deepwalk_space.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "from tqdm import tqdm\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can get the dataset from https://densitydesign.github.io/strumentalia-seealsology/\n",
    "\n",
    "__Steps to download:__\n",
    "\n",
    "a) Enter the following links:\n",
    "\n",
    "https://en.wikipedia.org/wiki/Space_research\n",
    "\n",
    "https://en.wikipedia.org/wiki/Space_Race\n",
    "\n",
    "https://en.wikipedia.org/wiki/Space_exploration\n",
    "\n",
    "b) Download the TSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"space_data.tsv\", sep = \"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>space exploration</td>\n",
       "      <td>discovery and exploration of the solar system</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>space exploration</td>\n",
       "      <td>in-space propulsion technologies</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>space exploration</td>\n",
       "      <td>robotic spacecraft</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>space exploration</td>\n",
       "      <td>timeline of planetary exploration</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>space exploration</td>\n",
       "      <td>landings on other planets</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              source                                         target  depth\n",
       "0  space exploration  discovery and exploration of the solar system      1\n",
       "1  space exploration               in-space propulsion technologies      1\n",
       "2  space exploration                             robotic spacecraft      1\n",
       "3  space exploration              timeline of planetary exploration      1\n",
       "4  space exploration                      landings on other planets      1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3328, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct an undirected graph\n",
    "G=nx.from_pandas_edgelist(df, \"source\", \"target\", edge_attr=True, create_using=nx.Graph())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2088"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(G) # number of nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to generate random walk sequences of nodes\n",
    "def get_randomwalk(node, path_length):\n",
    "    \n",
    "    random_walk = [node]\n",
    "    \n",
    "    for i in range(path_length-1):\n",
    "        temp = list(G.neighbors(node))\n",
    "        temp = list(set(temp) - set(random_walk))    \n",
    "        if len(temp) == 0:\n",
    "            break\n",
    "\n",
    "        random_node = random.choice(temp)\n",
    "        random_walk.append(random_node)\n",
    "        node = random_node\n",
    "        \n",
    "    return random_walk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['space exploration',\n",
       " 'atmospheric reentry',\n",
       " 'mars pathfinder',\n",
       " 'scientific information from the mars exploration rover mission',\n",
       " 'opportunity rover',\n",
       " 'spirit rover',\n",
       " 'aeolis quadrangle',\n",
       " 'mars science laboratory',\n",
       " 'mars 2020',\n",
       " 'exomars']"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_randomwalk('space exploration', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2088/2088 [00:00<00:00, 7717.75it/s]\n"
     ]
    }
   ],
   "source": [
    "all_nodes = list(G.nodes())\n",
    "\n",
    "random_walks = []\n",
    "\n",
    "for n in tqdm(all_nodes):\n",
    "    for i in range(5):\n",
    "        random_walks.append(get_randomwalk(n,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10440"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of sequences\n",
    "len(random_walks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train word2vec model\n",
    "model = Word2Vec(window = 4, sg = 1, hs = 0,\n",
    "                 negative = 10, # for negative sampling\n",
    "                 alpha=0.03, min_alpha=0.0007,\n",
    "                 seed = 14)\n",
    "\n",
    "model.build_vocab(random_walks, progress_per=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(982438, 1015840)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.train(random_walks, total_examples = model.corpus_count, epochs=20, report_delay=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word2Vec(vocab=2088, size=100, alpha=0.03)\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('reduced-gravity aircraft', 0.9756266474723816),\n",
       " ('micro-g environment', 0.9612352252006531),\n",
       " ('spaceflight osteopenia', 0.8710659742355347),\n",
       " ('microgravity university', 0.8698078393936157),\n",
       " ('space flight participant', 0.8578461408615112),\n",
       " ('space adaptation syndrome', 0.8436012268066406),\n",
       " ('space tourism society', 0.8100888729095459),\n",
       " ('lagrange point colonization', 0.7876768112182617),\n",
       " ('stanford torus', 0.7843056321144104),\n",
       " ('lists of space programs', 0.7734896540641785)]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find top n similar nodes\n",
    "model.similar_by_word('astronaut training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "terms = ['lunar escape systems','soviet moonshot', 'soyuz 7k-l1', 'moon landing',\n",
    "         'space food', 'food systems on space exploration missions', 'meal, ready-to-eat',\n",
    "         'space law', 'metalaw', 'moon treaty', 'legal aspects of computing',\n",
    "         'astronaut training', 'reduced-gravity aircraft', 'space adaptation syndrome', 'micro-g environment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_nodes(word_list):\n",
    "    X = model[word_list]\n",
    "    \n",
    "    # reduce dimensions to 2\n",
    "    pca = PCA(n_components=2)\n",
    "    result = pca.fit_transform(X)\n",
    "    \n",
    "    \n",
    "    plt.figure(figsize=(12,9))\n",
    "    # create a scatter plot of the projection\n",
    "    plt.scatter(result[:, 0], result[:, 1])\n",
    "    for i, word in enumerate(word_list):\n",
    "        plt.annotate(word, xy=(result[i, 0], result[i, 1]))\n",
    "        \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hoaHat2+f4uPjvWs8AgICNHnyZIWHh8vj8ejrr7+WJKWlpalr164KDw/XAw88oAYNGujYsWMX/BwB4Jp2Je4Py3MegN+/gPErrUEJPwHjV17p0s5T9OyEtWvX2rBhw6ywsNAKCgosNjbWPv74Y9u2bZuFhIRYdna2nTx50v7jP/7DZsyYYWZmx44d8/bz17/+1ebMmWNmZ5/bUNTmXCdOnLDCwkIzM5s3b56NHTvWzMy6d+9umzZtMjOzzMxMy8vLs4SEBKtYsaLt27fP8vPzrUuXLvbWW2+V+jyH06dPW2BgoG3dutXMzDIyMiwvL8/mz59vo0eP9tZQ0ljnSkxMtKCgIMvKyrLMzExr1qyZbd++3czMGjRoYGlpacWO64UXXrA+ffp4+zp+/Ljl5OTYTTfdZHv37jUzs0GDBtmsWbO8/bzwwgtmZvbnP//ZPB6PnTx50o4ePWq1atUyMyv1+M/9zMzM3nrrLRs8eLCZmQ0ePNjb5qevGzRo4P18nn/+efvjH/9oZmajR4+2p556yszM1qxZY5JKPEYAv13iOQ9l+sOaBwCXxLlrBX66/Wq0bt06rVu3TmFhYZKkrKwsffvtt8rMzFTPnj3l53e27rvuusu7z549e/TYY48pPT1dWVlZ6tat2wXHOHjwoPr3768ffvhBZ86cUWBgoCSpdevWGjt2rAYOHKg+ffropptukiS1aNFCDRs2lCQNGDBAmzZtUqVKlbzPc5CkM2fOKCYmRnv37lWdOnW8C6JLu4tTaWMV2bRpk3r37u29DWyfPn20ceNG73kpyfr16zVixAjvrV1r1qypnTt3KjAwUI0bN5YkDR48WM8//7z+/Oc/S5J69OghSfJ4PMrKylLVqlVVtWpVVapUSenp6aUe/913333Bc3whRU/MjoiI0LJly7zHu3z5cknS7bffrho1avzi/gHgWsC0JQCXxNW8VqDooW+BE1YpJ69AK3YckpnpkUce8T4n4bvvvtMf//jHEp/NUCQ+Pl5z587V7t27NXny5FKfn1DkwQcf1JgxY7R792794x//8LafMGGCXnnlFeXk5Khly5beKTVFz4AoUvSsiJKe52BmxdqXpLSxilzoeEtT0tgX6+fcxfLnPuPi3OdllHT8P91+sXNe0pjnPkfjlxwvAFzLCA8ALoleYfX0dB+P6lX3kyOpXnU/Pd3Ho15h9a5oXT9dyG0mPbJst6reEqFXX33Vu37g0KFDOnr0qNq0aaP33ntPubm5ysrK0qpVq7x9ZWZmqk6dOsrLy3O1iPjcRdzn3qZ137598ng8Gj9+vCIjI71f6Ldu3aqUlBQVFhZq8eLFatOmTanPc2jatKkOHz7svdtSZmam8vPziy02L22sIu3atdOKFSuUnZ2tU6dOafny5aUuJi9y22236aWXXvJ+IT9x4oSaNm2q1NRUb52vvfaa2rdvf9FzdK6Sjl+SbrjhBn311VcqLCz0XjWQii+sd6NNmzZasmSJpLNXn3788ceftT8AXGsIDwAumV5h9bR5QielTI/V5gmdrnhwkEp/6NsHJ2/Uvffeq5iYGHk8Ht19993KzMxUVFSUevTooZCQEPXp00eRkZGqVq2aJOmJJ55QdHS0unbtqqZNm1507ClTpiguLk5t27aVv7+/d/vs2bMVFBSkkJAQ+fn56Y477pAkxcTEaMKECQoKClJgYKB69+593vMcgoODvVcPKlSooMWLF+vBBx9USEiIunbtqtzc3GKLzUsbq0h4eLji4+PVokULRUdH6/7777/glCVJuv/++3XzzTcrODhYISEh+te//qVKlSpp/vz5iouLk8fjkY+Pj0aMGOHqMypS0vFL0vTp09W9e3d16tRJderU8ba/5557NGPGDIWFhWnfvn2uxpg8ebLWrVun8PBwrVmzRnXq1FHVqlV/Vp0AcC1xrsQl28jISEtMTLzs4wJA4IRVKul/9RxJKdNjS9wnKytLVapUUXZ2ttq1a6eXX35Z4eHhl7TODRs2aObMmVq5cuUlHedqdbmO//Tp0/L19VW5cuW0ZcsWjRw5UklJSZd0TACXl+M4X5hZ5JWu4/eCBdMArim/ZCH38OHDlZycrNzcXA0ePPiSBwdcPgcOHFC/fv1UWFioChUqaN68eVe6JAC4qnHlAcA1pWjNw7lTl/zK+14V6zEAAGWPKw9l61eveXAcp77jOAmO43zlOM6XjuP8qSwKA4BL4WpdyA0AwG9BWUxbypf0/8xsu+M4VSV94TjOB2aWXAZ9A0CZ6xVWj7AAAMAv8KuvPJjZD2a2/X9/z5T0lST+XxkAAAD4nSnTW7U6jhMgKUzS52XZLwAAAIArr8zCg+M4VSS9LenPZnayhPeHO46T6DhOYlpaWlkNCwAAAOAyKZPw4DhOeZ0NDovMbFlJbczsZTOLNLPIWrVqlcWwAAAAAC6jsrjbkiPpn5K+MrO//fqSAAAAAFyNyuLKQ2tJgyR1chwn6X9/7iyDfgEAAABcRX71rVrNbJMkpwxqAQAAAHAVK9O7LQEAAAD4/SI8AAAAAHCF8AAAAADAFcIDAAAAAFcIDwAAAABcITwAAAAAcIXwAAAAAMAVwgMAAAAAVwgPAAAAAFwhPAAAAABwhfAAAAAAwBXCAwAAAABXCA8AAAAAXCE8AAAAAHCF8AAAAADAFcIDAAAAAFcIDwAAAABcITwAAAAAcIXwAAAAAMAVwgMAAAAAVwgPAAAAAFwhPAAAAABwhfAAAAAAwBXCAwAAAABXCA8AAAAAXCE8AAAAAHCF8AAAAADAFcIDAAAAAFcIDwAAAABcITwAAAAAcIXwAAAAAMAVwgMAAAAAVwgPAAAAAFwhPAAAAABwhfAAAAAAwBXCAwAAAABXCA8AAAAAXCE8AAAAAHCF8AAAAADAFcIDAAAAAFcIDwAAAABcITwAAAAAcIXwAAAAAMAVwgMAAAAAVwgPAAAAAFwhPAAAAABwhfAAAAAAwBXCAwAAAABXCA8AAAAAXCE8AAAAAHCF8AAAAADAFcIDAAAAAFcIDwAAAABcITwAAAAAcIXwAAAAAMAVwgMAAAAAVwgPAAAAAFwhPAAAAABwhfAAAAAAwBXCAwAAAABXCA8AAAAAXCE8AAAAAHCF8AAAAADAFcIDAAAAAFcIDwAAAABcITwAAAAAcIXwAAAAAMAVwgMAAAAAVwgPAAAAAFwhPAAAAABwhfAAAAAAwBXCAwAAAABXCA8AAAAAXCE8AAAAAHCF8AAAAADAFcIDAAAAAFcIDwAAAABcITwAAAAAcIXwAAAAAMAVwgMAAAAAVwgPAAAAAFwhPAAAAABwhfAAAAAAwBXCAwAAAABXCA8AAAAAXCE8AAAAAHCF8AAAAADAFcIDAAAAAFfKJDw4jvOq4zhHHcfZUxb9AQAAALj6lNWVhwWSbi+jvgAAAABchcokPJjZJ5JOlEVfAAAAAK5OrHkAAAAA4MplCw+O4wx3HCfRcZzEtLS0yzUsAAAAgDJy2cKDmb1sZpFmFlmrVq3LNSwAAACAMsK0JQAAAACulNWtWt+QtEVSE8dxDjqO88ey6BcAAADA1aNcWXRiZgPKoh8AAAAAVy+mLQEAAABwhfAAAAAAwBXCAwAAAABXCA8AAAAAXCE8AAAAAHCF8AAAAADAFcIDAAAAAFcIDwAAAABcITwAAAAAcIXwAAAAAMAVwgMAAAAAVwgPAAAAAFwhPAAAAABwhfAAAAAAwBXCAwAAAABXCA8AAAAAXCE8AAAAAHCF8AAAAADAFcIDAAAAAFcIDwAAAABcITwAAAAAcIXwAAAAAMAVwgMAAAAAVwgPAAAAAFwhPAAAAABwhfAAAAAAwBXCAwAAAABXCA8AAAAAXCE8AAAAAHCF8AAAAADAFcIDAAAAAFcIDwAAAABcITwAAAAAcIXwAAAAAMAVwgMAAAAAVwgPAAAAAFwhPAAAAABwhfAAAAAAwBXCAwAAAABXCA8AAAAAXCE8AAAAAHCF8AAAAADAFcIDAAAAAFcIDwAAAABcITwAAAAAcIXwAAAAAMAVwgMAAAAAVwgPAAAAAFwhPFwBGzZsUPfu3S/pGAEBATp27NglHeOn7rzzTqWnpys9PV0vvPBCmfXbqlWrMuvrpwYMGKDg4GDNmjVLCxYs0OHDhy/ZWAAAAL915a50Ab8XZiYzk4/Pbz+P/dJjWb16tSQpNTVVL7zwgkaNGlUm9Xz66afFthUUFMjX1/ei++bn56tcuZL/mf/P//yPPv30U+3fv1+S1KFDBwUFBalu3bq/rmAAAIDfqd/+N90rKDU1VbfeeqtGjRql8PBwff/991q3bp1iYmIUHh6uuLg4ZWVlSZLef/99NW3aVG3atNGyZcu8fUyZMkUzZ870vg4KClJqaqok6b//+78VHByskJAQDRo0SJKUlpamvn37KioqSlFRUdq8ebMk6fjx47rtttsUFhamBx54QGZWYs1paWnq2rWrwsPD9cADD6hBgwY6duxYiccycuRIRUZGqnnz5po8ebIkac2aNerXr5+3vw0bNuiuu+6S9H9XOyZMmKB9+/YpNDRU48aN06BBg/TOO+949xk4cKDefffd8+rKyspS586dFR4eLo/Hc177KlWqeMfq2LGj7r33Xnk8nlLPUXx8vMaOHauOHTtq/Pjx2rp1q1q1aqWwsDC1atVKe/fulSTddtttOnr0qEJDQ/XEE08oMTFRAwcOVGhoqHJyci7+DwAAAOBaU/RX5sv5ExERYb8HKSkp5jiObdmyxczM0tLSrG3btpaVlWVmZtOnT7fHH3/ccnJy7KabbrJvvvnGCgsLLS4uzmJjY83MbPLkyTZjxgxvn82bN7eUlBTbs2ePNW7c2NLS0szM7Pjx42ZmNmDAANu4caOZme3fv9+aNm1qZmYPPvigPf7442ZmtnLlSpPk3fdco0ePtqeeesrMzNasWeNt99NjOXfM/Px8a9++ve3cudPy8vKsfv363mMcMWKEvfbaa2Zm1qBBA29fzZs39/azYcMG69mzp5mZpaenW0BAgOXl5Z1XV15enmVkZHjP4y233GKFhYVmZla5cmUzM0tISLDrrrvO/v3vf5uZlXqOBg8ebLGxsZafn29mZhkZGd7xPvjgA+vTp4/38zu3zvbt29u2bduKnTMAAPDbJSnRrsD33d/rD9OWfqUGDRqoZcuWkqTPPvtMycnJat26tSTpzJkziomJ0ddff63AwEA1atRIknTffffp5ZdfvmC/H330ke6++275+/tLkmrWrClJWr9+vZKTk73tTp48qczMTH3yySfeKxqxsbGqUaNGif1u2rRJy5cvlyTdfvvt57U791gkacmSJXr55ZeVn5+vH374QcnJyQoODtbtt9+u9957T3fffbdWrVqlZ5999oLH0r59e40ePVpHjx7VsmXL1Ldv32JTicxMjz76qD755BP5+Pjo0KFDOnLkiG688cbz2rVo0UKBgYEXPEeSFBcX553WlJGRocGDB+vbb7+V4zjKy8u7YL0AAAAoGeHhF1ix45BmrN2r/ftTdSLn7OteYfVkZurataveeOON89onJSXJcZwS+ypXrpwKCwu9r3NzcyWd/TJd0j6FhYXasmWL/Pz8ir1XUvvnn39e8+bNk3R2TYKVMp1JkipXruz9PSUlRTNnztS2bdtUo0YNxcfHe2vr37+/nn/+edWsWVNRUVGqWrVqqX0WGTRokBYtWqQ333xTr776arH3Fy1apLS0NH3xxRcqX768AgICvOOVVmNp5+in7SZOnKiOHTtq+fLlSk1NVYcOHS5aLwAAAIpjzcPPtGLHIT2ybLcOpZ+dE59fUKhHlu3Wih2H1LJlS23evFnfffedJCk7O1vffPONmjZtqpSUFO3bt0+SzgsXAQEB2r59uyRp+/btSklJkSR17txZS5Ys0fHjxyVJJ06ckHR2nv7cuXO9+yclJUmS2rVrp0WLFkk6uy7hxx9/lCSNHj1aSUlJSkpKUt26ddWmTRstWbJEkrRu3Tpvu586efKkKleurGrVqunIkSNas2aN970OHTpo+/btmjdvnvr3719s36pVqyozM/O8bfHx8Zo9e7YkqXnz5sX2ycjIUO3atVW+fHklJCR4FzFfSGnnqKS+69WrJ0lasGBWt7vtAAAgAElEQVRBqf2VVDcAAAD+D+HhZ5qxdq9y8grO25aTV6AZa/eqVq1aWrBggff2ny1bttTXX3+tSpUq6eWXX1ZsbKzatGmjBg0aePft27evTpw4odDQUL344otq3LixpLNfsP/617+qffv2CgkJ0dixYyVJc+bMUWJiooKDg9WsWTO99NJLkqTJkyfrk08+UXh4uNatW6ebb765xPonT56sdevWKTw8XGvWrFGdOnVKvHIQEhKisLAwNW/eXEOHDvVOxZIkX19fde/eXWvWrCnxlrN/+MMf1Lp1awUFBWncuHGSpBtuuEG33nqrhgwZUmJdAwcOVGJioiIjI7Vo0SI1bdq01M+gSGnn6KcefvhhPfLII2rdurUKCgpKbCOdDTgjRoxgwTQAAEApnAtNY7lUIiMjLTEx8bKPWxYCJ6xSSWfMkZQyPfZyl/OznT59Wr6+vipXrpy2bNmikSNHeq9eXErZ2dnyeDzavn27qlWrdsnHAwAAkCTHcb4ws8grXcfvBWsefqa61f28U5Z+uv234MCBA+rXr58KCwtVoUIF73qIS2n9+vUaOnSoxo4dS3AAAAD4DePKw89UtObh3KlLfuV99XQfj3qF1buClQEAAOCnuPJQtrjy8DMVBYQZa/fqcHqO6lb307huTQgOAAAA+N0jPPwCvcLqERYAAABwzeFuSwAAAABcITwAAAAAcIXwAAAAAMAVwgMAAAAAVwgPAAAAAFwhPAAAAABwhfAAAAAAwBXCA8rUggULdPjw4Staw+zZs5Wdnf2z37uQSZMmaf369Rds8+6772r69Ok/u28AAIDfCsfMLvugkZGRlpiYeNnHxaXXoUMHzZw5U5GRxZ8CX1BQIF9f30teQ0BAgBITE+Xv7/+z3rtc9QEAgMvHcZwvzKz4FxP8Ilx5wEX16tVLERERat68uV5++WVJZ79ox8fHKygoSB6PR7NmzdLSpUuVmJiogQMHKjQ0VDk5OQoICNDUqVPVpk0bvfXWW0pKSlLLli0VHBys3r1768cff5R0NnSMHz9eLVq0UOPGjbVx40ZJUmpqqtq2bavw8HCFh4fr008/lSRt2LBB3bt399Y4ZswYLViwQHPmzNHhw4fVsWNHdezY8bzjKOm9KlWqaNKkSYqOjtaWLVs0depURUVFKSgoSMOHD1dRuI6Pj9fSpUslnQ0gkydPVnh4uDwej77++mtJZ6+6jBkzxtv+oYceUqtWrdSwYUPvvoWFhRo1apSaN2+u7t2768477/S+BwAAcLUjPOCiXn31VX3xxRdKTEzUnDlzdPz4cSUlJenQoUPas2ePdu/erSFDhujuu+9WZGSkFi1apKSkJPn5+UmSKlWqpE2bNumee+7Rf/7nf+qZZ57Rrl275PF49Pjjj3vHyc/P19atWzV79mzv9tq1a+uDDz7Q9u3btXjxYj300EMXrPWhhx5S3bp1lZCQoISEhIu+d+rUKQUFBenzzz9XmzZtNGbMGG3btk179uxRTk6OVq5cWeI4/v7+2r59u0aOHKmZM2eW2OaHH37Qpk2btHLlSk2YMEGStGzZMqWmpmr37t165ZVXtGXLFhefAAAAwNWB8ICLmjNnjkJCQtSyZUt9//33+vbbb9WwYUP9+9//1oMPPqj3339f119/fan79+/fX5KUkZGh9PR0tW/fXpI0ePBgffLJJ952ffr0kSRFREQoNTVVkpSXl6dhw4bJ4/EoLi5OycnJZXpsvr6+6tu3r/d1QkKCoqOj5fF49NFHH+nLL78scb+Sav2pXr16ycfHR82aNdORI0ckSZs2bVJcXJx8fHx04403Frs6AgAAcDUrd6ULwNVpxY5DmrF2r/bt+lzZn76tef9arv6t/kMdOnRQbm6uatSooZ07d2rt2rV6/vnntWTJEr366qsl9lW5cmVXY1asWFHS2S/0+fn5kqRZs2bphhtu0M6dO1VYWKhKlSpJksqVK6fCwkLvvrm5ub/oOCtVquRd55Cbm6tRo0YpMTFR9evX15QpU0rtt6RaS2sjyTv96UqsMQIAACgrXHlAMSt2HNIjy3brUHqOCk9nK7+cn6as+U5zl32szz77TJJ07NgxrVy5Ut9++62eeOIJbd++XZJUtWpVZWZmlthvtWrVVKNGDe96htdee817FaI0GRkZqlOnjnx8fPTaa6+poKBAktSgQQMlJyfr9OnTysjI0Icffujd50I1lPRe0V2SioKCv7+/srKyLslahDZt2ujtt99WYWGhjhw5og0bNrje96mnnirzegAAAH4OrjygmBlr9yon7+yXdL/ACGXuWKN9/xipKTfcrJYtW0qSDh06pGeffVaFhYV688039fTTT0s6u1B4xIgR8vPzO28+v5nJzLRw4UKNGDFC2dnZatiwoebPn3/BWkaNGqW+ffvqrbfeUseOHb1XMerXr69+/fopODhYjRo1UlhYmHef4cOH64477lCdOnWKrXso6b0ePXqoR48ekuSdIhUQEKCIiIhfcxpL1LdvX3344YcKCgpS48aNFR0drWrVqrna96mnntKjjz5a5jUBAAC4xZUHFLN/f6oOzRuh42vm6IeFf5KPXxXV6HS/sn5M06FDh3TdddcpJCREDz30kNq0aaOkpCSFh4erd+/emjp1qipVqqQXXnhBR44ckZ+fnyZNmqTw8HB9//33+uqrr3Tq1CkVFhaqSZMmqlGjhqSzd08qur3rddddpxYtWig4OFiPPfaY/Pz8NHfuXD399NPKysry1tm/f3/deOON+uGHH3Tq1Cl169ZNkvT222+rZ8+eOnXq1Hl3boqOjlanTp309ddfKyEhQR06dNDHH3983l2SDh48qB49eqigoED+/v566KGH1KtXL23fvl0zZ87Url27lJqaqrlz52ro0KH6y1/+ogMHDmjOnDmKj4/XX/7yFzVt2lTlypXTlClTNHDgQK1fv14hISFq1KiREhMTNXPmTG3btk2VK1dWQkKCxo4dq3feeUfS2Ts29enTR7fffrsaNWqkhx9+WJI0YcIE5eTkKDQ0VAMHDrw8/xAAAAB+qugvwpfzJyIiwnD1Cn94kcnxsTpD59rND79rFW64xSp7uljMU+ttxYoV1rNnTzMzmz9/vo0ePdrMzPr162ezZs0yM7P8/HxLT0+3lJQUcxzHtmzZYmZmhw4dsvr169vRo0ctLy/POnbsaMuXLy82/owZM2z48OFmZrZ7927z9fW1bdu2ndfmzJkzFhMTY0ePHjUzszfffNOGDBliZmbt27e3sWPHmpnZqlWrrHPnzmZm9re//c0mTZpkZmaHDx+2Ro0aFTuOwYMHW2xsrOXn55uZ2ZgxY2zKlClmZvbhhx9aSEiImZlNnjzZYmJiLDc319LS0qxmzZp25swZS0lJMV9fX9u1a5cVFBRYeHi4DRkyxAoLC73nrn379la7dm2rW7euzZ8/33788Udr1KiRZWVl2fz58y0wMNDS09MtJyfHbr75Zjtw4ICZmVWuXPmXfqQAAFyzJCXaFfi++3v94coDinmgXUOVr36jKtQKkOP4qLz/zbq+YZgevr2pPB5PiXcX+uijjzRy5EhJZxcRF03FadCggXeq07Zt29ShQwfVqlVL5cqV08CBA8+721KRotu6SlJQUJCCg4OLtdm7d6/27Nmjrl27KjQ0VNOmTdPBgwe975d0N6R+/frprbfekiQtWbJEcXFxJR5/XFycdxH1pk2bNGjQIElSp06ddPz4cWVkZEiSYmNjVbFiRfn7+6t27dreOyoFBgbK4/HIx8dHzZs3V+fOneU4jvfcbdiwQfXr11eNGjU0e/Zs7yL0AwcOSJI6d+6satWqqVKlSmrWrJn2799fYp0AAACXG2se4FV0h6X9+1PlW768qvuVV0ZOnq6rWF73tb5FvcLqKTU1tdS7C5Xk3DstWSl3Glq+fLn3uQ6vvPKKqzsSmZmaN29e6nMSSrobUr169fSHP/xBu3bt0uLFi/WPf/zjF9XsOM55Y/x0nHO3+/j4eF/7+Ph425iZ3n77bTVp0uS8vj///PNS+wUAALjSuPIASeffYUmSCgpNp/MLNat/qO701FGLwD9ccP/OnTvrxRdfPLtvQYFOnjxZrE10dLQ+/vhjHTt2TAUFBXrjjTfUvn179e7dW0lJSUpKSlJkZKTatGmjJUuWSJKSk5O1e/fuYn01adJEaWlp3vCQl5dX6jMZznXPPffo2WefVUZGhjwez0Xbt2vXTosWLZJ0dl2Gv7//BZ9p4Va3bt3097//3RtOduzYcdF9ypcvr7y8vF89NgAAwC9FeICk8++wVCQnr0Az1u51tf9zzz2nhIQEeTweRURElPhFvk6dOnr66afVsWNHhYSEKDw8XD179izWbtSoUUpLS1NwcLCeeeYZBQcHF7sjUYUKFbR06VKNHz9eISEhCg0N1aeffnrROu+++269+eab6tevn6vjmjJlihITExUcHKwJEyZo4cKFrva7mIkTJyovL0/BwcEKCgrSxIkTL7rP8OHDFRwczIJpAABwxThupoiUtcjISEtMTLzs46J0gRNWqaR/CY6klOmxl7WWgoIC5eXlqVKlStq3b586d+6sb775RhUqVLisdQAAgN8+x3G+MLPIK13H7wVrHiBJqlvdzztl6afbL7fs7Gx17NhReXl5MjO9+OKLBAcAAICrAOEBkqRx3ZrokWW7z5u65FfeV+O6NbnAXpdG1apVxZUpAACAqw/hAZKkXmH1JJ1d+3A4PUd1q/tpXLcm3u0AAAAA4QFevcLqERYAAABQKu62BAAAAMCVMgkPjuPc7jjOXsdxvnMcZ0JZ9AkAAADg6vKrw4PjOL6Snpd0h6RmkgY4jtPs1/YLAAAA4OpSFlceWkj6zsz+bWZnJL0pqfiTv/CzValS5RfvO3v2bGVnZ//sdnfeeafS09N/8biX0pQpUzRz5swrXQYAAMA1qyzCQz1J35/z+uD/bsMV9EvDw+rVq1W9evVLWVqZy8/Pv9IlAAAAXBPKIjw4JWwr9rBix3GGO46T6DhOYlpaWhkM+8ucOnVKsbGxCgkJUVBQkBYvXixJCggI0Pjx49WiRQu1aNFC3333nSTpvffeU3R0tMLCwtSlSxcdOXJEkpSVlaUhQ4bI4/EoODhYb7/9tiRp3bp1iomJUXh4uOLi4pSVlVWshnnz5ikqKkohISHq27ev98t7SkqKYmJiFBUVpYkTJ3rbZ2VlqXPnzgoPD5fH49E777wjSUpNTVXTpk01ePBgBQcH6+6771Z2drbmzJmjw4cPq2PHjurYsaMkaeTIkYqMjFTz5s01efJkSSqxXUBAgI4dOyZJ+tvf/qagoCAFBQVp9uzZ3jFvvfVWDRs2TM2bN9dtt92mnJziD5d76623FBQUpJCQELVr106S1LZtWyUlJXnbtG7dWrt27dKUKVM0dOhQdejQQQ0bNtScOXO8bZ588kk1adJEXbp00d69e73bO3TooEcffVTt27fXc889p/3796tz584KDg5W586ddeDAAUlSfHy8Ro4cqY4dO6phw4b6+OOPNXToUN16662Kj4/39ufmcwMAALjmmdmv+pEUI2ntOa8fkfTIhfaJiIiwK2Xp0qV2//33e1+np6ebmVmDBg1s2rRpZma2cOFCi42NNTOzEydOWGFhoZmZzZs3z8aOHWtmZg8//LD96U9/8vZz4sQJS0tLs7Zt21pWVpaZmU2fPt0ef/zxYjUcO3bM+/tf//pXmzNnjpmZ3XXXXbZw4UIzM5s7d65VrlzZzMzy8vIsIyPDzMzS0tLslltuscLCQktJSTFJtmnTJjMzGzJkiM2YMcN7PGlpad5xjh8/bmZm+fn51r59e9u5c2eJ7YpeJyYmWlBQkGVlZVlmZqY1a9bMtm/fbikpKebr62s7duwwM7O4uDh77bXXih1jUFCQHTx40MzMfvzxRzMzW7Bggfec7d2714r+HUyePNliYmIsNzfX0tLSrGbNmnbmzBlvDadOnbKMjAy75ZZbvMfXvn17GzlypHe87t2724IFC8zM7J///Kf17NnTzMwGDx5s/fv3t8LCQluxYoVVrVrVdu3aZQUFBRYeHm47duxw/bkBAIDfHkmJ9iu/7/Lzfz9lceVhm6RGjuMEOo5TQdI9kt4tg34vCY/Ho/Xr12v8+PHauHGjqlWr5n1vwIAB3v9u2bJFknTw4EF169ZNHo9HM2bM0JdffilJWr9+vUaPHu3dt0aNGvrss8+UnJys1q1bKzQ0VAsXLtT+/fuL1bBnzx61bdtWHo9HixYt8va5efNmbw2DBg3ytjczPfroowoODlaXLl106NAh7xWQ+vXrq3Xr1pKk++67T5s2bSrxuJcsWaLw8HCFhYXpyy+/VHJy8gXP06ZNm9S7d29VrlxZVapUUZ8+fbRx40ZJUmBgoEJDQyVJERERSk1NLbZ/69atFR8fr3nz5qmg4OxTq+Pi4rRy5Url5eXp1VdfPe8v/7GxsapYsaL8/f1Vu3ZtHTlyRBs3blTv3r113XXX6frrr1ePHj3OG6N///7e37ds2aJ7773Xe+7OPQ933XWXHMeRx+PRDTfcII/HIx8fHzVv3lypqamuPzcAAIBr3a9+SJyZ5TuOM0bSWkm+kl41sy9/dWVlbMWOQ96nJ9caNEunKxzQI488ottuu02TJk2SJDnO/83AKvr9wQcf1NixY9WjRw9t2LBBU6ZMkXT2C/257Yu2de3aVW+88cYFa4mPj9eKFSsUEhKiBQsWaMOGDcXGPdeiRYuUlpamL774QuXLl1dAQIByc3NLbF/S/ikpKZo5c6a2bdumGjVqKD4+3rt/ac4G9ZJVrFjR+7uvr2+J05Zeeuklff7551q1apVCQ0OVlJSkP/zhD+rataveeecdLVmyRImJiaX2WbSOoaTjKVK5cuVS3zt3v6K+fXx8zhvHx8dH+fn58vX1dfW5AQAAXOvK5DkPZrbazBqb2S1m9mRZ9FmWVuw4pEeW7dah9BzlZR7XkWzT2tON1bbPEG3fvt3brmj9w+LFixUTEyNJysjIUL16Z9d/L1y40Nv2tttu09y5c72vf/zxR7Vs2VKbN2/2rpfIzs7WN998U6yezMxM1alTR3l5eVq0aJF3e+vWrfXmm29K0nnbMzIyVLt2bZUvX14JCQnn/VX8wIED3qskb7zxhtq0aSNJqlq1qjIzMyVJJ0+eVOXKlVWtWjUdOXJEa9as8e5/brtztWvXTitWrFB2drZOnTql5cuXq23bthc4y+fbt2+foqOjNXXqVPn7++v778+uqb///vv10EMPKSoqSjVr1rxgH+3atdPy5cuVk5OjzMxMvffee6W2bdWq1Xnnrug8uOH2cwMAALjW/eorD78FM9buVU7e2akzeWmpOrphvuQ4+nv5Ctqw4nVvu9OnTys6OlqFhYXev0JPmTJFcXFxqlevnlq2bKmUlBRJ0mOPPabRo0crKChIvr6+mjx5svr06aMFCxZowIABOn36tCRp2rRpaty48Xn1PPHEE4qOjlaDBg3k8Xi8X96fe+453XvvvXruuefUt29fb/uBAwfqrrvuUmRkpEJDQ9W0aVPve7feeqsWLlyoBx54QI0aNdLIkSMlScOHD9cdd9yhOnXqKCEhQWFhYWrevLkaNmzoneZUUrsi4eHhio+PV4sWLSSd/dIfFhZW4hSlkowbN07ffvutzEydO3dWSEiIpLPTnK6//noNGTLkon2Eh4erf//+Cg0NVYMGDS4YXubMmaOhQ4dqxowZqlWrlubPn++qTkmqVauWq88NAADgWudcaHrKpRIZGWnnTlm51AInrCp++yedvU1UyvRYSWfvMpSYmCh/f//LVtevlZqaqu7du2vPnj1XuhTXDh8+rA4dOujrr7+Wj0+ZXPgCAAAoleM4X5hZ5JWu4/fimvj2Vre638/ajkvjv//7vxUdHa0nn3yS4AAAAPAbdE1ceSha81A0dUmS/Mr76uk+HvUK43l2AAAAv1dceShb18Sah6KAUHS3pbrV/TSuWxOCAwAAAPAzXBPhQTobIAgLAAAAwC/HxHMAAAAArhAeAAAAALhCeAAAAADgCuEBAAAAgCuEBwAAAACuEB4AAAAAuEJ4AAAAAOAK4QEAAACAK4QHAAAAAK4QHgAAAAC4QngAAAAA4ArhAQAAAIArhAcAAAAArhAeAAAAALhCeAAAAADgCuEBAAAAgCuEBwAAAACuEB4AAAAAuEJ4AAAAAOAK4QEAAACAK4QHAAAAAK4QHgAAAAC4QngAAAAA4ArhAQAAAIArhAcAAAAArhAeAAAAALhCeAAAAADgCuEBAAAAgCuEBwAAAACuEB4AAAAAuEJ4AAAAAOAK4QEAAACAK4QHAAAAAK4QHgAAAAC4QngAAAAA4ArhAQAAAIArhAcAAAAArhAeAAAAALhCeAAAAADgCuEBAAAAgCuEBwAAgN+gO++8U+np6Rds89RTT12man6ZDh06KDEx0XX7pKQkrV69+hJWhIshPAAAAPwGrV69WtWrV79gm6s9PPxchIcrj/AAAABwGZ06dUqxsbEKCQlRUFCQFi9eLEn68MMPFRYWJo/Ho6FDh+r06dNas2aN+vXr5913w4YNuuuuuyRJAQEBOnbsmCTp9ddfV4sWLRQaGqoHHnhABQUFmjBhgnJychQaGqqBAwcWq6NKlSoaP368IiIi1KVLF23dulUdOnRQw4YN9e6770qScnNzNWTIEHk8HoWFhSkhIeGC2xcsWKA+ffro9ttvV6NGjfTwww9LkgoKChQfH6+goCB5PB7NmjXLW8dbb72lFi1aqHHjxtq4cWOp/Z85c0aTJk3S4sWLFRoa6j1vuMzM7LL/REREGAAAwLVo6dKldv/993tfp6enW05Ojt100022d+9eMzMbNGiQzZo1y/Ly8qx+/fqWlZVlZmYjRoyw1157zczMGjRoYGlpaZacnGzdu3e3M2fOmJnZyJEjbeHChWZmVrly5VLrkGSrV682M7NevXpZ165d7cyZM5aUlGQhISFmZjZz5kyLj483M7OvvvrK6tevbzk5OaVunz9/vgUGBnqP6eabb7YDBw5YYmLi/9/enYdXVZ3v/3+vhJRJJoWqxAHwxyDkhExAIGUIYaqhDNGIlFIIH5xx/IpInRCwpkKpxalWhaBSSEWIAlUQFBFEMUAAZRQIQrDIGAmTGZ7fHwmnCSRwkIRAuF/XlctzVvZe69l7Rz33WXuwLl26eMc+cOCAmZl17NjRHn74YTMzmzt3rsXExJx23MmTJ9u99957VvsbSLVy+LxbUX808yAiIiJyHnk8HhYsWMCIESP4/PPPqVWrFhs3bqRhw4Y0adIEgEGDBrF48WIqVapEjx49mD17Njk5OcydO5fevXsX6W/hwoWsWLGCVq1aERISwsKFC9m6desZ6/jVr35Fjx49vDV17NiRgIAAPB4P6enpACxZsoSBAwcC0KxZM66//no2bdpUYjtATEwMtWrVokqVKjRv3pzt27fTqFEjtm7dyn333cdHH31EzZo1vXXExcUBEB4efsZxpfxVKu8CRERERC4FKasyGDdvI7sOHqXewL9x/FffM3LkSLp160avXr1KXK9fv368/PLLXH755bRq1YoaNWoU+b2ZMWjQIJ577rmzqicgIADnHAB+fn5UrlzZ+zonJ8fbd3FKage8/QD4+/uTk5NDnTp1WL16NfPmzePll1/m3//+N5MmTSqy/Illz9S/lC/NPIiIiIiUsZRVGYycuZaMg0fJPrSP3UeMeceb0D4ugZUrV9KsWTPS09P57rvvAHj77bfp2LEjkH9HopUrV/L666/Tr1+/U/qOiYlhxowZ/PjjjwDs37+f7du3A/kBITs7+xfX3aFDB6ZOnQrApk2b+P7772natGmJ7SXZu3cveXl53HzzzYwZM4aVK1f+onFr1KjBoUOHfvH2yLlTeBAREREpY+PmbeRodi4A2XvS+eGth9nyz3t4ccI4nnjiCapUqcLkyZOJj4/H4/Hg5+fHXXfdBeR/I9+zZ08+/PBDevbseUrfzZs3Z+zYsXTr1o3g4GC6du3KDz/8AMAdd9xBcHBwsRdM++Kee+4hNzcXj8dDv379SEpKonLlyiW2lyQjI4NOnToREhLC4MGDzzhLUlL/0dHRrFu3ThdMlyNXHtNCERERdjb39BURERG5mDV8bC7FfeJywLbE2PNdziXFObfCzCLKu46KQjMPIiIiImWsfu2qZ9UucqFSeBAREREpY8O7N6VqgH+RtqoB/gzvXvJ1AiIXIt1tSURERKSM9QkNBPDebal+7aoM797U2y5ysVB4EBERETkP+oQGKizIRU+nLYmIiIiIiE8UHkRERERExCcKDyIiIiIi4hOFBxERERER8YnCg4iIiIiI+EThQUREREREfKLwICIiIiIiPlF4EBERERERnyg8iIiIiIiITxQeRERERETEJwoPIiIiIiLiE4UHERERERHxicKDiIiIiIj4ROFBRERERER8ovAgIiIiIiI+UXgQERERERGfKDyIiIiIiIhPFB5ERERERMQnCg8iIiIiIuIThQcREREREfGJwoOIiIiIiPhE4UFERERERHyi8CAiIiIiIj5ReBAREREREZ8oPIiIiIiIiE8UHkRERETKwdSpUwkJCfH++Pn5kZaWBsBll112xvVHjRrF+PHjAXj33Xdp0aIFfn5+pKamlmndcmlTeBAREREpBwMGDCAtLY20tDTefvttGjRoQEhIyC/qKygoiJkzZ9KhQ4dSrvLcNWjQgL1795ZKXydC1a5du7jllltKpU85OwoPIiIickk6fPgwsbGxtGzZkqCgIJKTkwFYuHAhoaGheDwehgwZwvHjx1m4cCF9+/b1rvvxxx8TFxcHFJ0lmDFjBoMHDwYoMqtQtWpVPvvssxJrmTZtGv379z+lfe/evbRt25a5c+eedltuvPFGmjZt6vO2XwlQ0UwAACAASURBVOzq16/PjBkzyruMS5LCg4iIiFySPvroI+rXr8/q1av55ptv6NGjB8eOHWPw4MEkJyezdu1acnJyePXVV+ncuTPr169nz549AEyePJmEhITT9n9iVmHMmDFERETQrl27EpdNTk4+JTzs3r2b2NhYRo8eTWxs7C/ezvT0dJo1a8bQoUMJCgpiwIABLFiwgKioKBo3bszy5csB2L9/P3369CE4OJjIyEjWrFlz2vZRo0YxZMgQOnXqRKNGjZg4ceIZa+nTpw/h4eG0aNGCf/7zn972yy67jMcff5yWLVsSGRnJ7t27Adi2bRtt27alVatWPPnkk0W2KSgoCICkpCTi4uLo0aMHjRs35tFHH/Uu9+abbwIEOecWOeded8699It3pADnGB6cc/HOuW+dc3nOuYjSKkpERESkrHk8HhYsWMCIESP4/PPPqVWrFhs3bqRhw4Y0adIEgEGDBrF48WKccwwcOJB33nmHgwcPsmzZMn7729+ecYzNmzczfPhwkpOTCQgIKHaZr776imrVqnk/DANkZ2cTExPD888/T9euXc95W7/77jseeOAB1qxZw4YNG/jXv/7FkiVLGD9+PH/+858BePrppwkNDWXNmjX8+c9/5o9//ONp2wE2bNjAvHnzWL58Oc888wzZ2dmnrWPSpEmsWLGC1NRUJk6cyL59+4D8WaDIyEhWr15Nhw4deP311wF44IEHuPvuu/n666+56qqrSuw3LS3NG/iSk5PZsWMHu3btYsyYMQDrga5As1+8A8XrXGcevgHigMWlUIuIiIhImUtZlUFU4id0n7SZegP/xvEagYwcOZLRo0djZiWul5CQwDvvvMO0adOIj4+nUqVKADjnvMscO3bM+/rw4cPceuutvP7669SvX7/EfqdPn37KrEOlSpUIDw9n3rx53rbHH3/cexrU2WrYsCEejwc/Pz9atGhBTEwMzjk8Hg/p6ekALFmyhIEDBwLQuXNn9u3bR2ZmZontALGxsVSuXJm6devy61//2jtjUJKJEyd6Zxd27NjB5s2bAfjVr35Fz549AQgPD/fWtHTpUu++OVFDcWJiYqhVqxZVqlShefPmbN++neXLl9OxY0eAXDPLBt496x0npzin8GBm681sY2kVIyIiIlKWUlZlMHLmWjIOHiX70D52HzHmHW9C+7gEVq5cSbNmzUhPT+e7774D4O233z7xAZT69etTv359xo4d672uAeDKK69k/fr15OXlMWvWLG97QkICCQkJtG/fvsR68vLyePfdd7ntttuKtDvnmDRpEhs2bCAxMRGAZ5991nsqlK/bGpX4Cb/5yydkHMohZVUGAH5+flSuXNn7OicnB6DY4OScK7Ed8PYD4O/v7+2rOIsWLWLBggUsW7aM1atXExoa6g1bAQEB3j5P7qdwOCtJcXWcLgjKL6drHkREROSSMW7eRo5m5wKQvSedH956mC3/vIcXJ4zjiSeeoEqVKkyePJn4+HjvN/V33XWXd/0BAwZw7bXX0rx5c29bYmIiPXv2pHPnzlx99dUAbN++nRkzZjBp0iTvbEFxt1BdvHgx11xzDY0aNTrld/7+/kyfPp1PP/2UV1555bTbNWvWLK655hqWLVtGbGwsoW07eUMSQE5uHiNnrvUGiOJ06NCBqVOnAvkf9OvWrUvNmjVLbD9bmZmZ1KlTh2rVqrFhwwa+/PLLM64TFRXF9OnTAbw1+Kp169YnLlL3d85VAm4+66LlFJXOtIBzbgFQ3Elmj5vZ+74O5Jy7A7gD4LrrrvO5QBEREZHSsqvgwzRA1UbhVG0UDoADIiLyL9+MiYlh1apVxa6/ZMkSbr/99iJtt9xyS7G3Dc3LyztjPZ06dSr2Q3RWVhaQfzpP4VOXChs1apT3dd++fYvcDSoq8RMOFNpWgKPZuYybt5HGJdQyatQoEhISCA4Oplq1akyZMuW07WerR48e/OMf/yA4OJimTZsSGRl5xnX+/ve/8/vf/56///3v3Hzz2X32DwwM5E9/+hN33nnnjcACYB2Q+YuKFy9XGlM6zrlFwCNm5tNTSSIiIkwPMBEREZHzLSrxE++38YUF1q7K0sc6n3bd8PBwqlevzscff1zkNJkLUcPH5lLcJzwHbEv85XduuthkZWVRo0aNFUAkMAuYZGazzrCanIZOWxIREZFLxvDuTaka4F+krWqAP8O7n/kZCStWrGDx4sUXfHAAqF+76lm1nw++PDW7tBXMzjQn/yY/24CU815EGXDONXDO/b48xj7XW7X2dc7tBNoCc51zxc+riYiIiFwA+oQG8lych8DaVXHkzzg8F+ehT2hgeZdWqs4lJF2sirtYe/z48QDrzKyZmd1vFecq6gbAxRcezGyWmV1jZpXN7Eoz615ahYmIiIiUhT6hgSx9rDPbEmNZ+ljnChcc4MIOSYsWLfLelhVg2LBhJCUlAdCgQQOefvppwsLC8Hg8bNiwAYDly5fTrl07QkNDadeuHRs35t/sMykpifj4eH73u9/RrVu3U8Z65513AG50zqU5515zzvkX/CQ5575xzq11zj0E4Jz7/5xzC5xzq51zK51zNzjnLnPOLSx4v9Y517tg2QbOuQ3OuSnOuTXOuRnOuWoFvwt3zn3mnFvhnJvnnLv65LoKnpX2TcFYiwvaPnfOhRRaZqlzLtg517Gg/jTn3CrnXA0gEWhf0PZQwTaNc859XVDPnQV9dCqo5d/OuU3OuUTn3ADn3PKC7bmhpHpKcsYLpkVERETk4tMnNPCCCAtnq27duqxcuZJXXnmF8ePH88Ybb9CsWTMWL15MpUqVWLBgAX/605947733AFi2bBlr1qzh8ssvL9LP+vXrSU5OBthgZuHOuVeAAcC3QKCZBQE452oXrDIVSDSzWc65KuR/yf4z0NfMfnLO1QW+dM59ULB8U+D/zGypc24ScI9z7u/Ai0BvM9vjnOsHPAsMOWkznwK6m1lGofHfAAYDDzrnmgCVzWyNc242cG/BOJcBx4DHyL/euGfBNtwBZJpZK+dcZWCpc25+Qb8tgRuB/cBW4A0za+2cewC4D3iwhHqKpfAgIiIiIheMuLg4IP8C9ZkzZwL5t3kdNGgQmzdvxjlX5EnWXbt2PSU4ACxcuJAVK1ZAwcwDUBX4EZgNNHLOvQjMBeYXfJsfeOJiajM7BuCcCwD+7JzrAOQBgcCVBUPsMLOlBa/fAe4HPgKCgI8Lnk/hD/xQzGYuBZKcc/8GZha0vQs86ZwbTn7YSCq07ATn3FRgppntLObZF92AYOfcidt+1QIakx9+vjazHwq2ZwtwIlSsBaJPU0+xdMG0iIiIiJSJEw+qa/jYXI5m55KyKoNKlSoVuY1t4adyw/8e+Fb4YXFPPvkk0dHRfPPNN8yePbvIOtWrVy92bDNj0KBBkH/NQ4iZNTWzUWZ2gPxv4xcB95L/jX9JT6IbANQDws0sBNgNVDkxxMlDFvTzbcF4IWbmMbNTzqcys7uAJ4BrgTTn3BVmdgT4GOgN3Ar8q2DZRGAo+eHnS+dcs2LqdMB9hcZtaGYnQsLxQsvlFXqfR8FEQnH1lLA/FB5EREREpPQVfpq3AWYwcuZaNhyqzLp16zh+/DiZmZksXLjwjH1lZmYSGJh/CtaJ6yPOJCYmhhkzZkDBB2Tn3OXOuesLTj/yM7P3gCeBMDP7CdjpnOtTsGzlgmsYagE/mlm2cy4auL7QENc559oWvO4PLAE2AvVOtDvnApxzLU6uzTl3g5l9ZWZPAXvJ/9AO+UFmIvmzBfsLLbvWzP4CpALNgENAjUJdzgPuLpgpwTnXxDlXfKoqxmnqOYXCg4iIiIiUusJP8z7haHYuk1cf4tZbbyU4OJgBAwYQGhp6xr4effRRRo4cSVRUFLm5uWdcHqB58+aMHTsWoIlzbg353+pfTf6pR4sKTmVKAkYWrDIQuL9g2S/If0jyVCDCOZdK/izEhkJDrAcGFSx/OfCqmf0M3AL8xTm3GkgD2hVT3riCC5a/ARYDqwHMbAXwEzC50LIPnriYGTgKfAisAXIKLnB+iPzQsQ5YWdDna5zd5QnF1lOcUnlI3NnSQ+JEREREKrYL5UF1zrkVZhZRyn02AOacuOi6FPutT/7pVM3M7MyPKC8HmnkQERERkVJ3IT6o7kLmnPsj8BXw+IUaHEDhQURERETKQEV+UJ2ZpZf2rIOZvWVm15rZu6XZb2nTrVpFREREpNSdeMbEuHkb2XXwKPVrV2V496YX5bMn5H8UHkRERESkTFysD6qTkum0JRERERER8YnCg4iIiIiI+EThQUREREREfKLwICIiIiIiPlF4EBERERERnyg8iIiIiIiITxQeRERERETEJwoPIiIiIiLiE4UHERERERHxicKDiIiIiIj4ROFBRERERER8ovAgIiIiIiI+UXgQERERERGfKDyIiIiIiIhPFB5ERERERMQnCg8iIiIiIuIThQcREREREfGJwoOIiIiIiPhE4UFERERERHyi8CAiIiIiIj5ReBAREREREZ8oPIiIiIiIiE8UHkRERERExCcKDyIiIiIi4hOFBxERERER8YnCg4iIiIiI+EThQUREREREfKLwICIiIiIiPlF4EBERERERnyg8iIiIiIiITxQeRERERETEJwoPIiIiIiLiE4UHERERERHxicKDiIiIiIj4ROFBRERERER8ovAgIiIiIiI+UXgQERERERGfKDyIiIiIiIhPFB5ERERERMQnCg8iIiIiIuIThQcREREREfGJwsMlLD09naCgoAt+zBdeeIEjR46UUUX5Dh48yCuvvFKmY4iIiIhc7BQexGdmRl5e3nkfV+FBRERE5MKg8HARSE9Pp1mzZgwdOpSgoCAGDBjAggULiIqKonHjxixfvhyAw4cPM2TIEFq1akVoaCjvv/++d/327dsTFhZGWFgYX3zxxVmNfeONN3LPPfcQFhbGjh07mD9/Pm3btiUsLIz4+HiysrIAGD16NK1atSIoKIg77rgDMwNgxYoVtGzZkrZt2/Lyyy97+27fvj1paWne91FRUaxZs6bI+BMnTmTXrl1ER0cTHR0NwLRp0/B4PAQFBTFixIhi687NzWX48OG0atWK4OBgXnvtNQCysrKIiYkhLCwMj8fj3UePPfYYW7ZsISQkhOHDh/u8f0REREQuKWZ23n/Cw8NNfLdt2zbz9/e3NWvWWG5uroWFhVlCQoLl5eVZSkqK9e7d28zMRo4caW+//baZmR04cMAaN25sWVlZdvjwYTt69KiZmW3atMlO7P9t27ZZixYtzji2c86WLVtmZmZ79uyx9u3bW1ZWlpmZJSYm2jPPPGNmZvv27fOu94c//ME++OADMzPzeDy2aNEiMzN75JFHvGMmJSXZAw88YGZmGzdutJL+Lq6//nrbs2ePmZllZGTYtddeaz/++KNlZ2dbdHS0zZo165R1XnvtNRszZoyZmR07dszCw8Nt69atlp2dbZmZmd5tueGGGywvL8+nfSEiIiIXHyDVyuHzbkX90czDRaJhw4Z4PB78/Pxo0aIFMTExOOfweDykp6cDMH/+fBITEwkJCaFTp04cO3aM77//nuzsbG6//XY8Hg/x8fGsW7furMa+/vrriYyMBODLL79k3bp1REVFERISwpQpU9i+fTsAn376KW3atMHj8fDJJ5/w7bffkpmZycGDB+nYsSMAAwcO9PYbHx/PnDlzyM7OZtKkSQwePPiMtXz99dd06tSJevXqUalSJQYMGMDixYtPWW7+/Pm89dZbhISE0KZNG/bt28fmzZsxM/70pz8RHBxMly5dyMjIYPfu3We1P0REREQuVZXKuwApWcqqDMbN28j27ensP5RDyqoM+oQG4ufnR+XKlQHw8/MjJycHyJ9Feu+992jatGmRfkaNGsWVV17J6tWrycvLo0qVKmdVR/Xq1b2vzYyuXbsybdq0IsscO3aMe+65h9TUVK699lpGjRrFsWPHMDOcc8X2W61aNbp27cr777/Pv//9b1JTUwHo3r07u3fvJiIigjfeeKPIOlZwKtTJZs2axTPPPAPAG2+8gZnx4osv0r179yLLJSUlsWfPHlasWEFAQAANGjTg2LFjZ7U/RERERC5Vmnm4QKWsymDkzLVkHDwKQE5uHiNnriVlVUaJ63Tv3p0XX3zR+wF71apVAGRmZnL11Vfj5+fH22+/TW5u7inrZmRkEBMTc8a6IiMjWbp0Kd999x0AR44cYdOmTd4P4HXr1iUrK4sZM2YAULt2bWrVqsWSJUsAmDp1apH+hg4dyv3330+rVq24/PLLAZg3bx5paWne4FCjRg0OHToEQJs2bfjss8/Yu3cvubm5TJs2jY4dO9K3b1/S0tJIS0sjIiKC7t278+qrr5KdnQ3Apk2bOHz4MJmZmfz6178mICCATz/91DtrUngMERERESmewsMFaty8jRzNLvoh/2h2LuPmbSxxnSeffJLs7GyCg4MJCgriySefBOCee+5hypQpREZGsmnTpiIzCSf88MMPVKp05omoevXqkZSURP/+/QkODiYyMpINGzZQu3Zt76lRffr0oVWrVt51Jk+ezL333kvbtm2pWrVqkf7Cw8OpWbMmCQkJJY55xx138Nvf/pbo6GiuvvpqnnvuOaKjo2nZsiVhYWH07t37lHWGDh1K8+bNCQsLIygoiDvvvJOcnBwGDBhAamoqERERTJ06lWbNmgFwxRVXEBUVRVBQkC6YFhERESmBK+k0kLIUERFhJ05RkeI1fGwuxR0ZB2xLjC318V566SWuu+46evXqVep9n86uXbvo1KkTGzZswM9PWVZERERKl3NuhZlFlHcdFYWuebhA1a9d1XvK0sntZWHYsGFl0u/pvPXWWzz++ONMmDBBwUFERETkIqBPbBeo4d2bUjXAv0hb1QB/hndvWsIaF58//vGP7Nixg/j4+PIuRURERER8oJmHC1Sf0EAg/9qHXQePUr92VYZ3b+ptFxERERE53xQeLmB9QgMVFkRERETkgqHTlkRERERExCcKDyIiIiIi4hOFBxERERER8YnCg4iIiIiI+EThQUREREREfKLwICIiIiIiPlF4EBERERERnyg8iIiIiIiITxQeRERERETEJwoPIiIiIiLiE4UHERERERHxicKDiIiIiIj4ROFBRERERER8ovAgIiIiIiI+UXgQERERERGfKDyIiIiIiIhPFB4uMhs2bCAkJITQ0FC2bNlyTn2NGjWK8ePHl1JlIiIiIlLRKTxcZFJSUujduzerVq3ihhtuKO9yREREROQSovBwFg4fPkxsbCwtW7YkKCiI5ORkABo0aMCIESNo3bo1rVu35rvvvgNg9uzZtGnThtDQULp06cLu3bsByMrKIiEhAY/HQ3BwMO+99x4A8+fPp23btoSFhREfH09WVlaR8f/zn//wwgsv8MYbbxAdHQ3AhAkTCAoKIigoiBdeeMG7bEntzz77LE2bNqVLly5s3Lix7HaWiIiIiFQ4lcq7gIvJRx99RP369Zk7dy4AmZmZ3t/VrFmT5cuX89Zbb/Hggw8yZ84cfvOb3/Dll1/inOONN97g+eef569//StjxoyhVq1arF27FoADBw6wd+9exo4dy4IFC6hevTp/+ctfmDBhAk899ZR3jJtuuom77rqLyy67jEceeYQVK1YwefJkvvrqK8yMNm3a0LFjR/Ly8kpsnz59OqtWrSInJ4ewsDDCw8PP704UERERkYuWwoMPUlZlMG7eRrZv3cfe9+awL/seHvq//rRv3967TP/+/b3/fOihhwDYuXMn/fr144cffuDnn3+mYcOGACxYsIDp06d7161Tpw5z5sxh3bp1REVFAfDzzz/Ttm3b09a1ZMkS+vbtS/Xq1QGIi4vj888/x8yKbc/Ly6Nv375Uq1YNgF69epXG7hERERGRS4ROWzqDlFUZjJy5loyDR6l0eSD1Bv6NLw9exh33/z9Gjx7tXc45d8rr++67j2HDhrF27Vpee+01jh07BoCZFVn+RFvXrl1JS0sjLS2NdevW8eabb562NjM7q/aT6xQRERERORsKD2cwbt5GjmbnApBzaB9+AZX5VbOOWFBPVq5c6V3uxPUPycnJ3hmDzMxMAgMDAZgyZYp32W7duvHSSy953x84cIDIyEiWLl3qvV7iyJEjbNq06bS1dejQgZSUFI4cOcLhw4eZNWsW7du3P237rFmzOHr0KIcOHWL27NmlsIdERERE5FKh05bOYNfBo97X2XvS+XHRZHAO51eJd2b/y/u748eP06ZNG/Ly8pg2bRqQfyvU+Ph4AgMDiYyMZNu2bQA88cQT3HvvvQQFBeHv78/TTz9NXFwcSUlJ9O/fn+PHjwMwduxYmjRpUmJtYWFhDB48mNatWwMwdOhQQkNDAUps79evHyEhIVx//fVFTrsSERERETkTd7pTXMpKRESEpaamnvdxf4moxE/IKBQgTgisXZWlj3UG8u+2lJqaSt26dc93eSIiIiJyGs65FWYWUd51VBTndNqSc26cc26Dc26Nc26Wc652aRV2oRjevSlVA/yLtFUN8Gd496blVJGIiIiISPk412sePgaCzCwY2ASMPPeSLix9QgN5Ls5DYO2qOPJnHJ6L89AnNNC7THp6umYdRERERKTCO6drHsxsfqG3XwK3nFs5F6Y+oYFFwoKIiIiIyKWoNO+2NAT4sBT7ExERERGRC8gZZx6ccwuAq4r51eNm9n7BMo8DOcDU0/RzB3AHwHXXXfeLihURERERkfJzxpkHM+tiZkHF/JwIDoOAnsAAO82tm8zsn2YWYWYR9erVK70tuERMnDiRG2+8kQEDBpxzXw0aNGDv3r2lUFVRKSkprFu3rtT7ldNLSkpi2LBhpdJXWloa//nPf7zvP/jgAxITE0ulb1/80vHatWtXBtWIiIjIyc7pmgfnXA9gBNDRzI6UTklSnFdeeYUPP/yQhg0blncpJUpJSaFnz540b968vEuR08jJyaFSpeL/1U9LSyM1NZWbbroJgF69etGrV6/zVtsvHe+LL74og2pERETkZOd6zcNLQA3gY+dcmnPuH6VQk5zkrrvuYuvWrfTq1Yu//e1v7N+/nz59+hAcHExkZCRr1qwBKLF93759dOvWjdDQUO68806KmyDKzc1l8ODBBAUF4fF4+Nvf/saWLVsICwvzLrN582bCw8MBeOyxx2jevDnBwcE88sgjfPHFF3zwwQcMHz6ckJAQtmzZwpYtW+jRowfh4eG0b9+eDRs2APkPsLv77ruJjo6mUaNGfPbZZwwZMoQbb7yRwYMHl1jPybZv305MTAzBwcHExMTw/fffe/u///77adeuHY0aNWLGjBmnrHv48GFiY2Np2bIlQUFB3ieEN2jQgBEjRtC6dWtat27tfeL37NmzadOmDaGhoXTp0oXdu3cDkJWVRUJCAh6Ph+DgYN577z0A5s+fT9u2bQkLCyM+Pp6srKxTaihp//Tu3Zu33noLgNdee80729SpUycefPBB2rVrR1BQEMuXLz+rffLwww8THR3NiBEjWL58Oe3atSM0NJR27dqxceNGfv75Z5566imSk5MJCQkhOTm5yKzGuezv9PR0mjVrxtChQwkKCmLAgAEsWLCAqKgoGjdu7N2WwuO9++67BAUF0bJlSzp06ADAt99+S+vWrQkJCSE4OJjNmzcDcNlllwFgZgwfPtz7d3PiuC5atIhOnTpxyy230KxZMwYMGOD99+Dkv2URERE5DTM77z/h4eEmZ+f666+3PXv2mJnZsGHDbNSoUWZmtnDhQmvZsuVp2++77z575plnzMxszpw5Bnj7OiE1NdW6dOnifX/gwAEzM+vUqZOtWrXKzMxGjhxpEydOtH379lmTJk0sLy+vyLKDBg2yd99919tH586dbdOmTWZm9uWXX1p0dLR3uX79+lleXp6lpKRYjRo1bM2aNZabm2thYWG2atWqEusprGfPnpaUlGRmZm+++ab17t3b2/8tt9xiubm59u2339oNN9xwyrozZsywoUOHet8fPHjQu5/Hjh1rZmZTpkyx2NhYMzPbv3+/d3tff/11e/jhh83M7NFHH7UHHnjA28/+/fttz5491r59e8vKyjIzs8TERO/+L6yk/fPf//7XbrjhBlu8eLE1btzY9u3bZ2ZmHTt29Nb82WefWYsWLczMbPLkyXbvvfeecZ/ExsZaTk6OmZllZmZadna2mZl9/PHHFhcXd0pfZ9P3mfb3tm3bzN/fv8hxTkhI8P4NnOir8HhBQUG2c+dOM/vf8R82bJi98847ZmZ2/PhxO3LkiJmZVa9e3czyj2uXLl0sJyfH/vvf/9q1115ru3btsk8//dRq1qxpO3bssNzcXIuMjLTPP/+8xL9lERGpOIBUK4fPuxX155xOW5LysWTJEu833J07d2bfvn1kZmaW2L548WJmzpwJQGxsLHXq1Dmlz0aNGrF161buu+8+YmNj6datGwBDhw5l8uTJTJgwgeTkZJYvX07NmjWpUqUKQ4cOJTY2lp49e57SX1ZWFl988QXx8fHetuPHj3tf/+53v8M5h8fj4corr8Tj8QDQokUL0tPT6dixY7H1FLZs2TLvdg0cOJBHH33U+7s+ffrg5+dH8+bNvbMEhXk8Hh555BFGjBhBz549ad++vfd3/fv39/7zoYceAmDnzp3069ePH374gZ9//tl7+tiCBQuYPn26d906deowZ84c1q1bR1RUFAA///wzbdu29Xn/XHnllYwePZro6GhmzZrF5ZdffkptHTp04KeffuLgwYM+75P4+Hj8/fMfeJiZmcmgQYPYvHkzzjmys7NP2UcnO5f9DdCwYcMixzkmJsb7N5Cenn7K8lFRUQwePJhbb72VuLg4ANq2bcuzzz7Lzp07iYuLo3HjxkXWWbJkCf3798ff358rr7ySjh078vXXX1OzZk1at27NNddcA0BISAjp6elERkae8W9ZREREN1HvDgAACRlJREFU/qc0b9UqpSxlVQZRiZ/Q8LG5/DfzGP9Z8wNAsacdOedKbC/8z5LUqVOH1atX06lTJ15++WWGDh0KwM0338yHH37InDlzCA8P54orrqBSpUosX76cm2++mZSUFHr06HFKf3l5edSuXZu0tDTvz/r1672/r1y5MgB+fn7e1yfe5+TklFjP6RTexsJ9FrdfmjRpwooVK/B4PIwcOZLRo0cX28+J1/fddx/Dhg1j7dq1vPbaaxw7dszb98n71szo2rWrd7vXrVvHm2++eVb7Z+3atVxxxRXs2rWrxG0s7v3p9kn16tW9r5988kmio6P55ptvmD17tnd7zsbZ7O+Tlyl83E8c85P94x//YOzYsezYsYOQkBD27dvH73//ez744AOqVq1K9+7d+eSTT4qsU9LYJ4/v7+/vvfbjTH/LIiIi8j8KDxeolFUZjJy5loyDRzEgJ88YM3cdKasy6NChA1On5t8Vd9GiRdStW5eaNWv61P7hhx9y4MCBU8bbu3cveXl53HzzzYwZM4aVK1cCUKVKFbp3787dd99NQkICkP+teWZmJjfddBMvvPACaWlpANSoUYNDhw4BULNmTRo2bMi7774L5H+oW716tc/bX1I9hbVr1877rf/UqVP5zW9+43P/u3btolq1avzhD3/gkUceKdL/ifPkk5OTvTMGmZmZBAbmPyhwypQp3mW7devGSy+95H1/4MABIiMjWbp0qfd6iSNHjrBp06Yi459u/yxfvpwPP/yQVatWMX78eLZt23ZKbUuWLKFWrVrUqlXrF+2TwtuTlJTkbS98DE92Lvv7l9iyZQtt2rRh9OjR1K1blx07drB161YaNWrE/fffT69evbzX9ZzQoUMHkpOTyc3NZc+ePSxevJjWrVuXOEZJf8siIiJSPJ22dIEaN28jR7Nzi7Qdy85l3LyNzB41ioSEBIKDg6lWrZr3w+yoEtqffvpp+vfvT1hYGB07diz2ORsZGRkkJCSQl5cHwHPPPef93YABA5g5c6b31KFDhw7Ru3dvjh07hpl5L2a+7bbbuP3225k4cSIzZsxg6tSp3H333YwdO5bs7Gxuu+02WrZs6dP2n66eEyZOnMiQIUMYN24c9erVY/LkyT71Dfnf7A8fPhw/Pz8CAgJ49dVXvb87fvw4bdq0IS8vj2nTpgH5+zY+Pp7AwEAiIyO9H+ifeOIJ7r33XoKCgvD39+fpp58mLi6OpKQk+vfv7z0VaezYsTRp0qRIDcXtn2bNmnH77bczefJk6tevz1//+leGDBni/Ya9Tp06tGvXjp9++olJkyb94n3y6KOPMmjQICZMmEDnzp297dHR0SQmJhISEsLIkSNLbX//EsOHD2fz5s2YGTExMbRs2ZLExETeeecdAgICuOqqq3jqqaeKrNO3b1+WLVtGy5Ytcc7x/PPPc9VVV3kvRj9ZSX/LIiIiUjx3umn+shIREWGpqannfdyLScPH5lLckXHAtsTY81rL+PHjyczMZMyYMed13PLQoEEDUlNTqVu3bnmXcopOnToxfvx4IiIiyrsUERGRi4ZzboWZ6X+epUQzDxeo+rWrknHwaLHt51Pfvn3ZsmXLKeeWi4iIiMilRzMPF6gT1zwUPnWpaoA/z8V56BMaWI6ViYiIiFw8NPNQujTzcIE6ERDGzdvIroNHqV+7KsO7N1VwEBEREZFyo/BwAesTGqiwICIiIiIXDN2qVUREREREfKLwICIiIiIiPlF4EBERERERnyg8iIiIiIiITxQeRERERETEJwoPIiIiIiLiE4UHERERERHxicKDiIiIiIj4ROFBRERERER8ovAgIiIiIiI+UXgQERERERGfKDyIiIiIiIhPFB5ERERERMQnCg8iIiIiIuIThQcREREREfGJwoOIiIiIiPhE4UFERERERHyi8CAiIiIiIj5xZnb+B3VuD7C9DIeoC+wtw/7lwqDjXPHpGFd8OsaXBh3niu9CPsbXm1m98i6ioiiX8FDWnHOpZhZR3nVI2dJxrvh0jCs+HeNLg45zxadjfOnQaUsiIiIiIuIThQcREREREfFJRQ0P/yzvAuS80HGu+HSMKz4d40uDjnPFp2N8iaiQ1zyIiIiIiEjpq6gzDyIiIiIiUsoqbHhwzo1zzm1wzq1xzs1yztUu75qkdDnn4p1z3zrn8pxzusNDBeOc6+Gc2+ic+84591h51yOlyzk3yTn3o3Pum/KuRcqOc+5a59ynzrn1Bf+9fqC8a5LS5Zyr4pxb7pxbXXCMnynvmqRsVdjwAHwMBJlZMLAJGFnO9Ujp+waIAxaXdyFSupxz/sDLwG+B5kB/51zz8q1KSlkS0KO8i5AylwP8PzO7EYgE7tW/yxXOcaCzmbUEQoAezrnIcq5JylCFDQ9mNt/McgrefglcU571SOkzs/VmtrG865Ay0Rr4zsy2mtnPwHSgdznXJKXIzBYD+8u7DilbZvaDma0seH0IWA8Elm9VUposX1bB24CCH11QW4FV2PBwkiHAh+VdhIj4LBDYUej9TvSBQ+Si5pxrAIQCX5VvJVLanHP+zrk04EfgYzPTMa7AKpV3AefCObcAuKqYXz1uZu8XLPM4+dOmU89nbVI6fDnGUiG5Ytr0TZbIRco5dxnwHvCgmf1U3vVI6TKzXCCk4PrSWc65IDPT9UwV1EUdHsysy+l+75wbBPQEYkz3pL0onekYS4W1E7i20PtrgF3lVIuInAPnXAD5wWGqmc0s73qk7JjZQefcIvKvZ1J4qKAq7GlLzrkewAigl5kdKe96ROSsfA00ds41dM79CrgN+KCcaxKRs+Scc8CbwHozm1De9Ujpc87VO3FHS+dcVaALsKF8q5KyVGHDA/ASUAP42DmX5pz7R3kXJKXLOdfXObcTaAvMdc7NK++apHQU3OxgGDCP/Ass/21m35ZvVVKanHPTgGVAU+fcTufc/5V3TVImooCBQOeC/xenOeduKu+ipFRdDXzqnFtD/hc/H5vZnHKuScqQnjAtIiIiIiI+qcgzDyIiIiIiUooUHkRERERExCcKDyIiIiIi4hOFBxERERER8YnCg4iIiIiI+EThQUREREREfKLwICIiIiIiPlF4EBERERERn/z/gAYCRTYi4HQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_nodes(terms)"
   ]
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}