{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random graphs\n",
    "\n",
    "<div style=\"display: flex; align-items: center;\">\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/yy/netsci-course/blob/master/docs/m08-randomgraphs/lab08.ipynb\">\n",
    "        <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" style=\"vertical-align: -8px; margin-right: 10px;\" />\n",
    "        Open this notebook in Google Colab\n",
    "    </a>\n",
    "</div>\n",
    "\n",
    "\n",
    "<div style=\"display: flex; align-items: center;\">\n",
    "    <a href=\"https://raw.githubusercontent.com/yy/netsci-course/master/docs/m08-randomgraphs/lab08.ipynb\" download>\n",
    "        <img src=\"http://yyahn.com/netsci-course/images/download_icon.png\" style=\"vertical-align: -8px; margin-right: 10px;\" />\n",
    "        Download this notebook (File -> Save As)\n",
    "    </a>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the random graph models?\n",
    "\n",
    "\"Random graph models\" may refer a wide range of models where randomness is involved in the generation of the graph, particularly in the edge formation. But, the most basic random graph models — that are usually referred as random graph models — are the Erdős-Rényi (ER) model and the configuration model. They both are commonly used as \"null models\" — the models that show us what we would expect if we don't know anything about the graph other than basic properties of the graphs — in network science. \n",
    "\n",
    "## Why random graphs?\n",
    "\n",
    "Probably the most important reason to study random graphs is that they provide a baseline for comparison, or act as \"null models\". Let's say you collected a network data and found that the average clustering coefficient of the network is 0.5. Is this high or low? \n",
    "\n",
    "<img src=\"https://yyiki.s3.us-east-2.amazonaws.com/public/imgs/is_four_a_lot-depends_on_contexts.jpg\" width=\"400\" />\n",
    "\n",
    "If the network is large and very sparse, 0.5 may be almost impossibly high. On the other hand, if the network is extremely dense, then 0.5 may be extremely low. So, it is hard to say! What could be useful contexts? \n",
    "\n",
    "## ER random graph\n",
    "\n",
    "One useful context can be an ER random graph with the same number of nodes and edges. This model answers the following question: \"if we only know the number of nodes and edges, and nothing else, what would be the average clustering coefficient that we would expect?\" We can create many instances of ER random graphs and calculate the average clustering coefficient. Then, we can compare the average clustering coefficient of the real network with the distribution of the average clustering coefficient of the ER random graphs!\n",
    "\n",
    "Let's try with the dolphins network. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "dolphin_net = nx.read_gml(\"dolphins.gml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: Shall we measure the average clustering coefficent of this network?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average clustering coefficient: 0.259\n"
     ]
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's then create many instances of ER random graphs and calculate the average clustering coefficient.\n",
    "\n",
    "**Q: plot the distribution of the average clustering coefficient of many (say 1,000) ER random graphs.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: where is the actual average clustering coefficient of the dolphins network in the distribution? Can you show it?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like the actual network has a much higher average clustering coefficient than the ER random graphs. This makes sense because the dolphins network is a social network, and social networks tend to have a high average clustering coefficient. \n",
    "\n",
    "But, let's think about the ER random graph model. Is the model a good null model for the dolphins network? Or, can it possibly be _too random_? Which of the important network properties does the ER random graph model not capture?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration model\n",
    "\n",
    "One very important property that the ER random graph model does not capture is, of course, the degree distribution. The degree distribution of the ER random graph is binomial, which is very different from the degree distribution of the real networks. Let's check how different the degree distribution of the dolphins network is from the binomial distribution.\n",
    "\n",
    "Before doing this, here's a useful plot for showing the degree distribution of a small network. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Say, there are 4 nodes with degree 1, 6 nodes with degree 2, etc. \n",
    "degree_counts = {1: 4, 2: 6, 3: 6, 4: 5, 5: 4, 6: 3, 7: 2, 8: 1, 9: 1, 12: 1}\n",
    "plt.ylim(0, 7)\n",
    "plt.xticks(range(1, 13))\n",
    "for d, c in degree_counts.items():\n",
    "    plt.plot([d, d], [0, c], 'k-')  # the pin/stem \n",
    "    plt.plot(d, c, 'ko')  # the \"head\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: can you plot the degree distribution of the dolphins network?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's generate an instance of ER random graph with the same number of nodes and edges, and plot the degree distribution of the ER random graph.\n",
    "\n",
    "**Q: can you plot the degree distribution of the ER random graph with the actual degree distribution of the dolphins network?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the difference is not dramatic, it seems to hint that the actual dolphin network may have slightly more nodes with high degrees than what we would expect from the ER random graph model. Let's check another network. This time, the famous karate club network. \n",
    "\n",
    "**Q: create the same plots for the karate club network.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8+PHntpX/d+vy5cvCwsJCjB8/XrO8Q4cOolGjRpr5K1euCLlcLmbPnq21nvPnzwsLCwut8u7du2t9r+eZN2+ekMvlmu+2hQsXCg8PD9GyZUsxZcoUIUTu58ne3l5MnDhR87q8z/6dO3c0ZWfPnhUVKlQQw4cP15Tl/QYNGjSoyH1rY2MjhBDiyJEjws7OTnTv3l3rN7g0XupTTnkqV66sudrp7t27OHDgAPr3748HDx7g9u3buH37Nu7cuYPAwEBcunRJM+p8z549aN26tdYgPAcHBwwZMqTIdry8vBAYGKhVtmXLFvj6+qJKlSqatm7fvg1/f3+o1WpNxrxlyxYolUp06dJFq56Pjw8qV66MgwcPvvA+AFDiVV/29vbIysrSOi1VVkXtg5K89dZbWj0c77zzDiwsLLBr165yx1Aau3btgrOzMwYNGqQpq1ixIsaPH4/MzEwcOnRIq/6AAQO0/vLK+yv5n3/+eW47LVu21Op6rVy5Mt566y1cuXIFf/75Z7m34caNGwBy93lRgxYBaP3l8/TpU9y5cwd16tSBvb09Tp8+Xaj+mDFjCq2rrOsoaO/evbh//z4GDRqk9dmWy+Vo1arVcz/bGRkZsLW1fW47umJhYYGxY8dq5i0tLTF27FjcvHkTp06dKvV6PvvsMzx79qzY0wlCCGzduhXBwcEQQmjtm8DAQKhUqufu3woVKqBNmzaa75GLFy/izp07+PjjjyGEwNGjRwHk/tX96quvasZo7dq1C3K5HOPHj9da3wcffAAhBHbv3q1V3qFDB7zyyitFxvDkyRNNz8quXbsQEBDw3H0DaH+u7t27B5VKBV9f3yK32d/fH7Vr19bMN2nSBHZ2dprjT61WIyYmBr1790bNmjU19Ro2bFim7yMrKyvs3bu30FTUezhq1ChUq1YNrq6u6Nq1K1QqFdauXas5PVhatWrV0vSgp6enF1knOjoaOTk56N+/v9bnxNnZGXXr1i3V74Ovry/UajV+++03ALmfCV9fX/j6+mp68y5cuID79+9rvt/S09MRHx+PESNGwMHBQbOuJk2aoEuXLkV+T7/99tvFxnDw4EEEBgaic+fOiI6OLvPYypf+lBMAZGZmwsnJCQCQmJgIIQSmTp2KqVOnFln/5s2bqFGjBpKTk9G6detCy+vUqVPk67y8vAqVXbp0CefOnUO1atWKbSuvnkql0sRZXL3yyjtHWdIPw7vvvovNmzcjKCgINWrUQEBAAPr371/i5cMFFbUPSlK3bl2t+cqVK8PFxUXvl14nJyejbt26hU7R5J0iSU5O1irP/yUJ/K9b+Xnjm5KTk9GqVatC5fnbKe/lmaGhoUhLS8MXX3yBqlWrYuLEiYXqPHr0CBEREVi1ahVSU1O1uoBVKlWh+kW9f2VdR0GXLl0CkDumrSjP68q3s7Mz6O0XXF1dCw1krFevHoDc0wqvv/56qdaT/4fq448/LrT81q1buH//PpYtW4Zly5YVuY7SHPe+vr6aUwFxcXFwcXFBs2bN4O3tjbi4OHTp0gVHjhxB//79Na9JTk6Gq6troe+D4j7/JR3XERERyMzMxO7du8t0/5+dO3di1qxZiI+P17qtRFGnmgsef0DuMZh3/N26dQuPHj0q9H0C5A7WLe0fSHK5HP7+/qWq+/nnn8PX1xeZmZnYtm0bNm7cWO7B+J999hnWrl2LOXPmaMYs5nfp0iUIIYrcPqB0F3g0a9YMlSpVQlxcHAIDAxEXF4cZM2bA2dkZ33zzDR4/fqxJbPL+AMv7HBQ1dKBhw4aIiYkpNPC3uM/K48eP0b17d/j4+GDz5s2wsCh7evLSJzTXrl2DSqXSJCF5A3E//PDDYjP34hKW5ynqPGBOTg66dOlS7Kj3vC/KnJwcODk5Yd26dUXWKy4hKq0LFy4AKHnbnJycEB8fj5iYGOzevRu7d+/GqlWrMHz48EKDZYtjyKu61Gq1wdoqrgdEFHOO2BAsLCywefNmdO3aFR988AHs7e0xcuRIrTrvv/8+Vq1ahbCwMLRu3RpKpRIymQwDBw4s8pL9ot6/sq6joLw6a9euLfJy1ud9sTVo0ADx8fF48uRJua5WK24sliE+P59++inWrl2LuXPnonfv3lrL8vbL0KFDERoaWuTrS3O7iXbt2uHp06c4evSo5q9uAJq/vP/66y/cunVLU14eJR3XgYGB2LNnD7788kt07NhR654txYmLi0PPnj3Rvn17fPfdd3BxcUHFihWxatUqrF+/vlB9Uzz+GjdurEl+evfujYcPH2LMmDFo166d1u0xSqNWrVoYOnRosclvTk4OZDIZdu/eXeS+KM1N6ypWrIhWrVrh8OHDSExMxPXr1+Hr64vq1avj6dOnOHbsGOLi4tCgQYMX+r0p7rOiUCjQrVs37NixA3v27NGM+SqLlz6hWbt2LQBokpdatWoByH1zn5eJe3h4IDExsVB5UWXFqV27NjIzM5/bVu3atbFv3z60bdtWL0lBwf1QHEtLSwQHByM4OBg5OTl49913sXTpUkydOhV16tTR+R03L126BD8/P818ZmYm0tPT0a1bN01ZlSpVCt047cmTJ4W6Z8sSm4eHB86dO4ecnBytv6r++usvzXJd8PDwQEJCQqFyXbVjZWWFn376CX5+fhgzZgzs7e01g9oBICoqCqGhofj66681ZY8fPy7TjehKu47i9n/eqQInJ6dS//WbX3BwMI4ePYqtW7dqnSIsrbzetILxFuyFyJOWllbor86///4bAMp8f53atWtj6NChWLp0aaGeumrVqsHW1hZqtfq5+6Wkz3bLli1haWmJuLg4xMXFYfLkyQByB8ovX74c+/fv18zn8fDwwL59+/DgwQOtXpryfC5ff/11vP322+jRowf69euHbdu2PTdJ3bp1K6ysrBATE6N12mHVqlWlbje/atWqwdraWtMbmF9Rx58+zJkzB9u2bcPs2bPLdFVcns8++ww//vij5oKM/GrXrg0hBLy8vDR/BBenpM+Kr68v5s6di3379qFq1apo0KABZDIZGjVqpPn85E808j4HxX2HVa1atdSXZctkMqxbtw69evVCv379ytyjB7zkl20fOHAAM2fOhJeXl2bci5OTEzp27IilS5cWeb7y1q1bmv8HBgbi6NGjWnfnvHv3brG9KEXp378/jh49ipiYmELL7t+/j2fPnmnqqdVqzJw5s1C9Z8+evdCdUNevX4/vv/8erVu3RufOnYutd+fOHa35ChUqaP5CzOsSzvvw6urOrMuWLcPTp08184sXL8azZ88QFBSkKatdu7bW6Py81xX8C7sssXXr1g3Xr1/Hpk2bNGXPnj3DN998g8qVK6NDhw7l2Zwi2zl+/LhmLAOQe7nssmXL4OnpWey4hLKws7PDnj17UKdOHQwaNEjzAwbk/mVb8K/Yb775pky9E6VdR3H7PzAwEHZ2dvjiiy+03us8+Y+5orz99ttwcXHBBx98oEks8rt58yZmzZpV7OvzEqr8nyG1Wl3saZ5nz55pXe775MkTLF26FNWqVdPc0K8sPvvsMzx9+hRffvmlVrlcLscbb7yBrVu3anpQ88u/X0r6bFtZWaFFixbYsGEDrl69qtVD8+jRIyxcuBC1a9eGi4uL5jXdunWDWq3Gt99+q7Wu//znP5DJZFrHX2n4+/tj48aN2LNnD4YNG/bcnju5XA6ZTKb1Gbpy5Qq2b99epnbzry8wMBDbt2/H1atXNeUXL14s8rtXH2rXro033ngDq1evxvXr18v1+rzkt+DrQ0JCIJfLMWPGjELHohBC67vbxsam2FPBvr6+yM7ORmRkJNq1a6dJfnx9fbF27VqkpaVp9eS5uLigadOmWLNmjdZn78KFC/jll1+0/vAsjbzL+Vu0aIHg4GAcP368TK9/aXpodu/ejb/++gvPnj3DjRs3cODAAezduxceHh746aeftLpBFy1ahHbt2qFx48YYM2YMatWqhRs3buDo0aO4du0azp49CwD46KOP8OOPP6JLly54//33NZdt16xZE3fv3i1Vj8DkyZPx008/oUePHhgxYgR8fHyQlZWF8+fPIyoqCleuXEHVqlXRoUMHjB07FhEREYiPj0dAQAAqVqyIS5cuYcuWLViwYEGpLluNiopC5cqV8eTJE82dgn/99Vd4e3tjy5YtJb529OjRuHv3Ljp16gQ3NzckJyfjm2++QdOmTTXn1ps2bQq5XI65c+dCpVJBoVCgU6dOxY79eZ4nT56gc+fO6N+/PxISEvDdd9+hXbt26Nmzp1Zcb7/9Nt544w106dIFZ8+eRUxMjNYNBMsa21tvvYWlS5dixIgROHXqFDw9PREVFYVff/0VkZGROhuE+vHHH2PDhg0ICgrC+PHj4eDggDVr1iApKQlbt27V2d18q1Wrhr1796Jt27bo3bs39u/fj5YtW6JHjx5Yu3YtlEolXnnlFRw9ehT79u3TXOpaGqVdR0n7f/HixRg2bBiaNWuGgQMHolq1arh69Sp+/vlntG3bttAPa35VqlTBtm3b0K1bNzRt2lTrTsGnT5/Ghg0bihzrlqdRo0Z4/fXXER4ejrt378LBwQEbN27U/DFRkKurK+bOnYsrV66gXr162LRpE+Lj47Fs2bJy3Toh74eqqNO2c+bMwcGDB9GqVSuMGTMGr7zyCu7evYvTp09j3759uHv3rmYd9vb2WLJkCWxtbWFjY4NWrVppxiv4+vpizpw5UCqVaNy4MYDcP97q16+PhISEQveACg4Ohp+fHz799FNcuXIF3t7e+OWXX7Bjxw6EhYVpDcAtrd69e2tOUdvZ2ZV4D5ju3btj/vz56Nq1KwYPHoybN29i0aJFqFOnDs6dO1fmtgFgxowZ2LNnD3x9ffHuu+9q/kBp1KhRqdf57NmzQvcsy9OnT5/n9kZMnjwZmzdvRmRkZKnvLZNf3inKhIQENGrUSFNeu3ZtzJo1C+Hh4bhy5Qp69+4NW1tbJCUlYdu2bXjrrbc0dxj38fHBpk2bMGnSJLRo0QKVK1dGcHAwAKB169awsLBAQkIC3nrrLc3627dvj8WLFwNAoVOT8+bNQ1BQEFq3bo0333xTc9m2Uqks8v5gz2NtbY2dO3eiU6dOCAoKwqFDh0o/jrBM10RJUN7lb3mTpaWlcHZ2Fl26dBELFizQXKJW0OXLl8Xw4cOFs7OzqFixoqhRo4bo0aOHiIqK0qp35swZ4evrKxQKhXBzcxMRERFi4cKFAoC4fv26pl7+yzULevDggQgPDxd16tQRlpaWomrVqqJNmzbiq6++KnQZ6LJly4SPj4+wtrYWtra2onHjxuKjjz7SXPpZnLxL5vImKysr4ebmJnr06CFWrlxZ5OVxBS/bjoqKEgEBAcLJyUlYWlqKmjVrirFjx4r09HSt1y1fvlzUqlVLczlk3mWvJe2D4i7bPnTokHjrrbdElSpVROXKlcWQIUO0Lg8UIvdSwilTpoiqVauKSpUqicDAQJGYmFhonSXFVvBSXiGEuHHjhhg5cqSoWrWqsLS0FI0bNy50WWz+S1MLQjGXkxd0+fJl0bdvX2Fvby+srKxEy5Ytxc6dO4tcX1ku2y6q7sWLF0XVqlWFg4ODuHDhgrh3755mGytXriwCAwPFX3/9Vez7UdTtD0q7DiGK3/9C5F7uGhgYKJRKpbCyshK1a9cWI0aMECdPnizVNqelpYmJEyeKevXqCSsrK1GpUiXh4+MjZs+eLVQqlaZeUe/15cuXhb+/v1AoFKJ69erik08+EXv37i3ysu1GjRqJkydPitatWwsrKyvh4eEhvv3221LFWNwxcOnSJc0+yX/ZthC5n8Nx48YJd3d3UbFiReHs7Cw6d+4sli1bplVvx44d4pVXXhEWFhaFLuH++eefBQARFBSk9ZrRo0cLAGLFihWFYnrw4IGYOHGicHV1FRUrVhR169YV8+bNEzk5OVr1ivusFXdsfPfddwKA+PDDD4veSf9asWKFqFu3rlAoFKJBgwZi1apVmu+x0rRf1Ofv0KFDwsfHR1haWopatWqJJUuWFLnOopR02TbyXfZf1OX3+XXs2FHY2dmJ+/fvF9tWScdbXhz5L9vOs3XrVtGuXTthY2MjbGxsRIMGDcS4ceNEQkKCpk5mZqYYPHiwsLe319zeIb8WLVoIAOLYsWOasmvXrgkAwt3dvch49+3bJ9q2bSusra2FnZ2dCA4OFn/++adWnZJuHZL/su08t2/fFq+88opwdnYWly5dKrLdgmRCGHHUlJkKCwvD0qVLkZmZWexgNSIiItKdl3oMjS48evRIa/7OnTtYu3Yt2rVrx2SGiIjIQF6aMTT60rp1a3Ts2BENGzbEjRs3sGLFCmRkZBR7DxsiIiLSPSY0L6hbt26IiorCsmXLIJPJ0KxZM6xYsULrEkgiIiLSL46hISIiIsnjGBoiIiKSPCY0REREJHlmP4YmJycHaWlpsLW11flt+YmIiEg/hBB48OABXF1dS3WTUbNPaNLS0sr8IDAiIiIyDSkpKXBzc3tuPbNPaPJuUZ+SkgI7OzsjR0NERESlkZGRAXd391I/asbsE5q800x2dnZMaIiIiCSmtMNFOCiYiIiIJI8JDREREUkeExoiIiKSPCY0REREJHlMaIiIiEjymNAQERGR5DGhISIiIsljQkNERESSx4SGiIiIJI8JDREREUkeExoiIiKSPCY0REREJHlMaIiIiEjyjJrQHD58GMHBwXB1dYVMJsP27du1lstksiKnefPmGSdgIiIiMklGTWiysrLg7e2NRYsWFbk8PT1da1q5ciVkMhneeOMNA0dKREREpszCmI0HBQUhKCio2OXOzs5a8zt27ICfnx9q1aql79CIiIhIQoya0JTFjRs38PPPP2PNmjXGDoWIiIhMjGQSmjVr1sDW1hYhISEl1svOzkZ2drZmPiMjQ9+hERERkZFJ5iqnlStXYsiQIbCysiqxXkREBJRKpWZyd3c3UIRERERkLJJIaOLi4pCQkIDRo0c/t254eDhUKpVmSklJMUCEREREZEySOOW0YsUK+Pj4wNvb+7l1FQoFFAqFAaIiIiIiU2HUhCYzMxOJiYma+aSkJMTHx8PBwQE1a9YEkDsGZsuWLfj666+NFSYRERGZOKMmNCdPnoSfn59mftKkSQCA0NBQrF69GgCwceNGCCEwaNAgY4RIREREEiATQghjB6FPGRkZUCqVUKlUsLOzM3Y4REREVApl/f2WxKBgIiIiopIwoSEiIiLJY0JDREREkseEhoiIiCSPCQ0RERFJHhMaIiIikjwmNERERCR5TGiIiIhI8pjQEBERkeQxoSEiIiLJM2pCc/jwYQQHB8PV1RUymQzbt28vVOfixYvo2bMnlEolbGxs0KJFC1y9etXwwRIREZHJMmpCk5WVBW9vbyxatKjI5ZcvX0a7du3QoEEDxMbG4ty5c5g6dSqsrKwMHCkRERGZMpN5OKVMJsO2bdvQu3dvTdnAgQNRsWJFrF27ttzr5cMpiYiIpMdsHk6Zk5ODn3/+GfXq1UNgYCCcnJzQqlWrIk9L5ZednY2MjAytiYiIiMybySY0N2/eRGZmJubMmYOuXbvil19+QZ8+fRASEoJDhw4V+7qIiAgolUrN5O7ubsCoiYiIyBhM9pRTWloaatSogUGDBmH9+vWaej179oSNjQ02bNhQ5Hqys7ORnZ2tmc/IyIC7uztPOREREUlIWU85WRggpnKpWrUqLCws8Morr2iVN2zYEEeOHCn2dQqFAgqFQt/hERERkQkx2VNOlpaWaNGiBRISErTK//77b3h4eBgpKiIiIjJFRu2hyczMRGJiomY+KSkJ8fHxcHBwQM2aNTF58mQMGDAA7du3h5+fH/bs2YP//ve/iI2NNV7QREREZHKMOoYmNjYWfn5+hcpDQ0OxevVqAMDKlSsRERGBa9euoX79+pgxYwZ69epV6jZ42TYREZH0lPX322QGBesLExoiIiLpMZv70BARERGVFhMaIiIikjwmNERERCR5TGiIiIhI8pjQEBERkeQxoSEiIiLJY0JDREREkseEhoiIiCSPCQ0RERFJnlETmsOHDyM4OBiurq6QyWTYvn271vIRI0ZAJpNpTV27djVOsERERGSyjJrQZGVlwdvbG4sWLSq2TteuXZGenq6ZNmzYYMAIiYiISAqM+rTtoKAgBAUFlVhHoVDA2dnZQBERERGRFJn8GJrY2Fg4OTmhfv36eOedd3Dnzh1jh0REREQmxqg9NM/TtWtXhISEwMvLC5cvX8Ynn3yCoKAgHD16FHK5vMjXZGdnIzs7WzOfkZFhqHCJiIjISEw6oRk4cKDm/40bN0aTJk1Qu3ZtxMbGonPnzkW+JiIiAjNmzDBUiERERGQCTP6UU361atVC1apVkZiYWGyd8PBwqFQqzZSSkmLACImIiMgYTLqHpqBr167hzp07cHFxKbaOQqGAQqEwYFRERERkbEZNaDIzM7V6W5KSkhAfHw8HBwc4ODhgxowZeOONN+Ds7IzLly/jo48+Qp06dRAYGGjEqImIiMjUGDWhOXnyJPz8/DTzkyZNAgCEhoZi8eLFOHfuHNasWYP79+/D1dUVAQEBmDlzJntgiIiISItMCCGMHYQ+ZWRkQKlUQqVSwc7OztjhEBERUSmU9fdbUoOCiYiIiIrChIaIiIgkjwkNERERSR4TGiIiIpI8JjREREQkeUxoiIiISPKY0BAREZHkMaEhIiIiyWNCQ0RERJLHhIaIiIgkz6gJzeHDhxEcHAxXV1fIZDJs37692Lpvv/02ZDIZIiMjDRYfERERSYNRE5qsrCx4e3tj0aJFJdbbtm0bfv/9d7i6uhooMiIiIpISoz5tOygoCEFBQSXWSU1Nxfvvv4+YmBh0797dQJERERGRlBg1oXmenJwcDBs2DJMnT0ajRo1K9Zrs7GxkZ2dr5jMyMvQVHhEREZkIkx4UPHfuXFhYWGD8+PGlfk1ERASUSqVmcnd312OEREREZApMNqE5deoUFixYgNWrV0Mmk5X6deHh4VCpVJopJSVFj1ESERGRKTDZhCYuLg43b95EzZo1YWFhAQsLCyQnJ+ODDz6Ap6dnsa9TKBSws7PTmoiIiMi8mewYmmHDhsHf31+rLDAwEMOGDcPIkSONFBURERGZIqMmNJmZmUhMTNTMJyUlIT4+Hg4ODqhZsyYcHR216lesWBHOzs6oX7++oUMlIiIiE2bUhObkyZPw8/PTzE+aNAkAEBoaitWrVxspKiIiIpIaoyY0HTt2hBCi1PWvXLmiv2CIiIhIskx2UDARERFRaTGhISIiIsljQkNERESSx4SGiIiIJI8JDREREUkeExoiIiKSPCY0REREJHlMaIiIiEjymNAQERGR5Bk1oTl8+DCCg4Ph6uoKmUyG7du3ay2fPn06GjRoABsbG1SpUgX+/v44duyYcYIlIiIik2XUhCYrKwve3t5YtGhRkcvr1auHb7/9FufPn8eRI0fg6emJgIAA3Lp1y8CREhERkSmTibI8TEmPZDIZtm3bht69exdbJyMjA0qlEvv27UPnzp1Ltd6816hUKtjZ2ekoWiIiItKnsv5+S2YMzZMnT7Bs2TIolUp4e3sbOxwiIiIyIUZ92nZp7Ny5EwMHDsTDhw/h4uKCvXv3omrVqsXWz87ORnZ2tmY+IyPDEGESERGREZl8D42fnx/i4+Px22+/oWvXrujfvz9u3rxZbP2IiAgolUrN5O7ubsBoiYiIyBhMPqGxsbFBnTp18Prrr2PFihWwsLDAihUriq0fHh4OlUqlmVJSUgwYLRERERmDyZ9yKignJ0frlFJBCoUCCoXCgBERERGRsRk1ocnMzERiYqJmPikpCfHx8XBwcICjoyNmz56Nnj17wsXFBbdv38aiRYuQmpqKfv36GTFqIiIiMjVGTWhOnjwJPz8/zfykSZMAAKGhoViyZAn++usvrFmzBrdv34ajoyNatGiBuLg4NGrUyFghExERkQkymfvQ6AvvQ0NERCQ9ZnsfGiIiIqLiMKEhIiIiyWNCQ0RERJLHhIaIiIgkjwkNERERSR4TGiIiIpI8JjREREQkeUxoiIiISPKY0BAREZHkMaEhIiIiyTNqQnP48GEEBwfD1dUVMpkM27dv1yx7+vQppkyZgsaNG8PGxgaurq4YPnw40tLSjBcwERERmSSjJjRZWVnw9vbGokWLCi17+PAhTp8+jalTp+L06dOIjo5GQkICevbsaYRIiYiIyJSZzMMpZTIZtm3bht69exdb58SJE2jZsiWSk5NRs2bNUq2XD6ckIiKSnrL+flsYICadUalUkMlksLe3L7ZOdnY2srOzNfMZGRkGiIyIiIiMSTKDgh8/fowpU6Zg0KBBJWZqERERUCqVmsnd3d2AURIREZExSCKhefr0Kfr37w8hBBYvXlxi3fDwcKhUKs2UkpJioCiJiIjIWEz+lFNeMpOcnIwDBw489zyaQqGAQqEwUHRERERkCkw6oclLZi5duoSDBw/C0dHR2CERERGRCTJqQpOZmYnExETNfFJSEuLj4+Hg4AAXFxf07dsXp0+fxs6dO6FWq3H9+nUAgIODAywtLY0VNhEREZkYo162HRsbCz8/v0LloaGhmD59Ory8vIp83cGDB9GxY8dStcHLtomIiKRHUpdtd+zYESXlUyZyixwiIiIycZK4yomIiIioJExoiIiISPKY0BAREZHkMaEhIiIiyWNCQ0RERJLHhIaIiIgkjwkNERERSR4TGiIiIpI8JjREREQkeSb9cEoqHbVajbi4OKSnp8PFxQW+vr6Qy+XGDouIiMhgjNpDc/jwYQQHB8PV1RUymQzbt2/XWh4dHY2AgAA4OjpCJpMhPj7eKHGasujoaHh6esLPzw+DBw+Gn58fPD09ER0dbezQiIiIDMaoCU1WVha8vb2xaNGiYpe3a9cOc+fONXBk0hAdHY2+ffvi2rVrWuWpqano27cvkxoiInppGPVp2/nJZDJs27YNvXv3LrTsypUr8PLywpkzZ9C0adMyrddcn7atVqvh6elZKJnJI5PJ4ObmhqSkJJ5+IiIiySnr77fZDQrOzs5GRkaG1mSO4uLiik1mgNwnlaekpCAuLs6AURERERmH2SU0ERERUCqVmsnd3d3YIelFenq6TusRERFJmdklNOHh4VCpVJopJSXF2CHphYuLi07rERERSZnZXbatUCigUCiMHYbe+fr6ws3NDampqShqGFTeGBpfX18jREdERGRYZtdD87KQy+VYsGABgNzkJb+8+cjISA4IJiKil4JRE5rMzEzEx8dr7i+TlJSE+Ph4XL16FQBw9+5dxMfH488//wQAJCQkID4+HtevXzdWyCYlJCQEUVFRcHV11Sp3c3NDVFQUQkJCjBQZERGRYRn1su3Y2Fj4+fkVKg8NDcXq1auxevVqjBw5stDyadOmYfr06aVqw1wv284vbxsBYNeuXQgICGDPDBERSVpZf79N5j40+vIyJDRZWVmoXLkygNxeLxsbGyNHRERE9GJe+vvQEBER0cunXAnNP//8o+s4iIiIiMqtXAlNnTp14Ofnhx9//BGPHz/WdUxEREREZVKuhOb06dNo0qQJJk2aBGdnZ4wdOxbHjx/XdWxEREREpVKuhKZp06ZYsGAB0tLSsHLlSqSnp6Ndu3Z49dVXMX/+fNy6dUvXcRIREREV64UGBVtYWCAkJARbtmzB3LlzkZiYiA8//BDu7u4YPnw4nyNEREREBvFCCc3Jkyfx7rvvwsXFBfPnz8eHH36Iy5cvY+/evUhLS0OvXr10FScRERFRscr1LKf58+dj1apVSEhIQLdu3fDDDz+gW7duqFAhNz/y8vLC6tWr4enpqctYiYiIiIpUroRm8eLFGDVqFEaMGFHs05ydnJywYsWKFwqOiIiIqDTKdcrp0qVLCA8PLzaZAQBLS0uEhoaWuJ7Dhw8jODgYrq6ukMlk2L59u9ZyIQQ+//xzuLi4wNraGv7+/rh06VJ5QiYiIiIzVq6EZtWqVdiyZUuh8i1btmDNmjWlXk9WVha8vb2xaNGiIpd/+eWXWLhwIZYsWYJjx47BxsYGgYGBvPcNERERaSlXQhMREYGqVasWKndycsIXX3xR6vUEBQVh1qxZ6NOnT6FlQghERkbis88+Q69evdCkSRP88MMPSEtLK9STQ0RERC+3ciU0V69ehZeXV6FyDw8PXL169YWDAoCkpCRcv34d/v7+mjKlUolWrVrh6NGjxb4uOzsbGRkZWhMRERGZt3IlNE5OTjh37lyh8rNnz8LR0fGFgwKA69evAwCqV6+uVV69enXNsqJERERAqVRqJnd3d53EQ0RERKarXAnNoEGDMH78eBw8eBBqtRpqtRoHDhzAhAkTMHDgQF3HWCbh4eFQqVSaKSUlxajxEBERkf6V67LtmTNn4sqVK+jcuTMsLHJXkZOTg+HDh5dpDE1JnJ2dAQA3btzQuprqxo0baNq0abGvUygUUCgUOomBiIiIpKFcPTSWlpbYtGkT/vrrL6xbtw7R0dG4fPkyVq5cCUtLS50E5uXlBWdnZ+zfv19TlpGRgWPHjqF169Y6aYOIiIjMQ7l6aPLUq1cP9erVK/frMzMzkZiYqJlPSkpCfHw8HBwcULNmTYSFhWHWrFmoW7cuvLy8MHXqVLi6uqJ3794vEjYRERGZmXIlNGq1GqtXr8b+/ftx8+ZN5OTkaC0/cOBAqdZz8uRJ+Pn5aeYnTZoEAAgNDcXq1avx0UcfISsrC2+99Rbu37+Pdu3aYc+ePbCysipP2ERERGSmZEIIUdYXvffee1i9ejW6d+8OFxcXyGQyreX/+c9/dBbgi8rIyIBSqYRKpYKdnZ2xw9GLrKwsVK5cGUBur5eNjY2RIyIiInoxZf39LlcPzcaNG7F582Z069atPC8nIiIi0qlyDwquU6eOrmMhIiIiKpdyJTQffPABFixYgHKcrSIiIiLSuXKdcjpy5AgOHjyI3bt3o1GjRqhYsaLW8ujoaJ0ER0RERFQa5Upo7O3ti3ygJBEREZExlCuhWbVqla7jICIiIiq3co2hAYBnz55h3759WLp0KR48eAAASEtLQ2Zmps6CIyIiIiqNcvXQJCcno2vXrrh69Sqys7PRpUsX2NraYu7cucjOzsaSJUt0HScRERFRscrVQzNhwgQ0b94c9+7dg7W1taa8T58+Ws9eIiIiIjKEcvXQxMXF4bfffiv0IEpPT0+kpqbqJDAiIiKi0ipXD01OTg7UanWh8mvXrsHW1vaFg8rvwYMHCAsLg4eHB6ytrdGmTRucOHFCp20QERGRtJUroQkICEBkZKRmXiaTITMzE9OmTdP54xBGjx6NvXv3Yu3atTh//jwCAgLg7+/PniAiIiLSKNfDKa9du4bAwEAIIXDp0iU0b94cly5dQtWqVXH48GE4OTnpJLhHjx7B1tYWO3bsQPfu3TXlPj4+CAoKwqxZs567Dj6ckoiISHoM8nBKNzc3nD17Fhs3bsS5c+eQmZmJN998E0OGDNEaJPyinj17BrVaDSsrK61ya2trHDlypMjXZGdnIzs7WzOfkZGhs3iIiIjINJUroQEACwsLDB06VJexFGJra4vWrVtj5syZaNiwIapXr44NGzbg6NGjxT4cMyIiAjNmzNBrXERERGRaynXK6Ycffihx+fDhw8sdUEGXL1/GqFGjcPjwYcjlcjRr1gz16tXDqVOncPHixUL1i+qhcXd35yknIiIiCSnrKadyJTRVqlTRmn/69CkePnwIS0tLVKpUCXfv3i3rKp8rKysLGRkZcHFxwYABA5CZmYmff/75ua/jGBoiIiLpKevvd7mucrp3757WlJmZiYSEBLRr1w4bNmwozyqfy8bGBi4uLrh37x5iYmLQq1cvvbRDRERE0lOuHprinDx5EkOHDsVff/2lq1UiJiYGQgjUr18fiYmJmDx5MqysrBAXF4eKFSs+9/XsoSEiIpIeg/TQFMfCwgJpaWm6XCVUKhXGjRuHBg0aYPjw4WjXrh1iYmJKlcwQERHRy6FcVzn99NNPWvNCCKSnp+Pbb79F27ZtdRJYnv79+6N///46XScRERGZl3IlNL1799aal8lkqFatGjp16oSvv/5aF3ERERERlVq5EpqcnBxdx0FERERUbjodQ0NERERkDOXqoZk0aVKp686fP788TRARERGVWrkSmjNnzuDMmTN4+vQp6tevDwD4+++/NXfyzSOTyXQTJREREVEJypXQBAcHw9bWFmvWrNHcNfjevXsYOXIkfH198cEHH+g0SCIiIqKSlOvGejVq1MAvv/yCRo0aaZVfuHABAQEBOr8XzYvgjfWIiIikxyA31svIyMCtW7cKld+6dQsPHjwozyqJiIiIyq1cCU2fPn0wcuRIREdH49q1a7h27Rq2bt2KN998EyEhIbqOkYiIiKhE5UpolixZgqCgIAwePBgeHh7w8PDA4MGD0bVrV3z33Xc6C06tVmPq1Knw8vKCtbU1ateujZkzZ0KHj58iIiIiM1CuQcGVKlXCd999h3nz5uHy5csAgNq1a+t87MbcuXOxePFirFmzBo0aNcLJkycxcuRIKJVKjB8/XqdtERERkXSVK6HJk56ejvT0dLRv3x7W1tYQQuj0Uu3ffvsNvXr1Qvfu3QEAnp6e2LBhA44fP66zNoiIiEj6ynXK6c6dO+jcuTPq1auHbt26IT09HQDw5ptv6vSS7TZt2mD//v34+++/AQBnz57FkSNHEBQUVOxrsrOzkZGRoTURERGReStXQjNx4kRUrFgRV69eRaVKlTTlAwYMwJ49e3QW3Mcff4yBAweiQYMGqFixIl577TWEhYVhyJAhxb4mIiICSqVSM7m7u+ssHiIiIjJN5UpofvnlF8ydOxdubm5a5XXr1kVycrJOAgOAzZs3Y926dVi/fj1Onz6NNWvW4KuvvsKaNWuKfU14eDhUKpVmSklJ0Vk8REREZJrKNYYmKytLq2cmz927d6FQKF44qDyTJ0/W9NIAQOPGjZGcnIyIiAiEhoYW+RqFQqHTGIiIiMj0lauHxtfXFz/88INmXiaTIScnB19++SX8/Px0FtzDhw9RoYJ2iHK5HDk5OTprg4iIiKSvXD00X375JTp37oyTJ0/iyZMn+Oijj/DHH3/g7t27+PXXX3UWXHBwMGbPno2aNWuiUaNGOHPmDObPn49Ro0bprA0iIiKSvnI9ywkAVCoVvv32W5w9exaZmZlo1qwZxo0bBxcXF50F9+DBA0ydOhXbtm3DzZs34erqikGDBuHzzz+HpaVlqdbBZzkRERFJT1l/v8uc0Dx9+hRdu3bFkiVLULdu3XIHaihMaIiIiKRH7w+nrFixIs6dO1eu4IiIiIj0oVyDgocOHYoVK1boOhYiIiKicinXoOBnz55h5cqV2LdvH3x8fAqd4pg/f75OgiMiIiIqjTIlNP/88w88PT1x4cIFNGvWDAA0jyXIo8tnORERERGVRpkSmrp16yI9PR0HDx4EkPuog4ULF6J69ep6CY6IiIioNMo0hqbgBVG7d+9GVlaWTgMiIiIiKqtyDQrOU85b2BARERHpVJkSGplMVmiMDMfMEBERkbGVaQyNEAIjRozQPPzx8ePHePvttwtd5RQdHa27CImIiIieo0wJTcEnXA8dOlSnwZCJUauBuDggPR1wcQF8fQG53NhR6YY5bxsR0UuoTAnNqlWr9BVHsTw9PZGcnFyo/N1338WiRYsMHs9LIzoamDABuHbtf2VubsCCBUBIiPHi0gVz3jYiopfUCw0KNoQTJ04gPT1dM+3duxcA0K9fPyNHZsaio4G+fbV/8AEgNTW3XMqnFM1524iIXmLlftq2sYSFhWHnzp24dOlSqQYk8+GUZaRWA56ehX/w88hkub0ZSUnSO0VjzttGRGRm9P5wSmN68uQJfvzxR4waNarYZCY7OxsZGRlaE5VBXFzxP/gAIASQkpJbT2rMeduIiF5ykkpotm/fjvv372PEiBHF1omIiIBSqdRM7u7uhgvQHKSn67aeKTHnbSMieslJKqFZsWIFgoKC4OrqWmyd8PBwqFQqzZSSkmLACM2Ai4tu65kSc942IqKXXLmetm0MycnJ2Ldv33PvcaNQKDT3yaFy8PXNHUeSmpp7CqagvHEmvr6Gj+1FmfO2ERG95CTTQ7Nq1So4OTmhe/fuxg7FvMnluZcvA7k/8PnlzUdGSnPQrDlvGxHRS04SCU1OTg5WrVqF0NBQWFhIplNJukJCgKgooOCpPTe33HIp36vFnLeNiOglJonsYN++fbh69SpGjRpl7FBeHiEhgL8/oFTmzu/aBQQEmEfvhTlvGxHRS0oSCU1AQACf7G0M+X/g27c3rx98c942IqKXkCROORERERGVhAkNERERSR4TGiIiIpI8JjREREQkeUxoiIiISPKY0BAREZHkMaEhIiIiyWNCQ0RERJLHhIaIiIgkz+QTmtTUVAwdOhSOjo6wtrZG48aNcfLkSWOH9dJSq9WIjY3Fhg0bEBsbC7VarasVA7GxwIYNuf/+u169tUdERGbFpB99cO/ePbRt2xZ+fn7YvXs3qlWrhkuXLqFKlSrGDu2lFB0djQkTJuDatWuaMjc3NyxYsAAhL/JQx+hoYMIEIN964eaG3wcNQr8NG3TfHhERmR2ZMOGHJH388cf49ddfERcXV+51ZGRkQKlUQqVSwc7OTofRmY6srCxUrlwZAJCZmQkbGxtdrRj4d7071q1Dn6FDCz1TSyaTAQCioqLKl2RERwN9+wIF1iv+nfoC2KbL9vLk2zZkZgK62mdERKQTZf39NulTTj/99BOaN2+Ofv36wcnJCa+99hqWL19u7LBeSpMnTy7yAaF5ZWFhYWU/HaRW5/bMFLFe2b//RkL7Q/pC7RERkdky6YTmn3/+weLFi1G3bl3ExMTgnXfewfjx47FmzZpiX5OdnY2MjAytiV5calpascuEEEhJSSl7T1pcnPZppgIqAKgJwFdX7RERkdky6TE0OTk5aN68Ob744gsAwGuvvYYLFy5gyZIlCA0NLfI1ERERmDFjhiHDpH+lp6eX9QWlquaiq/aIiMhsmXQPjYuLC1555RWtsoYNG+Lq1avFviY8PBwqlUozpaSk6DtM+peLS3GpR7EvKFW14tKWMrdHRERmy6R7aNq2bYuEhAStsr///hseHh7FvkahUEChUOg7tJdODVdXJKanFzmORiaTwc3NDb6+BU8OPYevL+DmBqSmFjmOJgfANQAFTyyVuz0iIjJbJt1DM3HiRPz+++/44osvkJiYiPXr12PZsmUYN26csUN76cybNw/A/64yypM3HxkZCblcXraVyuXAggV5K9JalJfeTERuYqOT9oiIyGyZdELTokULbNu2DRs2bMCrr76KmTNnIjIyEkOGDDF2aC+dXr16ISoqCq6urlrlbm5uL3YJdUgIEBUFFFivzN0dxydPxrEaNXTbHhERmSWTvg+NLvA+NC+04kL3asnbnwCwa9cuBAQE6KanJCMD+He92LULCAgA5HL9tcf70BARmTSzug8NmZ78yUT79u11d9on/3rat9fM6609IiIyK0xoiIiISPKY0BAREZHkMaEhIiIiyWNCQ0RERJLHhIaIiIgkjwkNERERSR4TGiIiIpI8JjREREQkeSb9cEoiKge1GoiLA9LTc59o7uurfeNCIiIzZPI9NNOnT4dMJtOaGjRoYOywiExTdDTg6Qn4+QGDB+f+6+mZW05EZMYk0UPTqFEj7Nu3TzNvYSGJsIkMKzoa6NsXKPh4ttTU3PKoqNyHgRIRmSFJZAYWFhZwdnY2dhhEpkutBiZMKJzMALllMhkQFgb06sXTT0Rklkz+lBMAXLp0Ca6urqhVqxaGDBmCq1evFls3OzsbGRkZWhOR2YuLA65dK365EEBKSm49IiIzZPIJTatWrbB69Wrs2bMHixcvRlJSEnx9ffHgwYMi60dERECpVGomd3d3A0dMZATp6bqtR0QkMSaf0AQFBaFfv35o0qQJAgMDsWvXLty/fx+bN28usn54eDhUKpVmSklJMXDEREbg4qLbekREEiOJMTT52dvbo169ekhMTCxyuUKhgEKhMHBUREbm6wu4ueUOAC5qHI1Mlrvc19fwsRERGYDJ99AUlJmZicuXL8OFf2kS/Y9cDixYkPt/mUx7Wd58ZCQHBBOR2TL5hObDDz/EoUOHcOXKFfz222/o06cP5HI5Bg0aZOzQiExLSEjupdmurtrlbm68ZJuIzJ7Jn3K6du0aBg0ahDt37qBatWpo164dfv/9d1SrVs3YoRGZnpAQwN8fUCpz53ftAgIC2DNDRGbP5BOajRs3GjsEImnJn7y0b89khoheCiZ/yomIiIjoeZjQEBERkeQxoSEiIiLJY0JDREREkseEhoiIiCSPCQ0RERFJHhMaIiIikjwmNERERCR5TGiIiIhI8iSV0MyZMwcymQxhYWHGDsU41GogNhbYsCH3X7Xa2BERERGZBJN/9EGeEydOYOnSpWjSpImxQzGO6GhgwgTg2rX/lbm55T5hOTDQeHERERGZAEn00GRmZmLIkCFYvnw5qlSpYuxwDC86GujbVzuZAYDUVKBvX8h37DBOXERERCZCEgnNuHHj0L17d/j7+xs7FMNTq3N7ZoQovOzfMsspU6TxRhIREemJyZ9y2rhxI06fPo0TJ06Uqn52djays7M18xkZGfoKzTDi4gr3zOQnBCpcuwZfAIcMFhQREZFpMek/7FNSUjBhwgSsW7cOVlZWpXpNREQElEqlZnJ3d9dzlHqWnl6qai56DoOIiMiUmXRCc+rUKdy8eRPNmjWDhYUFLCwscOjQISxcuBAWFhZQF3GVT3h4OFQqlWZKSUkxQuQ65FK6VKV0aQ8REZF5MulTTp07d8b58+e1ykaOHIkGDRpgypQpkMvlhV6jUCigUCgMFaL++frmXs2Umlr0OBqZDDk1aiCupNNSREREZs6kExpbW1u8+uqrWmU2NjZwdHQsVG625PLcS7P79gVkMu2kRiYDADyZOxc5Q4YYKUAiIiLjM+lTTvSvkBAgKgpwddUud3MDoqKg7tXLOHERERGZCJPuoSlKbGyssUMwjpAQwN8fUCpz53ftAgICcntwsrKMGxsREZGRsYdGSvKPGWrfXnueiIjoJcaEhoiIiCSPCQ0RERFJHhMaIiIikjwmNERERCR5TGiIiIhI8pjQEBERkeQxoSEiIiLJY0JDREREkie5OwUT6ZNarUZcXBzS09Ph4uICX1/fIh+CSvmo1UBcHJCenvt0eF9f3vSRiAzO5HtoFi9ejCZNmsDOzg52dnZo3bo1du/ebeywyAxFR0fD09MTfn5+GDx4MPz8/ODp6Yno6Ghjh2a6oqMBT0/Azw8YPDj3X0/P3HIiIgMy+YTGzc0Nc+bMwalTp3Dy5El06tQJvXr1wh9//GHs0MiMREdHo2/fvrh27ZpWeWpqKvr27cukpijR0blPgS+wz5CamlvOfUZEBmTyCU1wcDC6deuGunXrol69epg9ezYqV66M33//3dihkZlQq9WYMGEChBCFluWVhYWFQa1WGzo006VWAxMmAEXsM01ZWFhuPSIiAzD5hCY/tVqNjRs3IisrC61bty6yTnZ2NjIyMrQmopL8+uuvhXpm8hNCICUlBXFxcQaMysTFxRXumclPCCAlJbceEZEBSCKhOX/+PCpXrgyFQoG3334b27ZtwyuvvFJk3YiICCiVSs3k7u5u4GhJaq5fv16qeunp6XqOREJKuy+4z4jIQCSR0NSvXx/x8fE4duwY3nnnHYSGhuLPP/8ssm54eDhUKpVmSklJMXC0JDXOzs6lqufi4qLnSCSktPuC+4yIDEQSl21bWlqiTp06AAAfHx+cOHECCxYswNKlSwvVVSgUUCgUhg6RJKxt27Zwc3NDampqkeNoZDIZ3Nzc4Ovra4ToTJSvL+DmljsAuKhxNDJZ7nLuMyIyEEn00BSUk5OD7OxsY4dBZkIul2PBggUAcpOX/PLmIyMjeT+a/ORy4N99hgL7TDMfGcn70RCRwZh8QhMeHo7Dhw/jypUrOH/+PMLDwxEbG4shQ4YYOzQyIyEhIYiKioKrq6tWuZubG6KiohASEmKkyExYSAgQFQUU2Gdwc8st5z4jIgMy+VNON2/exPDhw5Geng6lUokmTZogJiYGXbp0MXZoZGZCQkLg7+8PpVIJANi1axcCAgLYM1OSkBDA3x/4d59h1y4gIIA9M0RkcCaf0KxYscLYIdBLJH/y0r59eyYzpZF/H7Vvz2SGiIzC5E85ERERET0PExoiIiKSPCY0REREJHlMaIiIiEjymNAQERGR5DGhISIiIsljQkNERESSx4SGiIiIJI8JDREREUmeySc0ERERaNGiBWxtbeHk5ITevXsjISHB2GERvTC1Wo3Y2Fhs2LABsbGxUKvVxg6JiEiyTD6hOXToEMaNG4fff/8de/fuxdOnTxEQEICsrCxjh0ZUbtHR0fD09ISfnx8GDx4MPz8/eHp6Ijo62tihERFJksk/y2nPnj1a86tXr4aTkxNOnTqF9u3bGykqovKLjo5G3759IYTQKk9NTUXfvn35dG8ionIw+R6aglQqFQDAwcHByJEQlZ1arcaECRMKJTMANGVhYWE8/UREVEaSSmhycnIQFhaGtm3b4tVXXy2yTnZ2NjIyMrQmIlMRFxeHa9euFbtcCIGUlBTExcUZMCoiIumTVEIzbtw4XLhwARs3biy2TkREBJRKpWZyd3c3YIREJUtPT9dpPSIiyiWZhOa9997Dzp07cfDgQbi5uRVbLzw8HCqVSjOlpKQYMEqikrm4uOi0HhER5TL5QcFCCLz//vvYtm0bYmNj4eXlVWJ9hUIBhUJhoOiIysbX1xdubm5ITU0tchyNTCaDm5sbfH19jRAdEZF0mXwPzbhx4/Djjz9i/fr1sLW1xfXr13H9+nU8evTI2KERlZlcLseCBQsA5CYv+eXNR0ZGQi6XGzw2IiIpM/mEZvHixVCpVOjYsSNcXFw006ZNm4wdGlG5hISEICoqCq6urlrlbm5uvGSbiKicJHHKicjchISEwN/fH0qlEgCwa9cuBAQEsGeGiKicTL6Hhshc5U9e2rdvz2SGiOgFMKEhIiIiyWNCQ0RERJLHhIaIiIgkjwkNERERSR4TGiIiIpI8JjREREQkeUxoiIiISPKY0BAREZHkmfydgqVIrVYjLi4O6enpcHFxga+vL2+aRqQLajUQFwekpwMuLoCvLyCXG/SYM+fj25y3zdzxGAAgTNyhQ4dEjx49hIuLiwAgtm3bVqbXq1QqAUCoVCr9BFjA1q1bhZubmwCgmdzc3MTWrVtffOWZmUIAuVNmZr7iTE1bmfnK9dGeIdsyq/aKDMGw26Y3hmpv61Yh3Nz+1xYghJubODp5sv6OuUIh6PH4NjJz3jZzZ8j3zpBtlfX32+QTml27dolPP/1UREdHv1hCs3atfgLMZ+vWrUImk2m90QCETCYTMpnsxd9wE/jRN5sEgwmNtNrbulUImUw7mQFEDiDUgOijr2NOKwQ9H99GZM7bZu4M+d4Z+nNidglNfi+U0NSoIcSzZ/oJTAjx7NmzQllrwTfc3d1dPHuRGEzgR99sEgwmNNJp79mzwj0z+SY1IJIBUUEfx5wmBAMc30Zizttm7gz53hnjc1LWhMbsBgVnZ2cjIyNDawIApKbmnnvXk7i4OFy7dq3Y5UIIpKSkIE6PMRCZpbg4oIRjqwKAmgB8C5Tr8pgz5+PbnLfN3BnyvZPC58TsEpqIiAgolUrN5O7u/r+F6el6aze9lOsubT0i+lcpjxmXYl/+4secOR/f5rxt5s6Q750UPidml9CEh4dDpVJpppSUlP8tdCnuK+/FuZRy3aWtR0T/KuUxU9zXqC6OOXM+vs1528ydId87KXxOzC6hUSgUsLOz05oAADVq5F7iqSe+vr5wc3ODTCYrcrlMJoO7uzt89RgDkVny9QXc3IBijq0cAFcBFOzo1uUxZ87Htzlvm7kz5Hsnhc+J2SU0xZozB9DjdfJyuRwLFiwAgEJveN58ZGSkaVyrTyQlcjnw77FVMKkR//47EbmJTR5dH3PmfHyb87aZO0O+d1L4nJh8QpOZmYn4+HjEx8cDAJKSkhAfH4+rV6+WbUU9e+o+uAJCQkIQFRUFV1dXrXI3NzdERUUhJCRE7zEQmaWQECAqCihwbMnc3XF88mQcq1FDq1wfx5w5H9/mvG3mzpDvncl/TnR2fZWeHDx4sMhLxEJDQ0v1ekPfWC9/mwDErl27dHcZmwlc2mw2l1Hzsm1ptqdS/a+tXbs0t2LQ2zFXZAiGa8vQzHnbzJ05HgNl/f02+UcfdOzYEUKI51c0Ifm73Nq3b8+uWiJdyX8stW+vmTfkMWfOx7c5b5u54zEggVNORERERM/DhIaIiIgkjwkNERERSR4TGiIiIpI8JjREREQkeUxoiIiISPKY0BAREZHkMaEhIiIiyWNCQ0RERJIniYRm0aJF8PT0hJWVFVq1aoXjx48bOyQiIiIyISaf0GzatAmTJk3CtGnTcPr0aXh7eyMwMBA3b940dmhERERkIkw+oZk/fz7GjBmDkSNH4pVXXsGSJUtQqVIlrFy50tihERERkYkw6YTmyZMnOHXqFPz9/TVlFSpUgL+/P44ePWrEyIiIiMiUmPTTtm/fvg21Wo3q1atrlVevXh1//fVXka/Jzs5Gdna2Zl6lUgEAMjIy9BdoAVlZWZr/Z2RkQK1W62rF//t/Rgbw73oN2Z45b5te2ysyBMNum94Ysj1zft9MgDlvm7kzx2Mg73dbCFG6FwgTlpqaKgCI3377Tat88uTJomXLlkW+Ztq0aQIAJ06cOHHixMkMppSUlFLlDCbdQ1O1alXI5XLcuHFDq/zGjRtwdnYu8jXh4eGYNGmSZv7+/fvw8PDA1atXoVQq9RovkJtRuru7IyUlBXZ2dmbTlqHb47ZJsz1z3jZDt8dtk2Z73DbdEULgwYMHcHV1LVV9k05oLC0t4ePjg/3796N3794AgJycHOzfvx/vvfdeka9RKBRQKBSFypVKpUHegDx2dnYGa8+QbRm6PW6bNNsz520zdHvcNmm2x23TjbJ0RJh0QgMAkyZNQmhoKJo3b46WLVsiMjISWVlZGDlypLFDIyIiIhNh8gnNgAEDcOvWLXz++ee4fv06mjZtij179hQaKExEREQvL5NPaADgvffeK/YU0/MoFApMmzatyNNQ+mDI9rht0myP28b2TK0tQ7fHbZNme4betrKSCVHa66GIiIiITJNJ31iPiIiIqDSY0BAREZHkMaEhIiIiyWNCQ0RERJJn9gnNokWL4OnpCSsrK7Rq1QrHjx/XSzuHDx9GcHAwXF1dIZPJsH37dr20AwARERFo0aIFbG1t4eTkhN69eyMhIUEvbS1evBhNmjTR3EipdevW2L17t17aKsqcOXMgk8kQFham83VPnz4dMplMa2rQoIHO28kvNTUVQ4cOhaOjI6ytrdG4cWOcPHlSL215enoW2j6ZTIZx48bpvC21Wo2pU6fCy8sL1tbWqF27NmbOnFn6Z7CU0YMHDxAWFgYPDw9YW1ujTZs2OHHihE7W/bxjWQiBzz//HC4uLrC2toa/vz8uXbqkt/aio6MREBAAR0dHyGQyxMfH66Wtp0+fYsqUKWjcuDFsbGzg6uqK4cOHIy0tTS/tAbnHYIMGDWBjY4MqVarA398fx44d01t7+b399tuQyWSIjIzUS1sjRowodOx17dq1XG2Vpj0AuHjxInr27AmlUgkbGxu0aNECV69e1XlbRX2vyGQyzJs3r5xbpxtmndBs2rQJkyZNwrRp03D69Gl4e3sjMDAQN2/e1HlbWVlZ8Pb2xqJFi3S+7oIOHTqEcePG4ffff8fevXvx9OlTBAQEaD0wTFfc3NwwZ84cnDp1CidPnkSnTp3Qq1cv/PHHHzpvq6ATJ05g6dKlaNKkid7aaNSoEdLT0zXTkSNH9NbWvXv30LZtW1SsWBG7d+/Gn3/+ia+//hpVqlTRS3snTpzQ2ra9e/cCAPr166fztubOnYvFixfj22+/xcWLFzF37lx8+eWX+Oabb3TeFgCMHj0ae/fuxdq1a3H+/HkEBATA398fqampL7zu5x3LX375JRYuXIglS5bg2LFjsLGxQWBgIB4/fqyX9rKystCuXTvMnTu3XOsvbVsPHz7E6dOnMXXqVJw+fRrR0dFISEhAz5499dIeANSrVw/ffvstzp8/jyNHjsDT0xMBAQG4deuWXtrLs23bNvz++++lvqV+edvq2rWr1jG4YcMGvbV3+fJltGvXDg0aNEBsbCzOnTuHqVOnwsrKSudt5d+m9PR0rFy5EjKZDG+88UaZ29KpF3l4pKlr2bKlGDdunGZerVYLV1dXERERodd2AYht27bptY38bt68KQCIQ4cOGaS9KlWqiO+//16vbTx48EDUrVtX7N27V3To0EFMmDBB521MmzZNeHt763y9xZkyZYpo166dwdoraMKECaJ27doiJydH5+vu3r27GDVqlFZZSEiIGDJkiM7bevjwoZDL5WLnzp1a5c2aNROffvqpTtsqeCzn5OQIZ2dnMW/ePE3Z/fv3hUKhEBs2bNB5e/klJSUJAOLMmTMv3M7z2spz/PhxAUAkJycbpD2VSiUAiH379umtvWvXrokaNWqICxcuCA8PD/Gf//xHL22FhoaKXr16vfC6S9vegAEDxNChQw3SVkG9evUSnTp10nnbZWW2PTRPnjzBqVOn4O/vrymrUKEC/P39cfToUSNGpnsqlQoA4ODgoNd21Go1Nm7ciKysLLRu3VqvbY0bNw7du3fXev/04dKlS3B1dUWtWrUwZMiQcnXPltZPP/2E5s2bo1+/fnBycsJrr72G5cuX6629/J48eYIff/wRo0aNgkwm0/n627Rpg/379+Pvv/8GAJw9exZHjhxBUFCQztt69uwZ1Gp1ob88ra2t9drDBgBJSUm4fv261udSqVSiVatWZve9AuR+t8hkMtjb2+u9rSdPnmDZsmVQKpXw9vbWSxs5OTkYNmwYJk+ejEaNGumljfxiY2Ph5OSE+vXr45133sGdO3f00k5OTg5+/vln1KtXD4GBgXByckKrVq30OvQhz40bN/Dzzz/jzTff1Htbz2O2Cc3t27ehVqsLPSKhevXquH79upGi0r2cnByEhYWhbdu2ePXVV/XSxvnz51G5cmUoFAq8/fbb2LZtG1555RW9tAUAGzduxOnTpxEREaG3NgCgVatWWL16Nfbs2YPFixcjKSkJvr6+ePDggV7a++eff7B48WLUrVsXMTExeOeddzB+/HisWbNGL+3lt337dty/fx8jRozQy/o//vhjDBw4EA0aNEDFihXx2muvISwsDEOGDNF5W7a2tmjdujVmzpyJtLQ0qNVq/Pjjjzh69CjS09N13l5+ed8d5v69AgCPHz/GlClTMGjQIL0+iHDnzp2oXLkyrKys8J///Ad79+5F1apV9dLW3LlzYWFhgfHjx+tl/fl17doVP/zwA/bv34+5c+fi0KFDCAoKglqt1nlbN2/eRGZmJubMmYOuXbvil19+QZ8+fRASEoJDhw7pvL381qxZA1tbW4SEhOi1ndKQxKMPqHjjxo3DhQsX9PqXaf369REfHw+VSoWoqCiEhobi0KFDeklqUlJSMGHCBOzdu7dc537LIn/vQZMmTdCqVSt4eHhg8+bNevlrIycnB82bN8cXX3wBAHjttddw4cIFLFmyBKGhoTpvL78VK1YgKCjohcYMlGTz5s1Yt24d1q9fj0aNGiE+Ph5hYWFwdXXVy7atXbsWo0aNQo0aNSCXy9GsWTMMGjQIp06d0nlbL6OnT5+if//+EEJg8eLFem3Lz88P8fHxuH37NpYvX47+/fvj2LFjcHJy0mk7p06dwoIFC3D69Gm99FIWNHDgQM3/GzdujCZNmqB27dqIjY1F586dddpWTk4OAKBXr16YOHEiAKBp06b47bffsGTJEnTo0EGn7eW3cuVKDBkyRO/f16Vhtj00VatWhVwux40bN7TKb9y4AWdnZyNFpVvvvfcedu7ciYMHD8LNzU1v7VhaWqJOnTrw8fFBREQEvL29sWDBAr20derUKdy8eRPNmjWDhYUFLCwscOjQISxcuBAWFhZ6+esmj729PerVq4fExES9rN/FxaVQEtiwYUO9nuYCgOTkZOzbtw+jR4/WWxuTJ0/W9NI0btwYw4YNw8SJE/XWy1a7dm0cOnQImZmZSElJwfHjx/H06VPUqlVLL+3lyfvuMOfvlbxkJjk5GXv37tVr7wwA2NjYoE6dOnj99dexYsUKWFhYYMWKFTpvJy4uDjdv3kTNmjU13y3Jycn44IMP4OnpqfP2CqpVqxaqVq2ql++XqlWrwsLCwuDfL3FxcUhISNDrd0tZmG1CY2lpCR8fH+zfv19TlpOTg/379+t9/Ie+CSHw3nvvYdu2bThw4AC8vLwM2n5OTg6ys7P1su7OnTvj/PnziI+P10zNmzfHkCFDEB8fD7lcrpd2ASAzMxOXL1+Gi4uLXtbftm3bQpfX//333/Dw8NBLe3lWrVoFJycndO/eXW9tPHz4EBUqaH+dyOVyzV+O+mJjYwMXFxfcu3cPMTEx6NWrl17b8/LygrOzs9b3SkZGBo4dOyb57xXgf8nMpUuXsG/fPjg6Oho8Bn19vwwbNgznzp3T+m5xdXXF5MmTERMTo/P2Crp27Rru3Lmjl+8XS0tLtGjRwuDfLytWrICPj4/exjyVlVmfcpo0aRJCQ0PRvHlztGzZEpGRkcjKysLIkSN13lZmZqZW5p2UlIT4+Hg4ODigZs2aOm1r3LhxWL9+PXbs2AFbW1vNuXulUglra2udthUeHo6goCDUrFkTDx48wPr16xEbG6u3LwBbW9tCY4FsbGzg6Oio8zFCH374IYKDg+Hh4YG0tDRMmzYNcrkcgwYN0mk7eSZOnIg2bdrgiy++QP/+/XH8+HEsW7YMy5Yt00t7QO6Pw6pVqxAaGgoLC/0d7sHBwZg9ezZq1qyJRo0a4cyZM5g/fz5GjRqll/ZiYmIghED9+vWRmJiIyZMno0GDBjo5tp93LIeFhWHWrFmoW7cuvLy8MHXqVLi6uqJ37956ae/u3bu4evWq5n4weT9azs7OZe4VKqktFxcX9O3bF6dPn8bOnTuhVqs13y0ODg6wtLTU6bY5Ojpi9uzZ6NmzJ1xcXHD79m0sWrQIqamp5b61wPP2ZcEErWLFinB2dkb9+vV12paDgwNmzJiBN954A87Ozrh8+TI++ugj1KlTB4GBgXrZtsmTJ2PAgAFo3749/Pz8sGfPHvz3v/9FbGysztsCchP5LVu24Ouvvy7X9uiFka+y0rtvvvlG1KxZU1haWoqWLVuK33//XS/tHDx4UAAoNIWGhuq8raLaASBWrVql87ZGjRolPDw8hKWlpahWrZro3Lmz+OWXX3TeTkn0ddn2gAEDhIuLi7C0tBQ1atQQAwYMEImJiTpvJ7///ve/4tVXXxUKhUI0aNBALFu2TK/txcTECAAiISFBr+1kZGSICRMmiJo1aworKytRq1Yt8emnn4rs7Gy9tLdp0yZRq1YtYWlpKZydncW4cePE/fv3dbLu5x3LOTk5YurUqaJ69epCoVCIzp07v9D+fV57q1atKnL5tGnTdNpW3mXhRU0HDx7U+bY9evRI9OnTR7i6ugpLS0vh4uIievbsKY4fP16utp7XXlFe5LLtktp6+PChCAgIENWqVRMVK1YUHh4eYsyYMeL69et63bYVK1aIOnXqCCsrK+Ht7S22b9+ut7aWLl0qrK2tdXbc6YJMCD3dypOIiIjIQMx2DA0RERG9PJjQEBERkeQxoSEiIiLJY0JDREREkseEhoiIiCSPCQ0RERFJHhMaIiIikjwmNERERCR5TGiISK9GjBgBmUwGmUyGihUronr16ujSpQtWrlyp92c9EdHLgwkNEeld165dkZ6ejitXrmD37t3w8/PDhAkT0KNHDzx79kxv7T558kRv6yYi08KEhoj0TqFQwNnZGTVq1ECzZs3wySefYMeOHdi9ezdWr14NALh//z5Gjx6NatWqwc7ODp06dcLZs2e11jNr1iw4OTnB1tYWo0ePxscff4ymTZtqlo8YMQK9e/fG7Nmz4erqqnnoYEpKCvr37w97e3s4ODigV69euHLlita6v//+ezRs2BBWVlZo0KABvvvuO33uEiLSMSY0RGQUnTp1gre3N6KjowEA/fr1w82bN7F7926cOnUKzZo1Q+fOnXH37l0AwLp16zB79mzMnTsXp06dQs2aNbF48eJC692/fz8SEhKwd+9e7Ny5E0+fPkVgYCBsbW0RFxeHX3/9FZUrV0bXrl01PTjr1q3D559/jtmzZ+PixYv44osvMHXqVKxZs8ZwO4SIXoyxn45JROYtNDRU9OrVq8hlAwYMEA0bNhRxcXHCzs5OPH78WGt57dq1xdKlS4UQQrRq1UqMGzdOa3nbtm2Ft7e3VlvVq1fXesr32rVrRf369UVOTo6mLDs7W1hbW4uYmBhNO+vXr9da98yZM0Xr1q3LvL1EZBwWxk6oiOjlJYSATCbD2bNnkZmZCUdHR63ljx49wuXLlwEACQkJePfdd7WWt2zZEgcOHNAqa9y4MSwtLTXzZ8+eRWJiImxtbbXqPX78GJcvX0ZWVhYuX76MN998E2PGjNEsf/bsGZRKpU62k4j0jwkNERnNxYsX4eXlhczMTLi4uCA2NrZQHXt7+zKt08bGRms+MzMTPj4+WLduXaG61apVQ2ZmJgBg+fLlaNWqldZyuVxepraJyHiY0BCRURw4cADnz5/HxIkT4ebmhuvXr8PCwgKenp5F1q9fvz5OnDiB4cOHa8pOnDjx3HaaNWuGTZs2wcnJCXZ2doWWK5VKuLq64p9//sGQIUPKvT1EZFxMaIhI77Kzs3H9+nWo1WrcuHEDe/bsQUREBHr06IHhw4ejQoUKaN26NXr37o0vv/wS9erVQ1paGn7++Wf06dMHzZs3x/vvv48xY8agefPmaNOmDTZt2oRz586hVq1aJbY9ZMgQzJs3D7169cL//d//wc3NDcnJyYiOjsZHH30ENzc3zJgxA+PHj4dSqUTXrl2RnZ2NkydP4t69e5g0aZKB9hIRvQgmNESkd3v27IGLiwssLCxQpUoVeHt7Y+HChQgNDUWFCrkXW+7atQuffvopRo4ciVu3bsHZ2Rnt27dH9erVAeQmJv/88w8+/PBDPH78GP3798eIESNw/PjxEtuuVKkSDh8+jClTpiAkJAQPHjxAjRo10LlzZ02PzejRo1GpUiXMmzcPkydPho2NDRo3boywsDC97hci0h2ZEEIYOwgiovLo0qULnJ2dsXbtWmOHQkRGxh4aIpKEhw8fYsmSJQgMDIRcLseGDRuwb98+7N2719ihEZEJYA8NEUnCo0ePEBwcjDNnzuDx48eoX78+PvvsM4SEhBg7NCIyAUxoiIiISPL46AMiIiKSPCY0REREJHlMaIiIiEjymNAQERGR5DGhISIiIsljQkNERESSx4SGiIiIJI8JDREREUkeExoiIiKSvP8H9UJRRxa5U5gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The discrepancy is even more dramatic in the karate club network. \n",
    "\n",
    "The thing is that the degree distribution is a very important property of the network. Degree distribution has an outsized impact on many network properties. For example, having hubs can dramatically decrease the average distance between nodes. Clustering coefficient is also affected by the degree distribution.\n",
    "\n",
    "In other words, the ER random graph model may be \"too random\" because it does not capture the degree distribution of the real networks. Is there any way to generate random graphs that exactly capture the degree distribution of the real networks, while keeping everything else random?\n",
    "\n",
    "That's where the \"configuration model\" comes in. The configuration model generates random graphs that have the same degree distribution as the real network. In other words, it keeps the degree sequence of the real network, in addition to the number of nodes and edges. You can picture the configuration model as follows:\n",
    "\n",
    "1. Break all the existing edges in the real network while keeping the \"ends\" of the edges (edge stubs) intact. If a node has degree $k$, then it keeps $k$ edge stubs.\n",
    "2. Randomly pick a pair of edge stubs and connect them. \n",
    "3. Repeat step 2 until all edge stubs are exhausted.\n",
    "\n",
    "Easy enough, right? Why don't you try a very small example by hand? You may encounter a few tricky cases. Did you find them? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details class=\"warning-class\"><summary>Solution</summary><p>Here's the tricky part. What if you are only left with two edge stubs connected to a single node? Should you connect them to create a self-loop? Or, should you just leave them unconnected? Or, what if two edge stubs that you pick are connected to the two nodes that are already connected? Should you connect them to create multiple edges between two nodes? Or should you find another pair? \n",
    "\n",
    "Another issue is that this process may generate a network with multiple components, even if the original network is connected. You want to be careful not to assume that the generated network is connected!\n",
    "</p></details>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create an instance of the configuration model using `nx.configuration_model` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiple edges: {(21, 29), (14, 42), (40, 51), (10, 36), (51, 59), (20, 45), (1, 42)}\n",
      "Self-loops: set()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_dolphin_net = nx.configuration_model([d for n, d in dolphin_net.degree()])\n",
    "\n",
    "# check multiple edges\n",
    "multiedges = set()\n",
    "for n1, n2 in random_dolphin_net.edges():\n",
    "    if random_dolphin_net.number_of_edges(n1, n2) > 1:\n",
    "        multiedges.add((n1, n2))\n",
    "print(\"Multiple edges:\", multiedges)\n",
    "\n",
    "# self loops\n",
    "selfloops = set()\n",
    "for n in random_dolphin_net.nodes():\n",
    "    if random_dolphin_net.has_edge(n, n):\n",
    "        selfloops.add(n)\n",
    "print(\"Self-loops:\", selfloops)\n",
    "\n",
    "nx.draw(random_dolphin_net, node_size=30, width=0.5, with_labels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you repeatedly run this code, you can probably see that the multiedges and self-loops are almost always present, and the network is sometimes disconnected. \n",
    "\n",
    "Always be aware of these issues! Don't just assume that the network generated from the configuration model is connected and has no multiedges or self-loops, all of which make it tricky to compute many network properties. \n",
    "\n",
    "You may be wondering, \"why don't we just rewire the multi-edges and self-loops until we don't have any?\" That's a good question! In fact, that's what people do to make the network more \"clean\". This can be done by 'edge swapping', a process that swaps a pair of edges so that all the degrees are preserved. We can keep doing this until we don't have any multi-edges or self-loops. The problem is that this process makes the network properties biased! So this is not perfect. To make the network truly random and unbiased, we need to allow multi-edges and self-loops."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's do some comparisons!\n",
    "\n",
    "Let's try the clustering coefficient comparison that we did above with two networks (dolphins and karate club) and two null models (ER and configuration model). For multi-edges and self-loops, let's do a quick-and-dirty thing: just remove them.\n",
    "\n",
    "You can do the following to remove them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiple edges: set()\n",
      "Self-loops: set()\n"
     ]
    }
   ],
   "source": [
    "g = nx.Graph(random_dolphin_net)\n",
    "g.remove_edges_from(nx.selfloop_edges(g))\n",
    "\n",
    "# check multiple edges\n",
    "multiedges = set()\n",
    "for n1, n2 in g.edges():\n",
    "    if g.number_of_edges(n1, n2) > 1:\n",
    "        multiedges.add((n1, n2))\n",
    "print(\"Multiple edges:\", multiedges)\n",
    "\n",
    "# self loops\n",
    "selfloops = set()\n",
    "for n in g.nodes():\n",
    "    if g.has_edge(n, n):\n",
    "        selfloops.add(n)\n",
    "print(\"Self-loops:\", selfloops)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: let's compare the average clustering coefficient of the real networks with the average clustering coefficient of the ER random graph and the configuration model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dolphins\n",
    "\n",
    "# YOUR SOLUTION HERE\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# karate club\n",
    "\n",
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting! For the dolphin network, both random graph models produce very similar distributions of the average clustering coefficient, which is much smaller than the actual average clustering coefficient of the dolphin network. However, for the karate club network, the two distributions are quite different. According to the ER random graph model, the average clustering coefficient of the karate club network is much higher than what we would expect. On the other hand, if we consider the configuration model, the average clustering coefficient of the karate club network is not at all surprising, but it is what's expected from the degree distribution! \n",
    "\n",
    "This difference is likely due to the fact that the Karate Club network has several hubs with large degrees, which is not captured by the ER random graph model. \n",
    "\n",
    "Let's try another comparison, this time with a statistical test. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing assortativity \n",
    "\n",
    "Lets try another mock hypothesis using the [dolphin social network](http://www-personal.umich.edu/~mejn/netdata/dolphins.zip). My hypothesis is that dolphins have a posh and very exclusive culture. Popular dolphins only hang with other popular dolphins, while the loners are stuck to mingle among themselves. A sad state of affairs, but social life is hard in the pods. How might we test this?\n",
    "\n",
    "There happens to be a similarity measure called [assortativity](https://en.wikipedia.org/wiki/Assortativity) where nodes of a certain type tend to be connected to nodes of the same type. In networkx there is a function called [degree assortativity](https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.assortativity.degree_assortativity_coefficient.html?highlight=degree_assortativity_coefficient#networkx.algorithms.assortativity.degree_assortativity_coefficient) which tells us how strongly nodes of similar degree are attached to each other. If the network has high degree assortativity, then low degree nodes will be connected to other low degree nodes, and high degree nodes will be connected to other high degree nodes. Conversely, low degree assortativity (or even disassortativity) would imply no (or negative) relationship. \n",
    "\n",
    "Excellent, so we have a measure, but what do we compare our graph too? It wouldn't really be appropriate to compare it to an ER graph because the nodes all have different degrees which are about the same and normally distributed. Instead, we want to compare our dolphin network to a graph with the same degree distribution, and that is where the configuration model comes in."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to do a rough test for our hypothesis by calculating the z-score for our dolphin network's degree assortativity coefficient. We will do this by generating a bunch of configuration models based on the dolphin degree sequence and calculating the assortativity coefficient for those null graphs. We can then compare our real network with the null model. If our z-score is high then it is unlikely that the dolphin network's assortativity can be accounted for by just the degree-sequence of a random graph, which means something more interesting is at work.\n",
    "\n",
    "Lets carry out this experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "dolphin_net = nx.read_gml(\"dolphins.gml\")\n",
    "dolphin_net = nx.Graph(dolphin_net)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the graph is loaded in lets calculate the degree assortativity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.04359402821531255\n"
     ]
    }
   ],
   "source": [
    "real_assortativity = nx.degree_assortativity_coefficient(dolphin_net)\n",
    "print(real_assortativity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting! So it appears that the network is disassortative, so it seems like our hypothesis about the dolphins is falling appart, but we still need to compare it to a null model in order to determine whether there is anything of interest to be pursued here.\n",
    "\n",
    "**Q: can you calculate the degree assortativity of the configuration model and compare it to the degree assortativity of the dolphin network?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can calculate the z-scores using scipy's [zscore](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.zscore.html) function. It will print out the z-scores for all the elements of the distribution. If we assume that the dolphin network came from the null distribution then we can calculate the z-score for the dolphin network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.028712520296263724\n"
     ]
    }
   ],
   "source": [
    "# Prepend the real assortativity to the model list and have scipy calculate the zscores\n",
    "zscores = stats.zscore([real_assortativity] + model_assortativity)\n",
    "\n",
    "# Just print out the first score which corresponds to the real network\n",
    "print(zscores[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A z-score corresponds to how many standard deviations out from the mean the sample is. A score of +1 would be one standard deviation above the mean. Since the score is close to zero we can safely conclude that the disassortativity we observed in the dolphin network is explainable entirely from the degree sequence and no other special properties of the network.\n",
    "\n",
    "However, this doesn't mean there aren't low-level features hidden in the network. The degree assortativity score is an aggregative measure, and it can obfuscate local deviations from assortativity that are non-random. There is also a score for the local degree assortativity, but we will not pursue that further here.\n",
    "\n",
    "Now that we have a distribution of null assortativities we can also visualize the distribution and plot our dolphin network's assortativity along with it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(model_assortativity, bins=50)\n",
    "plt.axvline(real_assortativity, lw=1, color=\"red\") \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And there we have it! We have fair reason to conclude that not only is the hypothesis wrong but the surprising disassortativity we found really wasn't anything special after all. [note: reference for [`axvline`](http://matplotlib.org/api/pyplot_api.html?highlight=axvline#matplotlib.pyplot.axvline) in matplotlib]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now try it yourself!\n",
    "\n",
    "1. Pick a graph of your choosing. You can use the graphs you are using for your project or any other real-world graph. Here are a couple places you can find some: [pajek datasets](http://vlado.fmf.uni-lj.si/pub/networks/data/), [Newman's datasets](http://www-personal.umich.edu/~mejn/netdata/). [note: if you use directed graphs makes sure you use the corresponding function [`directed_configuration_model`](https://networkx.github.io/documentation/stable/reference/generated/networkx.generators.degree_seq.directed_configuration_model.html?highlight=configuration_model#networkx.generators.degree_seq.directed_configuration_model)]\n",
    "\n",
    "2. Construct two simple hypotheses around two different measures that you can calculate from the graph. Here is a [list of algorithms that networkx has](https://networkx.github.io/documentation/stable/reference/algorithms/index.html). You aren't limited by measures networkx can calculate. However, do not use a measure that depends entirely on the degree sequence. For instance, it would be silly to compare average degree to the random networks generated by the configuration model because it uses the same degree sequence and so will have the same average degree.\n",
    "\n",
    "3. Carry out a simple hypothesis test for both hypotheses. It can be similar to what I showed here. You are welcome to carry it out in a more robust manner, but the focus of this assignment is getting a hang of using configuration models as null models. Here is a link to [Statistics for Hackers](https://speakerd.s3.amazonaws.com/presentations/7e68b43159d646cf81eda9e1bded8213/Statistics_for_Hackers_-_PyCon2016.pdf). It has a nice little python tutorial for how you can carry out hypothesis testing without all the hard math. Even if you don't use it for this assignment I highly recommend checking it out.\n",
    "\n",
    "4. Provide quantitative analysis and a graphical illustration of your results. It should be clear that your hypothesis was validated/invalidated/inconclusive.\n",
    "\n",
    "5. Answer the following questions:\n",
    "  * What graph are you using?\n",
    "  * What are you hypotheses?\n",
    "  * What measures will you be using to test your hypotheses? How do these measures accomplish this?\n",
    "  * Explain your results. Were they surprising? Did they confirm or reject your hypotheses?\n",
    "  * From these tests, what have you learned about the structure of the network you were investigating?\n",
    "\n",
    "6. Once you are complete submit your Jupyter notebook to Canvas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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