDMmTPQ19eHt7c3zMzM8h3PO8fAwKDQmaUDBgyAgYEB2rZti549e+LSpUuoWLFike8tOzsbly9fFsQgISEBLVu2hJeXF1q0aAFjY+PPnn/9+nUMHjwY1tbWWLt2rTC2UFQSEhKwefNmrFu3Dra2thg1ahQ6duyYz8Vwbm4u+vXrh9evX+Pff//NV3v/Fpw+fRre3t6QSCTQ0tLCwIED8csvvwjmuJ8iMzMTT58+FVxr54Xw8HCYmpqiYsWKcHR0hJ2dHSwsLGBqagplZeVidaPkbWdmZkJdXV343wDvRdjCwgJVq1YtdADzS9saGho/xPoQpQVJbN68GdOmTcPKlSvxyy+/FCudrKwsREZGonv37njy5AkGDhyIjIwMREVFISoqCrGxsTAxMYG9vT1cXFzg4uKCFStWYNOmTfDw8IC5uTnu3LkjWCfl8erVKwQEBAiWVRUqVECrVq3QunVr1KhRo0BPwLVr1zBq1Cioq6tj48aNcHV1Lfaz+RJyYfiA+NQsDFhzEWLp/28xqCkrYkCziuhU+/8XhDKZDBKJBGKxuMBnVlbWJ2tk7969g5+fHx4/fozq1atDJBIVKBQSExORlZUFIyMjmJiYCAX2tWvX0LdvX+zevRtTpkyBp6cnjI2NYWxs/MkB0g/dTly5cgXu7u6CKWm1atW+uhDIzc0VTFQnTZqEMWPGfPWCRRKJBEePHsXq1avx4sULDBs2DIMGDRJaPjKZDKNHj8aVK1cQEBBQqrNbP8Xbt2+xceNGbNiwQRhkvH37NmbNmoVGjRqVqHslNTUVGRkZgplhno26oqIitLW1hdXGDAwMIBKJYGxsDAsLC4hEIsFipbBCXEtLq0DBER8fj4EDByI2Nhb79u37oqD9XyE4OBjdu3dH/fr18ddffxVoxaWmpiIqKgqRkZFCYZ8XIiMj8e7dO5iYmCA2NhaampqoXr06+vbtCxsbG9jY2MDS0rLAe9C6dWthBTcPDw9MnDgRHh4euHHjhtAqiI6OhqenJ1q3bo2WLVt+sqs1JiYGkyZNwuXLl7F48WL06tWrzFthcmH4iH1Xn+PvK8+goAAoKijA0lAby/rVhbpK6c06vXr1KkaNGgVNTU38+eefwrJ9eWRnZwuzR/MW6XBwcMCFCxdQrVo1aGlpCcfi4uKgqakJY2NjmJiYQE1NTfijy2QyeHh4oFOnTvD09PzkYj1fS3h4OIYNG4a3b99i06ZNxTbLDQoKwl9//YXDhw+jY8eOGDVqFKpWrQqSmD9/PrZv347Tp0/D3t7+k2nkNdWLU2i/efMGERERSEpKgqamJpSUlJCVlSXYtWdlZUFDQwMODg7Q19cvdreKtrZ2voKDJN68eYPQ0FA8ffpUcDuRt0BMTEwMpFKp4Kfow88Pt01MTAqIA0lhacnly5fD29v7hx2YLitIIiUlJd8KdDExMdiyZQuio6PRpEkTZGdnIyYmBlFRUcjNzRUKeVtbW2E777uCggLq1auHefPmwcPDA7Vr18ayZcvyrVT4MUOHDoW7uzu6dOmCPn36ICEhAdHR0bC1tRVaBbVr1/7s2FR2djaWLVuGlStXYtiwYZg8efI3c+vy0wmDgoKCF4A/ASgB2EJy0afiFtdcNSYxHcFRSTDR00BVO9FXTQwpKlKpFDt27MD06dOF9Vs/VTuOiYlBv3798Ntvv6FXr17C/jy3E0ePHoWfnx+CgoJgY2MDc3NzqKurIykpCZGRkUhOToa1tTXs7OxgZ2cHW1tbYdvOzg4ikeirCw+S2LdvH8aPH48uXbpgwYIFX/TRJJPJhNr3hwX1q1evcPz4cQQEBEBPTw+1atWClZUV7ty5g9u3b6Nu3bqFdr2kpaUhNze3QHfc57Y1NDTw9OlTnD17FomJiejWrRt69eoFS0tLIY6qqioUFBSQk5ODefPmYdOmTViyZAn69ev3zQrZ1NRUQSgKE45Xr14hKSkJJiYm+Zzb5dnCZ2RkYN++fShfvjzGjRsHMzOzfG4mtLW1f2jBICm0wDMzM5GVlYXU1FShoP/w8+N9iYmJ0NTUhEgkgpGREUQikRCePXuGy5cvY8aMGWjcuDFsbGxgaGj4yWeRmZmJpk2bolWrVpg9ezaA9xWa5s2b486dOwW6VKVSKW7fvo1p06YhJCQE2dnZsLOzg46ODvbv3w9zc/Mi3fuRI0cwfvx4VK1aFcuWLSvTCbOF8VMJg4KCghKApwBaAIgBcAdAL5IhhcUv6TyGzMxMXLlyBRYWFmXWn5eSkoI//vgDu3fvRmRkJJRVVLHlbChOBcWAIFq4W2JQC2eoKClCQUEBKSkp+dxOKCsrC4PGTZs2LdQlRl4f6YsXLwoNEokELi4ucHNzg5ubG1xdXeHq6godHZ0v1rzfvn2L48eP4/nz56hVq5bQPVZY/MzMTGhqan6y4NbU1ERcXBweP36MhIQEuLu7w8jICJcuXcK0adNQv379Qgv6ohRwSUlJ2Lx5M9auXQsbGxuMGjUKnTp1KpI10f379zFw4ECYmJhg06ZNP4xbitzcXMFj6ps3bwrMDE5KSsKVK1fw5s0b2NnZQSaTCcezsrKgo6NTqH8iVVXVfLN7P/4s6rG8mcF5BXteKOp3RUVFaGhoQENDA5qamtDR0REK+g8L/I/3GRoafnZiYZ4p9e7du9GyZctPxpPJZOjevTs0NDSwa9eufP+zJUuWICAgAGfPnkVSUhJOnToFPz8/nDp1Cubm5rC2tkZ2djb8/f1x48YNTJ48GdevX//ib/ro0SOMHj0ab9++xZ9//gkPD4+v+1OUEj+bMNQFMJtky/++TwEAkgsLi18SYXj37h2qVq2KWbNmYceOHbhw4UKZ1rCys7Ohrq6O9acew+9etGAZpaqsiObulhjdxg1Lly7FvHnzUL9+fWGsIM8pXG5ubrGtUZKTk5GQkIB3794J1ilS6Xt/KyoqKtDQ0BAGxo2NjVGuXLkCBXtcXBwOHjwIMzMz+Pj4oHz58oX2ixd1XOPly5c4ePAgfH19ERISgtzcXEycOBEzZsz4KtPQpKQkLF++HBs2bEDbtm0xatSoAl13RUEsFgtuKWbOnImhQ4d+9fjK9+Lw4cMYNmwYRo8ejUmTJkFJSQkSiQRpaWmFuprIc9sgk8kKfBa273PHVFVVoampKRTweYX8h98/ta8sn++1a9fQuXNnLF68GP379y80zqRJk3Djxg2cOXMmn9AkJibiypUrGDFiBFRUVJCcnIymTZuidevWaNWqFaysrAST8itXriAhIQEODg5ITk7+ZBmSlJSEWbNmYf/+/Zg5cyaGDRv2XUyg8/jZhKErAC+Sv/333RtAbZIjP4gzGMBgALC2tq4eFRVVrGvJZDJhwkytWrWwfv36b6LehTnBUlFSxPEpXvD398fSpUuFafcfFvQAPjtY+bXbWlpaePv2LR4+fIiHDx/i0aNHePjwIcLCwmBubg5XV1dUqVIFderUQZ06daCnp4ecnBwsXboUq1atwtChQ/H7779/1dyHTxEVFYVVq1Zh7dq1UFNTQ+/evdG9e3c0btz4ky9PcnIyVqxYIfgWmjZtWqmsmRESEoLRo0cjOjoaCxcuRKdOnX7oLpk8Xr58iV9++QVKSkrYvXs3LCwsvneWvjuhoaFo1aoVBg0ahKlTp+b7HTdv3oylS5fi+vXrSE9Px9WrV3H16lVcuXIFL1++RJ06dVCxYkXs3LkTN27cECa/5REREYGmTZsir/wxNjZGUFBQga6klJQUrFq1Cn/99Re6deuGefPmfTMPzZ+juMLwXVZkA9AV78cV8r57A1jzqfglXcHN1dWV9+/f599//806dep8kzWe2y3wo+fcE/lC6z9OUiaTMTExkadPn+aNGzf46NEjRkZGMiEh4asW+S4pYrGYoaGh9PX15ZQpU9ikSRNqa2uzUqVKHDRoELdv387z58+zf//+FIlEXLRoUYFVpYrL06dPaWlpSS8vL1avXp3GxsYcMmQIz507J6wNnJyczFmzZtHQ0JADBw5kREREqVz7Y06dOsXKlSuzbt26vHLlSplco7SRSCScN28eTUxMePTo0e+dnR+C169fs0qVKhwyZAjFYjElEgnXr19PHR0dtmnThpaWljQxMWGXLl24atUqBgYG5lsmdfny5WzUqFGBlQWzs7OpoqIi/C+bNGnC06dPC8dTUlI4d+5cikQi9u3bt9irJJYV+JmW9gRQF8CpD75PATDlU/FLKgydOnWir68vpVIpXV1defz48RKlVxRWHn/Ath+IQ9sFflxy9D5J8u7du0VeP/lbkpuby8DAQK5evZo9e/aktbU1DQ0N2bhxY7q6ulIkEnHVqlWlImCvXr2im5sbx4wZw2fPnnHx4sWsXr06RSKRsBZyv379+Pz581K4s88jlUq5a9cuWltbs0OHDp9dkP1H4tq1a7S1teXw4cOZmZn5vbPzXcnKyqK/vz8dHBxobGxMLS0tKikpsXXr1ty+fTufPXv22QqhRCJh/fr1+eeffxY4ZmZmxpcvX5Ikhw8fzlWrVjE1NZXz58+nkZERf/nlF4aFhZXZvZWEn00YlAFEALADoArgAYBKn4pfUmGYMGECFyxYQJI8efIkHRwcyvxFyhFLuPRYEFv/cZKt/jjJRYfvMyv3fa2jR48etLS05MqVK8tk/dvS5OXLl/T19eXYsWPp6upKRUVFqqmpsWXLljxx4kSJnmNycjIbNGjAPn36MCEhgfPmzaO+vj6rVq1KFxcXmpqacsCAAdy+fXuBtZHLgqysLC5dupQikYiDBw8udFH2H43k5GT26NGDrq6ufPjw4ffOzjcjMTGR//57nIOmLmGz0WtYo9881m3dk6NGjWLdunWpqqrKdevWfVWaT58+paGhIZ8+fZpvf61atXjt2jWS71sWtWrVopGREXv16vXDVyJ+KmF4n1+0xnvLpHAA0z4Xt6TCsGnTJg4YMED43q1bN06bNq1EaRYVqUxGaSEFWmBgILt06UIjIyPOmzePSUlJ3yQ/JSUzM5OrV6+mlZUVNTU1qa6uzpYtW3LVqlUMCwv76sI7KSmJzs7OVFVVZY8ePfjkyRPh2NOnT7l27Vr26NGDpqamtLS0ZO/evblx40aGhoaWmVAkJiby999/p4GBAWfOnMnU1NQyuU5pIZPJuH37dopEIq5Zs+abdJV+S5KTk3n58mWuWbOGgwYNYqVKlaijo8PmAybTa84xoVXebqE//e6Es2bNmmzYsCEdHR0ZHh7+VddatWoV69evL3QdkWTXrl25Y8cOLl68mHp6ehSJRHz8+HFp32aZ8NMJw9eEkgrDhQsX2KBBA+H769evKRKJfogaVmhoKPv3708DAwNOmjSJsbGx3ztLRUImk/H48eOsVKkSHR0d2apVK1pYWNDOzo7Dhw/nv//+y7S0tM+ef+jQIdrZ2bFjx47s2LEj69Spw8TExE/Gf/r0Kbds2cK+ffvSxsaGRkZG7NKlC//8808GBQXle5lLg8jISHp7e9PU1JRr165lbm5uqaZf2oSFhbFatWps37494+Pjv3d2vpq8ca9//vmHU6dOZdu2bWltbU0tLS3WqVOHgwcP5l9//cU7d+4wOyeXHRb5FxjHazltH729vSmTybh27VqamZnxzp07Rc6DVCplw4YNuXz5cpLvRalhw4bU1tZmt27deOnSJerr6/804isXhs8QExNDExOTfPvWr1/PevXqFRhs+l5ERkZy5MiR1NfX54gRIxgZGfm9s1QkpFIp//77bzo4ONDDw4N79uzhkiVL2KxZM2pra9PDw4NLly7lo0ePhJfp4cOHbNasGStVqsSzZ8+SfF/wT5gwgS4uLkJ/7peIjIzk7t27OWjQIFaoUIF6enps27YtlyxZwps3b5ZaQX7//n16enrS0dGR27Zt+yHHh/LIycnh+PHjaWlpyfPnz3/v7HyS+Ph4njt3jitXruSAAQNYvXp1ampq0t7enp06deKsWbN46NAhPnv2rNB3NDtXQq95JwoIQ4tZh/P9PkeOHKFIJBK6gorC8+fPqaenx969e1NPT4/VqlVjz549Sb7/nxoaGvLNmzclfwjfALkwfAapVEpNTc18XQJSqZT16tXj+vXrS5R2aRMbG8vJkyfTwMCA/fr1Y0hIyPfOUpHIzc3lxo0baWFhwY4dO/LWrVtMTU3lsWPHOGzYMFpbW7NChQqsXbs2DQwM+Ndff+WzCsljyZIltLGxKVbf7Zs3b+jr68uRI0fSzc2NOjo6bNGiBSdMmMBt27bxxo0bTE5OLvY9njt3ji1btqS5uTkXLlz4Q3f/BQQE0MzMjFOmTPmuLZ2cnBwGBwdzz549nDhxIr28vGhmZsZy5cqxYcOGHDFiBDdu3MgbN258toVZGMM3XWbLD0Vh9jFO2VVQAPz8/GhkZMT79+9/Nj2pVEo/Pz96eXlRS0uLLi4ufPXqFQ8dOsQOHToI8Ro2bMhz5859VV6/F3Jh+AJubm68d+9evn2PHj2iSCTiq1evSpx+aZOcnMw//viDxsbG7NKlCwMDA793lopEZmYmV65cSRsbG9atW5f//PMPs7KyuGbNGurp6dHNzY1GRkasXr06ly1bVmjrYPv27TQxMeGtW7dKlJfExEQeO3aMf/zxB/v06cPq1atTS0uLpqambNq0KYcNG8bVq1fzzJkzfPnyZZG7Bx48eMC+fftSX1+fo0eP5osXL0qUz7Li7du3bNWqFWvXrv3Vfe1FRSqV8vXr17xx4wb379/PxYsXc/jw4WzTpg0rVapEdXV1VqxYkd27d+cff/zBf//9l5GRkaXSFfMyIY29V55lmz9O0GP6QXqvOs2E1KxC4/r6+tLU1DTfGFYeaWlpXLNmDZ2cnFilShVu376dCQkJNDExYVBQEO/cucOqVasK8YcOHcrVq1eXOP/fArkwfIHOnTvzn3/+KbB/2rRp7Nq1a4nTLyvS09O5atUqWlpasmXLlrx06dJP0b8pFot56NAhuru7U0VFheXLl+fly5eFY2fOnOHAgQNpYGDARo0acf369fn6xf/9918aGRnx1KlTpZovqVTK6Ohonjp1in/++SeHDh3Kxo0b08TEhNra2qxRowa9vb25YMECHj58mKGhoZ+scb98+ZITJkyggYEBe/bs+UOKt1Qq5cqVKykSibhnz56vPj83N5cRERG8cOECd+zYwTlz5nDgwIH08PCgg4MD1dTUaGRkxBo1arBLly4cN24c//zzTx49epT3798vdes/mUzGOXPmcNasWSTJoAcPaONam75+F7/4XmzdupVWVlZCN214eDjHjh1LAwMDdunShZcvX86XxsqVK9m+fXvGxsbS0NBQ2L969WoOHTq0VO+rrCiuMPzPelf9mEmTJkFXVxfTpk3Ltz8rKwvu7u5YtmwZOnToUKJrlCU5OTnYs2cPFi1aBBMTE0ydOhWtWrX6YWfrRkVF4ffff8edO3cwbNgwPH78GMePH0fv3r0xatQoYcGVnJwcnDp1Cvv27YO/vz/q1q2LXr16oVOnTnjw4AE6d+6M1atXo2fPnmWe5+TkZDx58kRYhyFvOyYmBtbW1jA2Ns7nzydvW1NTE1evXoWvry8cHBwwceJEtGrV6odaE+H+/fvo1asXateujaVLl0ImkwmuM/JCYmJiAXfVb9++hampaT5PpR8Ga2vrMlvE6GPEYjGGDBmChw8f4sSJE5DJZKhbty4WLlyYzynl51i+fDlWrFgBNzc33L17FwMHDsTw4cMLnU2fnZ0NR0dHHDhwAE2aNBG89547dw5z587FpUuXSvsWS52fyiXG11IawrBlyxZcu3YN27dvL3Asz9/KzZs3v3rhmm+NVCrFwYMHhXWVp0yZgq5du5Z4WdDSQiqVYu3atZg7dy58fHwwceJEYRW3N2/eYN26ddi4cSNq1qyJMWPGoHnz5oK4paen4/jx49i3bx+uXr2KXr16oXnz5vDx8cHkyZMxcuTIz126zMjOzkZkZCTi4+OFkOcB9MPtuLg4vH37FlKpFMrKyjA3N4eTk5MgKEZGRjA0NCzg3O7jUNT9AJCRkVGggP9UePfuHZKSkkASenp6EIlEgsO9cuXKwdDQENbW1vkKfgsLix/Cl1Rqaiq6desGFRUV/PPPPwCAJk2aoH379pgxY8YXz3/w4AF27dqFv//+W3hXbty4ASsrq8+et3HjRhw+fBgRERE4ceIEnJycEBsbC1dXV8THx/+wFbM85MLwBfI8e169erXQ43/++Sd27tyJa9eufdPlKIsLSfj5+WH+/PlISEjApEmT4O3tXSbLAxaV0NBQ/Pbbb1BUVMSWLVs+uQxjVlYW/v77b6xatQokMWbMGPTp0yffc3/16hU2btyIzZs3w87ODi9evMBvv/2GuXPn/tAvI0mkp6fj2LFjWLduHZ48eYKmTZvC2dkZEokECQkJEIvFgrO6j53WfW5fYXG0tbXzFe5FCcePH8eIESPw+++/4/fff/+hWjaF8fr1a7Ru3Rp16tTBmjVroKCggK5du0JXVxc7duz45P/h9evX2Lt3L3bv3o3U1FT88ssv8Pb2RoUKFfD777/j2rVrOHPmTKGejPPIzc1FxYoVoaenh8WLF6NFixYgCUNDQzx58uSLKyZ+b34qX0lfG0pjjCEmJobGxsafPC6TydizZ0/279//p+jDz0Mmk/HixYv09PSkpaUlV61a9c1nU+fm5nL+/Pk0NDTkmjVrimwCLJPJePbsWbZt25ZGRkacNm1aAUOAnJwc/v3336xZsyZVVFRYq1atn2JGch7BwcEcPHgw9fX12bJlS+7fv59ZWYUPkH5LIiMjWb9+fTZv3vyHfp6PHj2itbU1Fy5cKLyX48ePZ5MmTQp1zZKens7du3ezRYsW1NfX56+//sqLFy8W+E/KZDL+9ttvbNas2Rd/jx07dtDY2JhbtmwR9tWvX58XLlwo+Q2WMZAPPn8emUxGTU1NpqSkfDJOeno6XV1dfzgT1qJy584ddu7cmcbGxvzjjz9KZJpZGLkSKS8+esWdF8J46+lbYUZ327Zt6enpWaK5F2FhYRwxYgT19PTYuXNnHj58uMB8gcuXL9PMzIwqKirs2bMnb9y48dOIeEZGBvfs2cPmzZvTwMCAw4YN4+3bt79r/sViMWfNmkVTU9Nv4j/sazl//jyNjY3zDZqvW7eOTk5O+SZCSiQSnjlzhn379mW5cuXYunVr7t+//4sD3xKJhD169GC7du0+a9IrFotpaGjIX375Rdg3ePBgrlmzpgR3922QC0MRcHd35927dz8b5+nTpzQyMuKNGzdK5Zrfg8ePH7Nv3740MDDg5MmTS2U2dY5YwuGbLrP9wvezTdsv9OfM/e8Ltjdv3pRaAffu3Ttu3ryZjRs3pqGhIYcOHcqrV68K6WdnZ7N9+/Z0cHCgra0tq1Wrxn/++eeHmahYFKKiojhv3jza29uzUqVKXLp06XedMHX58mVaW1vTx8fnh2jNkOTevXtpZGSUb76Av78/TU1N+fz5c6ampvLgwYPs168fjYyMWK1aNa5cufKr/+s5OTls3bo1e/fu/dn/0KBBgygSiYT/4apVqzh8+PDi3dw3RC4MRaBLly7cv3//F+MdO3aMlpaWfPv2balc93vx4sULDh8+nPr6+hw5cmSBGr1MJmNaWhpjYmL4/PlzhoeHMyIigi9evGBUVBSjo6P57t07kuTpoJeCKOSF9gv9GRxVuAuL0iAyMpILFiygs7Mzy5cvzxkzZvDp06eUSCQcMmQIq1evzt27d7NmzZp0dXXlgQMHfiqBkMlkvHz5MgcMGCDM2j506NA3db+eR1JSErt27Up3d/fvOqlSJpNx4cKFtLa2zuey5sGDBzQ0NOTYsWPZsmVL6ujo0NPTk3/99VeJvQRkZmaycePGHDp06CcrOH5+ftTW1hbcnJ8+fZpNmjQp0XW/BcUVhv8zg88AhEW4p0+f/sW406dPFwanvucKTF9DWloanj17hmfPnuHVq1d49+4dUlJSEBsbi3v37iEyMhK6urrQ0NBAVlYWUlJSoKamhnLlykFdXb3An0Mmk6Fy5co4efIk1gY8wr938i+WpKqsiN+aO6NDTdsyvS+SuH//Pnbv3o19+/bB1tYWffr0QXR0NI4dO4ZTp04hJCQEs2bNglgsxuzZs9GxY8cfepD6Y9LT03Ho0CFs374djx8/Ru/evTFgwABUqVLlm+WBJLZu3YopU6Zg/vz5GDRo0Dd9hhKJBKNGjcK1a9fg5+cHMzMz3L17F3v37sW6deugpqaGzp07o127dvD09Pzi2uRfQ2pqKho0aIBhw4Zh2LBhBY6HhobCw8MDTk5OuHDhAl69eoWqVasiLi6u1PJQFsitkorA1q1bceXKFezYseOLcaVSKdq0aQMLCwts3rz5h7HcyMrKQnh4uCAAT58+FT5TUlLg4OCAChUqwNLSEnp6etDT00O5cuWgp6cHRUVFBAQEwNfXF/Xr18f06dNRq1atIl33/MNX+PPkw3yr0qmrKGFBn1ow0wKaNm0KV1dX1K1bF3Xr1oW7u3uZmDlKJBKcPXsWu3fvxsmTJ2FlZYXXr18jICAANWrUwPHjxzF79myQxOzZs9G+ffufSiCA96uG7dy5Ezt37oS2tjY6deqEjh07olq1at/kXkJDQ9GrVy/Y29tj8+bNMDAwKPNrZmRkoGfPnkhPT8egQYNw4cIFnDhxArq6ukhJSUH79u2xfv36MjXLfv78OerVq4djx46hbt26+Y6lp6fDyMgIWlpauHPnDmxtbaGvr49nz57ByMiozPJUUuRWSUXg0qVLrFevXpHjp6WlsW7duvTx8fkug4QSiYR3797lihUr2KFDB9rY2FBNTY1OTk5s27Ytx40bx/Xr1/PcuXOMjo4ucjdKeno6V6xYQQsLC3p5eQkzkj9HrkTK0VuvssMif3rNe9+N9MfBu8JzefLkCbdu3cpff/2VLi4u1NbWZuPGjTl58mQeO3asTPrQU1NTuXPnTrq6ulJBQYFt2rThhQsXKJFIeOTIEVauXJnVq1fn8ePHf5pB6g+RSqW8fv06J06cyAoVKtDS0pIjRozgmTNnytz/UVZWFkePHk0rKyteunSpzK7z6tUrbtiwgcbGxtTT06O2tjY9PT25YsUKPnnyhB06dPimloJ53ciFjVXkWTnNnj2bJFm3bt0yfTalAeRjDF/m1atXNDIy+qpzkpOTWa1aNU6ePLnM/5y5ubm8efMmFy9ezDZt2rBcuXJ0dnbm0KFDuW/fPj579qxQx3PFJTs7m5s3b6a9vT0bNGhAPz+/z69yJZXy+pNY7r/6jPcj4j8bNzk5mQEBAZw5c6ZgOmhiYkIvLy9OnjyZ//zzD8PCwkptTGDv3r3U1NSkra0tTUxMOGDAAB44cIB79uyhm5sba9asWSQB/JEJDQ3lwoULWbt2berr67NPnz709fUt0/UiTp48SVNTU86YMaPE/z2ZTMaIiAju2LGDAwcOpIODA3V1dampqckWLVrw9u3b+a4xduxYNm3a9JuPuUybNo1NmjQpcL+VK1fmnj17aGdnR6lUyl9//fWHt2CUC0MRkMlk1NLSEgZUi0p8fDwrVarEefPmlUo+PszP/fv3OX/+fHp6elJHR4dubm4cOXIkDxw48M0Gv8ViMfft20c3NzdWqVKFvr6+BdY2WLFiRYkWJ5HJZIyKiuKxY8c4Z84cduzYkba2ttTW1ma9evU4YsQIbt68mYGBgcW2jLl58yZNTEy4ZMkSrl69WnimHh4e9Pb2pomJCfv27fvTrHnxOfJq2l5eXtTR0WHr1q25adOmMmmZvX79mi1atGC9evW+ymFgTk4O79+/z40bN7JPnz60srKiiYkJu3fvzjVr1nDnzp00MTHJNz8gj7Vr19LJyem7eLCVSCT09PTkhAkT8u1v27Ytjxw5Qjc3N164cIErVqzgyJEjv3n+vga5MBSRypUrF8vZ2evXr+no6MgVK1aUOA+vXr3ikiVL6OrqSltbW44ZM4ZHjhxhQkJCidMuCXmL79SpU4cVKlTgtm3bmJOTw1u3btHU1PSTi+iUhKSkJF64cIErV65k37596e7uTg0NDbq6utLb25vLly/n6dOnGRkZWaTWRUhICK2trbl06VKS77sDjx49yt9++3/tnXdcFMf7xz8LqFhockdXBBURC2IXjBoLKmIvUSNi71GiSbDFkhgb1thQ+SkaiZXYC/aKiopg74iKoiIKUvTg7vP7g/O+XADpcJp9v177ur3Z2ZnPXtlnZ+aZZ4bQ3NycRkZG1NXV5ZgxY5iYmFjg11McxMXFccuWLezduzcNDQ3ZpEkTzps3r0DXIZbL5fTx8aFUKs3Us+/Nmzc8fvw4Fy1aRE9PTzo6OlJXV5fVq1enh4cH165dy3v37qlamTt27KBEIuHBgwczlPWplVJYEWFzQkxMDK2trbljxw5V2qhRo/jnn39y4cKF9PT05KFDh9iyZcti05gTRMOQQ3r06MHNmzfn6dzIyEhaW1vT19c31+cmJCRw06ZNdHV1VfVVnjp1SiPdKxUKBU+cOME2bdrQysqKUqmUGzZsKLL6k5OTeeXKFf7f//0fx4wZwxYtWtDS0pKlS5dmjRo12LVrV3p7e9PPz4+nT59mdHS0WrfWkydPWL16df78889q6QqFgqGhoRwzZgz19PSopaXFFi1acN26dV9FK4JMe0oPCgriyJEjaWFhwSpVqnDo0KH8+++/C6Q1cfHiRVpbW/Pbb7+lt7c3O3bsyIoVK7JcuXJ0cXHh6NGjuWbNGoaEhGQ5wWzx4sW0tLTMEAafJMPCwiiRSBgcHJxvrfnl0qVLlEgkqrVB5s6dy59++onR0dE0MDDg7du3aWZmVswqP49oGHLIxIkT+dtvv+X5/AcPHtDS0pIbN27MNq9cLuexY8fo6elJQ0NDurm5cfPmzQUeiriwUCgUbNWqFStVqkQTExP+8ccfBT6bOjckJCQwLCyM27Zt4x9//EFPT082adKEEomEenp6rFevHvv06cNp06Zx1apVrFmzJnv37p3pQK1CoeCqVatoaGjISpUq0cDAgA0aNOCMGTN46dIljTTYuUUulzM8PJxLlixh586daWRkRHt7e44cOZLbtm3jq1evMj0vJSWFERERPHbsGNeuXctJkybxu+++Y4MGDaivr09LS0taWVmxfPnynD9/fparrGWmx8vLi9WrV8907kFUVBQrVKiQo7lGRYWfnx/t7e0ZHx/PgIAAfvfddyTJTp06cd26ddTT0yuUlnRBkVfD8GU46BcgVatWxcmTJ/N8fuXKlXH48GG0bt0a8fHxGD16dIY8CoUC//zzD6ZNm4YSJUpgwIABmDt3LszMzPKhvOj5888/8fbtW9y+fRsPHz7EvHnzULlyZQwfPhxeXl5FHkCsbNmycHR0hKOjY4ZjsbGxai68J0+ehLa2NgIDA7Ft2zZUqFABVlZWsLCwgKWlJSwtLWFhYYGAgADs2LEDe/bsQdOmTREXFwcPDw/ExMSgadOmqs3JyalYAxTmBS0tLdSuXRu1a9fGuHHjIJfLER4ejuPHj2Pt2rUYOHAgjI2NUaFCBZQtWxYymQxPnz7F06dPYWpqCltbW9XWqVMn2NraomrVqjA2NgYAbNmyBWPHjoW2tja8vLw+qyU5OVn1uZ47dw5GRkZqxxMSEuDu7o6RI0fiu+++K6yPJNcMHjwYFy5cwKBBgzB27Fg8efIEADBgwAAsWbIEDg4OuHXrFpo2bVrMSguW/9Q8BgA4c+YMvL29ERwcnK9yHj16BDc3N3To0AE+Pj7Q0tICSRw6dEg1gW7WrFlo167dF+dHDwABAQH45ZdfcPbsWbVQ5BEREfDx8cGWLVvQr18//PTTT6hYsWIxKv08KSkp8PDwwIMHDzBz5ky8f/8eUVFRiIqKwvPnz9VeU1NTUapUKdSrVw9mZmZITExETEwMoqKi8ObNG1SpUgVOTk5o1KgRmjZtigoVKkBfX7/YJ0B+itqaPiz457bY2Fjo6+vDysoKRkZGUCgUiImJQWRkJGxsbNCmTRu0bt0a33zzDQwMDD5bd0REBPr27QsDAwNs2LABpqamGfK8efMGnTp1QsWKFeHv749SpUqpHZfL5ejatSukUin8/Pw07v/y4cMHfPPNN2jbti38/f3x7NkzyGQyWFpaonnz5nB1dcWwYcOKW2amiBPccsiLFy9Qu3ZtvH79Ot9lxcbGolu3bjA2NsawYcPw+++/IzY2Fr///ju6deumcT/wnLJ9+3aMGzcOR48ehYODQ6Z5Xrx4gcWLF8PPzw9dunSBt7d3lmG2ixuFQoHx48fj+PHjCAoKgrm5eYY8JPH69WvMmjULGzduxPfffw8bGxvV7PHXr18jMjISz58/x5s3b5CUlAQtLS0oFAqULFkSRkZGKF++PAwNDVG2bFno6OhAR0cH2traaq+fSxMEAR8+fFBtycnJmb7+Oy0lJQXly5dXrfmQ3WZsbJzp5EOZTIZLly7hxIkTOHHiBC5evAgHBwc0a9YMDRo0QP369WFra5vhd52SkoKZM2di3bp1WLduHdq1a6c69ujRI7Rv3x5du3bF7NmzM50o+uOPP+LatWs4ePCgxrbKIiMj0ahRI8TExCA5ORklSpTAuHHjcOvWLTg4OGDp0qXFLTFTRMOQQ0hCX18fT58+haGhYb7LO3fuHHr27InY2Fj4+Phg1KhRGrNoTl7YtWsXRowYgcOHD6N27drZ5o+NjcXy5cuxfPlytGjRApMmTYKTk1MRKM0dJDF37lysXbsWQUFBqFq1apZ5L1y4gL59+8LV1RWLFi3KdIUymUyGq1ev4syZMzh16hSCg4Ohq6uLmjVrolq1arC3t4elpSUUCgVSU1Mhl8uRmpr62X2S0NXVha6uLkqXLq32ml1aYczM//DhAy5evIizZ8/i8uXLuHz5MhITE1GvXj3Ur19fZSwqVKgAQRBw8uRJeHh4oGfPnpgzZw6uXbuGzp07Y+rUqRg1alSmdSxfvhwrV65EcHBwgfwfC5PDhw/Dzc0NoaGhqF27Ns6dOwdPT0/Y2NjgyJEjxS0vU8SZz7mgTp06vHTpUr7KSE5O5ujRo2lhYcEVK1ZwypQptLGxKdYAZPll9+7dNDExyTYCbWa8f/+eCxcupIWFBdu3b88zZ84UgsL8s3btWpqbm2d7je/evWPfvn1ZvXp1hoWFZVuuQqHgnTt36OfnxwEDBrBKlSo0NDRku3btOGnSJG7evJk3b94s0AmKxcGLFy+4b98+zpgxg+7u7jQ1NaVUKmX79u3566+/8q+//mLbtm1pa2tLIyMj7t69O8uy9u3bRzMzMz569KgIryB/mJqa0s3NjWTahFQ9PT2ampoWs6qsgeiVlHN69OjBv//+O8/n3759m7Vr12bPnj3VvHTWr19PExOTL2IBj/TIZDJ6e3vT0tKSFy9ezFdZHz584OrVq2lra8tvvvmGBw8e1LhwFP/88w+lUimPHz+ebd6NGzdSIpFw6dKlub6OFy9ecOfOnZw5cya7d+/OqlWrsnTp0nRycqKnpycXLlzII0eOfNFRfBUKBZ89e8Zdu3Zx6tSpbNu2LcuWLUtBEKijo8POnTtz//79Ga7x6tWrX2R4+27dulEqlfLAgQMkyY4dO7JUqVLF6q33OUTDkAsmTZrEmTNn5vo8hULB9evXUyKRcPXq1ZneKI4ePUqpVMq1a9dq3A0xMx49esTGjRuzffv2Wbov5oWUlBQGBASwZs2adHJy4vbt2zPMpi5OTpw4QalUysDAwGzz3r9/nw0aNGCHDh3y/RklJCTw4sWLXLt2LX/44Qc2b96choaGNDU1ZZs2bThhwgRu2LCBV69ezbBQkaYjl8s5ceJEVq1alSdPnuSiRYsolUppYmJCAwMDWllZ0dXVlYMHD6aRkRFnzJjBly9ffhH/k0/8/PPPHDJkCCtUqMB3795x2bJllEgkPHfuXHFLyxTRMOSCdevWqa3GlBPi4+P5/fffs0aNGmpx4jPj1q1brFmzJnv16qWxTxIkuW3bNkqlUi5cuLDQ/Pblcjl3797NRo0asVq1aly/fn2hB4DLKaGhoTQ3N+fq1auzzfvx40d6e3uzYsWKvHbtWoHqUCgUfPr0Kffv38/Zs2ezd+/edHBwoK6uLmvUqME+ffpwzpw53LdvH+/evauRBuPDhw/s27cvmzRpwtevX6vSk5KSOGbMGFasWJFbt27l9u3baWlpyfr169PZ2ZlGRkY0MjKis7MzBw0aRB8fH+7bt48PHjzQqAeJT/z5558cNWoUR4wYwcGDB/POnTssW7Ys16xZU9zSMkU0DLngzJkzbNy4cY7zX7lyhVWqVOGwYcNyHEYhKSmJo0ePprW1Nc+ePZtXqYVCYmIihw0bxsqVKzMkJKRI6lQoFDx+/Dhbt27NihUrctmyZRox0e/+/fu0tbXlrFmzcvTk+vfff1MqlTIoKKjQtSUnJzM0NJT+/v4cP34827RpQ1tbW5YsWZJWVlZs1qwZPT09OXPmTG7cuJFnz55lVFRUkU/Oe/v2LVu0aMGuXbtm+Z1+Gr+ys7PjoEGDVJ+1QqHgy5cvefLkSfr6+nLcuHF0dXVlxYoVWbp0adauXZu9evXi9OnTuWXLFoaFhRXr72bXrl10d3dnfHw8ra2tefDgQZqYmOSpB6IoEA1DLnjx4gWNjY1zlDc4ODjL+DA5Yc+ePTQ1NeWMGTM0YuDx6NGjqqfQz61/XZhcvHiRXbp0oampKefMmZProIYFzfPnz1m7dm3+8MMPObqpnj59mqamply7dm0RqMvIp5nJx48fp5+fH6dMmcI+ffqwcePGNDExYenSpWlvb083NzeOHj2aCxcu5D///MOwsLAC/84jIyNZo0YNjh07Ntsn/EGDBtHIyIguLi6MjIzMtuz379/z8uXL3LRpE6dMmcJu3brRwcGBpUqVoo2NDVu1asWBAwdyxowZXLduHY8ePcr79+8X6vKkoaGhrF27Nsm0B8x169ZpxP86K/JqGP5z7qrA/1xWnzx5kmEGZnouX74MNzc3bNiwAe3bt89zfc+fP0f//v3x8eNHbNq0CdbW1nkuK6+cP38eU6ZMwdOnTzFr1iz06tWr2OdZ3LhxA/PmzcPBgwcxYsQIjBs3rtgWPXn37h06d+4MS0tL+Pv7Z+tPf+/ePXTo0AE9e/bErFmzNGYhJyBt0ZuIiAhERETg0aNHGV5Lly4NGxsb2NrawsLCAiYmJpluZcuW/Ww9YWFhcHd3x4QJE/Djjz9+Nu+yZcuwatUqnDlzBmvXrsXixYuxcuVKdO/ePdfXl5KSorq+J0+eqLbIyEg8efIEUVFRMDIyQsWKFWFtbY2KFStm2Dc2Ns7T7z8mJgZ2dnaIjY1Vpd1/EYczt16gjK4O2tS2grGebq7LLSzEeQy5pG7duvD19c1yBbOwsDC0bdsWa9euRadOnfJdn0KhwIIFC7BgwQKsWLECPXv2zHeZOSE8PBxTp05FeHg4pk2bBk9Pz0JZWS0/PHr0CD4+Pti6dSv69++PCRMmoEKFCkWuIzk5GX369EFycjICAwNRrly5z+Z//fo1unTpggoVKsDf3x+6uppzQ8gKMm0i3ydDER0djVevXuHVq1d4+fKl2r6WllaWRuPly5fw9fXFlClT0K9fP0gkkix/V/v27cOwYcNw7tw51Sz6kJAQ9OnTB61atcKSJUsynSuSV+RyOaKjozM1Gp/2ZTKZykh8MhpWVlYwMTGBqamp6jr/PUubJMqWLYuXL19CT08PB0Ij4Rt0C7JUBXS0taCjLWDRAGfYmhbcsqP5QTQMuaRXr17o0qUL+vbtm+HYzZs30bp1ayxbtgw9evQo0HovXbqEvn37omnTpliwYIEq7kxBc+/ePUybNg0nT57EpEmTMHz4cI2/cT1//hyLFy/GunXr0LVrV3h7e392IlphkJqaiuHDh+PGjRvYv38/JBLJZ/N/+PABnp6eiIqKwq5du7LN/6VAEomJiSpDkX47ceIEzp49CwcHB8hkMrx69Qpv3ryBgYEBTExMYGhoiHLlyqniL504cQJdu3ZFlSpVVOnlypWDIAjYsGGDKg6Xk5OT6vin2eOFRXx8PJ4+fapmNJ49e6ZmIF+/fo0yZcqoDMWn123btiEwMBBNXJqi54IjasvdAoCTjTHm9mtcaNpzQ14Nw38uiN4nqlativv372dIj42Nhbu7O3x8fArcKABAgwYNEBoaismTJ6NGjRr4448/MHDgwALpiiCJc+fOwc/PD/v378ePP/4IPz+/bJ98NQULCwv4+Phg0qRJWLZsGZydndGyZUtMmjQJderUKRINOjo68PPzw+TJk/HNN98gKCjos7GgdHV1sXnzZkyePBnOzs44cuRIsXQVFjSCIKBcuXIoV64cbG1tAaT9vmbOnImHDx8iLCxMLQSKXC5HbGwsXr58ifj4eCQkJCAyMhITJ06Eh4cH7OzskJiYiNevX+Px48dISEhAYmIidHR0ULJkSXz//fcwMjKCjo4OEhMTkZiYiBIlSqgZkpIlS0JLSyvDJghCjtI+ly6Xy5GSkgIdHR1IpVLo6+vDzMwM79+/R0JCAmJiYhAWFoaUlBTIZDJs27YN9o4NQGR8sH78KqHIvqfC4j9tGI4ePaqWplAo4Onpia5du6Jfv36FVreenh6WLVuGgQMHYvTo0fDz88PKlSvzHEri4cOH+Ouvv/DXX39BV1cX/fv3x+LFiz87fqLJlC9fHtOnT8f48eOxZs0auLm5wcnJCZMnT4aLi0uh1y8IAubMmQOpVIqmTZsiKCgI1atXzzK/lpYW5s6dC3Nzc7Rq1QqnT5+GhYVFoessSlJSUjB8+HBcu3YNwcHBGSIFa2trq2IxAcD79+/h7e0Nb29v/PLLL9mW/+DBA/Tt2xdSqRTr16+HVCrFhw8fVAYkISEBMpkMJKFQKDJsmaVnlVcul+PNmzeqoIIxMTGIj4/H27dvERsbq3pvbGwMU1NTWFpawtjYWNW6P3jwIABAoq+LEtpa+JiiUF2HIAD2VoYF9KkXI3kZsS7qraC9kkjy7NmzbNiwoVranDlz6OzsXKR+9nK5nH5+fjQxMeGYMWNyPO8hNjaWvr6+dHFxoYmJCceNG8fLly9/UZOFckpycjJ9fX1pY2PDZs2a8dChQ0V2nRs3bqSpqSkvXLiQo/yzZ89m9erVC3SyYHETFxdHV1dXurm58f3799nmT0lJoZubG4cMGZKr7+nTXBELCwsePnw4P5JJps09+uTV9Ouvv7Jnz56qFQLNzMzYoEEDduvWjePGjeOCBQu4detWBgcH8+nTpypPo+TkZB48eFDlel6pUiU2bNiQI0eOJEmevf2c7rMP0H32AXaac5A9fIL4PFZzVgZEcbirAvABcAfANQA7ARimOzYJwAMAdwG0TZfeTpn2AMDEnNRTGIYhOjpazWX15s2blEgkfPLkSYHXlRPevHnD4cOH08zMjBs2bMj0D/XmzRsGBgayR48e1NfXZ8+ePbl3716NmTBW2KSkpHDTpk2sUaMG69atyx07dhSJz/6+ffsolUp56NChHOWfMmUKHR0di2W94oImKiqKjo6OHDZsWI7dMseMGcPWrVvn+Xd59OhRWlpa8ueff+bHjx+zzf/hwwdevnyZq1ev5qhRo9iyZUvVin+Ojo7s1asXp02bxoCAAF65ciVb4/bixQv6+fmxS5cu1NfXp4uLC+fMmcMbN25QoVBwwYIFNDMzUxmvl++SuOdSBI+GP2XSR81yXS0uw+AKQEe5Pw/APOW+A4BwAKUA2AB4CEBbuT0EYAugpDKPQ3b1FIZhUCgUPHnypOr90KFD87RkZ0ETEhLC+vXrs2nTpjx58iQDAwM5duxYOjo6Uk9Pj23atOHq1au/iptOXpHL5dy1axcbNmxIe3t7+vv7F7pxPHfuHE1MTHIUY0uhUNDLy4uNGjVifHx8oeoqTG7cuMGKFSty9uzZOX7yX7p0KR0cHPI9N+XVq1d0d3dn/fr1ee/ePVX6hw8fGBISQl9fXw4dOpR169ZVrRHu6enJJUuW8NChQzleI5z835Kvv/32Gxs0aEBDQ0P26tWLGzduVJvFTaaFUilfvjxtbGxU5RfEkqmFRbEYBrWCgK4AAvi/1sKkdMeCADRRbkHp0tXyZbUVhmEgyYRkGV/HJVOhUGjE9Pvo6Ghu3bqVI0aMoLm5OQVBoIWFBX/66SeeP3/+P9MyyCkKhYJHjx5ly5YtaW1tzeXLlxfqrNjr16/TysqKS5cuzZG2YcOGsVmzZjmeLa9JnDhxgiYmJvzrr79yfM7u3btpbm7OiIiIAtGgUCj4xx9/UE9Pjy1btqSTkxNLly7NWrVqccCAAVy2bBmDg4Pz9PkmJSVx7969HD58OC0tLVm5cmV6eXnx2LFjmbZSFAoF58+fTzMzM65fv56VK1cmmdZdZWhoqBH3j8zQBMOwF0A/5f7yT/vK9/8HoIdy80uX7gFgeRblDQNwGcDlihUrFuiHlSqX02d3GDv8kdY3OGjFCT55nX3faUEgl8v55MkTHjt2jKtWreL48ePp7u6uCtPcsWNHLliwgJcuXWJMTAx/++03SiQSenp68sGDB0Wi8UvkwoUL7Ny5M83MzDh37txCm9X9+PFj2tnZcerUqdk+Rcvlcvbr14+urq4aGd8oKwICAiiVSnns2LEcn3PlyhVKJJJ8R+d9/vw5N2/ezOHDh9Pe3p6GhoZs3rw5TUxM2KZNm3w9nT979oyrV6+mu7s79fT02Lx5c/r4+PDOnTuf/S7j4uLYrVs3NmjQgJGRkUxMTGSpUqUol8u5c+dOtm7dOs+aCptCMwwAjgK4kcnWOV2eKcoxhk/zIvJtGNJvBd1i2HruPjvOPkDX3/bR9bd9bPvbPnosPZbvAU25XM64uDg+efKE169f56lTp7hu3TpOnDiR3bt3Z61atVimTBmam5uzWbNmHDJkCOfPn8+dO3fy5s2bWT51vH37ltOnT6exsTEHDhzIhw8f5kvn18z169f5/fff09jYmFOnTs3QFVAQvHr1ivXr1+ewYcOyfVJMSUlh9+7d2bVr1yKPYZRbFAoF58yZwwoVKmQbKDI9T548oaWlJXfs2JHrOhMSErhz504OHz6c1apVo5GRETt37sxFixYxNDRU9fkmJiZyxIgRtLGxyXGobrlczpCQEE6bNo1OTk40MjJinz59+Pfff/PNmzc5KuPGjRu0s7PjiBEj1Iy7RCJhdHQ0hwwZwsWLF+f6uouKvBqGfE9wEwRhAIDhAFqRTFKmTQIAknOU74MAzFCeMoNk28zyZUVBT3AbuuoUnsSo+xrrltDG8iFNUVKeiEWLFkGhUKh8nj+9ksT79+8RFxeHuLg41bKPn17fv38PXV1dGBoawsDAAEZGRqoF1O3s7GBnZ4cqVapAT08vT7rfvn2LxYsXY8WKFejatSumTp2KSpUqFcAn8vXx8OFD+Pj4YNu2bfD09MSECRNgZWVVYOW/f/8e3bp1g76+PgICAj47eVAmk6FVq1Zo06YNpk2bVmAaCpLU1FSMHTsW586dw4EDB2BpaZmj896/f4+mTZuiX79++Pnnn3N0zvPnz7Fv3z7s2bMHp0+fRsOGDdGhQwd8++23qFWr1mdXQNy5c6cqfIq3t3eGvImJiTh69Cj27t2L/fv3w9DQEO7u7ujYsSOcnZ1zNWluy5Yt+OGHH7BgwQJ4enqqHatXrx6WLl2Kzp07IzQ0VGPnrhTLzGdBENoBWASgOcnX6dJrAPgbQEMAFgCOAagKQABwD0ArAFEALgHoS/Lm5+opaMMwbt053Il6p5ZWQlsL/mO+hVZqEvz9/VVLLZJU+USTaTGWDAwMYGBgoDIAn/aLamH42NhYLFq0CKtWrUL37t0xZcoUjf1hFjfPnz/HokWLsG7dOnTv3h2//PJLgc2m/vjxI/r3749Xr15h9+7d0NfPOgzCixcvUL9+ffj5+eUr7lZhkJiYqBYK5HPXkZ7U1FR06tQJFSpUgK+vb5axh0ji+vXr2LNnD/bs2YP79++jffv26NSpE9q1a5frJT2fPXuGfv36QRAEbNq0CXK5HPv27cPevXtx9uxZNGzYEB07doS7uzuqVKmSq7KBNEP+888/Y9++fQgMDMx0cmWXLl1gZWWFx48fY9++fbmuo6golqU9keZy+hRAmHLzTXdsCtI8kO4CaJ8u3Q1pxuEhgCk5qaegu5LO341W60rq8McBTtqUv77R4uD169ecOHEijYyM2LNnT546deqrnMdQEMTExHD69OmUSCTs3bs3w8PDC6Tc1NRUjho1ik5OToyOjv5s3jNnztDExESjugKjo6PZoEEDenp65sg19BMKhYKjRo2iq6trlk4Rjx8/5qxZs2hnZ8dKlSpx7NixPHbsWL6dKFJTU3n27Fk2a9aMOjo61NPTo4eHB7dt25Zvb6ioqCg6OzvT3d39s55/P/zwA62trbl379581VfYoLgHnwtzK5QJbrdfcLjvKfZdfJS+QTeZLNNMr4Kc8O7dO/7555+sVq0aa9WqxdWrVzMhIaG4ZWkk8fHx9PHxobm5Od3d3RkcHJzvMhUKBWfMmMEqVapke9NfunQpHR0dNcJT6e7du7S1teW0adNy/UCxePFi1qhRI8ONOD4+nuvXr2eLFi1obGzMkSNH8vz58/l+YImLi+OOHTs4YMAAmpiYsEaNGpw4cSJ9fX1pbW3NkSNH5tsj7eTJkzQ3N+fvv/+e7XiQl5cX9fT0NNYb6ROiYRChQqHgkSNH2LlzZ5YvX55eXl5qPuAi/yM5OZmrVq1ipUqV2KJFCwYFBeX75rVixQpaWlp+tjWiUCjYt29fenh4FGvr7uzZszQ1NaWfn1+uz921axctLCz4+PFjkmmDvIcPH+b3339PAwMDdurUiYGBgfn2xHr06BGXLl3KNm3asFy5cnR1deWyZcv46NEjtXzv3r1j7969c7S6YmYoFAr6+PjQ1NQ0xwswtW7dmg4ODrmuq6gRDYOIGo8fP+bEiRMplUrZtm1b7t27V+OfboqDlJQU/vXXX3RwcGC9evUYGBiYL++hrVu3UiqV8vTp01nmSUhIYK1atbhixYo815MfduzYQYlEwoMHD+b63MuXL1MikTAkJIQJCQlcuXIlq1Wrxjp16vDPP//MVyiQlJQUnjlzht7e3nRwcKCJiQkHDhzIwMDAbCcKpl+Pffny5Tk2unFxcezevTvr16+vMnTZ8ezZM+rr67NOnTo5yl+ciIZBJFOSk5O5YcMGNmzYkJUqVeIff/yR4YlLhCqf9AYNGrB69ercuHFjnvvCjxw5QqlUyt27d2eZ5/79+5RKpQXSlZUbFi9eTEtLS4aGhub63MjISFpaWnLNmjX09vamRCJhly5d8jW29e7dO27ZsoX9+vWjsbExHR0dOXXqVF64cCFPBvru3busW7cuO3XqlK2r8o0bN1itWjUOHz48V62bQYMGcfTo0TQ3N8+1vqJGNAwi2XLx4kWOGDGCEomEjRs35tKlSzV6On9x8Kk77ttvv6W1tTVXrFiRp77rkJAQmpmZcd26dVnm2b17NytVqlQk40FyuZxeXl6sXr16jp+M0xMXF8cqVaqwTp06NDIy4tixY/M84fLevXtctGgRv/32W+rp6dHNzY0rV64ssDhlHz9+5IQJE2hlZcXjx49nmmfLli2USCRcv359rsoODw+nqakp37x5wxIlSmj8xEXRMIjkGJlMxgMHDrB///40NDRky5YtuXbt2v90/KXMCA4OZseOHWlmZsZ58+blejb1nTt3aG1tzXnz5mWZp2/fvvzpp5/yK/WzJCUlsXv37mzWrFmevuOQkBBKJBKWLVuWPj4+OY4A/AmZTMYTJ05wwoQJtLOzo7m5OYcOHcrdu3cXqlE8dOgQzc3NOWnSJFXrTyaTcdy4cbS1teXVq1dzXaarqyuXL19OkrS2ttYoD7PMEA2DSJ5ISkrijh072L17d+rr67Njx478+++/Ra+mdISHh7NPnz6USCT89ddfczWb+tmzZ6xRowZ/+umnTLtGXr58SalUmqebVE6IiYmhs7Mze/funeun27t377Jnz54sXbo0q1evnqvfxJs3bxgQEMDevXvTyMiI9erV44wZM3j58uUinQH+8uVLtm/fno0aNWJwcDBdXFzYoUOHPBnIQ4cO0c7OTmVkPgW61GREwyCSb+Li4rhhwwa2a9eOBgYG7N27NwMCAgolrMSXyP379zl06FAaGRnxxx9/5LNnz3J03ps3b9ikSRN6enpmOm7h5+fH+vXrF7hzwMOHD2lnZ8dffvklVzfjJ0+ecMiQITQ2Nmb79u1Zo0aNbFtLCoWCt27d4vz58/nNN99QT0+PnTp14po1axgVFZXfS8kXcrmco0ePppaWFnv06JEnwxQfH88qVapwz549qrS+ffty48aNBSm1wBENg0iB8vr1a65evZqdO3emvr4+GzZsyGnTpvH8+fP/ee+mZ8+e8ccff6SRkRGHDh3K+/fvZ3tOYmIi3dzc6O7unmEOg0KhYPPmzXMUtTWnhISE0NzcXNXtkRMSEhL4yy+/sHz58pw4cSL/+usvWlpaMjIyMtP8Hz9+5JEjRzhu3DhWrlyZVlZWHDlyJPfv31+oUW5zw6f1E0xNTbl8+XJWq1aNHh4euQ6HPmDAAA4ZMkQtbeLEiZw1a1ZByi1wRMMgUmh8/PiRx48f588//8xatWrR2NiYvXv3pr+//3968Pr169ecNm0aJRIJ+/Tpk+1saplMRg8PD7q4uGToyrhz5w6NjY0LZAB27969lEgk3LVrV47POXDgACtVqsS+ffvy+fPnvHTpEiUSCS9duqSW79WrV9ywYQN79OhBAwMDNmrUiL///jvDwsI0btZ9fHw8e/TooeaKmpCQwCFDhrBy5coMCQnJUTmbN2+mnZ1dhq60FStWcPjw4QWuuyARDYNIkfH06VP6+fmxe/fuNDQ0pJOTEydNmsTTp0/nKqzC10JcXJwqVn/Hjh0/G/1TLpdz/PjxrFmzZoauqBkzZrBTp075usGuWrWKZmZmOV6K9MWLF/zuu+9oY2OjWqEuMjKSFhYW3LlzJxUKBa9du8bZs2fT2dmZ+vr67NatG9etW5dtCJDi5NatW7S3t+ewYcOYnJyc4fi2bdsolUo5d+7cz3YtPX78mFKplJcvX85wbO/evWzfvn2B6i5oRMMgUizIZDKeOXOGkydPZt26dVm2bFl+88039Pb25u7du7+qtY+zIykpiStXrqS1tTW//fZbHjlyJNObvEKh4Lx581ipUiXevXtXlf7hwwfa29szMDAw13XL5XJOnDiRVatWzZEbqVwu5+rVqymRSOjt7a3q3oqLi6ODgwOHDRumWufY2tqaY8aMYVBQkMa7Z5JpkwwlEslnXYXJtJu+i4sLW7Vqlek4iEwmY9OmTTl//vxMzw8PD2fNmjULRHNhIRoGEY0gLi6Ohw8f5owZM9i2bVsaGBiwatWq7N+/P319fXnt2rWvfoxCJpNx48aNrF69Ohs0aMB//vkn06fS//u//6OZmZlad82JEydYqVKlXLW8Pnz4wL59+7Jx48Y5MsTR0dFs3bo1GzZsyLCwMFXamjVraGJiwpIlS6rWOb5+/brGdRFlhUwmo5eXF21sbHI8gS8lJYXTp0+nqampWkA8hULBAQMGsEOHDlm2KGJjY6mvr18g2gsL0TCIaCSpqam8fv06V69eTU9PT1atWpUGBgZ0dXXl9OnTGRQUlONFU7405HI5//nnH9arV48ODg7cuHEjU1LUF4vftWsXpVIpjx49qkpr27YtV61alaM63r59yxYtWrBr1645Csx38uRJWlhYcPLkybx06RJ/++03NmzYkIaGhrS1tWWtWrW+yHGj58+fs2nTpnRzc8uTK+rp06dZsWJF/vDDD0xOTuaUKVPYsGHDz7roKhQKlitXLt8RXQsT0TCIfDG8evWKe/bs4cSJE9msWTPq6+vT3Nycrq6unDBhAv39/Xn58mWN8WzJLwqFgocPH2aLFi1oY2PDVatWqfV7nzp1ilKplNu2bSOZ5lFkaWmZad94eiIjI1mjRg2OHTs221aYXC7n9OnTaWhoyA4dOtDKykptneO5c+eydu3ahbYkamFy+vRpWlhY8LfffsvXHInY2Fj26NGDFhYWtLa2zlHry8HBIU+B+4oK0TCIfLEoFAo+fvyY+/bt45w5c9i3b1/WqlWLurq6tLOzY/fu3Tl9+nTu2LGDd+/e/aK7os6dO0d3d3eam5tz/vz5KrfJsLAwWlhYcOXKlSTJTp06ccmSJVmWc/XqVVpZWXHhwoWf7eqJioriggULKJVKqa2tzcaNG9PHx4e3b99WnRcYGEhLS8sCC0lRVCgUCi5cuJCmpqaqgfP8smPHDhoaGtLQ0JC+vr7ZdqO1bduW+/fvL5C6CwPRMIh8dchkMt64cYObN2/m5MmT2bFjR9rY2LBMmTKsW7cue/XqRW9vb/r6+jIoKIj37t37IgZHyTRD0Lt3b0okEk6bNo0xMTF8+PAhK1euzJkzZzI8PJyDBw/O9NygoCBKJBJu3bo1wzG5XM5Lly5x+vTprFu3Lg0MDFimTBl27NiRL1++zJD/U7iLzLxuNJn4+Hj27NmT9erVY0RERIGUuW/fPkqlUoaGhvLWrVt0dHRkt27dPtvVOXTo0Bx3+xUHomEQ+c8QHx/PCxcuMCAggLNmzeLgwYPZsmVL2tjYsGTJkrSysuI333xDDw8PTps2jevXr+fJkycZGRmpca2Ne/fucciQITQyMuL48eMZFhbGhg0bqtxNU+UKxsQnMyU1rYvkzZs3NDMzUwvrnZCQwF27dnHIkCE0Nzenvb09f/rpJ65cuZImJib09/fPtO7Hjx/TwsLis1FgNZFPrqhDhgzJtrstp/j7+9PU1FTNzTc5OZleXl6sUKFClqEvfv/9d06ePLlANBQGeTUM+Vrzuago6DWfRb5eUlNT8ezZM0RERCAiIgKPHz9W7UdERCAmJgYWFhYwMTFRbaampmrvP6UZGxsXyRreQNo6xgsXLsSGDRvg5+eHbt264cK9l1i4JxwfUuTQ0dbCCFcHtK1TAXK5HM+fP1db57hBgwZq6xwfOnQIHh4e8Pf3R4cOHTLUFxcXBxcXFwwZMgReXl5Fco0Fwfbt2zFq1CjMmzcPgwYNKpAyfXx8sGLFChw6dAj29vYZjh84cACDBw/G0KFDMW3aNLXfxJYtW3Dy5En4+voWiJaCJq9rPouGQeQ/xYcPHxAVFYVXr159dnv58iXevn0LQ0NDNYMhlUqhp6eHcuXKqbZ/v0+fVrZsWWhpaeVYX2xsLLS1tZEilMLAFScgS1WojpXS0cLigS4wLacFJycnNGnSBO7u7mjbti0MDAxU+QICAjB+/Hjs3LkTzs7OGepISUlBhw4dYGdnh2XLlkEQhPx9qEVASkoKvL29sWvXLuzYsQN169bNd5kKhQLe3t44ePAgDh06BCsrqyzzRkdHo3///khMTERAQAAqVaoEAJDL5ZDJZChdunS+9RQGeTUMRfM4JCKiIejq6qJy5cqoXLlytnnlcjnevHmjZjBev36NhIQEJCQk4NWrV3j//r3q/actfVpiYiJKly6tMhZlypRBiRIloKOjo/b6aV8qleL//u//sPdyJP59u06RK3D61nMMbGmP+/fvZ6p5zZo1+P3333H8+HHUqFEjw3GSGD16NEqUKIElS5Z8EUbhxYsX+O6771CuXDlcvnwZ5cuXz3eZSUlJGDZsGCIiInD69OlsyzQzM8OhQ4ewaNEiNGzYEA8ePIC+vj5iE2UIi3gDib4uHCsZQ+sL+DxzgmgYRESyQFtbW9VSyCsKhQJJSUlqhiI1NRUpKSlISUlR7X96LVu2LABAt4R2hpu2lpYWSpfM+i/7zz//YMaMGThz5kyWhm/BggUICQnBmTNniqybLD+cOXMGvXv3xrBhw/Drr7/mqvWVFXfu3EHPnj1Rp04dHDlyBGXKlMnReVpaWvjpp58waNAg6Ovr42BoJFYG3YKWlgABgLlRGSz0dEaZUpr/uWbHl38FIiIajJaWlqp7KTe42Jth9ZFb+Jgix6fO3hLaAlrVtsw0/5kzZzBixAgcOnQoS6MQGBiIpUuX4sKFC9DT08uVnqKGJJYsWYK5c+diw4YNaNeuXYGUGxAQAC8vL8yZMweDBw/OU4upfPnyeJ+cgpVBt9S6+p69ScQ/Fx+hXzO7AtFanIiGQUREAylTSgdLBjrD9/At3I16B2upHoa7OkCqn7Ev++bNm+jRowcCAgKy7Hu/ePEiRowYgaCgoM/2pWsC79+/x5AhQ/DgwQNcvHhR1Z+fH5KTkzFu3DicOnUKx44dQ+3atfNV3sPoOOhoa6kZBlmqAlcexoiGQUREpPCwMi6HWX0afjbP06dP0b59eyxatAht2rTJNM/jx4/RtWtXrFu3rkAGbQuT27dvo3v37nB2dsa5c+egq6ub7zLDwsLg6ekJBwcHXL58OcetpeTkZNy8eRMXL17EkSNHEBYWhhcvXmD79u1o1Kw1UuUKtfzaAmBjotktsZwiGgYRkS+UlJQU9OzZEyNHjsT333+faZ53796hQ4cOmDhxIjp27FjECnPHjh07MHLkSMydOxeDBw/Od3mJiYmYPn06Nm7ciHnz5mHAgAGZdh2RRHR0NMLDw3H16lUEBwcjNDQUL1++RIkSJZCSkoKKFSuiUaNGcHNzg5ubG3R0dPBtTUucuvkcH1LkKKGthVIltPGdS/ZODV8ComEQEflC+e2332BoaAhvb+9Mj38yHK1atcLYsWOLWF3OSU1NxcSJExEYGIhDhw6hXr16+S7zwIEDGDVqFJo2bYobN26oHAhkMhnu3LmD8PBwlSEIDQ2FTCZD2bJlkZSUhBIlSqB+/frw8vJCs2bN4OTkhJIlS2aow8u9FhpVNUHw3WiYGZaBW92KMNbLfwtHExANg4jIF8jp06fh5+eHq1evZuqpQxIjR45EqVKlsHjx4mJQmDOio6Px3XffoUyZMrhy5Uq+XVGjo6Mxbtw4XLlyBQsWLICRkRE2bdqkMgR3796FsbExypUrhw8fPiA6OhpVq1ZFs2bN4OLiAhcXF1SsWDFHg9JaggAXezO42JvlS7MmIhoGEZEvjLdv38LDwwN+fn4wM8v8pjR//nxcuXIFZ86cgba2dhErzBlnz55F7969MWTIEEybNi3PrqhyuRxXr16Fj48P9u7dC3NzcyQnJ2Pw4MGoXLky9PX18fHjR8TExEBXVxe1a9eGs7MznJ2d0bBhw1x7jP0XEA2DiMgXxsiRI9G5c+dMQ10AaX31y5cvx4ULFzTypkcSf/75J2bPng1/f3+0b98+x+fGxcWpnv4/dQVdv34dCoUCpqamcHV1ha6uLp49e4br168jKSkJderUURkCe3v7ApkL8bUjGgYRkS+Io0eP4tKlS7hx40amxy9cuICRI0fiyJEjsLTMfM5DUUIS75NToFtSGyV10lou48ePx+nTp3HhwgXY2Nhkep5CoUBERISaEQgLC0NMTAxq1qyJmjVrIiYmBg8fPoSpqSlKly6N58+f4927d3B2dka/fv3QuHFjSCSSorzcrwYxVpKIyBeCQqFAvXr1MGXKFPTo0SPD8YiICDg7O2Pt2rVwd3cvBoXqPH71Hr/vuILot0nQ0hLQrZENBnxbDbdu3YKtra0qvlBiYiKuX7+uZgSuX78OQ0NDODo6wtHREdWrV4cgCIiIiMDOnTsRFhYGHR0duLi4oHPnznB2doajoyNKlChRzFetWYixkkREvnI2bdqE0qVLo3v37hmOfXJLnTJlikYYBblCgYmbLuBtoiwtQUHsCnkMa6keWtaqgYCAAOzatQvh4eF49uwZqlevrjICvXr1glQqxe3btxEcHIyjR49i8eLFKF++PN6+fQtra2usW7cOHh4eX0Sspy8R0TCIiHwBJCUlYcqUKdi2bVuGm6FMJkP37t3Rpk0bjBkzppgUqnP3eRw+pKhPAPuQIseB0CdoWcsSurq66Nq1K2bMmAFbW1uVEQgODsbixYuRkJAAZ2dn2NnZwczMDHfu3EHLli3h5eUFJyenYrqq/w7iKIyIyBfAkiVL0LhxYzRp0kQt/ZNbatmyZbFo0aJiUpeRUjrayKybWrekNt6+fYvSpUvj1q1b+OGHH2BiYgIPDw+Eh4ejTZs22L9/PzZu3AiFQoGNGzeiVq1auH37NjZs2CAahSKiQFoMgiBMALAAgJRkjJD2SLMUgBuAJAADSIYq83oCmKo8dRbJDQWhQUTka0Umk2Hp0qU4depUhmPz5s3D1atXcfr0aY1yS7U11YOFURk8iUlAqiLNQAiKVBxYMwt/Dj+JBg0awNnZGT///DMaN24MIyMjXLt2DRs3bsTkyZNhbW2NwYMHY/v27Rq71sHXTL4NgyAIFQC4AniSLrk9gKrKrRGAVQAaCYJQHsB0APUBEMAVQRD2kHybXx0iIl8rO3fuRM2aNTOsLrZt2zasXLkS58+f1xi31KSkJFy6dAnBwcF4GhKKl6WrwtCmDkoKqahr/BHdF81ErVq1VCG/o6Oj4e/vjw0bNiA2NhYeHh44ceJEpiupiRQdBdFiWAzgFwC706V1BrBRueboBUEQDAVBMAfQAsARkrEAIAjCEQDtAGwuAB0iIl8lvr6+GDVqlFra+fPnMWbMmGJ3S3369KlqbCA4OBi3bt1STSAb2K83mjRpAgsLC7VzXr58if379yMwMBDnzp1Dly5dsGjRIrRo0UKcY6Ah5MswCILQGUAUyfB/DYhZAnia7v0zZVpW6ZmVPQzAMACoWLFifmSKiHyx3LlzB3fu3EHnzp1VaY8ePUK3bt3g7+8PR0fHItOSkpKCsLAwNUPw8eNHuLi4wNnZGUuWLEG9evUyREQlidu3b2PPnj3Ys2cPbt26BVdXV/Tt2xfbtm1TLU4kojlkaxgEQTgKILN591MATEZaN1KBQ3INgDVA2jyGwqhDRETTWb16NQYNGqQK4vb27Vt06NABU6dOhZubW6HWHRMTgwsXLuDcuXMIDg7GlStXULlyZTg7O8Pd3R2zZ8+Gra1tpi6jMpkMwcHBKmMgk8nQqVMnzJgxA82bN0epUqUKVbtI/sjWMJBsnVm6IAi1ANgA+NRasAIQKghCQwBRACqky26lTItCWndS+vSTedAtIvLVI5fLsWnTJly8eBFA2s22R48eaNeuHUaPHl2gdSkUCty5c0etNfDixQs0atQIzs7OmDJlCho1agQDA4NMz5fJZLh06RJOnjyJU6dO4fz586hWrRo6deqEHTt2wNHRUZxz8AWR564kktcBqBbDFQThMYD6Sq+kPQDGCIKwBWmDz3EkXwiCEARgtiAIRsrTXAFMyrN6EZGvmJCQEJiZmcHW1hYkMWLECJQrVw4LFizId9kJCQkICQlRGYHz58/D2NhYFVPIy8sLNWrUyNLT6ePHj2qG4MKFC7Czs0Pz5s0xZswYbN26FUZGRpmeK6L5FNYEtwNIc1V9gDR31YEAQDJWEITfAVxS5vvt00C0iIiIOgcOHFAFypszZw7Cw8Pz5JZKEpGRkWqtgXv37qmCyw0bNgzr16+Hqalppuenpqbi1q1buHLlimq7fv067O3t0aJFC4wdOxbbt2+HoaFhfi9ZREMQYyWJiGgoBw4cQPXq1REaGorx48fj/PnzGTx8MuPjx4+qlcg+bSRVg8TOzs5wcnLKtJ9fJpPh9u3bGYxAhQoVUK9ePdXm5OQEfX39wrhskQIkr7GSRMMgIqJhKEgkfEhB2VIloK0lYPv27ahevTpq1qyZaf5Xr17h/PnzCA4Oxrlz5xAWFgY7OzuVEXB2doa1tbWqj58kYmJicPfuXdy5c0ft9cmTJ7CxsclgBHK6TrKIZiEaBhGRr4BLD15h4Z5wvE9OQelSOvihfU00r/G/VoJcLsetW7fUWgMxMTFo3LixqkXQoEEDaGtr4+nTp2rb48ePVQZAoVDA3t4e9vb2qFatmuq1cuXKmS5jKfJlIhoGEZEvnJj4Dxi04gQ+pv4v+FwpHS0sG9IUVuXLoFu3bjh16hTKly8PBwcH2NrawszMDCQRFRWlZgSSkpJgZWWFChUqqDZra2uVAZBKpaKX0H8AMey2iMgXzrm70fj3Y1qqXIFTN1+gfws73L17Fx8/foRcLkd0dDTkcjnevXsHc3NzODg4oG3btiojIJFIxBu/SJ4RDYOIiIZQSkcLWv+6mWtpaUG3RFqYiJs3b2pUoDyRrxcxMImIiIbQtLo5tLXUDYO2loBva6VFjRGNgkhRIRoGERENoZxuCSwa4AxH6/IoU0oH9paGmN+/MaT6YthpkaJF7EoSEdEgKpnoYX7/JtlnFBEpRMQWg4iIiIiIGqJhEBERERFRQzQMIiIiIiJqiIZBREREREQN0TCIiIiIiKghGgYRERERETW+iFhJgiC8BhCZw+wSADGFKCe/aLo+QPM1ivryh6gvf3xJ+qxJSnNbwBdhGHKDIAiX8xI0qqjQdH2A5msU9eUPUV/++C/oE7uSRERERETUEA2DiIiIiIgaX6NhWFPcArJB0/UBmq9R1Jc/RH3546vX99WNMYiIiIiI5I+vscUgIiIiIpIPRMMgIiIiIqLGV2EYBEGYIAgCBUGQKN8LgiD8KQjCA0EQrgmCUDddXk9BEO4rN89C1vW7sv4wQRAOC4JgoWH6fARBuKPUsFMQBMN0xyYp9d0VBKFtuvR2yrQHgiBMLGR9PQVBuCkIgkIQhPr/Olbs+jLRW2x1/0vHOkEQXgmCcCNdWnlBEI4of1dHBEEwUqZn+VssJG0VBEE4IQjCLeV3O06T9Cnr1BUEIUQQhHClxpnKdBtBEC4qtWwVBKGkMr2U8v0D5fFKha1RWa+2IAhXBUHYV+D6SH7RG4AKAIKQNgFOokxzA3AQgACgMYCLyvTyAB4pX42U+0aFqE0/3f5YAL4aps8VgI5yfx6Aecp9BwDhAEoBsAHwEIC2cnsIwBZASWUeh0LUVx1ANQAnAdRPl64R+v6ltdjqzkRLMwB1AdxIlzYfwETl/sR033Wmv8VC1GYOoK5yXw/APeX3qRH6lHUKAMop90sAuKisexuA3sp0XwAjlfuj0v23ewPYWkTf83gAfwPYp3xfYPq+hhbDYgC/AGrrqHcGsJFpXABgKAiCOYC2AI6QjCX5FsARAO0KSxjJ+HRvy6bTqCn6DpNMVb69AMAqnb4tJD+SjADwAEBD5faA5COSMgBblHkLS99tknczOaQR+v5FcdatBsnTAGL/ldwZwAbl/gYAXdKlZ/ZbLCxtL0iGKvffA7gNwFJT9Cl1kWSC8m0J5UYALQHsyELjJ+07ALQShH8t3l3ACIJgBaADAD/le6Eg9X3RhkEQhM4AokiG/+uQJYCn6d4/U6ZllV6YGv8QBOEpgO8BTNM0fekYhLQnM3xGR3HqS48m6tOUzyYrTEm+UO5HAzBV7hebbmWXhhPSnsg1Sp+ymyYMwCukPaA9BPAu3YNUeh0qjcrjcQCMC1niEqQ9ECuU740LUp/GL+0pCMJRAGaZHJoCYDLSukOKjc/pI7mb5BQAUwRBmARgDIDpmqRPmWcKgFQAAUWpTVl3tvpEChaSFAShWP3UBUEoByAQgBfJ+PQPsJqgj6QcQB3luNtOAPbFqSc9giC4A3hF8oogCC0Kow6NNwwkW2eWLghCLaT1L4crf1RWAEIFQWgIIAppYw+fsFKmRQFo8a/0k4WhLxMCABxAmmHQGH2CIAwA4A6gFZWdkJ/Rh8+kF4q+LCgyfQWkSRN4KQiCOckXyq6YV8r0ItctCEIJpBmFAJL/aJq+9JB8JwjCCQBNkNaNpaN86k6v45PGZ4Ig6AAwAPCmEGW5AOgkCIIbAF0A+gCWFqi+ohgkKYoNwGP8b/C5A9QHrEKU6eUBRCBtYNdIuV++EDVVTbf/A4AdGqavHYBbAKT/Sq8B9cHdR0gbXNVR7tvgfwOsNYrguz0J9cFnjdKn1FRsdWehpxLUB599oD64O/9zv8VC1CUA2Ahgyb/SNUKfsk4pAEPlfmkAZ5D28LQd6oO7o5T7o6E+uLutCL/nFvjf4HOB6SuWH20hfUCP8T/DIABYgbR+wev/uqkMQtpg5QMAAwtZUyCAGwCuAdgLwFLD9D1AWt9jmHLzTXdsilLfXQDt06W7Ic2T5CHSunsKU19XpPWVfgTwEkCQJunLRG+x1f0vHZsBvACQovz8BiOtT/kYgPsAjkL5wPG532IhaWuKtIHca+l+d26aok9ZZ20AV5UabwCYpky3BRCi/N9sB1BKma6rfP9Aedy2CL/rFvifYSgwfWJIDBERERERNb5oryQRERERkYJHNAwiIiIiImqIhkFERERERA3RMIiIiIiIqCEaBhERERERNUTDICIiIiKihmgYRERERETU+H+G152GamedBgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#layout = g.layout(\"kk\")\n",
    "#layout = igraph_layout_from_cx_cartesian_layout(g[\"cartesian_layout\"])\n",
    "\n",
    "l = igraph_layout_from_cx_cartesian_layout(test_network.get_opaque_aspect(ndex2.constants.CARTESIAN_LAYOUT_ASPECT))\n",
    "print(l)\n",
    "draw_igraph_with_cx_layout(g, test_network.get_opaque_aspect(ndex2.constants.CARTESIAN_LAYOUT_ASPECT))\n",
    "\n",
    "#G = g.to_networkx()\n",
    "#nx.draw(G, node_size=50)\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make a fresh gameboard\n",
    "initialized to zeros\n",
    "a square numpy array with area scaled to the sqrt of the node count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_gameboard (cx_network, sparsity=15):\n",
    "    node_count = len(list(cx_network.get_nodes()))\n",
    "    dimension = round(math.sqrt(node_count * sparsity))\n",
    "    return np.zeros((dimension, dimension), dtype=np.int16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gb = make_gameboard(test_network)\n",
    "sns.heatmap(gb)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "make a fresh cartesian coordinates aspect and assign random coordinates to the nodes\n",
    "but no two nodes can have the same coordinates\n",
    "check this by placing ones on the board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_coords (cx_network, gameboard):\n",
    "    dimension = gameboard.shape[0]\n",
    "    coords = {}\n",
    "    nodes = cx_network.get_nodes()\n",
    "    for node_id, node in nodes:\n",
    "        #print(node)\n",
    "        placed = False\n",
    "        while not placed:\n",
    "            x = randint(0,dimension-1)\n",
    "            y = randint(0,dimension-1)\n",
    "            if gameboard[x, y] == 0:\n",
    "                gameboard[x, y] = 1\n",
    "                placed = True\n",
    "        coords[node_id]= {\n",
    "            'x' : x,\n",
    "            'y' : y,\n",
    "            'adj' : set()\n",
    "       }\n",
    "    for edge_id, edge in cx_network.get_edges():\n",
    "        #print(edge)\n",
    "        coords[edge[\"s\"]]['adj'].add(edge['t'])\n",
    "        coords[edge[\"t\"]]['adj'].add(edge['s'])\n",
    "    return coords\n",
    "\n",
    "def igraph_layout_from_coords(coords):\n",
    "    igraph_layout = [(0,0)] * len(coords)\n",
    "    for i, node in coords.items():\n",
    "        #igraph_node_id = node[i]\n",
    "        igraph_layout[i] = (node[\"x\"],node[\"y\"])\n",
    "    return igraph.Layout(igraph_layout)\n",
    "\n",
    "def draw_igraph_with_coords(g, coords):\n",
    "    l = igraph_layout_from_coords(coords)\n",
    "    fig, ax = plt.subplots()\n",
    "    igraph.plot(g, layout=l, target=ax)\n",
    "    #igraph.plot(g, l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gb = make_gameboard(test_network)\n",
    "coords = make_coords(test_network, gb)\n",
    "draw_igraph_with_coords(g, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD8CAYAAADUv3dIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbPUlEQVR4nO3de9xdVX3n8c83CSAECAqKkKCgopWqL8SU2lYpFmwDOjBO6wjoeBk0nVeLl6q1dPClqKMj7SDDKGojF4UqiNhqqlTwAkovQKICJiAY4yUJchOEERhJcr7zx97Bw/PKOfvsc93n8H3z2q9nn733WufH85ysZz1rr/Xbsk1ERIzHvEkHEBHxaJJGNyJijNLoRkSMURrdiIgxSqMbETFGaXQjIsYojW5ERAeSzpV0h6Q1Hc5L0v+RtE7SDZIOqaozjW5ERGefBJZ1OX8UcGC5LQc+VlVhGt2IiA5sfwu4u8slxwLnu3A1sIekfbrVuWCYAW73DXZcnCVvEdGTLQ9t0qB1bL5rfc9tzo6Pf+qfUvRQt1lhe0WNt1sMbGh7vbE89rNOBUbe6EZEjFVra8+Xlg1snUZ2YGl0I2K2uDXOd9sE7Nf2ekl5rKOM6UbEbGm1et8GtxJ4dTmL4fnAvbY7Di1AeroRMWM8xJ6upAuBw4G9JG0E3g3sULyPPw5cChwNrAMeAF5XVWca3YiYLVu3DK0q28dXnDfw53XqrGx0Jf0GxbSIxeWhTcBK2zfVeaOIiLGocSNtErqO6Ur6K+AiQMC15SbgQkkndym3XNJqSatbrfuHGW9ERHdu9b5NgLo9OULSLcBv2t485/iOwFrbB1a9QebpRkSvhjFP96H11/Y+T/cphw78fnVVDS+0gH2Bn8w5vk95LiKiUYZ5I20UqhrdtwBfl/QDfr3q4knA04CTRhhXRER/hjMVbGS6Nrq2vyLp6cChPPJG2irbzR6tjohHp62bq6+ZoMrZCy766lePIZaIiMFN+fBCRMR0mebhhYiIqZOebkTEGKWnGxExPm5N+Y20iIipkp5uRMQYZUw3ImKMGp7wJo1uRMyWhvd0+35yhKSOyXqTZSwiJma8T46obZDH9byn0wnbK2wvtb103ryFA7xFRERNW7f0vk1A1+EFSTd0OgXsPfxwIiIGNOWzF/YG/gi4Z85xAf82kogiIgbQ9FxcVY3ul4BdbV8394SkK0cRUETEQKa5p2v7xC7nThh+OBERA2r47IVMGYuI2TLNPd0YrgdvvWrgOnbe94VDiCSapCmfi0HjaMxnc0KzEnqVRjciZkuGFyIixijDCxERY5RGNyJijBo+vFC5DFjSb0g6QtKuc44vG11YERF9avgy4K6NrqQ3AV8E3giskXRs2+kPdCmXhDcRMRkNT3hTNbzwBuB5tn8paX/gEkn72z6TYinwdtleAawAWLDjYg8r2IiISg0fXqhqdOfZ/iWA7R9LOpyi4X0yXRrdiIiJafiNtKox3dslHbztRdkAvxTYC3j2COOKiOhPw4cXqhrdVwO3tR+wvcX2q4HDRhZVRES/7N63CahKeLOxy7l/HX44ERED2pJlwBER4zPlN9JiiBqTECQapSmfi6bEMbAhjtWW6xHOBOYDZ9v+4JzzTwI+BexRXnOy7Uu71TnIM9IiIppnSGO6kuYDZwFHAQcBx0s6aM5l7wQutv1c4Djgo1XhpacbEbNleD3dQ4F1ttcDSLoIOBa4se0aA7uX+4uAW6sqTaMbEbOlRqMraTmwvO3QinJxF8BiYEPbuY3Ab8+p4lTgcklvBBYCR1a9ZxrdiJgp3tr7gynbV8/26Xjgk7ZPl/Q7wAWSnmV3vpuXRjciZsvwhhc2Afu1vV5SHmt3IrAMwPa/S3oMxeKxOzpV2kuWsUMl/Va5f5Ckt0o6umbwERHj4VbvW3ergAMlHSBpR4obZSvnXPNT4AgASc8EHgPc2a3Srj1dSe+muHO3QNJXKcYzrgBOlvRc2+/vUO7hcRLNX8S8eQsr/t8iIoakNZyVZra3SDoJuIxiOti5ttdKei+w2vZK4G3AJyT9BcVNtdfa3adFqNt5Sd8DDgZ2olgOvMT2fZJ2Bq6x/ZyqwJNlLCJ6teWhTQMn0nrgw3/Wc5uzyxs/OvbEXVVjultsbwUekPRD2/cB2H5QUrOXfUTEo1ONG2mTUNXoPiRpF9sPAM/bdlDSIiCNbkQ0T8NTO1Y1uofZ/hXAnCkQOwCvGVlUERH9GtKY7qhUZRn7VYfjdwF3jSSiiIhBJOFNRMQYTXNPN37twVuvGriOmcniNAT5fsaoeMrHdCMipsuUz16IiJguGV6IiBijDC9ERIxRw3u6tZ8cIen8UQQSETEUw0t4MxJVCW/mZtQR8CJJewDYPmZEcUVE9KfhPd2q4YUlFI+mOJsig46ApcDp3Qoly1hETIq3NHv2QtXwwlLg28ApwL22rwQetP1N29/sVMj2CttLbS9NgxsRY9Vy79sEVC0DbgFnSPpc+fX2qjIRERM1C8uAbW8EXi7pJcB9ow0pImIAUz6m+wi2vwx8eUSxREQMzLPU6EZENF7Db6Sl0e1RkqsMV76fvzaM5D/DMDM/k/R0IyLGKI1uRMT4VDyMd+LS6EbEbElPNyJijNLoRkSMj7fMwOKIbSS9ADgUWGP78tGEFBExgGa3ud1zL0i6tm3/DcBHgN2Ad0s6uUu55ZJWS1rdat0/tGAjIqq45Z63SahKeLND2/5y4MW23wP8IfDKToWS8CYiJmaaE94A8yQ9lqJxlu07AWzfL2nLyKOLiKir4cMLVY3uIorUjgIsaR/bP5O0a3ksIqJRpjr3gu39O5xqAS8bejQREQPyliludDux/QDwoyHHEhExuCkfXoiImCoNz2E++kZ3GBmUZib7UQzVrHy2mhDDTHm0N7oREePU9J5u1TzdiIip4i29b1UkLZN0s6R1nRaESfrPkm6UtFbSZ6rqTE83ImbKsHq6kuYDZwEvBjYCqySttH1j2zUHAn8N/J7teyQ9oare9HQjYqa41ftW4VBgne31th8CLgKOnXPNG4CzbN8DYPuOqkrT6EbEbLF63trzxJTb8raaFgMb2l5vLI+1ezrwdEn/KulqScuqwus6vCDpt4GbbN8naWfgZOAQ4EbgA7bv7eFbEBExNnWGF2yvAFYM8HYLgAOBw4ElwLckPdv2LzoVqOrpngs8UO6fSbEs+LTy2HmdCrX/9jj7/At7jj4iYlBuqeetwiZgv7bXS8pj7TYCK21vtv0j4BaKRrijyoQ39sP3+JbaPqTc/xdJ13Uq1P7bY/Nd65u9Ji8iZkpr69DSwqwCDpR0AEVjexxwwpxrvgAcD5wnaS+K4Yb13Sqt6umukfS6cv96SUsBJD0d2Fwr/IiIMRjWjbSyw3kScBlwE3Cx7bWS3ivpmPKyy4CfS7oRuAL4S9s/71ZvVU/39cCZkt4J3AX8u6QNFIPLr68oGxExdj0MG/Rel30pcOmcY+9q2zfw1nLrSVWWsXuB10raHTigvH6j7dtrxB0RMTYNfwJ7b4sjbN8HXD/iWCIiBjbMnu4ojHxFWpJ5xKjksxXbM8QbaSORZcARMVMe9T3diIhxstPoRkSMTdNTO6bRjYiZ0kpPNyJifJo+vNB1RZqkN0nar9s1ERFN0tqqnrdJqFoG/D7gGklXSfozSY/vpdL2hDet1v2DRxkR0aMhJrwZiapGdz1FZp33Ac8DbpT0FUmvkbRbp0K2V9heanvpvHkLhxhuRER3LavnbRKqGl3bbtm+3PaJwL7AR4FlVGTSiYiYBFs9b5NQdSPtEVHZ3gysBFZK2mVkUUVE9Gnacy+8otMJ2w90OhcRMSlTPWXM9i3jCiQiYhhaWQYcETE+U93THYYHb71q4DqGkU1q0DiS0Wo25XMxe5q+OCI93YiYKY/6nm5ExDg1fPJCGt2ImC1bW1XLDyYrjW5EzJSGZ3ZMoxsRs8VM8ZiupB2B44BbbX9N0gnA71I8A35FuUItIqIxWg0f1K0a/DgPeAnwZkkXAC8HrgF+Czi7U6H2LGNnn3/h0IKNiKjSQj1vk1A1vPBs28+RtADYBOxre6ukv6fLI9ltrwBWAGy+a33Df+9ExCyZ6uEFYF45xLAQ2AVYBNwN7ATsMOLYIiJq2zrlje45wPeB+cApwOckrQeeD1w04tgiImqb6tkLts+Q9Nly/1ZJ5wNHAp+wfe04AoyIqGOqG10oGtu2/V8Al4wyoIiIQUz7mG5ExFRpeGbH0Te6TcnC1JQ4olnyuZg9k5oK1qv0dCNipmyddAAV0uhGxExpKT3diIixafpqrDS6ETFTmj5lrDLxpKSnSHq7pDMlfUjSf5O0+ziCi4ioq6XetyqSlkm6WdI6SSd3ue6PJVnS0qo6uza6kt4EfBx4DEWSm52A/YCrJR3epdzDCW9arfurYoiIGJqtqOetG0nzgbOAo4CDgOMlHbSd63YD3kyRDKxSVU/3DcBRtv8HxUq037R9CrAMOKNTIdsrbC+1vXTevIW9xBERMRRD7OkeCqyzvd72QxSpD47dznXvA04D/l8v8fXyXItt4747AbsC2P4pSXgTEQ3UqrG1/1VebsvbqloMbGh7vbE89jBJhwD72f5yr/FV3Ug7G1gl6RrghRStOZIeT5FtLCKiUerMXmhPQ1uXpHnAh4DX1ilXlfDmTElfA54JnG77++XxO4HD+gk0ImKUhrgMeBPFPaxtlpTHttkNeBZwpYq5wU8EVko6xvbqTpX2kvBmLbC2n4gjIsZtiFPGVgEHSjqAorE9Djhh20nb9wJ7bXst6Urg7d0aXMg83YiYMVuH1NO1vUXSScBlFDnFz7W9VtJ7gdW2V/ZT78gb3QdvvWrgOpKUZLgG/ZnM0s8j34vZM8zFEbYvBS6dc+xdHa49vJc609ONiJnS9BVpaXQjYqYk90JExBg96pOYR0SMU9OHF6pyLyyS9EFJ35d0t6SfS7qpPLbHmGKMiOjZ1hrbJFQtA74YuAc43PbjbO8JvKg8dvGog4uIqGuYWcZGoarR3d/2abZv23bA9m22TwOe3KlQ+3rms8+/cFixRkRUqpN7YRKqxnR/IukdwKds3w4gaW+KtcYbOhVqX8+8+a71Tb+ZGBEzpOkNTlVP9xXAnsA3yzHdu4ErgccBLx9xbBERtbVwz9skVCW8uQf4q3J7BEmvA84bUVwREX1p+tOAe8mn28l7hhZFRMSQTPWYrqQbOp0C9h5+OBERg5n2xRF7A39EMUWsnYB/G0lEEREDmNRYba+qGt0vAbvavm7uiTJ3ZKVkYWqe/Ex+Ld+L2dPsJrf6RtqJXc6d0OlcRMSkNH0ZcHIvRMRM2drwvm4a3YiYKenpRkSMUdNvpPU9T1fSPw8zkIiIYXCNbRKq5uke0ukUcPDQo4mIGNC0Dy+sAr5J0cjOtUenQpKWA8sBNH8R8+Yt7De+iIhapv1G2k3An9r+wdwTknrKMrZgx8XN/g5ExExp+phuVaN7Kp3Hfd843FAiIgbX7Ca3enHEJV1OP3bIsUREDKzpPd1kGYuImZIsYxERY+SG93STZSwiZsq0z14YOMtYRMQ4TfU83WQZi4hp0/J093QjIqZKs5vcNLoRMWOmesqYpN0l/U9JF0g6Yc65j442tIiI+lzjv0momqd7HsVMhc8Dx0n6vKSdynPPH2lkERF92IJ73iahqtF9qu2TbX/B9jHAd4BvSNqzWyFJyyWtlrS61bp/aMFGRFSZ9p7uTpIevsb2+4FPAN8COja8tlfYXmp7aTKMRcQ4DXNFmqRlkm6WtE7Syds5/1ZJN0q6QdLXJT25qs6qRvefgD9oP2D7k8DbgId6iDkiYqxs97x1I2k+cBZwFHAQcLykg+Zc9l1gqe3nAJcAf1MVX9U83Xd0OP4VSR+oqjwiYtyGOHvhUGCd7fUAki4CjgVu3HaB7Svarr8aeFVVpUl4ExEzZSvueWu//1Ruy9uqWgy05w3fWB7r5ESg8jFmSXgTETOlTk+3/YELg5D0KmAp8PtV1ybhTUTMlKqx2ho2Afu1vV5SHnsESUcCpwC/b/tXVZWOPOHNg7de1ctlXe287wsHriMiHh2GmPBmFXCgpAMoGtvjgLmLxJ4L/B2wzPYdvVSahDcRMVOGNf/W9hZJJwGXAfOBc22vlfReYLXtlcDfArsCn5ME8NNyTUNHyb0QETNlmLkXbF8KXDrn2Lva9o+sW2ca3YiYKVvd7Iy6aXQjYqY0/XE9VVnGnijpY5LOkrSnpFMlfU/SxZL2GVeQERG9atk9b5NQtTjikxSrLzYAVwAPAkcDVwEfH2lkERF9cI1tEqoa3b1tf9j2B4E9bJ9me4PtDwMdEzu0r/I4+/wLhxpwREQ3LdzzNglVY7rtjfL5c87N71SofZXH5rvWN3uAJSJmStOfHFHV6H5R0q62f2n7ndsOSnoacPNoQ4uIqG+qZy+0z0ebc3ydpC+PJqSIiP5N9eyFCskyFhGNM6x8uqOSLGMRMVOmfUw3WcYiYqpMqgfbq5FnGUuGsIgYp63DzDM2AskyFhEzZVIrzXqV3AsRMVOaPnshjW5EzJSm93RrTxmT9IRRBBIRMQyu8d8kVE0Ze9zcQ8C15SMqZPvukUUWEdGHpvd0q4YX7gJ+MufYYuA7FEl6nrK9QuVjjJcDaP4i5s1bOGCYERG9afoy4Krhhb+kyLFwjO0DbB8AbCz3t9vgQpHwxvZS20vT4EbEOE318ILt0yV9FjhD0gbg3UwuDWVERCU3vKdbOXvB9kbg5ZKOAb4K7DLyqCIi+tT0ZcA9z14oHzf8IuBIAEmvG1VQERH9anrCm1pTxmw/aHtN+TJZxiKicab6yRHJMhYR02Zra7rHdJNlbIgevPWqgetIAqHhGvRnkp9H80z7MuCBs4xFRIzTVKd2TJaxiJg2TZ+9kIQ3ETFTprqnGxExbZp+I63rlDFJy9r2F0k6R9INkj4jKbMXIqJxmj5lrGqe7gfa9k8Hfgb8B2AV8HejCioiol9NXxxRZ3hhqe2Dy/0zJL2m04XJMhYRkzLtqR2fIOmtFPNyd5ck//rXQ8desu0VwAqABTsubvZ3ICJmyrTP0/0EsFu5/ylgL+BOSU8ErhthXBERfZnqnq7t7eZXsH2bpCtGE1JERP9aDU/tWPsZaW2S8CYiGmeYN9IkLZN0s6R1kk7ezvmdJH22PH+NpP2r6kzCm4iYKcOalSBpPnAW8GJgI7BK0krbN7ZddiJwj+2nSToOOA14Rbd6k/AmImbKEEd0DwXW2V4PIOki4FigvdE9Fji13L8E+MicCQfbCbB71/sc4AUdzn2mTje+4n2WT7L8LNXRhBjy/5HvxSjrGOZGMbV1ddu2vO3cnwBnt73+L8BH5pRfAyxpe/1DYK9u79l1TNf2ibb/pcO5YSa8WT7h8rNURxNiGEYdTYihKXU0IYYm1TE0bnuIbrmtGPV7DnIjLSJilm0C9mt7vaQ8tt1rJC0AFgE/71ZpGt2IiO1bBRwo6QBJOwLHASvnXLMS2LY690+Ab7gcZ+ikKVnGBu3SD+NPglmpowkxDKOOJsTQlDqaEEOT6hgL21sknQRcBswHzrW9VtJ7gdUuHtZ7DnCBpHXA3RQNc1eqaJQjImKIMrwQETFGaXQjIsZooo1u1RK7HsqfK+kOSWsGiGE/SVdIulHSWklvrln+MZKulXR9Wb7v5dGS5kv6rqQv9Vn+x5K+J+k6Sav7rGMPSZdI+r6kmyT9To2yzyjfe9t2n6S39BHDX5TfyzWSLpT0mD7qeHNZfm2vMWzv8yTpcZK+KukH5dfH1iz/8jKGlqSlfcbwt+XP4wZJ/yhpjz7qeF9Z/jpJl0vat24dbefeJsmS9qoZw6mSNrV9Po7uFsPMmuCk5PkUE4mfAuwIXA8cVLOOw4BDgDUDxLEPcEi5vxtwS504KFbn7Vru7wBcAzy/z1jeCnwG+FKf5X9MxcTsHur4FPD6cn9HYI8Bfr63AU+uWW4x8CNg5/L1xcBra9bxLIpJ67tQ3Cz+GvC0fj5PwN8AJ5f7JwOn1Sz/TOAZwJUUOan7ieEPgQXl/mndYuhSx+5t+28CPl63jvL4fhQ3ln7S7bPWIYZTgbcP8vmchW2SPd2Hl9jZfgjYtsSuZ7a/RXHHsG+2f2b7O+X+/wVuoviH32t52/5l+XKHcqt9d1LSEuAlwNl1yw6LpEUU/1jOAbD9kO1f9FndEcAPbf+kj7ILgJ3LeY+7ALfWLP9M4BrbD9jeAnwT+E9VhTp8no6l+EVE+fU/1ilv+ybbN/caeIc6Li//PwCuppgvWreO+9peLqTiM9rl39YZwDsGKP+oN8lGdzGwoe31Rmo0dqNQZgh6LkVvtU65+ZKuA+4Avmq7VvnS/6b4MA+Sl87A5ZK+XT69o64DgDuB88phjrMl9fvYj+OAC+sWsr0J+F/ATykeD3Wv7ctrVrMGeKGkPSXtAhzNIye517G37Z+V+7cx+URP/xX4534KSnq/pA3AK4F39VH+WGCT7ev7ef/SSeUwx7ndhmpmWW6klSTtCnweeMucXkEl21tdPMpoCXCopGfVfO+XAnfY/nadctvxAtuHAEcBfy7psJrlF1D8Sfgx288F7qf4k7qWciL5McDn+ij7WIre5QHAvsBCSa+qU4ftmyj+DL8c+ApFwv2tdWPZTr1mqPlU6pF0CrAF+HQ/5W2fYnu/svxJNd97F+C/00dj3eZjwFOBgyl+oZ4+QF1Ta5KNbi9L7MZC0g4UDe6nbf9Dv/WUf4pfASyruHSu3wOOkfRjimGWP5D09328/6by6x3AP1IM4dSxEdjY1lO/hKIRruso4Du2b++j7JHAj2zfaXsz8A/A79atxPY5tp9n+zCKLHm39BELwO2S9gEov97RZz0DkfRa4KXAK8vGfxCfBv64ZpmnUvwivL78nC4BvqPiKTI9sX172UFpUTyVpu7ncyZMstHtZYndyEkSxRjmTbY/1Ef5x2+7myxpZ4rcm9+vU4ftv7a9xPb+FN+Hb9iu1buTtFDSbtv2KW6+1JrVYfs2YIOkZ5SHjuCRaex6dTx9DC2Ufgo8X9Iu5c/mCIpx9lokPaH8+iSK8dzP9BlP+zLP1wBf7LOevklaRjH0dIztB/qs48C2l8dS/zP6PdtPsL1/+TndSHED+rYaMezT9vJl1Px8zoxJ3sWjGGu7hWIWwyl9lL+Q4s+UzRQfghP7qOMFFH8y3kDxZ+h1wNE1yj8H+G5Zfg3wrgG/J4fTx+wFilkg15fb2n6+n2U9B1OkuLsB+ALw2JrlF1Ik/Fg0wPfgPRSNwhrgAmCnPuq4iuIXxvXAEf1+noA9ga8DP6CYBfG4muVfVu7/CrgduKyPGNZR3P/Y9vmsmnmwvTo+X34/bwD+CVhct445539M99kL24vhAuB7ZQwrgX0G+bcyrVuWAUdEjFFupEVEjFEa3YiIMUqjGxExRml0IyLGKI1uRMQYpdGNiBijNLoREWP0/wFS8PZkdFkibAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(gb)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def energy_field(radius, attract=True):\n",
    "    dimension = (2*radius)+1\n",
    "    ef = np.zeros((dimension, dimension), dtype=np.int16)\n",
    "    for x in range(0, dimension):\n",
    "        dx = abs(radius - x)\n",
    "        for y in range(0, dimension):\n",
    "            dy = abs(radius - y)\n",
    "            distance = int(math.sqrt(dx**2 + dy**2))\n",
    "            if attract:\n",
    "                ef[x,y] = distance\n",
    "            else:\n",
    "                energy = abs(distance - radius)\n",
    "                ef[x,y] = energy\n",
    "    return ef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf=energy_field(4, attract=False)\n",
    "sns.heatmap(rf)\n",
    "plt.show()\n",
    "af=energy_field(15)\n",
    "sns.heatmap(af)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_field(r_field, g_field, x, y, remove=False, show_coords=False):\n",
    "    if show_coords: print(\"coords\", x,y)\n",
    "    gx_max = g_field.shape[0] - 1\n",
    "    gy_max = g_field.shape[1] - 1\n",
    "    rx_max = r_field.shape[0] - 1\n",
    "    ry_max = r_field.shape[1] - 1\n",
    "    #\n",
    "    #   0.0 1.0 2.0\n",
    "    #   0.1 1.1 2.1\n",
    "    #   0.2 1.2 2.2\n",
    "    #\n",
    "    x_offset = int(r_field.shape[0]/2)\n",
    "    y_offset = int(r_field.shape[1]/2)\n",
    "    #print(\"gb shape\", g_field.shape)\n",
    "    #print(\"rf offset\", x_offset, y_offset)\n",
    "    gtx = x - x_offset\n",
    "    gty = y - y_offset\n",
    "    gbx = x + x_offset\n",
    "    gby = y + y_offset\n",
    "    #print(\"gb target\", gtx,gty,gbx,gby)\n",
    "    rtx = 0\n",
    "    rty = 0\n",
    "    rbx = rx_max\n",
    "    rby = ry_max\n",
    "    #print(\"rf source\", rtx,rty,rbx,rby)\n",
    "    # trim if r_field goes off board negative\n",
    "    if gtx < 0:\n",
    "        #print(\"negative x\")\n",
    "        rtx = -gtx\n",
    "        gtx = 0        \n",
    "    if gty < 0:\n",
    "        #print(\"negative y\")\n",
    "        rty = -gty\n",
    "        gty = 0\n",
    "    # trim if r_field goes off board positive\n",
    "    if gbx > gx_max:\n",
    "        dx = gbx - gx_max\n",
    "        #print(\"positive x\", dx)\n",
    "        gbx = gx_max\n",
    "        rbx = rx_max - dx\n",
    "    if gby > gy_max:\n",
    "        dy = gby - gy_max\n",
    "        #print(\"positive y\", dy)\n",
    "        gby = gy_max\n",
    "        rby = ry_max - dy\n",
    "    #print(\"adj gb target\", gtx,gty,gbx,gby) \n",
    "    #print(\"adj rf source\", rtx,rty,rbx,rby)\n",
    "    #gameboard[gtx, gty] = 1\n",
    "    #gameboard[gbx, gby] = 1\n",
    "    # add the r_field to the g_field\n",
    "    #print(g_field[gtx:gbx+1, gty:gby+1])\n",
    "    #print(r_field[rtx:rbx+1, rty:rby+1])\n",
    "    if remove:\n",
    "        g_field[gtx:gbx+1, gty:gby+1] -= r_field[rtx:rbx+1, rty:rby+1]\n",
    "    else:\n",
    "        g_field[gtx:gbx+1, gty:gby+1] += r_field[rtx:rbx+1, rty:rby+1]\n",
    "\n",
    "# wrapper for readability\n",
    "def subtract_field(r_field, g_field, x, y, show_coords=False):\n",
    "    add_field(r_field, g_field, x, y, remove=True, show_coords=show_coords)\n",
    "        \n",
    "def layout_node_step(repulsion_board, s_field, r_field, a_field, node, coords):\n",
    "    # make a scratchpad board where we add all the attraction fields\n",
    "    # and the use it to select the destination and update the repulsion board\n",
    "    s_field = np.copy(repulsion_board)\n",
    "    for adj_node_id in node['adj']:\n",
    "        # add an attraction field for each, to the scratchpad field\n",
    "        adj_node = coords[adj_node_id]\n",
    "        add_field(a_field, s_field, adj_node[\"x\"], adj_node[\"y\"])\n",
    "    # select the destination\n",
    "    # idea #1: choose a location with the minimum value\n",
    "    # argmin returns the index of the first location containing the minimum value in a flattened \n",
    "    # version of the array\n",
    "    # unravel_index turns the index back into the coordinates\n",
    "    destination = np.unravel_index(np.argmin(s_field, axis=None), s_field.shape)\n",
    "    #print(node)\n",
    "    #print(\"destination: \", destination)\n",
    "\n",
    "    # move the node on the repulsion board\n",
    "    # subtract it at it's current location\n",
    "    subtract_field(r_field, repulsion_board, node['x'], node['y'])\n",
    "    # add it at the destination\n",
    "    add_field(r_field, repulsion_board, destination[0], destination[1])\n",
    "    # update its coordinates\n",
    "    node[\"x\"] = destination[0]\n",
    "    node[\"y\"] = destination[1]\n",
    "\n",
    "# subtract the r_field from the repulsion board in the old location\n",
    "# update the coordinates\n",
    "# add the r_field to the repulsion board at the destination\n",
    "    \n",
    "def layout_round(repulsion_board, s_field, r_field, a_field, coords):\n",
    "    for node_id, node in coords.items():\n",
    "        layout_node_step(repulsion_board, s_field, r_field, a_field, node, coords)\n",
    "    igraph\n",
    "    print(\"----\")\n",
    "    sns.heatmap(af)\n",
    "    plt.show()\n",
    "          \n",
    "def layout_network(cx_network, rounds=1, sparsity=15, repulsion_radius=4):\n",
    "    # initialize the gameboard\n",
    "    gameboard = make_gameboard(cx_network, sparsity=sparsity)\n",
    "    coords = make_coords(cx_network, gameboard)\n",
    "    r_field = energy_field(repulsion_radius, attract=False)\n",
    "    a_field = energy_field(gameboard.shape[0])\n",
    "    for node_id, node in coords.items():  \n",
    "        add_field(r_field, gameboard, node[\"x\"], node[\"y\"])\n",
    "    print(\"----\")\n",
    "    sns.heatmap(gameboard)\n",
    "    plt.show()\n",
    "    # perform the rounds of layout\n",
    "    for n in range(0, rounds):\n",
    "        print(\"round \", round)\n",
    "        layout_round(gameboard, np.copy(gameboard), r_field, a_field, coords)\n",
    "    for node_id, node in coords.items():\n",
    "        print(node['x'], node['y'])\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "round  <built-in function round>\n",
      "----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 10\n",
      "8 9\n",
      "10 5\n",
      "5 3\n",
      "13 3\n",
      "4 8\n",
      "11 9\n",
      "5 15\n",
      "3 7\n",
      "7 8\n",
      "8 6\n",
      "5 11\n",
      "9 2\n",
      "15 4\n",
      "14 6\n",
      "11 9\n",
      "14 12\n",
      "1 3\n"
     ]
    }
   ],
   "source": [
    "layout_network(test_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def make_networkx_pos_from_cx_coordinates(cx_network):\n",
    "    cart_coords = cx_network.get_opaque_aspect(ndex2.constants.CARTESIAN_LAYOUT_ASPECT)\n",
    "    pos = {}\n",
    "    for node in cart_coords:\n",
    "        pos[node[\"node\"]] = (node[\"x\"],node[\"y\"])     \n",
    "    return pos\n",
    "\n",
    "def make_networkx_from_coordinates(coords):\n",
    "    G2 = nx.Graph()\n",
    "    for node_id, node in coords.items():\n",
    "        G2.add_node(node_id, pos=(node['x'], node['y']))\n",
    "    return G2\n",
    "    \n",
    "    \n",
    "c = make_coordinates(test_network, gb)\n",
    "for node_id, node in c.items():\n",
    "    print(node['x'], \" \", node['y'])\n",
    "    \n",
    "netx = make_networkx_from_coordinates(c)\n",
    "nx.draw(netx)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{900: (0, 0), 901: (1, 0), 902: (0, 1), 903: (1, 1), 904: (0, -1)}\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G2 = nx.Graph()\n",
    "G2.add_node(900,pos=(0,0))\n",
    "G2.add_node(901,pos=(1,0))\n",
    "G2.add_node(902,pos=(0,1))\n",
    "G2.add_node(903,pos=(1,1))\n",
    "G2.add_node(904,pos=(0,-1))\n",
    "node_pos=nx.get_node_attributes(G2,'pos')\n",
    "print(node_pos)\n",
    "nx.draw(G2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating CX\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d29f6dca43f64c7a9168525a3f02579a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Cytoscape(data=[{'numberVerification': [{'longNumber': 281474976710655}]}, {'metaData': [{'name': 'nodes', 'el…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_cx = test_network.to_cx()\n",
    "Cytoscape(data=test_cx, format='cx')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}