grapho.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graphomaly.preprocessing.egonet import EgonetFeatures\n",
    "from graphomaly.preprocessing.graph_to_features import graph_to_egonet_features,graph_to_rwalk_features\n",
    "import networkx as nx\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.read_graphml(\"data/test_graph.graphml\",node_type=int)\n",
    "# directed according to the graphml file\n",
    "mapping = {old_id: new_id for new_id, old_id in enumerate(G.nodes())}\n",
    "\n",
    "# Step 2: Relabel the nodes in the graph\n",
    "G = nx.relabel_nodes(G, mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_edges_with_node(graph, node_id):\n",
    "    # Get all edges in the graph\n",
    "    all_edges = graph.edges(data=True)\n",
    "    \n",
    "    # Filter edges that involve the given node ID\n",
    "    edges_with_node = [edge for edge in all_edges if node_id in edge[:2]]\n",
    "    \n",
    "    return edges_with_node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ego_graph(G,id):\n",
    "    egonet = nx.ego_graph(G, id, radius=1, center=True, undirected=True)\n",
    "    pos = nx.circular_layout(egonet)\n",
    "    nx.draw(egonet, pos, with_labels=True, node_color='skyblue', node_size=600, font_size=10)\n",
    "    nx.draw_networkx_edges(egonet, pos, edge_color='black')\n",
    "    edge_labels = {(u, v): f\"{d['cumulated_amount']}\" for u, v, d in egonet.edges(data=True)}\n",
    "    nx.draw_networkx_edge_labels(egonet, pos, edge_labels=edge_labels)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges_with_node = get_edges_with_node(G,86)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert nodes and their attributes to a pandas DataFrame\n",
    "node_data = dict(G.nodes(data=True))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_dict(node_data, orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1687</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1792</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46726</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46976</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47131</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47601</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47920</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       label\n",
       "86         1\n",
       "684        1\n",
       "1687       1\n",
       "1725       1\n",
       "1792       1\n",
       "...      ...\n",
       "46726      1\n",
       "46976      1\n",
       "47131      1\n",
       "47601      1\n",
       "47920      1\n",
       "\n",
       "[109 rows x 1 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['label']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through each edge and change 'total' to 'cumulated_amount'\n",
    "for u, v, data in G.edges(data=True):\n",
    "    if 'total' in data:\n",
    "        # Assign the value of 'total' to 'cumulated_amount'\n",
    "        data['cumulated_amount'] = data.pop('total')\n",
    "\n",
    "for u, v, data in G.edges(data=True):\n",
    "        data['n_transactions'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "egonet = nx.ego_graph(G, 86, radius=1, center=True, undirected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "circulant_graph() missing 1 required positional argument: 'offsets'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[145], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m pos \u001b[38;5;241m=\u001b[39m \u001b[43mnx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcirculant_graph\u001b[49m\u001b[43m(\u001b[49m\u001b[43megonet\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m nx\u001b[38;5;241m.\u001b[39mdraw(egonet, pos, with_labels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, node_color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mskyblue\u001b[39m\u001b[38;5;124m'\u001b[39m, node_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m600\u001b[39m, font_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m)\n\u001b[0;32m      3\u001b[0m nx\u001b[38;5;241m.\u001b[39mdraw_networkx_edges(egonet, pos, edge_color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[1;31mTypeError\u001b[0m: circulant_graph() missing 1 required positional argument: 'offsets'"
     ]
    }
   ],
   "source": [
    "\n",
    "pos = nx.circulant_graph(egonet)\n",
    "nx.draw(egonet, pos, with_labels=True, node_color='skyblue', node_size=600, font_size=10)\n",
    "nx.draw_networkx_edges(egonet, pos, edge_color='black')\n",
    "edge_labels = {(u, v): f\"{d['cumulated_amount']}\" for u, v, d in egonet.edges(data=True)}\n",
    "nx.draw_networkx_edge_labels(egonet, pos, edge_labels=edge_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "features,ids,labels=graph_to_egonet_features(G, FULL_egonets=True,  summable_attributes=[], labels=None, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f_degree_in                   2.000000\n",
       "f_degree_out                  3.000000\n",
       "f_amount_in               58049.000000\n",
       "f_amount_out             101843.417603\n",
       "f_nr_trans_in                 2.000000\n",
       "f_nr_trans_out                3.000000\n",
       "f_ego_nr_nodes                6.000000\n",
       "f_ego_nr_edges               10.000000\n",
       "f_egored_degree_in            2.000000\n",
       "f_egored_degree_out           2.000000\n",
       "f_egored_amount_in        58049.000000\n",
       "f_egored_amount_out       45894.000000\n",
       "f_egored_nr_trans_in          2.000000\n",
       "f_egored_nr_trans_out         2.000000\n",
       "f_egored_nr_nodes             5.000000\n",
       "f_egored_nr_edges             9.000000\n",
       "Name: 46726, dtype: float64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.loc[46726,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = get_edges_with_node(G,49998)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zqPzQ/QORLmPlTXpt6tSpWLx4MSQSCWp36YPu0xbB2NRMJacLkctkEOQy9KhUqtAzD6zCiT5OQdX2lClTMGvWrCL9ztyMS8fxZ8mQKaCSpToSZB8z2b4ca24yDPzEIr02ZcoUdOzYEYIgIOzQTvzwSWOEXzkLAIVeAZ7zuNcPb2FJjwa4c/JAocfHGUuiD/MhM5JFUd3OHCO8bFHeOnsFdlH3D+WtTTDCy5ZhkgwGZyhJL2VmZmLVqlWYNWsWkpOTkffHfP/5UGQ4VEBEUhYk+LAZiZzt3K1NUNfeAg7SDDg4ZF/rNyYmBlZWVkUeN2csiXJT14zkuwiCgIjELITGpBVp/+BuY8LV3GRQGChJ71y9ehWffvopHj16lCtIlilTBqtWrcKmTZuwefNmWFtbIyFDjtvxGbj/KgERcUkoZmuX7/ksjCRwKmYMp2ImqGprlusKFzt27ICvry+8vb1x+vRplb0HBksydJoOkgXJ2T+8SMnCixQZ0uT5Py7ft38gMiQMlKR31q5di5EjR0IikeQKlM2bN0dQUFCBjxk3bhxWrFiB4g5lYG5dHEamplBkZSEl4TXuXA1BuXLl3vl63t7eCAoKwo4dO+Dj46PS98JgSYZGG4LkuyRnKZAmU0AuAEYSwMJYCisT/h4SAQyUpKcOHz6MPn36IDU1FQBgbGyMAQMGYOPGjQVuHxYWhsDAQOzatQs3b94EAIwZMwaenp4YNWoUzMzM3vlaycnJsLe3B6C66jsvBkvSd9ocJInov/G3lPRS5cqVkZ6ergyCMpkMrq6u79y+Tp066N27N27dugUg+6oaz549w/jx498bJgHAysoKGzduRHp6Orp27aqy9/BvXLxD+krdi22ISDP4m0p6qV27dlAoFDh//jwOHTqEKlWqwNvb+72PmTdvHoyMso9/EgQBBw8exNWrVz/o9fr27YsWLVrgzJkz2LlzZ5HH/y4MlqQvGCSJ9Asrb9I7/v7+WLBgAYYPH461a9d+0GPu3r2LqlWr5jrm0tjYGJ07d8aBAx92WiBNVN95sQonXcNqm0g/MVCSXgkPD4eHhwdKlSqFly9ffvAHVM6iHCMjI8jlcuXpPgRBwJMnT+Ds7PxBz6OuVd//hcGStB2DJJF+Y6AkveLu7o5Hjx4hJCQEdevW/eDHXbt2DUePHgUATJ8+HR4eHhg6dChsbGzw+eef/+dxlP+mzlXf/4XBkrQNgySRYWCgJL1RmKq7IFKpFB06dMCRI0cK9Xgxqu+8GCxJbAySRIaFv9WkF8LDw7Fw4UI4ODjg119/FXUsmlj1/V+4eIfEwsU2RIaJv9mkF3JWdQcGBmrFB5amVn3/FwZL0hQGSSLDxt9w0nn+/v549OgRhg8f/lHHTarb4cOHYW5ujiFDhiA5OVnUsTBYkrowSBIRwEBJOk6bqu68tKH6zovBklSFQZKI/o2/8aTTtK3qzktbqu+8GCypsBgkiagg/M0nnaWtVXde2lR958VgSR+KQZKI3od7ANJJ2lx156WN1XdeDJb0LgySRPQhuCcgnaTtVXde2lp958VgSTkYJInoY3CPQDpHV6ruvLS5+s6LwdJwMUgSUWFwz0A6RZeq7rx0ofrOi8HScDBIElFRcA9BOkXXqu68dKX6zovBUn8xSBKRKnBPQTpDV6vuvHSp+s6LwVJ/MEgSkSpxj0E6QZer7rx0sfrOi8FSdzFIEpE6cM9BOkHXq+68dLX6zovBUncwSBKROnEPQlpPX6ruvHS5+s6LwVJ7MUgSkSZwT0JaTZ+q7rz0ofrOi8FSezBIEpEmcY9CWk3fqu689KX6zovBUjwMkkQkBu5ZSGvpa9Wdlz5V33kxWGoOgyQRiYl7GNJK+lx156WP1XdeDJbqwyBJRNqAexrSSvpedeelr9V3XgyWqsMgSUTahHsc0jqGUnXnpc/Vd14MloXHIElE2oh7HtIqhlR152UI1XdeDJYfjkGSiLQZ90CkVQyt6s7LUKrvvBgs341Bkoh0AfdEpDUMterOy5Cq77wYLN9ikCQiXcI9EmkFQ6668zLE6jsvQw6WDJJEpIu4ZyKtYOhVd16GWn3nZUjBkkGSiHQZ91AkOlbdBTPk6jsvfQ6WDJJEpA+4pyJRsep+N1bf+elTsGSQJCJ9wj0WiYpV9/ux+i6YLgdLBkki0kfcc5FoWHV/GFbf76ZLwZJBkoj0GfdgJApW3R+O1fd/0+ZgySBJRIaAezISBavuj8Pq+8NoU7BkkCQiQ8I9Gmkcq+7CYfX94d4XLHfs2AFBENT22gySRGSIJII696xEeYSHh8PDwwOlSpXCy5cvtfLDVSqVokOHDjhy5IjYQ8lnx44d8PX1hbe3N06fPi32cHSGQqHApk2b4OHhgWbNmkEmk8HY2FjlrxEQEIB58+YhMTERlpaWmDJlCmbNmqWVP+dERKrEQEka5e7ujkePHiEkJERrZye1OVACgLe3N4KCgrBjxw74+PiIPRydIggCJBKJSp+TQZKIiJU3aRCrbtVg9V14qgyTrLaJiN7iHo80gqu6VYervsXFIElElB/3fKQRXNWtWlz1rXrp6envXawjCALi4+NhZ2fHIElElAf3gKR2rLrVg9W3asTGxmLUqFEYMGAAQkND37mdRCKBra0tevbsySBJRJQH94SkVqy61YfVd+HlzERu3boVXl5eCA8Px8CBA2Ftbf3exykUCmzYsIFBkogoD+4RSa1YdasXq+/CkUgkSE9Px4YNGzBz5kycPHkSPXr0gKenp3KbgupvqVQKiUSiVZd0JCLSBvyEJ7Vh1a0ZrL4L5+TJk4iLi8PIkSNx6dIlDBkyBCNHjsSqVauQmZn5zhXhcrkcjx8/xrJlyxgsiYj+j4GS1IJVt+aw+n6/uLg47Nq1C/fv30d6errydrlcjidPnuDixYsYMmQIihUrhpSUFMydOxdDhw5FRkZGgc9nZGQEd3d3hIaGasW1womItAEDJakFq27NYvWdX3p6OiZNmgQXFxf88MMP6NatGwYNGoTMzEwAQMmSJeHi4oIxY8agb9++WLlyJbZs2YLDhw9j+/btOH78+DufWxAE/Pzzz1AoFKJdK5yISJvwk55UjlW3OFh953bgwAFcvnwZx44dQ1BQEFasWIFr165h2LBhyMrKgrOzM+zt7fHw4UP06tULQPaim3r16qF169bYvXu38ra8JBIJ7Ozs8ObNm3zXCmewJCJDxEBJKsWqWzysvnMLCAhA5cqV0axZM0ilUrRr1w4DBgzAvn37sGfPHri6uqJr166QyWQ4e/YsgOyZR4VCgRIlSiAtLQ0A3jvDLpVKMXfuXCQlJTFYEpFBY6AklWLVLS5DrL5fv36NP/74A7dv30Z8fDwAIDExEW5ubspAZ2xsDAB4/vy58tQ/aWlp+PTTT+Hj4wN/f3+EhYUhKysL9+/fR3h4OAYNGvTBY2CwJCJDx098UhlW3drBUKrvxMREfPHFF3B1dcWyZcvQsWNH+Pr6AgBsbGzg5eWFy5cvY9GiRXj27BmmTZuGmzdv4ocffsBff/2FZ8+eoWzZsliyZAnq1KmDbt26oUWLFqhbty4aNmwIb2/vjx4TgyURGSyBSAUePnwoSKVSwcHBQZDL5WIPp0gkEonQsWNHsYdRJNu3bxcACN7e3mIPRS1evnwp9OnTR+jUqZNw/vx5ISEhQdi7d69gb28vLF++XBAEQXj69Kkwc+ZMoVKlSoKFhYVQr1494cyZM0JmZqZQrlw54bffflM+X1JSknD16lVh5cqVwv3791U2TrlcLsyaNUuwtLQUAAg2NjbCkiVLdP53hIgoL4kgvOfitUQfyN3dHY8ePUJISIjOz05KpVJ06NABR44cEXsoReLt7Y2goCDs2LEDPj4+Yg9HpV69eoWJEydixIgRaN26NQAgKysLw4YNg5GRETZt2qTcNiIiAsbGxnBxcQEAXLt2DR07dsT+/fvRqFEjANkLb9R5iIZCocDcuXOxdOlSpKamwsbGBjNnzsTkyZN5aAgR6QXuyajI5HI5+vXrh1GjRul8mNQn+lJ9x8bGIiAgAKdPn8bTp08BAHZ2dlixYoUyTAKAiYkJ7t+/j8qVKwMAZDIZgOw/dnLCJAD8/fffqFChAipVqqS8Td2hjlU4Eek7BkoqMiMjI8ybNw+//PKL2EOhf9HlVd+CICArKwvz5s2Dq6sr/vjjD/j5+aFly5Y4d+4cjI2NYWdnlyuIRUVFITo6GjVr1gTwdiEOAISEhGDHjh0YNGgQ5s+fj88//xx2dnYaf18MlkSkrxgoSSVyrnFM2kVXV31LJBKEhYVh3759OHjwIIKDg3H27FlUqFABX331Fc6dO6fcNueonUuXLsHU1DTXrGUOY2NjzJkzBykpKbhw4QKGDRumsfdSEAZLItI3DJREek5Xqu+EhARs27ZN+fW6detQvHhxNGnSBGlpabCwsMC4ceNw5coV5TlOpVIp5HI5AGDXrl1o0aIFzMzMAADPnj1DYmIiAKBWrVoIDg7Gnj174OHhoeF39m4MlkSkLxgo6YNx/ZZu0vbqOyYmBgMGDEDJkiUxe/Zs5XGSjo6OiI6Ohrm5OSwsLABk19qenp74559/lIumjI2N8fr1a1y6dAnDhg3DkydPMHDgQLi4uODo0aPK1ylZsqTm39wHYrAkIl3HQEn/ae/evfjiiy+wbNky3L59W+zhUCFoY/WdEyTLlCmD9PR0TJw4ERkZGXB2dgYA9O/fXxkOT548iW+++QYLFy5Enz59IAgC7t27p3yukydPIjY2FnPmzEGFChUQFxeHW7duoU+fPmK9vUJhsCQiXcVASe81btw4fPfddwgNDcWGDRu48EaHaVP1HRYWhho1aiA2NhaXL1/G7t270a1bN6SkpCj/aKlcuTLWrl2L6Oho9O/fHydOnMCvv/6KuXPnQiaTITo6Wvl84eHhSExMRMmSJXHjxg0EBgaiSpUqYr29ImOwJCJdw0BJ73Tq1Cn8/vvv+OWXX3D58mXMnTsXEREROH/+PPbv3499+/aJPUT6CNpUfdepUwfBwcE4evQo6tSpAyD7GEorKyukp6crt/P19cWff/6JGzdu4PLly2jXrh1SU1MBQHmsJAD06dMHjx8/xvbt2+Hl5aXZN6NGDJZEpCt4YnN6p8aNG6Ndu3aYN28egOwTQjdr1gzly5eHmZkZUlNT0bdvX3zzzTcwMTERebSqoy8nNn8XTZ/wPDY2Fr/++itKlSqF2rVro3bt2rl+XnJOKv7s2TO4urri7NmzaNy4cb6TjQuCAIlEgj179mD27Nk4ePAg3N3d1T5+baKKE6TnfB+JiFSJM5RUoIcPH6JFixbo3r278rb+/fujU6dOOHbsGI4dO4bu3btjy5YtiI+PF3Gk9LE0VX2npqZi+vTpcHd3x99//41NmzahV69e+Q6byAlCaWlpqFy5Mq5evZrrdgC4c+cO9u7diz59+mDo0KHo27cvXFxcDG6hmCpmLCUSCTIyMjQwWiIyJAyUVKAKFSpg2LBhyuPQHj16hDJlymDFihUoV64c7O3t0bVrV1hZWSlPzUK6QRPVd2ZmJjZs2IALFy5gz549OHnyJE6cOIEePXogMDAQT548yfeYUqVK4fXr17C0tASQ+6wCRkZGOH78OCQSCa5cuQJ/f38YGxsb7ExbQcHy66+/xv37998bKsPCwjB27Fh069YNo0ePRkxMjAZHTUT6jIGS8sn54Pb09FR+uLu5ueH48eMoXbq08oP+zZs3yMjIgJWVlZjDpUJQx6rvnPNBAtkBMDk5GaNHj0arVq0gk8lgZWWFxo0b4/79+/muUqNQKGBra4vSpUvj5s2bAHIHSk9PTwQEBGDnzp3w9PRUyXj1wb+D5bZt21C5cuV3Vt8pKSmYMGECLly4gD59+uDZs2eoXr06/vjjDwA8LRgRFQ0DJeUyZswYdOzYUXn8YE5I+PdxVxKJBG/evMG0adPg4+MDR0dH0cZLhZdTfa9bt0553evCiI2Nxbhx4zBkyBBs2rQJT548gZGREcaMGYO+ffvC2NgYRkZGAABzc3OULFkyX3iRSqVISUmBu7s7EhIS8h0/CUD5xw3lJ5VK0bdv3/eGwgcPHuD58+dYuHAhhg8fjkOHDmHIkCFYt24dACh/vxksiagwGChJ6ciRI1i7di28vLywYsUKCIKgDAISiUT5gRMcHIyvvvoKFhYWmDNnjogjpqKwsrJCZGQkTpw4UejnCAoKQu3atREeHq68vGGfPn2QkpICGxsbALn/GNm1axdq164NKyurfNVssWLF8PjxY8THxxtslV1U7/u+ubm5oU6dOrm2sba2RlRUFP75559cz5GWlqbWcRKR/mGgJKVBgwZh5cqVOHr0KEJCQjBixAjlKVr+7eXLlzAxMVFWZaS7HBwcAGRfbeZj5MxcHz58GJ6enggMDMSGDRtw5swZ3L9/H1OnTkVcXByAtzNe0dHROHv2LPr16wcg96KbnOdbsmQJli1bxkCpYocPH8ann36K+/fvo0OHDvjss8+wcuVK/PLLL6hbty7Kli0LAPjuu+/g4+ODli1bYsSIEYiNjRV55ESkKxgoCUD2MXUVK1bEyJEj4ezsjBkzZuDkyZMICQnJt23v3r2xZMkSuLm5iTBSEsvr169x8OBBAFDOXB89ehTNmzcHkB0Ky5cvjx9//BF79+7F+fPnAbwNjpcvX4aDgwMaNWoEAIiMjMSmTZtyPV/r1q35c6Vi8fHx8PX1RZs2bbBz506cOXMGkZGROH78OJYvX46FCxfC2toaa9aswdy5c1G6dGl8/fXXiIqKgre3N8LDw8V+C0SkAxgoCdeuXcOJEyeUH+5A9hVyqlevjn79+uHWrVvK23Nmm7gQx3C8efMGw4cPh7u7O1asWIHnz58r7/Pw8MClS5dybT9o0CA4ODjgwIEDuc4AcPLkSbRu3Rrx8fHo378/3Nzc8Pfff/MUNmq2ceNG1K1bF2PHjoWXlxeaN2+OOXPmIDQ0FE2bNkXp0qUBAD/++CP8/Pzw008/oXfv3ti+fTucnZ1x4MABkd8BEekCBkqCpaUlDh06BE9PTwiCoFwQsXz5cjg7O2P16tViD5FEIggCJk+ejIcPH+Lo0aPYvXs3SpUqpbyvdevWuHHjBh4+fAhjY2NlOBw7dmyuKynFxMTg999/x/bt21G+fHnExMTg9u3b+O2333Jd8YZUz9zcHI8fP8br16+Vt5UrVw52dna4du0agOx/n7i4OLi4uADIPoeolZUVLCwsClx1X9DXRGTYGCgNWM4HgoeHBxo1aqRcPJFTUbq7u2PIkCFYtWqV8mTUPLZN//07KJw5cwbHjh3Dtm3b0KhRI0RFRSEuLg7JycmQSCSoXbs2XFxcEBAQAOBtdd21a1ekp6crr8sdGRkJOzs7tG3bFrdu3cLx48f16hKJ2qxNmzYoXbo0ZsyYgbt37+Ls2bNYtWoVYmNjUbduXQDZ5wDt2bMntm7dCiD7j8yHDx8iNDQUzs7OUCgUkEgkuX42kpKSeA5aIlJioDRgOeHw9evXuVZxA1CuwB01ahTmzp2L77//HleuXBFlnKQZcXFxmDdvHtavX6+87fHjx6hTpw5SU1PRq1cvtGnTBp07d0a7du1w9+5dNGrUCD169MCOHTtw48YN5eKe4OBglCpVChYWFgCA2rVr4/z589i6dSvPI6lhnp6e2L59O5KSktCqVSuMHj0aoaGhmD9/Puzt7ZV/SE6bNg1yuRwmJiYYOHAgunfvjujoaHh4eCj/yMzZRxw8eBCjRo1CiRIl8Pvvv4v59ohIS3zc0k7SK7NmzUJYWBiePn2KatWq4YsvvkDVqlVha2sLqVSq/KDp2bMnfv/9d9jb24s9ZFKDmJgYTJ48WTk79cMPPyjvi4yMxIsXL7Bjxw5YW1vj0KFDeP36NaZOnYqJEydi5cqV+PLLL3Hu3Dn07t0bEyZMQIsWLbBx40bUq1cPNWrUAJA9c5mzopw0z83NDQcOHMDTp0/x5MkT1K5dG+bm5gCyL7Navnx5uLm54fz58wgNDcWRI0cQFRUFOzs7tGvXTvk8u3fvxr59+5CQkABXV1cYGRnB2NiY1wcnIs5QGqphw4bh+PHjqF+/Pr766iuEhoaiT58+WLp0KSIjIwG8PZVL9erVcePGDbi6uoo4YlK1uLg49O/fH46OjoiNjUVoaCi6d++OGzduKLfp27cvrl27hjlz5sDX1xc1a9ZEq1at8P333yMpKQkHDx6ElZUVtm3bhqZNm2LTpk1o37495HI5li9f/s6rtpA4nJ2d0bRpU1haWkIqleLNmzfYtm0bTp8+DSD7cIe6desiKysLMTExGDhwIEqXLo3g4GAMHjwYs2bNQqdOnXD48GHExMSgZcuWaNWqlTJMKhQKxMfHIykpScR3SURi4AylAQoLC8OhQ4dw+vRp5bW6FQoFJk+ejMWLF+Pq1as4dOiQsr5UKBTK6pL0x4oVKxAdHY2QkBDUrl0bQHagSE9PR3p6OszNzVGmTBl8+umnOHLkSK4Z6tatWyM9PR2ZmZkAsk+QvWnTJkRHR0OhUPDqSTrC3NwcRkZG6NmzJ3r16oWqVavizJkz+Pvvv7Fs2TIMGzYMO3fuxOTJkzF27FisW7cOJiYmCAkJwe7du7F7927lKvHIyEj4+/vj+fPnePToEVq3bo0lS5agZMmSIr9LItIETh8YoNevX8PLy0u5ohMAOnXqpLzk4t27dzF+/HjlAficZdIPOf+eOZdZ9Pf3x4kTJ1C7dm3lbcWKFUNsbCzMzc0hCAJsbGwwevRopKSk4OTJk8pV3MnJyZDJZMraNIe9vT3DpA4xMzODv78/rl69CgC4cuUKqlSpgn379mHcuHEwNjZG8eLFUbx4cfzyyy/KwyLmzp0Lb29vNGjQAFKpFFFRUZg9ezYuXbqEwYMHY926dXj16hV8fX2VJ7gnIj0nkMG5dOmSIJVKhXXr1ilvGzJkiFCnTh1BEARhxYoVgqenpxAfHy/SCMUlkUiEjh07ij0MlYmLixP8/f2F33777Z3byOVyQRAE4fvvvxcqVKggZGRk5Lp/4sSJQvny5QVfX1/h77//Fnx9fYV69eoJz58/V+vYSbPS0tJyfa1QKARBEIRXr14JCxYsEJycnITSpUsLEolECAwMVG536dIloXbt2sKWLVuUt/3zzz9CxYoVhaCgoFzPRUT6iVNPBqhu3brw8/PD6tWr0bhxY/Tq1Qs7d+5Uzj5UqVIFZmZmBV52kXSHQqHA5s2b0aRJEyxYsAD79u3Do0ePAOQ/h2DOLHRWVhZKliyJV69e5dpuyZIlmDlzJqKjozF27FgkJydj586dcHJy0uA7InXLmZnOkXNspIODA2bMmIHnz5+jRIkSMDY2xpgxY5Tnscw5X2XOCn65XI4KFSrA3t4eFy9eVD7X3bt3MW3aNMTExGj2jRGR2jFQGiAjIyOMHj0agwcPhpeXFzw8PHDy5ElUrlwZAJCQkICkpCRW3TouOTkZ9+/fR58+fbB582aEhYXh7NmzAPKfTzTnNFE1atTAzZs3YWdnp9xOEAQYGxvj888/x6FDh3D27FkcPHgQ7u7umn1DpBEFrdbOWaB348YNREREYO3atZg5c6by58TR0RFNmzbFsmXLlI/ZvXs3Ll68iPr16ytv+/bbb7F48WKUK1cOgwYNQnR0tJrfDRFpjLgTpKQpR48eFaZPny507dpVCA4OLnAbhUIh3Lx5U6hatarwzTffaHiE2kNXK++YmBjhzJkzgiBkV9gymUy4c+eO8OrVK0EQBKF9+/ZCr169hIiICEEQCq4gT58+LZQpU0ZZU+bF2tKwtWzZUqhevbryZ+rf7t69K9SvX1+QSCTC4MGDBSMjI6F27dpCZGSkIAiCcOvWLcHMzEwIDg4Wrl69KnTs2FGQSCTCsmXLNP02iEgNuMrbAPz1118YNWoUqlevjtTUVLRo0QI3btzId6WSFy9e4Ntvv4W9vT3mz58v0mipMCIjI+Hm5gYg+7J5OYtl/v1v7Ofnh2HDhuHs2bNwc3MrcCbKwsICWVlZMDU1LfB1eK5Bw7ZhwwbExcXlWvH/zz//wM3NDZUrV8bly5dx48YNPHr0CFu3bkXv3r2Vl+pcs2YNMjMzIZfLUatWLRw5cgRhYWHIysoS6+0QkQqx0zQAgwcPxtixY7Fz504EBgaidevWePHiBX7++Wfs378f9+/fBwCULVsWa9euxe7du0UeMX2s0NBQNGnSBJ6enso/BnJWbudo164dqlSpgv379yMiIgJA/mMpy5Yti/j4eOVqbqJ/c3NzQ7169ZR/WCQmJmLy5Mm5rqLl7OyMzZs3w8PDA5999hksLS2RkpKCSpUq4fPPP0e/fv3g6+uLV69eoU6dOmjYsKFYb4eIVIiBUs8tWLAAFSpUwNixY2FpaQkzMzNERkZizJgx2LBhA3x9ffH1118rA4SNjY3yuCjSfjnHtj179gzOzs4YNWoU1qxZg9TUVOUVTP693ZQpU3DlyhWcP38eQPaMY865JAGgZMmSePHiBVq0aKHhd0K6SCKRwMnJCZ06dUKvXr0wevRoVK1aFbdv38amTZvg4eEBIPt0VGPGjMHatWuxa9cuPH36FPv37xd38ESkUgyUeiwrKwtyuRy9evVSnqR80aJFiI6Oxu+//46rV6/ixo0bOHjwIDZt2iTuYKlQjIyMAGSfP7B3797o1KkTrKys8P333xe4Xdu2bVG1alUcPHgQu3fvhq+vLxYvXqzczsLC4oMukZgTUMmwWVtbY926dQgJCYGDgwNKlCgBf39/nDx5EvXr18/1c5JzOddGjRrBxcUFmzZtyvXHDBHpNh5DqcdMTEwwa9YsvHz5EsbGxkhPT0diYiJ27typrJlcXV3Rpk0brujWUQqFAlKpFElJSTA2NoarqyvGjh2LxYsXw9HRETKZDJ9//jmKFSum3LZz586YOHEi9uzZg44dO2LAgAEf/bphYWFITU2Ft7e3Gt4V6RpPT0/8+uuv+a7pfeHCBZiamqJBgwbKnz8AqFatGiIiIpCUlMRGhEhPMEUYgDJlygDIPsfc1KlT0a5dO+V9aWlpiI2NhaWlpVjDo3dITk7+z2si54TJq1evomfPnjAzM0N8fDzevHmDsWPHonz58srZyYyMDPTq1QsTJ06Er68vHjx4gMDAwI++RntaWhpatGiBjh07Ijk5ubBvj/TQv8OkXC7HjRs3sGDBAly+fFkZJq9fv45Tp06hXLlyucKkIAj5juklIt3BQKmH0tLSEBERgStXrigDSc6O2sbGJte2c+bMgUwmQ//+/TU+TipYQkIChg0bhsqVK6NXr16YNGkSnj59CuDt+SL/7cmTJ6hbty5mz56N8uXLY+3atWjevDns7e3RtWtXmJubK6/H3qZNG9y4cQNbt25FhQoVCjU+CwsLbNiwAenp6ejatWuR3ivpLyMjI3z22WewsrJC69at4evrCz8/P7Rv3x6vX7/GrFmzcm0vkUjw5ZdfYunSpQX+nBORdpMI/JNQ7/j6+uL69ev4559/0KhRI2zfvh3ly5fPtc2LFy+watUqbNiwAceOHUP16tVFGq32kUql6NChA44cOaLx146OjsawYcMAAF999RUiIyPxyy+/oESJEti6dStKlSqVqzoEsk82XatWLbi7u2PEiBH44osvEBkZiU8++QRdunTBjz/+mO8xquDt7Y2goCDs2LEDPj4+Kn1u0i/379/H0qVLYWRkBEdHR3zyySe59jmCIODNmzcoX748EhMTYWNjg5kzZ2Ly5Mk8HIdIRzBQ6pnhw4fj/v37mDp1KqpUqYL+/ftDIpHg5MmTMDMzU+6cnz9/jq1bt6Js2bKcncxDjEApl8thZGSEw4cP44svvsCpU6dQqVIlAMC+ffswZMgQjBo1CosXL1Zum/OrK5FIcOLECdSoUQOlS5cGAKSkpGDhwoVISEjAihUr1DLm5ORk5fkIY2JiYGVlpZbXIf0hk8mUCwQLolAoMG/ePCxZsgSpqakMlkS6RJTTqZNaXLlyRShTpowQGhqqvO3cuXOCq6ur8moV9N80daWc2NhYYenSpblumzt3rtC0aVMhNjZWedvly5cFiUQiSKVS4fnz54IgCIJMJvvP58/IyFDtgAuwbds2AYDg7e2t9tciwyGXy4XZs2cLlpaWAgDBxsZGWLJkiSCXy8UeGhG9A//k0yNnzpxBo0aNch0b17RpU1hZWeHixYvK2x4/fpzrRMQkjo4dO2LKlClYt26d8jZvb2/cuHFDeY4+QRCwfv16DBo0CDVq1MDy5csBZB+flpKSglWrViExMRFA/uMr33W1G1Xy9fVFixYtcObMGezcuVPtr0eGQSqVYs6cOUhKSsLs2bMhk8kwZcoU2Nra8hhLIi3FQKlHhg4dCg8PDxQvXhwAlOd4q169Oo4ePQogu3Jq2bIlwsLCRBsnZV8qURAE9O/fHwsXLlTe7u3tjT59+mDevHmoU6cOjI2NcevWLUyePBleXl548uSJ8t81JCQE/v7++O677wBAtErw8OHDMDc3x5AhQ7jqm1SKwZJIdzBQ6pGSJUsqT2itUChgYmICAGjUqBEePnwIIPtKKUZGRhg1apRo4zREQp5DlTMyMmBjYwNfX19kZWVh1apVyvsCAgKwf/9++Pr64uLFizh37hxq1KiBjIwMmJmZKWcea9asiZ9++gljx47V6HvJy8rKiqu+Sa0YLIm0HwOlnpJKpcpzwlWvXh3Jyck4c+YMfvrpJ+zdu1fk0RmGN2/e4ObNmwDenp8vJ1j+9ddfcHJyQufOndGpUycsW7ZM+TgLCwvUrl0bU6ZMQf369QEA4eHhuHv3LmrWrKncrkSJEhgwYADKlSunqbf0Tqy+SRMYLIm0FwOlAahatSpiYmLQqlUrfP3117lCCeXXokWLXJcj/FiJiYkYNWoU3N3dMWjQIPTs2VMZ4nM+8ORyuXKF9IQJE5CRkQEfHx/4+vri/v37ALIPWXj8+DFu376NGTNmoGTJkujXr18R3536sPomTWGwJNI+DJR6Jm+1CmRX4eXLl4e7u3uu4/WoYKdPn0aVKlUK9ViFQoEZM2bgwYMHOHHiBAICAuDq6gpfX19cvXpVeZxjYGAgmjdvDgA4ceIEkpKS8Mcff6B+/frw8PAAkH2c5fLly9GwYUOkpqZiy5YtytMCaSNW36RpDJZE2oPnodQzr169gp2dXb5zvQmCgNevX/O6uWqQc15IALh16xaaNm2KAwcOoGXLlgCyr1xUvXp1VK9eHcuWLUPZsmXRp08fWFtbIygoCFlZWejZsycOHjyIuXPnYvjw4QCyZyivXbsGBweHj748oph4wnMSC89jSSQe/obpEX9/f1SqVAnnz5/Pd59EImGYVLGYmBiMHDkSCxYsUN6WlZWFUqVK5TplT0JCAhQKBY4ePYqLFy/CzMwMUVFROHPmDMaPH6+8ikjOZRZzmJqaokGDBjoVJgFW3yQezlgSiYeBUk+Eh4dj4cKFsLCwUFappB5xcXEYOHAgypQpg4cPH6Jv377K+0xMTODh4YGFCxciJiYGGRkZWLhwIXr37o1q1aph69atAIBdu3YhNDQUfn5+sLa2RrFixfDpp59i6NChSEpKKvDQBV3B6pvExmBJpHmsvPWEu7s7Hj16hJCQENStW1fs4eitPXv2YMCAAfD09MSOHTtQuXLlfNv8+eefGDduHCwtLfHgwQNUrlwZBw4cwPnz5zFx4kTExcXl2l4QBEgkEuX/6wtW36QtWIUTqR9/k/SAv78/Hj16hOHDhzNMqkFMTAzOnj0LALCzs4O7uztGjx6NypUr4/r161izZg2Cg4Px4sULAEC3bt1w/vx5fPfddwgNDcXNmzfh7u6OV69ewcvLS3llmxw5IVKfwiTA6pu0B2csidSPM5Q6Ljw8HB4eHihVqhRevnzJv7ZVKCYmBpMnT8bWrVsxYcIEBAQEAABGjhyJ69evAwBevHgBNzc33LlzB+XLl0dgYGCBK7FTUlLg4+MDJycnrFmzRqPvQ0zbt29Hv3794O3tjdOnT4s9HCIAnLEkUgf+5ui4du3aQaFQIDAwkDtCFUlNTcXYsWNRunRpxMTE4NKlSwgICFAe1zh06FCkp6ejTp06OHHiBP744w+cPHkSycnJmDhxIlJSUgBkr/4ODg7GtWvXMHToUISHh2PMmDFivjWN4wnPSRtxxpJIDQTSWd98840AQBg+fLjYQ9ErZ86cEWxtbQU/Pz/lba9evcq1ze7du4WIiIhctx09elQwMTERIiMjlbdNmTJFsLOzE7p16yY8ePBAvQPXUklJSYK5ublgbm4uJCUliT0conzkcrkwe/ZswdLSUgAg2NjYCEuWLBHkcrnYQyPSGay8dRSrbvXJzMzEhAkT8PDhQ8ydOxc//PADnjx5ghIlSqBTp065Tu3zbyEhIWjevDkCAwPRqlUrANmVuFwuh7OzsybfgtZh9U26gFU4UeHxN0RHsepWH1NTU/j4+ODhw4fo2rUrnJ2dMWLECLi6usLf3x8LFy5Eenp6vsft2bMHDRo0QMOGDZW3OTk5GXyYBFh9k25gFU5UeJyh1EH+/v5YsGABhg8fjrVr14o9HL2UmZmJgIAAVK5cGd27dweQvQrbz88P58+fR0BAABo1aoTg4GCYm5sjICAAZ8+exbfffouBAweKPHrtlJycrLx+eUxMDKysrEQeEdH7ccaS6MMxUOoYVt2a8/r1a5QoUQJSqRQKhQJSqRSRkZGoXLkyTp8+DXd3d0yaNAnHjx9H48aNsXTpUlSqVEnsYWs1Vt+kixgsif4bfxN0DKtu1VMoFEhJSclXZ5UsWVL5Pc75/2vXrgHInq20t7eHn58fQkJCcODAAYbJD8Dqm3QRq3Ci/8ZEokN4AnPVUigUWLZsGWxtbTFlypR3bpeZmQkAuH//Pn799VcMGTIEDRo0AADUqlULLi4uGhmvvuAJz0lXMVgSvRsDpY7IuVa3g4MDfv31V7GHo9P+HST9/Pwgk8lQpkyZAq9Uk5mZCT8/P/j4+KBmzZooUaIEZs+eLcKo9Qev9U26jsGSKD8GSh3BqrvoCgqSs2fPRlJSEmbOnFlgoDQ1NUW9evXg4OCA69evY9u2bXB0dBRh9PqF1TfpAwZLore4KEcHcFV30SgUCgQEBGDevHlITEyEpaUlpkyZglmzZhUYznMW4OQQBEHvrrOtDbjqm/QNF++QIWOg1HJc1V14Hxsk//04fp81g6u+SR8xWJIh4k+2lmPV/fHeV23PmTPnP7+P0dHRrKo0hNU36SNW4WSImFC0GFd1f5yiBskc3377LStuDeKqb9JXDJZkSFh5aylW3R+usNX2u5iamuLBgwcoX768GkZLBWH1TYaAVTjpM/4EaylW3f9NVTOSeclkMkyfPl3Fo6X3YfVNhoAzlqTPmFS0EKvu91NXkPy3+Ph4FYyUPgarbzIUDJakjxgotQxPYP5umgiSJB6e8JwMDYMl6RN+AmsZVt35MUgaDlbfZIgYLEkf8JNYi7Dqzo1B0jCx+iZDxWBJuoyfyFqCVfdbDJKGjdU3GToGS9JF/GTWEqy6GSTpLVbfRAyWpFv4Ca0FDL3qZpCkgrD6JsrGYEm6gJ/UIjPkqptBkt6H1TdRbgyWpM34iS0yQ6y6GSTpQ7H6JsqPwZK0ET+5RWRoVTeDJBUGq2+igjFYkjbhJ7hIDKnqZpCkomD1TfR+DJakDfhJLhJDqLoZJElVWH0T/TcGSxITP9FFoO9VN4MkqQOrb6IPw2BJYuAnu4bpc9XNIEnqxOqb6OMwWJIm8RNew/Sx6maQJE1h9U308RgsSRP4Sa9B+lZ1M0iSGFh9ExUOgyWpEz/xNUSfqm4GSRITq2+iomGwJHXgJ7+G6EPVzSBJ2oLVN1HRMViSKjEBaICuV90MkqSNWH0TqQaDJakCk4Ca6XLVzSBJ2ozVN5FqMVhSUTARqJkuVt0MkqQrWH0TqR6DJRUGk4Ea6VrVzSBJuojVN5F6MFjSx2BCUBNdqroZJEmXsfomUi8GS/oQTApqogtVN4Mk6QtW30Tqx2BJ78PEoAbaXnUzSJI+YvVNpBkMllQQJgcV0+aqm0GS9BmrbyLNYrCkf2OCUDFtrLoZJMlQsPom0jwGSwIYKFVK26puBkkyRKy+icTBYGnYmChURJuqbgZJMmSsvonExWBpmCSCIAhiD0IfuLu749GjRwgJCRFtdlKhUCAgIADz5s1DYmIiLC0tMWXKFMyaNYsh8iM8efIE5ubmcHBwEHsoVATe3t4ICgrCjh074OPjI/ZwiAyWQqHAvHnzsGTJEqSmpsLGxgYzZ87E5MmT+dmkRxgoVcDf3x8LFizA8OHDsXbtWo2/PoMkUX7Jycmwt7cHAMTExMDKykrkEREZNgZL/cZAWUTh4eHw8PBAqVKl8PLlS43+UjBIEr3f9u3b0a9fP3h7e+P06dNiD4eIwGCprxgoi0iMqptBkujDsfom0k4MlvqF/2JFoOlV3VxsQ/TxDh8+DFdXV1hbW4s9FCL6Fy7e0S+coSwkTVbdnJEkKpqsrCwAgImJicgjIaJ34YylbmOgLCRNVN0MkkREZGgYLHUT/2UKQd1VN6ttcYWEhKB///5wdXWFmZkZLCwsULVqVUybNg0JCQliD4/UJCQkhFfXIdICrMJ1E2coP5I6q27OSIovMDAQvXr1QqNGjeDp6Ym7d+8iNDQUX3zxBUJCQvDkyROcP38eTk5OYg+VVCQlJQVbtmzBxo0bcfnyZRw9ehTt27cXe1hE9H+csdQNBh0oBUFAikxAqkwBhQBIJYClsRRWJu/+AVVH1c0gqT1q1KgBX19fTJ8+XXnbzJkzce/ePezatQvDhw9HRkYGNm/eLOIoSVUiIyOxaNEi/PnnnxgyZAgyMzOxceNGREdHiz00IspDm4JlYfKDvjO4QJmQIcft+Aw8T8nCyxQZ0uT5376FkQRlihmjbDETVLU1QwkzIwCqP4E5g6T2sbS0xM2bN1GhQgXlbdevX0f79u3x6tUrhIaGokOHDoiNjRVxlKQqT58+xcqVK7FmzRq8fv0aAFCvXj14eXnxjwYiLSVWsCxKfjAEBhEoBUFARGIWQmLS8CgpCxIAH/Kmc7ZzszZBWVkCvKu6q6TqZpDUXlWrVsU333yDfv36KW9bu3YtfvnlF4SFhSEyMhJeXl5ITU0VcZRUWIIg4MGDB/Dw8FDe9ubNGwwaNAhlypTB6tWrce/ePdSqVQvnz59HnTp1RBwtEb2PJoKlKvJDPXsLuNuYQCKRqGRM2krvA2ViphyBT5Lx+CN+EPLKedz9C3/j84aV0LRurUKNhUFS+23YsAETJkzA8OHDlcdQrl27Fj/99BOGDx+OkydP4quvvsL169fFHip9JIVCgcmTJwMAAgICcu3c169fjzVr1mDfvn1wcnLClStXUL9+fbGGSkQfQV3BUpX5wdXaBJ1drGBjqr8zlnqdYm7GpWPt3XhEJmWfg66wyTnncZUaeuOicTncjEv/qMdz1bbuGDZsGNauXYsrV65gwYIFCAkJwa+//orhw4cDACpUqICNGzeKPEoqDKlUivLly+PkyZNIS0vLdV9CQgKSk5OVi60YJol0x8esCl+yZAlat26N9PT3f46rOj9EJmVh7d34j84PukQvZygFQcDZqFQER6X998aF1MTRAs0dLd87hc0ZSSLtU61aNbRv3x5+fn5wcnLCiRMn8Pnnn6N169bYtGkTnj17hgsXLuDEiRPo3Lkz6tatC2dnZ7GHTUQf6F0zlgMHDoSrqyvS09PxxRdfYNWqVfkeqy35QRfpZaAMepmi1h+GHE0dLdC8TLF8tzNI6q6MjAzI5XJYWlqKPRRSk6ysrFyLqgRBgImJCUqUKAETExPExsYiNTUVpqamym0cHR3FGCoRFUHeYGlqaoqsrCzkxJ6dO3eiS5cu+OqrrzBp0iR4enqKnh90md4Fyptx6Tj8JFljr9fFxQrV7cwBMEjqg969e6Ny5cpYsGBBgffv2LED9+7dw+zZszU8MiIiKgyFQoGpU6di6dKluW63sLDAuHHjsHjxYnh6emLr35dxIipTY+P6d37QB3qVchIz5Tj+THNhEgCOP0vGm3QZj5HUE5cvX0b37t2VX6elpeHw4cPKrx0cHLBjxw4xhkZERIUglUqV//u3tLQ0LFmyBAAQnZiCY0+TNDqu48+SkZgp1+hrqpPezFAKgoCd4YmITMoq9MGzhaJQICL0HNaO6s0ZST1gYWGBe/fuwcXFBQAQERGBmjVrIikpe0fz+PFjVKtWDcnJmv3DhYiICq9EiRJ48+bNO+8funIXKtRvDiNjY42NSQKgvLUJfCrY6MXxlJr7zqlZRGIWHv9/NZZGSaVwr98Cc1dugP/owQySOs7Ozg6xsbHKQJlzPF1WVhZMTEyQlpaW69g6IiLSfjt37sSTJ0+UX2dkZODrr79GWloaPJu2hUfjVhofkwDgcVIWIhKzUKG47n+u6E36CYlJg1j5XgKgcvtPGCb1QP369bFixQrl12vXroWTkxOWL1+OpKQk/PDDDyq75CYREWlGhw4dMGLECOX/JBIJ0tLSIJVK0cR3BOQymSjjkgAIjVH/IiBN0IvKOyFDjtV34sUeBkZXsTWoyyzpo7CwMDRr1gxOTk6QSqWQyWT4448/0K5dOyQkJMDGxgbHjx9HgwYNxB4qEREV0uXLl/HLL7/AxKYk3AZPE71y1of8oPNTat9//z1szY1xaMk3uW6PvH4Fa0f2wqwm5TGnuRt+/bwbstKz/wqICDmP6XXsC/zf09tXldv8PmkgvmtfFbOalMdPfVviauDufK+flvQGBxZ+je/aV4WDjSU8PDwQGBio/jdOalGnTh2EhoZi0KBB+Pzzz3H58mXUrVsXDx8+xKlTpxAeHs4wqeUWLlyIBg0awMbGBqVLl0avXr1w//79XNusXbsWrVq1QvHixSGVSpGYmJjvecLCwtC+fXvY2trC3t4eo0aNQkpKivL+GzduoF+/fnBxcYGlpSWqVq2Kn376KddzREVFoX///vD09ISRkZHySj1EJK4GDRpg48aNkFoVx4y6DvgzT4YAstdmbBzrg+l17HH774I/11MSXmNhxxqYXsceaUlvj9FMjInCjhmjsLRnw3c+/5oRPZTZw9bcGBKJBBKJBF26dFHdG9UgnT6G8sqVK/j111/hUrlqroU4kdevYOM4H7QcOgHdpy6E1MgYL+/fguT/lbRLzfqYcfxWruc68cv3eHj5LMpVqfX/57gMx0pV4D1kHKxK2uOfs8fxx6wvYW5lA68W7QEAsqxMrP/iU1iVLIV+izegurszqiEeJUqU0MC7J3Xx8vLCrFmzct1WsmRJtGql+WNs6OOdPXsW48aNQ7169SCTyTB9+nS0b98ed+/ehYWFBYDs1Z2dOnVCp06dMH369HzP8fLlS7Rr1w6+vr5YuXIlEhMTMWHCBAwZMgR//PEHACA0NBSlS5fG1q1b4ezsjODgYIwYMQLGxsYYM2YMgOzjtBwcHDBz5kwEBARo7ptARACA5ORk7Nu3Dz169ICNjQ0AIDExEQcPHoSzszP2/r4ejpWqFvjY81t/Bf5j5nLvvIlwrFQFidEvc90uz8pEMVs7tB4+Gee2ri7wsQOWboI8K/s0Ra7WJmhinYWaNWvis88++9i3qRV0NlAmJyejf//+WLNmDb6YMSfXfYeXzUSTviPQcugE5W32rhWV/21sYgrrUqWVX8uzsnDn9FE07jtcOe3d6vNJuZ6zab9ReHDxNG7/dUgZKEMPbENaYgK+2BgIIxMTyIwk8K5RQ9VvlUTw7NkzjBs3Dq1atcL48ePFHg59hLwNwaZNm+Dg4IDQ0FA0a9YMAJT/pmfOnCnwOQ4dOgRTU9Ncx9OuXr0aNWrUQEREBNzd3TF06NBcj3F1dUVwcDD27t2rDJTly5dXBsn169er5g0S0QcLDAzEoEGDULx4cXz99dcYO3YsDh48iIEDB0IqlWLoj1vw128r8j3uxb2bOLtlFcZuOYHv2lcr8Lkv/rERaUlv0GaEH+6fP5XrPlsnF3Sb8h0AIOTAtgIfb1ncVvnfqUYSnDixGZaWljobKHW28v7yyy/RpUsXNG7ZBop/TU8mv47B01uhsCpZCr8M6YwFbatgzfDueHz14juf627QUaS+eY163X3f+5rpyYm5fgDunDkKl+r1cOD7qVjQtgoWftIMs79dALlcf84rZagSExNx6NAh7N69G507d8arV6/EHhIVUkJCAiQSCUqWLPnBj8nIyMi3mt/cPPsExOfOnXvn4968efNRr0NE6pXzefzmzRvMnDkTLi4u2L9/PwDAyNQcFZu1y/eYzLRU7JwxGj2mLco1+fRvryLu4a+1S9Fn3kpl+1kUaXIBa9etR9++fVGsmG5eQUcnZyh37NiBsLAwXLlyBYkyRa77Xj+LBACc/HUJOk+cAyfPagg7tAvrRvfGxD+CUMqlQr7nu7J/Kyo1boXipZ3e+Zo3ju/Hs9vX0OubZcrb4p9HIuLKOdTq1BtDftqOuKcRWOA/BssWLYSVlZWK3i0Vlb29PW7evFmox54+fRqLFi1CvXr1sGbNGnTq1CnX/TKZDHv37uUspgju37+vrLDeRRAETJw4Ec2aNUOVKlU++Llbt26Nr776CkuXLsWECROQnJyM6dOnQyKR4OXLlwU+Jjg4GLt27SrSMdSJiYnw8PAo9OOJKLf09HTlfysUCrx58wZ79uwBANg6ORf4mMPLZsKlZn1UadmpwPtlmRnYMX0UOk2YgxJlyuH188gij/PprTDcuX0LGzfobpOhczOUT58+xYQJE7B161aYm5vnmp0EAEHIDpgNPxmEej36walyDXT1mw/78hULnHZ+8+oFHlz4G/V79n/na4ZfOYfdcybgk5k/oHSFysrbFQoFipUshV7+P6BslZqo0aEX7J3LIy1NP04BQNlXWJg+fToOHjwIPz8/TJw4EZmZmrs0FxXNmDFjcOfOnY++ulGVKlXw22+/4YcffoClpSWcnJzg7u4OBweHAk8PduvWLfTs2RNz5sxBmzZtVDV8IlIjYzOzfLfdOXMU4VfOoqvf/Hc+7ujP8+HgVgm1u6iumg7ZvxVeVavp9KJPnZuhDA0NRXR0NOrUqaO8TS6X43HYBVzctR6T914AADi4e+Z6nL1bJSREPc/3fCEHt8OyeEl4tehY4OtFhJ7H7xP7o+tX36JOV59c99mUKg2psQmkRm+X+gcE/AjfXt3w5MkTngBbB0VGZv+l+eLFCwDZV8bJqUv37NkDPz8/NGjQANeuXQMAGBsbo0+fPujTp49YQ6Z3GDt2LAIDA3H27FmUKVPmox/ft29f9O3bFzExMcoKatmyZXB3d8+13Z07d9C2bVuMHj26wAU+H8PGxgZRUVFFeg4iemv79u3o168fgOwJAnNzc6SmpgIAXty9gW/qO0LxrwzR8NMheP3sMeZ5V8z1PFunDIVr7UYYufYAIq6cRdTDu7hV/08A2U0IAMxv7YmWwyah3RdTP2qMmWkpuH58H2bOnlPEdysunQuUbdq0yVVfpmYp8MmAwbB3rQTvIeNQspwrbOwdERP5MNfjYp+Ew7NJ7pkDQRAQenA76nTtAyMTk3yvFRFyHr9N6IeO42ehQe9B+e4vX7MBrh3dC4VCoZy1eBLxEGXKlGGY1FHu7u4QBAESiQSCIOQLDzm3k3YbO3YsDhw4gDNnziivelRY9vb2AIANGzbAwsIC7dq9Pebq9u3baNOmDYYOHYp58+YV6XWISPWM/j/hk7MoZ/Dgwfj9998xY8YM2Jd1Rr+Ardg9Z7wyQxQrURINeg/O9RzL+7RAl6++hVeLDgCA/ks2IivjbZX+7PZV7Jk7ASPX/Qk7Z9ePHuPNEwchz8zEgAEDC/9GtYDOBUpra2tUq/Z2xZUgCDC3tIRlcVs4VvQCADQf9CVO/roYZTyqooxHNYQd2omYxw/Rf/GGXM8Vfvks4p9Hol7PAfleJ/zKOfw2oT+a+o5AtTZdkRSbvSjDyMRUuTCn4WdDcWHXehxaMgON+45A0rMIHFi0kMfT6bCrV/9/HtKICPTp0wchISEij4g+1pgxY7B9+3YcPHgQxYoVUy6oKl68uHJhzatXrxAVFYUHDx5AEATcuHED1tbWcHFxga1t9u/3ypUr0aRJE1hZWeH48eP4+uuvsXjxYuVxm7du3ULr1q3RqVMnTJw4Ufk6RkZGKFWqlHI8169fhyAISE5ORkxMDK5fvw5TU1N4eXlp8ttCZJA6d+6MzZs3o3v37srf3S+//BIuLi7o3r07NjzKhKlF7gxR0EKcEo7lULJseQCAnbNbrvtSE14DABzcPWBhXVx5+4t72ZNfmakpSEmIw4t7N2FkYorSeRrUkP1bUb1VJ5R3tFfRuxaHzgXKvCQSCcykuc8T1az/aMgyM3B42UykvklAGY+q+HzVH/l+CEIObEX5mvXh4FYp3/OGHdqBrPRUnN64HKc3Llfe7la3CUauPQAAKOFYFkNX7MLhZTPxk4837EqXwYQJEzB16sdNd5P2qPH/0z6VKlUK/fv3V35NumP16tWQSCRo2bJlrts3btyIQYMGKbeZO3eu8kTC3t7e+ba5fPky5syZg+TkZFSuXBlr165VVmcAsGfPHsTFxWHLli3YsmWL8vby5csjIiJC+XXt2rWVpyMLCwvDtm3b8m1DROphZWWFAQNyTxrZ2Nigf//sdRNlir0p6GEq8bNva+V/P797HdeP7EGJMs6YejhMeXvM44d4fO0SBmzap7ZxaIpeXHrxfFQqzr1MhZhvRFAoEHfpBPrUq6TTB9USEREZCm3IDxIAzcpYoqmjpYijKDqdW+VdkKq2ZqL+MOTYsGAGrly5IvYwSI1SU1ORkZEh9jCIiEgFtCE/CP8fh67T+cobAEqYGcHN2gSPk7JE+cGQy2R4cPE0MhJiIQgC1q5dq7zPxcUFHTp0EGFUVBhubm4IDAx85/FtU6ZMQWpqKjZu3KjhkRERkaqJnR8kANysTVDCzOg/t9V2ehEoAaCevQUeJWWJ8tpGxsa4uHM9UlNTMW7cuFz3lShRAvHx8aKMiz7ekydP3jsDWadOHSxfvvyd9xMRkW4RMz8IAOraW4jy2qqmN4HS3cYErtYmiNTwXxlymQzhl4NwP/hUvvskEglGjx6twdGQKsyePfudl8979eoV7t69q+ERERGRuoiVHyQAXK1N4G6T/7SFukgvjqEEssNbZxcrGGv4HSmyMrFn3kRMmTIFFha5/8oQBAFZWVlQKBTveDRpo+TkZLx586bA/5mbm6Nbt25iD5GIiFRErPxgLAU6uVgpzwKh6/Rilfe/3YxLx+EnyRp7vTqmydj78/dYtmwZDh8+DB+f7KvpSCQSmJiYIDMzE5aWlvDz88Ps2bMLvGwbaQ+pVIqrV6+iZs2aYg+FtNSrV69QokQJmP3/sm1yuRxJSUlISUlRXomjWLFiSElJQWpqKkqWLJnvj00i0j6azg9dXKxQ3c5cY6+nbnqXbqrbmaOJo2Z23k0dLdC+qitWr16NYsWKoU+fPvjiiy8AAGZmZnj69CmWLl0KY2NjzJs3D9bW1pg9ezZnLLWYvvylSOpTsmRJZZhMSUnBV199BRcXF9SvXx9t27bFmDFjEBYWBltbW2zYsAEeHh4ij5iIPoSm84M+hUlADwMlADR3tERTNf1QCP8Pg5Yv76FZAeeM+uGHH9C6dWvMnz8fDg4O+OqrrxAfH89gqSPkcjlnJ+m9TExMlJff/PPPP7F69WqMHz8eL168wNGjR1GvXj34+voiMzMTkyZNQsOGDXHy5EmRR01EH+LBkZ04//sKAG8/71WtqaNFgflB1+ld5f1vN+PScfxZMmQKqORAW7lMBllmBg5+PxW3ju9DXFwcrKysPvjxCoUCAQEBmDdvHhITE1mFE+k4Hx8fSCQS7NixQ3nbs2fP0KNHDyxatAgtW7ZEaGgoKlasCDs7OxFHSkT/JTk5GSVLlkRWVhbqdPVB92mLYGxqBiPjoq9fliD7mMn25fSr5v43vU4x1e3MMcLLFuWts1dQFbbMlMtkAIDwK2fxwyeN8ezy3zh9+vRHhUkg+/g8zljqtqSkJAwdOlTsYZAWSE9PR3x8vPLynDm/v//88w9evnyJcuXKwdjYGA0bNmSYJNIBVlZWOH36NOzt7RF2aCd++KQxwq+cBQBICjktlZM7ylubYISXrd6GSUDPZyhzCIKAiMQshMakISIpCxJ82Ixlznb3z59C8I51uHf+bW3l5eWFn3/+GW3atCn0uDhjqX0SExNhY2Pzzvujo6Ph6OjI8E8AgLlz5+KPP/5AYGAgrK2t8fz5cwwdOhQSiQSHDx9GqVKleFwukY44deoUxo4di3/++SfX7VW8O2DWup14VIj84G5tgrr2FnC3MdH7fYFBBMp/S8iQ43Z8Bl6kZOFFigxp8vxv38JIAqdixnAqZoKqtmZo2bAu0tLSULlyZRw8eBBA9uINQRDQtWtXBAQEoGLFioUeE4Ol9rCyskKvXr3w+eefo2XLlvnuj46ORpkyZSCXyzU/ONJKPXr0wKNHjwAAt2/fRosWLbB3717Y2tqKPDIi+hAPHz7EpEmTcOjQIeVnOwB0794dd+/eRbFixXD16tVC5Qd9uALOhzK4QJlXcpYCaTIF5AJgJAEsjKWwMskd4lJTU2FqaoqFCxdi7ty5+cJE/fr1cfny5SKPhcFSfBs2bMC6detw6dIlVKhQAUOHDsWQIUNQpkwZAJyhpPwSEhJw584dhIaGolKlSujYsSOA7AVeRkaG82FCpKsaNGiAK1eu5LrNyMgIc+bMwbRp05Sn/8vrQ/KDITH4QPkxNm/ejEGDBuW6zcvLC1u3bkXt2rVV9joMluK7e/cu1q9fj99//x3x8fHo1KkThg0bhgYNGqBcuXIMlEREeuLq1avo379/vqugbd68GQMGDBBpVLqHgfIjnD17Fi1atIBUKlUGiuDgYDRu3Fgtr8dgKT6ZTIYDBw5g7dq1OHnyJEqUKIHXr18zUBIR6ZELFy6gSZMmAKD8jD979iyaNWsm8sh0B1PJR6hYsSKkUik8PT2xf/9+SKVS9OzZU23hgqvCxWdsbIzevXvj6NGjePToESZMmICqVauKPSzSETKZDFlZWWIPg4jeQ6FQoEePHpBKpTh48CA8PT0hlUpRoUIFsYemUxgoP0KZMmVw48YNXLt2DT169MCMGTMQHR2NUaNGqfV1GSy1g7OzM2bOnImbN2+KPRTSEceOHYOHhweSkzV3OTci+jgjR45ETEwMvvnmG3Tr1g3Xrl3DjRs3lMfO04dh5V1E7u7uePToEa5cuYJ69epp5DVZhRPphu3bt6Nfv37w9vbG6dOnxR4OEeUREhKC+vXrw93dHeHh4WIPR6cxUBZReHg4PDw8UKpUKbx8+VKjgY7Bkkj7eXt7IygoCDt27ICPj4/YwyGi/1MoFHB0dERcXBwePnwINzc3sYek05g6iqhChQoaq77zYhVOpP0OHz4Mc3NzDBkyhNU3kRb5d9XNMFl0nKFUETGq77w4Y6kaT548gbm5ORwcHMQeCukJVt9E2oVVt+oxUKqImNV3XgyWRSOVStGhQwccOXJE7KGQHmH1TaQdWHWrB9OFiohZfefFKpxI+7D6JtIOrLrVgzOUKqYN1XdenLH8OJyhJHVh9U0kLlbd6sM0oWInTpyAVCpFly5dtGYmkDOWRNrB19cXLVq0wJkzZ7Bz506xh0NkUBQKBTp37gypVIqTJ0+KPRy9w0CpYtpUfefFYEkkPlbfROJg1a1erLzVRBur77xYhReMlTepG6tvIs1i1a1+hpsa1Ewbq++8OGNJJA5W30Saw6pbMxgo1USbq++8GCyJNI/VN5FmsOrWDFbeaqYL1Xdehl6Fs/ImTWH1TaRerLo1R//Tgch0ofrOizOWRJrB6ptIfVh1axYDpZrpUvWdF4Mlkfqx+iZSD1bdmsXKW0N0sfrOy1CqcFbepGmsvolUi1W35ulPCtByulh958UZSyL1YPVNpDqsusXBQKkhulx958VgSaR6rL6JVINVtzhYeWuYPlTfeelbFc7Km8TC6puoaFh1i0f3Pu11nD5U33lxxpJINVh9ExUeq25xMVBqmD5V33kxWBIVHatvosJh1S0uVt4i0cfqOy9drcJZeZPYWH0TfRxW3eLT3k91PaeP1XdenLEkKhxW30QfjlW3dmCgFIk+V995MVgSfTxW30QfhlW3dmDlLTJDqL7z0vYqnJU3aQtW30Tvx6pbe4j/6W3gDKH6zoszlkQfhtU30bux6tYuDJQiM6TqOy8GS6L/xuqbqGCsurULK28tYYjVd17aUoWz8iZtw+qbKDdW3dqHM5RawhCr77w4Y0lUMFbfRG+x6tZODJRawpCr77wYLInyY/VNlI1Vt3Zi5a1lWH3np+kqnJU3aStW32ToWHVrL85QahlW3/lxxpIoG6tvMmSsurUbA6WWYfX9bgyWRKy+yXCx6tZurLy1FKvv/6auKpyVN2k7Vt9kaFh1az/OUGopVt//jTOWZKhYfZMhYdWtGxgotRSr7w/HYEmGiNU3GQpW3bqBlbeWY/X98YpahbPyJl3B6pv0Hatu3cEZSi3H6vvjccaSDAWrb9JnrLp1CwOllmP1XXgMlmQIWH2TvmLVrVtYeesIVt9F96FVOCtv0jWsvknfsOrWPZyh1BGsvouOM5akr1h9kz5h1a2bGCh1BKtv1fmvYMlJe9JFrL5JX7Dq1k2svHUMq2/Vy1uFA9kB/v79+2q5VjiRurD6Jl3Hqlt3MVDqmPDwcHh4eKBUqVJ4+fIlA48K5QRLPz8/AFDZlXeINMnb2xtBQUHYsWMHfHx8xB4O0QdTKBRwdHREXFwcHj58yNlJHcNPSR3D6lt9cqpwAPD09OQxlqSTWH2TrmLVrdsYKHXQt99+Czc3N6xbtw4hISFiD0fvSCQSuLm5cfEO6SQrKyts2LAB6enp6Nq1q9jDIfogISEhWL9+Pdzd3TFv3jyxh0OFwECpo7jqW/24Kpx0FVd9ky7hqm79wECpo1h9aw6DJekiVt+kK1h16wcuytFxXPWtev91YvOiXiucSFO46pu0HVd16w9++uk4Vt+axxlL0hWsvkmbserWLwyUOo7Vt3gYLEkXsPombcWqW7+w8tYTrL5Vp7DX8mYVTtqK1TdpG1bd+oefcnqC1bf4OGNJ2orVN2kTVt36iYFST+RU32lpaTh37pzYwzFoDJakjVh9k7a4du0aJBIJq249w8pbz7x69Qp2dnYwNjYWeyg6q7CV97uwCidtweqbtEFWVhbS0tJgY2Mj9lBIhfhppmccHBwYJrUMZyxJW7D6Jm1gYmICa2trsYdBKsZAqWckEsl/bnPr1i0EBgZqYDT0bwyWpA1YfZM2+JDPKtItDJQGJjU1FWFhYZgyZQorL5EwWJKYeK1vIlIHBkoDY2lpiU8++QR9+/bF4MGDxR6OQWOwJLGw+iYiVWOgNAA5667u3r0LIHuG4ptvvkGZMmUwd+5cMYdGYLAkcbD6JnXiel/Dw0CppwRBwIMHDwBkH6uSkZGBwYMHY968eQCyQ0y1atWQlpbGX3wtwWBJmsTqm9Th1atXAHiMpCFioNRDCoUCM2bMwJo1a5Rh0czMDH5+fpg3bx62bt2KjRs34vfff4etrS1/8bUMgyVpCqtvUqXg4GAMGzYM+/btQ2JiovJ2TloYBgZKPSSVSuHk5ITjx48jNTVV+cvs7e2NatWq4ezZsxg/fjyGDBmCL774QuTR0ruoIlhmZmbi5MmTSEhIUP+ASSex+iZVGTFiBE6dOoVZs2bhxx9/xJ07dwBkz1bmDZX8w1j/MFDqqXHjxkEmk2HWrFnIyMgAAFy/fh3h4eH4+uuvERUVhTVr1sDExARJSUlITU0VecT0Lu8Llnfu3HnvX/8rVqzAnDlzUL16dQwfPhyvX7/W4MhJF7D6JlXYtWsXrK2tERQUhE8//RRr1qzB9OnTceDAAbx580bZhCUnJ0OhUPCiDnqIV8rRY3fu3EHPnj1Rv359AMCTJ09QsWJFbNy4EY8ePcL69euxefNmODk5QS6Xw8fHB1999ZXIoxafqq+Uo2o5V97Zs2cPgoOD37vto0ePkJqailevXuHHH39EbGwsdu3ahXLlymlotKQrvL29ERQUhB07dsDHx0fs4ZCOuXr1Ko4cOYIvv/wSxYsXx+XLl/HVV18hOjoa/fv3x6effooqVapg4sSJkEql+OGHH8QeMqkYA6WeO3fuHA4ePIizZ8+iZs2aGDlyJKKiojBz5kxcvXoVs2fPRqNGjfDy5UusWLECtWvXxtq1a8Uetqi0PVDmEAQBCoUCRkZGH7T9kydP0KlTJ3z55ZcYM2aMmkdHuiY5ORn29vYAgJiYGFhZWYk8ItIlMpkMSUlJsLW1hVwuV+6X5syZg3Xr1qFBgwaoWLEili5dimvXrqFGjRoij5hUjXPOeq5Zs2ZYvHgxzp49i9WrV+Pu3bvo0aMHkpOTUalSJdy6dQtnz57FkCFDsH79euzatYsnPNcREonkP8Nkzt+LMpkMLi4ukEqliIiIUN73zz//YO/evYiPj1f7eEm7sfqmojA2Ns4VJmUyGYDsQLl//35ERUVh6dKlmDNnDsOknmKgNBDGxsa4ceMG/P39MWHCBBw8eBCXLl3CV199hU2bNuHw4cOoWbMm+vbti2PHjok9XCoihUIBuVyuPG4pKysLv/32G27fvo3OnTsDABISEvDjjz9iyZIl8PT0xPDhw7l4x8Bx1Td9jISEBFy6dAnHjh1TNjo5f+QaGxtDoVBAEATUq1cPXl5eqFu3LmbNmiXmkEmNWHkbkI0bN2LGjBk4e/YsKlasqLzdx8cHtWvXxrRp0xAeHg43NzeDPmBaVyrvDxEeHo7Dhw9j48aNePPmDYYNGwZ/f3/l/U+fPoVCocDz58+xYMECGBsb47fffkOJEiXEGzSJitU3fag2bdogNjYWGRkZyMjIQKlSpfDjjz+iadOmAKCcrbx48SKaNGmCkJAQ1KlTR+RRk7oYbmowQFFRUcrjWHIkJSXhxIkTMDY2BgBUqFABUqmU5w3TYZGRkVi9ejW8vb3Rrl07bN68Gd26dUNwcHCuMCkIApydnVG+fHk0adIE/v7+CAsLw+XLl3M9H38WDAurb/oQI0aMQFpaGo4cOYLjx49j5syZCA0NRfPmzTFlyhQAb2crGzRogCtXrjBM6jkGSgPSo0cPnDhxAvv370d6ejrOnz+PUaNGwd3dHQMHDsy1bVxcnEijpKJISEiAm5sb/P390bt3b5w6dQpnz57FvHnz4OjomOvcbzl1eE5gdHd3h7GxMe7fv5/veVNSUjTzBkgrsPqm93nx4gXCwsKwZMkSODk5wcXFBf3790fPnj0xceJEbNy4EePHj4dMJlOeIqhu3bpiD5vUjIHSgFSpUgVz5szBwoUL4ejoiCFDhiAmJgaTJk1C6dKlce/ePWzZsgVt27ZFgwYN0Lt3b/z6669iD5s+gkwmQ69evZCQkIB//vkHAGBubq68P+dQBrlcDrlcDiA7WD59+hRLlizBq1ev0L9/f+X2ixYtwieffIJatWphyJAhXLxjQHjCc3oXc3NzpKSk4MqVK8rbzMzMcPHiRbRu3RozZ87E33//jfj4eIM+fMrQ8F/awHz99dfYtWsXVq5cifXr1yMgIAD9+/fH8ePH8c0338DPzw/W1taYOXMmypcvjylTpuD48eNiD5s+UKlSpbBnzx7cunUL4eHh8PT0RO/evREdHZ1rOyMjIxgZGeHcuXMYOXIkWrZsiQsXLmD16tWwtbXFq1ev8O2332LGjBlwd3fHwoUL8fz5cwwYMABv3rwR6d2RJrH6pnexsbFBs2bNcPr0aRw8eBC3bt3CZ599hrJly6Jr164YOXIknj9/jgsXLog9VNIkgQxeYGCg4OXlJTRu3Fg4fvx4rvv8/f2FNm3aiDQycUgkEqFjx45iD0Ml7ty5I3Tv3l3YuHGjIJfLBUEQhOPHjwt+fn6Cl5eX4OzsLPTs2VPYvHmzkJGRoXzcL7/8ItSoUUP44YcflLedO3dOsLOzE65cuaLx90HiadGihQBA2LFjh9hDIS0SHBwsNG7cWKhcubLg6Ogo1KhRQ4iKihIEQRDi4uKEZs2aCVu2bBF5lKRJxmIHWhJXaGgofHx80KVLF/z+++8wMTGBIAjIysqCqakpoqKiULx4cQDZx9rlHHdHusHLywsHDhyATCaDVCrFihUrMH78eFSvXh2LFi1Cy5YtYW1tnesxjx49wpEjR+Du7o5x48Ypb3dzc4OtrS1u376NevXqafqtkEgOHz4Me3t7DBkyBF26dOGqbwOWlJSEW7duoVatWmjcuDFOnz6N06dPw8nJCba2tihdujSA7Ku0Xb9+HQ0aNBB5xKRJrLwN3OHDh1G5cmVs374dJiYmyMjIgCAIMDU1RUJCAoKCglCpUiUAYJjUYTmr+Hv16oXevXvjzp07OHHiRL4qHADOnz+PZ8+ewcfHB8bGxsjKygIAxMbGIjw8HI0aNQKQ+zhMuVyOO3fuIDY2VkPviDSF1TcBwLJly9CpUyf4+PigXr16uHDhAkxNTdG+fXtUq1YNZcuWhSAIOHr0KAYNGoTx48crPzvIMDBQGjC5XI7w8HDUqlULQPbJsM3MzCCVShEUFISaNWvCwsIC06ZNE3egpDJly5bFH3/8gRs3buDevXuoWrUqunfvjtevXyu3efjwITIyMtC7d28Ab/+QCAgIQO3atVG2bFkAb4/DvHnzJnr16oV27drBwcEh14H6pB+46tuwHT9+HAsWLMDgwYPx008/wc7ODrdu3QIAZGZmKrdLSUmBmZkZ+vXrh/nz54s1XBIJA6UBMzIygpeXF86cOYNXr14hNTUVGRkZmDBhAnr37o26deti//79PMm1HvLy8sKxY8dw9epVKBQKREVFKe8LCQlBjRo1YGJiAoVCAWNjYzx69AibN2/GyJEjYWlpCQC4cOECxowZg+nTp6Ns2bLo0KEDatWqhWrVqon1tkiNuOrbcE2YMAFTp07FiBEj0LNnT7Rv3x6HDx9Gt27dULVqVWV4tLKyQqtWrTB37lyRR0xiYKA0cNOmTYOjoyNatGiBFi1aoESJEjhw4ACmTZuGDRs2wNnZGX/++SeWLl2Kzz77DBs2bMDDhw/FHjapiJeXFw4dOoQqVaooz0dZr149PHv2DCkpKZBKpYiLi8OYMWNQp04dDB06FI8fP8b8+fPxySefwM7ODhs3bkTPnj1x9+5dDBgwABYWFsoqnPQHq2/DtGXLFlhZWeU6V/HOnTsRFxeHBg0aYOzYsfjuu+/wzTffAMg+1j7nhOZkWHjpRUJsbCxCQ0Nx5coVuLm5oWvXrihevDiuX7+ORYsWITw8HPHx8XBzc0NycjJiYmIKPPm1vtCnSy8WRlhYGHr16oXmzZujQYMG+O233/DixQucPHkSVatWRYsWLfDs2TMEBgaicuXKiIuLw7Rp03Dt2jWcOXMGlpaWEAQBCoVC+cESGxuLkJAQ1K9fH3Z2diK/QyoKb29vBAUFYceOHfDx8RF7OKRmsbGxCA4ORqtWrWBtbY1NmzZh0aJF+Ouvv1CmTBkAwODBg5GcnIw9e/aIPFoSE2coCaVKlUKHDh3g7++P/v37w8bGBrt378bYsWMRExMDX19fnD9/HseOHUNgYCBsbW0xY8YMsYdNalKnTh2cPHkSKSkp2LdvH9q0aYM9e/agatWqymMrFQoF2rVrh61btyIkJATXrl1D//79YWlpCZlMBolEogyTW7ZswcCBA9G5c2c0atQIDx48EPkdUlGw+jYcCoUCpUqVQteuXZVng/Dy8sLWrVuVYTLnNhMTE6Snp4s1VNICPG0Q5fP8+XMsXboU5cqVw4QJE9C8eXPlfcWLF0e1atUQHx8PuVzOakNPVapUCfv27UNKSgqKFSumvN3MzAwTJkyAr68vtmzZgsmTJyMtLQ12dnaYOHEigLcryjdt2oTAwECkpKSga9euSE9PR9WqVeHk5CTGWyIVyam++/Xrh65du+L06dNiD4nUJOeyif++2k3Dhg1zbZOWloYtW7ZgyJAhua7KRYaHM5SUz8yZMxEdHY21a9cqw2TONaDv3buHM2fOwMvLi2HSABQrVgwFHRXj4OCAyZMnY9myZShevDgiIyPh6+uL5ORkXLx4EYMHD0ZAQAB69OiBQ4cOISMjA5mZmejYseM7n5N0B1d967fbt29j/vz5aNu2LQYOHIj9+/cjIiJCeX/O58Hz58/h5+cHiUQCPz8/sYZLWoIzlJSLQqFAUlISPv/8c9ja2gLIPshaKpUiKioK06dPBwC0atVKzGGSBuU9/2jOCe5jYmIQHBwMZ2dnnDt3Dunp6YiMjMTo0aOhUChw/fp1SCQSREZGYteuXahfvz5/bvQIT3iuv7p16wY3NzdUq1YN165dw8SJE9GoUSP06dMH3bp1g4mJCeLi4rBr1y6cP38eu3btEnvIpAU4Q0m5SKVSJCYmIjIyUnlbamoqDh06hObNmyM0NBS7du1C9erVAQBPnjzBq1evxBouiSAnYD59+hR///03OnfujPLly8PDwwMVK1bEyJEjERsbC1dXV+zYsQPbtm2Dubk5OnfurJyd5EnydR9Xfeun+fPnw97eHqdOncKKFStw7tw5BAQEICoqCosXL1aGRwsLC7Rq1Qq///47vLy8RB41aQOu8qZ8bty4gRYtWqBz584wMzNDVFQUjh07hoEDB2Lx4sUoXbo0UlJScPPmTSxfvhz37t1DWFiY2MNWGUNf5f0xXrx4ATs7O5iZmeUKitHR0di6dSsWLlyIxMRENGvWDCdPngTAS3jqG6761i/ffPMNLl26hMOHD8PExER5/GRcXBwmTJiAQ4cO4c8//8x1bD0RwBlKKkCNGjWwY8cO2NraIikpCcWLF8elS5fw22+/oXTp0nj+/Dm2bt2KoUOH4ubNm3jx4gUGDBgg9rBJBE5OTjA1NQWQuxq3t7fHpEmTMHToUDg5OeGvv/7CkCFDcm3Hv2X1A1d965dq1arhwYMHeP78OaRSKWQyGbKysmBnZ4ctW7bA29sbixYtEnuYpIUYKKlAHTt2xMqVK7Flyxbs2LED9evXVwaAqKgo7N69G/Hx8bh16xbu3r2LXbt24fjx4yKPmsTwrmMsIyMjcebMGfTq1QsRERFYunSp8n4g+9KfP/74o/IAf9JNrL71S+fOneHs7IxWrVrh2rVrMDY2homJCWQyGQCgdevWSExMRFJSEv8opFwYKOm9ck4DERsbqwwO1atXx4wZM1C8eHEcO3YMtra2mDt3Ljp27CjmUElL5PychIWFQSaToVmzZnB1dVWe0FwikUChUGDbtm2YNGkSrK2tMXv2bAZLHcZV37otLi4Of/31FzZv3ozXr19j7969qFWrFho2bAh/f38Ab08H9ujRIwCAtbU1D12hXHgMJb1XRkYGli1bhhIlSmDMmDG57ps5cybOnz+PEydOwMjICH5+fvD394eNjU2u85bpGh5DqTqvX79GsWLF8h1jCWSfUSAgIADz5s1DYmIiLC0t4efnh9mzZ+v0z4+hSk5Ohr29PQAgJiaGq751SOvWrfH69WvcuXMHlStXxpEjRyCRSLB582asWrUKycnJaNu2LV68eIH79+/jxIkTqFGjhtjDJi3DvTa9l5mZGe7evYurV68CyA4BOX+DWFhYwMjISPn10qVLUaJECYYBUipZsiTMzMwA5K/GpVIpvvrqK8THx2Pp0qUwNjbGvHnzOGOpo1h966bhw4cjMzMTu3fvRlxcHFJSUnDt2jXIZDK0a9cOBw4cwHfffQdTU1N07twZO3fuZJikAnGGkv7TrVu3UK9ePZw4cUK5si8uLg5dunRB8eLFERgYqDzJuT6s4OUMpTg4Y6kfuOpbd1y8eBHdu3fHqVOnUK1aNUgkEgwdOhT379/HjRs3ULVqVbi7u2PDhg0wMTHhxSzovbiXpv9UrVo1zJgxA1988QX8/f3x008/YcSIEZDL5Zg1a1aunYyuh0kSD2cs9QNXfeuOixcvYsiQIXBzc4NEIkFERAS2bt2KTp06ISgoCCNGjEBQUBA2btzIMEn/iTOU9MGWLVuGffv2ISIiAg0aNECvXr0waNAgvQuRnKHUDpyx1F3bt29Hv3794O3tzWt9a7HU1FT8888/qFOnDgBg6tSpeP78ObZs2aLcpmPHjqhRowYWL14s1jBJRzBQ0kdJS0uDXC5HRkaGctWuPtTc/8ZAqV0YLHUTq2/dk5iYCDMzM5iZmUEmk8HIyAgDBw5EmTJlsGTJErGHR1qOgZIoDwZK7cRgqVu46ls3/XuC4MyZM+jRowcuXryIypUrizwy0nbcCxORTuAxlrqFq751k0QigSAI+OuvvzB+/Hh8+eWXDJP0QRgoSSX+fTohInVisNQdPOG5dhMEAXK5PN/taWlp2Lt3L2rWrIkFCxaIMDLSRay8qchyVnvHxcVh9erVYg+nyFh56xZW4dqN1bf2GjNmDCpVqoSJEycWeAnV9PR0WFhYiDQ60jXc21KRGRkZYfv27fj1118REhIi9nDIwHDGUrux+tZOISEh+OWXX7BixYoCF1VKJBKGSfooDJSkEidOnIBUKkWXLl34AU6iYLDUXqy+tYtCoUDnzp0hlUpx8uRJsYdDeoKBklSiQoUKmDFjBqKjozFq1Cixh0MGjMFSO/GE59pj5MiRiImJwTfffAM3Nzexh0N6gsdQkkq5u7vj0aNHuHLlCurVqyf2cAqFx1DqFx5jqT14wnPxhYSEoH79+nB3d0d4eLjYwyE9wr0pqRSrb9I2nLHUHqy+xcWqm9SJgZJUitU3aSsGS+3A6ls8rLpJnVh5k1rocvXNytswsAoXD6tvzWPVTerGvSapBatv0nacsRQPq2/NYtVNmsBASWrB6pt0BYOlOFh9aw6rbtIEVt6kVhUqVEBERIROVd+svA0bq3DNYfWtfqy6SVO4dyS1On78OKtv0imcsdQcVt/qxaqbNImBktSK1TfpKgZLzWD1rT6sukmTWHmTRuhS9c3KmwrCKlx9WH2rHqtu0jTuBUkjWH2TruOMpfqw+lYtVt0kBgZK0ghW36QvGCzVg9W36rDqJjGw8iaN0oXqm5U3fQxW4arD6rvoWHWTWLi3I41i9U36hjOWqsPqu2hYdZOYGChJo1h9k75isFQNVt+Fx6qbxMTKm0ShzdU3K29SBVbhhcfq++Ox6iaxca9GomD1TfqOM5aFx+r747DqJm3AQEmiYPVNhoLBsnBYfX84Vt2kDVh5k6i0sfpm5U3qxCr8w7H6/m+suklbcO9FomL1TYaGM5YfjtX3+7HqJm3CQEmiYvVNhorB8sOw+n43Vt2kTVh5k1bQpuqblTeJgVX4u7H6zo9VN2kbw95LkdZg9U2GjjOW78bqOzdW3aSNGChJK7D6JsrGYFkwVt9vseombcTKm7SKNlTfrLxJm7AKf4vVN6tu0l6GtTcircfqmyg3zli+ZejVN6tu0mYMlKRVWH0TFYzBMpshV9+sukmbsfImrSRm9c3Km3SBIVfhhlh9s+ombaffex3SWay+id7PkGcsDa36ZtVNuoCBkrQSq2+iD2OowdKQqm9W3aQLWHmTVhOj+mblTbrMkKpwQ6i+WXWTrtCvvQvpHVbfRB/HkGYs9b36ZtVNuoSBkrQaq2+iwjGUYKnP1TerbtIlrLxJJ2iy+mblTfpIn6twfay+WXWTrtHtvQgZjJzqu3Pnznozs0KkSfo8Y6lv1TerbtJFDJSkE3Kq75iYGFbfREWgr8FSn6pvVt2ki1h5k07RRPXNypsMiT5V4fpQfbPqJl2lW3sLMngnTpxg9U2kQvo0Y6nr1TerbtJlDJSkU9zd3Vl9E6mBvgRLXa6+WXWTLmPlTTpJndU3K28i3a7CdbH6ZtVNuk679wpE78Dqm0i9dHnGUteqb1bdpA8YKEknsfom0gxdDZa6VH2z6iZ9wMqbdJo6qm9W3kTvpktVuC5U36y6SV9o128/0Udi9U2kWbo0Y6nt1TerbtInDJSk01h9E4lDV4KlNlffrLpJn7DyJr2gyuqblTfRx9PmKlwbq29W3aRvOENJeoHVN5G4tHnGUtuqb1bdpI8YKEkvsPom0g7aGiy1qfpm1U36iJU36RVVVN+svIlUR5uqcG2ovll1k77iDCXpFVbfRNpFm2Ysxa6+WXWTPmOgJL3C6ptIO2lLsBSz+mbVTfqMlTfppaJU36y8idRPzCr839X37t27MX78eGRlZeGPP/5Q22uy6iZ9x0BJeikiIgKVKlWCnZ0doqKiPuoDioGSSHPECpbe3t4ICgpCsWLFkJKSAjMzM6SlpUEikaj8tRQKBRwdHREXF4eHDx9ydpL0Eitv0kusvol0gxhVeGxsLOzs7AAAKSkpAICMjAzExMSo/LUAVt1kGDhDSXqtMNU3ZyiJxKPuGcuUlBS4u7sjOjo6332XLl1CgwYNivwa/8aqmwwFZyhJr33Mqm9BEJCcpUDpil6wcnJFdJoMyVlcKU6kSeqesbS0tMSnn34KiUQCIyOjXPfduHEDn3/+ObZu3ZrvcTn7h+g0GaJSZR+0f+CqbjIknKEkvTdz5kzMnz8fw4cPx9q1a3Pdl5Ahx+34DDxPycLLFBnS5Pl/HSyMJChTzBhli5mgqq0ZSpgZ5duGiNRDXTOW169fx/jx4xEUFKS8zdzcHOnp6ejcuTMOHz5c5P3D8OHDsX79esycORPz5s0r9FiJdAEDJRmEf1ffdevWRURiFkJi0vAoKQsSAB/yS5CznZu1CerZW8DdxkQtB/ATUX7qCJaCIOC3337DqFGjkJmZqby9dZ/BGLlgeZH2D68f3EADVt1kQBgoySDkrPp2rVwVCw6cxeOP+KDIK+dxrtYm6OxiBRtTzlgSaYoqg+W9e/fQqFEjJCUlQS6Xw8ahDHrPXg6Pxq2KvH94HHoeO2d+ibDgs1yIQwaBgZIMxvwNOyH3bAQzCwtAUvTDhyUAjKVA+3JWqG5nXvQBEtEHU0WwjIyMRKtWrfD48WPU7tIH3actgrGpGYyMjYs8PrlMBikEdHW35f6BDAIDJek9QRBwNioVwVFpEARBLTV1E0cLNHe0ZAVOpGFFDZYZGRlYHngOCteaEBQKSNRw7kvuH8gQcJU36b2cMAlAbTv04Kg0nItKVctzE9G7FXVV+KXXMihcawKAWsIkwP0DGQYGStJrN+PSlWFS3c5HpeFmXLpGXouIcitMsOT+gUh1GChJbyVmynH8WbJGX/P4s2QkZso1+ppE9NaHBkvuH4hUi8dQkl4SBAE7wxMRmZRVqJWahSUBUN7aBD4VbHi8FJEWeNcxll4DJiEyWcb9A5GKcIaS9FJEYhYeazhMAv8/XUhSFiISszT8ykRUkIJmLHeeCsZjDYdJgPsH0m+coSS9tPPhG1ECJZA9C+FmbYI+FYuL8OpE9D4KhQJL/74JWfHSkBoV/fRAH4v7B9JXnKEkvZOQIccjkcIkkD0LEZGUhYQMHitFpG0SswQoSpYVJUwC3D+Q/mKgJJ23cOFC1K9fH9bW1nBwcED3Hj0R+/ih8v7UN/E4uGgalvVqhJmNnfF951o4uHg60pMScz3Pw0tB+GVIZ8xu5ooF7argyPJ5kMtkuba5H/wXVg3qiNnNXDG/dWVs8RuC+BdP8o1JAmD7sdMwNjZGrVq11PG26QN9//33kEgkmDhxYr77BEFAp06dIJFIsH//fuXt169fh6+vL5ydnWFhYQEvLy8sX778na9x/vz5Av+tk5KSMHHiRJQvXx4WFhZo0qQJrly5oqJ3Rh/il19+QY0aNWBjYwMbGxs0a9oE98+fVN6/ZkQPTK9jn+t/+xb45XqOhJfPsGm8L2Y1ccH8Nl4IDJiTb98gy8zAsRULsKhzbfg3LItFXeogZP9W5f23Th3Civ5tMbdFBcxqUh7169bG5s2b1fvmiTRInD/RiFTozJkz+PLLL1G/fn3IZDIMnfg11o/5DJP2nIOpRTEkxkQhMSYKnSfOhYO7BxJePsO+7/yQFBOF/ks2AgBe3r+FTeN90erzSfhs3gokxrzE/gVTICjk6DxpLgDg9fNIbJ48CM36j4bPgl+QnpyIw8tmYovfEIzb9leuMaUmvUHA2OFo06YNXr16pfHvCWW7cuUKfv31V9SoUaPA+3/88ccCF0eEhobCwcEBW7ZsgbOzM4KDgzFy5EgYGRlh7NixubZNSEjAoEGDCvy3Hj58OG7duoXNmzfDyckJW7ZsQdu2bXHnzh2ULVtWdW+UlBQKBWQyGUxNTQEA5cqVw3fffQc3NzeYmJhg2o9r8PukQRi3/S+UrlAZAFC/10C0+2Kq8jlMzC3fPp9cjk0T+sHazgGjNx5GUuwr/DFzLIyMjdFhnL9yu21ThyM5Lga9Z/8IO2c3JMa8giC8PVWRZXFbtPp8EuxdK8HIxASxV05h6NChcHBwQIcOHdT9bSFSO85Qks47evQohgwZgqpVq6JGjRroPfsnJEQ9w/M71wEAjhW9MGDpJnh5d4CdsxsqNGiODl/OwN2g48pZhhvH9sOxUhW0GemHUi7ucK/bFJ0mzMaFXRuQkZJ9apHnd69DoZCj3ZczYOfshrJeNdF84Jd4ee8W5Fm5D7Lfv8APNTv2RuPGjTX7zSCl5ORk9O/fH2vXroWtrW2++69du4Zly5Zhw4YN+e4bNmwYli9fDm9vb7i7u2PAgAEYOnQo9u7dm2/b0aNHo1+/fvn+rdPS0rBnzx4sXrwYLVq0QMWKFTFnzhxUrFgRv/zyi+reKOUyf/58lCxZEjNnzsTr16/RrVs3XLp0CY0aNcLWrVvRYtQ0mFoWw5ObIcrHmJhbwLpUaeX/zK2slfc9uPg3oiPuoc/8VXDyrA7Ppm3Rbsw0XPhjA2RZmQCAe+dP4VFoMIb8vB0VG3rD1skF5WvWh2uthsrnca/XFFVbd4GDuwfsnN1Q67MRqFGjBs6dO6e5bw6RGjFQkl5JkQmIf/MGAGBRPH+IyJGenAjzYtbKa/bKsjJhYmqWaxsTc3PIMtLx/G52MC3rVRMSiRShB7dBIZcjPSkRVwN3oUJDbxiZmCgfF3JgG14/j0SLEX7IlHPNm1i+/PJLdOnSBW3bts13X2pqKvr164eVK1fC0dHxg57vzZs3KFmyZK7bNm7ciIiICMyePTvf9jKZDHK5HObmua/jbGFhwRChRq9evUJqaiq+++47ODs7Y+bMmXjy5AlSUlLw46pfcenIPmSmpcKlRn3lY64f2YNvW3vix8+a4+jP3yIz7e1VbZ7cCIFjRS9Y2zkob6vUuBUykpMQHf4PAOBu0DGUrVILQb/9jIUdqmNpz4YIDJiNrPSCT5ouCAJuBp/BvXv30KJFCzV9J4g0i5U36ZXkTBkOLfVH+VoN4FjRq8BtUuLj8NfaH1D/k4HK2yo1boXz237FtaN7UaNdDyTFRePUmqUAgMTY7BqzZNnyGLZqF7ZPHYH9C/ygkMvhUqM+hvy8Xfk8sU/Ccezn+Ri5/k8YGRvjrzNBeP78OcaNG6fGd22Ypk2b9s7aeMeOHQgLC3vn8YqTJk1CkyZN0KNHjw96reDgYOzcuROHDx9W3vbgwQNMmzYNZ8+ehbFx/l2ptbU1GjdujG+//RZeXl4oXbo0tm/fjgsXLqBixYoFvs7z58/x/ffff9CYqGBBQUEAsqvv1NRUzJ8/X3lfYuwr7F/ghwHLNqG0uycAoFbH3ihRphxs7B3x8sEdHP1pHmIfh2PAsk0AgKTYaFiVtM/1GjlfJ8VFAwBeP3uMyGuXYGxqhgHLfkNKQhwOLJyK1ITX+HTuz8rHpSclYmHH6pBlZUIqNcLS5T+jXbt2avteEGkSAyXplakTx+FV+D8YveFQgfenJydh04R+cHD3QNtRXytv92jcCp0mzsH+7/zwx8wxMDIxQ+sRk/H46kVI/3+MXVLsK+z9djLqdPVBzY69kJGSjBOrF2HrlGH4/JfdEBQK7JgxGm1Hfw378hUAALfv3kFybCxWrFih/jdvYIYPH15goHz69CkmTJiAEydO5JsdBICDBw/ir7/+wtWrVz/odW7duoUePXpg9uzZaN++PQBALpejX79+mDt3Ljw8PN752M2bN2PYsGEoW7YsjIyMUKdOHfj6+iI0NLTA7WP5s6JWpVwromqrztg9axxGrDuA0u6eaNB7kPJ+x0pVYFOqNNaN/gRxTx/Bztntg55XEARAIkHfBathbm0DAJBNzsC2r4ehx/TFMDG3AACYFrPCuO1/IzMtBeGXz2LWtCmoWbkSWrZsqfL3SqRpDJSkN8aOHYuTRwIxYs1+FC/tlO/+jJRkbBzrAzNLKwxY9luumhoAmg/4As36j0ZS7CtYWBdH/IunOPbzfNiWcwUAXNi1AeZWNug08W296TP/F3zfqSae3gyFvVslPL9zDS/v3cTBRdMAAML/L/NmZGSEnTt3onnz5mp694anoOMigewFNdHR0ahTp47yNrlcjqCgIKxYsQJffPEFwsPDUaJEiVyP6927N5o3b47Tp08rb7tz5w7atGmDkSNHwt//7QKMpKQkhISE4OrVq8pFOgqFAoIgwNjYGMePH0fr1q1RoUIFnDlzBikpKUhMTESZMmXg4+MDd3f3AsdepUoVLuIqoqlTp2Lz5s2Qy+WQSqVQKBQoU6YMoqOjYWRsgo7jZuLZ7WsI3rYGvfyX5Xu8c/Xsn5ucQGldygHPbofl2ib5dQwAKGtw61IOsLF3VIZJAHBw84AgCHgT/QKlXLL/wJRKpSjlkv1v7+RZHbavH2PhwoUMlKQXGChJ5wmCgHHjxmHfvn04fOIvHM2wy7dNenISNnzZB8amphgUsBkmZvlnrgBAIpHAxj77mLrrx/aiuGNZlK2cvUI4Kz0NEmnuw45zvhYEBcyKWWPCrqBc96ec2oazp//G7t274ebmhmLFihX5/dL7tWnTBjdv3sx129ChQ1G5cmVMnToVpUqVwqhRo3LdX716dQQEBKBbt27K227fvo3WrVtj8ODBWLBgQa7tbWxs8r3GqlWr8Ndffyn/rf+tWLFiKFasGOLj43Hs2DEsXry4wLGbmJjAwcGhwPvow1haWkIuzz7HY7t27TBv3jysW7cOa9euRWrCawDZf+jJsjIKfPyLe7cAANalSgMAXGrUw9/rA5D8OkZZdT+8eAZmVtZw+H9t7lqzIW6d/BMZqckws7QCkH34i0QqRXGH/H/c5pBAQEZGweMg0jUMlKTzvvzyS2zbtg0HDhxAaVsbZN2NRrpcgLmVDUzMLbLD5JjPkJWeBp/5q5CRkoSMlCQAQDHbUpAaGQEAgn5bAY8mrSGRSnHrr8M4s/En+C5ap7y/crN2OL91NU6tWaqsvI+tWIASZZzh5FkdUqk013GbFkYSxN8uDXNzc1SrVk3z3xgDZW1tne/7XaxYMdjZ2SlvL2ghjouLizII3rp1C61bt0aHDh0wefJkREVFAcieaba3t4dUKs33Gg4ODvn+rY8dOwZBEODp6YmHDx9iypQpqFy5MoYOHarS90xvtW7dGjExMfDz80ODBg0wffp0ODk5oUuXLug/YADWr5iPR6HnMXTlLsQ9fYRrR/eictO2sCxhi5cP7uDwsplwq9MYZTyqAgAqNWoFB3dP7PIfg04TZyMpNhrHVy1E48+Gwfj/C/lqdvoEf61bht1zxqPt6KlIjY9D4I9zUK9HP2XdfXrDjyhbpRbsyrlClpmJiOCTOLx1C1f8k95goCSdl7NDzlsbfTrnJ9Tt7osX/9zA01vZx6wt7dEg1zZfHwqFrZMLgOxTf/y9PgCyrEyUqVQVAwN+h2fTtyuEKzRoDp/vfkXQbz8j6LefYWJuCZca9TB0xU7lh8a/ORUzRrwq3yhpzO7duxETE4MtW7Zgy5YtytvLly+Px48ff/DzvHnzBtOnT8ezZ89QsmRJ9O7dGwsWLIBJnsMtSHV69+6N3r17K7+Ojo7GqVOn8PLlS1y+fBmlK1XB0JW7UKlRSyREPUf4pTM4v+1XZKWlonhpJ1Rr3RWthk9WPl5qZITBP27F/oVT8MuQzjA1t0Sdbj5o+8U05TZmllYYtmo3/lw8HSsHtINlcVtUb9cD7cdMV26TmZaKAwu/xpvolzAxM4dzBQ9s2bIFPj4+mvnGEKkZr+VNeud8VCrOvUwV7dKLQPaVcpqVsURTR8v/3JaINIf7ByL14HkoSe9UtTUT9cMCyL5eb1Vbs//cjog0i/sHIvVgoCS9U8LMCG7WJsh/QT3NkABwtzZBCTMjkUZARO/C/QORejBQkl6qZ28h2iyEAKCuff5jKolIO3D/QKR6DJSkl9xtTOAqwiyEBICbtQncbbjogkhbcf9ApHoMlKSXJBIJOrtYwVjDP+HGUqCTixUkErEKNSL6L9w/EKkeAyXpLRtTI7QvZ6XR12xfzgo2pjw2ikjbcf9ApFoMlKTXqtuZo4mjZo5Xaupogep2BV+Bh4i0D/cPRKrDQEl6r7mjJZqq+UOjqaMFmvGcckQ6h/sHItXgic3JYNyMS8fxZ8mQKaCSFZ4SZB8T1b6cFWceiHQc9w9ERcNASQblf+3dvUoDQRSG4ROTRUKMYGFjIZLW0rvwgr0LS9sgFmkslJgQYn7WKiAIovm65Hnq3XLOvAs7zPRzUw8vs3r+WFWn9ts4du/dDJu6v/ZPFBwK8wH2Jyg5Om3b1ni6qsfXRY3/sXHsnhsNm7q77NfovHFaEw6M+QD7EZQctfflpp7eljWZr2oyX9di83M59Luduhr06mrQ1O3FqRsu4EiYD/B3ghK+ma22tVhva9NWdTtV/d5JnTXOrgHmA/xGUAIAEPFpBQBARFACABARlAAARAQlAAARQQkAQERQAgAQEZQAAEQEJQAAEUEJAEBEUAIAEBGUAABEBCUAABFBCQBARFACABARlAAARAQlAAARQQkAQERQAgAQEZQAAEQEJQAAEUEJAEBEUAIAEBGUAABEBCUAABFBCQBARFACABARlAAARAQlAAARQQkAQERQAgAQEZQAAEQEJQAAkS/FtLXnFpDM9gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ego_graph(G,46726)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_list = [\"f_degree_in\",'f_degree_out', \"f_ego_edge_density\", \"f_egored_edge_density\",\n",
    "           \"f_amount_in\", \"f_egored_amount_in\",'f_amount_out','f_egored_amount_out']\n",
    "ego = EgonetFeatures()\n",
    "ego.fit(G,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xf = ego.transform(egonet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Wrong data type for G\n"
     ]
    }
   ],
   "source": [
    "features,ids = graph_to_rwalk_features(G, rwalk_len=15, rwalk_reps=100, \n",
    "                                       prob_edge=None, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f_rwalk_start_amount</th>\n",
       "      <th>f_rwalk_transfer_out</th>\n",
       "      <th>f_rwalk_out_back</th>\n",
       "      <th>f_rwalk_out_back_max</th>\n",
       "      <th>f_rwalk_end_amount</th>\n",
       "      <th>f_rwalk_transfer_in</th>\n",
       "      <th>f_rwalk_in_back</th>\n",
       "      <th>f_rwalk_in_back_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>18163.138367</td>\n",
       "      <td>9537.451895</td>\n",
       "      <td>358.616366</td>\n",
       "      <td>10906.238248</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>2879.218618</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33494.053579</td>\n",
       "      <td>3357.203201</td>\n",
       "      <td>561.261305</td>\n",
       "      <td>8280.979091</td>\n",
       "      <td>28200.067993</td>\n",
       "      <td>24490.817549</td>\n",
       "      <td>23.160771</td>\n",
       "      <td>1898.255626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>144438.368843</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>499.574386</td>\n",
       "      <td>391.756950</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>3712.777470</td>\n",
       "      <td>1917.609934</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>8598.857905</td>\n",
       "      <td>1868.665319</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>55166.325198</td>\n",
       "      <td>24657.153606</td>\n",
       "      <td>24039.990000</td>\n",
       "      <td>121899.000000</td>\n",
       "      <td>43328.167295</td>\n",
       "      <td>23059.481732</td>\n",
       "      <td>10647.970000</td>\n",
       "      <td>121899.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>14905.671502</td>\n",
       "      <td>1634.052774</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       f_rwalk_start_amount  f_rwalk_transfer_out  f_rwalk_out_back  \\\n",
       "0              32814.753080          12723.298571          0.000000   \n",
       "1                  0.000000              0.000000          0.000000   \n",
       "2                  0.000000              0.000000          0.000000   \n",
       "3              33494.053579           3357.203201        561.261305   \n",
       "4                  0.000000              0.000000          0.000000   \n",
       "...                     ...                   ...               ...   \n",
       "49995            499.574386            391.756950          0.000000   \n",
       "49996           3712.777470           1917.609934          0.000000   \n",
       "49997           8598.857905           1868.665319          0.000000   \n",
       "49998          55166.325198          24657.153606      24039.990000   \n",
       "49999          14905.671502           1634.052774          0.000000   \n",
       "\n",
       "       f_rwalk_out_back_max  f_rwalk_end_amount  f_rwalk_transfer_in  \\\n",
       "0                  0.000000        18163.138367          9537.451895   \n",
       "1                  0.000000        11912.188536          2879.218618   \n",
       "2                  0.000000         6287.124229          3875.109018   \n",
       "3               8280.979091        28200.067993         24490.817549   \n",
       "4                  0.000000       144438.368843        143333.478507   \n",
       "...                     ...                 ...                  ...   \n",
       "49995              0.000000            0.000000             0.000000   \n",
       "49996              0.000000            0.000000             0.000000   \n",
       "49997              0.000000            0.000000             0.000000   \n",
       "49998         121899.000000        43328.167295         23059.481732   \n",
       "49999              0.000000            0.000000             0.000000   \n",
       "\n",
       "       f_rwalk_in_back  f_rwalk_in_back_max  \n",
       "0           358.616366         10906.238248  \n",
       "1             0.000000             0.000000  \n",
       "2             0.000000             0.000000  \n",
       "3            23.160771          1898.255626  \n",
       "4             0.000000             0.000000  \n",
       "...                ...                  ...  \n",
       "49995         0.000000             0.000000  \n",
       "49996         0.000000             0.000000  \n",
       "49997         0.000000             0.000000  \n",
       "49998     10647.970000        121899.000000  \n",
       "49999         0.000000             0.000000  \n",
       "\n",
       "[50000 rows x 8 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ego_graph(G,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graphomaly.preprocessing.spectrum import SpectrumFeatures\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SpectrumFeatures(n_values=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SpectrumFeatures</label><div class=\"sk-toggleable__content\"><pre>SpectrumFeatures(n_values=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SpectrumFeatures(n_values=3)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = [\"f_spectrum_Laplacian_amount\"]\n",
    "sp = SpectrumFeatures(n_values=3)\n",
    "sp.fit(G, feature_list=my_list)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Graphomaly",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}