{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from socket import error as socket_error\n",
    "from urllib.request import urlopen\n",
    "from dns.query import udp, tcp\n",
    "from bs4 import BeautifulSoup\n",
    "from dns.resolver import dns\n",
    "from uuid import uuid4\n",
    "from tqdm import tqdm\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "\n",
    "import subprocess\n",
    "import socket\n",
    "import copy\n",
    "import time\n",
    "import json\n",
    "import csv\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pie = (6, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def write_list(fn, data):\n",
    "    '''Writes a list to a file with each value on a new line'''\n",
    "    with open(fn, 'w') as f:\n",
    "        for datum in data:\n",
    "            f.write(datum + newline)\n",
    "        \n",
    "def append_list(fn, data):\n",
    "    '''Appends a list to a file with each value on a new line'''\n",
    "    with open(fn, 'a') as f:\n",
    "        for datum in data:\n",
    "            f.write(datum + newline)\n",
    "    \n",
    "def read_list(fn):\n",
    "    '''Reads a file and '''\n",
    "    with open(fn, 'r') as f:\n",
    "        return [line.strip(newline) for line in f]\n",
    "    \n",
    "def write_json(fn, data):\n",
    "    with open(fn, 'w') as f:\n",
    "        f.write(json.dumps(data))\n",
    "            \n",
    "def read_json(fn):\n",
    "    '''Read a json file (fn) and returns it as a dictionary'''\n",
    "    with open(fn, 'r') as f:\n",
    "        return json.loads(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top-Level Domains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_tlds = read_json('data/data_tlds.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A24gSRiPwCHDA3bvDbrVA3zs0F9geXtsd9p8xlhhERDLJc3uaAZhVEs8c4/2NZMLaLwOP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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5ba4b0320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tlds = pd.DataFrame(data_tlds)\n",
    "df_tlds['creation_date'] = pd.to_datetime(df_tlds['creation_date'])\n",
    "ax = df_tlds.creation_date.value_counts().sort_index().cumsum().plot()\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel('Number of TLDs')\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"imgs/tld_growth.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>creation_date</th>\n",
       "      <th>organisations</th>\n",
       "      <th>tld</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-08-13</td>\n",
       "      <td>[ American Automobile Association, Inc.,  Mark...</td>\n",
       "      <td>AAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-10-22</td>\n",
       "      <td>[ AARP,  FairWinds Partners, LLC,  Verisign, I...</td>\n",
       "      <td>AARP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>[ Fiat Chrysler Automobiles N.V.,  FCA Italy S...</td>\n",
       "      <td>ABARTH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-04-09</td>\n",
       "      <td>[ ABB Ltd,  ABB Schweiz AG,  Verisign, Inc.]</td>\n",
       "      <td>ABB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-11-20</td>\n",
       "      <td>[ Abbott Laboratories, Inc.,  FairWinds Partne...</td>\n",
       "      <td>ABBOTT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  creation_date                                      organisations     tld\n",
       "0    2015-08-13  [ American Automobile Association, Inc.,  Mark...     AAA\n",
       "1    2015-10-22  [ AARP,  FairWinds Partners, LLC,  Verisign, I...    AARP\n",
       "2    2016-07-14  [ Fiat Chrysler Automobiles N.V.,  FCA Italy S...  ABARTH\n",
       "3    2015-04-09       [ ABB Ltd,  ABB Schweiz AG,  Verisign, Inc.]     ABB\n",
       "4    2014-11-20  [ Abbott Laboratories, Inc.,  FairWinds Partne...  ABBOTT"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tlds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_tlds = df_tlds[(df_tlds.creation_date >= '2013-10-23')].tld\n",
    "old_tlds = df_tlds[(df_tlds.creation_date < '2013-10-23')].tld"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tld_groups = {'all': df_tlds.tld, 'old': old_tlds, 'new': new_tlds}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Name Servers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_nss = read_json('data/data_ns.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_nss = pd.DataFrame(data_nss)\n",
    "maxc = df_nss.tld.value_counts().max()\n",
    "minc = df_nss.tld.value_counts().min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5b9a3ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_nss.tld.value_counts().hist(bins = maxc - minc, range = (minc, maxc))\n",
    "ax.set_ylabel('Number of TLDs')\n",
    "ax.set_xlabel('Number of name servers per TLD')\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"imgs/nss_per_tld.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QyR9HxF27+flcXfV+4QBjHGrqfxUrkhOE7c5FZGP9/EtV2S5St6SkPYDq/uHn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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5ba038ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_nss[df_nss.tld.isin(new_tlds)].tld.value_counts().hist(bins = maxc - minc, range = (minc, maxc))\n",
    "ax.set_ylabel('Number of TLDs')\n",
    "ax.set_xlabel('Number of name servers per TLD')\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"imgs/nss_per_tld_new.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SxWn5NEmrJG1Kr/vnFYOZmQ0tzxbBHmBBRBxB1lvphZKOABYCqyPicGB1mjczs5Lklggi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5ba558ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_nss[df_nss.tld.isin(old_tlds)].tld.value_counts().hist(bins = maxc - minc, range = (minc, maxc))\n",
    "ax.set_ylabel('Number of TLDs')\n",
    "ax.set_xlabel('Number of name servers per TLD')\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"imgs/nss_per_tld_old.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network Diversity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_asns = read_json('data/data_asns.json')\n",
    "data_ips = read_json('data/data_ips.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_asns = pd.DataFrame(data_asns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxc = df_asns.groupby('tld').asn.nunique().max()\n",
    "minc = df_asns.groupby('tld').asn.nunique().min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5b8113550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_asns.groupby('tld').asn.nunique().hist(bins = maxc - minc, range = (minc, maxc))\n",
    "ax.set_ylabel('Number of TLDs')\n",
    "ax.set_xlabel('Number of ASs per TLD')\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"imgs/asns_per_tld.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zM6tWs30QlwCnUUyMd0hEbB22VGZmVrlmJ8pdCBwAfB74haRfptvzkn45PPHMzKwqzfZB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\nXCDMzCzLBcLMzLJcIMzMLMsFwszMsv4L/wOx9VTxtRsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5b811acf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_asns[df_asns.tld.isin(new_tlds)].groupby('tld').asn.nunique().hist(bins = maxc - minc, range = (minc, maxc))\n",
    "ax.set_ylabel('Number of TLDs')\n",
    "ax.set_xlabel('Number of ASs per TLD')\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"imgs/asns_per_tld_new.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zs5LLrBBIOkXSeklPSHpc0g3p8uMkrZW0Nf1+bFYZzMyssSyPCPYD8yPiDJJuqz8k6Qxg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5b9a3c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_asns[df_asns.tld.isin(old_tlds)].groupby('tld').asn.nunique().hist(bins = maxc - minc, range = (minc, maxc))\n",
    "ax.set_ylabel('Number of TLDs')\n",
    "ax.set_xlabel('Number of ASs per TLD')\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"imgs/asns_per_tld_old.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maxc = df_asns.groupby('asn').tld.nunique().max()\n",
    "minc = df_asns.groupby('asn').tld.nunique().min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WAQwNDeXy5csn/VrDw8OsvemRST9vpr3/6CdYu/WZv6Idb1m+e/gda67rOq+Xzuf1+5xu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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5b9ba0d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_asns.groupby('asn').tld.nunique().hist(bins = int((maxc - minc) / 4), range=(minc, maxc))\n",
    "ax.set_yscale('log')\n",
    "ax.set_ylabel('Number of ASs')\n",
    "ax.set_xlabel('Number of TLDs per AS')\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"imgs/tlds_per_asn.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hz9jEppxtwKEd9w8pp0mShkgTBXEjcEREHBYRewOnAVdM5QUiYllErN6+vb9NPJKkqav7\nMNdLgK8DR0bEvRFxemY+BpwJXA3cAVyambdP5XUzc11mrpgzZ3i3y0tS29W6DyIzl3eZvh5YX+d7\nS5J2joeTSpIqtbIg3AchSfVrZUG4D0KS6tfKgpAk1a/2M6nrFBEPAP8+jaceCDw4w3HqYM6Z04aM\n0I6cbcgI7cjZVMYXZObzJpup1QUxXRFxUz+nmTfNnDOnDRmhHTnbkBHakXPYM7qJSZJUyYKQJFXa\nXQtiddMB+mTOmdOGjNCOnG3ICO3IOdQZd8t9EJKkye2uaxCSpEnsdgXR5XrYjYuIrRGxMSJujYib\nymkHRMSXIuLu8vtzGsj1Y9cV75YrCh8tl+1tEXFswzk/GBHbymV6a0S8ruOxD5Q574yI1w4o46ER\nsSEivhURt0fEe8vpQ7U8e+QcmuUZEc+IiBsi4ptlxt8vpx8WEdeXWT5TjhhNROxT3t9cPj6v7oyT\n5FwTEfd0LMtjyumN/QxVyszd5guYBXwHmA/sDXwTWNh0rjLbVuDACdP+BFhZ3l4JXNBArlcAxwKb\nJssFvA74AhDAS4HrG875QeB3KuZdWP7b7wMcVv6fmDWAjAcBx5a39wPuKrMM1fLskXNolme5TGaX\nt/cCri+X0aXAaeX0i4B3lbd/A7iovH0a8JkBLctuOdcAp1TM39jPUNXX7rYG0e162MPqJOCT5e1P\nAm8YdICsvq54t1wnAZ/Kwr8B+0fEQQ3m7OYkYG1mPpKZ9wCbKf5v1Coz78vMW8rbD1MMd38wQ7Y8\ne+TsZuDLs1wmY+XdvcqvBF4FXFZOn7gsx5fxZcAvRETUmXGSnN009jNUZXcriIOB73bcv5fe//EH\nKYFrIuLmKK67DTA3M+8rb/8XMLeZaD+mW65hXL5nlqvqH+/YRNd4znITx89S/EU5tMtzQk4YouUZ\nEbMi4lbgfuBLFGsuP8zimjMTczyZsXx8O/DcujNW5czM8WX5R+Wy/POI2GdizlKjP0O7W0EMs5dn\n5rHACcC7I+IVnQ9msf45dIecDWuu0t8ChwPHAPcBq5qNU4iI2cBngd/KzIc6Hxum5VmRc6iWZ2Y+\nnpnHUFy2+Hjgp5rM083EnBFxFPABirwvBg4AzmowYle7W0EM7fWwM3Nb+f1+4J8p/sN/b3z1svx+\nf3MJn6ZbrqFavpn5vfKH8wng73lqs0djOSNiL4pfup/OzM+Vk4dueVblHMblWeb6IbABeBnFJpnx\nC6F15ngyY/n4HOC/B5VxQs4l5Wa8zMxHgE8wJMtyot2tIHb6eth1iIh9I2K/8dvALwKbKLK9pZzt\nLcDnm0n4Y7rlugJ4c3kkxkuB7R2bTgZuwrbbX6JYplDkPK08suUw4AjghgHkCeBi4I7M/LOOh4Zq\neXbLOUzLMyKeFxH7l7efCbyGYl/JBuCUcraJy3J8GZ8CfLVcW6tVl5zf7viDICj2k3Quy6H5GWps\n73hTXxRHCdxFsb3y7KbzlJnmUxwF8k3g9vFcFNtIvwLcDXwZOKCBbJdQbE74P4rtoad3y0Vx5MWF\n5bLdCIw0nPMfyhy3UfzgHdQx/9llzjuBEwaU8eUUm49uA24tv143bMuzR86hWZ7AzwDfKLNsAs4r\np8+nKKfNwD8B+5TTn1He31w+Pn9Ay7Jbzq+Wy3IT8I88daRTYz9DVV+eSS1JqrS7bWKSJPXJgpAk\nVbIgJEmVLAhJUiULQpJUyYLQwEVERsSqjvu/ExEfnKHXXhMRp0w+506/z6kRcUdEbOiYdnTH6Jzf\n7xit88sRMS86RpqdkPeecrTPuyLiUxFxSN35e4mIY8p/oyUTpp9djkh6W/m5XtJURg2GBaEmPAKc\nHBEHNh2kU8cZuP04HTgjMxePT8jMjZl5TBbDKlwBvL+8/+pJXuv9mfki4EiKY+a/Wp7IWasen3c5\ncF35fXzelwGvpxjl9WeAV/P0MYO0C7Ig1ITHKC61+NsTH5i4BhARY+X3RRFxbUR8PiK2RMT5EfHG\nKMba3xgRh3e8zKsj4qbyL/LXl8+fFREfiYgby7+A39Hxuv8SEVcA36rIs7x8/U0RcUE57TyKk8ku\njoiPzNRCycKfUwzYd0KZeU353hsjotvyumgGP28ApwJvBV4TEc8oHzoIeDCLoSHIzAcz8z9n6rNr\nOE3lLyZpJl0I3BYRfzKF57wI+GmKYb23AB/LzOOjuKDNe4DfKuebRzG2zeHAhohYALyZYtiCF0cx\ncua/RsQ15fzHAkdlMVT1kyLi+cAFwHHADyhG231DZv5BRLyK4toIN035k0/uFoqB3O4FDs7Mo8o8\n+3eZfx4z8HlLPwfck5nfiYhRYCnFmEzXAOdFxF0UZ3t/JjOv3fmPqmHmGoQakcXooJ8CfnMKT7sx\ni0HOHqEYimD8F95Gil+S4y7NzCcy826KIvkpivGt3hzFsMvXUwxvcUQ5/w1dflm+GBjNzAeyGCL6\n0xQXJqrb+HUKtgDzI+Kvyv0BD3WZf6Y+LxSbldaWt9eW98nimgbHASuAB4DPRMRbp/n51BKuQahJ\nf0Hx1/InOqY9RvmHS0TsQXHlv3GPdNx+ouP+Ezz9//LE8WOS4pfuezLz6s4HImIRsGN68Wvzs8BX\nMvMHEfEi4LXAO4FfAX69Yv4Z+bwRMQv4ZeCkiDi7fI3nRsR+mflwZj4OjAKjEbGRYvC7NdP6hGoF\n1yDUmMz8PsUlIk/vmLyV4i9VgBMprsA1VadGxB7lfon5FAPIXQ28K4phrImIF0Yxcm4vNwCvjIgD\ny1+ey4HaNquUI3j+JsX2/i+WO/H3yMzPAudQbBqqMlOf9xeA2zLz0Mycl5kvoNi89EsRcWREHNEx\n7zHAv0/3s6odXINQ01YBZ3bc/3vg8xHxTeCLTO+v+/+g+OX+bOCdmfmjiPgYxWaoW8odsQ8wySVc\nM/O+iFhJMYR0AFdl5s4MuX5kRNzbcX98p/NHIuJc4FnAvwGLM/PRiDgY+ES5JgXFRWaqzMjnpSjA\nf54w7bPAuyhGGf6rcj/IYxSjoq5AuzRHc5VaLCLWAFdm5mWTzStNlZuYJEmVXIOQJFVyDUKSVMmC\nkCRVsiAkSZUsCElSJQtCklTJgpAkVfp/xd3PAf/x1mIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5b97785c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_asns[df_asns.tld.isin(old_tlds)].groupby('asn').tld.nunique().hist(bins = int((maxc - minc) / 4),\n",
    "                                                                           range=(minc, maxc))\n",
    "ax.set_yscale('log')\n",
    "ax.set_ylabel('Number of ASs')\n",
    "ax.set_xlabel('Number of TLDs per AS')\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"imgs/tlds_per_asn_old.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+W2f8i3r+vhFxRZ3l0/WI0UTETvX0+vr+hU1nnCbnORFxc8+yPLie39rvUFFmbjcfwDzg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\nOhPITuEa2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5b99facc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_asns[df_asns.tld.isin(new_tlds)].groupby('asn').tld.nunique().hist(bins = int((maxc - minc) / 4),\n",
    "                                                                           range=(minc, maxc))\n",
    "ax.set_yscale('log')\n",
    "ax.set_ylabel('Number of ASs')\n",
    "ax.set_xlabel('Number of TLDs per AS')\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"imgs/tlds_per_asn_new.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cred = read_json('data/data_cred.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2017-06-14 14:20:12--  https://www.iana.org/assignments/dns-sec-alg-numbers/dns-sec-alg-numbers-1.csv\n",
      "Loaded CA certificate '/etc/ssl/certs/ca-certificates.crt'\n",
      "Resolving www.iana.org... 2620:0:2d0:200::8, 192.0.32.8\n",
      "Connecting to www.iana.org|2620:0:2d0:200::8|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: unspecified [text/csv]\n",
      "Saving to: ‘data/lists/dnssec_algorithm_numbers.csv’\n",
      "\n",
      "data/lists/dnssec_a     [ <=>                ]   1.52K  --.-KB/s    in 0s      \n",
      "\n",
      "2017-06-14 14:20:13 (8.03 MB/s) - ‘data/lists/dnssec_algorithm_numbers.csv’ saved [1559]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://www.iana.org/assignments/dns-sec-alg-numbers/dns-sec-alg-numbers-1.csv -O data/lists/dnssec_algorithm_numbers.csv\n",
    "reader = csv.reader(open('data/lists/dnssec_algorithm_numbers.csv', 'r'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_cred = pd.DataFrame(data_cred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "algorithms = {}\n",
    "\n",
    "for row in reader:\n",
    "   k, _, v, _, _, _ = row\n",
    "   algorithms[k] = v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for algorithm_number in algorithms:\n",
    "    df_cred['algorithm'] = df_cred['algorithm'].replace(algorithm_number, algorithms[algorithm_number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L1H5XtEUBXOs7QC4daM+hLbmAymHjqBg4/IDrOOfYdP+PqBw2noFvunCfr3Xs2Lznzy0v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\nZKWiEBGRrP5/wizZCSe1oIIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5b31ccba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_cred.algorithm.value_counts().plot.pie(figsize = pie, autopct='%.2f')\n",
    "ax.set_ylabel(\"\")\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"imgs/dnssec_algs.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "dnssec_record_counts = [df_cred.loc[df_cred.dnskey == x].loc[df_cred.ds == y].count().tld \n",
    "                        for x in [True, False] for y in [True, False]]\n",
    "\n",
    "df_cred2 = pd.DataFrame(dnssec_record_counts, \n",
    "                        columns = ['dnssec_record_counts'],\n",
    "                        index = ['ds + dnskey', 'ds', 'dnskey', 'none'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tBWNum+9X9zzfwNi4F/dJzTHwJuBTQ+2PGjdddY/rex1wTtt+1GvJ6jkf1WvOmuoGvgy8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\numEoSZK68f92FJIfqsZySQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5b312bda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cred2.dnssec_record_counts.plot.barh()\n",
    "ax.set_ylabel('Available records')\n",
    "ax.set_xlabel('Number of TLDs')\n",
    "fig = ax.get_figure()\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"imgs/dnssec.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>dnskey</th>\n",
       "      <th>ds</th>\n",
       "      <th>tld</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>XN--MGBAI9AZGQP6J</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     algorithm dnskey    ds                tld\n",
       "1448      None  False  True  XN--MGBAI9AZGQP6J"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cred.loc[df_cred.dnskey == False].loc[df_cred.ds]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}