chutten/problematic_client.ipynb Secret

## problematic_client.ipynb
{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "---\n",
    "title: One Problematic Aurora 51 Client\n",
    "authors:\n",
    "- chutten\n",
    "tags:\n",
    "- aurora\n",
    "- firefox\n",
    "created_at: 2017-02-22\n",
    "updated_at: 2017-02-23\n",
    "tldr: Taking a look at one problematic client on Aurora leads to a broad examination of the types of hosts that are sending us this data and some seriously-speculative conclusions.\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Problematic Aurora 51 Client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Motivation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is one particular client, whose `client_id` I've obscured, that seems to be sending orders of magnitude more \"main\" pings per day than is expected, or even possible.\n",
    "\n",
    "I'm interested in figuring out what we can determine about this particular client to see if there are signifiers we can use to identify this anomalous use case. This identification would permit us to:\n",
    "* filter data from these clients out of derived datasets that aren't relevant\n",
    "* identify exceptional use-cases for Firefox we don't currently understand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many pings are we talking, here?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hadoop/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unable to parse whitelist (/home/hadoop/anaconda2/lib/python2.7/site-packages/moztelemetry/histogram-whitelists.json). Assuming all histograms are acceptable.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from moztelemetry.dataset import Dataset\n",
    "from moztelemetry import get_pings_properties, get_one_ping_per_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_pings = Dataset.from_source(\"telemetry\") \\\n",
    "    .where(docType='main') \\\n",
    "    .where(appBuildId=lambda x: x.startswith(\"20161014\")) \\\n",
    "    .where(appUpdateChannel=\"aurora\") \\\n",
    "    .records(sc, sample=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pings = all_pings.filter(lambda p: p['clientId'] == '<omitted>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "submission_dates = get_pings_properties(pings, [\"meta/submissionDate\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "ping_counts = submission_dates.map(lambda p: (datetime.strptime(p[\"meta/submissionDate\"], '%Y%m%d'), 1)).countByKey()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
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MjaIo42Rbaxq3AV27QmuaV+68885sv/32vPLKKxmPKSoq4rXXXmvw9oqKik3vB3+VMMD/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\ns3w53HornHSSfHclc7vsMthxRzj55Mwv5KxeLXc//e1vcOWVcO656b0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/3xj/CHP8C550pWcyYkcvvkEynxcOqpma07Ge3bw/HHS9mwVavSc2uO6uposoxd2NeSoW7hEUTQ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\nU2DaNAsQr7++lUnRgfuB7GudxqubVy+QW168unn1ArnlxaubVy+QW168u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F5y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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5d2b44890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(ping_counts.items(), columns=[\"date\", \"count\"]).set_index([\"date\"])\n",
    "df.plot(figsize=(17, 7))\n",
    "plt.xticks(np.arange(min(df.index), max(df.index) + timedelta(3), 3, dtype=\"datetime64[D]\"))\n",
    "plt.ylabel(\"ping count\")\n",
    "plt.xlabel(\"date\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just about 100k main pings submitted by this client on a single day? (Feb 16)... that is one active client.\n",
    "\n",
    "Or _many_ active clients."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What Can We Learn About These Pings?\n",
    "\n",
    "Well, since these pings all share the same clientId, they likely are sharing user profiles. This means things like profile `creationDate` and so forth won't change amongst them.\n",
    "\n",
    "However, here's a list of things that might change in interesting ways or otherwise shed some light on the purpose of these installs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subset = get_pings_properties(pings, [\n",
    "        \"meta/documentId\",\n",
    "        \"meta/submissionDate\",\n",
    "        \"meta/geoCountry\",\n",
    "        \"meta/geoCity\",\n",
    "        \"environment/addons/activeAddons\",\n",
    "        \"environment/settings/isDefaultBrowser\",\n",
    "        \"environment/system/cpu/speedMHz\",\n",
    "        \"environment/system/os/name\",\n",
    "        \"environment/system/os/version\",\n",
    "        \"payload/info/sessionLength\",\n",
    "        \"payload/info/subsessionLength\",        \n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4641520"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Non-System Addons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pings_with_addon = subset\\\n",
    "    .flatMap(lambda p: [(addon[\"name\"], 1) for addon in filter(lambda x: \"isSystem\" not in x or not x[\"isSystem\"], p[\"environment/addons/activeAddons\"].values())])\\\n",
    "    .countByKey()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(u'Random Agent Spoofer', 4640949),\n",
       " (u'Alexa Traffic Rank', 477049),\n",
       " (u'Firefox Hotfix', 1)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(pings_with_addon.items(), key=lambda x: x[1], reverse=True)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nearly every single ping is reporting that it has an addon called 'Random Agent Spoofer'. Interesting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Session Lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "SESSION_MAX = 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "session_lengths = subset.map(lambda p: p[\"payload/info/sessionLength\"] if p[\"payload/info/sessionLength\"] < SESSION_MAX else SESSION_MAX).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7k4xaa++ZrnlOkrcmeUxr7Zaqem6SNyd5dGvt6HTN9yZ5ZZK/3lr7fFW9JMkr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5d3fd24d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(session_lengths).hist(bins=250, figsize=(17, 7))\n",
    "plt.ylabel(\"ping count\")\n",
    "plt.xlabel(\"session length in seconds\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "215    2769089\n",
       "135     474275\n",
       "284     273834\n",
       "27      258250\n",
       "40      257293\n",
       "64      172719\n",
       "85      160488\n",
       "25      160317\n",
       "62       30421\n",
       "63       27714\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(session_lengths).value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The session lengths for over half of all the reported pings are exactly 215 seconds long. Two minutes and 35 seconds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Is this Firefox even the default browser?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(int, {False: 4641520})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset.map(lambda p: (p[\"environment/settings/isDefaultBrowser\"], 1)).countByKey()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CPU speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "MHZ_MAX = 5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mhzes = subset.map(lambda p: p[\"environment/system/cpu/speedMHz\"] if p[\"environment/system/cpu/speedMHz\"] < MHZ_MAX else MHZ_MAX).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OAjly5MjYEWDgiSeeGDsCDFgnqUQfqUYnqUYnqUQfqWA6nWZpaWnHb64+18Pf\nlZWVHD16NJPJZOwo4B8hKOexxx4bOwIMWCepRB+pRiepRiepRB+pYDKZ5OjRo1lZWdnR/c718Bcq\nefTRR8eOAAPve9/7xo4AA9ZJKtFHqtFJqtFJKtFH5pnhLwAAAADADDL8BQAAAACYQYa/AAAAAAAz\nyPAXijhw4MDYEWDg4MGDY0eAAesklegj1egk1egklegj88zwF4rYv3//2BFg4MYbbxw7AgxYJ6lE\nH6lGJ6lGJ6lEH5lnhr9QxGQyGTsCDLzpTW8aOwIMWCepRB+pRiepRiepRB+ZZ4a/AAAAAAAzyPAX\nAAAAAGAGGf5CEceOHRs7Agw8+eSTY0eAAesklegj1egk1egklegj88zwF4q45557xo4AAw8//PDY\nEWDAOkkl+kg1Okk1Okkl+sg8M/yFIh555JGxI8DAe9/73rEjwIB1kkr0kWp0kmp0kkr0kXlm+AtF\n7Nq1a+wIMHD11VePHQEGrJNUoo9Uo5NUo5NUoo/MM8NfAAAAAIAZZPgLAAAAADCDDH+hiNtvv33s\nCDBw3333jR0BBqyTVKKPVKOTVKOTVKKPzDPDXyhiz549Y0eAgeuuu27sCDBgnaQSfaQanaQanaQS\nfWSeGf5CEbfddtvYEWDg7W9/+9gRYMA6SSX6SDU6STU6SSX6yDwz/AUAAAAAmEGGvwAAAAAAM8jw\nF4o4ceLE2BFg4Omnnx47AgxYJ6lEH6lGJ6lGJ6lEH5lncz38XV5eztLSUqbT6dhRIHfcccfYEWDg\n/e9//9gRYMA6SSX6SDU6STU6SSX6SAXT6TRLS0tZXl7e0f1etaN7K2ZlZSULCwtjx4AkyQMPPDB2\nBBjwBySqsU5SiT5SjU5SjU5SiT5SwWQyyWQyyerqahYXF3dsv3N95i9UsmfPnrEjwMD1118/dgQY\nsE5SiT5SjU5SjU5SiT4yzwx/AQAAAABmkOEvAAAAAMAMMvyFIg4dOjR2BBh46KGHxo4AA9ZJKtFH\nqtFJqtFJKtFH5pnhLxRx+vTpsSPAwJkzZ8aOAAPWSSrRR6rRSarRSSrRR+aZ4S8Ucdddd40dAQbe\n9a53jR0BBqyTVKKPVKOTVKOTVKKPzDPDXwAAAACAGXTVZr6ptfbSJNcl2ZXkM733393SVAAAAAAA\nvCiXfOZva+0rWmvf1Vr7aJLfT/KbSZ5K8pnW2n9orf1wa+2125QTZt7a2trYEWDg2WefHTsCDFgn\nqUQfqUaAe+1wAAAgAElEQVQnqUYnqUQfmWeXNPxtrf3dnB32Hkjy00nemuTrk3xtkn1J7srZs4gf\nb619pLX2NduSFmbYzTffPHYEGLj77rvHjgAD1kkq0Ueq0Umq0Ukq0Ufm2aVe9uG1SV7fe/+1L/L1\nX0jyI621d+XsgPh1SX59C/LB3Dh48ODYEWDgne9859gRYMA6SSX6SDU6STU6SSX6yDy7pOFv731y\nic/7z0l+4EUlgjm1sLAwdgQY2Lt379gRYMA6SSX6SDU6STU6SSX6yDy75Gv+AgAAAABw5bjUyz58\nQWvtXyTpF/lST3ImyW8k+bHe+ydfZDYAAAAAADZpM2f+nkryl5Is5OzAtyf5hvVtVyW5Kckvtda+\neatCXo7W2tWttd9srd0zxv5hsw4fPjx2BBg4cuTI2BFgwDpJJfpINTpJNTpJJfrIPNvM8PdTSX4s\nySt673+t9/7XkvyZJB9K8n8n2Zvkg0kObVnKy/MPkvzcSPuGTVtdXR07AgycOHFi7AgwYJ2kEn2k\nGp2kGp2kEn1knm1m+PsdSe7rvT93bsP6x/cn+Y7ee0/yQJKv25qIl6619sokNyT5Vzu9b3ixHnzw\nwbEjwMC73/3usSPAgHWSSvSRanSSanSSSvSRebaZ4e9Lk7zqIttfleTL1j8+k4tfF3i7/eMkfz9J\nG2HfAAAAAABlbGb4+8+SHG6tLbfWvmX9sZzkcJKH15/zF5L82qW+YGvtda21o621T7XWnmutLV3k\nObe01p5urX22tfbzrbXXbvj6UpJP9t5/49ymTRwbAAAAAMBMuGoT37Oc5NNJ7kjy8vVtn06ykuev\n8/t4ko9cxmtek+QTOTtA/ucbv9hauynJvUnemeQX1jM81lr72t772vrTbkzy9tbaf5/kK5Jc1Vo7\n1Xv/ny8jBwAAAADATLjsM39775/vvf8vvffrk3xlkq/svV/fe39v7/3z68852Xv/7ct4zY/03t/T\ne/+JXPyM3eUkP9h7f7j3fiLJu5KcTnLzea/xfb33r+q9vyLJ9yb5YYNfriRLSxec8A6jWl5eHjsC\nDFgnqUQfqUYnqUYnqUQfmWebuexDkqS19seT/Nkkf7a1du3WRbpgPy9NspjkZ85tW7+p3E8n2bdd\n+4Wdduutt44dAQbe9ra3jR0BBqyTVKKPVKOTVKOTVKKPzLPLHv621q5prf1IkmeS/Ov1xzOttcOt\ntV1bHTDJtTl7I7lPb9j+6STXXewbeu8f7L3f8aVe+M1vfnOWlpYGj3379uXIkSOD5z3++OMX/Vei\nW265JYcPHx5sW11dzdLSUtbW1gbb77zzzhw6dGiw7eTJk1laWsqJEycG2++///7cfvvtg22nT5/O\n0tJSjh07Ntg+nU5z4MCBC7LddNNN23Ic999//wXfnzybM2dOX1HHUfHnsX///pk4jnMcx5VzHM8+\n++yGZ/9AkmTfvuf/fe1KOI5Z+Xk4ji9+HA888MBMHMes/Dzm/TjOvW9f6cdxjuO48o9jYWFhJo5j\nVn4ejiP55Cc/ORPHMSs/j3k/jnPv21f6cZxvJ4/jmWeeWf/o6Qte+7777rtijmOMn8fi4uIFs8e3\nvOUtF3z/dmpnT6K9jG9o7QeT/NdJbk3ys+ubvyXJ+5P8VO/9u15UoNaeS/LW3vvR9c+vT/KpJPt6\n7x8/73mHkry+937ZZ/+21haSHD9+/PgFf0jihT311FN59atfnbM/+m9a33pvrrnm7vzBH5waMRmw\nWaurq1lcXExyPMlCktUki7FGAgAAcOHfGRN/b9y8538/s9h7X93u/W3mhm9/Lclf773/n+dt+8nW\n2meTfDjJixr+XsRaks/n+ZvLnfPyJL+zxfsCAAAAAJgJm7nm765ceAmGJPl/17+2pXrvn8vZf1p4\nw7ltrbW2/vnHtnp/MJaN/z0AxvbEE0+MHQEGrJNUoo9Uo5NUo5NUoo/Ms80Mf38uyV2ttZed29Ba\nuzrJnetfu2zr1xF+TWvt69c3vWL98z+1/vn3J/mO1tq3tdZelbMXpNyV5KHN7A8qmk6nY0eAgcce\ne2zsCDBgnaQSfaQanaQanaQSfWSebeayD38nyWNJfru19kvr216T5EySN24yxzcmeSJJX3/cu779\ng0lu7r1/uLV2bZK7c/ZyD59I8sbe+2c2uT8o59FHHx07Agy8733vGzsCDFgnqUQfqUYnqUYnqUQf\nmWeXPfztvf9qa+1rkvzNJK9a3zxN8qO9989uJkTv/aP5Emch994/kOQDm3l9AAAAAIB5s5kzf9N7\nP53kh7c4y45bXl7O7t27M5lMMplMxo4DAAAAAMyg6XSa6XSaU6dO7eh+L2n421pbutQX7L0f3Xyc\nnbWyspKFhYWxYwAAAAAAM+zcyaerq6tZXFzcsf1e6g3fjlzi419sQ0aYCwcOHBg7AgwcPHhw7Agw\nYJ2kEn2kGp2kGp2kEn1knl3Smb+990sdEgObtH///rEjwMCNN944dgQYsE5SiT5SjU5SjU5SiT4y\nzwx1oQjXnaaaN73pTWNHgAHrJJXoI9XoJNXoJJXoI/Pskoa/rbW3X+oLttb+VGvtmzcfCQAAAACA\nF+tSz/z9rtbaU621O1prezd+sbW2u7X25tbajyVZTfLHtjQlAAAAAACX5ZKGv733v5Dkf0ryrUl+\ntbX2+621X2+t/Upr7beT/MckP5LkZJKv670f3bbEMKOOHTs2dgQYePLJJ8eOAAPWSSrRR6rRSarR\nSSrRR+bZJV/zt/d+tPf+rUlenuTbkjyQ5EeTHEzy55P8yd77u3vvn96OoDDr7rnnnrEjwMDDDz88\ndgQYsE5SiT5SjU5SjU5SiT4yz6663G/ova8lObINWWCuPfLII2NHgIH3vve9Y0eAAesklegj1egk\n1egklegj8+yyh7+zZHl5Obt3785kMnHnR0a3a9eusSPAwNVXXz12BBiwTlKJPlKNTlKNTlKJPlLB\ndDrNdDrNqVOndnS/cz38XVlZycLCwtgxAAAAAIAZdu7k09XV1SwuLu7Yfi/5mr8AAAAAAFw5DH+h\niNtvv33sCDBw3333jR0BBqyTVKKPVKOTVKOTVKKPzLPLHv621t7TWrvgYimttatba+/Zmlgwf/bs\n2TN2BBi47rrrxo4AA9ZJKtFHqtFJqtFJKtFH5tlmrvl7Z5IfSHJ6w/Zd61+7+8WGgnl02223jR0B\nBl7zmtdkdXX1C59fe+21/tDEqKyTVKKPVKOTVKOTVKKPzLPNDH9bkn6R7a9J8rsvLg4A43smyUvy\njne8Y7D1ZS/blU9+8ikDYAAAALhCXPLwt7X2bM4OfXuSf99aO38A/GVJvjxnzwgG4Ir2e0meS/Kh\nJHvXtz2VM2fekbW1NcNfAACAGXfy5Mmsra0lSZ566qmR0/BiXM6Zv9+Ts2f9/kjOXt7h1Hlf+8Mk\nv9l7/7ktzAZz5cSJE3nVq141dgw4z8uSLIwdAr7AOkkl+kg1Okk1Okkl+nh5Tp48mRtu2JszZzZe\n8ZUr0SXf8K33/sHe+0NJ/mKSf7r++bnH1OAXXpw77rhj7AiwwfvHDgAD1kkq0Ueq0Umq0Ukq0cfL\ns7a2tj74/VCS40n+4ciJeDEu+5q/vfePttZe0lr72iR/IhsGyL33f71V4WCePPDAA2NHgA38AYla\nrJNUoo9Uo5NUo5NUoo+btTdn/zeoyz5cyS57+NtauzHJjyX5qpy9DMT5es5e/xe4TK6jSj3Xjx0A\nBqyTVKKPVKOTVKOTVKKPzLPLHv7m7E3dfjHJX8nZW8L3F346AAAAAAA7bTPD369J8td777+x1WF2\n2vLycnbv3p3JZJLJZDJ2HAAAAABgBk2n00yn05w6dWpH93vJN3w7z8eTvHKrg4xhZWUlR48eNfil\nhEOHDo0dATZ4aOwAMGCdpBJ9pBqdpBqdpBJ9pILJZJKjR49mZWVlR/e7mTN/709yb2vtuiS/kuRz\n53+x9/7LWxEM5s3p06fHjgAbnBk7AAxYJ6lEH6lGJ6lGJ6lEH5lnmxn+/vj6rz9y3raeszd/c8M3\n2KS77rpr7AiwwbvGDgAD1kkq0Ueq0Umq0Ukq0Ufm2WaGv1+95SkAAAAAANhSlz387b3/h+0IAgAA\nAADA1rns4W9r7dte6Ou994c3Hwfm19raWq699tqxY8B5nh07AAxYJ6lEH6lGJ6lGJ6lEH5lnL9nE\n9/yTDY8P5Owt4X8oyX1blgzmzM033zx2BNjg7rEDwIB1kkr0kWp0kmp0kkr0kXm2mcs+/NGN21pr\nX5Pknyb5R1sRCubRwYMHx44AG7xz7AAwYJ2kEn2kGp2kGp2kEn1knm3mzN8L9N5/Pcm7c/ZMYGAT\nFhYWxo4AG+wdOwAMWCepRB+pRiepRiepRB+ZZ1sy/F33/yX5k1v4egAAAAAAbNJmbvi2tHFTkuuT\n3JrkZ7ciFAAAAAAAL85mzvw9suHxz5McTPLLSVxBGzbp8OHDY0eADY6MHQAGrJNUoo9Uo5NUo5NU\noo/Ms8se/vbeX7Lh8WW99+t673+j9/7MdoSEebC6ujp2BNjgxNgBYMA6SSX6SDU6STU6SSX6yDx7\nUdf8beu2KgzMswcffHDsCLDBu8cOAAPWSSrRR6rRSarRSSrRR+bZpoa/rbVva639SpLPJvlsa+2X\nW2t/a2ujAQAAAACwWZu54dvfTfIPkzyQ52/w9i1JfqC1dm3vfWUL8wEAAAAAsAmXPfxNcluS7+q9\nP3zetqOttV/L2Ru/Gf4CAAAAAIxsM5d9uD7Jxy6y/WPrXwM2YWlpaewIsMHy2AFgwDpJJfpINTpJ\nNTpJJfrIPNvM8Pc3krztIttvSvLrLy4OzK9bb7117AiwwcWWehiPdZJK9JFqdJJqdJJK9JF5tpnL\nPtyZ5NHW2uvz/DV/vznJG2JSAJu2f//+sSPABvvGDgAD1kkq0Ueq0Umq0Ukq0Ufm2WWf+dt7//Ek\nfz7JWpK3rj/Wkvy53vu/2Np4AAAAAABsxmbO/E3v/XiSd2xxFgAAAAAAtshln/nbWntza+2NF9n+\nxtbaX96aWDB/jhw5MnYE2OCJsQPAgHWSSvSRanSSanSSSvSRebaZG76974tsby/wtZKWl5eztLSU\n6XQ6dhTQQwp6bOwA8AUnT57MBz7wgayurn7hcfLkybFjMce8b1ONTlKNTlKJPlLBdDrN0tJSlpeX\nd3S/m7nsw9ck+eRFtp9I8soXF2dnraysZGFhYewYkCR59NFHx44AG1xR/57HDDt58mRuuGFvzpw5\nnZ/6qZ/6wvaXvWxXPvnJp7Jnz54R0zGvvG9TjU5SjU5SiT5SwWQyyWQyyerqahYXF3dsv5s58/dU\nkldcZPsrk/ynFxcHAGBobW0tZ86cTvKhJMfXHx/KmTOns7a2Nm44AACAwjZz5u9PJLmvtfbf9t7/\nryRprb0yyb1Jjm5lOACA5+1N4n/sAAAAXKrNnPl7R86e4XuitfZ0a+3pJE8l+Y9JvncrwwEAAAAA\nsDmXPfztvZ9K8k1J/kqSD+TsGb9v6L3/pd77721xPpgbBw4cGDsCbHBw7ACwwcGxA8AXeN+mGp2k\nGp2kEn1knm3msg/pvfckj68/gC2wf//+sSPABjeOHQA20Enq8L5NNTpJNTpJJfrIPNvU8BfYepPJ\nZOwIsMGbxg7AHDt58uQXbub21FNPrW/VSerwvk01Okk1Okkl+sg8M/wFAEo5efJkbrhhb86cOT12\nFAAAgCvaZm74BgCwbdbW1tYHvx9KcjzJPxw5EQAAwJXJ8BeKOHbs2NgRYIMnxw7A3NubZCHJV69/\nrpPU4X2banSSanSSSvSReXbZw9/W2h/5Io+vaK39F9sREubBPffcM3YE2ODhsQPABjpJHd63qUYn\nqUYnqUQfmWebuebv7yXpX+yLrbXfTvJQkrt6789tMhfMnUceeWTsCLDBe8cOABvoJHV436YanaQa\nnaQSfWSebWb4+z/k7N++HkryC+vb/tz69v8lybVJvjfJf46/pcEl27Vr19gRYIOrxw4AG+gkdXjf\nphqdpBqdpBJ9ZJ5tZvj7t5L8vd77h8/b9r+31n4lyXf23t/QWjuZ5B/E8BcAAAAAZtJTTz01+Pza\na6/Nnj17RkrDxWxm+PstSW65yPYnk+xb//hYEj9pAAAAAJg5zyR5Sd7xjncMtr7sZbvyyU8+ZQBc\nyGXf8C3Jbyf59ots//Ykv7X+8R9L8uxmQ21Ga213a+3fttZWW2u/3Fr72zu5f3ixbr/99rEjwAb3\njR0ANtBJ6vC+TTU6STU6SSX6uB1+L8lzST6U5Pj640M5c+Z01tbWRk3G0GbO/P3eJP9ba+0vJ/m3\n69u+Mcmrkvz19c9fm+TRFx/vsvx+ktf13s+01q5O8muttR/vve/oEBo2y7+KUc91YweADXSSOrxv\nU41OUo1OUok+bqe9SRbGDsELuOzhb+/9aGvtVUm+M8nXrm/+V0ne2nv/zfXn/NMtS3jpuXqSM+uf\nnrsjTNvpHLBZt91229gRYIO3jx0ANtBJ6vC+TTU6STU6SSX6yDzbzJm/6b0/neTdW5zlRWut7U7y\n0SSvTHJ77/13R44EAAAAADCKzVzzN621r2yt7W+tvaO19m3nPzb5eq9rrR1trX2qtfZca23pIs+5\npbX2dGvts621n2+tvXbjc3rvp3rvX5/kq5P8zdbaH99MHgAAAACAK91lD39ba/9NkpNJPpLkgST/\n5LzHZu/Eck2STyT57iT9Ivu8Kcm9Se5M8g1JfinJY621ay/2Yr33z6w/53WbzAM77sSJE2NHgA2e\nHjsAbKCT1OF9m2p0kmp0kkr0kXm2mTN/703yI0m+vPf+lb33P3re47/cTIje+0d67+/pvf9ELn6d\n3uUkP9h7f7j3fiLJu5KcTnLzuSe01v5Ea+3L1z/eneT1ST65mTwwhjvuuGPsCLDB+8cOABvoJHV4\n36YanaQanaQSfWSebWb4+18leX/v/fRWh7mY1tpLkywm+Zlz29Zv7vbTSfad99SvSvJvWmtP5ux1\nf/9J7/3XXui13/zmN2dpaWnw2LdvX44cOTJ43uOPP56lpQuuRJFbbrklhw8fHmxbXV3N0tJS1tbW\nBtvvvPPOHDp0aLDt5MmTWVpauuBfoO6///7cfvvtg22nT5/O0tJSjh07Ntg+nU5z4MCBC7LddNNN\n23Ic999//wXfnzybM2dOX1HHUfHn8cADD8zEcZzjOK6c43j22Wc3PPvH1389/w9IzyRJnn56eOZl\npeOYlZ+H40geeeSRC/Z11pkkw+P4yEc+UvY4ZuXn4Tgufhzn3rev9OM4x3Fc+cdx9913z8RxzMrP\nw3Eki4uLM3Ecs/LzmPfjOPe+faUfx/m28zgeeuihDa9w7jkX+594F14EYHl5ucRxVPh5LC4uXjB7\nfMtb3nLB92+ndnaOehnf0No/T/JI7/3D2xKoteeSvLX3fnT98+uTfCrJvt77x8973qEkr++977v4\nK73gPhaSHD9+/HgWFha2KPl8eOqpp/LqV786yc8m+ab1rffmmmvuzh/8wakRkwGbtbq6msXFxSTH\nkywk+dEk7zjv8yRZTbIY6yY7QScBAGA8l/bncX9G36znf3+z2Htf3e79XbWJ7/mXSf5Ra+3VSX4l\nyefO/+K5oS0AAAAAAOPZzPD3h9d/fc9FvtaTfNnm41zUWpLPJ3n5hu0vT/I7W7wvAAAAAICZcNnX\n/O29v+QFHls9+E3v/XM5ew75G85ta6219c8/ttX7g7FsvBYNjO+hsQPABg+NHQC+wPs21egk1egk\nlegj82wzZ/5uudbaNUlemaStb3pFa+01SX639/5bSb4/yUOtteNJfiHJcpJd8bdAZsjp0ztyD0W4\nDGfGDgAb6CR1eN+mGp2kGp2kEn1knl3S8Le19j8m+aHe+5n1j7+o3vv7N5HjG5M8kbOXjehJ7l3f\n/sEkN/feP9xauzbJ3Tl7uYdPJHlj7/0zm9gXlHTXXXeNHQE2eNfYAWADnaQO79tUo5NUo5NUoo/M\ns0s983c5Z2/jd2b94y+mJ7ns4W/v/aP5Epeg6L1/IMkHLve1AQAAAADm0SUNf3vvX32xj690y8vL\n2b17dyaTSSaTydhxAAAAAIAZNJ1OM51Oc+rUqR3d74u65u/6jdfSe+9bE2dnraysZGFhYewYkCRZ\nW1vLtddeO3YMOM+zYweADXSSOrxvU41OUo1OUok+UsG5k09XV1ezuLi4Y/t9wUstfDGttW9vrf1q\nzl4G4kxr7Vdba397a6PBfLn55pvHjgAb3D12ANhAJ6nD+zbV6CTV6CSV6CPz7LLP/G2t3Z3k7ya5\nP8nPrW/el2Sltban9/6eLcwHc+PgwYNjR4AN3jl2ANhAJ6nD+zbV6CTV6CSV6CPzbDOXffiuJN/R\ne5+et+1oa+2Xc3YgbPgLm+ASJNSzd+wAsIFOUof3barRSarRSSrRR+bZZi778NIkv3iR7cfzIq8h\nDAAAAADA1tjM8Pef5ezZvxu9M8mPvrg4AAAAAABshU3d8C3Jt6/f5O1/XX/8SpLvSPJca+37zz22\nMCfMvMOHD48dATY4MnYA2EAnqcP7NtXoJNXoJJXoI/NsM8Pfr0uymuQzSf7M+mNtfdvXJfmG9cfX\nb1FGmAurq6tjR4ANTowdADbQSerwvk01Okk1Okkl+sg8u+xr9Pbe/+J2BBnD8vJydu/enclkkslk\nMnYc5tyDDz44dgTY4N1jB4ANdJI6vG9TjU5SjU5SiT5SwXQ6zXQ6zalTp3Z0v3N9g7aVlRV3fAQA\nAAAAttW5k09XV1ezuLi4Y/vd7DV/AQAAAAAozPAXAAAAAGAGGf5CEUtLS2NHgA2Wxw4AG+gkdXjf\nphqdpBqdpBJ9ZJ4Z/kIRt95669gRYIO3jR0ANtBJ6vC+TTU6STU6SSX6yDwz/IUi9u/fP3YE2GDf\n2AFgA52kDu/bVKOTVKOTVKKPzDPDXwAAAACAGWT4CwAAAAAwgwx/oYgjR46MHQE2eGLsALCBTlKH\n922q0Umq0Ukq0UfmmeEvFDGdTseOABs8NnYA2EAnqcP7NtXoJNXoJJXoI/PM8BeKePTRR8eOABu8\nb+wAsIFOUof3barRSarRSSrRR+aZ4S8AAAAAwAwy/AUAAAAAmEFXjR1gTMvLy9m9e3cmk0kmk8nY\ncQAAAACAGTSdTjOdTnPq1Kkd3e9cn/m7srLy/7d399GWnXWd4L8/CG8Vh1InQuJL2Y0vGNRG6ipQ\nCqjNdMHQ9G3stgnXRiRRaQYSXdfuRJxekgozMlZsKBZJGF1jdYco3MRppai1mkmiNHQbUBhzeRkh\nRTtDtcHwotWEAiyCmDzzxzk33Nr1mqTu3U+d8/mstVfds89z9/7te785z9m/u7NP9u/fr/FLFy6+\n+OKxS4CBXWMXAAO7xi4A7mfepjcySW9kkp7IIz1YWlrK/v37s2fPnk3d71w3f6EnO3fuHLsEGHj6\n2AXAgEzSD/M2vZFJeiOT9EQemWeav9AJV6DTn+eOXQAMyCT9MG/TG5mkNzJJT+SReab5CwAAAAAw\ngzR/AQAAAABmkOYvdOK2224buwQY+MDYBcCATNIP8za9kUl6I5P0RB6ZZ5q/0Imrr7567BJg4Iax\nC4ABmaQf5m16I5P0RibpiTwyzzR/oRM33njj2CXAwGvHLgAGZJJ+mLfpjUzSG5mkJ/LIPNP8hU5s\n2bJl7BJg4DFjFwADMkk/zNv0RibpjUzSE3lknmn+AgAAAADMIM1fAAAAAIAZpPkLnbj88svHLgEG\n3jB2ATAgk/TDvE1vZJLeyCQ9kUfmmeYvdGLbtm1jlwAD549dAAzIJP0wb9MbmaQ3MklP5JF5pvkL\nnbjsssvGLgEGXjR2ATAgk/TDvE1vZJLeyCQ9kUfmmeYvAAAAAMAM0vwFAAAAAJhBmr/QiQMHDoxd\nAgwcHLsAGJBJ+mHepjcySW9kkp7II/NM8xc6ccUVV4xdAgy8cewCYEAm6Yd5m97IJL2RSXoij8wz\nzV/oxLXXXjt2CTDgDRK9kUn6Yd6mNzJJb2SSnsgj80zzFzqxbdu2sUuAgQvGLgAGZJJ+mLfpjUzS\nG5mkJ/LIPNP8BQAAAACYQZq/AAAAAAAzaK6bv8vLy1lcXMzKysrYpUB27949dgkwcP3YBcDA9WMX\nAPczb9MbmaQ3MklP5JEerKysZHFxMcvLy5u633M2dW+d2bNnT7Zv3z52GZAkOXLkyNglwMA9YxcA\nAzJJP8zb9EYm6Y1M0hN5pAdLS0tZWlrK6upqFhYWNm2/c33lL/TkqquuGrsEGHj52AXAgEzSD/M2\nvZFJeiOT9EQemWeavwAAAAAAM0jzFwAAAABgBmn+QicOHTo0dgkwcPfYBcCATNIP8za9kUl6I5P0\nRB6ZZ5q/0IlLLrlk7BJg4DVjFwADMkk/zNv0RibpjUzSE3lknmn+Qid27do1dgkw8LKxC4ABmaQf\n5m16I5P0RibpiTwyzzR/oRPbt28fuwQYuHDsAmBAJumHeZveyCS9kUl6Io/MM81fAAAAAIAZpPkL\nAAAAADCDNH+hE3v37h27BBjYN3YBMCCT9MO8TW9kkt7IJD2RR+aZ5i90YnV1dewSYODA2AXAgEzS\nD/M2vZFJeiOT9EQemWeav9CJ6667buwSYOBVYxcAAzJJP8zb9EYm6Y1M0hN5ZJ5p/gIAAAAAzCDN\nXwAAAACAGTQzzd+q+uaqeldVfaSqPlhVPz52TQAAAAAAY5mZ5m+Sv03y8621707ynCRvqKrHjFwT\nnLbFxcWxS4CB5bELgAGZpB/mbXojk/RGJumJPDLPZqb521r7dGvtw9OvP5PkUJKvH7cqOH2XXnrp\n2CXAwAvHLgAGZJJ+mLfpjUzSG5mkJ/LIPJuZ5u96VbWQ5GGttbvGrgVO186dO8cuAQZ2jF0ADMgk\n/TBv0xuZpDcySU/kkXnWRfO3qp5ZVfur6q6quq+qjrkev6peWVUHq+pLVfXHVfUDJ9jW1yd5c5Kf\n3fQ8P0QAACAASURBVOi6AQAAAAB61UXzN8m5ST6Y5BVJ2vDJqrooyeuSXJnkKUk+lOSWqjpvMO6R\nSd6W5LWttfdtdNEAAAAAAL3qovnbWru5tfbq1trbk9Rxhiwn+Y3W2g2ttQNJXp7kSJJLBuPenOSd\nrbW3bmzFcObt27dv7BJg4F1jFwADMkk/zNv0RibpjUzSE3lknnXR/D2ZqnpEkoUk71xb11prSf4g\n627+V1U/lOSfJXlBVX2gqlar6rs3u154sFZWVsYuAQZuGbsAGJBJ+mHepjcySW9kkp7II/Os++Zv\nkvOSPDzJZwbrP5Pk/LUHrbX3tNbOaa1tb609ZfrvR0624ec973lZXFw8atmxY8cxfxG69dZbs7h4\nzG2I88pXvjJ79+49at3q6moWFxdz6NCho9ZfeeWV2b1791Hr7rzzziwuLubAgQNHrb/mmmty+eWX\nH7XuyJEjWVxczG233XbU+pWVlVx88cXH1HbRRRdtyHFcc801x3x/cnfuuefIWXUcPf4+brrpppk4\njjWO4+w5jrvvvnsw+nen//7qunWfSpIcPHiw2+OYld+H40huvPHGY/Y18eUkRx/HzTff3O1xzMrv\nw3Ec/zjW5u2z/TjWOI6z/ziuu+66mTiOWfl9OI7kGc94xkwcx6z8Pub9ONbm7bP9ONbbyOO4/vrr\nB1tYG3Mwx3rDMWuWl5e7OI4efh8LCwvH9B6f//znH/P9G6kmF9H2o6ruS/KC1tr+6eMLktyVZMf6\n+/hW1e4kz2qtPeCP/q6q7Uluv/3227N9+/YzVPl8uOOOO/KkJz0pyXuS/OB07ety7rmvyRe/eHjE\nyoAHa3V1NQsLC0luT7I9yVuSvHjd4yRZTbIQr5tsBpkEAIDxnN77ce/RH6yv/nyz0Fpb3ej9nQ1X\n/h5Kcm+Sxw/WPz7Jpze/HAAAAACA/nXf/G2tfSWTPyM8e21dVdX08XvHqgsAAAAAoGddNH+r6tyq\nenJVfd901ROmj79l+vj1SX62ql5SVd+V5NeTbEly/QjlwoY43j1jYFy7xi4ABnaNXQDcz7xNb2SS\n3sgkPZFH5tk5Yxcw9f1J3pWkTZfXTde/OcklrbXfqarzkrwmk9s9fDDJc1prfzVGsbARdu7cOXYJ\nMPD0sQuAAZmkH+ZteiOT9EYm6Yk8Ms+6aP621v5TTnEVcmvtTUnetDkVweZbWloauwQYeO7YBcCA\nTNIP8za9kUl6I5P0RB6ZZ100f8eyvLycrVu3ZmlpyQsBAAAAALAhVlZWsrKyksOHD2/qfue6+btn\nz55s37597DIAAAAAgBm2dvHp6upqFhYWNm2/XXzgG5DcdtttY5cAAx8YuwAYkEn6Yd6mNzJJb2SS\nnsgj80zzFzpx9dVXj10CDNwwdgEwIJP0w7xNb2SS3sgkPZFH5pnmL3TixhtvHLsEGHjt2AXAgEzS\nD/M2vZFJeiOT9EQemWeav9CJLVu2jF0CDDxm7AJgQCbph3mb3sgkvZFJeiKPzDPNXwAAAACAGaT5\nCwAAAAAwgzR/oROXX3752CXAwBvGLgAGZJJ+mLfpjUzSG5mkJ/LIPNP8hU5s27Zt7BJg4PyxC4AB\nmaQf5m16I5P0RibpiTwyz84Zu4AxLS8vZ+vWrVlaWsrS0tLY5TDnLrvssrFLgIEXjV0ADMgk/TBv\n0xuZpDcySU/kkR6srKxkZWUlhw8f3tT9znXzd8+ePdm+ffvYZQAAAAAAM2zt4tPV1dUsLCxs2n7d\n9gEAAAAAYAZp/kInDhw4MHYJMHBw7AJgQCbph3mb3sgkvZFJeiKPzDPNX+jEFVdcMXYJMPDGsQuA\nAZmkH+ZteiOT9EYm6Yk8Ms80f6ET11577dglwIA3SPRGJumHeZveyCS9kUl6Io/MM81f6MS2bdvG\nLgEGLhi7ABiQSfph3qY3MklvZJKeyCPzTPMXAAAAAGAGaf4CAAAAAMwgzV/oxO7du8cuAQauH7sA\nGLh+7ALgfuZteiOT9EYm6Yk8Ms80f6ETR44cGbsEGLhn7AJgQCbph3mb3sgkvZFJeiKPzDPNX+jE\nVVddNXYJMPDysQuAAZmkH+ZteiOT9EYm6Yk8Ms80fwEAAAAAZtA5YxcwpuXl5WzdujVLS0tZWloa\nuxwAAAAAYAatrKxkZWUlhw8f3tT9znXzd8+ePdm+ffvYZUCS5NChQznvvPPGLgPWuXvsAmBAJumH\neZveyCS9kUl6Io/0YO3i09XV1SwsLGzaft32ATpxySWXjF0CDLxm7AJgQCbph3mb3sgkvZFJeiKP\nzDPNX+jErl27xi4BBl42dgEwIJP0w7xNb2SS3sgkPZFH5pnmL3TCLUjoz4VjFwADMkk/zNv0Ribp\njUzSE3lknmn+AgAAAADMIM1fAAAAAIAZpPkLndi7d+/YJcDAvrELgAGZpB/mbXojk/RGJumJPDLP\nNH+hE6urq2OXAAMHxi4ABmSSfpi36Y1M0huZpCfyyDzT/IVOXHfddWOXAAOvGrsAGJBJ+mHepjcy\nSW9kkp7II/NM8xcAAAAAYAZp/gIAAAAAzCDNXwAAAACAGaT5C51YXFwcuwQYWB67ABiQSfph3qY3\nMklvZJKeyCPzTPMXOnHppZeOXQIMvHDsAmBAJumHeZveyCS9kUl6Io/MM81f6MTOnTvHLgEGdoxd\nAAzIJP0wb9MbmaQ3MklP5JF5pvkLAAAAADCDNH8BAAAAAGaQ5i90Yt++fWOXAAPvGrsAGJBJ+mHe\npjcySW9kkp7II/NM8xc6sbKyMnYJMHDL2AXAgEzSD/M2vZFJeiOT9EQemWeav9CJm266aewSYOBX\nxy4ABmSSfpi36Y1M0huZpCfyyDzT/AUAAAAAmEGavwAAAAAAM0jzFwAAAABgBs1183d5eTmLi4tu\n/E0XLr744rFLgIFdYxcAA7vGLgDuZ96mNzJJb2SSnsgjPVhZWcni4mKWl5c3db/nbOreOrNnz55s\n37597DIgSbJz586xS4CBp49dAAzIJP0wb9MbmaQ3MklP5JEeLC0tZWlpKaurq1lYWNi0/c71lb/Q\nk6WlpbFLgIHnjl0ADMgk/TBv0xuZpDcySU/kkXmm+QsAAAAAMIM0fwEAAAAAZpDmL3TitttuG7sE\nGPjA2AXAgEzSD/M2vZFJeiOT9EQemWeav9CJq6++euwSYOCGsQuAAZmkH+ZteiOT9EYm6Yk8Ms80\nf6ETN95449glwMBrxy4ABmSSfpi36Y1M0huZpCfyyDzT/IVObNmyZewSYOAxYxcAAzJJP8zb9EYm\n6Y1M0hN5ZJ5p/gIAAAAAzCDNXwAAAACAGaT5C524/PLLxy4BBt4wdgEwIJP0w7xNb2SS3sgkPZFH\n5pnmL3Ri27ZtY5cAA+ePXQAMyCT9MG/TG5mkNzJJT+SReab5C5247LLLxi4BBl40dgEwIJP0w7xN\nb2SS3sgkPZFH5pnmLwAAAADADNL8BQAAAACYQTPV/K2q36uqz1bV74xdCzxQBw4cGLsEGDg4dgEw\nIJP0w7xNb2SS3sgkPZFH5tlMNX8z+Rjwnxy7CHgwrrjiirFLgIE3jl0ADMgk/TBv0xuZpDcySU/k\nkXk2U83f1tp/TvLFseuAB+Paa68duwQY8AaJ3sgk/TBv0xuZpDcySU/kkXk2U81fOJtt27Zt7BJg\n4IKxC4ABmaQf5m16I5P0RibpiTwyz7po/lbVM6tqf1XdVVX3VdXicca8sqoOVtWXquqPq+oHxqgV\nAAAAAOBs0EXzN8m5ST6Y5BVJ2vDJqrooyeuSXJnkKUk+lOSWqjpvM4sEAAAAADhbdNH8ba3d3Fp7\ndWvt7UnqOEOWk/xGa+2G1tqBJC9PciTJJccZWyfYBnRt9+7dY5cAA9ePXQAMXD92AXA/8za9kUl6\nI5P0RB6ZZ100f0+mqh6RZCHJO9fWtdZakj9IsmMw9veT3JTkf6yqO6vqaSfb9vOe97wsLi4etezY\nsSP79u07atytt96axcVj7kSRV77yldm7d+9R61ZXV7O4uJhDhw4dtf7KK6885sXmzjvvzOLiYg4c\nOHDU+muuuSaXX375UeuOHDmSxcXF3HbbbUetX1lZycUXX3xMbRdddNGGHMc111xzzPcnd+eee46c\nVcfR4+/jyJEjM3EcaxzH2XMcd99992D0707/vWfduk8lSQ4ePNjtcczK78NxJDfeeOMx+5p4e5Kj\nj+Pmm2/u9jhm5ffhOI5/HGvz9tl+HGsch+NwHI7jTB/Hu9/97pk4jln5fcz7cazN22f7cay3kcdx\n/fXXD7awNuZgjvWGY9YsLy93cRw9/D4WFhaO6T0+//nPP+b7N1JN+qj9qKr7krygtbZ/+viCJHcl\n2dFae9+6cbuTPKu1tuP4WzrpPrYnuf3222/P9u3bz1Dl8+GOO+7Ik570pCTvSfKD07Wvy7nnviZf\n/OLhESsDHqzV1dUsLCwkuT3J9iRvSfLidY+TZDXJQrxushlkEgAAxnN678e9R3+wvvrzzUJrbXWj\n99f9lb8AAAAAADxwZ0Pz91CSe5M8frD+8Uk+vfnlAAAAAAD0r/vmb2vtK5lcQ/7stXVVVdPH7x2r\nLjjThvengfEN7wUMY5NJ+mHepjcySW9kkp7II/Osi+ZvVZ1bVU+uqu+brnrC9PG3TB+/PsnPVtVL\nquq7kvx6ki3xsd/MkEsuuWTsEmDgNWMXAAMyST/M2/RGJumNTNITeWSenTN2AVPfn+RdSdp0ed10\n/ZuTXNJa+52qOi+Ts77HJ/lgkue01v5qjGJhI+zatWvsEmDgZWMXAAMyST/M2/RGJumNTNITeWSe\nddH8ba39p5ziKuTW2puSvGlzKoLN55Mw6c+FYxcAAzJJP8zb9EYm6Y1M0hN5ZJ510fwdy/LycrZu\n3ZqlpaUsLS2NXQ4AAAAAMINWVlaysrKSw4cPb+p+57r5u2fPHn/9AQAAAAA21NrFp6urq1lYWNi0\n/XbxgW9Asnfv3rFLgIF9YxcAAzJJP8zb9EYm6Y1M0hN5ZJ5p/kInVldXxy4BBg6MXQAMyCT9MG/T\nG5mkNzJJT+SReab5C5247rrrxi4BBl41dgEwIJP0w7xNb2SS3sgkPZFH5pnmLwAAAADADNL8BQAA\nAACYQZq/AAAAAAAzSPMXOrG4uDh2CTCwPHYBMCCT9MO8TW9kkt7IJD2RR+bZOWMXMKbl5eVs3bo1\nS0tLWVpaGrsc5tyll146dgkw8MKxC4ABmaQf5m16I5P0RibpiTzSg5WVlaysrOTw4cObut+5bv7u\n2bMn27dvH7sMSJLs3Llz7BJgYMfYBcCATNIP8za9kUl6I5P0RB7pwdrFp6urq1lYWNi0/brtAwAA\nAADADNL8BQAAAACYQZq/0Il9+/aNXQIMvGvsAmBAJumHeZveyCS9kUl6Io/MM81f6MTKysrYJcDA\nLWMXAAMyST/M2/RGJumNTNITeWSeaf5CJ2666aaxS4CBXx27ABiQSfph3qY3MklvZJKeyCPzTPMX\nAAAAAGAGaf4CAAAAAMwgzV8AAAAAgBmk+QuduPjii8cuAQZ2jV0ADOwauwC4n3mb3sgkvZFJeiKP\nzDPNX+jEzp07xy4BBp4+dgEwIJP0w7xNb2SS3sgkPZFH5pnmL3RiaWlp7BJg4LljFwADMkk/zNv0\nRibpjUzSE3lknmn+AgAAAADMoHPGLmBMy8vL2bp1a5aWlvwVCAAAAADYECsrK1lZWcnhw4c3db9z\nfeXvnj17sn//fo1funDbbbeNXQIMfGDsAmBAJumHeZveyCS9kUl6Io/0YGlpKfv378+ePXs2db9z\n3fyFnlx99dVjlwADN4xdAAzIJP0wb9MbmaQ3MklP5JF5pvkLnbjxxhvHLgEGXjt2ATAgk/TDvE1v\nZJLeyCQ9kUfmmeYvdGLLli1jlwADjxm7ABiQSfph3qY3MklvZJKeyCPzTPMXAAAAAGAGaf4CAAAA\nAMwgzV/oxOWXXz52CTDwhrELgAGZpB/mbXojk/RGJumJPDLPNH+hE9u2bRu7BBg4f+wCYEAm6Yd5\nm97IJL2RSXoij8wzzV/oxGWXXTZ2CTDworELgAGZpB/mbXojk/RGJumJPDLPNH8BAAAAAGaQ5i8A\nAAAAwAzS/IVOHDhwYOwSYODg2AXAgEzSD/M2vZFJeiOT9EQemWeav9CJK664YuwSYOCNYxcAAzJJ\nP8zb9EYm6Y1M0hN5ZJ5p/kInrr322rFLgAFvkOiNTNIP8za9kUl6I5P0RB6ZZ5q/0Ilt27aNXQIM\nXDB2ATAgk/TDvE1vZJLeyCQ9kUfmmeYvAAAAAMAM0vwFAAAAAJhBmr/Qid27d49dAgxcP3YBMHD9\n2AXA/czb9EYm6Y1M0hN5ZJ5p/kInjhw5MnYJMHDP2AXAgEzSD/M2vZFJeiOT9EQemWeav9CJq666\nauwSYODlYxcAAzJJP8zb9EYm6Y1M0hN5ZJ5p/gIAAAAAzKBzxi4AAAAAAJgNd9xxx/1fn3feedm2\nbdtRz9955505dOjQUeuON44zY66bv8vLy9m6dWuWlpaytLQ0djnMuUOHDuW8884buwxY5+6xC4AB\nmaQf5m16I5P0RibpiTxulk8leVhe/OIX37/m0Y/eko997I77G7t33nlnnvjEC3PPPUffh3k4bhat\nrKxkZWUlhw8f3tT9zvVtH/bs2ZP9+/dr/NKFSy65ZOwSYOA1YxcAAzJJP8zb9EYm6Y1M0hN53Cyf\nS3Jfkt9OcnuS38499xw56irfQ4cOTRu/a2OOP24WLS0tZf/+/dmzZ8+m7neur/yFnuzatWvsEmDg\nZWMXAAMyST/M2/RGJumNTNITedxsFybZfgbGcCbM9ZW/0JPt273o0ZsLxy4ABmSSfpi36Y1M0huZ\npCfyyDzT/AUAAAAAmEGavwAAAAAAM0jzFzqxd+/esUuAgX1jFwADMkk/zNv0RibpjUzSE3lknmn+\nQidWV1fHLgEGDoxdAAzIJP0wb9MbmaQ3MklP5JF5pvkLnbjuuuvGLgEGXjV2ATAgk/TDvE1vZJLe\nyCQ9kUfmmeYvAAAAAMAM0vwFAAAAAJhBmr8AAAAAADNI8xc6sbi4OHYJMLA8dgEwIJP0w7xNb2SS\n3sgkPZFH5pnmL3Ti0ksvHbsEGHjh2AXAgEzSD/M2vZFJeiOT9EQemWeav9CJnTt3jl0CDOwYuwAY\nkEn6Yd6mNzJJb2SSnsgj80zzFwAAAABgBs1U87eqnl9VB6rqY1X102PXAwAAAAAwlplp/lbVw5O8\nLsmPJFlI8otV9XWjFgUPwL59+8YuAQbeNXYBMCCT9MO8TW9kkt7IJD2RR+bZzDR/kzw1yZ+21j7d\nWvtikv+QxE1dOGvs3r177BJg4PqxC4CB68cuAO5n3qY3MklvZJKeyCPzbJaav9+Y5K51j+9K8k0j\n1QIP2Dd8wzeMXQIMfP3YBcCATNIP8za9kUl6I5P0RB6ZZ100f6vqmVW1v6ruqqr7qmrxOGNeWVUH\nq+pLVfXHVfUDY9QKAAAAAHA26KL5m+TcJB9M8ookbfhkVV2Uyf18r0zylCQfSnJLVZ23btgnk3zz\nusffNF0HAAAAADB3umj+ttZubq29urX29iR1nCHLSX6jtXZDa+1AkpcnOZLkknVj3p/ku6vqgqr6\nmiTPTXLLRtcOAAAAANCjc8Yu4FSq6hFJFpK8dm1da61V1R8k2bFu3b1V9S+TvDuTBvLu1trdJ9js\no5Pkjjvu2KiyZ9bBgwenX30s0x9jkr/Ivffem9XV1ZGqmg3vf//7/QwZxVdfC9f+Xfvv/E+TrB71\nnNdNNoNMcjYwb9MbmaQ3MklP5PGBOfH78fXvvU9n3eTfd7zjHfdv86t9pfXfN1/v7dcd56NPNu5M\nqdaOucvCqKrqviQvaK3tnz6+IJMPb9vRWnvfunG7kzyrtbbj+Fs66T5+IslbzlDJAAAAAAAPxD9v\nrb11o3fS/ZW/G+SWJP88yX9Ncs+4pQAAAAAAc+LRSf5ONul2tWdD8/dQknuTPH6w/vFJPv1gNtha\n+29JNryzDgAAAAAw8N7N2lEXH/h2Mq21ryS5Pcmz19ZVVU0fb9oPCgAAAADgbNLFlb9VdW6Sb8/k\ng9qS5AlV9eQkn22tfSLJ65NcX1W3J3l/kuUkW5JcP0K5AAAAAADd6+ID36rqh5O8K8mwmDe31i6Z\njnlFkisyud3DB5Nc1lr7k00tFAAAAADgLNFF8xcAAAAAgDOr+3v+nkhV/VJVvb+qPl9Vn6mqt1XV\ndx5n3Guq6pNVdaSqfr+qvn3w/KOq6rqqOlRVX6iqf19VjxuM+bqqektVHa6qu6vqN6e3qoAkp85j\nVZ1TVbur6sNV9cWququq3lxVFwy2I4+cEaf7Grlu/K9X1X1V9XOD9TLJGfEA5u0Lq+rtVfW56evl\n+6rqm9c9L5M8ZKeTx6o6t6qurapPTN9HfqSq/sVgjDxyRlTVy6vqQ9OcHK6q91bVcwdjnNewaU6W\nSec2jOF0XifXjXVuw4Y6zXm7m/Oas7b5m+SZSa5J8rQk/0OSRyS5taoeszagqn4xyaVJXpbkqUn+\nOsktVfXIddt5Q5J/mOSfJnlWkm9M8ruDfb01yYWZfMjcP5yO+40zf0icxU6Vxy1Jvi/JVUmekuTH\nkjwxydsH25FHzpRTvkauqaofm4676zjbkUnOlNOZt78tyR8m+WgmOfreJP9LknvWbUcmORNO5zVy\nT5KdSX4iyXdNH19bVc9fN0YeOVM+keQXk2xPspDkPyZ5e1VdmDivYRQny6RzG8Zw0tfJNc5t2CSn\nmrf7Oq9prc3EkuS8JPcleca6dZ9Msrzu8WOTfCnJC9c9/nKSH1s35onT7Tx1+vjC6eOnrBvznCR/\nm+T8sY/b0udyvDweZ8z3J7k3yTdPH8ujZcOWE2UyyTcluXOarYNJfm7dczJp2bDlBPP2Sib3+z/R\n98ikZUOWE+Tx/0nyrwfj/iTJa6Zfy6NlQ5ck/y3JxdOvnddYRl/WZ/I4zzm3sWz6MsykcxvLmMtg\n3r6xp/Oas/nK36GvzeQD4z6bJFX1d5Ocn+SdawNaa59P8r4kO6arvj/JOYMxH8vkxWJtzNOT3N1a\n+8C6ff3BdF9P24gDYSYclcdTjPnc9PFC5JGNc0wmq6qS3JDk6tbaHcf5HplkIw3n7crkr9l/VlU3\n1+R/xf/jqvrH675HJtkox5u335tksaq+MUmq6keTfEeSW6bPyyMboqoeVlUvyuTqyvc6r2Fsg0z+\n0QmGObdh0xwvk85tGMtx5u1K8rx0dF4zE83f6Q/2DUlua619dLr6/Ex+IJ8ZDP/M9LkkeXySv5m+\neTrRmPOT/OX6J1tr92ZycnB+YOAEeRyOeVSSX03y1tbaF6erz488sgFOkslXZZK5a0/wrTLJhjhB\nJh+X5Gsy+d+n3pHkHyR5W5Lfq6pnTsfIJGfcSV4jL0tyR5K/qKq/ySSXr2ytvWf6vDxyRlXV91TV\nFzK5EuhNmVwN9LE4r2EkJ8jkgeOMc27DpjhFJp3bsKlOMm93d15zzgMZ3LE3JXlSkh8auxDIKfJY\nVeck+T8zeRP/ik2si/l1TCaraiHJz2VynzbYbMd7nVz7g/S+1tobp19/uKp+MMnLM7lnFmyEE83b\nP5fJVRXPz+QqjGcleVNVfbK19h83t0TmxIEkT06yNcmPJ7mhqp41bknMueNmcn0D2LkNm+xEr5Pn\nxrkNm+9EeTw8fb6b85qz/srfqro2k8upf6S19ql1T306SWXyV/D1Hj99bm3MI6vqsacYM/y0vYcn\n+fp1YyDJSfO49vzam6NvSbJz3V/GE3lkA5wkk89I8g1JPlFVX6mqryT51iSvr6qPT8fIJGfcSTJ5\nKJP7Vw3/N707kmybfi2TnFEnymNVPTrJryT5hdbaO1prf9pae1OSm5L8q+kweeSMaq39bWvt4621\nD7TW/nWSDyX5+TivYSQnyWQS5zZsvpNk0rkNm+4keezuvOasbv5O37D/4yQ/2lq7c/1zrbWDmfww\nnr1u/GMzuYLjvdNVt2fyC1k/5omZ/DLW7mX0R0m+tqrW/wXp2Zm8AXvfmTwezm4ny+P0+bU3R09I\n8uzW2t2DIfLIGXWKTN6Q5O9l8pfKteWTSa7O5CbyiUxyhp1i3v5Kkv87kw86WO87k/z59GuZ5Iw5\nxWvkI6bLvYP19+ar75/lkY32sCSPcl5DRx6W5FGJcxu6sZZJ5zb0YG3e7u+85oF8OlxPSyb/i97d\nSZ6ZSWd8bXn0ujFXZPJpe/8oyfcm2Zfkz5I8crCdg0l+JJMbLr8nyR8O9vWOTD7d+Qcy+V8CP5bk\nt8b+GVj6WU6Vx0xusfL26X/o3zsY84jBduTR8pCX03mNPM73HPWJuOu2I5OWh7yc5rz9giT3JPmZ\nJN+W5NIkf5Nkx2A7Mml5SMtp5vFdST6c5IeT/J0kL01yJMnLBtuRR8tDXpK8dprHb03yPUn+t0xO\nCv/+9HnnNZZNXU6WyTi3sYywnOp18jjjndtYNmw5jXm7q/Oa0X9gD+EHfV8mV18Ml5cMxu3K5C8+\nRzL5dOZvHzz/qCTXZHJZ9hcy+evl4wZjvjbJb2dy3467k/wfSbaM/TOw9LOcKo/TF4Thc2vf86x1\n25FHyxlZTvc1cvA9Hz/OGySZtJyR5QHM2y9N8l+S/HWS1STPHzwvk5aHvJxOHjP53+z2JvnENI8f\nTfLzg+3Io+WMLEl+czoPfymTq3xvzaChEec1lk1cTpbJOLexjLCczuvkYLxzG8uGLac5b780nZzX\n1HRjAAAAAADMkLP6nr8AAAAAAByf5i8AAAAAwAzS/AUAAAAAmEGavwAAAAAAM0jzFwAAAABgBmn+\nAgAAAADMIM1fAAAAAIAZpPkLAAAAADCDNH8BAAAAAGaQ5i8AABxHVb2rql5/kuevrKrVzawJAAAe\nCM1fAAB4cH4tybMfygaq6qeq6r6q+shxnvtn0+c+Phh/9wm2dV9VLT6UegAAmC2avwAA8CC0lDy2\ncAAABO9JREFU1o601o7biH2A/jrJ46rqaYP1lyT58+Pt+gzsEwCAOaD5CwDAqKrqx6vqw1V1pKoO\nVdWtVfWY6XP/rqreVlWvrqq/rKrDVfW/V9U5676/quqXqurj0218oKr+6WAf31NV76iqL1TVp6vq\nhqr679c9v2W67gtVdVdV/cJp1H1lVX1g3eO1Wv9lVX1yeizXVtXDT7Gpv03y1iQ/vW5b35TkR6br\nH7BpbfdV1b3Tf9eWlzyY7QEAcHbS/AUAYDRVdX4mDc7fTPJdSX44ye8lqXXDnr3uuRcl+SdJrlz3\n/P+c5MVJXpbkSUn2JPmtqnrmdB9bk7wzye1Jtid5TpLHJfmdddv4N0memeQfJdmZSeN1+2kcwvAq\n3B9N8oTp978kyUuny6m28W+TvLCqHj1d99Ik/1eSvzyNGo7n15Kcn+SC6b//KpMrjP/kQW4PAICz\n0DmnHgIAABvmgiQPT/K21tonpuuG97/9cpKLW2tfTnJHVb06ydVJfrmqHpnkl5I8u7X2vun4/zpt\n/P6LJH+Y5LIkq621X17bYFX9TJI7q+rbk3wqk1ss/ERr7d3T538qyV88iOP5bJJLW2styX+pqv+Q\nSfN678m+qbX2oem9fX88yW9n0vxdTvJtxxn+tVX1+RzdIE/WNaJba0eSHJkey9OT/K9JfrK19tEH\ncUwAAJylNH8BABjThzK5KvdPq+qWJLcm+fettc+tHzNt/K75oyRfU1XfkuS/S7Ilye9X1fpm6COS\nrE6//ntJ/n5VfWGw75ZJc3XLdPz773+itbur6mMP4ng+Mm38rvlUku85ze/9t0kuqapPTGt6RyaN\n66HPJ3lKjm3+/r/DgVW1LcnbklzdWvvd06wDAIAZofkLAMBoWmv3JdlZVTsyud3CZUl+paqe2lo7\n3oedDX3N9N/nJfnk4LkvrxuzP8kVObZh+qkk3/Fgaj+Brwwet5z+rdbekskVzbuS/FZr7b6j+9n3\nu6+1dnC4cji2qrZkctzvaa3tOs0aAACYIe75CwDA6Fprf9RauyqTK1r/JsmPrXv6yVX1qHWPdyT5\n4vQ2ER/NpMn7ra21jw+Wu6bjV5N8d5I/P86YLyX5/zL50LWnre2gqr4uyXdu1PEeT2vt7kyatc/K\nKW4TcZrekknz+SfPwLYAADgLufIXAIDRVNVTM7kn7q2ZfLjZ05Ocl0lTd80jk+ytql9J8nczuTL2\nmiRprX2xqv5Nkj1V9fAktyXZmuSHkhxurf1WkuuS/EySG6vq6kzuy/sdSS5K8tOttb+uqr1Jfq2q\nPpvkrzK5R+69G3nsJ/BTSf6naSP4QauqqzL5uf6DJI+tqsdOnzrcWrvnIdYIAMBZQvMXAIAxfT6T\nK11/Psljk/x5kl9ord26bsw7k/xZkv+cSSP4rUmuWnuytfbLVfWXSV6V5AlJPpfJ1b6vnT7/qar6\noSS7k9yS5FHT/dy87v68lyc5N5Mrb7+Q5HXTejbV9N7GXz7lwBN8e776oW/PyuR43jsYc3GSGx7k\n9gEAOMvU0Z9HAQAA/aiqf5dka2vtn4xdCwAAnG3c8xcAAAAAYAZp/gIAAAAAzCC3fQAAAAAAmEGu\n/AUAAAAAmEGavwAAAAAAM0jzFwAAAABgBmn+AgAAAADMIM1fAAAAAIAZpPkLAAAAADCDNH8BAAAA\nAGaQ5i8AAAAAwAz6/wGhCTkT+QcicQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5d3418950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = pd.Series(mhzes)\n",
    "ds.hist(bins=250, figsize=(17, 7))\n",
    "plt.ylabel(\"ping count (log)\")\n",
    "plt.xlabel(\"speed in MHz\")\n",
    "plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3504    2808736\n",
       "2397    1030407\n",
       "3503     506650\n",
       "2097     274870\n",
       "2400       4875\n",
       "2399       3835\n",
       "3495       3284\n",
       "2396       1963\n",
       "2600       1907\n",
       "2599       1540\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(mhzes).value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be a family gathering of different hardware configurations this client is running on, most on a particular approximately-3.5GHz machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Operating System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def major_minor(version_string):\n",
    "    return version_string.split('.')[0] + '.' + version_string.split('.')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pings_per_os = subset\\\n",
    "    .map(lambda p: (p[\"environment/system/os/name\"] + \" \" + major_minor(p[\"environment/system/os/version\"]), 1))\\\n",
    "    .countByKey()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(u'Windows_NT 5.1', 4641520)]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print len(pings_per_os)\n",
    "sorted(pings_per_os.items(), key=lambda x: x[1], reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the pings come from Windows XP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Physical Location (geo-ip of submitting host)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pings_per_city = subset\\\n",
    "    .map(lambda p: (p[\"meta/geoCountry\"] + \" \" + p[\"meta/geoCity\"], 1))\\\n",
    "    .countByKey()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "419\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(u'US Costa Mesa', 599161),\n",
       " (u'US Phoenix', 451711),\n",
       " (u'FR Paris', 246284),\n",
       " (u'GB ??', 237285),\n",
       " (u'FR ??', 191953),\n",
       " (u'GB London', 190846),\n",
       " (u'DE ??', 145394),\n",
       " (u'US Los Angeles', 124703),\n",
       " (u'US Houston', 99759),\n",
       " (u'US New York', 94702)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print len(pings_per_city)\n",
    "sorted(pings_per_city.items(), key=lambda x: x[1], reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These pings are coming from all over the world, mostly from countries where Firefox user share is already decent. This may just be a map of Browser use across the world's population, which would be consistent with a profile that is inhabiting a set %ge of the browser-using population's computers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Document IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pings_per_docid = subset\\\n",
    "    .map(lambda p: (p[\"meta/documentId\"], 1))\\\n",
    "    .countByKey()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128110\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(u'358f9afc-75bc-406b-be35-489e1b82804c', 2769088),\n",
       " (u'975fae0b-70f3-4a57-823c-8a4c791655e9', 474255),\n",
       " (u'a533f66f-a7a8-429c-9ddb-ecfc4ca7608b', 273833),\n",
       " (u'b810196c-38f1-479e-b528-ceef036ba109', 258250),\n",
       " (u'89a3a6cd-7410-4735-877d-24720ae51f52', 257292),\n",
       " (u'd30bf2c6-24cf-436c-9779-02bf670ea9d4', 160317),\n",
       " (u'81b001e9-7c44-4bc5-85e6-901707a0345e', 160026),\n",
       " (u'0903724b-1bdc-4eb0-8ccc-44eeb1c53415', 160000),\n",
       " (u'effa9557-75c5-4e6e-9967-b46a596ecb5a', 101),\n",
       " (u'56b29ce3-1d05-4bd2-a057-f0ef17fe4160', 18)]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print len(pings_per_docid)\n",
    "sorted(pings_per_docid.items(), key=lambda x: x[1], reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That same half of pings reporting 215s sessions? The same ping. Maybe a ping stored in the profile directory before it was distributed? But then how are we seeing hundreds-of-thousands of copies of seven others, too..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "None of this is concrete, but if I were invited to speculate, I'd think there's some non-Mozilla code someplace that has embedded a particular (out-of-date) version of Firefox Developer Edition into themselves, automating it to perform a 2-minute-and-35-second task on Windows XP machines, possibly while masquerading as something completely different (using the addon).\n",
    "\n",
    "This could be legitimate. Firefox contains a robust networking and rendering stack so it might be desireable to embed it within, say, a video game as a fully-featured embedded browser. The user-agent-spoofing addon could very well be used to set a custom user agent to identify the video game's browser, and of course it wouldn't be the user's default browser.\n",
    "\n",
    "However, I can't so easily explain this client's broad geographical presence and Windows XP focus."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}

## problematic_client.py

# coding: utf-8
---
title: One Problematic Aurora 51 Client
authors:
- chutten
tags:
- aurora
- firefox
created_at: 2017-02-22
updated_at: 2017-02-23
tldr: Taking a look at one problematic client on Aurora leads to a broad examination of the types of hosts that are sending us this data and some seriously-speculative conclusions.
---
# ## One Problematic Aurora 51 Client

# ### Motivation

# There is one particular client, whose `client_id` I've obscured, that seems to be sending orders of magnitude more "main" pings per day than is expected, or even possible.
#
# I'm interested in figuring out what we can determine about this particular client to see if there are signifiers we can use to identify this anomalous use case. This identification would permit us to:
# * filter data from these clients out of derived datasets that aren't relevant
# * identify exceptional use-cases for Firefox we don't currently understand

# ### How many pings are we talking, here?

# In[1]:

import pandas as pd
import numpy as np
import matplotlib

from matplotlib import pyplot as plt
from moztelemetry.dataset import Dataset
from moztelemetry import get_pings_properties, get_one_ping_per_client


# In[2]:

all_pings = Dataset.from_source("telemetry")     .where(docType='main')     .where(appBuildId=lambda x: x.startswith("20161014"))     .where(appUpdateChannel="aurora")     .records(sc, sample=1)


# In[5]:

pings = all_pings.filter(lambda p: p['clientId'] == '<omitted>')


# In[6]:

submission_dates = get_pings_properties(pings, ["meta/submissionDate"])


# In[7]:

from datetime import datetime
ping_counts = submission_dates.map(lambda p: (datetime.strptime(p["meta/submissionDate"], '%Y%m%d'), 1)).countByKey()


# In[8]:

from datetime import timedelta


# In[9]:

df = pd.DataFrame(ping_counts.items(), columns=["date", "count"]).set_index(["date"])
df.plot(figsize=(17, 7))
plt.xticks(np.arange(min(df.index), max(df.index) + timedelta(3), 3, dtype="datetime64[D]"))
plt.ylabel("ping count")
plt.xlabel("date")
plt.grid(True)
plt.show()


# Just about 100k main pings submitted by this client on a single day? (Feb 16)... that is one active client.
#
# Or _many_ active clients.

# ### What Can We Learn About These Pings?
#
# Well, since these pings all share the same clientId, they likely are sharing user profiles. This means things like profile `creationDate` and so forth won't change amongst them.
#
# However, here's a list of things that might change in interesting ways or otherwise shed some light on the purpose of these installs.

# In[13]:

subset = get_pings_properties(pings, [
        "meta/documentId",
        "meta/submissionDate",
        "meta/geoCountry",
        "meta/geoCity",
        "environment/addons/activeAddons",
        "environment/settings/isDefaultBrowser",
        "environment/system/cpu/speedMHz",
        "environment/system/os/name",
        "environment/system/os/version",
        "payload/info/sessionLength",
        "payload/info/subsessionLength",
    ])


# In[14]:

subset.count()


# #### Non-System Addons

# In[18]:

pings_with_addon = subset    .flatMap(lambda p: [(addon["name"], 1) for addon in filter(lambda x: "isSystem" not in x or not x["isSystem"], p["environment/addons/activeAddons"].values())])    .countByKey()


# In[19]:

sorted(pings_with_addon.items(), key=lambda x: x[1], reverse=True)[:5]


# Nearly every single ping is reporting that it has an addon called 'Random Agent Spoofer'. Interesting.

# #### Session Lengths

# In[20]:

SESSION_MAX = 400


# In[21]:

session_lengths = subset.map(lambda p: p["payload/info/sessionLength"] if p["payload/info/sessionLength"] < SESSION_MAX else SESSION_MAX).collect()


# In[22]:

pd.Series(session_lengths).hist(bins=250, figsize=(17, 7))
plt.ylabel("ping count")
plt.xlabel("session length in seconds")
plt.show()


# In[23]:

pd.Series(session_lengths).value_counts()[:10]


# The session lengths for over half of all the reported pings are exactly 215 seconds long. Two minutes and 35 seconds.

# #### Is this Firefox even the default browser?

# In[24]:

subset.map(lambda p: (p["environment/settings/isDefaultBrowser"], 1)).countByKey()


# No.

# #### CPU speed

# In[25]:

MHZ_MAX = 5000


# In[26]:

mhzes = subset.map(lambda p: p["environment/system/cpu/speedMHz"] if p["environment/system/cpu/speedMHz"] < MHZ_MAX else MHZ_MAX).collect()


# In[27]:

ds = pd.Series(mhzes)
ds.hist(bins=250, figsize=(17, 7))
plt.ylabel("ping count (log)")
plt.xlabel("speed in MHz")
plt.yscale("log")
plt.show()


# In[28]:

pd.Series(mhzes).value_counts()[:10]


# There seems to be a family gathering of different hardware configurations this client is running on, most on a particular approximately-3.5GHz machine

# #### Operating System

# In[29]:

def major_minor(version_string):
    return version_string.split('.')[0] + '.' + version_string.split('.')[1]


# In[30]:

pings_per_os = subset    .map(lambda p: (p["environment/system/os/name"] + " " + major_minor(p["environment/system/os/version"]), 1))    .countByKey()


# In[31]:

print len(pings_per_os)
sorted(pings_per_os.items(), key=lambda x: x[1], reverse=True)[:10]


# All of the pings come from Windows XP.

# #### Physical Location (geo-ip of submitting host)

# In[32]:

pings_per_city = subset    .map(lambda p: (p["meta/geoCountry"] + " " + p["meta/geoCity"], 1))    .countByKey()


# In[33]:

print len(pings_per_city)
sorted(pings_per_city.items(), key=lambda x: x[1], reverse=True)[:10]


# These pings are coming from all over the world, mostly from countries where Firefox user share is already decent. This may just be a map of Browser use across the world's population, which would be consistent with a profile that is inhabiting a set %ge of the browser-using population's computers.

# #### Document IDs

# In[34]:

pings_per_docid = subset    .map(lambda p: (p["meta/documentId"], 1))    .countByKey()


# In[35]:

print len(pings_per_docid)
sorted(pings_per_docid.items(), key=lambda x: x[1], reverse=True)[:10]


# That same half of pings reporting 215s sessions? The same ping. Maybe a ping stored in the profile directory before it was distributed? But then how are we seeing hundreds-of-thousands of copies of seven others, too...

# ### Conclusion
#
# None of this is concrete, but if I were invited to speculate, I'd think there's some non-Mozilla code someplace that has embedded a particular (out-of-date) version of Firefox Developer Edition into themselves, automating it to perform a 2-minute-and-35-second task on Windows XP machines, possibly while masquerading as something completely different (using the addon).
#
# This could be legitimate. Firefox contains a robust networking and rendering stack so it might be desireable to embed it within, say, a video game as a fully-featured embedded browser. The user-agent-spoofing addon could very well be used to set a custom user agent to identify the video game's browser, and of course it wouldn't be the user's default browser.
#
# However, I can't so easily explain this client's broad geographical presence and Windows XP focus.

	# coding: utf-8
	---
	title: One Problematic Aurora 51 Client
	authors:
	- chutten
	tags:
	- aurora
	- firefox
	created_at: 2017-02-22
	updated_at: 2017-02-23
	tldr: Taking a look at one problematic client on Aurora leads to a broad examination of the types of hosts that are sending us this data and some seriously-speculative conclusions.
	---
	# ## One Problematic Aurora 51 Client

	# ### Motivation

	# There is one particular client, whose `client_id` I've obscured, that seems to be sending orders of magnitude more "main" pings per day than is expected, or even possible.
	#
	# I'm interested in figuring out what we can determine about this particular client to see if there are signifiers we can use to identify this anomalous use case. This identification would permit us to:
	# * filter data from these clients out of derived datasets that aren't relevant
	# * identify exceptional use-cases for Firefox we don't currently understand

	# ### How many pings are we talking, here?

	# In[1]:

	import pandas as pd
	import numpy as np
	import matplotlib

	from matplotlib import pyplot as plt
	from moztelemetry.dataset import Dataset
	from moztelemetry import get_pings_properties, get_one_ping_per_client


	# In[2]:

	all_pings = Dataset.from_source("telemetry") .where(docType='main') .where(appBuildId=lambda x: x.startswith("20161014")) .where(appUpdateChannel="aurora") .records(sc, sample=1)


	# In[5]:

	pings = all_pings.filter(lambda p: p['clientId'] == '<omitted>')


	# In[6]:

	submission_dates = get_pings_properties(pings, ["meta/submissionDate"])


	# In[7]:

	from datetime import datetime
	ping_counts = submission_dates.map(lambda p: (datetime.strptime(p["meta/submissionDate"], '%Y%m%d'), 1)).countByKey()


	# In[8]:

	from datetime import timedelta


	# In[9]:

	df = pd.DataFrame(ping_counts.items(), columns=["date", "count"]).set_index(["date"])
	df.plot(figsize=(17, 7))
	plt.xticks(np.arange(min(df.index), max(df.index) + timedelta(3), 3, dtype="datetime64[D]"))
	plt.ylabel("ping count")
	plt.xlabel("date")
	plt.grid(True)
	plt.show()


	# Just about 100k main pings submitted by this client on a single day? (Feb 16)... that is one active client.
	#
	# Or _many_ active clients.

	# ### What Can We Learn About These Pings?
	#
	# Well, since these pings all share the same clientId, they likely are sharing user profiles. This means things like profile `creationDate` and so forth won't change amongst them.
	#
	# However, here's a list of things that might change in interesting ways or otherwise shed some light on the purpose of these installs.

	# In[13]:

	subset = get_pings_properties(pings, [
	"meta/documentId",
	"meta/submissionDate",
	"meta/geoCountry",
	"meta/geoCity",
	"environment/addons/activeAddons",
	"environment/settings/isDefaultBrowser",
	"environment/system/cpu/speedMHz",
	"environment/system/os/name",
	"environment/system/os/version",
	"payload/info/sessionLength",
	"payload/info/subsessionLength",
	])


	# In[14]:

	subset.count()


	# #### Non-System Addons

	# In[18]:

	pings_with_addon = subset .flatMap(lambda p: [(addon["name"], 1) for addon in filter(lambda x: "isSystem" not in x or not x["isSystem"], p["environment/addons/activeAddons"].values())]) .countByKey()


	# In[19]:

	sorted(pings_with_addon.items(), key=lambda x: x[1], reverse=True)[:5]


	# Nearly every single ping is reporting that it has an addon called 'Random Agent Spoofer'. Interesting.

	# #### Session Lengths

	# In[20]:

	SESSION_MAX = 400


	# In[21]:

	session_lengths = subset.map(lambda p: p["payload/info/sessionLength"] if p["payload/info/sessionLength"] < SESSION_MAX else SESSION_MAX).collect()


	# In[22]:

	pd.Series(session_lengths).hist(bins=250, figsize=(17, 7))
	plt.ylabel("ping count")
	plt.xlabel("session length in seconds")
	plt.show()


	# In[23]:

	pd.Series(session_lengths).value_counts()[:10]


	# The session lengths for over half of all the reported pings are exactly 215 seconds long. Two minutes and 35 seconds.

	# #### Is this Firefox even the default browser?

	# In[24]:

	subset.map(lambda p: (p["environment/settings/isDefaultBrowser"], 1)).countByKey()


	# No.

	# #### CPU speed

	# In[25]:

	MHZ_MAX = 5000


	# In[26]:

	mhzes = subset.map(lambda p: p["environment/system/cpu/speedMHz"] if p["environment/system/cpu/speedMHz"] < MHZ_MAX else MHZ_MAX).collect()


	# In[27]:

	ds = pd.Series(mhzes)
	ds.hist(bins=250, figsize=(17, 7))
	plt.ylabel("ping count (log)")
	plt.xlabel("speed in MHz")
	plt.yscale("log")
	plt.show()


	# In[28]:

	pd.Series(mhzes).value_counts()[:10]


	# There seems to be a family gathering of different hardware configurations this client is running on, most on a particular approximately-3.5GHz machine

	# #### Operating System

	# In[29]:

	def major_minor(version_string):
	return version_string.split('.')[0] + '.' + version_string.split('.')[1]


	# In[30]:

	pings_per_os = subset .map(lambda p: (p["environment/system/os/name"] + " " + major_minor(p["environment/system/os/version"]), 1)) .countByKey()


	# In[31]:

	print len(pings_per_os)
	sorted(pings_per_os.items(), key=lambda x: x[1], reverse=True)[:10]


	# All of the pings come from Windows XP.

	# #### Physical Location (geo-ip of submitting host)

	# In[32]:

	pings_per_city = subset .map(lambda p: (p["meta/geoCountry"] + " " + p["meta/geoCity"], 1)) .countByKey()


	# In[33]:

	print len(pings_per_city)
	sorted(pings_per_city.items(), key=lambda x: x[1], reverse=True)[:10]


	# These pings are coming from all over the world, mostly from countries where Firefox user share is already decent. This may just be a map of Browser use across the world's population, which would be consistent with a profile that is inhabiting a set %ge of the browser-using population's computers.

	# #### Document IDs

	# In[34]:

	pings_per_docid = subset .map(lambda p: (p["meta/documentId"], 1)) .countByKey()


	# In[35]:

	print len(pings_per_docid)
	sorted(pings_per_docid.items(), key=lambda x: x[1], reverse=True)[:10]


	# That same half of pings reporting 215s sessions? The same ping. Maybe a ping stored in the profile directory before it was distributed? But then how are we seeing hundreds-of-thousands of copies of seven others, too...

	# ### Conclusion
	#
	# None of this is concrete, but if I were invited to speculate, I'd think there's some non-Mozilla code someplace that has embedded a particular (out-of-date) version of Firefox Developer Edition into themselves, automating it to perform a 2-minute-and-35-second task on Windows XP machines, possibly while masquerading as something completely different (using the addon).
	#
	# This could be legitimate. Firefox contains a robust networking and rendering stack so it might be desireable to embed it within, say, a video game as a fully-featured embedded browser. The user-agent-spoofing addon could very well be used to set a custom user agent to identify the video game's browser, and of course it wouldn't be the user's default browser.
	#
	# However, I can't so easily explain this client's broad geographical presence and Windows XP focus.