Codecademy_IDA/musclehub_project/musclehub.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone Project 1: MuscleHub AB Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Get started with SQL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like most businesses, Janet keeps her data in a SQL database.  Normally, you'd download the data from her database to a csv file, and then load it into a Jupyter Notebook using Pandas.\n",
    "\n",
    "For this project, you'll have to access SQL in a slightly different way.  You'll be using a special Codecademy library that lets you type SQL queries directly into this Jupyter notebook.  You'll have pass each SQL query as an argument to a function called `sql_query`.  Each query will return a Pandas DataFrame.  Here's an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This import only needs to happen once, at the beginning of the notebook\n",
    "from codecademySQL import sql_query\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Here's an example of a query that just displays some data\n",
    "sql_query('''\n",
    "SELECT *\n",
    "FROM visits\n",
    "LIMIT 5\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Here's an example where we save the data to a DataFrame\n",
    "df = sql_query('''\n",
    "SELECT *\n",
    "FROM applications\n",
    "LIMIT 5\n",
    "''')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Get your dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get started!\n",
    "\n",
    "Janet of MuscleHub has a SQLite database, which contains several tables that will be helpful to you in this investigation:\n",
    "- `visits` contains information about potential gym customers who have visited MuscleHub\n",
    "- `fitness_tests` contains information about potential customers in \"Group A\", who were given a fitness test\n",
    "- `applications` contains information about any potential customers (both \"Group A\" and \"Group B\") who filled out an application.  Not everyone in `visits` will have filled out an application.\n",
    "- `purchases` contains information about customers who purchased a membership to MuscleHub.\n",
    "\n",
    "Use the space below to examine each table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Examine visits here\n",
    "sql_query('''\n",
    "select *\n",
    "from visits\n",
    "limit 5;''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email</th>\n",
       "      <th>gender</th>\n",
       "      <th>fitness_test_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Kim</td>\n",
       "      <td>Walter</td>\n",
       "      <td>KimWalter58@gmail.com</td>\n",
       "      <td>female</td>\n",
       "      <td>2017-07-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Tom</td>\n",
       "      <td>Webster</td>\n",
       "      <td>TW3857@gmail.com</td>\n",
       "      <td>male</td>\n",
       "      <td>2017-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Marcus</td>\n",
       "      <td>Bauer</td>\n",
       "      <td>Marcus.Bauer@gmail.com</td>\n",
       "      <td>male</td>\n",
       "      <td>2017-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Roberta</td>\n",
       "      <td>Best</td>\n",
       "      <td>RB6305@hotmail.com</td>\n",
       "      <td>female</td>\n",
       "      <td>2017-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Carrie</td>\n",
       "      <td>Francis</td>\n",
       "      <td>CF1896@hotmail.com</td>\n",
       "      <td>female</td>\n",
       "      <td>2017-07-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index first_name last_name                   email  gender  \\\n",
       "0      0        Kim    Walter   KimWalter58@gmail.com  female   \n",
       "1      1        Tom   Webster        TW3857@gmail.com    male   \n",
       "2      2     Marcus     Bauer  Marcus.Bauer@gmail.com    male   \n",
       "3      3    Roberta      Best      RB6305@hotmail.com  female   \n",
       "4      4     Carrie   Francis      CF1896@hotmail.com  female   \n",
       "\n",
       "  fitness_test_date  \n",
       "0        2017-07-03  \n",
       "1        2017-07-02  \n",
       "2        2017-07-01  \n",
       "3        2017-07-02  \n",
       "4        2017-07-05  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine fitness_tests here\n",
    "sql_query('''\n",
    "select *\n",
    "from fitness_tests\n",
    "limit 5;''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email</th>\n",
       "      <th>gender</th>\n",
       "      <th>application_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Roy</td>\n",
       "      <td>Abbott</td>\n",
       "      <td>RoyAbbott32@gmail.com</td>\n",
       "      <td>male</td>\n",
       "      <td>2017-08-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Agnes</td>\n",
       "      <td>Acevedo</td>\n",
       "      <td>AgnesAcevedo1@gmail.com</td>\n",
       "      <td>female</td>\n",
       "      <td>2017-09-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Roberta</td>\n",
       "      <td>Acevedo</td>\n",
       "      <td>RA8063@gmail.com</td>\n",
       "      <td>female</td>\n",
       "      <td>2017-09-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Darren</td>\n",
       "      <td>Acosta</td>\n",
       "      <td>DAcosta1996@hotmail.com</td>\n",
       "      <td>male</td>\n",
       "      <td>2017-07-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Vernon</td>\n",
       "      <td>Acosta</td>\n",
       "      <td>VAcosta1975@gmail.com</td>\n",
       "      <td>male</td>\n",
       "      <td>2017-07-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index first_name last_name                    email  gender  \\\n",
       "0      0        Roy    Abbott    RoyAbbott32@gmail.com    male   \n",
       "1      1      Agnes   Acevedo  AgnesAcevedo1@gmail.com  female   \n",
       "2      2    Roberta   Acevedo         RA8063@gmail.com  female   \n",
       "3      3     Darren    Acosta  DAcosta1996@hotmail.com    male   \n",
       "4      4     Vernon    Acosta    VAcosta1975@gmail.com    male   \n",
       "\n",
       "  application_date  \n",
       "0       2017-08-12  \n",
       "1       2017-09-29  \n",
       "2       2017-09-15  \n",
       "3       2017-07-26  \n",
       "4       2017-07-14  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine applications here\n",
    "sql_query('''\n",
    "select *\n",
    "from applications\n",
    "limit 5;''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email</th>\n",
       "      <th>gender</th>\n",
       "      <th>purchase_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Roy</td>\n",
       "      <td>Abbott</td>\n",
       "      <td>RoyAbbott32@gmail.com</td>\n",
       "      <td>male</td>\n",
       "      <td>2017-08-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Roberta</td>\n",
       "      <td>Acevedo</td>\n",
       "      <td>RA8063@gmail.com</td>\n",
       "      <td>female</td>\n",
       "      <td>2017-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Vernon</td>\n",
       "      <td>Acosta</td>\n",
       "      <td>VAcosta1975@gmail.com</td>\n",
       "      <td>male</td>\n",
       "      <td>2017-07-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Darren</td>\n",
       "      <td>Acosta</td>\n",
       "      <td>DAcosta1996@hotmail.com</td>\n",
       "      <td>male</td>\n",
       "      <td>2017-07-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Dawn</td>\n",
       "      <td>Adkins</td>\n",
       "      <td>Dawn.Adkins@gmail.com</td>\n",
       "      <td>female</td>\n",
       "      <td>2017-08-24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index first_name last_name                    email  gender purchase_date\n",
       "0      0        Roy    Abbott    RoyAbbott32@gmail.com    male    2017-08-18\n",
       "1      1    Roberta   Acevedo         RA8063@gmail.com  female    2017-09-16\n",
       "2      2     Vernon    Acosta    VAcosta1975@gmail.com    male    2017-07-20\n",
       "3      3     Darren    Acosta  DAcosta1996@hotmail.com    male    2017-07-27\n",
       "4      4       Dawn    Adkins    Dawn.Adkins@gmail.com  female    2017-08-24"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine purchases here\n",
    "sql_query('''\n",
    "select *\n",
    "from purchases\n",
    "limit 5;''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'd like to download a giant DataFrame containing all of this data.  You'll need to write a query that does the following things:\n",
    "\n",
    "1. Not all visits in  `visits` occurred during the A/B test.  You'll only want to pull data where `visit_date` is on or after `7-1-17`.\n",
    "\n",
    "2. You'll want to perform a series of `LEFT JOIN` commands to combine the four tables that we care about.  You'll need to perform the joins on `first_name`, `last_name`, and `email`.  Pull the following columns:\n",
    "\n",
    "\n",
    "- `visits.first_name`\n",
    "- `visits.last_name`\n",
    "- `visits.gender`\n",
    "- `visits.email`\n",
    "- `visits.visit_date`\n",
    "- `fitness_tests.fitness_test_date`\n",
    "- `applications.application_date`\n",
    "- `purchases.purchase_date`\n",
    "\n",
    "Save the result of this query to a variable called `df`.\n",
    "\n",
    "Hint: your result should have 5004 rows.  Does it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows, Columns: (5004, 8)\n",
      "Columns: First_Name, Last_Name, Gender, email, Visit, Fitness_Test, Application, Purchase\n"
     ]
    }
   ],
   "source": [
    "df = sql_query('''\n",
    "select  a.first_name as First_Name,\n",
    "        a.last_name as Last_Name,\n",
    "        a.gender as Gender,\n",
    "        a.email as email,\n",
    "        a.visit_date as Visit,\n",
    "        b.fitness_test_date as Fitness_Test,\n",
    "        c.application_date as Application,\n",
    "        d.purchase_date as Purchase\n",
    "\n",
    "from \n",
    "\n",
    "(select first_name, last_name, gender, email, visit_date\n",
    "from visits\n",
    "where visit_date >='7-1-17') a\n",
    "\n",
    "left join \n",
    "\n",
    "(select first_name, last_name, email, fitness_test_date\n",
    "from fitness_tests) b\n",
    "\n",
    "on a.first_name = b.first_name\n",
    "    and a.last_name = b.last_name\n",
    "    and a.email = b.email\n",
    "    \n",
    "left join\n",
    "\n",
    "(select first_name, last_name, email, application_date\n",
    "from applications) c\n",
    "\n",
    "on a.first_name = c.first_name\n",
    "    and a.last_name = c.last_name\n",
    "    and a.email = c.email\n",
    "    \n",
    "left join\n",
    "\n",
    "(select first_name, last_name, email, purchase_date\n",
    "from purchases) d\n",
    "\n",
    "on a.first_name = d.first_name\n",
    "    and a.last_name = d.last_name\n",
    "    and a.email = d.email;\n",
    "''')\n",
    "\n",
    "print('Rows, Columns: {}'.format(df.shape))\n",
    "print('Columns: {}'.format(', '.join(df.columns)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Investigate the A and B groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have some data to work with! Import the following modules so that we can start doing analysis:\n",
    "- `import pandas as pd`\n",
    "- `from matplotlib import pyplot as plt`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to add some columns to `df` to help us with our analysis.\n",
    "\n",
    "Start by adding a column called `ab_test_group`.  It should be `A` if `fitness_test_date` is not `None`, and `B` if `fitness_test_date` is `None`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['AB_Test_Group'] = df['Fitness_Test'].apply(lambda x: 'B' if x == None else 'A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First_Name</th>\n",
       "      <th>Last_Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>email</th>\n",
       "      <th>Visit</th>\n",
       "      <th>Fitness_Test</th>\n",
       "      <th>Application</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>AB_Test_Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kim</td>\n",
       "      <td>Walter</td>\n",
       "      <td>female</td>\n",
       "      <td>KimWalter58@gmail.com</td>\n",
       "      <td>7-1-17</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tom</td>\n",
       "      <td>Webster</td>\n",
       "      <td>male</td>\n",
       "      <td>TW3857@gmail.com</td>\n",
       "      <td>7-1-17</td>\n",
       "      <td>2017-07-02</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Edward</td>\n",
       "      <td>Bowen</td>\n",
       "      <td>male</td>\n",
       "      <td>Edward.Bowen@gmail.com</td>\n",
       "      <td>7-1-17</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Marcus</td>\n",
       "      <td>Bauer</td>\n",
       "      <td>male</td>\n",
       "      <td>Marcus.Bauer@gmail.com</td>\n",
       "      <td>7-1-17</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>2017-07-05</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Roberta</td>\n",
       "      <td>Best</td>\n",
       "      <td>female</td>\n",
       "      <td>RB6305@hotmail.com</td>\n",
       "      <td>7-1-17</td>\n",
       "      <td>2017-07-02</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First_Name Last_Name  Gender                   email   Visit Fitness_Test  \\\n",
       "0        Kim    Walter  female   KimWalter58@gmail.com  7-1-17   2017-07-03   \n",
       "1        Tom   Webster    male        TW3857@gmail.com  7-1-17   2017-07-02   \n",
       "2     Edward     Bowen    male  Edward.Bowen@gmail.com  7-1-17         None   \n",
       "3     Marcus     Bauer    male  Marcus.Bauer@gmail.com  7-1-17   2017-07-01   \n",
       "4    Roberta      Best  female      RB6305@hotmail.com  7-1-17   2017-07-02   \n",
       "\n",
       "  Application    Purchase AB_Test_Group  \n",
       "0        None        None             A  \n",
       "1        None        None             A  \n",
       "2  2017-07-04  2017-07-04             B  \n",
       "3  2017-07-03  2017-07-05             A  \n",
       "4        None        None             A  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do a quick sanity check that Janet split her visitors such that about half are in A and half are in B.\n",
    "\n",
    "Start by using `groupby` to count how many users are in each `ab_test_group`.  Save the results to `ab_counts`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AB_Test_Group</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>2504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  AB_Test_Group  Counts\n",
       "0             A    2504\n",
       "1             B    2500"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_counts = df.groupby('AB_Test_Group').size().reset_index(name = 'Counts')\n",
    "ab_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll want to include this information in our presentation.  Let's create a pie cart using `plt.pie`.  Make sure to include:\n",
    "- Use `plt.axis('equal')` so that your pie chart looks nice\n",
    "- Add a legend labeling `A` and `B`\n",
    "- Use `autopct` to label the percentage of each group\n",
    "- Save your figure as `ab_test_pie_chart.png`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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r9iXz7sP1XveH2a2KCbd73b+zpH+eFzNHDNO1XyiTf/Xuri5rYJ45Kr7becty\nyztLumNW7a1QaW2z0mLDO7fZrBbNHDlMK/dU9Ete9J/8ykbd/fo2Pbp0r751/mh9dfYIRTr41QgA\nAADM4e1jAAAAMKK51a3/XV2guQ+v1E9e3UZJFyAum5SqqAH+pbavkWeBKszuewrKZlf3ddt6K6XG\npnSfLvNECZGhSor2Pjqtv0qvrtf0XdSNTYnyuX98L99Tb9fvC4tF+tXCSbJaj6/x91FumVbsKe9y\nXOawiG7n7vJy/7ml3cvHzLjwHo6Evzhc16LfLM7VuQ8t16NL96rmCwUsAAAAMFgo6gAAADCo6ppd\nenz5fp330HLd9+4uFfcy0gj+5bpBKNEWTEuX7YQSJRhMyYz1ub+yvvt0kJUNrSqobPR6zvThcT6v\nOWOE7/2fH6jxuf9U5JbW+Vxbr/fMw3zu//xA9SnlOtHXzhqpyRnH/zycLrfue3dXt+NiwrsX0t6m\nCj3S1L3k8TUyEP7jSJNLj320T+f8drl+9e5OlfB/EgAAAAYZ8zsAAABgUFTUO/XMp/l6ae1B1TtZ\ntykQJUU5dO6YxIG/n2iHzh+b6HXawMxh4fr07ou9nn/jU2u0Nv/0C50vumRiiqwWacnOspM6b3JG\njM7wUUC1uT3Kq+x5ROmbm4v0o0vG97hvakasMuLCvZbdV05J83qfdS0uLdl52Ov+H84fqx/OH9fj\nvqKaJp330Ioe9znbPPpgx2F9ZdbwHvfPHZ+scLutxxGEvWXOr2jQpoNHvO7vi7gIu358Sdfv66lP\n8nWwuqnbsS0uT7dtdlvPBXKorft7YJ1t3c+H/2p2ufXs6gN6YW2hFk3P0HcvzFZ2su8RoAAAAEB/\nYEQdAAAABtSh6ib9/K0dOu+h5frLqnxKugB2zfR0hfRQSAyEwRi5d7KGDwvXX2+epdfvOEfzc5LV\nl0F/abFheuKmmV2mWfyijQdrVNfc89+LNzYVy+PpeSE5q9Wiuy7vucSbkBqtBdPSvd7n+9tLB6xI\nen1jkdd9UY4Qfe/iMT3umzs+SbNHd18X7pg3Nhefdra7LhuvYSesB1h8pFlPrNjf47FfXItO6iiJ\ne5LRw/aqBqZSDEQud7te3Vik+Y+u0u0vbNTWQ6dXDgMAAAC9YUQdAAAABsTesno9uSJP720rUZuX\nogGBxdfacfvK6nXJox+f1PUevG6Kvjp7RI/7LpmYqshQmxpbex55ZdIZI4fp6VvOVFFNk97ZUqLV\neZXaVHjj5qCUAAAgAElEQVSkyyix9NgwLZiWrjvmZisuItTH1aTXfBRbRTXNemtLsa6bmdnj/oXT\nM9TU6tYjS/ao6mixdOG4JD38pamyeylVnS63/roqv7dv85StK6jWuoIqnTU6ocf9/3bRGLk97Xrq\n43w1ONtktUhXT03XA9dN8XrN6sZWvbi28LRyTc6I0Q1ndn2+3b94V48j5yRpe9ER3XBm15GB541J\n0kPa02VbbLhdk9K7T226vZiCJ5C1t0tLdh7Wkp2HdU52gu6Ym63zxyaZjgUAAIAgRFEHAACAfrWj\nuFaPfbRPy3LL1E4/FzTGJEd1Wdfriz7Y4X0aRW+W7DzstagLD7XpyilpetVHiWVa5rAI3XnRGN15\nUccIsbpml+paXIoOsyu2j+uT7Syp9VnUSdL9i3M1LyfF6zW/OnuEvjJruCobnIp0hCjK4ftl3l9W\n5flc+64//PzNHVr8/fMVGtJzWfj9eWN159xsVTQ4FRceqvBQm8/rPfh+ro40eV/7ri9+dc3kLmsf\nrt5fqfe3e3/ertpXIY+nvctoyCmZsVo0PUNvbTk+uu+nl43v9n0eqm5SXsXAPsYYPJ/lVemzvCrN\nHBGnuy+foLOyei6hAQAAgFPB1JcAAADoFwWVjfrey5u04PFPtXQXJV2w6W0qSl/rnXnz2f4q1bd4\nL1+unel/01/6EhNuV+awiD6XdDWNrfqP/9vS69+VqsZW/fdbO3weY7NalBIT1mtJt63oiJ5cmden\nfKdjX3mD/rBsr89jQmxWpcWG91rSLcstO+3C9stnZGrmyOPrBLrcHt37zk6f5xyqbtay3O7rEf7u\nK9P0+E0z9NPLxuv/bp+jr88Z2e2YZz4tOK288E+bDh7RDU+t1a3PrteuklrTcQAAABAkGFEHAACA\n01JR36LHlu3TPzYcYorLILbQR1F3sKpJO0vqTvqarW6PVuwu1zXTe772nNEJSosNU2lty0lf299V\nNjj1//6+QXvLGvp0/DtbS5QYFaqfXzXR53p3vuSW1um2v28YsLXpvujJlXmKjwzVt87POuVrfJZX\nqX9/efNp5Yh2hOiuyyd02fbcZwe0v7z3x/6+d3dp9uj4LtOX2qwWXT3V+/p/mwpr9MJpTtMJ/7Zy\nb4VW7avQgqnp+vGl4zQyIdJ0JAAAAAQwRtQBAADglNS1uPTwkj264H9W6sV1BynpgtjZWQnKiAv3\nuv+DHaWnfO0lO7uPWDrGarVokZcSz4TtxbXaUXz6o2g+2FGqyx79WFuLTu5az64+oG+/8Lkq6k++\nuHxzc7G+/Jc1qmxoPelzT8dvFufqrte2+Rw52RO3p11Pf5KvW55d32Xtv1Pxo0vHKSna0Xm7or5F\nf1i2r0/nFh9p1ree+1w1jX173HaW1Or2FzfKzb+HQa+9vaNAn//7Vfr5WztO6e8lAAAAIDGiDgAA\nACfJ2ebW82sK9eSK/ao5zTWjEBh6m4LyVKa9PGbFnnI5XW457D1Pf3jtzAz9edXAT9XYFxsO1Ojq\nP32q7KRIXToxVWdnJ2jWqGGKCO39ZVVZXYs+3FmmF9cWak9Z/Sln+Ci3XHMfXqmbzx6pr8warqyk\nKK/HOl1ufbS7XE9/UqBNB2tO+T5P1z8/P6RluWW67dzRuv6MDKXFei99G5xt+mB7qZ76OF/7+jDi\nrTfjU6K7TU352w92q8HZ1udrfF5YowWPf6qfXDpeV09NU4it+/td61tceu6zQj25cr+aWk+vWERg\ncbnb9eLaQr2+sUjfPHeUvjs3WzFhfZv+FgAAAJAkS3s7q4cAAACgdx5Pu97YXKRHl+5T8ZFm03EA\nv2C1SJnDIpSVGKmU2I414sJDbXK6PKprcamqwamdJXUDNn3n8PhwTUqPVUZcx1pvbne7appalV/Z\nqK2HjgzaNJcnIzspSjlp0UqNDVO43SaX26OqhlbtL2/Q9uLafh2dOy8nWVMyYjtvN7W69dTH+ad8\nvZiwEM3JSlB6XLgiQm2qa3Ypr7JRGw/UqNXtf481Bl9chF13XJitW88Z5fUNCAAAAMCJKOoAAADQ\nq6W7DuvhJXv6vJ4WAABDWWpMmH4wf6y+Mmu4bKe4riQAAACGBoo6AAAAeLW+oFoP/Wu3NhaamzYP\nAIBAlZ0UqR9dMl5XTU0zHQUAAAB+iqIOAAAA3ew5XK//WbJbH+WWm44CAEDAm5oZq7sum6Dzxiaa\njgIAAAA/Q1EHAACATkU1TXp06V69ublY/bhMFAAAkHTumATdffkETc2MMx0FAAAAfoKiDgAAAKpu\nbNUTK/brxbWFcrZ5TMcBACCoXTE5VT+5bLyyk6JMRwEAAIBhFHUAAABDWFNrm57+pEB/+zhf9c42\n03EAABgybFaLbjxzuH5y6XgNiww1HQcAAACGUNQBAAAMUe9tLdFvFufqcF2L6SgAAAxZcRF23XXZ\neN145ghZrRbTcQAAADDIKOoAAACGmP3lDbr3nR1avb/KdBQAAHDUlIxY/XrhJE0fMcx0FAAAAAwi\nijoAAIAhotHZpj9+tE/Pri6Qy82PgAAA+BuLRfryGcN19+XjlRDlMB0HAAAAg4CiDgAAYAhgmksA\nAAJHbLhdP7pknL4+Z6RsTIcJAAAQ1CjqAAAAglheeYN+wTSXAAAEpEnpMbrvmkmaNSredBQAAAAM\nEIo6AACAIMQ0lwAABAeLRbpuRobuuSJHSdFMhwkAABBsKOoAAACCzHvbSnT/4lyV1jLNJQAAwSIm\nLEQ/nD9O3zh7pEJsVtNxAAAA0E8o6gAAAIIE01wCABD8JqRG676Fk3TW6ATTUQAAANAPKOoAAAAC\nHNNcAgAw9Cycnq6fXZmj5Jgw01EAAABwGijqAAAAAhjTXAIAMHRFOUL0/Xlj9c1zR8nOdJgAAAAB\niaIOAAAgADHNJQAAOGZMcpR+tXCSzslONB0FAAAAJ4miDgAAIIA0tXZMc/nMp0xzCQAAurp6apru\nXTBRSdFMhwkAABAoKOoAAAACxMo95frPN7YzzSUAAPAqLsKuexdM0rUzMkxHAQAAQB9Q1AEAAPi5\nuhaXfv3eLr36eZHpKAAAIEBcMjFF9y+arOQYRtcBAAD4M4o6AAAAP7ZqT7nuYRQdAAA4BbHhdt27\nYKKum5lpOgoAAAC8oKgDAADwQ/UtLv1mca7+b8Mh01EAAECAm5+TrAeuncLoOgAAAD9EUQcAAOBn\nPt5boXte36YSRtEBAIB+Ehtu1y8WTNT1jK4DAADwKxR1AAAAfqLB2ab7F+/SK+sZRQcAAAbGvKOj\n61IYXQcAAOAXKOoAAAD8wCf7KnTP69tVfKTZdBQAABDkYsJDdO/Vk3T9GYyuAwAAMI2iDgAAwKAG\nZ5seWJyrl9cfNB0FgB+xWS0KtVlltUhWi0UWa8fnjg/JcvSz1WKRRZKnXXK3t6u9vV2edsnT3i5P\ne7vaPce+llxuj9o8vPwDcNzFEzpG16XGMroOAADAFIo6AAAAQ1bvr9Tdr29TUQ2j6IBAZ7VI0WF2\nRTlCFOmwKTI0RJGOYx8dt6McIYpw2I4eE6KI0ONfR4bajh8fGqLwUNuA5HS63GpsdavR2aYGZ5ua\nunzdpgZnx+1GZ1vnccc+Gpzuo8d03K5vaaP4A4JATHiI/vuqifryrOGmowAAAAxJFHUAAACDrNHZ\npgffz9VL6w+Kn8QA/2azWpQQGaqUmDAlxziUHO3o+DraoaToMKXEOJQcHabEqFCF2Kym4w4qj6dd\nVY2tqqhvUXm9U2V1TpXXt6isztmxrc6psroWVTQ45XLzjx3g7+aOT9Jvr5vK6DoAAIBBRlEHAAAw\niD7Lq9RdrzGKDjDNYpFSjhVtMWFKiXYo6WgBd6yIS452KCHKIZvVYjpuwKtubO0o7eo7yrvyeufR\ncq+j0Cuvb9Hh2hZG6AGGxYSF6L+vZnQdAADAYKKoAwAAGASNzjY9+MFuvbSukFF0wCCKjwzV6MRI\nZSVGanRipEYnHf2cECmHfWCml8Spcbk9OljdpILKxo6Pio7P+ZUNKqtzmo4HDClzxyfpweumKC02\n3HQUAACAoEdRBwAAMMDW5FXprte36lA1o+iAgRBut3WUbycUcVmJkcpKilJsuN10PPSDRmebDlQ1\nKr+iUfmdJV6DCiobVdfSZjoeEJRiwkL086sn6iuMrgMAABhQFHUAAAADxOly64EPduv5NQcYRQf0\ngxHxEcpOilLWsVFxiZHKSopkxMcQV9ng7ByFl1/RUd7lHR2N52YqTeC0XTklVb+9fqpiwnjjAwAA\nwECgqAMAABgAeRUN+t7Lm5RbWm86ChBwLBZpZHyEpmTEanJGrKZkxmpyeqxiGB2Hk9Dc6tau0jpt\nL67VjuJabS+q1f6KBso74BQMjw/Xn746U9OHx5mOAgAAEHQo6gAAAPrZm5uL9fM3t6ux1W06CuD3\nLBZpVELk8VIuI1aTMmIYuYEB0dzqVu6J5V1xrfaVU94BfWG3WfSTS8fr9guzTUcBAAAIKhR1AAAA\n/aS51a1fvL1Dr24sMh0F8EsWizQ6IbJzlNyUjFhNTKeUg1ktruPl3bECb19Zg9oo74AezR2fpN99\neZoSohymowAAAAQFijoAAIB+sOdwvf7t5U3aX95gOgrgN9JjwzRrVLymnlDKRVPKIQA4O8u7jgJv\nY2GN8ir49x04JiXGocdunKE5WQmmowAAAAQ8ijoAAIDT9Mr6g7rv3Z1qcXlMRwGMSo8N05zsBM3J\nStCc0QkakRBhOhLQbyrqnVqXX6U1+VVam1+lvIpG05EAo6wW6fvzxurfLx4rm9ViOg4AAEDAoqgD\nAAA4RQ3ONv3XG9v1ztYS01EAI9JiwzpKuawEzcmK18iESNORgEFTUd+itfnVWptfpTV5VcqvpLjD\n0DQnK15/uGGGUmPDTEcBAAAISBR1AAAAp2BHca2+9/ImHahqMh0FGDQUc4B35XUtWptf1VneUdxh\nKImPDNUjX56miyckm44CAAAQcCjqAAAATtLfPzugB9/PlbONqS4R3FJjjhZz2fGaMzpBoxIp5oC+\nOlbcrTla3hVQ3CHIWSzS/ztvtO6+fILsNqvpOAAAAAGDog4AAKCP6ppd+ulr27Rk52HTUYABEeUI\n0QXjEnX+2CSdnUUxB/SnsqPF3af7KrV8d7mqGltNRwIGxLTMWP3pqzNZpxQAAKCPKOoAAAD6YMvB\nGn3vlc0qqmk2HQXoVxlx4ZqXk6z5OSmak5Wg0BBGQQADzeNp1+ZDR7RsV5mW5ZZpX3mD6UhAv4oJ\nC9ED103R1VPTTUcBAADwexR1AAAAvfjrqjw98uEeudz82ITAZ7FIUzJiNT8nRZdMTFFOWozpSMCQ\nV1jVqGW5ZVqWW64NBdVq8/D/DYLDTbNH6BcLJirMbjMdBQAAwG9R1AEAAHhR3diqH/9zq1bsKTcd\nBTgtYXarzh2TqHkTUjQ/J1nJMWGmIwHwoq7ZpZV7yrUst1wr95SrrqXNdCTgtIxPidYTX5upMclR\npqMAAAD4JYo6AACAHqwrqNIPXtmiw3UtpqMApyQpyqGLc5I1PydZ541JUngooxmAQONye7ThQPXR\nKTLLdbC6yXQk4JSE2226b+EkfWXWcNNRAAAA/A5FHQAAwBf8afk+/WHZPrmZegwBZnxKtOZP7Bg1\nN2PEMNNxAPSzvWX1naXdlkM14r8pBJobzhyuXy+czHqoAAAAJ6CoAwAAOKrR2aYf/XOLluwsMx0F\n6LMJqdFaND1DV01N0/D4CNNxAAySyganluw4rLe2FOvzwhrxyh6BYtbIYfrz189QUrTDdBQAAAC/\nQFEHAAAg6VB1k7713OfaU1ZvOgrQq7TYMC2cnq5FMzI0ITXGdBwAhhXVNOntLSV6c3Ox9pc3mI4D\n9Co9NkxPfWOWJmfEmo4CAABgHEUdAAAY8tbmV+mOFzeqpsllOgrgVUx4iK6cnKZFMzI0JyvBdBwA\nfmpnSa3e3Fysd7eWqKzOaToO4FWY3aqHvzRNC6alm44CAABgFEUdAAAY0l5aW6hfvrtTLjc/EsH/\nOEKsumhCshZNz9BFE5LkCLGZjgQgQHg87VqTX6W3NhfrXzsOq97ZZjoS0KM752brJ5eOl9VqMR0F\nAADACIo6AAAwJLW5PfrlOzv14rqDpqMAXVgs0lmj47VoRoaunJymmHC76UgAApzT5dbS3DK9vaVE\nK/eU8+YU+J15Ocl67MYZinKEmI4CAAAw6CjqAADAkFPT2Ko7X9qkNflVpqMAnSakRmvR9AxdMz1d\n6XHhpuMACFI1ja16f3up3txcrI0Ha8RvBOAvxiZH6W/fmKVRiZGmowAAAAwqijoAADCk7D5cp28/\n/7kOVTebjgIoNSZMi2aka+H0DOWkxZiOA2CIKapp0ttbSvTm5mLtL28wHQdQbLhdj980Q+ePTTId\nBQAAYNBQ1AEAgCFj6a7D+uE/tqix1W06CoYwi0U6f0yibj57pC6ekCIba/IA8APrCqr04ppC/Wvn\nYabGhFE2q0U/uzJHt5032nQUAACAQUFRBwAAhoQ/Ld+n3y/dyxRfMCYuwq4vnZGpr581kmm9APit\ninqn/rHhoF5Zd1AltS2m42AI+/KsTN2/aIpCQ6ymowAAAAwoijoAABDUWlxu/fTVrXp3W6npKBii\npmXG6uazR2rB1HQ57DbTcQCgT9yedi3fXaYX1hTqk/2VvNEFRswcMUx/vXmmkqLDTEcBAAAYMBR1\nAAAgaJXWNuvbz3+uHcV1pqNgiAmzW3XNtAzdPGekpmTGmo4DAKflQGWjXlp3UK9uPKQjTS7TcTDE\npMWG6a83n6GpmXGmowAAAAwIijoAABCUNhXW6PYXNqqiwWk6CoaQrMRIfX3OSF1/RqZiw+2m4wBA\nv3K63Hp3W4leXHtQWw4dMR0HQ4gjxKr/+dJULZyeYToKAABAv6OoAwAAQee1jYf0X2/sUKvbYzoK\nhgCb1aL5OSm6ec5InTc20XQcABgU24tq9cLaQr2ztVgtLv6/xeD47oVZuuuyCbJaLaajAAAA9BuK\nOgAAEDTcnnY98H6unvm0wHQUDAHJ0Q59dfYI3Th7uNJiw03HAQAjaptden1jkV5cW6j8ykbTcTAE\nXDwhWY/dOF3RYYxcBwAAwYGiDgAABIW6Zpf+/ZXNWrW3wnQUBLmpmbH6zgVZunxSqkJsVtNxAMBv\nfLqvUk9/kq+V/F+MATY2OUrP3TZb6XG8UQYAAAQ+ijoAABDwSmub9Y1n1mtfeYPpKAhi545J0B0X\njmF6SwDoxc6SWv15ZZ7e314qD79xwABJiw3Tc7fN1riUaNNRAAAATgtFHQAACGj7yxv0jWfWqaS2\nxXQUBCGLRbpsUqruuDBb04bHmY4DAAHlQGWj/vpxnt7YVCxnG+vYof/Fhtv19C2zdOaoeNNRAAAA\nThlFHQAACFibD9bom3/foCNNLtNREGTsNosWTc/Qdy/MVnZylOk4ABDQyupa9MynBXppbaEaW92m\n4yDIOEKsevymGbpkYqrpKAAAAKeEog4AAASkFbvLdedLm9Ts4hd+6D/hdpu+Onu4vnV+FuveAEA/\nq2126bnPDujvnx1QdWOr6TgIIjarRb9eOFk3nTXCdBQAAICTRlEHAAACzuubinT3a9vUxsI36Cdx\nEXbdcvYo3XrOKA2LDDUdBwCCWnOrW//YcFBPf1Kg4iPNpuMgiPzHJeP0g3ljTccAAAA4KRR1AAAg\noPxlVZ4e+tdu8RMM+kNqTJi+df5ofXX2CEU6QkzHAYAhxeX26J0tJfrzqjztL28wHQdB4utnjdCv\nFk6W1WoxHQUAAKBPKOoAAEDA+M3iXXr6kwLTMRAERidG6vYLs3TdjEyFhlhNxwGAIe/DnYf15Mo8\nbTl0xHQUBIHLJqXqjzdOl8NuMx0FAACgVxR1AADA77ncHv301W16a0ux6SgIcJPSY3Tn3DG6YnIq\n77QHAD/0WV6lnlyRp0/3V5qOggB31uh4/e2WWYoJs5uOAgAA4BNFHQAA8GtNrW2688VNWrm3wnQU\nBLDRiZH68aXjdPXUdNNRAAB9sDa/Sg99sFubGWGH0zAhNVrP3TZbKTFhpqMAAAB4RVEHAAD8VnVj\nq277+wamwcIpS4lx6Afzxuors4YrxMYUlwAQaJbsPKyHl+xhDTucsoy4cD1/22xlJ0eZjgIAANAj\nijoAAOCXimqadMuz65VX0Wg6CgJQbLhdd1yYrVvPHaUw1qcBgIDm9rTrjU1FenTpXpXUtpiOgwA0\nLMKuZ289UzNGDDMdBQAAoBuKOgAA4Hd2H67TLc+uV1md03QUBJgwu1XfPGe0vjs3W7HhrEkDAMHE\n2ebWC2sK9cSK/appcpmOgwATbrfpia/N1MUTkk1HAQAA6IKiDgAA+JX1BdX61vMbVNfcZjoKAkiI\n1aIbzhyu788byzo0ABDk6ltc+tsn+Xr6kwI1tbpNx0EACbFa9Nvrp+hLZww3HQUAAKATRR0AAPAb\nS3cd1vde3ixnm8d0FAQIi0W6akqafnzpeI1OjDQdBwAwiCobnPrT8v16Zd1Btbr52QF9d9dl43Xn\nRWNMxwAAAJBEUQcAAPzEPzYc1M/e3CG3hx9N0DcXjE3UXZdP0OSMWNNRAAAGHapu0u+X7tXbW4rF\njxHoq1vPGaVfXD1RVqvFdBQAADDEUdQBAADj/rR8n3734V7TMRAgpg+P092XT9DZ2QmmowAA/Ehu\naZ0eXrJHy3eXm46CAHHNtHQ9esN02SjrAACAQRR1AADAqPsX5+pvn+SbjoEAkJ0UpZ9eNl6XT041\nHQUA4Mc2HKjWQx/s1ueFNaajIABcOSVVf7xxhkJsVtNRAADAEEVRBwAAjPnVuzv17OoDpmPAz8WE\nh+jHl4zX1+eM5B3vAIA++2BHqX797i6V1LaYjgI/d9mkVP1/9u78we6yvvv/e/YZJjNZJ2QPSchG\nSAjZCCCIyFZBdhSURbBqXWrdqL2tULVWXFqr3ijgjlRRFgXRogWURY0gmAAJCYEsJGTfJ8kks535\n/lC+lpuyJJNzznWWx+MveP52znVe53N9rn3bkVFjrAMAEjDUAQBJXH3nwvjBvOdSZ1DAKioiLpg5\nMj5+2sQY2KcudQ4ARWhPR3dc+9tn41sPLo+O7kzqHArYGycPjm+8fUbUVVelTgEAyoyhDgDIu0/+\n7Mn4j4dXpc6ggE0d3jc+c9aUOHJU/9QpAJSAFZt3x6fvWhT3P70pdQoF7IQJLXHDJTOjrsZYBwDk\nj6EOAMirf7j9ifjxn1anzqBA9TuoJq48ZWJcNGdUVLrmEoAsu+ep9fHpu56K57ftSZ1CgTpu/KD4\n1qWzot5YBwDkiaEOAMiLTKYnPv7TJ+LWR59PnUIBqqyIuHD2qLjy1InRv7E2dQ4AJWxvZ3dcd/+y\nuP6BZdHe5TpM/rejxw2M71w2Kw6qrU6dAgCUAUMdAJBzmUxPfOy2x+Onf16TOoUCNH1kv/jMWVNi\n2oh+qVMAKCOrtrTFZ36xKO5dvDF1CgVozpgB8b13zI7GOmMdAJBbhjoAIKe6Mz3xkVsWxJ0L1qZO\nocAMaKyNj582Kd46e2TqFADK2G+WbIxP37UontvSljqFAjNzdP+48Yo50cdYBwDkkKEOAMiZ7kxP\nfPgnC+Lnjxvp+B+VFRFvP2p0fOzUidG3oSZ1DgBEe1d3fPPB5fH13z4beztdh8n/mPXCWOfJOgAg\nVwx1AEBOZDI98WFP0vESM0f3j8+cNSWmDOubOgUA/pfnt7XFP/9icfx60frUKRSQOWMGxPcvn+2d\ndQBAThjqAICsy2R64srbHo/bvZOOF7T0qYt/+KtJcd7MEalTAOA1Pbh0U3zq54ti+ebdqVMoEHPH\nDojvvWNONNRWpU4BAEqMoQ4AyLq/v+2JuOXR1akzKBBvnT0y/vH0ydFc75pLAIpHR1cmvnH/s/H1\n3z4bnd1+OiHi6HED43vvmB31NcY6ACB7DHUAQFZ94qdPxo8eWZU6gwIwrG99fP68aXH8hJbUKQDQ\na0+t3REfu/WJeGpda+oUCsCxhw6M71xmrAMAssdQBwBkzVV3LIyb/vhc6gwKwEVzRsYn3jQ5mjxF\nB0AJ6OzOxDd++2xc6+k6IuK48YPi25fOijpjHQCQBYY6ACArPn3Xovje71emziCx4f0a4vPnTY3j\nxnuKDoDSs3hda3zs1sdj0VpP15W7109oiW9eOjPqqo11AMCBMdQBAAfss798Kr790IrUGST2tjmj\n4hOnT44+ddWpUwAgZ7q6M/H1+5fFtb95xtN1Ze4NEwfHDZfMjNrqytQpAEARM9QBAAfk83cvjusf\nWJ46g4RG9G+Iz587LV43flDqFADIm8XrWuPK2x6PhWs8XVfOTp1ycHzj7TOjqrIidQoAUKQMdQBA\nr339t8/Gl379dOoMEqmo+O+n6P7PmzxFB0B56urOxHUPLIv/e9+z0dGdSZ1DIhfNGRnXnDstdQYA\nUKQMdQBAr/zkT6vj47c/kTqDREb0b4gvnDctjj3UU3QAsGR9a1x56xPx5JodqVNI5G9PPDQ+esrE\n1BkAQBEy1AEA++3exRviPTc9Ft0ZXyPKTUVFxNuPGh3/568mRaOn6ADgL7q6M3H9A8vja/c94+m6\nMvXpM6fEZccckjoDACgyhjoAYL88unJrXPydh2Nvpx+gys2I/g3xxfOnxTHjPEUHAK/k6fU742O3\nPu7pujJUWRHxtQuPjDOOGJY6BQAoIoY6AGCfLd2wMy64fl7s2NOZOoU8qqiIuPio0fEPnqIDgH3S\n1Z2JGx5cHl+919N15aa2qjK++47Z8brx/tgEAOwbQx0AsE/Wbt8T5133h1i3Y2/qFPKopU9dfPmt\nR8Rx41tSpwBA0VmyvjU+8KP58ezGXalTyKPG2qq4+d1zY9qIfqlTAIAiYKgDAF7T9raOOP/6eX5k\nKjPHjR8UX37L9GhpqkudAgBFa09Hd1z984Vx66PPp04hjwY21satf3N0jG3pkzoFAChwhjoA4FXt\n6cYVN00AACAASURBVOiOt3/7j/HnVdtTp5An1ZUV8ZGTJ8T73nBo6hQAKBl3zF8Tn7xjYexq70qd\nQp6M6N8QP33vMTG4uT51CgBQwAx1AMAr6urOxLtveix+s2Rj6hTyZHi/hvjaRUfGzNH9U6cAQMlZ\nsXl3/O3Nf46Fa1pTp5Ank4c2xU/ec3Q019ekTgEACpShDgB4RVfe+njc+phrmsrFqVMOji+ef0T0\nbfBDEgDkSkdXJq65e3F87/crU6eQJ3PGDIibrpgTdTVVqVMAgAJkqAMAXtYX7l4S1z2wLHUGeVBX\nXRmfeNPkuOyYQ1KnAEDZuOep9XHlbU/E9rbO1CnkwSmHHRzXXTwzqiorUqcAAAXGUAcA/C/f+/2K\n+PRdT6XOIA/GDGqMa992ZEwZ1jd1CgCUnbXb98QHb54fjz63LXUKefDW2SPjC+dNS50BABQYQx0A\n8P+46/G18cEfzw/fEErfOUcOj8+efXg01lWnTgGAstXVnYl/v/eZuO7+ZyPj+1fJe/8bDo0rT52Y\nOgMAKCCGOgDgLx56ZlO88/uPRkd3JnUKOXRQbVV85qwpcf7MkalTAIAX/O6ZzfHhWxbEpp3tqVPI\nsX9682Fx+bFjUmcAAAXCUAcARETEk8/viAu/OS92d3SnTiGHJg9timsvmhHjBvdJnQIAvMSmne3x\n0VsWxIPPbE6dQg5VVER89a3T48zpw1OnAAAFwFAHAMTKzbvj/Ov/EJt3daROIYcunjs6rjp9ctTV\nVKVOAQBexXX3Pxv/9l9Lo8tdmCWrpqoivvuO2XHc+JbUKQBAYoY6AChzm3bujfOumxertralTiFH\nmuur4/PnTYs3TR2aOgUA2EePPbctPnjz/FizfU/qFHKksbYqfvSuuXHEyH6pUwCAhAx1AFDGdrV3\nxVtvmBeL1ramTiFHxrX0iW9fNivGDGpMnQIA7Ketuzvi/T/8c8xbviV1CjnS0lQXP//AsTG0b0Pq\nFAAgkcrUAQBAGplMT3zw5vlGuhJ24qTBccf7jzHSAUCRGtBYGze9c05cMnd06hRyZNPO9njXDx6N\nPd4TDQBly1AHAGXqi79eEr9ZsjF1Bjny3tePi29fOiua6mtSpwAAB6C6qjL++ezD43PnTI2aqorU\nOeTAwjWtceVtj6fOAAAScfUlAJShO+aviQ/9ZEHqDHKgvqYyvnDetDhr+vDUKQBAlj2yYmu89z8e\niy27O1KnkAMfOXlCfPCN41NnAAB5ZqgDgDLzxPPb44Lr50V7VyZ1Clk2tG99fPOSWTF1RN/UKQBA\njjy/rS3ec9Njri8vQRUVEddfPDNOnTIkdQoAkEeGOgAoIxtb98aZ1/4+1rfuTZ1Cls0Y1T9uuGRG\ntDTVp04BAHJsT0d3fOzWx+OXT65LnUKWHVRbFbe/95iYPLQ5dQoAkCfeUQcAZaK9qzvec9NjRroS\ndMGsEfHjd8810gFAmWiorYqvv31GfPSUCVHhtXUlpa2jO971g0djy6721CkAQJ54og4AysRHb1kQ\nt/95TeoMsqiqsiI+efrkuPzYMalTAIBE/mvR+vjwTxbE7o7u1Clk0ZwxA+KHf31U1FT5jz0AlDqf\n9gBQBr710HIjXYnpd1BN3Hj5HCMdAJS5U6YMiZ++79gYNeCg1Clk0SMrtsZVdyxMnQEA5IGhDgBK\n3ANPb4zP370kdQZZNH5wn7jz/cfG68YPSp0CABSAiUOa4ucfODaOGTcwdQpZ9OM/rY7v/X5F6gwA\nIMdcfQkAJWzZpl1xztd/H617u1KnkCUnTR4cX7nwyOhTV506BQAoMF3dmfjsLxfH9/+wMnUKWVJV\nWRHfv3x2HDe+JXUKAJAjhjoAKFGtezrj7K//PpZv3p06hSx5/xsOjY+ePCEqKytSpwAABezHf1oV\nV9+xKDq6M6lTyIK+DTVxx/uPjTGDGlOnAAA5YKgDgBKUyfTEFd//U9y/dFPqFLKgrroyvnT+tDhz\n+vDUKQBAkXh05dZ41w8ejW1tnalTyIJxLX3iZ+8/Jprra1KnAABZ5h11AFCCrrl7iZGuRDTVVceN\nV8wx0gEA+2XWIQPitr85Jkb0b0idQhYs27QrPnjz/Mhk/N8eAEqNoQ4ASsztf34+vvXQ8tQZZMHg\nprq45W+OjrljB6ZOAQCK0LjBfeL29x4Tk4Y0pU4hC+5/elNcc/eS1BkAQJYZ6gCghCxYtS0+8dMn\nU2eQBWMHNcZP33dMTB7anDoFAChiBzfXxy3vOTqOGjMgdQpZ8K2Hlsftjz2fOgMAyCJDHQCUiPU7\n9sa7b3os2rsyqVM4QEeM6Bu3vfeYGNH/oNQpAEAJaG6oiR9cMSdOnTIkdQpZ8ImfPRl/fm5b6gwA\nIEsMdQBQAto7u+PdNz0aG3e2p07hAJ0woSVufvfcGNBYmzoFACghdTVVcd3bZ8TFR41KncIBau/K\nxHtueizWbt+TOgUAyAJDHQCUgI/f/mQ88fyO1BkcoHNnDI9vXTYrDqqtTp0CAJSgysqK+Ow5U+PD\nJ41PncIB2rSrPd5z02PR3tWdOgUAOECGOgAoct9+aHncsWBN6gwO0LuPHxtffsv0qKny9QwAyK2/\nO2lCfO6cw6OqsiJ1CgfgyTU74l9+uTh1BgBwgPwSBABFbMGqbfGFXy1JncEBqKiI+OTpk+MTb5qc\nOgUAKCNvO2p0XPf2GVFX7aehYvaDec/F3QvXpc4AAA6Ab2MAUKRa93TGB26eH53dPalT6KWaqor4\n97dMj78+bmzqFACgDJ0yZUjc9M6jornBtdvF7OO3PRGrt7alzgAAeslQBwBF6uO3PxHPb/MC+WLV\nWFsV37lsdpx95PDUKQBAGZszZkDc+p5jYkhzfeoUeql1b1f87c3zo7M7kzoFAOgFQx0AFKGb5j0X\ndy9cnzqDXhrYWBs3v3tuHD+hJXUKAEBMHNIUt7/vmBjX0id1Cr20YPX2+PzdrsQHgGJkqAOAIrN4\nXWt89pdPpc6gl0YOaIjb3ntMTBvRL3UKAMBfDO/XELf9zdExY5TvKMXqO79bEfcu3pA6AwDYT4Y6\nACgiu9u74gM/+nO0d7nWphhNGdYcP33vMTFmUGPqFACA/6V/Y2388K/nxomTBqdOoZc+duvjsXa7\n6/EBoJgY6gCgiHzyjoWxbNPu1Bn0wvSR/eLmd82NlibvfwEACldDbVV885KZ8aapQ1Kn0Avb2zrj\nb2+eH13eVwcARcNQBwBF4rbHVsfP5q9JnUEvzBzdP25655xobqhJnQIA8JqqqyrjaxceGWceMSx1\nCr3w2HPb4t/+a2nqDABgHxnqAKAIPLtxV1x956LUGfTCnDED4gdXzImmeiMdAFA8qqsq49/fOj3O\nOXJ46hR64foHl8UDT29MnQEA7ANDHQAUuPbO7vjAj/4cbR3dqVPYT0ePGxjfv3x2NNZVp04BANhv\nVZUV8W8XHBEXzBqROoX91NMT8ZFbHo8NrXtTpwAAr8FQBwAF7tN3PRVL1u9MncF+et2hg+J775gd\nB9Ua6QCA4lVZWRFfOv+IuGjOyNQp7KctuzvigzfPj+5MT+oUAOBVGOoAoID94om18aNHVqXOYD+d\nMKElvnPZrKivqUqdAgCQFdecOy0umTs6dQb76eEVW+Or93lfHQAUMkMdABSoVVva4v/c/mTqDPbT\niZMGxw2Xzow6Ix0AUGL++ezD44pjD0mdwX669jfPxu+f3Zw6AwB4BYY6AChAHV2Z+MDNf46d7V2p\nU9gPJ04aHNdfPDPqqo10AEBpuvrNU4x1RSbTE/GhHy+ITTvbU6cAAC/DUAcABegLv1oSTzy/I3UG\n++GECS1x3cUzorba1ysAoLRd/eYpcenRrsEsJpt2tceHfjI/Mt5XBwAFxy9JAFBg7l28Ib7zuxWp\nM9gPx48fFDdc4kk6AKB8fOasw+Pio0alzmA//P7ZLfH1+59NnQEAvIShDgAKyNrte+Jjtz6eOoP9\ncOyhA+Obl87yTjoAoOx89pypcdGckakz2A9fufeZeHjFltQZAMCLGOoAoEB0dWfigzfPj+1tnalT\n2EdHjxsY3750dtQb6QCAMnXNudPiLbOMdcWiO9MTf3fzgtje1pE6BQB4gaEOAArEV+97Jh59blvq\nDPbRUWMGxHcumxUNtUY6AKC8ff7cqXH+zBGpM9hH61v3xtV3LkqdAQC8wFAHAAXg8dXb4xv3L0ud\nwT6afUj/+O47ZsdBtdWpUwAAkqusrIgvnjctzjlyeOoU9tHPH18bv1q4PnUGABCGOgBIrr2rO668\n7fHozvSkTmEfTBnWHN99x+xorDPSAQD8/yorK+JfLzgiTp0yJHUK++iTdzwZW3e7AhMAUjPUAUBi\nX733mVi6YVfqDPbB6IEHxfcvnxNN9TWpUwAACk5VZUV87cLpcdSYAalT2Aebd3XE1XcuTJ0BAGXP\nUAcACT2+envc8ODy1Bnsg5Y+dfGDK+ZES1Nd6hQAgIJVV1MV37psVkwe2pQ6hX3wiyfWxS+fWJc6\nAwDKmqEOABJp7+qOj93qysti0FRXHTdeMTtGD2xMnQIAUPCa62vixsvnxIj+DalT2AdX3bkwNu9q\nT50BAGXLUAcAiXz13mfimY2uvCx0ddWV8c1LZ8Vhw/qmTgEAKBqDm+vjpnceFQMba1On8Bq27u6I\nq+5wBSYApGKoA4AEXHlZHCorIv79rdPj6HEDU6cAABSdMYMa43uXz47G2qrUKbyGuxeuj7seX5s6\nAwDKkqEOAPLMlZfF4zNnHR5vmjo0dQYAQNGaNqJfXH/JzKipqkidwmu4+s6FsWmnKzABIN8MdQCQ\nZ668LA5/98bxcfHc0akzAACK3nHjW+LfLjgiKmx1BW1bW2d88o4nU2cAQNkx1AFAHi1w5WVRePtR\no+LDJ09InQEAUDLOnD48rj7jsNQZvIZfL9oQd8xfkzoDAMqKoQ4A8qS9qzuudOVlwfurw4fEP591\neOoMAICSc/mxY+J9J4xLncFr+NRdi2Jj697UGQBQNgx1AJAnrrwsfHPHDoivXDg9KivdywQAkAt/\nf9qkeMuskakzeBXb2zrjEz9zBSYA5IuhDgDywJWXhe+woc3xzUtnRV11VeoUAICSds25U+OkyYNT\nZ/Aq7l28MW7/8/OpMwCgLBjqACDH2ru642OuvCxoIwc0xI1XzI7m+prUKQAAJa+qsiKufduMmDW6\nf+oUXsWn71oUG1yBCQA5Z6gDgBz7yj3PxLOuvCxYg/rUxk1XHBUtTfWpUwAAykZ9TVV85x2zY+LB\nTalTeAWte7riH253BSYA5JqhDgByaMHq7fHNh1x5Wagaa6vie++YE4cMakydAgBQdvo21MSNV8yJ\n4f0aUqfwCn779Ma45dHVqTMAoKQZ6gAgR1x5WdgqKyK+etGRMXVE39QpAABla0jf+vj2ZbOisdZ7\nggvVZ3/xVKzbsSd1BgCULEMdAOSIKy8L28dOmRgnTT44dQYAQNmbPLQ5vvzW6VFRkbqEl9O61xWY\nAJBLhjoAyAFXXha2M48YFu97w6GpMwAAeMGpU4bEh06akDqDV/DA0k1xqyswASAnDHUAkGWd3Zm4\n0pWXBWvaiL7xxfOnpc4AAOAl/u6N4+P0qUNTZ/AKrrl7SezY05k6AwBKjqEOALLsu79bEc+48rIg\nDW6qi29eMivqa7wDBQCgEP3rBUfElGHNqTN4GVt3d8SXfrUkdQYAlBxDHQBk0fode+Nr9z2TOoOX\nUVddGd+8ZGYM6VufOgUAgFfQUFsV37p0VgzqU5s6hZfxo0dWxZPP70idAQAlxVAHAFn0uf9cHLs7\nulNn8DKuOXdqTB/VP3UGAACvYVi/hrjhkllRW+Vnq0KT6Ym46s6FqTMAoKT4xgMAWTJv2Zb4+eNr\nU2fwMt59/Ng4d8aI1BkAAOyjmaP7x7+cc3jqDF7GgtXb4+ZHVqXOAICSYagDgCzo7M7E1f5ZWpBO\nmNgS/3DapNQZAADspwtmjYwrjj0kdQYv44u/WhLb2zpSZwBASTDUAUAWfP8PK+OZjbtSZ/AS41r6\nxNcuOjIqKytSpwAA0Av/ePphcfz4QakzeIltbZ3xxV89nToDAEqCoQ4ADtDG1r3x1XufSZ3BS/Rt\nqIlvXzYrmutrUqcAANBLVZUVce3bZsSYQY2pU3iJH/9pVTy+envqDAAoeoY6ADhA//Kfi2NXe1fq\nDF7kv3/QOdIPOgAAJaC5oSa+femsaK6vTp3Ci2R6Iq66c2FkMj2pUwCgqBnqAOAAPLx8S9y5YG3q\nDF7ik6dPjuPGt6TOAAAgS8YNfuFKczeaF5Qnnt8RNz+yKnUGABQ1Qx0A9FJXdyauvnNR6gxe4q2z\nR8blx45JnQEAQJadMHFw/MNfTU6dwUt86b+ejq27O1JnAEDRMtQBQC/dOG9lPL1hZ+oMXmTW6P7x\nz2cdnjoDAIAceffxY+O8GcNTZ/Ai29s64wu/WpI6AwCKlqEOAHph08698ZV7nkmdwYu0NNXFdRfP\njNpqX28AAErZ586dGocPb06dwYvc8ujqWLBqW+oMAChKfskCgF743H8uiZ3tXakzeEFlRcRXL5we\nLU11qVMAAMixuuqquPaiGdGnrjp1Ci/o6Yn45J0LI5PpSZ0CAEXHUAcA++mRFVvjZ/PXpM7gRT5w\n4qFxzLhBqTMAAMiTQwY1xjXnTk2dwYssXNMaP3x4VeoMACg6hjoA2A/dmZ74p58vTJ3Bi8wdOyD+\n7o0TUmcAAJBnbz5iWFw4e2TqDF7kX//r6diyqz11BgAUFUMdAOyHH8xbGYvX7UydwQsGNNbGVy88\nMqoqK1KnAACQwKfOnBITD25KncELduzpjM//aknqDAAoKoY6ANhHm3a2x7/fszR1Bi+oqIj48luO\niIOb61OnAACQSH1NVXz97TOioaYqdQovuO2x5+PPz21LnQEARcNQBwD76PN3L47WvV2pM3jBu48b\nGydMHJw6AwCAxA4d3Cc+c9aU1Bm8oKcn4qo7F0Z3pid1CgAUBUMdAOyDR1dujZ/OX5M6gxfMGNU/\nrjx1YuoMAAAKxAWzRsa5M4anzuAFi9a2xn/88bnUGQBQFAx1APAaujM9cfWdi6LHH0ILQt+Gmvja\nRdOjusrXGAAA/sc/n3V4jGtpTJ3BC75y79Jo3duZOgMACp5fuADgNdz22PPx1LrW1Bm84EvnT4sR\n/Q9KnQEAQIFprKuOa982I+qq/dxVCLa1dcYNDyxPnQEABc83FwB4Fe2d3fGVe5emzuAF7zjmkDhl\nypDUGQAAFKjJQ5vjqjMOS53BC777uxWxsXVv6gwAKGiGOgB4FTfOey7W7XCwLARTh/eNT7xpcuoM\nAAAK3MVzR8fpU4emziAi9nR2x9fueyZ1BgAUNEMdALyC1r2d8Y37n02dQUQ01VXHtW87MmpdYwQA\nwD645rypMWqA69ILwY//tDpWbN6dOgMACpZfuwDgFdzwwPLY3ubl54XgmnOnxuiBjakzAAAoEs31\nNf/9R68qP32l1pXpiX/99dOpMwCgYPm2AgAvY9POvfHd361InUFEXDRnZJxxxLDUGQAAFJlpI/rF\nx/9qYuoMIuI/F66LJ57fnjoDAAqSoQ4AXsZX73s29nR2p84oe5OGNMU/vXlK6gwAAIrUO183Nk6a\nPDh1Rtnr6Yn4/N1LUmcAQEEy1AHAS6zcvDt+/Miq1Bllr7aqMr564ZFRX1OVOgUAgCL2pfOPiJY+\ndakzyt4flm2JB5duSp0BAAXHUAcAL/Hle5ZGV6YndUbZ+9BJ42PikKbUGQAAFLn+jbXxL+ccnjqD\niPjCrzxVBwAvZagDgBdZtHZH3PXE2tQZZe+IEX3jPa8flzoDAIASccqUIXHWdO89Tm3R2tb4+YI1\nqTMAoKAY6gDgRb74q6ejx8N0SdVVV8a/XnBEVFVWpE4BAKCEfPrMKdHS5ArM1P7tnqXR2Z1JnQEA\nBcNQBwAvmLdsSzzgnQnJfeik8TH+YFdeAgCQXf0Oqo3PnTM1dUbZe25LW9zsneAA8BeGOgB4wRe9\nLyG56SP7xbuPd+UlAAC5cfJhB8c5Rw5PnVH2/u99z8bu9q7UGQBQEAx1ABARv1q4Puav3p46o6y5\n8hIAgHz41JlTYrArMJPatKs9vv27FakzAKAgGOoAKHvdmZ740q+fTp1R9j588oQ4dHCf1BkAAJS4\nvg018blzXYGZ2rceXB5bdrWnzgCA5Ax1AJS92x57PpZt2pU6o6wdObJfvOu4sakzAAAoEydNPjjO\nneEKzJR2tXfFtb99NnUGACRnqAOgrLV3dsdX7l2aOqOsufISAIAUPvXmKXFwsyswU/rhH1fF6q1t\nqTMAIClDHQBl7cZ5z8W6HXtTZ5S1j5w8Ica58hIAgDxrbqiJa86dljqjrHV0Z+LL9/jjJADlzVAH\nQNlq3dsZ37jfVSspzRjV35WXAAAkc+KkwXH+zBGpM8ranQvWxOJ1rakzACAZQx0AZeuGB5bH9rbO\n1Bllq666Mr50wbSodOUlAAAJXX3GYa7ATCjTE/GFXy1JnQEAyRjqAChLm3buje/+bkXqjLL2sVMn\nxrgWV14CAJBWc0NNfN4VmEnd//SmWLBqW+oMAEjCUAdAWbr+geWxp7M7dUbZmjm6f7zz2DGpMwAA\nICIi3jBpcFwwyxWYKX3j/mWpEwAgCUMdAGVn2+6OuPmRVakzylZ9TWV86XxXXgIAUFiuOuOwGNq3\nPnVG2bpn8YZYumFn6gwAyDtDHQBl58Z5K6Otw9N0qXzk5Ikx1pWXAAAUmOb6mrjm3KmpM8pWT0/E\ndZ6qA6AMGeoAKCu727vi+39YmTqjbE0a0hRXHHtI6gwAAHhZJ0wcHKcdPiR1Rtm66/G1sXprW+oM\nAMgrQx0AZeXmR1bF9rbO1Bll6zNnHR7VVb5+AABQuK4647BoqKlKnVGWujI9ccODy1NnAEBe+aUM\ngLLR0ZWJbz+0InVG2Tp7+vCYM2ZA6gwAAHhVw/s1xAdOPDR1Rtm69dHVsWnn3tQZAJA3hjoAysbP\n5q+J9a0OfCk01VXHP54+KXUGAADsk3cdNzbGDmpMnVGW2v3BEoAyY6gDoCxkMj1xwwNeTJ7Kh04e\nHy1N9akzAABgn9RWV8anzpySOqNs/fDhVbFjj1cWAFAeDHUAlIW7F66P5Zt3p84oSxMPborLjj4k\ndQYAAOyX4ye0xGmHD0mdUZZ2tXfFjX9YmToDAPLCUAdAWbjugWdTJ5Stz5w9JaqrfOUAAKD4XHXG\nYdFQU5U6oyx9/w8rY09Hd+oMAMg5v5oBUPIeXLopFq5pTZ1Rls6ePjyOGjMwdQYAAPTK8H4N8YET\nD02dUZa27u6Imx9ZlToDAHLOUAdAybvufu+mS6Gprjr+8fRJqTMAAOCAvOu4sTF2UGPqjLL0rYeW\nR0dXJnUGAOSUoQ6AkjZ/1baYt3xL6oyy9KGTx0dLU33qDAAAOCC11ZXxqTOnpM4oS+t27I2fzV+T\nOgMAcspQB0BJ+4an6ZKYeHBTXHb0IakzAAAgK46f0BKnHT4kdUZZuv6BZZHJ9KTOAICcMdQBULKW\nbtgZ9y7ekDqjLH3m7ClRXeVrBgAApeOqMw6Lhpqq1BllZ8Xm3fGfC9elzgCAnPELGgAl6/oHlkWP\nP17m3dnTh8dRYwamzgAAgKwa3q8hPnDioakzypL3jgNQygx1AJSk57e1xc8XrE2dUXaa6qrjE2+a\nlDoDAABy4l3HjY0xgxpTZ5SdRWtb4/6nN6bOAICcMNQBUJK++eDy6PIeg7z70MnjY3BzfeoMAADI\nidrqyvj0mVNSZ5Slr//22dQJAJAThjoASs7mXe1xy6OrU2eUnYkHN8VlRx+SOgMAAHLq+Aktcdrh\nQ1JnlJ0/rdwWf1q5NXUGAGSdoQ6AkvO936+MvZ2Z1Bll59NnTYnqKl8tAAAofVedcVjUVfvum2+e\nqgOgFPlGAUBJ2bm3M26atzJ1Rtl5w8TBMXfswNQZAACQF8P7NcRlxxySOqPs3P/0pli8rjV1BgBk\nlaEOgJLyw4dXRevertQZZaWyIuLjfzUxdQYAAOTV+04YF8311akzys53f78idQIAZJWhDoCS0Z3p\niR/8YWXqjLJz9pHDY9KQ5tQZAACQV/0Oqo2/ef241Bll5+cL1sa23R2pMwAgawx1AJSMe57aEGt3\n7E2dUVZqqyrjIydPSJ0BAABJXH7smBjcVJc6o6y0d2XiJ39anToDALLGUAdAybjR03R5d/HcUTGi\n/0GpMwAAIImG2qr40EnjU2eUnZv++Fx0Z3pSZwBAVhjqACgJSzfsjHnLt6TOKCt96qrj/W84NHUG\nAAAk9ZZZI2PsoMbUGWVlzfY9ce/iDakzACArDHUAlATvpsu/dx0/Ngb2cc0PAADlrbqqMj56ysTU\nGWXn+86AAJQIQx0ARa91b2f8dP6a1BllZVCf2vjr141JnQEAAAXh9GlDY9qIvqkzysq8ZVvi6fU7\nU2cAwAEz1AFQ9G5/7Plo6+hOnVFW/vbE8dFYV506AwAACsbHT5uUOqHs/GDeytQJAHDADHUAFL0f\nzHsudUJZGTXgoHjbUaNSZwAAQEE59tBBcdz4QakzysrP5q+J1j2dqTMA4IAY6gAoag88vTFWbN6d\nOqOsfPSUCVFT5SsEAAC81MdPmxQVFakrykdbR3fc8ujq1BkAcED8ygZAUbvR03R5NWVYc5w1fXjq\nDAAAKEiHD+8bZ0wdmjqjrLhhBYBiZ6gDoGit2tIW9z+9MXVGWfn7UyemTgAAgIL20VMmRk2Vx+ry\nZdXWtnjAuRCAImaoA6Bo3fzIqsj0pK4oH0ePHRivnzg4dQYAABS0QwY1xltne6dzPv3w4VWpEwCg\n1wx1ABSljq5M3PqYdxHk08dP8zQdAADsi79746FxUG1V6oyycd+SjbF+x97UGQDQK4Y6AIrSfy1a\nH5t3daTOKBunHT4kpo/qnzoDAACKQktTfVzxujGpM8pGd6YnfvInT9UBUJwMdQAUpR8+4hCWckpv\nsAAAIABJREFUL1WVFXHlKZ6mAwCA/fGe48dG/4NqUmeUjR//aXV0ezcCAEXIUAdA0Vm+aVfMW7Yl\ndUbZOGPa0Bg3uE/qDAAAKCpN9TWeqsujdTv2xm+WbEidAQD7zVAHQNG5+RHvpsuXioqI954wLnUG\nAAAUpcuOPiT61FWnzigbP/yjm1cAKD6GOgCKSntXd9z2mKEuX944aXBMGtKcOgMAAIpSc0NNXDx3\nVOqMsvHgM5ti9da21BkAsF8MdQAUlbufXB/b2jpTZ5SN951waOoEAAAoau983Zioq/YTXD5keiJu\n9j5zAIqMbwkAFJUfOXTlzdyxA2LG6P6pMwAAoKi1NNXHBTNHpM4oG7c8ujo6uzOpMwBgnxnqACga\nz2zYGY+s2Jo6o2x4mg4AALLjPa8fF1WVFakzysLmXR3x60XrU2cAwD4z1AFQNFxhkj+HD2+O4ye0\npM4AAICSMHLAQfHmacNSZ5SN2x9bkzoBAPaZoQ6AotDVnYk7F6xNnVE2PE0HAADZ9d4TxkWFh+ry\n4qFnNsWWXe2pMwBgnxjqACgKDz2zObbs7kidURbGtTTGaVOGpM4AAICSMnFIU5w0+eDUGWWhK9MT\ndz2xLnUGAOwTQx0AReGOBa4uyZe/ef24qPT+DAAAyLr3nTAudULZuHO+MyQAxcFQB0DBa+voinue\n2pA6oywM61sfZx85PHUGAACUpCNH9Y+jxw5MnVEW5q/eHis3706dAQCvyVAHQMH79aIN0dbRnTqj\nLLzr+LFRU+XrAQAA5Mr73uCpunxxMwsAxcAvcQAUPFeW5MeAxtq4cPao1BkAAFDSjhvfElOH902d\nURbucJYEoAgY6gAoaJt3tcdDz25OnVEWLj/2kGiorUqdAQAAJc9TdfmxcktbzF+1LXUGALwqQx0A\nBe0XT6yL7kxP6oyS16euOi47+pDUGQAAUBZOPWxIjGvpkzqjLPzMU3UAFDhDHQAFzbWX+XHx3FHR\n3FCTOgMAAMpCZWVFvPeEsakzysIvnlgXXd2Z1BkA8IoMdQAUrJWbd8f81dtTZ5S8uurKeOfrxqTO\nAACAsnLW9OExrG996oySt3V3RzywdFPqDAB4RYY6AArWnY+vTZ1QFs6fOSJamvxAAAAA+VRTVRnv\nOt5Tdflwh5taAChghjoACpZrL/PjHccckjoBAADK0vkzR0RjbVXqjJJ3z+INsau9K3UGALwsQx0A\nBenx1dtj+ebdqTNK3tyxA2L8wU2pMwAAoCw11dfEOUcOT51R8vZ2ZuLXC9enzgCAl2WoA6Ag3bnA\n03T5cPHc0akTAACgrF18tO/k+fAzN7YAUKAMdQAUnO5MT9z1+LrUGSVvcFNdnDplSOoMAAAoa5OG\nNMecMQNSZ5S8PyzbHBtb96bOAID/xVAHQMH5/bObY9Ou9tQZJe+iOaOipspXAQAASO0SN13kXKYn\n4s7H16bOAID/xa9zABQc117mXnVlRbztqFGpMwAAgIg47fAh0dJUlzqj5N3h+ksACpChDoCCsqej\nO369aEPqjJJ38mEHx8HN9akzAACAiKipqowLZ49MnVHyFq1tjaUbdqbOAID/h6EOgIJy7+INsau9\nK3VGybvEC+sBAKCgvO2oUVFVWZE6o+R5qg6AQmOoA6Cg3OHay5w7dHCfOGbcoNQZAADAiwzt2xAn\nTT44dUbJu3OB99QBUFgMdQAUjG27O+LBpZtSZ5S8i72oHgAACtIlvqvn3Jrte+Lh5VtSZwDAXxjq\nACgYv3xyXXR296TOKGkH1VbFeTOGp84AAABexuvGD4pxLY2pM0reXU+sS50AAH9hqAOgYNz1uCtI\ncu2cI4dHU31N6gwAAOAVuAEj9+59akPqBAD4C0MdAAVh2+6OePS5bakzSp5DPwAAFLbzZo6Ihpqq\n1BklbX3r3njy+R2pMwAgIgx1ABSI3z69Mbozrr3MpVmj+8fkoc2pMwAAgFfRXF8TZ00fljqj5N27\n2FN1ABQGQx0ABcEhKfcuPdrTdAAAUAx8d889Z1AACoWhDoDkOroy8eDSzakzSlpLn7o47fChqTMA\nAIB9cNiwvjFjVP/UGSVt0drWWLt9T+oMADDUAZDewyu2xK72rtQZJe0ts0dGbbWPfQAAKBaXeKou\n5+5bvDF1AgAY6gBIz+Eot6oqK+JtR41KnQEAAOyHN00dEgMba1NnlDTXXwJQCAx1ACTncJRbx4wb\nGMP7NaTOAAAA9kNddVWcOX1Y6oySNm+Z210ASM9QB0BSS9a3xvPbvBcgl84+cnjqBAAAoBfOnu67\nfC51dGfiwaWbUmcAUOYMdQAk5drL3GqoqYrTpgxJnQEAAPTCESP7xbiWxtQZJc0NLwCkZqgDIKn7\nHIpy6uTDDo7GuurUGQAAQC+d5am6nPrtko3RnelJnQFAGTPUAZDM5l3tsWD19tQZJe0c114CAEBR\n850+t7a1dcajz21NnQFAGTPUAZDMb5dsDH9czJ1BfWrjuPGDUmcAAAAHYOSAg2Lm6P6pM0ravU95\nJQMA6RjqAEjGuwBy64xpw6K6ykc9AAAUO0/V5ZazKQAp+fUOgCTau7rjoWc2p84oaWdPH5Y6AQAA\nyILTpw6NmqqK1Bkla8Xm3bFs467UGQCUKUMdAEnMW7Yl2jq6U2eUrDGDGmP6KNfjAABAKejfWBuv\nn9CSOqOk3eOpOgASMdQBkMS9i70DIJc8TQcAAKXlbNdf5tS9TxnqAEjDUAdAEr/xb8WccogHAIDS\ncvLkg6Oprjp1Rsn686ptsXV3R+oMAMqQoQ6AvFu0dkes3bE3dUbJmjGqX4we2Jg6AwAAyKK6mqo4\nbeqQ1BklK9MT8dslbn4BIP8MdQDknWsvc8vTdAAAUJrO8V0/p7ynDoAUDHUA5N19Dj85U1NVEWdM\n8346AAAoRXPHDIyhfetTZ5Ssh5Zuivau7tQZAJQZQx0AebWxdW88uWZH6oySdfyElhjQWJs6AwAA\nyIHKyoo48wh/zMuV3R3dMW/ZltQZAJQZQx0AeXXfko3R05O6onSdM91VOAAAUMpcdZ9b9z+9KXUC\nAGXGUAdAXt3/tPfT5UpTXXWcdNjBqTMAAIAcmjy0OSYNaUqdUbL+uNwTdQDkl6EOgLzJZHrij8u3\nps4oWacdPiTqa6pSZwAAADnmqbrceXrDzti6uyN1BgBlxFAHQN48vWFn7NjTmTqjZL3ZuyoAAKAs\neE9d7vT0RDy8wlN1AOSPoQ6AvHGFSO401VXH3LEDU2cAAAB5MKxfQ0wZ1pw6o2S5CQaAfDLUAZA3\nhrrcOX5CS9RW+1gHAIBycdJk76fOlT8uc3YFIH/8ogdA3jyywr8Sc+WkwxzSAQCgnBjqcmfpRu+p\nAyB/DHUA5MXida2xrc376XKhurIiTpw0OHUGAACQR1NH9I0hzfWpM0pST48bYQDIH0MdAHnhkJM7\nsw8ZEH0balJnAAAAeeZmjdxxhgUgXwx1AOTFw669zJmTDvM0HQAAlKOTJzsL5IqhDoB8MdQBkBfe\nT5c7J08ekjoBAABI4Ohxg6JPXXXqjJK0dMOu2LKrPXUGAGXAUAdAzi1Z3+pF3DkyfnCfGDXwoNQZ\nAABAArXVlXHc+EGpM0rWH5f7wykAuWeoAyDnHna4yZmTvZMCAADKmvfU5Y7rLwHIB0MdADnncJM7\nJ012KAcAgHJ24sTBUVVZkTqjJM1zlgUgDwx1AOSc99PlRkufupg+sl/qDAAAIKH+jbUxc3T/1Bkl\n6dmNu2Kz99QBkGOGOgByaumGnbHF++ly4g2TBkelf84CAEDZc9NG7rghBoBcM9QBkFMONblz8mGD\nUycAAAAFwLurc+eP3rkOQI4Z6gDIqYcdanKivqYyXndoS+oMAACgAIwZ1BjjWhpTZ5Skecv8+RSA\n3DLUAZBTD69wqMmFYw8dFA21VakzAACAAuH6y9xYtmlXbNrpPXUA5I6hDoCceWbDzti8y/vpcsEh\nHAAAeLGTXH+ZMw97pQMAOWSoAyBnHl7h2stcqKiIOGmy99MBAAD/Y+ao/jGgsTZ1RkmaZ6gDIIcM\ndQDkzB8dZnLiiBH9oqWpPnUGAABQQCorK+LESf7QlwvOtgDkkqEOgJx5eLkn6nLhDRNbUicAAAAF\n6ARnhZxYtml3bNq5N3UGACXKUAdATizbuCs27fLC7VyYO25g6gQAAKAAzR3rrJArj6zYljoBgBJl\nqAMgJx5Z6Wm6XKivqYzpI/ulzgAAAArQoD51MX5wn9QZJWnhmh2pEwAoUYY6AHLiiee3p04oSTNG\n9Y+66qrUGQAAQIE62g0cOfGkoQ6AHDHUAZATC9e0pk4oSa6yAQAAXo0zQ24sXGuoAyA3DHUAZF1n\ndyaeXr8zdUZJ8u9YAADg1cwdOzAqKlJXlJ7tbZ2xaktb6gwASpChDoCsW7phZ3R0Z1JnlJyGmqo4\nYoT30wEAAK9sQGNtTBjclDqjJLn+EoBcMNQBkHUOL7kxc3T/qK320Q0AALw6N3HkxkJnXQBywK99\nAGSd99PlhndNAAAA+8LZITf8KRWAXDDUAZB1ixxecsK/YgEAgH1x1JgB3lOXAwvXOusCkH2GOgCy\nqqs7E0+t80Rdth1UWxXTRvRNnQEAABSB/o21MWmI99Rl2/a2zli9tS11BgAlxlAHQFY9u2lXtHdl\nUmeUnFmj+0dNlY9tAABg3xzt+succP0lANnmFz8AssrLtXPDtZcAAMD+cIbIjSefd+YFILsMdQBk\n1cI1rr3MBS+DBwAA9secMQOj0nvqss4TdQBkm6EOgKxyaMm+xtqqmDrc++kAAIB917ehJiYPbU6d\nUXIWrnXmBSC7DHUAZE0m0xOL13miLttmHzIgqr2fDgAA2E9u5si+7W2dsXprW+oMAEqIX/0AyJrl\nm3dHW0d36oySM9e7JQAAgF7wnrrccJMMANlkqAMgaxxWcsO/YAEAgN6YM2aA99TlwEJnXwCyyFAH\nQNY4rGRfU12199MBAAC90lxfE1OGOU9kmz+pApBNhjoAssZQl30zRvePKn+BBQAAemnOmAGpE0qO\noQ6AbDLUAZA1T61tTZ1QcqaO8O9XAACg99zQkX3b2zpj9da21BkAlAhDHQBZsWLz7tjZ3pU6o+Q4\nVAMAAAfCmSI3PFUHQLYY6gDICtde5oZDNQAAcCDGDe4TfeqqU2eUHGdgALLFUAdAVjikZN/AxtoY\n1q8hdQYAAFDkpgxrTp1QcjxRB0C2GOoAyAqHlOzzNB0AAJANhztbZJ0zMADZYqgDICsWrW1NnVBy\npo5wmAYAAA7cNGeLrNve1hmbd7WnzgCgBBjqADhgG1r3xo49nakzSo4n6gAAgGzwRF1urNi8O3UC\nACXAUAfAAVvpcJITDtMAAEA2jGvpE33qqlNnlBxDHQDZYKgD4ICt2OJwkm2D+tTGsH4NqTMAAIAS\nMWVYc+qEkrNik7MwAAfOUAfAAXM4yT5P0wEAANnkHdjZt9wTdQBkgaEOgAO20hN1Wef9dAAAQDY5\nY2Tf8k27UicAUAIMdQAcsOWeqMs6h2gAACCb3NqRfau2tkUm05M6A4AiZ6gD4IBkMj2xamtb6oyS\n41oaAAAgm8a19ImmuurUGSWlvSsTa7bvSZ0BQJEz1AFwQNbu2BPtXZnUGSVlUJ/aGNq3IXUGAABQ\nYqYMb06dUHJWeE8dAAfIUAfAAXEoyT5X0gAAALngrJF9y52JAThAhjoADoihLvumufYSAADIgWmG\nuqxzJgbgQBnqADggKx1Ksm6qwzMAAJADnqjLvhWbdqVOAKDIGeoAOCCu+cg+h2cAACAXxrb0iaa6\n6tQZJcWZGIADZagD4IB4oi67BvWpjaF9G1JnAAAAJWrK8ObUCSVl7fY90d7ZnToDgCJmqAOg1zq7\nM/H8tj2pM0rKoYP7pE4AAABK2KEtzhzZlOmJWLmlLXUGAEXMUAdAr63e2hZdmZ7UGSVlzKDG1AkA\nAEAJG9PizJFtK9w0A8ABMNQB0GsOI9k31r9bAQCAHBo7yJkj21Zs3pU6AYAiZqgDoNdWbjHUZZsn\n6gAAgFxy5si+5f7ECsABMNQB0GsrNruHP9scmgEAgFwaOeCgqKmqSJ1RUlZsMtQB0HuGOgB6zfUe\n2VVdWRGjBhyUOgMAAChhVZUVMWqAPwhmk9dCAHAgDHUA9NpKT9Rl1X//s9VHMwAAkFtjWwx12bRl\nd0e07ulMnQFAkfJrIAC90t7ZHWt37EmdUVJcewkAAOSDs0f2eU8dAL1lqAOgV1ZuaYuentQVpcW/\nWgEAgHxw9sg+118C0FuGOgB6xSEk+8b6VysAAJAHnqjLvlVbnJEB6B1DHQC98pxDSNaNGdQndQIA\nAFAG/Ekw+za0tqdOAKBIGeoA6JUNrXtTJ5Qc188AAAD50NJUH0111akzSsrGnc7IAPSOoQ6AXtm4\n078Fs6mxtioObq5PnQEAAJSJMf4omFWeqAOgtwx1APTKJkNdVh3i6hkAACCPvKcuuzxRB0BvGeoA\n6BVP1GXXuBbvpwMAAPJnrDNIVm3e1RGZTE/qDACKkKEOgF7Z6B11WeXfrAD8f+zdd3gVZfr/8U9I\nA0ISCAEEEjoEAgEjLaEISpEiiGVFAWliZRfUdVcXdBF+im1VRFdBVLCACio2EJU1rEjvVaQIofck\nENKT+f3BwhdIcmbmnElOEt6va8/lcs5T7lMnM/dTAAAoTg04B3FUbp6hU+cY0AoAsI9EHQDAtrSs\nHJ3LyvV2GGUKiToAAAAAxYlzEOex8gwAwB0k6gAAth1nk2zHNWAjdwAAAADFiESd8zhXBgC4g0Qd\nAMA2Rgk6j5NkAAAAAMUpKNBPNUICvR1GmXLsLFtEAADsI1EHALDtBIk6R4UFBSi4vL+3wwAAAABw\nlalblQGDTmJGHQDAHSTqAAC2HWeUoKMYxQoAAADAG2oEcy7iJFafAQC4g0QdAMA2ZtQ5q3pweW+H\nAAAAAOAqVD2EcxEnHT/DoFYAgH0k6gAAtpGoc1Z1ZtQBAAAA8ILqzKhzFDPqAADuIFEHALCNkw9n\nMaMOAAAAgDcwo85Zx5hRBwBwA4k6AIBt7FHnLPaoAwAAAOANnIs462Qqg1oBAPaRqAMA2MbSl85i\nRh0AAAAAb+BcxFnZuYZOkawDANhEog4AYEtObp5OncvydhhlCqNYAQAAAHgD+2U7j60iAAB2kagD\nANhyMjVLhuHtKMqWamzgDgAAAMALQsr7q4K/r7fDKFOOs08dAMAmEnUAAFtY9tJ5LDcDAAAAwFuY\nVecsZtQBAOwiUQcAsOX4WUYHOqlKRX8F+HE4BgAAAOAd1Vnhw1HHzpCoAwDYw5VBAIAtjA50Vo0Q\nZtMBAAAA8B7OSZzF4FYAgF0k6gAAtrD0pbMYvQoAAADAmzgncRaDWwEAdpGoAwDYwkmHsxi9CgAA\nAMCbqnNO4qjjZ5hRBwCwh0QdAMCWEyzj4ahqjF4FAAAA4EXMqHPW6XNZ3g4BAFDKkKgDANhyKpWT\nDicxow4AAACAN3FO4qxzWbneDgEAUMqQqAMA2HIuK8fbIZQp1UMYvQoAAADAe5hR56xzmZwzAwDs\nIVEHALDlXCajA51UI5jRqwAAAAC8hxl1zkrPzlVenuHtMAAApQiJOgCALWnMqHMUM+oAAAAAeFNI\nBX+V9+cSoVMMQ0rLZoArAMA6jsIAAFuYUeesaiwzAwAAAMDLqrPSh6NY/hIAYAeJOgCAZXl5hjJy\nSNQ5JSjAV4F+vt4OAwAAAMBVrkpFf2+HUKaQqAMA2EGiDgBgWVp2rgyW2ndMUKCft0MAAAAAAM5N\nHMZKNAAAO0jUAQAsS2NUoKM4GQYAAABQEnBu4qxz7O0OALCBRB0AwLJzWYwKdFJQIMteAgAAAPC+\noAASdU5i6UsAgB0k6gAAlqUxKtBRlRi1CgAAAKAEqMQgQkeRqAMA2EGiDgBgGevsO6sio1YBAAAA\nlAAVGUToqFTOnQEANpCoAwBYxow6ZzGjDgAAAEBJwB51zuLcGQBgB4k6AIBlzKhzFnvUAQAAACgJ\nWPrSWSx9CQCwg0QdAMAyRgU6i1GrAAAAAEoCzk2cxdKXAAA7SNQBACw7l8XJhpOC2KMOXuTj4+Py\n1rVrV2+HCNiSlZWlefPm6aGHHlJcXJwiIiIUHBzs8nMOACg5+NvEuzg3cRaDXAEAdnAUBgBYlsby\nHY5i1CoAOGP69Ol66qmndPLkSW+HgqtQVlaWfvvtNyUmJurw4cNKTU1VRkaGAgMDFRwcrNDQUDVo\n0EDNmjVTSEiIt8MFgAJxbuKsVM6dAQA2cBQGAFjGjDpnFfU+ELNmzdKIESMsl+/UqZOWLl3qdn8p\nKSmKiIhQamqq5Tp79+5VvXr13O4TwNXNMAwNGzZMH330kbdDsWXKlCl69NFHLZfftWuXGjVqVIQR\nnffII4/o9ddfv+y+Ro0aadeuXZfdZ/f4cik/Pz8FBAQoJCRE4eHhioyMVJMmTRQbG6uuXbuqbt26\nbsdfnNasWaOvv/5aP/zwgzZt2qTs7GxL9erUqaNu3bqpV69e6tmzpypXrlzEkQKANeyf7Sz2qAMA\n2MHSlwAAy1i+w1kVS9jyMr/++qs2bNjgdv333nvPVpIOADz18ssvl7oknXQ+0VWU5d311Vdf5btv\nwIABjvaRk5OjtLQ0HT16VFu3btX333+v119/XcOHD1e9evXUunVrvfvuu8rJKXl/c+Tl5enDDz/U\ntddeq3bt2um5557T2rVrLSfpJGn//v2aOXOmBg4cqJo1a2r48OFauXJlEUbtLJYmBMoulr50FoNc\nAQB2kKgDAFh2jg2xHVUSl5eZOnWqW/Xy8vL05ptvOhwNABQuOTlZkydP9nYYtm3cuFGbNm2yVeej\njz5SXl5eEUV03oYNG5SYmJjv/ltvvbVI+73S+vXrdd999+naa6/VH3/8Uax9u7J8+XLFxMRo2LBh\ntt+/wmRkZOiDDz5QfHy8I+0BgCdK4rlJacaMOgCAHSTqAACWMaPOWZVK4Mnwp59+6tYeT9988432\n7t1bBBEBQMG++uorpaSkeDsM29yZHbd//379/PPPzgdziYJm09WoUUNxcXFF2m9htm3bpvj4eG3f\nvt0r/V9gGIYmTJigzp07ez0WAChKRb0s/9WGRB0AwI6Sd4UQAFBiMaPOWRUDSt7JcEZGhqZPn67x\n48fbqnflnkYAUNSWLFliWuaaa67RI488opiYGFWsWLHogzKRnZ2tOXPmuFV31qxZ6t69u8MR/Z+C\nEnX9+/dXuXLeG9t5/PhxDRs2TCtXrpSvb/EfM3NycjRkyBB99tlnxd43ABS3qpUCteTxrt4Oo8yo\nUALP9QAAJReJOgCAZY90b6RbY2tJ8rnwP/n4nH/MRz7y+d99Fx/T+QL/K5Kv7P+Knv//Fsqdf+zK\nuj7/93hh9/+vzQs9XKh/5f3/9+9L4/C57DldeOD/nt//xXp57Jf2UfBjEVW8f9G4IG+//baeeOIJ\n+flZ+zNh8+bNli6YA4CTdu3aZVpm7ty56ty5czFEY82CBQt04sQJt+rOnz9fZ86cUUhIiMNRSXv3\n7tXmzZvz3V/cy14WZO3atfr00081ePDgYu3XMAwNHTrUrSRdWFiYqlevrrCwMKWkpOjUqVM6efJk\nidx3DyhJDMPwdghXtXI+PqoXHuTtMAAAuCqRqAMAWNYqsopaRVbxdhgoYocOHdIXX3yhgQMHWirP\nbDoA3nD69GmXjwcFBZWoJJ3k3rKXF6SlpWnu3LkaNWqUcwH9z/z58/PdFxwcrG7dunnUbkJCQr77\ncnJydOTIEf3888+aPXu2srOzTdv5/PPPiz1RN3nyZH3yySeWy0dFRenhhx9Wnz591KhRo3yPp6Wl\nafny5frll1/0ySefaPfu3U6GCwAAAKAUI1EHAADyef311y0l6k6ePOn2Mm4A4In09HSXj4eHhxdT\nJNacOHFCCxcu9KiNWbNmFUmirqBlL/v06aOAgACP2u3atWuhj91zzz166KGH1K1bN6Wmprps57//\n/a9Hcdi1bt06TZgwwVLZwMBAvfzyy3rooYdczkSvWLGiunfvru7du2vixIlatGiRXnnlFf3nP/9x\nKmwAAAAApRSJOgAAkM+KFSu0du1atWnTxmW56dOnKyMjo5iiKlh2drY2bNiggwcP6vTp0xdn2YSF\nhalq1apq1KiRWrRocXEJ0uKUk5Oj1atXa8uWLTp16pQCAgJUu3ZtxcXFqX79+pbb2bFjh9auXavD\nhw/LMAxVq1ZN9erVU8eOHRUYGFiEzyC/lJQUrVy5Urt27VJKSoqCg4NVq1YttWjRQk2bNi2yftPT\n07V+/XodOXLk4vvs5+enqlWrqmrVqmrWrJkaN25cZP2bOXjwoNauXat9+/bp7NmzMgxDlStX1oMP\nPmia7MjLy1NiYqJ+//13HTx4UOfOndO5c+cknb+4HxwcrIiICEVGRqp+/fqqUKFCcTwltxw7dkwb\nN27UqVOndPr0aaWkpKhSpUoKCwtTWFiYoqOjbX32yxIrs8ciIiJ08ODBQh9ftmyZdu/eXeCMLXed\nPHlSy5cvz3f/gAEDHOujMO3atdOf//xnvfDCCy7LJSUlKTMzs9h+70aPHq3cXPN9eUNCQvT111+7\nTEgWxMfHR71791bv3r31+eef69FHH7Udo2EYOnz4sA4ePKhDhw7p+PHjSk9PV3p6unJzcxUYGKjQ\n0FBVr15dDRs2VFRUVLEfL5yWl5enLVu2aO/evTp9+rSSkpKUlZV18ThQt25dxcbGFtl+hmfOnNHy\n5ct16NAhHTt2TIGBgapdu7ZiY2MVFRVVJH1akZqaqvXr1+vo0aNKSkrS6dOn5e/vf/F3t379+oqJ\nifHafpPZ2dlau3attm/frpMnTyojI0MBAQHq0qWLOnToUOzxGIahHTt2aPfu3Rf/nkhPT7/4d2NE\nRIRat27t8UCFgqSlpWnnzp3auXOnTp8+rXPnzik9PV0BAQGqWLGiqlatqsjISEVGRqquDsVYAAAg\nAElEQVROnTpe+dsVAAB4iQEAAMqkmTNnGpJc3vz9/Qt97J577nHZflZWllGrVq1C6wcEBJj2v3fv\nXreeW1JSkvHyyy8bXbt2NSpUqGDaT1hYmDFgwADj22+/NfLy8mz3l5CQYNrHhAkTLpZPTk42xo0b\nZ1StWrXQ8u3btzcWLlxYaJ85OTnGe++9Z0RHRxfaRsWKFY3Bgwcbe/bssf2czJ5Ply5dLiu/fPly\no1+/foafn1+hdVq0aGG8+uqrRlZWlu14CnLkyBFjwoQJRlxcnMvP6oVbzZo1jUGDBhm//PKLW/3Z\nfZ8NwzA+//xzo3379oWWT0pKKrCv3NxcY+7cucadd95phISEmPZ74ebr62s0bdrUGDhwoDFlyhRj\n/fr1Rm5urlvP1ykrVqwwRo4caTRq1MjSc6hVq5YxcOBAY+HChZa/j1beG7u3hISEon1hrtCqVSuX\n8VSqVMn4+uuvTeMeP368o3G99957+foICAgwUlJSCq1j5fgiWTvV/O677yy1dejQIaeeskuLFi2y\n/Bly9RtuR2pqqsvH8/LyjO3btxszZswwRo4cabRt29aoVKmSrc97QECA0aFDB+PFF180jh49aiku\nq++z3duVxxdX0tLSjGnTphm9evWy9FsZHBxs9OrVy/j444+N7Oxsy/248uuvvxo333yzERgYWGi/\njRs3Nl5//fXLjn9WXr+ZM2e6FdO+ffuMJ554wmjdurXh6+tr2k9ISIjRq1cvY/r06UZ6erpbfXbp\n0sXWd/7w4cPGmDFjjNDQ0ALLjh07Nl8fTn52LpWdnW3MmTPHuOWWW4zw8HDTfsqXL2907drV+Pe/\n/22kpaW51ecFx48fNyZPnmzEx8cb5cqVs/w9qVSpktGhQwdj9OjRxieffFJsv4EAAMA7SNQBAFBG\nWblAdPfddxs+Pj6FXtQ7duxYoe3PmTOn0Hbr1q1rdOrUybR/u4m65ORk45FHHrF9gfLSW/PmzY1v\nv/3WVr92EjgJCQnGNddcYzmeESNG5Ets7du3z4iLi7PcRkBAgDF79mxbz8nqxbCMjAzjgQceKPRz\nUtCtRYsWxtq1a23Fc6lDhw4ZI0aMsJTsLewWHx9vLFu2zFa/dt7nlJQUo3///qblC0rULVu2zGje\nvLnbz+3KW1xcnNuvtSf+85//GB06dPAo9ubNmxtz58417au0J+o2bNhgGs+QIUOMnJwco3r16i7L\n1alTx9HkbL9+/fL10atXL5d1vJGoS05OduLpmurdu7eleB5++OFiiccwzifDnfzsBwQEGI8++qhp\ngtCbibqMjAzjmWeesZRUKexWt25dtxNhhnE+gTpy5Ehbx79mzZoZW7Zssfz62Y1vz549xsCBAy0l\n5wq7Va9e3XjuuedsD6qxk6ibP3++UblyZZdliyNRl5uba7z++utGZGSk269XtWrVjJdfftn2725e\nXp4xefJkj/5mvfI2bdo0WzEAAIDSwztrHwAAgBKhSZMm6tWrV4GPZWVladq0aYXWff311wt9bPTo\n0Y4vPbV69WrFxsZqypQppvsZubJt2zb169dPjz32mOkydHZ9+eWX6tmzp44ePWq5zsyZMzVw4EAZ\nhiFJ2r59u+Li4rRy5UrLbWRlZWnIkCGO7xd49uxZ9ejRQ9OnT78YnxVbt25V165d9euvv9ruc+HC\nhWrVqpVmzpyprKws2/UvWLFihbp06aKXXnrJVuxWnDlzRl27dtU333xju+63336rbt26adu2bY7F\nY7ZXm9NycnI0btw4de/evcAlE+3Ytm2b7rzzTt17771KS0tzKMKSZ9asWaZlBg0aJF9fX915550u\ny+3fv18///yzI3GdO3dOP/30U777i2PZywuWLVtmWuaaa65RaGhokcdy+vTpAl+PKwUEBOif//xn\nkcdTVLKysvTaa6+pXbt2OnHihLfDyWfnzp2Ki4vTM888o5MnT7rdTmJiokaMGKFBgwbp7Nmztuqe\nOXNGPXv21Pvvv2/rGPLbb78pPj5ev/zyi91wTc2dO1exsbH67LPPLC3NWpjjx49r/Pjx6ty5sxIT\nEx2M8LxZs2bp9ttvV3JysuNt23H06FH17NlTY8eO1YEDB9xu58SJE/rb3/6mnj176tixY5bqGIah\ne+65R+PGjfPob9YrFffxHgAAFB8SdQAAXOXGjh1b6GPTpk0rMJm1atUqrVq1qsA6FStW1KhRoxyL\nT5Lmz5+vTp06ae/evY61+dprr2nAgAEeXey61KpVqzRo0CC3kn/z58/XlClTdOzYMd100022En0X\nGIahhx9+2KOLUZfKycnRbbfdpqVLl7pVPzU1Vb169dKGDRss13njjTd08803e3Rh9lI5OTl64okn\n9OCDDzrSnnT+db7zzjttPa8Ljhw5omHDhnl9X0dP5OTkqH///nr++ecdTYC+//776ty5s6MXNEuK\n7Oxs0yR6eHi4evToIUm6++67Tdu0kvizYtGiRfk+jz4+Prrlllscad/MunXr9Oabb5qW69OnTzFE\nI/3000/KyckxLTdw4EDVqFGjGCIqWtu3b1f//v1L1MX/lStXqnXr1tq4caNjbX7yySfq2rXrxb0/\nzWRnZ6tXr15uD0RITU3VzTffrK1bt7pVvyDPPfecBg4cqDNnzjjW5qpVqxQbG+vowJH//ve/uu++\n+5SXl+dYm+7Ys2ePYmNj9Z///MexNv/zn/+offv2lv5Gmz59umbPnu1Y3wAAoOwjUQcAwFWuZ8+e\natq0aYGPHTlyRHPnzs13v6vZdPfcc4+qVKniWHyrVq3S4MGDHZ/9Jp2fvTV69GhH2lq0aJEyMzPd\nrj9x4kTdddddOnjwoNttpKSk6IUXXnC7/qWWLVumxYsXe9TGuXPnNHToUEsz47766is98sgjjs9+\nk6R33nnHsdfl/fff1w8//OBW3ddee01JSUmOxOEtDz/8sL7//vsiaXv9+vUaOHCgY8nzkmLBggWm\ns5b+9Kc/yc/PT5IUHx+vunXruiw/f/58Ry7Yf/XVV/nui4uL0zXXXONx25K0ZMmSfLfFixdr9uzZ\nuvfee9WhQwfTmU6+vr567LHHHInHzIoVKyyV69+/fxFHUnxWrlzp2O+jp/bs2aP+/fsXScLezu/L\nxIkTLX8WCnP27Fm98sorHrVxwezZs/XUU0850taVkpKS1LdvX8szxcwMGTLEUrK7KJ06dUq9e/d2\na9CTmcTERN18880uk755eXmaOHGi430DAICyjUQdAABXOR8fH/3lL38p9PGpU6de9u/Dhw/r888/\nL7T8mDFjHIvt7NmzGjBggOXR/nXr1lVcXJzatWtnebbD9OnTC7xY7anw8HC1a9dOMTExKlfO/E+u\nlJQULVmyJN/9jRo1Unx8vOUL5x9//LHlWQN2+Pj4qHHjxoqPj1dUVJSl5ySdXwZz8uTJLsskJiZq\n8ODBlkbglytXTo0aNVKHDh3Upk0by0nhcePGaf369ZbKuuJJIvWLL76wVC4iIkJt27ZV586dFRsb\nq4YNGyowMNDtfp3ywQcfaMaMGZbKXvp5iY6OvpiEMrNw4UI9//zznoRZ4liZ/XbpLDofHx/ddddd\nLsunpaUVOIjCjpycHC1YsCDf/U4ue3nDDTfku/Xo0UNDhgzR+++/bymJ/+yzz6p58+aOxeTKli1b\nLJWLj48v4kis8fX1VWRkpFq0aKF27dqpc+fOiouLU4sWLRQWFma5nalTpzo6U8sdeXl5uu222ywv\nxVmrVi21a9dO8fHxioyMtFRnwYIFeuONN1yW2bx5s63EZbVq1dSuXTu1atVKQUFBlutZtWvXLo0c\nOdJy+Vq1aql9+/Zq1aqVKlWqZKnOhWOwEzw5Rjpl2LBh2rVrl6Wy4eHhatOmjTp06KAGDRpYqrNu\n3TqNHz++0MeXLVtmKUkYEBCgpk2bKj4+Xh06dFDz5s1Vq1Yt+fj4WIoDAACUMV7bHQ8AABSpmTNn\nmm5KP2HCBMMwDCM1NdUIDQ0ttNzKlSsvtjt+/PhCy3Xv3v1iuS5dupj2v3fvXpfP4ZlnnjFto2LF\nisbEiRONQ4cO5au/du1ao0ePHqZtNG3a1MjJySk0joSEBNM2LtyqV69ufP7555e198cffxgtWrSw\n3IYko2PHjsb27dsvtpGXl2e89dZbho+Pj2ndxYsXm308bMVy3333GQcOHLis/pEjR4y//OUvluIJ\nCwsz0tLSCo1l+PDhpm1UrVrVmDJlinHq1KnL6ubl5RkJCQlG69atTdu49PPp6ft84ebv72906dLF\neOihh4zx48cbY8eONfr06WNUrlzZkGQkJSUZhmEYmZmZpq/Vn//853yv8wU5OTnGrl27jPnz5xuP\nPvqoERsba5QrV86QZLRq1cr0/fZUZmamUadOHdPXo1y5csaYMWPyfR9PnTplTJo0yQgMDDRtIyQk\nJN/7nJSUZCQkJFx2q1Gjhst2atSoka/OpbcL701ROn78uOHv7+8yzsjISCMvL++yeps2bbL0G+GJ\nxYsXF9juzp07TetaOb54evP39zdefPFFj56jXQ0bNrT0W1TcVqxYYZQrV87o0KGDMX78eOPrr782\nEhMTXR63DMMwDh06ZEybNs2IiIgwfV7vvvtuvvpHjhzJ970xa6dVq1Yuv3cJCQnGhg0b8vU1a9Ys\n07b9/PyMRx55xNi9e3e++jt27DDuvvtuS+9fSkpKoa/Z0KFDLX0+Y2JijISEhMu+u2lpaca7775r\nhIWFWf6cz5w50+V7OHDgQEvt9OzZM9/rmpGRYcyePdu45pprLLXx008/uYzFyt91V95q165tDB48\n2Hj88ceNJ5980hg+fLjRsmVLw8fHxxg7dmy+Psza69Kli8sYlyxZYimuYcOGGZs3b85Xf//+/caf\n/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5d32X0VKlRQr169iigq66wkmj2ZoWRF1apVLS1Vl5mZqWeffbZIY5GkpUuXmpb529/+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\nXjo7LvznhdkZAACUqOuevi4mzpuYnVHxaqpq4rTtT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5lx1IijslMAgAo1dtbYOOvBs6Kh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\nv7f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      "text/plain": [
       "<matplotlib.figure.Figure at 0x106e4dd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),    \n",
    "             (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),    \n",
    "             (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),    \n",
    "             (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),    \n",
    "             (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]\n",
    "for i in range(len(tableau20)):    \n",
    "    r, g, b = tableau20[i]    \n",
    "    tableau20[i] = (r / 255., g / 255., b / 255.)\n",
    "\n",
    "angle = 0\n",
    "    \n",
    "plt.figure(dpi=300, facecolor='w')\n",
    "_, texts = plt.pie(ab_counts['Counts'],\n",
    "        labels=['A: {:.1f}%'.format(\n",
    "                    ab_counts.loc[0,'Counts'] * 100.0 / ab_counts['Counts'].sum() ),\n",
    "                'B: {:.1f}%'.format(\n",
    "                    ab_counts.loc[1,'Counts'] * 100.0 / ab_counts['Counts'].sum() )\n",
    "               ],\n",
    "        labeldistance = 0.88,\n",
    "        colors = tableau20[::4],\n",
    "        wedgeprops = { 'linewidth' : 7, 'edgecolor' : 'white' },\n",
    "        startangle = angle\n",
    "       )\n",
    "\n",
    "\n",
    "for text in texts:\n",
    "    text.set_color('white')\n",
    "    text.set_fontweight('bold')\n",
    "    text.set_rotation(angle)\n",
    "    text.set_horizontalalignment('center')\n",
    "    \n",
    "plt.axis('equal')\n",
    "p = plt.gcf()\n",
    "p.gca().add_artist(plt.Circle( (0,0), 0.8, color='white'))\n",
    "plt.title('Members of A/B Categories', y = 0.462, fontweight='bold', fontsize = 12)\n",
    "plt.subplots_adjust(right=0.9)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.savefig('ab_test_pie_chart.png', dpi=600)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Who picks up an application?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that the sign-up process for MuscleHub has several steps:\n",
    "1. Take a fitness test with a personal trainer (only Group A)\n",
    "2. Fill out an application for the gym\n",
    "3. Send in their payment for their first month's membership\n",
    "\n",
    "Let's examine how many people make it to Step 2, filling out an application.\n",
    "\n",
    "Start by creating a new column in `df` called `is_application` which is `Application` if `application_date` is not `None` and `No Application`, otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['is_application'] = df['Application'].apply(\n",
    "    lambda x: 'Application' if x != None else 'No Application')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, using `groupby`, count how many people from Group A and Group B either do or don't pick up an application.  You'll want to group by `ab_test_group` and `is_application`.  Save this new DataFrame as `app_counts`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AB_Test_Group</th>\n",
       "      <th>is_application</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>Application</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>No Application</td>\n",
       "      <td>2254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B</td>\n",
       "      <td>Application</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>No Application</td>\n",
       "      <td>2175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  AB_Test_Group  is_application  Count\n",
       "0             A     Application    250\n",
       "1             A  No Application   2254\n",
       "2             B     Application    325\n",
       "3             B  No Application   2175"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_counts = df.groupby(['AB_Test_Group','is_application']).\\\n",
    "    First_Name.count().\\\n",
    "    reset_index(name = 'Count')\n",
    "    \n",
    "app_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to want to calculate the percent of people in each group who complete an application.  It's going to be much easier to do this if we pivot `app_counts` such that:\n",
    "- The `index` is `ab_test_group`\n",
    "- The `columns` are `is_application`\n",
    "Perform this pivot and save it to the variable `app_pivot`.  Remember to call `reset_index()` at the end of the pivot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>is_application</th>\n",
       "      <th>AB_Test_Group</th>\n",
       "      <th>Application</th>\n",
       "      <th>No Application</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>250</td>\n",
       "      <td>2254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>325</td>\n",
       "      <td>2175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_application AB_Test_Group  Application  No Application\n",
       "0                          A          250            2254\n",
       "1                          B          325            2175"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_pivot = app_counts.pivot(\n",
    "    index = 'AB_Test_Group',\n",
    "    columns = 'is_application',\n",
    "    values = 'Count').\\\n",
    "    reset_index()\n",
    "app_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a new column called `Total`, which is the sum of `Application` and `No Application`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "app_pivot['Total'] = app_pivot['Application'] + app_pivot['No Application']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate another column called `Percent with Application`, which is equal to `Application` divided by `Total`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "app_pivot['Percent with Application'] = app_pivot['Application'] / app_pivot['Total']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like more people from Group B turned in an application.  Why might that be?\n",
    "\n",
    "We need to know if this difference is statistically significant.\n",
    "\n",
    "Choose a hypothesis tests, import it from `scipy` and perform it.  Be sure to note the p-value.\n",
    "Is this result significant?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>is_application</th>\n",
       "      <th>AB_Test_Group</th>\n",
       "      <th>Application</th>\n",
       "      <th>No Application</th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent with Application</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>250</td>\n",
       "      <td>2254</td>\n",
       "      <td>2504</td>\n",
       "      <td>0.09984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>325</td>\n",
       "      <td>2175</td>\n",
       "      <td>2500</td>\n",
       "      <td>0.13000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_application AB_Test_Group  Application  No Application  Total  \\\n",
       "0                          A          250            2254   2504   \n",
       "1                          B          325            2175   2500   \n",
       "\n",
       "is_application  Percent with Application  \n",
       "0                                0.09984  \n",
       "1                                0.13000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.000001 | 0.000965 -  null hypothesis can be rejected!\n",
      "Signficant difference from untreated group (A).\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import binom_test, chi2_contingency\n",
    "\n",
    "p_val = binom_test(x = [app_pivot.loc[1,'Application'], app_pivot.loc[1,'No Application']],\n",
    "           p = app_pivot.loc[0,'Percent with Application'], alternative='greater')\n",
    "\n",
    "\n",
    "chi_p_val= chi2_contingency([app_pivot.loc[0,['Application','No Application']],\n",
    "                 app_pivot.loc[1,['Application','No Application']]])[1]\n",
    "\n",
    "\n",
    "print('p-value: {:.6f} | {:.6f} - '.format(p_val, chi_p_val)),\n",
    "if p_val > 0.05 or chi_p_val > 0.05:\n",
    "    print('null hypothesis cannot be rejected.')\n",
    "else:\n",
    "    print('null hypothesis can be rejected!\\nSignficant difference from untreated group (A).')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Who purchases a membership?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of those who picked up an application, how many purchased a membership?\n",
    "\n",
    "Let's begin by adding a column to `df` called `is_member` which is `Member` if `purchase_date` is not `None`, and `Not Member` otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['is_member'] = df['Purchase'].apply(\n",
    "                    lambda x: 'Member' if x != None else 'Not Member')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's create a DataFrame called `just_apps` the contains only people who picked up an application."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First_Name</th>\n",
       "      <th>Last_Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>email</th>\n",
       "      <th>Visit</th>\n",
       "      <th>Fitness_Test</th>\n",
       "      <th>Application</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>AB_Test_Group</th>\n",
       "      <th>is_application</th>\n",
       "      <th>is_member</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Edward</td>\n",
       "      <td>Bowen</td>\n",
       "      <td>male</td>\n",
       "      <td>Edward.Bowen@gmail.com</td>\n",
       "      <td>7-1-17</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>B</td>\n",
       "      <td>Application</td>\n",
       "      <td>Member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Marcus</td>\n",
       "      <td>Bauer</td>\n",
       "      <td>male</td>\n",
       "      <td>Marcus.Bauer@gmail.com</td>\n",
       "      <td>7-1-17</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>2017-07-05</td>\n",
       "      <td>A</td>\n",
       "      <td>Application</td>\n",
       "      <td>Member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Salvador</td>\n",
       "      <td>Cardenas</td>\n",
       "      <td>male</td>\n",
       "      <td>SCardenas1980@gmail.com</td>\n",
       "      <td>7-1-17</td>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>2017-07-06</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>Application</td>\n",
       "      <td>Not Member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Valerie</td>\n",
       "      <td>Munoz</td>\n",
       "      <td>female</td>\n",
       "      <td>VMunoz1998@gmail.com</td>\n",
       "      <td>7-1-17</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>2017-07-05</td>\n",
       "      <td>2017-07-06</td>\n",
       "      <td>A</td>\n",
       "      <td>Application</td>\n",
       "      <td>Member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Burks</td>\n",
       "      <td>male</td>\n",
       "      <td>MB9820@gmail.com</td>\n",
       "      <td>7-1-17</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>2017-07-13</td>\n",
       "      <td>B</td>\n",
       "      <td>Application</td>\n",
       "      <td>Member</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First_Name Last_Name  Gender                    email   Visit Fitness_Test  \\\n",
       "2      Edward     Bowen    male   Edward.Bowen@gmail.com  7-1-17         None   \n",
       "3      Marcus     Bauer    male   Marcus.Bauer@gmail.com  7-1-17   2017-07-01   \n",
       "9    Salvador  Cardenas    male  SCardenas1980@gmail.com  7-1-17   2017-07-07   \n",
       "11    Valerie     Munoz  female     VMunoz1998@gmail.com  7-1-17   2017-07-03   \n",
       "35    Michael     Burks    male         MB9820@gmail.com  7-1-17         None   \n",
       "\n",
       "   Application    Purchase AB_Test_Group is_application   is_member  \n",
       "2   2017-07-04  2017-07-04             B    Application      Member  \n",
       "3   2017-07-03  2017-07-05             A    Application      Member  \n",
       "9   2017-07-06        None             A    Application  Not Member  \n",
       "11  2017-07-05  2017-07-06             A    Application      Member  \n",
       "35  2017-07-07  2017-07-13             B    Application      Member  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "just_apps = df[df['is_application'] == 'Application']\n",
    "just_apps.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Now, let's do a `groupby` to find out how many people in `just_apps` are and aren't members from each group.  Follow the same process that we did in Step 4, including pivoting the data.  You should end up with a DataFrame that looks like this:\n",
    "\n",
    "|is_member|ab_test_group|Member|Not Member|Total|Percent Purchase|\n",
    "|-|-|-|-|-|-|\n",
    "|0|A|?|?|?|?|\n",
    "|1|B|?|?|?|?|\n",
    "\n",
    "Save your final DataFrame as `member_pivot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>is_member</th>\n",
       "      <th>AB_Test_Group</th>\n",
       "      <th>Member</th>\n",
       "      <th>Not Member</th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>200</td>\n",
       "      <td>50</td>\n",
       "      <td>250</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>250</td>\n",
       "      <td>75</td>\n",
       "      <td>325</td>\n",
       "      <td>0.769231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_member AB_Test_Group  Member  Not Member  Total  Percent Purchase\n",
       "0                     A     200          50    250          0.800000\n",
       "1                     B     250          75    325          0.769231"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_pivot = just_apps.groupby(['is_member','AB_Test_Group']).\\\n",
    "    First_Name.count().\\\n",
    "    reset_index(name = 'Count').pivot(\n",
    "    index = 'AB_Test_Group',\n",
    "    columns = 'is_member',\n",
    "    values = 'Count').\\\n",
    "    reset_index()\n",
    "    \n",
    "member_pivot['Total'] = member_pivot['Member'] + member_pivot['Not Member']\n",
    "member_pivot['Percent Purchase'] = member_pivot['Member'] / member_pivot['Total']\n",
    "member_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like people who took the fitness test were more likely to purchase a membership **if** they picked up an application.  Why might that be?\n",
    "\n",
    "Just like before, we need to know if this difference is statistically significant.  Choose a hypothesis tests, import it from `scipy` and perform it.  Be sure to note the p-value.\n",
    "Is this result significant?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.925357 | 0.432586 -  null hypothesis cannot be rejected.\n"
     ]
    }
   ],
   "source": [
    "p_val = binom_test(\n",
    "        x = [member_pivot.loc[1,'Member'], member_pivot.loc[1,'Not Member']],\n",
    "        p = member_pivot.loc[0,'Percent Purchase'], alternative='greater')\n",
    "\n",
    "\n",
    "chi_p_val= chi2_contingency([member_pivot.loc[0,['Member','Not Member']],\n",
    "                 member_pivot.loc[1,['Member','Not Member']]])[1]\n",
    "\n",
    "\n",
    "print('p-value: {:.6f} | {:.6f} - '.format(p_val, chi_p_val)),\n",
    "if p_val > 0.05 or chi_p_val > 0.05:\n",
    "    print('null hypothesis cannot be rejected.')\n",
    "else:\n",
    "    print('null hypothesis can be rejected!\\nSignficant difference from untreated group. (A)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previously, we looked at what percent of people **who picked up applications** purchased memberships.  What we really care about is what percentage of **all visitors** purchased memberships.  Return to `df` and do a `groupby` to find out how many people in `df` are and aren't members from each group.  Follow the same process that we did in Step 4, including pivoting the data.  You should end up with a DataFrame that looks like this:\n",
    "\n",
    "|is_member|ab_test_group|Member|Not Member|Total|Percent Purchase|\n",
    "|-|-|-|-|-|-|\n",
    "|0|A|?|?|?|?|\n",
    "|1|B|?|?|?|?|\n",
    "\n",
    "Save your final DataFrame as `final_member_pivot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>is_member</th>\n",
       "      <th>AB_Test_Group</th>\n",
       "      <th>Member</th>\n",
       "      <th>Not Member</th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>200</td>\n",
       "      <td>2304</td>\n",
       "      <td>2504</td>\n",
       "      <td>0.079872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>250</td>\n",
       "      <td>2250</td>\n",
       "      <td>2500</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_member AB_Test_Group  Member  Not Member  Total  Percent Purchase\n",
       "0                     A     200        2304   2504          0.079872\n",
       "1                     B     250        2250   2500          0.100000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_member_pivot = df.groupby(['is_member','AB_Test_Group']).\\\n",
    "    First_Name.count().\\\n",
    "    reset_index(name = 'Count').pivot(\n",
    "    index = 'AB_Test_Group',\n",
    "    columns = 'is_member',\n",
    "    values = 'Count').\\\n",
    "    reset_index()\n",
    "    \n",
    "final_member_pivot['Total'] = final_member_pivot['Member'] +\\\n",
    "                                final_member_pivot['Not Member']\n",
    "final_member_pivot['Percent Purchase'] = final_member_pivot['Member'] /\\\n",
    "                                final_member_pivot['Total']\n",
    "final_member_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previously, when we only considered people who had **already picked up an application**, we saw that there was no significant difference in membership between Group A and Group B.\n",
    "\n",
    "Now, when we consider all people who **visit MuscleHub**, we see that there might be a significant different in memberships between Group A and Group B.  Perform a significance test and check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.000186 | 0.014724 -  null hypothesis can be rejected!\n",
      "Signficant difference from untreated group. (A)\n"
     ]
    }
   ],
   "source": [
    "p_val = binom_test(\n",
    "        x = [final_member_pivot.loc[1,'Member'], final_member_pivot.loc[1,'Not Member']],\n",
    "        p = final_member_pivot.loc[0,'Percent Purchase'], alternative='greater')\n",
    "\n",
    "\n",
    "chi_p_val= chi2_contingency([final_member_pivot.loc[0,['Member','Not Member']],\n",
    "                 final_member_pivot.loc[1,['Member','Not Member']]])[1]\n",
    "\n",
    "\n",
    "print('p-value: {:.6f} | {:.6f} - '.format(p_val, chi_p_val)),\n",
    "if p_val > 0.05 or chi_p_val > 0.05:\n",
    "    print('null hypothesis cannot be rejected.')\n",
    "else:\n",
    "    print('null hypothesis can be rejected!\\nSignficant difference from untreated group. (A)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Summarize the acquisition funel with a chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'd like to make a bar chart for Janet that shows the difference between Group A (people who were given the fitness test) and Group B (people who were not given the fitness test) at each state of the process:\n",
    "- Percent of visitors who apply\n",
    "- Percent of applicants who purchase a membership\n",
    "- Percent of visitors who purchase a membership\n",
    "\n",
    "Create one plot for **each** of the three sets of percentages that you calculated in `app_pivot`, `member_pivot` and `final_member_pivot`.  Each plot should:\n",
    "- Label the two bars as `Fitness Test` and `No Fitness Test`\n",
    "- Make sure that the y-axis ticks are expressed as percents (i.e., `5%`)\n",
    "- Have a title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C+nmAjYHbm5ZJMzP7kKbVWUg6HdgV2BGYIqlWR/FWRLwVESHpZOAwSY8BTwA/B94CLmxW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\nzMyskoOFmZlV+v850uzG0PEcRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108fd8810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "plt.bar(\n",
    "    range(app_pivot.shape[0]),\n",
    "    app_pivot['Percent with Application']*100,\n",
    "    yerr = None,\n",
    "    capsize=5,\n",
    "    color = tableau20[5],\n",
    "    alpha = 1)\n",
    "plt.title('Percentage of Visitors Who Apply', fontsize=16, y = 1.1)\n",
    "ax.set_xticks(range(app_pivot.shape[0]))\n",
    "ax.set_yticks(range(0,int(app_pivot['Percent with Application'].max()*100 + 10),5))\n",
    "ax.set_xticklabels(['Fitness Test', 'No Fitness Test'])\n",
    "ax.set_ylabel('Applied (%)', fontsize=14)\n",
    "ax.set_xlabel('Test Group', fontsize=14)\n",
    "ax.get_xaxis().tick_bottom()  \n",
    "ax.get_yaxis().tick_left()  \n",
    "ax.spines[\"top\"].set_visible(False)  \n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "plt.xticks(fontsize=14, rotation=0)  \n",
    "plt.yticks(fontsize=14) \n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KiBtK44eSukR/Y3kRMRe4Edi8yXWYmdlipunGCJK25s0uy++LiH80Me93gLVJ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\nQDIzs0pwQDIzs0r4f2JO7LGjJEw0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108fcbe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "plt.bar(\n",
    "    range(member_pivot.shape[0]),\n",
    "    member_pivot['Percent Purchase']*100,\n",
    "    yerr = None,\n",
    "    capsize=5,\n",
    "    color = tableau20[7],\n",
    "    alpha = 1)\n",
    "plt.title('Percentage of Applicants Who Become Members', fontsize=16, y = 1.1)\n",
    "ax.set_xticks(range(member_pivot.shape[0]))\n",
    "ax.set_yticks(range(0,int(member_pivot['Percent Purchase'].max()*100 + 10),10))\n",
    "ax.set_xticklabels(['Fitness Test', 'No Fitness Test'])\n",
    "ax.set_ylabel('Members (%)', fontsize=14)\n",
    "ax.set_xlabel('Test Group', fontsize=14)\n",
    "ax.get_xaxis().tick_bottom()  \n",
    "ax.get_yaxis().tick_left()  \n",
    "ax.spines[\"top\"].set_visible(False)  \n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "plt.xticks(fontsize=14, rotation=0)  \n",
    "plt.yticks(fontsize=14) \n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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F8c7K453SQv8jS+vle3k91Nb/ONKBu2BL6zl3OyKvn1qJcFATy7ZJHmfzOvvLeXmcd0nV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\ni5mZVc7JxczMKufkYmZmlfv/DPd/bGha4SoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d0418d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "plt.bar(\n",
    "    range(final_member_pivot.shape[0]),\n",
    "    final_member_pivot['Percent Purchase']*100,\n",
    "    yerr = None,\n",
    "    capsize=5,\n",
    "    color = tableau20[9],\n",
    "    alpha = 1)\n",
    "plt.title('Percentage of Visitors Who Become Members', fontsize=16, y = 1.1)\n",
    "ax.set_xticks(range(final_member_pivot.shape[0]))\n",
    "ax.set_yticks(range(0,int(final_member_pivot['Percent Purchase'].max()*100 + 10),5))\n",
    "ax.set_xticklabels(['Fitness Test', 'No Fitness Test'])\n",
    "ax.set_ylabel('Members (%)', fontsize=14)\n",
    "ax.set_xlabel('Test Group', fontsize=14)\n",
    "ax.get_xaxis().tick_bottom()  \n",
    "ax.get_yaxis().tick_left()  \n",
    "ax.spines[\"top\"].set_visible(False)  \n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "plt.xticks(fontsize=14, rotation=0)  \n",
    "plt.yticks(fontsize=14) \n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show Funnel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([2504, 250, 200], [2500, 325, 250])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [\n",
    "    app_pivot[app_pivot['AB_Test_Group'] == 'A']['Total'].values[0],\n",
    "    app_pivot[app_pivot['AB_Test_Group'] == 'A']['Application'].values[0],\n",
    "    final_member_pivot[final_member_pivot['AB_Test_Group'] == 'A']['Member'].values[0],\n",
    "]\n",
    "\n",
    "b = [\n",
    "    app_pivot[app_pivot['AB_Test_Group'] == 'B']['Total'].values[0],\n",
    "    app_pivot[app_pivot['AB_Test_Group'] == 'B']['Application'].values[0],\n",
    "    final_member_pivot[final_member_pivot['AB_Test_Group'] == 'B']['Member'].values[0],\n",
    "]\n",
    "a,b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jj/Tr14/777+fli1bUqNGDT7++ONT/V566SWaNm1KVFQUw4YNAyAxMZEuXbrQsGFD6tev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\nzHXr1Z2e3ns9wSI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zNjRT0NSuEM7YWxpx+7RfqVyqF4MaRlOvXD1fh2WM8XNZGUyx3+v1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d11a350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "plt.plot(\n",
    "    range(len(a)),\n",
    "    a,\n",
    "    color = tableau20[0],\n",
    "    alpha = 1,\n",
    "    marker = 'o',\n",
    "    label = 'Fitness Test')\n",
    "plt.plot(\n",
    "    range(len(b)),\n",
    "    b,\n",
    "    color = tableau20[4],\n",
    "    alpha = 1,\n",
    "    linestyle= '--',\n",
    "    marker = 'o',\n",
    "    label = 'No Fitness Test')\n",
    "plt.title('Visitors in Each Test Group', fontsize=16, y = 1.1)\n",
    "ax.set_xticks(range(len(a)))\n",
    "ax.set_yticks(range(0,int(max(max(a,b))*1.10),int(round(max(max(a,b))*.1,-2))))\n",
    "ax.set_xticklabels(['Vistors in Group','Applications', 'Members'])\n",
    "ax.set_ylabel('Number', fontsize=14)\n",
    "ax.set_xlabel('Step in Funnel', fontsize=14, fontweight = 'bold')\n",
    "ax.get_xaxis().tick_bottom()  \n",
    "ax.get_yaxis().tick_left()  \n",
    "ax.spines[\"top\"].set_visible(False)  \n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "plt.xticks(fontsize=14, rotation=0)  \n",
    "plt.yticks(fontsize=14) \n",
    "plt.legend()\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([2504, -2254, -50, -200], [2500, -75, -2175, -250])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_flow = [app_pivot[app_pivot['AB_Test_Group'] == 'A']['Total'].values[0],\n",
    "         -app_pivot[app_pivot['AB_Test_Group'] == 'A']['No Application'].values[0],\n",
    "         -member_pivot[member_pivot['AB_Test_Group'] == 'A']['Not Member'].values[0],\n",
    "         -final_member_pivot[final_member_pivot['AB_Test_Group'] == 'A']\\\n",
    "          ['Member'].values[0]]\n",
    "#a_flow = [float(x) * 100 / a_flow[0] for x  in a_flow]\n",
    "\n",
    "b_flow = [app_pivot[app_pivot['AB_Test_Group'] == 'B']['Total'].values[0],\n",
    "         -member_pivot[member_pivot['AB_Test_Group'] == 'B']['Not Member'].values[0],\n",
    "         -app_pivot[app_pivot['AB_Test_Group'] == 'B']['No Application'].values[0],\n",
    "         -final_member_pivot[final_member_pivot['AB_Test_Group'] == 'B']\\\n",
    "          ['Member'].values[0]]\n",
    "#b_flow = [float(x) * 100/ b_flow[0] for x  in b_flow]\n",
    "\n",
    "a_flow, b_flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.sankey import Sankey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lCSwBAAAAABZWVX+c5JdX3cca7ZVpMe+yYRsjgjZusaJ9R9VZ9mc4IviF5fzB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\nwpZlPgw72sszzX9Yn2tmWsC51kWcq/CFTIFMa7JYJPpzmRb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dlzwIldLyhJmqNl5kzgvNYHEbEC8ApgcWBBYBrFe/39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\n0qu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ATmALOBbqANWA88EBGdIxZQkjTenAL8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iC+B9RXUzdz+c9s7ymSaS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NsD+DDw1j94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pqj4i2O+k/Zi2x7Rmyv96TOEkSdKuLgEu6HYISZIktc6FJZIkTT7vBjYXFdT6a6z61qqK4kiS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\ndOA4YGFE/CAiDoyIaEl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bJtjy5S24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KGFdstOREREREREREQWsoYjGmg9qTWrrqS/V6EvLYiIiIiIiIiIiIiIiCxy+963ygvEzM4C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\nblKjuf+uLW7Ea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ngNsi4vsR8cmIeFtEvDYi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\nBNqft+1UHccfrFmvXzo9L0ezKQwdWAKwH88LLImI8cBebfbPB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d3be750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['', 'Did Not Apply', 'Applied but\\nDid Not\\nBecome Member' , 'Became\\nMember']\n",
    "labels2 = ['', 'Applied but\\nDid Not\\nBecome Member', 'Did Not Apply', 'Became\\nMember']\n",
    "\n",
    "plt.close()\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(7,7.2), dpi=600, frameon=False)\n",
    "\n",
    "ax = fig.add_subplot(2, 1, 1, xticks=[], yticks=[], frameon=False,)\n",
    "ax.set_title(\"Flow Diagram of MuscleHub Visitors\",y = 1.1, fontweight='bold')\n",
    "ax2 = fig.add_subplot(2,1,2, xticks=[], yticks=[], frameon=False,)\n",
    "\n",
    "sankey = Sankey(ax=ax, scale=0.0005, offset=0.65, head_angle=120,\n",
    "                format = '%d', unit=' visitors', gap=1, \n",
    "                radius=0.05, shoulder=0.05,)\n",
    "sankey.add(flows = a_flow,\n",
    "            labels = labels,\n",
    "            orientations= [0, 1, 1, 0],\n",
    "            pathlengths=[0, 1, 2.5, 1],\n",
    "            trunklength=5.,\n",
    "            facecolor=tableau20[0],\n",
    "            label=\"Fitness Test\"\n",
    "          ).finish()\n",
    "\n",
    "sankey2 = Sankey(ax=ax2, scale=0.0005, offset=0.65, head_angle=120,\n",
    "                format = '%d', unit=' visitors', gap=1, \n",
    "                radius=0.04, shoulder=0.05,)\n",
    "sankey2.add(flows = b_flow,\n",
    "            labels = labels2,\n",
    "            orientations= [0, -1, -1, 0],\n",
    "            pathlengths=[0, 2.5, 1, 1],\n",
    "            trunklength=5.,\n",
    "            facecolor=tableau20[4],\n",
    "            label=\"No Fitness Test\"\n",
    "          ).finish()\n",
    "\n",
    "ax.text(0.25, 0.25, 'Fitness Test', horizontalalignment='center',\n",
    "        verticalalignment='center', transform=ax.transAxes, fontweight='bold')\n",
    "ax2.text(0.25, -0.3, 'No Fitness Test', horizontalalignment='center',\n",
    "        verticalalignment='center', transform=ax.transAxes, fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py27]",
   "language": "python",
   "name": "conda-env-py27-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}