ShiftSummary_September2015.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import seaborn\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"rundbfills.csv\")\n",
    "df[\"time_total\"] = [datetime.timedelta(seconds=duration) for duration in df[\"time_total\"]]\n",
    "df[\"endtime_formatted\"] = [datetime.datetime.strptime(timestamp, \"%Y-%m-%d %H:%M:%S\") for timestamp in df[\"timestamp\"]]\n",
    "df[\"starttime_formatted\"] = [endtime - duration for endtime, duration in zip(df[\"endtime_formatted\"], df[\"time_total\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dmshiftstarts_moritz = [(\"2015-09-08\", \"night\"),\n",
    "                        (\"2015-09-09\", \"night\"),\n",
    "                        (\"2015-09-10\", \"night\"),\n",
    "                        (\"2015-09-14\", \"morning\"),\n",
    "                        (\"2015-09-15\", \"morning\"),\n",
    "                        (\"2015-09-16\", \"evening\"),\n",
    "                        (\"2015-09-17\", \"evening\"),\n",
    "                        (\"2015-09-18\", \"night\"),\n",
    "                        (\"2015-09-19\", \"night\"),\n",
    "                        (\"2015-09-20\", \"night\")]\n",
    "\n",
    "dmshiftstarts_alex = [(\"2015-09-16\", \"morning\"),\n",
    "                      (\"2015-09-17\", \"morning\"),\n",
    "                      (\"2015-09-18\", \"morning\"),\n",
    "                      (\"2015-09-19\", \"morning\"),\n",
    "                      (\"2015-09-20\", \"morning\"),\n",
    "                      (\"2015-09-21\", \"evening\")]\n",
    "\n",
    "dmshiftstarts_timon = [(\"2015-09-09\", \"morning\"),\n",
    "                       (\"2015-09-10\", \"morning\"),\n",
    "                       (\"2015-09-11\", \"evening\"),\n",
    "                       (\"2015-09-12\", \"evening\"),\n",
    "                       (\"2015-09-13\", \"evening\"),\n",
    "                       (\"2015-09-14\", \"night\")]\n",
    "\n",
    "dmshiftstarts_vanessa = [(\"2015-06-24\", \"morning\"),\n",
    "                         (\"2015-06-25\", \"morning\"),\n",
    "                         (\"2015-06-26\", \"morning\"),\n",
    "                         (\"2015-06-27\", \"evening\"),\n",
    "                         (\"2015-06-28\", \"evening\"),\n",
    "                         (\"2015-06-29\", \"night\")]\n",
    "\n",
    "daytimetitle_daytime_dict = {\"morning\":\"06:30:00\", \"evening\":\"14:30:00\", \"night\":\"22:30:00\"}\n",
    "\n",
    "shift_duration = (8,0,0) #hours, minutes, seconds\n",
    "shift_duration = datetime.timedelta(hours=shift_duration[0], minutes=shift_duration[1], seconds=shift_duration[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def format_shift(shifts, translator=daytimetitle_daytime_dict):\n",
    "    return [datetime.datetime.strptime(shift[0]+\" {0}\".format(translator[shift[1]]), \"%Y-%m-%d %H:%M:%S\") for shift in shifts]\n",
    "\n",
    "def time_overlap(ti_1, ti_2):\n",
    "    if ti_1[1]>=ti_2[0] and ti_2[1]>=ti_1[0]:\n",
    "        if ti_1[0] <= ti_2[0] <= ti_1[1]: start = ti_2[0]\n",
    "        else: start = ti_1[0]\n",
    "        stop = min(ti_1[1], ti_2[1])\n",
    "        return abs(start-stop)\n",
    "    else: return datetime.timedelta(hours=0)\n",
    "\n",
    "def check_for_matching_time(df, shifs, shift_dur):\n",
    "    shift_time = []\n",
    "    run_time = []\n",
    "    total_lumi = []\n",
    "    logged_lumi = []\n",
    "    inefficiency = []\n",
    "    deadtime = []\n",
    "    for start_shift in format_shift(shifs):\n",
    "        shift_time.append(shift_dur)\n",
    "        for cnt, (start_run, end_run) in enumerate(zip(df[\"starttime_formatted\"], df[\"endtime_formatted\"])):\n",
    "            temptime = time_overlap([start_run, end_run], [start_shift, start_shift + shift_dur])\n",
    "            if temptime: \n",
    "                run_time.append(temptime)\n",
    "                total_lumi.append(temptime/df[\"time_total\"][cnt]*df[\"lumi_total\"][cnt])\n",
    "                logged_lumi.append(temptime/df[\"time_total\"][cnt]*df[\"lumi_logged\"][cnt])\n",
    "                #print \"start_run={0}, end_run={1}, start_shift={2}, dur={3}\".format(start_run, end_run, start_shift, temptime)\n",
    "    return (run_time, shift_time, total_lumi, logged_lumi)\n",
    "            \n",
    "def timedelta_sum(timedeltas):\n",
    "    time_sum = datetime.timedelta(0)\n",
    "    for timedelta in timedeltas:\n",
    "        time_sum = time_sum + timedelta\n",
    "    return time_sum\n",
    "    \n",
    "def show_shift_efficiency(runtime, shifttime, name=\"\"):\n",
    "    appendix = \"'s\" if name[-1] in [\"x\", \"s\", \"z\"] else \"s\"\n",
    "    print \"{0} shift efficiency: {1}%. \" \\\n",
    "          \"Time data taking: {2}\".format(name+appendix, \n",
    "                                         round(timedelta_sum(runtime)/timedelta_sum(shifttime),2), \n",
    "                                         timedelta_sum(runtime))    \n",
    "        \n",
    "#  stolen from http://stackoverflow.com/questions/8906926/formatting-python-timedelta-objects  \n",
    "def strfdelta(tdelta, fmt):\n",
    "    d = {\"days\": tdelta.days}\n",
    "    d[\"hours\"], rem = divmod(tdelta.seconds, 3600)\n",
    "    d[\"minutes\"], d[\"seconds\"] = divmod(rem, 60)\n",
    "    return fmt.format(**d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class Shifter(dict):\n",
    "    def __init__(self, **kwargs):\n",
    "        self[\"name\"] = kwargs.pop(\"name\", \"John Doe\")\n",
    "        self[\"dlumi\"] = sum(kwargs.pop(\"dlumi\", [0.0]))/1e6 #pb^-1\n",
    "        self[\"clumi\"] = sum(kwargs.pop(\"clumi\", [0.0]))/1e6 #pb^-1\n",
    "        self[\"runtime\"] = kwargs.pop(\"runtime\", [datetime.timedelta(hours=0)])\n",
    "        self[\"shifttime\"] = kwargs.pop(\"shifttime\", [datetime.timedelta(hours=0)])\n",
    "        self[\"digits\"] = kwargs.pop(\"digits\", 2)\n",
    "        self[\"shifteff\"] = timedelta_sum(self[\"runtime\"])/timedelta_sum(self[\"shifttime\"])\n",
    "        self[\"ineff\"] = self[\"clumi\"]/self[\"dlumi\"]\n",
    "        \n",
    "        self[\"runtime\"] = timedelta_sum(self[\"runtime\"])\n",
    "        self[\"shifttime\"] = timedelta_sum(self[\"shifttime\"])\n",
    "        \n",
    "        \n",
    "    def get_name(self):\n",
    "        return self[\"Name\"]\n",
    "    \n",
    "    def get_dlumi(self):\n",
    "        return self[\"dlumi\"]\n",
    "    \n",
    "    def get_clumi(self):\n",
    "        return self[\"clumi\"]\n",
    "    \n",
    "    def get_runtime(self):\n",
    "        return self[\"runtime\"]\n",
    "    \n",
    "    def get_shifttime(self):\n",
    "        return self[\"shifttime\"]\n",
    "    \n",
    "    def get_shifteff(self):\n",
    "        return self[\"shifteff\"]\n",
    "    \n",
    "    def get_ineff(self):\n",
    "        return self[\"ineff\"]\n",
    "    \n",
    "    def describe(self):\n",
    "        print \"Name: {0}\\nTotal time shifted: {1},\" \\\n",
    "        \" time in stable beams condition: {2} ({3}%)\" \\\n",
    "        \"\\nDelivered Lumi: {4} pb^-1, collected Lumi: {5} pb^-1 ({6}%)\\n\".format(self[\"name\"],\n",
    "                                                                  str(self[\"shifttime\"]).split(\".\")[0],\n",
    "                                                                  str(self[\"runtime\"]).split(\".\")[0], \n",
    "                                                                  round(self[\"shifteff\"]*100,self[\"digits\"]), \n",
    "                                                                  round(self[\"dlumi\"],self[\"digits\"]),\n",
    "                                                                  round(self[\"clumi\"],self[\"digits\"]),\n",
    "                                                                  round(self[\"ineff\"]*100,self[\"digits\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "runtime_alex, shifttime_alex, totlumi_alex, loglumi_alex = check_for_matching_time(df, dmshiftstarts_alex, shift_duration)\n",
    "runtime_timon, shifttime_timon, totlumi_timon, loglumi_timon = check_for_matching_time(df, dmshiftstarts_timon, shift_duration)\n",
    "runtime_moritz, shifttime_moritz, totlumi_moritz, loglumi_moritz = check_for_matching_time(df, dmshiftstarts_moritz, shift_duration)\n",
    "runtime_vanessa, shifttime_vanessa, totlumi_vanessa, loglumi_vanessa = check_for_matching_time(df, dmshiftstarts_vanessa, shift_duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: Timon\n",
      "Total time shifted: 2 days, 0:00:00, time in stable beams condition: 0 days 20:20:33 (42.38%)\n",
      "Delivered Lumi: 7.67 pb^-1, collected Lumi: 6.08 pb^-1 (79.35%)\n",
      "\n",
      "Name: Alex\n",
      "Total time shifted: 2 days, 0:00:00, time in stable beams condition: 0 days 20:08:35 (41.97%)\n",
      "Delivered Lumi: 11.74 pb^-1, collected Lumi: 9.84 pb^-1 (83.82%)\n",
      "\n",
      "Name: Moritz\n",
      "Total time shifted: 3 days, 8:00:00, time in stable beams condition: 0 days 16:26:07 (20.54%)\n",
      "Delivered Lumi: 8.57 pb^-1, collected Lumi: 7.08 pb^-1 (82.56%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "alex = Shifter(name=\"Alex\",\n",
    "               dlumi=totlumi_alex,\n",
    "               clumi=loglumi_alex,\n",
    "               runtime=runtime_alex, \n",
    "               shifttime=shifttime_alex)\n",
    "\n",
    "timon = Shifter(name=\"Timon\",\n",
    "               dlumi=totlumi_timon,\n",
    "               clumi=loglumi_timon,\n",
    "               runtime=runtime_timon, \n",
    "               shifttime=shifttime_timon)\n",
    "\n",
    "moritz = Shifter(name=\"Moritz\",\n",
    "               dlumi=totlumi_moritz,\n",
    "               clumi=loglumi_moritz,\n",
    "               runtime=runtime_moritz, \n",
    "               shifttime=shifttime_moritz)\n",
    "\n",
    "#vanessa = Shifter(name=\"Vanessa\",\n",
    "#                dlumi=totlumi_vanessa,\n",
    "#                clumi=loglumi_vanessa,\n",
    "#                runtime=runtime_vanessa, \n",
    "#                shifttime=shifttime_vanessa)\n",
    "\n",
    "timon.describe()\n",
    "alex.describe()\n",
    "moritz.describe()\n",
    "#vanessa.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FknYucFlmPtb1CCVJkmqi2cz/Pwd+DhwWEdOB3SiStHcBhwIPRMSFFEnaeZl5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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10acc15d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def configure_barchart(ylabel=\"default\", names=(\"Alex\", \"Moritz\", \"Timon\"), xticks=[0.5,1.5,2.5], appendix = [\"\", \"\", \"\"]):\n",
    "    fig, ax = plt.subplots()\n",
    "    new_names = [name+app for name, app in zip(names,appendix)]\n",
    "    ax.set_ylabel(ylabel,fontsize=20)\n",
    "    ax.set_xticks(xticks)\n",
    "    ax.set_xticklabels(new_names, fontsize=20)\n",
    "    fig.set_size_inches(10,8)\n",
    "    return fig, ax\n",
    "    \n",
    "fig_shifttime, ax_shifttime = configure_barchart(\"Total time shifted / time with stable beams [h]\", \n",
    "                                                  appendix=[\" ({0}%)\".format(round(alex.get_shifteff()*100,2)),\n",
    "                                                            \" ({0}%)\".format(round(moritz.get_shifteff()*100,2)),\n",
    "                                                            \" ({0}%)\".format(round(timon.get_shifteff()*100,2)),])\n",
    "\n",
    "shifttime = [alex.get_shifttime().total_seconds()/3600., #time in h\n",
    "             moritz.get_shifttime().total_seconds()/3600.,\n",
    "             timon.get_shifttime().total_seconds()/3600.]\n",
    "\n",
    "runtime = [  alex.get_runtime().total_seconds()/3600.,\n",
    "             moritz.get_runtime().total_seconds()/3600.,\n",
    "             timon.get_runtime().total_seconds()/3600.]\n",
    "\n",
    "ax_shifttime.bar([0.1,1.1,2.1] , shifttime, color=['r','g','b'], alpha=0.5)\n",
    "ax_shifttime.bar([0.1,1.1,2.1] , runtime, color=['r','g','b'])\n",
    "\n",
    "plt.show()\n",
    "fig_shifttime.savefig(\"shifttime.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11.744171997662921, 8.5748477077198793, 7.6667133209859806]\n"
     ]
    },
    {
     "data": {
      "image/png": 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PActExN8p6516C1aSJC2yakePDkRm3g38F/A7yrJZ92bmhYONSpIkaeaMtVqtQcfQtYhY\nGzgL2JwyWvVU4LTMPLlT+RvGxlrrLMD4JIAbgHvmzGGTTTYZdCgd3XXXXRx+2eEs85RlBh2KRsxf\n5v2F/Tfbn5VWWmnQoUgLq7FOG4f19uiLgR9l5l0AEfFtYFOgY9ImDcrs2bM3abVaVw46jk5mzZq1\nEu/jw6zIXwcdy7A6aMuDDjrkkkMOGXQcQ+dulv7Maz/z6VarddegQ9GMaTFJ4qHeDWvS9ivgwIhY\nGniAMoHvQnlhlCRJ6oe+9WmLiGUjYtOI2LF5vWK/jj1RZl4LnAT8GLiu2Xz8TNUnSZI0aNUtbRGx\nKvBZYBdgcUqT6OLAuyPi7cAemXlpbT0TZeangU/3+7iSJEkLo6qWtohYGbgc2BW4AriaR+9h3wus\nCZwXERvU1CNJkjTqam+PHgKsAeycmZsBZ4/vyMxjgG0orXkHVtYjSZI00mqTtp2AMzLzrE47M/MS\n4NvA7Mp6JEmSRlpt0jYLuGmaMrcCq1TWI0mSNNJqk7bbgI2mKbMxJXGTJElSj2qTtlOBbSNir047\nI2I/yqoFZ1TWI0mSNNJqp/w4DNgRODYi9h4/XkScSFm1YH3gxqacJEmSelSVtGXmfRGxGSUp2w14\nUrNrN+BBygS4H8zMe6qilCRpARkbG1scWH7QcQyzO++8syyVp55MtsRb9eS6mTkP2Cci9gWC8kG/\nv+zKB2qPL0nSArY8/OAgWM11eXt0+OEAv/7woOMYTrctDbyv056+rT2amQ8Dv+jX8SRJGpzV/grP\nNmnr0TLLAKzkv1+fdZW0RcRyvVaUmff1+l5JkqRR121L272UtUW7Mcaj65FKkiSpB90mbb0u/N5t\noidJkqQ2XSVtmbnVDMUhSZKkKXTbp21D4PbMvKPt9XzJzOu6jE2SJEmNbm+PXgMcDBza9np+2KdN\nkiSpQrdJ20nAtRNezw/7tEmSJFXotk/b7lO9liRJ0syoXTBekiRJC0D1iggR8RrgHcBawLKUedke\nJzOfVVuXJEnSqKpK2iLi9cC3mpct4E+TFLVPmyRJUoXalrYDgAeANwHnZuZD9SFJkiRpotqkbT3g\n5Mz8bj+CkSRJUme1AxHmAS4EL0mSNMNqk7bTgF0iYpl+BCNJkqTOelnGqt1pwK7AJRFxJPBrSh+3\nx3EZK0mSpN71soxVJ6sAX53ifS5jJUmSVKGXZax64ZQfkiRJFaqWsZI01JbkVlbgXuyT2qO7nnUX\n/IaVBh3H0LmfpYAlBx2GNGxqJ9ddMzNv6VMskhasZ7AM72K5QYcxvI6+8mhYjvcMOo6h8wgAXwRu\nH2wg0nCpnaftNxHxS+Cc5nFZZj5SH5akBWJ5YNagg5AkzY/aKT+OoiR+HwJ+ANwZEd+IiLdEhLcM\nJEmS+qSqpS0z3w/lNinwSmB7YAfgDcAjEXEFTStcZl5bF6okSdLoqr09CkDTr+1/gP+JiCWBlwI7\nAns2f/4ETvkhSZLUs74kbQARsQTwEmALYEvgZcCTm91z+1WPJEnSKKodPbo1JUnbAtgEWLrZdQdw\nHqWf2w8y81c19UiSJI262pa2C5vn+ylLWl0OXJKZ11ceV5IkSW1qR4/+lLLawbKUAQjbAttExPNq\nA5MkSdKjakePvigiVgC2ArZuHrsARMTdwCU8eov0Z1WRSpIkjbDqgQiZeQ9wRvMgIp5KSeI2A14N\nvA4XjJckSapSe3v0MSLiycDGwKaUxO2ZzS5Hj0qSJFWoHT26FGVqj22ax4soLWqPAFcCHwfOzcyr\nK+OUJEkaabW3R+8Flmr+fCfwTeBc4ILMvKvy2JIkSWrUJm0/pyRp5wJXZmarPiRJkiRNVDt69CX9\nCkSSJEmT6+tABEmSJM0MkzZJkqQhYNImSZI0BEzaJEmShoBJmyRJ0hDoavRoRGwIzM3MuW2v50tm\nXtdlbJIkSWp0O+XHNcDBwKFtr+eHa49KkiRV6DZpOwm4dsLr+eGku5IkSRW6Stoyc/epXkuSJGlm\n1C5jRUQsDWwBrMmj65A+TmYeVVuXJEnSqKpK2pqBCOcAq01TtAWYtEmSJPWotqXtSErCdiJwJfDA\nJOXs0yZJklShNml7EXBqZu7Rj2AkSZLUWe3kun8G/tCPQCRJkjS52qTtq8BrI+JJ/QhGkiRJndXe\nHv04sD5wbUQcD9wMPNipYGaeWVmXJEnSyKpN2lYD1gaeBfznFOVcEUGSJKlCbdJ2LLAu8CPgckof\nt04cPSpJklShNmnbFLggM7fvRzCSJEnqrHYgwoM8di1SSZIkzYDalrbvADtExMcy86F+BDS/ImJ5\n4IvAcym3X/fIzDkLMgZJ0iJpSbhyBfjdMoMOZFjdddfqwK0rDTqO4XT7UvDsjntqk7b9gYuBSyLi\nOOBGJunXlpnXVdY10WeBczPz9RGxBOC0I5KkfngGrPQuWH3QcQyto48GWP09g45jOD0EsGenPbVJ\n21xgjHKbdfYU5fo6ejQingJsnplvA8jMh4F5/Tq+JGnUrQWsM+ggpMeoTdpOns9y/R49uhbwx4g4\nAXg+8BNg38z8S5/rkSRJWihUJW2ZuXuf4ujWEsBGwHsy86qIOBL4CGWyX0mSpEVO7ejRQbkVuDUz\nr2pen0ZJ4qSFypw5c66gtDQvdI8mNmkgPDek7o21Wr3fuYyIM5j+1ucY0MrMXXquqHPdlwLvzMwb\nIuJgYOnM3L9T2RvGxlr2TNCCdgMQsEmr1bpy0LF0MjY2tjHv4QpmDToSjZw7gaMX8nODvMI+bRqM\nG2i11hnrtKe2T9vO81Hmz8DDlfV08l7g5Ih4AnAT8PYZqEOSJGmhUJu0PWuS7ctQ1iT9UPPnbSrr\neZzMvBZ4Sb+PK0mStDCqHYhwyxS7fxkR3wd+DnwK2LumLkmSpFE2owMRMvMB4LvA62ayHkmSpEXd\nghg9uhLwlAVQjyRJ0iKr6vZoRCw3ya7FKMtKvQZ4E/DjmnokSZJGXe1AhHuZfMqP8eGqfwcOrqxH\nkiRppNUmbZdOsr0F/A24HvjyDCwWL0mSNFJqR49u1ac4JEmSNIVhXcZKkiRppHTV0jbFwINpZeZ9\nvb5XkiRp1HV7e3SqgQeTGWves3iX75MkSVKj26RtsoEH0+l9VXpJkiR1l7Q58ECSJGkwHIggSZI0\nBEzaJEmShoBJmyRJ0hAwaZMkSRoCJm2SJElDwKRNkiRpCJi0SZIkDYFul7E6gR4nys3MPXp5nyRJ\nkrpfEeFtFXWZtEmSJPWo29uj60x4vAy4HfgZsAuwKvAEYAXglcAc4KamrCRJknrU7TJWN7a/joiP\nAw8DL8/Mu9t2zQO+HxFzgGuAT1OSOkmSJPWgdiDCa4DvTkjY/iEz/wScDWxbWY8kSdJIq03aHgFW\nnKbM6sADlfVIkiSNtNqk7VLg9RGxTaedEfFPwGuB8yvrkSRJGmndjh6d6EBga+B7EXE+cCXwJ+Ap\nwGbNvrnAxyrrkSRJGmlVSVtm/jwitgQ+C+zQPMa1gPOA92Tmb2vqkSRJGnW1LW1k5jXAlhGxGrAh\nZbqPe4CrM3Nu7fElSZLU32Ws5o0/MvM84KE+HluSJGmkVSdtEbFqRJxCaV27DDiz2fXuiLgxIrao\nrUOSJGnUVSVtEbEycDmwK3AFcDUw1uy+F1gTOC8iNqipR5IkadTVtrQdAqwB7JyZm1Em0gUgM48B\ntqH0mzuwsh5JkqSRVpu07QSckZlnddqZmZcA3wZmV9YjSZI00mqTtlmUBeGnciuwSmU9kiRJI602\nabsN2GiaMhtTEjdJkiT1qDZpOxXYNiL26rQzIvYDNgfOqKxHkiRppNVOrnsYsCNwbETsPX68iDgR\neDGwPnBjU06SJEk9qmppy8z7KGuMHgesBazb7NoNWBs4Cdg0M++pqUeSJGnU1c7T9gyAzNyHsnzV\nBpTboS8EVsjM3YElIuLllXFKkiSNtNrbo7cABwOHZubDwC86lHk/sDewXGVdkiRJI6urpC0itgPW\na16Or3wwOyLeN8lblgL+GXikt/AkSZIE3be03QscwaMJG8D2zWMqx3RZjyRJktp0lbRl5lURsROw\ncrPpy8B3m8dELeAh4NbMvLQqSkmSpBHXdZ+2zDxn/M8RsRVlGatOSZskSZL6pHbKj92BsyPitRGx\nSfu+iPh8RLy+5viSJEkqaqf8eBLwPcqi8K+esH1P4FsRcXpELFkVpSRJ0oirXcbqAGAb4HjgC+Mb\nM/PPwBqUSXdfB3yssh5JkqSRVjtP267ARZn5uLVHM/M2YJ+IeB7wVuCgyrokSZJGVm1L2xrA1dOU\nuRJYvbIeSZKkkVabtM0FNpqmzHOBOyrrkSRJGmm1Sdt3gK0nWxEhIt5JmXj3rMp6JEmSRlptn7ZP\nUgYaHBkRewOXA/cBTwY2BtYHfk9Zn1SSJEk9qp2n7U5gNvA1Sr+1twHvBXYHngN8A5idmXPrwpQk\nSRpttS1tZObtwG4R8S5gbWAF4P6yKx+oPb4kSZLq+7S1WxJYHlg+M68FlunjsSVJkkZaddIWEatG\nxCnAPcBlwJnNrndHxI0RsUVtHZIkSaOudhmrlSmDD3YFrqDM2TbW7L4XWBM4LyI2qKlHkiRp1NW2\ntB1CmWB358zcDDh7fEdmHkNZ4moJ4MDKeiRJkkZabdK2E3BGZnachy0zL6EsJj+7sh5JkqSRVpu0\nzQJumqbMrcAqlfVIkiSNtNqk7TamX8ZqY0riJkmSpB7VJm2nAttGxF6ddkbEfsDmwBmV9UiSJI20\n2sl1DwN2BI5tlrFaAiAiTgReTFnG6samnCRJknpUu4zVfcBmwHHAWsC6za7dKKsjnARsmpn31NQj\nSZI06vqxjNU8YJ+I2BcIyqoILmMlSZLUR9VJ27jMfBj4Rb+ON52IWBz4MXBrZr5mQdUrSZI0CF0l\nbRGxXK8VNbdS+2lf4JfAk/t8XEmSpIVOty1t9wKtLt8z1rxn8S7fN6mIWB3YAfgksF+/jitJkrSw\n6jZpu7THerpN9KZzBPAhoOeWP0mSpGHSVdKWmVvNUBzzLSJeDdyRmT+NiIHHI01lzpw5Vww6hsnM\nmTOH2V9zhTkNxkJ/bnhqaCE01mrNfyPYwtCnLSIOA94KPAw8kdLadnpm7jbZe24YG2ut04/KpS7c\nAARs0mq1rhx0LJ2MjY1tzHu4glmDjkQj507g6IX83CCvAK8cGoQbaLXWGeu0Z+j6tGXmR4GPAkTE\nlsAHp0rYJEmSFgXD2qdtQR1bkiRpoTB0fdraZeYlwCWDjkOSJGmm9W1y3Yh4BvB8YBngLuCXmfmH\nfh1fkiRplFUnbRGxFvAFYOsJux6JiIuBPTPz5tp6JEmSRllV0hYRqwKXAU8DrgJ+BPwBWAHYAtgG\nuDQiXpiZd1bGKkmSNLJqW9oOoiRse2fm5yfujIh3AscD/w58oLIuSZKkkbVY5ft3BC7slLABZOYX\ngQuBnSrrkSRJGmm1SdtTgWunKfMzYLXKeiRJkkZabdJ2B7DhNGU2oMx/LUmSpB7VJm3nANtFxB6d\ndkbEXsC2TTlJkiT1qHYgwiHAa4EvRsRulBUT5lFuh24GvBiYCxxaWY8kSdJIq0raMvP/IuJllBGi\nL6dM89HuYuBfM/O2mnokSZJGXfXkupl5E7BNRKwBvABYDvgT8NPM/H3t8SVJktSfFREWB14D/F9m\nntW2/fMRcWFmnlZbhyRJ0qirGogQEU8Cvgd8G3j1hO17At+KiNMjYsmqKCVJkkZc7ejRAyhLVR1P\nWX8UgMz8M7AGcBzwOuBjlfVIkiSNtNrbo7sCF2XmXhN3NIMP9omI5wFvpSx5JUmSpB7UtrStAVw9\nTZkrgdUr65EkSRpptUnbXGCjaco8l7JygiRJknpUm7R9B9g6It7XaWdEvBPYHjir035JkiTNn9o+\nbZ+kDDQ4MiL2Bi4H7gOeDGwMrA/8Hji4sh5JkqSRVtXSlpl3ArOBr1H6rb0NeC+wO/Bs4BvA7Myc\nWxemJEnSaOvHigi3A7tFxLuAtYEVgPvLrnyg9viSJEnqQ9I2LjMfBH7Zr+NJkiTpUbUDESRJkrQA\nmLRJkiQNAZM2SZKkIWDSJkmSNAS6StoiYu+IWHumgpEkSVJn3Y4e/TRwdETcDJzfPC7KzD/1PTJJ\nkiT9Q7eQlXc4AAAgAElEQVRJ24rAZsArm8e/An+PiDmUBO6CzLyqvyFKkiSpq6QtM/8GXNQ89o+I\nVYFXUBK4fYFPRMRdwIXABcD5mfmH/oYsSZI0eqom121WQzgJOCkixoCNeLQV7vPAkhHxC0oL3L/V\nBitJkjSq+rkiQgv4SfM4LCKeDGxNSeB2BkzaJEmSetS3pG2iZnDCd5uHJEmSKjhPmyRJ0hAwaZMk\nSRoCJm2SJElDwKRNkiRpCJi0SZIkDQGTNkmSpCHQ1ZQfEXEC0Oqloszco5f3SZIkqft52t5WUZdJ\nmyRJUo+6vT26zoTHy4DbgZ8BuwCrAk8AVqCshDAHuKkpK0mSpB51u2D8je2vI+LjwMPAyzPz7rZd\n84DvR8Qc4Brg05SkTpIkST2oHYjwGuC7ExK2f2iWsjob2LayHkmSpJFWm7Q9Aqw4TZnVgQcq65Ek\nSRpptUnbpcDrI2KbTjsj4p+A1wLnV9YjSZI00rodPTrRgcDWwPci4nzgSuBPwFOAzZp9c4GPVdYj\nSZI00qqStsz8eURsCXwW2KF5jGsB5wHvyczf1tQjSZI06mpb2sjMa4AtI2I1YEPKdB/3AFdn5tza\n40uSJKm/y1jNG39k5nnAQ308tiRJ0kirTtoiYtWIOIXSunYZcGaz690RcWNEbFFbhyRJ0qirStoi\nYmXgcmBX4ArgamCs2X0vsCZwXkRsUFOPJEnSqKttaTsEWAPYOTM3o0ykC0BmHgNsQ+k3d2BlPZIk\nSSOtNmnbCTgjM8/qtDMzLwG+DcyurEeSJGmk1SZtsygLwk/lVmCVynokSZJGWm3Sdhuw0TRlNqYk\nbpIkSepRbdJ2KrBtROzVaWdE7AdsDpxRWY8kSdJIq51c9zBgR+DYiNh7/HgRcSLwYmB94MamnCRJ\nknpU1dKWmfdR1hg9DlgLWLfZtRuwNnASsGlm3lNTjyRJ0qjrxzJW84B9ImJfIIDlgfvLrnyg9viS\nJEmqTNqalRAuzcxjMvNh4Bf9CUuSJEntalvaXg38sR+BSJIkaXK1o0f/CCzXj0AkSZI0udqWtr2B\nb0bEZ4DTgZuBv3Yq2AxakCRJUg9qk7Zjmud/ax6tDmXGmu2LV9YlSZI0smqTtt8Ct1ASs6l0SuZ6\nFhFrUKYTWaU59vGZeVQ/65AkSVqYVCVtmblVn+Lo1kPABzLzmohYFvhJRHw/M68fUDySJEkzqnYg\nwj9ExLIR8dKI2LF5vWK/jj1RZt6emdc0f74fuB54+kzVJ0mSNGjVSVtErNrM13YP8L/Amc2ud0fE\njRGxRW0d09S/JvBC4IqZrEeSJGmQqpK2iFgZuBzYlZI0Xc2j/dvuBdYEzouIDWrqmaL+ZYHTgH2b\nFjdpoTJnzpwrKP0uF7pHE5s0EJ4bUvdqW9oOAdYAds7MzYCzx3dk5jHANpR+cwdW1vM4EbEkZZqR\nr2Xmd/p9fKkfZs+evQnlh8xC92hikwbCc0PqXm3SthNwRmae1WlnZl4CfBuYXVnPY0TEGPAl4JeZ\neWQ/jy1JkrQwqp3yYxZw0zRlbqVMzdFPLwPeAlwXET9tth2Qmd/rcz2SJEkLhdqk7TZgo2nKbExJ\n3PomMy+jjyNfJUmSFna1ic+pwLYRsVennRGxH7A5cEZlPZIkSSOttqXtMGBH4NiI2Hv8eBFxIvBi\nYH3gxqacJEmSelTV0tYsAr8ZcBywFrBus2s3YG3KUlObZuY9NfVIkiSNutqWNjJzHrBPROwLBLA8\ncH/ZlQ/UHl+SJEmVSVtEHE2ZJ21OZj4M/KI/YUmSJKldbUvb3sDeEfEb4OuUBO6G+rAkSZLUrnb0\n6KbAUcDSwMeAX0XEVRHx/oh4anV0kiRJAuoHIszJzPdTlrJ6OfA/wDOB/wZui4jzI2K3Zo1QSZIk\n9agvE9Rm5iOZeUlmvht4OrA9cAKwIXAiMLcf9UiSJI2qmVhVYAx4YvPcarY9NAP1SJIkjYzqKT8A\nImIJ4BXAG4CdgeUoidr3gK8BZ/ajHkmSpFFVO+XHK4B/Bl4HrNBsvpySqJ2SmXfXhSdJkiSob2n7\nXvN8A3AkZcqPmyuPKUmSpAlqk7ajga9m5lX9CEaSJEmddZW0RcSGwNzMHB8N+iWg1WyfUmZe10N8\nkiRJovuWtmuAg4FDm9c/nc/3tYDFu6xLkiRJjW6TtpOAaye8nh+t6YtIkiRpMl0lbZm5+1SvJUmS\nNDNmYnJdSZIk9Vm3AxGW67WizLyv1/dKkiSNum77tN1L9/3TxpezciCCJElSj7pN2i7tsR4HIkiS\nJFXodiDCVjMUhyRJkqbQt4EIEbFsRLw0InZsXq/Yr2NLkiSNuuqkLSJWjYhTgHuA/wXObHa9OyJu\njIgtauuQJEkadVVJW0SsDFwO7ApcAVxNGXgAZdDCmsB5EbFBTT2SJEmjrral7RBgDWDnzNwMOHt8\nR2YeA2xD6Td3YGU9kiRJI602adsJOCMzz+q0MzMvAb4NzK6sR5IkaaTVJm2zgJumKXMrsEplPZIk\nSSOtNmm7DdhomjIbUxI3SZIk9ag2aTsV2DYi9uq0MyL2AzYHzqisR5IkaaR1uyLCRIcBOwLHRsTe\n48eLiBOBFwPrAzc25SRJktSjqpa2ZhH4zYDjgLWAdZtduwFrAycBm2bmPTX1SJIkjbraljYycx6w\nT0TsCwSwPHB/2ZUP1B5fkiRJlUlbRCwOvABYFViOkqz9BvhlZrpIvCRJUp/0lLRFxNqUCXN3AZbt\nUOSeiPgWcFhm/r4iPkmSJNFDn7aI2AG4htJvbUnKMlanASdT1h29jtLq9q/AzyPiVX2LVpIkaUR1\n1dIWEUGZ5mNxYH/g6Mz8a4dyKwJ7Ah8HTo2IDTLz5j7EK0mSNJK6vT26H7A0sGNmnjdZocy8G/jP\niLgSuBB4P7Bvz1FKkiSNuG5vj74c+MFUCVu7zLwIuAzYutvAJEmS9Khuk7anU/qzdeNqYM0u3yNJ\nkqQ23SZtSwP3dfme+4AndfkeSZIktek2aRsDHunyPd2WlyRJ0gS1C8ZLkiRpAehlct0XRMRuXZR/\nPuDqCJIkSRV6Sdpe2zwkSZK0gHSbtB3aYz22tEmSJFXoKmnLzINnKA5JkiRNwYEIkiRJQ8CkTZIk\naQiYtEmSJA0BkzZJkqQhYNImSZI0BEzaJEmShoBJmyRJ0hAwaZMkSRoCJm2SJElDwKRNkiRpCJi0\nSZIkDQGTNkmSpCFg0iZJkjQETNokSZKGgEmbJEnSEDBpkyRJGgImbZIkSUPApE2SJGkILDHoAHoV\nEdsDRwKLA1/MzMMHHJIkSdKMGcqWtohYHDga2B5YH3hTRKw32KgkSZJmzlAmbcDGwI2ZeUtmPgR8\nE9h5wDFJkiTNmGG9Pboa8Pu217cCm0xW+OYZD0d6vKH43N076AA0kobiczcUZ7AWSTcD63TcM6xJ\nW6ubwq9stcZmKhBpMuvQ5Qd1AWu1WlcCnhsajK8OOoDJeW5osDonbDC8t0dvA9Zoe70GpbVNkiRp\nkTSsLW0/Bp4TEWsCfwDeALxpoBFJkiTNoKFsacvMh4H3AOcDvwROyczrBxuVJEnSzBlrtRbmXjeS\nJEmCIW1pkyRJGjUmbZIkSUPApE2SJGkIDOvo0YVGRJwHvBI4LzN37LB/d+DLwAcy87MLOLz2OJ4N\n/BTYKjN/0mxbAvgg8HbgmcCfgAuAf8/MWzoc463AvpRJZP5CGQjy8cz87XzGsAxwMLAr8HTgTuCs\npr67JpR9QhPbW4C1gAeBK4FPZuYlE8q+FDgOeA7wc+D9mXl5h+PdAJyamR/qENs7gI8Cz8vMv87P\n32fURMTBwMeblwdm5ienKHsUZbAQwJqZ+bs+xrEVcBFwZGbu17b9FcDdmfnjftXVHPcJwNXAlzLz\niLbtuwAfBjYE/k75fB468fPZlN0W2B94CfBE4CbgJOD/Zebf5yOGtYFfT1HkiZn5t7byTwcOBXYE\nlmvqOx44rr2+iFgV+BKwNWXC8k9n5hc71P8N4NmZ+ZIO+9YFfgLMzsyfTfd3WdRMOC+m81tgK8rs\nqd/NzNfNUFgLTHNtuQbYMjN/EhE/ALaY5m2HZOYhbcd4F7A3EMBdwHnAJzLz95O8f2IMT6Kci28A\nnkGZVeIUyvXiLxPKbgN8f5JDzc3Mp7WVXeiuLSZtFZovvO0oCcwrI2K1zLxtkuIDG/EREWPAF4Az\nxhO2xteAfwauBz5HSY7eCGwbES9pv9BGxCeBA5qyxwOrNGVfExGbZOZUF5Tx9WIvADYFrgJOpVzs\n9gRe3tR3X1N2MUoytx1wHXAssEIT6/8XEW/MzNOasksDZwAPNOV2AL4XEetk5ty2EPZsjvGfk4R4\nAiUh/Q/g36b6uwiAXYCOSVvzeduleTkTn/ubKcn/nLY63w0cA7x2Bur7d0qi9bm2+vaifN7upiQ9\nSwL/Qvl87pyZ57SVfQslQZsHnAbcR/mh9yngpfMZ8/Ob528Cv+qwvz0ReybwI2Dlpr4/UJK3oygX\nn33b3nc8JYn4AuWCeXxE/CEzz2073oaUc2+HToFl5q8i4kTgSxExOzMfmY+/z6LkYmDi33n8h/CR\nPHb9h3ubxyGU79Kh1nZt+XbbteUEyo+qiRajfLc+Efhh2zGOAd4N3AF8BXgCj15bts3MX0wTwxLA\nOZRE8SLgO8ALKderV0TEZpn5YNtbxs+lzwO3Tzjc/W3HXSivLSZtdd5M+SAeTjkJ9wA+MdCIOtsN\n2IzypQxARGxM+SK+AtiiWcN1/BfP/1Auins024JyAswBNh//pR4RXwO+R7l4//M0MfwTJWH7dma+\nvi2O8WTw/ZSWAZpjbQecDrxh/CIQEYdTEr5jIuLMpmXh1ZQEcpPMvCoijgB+R/m/+e/mfctQLrxH\nTWzRG5eZj0TEgcDpEfGVzLxumr/PKLsdeGFEPHOSVtaXUlpS7wee1O/KmzoPnbD5qf2uByAingN8\nBNizmWpo/Jf1pygX343Gf9xExNGUz+dRlIvI+Bf/Z5uyLxz/92ouNN8FdoqI12XmGdOEsmHzfFhm\n/nyassdQ/j12Gk++IuLfgcuA90bEf2fmbyNiFcr5s39mfqYp90tgL+DctuN9AvjfzDx/ijo/AfyG\ncvE9Zpr4FilNy+rE1v+taZK2SVqZD+mwbRg97tqSmV/pVDAi9qN8H3wiMy9qtm1F+czcRLm23N5s\n/wzlXPoSMHuaGPagJGz/nZkfbKvvMMq5+w5K0jVu/Fz6cGbez+QWymuLfdrq7Eb5pf0Zyq/ntw82\nnMdrfgntD5ybmb9p27VR83zyeMLWOIHyq719LdfnUz6sj7mVk5kXUC5G051U7fWdOGH78c1ze327\nUFpoDmr/1Z6ZSWnyXhl4UbN5reb52qbMH4A/Amu2He89lF9vn5kmxjMpq23sP025Ufed5nmyWzuv\np7Qq/ZAFvxRQv+v7N8rf5eS2bc8CngJc1H5BbpKpK4C1ImLlZvPLKb/Cv9ie4DYJ4GHNy+3nI44N\ngb8xTetMRKxFaRH4entrWdPS8HFKq8hTms1rNs/Xth3iOh49p8Z/3L0G+NhU9TYX29OBDzWt6lrE\nTXFt6VR2bcrn/Rc89gfXG5vnj40nbFBabyktYRtHxHS3Wp9N+c6f2NL1zeZ54vVpQ+CWaRI2WEiv\nLba09Sging9sAHwrMx+IiDOAt0XEdpk52f3y9vevSvkS3QmYRbmF8S3gP8Y/TBGxOeUX3O+B9TPz\nz832J1D6kKxH6Ufwv1NU9SpgXeCgCdvHm3fXnLD9qcDilA8nAJn5rSa2iX+HpwLLU34lTWey+lZv\nnv/Ytu0UysXphg7HGe+3s2zzfE/z/GTgruaLZFnKhZaIWI5yovzX+O3XyWRmq+m7s19EfGR++1OM\noIsoX7a7UG7/TPRPlFakp3TYR0RsR+l/sgnlO+h64IvA5zOz1VbuEcrtkl835VuUFoprmhg+m5kf\nmNCH5oyIIDMXi4hbKP1bJvOVzJz0h1ZErET5YXbCeCtb449NLGtOKD8GPI3yGZ3XbP4NpSX5cf3c\nePxneSobAr+aj/5vr2qeT5+4IzPPo/QVGtd+7oxbjsfezvskcGFmXjofMZ5MaYXYlUcvmJqgWcnn\nN7T1aWtuL7+Z8v17OOWW+VKU29x7Uy74hwJvpbRW/YTSv+q6Ccd+CaXlZ3Ngmaaer1G+/9r7PP6A\n0hK4GSXheCXltuWPKf2UO31eJ5rs2tLJpyjJzfsmfIbHE6M5j38L43+3zYBJP3+Z+WHK98NE6zbP\n/7iV2fygWJ9yh2g6C+W1xaStd7s1z6c0z98E3ga8k8k7OQIQEc8A/pdyC+lMykXrhZQP3nYRsXlm\n/iUzfxgRnwPeS7lYjTf9HgI8Fzh8moQNyvJef+8Q0zlAAntHxE+As5t4jqf0z+h0MR6PfxlgY0oT\n8SOUE3I6Jzd/v49HxE2Uk3Bdyq3YB2m7pZKZp9PhotMkqztQLpi/bDbPaV7vHxGHUprCn0T59wXY\nD3h4qr/PBOc3cb4eOGKasqPqIcrn9i0RsUpm3jG+o2mZWYPSZ/FdE98YEe/l0duFp1Nuob6K8v+/\nBY9fjm57SoveCcCqwOXA0s2+8QTvhObPW/LYPl9H8PjEcQzYB1iJRz8jk3kt5UL2mNuCmXlXRHwF\n2D0iPgX8F+WuxccpfcaOHL9ANi0GnfqgwaMtldP12XkSpXXv4qb/z46U2zbXUy4YX28r/rzxMCPi\n/cC/UpLLWyhJ7ufbyt5CuaDt01zEg9Iy+Nmm3q2AbZi/lnQoiemDlP9Dk7bpTezvOUbpH7cYZfDa\n84FXUL6bb6L8355C+Z7eFTin6V/1V4CIeC3lvHuI0hp+O7AtJfHevukfNn5XpUVJQH4I/JlyDj2N\n0pH//IjYKDPHv2MnM9m15TEi4kWU79MLMvPiCbvH+5ot1eGtyzXPa04Tx8T6VqR8bxxFSbzab40G\nJXl8MCK+ShmAszxloNF/TOgCsFBeW7w92oMmW/8Xyi3R8Q7HF1J+ge/c/EKfynGUE+TVmfm6zPxo\nZr6K0lnxhTz2l8sBlF9L74uIDZqL4ocoTbYHzke4WwE3Z2b7r2eai8rmlA/myZRfD9dTkrFdM/M7\ndNA0c99Pael4AbBfZn57uiCaC/tLKV8k51BGql7V/Dtsm5lXzcff5QDKr8Pzxgd8NKPVjqUktPdR\nToZTMvN7zf/DByjJ7V+a+Kf7zI93pt1yPuIZVS1KwrUYsPOEfeO3Rh/3RR4Rz6Ik+r+l9AV7e2a+\nl9KKdBHwhqbTfrtVgDdn5gcy802ZecXE4zZ9aMZbBr6ZmYc22z+bmYe2PyiDhmZRWtkeN0pygq2a\n506jUd9FSW72p3Sgvp3SInI489HZOCLWo5zvD1BaE6eyQfP8ckq/0FOax5rA16KMXhz39Ob5M5SL\n9RWUVsxlgWObJBOA5gL+AUpLxh8pfd5+w6O3mT4JnJ2ZV7bFPent5yZ5uJ7yvaLuLUZJoDbKzP0z\nc3tKS9u6lLsqz2vOgzdQupmsRvM91bT6fJny3bxpZv5LlpHVL6R8v2/OY2/NjVF+uFwDvCAzP5iZ\nb6ZcU55AadGbzlZ0uLZ08IHm+fAO+8a/93fpsG+n5rlji30nUUZq3klpXVyKco29ua3IeH+2XSnX\nkq9SEtyNgHMj4h8t7wvrtcWkrTfbUZqxz2j7Rf13yi3EJ/BoK9zjRMTTKC0L5za3K9odA9wK7D6+\noflAvINyy/Joyon5EPDWCbdsOtW1MuXEftwvpohYivKB3IoyVcF/NfEvBhzV3P7tZHHKhXd85M0R\nETFlf5emvhUoF6fnUS7Q/4/yC3J5yoi1NaZ5/26UZPZeSkvJPzQX/m0pF8tXZeZ4a83+lOTwmIhY\nJcr0LA9GxNyIeA8dNM3ct/Fonzl1dgHlAjHxy/afgPFBIhO9mfL5OSTbppRpPuPva16+Y8J7/spj\nO8X3LCJeRUlIfkzpbD+djYD7mr4sE32Q8jn8LeW8/CLlYvFeprngRcTqlL/T0sABOfmI83HLUVrF\nP5eZL2wu6G+nJHO/BQ6MMsITHh348XJKB+rdm/PjBZTbzPu3lSUzv9ns25fy//OizLw3InagtLB9\nLCIWi4ijIuLPwP0R8fmm1buTXwDLNz/u1L3jJvQxHp9e4vjx7jGN8UT6mc3zzpTv0s9m5jXjhZrr\n0gco59HEcwtKS2377crxa9IzO5T9h6muLRPKPZWSIP2kQysblPPmPsodmH0iYlZErN50+t+0KdNN\nP9U7KOf41yl3Ei+IMhXQuCcCNwIfycwtMvMjTbL6Ysq14ugmZmDhvLaYtPVmPCmbeAtg/DZFp5Nj\n3HiH/FkRcXD7g3J75W/AylHmWQL+MTrpOMqvpfUpc2RNN4IMSisFlIvJRAdSWkWOzszZmfmhzHwj\n5RbVysBZEbHkxDdl5g3Nr7K9KQnYDcChTV+KqXyuif/DmbltZn44M3dqYliP0qzfUZQRrSdQvnhe\nlx1GLGbmRZl5xHjzdpMc70OZp+fBpv71KEnG5yiJ6VaTVHknpTVGk2j+Tc8Btm5+5RMRG1H6qEz2\nf/mC5vlx/VOaWzHzePSX8Ljft/dz61VEBPANSovSLpMklROtQodzJ8qca5+iJH/Py8z3Zeae/P/t\nnX/MlWUZxz8s5qQfCinNhpSRdAnSokZFBrilMpEghCAikhSCSaVCVlYor0SNmW4RTqYINCWQCSHR\ntEg2p8Vcm9FUcFfQbKugjDBDmlb69sf3vjnPec79nHNeZPx67892duA893PO855z/7ju68f30Xj4\nC7DKzC6suI7zUUjq3WiBbqnd6O5b3X2Qu19fen0vSpXogcJaUJOeuLs4R7gq26JES12lt7vvcvdl\n7r7OlZ/bA8kTbHDlTM0Jj/nIwzgV5U2liN9X34rjmebsKf0/GmrPl15/JTzHsGKzsbUfzdPvMrNi\n/mInjXnDMRczFa4s0mxtKTIVyeHckzro7vtQGsIhNC+/gIrerqSWXvHv1LkV77clRK6mI6OvJ3B/\nSOnB3X/k7u9z99tK5z2Hwpy9KEUPTrS1JRttXSR0+qir9IiZvR4f1GLdg02ifCl6h+fhyEgrPwag\nwdSndF4MQXZS2321IrqVU51+GjKC6sQAQ/hpJSoQuLTZm7v7AWoSJ+Or2pnkDaYgV/rtpffYhHZ3\nHwkho/K5HSjv7RAwzttLkAUtKn8DVgQv30Tg+2FQL0ZCw1+qOPcQcJpJriFTzUY0IX8y/P/TaNdc\nJQ1xBuq/L1Uc34uSp4u8YTFKMzsTFUb0QhIyf27z1DOpHjsgL9lh70dIAViA5tUGb3vY2GynZrBV\n9b+usCM8x4Tu+N0+lWgbq0QHtHjPSch4joKxX0Te07td+XMrUSg4Rfw+yvNXpj0OVbz+asXrkZj/\n1Wxs9aBxfJXfN26QWnm3mq0tRcajvK/KFBp3fwxVgM5AKTBTUO7ZvtCkrKXWFu6+A4U/+6LUnFbE\nsXRei3bHdW3JhQhdZzJysf4GJS+WuQCFHGeRNq5imfEid+9o5wPN7HQUOo0L2L1mNtTrBQNTHAjP\nqZyAfsAfKt4jurz7h8+/ECXFbky0j5IHzfL4+qK+5hXHd6GQcX+CpEHY7S9HwoX/AK7w9vLeorjo\nLOBad/9fyKV6E/WK8rvRnR1SRMP6lYrjGfEIwfuJvMyTgC2l8E6Rg2gx6Id+0zJ9Kl4/YkKeyTr0\nW8/rgtEPGj9VYwfS8ht1Y6dwHZchoc5eKOG5XQX9KONxHvCkNyqqx8k/9tXYx1Phy+g5r1xow/e1\nCEkBxfF6PvXVdruBs8zsbe5+sPQWcezku4ocW+Lv0I+0wd4HGWRHa3w1W1uAww6OkcDjXqFhFnH3\nl5CBVTx/WPhnq0KdUUBvd/9p4nDd+hQ87ue6+7ZE2/JYSn3WcV9bsqet68Qd9Hx3n1t+oN1CJzDZ\nzFKl/LGMORlONLMFZva1UmhyEdp53Iq0boxGcdEUcaeScsfuA84NBmGZgeE57nBuQImdlyXaxty3\nZrIfB1DYt6ojlz8PlGM3G+X4jWzXYAssRJVxMcG7Z+kZZHhXKbefDew9GmG5U5ngZfoFMCZ4kQbS\nJMxNbSfbkKgewobn0GKCbkLVb7UEVZKtaScUWWIf6c1IzHGzxLGGvmxmw1Gy8+lIpqFtgy2wCNhG\nWs9tRHiOxRIxPHZJom1cBJuJe05HRlpH4bWeNI4dSH/nca7JcjnHlmZj6wwUPt3TKg+6CzRbWyIf\nQv3mV1UNzOzKkAc2KXF4IsrfbiWhtRLYYGa9E8fK69O9wC/N7IOJtuWxlOK4ry3ZaOsCwcoehcJ8\n21NtXPor21C1Vlm+AFcly+NooavrqGb2OTRBj/HaHQo+inJJnkGGzG1ohz+/VR5Z2L38CcmDlHkg\nXOPi0jW8H4VD/o4qYqEma7KoaOQFD8DNaFe9rsl1vIoWrQHlJM3ggRgH7Ar5M5jZeGQo7kc6dFWS\nCQ2EndTngQ6vCfPuRoPoY6FNT7SANejAhQTbd1AvOJqpZiMKuSxFu/1m+kdrUKjkW6HvAIclLaLk\ny31HeB3Ru3c4FyeMpxvRJNwgP9IGzwJvLl5rII6H7xbDHGHRWISMmQfCa28N7Xuhjd4yus6G8HxL\nzM0J721I8f0AtXzaR1EF6FQriJKG5OqbUHgmXn8dYaPYAazy+oo7pz68NBz4q6fFSYegEHlTsdVu\nwrHc9D2EQqNziwZJmOuWIkPiSMdWAy3Wlki8jlREKrIDGTJ14XYzuwb1ufu9ILpbwXpkNNVJT5nZ\nWOT9f9prt9iKY2mxFUSgzezjaI7YQ8UcdqKsLTk82jViVdjapq2UNH8pcqPelTg+GyUjPxiqTnai\nXftY5L6eC4crPFfHc0KVz2um+x4+Bqw26ek0S6p+GJhjZv29XtDvO8hzNj902CeQXEA0JK/xUMrs\n7o+a2Wp0x4edZrYFuXknoslghhcq7Ex6QUNRdW3soPOQmOoPg1G2A+3oJ6DFfkbh2qIh+TQSLE79\nXRcWdiwAAAQTSURBVOsK4ZsityID8HCRiLsfMLOHgK+b2dmomOOdFO4lWSBWLLUUSM4Aukfsf9FC\nvrZZX3T3583sq2gR+W34TQ6h0Ph70G/646rzWxDz1BaYdKFWo111JzJk5oXxVMzVebGF9+1nqKJy\nBIVEcFfJ/wo0ye8ys83IWByPvIVLvCaTMRuFSg8AfaxeniPynLuvBzCzoWhM7HD3zeHzNpvZT9B4\nezaMvz4oLH0aKqz4Z2j7mpnNQB7QrWa2AWlVTQzXdq3X3zexyEw0Lsq34luJxu0mlN4xBd0Au46Q\n3zMYicZmL/UxvBuIux8Mhs56YHv4rV5AOmRDkKOgLLnxRq+vam2JxAriVPV1vO4/mtlS4AYzexJJ\n9wxCa+EzlHKuK9aWJSivdo6pMno78niPQ86HaYW3WI767xjgd2a2FY3PCcj5MM2r7517Qqwt2dPW\nNaajRWBNi3ab0K5nGFJTrpvA3P33qOx3BUr4vQ6V798HfLjgWepAxtw9XtCncvcnkPTHIFqrUW8O\nz8Wy5xjaGoXCrWehkv/L0S5jhBdueB3az0TJlS8juYTxaIBd7O5lL9unkAfuA4Xz96GQ8J3hb5qH\nOvFaYFjcCQVvxRD0nX2CdLHGzSRCUyaZkskV38lM9LtchfKDZrl7KudwNPIGNdwBIkMnjX35X8go\n6qS2i23WfhmaMJ9ChsRVaGKd5Sq9P1LWo9/svajS8WJqXrebUD9fSH0/ur7xber4OUrUHl0+4O5z\nkNG2PzxPR2GTz7p7sbJyJLXCovLnx8dnCu2HhtfK+ndTkOzAofD3jUXSORclxuqvkd7iFjSmvxCu\nbYK7J6v4ggf926jqtE6CxN3vRIbcyPC5P0Ae/zKxcKnVprY70ND3j7B926+Hoq4RyCi4HPXL15G3\n+ZJSaLSr15ciubYUeDvNC48iN6LIylvQbaEuQP1rlLu/WGqbWlteRn/3HcjxcB1aa1YhCZtdhbb/\nQakDi9H88GU0VzwY2iZDoyfS2tKjszNviE51zGwn8iqMaNm4GxMWrr0omX5Gq/aZUx8zW44m43MS\nSfeZAmb2MNpMDWzircicQuS1pT2O5tqSPW3dg+8BF6UkNTJ1TELVUO3clivTPViCqi7Ld2rIFDDd\nmm80UojPBlv3Ia8t7XHU1pZstHUP1qI4fzsVp92SkEB6C3BXVwofMqc2LiHnO4Bvhpy4TJoOlIO6\n8jhfR+bYkteWFhzttSUbbd2AkBR8NXBFq4rTbsxM5FH5RquGmW7HQlQs85XjfSEnImY2GFXKX+31\nt0TKnOLktaUtjuraknPaMplMJpPJZE4Csqctk8lkMplM5iQgG22ZTCaTyWQyJwHZaMtkMplMJpM5\nCchGWyaTyWQymcxJQDbaMplMJpPJZE4C/g8cf6yTawe/lQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10870fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dellumi = [alex.get_dlumi(),\n",
    "           moritz.get_dlumi(),\n",
    "           timon.get_dlumi()]\n",
    "\n",
    "print dellumi\n",
    "\n",
    "collumi = [alex.get_clumi(),\n",
    "           moritz.get_clumi(),\n",
    "           timon.get_clumi()]\n",
    "\n",
    "fig_lumi, ax_lumi = configure_barchart(r\"Delivered / collected lumi while on shift [$pb^{-1}$]\",\n",
    "                                       appendix=[\" ({0}%)\".format(round(alex.get_ineff()*100,2)),\n",
    "                                                \" ({0}%)\".format(round(moritz.get_ineff()*100,2)),\n",
    "                                                \" ({0}%)\".format(round(timon.get_ineff()*100,2))])\n",
    "\n",
    "ax_lumi.bar([0.1,1.1,2.1], dellumi, color=['r','g','b'], alpha=0.5)\n",
    "ax_lumi.bar([0.1,1.1,2.1], collumi, color=['r','g','b'])\n",
    "\n",
    "plt.show()\n",
    "fig_lumi.savefig(\"coll_lumi.pdf\")"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}