ilanasegall/stylo_metrics.ipynb Secret

## stylo_metrics.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This script is intended for variables not yet available in the [Experiments Viewer](https://moz-experiments-viewer.herokuapp.com/). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/anaconda2/lib/python2.7/site-packages/IPython/core/magics/pylab.py:161: UserWarning:\n",
      "\n",
      "pylab import has clobbered these variables: ['rand', 'cbrt', 'rank', 'cosh', 'hypot', 'array', 'tan', 'size', 'isnan', 'randn', 'log', 'floor', 'sum', 'sqrt', 'split', 'rint', 'log10', 'sin', 'log2', 'cos', 'ceil', 'broadcast', 'sinh', 'repeat', 'trunc', 'expm1', 'tanh', 'exp', 'log1p', 'mean']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.plotly as py\n",
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import ttest_ind\n",
    "from collections import defaultdict as dd\n",
    "\n",
    "from pyspark.sql import Row\n",
    "from pyspark.sql import SQLContext\n",
    "from pyspark.sql.types import *\n",
    "from pyspark.sql.functions import *\n",
    "import itertools\n",
    "\n",
    "from moztelemetry.dataset import Dataset\n",
    "\n",
    "from scipy.stats import mannwhitneyu\n",
    "\n",
    "py_max = __builtin__.max\n",
    "py_map = __builtin__.map\n",
    "\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Experiment Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "experiment_probes = {}\n",
    "experiment_probes[\"payload/histograms\"] =  [   \n",
    "                \"TIME_TO_DOM_INTERACTIVE_MS\", \n",
    "                \"TIME_TO_DOM_COMPLETE_MS\",\n",
    "                \"MEMORY_TOTAL\", \n",
    "                \"MEMORY_UNIQUE\", \n",
    "                \"MEMORY_VSIZE\",\n",
    "                \"GHOST_WINDOWS\",\n",
    "                ]\n",
    "\n",
    "experiment_probes[\"payload/processes/content/histograms\"] = [\n",
    "                \"TIME_TO_DOM_LOADING_MS\", \n",
    "                 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "probe_names = list(itertools.chain(*experiment_probes.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To investigate:\n",
    "\n",
    "TOTAL_SCROLL_Y always = 0.0\n",
    "no entries for IDLE_NOTIFY_BACK_LISTENERS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "EXPERIMENT_SLUG = \"pref-flip-quantum-css-style-r1-1381147\"\n",
    "START_DATE = \"20170725\" #YYYYMMDD\n",
    "END_DATE = \"20170823\" #YYYYMMDD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#modified from https://stackoverflow.com/questions/13852896/using-a-single-variable-to-index-into-nested-dictionaries\n",
    "def recursive_get(d, keys):\n",
    "    if len(keys) == 1:\n",
    "        return d.get(keys[0],{})\n",
    "    return recursive_get(d.get(keys[0],{}), keys[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def median(lst):\n",
    "    return lst[(len(lst))/2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'submissionDate', u'docType', u'experimentId', u'experimentBranch']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts = Dataset.from_source(\"telemetry-cohorts\")\n",
    "cohorts.schema"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fetch experiment data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fetching 21323.90301MB in 6523 files...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1036529"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pings = cohorts.where(submissionDate = lambda x: x >= START_DATE and x <= END_DATE) \\\n",
    "               .where(experimentId=EXPERIMENT_SLUG) \\\n",
    "               .records(sc, sample=.1)\n",
    "        \n",
    "pings.cache()\n",
    "pings.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What were the different ping types observed for this experiment on this day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pings.map(lambda f: f[\"meta\"].get(\"docType\", \"None\")).countByValue()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "main_pings = pings.filter(lambda x: x[\"meta\"][\"docType\"]==\"main\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PythonRDD[5] at RDD at PythonRDD.scala:48"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_pings.cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (testing samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# subset = main_pings.take(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# subset = sc.parallelize(subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# single = sc.parallelize(main_pings.take(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# single.first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def extract_probes(p):\n",
    "    branch = p.get(\"environment\",{}).get(\"experiments\",{}).get(EXPERIMENT_SLUG, {}).get(\"branch\", \"warning\")\n",
    "    \n",
    "    output = []\n",
    "    for path, probe_names in experiment_probes.iteritems():\n",
    "        for probe_name in probe_names:\n",
    "            probe = recursive_get(p, path.split(\"/\")).get(probe_name, {})\n",
    "            #take all entries in histogram right now. this is probably problematic. inspect more\n",
    "            for k,v in probe.get(\"values\",{}).iteritems():\n",
    "                output.extend([{\"probe\": probe_name, \"branch\": branch, \"val\": float(k)}] * int(v))\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# get the bins we use for the histogram for a probe by looking at all branches\n",
    "def get_bins(probe_name, logscale=False):\n",
    "    \n",
    "    all_branches = [r.val for r in df.where(df.probe == probe_name)\\\n",
    "                                 .collect()]\n",
    "\n",
    "    #remove top 0.5%, bottom 0.5% for easy outlier\n",
    "    trim = int(len(all_branches)/200.0)\n",
    "    all_branches_trimmed = sorted(all_branches)\n",
    "    all_branches_trimmed = all_branches_trimmed[trim:-1*trim]\n",
    "    \n",
    "    if logscale:\n",
    "        if all_branches_trimmed[0] < 1:\n",
    "            all_branches_trimmed = py_map(lambda d: d+1, all_branches_trimmed)\n",
    "        return list(np.linspace(np.log10(all_branches_trimmed[1]), np.log10(all_branches_trimmed[-1]), 10))\n",
    "    \n",
    "    n,b = np.histogram(all_branches_trimmed,10)\n",
    "    return b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# return (proceed bool, reason)\n",
    "def can_chart_pref(pref_name):\n",
    "    n = df.where(df.probe == pref_name).count()\n",
    "    if n==0:\n",
    "        return (False, \"0 entries for pref %s\"%pref_name)\n",
    "    elif n>10000000:\n",
    "        return (False, \"%i values for pref %s\"%(n,pref_name))\n",
    "    return (True, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# get values for branch of experiment for pref, and trim off outliers\n",
    "def get_vals(pref_name, branch):\n",
    "    x_vals = [r.val for r in df.where(df.probe == pref_name)\\\n",
    "                                 .where(df.branch == branch)\\\n",
    "                                 .collect()] \n",
    "    trim = int(len(x_vals)/200.0)\n",
    "    x_trimmed = sorted(x_vals)[trim:-1*trim]\n",
    "    return x_trimmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# return (pval, direction) if significant p value for mannwhitneyu vs control\n",
    "def test_unequal(branch_vals, control_vals, p_threshold=.05):\n",
    "    \n",
    "    try:\n",
    "        r = mannwhitneyu(branch_vals, control_vals)\n",
    "    except:\n",
    "        return None, None\n",
    "    \n",
    "    prefix = \"\"\n",
    "    if r.pvalue < p_threshold:\n",
    "        prefix = \"***\"\n",
    "    \n",
    "    if median(branch_vals) > median(control_vals):\n",
    "        return (prefix + str(r.pvalue), \"> control\")\n",
    "   \n",
    "    return (prefix + str(r.pvalue), \"< control\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# chart histograms for all branches of a probe, log/std, and calculate if any branches vary from the mean\n",
    "def chart_pref(pref_name, logscale):\n",
    "   \n",
    "    sig_branches = []    \n",
    "    fig, axarr = plt.subplots(n_branches, 1, sharex=True)\n",
    "\n",
    "    b = get_bins(pref_name, logscale)\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    print pref_name + \" (logscale=\" + str(logscale) + \")\" + \":\"\n",
    "\n",
    "    control_vals = get_vals(pref_name, \"gecko\")\n",
    "    \n",
    "    for i in range(n_branches):\n",
    "        \n",
    "        if branches[i] == \"control\":\n",
    "            x = control_vals\n",
    "            \n",
    "        else:\n",
    "            x = get_vals(pref_name, branches[i]) \n",
    "        \n",
    "        if logscale: #always assume 0 as lowest val for now\n",
    "            x_trans = py_map(lambda d: d+1, x)\n",
    "            ap,bp,cp = axarr[i].hist(np.log(x_trans), bins=b)\n",
    "            \n",
    "        else: \n",
    "            axarr[i].hist(x, bins=b)\n",
    "            \n",
    "        axarr[i].set_title(branches[i])\n",
    "        \n",
    "        if branches[i] == \"gecko\": continue\n",
    "        \n",
    "        p, direction = test_unequal(x, control_vals)\n",
    "        if p is not None and p.startswith(\"***\"):\n",
    "            print branches[i], p #, direction\n",
    "            \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "probe_dicts = main_pings.flatMap(extract_probes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = sqlContext.createDataFrame(probe_dicts.map(lambda d: Row(**d)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stylo : 169106\n",
      "gecko : 37951\n"
     ]
    }
   ],
   "source": [
    "#check on branch distribution\n",
    "for b,v in main_pings.map(lambda x: x.get(\"environment\",{}).get(\"experiments\",{}).get(EXPERIMENT_SLUG, {}).get(\"branch\", \"warning\")).countByValue().iteritems():\n",
    "    print b, \":\", v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "branches = [i.branch for i in df.select(\"branch\").distinct().collect()]\n",
    "n_branches = len(branches)\n",
    "\n",
    "#for this one - set order\n",
    "branches = [\"gecko\", \"stylo\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIME_TO_DOM_INTERACTIVE_MS (logscale=False):\n",
      "stylo ***6.20131183394e-260\n"
     ]
    },
    {
     "data": {
      "image/png": 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BWf2c5xlgZn/9MTMzM6sSb9tlZmZmVjEOeGZmZmYV44BnZmZmVjEOeGZmZmYV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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f165c6ecb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIME_TO_DOM_INTERACTIVE_MS (logscale=True):\n",
      "stylo ***6.20131183394e-260\n"
     ]
    },
    {
     "data": {
      "image/png": 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zRGxpRFvNzGo5ITOzcSMi+iLiv0fE7wHHAz8B/rJavYfffQ9YD1wo6XRgGnt+\nXQnwb6TFAdOHqJue683M9okTMjMb8yQdJmlSsSwi+oGXgYm5aCtppeVw7gTmklZJ9pMm+u/Jd4BX\ngc9KenuhLWcBLaSJ/WZm+8TbXpjZePAO4CVJy4CngJ8Cv02aeP9nOWYd8PuS/h74P6RtL4pJ0z3A\n9aTXljdHxM4h7qPqP0REdY+y/wF8U1IFmAr8CfBDYHEd+2dm45y3vTCzMS+PUC0CziTNIzsMeA74\n7xHxDznmSNJeZR8HJgP/NsQWGA8CZwH/KSLW1NTttu1FofyTpD3I3k8ahVsB/HlEvNKArprZOOWE\nzMwsk/Q/gQ9GxK+U3RYzO7Qc0BwySX+ejyC5oab8Wkkv56NEHpF0Yk39RElLJfVL2iJpmaRja2KO\nlnS3pEFJA5Juk3RUTcx7JS2XtFVSr6TrJXlenJmNWN6/7GzSikszs4Nqv5MXSacCl5LmaxTLryQt\nO78UmEEawl8paUIhbDHpX3znAbOB44D7am5xD2li7JwcO5v0uqF6n8NIrwbeBswCPg1cBFy7v30y\ns0OPpPdJmgdUgO3AP5TcJDM7BO3XK0tJP0+aIHsZcDWwPiL+LNe9DPxdRHTm75OAPuDTEXFv/v5j\n4PyIuD/HTCedPTcrItZKagG+B7RFxPocMxdYDrwnInrzSqavAe/Oq6mQ9EfAdcC7ImLH/j0SMzuU\nSPo08GXgBeC/Vv+9ZGZ2MO3vCNlS0qaJjxUL867YU4FHq2X5cN01wGm56BTSqFYxZiPQU4iZBQxU\nk7FsFWkfoZmFmGeqyVi2krSs/QP72S8zO8RExFci4rCIOMHJmJmVZcTbXkg6H/h1UmJVayopaeqr\nKe/LdQDNwPacqA0XM5W0v8/PRMROSZtqYoa6T7XuKczMzMzGgBElZJLeQ5r/9VsR8WZjmtQ4kt5J\n2vjxBWBbua0xMzOzMegI4H3Ayoj4Sb0uOtIRsjbgXUCXpOoGiYcDsyVdAZxE2jixmd1Hr5pJx5IA\n9AITJE2qGSVrznXVmNpVl4cDx9TEnFrTvuZC3VDmkg4RNjMzMzsQF5IWINbFSBOyVcCv1pTdQZqQ\nf11E/FBSL2ll5NPws0n9M0nzziAtBtiRY4qT+qcBq3PMamCypJML88jmkJK9NYWYz0uaUphHdiYw\nCDw7TPtfALjrrrtoaWkZUcfHm46ODjo7O8tuxqjgZ5H4OSR+Dm/xs0j8HN7iZwHd3d3MmzcPck5R\nLyNKyCK7erY8AAAgAElEQVRiKzXJjqStwE8iojsXLQaukvQcqbGLgJeAB/I1Nku6HbhB0gCwBbgR\neCIi1uaYDZJWArdKugyYANwEVCKiOvr1cG7LnXmrjXfney3Zw+vUbQAtLS20traOpOvjTlNT0yH/\nDKr8LBI/h8TP4S1+Fomfw1v8LHZT16lP9TjLcrd9MyLi+sIRJZOBbwFnRcT2QlgHsBNYRjr49yHg\n8prrXgAsIY3K7cqxCwr32SXpHOBLwJOk/c7uAK6pQ5/MzMzMDpoDTsgi4jeHKFsILNzDb94A5ufP\ncDGvAfP2cu8XgXP2salmZmZmo5KPGTIzMzMrmROyQ1R7e3vZTRg1/CwSP4fEz+EtfhaJn8Nb/Cwa\nZ7+OThqrJLUC69atW+dJiWZmZjZiXV1dtLW1QTresate1/UImZmZmVnJnJCZmZmZlcwJmZmZmVnJ\nnJCZmZmZlcwJmZmZmVnJnJCZmZmZlWxECZmkz0p6StJg/jwp6WOF+i9L2lXzWVFzjYmSlkrql7RF\n0jJJx9bEHC3p7nyPAUm3STqqJua9kpZL2iqpV9L1kpxgmpmZ2Zgz0gTmReBKoBVoAx4DHpDUUoj5\nOtAMTM2f2l3kFgNnA+cBs4HjgPtqYu4BWoA5OXY26WxMAHLitYJ09NMs4NPARcC1I+yPmZmZWelG\ndJZlRCyvKbpK0mWkpKg7l70RET8e6veSJgGXAOdHxOO57GKgW9KMiFibk7u5pA3X1ueY+cBySZ+L\niN5cfxLw0YjoB56RdDVwnaSFEbFjJP0yMzMzK9N+v+KTdJik84EjgScLVR+R1Cdpg6SbJR1TqGsj\nJYGPVgsiYiPQA5yWi2YBA9VkLFsFBDCzEPNMTsaqVgJNwAf2t09mZmZmZRjRCBmApA8Cq4EjgC3A\n7+akCtLryvuA54FfAv4WWCHptEhnNE0FtkfE5prL9uU68t9Xi5URsVPSppqYviGuUa17aqT9MjMz\ns6Snp4f+/v69B44xU6ZMYdq0aWU3Y0gjTsiADcCHSKNRnwS+Kml2RGyIiHsLcd+T9AzwA+AjwDcO\ntLFmZmbWWD09PUyf3sK2ba+X3ZS6O+KII9m4sXtUJmUjTsjy/Kwf5q/rJc0AFgCXDRH7vKR+4ERS\nQtYLTJA0qWaUrDnXkf/Wrro8HDimJubUmts1F+r2qKOjg6ampt3K2tvbfYq9mZmNyHgcSeru7s7J\n2F2k9XXjRTfbts2jv79/nxOySqVCpVLZrWxwcLARjduvEbJahwETh6qQ9B7gncAruWgdsIO0evL+\nHDMdmEZ6DUr+O1nSyYV5ZHMAAWsKMZ+XNKUwj+xMYBB4dm8N7uzspLW1dZ87aGZmVms8jyQlLaRN\nFQ5dQw3WdHV10dbWVvd7jSghk/QF0jyxHuAdwIXAGcCZeZ+wa0hzyHpJo2JfBL5PmnBPRGyWdDtw\ng6QB0hy0G4EnImJtjtkgaSVwa17BOQG4CajkFZYAD5MSrzslXQm8G1gELImIN/frSZiZmY1Af3//\nOB1JWgFcXXYjDjkjHSE7FvgKKQEaBJ4GzoyIxyQdAfwa8ClgMvAyKRH7q5okqQPYCSwjjaw9BFxe\nc58LgCWk1ZW7cuyCamVE7JJ0DvAl0grPrcAdpITQzMzsIBpvI0ndew+xuhvpPmSf2UPdNuBjw9UX\n4t4A5ufPcDGvAfP2cp0XgXP2dj8zMzOz0c5HDZmZmZmVzAmZmZmZWcmckJmZmZmVzAmZmZmZWcmc\nkJmZmZmVzAmZmZmZWcmckJmZmZmVzAmZmZmZWcmckJmZmZmVbEQJmaTPSnpK0mD+PCnpYzUx10p6\nWdLrkh6RdGJN/URJSyX1S9oiaZmkY2tijpZ0d77HgKTb8lmZxZj3SlouaaukXknXS3KCaWZmZmPO\nSBOYF4ErSYd2tQGPAQ9IagHIB31fAVwKzCCdMblS0oTCNRYDZwPnAbOB40gHkhfdQzocbE6OnQ3c\nUq3MidcK0tFPs4BPAxcB146wP2ZmZmalG1FCFhHLI+KhiPhBRDwXEVcBPyUlRZAOAF8UEQ9GxL+S\nDho/DjgXQNIk4BKgIyIej4j1wMXAhyXNyDEtwFzgDyPiOxHxJOncy/MlTc33mQucBFwYEc9ExErS\n0fSXSxrpgelmZmZmpdrvV3ySDpN0PnAk8KSk44GpwKPVmIjYDKwBTstFp5BGtYoxG4GeQswsYCAn\na1WrgABmFmKeiYj+QsxKoAn4wP72yczMzKwMI07IJH1Q0hbgDeBm4HdzUjWVlDT11fykL9cBNAPb\nc6I2XMxU4NViZUTsBDbVxAx1HwoxZmZmZmPC/rze2wB8iDQa9Ungq5Jm17VVDdbR0UFTU9NuZe3t\n7bS3t5fUIjMzMxttKpUKlUplt7LBwcGG3GvECVlE7AB+mL+uz3O/FgDXAyKNghVHr5qB6uvHXmCC\npEk1o2TNua4aU7vq8nDgmJqYU2ua1lyo26POzk5aW1v3FmZmZmaHsKEGa7q6umhra6v7veqxTcRh\nwMSIeJ6UDM2pVuRJ/DOBJ3PROmBHTcx0YBqwOhetBiZLOrlwjzmkZG9NIeZXJU0pxJwJDALP1qFP\nZmZmZgfNiEbIJH0B+DppEv47gAuBM0jJEKQtLa6S9BzwArAIeAl4ANIkf0m3AzdIGgC2ADcCT0TE\n2hyzQdJK4FZJlwETgJuASkRUR78eJiVed+atNt6d77UkIt4c8VMwMzMzK9FIX1keC3yFlAANAk8D\nZ0bEYwARcb2kI0l7hk0GvgWcFRHbC9foAHYCy4CJwEPA5TX3uQBYQlpduSvHLqhWRsQuSecAXyKN\nvm0F7gCuGWF/zMzMzEo3ooQsIj6zDzELgYV7qH+DtK/Y/D3EvAbM28t9XgTO2Vt7zMzMzEY7HzVk\nZmZmVjInZGZmZmYlc0JmZmZmVjKf+2hmNor09PTQ39+/98AxZsqUKUybNq3sZpiNWk7IzMxGiZ6e\nHqZPb2HbttfLbkrdHXHEkWzc2O2kzGwYTsjMzEaJ/v7+nIzdBbSU3Zw66mbbtnn09/c7ITMbhhMy\nM7NRpwXw8W5mhxJP6jczMzMr2YgSMkl/IWmtpM2S+iTdL+lXamK+LGlXzWdFTcxESUsl9UvaImmZ\npNoDxY+WdLekQUkDkm6TdFRNzHslLZe0VVKvpOslOck0MzOzMWWkryxPJ50r+Z38278FHpbUEhH/\nXoj7OnAR6UBwgDdqrrMYOAs4D9gMLAXuy9evugdoJh0sPoF0NNIt5B38c+K1AngZmAUcB9wJbAeu\nGmG/zGyMGY+rEbu7u8tugpmVZKRHJ328+F3SRcCrQBvw7ULVGxHx46GuIWkScAlwfkQ8nssuBrol\nzYiItZJagLlAW0SszzHzgeWSPpcPGZ8LnAR8NCL6gWckXQ1cJ2lhROwYSd/MbOwYz6sRzezQdKCT\n+icDAWyqKf+IpD5gAHgMuCoiqjFt+b6PVoMjYqOkHuA0YC1pxGugmoxlq/K9ZgIP5JhncjJWtZJ0\n4PgHgKcOsG9mNkqN39WIK4Cry25Ew4y3EcDx1h8r134nZJJEevX47Yh4tlD1ddLrx+eBXyK91lwh\n6bSICGAqsD0iNtdcsi/Xkf++WqyMiJ2SNtXE9A1xjWqdEzKzcW+8rUYcr/8D/wpwGPPmzSu7IWaj\n1oGMkN0MvB/4cLEwIu4tfP2epGeAHwAfAb5xAPczM7Mx6TVgFx7RNBvefiVkkpYAHwdOj4hX9hQb\nEc9L6gdOJCVkvcAESZNqRsmacx35b+2qy8OBY2piTq25XXOhblgdHR00NTXtVtbe3k57e/uefmZm\nZgfEI5o2tlQqFSqVym5lg4ODDbnXiBOynIz9DnBGRPTsQ/x7gHeSxqwB1gE7SKsn788x04FpwOoc\nsxqYLOnkwjyyOaRVm2sKMZ+XNKUwj+xMYBAovkL9Dzo7O2ltHU//UjAzM7N6G2qwpquri7a2trrf\na0QJmaSbgXbgE8BWSdURqcGI2Jb3CbuGNIeslzQq9kXg+6QJ90TEZkm3AzdIGgC2ADcCT0TE2hyz\nQdJK4FZJl5G2vbgJqOQVlgAPkxKvOyVdCbwbWAQsiYg39+NZmJmZmZVipCNknyWtdPyXmvKLga8C\nO4FfAz5FWoH5MikR+6uaJKkjxy4DJgIPAZfXXPMCYAlpdeWuHLugWhkRuySdQ1pV+SSwlbRX2TUj\n7JOZmZlZqUa6D9ked8GPiG3Ax/bhOm8A8/NnuJjXyJvA7iHmReCcvd3PzMzMbDTzMUNmZmZmJXNC\nZmZmZlYyJ2RmZmZmJXNCZmZmZlYyJ2RmZmZmJXNCZmZmZlYyJ2RmZmZmJXNCZmZmZlYyJ2RmZmZm\nJRtRQibpLyStlbRZUp+k+yX9yhBx10p6WdLrkh6RdGJN/URJSyX1S9oiaZmkY2tijpZ0t6RBSQOS\nbstnZRZj3itpuaStknolXS/JSaaZmZmNKSM9y/J00iHf38m//VvgYUktEfHvAPmg7ytI51m+APwN\nsDLHbM/XWQycBZwHbAaWkg4kP71wr3uAZmAO6XDxO4BbyMcp5cRrBem8zFnAccCdwHbgqhH2y2zc\n6unpob+/v+xm1FV3d3fZTTAzq6uRnmX58eJ3SRcBrwJtwLdz8QJgUUQ8mGM+BfQB5wL3SpoEXAKc\nHxGP55iLgW5JMyJiraQWYC7QFhHrc8x8YLmkz0VEb64/CfhoRPQDz0i6GrhO0sKI2DHSh2E23vT0\n9DB9egvbtr1edlPMzGwPRjpCVmsyEMAmAEnHA1OBR6sBEbFZ0hrgNOBe4JR832LMRkk9OWYtacRr\noJqMZavyvWYCD+SYZ3IyVrUS+BLwAeCpA+yb2ZjX39+fk7G7gJaym1NHK4Cry26EmVnd7HdCJkmk\nV4/fjohnc/FUUtLUVxPel+sgvYbcHhGb9xAzlTTy9jMRsVPSppqYoe5TrXNCZvYzLUBr2Y2oI7+y\nNLPx5UBGyG4G3g98uE5tMTMzMzsk7VdCJmkJ8HHg9Ih4pVDVC4g0ClYcvWoG1hdiJkiaVDNK1pzr\nqjG1qy4PB46piTm1pmnNhbphdXR00NTUtFtZe3s77e3te/qZmZmZHUIqlQqVSmW3ssHBwYbca8QJ\nWU7Gfgc4IyJ6inUR8bykXtLKyKdz/CTSvK+lOWwdsCPH3J9jpgPTgNU5ZjUwWdLJhXlkc0jJ3ppC\nzOclTSnMIzsTGASqr1CH1NnZSWvreHp9Y2ZmZvU21GBNV1cXbW1tdb/XiBIySTcD7cAngK2SqiNS\ngxGxLf/zYuAqSc+Rtr1YBLxEmohfneR/O3CDpAFgC3Aj8ERErM0xGyStBG6VdBlp24ubgEpeYQnw\nMCnxujNvtfHufK8lEfHmCJ+DmZmZWWlGOkL2WdKk/X+pKb8Y+CpARFwv6UjSnmGTgW8BZxX2IAPo\nAHYCy4CJwEPA5TXXvABYQlpduSvHLqhWRsQuSeeQVlU+CWwl7VV2zQj7ZGZmZlaqke5Dtk+74EfE\nQmDhHurfAObnz3Axr5E3gd1DzIvAOfvSJjMzM7PRyscMmZmZmZXMCZmZmZlZyZyQmZmZmZXMCZmZ\nmZlZyZyQmZmZmZXMCZmZmZlZyZyQmZmZmZXMCZmZmZlZyZyQmZmZmZVsxAmZpNMlfU3SjyTtkvSJ\nmvov5/LiZ0VNzERJSyX1S9oiaZmkY2tijpZ0t6RBSQOSbpN0VE3MeyUtl7RVUq+k6yU5yTQzM7Mx\nZX+Sl6OA7wJ/TDrXcihfB5qBqfnTXlO/GDgbOA+YDRwH3FcTcw/QAszJsbNJ52MCkBOvFaTjn2YB\nnwYuAq7djz6ZmZmZlWakh4sTEQ+RDgNHkoYJeyMifjxUhaRJwCXA+RHxeC67GOiWNCMi1kpqAeYC\nbRGxPsfMB5ZL+lxE9Ob6k4CPRkQ/8Iykq4HrJC2MiB0j7ZuZmZlZGRr1eu8jkvokbZB0s6RjCnVt\npETw0WpBRGwEeoDTctEsYKCajGWrSCNyMwsxz+RkrGol0AR8oK69MTMzM2ugRiRkXwc+Bfwm8N+A\nM4AVhdG0qcD2iNhc87u+XFeNebVYGRE7gU01MX1DXINCjJmZmdmoN+JXlnsTEfcWvn5P0jPAD4CP\nAN+o9/32R0dHB01NTbuVtbe3095eO9XNzMzMDlWVSoVKpbJb2eDgYEPuVfeErFZEPC+pHziRlJD1\nAhMkTaoZJWvOdeS/tasuDweOqYk5teZ2zYW6YXV2dtLa2jrSrpiZmdkhZKjBmq6uLtra2up+r4Zv\nESHpPcA7gVdy0TpgB2n1ZDVmOjANWJ2LVgOTJZ1cuNQcQMCaQsyvSppSiDkTGASerXM3zMzMzBpm\nxCNkeS+wE0nJEcAJkj5Emt+1CbiGtIVFb477IvB90oR7ImKzpNuBGyQNAFuAG4EnImJtjtkgaSVw\nq6TLgAnATUAlr7AEeJiUeN0p6Urg3cAiYElEvDnSfpmZmZmVZX9eWZ5CevUY+fP3ufwrpL3Jfo00\nqX8y8DIpEfurmiSpA9gJLAMmkrbRuLzmPhcAS0irK3fl2AXVyojYJekc4EvAk8BW4A5SQmhmZmY2\nZuzPPmSPs+dXnR/bh2u8AczPn+FiXgPm7eU6LwLn7O1+ZmZmZqOZjxkyMzMzK5kTMjMzM7OSOSEz\nMzMzK5kTMjMzM7OSOSEzMzMzK5kTMjMzM7OSOSEzMzMzK5kTMjMzM7OSjTghk3S6pK9J+pGkXZI+\nMUTMtZJelvS6pEcknVhTP1HSUkn9krZIWiap9jDxoyXdLWlQ0oCk2/KxTcWY90paLmmrpF5J10ty\nkmlmZmZjyv4kL0cB3yUdkxS1lflcySuAS4EZpCONVkqaUAhbDJwNnAfMBo4jnX9ZdA/QQjpU/Owc\nd0vhPocBK0inDcwCPg1cBFy7H30yMzMzK83+HJ30EOnsSSRpiJAFwKKIeDDHfAroA84F7pU0CbgE\nOD8fw4Ski4FuSTMiYq2kFmAu0BYR63PMfGC5pM/lA8bnAicBH42IfuAZSVcD10laGBE7Rto3MzMz\nszLU9fWepOOBqcCj1bKI2AysAU7LRaeQEsFizEagpxAzCxioJmPZKtKI3MxCzDM5GataCTQBH6hT\nl8zMzMwart7zraaSkqa+mvK+XAfQDGzPidpwMVOBV4uVEbET2FQTM9R9KMSYmZmZjXqeAG9mZmZW\nshHPIduLXkCkUbDi6FUzsL4QM0HSpJpRsuZcV42pXXV5OHBMTcypNfdvLtQNq6Ojg6ampt3K2tvb\naW9v39PPzMzM7BBSqVSoVCq7lQ0ODjbkXnVNyCLieUm9pJWRTwPkSfwzgaU5bB2wI8fcn2OmA9OA\n1TlmNTBZ0smFeWRzSMnemkLM5yVNKcwjOxMYBJ7dUzs7OztpbW09kK6amZnZODfUYE1XVxdtbW11\nv9eIE7K8F9iJpOQI4ARJHwI2RcSLpC0trpL0HPACsAh4CXgA0iR/SbcDN0gaALYANwJPRMTaHLNB\n0krgVkmXAROAm4BKXmEJ8DAp8bozb7Xx7nyvJRHx5kj7ZWZmZlaW/RkhOwX4BmnyfgB/n8u/AlwS\nEddLOpK0Z9hk4FvAWRGxvXCNDmAnsAyYSNpG4/Ka+1wALCGtrtyVYxdUKyNil6RzgC8BT5L2O7sD\nuGY/+mRmZmZWmv3Zh+xx9rIYICIWAgv3UP8GMD9/hot5DZi3l/u8CJyzpxgzMzOz0c6rLM3MzMxK\n5oTMzMzMrGROyMzMzMxK5oTMzMzMrGROyMzMzMxK5oTMzMzMrGROyMzMzMxK5oTMzMzMrGROyMzM\nzMxKVveETNI1knbVfJ6tiblW0suSXpf0iPT/t3f3MXJVZRzHvz8IbS3YoiLd8mJAgaZGU2iRFwsF\nLIJKghqJUjAiGLBasWlCfIkaURIMEFreo4gGCDCGYAiC1SWABIG+SMtL0JZGW1qKtLAUFwIWSnn8\n496l03Fnd+52bu/O2d8nmbRz59ydc548nT577pl7dFDD66MlXSupR9Jrku6QtHdDm/dJulVSr6RX\nJN2Q77NpZmZm1lHKmiF7GpgAdOWPY/peyDcC/w5wHnAE2R6U3ZJG1Z1/BXAK8CVgBrAP8PuG97gN\nmAzMzNvOINs/08zMzKyjDGVz8Va8HREvNXltLnBRRNwDIOlrwEbgC8DtksYB5wCn5/tmIulsYIWk\nIyJiqaTJwMnAtIh4PG9zPvBHSRdExIaSxmVmZmbWdmXNkB0s6XlJ/5J0i6T9ASQdSDZjdn9fw4h4\nFVgCHJ0fOpysUKxv8wywrq7NUcArfcVY7j4ggCPLGZKZmZlZOcooyBYDXyebwZoNHAg8lK/v6iIr\nmjY2nLMxfw2yS51v5YVaszZdwIv1L0bEVmBTXRszMzOzjtD2S5YR0V339GlJS4G1wJeBle1+PzMz\nM7NOV9YasndFRK+kVcBBwIOAyGbB6mfJJgB9lx83AKMkjWuYJZuQv9bXpvFbl7sC769r09S8efMY\nP378dsdmzZrFrFmzWhyVmZmZpa5Wq1Gr1bY71tvbW8p7lV6QSdqDrBi7KSLWSNpA9s3Ip/LXx5Gt\n+7o2P2UZ8Hbe5s68zSTgQ8CivM0iYE9Jh9WtI5tJVuwtGaxPCxYsYOrUqW0YnZmZmaWqv8ma5cuX\nM23atLa/V9sLMkmXAXeTXabcF/gZsAX4Xd7kCuDHkv4JPAtcBKwH7oJskb+k3wDzJb0CvAZcBTwS\nEUvzNisldQO/lvQtYBRwNVDzNyzNzMys05QxQ7Yf2T3CPgC8BDwMHBURLwNExKWSxpLdM2xP4K/A\nZyPirbqfMQ/YCtwBjAb+DMxpeJ8zgGvIvl35Tt52bgnjMTMzMytVGYv6B12IFREXAhcO8PqbwPn5\no1mb/wBfLd5DMzMzs+HFe1mamZmZVcwFmZmZmVnFXJCZmZmZVcwFmZmZmVnFXJCZmZmZVcwFmZmZ\nmVnFXJCZmZmZVcwFmZmZmVnFSt/L0oanWq3mzdRzfbFYt24dPT09VXenrVasWFGgdQ1wTjgO9RyL\njOOwjWNRlo4vyCTNAS4AuoAngfMj4m/V9mr4c0G2Ta1WY/r06UyaNJnNm9+oujsV8gdtxnHYxrHI\nOA7bOBZl6eiCTNJXgMuB84ClZHtgdks6JCLSmuqwUvX09OTF2C3A5Kq700YLgZ9U3QkzMxtERxdk\nZAXYryLiZgBJs4FTgHOAS6vsmHWqycDUqjvRRkUuWZqZWVU6dlG/pN2AacD9fcciIoD7gKOr6peZ\nmZlZUZ08Q7YXsCuwseH4RmBSk3PGQNGFzkOzevVqNm3aVPr7DNXatWu5/vrrC583duxYJJXQo+qs\nX7+ehQsX5s8Wktas0iP5n62Maz1wa7ndaZsi4yqqyjiUOa6haFcshtu4imoWh04fVzMDjauTPica\nrQF2vAaoO3/MjvVne8omlTqPpInA88DREbGk7vglwIyI+L9ZMkln0LmZZGZmZsPHmRFxW7t+WCfP\nkPUAW4EJDccnABuanNMNnAk8C2wurWdmZmaWqjHAAWQ1Rdt07AwZgKTFwJKImJs/F7AOuCoiLqu0\nc2ZmZmYt6uQZMoD5wI2SlrHtthdjgRur7JSZmZlZER1dkEXE7ZL2An5OdqnyCeDkiHip2p6ZmZmZ\nta6jL1mamZmZpaBj70NmZmZmlorkCjJJcyStkfRfSYslfWKAtsdJeqfhsVXS3juzz+0m6VhJf5D0\nfD6mU1s453hJyyRtlrRK0lk7o69lKhqHhPPhh5KWSnpV0kZJd0o6pIXzksqJocQh4ZyYLelJSb35\n41FJnxnknKTyAYrHIdV8aCTpB/nY5g/SLrmcaNRKLNqVF0kVZHV7W/4UOIxss/HufJ1ZMwEcTLY5\neRcwMSJeLLuvJdudbD3dt8nGNyBJBwD3kO16MAW4ErhB0qfL6+JOUSgOuRTz4VjgauBI4ERgN+Be\nSe9pdkKiOVE4DrkUc+I54Ptk+4RNAx4A7pLU70auieYDFIxDLsV8eFc+iXEe2f+fA7U7gDRz4l2t\nxiK343kREck8gMXAlXXPRXZb4e81aX8c2b3MxlXd9xJj8g5w6iBtLgGeajhWAxZW3f+dHIfk8yEf\n5155PI4Z4TnRShxGRE7kY30ZOHuk5kOLcUg6H4A9gGeATwF/AeYP0DbpnCgYi7bkRTIzZDuwt6WA\nJyT9W9K9kj5Zbk+HpaPI4lSvm5G5J+hIyIc9yX6bG2hvr5GQE63EARLPCUm7SDqd7JZBi5o0Sz4f\nWowDpJ0P1wJ3R8QDLbRNPSeKxALakBcdfduLBkPZ2/IF4JvAY8Bo4FzgQUlHRMQTZXV0GOqi/7iN\nkzQ6It6soE9VSD4fJAm4Ang4Iv4xQNOkc6JAHJLNCUkfIys8xgCvAV+MiJVNmiebDwXjkHI+nA4c\nChze4ikp50TRWLQlL1IqyAqLiFXAqrpDiyV9hOwGs8ktTrSBjZB8uA74KDC96o5UrKU4JJ4TK8nW\n/owHTgNuljRjgGIkVS3HIdV8kLQf2S8oJ0bElqr7U6WhxKJdeZHMJUuGtrdlf5YCB7WrUx1iA/3H\n7dVO/i2nTZLJB0nXAJ8Djo+IFwZpnmxOFIxDf5LIiYh4OyJWR8TjEfEjsoXLc5s0TzYfCsahPynk\nwzTgg8BySVskbSFbFzVX0lv5jHKjVHNiKLHoT+G8SKYgyyvZZcDMvmN54GYCjxb4UYeSTT+OJIuo\ni1vuJAZeRzFSJJEPeRHyeeCEiFjXwilJ5sQQ4tCfJHKiH7uQXW7pT5L50MRAcehPCvlwH/BxsrFM\nyR+PAbcAU/L12I1SzYmhxKI/hfMitUuWA+5tKekXwD4RcVb+fC6wBvg72fqBc4ETgI7+2q6k3ckq\n875K/sOSpgCbIuK5xjgAvwTmSLoE+C3ZP7LTyGYROlbROCScD9cBs4BTgdcl9f1W2xsRm/M2FwP7\nppwTQ4lDwjlxMfAnYB3wXuBMslmAk/LXR8pnRKE4pJoPEfE6sN1aSkmvAy9HxIr8efKfETC0WLQr\nL0K3V1UAAADuSURBVJIqyGLwvS27gP3rThlFdt+yfYA3gKeAmRHx0M7rdSkOJ/uabuSPy/PjNwHn\n0BCHiHhW0inAAuC7ZLcK+UZENH6DptMUigPp5sNssvE/2HD8bODm/O8TST8nCseBdHNib7J/BxOB\nXrJxnVT3jbKR8hlRKA6kmw/9aZwJGgmfEc0MGAvalBfey9LMzMysYsmsITMzMzPrVC7IzMzMzCrm\ngszMzMysYi7IzMzMzCrmgszMzMysYi7IzMzMzCrmgszMzMysYi7IzMzMzCrmgszMzMysYi7IzMzM\nzCrmgszMzMysYi7IzMzMzCr2PyyHwUDeTmNQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f167d839ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "TIME_TO_DOM_COMPLETE_MS (logscale=False):\n",
      "stylo ***0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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ApcBI4G7gotJ3ngksIs2e7c7Z2dXGiOiWNAO4DlhJWm9vCXDFXl6jmZmZ2aBV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1674b78310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIME_TO_DOM_COMPLETE_MS (logscale=True):\n",
      "stylo ***0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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g4DHgt9pQBc3tyk8B9wE/B/xn4PokJ7SvJ1kKPFVVj/addlPbRvv9od7Gqtqa\n5OG+mk1TnGOy7c7pjkuSJKkru7MP2d3Ay2hmo34H+ESSlVV1d1Vd3VP3jSR3Ad8GXgV8aU87u6eS\nPJdm48f7gS3d9kaSJM1CBwEvAtZV1Y9m6qTTDmTt+qzvtD/ekeRY4GzgjClq70syDhxFE8g2AguS\nLOybJVvSttF+73/qcn/g0L6al/ddbklP246cSPMSYUmSpD3xRpqHEGfETOzUvx//59Uk20nyAuC5\nwOQGibcDT9M8Pdm7qP8ImtugtN8XJzm6Zx3ZKpodsm/rqfnjJIf1rCN7DTABfHMnfb0f4IorrmD5\n8uU7KZsb1qxZw9q1a7vuxl7hWOcmxzo3Oda5ab6MdXR0lNWrV0ObKWbKtAJZkj+jWSc2RvOqkjcC\nrwRe0+4T9j6aNWQbaWbF/hz4Fs2Ce6rq0SQfAS5MsplmDdrFwC1Vtb6tuTvJOuBDSc4AFgCXACNV\nNTn79Xma4PXJ9tUlz6PZpfvSqvrxToawBWD58uWsWLFiOkOflRYtWjQvxgmOda5yrHOTY52b5tNY\nWzO69Gm6M2SHAx+nCUATwNeB11TVF5McBPwy8Caa15I8SBPE/qQvJK0BtgLX0Mys3QCc2Xed04BL\naZ6u3NbWnj3ZWFXbkrwB+CDNE56PAx+jCYSSJEmzyrQCWVW9bSdtW4DX7qi9p+5J4Kz2a0c1jwCr\nn+E83wPe8EzXkyRJ2tf5qiFJkqSOGcjmsOHh/rdWzV2OdW5yrHOTY52b5tNYB2GPX500myRZAdx+\n++23z7eFh5IkaQZs2LCBoaEhaF7xuGGmzusMmSRJUscMZJIkSR0zkEmSJHXMQCZJktQxA5kkSVLH\nDGSSJEkdM5BJkiR1zEAmSZLUMQOZJElSx6b1cvEk7wDOAF7UHvoGcF5V3dBTcx7wNmAxcAtwRlXd\n29N+IHAh8HvAgcA64J1V9VBPzXOAS2leHr4N+BRwdlU93lPzQuCvgFcBjwGfAP6wqrZNZ0ySJI2N\njTE+Pt51N/bYYYcdxhFHHNF1N7QbphXIgO8B5wD/GwjwFuDTSX6lqkaTnAO8C3gTcD/wn4B1SZZX\n1VPtOS4CTgJOAR4FLqMJXK/ouc5VwBJgFbAA+BhwObAaIMl+wPXAg8DxwPOBTwJPAe+Z5pgkSfPY\n2NgYy5YtZ8uWJ7ruyh476KCDueeeUUPZLDStQFZV1/Udek+SM2hC0ShwNnB+VX0WIMmbgE3AycDV\nSRYCpwPBDl2nAAAcB0lEQVSnVtXNbc1bgdEkx1bV+iTLgRNp3hF1R1tzFnBdkndX1ca2/SXAq6tq\nHLgryXuB9yc5t6qe3o3fC0nSPDQ+Pt6GsSuA5V13Zw+MsmXLasbHxw1ks9B0Z8h+op2l+l3gYOCr\nSY4ElgI3TdZU1aNJbgNOAK4Gjmmv2VtzT5KxtmY9TbjbPBnGWjcCBRwHfLqtuasNY5PWAR8EXgrc\nubvjkiTNV8uBFV13QvPUtBf1J/mlJI8BTwIfAH6rqu6hCWNFMyPWa1PbBs1tyKeq6tGd1CwFHupt\nrKqtwMN9NVNdh54aSZKkWWF3ZsjuBl4GLAJ+B/hEkpUz2itJkqR5ZNqBrF2f9Z32xzuSHEuzduwC\nmoX+S9h+9moJMHn7cSOwIMnCvlmyJW3bZM3hvddMsj9waF/Ny/u6tqSnbafWrFnDokWLtjs2PDzM\n8PDwM31UkqR92ujoaNdd2GP7ytOiIyMjjIyMbHdsYmJiINfa7TVkPfYDDqyq+5JspHky8usA7SL+\n42iepAS4HXi6rbm2rVkGHAHc2tbcCixOcnTPOrJVNGHvtp6aP05yWM86stcAE8A3n6nDa9euZcUK\n1wlIkuaSHwD7sXr16q47ssf2ladFp5qs2bBhA0NDQzN+renuQ/ZnwOeAMeDZwBuBV9KEIWi2tHhP\nkntptr04H3iAZiH+5CL/jwAXJtlMs3/YxcAtVbW+rbk7yTrgQ+0TnAuAS4CR9glLgM/TBK9Ptltt\nPK+91qVV9eNp/y5IkjTrPUKzdadPi85G050hOxz4OE0AmqCZCXtNVX0RoKouSHIwzZ5hi4EvAyf1\n7EEGsAbYClxDszHsDcCZfdc5jWZj2Btp/nZdQ3NblPY625K8geapyq8Cj9PsVfa+aY5HkqQ5xqdF\nZ6Pp7kP2tl2oORc4dyftTwJntV87qnmEdhPYndR8j2Ynf0mSpFnNd1lKkiR1zEAmSZLUMQOZJElS\nxwxkkiRJHTOQSZIkdcxAJkmS1DEDmSRJUscMZJIkSR0zkEmSJHXMQCZJktQxA5kkSVLHphXIkvxR\nkvVJHk2yKcm1SX6hr+ajSbb1fV3fV3NgksuSjCd5LMk1SQ7vq3lOkiuTTCTZnOTDSQ7pq3lhkuuS\nPJ5kY5ILkhgyJUnSrDLd8PIK4BLgOODXgWcBn0/yU311nwOWAEvbr+G+9ouA1wOnACuB5wOf6qu5\niuaV9ava2pXA5ZONbfC6nuYF6ccDbwbeApw3zTFJkiR16oDpFFfV63p/TvIW4CFgCPhKT9OTVfXD\nqc6RZCFwOnBqVd3cHnsrMJrk2Kpan2Q5cCIwVFV3tDVnAdcleXdVbWzbXwK8uqrGgbuSvBd4f5Jz\nq+rp6YxNkvamsbExxsfHu+7GHjvssMM44ogjuu6GNOtNK5BNYTFQwMN9x1+VZBOwGfgi8J6qmqwZ\naq9702RxVd2TZAw4AVhPM+O1eTKMtW5sr3Uc8Om25q42jE1aB3wQeClw5x6OTZIGYmxsjGXLlrNl\nyxNdd2WPHXTQwdxzz6ihTNpDux3IkoTm1uNXquqbPU2fo7n9eB/wc8B/Bq5PckJVFc0tzKeq6tG+\nU25q22i/P9TbWFVbkzzcV7NpinNMthnIJO2TxsfH2zB2Bc3KjNlqlC1bVjM+Pm4gk/bQnsyQfQD4\nReBXew9W1dU9P34jyV3At4FXAV/ag+tJ0hyzHFjRdSck7QN2K5AluRR4HfCKqvrBzmqr6r4k48BR\nNIFsI7AgycK+WbIlbRvt9/6nLvcHDu2reXnf5Zb0tO3QmjVrWLRo0XbHhoeHGR7uf/ZAkiTNVyMj\nI4yMjGx3bGJiYiDXmnYga8PYbwKvrKqxXah/AfBcYDK43Q48TfP05LVtzTLgCODWtuZWYHGSo3vW\nka0CAtzWU/PHSQ7rWUf2GmAC6L2F+n9Zu3YtK1b4X6WSJGnHppqs2bBhA0NDQzN+rWkFsiQfoNnC\n4jeAx5NMzkhNVNWWdp+w99GsIdtIMyv258C3aBbcU1WPJvkIcGGSzcBjwMXALVW1vq25O8k64ENJ\nzgAW0Gy3MdI+YQnweZrg9ckk5wDPA84HLq2qH+/G74UkSVInpjtD9g6aJx3/tu/4W4FPAFuBXwbe\nRPME5oM0QexP+kLSmrb2GuBA4AbgzL5zngZcSvN05ba29uzJxqraluQNNE9VfhV4HPgYTSCUJEma\nNaa7D9lON5Ktqi3Aa3fhPE8CZ7VfO6p5BFj9DOf5HvCGZ7qepLljLuzfNTo62nUXJO1j9nQfMkna\na+bS/l2S1MtAJmnWmDv7d10PvLfrTkjahxjIJM1Cs33/Lm9ZStredF8uLkmSpBlmIJMkSeqYgUyS\nJKljBjJJkqSOGcgkSZI6ZiCTJEnqmIFMkiSpYwYySZKkjhnIJEmSOjatQJbkj5KsT/Jokk1Jrk3y\nC1PUnZfkwSRPJPlCkqP62g9MclmS8SSPJbkmyeF9Nc9JcmWSiSSbk3w4ySF9NS9Mcl2Sx5NsTHJB\nEkOmJEmaVaYbXl4BXAIcB/w68Czg80l+arIgyTnAu4C3A8cCjwPrkizoOc9FwOuBU4CVwPOBT/Vd\n6yqa96OsamtXApf3XGc/mhfCHQAcD7wZeAtw3jTHJEmS1Klpvcuyql7X+3OStwAPAUPAV9rDZwPn\nV9Vn25o3AZuAk4GrkywETgdOraqb25q3AqNJjq2q9UmWAycCQ1V1R1tzFnBdkndX1ca2/SXAq6tq\nHLgryXuB9yc5t6qenu5vhiRJUhf29PbeYqCAhwGSHAksBW6aLKiqR4HbgBPaQ8fQBMHemnuAsZ6a\n44HNk2GsdWN7reN6au5qw9ikdcAi4KV7OC5JkqS9ZrcDWZLQ3Hr8SlV9sz28lCY0beor39S2ASwB\nnmqD2o5qltLMvP1EVW2lCX69NVNdh54aSZKkfd60bln2+QDwi8CvzlBfJEmS5qXdCmRJLgVeB7yi\nqn7Q07QRCM0sWO/s1RLgjp6aBUkW9s2SLWnbJmv6n7rcHzi0r+blfV1b0tO2Q2vWrGHRokXbHRse\nHmZ4eHhnH5MkTWF0dLTrLuyR2d5/Dc7IyAgjIyPbHZuYmBjItaYdyNow9pvAK6tqrLetqu5LspHm\nycivt/ULadZ9XdaW3Q483dZc29YsA44Abm1rbgUWJzm6Zx3ZKpqwd1tPzR8nOaxnHdlrgAlg8hbq\nlNauXcuKFSumO3RJ0nZ+AOzH6tWru+6INBBTTdZs2LCBoaGhGb/WtAJZkg8Aw8BvAI8nmZyRmqiq\nLe2vLwLek+Re4H7gfOAB4NPQLPJP8hHgwiSbgceAi4Fbqmp9W3N3knXAh5KcASyg2W5jpH3CEuDz\nNMHrk+1WG89rr3VpVf14mr8PkqRpewTYBlxBs0vRbHU98N6uO6F5brozZO+gWbT/t33H3wp8AqCq\nLkhyMM2eYYuBLwMnVdVTPfVrgK3ANcCBwA3AmX3nPA24lObpym1t7dmTjVW1LckbgA8CX6XZ7+xj\nwPumOSZJ0h5ZDszmuw7eslT3prsP2S49lVlV5wLn7qT9SeCs9mtHNY8AO50Hr6rvAW/YlT5JkiTt\nq3zNkCRJUscMZJIkSR0zkEmSJHXMQCZJktQxA5kkSVLHDGSSJEkdM5BJkiR1zEAmSZLUsd16ubik\n2WdsbIzx8fFnLtyH+RJoSXOVgUyaB8bGxli2bDlbtjzRdVckSVMwkEnzwPj4eBvGfAm0JO2Lph3I\nkrwC+P+AIeB5wMlV9Zme9o8Cb+772A1V9bqemgOBC4Hfo3m5+DrgnVX1UE/Nc2heLv4GmpeLfwo4\nu6oe76l5IfBXwKuAx2hecP6HVbVtuuOS5gdfAi1J+6LdWdR/CPA14J1A7aDmc8ASYGn7NdzXfhHw\neuAUYCXwfJrA1esqmv/3WNXWrgQun2xMsh/Nfy4fABxPEwLfApy3G2OSJEnqzLRnyKrqBuAGgCTZ\nQdmTVfXDqRqSLAROB06tqpvbY28FRpMcW1XrkywHTgSGquqOtuYs4Lok766qjW37S4BXV9U4cFeS\n9wLvT3JuVT093bFJkiR1YVDbXrwqyaYkdyf5QJJDe9qGaILgTZMHquoeYAw4oT10PLB5Moy1bqSZ\nkTuup+auNoxNWgcsAl46o6ORJEkaoEEEss8BbwL+NfAfgFcC1/fMpi0FnqqqR/s+t6ltm6x5qLex\nqrYCD/fVbJriHPTUSJIk7fNm/CnLqrq658dvJLkL+DbNwvsvzfT1JEmSZruBb3tRVfclGQeOoglk\nG4EFSRb2zZItadtovx/ee54k+wOH9tW8vO9yS3radmjNmjUsWrRou2PDw8MMD/c/eyBJkuarkZER\nRkZGtjs2MTExkGsNPJAleQHwXOAH7aHbgadpnp68tq1ZBhwB3NrW3AosTnJ0zzqyVUCA23pq/jjJ\nYT3ryF4DTADf3Fmf1q5dy4oVs/nRf0mSNGhTTdZs2LCBoaGhGb/W7uxDdgjNbNfkmrAXJ3kZzfqu\nh4H30WxhsbGt+3PgWzQL7qmqR5N8BLgwyWaa/cMuBm6pqvVtzd1J1gEfSnIGsAC4BBhpn7AE+DxN\n8PpkknNo9kQ7H7i0qn483XFJkiR1ZXdmyI6hufVY7ddftsc/TrM32S/TLOpfDDxIE8T+pC8krQG2\nAtfQbAx7A3Bm33VOo9kY9kaajWGvAc6ebKyqbUneAHwQ+CrwOPAxmkAoSZI0a+zOPmQ3s/OnM1+7\nC+d4Ejir/dpRzSPA6mc4z/dodvKXJEmatQa1D5kkSZJ2kYFMkiSpYwYySZKkjhnIJEmSOmYgkyRJ\n6piBTJIkqWMGMkmSpI4ZyCRJkjpmIJMkSeqYgUySJKljBjJJkqSOGcgkSZI6Nu1AluQVST6T5PtJ\ntiX5jSlqzkvyYJInknwhyVF97QcmuSzJeJLHklyT5PC+muckuTLJRJLNST6c5JC+mhcmuS7J40k2\nJrkgiSFTkiTNKrsTXg4Bvga8E6j+xiTnAO8C3g4cCzwOrEuyoKfsIuD1wCnASuD5wKf6TnUVsBxY\n1dauBC7vuc5+wPXAAcDxwJuBtwDn7caYJEmSOnPAdD9QVTcANwAkyRQlZwPnV9Vn25o3AZuAk4Gr\nkywETgdOraqb25q3AqNJjq2q9UmWAycCQ1V1R1tzFnBdkndX1ca2/SXAq6tqHLgryXuB9yc5t6qe\nnu7YJEmSujCjt/eSHAksBW6aPFZVjwK3ASe0h46hCYK9NfcAYz01xwObJ8NY60aaGbnjemruasPY\npHXAIuClMzQkSZKkgZvp9VZLaULTpr7jm9o2gCXAU21Q21HNUuCh3saq2go83Fcz1XXoqZEkSdrn\nTfuW5VywZs0aFi1atN2x4eFhhoeHO+qRJEna14yMjDAyMrLdsYmJiYFca6YD2UYgNLNgvbNXS4A7\nemoWJFnYN0u2pG2brOl/6nJ/4NC+mpf3XX9JT9sOrV27lhUrVjzjYCRJ0vw11WTNhg0bGBoamvFr\nzegty6q6jyYMrZo81i7iPw74anvoduDpvpplwBHAre2hW4HFSY7uOf0qmrB3W0/Nv0xyWE/Na4AJ\n4JszNCRJkqSBm/YMWbsX2FE04QjgxUleBjxcVd+j2dLiPUnuBe4HzgceAD4NzSL/JB8BLkyyGXgM\nuBi4parWtzV3J1kHfCjJGcAC4BJgpH3CEuDzNMHrk+1WG89rr3VpVf14uuOSJEnqyu7csjwG+BLN\n4v0C/rI9/nHg9Kq6IMnBNHuGLQa+DJxUVU/1nGMNsBW4BjiQZhuNM/uucxpwKc3Tldva2rMnG6tq\nW5I3AB+kmX17HPgY8L7dGJMkSVJndmcfspt5hludVXUucO5O2p8Ezmq/dlTzCLD6Ga7zPeANO6uR\nJEna1/maIUmSpI4ZyCRJkjpmIJMkSeqYgUySJKljBjJJkqSOGcgkSZI6ZiCTJEnqmIFMkiSpYwYy\nSZKkjhnIJEmSOmYgkyRJ6tiMB7Ik70uyre/rm3015yV5MMkTSb6Q5Ki+9gOTXJZkPMljSa5Jcnhf\nzXOSXJlkIsnmJB9OcshMj0eSJGnQBjVD9o/AEmBp+/Vrkw1JzgHeBbwdOBZ4HFiXZEHP5y8CXg+c\nAqwEng98qu8aVwHLgVVt7Urg8gGMRZIkaaAOGNB5n66qH+6g7Wzg/Kr6LECSNwGbgJOBq5MsBE4H\nTq2qm9uatwKjSY6tqvVJlgMnAkNVdUdbcxZwXZJ3V9XGAY1LkiRpxg1qhuznk3w/ybeTXJHkhQBJ\njqSZMbtpsrCqHgVuA05oDx1DExR7a+4Bxnpqjgc2T4ax1o1AAccNZkiSJEmDMYhA9vfAW2hmsN4B\nHAn8Xbu+aylNaNrU95lNbRs0tzqfaoPajmqWAg/1NlbVVuDhnhpJkqRZYcZvWVbVup4f/zHJeuC7\nwO8Cd8/09SRJkma7Qa0h+4mqmkjyLeAo4G+B0MyC9c6SLQEmbz9uBBYkWdg3S7akbZus6X/qcn/g\n0J6aHVqzZg2LFi3a7tjw8DDDw8O7OCpJkjTXjYyMMDIyst2xiYmJgVxr4IEsyU/ThLGPV9V9STbS\nPBn59bZ9Ic26r8vaj9wOPN3WXNvWLAOOAG5ta24FFic5umcd2SqasHfbM/Vp7dq1rFixYgZGJ0mS\n5qqpJms2bNjA0NDQjF9rxgNZkr8A/obmNuXPAH8K/Bj4b23JRcB7ktwL3A+cDzwAfBqaRf5JPgJc\nmGQz8BhwMXBLVa1va+5Osg74UJIzgAXAJcCIT1hKkqTZZhAzZC+g2SPsucAPga8Ax1fVjwCq6oIk\nB9PsGbYY+DJwUlU91XOONcBW4BrgQOAG4My+65wGXErzdOW2tvbsAYxHkiRpoAaxqP8ZF2JV1bnA\nuTtpfxI4q/3aUc0jwOrp91CSJGnf4rssJUmSOmYgkyRJ6piBTJIkqWMGMkmSpI4ZyCRJkjpmIJMk\nSeqYgUySJKljA391kjTbjY2NMT4+3nU39sjo6GjXXZAk7YSBTNqJsbExli1bzpYtT3TdFUnSHGYg\nk3ZifHy8DWNXAMu77s4euB54b9edkCTtgIFM2iXLgRVdd2IPeMtSkvZlLuqfw0ZGRrruwl4zn8YK\njnVucqxzk2PVrpn1gSzJmUnuS/LPSf4+ycu77tO+Yj6FlPk01vn1j55jnZsc69w0n8Y682Z1IEvy\ne8BfAu8DjgbuBNYlOazTjkmSJE3DrA5kwBrg8qr6RFXdDbwDeAI4vdtuSZIk7bpZG8iSPAsYAm6a\nPFZVBdwInNBVvyRJkqZrNj9leRiwP7Cp7/gmYNkOPnMQ7NubZD788MN85zvfmZFzffe73+Wv//qv\nZ+Rc03XooYfy7Gc/e69tqPrAAw9w5ZVXzvh577vvvvZX17PvPKn4ADDdsd7Sft+XxrEr+sc6W8fR\nb6px7M6fa9d2989jXxvrIP9e7c2xdv2/j5kaa/Pv7r76/9U9/TpoJs+bZlJp9knyPOD7wAlVdVvP\n8T8HVlbV/zVLluQ09q1/BSRJ0uz0xqq6aqZONptnyMaBrcCSvuNLgI07+Mw64I3A/cCWgfVMkiTN\nVQcBL6LJFDNm1s6QAST5e+C2qjq7/TnAGHBxVf1Fp52TJEnaRbN5hgzgQuBjSW4H1tM8dXkw8LEu\nOyVJkjQdszqQVdXV7Z5j59HcqvwacGJV/bDbnkmSJO26WX3LUpIkaS6YtfuQSZIkzRVzLpBN592W\nSV6ZZFvf19Ykh+/NPk9Xklck+UyS77d9/o1d+MyrktyeZEuSbyV5897o656a7lhn658pQJI/SrI+\nyaNJNiW5Nskv7MLnZt2f7e6Mdbb+2Sb5/9u7v1CpqiiO498llIZigVneUBJM6M+NewkpMbVSkygw\ngpCeIotKfMieKuihngqJLKmEIAhF6jmKIAsTEb2ImYmECUV/IKOwuEVBXmn1sLc2Dmf+nHHGc9fp\n94ED3pk9utf85uCac/c5Z72ZfWFm43nbZ2Z3dXhNuEyhfK1RMy1iZs/k+W/uMC5kto26qTVqtmb2\nXMG8v+zwmr5kWquGrMd7WzqwEJiTtyF3/3nQcz1P00nr5TaQ5t+Wmc0HPiDd1WAE2AK8ZWZ3Dm6K\nfVOq1ixipgDLgNeAW4BVwEXATjO7pNULAmdbutYsYrY/AE8DN5HuLrILeM/MrisaHDhTKFlrFjHT\nc+Qv/o+R/s9pN24+cbMFuq81i5rtUdK69DPzXtpqYF8zdffabMAYsKXhZyNdOvipFuNvI13LbGbV\ncz+Pmv8B1nQYswk40vTYu8CHVc9/ALWGz7ShlstzzUv/B9l2U2udsj0JrKtzpl3WGj5TYAbwFbAC\n+BTY3GZs6GxL1hoyW9IBnUMlxvct09ocIbPe721pwGEz+9HMdprZksHOtBKLSe9Do4+o7z0/65Lp\nZaRvmL+2GVOXbLupFYJna2ZTzOwB0uV59rcYVotMu6wVgmcKvAG87+67uhgbPdsytULcbBfmZTJf\nm9kOM5vXZmzfMg192Ysmvdzb8gTwOHAQmAo8Cuw2s5vd/fCgJlqBORS/LzPNbKq7/13BnAalFpma\nmQGvAnvdvd36hfDZlqg1bLZmNkxqSqYBfwD3ufuxFsNDZ1qy1rCZAuSGcxRY1OVLwmbbQ61Rsx0D\nHiIdCRwCngf2mNmwu/9ZML5vmdapISvN3Y8DxxseGjOzBaQLzIZbaCm1ynQrcD1wa9UTuQC6qjV4\ntsdI60suBe4HtpvZ8jaNSmRd1xo5UzObS/oiscrdJ6qezyD1UmvUbN298XZIR83sAPAdsBZ4e5D/\ndm1+ZUlv97YscgC4pl+TmiR+ovh9+X0yfyPro1CZmtnrwN3A7e5+osPw0NmWrLVIiGzd/bS7f+Pu\nn7v7s6QF0RtbDA+daclai4TIlLREZjZwyMwmzGyCtG5qo5mdykd+m0XNtpdai0TJ9ix3Hyc1lq3m\n3bdMa9OQ5a79M2Dlmcfyh2QlsK/EXzVKOtRaJ/tpeF+y1bRf11EnYTLNDcq9wB3u/n0XLwmbbQ+1\nFgmTbZMppF/jFAmbaQvtai0SJdNPgBtJ8x3J20FgBzCS1zA3i5ptL7UWiZLtWWY2g9SMtZp3/zKt\n+oyGPp8dsRb4C3gQuBZ4k3SGz+z8/IvAtobxG4E1wALgBtIh2QnSt/XK62lT53TSDjFKOjPtyfzz\nvBZ1ziet5dhEWk+3AThFOvxceT19rjVkpnnuW4HfSJeEuLJhm9Yw5oU6ZNtjrSGzzXUsA64GhvNn\n9jSwIj9fp/21bK0hM21T/zlnHtZlf+2x1pDZAi8By/NneAnwMWlN2KxBZ1qrNWTe+d6Wc4DGsyUu\nJl237CpSI3cEWOnuey7crHuyiLQzeN5ezo9vAx6mqU53/9bM7gFeAZ4gXQrkEXdvPjNkMipVK3Ez\nBVhPqnF30+PrgO35z0PUI9vStRI32ytIn9chYJw079X+35lqddpfS9VK3ExbaT5SVJf9tUjbWomb\n7VzgHWAW8AuwF1js7ifz8wPLVPeyFBEREalYbdaQiYiIiESlhkxERESkYmrIRERERCqmhkxERESk\nYmrIRERERCqmhkxERESkYmrIRERERCqmhkxERESkYmrIRERERCqmhkxERESkYmrIRERERCqmhkxE\nRESkYv8CUfpCT8MdVhQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1674d35ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEMORY_TOTAL (logscale=False):\n",
      "stylo ***0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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xoBmYFhE3bdW3NzMzM9sBVGOdxC6NTkbENGBaB+3rgan5p73MG+SFszvIvETa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f167c33d3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MEMORY_TOTAL (logscale=True):\n",
      "stylo ***0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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QAzHUIFZVP08yCewP/D38crH+/sB5fZp9jeZyY68D2vJpX6V5MG+vXYF7Zglh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\nVZ9O8gxg+xnNvklzI8CVVfX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f167d3b7290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEMORY_UNIQUE (logscale=False):\n",
      "stylo ***0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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SXfkYayXdJmnvUuZASfMkrZe0StJUSQNKmSMlLZD0tqQXJU3q6WtiZmZm1tdU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\nxk9InzDeJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1656eeaad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MEMORY_UNIQUE (logscale=True):\n",
      "stylo ***0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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DOwFrqurH89XooBOxe4BHgZWTylcCa/vUWdsn/oGqerh9fyfwSG28wO1GYFWS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\n74ERAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f167c27b9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEMORY_VSIZE (logscale=False):\n",
      "stylo ***0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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RsUzSGGAq0BART+XMRcBCSV/Ne79OBQ4BPh0RbcCzkq4CrpXUHBFbSLtR7AGck9+3ShpL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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1656c9f810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MEMORY_VSIZE (logscale=True):\n",
      "stylo ***0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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pNmu8A3hoAfspSZI2DU8EdgFWVdWP56vRQSdi9wKPAssnlS8HVveps7pP/ANV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\nHOM/AHx8hpjbgNXA03oLk2w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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f167d3bc950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "GHOST_WINDOWS (logscale=False):\n",
      "stylo ***0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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MwcADwKPAMcA3gJmSPr2V/WFmZmbWbXp19gcR8RDwEIAktRG5HJgcEQ/kzJeAlcApwD2S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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16747f3a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GHOST_WINDOWS (logscale=True):\n",
      "stylo ***0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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AZwLTCpldgRZgOTAGmAJMlXROIbMPcDfwAHAw8A1gjqRPbub1MDMzM+tzA3r6g4i4F7gX\nQJK6iFwMXBURd+fM54AVwInA7ZIGA2cBp0TEgznzeaBN0mERsUTSKGAS0BgRj+XMhcACSV+KiPbc\nPhI4JiI6gCckXQFcI2lqRKwFTgd2As7O39skjQYuAebk/p4LPBsRX87fl0n6BDAZuL+n18fMzMys\nL/TqM3WS9gWGk0a9AIiIVcBi4PC86RBSMVnMLANeKGTGAStrBV22kDQyOLaQeSIXdDUtwBDgwEJm\nUS7oipkRkoYUMgtLp9JS6IuZmZnZNq+3J0oMJxVeK0rbV+Q2gAZgTS72NpYZDrxSbIyIdcCrpUxX\nx6GXMoMlDcLMzMxsO7Cjzn7t6raxmZmZ2Xarx8/UdaOdVDA1sOHoVwPwWCEzUNLg0mhdQ26rZcqz\nYfsDu5cyh5aO31Boq/1t6CITm5FZFRFv//EpFk0m3e0tasofMzMzM2hubqa5uXmDbZ2dnb1+nF4t\n6iJiuaR20ozVnwPkiRFjgdk5thRYmzN35swIYG/gkZx5BBgqaXThuboJpIJxcSFzmaRhhefqJgKd\nwFOFzNWS+ufbt7XMsojoLGSOLZ3KxEJfNmEGaVKtmZmZWdeamppoatpwwKe1tZXGxsZePc6WrFO3\ni6SDJX08b9ovf98rf58JXC7pU5I+BnwXeAm4C96dOHEzcF1eH64RmAs8FBFLcuZp0mSFmyQdKukI\n4HqgOc98BbiPVLzdmteimwRcBcyKiHdyZh6wBpgr6QBJnwUuAr5eOKVv5XO4VtIISecBJwPX9fTa\nmJmZmfWVLRmpOwT4D9ItzOAPBdJ3gLMiYrqknUlryg0FfgIcGxFrCvuYDKwD5gODSEuknF86zqnA\nLNLM1PU5e3GtMSLWSzoBuAF4mLQe3i3AlYXMKkkTSaOEjwIdwNSIuLmQeU7S8aRht4tIBejZEVGe\nEWtmZma2zVLEH60fbN2QNAZYmu4kV+n260+BI2lra2PkyJF93RkzM7PKKtx+bYyI1t7Y5446+9XM\nzMysUlzUmZmZmVWAizozMzOzCnBRZ2ZmZlYBLurMzMzMKsBFnZmZmVkFuKgzMzMzqwAXdWZmZmYV\n4KLOzMzMrAJc1JmZmZlVgIs6MzMzswro9aJOUj9JV0l6VtIbkn4l6fIuctMkvZwz90vav9Q+SNJs\nSR2SXpc0X9Iepcxukm6T1ClppaQ5knYpZfaStEDSakntkqZL6lfKHCRpkaQ3JT0vaUpvXhMzMzOz\neqvHSN1XgL8DzgNGAl8GvizpglpA0qXABcAXgMOA1UCLpIGF/cwEjgdOAsYDewJ3lI41DxgFTMjZ\n8cCNheP0A+4BBgDjgDOAM4FphcyuQAuwHBgDTAGmSjpnyy+BmZmZ2dY1oA77PBy4KyLuzd9fkHQq\nqXiruRi4KiLuBpD0OWAFcCJwu6TBwFnAKRHxYM58HmiTdFhELJE0CpgENEbEYzlzIbBA0pcioj23\njwSOiYgO4AlJVwDXSJoaEWuB04GdgLPz9zZJo4FLgDl1uD5mZmZmva4eI3UPAxMk/RmApIOBI0gj\nZkjaFxgOPFD7QUSsAhaTCkKAQ0gFZzGzDHihkBkHrKwVdNlCIICxhcwTuaCraQGGAAcWMotyQVfM\njJA0pKcnb2ZmZtYX6jFSdw0wGHha0jpS4fiPEfH93D6cVHitKP1uRW4DaADW5GJvY5nhwCvFxohY\nJ+nVUqar49TaHs9/n91EprPr0zQzMzPbdtSjqPsscCpwCvAU8HHgG5Jejohb63A8MzMzsx1ePYq6\n6cC/RMQP8vdfSNoH+CpwK9AOiDQaVxxFawBqt1LbgYGSBpdG6xpyWy1Tng3bH9i9lDm01L+GQlvt\nb0M3mY2YTLqTW9SUP2ZmZmbQ3NxMc3PzBts6O3v/RmA9irqdgXWlbevJz+9FxHJJ7aQZqz8HyBMj\nxgKzc34psDZn7syZEcDewCM58wgwVNLownN1E0gF4+JC5jJJwwrP1U0k3VJ9qpC5WlL/iFhXyCyL\niG6u+AzShFkzMzOzrjU1NdHUtOGAT2trK42Njb16nHpMlPgRcLmk4yR9RNKnSUNa/17IzMyZT0n6\nGPBd4CXgLnh34sTNwHWSjpbUCMwFHoqIJTnzNGlCw02SDpV0BHA90JxnvgLcRyrebs1r0U0CrgJm\nRcQ7OTMPWAPMlXSApM8CFwFfr8O1MTMzM6uLeozUXUAqnGaTbo++DNyQtwEQEdMl7UxaU24o8BPg\n2IhYU9jPZNKI33xgEHAvcH7pWKcCs0izXtfn7MWF46yXdEI+/sOk9fBuAa4sZFZJmpj7+yjQAUyN\niJvfy0UwMzMz25oUEX3dh+2OpDHA0nSXuEq3X38KHElbWxsjR47s686YmZlVVuH2a2NEtPbGPv3u\nVzMzM7MKcFFnZmZmVgEu6szMzMwqwEWdmZmZWQW4qDMzMzOrABd1ZmZmZhXgos7MzMysAlzUmZmZ\nmVWAizozMzOzCnBRZ2ZmZlYBLurMzMzMKsBFnZmZmVkF1KWok7SnpFsldUh6Q9LjksaUMtMkvZzb\n75e0f6l9kKTZeR+vS5ovaY9SZjdJt0nqlLRS0hxJu5Qye0laIGm1pHZJ0yX1K2UOkrRI0puSnpc0\npbeviZmZmVk99XpRJ2ko8BDwNjAJGAX8A7CykLkUuAD4AnAYsBpokTSwsKuZwPHAScB4YE/gjtLh\n5uX9T8jZ8cCNheP0A+4BBgDjgDOAM4FphcyuQAuwHBgDTAGmSjpniy+CmZmZ2VY2oA77/ArwQkQU\ni6LnS5mLgasi4m4ASZ8DVgAnArdLGgycBZwSEQ/mzOeBNkmHRcQSSaNIRWNjRDyWMxcCCyR9KSLa\nc/tI4JiI6ACekHQFcI2kqRGxFjgd2Ak4O39vkzQauASY09sXx8zMzKwe6nH79VPAo5Jul7RCUmtx\n1EvSvsBw4IHatohYBSwGDs+bDiEVnMXMMuCFQmYcsLJW0GULgQDGFjJP5IKupgUYAhxYyCzKBV0x\nM0LSkJ6evJmZmVlfqEdRtx9wLrAMmAjcAHxT0n/L7cNJhdeK0u9W5DaABmBNLvY2lhkOvFJsjIh1\nwKulTFfHoYcZMzMzs21aPW6/9gOWRMQV+fvjkj4KfBG4tQ7HMzMzM9vh1aOo+w3QVtrWBnwm/7sd\nEGk0rjhC1gA8VsgMlDS4NFrXkNtqmfJs2P7A7qXMoaW+NBTaan8buslsxGTSndyipvwxMzMzg+bm\nZpqbmzfY1tnZ2evHqUdR9xAworRtBHmyREQsl9ROmrH6c4A8MWIsMDvnlwJrc+bOnBkB7A08kjOP\nAEMljS48VzeBVDAuLmQukzSs8FzdRKATeKqQuVpS/3z7tpZZFhHdXPEZpAmzZmZmZl1ramqiqWnD\nAZ/W1lYaGxt79Tj1eKZuBjBO0lcl/RdJpwLnALMKmZnA5ZI+JeljwHeBl4C74N2JEzcD10k6WlIj\nMBd4KCKW5MzTpAkNN0k6VNIRwPVAc575CnAfqXi7Na9FNwm4CpgVEe/kzDxgDTBX0gGSPgtcBHy9\nDtfGzMzMrC56faQuIh6V9GngGuAK0vpvF0fE9wuZ6ZJ2Jq0pNxT4CXBsRKwp7GoysA6YDwwC7gXO\nLx3uVFKxuBBYn7MXF46zXtIJpMkaD5PWw7sFuLKQWSVpImmU8FGgA5gaETe/tythZmZmtvXU4/Yr\nEXEPadHfTWWmAlM30f42cGH+bCzzGmmduU0d50XghG4yTwJHbSpjZmZmti3zu1/NzMzMKsBFnZmZ\nmVkFuKgzMzMzqwAXdWZmZmYV4KLOzMzMrAJc1JmZmZlVgIs6MzMzswpwUWdmZmZWAS7qzMzMzCrA\nRZ2ZmZlZBbioMzMzM6uAuhd1kr4iab2k60rbp0l6WdIbku6XtH+pfZCk2ZI6JL0uab6kPUqZ3STd\nJqlT0kpJcyTtUsrsJWmBpNWS2iVNl9SvlDlI0iJJb0p6XtKU3r4OZmZmZvVU16JO0qHAF4DHS9sv\nBS7IbYcBq4EWSQMLsZnA8cBJwHhgT+CO0iHmAaOACTk7HrixcJx+wD3AAGAccAZwJjCtkNkVaAGW\nA2OAKcBUSeds8YmbmZmZbWV1K+okfQD4HnAO8Fqp+WLgqoi4OyKeBD5HKtpOzL8dDJwFTI6IByPi\nMeDzwBGSDsuZUcAk4OyIeDQiHgYuBE6RNDwfZxIwEjgtIp6IiBbgCuB8SQNy5nRgp7yftoi4Hfgm\ncElvXxMzMzOzeqnnSN1s4EcR8ePiRkn7AsOBB2rbImIVsBg4PG86hDS6VswsA14oZMYBK3PBV7MQ\nCGBsIfNERHQUMi3AEODAQmZRRKwtZUZIGtKTEzYzMzPrK3Up6iSdAnwc+GoXzcNJhdeK0vYVuQ2g\nAViTi72NZYYDrxQbI2Id8Gop09Vx6GHGzMzMbJs2oPtIz0j6MOl5uL+MiHd6e/9mZmZm9sd6vagD\nGoEPAq2SlLf1B8ZLuoD0jJtIo3HFEbIGoHYrtR0YKGlwabSuIbfVMuXZsP2B3UuZQ0v9ayi01f42\ndJPZiMmkO7lFTfljZmZmBs3NzTQ3N2+wrbOzs9ePU4+ibiHwsdK2W4A24JqIeFZSO2nG6s/h3YkR\nY0nP4QEsBdbmzJ05MwLYG3gkZx4BhkoaXXiubgKpYFxcyFwmaVjhubqJQCfwVCFztaT++fZtLbMs\nIrq54jNIE2bNzMzMutbU1ERT04YDPq2trTQ2NvbqcXq9qIuI1fyhYAJA0mrgdxHRljfNBC6X9Cvg\nOeAq4CXgrryPVZJuBq6TtBJ4nTQj9aGIWJIzT0tqAW6SdC4wELgeaI6I2gjbfbkvt+ZlVD6UjzWr\ncGt4HvBPwFxJ15IK0otIM3TNzMzMtgv1GKnrSmzwJWK6pJ1Ja8oNBX4CHBsRawqxycA6YD4wCLgX\nOL+031OBWaTRwfU5+24xFhHrJZ0A3AA8TFoP7xbgykJmlaSJpFHCR4EOYGpE3PzeTtnMzMxs69kq\nRV1E/EUX26YCUzfxm7dJ685duInMa6R15jZ17BeBE7rJPAkctamMmZmZ2bbM7341MzMzqwAXdWZm\nZmYV4KLOzMzMrAJc1JmZmZlVgIs6MzMzswpwUWdmZmZWAS7qzMzMzCrARZ2ZmZlZBbioMzMzM6sA\nF3VmZmZmFeCizszMzKwCXNSZmZmZVUCvF3WSvippiaRVklZIulPSf+0iN03Sy5LekHS/pP1L7YMk\nzZbUIel1SfMl7VHK7CbpNkmdklZKmiNpl1JmL0kLJK2W1C5puqR+pcxBkhZJelPS85Km9OY1MTMz\nM6u3eozUHQlcD4wF/hLYCbhP0vtrAUmXAhcAXwAOA1YDLZIGFvYzEzgeOAkYD+wJ3FE61jxgFDAh\nZ8cDNxaO0w+4BxgAjAPOAM4EphUyuwItwHJgDDAFmCrpnC2/BGZmZmZb14De3mFEHFf8LulM4BWg\nEfhp3nwxcFVE3J0znwNWACcCt0saDJwFnBIRD+bM54E2SYdFxBJJo4BJQGNEPJYzFwILJH0pItpz\n+0jgmIjoAJ6QdAVwjaSpEbEWOJ1UeJ6dv7dJGg1cAszp7etjZmZmVg9b45m6oUAArwJI2hcYDjxQ\nC0TEKmAxcHjedAip4CxmlgEvFDLjgJW1gi5bmI81tpB5Ihd0NS3AEODAQmZRLuiKmRGShmzB+ZqZ\nmZltdXUt6iSJdBv1pxHxVN48nFR4rSjFV+Q2gAZgTS72NpYZThoBfFdErCMVj8VMV8ehhxkzMzOz\nbVqv334t+VfgAOCIOh/HzMzMbIdWt6JO0izgOODIiPhNoakdEGk0rjhC1gA8VsgMlDS4NFrXkNtq\nmfJs2P7A7qXMoaWuNRTaan8buslsxGTSndyipvwxMzMzg+bmZpqbmzfY1tnZ2evHqUtRlwu6vwGO\niogXim0RsVxSO2nG6s9zfjDpObjZObYUWJszd+bMCGBv4JGceQQYKml04bm6CaSCcXEhc5mkYYXn\n6iYCncBThczVkvrn27e1zLKI6OaKzyBNmDUzMzPrWlNTE01NGw74tLa20tjY2KvHqcc6df8KnAac\nCqyW1JA/7yvEZgKXS/qUpI8B3wVeAu6CdydO3AxcJ+loSY3AXOChiFiSM0+TJjTcJOlQSUeQllJp\nzjNfAe6yE/azAAAgAElEQVQjFW+35rXoJgFXAbMi4p2cmQesAeZKOkDSZ4GLgK/39rUxMzMzq5d6\njNR9kTQR4n+Xtn+eVLwREdMl7UxaU24o8BPg2IhYU8hPBtYB84FBwL3A+aV9ngrMIs16XZ+zF9ca\nI2K9pBOAG4CHSevh3QJcWciskjSRNEr4KNABTI2Im7fo7M3MzMz6QD3Wqdus0b+ImApM3UT728CF\n+bOxzGukdeY2dZwXgRO6yTwJHLWpjJmZmdm2zO9+NTMzM6sAF3VmZmZmFeCizszMzKwCXNSZmZmZ\nVYCLOjMzM7MKcFFnZmZmVgEu6szMzMwqwEWdmZmZWQW4qDMzMzOrABd1ZmZmZhXgos7MzMysAlzU\nZZLOl7Rc0puS/o+kQ/u6Tzuy5ubmvu5C5fka15evb/35GteXr+/2x0UdIOmzwNeBK4HRwONAi6Rh\nfdqxHZj/Y1J/vsb15etbf77G9eXru/1xUZdMBm6MiO9GxNPAF4E3gLP6tltmZmZmm2eHL+ok7QQ0\nAg/UtkVEAAuBw/uqX2ZmZmY9MaCvO7ANGAb0B1aUtq8ARmz97pjZjuKFF16go6Ojr7vR64YNG8be\ne+/d190w2+G4qNsy70t//l9STVgVvwHgRz/6EUuXLu3Tnrz00kvcdtttvbrPfv36sX79+l7d57Zi\nS86tHte4HrbX/7t1d307OjqYMuUrvPPOW1uxV1vHTjsN4mtfu5Zhw+r738e++N/w9vq/x81RPrft\n5b8Rm2PYsGF88IMf7OtubKCtra32z/f11j6V7jTuuPLt1zeAkyLih4XttwBDIuLTXfzmVKAa/0s3\nMzOzvnRaRMzrjR3t8CN1EfGOpKXABOCHAJKUv39zIz9rAU4DngOq9/9mm5mZWb29D9iHVFP0ih1+\npA5A0t8Ct5BmvS4hzYY9GRgZEb/tw66ZmZmZbZYdfqQOICJuz2vSTQMagJ8Bk1zQmZmZ2fbCI3Vm\nZmZmFbDDr1NnZmZmVgUu6szMzMwqwEWdmZmZWQW4qDMz24okHSVpvaTxfd0XM6sWF3VmZj0gqUnS\nxe9xN56hZma9zkWdmVnPnAq816LOzKzXuagzMzMzqwAXdWZmBZI+IGmmpOWS3pK0QtJ9kkZL+g/g\neOAj+bm49ZKelbSLpN9LmtHF/v5U0lpJl3Zz3P9H0qOS3pD0W0m3StqzXudpZtXjN0qYmW3oRuAz\nwPVAG/AnwCeAkcDVwBDgT4G/BwT8PiJWS7oT+KykS2LDVd1PzX+/t7EDSjoTmAssBr5CerPN3wN/\nLml0RKzqvdMzs6ryGyXMzAokrQRujYiLNtL+I+DAiNivtP2TwL3AsRFxX2H7z4BXI+Iv8vejgB8D\nx0TEIkkDgJeAduCwiFiTc8cBdwP/HBH/3NvnaWbV49uvZmYbeg0YK+lDPfzdQuA3wGm1DZI+ChwE\n3LqJ3x0C7AH8a62gA4iIe4CnSbd7zcy65aLOzGxDXwY+CrwoabGkKyXt292P8i3X24ATJb0vbz4N\neBOYv4mffoS0xMkzXbQ9ndvNzLrlos7MrCAifgDsB1wA/Br4EvALSZM24+ffBXYFTszfm4AfRcTr\n9eirmVmRizozs5KIWBER34qIzwD7Ar8D/rHWvInf/QJ4DDhN0pHA3mz61ivA86QJFyO6aBuR283M\nuuWizswsk9RP0uDitojoAF4GBuVNq0kzYDfmVmASafZqB2nyxKY8CrwCfFHSToW+HAuMIk2WMDPr\nlpc0MTP7g12BlyTNBx4Hfg98kjSZ4ZKcWQr8raSvA/+XtKRJsfCaB0wn3YL914hY18VxVPtHRNTW\nsJsLLJLUDAwHLgKeBWb24vmZWYV5SRMzsyyPlF0FTCQ9V9cP+BXwrYj4t5zZmbSW3XHAUOD5LpY3\nuRs4FvjziFhcattgSZPC9pNJa9QdQBoNvAf4SkT8pg6namYV5KLOzKyXSfp34KMR8V/7ui9mtuPo\n8TN1kvbMr6/pyK+zeVzSmFJmmqSXc/v9kvYvtQ+SNDvv43VJ8yXtUcrsJuk2SZ2SVkqaI2mXUmYv\nSQskrZbULmm6pH6lzEGSFkl6U9LzkqZ0cU5HS1qaXwn0jKQzenpdzMwA8vp2x5NmwpqZbTU9Kuok\nDQUeAt4mPQg8CvgHYGUhcylpKYAvAIeRbiO0SBpY2NVM0n/0TgLGA3sCd5QONy/vf0LOjifd8qgd\npx/p9sQAYBxwBnAmMK2Q2RVoAZYDY4ApwFRJ5xQy+5AeRH4AOBj4BjAnrw5vZrZZJO0j6XSgGVgD\n/Fsfd8nMdjA9uv0q6Rrg8Ig4ahOZl4GvRcSM/H0wsAI4IyJuz99/C5wSEXfmzAjSOxbHRcQSSaOA\nXwCNEfFYzkwCFgAfjoj2PDPsh8CH8uw0JP0dcA3wwfzw8bmk52OGR8TanPkX4G8i4oD8/VrSa30O\nKpxDMzAkIo7b7ItjZju0PML/beA54B9q/30zM9taenr79VPAo5Jul7RCUmtp1Gtf0qytB2rb8ouo\nFwOH502HkEbXipllwAuFzDhgZa2gyxaS1ocaW8g8USvoshbSUgMHFjKLagVdITNC0pBCZmHpPFsK\nfTEz61ZEfCci+kXEfi7ozKwv9LSo2w84F1hGmh12A/BNSf8ttw8nFV4rSr9bkdsAGoA1udjbWGY4\nad2md+VlAV4tZbo6Dr2UGSxpEGZmZmbbgZ6uU9cPWBIRV+Tvj+cXVn+R7ldN35ao+8gmfiz9CemZ\nwueAt3qjQ2ZmZrZDeR+wD9ASEb/rjR32tKj7DenZt6I24DP53+2kgqmBDUe/GkivzqllBkoaXBqt\na8httUx5Nmx/YPdS5tBSXxoKbbW/DV1kYjMyqyLibbo2ifTibjMzM7P34jTS5ND3rKdF3UP88fsJ\n3303YUQsl9ROmrH6c3h3osRYYHbOLwXW5kxxosTewCM58wgwVNLownN1E0gF4+JC5jJJwwrP1U0E\nOoGnCpmrJfUvrOo+EVgWEZ2FzLGlc5pY6EtXngP43ve+x6hRozYRsy01efJkZsyY0dfdqDRf4/ry\n9a0/X+P68vWtr7a2Nk4//XTINUVv6GlRNwN4SNJXgdtJxdo5wH8vZGYCl0v6FamjVwEvAXdBmjgh\n6WbgOkkrgdeBbwIPRcSSnHlaUgtwU57BOhC4HmiOiNoI232k4u3WvIzKh/KxZkXEOzkzD/gnYG6e\n5fox0qt3Li7091vA+bl9Lql4PJm0WvzGvAUwatQoxowZs4mYbakhQ4b42taZr3F9+frWn69xffn6\nbjW99hhXj4q6iHhU0qdJy4ZcQVr/7eKI+H4hM73wGp2hwE9IS4asKexqMrAOmE96Sfa9wPmlw50K\nzCLNTF2fs+8WYxGxXtIJpMkaD5PWw7sFuLKQWSVpImmU8FHSy7WnRsTNhcxzko4nFawXkQrQsyOi\nPCPWzMzMbJvV05E6IuIe0qK/m8pMBaZuov1t4ML82VjmNeD0bo7zInBCN5kngY2uq5czi4DGTWW6\nsnz5ct7//vf39GfbtN13352GhvIjhmZmZrat63FRZ39w8skn93UXet3gwbvxy18+zR577NF92MzM\nzLYZLurek7n88byR7dnjrFp1Hq+++mqfF3VNTU19evwdga9xffn61p+vcX35+m5/evSaMEskjQGW\npom8VXqI9KfAkbS1tTFy5Mi+7oyZmVlltba20tjYCOmVqK29sc+evlHCzMzMzLZBLurMzMzMKsBF\nnZmZmVkFuKgzMzMzqwAXdWZmZmYV4KLOzMzMrAJc1JmZmZlVgIs6MzMzswpwUWdmZmZWAS7qzMzM\nzCrARZ2ZmZlZBbioMzMzM6uAHhV1kq6UtL70eaqUmSbpZUlvSLpf0v6l9kGSZkvqkPS6pPmS9ihl\ndpN0m6ROSSslzZG0Symzl6QFklZLapc0XVK/UuYgSYskvSnpeUlTujinoyUtlfSWpGckndGTa2Jm\nZma2LdiSkbongQZgeP58otYg6VLgAuALwGHAaqBF0sDC72cCxwMnAeOBPYE7SseYB4wCJuTseODG\nwnH6AfcAA4BxwBnAmcC0QmZXoAVYDowBpgBTJZ1TyOwD3A08ABwMfAOYI+mTPbskZmZmZn1rwBb8\nZm1E/HYjbRcDV0XE3QCSPgesAE4Ebpc0GDgLOCUiHsyZzwNtkg6LiCWSRgGTgMaIeCxnLgQWSPpS\nRLTn9pHAMRHRATwh6QrgGklTI2ItcDqwE3B2/t4maTRwCTAn9/dc4NmI+HL+vkzSJ4DJwP1bcG3M\nzMzM+sSWjNT9maRfS/pPSd+TtBeApH1JI3cP1IIRsQpYDByeNx1CKiSLmWXAC4XMOGBlraDLFgIB\njC1knsgFXU0LMAQ4sJBZlAu6YmaEpCGFzMLS+bUU+mJmZma2XehpUfd/SLc5JwFfBPYFFuXn3YaT\nCq8Vpd+syG2QbtuuycXexjLDgVeKjRGxDni1lOnqOPRSZrCkQZiZmZltJ3p0+zUiWgpfn5S0BHge\n+Fvg6d7sWB2prztgZmZm1tu25Jm6d0VEp6RngP2B/00qmBrYcPSrAajdSm0HBkoaXBqta8httUx5\nNmx/YPdS5tBSdxoKbbW/DV1kYjMyqyLibbo1mXTHt6gpf8zMzMygubmZ5ubmDbZ1dnb2+nHeU1En\n6QOkgu47EbFcUjtpxurPc/tg0nNws/NPlgJrc+bOnBkB7A08kjOPAEMljS48VzeBVDAuLmQukzSs\n8FzdRKATeKqQuVpS/3z7tpZZFhGdhcyxpdOaWOhLN2aQJtaamZmZda2pqYmmpg0HfFpbW2lsbOzV\n4/R0nbqvSRov6SOS/pxUmL0DfD9HZgKXS/qUpI8B3wVeAu6CdydO3Axcl9eHawTmAg9FxJKceZo0\nWeEmSYdKOgK4HmjOM18B7iMVb7fmtegmAVcBsyLinZyZB6wB5ko6QNJngYuArxdO6VvAfpKulTRC\n0nnAycB1PbkuZmZmZn2tpyN1HyYVS38C/Bb4KTAuIn4HEBHTJe1MWlNuKPAT4NiIWFPYx2RgHTAf\nGATcC5xfOs6pwCzSzNT1OXtxrTEi1ks6AbgBeJi0Ht4twJWFzCpJE0mjhI8CHcDUiLi5kHlO0vGk\nIbeLSAXo2RFRnhFrZmZmtk1TRPR1H7Y7ksYAS9Pd5Crdfv0pcCRtbW2MHDmyrztjZmZWWYXbr40R\n0dob+/S7X83MzMwqwEWdmZmZWQW4qDMzMzOrABd1ZmZmZhXgos7MzMysAlzUmZmZmVWAizozMzOz\nCnBRZ2ZmZlYBLurMzMzMKsBFnZmZmVkFuKgzMzMzqwAXdWZmZmYV4KLOzMzMrAJc1JmZmZlVgIs6\nMzMzswpwUWdmZmZWAe+pqJP0FUnrJV1X2j5N0suS3pB0v6T9S+2DJM2W1CHpdUnzJe1Ryuwm6TZJ\nnZJWSpojaZdSZi9JCyStltQuabqkfqXMQZIWSXpT0vOSpnRxHkdLWirpLUnPSDrjvVwXMzMzs61t\ni4s6SYcCXwAeL22/FLggtx0GrAZaJA0sxGYCxwMnAeOBPYE7SoeYB4wCJuTseODGwnH6AfcAA4Bx\nwBnAmcC0QmZXoAVYDowBpgBTJZ1TyOwD3A08ABwMfAOYI+mTPbkeZmZmZn1pi4o6SR8AvgecA7xW\nar4YuCoi7o6IJ4HPkYq2E/NvBwNnAZMj4sGIeAz4PHCEpMNyZhQwCTg7Ih6NiIeBC4FTJA3Px5kE\njAROi4gnIqIFuAI4X9KAnDkd2Cnvpy0ibge+CVxS6O+5wLMR8eWIWBYRs4H5wOQtuTZmZmZmfWFL\nR+pmAz+KiB8XN0raFxhOGvUCICJWAYuBw/OmQ0ija8XMMuCFQmYcsDIXfDULgQDGFjJPRERHIdMC\nDAEOLGQWRcTaUmaEpCGFzMLS+bUU+mJmZma2zetxUSfpFODjwFe7aB5OKrxWlLavyG0ADcCaXOxt\nLDMceKXYGBHrgFdLma6OQy9lBksahJmZmdl2YED3kT+Q9GHS83B/GRHv1KdLW4X6ugNmZmZmvalH\nRR3QCHwQaJVUK4z6A+MlXUB6xk2k0bji6FcDULuV2g4MlDS4NFrXkNtqmfJs2P7A7qXMoaX+NRTa\nan8busjEZmRWRcTbbNJk0t3eoqb8MTMzM4Pm5maam5s32NbZ2dnrx+lpUbcQ+Fhp2y1AG3BNRDwr\nqZ00Y/Xn8O7EiLGk5/AAlgJrc+bOnBkB7A08kjOPAEMljS48VzeBVDAuLmQukzSs8FzdRKATeKqQ\nuVpS/3z7tpZZFhGdhcyxpXOaWOjLJswgTao1MzMz61pTUxNNTRsO+LS2ttLY2Nirx+lRURcRq/lD\nwQSApNXA7yKiLW+aCVwu6VfAc8BVwEvAXXkfqyTdDFwnaSXwOmlG6kMRsSRnnpbUAtwk6VxgIHA9\n0BwRtRG2+3Jfbs3LqHwoH2tW4dbwPOCfgLmSriUVpBeRZujWfIs0Y/ZaYC6peDwZOK4n18bMzMys\nL/V0pK4rscGXiOmSdiatKTcU+AlwbESsKcQmA+tIS4cMAu4Fzi/t91RgFml0cH3OvluMRcR6SScA\nNwAPk9bDuwW4spBZJWkiaZTwUaADmBoRNxcyz0k6njTsdhGpAD07IsozYs3MzMy2WYqI7lO2AUlj\ngKXpTnKVbr/+FDiStrY2Ro4c2dedMTMzq6zC7dfGiGjtjX363a9mZmZmFeCizszMzKwCXNSZmZmZ\nVYCLOjMzM7MKcFFnZmZmVgEu6szMzMwqwEWdmZmZWQW4qDMzMzOrABd1ZmZmZhXgos7MzMysAlzU\nmZmZmVWAizozMzOzCnBRZ2ZmZlYBLurMzMzMKsBFnZmZmVkF9Kiok/RFSY9L6syfhyX9VSkzTdLL\nkt6QdL+k/UvtgyTNltQh6XVJ8yXtUcrsJum2fIyVkuZI2qWU2UvSAkmrJbVLmi6pXylzkKRFkt6U\n9LykKV2c09GSlkp6S9Izks7oyTUxMzMz2xb0dKTuReBSYAzQCPwYuEvSKABJlwIXAF8ADgNWAy2S\nBhb2MRM4HjgJGA/sCdxROs48YBQwIWfHAzfWGnPxdg8wABgHnAGcCUwrZHYFWoDlub9TgKmSzilk\n9gHuBh4ADga+AcyR9MkeXhczMzOzPjWgJ+GIWFDadLmkc0mFVRtwMXBVRNwNIOlzwArgROB2SYOB\ns4BTIuLBnPk80CbpsIhYkgvESUBjRDyWMxcCCyR9KSLac/tI4JiI6ACekHQFcI2kqRGxFjgd2Ak4\nO39vkzQauASYk/t/LvBsRHw5f18m6RPAZOD+nlwbMzMzs760xc/USeon6RRgZ+BhSfsCw0mjXgBE\nxCpgMXB43nQIqZAsZpYBLxQy44CVtYIuWwgEMLaQeSIXdDUtwBDgwEJmUS7oipn/v737D7KrrO84\n/v4kSCgMSWSCG51CgVET8EeEJfyYmKCTumkEFEcsbLCGH46DAUljRVurQwpUJQ4Ra1AoSQpYspVi\nGQUcAviLH4nJkGDBYQmtgBg0oSm4QX4kkP32j+dZenLYTfZe7u7de/J5zZxJ7jnfe8/3PLlzzzfP\nOc9zJkkaV4i5q3RoKwu5mJmZmbWEmos6Se+U9BywDfg28JFcmE0kFV6bS2/ZnLcBtAHbc7E3UMxE\n4OnixojYATxTiulvPzQoZqykMZiZmZm1iJouv2aPkO4/GwecClwvaUZDsxpaanYCZmZmZo1Wc1GX\nL2c+ll8+IOkY0r10i0gFUxs79361AX2XUjcBe0saW+qta8vb+mLKo2FHAweUYqaWUmsrbOv7s62f\nmBhEzNaI2MZuLSDVtkWdeTEzMzODrq4uurq6dlrX09PT8P3U01NXNgoYExGPS9pEGrH6IEAeGHEs\ncGWOXQe8kmNuzjGTgIOB1TlmNTBe0pGF++pmkgrGNYWYL0qaULivrgPoAR4uxFwqaXS+fNsXsyEi\negoxs0vH01HIZTe+QRpYa2ZmZta/zs5OOjt37vBZv3497e3tDd1PrfPUfUXSdEl/lu+t+ypwAvCv\nOeQK0ojYkyW9C7ge2Aj8AF4dOLEMWJznh2sHlgP3RcTaHPMIabDCNZKmSpoGfAvoyiNfAe4gFW/f\nzXPRzQIuAZZExMs5ZgWwHVgu6QhJpwEXAJcXDukq4DBJl0maJGke6ZLy4lraxczMzKzZau2pexNw\nHfBmUq/Yg0BHRPwEICIWSdqXNKfceOAeYHZEbC98xgJgB3ATMAa4HTivtJ85wBLSyNTeHDu/b2NE\n9Eo6CfgOsIo0H961wEWFmK2SOki9hPcDW4CFEbGsEPOEpBNJXW4XkArQcyKiPCLWzMzMbERTRDQ7\nh5Yj6ShgXbqaXKXLr/cC0+nu7mby5MnNTsbMzKyyCpdf2yNifSM+089+NTMzM6sAF3VmZmZmFeCi\nzszMzKwCXNSZmZmZVYCLOjMzM7MKcFFnZmZmVgEu6szMzMwqwEWdmZmZWQW4qDMzMzOrABd1ZmZm\nZhXgos7MzMysAlzUmZmZmVWAizozMzOzCnBRZ2ZmZlYBLurMzMzMKqCmok7S30laK2mrpM2Sbpb0\n9n7iLpb0O0kvSLpT0ltL28dIulLSFknPSbpJ0ptKMW+UdIOkHknPSloqab9SzEGSbpP0vKRNkhZJ\nGlWKebekuyW9KOk3ki7sJ9/3SVon6SVJj0qaW0u7mJmZmTVbrT1104FvAccCfw68AbhD0p/0BUj6\nAnA+8CngGOB5YKWkvQufcwVwIvBRYAbwFuD7pX2tAA4HZubYGcDVhf2MAn4E7AUcB8wFzgQuLsTs\nD6wEHgeOAi4EFkr6ZCHmEOBW4MfAFOCbwFJJH6ipZczMzMyaaK9agiPig8XXks4EngbagXvz6vnA\nJRFxa475BLAZOAW4UdJY4Gzg9Ij4eY45C+iWdExErJV0ODALaI+IB3LMZ4DbJH0uIjbl7ZOB90fE\nFuAhSV8GviZpYUS8AnycVHiek193SzoS+CywNOf7aeCxiPh8fr1B0nuBBcCdtbSPmZmZWbO83nvq\nxgMBPAMg6VBgIqnXC4CI2AqsAY7Pq44mFZPFmA3Ak4WY44Bn+wq67K68r2MLMQ/lgq7PSmAc8I5C\nzN25oCvGTJI0rhBzV+m4VhZyMTMzMxvx6i7qJIl0GfXeiHg4r55IKrw2l8I3520AbcD2XOwNFDOR\n1AP4qojYQSoeizH97YcGxYyVNAYzMzOzFlDT5deSbwNHANMalMtwUrMTMDMzM2ukuoo6SUuADwLT\nI+L3hU2bSAVTGzv3frUBDxRi9pY0ttRb15a39cWUR8OOBg4oxUwtpdZW2Nb3Z1s/MTGImK0RsY1d\nWkC62lvUmRczMzMz6Orqoqura6d1PT09Dd9PzUVdLug+DJwQEU8Wt0XE45I2kUasPpjjx5Lug7sy\nh60DXskxN+eYScDBwOocsxoYL+nIwn11M0kF45pCzBclTSjcV9cB9AAPF2IulTQ6X77ti9kQET2F\nmNmlw+wo5LIL3yANqjUzMzPrX2dnJ52dO3f4rF+/nvb29obup9Z56r4NnAHMAZ6X1JaXfQphVwBf\nknSypHcB1wMbgR/AqwMnlgGL8/xw7cBy4L6IWJtjHiENVrhG0lRJ00hTqXTlka8Ad5CKt+/muehm\nAZcASyLi5RyzAtgOLJd0hKTTgAuAywv5XgUcJukySZMkzQNOBRbX0jZmZmZmzVRrT925pEuXPyut\nP4tUvBERiyTtS5pTbjxwDzA7IrYX4hcAO4CbgDHA7cB5pc+cAywhjUztzbHz+zZGRK+kk4DvAKtI\n8+FdC1xUiNkqqYPUS3g/sAVYGBHLCjFPSDqR1O12AakAPSciyiNizczMzEasWuepG1TPXkQsBBbu\nYvs24DN5GSjmD6R55na1n98CJ+0m5lfACbuJuZs0156ZmZlZS/KzX83MzMwqwEWdmZmZWQW4qDMz\nMzOrABd1ZmZmZhXgos7MzMysAlzUmZmZmVWAizozMzOzCnBRZ2ZmZlYBLurMzMzMKsBFnZmZmVkF\nuKgzMzMzqwAXdWZmZmYV4KLOzMzMrAJc1JmZmZlVgIs6MzMzswqouaiTNF3SDyU9JalX0of6iblY\n0u8kvSDpTklvLW0fI+lKSVskPSfpJklvKsW8UdINknokPStpqaT9SjEHSbpN0vOSNklaJGlUKebd\nku6W9KKk30i6sJ983ydpnaSXJD0qaW6t7WJmZmbWTPX01O0H/BKYB0R5o6QvAOcDnwKOAZ4HVkra\nuxB2BXAi8FFgBvAW4Pulj1oBHA7MzLEzgKsL+xkF/AjYCzgOmAucCVxciNkfWAk8DhwFXAgslPTJ\nQswhwK3Aj4EpwDeBpZI+MMj2MDMzM2u6vWp9Q0TcDtwOIEn9hMwHLomIW3PMJ4DNwCnAjZLGAmcD\np0fEz3PMWUC3pGMiYq2kw4FZQHtEPJBjPgPcJulzEbEpb58MvD8itgAPSfoy8DVJCyPiFeDjwBuA\nc/LrbklHAp8FluZ8Pw08FhGfz683SHovsAC4s9b2MTMzM2uGht5TJ+lQYCKp1wuAiNgKrAGOz6uO\nJhWTxZgNwJOFmOOAZ/sKuuwuUs/gsYWYh3JB12clMA54RyHm7lzQFWMmSRpXiLmrdCgrC7mYmZmZ\njXiNHigxkVR4bS6t35y3AbQB23OxN1DMRODp4saI2AE8U4rpbz80KGaspDGYmZmZtYA9dfRrf5eN\nzczMzFpWzffU7cYmUsHUxs69X23AA4WYvSWNLfXWteVtfTHl0bCjgQNKMVNL+28rbOv7s62fmBhE\nzNaI2PbaQyxaQLraW9SZFzMzMzPo6uqiq6trp3U9PT0N309Di7qIeFzSJtKI1QcB8sCIY4Erc9g6\n4JUcc3OOmQQcDKzOMauB8ZKOLNxXN5NUMK4pxHxR0oTCfXUdQA/wcCHmUkmj8+XbvpgNEdFTiJld\nOpSOQi678A3SoFozMzOz/nV2dtLZuXOHz/r162lvb2/ofuqZp24/SVMkvSevOiy/Pii/vgL4kqST\nJTZ4gvwAAAgASURBVL0LuB7YCPwAXh04sQxYnOeHaweWA/dFxNoc8whpsMI1kqZKmgZ8C+jKI18B\n7iAVb9/Nc9HNAi4BlkTEyzlmBbAdWC7pCEmnARcAlxcO6ap8DJdJmiRpHnAqsLjWtjEzMzNrlnp6\n6o4Gfkq6hBn8f4F0HXB2RCyStC9pTrnxwD3A7IjYXviMBcAO4CZgDGmKlPNK+5kDLCGNTO3NsfP7\nNkZEr6STgO8Aq0jz4V0LXFSI2Sqpg9RLeD+wBVgYEcsKMU9IOpHU7XYBqQA9JyLKI2LNzMzMRixF\nvGb+YNsNSUcB69KV5Cpdfr0XmE53dzeTJ09udjJmZmaVVbj82h4R6xvxmXvq6FczMzOzSnFRZ2Zm\nZlYBLurMzMzMKsBFnZmZmVkFuKgzMzMzqwAXdWZmZmYV4KLOzMzMrAJc1JmZmZlVgIs6MzMzswpw\nUWdmZmZWAS7qzMzMzCrARZ2ZmZlZBbioMzMzM6sAF3VmZmZmFeCizszMzKwCXNTZiNTV1dXsFCrP\nbTy03L5Dz208tNy+rcdFXSbpPEmPS3pR0i8kTW12Tnsy/5gMPbfx0HL7Dj238dBy+7YeF3WApNOA\ny4GLgCOB/wRWSprQ1MTMzMzMBslFXbIAuDoiro+IR4BzgReAs5ublpmZmdng7PFFnaQ3AO3Aj/vW\nRUQAdwHHNysvMzMzs1rs1ewERoAJwGhgc2n9ZmDSAO/ZJ/3xH8D9Q5VXE/w3ALfccgvr1q1raiYb\nN27khhtuaOhnjho1it7e3oZ+5khRz7ENRRsPhVb9dxtM+7bqse3OcB1XM77DVf03g9ceW6v8RgzG\nhAkTOPDAA5udxk66u7v7/rpPoz5TqVNqzyXpzcBTwPERsaaw/jJgRkS8prdO0hygGt90MzMza6Yz\nImJFIz7IPXWwBdgBtJXWtwGbBnjPSuAM4AngpSHLzMzMzKpqH+AQUk3REHt8Tx2ApF8AayJifn4t\n4EngnyLi601NzszMzGwQ3FOXLAaulbQOWEsaDbsvcG0zkzIzMzMbLBd1QETcmOeku5h02fWXwKyI\n+J/mZmZmZmY2OL78amZmZlYBe/w8dWZmZmZV4KKuH7U+B1bS+yStk/SSpEclzR2uXFtVLW0s6SOS\n7pD0tKQeSaskdQxnvq2o3ucZS5om6WVJ64c6x1ZWx+/E3pL+UdIT+bfiMUlnDlO6LamONj5D0i8l\nPS/pd5KWSTpguPJtJZKmS/qhpKck9Ur60CDe43PdINXavo06z7moK6n1ObCSDgFuJT2RYgrwTWCp\npA8MR76tqI5n7c4A7gBmA0cBPwVukTRlGNJtSfU+z1jSOOA60hNVbAB1tu+/A+8HzgLeDnQCG4Y4\n1ZZVx2/xNNJ39xrgCOBU4Bjgn4cl4dazH+n+8XnAbu/D8rmuZjW1Lw06z/meupIBpjf5LWl6k0X9\nxF8GzI6IdxfWdQHjIuKDw5R2S6m1jQf4jF8B/xYRlw5dpq2r3jbO391HgV7gwxFx1HDk22rq+J34\nC2AFcFhE/GFYk21RdbTx3wDnRsTbCuvOBz4fEQcPU9otSVIvcEpE/HAXMT7X1Wkw7TvA+2o+z7mn\nrqDO58Aex2t7NVbuIn6P1ohn7eYf9/2BZ4Yix1ZXbxtLOgs4FPiHoc6xldXZvieTnin4BUkbJW2Q\n9HVJDXs8UJXU2cargYMkzc6f0QZ8DLhtaLPdY/hcN4zqPc+5qNvZrp4DO3GA90wcIH6spDGNTa8S\n6mnjsgtJXds3NjCvKqm5jSW9DfgK6XE11XywZePU8x0+DJgOvAM4BZhPujx45RDl2OpqbuOIWAV8\nHPiepO3A74FngfOHMM89ic91w6uu85yLOmspSs/d/TLwsYjY0ux8qkDSKNKzjC+KiF/3rW5iSlU0\ninRJe05E3B8RtwOfBeb6hNgYko4g3ee1kHRP0ixSz/PVTUzLrGav5zznyYd3Vs9zYDcNEL81IrY1\nNr1KqKeNAZB0Oumm51Mj4qdDk14l1NrG+wNHA++R1NdzNIp0BWA70BERPxuiXFtRPd/h3wNPRcQf\nC+u6ScXznwK/7vdde6562vhvgfsiYnF+/StJ84B7JP19RJR7maw2PtcNg9d7nnNPXUFEvAysA2b2\nrcvXtWcCqwZ42+pifNaR11tJnW2MpE5gGXB67uWwAdTRxluBdwLvIY1qmwJcBTyS/75miFNuKXV+\nh+8D3iJp38K6SaTeu41DlGrLqrON9wVeKa3rJY08dM/z6+dz3RBryHkuIrwUFuAvgReATwCTSV33\n/wscmLd/FbiuEH8I8BxwGelHeh6wHfjzZh/LSF3qaOM5uU3PJf3PsG8Z2+xjGalLrW3cz/svAtY3\n+zhG6lLHd3g/4DfA94DDSdMXbACuavaxjNSljjaeC2zLvxOHAtNIz/Je1exjGYlL/k5OIf1nrhf4\n6/z6oAHa1+e6oW3fhpznmn7gI3HJX9YngBdJ/ws5urDtX4CflOJnkP5X+SLwX8BfNfsYRvpSSxuT\n5uvZ0c+yvNnHMZKXWr/Hpfe6qGtw+5LmplsJ/DEXeIuAMc0+jpG81NHG5wEP5TbeSJq37s3NPo6R\nuAAn5GKj399Vn+uGt30bdZ7zPHVmZmZmFeB76szMzMwqwEWdmZmZWQW4qDMzMzOrABd1ZmZmZhXg\nos7MzMysAlzUmZmZmVWAizozMzOzCnBRZ2ZmZlYBLurMzMzMKsBFnZmZmVkFuKgzMzMzqwAXdWZm\nZmYV8H9NKaa885/vlQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f167c33d150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "TIME_TO_DOM_LOADING_MS (logscale=False):\n",
      "stylo ***5.71088173861e-16\n"
     ]
    },
    {
     "data": {
      "image/png": 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zDM2XFUuSJD0er6KZqDhQAw1mVXVbknU0Mym/Cf/+8P+hwEVt2WrgJ21N78P/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\nMGgDOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16774c69d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIME_TO_DOM_LOADING_MS (logscale=True):\n",
      "stylo ***5.71088173861e-16\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f165a792310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for p in probe_names:\n",
    "    can_chart, reason = can_chart_pref(p)\n",
    "    if can_chart:\n",
    "        chart_pref(p, False)\n",
    "        chart_pref(p, True)\n",
    "    else:\n",
    "        print reason\n",
    "    print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "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": 1
}

## stylo_metrics.py

# coding: utf-8

# This script is intended for variables not yet available in the [Experiments Viewer](https://moz-experiments-viewer.herokuapp.com/).

# In[1]:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import plotly.plotly as py
from scipy.stats import chi2_contingency
from scipy.stats import ttest_ind
from collections import defaultdict as dd

from pyspark.sql import Row
from pyspark.sql import SQLContext
from pyspark.sql.types import *
from pyspark.sql.functions import *
import itertools

from moztelemetry.dataset import Dataset

from scipy.stats import mannwhitneyu

py_max = __builtin__.max
py_map = __builtin__.map

get_ipython().magic(u'pylab inline')


# ### Define Experiment Variables

# In[2]:

experiment_probes = {}
experiment_probes["payload/histograms"] =  [
                "TIME_TO_DOM_INTERACTIVE_MS",
                "TIME_TO_DOM_COMPLETE_MS",
                "MEMORY_TOTAL",
                "MEMORY_UNIQUE",
                "MEMORY_VSIZE",
                "GHOST_WINDOWS",
                ]

experiment_probes["payload/processes/content/histograms"] = [
                "TIME_TO_DOM_LOADING_MS",
                 ]


# In[3]:

probe_names = list(itertools.chain(*experiment_probes.values()))


# To investigate:
#
# TOTAL_SCROLL_Y always = 0.0
# no entries for IDLE_NOTIFY_BACK_LISTENERS
#

# In[4]:

EXPERIMENT_SLUG = "pref-flip-quantum-css-style-r1-1381147"
START_DATE = "20170725" #YYYYMMDD
END_DATE = "20170823" #YYYYMMDD


# ### Simple Utils

# In[5]:

#modified from https://stackoverflow.com/questions/13852896/using-a-single-variable-to-index-into-nested-dictionaries
def recursive_get(d, keys):
    if len(keys) == 1:
        return d.get(keys[0],{})
    return recursive_get(d.get(keys[0],{}), keys[1:])


# In[6]:

def median(lst):
    return lst[(len(lst))/2]


# ### Get data

# In[7]:

cohorts = Dataset.from_source("telemetry-cohorts")
cohorts.schema


# Fetch experiment data

# In[8]:

pings = cohorts.where(submissionDate = lambda x: x >= START_DATE and x <= END_DATE)                .where(experimentId=EXPERIMENT_SLUG)                .records(sc, sample=.1)

pings.cache()
pings.count()


# What were the different ping types observed for this experiment on this day?

# In[9]:

# pings.map(lambda f: f["meta"].get("docType", "None")).countByValue()


# In[10]:

main_pings = pings.filter(lambda x: x["meta"]["docType"]=="main")


# In[11]:

main_pings.cache()


# #### (testing samples)

# In[12]:

# subset = main_pings.take(1000)


# In[13]:

# subset = sc.parallelize(subset)


# In[14]:

# single = sc.parallelize(main_pings.take(1))


# In[15]:

# single.first()


# ### Utility functions

# In[16]:

def extract_probes(p):
    branch = p.get("environment",{}).get("experiments",{}).get(EXPERIMENT_SLUG, {}).get("branch", "warning")

    output = []
    for path, probe_names in experiment_probes.iteritems():
        for probe_name in probe_names:
            probe = recursive_get(p, path.split("/")).get(probe_name, {})
            #take all entries in histogram right now. this is probably problematic. inspect more
            for k,v in probe.get("values",{}).iteritems():
                output.extend([{"probe": probe_name, "branch": branch, "val": float(k)}] * int(v))
    return output


# In[17]:

# get the bins we use for the histogram for a probe by looking at all branches
def get_bins(probe_name, logscale=False):

    all_branches = [r.val for r in df.where(df.probe == probe_name)                                 .collect()]

    #remove top 0.5%, bottom 0.5% for easy outlier
    trim = int(len(all_branches)/200.0)
    all_branches_trimmed = sorted(all_branches)
    all_branches_trimmed = all_branches_trimmed[trim:-1*trim]

    if logscale:
        if all_branches_trimmed[0] < 1:
            all_branches_trimmed = py_map(lambda d: d+1, all_branches_trimmed)
        return list(np.linspace(np.log10(all_branches_trimmed[1]), np.log10(all_branches_trimmed[-1]), 10))

    n,b = np.histogram(all_branches_trimmed,10)
    return b


# In[27]:

# return (proceed bool, reason)
def can_chart_pref(pref_name):
    n = df.where(df.probe == pref_name).count()
    if n==0:
        return (False, "0 entries for pref %s"%pref_name)
    elif n>10000000:
        return (False, "%i values for pref %s"%(n,pref_name))
    return (True, None)


# In[28]:

# get values for branch of experiment for pref, and trim off outliers
def get_vals(pref_name, branch):
    x_vals = [r.val for r in df.where(df.probe == pref_name)                                 .where(df.branch == branch)                                 .collect()]
    trim = int(len(x_vals)/200.0)
    x_trimmed = sorted(x_vals)[trim:-1*trim]
    return x_trimmed


# In[29]:

# return (pval, direction) if significant p value for mannwhitneyu vs control
def test_unequal(branch_vals, control_vals, p_threshold=.05):

    try:
        r = mannwhitneyu(branch_vals, control_vals)
    except:
        return None, None

    prefix = ""
    if r.pvalue < p_threshold:
        prefix = "***"

    if median(branch_vals) > median(control_vals):
        return (prefix + str(r.pvalue), "> control")

    return (prefix + str(r.pvalue), "< control")


# In[43]:

# chart histograms for all branches of a probe, log/std, and calculate if any branches vary from the mean
def chart_pref(pref_name, logscale):

    sig_branches = []
    fig, axarr = plt.subplots(n_branches, 1, sharex=True)

    b = get_bins(pref_name, logscale)
    plt.tight_layout()

    print pref_name + " (logscale=" + str(logscale) + ")" + ":"

    control_vals = get_vals(pref_name, "gecko")

    for i in range(n_branches):

        if branches[i] == "control":
            x = control_vals

        else:
            x = get_vals(pref_name, branches[i])

        if logscale: #always assume 0 as lowest val for now
            x_trans = py_map(lambda d: d+1, x)
            ap,bp,cp = axarr[i].hist(np.log(x_trans), bins=b)

        else:
            axarr[i].hist(x, bins=b)

        axarr[i].set_title(branches[i])

        if branches[i] == "gecko": continue

        p, direction = test_unequal(x, control_vals)
        if p is not None and p.startswith("***"):
            print branches[i], p #, direction

    plt.show()


# # Results

# In[44]:

probe_dicts = main_pings.flatMap(extract_probes)


# In[45]:

df = sqlContext.createDataFrame(probe_dicts.map(lambda d: Row(**d)))


# In[46]:

#check on branch distribution
for b,v in main_pings.map(lambda x: x.get("environment",{}).get("experiments",{}).get(EXPERIMENT_SLUG, {}).get("branch", "warning")).countByValue().iteritems():
    print b, ":", v


# In[47]:

branches = [i.branch for i in df.select("branch").distinct().collect()]
n_branches = len(branches)

#for this one - set order
branches = ["gecko", "stylo"]


# In[48]:

for p in probe_names:
    can_chart, reason = can_chart_pref(p)
    if can_chart:
        chart_pref(p, False)
        chart_pref(p, True)
    else:
        print reason
    print


# In[ ]:


# In[ ]:

	# coding: utf-8

	# This script is intended for variables not yet available in the [Experiments Viewer](https://moz-experiments-viewer.herokuapp.com/).

	# In[1]:

	import matplotlib.pyplot as plt
	import pandas as pd
	import numpy as np
	import plotly.plotly as py
	from scipy.stats import chi2_contingency
	from scipy.stats import ttest_ind
	from collections import defaultdict as dd

	from pyspark.sql import Row
	from pyspark.sql import SQLContext
	from pyspark.sql.types import *
	from pyspark.sql.functions import *
	import itertools

	from moztelemetry.dataset import Dataset

	from scipy.stats import mannwhitneyu

	py_max = __builtin__.max
	py_map = __builtin__.map

	get_ipython().magic(u'pylab inline')


	# ### Define Experiment Variables

	# In[2]:

	experiment_probes = {}
	experiment_probes["payload/histograms"] = [
	"TIME_TO_DOM_INTERACTIVE_MS",
	"TIME_TO_DOM_COMPLETE_MS",
	"MEMORY_TOTAL",
	"MEMORY_UNIQUE",
	"MEMORY_VSIZE",
	"GHOST_WINDOWS",
	]

	experiment_probes["payload/processes/content/histograms"] = [
	"TIME_TO_DOM_LOADING_MS",
	]


	# In[3]:

	probe_names = list(itertools.chain(*experiment_probes.values()))


	# To investigate:
	#
	# TOTAL_SCROLL_Y always = 0.0
	# no entries for IDLE_NOTIFY_BACK_LISTENERS
	#

	# In[4]:

	EXPERIMENT_SLUG = "pref-flip-quantum-css-style-r1-1381147"
	START_DATE = "20170725" #YYYYMMDD
	END_DATE = "20170823" #YYYYMMDD


	# ### Simple Utils

	# In[5]:

	#modified from https://stackoverflow.com/questions/13852896/using-a-single-variable-to-index-into-nested-dictionaries
	def recursive_get(d, keys):
	if len(keys) == 1:
	return d.get(keys[0],{})
	return recursive_get(d.get(keys[0],{}), keys[1:])


	# In[6]:

	def median(lst):
	return lst[(len(lst))/2]


	# ### Get data

	# In[7]:

	cohorts = Dataset.from_source("telemetry-cohorts")
	cohorts.schema


	# Fetch experiment data

	# In[8]:

	pings = cohorts.where(submissionDate = lambda x: x >= START_DATE and x <= END_DATE) .where(experimentId=EXPERIMENT_SLUG) .records(sc, sample=.1)

	pings.cache()
	pings.count()


	# What were the different ping types observed for this experiment on this day?

	# In[9]:

	# pings.map(lambda f: f["meta"].get("docType", "None")).countByValue()


	# In[10]:

	main_pings = pings.filter(lambda x: x["meta"]["docType"]=="main")


	# In[11]:

	main_pings.cache()


	# #### (testing samples)

	# In[12]:

	# subset = main_pings.take(1000)


	# In[13]:

	# subset = sc.parallelize(subset)


	# In[14]:

	# single = sc.parallelize(main_pings.take(1))


	# In[15]:

	# single.first()


	# ### Utility functions

	# In[16]:

	def extract_probes(p):
	branch = p.get("environment",{}).get("experiments",{}).get(EXPERIMENT_SLUG, {}).get("branch", "warning")

	output = []
	for path, probe_names in experiment_probes.iteritems():
	for probe_name in probe_names:
	probe = recursive_get(p, path.split("/")).get(probe_name, {})
	#take all entries in histogram right now. this is probably problematic. inspect more
	for k,v in probe.get("values",{}).iteritems():
	output.extend([{"probe": probe_name, "branch": branch, "val": float(k)}] * int(v))
	return output


	# In[17]:

	# get the bins we use for the histogram for a probe by looking at all branches
	def get_bins(probe_name, logscale=False):

	all_branches = [r.val for r in df.where(df.probe == probe_name) .collect()]

	#remove top 0.5%, bottom 0.5% for easy outlier
	trim = int(len(all_branches)/200.0)
	all_branches_trimmed = sorted(all_branches)
	all_branches_trimmed = all_branches_trimmed[trim:-1*trim]

	if logscale:
	if all_branches_trimmed[0] < 1:
	all_branches_trimmed = py_map(lambda d: d+1, all_branches_trimmed)
	return list(np.linspace(np.log10(all_branches_trimmed[1]), np.log10(all_branches_trimmed[-1]), 10))

	n,b = np.histogram(all_branches_trimmed,10)
	return b


	# In[27]:

	# return (proceed bool, reason)
	def can_chart_pref(pref_name):
	n = df.where(df.probe == pref_name).count()
	if n==0:
	return (False, "0 entries for pref %s"%pref_name)
	elif n>10000000:
	return (False, "%i values for pref %s"%(n,pref_name))
	return (True, None)


	# In[28]:

	# get values for branch of experiment for pref, and trim off outliers
	def get_vals(pref_name, branch):
	x_vals = [r.val for r in df.where(df.probe == pref_name) .where(df.branch == branch) .collect()]
	trim = int(len(x_vals)/200.0)
	x_trimmed = sorted(x_vals)[trim:-1*trim]
	return x_trimmed


	# In[29]:

	# return (pval, direction) if significant p value for mannwhitneyu vs control
	def test_unequal(branch_vals, control_vals, p_threshold=.05):

	try:
	r = mannwhitneyu(branch_vals, control_vals)
	except:
	return None, None

	prefix = ""
	if r.pvalue < p_threshold:
	prefix = "***"

	if median(branch_vals) > median(control_vals):
	return (prefix + str(r.pvalue), "> control")

	return (prefix + str(r.pvalue), "< control")


	# In[43]:

	# chart histograms for all branches of a probe, log/std, and calculate if any branches vary from the mean
	def chart_pref(pref_name, logscale):

	sig_branches = []
	fig, axarr = plt.subplots(n_branches, 1, sharex=True)

	b = get_bins(pref_name, logscale)
	plt.tight_layout()

	print pref_name + " (logscale=" + str(logscale) + ")" + ":"

	control_vals = get_vals(pref_name, "gecko")

	for i in range(n_branches):

	if branches[i] == "control":
	x = control_vals

	else:
	x = get_vals(pref_name, branches[i])

	if logscale: #always assume 0 as lowest val for now
	x_trans = py_map(lambda d: d+1, x)
	ap,bp,cp = axarr[i].hist(np.log(x_trans), bins=b)

	else:
	axarr[i].hist(x, bins=b)

	axarr[i].set_title(branches[i])

	if branches[i] == "gecko": continue

	p, direction = test_unequal(x, control_vals)
	if p is not None and p.startswith("***"):
	print branches[i], p #, direction

	plt.show()


	# # Results

	# In[44]:

	probe_dicts = main_pings.flatMap(extract_probes)


	# In[45]:

	df = sqlContext.createDataFrame(probe_dicts.map(lambda d: Row(**d)))


	# In[46]:

	#check on branch distribution
	for b,v in main_pings.map(lambda x: x.get("environment",{}).get("experiments",{}).get(EXPERIMENT_SLUG, {}).get("branch", "warning")).countByValue().iteritems():
	print b, ":", v


	# In[47]:

	branches = [i.branch for i in df.select("branch").distinct().collect()]
	n_branches = len(branches)

	#for this one - set order
	branches = ["gecko", "stylo"]


	# In[48]:

	for p in probe_names:
	can_chart, reason = can_chart_pref(p)
	if can_chart:
	chart_pref(p, False)
	chart_pref(p, True)
	else:
	print reason
	print


	# In[ ]:




	# In[ ]: