{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from prayas import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiments provide a simple way to continuously monitor the experiment performance and to decide when to stop based on the maximum potential loss of the variants. We first set up the experiment by defining the model, baseline, and measures of interest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "e = Experiment(\"moonshot1\")\n",
    "\n",
    "e.setup = OneOptionModel([\"apollo\", \"gemini\"], baseline=\"apollo\")\n",
    "e.setup.add_measure(\"revenue\", [90, 80])\n",
    "e.setup.primary_measure = \"revenue\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The specification of the experiment is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment with a One option model\n",
      "Variants              : apollo, gemini\n",
      "Baseline              : apollo\n",
      "Measures              : conversion, revenue\n",
      "Primary measure       : revenue\n",
      "Maximum loss threshold: 5            \n"
     ]
    }
   ],
   "source": [
    "print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we add the first daily data we collected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "e.add_data(\"20190730\", [ 0,  0], [ 186,  180])\n",
    "e.add_data(\"20190731\", [ 7,  1], [ 714,  652])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compute the daily scoring against the baseline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/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>Date</th>\n",
       "      <th>Variant</th>\n",
       "      <th>Measure</th>\n",
       "      <th>ProbabilityToBeBest</th>\n",
       "      <th>ProbabilityToBeatBaseline</th>\n",
       "      <th>UpliftFromBaseline</th>\n",
       "      <th>PotentialLossFromBaseline</th>\n",
       "      <th>MaxUplift</th>\n",
       "      <th>MaxPotentialLoss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20190730</td>\n",
       "      <td>apollo</td>\n",
       "      <td>revenue</td>\n",
       "      <td>0.52400</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.784740</td>\n",
       "      <td>47.546597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20190730</td>\n",
       "      <td>gemini</td>\n",
       "      <td>conversion</td>\n",
       "      <td>0.50270</td>\n",
       "      <td>0.5119</td>\n",
       "      <td>3.599060</td>\n",
       "      <td>49.631761</td>\n",
       "      <td>2.128436</td>\n",
       "      <td>48.418039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20190730</td>\n",
       "      <td>apollo</td>\n",
       "      <td>conversion</td>\n",
       "      <td>0.49730</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.084078</td>\n",
       "      <td>50.932533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20190730</td>\n",
       "      <td>gemini</td>\n",
       "      <td>revenue</td>\n",
       "      <td>0.47600</td>\n",
       "      <td>0.4783</td>\n",
       "      <td>-7.189932</td>\n",
       "      <td>52.351470</td>\n",
       "      <td>-8.912659</td>\n",
       "      <td>52.510509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20190731</td>\n",
       "      <td>apollo</td>\n",
       "      <td>revenue</td>\n",
       "      <td>0.98215</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>314.532699</td>\n",
       "      <td>0.888369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20190731</td>\n",
       "      <td>apollo</td>\n",
       "      <td>conversion</td>\n",
       "      <td>0.97250</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>265.239365</td>\n",
       "      <td>1.642050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20190731</td>\n",
       "      <td>gemini</td>\n",
       "      <td>conversion</td>\n",
       "      <td>0.02750</td>\n",
       "      <td>0.0257</td>\n",
       "      <td>-72.822973</td>\n",
       "      <td>73.236028</td>\n",
       "      <td>-72.620695</td>\n",
       "      <td>72.950166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20190731</td>\n",
       "      <td>gemini</td>\n",
       "      <td>revenue</td>\n",
       "      <td>0.01785</td>\n",
       "      <td>0.0183</td>\n",
       "      <td>-75.656182</td>\n",
       "      <td>75.981442</td>\n",
       "      <td>-75.876451</td>\n",
       "      <td>75.889115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date Variant     Measure  ProbabilityToBeBest  \\\n",
       "0  20190730  apollo     revenue              0.52400   \n",
       "1  20190730  gemini  conversion              0.50270   \n",
       "2  20190730  apollo  conversion              0.49730   \n",
       "3  20190730  gemini     revenue              0.47600   \n",
       "4  20190731  apollo     revenue              0.98215   \n",
       "5  20190731  apollo  conversion              0.97250   \n",
       "6  20190731  gemini  conversion              0.02750   \n",
       "7  20190731  gemini     revenue              0.01785   \n",
       "\n",
       "   ProbabilityToBeatBaseline  UpliftFromBaseline  PotentialLossFromBaseline  \\\n",
       "0                     0.0000            0.000000                   0.000000   \n",
       "1                     0.5119            3.599060                  49.631761   \n",
       "2                     0.0000            0.000000                   0.000000   \n",
       "3                     0.4783           -7.189932                  52.351470   \n",
       "4                     0.0000            0.000000                   0.000000   \n",
       "5                     0.0000            0.000000                   0.000000   \n",
       "6                     0.0257          -72.822973                  73.236028   \n",
       "7                     0.0183          -75.656182                  75.981442   \n",
       "\n",
       "    MaxUplift  MaxPotentialLoss  \n",
       "0    9.784740         47.546597  \n",
       "1    2.128436         48.418039  \n",
       "2   -2.084078         50.932533  \n",
       "3   -8.912659         52.510509  \n",
       "4  314.532699          0.888369  \n",
       "5  265.239365          1.642050  \n",
       "6  -72.620695         72.950166  \n",
       "7  -75.876451         75.889115  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e.monitor_score_baseline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To easily compare the performance of the variants over time, we suggest to look at the maximum potential loss per variant:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 431.625x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "e.monitor_plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot shows the maximum potential loss over time; the dashed line is the maximum loss threshold set for the experiment (default: 0.05).\n",
    "\n",
    "As the experiment continues, we add more daily data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "e.add_data(\"20190730\", [ 0,  0], [ 186,  180])\n",
    "e.add_data(\"20190731\", [ 7,  1], [ 714,  652])\n",
    "e.add_data(\"20190801\", [13,  5], [1233, 1141])\n",
    "e.add_data(\"20190802\", [15,  8], [1744, 1681])\n",
    "e.add_data(\"20190803\", [21, 13], [2304, 2146])\n",
    "e.add_data(\"20190804\", [26, 16], [2835, 2719])\n",
    "e.add_data(\"20190805\", [29, 20], [3275, 3260])\n",
    "e.add_data(\"20190806\", [36, 23], [3741, 3805])\n",
    "e.add_data(\"20190807\", [43, 26], [4343, 4354])\n",
    "e.add_data(\"20190808\", [51, 32], [4863, 4921])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we continue to investigate the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 431.625x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "e.monitor_plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, the loss of the 'apollo' variant is below the threshold after a few days and continues to stay below. We decide to stop an experiment with a result if a variant is below the threshold for a consecutive number of days:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/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>Timestamp</th>\n",
       "      <th>Loss apollo</th>\n",
       "      <th>Loss gemini</th>\n",
       "      <th>Runs apollo</th>\n",
       "      <th>Runs gemini</th>\n",
       "      <th>Max loss apollo</th>\n",
       "      <th>Max loss gemini</th>\n",
       "      <th>Stop apollo</th>\n",
       "      <th>Stop gemini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20190730</td>\n",
       "      <td>48.266853</td>\n",
       "      <td>51.491613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20190731</td>\n",
       "      <td>1.069647</td>\n",
       "      <td>75.996100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20190801</td>\n",
       "      <td>0.717000</td>\n",
       "      <td>59.209678</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20190802</td>\n",
       "      <td>1.208869</td>\n",
       "      <td>48.692807</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20190803</td>\n",
       "      <td>1.305198</td>\n",
       "      <td>40.086982</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.266853</td>\n",
       "      <td>75.996100</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20190804</td>\n",
       "      <td>0.598810</td>\n",
       "      <td>42.075643</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.305198</td>\n",
       "      <td>75.996100</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20190805</td>\n",
       "      <td>0.677079</td>\n",
       "      <td>37.922837</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.305198</td>\n",
       "      <td>59.209678</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20190806</td>\n",
       "      <td>0.154403</td>\n",
       "      <td>43.471300</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.305198</td>\n",
       "      <td>48.692807</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20190807</td>\n",
       "      <td>0.052977</td>\n",
       "      <td>45.669064</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.305198</td>\n",
       "      <td>45.669064</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20190808</td>\n",
       "      <td>0.037551</td>\n",
       "      <td>44.192609</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.677079</td>\n",
       "      <td>45.669064</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Timestamp  Loss apollo  Loss gemini  Runs apollo  Runs gemini  \\\n",
       "0  20190730    48.266853    51.491613          NaN          NaN   \n",
       "1  20190731     1.069647    75.996100          NaN          NaN   \n",
       "2  20190801     0.717000    59.209678          NaN          NaN   \n",
       "3  20190802     1.208869    48.692807          NaN          NaN   \n",
       "4  20190803     1.305198    40.086982          4.0          0.0   \n",
       "5  20190804     0.598810    42.075643          5.0          0.0   \n",
       "6  20190805     0.677079    37.922837          5.0          0.0   \n",
       "7  20190806     0.154403    43.471300          5.0          0.0   \n",
       "8  20190807     0.052977    45.669064          5.0          0.0   \n",
       "9  20190808     0.037551    44.192609          5.0          0.0   \n",
       "\n",
       "   Max loss apollo  Max loss gemini  Stop apollo  Stop gemini  \n",
       "0              NaN              NaN        False        False  \n",
       "1              NaN              NaN        False        False  \n",
       "2              NaN              NaN        False        False  \n",
       "3              NaN              NaN        False        False  \n",
       "4        48.266853        75.996100        False        False  \n",
       "5         1.305198        75.996100         True        False  \n",
       "6         1.305198        59.209678         True        False  \n",
       "7         1.305198        48.692807         True        False  \n",
       "8         1.305198        45.669064         True        False  \n",
       "9         0.677079        45.669064         True        False  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e.monitor_decision(days=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following this rule, we could have stopped the experiment already on `2019-08-04`. We can access the last model of the experiment and do the same analysis as already shown in the other example notebooks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/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>Variant</th>\n",
       "      <th>Measure</th>\n",
       "      <th>ProbabilityToBeBest</th>\n",
       "      <th>ProbabilityToBeatBaseline</th>\n",
       "      <th>UpliftFromBaseline</th>\n",
       "      <th>PotentialLossFromBaseline</th>\n",
       "      <th>MaxUplift</th>\n",
       "      <th>MaxPotentialLoss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apollo</td>\n",
       "      <td>revenue</td>\n",
       "      <td>0.99670</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>79.254505</td>\n",
       "      <td>0.037169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>apollo</td>\n",
       "      <td>conversion</td>\n",
       "      <td>0.98365</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59.268571</td>\n",
       "      <td>0.186129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gemini</td>\n",
       "      <td>conversion</td>\n",
       "      <td>0.01635</td>\n",
       "      <td>0.01495</td>\n",
       "      <td>-37.287659</td>\n",
       "      <td>37.448505</td>\n",
       "      <td>-37.212973</td>\n",
       "      <td>37.387003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gemini</td>\n",
       "      <td>revenue</td>\n",
       "      <td>0.00330</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>-44.270473</td>\n",
       "      <td>44.282816</td>\n",
       "      <td>-44.213397</td>\n",
       "      <td>44.089087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Variant     Measure  ProbabilityToBeBest  ProbabilityToBeatBaseline  \\\n",
       "0  apollo     revenue              0.99670                    0.00000   \n",
       "1  apollo  conversion              0.98365                    0.00000   \n",
       "2  gemini  conversion              0.01635                    0.01495   \n",
       "3  gemini     revenue              0.00330                    0.00380   \n",
       "\n",
       "   UpliftFromBaseline  PotentialLossFromBaseline  MaxUplift  MaxPotentialLoss  \n",
       "0            0.000000                   0.000000  79.254505          0.037169  \n",
       "1            0.000000                   0.000000  59.268571          0.186129  \n",
       "2          -37.287659                  37.448505 -37.212973         37.387003  \n",
       "3          -44.270473                  44.282816 -44.213397         44.089087  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e.result.score_baseline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HjfPueRInWMW6tB9dqaDgziiX0cDvRSQEK1G/ZIx5Q0RWARhjVgO3AV8XkXagCVhhtGzawFUUWrM5Q7xcasfhcE0w0hmjSgWDPjOGMWYPMLuH51d3uf8E8IRnQxvCKgtg5CTfnCttLnzwI2iug8gY35xTKeUVOlPU33S0WzVcvN1/3iktDzBQssM351NKeY0mdH9TcwKcbTByom/Ol+paa7ZI+9GVCnSa0P3NuREuPkrow+Jg5GS9MKpUENCE7m86x4T7qoUOrglGn4Jex1YqoGlC9zcVBTAsAaISfHfOtLnQVAVVR313TqWUx2lC9zeVhb5tnYOV0EEnGCkV4DSh+5uKAt/1n3dKmgLhI7QfXakApwndnzTXQkMZjPTRkMVOjhBIy9UVjJQKcJrQ/UlFobX1dQsdrLoupfugpb7vfZVSfkkTuj+xY4RLp/R5YJw6wUipAKYJ3Z9UFICEQLyXlp27kLTOCUba7aJUoNKE7k8qCyB+HISG+/7cw+Kt+jFaqEupgKUJ3Z9UHfVdDZeejJkDJTvtO79SalA0ofsLY6D6hD3dLZ3GzLaWvqs7bV8MSqkB04TuLxqroKUO4jPsi2HMLGt7epd9MSilBsydFYsiReQT1wLQ+0TkkR72ERF5TEQKRWSPiMzxTrhBrPq4tbUzoadMB3FAiSZ0pQKRO0vitABXGmPqRSQM2CIi640xW7vscx0w0XW7CHjStVXuqj5mbe1M6OHDrcqL2o+uVEByZ8UiA3TONglz3bqX5VsOPOvad6uIxInIaGOMdsa6yx9a6GD1oxdusPr0rWVilUtbh5O395VypLyeqoZWpo6JYeHkJJJHRNodmlKAey10XOuJbgcmAL80xmzrtksqUNTlcbHrufMSuoisBFYCjB07doAhB6nq4xA9CsKj7I1j9AzY/QI0lEN0sr2x+JFXdhbz07cPU1zdBEBUeAiNrR04BL6+cDzfvnoSoSF6SUrZy62EbozpAGaJSBzwiohMM8bs7bJLT025LxTXNsasAdYA5OXlafHtrqqP2986B0ieam3L9mtCB4wx/PTtwzzxfiEz0mJ55MYcLps4kvAQBwdOn+W3Hx7jl+8fYdvRKp7+ylxio8LsDlkNYf1qUhhjaoAPgCXdXioG0rs8TgNKBhXZUONvCb10v71x+IkfvHGAJ94v5M556bzywKVclT2KiNAQRISpY2L48e0z+cWKWewuruGBF7bT1uG0O2Q1hLkzyiXJ1TJHRIYBVwMHu+32GnCPa7TLfKBW+8/7ob0Vaov9I6FHJ0HUSKuFPsSt3XmKZz48xr2XZPBfN08nxNHzNYXls1L5r5un82FhJY+8vs/HUSr1OXe6XEYDv3f1ozuAl4wxb4jIKgBjzGpgHbAUKAQagfu8FG9wqi0CjH8kdIDkbCg7YHcUtjpW0cC/vfIZczPi+f712UgfF4hvz0unsKyeX286ymUTklgyLcVHkSr1OXdGuewBZvfw/Oou9w3wDc+GNoScG7Jo4yzRrpKnws4/gNMJjqF3oc/pNHznpV2EhTr4xYrZbl/s/O61k9lUUMHDr+7lkgmJxERqf7ryraH3afVHVX4wBr2r5Gxoa4Dak3ZHYou1u06x82QN379+KmPihrn9vrAQB/9963Qq6lv40fruvZJKeZ8mdH9QfRxCI61hi/5gVI61HYLdLg0t7fz33w4yMy2WW2an9vv9M9LiuO/STF7YdpK9p2q9EKFSvdOE7g+qj0PcOP/p3uis+FhRYG8cNli98QildS08vCwHRy8XQfvyrasnEh8Vxg/XH8DqjVTKN/wkgwxx1Sf8p7sFICoBhiVAZaHdkfhUUVUjv950lOWzxpA7Ln7Ax4mJDOP/XDWRDwsr2Xi43IMRKnVhmtDtZozVQk/wkwuinRInWPXZh5AfrT+IQ+ChJVMGfay7LhrH2IQofrT+IE6nttKVb2hCt1tjFbSe9a8WOkDieKg8YncUPrPtaCVvfnaaVVeM79eF0N6Ehzr4zjWTOHjmLG/vL/VAhEr1TRO63fyhymJPEsfD2RJobbA7Eq/rcBoefWM/Y2Ij+YfLx3vsuDfMGE1GYhSPv1egfenKJzSh281fqix2l+BKbEOg2+Uv24vYV1LHvy7NZlh4iMeOGxri4IFFE9hXUsf7h8o8dlyleqMJ3W6dLfS4cfbG0V3nSJcg73Y529zGj986RN64eJbNGO3x4988O5W0+GE8/t7QusCs7KEJ3W7+Uja3u4QsaxvkI11+9cERKupbeXjZ1D6n9w9EWIiD+y/LZOfJGnaerPb48ZXqShO63exeGLo3EdEwYnRQd7mU1DTxzJZj3DI7lRlpcV47z2156YyICOW3Hx732jmUAk3o9qs65n/9550Sgnuky0/ePoQBHrx2slfPEx0Ryh1z01n32WnO1DZ79VxqaNOEbqf2Fqg75b8JPTEraLtc9pXU8srOU9x3aQapHhim2JevXJxBhzE8t/W418+lhi5N6Haq8bOyud0lToDGCmiqsTsSj/vR+oPEDgvjgYUTfHK+sYlRXDVlFH/6tIjWdl0EQ3mHJnQ7+euQxU7nhi4GV7fLpsPlbC6o4JtXTiR2mO9K3P7dRelU1Lfy7gGdaKS8w50Vi9JF5H0ROSAi+0TkWz3ss1BEakVkl+v2sHfCDTL+Oqmo07mhi8FzYbTDafivdQcYmxDFl+f7dqjoFZOSGR0byYufFvW9s1ID4E4LvR140BiTDcwHviEiU3vYb7MxZpbr9qhHowxWnWVzR/jp6jbxGYAEVQv9jT0lHDxzln++djLhob79ghriEG7PS2dzQTlFVY0+PbcaGvr8jTbGnDbG7HDdPwscAPpfKFp9UefC0F4Y/+wRYZEQlx40F0aNMazeeJQJydFcP93zk4jccUdeGgB/ztdWuvK8fjVRRCQDazm6bT28fLGI7BaR9SKS44HYgl9nQvdnQTR0cXNBBQdO17Hy8qwB1zofrLT4KK6YlMRL+cW0d+jFUeVZbid0EYkGXgb+yRhT1+3lHcA4Y8xM4HFgbS/HWCki+SKSX14+xOtEd5bN9feEnjjBSuhBUFzq15uOMComguWzxtgax4q5YzlT16y10pXHuZXQRSQMK5k/b4z5a/fXjTF1xph61/11QJiIjOxhvzXGmDxjTF5SUtIgQw9wjZXQWh8ACX08tNRa8Qaw/SV1fFhYyX2XZhIR6rkCXANxVXYyI6MjePGToblmq/Ied0a5CPA0cMAY87Ne9klx7YeIzHMdN7AzgLf5+5DFTp1DFwO82+Wl/CLCQx2smJtudyiEhTi4PS+N9w6W6cxR5VHutNAvBb4MXNllWOJSEVklIqtc+9wG7BWR3cBjwAqjBaAvrKpzyKIf1nHpKrEzoQfuhdGW9g7W7jrF4qmjiIsKtzscAFbMTcdp9OKo8qzQvnYwxmwBLngFyRjzBPCEp4IaEjpb6HFjbQ2jT3HjwBEa0EMXN+wvo6axjdvz7G+ddxqXOJxLxifyp/wivrFogm0XaVVw0Zmidqk+DtEp/lc2t7uQUKtbKIBb6H/eXsTo2Egum/CFyzq2unPeWIqrm9hSWGF3KCpIaEK3SyCMcOmUMD5gZ4uW1TWz6XA5t85JI8TPWsGLc0YRHxXGHz/Vi6PKMzSh2yWQEnriBKvLJQAvi7y1vxSngRttHqrYk4jQEG6dk8bb+0opP9tidzgqCGhCt4O/l83tLjEL2hrh7Gm7I+m3t/edIXPkcCYmR9sdSo9WzEun3Wl4eUex3aGoIKAJ3Q7VJ/DrsrndJQTmSJfaxjY+PlLJtTkpXllezhMmJI9gbkY8f/q0CB0YpgZLE7odOqssdg4J9HcjJ1rbisP2xtFP7x4spd1puDZnlN2hXNCKuWM5VtHA1qNVdoeiApwmdDt0rtPp72PQO8WkQng0lAdWQn9r3xlSYiKZ6cX1Qj3h+hmjiYkM1YujatA0oduh6iiEj4Dh/jWMrlcikDQZyg/YHYnbmts62Hi4nMU5o/x+jHdkWAg3z05l/d4z1DS22h2OCmCa0O1QdQwSMv23bG5PkrKh/JDdUbht27EqmtucLJqSbHcoblkxbyyt7U7+uuOU3aGoAKYJ3Q5VR62EHkiSJkN9KTQGRj/vpsPlhIc6mJ+ZaHcobskeHcPM9Dhe/OSkXhxVA6YJ3dc62qHmBCRk2R1J/yRNsbYBcmF0c0E58zISGBZub2XF/rhzbjoFZfXsOFltdygqQGlC97W6YnC2B2BCn2xtyw/aG4cbTtc2cbi0nssnBcg1CpdlM8cwPDyEFz/Rgl1qYDSh+1rnCJdAS+ix6RAWBWX+n9A3H7Zqo1w+KbBq7g+PCOXGWam8saeEuuY2u8NRAUgTuq8F2pDFTg6H1e1SutfuSPq0saCcUTERTB41wu5Q+u3Oeek0tzl5dVeJ3aGoAKQJ3deqjkFoJIywZ5HiQRk9A87s8euaLh1Ow5aCChZMTPLb2aEXMj01lqmjY3hxm14cVf2nCd3Xqo5ZrXNHAP7Tj54JzbXWRV0/tae4htqmtoDrbukkItw5L539p+v47FSt3eGoAOPOEnTpIvK+iBwQkX0i8q0e9hEReUxECkVkj4jM8U64QSAQhyx2Gj3T2p7eY28cF7DpcAUisMDPap/3x/LZqUSGOfTiqOo3d5qJ7cCDxphsYD7wDRGZ2m2f64CJrttK4EmPRhksnE6rbG6gXRDtlJwDEgKnd9sdSa82FZQzIzWW+OH+sdTcQMREhnHDjDG8tusUDS3tdoejAkifCd0Yc9oYs8N1/yxwAEjtttty4Flj2QrEiUgAdhJ7Wf0ZaG8K3BZ6WCQkZ/ttQq9tamNXUQ0LJgZmd0tXd+Sl09DawbsHy+wORQWQfnXkikgGMBvY1u2lVKDr98Nivpj0EZGVIpIvIvnl5eX9izQYBMrC0BeSMsNK6H54we6jwgo6nCZg+8+7yhsXT/KICNZ/Fng16JV93E7oIhINvAz8kzGmrvvLPbzlC594Y8waY0yeMSYvKSnwP3T9Fqhj0LsaMwsayqwFOvzMpoJyoiNCmT3Wv6srusPhEK7NSeH9Q2U0tmq3i3KPWwldRMKwkvnzxpi/9rBLMdB1SfU0QAfSdld1FByh1iSdQJWaZ22L8+2NoxtjDJsOV3DJ+ETCQgJwBFEPrpueQnObkw8ODcFvs2pA3BnlIsDTwAFjzM962e014B7XaJf5QK0xRr8rdld9DOLGQkio3ZEMXMo0CAmHU/6V0I9WNHCqpikouls6zctIIHF4OOv3nrE7FBUg3MkslwJfBj4TkV2u574HjAUwxqwG1gFLgUKgEbjP86EGgcrCwO5uAQiNsPrRi7fbHcl5Nh22WrFXBFFCDw1xsDgnhVd3naK5rYPIsMApNKbs0WdCN8Zsoec+8q77GOAbngoqKDmdUFEIGZfbHcngpeXBjmetypF+8m1j0+FyMkcOJz0hyu5QPOranFG8+MlJPj5SGTC13ZV9gqOzMRDUFVtDFjvX5wxkaXOhrRHK9tsdCQAt7R1sPVrF5RMDdzJRb+ZnJRIVHsK7B0vtDkUFAE3ovtJZR3zkJHvj8ITUXGvrJ/3o+ceraWrrCKr+806RYSFcNmEk7x0o09ouqk+a0H2losDaBkNCj8+AqES/6UffdLicsBBhflZgrE7UX1dlJ1NS28yB02ftDkX5OU3ovlJxGCLjAmdh6AsRsYYv+kkL/YND5eSOi2d4hH/053taZ9/5uwe020VdmCZ0X6kosFrnAVjStUdpedai0c3d55j5VlFVI4dKz3J19ihb4/Cm5BGRzEyL1TIAqk+a0H2lM6EHi9RcwEDJDlvD2OBqtV4VxAkdrJ9vd3EN5Wdb7A5F+TFN6L7QXGsV5gqGES6dOi+M2jxj9N0DZYxPGk7myOG2xuFtV05Jxhh4/5C20lXvNKH7QkWhtQ2mFvqwOEicCKfsuzBa19zG1qOVXD01uFvnADljYkiJieS9A5rQVe80oftCMA1Z7Cotz2qh2zScbuOhctqdJqj7zzuJCFdmJ7O5oJyW9g67w1F+ShO6L1QcBkcYxI+zOxLPSs21Ki/W2rOyzrsHSomPCmPO2Hhbzu9rV2cn09DawbajVXaHovyUJnRfqDhsLWoREmZ3JJ6VZl/lxXSXvFEAABs9SURBVPYOJ+8fKufKKaMIcQTJyKE+XDJ+JJFhDh2+qHqlCd0Xgm2ES6dR0yA00pZ+9PwT1dQ2tXF19tCpb9I5a/TdgzprVPVME7q3dbRZddCDaYRLp5Awa+FoG1roG/aXEh7iYEEQTve/kCunjKK4uonDpfV2h6L8kCZ0b6s+Ac624GyhgzVj9PQu6w+Xjxhj2HCglPnjE4kO0tmhvbmyc9aoFutSPdCE7m3BOsKlU1outDdD6T6fnfJIeQPHKxu5Zgh1t3RKiY1kWmoM7+rwRdUDd1YsekZEykRkby+vLxSRWhHZ5bo97PkwA1j5QWsbjF0u8PmSdD6s69I5O/TKITBcsSdXTRnFjpPVVDW02h2K8jPutNB/ByzpY5/NxphZrtujgw8riJQfhJhUiIy1OxLviBsLw5N8WnnxrX1nyBkTQ2rcMJ+d059cle2aNaq1XVQ3fSZ0Y8wmQAe+DlTZfkiaYncU3uPjyouna5vYebKGpdNH++R8/mjamFiSR0RoP7r6Ak/1oV8sIrtFZL2I5PS2k4isFJF8EckvLx8CK5k7O6whi8nZdkfiXWm51rWCphqvn+pvrgWTl0xL8fq5/JXDIVyVPYoPDpXT1KqzRtXnPJHQdwDjjDEzgceBtb3taIxZY4zJM8bkJSUNgeFm1cetC4bBntDP9aN7v9tl/d4zTBoVzfikaK+fy58tmzmaxtYObaWr8ww6oRtj6owx9a7764AwEQmCVRw8oHPNzaBP6HMAgVPeLaVbdraZT49Xcd20odvd0umizESSRkTw+u4Su0NRfmTQCV1EUkSsVRtEZJ7rmJWDPW5QKOsc4TLZ3ji8LTLWGpbp5X70t/eVYgxcN33odrd0CnEI108fzfuHyqlr9t0cAOXf3Bm2+CLwMTBZRIpF5KsiskpEVrl2uQ3YKyK7gceAFUbnJVvK9kPcOIgYAt0DqblWl4sX/+v/tvcMmSOHM3nUCK+dI5DcOGsMre1O3tmn3S7K0uc0O2PMnX28/gTwhMciCiblB4O/u6VTWi7sfgFqTnqlqmR1QysfH61k5eVZSLAs4zdIs9PjSIsfxtpdp7g1N83ucJQf0Jmi3tLRNjRGuHTqXMHIS90u7+wvpcNpWKr95+eICLflprG5oIKTlY12h6P8gCZ0b6k8YtVwSRoiCb2z8qKXJhit33uatPhhTEuN8crxA9WKuWMJcQgvfHLS7lCUH9CE7i1DZYRLp5AwGD0Lij/x+KHrmtvYUljBkpwU7W7pJiU2kqumJPPn/CJa2512h6NspgndW8oPgjiCtyhXT8bOh5Jd0Nbk0cO+e6CUtg6jo1t6cdf8cVQ2tPLWvjN2h6JspgndW8r2Q0IWhEXaHYnvjL3Y6mby8Hj0N/ecISUmktnpQ2Opuf5aMGEk4xKjeHrLMV34YojThO4tZQeDu4ZLT9LnWduTH3vskGeb29hUUM5101NwDJGl5vrL4RC+tiCLXUU1fHxUp4AMZZrQvaGtGaqOQPJUuyPxragE6yLwya0eO+S7B8pobXdy/RAuxuWO23LTSBoRwa/eP2J3KMpGmtC9oeIQGOfQuSDa1dj5ULTNKkzmAW9+dpqUmEjmjNXulguJDAvh/ssy2VJYwa4i7xdJU/5JE7o3nHGtBZIy3d447JC5AFrqoGTnoA91trmNjYfLWTJNu1vccdf8ccQOC+MXGw7bHYqyiSZ0byjdC6HDrIuiQ03mQkDgyPuDPtR7B13dLTO0u8Ud0RGhrLpiPO8fKif/uC5hMBRpQveG0r1Wd4sjxO5IfG94IoyeCUfeG/Sh3txzmlExEeRqd4vbvnLJOEZGR/A/bx3SES9DkCZ0TzPG6nJJmWZ3JPYZv8iaYNRydsCHqG9p54PD5Vw3bbR2t/RDVHgo37xyAp8cq2JzQYXd4Sgf04TuaWfPQFOVNRV+qMpaBM52OL5lwId490CpdrcM0Ip56aTGDePH2kofcjShe1qp64LoUE7oY+db1xAG0Y++7jPtbhmoiNAQ/unqiXx2qlZnjw4xmtA97VxCH2Jj0LsKjYCMSwfcj17f0s77h7S7ZTBunp3K+KTh/OTtw3Q4tZU+VGhC97QzeyE2HYYN8Zbl+CuhsgBqivr91s7ulqU6mWjAQkMcPLh4MoVl9by665Td4SgfcWfFomdEpExE9vbyuojIYyJSKCJ7RGSO58MMIKX7hnZ3S6esRdb2aP+7XdZ9dprkERHkjRvifxQHaUlOCtNSY/jfDYe1EuMQ4U4L/XfAkgu8fh0w0XVbCTw5+LACVFszVByGUTl2R2K/5GyIToHCd/v1toaWdj44VM51Oplo0BwO4buLJ1NU1cSf8vv/TUkFnj4TujFmE3ChWQrLgWeNZSsQJyJD87tyxSEwHUN7yGInEZh4tXVhtMP9RYzfPVhGi3a3eMwVk5KYl5HA4+8W0NTqmXIMyn95og89Fej657/Y9dwXiMhKEckXkfzy8nIPnNrPnNERLueZuBhaaqHI/UUv1u05TdKICPIyErwY2NAhInz32smUnW3hRV3VKOh5IqH39L24x8vqxpg1xpg8Y0xeUlKSB07tZ4bylP+eZC0CRxgUvOXW7g0t7bx/qIyl01II0e4Wj5mXmcC8zASe2nxU+9KDnCcSejGQ3uVxGlDigeMGnjOfDd0p/z2JjIFxF8Pht93a/Z39pbS0O7l+xhgvBzb0PLBwPKdrm1m7U0e8BDNPJPTXgHtco13mA7XGmNMeOG5gcTrh9G4YM9vuSPzLxGuh/ADU9P11f+2uU6TGDdPRLV5wxaQkcsbEsHrjER2XHsTcGbb4IvAxMFlEikXkqyKySkRWuXZZBxwFCoGngAe8Fq0/qzpilY3VhH6+Sdda24ILt9Ir61vYXFDBjbPG6OgWLxARHlg4gaMVDTp7NIiF9rWDMebOPl43wDc8FlGg6qz/rQn9fIkTID7D6naZe3+vu7352Wk6nIbls7S7xVuWTEsha+Rwfvl+IddNS0FE/3AGG50p6imndlgXRIfaOqJ9EbG6XY5tgramXndbu/MUU1JGMCUlxofBDS0hDmHVFePZV1LHJq3EGJQ0oXtKyU4YPQNC+vzSM/RMWgztTVZS78HJykZ2nKzhRm2de91Ns1MZHRvJr94vtDsU5QWa0D2hox3O7NHult5kLIDwEXDwjR5ffm23NfLixpma0L0tPNTB/Quy2Hasih0nq+0OR3mYJnRPKNsHbY2Qmmt3JP4pNMJqpR9c94XFo40xrN1VwryMBNLio2wKcGhZMTedmMhQntp01O5QlIdpQveEk9us7dj59sbhz6bcAI0VULTtvKf3n66jsKxeu1t8aHhEKHfPH8ff9p3hRGWD3eEoD9KE7gknP4YRY6yyuapnE6+BkAjY/9p5T7+6q4RQh3C91m7xqXsvySDM4eDpLcfsDkV5kCZ0TyjaBmMvskZ0qJ5FjIAJV8O+V851u3Q4Da/tKmHh5CTih4fbHODQkhwTyU2zx/BSfhFVDa12h6M8RBP6YNUUQd0pGHux3ZH4vxl3QP2Zc6NdNheUc6aumZtnp9kc2NB0/4Ismtuc/GHrCbtDUR6iCX2wTm61tukX2RtHIJi0BCJiYM9LAPw5v5j4qDCunppsc2BD06RRI1g0OYnff3Sc5jYtrRsMNKEP1rGNEBELKdPtjsT/hUXC1OVw4DWqqip5e/8Zbp6dRkSoFjOzy9cuz6KyoZW/7tCiXcFAE/pgHdsImQu0wqK78u6D1noO/e3XtHUY7pir3S12ujgrkWmpMTy1+agW7QoCmtAHo+qYVUUwa6HdkQSO1FxM2lzSC59jZqpO9bdbZ9GuYxUNvL57aFa9Diaa0Afj2EZrm3mFvXEEmCOZd5HmLOE7WcV2h6KwFpOekjKCx94toL1DF8AIZJrQB+PoB9b485ET7Y4koDx2eirlxHFZ1ct2h6KwFpP+p6sncrSigde0lR7QNKEPVEcbHHkPxl+p48/74UxtM2/ur+RA6u2EHNkAFVokyh8snppC9ugYfr6hQEe8BDC3ErqILBGRQyJSKCL/2sPrC0WkVkR2uW4Pez5UP1O0DZprYfISuyMJKC9sO4HTGLKWfNNab3Tbk3aHpLBa6d9bOoWTVY38ZrPWeAlU7qxYFAL8ErgOmArcKSJTe9h1szFmluv2qIfj9D+H37ISUtZCuyMJGI2t7Ty39QRXTUkmLX0czPwS7HgOzuoKOv5gwcQkrs0ZxS/fP0JJTe+165X/cqeFPg8oNMYcNca0An8Elns3rABw+C3IuMya0q7c8sdPiqhubGPVFeOtJxY8CM52+OhxewNT53z/+qk4jeE/XtuHtRiZCiTuJPRUoKjL42LXc91dLCK7RWS9iOT0dCARWSki+SKSX15ePoBw/URFIVQcsmY+Kre0tjt5avNR5mUmkJeRYD2ZkGWVA/j0aagP4N+HIJKeEMV3rpnE2/tL+eOnRX2/QfkVdxJ6T1f8uv/p3gGMM8bMBB4H1vZ0IGPMGmNMnjEmLykpqX+R+pP9r1jb7GX2xhFA1u46xenaZh5YOP78FxY8CB0t8LG20v3F1xZkcdmEkTzy+j4KSs/aHY7qB3cSejHQtS5sGnDe2CZjTJ0xpt51fx0QJiIjPRalv9m31qrdEtvTFxXVXUt7B4+9W8C01BiumNTtD/nIiZBzC3zyG2iotCdAdR6HQ/jZHTMZHh7K157Np6K+xe6QlJvcSeifAhNFJFNEwoEVwHlFrUUkRVxLiIvIPNdxg/PTWVEApXth6k12RxIwnt96kuLqJv7l2ik9rzR/+T9ba45u+rHvg1M9So6JZM09uZyubearv/uUxtZ2u0NSbugzoRtj2oF/BN4CDgAvGWP2icgqEVnl2u02YK+I7AYeA1aYYL2i8tmfAbGKTKk+1TW38fh7BVw6IZEFE3v50pY8BeZ8BT59yvqDqfxC7rgEHr9zNp+dquW+335KfYsmdX/n1jh0Y8w6Y8wkY8x4Y8x/up5bbYxZ7br/hDEmxxgz0xgz3xjzkTeDto2zA3Y+D+MXaXeLm5784AjVjW3865LsnlvnnRb9G4RFwbrvQpC2BQLR4pwU/vdLs8g/Uc1dT22lWhfD8Gs6U7Q/jm2EumKYfbfdkQSEwrKz/GbzUW6Zk8r0tNgL7xydBFc9bJVT2PW8T+JT7lk+K5XVd+dy4MxZVqzZSllds90hqV5oQu+PHc/CsHhrwWN1QcYYvr92L1HhoXxvabZ7b8r7Koy7FN76nk428jPXTB3Fb++dS1F1I7f/+mNOVjbaHZLqgSZ0d9UUWQscz7oLQiPsjsbvvbzjFFuPVvEvSyYzMtrNfy+HA258HNpb4M0HtevFz1w6YSR/uP8iahrbuPlXH7LjZLXdIaluNKG7a9tqa3vRqgvvpzhV08Qjr+1jbkY8d84d2783J46HRd+Dg2/AXq3G6G/mjI3nrw9cwvCIUO5cs5VXdmoJZH+iCd0dTTVWd0vOzRCX3vf+Q5jTafjuS7txGsNPb5+FwzGASpTzvwGpudYF0rOlng9SDcr4pGheeeASZqbH8e0/7ebhV/dqhUY/oQndHVt/BS11cOm37I7E763edISPj1byf2+YytjEqIEdJCQUbnoSWhvhjW9r14sfSoyO4Pn7L+L+yzJ59uMTLHt8C3uKa+wOa8jThN6Xxir4+FeQfSOMnmF3NH5tc0E5P3nrENfPGM2X5g7ym0zSZLjy+3DoTdfYf+VvwkIcfP+GqfzuvrmcbW7n5l99xM/ePkRru656ZBdN6H3Z8jNorYeFXygDr7o4UdnAN1/cycTkEfz4thkXHnPurou/AWnzrK6XmpODP57yioWTk3nr25ezfNYYHnuvkOW//JDdRdpat4Mm9AupPg7bfg2z/g5G9VhAUgGldc3c/fQ2BPj1l3OJCg/1zIEdIXDLr8HphL981VolSvml2GFh/OyOWTx1Tx5VDS3c/KsPeeT1fTq71Mc0oV/IO/8OEmLNYlQ9Kj/bwpef3kZVfSu/u28eGSOHe/YECVlw4y+g+BPtTw8A10wdxTvfuYK7LhrH7z46zuKfbWTDfr2w7Sua0HtTuAH2r4XLvq3T/HtxtLyeW5/8iJNVjTz1lTxmpsd550TTbrUKeO18Dt7+vtViV34rJjKMH9w0jb+suoQRkWHc/2w+Dzy/nVKdYep1YlcNrby8PJOfn2/LufvU2gBPXmp95f/6RzqRqAfvHSzlwZd2IyI8/ZU8Zo+N9+4JjYF1/2wV8Mq+EZb9AqISvHtONWidC5v84t0CHAL3XpLJqiuyiIsKtzu0gCUi240xeT2+pgm9B69+wyrCde8b1jJz6pyzzW389O3D/O6j40xJGcGTd+eS6elult4YAx8/ARv+wyrBsOC7kPsVCBvmm/OrATtR2cD/vnOYV3eXEB0eysrLs7j30gxGRIbZHVrA0YTeH9t/D6//H+sr/pXftzsav9He4eSVnaf4n7cOUX62hXsvyeBfr5tCZFiI74M5sxfW/wuc+BCGJ8PFD8DsL8Pw4F1TJVgcPFPHT98+zDv7SxkeHsItc9K4LTeNGWmxnhkZNQRoQnfXnj/DKyshaxH83UvWBJchrr6lnbU7T7Fm01FOVjUyMy2WR5ZPY5a3+sv74/iHsOl/rAqNjjDIvgGm3w6ZV0BEtN3RqQvYU1zD7z46zht7TtPa7iQ1bhiXTRjJ3MwEpqSMICtpuOdGSwUZTeh9aaqBD35o1WsZewnc/TKED3CWYxBoae/go8JK3vzsNOs+O01jawcz0mL5x0UTuDp71MCm83tT2UGrNMPuF6CpGkLCYex8GDXdqg2TOB5i0yE2Ta+H+JnaxjbeOVDKW/vO8MmxKmqbPh+amho3jKyk4YxLjGJsQhRjE4Zb28QooiOGbrIfdEIXkSXAL4AQ4DfGmB91e11cry8FGoF7jTE7LnRM2xN6QwWU7ITDb8FnL0FzLcz7B1j8/yB06FywMcZQXt/CoTNn+fRYFduOVbGrqIaWdicjIkK5bnoKd84by6z0OP//StzeCkVboeBtOLrRWv2ovanLDmIl99Q8q1ZMWh6Mmjak/r/9mdNpOFJeT2GZdTtSXs/RigZOVDael+gBEoeHk54Q1SXZW7cxccNIjokgItSGrkAfGVRCF5EQ4DBwDdaC0Z8Cdxpj9nfZZynwTayEfhHwC2PMRRc6rkcTujFgnNaKQu3N0HL281tDOdSdgroS1+2UNWGotsh6b2gkTL4OLvtOQE7tN8bgNNDhNLR2OGloaae+pZ3Glg5r22o9bmjpoLapjZrGVmoa26hsaKGoqomTVY00uQorOQRyxsQyLzOBSyckcumEkYH9wXA64WwJVB21yh/XnIAzn0FxPjSUWfuERFhL4MWkwogU62JreDREjHBto7s8Hm7dD4uyRkA5QsARas1VcIRa5X+VV9Q2tlFU3ciJykZOVjVysqqBk1XW45KaJpzd0lh8VBijYiJJjokkeUQEscPCiI4IZUSkdYuOCCMqIoSIEAfhoZ/fwkIchIc4iOi8H+ogxPWNVASErvdBRFxbfNbguVBCd+d7yzyg0Bhz1HWwPwLLgf1d9lkOPOtaR3SriMSJyGhjzOlBxv5FB16Hv/4DmA4rgZsOK5n3xREKI8ZAzBhIvwjmrYQxs61bAPS3/nVHMf/2yl6cxrhu4DSm3/NswkMdxEeFER9ltXAumziSsQlRZCUNZ1Z6XHCNOnA4rG6W2LTznzfG+oN+aruV3MsPWqUFTm6F5hr3fp96PWeXBH/vG5A6Z3A/gwIgNiqM2KhYpqV+ceWrtg4nJTVNnKhs5ExdM2V1zZTWtZy7X1B6lrPN7T6btdqZ7B0in/8ROPcHwHr8tQWZfGfxZI+f252EngoUdXlcjNUK72ufVOC8hC4iK4GVrof1InKoX9F+0Uigwv3dq4C9gzzlgPUzVtsESpzg77F+P7fznn/H+blAiRMCJ9Ye43zQdRugcb294E5C7+l7RPd2oTv7YIxZA6xx45xuEZH83r56+JtAiTVQ4oTAiVXj9LxAidXXcbrT6VcMdK2FmgaUDGAfpZRSXuROQv8UmCgimSISDqwAXuu2z2vAPWKZD9R6pf9cKaVUr/rscjHGtIvIPwJvYQ1bfMYYs09EVrleXw2swxrhUog1bPE+74V8Ho913/hAoMQaKHFC4MSqcXpeoMTq0zhtm1iklFLKs3TgrFJKBQlN6EopFSQCIqGLyBIROSQihSLyhcU9ReQuEdnjun0kIjP9Mc4u+80VkQ4Ruc2X8XWLoc9YRWShiOwSkX0istHXMbpi6Ov/PlZEXheR3a44fXX9pnscz4hImYj0ONHBNWDgMdfPsUdEbJtx5Eas/vJ5umCcXfaz9fPkTpw++ywZY/z6hnUh9giQBYQDu4Gp3fa5BIh33b8O2OaPcXbZ7z2sC8m3+fG/aRzWbOCxrsfJfhrn94D/dt1Pwpo9Fm5DrJcDc4C9vby+FFiPNWdjvh2/o/2I1fbPkztxdvkdsfvz1Ne/p88+S4HQQj9XesAY0wp0lh44xxjzkTGm2vVwK9Y4eF/rM06XbwIvA2W+DK4bd2L9O+CvxpiTAMYYO+J1J04DjHAViIvGSug+X5nYGLPJde7enCuPYYzZCsSJyGjfRHe+vmL1k8+TO/+m4AefJzfi9NlnKRASem9lBXrzVayWkK/1GaeIpAI3A6t9GFdP3Pk3nQTEi8gHIrJdRO7xWXSfcyfOJ4BsrIlsnwHfMmYwxVi8pr+/x/7Crs9Tn/zo89QXn32WAqGosFtlBQBEZBHWL6Ad68a5E+fPgYeMMR02l6J1J9ZQIBe4ChgGfCwiW40xh70dXBfuxHktsAu4EhgPvCMim40xdd4Orp/c/j32FzZ/ntzhL5+nvvjssxQICd2tsgIiMgP4DXCdMabSR7F15U6cecAfXb98I4GlItJujFnrmxDPcbecQ4UxpgFoEJFNwEysUsq+4k6c9wE/MlbnZKGIHAOmAJ/4JkS3BVR5DD/4PLnDXz5PffHZZykQulz6LD0gImOBvwJf9nELsqs+4zTGZBpjMowxGcBfgAds+uVzp5zDq8ACEQkVkSisCpsH/DDOk1gtH0RkFDAZOOrTKN0TMOUx/OTz1Cc/+jz1xWefJb9voRv3Sg88DCQCv3L9tW43Pq7E5macfsGdWI0xB0Tkb8AewIm1UpVPaw+7+W/6A+B3IvIZVrfGQ8YYn5dVFZEXgYXASBEpBv4dCOsSp13lMb7AjVht/zy5Gadf6CtOX36WdOq/UkoFiUDoclFKKeUGTehKKRUkNKErpVSQ0ISulFJBQhO6UkoFCU3oSikVJDShK6VUkPj/AZGM2ZtZFhSVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "e.result.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/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>Variant</th>\n",
       "      <th>Measure</th>\n",
       "      <th>ProbabilityToBeBest</th>\n",
       "      <th>ProbabilityToBeatBaseline</th>\n",
       "      <th>UpliftFromBaseline</th>\n",
       "      <th>PotentialLossFromBaseline</th>\n",
       "      <th>MaxUplift</th>\n",
       "      <th>MaxPotentialLoss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apollo</td>\n",
       "      <td>revenue</td>\n",
       "      <td>0.99595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.510021</td>\n",
       "      <td>0.03168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Variant  Measure  ProbabilityToBeBest  ProbabilityToBeatBaseline  \\\n",
       "0  apollo  revenue              0.99595                        0.0   \n",
       "\n",
       "   UpliftFromBaseline  PotentialLossFromBaseline  MaxUplift  MaxPotentialLoss  \n",
       "0                 0.0                        0.0  79.510021           0.03168  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e.result.decision()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the primary measure defined for this experiment, we would go with the variant 'apollo'."
   ]
  }
 ],
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