chutten/beta47_plugin_block.ipynb Secret

## beta47_plugin_block.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plugin Block Experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a very a brief introduction to Spark and Telemetry in Python. You should have a look at the [tutorial](https://gist.github.com/vitillo/25a20b7c8685c0c82422) in Scala and the associated [talk](http://www.slideshare.net/RobertoAgostinoVitil/spark-meets-telemetry) if you are interested to learn more about Spark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import ujson as json\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.plotly as py\n",
    "import IPython\n",
    "\n",
    "from __future__ import division\n",
    "from montecarlino import grouped_permutation_test\n",
    "\n",
    "from moztelemetry.spark import get_pings, get_one_ping_per_client, get_pings_properties\n",
    "\n",
    "%pylab inline\n",
    "IPython.core.pylabtools.figsize(16, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def chi2_distance(xs, ys, eps = 1e-10, normalize = True):\n",
    "    histA = xs.sum(axis=0)\n",
    "    histB = ys.sum(axis=0)\n",
    "    \n",
    "    if normalize:\n",
    "        histA = histA/histA.sum()\n",
    "        histB = histB/histB.sum()\n",
    "    \n",
    "    d = 0.5 * np.sum([((a - b) ** 2) / (a + b + eps)\n",
    "        for (a, b) in zip(histA, histB)])\n",
    "\n",
    "    return d\n",
    "\n",
    "def median_diff(xs, ys):\n",
    "    return np.median(xs) - np.median(ys)\n",
    "\n",
    "def compare_histogram(histogram, e10s, none10s, branch_one, branch_two):\n",
    "    # Normalize individual histograms\n",
    "    e10s = e10s.map(lambda x: x/x.sum())\n",
    "    none10s = none10s.map(lambda x: x/x.sum())\n",
    "    \n",
    "    pvalue = grouped_permutation_test(chi2_distance, [e10s, none10s], num_samples=100)\n",
    "    \n",
    "    eTotal = e10s.sum()\n",
    "    nTotal = none10s.sum()\n",
    "        \n",
    "    eTotal = 100*eTotal/eTotal.sum()\n",
    "    nTotal = 100*nTotal/nTotal.sum()\n",
    "        \n",
    "    fig = plt.figure()\n",
    "    fig.subplots_adjust(hspace=0.3)\n",
    "        \n",
    "    ax = fig.add_subplot(1, 1, 1)\n",
    "    ax2 = ax.twinx()\n",
    "    width = 0.4\n",
    "    ylim = max(eTotal.max(), nTotal.max())\n",
    "        \n",
    "    eTotal.plot(kind=\"bar\", alpha=0.5, color=\"green\", label=\"e10s\", ax=ax, width=width, position=0, ylim=(0, ylim + 1))\n",
    "    nTotal.plot(kind=\"bar\", alpha=0.5, color=\"blue\", label=\"non e10s\", ax=ax2, width=width, position=1, grid=False, ylim=ax.get_ylim())\n",
    "        \n",
    "    ax.legend(ax.get_legend_handles_labels()[0] + ax2.get_legend_handles_labels()[0],\n",
    "              [\"{} ({} samples)\".format(branch_one, len(e10s)), \"{} ({} samples)\".format(branch_two, len(none10s))],\n",
    "             loc=\"best\")\n",
    "\n",
    "    # If there are more than 100 labels, hide every other one so we can still read them\n",
    "    if len(ax.get_xticklabels()) > 100:\n",
    "        for label in ax.get_xticklabels()[::2]:\n",
    "            label.set_visible(False)\n",
    "    \n",
    "    plt.title(histogram)\n",
    "    plt.xlabel(histogram)\n",
    "    plt.ylabel(\"Frequency %\")\n",
    "    plt.show()\n",
    "        \n",
    "    print \"The probability that the distributions for {} are differing by chance is {:.2f}.\".format(histogram, pvalue)\n",
    "\n",
    "def normalize_uptime_hour(frame):\n",
    "    frame = frame[frame[\"payload/simpleMeasurements/uptime\"] > 0]\n",
    "    frame = 60 * frame.apply(lambda x: x/frame[\"payload/simpleMeasurements/uptime\"]) # Metric per hour\n",
    "    frame.drop('payload/simpleMeasurements/uptime', axis=1, inplace=True)\n",
    "    return frame\n",
    "    \n",
    "def compare_count_histograms(pings, *histograms_names):\n",
    "    properties = histograms_names + (\"payload/simpleMeasurements/uptime\", \"e10s\")\n",
    "\n",
    "    frame = pd.DataFrame(get_pings_properties(pings, properties).collect())\n",
    "\n",
    "    e10s = frame[frame[\"e10s\"] == True]\n",
    "    e10s = normalize_uptime_hour(e10s)\n",
    "    \n",
    "    none10s = frame[frame[\"e10s\"] == False]\n",
    "    none10s = normalize_uptime_hour(none10s)\n",
    "    \n",
    "    for histogram in e10s.columns:\n",
    "        if histogram == \"e10s\" or histogram.endswith(\"_parent\") or histogram.endswith(\"_children\"):\n",
    "            continue\n",
    "            \n",
    "        compare_scalars(histogram + \" per hour\", e10s[histogram].dropna(), none10s[histogram].dropna())\n",
    "\n",
    "        \n",
    "def compare_histograms(pings, branch_one, branch_two, *histogram_names):\n",
    "    frame = pd.DataFrame(get_pings_properties(pings, histogram_names + (\"environment/addons/activeExperiment/branch\",)).collect())\n",
    "    e10s = frame[frame[\"environment/addons/activeExperiment/branch\"] == branch_one]\n",
    "    none10s = frame[frame[\"environment/addons/activeExperiment/branch\"] == branch_two]\n",
    "    \n",
    "    for histogram in none10s.columns:\n",
    "        if histogram == \"environment/addons/activeExperiment/branch\":\n",
    "            continue\n",
    "            \n",
    "        has_one = np.sum(e10s[histogram].notnull()) > 0\n",
    "        has_two = np.sum(none10s[histogram].notnull()) > 0\n",
    "        \n",
    "        if has_one and has_two:\n",
    "            compare_histogram(histogram, e10s[histogram].dropna(), none10s[histogram].dropna(), branch_one, branch_two)\n",
    "\n",
    "def compare_scalars(metric, *groups):\n",
    "    print \"Median difference in {} is {:.2f}, ({:.2f}, {:.2f}).\".format(metric,\n",
    "                                                                        median_diff(*groups), \n",
    "                                                                        np.median(groups[0]),\n",
    "                                                                        np.median(groups[1]))\n",
    "    print \"The probability of this effect being purely by chance is {:.2f}.\". \\\n",
    "        format(grouped_permutation_test(median_diff, groups, num_samples=10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.defaultParallelism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pings = get_pings(sc, app=\"Firefox\", channel=\"beta\", version=\"47.0\", build_id=(\"20160510000000\", \"20160517999999\"), fraction=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def experiment(p):\n",
    "    return p.get(\"environment\", {}).get(\"addons\", {}).get(\"activeExperiment\", {})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "participants = pings.filter(lambda p: experiment(p).get(\"id\", None) == \"plugin-block-beta47@experiments.mozilla.org\" and experiment(p).get(\"branch\", None) is not None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(int,\n",
       "            {u'aggressive': 178872,\n",
       "             u'conservative': 176812,\n",
       "             u'control': 179069})"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "participants.map(lambda p: (experiment(p).get(\"branch\", None), p)).countByKey()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "subset = get_one_ping_per_client(participants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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erlNOOUVz5swJL++5555TixYtVLNmTbVo0UJTpkyRJC1fvlzdu3dX7dq1Vb9+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\ns2ePfve73ykrK0uNGjXStdd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6ibALAACAOY29gYFuohszAAAAAKBzCLsAAAAA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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f78999bf050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/keyedHistograms/BLOCKED_ON_PLUGIN_INSTANCE_DESTROY_MS/Shockwave Flash21.0.0.242 are differing by chance is 0.46.\n"
     ]
    },
    {
     "data": {
      "image/png": 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Unp6u00477ZDyPv74Yznn1KZNGzVu3FgDBw7Uli1b4i770UcfVZMmTbRp0yZ9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\n7rvvIhcBJKlFixb66KOPtH3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gTDKzNmb2uKSvJZ0jqbdzrlXw+vGC5uWeXQAAAABAWfU7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\ndv4f/KT3IYbcTCwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7897fd4f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/keyedHistograms/BLOCKED_ON_PLUGIN_INSTANCE_INIT_MS/Shockwave Flash21.0.0.242 are differing by chance is 0.96.\n"
     ]
    },
    {
     "data": {
      "image/png": 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+/PncfffdHHrooSXX/eMf/5jevXszd+5c3n33Xa688srCvLqOJcCUKVPYdddd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\n4cOH8+yzzzJ16lRuvfVWzj7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eUjoEWAA81JAMrNmVJEmSJLU0mwDTgPZA26qJKaVxEXFn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\nDmUXAAAAAFAcyi4AAAAAoDiUXQAAAABAcSi7AAA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f78994ed050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/keyedHistograms/BLOCKED_ON_PLUGIN_MODULE_INIT_MS/Shockwave Flash21.0.0.242 are differing by chance is 0.92.\n"
     ]
    },
    {
     "data": {
      "image/png": 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cqPnqkCFD1KVLF+Xm5iojI0PTp0/fq3nr+++/r//+97+aM2eO1q1bp7POOkvj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\nddS7d2/l5OTEjCk/P1+/+93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Q0cyS9KKkdyVNd8594JzrJylP0lvxFEDNLgAAAACgojlC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7898fb1050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/keyedHistograms/BLOCKED_ON_PLUGIN_STREAM_INIT_MS/Shockwave Flash21.0.0.242 are differing by chance is 0.47.\n"
     ]
    }
   ],
   "source": [
    "compare_histograms(subset,\n",
    "                   \"aggressive\",\n",
    "                   \"control\",\n",
    "                   \"payload/keyedHistograms/BLOCKED_ON_PLUGIN_INSTANCE_DESTROY_MS/Shockwave Flash21.0.0.242\",\n",
    "                   \"payload/keyedHistograms/BLOCKED_ON_PLUGIN_INSTANCE_INIT_MS/Shockwave Flash21.0.0.242\",\n",
    "                   \"payload/keyedHistograms/BLOCKED_ON_PLUGIN_MODULE_INIT_MS/Shockwave Flash21.0.0.242\",\n",
    "                   \"payload/keyedHistograms/BLOCKED_ON_PLUGIN_STREAM_INIT_MS/Shockwave Flash21.0.0.242\"\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ktW/fXpmZZX/BMX78eN11111q2rSpdu/erYsvvljdunXTsGHDdPjwYV1xxRV68MEHdf31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\nqaCgQJ06dVJqaqo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757qL6rYYAABQ55jGDAAAAADwHcIuAAAAAMB3CLsAAAAA\nAN8h7AIAAAAAfIewCwAAAADwHcIuAAAAAMB3CLsAAAAAAN8h7AIAAAAAfIewCwAAAADwHcIuAAAA\nAMB3CLsAAAAAAN8h7AIAAAAAfIewCwAAAADwHcIuAAAAAMB3CLsAAAAAAN8h7AIAAAAAfIewCwAA\nAADwnfrHuwAAAIDaNu0P05S5J7PMffFt4jVjyow6rggAUNsIuwAAwPcy92QqcVhimfvSl6TXaS0A\n8P/bO/NwOaoy/3/eBJBNCHtECGEZRUf2RRxRAoIgjLIv8kOIOC6DoiCjojKyKosLIIqjsgkIKLLL\njhJEGYYAIQkM+xZBUBhBdkHy/v54T3Mrnb6dOt3n3u578/08Tz23uqrre0+dqq4671m+RwwPCnaF\nEEKIHqNWRyGEEKI8CnaFEEKIHqNWRyGEEKI8XQW7ZvYI8DdgNvCau29cIlFCCCGEEEIIIUY2vY4X\nu23ZnQ1McvdnSiRGCCGEEKJTvvGNE5g169mW+6bd/eigredCCCGGjJ7Gi90Gu4amLxJCCCFEHzBr\n1rNMnHhYy32/v2XL4U2MEEII6HG82O0/duBaM5tqZp8skSAhhBBCCCGEEKOCnsaL3bbsvtfdnzCz\n5YiTuNvdf9/8pcMOO+yN9SeffJKJE7v8r0IIIYSYL2jnVA3D71Yt52whhIApU6YwZcqUOl+tFS8O\nFV0Fu+7+RPr7lJldBGwMtA12J08+rHm3EEIIIURL2jlVw9C4Vbcf+3sjOx672bClRQgh+pFJkyYx\nadKkNz4ffvjhLb9XN14cKjoOds1sUWCMu79gZosBHwRan6UQQgghxAhBY3+FEKJ7+iFe7KZldwXg\nIjPzpPNzd7+mTLKEEEKI0YWcgoUQQsxn9Dxe7DjYdfeHgXULpkUIIYQYtai1UAwFGkMshOhX+iFe\n7NagSgghhBBC9Ih2Y5o1hlgIMb+jOXKFEEIIIYQQQow6FOwKIYQQQgghhBh1qBuzEEIIIXqKzLva\no/wRQojOULArhBBCiJ4i8672KH+EEKIz1I1ZCCGEEEIIIcSoQ8GuEEIIIYQQQohRh7oxCyGEEB2i\nOU6FEEKI/kXBrhBCCNEhmuNUCCGE6F8U7AohhBBiDtRiLYQQYjSgYFcIIYQQc6AWayGEEKMBBbtC\nCCFEG0brHKej9byEEEKIBgp2hRBCiDaM1jlOR+t5CSGEEA0U7AohhBCjBI21FUIIIQZQsCuEEEKM\nEjTWVgghhBhgTK8TIIQQQgghhBBClEbBrhBCCCGEEEKIUYeCXSGEEEIIIYQQow6N2RVCCCFGEJoy\nSAghhKiHgl0hhBBiBKEpg4QQQoh6KNgVQgghhJjP0bRVQojRiIJdIYQQQoj5HE1bJYQYjSjYFUII\nIYQYZtSSKoQQQ4+CXSGEEEKIIaC9mdiN7HjsZi33DVVLqszNhBDzGwp229Cu1hVU8yqEEFXaFaQn\nTBjHEUccMMwpEqK39JuZWL+lRwghhhoFu21oN34FNIZFCCGqtCtIP/JI6+1CCCGEEEOFgl0hhBBD\nzrQZ05h8wOSW+9RLRgghhBBDgYJdIYQQQ86Lr7zIxB3Wa7lPvWSEEEIIMRTM98GuzBqEEEKIoUGO\nw0IIIXrJfB/syqxBCCGE6Ix2FcbQG8dhIYQQosF8H+wKIYQQojPaVRiDKo2FEEL0FgW7Qggh+op2\nrYUPzbqD1dYe13KfusUKIYQQooqCXSGEEH3FvIaXbPGNdVvuU7dYIYQQQlRRsCuEEKOUEuZA7TRy\ndIQQ8wcyJRNC9BMKdoUQYoRSyhyovSv94BrNOkKI+YNOnxl6XgghhhsFu0IIMUIpZQ4kV3ohRA56\nZgghRgpjep0AIYQQQgghhBCiNGrZFUIIIYQQw0677tATJozjiCMOGOYUCSFGGwp2hRCiz5DBixBi\nfqBdd+iLLt2eWc/d0XKfnoNCiLoo2BVCjFhGalBYylhKCCFGKy++8iITd1iv5T49B4UQdVGwK4QY\nscz68ywm7jCx5b5+LgyVMpYSQgghhBCDo2BXCNHXtJ/i4tFBg90cnYdm3cFqa49ruS+nhXiktjQL\nIYQQQoxGFOwKMUoYSUYfOUFhqSku5qWzxTfWbbkvp4V4pLY0CyGEEEKMRroKds1sG+AEYgqjU939\n2G4T9PKLL3crwZQpU5g0aVLXOiXSAuXSI50yOvMaL3n79Cmsv9nElvuqgVg7nXYazTrtyMmbdsHc\nzTd/ppbGvNj7k3szZrHWM5bltFzectstbPK5TVruywkKS/1Gc3TaXfcb/+eu2i3NpdIz1Dr9lBbp\nzJ86/ZQW6fSfTql3ejtGanlHOqNHp5/SkqMzFLFiLh0Hu2Y2BvgB8AHgT8BUM7vE3e/pJkEvv9RH\nwW6BtMDIvUFHms6hRx7Kqhev2nJf9YU2r/GSl16+PjvVaJ1rp9NOo1mnHaXyZtr0aUw+YHLLfc0v\n+2kzgesAACAASURBVHYFh2uuncpnfr57y33N59RO547/fnTQYDeHUr/RHJ321/3SYU/PUOv0U1qk\nM3/q9FNapNN/OqXe6e1oV74ABc3SGXqdfkpLXZ2hihVz6aZld2Pgfnd/FMDMzgO2B4b1BMTQ0i5g\nuf6GS3jk2Uda7huq8Ynt0nPvPX9h8//cvOW+fu5C2mkeQ/18fu0fr9XuXtuu4PDqq/WDuVI6Qggh\nxFDTafkC6pcxrr/xeh45YPDvyttBjDL6IlbsJth9K/DHyufHiJMaFoYjQBiO9OS0qpUKLts9bHPG\nSz5/+aW1A6hS5zUaA6hO8xj6O4gXQgghRgqlyhfDETSXIqc8KEQH9DRWbGDu3tmBZjsDW7v7p9Ln\nvYCN3f3zTd/r7B8IIYQQQgghhBgRuLs11uvGikNNNy27jwMTKp9XStvmoHrSQgghhBBCCCFGPbVi\nxaGmta1qPaYCa5jZKma2ELAHMDL7kQohhBBCCCGEKEVfxIodt+y6++tm9jngGgbspO8uljIhhBBC\nCCGEECOOfokVOx6zK4QQQgghhBBC9CvddGMWQgghhBBCCCH6km4MqrrGzNYk5lt6a9r0OHCpukML\nIYQQQgghhOiGnrXsmtlXgPMAA25JiwHnmtnBGToLmVnV5npzMzvIzD7UQZrGmNmYiu76ZrZ0psba\nuf93iNNTLH9aaO/XrcZowcy2NrNPmNnEpu37Zmgs2/R5LzP7vpl9qnoNa+h8z8zeW/f7bXTMzHYz\ns13T+gdSevZr3JcZWpub2Q/M7BIzu9DMjjGzNTpIU9f53ELzt50eO5oxs6XMbIkCOkvnPreE6BYz\nm2BmC6d1M7OPm9lJZvbvZpZV0W9mi5vZLmZ2oJl93sy2yX0GttBc1cx2SpX+dY/5SOOcSmBmS5rZ\n7mb2xbTsbmbjCuh+K/P7xa7VaMTMFjWzL5vZl8xsYTObbGaXmtlxZrZ4hs7alfUFzeyQpPMtM1u0\nyzTel/n91czsNDM7Kv2+fmpmd5rZ+c3v9y7T9ZPh1Ch1rURZetmN+RPARu5+jLufnZZjiMmGP5Gh\nMxUYB2BmXwK+CSwCfNHMjq4rYmY7AE8Aj5vZ9sCNwLeBGWb24Yz0TDOz+83sSDN7Z8ZxQ5WeUvnz\nxablIOCIxucMnR0bBV8zW87MzjSzmWb2CzNbKUPHrE8CsfRi/zqwFvAbM9u/svtzGUm5pqJ5CPAx\n4DZgK+B7GTofA040s0fTA3a9jGOr/BDYLemdBXyGuJ/eDxxfVyTdZ3sDNwOvAQ+m5Xwz2zVDp+t8\nNrMZTctM4L2Nz3XTMoj2Ema2gZkt1Y1OKTpJj5mtmH6TfwOeBu40s1lmdpiZLZihM8HMzjOzp4D/\nAW4xs7+kbRMzdP5qZqek33fH09iZ2crpf99oZl+rnouZXZyhs6aZXWlml5vZ6mZ2hpk9a2a3mNk7\nMnT2rayvZGa/STo3mdnbhjMtFb0lzGz1FtuzKnBL6bQ4PiuAAq5goIxzDLAdcS9uBOQUXncDfgts\nQzxnNiKeiXeY2VoZOhdX1rdPmh8GLjGzyTVlfgE8ZmZnmdm2Zja27v9vkZ69gduBScCiadkcuC3t\nq6vz/ablJGC/xueaMkWuVUpP3wSGJTQSZwArAKsClwMbEuVBA36UqdPgGGAN4LtEmfC/6oqY2fNm\n9lxanjez54HVG9sz0jIVeIEoG9wDfAi4CjitblpSepYeZFkG2Ha4NCrnVeJaDZbOj3SrkXS6qoA2\nszXMbGfrIs4ZVty9JwtxY6/SYvsqwL0ZOndW1m8FFknrCwAzMnSmAeOJG/Q54O2V9NyaqfMuIqh8\nAJgOHAxMzMyfUukplT/PEy/abwCHpuWZxnqGzv9W1n8BHEjMuzUZuDZD52TgV4SF+dnA+UQB5Dzg\nxAydo4HTgb2S3reBT6b837WmxkxggbQ+jnhxH9+4jjnXvLJ+O7BYWl8QmJmrA7wN+E/grvR7OxR4\nW4bOzMr//z9goQ7vnZmV9QWAP6T1par353Dkc+V+WTP9liYCf0zrq9RNS9I6G1g2rW8NzAKuAx6t\ne++kY1dO9+2NwNeABSv7Lh7O9BAF8ElpfSeiUmMx4CjgJxlp+W9gd2BsZdtYYtqBmzN07iUCjD8Q\nw1xOBDbJuU5J51qismZd4CTgJmCZnHsnffd3RIDy0ZSvexCFmA8Dv8nQub2y/kvgU0Rhf8e6OqXS\nkrR2A/4E3EE8LzZqldZh1Pl+03IS8Gzjc02N6rvmNmBM5fP0jLTMABZN68sCV6f1tYGbMnSqz/eb\ngFUrmrXSQ7yXliLeUb8B/kwEKZt18Ju4FxjXYvtSwH0ZOn8knj17A/uk5anG+nBeq/T9XxIB3Mkp\nj34AvI94t5+VoVP9jX6XCGI2I56JZw6XRjr2jvTXgCcZMJc1Msu5VU3Su6YDne8DZwIrVLY9nHmd\nqmmZNdi+mlqvAw8BD1eWxudXh0uj5LVKx+zUtOycNHcCdsrQOaSy/k7gvnRejwDvrqlxPQPli48l\njVOIctn+OefVi6V3/zhqSR8AriRq7n5C1Og8AGyToXMT8K60fhWwVFpfmLyCdPWHd2fTvpyX9O1N\nnzcmWuUeo/MXYzfpKZU/E4iA8lgGXvwPdXDd762s39a0744Mnb4JxIC7mz6PBU5N+XVXRlruAdYD\nNqApuM3Mm7nuD6JgdjTwQIf34FVdpGc6sHTlPrq5si8nf0rl845EoPCR9Dn7Pm5x79xEqtAio/Ca\nvl8qEOs6Pc3fq/5GgXsy0nJ/J/tafLdaWJwAfJmoCHoI+FaGzh1Nn/cigrHVW/1e2uhUfxMPDJbW\nzPNqzvO6FTdF0tLIH+AtaX3j9CzasYN7sJROiQDqamCLtH4BqTILWCbz9zmTgQLrIrR5N2dc81ub\n9tW95s3li/HA54nKpT9mXvP7gCVbbF8y8zf6ZuAE4BxgxbQt65la6lo17sH0t+eBYQmN6jml9dOa\n9uXcyw8R77+daWpU6iCfNyAqRz9PVNTlXvPbiAr5jYheRBum7Wvk5E065n5gwiD7av0uSmiUvFbp\n+68BvyZauk9Py/Pp72kZOtVnz+XAh9L6xtSMS5iz4WwqA2WURXOvVy+Wno2FcPerUnetjZnToGqq\nu7+eIfUZ4OdmNh34C3Crmf2O6OqYO25kjLvPBqpdzMYCC+XIVD+4+y1EF76DiO6fw52eIvnj7rOA\nXVP3q2vNrHY31iammNkRROA1xcx2dPeLzGxz4G8ZOv9I6XrNzKa6+6vp8z/MbHaGzmwzW9rd/wqs\nSARQuPszGd0mHzSzzdz9hnTs68AnzOwo4qVSlycY6K78tJm9xd2fSN1o/pGhM1e63X0G0ULx1Qyd\nJ81scXd/wd23eUPcbDzwaobOt4ju/fcBbwf+PeksRwTCdSmSz+l+uwY40sw+Qd7vqcoYM1vC3Z8D\nZhMtqbj705Y3zmw5d290IdvfzPYCfpe6K/kwp+ep9P+vJ2qPH4EYNkDesJfbzOxk4GdE4ALRgr0P\n0TpVlzfu5fQMOg44zmKc4+4ZOgua2cLu/krSOtvMniQK2Ytl6FS7jTYPLci5j1ZKXTwNWNbMFnT3\n1xppHea0QLTAPwHxzkrP41+b2crk3YOldN4JHElUiv+Hu//JzA51959laPwbcKaZHUa8W+4wszuI\nXiG1h94QPUiuSu/NbYjKtUY3wJyu9eukLp4GvKnyfF+IOa9lO5rLF0+SWrvNbJWMtED0Prs9PQsb\nv9EJxLCZI+uKuPvzwAFmtgFR1ric/CFypa5VNV1uZld4KpGnzzn34JJmtiNxLos0fp+ZOiU0IMpt\njXdxtTy4OhH81OUGoNEN9iYzW8Hd/5ze6U9n6ODut5nZlkTPmxuIBpQcvgxcRryrdgC+ambrAEsQ\nPRdyOIFooJjVYt9xw6gB5a4VwL8Q3c2nuvuPks4kd/94pk6Vt7r7lfDGM3qRmse9ZmZvdffHia7n\nL6btf6f+86t39DraLrEQGf0h4AvAQUQhaK7uOfPQ2AhYuMX2icBeGTp7FjqnIukplT9NeosRXYJ+\n18GxCwKHEQ+UWcSD7nmiVrhlrdogOlcCi7fYPh64JUNnd6IL4LUpPdul7csB59TUWITUPbzFvrcW\nuBfGklrTa35/rnwpuaTrv3zmMUsTY1e6ue+K5zOwDvCZDo/djaid3pfo8XABEcydAXw3Q+eu5t86\nsCXRy+WJ4UwPUdj9JXAn0bLWaKVbBtg5Iy0LEZUaVxEtYzPT+n7AmzJ0vlfonj2QFl09iZ4UOcMn\nPj3Ic2cN4IQMnX2alkaPm/HUbLEulZZ0zE3A6k3b3kx0A/37cOtUjt2AqHj5D+CRDq/9O4hZH3YG\n3k2li2yGxrYpDVtVto3JuZfbaI8D3lPzu5O6/X9NeksR3d8PSssejXuxQz0DPguc3cNrdcogv4vV\ngd9n6JzetKyQto+n/lCDrjXq5HnJe6LDNLwF2LaAzrJUhr6MtqWTa5WeM19Iz8GN6axH5bPEEK7L\niEqNRSv76vZgnESUVY4ghgbcRAyNu5aokOx5/rZbGt07xHyEmS3v7n/pg3QsSYzB/L+CmosRY11r\nn1+qoV+N6Ar4bIE0LE50z3koV8/MJgDPufuzFkY+GxLdR+/sUXqMuXtf3OIZD47UcvFa45jU2rM+\nMU7rypz0tPkfa7r7PTW/2/U5VbTWIGqh30Z0g3+MGGd7dYbGgUQ3oxuatq8HHOfuWw1nesT8R2pR\necnd72/aviCwm7v/fDh1mo41opLkPe6+V+7xQ4GZre/ut/c6Hf2IhYP7PxHvm2d6nZ4qZmadPOd7\nTerlc42nnild6EwA/uLur6Tf1WTSuxj4qbvX7kGWWoNx9ydTL633EV2j78rQWJzoLbEyMWb2PuI8\nc3rnDab9LXf/WuYxS6b0VMsGV3dQbiqi06T5VmKs94buvlrmsZs1bbrN3V8wsxWAXdz9hzV1lgT2\nZM7yxSV1y169ZMQHu2a2jbtfldaXJLpzbUS0TBzo7n+uqbME0cVzJeBKdz+nsu9kd681zU5Kw1eJ\nbhnLE123/gJcAhxTKJi60t1rTR1krR3XbidaNMyj++6wpadyTLXbXmPbsu6e1ZXGzDak8qDs9EfX\nTXqq94eZbUq0Uj9ItLB82t2vqJmGg4nWmr8D3yFaEv4AbAKc6u61HJkLpueDhMnH/cTDGuL3sQaw\nn7tfM9ixTTrTiRaJZywcwXckugZuRjx0a0811uZ/zHL3CTW+V+ScRiupy90N7v7XVID5LvGs+F/g\nIHd/rAvt37r7Fh0ctzXxPK0WHC5pPPdrahiwK/E8/hWwBdGCdA/wX3ULV2b2PeACd/9D/TMYVKur\n82p+PqXu5xsT776f9rpQb+EC/rpHt/oSelnvh9TV/Xii99DnCbO+HYgC9T7ufndNnfWbNxHv8w8T\n79BaQW/qyv1t4npfCXy78c4xs4vdfYc6Om30Z7p7jjt0qfw5GzjAY7jE1sBPk8Y/ES0+59fQKJo3\nqTy3nLs/2LR9bY8hPdmY2aqkZ2FOOaNQUPgy0W30SuBcInDKGe7X0LkT2NjdXzKzY4nW7ouJ5yFe\n6XY7D51PE8arRvQimkw8dzYlKmhPraGxG1HGmUG4gN9EtGKuRfRerH2dbG7XbyNMlM4EcPfP19DY\nm2ipvIY5ywZbAYe7+5k101JERxRmqJuOh3phzoHXpxCuoasQ3dZynEwvIPrG70A0919A6qJEnvHI\n1cBXgPGVbePTtmsydNYfZNmAvK6Ns5nTXe5hYtD7w2R0hyiYns2J2qCniYfBxFbXsobOZoS79HWE\nK/SvicBwCrDycKan6R68Hlg/ra9GnnP2XURX3WWIrt3Lpe2L0bkRSjfpuZsWLuKEQ/jdGTqlHMGb\nHVqrTq3PDec5pWOM6Dq8a1r/QErPfmR2vyPckz/RnDZg3w50fkQ8wy5N6zmGf6Xc0mc0LTOJSpwZ\nmdf8BKJiZA+iILVpWr+CPNf1Uu7tT6V7+FFiHNd6Oden5Hk1/c4PId49+6RzOz4zPWsSBenLiQLw\nGUTXt1uAd2TorEgUMP9GVEA2hqscRsVlvIbOh4h31O+JAOMuolv/Y8AHamqUcs6eTRTEr68sL6e/\nv83Q6dqIjrndWasurU9lXvNS+VPCGK+ISV/6filH8Isr69un+/F0wsV6ck2NTzPgevvvxHRKpyaN\nT2SkpYgLN+UcymcSxkTLEOM3x6ftS1HTvJJCLufpmBKGdqXcyYvopGMWSPfQVQy8S69Mv5Wc5+nn\nGHBSXiP99p9N9+O7ctI0iH7tmRp6tfQ8AQUyufrCb3bczHGMbT7260TwtEzmA3LQaZPa7Wvx3dcJ\np7vrWywvZ+gclH4oa1W2PdxBPpdKz1Tgn9P6LkQL2ybpc9Y0PQwEg6sCF6X1rcirVOg6PU33YLNb\nZs69MyP9HUv0Bqi+iDoNdrtJz/2kqX6ati9EnqtzKUfw54kpWvZpsTw9nOeUjik5/dXviADoQSo2\n/pnXq0QAVcotvcgUTwxSOCAK5TmOsaXc20tN69X1eVFoqrJ0TKnAp9TUVXcQ4zffk65X45n8jrq/\nCco5Z+9MGPB8qLLt4Zz8bZxT0+dsR3CiovoM5h4LejrwfGZ6SuXPXcASaf33zPnequWSXyJvmu6d\nEo7gJaaK6joobHU96NCFm3IO5SXc5Iu4nKfvl3AEL+VOXkQnHXMuUWG9CVHpvFJa/xHwiwyduyrr\nl1d+D5NIM5DU0Fh6kGUZ4LGc8+rF0jM35oIsb2ZfJF7MSzaNychxBHyTDbgf4+7fNLPHiUJA7YnI\ngUfN7MvAzzx1oU794icz4HhYh7uJLqf3N+8ws9o67v5dM/sFcHw67lDyXDGLpocoZN6V0vYrM7sb\nuNDMvpKZrrHu/lRan0UUonH3a83shGFOz5pmNoO4Byea2VIeXXbHkOeKeruZnUMUDn8D/MzMriK6\nGP1vhk6p9JwGTDWz85jTUXcPona6LqUc06cSL8GbmndYuHjWodQ5AbzP3ddK4xGfJApYr5rZuUTw\nUZd/JVoI/5HO4xwzW83dDyTP7XVbd39b88b0+7+PMLmYF0Xc0t39I6lL9E+A77j7pWb2mrs/Wlcj\n8YqZbeTuU5u2bwTkjF8r5d7u6bj7CMfaI81sbSJIvIKoNa9DifNaxGJc9xiilv/FlLbXzCy3i+Ob\n3f0yADM70t3PS9svM7PDM3SWcfcpKR0XmtnXU7oOMbOcISazPXWjNbOX3P3mpHl3eo7VoYhbtbtf\nYGZXE9d6X6ICuZN3aAlH8BnE72kuDwcLZ9wcSrl5Hw5cb2Y/JBoIzjezS4leU3WHGpRyS4dyjuDV\n7y7k7g8nzacznhmvuftLwEtm9qCHezbpfZyTllIu3KVcr70y9Gu7NxJptjD1y92lXM7xMo7gRdzJ\nC+oAbNDinf4YcLPF7BZ1qcZ6y7v7RQDuPsXM3lxT4ymiIrR6bTx9Xj4jLT1hNAS7PyVqdSBqPJcl\nps8YT9Tw1eUyIqi4rrHB3c9ID9yTMnR2J8Yy3JCCXIiC8GVE95q6HMbgP9b9M3TwGGe3q4XJwbVE\nTWMupdLzmpmNrzz07zKzDxDdkFfP0LnVzE4lWhI+QnRfxswWJc8GvUR63tH0uWHJvjTwjYy0/Btz\njivcmDADuBeoZSAwSHpe6CQ97n60mV1MdOF6T9r8OPD/3L128O3uM9K4tw8SLWLTiQf2gZ43hn0X\nBgkE3H3Vmmk52swuIe6Zjs8pUSqAWsCTMYiHMdmHgZ+Y2fnkFTpLBFCfI3q13Js+H2hmLxLPr49l\npAUvM8XTZOBH6YXcGC+8MlFQm5yhU2oarVLTek2m+/MqNVUZlAt8Sk1d9WwaF7gE8IyFidsvCZfy\nF9oeOcAPK9f85MZGCxO369ocNxfu/gLxW1iPmE6rbgGxyimEw/AbRnTufp2Z7Ur9qU0OAAYbA71j\nZnqK5I+7/9LMbmdOY7xNgHO9vjFeibxp8LyZre5pvG76TUwixqb+c4ZOiamiSgSFEMNJWv+DjApE\nd/8jsLmZvYO4VmcQz5+pnmcK9ca95nP6OCxDVAbVSctXzGxbYpqxI9z92rTrWWKIXDYe0yFtQQwl\n+n3msT9LlTRbM+CjMAX4qmcYrZXSSfw1/QYuaFyfVNm3KzF8ry6/MrMzCCfli8zsAOAiIuZpNdVS\nKx4ihpDM9X3La/DqCSPeoAreMFp4K/A/6aXU2P6GeVVNnY2JKdCmmtk7idqme7ymoU8b3bPcPaug\n2EJjU5L5iGeY6JjZu4mxiM+lQPAw4kFyGzHFRa3WGjN7ExHI/ym9hPYk5gC7m+ie9lpbgQGdLYmx\nRdObto8DPuvu36ypsyDxcn0nETyd5u6vW8wZtnzdF0Cp9Ij5DzO7Eti1+sxJ28cDl7r7xjV1fk0Y\nsjS7MR8FfM3daxWKUoXCj4iCeHMA9Vl3v62OTkWvmFu6hUvve3xgPuHc48dTMXJqVE4VSFeWe3sj\nOCjxv5Ne8fNKhaGFU4tS3WM+Dfy8xb28BvA5dz+gps4EwlzvnURl85cqAfgkd7+gps7KxDjk2UTL\n4UeJMe2PEqZHtcyThoIUuL/ZCxlvibKkZ82L7v5A0/aOHcGbdMYR49j/u8Z3JxCeJs3ml29NGlmV\nLunYosZv3ZJaYvECZqelsD52BM/BYkaOY4mg9BlSD1aiMvHgRm+DmlqTiXHjqwNvIlqdLwaOrRMH\nmNlniam7prfYt7+75zQKDjsjPtg1s/2J1oi7CXODL7j7JWnf7e5eq4bIzA4lTDEWIFo/303cUFsR\nA+frBmGXtti8BdECibt/pMX+Vjq3NArLZvZJYu66i4jWscvc/ZiaOncB66TWpp8QrY4XEGY667j7\nTjV1fk7kzaJEzdviwIVJx9x9nzo6IwEzWyangG+FHLhtTmfxcYQTbs+cxefxP7IduLvVsQLO6015\nc4W7n1vZVypvcgOoRQDc/eUW+xqTuOf8/44DKCs4TVQKCrqe4slaT8d1t2e4mSadMQDuPjud57uI\nuVuzCmklzqtNPt9Vt4K25LUarVi4A69EjDt+pLJ9X3c/raZGKSfvIXHPts5dzos5r6d7b2fmnELm\nlOaAs83xxVzORyNmtiJhoLo9UfZqvBNOA75Zt7FhHv+jtpt3eiYfR5T/niWCsCWIcu7B1d9aG40i\nbuBJq28cwc3sr0TZ+FzCwK5IkJUqDClR+Txf4n0wcLibhRjkvnhan0g4ZX4hfc4xI5hJdE1ZlOgm\n1DBcWIQ8A5PbCWOWSYRj8CSiu9lmZDjnMedg/anM6cxb23yEisMsc5sc5BgkNMyTFiCcAMemz5aZ\nP0sSD+17gL8S5iN3p21zOdi10VmCGFd4FrBn076TM3SOYcClbkOiq8YDRAtCretFOQfufnMWL+XA\nXUqn6/wplTcVvTEkQxaiu+f6wNId6BhRwdZwV3035E9A30J3v8zvT2fAQOxLhDHLIUQF4NEZOh9M\nv6Mr07U6hRi/9wDwwQydgwk303uIbv73EGOr7wK+mKGzA/HceoIoMP4PMS7+MeDDPTivrvN5HhrH\ndHvvVP7PN3qhQ5cO5ZQzfitlRNe1ezaFXM6TVinn9aMJg6y9Uj59m+h1NY3o+VJHo4jLeY3/k1N2\n+mv6fTcq9Dv5fyun++RG4GtUHHTJe6eXMn4r4uZNGGPtTioHpm1jCe+Lm2tqFDHFa76u9NgRnBgC\n9Dli/PrjwIkkg70O76HFiSFcBxKVAtuQP9vDBKK3DymPP57O8d9pYdjZRmcJYPUW29fu9PyGa+l5\nAro+gSa3v3RjXEW0+uQEc9NarafPOTpj0k15LbBu2pblCpeOmU449s3lBp35wzsf+HhaP52YkBpi\nvMbUDJ07icL8UoQr7tJp+8LkTUNTKjAsFdBVH5LXk6YpSPlTa5oeyjlw95uzeCkH7lI6XedPqbxJ\nx/ZbAPXFFsvTjfWaGqWmiSo1bVWp6bimpefMqkRl5tvT9lXq/s4Ln1fX+VzqWtX4P7OGW4cwrusq\nUCUCwQXS+jjCEOf4xv2QkZaiTt6Nc6AD92wKuZwnrVLO69V36AIkd1eirFDrN0ohl/OkUSqg6zpo\noVwA1ex4fFtl/Z4MnSJu3rRxFW63r9U1T+sdu4Gn7/eNIzhzllMmAF9Ov/eHiKGDOee1GzH92ynE\nc/As4OdE5dZaGTp3MjDN07FEpdReRM+A0zLS0vWUXr1aRoNB1Z/NbF13vwPCSMLM/pW4iLUnWAde\nNbNFPcY5bdDYmLpL1h6479Gl6XgLc5njzezPdGYEtiQxrtYIk4OGOcLi5DnV/RtwopkdQhR8/zsN\nJv9j2leXU4mXz1giQDjfzB4ijCjOa3dgExPd/djqBo9ulsdaOF3WZXV33zmtX2xmXwd+a2HClcMC\nZtYwCFrEk7mPu9+XxinX4VEr48Ddb87ipRy4S+mUyJ9SeQNREFuHCMamEw//ey3cMS8gTJ3qcCKw\npTd1/TKzVYkCerPh2GAcnr5/FwPPiLHkmek8Z2bv8nB7fZqozHqZeIbl3IMLMDBuuMrjROG+Lq+7\n+8tm9mpKx/8BuPuL0bu0Puk5g5nNcvd707ZHrb67L5Q7rxL5XOpaYWHE03IXcX8Pqw7RutOtQ3kp\n47dSRnRdu2d7OZdzKOS8Dsw2s6U9hgOsSDJv8nAcrnutPB3Trcs5RCv1zxuaTSycofOiu/8A+EHq\ntrsHcHIaYnSeu3+thsZyPuBTsH/quv67VE5plb7BKGX8VsrN+zYzO5kwa6vOarAPUbFYh1KmeNBf\njuBv3PMeZk7HAcelbtu7Z+hA9ADZxN1fMrNlCV+FrdNv48eEb04dxviAh8OWRFllNnC2xSwZdfga\n4Q79hIW/0Vlm9lUPZ+e8l3EPGA3B7t40OU+mF9zeZvbjDJ33u/vf0/HVF9iCxA84Cx9wQN6Owd0T\n2x0/cZBds8lwXfQYeD45jVVclVRY85rjPys6x1tMYYK7/8nMziR+ND9191sypEoFhqWClpOB5LDQ\nyQAAE5tJREFUK8zsGMIG/0RivMUW1HfzbnbgdqLF71LyHLj7zVn8MMo4cJfSKZE/pfKmcWw/BVD/\nTIy7Www4PL0g93H3nKljSk0T1WqKpwnEbyVniqdS03FReV7sW9k2lpE7HVepawUx7m6jVu+FzAqp\nUjolAtUHzWwzT8Zv7v468AkL47ed2x86B6WcvIu4Z3sZl3Mo57z+LWCaxVQobye6RmIxDrhuQbqU\nyzmUC+hKBC2lAqh9CeO3g4l33efS9qXJy59Sbt57E0MMDmdO74JLqf8cLOmW3k+O4NcPksZ7iPzK\nwYgKTAi/neWT1oxUpq/LH81sC3f/LVFRsjJRFl8mQ6PUlF49YcQbVImRhYWT4MFEt8/G3FyNwPAY\nr+mcZ2bHEd2er2vavg1wkrv/U0aaJhEv6MZDsuFSd1qjwFVDY01ivNPN3p0jeL87i5/p7nt3qdGN\ns/g97v43C2fxgxkwVKnlLG6FXMWT1jSipnO2mW3cqPRJAdR0d39XTZ2vEpUirQKoX7r70XXTlPS2\nJ7pOHQ8c5+6rZR4/loFpohqB+NWeN00UFtNbbE9TYcgzpngyswVoPR3XLOCHjdaxGjobEd0tX2na\nPhHY1N3PzkjTO4mpqzo+r6TTdT4XvFZHEecwV8WlmR3r7l8ZZp2uHcqtsPFbC40sI7o2OmOJITi1\n3bMrx3blcl7R6cp53cKRdzWiO2rWvZeOL+ZybmbvAx711lOkbOjut9bU+Z6758w920rjQKKLZ/N9\nvB7xbN6qG30xujGzY4ku8I25iK9092+l39uN7l5rKq0UkJ5JtKb/DdiUqDQZRxh4/aaGxk3AxzxN\n6ZW2vZkoK2/q7nV7QvYEBbuibzCzj7v76SNNx8w+T7hld+sI3u/O4kZ0C+oXZ/GXiACotrO4tXcV\nx90n10lL0uq7AKqitxjRov5ud39/JxqlsUyXczH/UipQtdZO3ve0avWrodWYL7W6bQ6H5RoaXTl5\n2xA7cJvZftVWtozjSjmvd53HoxEr5FadrlPXzuJJa2vCt6J6zS+pWynffF2tC3fyUlrWpat4aZ2k\n1ZiLeLqnuYgteo4t6Kk3aoZWY37lRsVo7fmVU8XaS940HM0KTek15HgfDBzWosW9N0YoJXQo6wje\nT87i0xhlzuIUchUfKQvJDCXj+6Xc0qsu5xsQ5hz3k+Fyno4t4ro+j/9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1kSN4qbQgZ/F5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\nhBBCCDHq+P9ba0aWSMt43QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7888c22310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/FLASH_PLUGIN_AREA are differing by chance is 0.00.\n"
     ]
    },
    {
     "data": {
      "image/png": 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/+OKLtWHDBhUUFKh9+/aaNGmSrrjiiuD+uXPn6u6779ZXX32lyy67TDNmzFCT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\n9Nw4vDY/8omkAcEqrd2tbS31F+IuxtbUTIxO3Ml8pA05N1J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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7894a291d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/FLASH_PLUGIN_HEIGHT are differing by chance is 0.00.\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAOA7LFCFw+JxNgAAAACORYRdHBaPswEAAABwLGIaMwAA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\nIiIiXUeNXREREREREek6/wfwPA2jFV+SWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f78895a6610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/FLASH_PLUGIN_WIDTH are differing by chance is 0.00.\n"
     ]
    }
   ],
   "source": [
    "compare_histograms(subset,\n",
    "                   \"aggressive\",\n",
    "                   \"control\",\n",
    "                   \"payload/histograms/FLASH_PLUGIN_AREA\",\n",
    "                   \"payload/histograms/FLASH_PLUGIN_HEIGHT\",\n",
    "                   \"payload/histograms/FLASH_PLUGIN_WIDTH\"\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/e/ZsJkyYwOOPP95gmYb+38pv3/D03sRcxixJkiQ1EytXruSBBx5g7dq1LFq0\niLKysppH5gA888wzvP7666SU+MMf/sA999zDUUcdtQVHvPm0adOGF1980aC7hQwaNGi9QbclMuxK\nkiRJzURKicmTJ1NaWspee+3FwIEDaz1r9c0332TEiBF06NCB8847j6uuuoohQ4ZswRHrk7Ixy54/\n7bxmV5IkSWom2rZtW+smTXUdccQRHHHEEZ/giNRcFF6breI4sytJkiRJyhzDriRJkiQpcwy7kiRJ\nkqTMMexKkiRJkjLHsCtJkiRJyhzDriRJkiTKyso4+eSTG9y/evVqBg4cyNKlSz/BUWXD1KlTOfDA\nAzepjdmzZzNs2LDNNKJPBx89JEmSpMybNOlnLFhQ0WTt9+nTmUsuOa/J2m9MWVkZ//znP5k2bdom\ntbO+Z7leffXVDB8+nO7duwMwa9YsLrnkEp599llKS0t5/fXXa5WfNGkSM2fO5JVXXuHiiy9m0qRJ\nNfvefPNNxo8fzzPPPMOSJUuYP38+ffr0qdm/fPlyJkyYwMMPP0xJSQmHHXYYV155Jdtuuy0AVVVV\nTJo0ieuuu44VK1awyy678Oc//5mOHTtu0vE3pU19Tu6gQYPo0qUL9913H4cffvhmGlW2GXYlSZKU\neQsWVNCv35Qma3/+/KZre3NJKW1S4Lrqqqu45pprat63b9+e008/nTFjxnDZZZetU36XXXbhxz/+\nMVddddU6+0pKSvjyl7/MRRddxP7777/O/v/8z//k3Xffpby8nKqqKr72ta8xZcoULr/8ciAXpJ98\n8kn+9re/0atXL15++WW22WabjT62lmLMmDFcddVVht0iuYxZkiRJ+gS98cYbHHPMMXTr1o3tt9+e\nc889F8iF0R/84Af069ePz3zmM5x22mlUVlYCUF5eTklJCdOmTaNv375069atJmA++OCDXHbZZdx6\n66106NCBPfbYA4CRI0fy/e9/nwMOOID27dszb948lixZwqhRo+jatSuf/exn+e1vf1vUmBcuXMi8\nefPYd999a7bts88+nHjiiey444711jn55JM57LDDamZjC3Xr1o0JEyaw9957k1JaZ//8+fM56qij\naN++PR06dODoo4/mpZdeAqCiooIrrriCa665hl69egHwuc99jjZt2tQ7jvvvv5+BAwfSsWNHevfu\nzU9+8pOado488ki6detG165dOfLII1m0aFFNvZEjR3LxxRczbNgwOnTowKhRo1i2bBknnXQSnTp1\nYt9992WrmcoAAAAgAElEQVTBggU15UtKSvjFL37BTjvtRLdu3fje977X4PmcM2cOhx56KF27dmW3\n3XZjxowZjY4XYMSIETz88MOsWbOmwbb1sU/dzO6GLmF57pVy+h3Vr+kGJEmSpE+NqqoqjjjiCA4+\n+GBuuukmSkpKeOaZZwC47rrrmDZtGo8++ijbb789J598MmeffXatpcmPP/44c+fOZc6cOQwdOpRj\njjmGww47jIsuuqjeZcw33ngjf/jDH/jsZz9LVVUVBx10EIMHD+bNN9/k5Zdf5pBDDmHnnXdmxIgR\n6x337Nmz6d+/PyUln8xc2be+9S1+/etfM3r0aFJK3HHHHRx11FE1Y2ndujUzZszgpz/9KZ06deLc\nc8/lrLPOqretb3zjG9x+++3sv//+vPvuu8ybNw/IfRbjxo3j9ttv56OPPmLcuHGcffbZ3HXXXTV1\nb731Vh566CG6du3Kfvvtx/7778+VV17JtGnTGDt2LGVlZfzud7+rKT9z5kyeffZZVqxYwUEHHcSu\nu+7KuHHjao3ngw8+4NBDD+UHP/gBDz74IC+88AIHH3wwgwYNYtddd21wvAA9e/akdevWvPrqq3z+\n85/fbOc7qz51YXdDl7D831MHN91gJEmS9Kny1FNPsWTJEn70ox/VBMfqZbzTp0/n/PPPp2/fvgD8\n8Ic/5POf/zzXX389kLvmc8qUKbRp04bBgwczZMgQnn/+eQYMGNBgf6eddhq77rorAIsXL+aJJ57g\ngQceoHXr1gwZMoRvfOMbTJs2rdGwW1FRQYcOHTbx6Iu35557snr1arp27UpEcNBBB3HmmWcCuZnx\niooK5s6dS3l5Oa+++ioHHXQQAwYM4KCDDlqnrTZt2vDSSy8xaNAgOnXqxO677w5AaWkpRx99NABb\nb701EydOXKf+2LFj6devHwBf/vKXeeWVVxg5ciQAxx13XK3rkAEuvPBCOnXqRKdOnTjvvPO4+eab\n1wm7v//979lxxx055ZRTABgyZAjHHHMMM2bM4OKLL25wvNU6dOhARUXTXX+eJS5jliRJkj4hCxcu\npG/fvvXOkC5evLgm6AL07duXjz76qNbdj6tvDgXQrl073nvvvfX217t371rtl5aW0q5du1p9FC7d\nbUiXLl1YsWJFo+U2l+OOO44BAwbw/vvvU1lZSf/+/TnxxBMBaNu2LRHB5MmTadOmDYMGDWL06NHc\nf//99bZ1xx13cN9999G3b19GjhzJk08+CcCHH37I+PHj6devH507d2b48OFUVFTUWlZdeL7btm27\nzvu65796WTXkzu3ixYvXGU95eTlPPvkkpaWllJaW0qVLF6ZPn17zOTc03morVqygc+fORZ3HTzvD\nriRJkvQJ6d27NwsWLKCqqmqdfT179qS8vLzmfXl5Oa1bt64VsBrS0I2nCrf37NmTZcuW8f7779ds\nW7BgATvssEOj7Q8ePJh58+bVO+6m8PzzzzN+/Hi22WYb2rVrx4QJE3jggQdqxlLX+m68tddeezFz\n5kzefvttRo0axfHHHw/A5Zdfzty5c3n66aepqKjgscceA6j3GuJiLVy4sOb1ggUL6Nmz5zplevfu\nzYgRI1i2bBnLli1j+fLlVFZW8stf/nK944XcHyzWrFmz3tl8fcywK0mSJH1Chg4dSo8ePbjwwgv5\n4IMPWLVqFU888QQAJ5xwAj/96U+ZP38+7733Hv/5n//J6NGja2aB1xfCunfvzvz589dbplevXuy/\n//5MnDiRVatW8cILL/C73/1uvc/WrbbDDjuw884789RTT9VsSymxatUqVq9eTVVVFatWrap146SP\nPvqIlStXUlVVxZo1a1i1alWtsLxq1SpWrlwJwMqVK1m1alWt8/Tb3/6WlStX8uGHH/Kb3/ymJuT2\n79+fAw88kEsvvZTVq1fzyiuvcMstt3DkkUeuM+41a9Ywffp0KisradWqFR06dKBVq1YAvPfee7Rt\n25aOHTuybNkypkyZ0uh5aMyPf/xjKioqWLhwIVdccQWjR49ep8wRRxzBP/7xD2688UY++ugj1qxZ\nwzPPPMOcOXPWO16ARx99lC9+8Yu0bt16k8f6afCpu2ZXkiRJnz59+nRu0scD9elT3LLSkpIS7r33\nXs455xz69OlDSUkJY8aMYf/992fcuHEsWbKEL3zhC6xatYovfelL/PznP6+pW3f2svD9cccdx403\n3kjXrl3p378/zzzzTL2znTfffDPjx4+nZ8+elJaW8l//9V8116A2Zvz48UybNo399tsPgMcee4yR\nI0fW9NOuXTuGDx/OI488AsAZZ5zB1KlTa/ZfdtllXHfddTXXqlYvR44Idt11VyKCtWvXAnDttddy\nzjnn1CwLHjp0KFOnTq11HOPGjaNr1650796dSy+9tMHrjm+44QbOOecc1q5dy4ABA5g+fToA5513\nHmPGjGG77bZjhx124IILLuCee+5p8HwXY9SoUey1115UVlYyduzYda7XBdh222156KGH+M53vsP5\n559PSokhQ4bU3HW57nhvuummmro33XQTEyZM2OBxfVrFpkzTF9VBRGrqPjbEaadN2aAbVN1428Gc\n9OsDiio7f+Z8rv/Z9Rs3MEmSJG0WEbFJS1FVv9WrV7Pnnnvy8MMPF7W0+tOmpKSE1157jf79+zdJ\n+7Nnz2bChAk8/vjjTdJ+MRr6fyu/feMf4txEnNmVJEmS1Kg2bdrw4osvbulhfGoNGjRoiwbdlqio\na3YjolNEzIiIVyLipYjYNyK6RMRDEfFqRDwYEZ2aerCSJEmS1BxtzLLnrNvSObLYG1RdAdyfUtoN\nGALMAS4E/pRSGgA8AkxsmiFKkiRJUvO2du3aJlvC3IJt0RzZaNiNiI7AgSml6wBSSh+llN4FRgHV\nV4lPBY5qqkFKkiRJklqO5pAji5nZ3RH4V0RcFxHPRsTVEdEO6J5SWgqQUnoT6NZUg5QkSZIktShb\nPEcWE3a3AvYEfpVS2hN4n9zUc93bcHnLO0mSJEkSNIMcWczdmN8AFqaUnsm/v4PcIJdGRPeU0tKI\n+AzwVkMNFD6gecSIEQ0+A0uSJEnaVH379vVmQVIT6Nu3LwCzZs1i1qxZjRXf5By5qRoNu/lBLIyI\nz6aU/gEcBLyU/zkN+B/gVODuhtooDLuSJElSU5o/f/6WHoKUaXUnMMvKytYpszly5KYq9jm75wI3\nRURr4HVgLNAKuC0ixgHlwPFNM0RJkiRJUgu0RXNkUWE3pfQ8sE89uw7evMORJEmSJGXBls6RxT5n\nV5IkSZKkFsOwK0mSJEnKHMOuJEmSJClzDLuSJEmSpMwx7EqSJEmSMsewK0mSJEnKHMOuJEmSJClz\nDLuSJEmSpMwx7EqSJEmSMsewK0mSJEnKHMOuJEmSJClzDLuSJEmSpMwx7EqSJEmSMsewK0mSJEnK\nHMOuJEmSJClzDLuSJEmSpMwx7EqSJEmSMsewK0mSJEnKHMOuJEmSJClzDLuSJEmSpMwx7EqSJEmS\nMsewK0mSJEnKHMOuJEmSJClzDLuSJEmSpMwx7EqSJEmSMsewK0mSJEnKHMOuJEmSJClzDLuSJEmS\npMwx7EqSJEmSMsewK0mSJEnKHMOuJEmSJClzDLuSJEmSpMwx7EqSJEmSMsewK0mSJEnKHMOuJEmS\nJClzDLuSJEmSpMwx7EqSJEmSMsewK0mSJEnKHMOuJEmSJClzDLuSJEmSpMwx7EqSJEmSMsewK0mS\nJEnKHMOuJEmSJClzDLuSJEmSpMwx7EqSJEmSMsewK0mSJEnKHMOuJEmSJClzDLuSJEmSpMwx7EqS\nJEmSMmerYgpFxHzgXaAKWJNSGhoRXYBbgb7AfOD4lNK7TTROSZIkSVILsqVzZLEzu1XAiJTSHiml\nofltFwJ/SikNAB4BJjbFACVJkiRJLdIWzZHFht2op+woYGr+9VTgqM01KEmSJElSi7dFc2SxYTcB\nf4yIpyPiG/lt3VNKSwFSSm8C3ZpigJIkSZKkFmmL5siirtkFhqWUlkTE9sBDEfEquYEXqvu+xpQp\nU2pejxgxghEjRmzgMCVJkiRJzcGsWbOYNWtWMUU3KUduqqLCbkppSf6/b0fETGAosDQiuqeUlkbE\nZ4C3GqpfGHYlSZIkSS1X3QnMsrKyesttao7cVI0uY46IdhGxbf51e+BQYDZwD3BavtipwN1NNEZJ\nkiRJUgvSHHJkMTO73YG7IiLly9+UUnooIp4BbouIcUA5cHxTDVKSJEmS1KJs8RzZaNhNKc0Ddq9n\n+zLg4KYYlCRJkiSp5WoOObLYuzFLkiRJktRiGHYlSZIkSZlj2JUkSZIkZY5hV5IkSZKUOYZdSZIk\nSVLmGHYlSZIkSZlj2JUkSZIkZY5hV5IkSZKUOYZdSZIkSVLmGHYlSZIkSZlj2JUkSZIkZY5hV5Ik\nSZKUOYZdSZIkSVLmGHYlSZIkSZlj2JUkSZIkZY5hV5IkSZKUOYZdSZIkSVLmGHYlSZIkSZlj2JUk\nSZIkZY5hV5IkSZKUOYZdSZIkSVLmGHYlSZIkSZlj2JUkSZIkZY5hV5IkSZKUOYZdSZIkSVLmGHYl\nSZIkSZlj2JUkSZIkZY5hV5IkSZKUOYZdSZIkSVLmGHYlSZIkSZlj2JUkSZIkZY5hV5IkSZKUOYZd\nSZIkSVLmGHYlSZIkSZlj2JUkSZIkZY5hV5IkSZKUOYZdSZIkSVLmGHYlSZIkSZlj2JUkSZIkZY5h\nV5IkSZKUOYZdSZIkSVLmGHYlSZIkSZlj2JUkSZIkZY5hV5IkSZKUOYZdSZIkSVLmGHYlSZIkSZlj\n2JUkSZIkZY5hV5IkSZKUOUWH3YgoiYhnI+Ke/PsuEfFQRLwaEQ9GRKemG6YkSZIkqSXZ0hlyQ2Z2\nvw28XPD+QuBPKaUBwCPAxM05MEmSJElSi7ZFM2RRYTciegFfAX5bsHkUMDX/eipw1OYdmiRJkiSp\nJWoOGbLYmd2fAt8FUsG27imlpQAppTeBbpt5bJIkSZKklmmLZ8itGisQEYcDS1NKf4+IEespmhra\nMWXKlJrXI0aMYMSI9TUjSZIkSWquZs2axaxZsxrcvzky5ObQaNgFhgFfjYivAG2BDhFxA/BmRHRP\nKS2NiM8AbzXUQGHYlSRJkiS1XHUnMMvKyuoW2eQMuTk0uow5pXRRSqlPSqk/MBp4JKV0MnAvcFq+\n2KnA3U02SkmSJElSi9BcMmQxM7sN+W/gtogYB5QDx2+eIUmSJEmSMmijM2REbE/u7s5tgatSSnMb\nq7NBYTel9CjwaP71MuDgDakvSZIkSfr02IwZ8n+Ba8hd5zsd2KexChvynF1JkiRJkppcRDwYEV8o\n2NQGmJ//2bqYNgy7kiRJkqTm5njgyIi4OSJ2Ai4GfghcAZxVTAObcs2uJEmSJEmbXUrpXeC7EdEf\nuBRYDJydUqootg3DriRJkiSpWcnP5p4JrAYuAHYCbo2I+4BfpZTWNtaGy5glSZIkSc3NzcCdwJ+B\nG1JKf0kpHQZUAA8V04Azu5IkSZKk5mZrYB6wLdCuemNKaVpEzCimAcOuJEmSJKm5ORP4JbllzBMK\nd6SUPiymAcOuJEmSJKlZSSk9ATyxKW14za4kSZIkKXMMu5IkSZKkzDHsSpIkSZKapYgYtLF1DbuS\nJEmSpObq1xHxVEScFRGdNqSiYVeSJEmS1CyllA4ETgR6A/8vIqZHxCHF1DXsSpIkSZKarZTSXOD7\nwH8Aw4GfR8SciPja+uoZdiVJkiRJzVJEDI6InwKvAF8Ejkwp7ZZ//dP11fU5u5IkSZKk5uoXwG+B\ni1JKH1ZvTCktjojvr6+iYVeSJEmS1FwdDnyYUloLEBElwDYppQ9SSjesr6LLmCVJkiRJzdWfgLYF\n79vltzXKsCtJkiRJaq62SSm9V/0m/7pdMRUNu5IkSZKk5ur9iNiz+k1E7AV8uJ7yNbxmV5IkSZLU\nXJ0HzIiIxUAAnwG+XkxFw64kSZIkqVlKKT0dEbsCA/KbXk0prSmmrmFXkiRJktSc7QP0I5df94wI\nUkrTGqtk2JUkSZIkNUsRcQOwE/B3YG1+cwIMu5IkSZKkFmtv4HMppbShFb0bsyRJkiSpuXqR3E2p\nNpgzu5IkSZKk5mo74OWIeApYVb0xpfTVxioadiVJkiRJzdWUja1o2JUkSZIkNUsppUcjoi+wS0rp\nTxHRDmhVTF2v2ZUkSZIkNUsRcQZwO/Cb/KYdgJnF1DXsSpIkSZKaq28Bw4BKgJTSXKBbMRUNu5Ik\nSZKk5mpVSml19ZuI2Ircc3YbZdiVJEmSJDVXj0bERUDbiDgEmAHcW0xFw64kSZIkqbm6EHgbmA2M\nB+4Hvl9MRe/GLEmSJElqllJKVcA1+Z8NYtiVJEmSJDVLETGPeq7RTSn1b6yuYVeSJEmS1FztXfB6\nG+A4oLSYil6zK0mSJElqllJK7xT8LEop/Qw4vJi6zuxKkiRJkpqliNiz4G0JuZneonKsYVeSJEmS\n1Fz9b8Hrj4D5wPHFVDTsSpIkSZKapZTSyI2ta9iVJEmSJDVLEXH++vanlH7S0D7DriRJkiSpudob\n2Ae4J//+SOApYG5jFQ27kiRJkqTmqhewZ0ppBUBETAHuSymd1FhFHz0kSZIkSWquugOrC96vzm9r\nlDO7kiRJkqTmahrwVETclX9/FDC1mIqGXUmSJElSs5RSujQiHgAOzG8am1J6rpi6LmOWJEmSJDVn\n7YDKlNIVwBsRsWMxlRoNuxGxdUT8LSKei4jZETE5v71LRDwUEa9GxIMR0WnTxi9JkiRJauk2Z4bM\n1/0PYGJ+U2vgxmLG0WjYTSmtAkamlPYAdge+HBFDgQuBP6WUBgCPFHQuSZIkSfqU2swZ8mjgq8D7\n+bYXAx2KGUdRy5hTSh/kX25N7jrfBIzi4wuDp5K7UFiSJEmS9Cm3GTPk6pRSytcnItoXO4aiwm5E\nlETEc8CbwB9TSk8D3VNKS/MH8ibQrdhOJUmSJEnZtRkz5G0R8Rugc0ScAfwJuKaYMRR1N+aUUhWw\nR0R0BO6KiIHkk3VhsYbqT5kypeb1iBEjGDFiRDHdSpIkSZKamVmzZjFr1qz1ltnUDFnQzuURcQhQ\nCQwAJqWU/ljMODfo0UMppcqImAV8CVgaEd1TSksj4jPAWw3VKwy7kiRJkqSWq+4EZllZWYNlNzZD\nAkREK3LX+I4Eigq4hYq5G/N21XfJioi2wCHAK8A9wGn5YqcCd29o55IkSZKkbNlcGTKltBao2tgn\n/xQzs9sDmBoRJeTC8a0ppfsj4kly66fHAeXA8RszAEmSJElSpmzODPkeMDsi/kj+jswAKaVzG6vY\naNhNKc0G9qxn+zLg4CIGJ0mSJEn6lNjMGfLO/M8G26BrdiVJkiRJamoR0SeltCClNLXx0vUr6tFD\nkiRJkiR9gmZWv4iIOzamAcOuJEmSJKm5iYLX/TemAcOuJEmSJKm5SQ28LprX7EqSJEmSmpshEVFJ\nboa3bf41+fcppdSxsQYMu5IkSZKkZiWl1GpT23AZsyRJkiQpcwy7kiRJkqTMMexKkiRJkjLHsCtJ\nkiRJyhzDriRJkiQpcwy7kiRJkqTMMexKkiRJkjLHsCtJkiRJyhzDriRJkiQpcwy7kiRJkqTMMexK\nkiRJkjLHsCtJkiRJyhzDriRJkiQpcwy7kiRJkqTMMexKkiRJkjLHsCtJkiRJyhzDriRJkiQpcwy7\nkiRJkqTMMexKkiRJkjLHsCtJkiRJyhzDriRJkiQpcwy7kiRJkqTMMexKkiRJkjLHsCtJkiRJyhzD\nriRJkiQpcwy7kiRJkqTMMexKkiRJkjLHsCtJkiRJyhzDriRJkiQpcwy7kiRJkqTMMexKkiRJkjLH\nsCtJkiRJyhzDriRJkiQpcwy7kiRJkqTMMexKkiRJkjLHsCtJkiRJyhzDriRJkiQpcwy7kiRJkqTM\nMexKkiRJkjLHsCtJkiRJyhzDriRJkiQpcwy7kiRJkqTMaTTsRkSviHgkIl6KiNkRcW5+e5eIeCgi\nXo2IByOiU9MPV5IkSZLU3DWHHFnMzO5HwPkppYHAvwHfiohdgQuBP6WUBgCPABObapCSJEmSpBZl\ni+fIRsNuSunNlNLf86/fA14BegGjgKn5YlOBo5pqkJIkSZKklqM55MgNumY3IvoBuwNPAt1TSksh\ndyBAt809OEmSJElSy7alcmTRYTcitgVuB76dT+apTpG67yVJkiRJn2JbMkduVUyhiNiK3ABvSCnd\nnd+8NCK6p5SWRsRngLcaqj9lypSa1yNGjGDEiBEbPWBJkiRJ0pYza9YsZs2a1Wi5Tc2Rm6qosAtc\nC7ycUrqiYNs9wGnA/wCnAnfXUw+oHXYlSZIkSS1X3QnMsrKyhopuUo7cVI2G3YgYBpwIzI6I58hN\nM1+UH9xtETEOKAeOb6pBtnSTJv2MBQsqii7fp09nLrnkvCYckSRJkiQ1neaQIxsNuymlx4FWDew+\nePMOJ5sWLKigX78pRZefP7/4spIkSZLU3DSHHLlBd2OWJEmSJKklMOxKkiRJkjLHsCtJkiRJyhzD\nriRJkiQpcwy7kiRJkqTMMexKkiRJkjLHsCtJkiRJyhzDriRJkiQpcwy7kiRJkqTMMexKkiRJkjLH\nsCtJkiRJyhzDriRJkiQpcwy7kiRJkqTMMexKkiRJkjLHsCtJkiRJyhzDriRJkiQpcwy7kiRJkqTM\n2WpLD0Dreu6F5zjtvNOKKtunex8umXhJ0w5IkiRJkloYw24z9P7K9+l31B5FlZ0/c37TDkaSJEmS\nWiCXMUuSJEmSMsewK0mSJEnKHMOuJEmSJClzDLuSJEmSpMwx7EqSJEmSMsewK0mSJEnKHMOuJEmS\nJClzDLuSJEmSpMwx7EqSJEmSMsewK0mSJEnKHMOuJEmSJClzDLuSJEmSpMwx7EqSJEmSMsewK0mS\nJEnKHMOuJEmSJClzDLuSJEmSpMwx7EqSJEmSMsewK0mSJEnKHMOuJEmSJClzDLuSJEn/v727j7Kj\nru84/v7GKEcaSSAIwYe4VaBaqwJS9LRaovgQq4VUsSKnlVCPtrYFg57jQ7Uh5CimtFp8wiJFBJRS\nlUqwyqMmEUUESiBAA1phWXyKqARqwBbIt3/8ZvWyZMPd7P3tvTv7fp0zJ7Mzs/czc/dm7u8785sZ\nSVLrWOxKkiRJklrHYleSJEmS1DoWu5IkSZKk1rHYlSRJkiS1jsWuJEmSJKl1LHYlSZIkSa1jsStJ\nkiRJah2LXUmSJElS61jsSpIkSZJax2JXkiRJktQ6j1jsRsTpEbEpIjZ0TNs1Ii6JiFsi4uKImFt3\nNSVJkiRJ08Ug1JHdnNk9A3j5mGnvAi7LzN8Cvga8u9crJkmSJEmatvpeRz5isZuZ3wDuGjP5MODM\nZvxMYEmP10uSJEmSNE0NQh25o9fs7pGZmwAy88fAHr1bJUmSJElSC01pHTm7R6+T25u5YsWKX40v\nWrSIRYsW9ShWkiRJkjSV1q5dy9q1a3vxUtutIydrR4vdTRGxZ2ZuiogFwE+2t3BnsStJkiRJmr7G\nnsA84YQTuv3VCdWRk9VtN+ZohlEXAEub8aOA1T1cJ0mSJEnS9NfXOrKbRw+dA1wB7BsRIxFxNLAK\neGlE3AIc0vwsSZIkSdJA1JGP2I05M48cZ9ZLerwukiRJkqQWGIQ6ckfvxixJkiRJ0sCy2JUkSZIk\ntY7FriRJkiSpdSx2JUmSJEmtY7ErSZIkSWodi11JkiRJUutY7EqSJEmSWsdiV5IkSZLUOha7kiRJ\nkqTWsdiVJEmSJLWOxa4kSZIkqXUsdiVJkiRJrWOxK0mSJElqHYtdSZIkSVLrWOxKkiRJklrHYleS\nJEmS1DoWu5IkSZKk1pnd7xVQHcuXn8zIyOaull24cB4rVy6rvEaSJEmSNHUsdltqZGQzQ0Mrulp2\neLi75SRJkiRpurAbsyRJkiSpdSx2JUmSJEmtYzdmsX7DepYuW9r18gv3XMjKd6+st0KSJEmSNEkW\nu2LLL7cwtGT/rpcfPn+43spIkiRJUg/YjVmSJEmS1DoWu5IkSZKk1rHYlSRJkiS1jsWuJEmSJKl1\nLHYlSZIkSa1jsStJkiRJah2LXUmSJElS61jsSpIkSZJax2JXkiRJktQ6FruSJEmSpNaZ3e8VUHss\nX34yIyObu1p24cJ5rFy5rPIaSZIkSZqpLHbVMyMjmxkaWtHVssPD3S0nSZIkSTvCYld9sX7DepYu\nW9r18gv3XMjKd6+st0KSJEmSWsViV32x5ZdbGFqyf9fLD58/XG9lJEmSJLWON6iSJEmSJLWOxa4k\nSZIkqXUsdiVJkiRJrWOxK0mSJElqHYtdSZIkSVLreDdmTWvLl5/MyMjmrpZduHAeK1cuq7xGkiRJ\nkgaBxa6mtZGRzQwNrehq2S9ecBgj91zX9Wv7bF9JkiRp+rLY1Yzhs30lSZKkmcNiV5qgiXSdvnXk\nOp767Hldv7ZnkyVJkjQVJtKmhYm3aweBxa40QRPpOv2Nq17Ci5fv1/VrezZZkiRJU2EibVp4hHbt\nh3uzTr1msSsNuIkedfNGXJIkSdIki92IWAycTHmE0emZ+fc9WatxrF27lkWLFtWMeJj7ttw3pXn9\nyJwJ2zidMyd61O3szx4y6RtxTbTA3rp1M2eddXLXy/dCP/YHMyFzJmyjmWaaOZh5Zppp5mBnTrRd\nO9W14rbscLEbEbOAjwGHAD8Ero6I1Zl5c69Wbqy+/FHv7UOBNMWZM2EbZ1LmXZvvYmjJC7tefltd\npydaYH/i1OexdNnSrpYd77rkiRbYt912PevWLep6+V6YCV9mM2EbzTTTzMHMM9NMMwc7cyLt2n7U\nitsymTO7BwHfzczbASLiXOAwYEo3QFL/3f/A/QwtGepq2fGuS55ogX3hxVNfYK9Zt5rhzcPTLlOS\nJGmKDUStOJli94nAHR0/f5+yUZJUXT8K7P/58gXTMnMiBfZEimvoTVFvZveZNQ+W9COzVwdo1ly+\nhmOiB/8AAAvsSURBVOFlk8vckd4lK1Z0vXhP9GI7B91EthGm73ZKM8BA1IqRmTv2ixGvAV6emW9u\nfv5T4KDMPHbMcjsWIEmSJEmaFjIzRse7rRVrm8yZ3R8ACzt+flIz7SE6N1qSJEmS1Hpd1Yq1zZrE\n714N7B0RT4mIxwBHABf0ZrUkSZIkSdPUQNSKO3xmNzMfjIi/AS7h17eT3tizNZMkSZIkTTuDUivu\n8DW7kiRJkiQNqsl0Y5YkSZIkaSBN5gZVVUXE0ynPYnpiM+kHwAVt6yrdbOcTgW9n5i86pi/OzIsq\nZR4EZGZeHRG/DSwGbs7Mr9TIG2cdzsrMN0xh3gsotzu/MTMvqZTxPGBjZt4TEY8F3gUcAPwXcGJm\n3l0h81jgi5l5xyMu3LvM0esufpiZl0XEkcDvARuBT2bm/RUynwq8Gngy8CDwHeCczLyn11mSJElq\nh4HsxhwR7wReD5xLeSYTlDt4HQGcm5mr+rBOR2fmGT1+zWOBv6YUCfsBb83M1c28azPzgF7mNa97\nPPAKyoGOS4HnAWuAlwIXZ+b7K2SOvRg9gBcBXwPIzEMrZF6VmQc142+ivM9fBF4GfKnGZygibgKe\nk5kPRMQngXuBLwCHNNNfXSHzbmAL8D3gX4HPZ+advc4Zk/lZyudnZ2AzMAf4d8p2RmYe1eO8Y4FX\nAV8H/hBY3+T+MfBXmbm2l3mSJNUWEXtk5k/6vR61RcT8zPxZv9dDM1hmDtxAOWvz6G1Mfwzw3T6t\n00iF17wBmNOMDwHXUApegPWVtuMG4FGUQuUeYJdm+mOBDZUyrwU+AywCDm7+/VEzfnClzPUd41cD\nj2/GfwO4oVLmxs5tHjPvulrbSbkc4WXA6cCdwEXAUcDjKmVuaP6dDWwCHtX8HDU+Q6Of2WZ8Z2Bt\nM76w1v8Th/YPwB79Xocp2s75/V4Hhx36u80FVgE3Az8HfkY5ML4KmNeH9bmw0uvuAnwAOBs4csy8\nUyplLgA+AXwcmA+saL5nPgfsVSlztzHDfGAY2BXYrVLm4jGfp9OBDcA5wJ6VMlcBuzfjBwK3Av8N\n3F6xvXct8F7gaTVefxt5B1JOEn2G0tvsUuDupq25f6XMOcBK4KYm607gSmBpxe0cqH3QZIZBvWZ3\nK/CEbUzfq5lXRURsGGe4AdizQuSsbLouZ+YwpQh8RUR8iFI41PBAZj6YmfcC38umG2hm3ke99/ZA\n4D+B9wB3ZzkTd19mrsvMdZUyZ0XErhExn1Io3QmQmVuABypl3hgRRzfj10fEgQARsS/Q8669jczM\nrZl5SWa+kfL/5hRK1/RbK2XOaroyP45SfM5tpu8EPLpS5uglFztRdvpk5kjFPCJibkSsioibI+Ln\nEfGziNjYTJtXK3c763NhpdfdJSI+EBFnN13SO+edUiFvQUR8IiI+HhHzI2JFRNwQEZ+LiL16nddk\n7jZmmA9c1ewjdquUubhjfG5EnN58n5wTETW+T2g+m7s34wdGxK3AtyPi9og4uFLmtRHx3oh4Wo3X\nHyfzwIhYExGfiYgnR8SlEXF3RFwdEftXypwTESsj4qYm686IuDIiltbIoxRedwGLMnO3zJxP6RF1\nVzOv5yLigHGG51J6n9VwBqW9cx5wREScFxE7NfOeXynz05TLi+6gFC33UXoNXQ78c6XMn1LaQaPD\nNZRL2K5txms4sWP8g5STDH9EKcpOrZT5ysz8aTP+D8DrMnNvSu/BD1bK3BWYB6yJiKsi4riI2FYN\n0SunACcBXwauAE7NzLmUS9d6/p3Z+CylTfdy4ATgI8CfAS+KiBO394uTMOX7oGr6XW2PczRhMeVI\n0IXAJ5vhomba4oq5myg79KeMGYYo1yf2Ou9rwH5jps0GzgIerLSN3wZ2bsZndUyfy5izkRWynwR8\nHvgYFc6Uj8kapuwYbmv+3auZPod6Z1nnUr5Ev9e8z/c32eso3ZhrZI57ZnP071wh87hmu24HjgW+\nCpxGOTJ+fIW8t1KORp9GOcJ4dDP98cDXK36GLgbeCSzomLagmXZJpcwDxhmeC/yoUuZ5lCO1SyjP\nvzsP2KmZ1/N9QrMvP4bSMNjQvJ9PbqatrrSNW5t9Qedw/+j+oVLmtR3j/wK8r/k+OQ44v1LmDR3j\na4Dfbcb3Ba6plHkb8I/ACHBVs31PqJHVkXkV5XKc11MKlsOb6YcA36qUuRpY2nyPvQ34O2Af4EzK\nPRl6nXfLjsybZOaDlHbJmm0M91XKvG7Mz+8Bvkk581mlTcJDe36NbG99epj59mbf96yOabfVyOp4\n/c590Nj3udZ2bgRmN+NXjplXq1dd53a+kFJw/rj53L55ij8/tXplXj/m56ubf2dR7rlTI3PK90G1\nhr6vwHbeyFmUo3qvaYbn03RlrJh5OvCCceadUyHvSXQ0pMfM+/1K27jTONN379wJV36fX1mjcdBl\n9s7Ab1bO2AV4DqVAqdJVqCNr3z69j08YbdBSjqgeDhxUMe+ZTcbTp3AbbWzWKXZtaNrQrPkZakVj\nk/Jcynd0fodQepi9E7is0jbeCOwzzrw7KmVupOPAezNtKaW75u21/5bA+8bMq/L/pHnt0QP+H6L0\njKpyoK0j7/uUAzNvpxyUio55tS5bO6b57L6Y0j38w5RL1k4Azq6U+bDvKcrleouBMyrkfYty6dhr\nKQf9lzTTD6beQcUraOoT4FDKPXZG59Vqj0z5PqjWMLB3Y87MrZT+6FOZ+cbtzDtyvHmTyPv+duZ9\ns9d5zev+7zjTf0rpZlNdZn6Z0v1jymXpvn1b5Yx7gOtrZnRkfWcqcraR+8OO8c2UG3HVzLuJ0viZ\nSrdHxDuAMzNzE0DT/XQp5WxSDRuBv8jM746dERG1MneKiFnNPpfMfH9E/IByQ7A5FfI6L585a8y8\nR1XIIzM/GBH/BvxT8z4eD2SNrA57RMTbKF0050ZEZNNaoN5j/04BvhIRq4CLIuLDlJvHvRi4rlLm\nr2Tm5cDlEXEMpdvi6yg9s3rtlxHxMkpvmoyIJZl5ftNV+8EKeQBbIuIFmfmNiDiUcg0bmbk1Impc\ndvQ6Su+Hdc1+Jym9zy4A/qRCHpTiZLzP5jGVMr9E+XxeNjohMz8dET8GPlopc3VEzMnMX2Tme0cn\nRsTewC2VMkfbfK9tPj+XUg6+13QapaiG0utsd+DOiFhApf1BZn60ufTvLZQeJbMpPSDOp/RuqeFh\n7aDMfJBygLPGU03+ktKNeSulW/FbIuLTlKfGvKlCHpT387SI2IfSFnojQEQ8nnLteQ392AdVMZB3\nY5akfouIXSk7+sOAPZrJozv6VZl5V4XMwylnFh7W4Bpt0FfIPInSLfuyMdMXAx/NzH16nLcSOCk7\nHrXWTN+b8r4e3su8beQfCvwtMJSZCyrmHD9m0imZOdrQPCkrPXotIhbx0IbmHZSG5qcys+f3K4iI\nczPziF6/7iNkPodfNzaPo2zvUTSNzcy8okLmsynd0Ucbm3+emd9pGpuvz8yPVMh8OuVs4JU5dY8m\n7MfjEMfLfEVm1rpXQV+3k3JQ5mmZeeMM+nu2JjMinkHp4TaV2/gMyjZO5f6g81Glz6ScLd+YU/io\n0p7o96llBwcHh+k20Fw3bOb0zKPcff53Zsr7aub0y6TcD+EWyoGKYeCwjnm1rmXtR+YxMyRzpry3\nrd/OZhtv7sP7OtWZx1N62F5DuWP6Vyn3Kvg68J4ambWGvq+Ag4ODw3QbqHyDtZmaORO20Uwzu3zN\nfj2a0EwzzRygvD5nTumjSmsNA3vNriT1U0RsGG8WdR5FNiMyZ8I2mmlmDzzk0YRN9/QvRMRTqPdo\nQjPNNHPw8vqV+UCWa5/vjYiHPKo0Iqo9BrYGi11J2rY9KTefGHttblDujGjm9Mgz08zpmLkpIvbL\nzOsAMvMXEfEq4FPAsyrkmWmmmYOZ16/M/4uInbPc2PW5oxMjYi7lfgnThsWuJG3bf1C6DT3srpUR\nsdbMaZNnppnTMfMNwENuKJblBmNviIhTK+SZaaaZg5nXr8w/yOYJLtk8raHxaMoNAacN78YsSZIk\nSWqdWs/6kyRJkiSpbyx2JUmSJEmtY7ErSZIkSWodi11JkiRJUuv8P0uSm4Z6O+pdAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f787151b050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/FLASH_PLUGIN_INSTANCES_ON_PAGE are differing by chance is 0.00.\n"
     ]
    },
    {
     "data": {
      "image/png": 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6xfVdHpxzEQWu2bNn6+mnnw6+Tk1N1dChQxUfH6+srCz1799fM2fO1Nix/sA/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\nAAAAgOcQdgEAAAAAnkPYBQA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lFrftqxua+WxJP0pyiu1TJH01yS+rJojfSbJV\nzREXYfvHki6R9O2ORlVrqtxjtX2S19WZrxvbv1HpFL2PyoWQRhdio97LT1mVGf3eborOHR9+shv6\nG1V2hzSyG3pLX+cbJO08uki0vb7K95IX1RJsDKN3A1W3m+yn8vW4IMkragvXg8vInl5N7b6QMmKr\nMdqWd4Tt4yVtqNJga2RG9LoqTWpvTovmGXdiZRfA0obRX4PRxvFf75F0gu3DJf1e0s9cxkTcVp1r\noukdHcUlPdGd+bO2393j79StbWOpHqlW8FZWGQu3R5Jzbb9aUlOnPjyhRd3Q2/g6T9WTRUGn29XQ\nWfMatRuoamp3he2PSmpq07U5kq7r0dTuyMHHGVfb8o7YJcmGow+6TN24SRLFLgC0AKO/BqON47/u\nl/RO28+StL6qD7LpMiuxQX5r++MqK7t3S5LtNSS9U09202yslhRi/yTpOJWLNDtKer/tb6kUNO+t\nMddYbhp9oBohcmH1q4na+Dp/U9KcqottZ/fa/VTm0DfR57odrO4xvmzAWSZqL0mPdDvR0O7tbcs7\n4hHbWyWZM+r4Vurx72kDtjEDANBStleVdKhK98znVofvVumeeWyS0TsYatfSsVQvkrS2Fu2GvlNb\n7mWzfWqS/evO0YvtD0r6fpLGX6TpVHWa302jute2vfFT09leremdr9vG9uaSvi5pJT25Y+F5KjOj\nP5DkV3Vl6wfFLgAAQ6ip3cXH0sTMVRF2oKR5ak839PNHH5K0ncr93Uqy28BDjcP2/Sozam9RuY/7\n7CT31ptq6WL7giQ7151jNNvHSvp8kt9XXbvPUtkBsKyk/ZM0akW6jY0DO1V9H564eDPSD6KtKHYB\nABhCbRxx0cTMtudKenlnN3RJpyU5ocGNk66SdL2kb6h80LZKAbmfJDWtOJCeyLyFpB1UtrPvJulX\nKrnPSfLHGuN1VRUFR6gUXjMkHazSCGyeykWRO2uM11W1etf1lKQfJFlrkHkmwvbcJC+pHl8q6eMp\n89s3lHR6ki3rTfhUbWwcOKJto7QmgmIXAICWaml38VZlbmk39CkqzWR2kfTPSa62fWuS59ccracu\nXYKXVZnV/WZJOyR5Tm3herB9oaQfSlpB0lskfUfS6Sorejsk2b3GeF1Vc3YvU/exdS9LsvyAI42r\nal73kpQZ6D9P8rKOc08Uwk1h+8Ykf7u45+o21igtSU0dpTUuil0AAFrK9t0ao7t4krUHn2psbcts\n+xJJH+lsamd7qkpzorcmWaa2cOOwva6kL6ncx71b01bNO421St7R2b1RxhmX1NQRa9dJ2jPJzV3O\n3ZbkeTXEGlPVwG5XSceqdIxeVWVW92skPT/J22uMtwjbF0n6ibo3Dnxtkh1qjNdTG0dpTQTdmAEA\naK82dhdvW+Y2dkOXJCX5naS9bb9e0gN15xnHvr1ONLHQrUzpeHzqqHNNvQhypJ6au9PBA8wxYUlO\nrG4neL/KHNipkl4o6VxJn6kzWw/7qjQOvKwqcqMnGwfuU2ewcbRxlNa4WNkFAAAAFpPtmZKO6+zQ\nXR3fQKUR0V71JBub7Y1U7slsTXdx21urTEiaY3tjSTtJuiHJj2qOtgjb20ial+R+289UKXw3V7mP\n/uhqzF3j2D5MpRjvNkrrrCTH1JWtHxS7AAAAwCRqYmdx6Ynu4h+QdIPa0138CJX7t6dKulilgdJs\nldncP05yVH3pFmX7ekmbVvcYn6TSZfx7kravjr+x1oBjGMZRWhS7AAAAwCRqYmdxqbXdxeeqFObL\nSbpL0rpJHrC9vMrq9Ca1BhzF9g0j97d2abzWyHu5hxn37AIAAACLaZzO4msMMstimDKydTnJfNvb\nSvqu7fXUvUNzEzyW5HFJD9m+JckDkpTkYdt/rTlbN9d1rOxfY3vLJL+sRiX9pe5wvbR9PnAvFLsA\nAADA4ltDY3QWH3ycCbnb9mYjDeKqFd43qHQXb9QInw6PdnTk3mLkYFWcNbHYfY+kE2wfLun3kn5m\n+zaV+2DfU2uysZ2lMh942y7zgc+S1Nj5wGNhGzMAAACwmGyfLOmUJD/tcu70JG+pIdaYqnFUj40U\nM6PO/X2S/6kh1phsL5fkz12Ory5prSRza4g1LtvPkrS+qi7HI2OImqqt84HHQ7ELAAAAAEuxts4H\nHk+vOVsAAAAAgKXDvpJWU5kPfJ/t+1S6Xj9b0t51BusHK7sAAAAAgK6aOkprIih2AQAAAABdNXWU\n1kTQjRkAAAAAlmItHaU1LopdAAAAAFi6tXGU1rgodgEAAABg6fYDSSuOzGDuZHv24ONMDu7ZBQAA\nAAAMHUYPAQAAAACGDsUuAAAAAGDoUOwCAAAAAIYOxS4AAAAAYOj8P+9pMEbNjS1GAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f786ec255d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/FX_PAGE_LOAD_MS are differing by chance is 0.15.\n"
     ]
    },
    {
     "data": {
      "image/png": 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LL79cl19++WGsCNEi/NpslAwjuwAAAACAwCHsAgAAAAACh7ALAAAAAAgcwi4A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\nBQAAAAAEDmEXAAAAABA4hF0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wCwAAAAAIHMIuAAAAACBwCLsAAAAAgMAh7AIAAAAAAoew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\nT9p3vqwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f786f022650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/INPUT_EVENT_RESPONSE_MS are differing by chance is 0.98.\n"
     ]
    }
   ],
   "source": [
    "compare_histograms(subset,\n",
    "                   \"aggressive\",\n",
    "                   \"control\",\n",
    "                   # \"payload/histograms/PLUGIN_BLOCKED_FOR_STABILITY\", # is 0 for control.\n",
    "                   \"payload/histograms/INPUT_EVENT_RESPONSE_MS\",\n",
    "                   \"payload/histograms/FLASH_PLUGIN_INSTANCES_ON_PAGE\",\n",
    "                   \"payload/histograms/FX_PAGE_LOAD_MS\"\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "compare_histograms(subset,\n",
    "                   \"aggressive\",\n",
    "                   \"control\",\n",
    "                   #\"payload/keyedHistograms/SUBPROCESS_CRASHES_WITH_DUMP/plugin\",\n",
    "                   #\"payload/keyedHistograms/SUBPROCESS_ABNORMAL_ABORT/plugin\"\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vbVDN77zzDnPnzuWAAw6oWrfjjjtSXFwMwBdffEFRURFvvvlmjXYppTr7mzRp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f78999e2ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/PLUGIN_HANG_TIME are differing by chance is 0.03.\n"
     ]
    },
    {
     "data": {
      "image/png": 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8GOuOnaT/mtk7ZjaiNgoCAAAAAARCnebFWKcx93HObTSz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\nIiIiIiLGzv8H9KNTam5ONdcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f786e5100d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/PLUGIN_HANG_UI_RESPONSE_TIME are differing by chance is 0.02.\n"
     ]
    },
    {
     "data": {
      "image/png": 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Bfy16PhZ4JKX0KeBR4NKGLEySJEmS1Kw1aoasV9iNiF7AicAdRYtP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\nAIDhiF0AAACGI3YBAAAYjtgFAABgOL8DgSilTWPT884AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7893a961d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/PLUGIN_HANG_UI_USER_RESPONSE are differing by chance is 0.56.\n"
     ]
    }
   ],
   "source": [
    "compare_histograms(subset,\n",
    "                   \"aggressive\",\n",
    "                   \"control\",\n",
    "                   #\"payload/histograms/PLUGIN_HANG_NOTICE_COUNT\",\n",
    "                   \"payload/histograms/PLUGIN_HANG_TIME\",\n",
    "                   \"payload/histograms/PLUGIN_HANG_UI_RESPONSE_TIME\",\n",
    "                   \"payload/histograms/PLUGIN_HANG_UI_USER_RESPONSE\"\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "zip argument #1 must support iteration",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-79-1a6d5a50f4c2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m                   \u001b[1;34m\"aggressive\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m                   \u001b[1;34m\"control\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m                   \u001b[1;34m\"payload/keyedHistograms/PLUGIN_ACTIVATION_COUNT/flash\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m                   \u001b[1;31m#\"payload/keyedHistograms/PLUGIN_ACTIVATION_COUNT/java\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m                   )\n",
      "\u001b[1;32m<ipython-input-39-c3ed49517847>\u001b[0m in \u001b[0;36mcompare_histograms\u001b[1;34m(pings, branch_one, branch_two, *histogram_names)\u001b[0m\n\u001b[0;32m     92\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhas_one\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mhas_two\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 94\u001b[1;33m             \u001b[0mcompare_histogram\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me10s\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnone10s\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbranch_one\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbranch_two\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     95\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     96\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcompare_scalars\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmetric\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mgroups\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-39-c3ed49517847>\u001b[0m in \u001b[0;36mcompare_histogram\u001b[1;34m(histogram, e10s, none10s, branch_one, branch_two)\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[0mnone10s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnone10s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m     \u001b[0mpvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrouped_permutation_test\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchi2_distance\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0me10s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnone10s\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_samples\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[0meTotal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me10s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/hadoop/anaconda2/lib/python2.7/site-packages/montecarlino.pyc\u001b[0m in \u001b[0;36mgrouped_permutation_test\u001b[1;34m(statistic, groups, num_samples, alternative)\u001b[0m\n\u001b[0;32m    107\u001b[0m         \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    108\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 109\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mpermutation_test\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatistic\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malternative\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    110\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    111\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_median_difference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/hadoop/anaconda2/lib/python2.7/site-packages/montecarlino.pyc\u001b[0m in \u001b[0;36mpermutation_test\u001b[1;34m(statistic, x, y, num_samples, alternative, grouped)\u001b[0m\n\u001b[0;32m     89\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m     \u001b[0mdensities\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mpool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_permutation_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatistic\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjob_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malternative\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrouped\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 91\u001b[1;33m     \u001b[0mdensities\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdensity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mdensity\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdensities\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     92\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m     \u001b[0mpool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/hadoop/anaconda2/lib/python2.7/multiprocessing/pool.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    565\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    566\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 567\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    568\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    569\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_set\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: zip argument #1 must support iteration"
     ]
    }
   ],
   "source": [
    "compare_histograms(subset,\n",
    "                  \"aggressive\",\n",
    "                  \"control\",\n",
    "                  #\"payload/keyedHistograms/PLUGIN_ACTIVATION_COUNT/flash\",\n",
    "                  #\"payload/keyedHistograms/PLUGIN_ACTIVATION_COUNT/java\"\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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edt2/V1+kC9LOCLsuc0Fm5RUFAAAAAMevSvNieXfsJL1h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\nJEnS2DHZlSRJkiSNHZNdSZIkSdLY+f+5WeMhiViv1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f78962e6090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/HTTP_REQUEST_PER_PAGE are differing by chance is 0.85.\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "zip argument #1 must support iteration",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-78-6adcef113778>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m                   \u001b[1;34m\"control\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m                   \u001b[1;34m\"payload/histograms/HTTP_REQUEST_PER_PAGE\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m                   \u001b[1;34m\"payload/histograms/PLUGIN_TINY_CONTENT\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m                   )\n",
      "\u001b[1;32m<ipython-input-39-c3ed49517847>\u001b[0m in \u001b[0;36mcompare_histograms\u001b[1;34m(pings, branch_one, branch_two, *histogram_names)\u001b[0m\n\u001b[0;32m     92\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhas_one\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mhas_two\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 94\u001b[1;33m             \u001b[0mcompare_histogram\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me10s\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnone10s\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbranch_one\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbranch_two\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     95\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     96\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcompare_scalars\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmetric\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mgroups\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-39-c3ed49517847>\u001b[0m in \u001b[0;36mcompare_histogram\u001b[1;34m(histogram, e10s, none10s, branch_one, branch_two)\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[0mnone10s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnone10s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m     \u001b[0mpvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrouped_permutation_test\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchi2_distance\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0me10s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnone10s\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_samples\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[0meTotal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me10s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/hadoop/anaconda2/lib/python2.7/site-packages/montecarlino.pyc\u001b[0m in \u001b[0;36mgrouped_permutation_test\u001b[1;34m(statistic, groups, num_samples, alternative)\u001b[0m\n\u001b[0;32m    107\u001b[0m         \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    108\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 109\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mpermutation_test\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatistic\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malternative\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    110\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    111\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_median_difference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/hadoop/anaconda2/lib/python2.7/site-packages/montecarlino.pyc\u001b[0m in \u001b[0;36mpermutation_test\u001b[1;34m(statistic, x, y, num_samples, alternative, grouped)\u001b[0m\n\u001b[0;32m     89\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m     \u001b[0mdensities\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mpool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_permutation_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatistic\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjob_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malternative\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrouped\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 91\u001b[1;33m     \u001b[0mdensities\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdensity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mdensity\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdensities\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     92\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m     \u001b[0mpool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/hadoop/anaconda2/lib/python2.7/multiprocessing/pool.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    565\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    566\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 567\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    568\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    569\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_set\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: zip argument #1 must support iteration"
     ]
    }
   ],
   "source": [
    "compare_histograms(subset,\n",
    "                  \"aggressive\",\n",
    "                  \"control\",\n",
    "                  \"payload/histograms/HTTP_REQUEST_PER_PAGE\",\n",
    "                  \"payload/histograms/PLUGIN_TINY_CONTENT\"\n",
    "                  )"
   ]
  }
 ],
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## beta47_plugin_block.py

# coding: utf-8

# ### Plugin Block Experiment

# This is a very a brief introduction to Spark and Telemetry in Python. You should have a look at the [tutorial](https://gist.github.com/vitillo/25a20b7c8685c0c82422) in Scala and the associated [talk](http://www.slideshare.net/RobertoAgostinoVitil/spark-meets-telemetry) if you are interested to learn more about Spark.

# In[38]:

import ujson as json
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import plotly.plotly as py
import IPython

from __future__ import division
from montecarlino import grouped_permutation_test

from moztelemetry.spark import get_pings, get_one_ping_per_client, get_pings_properties

get_ipython().magic(u'pylab inline')
IPython.core.pylabtools.figsize(16, 7)


# In[39]:

def chi2_distance(xs, ys, eps = 1e-10, normalize = True):
    histA = xs.sum(axis=0)
    histB = ys.sum(axis=0)

    if normalize:
        histA = histA/histA.sum()
        histB = histB/histB.sum()

    d = 0.5 * np.sum([((a - b) ** 2) / (a + b + eps)
        for (a, b) in zip(histA, histB)])

    return d

def median_diff(xs, ys):
    return np.median(xs) - np.median(ys)

def compare_histogram(histogram, e10s, none10s, branch_one, branch_two):
    # Normalize individual histograms
    e10s = e10s.map(lambda x: x/x.sum())
    none10s = none10s.map(lambda x: x/x.sum())

    pvalue = grouped_permutation_test(chi2_distance, [e10s, none10s], num_samples=100)

    eTotal = e10s.sum()
    nTotal = none10s.sum()

    eTotal = 100*eTotal/eTotal.sum()
    nTotal = 100*nTotal/nTotal.sum()

    fig = plt.figure()
    fig.subplots_adjust(hspace=0.3)

    ax = fig.add_subplot(1, 1, 1)
    ax2 = ax.twinx()
    width = 0.4
    ylim = max(eTotal.max(), nTotal.max())

    eTotal.plot(kind="bar", alpha=0.5, color="green", label="e10s", ax=ax, width=width, position=0, ylim=(0, ylim + 1))
    nTotal.plot(kind="bar", alpha=0.5, color="blue", label="non e10s", ax=ax2, width=width, position=1, grid=False, ylim=ax.get_ylim())

    ax.legend(ax.get_legend_handles_labels()[0] + ax2.get_legend_handles_labels()[0],
              ["{} ({} samples)".format(branch_one, len(e10s)), "{} ({} samples)".format(branch_two, len(none10s))],
             loc="best")

    # If there are more than 100 labels, hide every other one so we can still read them
    if len(ax.get_xticklabels()) > 100:
        for label in ax.get_xticklabels()[::2]:
            label.set_visible(False)

    plt.title(histogram)
    plt.xlabel(histogram)
    plt.ylabel("Frequency %")
    plt.show()

    print "The probability that the distributions for {} are differing by chance is {:.2f}.".format(histogram, pvalue)

def normalize_uptime_hour(frame):
    frame = frame[frame["payload/simpleMeasurements/uptime"] > 0]
    frame = 60 * frame.apply(lambda x: x/frame["payload/simpleMeasurements/uptime"]) # Metric per hour
    frame.drop('payload/simpleMeasurements/uptime', axis=1, inplace=True)
    return frame

def compare_count_histograms(pings, *histograms_names):
    properties = histograms_names + ("payload/simpleMeasurements/uptime", "e10s")

    frame = pd.DataFrame(get_pings_properties(pings, properties).collect())

    e10s = frame[frame["e10s"] == True]
    e10s = normalize_uptime_hour(e10s)

    none10s = frame[frame["e10s"] == False]
    none10s = normalize_uptime_hour(none10s)

    for histogram in e10s.columns:
        if histogram == "e10s" or histogram.endswith("_parent") or histogram.endswith("_children"):
            continue

        compare_scalars(histogram + " per hour", e10s[histogram].dropna(), none10s[histogram].dropna())


def compare_histograms(pings, branch_one, branch_two, *histogram_names):
    frame = pd.DataFrame(get_pings_properties(pings, histogram_names + ("environment/addons/activeExperiment/branch",)).collect())
    e10s = frame[frame["environment/addons/activeExperiment/branch"] == branch_one]
    none10s = frame[frame["environment/addons/activeExperiment/branch"] == branch_two]

    for histogram in none10s.columns:
        if histogram == "environment/addons/activeExperiment/branch":
            continue

        has_one = np.sum(e10s[histogram].notnull()) > 0
        has_two = np.sum(none10s[histogram].notnull()) > 0

        if has_one and has_two:
            compare_histogram(histogram, e10s[histogram].dropna(), none10s[histogram].dropna(), branch_one, branch_two)

def compare_scalars(metric, *groups):
    print "Median difference in {} is {:.2f}, ({:.2f}, {:.2f}).".format(metric,
                                                                        median_diff(*groups),
                                                                        np.median(groups[0]),
                                                                        np.median(groups[1]))
    print "The probability of this effect being purely by chance is {:.2f}.".         format(grouped_permutation_test(median_diff, groups, num_samples=10000))


# In[40]:

sc.defaultParallelism


# In[64]:

pings = get_pings(sc, app="Firefox", channel="beta", version="47.0", build_id=("20160510000000", "20160517999999"), fraction=0.5)


# In[68]:

def experiment(p):
    return p.get("environment", {}).get("addons", {}).get("activeExperiment", {})


# In[69]:

participants = pings.filter(lambda p: experiment(p).get("id", None) == "plugin-block-beta47@experiments.mozilla.org" and experiment(p).get("branch", None) is not None)


# In[70]:

participants.map(lambda p: (experiment(p).get("branch", None), p)).countByKey()


# In[71]:

subset = get_one_ping_per_client(participants)


# In[72]:

compare_histograms(subset,
                   "aggressive",
                   "control",
                   "payload/keyedHistograms/BLOCKED_ON_PLUGIN_INSTANCE_DESTROY_MS/Shockwave Flash21.0.0.242",
                   "payload/keyedHistograms/BLOCKED_ON_PLUGIN_INSTANCE_INIT_MS/Shockwave Flash21.0.0.242",
                   "payload/keyedHistograms/BLOCKED_ON_PLUGIN_MODULE_INIT_MS/Shockwave Flash21.0.0.242",
                   "payload/keyedHistograms/BLOCKED_ON_PLUGIN_STREAM_INIT_MS/Shockwave Flash21.0.0.242"
                  )


# In[73]:

compare_histograms(subset,
                   "aggressive",
                   "control",
                   "payload/histograms/FLASH_PLUGIN_AREA",
                   "payload/histograms/FLASH_PLUGIN_HEIGHT",
                   "payload/histograms/FLASH_PLUGIN_WIDTH"
                  )


# In[74]:

compare_histograms(subset,
                   "aggressive",
                   "control",
                   # "payload/histograms/PLUGIN_BLOCKED_FOR_STABILITY", # is 0 for control.
                   "payload/histograms/INPUT_EVENT_RESPONSE_MS",
                   "payload/histograms/FLASH_PLUGIN_INSTANCES_ON_PAGE",
                   "payload/histograms/FX_PAGE_LOAD_MS"
                  )


# In[75]:

compare_histograms(subset,
                   "aggressive",
                   "control",
                   #"payload/keyedHistograms/SUBPROCESS_CRASHES_WITH_DUMP/plugin",
                   #"payload/keyedHistograms/SUBPROCESS_ABNORMAL_ABORT/plugin"
                  )


# In[76]:

compare_histograms(subset,
                   "aggressive",
                   "control",
                   #"payload/histograms/PLUGIN_HANG_NOTICE_COUNT",
                   "payload/histograms/PLUGIN_HANG_TIME",
                   "payload/histograms/PLUGIN_HANG_UI_RESPONSE_TIME",
                   "payload/histograms/PLUGIN_HANG_UI_USER_RESPONSE"
                  )


# In[79]:

compare_histograms(subset,
                  "aggressive",
                  "control",
                  #"payload/keyedHistograms/PLUGIN_ACTIVATION_COUNT/flash",
                  #"payload/keyedHistograms/PLUGIN_ACTIVATION_COUNT/java"
                  )


# In[78]:

compare_histograms(subset,
                  "aggressive",
                  "control",
                  "payload/histograms/HTTP_REQUEST_PER_PAGE",
                  "payload/histograms/PLUGIN_TINY_CONTENT"
                  )

	# coding: utf-8

	# ### Plugin Block Experiment

	# This is a very a brief introduction to Spark and Telemetry in Python. You should have a look at the [tutorial](https://gist.github.com/vitillo/25a20b7c8685c0c82422) in Scala and the associated [talk](http://www.slideshare.net/RobertoAgostinoVitil/spark-meets-telemetry) if you are interested to learn more about Spark.

	# In[38]:

	import ujson as json
	import matplotlib.pyplot as plt
	import pandas as pd
	import numpy as np
	import plotly.plotly as py
	import IPython

	from __future__ import division
	from montecarlino import grouped_permutation_test

	from moztelemetry.spark import get_pings, get_one_ping_per_client, get_pings_properties

	get_ipython().magic(u'pylab inline')
	IPython.core.pylabtools.figsize(16, 7)


	# In[39]:

	def chi2_distance(xs, ys, eps = 1e-10, normalize = True):
	histA = xs.sum(axis=0)
	histB = ys.sum(axis=0)

	if normalize:
	histA = histA/histA.sum()
	histB = histB/histB.sum()

	d = 0.5 * np.sum([((a - b) ** 2) / (a + b + eps)
	for (a, b) in zip(histA, histB)])

	return d

	def median_diff(xs, ys):
	return np.median(xs) - np.median(ys)

	def compare_histogram(histogram, e10s, none10s, branch_one, branch_two):
	# Normalize individual histograms
	e10s = e10s.map(lambda x: x/x.sum())
	none10s = none10s.map(lambda x: x/x.sum())

	pvalue = grouped_permutation_test(chi2_distance, [e10s, none10s], num_samples=100)

	eTotal = e10s.sum()
	nTotal = none10s.sum()

	eTotal = 100*eTotal/eTotal.sum()
	nTotal = 100*nTotal/nTotal.sum()

	fig = plt.figure()
	fig.subplots_adjust(hspace=0.3)

	ax = fig.add_subplot(1, 1, 1)
	ax2 = ax.twinx()
	width = 0.4
	ylim = max(eTotal.max(), nTotal.max())

	eTotal.plot(kind="bar", alpha=0.5, color="green", label="e10s", ax=ax, width=width, position=0, ylim=(0, ylim + 1))
	nTotal.plot(kind="bar", alpha=0.5, color="blue", label="non e10s", ax=ax2, width=width, position=1, grid=False, ylim=ax.get_ylim())

	ax.legend(ax.get_legend_handles_labels()[0] + ax2.get_legend_handles_labels()[0],
	["{} ({} samples)".format(branch_one, len(e10s)), "{} ({} samples)".format(branch_two, len(none10s))],
	loc="best")

	# If there are more than 100 labels, hide every other one so we can still read them
	if len(ax.get_xticklabels()) > 100:
	for label in ax.get_xticklabels()[::2]:
	label.set_visible(False)

	plt.title(histogram)
	plt.xlabel(histogram)
	plt.ylabel("Frequency %")
	plt.show()

	print "The probability that the distributions for {} are differing by chance is {:.2f}.".format(histogram, pvalue)

	def normalize_uptime_hour(frame):
	frame = frame[frame["payload/simpleMeasurements/uptime"] > 0]
	frame = 60 * frame.apply(lambda x: x/frame["payload/simpleMeasurements/uptime"]) # Metric per hour
	frame.drop('payload/simpleMeasurements/uptime', axis=1, inplace=True)
	return frame

	def compare_count_histograms(pings, *histograms_names):
	properties = histograms_names + ("payload/simpleMeasurements/uptime", "e10s")

	frame = pd.DataFrame(get_pings_properties(pings, properties).collect())

	e10s = frame[frame["e10s"] == True]
	e10s = normalize_uptime_hour(e10s)

	none10s = frame[frame["e10s"] == False]
	none10s = normalize_uptime_hour(none10s)

	for histogram in e10s.columns:
	if histogram == "e10s" or histogram.endswith("_parent") or histogram.endswith("_children"):
	continue

	compare_scalars(histogram + " per hour", e10s[histogram].dropna(), none10s[histogram].dropna())


	def compare_histograms(pings, branch_one, branch_two, *histogram_names):
	frame = pd.DataFrame(get_pings_properties(pings, histogram_names + ("environment/addons/activeExperiment/branch",)).collect())
	e10s = frame[frame["environment/addons/activeExperiment/branch"] == branch_one]
	none10s = frame[frame["environment/addons/activeExperiment/branch"] == branch_two]

	for histogram in none10s.columns:
	if histogram == "environment/addons/activeExperiment/branch":
	continue

	has_one = np.sum(e10s[histogram].notnull()) > 0
	has_two = np.sum(none10s[histogram].notnull()) > 0

	if has_one and has_two:
	compare_histogram(histogram, e10s[histogram].dropna(), none10s[histogram].dropna(), branch_one, branch_two)

	def compare_scalars(metric, *groups):
	print "Median difference in {} is {:.2f}, ({:.2f}, {:.2f}).".format(metric,
	median_diff(*groups),
	np.median(groups[0]),
	np.median(groups[1]))
	print "The probability of this effect being purely by chance is {:.2f}.". format(grouped_permutation_test(median_diff, groups, num_samples=10000))


	# In[40]:

	sc.defaultParallelism


	# In[64]:

	pings = get_pings(sc, app="Firefox", channel="beta", version="47.0", build_id=("20160510000000", "20160517999999"), fraction=0.5)


	# In[68]:

	def experiment(p):
	return p.get("environment", {}).get("addons", {}).get("activeExperiment", {})


	# In[69]:

	participants = pings.filter(lambda p: experiment(p).get("id", None) == "plugin-block-beta47@experiments.mozilla.org" and experiment(p).get("branch", None) is not None)


	# In[70]:

	participants.map(lambda p: (experiment(p).get("branch", None), p)).countByKey()


	# In[71]:

	subset = get_one_ping_per_client(participants)


	# In[72]:

	compare_histograms(subset,
	"aggressive",
	"control",
	"payload/keyedHistograms/BLOCKED_ON_PLUGIN_INSTANCE_DESTROY_MS/Shockwave Flash21.0.0.242",
	"payload/keyedHistograms/BLOCKED_ON_PLUGIN_INSTANCE_INIT_MS/Shockwave Flash21.0.0.242",
	"payload/keyedHistograms/BLOCKED_ON_PLUGIN_MODULE_INIT_MS/Shockwave Flash21.0.0.242",
	"payload/keyedHistograms/BLOCKED_ON_PLUGIN_STREAM_INIT_MS/Shockwave Flash21.0.0.242"
	)


	# In[73]:

	compare_histograms(subset,
	"aggressive",
	"control",
	"payload/histograms/FLASH_PLUGIN_AREA",
	"payload/histograms/FLASH_PLUGIN_HEIGHT",
	"payload/histograms/FLASH_PLUGIN_WIDTH"
	)


	# In[74]:

	compare_histograms(subset,
	"aggressive",
	"control",
	# "payload/histograms/PLUGIN_BLOCKED_FOR_STABILITY", # is 0 for control.
	"payload/histograms/INPUT_EVENT_RESPONSE_MS",
	"payload/histograms/FLASH_PLUGIN_INSTANCES_ON_PAGE",
	"payload/histograms/FX_PAGE_LOAD_MS"
	)


	# In[75]:

	compare_histograms(subset,
	"aggressive",
	"control",
	#"payload/keyedHistograms/SUBPROCESS_CRASHES_WITH_DUMP/plugin",
	#"payload/keyedHistograms/SUBPROCESS_ABNORMAL_ABORT/plugin"
	)


	# In[76]:

	compare_histograms(subset,
	"aggressive",
	"control",
	#"payload/histograms/PLUGIN_HANG_NOTICE_COUNT",
	"payload/histograms/PLUGIN_HANG_TIME",
	"payload/histograms/PLUGIN_HANG_UI_RESPONSE_TIME",
	"payload/histograms/PLUGIN_HANG_UI_USER_RESPONSE"
	)


	# In[79]:

	compare_histograms(subset,
	"aggressive",
	"control",
	#"payload/keyedHistograms/PLUGIN_ACTIVATION_COUNT/flash",
	#"payload/keyedHistograms/PLUGIN_ACTIVATION_COUNT/java"
	)


	# In[78]:

	compare_histograms(subset,
	"aggressive",
	"control",
	"payload/histograms/HTTP_REQUEST_PER_PAGE",
	"payload/histograms/PLUGIN_TINY_CONTENT"
	)