notebook.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "source": "This notebook demonstrates **ISP latency assessment**, based on the paper \"General Internet Service Assessment by Latency Including Blocked Measurements\".\n\nIf custom data are used, please specify the variables below.",
   "metadata": {
    "cell_id": "5eb7386e-4192-4297-b58c-f565c79655b7",
    "tags": [],
    "deepnote_cell_type": "markdown",
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   "metadata": {
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    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "97a6a483",
    "execution_start": 1656072635432,
    "execution_millis": 37,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 315
   },
   "source": "#RIPE Atlas probes\natlasfile = '20220202.json'\n\n#country ISO A2 and A3 codes\ncountrya2 = 'CZ'; countrya3 = 'CZE'\n\n#max latency observation points in country\npointmax = 200\n\n#approx. location of the host OR set geo plot to False\ngeographicplot = False\nendhostlat = 50; endhostlon = 16\n\n#inputs are not validated for code simplicity",
   "execution_count": 1,
   "outputs": []
  },
  {
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    "cell_id": "686148eb-f1e6-4216-8d3f-ae3e4b0d9e61",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "615f54c",
    "execution_start": 1656072635470,
    "execution_millis": 16971,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 189
   },
   "source": "%%capture\n!pip install numpy\n!pip install shapely\n!pip install pandas\n!pip install matplotlib\n!pip install geopandas\n!pip install lifelines",
   "execution_count": 2,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
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    "cell_id": "00001-e7c668c3-76bd-43cf-a35c-cafe1e2a42ac",
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    "source_hash": "998b903a",
    "execution_start": 1656072652441,
    "execution_millis": 1759,
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    "deepnote_cell_type": "code",
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   },
   "source": "#standard modules \nimport sys; import os; import json; import glob\n\n#data processing\nimport numpy as np; import pandas as pd \nimport matplotlib.pyplot as plt\n\n#geography\nimport geopandas as gpd\nfrom shapely.geometry import LineString\nfrom matplotlib.lines import Line2D\n\n#survival analysis\nfrom lifelines import KaplanMeierFitter\nfrom lifelines.utils import restricted_mean_survival_time\nfrom lifelines.plotting import plot_lifetimes\nfrom lifelines.plotting import rmst_plot\nfrom lifelines.plotting import add_at_risk_counts",
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "Process RIPE Atlas probes in country into observation points",
   "metadata": {
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    "tags": [],
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    "source_hash": "eb038578",
    "execution_start": 1656072654210,
    "execution_millis": 624,
    "deepnote_app_coordinates": {
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     "filters": [
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       "value": "",
       "type": "contains"
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     "sortBy": []
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    "deepnote_cell_type": "code",
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   },
   "source": "#load RIPE Atlas file with list of probes\ntry:\n    probes_js = json.load(open('atlas/'+atlasfile))\nexcept:\n    print('RIPE Atlas file'+' atlas/'+atlasfile+' does not exist or cannot be open')\n    raise\n\n#parse Atlas data\nprobes_pf = pd.DataFrame(probes_js['objects'])\nprobes_pf = probes_pf.filter(['id','address_v4','status_name','country_code','latitude','longitude'])\nprobes_pf = probes_pf.rename(columns={'id':'pointid','address_v4':'ip','status_name':'sts','country_code':'cntr','latitude':'lat','longitude':'lon'})\nprobes_pf.pointid = probes_pf.pointid.astype('str') \n\n#filter Atlas probes with IPv4 address, in specified country, and currently functional\npoints_pf = probes_pf.loc[(pd.notna(probes_pf.ip)) & (probes_pf.cntr == countrya2) & (probes_pf.sts == 'Connected')] \n\ndel probes_pf\n\n#reduce number of observation points for latency measurement if above pointmax\nif len(points_pf) > pointmax:\n    pointsmsr_pf = points_pf.sample(pointmax)\nelse:\n    pointsmsr_pf = points_pf\n\n#custom data = Atlas file of different name is uploaded\nif atlasfile != '20220202.json':\n\n    #store new latency observation points\n    pointsmsr_pf.to_csv('points.csv',header=False,index=False)\n\n#show latency observation points\n#pointid - Atlas probe ID\n#ip - Atlas probe IP address\n#sts - Atlas probe current status\n#cntr - Atlas probe country ISO A2\n#lat,lon - Atlas probe coordinates\npoints_pf",
   "execution_count": 4,
   "outputs": [
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     "output_type": "execute_result",
     "execution_count": 4,
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      "text/plain": "       pointid              ip        sts cntr      lat      lon\n38     1000034    81.201.60.30  Connected   CZ  50.0895  14.4885\n60     1000056  176.74.145.221  Connected   CZ  49.3105  16.6195\n61     1000057  90.177.221.181  Connected   CZ  49.1905  16.5795\n63     1000059   46.28.109.126  Connected   CZ  49.0515  14.4295\n67     1000063    84.242.77.74  Connected   CZ  50.0085  14.4505\n...        ...             ...        ...  ...      ...      ...\n34943     6810  78.128.211.119  Connected   CZ  50.0975  14.3875\n34993     6856   188.92.41.251  Connected   CZ  50.0805  14.4495\n35112     6963  185.178.172.32  Connected   CZ  50.0785  14.5205\n35180     7024  93.190.128.178  Connected   CZ  50.0595  14.4785\n35242      721    217.31.205.1  Connected   CZ  49.8275  18.1785\n\n[254 rows x 6 columns]",
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     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Read provided latency data OR custom latency data",
   "metadata": {
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    "source_hash": "94a6e60d",
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    "execution_millis": 4,
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    "deepnote_cell_type": "code",
    "deepnote_cell_height": 81
   },
   "source": "latencyfiles_ls = sorted(glob.glob(\"latency/*.json\"),reverse = False)",
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "Process latency data for survival analysis",
   "metadata": {
    "cell_id": "4df1cc83-de60-4a14-bd60-751322336293",
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   "source": "#ping measurements\ndef ping(file):\n    latency = np.nan\n    pointip = np.nan\n    observed = False\n\n    msr_pf = pd.read_json(file)\n\n    #response is present\n    if len(msr_pf) > 0: \n        pointip = msr_pf.destination_ip.iloc[0]\n\n        if msr_pf.packets_received.iloc[0] > 0:\n            latency = round(msr_pf.round_trip_ms_avg.iloc[0])\n            observed = True\n\n    return [latency,observed,pointip]\n\n#traceroute measurements\ndef hop(path):\n    latency = np.nan\n    latency_ls = []\n\n    try:\n        for i in range(3):  \n            latency_ls.append(path[i]['rtt'])\n\n        latency  = round(sum(latency_ls)/len(latency_ls))\n\n    except:\n        return latency  \n    \n    return latency\n\ndef trace(file):\n    latency = np.nan\n    pointip = np.nan\n    observed = False\n\n    msr_pf = pd.read_json(file)\n\n    if len(msr_pf) > 0:\n\n        pointip = msr_pf.destination_ip.iloc[0]\n\n        msr_pf = msr_pf.reset_index(drop=True)\n        msr_pf = msr_pf.join(pd.json_normalize(msr_pf.hops))\n\n        #avg latency for each hop\n        msr_pf['latency'] = msr_pf.probes.apply(hop)\n\n        #max unobserved latency\n        latency = msr_pf.latency.max()\n\n        #it is possible that ping failed but trace reached the end, we still keep max latency on the path\n        lastip = msr_pf.loc[msr_pf.latency == latency].probes.values[0][0]['ip']\n\n        if lastip == pointip:\n            observed = True\n        else:\n            observed = False\n    \n    return [latency,observed,pointip]\n\nlatency_pf = pd.DataFrame(columns=['latency','observed','pointip','latencytype','pointid','hostid','filename'])\n\nfor path in latencyfiles_ls:\n\n    latency_ls = []\n\n    filename = os.path.basename(path)\n    basename = os.path.basename(path).split('.')[0]\n    hostid = basename.split('_')[0]\n    pointid = basename.split('_')[1]\n    latencytype = basename.split('_')[2]\n\n    if latencytype == 'ping':\n        pointlatency_ls = ping(path)\n    \n    #if trace exists it has preference\n    if latencytype == 'trace':\n        pointlatency_ls = trace(path)\n\n    if len(pointlatency_ls) > 0:\n\n            pointlatency_ls.extend([latencytype,pointid,hostid,filename])\n            \n            latency_pf.loc[len(latency_pf.index)] = pointlatency_ls \n\n    else:\n        print('Unknown latency type',path)\n\n#remove lat <= 1, just getway is reached\nlatency_pf = latency_pf.loc[latency_pf.latency > 1]\n\n#remove lat = NA\nlatency_pf = latency_pf.drop(latency_pf.loc[pd.isna(latency_pf.latency)].index)\n\n#retype\nlatency_pf.latency = latency_pf.latency.astype('int')\nlatency_pf.observed = latency_pf.observed.astype('bool') #for lifelines\n\n#show number of observed and unobserved latency values\nprint('Latency observed:\\n', latency_pf.observed.value_counts())\n\n#show latency values\n#pointip - Atlas probe IP\n#pointid - Atlas probe ID\n#hostid - end host ID from where latency was measured\n#filename - original file with stored latency measurement\nlatency_pf",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "text": "Latency observed:\n True     189\nFalse     63\nName: observed, dtype: int64\n",
     "output_type": "stream"
    },
    {
     "output_type": "execute_result",
     "execution_count": 6,
     "data": {
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       "rows": [
        {
         "latency": 19,
         "observed": false,
         "pointip": "78.80.123.82",
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         "pointid": "1000402",
         "hostid": "ef4add9f2d1c428e82690c5db7f404ba",
         "filename": "ef4add9f2d1c428e82690c5db7f404ba_1000402_trace.json",
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        {
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         "observed": false,
         "pointip": "85.239.64.22",
         "latencytype": "trace",
         "pointid": "29746",
         "hostid": "ef4add9f2d1c428e82690c5db7f404ba",
         "filename": "ef4add9f2d1c428e82690c5db7f404ba_29746_trace.json",
         "_deepnote_index_column": 233
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        {
         "latency": 20,
         "observed": false,
         "pointip": "176.98.252.19",
         "latencytype": "trace",
         "pointid": "22585",
         "hostid": "ef4add9f2d1c428e82690c5db7f404ba",
         "filename": "ef4add9f2d1c428e82690c5db7f404ba_22585_trace.json",
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        },
        {
         "latency": 20,
         "observed": true,
         "pointip": "212.96.172.171",
         "latencytype": "ping",
         "pointid": "20171",
         "hostid": "ef4add9f2d1c428e82690c5db7f404ba",
         "filename": "ef4add9f2d1c428e82690c5db7f404ba_20171_ping.json",
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        },
        {
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         "observed": true,
         "pointip": "178.17.7.25",
         "latencytype": "ping",
         "pointid": "1003257",
         "hostid": "ef4add9f2d1c428e82690c5db7f404ba",
         "filename": "ef4add9f2d1c428e82690c5db7f404ba_1003257_ping.json",
         "_deepnote_index_column": 82
        },
        {
         "latency": 20,
         "observed": false,
         "pointip": "45.87.237.252",
         "latencytype": "trace",
         "pointid": "3695",
         "hostid": "ef4add9f2d1c428e82690c5db7f404ba",
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         "_deepnote_index_column": 279
        },
        {
         "latency": 20,
         "observed": true,
         "pointip": "62.129.51.6",
         "latencytype": "ping",
         "pointid": "53824",
         "hostid": "ef4add9f2d1c428e82690c5db7f404ba",
         "filename": "ef4add9f2d1c428e82690c5db7f404ba_53824_ping.json",
         "_deepnote_index_column": 302
        },
        {
         "latency": 20,
         "observed": true,
         "pointip": "95.85.255.19",
         "latencytype": "ping",
         "pointid": "1002507",
         "hostid": "ef4add9f2d1c428e82690c5db7f404ba",
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         "_deepnote_index_column": 58
        },
        {
         "latency": 20,
         "observed": true,
         "pointip": "178.248.11.5",
         "latencytype": "ping",
         "pointid": "16657",
         "hostid": "ef4add9f2d1c428e82690c5db7f404ba",
         "filename": "ef4add9f2d1c428e82690c5db7f404ba_16657_ping.json",
         "_deepnote_index_column": 134
        },
        {
         "latency": 20,
         "observed": true,
         "pointip": "185.218.92.12",
         "latencytype": "ping",
         "pointid": "18766",
         "hostid": "ef4add9f2d1c428e82690c5db7f404ba",
         "filename": "ef4add9f2d1c428e82690c5db7f404ba_18766_ping.json",
         "_deepnote_index_column": 162
        }
       ]
      },
      "text/plain": "     latency  observed         pointip latencytype  pointid  \\\n0         15      True    81.201.60.30        ping  1000034   \n1         18      True  176.74.145.221        ping  1000056   \n2         22      True  90.177.221.181        ping  1000057   \n3         16      True   46.28.109.126        ping  1000059   \n4         17      True    84.242.77.74        ping  1000063   \n..       ...       ...             ...         ...      ...   \n317       13      True  78.128.211.119        ping     6810   \n318       12      True   188.92.41.251        ping     6856   \n319       14      True  185.178.172.32        ping     6963   \n320       13      True  93.190.128.178        ping     7024   \n321       12      True    217.31.205.1        ping      721   \n\n                               hostid  \\\n0    ef4add9f2d1c428e82690c5db7f404ba   \n1    ef4add9f2d1c428e82690c5db7f404ba   \n2    ef4add9f2d1c428e82690c5db7f404ba   \n3    ef4add9f2d1c428e82690c5db7f404ba   \n4    ef4add9f2d1c428e82690c5db7f404ba   \n..                                ...   \n317  ef4add9f2d1c428e82690c5db7f404ba   \n318  ef4add9f2d1c428e82690c5db7f404ba   \n319  ef4add9f2d1c428e82690c5db7f404ba   \n320  ef4add9f2d1c428e82690c5db7f404ba   \n321  ef4add9f2d1c428e82690c5db7f404ba   \n\n                                              filename  \n0    ef4add9f2d1c428e82690c5db7f404ba_1000034_ping....  \n1    ef4add9f2d1c428e82690c5db7f404ba_1000056_ping....  \n2    ef4add9f2d1c428e82690c5db7f404ba_1000057_ping....  \n3    ef4add9f2d1c428e82690c5db7f404ba_1000059_ping....  \n4    ef4add9f2d1c428e82690c5db7f404ba_1000063_ping....  \n..                                                 ...  \n317    ef4add9f2d1c428e82690c5db7f404ba_6810_ping.json  \n318    ef4add9f2d1c428e82690c5db7f404ba_6856_ping.json  \n319    ef4add9f2d1c428e82690c5db7f404ba_6963_ping.json  \n320    ef4add9f2d1c428e82690c5db7f404ba_7024_ping.json  \n321     ef4add9f2d1c428e82690c5db7f404ba_721_ping.json  \n\n[252 rows x 7 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>latency</th>\n      <th>observed</th>\n      <th>pointip</th>\n      <th>latencytype</th>\n      <th>pointid</th>\n      <th>hostid</th>\n      <th>filename</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>15</td>\n      <td>True</td>\n      <td>81.201.60.30</td>\n      <td>ping</td>\n      <td>1000034</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba_1000034_ping....</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>True</td>\n      <td>176.74.145.221</td>\n      <td>ping</td>\n      <td>1000056</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba_1000056_ping....</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>22</td>\n      <td>True</td>\n      <td>90.177.221.181</td>\n      <td>ping</td>\n      <td>1000057</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba_1000057_ping....</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>16</td>\n      <td>True</td>\n      <td>46.28.109.126</td>\n      <td>ping</td>\n      <td>1000059</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba_1000059_ping....</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17</td>\n      <td>True</td>\n      <td>84.242.77.74</td>\n      <td>ping</td>\n      <td>1000063</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba_1000063_ping....</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>317</th>\n      <td>13</td>\n      <td>True</td>\n      <td>78.128.211.119</td>\n      <td>ping</td>\n      <td>6810</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba_6810_ping.json</td>\n    </tr>\n    <tr>\n      <th>318</th>\n      <td>12</td>\n      <td>True</td>\n      <td>188.92.41.251</td>\n      <td>ping</td>\n      <td>6856</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba_6856_ping.json</td>\n    </tr>\n    <tr>\n      <th>319</th>\n      <td>14</td>\n      <td>True</td>\n      <td>185.178.172.32</td>\n      <td>ping</td>\n      <td>6963</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba_6963_ping.json</td>\n    </tr>\n    <tr>\n      <th>320</th>\n      <td>13</td>\n      <td>True</td>\n      <td>93.190.128.178</td>\n      <td>ping</td>\n      <td>7024</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba_7024_ping.json</td>\n    </tr>\n    <tr>\n      <th>321</th>\n      <td>12</td>\n      <td>True</td>\n      <td>217.31.205.1</td>\n      <td>ping</td>\n      <td>721</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba</td>\n      <td>ef4add9f2d1c428e82690c5db7f404ba_721_ping.json</td>\n    </tr>\n  </tbody>\n</table>\n<p>252 rows × 7 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Plot latency observation links in country",
   "metadata": {
    "cell_id": "ab30716f-960b-40f6-9bfd-880cefa70413",
    "tags": [],
    "deepnote_app_coordinates": {
     "x": 0,
     "y": 0,
     "w": 12,
     "h": 5
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    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "3fe46fc5-289c-4d17-9b3a-243758c627d6",
    "tags": [],
    "deepnote_app_coordinates": {
     "x": 0,
     "y": 0,
     "w": 12,
     "h": 5
    },
    "deepnote_to_be_reexecuted": false,
    "source_hash": "75da8751",
    "execution_start": 1656072658796,
    "execution_millis": 5,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 945
   },
   "source": "%%capture --no-display\n\nif geographicplot:\n\n    #fill host location\n    latency_pf['hostlat'] = endhostlat;latency_pf['hostlon'] = endhostlon\n\n    #fill observation point (Atlas probe) location \n    latency_pf['pointlat'] = np.nan;latency_pf['pointlon'] = np.nan\n    for pointid in latency_pf.pointid:\n        latency_pf.loc[latency_pf.pointid == pointid,'pointlat'] = points_pf.loc[points_pf.pointid == pointid].lat.values[0]\n        latency_pf.loc[latency_pf.pointid == pointid,'pointlon'] = points_pf.loc[points_pf.pointid == pointid].lon.values[0]\n\n    #clip map to country (ISO A3)\n    world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) \n    world = world[world['iso_a3'].isin([countrya3])]\n    ax = world.plot(color='white', edgecolor='black',figsize=(8, 12))\n    ax.set_aspect('equal')\n    ax.ticklabel_format(style='plain', useOffset=False, axis='both')\n\n    #transform to geopandas\n    latency_gpf = gpd.GeoDataFrame(latency_pf)\n    latency_gpf['pointcrd'] = gpd.points_from_xy(latency_pf.pointlon,latency_pf.pointlat)\n    latency_gpf['hostcrd'] = gpd.points_from_xy(latency_pf.hostlon,latency_pf.hostlat)\n\n    #create lines between host and observation points\n    latency_gpf['lines'] = gpd.GeoSeries(latency_gpf.apply(lambda row: LineString([row.pointcrd,row.hostcrd]),axis=1))\n    latency_gpf.lines = latency_gpf.lines.set_crs(world.crs)\n\n    #plot observation points and host\n    latency_gpf.pointcrd.plot(ax=ax, marker='+', color='black', markersize=150)\n    latency_gpf.hostcrd.plot(ax=ax, marker='o', color='black', markersize=150)\n    \n    #plot observation links - observed and unobserved latency\n    latency_gpf.loc[latency_gpf.observed == True].lines.plot(ax=ax, color='brown')\n    latency_gpf.loc[latency_gpf.observed == False].lines.plot(ax=ax,  linestyle='dashed',color='royalblue')\n\n    legend_elements = [\n        Line2D([0], [0], color='black', marker='+', linestyle='none', lw=2, markersize=15, label='Latency observation point'),\n        Line2D([0], [0], color='black', marker='o', linestyle='none', markersize=5,label='Host analyzed'),\n        Line2D([0], [0], color='brown', lw=2, label='Observed latency'),\n        Line2D([0], [0], color='royalblue', lw=2, linestyle='--', label='Unobserved latency (censored)'),\n        Line2D([0], [0], color='black', lw=2, label='Country border')\n        ]\n\n    ax.legend(handles=legend_elements, loc='upper right')\n    plt.axis('off')\n\n    plt.show()",
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "Process survival latency and show median survival",
   "metadata": {
    "cell_id": "9ff5b839-b30b-4486-a42c-d8f627868635",
    "tags": [],
    "deepnote_app_coordinates": {
     "x": 0,
     "y": 81,
     "w": 12,
     "h": 5
    },
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "736a64fc-db3c-44a9-a463-7c77e856a47c",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e9041396",
    "execution_start": 1656072658813,
    "execution_millis": 930,
    "deepnote_app_coordinates": {
     "x": 0,
     "y": 87,
     "w": 12,
     "h": 5
    },
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 737.1875,
    "deepnote_output_heights": [
     null,
     271
    ]
   },
   "source": "#use Kaplan-Meier estimator\nsurvivallatency = KaplanMeierFitter().fit(latency_pf.latency,latency_pf.observed)\n\n#get median survival\nsurvivalmedian = survivallatency.median_survival_time_\n\nprint(survivalmedian)\n\nax = survivallatency.plot(label='Median survival latency',show_censors=True)\nax.set_xlabel('Latency [ms]')\nax.set_ylabel('P(X>x)')\nax.vlines(survivalmedian,0,1, lw=2, linestyles='--',color='black')\nax.legend(loc='upper right')\nax.set_xlim([0, 50])\n\n#add event table (sample)\nadd_at_risk_counts(survivallatency,ax=ax)\n\nplt.tight_layout()",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "text": "19.0\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": 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     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 424,
       "height": 269
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Show full survival latency table",
   "metadata": {
    "cell_id": "7c918264-c224-48a4-81e4-c7fb0415cf83",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "fa908daf-18eb-45fc-85db-9f971180bc31",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c7f96008",
    "execution_start": 1656072659363,
    "execution_millis": 27,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 600
   },
   "source": "survivallatency.event_table",
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 9,
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      },
      "text/plain": "          removed  observed  censored  entrance  at_risk\nevent_at                                                \n0.0             0         0         0       252      252\n9.0             1         1         0         0      252\n11.0            4         4         0         0      251\n12.0           14        14         0         0      247\n13.0           23        23         0         0      233\n14.0           17        17         0         0      210\n15.0           19        19         0         0      193\n16.0           19        19         0         0      174\n17.0           16        16         0         0      155\n18.0           11        11         0         0      139\n19.0           18        13         5         0      128\n20.0           11         9         2         0      110\n21.0           17         7        10         0       99\n22.0           14         5         9         0       82\n23.0           10         7         3         0       68\n24.0            5         3         2         0       58\n25.0            9         2         7         0       53\n26.0           10         4         6         0       44\n27.0            4         1         3         0       34\n28.0            4         2         2         0       30\n29.0            3         1         2         0       26\n30.0            5         3         2         0       23\n31.0            1         0         1         0       18\n32.0            2         2         0         0       17\n33.0            4         1         3         0       15\n35.0            3         2         1         0       11\n37.0            1         1         0         0        8\n39.0            1         0         1         0        7\n53.0            1         1         0         0        6\n55.0            1         0         1         0        5\n69.0            1         0         1         0        4\n102.0           1         0         1         0        3\n165.0           1         1         0         0        2\n4276.0          1         0         1         0        1",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>removed</th>\n      <th>observed</th>\n      <th>censored</th>\n      <th>entrance</th>\n      <th>at_risk</th>\n    </tr>\n    <tr>\n      <th>event_at</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0.0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>252</td>\n      <td>252</td>\n    </tr>\n    <tr>\n      <th>9.0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>252</td>\n    </tr>\n    <tr>\n      <th>11.0</th>\n      <td>4</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>251</td>\n    </tr>\n    <tr>\n      <th>12.0</th>\n      <td>14</td>\n      <td>14</td>\n      <td>0</td>\n      <td>0</td>\n      <td>247</td>\n    </tr>\n    <tr>\n      <th>13.0</th>\n      <td>23</td>\n      <td>23</td>\n      <td>0</td>\n      <td>0</td>\n      <td>233</td>\n    </tr>\n    <tr>\n      <th>14.0</th>\n      <td>17</td>\n      <td>17</td>\n      <td>0</td>\n      <td>0</td>\n      <td>210</td>\n    </tr>\n    <tr>\n      <th>15.0</th>\n      <td>19</td>\n      <td>19</td>\n      <td>0</td>\n      <td>0</td>\n      <td>193</td>\n    </tr>\n    <tr>\n      <th>16.0</th>\n      <td>19</td>\n      <td>19</td>\n      <td>0</td>\n      <td>0</td>\n      <td>174</td>\n    </tr>\n    <tr>\n      <th>17.0</th>\n      <td>16</td>\n      <td>16</td>\n      <td>0</td>\n      <td>0</td>\n      <td>155</td>\n    </tr>\n    <tr>\n      <th>18.0</th>\n      <td>11</td>\n      <td>11</td>\n      <td>0</td>\n      <td>0</td>\n 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<th>26.0</th>\n      <td>10</td>\n      <td>4</td>\n      <td>6</td>\n      <td>0</td>\n      <td>44</td>\n    </tr>\n    <tr>\n      <th>27.0</th>\n      <td>4</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>34</td>\n    </tr>\n    <tr>\n      <th>28.0</th>\n      <td>4</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>29.0</th>\n      <td>3</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>26</td>\n    </tr>\n    <tr>\n      <th>30.0</th>\n      <td>5</td>\n      <td>3</td>\n      <td>2</td>\n      <td>0</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>31.0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>32.0</th>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>33.0</th>\n      <td>4</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>35.0</th>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>37.0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>39.0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>53.0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>55.0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>69.0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>102.0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>165.0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4276.0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Process mean traditional latency and restricted mean survival latency",
   "metadata": {
    "cell_id": "893eb047-58ca-415f-a657-431e6c46024f",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "52d0d6bf-eb3c-4de0-ac7c-25365382ece8",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "cfe53035",
    "execution_start": 1656072659437,
    "execution_millis": 309,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 653,
    "deepnote_output_heights": [
     268
    ]
   },
   "source": "%%capture --no-display\n\nlimit = 150 # to exclude latency caused by temporary networking problems\n\n#use the limit also for observed latency\n#mean latency from observed latency only\nsimplemean = round(latency_pf.loc[(latency_pf.observed == True) & (latency_pf.latency < limit)].latency.mean())\n\n#survival mean latency including unobserved values\nrestrictedsurvivalmean = round(restricted_mean_survival_time(survivallatency, t=limit))\n\nax = rmst_plot(survivallatency, t=limit, label='Restricted mean survival latency (limit 150ms)')\nax.set_ylabel('P(X>x)')\nax.set_xlabel('Latency [ms]')\nax.set_xlim([0, 200])\n\nplt.show()",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 395,
       "height": 266
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Show ISP latency assessment by **mean traditional latency** and by **restricted mean survival latency**",
   "metadata": {
    "cell_id": "ed7acec2-620a-4cdd-b046-8f181258e561",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "6d1d5477-0154-4180-bfcd-858edd7bad52",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ee515f99",
    "execution_start": 1656072659662,
    "execution_millis": 84,
    "owner_user_id": "4e26377f-f822-4767-b8d0-46e25b3f5e16",
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 222.765625
   },
   "source": "print('Mean traditional latency:',simplemean,'ms')\nprint('Restricted mean survival latency:',restrictedsurvivalmean,'ms')\nprint()\nprint('Difference of mean traditional and survival latency:',restrictedsurvivalmean-simplemean,'ms')",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "text": "Mean traditional latency: 18 ms\nRestricted mean survival latency: 33 ms\n\nDifference of mean traditional and survival latency: 15 ms\n",
     "output_type": "stream"
    }
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