plot-success-rate-cdf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "import filterbyip as fbi\n",
    "import clusterbycolumn as cbc\n",
    "import requests\n",
    "import asinfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    padding: 0.5em;\n",
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       "\n",
       ".CodeMirror {\n",
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       "\n",
       "div.output_area pre {\n",
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       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "css = open('style-table.css').read() + open('style-notebook.css').read()\n",
    "HTML('<style>{}</style>'.format(css))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def asn_from_probe_name(probe_name):\n",
    "    base = 'http://leone-collector.eecs.jacobs-university.de/msmpoint'\n",
    "    msm_metadata = requests.get('%s/%s'%(base, probe_name))\n",
    "    asn4 = str(msm_metadata.json()['asn4'])\n",
    "    asn6 = str(msm_metadata.json()['asn6'])\n",
    "    return asn4,asn6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dailydb_location = '/data/vbajpai/datasets/youtube-data-collection-python3/youtube.db'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query = '''select   msmpoint.name as probe\n",
    "                  , dtime\n",
    "                  , video_address\n",
    "                  , successes\n",
    "                  , failures\n",
    "           from youtube, msmpoint\n",
    "           where youtube.unit_id = msmpoint.skunitid\n",
    "           and error_code <> 607\n",
    "           and msmpoint.skunitid <> ''\n",
    "           order by dtime DESC\n",
    "        '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "con = sqlite3.connect(dailydb_location)\n",
    "df = pd.read_sql(query, con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probe</th>\n",
       "      <th>dtime</th>\n",
       "      <th>video_address</th>\n",
       "      <th>successes</th>\n",
       "      <th>failures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>samsbox08</td>\n",
       "      <td>2017-06-27 23:37:44</td>\n",
       "      <td>2001:b30:ef00:99ca::c</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>samsbox08</td>\n",
       "      <td>2017-06-27 23:36:40</td>\n",
       "      <td>195.95.178.204</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>samsbox45</td>\n",
       "      <td>2017-06-27 23:36:12</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>samsbox03</td>\n",
       "      <td>2017-06-27 23:36:00</td>\n",
       "      <td>2001:1a80:801::c</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>samsbox45</td>\n",
       "      <td>2017-06-27 23:35:09</td>\n",
       "      <td>173.194.20.202</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       probe                dtime          video_address  successes  failures\n",
       "0  samsbox08  2017-06-27 23:37:44  2001:b30:ef00:99ca::c          1         0\n",
       "1  samsbox08  2017-06-27 23:36:40         195.95.178.204          1         0\n",
       "2  samsbox45  2017-06-27 23:36:12                   None          0         1\n",
       "3  samsbox03  2017-06-27 23:36:00       2001:1a80:801::c          1         0\n",
       "4  samsbox45  2017-06-27 23:35:09         173.194.20.202          1         0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_probes = len(set(df['probe']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def round_time_to_nearest_hour(dtime):\n",
    "    import datetime;    \n",
    "    d = datetime.datetime.strptime(dtime, \"%Y-%m-%d %H:%M:%S\")\n",
    "    d = d.replace(minute=0, second=0)\n",
    "    dtime = d.strftime('%Y-%m-%d %H:%M:%S')\n",
    "    return dtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['dtime'] = df['dtime'].apply(round_time_to_nearest_hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>samsbox08</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>samsbox45</td>\n",
       "      <td>2017-06-27 23:00:00</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>samsbox03</td>\n",
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       "      <th>4</th>\n",
       "      <td>samsbox45</td>\n",
       "      <td>2017-06-27 23:00:00</td>\n",
       "      <td>173.194.20.202</td>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "       probe                dtime          video_address  successes  failures\n",
       "0  samsbox08  2017-06-27 23:00:00  2001:b30:ef00:99ca::c          1         0\n",
       "1  samsbox08  2017-06-27 23:00:00         195.95.178.204          1         0\n",
       "2  samsbox45  2017-06-27 23:00:00                   None          0         1\n",
       "3  samsbox03  2017-06-27 23:00:00       2001:1a80:801::c          1         0\n",
       "4  samsbox45  2017-06-27 23:00:00         173.194.20.202          1         0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>probe</th>\n",
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       "    <tr>\n",
       "      <th>4886447</th>\n",
       "      <td>samsbox03</td>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
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       "      <td>samsbox02</td>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
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       "      <th>4886449</th>\n",
       "      <td>samsbox02</td>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>64.15.112.79</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4886450</th>\n",
       "      <td>samsbox06</td>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>2a00:1450:4002:9::12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4886451</th>\n",
       "      <td>samsbox06</td>\n",
       "      <td>2014-12-10 12:00:00</td>\n",
       "      <td>149.3.177.111</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             probe                dtime         video_address  successes  \\\n",
       "4886447  samsbox03  2014-12-10 12:00:00   2001:1a80:100:1b::e          1   \n",
       "4886448  samsbox02  2014-12-10 12:00:00  2a00:1450:4005:6::12          1   \n",
       "4886449  samsbox02  2014-12-10 12:00:00          64.15.112.79          1   \n",
       "4886450  samsbox06  2014-12-10 12:00:00  2a00:1450:4002:9::12          1   \n",
       "4886451  samsbox06  2014-12-10 12:00:00         149.3.177.111          1   \n",
       "\n",
       "         failures  \n",
       "4886447         0  \n",
       "4886448         0  \n",
       "4886449         0  \n",
       "4886450         0  \n",
       "4886451         0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "probe            4886452\n",
       "dtime            4886452\n",
       "video_address    4378415\n",
       "successes        4886452\n",
       "failures         4886452\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_af_from_endpoint(ep):\n",
    "    if ep is None: return None\n",
    "    elif fbi.if_ipv6(ep): return 'v6'\n",
    "    elif fbi.if_ipv4(ep): return 'v4'\n",
    "    else: print('.'); return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".\n"
     ]
    }
   ],
   "source": [
    "df['af'] = df['video_address'].apply(get_af_from_endpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "probe            4886452\n",
       "dtime            4886452\n",
       "video_address    4378415\n",
       "successes        4886452\n",
       "failures         4886452\n",
       "af               4378414\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>1</td>\n",
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       "      <td>v6</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>samsbox08</td>\n",
       "      <td>2017-06-27 23:00:00</td>\n",
       "      <td>195.95.178.204</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>v4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>samsbox45</td>\n",
       "      <td>2017-06-27 23:00:00</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>samsbox03</td>\n",
       "      <td>2017-06-27 23:00:00</td>\n",
       "      <td>2001:1a80:801::c</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>v6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>samsbox45</td>\n",
       "      <td>2017-06-27 23:00:00</td>\n",
       "      <td>173.194.20.202</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>v4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       probe                dtime          video_address  successes  failures  \\\n",
       "0  samsbox08  2017-06-27 23:00:00  2001:b30:ef00:99ca::c          1         0   \n",
       "1  samsbox08  2017-06-27 23:00:00         195.95.178.204          1         0   \n",
       "2  samsbox45  2017-06-27 23:00:00                   None          0         1   \n",
       "3  samsbox03  2017-06-27 23:00:00       2001:1a80:801::c          1         0   \n",
       "4  samsbox45  2017-06-27 23:00:00         173.194.20.202          1         0   \n",
       "\n",
       "     af  \n",
       "0    v6  \n",
       "1    v4  \n",
       "2  None  \n",
       "3    v6  \n",
       "4    v4  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfgroupby = cbc.group(df, column=['probe', 'af'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_agg = cbc.aggregate(dfgroupby, ['successes', 'failures'], ['sum', 'count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_merged = cbc.merge(df, df_agg, column=['probe', 'af'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df_merged.drop(['dtime', 'video_address', 'successes', 'failures'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.rename( columns={  df.columns[-1]: 'failures_count'\n",
    "                    , df.columns[-2]: 'failures_sum'\n",
    "                    , df.columns[-3]: 'successes_count'\n",
    "                    , df.columns[-4]: 'successes_sum'}\n",
    "                , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfv6 = df[df.af == 'v6']\n",
    "dfv4 = df[df.af == 'v4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probe</th>\n",
       "      <th>af</th>\n",
       "      <th>successes_sum</th>\n",
       "      <th>successes_count</th>\n",
       "      <th>failures_sum</th>\n",
       "      <th>failures_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>samsbox08</td>\n",
       "      <td>v6</td>\n",
       "      <td>24245</td>\n",
       "      <td>24554</td>\n",
       "      <td>309</td>\n",
       "      <td>24554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>samsbox03</td>\n",
       "      <td>v6</td>\n",
       "      <td>35640</td>\n",
       "      <td>35730</td>\n",
       "      <td>90</td>\n",
       "      <td>35730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>samsbox97</td>\n",
       "      <td>v6</td>\n",
       "      <td>14350</td>\n",
       "      <td>14353</td>\n",
       "      <td>3</td>\n",
       "      <td>14353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>samsbox51</td>\n",
       "      <td>v6</td>\n",
       "      <td>27986</td>\n",
       "      <td>28323</td>\n",
       "      <td>337</td>\n",
       "      <td>28323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>samsbox33</td>\n",
       "      <td>v6</td>\n",
       "      <td>28717</td>\n",
       "      <td>29152</td>\n",
       "      <td>435</td>\n",
       "      <td>29152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       probe  af  successes_sum  successes_count  failures_sum  failures_count\n",
       "0  samsbox08  v6          24245            24554           309           24554\n",
       "3  samsbox03  v6          35640            35730            90           35730\n",
       "6  samsbox97  v6          14350            14353             3           14353\n",
       "8  samsbox51  v6          27986            28323           337           28323\n",
       "9  samsbox33  v6          28717            29152           435           29152"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cal_success_rate(x):\n",
    "    success = x['successes_sum']\n",
    "    total   = x['successes_count']    \n",
    "    \n",
    "    rate = (success / total) * 100\n",
    "    return rate\n",
    "\n",
    "dfv6['success_rate'] = dfv6.apply(cal_success_rate, axis = 1)\n",
    "dfv4['success_rate'] = dfv4.apply(cal_success_rate, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probe</th>\n",
       "      <th>af</th>\n",
       "      <th>successes_sum</th>\n",
       "      <th>successes_count</th>\n",
       "      <th>failures_sum</th>\n",
       "      <th>failures_count</th>\n",
       "      <th>success_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>samsbox08</td>\n",
       "      <td>v4</td>\n",
       "      <td>29529</td>\n",
       "      <td>29844</td>\n",
       "      <td>315</td>\n",
       "      <td>29844</td>\n",
       "      <td>98.944511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>samsbox45</td>\n",
       "      <td>v4</td>\n",
       "      <td>33295</td>\n",
       "      <td>33604</td>\n",
       "      <td>309</td>\n",
       "      <td>33604</td>\n",
       "      <td>99.080467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>samsbox03</td>\n",
       "      <td>v4</td>\n",
       "      <td>34424</td>\n",
       "      <td>34454</td>\n",
       "      <td>30</td>\n",
       "      <td>34454</td>\n",
       "      <td>99.912927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>samsbox97</td>\n",
       "      <td>v4</td>\n",
       "      <td>15413</td>\n",
       "      <td>15413</td>\n",
       "      <td>0</td>\n",
       "      <td>15413</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>samsbox51</td>\n",
       "      <td>v4</td>\n",
       "      <td>28422</td>\n",
       "      <td>28831</td>\n",
       "      <td>409</td>\n",
       "      <td>28831</td>\n",
       "      <td>98.581388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        probe  af  successes_sum  successes_count  failures_sum  \\\n",
       "1   samsbox08  v4          29529            29844           315   \n",
       "4   samsbox45  v4          33295            33604           309   \n",
       "5   samsbox03  v4          34424            34454            30   \n",
       "7   samsbox97  v4          15413            15413             0   \n",
       "12  samsbox51  v4          28422            28831           409   \n",
       "\n",
       "    failures_count  success_rate  \n",
       "1            29844     98.944511  \n",
       "4            33604     99.080467  \n",
       "5            34454     99.912927  \n",
       "7            15413    100.000000  \n",
       "12           28831     98.581388  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import Cdf\n",
    "import Pmf\n",
    "samplev4 = dfv4['success_rate'].values\n",
    "samplev6 = dfv6['success_rate'].values\n",
    "\n",
    "c4 = Cdf.MakeCdfFromList(samplev4)\n",
    "c6 = Cdf.MakeCdfFromList(samplev6)\n",
    "\n",
    "cc4 = [1 - p for x, p in c4.Items()]\n",
    "cc6 = [1 - p for x, p in c6.Items()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vdd68eZqRkXFwe9/ryy+/rLf/P//5z5qXl6c7duwImrdE/32aFhaoAZamlb9F9QwuYMGs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\nGJOGrPI3xpg0ZJW/McakIav8jTEmDf1/gnfTxau53P0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efca21a3dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 3))\n",
    "plt.rcParams['font.size'] = 12\n",
    "\n",
    "yticks = np.arange(0,1.1,0.2)\n",
    "xticks = np.arange(80,110,10)\n",
    "\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax2 = fig.add_subplot(212, sharex=ax1)\n",
    "\n",
    "ax1.spines['right'].set_color('none')\n",
    "ax1.spines['top'].set_color('none')\n",
    "ax1.yaxis.set_ticks_position('left')\n",
    "ax1.xaxis.set_ticks_position('bottom')\n",
    "ax1.spines['bottom'].set_position(('axes', -0.05))\n",
    "ax1.spines['left'].set_position(('axes', -0.05))\n",
    "\n",
    "ax2.spines['right'].set_color('none')\n",
    "ax2.spines['top'].set_color('none')\n",
    "ax2.yaxis.set_ticks_position('left')\n",
    "ax2.xaxis.set_ticks_position('bottom')\n",
    "ax2.spines['bottom'].set_position(('axes', -0.05))\n",
    "ax2.spines['left'].set_position(('axes', -0.05))\n",
    "\n",
    "ax1.plot(c6.xs, cc6, marker='s', linewidth=0, markersize=5, fillstyle='none', color = 'Red')\n",
    "ax2.plot(c4.xs, cc4, marker='o', linewidth=0, markersize=5, fillstyle='none', color = 'Blue')\n",
    "\n",
    "ax1.set_xlim([80,101])\n",
    "ax1.set_yticks(yticks)\n",
    "ax1.set_xticks(xticks)\n",
    "ax1.set_xscale('linear')\n",
    "ax1.set_xlabel('', fontsize=12)\n",
    "\n",
    "ax1.grid(False)\n",
    "ax1.legend(['IPv6 (102)'], fontsize=12, loc='best')\n",
    "\n",
    "ax2.set_xlim([80,101])\n",
    "ax2.set_yticks(yticks)\n",
    "ax2.set_xticks(xticks)\n",
    "ax2.set_xscale('linear')\n",
    "ax2.set_xlabel('', fontsize=12)\n",
    "\n",
    "ax2.grid(False)\n",
    "ax2.legend(['IPv4 (102)'], fontsize=12, loc='best')\n",
    "\n",
    "ax1_ = ax1.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('[\\'14 - \\'17]', fontsize = 12)\n",
    "ax1_.yaxis.set_label_coords(1.05, -0.2)\n",
    "plt.setp(ax1_.get_yticklabels(), visible=False)\n",
    "\n",
    "fig.subplots_adjust(hspace = 0.3)\n",
    "\n",
    "def to_percent(x, position): return str(int(x)) + '%'\n",
    "ax1.xaxis.set_major_formatter(FuncFormatter(to_percent))\n",
    "ax2.xaxis.set_major_formatter(FuncFormatter(to_percent))\n",
    "\n",
    "plt.setp(ax1.get_xticklabels(), visible=False)\n",
    "\n",
    "ax1.yaxis.set_label_coords(-0.18, -0.2)\n",
    "ax1.set_ylabel('CCDF', fontsize=12)\n",
    "\n",
    "ax1.set_title('Success Rate', fontsize=12, y=1.05)\n",
    "plt.savefig('yt-success-rate-ccdf.pdf', format='pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('yt-success-rate-v4.csv', 'w') as f: \n",
    "    f.write('values; percentile\\n')\n",
    "    for x, y in zip(c4.xs, cc4): f.write('%.2f; %.2f\\n'%(x,y))\n",
    "        \n",
    "with open('yt-success-rate-v6.csv', 'w') as f: \n",
    "    f.write('values; percentile\\n')\n",
    "    for x, y in zip(c6.xs, cc6): f.write('%.2f; %.2f\\n'%(x,y))        "
   ]
  }
 ],
 "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}