ShakoHo/javascript.options.baselinejit.threshold.ipynb

## javascript.options.baselinejit.threshold.ipynb
{
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  },
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os \n",
      "import json\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "from datetime import datetime\n",
      "from IPython.display import display, display_markdown\n",
      "\n",
      "#\n",
      "# Hide the plotbox if case miss any one data source\n",
      "#\n",
      "HIDE_MISSING_CASE = False\n",
      "\n",
      "DATA_LIST = 'time_list'\n",
      "DATA_NAME = 'run_time'\n",
      "DELETE_KEY = \"video-recording-fps\"\n",
      "\n",
      "#\n",
      "# Input data source\n",
      "#\n",
      "input_json_fp = os.getenv(\"input_json_fp\")\n",
      "#input_json_fp = \"output/javascript.options.baselinejit.threshold.json\"\n",
      "with open(input_json_fp) as read_fh:\n",
      "    data_dict = json.load(read_fh)\n",
      "    for item in data_dict:\n",
      "        if DELETE_KEY in data_dict[item]:\n",
      "            data_dict[item].pop(DELETE_KEY)\n",
      "\n",
      "\n",
      "#\n",
      "# Definde layout and figure size\n",
      "#\n",
      "layout_row = 1\n",
      "layout_column = 1\n",
      "fig_size_w = 10\n",
      "fig_size_h = 5\n",
      "matplotlib.rcParams.update({'figure.max_open_warning': 0})\n",
      "\n",
      "#\n",
      "# generate merged data\n",
      "#\n",
      "casename_list = list(set([casename for lable, data in data_dict.items() for casename in data.keys()]))\n",
      "\n",
      "case_result = []\n",
      "for casename in casename_list:\n",
      "    result = {\n",
      "        \"casename\": casename,\n",
      "        \"result\": {}\n",
      "    }\n",
      "    for lable, data in data_dict.items():\n",
      "        result[\"result\"][lable] = {}\n",
      "    \n",
      "    for lable, data in data_dict.items():\n",
      "        if data.get(casename):\n",
      "            result[\"result\"][lable][DATA_LIST] = data.get(casename).get(DATA_LIST)\n",
      "        else:\n",
      "            result[\"result\"][lable][DATA_LIST] = []\n",
      "    case_result.append(result)\n",
      "\n",
      "nightly_better_list = []\n",
      "for case in sorted(case_result):\n",
      "    casename = case.get('casename')\n",
      "    result = case.get('result')\n",
      "    d = pd.DataFrame(result)\n",
      "    \n",
      "    # drop empty 'DATA_LIST'\n",
      "    for c in d:\n",
      "        if (d[c][DATA_LIST] == []) :\n",
      "            d.drop(c, axis=1, inplace=True)\n",
      "\n",
      "    # Retrive the value of 'DATA_NAME' from each run\n",
      "    value = pd.DataFrame([pd.DataFrame(d[c][DATA_LIST])[DATA_NAME] for c in d]).T\n",
      "    value.columns = d.columns\n",
      "    \n",
      "    # Plot input latency boxplot\n",
      "    value_min = min(value.min())\n",
      "    value_max = max(value.max())\n",
      "    value_median = pd.DataFrame([pd.DataFrame(d[c][DATA_LIST])[DATA_NAME].median() for c in d]).T\n",
      "    value_median.columns = d.columns\n",
      "    \n",
      "    # When HIDE_MISSING_CASE is enabled, skip the case if the case data less then input data amount.\n",
      "    if HIDE_MISSING_CASE and len(INPUT_SOURCE) > len(value.T):\n",
      "        # display summary\n",
      "        display_markdown('### {}\\nskip draw plot box'.format(casename), raw=True)\n",
      "        display(value.describe())\n",
      "    else:\n",
      "        \n",
      "        fig, ax = plt.subplots()\n",
      "        ax.plot(list(range(1,len(list(value_median.median()))+1)), list(value_median.median()), \"r:\")\n",
      "        \n",
      "        value.plot.box(layout=(layout_row, layout_column),\n",
      "                       sharey=True, sharex=True, figsize=(fig_size_w, fig_size_h),\n",
      "                       ylim=(0, value_max*1.1), ax=ax)\n",
      "        plt.title(casename)\n",
      "        if \"Nightly\" in value_median and \"ESR52\" in value_median:\n",
      "            if value_median['Nightly'][0] < value_median['ESR52'][0]:\n",
      "                nightly_better_list.append(casename)\n",
      "        # display summary\n",
      "        display(casename)\n",
      "        display(value.describe().T)"
     ],
     "language": "python",
     "metadata": {
      "scrolled": false
     },
     "outputs": [
      {
       "output_type": "display_data",
       "text": [
        "u'test_firefox_outlook_ail_type_composemail_0'"
       ]
      },
      {
       "html": [
        "<div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
        "\n",
        "    .dataframe thead th {\n",
        "        text-align: right;\n",
        "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>min</th>\n",
        "      <th>25%</th>\n",
        "      <th>50%</th>\n",
        "      <th>75%</th>\n",
        "      <th>max</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>10.0</td>\n",
        "      <td>17.222222</td>\n",
        "      <td>8.466022</td>\n",
        "      <td>5.555556</td>\n",
        "      <td>11.111111</td>\n",
        "      <td>16.666667</td>\n",
        "      <td>22.222222</td>\n",
        "      <td>33.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>10.0</td>\n",
        "      <td>28.888889</td>\n",
        "      <td>19.737648</td>\n",
        "      <td>11.111111</td>\n",
        "      <td>13.888889</td>\n",
        "      <td>27.777778</td>\n",
        "      <td>33.333333</td>\n",
        "      <td>77.777778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>10.0</td>\n",
        "      <td>22.222222</td>\n",
        "      <td>13.857990</td>\n",
        "      <td>11.111111</td>\n",
        "      <td>11.111111</td>\n",
        "      <td>22.222222</td>\n",
        "      <td>22.222222</td>\n",
        "      <td>55.555556</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>10.0</td>\n",
        "      <td>20.000000</td>\n",
        "      <td>12.339505</td>\n",
        "      <td>5.555556</td>\n",
        "      <td>11.111111</td>\n",
        "      <td>16.666667</td>\n",
        "      <td>33.333333</td>\n",
        "      <td>33.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td>10.0</td>\n",
        "      <td>20.000000</td>\n",
        "      <td>16.186499</td>\n",
        "      <td>5.555556</td>\n",
        "      <td>11.111111</td>\n",
        "      <td>11.111111</td>\n",
        "      <td>30.555556</td>\n",
        "      <td>55.555556</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "display_data",
       "text": [
        "    count       mean        std        min        25%        50%        75%  \\\n",
        "1    10.0  17.222222   8.466022   5.555556  11.111111  16.666667  22.222222   \n",
        "10   10.0  28.888889  19.737648  11.111111  13.888889  27.777778  33.333333   \n",
        "20   10.0  22.222222  13.857990  11.111111  11.111111  22.222222  22.222222   \n",
        "5    10.0  20.000000  12.339505   5.555556  11.111111  16.666667  33.333333   \n",
        "50   10.0  20.000000  16.186499   5.555556  11.111111  11.111111  30.555556   \n",
        "\n",
        "          max  \n",
        "1   33.333333  \n",
        "10  77.777778  \n",
        "20  55.555556  \n",
        "5   33.333333  \n",
        "50  55.555556  "
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# show plot\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {
      "scrolled": false
     },
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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VYJiSJEmqwTAlSZJUg2FKkiSpBsOUJElSDYYpSZKkGgxTkiRJNRimJEmSajBMSZIk1dBh\nmIqI10fE79t8PRMRx0XExhFxXUTcV33fqDcKliRJ6k86DFOZeW9m7p6ZuwN7AS8AlwGTgRsycwfg\nhqotSZI0oKzuYb6DgD9n5kPAocDU6v6pwGHdWZgkSVIrWN0wdRQwrbo9MjMXVLcfBUa294SIODYi\nZkbEzIULF3axTEmSpP6p02EqIoYB7wP+e8XHMjOBbO95mXlOZo7LzHEjRozocqGSJEn90er0TB0C\n3J6Zj1XtxyJic4Dq++PdXZwkSVJ/tzphajzNQ3wAVwATqtsTgMu7qyhJkqRW0akwFRHrAm8Dftbm\n7tOAt0XEfcDBVVuSJGlAGdKZhTLzeWCTFe57gnJ2nyRJ0oDlDOiSJEk1GKYkSZJqMExJkiTVYJiS\nJEmqwTAlSZJUg2FKkiSpBsOUJElSDYYpSZKkGgxTkiRJNRimJEmSajBMSZIk1WCYkiRJqsEwJUmS\nVINhSpIkqQbDlCRJUg2GKUmSpBoMU5IkSTUYpiRJkmowTEmSJNVgmJIkSarBMCVJklSDYUqSJKmG\nToWpiNgwIn4SEfdExJyI2DciNo6I6yLivur7Rj1drCRJUn/T2Z6pbwLXZOZOwG7AHGAycENm7gDc\nULUlSZIGlA7DVERsAPwf4HyAzHwlM58CDgWmVotNBQ7rqSIlSZL6q870TG0LLAR+EBF3RMR5EbEu\nMDIzF1TLPAqM7KkiJUmS+qvOhKkhwJ7AdzNzD+B5Vjikl5kJZHtPjohjI2JmRMxcuHBh3XolSZL6\nlc6EqXnAvMy8tWr/hBKuHouIzQGq74+39+TMPCczx2XmuBEjRnRHzZIkSf1Gh2EqMx8FHo6I11d3\nHQTcDVwBTKjumwBc3iMVSpIk9WNDOrncJODHETEMeAD4GCWIXRoRE4GHgCN6pkRJkqT+q1NhKjN/\nD4xr56GDurccSZKk1uIM6JIkSTUYpiRJkmowTEmSJNVgmJIkSarBMCVJklSDYUqSJKkGw5SkljVt\n2jTGjh3L4MGDGTt2LNOmTevrkiQNQJ2dtFOS+pVp06YxZcoUzj//fPbff39mzJjBxIkTARg/fnwf\nVydpIIlyjeLeMW7cuJw5c2avbU/Smmvs2LGcffbZvPWtb331vunTpzNp0iTuuuuuPqxM0poiImZl\nZnuTli+/nGFKUisaPHgwL730EkOHDn31vsWLF7PWWmuxdOnSPqxMaj0R0evb7M380VWdDVOOmZLU\nksaMGcOMGTOWu2/GjBmMGTOmjyqSWldmdulrmxOu7PJz1ySGKUktacqUKUycOJHp06ezePFipk+f\nzsSJE5kyZUpflyZpgHEAuqSW1BhkPmnSJObMmcOYMWM49dRTHXwuqdcZpiS1rPHjxxueJPU5D/NJ\nkiTVYJiSJEmqwTAlSZJUg2FKkiSpBsOUJElSDYYpSZKkGgxTkiRJNRimJEmSajBMSZIk1dCpGdAj\nYi7wLLAUWJKZ4yJiY+ASYDQwFzgiMxf1TJmSJEn90+r0TL01M3fPzHFVezJwQ2buANxQtSVJkgaU\nOof5DgWmVrenAofVL0eSJKm1dDZMJXBtRMyKiGOr+0Zm5oLq9qPAyG6vTpIkqZ/r1JgpYP/MnB8R\nmwHXRcQ9bR/MzIyIbO+JVfg6FmDrrbeuVawkSVJ/06meqcycX31/HLgM2Bt4LCI2B6i+P76S556T\nmeMyc9yIESO6p2pJkqR+osMwFRHrRsTwxm3g7cBdwBXAhGqxCcDlPVWkJElSf9WZw3wjgcsiorH8\nRZl5TUT8Drg0IiYCDwFH9FyZkiRJ/VOHYSozHwB2a+f+J4CDeqIoSZKkVuEM6JIkSTUYpiRJkmow\nTEmSJNVgmJIkSarBMCVJklSDYUqSJKkGw5QkSVINhilJkqQaDFOSJEk1GKYkSZJqMExJkiTVYJiS\nJEmqwTAlSZJUg2FKkiSpBsOUJElSDYYpSZKkGgxTkiRJNRimJEmSajBMSZIk1WCYkiRJqsEwJUmS\nVINhSpIkqQbDlCRJUg2dDlMRMTgi7oiIK6v2thFxa0TcHxGXRMSwnitTkiSpf1qdnql/Bua0aZ8O\nfD0ztwcWARO7szBJkqRW0KkwFRFbAu8GzqvaARwI/KRaZCpwWE8UKEmS1J91tmfqG8AXgGVVexPg\nqcxcUrXnAaO6uTZJkqR+r8MwFRHvAR7PzFld2UBEHBsRMyNi5sKFC7uyCkmSpH6rMz1T+wHvi4i5\nwMWUw3vfBDaMiCHVMlsC89t7cmaek5njMnPciBEjuqFkSZKk/qPDMJWZX8zMLTNzNHAUcGNmfhiY\nDhxeLTYBuLzHqpQkSeqn6swzdQLw2Yi4nzKG6vzuKUmSJKl1DOl4kabMvAm4qbr9ALB395ckSZLU\nOpwBXZIkqQbDlCRJUg2GKUmSpBoMU5IkSTUYpiRJkmowTEmSJNVgmJIkSarBMCVJklSDYUqSJKkG\nw5QkSVINhilJkqQaDFOSJEk1GKYkSZJqMExJkiTVYJiSJEmqwTAlSZJUw5C+LqA/iohe32Zm9vo2\n+7PdTr6Wp19cvNrPe+j09/RANau2zQlXrvZzNlh7KH/48tt7oJrW5nuvf+jK+8/3ngYyw1Q7uvrh\nOnryVcw97d3dXM3A9PSLi7v2szytNf4wjp58VV+X0C/53usfuvT+872nAczDfJIkSTUYpiRJkmow\nTEmSJNVgmJIkSarBMCVJklRDh2EqItaKiNsi4g8RMTsiTq7u3zYibo2I+yPikogY1vPlSpIk9S+d\n6Zl6GTgwM3cDdgfeGRH7AKcDX8/M7YFFwMSeK1OSJKl/6nCeqSwTvzxXNYdWXwkcCHyoun8qcBLw\n3e4vUZIkdUZXJzyuozfn7uqvk652atLOiBgMzAK2B74N/Bl4KjOXVIvMA0at5LnHAscCbL311nXr\nlSRJK9HlCY9bRH+ddLVTA9Azc2lm7g5sCewN7NTZDWTmOZk5LjPHjRgxootlSpIk9U+rdTZfZj4F\nTAf2BTaMiEbP1pbA/G6uTZIkqd/rzNl8IyJiw+r22sDbgDmUUHV4tdgE4PKeKlKSJKm/6syYqc2B\nqdW4qUHApZl5ZUTcDVwcEacAdwDn92CdkiRJ/VJnzub7I7BHO/c/QBk/JUmSNGA5A7q0dGlfVyBJ\namGGKQ1sX/savO51zUD1ta/BkUc2H58/H55+um9qkyS1hE7NMyX1tuFjJrPr1Mk9v6FNgZOHw492\nb7bfBUzdtUc3O3wMwJo7F4wkDSSGKfVLz845rXsmnnvySfje9+C974Vdd4Xbb4ePfAQuvBD22qvj\n5990U1nH+99f2m9+M6y3Hvzyl6V98MGw447wne+U9q9/DVtvDdtuu8rV9teJ5yRJq88wpTVLJtx2\nGwwdCnvuCRFw0kkwfHgJU3vsAXff3fn1HXDA8u3f/AZeeKHZ3ntvGNVm8v8PfKAEr3POKe2PfxwO\nOQQ++MHSfu65EsYkSWsMw5Ra3+LFMG9e6Q3KLGFmn33gpz+FjTaCRx+FjTcuy0bU21YErLtus/2V\nrzRvZ8I11zQff/lluPVW2GWX0n7xRdhgAzj9dGAMvPIKnHsuvOMdsP329eqSJPUZB6CrNS1Z0rz9\n/veXw3gAgwbBZZeVkNLQCFI9LQLGjYMxY0r7Na+B2bPhc59r1nzqqeVQIcCf/wyf+hTccktpP/gg\nvOlNpfcL4Pnn4d57l3+tkqR+xzCl1vOd78CWW5aeH4BJk+CUU0rPEJRDb70VoFbH8OEweXIJTAA7\n7VTOFnzf+0r7hRfKMo2erd/+tiwzY0Zp33UXTJkCCxaUduP1SpL6lGFK/d8dd5SB3g89VNo77wyH\nH17GHwG8/e1w2GH1D+H1tgjYYgtYf/3S3mUXuP765sD4sWNh6lTYvTrT8M474Ywzmj1VF1xQQuUj\nj5T2n/4EN95YDntKknqNYUr9zzPPlO+33Va+Dx9exkTNm1faBxwA3/oWbLJJn5TXazbfHI45Bjbc\nsLTHjy+9V1tuWdrbbFNC5mablfYPf1iC5bJlpf2DH8DHPtZsP/WUQUuSeoBhSn0vE2bNKoO1oZyJ\nB3DDDeX79tvDPffAfvv1TX39ydChzR64Aw8svVNDqvNIPv3pMpXDa15T2o88AnPmlHFkAJ/5zPID\n3X/+8zJIX5JUi2fzqW8sWVIO273udaX9oQ+VnpZrr4W11y73ffGLfVdfK9pss2YvFZTxVVOmNNtH\nHAH77ttsf+MbZT984AOlffTR5ezHs84q7T/+EV772uXXKUn6G4Yp9Z4lS5q9KEcfXQZYz51belou\nughGj+7L6tZ8hxyyfPvaa8uEpA0jRpSpGxoOO6wMlp82rbQnTy5TThx2WGkvW9bs9ZKkAcxPQvWO\nCy6AkSOb46H+8R/hzDOb43n22mvNHwPV3wwbVnqeGs48E7785Wb73HPhuOPK7WXLSqj63e9Ke+nS\n0ot1xhmlnVkCceMkAUkaQAxT6hl3310GQ8+eXdo771wuINyYPfwtbymHlwYP7rsatWoHHdScxmHQ\noBKU/v3fS/vll+Gf/qnMMg9liocPfxiuvrq0Fy4sk5HedFNpv/IKPPaY0zlIWiMZptQ9XngB/uM/\nyrXpoPRaPPRQc06kvfcu80O17QlR62kc1ltnHTjttHI2IZT9Ont2c/zVk0/CE080ex5vv70sc1V1\nTcK5c8vvS+P3Q5JamGFKXff738P//E+5PWxYuUzKddeV9uabl9m7G39stWYbMqT0PjYGq7/+9TBz\nZjnjEGCrreCb32zOoTVzJnzhC80xW5dfXq6dOHduac+fX+YXc/Z3SS3AAejqvKVL4YEHYIcdSvsT\nnyh/RP/3f8v3++9vzokktTVqVJm6oeHww0uQakxYut565dqKI0aU9o9/DCecUObG2mCDErZuuqkE\n9mHDymHDttNESFIfsmdKq9Z2ksd//Ef4u79r9hacdx5ccUXzcYOUVsdGGzXHzB10UPldalxKZ/z4\nMg9W4+zCO++En/2sOQfZ8cfDdtstPwbr2mt7r3ZJamON7pna7eRrefrF3p3xefTkq3ptWxusPZQ/\nfPntPbeBSy+FY48th+tGjoSJE8ug8sYfsN1267ltq6XtOnXX7lnR1C+V71sB/7Y+/PANpb0XsNd6\nr7aHj4FdFwBTu2eznXHnhDt7b2OS+rU1Okw9/eJi5p727r4uo8d0e3D785/LRYO//OVyFtcuu5Qz\n8F55pTy+zz7lS+rAs3NO69333qJF5QzCHXcs7U9+shwC/Na3Snu33cqhxsbZhl//ehnX9a53dWlz\nvflPk6T+z8N8A9krr8DXvgbXXFPam24KDz5Y/ihBCVP/9V9l8LDUn220UTNIAXz7280gBXDyyc05\nszLL2Ktf/rL5+E47NefMgnIixaOP9mzNktYYHYapiNgqIqZHxN0RMTsi/rm6f+OIuC4i7qu+b9Tz\n5aq2P/4Rbryx3B46tJxh1QhTG2xQruX2nvf0XX1STzjssHKIGkqP1SOPlKkdoPxT8Za3lAHwAE8/\nXZadWh0zfOGF0kPbmPZj6dLerV1Sv9eZw3xLgM9l5u0RMRyYFRHXAR8FbsjM0yJiMjAZOKHnSlWX\nLF0K991X/vOGcrHbxx6Du+4qf1TuvHP5S4hIA8GgQc3B7sOGlR7YhrXXhhkzYMstS/vRR8tUDk88\nUdr33lu+X3YZ/P3fl/fTFVfAe9/rPGrSANVhz1RmLsjM26vbzwJzgFHAoTSHe04FDuupIrWa2p6B\n9/nPwxvfCC+9VNpnn93smQKDlLSiYcNgv/3KhbehnDX45z/D+99f2o3pHN5QDYa/7bZyokZjjqxf\n/xre/ObyTwyUEPbgg80JTCWtcVZrzFREjAb2AG4FRmZmY/riR4GR3VqZuubKK8s17h58sLSPOaZM\nYdCYj6ftxIqSVl+jx+p1ryvf3/WuMv/aHnuUduOfmcY/KpdeWgLZI4+U9vTpZQxX49JKXmJHanmd\nDlMRsR7wU+C4zHym7WOZmUC7nwgRcWxEzIyImQsbA5vVfebNK2OcbrihtBtn4DX+C95999J+zWv6\nrkZpTTZ4cBlv1XiPHXxwuTJA45+Wt72tXDR61KjSvvnmMgC+sfy//ms5DN94z95xR5kIV1LL6NTU\nCBExlBKkfpyZP6vufiwiNs/MBRGxOfB4e8/NzHOAcwDGjRvnv2B1LV1aDtWNHg0MLWfg/eUv5dRw\nKB/q557blxVKamv77ctXw5dw8twUAAAMUElEQVS+VA6/NyYsHTsWnnmmed3D008vhw4feKC0TzsN\nnn0WTj21tBctKr1egzwZW+ovOgxTERHA+cCczDyzzUNXABOA06rvl/dIhSoXkH34YXjnO8sH8Dnn\nlJnIN/17WGutcoaepNbRtqf4yCPLV8OKF4B+8MHmNQwB3ve+8vzrry/tH/6wHHpsXAdRUq/rTM/U\nfsDRwJ0R8fvqvhMpIerSiJgIPAQc0TMlDkDLlsE995TxTQAnngh/+EP5UI2AW24pg2CdOFBa82y1\n1fJzu7U90xDKZZ0al9WB8vlw8MHNMHXggWUc1/HHl/btt5fxXZ5sIvWYDsNUZs4AVnY10YO6t5wB\nrO2FW086qXTtL1xYPgDPOKN8bwwib5xNJGng+fCHl2//6U/w/PPl9rJlZaxWIzi98grsvTdMngyn\nnFKGCUyaBB/6EOy/f3PwuxeMlmrxoHt/MH16+QC8s7rW11FHwQUXlFO0oVz2wvlrJLVnnXVgxIhy\ne9AguPhi+MQnmo///OclPEGZM+vii5vTNjz8MAwfXs44hDJh6U9/2nu1S2sIw1RfWLgQDj0ULq+G\nme28c5nDphGedt65fPitvXbf1Sip9Q0bVs72bQwZGDWqzHt1zDGlPWgQ/MM/lH/YoJxJePjhzeff\ncUf5rLrnntJ+/nl46qneq19qEYap3pBZrhM2bVppb7xxmdLgmWqGiZEj4fvfb85SLkk9JaJ5JuGW\nW8I3vlEuBA3lQuazZjWXXbSoTFjaGKP185+X6yA2wtXtt8NZZ8Fzz/Ve/VI/ZJjqKffcA7/4Rbkd\nAT/6UbnkBJQPslmz4Oij+64+SVrRWmvBnns22wceWC491ZigdM89y9QN221X2r/6VbmAdGOahrPO\nKldceOWV0r7/frj7bicm1RrPMNVdli0rHzoNp54KH/9486Ko113X7JmSpFY0Zgx84QvNIQmTJ5dr\nE66zTmlvumkJXo3HzzijXES6McD93HPLQPiGtpe+klqYYaq7fO1r5Vpdjz5a2iefXKYzaHSnDx/e\nd7VJUk+IaA5+hzLW8+KLm+3Pfrb0yjfcckvpzWo49NAyQ3zDVVeVZaQW06kZ0FvV8DGT2XXq5N7Z\n2GbAD3aBX72tw0W7y/AxAO/ute1JGhi6/bNzajXn1f+pvqbuWtqNuUobbYC/Avd236ZX5OemesIa\nHaaenXMac09bc980o520U1IP6LPPzgUL4KWXymWxAN773nK1hy9+sYy7Gj68DJ8466zSPumkcmWI\nffft9Cb83FRPWKPDlCSphWy++fLtxkk8UMaffve7sOOOpb1oEXzlK+Xs6H33LVM27LJLGXJx1FEl\nlN18M+y1F2y4Ye+9Bg1IjpmSJPV/Q4aUM6Df9KbS3nhjeOGF5gSlL75YLqszalRp3313ad9wQ2nf\nfz9MmNBc3yuvOABe3cYwJUlqTUOHNs8k3HxzmDoV3vzm0t5xx3Ix6Le8pbTnz28GK4BrrinP/cMf\nSvvee+HCC+HZZ3uvfq0xDFOSpDXPeuvBQQeV6RqghKp585qPb7cdfP7zzfFZV19dZoZ/+eXS/tGP\nypmGjXC1YEH5cs4stcMwJUkaeMaOLWOuGheO/9SnymTLjfCVWQ4Drrdeaf/nf5YAtmxZaf/kJ3Dm\nmb1ft/olw5QkSUOHNq9RCGV81k03NScc/chH4Pzzm3MHXnUVnHdec/kJE+CQQ5rt3/62efF6rfE8\nm0+SpI7ssUf5avjBD8oZgw1vfOPyF4E+7jjYYINy9QsoE5hut13pAYOy7AYbNMOaWpphSv3Wmjwf\nzAZrD+3rEiTVtdZazduNkNRw4YXLh63Zs5cPTmPGwGGHlekeAL7znXKh6bbXRlTLMEypX+rtCQNH\nT75qjZ7gVVIva3vIEJa/jE4mnHgi7LRTab/wQglj//ZvJUy9/DLsvXeZrPSoo2DJEvjjH0sAW3vt\n3nsN6jTHTEmS1JsiYNKk5nUJ11kHnnwSPvnJ0n7mGRg9unlN1wcfLJOPXnJJaS9YUA4j3n13aWd6\nlmEfM0xJktTXNtwQNtqo3B4xAi6/HN5d9ZaPHFnOHjzooNJ+4AE491xYuLC0b7oJNtkEbruttOfN\ngyuvhOee69WXMJAZpiRJ6s/WXx8+8AHYaqvS3m+/Mv/V/vuX9iabwJFHwtZbl/Y115TrGj7+eGlf\nfTUccQQ88URpP/00PP98776GNZxhSpKkVjNoUHOahje8oQxkf+1rS/uII8rUDNtsU9p//WsZc9WY\nU+sb3yhnEjYGyF9/PZxzjocKazBMSZK0Jll//XLx50bYOuaYMiHp0Oos4ne8o0xC2jgb8aKLyuD3\nxtmGxx8Phx7aXN/s2WXcllbKs/kkSRpI9tmnfDWcd17pvWp47WvLhaAbPv3pcljwlltK+6tfLYcW\njz22d+ptAR2GqYj4PvAe4PHMHFvdtzFwCTAamAsckZmLeq5MSZLUIwYNgs02a7aPP375x08/vUzf\n0PCrX5XxW4apV3WmZ+oC4FvAD9vcNxm4ITNPi4jJVfuE7i+vPid+HFiixmzCcXrXnpeOM2hXV957\nD53+nh6oZNW2OeHK1X7OQHjvramfnWv6vhs+ZjK7Tp3cMytvHOn7GMATMHXXntnOKgwfA9D/5gSM\nzvwhiIjRwJVteqbuBQ7IzAURsTlwU2a+fhWrAGDcuHE5c+bMehX3Y078KEmrx8/N1ram77+ImJWZ\n4zparqsD0Edm5oLq9qPAyC6uR5IkqaXVPpsvS9fWSru3IuLYiJgZETMXNiYYkyRJWkN0NUw9Vh3e\no/r++MoWzMxzMnNcZo4bMWJEFzcnSZLUP3U1TF0BTKhuTwAu755yJEmSWkuHYSoipgH/C7w+IuZF\nxETgNOBtEXEfcHDVliRJGnA6nBohM8ev5KGDurkWSZKkluPlZCRJkmowTEmSJNVgmJIkSarBMCVJ\nklSDYUqSJKkGw5QkSVINhilJkqQaDFOSJEk1GKYkSZJq6HAG9IEoIrr+3NO79rzM7PI2Jamv+bnZ\n2tx/9Rim2rEm7WBJ6g1+brY29189HuaTJEmqwTAlSZJUg2FKkiSpBsOUJElSDYYpSZKkGgxTkiRJ\nNRimJEmSajBMSZIk1WCYkiRJqsEwJUmSVINhSpIkqQbDlCRJUg21wlREvDMi7o2I+yNicncVJUmS\n1Cq6HKYiYjDwbeAQYGdgfETs3F2FSZIktYI6PVN7A/dn5gOZ+QpwMXBo95QlSZLUGuqEqVHAw23a\n86r7JEmSBowhPb2BiDgWOLZqPhcR9/b0NvvQpsBf+7oIdYn7rrW5/1qX+661ren7b5vOLFQnTM0H\ntmrT3rK6bzmZeQ5wTo3ttIyImJmZ4/q6Dq0+911rc/+1Lvdda3P/FXUO8/0O2CEito2IYcBRwBXd\nU5YkSVJr6HLPVGYuiYhPAb8CBgPfz8zZ3VaZJElSC6g1Ziozrwau7qZa1gQD4nDmGsp919rcf63L\nfdfa3H9AZGZf1yBJktSyvJyMJElSDYapbhAR34+IxyPirr6uRR1rb39FxMYRcV1E3Fd936gva1T7\nImKriJgeEXdHxOyI+OfqfvdfC4qIuRFxZ0T8PiJm9nU96lh7+8z3n2Gqu1wAvLOvi1CnXcDf7q/J\nwA2ZuQNwQ9VW/7ME+Fxm7gzsA3yyuoyV+691vTUzd/f0+pay4j4b8O8/w1Q3yMzfAE/2dR3qnJXs\nr0OBqdXtqcBhvVqUOiUzF2Tm7dXtZ4E5lCsvuP+kvjPg33+GKakYmZkLqtuPAiP7shh1LCJGA3sA\nt+L+a1UJXBsRs6qrZaj/a2+fDfj3X49fTkZqNZmZEeFprv1YRKwH/BQ4LjOfiYhXH3P/tZT9M3N+\nRGwGXBcR91Q9x+q//maftX1woL7/7JmSisciYnOA6vvjfVyPViIihlKC1I8z82fV3e6/FpSZ86vv\njwOXAXv3bUXqyEr22YB//xmmpOIKYEJ1ewJweR/WopWI0gV1PjAnM89s85D7r8VExLoRMbxxG3g7\n4BnR/dgq9tmAf/85aWc3iIhpwAGUq2c/Bnw5M8/v06K0Uu3tL+DnwKXA1sBDwBGZ6UkF/UxE7A/8\nD3AnsKy6+0TKuCn3XwuJiO0oPRtQhpxclJmn9mFJ6sDK9llEbMIAf/8ZpiRJkmrwMJ8kSVINhilJ\nkqQaDFOSJEk1GKYkSZJqMExJkiTVYJiSJEmqwTAlSZJUg2FKkiSphv8PI6ZjMA7fBLIAAAAASUVO\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110294cd0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Case list which Nightly peform better than ESR52"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nightly_better_list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    }
   ],
   "metadata": {}
  }
 ]
}