Analysis_of_the_network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Signed Directed Bitcoin OTC web of trust network\n",
    "\n",
    "### Dataset information:\n",
    "This is who-trusts-whom network of people who trade using Bitcoin on a platform called Bitcoin OTC. Since Bitcoin users are anonymous, there is a need to maintain a record of users' reputation to prevent transactions with fraudulent and risky users. Members of Bitcoin OTC rate other members in a scale of -10 (total distrust) to +10 (total trust) in steps of 1. This is the first explicit weighted signed directed network available for research.\n",
    "\n",
    "<img src=\"data/image.png\" alt=\"Drawing\" style=\"width: 350px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each line has one rating, sorted by time with the following format: \n",
    "    `SOURCE, TARGET, RATING, TIME`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some draws of the network from GEPHI:\n",
    "<img src=\"output/gephi_draws/screenshot_121233.png\" alt=\"Drawing\" style=\"width: 650px;\"/>\n",
    "<img src=\"output/gephi_draws/screenshot_121958.png\" alt=\"Drawing\" style=\"width: 650px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data and build network using NetworkX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import collections\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import networkx as nx\n",
    "import powerlaw\n",
    "\n",
    "from data_tools import plot_functions as func\n",
    "\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1.289242e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.289242e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1.289243e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1.289245e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>1.289254e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source  Target  Rating          Time\n",
       "0       6       2       4  1.289242e+09\n",
       "1       6       5       2  1.289242e+09\n",
       "2       1      15       1  1.289243e+09\n",
       "3       4       3       7  1.289245e+09\n",
       "4      13      16       8  1.289254e+09"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read file\n",
    "register_matplotlib_converters()\n",
    "data = pd.read_csv('data/soc-sign-bitcoinotc.csv', sep=',', decimal='.', parse_dates=True, infer_datetime_format=True)\n",
    "# time is in epochtime format, can be transformed to date time\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the graph\n",
    "graph = nx.from_pandas_edgelist(data, source='Source', target='Target', edge_attr='Rating', create_using=nx.DiGraph)\n",
    "n_nodes = graph.number_of_nodes()\n",
    "n_edges = graph.number_of_edges()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contents:\n",
    "* 1 - Number of nodes and edges\n",
    "\n",
    "\n",
    "* 2 - Edge weights (ratings) frequency and distribution \n",
    "\n",
    "\n",
    "* 3 - Degree centrality:\n",
    "     * 3.1 Degree centrality (considering graph as undirected)\n",
    "     * 3.2 In-Degree centrality\n",
    "     * 3.3 Out-Degree centrality\n",
    "     \n",
    "     \n",
    "* 4 - Weighted degree centrality:\n",
    "     * 4.1 Weighted degree centrality (considering graph as undirected)\n",
    "     * 4.2 Weighted In-Degree centrality\n",
    "     * 4.3 Weighted Out-Degree centrality\n",
    "     \n",
    "     \n",
    "* 5 - Average node rating\n",
    "     * 5.1 In & out average rating distributions\n",
    "     * 5.2 Absolute difference between in & out average ratings distribution\n",
    "     * 5.3 In & out average ratings 2d plane projection\n",
    "     \n",
    "     \n",
    "* 6 - Stats summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Table to store stats for each node.\n",
    "stats = pd.DataFrame(graph.nodes()) \n",
    "stats.columns = ['Node']\n",
    "#stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Number of nodes and edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 5881\n",
      "Number of edges: 35592\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of nodes:\", n_nodes)\n",
    "print(\"Number of edges:\", n_edges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Edge weights (ratings) frequency and distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>2413</td>\n",
       "      <td>0.067796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-9</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-8</td>\n",
       "      <td>31</td>\n",
       "      <td>0.000871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-7</td>\n",
       "      <td>14</td>\n",
       "      <td>0.000393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-6</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>-5</td>\n",
       "      <td>179</td>\n",
       "      <td>0.005029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>-4</td>\n",
       "      <td>27</td>\n",
       "      <td>0.000759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>-3</td>\n",
       "      <td>91</td>\n",
       "      <td>0.002557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>-2</td>\n",
       "      <td>182</td>\n",
       "      <td>0.005114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>-1</td>\n",
       "      <td>601</td>\n",
       "      <td>0.016886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>20048</td>\n",
       "      <td>0.563273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>5562</td>\n",
       "      <td>0.156271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2561</td>\n",
       "      <td>0.071954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>967</td>\n",
       "      <td>0.027169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>1268</td>\n",
       "      <td>0.035626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>265</td>\n",
       "      <td>0.007445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>208</td>\n",
       "      <td>0.005844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>277</td>\n",
       "      <td>0.007783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>108</td>\n",
       "      <td>0.003034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>765</td>\n",
       "      <td>0.021494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rating  Frequency  Probability\n",
       "0      -10       2413     0.067796\n",
       "1       -9         20     0.000562\n",
       "2       -8         31     0.000871\n",
       "3       -7         14     0.000393\n",
       "4       -6          5     0.000140\n",
       "5       -5        179     0.005029\n",
       "6       -4         27     0.000759\n",
       "7       -3         91     0.002557\n",
       "8       -2        182     0.005114\n",
       "9       -1        601     0.016886\n",
       "10       1      20048     0.563273\n",
       "11       2       5562     0.156271\n",
       "12       3       2561     0.071954\n",
       "13       4        967     0.027169\n",
       "14       5       1268     0.035626\n",
       "15       6        265     0.007445\n",
       "16       7        208     0.005844\n",
       "17       8        277     0.007783\n",
       "18       9        108     0.003034\n",
       "19      10        765     0.021494"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = data.groupby(['Rating']).size().reset_index(name='Frequency')\n",
    "counts['Probability'] = counts['Frequency'] / counts['Frequency'].sum()\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches(10.0, 7.0)\n",
    "\n",
    "bins = np.linspace(-10,10,21)\n",
    "\n",
    "# Ratings frequency (count for each rating).\n",
    "ax = plt.subplot(2, 1, 1)\n",
    "\n",
    "plt.bar(counts['Rating'], counts['Frequency'])\n",
    "\n",
    "plt.title('Ratings frequency count')\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "ax.xaxis.set_ticks(bins)\n",
    "\n",
    "plt.grid(True, axis='y')\n",
    "\n",
    "# Ratings probability (distribution).\n",
    "ax = plt.subplot(2, 1, 2)\n",
    "\n",
    "plt.bar(counts['Rating'], counts['Probability'])\n",
    "\n",
    "plt.title('Ratings distribution')\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Probability')\n",
    "\n",
    "ax.xaxis.set_ticks(bins)\n",
    "\n",
    "plt.grid(True, axis='y')\n",
    "\n",
    "#fig.savefig(\"output/ratings_frequency.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Degree centrality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1) Degree centrality (considering graph as undirected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert dictionary into list of tuples\n",
    "degree_list = [v for k, v in nx.degree_centrality(graph).items()] \n",
    "in_degree_list = [v for k, v in nx.in_degree_centrality(graph).items()] \n",
    "out_degree_list = [v for k, v in nx.out_degree_centrality(graph).items()] \n",
    "\n",
    "# Store node degree centrality (considering graph as unirected)\n",
    "stats['Degree'] = degree_list\n",
    "\n",
    "# Store node in-degree centrality (considering graph as directed)\n",
    "stats['InDegree'] = in_degree_list\n",
    "\n",
    "# Store node out-degree centrality (considering graph as directed)\n",
    "stats['OutDegree'] = out_degree_list\n",
    "\n",
    "# Transform normalized (%) to absolute value.\n",
    "stats['Degree'] = round(stats['Degree'] * (n_nodes-1))\n",
    "stats['InDegree'] = round(stats['InDegree']  * (n_nodes-1))                \n",
    "stats['OutDegree'] = round(stats['OutDegree'] * (n_nodes-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 nodes with highest degree Centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Node</th>\n",
       "      <th>Degree</th>\n",
       "      <th>InDegree</th>\n",
       "      <th>OutDegree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>763.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2571</td>\n",
       "      <td>2642</td>\n",
       "      <td>818.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>406.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1785</td>\n",
       "      <td>1810</td>\n",
       "      <td>715.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2071</td>\n",
       "      <td>2125</td>\n",
       "      <td>577.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>397.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980</td>\n",
       "      <td>2028</td>\n",
       "      <td>572.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>945</td>\n",
       "      <td>905</td>\n",
       "      <td>528.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4071</td>\n",
       "      <td>4172</td>\n",
       "      <td>486.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>448.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>441.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4089</td>\n",
       "      <td>4197</td>\n",
       "      <td>405.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Node  Degree  InDegree  OutDegree\n",
       "23      35  1298.0     535.0      763.0\n",
       "2571  2642   818.0     412.0      406.0\n",
       "1785  1810   715.0     311.0      404.0\n",
       "2071  2125   577.0     180.0      397.0\n",
       "1980  2028   572.0     279.0      293.0\n",
       "945    905   528.0     264.0      264.0\n",
       "4071  4172   486.0     222.0      264.0\n",
       "10       7   448.0     216.0      232.0\n",
       "3        1   441.0     226.0      215.0\n",
       "4089  4197   405.0     203.0      202.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.sort_values(by='Degree', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Degree distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\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>Degree</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>829</td>\n",
       "      <td>0.140962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1622</td>\n",
       "      <td>0.275803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>380</td>\n",
       "      <td>0.064615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>621</td>\n",
       "      <td>0.105594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>259</td>\n",
       "      <td>0.044040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>324</td>\n",
       "      <td>0.055093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>153</td>\n",
       "      <td>0.026016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>185</td>\n",
       "      <td>0.031457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>112</td>\n",
       "      <td>0.019044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>139</td>\n",
       "      <td>0.023635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Degree  Frequency  Probability\n",
       "0     1.0        829     0.140962\n",
       "1     2.0       1622     0.275803\n",
       "2     3.0        380     0.064615\n",
       "3     4.0        621     0.105594\n",
       "4     5.0        259     0.044040\n",
       "5     6.0        324     0.055093\n",
       "6     7.0        153     0.026016\n",
       "7     8.0        185     0.031457\n",
       "8     9.0        112     0.019044\n",
       "9    10.0        139     0.023635"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = stats.groupby(['Degree']).size().reset_index(name='Frequency')\n",
    "counts['Probability'] = counts['Frequency'] / counts['Frequency'].sum()\n",
    "counts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating best minimal value for power law fit\n",
      "c:\\users\\hp\\.virtualenvs\\network_science-3ktgw-v0\\lib\\site-packages\\powerlaw.py:700: RuntimeWarning: invalid value encountered in true_divide\n",
      "  (Theoretical_CDF * (1 - Theoretical_CDF))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha=  2.000700901043796\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches(14.0, 14.0)\n",
    "\n",
    "# Linear scale.\n",
    "ax = plt.subplot(2, 1, 1)\n",
    "\n",
    "plt.scatter(counts['Degree'], counts['Probability'])\n",
    "\n",
    "plt.title('Degree distribution (linear scale)')\n",
    "plt.xlabel('Degree')\n",
    "plt.ylabel('Probability')\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "# Log scale.\n",
    "ax = plt.subplot(2, 1, 2)\n",
    "\n",
    "plt.scatter(counts['Degree'], counts['Probability'])\n",
    "\n",
    "plt.title('Degree distribution (log scale)')\n",
    "plt.xlabel('log(Degree)')\n",
    "plt.ylabel('log(Probability)')\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.set_ylim(ymin=0.0001)\n",
    "\n",
    "# This is the only way I found to make it work.\n",
    "# Matplotlib log scale only works for positive values.\n",
    "# Can't make symlog scale to work either.\n",
    "ax.set_yscale('log')\n",
    "ticks = [1,1e-01, 1e-02, 1e-03, 1e-04]\n",
    "ax.set_yticks(ticks)\n",
    "ax.get_yaxis().set_major_formatter(mpl.ticker.ScalarFormatter())\n",
    "#ax.set_yticklabels([str(t) for t in ticks])\n",
    "plt.grid()\n",
    "#fit.plot_pdf( color= 'b', ax = ax)\n",
    "data = np.array(stats['Degree']) # data can be list or numpy array\n",
    "\n",
    "\n",
    "#part responsible for power law\n",
    "fit = powerlaw.Fit(np.array(stats['Degree'])+1, discrete=True)\n",
    "# OR WE CAN DEFINE XMIN PARAMETER TO ADJUST BETTER POWER LAW \n",
    "#The data value beyond which distributions should be fitted. If None an optimal one will be calculated.\n",
    "#fit = powerlaw.Fit(np.array(stats['Degree'])+1, xmin=1.7, discrete=True)\n",
    "fit.power_law.plot_pdf( color= 'b',linestyle='--',label='fit ccdf')\n",
    "#fit.plot_pdf( color= 'r')\n",
    "\n",
    "#fig.savefig(\"output/degree_distribution.pdf\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print('alpha= ',fit.power_law.alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2) In-Degree centrality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 nodes with highest in-degree centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Node</th>\n",
       "      <th>Degree</th>\n",
       "      <th>InDegree</th>\n",
       "      <th>OutDegree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>763.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2571</td>\n",
       "      <td>2642</td>\n",
       "      <td>818.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>406.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1785</td>\n",
       "      <td>1810</td>\n",
       "      <td>715.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980</td>\n",
       "      <td>2028</td>\n",
       "      <td>572.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>945</td>\n",
       "      <td>905</td>\n",
       "      <td>528.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>441.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4071</td>\n",
       "      <td>4172</td>\n",
       "      <td>486.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>448.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4089</td>\n",
       "      <td>4197</td>\n",
       "      <td>405.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>401.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Node  Degree  InDegree  OutDegree\n",
       "23      35  1298.0     535.0      763.0\n",
       "2571  2642   818.0     412.0      406.0\n",
       "1785  1810   715.0     311.0      404.0\n",
       "1980  2028   572.0     279.0      293.0\n",
       "945    905   528.0     264.0      264.0\n",
       "3        1   441.0     226.0      215.0\n",
       "4071  4172   486.0     222.0      264.0\n",
       "10       7   448.0     216.0      232.0\n",
       "4089  4197   405.0     203.0      202.0\n",
       "7       13   401.0     191.0      210.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.sort_values(by='InDegree', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### InDegree distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>InDegree</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>0.003911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2427</td>\n",
       "      <td>0.412685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1042</td>\n",
       "      <td>0.177181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>565</td>\n",
       "      <td>0.096072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>335</td>\n",
       "      <td>0.056963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>235</td>\n",
       "      <td>0.039959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>189</td>\n",
       "      <td>0.032137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>142</td>\n",
       "      <td>0.024146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>95</td>\n",
       "      <td>0.016154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>87</td>\n",
       "      <td>0.014793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   InDegree  Frequency  Probability\n",
       "0       0.0         23     0.003911\n",
       "1       1.0       2427     0.412685\n",
       "2       2.0       1042     0.177181\n",
       "3       3.0        565     0.096072\n",
       "4       4.0        335     0.056963\n",
       "5       5.0        235     0.039959\n",
       "6       6.0        189     0.032137\n",
       "7       7.0        142     0.024146\n",
       "8       8.0         95     0.016154\n",
       "9       9.0         87     0.014793"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = stats.groupby(['InDegree']).size().reset_index(name='Frequency')\n",
    "counts['Probability'] = counts['Frequency'] / counts['Frequency'].sum()\n",
    "counts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating best minimal value for power law fit\n",
      "c:\\users\\hp\\.virtualenvs\\network_science-3ktgw-v0\\lib\\site-packages\\powerlaw.py:700: RuntimeWarning: invalid value encountered in true_divide\n",
      "  (Theoretical_CDF * (1 - Theoretical_CDF))\n",
      "c:\\users\\hp\\.virtualenvs\\network_science-3ktgw-v0\\lib\\site-packages\\powerlaw.py:700: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  (Theoretical_CDF * (1 - Theoretical_CDF))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha=  2.0906882890864873\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches(14.0, 14.0)\n",
    "\n",
    "# Linear scale.\n",
    "ax = plt.subplot(2, 1, 1)\n",
    "\n",
    "plt.scatter(counts['InDegree'], counts['Probability'])\n",
    "\n",
    "plt.title('InDegree distribution (linear scale)')\n",
    "plt.xlabel('InDegree')\n",
    "plt.ylabel('Probability')\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "# Log scale.\n",
    "ax = plt.subplot(2, 1, 2)\n",
    "\n",
    "plt.scatter(counts['InDegree'], counts['Probability'])\n",
    "\n",
    "plt.title('InDegree distribution (log scale)')\n",
    "plt.xlabel('log(InDegree)')\n",
    "plt.ylabel('log(Probability)')\n",
    "\n",
    "ax.set_xscale('log')\n",
    "\n",
    "# This is the only way I found to make it work.\n",
    "# Matplotlib log scale only works for positive values.\n",
    "# Can't make symlog scale to work either.\n",
    "ax.set_yscale('log')\n",
    "ticks = [1, 1e-01 ,1e-02, 1e-03, 1e-04]\n",
    "ax.set_yticks(ticks)\n",
    "xticks = [1, 1e+01, 1e+02, 1e+03, 1e+04]\n",
    "ax.set_xticks(xticks)\n",
    "ax.get_yaxis().set_major_formatter(mpl.ticker.ScalarFormatter())\n",
    "#ax.set_yticklabels([str(t) for t in ticks])\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "fit = powerlaw.Fit(np.array(stats['InDegree'])+1, discrete=True)\n",
    "fit.power_law.plot_pdf( color= 'b',linestyle='--',label='fit ccdf')\n",
    "#fit.plot_pdf( color= 'r')\n",
    "\n",
    "#fig.savefig(\"output/in_degree_distribution.pdf\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print('alpha= ',fit.power_law.alpha)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3) Out-Degree centrality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 nodes with highest out-degree centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Node</th>\n",
       "      <th>Degree</th>\n",
       "      <th>InDegree</th>\n",
       "      <th>OutDegree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>763.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2571</td>\n",
       "      <td>2642</td>\n",
       "      <td>818.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>406.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1785</td>\n",
       "      <td>1810</td>\n",
       "      <td>715.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2071</td>\n",
       "      <td>2125</td>\n",
       "      <td>577.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>397.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980</td>\n",
       "      <td>2028</td>\n",
       "      <td>572.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>945</td>\n",
       "      <td>905</td>\n",
       "      <td>528.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4071</td>\n",
       "      <td>4172</td>\n",
       "      <td>486.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>448.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>441.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3032</td>\n",
       "      <td>3129</td>\n",
       "      <td>229.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>212.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Node  Degree  InDegree  OutDegree\n",
       "23      35  1298.0     535.0      763.0\n",
       "2571  2642   818.0     412.0      406.0\n",
       "1785  1810   715.0     311.0      404.0\n",
       "2071  2125   577.0     180.0      397.0\n",
       "1980  2028   572.0     279.0      293.0\n",
       "945    905   528.0     264.0      264.0\n",
       "4071  4172   486.0     222.0      264.0\n",
       "10       7   448.0     216.0      232.0\n",
       "3        1   441.0     226.0      215.0\n",
       "3032  3129   229.0      17.0      212.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.sort_values(by='OutDegree', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OutDegree Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OutDegree</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1067</td>\n",
       "      <td>0.181432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1793</td>\n",
       "      <td>0.304880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>821</td>\n",
       "      <td>0.139602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>479</td>\n",
       "      <td>0.081449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>311</td>\n",
       "      <td>0.052882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>235</td>\n",
       "      <td>0.039959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>147</td>\n",
       "      <td>0.024996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>116</td>\n",
       "      <td>0.019725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>120</td>\n",
       "      <td>0.020405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>77</td>\n",
       "      <td>0.013093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OutDegree  Frequency  Probability\n",
       "0        0.0       1067     0.181432\n",
       "1        1.0       1793     0.304880\n",
       "2        2.0        821     0.139602\n",
       "3        3.0        479     0.081449\n",
       "4        4.0        311     0.052882\n",
       "5        5.0        235     0.039959\n",
       "6        6.0        147     0.024996\n",
       "7        7.0        116     0.019725\n",
       "8        8.0        120     0.020405\n",
       "9        9.0         77     0.013093"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = stats.groupby(['OutDegree']).size().reset_index(name='Frequency')\n",
    "counts['Probability'] = counts['Frequency'] / counts['Frequency'].sum()\n",
    "counts.head(10)\n",
    "\n",
    "counts2 = stats.groupby(['InDegree']).size().reset_index(name='Frequency')\n",
    "counts2['Probability'] = counts2['Frequency'] / counts2['Frequency'].sum()\n",
    "counts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating best minimal value for power law fit\n",
      "c:\\users\\hp\\.virtualenvs\\network_science-3ktgw-v0\\lib\\site-packages\\powerlaw.py:700: RuntimeWarning: invalid value encountered in true_divide\n",
      "  (Theoretical_CDF * (1 - Theoretical_CDF))\n",
      "c:\\users\\hp\\.virtualenvs\\network_science-3ktgw-v0\\lib\\site-packages\\powerlaw.py:700: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  (Theoretical_CDF * (1 - Theoretical_CDF))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha=  2.014455707503751\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches(14.0, 14.0)\n",
    "\n",
    "# Linear scale.\n",
    "ax = plt.subplot(2, 1, 1)\n",
    "\n",
    "plt.scatter(counts['OutDegree'], counts['Probability'])\n",
    "\n",
    "plt.title('OutDegree distribution (linear scale)')\n",
    "plt.xlabel('OutDegree')\n",
    "plt.ylabel('Probability')\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "# Log scale.\n",
    "ax = plt.subplot(2, 1, 2)\n",
    "\n",
    "plt.scatter(counts['OutDegree'], counts['Probability'])\n",
    "\n",
    "plt.title('OutDegree distribution (log scale)')\n",
    "plt.xlabel('log(OutDegree)')\n",
    "plt.ylabel('log(Probability)')\n",
    "\n",
    "ax.set_xscale('log')\n",
    "\n",
    "# This is the only way I found to make it work.\n",
    "# Matplotlib log scale only works for positive values.\n",
    "# Can't make symlog scale to work either.\n",
    "ax.set_yscale('log')\n",
    "ticks = [1, 1e-01 ,1e-02, 1e-03, 1e-04]\n",
    "ax.set_yticks(ticks)\n",
    "xticks = [1, 1e+01, 1e+02, 1e+03, 1e+04]\n",
    "ax.set_xticks(xticks)\n",
    "ax.get_yaxis().set_major_formatter(mpl.ticker.ScalarFormatter())\n",
    "#ax.set_yticklabels([str(t) for t in ticks])\n",
    "plt.grid()\n",
    "\n",
    "fit = powerlaw.Fit(np.array(stats['OutDegree'])+1, discrete=True)\n",
    "fit.power_law.plot_pdf( color= 'b',linestyle='--',label='fit ccdf')\n",
    "#fit.plot_pdf( color= 'r')\n",
    "\n",
    "\n",
    "#fig.savefig(\"output/out_degree_distribution.pdf\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print('alpha= ',fit.power_law.alpha)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) Weighted degree centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert dictionary into list of tuples\n",
    "weighted_degree_list = [v for k, v in graph.degree(weight='Rating')] \n",
    "weighted_in_degree_list = [v for k, v in graph.in_degree(weight='Rating')] \n",
    "weighted_out_degree_list = [v for k, v in graph.out_degree(weight='Rating')] \n",
    "\n",
    "# Store node degree centrality (considering graph as unirected)\n",
    "stats['WeightedDegree'] = weighted_degree_list\n",
    "\n",
    "# Store node in-degree centrality (considering graph as directed)\n",
    "stats['WeightedInDegree'] = weighted_in_degree_list\n",
    "\n",
    "# Store node out-degree centrality (considering graph as directed)\n",
    "stats['WeightedOutDegree'] = weighted_out_degree_list\n",
    "\n",
    "# stats2 - auxiliary dataframe \n",
    "stats2 = pd.DataFrame([x for x in stats['WeightedInDegree'] if x >= 0], columns = ['WeightedInDegreePos'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1) Weighted degree centrality (considering graph as undirected)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 nodes with highest weighted degree centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Node</th>\n",
       "      <th>Degree</th>\n",
       "      <th>InDegree</th>\n",
       "      <th>OutDegree</th>\n",
       "      <th>WeightedDegree</th>\n",
       "      <th>WeightedInDegree</th>\n",
       "      <th>WeightedOutDegree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>763.0</td>\n",
       "      <td>1890</td>\n",
       "      <td>1016</td>\n",
       "      <td>874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2571</td>\n",
       "      <td>2642</td>\n",
       "      <td>818.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>1805</td>\n",
       "      <td>1041</td>\n",
       "      <td>764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>441.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>1234</td>\n",
       "      <td>801</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>448.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>1125</td>\n",
       "      <td>614</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>967</td>\n",
       "      <td>1018</td>\n",
       "      <td>382.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>775</td>\n",
       "      <td>471</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4169</td>\n",
       "      <td>4291</td>\n",
       "      <td>330.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>697</td>\n",
       "      <td>360</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4089</td>\n",
       "      <td>4197</td>\n",
       "      <td>405.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>677</td>\n",
       "      <td>416</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>401.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>627</td>\n",
       "      <td>341</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980</td>\n",
       "      <td>2028</td>\n",
       "      <td>572.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>578</td>\n",
       "      <td>202</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1350</td>\n",
       "      <td>1396</td>\n",
       "      <td>280.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>518</td>\n",
       "      <td>237</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Node  Degree  InDegree  OutDegree  WeightedDegree  WeightedInDegree  \\\n",
       "23      35  1298.0     535.0      763.0            1890              1016   \n",
       "2571  2642   818.0     412.0      406.0            1805              1041   \n",
       "3        1   441.0     226.0      215.0            1234               801   \n",
       "10       7   448.0     216.0      232.0            1125               614   \n",
       "967   1018   382.0     179.0      203.0             775               471   \n",
       "4169  4291   330.0     158.0      172.0             697               360   \n",
       "4089  4197   405.0     203.0      202.0             677               416   \n",
       "7       13   401.0     191.0      210.0             627               341   \n",
       "1980  2028   572.0     279.0      293.0             578               202   \n",
       "1350  1396   280.0     118.0      162.0             518               237   \n",
       "\n",
       "      WeightedOutDegree  \n",
       "23                  874  \n",
       "2571                764  \n",
       "3                   433  \n",
       "10                  511  \n",
       "967                 304  \n",
       "4169                337  \n",
       "4089                261  \n",
       "7                   286  \n",
       "1980                376  \n",
       "1350                281  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.sort_values(by='WeightedDegree', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 nodes with lowest weighted degree centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Node</th>\n",
       "      <th>Degree</th>\n",
       "      <th>InDegree</th>\n",
       "      <th>OutDegree</th>\n",
       "      <th>WeightedDegree</th>\n",
       "      <th>WeightedInDegree</th>\n",
       "      <th>WeightedOutDegree</th>\n",
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       "  <tbody>\n",
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       "      <td>1785</td>\n",
       "      <td>1810</td>\n",
       "      <td>715.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>-706</td>\n",
       "      <td>230</td>\n",
       "      <td>-936</td>\n",
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       "    <tr>\n",
       "      <td>3647</td>\n",
       "      <td>3744</td>\n",
       "      <td>113.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-595</td>\n",
       "      <td>-675</td>\n",
       "      <td>80</td>\n",
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       "      <td>2071</td>\n",
       "      <td>2125</td>\n",
       "      <td>577.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>-560</td>\n",
       "      <td>439</td>\n",
       "      <td>-999</td>\n",
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       "    <tr>\n",
       "      <td>2206</td>\n",
       "      <td>2266</td>\n",
       "      <td>194.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>-508</td>\n",
       "      <td>26</td>\n",
       "      <td>-534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2692</td>\n",
       "      <td>2691</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>-373</td>\n",
       "      <td>4</td>\n",
       "      <td>-377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3689</td>\n",
       "      <td>3789</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>-350</td>\n",
       "      <td>-60</td>\n",
       "      <td>-290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2349</td>\n",
       "      <td>2351</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>-342</td>\n",
       "      <td>6</td>\n",
       "      <td>-348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3690</td>\n",
       "      <td>3790</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-340</td>\n",
       "      <td>-60</td>\n",
       "      <td>-280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3693</td>\n",
       "      <td>3791</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-340</td>\n",
       "      <td>-60</td>\n",
       "      <td>-280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3686</td>\n",
       "      <td>3786</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-340</td>\n",
       "      <td>-60</td>\n",
       "      <td>-280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Node  Degree  InDegree  OutDegree  WeightedDegree  WeightedInDegree  \\\n",
       "1785  1810   715.0     311.0      404.0            -706               230   \n",
       "3647  3744   113.0      81.0       32.0            -595              -675   \n",
       "2071  2125   577.0     180.0      397.0            -560               439   \n",
       "2206  2266   194.0      53.0      141.0            -508                26   \n",
       "2692  2691    45.0       4.0       41.0            -373                 4   \n",
       "3689  3789    35.0       6.0       29.0            -350               -60   \n",
       "2349  2351    48.0       4.0       44.0            -342                 6   \n",
       "3690  3790    34.0       6.0       28.0            -340               -60   \n",
       "3693  3791    34.0       6.0       28.0            -340               -60   \n",
       "3686  3786    34.0       6.0       28.0            -340               -60   \n",
       "\n",
       "      WeightedOutDegree  \n",
       "1785               -936  \n",
       "3647                 80  \n",
       "2071               -999  \n",
       "2206               -534  \n",
       "2692               -377  \n",
       "3689               -290  \n",
       "2349               -348  \n",
       "3690               -280  \n",
       "3693               -280  \n",
       "3686               -280  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.sort_values(by='WeightedDegree').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weighted degree distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>WeightedDegree</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-706</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-595</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-560</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-508</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-373</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>-350</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>-342</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>-340</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>-335</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>-330</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WeightedDegree  Frequency  Probability\n",
       "0            -706          1      0.00017\n",
       "1            -595          1      0.00017\n",
       "2            -560          1      0.00017\n",
       "3            -508          1      0.00017\n",
       "4            -373          1      0.00017\n",
       "5            -350          1      0.00017\n",
       "6            -342          1      0.00017\n",
       "7            -340          4      0.00068\n",
       "8            -335          1      0.00017\n",
       "9            -330          3      0.00051"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = stats.groupby(['WeightedDegree']).size().reset_index(name='Frequency')\n",
    "counts['Probability'] = counts['Frequency'] / counts['Frequency'].sum()\n",
    "counts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Values less than or equal to 0 in data. Throwing out 0 or negative values\n",
      "Calculating best minimal value for power law fit\n",
      "c:\\users\\hp\\.virtualenvs\\network_science-3ktgw-v0\\lib\\site-packages\\powerlaw.py:700: RuntimeWarning: invalid value encountered in true_divide\n",
      "  (Theoretical_CDF * (1 - Theoretical_CDF))\n",
      "c:\\users\\hp\\.virtualenvs\\network_science-3ktgw-v0\\lib\\site-packages\\powerlaw.py:700: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  (Theoretical_CDF * (1 - Theoretical_CDF))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha=  2.1391550275873072\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches(14.0, 14.0)\n",
    "\n",
    "# Linear scale.\n",
    "ax = plt.subplot(2, 1, 1)\n",
    "\n",
    "plt.scatter(counts['WeightedDegree'], counts['Probability'])\n",
    "\n",
    "plt.title('WeightedDegree distribution (linear scale)')\n",
    "plt.xlabel('WeightedDegree')\n",
    "plt.ylabel('Probability')\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "# Log scale.\n",
    "ax = plt.subplot(2, 1, 2)\n",
    "\n",
    "plt.scatter(counts['WeightedDegree'], counts['Probability'])\n",
    "\n",
    "plt.title('WeightedDegree distribution (log scale)')\n",
    "plt.xlabel('log(WeightedDegree)')\n",
    "plt.ylabel('log(Probability)')\n",
    "\n",
    "ax.set_xscale('log')\n",
    "\n",
    "# This is the only way I found to make it work.\n",
    "# Matplotlib log scale only works for positive values.\n",
    "# Can't make symlog scale to work either.\n",
    "ax.set_yscale('log')\n",
    "ticks = [1, 1e-01 ,1e-02, 1e-03, 1e-04]\n",
    "ax.set_yticks(ticks)\n",
    "xticks = [1, 1e+01, 1e+02, 1e+03, 1e+04]\n",
    "ax.set_xticks(xticks)\n",
    "ax.get_yaxis().set_major_formatter(mpl.ticker.ScalarFormatter())\n",
    "#ax.set_yticklabels([str(t) for t in ticks])\n",
    "plt.grid()\n",
    "\n",
    "fit = powerlaw.Fit(np.array(stats['WeightedDegree'])+1, discrete=True)\n",
    "fit.power_law.plot_pdf( color= 'b',linestyle='--',label='fit ccdf')\n",
    "#fit.plot_pdf( color= 'r')\n",
    "\n",
    "\n",
    "#fig.savefig(\"output/weighted_degree_distribution.pdf\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print('alpha= ',fit.power_law.alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2) Weighted In-Degree centrality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 nodes with highest weighted in-degree centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Node</th>\n",
       "      <th>Degree</th>\n",
       "      <th>InDegree</th>\n",
       "      <th>OutDegree</th>\n",
       "      <th>WeightedDegree</th>\n",
       "      <th>WeightedInDegree</th>\n",
       "      <th>WeightedOutDegree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2571</td>\n",
       "      <td>2642</td>\n",
       "      <td>818.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>1805</td>\n",
       "      <td>1041</td>\n",
       "      <td>764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>763.0</td>\n",
       "      <td>1890</td>\n",
       "      <td>1016</td>\n",
       "      <td>874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>441.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>1234</td>\n",
       "      <td>801</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>448.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>1125</td>\n",
       "      <td>614</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4071</td>\n",
       "      <td>4172</td>\n",
       "      <td>486.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>255</td>\n",
       "      <td>472</td>\n",
       "      <td>-217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>967</td>\n",
       "      <td>1018</td>\n",
       "      <td>382.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>775</td>\n",
       "      <td>471</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2071</td>\n",
       "      <td>2125</td>\n",
       "      <td>577.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>-560</td>\n",
       "      <td>439</td>\n",
       "      <td>-999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4089</td>\n",
       "      <td>4197</td>\n",
       "      <td>405.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>677</td>\n",
       "      <td>416</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4169</td>\n",
       "      <td>4291</td>\n",
       "      <td>330.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>697</td>\n",
       "      <td>360</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>401.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>627</td>\n",
       "      <td>341</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Node  Degree  InDegree  OutDegree  WeightedDegree  WeightedInDegree  \\\n",
       "2571  2642   818.0     412.0      406.0            1805              1041   \n",
       "23      35  1298.0     535.0      763.0            1890              1016   \n",
       "3        1   441.0     226.0      215.0            1234               801   \n",
       "10       7   448.0     216.0      232.0            1125               614   \n",
       "4071  4172   486.0     222.0      264.0             255               472   \n",
       "967   1018   382.0     179.0      203.0             775               471   \n",
       "2071  2125   577.0     180.0      397.0            -560               439   \n",
       "4089  4197   405.0     203.0      202.0             677               416   \n",
       "4169  4291   330.0     158.0      172.0             697               360   \n",
       "7       13   401.0     191.0      210.0             627               341   \n",
       "\n",
       "      WeightedOutDegree  \n",
       "2571                764  \n",
       "23                  874  \n",
       "3                   433  \n",
       "10                  511  \n",
       "4071               -217  \n",
       "967                 304  \n",
       "2071               -999  \n",
       "4089                261  \n",
       "4169                337  \n",
       "7                   286  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.sort_values(by='WeightedInDegree', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 nodes with lowest weighted in-degree centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Node</th>\n",
       "      <th>Degree</th>\n",
       "      <th>InDegree</th>\n",
       "      <th>OutDegree</th>\n",
       "      <th>WeightedDegree</th>\n",
       "      <th>WeightedInDegree</th>\n",
       "      <th>WeightedOutDegree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3647</td>\n",
       "      <td>3744</td>\n",
       "      <td>113.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-595</td>\n",
       "      <td>-675</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2430</td>\n",
       "      <td>2498</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-256</td>\n",
       "      <td>-256</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1337</td>\n",
       "      <td>1383</td>\n",
       "      <td>162.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>-46</td>\n",
       "      <td>-232</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4405</td>\n",
       "      <td>4531</td>\n",
       "      <td>47.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-238</td>\n",
       "      <td>-230</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1962</td>\n",
       "      <td>2017</td>\n",
       "      <td>54.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-214</td>\n",
       "      <td>-229</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4526</td>\n",
       "      <td>4654</td>\n",
       "      <td>46.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-233</td>\n",
       "      <td>-186</td>\n",
       "      <td>-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4600</td>\n",
       "      <td>4744</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-199</td>\n",
       "      <td>-179</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4553</td>\n",
       "      <td>4681</td>\n",
       "      <td>39.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-223</td>\n",
       "      <td>-176</td>\n",
       "      <td>-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4544</td>\n",
       "      <td>4672</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-256</td>\n",
       "      <td>-169</td>\n",
       "      <td>-87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4558</td>\n",
       "      <td>4686</td>\n",
       "      <td>38.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-242</td>\n",
       "      <td>-167</td>\n",
       "      <td>-75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Node  Degree  InDegree  OutDegree  WeightedDegree  WeightedInDegree  \\\n",
       "3647  3744   113.0      81.0       32.0            -595              -675   \n",
       "2430  2498    45.0      45.0        0.0            -256              -256   \n",
       "1337  1383   162.0      96.0       66.0             -46              -232   \n",
       "4405  4531    47.0      25.0       22.0            -238              -230   \n",
       "1962  2017    54.0      45.0        9.0            -214              -229   \n",
       "4526  4654    46.0      25.0       21.0            -233              -186   \n",
       "4600  4744    21.0      19.0        2.0            -199              -179   \n",
       "4553  4681    39.0      22.0       17.0            -223              -176   \n",
       "4544  4672    30.0      18.0       12.0            -256              -169   \n",
       "4558  4686    38.0      25.0       13.0            -242              -167   \n",
       "\n",
       "      WeightedOutDegree  \n",
       "3647                 80  \n",
       "2430                  0  \n",
       "1337                186  \n",
       "4405                 -8  \n",
       "1962                 15  \n",
       "4526                -47  \n",
       "4600                -20  \n",
       "4553                -47  \n",
       "4544                -87  \n",
       "4558                -75  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.sort_values(by='WeightedInDegree').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weighted In-Degree distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>WeightedInDegree</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-675</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-256</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-232</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-230</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-229</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>-186</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>-179</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>-176</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>-169</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>-167</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WeightedInDegree  Frequency  Probability\n",
       "0              -675          1      0.00017\n",
       "1              -256          1      0.00017\n",
       "2              -232          1      0.00017\n",
       "3              -230          1      0.00017\n",
       "4              -229          1      0.00017\n",
       "5              -186          1      0.00017\n",
       "6              -179          1      0.00017\n",
       "7              -176          1      0.00017\n",
       "8              -169          1      0.00017\n",
       "9              -167          1      0.00017"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = stats.groupby(['WeightedInDegree']).size().reset_index(name='Frequency')\n",
    "counts['Probability'] = counts['Frequency'] / counts['Frequency'].sum()\n",
    "counts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches(10.0, 7.0)\n",
    "\n",
    "# Linear scale.\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "\n",
    "plt.scatter(counts['WeightedInDegree'], counts['Probability'])\n",
    "\n",
    "plt.title('WeightedInDegree distribution (linear scale)')\n",
    "plt.xlabel('WeightedInDegree')\n",
    "plt.ylabel('Probability')\n",
    "ax.set_ylim(ymax=0.10)\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "#fig.savefig(\"output/weighted_in_degree_distribution_linear.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>WeightedInDegreeNeg</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>0.081081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
       "      <td>0.084767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0.020885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>0.027027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0.030713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>0.022113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>0.034398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>0.038084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>55</td>\n",
       "      <td>0.067568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>117</td>\n",
       "      <td>0.143735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WeightedInDegreeNeg  Frequency  Probability\n",
       "0                    1         66     0.081081\n",
       "1                    2         69     0.084767\n",
       "2                    3         17     0.020885\n",
       "3                    4         22     0.027027\n",
       "4                    5         25     0.030713\n",
       "5                    6         18     0.022113\n",
       "6                    7         28     0.034398\n",
       "7                    8         31     0.038084\n",
       "8                    9         55     0.067568\n",
       "9                   10        117     0.143735"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stats_pos, stats_neg - auxiliary dataframe \n",
    "stats_pos = pd.DataFrame([x for x in stats['WeightedInDegree'] if x >= 0], columns = ['WeightedInDegreePos'])\n",
    "counts = stats_pos.groupby(['WeightedInDegreePos']).size().reset_index(name='Frequency')\n",
    "counts['Probability'] = counts['Frequency'] / counts['Frequency'].sum()\n",
    "counts.head(10)\n",
    "\n",
    "stats_neg = pd.DataFrame([abs(x) for x in stats['WeightedInDegree'] if x < 0], columns = ['WeightedInDegreeNeg'])\n",
    "counts2 = stats_neg.groupby(['WeightedInDegreeNeg']).size().reset_index(name='Frequency')\n",
    "counts2['Probability'] = counts2['Frequency'] / counts2['Frequency'].sum()\n",
    "counts2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Values less than or equal to 0 in data. Throwing out 0 or negative values\n",
      "Calculating best minimal value for power law fit\n",
      "c:\\users\\hp\\.virtualenvs\\network_science-3ktgw-v0\\lib\\site-packages\\powerlaw.py:700: RuntimeWarning: invalid value encountered in true_divide\n",
      "  (Theoretical_CDF * (1 - Theoretical_CDF))\n",
      "c:\\users\\hp\\.virtualenvs\\network_science-3ktgw-v0\\lib\\site-packages\\powerlaw.py:700: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  (Theoretical_CDF * (1 - Theoretical_CDF))\n",
      "Calculating best minimal value for power law fit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive values: alpha=  2.0531865037663066\n",
      "negative values: alpha=  2.294420507313797\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches(10.0, 7.0)\n",
    "\n",
    "# Log scale.\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "\n",
    "plt.scatter(counts['WeightedInDegreePos'], counts['Probability'])\n",
    "plt.scatter(counts2['WeightedInDegreeNeg'], counts2['Probability'], marker='o', c='r')\n",
    "\n",
    "plt.title('WeightedInDegree distribution (log scale)')\n",
    "plt.xlabel('log(WeightedInDegree)')\n",
    "plt.ylabel('log(Probability)')\n",
    "\n",
    "ax.set_xscale('log')\n",
    "\n",
    "# This is the only way I found to make it work.\n",
    "# Matplotlib log scale only works for positive values.\n",
    "# Can't make symlog scale to work either.\n",
    "ax.set_yscale('log')\n",
    "ticks = [1,1e-01, 1e-02, 1e-03, 1e-04]\n",
    "ax.set_yticks(ticks)\n",
    "xticks = [1, 1e+01, 1e+02, 1e+03, 1e+04]\n",
    "ax.set_xticks(xticks)\n",
    "ax.get_yaxis().set_major_formatter(mpl.ticker.ScalarFormatter())\n",
    "#ax.set_yticklabels([str(t) for t in ticks])\n",
    "plt.grid()\n",
    "\n",
    "fit = powerlaw.Fit(np.array(stats_pos['WeightedInDegreePos']), discrete=True)\n",
    "fit.power_law.plot_pdf( color= 'b',linestyle='--',label='fit ccdf')\n",
    "#fit.plot_pdf( color= 'r')\n",
    "print('positive values: alpha= ',fit.power_law.alpha)\n",
    "\n",
    "fit = powerlaw.Fit(np.array(stats_neg['WeightedInDegreeNeg']), discrete=True)\n",
    "fit.power_law.plot_pdf( color= 'r',linestyle='--',label='fit ccdf')\n",
    "print('negative values: alpha= ',fit.power_law.alpha)\n",
    "\n",
    "#fig.savefig(\"output/weighted_in_degree_distribution.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3) Weighted Out-Degree centrality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 nodes with highest weighted out-degree centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Node</th>\n",
       "      <th>Degree</th>\n",
       "      <th>InDegree</th>\n",
       "      <th>OutDegree</th>\n",
       "      <th>WeightedDegree</th>\n",
       "      <th>WeightedInDegree</th>\n",
       "      <th>WeightedOutDegree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>763.0</td>\n",
       "      <td>1890</td>\n",
       "      <td>1016</td>\n",
       "      <td>874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2571</td>\n",
       "      <td>2642</td>\n",
       "      <td>818.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>1805</td>\n",
       "      <td>1041</td>\n",
       "      <td>764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>448.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>1125</td>\n",
       "      <td>614</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>441.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>1234</td>\n",
       "      <td>801</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>212</td>\n",
       "      <td>257</td>\n",
       "      <td>111.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>477</td>\n",
       "      <td>57</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980</td>\n",
       "      <td>2028</td>\n",
       "      <td>572.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>578</td>\n",
       "      <td>202</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4169</td>\n",
       "      <td>4291</td>\n",
       "      <td>330.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>697</td>\n",
       "      <td>360</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>967</td>\n",
       "      <td>1018</td>\n",
       "      <td>382.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>775</td>\n",
       "      <td>471</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>401.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>627</td>\n",
       "      <td>341</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1350</td>\n",
       "      <td>1396</td>\n",
       "      <td>280.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>518</td>\n",
       "      <td>237</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Node  Degree  InDegree  OutDegree  WeightedDegree  WeightedInDegree  \\\n",
       "23      35  1298.0     535.0      763.0            1890              1016   \n",
       "2571  2642   818.0     412.0      406.0            1805              1041   \n",
       "10       7   448.0     216.0      232.0            1125               614   \n",
       "3        1   441.0     226.0      215.0            1234               801   \n",
       "212    257   111.0      18.0       93.0             477                57   \n",
       "1980  2028   572.0     279.0      293.0             578               202   \n",
       "4169  4291   330.0     158.0      172.0             697               360   \n",
       "967   1018   382.0     179.0      203.0             775               471   \n",
       "7       13   401.0     191.0      210.0             627               341   \n",
       "1350  1396   280.0     118.0      162.0             518               237   \n",
       "\n",
       "      WeightedOutDegree  \n",
       "23                  874  \n",
       "2571                764  \n",
       "10                  511  \n",
       "3                   433  \n",
       "212                 420  \n",
       "1980                376  \n",
       "4169                337  \n",
       "967                 304  \n",
       "7                   286  \n",
       "1350                281  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.sort_values(by='WeightedOutDegree', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 nodes with lowest weighted out-degree centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Node</th>\n",
       "      <th>Degree</th>\n",
       "      <th>InDegree</th>\n",
       "      <th>OutDegree</th>\n",
       "      <th>WeightedDegree</th>\n",
       "      <th>WeightedInDegree</th>\n",
       "      <th>WeightedOutDegree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2071</td>\n",
       "      <td>2125</td>\n",
       "      <td>577.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>-560</td>\n",
       "      <td>439</td>\n",
       "      <td>-999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1785</td>\n",
       "      <td>1810</td>\n",
       "      <td>715.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>-706</td>\n",
       "      <td>230</td>\n",
       "      <td>-936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2206</td>\n",
       "      <td>2266</td>\n",
       "      <td>194.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>-508</td>\n",
       "      <td>26</td>\n",
       "      <td>-534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010</td>\n",
       "      <td>2067</td>\n",
       "      <td>303.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>-267</td>\n",
       "      <td>197</td>\n",
       "      <td>-464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2692</td>\n",
       "      <td>2691</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>-373</td>\n",
       "      <td>4</td>\n",
       "      <td>-377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2349</td>\n",
       "      <td>2351</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>-342</td>\n",
       "      <td>6</td>\n",
       "      <td>-348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2894</td>\n",
       "      <td>2934</td>\n",
       "      <td>126.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>-174</td>\n",
       "      <td>128</td>\n",
       "      <td>-302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4387</td>\n",
       "      <td>4458</td>\n",
       "      <td>49.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-335</td>\n",
       "      <td>-35</td>\n",
       "      <td>-300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3689</td>\n",
       "      <td>3789</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>-350</td>\n",
       "      <td>-60</td>\n",
       "      <td>-290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3686</td>\n",
       "      <td>3786</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-340</td>\n",
       "      <td>-60</td>\n",
       "      <td>-280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Node  Degree  InDegree  OutDegree  WeightedDegree  WeightedInDegree  \\\n",
       "2071  2125   577.0     180.0      397.0            -560               439   \n",
       "1785  1810   715.0     311.0      404.0            -706               230   \n",
       "2206  2266   194.0      53.0      141.0            -508                26   \n",
       "2010  2067   303.0     117.0      186.0            -267               197   \n",
       "2692  2691    45.0       4.0       41.0            -373                 4   \n",
       "2349  2351    48.0       4.0       44.0            -342                 6   \n",
       "2894  2934   126.0      48.0       78.0            -174               128   \n",
       "4387  4458    49.0       9.0       40.0            -335               -35   \n",
       "3689  3789    35.0       6.0       29.0            -350               -60   \n",
       "3686  3786    34.0       6.0       28.0            -340               -60   \n",
       "\n",
       "      WeightedOutDegree  \n",
       "2071               -999  \n",
       "1785               -936  \n",
       "2206               -534  \n",
       "2010               -464  \n",
       "2692               -377  \n",
       "2349               -348  \n",
       "2894               -302  \n",
       "4387               -300  \n",
       "3689               -290  \n",
       "3686               -280  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.sort_values(by='WeightedOutDegree').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weighted Out-Degree distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WeightedOutDegree</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-936</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-534</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-464</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-377</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>-348</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>-302</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>-300</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>-290</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>-280</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WeightedOutDegree  Frequency  Probability\n",
       "0               -999          1      0.00017\n",
       "1               -936          1      0.00017\n",
       "2               -534          1      0.00017\n",
       "3               -464          1      0.00017\n",
       "4               -377          1      0.00017\n",
       "5               -348          1      0.00017\n",
       "6               -302          1      0.00017\n",
       "7               -300          1      0.00017\n",
       "8               -290          1      0.00017\n",
       "9               -280          6      0.00102"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = stats.groupby(['WeightedOutDegree']).size().reset_index(name='Frequency')\n",
    "counts['Probability'] = counts['Frequency'] / counts['Frequency'].sum()\n",
    "counts.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches(10.0, 7.0)\n",
    "\n",
    "# Linear scale.\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "\n",
    "plt.scatter(counts['WeightedOutDegree'], counts['Probability'])\n",
    "\n",
    "plt.title('WeightedOutDegree distribution (linear scale)')\n",
    "plt.xlabel('WeightedOutDegree')\n",
    "plt.ylabel('Probability')\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "# Log scale.\n",
    "\n",
    "#fig.savefig(\"output/weighted_out_degree_distribution_linear.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>WeightedOutDegreeNeg</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.035461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>0.060284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0.049645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>0.056738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0.031915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>0.042553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>0.049645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>0.078014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>0.074468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>0.049645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WeightedOutDegreeNeg  Frequency  Probability\n",
       "0                     1         10     0.035461\n",
       "1                     2         17     0.060284\n",
       "2                     3         14     0.049645\n",
       "3                     4         16     0.056738\n",
       "4                     5          9     0.031915\n",
       "5                     6         12     0.042553\n",
       "6                     7         14     0.049645\n",
       "7                     8         22     0.078014\n",
       "8                     9         21     0.074468\n",
       "9                    10         14     0.049645"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stats_pos, stats_neg - auxiliary dataframe \n",
    "stats_pos = pd.DataFrame([x for x in stats['WeightedOutDegree'] if x >= 0], columns = ['WeightedOutDegreePos'])\n",
    "counts = stats_pos.groupby(['WeightedOutDegreePos']).size().reset_index(name='Frequency')\n",
    "counts['Probability'] = counts['Frequency'] / counts['Frequency'].sum()\n",
    "counts.head(10)\n",
    "\n",
    "stats_neg = pd.DataFrame([abs(x) for x in stats['WeightedOutDegree'] if x < 0], columns = ['WeightedOutDegreeNeg'])\n",
    "counts2 = stats_neg.groupby(['WeightedOutDegreeNeg']).size().reset_index(name='Frequency')\n",
    "counts2['Probability'] = counts2['Frequency'] / counts2['Frequency'].sum()\n",
    "counts2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Values less than or equal to 0 in data. Throwing out 0 or negative values\n",
      "c:\\users\\hp\\.virtualenvs\\network_science-3ktgw-v0\\lib\\site-packages\\powerlaw.py:700: RuntimeWarning: invalid value encountered in true_divide\n",
      "  (Theoretical_CDF * (1 - Theoretical_CDF))\n",
      "Calculating best minimal value for power law fit\n",
      "c:\\users\\hp\\.virtualenvs\\network_science-3ktgw-v0\\lib\\site-packages\\powerlaw.py:700: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  (Theoretical_CDF * (1 - Theoretical_CDF))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive values: alpha=  1.6542514079557757\n",
      "negative values: alpha=  1.6591387209616981\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches(10.0, 7.0)\n",
    "\n",
    "# Log scale.\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "\n",
    "plt.scatter(counts['WeightedOutDegreePos'], counts['Probability'])\n",
    "plt.scatter(counts2['WeightedOutDegreeNeg'], counts2['Probability'], marker='o', c='r')\n",
    "\n",
    "plt.title('WeightedOutDegree distribution (log scale)')\n",
    "plt.xlabel('log(WeightedOutDegree)')\n",
    "plt.ylabel('log(Probability)')\n",
    "\n",
    "ax.set_xscale('log')\n",
    "\n",
    "# This is the only way I found to make it work.\n",
    "# Matplotlib log scale only works for positive values.\n",
    "# Can't make symlog scale to work either.\n",
    "ax.set_yscale('log')\n",
    "ticks = [1,1e-01, 1e-02, 1e-03, 1e-04]\n",
    "ax.set_yticks(ticks)\n",
    "xticks = [1, 1e+01, 1e+02, 1e+03, 1e+04]\n",
    "ax.set_xticks(xticks)\n",
    "ax.get_yaxis().set_major_formatter(mpl.ticker.ScalarFormatter())\n",
    "#ax.set_yticklabels([str(t) for t in ticks])\n",
    "plt.grid()\n",
    "\n",
    "fit = powerlaw.Fit(np.array(stats_pos['WeightedOutDegreePos']), xmin=2, discrete=True)\n",
    "fit.power_law.plot_pdf( color= 'b',linestyle='--',label='fit ccdf')\n",
    "#fit.plot_pdf( color= 'r')\n",
    "print('positive values: alpha= ',fit.power_law.alpha)\n",
    "\n",
    "fit = powerlaw.Fit(np.array(stats_neg['WeightedOutDegreeNeg']), discrete=True)\n",
    "fit.power_law.plot_pdf( color= 'r',linestyle='--',label='fit ccdf')\n",
    "print('negative values: alpha= ',fit.power_law.alpha)\n",
    "\n",
    "#fig.savefig(\"output/weighted_out_degree_distribution.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Average node rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Node</th>\n",
       "      <th>Degree</th>\n",
       "      <th>InDegree</th>\n",
       "      <th>OutDegree</th>\n",
       "      <th>WeightedDegree</th>\n",
       "      <th>WeightedInDegree</th>\n",
       "      <th>WeightedOutDegree</th>\n",
       "      <th>inAvgRating</th>\n",
       "      <th>outAvgRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>84.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>158</td>\n",
       "      <td>61</td>\n",
       "      <td>97</td>\n",
       "      <td>3.54</td>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>86.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>266</td>\n",
       "      <td>123</td>\n",
       "      <td>143</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>441.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>1234</td>\n",
       "      <td>801</td>\n",
       "      <td>433</td>\n",
       "      <td>3.11</td>\n",
       "      <td>2.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>28.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>2.33</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>117.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>343</td>\n",
       "      <td>168</td>\n",
       "      <td>175</td>\n",
       "      <td>1.39</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6</td>\n",
       "      <td>-6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>401.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>627</td>\n",
       "      <td>341</td>\n",
       "      <td>286</td>\n",
       "      <td>5.67</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>86</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Node  Degree  InDegree  OutDegree  WeightedDegree  WeightedInDegree  \\\n",
       "0     6    84.0      44.0       40.0             158                61   \n",
       "1     2    86.0      41.0       45.0             266               123   \n",
       "2     5     6.0       3.0        3.0              14                 7   \n",
       "3     1   441.0     226.0      215.0            1234               801   \n",
       "4    15    28.0      13.0       15.0              44                20   \n",
       "5     4   117.0      54.0       63.0             343               168   \n",
       "6     3    21.0      21.0        0.0              -6                -6   \n",
       "7    13   401.0     191.0      210.0             627               341   \n",
       "8    16     1.0       1.0        0.0               8                 8   \n",
       "9    10    13.0       5.0        8.0              86                30   \n",
       "\n",
       "   WeightedOutDegree  inAvgRating  outAvgRating  \n",
       "0                 97         3.54          2.01  \n",
       "1                143         3.00          3.18  \n",
       "2                  7        -0.29          0.00  \n",
       "3                433         3.11          2.78  \n",
       "4                 24         2.33          2.33  \n",
       "5                175         1.39          2.42  \n",
       "6                  0         2.84          2.20  \n",
       "7                286         5.67          3.00  \n",
       "8                  0         2.00         10.00  \n",
       "9                 56         6.00          7.00  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count weighted degree distribution\n",
    "weighted_in_degree = dict()\n",
    "weighted_out_degree = dict()\n",
    "#reread data file\n",
    "data = pd.read_csv('data/soc-sign-bitcoinotc.csv', sep=',', decimal='.', parse_dates=True, infer_datetime_format=True)\n",
    "for instance in data.values:\n",
    "    weighted_in_degree[instance[1]] = weighted_in_degree.get(instance[1], 0) + instance[2]\n",
    "    weighted_in_degree[instance[0]] = weighted_in_degree.get(instance[0], 0)\n",
    "    weighted_out_degree[instance[0]] = weighted_out_degree.get(instance[0], 0) + instance[2]\n",
    "    weighted_out_degree[instance[1]] = weighted_out_degree.get(instance[1], 0)\n",
    "        \n",
    "in_degree_list_weight = list()\n",
    "\n",
    "for key, value in weighted_in_degree.items():\n",
    "    #third value is average value of ratings that node received from other nodes\n",
    "    if graph.in_degree[key] != 0:\n",
    "        in_degree_list_weight.append((int(key), value / graph.in_degree[key]))\n",
    "    else:\n",
    "        in_degree_list_weight.append((int(key), 0))\n",
    "\n",
    "out_degree_list_weight = list()\n",
    "for key, value in weighted_out_degree.items():\n",
    "    #third value is average value of ratings that node gave to other nodes\n",
    "    if graph.out_degree[key] != 0:\n",
    "        out_degree_list_weight.append((int(key), value / graph.out_degree[key]))\n",
    "    else:\n",
    "        out_degree_list_weight.append((int(key), 0))\n",
    "\n",
    "stats['inAvgRating'] = [round(v,2) for k, v in sorted(in_degree_list_weight, key=lambda tup: tup[0])]\n",
    "stats['outAvgRating'] = [round(v,2) for k, v in sorted(out_degree_list_weight, key=lambda tup: tup[0])]\n",
    "\n",
    "stats.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  5.1) In & out average rating distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches(14.0, 7.0)\n",
    "\n",
    "# Linear scale.\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "\n",
    "# Plot the histogram.\n",
    "stats['inAvgRating'].plot(kind='density', legend=True)\n",
    "stats['outAvgRating'].plot(kind='density', legend=True)\n",
    "\n",
    "plt.title('Average in & out -rating distribution')\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Probability')\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "#fig.savefig(\"output/avg_in_out_rating_distribution.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2) Absolute difference between in & out average ratings distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff = np.abs(stats['inAvgRating'] - stats['outAvgRating'])\n",
    "#diff.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5881.000000\n",
       "mean        1.509163\n",
       "std         2.557575\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.500000\n",
       "75%         1.500000\n",
       "max        20.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches(10.0, 7.0)\n",
    "\n",
    "# Linear scale.\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "\n",
    "# Plot the histogram.\n",
    "diff.plot(kind='density')\n",
    "\n",
    "plt.title('Absolute difference between in & out - average ratings distribution')\n",
    "plt.xlabel('Rating difference')\n",
    "plt.ylabel('Probability')\n",
    "\n",
    "ax.set_xlim([0,20])\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "#fig.savefig(\"output/abs_diff_avg_in_out_rating_distribution.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3) In & out average ratings 2d plane projection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "* 1st quadrant: User rates others positively. Others rate user positively.\n",
    "* 2nd quadrant: User rates others positively. Others rate user negatively. (!!!)\n",
    "* 3rd quadrant: User rates others negatively. Others rate user negatively.\n",
    "* 4th quadrant: User rates others negatively. Others rate user positively. (!!!)\n",
    "\n",
    "See counts below!\n",
    "\n",
    "\n",
    "(Compare with y=x line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GUd4EQRAEIy6retU3TRcEIRrEYEEQBCHFNBgIrJgbeog50zJ6V8zFuicH67ZOO4iM40fGURehfVn90AvYdfhE7XMrh6mUlTdBEISUojIQWPfkYKgGAn7KUMWPLE4rGClgcdRFaF+cihsA7Dp8AqsfeiEhiaJFlDdBEISUojIQGB4ph2og4LeMnoVF7Fp7DV7ZvBK71l6DzkI+knIEwQ9Oxc0rPeuI8iYIgpBS4jAQiMsIQYwdBCE8RHkTBEFIKXEYCMRlhCDGDoIQHqK8CYIgpJTeFXNRyOfq0gr5nLGBQFrKiLMcoT1ZNme6r/SsI9amgiBkgna0VLTqF2W94ygjznKE9mTrLVe3lbWpKG+CIKQey1LROvBuWSoCaPmXf8/CYuR1jKOMOMsR2pNWVdRUyLapIAipRywVBUEQLiDKmyAIgekbKGHZ5p0YLJ3Css07G3x2Wd9fsfYZ5femiKWiIAjCBUR5EwQhEHanq0Cj09UwnbKKpaIgCMIFRHkTBCEQXluZYW51iqWiIAjCBcRgQRCEQHhtZYa51SmWioIgCBcQ5U0QhEBc1lmobZk6002+94tYKgqCIFSQbVNBEALhtZUpW52CIAjRICtvgiAEwr6VCZxG0bGVKVudgiAI0SDKmyAIgbG2Mvv7+3Hb6m7t94IgCEJ4iPImCBHTymGd1vcNYtueY7j9vSP4/XU7sGrJTGzs6UpaLNc2D7M/rLxunnkad2/eGUnfvmfdMxjlC5/HEfDjTStDLcPqxzIzckSR9WMc4YuC9G8cz2irlBGUVgldFcczb4KceROECAnT11naWN83iId3H0WZK5pFmRkP7z6K9X2Dicrl1uZh9oeXn7swcCpuADDKlfSwiKsfnS9vANh1+ARWP/RCaGUE6d84ntFWKSMocfR9HMTxzJsiypsgREgrh3XatueYr/S4cGvzMPsjjr51Km5e6UGIqx+dL2+v9CAE6ZM4+rFVyghKHH0fB2lqY9k2FYQIaeWwTtZKjWl6XARp8yD90Sp9m9Z+DEKYfR9mP7ZKGe1OmtpYVt4EIUJaOaxTjshXely4tXmY/dEqfZvWfgxCkD6Jox9bpYx2J01tLMqbIERIK/s6W7Vkpq/0uHBr8zD7I46+HafRn3TpQYirH5fNme4rPQhB+iSOfmyVMoISR9/HQZraWJQ3QYiQnoVFbLq+C8XOAghAsbOATdd3pcYCrBk29nRhzdJZtRWaHBHWLJ2VuLWpW5uH2R/2vNBkXjp+vGllg6IWtrVpXP249ZarG17WYVscBunfOJ7RVikjKHH0fRzE8cybQpzBcw1BWbx4Me/duzfSMvr7+9Hd3R1pGWmmnesvde9OWozEaOf6S927kxYjMdq5/nHVnYj2MfNiZ7qsvAmCIAiCIGSIVFqbEtFcANttSe8G8EVm/ortmm4AfwvglWrSk8x8X2xCCoIgxECaHa8K2UfGVzZJpfLGzIcAXAUARJQDUALwHcWl/8jMH4lTNkEQhLiwnIJavqUsp6AA5AUrNI2Mr+yShW3T3wBwmJlfTVoQQRCEOEmTU1Ch9ZDxlV1Sb7BARN8E8CIzf82R3g3gCQA/BfAagM8z80HF/bcCuBUAZsyYseiRRx6JVN4zZ85gypQpkZaRZtqx/kPDIzh+6k1MGz+Gk+c7MGPqRHQW8kmLFSvt2O92oqr/YOmU9ruu4tTQywtCO/d91uve7PjKev2bIa66L1++XGmwkGrljYjGo6KYzWfm447v3gZgjJnPENF1AL7KzFe65SfWptHTbvW3bzvc1TWKBwbHoZDPpcZEPy7ard8t7EGqHzl2UejnhZZt3lmLo2in2FnArrXXhFZOM7Rr3wPZr3uz4yvr9W8GsTZ158OorLodd37BzL9k5jPVv3cAyBPRxXELKLQ3su3QvsQRpDpNTkGF1kPGV3ZJu/K2CsA21RdE9A6iildJIvoAKnX5RYyyCUKqYt0J8RKH4p5mx6tC9pHxlV1SaW0KAEQ0CcC1AP7AlvZZAGDmrwP4OIA/JKJRAMMAbuY07wELLcllnQXltoPEE2x94lLcrcgQghAFMr6ySWpX3pj5HDO/nZlP2dK+XlXcwMxfY+b5zLyAmZcy8/eTk1ZoV2TboX1JU5BqQRDai9SuvGUN+8HluzfvFEeHHrSKY0hL5spW2WkUFXVplbraaajTgrL3TS1G74q5dT6ygGgU92sf7MfLr5+tfb7y0sl47s5u13v8jrkgYzTInBfHs+BWRt9ACfc+fRAnz40AADoLeWz42HzfMsQ136/vG8S2PcdQZkaOCKuWzAw95myQ8SUkjyhvIVDn6HCmODr0otUcQ1rbDv39/bhtdXfdd61WV0Bdp9LJMvoGSpmtUxBMFPdmcb5YAeDl18/i2gf7tS9Yv2MuyBgNMufF8Sy4lQEAvY8fwEj5wumaoeER9D52wJcMcc336/sG8fDuo7XPZeba57AUuCDjS0gHqd02zRJiceiPdmqvVqyrqk5jzJmuU1B6Fhaxa+016CpOxa6114SuvDpfrF7pgP8xF2SMxnWPX9zK2PLsoTrFzWJkzN/YjeuZ3rbnmK/0IAQZX0I6kJW3EBCLQ3+0U3u1Yl1bsU6thN/+CdKfcd3jl6Bl+JEhrvFf1tjf6dKF9kJW3kJADi77o53aqxXr2op1aiX89k+Q/ozrHr+4lRFENr9lhEmu4gnLOF1oL0R5CwGxOPRHO7VXK9ZVVacOokjr1DdQwrLNO3HF2mewbPPOUB3hppkrL53sKx3wP+aCjNG47nFDNSbcyuhdMRf5XKPik+/wN3a96hHWWF21ZKav9CAEGV9COhDlLQTsjg4BcXToRTs5hmzFuqrqVJxWiKxO9kgGjGgiGaSV5+7sxoyLxtelzbhovOthcr9jLsgYDTLnhfks6MYEAG0ZPQuL2PLxBZg26ULc4c5CHltuXOBLBre6hzlWN/Z0Yc3SWbWVthwR1iydFaq16XN3djcoamJtmg1SHds0bCS2afS0c/2l7t2R5N3O8T2d1pMAUhc7N4lxn5Yx4ax7WuSKC5nzuiMvRxfbVAwWBEFIBTr/XO1sIOFm2Rim8haGn7fl8y7B8y+9EaoPt7jGRFj+59Iql9B6iPImCELiuPnnaucQZHEormH5ebP7JAvD91lcYyJM/3NplUtoPeTMmyAIieO2wtSKRh+mxGHZGJbPNifN+j6La0yE6bctrXIJrYesvAmCkDhuK0z2SAbttn0URwiuMH22Bb3Oz71hj4kwVzfjlku2VdsXUd4EQUgcr+0my1qw3YhDcQ2y1ae7x08ezcoV1pgIe1s+LrlkW7W9kW1TQRASp523Rr2wQnC9snllJCG4wvLZ5qTZ/otrTMRVjl//b15yybZqeyPKmyAIiZNVf3jWC3mwdCqzzoPD8vO2ZumsUPsvrjERRzlB/L95ydXOVtiCbJsKgpASsrY1qrK4zOq2VZC2t+7p7+/Hbau7UyNXGssJ6vLFTa52tsIWZOVNEAQhELJtJZgSxSqZHDVob2TlTRBSQCtajaW9Ts3KF9e2Vdrb0Q/r+waxbc8xlJmRI8KqJTNDDfcEeLeX6nvAn1GI00Gx1/Vuq2Ru8l77YD9efv1s7Xp76Kp2tsIWRHkThMRpRauxtNcpDPkK+Q6cGxlTpqdJzrSwvm+wzpFvmbn2OSwFzqu9VN/3Pn4AYGBkjJX3uJZhuF2uc/myfN4lWnn/+/Mv1yluAPDy62dx7YP9dQpc1saBEA6ybSoICZOm7Te/FnE60lQnFWHINzzaqLi5pQch7e3oh217jvlKD4JXe6m+HylzTXFT3eO3DBU644PnX3pDm5dTcbPQpQvthay8CULCpMVqLMxVnrTUSUcY8jH7Sw9C2tvRD2VNw+jSg+DVXn7azW/be+WtWiW7Y/v+QHkJgqy8CULCxBECyYQwV3nSUicdYciXI/KVHoS0t6Mf0tBeftrNb9sH6ZNW6l8hXkR5E4SESYvVWJirPGmpk44w5Fu1ZKav9CCkvR39kIb2Un2fzxHyHaS9x28ZYcl75aWTlffo0oX2QrZNBSFh0mI1FqbfqLTUSUcY8lmH7K0zW1FYT6a9He14WXna2ysqa1Ov9tJ9r7vHqlNpaBg5IpSZUews4IZFRTz/0hsATqPYRJ+4yduzsOhqbSq0N8RhHtBIOYsXL+a9e/dGWkZ/fz+6u7sjLSPNtHP9s15355k3oLIKYOJtPut1b5Z2rn9/fz+Gpl4ZeOykFdXzYGHVrfPUy5H2e9rdxLT7uI+j7kS0j5kXO9Nl5U0QBADZWuWJAueLcvm8S/D8S28k3hZXrH0G9p/YBOCVzStDLWP1Qy9g1+ETtc/L5kzH1luudr3H7uvsK88eaDA8UEUQiMPPm1cZJjL0DZRw16ONdXLW7f6l5ieP5t29A2+WL+Q3MUd46f7rtNe3kpuYVsKvj7+okDNvgiDUiDoIelpRxZ58ePdRX7Eoo8CpuAEAV9PDwqm4AcCuwyew+qEXtPfY2wvQW4zaz0taft6say0/b+v7BputgnEZJjJYdfOygvVzFtSpuAHAm2XGvLt3aO9pJTcxrYJz3Cc1LwCivIVGKwSoFoR25d6nDyq3x+wk8eLUqQ9hHnZxKm5e6YBasVBhPy+p8+f27T1HQ/Et6FaGlW7ia860bh1ExvO9U3HzSgday01Mq5AmhVqUtxBIkzYuCII/+gZKOHluxOhaeXFWMGkHpwWmbiVrjBHaCqeXLzkTX3OmfWzdE9V8L25E0keaFGpR3kIgTdq4IAj+8POcyouzgq4dckR1EQTs2+6m/tyamTu9fMmZ+Jrz6mNVHlHM963kJqZVSJNCLcpbCKRJGxcEwR+mz2kSL06duhOeW9uKcYKf9L6BEs6+NdqQXsjn8MAnFmjPS/rx5xZ07vTyJWfia06nNH3lpqtwZPNKjBmc73MyMafuMV06oA+p1S7nUNNImhRqUd5CIE3auCAI/tA9p4V8R+Ivzlc2r2xQ1MK2Nt16y9UNiprO2tQ6IjI0XL/NPG1S3rN9NvZ0Yc3SWXWrYIW8+hUUdO5UlbFm6ayaNanX94C30hRkvn/p/usaFDUva1NLlnY0IEor9rEBJKtQi6uQEOhdMVfp40iWtwUh/eie37SscoTtFkSFl1sQC91h/knjxxm11caerjpFSedbsJm501mG3+8BdRxSi6DzvZeiJmQDa2z09/fjttXdickhylsI2P1jNetxWxCEeGl3/3Z+CPuISBbbXuZ7IQ2kVnkjoiMATgMoAxh1ehgmIgLwVQDXATgH4FPM/GLcclqkRRsXwqdZL+f2+6cW8iACTp4bqQu345Vn2j2t61A5RF18+XTXuiRRV7eVlqSZrfDpdsTnapxXm5o6kG02hJqqLsXOQp1j5C3PHsId2/cH7vsl9z+H46fP1z7PuGg89tx9ra88vLh9+/7KHzMr1qa3b9/fIGdanT4nRRBH0IKetJ95W87MV6lCQwD4MIArq/9uBfDnsUomtAUq561+3AI47x8aHqm5pTB1NdCsDEmhc4h612MHtHXJal2jQqXsuKWr8GpTPw5kl8+7RFmGLt1E5jAdIzsVNwA4fvo8ltz/nHEeXpj0SVqdPidFEEfQgjtpV97c+E0Af8MVdgPoJKJ3Ji2U0Fo06wbG1OGnW55ZdUWjc4haHlOHUQKyW9c049WmfhzIVoKxN6JLbxa/fe9U3LzSo8LkuW+ncR3EEbTgTmoD0xPRKwBOouJM/C+Y+S8d338XwGZm/qfq578D8MfMvNdx3a2orMxhxowZix555JFI5T5z5gymTJkSaRlpptXqP1g6pf2uqzi17rOq7m73m+TpV4akCKvuWairiqjGfRjt4ZWHnzJU184oAMeHgZnTJ+H4qTdxvjyG8bkOzJg6EZ2FvJEcXoRV1zCwl2HV3VmGn7qmeVx7YTrus/pcuxHXu2758uXKwPRpVt4uY+bXiOhSAM8BuI2Z/8H2/TMANjmUtz9i5n26PBcvXsx79+7VfR0K/f396O7ujrSMNNNq9V+2eafyjE+xs4Bda68BUB+o+JFjF9WdZdHdr8Kep18ZkkbV73PW7fCMD2lh1SULdVUR1bh32x41Pffm1aZ+ylDldVfXKL55uIA3R8ZcLXb9bPWq5LSjOx/3GL8AACAASURBVMMXRns5cZZlr/9dXaN4YPDC0XGrDNPnPu3j2gvTcR9FvyRNXO86IlIqb6ndNmXm16r/vw7gOwA+4LjkpwDsHhffBeC1eKQT2gUvp4xeodFU96twczWQtGNIK26v39iTOoeouY56f1f2uiRd11bEq039OJBV5dVBBGZEst2t6nu3M3wzLhqvzEeX7oWqLBNMnvt2Gtd+HUEL3qRSeSOiyUR0kfU3gA8C+D+Oy54C8LtUYSmAU8z8s5hFFVocL4edXueJnPd3FvKYNqmylWQ5CvVy9Jikp/VmDAh0DlEfuHGBti7iVb4e3aqE12qFXeHe8uwh3LCoqG3Tl+6/DuMceto4UvslU/VPcVoBpxxOey3sLkR0MtvzWrN0lmff3/v0Qe0zt+fua/G2CfVK09sm5AJbm5qeWQXq66dqJ5O6tSp+HEELZqTVVcgMAN+peAPBOADfZub/TUSfBQBm/jqAHai4CfkxKq5CPp2QrEKL4+ZGwsTvVRhuKJJyZeGmnAZxymrhdm+a3XZEiWorcO+r6gPd6/sGtY5mnY5vS0PDeGJfSass9A2UkB+Xw6itn4kIC+/7HobOjTS4tXD2T39/Py7rzBm5EGl2i6xvoFSz1nby2tAw+gZKGBmrTx8Zq9wXZEz5CZ3mLMNrHFsKdru4DhFFLVxSufLGzD9h5gXVf/OZ+f5q+terihuqVqb/mZnnMHOX01BBEOKg1UOjSdzeeNCtcG7dc1R5vc6SF/Bvsau6fmSMcfLciPFqa9Dtbr9b8m7bsJd1FkK3VjZ9jv2WIS5xhGZJ68qb0OJk1emsk94Vc9H72AGM2Nxf5DuoZc6yNOOUNUznxlkeI3Z0ddIpHTrshiBuB+rtNKOID4+Uce/TB5VOZ2+eeRqP7D6Ed02biJdfP1u75/2zpro6ru2clMeZN0drz46lwAD6lVk3WXtXzMUdlvNcn3XU9YsqFJYOPz9oml3RFoRUrrwJrU3L/epURQ5vEZbPu6ShOqYrKmE6N878GIF7nYKuZPo5UD/V5rbDji44vJOT50aUTmdR/WxX3ICKD6/1fYN1svY+fsFB88lzI3U/egDvFSzdj4bOQh49C4uBVsLd+kV1dq1T045+VttlRVtoFlHehNhpJUesW549hBGHM9ORMmeyLk76Bkp4Yl8J9toRgBsWeZ9Ji8K5cVbHiIVbnYJus/s5UE+aHxXDo2PqL0LAvr1779MHG54VFV6ra6rt2Q0fm+/6vduPDROjo11rr8Erm1di19prsOFj85u2iG714xZC9IjyJsROnL86g7q5MCUNv6CjqqPqpcYw86bfbLukoV3Dxq1OOqUjaJ4qhjQH/aN09Wnf3tUZGjixKzDOsQ3A1Ro5iLWy37HWs7CIGxYV66yoTX7Q2BGXOEKziPImxE5cvzrj2HpL+hd0lHVsRoFqtl2SbtcocKuTTukImqefa3O6JTmgJktQ3PJW4fSh2OuIg9v72AEAqFsJa/aMmN+xZq1I22P2PrGv5OuZE5c4QrOI8ibETly/OuPYekv6F3SUddSdkdKl22m2XZJu1yjwqpNze65nYdHTuanqTGK+g5B3ONh1KkX21ayl756mLGPN0lk1WXTnvLyYmO+oKTW6PAhQKjAbnjrYcCZuZIyx4amD2vKC/JjxO9bCeuZU/S0Ipoi1qRA7dge3UVoS6laISkPDoflXstcFOI1izFaRUW4v6hZNTBZTmu3juMZInASp09Zbrsbqh16oC+BtOTfVnUm86QMzsfjy6cpyVD7gTpw9j2VzpmP3T06izIwcEVYtmVnnR27Dx+Y3WFV3AJg6KQ9gVCv/2fPlmgWpKo98B2HLjQuUbTCkcfyrSweCWXH67ZdW3NIXsocob0IixOGIVec2gYA6Kzkv9wReWHXp7+/Hbau7g4obiGZceXihOyOlS3fSbB+3orPeIHXSOTd1O5O4sUe9BadTbo78YhiHNzVGVLDLbd3vdBXihaU8WTE8o1TIgypWfvrF7ZlrRfc2QjoR5U2IlCgnMyvv0tAwckQoM9etfPWumIvexw80WLg5z2dn1b9S30AJZ99qXPWwtnyabfugimEUfe43T5Prs/Kifc+6ZzBqG7TjCPjxppWuikrfQAkbnjpYW6WaNimPez4633U12hk83IqG4HzOGMC586N4eHfVgbA6hG1D/ldU82db2qYdPwRwQaGbWsiDqPIDgdD4rALAhHEdmLNuh3KVMKwfM+v7BrFtz7E6gwtrblHNK/kcYfm8SxpWNe0/DO15qlY3VcQxRrPyHAj1iPImRIZqi6bZVS5d3tYkay8DgHr2V5C1LQ9n/S2slzSAptte5aDU69xZFH3uN0+T66Mcm2HiVNwAYJQr6TpFZWoh37A9efLcCHofP4CphbzrtqOd2WufwVduukr5nJlajtpRPYrHT5/H7TbHul6yEYC3bK5Nysw1JXJjT5fWz51pQHmgorjVFFNHHuueHMQNi4qNlWHgmR/8TLtlu/fVE3V5OuVWEccYzcpzIDQiBgtCZER5mN7Nv5VVxpZnDzUceNaRNStGXf0njR/n6rHfT9sHsYiLos/DCPfkvD4OY5YgLlyc9zgVN4tR1h+0J4Jy3I+UGURmLkgs/PiRs4jSR7XuaXYLF+YXt7yGR8rYtueY0pDCLeaqLs8ww5wFoRX9KbYLsvImREaUB3u98vBTRhatGL3aNqy293tGK4o+95unSXrUh86DrGio7nFDd9BeFyIKqGxHfvmmq4zCaQHB2sOef4Qu5Oooh+iszisvv2W5tbFbXnEYRojxRXaRlTchMqL01eWVx2WdBddQOln3r+TVtkn5SYuiXL95mqRH3T5BVjSCrHKp3E241cHyKWe/xw2/7dFB9TI14yPOD379yTWTl+77zkJe63JEd49bWXE8w63oT7FdEOVNiIwofXWp8naW4RZKJ+v+lbzaNik/aVGU6zdPk+ujbp8gKxp+VjvGkX5btnfFXOQ7GpWCfI5818/tOVNRyOfqtoeXz7vEV3lBWbXEwGrCgL6BEsaP0ytUhXwOq5bM1M4rumMGOvnc5I7jGW5Ff4rtgmybCpERpa8ue946a1OLVrSk8mrbpPykRVGu3zxNro+6ffxaPfYNlNBRHcNejCPgS5+4qmGL9fbt+/GFJ3+ACflcw5msyeNzuP+31CvMnRojBivYO3DhObPIEeHiKXkcP32+7p6z58u4Y/t+7H31BDb2dBm5ElmzdBa+e+BnNRk6CBhjoJDvwPCId9zVDlw49H9k88oGq1kr3YsL29bqMu1zi+VHT+XbUdXGlnx+rE3jeIbjnCfEqjVciKMMbJcyFi9ezHv37o20jP7+fnR3d0daRppp5/pL3buV37XDpO2sv8oauJDPKbfodZbDToqdhdo257LNO31ZUOrKBqBUdizs7kJ0Mt7VNYoHBoOvA0yblMebI2MNbTVhXIexZeyapbOwsadL2+43LCri+ZfecB2Dbm2qa792fuYB8/r7eR6yQlx9T0T7mHmxM122TQVBiIw44sumET+WuiZn3ZxbWX4PlA+PlHHXowd8Wb76lTEoJ8+NKM8HmipuAPDtPRW3G7qzhlt3H/Ucg25tKhaYzSFWreEj26aC4IN2WEUKkyDhiuIkyv40tdQ1UcSGR8q4fft+3L59P2ZcNN7TSlSF3Rdi7+MHajJOm5RXurkgAq5Yq/cnlyasXWJdW5o45jaxvG0YLwuiUWhbDbFqDR9ZeRMEQ9p1FakZTCbtIP7QwiAt/enXsu/46fM4PXxeaZRgykiZce/TlQDv43PqfJhRa5cofbfpmDDO/+upc1Le+Frn2PQyzphayDeMl9LJYXn+DRCr1vAR5U0QDEnr0n9Syo8JXpN2kgpUWvrTr0UnAPzyrTKmTGxu48RabXMaHahgNOd8N0eEzoJasVLlW8jncH7U21jhwvWVV5mfI9zOsWltdavktJwfO8fLGHPiz38WEKvW8BHlLSSsF+hg6VTqXqBCOKRp6d8ab7PXPoM7tu9PfPVIh9eknaQC5dWfcSnF9vNxfhgKEKKqGRjQKmBelJlx9vyocrXQqW9Nm5THpuu7fDn43XT9+wAApwzPyREqz4qzX3sWFrH/ng/iKzdd1XBeUdfesvXnTZBoLYI7cuYtBOosaWZKfDgTsnh2LKyg183itNzSnecBkneT4uWKIEmF2K0/w4r5aDrOrfNxfqxImz2L5lcRs5RLP4YEdkbKjGmT8pg0fhxeGxrWukb55fAoANTc/7hhxfK12lTXJla51hawlauuX1XnFZ3uUixk688Mv9FaBHdk5S0E0rL9khXSctbIL2lZ+jex/LPaNA1trIoCYJHkWRi3/tzw1MGmn+kg49zPFmqQ7VaLfAdhw8fm+7qnd8XcppXqk+dGamNBp5iVmdH72AEsffc05fdrls7Ckc0rcWTzSgx88YN140nloDjfQVj5vnfWPrv92HFD1d4d5N/xsSCEgShvIZCm7bQskFVlNy1L/ybjKkekbONm3EVEQbMKcTNbm7r+BPSrS36e6SDj3CmTG3ds348J4zowebw/BS5HhC03LvA1bi2HvWEo1Vese8bVtxxQCfR+8LXTWLN0Vi2EVI6o5s/NiTUObt++v8FB8RiA7f98rOkYrqrxUpxWkNUkIRFk2zQE0rKdlhWSVnab2bJNw9L/VI1HfItCPqddmbO7i0jD1n4zHt7D2NpU9eeyzTu113cQ1dxneMkZdJzbZXJTchjBtjDHmH31uRX6CQBmv715tyGmRgVDwyPY2NPlGoUA8HZyXB5jeDn06CBC30DJs12c46W/v98jZ0GIBll5C4G0bKdlhSS3yrK6ZWvRN1DC2fOj2u+t1SOTw+9pWe1021Z1I6oVXDflqsxsPG7S6h5hquOsm5vHEefq8vcPn4hStECE4UC4zJypeUAQZOUtBHoWFvHY3qPYZZvY3j9rauIrNGlAtcrVu2KuMlRKHMpukk5jm1nxs+7VrXpMm5THwBc/WJfW+/gBjJTdlzmcisr6vkGj+IsmzkrDNEpx5rV83iXatmh2BdfUEMBr3KjGeb6DcO78qOvqnb2uJof2/TI0PIIl9z+HcbkcXhsaxkRNDNFlc6bjyC+Gccf2/bj36YNKR75R0kGNK4/L5kzH1luurksLa8U+bufRWTTaEtKDrLyFwPq+wTrFDQB2HT6B9X2DCUmUDnSrXAASOzuW1JZtMyt+9nt1KN0YGLzz7atA6/sG8fDuozVlocyMh3cfbRjHqro4nZWGucKpyuvh3UeN6hQEP4YAbuPGeUaqs5AHqHJoX9cmzrqGrbhZHD99vlaGLhD7rsMnatfErbgBF6ImOGX6lbt31J1xDHMlM86jG1neARCSR5S3ENi255iv9Kxjekjca5UryFZZsyS1ldXMFp/JtpBT/i3PHmo4uO3EudppOo5V8jidlYa5pelnWyyMFVzVwfRpGs/9XuPGPs4nTxjXsBLqbJMoY4i2CufL9VvXy+ddYqRsh9GfYZFVoy0hPci2aQi4mby3Gn4OiSdtmKAiqS3bZtrC6xqV/G73EKDcpjEdx7q8S0PDnrEwg/S9n3vCWsF1HkxXHYr3O25MxoBYqPtjeKSM5196A5uu73I9VgAAu9ZeU/s7jP5shjTOjUK2kJW3ELBM2U3Ts4yfX4xpPLCdlLuPZtrC7Rqd/Lp7ip0F7Wqn6Th2k8crFmaQvje9p9jZnNsGtxXlMMaNLu6mPT1pY4Ys8trQcG2F05Sk3f6kcW4UsoWsvIXAqiUzlWdwVi2ZmYA00eLnF2OShgluJOHuw6st3A4v6+51e9n0rpiL3scO1G2d5jvcHYqajmOVPE6sWJj2NbugfW9SXrPjqm+gVNdepaFh9D52AMCFFeVmx81bGvnPvjWKZZt34rWhYUwt5JHPkaehiXABy3rW7bzYsjnTlc+YH4UvTNI6NwrZQVbeQmBjT5exM8ms4+cXY9K/btOEW1t4HV4O3I7O5S+PhWDTcWzqSJaBUPpeVf81S2eFOq42PHWw4YzgyBhjw1MHA+fp5JzGMOB8mWt9PzQ8AnDFerj11u2j4ez50ZpipiLfAdy4eFaqDARkbhSaRVbeQmLx5dPx/EtvABjBO6ZOxOLLpyctUiT4/cWYBqe2aUHXFibuS/y245ZnDzWs3oyU2dMVgolTVKc8Fae2pxuuKXYWQlvZUNXfvpJivbiDjjWds9ugcTybYWSMMWn8OAx88YOekQiEC+NatyswOpasiyAdMjcKzZA65Y2IZgL4GwDvQCWyyV8y81cd13QD+FsAr1STnmTm++KU007WAtM3G2EACC/geZZ8HUUlaxSHl+M8EN27Yi5KP9pXlxb1FlBYgePjpKDxp6ZCDq77ozQ0jKJLpJskDASyNLcJ2SON26ajAO5i5l8FsBTAfyaiX1Nc94/MfFX1X2KKG5Ats+8w/AuF5eYjS76OopQ1isPLTi/6XunN0LOwiOK0QqxbQGE/czrXEbr0IEz0EUReDq77I1cNEK+LdBO3gUCW5jYhm6ROeWPmnzHzi9W/TwP4EYBU/1zJktl3mhTNNMniRZSyRhFeTWfoHJUBdGchH6nfPqclaNjRFe756Hzkc/WNk88R7vno/ED5qVA6UlYgB9fVuCnS5Wq8Vt05srhDGGZpbhOyCXGKfZER0WwA/wDgvcz8S1t6N4AnAPwUwGsAPs/MypPFRHQrgFsBYMaMGYseeeSR0OU89PPTOF+ubIfMKADHq++P8bkOzH3HRaGX1wyDpVPa77qKU5vO/8yZM5gyZUoqZAkTE1n91N3J0PAIjp96E+fLYxif68CMqRMrHvkDElXb6uRspu4mZZZODmPMYK5q5plrpg9M6m+fJ+zkOgg5ImW5bv2YFuxzXpR0EKGDgFGF82mTfg/7GQP0/Z6lua0Zonzu005cdV++fPk+Zl7sTE+t8kZEUwD8PYD7mflJx3dvAzDGzGeI6DoAX2XmK73yXLx4Me/duzd0We3nb+7qGsUDg+M8XTkkhW7VIqzD5f39/eju7k6FLGFiIqufukdNFG2rc2y66foudJ56ObS6O+Orjh9HyrNiKlckST1zJn3v1n46mbNgsGDNeXHQWcjjrdExX20YJbp+z9Lc1gxpmvPiJq66E5FSeUvdtikAEFEelZW1rU7FDQCY+ZfMfKb69w4AeSK6OGYxa9iX64F0m33HvX2QFVm8CCqraSixsImibU23gpqpsyq+qu6Qf1iuSFRE0W/iHqJ5Tg2PxNKGzfZ/luY2IZuk0dqUAHwDwI+Y+UHNNe8AcJyZmYg+gIoS+osYxcwsYVuLtoosXgSRNUmLyCja1v1s52QAVWe3jx+ouSkpDQ2j9/F6Z7du+IkH7LaK0Yyln1e/qfLuNJRZ3EN4Y/0I1lmORt2GYTy3WZrbhGySOuUNwDIAvwNgkIj2V9O+AGAWADDz1wF8HMAfEtEogGEAN3OC+79ZcxWSphdImmTxIoivtSR9S4XdtrqYpYzKea6hgRLuffqg0r/cvU8fNJLFNB6w2ypGsy9frxVGZ953bN+PO7tGcffmndoXtLiNMIOAWr8mFYEgrOc2S3ObkD1St23KzP/EzMTM77O5AtnBzF+vKm5g5q8x83xmXsDMS5n5+0nKLJZFgoosWSGboNoKsjhfHsO6JwdxUmNRqUt3oouv2kHmW6TNPo9u/abK21I3de4gVG4j7ti+H7Nj3krPAgzU+impLeZWe26F1iSNK2+Zo9Ue9jhWCcIow3mwfdWSmU2HJDORy1R23UrVZZ0FrH7oBew6fKKWduWlk3Hu/BhKQ8PIEaHMjKItb1O57n36YE1R6izkseFj82vXNdte9q0gVb3cYo865dTVRRdf9beX1Ifpss4kqfLw8zyqZHHrN69nenikjLserd8m3vDUQU+Fz369XbZ2pDQ0jDsf3Q8GYC3EloaGcfv2/eh9bD+23HiV61zR7Dh3639nGbe/dwS/v25HKHOPIPhBlLcQ6JyUV64sdIbo4DMu4jinFUYZ1sF2izJz7XPQSdRELrdrnOeedKHEJo3vqFPcAODl18/W1cWe995XT+CJfSVPuexnzYCKawQruPreV0+E0l7WVtAVa5+Bn3MKnbbg4W5tbMni9vL1ysP0edTlc8OiYl17Axe27HSKq50yc00ewDvE1vBIGbdv348tzx6qU9btebQbCm8gAICRMeD27ZXTNKq5Iox5wSsEYBRzjyD4JXXbpllEd0wnpV5YXIljCziMMnQH2/0ceA8ilx/ZddaFdkXNi+GRMrbtOWYkl/OsGVCJk7nl2UOhtlffQAkdmu3NzkIe+Q6Hs9sOwoaPza/J6VWXjT1dOLzpOhzZvBKHN13X8EL0ysP0edTl8/xLb/hy9qrCksfPmLaMO66693u4fft+45XMdkTXrmGMcy+r4CjmHkHwi6y8hcApzS9rXXqaiWMLOIwydAfbTQ+8+ynfnm5icWlHdWjZWjkwRVcnE7ms73StUmbGFWufMd66tlaEVDIV8rk6Ja3ZLU23+rilmz6PbvnoDpv3LCxi76snsHX3Uc+VxyDPzEiZPVfqskZnIY9TwyO+Vmq90LVtWPOCm7FBFHOPIPhFlLcQMDkjkRXiqEsYZVjnwlTpUcqVRF/r6moiF1DxTO/2YrHHXgTct65V57cAgEB1qxO6PMJoP688TMvQXddBVDtvplJCn3/pDSNFhEi//ddO7L/ngwDCdTisGy9RzAtOOjT92hFR6DlBUCHbpiHQSg4Z46hLGGWsWjLTV3pYcoUh+7I5042vLeRzWLVkppFczticFqYrAl5b130DJe2qEIONziuG0X5eeZiWodsCLTOj9/ED6H3sgDKwuOmKmihu0aEbL1HMC04mjFO/NnXpghAFsvIWAnYrPOB0nZVg1ojDuWQYZZgcbI9CLqfFZY6opvT0LjA7o7T1lqt9W5suvny6kVx2a1Nn+CgT3BQTN8VufM7sxeXVxnbrz6mFPIgqAd3t13nlYTq+rM93PXqgQcFVnR+0+tltlVNoZPbaZ1DsLOArN13l+8iAijVLZ2nnio09XXjljTN1z9ayOdNDNSR4UxPxQ5cuCFFgpLwR0Wk0vgdOAdgL4C5m/knYgmUN66XS39+P21Z3Jy1OU8ThXDKMMjb2dIVu3WUil/W901KxdLKMvoGSUb223nJ1JHLZlSDdi5IQbPvSTWGZMXWiq2w6Oe04rT/tq3zObV2v9jAdXz0Li7jDh0JhtUEQxbidsYwx8h2EEduSZNjt2DdQwotH64PCv3j0lPFzaUIrHZMRsovpOu+DAHoBFAG8C8DnATwE4BEA34xGNEFILypLxTHm1DhmdpPDWonyu33p5kDXcgXSDKo2tWNqkew3LmWQly6jongAFWvEnBx48mSkzHWKG1Dfjhb5DtIeAQDcrTrjsJZvpWMyQnYx3Tb9EDMvsX3+SyLazcz3EdEXohAsa1jbPTfPPO0aJkeIBp3j16gcDodllWsqn1/nwW6rGfZ7/bSN7uyc9T5utq1N2q40NIw5Lk5Rg/gQVPn1MoEBFPId+PmpN1GWA26BYVQUYPu4AfRW2W5nOOOwlrcsji0lMkeEGxZJKCwhXkyVtzEi+gSAx6ufP277ru1nrazFNo2KvoESjv/8ND7tw/VEWOWqXtgmzm2DEsbWiamiEcR5sI7OQr7ubJifdpimcX47bVIeQ8MjWPd3zTleNj1L5uYUVbfy4hZb1R4Jwa+bjmE559Q0hXwHdq29piH9zkf3+7bqjGNLs2+ghCf2lWpKZJkZT+wrYfHl09tqvheSxXTbdDUqweJfB3C8+vcaIioA+FxEsmUGiW16QXk4Xx5rsM4LswzVdpiu/U2c2wZFtXXSQeRr68R03AR1HuzE7octCG7Ob4+ferPptjZ1gGuh2j7TrbCcPDdSiyW6vm9QOY7eGo1HEQvTbUUroGv3IFadcWxpynwvpAGjlbeqQcJHNV//U3jiZJNWi20aBLcJLYxfo26rT34ddobRL6ptx+K0cijbhM70ZpwHAxcMFJrdSnZzfnu+PAbVb0GvtnbKcsOiih81u7WpLqi9s3+tyA9u22qloeG60EZWzMy4KORzbRk5oeiyqjrGaIhTC+hXNd2sOu1bmpYVethbmjLfC2nA1Nr0EgC3AJhtv4eZfy8asbKFWB9FP6G5KYe69jdxbtsMzm3H/v5+X/c360zWus5NaSl2FmpbUn0DJSy873t1ypDX9qZdudKVcVlnAeNz6pBfDDScUbPyLA0N11kbloaG8cS+Up2zXwC4Yt0zylU/In1eaaUdFbfJ43PYtfYazFm3Q6tYW+O7NDRcicfrsTWqI44tTZnvhTRgum36twCmAvh/ATxj+ycAWD7vEl/prYhu4gprQnNTDnVbJW7Obf1aJEZBM85k7fVwC1dl5WVdp1rF0m35WPdYjmpVZeQ7KlvFM6ZO1G55WmfU1vcN1uUJNCpbKlkKmm2yDgB3bN+vzSsMpk3K18W4DEK7u249PzqGvoGSsaPckTFW+tkDvLdAxdpUaBdMDRYmMfMfRypJhnn+pTd8pbcilsUeMFpLC3NCc/u162Y5qXJuCzT6aEvCwMSvM1nVdcs271Su5uSoPlyV15k4lXJsco7OWiHpLOSx6fpfq62Cqdi25xief+kNzzydsui2zzTv99AgAPd8dH7dVnPgjNK+JBghI2MVFzrWCrB929ovzlVZJ3FZmwKt4ZRdyC6mytt3ieg6Zt4RqTQZRc5AXJjQjh96seGMVRio3DnYlUO3QOLOdJXCE+b5PD/4cSaruk43xsa4PlyV11hUrZCajN+RcuXFfP/SjpqMuhiWZWajPE23jaOGUXlB3759f1P6l3gRuTCWNvZ0Yevuo4Hasmj7oaYjri3NVnLKLmQTU+XtvwD4AhG9BWAE1d+SzPy2yCTLEHGegQjDL9j6vkHXsFJBD7T3LCyi/9TLeGVzdy3NGQZq2ZzpU9hnjAAAIABJREFU2HrL1UoZ3EJAOQ8iAxWF6/bt+2sHzifmCC/df52nnDoFojQ0jCsUbk76Bkp1biSmTcrXVmT84OWHze7ravm8S2oH9y/rLGDS+A68/PqFc2VWO7oFV7/2wf66e9x47dQw1vcN1o0DU6WpNDSMwdIofr96tk131tA0z9LQcE0BdMsrDqLckm0nGMD8L/5vnD0f/MxfaWgY8+7e4fqM966YqzRA8doB8JoTnQTx6+m3jLiIyhemEC2m1qYXRS1IltG9jMJeLQjDL9jeV0/UbVs4fWYFcXKqw6m4AcCuwyew5P7ncPz0+QYZtv3zsZqzU2e5zoPIKt4ss+fkDgBTC3mXAOv1ZQNA72MH6jzDnzw3gt7HD9RkM8HED5v90LbTItLJrsMnsPqhFzBpvPpEVZnZWHEDKu4+nL7Tls+7xNcWl9WPV146WVv2L06/aZyflaeTYmcB586Paq1QhXTSjOJm4fWMf/5RteXw5x/dr31W1/cNus6JToL49fRbRlyEOd8L8eJ6lpaI5lX/f7/qXzwiChZh+AXThZax0sM88OtU3Czsipsdp5d6e7lG569Qmdy9MHGzZZW95dlDDSF9gAvbhaaYyu+HXYdP+FLQTLCPj6BnNn/yxjntdyb940au6ksvwcU4IWHcxtCo5itdOqAPt6VLDzJH+i0jLsRnXXbxWnm7E8CtAB5QfMcAGt1iC5ERhl8w3RxmrXCk7fyeVW6Y5Q8Zrth4lelHpqycf7SvdAWVOcptzjJzoFBWgqBDN179+ol0e178lhEXaZvvBXNcV96Y+dbqnx9m5uX2fwC8DxcJoWLqjsPtOp13dys9apcffrHKDbN807wu6yy4XutHpqz4gLKPj6AyRx1BQBQ3IUy85kQnQeZIv2XERdrme8EcUxdE3zdMEyIkDL9gOl9LVnqYPoyWzZmuTJ9x0Xhles4RtNBermnopIk578nQJC+r7N4Vc5FXBFPM5/yFwvIb+smEZXOma9s4KPbxEVTmVUtm4spLJyu/e9uEcNtAaD/cnvFxmq906QA850QnpnOk3ZfkxLz6VWvq+y4qxGdddvE68/YOIloEoEBEC23n3boBTIpFwgxwZPNKX+lB6VlYxKbru+qchqr8Hrldt7GnC2uWzqr94ssRYc3SWbVDs6ZlmLD1lqsblItlc6Zjz93XKmV44MYF2nLtcln3ODG1NlXVcc3SWcqyexYWseXGBegs5Gv3T5uUx5aPL/DVJs4yVbjJ41SGLGtTVRtDc4/9nedsPuc4UMlsbwM7pMjjuTu7G8q/8tLJ+MG9H2roe50yb31vz9vEUe6yOdM9lcQOUk9+41yinie9SpIFdM+nV8upxkCxs9CgdHk94z/etLLhnnFUSdfhNSc6cc5FqjnS6eD67Pkych2kfFaSJMz5XogXYpc9dyL6JIBPAVgMYK/tq9MA/pqZn4xUupBZvHgx79271/tCn9gtdu7qGsUDg+NQyOfa8iHo7+9Hd3d30mIkQjvUXedWwE/dnXk43aK4uSpQWe0GedaWbd6ptOK1hxMzLR9A7blvB5xtZNr3Qds8zejq3op1VdEOc56OuOpORPuYebEz3XW2YeZvAfgWEd3AzE9EJl3GiToouyCkBVOnwjpUrgmcblHcXBWYRqXwIqh7H1X5STgQThLrMLtfX2dejrZbCTEEEKLG1M/bE0S0EsB8ABNt6fdFJViWkAdViJusOtY0cZni9cOnWQUS0Dv/NdkadZa/bPNOVDYj6rHCJrmFDMsil3UWAvk6czrazhHhhkXqvszq+LaQ4PVC1BgZLBDR1wHcBOA2VI4v3Ajg8gjlyhRisSPEifM8jfXi7BsoJS2aJ6Y/aEyvsx8KX7Z5p3EbuLluWN836CvP3hVz0eFQ+qwVpZ6FxZZaWSKgppD69Q/mdLRdZsYT+0oN7Zvl8W0hhgBC1Jham/4bZv5dACeZ+V4AVwNI1kwmRciDKsRJlh1r+nHT4kUzL3k3w4eHdx/1lWfPwiKK0wp1hh0T8x24Y/t+LNu8ExueOugpTxYgAKuXzkLPwmKg3YYwnIxnBTEEEKLGVHmznshzRHQZKvFNr4hGpOxhYn0kJEPQlZk0k+Vtej9uWrxo5iXvxw2KSZ6dhTx2rb0GX77pKrw1OoaT50Zqyp8uFJvFmqWzjKJ+JIHd+HZqIY/Fl1csm4PsNoThZNxJUs+3Ve5g6ZS23J6FRexaew1e2bwSu9ZeE8n7oBXnN8EMU/Oo7xJRJ4AtAF5EJbrC/4hMqgxinYPp7+/Hbau7kxZHQHbj9nmd98nyeRrVgX8/1qZ23F7y9jacWsiDqBJZw5m/Koi5n7KcBAmD9vxLb6Q23Jc9MtzQ8Aju2L4ft2/fj2mT8sh3UF3oOC+l23Tcml6X1PMd5Lxf5HIgO/ObEA6mBgv/tfrnE0T0XQATmflUdGIJQvNk0QrYZELOutVeGAYHgP4l3zkpX9c+9pUvZ3uaGhOYKsZBVj+zZMxgqWonz40gn6Oq77/RmnFGGNamptcl9XynZV5JixxCMphum9Zg5rcAfICInotAHkEIjSxuL5psBbbieZog2z+6s6bM7iG07O3Zu2KupwNZP4pxFlY/w2KkzJg8YRy6ilONtgXDcDJuJ6nnOy3zSlrkEJLBdeWNiK4B8HUAlwHoA/AnAP4GlbOr90cunSA0QRa3F00n5LBWr9JA0O0fnc+3Owy2Qq32tNxXbN19FPadS0JllclkRclO74q5xluxrUClHdWh0FSYjluT65J6vtMyr6RFDiEZvFbeHgBwK4C3A3gcwG4A/5OZF0UdXYGIPkREh4jox0S0VvH9BCLaXv1+DxHNjlIeIXtk0Qq4Hd3ONGN4oDoUbtJW9ms29nThyzddVbfS8+WbrsKRAAfNexYWtWHEWpEkx2VSz3da5pW0yCEkg1d4rBeZ+f22z4eZeU7kQhHlAPx/AK4F8FMA/wJgFTP/0HbNfwLwPmb+LBHdDOC3mPkmt3yjCo8FALPXPgOgPkyOM7ZpGI4ndXn0DZSw4amDtfM90yblcc9H5wPw741+fd9gnSPNVUtmesbgW/3QC9h1+ESt/lbsTb/1cGPJ/c/h+Onztc8Tc4SRMbjKee2D/Xj59bO1z1deOhnP3dmtlUG1auIVo/Y9657BKF/oe2csRVV72qMKWBQ1v6QJwKUXja+r+7I503Hj4lmubagLQ1UaGq45qXWuLLmNL9X5MKs97aFi7Hl0TsqDGTg1POLaxjos+YD6cTz77QXs/snJWpsuffc0HPnFcK3Mk+f0Fp5WOK3H9h7FrsMn6upy7vxYXd2d5TrbeN7dO/BmmWt9PzFH2PzxBeh97EDdYf4OAGPGtc4WutBgk8fn8FvvL9YZo6jG91duugoAlONrwrgO/OkN76t9rxuXfp5h1T3O/L2MaEzmeydBwsK5zV9B83QSZL638BMiyqsuYRBHGUH6vhl04bG8lLefAPi8LelL9s9Rrb4R0dUANjDziurnddXyNtmuebZ6zQtENA7AzwFcwi4Vikp5szoTaJzIrE4NIyajLo8bFhWx/Z+P1b0sgIqZf66DMFKutwhzK3N936BSsXALomwpbkB9/XUKXJC2cCpuOuxy2uWyYyk+qhiVOnQPp6W4AfV1txQ4XXtGgb0NdTE43e4DoB1fT+wrafO68tLJuPv9QHd3t69yTcl3EECoG8dBsZRBp+KmLDdHAKPBotJqY0txAxr7nhzPXT5XCUp+PoQ6pI0w4rrmc6TtXwIwTmHZqpov7HOxkyObVyrHp6qfndjLM5nvnZg8F846ORURC51CEmReDTLf2zFV3vzWJQhxlBGk75tFp7x5bZv+PYCP2v7ZP38kbCFtFAEcs33+aTVNeQ0zjwI4hcr2bioJw/GkLo9texoVN6Bi5u+cEL3K3LbnmK90ANqXoC49SFuYKG5AvZxucgVx6aBiVDPfW+lu7RY29jb0Uz/rPrfx5ZaXfcIMq13tjIxxKIobgNo2qJfiBlSeHedzZW/jNzUyjSqeO1VewgXc+pfRqFgFddqrGp8mfdOsk2A/YeEsVIqIW3qQeTXIfB8Ev3VJaxlpwnXlLSmI6EYAK5j5M9XPvwPgA8x8m+2ag9Vrflr9fLh6zS8ced2Kyrk9zJgxY9EjjzwSuryDpQteU2YUgOO2lf+u4tSGa5xY1/gpp1l0ZQaR06T+YZbhhUmb+yVo3cOUwZQkyr1iag5TpkxpqtzxuQ6cL0e7uRjW2HC2sbPv24kk6+58Lr3mlqj7Pa75K4551eS9dObMGUyZMsXzujDef2krw7Tvm2X58uX+t01rFxHdqUg+BWAfM4duWtWK26bLNu9Unvcodhawa+01RuXo8tAF2dbhVuacdTu0AbsPb7pOeY/fpeQgbeG2HaKT0+2ezkLe0/O9Hd2SuFfdde0ZFVYb6trY7T5A7XPMZHz99Ycmo7u723e5do5sXtnU/aZlAObjSUWOCA98YkHd2SqTrUO/z2lWCGPbNAiq+cJr27SZ8WWVF2TrzLRce5286mJaRtjzvR3TbVO/dQlC3GWkfdvUYjGAz6KyVVlEZSWrG8BDRPRHYQlp418AXElEVxDReAA3A3jKcc1TAD5Z/fvjAHa6KW5JE4ZlkC6PVUtmVs4FOeig6nkOH2WuWqIOWatLBypnyPykB2mLGReN135nxy6nrvwrL52Ms+dHjfLzYpzGSZiV7tZuYWNvQz/hn6z73MaXW15XXnrBVYSfclWo7s93UMM4bhbd2KgrN0fK56rMjHVPDrr2fUMdcoTxuhsE1/4loKEfglpVKsdXjjxfhM1acQYJC2d/ruzo0oPMq0Hm+yD4rUtay0gTpsrb2wG8n5nvYua7UFHmLgHw7wF8KmyhqmfYPgfgWQA/AvAoMx8kovuI6GPVy74B4O1E9GMAdwJocCcSFzqN257u17GqymmpLo+NPV3YcuOCOhcF0ybl8eAnrsKWjy/w5cx1Y08X1iydhVw12GKOyPPw6tZbrm54GbpZmwZxMrvn7msbFLiJOXKVUyfXufNjyjM20yapXTy4/aL68aaVDS9xu7Wpqj11ioNVvv1aoNI+zglo2Zzp+IrDvYW9DVVtvGbprNoKmz1v6z638WWP3WvHeRjYmce0SXl0FvK1/HSQ5v5iZwFbblzQMI6XzZne0Kb271V85aaras/UkV8MN7TplZdOri/34wuw5cYFtXLsDI+UMWNqARMVSseMqQXcsKhY1wblMmN4xH1LOKdQFNPEOJ/yTR6fq405r36x+tfJhHEd+PJNV2HLjWbzmNdcrBpfN/36TOQc/ZjroLpxay/PZL534vY86ur03J3dyjGqO3wfZF4NMt8HwW9d0lpGkL6PCtNt0x8BWMDM56ufJwDYz8y/SkQDzLwwYjlDIUpXIRZ+TKd1hGGZmhRh1D9qrlj7DFSjngC80sRDaFr3MLbQ00ba697MM+U2Xr5801W49+mD+L05w3VbKPa8r7r3e9ot+mmT8sqYqxbNbrvbx7TpFpnbFt80hRsWa/soq+O3mTGZhfkuStq5/nHVXbdtanpQ4dsAdhPR31Y/fxTANiKaDOCH+tuEIEjMumhJ2jN5O4e1SSouq+6Zuvfpg4E9+U8t5LXuH+zPq9vZyknjx2Hgix/Uft/sGTkGsPC+7+HMmyPQGXSWmev8hbmV6OY/L6vjt52fRyG7GG2bVgPT3wJgCBVDhc8y833MfJaZV0cpYDsik0m0JO2ZvB2jKFi4be0EiW9qiu7ZOXluxLMc3Xghco+havK8xvFMnzw3Arcd286qElryUNy8yOr4befnUcguRsobEX0VwARm/iozf4WZo917bHNkMomWpAO7J608Jo0qpJW1rWkpEFZ807AUOLdnx8t/l268DLmsQtnL1J2l9JIrDkyUUFOyOn7b/XkUsonptumLANYT0a8A+A6A7aLARUdSW0vtRJKB3XUB1dt5SzzqowJuYblMVr9U40UVzsnC/rze89H5uOuxAyg7HMHmO6h2TRih84Kw6fou3OEjXJmOaZPymR2/8jwKWcRIeWPmbwH4FhFNB3ADgD8lolnMfGWk0rUpMpm0Pkkqj2kk6qMCPQuLdbF/7QRd/Vo+7xJlaKFCvqPB8hcA7n36YO3MWGchjw0fm19bdbTHQi0NDaP3sQOBZPJDjgg9C4uuSqgJHUS1OMpZRZ5HIWv49az4HgDzAMyGGCpEikwmQjsRhxHJho/ND3VF+/mX3lCmT588oeHZdXueNzx1sCE808gYY8NTB13LtyxVg55Ts3x5qVb6CXDN13I4XOwsoDitnOq5KqlVTUGIEtMzb39KRC8DuA/AQQCLmPmjkUomCELbEMe5o7DPOupWBf2uYumsUb0igLw5MhZIceug+sDjqnb5N3Omw82rW5m51j92/5JpI+qzlIKQFKYrb68AuJqZ/zVKYQRBaE/iOioQ5oq2brWQgJpT7ShRGRnkiDBhHOGcwry0s5DH/nvUbkns7WIpPF6KoXUm8f6lpr7e40fcLgmtiumZt69bfxPRHACrANzMzO+NSjBBENqLrB0V6F0xF3ds39+g5DDgSzlQOb51S3djjBnDI2q165RhLF+VwqOjsvqY3vBD4nZJaFVMt03fSUS3E9E/o7JtmkNFgRMEIQBR+jQTzGmmH3oWFrWrU36Ug3s+Or8htmc+F8wIoIMIk8ZrYmgSjOrpR/akXZ14IW6XhFbFVXkjoluIaCeAvwdwMYDPAPgZM9/LzINxCCgIrYacw0kHYfSDLl6nH+WgZ2GxIXbrlo8vQM/CIvyGOy0z4+x59aoZM4zqqZPdKUoW3BeJDzehVfFaefvvqKyy/TYzr2fmH8DdCEkQBA/czuEIZoSxchlGP4SlHKgcFwPAWESzrVs9dXVa7RFIPY0k7ZBbEKLC68zbZQBuBPAgEc0A8CiA9JoWCZEh5vbh4WaluGzzTmlbD5xB5q2VJACu7WaN4dLQcM3VhQo/24ZWeccPvQgCMvVs6Orp13hkaHgEyzbvTO3ckLWzlIJggqvyVrUu/XMAf05E7wJwM4DXiehHAL7DzF+IQUYhYYK+LAU1OitFQNrWju4HQxALQucYdgv4zoAvJbpnYRH9p17GK5u7jeuWBty2dk0Vnr6BEkonh1EaqqzUpWH8yg9NoR0wNViYwMw/ZeYvMfMiAD1oPAIhtCiyzRcuqm0pO9K27ufRglgQ+rGgBMI5hxjG1u6yOdN935PvIOQMDsstn3eJ77ydbHn2EMYcinCS41fOkwrtgqmDnhfsH5j5EIAV4YsjpBExtw8X+zkcHe3etm4/GIJYEAZpz2aUkLCUiK23XI0rL613xeH87GTLjQvwwI0XDCBypFbkdBEi/OB3bojaylp+aArtgpe16TuIaBGAAhEtJKL3V/91A5gUi4RC4oi5ffhYB9TDsFZsRdyUgiBGAkHbM6gSHZYS0TdQwk9PvlmXduRfz3neZzeAcK6MWYTxA8HP3BDHqpj80BTaBa+VtxUAvgTgXQAeBPBA9d+dAOS8W5uQVnP79X2DmLNuB2avfQZz1u3A+r7sea9Ja9smjZtSEMSC0G2rupDPaUM8ha30+VEi+gZKuOvRAw1KoDMOqhOnghjlj6/eFXPR4VjZ043fOFbF5Iem0C54GSx8C8C3iOgGZn4iJpmElBFX6CI/rO8bxMO7j9Y+l5lrn62YjVkgjW2bBlTB0u1KgV8LQns7261Ni9X23vvqibrxZBH0XJjOKMVUiVjfN4itu48G8svkVBC92rIZehYW0ffzH6LYmfMcv3GsikVZV0FIE6axTd9LRA3uvpn5vpDlEVJK2sztt+05pk3PkvIGpK9t00AUSq1bO+tWf4KeC2tGiegbKAVW3IBGBdHZllMLeRABd2zfjy3PHjJuV50VZ2chj11ru43kakahNUF+DAntgqnydsb290QAHwHwo/DFEQQzdK4e3FxACNkiTqU27FWhZpSILc8eCqy46RREqy2b8ZGnu6/TULa4VsXkx5DQDpgGpn/A/pmIvgTgqUgkEgQDdE5WdZZ1guBGFKtCfpQI+6qWqeJWrMrs3AJ2K9Ovjzy7Y2Mn1n33LzVzWiCrYoIQHqYrb04mAXh3mIIIgh9WLZmpPKO0asnM2GWxXnA3zzyNu12cu/p1HtpuzkaTrK9qVYgQftQLVR0BNJTtxbI507H1lqt9l+9nhdG52qa/z911iR1ZFROEcDBS3ohoEBdimnYAuBTAf41KKEHwwjrXtm3PMZSZkSPCqiUzYz/vVveCm6nfhvK7XdVuUS2Srq/ToIFwYcILSxZdHSfmO3wpbgBwxSVTAsngZ4XRxLGxWHEKQjKYOun9CIDfBfAQgO0APszM/y0yqQTBgI09XTi86Toc2bwShzddl4ihgqn7A79uEtrN2Wga6mv3vefcugxDFl0dT54b8Z2XzmDHCy/XNHYnuroQbqr7BEGIF1Pl7TcB/E8AF6MSmP6viOi2yKQShIxgug3l90B8uzkbTVN9o5LF7/1u5zeDGua4+chzOtF1w8S3niAI0WF65u0zAJYy81kAIKI/RSVklqy+VTE99yS0FqbbUKbXWeNI9/J026byOjOWljN0KjnicCNhIkfPwmJksujy7Szk8dboWIMVptuWZTOGOapzZ06/iToK+ZwobYKQAkxX3giAfSYpQwLT17D/YgUkGHI7YRohweQ65zhy4rZN5RV6KC0Bu3VyLJ93SayRJtzaI6qoF7p8N3xsvnI1zC32rR/DHK94oiaKm2kkC0EQ4sF05e2vAOwhou9UP/cA+EY0ImUPv+b3QutgP+gOnNa6azBxk+B2QNzLDYTXGEzLGNXJ8fxLb2DT9V2xrQy6tceutdfUrglTFq8xoMpfZe25bM504/OdJoYgXufnip2FWpsIgpAOTP28PUhE/QD+LSo/wj7NzANRCpYl0nReRzAjzC1Eaxuqv78ft63u9rxOh268EOD58vQag2kZo25ypMkpb1Sy+Mk3DL9oJkq72/k5MUoQhHRi7OeNmV8E8GKEsmSWJM7rpJG+gRKO//w0Pr32Gc8XTRjKU9A8knZLoaOZceR1r9+8ozofl5ZnJS1yeOFU4CyLV9O+MFHadQ6vAcg2qSCkFNMzb4ILUZ2RSRqvszLOa9c9OYjz5THPM1VhnL9qJo80uKVQ0cw48rrXT95Rno9Ly7OSFjm8aLYvdMqoPV13fm7N0lmiuAlCShHlLQTs5vdAaxzs9fvS8KMQhaE8NZNHWrYQnbi5cWj2Xj95R6ncNlPHMEmLHBa6H0p++8KZj8oQhAAsn3dJ7fPGni4smzO97ho/5+oEQYifoOGxBAem556ygt8D7n4UojCUp2bySPOWWTNnrbzuNc07auU2LSGSwpAjDBdBbtv4OstjVboqnyf2lfD+WVPx/cMnau5nGMAT+0pYfPn0mn+3F4+eqsvrxaOn0DdQSkU/CYLQiKy8tQh+tjhN8PsCN9meCXKtjmbyyMqWWVKE0T/tQFgugtx+KOn8uanSdfns/slJ14gRaT1GIAiCnlQpb0S0hYheIqIfENF3iKhTc90RIhokov1EtDduOdNGFGeU/L7A/ShE/397dx8lV13nefz9TdOBDsmmEx8a0gRBhxMHzAFMD+BkdzZBFMQHIqImCy6sMzLOHp1lVrMTTlzFEYZolpnjrqMOOh7dBQkjSETFwbihj2fdTSRAYkDJEh4G6GjAJR0TaUnT+e4fdSuprr63uh7uY93P65w+XfWrW/f+fnUf6lu/pxtH8NTJOvLWZJY3cQW3cf+gyJu4gp5GP5SiBhKEpUetJ2odnYxErt23u351oOv2rUje5a3ZdBNwrbu/HNzF4VrgLyOWXe7uv04va/mVxBxeqy9cNGWOqUZf4NXt7N31IAYNRyjGMQVCp+vIS9NdHjX6bJsdhZrXEb1xiqt5ebpm/LDXwibwjVrPDIPDIfFb/6zeprZfr37fHpo43HX7ViTvchW8ufsPa55uAS7LKi9FkkQfpXaCoxVnDzK8/zGeXLesqfXHMempviySEfbZNgrI6qvI8zIpcJLi6js53Q+lZn9ERa0HnLHxw1OWr1bItfpDrQz7ViTvzNu8wXHSzOy7wO3ufkvIa08C+6j0vf17d7+5wXquBq4GGBgYWLJhw4aEclxx8OBBZs+eneg26u361QEOTUy9OM/smcGiE+akmpcsyp+10bFx9u7/HfNmHmbfoRkMzD2O/r7ehu/ZMzrGC78dx3EMY/7xvbnsU1YtW9jxVTWzZwaDs23Sft85sj9y+YXzZx1Z58ye5j6vLNSWPSyfo2PjjOwb47A7A32wdwxmmDE4r6/l8jTa1s/3/GZS02ePGacv+Beh7+2ZYRjw8mE/sp5nXngxcruLB+c2VdZa9fu2Wvba9bVTziIq4/WuVpnLn1bZly9f/oC7D9Wnpx68mdmPgBNCXlrr7t8JllkLDAGXekgGzWyBu+8xs1dTaWr9qLv/eLptDw0N+bZtyXaRGx4eZtmyZYluo159jQh0dgPpTiZozaL8War97D+2+GVu2nnMtJ991L0krzjv5FxNzxB2XEX5+kXHT9rvS9dtDq2Vmjerl9+NT70Je976HDZ7TtWONt3wzJzQc6WT8+ncGzax98ChKekDc2ayde1bmspn1L5o97ZX9eurHvfTrS/u61QelO16V6/M5U+r7GYWGrylPmDB3S9w9zeE/FUDtyuBdwCXhwVuwTr2BP+fA+4Czkkr/3kUZwf8vNzAvCja6bQedS/J6e4xmbZG91qtFTbyMWrQgzuFGNnY7H5dcfYgP1lzPosH5/KTNedHNjW3ez6FBW616c3kM+7R1e2uT6NaReKTqz5vZnYRlQEK/9rdQ+v6zex4YIa7HwgevxX4qxSzmUtx9f+KusB++ruPpHbT8CJpp79hKyMIs9Rsn8mwfEf1mfyL27d3tK20dNqPtFrbFlbjFWf/sGbyGccAoVr165uXQK7hAAAflklEQVTZM6OpH4t5nRxbpIhyFbwBXwCOBTZZ5df8Fnf/sJktAL7q7hcDA8BdwevHAN9093/KKsPdJupCuu/Fcfa9OA5058jBdrXTaT3qXpJRc3plJaps9cJGPkL4D4qogCZv/f06GYzQTHNzXAFLs/mMe3BP7fqGh4dZ1sS68zw5tkjR5GqeN3f/PXdf6O5nBX8fDtL3BIEb7v6Eu58Z/J3h7jdkm+vu0uyFVM0dFe00IUXdSzIqPSthZauX5vx8aeokn800N8cVsOTl82xmTr+85FWkG+QqeJPsNfOFXaXmjvbuazv0mvlTTrwZQXqe1JetWjNY/d9O38qiTJDcST6nOy9aCViuOO/khul5+DxHx8ab6teXh7yKdIu8NZtKxmr7s4yMjkU28YGaO6pava/t+nt3UT/xxuEgPW9fZEnMpVeU+fnazWej5ubBFvub3ffo89OmV/NZ7Wf3F7dvZ/29u1Lrl7p3/+8YG5/8cySqX19R9r1I3il4kymqF9dG/XbU3NE+ddzublGT3rZTy9TssZLlHS0qcwBObcTR8SySHDWbSqhG/XbU3NEZ3fi9u8XZPNjssZLlNBwze8K/RnQ8iyRHNW8SKupXs0FbE3vKUa3ejkiKJ67mwWaPlSxrcwfmHkdf74SOZ5EUqeZNQql2KDnquC3NWnH2IO9ZMjhpsMh7lkwNDLM8X/v7enU8i6RMNW8SKuwXv1HpS7N03WZN0tshddzuDrW3x1qbwHmx8aER7nxg5MigoQl37nxghKHXzJ+0naxrc3U8i6RLwZuEqh91akB1zGk3T9LbyX0o09ZqXotUtiKYNEhgYTLnRaO+bLXbiPsuCiKSbwreJFL113TYja3jvMVPXmQ5Yq9Vrea1SGUrimYDq0600pdNtV8i5aE+bzKtskxtUaQbZ7ea1yKVLUozs/inKY3zon9Wb0vpIlIOCt5kWmUZvFCkILXVvBapbGGqNYfTzeKfpjTOi4j5sSPTRaQcFLzJtMpyT8IiBamt5rVIZQuTx5rDNM6L/WPjLaXnQd5qSEW6kYI3mVZZprYoUpDaal6LVLYweaw5bOe+tq0qWtCdxxpSkW6kAQvSlDJ0hi7SiL1W81qksoWJul9o1kFMq/e1bXXEb9ZTgLQqjUEcIqLgTWSSIgWprea1SGWrV7QgJkw7I36LFHRvfGgkNMCG4vStFCkKBW8Fp7m7pAyKFMREabdWqghBdzUwjZJ1DalIt1HwVmCau0vKpAhBTCN57LcXl7DAtKpoNaQiRaDgrcDUv6RzqrmUtLTbb+8TG3dy29ZnmHCnx4xV5y7k+hWLk8pmWxoFoJ0M4tD5KRJOo00LrJt/yadBI+MkTctf/6qW0qESuN2y5elJ9za9ZcvTfGJjdBNlFqIC0MH+vo4CN52fIuEUvBVY0aYRyJs8zh0m3eu+R59vKR3gtq3PtJSelSSmotH5KRJNwVuBFX3urqyp5lLS1M7xNhFxK4Wo9KwkMRekzk+RaOrzVmDdMAIvS3mdO0y6UzvHW49ZaKDWYxZr3uIQ94ASnZ8i0RS8FVzWI/CqHYpHRsfoMeOaN4yzdt3mQgSR3TB3mBRHO8fbqnMXcsuWp0PTm1HkDv86P0WiKXgrqTgu6hsfGmH1t3YwfvhoZ2oozpQlqrmUNLVzvFVHlbYz2rToUwnp/BSJpuCthOK6qF939yNHArd6RZmyJOuaSymXdo6361csbmtqkG6YSkjnp0g4BW8lFNdFfXRsvOHrSXcsTrtJKM7tJTF3V5GbyCTaKWu+PyXtqXVvn/Z96vAv0r002rSE0rqoJ9mxOGwOqNV37OCsT/+QU9d8n6XrNsc6H1Scc04lMXeX5sTqTmGBW6P0WppKSKR7KXgrobgu6vNm9Ua+lnTH4rDaw/EJZ3RsPJHgJc45p5KYu0tzYkk9TSUk0r0UvJVQXBf1T73zDHp7pk5Z0N/X2/EcT9NpppYwzuAlztrKJObuUhOZ1Eti7jURyQf1eSuhuEZxha1n4fwJtn/qLbHnuV7UHFD14gpe4pxzKom5uzQnloRRh3+R7qTgraTiuqjXr2d4eLjjdTYjbA6oMHEFL3HOOdXp3F1J509ERPJNwZsUUn2tX/+sXg7+7uVJU5fEGbzEOedUJ3N3pZE/yQ8DwhrT83d/BRFJk4I3Kaz6Wr+kp8qIswmq3bm7GlETWfdRc7iIhFHwJi3J81xiCl6k26g5XETC5G60qZldZ2YjZrY9+Ls4YrmLzGyXme02szVp57PexodGWLpuMztH9sc+x1heaC4xkXRpxKiIhMlrzdvfuvt/iXrRzHqAvwPeAjwL3G9md7v7z9PKYK1Jt5taWLx7CDarG263I1I0qlEWkXq5q3lr0jnAbnd/wt0PARuAS7LKTFkmSNVcYiIiItkz72Bi0CSY2XXAVcBvgG3Ax9x9X90ylwEXufufBM8/AJzr7h8JWd/VwNUAAwMDSzZs2BB7nneO7D/yeKAP9tbEMosH58a+vazs+tUBDk0cnpI+s2cGi06YA8DBgweZPXt22lnLBZW9nGWHcpdfZS9n2aHc5U+r7MuXL3/A3Yfq0zNpNjWzHwEnhLy0FvgS8BkqI+Q/A9wEfLB+FSHvDY1C3f1m4GaAoaEhX7ZsWXuZbmDtus1HRoR9bPHL3LSz8rEO9vfx0cvj315WRmubhwN9vT3ceOlilgXNOsPDwyTxGReByr4s62xkpszlV9mXZZ2NzJS5/FmXPZPgzd0vaGY5M/sK8L2Ql54Famc0PQnYE0PW2lKWEWGaS0zyJs+jn0VEkpK7AQtmdqK7/zJ4+m7g4ZDF7gdOM7NTgRFgJfBvUsriFLVBDRxgsIu/RNR5OhsKUqbaWFcT3K0DhURE6uUueAM+Z2ZnUWkGfQr4UwAzWwB81d0vdveXzewjwL1AD/A1d38kqwzD0aBmeHi4q5pKJXsKUsJp9LOIlFXugjd3/0BE+h7g4prn9wD3pJUvkawoSAmn0c8iUlZFnSpEpDQUpISLukWUbh0lIt1OwZt0jepdLk5d8/2uusvF3L7eltLLYvWFi+jr7ZmU1o0DhURE6uWu2VSkHd3cL8zCJsZpkF4WGv0sImWl4E26Qjf3Cxt9cbyl9DLR6GcRKSMFb5JLrU6N0c39whb09x2ZBLo+XUREykd93iR3qk2gI6NjOEebQBv1Yevmzuvq2yUiIrUUvEnuNGoCjdLNAc6Kswe58dLFDPb3YVRuu3bjpYvVXCgiUlJqNpXcaacJtNs7r6tvl4iIVCl4i0m1j9bKhQdYu25zoQKHvN16qd0+XnkOcPL0GecpLyIi0jo1m8agto8WNNdHKy/a6V+WtG5rAs3TZ5ynvIiISHsUvMWgnT5aeZHHvHdbH688fcZ5ykuzunXyZRGRdqnZNAZFnqYir3nPcxNoq/L0GecpL83o5smXRUTapZq3GBR5mooi570o8vQZ5ykvzShiTaGISNIUvMWgyH20ipz3osjTZ5ynvDSjaDWFIiJpULNpDGqnqYADDBZoBF+Rp9goyqjJPH3GecpLM+K8u0RRjpd6Rc23iCRHwVtMqn20hoeH+ejly7LOTkuK2L+saH2h8vQZ5ykv01l94aJJ+xnaqyks2vFSVdR8i0iy1GwqhaS+UOUQ18jjoh4vRc23iCRLNW9SSOoLVR5x1BQW9Xgpar5FJFmqeZNCKtqoSclWUY+XouZbRJKl4E0KqWijJiVbRT1eippvEUmWmk2lKXkb8dbsqMlPbNzJbVufYcKdHjNWnbuQ61csziLLkqGijbKtKmq+RSRZCt5kWnkd8TZdX6hPbNzJLVuePvJ8wv3IcwVw5ZOHUbbt/AjKQ75FJF/UbCrTKuqIt9u2PtNSepp0v87yqf4IGhkdwzn6I0j7XkRapeBNplXUEW8T7i2lp0Vf4uVU1B9BIpI/Ct5kWkUd8dZj1lJ6WvQlXk5F/REkIvmj4E2mVdQRb6vOXdhSelr0JV5ORf0RJCL5o+BNphXXLPet6rRf2PUrFnPFeScfqWnrMeOK807OfLCCvsTLqag/gkQkfzTaVJqS9oi3uEa4Xr9icebBWr247tcpxaJpP0QkLgreJJca9Qsr+pedvsTLS9N+iEgcFLxJLo1E9P+KSi8afYlLt8rbhN4i3UjBW07EccHL40Wz2TzVL2dA2IQeWY8UFZFoeZ3QW6TbaMBCDsQx71ce5w5rNk9hy0XNxJb1HG0iEk3T4IikQ8FbDsRxwfv0dx/J3UWz2XKFLRdlUCMyRXJL0+CIpEPBWw50esHb+NAI+14c72gdSWi2XK3kUSMyRfJL0+CIpCNXwZuZ3W5m24O/p8xse8RyT5nZzmC5bWnnM26dXvAa1a5ledFstlzN5rG/r1f9ZtB9USW/NJedSDpyFby5+/vd/Sx3Pwu4E/h2g8WXB8sOpZS9xHR6wWtUc5XlRXP1hYvonTF5gEHvDGP1hYsmBSC/fellensaD0To6+3hunedkWR2CyGPfRtFqrKa0FukbHI52tTMDHgfcH7WeUlDp/N+LejvC51CIxc1VfUxmcG2f36BOx8YOdLPbXRsnN4ZxrxZvYy+OM6C/j6Wv/5V3Pfo87kaOZsH3Tz/nXQHTYMjkjzzHI7eM7M/Av4mqlbNzJ4E9lGZTeLv3f3mBuu6GrgaYGBgYMmGDRsSyPFRBw8eZPbs2Yluo97o2Dgj+8Y4XLMvZ5gxOK+P/r7eVPNSW/5dvzrAoYnDU5YxDA8ZTzqzZwaLTpiTeB6Tksa+3zmyP/K1xYNzE912I1kc93lS5vKr7OUsO5S7/GmVffny5Q+ExUKpB29m9iPghJCX1rr7d4JlvgTsdvebItaxwN33mNmrgU3AR939x9Nte2hoyLdtS7aL3PDwMMuWLUt0G2HyMsdbbflPXfP9yCk/whjw5Lq3J5GtVKSx75eu2xxayzrY38dP1mRXUZ3VcZ8XZS6/yr4s62xkpszlT6vsZhYavKXebOruFzR63cyOAS4FljRYx57g/3NmdhdwDjBt8NbN6pteq4MYsmy+iGrO7TELna9NI9Kmp/uiiohIrgYsBC4AHnX3Z8NeNLPjzWxO9THwVuDhFPOXS3nsyB41EGPVuQs1Iq1N6hAuIiJ5HLCwEritNsHMFgBfdfeLgQHgrsqYBo4Bvunu/5R6LnMmjx3ZGw3EGHrN/Fw08xaROoTLdPLSjUJEkpG74M3drwpJ2wNcHDx+Ajgz5WzlXl5nNo8KNBSAiCRD9xcV6X55bDaVNmhmcxEB3V9UpAwUvHUJzWwuIpDfWngRiY+Cty6hjuwiAqqFFymD3PV5k/apH5mIaDoZke6n4E1EpIt0ers9Eck/BW8iIl1GtfAi3U193kREREQKRMGbiIiISIEoeBMREREpEAVvIiIiIgWi4E1ERESkQBS8iYiIiBSIgjcRERGRAlHwJiIiIlIgCt5ERERECkTBm4iIiEiBKHgTERERKRAFbyIiIiIFouBNREREpEAUvImIiIgUiII3ERERkQI5JusMiIhItI0PjbD+3l3sGR1jQX8fqy9cxIqzB7POlohkSMGbiEhObXxohGu/vZOx8QkARkbHuPbbOwEUwImUmJpNRURyav29u44EblVj4xOsv3dXRjkSkTxQ8CYiklN7RsdaSheRclDwJiKSUwv6+1pKF5FyUPAmIpJTqy9cRF9vz6S0vt4eVl+4KKMciUgeaMCCiEhOVQclaLSpiNRS8BaT6nD+lQsPsHbdZl1gRSQWK84e1LVERCZR8BaDScP5F2o4v4iIiCRHfd5ioOH8IuW08aERlq7bzM6R/Sxdt5mND41knSURKQHVvMVAw/lFykc17iKSFdW8xUDD+UXKRzXuIpIVBW8x0HB+kfJRjbuIZCWTZlMzey9wHfD7wDnuvq3mtWuBPwYmgD9393tD3n8qsAGYDzwIfMDdD6WQ9VArzh7kmtu3T0obG59ouukk7htPp3Uj67Dt9Me47uvufoTRsXEA5s3q5VPvPGNSOcK2D5OnVTjlFX1seWIfE+70mLHq3IVcv2Jxx3mr3cby17+K+x59PlcjjVs9Brrt5udplGdBfx8jIYFa3DXu596wib0Hjl7eBubMZOvat8S6DRFpTl5mlsiq5u1h4FLgx7WJZnY6sBI4A7gI+KKZ9Ux9O58F/tbdTwP2UQn2MnPKmu+3lF6r2m9mZHQM52i/mXY7Pse9vla3Uw22Ol336m/tmLSufS+Os/qOHUfKEbb91d/aweo7dkxK+8njLzDhDsCEO7dseZpPbNzZUd7qt3vLlqePfIkn9Xl3msdGeUrrmElLWuWZNTP88hmV3o76wA1g74FDnHvDpti2ISLNqb22QLbXykyCN3f/hbuHdQy5BNjg7i+5+5PAbuCc2gXMzIDzgTuCpG8AK5LMb5Li7jeTVj+cqO3s3f+7WNY9ftinpI9P+JFyhG1//LAzPjH1ffVu2/pMR3mr3269rPs9tXoMdFvfrbTK89hzv20pvR31gdt06SKSnDxdK819+i+7xDZuNgx8vNpsamZfALa4+y3B838AfuDud9S855XBMr8XPF8YLPOGiG1cDVwNMDAwsGTDhg2xl2PnyP4jjwf6YG9NS8riwblNv7fedO9NY32tbmegD149v7PtNCoDVMox3TLTafezaLTdVvd9Ulo9BuI4Zg4ePMjs2bObWjZpWZwDSe37tMrSiTzt+7SVuexQvvKncc7XW758+QPuPlSfnlifNzP7EXBCyEtr3f07UW8LSauPLptZ5ugL7jcDNwMMDQ35smXLohZt21U1zaMfW/wyN+08+rE+dXnj7a1dtzm038xgfx8fnea9aayv1e1ce9Zh3tfhZxy1bjhajkbLTKfHjMfb/Cwabbd238f9ebei1WMgjmNmeHiYJM6tdqR1DnRy3rezjXpxbaNTedr3aStz2aF85a+9tmR9vU+s2dTdL3D3N4T8RQVuAM8CC2uenwTsqVvm10C/mR3TYJnCiHukalojX6O2MzD3uFjW3Ttjaoze22NHyhG2/d4ZRm9PWGw/2apzF067TKO81W+3XtYjjVs9BrpttHRa5Vn6uvktpbdjYM7MltJFJDl5ulbmbaqQu4GVZnZsMKL0NOCntQt4pZ33PuCyIOlKoFFAmLin1r29pfRaK84e5MZLFzPY34dRieBvvHRx26NX4l5fq9vp7+uNZd3r33vmpHXNm9XL+svOPFKOsO2vf++ZrL/szElpS183nx6rBHQ9Zlxx3skdjTYN2+4V553MYDDCMKnPu9M8NspTWsdMWtIqz60fetOUQG3p6+Zz64feFNs2tq59y5RATaNNRbJRe22BjK+V7p76H/BuKrVsLwF7gXtrXlsLPA7sAt5Wk34PsCB4/FoqQd1u4FvAsc1sd8mSJZ60++67L/Ft5FmZy6+yl1eZy6+yl1eZy59W2YFtHhLPZDLPm7vfBdwV8doNwA0h6RfXPH6CulGoIiIiImWQt2ZTEREREWlAwZuIiIhIgSh4ExERESkQBW8iIiIiBaLgTURERKRAFLyJiIiIFIiCNxEREZECUfAmIiIiUiAK3kREREQKRMGbiIiISIEoeBMREREpEAVvIiIiIgVilZvWl4OZPQ/8c8KbeSXw64S3kWdlLr/KXl5lLr/KXl5lLn9aZX+Nu7+qPrFUwVsazGybuw9lnY+slLn8Kns5yw7lLr/KXs6yQ7nLn3XZ1WwqIiIiUiAK3kREREQKRMFb/G7OOgMZK3P5VfbyKnP5VfbyKnP5My27+ryJiIiIFIhq3kREREQKRMGbiIiISIEoeGuDmb3XzB4xs8NmNlT32rVmttvMdpnZhRHvP9XMtprZY2Z2u5nNTCfn8Qryvj34e8rMtkcs95SZ7QyW25Z2PpNiZteZ2UjNZ3BxxHIXBcfDbjNbk3Y+k2Bm683sUTP7mZndZWb9Ect1zb6fbj+a2bHBObE7OL9PST+XyTCzhWZ2n5n9Irj2/YeQZZaZ2f6a8+GTWeQ1CdMdx1bxX4N9/zMze2MW+UyCmS2q2afbzew3ZnZN3TJds+/N7Gtm9pyZPVyTNt/MNgXf2ZvMbF7Ee68MlnnMzK5MNKPurr8W/4DfBxYBw8BQTfrpwA7gWOBU4HGgJ+T9/wisDB5/GfizrMsUw2dyE/DJiNeeAl6ZdR4TKPN1wMenWaYnOA5eC8wMjo/Ts857DGV/K3BM8PizwGe7ed83sx+Bfw98OXi8Erg963zHWP4TgTcGj+cA/zek/MuA72Wd14TK3/A4Bi4GfgAYcB6wNes8J/Q59AC/ojJxbFfue+CPgDcCD9ekfQ5YEzxeE3a9A+YDTwT/5wWP5yWVT9W8tcHdf+Huu0JeugTY4O4vufuTwG7gnNoFzMyA84E7gqRvACuSzG/SgjK9D7gt67zk0DnAbnd/wt0PARuoHCeF5u4/dPeXg6dbgJOyzE8KmtmPl1A5n6Fyfr85ODcKz91/6e4PBo8PAL8ABrPNVa5cAvx3r9gC9JvZiVlnKgFvBh5396TvVJQZd/8x8EJdcu25HfWdfSGwyd1fcPd9wCbgoqTyqeAtXoPAMzXPn2XqBe4VwGjNF1/YMkXzr4C97v5YxOsO/NDMHjCzq1PMVxo+EjSTfC2iKr2ZY6LoPkil1iFMt+z7ZvbjkWWC83s/lfO9qwTNwWcDW0NefpOZ7TCzH5jZGalmLFnTHcdlOM+hUqMc9SO9W/c9wIC7/xIqP2SAV4csk+oxcExSKy46M/sRcELIS2vd/TtRbwtJq5+LpZllcqPJz2EVjWvdlrr7HjN7NbDJzB4Nft3kXqPyA18CPkNl/32GStPxB+tXEfLe3O7vWs3sezNbC7wM3BqxmsLu+zpdd263w8xmA3cC17j7b+pefpBKc9rBoP/nRuC0tPOYkOmO4zLs+5nAu4BrQ17u5n3frFSPAQVvEdz9gjbe9iywsOb5ScCeumV+TaVK/Zjg13nYMrkx3edgZscAlwJLGqxjT/D/OTO7i0oTVCG+wJs9DszsK8D3Ql5q5pjIpSb2/ZXAO4A3e9DpI2Qdhd33dZrZj9Vlng3Oi7lMbX4pLDPrpRK43eru365/vTaYc/d7zOyLZvZKdy/8jcubOI4Le5634G3Ag+6+t/6Fbt73gb1mdqK7/zJoDn8uZJlnqfT9qzqJSr/4RKjZNF53AyuDUWenUvnl8dPaBYIvufuAy4KkK4GomrwiuAB41N2fDXvRzI43sznVx1Q6uj8ctmzR1PVpeTfh5bofOM0qI4xnUml2uDuN/CXJzC4C/hJ4l7u/GLFMN+37Zvbj3VTOZ6ic35ujgtqiCfru/QPwC3f/m4hlTqj28TOzc6h8v/y/9HKZjCaP47uBfxuMOj0P2F9tZusikS0s3brva9Se21Hf2fcCbzWzeUEXmrcGaclIeyRHN/xR+aJ+FngJ2AvcW/PaWiqj0nYBb6tJvwdYEDx+LZWgbjfwLeDYrMvUwWfxdeDDdWkLgHtqyroj+HuESpNb5vmOqez/A9gJ/IzKyX1iffmD5xdTGZ33eLeUPzh2nwG2B3/VUZZdu+/D9iPwV1QCWIDjgvN5d3B+vzbrPMdY9n9JpQnoZzX7/GLgw9XzH/hIsJ93UBnE8odZ5zumsocex3VlN+DvgmNjJzWzEHTDHzCLSjA2tyatK/c9lQD1l8B48D3/x1T6rv5P4LHg//xg2SHgqzXv/WBw/u8G/l2S+dTtsUREREQKRM2mIiIiIgWi4E1ERESkQBS8iYiIiBSIgjcRERGRAlHwJiIiIlIgCt5EpGuY2f9ucrlXmdm4mf1ph9v7upk9aWbbg1sDvbmJ91xlZgtqnn/VzE7vJB8iUi4K3kSka7j7Hza56HupzEe1KobNrnb3s4BrgC83sfxVVObDA8Dd/8Tdfx5DPkSkJBS8iUjXMLODwf9lZjZsZneY2aNmdmt1BvjAKuBjwElmNhi858/M7HM167rKzP5b8Pg/B+vZZGa3mdnHQzb/f6i5EbWZfdLM7jezh83s5mD2/cuoTOx5a1Bb1xfkc6iafzO7IajF22JmA0H664Ln95vZX1XLKSLlpOBNRLrV2VRqw06nMkv+UgAzWwic4O4/Bf4ReH+w/B1U7tNb9X7g9iCwek+wvkupBF9hLqJyQ+6qL7j7H7j7G4A+4B3ufgewDbjc3c9y97G6dRwPbHH3M6ncO/NDQfrngc+7+x/QfffMFJEWKXgTkW71U3d/1t0PU7md0ylB+koqQRvABoKmU3d/HnjCzM4zs1cAi4CfULk11HfcfczdDwDfrdvOejN7ArgF+Oua9OVmttXMdgLnA2c0kedDwPeCxw/U5PlNVG69BfDNJtYjIl3smKwzICKSkJdqHk9w9Hq3Chgws8uD5wvM7DR3fwy4HXgf8Chwl7t7XXNrmNXAt4E/B74BLDGz44AvUrnH5TNmdh2Ve59OZ9yP3rOwNs8iIkeo5k1ESsPMFgHHu/ugu5/i7qcAN1KpjYNKELaCSoB3e5D2v4B3mtlxZjYbeHv9eoPavc8DM8zsQo4Gar8O3nNZzeIHgDktZn0LlaZbavIqIiWl4E1EymQVcFdd2p0cbTrdB/wceE3QJw53vx+4G9hBJbjbBuyvX3FQY3Y98J/cfRT4CrCTSj+4+2sW/Trw5eqAhSbzfQ3wH83sp8CJYdsXkfKwozX0IiISxsxmu/tBM5tFZSDB1e7+YIrbnwWMBc24K4FV7n5JWtsXkXxRfwoRkendHEykexzwjTQDt8AS4AtB/7tR4IMpb19EckQ1byIiIiIFoj5vIiIiIgWi4E1ERESkQBS8iYiIiBSIgjcRERGRAlHwJiIiIlIg/x9BIrg9mT1WDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches(10.0, 7.0)\n",
    "\n",
    "# Linear scale.\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "\n",
    "plt.scatter(stats['inAvgRating'], stats['outAvgRating'])\n",
    "\n",
    "plt.title('In & out - average ratings')\n",
    "plt.xlabel('inAvgRating')\n",
    "plt.ylabel('outAvgRating')\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "#fig.savefig(\"output/avg_in_out_rating_2d_projection.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quad 1: 4916\n",
      "Quad 2: 353\n",
      "Quad 3: 476\n",
      "Quad 4: 143\n",
      "Total check: 5881\n"
     ]
    }
   ],
   "source": [
    "zeroes = len(stats[(stats['inAvgRating'] == 0) & (stats['outAvgRating'] == 0)])\n",
    "quad_1 = len(stats[(stats['inAvgRating'] >= 0) & (stats['outAvgRating'] >= 0)])\n",
    "quad_2 = len(stats[(stats['inAvgRating'] < 0) & (stats['outAvgRating'] > 0)])\n",
    "quad_3 = len(stats[(stats['inAvgRating'] <= 0) & (stats['outAvgRating'] <= 0)])\n",
    "quad_4 = len(stats[(stats['inAvgRating'] > 0) & (stats['outAvgRating'] < 0)])\n",
    "\n",
    "print(\"Quad 1: {0}\".format(quad_1))\n",
    "print(\"Quad 2: {0}\".format(quad_2))\n",
    "print(\"Quad 3: {0}\".format(quad_3))\n",
    "print(\"Quad 4: {0}\".format(quad_4))\n",
    "print(\"Total check: {0}\".format(quad_1 + quad_2 + quad_3 + quad_4 -  zeroes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of nodes with only in-links or only out-links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average number of links for only in-linked nodes 1.711340206185567\n",
      "Quantity of nodes with only in-links: 1067\n",
      "Average rating of nodes with only in-links: -0.6296651166700277\n",
      "Sum of ratings of only in-nodes: -1364.0\n",
      "\n",
      "Average number of links for only out-linked nodes 6.925681431005111\n",
      "Quantity of nodes with just out-links: 4696\n",
      "Average rating of nodes with only out-links: 1.6359049766932803\n",
      "Sum of ratings of only in-nodes: 31069.0\n"
     ]
    }
   ],
   "source": [
    "weighted_in_degree = dict()\n",
    "weighted_out_degree = dict()\n",
    "for instance in data.values:\n",
    "    weighted_in_degree[instance[1]] = weighted_in_degree.get(instance[1], 0) + instance[2]\n",
    "    weighted_out_degree[instance[0]] = weighted_out_degree.get(instance[0], 0) + instance[2]\n",
    "# COUNT NUMBERS OF NODES WITH ONLY IN EDGES, COUNT OF ITS SUM\n",
    "sum_just_in = 0\n",
    "sum_just_out = 0\n",
    "avg_just_in = 0\n",
    "avg_just_out = 0\n",
    "i = 0\n",
    "number_of_edges = list()\n",
    "just_in_nodes = dict()\n",
    "just_out_nodes = dict()\n",
    "\n",
    "for key, value in weighted_in_degree.items():\n",
    "    if key not in weighted_out_degree:\n",
    "        just_in_nodes[key] = value\n",
    "\n",
    "        number_of_edges.append(graph.in_degree[key])\n",
    "        i += 1\n",
    "        sum_just_in += value\n",
    "        avg_just_in += value / graph.in_degree[key]\n",
    "\n",
    "print(\"Average number of links for only in-linked nodes\",sum(number_of_edges)/len(number_of_edges))\n",
    "print(\"Quantity of nodes with only in-links:\", i)\n",
    "print(\"Average rating of nodes with only in-links:\", avg_just_in / i)\n",
    "print(\"Sum of ratings of only in-nodes:\",sum_just_in)\n",
    "\n",
    "# COUNT NUMBERS OF NODES WITH ONLY OUT EDGES, COUNT OF ITS SUM\n",
    "i = 0\n",
    "number_of_edges = list()\n",
    "for key, value in weighted_out_degree.items():\n",
    "    if key not in weighted_in_degree_list:\n",
    "        just_out_nodes[key] = value\n",
    "        i += 1\n",
    "        sum_just_out += value\n",
    "        avg_just_out += value / graph.out_degree[key]\n",
    "        number_of_edges.append(graph.out_degree[key])\n",
    "print()\n",
    "print(\"Average number of links for only out-linked nodes\", sum(number_of_edges) / len(number_of_edges))\n",
    "print(\"Quantity of nodes with just out-links:\", i)\n",
    "print(\"Average rating of nodes with only out-links:\", avg_just_out / i)\n",
    "print(\"Sum of ratings of only in-nodes:\",sum_just_out)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6) Stats summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Degree</th>\n",
       "      <th>InDegree</th>\n",
       "      <th>OutDegree</th>\n",
       "      <th>WeightedDegree</th>\n",
       "      <th>WeightedInDegree</th>\n",
       "      <th>WeightedOutDegree</th>\n",
       "      <th>inAvgRating</th>\n",
       "      <th>outAvgRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>5881.000000</td>\n",
       "      <td>5881.000000</td>\n",
       "      <td>5881.000000</td>\n",
       "      <td>5881.000000</td>\n",
       "      <td>5881.000000</td>\n",
       "      <td>5881.000000</td>\n",
       "      <td>5881.000000</td>\n",
       "      <td>5881.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>12.104064</td>\n",
       "      <td>6.052032</td>\n",
       "      <td>6.052032</td>\n",
       "      <td>12.249617</td>\n",
       "      <td>6.124809</td>\n",
       "      <td>6.124809</td>\n",
       "      <td>0.725761</td>\n",
       "      <td>1.345783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>38.298915</td>\n",
       "      <td>17.675082</td>\n",
       "      <td>21.088387</td>\n",
       "      <td>66.453676</td>\n",
       "      <td>38.843600</td>\n",
       "      <td>38.521048</td>\n",
       "      <td>2.821867</td>\n",
       "      <td>2.208497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-706.000000</td>\n",
       "      <td>-675.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.690000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1298.000000</td>\n",
       "      <td>535.000000</td>\n",
       "      <td>763.000000</td>\n",
       "      <td>1890.000000</td>\n",
       "      <td>1041.000000</td>\n",
       "      <td>874.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Degree     InDegree    OutDegree  WeightedDegree  \\\n",
       "count  5881.000000  5881.000000  5881.000000     5881.000000   \n",
       "mean     12.104064     6.052032     6.052032       12.249617   \n",
       "std      38.298915    17.675082    21.088387       66.453676   \n",
       "min       1.000000     0.000000     0.000000     -706.000000   \n",
       "25%       2.000000     1.000000     1.000000        2.000000   \n",
       "50%       4.000000     2.000000     2.000000        4.000000   \n",
       "75%       9.000000     5.000000     4.000000       12.000000   \n",
       "max    1298.000000   535.000000   763.000000     1890.000000   \n",
       "\n",
       "       WeightedInDegree  WeightedOutDegree  inAvgRating  outAvgRating  \n",
       "count       5881.000000        5881.000000  5881.000000   5881.000000  \n",
       "mean           6.124809           6.124809     0.725761      1.345783  \n",
       "std           38.843600          38.521048     2.821867      2.208497  \n",
       "min         -675.000000        -999.000000   -10.000000    -10.000000  \n",
       "25%            1.000000           1.000000     1.000000      0.690000  \n",
       "50%            2.000000           2.000000     1.000000      1.000000  \n",
       "75%            6.000000           7.000000     1.690000      2.000000  \n",
       "max         1041.000000         874.000000    10.000000     10.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_description = stats.describe().drop('Node', axis=1)\n",
    "stats_description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "func.correlation_analysis(stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connected components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.is_strongly_connected(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1144"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The condensation of G is the graph with each of the strongly connected components contracted into a single node.\n",
    "condensated_graph = nx.condensation(graph)\n",
    "len(nx.nodes(condensated_graph))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1215"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reciprocal (bool (optional)) – If True only keep edges that appear in both directions in the original digraph.\n",
    "nx.number_connected_components(nx.DiGraph.to_undirected(graph, reciprocal=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.number_connected_components(nx.DiGraph.to_undirected(graph))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7923128792987189\n"
     ]
    }
   ],
   "source": [
    "rep = nx.algorithms.reciprocity(graph)\n",
    "type(rep)\n",
    "print(rep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}