twitter_retweet_network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as pl\n",
    "from collections import defaultdict\n",
    "from pprint import pprint\n",
    "import itertools\n",
    "import random\n",
    "\n",
    "pl.rcParams['figure.figsize'] = [12, 12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 18470\n",
      "Number of edges: 48053\n",
      "Average degree:   5.2034\n",
      "{'cen': 0, 'con': 7115, 'lib': 11355, 'total': 18470}\n"
     ]
    }
   ],
   "source": [
    "g = nx.read_graphml('retweet.graphml')\n",
    "if g.is_directed():\n",
    "    g = g.to_undirected()\n",
    "# convert from multigraph to standard graph\n",
    "g = nx.Graph(g)\n",
    "nodes = g.nodes(data=True)\n",
    "edges = g.edges()\n",
    "info = nx.info(g)\n",
    "print(info)\n",
    "nodes = {k: v for k, v in nodes}\n",
    "edges = tuple((k, v) for k, v in edges)\n",
    "fname = 'cluster'\n",
    "fcon = 'right'\n",
    "flib = 'left'\n",
    "fcen = '-'\n",
    "con = [k for k, v in nodes.items() if v[fname] == fcon]\n",
    "lib = [k for k, v in nodes.items() if v[fname] == flib]\n",
    "cen = [k for k, v in nodes.items() if v[fname] == fcen]\n",
    "pprint(dict(con=len(con), lib=len(lib), cen=len(cen), total=(len(con) + len(lib) + len(cen))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'modularity': 0.4753748002121123}\n"
     ]
    }
   ],
   "source": [
    "# calc modularity of network\n",
    "from networkx.algorithms.community.quality import modularity\n",
    "q =  modularity(g, [con, lib, cen])\n",
    "\n",
    "pprint(dict(modularity=q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edge Sample:\n",
      "[('n5461', 'n12407'),\n",
      " ('n12356', 'n16645'),\n",
      " ('n3558', 'n10177'),\n",
      " ('n3740', 'n14671'),\n",
      " ('n12033', 'n14025'),\n",
      " ('n6555', 'n15394'),\n",
      " ('n4167', 'n15341'),\n",
      " ('n5525', 'n7499'),\n",
      " ('n3339', 'n12711'),\n",
      " ('n764', 'n14553'),\n",
      " ('n1126', 'n17445'),\n",
      " ('n5010', 'n10255'),\n",
      " ('n14358', 'n15141'),\n",
      " ('n7406', 'n16397'),\n",
      " ('n8190', 'n16282'),\n",
      " ('n5081', 'n11150'),\n",
      " ('n13014', 'n13456'),\n",
      " ('n1209', 'n13417'),\n",
      " ('n2031', 'n18451'),\n",
      " ('n11993', 'n16260'),\n",
      " ('n10277', 'n13580'),\n",
      " ('n4367', 'n5008'),\n",
      " ('n1110', 'n6438'),\n",
      " ('n764', 'n4864'),\n",
      " ('n9463', 'n16348')]\n",
      "Nodes from Edges Sample:\n",
      "{'-': {},\n",
      " 'left': {'n10277': (...,),\n",
      "          'n1110': (...),\n",
      "          'n11150': (...),\n",
      "          'n11993': (...),\n",
      "          'n12033': (...),\n",
      "          'n12356': (...),\n",
      "          'n13580': (...),\n",
      "          'n14025': (...),\n",
      "          'n15341': (...),\n",
      "          'n16260': (...),\n",
      "          'n16282': (...),\n",
      "          'n16645': (...,),\n",
      "          'n18451': (...),\n",
      "          'n2031': (...),\n",
      "          'n4167': (...),\n",
      "          'n4367': (...,),\n",
      "          'n5008': (...),\n",
      "          'n5081': (...),\n",
      "          'n5525': (...),\n",
      "          'n6438': (...,),\n",
      "          'n7499': (...),\n",
      "          'n8190': (...)},\n",
      " 'right': {'n10177': (...),\n",
      "           'n10255': (...),\n",
      "           'n1126': (...),\n",
      "           'n1209': (...),\n",
      "           'n12407': (...),\n",
      "           'n12711': (...),\n",
      "           'n13014': (...),\n",
      "           'n13417': (...),\n",
      "           'n13456': (...),\n",
      "           'n14358': (...),\n",
      "           'n14553': (...),\n",
      "           'n14671': (...),\n",
      "           'n15141': (...),\n",
      "           'n15394': (...),\n",
      "           'n16348': (...),\n",
      "           'n16397': (...),\n",
      "           'n17445': (...),\n",
      "           'n3339': (...),\n",
      "           'n3558': (...),\n",
      "           'n3740': (...),\n",
      "           'n4864': (...),\n",
      "           'n5010': (...),\n",
      "           'n5461': (...),\n",
      "           'n6555': (...),\n",
      "           'n7406': (...),\n",
      "           'n764': (...),\n",
      "           'n9463': (...)}}\n"
     ]
    }
   ],
   "source": [
    "esample = random.sample(edges, 25)\n",
    "print(\"Edge Sample:\")\n",
    "pprint(esample)\n",
    "\n",
    "nsample = {'left': {}, 'right': {}, '-': {}}\n",
    "for u, v in esample:\n",
    "    uclust = g.nodes[u]['cluster']\n",
    "    vclust = g.nodes[v]['cluster']\n",
    "    if u not in nsample[uclust]:\n",
    "        nsample[uclust][u] = tuple(g.neighbors(u))\n",
    "    if v not in nsample[vclust]:\n",
    "        nsample[vclust][v] = tuple(g.neighbors(v))\n",
    "\n",
    "print(\"Nodes from Edges Sample:\")\n",
    "pprint(nsample, depth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n3189 10\n"
     ]
    }
   ],
   "source": [
    "def ratio_calc(graph, node, sweight=1, oweight=0, nweight=0.5, \n",
    "               field='cluster', neutral='-', right='right', left='left', \n",
    "               no_alignment_value=None):\n",
    "    \"\"\"\n",
    "    Perform a ratio calculation on given node in graph.\n",
    "    \"\"\"\n",
    "    nbors = tuple(graph.neighbors(node))\n",
    "    nclust = graph.nodes[node][field]\n",
    "    \n",
    "    if nclust == neutral:\n",
    "        return no_alignment_value\n",
    "    \n",
    "    counts = { k: float(len(list(v))) for k,v in itertools.groupby(nbors, key=lambda y: graph.nodes[y][field]) }\n",
    "    nother = right if nclust == left else left\n",
    "    ratio = ((sweight * counts.get(nclust, 0)) + (oweight * counts.get(nother, 0)) + (nweight * counts.get(neutral, 0))) \\\n",
    "                / len(nbors)\n",
    "    return ratio\n",
    "\n",
    "ratios = { n: ratio_calc(g, n) for n in g.nodes() }\n",
    "ratios = { k: v for k, v in ratios.items() if v }\n",
    "#pprint(ratios)\n",
    "print(\"n3189\", len(list(g.neighbors(\"n3189\"))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JfjxJuvu6qro0yfVJ7k7yiu6+Z1rPK5NcnmRLkou7+7ppE/8qybur6ueTfCzJ29dq5wAA4FCp2Unhw8/CwkLv2rVro5sBAMCDWFVd090LS03zzYYAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwIADBumquriqbq2qT87V3lBVf1JV11bVb1bVMVP9lKq6s6o+Pt1+dW6ZZ1TVJ6pqd1W9uapqqj+2qq6oqk9NP489BPsJAABraiVnpN+R5Mx9alck+bbu/vYkf5rk1XPTPt3dp023l8/V35rkZUl2TLfFdV6Q5Mru3pHkyukxAABsagcM0t39kSS371P77e6+e3p4VZIT97eOqjohyaO7+6ru7iTvTHL2NPmsJJdM9y+ZqwMAwKa1Fn2k/2mSD849PrWqPlZVH66q755q25PsmZtnz1RLkuO7++bp/heSHL8GbQIAgEPqyNUsXFU/k+TuJL82lW5OcnJ3f6mqnpHk/VX1lJWur7u7qno/2zs/yflJcvLJJ483HAAAVmn4jHRV/ViSf5DkRVN3jXT317v7S9P9a5J8OskTk+zN/bt/nDjVkuSWqevHYheQW5fbZndf1N0L3b2wbdu20aYDAMCqDQXpqjozyb9M8oLu/tpcfVtVbZnuf0tmFxXeOHXd+GpVnT6N1vHiJB+YFtuZ5Lzp/nlzdQAA2LQO2LWjqt6V5NlJjquqPUkuzGyUjocluWIaxe6qaYSO70ny2qq6K8m9SV7e3YsXKv5EZiOAHJ1Zn+rFftWvT3JpVb00yeeSvHBN9gwAAA6hmnplHHYWFhZ6165dG90MAAAexKrqmu5eWGqabzYEAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMCAFQXpqrq4qm6tqk/O1R5bVVdU1aemn8dO9aqqN1fV7qq6tqqePrfMedP8n6qq8+bqz6iqT0zLvLmqai13EgAA1tpKz0i/I8mZ+9QuSHJld+9IcuX0OEmel2THdDs/yVuTWfBOcmGS70zyzCQXLobvaZ6XzS2377YAAGBTWVGQ7u6PJLl9n/JZSS6Z7l+S5Oy5+jt75qokx1TVCUnOSHJFd9/e3V9OckWSM6dpj+7uq7q7k7xzbl0AALApraaP9PHdffN0/wtJjp/ub09y09x8e6ba/up7lqgDAMCmtSYXG05nknst1rU/VXV+Ve2qql233Xbbod4cAAAsazVB+papW0amn7dO9b1JTpqb78Sptr/6iUvUH6C7L+ruhe5e2LZt2yqaDgAAq7OaIL0zyeLIG+cl+cBc/cXT6B2nJ/nK1AXk8iTPrapjp4sMn5vk8mnaV6vq9Gm0jhfPrQsAADalI1cyU1W9K8mzkxxXVXsyG33j9UkuraqXJvlckhdOs1+W5PlJdif5WpKXJEl3315VP5fk6mm+13b34gWMP5HZyCBHJ/ngdAMAgE2rZt2bDz8LCwu9a9eujW4GAAAPYlV1TXcvLDXNNxsCAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAOGg3RVPamqPj53+2pV/VRVvaaq9s7Vnz+3zKurandV3VBVZ8zVz5xqu6vqgtXuFAAAHGpHji7Y3TckOS1JqmpLkr1JfjPJS5K8qbt/eX7+qnpyknOSPCXJ45P8TlU9cZr8liTfl2RPkquramd3Xz/aNgAAONSGg/Q+npPk0939uapabp6zkry7u7+e5DNVtTvJM6dpu7v7xiSpqndP8wrSAABsWmvVR/qcJO+ae/zKqrq2qi6uqmOn2vYkN83Ns2eqLVd/gKo6v6p2VdWu2267bY2aDgAAB2/VQbqqjkrygiT/dSq9NckTMuv2cXOSN652G4u6+6LuXujuhW3btq3VagEA4KCtRdeO5yX5w+6+JUkWfyZJVb0tyX+bHu5NctLccidOteynDgAAm9JadO04N3PdOqrqhLlpP5jkk9P9nUnOqaqHVdWpSXYk+YMkVyfZUVWnTme3z5nmBQCATWtVZ6Sr6hGZjbbx43PlX6qq05J0ks8uTuvu66rq0swuIrw7ySu6+55pPa9McnmSLUku7u7rVtMuAAA41Kq7N7oNQxYWFnrXrl0b3QwAAB7Equqa7l5YappvNgQAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwIBVB+mq+mxVfaKqPl5Vu6baY6vqiqr61PTz2KleVfXmqtpdVddW1dPn1nPeNP+nquq81bYLAAAOpbU6I/33uvu07l6YHl+Q5Mru3pHkyulxkjwvyY7pdn6Styaz4J3kwiTfmeSZSS5cDN8AALAZHaquHWcluWS6f0mSs+fq7+yZq5IcU1UnJDkjyRXdfXt3fznJFUnOPERtAwCAVVuLIN1Jfruqrqmq86fa8d1983T/C0mOn+5vT3LT3LJ7ptpydQAA2JSOXIN1/J3u3ltVfyPJFVX1J/MTu7urqtdgO5mC+vlJcvLJJ6/FKgEAYMiqz0h3997p561JfjOzPs63TF02Mv28dZp9b5KT5hY/caotV993Wxd190J3L2zbtm21TQcAgGGrCtJV9YiqetTi/STPTfLJJDuTLI68cV6SD0z3dyZ58TR6x+lJvjJ1Abk8yXOr6tjpIsPnTjUAANiUVtu14/gkv1lVi+v69e7+v6rq6iSXVtVLk3wuyQun+S9L8vwku5N8LclLkqS7b6+qn0ty9TTfa7v79lW2DQAADpnqXpPuy+tuYWGhd+3atdHNAADgQayqrpkb4vl+fLMhAAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGDAfpqjqpqj5UVddX1XVV9ZNT/TVVtbeqPj7dnj+3zKurandV3VBVZ8zVz5xqu6vqgtXtEgAAHHpHrmLZu5P8dHf/YVU9Ksk1VXXFNO1N3f3L8zNX1ZOTnJPkKUken+R3quqJ0+S3JPm+JHuSXF1VO7v7+lW0DQAADqnhIN3dNye5ebr/51X1x0m272eRs5K8u7u/nuQzVbU7yTOnabu7+8Ykqap3T/MK0gAAbFpr0ke6qk5J8rQkH51Kr6yqa6vq4qo6dqptT3LT3GJ7ptpydQAA2LRWHaSr6pFJ3pvkp7r7q0nemuQJSU7L7Iz1G1e7jbltnV9Vu6pq12233bZWqwUAgIO2qiBdVVszC9G/1t3vS5LuvqW77+nue5O8LX/dfWNvkpPmFj9xqi1Xf4Duvqi7F7p7Ydu2batpOgAArMpqRu2oJG9P8sfd/W/n6ifMzfaDST453d+Z5JyqelhVnZpkR5I/SHJ1kh1VdWpVHZXZBYk7R9sFAADrYTWjdjwryT9J8omq+vhU+9dJzq2q05J0ks8m+fEk6e7rqurSzC4ivDvJK7r7niSpqlcmuTzJliQXd/d1q2gXAAAcctXdG92GIQsLC71r166NbgYAAA9iVXVNdy8sNc03GwIAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwQJAGAIABR250AwA4tN7/sb15w+U35PN33JnHH3N0XnXGk3L207ZvdLMADnuCNMCD2Ps/tjevft8ncudd9yRJ9t5xZ179vk8kiTANsEqCNLBmnPncfN5w+Q33hehFd951T95w+Q2OzQbyXoEHh00TpKvqzCT/LsmWJP+5u1+/wU1ac4sfnHvvuDOVpKf6w7cekYdt3ZIvf+2ubKnKPd0P+Ll9Px+08x/Ijzl6a6qSO752Vx5z9NZ84+578rW77n3Adua3f0Ql93bu20aSJdf3+GOOzt/7n7blQ39y230f/gd6vL9fDkv9Ilnc9t477tzvc7C/fV7q/nzbllr3MfvMu9Jfavvbh6Wev6XWu9J17HvcFo/nUsf62Idvzfd/+wn7PRYH2u7jl3i+93dckhzUmc/3f2xvXrPzutxx5133tfnCH3jKA47vvm3fd9pSx3X7Mq/F5fZvpcd2qTbMH5+Vvq4OFKLWMmR9/o47D6q+kudgpe/d5d6z+zseS9WWO/Yjx28l61jq9b7U5+Nyr78DfZ6s5L8EI6+Bg/08Wu7z+0Cfvyt9rkfbudrP4AN9Vuzvs3l/y+zv82Yln58Hs78H2/al1rdvbbnfzyt5Dvd9fe/7+2bxs3s1x+twVd194LkOdSOqtiT50yTfl2RPkquTnNvd1y+3zMLCQu/atWudWrh8CJ43/2J60dt+P//j07evW/seDLYeUbnr3o1/PSbJ0Vu35B8+Y3v+2x/dfF/Qm3fUlkp3Z/ocOWiLr5Vdn7s9v3bVn93v9bT1iEoqueueQ/NcPHzrEbnrnnuH236wlvqj7Zijty75vC7n6K1b8gs/9NQk9w/rh6Mtlaz20C4V1JLk1e+7NndOB/aISv7xd5583y/9pdbxiIcdmc/fcWe+aesR9y23P4vvi/des3fVx+BgXudHbal8Y4VP2nKfz0dv3ZKnn/yY/N6nb19y+vy2Oofu/bc/xxy9NUmWfW8sfm4kecAfoU8+4VEH3LfVWnwf7vrc7XnXR2/KPd2pJEccUbln7rN78XWy+Nrb9wTAXff2/Z7f5V4LR1TyhG2PyKdu/cv71R9x1JZ87Rv35DFHb81ffuPu+y23uK1jlpi2P4vLLff6OZCl9mHrEZWtW+q+wLm/edfaarYx8nl7RCWPOXrrkuF+f7mpkvzPT3hsrvv8n9/vdT9/omgzBO6quqa7F5actkmC9HcleU13nzE9fnWSdPcvLLfMegbpfc8e7M/WLZVTHvfwB7zxYV+LoZKV2X7M0UmyZCh8qNvfH6HPesJj84d/9pX7fX6t5pfs4lkpNsbWI2bP/0Z9dhy9wj+6RgMpm8NqP2/X+o+FxXC/UWF6f0F6swx/tz3JTXOP90y1TWGpPobLueueFqJZESH64Hz+jjtX1B3hoWh//8m56sYv5xd+6KnZfszRqcx+QT7ym44c/gUnRG+su+7duBCdZEUhOhGiD3er/bzd978Oq7V4XcdmtGn6SK9EVZ2f5PwkOfnkk9dtu355w8Z7vDPSQ+7pztlP236/MzmnXvBbw+tzRhoe/Dbj5+1mzWKb5Yz03iQnzT0+cardT3df1N0L3b2wbdu2dWvc4gsK1svWLbXRTVi1LbW2+/CqM56UV53xpBy9dcuarvfBbqnjMPqZdvTWLTn3O09ak2Ow2H90vWzmd9T2Y47OsQ/futHNOKDKrEvaSuY7GOv9WjgUDmYf1mt/R7dx9NYtm/LzdrNmsc0SpK9OsqOqTq2qo5Kck2TnBrfpPgfzYtq6pbLjbzziELfowWn0eTvm6K059uFbU5n131utyqxf6daV/MZYhSPqgR90leRHTz85b/jh77jvX/GL+7c4fdEa7OohcfTWLfmVHzktb3zhd6zZc/isJzz2vrOqv/BDT73voqyVOrx/RR/Y/p7nc7/zpAfURn5Bbj/m6PzCDz01P3/2U4eOwb7resM/+o77XueH2vZjjs6LTj95xftcyYqC7fZjjs6znvDY4dfX4nvlf1zw93PhDzxlRe+XrUfUisLsiGc94bH7/Qx90ekn5x9/59L/DX7EUVvu6zp0oOf6iMp9n9nzr4WRz++tR9SSn4+L0x6+hh+Uxz58a3709JPve80u/pG67+t58XN7qSB77MO33jfvSt9D2485Oj96+skH9Z571hMee7/310pfMsc+fOt9fZFX+nn78K1HPGBf1/qPhcVwvxltiosNk6Sqnp/kVzIb/u7i7n7d/uY/3EftePjWI/JXd92bpXqbrcVFGo84akte94Ozq273HbpmpeuvzILevlfLb6m675fzviNOLNp6RFY8KsT88/az7//E/dZ5vyuwv37XfetcHJHg589+6gPWt9SwQUuNvlFJHn7UlvzlN+5Zdriu+Svj5x21pXLkEbXscHNHHmD/56++X81QQCsZVmnf/V/8xbLvsIjzQwbuO9Teoq1HzEacmO+jub+hvvZ9DueHWtx3OKf54ZQWrfQ4LzXqxPyyyz1Pyx3fAznQUH37W/dKn+/5kV0WR0jY33OeLD1qx1LP3Uqev+Sv3+v7W8dyQxju+15e/Eza33B1S71Wlhpq6/u//YS895o9S7Z58Xlcqu3LDVs4b992rmQ/lhoibLl2LVpqOLnlPnOWGqJ03+d98fNn399RSw2Fue9nwr7Dlx1on3/2/Z+47zW53Gtkud+ZBxoqbd91f8u2B168vzhqx0qHRzzQ8J2rHSpzOSsdanGpY77c87TUOuc/Iw50PJYbsnN/wxuuZF8ONCTmckO3Lvc72qgd62C9gzQAAA89h8OoHQAAcFgRpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYftF7JU1W1JPreOmzwuyRfXcXtsDMf5ocFxfuhwrB8aHOeHho06zt/c3duWmnDYBun1VlW7lvtWGx48HOeHBsf5ocOxfmhwnB8aNuNx1rUDAAAGCNIAADBAkF65iza6AawLx/mhwXF+6HCsHxoc54eGTXec9ZEGAIABzkgDAMAAQXofVXVmVd1QVbur6oIlpj+sqt4zTf9oVZ2yAc1klVZwnP/Xqrq+qq6tqiur6ps3op2szoGO89x8/7Cquqo21dXgrMxKjnNVvXB6T19XVb++3m1kbazgs/vkqvpQVX1s+vx+/ka0k3FVdXFV3VpVn1xmelXVm6fXwLVV9fT1buM8QXpOVW1J8pYkz0vy5CTnVtWT95ntpUm+3N3fmuRNSX5xfVvJaq3wOH8syUJ3f3uS30jyS+vbSlZrhcc5VfWoJD+Z5KPr20LWwkqOc1XtSPLqJM/q7qck+an1biert8L39M8mubS7n5bknCT/cX1byRp4R5Iz9zP9eUl2TLfzk7x1Hdq0LEH6/p6ZZHd339jd30jy7iRn7TPPWUkume7/RpLnVFWtYxtZvQMe5+7+UHd/bXp4VZIT17mNrN5K3s9J8nOZ/UH8V+vZONbMSo7zy5K8pbu/nCTdfes6t5G1sZJj3UkePd1/TJLPr2P7WAPd/ZEkt+9nlrOSvLNnrkpyTFWdsD6teyBB+v62J7lp7vGeqbbkPN19d5KvJHncurSOtbKS4zzvpUk+eEhbxKFwwOM8/UvwpO7+rfVsGGtqJe/nJyZ5YlX9j6q6qqr2d7aLzWslx/o1SX60qvYkuSzJP1+fprGODvZ3+CF15EZtGA4HVfWjSRaS/N2Nbgtrq6qOSPJvk/zYBjeFQ+/IzP4N/OzM/rv0kap6anffsZGN4pA4N8k7uvuNVfVdSf5LVX1bd9+70Q3jwckZ6fvbm+SkuccnTrUl56mqIzP719GX1qV1rJWVHOdU1fcm+ZkkL+jur69T21g7BzrOj0rybUl+t6o+m+T0JDtdcHjYWcn7eU+Snd19V3d/JsmfZhasObys5Fi/NMmlSdLdv5/km5Icty6tY72s6Hf4ehGk7+/qJDuq6tSqOiqzCxV27jPPziTnTfd/OMl/b4NxH24OeJyr6mlJ/lNmIVp/ysPTfo9zd3+lu4/r7lO6+5TM+sK/oLt3bUxzGbSSz+33Z3Y2OlV1XGZdPW5cxzayNlZyrP8syXOSpKr+VmZB+rZ1bSWH2s4kL55G7zg9yVe6++aNaoyuHXO6++6qemWSy5NsSXJxd19XVa9Nsqu7dyZ5e2b/KtqdWWf4czauxYxY4XF+Q5JHJvmv07Wkf9bdL9iwRnPQVnicOcyt8DhfnuS5VXV9knuSvKq7/SfxMLPCY/3TSd5WVf8iswsPf8zJrsNLVb0rsz98j5v6ul+YZGuSdPevZtb3/flJdif5WpKXbExLZ3yzIQAADNC1AwAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAFHbnQDRh133HF9yimnbHQzAAB4ELvmmmu+2N3blpp22AbpU045Jbt27droZgAA8CBWVZ9bbpquHQAAMECQBgCAAYI0AAAMOGCQrqqLq+rWqvrkXO09VfXx6fbZqvr4VD+lqu6cm/arc8s8o6o+UVW7q+rNVVVT/bFVdUVVfWr6eewh2E8AAFhTKzkj/Y4kZ84XuvtHuvu07j4tyXuTvG9u8qcXp3X3y+fqb03ysiQ7ptviOi9IcmV370hy5fQYAAA2tQMG6e7+SJLbl5o2nVV+YZJ37W8dVXVCkkd391Xd3UnemeTsafJZSS6Z7l8yVwcAgE1rtX2kvzvJLd39qbnaqVX1sar6cFV991TbnmTP3Dx7plqSHN/dN0/3v5Dk+FW2CQAADrnVjiN9bu5/NvrmJCd395eq6hlJ3l9VT1npyrq7q6qXm15V5yc5P0lOPvnkwSYDAMDqDZ+Rrqojk/xQkvcs1rr76939pen+NUk+neSJSfYmOXFu8ROnWpLcMnX9WOwCcuty2+zui7p7obsXtm1b8gtmAABgXayma8f3JvmT7r6vy0ZVbauqLdP9b8nsosIbp64bX62q06d+1S9O8oFpsZ1JzpvunzdXBwCATWslw9+9K8nvJ3lSVe2pqpdOk87JAy8y/J4k107D4f1Gkpd39+KFij+R5D8n2Z3ZmeoPTvXXJ/m+qvpUZuH89eO7AwAA66Nmg2gcfhYWFnrXrl0b3QwAAB7Equqa7l5YatpqLzYEAOAQOOWC39roJmwqn3399290Ex7AV4QDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMOGKSr6uKqurWqPjlXe01V7a2qj0+3589Ne3VV7a6qG6rqjLn6mVNtd1VdMFc/tao+OtXfU1VHreUOAgDAobCSM9LvSHLmEvU3dfdp0+2yJKmqJyc5J8lTpmX+Y1VtqaotSd6S5HlJnpzk3GneJPnFaV3fmuTLSV66mh0CAID1cMAg3d0fSXL7Ctd3VpJ3d/fXu/szSXYneeZ0293dN3b3N5K8O8lZVVVJ/n6S35iWvyTJ2Qe3CwAAsP5W00f6lVV17dT149iptj3JTXPz7Jlqy9Ufl+SO7r57nzoAAGxqo0H6rUmekOS0JDcneeNaNWh/qur8qtpVVbtuu+229dgkAAAsaShId/ct3X1Pd9+b5G2Zdd1Ikr1JTpqb9cSptlz9S0mOqaoj96kvt92Lunuhuxe2bds20nQAAFgTQ0G6qk6Ye/iDSRZH9NiZ5JyqelhVnZpkR5I/SHJ1kh3TCB1HZXZB4s7u7iQfSvLD0/LnJfnASJsAAGA9HXmgGarqXUmeneS4qtqT5MIkz66q05J0ks8m+fEk6e7rqurSJNcnuTvJK7r7nmk9r0xyeZItSS7u7uumTfyrJO+uqp9P8rEkb1+rnQMAgEPlgEG6u89dorxs2O3u1yV53RL1y5JctkT9xvx11xAAADgs+GZDAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMOGKSr6uKqurWqPjlXe0NV/UlVXVtVv1lVx0z1U6rqzqr6+HT71bllnlFVn6iq3VX15qqqqf7Yqrqiqj41/Tz2EOwnAACsqZWckX5HkjP3qV2R5Nu6+9uT/GmSV89N+3R3nzbdXj5Xf2uSlyXZMd0W13lBkiu7e0eSK6fHAACwqR0wSHf3R5Lcvk/tt7v77unhVUlO3N86quqEJI/u7qu6u5O8M8nZ0+Szklwy3b9krg4AAJvWWvSR/qdJPjj3+NSq+lhVfbiqvnuqbU+yZ26ePVMtSY7v7pun+19IcvwatAkAAA6pI1ezcFX9TJK7k/zaVLo5ycnd/aWqekaS91fVU1a6vu7uqur9bO/8JOcnycknnzzecAAAWKXhM9JV9WNJ/kGSF03dNdLdX+/uL033r0ny6SRPTLI39+/+ceJUS5Jbpq4fi11Abl1um919UXcvdPfCtm3bRpsOAACrNhSkq+rMJP8yyQu6+2tz9W1VtWW6/y2ZXVR449R146tVdfo0WseLk3xgWmxnkvOm++fN1QEAYNM6YNeOqnpXkmcnOa6q9iS5MLNROh6W5IppFLurphE6vifJa6vqriT3Jnl5dy9eqPgTmY0AcnRmfaoX+1W/PsmlVfXSJJ9L8sI12TMAADiEDhiku/vcJcpvX2be9yZ57zLTdiX5tiXqX0rynAO1AwAANhPfbAgAAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBgwIqCdFVdXFW3VtUn52qPraorqupT089jp3pV1ZurandVXVtVT59b5rxp/k9V1Xlz9WdU1SemZd5cVbWWOwkAAGttpWek35HkzH1qFyS5srt3JLlyepwkz0uyY7qdn+StySx4J7kwyXcmeWaSCxfD9zTPy+aW23dbAACwqawoSHf3R5Lcvk/5rCSXTPcvSXL2XP2dPXNVkmOq6oQkZyS5ortv7+4vJ7kiyZnTtEd391Xd3UneObcuAADYlFbTR/r47r55uv+FJMdP97cnuWluvj1TbX/1PUvUAQBg01qTiw2nM8m9Fuvan6o6v6p2VdWu22677VBvDgAAlrWaIH3L1C0j089bp/reJCfNzXfiVNtf/cQl6g/Q3Rd190J3L2zbtm0VTQcAgNVZTZDemWRx5I3zknxgrv7iafSO05N8ZeoCcnmS51bVsdNFhs9Ncvk07atVdfo0WseL59YFAACb0pErmamq3pXk2UmOq6o9mY2+8fokl1bVS5N8LskLp9kvS/L8JLuTfC3JS5Kku2+vqp9LcvU032u7e/ECxp/IbGSQo5N8cLoBAMCmtaIg3d3nLjPpOUvM20lescx6Lk5y8RL1XUm+bSVtAQCAzcA3GwIAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMCA4SBdVU+qqo/P3b5aVT9VVa+pqr1z9efPLfPqqtpdVTdU1Rlz9TOn2u6qumC1OwUAAIfakaMLdvcNSU5LkqrakmRvkt9M8pIkb+ruX56fv6qenOScJE9J8vgkv1NVT5wmvyXJ9yXZk+TqqtrZ3dePtg0AAA614SC9j+ck+XR3f66qlpvnrCTv7u6vJ/lMVe1O8sxp2u7uvjFJqurd07yCNAAAm9Za9ZE+J8m75h6/sqquraqLq+rYqbY9yU1z8+yZasvVH6Cqzq+qXVW167bbblujpgMAwMFbdZCuqqOSvCDJf51Kb03yhMy6fdyc5I2r3cai7r6ouxe6e2Hbtm1rtVoAADhoa9G143lJ/rC7b0mSxZ9JUlVvS/Lfpod7k5w0t9yJUy37qQMAwKa0Fl07zs1ct46qOmFu2g8m+eR0f2eSc6rqYVV1apIdSf4gydVJdlTVqdPZ7XOmeQEAYNNa1RnpqnpEZqNt/Phc+Zeq6rQkneSzi9O6+7qqujSziwjvTvKK7r5nWs8rk1yeZEuSi7v7utW0CwAADrVVBenu/sskj9un9k/2M//rkrxuifplSS5bTVsAAGA9+WZDAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAANWHaSr6rNV9Ymq+nhV7Zpqj62qK6rqU9PPY6d6VdWbq2p3VV1bVU+fW8950/yfqqrzVtsuAAA4lNbqjPTf6+7TunthenxBkiu7e0eSK6fHSfK8JDum2/lJ3prMgneSC5N8Z5JnJrlwMXwDAMBmdKi6dpyV5JLp/iVJzp6rv7NnrkpyTFWdkOSMJFd09+3d/eUkVyQ58xC1DQAAVm0tgnQn+e2quqaqzp9qx3f3zdP9LyQ5frq/PclNc8vumWrL1QEAYFM6cg3W8Xe6e29V/Y0kV1TVn8xP7O6uql6D7WQK6ucnycknn7wWqwQAgCGrPiPd3Xunn7cm+c3M+jjfMnXZyPTz1mn2vUlOmlv8xKm2XH3fbV3U3QvdvbBt27bVNh0AAIatKkhX1SOq6lGL95M8N8knk+xMsjjyxnlJPjDd35nkxdPoHacn+crUBeTyJM+tqmOniwyfO9UAAGBTWm3XjuOT/GZVLa7r17v7/6qqq5NcWlUvTfK5JC+c5r8syfOT7E7ytSQvSZLuvr2qfi7J1dN8r+3u21fZNgAAOGRWFaS7+8Yk37FE/UtJnrNEvZO8Ypl1XZzk4tW0BwAA1otvNgQAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwYDhIV9VJVfWhqrq+qq6rqp+c6q+pqr1V9fHp9vy5ZV5dVbur6oaqOmOufuZU211VF6xulwAA4NA7chXL3p3kp7v7D6vqUUmuqaorpmlv6u5fnp+5qp6c5JwkT0ny+CS/U1VPnCa/Jcn3JdmT5Oqq2tnd16+ibQAAcEgNB+nuvjnJzdP9P6+qP06yfT+LnJXk3d399SSfqardSZ45Tdvd3TcmSVW9e5pXkAYAYNNakz7SVXVKkqcl+ehUemVVXVtVF1fVsVNte5Kb5hbbM9WWqwMAwKa16iBdVY9M8t4kP9XdX03y1iRPSHJaZmes37jabcxt6/yq2lVVu2677ba1Wi0AABy0VQXpqtqaWYj+te5+X5J09y3dfU9335vkbfnr7ht7k5w0t/iJU225+gN090XdvdDdC9u2bVtN0wEAYFVWM2pHJXl7kj/u7n87Vz9hbrYfTPLJ6f7OJOdU1cOq6tQkO5L8QZKrk+yoqlOr6qjMLkjcOdouAABYD6sZteNZSf5Jkk9U1cen2r9Ocm5VnZakk3w2yY8nSXdfV1WXZnYR4d1JXtHd9yRJVb0yyeVJtiS5uLuvW0W7AADgkFvNqB3/T5JaYtJl+1nmdUlet0T9sv0tBwAAm41vNgQAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABgjSAAAwQJAGAIABgjQAAAwQpAEAYIAgDQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGCAIA0AAAMEaQAAGCBIAwDAAEEaAAAGCNIAADBAkAYAgAGCNAAADBCkAQBggCANAAADBGkAABggSAMAwABBGgAABhy50Q0AeCg45YLf2ugmbBqfff33b3QTANbEpgnSVXVmkn+XZEuS/9zdr9/gJsFBE5b+mrAEwIPdpgjSVbUlyVuSfF+SPUmurqqd3X39xraMAxEcgYPlc+P+/NEJh69NEaSTPDPJ7u6+MUmq6t1Jzkqy6YK0XwCwMt4rsDLeK3D42iwXG25PctPc4z1TDQAANqXNckZ6Rarq/CTnTw//oqpuWMfNH5fki+u4PTaG4/zQ4Dg/dDjWDw2O80NA/eKGHedvXm7CZgnSe5OcNPf4xKl2P919UZKL1qtR86pqV3cvbMS2WT+O80OD4/zQ4Vg/NDjODw2b8Thvlq4dVyfZUVWnVtVRSc5JsnOD2wQAAMvaFGeku/vuqnplksszG/7u4u6+boObBQAAy9oUQTpJuvuyJJdtdDv2Y0O6lLDuHOeHBsf5ocOxfmhwnB8aNt1xru7e6DYAAMBhZ7P0kQYAgMOKIL2Pqjqzqm6oqt1VdcES0x9WVe+Zpn+0qk7ZgGaySis4zv9rVV1fVddW1ZVVtezQN2xeBzrOc/P9w6rqqtpUV4OzMis5zlX1wuk9fV1V/fp6t5G1sYLP7pOr6kNV9bHp8/v5G9FOxlXVxVV1a1V9cpnpVVVvnl4D11bV09e7jfME6TlzX1X+vCRPTnJuVT15n9lemuTL3f2tSd6U5BfXt5Ws1gqP88eSLHT3tyf5jSS/tL6tZLVWeJxTVY9K8pNJPrq+LWQtrOQ4V9WOJK9O8qzufkqSn1rvdrJ6K3xP/2ySS7v7aZmNAPYf17eVrIF3JDlzP9Ofl2THdDs/yVvXoU3LEqTv776vKu/ubyRZ/KryeWcluWS6/xtJnlNVtY5tZPUOeJy7+0Pd/bXp4VWZjW3O4WUl7+ck+bnM/iD+q/VsHGtmJcf5ZUne0t1fTpLuvnWd28jaWMmx7iSPnu4/Jsnn17F9rIHu/kiS2/czy1lJ3tkzVyU5pqpOWJ/WPZAgfX8r+ary++bp7ruTfCXJ49aldayVg/1K+pcm+eAhbRGHwgGP8/QvwZO6+7fWs2GsqZW8n5+Y5IlV9T+q6qqq2t/ZLjavlRzr1yT50arak9lIYP98fZrGOjrY3+GH1KYZ/g42o6r60SQLSf7uRreFtVVVRyT5t0l+bIObwqF3ZGb/Bn52Zv9d+khVPbW779jIRnFInJvkHd39xqr6riT/paq+rbvv3eiG8eDkjPT9reSryu+bp6qOzOxfR19al9axVlb0lfRV9b1JfibJC7r76+vUNtbOgY7zo5J8W5LfrarPJjk9yU4XHB52VvJ+3pNkZ3ff1d2fSfKnmQVrDi8rOdYvTXJpknT37yf5piTHrUvrWC8r+h2+XgTp+1vJV5XvTHLedP+Hk/z3Nhj34eaAx7mqnpbkP2UWovWnPDzt9zh391e6+7juPqW7T8msL/wLunvXxjSXQSv53H5/ZmejU1XHZdbV48Z1bCNrYyXH+s+SPCdJqupvZRakb1vXVnKo7Uzy4mn0jtOTfKW7b96oxujaMWe5ryqvqtcm2dXdO5O8PbN/Fe3OrDP8ORvXYkas8Di/Ickjk/zX6VrSP+vuF2xYozloKzzOHOZWeJwvT/Lcqro+yT1JXtXd/pN4mFnhsf7pJG+rqn+R2YWHP+Zk1+Glqt6V2R++x0193S9MsjVJuvtXM+v7/vwku5N8LclLNqalM77ZEAAABujaAQAAAwRpAAAYIEgDAMAAQRoAAAYI0gAAMECQBgCAAYI0AAAMEKQBAGDA/wvoDRNRWjsTuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = { k: len(tuple(v)) for k, v in itertools.groupby(sorted(ratios.values())) }\n",
    "plot, (ap, bp) = pl.subplots(2)\n",
    "plot.suptitle('Retweet Ratio Distribution')\n",
    "ap.scatter(d.keys(), d.values())\n",
    "bp.hist(ratios.values(), bins=9)\n",
    "plot.show()"
   ]
  },
  {
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}