Project7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project description\n",
    "\n",
    "You are an analyst at a big online store. Together with the marketing department, you've compiled a list of hypotheses that may help boost revenue.\n",
    "You need to prioritize these hypotheses, launch an A/B test, and analyze the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1. Prioritizing Hypotheses\n",
    "\n",
    "- The file hypotheses_us.csv contains nine hypotheses on boosting an online store's revenue with Reach, Impact, Confidence, and Effort specified for each."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plot\n",
    "from math import factorial\n",
    "from scipy import stats as st\n",
    "from functools import reduce\n",
    "import math\n",
    "import random\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading, optimizing, and examining Hypotheses dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9 entries, 0 to 8\n",
      "Data columns (total 5 columns):\n",
      "Hypothesis    9 non-null object\n",
      "Reach         9 non-null int64\n",
      "Impact        9 non-null int64\n",
      "Confidence    9 non-null int64\n",
      "Effort        9 non-null int64\n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 488.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "hypotheses = pd.read_csv('/datasets/hypotheses_us.csv', sep=';')\n",
    "hypotheses.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are 9 hypothesis in our data. No missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "hypotheses.columns = hypotheses.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Add two new channels for attracting traffic. T...\n",
       "1    Launch your own delivery service. This will sh...\n",
       "2    Add product recommendation blocks to the store...\n",
       "3    Change the category structure. This will incre...\n",
       "4    Change the background color on the main page. ...\n",
       "5    Add a customer review page. This will increase...\n",
       "6    Show banners with current offers and sales on ...\n",
       "7    Add a subscription form to all the main pages....\n",
       "8    Launch a promotion that gives users discounts ...\n",
       "Name: hypothesis, dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypotheses['hypothesis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    10\n",
       "3     8\n",
       "2     8\n",
       "6     5\n",
       "5     3\n",
       "4     3\n",
       "0     3\n",
       "1     2\n",
       "8     1\n",
       "Name: reach, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypotheses['reach'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Hypothesis 7 has the most reach value with a 10 while hypothesis 3 and 2 have 8 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "8     9\n",
       "7     7\n",
       "1     5\n",
       "6     3\n",
       "3     3\n",
       "2     3\n",
       "5     2\n",
       "4     1\n",
       "Name: impact, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypotheses['impact'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Hypothesis 0 has the most impact and then 8 with 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8    9\n",
       "7    8\n",
       "6    8\n",
       "0    8\n",
       "2    7\n",
       "1    4\n",
       "3    3\n",
       "5    2\n",
       "4    1\n",
       "Name: confidence, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypotheses['confidence'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Hypothesis 8 has the most confidence score at 9. 7, 6, and 0 are next all with a score of 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    10\n",
       "3     8\n",
       "0     6\n",
       "8     5\n",
       "7     5\n",
       "6     3\n",
       "5     3\n",
       "2     3\n",
       "4     1\n",
       "Name: effort, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypotheses['effort'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Hypothesis 1 will require the most effort to accomplish by launching our own delivery service with hypothesis 3 coming in with a score of 8 while all the rest of the hypothesis would require 6 or less of an effort to accomplish"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying the ICE framework to prioritize hypotheses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making new ICE column\n",
    "hypotheses['ICE']= (hypotheses['impact'] * hypotheses['confidence']) / hypotheses['effort']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                          hypothesis        ICE\n",
      "8  Launch a promotion that gives users discounts ...  16.200000\n",
      "0  Add two new channels for attracting traffic. T...  13.333333\n",
      "7  Add a subscription form to all the main pages....  11.200000\n",
      "6  Show banners with current offers and sales on ...   8.000000\n",
      "2  Add product recommendation blocks to the store...   7.000000\n",
      "1  Launch your own delivery service. This will sh...   2.000000\n",
      "5  Add a customer review page. This will increase...   1.333333\n",
      "3  Change the category structure. This will incre...   1.125000\n",
      "4  Change the background color on the main page. ...   1.000000\n"
     ]
    }
   ],
   "source": [
    "print(hypotheses.loc[:,['hypothesis', 'ICE']].sort_values(by= 'ICE', ascending= False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Hypothesis 8 is highest priority with the ICE framework at 16.20 while 0 and 7 have 13.33 and 11.20 respectively.\n",
    "- The rest of the hypothesis fall under a score of 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying the RICE framework to prioritize hypotheses. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making new RICE column\n",
    "hypotheses['RICE']= (hypotheses['reach'] * hypotheses['impact']) * hypotheses['confidence'] / hypotheses['effort']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                          hypothesis   RICE\n",
      "7  Add a subscription form to all the main pages....  112.0\n",
      "2  Add product recommendation blocks to the store...   56.0\n",
      "0  Add two new channels for attracting traffic. T...   40.0\n",
      "6  Show banners with current offers and sales on ...   40.0\n",
      "8  Launch a promotion that gives users discounts ...   16.2\n",
      "3  Change the category structure. This will incre...    9.0\n",
      "1  Launch your own delivery service. This will sh...    4.0\n",
      "5  Add a customer review page. This will increase...    4.0\n",
      "4  Change the background color on the main page. ...    3.0\n"
     ]
    }
   ],
   "source": [
    "print(hypotheses.loc[:,['hypothesis', 'RICE']].sort_values(by= 'RICE', ascending= False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Hypothesis 7 is highest priority with the RICE framework at 112 while 2 gets a score of 56. The rest fall under a score of 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Showing how the prioritization of hypotheses changes when you use RICE instead of ICE. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hypothesis</th>\n",
       "      <th>reach</th>\n",
       "      <th>impact</th>\n",
       "      <th>confidence</th>\n",
       "      <th>effort</th>\n",
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       "      <th>RICE</th>\n",
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       "      <td>8</td>\n",
       "      <td>Launch a promotion that gives users discounts ...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
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       "      <td>16.200000</td>\n",
       "      <td>16.2</td>\n",
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       "      <td>0</td>\n",
       "      <td>Add two new channels for attracting traffic. T...</td>\n",
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       "      <td>10</td>\n",
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       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>11.200000</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Show banners with current offers and sales on ...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>40.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Add product recommendation blocks to the store...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          hypothesis  reach  impact  \\\n",
       "8  Launch a promotion that gives users discounts ...      1       9   \n",
       "0  Add two new channels for attracting traffic. T...      3      10   \n",
       "7  Add a subscription form to all the main pages....     10       7   \n",
       "6  Show banners with current offers and sales on ...      5       3   \n",
       "2  Add product recommendation blocks to the store...      8       3   \n",
       "\n",
       "   confidence  effort        ICE   RICE  \n",
       "8           9       5  16.200000   16.2  \n",
       "0           8       6  13.333333   40.0  \n",
       "7           8       5  11.200000  112.0  \n",
       "6           8       3   8.000000   40.0  \n",
       "2           7       3   7.000000   56.0  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypotheses.sort_values(by = 'ICE', ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hypothesis</th>\n",
       "      <th>reach</th>\n",
       "      <th>impact</th>\n",
       "      <th>confidence</th>\n",
       "      <th>effort</th>\n",
       "      <th>ICE</th>\n",
       "      <th>RICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Add a subscription form to all the main pages....</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
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       "      <td>11.200000</td>\n",
       "      <td>112.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>Add product recommendation blocks to the store...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>56.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>Add two new channels for attracting traffic. T...</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>40.0</td>\n",
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       "      <td>6</td>\n",
       "      <td>Show banners with current offers and sales on ...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
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       "      <td>8.000000</td>\n",
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       "      <td>8</td>\n",
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       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>16.200000</td>\n",
       "      <td>16.2</td>\n",
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      ],
      "text/plain": [
       "                                          hypothesis  reach  impact  \\\n",
       "7  Add a subscription form to all the main pages....     10       7   \n",
       "2  Add product recommendation blocks to the store...      8       3   \n",
       "0  Add two new channels for attracting traffic. T...      3      10   \n",
       "6  Show banners with current offers and sales on ...      5       3   \n",
       "8  Launch a promotion that gives users discounts ...      1       9   \n",
       "\n",
       "   confidence  effort        ICE   RICE  \n",
       "7           8       5  11.200000  112.0  \n",
       "2           7       3   7.000000   56.0  \n",
       "0           8       6  13.333333   40.0  \n",
       "6           8       3   8.000000   40.0  \n",
       "8           9       5  16.200000   16.2  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypotheses.sort_values(by = 'RICE', ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "hypotheses['hypothesisint'] = [0, 1, 2, 3, 4, 5, 6, 7, 8,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = hypotheses['hypothesisint']\n",
    "y1 = hypotheses['ICE']\n",
    "y2 = hypotheses['RICE']\n",
    "\n",
    "# Plot Line1 (Left Y Axis)\n",
    "fig, ax1 = plt.subplots(1,1,figsize=(16,9), dpi= 80)\n",
    "ax1.plot(x, y1, color='tab:red')\n",
    "\n",
    "# Plot Line2 (Right Y Axis)\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "ax2.plot(x, y2, color='tab:blue')\n",
    "\n",
    "# Decorations\n",
    "# ax1 (left Y axis)\n",
    "ax1.set_xlabel('Hypotheses', fontsize=20)\n",
    "ax1.tick_params(axis='x', rotation=0, labelsize=12)\n",
    "ax1.set_ylabel('ICE', color='tab:red', fontsize=20)\n",
    "ax1.tick_params(axis='y', rotation=0, labelcolor='tab:red' )\n",
    "ax1.grid(alpha=.4)\n",
    "\n",
    "# ax2 (right Y axis)\n",
    "ax2.set_ylabel(\"RICE\", color='tab:blue', fontsize=20)\n",
    "ax2.tick_params(axis='y', labelcolor='tab:blue')\n",
    "ax2.set_xticks(np.arange(0, len(x), 60))\n",
    "ax2.set_xticklabels(x[::60], rotation=90, fontdict={'fontsize':10})\n",
    "ax2.set_title(\"Difference of ICE and RICE\", fontsize=22)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<b>Reviewer's comment: </b> Nice plot! I like color scheme and double Y-axis. Probably barplot or scatter plot will be more useful, because lines show false trend.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The top five prioritized hypotheses with ICE are 8, 0, 7, 6, and 2 in order from highest to lowest priority.\n",
    "- The top five prioritized hypotheses with RICE are 7, 2, 0, 6, 8 in order from highest to lowest priority.\n",
    "- The highest priority goes to hypothesis 8 when using ICE as a method of prioritization\n",
    "- The highest priority goes to hypothesis 7 when using the RICE method of prioritization\n",
    "- The reason for this change is because with RICE we added the amount of significance of reach to users and new audience would be. \n",
    "- Even though with ICE hypothesis 8 would have medium amount of effort and would work very well for users that are already in the system it would not reach any new audience because it would only be targeting our users that we have already. Therefore it ends up having the lowest priority with RICE.\n",
    "- With RICE we are showing how much reach to users and new audience we will also have so adding a subscription for for all our main pages will reach new prospective users as well and adding product recommendation blocks to our store will be seen by anyone.\n",
    "- Hypothesis 0 will have the most impact on our profit because it can help attract new customers but it isn't meant for our users so again it will have little reach. It has the most effort though.\n",
    "- Reach is a very important aspect for RICE and that is why hypothesis 8 which has the most reach of all and a high impact and confidence jumps from the 5th lowest priority in ICE to highest priority in RICE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2. A/B Test Analysis\n",
    "\n",
    "-You carried out an A/B test and got the results described in the files orders_us.csv and visits_us.csv."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading, optimizing, and examining Orders dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1197 entries, 0 to 1196\n",
      "Data columns (total 5 columns):\n",
      "transactionId    1197 non-null int64\n",
      "visitorId        1197 non-null int64\n",
      "date             1197 non-null object\n",
      "revenue          1197 non-null float64\n",
      "group            1197 non-null object\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 46.9+ KB\n"
     ]
    }
   ],
   "source": [
    "#orders = pd.read_csv('B:\\Yandex\\projects\\Project7\\orders_us.csv')\n",
    "orders= pd.read_csv('/datasets/orders_us.csv')\n",
    "orders.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transactionId    0\n",
       "visitorId        0\n",
       "date             0\n",
       "revenue          0\n",
       "group            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are 1197 rows. No missing values\n",
    "- Group A and B then have 1197 visitors between them\n",
    "- We will have to check if there are any visitors that somehow made it into both groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders['date'] = orders['date'].map(lambda x: dt.datetime.strptime(x, '%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders.columns = orders.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transactionid</th>\n",
       "      <th>visitorid</th>\n",
       "      <th>date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>group</th>\n",
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       "  <tbody>\n",
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       "      <td>844</td>\n",
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       "      <td>2019-08-05</td>\n",
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      ],
      "text/plain": [
       "     transactionid   visitorid       date  revenue group\n",
       "844     3734714128   471551937 2019-08-05    355.1     A\n",
       "711      672040400   723038324 2019-08-20     50.9     B\n",
       "335     4006427057   612505725 2019-08-25     20.2     A\n",
       "642      369801982   954785863 2019-08-04    155.8     B\n",
       "450     1750160666  1333886533 2019-08-06     25.5     A"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3574407166    1\n",
       "2470658885    1\n",
       "1476831880    1\n",
       "3709669952    1\n",
       "2764584088    1\n",
       "             ..\n",
       "1168756094    1\n",
       "1185885568    1\n",
       "11068804      1\n",
       "486237576     1\n",
       "1192185169    1\n",
       "Name: transactionid, Length: 1197, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['transactionid'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiples = orders['visitorid'].value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A few users made multiple transactions. Let's see if any of them are in both groups\n",
    "- So there were 1197 transactions made by 1031 users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiples.columns = ['id', 'amount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiple_visits = multiples.query('amount > 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2780786433</td>\n",
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       "      <td>393266494</td>\n",
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       "      <td>3967698036</td>\n",
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       "      <td>249864742</td>\n",
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      ],
      "text/plain": [
       "            id  amount\n",
       "0   4256040402      11\n",
       "1   2458001652      11\n",
       "2   2378935119       9\n",
       "3   2038680547       8\n",
       "4   3717692402       5\n",
       "5   3062433592       5\n",
       "6    199603092       5\n",
       "7   1230306981       5\n",
       "8   3803269165       5\n",
       "9    237748145       5\n",
       "10  1614305549       4\n",
       "11   457167155       4\n",
       "12  2587333274       4\n",
       "13  2988190573       3\n",
       "14  3908431265       3\n",
       "15  1668030113       3\n",
       "16  2742574263       3\n",
       "17  1294878855       3\n",
       "18  4186807279       3\n",
       "19  3891541246       3\n",
       "20  2044997962       3\n",
       "21  2780786433       3\n",
       "22   393266494       3\n",
       "23  3984495233       3\n",
       "24   611059232       3\n",
       "25  2108163459       3\n",
       "26  3967698036       3\n",
       "27  1404934699       3\n",
       "28  1738359350       3\n",
       "29   249864742       3"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiple_visits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 30 users made multiple visits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "visitor4256040402 = orders.query('visitorid == \"4256040402\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>60</td>\n",
       "      <td>1421016313</td>\n",
       "      <td>4256040402</td>\n",
       "      <td>2019-08-16</td>\n",
       "      <td>875.5</td>\n",
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       "      <td>90.2</td>\n",
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       "      <td>187</td>\n",
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       "    <tr>\n",
       "      <td>263</td>\n",
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       "      <td>2019-08-18</td>\n",
       "      <td>170.8</td>\n",
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       "    <tr>\n",
       "      <td>588</td>\n",
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       "      <td>2519737434</td>\n",
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       "      <td>2019-08-08</td>\n",
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       "      <td>2019-08-27</td>\n",
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       "      <td>1019</td>\n",
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       "      <td>2019-08-31</td>\n",
       "      <td>30.5</td>\n",
       "      <td>B</td>\n",
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       "    <tr>\n",
       "      <td>1091</td>\n",
       "      <td>750632879</td>\n",
       "      <td>4256040402</td>\n",
       "      <td>2019-08-12</td>\n",
       "      <td>5.8</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      transactionid   visitorid       date  revenue group\n",
       "60       1421016313  4256040402 2019-08-16    875.5     B\n",
       "114      1120327437  4256040402 2019-08-01     90.2     A\n",
       "187      2048878902  4256040402 2019-08-17     25.8     A\n",
       "253      1610091760  4256040402 2019-08-23    265.8     A\n",
       "263      2575861846  4256040402 2019-08-18    170.8     B\n",
       "588      3700356838  4256040402 2019-08-20    275.5     A\n",
       "873      2519737434  4256040402 2019-08-08    900.7     B\n",
       "901      3402467119  4256040402 2019-08-27     30.1     A\n",
       "1018     1415970216  4256040402 2019-08-31     80.5     A\n",
       "1019      453824282  4256040402 2019-08-31     30.5     B\n",
       "1091      750632879  4256040402 2019-08-12      5.8     A"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visitor4256040402"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that this user is in both groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    973\n",
       "2     58\n",
       "Name: group, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_grouped = orders.groupby('visitorid').agg({'group': 'nunique'})\n",
    "users_grouped['group'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- And it looks like we have 58 instances where a user ended up in both group A and B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_one_group= users_grouped.query('group == 1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We made a new dataset with users that only were in 1 group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders = orders[orders['visitorid'].isin(users_one_group.index)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Made sure that this new dataset becomes part of our orders dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>visitorid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A</td>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B</td>\n",
       "      <td>528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       visitorid\n",
       "group           \n",
       "A            445\n",
       "B            528"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visitors = orders.groupby('group').agg({'visitorid': 'nunique'})\n",
    "visitors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- But now A and B need to have same numbers in order for us to compare them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Updated observation. Group B has more visitorids in its group. We can change it that they will be equal in number but since the test is already ongoing and these are the numbers we have we should check based on what we have instead of possibly altering the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "#B_group = pd.Series(orders[orders['group'] == 'B']['visitorid'].unique()).sample(445)\n",
    "\n",
    "#orders_B = orders.query('visitorid in @B_group & group == \"B\"')\n",
    "\n",
    "#orders_A = orders[orders['group'] == 'A']\n",
    "\n",
    "#len(orders_A['visitorid'].unique()) == len(orders_B['visitorid'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_B = orders[orders['group'] == 'B']\n",
    "orders_A = orders[orders['group'] == 'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Separated the groups and did not change Group B's size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>transactionid</th>\n",
       "      <th>visitorid</th>\n",
       "      <th>date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [transactionid, visitorid, date, revenue, group]\n",
       "Index: []"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visitor4256040402fixedA = orders_A.query('visitorid == \"4256040402\"')\n",
    "visitor4256040402fixedA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>transactionid</th>\n",
       "      <th>visitorid</th>\n",
       "      <th>date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [transactionid, visitorid, date, revenue, group]\n",
       "Index: []"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visitor4256040402fixedB = orders_B.query('visitorid == \"4256040402\"')\n",
    "visitor4256040402fixedB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We were able to get rid of this user and the others that were mixed in with both groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     468.000000\n",
       "mean      113.700855\n",
       "std       171.372117\n",
       "min         5.000000\n",
       "25%        20.300000\n",
       "50%        50.100000\n",
       "75%       131.925000\n",
       "max      1425.800000\n",
       "Name: revenue, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_A['revenue'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      548.000000\n",
       "mean       145.348905\n",
       "std        868.228227\n",
       "min          5.100000\n",
       "25%         20.800000\n",
       "50%         45.900000\n",
       "75%        125.200000\n",
       "max      19920.400000\n",
       "Name: revenue, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_B['revenue'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Max for Group A is 1425.80 and for Group B is 19920.40. That is a huge difference of revenue\n",
    "- There are definitely some outliers and abnormalities here in the data that can skew the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 468 entries, 7 to 1194\n",
      "Data columns (total 5 columns):\n",
      "transactionid    468 non-null int64\n",
      "visitorid        468 non-null int64\n",
      "date             468 non-null datetime64[ns]\n",
      "revenue          468 non-null float64\n",
      "group            468 non-null object\n",
      "dtypes: datetime64[ns](1), float64(1), int64(2), object(1)\n",
      "memory usage: 21.9+ KB\n"
     ]
    }
   ],
   "source": [
    "orders_A.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 548 entries, 0 to 1196\n",
      "Data columns (total 5 columns):\n",
      "transactionid    548 non-null int64\n",
      "visitorid        548 non-null int64\n",
      "date             548 non-null datetime64[ns]\n",
      "revenue          548 non-null float64\n",
      "group            548 non-null object\n",
      "dtypes: datetime64[ns](1), float64(1), int64(2), object(1)\n",
      "memory usage: 25.7+ KB\n"
     ]
    }
   ],
   "source": [
    "orders_B.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - We started with 1197 rows but now are down to 468 for Group A and 548 for Group B\n",
    "- We also have noticed a few outliers like Group B having revenue of 19920.40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_fix = orders_A.append(orders_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1016 entries, 7 to 1196\n",
      "Data columns (total 5 columns):\n",
      "transactionid    1016 non-null int64\n",
      "visitorid        1016 non-null int64\n",
      "date             1016 non-null datetime64[ns]\n",
      "revenue          1016 non-null float64\n",
      "group            1016 non-null object\n",
      "dtypes: datetime64[ns](1), float64(1), int64(2), object(1)\n",
      "memory usage: 47.6+ KB\n"
     ]
    }
   ],
   "source": [
    "orders_fix.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>transactionid</th>\n",
       "      <th>visitorid</th>\n",
       "      <th>date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>986</td>\n",
       "      <td>218256919</td>\n",
       "      <td>91069833</td>\n",
       "      <td>2019-08-10</td>\n",
       "      <td>15.6</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>545</td>\n",
       "      <td>2172704731</td>\n",
       "      <td>2042457747</td>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>280.8</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>756</td>\n",
       "      <td>125335873</td>\n",
       "      <td>3423937755</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>5.8</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>365</td>\n",
       "      <td>4288552432</td>\n",
       "      <td>2971471657</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>10.8</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1138</td>\n",
       "      <td>2974379446</td>\n",
       "      <td>3160565905</td>\n",
       "      <td>2019-08-13</td>\n",
       "      <td>25.2</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      transactionid   visitorid       date  revenue group\n",
       "986       218256919    91069833 2019-08-10     15.6     B\n",
       "545      2172704731  2042457747 2019-08-29    280.8     B\n",
       "756       125335873  3423937755 2019-08-30      5.8     B\n",
       "365      4288552432  2971471657 2019-08-19     10.8     A\n",
       "1138     2974379446  3160565905 2019-08-13     25.2     A"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_fix.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Brought the A and B groups back together in order for us to continue testing and have 1016 rows\n",
    "- We have a few outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading, optimizing, and examining Visits dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 62 entries, 0 to 61\n",
      "Data columns (total 3 columns):\n",
      "date      62 non-null object\n",
      "group     62 non-null object\n",
      "visits    62 non-null int64\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 1.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#visits = pd.read_csv('visits_us.csv')\n",
    "visits = pd.read_csv('/datasets/visits_us.csv')\n",
    "visits.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We have 62 rows here. That is 62 dates. So 31 for each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "visits['date'] = visits['date'].map(lambda x: dt.datetime.strptime(x, '%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>group</th>\n",
       "      <th>visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>2019-08-11</td>\n",
       "      <td>B</td>\n",
       "      <td>721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>B</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>2019-08-08</td>\n",
       "      <td>B</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2019-08-08</td>\n",
       "      <td>A</td>\n",
       "      <td>610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>2019-08-12</td>\n",
       "      <td>B</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date group  visits\n",
       "41 2019-08-11     B     721\n",
       "45 2019-08-15     B     544\n",
       "38 2019-08-08     B     654\n",
       "7  2019-08-08     A     610\n",
       "42 2019-08-12     B     543"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                      62\n",
       "unique                     31\n",
       "top       2019-08-02 00:00:00\n",
       "freq                        2\n",
       "first     2019-08-01 00:00:00\n",
       "last      2019-08-31 00:00:00\n",
       "Name: date, dtype: object"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits['date'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The testing took place in the month of August. August 1, 2019 until August 31, 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     62.000000\n",
       "mean     607.290323\n",
       "std      114.400560\n",
       "min      361.000000\n",
       "25%      534.000000\n",
       "50%      624.500000\n",
       "75%      710.500000\n",
       "max      770.000000\n",
       "Name: visits, dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits['visits'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Maximum visits were 770 in one day. Most of the visits were over 600 visitors in a day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc1bc015b50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visits.hist(log= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordersByUsersA = orders_fix[orders_fix['group']=='A'].groupby('visitorid', as_index=False).agg({'transactionid' : pd.Series.nunique})\n",
    "ordersByUsersA.columns = ['visitorid', 'orders']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>visitorid</th>\n",
       "      <th>orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>11685486</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>54447517</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>66685450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>78758296</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>85103373</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   visitorid  orders\n",
       "0   11685486       1\n",
       "1   54447517       1\n",
       "2   66685450       1\n",
       "3   78758296       1\n",
       "4   85103373       1"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordersByUsersA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordersByUsersB = orders_fix[orders_fix['group']=='B'].groupby('visitorid', as_index=False).agg({'transactionid' : pd.Series.nunique})\n",
    "ordersByUsersB.columns = ['visitorid', 'orders'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>visitorid</th>\n",
       "      <th>orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5114589</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6958315</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>39475350</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>47206413</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>48147722</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   visitorid  orders\n",
       "0    5114589       1\n",
       "1    6958315       1\n",
       "2   39475350       1\n",
       "3   47206413       1\n",
       "4   48147722       1"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordersByUsersB.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# building an array with unique paired date-group values\n",
    "datesGroups = orders_fix[['date','group']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>2019-08-16</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>2019-08-22</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>2019-08-17</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>930</td>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1043</td>\n",
       "      <td>2019-08-11</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1067</td>\n",
       "      <td>2019-08-12</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1114</td>\n",
       "      <td>2019-08-13</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1149</td>\n",
       "      <td>2019-08-14</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date group\n",
       "7    2019-08-15     A\n",
       "45   2019-08-16     A\n",
       "55   2019-08-01     A\n",
       "86   2019-08-22     A\n",
       "124  2019-08-17     A\n",
       "...         ...   ...\n",
       "930  2019-08-31     B\n",
       "1043 2019-08-11     B\n",
       "1067 2019-08-12     B\n",
       "1114 2019-08-13     B\n",
       "1149 2019-08-14     B\n",
       "\n",
       "[62 rows x 2 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datesGroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting aggregated cumulative daily data on orders \n",
    "ordersAggregated = datesGroups.apply(lambda x: orders_fix[np.logical_and(orders_fix['date'] <= x['date'], orders_fix['group'] == x['group'])].agg({'date' : 'max', 'group' : 'max', 'transactionid' : pd.Series.nunique, 'visitorid' : pd.Series.nunique, 'revenue' : 'sum'}), axis=1).sort_values(by=['date','group'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting aggregated cumulative daily data on visitors  \n",
    "visitsAggregated = datesGroups.apply(lambda x: visits[np.logical_and(visits['date'] <= x['date'], visits['group'] == x['group'])].agg({'date' : 'max', 'group' : 'max', 'visits' : 'sum'}), axis=1).sort_values(by=['date','group'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        date group  orders  buyers  revenue  visits\n",
      "0 2019-08-01     A      23      19   2266.6     719\n",
      "1 2019-08-01     B      17      17    967.2     713\n",
      "2 2019-08-02     A      42      36   3734.9    1338\n",
      "3 2019-08-02     B      40      39   3535.3    1294\n",
      "4 2019-08-03     A      66      60   5550.1    1845\n"
     ]
    }
   ],
   "source": [
    "# merging the two tables into one and giving its columns descriptive names\n",
    "cumulativeData = ordersAggregated.merge(visitsAggregated, left_on=['date', 'group'], right_on=['date', 'group'])\n",
    "cumulativeData.columns = ['date', 'group', 'orders', 'buyers', 'revenue', 'visits']\n",
    "\n",
    "print(cumulativeData.head()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Graph cumulative revenue by group. Make conclusions and conjectures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc1b9198850>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#DataFrame with cumulative orders and cumulative revenue by day, group A\n",
    "cumulativeRevenueA = cumulativeData[cumulativeData['group']=='A'][['date','revenue', 'orders']]\n",
    "\n",
    "# DataFrame with cumulative orders and cumulative revenue by day, group B\n",
    "cumulativeRevenueB = cumulativeData[cumulativeData['group']=='B'][['date','revenue', 'orders']]\n",
    "\n",
    "\n",
    "#Increasing size of graph and rotating axis\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Cumulative Revenue by Group A and B', fontdict={'size':15})\n",
    "plt.ylabel('Amount of Revenue', fontdict={'size': 15})\n",
    "plt.xlabel('Dates in August', fontdict={'size': 15})\n",
    "\n",
    "# Plotting the group A revenue graph \n",
    "plt.plot(cumulativeRevenueA['date'], cumulativeRevenueA['revenue'], label='A')\n",
    "\n",
    "# Plotting the group B revenue graph \n",
    "plt.plot(cumulativeRevenueB['date'], cumulativeRevenueB['revenue'], label='B')\n",
    "\n",
    "\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see here that both Group A and B are rising steadily in revenue. Group A had a little more revenue by August 13 than Group B but then after August 17 Group B shot away from Group A and had  a steady stream of revenue over 50000 the rest of the month\n",
    "- Group B is definitely the leader in the second half of the month.\n",
    "- Of course we know Group B had more orders than Group A at some point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Graph cumulative average order size by group. Make conclusions and conjectures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc1b9290410>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l5y6cLamjMyKuBN5bzGlSIg8vWtX3PbkG+HpEfAy4mTws5KiKc6+NiAeAV0fEAnJodFcf5/wx8G7yHDpXlA+TSin9IyK+Sb760xfIf7g3kwOW3VJKb+3lnKcB96SUflC5IyKmkbtZjiB39ECeX+ncYrk+pfRMN0ZKqRT5kvJfjYgdi/vUked+OTKlVBmuVLoGOCsibgbuIwdLu1QccxV5YvGfRMTZ5Pfhk+RulfKOrXPIV1T7dURcSO7I2R54MXBRSum6ngpIKc0v3rPPF6HVpeQQ8ATya//NlFLlnDQ9+Qo5KPlV8TnejhxYPfO5KT5b7wcujohJ5HBjLXno3svJk7U/vdGZ+3Z1RNxDHta4mDzU6d3kLptv9XSHiHgtuVvps8C0iDikbPcdA/C+riK/D+eSh4adS55b6G8Vx32X/Lu9imL+r76klK6MiK8AF0XEkcVzfow8UXVX8FMZlFWeo7N4bt+KiOXkz+ALgP8HfLQsSLqCPMn4dyLiInLoU9nh1WUieX6qb5N//z4OfD2l1Nt30knkqwF+NaV0c/mOooPzY+Tf043CpZTSExFxK/CJiFhB/h34CHl47KS+nnsP5+oovju+GBGPkSfjfiVlwXo/7FeE73Xkz/HHyaH1JucLkySNcNWeUdzFxcXFxaWvhfzHzR/IfyytBf5Jnpdndtkxu5DnJ3mK3A1yEj1fLa7yak9nkP/vfvmVrFrY+ApQ25DDrRXkbqW3V56PiqvFkbuIvkjufFpBDmkO7uHcx5ADpdXFvpaeaiiODfIfagl4SQ+vVZDnLVpI7mR4lDyc7vReXtuuq8J9tJf9TeSrV1VePepP9HE1P+D15KF0q4r73wy8r6/3otg+AfgeORh8nDyk7QQqrgZIno/n/4rXrOv9+C05cCs/3x7kOYQeL2q5lxywzOnH5+7E4jO0knylr1uAN1Fx5a7enkux74XFe7uGfAW355HDj3MqjjuO/If8U8Vn5U7yJewbevpsbaLu04rP6uLicZeQLz1/UMVxz1wtju6r0fW0tPT3fe2lnkXkicXPIQeAT5E7dKb0cvwS4H838zviFeTg5XFyl+GD5N+343p7zj2c49+Kz8da8hX23tvDMWeQQ8+nyUO8DqPnq8W9j9wB107+3vo60NRH/b8E/tnH/vPJoXaP5yB///2+eG3bgA9Vfmbo4buu7P35YsV3yKfJ3x0ryRPqv7bys9DLZ738c9NZvJeXUHaFThcXFxeX0btESgN9IR5JkqShU8wbdT9wXkrpk9WuR1ummF9rIXB0Sun31a5nS0REAv4tpXRetWuRJGkoOSxOkiSNKBHxTnJnxL/IE3m/j9xl1dfVxTRMRcR0YHdyx8wC8nxekiRpBDFckiRJI81q4MPk4XGJPGTt6JRSXxOsa/g6kRwM3gO8IdlWL0nSiOOwOEmSJEmSJG2xumoXIEmSJEmSpJFr1A2LmzFjRmppaal2GZIkSZIkSaPG/PnzH0spzexp36gLl1paWrjtttuqXYYkSZIkSdKoERG9zm/psDhJkiRJkiRtMcMlSZIkSZIkbTHDJUmSJEmSJG2xUTfnkiRJkiRJ0nCzbt06lixZwurVq6tdSp+am5uZM2cOjY2N/b6P4ZIkSZIkSdIgW7JkCRMnTqSlpYWIqHY5PUopsXz5cpYsWcK8efP6fT+HxUmSJEmSJA2y1atXM3369GEbLAFEBNOnT9/s7irDJUmSJEmSpCEwnIOlLltSo+GSJEmSJEmStpjhkiRJkiRJUo244ooriAjuueeeATun4ZIkSZIkSVKNuOSSSzj88MO55JJLBuychkuSJEmSJEk14Mknn+RPf/oT3/3ud7n00ksH7LwNA3YmSZIkSZIkbdKnfrmQvz24YkDPudd2k/jkiXv3ecyVV17Jsccey2677cb06dOZP38+BxxwwFY/tp1LkiRJkiRJNeCSSy7h1FNPBeDUU08dsKFxdi5JkiRJkiQNoU11GA2Gxx9/nGuvvZa7776biKCjo4OI4NxzzyUiturcdi5JkiRJkiSNcpdddhlveMMbaG1tZdGiRSxevJh58+Zxww03bPW5DZckSZIkSZJGuUsuuYRXvOIVG2x75StfOSBD4xwWJ0mSJEmSNMr94Q9/2GjbWWedNSDntnNJkiRJkiRJW8xwSZIkSZIkSVvMcEmSJEmSJElbzHBJkiRJkiRJW8xwSZIkSZIkSVvMq8VJkiRJAB3r4f8+DJPnwD6vhCk7VLsiSZJGBMMlSZIkCWD5vXDrd/LPvzsHdjg0h0x7vwLGz6hqaZIkDYT6+nr23XdfUkrU19dz3nnncdhhh231eQ2XJEmSJIBSa16/8rv557svg6s+AL/5MOz8Itj3FNjjeGiaWN06JUnaQmPHjuXOO+8E4Oqrr+bss8/mj3/841af13BJkiRJAii15XXLETDxFDji/bBsYQ6Z7r4MLn8HNDTD7sfBPqfAri+Ghqbq1ixJ0hZasWIFU6dOHZBzGS5JkiRJAO2Lcng0YVb3tm32zstRn4Alt8LdP4UFP4eFl0PTZNjrZbmjqeUIqKuvWumSpBHmNx+Bh+8e2HPO3heO+88+D1m1ahX7778/q1ev5qGHHuLaa68dkIc2XJIkSZIgD4WbsgNEbLwvAuYelJeXfB4euA7u/hksvALuuBgmzIZ9Ts4dTds/p+dzSJJUZeXD4m666SZOP/10FixYQGzlf7cMlyRJkiTIw+L6c4W4+gbY5ei8nPBl+OfVsOCyPBn4X86HqfNg31fljqaZuw9+3ZKkkWcTHUZD4dBDD+Wxxx7j0UcfZdasWZu+Qx8MlyRJkiSA9lbY/sDNu0/jWNj75XlZVYJ7fpWHzt3wRbj+C3mIwr6vyledmzxncOqWJGkL3HPPPXR0dDB9+vStPpfhkiRJkrT6CVhdgqk7bvk5xk6BZ78+LyuX5XmZ7v4pXPOJvOxwWO5m2uvlMH7r/yE/qNashJUPAwEzdql2NZKkAdI15xJASonvf//71Ndv/ZyBhkuSJElS15Xi+jMsrj8mbgOHvDMvj98PC36Wrzj36/fBbz4EO78odzTt/lJomjAwj7kpKcGaFTk0WvkwPLkMVj6Ug7CVDxW3i33rnuq+37bPggPOyPNJNU8amlolSYOio6NjUM5ruCRJkiS1t+b1lK3oXOrNtJ3g+R+EIz4AyxYWV5z7Gfz8bdAwFnY/LgdNuxwNDWM2//wp5a6rDUKiXkKj9as2vn/jOJg4O09Kvu2zYLeXwIRtYOK2sKo9T1j+q/fC1f+eJy0/4E1OWi5J2oDhkiRJktTVuTS1ZfAeIwJm75OXoz4Ji2/OE4EvvBwW/hyap8BeJ+Whczs+D6IuhzsrH944NHry4Q07kNav3vjxxkzMHVQTZsP2B+QAqStEmliERxO2gaaJfQdFB78Dlt4O87+XQ7E7LoZt9sndTPu+Kg8HlCTVtCENlyLiQuAE4JGU0j5l2/8NOBPoAH6dUvpQsf1s4C3F9rNSSlcPZb2SJEmqEaVWGDMBxk4dmserq4MdD83Lsf8J91+Xh80t+Bnc/n1ompy7jDrWbnzfpslFODQb5h68YVA0cdsiQNpm4IbbRcCcA/Lyks/lQGz+RXDVB+C3H8/dTM95I8w9yG4mSdqElBIxzL8rU0qbfZ+h7ly6CDgP+EHXhog4EjgJeFZKaU1EzCq27wWcCuwNbAf8LiJ2SykNzgBBSZIk1a721jwkrhr/4K9vhF1fnJe1T8O/rs5hU/PkitCo6EIaM27oa+zSPAkOfHNeHrwD5n8/D/O784cwc0844I2w32tg3LTq1ShJw1RzczPLly9n+vTpwzZgSimxfPlympubN+t+sSWJ1NaIiBbgV12dSxHxE+CClNLvKo47GyCl9Pni9tXAOSmlm/o6/4EHHphuu+22QahckiRJo9b5h+UrxZ12SbUrGXnWPJmH9c2/CJbOh/om2PvluZtpx8PsZpKkwrp161iyZAmrV/cwlHkYaW5uZs6cOTQ2Nm6wPSLmp5QO7Ok+w2HOpd2AIyLis8Bq4AMppVuB7YG/lB23pNi2kYh4O/B2gB12GKArfEiSJKk2pJSHxc07otqVjExNE+A5p+fl4btzN9NdP87L9F1zN9OzXgvjp1e7UkmqqsbGRubNm1ftMgZFXbULIAdc04BDgA8CP4nN7A9LKV2QUjowpXTgzJkzB6NGSZIkjVZPPw5rnxycK8XVmtn7wvFfhPffAyedn4fH/fbf4ct7wGVvhvv/CJ2d1a5SkjTAhkPn0hLg5ymPz7slIjqBGcBSYG7ZcXOKbZIkSdLAKbXm9RQ74AfMmPHw7NflZdnf8iTlf700T1g+bafc5bT/62DCrGpXKkkaAMOhc+kK4EiAiNgNGAM8BvwCODUimiJiHrArcEvVqpQkSdLo1BUuTbVzaVBssxcc91+5m+kVF+TJyX93Dnx5T/jJ6XDv7+1mkqQRbkg7lyLiEuCFwIyIWAJ8ErgQuDAiFgBrgTcWXUwLi8m+/wasB870SnGSJEkacO12Lg2JxrHwrNfk5dF/5m6mO38Ef7syD0l8zhtg/9fDpG2rXakkaTMN+dXiBptXi5MkSdJm+dX78nCtj7RWu5Las34N/P2X+Upzi26AqIfdj4MDzoCdXwR19dWuUJJUGO5Xi5MkSZKqp9TqkLhqaWiCfU/Jy/L7cjfTHT+Ee34Fk+fCs98Az349TO7xotGSpGFiOMy5JEmSJFVPqc0rxQ0H03eGF/8HvO/v8Krvw/Rd4LrPwVf2gR+8HG76OjzydxhlIy8kaTSwc0mSJEm1K6UcLu16TLUrUZeGMbD3y/Py+ANwx8V56NzVH837J20POx+Zh83tdCSMm1bdeiVJhkuSJEmqYU8ug/WrYWpLtStRT6bNg6M+kZfSYrjvWrjv9zlsuuN/gYDtng27HAU7HwVzDoT6xmpXLUk1x3BJkiRJtavUltdeKW74mzIXDnhjXjo7YOnt3WHTDV+C68+Fpkkw7/m5q2nnF+VwSpI06AyXJEmSVLvaiyvEOefSyFJXD3Ofm5cXfhhWleCB63PQdO+1eUJwgGk75Y6mnV8E846AponVrVuSRinDJUmSJNWu0qK8tnNpZBs7BfZ6WV5Syleeu+/3ubPpzh/Brd+GugaYe3AOmnY5CmY/C+q8vpEkDQTDJUmSJNWuUhuMnwljxlW7Eg2UCJixS14OfgesXwOLb85B072/h2s/nZdx0/OE4LscldeTtq125ZI0YhkuSZIkqXa1tzokbrRraMrzMM17Phx9Djz5CNx/XQ6a7rsWFlyWj5u1d74K3S5HwQ6HQWNzFYuWpJHFcEmSJEm1q9QG2z+n2lVoKE2YBfu9Oi8pwbIF3UHTLRfATedBQzPs+Lzuq9DN3D13REmSemS4JEmSpNrU2QFPLIG9X17tSlQtETB737wc/h5Y+xQsurH7KnRXfzQfN2n73NW005HQcjhMnF3duiVpmDFckiRJUm1a8SB0rnNYnLqNGQ+7HZMXgNLi7qDp77+EO/43b5++a776XMvh0HJE7oaSpBpmuCRJkqTaVGrLa68Up95MmQsHvDEvnR3w8F3wwA2w6E9w10/htgvzcTN27w6bdjwcJsysbt2SNMQMlyRJklSbSq15PbWlqmVohKirh+2enZfnnQUd6+Hhv3aHTX+9FG79Tj525p45aJp3RA6bxk+vbu2SNMgMlyRJklSb2luBgMlzql2JRqL6Btj+gLwc/h7oWAcP/RUeuD6HTXf+CG79dj521t5lYdPzYNy06tYuSQPMcEmSJEm1qdQGE7fNl6qXtlZ9I8w5MC9HvC+HTQ/e0R023XEx3PItIGCbfcrCpsNg7NRqVy9JW8VwSZIkSbWp1ApTncxbg6S+EeYelJfnfwDWr4UHb4dFN+ShdPO/Bzd/AyiuWNdyRA6bdjgUxk6pdvWStFkMlyRJklSbSm15iJI0FBrGwA6H5OX5H4T1a2Dp/NzV9MD1eb6mv3wdog5m71d0Nj0/H988udrVS1KfDJckSZJUezrWwYqlXilO1dPQlIfE7XgYvOBDsG41LL2te4LwWy6Am87LYdO2+28YNjVNrHb1krQBwyVJkiTVnicWQ+p0WJyGj8bmHCC1HJ5vr1sFS24tOptugLRzQsgAACAASURBVL98A/78NYh6mLlHHkpXvjhJuKQqMlySJElS7Sm15fUUwyUNU41jc6fSvOfDkcDap2HJLbDoxu6r0t11affxk+ZsHDhN2RHq6qr2FCTVDsMlSZIk1Z721rx2WJxGijHjYKcX5qXLU4/Bw3dvuPzrt5A68v6mSfnKdM8ETvvAzD1zl5QkDSDDJUmSJNWeUmseXjRp+2pXIm258TNg5yPz0mXdKnjk7xsGTnf+ENY+mfdHPczcvaLLaT+H1UnaKoZLkiRJqj2lNpi8PdT7z2GNMo1jYfvn5KVLZye0P7Bh4PTADXDXj7uPmbR9D8PqWhxWJ6lf/K+pJEmSak97q/MtqXbU1cH0nfOy98u7t/c4rO6a7mF1YybmoXTlgZPD6iT1wHBJkiRJtafUCru+uNpVSNW1qWF1yxYUw+p+1POwuvL5nMbPqM5zkDQsGC5JkiSptqxbBU8us3NJ6klvw+pKi/oeVjdx2w07nLbZF6bt5LA6qUYYLkmSJKm2lBbnteGS1D91dTkomrYT7HVS9/anlsOyu+HhBd2h033XQuf6vL9xPGyzd9nQuv1g1l75yneSRhXDJUmSJNWWUlteTzVckrbK+Omw0wvz0mX9Gnj0nrIupwVw98/gtguLAwKm77Lx5OETtoGIIX8KkgaG4ZIkSZJqS2lRXk/ZoaplSKNSQxNs+6y8dEkph7rLyjqcls6HhT/vPmb8zLI5nPbL3U7Td/WKjtII4W+qJEmSakt7K9SPgQmzq12JVBsicqfg1B1hj+O7t68qwbKFReh0Vw6dbv4mdKzN++ubYJu9itBpv2Iup72heVJ1nkelzo5ca8daaBgLDWOqXZFUNYZLkiRJqi2lNpg814mGpWobOwVanpeXLh3r4LF/FR1Od+Xg6R9XwR0Xdx8ztaV70vAZuwIJOtZ3Bz0d66BzXffPz6zXdR/TWXF8R8XxnZXbetifOrtrap4CL/kc7P9ah/epJhkuSZIkqbaUWp1vSRqu6huLbqW94FmvydtSgpUPFROHFx1OyxbA338FpE2fM+pzt2L9mHz+Z5YxUFf2c/2Y3H3UNKHY19DD/Yqf6xo33P73X8GV74IFl8EJX/E7RjXHcEmSJEm1pb0Vtt2/2lVI6q8ImLRdXnY7pnv7midzWFzX0HsQVNc4NF2Kh5wJt34HfncOnH8oHP1JeO7b7JBUzTBckiRJUu1YsxJWPe5k3tJo0DQhz8E0HNTVwcFvh92PhV++B37zIVjwM3jZ/8DM3atdnTTojFElSZJUO0ptee2QFUmDYcoO8Pqfwcu/CY/9E755OFx/bp6rSRrFDJckSZJUO7rCpSktVS1D0igWAfufBmfeAru/FK79DFzwQnjwjmpXJg0awyVJkiTVjvbWvHZYnKTBNmEWvPr78Jr/hacehW8fBdd8AtatqnZl0oAzXJIkSVLtKLVC4zgYP6PalUiqFXueCGfeDPu/Fm78KnzjebDoT9WuShpQhkuSJEmqHaW23LUUUe1KJNWSsVPhpPPg9Cuhcz1cdDz86r2wekW1K5MGhOGSJEmSakd7K0xxMm9JVbLTC+FdN8EhZ8Jt34PzD4F/Xl3tqqStZrgkSZKk2pBSHhbnleIkVdOY8XDs5+At10DTRPjRq+Fnb4WnHqt2ZdIWM1ySJElSbVhdgjUrnMxb0vAw97nwjuvhBR+BhVfA1w+Cuy/LQbg0whguSZIkqTY8c6U4O5ckDRMNTXDk2fCOP+bvpp+9BS45DZ5YWu3KpM1iuCRJkqTaUGrLa4fFSRputtkb3vo7OOazcP91eS6m274HnZ3VrkzqF8MlSZIk1YZSV+eSw+IkDUN19XDYu+Fdf4ZtnwW/eg/84GWw/L5qVyZtkuGSJEmSakN7KzRNzpcEl6ThatpO8MZfwolfg4f+Ct84DG78GnSsr3ZlUq8MlyRJklQbSm0w1a4lSSNABBzwRjjzZtj5RXDNx+G7R8PDC6pdmdQjwyVJkiTVhlKrk3lLGlkmbQen/ghO+R6UFsMFL4BrPwvr11S7MmkDhkuSJEka/VLKnUuGS5JGmgjY52R4962wzylw/Rfgm0fA4luqXZn0DMMlSZIkjX5PPQbrnnYyb0kj17hpcPK34HWXwdqn4LvHwG8+kn+WqsxwSZIkSaNf15Xiptq5JGmE2/XF8K6b4LlvgZu/AecfAvf9odpVqcYZLkmSJGn0a1+U1w6LkzQaNE+C478Eb/oN1I+Bi18OV54JK5dVuzLVqIZqFyBJkiQNulJbXjssTtJosuNh8M4b4Y//CTd+De74X2ieAtN2ysv0nYufi/W4aXkOJ2mAGS5JkiRp9Cu1wrjp0DSh2pVI0sBqbIajz4F9Xw33/wGW3weP3w9LboGFP4fU2X1s8+QNw6byAGrcdIMnbTHDJUmSJI1+XilO0mi3zV55Kbd+Tf7+6wqcHu8Knm7dOHhqmgzT5m3c7TRtJxg/Y/gHTynB+tWwZiWsXgHrnoJJc2D89GpXVhMMlyRJkjT6tbfC7H2rXYUkDa2GJpixa14qrV+buzofv3/D8GnpfFh4eUXwNCkHT12BU3kANRDB0/o1ORBasyKHQ8+sV25i+8oN93Wu3/jcE2YXwdveMGvv/POM3XPHlwaM4ZIkSZJGt85OeGIx7HlCtSuRpOGjYcwmgqe2Dbudlt8HD94Bf7sSUkf3sWMmwvSdurucpu0EdY39CITKtnes3XS99U3QNDEvzZNy4DVlbl5vsH1i3tY4Nl/MYdnCvNx8AXSsyeeKepi+y8ah05Qdh3+H1jBluCRJkqTR7cmH8x8uTuYtSf3TMAZm7JKXSuvX5sC+cqjdQ3+Fv/1iw+AJoK6hLACalAOgSdtB0+4VwdDk7mMqQ6SmibkLa2t0rM+1LlsIj/wtr5fenru0uoyZCLP2zIHTNnvDrGKo4dipW/fYNcBwSZIkSaNbe2teT2mpahmSNCo0jMnD4qbvvPG+jnW54yml7i6ihubh0Q1U3wAzd88LJ3dvX7MSHvn7hqHTwsth/ve6j5m0fRE0lYVOM3bLr4UAwyVJkiSNdqUiXJrqhN6SNKjqG3sOnYazpokw96C8dEkJVj7UPaSuK3S6/zroXJePqWvIAVNl6DR5zvAI04aY4ZIkSZJGt1JbXk+eW906JEkjQ0QeujdpO9j1xd3bO9bB8ns3DJ0W3wwLLus+pmlyHkpXHjrN3g/GjBv65zGEDJckSZI0urW35qsFeWUgSdLWqG/MczLN2hP2PaV7+6pSHlr3yEJY9rccOt39U7jtu3n/GVdBy/OqU/MQMVySJEnS6FZqdUicJGnwjJ0COx6aly4pwRNLctC07bOqV9sQMVySJEnS6FZqhbkHV7sKSVItiYApc/NSA+qqXYAkSZI0aDrWwxNLYYqdS5IkDRbDJUmSJI1eK5ZC6oApO1S7EkmSRi3DJUmSJI1epda8ds4lSZIGjeGSJEmSRq/2IlxyWJwkSYPGcEmSJEmjV6kNog4mz6l2JZIkjVqGS5IkSRq9Sq0waXuob6x2JZIkjVqGS5IkSRq9Sm0OiZMkaZAZLkmSJGn0am/1SnGSJA0ywyVJkiSNTuvXwMqHvFKcJEmDzHBJkiRJo9MTS4DksDhJkgaZ4ZIkSZJGp/ZFee2wOEmSBpXhkiRJkkanUmteOyxOkqRBZbgkSZKk0anUBnWNMHHbalciSdKoZrgkSZKk0am9FSbPgbr6alciSdKoZrgkSZKk0anU6pA4SZKGgOGSJEmSRqdSm5N5S5I0BAyXJEmSNPqsfQqeehSm2LkkSdJgM1ySJEnS6FNanNdTW6pahiRJtcBwSZIkSaNPqTWvHRYnSdKgG9JwKSIujIhHImJBD/veHxEpImYUtyMivhYR90bEXRHxnKGsVZIkSSNYe1e45LA4SZIG21B3Ll0EHFu5MSLmAscAbWWbjwN2LZa3A98YgvokSZI0GpRaoaEZJsyqdiWSJI16QxoupZSuBx7vYdd/Ax8CUtm2k4AfpOwvwJSI2HYIypQkSdJIV2rNQ+Iiql2JJEmjXtXnXIqIk4ClKaW/VuzaHlhcdntJsa2nc7w9Im6LiNseffTRQapUkiRJI0Z7q0PiJEkaIlUNlyJiHPBR4BNbc56U0gUppQNTSgfOnDlzYIqTJEnSyFVqczJvSZKGSEOVH39nYB7w18gty3OA2yPiIGApMLfs2DnFNkmSJKl3q5+A1SWYaueSJElDoaqdSymlu1NKs1JKLSmlFvLQt+eklB4GfgGcXlw17hDgiZTSQ9WsV5IkSSNAqbhGjMPiJEkaEkMaLkXEJcBNwO4RsSQi3tLH4VcB9wP3At8G3jUEJUqSJGmka2/Na4fFSZI0JIZ0WFxK6bRN7G8p+zkBZw52TZIkSRplSkW4NLWlqmVIklQrqn61OEmSJGlAldpgzEQYO7XalUiSVBMMlyRJkjS6tLfmIXH5gjGSJGmQGS5JkiRpdCm1eqU4SZKGkOGSJEmSRo+U8rA4J/OWJGnIGC5JkiRp9Hj6cVj7JEyxc0mSpKFiuCRJkqTR45krxRkuSZI0VAyXJEmSNHp0hUsOi5MkacgYLkmSJGn0aO8Kl+xckiRpqBguSZIkafQotcHYqdA8qdqVSJJUMwyXJEmSNHqUWh0SJ0nSEDNckiRJ0ujR3uqQOEmShpjhkiRJkkaHlOCJxXYuSZI0xAyXJEmSNDo8uQzWr4apLdWuRJKkmmK4JEmSpNHBK8VJklQVhkuSJEkaHUptee2wOEmShpThkiRJkkaH0qK8NlySJGlIGS5JkiRpdCi1wfhZMGZctSuRJKmmGC5JkiRpdGhvtWtJkqQqMFySJEnS6FBqhalO5i1J0lAzXJIkSdLI19kBTyyxc0mSpCowXJIkSdLIt+JB6FwPU+xckiRpqBkuSZIkaeQrtea1w+IkSRpyhkuSJEka+UpteW3nkiRJQ85wSZIkSSNfeysQMHlOtSuRJKnmGC5JkiRp5Cu1waTtoKGp2pVIklRzDJckSZI08pVavVKcJElVYrgkSZKkka+91fmWJEmqEsMlSZIkjWzr18LKB71SnCRJVWK4JEmSpJFtxRJInQ6LkySpSgyXJEmSNLK1t+a1w+IkSaoKwyVJkiSNbKW2vLZzSZKkqjBckiRJ0shWaoWoh0nbV7sSSZJqkuGSJEmSRrZSG0yeA/UN1a5EkqSaZLgkSZKkka291SFxkiRVkeGSJEmSRrZSK0x1Mm9JkqrFcEmSJEkj17pV8OQymNJS7UokSapZhkuSJEkauUqL89phcZIkVY3hkiRJkkauUmteOyxOkqSqMVySJEnSyNUVLtm5JElS1RguSZIkaeRqb4X6Jpgwu9qVSJJUswyXJEmSNHKVWmHKXKjzn7WSJFWL/xWWJEnSyFVqc0icJElVZrgkSZKkkau9FaY4mbckSdVkuCRJkqSRac1KWPW4V4qTJKnKDJckSZI0MpXa8tphcZIkVZXhkiRJkkam9ta8ntJS1TIkSap1hkuSJEkamexckiRpWDBckiRJ0shUaoXGcTB+RrUrkSSpphkuSZIkaWTqulJcRLUrkSSpphkuSZIkaWQqtTkkTpKkYcBwSZIkSSNPSnlY3NQdq12JJEk1z3BJkiRJI8/qEqxZkYfFSZKkqjJckiRJ0sjT3prXDouTJKnqDJckSZI08pSKcMlhcZIkVZ3hkiRJkkaeUlteOyxOkqSqM1ySJEnSyNPeCk2TYeyUalciSVLNM1ySJEnSyFNqhanOtyRJ0nBguCRJkqSRp9TmkDhJkoYJwyVJkiSNLCkZLkmSNIwYLkmSJGlkeeoxWPe0V4qTJGmYMFySJEnSyFJqzespzrkkSdJwYLgkSZKkkaV9UV47LE6SpGHBcEmSJEkjS6ktr+1ckiRpWDBckiRJ0shSaoVx06FpQrUrkSRJGC5JkiRppGlvdUicJEnDiOGSJEmSRpZSm0PiJEkaRgyXJEmSNHJ0dsITi2GqnUuSJA0XhkuSJEkaOVY+BB1rHRYnSdIwYrgkSZKkkeOZK8UZLkmSNFz0K1yKiFkRMa/sdkTE2yPiKxFx4uCVJ0mSJJUptea1w+IkSRo2+tu5dBHw3rLb/wGcDxwLXB4RZwxsWZIkSVIPujqXJs+tbh2SJOkZ/Q2XngNcCxARdcA7gY+mlPYAPgu8Z3DKkyRJksq0t8KE2dDYXO1KJElSob/h0mRgefHzAcA04IfF7WuBXQa4LkmSJGljpVaHxEmSNMz0N1xaAuxV/Hw8cE9KaWlxezKweqALkyRJkjZSaoUpO1S7CkmSVKahn8ddCJwbEUeTw6Wzy/YdAvx9oAuTJEmSNtCxHp5YCvvauSRJ0nDSr3AppfT5iFgCHAT8Gzls6jIN+M4g1CZJkiR1W7EEUofD4iRJGmY2GS5FRBPwAeBXKaWLK/enlN45GIVJkiRJG+i6UpzD4iRJGlY2OedSSmkN8DFgyuCXI0mSJPWivTWvp9i5JEnScNLfCb1vBp4zmIVIkiRJfSq1QdTB5DnVrkSSJJXp74TeHwJ+FBHrgKuAZUAqPyCl9PQA1yZJkiR1K7XCpO2hvrHalUiSpDL9DZduLtZfA77ayzH1W1+OJEmS1Iv2VofESZI0DPU3XHozFZ1KkiRJ0pAqtcHOR1a7CkmSVKFf4VJK6aJBrkOSJEnq3fo1sPIhrxQnSdIw1N/OJQAiYi/gAGAucGFK6eGI2AVYllJaORgFSpIkSZQWA8lhcZIkDUP9ulpcREyIiJ8AC4DvAJ8Gtit2fw74ZD/Pc2FEPBIRC8q2nRsR90TEXRFxeURMKdt3dkTcGxH/iIiX9PdJSZIkaZQptea1nUuSJA07/QqXgC8DhwFHAROBKNt3FXBsP89zUQ/HXgPsk1LaD/gncDY80yV1KrB3cZ/zI8JJwyVJkmpRV7g01c4lSZKGm/6GSycDH04p/QHoqNjXCvTrv/IppeuBxyu2/TaltL64+RdgTvHzScClKaU1KaUHgHuBg/pZryRJkkaTUhvUNcLEbatdiSRJqtDfcGkssLyXfRPZOHDaUm8GflP8vD2wuGzfkmKbJEmSak17K0yeA3U2skuSNNz0N1y6FTi9l32nAH/e2kIi4mPAeuCHW3Dft0fEbRFx26OPPrq1pUiSJGm4KbU6JE6SpGGqv+HSx4GTI+J3wFuBBLw0Ii4GXkU/J/TuTUScAZwAvC6llIrNS8lXpesyp9i2kZTSBSmlA1NKB86cOXNrSpEkSdJwVGrzSnGSJA1T/QqXUko3kCfzbgLOI0/o/SlgJ+DolNKtW1pARBwLfAh4WUrp6bJdvwBOjYimiJgH7ArcsqWPI0mSpBFq7VPw1KNeKU6SpGGqob8HppRuBI6IiLHAVKBUEQZtUkRcArwQmBERS8gdT2eTQ6trIgLgLymld6aUFkbET4C/kYfLnZlSGqi5nSRJkjRSlNryempLVcuQJEk963e41CWltApYtSUPllI6rYfN3+3j+M8Cn92Sx5IkSdIo0RUu2bkkSdKw1Gu4FBEXbs6JUkpv3vpyJEmSpArtrXntnEuSJA1LfXUu7VtxewdgJvBIscwqlkeB1kGpTpIkSSq1QkMzTJhV7UokSVIPep3QO6X03K4F+A/gSeDwlNLslNJ+KaXZwBHASuAzQ1OuJEmSak6pNQ+Jy/NzSpKkYaZfV4sD/hP495TSn8s3FpN8fwL4r4EuTJIkSQLysDiHxEmSNGz1N1zaCejtynBPAy0DUo0kSZJUqdQGUw2XJEkarvobLt0OnBMR25ZvjIjtgHOA+QNclyRJkgSrn4DVJa8UJ0nSMNbXhN7l3gFcDSyKiPl0T+h9ALAceP3glCdJkqSa5pXiJEka9vrVuZRSWgDsDLwX+AfQVKzfC+xc7JckSZIGVqktr+1ckiRp2Npk51JENAGnALeklM4f/JIkSZKkQqnoXJraUtUyJElS7zbZuZRSWgN8B9hu8MuRJEmSyrS3wpiJMHZqtSuRJEm96O+E3ncDuw1mIZIkSdJGSm15SFxEtSuRJEm96G+49F7gQxFxQkT0dxJwSZIkaeuUWmGqk3lLkjSc9TcougIYB1wJpIhoB1L5ASmlWQNcmyRJkmpZSrlzad4Lql2JJEnqQ3/Dpa9TESZJkiRJg+rpx2Htk14pTpKkYa5f4VJK6ZxBrkOSJEnaUGlRXjssTpKkYa3f8ydFxHbA9sXNpSmlBwenJEmSJIk8JA5giuGSJEnDWZ8Tekd2VkTcCywG/lIsiyPi3oh4d4SX7pAkabi78d7HeOiJVdUuQ9o87a157bA4SZKGtV47l4qrwv0cOAG4DvgaUPwXnh2Bk4ptL46Ik1NKHYNbqiRJ2hK3LXqc133nZiaPbeQLp+zHS/aeXe2SpP4ptcLYqdA8qdqVSJKkPvQ1LO7fgKOAl6aU/q+H/V+LiGPIAdS7ga8OQn2SJGkrdHQmzvnlQmZPambmxCbecfF8zjishbNfugdNDfXVLk/qW6nNriVJkkaAvobFnQF8oZdgCYCU0m+Bc4E3D3BdkiRpAPzktsUsWLqCjx6/J5f9v0N58/PmcdGfF3Hy+X/mgceeqnZ5Ut/aW51vSZKkEaCvcGlX8nC4TbmuOFaSJA0jTzy9jnOv/gcHtUzjxP22pamhnk+cuBffOf1AlpZWccLXbuDKO5dWu0ypZynBE4u9UpwkSSNAX+HSKmByP84xuThWkiQNI1/5/T8pPb2WT75sL8qvv3H0Xttw1VlHsNd2k/j/Lr2TD132V55eu76KlUo9eHIZrF9t55IkSSNAX3Mu3QS8FfjFJs7xVuDPA1aRJEnaav9ctpIf3NTKaQftwN7bbfz/irabMpZL3nYIX/39vzjvD/dye1uJr7/2Oew+e2IVqpV68MyV4gyXJEkbSimxel0nK1avY8WqdcV6fdnt/DNAY10dDfVBQ13QUF+X18XPjfVBwzP7NzyusVjX10XFcUFjff65vi7Kzt+9v/x/6tWKvsKlzwPXRcQPgY+mlFrLd0bEDsBngeOA5w9eiZIkaXOklPjULxcyfkw97z9m916Pa6iv4/3H7M7B86bznh/fycvO+xOfPHFvTjtobk3+o0jDTKktrx0WJ0mjTkqJVes6KgKhHgKiPrav70x9PsaY+jxQa31nJ5s4dMA11BXBUxFCXfSmg9h/7pShLWKI9RoupZRujIg3At8CXhURdwFdAdOOwL7AWuD0lNJNg16pJEnql6sXLuPGe5fzqZftzbTxYzZ5/OG7zuA3/98RvO8nd/LRy+/mxvse4/Mn78uk5sYhqFbqRWlRXk+eW9UyJEn999Sa9fzu78tY/PjTWx0ONTfWMam5kUljG5nY3MDUcWPYcfp4JjU3MGlsY7Gv4ZljyrdPbG6gubH7qridnYl1nZ10dCbWdSTWd3SyvjPlpaOTdR2p2Je3d3R2Fscl1nd2PrOuPK7r/nl/ca7i/us7isfq7GR6P/49NtL11blESulHEXEd8DbgCGCvYtdSctfSd1JKDw5qhZIkqd9Wr+vgM7/+G7tvM5HXHdz/S7jPnNjE9990EN+8/j6+9Nt/cveSJ/if057Ns0b5/2XTMNbeCuNnwZhx1a5EktSH9R2d3Hjfci6/fQlXL1zGqnUdAIxtrGfS2AYmNufgZ9r4MbRMH18RCPUcEE1sbqCpoX4Tj9x/dXVBU93AnU8b6zNcAijCo08NQS2SJGkrXXD9/SxpX8WP3nYwDfV9XbdjY3V1wbteuAsHz5vGWZfcySnf/DMfPnYP3nL4vP+fvfsOj6pM3zj+Pcmk90ILBEKV3kFAUMECdnHXXlGxt3X9rXVX1921rK5txbYqiAUVRewNRQWl9yolgST09N5m3t8fZ0hCD2SSk3J/rmuuKefkzCMEZO487/NqmZzUv5xUiK55QCoiIvXHGMOa7Xl8smwbn63Yzp78UqJCAhg/sC3jB7SlX7toAl1H9+8QadyOGC6JiIhI47A9p5iXftrEmX1aM6Jz/DFfZ1CHWL68YyR/+Wgl//xyHfM2Z/LUhf1qtMROxGdytkLbQU5XISIi1WzPKWbm8m18snQbG3cXEOBvMaZ7S8YPaMfo7i182m0kjYvCJRERkSbisa/WYQw8cGaPWl8rOjSQV68cxFu/beGxr9Zz5vNzeOHSAQztGOuDSkWOwOOG3HTodYHTlYiINHt5JeV8s2onM5alsyAlC2NgcIcY/jW+N2f1aUN0qH74JAqXREREmoT5yZl8sXIHd57SlXYxvplRY1kW15zQkcFJsdz23lIueW0ed53ajVtHd8HfT8vkpA7lbQdPhZbFiYg4pNzt4ZcNe5ixbBuz1u6itMJDx/gw/nRqN87v35b2cZqHJ/tSuCQiItLIVbg9PPLZGtpGh3DTSZ19fv3ebaP44o5RPPTJKp75fgPzkzN57uL+tIwM9vl7iQD2kjiAmA7O1iEi0owYY1iRnssnS9P5fOUOsgrLiAkN4OIhiYwf0Jb+idGawSiHdMhwybKsH4FbjDHrLcu6CvjSGJNZf6WJiIhITUxblMb6nfm8dPlAQgLrZtZBeJCLZy/uz4gu8Tz86RrOeH4Oz1zcn5O6taiT95NmLifVvo9WuCQiUtfSsor4ZNk2Zi7bRnJGIYEuP07r2Yrx/dty0nEtCDjKDUKkeTpc59IoYO/+w5OB4YDCJRERkQYkp6iM/3z3O8M6xXJG79Z1+l6WZXHR4EQGJEZz23vLuPrNhdx4UifuOf04/cNTfCt7K2BBVDunKxERaZJyi8r5YtV2Zi7bxqIt2QAM6xTLjSd14ow+bYgMDnC4QmlsDhcupQEXWpZVAFhAR+/jgzLGrPV1cSIiInJ4z3y/gbzich45t1e9tap3bRXBp7edwN8/X8urPyezMCWLFy4ZQGKs5i+Ij+RshcgEcAU5XYmISJNRWuFm9vo9fLIsndnr91Dm9tClZTj/N/Y4zh/QlrbRIU6XKI3Y4cKlx4GXgLsAA7x35PSk9AAAIABJREFUiPMs73HtOSgiIlKP1u3I4535W7lyWAe6t46s1/cODvDn8Qv6MKJzHA/MWMVZL8zh33/sy7jebeq1DmmiclI1zFtExAeMMSzZms0ny7bxxcod5BaXEx8exBXDOnDBwLb0SojUHCXxiUOGS8aY/1mW9RnQFfgFuBVQd5KIiEgDYIzhkc/WEBUSwJ9O6+ZYHef0S6Bfu2hum7aUm95ZylXDO/DAmT0IDtDPnKQWsrdC0kinqxAROYAxhpyiclKzikjNKiItu4i0rCKyC8sJCfQn1HsLCXQRtt9j+7ir8pzQQBehQf6EBvjj8vHy8pSMwso5SqlZRQQH+DG2V2vGD2jLyC7xPn8/kcPuFmeM2QXssizr78Cnxpjt9VOWiIiIHM5Xq3ayICWLf57fm+jQQEdraR8Xykc3jeDJb9bzxtwUFm/J5sXLBtCpRbijdUkjVVEGedu0U5yIOKa0ws227GI7PPKGSPbjYtKyisgvrdjn/PjwQGJCAykud1Nc5qawrIKScs9RvWegv19l0BQS6E9YkIuQgGoh1CGCqrAgf+95LkIC/VmzPZcZS7exPC0Hy4ITOsdz5yldGdu7NeFB2ixe6k6NvruMMX8HsCwrEOgDxAJZwCpjTFndlSciIiL7Ky5z868v19KjTSSXDm0YS4cCXX789eyejOgcx5+nr+Ds/87ln+f35oKBGsgsRykvHTBaFicidcYYw56C0srgKC2ruFqAVMTOvBKMqTo/yOVHYmwo7WNDGdoxlsTYUBJjQmgfF0piTChhBwltPB5DcbkdNBWXuSmqvFVQVFYVQh3uWFGZm4yCMgrLiirPKy5zU+Y+fHDVvXUED5zZnXP7taV1VLCvf/lEDqrG0aVlWX8B7gciqZqzlGdZ1mPGmKfqqD4RERHZz8s/b2Z7bgnPXTIAf7+GNSfhlB6t+PrOUdw5bTl3f7iCXzdl8uh5vQ76D++6Yowhu6icXXkl7MorYXdeqf04v4RdeaXsziuhsMzNca0j6Ncuir7toundNko/0W0osrfa99HqXGpuSsrdpGYVkZJRyJaMQrZkFnofFxEe7OKGUZ04f0BbAl1aziNHVlRWUdlpVD042ruUbf/OotaRwbSPDWV45zjae4Okvbf48CD8jvL/t35+FmFBrjr5/1+523PQgKqwrIKEqBCOax3h8/cUOZIafadblnUX9oDvV4APgF1AK+Bi4HHLskqNMS/UWZUiIiICQFpWEa/+vJlz+iUwtGOs0+UcVJuoEN6beDwv/LCR/87exPK0bF68bCA92tRu6LgxhvzSCnbn2SHRrrwSdlYPj7yv78kvPehPdaNDA2gdGUzLyGBaRvqxPDWHL1fuAMCyoEuLcPq2i6Zfoh049WgTQZBLs6PqXU6qfa9lcU1SWYWHtOwiUvZUC48y7QBpe27xPt0isWGBJMWFMqJLHL/vzOcvH6/kuVkbuPGkzlw8JFGz3YTC0gpWb8tla1YR6dVCpNSsYjIKSvc5NyzQn8TYUDrGh3FStxaVXUeJsaG0iwlpVN9PAf5+RIX4ERUS4HQpIpUsU/1v8EOdZFkbgQ+NMQ8e5Ni/gIuNMV3qoL6jNnjwYLN48WKnyxAREakTN7+zhJ9+38MPfz6JhEawZfBvmzK484Pl5BaX87eze3L58e0PuitNYWkFu/NLD9JtVOp9bgdHxeXuA742IshFy8ggWkUG0yoy2H4cEex9br/eIiLooB8cMgpKWZWey4r0HFam57IyPYeMAnvFf4C/RffWkfRtF0W/dtH0TYyiS4twDUGtaz88CnOfg4d2g7+6yRqjCreH9OxiUjK9HUgZhaRkFrElo5D07CI81T5+RAa76BgfRsf4MJL23sfZj6t/cDbG8NPve3hx9iaWbM0mPjyIiaM6cvmwDuo6bEZKyt0s2ZrNvM2Z/LY5g5XpuVR4v6H8LEiIDqnsNkr03vY+jwkN0K5oIrVkWdYSY8zggx6rYbhUApxtjJl1kGOnAZ8bYxrEYk6FSyIi0lT9timDy15fwD2nd+O2MV2dLqfGMgpK+dMHy5mzMYPTeraiU4uwfbqNdueVHjAcFSA4wK+y06hVZDCtIoKqwqO9QVJEkE+XHBhj2J5bwsq0HFZ4w6ZV6bmV9YUE+NO7bSR920XT17ukLikuVB9YfOmj6yB9Edy10ulK5DDcHsP2nOLK7qOqpWz20qOKaglSeJCLpPhQkuL2DY86xocd9Qd+Ywzzk7OYNHsTczdlEBUSwIQTkrhmRJLjmxuI75VWuFmWmsO8zZnMS85keWoOZW4P/n4WfdpGMbxzHEM7xtIpPoyE6BACFP6L1ClfhEsbgJnGmL8c5Ni/gfONMc7tg1yNwiUREWmKKtweznxhDsXlbr7/00mNqn0f7MGmr81J5j/f/Y6FVS0gCqLlfl1GrSKDaBkZTESQq0GENh6PISWzcJ8OpzXbcyvndUQGu/YJm/olRtE6MrhB1N4ovX4aBATD1Z87XUmzZ4xhZ14JKXsKK7uQUjKK2JJZSGpm0T7LT0MC/L2BUeg+4VFSXBjx4YF18udhWWo2k2ZvZta6XYQF+nPl8CSuG9mRFhFBPn8vqR/lbg8r06vCpMVbsimt8GBZ0DvBDpOGd4pjcFIMEcFaEiZS33wRLt0GvAC8CXyEPXOpJXAhcA1wpzFmkq8Krg2FSyIi0hRN+TWFRz5fy6tXDmJsr9ZOl3PMyio8BPhbjT54qXB72LCrgJXpVR1Ov+/Mr+zWaBERVDksfO+yupgwdVXUyNPdoOtpcF6D+Kdls5JfUs7K9FyWpWazLDWHZWk5ZBVWbQwd6PIjKa5aB5I3POoYH0aryCDH/lyv25HHpNmb+HLVDgL9/bh0aHtuOLFTo1g63Ny5PYbV23KZl5zJvM2ZLNqSRVGZvfy5e+uIyjDp+I5xRIUqTBJxWq3DJe9FJgIPAwnYO8VZwHbgEWPM6z6qtdYULomISFOTWVDK6Kd/ol9iNFOvHdrog5mmqqTczdodeaxMs7ubVqTnkJxRWDmgODE2xO5s0g51h1ZeDP9qDaMfgpP+z+lqmjS3x7Bpd0G1ICmbjbsLKr9fu7QMZ0CiHY52ahFOUnwYbSKDj3rHrPqUvKeAl3/azCfLtmFZ8IeB7bjppM4kxYc5XZp4eTyGdTvz7M6kzZksTMmqXHbcpWU4wzvFMbxzHMd3jCUuXB1oIg2NT8Il74UsoB3QBtgBpJujuUA9ULgkIiJNzf0zVvHh4jS+uXMUXVtpe+HGJL+knNXb8liZXhU4pWcXA/YOdW2jQ4gMDiA82EVEkIvwYBfh3vuIoL2PAwgPchGx/7FgFyEB/k0rbNyzASYNgfGvQb+Lna6mSckoKGV5ag7L0+wgaUVaLgXeD/XRoQH0T4xmQGIMA9pH0y8xulHvQpWeXcSrPyfzweI0KtwezumXwK2ju9BNf3/WO2MMG3cX8NumDOYlZ7IgJYuconIAkuJCGd45jmGd7O6klpENYoSviBzG4cKlo/pxmTdISvPeREREpI6t3pbL+4tSmTCio4KlRigiOMBe1tE5rvK1zIJSVm7LZWVaLikZBRSUuikoLWdXfgmb91RQUFpBfkkFpRWew1zZ5mfhDZ4CKoOnA8MpV7VwKmC/597jga6G0ZGSs9W+j+ngbB2NXFmFh3U78uyupLQclqXmkJpVBIC/n0WPNhGMH9CWAe2jGdA+pskNpW8XE8o/zu/N7WO68PrcFN6Zv5VPl2/n9J6tuG1MF/q2i3a6xCbLGENyRmHlzKQFyZmVO3C2jQ7h1B6tGOENlLRsUaRpOarOpcZAnUsiItJUGGO48JV5pGQU8uM9JzfqTgI5emUVHgpLq8KmgtIKCkrLqx6X7Hssv6S88vX8asf3zi85nIhgl9250j6Gge3tDhZH5psseh2+/DPcvR4i29T/+zdCe3c43Lu8bXlaDqu25VLmDSdbRQYxsH1M5e9vn7ZRhAQ2rg0Baiu7sIzJv6Yw5bct5JVUcGK3Ftw2ugtDO8Y6XdpRK3d7SMkopLTc3jEtwN/y3vvh72fh8rdw+fl57+3HdTnnzhhDWlYx85IzKgOlXXmlALSODK6cmTS8cxyJsaF1UoOI1B+fdS6JiIhI/flsxXYWb83miQv6KFhqhgJdfgS6Ams9CNztMd5gam/gtG9AlV9SQXJGIctSs3nxx43s3UG+c4swb9hkL5Xq1ioC/7rubsreCv5BEN6qbt/Hy+MxbM0qYu32PNbuyGXt9jw27CrA5W8RFRJAVEgAkXvvgwMqX6s65qp8HBEcUPe/PkBRWQWr0nO9HUl2oLQ73/4wH+Tyo0/bKK4e3oEB3t+3NlHqDokJC+Tu049j4omdeHv+Vt6Yk8JFr85jaMdYbhvdhVFd4xtk51ZxmZv1O/NYvT2PtdtzWbM9j/U78yuDw6PhZ4HL388bOFkHf+xv4e8No/YGU679wquAvef4WZR7DEu3ZrMtx17qGx8eaC9x8wZKHePDGuSvq4jUDXUuiYiINECFpRWc8p+faRERxMxbT6iXD60iBaUVrEzLYak3tFiamk22dz5KWKA//RKjK8OmAe1jiPX1DngfXgW71sDtS3x7XeyB67/vzGftjjxvmJTHuh15lZ1d/n4WXVuGV87lyS0uJ7e4nLzicvJK7Mfl7sP/uzki2HXEECqyWmAVVS24CnT5HXA9j8eQklloD9z2/p78visftzcBTIoLrQyR+idG07115EGvI/sqLnMzbWEqr/2SzM68Evq2i+LW0V04rUcrx5aH5haXs3Z7Hmu8IdKa7bls2l1QGfZGhQTQKyGS3m2j6NkmkrAgF26Ph3K3ocLjocJtqPB4b24Pbo+h3G0qz3F7DOUeD+7K8+yvqTzHY7zHPN5r7H/dqsf2te2Aq3dCFMM7xzGicxxdWoYrTBJp4nw20LsxULgkIiJNwVPfrmfS7M18fPNwBnVofEs3pGkwxrA1s2ifsGn9zn3DjephU/fWEbj8axFuvHYyhMTClTNqVXdmQek+IdLa7Xls3lP1QT08yEXPNpH0TIisvO/SMpzggEMvFzPGUFzurhY6VVQ+3ieIqv68pOpxSfnhu01CAvz3CZz8/SzW7sgjt9gO9yKCXPRLjPb+WkfTP7EOwr1mprTCzYyl23j5p82kZhVxXKsIbhndmbP7JtRpoL87v4Q126qCpNXbc0nLKq483ioyiN4JUfRKiKRnQhS920bSNjpEwY2IOK7W4ZJlWT8Ctxhj1h/kWDfgFWPMmFpX6gMKl0REpLHbmlnIac/8wll92/Dsxf2dLkdkH0VlFaxMz60Mm5alZlcO7A0J8Kdvu6iq2U3tY2gRcRTbiT/ZEXqeB+c8V6PTD7asbe2OvMqZLwAJUcH7hEg920TRLiak3jtUSivc+4RSefuFUrn7hVIl5R56tImo3MGtc4vwhjF0vQmqcHv4fOV2Js3ezKbdBSTFhXLzyZ0ZP6BdrTrBjDGkZxezeltViLRmex578qu+PzvEhdI7IYqeCZH0SoikV0LU0f2ZERGpR74IlzzAMGPMwoMcGwzMN8Y0iPlNCpdERKSxmzh1Mb9uymD2PSfTSlsz+17Bblj2Dvi5ICgCgiMhaO9t7/MICIwAPy0xOpK9H6D3djctS81mzfY8KrxtQomxIQxIrAqberQ5xNKt0nx4vB2c+giM/NMBh2u6rK16R1KPNpG1nlklzYfHY/hu7U5enL2J1dvySIgK5saTOnPxkMTDdrWBHVAlZxTa3Ujb7CBp7fY88koqgGrfn94AqXdCJD0SIokM1jw9EWk8fDXQ+4AUyrKsQGAMsPMYaxMREZFqft6wh+/X7uLecd0VLNWFoiyYeh7sXluz8wMj9g2c9gmgDhJIHey1gFBowstZLMsiMTaUxNhQzuvfFrCDoNXbcisDpwUpmXy2YjtQNXR6YIcYBiRGM7BDjP29npNqXzC6fY2XtV00OLHGy9pEjsTPz2Jc7zaM7dWanzbsYdKPm3j4szX898dNXD+qI1cM60B4kIuScjcbduXb3Ujb9g7azqtc+hjk8qN7m0jO7pdQubztuNYR+v4UkSbtkJ1LlmU9DPythtd5yhhzn8+qqgV1LomISGNV7vYw7rlfcHsM3/7pRIJc+iDiU2WFdrC0YwVc9gG0GwIleXbHTGmefat8vve1fO9reVXPK1/Lh/LCI7+v5X/oQCowHILC7RArMMz7OLza62H7HgsIa5TdVMYYduSW7LOUbvW2PMq8Q4ETooK5KnYtN+14iAmuJ5hd0L7yaxvKsjZpfowxLEjJYtLsTczZmEFUSABtooLZtLugsjMvIshFT++g7b3L2jq3CKvd7DERkQbqWDuXvgIyAAt4AfgPsGW/c8qA9caYOT6oU0SkdtwVUFECFaX2vbu06nFF2X7H9j4/3LHqX3+oY97XPRXQph90GwtdT4f4bk26U0Hqxlu/bWHznkLeuHqwgiVfqyiF9y+HbUvgwregs3dUZFBE7a7rroCy/EOEUrn7Pq8eVBXshMyN3tcLoKL4yO+1V0BYteAp3LuEz/s4MMz7PPwQYVX4fsciwL/uJxtYlkVCdAgJ0SGc1bcNYM8gWrs9j6XepXSlKckAtOvYg4fat9eyNnGcZVkM6xTHsE5xLE/L4X9zkikoqeCUHi3p5e1ISowJVdApIkLNZy5dDXxpjMmo+5JqR51LIs1ISR5MuwS2L7MDHnP4nXhqxD8IXHtvwfa9/37Pqx/3D7TvMbD1t6qlNtEdvEHTWEgaCQFa3iSHtye/lDFP/8SgpBgmXzNEuwL5kscNH02AtZ/CuS/CwCudruhAHjeUFdhBU1mhHVhVPi6odsz7Wml+tccF+53j/ZoDJxocnH+Q3UUVGmfv1BYaCyEx3vtY+/XKx977kBjfh1Lf3A9LpsAD2xXOi4iINEC1nrlkjHnLtyWJiNSSxw0zJkLqfBhyvf2T+srwJxhcgfs+3xsCHS4g8g+s/XKTnDTY+J19W/o2LHwNXCHQ6SS7o6nr6RCd6JtfA2lSnvp2PcXlbv56dk8FS75kDHxxlx0snf6vhhksAfj5Q3CUffMFjwfKiw4RTh0kqCrJheIsKMqGrGQoyrTnU3nKD/0ewVH7Bk6hsdUCqpj9jnkDqoCQQ18ve6sdzOv7X0REpNGpUbhkWVYAcCdwAdAOOOBH8MaYlr4tTUTkMH74O2z4Bs58GoZOdLqaKtGJMOQ6+1ZeDFvm2kHThm/tegFa9oKup9mdTe2G1suSFGnYVqTlMH1JOhNHdaJzi3Cny2laZj0CS6fCqHtgxG1OV1N//PzsZW9B4UCrY7uGMXYAVZTlDZ6yoDi7KniqfC0LCvdAxu/287KCQ1/TFVItcIrZt1tqx3Jo3efYahURERFH1fQTzbPAjcAXwGzsWUsiIs5YPg1+fR4GX9uwgqX9BYTYIVLX0+CMf0PGBjtk2vgdzHsRfn0OgqOhyyl2R1OX0yAszumqpZ55PIZHPl9DXFgQt4/p4nQ5TcvcZ+0/Z4OvhTEPOV1N42NZ3h3wIiCmQ82/rqLUG0LtF0AVZdnBVPVjO1fb98XZ9tLmAQ20s0xEREQOq6bh0oXAfcaY/9RlMSIiR5S2ED6/A5JG2YFNY2FZ0OI4+3bCHfYSlM2zq5bQrf4YsKDdYHtOU7fToXVfLQ9pBj5Zto1lqTk8fWE/IoIDnC6n6Vgyxe5a6v0Hu8NRf5bqjysIIlrbt5ryeOxB575aFigiIiL1qqYDvXcDlxtjvq/7kmpHA71FmrDcdHhtNASGwsTZ9jKKpsDjgR3LYIM3aNq+1H49vHXV8rlOJ9d+VytpcApKKxj99E8kRIfwyc0jtOOQr6z5BKZPgC6nwiXv2TPYRERERKRWaj3QG/gfcCnQ4MMlEWmiygph2qX2HKOrP286wRLYs1HaDrJvo++Hgt2w8XvY+K09hHjZ2+AXAEkneIeCj4V4LZ9qCv7740b25Jfyv6sGK1jylU0/wMcTIfF4uGiqgiURERGRelDTcGkXcLllWbOxA6ac/Y4bY8zLPq1MRGQvjwc+uQl2roLLPoSW3Z2uqG6Ft4QBl9s3d7m9I97Gb+3Opm8fsG+xneyQqetpkDTSXoYijUryngLenJvChYPa0T8x2ulymoa0hfDBFdCiO1z2gd3lKCIiIiJ1rqbL4jxHOMUYY/x9U1LtaFmcSBM0+3H4+Qk4/Z8w4nanq3FW9hbv8rlvIWUOuEshIMxeNtftdLuzKTLB4SKlJq6dsoiFKVn8eM9JtIw4YBNWOVo7V8OUM+3dx6791g5pRURERMRnar0szhjj59uSRERqaM0ndrDU/3IY3oy2ET+UmCQ4/gb7VlYEKb9UdTX9/iVgQbdxMOxm6Hiihhg3UD+u38WP63fz4Jk9FCz5QlYyvHMBBITClTMVLImIiIjUs5ouixMRqX/bl8MnN0O7oXD2swpK9hcYCseNs2/GwO51sGYGLJ4MU7+Glr3skKnPhRCgAKOhKKvw8I8v1tGpRRhXj0hyupzGL28HTD0f3GUw4RuI6eB0RSIiIiLNTo3CJcuyzjzSOcaYr2pfjoiIV/5OeP8ye4nLJe9qptCRWBa06mnfRt0Dqz+C+S/DZ7fBrIdh8LUw5Pqj2xpc6sTkX1NIyShkyoQhBLrUGFwrRVl2x1Jhhj3ov6nPYxMRERFpoGraufQFYID92waqD2xqEDOXRKQJKC+B9y+H4mzNTjkWAcEw4Ap7KeGWOXbI9MvTMPc56H2B3c2UMMDpKpul3XklvPDDRk7t0ZKTj9P3da2UFsB7F0HmJrh8OrQb5HRFIiIiIs1WTcOljgd5LQYYC0wArvFVQSLSzBkDn98B2xbDRW9Dm75OV9R4WZY9d6njiZC5GRa+BsvegZUfQPvhdsh03FngrxXS9eWJb9ZT7jY8dFZPp0tp3CpK7V3hti2Bi6baA+1FRERExDE1Hei99SAvbwWWW5blBh4AzvVlYSLSTP36nB1+jH4IeuqvFZ+J6wxnPAmjH7ADpgWvwIdXQVR7ezj4gCshJNrpKpu0panZzFi6jZtP7kxSfJjT5TReHjfMmAjJs+G8SdDjHKcrEhEREWn2fDHsYRkwxgfXEZHm7vevYdbfodcFcOI9TlfTNAVHwfBb4Y7lcPG7EN0evnsInukJX94DGZucrrBJ8ngMj3y2hlaRQdw2uovT5TRexsAXd8HaT2HsY/byTxERERFxXK3WQliWFYi9JG6HT6oRkeZr11r4+Hpo08/uRtDOcHXLzx96nG3fdqyA+a/A0rdg0f+g61h7yVynk/X74CMfLUlnZXouz13cn7AgLUM8ZrMehqVT7aH1w291uhoRERER8arpbnGL2Hd4N0AgkAREYM9dEhE5NoUZMO1iCAyHS6dBYKjTFTUvbfrB+Jfh1Edg8Zuw+A14+3xo0cMOmfpeBAEhTlfZaOWVlPPvb9czqEMM5/VPcLqcxmvus/Dr8zD4OhjzkNPViIiIiEg1Nf3x6RoODJdKgOnATGPMGp9WJSLNR0WZPfsnfxdM+Boi9eHbMRGtYPT9MPJPsPpje5e5z++AWY/A4Akw5Hr9/hyDZ7/fQGZhGZOvGYqlTrBjs2SK/X3Y+49w5tPqqBMRERFpYGo60PuaOq5DRJojY+CrP8PWX+GC17WVeEMREAwDLof+l9m/N/NfhjnP2F0jvcbb3Uxt9XtVEx8vSWfyr1u4clgH+rSLcrqcxmn1DPj8Luh6Oox/Bfx8MS5SRERERHzpqAY/eGcs9QFigSxglTGmrC4KE5FmYMGr9vyUkXdD3wudrkb2Z1mQNNK+ZaXAwtdg6duwajokHm+HTN3PAX/NEDqYuRszuPfjlYzoHMdfz+7pdDmN06ZZMOMGaD8MLnwL/AOcrkhEREREDqLGP/6zLOsvwC5gIfAtsAjYZVnW/9VRbSLSlG36Ab69H447C8b81elq5EhiO8K4x+HutTDuCSjYBdOvgef72R1NxdlOV9igrNuRx03vLKFzi3BeuXIQgS512xy11AXwwZXQojtc+r5msYmIiIg0YDX6165lWXcBjwPvAaOBHsDJ3uePW5Z1R10VKCJNUMZGmD7BHhh9wata5tKYBEfaHUu3L4VLptmh0/d/g2d6whd3w54NTlfouB25xUyYvIiwIH8mTxhCZLC6bY7aztXw3oUQ0RqunAEh0U5XJCIiIiKHYRmz/5zug5xkWRuBD40xDx7k2L+Ai40xXeqgvqM2ePBgs3jxYqfLEJFDKc6G10+17yfOhpgOTlcktbVzFcx/BVZ9CO4y6HKaPfw7aSQEhTtdXb3KKynnolfmkZ5dzIc3DqdnQqTTJTU+Wcnw5jiw/OHab/R3hIiIiEgDYVnWEmPM4IMdq+mgjERg9iGO/QT8+RjqEpHmxl1hdyxlb4WrP9OHxqaidR84fxKc+jAsngyLXodpF9vhQJu+0H4EdBgO7YdDWLzT1daZsgoPt7yzlE27C5g8YYiCpWORtwOmnm+HlBMULImIiIg0FjUNl1KB04FZBzl2mve4iMjhffcgJM+Gc/8LHUY4XY34WnhLOPleGHkXbJkLqfNg6zxY/AbMn2SfE9/NDpnaD7cDp+gOTWJbeWMM981YydxNGTz1x76M6trC6ZIan6IseHs8FGXCVZ9By+5OVyQiIiIiNVTTcOkF4AXLsmKBj7AHe7cELgSuAe6sk+pEpOlYMgUWvALDboGBVzldjdQlVxB0OcW+AVSUwvblkPqbHTatnQlL37KPRSRUdTV1GGHP4WqEM7ie/X4DM5Zu40+nduPCwYlOl9P4lBbAexdB1ma4/CNoN8jpikRERETkKNRo5hKAZVkTgYeBBMAAFrAdeMQY83qdVXiUNHNJpAHaMhemngcdT4LLPtTW9c2dxwO713o7m36z7/N32MeCo+1t5/d2NyUMAFegs/UewfsLU7lvxiouGtyOJ//QF6sJdGLVq4pSeO9iSPkZLnobepztdEUiIiL8nrmYAAAgAElEQVQichCHm7lU43DJeyELaAe0AXYA6eYoLmBZ1pvA2cBuY0xv72uxwAdAErAFuMgYk+19r+eBM4Ei4BpjzNIjvYfCJZEGJnsLvDYaQuPg+lna9UkOZIz9fZI6v6q7KXOjfcwVDG0HV3U3JQ6FoAhHy63up993c91bizmhSzxvXD2YAP/G13XlKI8bpl8D6z6D816CAZc7XZGIiIiIHILPwiUfFHIiUABMrRYu/RvIMsY8YVnWfUCMMeZey7LOBG7HDpeOB543xhx/pPdQuCTSgJTkwRun210pE3+EuM5OVySNRcEeu6Npb3fTzpVgPGD5Qeu+9hK69sPsYeHhzsw3Wr0tl4tfnUeHuDA+vGk44UHqyDsqxsBnt8Oyt2HsYzD8VqcrEhEREZHD8MVucViWlQCcA7QFgvc7bIwx9x7pGsaYXyzLStrv5fOAk72P38Lefe5e7+tTvZ1R8y3LirYsq40xZkdNaxYRB3ncMGMiZGyAKz5WsCRHJ7wF9DzXvgGU5kPaQm/gNB8WvwnzX7KPxXWpmtnUfjjEJNX5kPD07CImTFlEdGggkycMUbB0LGY9bAdLJ/6fgiURERGRRq5G/xq2LOsS7ODHAvYAZfudYrADoWPRqlpgtBNo5X3cFkirdl669zWFSyKNwQ+PwoZv4IynoPNop6uRxi4oYr8h4WWwY3nVzKZ1n9lBBUBEm6qZTe0GQWxnny7HzC0q55rJiygpd/Pu9cfTKnL/n7fIEc19Fn59HoZcD6MfdLoaEREREamlmv6o9V/Ax8BNxpi8uirGGGMsyzrqdXqWZd0A3ADQvn17n9clIkdpxQfw63MwaAIMneh0NdIUuQLt+UuJQ4G77CHhe9ZVhU1b58GaGVXnh8bZIVNsJ7uLLrZT1ePgqBq/bWmFmxveXszWzELeunYo3Vo1nPlPjYIxsGQyzHoE+lxoh88agC4iIiLS6NU0XIoD3qijYGnX3uVulmW1AXZ7X98GVN/PuZ33tQMYY14DXgN75lId1CgiNZW2yJ6jkjQKztQHR6knfn7Qqpd9GzrRDjFytsLO1fb29lnJkLkZtsyBle/v+7Wh8dUCp84Q16nqcXBk5Wkej+H/pq9kQUoWz1/SnxGd4+v5P7IRMQbyttuB357fYc/6qvuSXOh6Opz/sv37JiIiIiKNXk3DpRnYc5F+qIMaPgOuBp7w3n9a7fXbLMt6H3ugd67mLYk0cLnp8P5lENkGLpoK/gFOVyTNlWXZs5dikg48VlZk706XtdkOnLKS7Vvyz7Bi2r7nhrWo7HiakxmJOzmQf58wlPN6RB543ebI44G8dDs42r1fkFSWX3VeaDy06A69/2gHgP0v098PIiIiIk1IjXaLsywrFHgDKAR+BHL2P8cY81UNrjMNO6SKB3YBDwMzgQ+B9sBW4CJjTJZlWRbwIjAOKAImGGOOuA2cdosTcUhZIbw5DrJS4PrvoWUPpysSOXplRZCd4g2d9nY8JVO0cwOhpbv3PTes5YFL7PY+Dmpiy+U8HrsTbJ8upHWwZwOUF1adF9YSWna3g6QWx3nvu0OYurxEREREGrvD7RZX03CpP/bMpY6HOMUYY/yPvUTfUbgk4gBjYPo1sPZTuOwD6DbW6YpEfGbW2l3c8PZixnaN4L/jonHlpFQts9vb9ZS/X2NteKt9l9lFtLEDp8pbpPcWAQEhDWf5qMdtd3XtWb/vUrY9G6CiuOq8iDb7hkd7w6TQWMdKFxEREZG6dbhwqabL4iYDecBZwCYO3C1OfOznDXsIDfRnSJL+oS6NwM//hrUz4bRHFSxJk7IiLYfbpy2jd9so/nPFMFyBLkjoe+CJZYX7BU6bITMZNs2C5TsP/yaWvx0yBVcLnCoDqGqPgyMPHVAdbUjlrrA7tPaGSLu9QVLGBnCXVp0X2dYOjgaPrBYmHefT3fdEREREpPGrabjUDbjAGPNtXRYjtnK3h79/voaCkgq+unMU8eFBTpckcmhrZsJPj0G/S2HEHU5XI+IzqZlFXPfWIuIjAnnj6iGEBh7mf5mBYdC6j33bX2kBFGVAab59K8nzPs7z3vL3O5YHBbvtoGrv8YqSIxfs5zp08BQUYdeYt90OkzI3gbvaz4mi2tuhUaeT7ACpZQ+I77bPQHMRERERkUOpabi0EHsmktSDAH8/Xrx0IONf+pU/fbCcKROG4u/XQJZMiFS3YwV8chO0GwpnP9dwlvaI1FJ2YRnXTF5IhccwZcJQWkTUIuQPCrdvtVFRBmUF9k5rlWFU3r73JfsFVaV5ULATMjdWvRbeyg6Pup5W1YUU363pzYgSERERkXpV03DpbmCKZVnFHHqgd5EvC2vueiZE8si5vbh/xiomzd7EHad0dbokkX3l74Jpl0JoHFz8DgQEO12RiE+UlLu5fupi0nOKeff64+ncopbBkC+4AsEVq5lGIiIiItIg1TRcWuK9f+sw5zSIgd5NySVDElmYksWzszYwuEMMI7potx1pIIyBGddDcTZc+w1EtHK6IhGf8HgMd3+4nKWp2bx46UDNvRMRERERqYGahkvXAkfeVk58yrIs/nl+b1am53DH+8v56s6RtIxQd4g0AOs+g5Rf4MynoU0/p6sR8Zl/fbWOr1bt5KGzenBW3zZOlyMiIiIi0ijUKFwyxkw53HHLsgJ8Uo0cICzIxUuXD+K8SXO5Y9oy3r1+mOYvibPKi+G7h6BlTxg0welqRHzmzbkpvDE3hWtGJHHdyI5OlyMiIiIi0mj4HesXWrZTLMt6Hdjlw5pkP8e1juCf5/dhfnIWz83a4HQ50tzNexFyUmHc4+Bf0+ZHkYbtm9U7+MeXaxnbqxV/PbsnlobTi4iIiIjU2FF/MrQsaxhwKXAh0ArIAqb5uC7Zzx8HtWNBciYvzt7EkKRYTuzWwumSpDnK2w5znoHuZ0Onk52uRsQnlmzN4s73l9M/MZrnLxmg7lARERERkaNUo84ly7L6WJb1mGVZycCvwA3YwdLdQBtjzK11WKN4PXpeb7q1jOCuD5azM7fE6XKkOZr1d/BUwOn/dLoSEZ9I3lPA9W8tpk1UMK9fNZjgAO1NISIiIiJytA4ZLlmW1cmyrActy1oNLAf+DKwBrgK6AhawzBhTUS+VCiGB/ky6fCAl5W5un7aUCrfH6ZKkOUlbBCvfh+G3Qazm0Ujjl1FQyjWTF2FZFlMmDCUuPMjpkkREREREGqXDdS5tAh4F8oAbgdbGmHOMMe8C+fVRnByoS8twHr+gD4u2ZPP0d5q/JPXE44Fv7oXw1jDqbqerEam14jI31721mN35Jbxx9WCS4sOcLklEREREpNE6XLi0Fbs7qTdwMjDCsixN720AzuvflkuHtueVnzfz43rNUpd6sPID2LYETn0EgiKcrkakVtwewx3vL2Nleg7PXzKAAe1jnC5JRERERKRRO2S4ZIzpCIwApgCnAJ8DuyzL+p/3uamPAuXgHj6nJz3bRHL3hyvYllPsdDnSlJXmw6xHoO0g6Hux09WI1Ioxhr9/vobv1+7ikXN6MbZXa6dLEhERERFp9A470NsYM98YcwfQFjgdmAn8AfjIe8pEy7IG122JcjDBAf68dPlAKtyG295bSlmF5i9JHZnzDBTshHFPgl+N9gAQabD+NyeZqfO2csOJnbh6RJLT5YiIiIiINAk1+qRojPEYY2YZY67D3iVuPPCh936BZVnr6rBGOYSk+DCe/ENflqXm8O9v1jtdjjRFWSkw70W7YylxiNPViNTK5yu289hX6zm7bxvuG9fd6XJERERERJqMo25DMMaUG2M+NcZcCrQErgQ2+rwyqZGz+rbh6uEdeH1uCt+t2el0OdLUfPcQ+LnsWUsijdiC5Ez+/OEKhibF8vSF/fDzs5wuSURERESkyajVGhdjTJEx5j1jzLm+KkiO3gNn9aBvuyj+PH0FaVlFTpcjTUXyT7D+C3t3uMgEp6uRRia7sIziMjfGOD+eb9PufCZOXUxibAivXTWI4AB/p0sSEREREWlStPtbExDk8mfSZQM584U53PreUqbfNJwglz48SS24K+Cb+yG6PQy/zelqpBEpKXdzz/QVfLFyBwAB/hYRwQFEBrvs+xAXEUHe++AAIoOrP646J9J7LDzYhX8tuox255dw9ZuLCHT5M2XCUKJDA331nyoiIiIiIl4Kl5qIxNhQnr6wHze+vYTHv1rPI+f2crokacyWTIbda+GiqRAQ4nQ10khkFJQycepilqflMHFUR2LCAskvqSCvuNy+L7Hvd+cVVD4vKnMf8brhQS4igu3AKSLYRWRIwBGfRwa7CHL5c/O7S8guKuODG4aTGBtaD78KIiIiIiLNj8KlJmRsr9ZcN7Ijb8xNYUhSLGf1beN0SdIYFWXB7McgaRT00IpXqZmNu/KZMGURGQWlvHz5IMb1bl2jr6twe8gvqagMm/JKyskrriC/pJy8Eu995XNvOJVfwuY9VaFVhefQS+/8LHjj6iH0aRflq/9UERERERHZj8KlJubecd1ZmprNvR+vpFdCJEnxYU6XJI3Nz09CSQ6MexwsDT2WI/t1UwY3vbOE4AB/PrhhOP0So2v8tS5/P2LCAokJO7blasYYisvdlR1SeXtDKm/w1K1VBEM7xh7TtUVEREREpGYULjUxgS4/XrxsIGe9MIdb3l3KjFtGaHit1Nzu9bDwfzDoGmjdx+lqpBH4YFEqD36yms4twnlzwhDaRtfvMkrLsggNdBEa6KJVZHC9vreIiIiIiNhqtVucNExto0N45qJ+rN2Rx6NfrHW6HGksjIFv7oOgcBj9oNPVSAPn8Rie/GY99368ihFd4vno5uH1HiyJiIiIiEjDoHCpiRrTvRU3ndSZ9xak8unybU6XI43Bhm8geTacfD+ExTtdjTRgJeVubp+2jJd/2sxlx7fnzasHExEc4HRZIiIiIiLiEC2La8LuOb0bS7Zmcf+MVfRKiKJLy3CnS5KGqqIUvn0A4rvBkOudrkYasD359o5wK9JzePDMHlw/qiOWZnOJiIiIiDRr6lxqwlz+fvz30oEEB/hz67tLKa7Blt/STC14BbKS7SHe/upAkYPbuCuf8S/9yvqdebxyxSAmnthJwZKIiIiIiChcaupaRwXz3MX92bA7n4c/W+10OdIQ5e+Cn5+CbuOgy6lOVyMN1NyNGVzw8m+UVnj48MbhjO3V2umSRERERESkgVC41Ayc2K0Ft43uwoeL0/loSbrT5UhD8+OjUFECp//L6UqkgXp/YSrXTF5IQlQIM289gb7top0uSUREREREGhCFS83EXad2Y1inWB6auYoNu/KdLkcaim1LYdm7cPyNEN/F6WqkgfF4DE98vZ77ZmhHOBEREREROTSFS82Ev5/FC5cMIDwogFveXUphaYXTJYnTjIFv7oPQODjpL05XIw1MSbmb26Yt5ZWfN3O5doQTEREREZHDULjUjLSMDOaFS/qTvKeAv85cjTHG6ZLESas/hrQFcMrfIDjK6WqkAdmTX8olr83n69U7eeisHvzz/N64/PW/CxEREREROTh9WmhmRnSJ585TujFj2TY+WJTmdDnilLIi+P5v0LovDLjC6WqkAdl/R7jrR2lHOBEREREROTyFS83QbWO6MKprPA9/toZ1O/KcLkec8OvzkLcNzngS/PydrkYaiLkbM7jgJe0IJyIiIiIiR0fhUjPk72fx7MX9iQ615y/ll5Q7XZLUp5w0+PU56HUBdBjhdDXSQOzdEa5tjHaEExERERGRo6NwqZmKDw/ihUsGsDWzkPtnrNL8pebk+7/Z96c96mwd0iB4PIbHv17HfTNWcUKXeKbfpB3hRERERETk6ChcasaO7xTHPWOP44uVO3hnQarT5Uh92PobrJkBJ9wF0YlOVyMOKyl3c+t7S3n152QuP749b2hHOBEREREROQYupwsQZ910YmcWpWTxj8/XMiAxmt5ttWtYk+Vxw9f3QmRbOOFOp6sRh+3JL+X6qYtZmZ7DQ2f14LqRHTW4W0REREREjok6l5o5Pz+LZy7qT3x4ILe8u5Q8zV9qupa9AztX2svhAkOdrkYctGFXPudP+pUNO/O1I5yIiIiIiNSawiUhJiyQ/142kO05xfxl+krNXzoYjwfmvQSvngQbZzldzdEryYUfHoXEYdD7D05XIw6as3EPf3jpN8rc2hFORERERER8Q+GSADCoQwz3juvON2t2MuW3LU6X07Bkb4G3zoFv77cfv/sH+OJuKCt0urKa+/nfUJQJZzwB6lBptqYtTOWayYsqd4Tr007LYEVEREREpPYULkml60d15LSerXjsq3UsT8txuhznGQNLpsDLJ9jLyc57Cf68HobfBovfhFdGQdoip6s8soyNsOAVGHAFJAxwuhpxwN4d4e6fsYqR2hFORERERER8TOGSVLIsi6f/2I9WkcHc+u5ScorKnC7JOXk74L2L4PM7oe1AuPk3GHA5BITA2H/B1Z+DuwzePB1+/CdUNOBfq28fBFcInPI3pysRBxSXVe0Id8Uw7QgnIiIiIiK+p3BJ9hEVGsCkywayO7+Ee5rr/KVVH8FLwyBlDpzxFFz5KUQn7ntOx1Fw86/Q71L45Sl441TYvd6Zeg9n4yzY+C2c9BcIb+l0NVLPdueXcMn/5vPNmp08dFYP/nFeb1z++mtfRERERER8S58y5AD9EqN58MwezFq3i9fnpDhdTv0pzITp18DH10F8V7hpLhx/A/gd4o9JcBSc/xJc/A7kpsOrJ8K8Sfbw74bAXW7PiYrtBMff5HQ1Us827Mpn/KTf2LAzn1e1I5yIiIiIiNQhl9MFSMN09YgkFm7J4olv1tO5ZRhjurdyuqS69fvX8NkdUJxtLx8bcSf41/CPR49zIPF4++u/fcC+1vkvQXT7uq35SBa9Dhkb4NL3wRXobC1Sr+Zs3MMt7ywlONCfD28crsHdIiIiIiJSp9S5JAdlWRZP/KEvXVuGc+2Uxdw/YyX5JeVOl+V7JXkw81aYdgmEt4IbfoJRf655sLRXeEu4dBqc+yJsX2YPAV/+nj0U3AmFGTD7ceg8BrqNc6YGcYR2hBMRERERkfqmcEkOKTI4gJk3D+PGEzvywaI0xj77Cz9v2ON0Wb6T/DO8PAJWvAej7oGJP0Lr3sd+PcuCgVfas5ha9YaZN8MHV9hBT32b/S8oK4Cxj9t1SbPw+pxk7QgnIiIiIiL1TuGSHFpFGcH/7cP9KRP46eTNxASUcfWbC7nv45XkNeYuprIi+OovMPVccAXBdd/DKX/13dKxmCS45gs47R+w8Tt7OPjvX/vm2jWxcxUsmQJDJ0LL7vX3vuKoDxel8c8v13Fmn9baEU5EREREROqVwiU5tB0roGAnlBbQft5f+aJ8ItM7zGTRkgWNt4spbRG8OgoWvmoPub5xDrQb7Pv38fOHE+6wl9mFt7aX3X16G5Tm+/69qjMGvrkfgqPh5Pvq9r2kwfhq1Q7um7GSUV3jefbi/toRTkRERERE6pU+gcihpc6z7yf+CNf/gHXcmQzZ8wk/BN7Dfyv+wdQpL3Pf9GWNo4upogx+eBTePB0qSuHqz+GMJyEwtG7ft1UvmPgDjLwblr9rL8Pb+lvdvd+6z2DLHBjzIITE1N37SIPxy4Y93Pn+Mga0j+HVKwcR5PJ3uiQREREREWlmLOPUwOE6MnjwYLN48WKny2ga3r8cdq2GO1dUvVawG5a+hVn0Blb+DlJNCz51nUH/825nVN9uztV6ODtXwyc32v8tA66w5xAFR9Z/Hanz7Tqyt8KI22HMQ/ayPF8pL4ZJQyEwAm785eiHkkujs2RrFle8vpCk+DDev2EYUSFaCiciIiIiInXDsqwlxpiDLv1R55IcnDF2GNJ++L6vh7eEE/8P665VcOFbxLbpzO3uqQz5eAQLnruM/C1LnKn3YNwVMOc/8NrJdih26ftw3iRngiWA9sPgpl9h0DXw2wt2XTtX+e76816EnFQY97iCpWZg3Y48JkxeROuoYKZeO1TBkoiIiIiIOEbhkhxcVjIUZUDi8Qc/7h8Avc4n/KZvKZ04l/WtzqZP9iwipowh58UxsOojeymaUzI2weRx9lK4HmfDrQvguDOcq2evoHA45zm4bDoUZcJro2HOM+Bx1+66edvt6/Q4Bzqd5JtapcFKySjkyjcWEhbk4u3rhtIiwocdcCIiIiIiIkdJ4ZIcXOp8+779sCOeGtS2D/1vmULylYt4Jfg6cnanw8fX4Xm2F8x+HPJ21HGx1Xg8sOBVeGUkZGyEP7wBF06B0Nj6q6Emup0ON8+D7mfCD3+HyWfagd6xmvWIHVCd9g+flSgN047cYq54fQEeY3j7uuNpF1PHc8NERERERESOQOGSHFzafAiOgvjjavwlvbt0YMI9TzF9xEyuLb+XeUXt4Ocn4Lne8NG1dmBVlzO+clLh7fPg679A0ki4ZT70+WPdvV9thcXBhW/B+Ndg9zp4eSQsnnz0v0Zpi2DlBzDiNojtWDe1SoOQVVjGlW8sJLe4nLcmDKVLy3CnSxIREREREdFAbzmEF4dCTAe4fPoxffnK9Bzumb6C0t2beLTNfE4s/AarNA9a94GhN0DvP/pupzZj7J3Yvr4PMDD2MRh4FViWb65fH3LTYeYtkPIzdB0L5/4XIlod+es8HnjjVMjdBrcvsZfdSZOUX1LOZf9bwIZd+Uy9dijHd4pzuiQREREREWlGNNBbjk5RFmT8fuh5SzXQt100n98+knNOHsm1O8dzsudl1g9+1A5DPrsdnukB3z0EWSm1qzV/F0y7FD69Fdr0g5t/hUFXN65gCSCqHVw5E8Y9aQdMLw2DNTOP/HUrP4BtS+C0vytYasJKyt1c/9Zi1u3I4+UrBipYEhERERGRBkXhkhwobaF9X4N5S4cT5PLnnrHHMfOWEwgOjWTc3C7cHTeJgks/g04nw7yX4IUB8N4lsOkHO3g6Gms+sUOY5Nkw9nG4+nOISapVzY7y84NhN8GNc+yuselXw4wboDjn4OeX5tuzltoOhj4X1WupUn/K3R5ufXcpC7dk8Z+L+jGmew062kREREREROqRwiU5UNp88HNBwkCfXK5Puyg+u/0Ebh/ThU9X7GDMR+XM6v1vuGsVnHgPbFsM71wAk4bA/FegJPfwFyzKgo+ug+nX2GHSjXNg+C12ONMUtOgG130PJ99v77r38ghI/unA8+Y8AwU74Ywnm85/u+zD4zHcM30FP6zfzT/O6815/ds6XZKIiIiIiMgBNHNJDvTmGeAuhYk/+vzSq7flcs/0Fazfmc8FA9ryt3N6Eh1oYO2nsPA1SF8EAWHQ7xIYOhFa9tj3Ahu+s5fVFWXASffByD+Bv8vndTYY25bAjBshcyMcfxOc+ggEhNjLCScNhV4XwAWvOl2l1AFjDH/7dA1vz9/K/409jltHd3G6JBERERERacYON3NJ4ZLsq6IMnkiEwdfBuMfq5C3KKjy8OHsTL83eRExYII+P78OpPb1LfbYthYX/g9Uf2wFXxxPtAeBJI+H7h2HpW9CyJ4x/xZ6x1ByUFdnL3xa+CvHdYPyrMOc/sHk23L4YIhOcrlDqwFPfrmfS7M3ceGIn7jujO1ZjmyMmIiIiIiJNisIlqbm0RfbuYxdNhZ7n1elbVe9iGj+gLQ+f05Po0ED7YGEGLJ0Ki96AvHSw/AEDI+6A0Q+AK6hOa2uQNv8IM2+Fgl1g3DDmr/ayQmlyXvtlM499tZ5Lhyby2Pg+CpZERERE5P/bu/Mwqcoz7+Pfu2mQVXYQlAYRBFdcMIK7UaNOjEsWlyRq3I0xEzNJNJmZzOQ1e2ISsxu3uMUYNYtbQoyKu40CiuKCC2K3AgI2ShQRGp73jyoiMpwSW7rP6a7v57r6Ol3nVJ26u8/Pouv2eZ6ScmdzSevvvp/BP74OX3oaerX+wsFrj2L6zhHbccDWazzvymZ4ehI8+w8Y90moa/kn2HUIby6GSV+DV54rLWDeuWveFWkDu+bBBr76p8f48PZD+NnRO9KpxsaSJEmSpPzZXNL6u+ZT8PJM+MKMNn3amS+9xleuf5Qn5y3h8B2G8r8f2Ya+Pbq0aQ1S3m55dB5n/n46e40eyEXHjadLrQu1S5IkSSqGSs0l37nobSlBQz3UTWzzp952097c8LndOWv/0dz86DwO+Mnd/P3x+W1eh5SXO2ct4Kw/PMzOdX254NM721iSJEmS1G747kVva5pd+hS2YflMPetSW8NZ+2/JDWfuzsBeG3HaldP4998/zOI3ludSj9RWps5p4vSrpjF6UC8u+cwudOvSKe+SJEmSJGm92VzS2xrqS9u6CbmWsc3Q3tx45u58cf8t+etj8zjgJ3cxaaajmNQxPT73NU647CGG9O7G5Sd+gN7dOuddkiRJkiS9JzaX9LbGeujaGwaMybsSOneq4Qv7j+bGM/dg8MZdOf2qaRzxq/v462PzaF65Ku/ypA1i9sLXOf7SB+m1US1XnbwrA3tV4acgSpIkSWr3bC7pbQ1TSlPiaooTi62HbsxfPrc75x62DU1vLOeM301nn/Pu5JJ7n+efy1bkXZ7UYnNffZNjL3mQlODKk3dl0z7d8i5JkiRJklqkOF0E5WtpEyyaldt6S5V07lTDcRNHcMeX9uE3x+7M0N7d+ObNT7Dbd+/g27c8wUuvvpl3idJ78srrb/HpS6aw5M0VXH7iB9hiYM+8S5IkSZKkFqvNuwAVROODpW3O6y1V0qkmOHCbTThwm02Y0fgql9z7PJfeN4dL75vDwdtuwsl7jmSHYX3yLlOqaMmyFRz/2wd5afGbXHnSrmy7ae+8S5IkSZKk98Xmkkoa66GmFobulHcl62XcsD787JgdOefgsVx+/xx+P6WBmx+dxy4j+nLSHiM5YOvBdKqJvMuU3uHN5Ss5+bKpPDXvn1x03Hg+sHm/vEuSJEmSpPctUkp517BBjR8/Pk2dOjXvMtqfSw+GlW/BKXfkXX4IGtQAACAASURBVEmLvP5WM9c+1Mil9z3Pi4vfpK5fd07cfQSfGD+MHhvZQ1X+ljev4rQrp3Ln0wv52dE78pFxQ/MuSZIkSZLWW0RMSymNX9cx11wSNC+HudNhWHGnxL2bnhvVcuIem3Pnl/fh15/aiQE9u/CNm55g4ndv57t/e5J5r7kuk/KzclXiS9fNYPKshXz78O1sLEmSJEnqUBzSIZg3A5qXQV3xFvN+r2o71XDwdkM4eLshTHthMZfe+zwX3T2bS+55nkO2H8LJe450jRu1qZQSX79hJjfNmMs5B43lk7vW5V2SJEmSJG1QNpcEDQ+Utu145NK67Dy8LzsP70tj01Iuu38Of3iokb88MpddN+/HyXuOZL+xg6hxXSa1sh/8fRZXT2ng9L234LP7bJF3OZIkSZK0wTktTtA4BfqOgF6D866kVQzr152vH7I193/tg/zXv21FY9NSTrliKvv9+C6urH+Bpcub8y5RHdQFdz3Hr+98jk/uWsc5B43JuxxJkiRJahU2l6pdStBQD3UT866k1W3ctTOn7DWSu8/el58fsyMbd63l63+ZyW7fu4Mf/v0pXl6yLO8S1YFcPaWB7/3tKQ7ZfgjfPGxbIhwlJ0mSJKljclpctWuaDUsXwbD2v97S+qrtVMNHxg3lkO1L6zJddM9sfnXnc1x492w+Mm4oJ+8xkq2Hbpx3mWrHbpoxl//6y2PsM2YgPz5yBzo5/VKSJElSB2Zzqdo11Je2dR1rvaX1ERGMH9GP8SP68cIrb/Db++Zw7dRG/jT9JXYf1Z+T9xjJ3lsOdF0mvSeTZy3gi394hF2G9+PXn9qZLrUOEJUkSZLUsfmup9o11kPX3jCguteDGd6/B984dBse+Op+fPXgsTy34A1OuOwhDvjJXVw9pYFlK1bmXaLagQefb+KzV01jzCa9uPgz4+nWpVPeJUmSJElSq7O5VO0appSmxNUYBYDe3Ttz+t5bcM85+3L+UTvQrUsn/vPPj7Hb9+7gx/94moX/fCvvElVQM196jZMue4ihvbtx+YkfYOOunfMuSZIkSZLahNPiqtnSJlg0C7Y/Mu9KCqdzpxoO33FTDtthKFOeb+Lie57n53c8wwV3PcdxE4Zzxr6j6NejS95lKmdLlq3gvmcWMXnWAv42cz69utZy5cm7MqDnRnmXJkmSJEltxuZSNWt8sLStwvWW1ldEMGFkfyaM7M/sha/zy8nPcel9z3PNQ42csudITtpzc3pu5H9G1SKlxDMLXmfyUwuYPGsBU+cspnlVolfXWvbaciBf+dAYNu3TLe8yJUmSJKlNFeZdcUR8ETgZSMBjwAnAEOAaoD8wDTg2pbQ8tyI7msZ6qKmFoTvlXUm7MHJgT3505DhO33sk5906i5/c9jRXPDCHMz84ik/uWsdGta6v0xEtXd7M/c++wuRZC7hz1kJeevVNAMZu0otT9hrJvmMGsWNdHzp3cmqpJEmSpOoUKaW8ayAiNgXuBbZOKb0ZEdcCfwX+DfhTSumaiLgAmJFS+nWlc40fPz5NnTq19YvuCC49GFa+BafckXcl7dLDDYv5waRZPDD7FTbt040vHrAlR+y4qR873wE8v+iNf41OmjK7ieUrV9G9Syd2HzWAfccMYp8xAxnqCCVJkiRJVSQipqWUxq/rWGFGLlGqpVtErAC6A/OADwKfLB+/HPgGULG5pPXUvBzmTofxJ+VdSbu1Y11frj5lV+59dhE/mDSLL183g9/c9RxfPnAMH9p6MBE2mdqLZStW8uDzTUyetYDJTy1gzitLARg5sAfHThzOvmMGscvmfR2dJkmSJEnrUIjmUkrppYg4D2gA3gRupTQN7tWUUnP5bi8Cm67r8RFxKnAqQF1dXesX3BHMmwHNy6Bu17wradcigj1HD2SPUQP428z5nHfrLE67cho7DOvD2QeNYbctBuRdojK8uHgpd85ayJ2zFnDfs6/w5oqVbFRbw8Qt+nPC7puzz5iBDO/fI+8yJUmSJKnwCtFcioi+wGHA5sCrwHXAQev7+JTShcCFUJoW1xo1djgND5S2w1zMe0OICP5tuyF8aOvB/HH6i5x/2zN88qIp7Dl6AF85cAzbb9Yn7xKr3oqVq5g6ZzF3zipNd3v65dcB2KxvNz4xfjP2HTOICSP7062Lo5MkSZIk6b0oRHMJ2B94PqW0ECAi/gTsDvSJiNry6KXNgJdyrLFjaZwCfUdAr8F5V9Kh1Haq4ahd6jhsh025qv4Ffjn5WQ79xX3823ab8KUPjWGLgT3zLrGqLFiyjDtnLWTyrAXc+8wi/vlWM507BR/YvB9Hjh/GPmMGscXAHk5hlCRJkqT3oSjNpQZgQkR0pzQtbj9gKjAZ+DilT4w7Hrghtwo7kpSgoR5GH5B3JR1W186dOHnPkRy1yzAuuud5LrlnNn9//GU+vtNmfGH/0S4G3UpWrko80riYyU+VGkqPz10CwCYbd+WQcUPYZ8wgdh81gJ4bFeWlT5IkSZLav0K8w0opTYmI64HpQDPwMKVpbrcA10TEt8r7Lsmvyg6kaTYsXQTDXG+ptfXq2pn/OGBLjps4nF9Ofpbf1Tfw50de4rgJwzlj31H069El7xLbvVeXLv/X6KS7nl7Iq0tX0Kkm2LmuL2cfNIZ9xwxi7Ca9HJ0kSZIkSa0kUupYSxSNHz8+TZ06Ne8yiu3h38ENZ8AZ9TBoq7yrqSovLl7K+bc9w5+mv0j3LrWcsudITtpzc0fStNDdTy/kzKuns2RZM/17dGHvMQPZd8wg9ho9kN7dO+ddniRJkiR1GBExLaU0fl3HfEdbjRrroWtvGDAm70qqzmZ9u3PeJ8Zx2l4jOe/WWfzktqe54oE5fG7fUXxqQp0fdb+eUkr89r45fOuWJxg9qBeXnbgdO2zWh5oaRydJkiRJUlurybsA5aBhSmlKXI2XPy+jB/fiN8eO589n7MaYTXpx7s1P8MHz7uK6qY2sXNWxRhNuaG81r+Srf3yMc29+gv22Gswfz9iNner62liSJEmSpJzYXag2S5tg0SzXWyqIHev6cvUpE7jqpF3p37MLX7n+UQ46/24mzZxPR5uyuiEsev0tPn3xFP4wtZEz9x3Fbz69s1MKJUmSJClnviurNo0PlrZ1E/KtQ++wx+gB7D5qdybNnM8Pb53F6VdNY9ywPpxz0Bh222JA3uUVwhNzl3DKFVNZ9Ppb/OyYHTl03NC8S5IkSZIk4cil6tNYDzW1MHSnvCvRWiKCg7cbwq1n7cUPPrY9C5Ys45MXTeHYS6bw6Iuv5l1eribNnMfHfn0/K1clrjt9oo0lSZIkSSoQm0vVpmEKDBkHXbrnXYky1Haq4chdhjH5y/vw3x/eisfnLuHQX9zHGb+bxrMLXs+7vDaVUuKntz3D6VdNZ8wmvbjxzN3ZfrM+eZclSZIkSVqD0+KqSfNymDsdxp+UdyVaD107d+LkPUdy1C7DuPie57n4ntlMmjmfT+w8jC/sP5qhfbrlXWKrenP5Sr583QxueWweH91xU77z0e3o2tlP05MkSZKkorG5VE3mzYDmZVDnYt7tSa+unfniAVty3MTh/HLyc1xV/wJ/eeQlPrvPFpy+9xYdsuEy99U3OeWKqTwxbwlfO3gsp+41kgg/DU6SJEmSishpcdWk4YHSdpiLebdH/XtuxP98ZGvu+PLeHLD1YM6/7Rn2+9FdTJo5r0N9sty0FxZz6C/u44VXlnLJ8eM5be8tbCxJkiRJUoHZXKomjVOg7wjoNTjvSvQ+bNa3O7/45E5cc+oEenWt5fSrpvPpS6bw9Mv/zLu09+26qY0cc2E9PTbqxJ/P2I0PjjWrkiRJklR0NpeqRUrQUA91E/OuRBvIhJH9ufnze3DuYdsw86UlHPzTezj3pid47c0VeZf2nq1clfj2LU/wlesfZfyIvvzljN0ZPbhX3mVJkiRJktaDay5Vi6bZsHQRDHO9pY6ktlMNx00cwSHbD+W8W2fx2/uf58YZL3H2gWP5+M6bUVNT/OlkS5at4PNXP8xdTy/kuInD+fohW9O5k31vSZIkSWovfAdXLRrqS9s611vqiPr16MJ3jtiOm87cg+H9e3D2Hx/liF/dx8MNi/MuraLnF73B4b+8j/ueXcS3j9iWcw/b1saSJEmSJLUzvourFo310LU3DBiTdyVqRdtu2pvrT5/I+UftwLzXlnHEr+7ny9fNYME/l+Vd2v9xzzMLOewX97L4jeVcdfKufGrX4XmXJEmSJElqAafFVYuGKaUpcTX2Ezu6iODwHTdl/60H84s7nuWSe2czaeZ8ztp/NMfvNiL3kUEpJS67fw7fuuVJRg3sycXHj2dYv+651iRJkiRJajk7DdVgaRMsmuV6S1Wm50a1fPXgsfz9rL0YP6Iv37rlSQ46/27ueWZhbjUtb17F1/70GP/vpifYd8wg/njGbjaWJEmSJKmds7lUDRofLG1db6kqjRzYk8tO+ACXHD+e5lWJYy95kFOvmErDK0vbtI5XXn+LT188hWseauRz+27BhcfuTM+NHDwpSZIkSe2d7+yqQWM91NTC0J3yrkQ52m+rwewxegAX3/M8v7jjWfb/yV2cvtdIPrvPKLp16dSqz/3E3CWccsVUFr3+Fj89egcO22HTVn0+SZIkSVLbceRSNWiYAkPGQRenH1W7jWo78bl9R3HHl/fm4G034Wd3PMt+P7qTmx+dS0qpVZ5z0sz5fPyC+2letYprT5toY0mSJEmSOhibSx1d83KYOx2GOSVObxvSuxs/PXpHrj1tIr27d+HMqx/mmIvqeWr+kg32HCklfn77M5x+1TRGD+7FjWfuwbhhfTbY+SVJkiRJxWBzqaObNwOal0Gdi3nr//rA5v24+fN78M3Dt+Wp+f/kwz+7l2/c+DivLV3xvs775vKVnPn7h/nRP57m8B2G8odTJzB4464bqGpJkiRJUpG45lJH1/BAaevIJWXoVBMcO2E4h2w3hB//42mueGAONzzyEl85cCxH7TKMTjXxns4399U3OfXKqTw+dwlfPXgsp+01koj3dg5JkiRJUvvhyKWOrnEK9B0BvQbnXYkKrm+PLnzz8G256fN7MHpQL/7zz49x2C/vZdoLTet9jukNizn0F/cxZ9FSLj5uPKfvvYWNJUmSJEnq4GwudWQpQUM91E3MuxK1I9sM7c0fTpvAT4/egUX/XM7Hfv0A//GHR1iwZFnFx/1x2osc/Zt6unfpxJ/O2I39trKhKUmSJEnVwGlxHVnTbFi6CIa53pLem4jgsB02Zf+tBvPLyc9y8T3P8/fH5/Pv+43mhN03p0vt233plasS35/0FBfePZuJI/vzq0/tRN8eXXKsXpIkSZLUlhy51JE11Je2da63pJbpsVEtZx80llu/uBcTRvbnu397ioPOv5vJsxYAsGTZCk6+/CEuvHs2x04YzhUnfcDGkiRJkiRVGUcudWSN9dC1NwwYk3claudGDOjBJZ/ZhclPLeDcm5/ghN8+xH5jB/FC01LmLHqDbx2+LZ+eMDzvMiVJkiRJObC51JE1TClNiatxgJo2jH3HDmL3UQO49L7n+fntz9C5toYrT9qViVv0z7s0SZIkSVJObC51VEubYNEs2P7IvCtRB9OltobT996CI8cPI6VE/54b5V2SJEmSJClHNpc6qsYHS1vXW1Ir6efaSpIkSZIkXNC742qsh5paGLpT3pVIkiRJkqQOzOZSR9UwBYaMgy7d865EkiRJkiR1YDaXOqLm5TB3OgxzSpwkSZIkSWpdNpc6onkzoHkZ1O2adyWSJEmSJKmDs7nUETXWl7aOXJIkSZIkSa3M5lJH1FAPfUdAr8F5VyJJkiRJkjo4m0sdTUql5lLdxLwrkSRJkiRJVcDmUkfTNBuWLoJhrrckSZIkSZJan82ljqahvN5SnestSZIkSZKk1mdzqaNprIeuvWHAmLwrkSRJkiRJVcDmUkfTMKU0Ja7GSytJkiRJklqfHYiOZGkTLJrlekuSJEmSJKnN2FzqSBofLG1db0mSJEmSJLURm0sdSWM91NTC0J3yrkSSJEmSJFUJm0sdScMUGDIOunTPuxJJkiRJklQlbC51FM3LYe50GOaUOEmSJEmS1HZsLnUU82ZA8zKoczFvSZIkSZLUdmwudRSN9aWtI5ckSZIkSVIbsrnUUTTUQ98R0Gtw3pVIkiRJkqQqYnOpI0ip1Fyqm5h3JZIkSZIkqcrYXOoImmbD0kUwzPWWJEmSJElS27K51BE0lNdbqnO9JUmSJEmS1LZsLnUEjfXQtTcMGJN3JZIkSZIkqcrYXOoIGqaUpsTVeDklSZIkSVLbshvR3i1tgkWzXG9JkiRJkiTlwuZSe9f4YGnrekuSJEmSJCkHNpfau8Z6qKmFoTvlXYkkSZIkSapCNpfau4YpMGQcdOmedyWSJEmSJKkK2Vxqz5qXw9zpMMwpcZIkSZIkKR82l9qzeTOgeRnUuZi3JEmSJEnKh82l9qyxvrR15JIkSZIkScqJzaX2rKEe+o6AXoPzrkSSJEmSJFUpm0vtVUql5lLdxLwrkSRJkiRJVczmUnvVNBuWLoJhrrckSZIkSZLyY3OpvWoor7dU53pLkiRJkiQpPzaX2qvGeujaGwaMybsSSZIkSZJUxWwutVcNU0pT4mq8hJIkSZIkKT92JtqjpU2waJbrLUmSJEmSpNzZXGqPGh8sbV1vSZIkSZIk5czmUnvUWA81tTB0p7wrkSRJkiRJVc7mUnvUMAWGjIMu3fOuRJIkSZIkVTmbS+1N83KYOx2GOSVOkiRJkiTlz+ZSezNvBjQvgzoX85YkSZIkSfmzudTeNNaXto5ckiRJkiRJBWBzqb1pqIe+I6DX4LwrkSRJkiRJsrnUrqRUai7VTcy7EkmSJEmSJMDmUvvSNBuWLoJhrrckSZIkSZKKweZSe9JQXm+pzvWWJEmSJElSMdhcak8a66FrbxgwJu9KJEmSJEmSAJtL7UvDlNKUuBovmyRJkiRJKga7FO3F0iZYNMv1liRJkiRJUqHYXGovGh8sbV1vSZIkSZIkFYjNpfaisR5qamHoTnlXIkmSJEmS9C82l9qLhikwZBx06Z53JZIkSZIkSf9SmOZSRPSJiOsj4qmIeDIiJkZEv4j4R0Q8U972zbvOXDQvh7nTYZhT4iRJkiRJUrEUprkE/BSYlFIaC4wDngS+CtyeUhoN3F6+XX3mzYDmZVDnYt6SJEmSJKlYCtFciojewF7AJQAppeUppVeBw4DLy3e7HDg8nwpz1lhf2jpySZIkSZIkFUwhmkvA5sBC4LcR8XBEXBwRPYDBKaV55fvMBwav68ERcWpETI2IqQsXLmyjkttQQz30HQG91vnjS5IkSZIk5aYozaVaYCfg1ymlHYE3WGsKXEopAWldD04pXZhSGp9SGj9w4MBWL7ZNpVRqLtVNzLsSSZIkSZKk/6MozaUXgRdTSlPKt6+n1Gx6OSKGAJS3C3KqLz9Ns2HpIhjmekuSJEmSJKl4CtFcSinNBxojYkx5137AE8CNwPHlfccDN+RQXr4ayust1bnekiRJkiRJKp7avAtYw+eB30VEF2A2cAKl5te1EXES8AJwZI715aOxHrr2hgFj3v2+kiRJkiRJbawwzaWU0iPA+HUc2q+taymUhimlKXE1hRhkJkmSJEmS9A52LIpsaRMsmuV6S5IkSZIkqbBsLhVZ44OlrestSZIkSZKkgrK5VGSN9VBTC0N3yrsSSZIkSZKkdbK5VGQNU2DIOOjSPe9KJEmSJEmS1snmUlE1L4e502GYU+IkSZIkSVJx2VwqqnkzoHkZ1LmYtyRJkiRJKi6bS0XVWF/aOnJJkiRJkiQVmM2lomqoh74joNfgvCuRJEmSJEnKZHOpqF5rhLqJeVchSZIkSZJUUW3eBSjDaXfDimV5VyFJkiRJklSRI5eKrHPXvCuQJEmSJEmqyOaSJEmSJEmSWszmkiRJkiRJklrM5pIkSZIkSZJazOaSJEmSJEmSWszmkiRJkiRJklrM5pIkSZIkSZJazOaSJEmSJEmSWszmkiRJkiRJklrM5pIkSZIkSZJazOaSJEmSJEmSWszmkiRJkiRJklrM5pIkSZIkSZJazOaSJEmSJEmSWszmkiRJkiRJklrM5pIkSZIkSZJazOaSJEmSJEmSWszmkiRJkiRJklrM5pIkSZIkSZJazOaSJEmSJEmSWszmkiRJkiRJklrM5pIkSZIkSZJazOaSJEmSJEmSWixSSnnXsEFFxELghbzr2EAGAIvyLkKFZDaUxWyoEvOhLGZDWcyGKjEfymI2imdDXJPhKaWB6zrQ4ZpLHUlETE0pjc+7DhWP2VAWs6FKzIeymA1lMRuqxHwoi9konta+Jk6LkyRJkiRJUovZXJIkSZIkSVKL2VwqtgvzLkCFZTaUxWyoEvOhLGZDWcyGKjEfymI2iqdVr4lrLkmSJEmSJKnFHLkkSZIkSZKkFrO5JEmSJEmSpBazuSRJkiRJkqQWs7kkSZIkSZKkFrO5JEmSJEmS3reI6BsRG+ddh9qezaV2ICL+lncNyk9EHLTG970j4pKIeDQiro6IwXnWpnxFRM+IODciHo+I1yJiYUTUR8Rn8q5N+YqIsRHxt4i4JSK2iIjLIuLViHgwIrbKuz7lKyKaIuLiiNgvIiLvetQ+RMRjedeg/ETEiWt8v1lE3F7+d+X+iNgyz9qUv4gYGhFXRMRrwCJgZkQ0RMQ3IqJz3vXpnVrr9TxSSq1xXr1HEbFT1iHg5pTSkLasR8UREdNTSjuVv78YmA9cBHwU2DuldHie9Sk/EXED8GfgNuBIoAdwDfDfwEsppf/MsTzlKCLuBn4I9AS+B5wD/AE4BDgrpbRfjuUpZxExC/g5cAwwArge+H1KqT7PupS/iPho1iHggpTSwLasR8Wx1t+j11L62+Ni4DDgTP9dqW4RcQdwbkrpzvLryJ6U/h79GjAopXRqrgVWoTxez20uFURErATuonSx1zYhpdStjUtSQaz1j/kjKaUd1jj2jtuqLhExI6U0bo3bD6WUdomIGuCJlNLYHMtTjiLi4ZTSjuXvn00pjVrj2L9eU1Sd1vp3pQ44uvzVB7jGxnT1iogVwO+Adb1B+HhKqVcbl6SCeJe/R//1b46q0zr+Jp2WUtq5/P1T/k3a9vJ4Pa/d0CdUiz0JnJZSembtAxHRmEM9Ko5BEfEflBqPG0dEpLe7wk5trW5vRMQeKaV7I+JQoAkgpbTKqS5Vr9Ma3/94rWNd2rIQFdK/Xh9SSg3AD4AfRMRY4KjcqlIRPAqcl1KaufaBiNg/h3pUHJtFxM8ovX4MjIjOKaUV5WNOe9LCiPg0MJnS7Io5AOW/R32/ko82fz23uVQc3yD7P7zPt2EdKp6LgNWd5cuBAZRewDcBHsmtKhXB6cDF5bUOZgInAUTEQOCXeRam3P0yInqmlF5PKf1q9c6IGEVpKoOq2+R17UwpPQX8vzauRcVyFrAk49gRbVmICucra3w/ldK068Xlv0dvzKckFciJwHnAVym9PzmzvL8fpalxantt/nrutDhJkiRJkiS1mCOXCiQiDgQOBzYt73oJuCGlNCm/qlQEZkNZzIbeq4j4n5TSuXnXoWIyHyr/u7IZcHtKac4a+09MKV2aW2HKndlQJWvk47aU0gtr7DcfOYiIWkqzGo4AhpZ3vwTcAFyyxrTWDfecjlwqhog4H9gSuAJ4sbx7M+A44JmU0hfyqk35MhvKYjbUEhHRkFKqy7sOFZP5qG4R8R1gD2A68BHg/JTSz8vH/DCAKhYR3wV2x2xoHXztKJ6I+D3wKqVlVdZ8n3A80C+ltMHXWLS5VBAR8XRKact17A/g6ZTS6BzKUgGYDWUxG8oSEVlz7APollJy5HIVMx/KEhGPATumlJojog9wNTArpfRFPxGsupkNVWI+iifrfcK7HXs/XLm9OJZFxC7r2L8LsKyti1GhmA1lMRvK8iowOqW08VpfvYB5eRen3JkPZalNKTUDpJRepTQCYeOIuA4/abLamQ1VYj6KpykiPhER/+r5RERNRBwFLG6NJ/T/TBXHZ4BfR0Qv3h62Ngx4rXxM1eszmA2t22cwG1q3K4DhwMvrOHZ1G9ei4jEfyvJcROydUroLIKW0EjgpIr4FfCzf0pQzs6FKzEfxHA18H/hVRKxuJvUF7igf2+CcFlcw5Y/z/NfCvCml+XnWo+IwG8piNiRJG0JEdANIKb25jmObppReavuqVARmQ5WYj2KLiP4AKaVXWvN5nBZXMCml+SmlaSmlacDpedej4jAbymI2tD4i4ht516DiMh+C0hvDtd8crs6Gbw6rm9lQJeaj2FJKr6SUXomIC1vzeWwuFduheRegwjIbymI2lMVsqBLzoSxmQ1nMhioxH8UzvjVPbnOp2CLvAlRYZkNZzIaymA1VYj6UxWwoi9lQJeajeBa05sldc6nAIiKSF0jrYDaUxWwoi9lQJeZDWcyGspgNVWI+qo8jlwoiIkZGxKUR8a2I6BkRFwGPRcR1ETEi5/KUI7OhLGZDWcyGKjEfymI2lMVsqBLzUTwRURMRJ0bELRExIyKmR8Q1EbFPaz2nzaXiuAx4CHgdqAeeAg4GJgGX5leWCuAyzIbW7TLMhtbtMsyGsl2G+dC6XYbZ0LpdhtlQtsswH0VzCVAHfBeYDNxc3vffEfH51nhCp8UVREQ8nFLasfx9Q0qpbl3HVH3MhrKYDWUxG6rEfCiL2VAWs6FKzEfxRMSjKaXt17hdn1KaEBEbAY+klLba0M/pyKXiWBURW0bELkD3iBgPEBGjgE75lqacmQ1lMRvKYjZUiflQFrOhLGZDlZiP4lkREVsARMROwHKAlNJbQKuMMKptjZOqRc4GbgJWAYcDX4uIccDGwCl5FqbcmQ1lMRvKYjZUiflQFrOhLGZDlZiP4vkKMDkillNq8B0NEBEDKU2R2+CcFldgETEAWJxSWpl3aon2wwAADQZJREFULSoWs6EsZkNZzIYqMR/KYjaUxWyoEvORv4gIoH9KaVFbPJ8jlwokIsYChwGblne9BNxAaUE0VTGzoSxmQ1nMhioxH8piNpTFbKgS81FIY4DDImLNa3JjSunJ1ngy11wqiIg4B7gGCODB8lcA10TEV/OsTfkyG8piNpTFbKgS86EsZkNZzIYqMR/FU+Ga/L61ronT4goiIp4GtkkprVhrfxfg8ZTS6HwqU97MhrKYDWUxG6rEfCiL2VAWs6FKzEfx5HFNHLlUHKuAoevYP6R8TNXLbCiL2VAWs6FKzIeymA1lMRuqxHwUT5tfE9dcKo6zgNsj4hmgsbyvDhgFnJlbVSoCs6EsZkNZzIYqMR/KYjaUxWyoEvNRPG1+TZwWVyARUQN8gHcugvaQK+zLbCiL2VAWs6FKzIeymA1lMRuqxHwUT1tfE5tLBRYRp6aULsy7DhWP2VAWs6EsZkOVmA9lMRvKYjZUifkonta+Jq65VGyn512ACstsKIvZUBazoUrMh7KYDWUxG6rEfBRPq14Tm0vFFnkXoMIyG8piNpTFbKgS86EsZkNZzIYqMR/F06rXxGlxBRYRm6WUXsy7DhWP2VAWs6EsZkOVmA9lMRvKYjZUifkonta+JjaXCiQiRgIfBYYBK4GngatTSktyLUy5MxvKYjaUxWyoEvOhLGZDWcyGKjEfxdPW18RpcQUREf8OXAB0BXYBNqIUgvqI2CfH0pQzs6EsZkNZzIYqMR/KYjaUxWyoEvNRPHlcE0cuFUREPAbskFJaGRHdgb+mlPaJiDrghpTSjjmXqJyYDWUxG8piNlSJ+VAWs6EsZkOVmI/iyeOaOHKpWGrL242AngAppQagc24VqSjMhrKYDWUxG6rEfCiL2VAWs6FKzEfxtOk1qX33u6iNXAw8FBFTgD2B7wNExECgKc/ClDuzoSxmQ1nMhioxH8piNpTFbKgS81E8bX5NnBZXIBGxDbAVMDOl9FTe9ag4zIaymA1lMRuqxHwoi9lQFrOhSsxH8bT1NbG51A5ERM+U0ut516HiMRvKYjaUxWyoEvOhLGZDWcyGKjEfxdNa18Q1l9qHJ/IuQIVlNpTFbCiL2VAl5kNZzIaymA1VYj6Kp1WuiWsuFURE/EfWIcqLb6k6mQ1lMRvKYjZUiflQFrOhLGZDlZiP4snjmjhyqTi+A/QFeq311ROvU7UzG8piNpTFbKgS86EsZkNZzIYqMR/F0+bXxJFLxTEd+EtKadraByLi5BzqUXGYDWUxG8piNlSJ+VAWs6EsZkOVmI/iafNr4oLeBRERY4CmlNLCdRwbnFJ6OYeyVABmQ1nMhrKYDVViPpTFbCiL2VAl5qN48rgmNpckSZIkSZLUYs5/LIiI6B0R34uIpyKiKSJeiYgny/v65F2f8mM2lMVsKIvZUCXmQ1nMhrKYDVViPoonj2tic6k4rgUWA/uklPqllPoD+5b3XZtrZcqb2VAWs6EsZkOVmA9lMRvKYjZUifkonja/Jk6LK4iImJVSGvNej6njMxvKYjaUxWyoEvOhLGZDWcyGKjEfxZPHNXHkUnG8EBFnR8Tg1TsiYnBEnAM05liX8mc2lMVsKIvZUCXmQ1nMhrKYDVViPoqnza+JzaXiOAroD9wVEYsjogm4E+gHHJlnYcqd2VAWs6EsZkOVmA9lMRvKYjZUifkonja/Jk6LK5CIGAtsBtSnlF5fY/9BKaVJ+VWmvJkNZTEbymI2VIn5UBazoSxmQ5WYj+Jp62viyKWCiIh/B24AzgRmRsRhaxz+Tj5VqQjMhrKYDWUxG6rEfCiL2VAWs6FKzEfx5HFNalvjpGqRU4CdU0qvR8QI4PqIGJFS+ikQuVamvJkNZTEbymI2VIn5UBazoSxmQ5WYj+Jp82tic6k4alYPVUspzYmIfSgFYDj+B1ntzIaymA1lMRuqxHwoi9lQFrOhSsxH8bT5NXFaXHG8HBE7rL5RDsIhwABgu9yqUhGYDWUxG8piNlSJ+VAWs6EsZkOVmI/iafNr4oLeBRERmwHNKaX56zi2e0rpvhzKUgGYDWUxG8piNlSJ+VAWs6EsZkOVmI/iyeOa2FySJEmSJElSizktTpIkSZIkSS1mc0mSJEmSJEktZnNJkiQVSkR8IyJS+WtVRCyOiIci4tsRsUkLz3l2+ZNSWl1EjCjXfsgGPu+d5fPuvyHP21oi4tSIODzvOiRJUuuzuSRJkoroNWAisBtwNPAn4FjgsYjYuQXnOxvYZ4NVV9k8SrXfu6FOGBGbAnuWbx6zoc7byk4FbC5JklQFbC5JkqQiak4p1Ze//p5S+i6wPaXGzTUR0Snn+jKllN4q1/3qBjztUUAAdwAfjYguG/DckiRJ74vNJUmS1C6UmzVnA6OAA1bvj4jvRcRjEfF6RLwYEb9bc/pcRMwB+gP/u8Z0u33Kx2oi4qsR8WxEvBURT0fE8Ws+b0TsERH3RMSS8tcjEfGJrDrXNS0uIuZExHkR8cVyjYsj4pqI6LOeP/4xQD3wfaAPcPBaz/mZ8nP2XGv/nIg4b43bERHfjIgF5Z/l0og4uvzYEeX77FO+ve1a57ozIq5f4/Y2ETEpIpoi4o2IeDIiPrf6vsDOwPFr/M4/s54/qyRJamdsLkmSpPbkTqAZmLDGvkHAd4APA2cBI4E7ImL13zlHUJpmdwml6WoTgenlYz8H/hu4sPz4PwOXrm4MRcTGwM3AbOBjwMeBKyk1eN6rI4H9KE0XOwc4pFx3RRExChgPXAPcDiyg5VPjzgL+E7iA0s/yJvCDFp7rJmAl8GngUEq/y17lY2cATwF/5e3f+S0tfB5JklRwtXkXIEmStL5SSssiYhEweI19J67+vjxd7gHgRWAP4O6U0sMR0Qy8mFKqX+O+o4DPAieklC4v774tIoYA/0upqbQl0Bs4M6X0z/J9bm1h+SuAw1NKzeXn35rSelJnvMvjjgFWAdemlFZGxHXACRHRI6X0xvo+efl3czZwQUrpf8q7b42IzYFh7+UHiYgBwObAYSmlx8q7b199PKX0RES8ASxc83cuSZI6JkcuSZKk9ibecSPi4Ii4PyJeozSq6cXyoS3f5Tz7UWra/Dkiald/UWqS7FBuxjwHvA5cHRGHvYdpbOsyeXVjqewJYFBEdH6Xxx0D3JVSml++fQ3QHTjsPT7/MGAT4Ma19q99e300AY3ABRFxVEQMasE5JElSB2FzSZIktRsR0ZXS+kkvl2/vQqk58iKlT5ObyNtT5rq+y+kGAJ0oTZlbscbXZZRGdw9JKS2mtL5TZ+BaYGFE3BIRI1tQ/toLfC+n1CjbKOsBETEO2Aq4OSL6lJtbj1Na2Py9To1bvQ7VwrX2r337XaWUVgEfAuYDlwLzy+tS7fhezyVJkto/p8VJkqT2ZF9Kf788UL59BKXmyFEppQQQEcPX81xNlEY67U5pBNPaFgCUp3UdFBHdgP2BHwNX8851n1rL6gbSj8pfazowIvqllJqAZeV9a3+KXN81vl898mngWvdZ+3alcy1afSOl9BTwsfLIqz0pLTZ+S0RsVm4+SZKkKuHIJUmS1C6UR+18H3gWuK28uxuwYnVjqexT63j4cv7vSKY7KI1c6p1SmrqOr+Vr3jml9GZK6SZKI3W23gA/UkUREZTWZJpMqam25tcnKY2m+lj57qunAm61xuN3BTZe45SNlBpMa0+nO3St2+s61zBg7LrqTCmtSCndQanpNoS3Fztf1+9ckiR1QI5ckiRJRVQbEatHBvWi9LH2n6W01tBBKaWV5WP/AM6KiPMpfXrZbpQ+vWxtTwEfjohJlNZQmpVSmhURFwDXRMQPgKmUmiHbAFumlE6OiA8DJwJ/ARqATYHTKDWmWttEYDhwTkrpzrUPRsTXKI1sugh4EHgJ+FlEfB3oR2nx7iWr719eDPyHwA8jYiFwH6XG0nblu6wq3+/FiJgKfDMillL6n5H/SWmk1+rn3h44D/gDpU/S60vpE/BmlEdSQel3fmBEHAi8AjyfUnrl/f5SJElS8ThySZIkFVFvSlPf7geuAz4OXAVsl1KatvpOKaW/UmpqfIzS2kt7A4es43xfAd4AbgEeotSsAvgc8E3gOOCvlNZb+jBwd/n4s0ACvkPpU+J+AEyi1HBqbcdQag5lLbh9FbB3RAwpj7I6glKD6HrgS5SacYvXesxPgO9S+oS6P1JqCn2nfGzJGvc7hlIz7ary8XOBWWscn09p3av/Av4G/Ap4kneOgvpWed+1lH7nH1mPn1mSJLVD8c5R5JIkSaomEXExcEBKaX3XqpIkSXoHp8VJkiRViYjYFjiK0oiwVcDBwAmURn9JkiS1iCOXJEmSqkREbE5pQfIdgB7AC8BvgB8l/yiUJEktZHNJkiRJkiRJLeaC3pIkSZIkSWoxm0uSJEmSJElqMZtLkiRJkiRJajGbS5IkSZIkSWoxm0uSJEmSJElqsf8Pdclkm5iGR9AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Cumulative Average Order Size by Group A and B', fontdict={'size':15})\n",
    "plt.ylabel('Amount of Orders', fontdict={'size': 15})\n",
    "plt.xlabel('Dates in August', fontdict={'size': 15})\n",
    "plt.plot(cumulativeRevenueA['date'], cumulativeRevenueA['revenue']/cumulativeRevenueA['orders'], label='A')\n",
    "plt.plot(cumulativeRevenueB['date'], cumulativeRevenueB['revenue']/cumulativeRevenueB['orders'], label='B')\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Well we can see here that Group A started with more orders than Group B but then dropped with a low point after August 5th. Though it rose up and stayed steady with orders after August 13.\n",
    "- Group B started slow but then around the time where the revenue shot up the orders did as well and they started to gradually lower after its high point right after August 17\n",
    "- That must be why the revenue was higher the second half of the month for Group B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Graph the relative difference in cumulative average order size for group B compared with group A. Make conclusions and conjectures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gathering the data into one DataFrame\n",
    "mergedCumulativeRevenue = cumulativeRevenueA.merge(cumulativeRevenueB, left_on='date', right_on='date', how='left', suffixes=['A', 'B'])\n",
    "\n",
    "# plotting a relative difference graph for the average purchase sizes\n",
    "plt.title('Relative Difference in Cumulative Average Order Size for Group B Compared with Group A', fontdict={'size':10})\n",
    "plt.ylabel('Relative Difference', fontdict={'size': 10})\n",
    "plt.xlabel('Dates in August', fontdict={'size': 10})\n",
    "plt.xticks(rotation=90)\n",
    "plt.plot(mergedCumulativeRevenue['date'], (mergedCumulativeRevenue['revenueB']/mergedCumulativeRevenue['ordersB'])/(mergedCumulativeRevenue['revenueA']/mergedCumulativeRevenue['ordersA'])-1)\n",
    "\n",
    "# adding the X axis\n",
    "plt.axhline(y=0, color='black', linestyle='--') \n",
    "plt.figure(figsize=(20,10))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As we can see Group B started out lower but then rose up and theres a major fluctuation between August 5 and then it settles by August 9 where we did see that Group A and B started having similar numbers of orders until after August 17 where Group B has a significant difference because of a large order.\n",
    "- Group B starts coming back after that huge leap though"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate each group's conversion rate as the ratio of orders to the number of visits for each day. Plot the daily conversion rates of the two groups and describe the difference. Draw conclusions and make conjectures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumulativeData['conversion'] = cumulativeData['orders']/cumulativeData['visits']\n",
    "\n",
    "cumulativeDataA = cumulativeData[cumulativeData['group']=='A']\n",
    "cumulativeDataB = cumulativeData[cumulativeData['group']=='B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2019-08-01', '2019-08-31', 0.024, 0.036]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graphs\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title('Conversion Rate for Group A and B', fontdict={'size':15})\n",
    "plt.ylabel('Relative Difference', fontdict={'size': 15})\n",
    "plt.xlabel('Dates in August', fontdict={'size': 15})\n",
    "plt.plot(cumulativeDataA['date'], cumulativeDataA['conversion'], label='A')\n",
    "plt.plot(cumulativeDataB['date'], cumulativeDataB['conversion'], label='B')\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend()\n",
    "\n",
    "# setting the axes' scale\n",
    "plt.axis([\"2019-08-01\", '2019-08-31', 0.024, 0.036]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fc1b8ddb210>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mergedCumulativeConversions = cumulativeDataA[['date','conversion']].merge(cumulativeDataB[['date','conversion']], left_on='date', right_on='date', how='left', suffixes=['A', 'B'])\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title('Conversion Rate for Group A and B', fontdict={'size':15})\n",
    "plt.ylabel('Relative Difference', fontdict={'size': 15})\n",
    "plt.xlabel('Dates in August', fontdict={'size': 15})\n",
    "plt.xticks(rotation=90)\n",
    "plt.plot(mergedCumulativeConversions['date'], mergedCumulativeConversions['conversionB']/mergedCumulativeConversions['conversionA']-1, label=\"Conversation daily rates\")\n",
    "plt.legend()\n",
    "\n",
    "plt.axhline(y=0, color='black', linestyle='--')\n",
    "plt.axhline(y=0.2, color='grey', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We still see Group B starting slow but here we see that the conversion ratio is pretty stable between the groups. So it must be that the outliers are causing the fluctuations in the testing\n",
    "- Group B shows a better conversion rate than Group A.\n",
    "- Group A started out strong but then faltered whereas Group B started at slowly but then gained more than Group A and they both stayed steady in their place and both seem to be gradually lowering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot a scatter chart of the number of orders per user. Make conclusions and conjectures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordersByUsers = orders_fix.drop(['group', 'revenue', 'date'], axis=1).groupby('visitorid', as_index=False).agg({'transactionid' : pd.Series.nunique})\n",
    "\n",
    "ordersByUsers.columns = ['visitorid','orders']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc1b8f88590>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_values = pd.Series(range(0, len(ordersByUsers['orders'])))\n",
    "plt.scatter(x_values, ordersByUsers['orders']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As we had seen before there were a few outliers. Here we can see that most users had 1 order and there were a few that had 2 orders. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate the 95th and 99th percentiles for the number of orders per user. Define the point at which a data point becomes an anomaly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 1. 2.]\n"
     ]
    }
   ],
   "source": [
    "print(np.percentile(ordersByUsers['orders'], [90, 95, 99]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Not more than 5% of users placed more than 1 , and not more than 1% of users made more than two orders.\n",
    "- I will define the abnormal point of being any orders above 2 or the 99th percentile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot a scatter chart of order prices. Make conclusions and conjectures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc1b9057350>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_values = pd.Series(range(0, len(orders_fix['revenue'])))\n",
    "plt.scatter(x_values, orders_fix['revenue']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Well we can see that we have two outliers one above 2500 and one above 20000. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate the 95th and 99th percentiles of order prices. Define the point at which a data point becomes an anomaly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[280.8   414.275 830.3  ]\n"
     ]
    }
   ],
   "source": [
    "print(np.percentile(orders['revenue'], [90, 95, 99]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Not more than 5% of orders was more than 414.275, and not more than 1% was more than 830.3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Well I think here we can say that anything above 830 is abnormal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the statistical significance of the difference in conversion between the groups using the raw data. Make conclusions and conjectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Our H0 is that Group A and Group B are statistically equal in their conversion using the raw data.\n",
    "- Our H1 is that Group A and Group B are statistically different in their conversion using the raw data\n",
    "- I will choose to use the significance level of Alpha at 0.05 which is what we have been using in our projects and which the common significance level to use. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.006\n",
      "0.160\n"
     ]
    }
   ],
   "source": [
    "sampleA = pd.concat([ordersByUsersA['orders'],pd.Series(0, index=np.arange(visits[visits['group']=='A']['visits'].sum() - len(ordersByUsersA['orders'])), name='orders')],axis=0)\n",
    "\n",
    "sampleB = pd.concat([ordersByUsersB['orders'],pd.Series(0, index=np.arange(visits[visits['group']=='B']['visits'].sum() - len(ordersByUsersB['orders'])), name='orders')],axis=0)\n",
    "\n",
    "print(\"{0:.3f}\".format(st.mannwhitneyu(sampleA, sampleB)[1]))\n",
    "\n",
    "print(\"{0:.3f}\".format(sampleB.mean()/sampleA.mean()-1)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The first row of the output gives us the p-value, 0.006, which is less than 0.05 which is our Alpha. So we will need to reject the null hypothesis that there's not a statistically significant difference in conversion between the groups. And the relative gain of Group B is 16%.\n",
    "- Well it seems from here that Group B is doing fairly better than Group A "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the statistical significance of the difference in average order size between the groups using the raw data. Make conclusions and conjectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Our H0 is that Group A and Group B are statistically equal in their average order size using the raw data.\n",
    "- Our H1 is that Group A and Group B are statistically different in their average order size using the raw data\n",
    "- I will choose to use the significance level of Alpha at 0.05 which is what we have been using in our projects and which the common significance level to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.43112\n",
      "0.27834\n"
     ]
    }
   ],
   "source": [
    "print(\"{0:.5f}\".format(st.mannwhitneyu(orders[orders['group']=='A']['revenue'], orders[orders['group']=='B']['revenue'])[1]))\n",
    "print(\"{0:.5f}\".format(orders[orders['group']=='B']['revenue'].mean()/orders[orders['group']=='A']['revenue'].mean()-1)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The p-value is considerably higher than 0.05 or 5%, which is our Alpha, at 43%, so there's no reason to reject the null hypothesis.\n",
    "- Therefore we can conclude that average order size is statistically equal between the groups. \n",
    "- The average order size for Group B is 28% larger than it is for Group A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filtering Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1099    148427295\n",
      "33      249864742\n",
      "58      611059232\n",
      "949     887908475\n",
      "744     888512513\n",
      "Name: visitorid, dtype: int64\n",
      "(19,)\n"
     ]
    }
   ],
   "source": [
    "usersWithManyOrders = pd.concat([ordersByUsersA[ordersByUsersA['orders'] > 2]['visitorid'], ordersByUsersB[ordersByUsersB['orders'] > 2]['visitorid']], axis = 0)\n",
    "usersWithExpensiveOrders = orders_fix[orders_fix['revenue'] > 830]['visitorid']\n",
    "abnormalUsers = pd.concat([usersWithManyOrders, usersWithExpensiveOrders], axis = 0).drop_duplicates().sort_values()\n",
    "print(abnormalUsers.head(5))\n",
    "print(abnormalUsers.shape) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Well we have 19 anomolous users that have contributed to the fluctuations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the statistical significance of the difference in conversion between the groups using the filtered data. Make conclusions and conjectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Our H0 is that Group A and Group B are statistically equal in their conversion using the filtered data.\n",
    "- Our H1 is that Group A and Group B are statistically different in their conversion using the filtered data\n",
    "- I will choose to use the significance level of Alpha at 0.05 which is what we have been using in our projects and which the common significance level to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampleAFiltered = pd.concat([ordersByUsersA[np.logical_not(ordersByUsersA['visitorid'].isin(abnormalUsers))]['orders'],pd.Series(0, index=np.arange(visits[visits['group']=='A']['visits'].sum() - len(ordersByUsersA['orders'])),name='orders')],axis=0)\n",
    "\n",
    "sampleBFiltered = pd.concat([ordersByUsersB[np.logical_not(ordersByUsersB['visitorid'].isin(abnormalUsers))]['orders'],pd.Series(0, index=np.arange(visits[visits['group']=='B']['visits'].sum() - len(ordersByUsersB['orders'])),name='orders')],axis=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.003\n",
      "0.192\n"
     ]
    }
   ],
   "source": [
    "print(\"{0:.3f}\".format(st.mannwhitneyu(sampleAFiltered, sampleBFiltered)[1]))\n",
    "print(\"{0:.3f}\".format(sampleBFiltered.mean()/sampleAFiltered.mean()-1)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Well with the filtered data the p-value changed from 0.006 to 0.003 which means we still can't reject the null hypothesis and we see that there is a positive conversion of Group B of 19% which is more than the earlier conversion rate by 3%\n",
    "- Group B is still doing pretty well in our test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the statistical significance of the difference in average order size between the groups using the filtered data. Make conclusions and conjectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Our H0 is that Group A and Group B are statistically equal in their average order size using the filtered data.\n",
    "- Our H1 is that Group A and Group B are statistically different in their average order size using the filtered data\n",
    "- I will choose to use the significance level of Alpha at 0.05 which is what we have been using in our projects and which the common significance level to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.438\n",
      "-0.014\n"
     ]
    }
   ],
   "source": [
    "print(\"{0:.3f}\".format(st.mannwhitneyu(\n",
    "    orders[np.logical_and(\n",
    "        orders['group']=='A',\n",
    "        np.logical_not(orders['visitorid'].isin(abnormalUsers)))]['revenue'],\n",
    "    orders[np.logical_and(\n",
    "        orders['group']=='B',\n",
    "        np.logical_not(orders['visitorid'].isin(abnormalUsers)))]['revenue'])[1]))\n",
    "\n",
    "print(\"{0:.3f}\".format(\n",
    "    orders[np.logical_and(orders['group']=='B',np.logical_not(orders['visitorid'].isin(abnormalUsers)))]['revenue'].mean()/\n",
    "    orders[np.logical_and(orders['group']=='A',np.logical_not(orders['visitorid'].isin(abnormalUsers)))]['revenue'].mean() - 1)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Where the data had 0.431 beforehand and we couldn't reject the null hypothesis here it to 0.438 which will lead us to the same conclusions as beforehand and we will not reject the null hypothesis.\n",
    "- Here though it shows that Group B has a loss of 1% which probably shows that Group B had a lot of the orders that were problematic in our tests and once we filtered them out the average order size for each Group was much closer than with the raw data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make a decision based on the test results. The possible decisions are: 1. Stop the test, consider one of the groups the leader. 2. Stop the test, conclude that there is no difference between the groups. 3. Continue the test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The raw and filtered data revealed that there is a statistically significant difference in conversion between the groups.\n",
    "\n",
    "- The raw and filtered data revealed that there is not a statistically significant difference in average order size between the groups.\n",
    "\n",
    "- The graph showing the difference in conversion between the groups tells us that Group B's results are much better than Group A's\n",
    "\n",
    "- The graph showing the difference in average order size between the groups tells us that group B's results were dramatically higher after August 17 but were on par for some of the time with Group A's results but towards the end of the month it was gradually lowering towards Group A.\n",
    "\n",
    "- According to our graphs and the conclusions we have made we can say that we can stop the test and say that Group B has done better than Group A and that its conversion rate was better than Group A's though it seems to be dropping towards the end."
   ]
  }
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