nb-cache_nocache.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cache vs Origin comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import Cdf\n",
    "import Pmf\n",
    "\n",
    "\n",
    "# define global plot parameters\n",
    "params = {'axes.labelsize' : 12, 'axes.titlesize' : 12,\n",
    "          'font.size' : 12, 'legend.fontsize' : 12,\n",
    "          'xtick.labelsize' : 12, 'ytick.labelsize' : 12}\n",
    "plt.rcParams.update(params)\n",
    "plt.rcParams.update({'figure.max_open_warning': 0})\n",
    "\n",
    "\n",
    "# connect to database\n",
    "conn = sqlite3.connect('data/youtube-traceroute.db')\n",
    "\n",
    "# read necessary tables from database\n",
    "src = pd.read_sql_query('select * from probes_asns', conn)\n",
    "dst = pd.read_sql_query('select * from dst_asn_mapping', conn)\n",
    "hostnames = pd.read_sql_query('select * from hostnames', conn)\n",
    "deltas = pd.read_sql_query('select * from deltas', conn)\n",
    "\n",
    "conn.close()\n",
    "\n",
    "# distinguish between IPv4 and IPv6 data\n",
    "dst_v6 = dst[dst.ip.str.contains(':')]\n",
    "dst_v4 = dst[~dst.ip.str.contains(':')]\n",
    "\n",
    "hostnames_v6 = hostnames[hostnames.ip.str.contains(':')]\n",
    "hostnames_v4 = hostnames[~hostnames.ip.str.contains(':')]\n",
    "\n",
    "\n",
    "# add ASNs based on probe ASN metadata\n",
    "deltas = deltas.merge(src, on = 'unit_id')\n",
    "\n",
    "# add destination ASNs\n",
    "deltas = deltas.merge(dst_v4, left_on = 'destination_v4', right_on = 'ip').rename(\n",
    "    columns = {'ip' : 'dst_v4', 'asn' : 'dst_asn_v4', 'holder' : 'dst_holder_v4'})\n",
    "deltas = deltas.merge(dst_v6, left_on = 'destination_v6', right_on = 'ip').rename(\n",
    "    columns = {'ip' : 'dst_v6', 'asn' : 'dst_asn_v6', 'holder' : 'dst_holder_v6'})\n",
    "\n",
    "# add hostnames\n",
    "deltas = deltas.merge(hostnames_v4, how = 'left', left_on = 'destination_v4', right_on = 'ip').rename(\n",
    "    columns = {'ip' : 'ip_hostname_v4', 'hostname' : 'hostname_v4'})\n",
    "deltas = deltas.merge(hostnames_v6, how = 'left', left_on = 'destination_v6', right_on = 'ip').rename(\n",
    "    columns = {'ip' : 'ip_hostname_v6', 'hostname' : 'hostname_v6'})\n",
    "\n",
    "\n",
    "# pick correct columns and rearrange order\n",
    "deltas = deltas[['unit_id', 'dtime',\n",
    "                 'src_asn_v4', 'src_holder_v4',\n",
    "                 'dst_v4', 'hostname_v4', 'dst_asn_v4', 'dst_holder_v4',\n",
    "                 'max(ttl)_v4', 'rtt_v4',\n",
    "                 'src_asn_v6', 'src_holder_v6',\n",
    "                 'dst_v6', 'hostname_v6', 'dst_asn_v6', 'dst_holder_v6',\n",
    "                 'max(ttl)_v6', 'rtt_v6',\n",
    "                 'ttl_delta', 'rtt_delta']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cache Identification\n",
    "In this case, a cache is identified by either having the same ASN for source and destination or having one of the following identifiers in its hostname (from reverse DNS lookup):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to identify caches, use hostname keywords and matching AS numbers for sources and destinations\n",
    "cache_names = 'ggc|cache'\n",
    "\n",
    "deltas['is_v4_cache'] = np.where(deltas['hostname_v4'].str.contains(cache_names, na = False) |\n",
    "                                 (deltas['src_asn_v4'] == deltas['dst_asn_v4']), True, False)\n",
    "deltas['is_v6_cache'] = np.where(deltas['hostname_v6'].str.contains(cache_names, na = False) |\n",
    "                                 (deltas['src_asn_v6'] == deltas['dst_asn_v6']), True, False)\n",
    "\n",
    "v4_cache = deltas.query('is_v4_cache == True')\n",
    "no_v4_cache = deltas.query('is_v4_cache == False')\n",
    "\n",
    "v6_cache = deltas.query('is_v6_cache == True')\n",
    "no_v6_cache = deltas.query('is_v6_cache == False')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'unit_id', u'dtime', u'src_asn_v4', u'src_holder_v4', u'dst_v4',\n",
      "       u'hostname_v4', u'dst_asn_v4', u'dst_holder_v4', u'max(ttl)_v4',\n",
      "       u'rtt_v4', u'src_asn_v6', u'src_holder_v6', u'dst_v6', u'hostname_v6',\n",
      "       u'dst_asn_v6', u'dst_holder_v6', u'max(ttl)_v6', u'rtt_v6',\n",
      "       u'ttl_delta', u'rtt_delta', u'is_v4_cache', u'is_v6_cache'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# get overview of columns\n",
    "print(v4_cache.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# putting related information together for list processing\n",
    "v4_dfs = [no_v4_cache,\n",
    "          v4_cache]\n",
    "\n",
    "v6_dfs = [no_v6_cache,\n",
    "          v6_cache]\n",
    "\n",
    "data = [('IPv4', v4_dfs, ['cyan', 'blue']), \n",
    "        ('IPv6', v6_dfs, ['orange', 'red'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots split out (side by side)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Side by side plots\n",
    "ttl_cdf_fig, ttl_cdf_axes = plt.subplots(figsize = (5, 4), nrows = 2, ncols = 1)\n",
    "rtt_cdf_fig, rtt_cdf_axes = plt.subplots(figsize = (5, 4), nrows = 2, ncols = 1, sharex = True)\n",
    "plt.subplots_adjust(hspace = 0.5)\n",
    "\n",
    "# helper variables\n",
    "i = 0\n",
    "cdfs = []\n",
    "\n",
    "# plot loop\n",
    "for version, dfs, colors in data:\n",
    "\n",
    "    ### TTL\n",
    "    ttl_no_cache = Cdf.MakeCdfFromList(dfs[0]['max(ttl)_%s' % version[-2:]])\n",
    "    ttl_cache = Cdf.MakeCdfFromList(dfs[1]['max(ttl)_%s' % version[-2:]])\n",
    "\n",
    "    ttl_cdf_axes[i].plot(ttl_no_cache.xs, ttl_no_cache.ps,\n",
    "                         label = 'no cache',\n",
    "                         marker = '^',\n",
    "                         linewidth = 0.5,\n",
    "                         markersize = 3.5,\n",
    "                         fillstyle = 'none',\n",
    "                         color = colors[0])\n",
    "    ttl_cdf_axes[i].plot(ttl_cache.xs, ttl_cache.ps, label = 'cache', marker = 's', linewidth = 0.5, markersize = 3.5, fillstyle = 'none', color = colors[1])\n",
    "    \n",
    "    \n",
    "    # PLOT FORMATTING\n",
    "    # customize axes and grid appearance\n",
    "    ttl_cdf_axes[i].set_ylabel('CDF')\n",
    "    ttl_cdf_axes[i].set_ylim([-0.05, 1.05])\n",
    "    ttl_cdf_axes[i].set_xlim([-0.5, 22.5])\n",
    "    ttl_cdf_axes[i].xaxis.set_ticks(range(0, 23, 2))\n",
    "\n",
    "    ttl_cdf_axes[i].legend(loc = 'lower right', fontsize = 'smaller')\n",
    "    \n",
    "    yticks = np.arange(0.0, 1.01, 0.2)\n",
    "    ttl_cdf_axes[i].set_yticks(yticks)\n",
    "    ttl_cdf_axes[i].grid(False)\n",
    "    ttl_cdf_axes[i].spines['right'].set_color('none')\n",
    "    ttl_cdf_axes[i].spines['top'].set_color('none')\n",
    "    ttl_cdf_axes[i].yaxis.set_ticks_position('left')\n",
    "    ttl_cdf_axes[i].xaxis.set_ticks_position('bottom')\n",
    "    ttl_cdf_axes[i].spines['bottom'].set_position(('axes', -0.02))\n",
    "    ttl_cdf_axes[i].spines['left'].set_position(('axes', -0.02))\n",
    "    \n",
    "    ax1_ = ttl_cdf_axes[i].twinx()\n",
    "    ax1_.spines['right'].set_color('none')\n",
    "    ax1_.spines['top'].set_color('none')\n",
    "    ax1_.spines['left'].set_color('none')\n",
    "    ax1_.spines['bottom'].set_color('none')\n",
    "    ax1_.yaxis.set_ticks_position('none')\n",
    "    ax1_.set_ylabel('%s' % version)\n",
    "    plt.setp(ax1_.get_yticklabels(), visible = False)\n",
    "    \n",
    "\n",
    "\n",
    "    ### RTT\n",
    "    rtt_no_cache = Cdf.MakeCdfFromList(dfs[0]['rtt_%s' % version[-2:]])\n",
    "    rtt_cache = Cdf.MakeCdfFromList(dfs[1]['rtt_%s' % version[-2:]])\n",
    "\n",
    "    rtt_cdf_axes[i].plot(rtt_no_cache.xs, rtt_no_cache.ps, label = 'no cache', marker = '^', linewidth = 0.5, markersize = 3.5, fillstyle = 'none', color = colors[0])\n",
    "    rtt_cdf_axes[i].plot(rtt_cache.xs, rtt_cache.ps, label = 'cache', marker = 's', linewidth = 0.5, markersize = 3.5, fillstyle = 'none', color = colors[1])\n",
    "\n",
    "    \n",
    "    # PLOT FORMATTING\n",
    "    # customize axes and grid appearance\n",
    "    rtt_cdf_axes[i].set_ylabel('CDF')\n",
    "    rtt_cdf_axes[i].set_ylim([-0.05, 1.05])\n",
    "    rtt_cdf_axes[i].minorticks_off()\n",
    "    rtt_cdf_axes[i].set_xscale('log')\n",
    "\n",
    "    rtt_cdf_axes[i].legend(loc = 'lower right', fontsize = 'smaller')\n",
    "\n",
    "    yticks = np.arange(0.0, 1.01, 0.2)\n",
    "    rtt_cdf_axes[i].set_yticks(yticks)\n",
    "    rtt_cdf_axes[i].grid(False)\n",
    "    rtt_cdf_axes[i].spines['right'].set_color('none')\n",
    "    rtt_cdf_axes[i].spines['top'].set_color('none')\n",
    "    rtt_cdf_axes[i].yaxis.set_ticks_position('left')\n",
    "    rtt_cdf_axes[i].xaxis.set_ticks_position('bottom')\n",
    "    rtt_cdf_axes[i].spines['bottom'].set_position(('axes', -0.02))\n",
    "    rtt_cdf_axes[i].spines['left'].set_position(('axes', -0.02))\n",
    "    \n",
    "    ax1_ = rtt_cdf_axes[i].twinx()\n",
    "    ax1_.spines['right'].set_color('none')\n",
    "    ax1_.spines['top'].set_color('none')\n",
    "    ax1_.spines['left'].set_color('none')\n",
    "    ax1_.spines['bottom'].set_color('none')\n",
    "    ax1_.yaxis.set_ticks_position('none')\n",
    "    ax1_.set_ylabel('%s' % version)\n",
    "    plt.setp(ax1_.get_yticklabels(), visible = False)\n",
    "    \n",
    "    \n",
    "    # adjust x-axis based on what metric we are looking at\n",
    "    if i == 0:\n",
    "        ttl_cdf_axes[i].set_xlabel('')\n",
    "        rtt_cdf_axes[i].set_xlabel('')\n",
    "        ttl_cdf_axes[i].set_xticklabels([])\n",
    "        rtt_cdf_axes[i].set_xticklabels([])\n",
    "    else:\n",
    "        ttl_cdf_axes[i].set_xlabel('TTL')\n",
    "        rtt_cdf_axes[i].set_xlabel('RTT [ms]')\n",
    "    \n",
    "    # increase helper variable to go to correct data/plot in next iteration\n",
    "    i = i + 1\n",
    "    \n",
    "    # append calculated CDFs to list for later use\n",
    "    cdfs.append(ttl_no_cache)\n",
    "    cdfs.append(ttl_cache)\n",
    "    cdfs.append(rtt_no_cache)\n",
    "    cdfs.append(rtt_cache)\n",
    "\n",
    "    \n",
    "# get \"zoomed in version\" of rtt\n",
    "rtt_cdf_axes[0].set_xscale('linear')\n",
    "rtt_cdf_axes[0].set_xlim([-0.5, 60.5])\n",
    "rtt_cdf_axes[1].set_xscale('linear')\n",
    "rtt_cdf_axes[1].set_xlim([-0.5, 60.5])\n",
    "\n",
    "#ttl_cdf_fig.savefig('plots/cache_nocache_ttl.pdf', bbox_inches = 'tight')\n",
    "#rtt_cdf_fig.savefig('plots/cache_nocache_rtt.pdf', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TTL and RTT in one plot each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# in one plot\n",
    "ttl_cdf_fig, ttl_cdf_ax = plt.subplots(figsize = (5, 2))\n",
    "rtt_cdf_fig, rtt_cdf_ax = plt.subplots(figsize = (5, 2))\n",
    "plt.subplots_adjust(hspace = 0.5)\n",
    "\n",
    "# plot loop\n",
    "for version, dfs, colors in data:\n",
    "\n",
    "    ### TTL\n",
    "    ttl_no_cache = Cdf.MakeCdfFromList(dfs[0]['max(ttl)_%s' % version[-2:]])\n",
    "    ttl_cache = Cdf.MakeCdfFromList(dfs[1]['max(ttl)_%s' % version[-2:]])\n",
    "\n",
    "    ### RTT\n",
    "    rtt_no_cache = Cdf.MakeCdfFromList(dfs[0]['rtt_%s' % version[-2:]])\n",
    "    rtt_cache = Cdf.MakeCdfFromList(dfs[1]['rtt_%s' % version[-2:]])\n",
    "\n",
    "    ttl_cdf_ax.plot(ttl_no_cache.xs, ttl_no_cache.ps,\n",
    "                    label = 'no cache (%s)' % version,\n",
    "                    marker = '^', linewidth = 0.5,\n",
    "                    markersize = 2.5,\n",
    "                    fillstyle = 'none',\n",
    "                    color = colors[0])\n",
    "    rtt_cdf_ax.plot(rtt_no_cache.xs, rtt_no_cache.ps,\n",
    "                    label = 'no cache (%s)' % version,\n",
    "                    marker = '^', linewidth = 0.5,\n",
    "                    markersize = 2.5,\n",
    "                    fillstyle = 'none',\n",
    "                    color = colors[0])\n",
    "    ttl_cdf_ax.plot(ttl_cache.xs, ttl_cache.ps,\n",
    "                    label = 'cache (%s)' % version,\n",
    "                    marker = 's', linewidth = 0.5,\n",
    "                    markersize = 2.5,\n",
    "                    fillstyle = 'none',\n",
    "                    color = colors[1])\n",
    "    rtt_cdf_ax.plot(rtt_cache.xs, rtt_cache.ps,\n",
    "                    label = 'cache (%s)' % version,\n",
    "                    marker = 's',\n",
    "                    linewidth = 0.5,\n",
    "                    markersize = 2.5,\n",
    "                    fillstyle = 'none',\n",
    "                    color = colors[1])\n",
    "\n",
    "# PLOT FORMATTING\n",
    "# customize axes and grid appearance\n",
    "ttl_cdf_ax.set_ylabel('CDF')\n",
    "ttl_cdf_ax.set_ylim([-0.05, 1.05])\n",
    "ttl_cdf_ax.set_xlim([-0.5, 22.5])\n",
    "ttl_cdf_ax.xaxis.set_ticks(range(0, 23, 2))\n",
    "\n",
    "ttl_cdf_ax.legend(loc = 'lower right', fontsize = 'smaller')\n",
    "\n",
    "yticks = np.arange(0.0, 1.01, 0.2)\n",
    "ttl_cdf_ax.set_yticks(yticks)\n",
    "ttl_cdf_ax.grid(False)\n",
    "ttl_cdf_ax.spines['right'].set_color('none')\n",
    "ttl_cdf_ax.spines['top'].set_color('none')\n",
    "ttl_cdf_ax.yaxis.set_ticks_position('left')\n",
    "ttl_cdf_ax.xaxis.set_ticks_position('bottom')\n",
    "ttl_cdf_ax.spines['bottom'].set_position(('axes', -0.02))\n",
    "ttl_cdf_ax.spines['left'].set_position(('axes', -0.02))    \n",
    "\n",
    "\n",
    "### RTT\n",
    "\n",
    "# PLOT FORMATTING\n",
    "# customize axes and grid appearance\n",
    "rtt_cdf_ax.set_ylabel('CDF')\n",
    "rtt_cdf_ax.set_ylim([-0.05, 1.05])\n",
    "rtt_cdf_ax.minorticks_off()\n",
    "rtt_cdf_ax.set_xscale('log')\n",
    "\n",
    "rtt_cdf_ax.legend(loc = 'lower right', fontsize = 'smaller')\n",
    "\n",
    "yticks = np.arange(0.0, 1.01, 0.2)\n",
    "rtt_cdf_ax.set_yticks(yticks)\n",
    "rtt_cdf_ax.grid(False)\n",
    "rtt_cdf_ax.spines['right'].set_color('none')\n",
    "rtt_cdf_ax.spines['top'].set_color('none')\n",
    "rtt_cdf_ax.yaxis.set_ticks_position('left')\n",
    "rtt_cdf_ax.xaxis.set_ticks_position('bottom')\n",
    "rtt_cdf_ax.spines['bottom'].set_position(('axes', -0.02))\n",
    "rtt_cdf_ax.spines['left'].set_position(('axes', -0.02))    \n",
    "\n",
    "# adjust x-axis based on what metric we are looking at\n",
    "ttl_cdf_ax.set_xlabel('TTL')\n",
    "rtt_cdf_ax.set_xlabel('RTT [ms]')\n",
    "\n",
    "# get \"zoomed in version\" of rtt\n",
    "rtt_cdf_ax.set_xscale('linear')\n",
    "rtt_cdf_ax.set_xlim([-0.5, 60.5])\n",
    "rtt_cdf_ax.set_xscale('linear')\n",
    "rtt_cdf_ax.set_xlim([-0.5, 60.5])\n",
    "\n",
    "\n",
    "# saving and showing plots\n",
    "ttl_cdf_fig.savefig('plots/combined_cache_nocache_ttl.pdf', bbox_inches = 'tight')\n",
    "rtt_cdf_fig.savefig('plots/combined_cache_nocache_rtt.pdf', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distributions of metrics for all cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"cdfs\" contains:\n",
    "#    - v4 TTL no cache\n",
    "#    - v4 TTL w/ cache\n",
    "\n",
    "#    - v4 RTT no cache\n",
    "#    - v4 RTT w/ cache\n",
    "\n",
    "#    - v6 TTL no cache\n",
    "#    - v6 TTL w/ cache\n",
    "\n",
    "#    - v6 RTT no cache\n",
    "#    - v6 RTT w/ cache\n",
    "\n",
    "info = zip(['ttl_v4_no_cache', 'ttl_v4_cache',\n",
    "        'rtt_v4_no_cache', 'rtt_v4_cache',\n",
    "        'ttl_v6_no_cache', 'ttl_v6_cache',\n",
    "        'rtt_v6_no_cache', 'rtt_v6_cache'], cdfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- ttl_v4_no_cache ---\n",
      "value; cdf\n",
      "\n",
      "4.00; 0.01454\n",
      "5.00; 0.06030\n",
      "6.00; 0.12833\n",
      "7.00; 0.23515\n",
      "8.00; 0.38299\n",
      "9.00; 0.47531\n",
      "10.00; 0.65216\n",
      "11.00; 0.83013\n",
      "12.00; 0.88533\n",
      "13.00; 0.91516\n",
      "14.00; 0.95114\n",
      "15.00; 0.96770\n",
      "16.00; 0.97926\n",
      "17.00; 0.98924\n",
      "18.00; 0.99856\n",
      "19.00; 0.99935\n",
      "20.00; 0.99940\n",
      "21.00; 0.99944\n",
      "22.00; 0.99997\n",
      "23.00; 1.00000\n",
      "\n",
      "\n",
      "--- ttl_v4_cache ---\n",
      "value; cdf\n",
      "\n",
      "2.00; 0.02410\n",
      "3.00; 0.04154\n",
      "4.00; 0.45734\n",
      "5.00; 0.72450\n",
      "6.00; 0.89859\n",
      "7.00; 0.97802\n",
      "8.00; 0.99044\n",
      "9.00; 0.99111\n",
      "10.00; 0.99248\n",
      "11.00; 0.99776\n",
      "12.00; 0.99982\n",
      "13.00; 0.99984\n",
      "14.00; 1.00000\n",
      "\n",
      "\n",
      "--- rtt_v4_no_cache ---\n",
      "value; cdf\n",
      "\n",
      "0.41; 0.00000\n",
      "0.64; 0.03888\n",
      "1.10; 0.04318\n",
      "1.36; 0.04774\n",
      "1.64; 0.05276\n",
      "1.89; 0.05622\n",
      "2.17; 0.05974\n",
      "2.44; 0.06275\n",
      "2.65; 0.07126\n",
      "2.85; 0.07534\n",
      "3.06; 0.07872\n",
      "3.26; 0.08242\n",
      "3.46; 0.08563\n",
      "3.67; 0.09341\n",
      "3.87; 0.10871\n",
      "4.07; 0.13017\n",
      "4.27; 0.15056\n",
      "4.48; 0.16643\n",
      "4.73; 0.16993\n",
      "4.94; 0.18576\n",
      "5.14; 0.19241\n",
      "5.40; 0.19421\n",
      "5.61; 0.19743\n",
      "5.82; 0.20008\n",
      "6.07; 0.20177\n",
      "6.28; 0.20926\n",
      "6.49; 0.21287\n",
      "6.69; 0.21902\n",
      "6.89; 0.22658\n",
      "7.10; 0.22940\n",
      "7.33; 0.23165\n",
      "7.56; 0.23393\n",
      "7.76; 0.25337\n",
      "7.96; 0.25900\n",
      "8.18; 0.26159\n",
      "8.39; 0.26456\n",
      "8.59; 0.26956\n",
      "8.79; 0.27560\n",
      "8.99; 0.28282\n",
      "9.19; 0.29267\n",
      "9.39; 0.30675\n",
      "9.59; 0.32868\n",
      "9.79; 0.34774\n",
      "9.99; 0.36575\n",
      "10.19; 0.37975\n",
      "10.39; 0.39007\n",
      "10.59; 0.40034\n",
      "10.79; 0.41128\n",
      "10.99; 0.42224\n",
      "11.19; 0.43051\n",
      "11.39; 0.43787\n",
      "11.59; 0.44523\n",
      "11.79; 0.45350\n",
      "11.99; 0.46071\n",
      "12.19; 0.46780\n",
      "12.39; 0.47531\n",
      "12.59; 0.48254\n",
      "12.79; 0.48894\n",
      "12.99; 0.49283\n",
      "13.19; 0.49677\n",
      "13.40; 0.50176\n",
      "13.60; 0.50779\n",
      "13.80; 0.51484\n",
      "14.00; 0.52013\n",
      "14.20; 0.52472\n",
      "14.40; 0.52983\n",
      "14.60; 0.53455\n",
      "14.80; 0.54042\n",
      "15.00; 0.54461\n",
      "15.20; 0.54866\n",
      "15.41; 0.55309\n",
      "15.61; 0.55804\n",
      "15.81; 0.56348\n",
      "16.01; 0.56785\n",
      "16.21; 0.57196\n",
      "16.41; 0.57640\n",
      "16.61; 0.58190\n",
      "16.81; 0.58839\n",
      "17.01; 0.59579\n",
      "17.21; 0.60938\n",
      "17.41; 0.62026\n",
      "17.61; 0.63365\n",
      "17.81; 0.64724\n",
      "18.01; 0.65956\n",
      "18.21; 0.67063\n",
      "18.41; 0.68157\n",
      "18.61; 0.69249\n",
      "18.81; 0.70117\n",
      "19.01; 0.70925\n",
      "19.21; 0.71455\n",
      "19.41; 0.71829\n",
      "19.62; 0.72149\n",
      "19.82; 0.72478\n",
      "20.03; 0.72749\n",
      "20.25; 0.73008\n",
      "20.47; 0.73249\n",
      "20.68; 0.73541\n",
      "20.88; 0.73857\n",
      "21.09; 0.74173\n",
      "21.29; 0.74492\n",
      "21.50; 0.74818\n",
      "21.71; 0.75146\n",
      "21.91; 0.75435\n",
      "22.11; 0.75730\n",
      "22.32; 0.76015\n",
      "22.53; 0.76303\n",
      "22.75; 0.76560\n",
      "22.96; 0.76826\n",
      "23.19; 0.77040\n",
      "23.42; 0.77228\n",
      "23.64; 0.77500\n",
      "23.84; 0.77900\n",
      "24.05; 0.78278\n",
      "24.26; 0.78586\n",
      "24.46; 0.78935\n",
      "24.67; 0.79550\n",
      "24.87; 0.80412\n",
      "25.07; 0.81210\n",
      "25.27; 0.81682\n",
      "25.48; 0.81996\n",
      "25.70; 0.82278\n",
      "25.92; 0.82563\n",
      "26.15; 0.82822\n",
      "26.40; 0.83011\n",
      "26.62; 0.83200\n",
      "26.86; 0.83410\n",
      "27.08; 0.83668\n",
      "27.28; 0.84076\n",
      "27.48; 0.84510\n",
      "27.69; 0.84849\n",
      "27.92; 0.85099\n",
      "28.16; 0.85287\n",
      "28.41; 0.85481\n",
      "28.63; 0.85733\n",
      "28.85; 0.86009\n",
      "29.08; 0.86231\n",
      "29.34; 0.86409\n",
      "29.59; 0.86565\n",
      "29.86; 0.86725\n",
      "30.13; 0.86873\n",
      "30.43; 0.87020\n",
      "30.77; 0.87154\n",
      "31.12; 0.87282\n",
      "31.49; 0.87412\n",
      "31.84; 0.87546\n",
      "32.19; 0.87681\n",
      "32.52; 0.87815\n",
      "32.84; 0.87945\n",
      "33.25; 0.88065\n",
      "33.62; 0.88192\n",
      "34.05; 0.88312\n",
      "34.47; 0.88435\n",
      "34.82; 0.88559\n",
      "35.22; 0.88684\n",
      "35.64; 0.88806\n",
      "36.07; 0.88923\n",
      "36.51; 0.89035\n",
      "37.03; 0.89142\n",
      "37.61; 0.89255\n",
      "38.17; 0.89361\n",
      "38.78; 0.89471\n",
      "39.45; 0.89578\n",
      "40.05; 0.89683\n",
      "40.72; 0.89784\n",
      "41.45; 0.89890\n",
      "42.23; 0.90003\n",
      "42.89; 0.90110\n",
      "43.37; 0.90237\n",
      "43.72; 0.90366\n",
      "44.28; 0.90475\n",
      "44.52; 0.90683\n",
      "44.91; 0.90804\n",
      "45.63; 0.90914\n",
      "46.51; 0.91016\n",
      "47.37; 0.91112\n",
      "48.37; 0.91210\n",
      "48.93; 0.91325\n",
      "49.51; 0.91429\n",
      "50.11; 0.91538\n",
      "50.56; 0.91675\n",
      "51.23; 0.91810\n",
      "51.87; 0.91933\n",
      "52.36; 0.92132\n",
      "52.97; 0.92286\n",
      "53.65; 0.92414\n",
      "54.14; 0.92567\n",
      "54.85; 0.92687\n",
      "55.41; 0.92806\n",
      "55.81; 0.92934\n",
      "56.17; 0.93077\n",
      "56.53; 0.93201\n",
      "56.89; 0.93327\n",
      "57.29; 0.93455\n",
      "57.65; 0.93593\n",
      "57.99; 0.93725\n",
      "58.30; 0.93862\n",
      "58.70; 0.94004\n",
      "59.25; 0.94127\n",
      "59.54; 0.94414\n",
      "59.82; 0.94763\n",
      "60.13; 0.94966\n",
      "60.44; 0.95184\n",
      "60.73; 0.95414\n",
      "61.05; 0.95570\n",
      "61.34; 0.95729\n",
      "61.66; 0.95873\n",
      "62.05; 0.96021\n",
      "62.52; 0.96141\n",
      "63.25; 0.96249\n",
      "64.43; 0.96350\n",
      "64.91; 0.96477\n",
      "65.16; 0.96659\n",
      "65.39; 0.96882\n",
      "65.61; 0.97111\n",
      "65.86; 0.97288\n",
      "66.23; 0.97422\n",
      "66.89; 0.97530\n",
      "67.51; 0.97639\n",
      "67.94; 0.97763\n",
      "68.19; 0.97940\n",
      "68.43; 0.98123\n",
      "68.70; 0.98294\n",
      "69.09; 0.98417\n",
      "69.94; 0.98522\n",
      "72.07; 0.98618\n",
      "75.15; 0.98708\n",
      "79.83; 0.98797\n",
      "85.95; 0.98889\n",
      "91.89; 0.98978\n",
      "98.65; 0.99066\n",
      "103.86; 0.99157\n",
      "111.42; 0.99246\n",
      "121.66; 0.99335\n",
      "130.82; 0.99423\n",
      "133.65; 0.99518\n",
      "138.72; 0.99607\n",
      "164.15; 0.99695\n",
      "210.76; 0.99784\n",
      "308.27; 0.99870\n",
      "566.26; 0.99957\n",
      "\n",
      "\n",
      "--- rtt_v4_cache ---\n",
      "value; cdf\n",
      "\n",
      "0.11; 0.00001\n",
      "0.33; 0.03161\n",
      "0.53; 0.04618\n",
      "0.73; 0.06103\n",
      "0.93; 0.06997\n",
      "1.13; 0.08773\n",
      "1.33; 0.10669\n",
      "1.53; 0.12024\n",
      "1.73; 0.16474\n",
      "1.93; 0.18437\n",
      "2.13; 0.19604\n",
      "2.34; 0.20010\n",
      "2.54; 0.20438\n",
      "2.74; 0.21348\n",
      "2.94; 0.22122\n",
      "3.14; 0.22651\n",
      "3.34; 0.24668\n",
      "3.54; 0.25953\n",
      "3.74; 0.28385\n",
      "3.94; 0.30091\n",
      "4.14; 0.31483\n",
      "4.34; 0.33194\n",
      "4.54; 0.34786\n",
      "4.75; 0.36579\n",
      "4.95; 0.38717\n",
      "5.15; 0.41278\n",
      "5.35; 0.42404\n",
      "5.56; 0.43666\n",
      "5.76; 0.44977\n",
      "5.96; 0.46361\n",
      "6.16; 0.47182\n",
      "6.36; 0.48690\n",
      "6.56; 0.49850\n",
      "6.76; 0.51146\n",
      "6.96; 0.52273\n",
      "7.16; 0.54136\n",
      "7.36; 0.56452\n",
      "7.56; 0.57735\n",
      "7.76; 0.58940\n",
      "7.96; 0.59436\n",
      "8.17; 0.59935\n",
      "8.37; 0.60494\n",
      "8.57; 0.61091\n",
      "8.77; 0.62086\n",
      "8.97; 0.64053\n",
      "9.17; 0.66057\n",
      "9.37; 0.67356\n",
      "9.57; 0.68768\n",
      "9.77; 0.69801\n",
      "9.97; 0.70362\n",
      "10.18; 0.70904\n",
      "10.38; 0.71495\n",
      "10.58; 0.71853\n",
      "10.79; 0.72207\n",
      "11.02; 0.72437\n",
      "11.25; 0.72686\n",
      "11.47; 0.73153\n",
      "11.67; 0.73626\n",
      "11.88; 0.74148\n",
      "12.09; 0.74482\n",
      "12.30; 0.74799\n",
      "12.51; 0.75124\n",
      "12.71; 0.75539\n",
      "12.93; 0.75837\n",
      "13.18; 0.76051\n",
      "13.41; 0.76292\n",
      "13.64; 0.76529\n",
      "13.87; 0.76797\n",
      "14.15; 0.76992\n",
      "14.39; 0.77229\n",
      "14.62; 0.77476\n",
      "14.84; 0.77911\n",
      "15.04; 0.78496\n",
      "15.25; 0.78934\n",
      "15.51; 0.79177\n",
      "15.80; 0.79386\n",
      "16.15; 0.79551\n",
      "16.55; 0.79713\n",
      "16.81; 0.79931\n",
      "17.04; 0.80309\n",
      "17.24; 0.80973\n",
      "17.44; 0.81821\n",
      "17.64; 0.82605\n",
      "17.86; 0.83010\n",
      "18.11; 0.83232\n",
      "18.32; 0.83876\n",
      "18.53; 0.84469\n",
      "18.73; 0.85033\n",
      "18.95; 0.85405\n",
      "19.20; 0.85726\n",
      "19.45; 0.86004\n",
      "19.72; 0.86328\n",
      "20.01; 0.86595\n",
      "20.31; 0.86786\n",
      "20.60; 0.86984\n",
      "20.96; 0.87163\n",
      "21.34; 0.87317\n",
      "21.61; 0.87516\n",
      "21.84; 0.87757\n",
      "22.06; 0.88024\n",
      "22.28; 0.88337\n",
      "22.48; 0.88778\n",
      "22.68; 0.89680\n",
      "22.88; 0.90237\n",
      "23.09; 0.90674\n",
      "23.30; 0.91159\n",
      "23.50; 0.91608\n",
      "23.70; 0.92190\n",
      "23.90; 0.92698\n",
      "24.11; 0.93032\n",
      "24.35; 0.93264\n",
      "24.61; 0.93467\n",
      "24.87; 0.93695\n",
      "25.17; 0.93896\n",
      "25.43; 0.94118\n",
      "25.67; 0.94363\n",
      "25.91; 0.94627\n",
      "26.13; 0.94892\n",
      "26.34; 0.95220\n",
      "26.55; 0.95698\n",
      "26.75; 0.96366\n",
      "26.96; 0.96713\n",
      "27.19; 0.96985\n",
      "27.47; 0.97174\n",
      "27.78; 0.97341\n",
      "28.33; 0.97476\n",
      "28.83; 0.97610\n",
      "29.51; 0.97739\n",
      "30.27; 0.97860\n",
      "32.16; 0.97977\n",
      "33.73; 0.98098\n",
      "35.02; 0.98222\n",
      "36.93; 0.98341\n",
      "40.39; 0.98452\n",
      "46.68; 0.98562\n",
      "51.92; 0.98684\n",
      "53.15; 0.98842\n",
      "54.12; 0.99000\n",
      "54.92; 0.99154\n",
      "56.11; 0.99290\n",
      "57.27; 0.99421\n",
      "59.01; 0.99546\n",
      "66.08; 0.99684\n",
      "91.22; 0.99792\n",
      "159.82; 0.99900\n",
      "\n",
      "\n",
      "--- ttl_v6_no_cache ---\n",
      "value; cdf\n",
      "\n",
      "4.00; 0.06104\n",
      "5.00; 0.15256\n",
      "6.00; 0.22505\n",
      "7.00; 0.28225\n",
      "8.00; 0.38169\n",
      "9.00; 0.52517\n",
      "10.00; 0.68169\n",
      "11.00; 0.82437\n",
      "12.00; 0.89212\n",
      "13.00; 0.93627\n",
      "14.00; 0.96611\n",
      "15.00; 0.97957\n",
      "16.00; 0.98533\n",
      "17.00; 0.99617\n",
      "18.00; 0.99855\n",
      "19.00; 0.99947\n",
      "20.00; 0.99952\n",
      "21.00; 0.99997\n",
      "22.00; 1.00000\n",
      "\n",
      "\n",
      "--- ttl_v6_cache ---\n",
      "value; cdf\n",
      "\n",
      "2.00; 0.00757\n",
      "3.00; 0.02791\n",
      "4.00; 0.51241\n",
      "5.00; 0.77571\n",
      "6.00; 0.88308\n",
      "7.00; 0.99363\n",
      "8.00; 0.99958\n",
      "9.00; 0.99986\n",
      "10.00; 0.99996\n",
      "11.00; 0.99998\n",
      "12.00; 1.00000\n",
      "\n",
      "\n",
      "--- rtt_v6_no_cache ---\n",
      "value; cdf\n",
      "\n",
      "0.45; 0.00000\n",
      "0.70; 0.03466\n",
      "1.09; 0.03678\n",
      "1.29; 0.04289\n",
      "1.50; 0.04789\n",
      "1.72; 0.05074\n",
      "1.95; 0.05268\n",
      "2.15; 0.06004\n",
      "2.37; 0.06286\n",
      "2.60; 0.06885\n",
      "2.90; 0.07339\n",
      "3.20; 0.07484\n",
      "3.45; 0.07636\n",
      "3.67; 0.08226\n",
      "3.87; 0.09241\n",
      "4.07; 0.11002\n",
      "4.27; 0.13883\n",
      "4.47; 0.15432\n",
      "4.70; 0.15629\n",
      "4.92; 0.15988\n",
      "5.14; 0.16366\n",
      "5.35; 0.16744\n",
      "5.56; 0.17025\n",
      "5.76; 0.17323\n",
      "6.00; 0.17497\n",
      "6.26; 0.17635\n",
      "6.48; 0.17888\n",
      "6.69; 0.18187\n",
      "6.89; 0.18636\n",
      "7.09; 0.19158\n",
      "7.29; 0.21002\n",
      "7.51; 0.21260\n",
      "7.72; 0.21616\n",
      "7.92; 0.22402\n",
      "8.12; 0.23285\n",
      "8.32; 0.23654\n",
      "8.52; 0.24233\n",
      "8.72; 0.25124\n",
      "8.92; 0.26813\n",
      "9.12; 0.28452\n",
      "9.32; 0.29537\n",
      "9.52; 0.31119\n",
      "9.72; 0.34155\n",
      "9.92; 0.35219\n",
      "10.12; 0.36086\n",
      "10.32; 0.36907\n",
      "10.52; 0.37735\n",
      "10.72; 0.38598\n",
      "10.92; 0.39304\n",
      "11.12; 0.39925\n",
      "11.32; 0.40541\n",
      "11.52; 0.41542\n",
      "11.72; 0.42978\n",
      "11.92; 0.44098\n",
      "12.12; 0.45005\n",
      "12.32; 0.45805\n",
      "12.52; 0.46593\n",
      "12.72; 0.47314\n",
      "12.92; 0.47780\n",
      "13.12; 0.48161\n",
      "13.32; 0.48581\n",
      "13.53; 0.49038\n",
      "13.73; 0.49686\n",
      "13.93; 0.50216\n",
      "14.13; 0.50691\n",
      "14.33; 0.51088\n",
      "14.53; 0.51511\n",
      "14.73; 0.52021\n",
      "14.93; 0.52407\n",
      "15.13; 0.52765\n",
      "15.33; 0.53197\n",
      "15.53; 0.53857\n",
      "15.73; 0.54542\n",
      "15.94; 0.54988\n",
      "16.14; 0.55391\n",
      "16.34; 0.55821\n",
      "16.54; 0.56282\n",
      "16.74; 0.56787\n",
      "16.94; 0.57294\n",
      "17.14; 0.57903\n",
      "17.34; 0.58658\n",
      "17.54; 0.59530\n",
      "17.74; 0.60383\n",
      "17.94; 0.61191\n",
      "18.14; 0.61911\n",
      "18.34; 0.62734\n",
      "18.54; 0.63548\n",
      "18.74; 0.64391\n",
      "18.94; 0.65114\n",
      "19.14; 0.65555\n",
      "19.34; 0.65879\n",
      "19.55; 0.66151\n",
      "19.75; 0.66431\n",
      "19.96; 0.66692\n",
      "20.17; 0.66942\n",
      "20.38; 0.67251\n",
      "20.58; 0.67578\n",
      "20.79; 0.67933\n",
      "20.99; 0.68289\n",
      "21.19; 0.68654\n",
      "21.39; 0.68929\n",
      "21.60; 0.69181\n",
      "21.81; 0.69494\n",
      "22.01; 0.69834\n",
      "22.22; 0.70133\n",
      "22.43; 0.70379\n",
      "22.64; 0.70644\n",
      "22.84; 0.70977\n",
      "23.05; 0.71284\n",
      "23.26; 0.71530\n",
      "23.48; 0.71733\n",
      "23.70; 0.71968\n",
      "23.91; 0.72311\n",
      "24.11; 0.72695\n",
      "24.31; 0.73089\n",
      "24.52; 0.73465\n",
      "24.72; 0.73954\n",
      "24.92; 0.74572\n",
      "25.12; 0.75218\n",
      "25.32; 0.75598\n",
      "25.54; 0.75841\n",
      "25.77; 0.76074\n",
      "25.99; 0.76298\n",
      "26.21; 0.76483\n",
      "26.47; 0.76654\n",
      "26.75; 0.76794\n",
      "27.04; 0.76932\n",
      "27.40; 0.77052\n",
      "27.73; 0.77178\n",
      "28.02; 0.77314\n",
      "28.25; 0.77503\n",
      "28.45; 0.77869\n",
      "28.65; 0.78493\n",
      "28.85; 0.79307\n",
      "29.05; 0.79983\n",
      "29.25; 0.80577\n",
      "29.45; 0.81025\n",
      "29.66; 0.81392\n",
      "29.86; 0.81754\n",
      "30.06; 0.82139\n",
      "30.26; 0.82494\n",
      "30.46; 0.82788\n",
      "30.68; 0.83016\n",
      "30.92; 0.83209\n",
      "31.18; 0.83376\n",
      "31.45; 0.83524\n",
      "31.74; 0.83662\n",
      "32.03; 0.83793\n",
      "32.26; 0.84526\n",
      "32.56; 0.84659\n",
      "32.86; 0.84787\n",
      "33.20; 0.84906\n",
      "33.52; 0.85027\n",
      "33.87; 0.85149\n",
      "34.26; 0.85262\n",
      "34.60; 0.85385\n",
      "34.97; 0.85496\n",
      "35.31; 0.85612\n",
      "35.65; 0.85738\n",
      "35.99; 0.85851\n",
      "36.39; 0.85968\n",
      "36.78; 0.86073\n",
      "37.22; 0.86179\n",
      "37.62; 0.86290\n",
      "38.00; 0.86403\n",
      "38.49; 0.86507\n",
      "38.96; 0.86609\n",
      "39.44; 0.86705\n",
      "39.90; 0.86816\n",
      "40.37; 0.86927\n",
      "40.86; 0.87033\n",
      "41.45; 0.87128\n",
      "41.96; 0.87225\n",
      "42.62; 0.87322\n",
      "43.26; 0.87414\n",
      "43.97; 0.87509\n",
      "44.75; 0.87598\n",
      "45.40; 0.87690\n",
      "45.90; 0.87789\n",
      "46.47; 0.87882\n",
      "47.15; 0.87975\n",
      "47.77; 0.88070\n",
      "48.02; 0.88248\n",
      "48.24; 0.88477\n",
      "48.47; 0.88688\n",
      "48.69; 0.88927\n",
      "48.90; 0.89175\n",
      "49.13; 0.89338\n",
      "49.60; 0.89457\n",
      "50.03; 0.89565\n",
      "50.34; 0.89825\n",
      "50.70; 0.90011\n",
      "51.03; 0.90263\n",
      "51.28; 0.90715\n",
      "51.72; 0.90854\n",
      "52.01; 0.91210\n",
      "52.45; 0.91358\n",
      "52.86; 0.91488\n",
      "53.16; 0.91692\n",
      "53.45; 0.91847\n",
      "53.69; 0.92044\n",
      "53.92; 0.92258\n",
      "54.15; 0.92454\n",
      "54.39; 0.92624\n",
      "54.65; 0.92799\n",
      "54.89; 0.92994\n",
      "55.12; 0.93204\n",
      "55.32; 0.93507\n",
      "55.52; 0.93919\n",
      "55.73; 0.94379\n",
      "55.93; 0.94797\n",
      "56.13; 0.95060\n",
      "56.36; 0.95255\n",
      "56.61; 0.95408\n",
      "56.88; 0.95561\n",
      "57.19; 0.95683\n",
      "57.48; 0.95823\n",
      "57.78; 0.95963\n",
      "58.13; 0.96072\n",
      "58.67; 0.96172\n",
      "59.33; 0.96268\n",
      "59.95; 0.96361\n",
      "60.56; 0.96464\n",
      "61.19; 0.96558\n",
      "61.72; 0.96672\n",
      "62.19; 0.96791\n",
      "62.70; 0.96899\n",
      "63.43; 0.96989\n",
      "64.27; 0.97078\n",
      "64.80; 0.97180\n",
      "65.37; 0.97284\n",
      "66.14; 0.97379\n",
      "67.26; 0.97464\n",
      "68.57; 0.97551\n",
      "69.87; 0.97638\n",
      "70.86; 0.97730\n",
      "71.47; 0.97836\n",
      "72.99; 0.97927\n",
      "75.21; 0.98016\n",
      "77.65; 0.98108\n",
      "80.48; 0.98197\n",
      "83.52; 0.98283\n",
      "87.15; 0.98368\n",
      "89.53; 0.98452\n",
      "91.00; 0.98539\n",
      "92.72; 0.98624\n",
      "96.03; 0.98706\n",
      "100.39; 0.98787\n",
      "106.46; 0.98865\n",
      "116.26; 0.98943\n",
      "125.86; 0.99023\n",
      "126.81; 0.99126\n",
      "128.90; 0.99220\n",
      "138.68; 0.99300\n",
      "157.67; 0.99377\n",
      "205.29; 0.99456\n",
      "271.80; 0.99535\n",
      "294.00; 0.99615\n",
      "336.87; 0.99694\n",
      "340.10; 0.99775\n",
      "345.11; 0.99856\n",
      "407.07; 0.99938\n",
      "\n",
      "\n",
      "--- rtt_v6_cache ---\n",
      "value; cdf\n",
      "\n",
      "0.15; 0.00001\n",
      "0.39; 0.02469\n",
      "0.60; 0.03902\n",
      "0.84; 0.04864\n",
      "1.04; 0.06506\n",
      "1.25; 0.07824\n",
      "1.45; 0.09208\n",
      "1.65; 0.10182\n",
      "1.85; 0.14612\n",
      "2.05; 0.17822\n",
      "2.25; 0.19056\n",
      "2.45; 0.19608\n",
      "2.65; 0.20557\n",
      "2.85; 0.21505\n",
      "3.05; 0.22443\n",
      "3.25; 0.23840\n",
      "3.45; 0.26281\n",
      "3.65; 0.28520\n",
      "3.85; 0.32089\n",
      "4.05; 0.33201\n",
      "4.25; 0.34392\n",
      "4.45; 0.35412\n",
      "4.65; 0.36333\n",
      "4.85; 0.38645\n",
      "5.05; 0.41410\n",
      "5.25; 0.43428\n",
      "5.45; 0.45042\n",
      "5.66; 0.45960\n",
      "5.86; 0.46491\n",
      "6.07; 0.47036\n",
      "6.26; 0.48664\n",
      "6.46; 0.51593\n",
      "6.67; 0.53963\n",
      "6.87; 0.55792\n",
      "7.07; 0.56779\n",
      "7.27; 0.57724\n",
      "7.47; 0.59470\n",
      "7.67; 0.61668\n",
      "7.87; 0.64490\n",
      "8.07; 0.65208\n",
      "8.27; 0.65700\n",
      "8.48; 0.66190\n",
      "8.68; 0.66848\n",
      "8.88; 0.67820\n",
      "9.08; 0.68870\n",
      "9.28; 0.69889\n",
      "9.48; 0.70566\n",
      "9.69; 0.71417\n",
      "9.89; 0.71813\n",
      "10.11; 0.72192\n",
      "10.31; 0.72618\n",
      "10.53; 0.72983\n",
      "10.75; 0.73370\n",
      "10.97; 0.73718\n",
      "11.19; 0.74072\n",
      "11.42; 0.74435\n",
      "11.63; 0.75092\n",
      "11.85; 0.75490\n",
      "12.08; 0.75789\n",
      "12.31; 0.76099\n",
      "12.53; 0.76416\n",
      "12.75; 0.76772\n",
      "13.01; 0.76999\n",
      "13.26; 0.77266\n",
      "13.50; 0.77505\n",
      "13.78; 0.77747\n",
      "14.13; 0.77912\n",
      "14.45; 0.78096\n",
      "14.67; 0.78423\n",
      "14.88; 0.78842\n",
      "15.10; 0.79418\n",
      "15.41; 0.79649\n",
      "15.79; 0.79849\n",
      "16.34; 0.80014\n",
      "16.62; 0.80378\n",
      "16.84; 0.81032\n",
      "17.04; 0.82055\n",
      "17.25; 0.82766\n",
      "17.46; 0.83321\n",
      "17.70; 0.83731\n",
      "18.12; 0.83919\n",
      "18.38; 0.84224\n",
      "18.59; 0.84774\n",
      "18.80; 0.85296\n",
      "19.05; 0.85734\n",
      "19.30; 0.86061\n",
      "19.57; 0.86382\n",
      "19.88; 0.86685\n",
      "20.18; 0.86886\n",
      "20.45; 0.87111\n",
      "20.68; 0.87410\n",
      "20.94; 0.87655\n",
      "21.27; 0.87845\n",
      "21.63; 0.88054\n",
      "21.93; 0.88247\n",
      "22.16; 0.88570\n",
      "22.37; 0.88950\n",
      "22.58; 0.89516\n",
      "22.79; 0.90202\n",
      "23.02; 0.90509\n",
      "23.27; 0.90739\n",
      "23.48; 0.91062\n",
      "23.69; 0.91559\n",
      "23.90; 0.91941\n",
      "24.13; 0.92241\n",
      "24.45; 0.92437\n",
      "24.71; 0.92677\n",
      "25.03; 0.92909\n",
      "25.34; 0.93112\n",
      "25.60; 0.93419\n",
      "25.84; 0.93708\n",
      "26.06; 0.94016\n",
      "26.27; 0.94405\n",
      "26.48; 0.94823\n",
      "26.68; 0.95666\n",
      "26.89; 0.96054\n",
      "27.11; 0.96402\n",
      "27.34; 0.96692\n",
      "27.59; 0.96970\n",
      "27.87; 0.97194\n",
      "28.25; 0.97372\n",
      "28.62; 0.97561\n",
      "29.01; 0.97741\n",
      "29.90; 0.97894\n",
      "32.12; 0.98025\n",
      "35.54; 0.98150\n",
      "36.95; 0.98310\n",
      "38.45; 0.98569\n",
      "40.57; 0.98722\n",
      "43.50; 0.98850\n",
      "46.82; 0.98982\n",
      "49.45; 0.99167\n",
      "55.93; 0.99296\n",
      "59.33; 0.99443\n",
      "66.69; 0.99572\n",
      "90.41; 0.99696\n",
      "147.39; 0.99822\n",
      "391.44; 0.99950\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for name, cdf in info:\n",
    "    \n",
    "    print('--- %s ---' % name)\n",
    "    print('value; cdf')\n",
    "    print('')\n",
    "    \n",
    "    if len(cdf.xs) > 100:\n",
    "        tup = list(zip(cdf.xs, cdf.ps))[0::200]\n",
    "    else:\n",
    "        tup = zip(cdf.xs, cdf.ps)\n",
    "    \n",
    "    for x, y in tup:\n",
    "        print('%.2f; %.5f' % (x, y))\n",
    "        \n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
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 },
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