Starbucks_Capstone_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Starbucks Capstone Challenge\n",
    "\n",
    "### Introduction\n",
    "\n",
    "This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks. \n",
    "\n",
    "Not all users receive the same offer, and that is the challenge to solve with this data set.\n",
    "\n",
    "Your task is to combine transaction, demographic and offer data to determine which demographic groups respond best to which offer type. This data set is a simplified version of the real Starbucks app because the underlying simulator only has one product whereas Starbucks actually sells dozens of products.\n",
    "\n",
    "Every offer has a validity period before the offer expires. As an example, a BOGO offer might be valid for only 5 days. You'll see in the data set that informational offers have a validity period even though these ads are merely providing information about a product; for example, if an informational offer has 7 days of validity, you can assume the customer is feeling the influence of the offer for 7 days after receiving the advertisement.\n",
    "\n",
    "You'll be given transactional data showing user purchases made on the app including the timestamp of purchase and the amount of money spent on a purchase. This transactional data also has a record for each offer that a user receives as well as a record for when a user actually views the offer. There are also records for when a user completes an offer. \n",
    "\n",
    "Keep in mind as well that someone using the app might make a purchase through the app without having received an offer or seen an offer.\n",
    "\n",
    "### Example\n",
    "\n",
    "To give an example, a user could receive a discount offer buy 10 dollars get 2 off on Monday. The offer is valid for 10 days from receipt. If the customer accumulates at least 10 dollars in purchases during the validity period, the customer completes the offer.\n",
    "\n",
    "However, there are a few things to watch out for in this data set. Customers do not opt into the offers that they receive; in other words, a user can receive an offer, never actually view the offer, and still complete the offer. For example, a user might receive the \"buy 10 dollars get 2 dollars off offer\", but the user never opens the offer during the 10 day validity period. The customer spends 15 dollars during those ten days. There will be an offer completion record in the data set; however, the customer was not influenced by the offer because the customer never viewed the offer.\n",
    "\n",
    "### Cleaning\n",
    "\n",
    "This makes data cleaning especially important and tricky.\n",
    "\n",
    "You'll also want to take into account that some demographic groups will make purchases even if they don't receive an offer. From a business perspective, if a customer is going to make a 10 dollar purchase without an offer anyway, you wouldn't want to send a buy 10 dollars get 2 dollars off offer. You'll want to try to assess what a certain demographic group will buy when not receiving any offers.\n",
    "\n",
    "### Final Advice\n",
    "\n",
    "Because this is a capstone project, you are free to analyze the data any way you see fit. For example, you could build a machine learning model that predicts how much someone will spend based on demographics and offer type. Or you could build a model that predicts whether or not someone will respond to an offer. Or, you don't need to build a machine learning model at all. You could develop a set of heuristics that determine what offer you should send to each customer (i.e., 75 percent of women customers who were 35 years old responded to offer A vs 40 percent from the same demographic to offer B, so send offer A)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Sets\n",
    "\n",
    "The data is contained in three files:\n",
    "\n",
    "* portfolio.json - containing offer ids and meta data about each offer (duration, type, etc.)\n",
    "* profile.json - demographic data for each customer\n",
    "* transcript.json - records for transactions, offers received, offers viewed, and offers completed\n",
    "\n",
    "Here is the schema and explanation of each variable in the files:\n",
    "\n",
    "**portfolio.json**\n",
    "* id (string) - offer id\n",
    "* offer_type (string) - type of offer ie BOGO, discount, informational\n",
    "* difficulty (int) - minimum required spend to complete an offer\n",
    "* reward (int) - reward given for completing an offer\n",
    "* duration (int) - time for offer to be open, in days\n",
    "* channels (list of strings)\n",
    "\n",
    "**profile.json**\n",
    "* age (int) - age of the customer \n",
    "* became_member_on (int) - date when customer created an app account\n",
    "* gender (str) - gender of the customer (note some entries contain 'O' for other rather than M or F)\n",
    "* id (str) - customer id\n",
    "* income (float) - customer's income\n",
    "\n",
    "**transcript.json**\n",
    "* event (str) - record description (ie transaction, offer received, offer viewed, etc.)\n",
    "* person (str) - customer id\n",
    "* time (int) - time in hours since start of test. The data begins at time t=0\n",
    "* value - (dict of strings) - either an offer id or transaction amount depending on the record\n",
    "\n",
    "**Note:** If you are using the workspace, you will need to go to the terminal and run the command `conda update pandas` before reading in the files. This is because the version of pandas in the workspace cannot read in the transcript.json file correctly, but the newest version of pandas can. You can access the termnal from the orange icon in the top left of this notebook.  \n",
    "\n",
    "You can see how to access the terminal and how the install works using the two images below.  First you need to access the terminal:\n",
    "\n",
    "<img src=\"images/pic1.png\"/>\n",
    "\n",
    "Then you will want to run the above command:\n",
    "\n",
    "<img src=\"images/pic2.png\"/>\n",
    "\n",
    "Finally, when you enter back into the notebook (use the jupyter icon again), you should be able to run the below cell without any errors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Workflows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have three assumptions belows:\n",
    "\n",
    "<img src=\"images/pic3.png\" width=\"600\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import json\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the json files\n",
    "portfolio = pd.read_json('data/portfolio.json', orient='records', lines=True)\n",
    "profile = pd.read_json('data/profile.json', orient='records', lines=True)\n",
    "transcript = pd.read_json('data/transcript.json', orient='records', lines=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1. Portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reward</th>\n",
       "      <th>channels</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>informational</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>[web, email]</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>discount</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>discount</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>discount</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>informational</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>bogo</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>discount</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
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      "text/plain": [
       "   reward                      channels  difficulty  duration     offer_type  \\\n",
       "0      10       [email, mobile, social]          10         7           bogo   \n",
       "1      10  [web, email, mobile, social]          10         5           bogo   \n",
       "2       0          [web, email, mobile]           0         4  informational   \n",
       "3       5          [web, email, mobile]           5         7           bogo   \n",
       "4       5                  [web, email]          20        10       discount   \n",
       "5       3  [web, email, mobile, social]           7         7       discount   \n",
       "6       2  [web, email, mobile, social]          10        10       discount   \n",
       "7       0       [email, mobile, social]           0         3  informational   \n",
       "8       5  [web, email, mobile, social]           5         5           bogo   \n",
       "9       2          [web, email, mobile]          10         7       discount   \n",
       "\n",
       "                                 id  \n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd  \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0  \n",
       "2  3f207df678b143eea3cee63160fa8bed  \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9  \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7  \n",
       "5  2298d6c36e964ae4a3e7e9706d1fb8c2  \n",
       "6  fafdcd668e3743c1bb461111dcafc2a4  \n",
       "7  5a8bc65990b245e5a138643cd4eb9837  \n",
       "8  f19421c1d4aa40978ebb69ca19b0e20d  \n",
       "9  2906b810c7d4411798c6938adc9daaa5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* offer: new  name\n",
    "* channels: categorial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>reward</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>id</th>\n",
       "      <th>offer</th>\n",
       "      <th>email</th>\n",
       "      <th>social</th>\n",
       "      <th>mobile</th>\n",
       "      <th>web</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>bogo_10_7_10</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "      <td>bogo</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>bogo_5_7_5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>discount</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>discount_20_10_5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   reward  difficulty  duration     offer_type  \\\n",
       "0      10          10         7           bogo   \n",
       "1      10          10         5           bogo   \n",
       "2       0           0         4  informational   \n",
       "3       5           5         7           bogo   \n",
       "4       5          20        10       discount   \n",
       "\n",
       "                                 id                offer  email  social  \\\n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd         bogo_10_7_10      1       1   \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0         bogo_10_5_10      1       1   \n",
       "2  3f207df678b143eea3cee63160fa8bed  informational_0_4_0      1       0   \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9           bogo_5_7_5      1       0   \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7     discount_20_10_5      1       0   \n",
       "\n",
       "   mobile  web  \n",
       "0       1    0  \n",
       "1       1    1  \n",
       "2       1    1  \n",
       "3       1    1  \n",
       "4       0    1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new id\n",
    "portfolio['offer'] = portfolio.offer_type + '_' + portfolio.difficulty.astype(str) +\\\n",
    "                    '_' + portfolio.duration.astype(str) + '_' + portfolio.reward.astype(str)\n",
    "\n",
    "# seperate \"channels\" into multiple columns\n",
    "channel_set = set()\n",
    "portfolio[\"channels\"].apply(lambda x: channel_set.update(set(x)))\n",
    "for channel in channel_set:\n",
    "    portfolio[channel] = portfolio[\"channels\"].apply(lambda x: 1 if channel in x else 0)\n",
    "portfolio.drop([\"channels\"], axis=1, inplace=True)\n",
    "\n",
    "# display\n",
    "portfolio.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2. Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "</div>"
      ],
      "text/plain": [
       "  gender  age                                id  became_member_on    income\n",
       "0   None  118  68be06ca386d4c31939f3a4f0e3dd783          20170212       NaN\n",
       "1      F   55  0610b486422d4921ae7d2bf64640c50b          20170715  112000.0\n",
       "2   None  118  38fe809add3b4fcf9315a9694bb96ff5          20180712       NaN\n",
       "3      F   75  78afa995795e4d85b5d9ceeca43f5fef          20170509  100000.0\n",
       "4   None  118  a03223e636434f42ac4c3df47e8bac43          20170804       NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Handle missing values\n",
    "* Create new column called \"member_days\"\n",
    "* Rename ID to customer_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender              2175\n",
      "age                    0\n",
      "id                     0\n",
      "became_member_on       0\n",
      "income              2175\n",
      "dtype: int64\n",
      "Number of invalid age:  2175\n",
      "=======================\n",
      "gender              0\n",
      "age                 0\n",
      "id                  0\n",
      "became_member_on    0\n",
      "income              0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Handling missing values\n",
    "print(profile.isnull().sum()) # isnull(), isna() functions are to detect missing values. Both of them do the same thing.\n",
    "\n",
    "print(\"Number of invalid age: \", len(profile[profile[\"age\"] == 118]))\n",
    "profile[profile[\"age\"] == 118].head() \n",
    "\n",
    "# Drop record with invalid ages, then all invalid income age are removed\n",
    "profile = profile[profile[\"age\"] != 118]\n",
    "print(\"=======================\")\n",
    "print(profile.isnull().sum()) # isnull(), isna() functions are to detect missing values. Both of them do the same thing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The last record is on date:  2018-07-26 00:00:00\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "   gender  age                                id    income  member_days\n",
       "1       F   55  0610b486422d4921ae7d2bf64640c50b  112000.0          376\n",
       "3       F   75  78afa995795e4d85b5d9ceeca43f5fef  100000.0          443\n",
       "5       M   68  e2127556f4f64592b11af22de27a7932   70000.0           91\n",
       "8       M   65  389bc3fa690240e798340f5a15918d5c   53000.0          167\n",
       "12      M   58  2eeac8d8feae4a8cad5a6af0499a211d   51000.0          257"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the last date \n",
    "profile[\"member_days\"] = pd.to_datetime(profile[\"became_member_on\"], format='%Y%m%d', errors='ignore')\n",
    "last_date = profile[\"member_days\"].max()\n",
    "print(\"The last record is on date: \", last_date)\n",
    "\n",
    "# Create member_days and drop became_member_on\n",
    "profile[\"member_days\"] = (last_date - profile[\"member_days\"]).dt.days\n",
    "profile.drop([\"became_member_on\"], axis=1, inplace=True)\n",
    "profile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>customer_id</th>\n",
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      "text/plain": [
       "   gender  age                       customer_id    income  member_days\n",
       "1       F   55  0610b486422d4921ae7d2bf64640c50b  112000.0          376\n",
       "3       F   75  78afa995795e4d85b5d9ceeca43f5fef  100000.0          443\n",
       "5       M   68  e2127556f4f64592b11af22de27a7932   70000.0           91\n",
       "8       M   65  389bc3fa690240e798340f5a15918d5c   53000.0          167\n",
       "12      M   58  2eeac8d8feae4a8cad5a6af0499a211d   51000.0          257"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.rename(columns = {'id':'customer_id'}, inplace = True)\n",
    "profile.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3. Transcript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fk_customer_id</th>\n",
       "      <th>event</th>\n",
       "      <th>value</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
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       "      <td>offer received</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8ec6ce2a7e7949b1bf142def7d0e0586</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     fk_customer_id           event  \\\n",
       "0  78afa995795e4d85b5d9ceeca43f5fef  offer received   \n",
       "1  a03223e636434f42ac4c3df47e8bac43  offer received   \n",
       "2  e2127556f4f64592b11af22de27a7932  offer received   \n",
       "3  8ec6ce2a7e7949b1bf142def7d0e0586  offer received   \n",
       "4  68617ca6246f4fbc85e91a2a49552598  offer received   \n",
       "\n",
       "                                              value  time  \n",
       "0  {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}     0  \n",
       "1  {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}     0  \n",
       "2  {'offer id': '2906b810c7d4411798c6938adc9daaa5'}     0  \n",
       "3  {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}     0  \n",
       "4  {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}     0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.rename(columns = {'person':'fk_customer_id'}, inplace = True) #fk means foreign key\n",
    "transcript.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'amount', 'reward', 'offer_id', 'offer id'}\n"
     ]
    }
   ],
   "source": [
    "# check transcript[\"value\"]\n",
    "key_set = set()\n",
    "transcript[\"value\"].apply(lambda x: key_set.update(x.keys()))\n",
    "print(key_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     fk_customer_id           event  time  \\\n",
       "0  78afa995795e4d85b5d9ceeca43f5fef  offer received     0   \n",
       "1  a03223e636434f42ac4c3df47e8bac43  offer received     0   \n",
       "2  e2127556f4f64592b11af22de27a7932  offer received     0   \n",
       "3  8ec6ce2a7e7949b1bf142def7d0e0586  offer received     0   \n",
       "4  68617ca6246f4fbc85e91a2a49552598  offer received     0   \n",
       "\n",
       "                           offer_id  amount  reward  \n",
       "0  9b98b8c7a33c4b65b9aebfe6a799e6d9     NaN     NaN  \n",
       "1  0b1e1539f2cc45b7b9fa7c272da2e1d7     NaN     NaN  \n",
       "2  2906b810c7d4411798c6938adc9daaa5     NaN     NaN  \n",
       "3  fafdcd668e3743c1bb461111dcafc2a4     NaN     NaN  \n",
       "4  4d5c57ea9a6940dd891ad53e9dbe8da0     NaN     NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert(dic):\n",
    "    if \"offer_id\" in dic.keys():\n",
    "        return dic[\"offer_id\"]\n",
    "    elif \"offer id\" in dic.keys():\n",
    "        return dic[\"offer id\"]\n",
    "    else:\n",
    "        return np.NaN\n",
    "\n",
    "# seperate value into small columns\n",
    "transcript[\"offer_id\"] = transcript[\"value\"].apply(convert)\n",
    "transcript[\"amount\"] = transcript[\"value\"].apply(lambda dic: dic[\"amount\"] if \"amount\" in dic.keys() else np.NaN)\n",
    "transcript[\"reward\"] = transcript[\"value\"].apply(lambda dic: dic[\"reward\"] if \"reward\" in dic.keys() else np.NaN)\n",
    "transcript.drop(['value'], axis=1, inplace=True)\n",
    "transcript.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>discount_20_10_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>discount_10_7_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8ec6ce2a7e7949b1bf142def7d0e0586</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>discount_10_10_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     fk_customer_id           event  time  amount  reward  \\\n",
       "0  78afa995795e4d85b5d9ceeca43f5fef  offer received     0     NaN     NaN   \n",
       "1  a03223e636434f42ac4c3df47e8bac43  offer received     0     NaN     NaN   \n",
       "2  e2127556f4f64592b11af22de27a7932  offer received     0     NaN     NaN   \n",
       "3  8ec6ce2a7e7949b1bf142def7d0e0586  offer received     0     NaN     NaN   \n",
       "4  68617ca6246f4fbc85e91a2a49552598  offer received     0     NaN     NaN   \n",
       "\n",
       "              offer  \n",
       "0        bogo_5_7_5  \n",
       "1  discount_20_10_5  \n",
       "2   discount_10_7_2  \n",
       "3  discount_10_10_2  \n",
       "4      bogo_10_5_10  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add \"offer type\" into transcript\n",
    "temp = portfolio[[\"id\", \"offer\"]]\n",
    "temp = pd.merge(transcript, temp, left_on='offer_id', right_on='id', how='left')\n",
    "temp.drop([\"id\", \"offer_id\"], axis=1, inplace=True)\n",
    "\n",
    "transcript = temp\n",
    "transcript.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transcript processing\n",
    "Now, we will create the action table by customers.\n",
    "* n_received: number of offers received by customers\n",
    "* n_responded: number of offers that the customer responded (receive, view and complete in order)\n",
    "* n_not_responded: number of offers that the customer did not responded (not complete in the valid time)\n",
    "* n_n_invalid_responded: number of offers that the customer completed without viewing\n",
    "\n",
    "<img src=\"images/pic4.png\" width=\"600\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fk_customer_id</th>\n",
       "      <th>event</th>\n",
       "      <th>time</th>\n",
       "      <th>amount</th>\n",
       "      <th>reward</th>\n",
       "      <th>offer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0020c2b971eb4e9188eac86d93036a77</td>\n",
       "      <td>offer received</td>\n",
       "      <td>408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0020c2b971eb4e9188eac86d93036a77</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0020c2b971eb4e9188eac86d93036a77</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00715b6e55c3431cb56ff7307eb19675</td>\n",
       "      <td>offer received</td>\n",
       "      <td>336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00715b6e55c3431cb56ff7307eb19675</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>00857b24b13f4fe0ad17b605f00357f5</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00857b24b13f4fe0ad17b605f00357f5</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>008d7088107b468893889da0ede0df5c</td>\n",
       "      <td>offer received</td>\n",
       "      <td>504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>008d7088107b468893889da0ede0df5c</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>558</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0091d2b6a5ea4defaa8393e4e816db60</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     fk_customer_id            event  time  amount  reward  \\\n",
       "0  0020c2b971eb4e9188eac86d93036a77   offer received   408     NaN     NaN   \n",
       "1  0020c2b971eb4e9188eac86d93036a77     offer viewed   426     NaN     NaN   \n",
       "2  0020c2b971eb4e9188eac86d93036a77  offer completed   510     NaN    10.0   \n",
       "3  00715b6e55c3431cb56ff7307eb19675   offer received   336     NaN     NaN   \n",
       "4  00715b6e55c3431cb56ff7307eb19675  offer completed   342     NaN    10.0   \n",
       "5  00857b24b13f4fe0ad17b605f00357f5   offer received     0     NaN     NaN   \n",
       "6  00857b24b13f4fe0ad17b605f00357f5     offer viewed     0     NaN     NaN   \n",
       "7  008d7088107b468893889da0ede0df5c   offer received   504     NaN     NaN   \n",
       "8  008d7088107b468893889da0ede0df5c     offer viewed   558     NaN     NaN   \n",
       "9  0091d2b6a5ea4defaa8393e4e816db60   offer received     0     NaN     NaN   \n",
       "\n",
       "          offer  \n",
       "0  bogo_10_5_10  \n",
       "1  bogo_10_5_10  \n",
       "2  bogo_10_5_10  \n",
       "3  bogo_10_5_10  \n",
       "4  bogo_10_5_10  \n",
       "5  bogo_10_5_10  \n",
       "6  bogo_10_5_10  \n",
       "7  bogo_10_5_10  \n",
       "8  bogo_10_5_10  \n",
       "9  bogo_10_5_10  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# re-order before processing\n",
    "transcript = transcript.sort_values(by=[\"offer\", \"fk_customer_id\", \"time\"]).reset_index(drop=True)\n",
    "transcript.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_number(row, n_received, n_viewed, n_responded, n_invalid_responded):\n",
    "    '''\n",
    "    Input\n",
    "        row: a record/row in transcripts\n",
    "        n_received: number of offers received\n",
    "        n_viewed: number of offers viewed\n",
    "        n_responded: number of offers responded\n",
    "        n_invalid_responded: number of invalid responsed offers (completed but not viewed)\n",
    "    Output\n",
    "        n_received, n_responded, n_not_responded, n_invalid_responded: these above number after being aware of the row\n",
    "    '''\n",
    "    # increase n_received whenever receiving an offer\n",
    "    if row[\"event\"] == \"offer received\":\n",
    "        n_received = n_received + 1\n",
    "        \n",
    "    # assume that the customer is aware of all offers when viewing \n",
    "    elif row[\"event\"] == \"offer viewed\":\n",
    "        n_viewed = n_received\n",
    "        \n",
    "    # assume that offer_completed event happens if and only if an offer is completed within the valid period\n",
    "    elif row[\"event\"] == \"offer completed\":\n",
    "        if n_viewed > n_responded:\n",
    "            n_responded = n_responded + 1\n",
    "        else:\n",
    "            n_invalid_responded = n_invalid_responded + 1\n",
    "    return n_received, n_viewed, n_responded, n_invalid_responded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_num_not_responded(max_recorded_time, deadline, n_received, n_responded, n_invalid_responded):\n",
    "    n_not_responded = n_received - n_responded - n_invalid_responded\n",
    "    if (deadline != None and deadline > max_recorded_time):\n",
    "        n_not_responded = n_not_responded - 1 #cannot justify whether he respond to the latest ad or not\n",
    "    return n_not_responded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>>>>>>>>> PROCESS for offer:  bogo_10_5_10\n",
      "5000 / 167581\n",
      "10000 / 167581\n",
      "15000 / 167581\n",
      ">>>>>>>>>>> PROCESS for offer:  bogo_10_7_10\n",
      "20000 / 167581\n",
      "25000 / 167581\n",
      "30000 / 167581\n",
      "35000 / 167581\n",
      ">>>>>>>>>>> PROCESS for offer:  bogo_5_5_5\n",
      "40000 / 167581\n",
      "45000 / 167581\n",
      "50000 / 167581\n",
      "55000 / 167581\n",
      ">>>>>>>>>>> PROCESS for offer:  bogo_5_7_5\n",
      "60000 / 167581\n",
      "65000 / 167581\n",
      "70000 / 167581\n",
      ">>>>>>>>>>> PROCESS for offer:  discount_10_10_2\n",
      "75000 / 167581\n",
      "80000 / 167581\n",
      "85000 / 167581\n",
      "90000 / 167581\n",
      ">>>>>>>>>>> PROCESS for offer:  discount_10_7_2\n",
      "95000 / 167581\n",
      "100000 / 167581\n",
      "105000 / 167581\n",
      ">>>>>>>>>>> PROCESS for offer:  discount_20_10_5\n",
      "110000 / 167581\n",
      "115000 / 167581\n",
      "120000 / 167581\n",
      ">>>>>>>>>>> PROCESS for offer:  discount_7_7_3\n",
      "125000 / 167581\n",
      "130000 / 167581\n",
      "135000 / 167581\n",
      "140000 / 167581\n",
      ">>>>>>>>>>> PROCESS for offer:  informational_0_3_0\n",
      "145000 / 167581\n",
      "150000 / 167581\n",
      "155000 / 167581\n",
      ">>>>>>>>>>> PROCESS for offer:  informational_0_4_0\n",
      "160000 / 167581\n",
      "165000 / 167581\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fk_customer_id</th>\n",
       "      <th>fk_offer</th>\n",
       "      <th>n_received</th>\n",
       "      <th>n_responded</th>\n",
       "      <th>n_not_responded</th>\n",
       "      <th>n_invalid_responded</th>\n",
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       "      <th>0</th>\n",
       "      <td>0020c2b971eb4e9188eac86d93036a77</td>\n",
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       "      <td>0</td>\n",
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       "      <td>00715b6e55c3431cb56ff7307eb19675</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>00857b24b13f4fe0ad17b605f00357f5</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>008d7088107b468893889da0ede0df5c</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0091d2b6a5ea4defaa8393e4e816db60</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     fk_customer_id      fk_offer n_received n_responded  \\\n",
       "0  0020c2b971eb4e9188eac86d93036a77  bogo_10_5_10          1           1   \n",
       "1  00715b6e55c3431cb56ff7307eb19675  bogo_10_5_10          1           0   \n",
       "2  00857b24b13f4fe0ad17b605f00357f5  bogo_10_5_10          1           0   \n",
       "3  008d7088107b468893889da0ede0df5c  bogo_10_5_10          1           0   \n",
       "4  0091d2b6a5ea4defaa8393e4e816db60  bogo_10_5_10          2           1   \n",
       "\n",
       "  n_not_responded n_invalid_responded  \n",
       "0               0                   0  \n",
       "1               0                   1  \n",
       "2               1                   0  \n",
       "3               1                   0  \n",
       "4               1                   0  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.DataFrame(columns=[\"fk_customer_id\", \"fk_offer\", \"n_received\", \"n_responded\", \"n_not_responded\", \"n_invalid_responded\"])\n",
    "\n",
    "max_recorded_time = max(transcript[\"time\"])\n",
    "offer_list = sorted(portfolio[\"offer\"].unique()) #sort is important\n",
    "\n",
    "count = 0 #to simplily display progress for each type of offer\n",
    "max_count = len(transcript[transcript[\"event\"] != \"transaction\"])\n",
    "\n",
    "for offer_idx, offer in enumerate(offer_list):    \n",
    "    print(\">>>>>>>>>>> PROCESS for offer: \", offer)\n",
    "    offer_duration = portfolio[portfolio[\"offer\"] == offer].iloc[0][\"duration\"]\n",
    "    sub_trans = transcript[transcript[\"offer\"] == offer]\n",
    "\n",
    "    customer_id = None\n",
    "    n_received = n_viewed = n_responded = n_invalid_responded = None\n",
    "    deadline = None\n",
    "\n",
    "    for idx, row in sub_trans.iterrows():\n",
    "        # display progress\n",
    "        count = count + 1\n",
    "        if (count % 5000 == 0): #every 1000 rows\n",
    "            print(count, \"/\", max_count)\n",
    "\n",
    "        # if this record is for a new customer, save the result for the previous customer\n",
    "        if (customer_id != row[\"fk_customer_id\"] and customer_id != None) or (idx == len(transcript)):\n",
    "            n_not_responded = get_num_not_responded(max_recorded_time, deadline,\\\n",
    "                                                    n_received, n_responded, n_invalid_responded)\n",
    "            temp = temp.append({\"fk_customer_id\": customer_id, \"fk_offer\": offer, \"n_received\": n_received, \"n_responded\": n_responded,\\\n",
    "                    \"n_not_responded\": n_not_responded, \"n_invalid_responded\": n_invalid_responded}, ignore_index=True)\n",
    "\n",
    "        # if this record is for a new customer, initilize the numbers\n",
    "        if (customer_id != row[\"fk_customer_id\"]):\n",
    "            customer_id = row[\"fk_customer_id\"]\n",
    "            n_received = n_viewed = n_responded = n_invalid_responded = 0\n",
    "            deadline = None\n",
    "\n",
    "        # update customer status and all other numbers\n",
    "        n_received, n_viewed, n_responded, n_invalid_responded =\\\n",
    "                compute_number(row, n_received, n_viewed, n_responded, n_invalid_responded)\n",
    "        \n",
    "        # if receive a new offer, calculate the latest deadline\n",
    "        if row[\"event\"] == 'offer received':\n",
    "            deadline = row[\"time\"] + offer_duration * 24\n",
    "        elif row[\"event\"] == 'offer completed':\n",
    "            deadline = None\n",
    "    \n",
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>0020c2b971eb4e9188eac86d93036a77</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00715b6e55c3431cb56ff7307eb19675</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00857b24b13f4fe0ad17b605f00357f5</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>008d7088107b468893889da0ede0df5c</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0091d2b6a5ea4defaa8393e4e816db60</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0099bf30e4cb4265875266eb3eb25eab</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00a794f62b9a48beb58f8f6c02c2f1a6</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>00ad4c2cace94f67a6354ec90d6c6f45</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>00b3c376db2a4115af3aef34a02f61d6</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>00c20a9202d5475190b31a24de6fb06d</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     fk_customer_id      fk_offer n_received n_responded  \\\n",
       "0  0020c2b971eb4e9188eac86d93036a77  bogo_10_5_10          1           1   \n",
       "1  00715b6e55c3431cb56ff7307eb19675  bogo_10_5_10          1           0   \n",
       "2  00857b24b13f4fe0ad17b605f00357f5  bogo_10_5_10          1           0   \n",
       "3  008d7088107b468893889da0ede0df5c  bogo_10_5_10          1           0   \n",
       "4  0091d2b6a5ea4defaa8393e4e816db60  bogo_10_5_10          2           1   \n",
       "5  0099bf30e4cb4265875266eb3eb25eab  bogo_10_5_10          1           0   \n",
       "6  00a794f62b9a48beb58f8f6c02c2f1a6  bogo_10_5_10          1           0   \n",
       "7  00ad4c2cace94f67a6354ec90d6c6f45  bogo_10_5_10          1           0   \n",
       "8  00b3c376db2a4115af3aef34a02f61d6  bogo_10_5_10          1           0   \n",
       "9  00c20a9202d5475190b31a24de6fb06d  bogo_10_5_10          1           1   \n",
       "\n",
       "  n_not_responded n_invalid_responded  \n",
       "0               0                   0  \n",
       "1               0                   1  \n",
       "2               1                   0  \n",
       "3               1                   0  \n",
       "4               1                   0  \n",
       "5               1                   0  \n",
       "6               1                   0  \n",
       "7               1                   0  \n",
       "8               0                   1  \n",
       "9               0                   0  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fk_customer_id</th>\n",
       "      <th>event</th>\n",
       "      <th>time</th>\n",
       "      <th>amount</th>\n",
       "      <th>reward</th>\n",
       "      <th>offer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0020c2b971eb4e9188eac86d93036a77</td>\n",
       "      <td>offer received</td>\n",
       "      <td>408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0020c2b971eb4e9188eac86d93036a77</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0020c2b971eb4e9188eac86d93036a77</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00715b6e55c3431cb56ff7307eb19675</td>\n",
       "      <td>offer received</td>\n",
       "      <td>336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00715b6e55c3431cb56ff7307eb19675</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>00857b24b13f4fe0ad17b605f00357f5</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00857b24b13f4fe0ad17b605f00357f5</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>008d7088107b468893889da0ede0df5c</td>\n",
       "      <td>offer received</td>\n",
       "      <td>504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>008d7088107b468893889da0ede0df5c</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>558</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0091d2b6a5ea4defaa8393e4e816db60</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0091d2b6a5ea4defaa8393e4e816db60</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0091d2b6a5ea4defaa8393e4e816db60</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0091d2b6a5ea4defaa8393e4e816db60</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0091d2b6a5ea4defaa8393e4e816db60</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0099bf30e4cb4265875266eb3eb25eab</td>\n",
       "      <td>offer received</td>\n",
       "      <td>168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0099bf30e4cb4265875266eb3eb25eab</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>00a794f62b9a48beb58f8f6c02c2f1a6</td>\n",
       "      <td>offer received</td>\n",
       "      <td>408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>00a794f62b9a48beb58f8f6c02c2f1a6</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>414</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>00ad4c2cace94f67a6354ec90d6c6f45</td>\n",
       "      <td>offer received</td>\n",
       "      <td>408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>00ad4c2cace94f67a6354ec90d6c6f45</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      fk_customer_id            event  time  amount  reward  \\\n",
       "0   0020c2b971eb4e9188eac86d93036a77   offer received   408     NaN     NaN   \n",
       "1   0020c2b971eb4e9188eac86d93036a77     offer viewed   426     NaN     NaN   \n",
       "2   0020c2b971eb4e9188eac86d93036a77  offer completed   510     NaN    10.0   \n",
       "3   00715b6e55c3431cb56ff7307eb19675   offer received   336     NaN     NaN   \n",
       "4   00715b6e55c3431cb56ff7307eb19675  offer completed   342     NaN    10.0   \n",
       "5   00857b24b13f4fe0ad17b605f00357f5   offer received     0     NaN     NaN   \n",
       "6   00857b24b13f4fe0ad17b605f00357f5     offer viewed     0     NaN     NaN   \n",
       "7   008d7088107b468893889da0ede0df5c   offer received   504     NaN     NaN   \n",
       "8   008d7088107b468893889da0ede0df5c     offer viewed   558     NaN     NaN   \n",
       "9   0091d2b6a5ea4defaa8393e4e816db60   offer received     0     NaN     NaN   \n",
       "10  0091d2b6a5ea4defaa8393e4e816db60     offer viewed     6     NaN     NaN   \n",
       "11  0091d2b6a5ea4defaa8393e4e816db60  offer completed     6     NaN    10.0   \n",
       "12  0091d2b6a5ea4defaa8393e4e816db60   offer received   576     NaN     NaN   \n",
       "13  0091d2b6a5ea4defaa8393e4e816db60     offer viewed   576     NaN     NaN   \n",
       "14  0099bf30e4cb4265875266eb3eb25eab   offer received   168     NaN     NaN   \n",
       "15  0099bf30e4cb4265875266eb3eb25eab     offer viewed   180     NaN     NaN   \n",
       "16  00a794f62b9a48beb58f8f6c02c2f1a6   offer received   408     NaN     NaN   \n",
       "17  00a794f62b9a48beb58f8f6c02c2f1a6     offer viewed   414     NaN     NaN   \n",
       "18  00ad4c2cace94f67a6354ec90d6c6f45   offer received   408     NaN     NaN   \n",
       "19  00ad4c2cace94f67a6354ec90d6c6f45     offer viewed   426     NaN     NaN   \n",
       "\n",
       "           offer  \n",
       "0   bogo_10_5_10  \n",
       "1   bogo_10_5_10  \n",
       "2   bogo_10_5_10  \n",
       "3   bogo_10_5_10  \n",
       "4   bogo_10_5_10  \n",
       "5   bogo_10_5_10  \n",
       "6   bogo_10_5_10  \n",
       "7   bogo_10_5_10  \n",
       "8   bogo_10_5_10  \n",
       "9   bogo_10_5_10  \n",
       "10  bogo_10_5_10  \n",
       "11  bogo_10_5_10  \n",
       "12  bogo_10_5_10  \n",
       "13  bogo_10_5_10  \n",
       "14  bogo_10_5_10  \n",
       "15  bogo_10_5_10  \n",
       "16  bogo_10_5_10  \n",
       "17  bogo_10_5_10  \n",
       "18  bogo_10_5_10  \n",
       "19  bogo_10_5_10  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine the first 15 rows\n",
    "transcript[transcript[\"offer\"] == \"bogo_10_5_10\"].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fk_customer_id</th>\n",
       "      <th>event</th>\n",
       "      <th>time</th>\n",
       "      <th>amount</th>\n",
       "      <th>reward</th>\n",
       "      <th>offer</th>\n",
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       "      <th>36284</th>\n",
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       "      <td>bogo_5_5_5</td>\n",
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       "      <th>36285</th>\n",
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       "      <th>36286</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bogo_5_5_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71617</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
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       "      <td>504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>discount_10_10_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71618</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
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       "      <td>528</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>discount_10_10_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71619</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>discount_10_10_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91858</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>discount_10_7_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91859</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>discount_10_7_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141515</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>offer received</td>\n",
       "      <td>168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>informational_0_3_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141516</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>informational_0_3_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155820</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>offer received</td>\n",
       "      <td>336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>informational_0_4_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155821</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>informational_0_4_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167581</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>transaction</td>\n",
       "      <td>228</td>\n",
       "      <td>22.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167582</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>transaction</td>\n",
       "      <td>414</td>\n",
       "      <td>8.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167583</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>transaction</td>\n",
       "      <td>528</td>\n",
       "      <td>14.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167584</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>transaction</td>\n",
       "      <td>552</td>\n",
       "      <td>13.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167585</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>transaction</td>\n",
       "      <td>576</td>\n",
       "      <td>10.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167586</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>transaction</td>\n",
       "      <td>660</td>\n",
       "      <td>12.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167587</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>transaction</td>\n",
       "      <td>690</td>\n",
       "      <td>28.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167588</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>transaction</td>\n",
       "      <td>696</td>\n",
       "      <td>18.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          fk_customer_id            event  time  amount  \\\n",
       "36284   0009655768c64bdeb2e877511632db8f   offer received   408     NaN   \n",
       "36285   0009655768c64bdeb2e877511632db8f  offer completed   414     NaN   \n",
       "36286   0009655768c64bdeb2e877511632db8f     offer viewed   456     NaN   \n",
       "71617   0009655768c64bdeb2e877511632db8f   offer received   504     NaN   \n",
       "71618   0009655768c64bdeb2e877511632db8f  offer completed   528     NaN   \n",
       "71619   0009655768c64bdeb2e877511632db8f     offer viewed   540     NaN   \n",
       "91858   0009655768c64bdeb2e877511632db8f   offer received   576     NaN   \n",
       "91859   0009655768c64bdeb2e877511632db8f  offer completed   576     NaN   \n",
       "141515  0009655768c64bdeb2e877511632db8f   offer received   168     NaN   \n",
       "141516  0009655768c64bdeb2e877511632db8f     offer viewed   192     NaN   \n",
       "155820  0009655768c64bdeb2e877511632db8f   offer received   336     NaN   \n",
       "155821  0009655768c64bdeb2e877511632db8f     offer viewed   372     NaN   \n",
       "167581  0009655768c64bdeb2e877511632db8f      transaction   228   22.16   \n",
       "167582  0009655768c64bdeb2e877511632db8f      transaction   414    8.57   \n",
       "167583  0009655768c64bdeb2e877511632db8f      transaction   528   14.11   \n",
       "167584  0009655768c64bdeb2e877511632db8f      transaction   552   13.56   \n",
       "167585  0009655768c64bdeb2e877511632db8f      transaction   576   10.27   \n",
       "167586  0009655768c64bdeb2e877511632db8f      transaction   660   12.36   \n",
       "167587  0009655768c64bdeb2e877511632db8f      transaction   690   28.16   \n",
       "167588  0009655768c64bdeb2e877511632db8f      transaction   696   18.41   \n",
       "\n",
       "        reward                offer  \n",
       "36284      NaN           bogo_5_5_5  \n",
       "36285      5.0           bogo_5_5_5  \n",
       "36286      NaN           bogo_5_5_5  \n",
       "71617      NaN     discount_10_10_2  \n",
       "71618      2.0     discount_10_10_2  \n",
       "71619      NaN     discount_10_10_2  \n",
       "91858      NaN      discount_10_7_2  \n",
       "91859      2.0      discount_10_7_2  \n",
       "141515     NaN  informational_0_3_0  \n",
       "141516     NaN  informational_0_3_0  \n",
       "155820     NaN  informational_0_4_0  \n",
       "155821     NaN  informational_0_4_0  \n",
       "167581     NaN                  NaN  \n",
       "167582     NaN                  NaN  \n",
       "167583     NaN                  NaN  \n",
       "167584     NaN                  NaN  \n",
       "167585     NaN                  NaN  \n",
       "167586     NaN                  NaN  \n",
       "167587     NaN                  NaN  \n",
       "167588     NaN                  NaN  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine a person's record\n",
    "\n",
    "# transcript[transcript['fk_customer_id']=='fe97aa22dd3e48c8b143116a8403dd52'].head(100)\n",
    "# transcript[transcript['fk_customer_id']=='6d2db3aad94648259e539920fc2cf2a6'].head(100)\n",
    "# transcript[transcript['fk_customer_id']=='33834c812b4e47e4955093ad03f6117a'].head(100)\n",
    "# transcript[transcript['fk_customer_id']=='01925607d99c460996c281f17cdbb9e2'].head(100)\n",
    "transcript[transcript['fk_customer_id']=='0009655768c64bdeb2e877511632db8f'].head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save\n",
    "temp.to_pickle('cache/customer_offer_response.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1. Customer response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50629, 6)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove information offers\n",
    "temp = pd.read_pickle('cache/customer_offer_response.pkl')\n",
    "number = temp[~temp[\"fk_offer\"].str.contains('info')]\n",
    "number.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.axis('equal')\n",
    "events = ['n_responded', 'n_not_responded', 'n_invalid_responded']\n",
    "numbers = [number[\"n_responded\"].sum(),number[\"n_not_responded\"].sum(),number[\"n_invalid_responded\"].sum()]\n",
    "ax.pie(numbers, labels = events, autopct='%1.2f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Findings:__\n",
    "* In general, 40% of offerred was not responded. \n",
    "* Interestinly, the customers completed up to 17% of offers without viewing those offer in advance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2. Customer type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = number.groupby([\"fk_customer_id\"])[[\"n_received\", \"n_responded\", \"n_not_responded\", \"n_invalid_responded\"]].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_received</th>\n",
       "      <th>n_responded</th>\n",
       "      <th>n_not_responded</th>\n",
       "      <th>n_invalid_responded</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fk_customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0009655768c64bdeb2e877511632db8f</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>dont_care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00116118485d4dfda04fdbaba9a87b5c</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>never_respond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0011e0d4e6b944f998e987f904e8c1e5</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>always_respond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0020c2b971eb4e9188eac86d93036a77</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>has_response</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0020ccbbb6d84e358d3414a3ff76cffd</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>always_respond</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  n_received  n_responded  n_not_responded  \\\n",
       "fk_customer_id                                                               \n",
       "0009655768c64bdeb2e877511632db8f           3            0                0   \n",
       "00116118485d4dfda04fdbaba9a87b5c           2            0                2   \n",
       "0011e0d4e6b944f998e987f904e8c1e5           3            3                0   \n",
       "0020c2b971eb4e9188eac86d93036a77           4            2                1   \n",
       "0020ccbbb6d84e358d3414a3ff76cffd           3            3                0   \n",
       "\n",
       "                                  n_invalid_responded            type  \n",
       "fk_customer_id                                                         \n",
       "0009655768c64bdeb2e877511632db8f                    3       dont_care  \n",
       "00116118485d4dfda04fdbaba9a87b5c                    0   never_respond  \n",
       "0011e0d4e6b944f998e987f904e8c1e5                    0  always_respond  \n",
       "0020c2b971eb4e9188eac86d93036a77                    1    has_response  \n",
       "0020ccbbb6d84e358d3414a3ff76cffd                    0  always_respond  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_type(row):\n",
    "    '''\n",
    "        type 1: respond to all\n",
    "        type 2: at least one response\n",
    "        type 3: dont care\n",
    "        type 3: never respond\n",
    "        type 4: no offer\n",
    "    '''\n",
    "    if row[\"n_responded\"] > 0:\n",
    "        if row[\"n_not_responded\"] + row[\"n_invalid_responded\"] == 0:\n",
    "            return \"always_respond\"\n",
    "        else:\n",
    "            return \"has_response\"\n",
    "    else:\n",
    "        if row[\"n_invalid_responded\"] > 0:\n",
    "            return \"dont_care\"\n",
    "        elif row[\"n_not_responded\"] > 0:\n",
    "            return \"never_respond\"\n",
    "        else:\n",
    "            return \"no_offer\"\n",
    "\n",
    "df[\"type\"] = df.apply(lambda row: get_type(row), axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has_response      63.64\n",
      "never_respond     27.64\n",
      "always_respond    15.45\n",
      "dont_care          7.07\n",
      "no_offer           0.38\n",
      "Name: type, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = round(df[\"type\"].value_counts() * 10000 / len(profile)) / 100\n",
    "temp.plot(kind='barh')\n",
    "print(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Findings:__\n",
    "* There are 0.38% customers who have offers but the valid period was not finished. so we dont consider them in _never_respond_ group\n",
    "* 7.1% customers are loyal users, they just bought products regardless of offers. Starbucks may send offer infrequently to save its cost\n",
    "* 27.7% customers have never responded to offers. Starbucks may create another offer which focuses on this group\n",
    "* 15.5% customers always respond to offers. Starbucks may create a campaign strategy to increase revenue or loan considering this group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3. Customer characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>income</th>\n",
       "      <th>member_days</th>\n",
       "      <th>n_received</th>\n",
       "      <th>n_responded</th>\n",
       "      <th>n_not_responded</th>\n",
       "      <th>n_invalid_responded</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>376</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>dont_care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>75</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>443</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>has_response</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>68</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>91</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>has_response</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>65</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>167</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>has_response</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>58</td>\n",
       "      <td>2eeac8d8feae4a8cad5a6af0499a211d</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>257</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>has_response</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender  age                       customer_id    income  member_days  \\\n",
       "0      F   55  0610b486422d4921ae7d2bf64640c50b  112000.0          376   \n",
       "1      F   75  78afa995795e4d85b5d9ceeca43f5fef  100000.0          443   \n",
       "2      M   68  e2127556f4f64592b11af22de27a7932   70000.0           91   \n",
       "3      M   65  389bc3fa690240e798340f5a15918d5c   53000.0          167   \n",
       "4      M   58  2eeac8d8feae4a8cad5a6af0499a211d   51000.0          257   \n",
       "\n",
       "   n_received  n_responded  n_not_responded  n_invalid_responded          type  \n",
       "0         1.0          0.0              0.0                  1.0     dont_care  \n",
       "1         3.0          2.0              0.0                  1.0  has_response  \n",
       "2         3.0          2.0              1.0                  0.0  has_response  \n",
       "3         6.0          5.0              1.0                  0.0  has_response  \n",
       "4         2.0          1.0              1.0                  0.0  has_response  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.merge(profile, df, left_on='customer_id', right_on='fk_customer_id', how='left')\n",
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Szspcsq6I+AMLgGcK7dEyFWgCROGY5fxWUUEZYz4yxrQ3xrQPC7uqH6ZSSilVRekAs1JKKaWUUkop5Zh13KDQ9/WBY87KiIg7EAicvlRdEfHAMbg82xjz1bkCxpiTxpg8Y0w+MA3HEh1KKaVUtaMDzEoppZRSSimllGNTv2YiEiEinsC9wKILyiwC/mS9vgdYYYwx1vF7RcRLRCKAZsAma33mj4Gdxpi3CzckInUKfXsXsK3cz0gppZSqBNVqDWallKqq7HY7R44cISsry9WhqMvk7e1N/fr18fDwcHUoSimlSkFz8ZXDVbnYWlP5SWAp4AZMN8ZsF5GJQLQxZhGOweKZ1iZ+p3EMQmOVm49j875cYLQxJk9EugMPAr+KSKzV1d+NMd8Db4hIFI6lNA4Aj1baySqlVAXQXHzlKG0u1gFmpZQqB0eOHKFGjRqEh4fjmKiiqiNjDElJSRw5coSIiAhXh6OUUqoUNBdfGVydi62B3+8vODa+0OssYLCTuq8Ar1xwbB1Fr8+MMebBy41XKaWqEs3FV4ay5GJdIkMppcpBVlYWISEhmkSrOREhJCRE77grpVQ1pLn4yqC5WCmlqi/NxVeGsuRiHWBWSqlyokn0yqA/R6WUqr70d/iVQX+OSilVfenv8CtDaX+OOsCslFJKKaWUUkoppZRSqkx0gFkppZRSSimllFJKKaVUmegAs1JKXeHCw8NJTEx0dRhVkr+/v6tDUEopdRXQXOyc5mKllFIVTfOwc+WVh3WAWSmlVKXKzc11dQhKKaXUVU1zsVJKKeU6V2Iednd1AEopdaWZ8O12dhxLLdc2W9YN4MU/XF9suYEDB3L48GGysrIYM2YMjzzySMF7b7zxBt7e3jz99NM8++yzxMXFsWLFCn788UdmzJjBrFmzePzxx9m8eTOZmZncc889TJgwgR9//JH33nuPr7/+GoAffviBqVOn8sUXX/DQQw8RHR2NiDBy5EieffbZIuPq3bs3Xbt2Zf369fTv359hw4bx2GOPcejQIQDeeecdunXrxurVqxkzZgzg2FRgzZo1xMTEMH78eEJCQvjtt9/o2bMn//3vf7HZbHz++ee8+uqrGGO44447eP311wHHXdgxY8bw3Xff4ePjw8KFC6lduzb79+/n/vvvJzc3l379+l3Wz0QppVTVpbn4YpqLlVJKVSZX5WLNw67JwzqDWSmlriDTp08nJiaG6OhopkyZQlJSUsF7PXv2ZO3atQBER0eTlpaG3W5n3bp19OjRA4BXXnmF6Ohotm7dyurVq9m6dSs33XQTO3fuJCEhAYAZM2YwYsQIYmNjOXr0KNu2bePXX39lxIgRl4wtOTmZ1atX85e//IUxY8bw7LPPsnnzZhYsWMDDDz8MwOTJk3n//feJjY1l7dq1+Pj4ALBp0ybeeustfv31V/bu3ctXX33FsWPHeP7551mxYgWxsbFs3ryZb775BoD09HQ6d+5MXFwcPXv2ZNq0aQCMGTOm4A+Ga665phw/eaWUUspBc7HmYqWUUq6jedg1eVhnMCulVDkryeymijJlypSCu6qHDx8mPj6+4L127doRExPD2bNn8fLyom3btkRHR7N27VqmTJkCwPz58/noo4/Izc3l+PHj7Nixg8jISB588EFmzZrFiBEj2LhxI5999hlnz55l3759PPXUU9xxxx306dPnkrENGTKk4PXy5cvZsWNHwfepqamcPXuWbt268ec//5mhQ4cyaNAg6tevD0DHjh1p3LgxAPfddx/r1q3Dw8OD3r17ExYWBsDQoUNZs2YNAwcOxNPTkzvvvLPgvH/44QcA1q9fz4IFCwB48MEHef7558v+YSullKqyNBcXTXOxUkqpyuKqXKx52DV5WAeYlVLqCrFq1SqWL1/Oxo0b8fX1pXfv3mRlZRW87+HhQXh4ODNmzKBr165ERkaycuVK9u7dS4sWLdi/fz+TJ09m8+bNBAcHM3z48IL6I0aM4A9/+APe3t4MHjwYd3d3goODiYuLY+nSpbz//vvMnz+f6dOnO43Pz8+v4HV+fj4bN24suBt7ztixY7njjjv4/vvv6dy5M8uXLwccjwYVJiIYY5z25eHhUVDHzc3tvDWuLmxLKaWUKi+ai3+nuVgppVRl0zz8u8rOw7pEhlJXIGMMGb/8wtlVq8jVnVKvGikpKQQHB+Pr68uuXbv46aefLirTs2dPJk+eTM+ePenRowcffPABUVFRiAipqan4+fkRGBjIyZMnWbJkSUG9unXrUrduXSZNmsTw4cMBSExMJD8/n7vvvpuXX36ZLVu2lDjWPn368N577xV8HxsbC8DevXtp3bo1zz//PO3bt2fXrl2A43Gg/fv3k5+fz7x58+jevTudOnVi9erVJCYmkpeXx+eff06vXr0u2W+3bt2YO3cuALNnzy5xvEpVFdEnotl0fBOpOeW7np1SqnxoLtZcrFRmbiZbE7ay4tAK7Hl2V4ej1FVF87Dr8rAOMCt1BcnPyuLM/PnsHzCQg/fdz5HHHie+ew/ie99I4ocfXfLulqr++vXrR25uLpGRkbzwwgt07tz5ojI9evTg+PHjdOnShdq1a+Pt7V2w1lSbNm244YYbuP766xk5ciTdunU7r+7QoUNp0KABLVu2BODo0aP07t2bqKgohg8fzmuvvVbiWKdMmUJ0dDSRkZG0bNmSDz74AHBsbNCqVSvatGmDj48Pt912GwBdunRh7NixtGrVioiICO666y7q1KnDa6+9xo033kibNm1o27YtAwYMuGS///nPf3j//ffp0KEDKSkpJY5XKVdLzUll7NqxjFg6goeWPUS3z7sx4JsBxJ6KdXVoSqlCNBdrLlZXL3u+nefXPE/nOZ0Z+v1Qxqwcw+BvB2uuVqoSaR52XR6W6jTg1L59exMdHe3qMJSqkvLOnuXQiJFkbduGR8MG1Lj5FtyvuYacAwfI2raNrF9/pcZt/aj76qvYLngEQ12+nTt30qJFC1eHUaGefPJJbrjhBh566KFK7XfVqlVMnjyZ7777rtL6LOrnKSIxxpj2lRZEFaR52DW2J27nmVXPcCrjFP0b96dxUGMOph5k7ZG1JGcnM7HbRO5ofIerw1TK5TQXVxzNxVWH5uKqKS8/j3HrxrFk/xJubngzzWs2J9/kM/+3+ZzOOs2z7Z5lRKtLb/6l1JXgSs/FV1MehtLlYl2DWakrQF5aGoceHkXWzp2Ejh6NT4cOBWvqeDdvTo2+fTm7ZAnJ8+dz4OAhGkydikftWi6OWlUn7dq1w8/Pj7feesvVoSh1VcnNz2XcunHk5OUwruM4mgQ1AaBVaCt61e/F+7HvM3btWI6lHWNU5CgXR6uUqkiai5WqmowxvPLzKyzZv4S7m9193k3fVqGtmL5tOu9seYfWoa1pf81VfX9EqWpN8/Cl6QCzUtVcfno6hx95lKxffyV09Gh821/8R4uIEHD77XjUrUvi1KkcffZZGs38DHFzc0HEqjqKiYkpUbnRo0ezfv36846NGTOGESPKPmOjd+/e9O7du8z1larOvt37LftT9jM6anTB4PI5/p7+/KX9X5i+bTpTfplCg4AG9Avv56JIlVIVTXOxUlXT4v2L+WL3F9wecftFTxT5uPswstVIJm6cyNi1Y1nQfwGBXoEuilQpdTk0D1+aDjArVc2dmPQKmbGxhDz2WJGDy4X5REVRc9gwkj76iKRp0wh97LFKilJdLd5//31Xh6DUFSM7L5v/xv2XiMAI2tZqW2QZd5s7I1uNJCEzgfHrx9MsqNlFA9FKqauL5mKlKk+6PZ23ot8iIjCCQc0GFVnGx92HRyIf4bWfX+OlDS/xdu+3C542VUpdea7WPKyb/ClVjaVv3EjK118TcMcd+HXqVKI6vl274tu5Mwnvvkfm1q0VHKFSSqmymv/bfE6kn2BQs0GXvBB1t7nzRJsn8LB5MGblGNJy0ioxSqWUUurq9WHchyRmJnJ/8/uxifPhlYjACAY0HcDyQ8vZmqjXYEqpK48OMCtVDZxKzeLbuGOsi09k14lU0rJzyc/K4vj4F3GvXZuA/v1L3JaIUHPYMNyCgzn61+fIT0+vwMiVUkqVRbo9nY+2fkSLmi24PuT6YssHewfzeJvHOXz2MK9tKvnu1UoppZQqm/0p+5m5cybd63Uv0dNDNzW8CS83L77c/WUlRKeUUpWrRAPMItJPRH4TkT0iMraI971EZJ71/s8iEm4dv1VEYkTkV+vfmwrVWWW1GWt96Y5jSl0gLTuXt5f9Rq83V/HU57/wwMc/0++dtXR8ZTlr/vkv7IcPU3PECGyenqVq1+bnR8ioUdgPHSJx2rQKil4ppVRZrTi0guTsZAY0HVDiOtfVvI7bIm5j0d5FrDu6rgKjU0oppdSbm9/Ew+bB3c3uLlF5H3cfOtXpxJL9Szibc7aCo1NKqcpV7ACziLgB7wO3AS2B+0Sk5QXFHgLOGGOaAv8GXreOJwJ/MMa0Bv4EzLyg3lBjTJT1deoyzkOpK85P+5Lo+cZKpqzYQ1SDIF4ecD3j72zJ0zc1pYskE7r4C+Ku60RK+LVlat+7eXN8O3bk9KefkZuUVM7RK6WUuhyrj6wm0CuQpkFNS1Wvf+P+1PGrw4SNE0i36xMqSimlVEX4+fjPrD26ljsb31mqTft61+9Ndl423+37rgKjU0qpyleSGcwdgT3GmH3GmBxgLnDhdJoBwKfW6y+Bm0VEjDG/GGOOWce3A94i4lUegSt1JdufmM6jM2Pw8XBj0sBWPH1zM5rWqkGLOgF0aRLKU0fXkO/hyeSmtzN45RmOZeSVqZ/Au+7CZGeT9JHOYr4SHDhwgFatWrk6DKXUZbLn21l3dB2RoZGXXM+xKB5uHoxoNYKT6Sd5J+adCopQKeWM5mKlrnz5Jp+3o98mxDuEWxreUqq64YHhNApoxBe/fYExpoIiVOrqprnYNUpy1VIPOFzo+yPWsSLLGGNygRQg5IIydwO/GGOyCx2bYS2P8YLoNqpKAZCSYWfkJ5sxxvBc3+toEuZ/3vu2wwfxXL8K07Un49oGkpVreHxDCmn2/FL35VG3Ln7dunHm88+xnzhRXqegrmC5ubmuDkGpK94vJ38h3Z5Om7A2ZarfNKgpNze8mbm/zSX2VGw5R6eUcjXNxUq51v/2/48dp3dwV7O78HDzKHX9XvV7EZ8cz6+Jv1ZAdEqpyqC5+GLuJShT1MDvhbfaLllGRK7HsWxGn0LvDzXGHBWRGsAC4EHgs4s6F3kEeASgYcOGJQhXqerLnpfPE3NiOHw6g3/c3oLaAd4XlfH+YhZ4eJDTrRcNfW38uZk7r+zK5a+bU3mvcyDuttLdqwkcOJD0jRtJ/O9U6kycUF6ncnVbMhZOlPMfjNe0htv+VWyxvLw8Ro0axYYNG6hXrx4LFy5k1qxZfPTRR+Tk5NC0aVNmzpyJr68vX3zxBRMmTMDNzY3AwEDWrFlTZJuffPIJixcvJisri/T0dFasWMGbb77J/Pnzyc7O5q677mLChAmkp6fzxz/+kSNHjpCXl8cLL7zAkCFDCA8PZ8iQIaxcuRKAOXPm0LRpUw4ePMjIkSNJSEggLCyMGTNm0LBhQ4YPH05AQADR0dGcOHGCN954g3vuuYfjx48zZMgQUlNTyc3NZerUqfTo0YNly5bx4osvkp2dTZMmTZgxYwb+/v5FnotS1cGaI2twt7nTMuTCFclKblCzQcScjOHln15m3p3zcLeV5E8+pa4gmos1FyvlRFZuFhM3TsSeb+fvnf5OsHdwqer+Z8t/aFCjAZ3rdC5T/53qdOLzXZ+zZP8SIsMiy9SGUtWC5uKrKheXZAbzEaBBoe/rA8eclRERdyAQOG19Xx/4GhhmjNl7roIx5qj171lgDo6lOC5ijPnIGNPeGNM+LCysJOekVLU1fd1+1u9J4qHuETSvE3DR+7aTx/FYuRR7h67gXwOAyEAbD0e4sfakncnbSr/epntoKP69e5O8YAE5hw8XX0FVafHx8YwePZrt27cTFBTEggULGDRoEJs3byYuLo4WLVrw8ccfAzBx4kSWLl1KXFwcixYtumS7Gzdu5NNPP2XFihUsW7aM+Ph4Nm3aRGxsLDExMaxZs4b//e9/1K1bl7i4OLZt20a/fvoRqlIAACAASURBVP0K6gcEBLBp0yaefPJJnnnmGQCefPJJhg0bxtatWxk6dChPP/10Qfnjx4+zbt06vvvuO8aOdewtO2fOHPr27UtsbCxxcXFERUWRmJjIpEmTWL58OVu2bKF9+/a8/fbb5f2xKlWpVh9ZzXXB1+HtfvFNxpLydvfmvub3sfvMbub9Nq8co1NKFUdzseZiVXWdyTrDw8se5rt93/HDwR+4e9Hd/Hz85xLXf2fLOxxLP8a9191b6mWszvFx96FRQCO2J20vU32lVPE0F1d+Li7JdJbNQDMRiQCOAvcC919QZhGOTfw2AvcAK4wxRkSCgMXAOGPM+nOFrUHoIGNMooh4AHcCyy/7bJSqxs6k5/Deyj3c0CCI3tfVKrKM15ezQQR7jxt/P2jyuTUMDqcLM/dmcktdT9qHepaq74A77yRt5UrOzJpN7XFjL+c0FJTojmpFiYiIICoqCoB27dpx4MABtm3bxj//+U+Sk5NJS0ujb9++AHTr1o3hw4fzxz/+kUGDBl2y3VtvvZWaNWsCsGzZMpYtW8YNN9wAQFpaGvHx8fTo0YO//vWvPP/889x555306NGjoP59991X8O+zzz4LOJLzV199BcCDDz7I3/72t4LyAwcOxGaz0bJlS06ePAlAhw4dGDlyJHa7nYEDBxIVFcXq1avZsWMH3bp1AyAnJ4cuXbpc3oeolAsdTD3IgdQD3N/8wj+1Sq9d7Xa0CmnFlC1T6NOoD2G+eqNeXUU0F2suVuoCaTlpPLjkQY6lHePxNo9Ty7cWH279kFHLRvFS15cY1OzS/w9uOLaB2Ttnc0vDW2gR0uKyYgkPCGfd0XXk5efhZnO7rLaUqrI0F19VubjYAWZjTK6IPAksBdyA6caY7SIyEYg2xiwCPgZmisgeHDOX77WqPwk0BV4QkResY32AdGCpNbjshmNwWXcZU1e1d1fsIT07l/s6Fr0UjJxOwvOHxeS27YQJDMbDnsQ1pxYRdno5bvnZdADSvbxZFt2T3O4P4O5fp8R9uwcH49uhA8kLviT0qadw8/crp7NSlc3L6/d9VN3c3MjMzGT48OF88803tGnThk8++YRVq1YB8MEHH/Dzzz+zePFioqKiiI2NJSTkwuXzHfz8fv9vwhjDuHHjePTRRy8qFxMTw/fff8+4cePo06cP48ePB6DwMvvOltwvfLzweZzbAKVnz56sWbOGxYsX8+CDD/Lcc88RHBzMrbfeyueff17cR6NUtbDmiOORvPJ4ZFZEGNpiKOM3jOfN6Dd5o+cbl92mUqp4mouVqpp+Pv4zB1MP8tQNT3FDLceA0PjO43k/9n1e3PAieSaPwdcOLrJuSnYKL6x/gTp+dbjn2nsuO5aIwAiWH1rOvpR9NAtudtntKaXOp7m48pXomQ5jzPfGmGuNMU2MMa9Yx8Zbg8sYY7KMMYONMU2NMR2NMfus45OMMX7GmKhCX6eMMenGmHbGmEhjzPXGmDHGmLyKO02lqrZDSRl8tvEAva6tRYOavkWW8Vz+PWK3k9OjFw2OziBy5xPUTlzCWe/mnAq4iVMBvTnhdT135P9IjbXD8dw6GXIzSxxDjT59yE9LJ+Wbb8rnpFSVcfbsWerUqYPdbmf27NkFx/fu3UunTp2YOHEioaGhHC7hEil9+/Zl+vTppKWlAXD06FFOnTrFsWPH8PX15YEHHuCvf/0rW7ZsKagzb968gn/P3Unt2rUrc+fOBWD27Nl07979kv0ePHiQWrVqMWrUKB566CG2bNlC586dWb9+PXv27AEgIyOD3bt3l/CTUarqWXNkDXX961LLt+gnWUqrtl9tbou4jSX7l5TqEWClVPnSXKyU68UlxuEmbrQKaVVwzMvdi6dueIrIsEgmbpzI1Nip5OTlnFfvUOohHlr6EEmZSYxqPQpPt9I9LVqU8IBwAF0mQ6lKpLm4YumOL0pVAW8s3YWbTbinXf2iC+Tn47n0W3IbNyU88wtCz6zhjF87EgJ6YncvtClFIPzt2GDaZK9i+NGl2NL2k9XuFfAKKjYGryZN8GzcmDOzZhJ8/32IrWxriqmq5+WXX6ZTp040atSI1q1bc/bsWQCee+454uPjMcZw880306ZNmxK116dPH3bu3FmQEP39/Zk1axZ79uzhueeew2az4eHhwdSpUwvqZGdn06lTJ/Lz8wvuqk6ZMoWRI0fy5ptvFmxmcCmrVq3izTffxMPDA39/fz777DPCwsL45JNPuO+++8jOzgZg0qRJXHvttaX+nJRytdz8XLac2kKv+r3Ktd3bI27np2M/MemnSXzV/6sy7XivlLo8mouVcr2tCVtpFNDoojzo4ebB6KjRzNg2g//G/ZdF+xbxRJsn8PXw5UT6CaZsmYJNbDx5w5OEB4aXSyy1/Wrj7ebN9sTtDGw6sFzaVEpdmubiiiXnplhXB+3btzfR0dGuDkOpcrXtaAp3vruOQTfUY3D7BkWWcY+Lwf/vY/C/pQ4NQmM4GXgziQFFD0CcyXXjsYMNedA3mr/l/RfjU4usDq9hfItfMiN940aSPviABh99iH/Pnpd1XlebnTt30qLF5a3FdqUKDw8nOjqa0NBQV4dSYkX9PEUkxhjT3kUhVQmahytW/Jl4Bi0axKjWo+hSt3zXTNuasJV3trzDmLZjeLj1w+XatlJVheZi5zQXXzk0F5eNPd9Olzld6FGvB/e3cL7PwbbEbXy+63OOpx8vONYksAmPtXmMEJ+iH5kvq9c3vY6nmydz7phTru0q5Uqai5270nOxzmBWysWmr9+Pj4eNOyKdDwB7Lv0W8XKjXnAMpwJ6Ox1cBgh2z+OBkNNMTehE7wgbHU99iPdPfyaz23/BK9hpPcCxDvO8uZyeOVMHmJVSqpLtPL0TgEYBjcq97ciwSNrWassHcR9wW8Rt1POvV+59KKWUUlVV/Jl4svOyaRLU5JLlWoW2YkLXCRxKPYSbzQ1Pmye1/Wpjk/J/ujM8MJyVh1Ziz7fjYdOni5RS1Zs+A6+UCyWlZfNt3DF6NAvD17Po+z2SmoLH+pUENUwhObAjCQE3Fttuv8AUanvYeelkGzKaPIrkpOAd+wrkX3qpc3F3x7/3jaSvXUfOwYNlOidVfS1dupSoqKjzvu66667LbvfAgQPV6i6tUq6yM2knXm5eXON3TYW0f19zx67VL298mer0BJtSVxPNxUpVjK0JWwGKHWAGcLe50zioMY0CGlHHv06FDC6DYx3mnPwc9ibvrZD2lVJlo7m4bHQGs1IuNHfzYex5hj7XOx9M8FjxPyQ3D/9mNg4H3gpOdho9r47AAzWTeOvkNSzJbMofGtyN16G5eMTPwH7dpR+N9uvZk5SFC0n+6mtqPftMqc9JVV99+/alb9++rg5DqavWjqQdNKjRoMIuZEN8Qri72d3M2TWHRXsXMaDpgArpRylVdpqLlaoYcQlxBHoFEuJdvstcXI6IwAjAsSxH85rNXRyNUuoczcVlozOYlXKR3Lx8Zv10kNb1AqkX5FN0IWPw+3YW3jVzOBPRh3ybV4nb71UjjXDPbKYcqkFmcAfsIV3w3DcPtxPrLlnPvWZNvFu3JuXrrzC5uaU5JaWUUmWUb/LZdXpXhSyPUdhNDW+iWVAzXt/8OgkZCRXal1JKKVVVxCXE0TiwMVKCyTqVJcwnDD93P7YnbXd1KEopddl0gFkpF1m+8xTHU7Lo07K20zKeOzZjTpzB5zo/Un2uL1X7bgLDQpI4lO3O1wk+5NQfSJ5vA7y2/RtyUi5Z179nT3JPJZC27tKD0UoppcrHodRDZORm0DCgYYX2YxMbI1qNICs3i0k/TdKlMpRSSl3xzmSd4fDZwzQNaurqUM4jIjQKbMT2RB1gVkpVfzrArJSLfLbxAKH+nrRt6HzjvZoL3wExpEb2LdHSGBfq6JdBC+9MPjjiTw7uZDccArlpeO766JL1fKKisAUEkLLgq1L3qZRSqvQKNvirUbEzmAGu8buGgU0HsuLwChbEL6jw/pRSSilX+jXxVwAaBzZ2cSQXa1ijIfHJ8eSbfFeHopRSl0UHmJVygT2nzrJhbxK3tqiNzVb0wLFbdgp5cfvxrOuFPaB+mfoRgXtrnuFkjjvfJvhgfOpgr3UjHkeXYUuKc17P3R2/rl05u2IFuUlJZepbKaVUye1M2om7zZ26/nUrpb++4X25PuR6Xvv5NXad3lUpfSqllFKuEHsqFpvYCA8Md3UoFwnxDiE3P5fTWaddHYpSSl0WHWBWygW+2nIUm0DPa8OclglZ8yH2NDfymre8rL7a+WbQzCuL/zvqT64B+zW3kO8Z4lgqIy/HaT3/nj0hL4+UhYsuq3+lqoLevXsTHR3t6jCUcmpH0g7q+9fH3VY5+y/bxMaoyFH4efjx51V/Ji0nrVL6VUpdvTQXK1fZn7Kf2r618XIr+X42lSXIOwiAkxknXRyJUupqUJG5WAeYlapk+fmGhbHHaF0/kCBfz6ILmXx8Vn4HAvYWUZfVnwgMrnmGQ9nuLE3yBpsnOQ3uxpZxFI/9853W86hXD8+mTUj+8ktdo1NdJLcEG0CWpIxSCowx7Dy9s8LXX75QgGcAj7Z5lKNpRxm7diz2fHu5tW2MYW/yXmZsm8HDSx/m3u/uZdj3w3hi+RMs3rcYe1759aXU1UpzsVIlk5iZSJBXkKvDKFJN75oAnEo/5eJIlFJlobn4d5UzTUYpVSD64BmOJmcyIMr5Y9BBR9eQsScHt/qh5Pt4X3afXfzSaeiZw0dH/LktJAsCriM3KBKPffOwN7gDvIpeB9q/ew9Of/IJWdt34NOqdJsMXs1e3/R6uT9y3rxmc57v+Pwlyxw4cIDbbruN7t27s2HDBurVq8fChQs5duwYo0ePJiEhAV9fX6ZNm0adOnVo06YN+/btw2azkZGRwXXXXce+ffs4dOjQReWbN2/O8OHDqVmzJr/88gtt27blrbfeuiiGl156iWPHjnHgwAFCQ0OZOXMmY8eOZdWqVWRnZzN69GgeffRRjh8/zpAhQ0hNTSU3N5epU6fSo0cP/P39efTRR1m5ciXBwcHMnTuXsLAwYmNjeeyxx8jIyKBJkyZMnz6d4OBgevfuTadOnVi5ciXJycl8/PHH9OjRg8zMTEaMGMGOHTto0aIFmZmZ5frzUKo8HUs/RmpOKo0CKn795QtdG3wt9zW/j9k7Z/OPdf/gte6v4WZzK3N7xhhWH1nN1Lip7EjaAUCDGg0I9gomKy+Lo6ePsvboWiZHT2ZU61Hc1/w+pAx7DChVHM3FmouVOichM4EGNRq4OowinRv4PpWhA8zqyqO5+OrKxTqDWalK9k3sUbzcbXQIr+m0TM21/4c93Z3clq3LpU+bwODg0+zJ9GDVGcejYTl1bof8HDz3zHRaz7djR3B3J2XRwnKJQ1W8+Ph4Ro8ezfbt2wkKCmLBggU88sgjvPvuu8TExDB58mSeeOIJAgMDadOmDatXrwbg22+/pW/fvnh4eBRZ/pzdu3ezfPnyIpPoOTExMSxcuJA5c+bw8ccfExgYyObNm9m8eTPTpk1j//79zJkzh759+xIbG0tcXBxRUY6Z+unp6bRt25YtW7bQq1cvJkyYAMCwYcN4/fXX2bp1K61bty44Do47wps2beKdd94pOD516lR8fX3ZunUr//jHP4iJiSn3z1qp8rIzqfI2+CvKzQ1v5u5md7Nk/xIm/jSxzBsNbTi6gSHfDeGpFU+RkJHA0OZDmdxzMhO6TuCZds/wXIfneLXHqzzT9hlCfUJ5bdNrTPppEnn5eeV8Rkq5luZizcWq6jDGkJiZSKBXoKtDKVKgVyA2sekSGUqVM83FlZ+LdQazUpUoJzefxVuP065RMN4eRc8Q8zp7CGK2gc0f06xJufXdq0Yac07bmXbUnxuDs8E7jNyQzrgf/h57+CCM38UbCdr8/PCJiiL1u8XU/tvfEHf9lVESxd1RrUgREREFSaldu3YcOHCADRs2MHjw4IIy2dnZAAwZMoR58+Zx4403MnfuXJ544gnS0tKclgcYPHgwbm6Xnt3Yv39/fHx8AFi2bBlbt27lyy+/BCAlJYX4+Hg6dOjAyJEjsdvtDBw4sCBmm83GkCFDAHjggQcYNGgQKSkpJCcn06tXLwD+9Kc/nRffoEGDzjtfgDVr1vD0008DEBkZSWRkZGk+RqUq1c7TO7GJjfo1yraha3m4o/Ed5OTl8FX8VyRmJDKx20RCfEJKVPe307/xdszbbDi2gVCfUEa0GkGXOl2KXE/aJjYiwyJpFdqKBfELmL97PqcyTvFGrzfwcfcp79NSVzHNxZqLlQJIt6eTnZdNoGfVHGC2iY1Ar0CdwayuSJqLr65crKNFSlWi1bsTSMm0061pqNMyYXu+5Owhb2hQB8pheYxz3AQGBiUzNSGMX8560DbATs41fXA/HYPn7ulk3zC+yHp+XbuSGR1N+vr1+Fu/yFTV5eX1++Ylbm5unDx5kqCgIGJjYy8q279/f8aNG8fp06eJiYnhpptuIj093Wl5AD8/v2JjKFzGGMO7775L3759Lyq3Zs0aFi9ezIMPPshzzz3HsGHDLipTkkfnz52zm5vbeetb6WP3qro4kHKAMJ8wPN2crMtfSQY2HUiAZwDzd8/n7kV382KXF+nVoBc2KfqBt7iEOP5v6/+x6sgqfN19GXLdEG5qeBMeNo9i+7KJjcHXDqamV03m7JrD+PXjeaPnG/r/rboiaC7WXKyqjoTMBIAqO4MZINgrWAeYlSpnmosrPxfrEhlKVaJvYo8S4O1OZH3nf+DUiFuCPd2d/GublXv/twSkUsOWx4xj1i86jxrYa/XC/cRabMk7i6zj06YNNn9/UhZ9W+7xqIoXEBBAREQEX3zxBeBIbHFxcQD4+/vTsWNHxowZw5133ombm9sly5dF3759mTp1Kna7Y0Ov3bt3k56ezsGDB6lVqxajRo3ioYceYsuWLQDk5+cX3NWdM2cO3bt3JzAwkODgYNauXQvAzJkzC+7aOtOzZ09mz54NwLZt29i6dWuZz0Gpinbo7CFq+dZydRiICDc3upkXOr+Aj7sPT698mn4L+jFlyxR+PPgjqw+v5seDP/L6ptcZ8M0AHvj+AaJPRtO/SX/+1eNf9A3vW6LB5cJubnQzg5oN4n8H/seC+AUVdGZKuZbmYs3FynUSMxOBqj3AHOQVpEtkKFXBNBdXfC7WGcxKVZKzWXaW7zhJz2vDcLcVfW/HO3U/ebtPAgHQuGG5x+BtM9wRlMK808EczHSjkU8e9lq98UjcgEf8Z2R3eO2iOuLujm/Hjpxdvpy8tHTc/Iu/U6eqltmzZ/P4448zadIk7HY79957L23atAEcjwMNHjyYVatWlah8aT388MMcOHCAtm3bYowhLCyMb775hlWrVvHmm2/i4eGBv78/n332GeC4y7t9+3batWtHYGAg8+bNA+DTTz8t2MygcePGzJgx45L9Pv7444wYMYLIyEiioqLo2LFjmeJXqqIZYziUeogudbu4OpQC9WvU54XOLxB9MpqNxzfy8a8fk8/v6zJ72Dy4Nvha7m9+P93rdcfb/fKetrkt4jZ2nd7Fa5teIzIskmuDr73cU1CqytFcrLlYuUZSZhJQtQeYg72D2X1mt6vDUOqKp7m4YnOxGGMqrPHy1r59exMdHe3qMJQqk4WxRxkzN5YX/9CS5tcEFFmm7vYP4c3/I4Pa5A+/t0LiOJPrxogD4QyqlcH4xqkAeJxcgeexxWR2mUJ+UIuL6mTHx3Ny0iTqvPoqQYPuqpC4qrudO3fSosXFn50qHX9/f9LS0lwdRpE/TxGJMca0d1FIVYLm4fKXmJnIjfNv5P7m93NLo1tcHU6RUnNSOZN1pmDzv/r+9fFwK91M5eKkZKfw0oaXqOlTk/l3zr/sQWt1ddJcXD40F1dtmotLZ+aOmbyx+Q2m3DgFf09/V4dTpMX7FrMgfgE/3/8zvh6+rg5Hqcuiubh8VMdcrEtkKFVJ/rftBEG+Hlxbu4bTMkG7l5CR6IVpHFFhcQS753FTjVS+OeXLabvjV4A9tBvG3Q+PPbOKrOPZtCnutWuTsmhRhcWllFJXo8NnDwNUiSUynAnwDKBRQCMiAiOICIwo98FlcMwse6j1Q+xP2c+snUXnIqWUUqq0EjMTcRM3/Dyq7lOYwd7BALoOs1KqWtMBZqUqQWZOHqt+S6B9o5rYnCyw7pFxEtmxGwyYClgeo7CBwclkG2HuCesOuZsX9rBeuCdswpby20XlRQTfLl3I+Pln7CdOVGhsqnqYMWMGUVFR532NHj36stutCndpy5OI9BOR30Rkj4iMLeJ9LxGZZ73/s4iEF3pvnHX8NxHpW1ybIhJhtRFvtelpHf+ziOwQka0i8qOINCpU509W+XgR+VNFfQ7KuYOpBwGo7VvbxZG4XqvQVkSFRTFt67SCR5qVUs5pLlaqeImZiQR5BVXpDSeDvXSAWanqSnPx73QNZqUqwerdCWTa8+gUUdNpmZqHfyDtmDd4e0Ldih1oaOhpp6NfOnNO+DKybhrebmAP64bHqVV47JlFdruXL6rj17Urqd98Q+p33xHy8MMVGp+q+kaMGMGIESNcHUaVJiJuwPvArcARYLOILDLG7ChU7CHgjDGmqYjcC7wODBGRlsC9wPVAXWC5iJxbmNZZm68D/zbGzBWRD6y2pwK/AO2NMRki8jjwhtVHTeBFoD1ggBirrTMV96moCx1KPYRNbIT4hLg6lCph8HWDGb9+PFPjpvLPzv90dThKVWmai5UqXmJmYpVefxkgyDsIQDf6U6oa0lz8O53BrFQl+N+24/h7udO8jvPlMYIPLuXscR9MRENwsglgeborKJkzuW4sSvRxHHDzxl6rJ+6nfsKWsuei8h61a+PZtAkpCxdSndZuV8qFOgJ7jDH7jDE5wFxgwAVlBgCfWq+/BG4WxxSbAcBcY0y2MWY/sMdqr8g2rTo3WW1gtTkQwBiz0hiTYR3/Cahvve4L/GCMOW0NKv8A9CvH81clcPjsYUJ9QnG36T1/gDp+dejdoDdf7v6Sfcn7XB2OUkqpai4hM4EAz6L3v6kqzs1g1gFmpVR1pgPMSlWwnNx8lu88RbtGwbg7GTh2y07Bc0cM+dmCadyoyDLlrbVPJk29svjkmD/51nixPaw7xuaNx/75Rdbx69qN7Pg9ZP928TIaSqmL1AMOF/r+iHWsyDLGmFwgBQi5RF1nx0OAZKsNZ32BY1bzklLEpyrYwdSD1PKpuusvu0L/Jv3xcvPi3zH/dnUoSimlqrnEDMcSGVWZt7s3Pu4+ukSGUqpa0wFmpSrY+r2JpGXn0jHc+fIYQcfWkHHMAwSIaFApcYnAoOBkDma5s+qMl+Ogmw/20M64nViNZFy81rJvp07g5kbKQt3sT6kSKGqxvwun/zsrU17Hf+9I5AEcy2G8WYr4EJFHRCRaRKITEhKKqKLKyhjDobOHqO2n6y8XVsOzBn0a9WHVkVXsOXPxEzVKKaVUSdjz7SRnJxPgVbVnMINjFrMOMCulqjMdYFaqgi3ddgIfDzda1XO+9lfgyZ8cy2PUrQ0+3pUWW3f/NGq52/nk2O+7KueG9QDA48CCi8q7+fvjExlJynffYfLyKi1OpaqpI0DhO0b1gWPOyoiIOxAInL5EXWfHE4Egq42L+hKRW4B/AP2NMdmliA9jzEfGmPbGmPZhYWHFnLIqjTPZZ0i3p1PLV2cwX+imhjfh5ebFJ9s/cXUoSimlqqkzWWcwmCq/BjM41mHWJTKUUtWZDjArVYFy8/JZuv0EUQ2D8HR3/r+b34ENZJ92x4RXzuzlc9wEBgQlE3PWi61nPQAwnkHkBrfF/fASyEm9ONZu3chLSCB940+VGqsqvZdeeonJkyeXul5sbCzff/99BUR01dkMNBORCBHxxLFp34XT/xcBf7Je3wOsMI5FzhcB94qIl4hEAM2ATc7atOqstNrAanMhgIjcAHyIY3C58NSYpUAfEQkWkWCgj3VMVZJDqYcAdIC5CP6e/vSo14PF+xdzIv3iJ2qUqi40FyvlOgmZjievqvoSGWDNYE7XGcxKlTfNw5VHB5iVqkDRB89wJsN+yeUxPDNOkL/PulvdsPKXP+0TmIqfLY9Pj/8+i9leqzeSn43HoW8vKu/Tpg3i60vqt7pMxpWqopNp3lUy+91aD/lJHIO2O4H5xpjtIjJRRPpbxT4GQkRkD/BnYKxVdzswH9gB/A8YbYzJc9am1dbzwJ+ttkKstsGxJIY/8IWIxIrIIquP08DLOAatNwMTrWOqkhw66xhgru2rS2QUpU94H/JNPrN2zHJ1KEpVOs3FSl2+pMwkgCq/yR9AsHcwSVlJ5OXr/5tKVQWah0tPtyxXqgL9sOMk7jahTX3nd80DTvxE+kkv8HCHOpU/i83XZrgtMJWvkoI4kuVGfe88jE8dcgNa4HHga+wR94CbV0F58fTEt0MHUpf9wDUvvojN17fSY67qTrz6Ktk7d5Vrm14tmnPN3/9ebLlXXnmFzz77jAYNGhAWFka7du2IjY3lscceIyMjgyZNmjB9+nSCg4Pp3bs3nTp1YuXKlSQnJ/Pxxx/TqVMnxo8fT2ZmJuvWrWPcuHEMGTLkon7S0tJ46qmniI6ORkR48cUXufvuu3n88cfZvHkzmZmZ3HPPPUyYMAGA8PBwRo4cybJly3jyySfp0KEDo0ePJiEhAV9fX6ZNm0bz5s3L9TOrCowx3wPfX3BsfKHXWcBgJ3VfAV4pSZvW8X1AxyKO33KJ+KYD052fgapIh1IPIQihPqGuDqVKCvUJpeM1Hfli9xc80uaRajFAoKoOzcWai5VKzEwEqBZLZAR7BZNn8kjKStInm9QVw1W5WPOwa+gMZqUqiDGGZdtPcH29AHw83ZyWCzj5E+knvTH164Cb83IV6Q9BydiAmRfOYran4P7/7N13eFzXeeD/77lTMYMyqCRIsAAkwd47TL+VGAAAIABJREFUKVHNqrYlS7YjOXEsK46ddexf1pF3N/Lajh3H3thZb1xlJ3IUWbbVOylSoiiKkkix994bQPQ+M5h+z+8PDCWSAEFgMMBggPfzPHgIzJx77juUiDPz3ve+p3pDp/HuZcvQgQDet98ewCjFtezatYtnn32WPXv28PLLL7Njxw4AvvCFL/CTn/yE/fv3M3PmzA8XOIBoNMr27dv5+c9/zj/90z9ht9v5wQ9+wP3338/evXu7XEgB/vmf/5mcnBwOHDjA/v37ufnmm4GOxXznzp3s37+f9957j/379394jNPpZNOmTTzwwAN85Stf4Ve/+hW7du3ipz/9KX/7t3/bj38zQgxO59vOU5hRiNWQ6/1Xc8f4O2iPtvPS8c77AggxGMlaLMTgUd/e0SIjHRLMHmdHQZJs9CdE38g6nDryiUaIfnK81kdFc4Dbpo/sdpz71GaavBb0/JIBiqyzAmuMG7K8vFSXyVdLvHhsGjNzAqazGOvZV4mOvh2U+nC8o7wcS0EBrStXkXP33d3MPDz1pLqpP2zcuJF7770XV7yq/O6778bv99PS0sINN9wAwIMPPshnP/tRwex9990HwPz58zl79myPz/X222/z7LPPfvhzbm4uAM8//zyPPfYY0WiU6upqDh8+zKxZswA+XJh9Ph+bN2++LI5QKIQQw81573kKXbJxYnfGZo9lkmcSLxx/gQenP4ihpDZC9IysxbIWC9EQaMBtc2MzbKkO5ZpyHR3/fmvba5nBjBRHI0RypGItlnU4dSTBLEQ/eftIR1/leWNzrzrG7r9A9HQjkAvjBr7/8qXuzW1hvTebF+pcfHm0H5QiUngdjooXMJoPYubN/HCsMgzcS5fStno1kbo6bEVyG9dgoS65ENATDkdH+xOLxUI0Gu3xcVrrTuc6c+YMP/3pT9mxYwe5ubl88YtfJBgMfvi8291RIW+aJh6Ph7179/YqViGGEq0159rOsXDkwlSHMujdOOZGfnfgd2yv2c6S4iVJmbO6NcC+ihZON/g5U++npi1IWyBCWzCKqTVWQ2G3GBRkOSjOcTIm18X88bnMHZPb7V1JQoCsxUIMFg2BBnLsg796GT6qYL5YdS2ESJysw6khZSBC9JO3DtUwsdBNntt+1THZNdtor3VAhh0K8wcwus5KHWHmudr5U7WbsNnxWDRvHtriwnbulU7j3cuWgWnStlp2Vh0sVqxYwSuvvEIgEMDr9bJq1Srcbje5ubls3LgRgD/+8Y8fXrm9mqysLLxeb7djbrvtNn79619/+HNzczNtbW243W5ycnKora3ljTfe6PLY7OxsSktLeeGFF4COhXnfvn29ealCpL2WUAu+iE82+OuBBSMWkGnL5Pljzyc8hz8UZc2Bah5+bi/Lf/wOS//lHf7bn3bzr28eY93hWqpbAmgNRVkORnsyKMh0kGG3UtUSYN3hWv5t3XH+/HfbmPn9tfzl49vYcKwO09RJfJViqJC1WIjBoyHQkBbtMQDc1o6kkzfc/b97IUT3ZB1OHalgFqIf1LYF2VfZyv0LxnQ7LrtmM211TvTYkstaUKTKfZ5mvlM1mtUNGdxbFADDTiR/Ebba91GBOnTGR5XKtlGjsJeV0fraa+Q/9MXUBS0+NG/ePO6//37mzJnDuHHjuP766wF48sknP9zQoKysjCeeeKLbeW666SZ+/OMfM2fOnKtuaPCd73yHr33ta8yYMQOLxcL3vvc97rvvPubOncv06dMpKytj+fLlVz3HU089xVe/+lV++MMfEolEeOCBB5g9e3bf/gKESCMV3goA2cinB2wWG8tGLWP9+fU0BBp6vCliKBpjw9E6Xt59gXeP1ROOmWQ5rUwrzuaWqUWUj8iiOMeJy37tt8P+UJRjtV6OVLex+VQjDz2xgwmFbr5151Q+Nk0uEoiPyFosxOBRH6hnTFb3n8cGC5vFhs2w0RZuS3UoQqQ1WYdTR2mdPtUXCxYs0Dt37kx1GEJc01PbzvHtVw7yr5+exZg811XHzfiv66h8CczbVsDsaQMYYde0hv+vYgwWw+DV2Q0oBSrURMbh/0Ok7H4ik7902XjvW2/R/NRTlK58DWd5eYqiHhyOHDnC1KlTUx2GSJKu/nsqpXZprRekKKRBQdbh5Fl3bh0Pv/sw31/6fcZmj011OINetb+ab2/6Nn839+/48qwvX3WcaWq2n21i5b4qVu+vpjUQweOysaQ0n4Xjc5k8MhuL0bcLutGYyZbTjazaV0VFc4AvLhvPt+6agsMqrTNSTdbioUXW4q7JWtwzWmsWPrWQG8fcyP2Tu96ga7B5+N2HuWXsLXx/2fdTHYoQCZO1eGjpzVosFcxC9IN1h2oZme2gJDfjqmPsvgtEzrYAHhib2v7LFykFn/K08LPaEXzQauc6TxjtyCOWMwNbxRoiE/8SLB+1/HAtWULzM8/QtmoVzm9+M4WRCyFEeqnx1wCQ58xLcSTpodhdzJS8Kbx4/EW+NPNLl232p7XmUFUbK/dVsXJfFTWtQRxWgwXjcrluUiEzR+f0Oal8KavF4PpJhSwpy+eZ7ef5/eaz7DjbxOMPLmRkjjNp5xFCCJE4f8RPKBYi256d6lB6zGV1SQWzECJtSYJZiCTzhaJ8cKqBW6eN7La5fFb97o7+y1kZ4Bk8b3xuyPLyZEM+T1Rlcp2nCYBIwVKspw5gqf2A2KibPhxryc4mY+ZMWleuovDv/x5lSFv3oeaJJ57gF7/4xWWPLV++nEcffTRFEQkxNNT6a7Ebdtw2d6pDSRsrSlbw2P7H2FGzg2m589h6uolNJ+p573g9ZxvbsRiKOSUePjOvhPnjcnHa+rei2GYx+MLS8UwflcOjG07yhf/axgt/s4wcl61fzyuGH1mLhei9i4naTFtmiiPpuQxbhvRgFmIQknW4ZyTBLESSvXesnkhMs2Bcbrfj3A178Tc40GWjB0X/5YtsCj7paeHJxgKO+q1McUcxsyZh2vOxVay+LMEM4Fq2jMbf/pb27TtwL1mcoqgHh652kU13Dz30EA899FCqwxhQ6dQ6SqSvmvYa8px5Q+53Rn/RWpOnZmLFycNrHqfmZCMxrXFYDaYUZ/HXU0tZVJpHlnPgk7vzx+Xy8K3l/OTNo/z1H3bwxy8t7vfktrg6WYuHBlmLRV/5Ij4AMqxXv6N0sJEKZjFUDLW1eDiuw9D7tVjKDYVIsrWHasjJsDF5RFa345wndxELGuiSUQMUWc/dmdOGU5k8WR2vrFMG0fxFWJr2ofyVl43NmDsXlZFB68qVKYh08HA6nTQ2NsoHojSntaaxsRGnU25zF/2r1l9LrrP7C5ECfEGTF7b6+cYfmvj2s34CLdPxGru5a1Ye3/34VH73hQU8csdUbpk6IiXJ5YtmjM7hb2+cwI6zzXzjub2yFqSIrMVDg6zFIhl84fRMMEsFs0h3shYPDYmsxVLBLEQShaMm7xytY+H4XIxu+j0qM4Jx8iyQCSXFAxZfT2VZTG7NbmNNQw5/P9ZLkd0kmr8IW/VarBVvEJny0QZLhsOBa/58vGvXYn73OxgZ6fMmLplKSkqorKykvr4+1aGIPnI6nZSUlKQ6DDHEVfurmeCZkOowBi1Ta947HORPm3x4A5pxhYpPzLOSnbeIVRd2MbH0PNMKJ6U6zMssnVBAoz/MU9vO89yOCh5YJJs3DjRZi4cOWYtFX12sYHZa0+dChcsmCWaR/mQtHjp6uxZLglmIJNpyuhFfKMqCcd1v2uRqPkqg1oAMG+R5Bii63rknt4XVrTk8Ve3m78d50bZsYjnTsVWuJVL+RTA+qhRzL1+Of9MmvO+8Q87HP566oFPIZrNRWlqa6jCEEGkgZsZoCDSwcOTCVIcyKPlDJj95rZUjVRHG5Cs+d52NYk/HTXdaTySrLpetdW+xqPCWFEfa2V0zi9lzvoUfrj7CjZOLZNO/ASZrsRDiIn/ED6RnBfNQay8ghhdZi4cvaZEhRBK9dagGp81gxuicbsdlNuylvd4Oo4oGVf/lSxXboizN9PNcrQt/rCPGaP5iVKQVS+3my8Y6pkzBkp9P28pVqQhVCCHSSkOggZiOSYuMLoQimh+/1sqx6gifnG/loZs+Si4DKGUwNWceh5t30BZuSmGkXTOU4svXlxGOmnz3tYNye6gQaUgpdYdS6phS6qRS6pEunncopZ6LP79NKTX+kue+FX/8mFLq9vhjY5RSG5RSR5RSh5RS//2S8XlKqXVKqRPxP2VhSJKLlcBplWC2uYjpGIFoINWhCCFEr0mCWYgkMU3NusO1zC7xYLd2/0/LdXYHEZ8Vs2TMAEWXmHs9LXhjBq/Wdbwxi2VPxrTnYqtYc9k4ZRi4Fi/Gt2kT0cbGVIQqhBBpo7a9FoA8Z/d3uww3kZjm/73eyrGqCPcttjK/zILRxUXYaZ4FmJhsr1+fgiivbWSOk88uKGHd4VrWHKhJdThCiF5QSlmAR4E7gWnA55RS064Y9iWgWWs9EfgZ8JP4sdOAB4DpwB3Ab+LzRYFvaq2nAkuAr10y5yPAeq31JGB9/GeRBOlawQzIRn9CiLQkCWYhkmRPRQt13hALx187YWA9fKDjmzGDr//ypaZmBJnqDPBktZuYpmOzv7wFGI17UMGGy8a6ly+HWIy21Wu6nkwIIQQANf6OpGOuQwrVLvX4Bi97zoX5xHwrM8ZYrjou3zmSImcJW+reGsDoeufOGcWUFbj5p1WHCEZiqQ5HCNFzi4CTWuvTWusw8CxwzxVj7gGejH//InCL6uhncA/wrNY6pLU+A5wEFmmtq7XWuwG01l7gCDC6i7meBD7VT69r2PFFfCgUDosj1aH0mMvWkWCWPsxCiHQkCWYhkuStwzVYDMWcMd33VLaE24iebwargqKCAYoucfd6WrgQsvJ2U0cfyWjeAhQaa9XllWP2khJs48bRunJlKsIUQoi0IRXMnZ2sibD+YJCl5Rbml109uXzRNM8CKvwnqGo/2//BJcBiKD63aCx13hDPbj+f6nCEED03Gqi45OdKPkoGdxqjtY4CrUB+T46Nt9OYC2yLPzRCa10dn6saKOoqKKXUV5RSO5VSO2XjrJ7xhX24rK606mV8sYJZEsxCiHQkCWYhkkBrzdqDNUwflY3b0f3emZkN+2ivt2OMzAXLtT9Ep9qSTD/FtghPVrkB0I4CYu7xWC+sgyt6S7qXLSN48CCh06dTEaoQQqSFGn8NdsOO2+ZOdSiDgtaa37/nxe2AG6b1bF2c4pmHwmDrIK5inj4qmykjs3j03VNSxSxE+ugqG3llM/Wrjen2WKVUJvAS8A2tda96IGitH9NaL9BaLygsLOzNocOWL+LDaU2vjValglkIkc4kwSxEEpys83G2sZ0F4659u7O7ciehFhvmmMHdf/kii4K7PS3s89nZ47UBEM2bj+E7h9F26rKx7iVLwDCkilkIIbpR468hLyMvraqq+tPWEyGOVUe5eYYVp61nfyduaxbjMyezrW4dpjb7OcLEKKX4zPwS6r0hnt4mVcxCpIlK4NI36SVA1dXGKKWsQA7Q1N2xSikbHcnlp7TWL18yplYpVRwfUwzUJe2VDHO+sC+t+i+D9GAWQqQ3STALkQRrD3X005w/7tq3O9sPbgOtBv0Gf5e6NbuNTCPGH6s7qu2injloZcVate6ycRaPB+f0abSuXIU2B+cHfiGESLXa9lrpvxwXjmr+uMnHiBzF3NLevS2d5llAc7ie4637+im6vps+KodpxVn8VqqYhUgXO4BJSqlSpZSdjk37rqycWAk8GP/+M8A7Wmsdf/wBpZRDKVUKTAK2x/szPw4c0Vr/WzdzPQi8lvRXNEz5I/60q2DOsHUkxKWCWQiRjiTBLEQSrD1Uy6SiTPLc9u4Hao06cbbjBrpRIwYitKTIMDS3ZbfxdqOT6pABVhexnGlYq94BM3rZWPfSZUSrqgjs3p2iaIUQYnCr8ddI/+W49QcD1LeZ3DbbitHLiu4J2TOwG85B3SYD4NPzx1DvkypmIdJBvKfy14G1dGzG97zW+pBS6gdKqbvjwx4H8pVSJ4GHgUfixx4CngcOA28CX9Nax4DlwF8CNyul9sa/7orP9WPgVqXUCeDW+M8iCbwR74cVwelCejALIdKZJJiF6KOqlgAHLrT2qD2Gvb2GUG0MS34G2G0DEF3yfNLTigaeqYlXMefOR4VbsDTsumxcxvz5KIeD1tekTYYQQlwpakZpCDSQ65QKZuhIMI/KVUwY0fu3pDbDzqTsWexqfJdQLNgP0SXHtOJsykdk8vvNZzHNK1u5CiEGG631Gq11udZ6gtb6R/HH/lFrvTL+fVBr/Vmt9USt9SKt9elLjv1R/LjJWus34o9t0lorrfUsrfWc+Nea+HONWutbtNaT4n82peI1D0W+cPr1YLYaVhwWhySYhRBpSRLMQvTRusO1ACwYf+1qNFfDQYKNNnRx+m3OUWSLssTt54VaF4EYxLKnoK1urFVvXzbOcDrJmD+ftjfewAyFUhStEEIMTg2BBmI6JhXMwJm6COcaYswZn/iGt9M8CwjFAuxr+iCJkSXfbdNGcr6pnfdP1Kc6FCGEGBZ8kfTrwQwdG/1JD2YhRDqSBLMQfbT2UA0luRmM8lz7DUzG8a2YUQNz9LgBiCz57sltoS1msKohAwwr0ZyZWOq2whWVY+5lyzB9Pnwb3k1NoEIIMUjVtndclJQKZnj3cBCLATPGJv52dIx7Alm23EHfJmNxaR6eDBt/2HIu1aEIIcSwkLYJZqtLKpiFEGlJEsxC9EFLe5htp5uY34P2GAC2IwcAMEeP7s+w+s10Z5AyR4inqt1oDdHcOahYEEvd9svGOadPx+Lx0LpqVYoiFUKIwanG37Ep7HCvYI7ENBuPBpk8ysBl713v5UspZTA1Zz6Hm3fQGm5MYoTJZbUY3DyliA1H66hoak91OEIIMaRFYhHCsXBaJpgzrBmSYBZCpCVJMAvRB+uP1BHTmoU9aI8BoM9UoRwKcnP6ObL+oRTc7WnhZMDGtjY7ZuYEtDULa817l48zDFxLluB77z2izc0pilYIIQafWn+8gtkxvCuY954N4w1q5ozv+1vRaZ4FmJhsr38nCZH1n1umjkAp+NNWqWIWQoj+5I/4AdIywSwtMoQQ6UoSzEL0wVuHa8hz2ykrcF9zrDXUQrg2grXI3ZGpTVM3ZPrItsR4psYFyiDqibfJiAYuG+detgyiUdreeCNFkQohxOBT016Dw+LAbbv2ujGUbTgcINNJQpv7XSnfOYKRGWPZWrc2CZH1nzy3nYXj83h2RwXBSCzV4QghxJDljXRUAKdlgtkqCWYhRHqSBLMQCWoPR3nveD0LxuWiepAwdlXtJtRqRRcXDUB0/cduaG7NbmNDk5PakNHRJsMMdySZL2EbOxZbSQltK6VNhhBCXFTrryXX2bN1Y6hqC5jsPhNm1lgLFiM5fw9TPfOp8J/kgv90UubrL7dNG0FrIMIbB6tTHYoQQgxZFyuYnVZniiPpPenBLIRIV5JgFiJBbx+pIxgxWVyW36PxzgMfAIpoyfh+jWsg3JndSgzFS3UuTHcppi27c5sMpXAvW0Zg717C5+R2YCGEgI4ezMO9PcaesyFiJkwfk7y3oVNy5mJgYWvduqTN2R+mFGdTlOXgxV2VqQ5FCCGGrIsJWpfVleJIes9lc+GL+NBapzoUIYToFUkwC5Gg1furyHXZmDIiq0fjrUcPAmCOHtefYQ2IYnuUea52Xqh1EcUg5pmNpX47xKsFLnItXQpK0SpVzEIIAUBdoA6P05PqMFJq//kILgcU5yavittlzWJ81hS21a/D1IO3/YShFNdPKmDzyUaqWgLXPkAIIUSvpXUFs82FqU3ao7IhrBAivUiCWYgEeIMRNhytZ3FpPkYPb+/VZ2uw5hjgdPRzdAPjrpxW6iIW3m1yEPXMRpmRTm0yrHl5OKZOpXXlSrkKL4QY9rTWNAYa8diHb4JZa83+cyFKiwyMJLcJmeaZT0u4gaOte5I6b7JdP6kQDbyy50KqQxFCiCHJF/EBaVrBHI+5LSR9mIUQ6UUSzEIk4O0jtYRjJksn9Kw9hhFpJ1wTxjJi6GzqtMjtp8Aa5dlaF6Z7HKYtB2vtpk7j3MuWEamoILB3bwqiFEKIwaMt3EbEjJDjyEl1KClT0RijpV0nZXO/K5VlTcdhZLBtkLfJGJHtZGpxFi/uqpSLr0II0Q984Y4Ec1pWMF9MMMtGf0KINCMJZiESsGpfNQWZdiYWZfZovOvEZmIhC7p4RD9HNnAsCm7PbmVLq5NzQRuxnOlY6ndALHTZONf8+WCz0bZ6TYoiFUKIwaEx0AhAtiM7xZGkzv7zYQDK+iHBbDPslOfMZlfDe4Rig7v9xPWTCjnT4GdPRUuqQxFCiCHnYgVzhjUjxZH0nsvWkWCWjf6EEOlGEsxC9FJre4T3j9ezqDS/x7f3Ovd3VPZGS8r6M7QBd3tOGwaaV+pdRHNmoMwQloZdl40xXC4yZs+mbc0adDSaokiFECL1GgINAOTYh28F8/7zYQqyFB5XcttjXDTNs4CwGWRP48Z+mT9ZFpfmYbcavCSb/QkhRNL5I34syoLNsKU6lF67WMEsCWYhRLrpUYJZKXWHUuqYUuqkUuqRLp53KKWeiz+/TSk1Pv74rUqpXUqpA/E/b77kmPnxx08qpX6pVJIb8QnRT9YeriFqapaW9aw9BoDl+BGURRMtGtuPkQ28fGuM+e52XqvLIJI5EW3JwFL7Qadx7qVLiTU14d+6LQVRCiHE4NAY7KhgHq4tMiJRzaHKMKVF/feWb7SrlBxbPlvq3uq3cySDy25l4fg8Vu2rIhQdvJsSCiFEOvKGvWRYM0jHFMOHFcwRSTALIdLLNRPMSikL8ChwJzAN+JxSatoVw74ENGutJwI/A34Sf7wB+KTWeibwIPDHS475LfAVYFL8644+vA4hBszr+6ooynIwobDn/ZT1+VpseQZYrf0YWWp8LKuNuoiFza0uYtlTsdZtAfPyD8sZs2ahXC7aXn89RVEKIUTqXaxgzrYPzxYZx6sjhKP0S//li5QymOqZz9GW3bSEGvrtPMlw3cQC2oJR3j8+uOMUQoh044/403KDP5AKZiFE+urJO/xFwEmt9WmtdRh4FrjnijH3AE/Gv38RuEUppbTWe7TWVfHHDwHOeLVzMZCttd6iO3Y3+QPwqT6/GiH6Wb03xAcnG1lSlt/zK+KRMJH6CJbCnvVrTjeL3X6yjBiv1mcQ9cxERbwYzQcvG6PsdlwLFuBdtw4zGExRpEIIkVoNgQYsyoLbNnQ2fO2NfefDGArGF/Vvh7ZpngVoTLbVv92v5+mrGaOzyXZaWbmv6tqDhRBC9Jgv7EvLDf7go77RssmfECLd9OQd/mig4pKfK+OPdTlGax0FWoEr+wd8GtijtQ7Fx1/adK6rOYUYdF7fX0VMa66bWNDjYzKOb0NHFXpkYT9Gljo2A27M8rK+yUlTxhS0smGt3dRpnHvJEky/H9+776UgSiGESL2GQAMehyctb9lNhgPnw4zOUzht/fv6cx2FjHKNZ1PtajrqGAYnq2GwqDSPtw/X0B6WPQqEECJZfJH0TTBbDAsZ1gypYBZCpJ2eJJi7+hRw5bv1bscopabT0Tbjb3ox58Vjv6KU2qmU2llfX9+DcIXoPy/vvkBpgZsxeT2/5cp5cDMAsdHj+iuslLs1u42IVqxu9hDLmtTRh/mKD/WOqVOxeDy0rV6doiiFECK1GgONw7Y9RjCiOV0XpbSfq5cvmpW7lNpABcda9w7I+RK1bEIBgYjJ20fqUh2KEEIMGb6IL21bZEBHm4y2kFQwCyHSS0/e5VcCYy75uQS48l6+D8copaxADtAU/7kEeAX4gtb61CXjS64xJwBa68e01gu01gsKC4dmBahIDyfrfBy40MryCT2vXgawHj+MYTUJF4zvn8AGgQnOMGWOEK/UZRDzzMQI1mO0nbxsjDIMXIsW4Xv3XWJeuSIvhBh+6gP1w3aDvzN1EUwNo/MGpnq7PGcOTouL92tWDsj5EjV5ZBZ5bjsr915IdShCCDFkeMPetK1gBqSCWQiRlnqSYN4BTFJKlSql7MADwJXv1lfSsYkfwGeAd7TWWinlAVYD39Jaf3BxsNa6GvAqpZaojvtEvwC81sfXIkS/enXPBQwFyyZe2f3lGs5VYc8DbXX0T2CDxMey2jjst3PcNguNwlK3tdMY1+LF6EgE34YNKYhQCCFSqyHQQLZjeFYwn6ztaAExOm9gKphthp3pnkXsbnyf1nDjgJwzEYZSLCnL591j9bS2R1IdjhBCDAn+iP/DXsbpyGVz4Y1IglkIkV6u+S4/3lP568Ba4AjwvNb6kFLqB0qpu+PDHgfylVIngYeBR+KPfx2YCHxXKbU3/lUUf+6rwH8CJ4FTwBvJelFCJJtpal7Zc4GZo3PIddl7fmA0SrQugKUwfd/g9NSNWV4MNCtbCjHd47DUd04w28vKsOTl0fbm2hREKIQQqRMzY7SEWsixD88K5lM1EXJcikznwPWfnpW3FFPH2FS7ZsDOmYhlE/KJmpq1h2pSHYoQQqQ9rTW+iC+9E8zSIkMIkYZ6VEaitV6jtS7XWk/QWv8o/tg/aq1Xxr8Paq0/q7WeqLVepLU+HX/8h1prt9Z6ziVfdfHndmqtZ8Tn/LoezLuwiGFv57lmLrQEWN6Lzf0ArKcOoWPAiF5WPaehHKvJXFc7axqcRLOnYmk9jgo1XTZGGQauBQvwb9pEzOdLUaRCCDHwmkPNmNocthXMJ2oiA9Ye46I8RxFj3eVsrF6FqWMDeu7eKCtwMzLbwWv7pE2GEEL0VdgMEzWjaZ1gzrBJiwwhRPoZmPsUhUhzr+ypxGkzWDg+r1fHZRzYCIA5quQaI4eGFVk+LoSsHLHOAsBSt73TGNeiRehwWNpkCCGGlcZAR5uG4VjB7A2Y1LWZjMrNhMrdAAAgAElEQVQd2AQzwOy8pTSF69jftGXAz91TSimWTihgy6lG6r2hVIcjhBBpzRfuKGJJ5wSzw+IgEA2kOgwhhOgVSTALcQ3BSIzX91ezcFweTpulV8faju7HsJmEhvAGf5da5vZhUyYvtE3AtOd23SZjwgRpkyGEGHYaAg0Aw3KTv5O1Hb2FB6r/8qUmZs8kx5bPm5VPM5hvlls2IR9Tw5oD1akORQgh0povkv4JZqfFKQlmIUTakQSzENew9lAN3mCUFeWFvT5Wna3AmRclas3th8gGH5dFs9DVzptNGUSypmJp2AWx8GVjPmyTsXGjtMkQQgwbwzrBXBNFQUoqmA1lYX7BDZz2HuZk24EBP39PleS6GJvnYuXeqlSHIoQQaW0oJJgdFgfBWJCYOXjbOwkhxJUkwSzENTy/s4KiLAfTRvWyb2YkTKzWi7XQAWrgP1Snyg1ZXhojFg5bZ6JiQSxN+zuNcS1cGG+T8e7AByiEEClwMcGcbR9+PZhP1UYozFY4bKlZC2fkLibDksmblU+n5Pw9tXRCPrvON1PZ3J7qUIQQIm0NlRYZAMFYMMWRCCFEz0mCWYhuXGgJsPlkI9dPKsDoZZLYcvokmKCKhkf18kUL3e24DJNn/bPRhg1L/bZOY+wTJ3a0yVj7ZgoiFEKIgdcQaMBpceK0OlMdyoDSWnOiJkJxCqqXL7IZdubmX8+B5q1U+k8nZU5X0yEmvf91pq+9n4LTr6Bife+dvKysY0Pg1/dLmwwhhEjUxQrmdF5vHdaOBLO0yRBCpBNJMAvRjZd2VaKBFZN63x7DcaRjgzuzuDjJUQ1uDkOz1O1jdXM2kcxJWOq2whV9L5VhkDF/Pv6NmzDbpVJLCDH0NQYayXYMv+rlBq9JW0CnpP/ypebkLcdmOFhb+Uyf5rEF6pn8zpeZvfqT5F54F4evkkkffJN5L1+Hp3J9n+YuynYyqSiTVfukTYYQQiTKH/EDQ6OCuT0in5OEEOlDEsxCXIVpal7YVcH0UdkUZff+Crj96J6ODf7yxvRDdIPbiiwfvpjBMdssjEANyn++0xjXvHnoUAj/5s0piFAIIQZWY7BxWLbH+GiDv9S2isqwupmVu5Tt9eup8p9JaA4jGmDKhi/jqdlE7YTPcvy6X3By2U85O+9bxKxuJm36Jvb2mj7FuXRCPoeq2jhVL3sUCCFEIrxhLzA0EsxSwSyESCeSYBbiKrafbaKiKcANCWzuB2CcOYMzN0LYPjLhGDSaFto5oC6wRh3kj8ZWfm1s4EfGG/yzsZp/Md7k/xpv8XtjC+vVUU5QRxQz4fMly2xXO24jxivBuQBY6nd1GuMoL8dwu/Guf2egwxNCiAFXH6gfthv8WQwY4ekiwaw12b4qys+9xbJ9v2HmiZcpbDqGMqP9EsuiwluwG3ZeOvsfvT9Ym0z84H/gbjxA5Yyv01B2L6bNDUrhz59J5az/jmGGmLD5HzrdtdMbi0vzUSCb/QkhRIKGQgWz09JR3NQelQpmIUT6sKY6ACEGqxd2VpJhs7CoNK/3B0ejmNXN2MoNTKPn1c8mJqdp4Iiq4biq5biqo1V9dOXapi1kajuZ2oEFRQxNRMU4oC4QNjp2Gc7WTpboMpabEyinCMXAV43ZFCxy+3mpZTTfzizC0rCDaOl9l41RVivOWbPwvbsBHYuhLJYBj1MIIQZKY6CR0uzSVIcx4M7URRiRo7Aal69F7vZ6btz1UwpbTgIQsmbgiFdqBW2ZbJ/+EKdLbkjqJrkuayaLCj/GxtrXOdqymymeeT0+dsy+n5N//g1qJv0F3qL5nZ4Pu4upmfTnjDr6BCNOPE1t+V8kFGOe2860Udms2lfFNz42CTWMNgkWQohk8EV82AwbViN9Ux3SIkMIkY7S97euEP3IF4qy5kA1S8rycVh7n/g0Ks9BTGMUXPt2aC9Bdqnz7FEVHFQX8KswAPmmi7JoHqPNbEaaWYwws8jC0eUcJpoWFeCC0coBaw0bLEd5y3qYKeYIPm8uZiJFvX4NfbUs088GbzZVjqmUNG2BWAgsl8fvmjeP9i1bCOzZg2vBggGPUQghBkI4FqYt3DYsK5jPN0YpLbo8STqi4RA37voplliY3aUfpyZ3Ij5nPo5oOwWtZ5lctZkVe39FadVmtsz6G9oz8pMWz7z8Fexr+oAXzvyGb895DENd+2a+rNrtlBz4Nc2jbqRx3F1XHddc8jGy63cxbuePaB59E2H3qIRiXDohn//ceIZDVW3MGD38/p8RQoi+CEaDHyZo05Vs8ieESEeSYBaiC2v2VxOIxLhxcmLtMazHDwGgR3ad2A0SYYs6zQfqFIdVNabS5JhOpkSLmBQrYIKZT5bu+RsjA0WedpEXczEzVkyIKLutlay3n+Q71pUsNct40FyCB1dCrycR81ztOJXJ29HZPGS+h6XpALHCy5PIzpkzwWrFu/4dSTALIYasxkAjwLBLMLe2m7S2a0bkfJTEHVu9lRt3/Rs+Zy7vTv8iXtdH62zI5uZCwXSq8qcysXorM8+9zcc3PcLapd+nLXN0UmKyGjauG/Fx1lT+ia11b7FsxB3djlexEGVbv03YWUj1lC90X1GtFFVT/4pJmx5mxIlnqZjzcEIxLhqfxxMfnGXVvipJMAshRC8FooH0TzBfrGCWFhlCiDQiPZiF6MLzOysY5enYzT0RjqM7URaTSEHJZY+fp4nHjI38N8tT/IdlIzW0siJSxtcDy3kkcBOfDc9iTmxUr5LLXZ4fK0uj4/mf7Tdyc3giO9VZ/sHyCofUwPV0dBqa+e52/ss7E62sWBp2dhpjZGTgnDYV7/r16D70rBRCiMGsIdAAMOw2+ato7OilXBRPMGe213Ld3l/TnDmK9bP+5rLk8qW0MjgxahnrZ30FSyzMnZu/i8fbebPYRE3JmUtxxjheOPMb2sJN3Y4ddegxXG2nqJ7yRXTMAqfPw7tbUE+/Chs2Q1PLZeMjGUX4CuZQdOJZlBlJKL4sp41ZJTms2leFacraKIQQvRGIBrBb7KkOo0+kRYYQIh1JglmIK5xp8LPzXDMrJhUm3PvQevIYTk+UkKMYgNM08FNjHf/L+jKb1EmmR0fy1cBSvhlYwR2RyZSYOf3SK9mBldsi5XwtsAyHtvBD4w1eUrsxGZgPrMvcPioiblqdZV0mmAEy5s4jcv484VOnBiQmIYQYaBcTzMOtgvl8w8UEs0KZUVbs/jlozZbyzxKxXnt/glb3SDbMeAi0yR2b/xFPW3KSzEoZ3D76AYKxdp469bOrXuB0tp2m5MCjtBQsxre7FfWN72H86JcYf3wJ3tuC8aeXMf7nD1Hf+39w4OiHxzWV3II92EBuxdsJx7hsQgFVrUF2n29OeA4hhBiOgtFg2ieYL27yJy0yhBDpRBLMQlzhxV0VGAqun5RYewxME11RgyMvSp3Vya+MDfxv66scUlXcEp7II+0dlcrjzNwB24BvpM7ma4FlzImO4gXLbn5rvEcUs9/Pu8jtx6Y025mJ4TuHCtR3GpMxdy4A3vXv9Hs8QgiRCg3BeILZPswSzI1RXA7IdMCcY89R1HycnRPvod2Z2+M5vK4iNsz4Ehr42LYfkhFMTsI13zmSZUV3sKdxIzsaulh/tKZ02/fwNziofimA8fzrMHok5j23Y37lz9Hf+GvMv7of84YlEAiifvE4rH0XtMZXMJuws4ARx59OOL75Y3OxWwxW7Ru4O4+EEGIoGAotMmwWGyAtMoQQ6UUSzEJcImZqXtxVyawSD3nuxK58G9UXIBxj72gXD1tfYqs6w83hifxD+43cGinHRWquqDuw8mfhWdwWLmejcZKfGW8TJtqv53RZNHNc7fzBPw8AS8OuTmOsubnYy8rwviMJZiHE0HSxB3OWIyvFkQys8w1RirIV+W1nmHXyFU4XzaeyYEav5/Fl5LNx6udxhL3csv3/YI0GkxLfgoIbKc4Yx9Onfk5LqOGy5/Iq1sIHe6hcl4VuD2F+6nb0fXdCeSnkZHf0Ys7PhUVz0J+/DyaVYjz/OuqJ5yBq0jz6Zjw1H+BsO5NQbBl2C3PHenh9fzXRWP9fEBZCiKGiPdqO3UjvCmZDGTgsDgIRqWAWQqQPSTALcYlNJxuobQtxQ3mC1ctA9Pg+AH5bamGUmcM3AtdxW6QcJ7ZkhZkwheLmyETuCU1nl3GeHxtrCZJYj8ieWur2sSk8nrA156ptMpyzZhHcv59os9wKLIQYepqCTbisLmxG6teBgaK1pqIxSlGOYu7RZwhbnewr7X5Dve60ZI5i6+Q/I6/1DCt2/xylY32O0VAWbi/5HBEzxH8c/T7ReM9kIxrA88QPqd7hQY8rQX/xz2BS6dU3+LPb0Hffil42H/XBTtRTL9My+ga0sjDixDMJx7d8QgGN/jAfnGpMeA4hhBhugtFg2lcwQ0cfZqlgFkKkE0kwC3GJF3dVkuWwMn9cz2/fvdQ533F2bPkVUQNudnr4UnAhhTqxjQL709LoOO4PzuaIquZnxnqi9P2D+tUsdLcDiuOWaR0VzF0kBTJmzQKt8X+wud/iEEKIVGkONg+7Df7q20yCEVhsO8GYut0cG31dj/oud6c6bzL7Su9kbO0O5h1JvP3EpfIdI7ht9AOc8h7k+TOPdjz2i7+jaTsYE0d0VC3be3BhQCn08oXoxXNRG7cT3XeOtsL5FJ56GczE1tg5Yz1kOqy8vLsyoeOFEGI4CkQD2K3pXcEMHX2YJcEshEgnkmAWIq4tGOGtQzUsnZCPzdL7fxp7Gzfxk31fo6Q6hMUTZYkeP2A9lhMxNzaae8Mz2GdU8hvj/X7b+C/PGqPcGeSNyGxU1IfRerzTGHtpKUZWFv6N7/dLDEIIkUrNwWYy7YPvYmN/qmiMAppPNz5DwJbJieIlSZn3RPESTo5cyMxTrzKhYkNS5pySM5f5+TfybvWr1Pz+ESLvHME1yU70nnvAYunVXHr5AvSoEagnX6DFNh1bqInMhr0JxWWzGCwpy2ftwRq8wf6920gIIYaKQDSAwxgCFcxWaZEhhEgvkmAWIu6NA9WEoibXTyro9bE76zfw70e/R4GjmCk1Ck9OCK81rx+iTK5F0bHcEZ7MZuMUTxpb0P2UZF7k8vN0+xw0Ckt95zYZyjBwzpiB7/2NaFN6TQohhpamYBNZ9uHXf/k64yDj2w5zpOQGYpYkVZMpxZ7Sj1ObU8ay/f9OUdPRpEy7YuQnuK1yJOUvbkKPiRC4614wEnibbLGgP3ELaE37SwcxtYW8yrcTjuuG8gKCUZM1B6oTnkMIIYaTQDSAPVlrTgrZLXapYBZCpBVJMAsR9/LuCxTnOJlQ2Lsqsy21a/ndsX+mOGMcf57zOSztEax5BmEjo58iTa4bImVcFyllrXGYN9ShfjnHIrefZrJptI3F0rCjyzEZM2cSa24meOhwv8QghBCp0hhsJMs2zBLMjVH+h/0l/A4Pp0cuSOrc2rCwZfL9tNtzuGnHT8hsr+vznJnnq/mr56q5UATf/HQWzfY+rOE52ejbb4CzF6g5N57cyvUJTzWhMJNRHicv7bqQeDxCCDFMaK2HTA9mp8VJICoVzEKI9CEJZiGAyuZ2tp1p4rqJBairbeLThd0N7/P7Ez9mjHsinx7/FXIr6gGIFLr7K9SkUyjuCk9henQEfzS2sU8lv9djmSNMvjXKFj0To+UYRLydxjhnzgSl8EmbDCHEEGJqk9ZQ67CrYHbWHmcOxzk+ahmmYU36/GGbi01T/wJLLMzN2/8Fax8+hFu9Pib/399is0TIvAmqnSa/NN7BpA931EyegC4bR9veCI76Uzi85xKaRinF9RML2X62iYomqWQTQojuhGIhNHroVDBH5Pe+ECJ9SIJZCOC1vVUAXDex5+0xznqP8vjxHzEyYxyfGvclbIYD9+nzgCZQOPjbY1zKQPFnodmMNLP4hbGeC7QkdX6lYKHLz3OBuShMLA17Oo2xZGdjLy3F//7GpJ5bCCFSyRv2EtOxYZVgjsY0t/vfJKTsnC2a22/n8boK2TL5fjy+Slbs/gXoBBLCWlP273/E3trG6OubCeet4FPh6Rw0qnjB2N2n+PT1i9DBKI1HMvtUxXz9pAIU8JJs9ieEEN26WPE7VCqYpUWGECKdSIJZDHtaa17eXcmUkVkUZfdsh/vGYC2/Pvy/cVky+dS4v8JmdFwlzzl9Ent2FJ+z932cU82BlS+E5mOg+FfLW/gJJXX+xW4/W2OTiBhOLA2d+zBDRxVzYP9+Yi3JTXALIUSqNAWbAIZVgrmxtplPGpvZmzWfiLVn62qianMnsrf0TsbW7mDpgd/1Osk88o0N5O49zIg5LZwfOxe/1cPC6BjmR0bzqtrHUWoSD64oHz11Ek3Hs8g5ujbhafIzHUwfnc1Luysxzf7ZK0EIIYaCYDQIDI0Es8PqkBYZQoi0IglmMewduNDKqXp/j6uXQ7EAvz78CKFYO/eO+2tc1o+SBq6zF3B6IrSlwQZ/XcnVGXw+OI96vPzWeD+pm/7NcgUwlMFxy9SOBLPuPHfGrFlgmvg++CBp5xVCiFRqDjYDwyvBnH3oTZwqwokRiwfkfCdHLubI6OuZfO4tlu3/jx4nmd2nzjL22VfJKgnQPqOY0xmzP3zu7vB0cnUGj1repZ1wwrHp5QvQGkLvnsASbkt4nhvKi6hoCrD5VGPCcwghxFB3MSE7FFpkOCwOaZEhhEgrkmAWw97Luy9gNRSLy/J7NP6507/mQvtZPjHmQfKdIz983Or1YWkN4MiL4rfm9le4/W68mcdd4SnsNM7xujqQtHmdhmZ2RoDXw3MwgvUo//lOY+xlZRhZWfg3bkraeYUQIpWGXYJZm0yrWMMOczIqf4Du5lGKA+Nu5XDJDZSff5vl+36DEYt0e4ilPcDkn/8OmyNKxlI7B3Ju6ujnFOfAyv2h2TTg4/fGlsRjy83BmD6WllMZ5Ox9NeFpFo3PI8tp5ZntnddOIYQQHYZSiwyHxUEwFsRMpP2TEEKkgCSYxbBmmprVB6qZO9ZDpuPamxDtaniXTbWrWVRwM+Ozplz2nOtsR29EM9+JqSz9Eu9AWR4dz8zoSJ42dnBIVSVt3kVuPytDcwCw1O/q9LwyDJxTp+LfvBndRYWzEEKkm8ZgR8XpcEkw51TspCBcw8vGzdgsA/h7XCkOjr2Fg2NuYlLFBj6x8R/IbT3b9VjTZNYv/g1bcysFy4PsHnEHpur8HmCcmctNkYm8b5zo0wa40etWoBTYVyWeYLZbDa6fVMjaQzXUe5PbwkoIIYaKoZZgho/afgghxGAnCWYxrO0810y9N8Ti0mtXLzcGa/nDiZ9SnDGOZSPu7PS8O55gDhWlb/XyRQrFp0MzKdAufmlsoIXk3J610N3OBQppsozA0rCjyzHO6dOJ1tURPnMmKecUQohUGm4VzAVH36SFLPa7Zg38yZXi8Nib2Tj182SEmvjExv/FooOPU1y/D0ssjDPUyujaXaz4r+9iP1iDa66FXeX3EDYyrjrlzZEJ5JoZPG1sx0y0bVRmJs4yB6H9tShv4m0ybplSRNTUvLCrIuE5hBBiKBtKLTKclo49DGSjPyFEupAEsxjW1hyoxm4xmDe2+6SwqWM8fvyHxHSUu0o+j6WLCuWs06exuaJ43em3wV9XnNj4i+Bc2gnxG+P9xD9YX6LQFqXMEWKLnomlaT/EOve1dE6bBoB/Sx9uSRZCiEGiOdRMhjUDm2FLdSj9zhLy4Tm3hZWxpeS6U3cXSnXeZNbO+ToVBTOYfHYtt2/9AX/xxud54K2/4vq3/pXw+63okgwOLruLkMXV7VxWLNwWLuecamKzOpVwTJY5ZeiowrX6TwnPMcqTwbTiLJ7Zfl42+xNCiC4MqQpma8drCERkoz8hRHqQBLMYti62x5g9JocMe/ctLd6rXsnJtgPcXHwvHkfXCeTMM+dw5EZos/asl3M6GKmz+Xh4KvuNSlYnqR/zIref14IzUWYYo+VIp+etRUVYCwslwSyEGBKagk3Dpno599R7GLEIL0RXUOjqvgdyfwvbXGwv/wyvLf4W70/9S46PWsq+UbdyYmcpEYeTc3esAKNnb4Nnx0YxKpbNc8ZOIsQSiidYMoOMghC2Na+DmXg/zZunjKCiKcCmkw0JzyGEEENVMNbRTmIoVDBfTJJLBbMQIl1IglkMWz1tj9Ecquflc48xLnMy0zwLuxxjBILY6ttwDrEEM8Di6FhmREfyrLGDk9T1eb6Fbj9bzOmYKCyNe7oc45g2jfat29DRaJ/PJ4QQqdQcbCbLNjwSzAXH19HiHMEBXUqRu/MdKqkQtTioyStn//jb8X0QxdLgp+72eZiunle3GSjuiEymXvlYpzpfGO2JsDWf7MkaGtuw7tya0BwAi0rzyHZa+dPWcwnPIYQQQ9XFat8hUcEsCWYhRJqRBLMYttYcqMZmUddsj/Hs6V8SM6N8bNRnUJfsMH8p1/kLABj5FiLd9HJMRwrFfaGZZGknv7RsIEDfkgbljhCGxcE5oxRL4+4uxzinT8f0+QgeOtSncwmRakqpO5RSx5RSJ5VSj3TxvEMp9Vz8+W1KqfGXPPet+OPHlFK3X2tOpVRpfI4T8Tnt8cdXKKV2K6WiSqnPXHH+mFJqb/xrZX/8HQx3w6WC2d5WTVbNQXZnLQFUyiuYr+TZfoy8bcdoWVROcExhr48vjxUyMZbPa8Y+oiRQgawUatJoLBkmjlUv9v74OJvF4KYpRaw7XMv5Rkk6CCHEpYZSD+aLCWZpkSGESBeSYBbDkmlq1hyoZs4YT7ftMfY2bmJP40aWFt2Ox3713sruMx0b7kQKs5Me62Dgwsb9odnU4+UPRuKVVwCGgoUuP+siMzFajkHE32mM9GEWQ4FSygI8CtwJTAM+p5SadsWwLwHNWuuJwM+An8SPnQY8AEwH7gB+o5SyXGPOnwA/01pPAprjcwOcB74IPN1FmAGt9Zz4191JeNniCs3B5mGRYM4/sR6Aty1LcVhMsuyJtZLoD/baZkY99z6B0fk0LypPeJ7lkfG0qgC7VWLVw+0ZE8ib6MO2eztG5fmE47ht2kgMQ/H7zWcTnkMIIYaiQEwqmIUQIlUkwSyGpV3nm6m7RnuMUCzIM6d+QaFzFPMLbux2PvfZ81gcMbxZQ6s9xqVKzTxuiExgg3GcHepsn+ZalOlnQ2wGCrNjs78rWLKysI0bh3+zJJhFWlsEnNRan9Zah4FngXuuGHMP8GT8+xeBW1THrRL3AM9qrUNa6zPAyfh8Xc4ZP+bm+BzE5/wUgNb6rNZ6PyRSdin6QmtNc2gYJJi1puDYOtoKJnIgMIJCd5ir3PAz4FQ4ytgn1oFhUHf7vB73Xe5KeayQHNPJ2+pYQsf7naV4JrSDobC/9XrCceS57Swpy+e5nefxBgdXpbgQQqRSIBrAZtgwVPqnOZxWJ/BRVbYQQgx26f+bV4gErN5/7fYY6y48R3O4npuLP41Fdb8JYPbpMzjzInhtV69yHgo+FpnE6FgOjxkbaSHxq+lzXe3sMycSwX71NhnTphHYswczIG+qRNoaDVRc8nNl/LEux2ito0ArkN/NsVd7PB9oic9xtXN1xamU2qmU2qqU+lRXA5RSX4mP2VlfX9+DKcVF3oiXqBkd8glmd/0xnG0XaCyZR1WbhaJB1B5j1IsbybjQSN1tc4ll9q2FlQWDBdESDqhK6vD2+viINRfTnYNjrAP7O2shlvg+A3fNGIk/FOO5HRXXHiyEEMNEIBIYEtXLcEkFc0QqmIUQ6UESzGLY0Vrz9pFaZozKuWp7jOZQPW9WPk159mxK3GXdzqfCEezVjTg9EdqsQzvBbMXg/tBsgkT4D2MjGp3QPC5DM9kVYw/lV93ozzl9OjoSoX1X1wloIdJAVzWcV/6judqYZD1+LWO11guAPwd+rpSa0GkSrR/TWi/QWi8oLOx979rhrDnYDDDkE8x5p97DVBZqimbTGLBS6BocG/zlbjlC3pajNC8sJzB+RFLmXBgdA8AGI/Eq5rwxjRjNjVj37ko4jrLCTKaMzOL3H5wlZia2FgshxFATjAWHXoJZWmQIIdKEJJjFsHOyzkdlc4B5465evfzquf8kpmOsGPnJa87nqqxGmRpbnonfMjR7MF+qSGdye3gye4wKNqqTCc+zyO1nfWQmhu8cKtjY6XlHeTlYrbRvlTYZIm1VAmMu+bkEqLraGKWUFcgBmro59mqPNwCe+BxXO1cnWuuq+J+ngXeBudd+WaKnPkww24Zwgllrck++S1vRZCqCHa9zMGzw56yoZ9TzG2kfU0jz4slJm9ejM5gcK2KDOkYsga4zfkcp2SNb0RlO7Ovf6FMsd80oprIlwNpDNX2aRwghhopANDAkNviDjzYqlBYZQoh0IQlmMeysP1oHwNwxni6fP+c7xpa6tczLX0GO/do9lV1nO25PjRZkwhDo99UTy6LjGR/L5ffGFprovElfTyx0+dlkzgDA6KKK2XA4cJSV4t++o0+xCpFCO4BJSqlSpZSdjk37Vl4xZiXwYPz7zwDvaK11/PEHlFIOpVQpMAnYfrU548dsiM9BfM7XugtOKZWrlHLEvy8AlgOH+/SKxWWagk0AZDuG7sVHd+0RHP56mkbPocrbcX0j1Qlmiy/AuMfXYjrt8b7LyW0IvSg6hhYVYI/qfXsKv2M8hgVs5YXYNr8P7YmtoQDzx+VSnOPk0Q0n6fgVIIQQw9tQSjAbysBhcUiLDCFE2hge2TAhLrH+SC3j813kZ3a+fUprzfOnf4PLmsniwlt7NJ/7TAWGzcTvyUt2qIOWgeIzoVlEiPKfxqaEWmUU26O02Yrx4v7/2bvv+Liu887/n3PvVGDQe2MBAXZSpERRopolS7ZsSbbs2HIslzhl4911nObUdRKv7cSJSzbOb7NJbGcTx+vYVollWb2LlESz9woSBEn0PmjT773n98eAFQCJAQb9eb9efIm6955zDyx0/K4AACAASURBVCiIg3nmud8zZkyGd8VKoseOYQ9N/A24EDNlOA/588DLwAngCa31MaXUV5VSHxy+7F+BAqVUPfAF4E+Hxx4DniBZ8H0J+C2ttT3WnMNz/QnwheG5CobnRil1s1KqGXgE+K5S6sL1q4C9SqlDJIvTX9daS4E5jRZCB3P+ma04hotg6VpaB0wUmsIZLDAry2bR/30ZV1+Ijgc24WSk/1HpFXYRmdrDTnU25bGWK4e4mUv20jgqEcfz9psTXodhKD54QznHWgfYWif56EIIEbHmTwYzJGMypINZCDFXSIFZLCh94Tj7zgfZOMbmfif793N64BC3Fr0Xr+kb15xZDWfx5c3/Df6uVqgzeW98BfuNJt6ZYFTGpowI79hrMLr3wyjdV75Vq8C2iRyQHGYxN2mtX9BaL9daL9Naf2342Je01s8M/z6qtX5Ea12jtd48HFVxYezXhset0Fq/eK05h483DM9RMzxnbPj4Hq11pdY6U2tdoLVeM3z8F1rrdVrrG4b/+a/T9eeyUFzoYJ63GczaIf/MNvqLV+K4fbQMusjzWbjNGeqm1Zryx98icKaNrvs2ECsdOwprMkwMVlhFHFRNOBOIyQh7F5OXcQa7qGTSMRl31BZSGPDwv18/LV3MQogFL2JF8Bjzo4MZkgVmyWAWQswVUmAWC8q2U104Gm5cNDIeQ2vNs43fJ+DOZV3ereOaT1k2/ub24QLz9eM05pvbrSUssnP5gbGTAaIpj785M8w7zlrMWDcq3DLivKemBkyTsMRkCCHmoN5oLz7Th9t0z/RSpkSg/RiecA+9FRsAaB10zWj3cuHrB8nfeZLg5uWEVlRO6b1W2sUMqRin6Ux5bNi7GI8zAOtW4Dp2CKP9unHpY3IZBh+8oZwDTX3sODNyPwMhhFhIIon518EsERlCiLlCCsxiQXn9RCfZfhfVRYER507276d+4CibC+/FZYyvGOBr7UBZDt68BAOuhVdgNlD8UmwtYeL82Nid8vg1/gj79GoAzO6RXcrJHOZqQrtTn1sIIWZaMBacv93LQH79VhzDTV/JarSG1gGTooz4jKwld1cdZT/fyVBteVo39RvLcrsQQyv2GxPJYV4MQEZNsgji3vrqpNbyruXF5GW4+Yc3Jr7xrhBCzAcRK4LHNb86mCUiQwgxV0iBWSwYlu2wta6TjVV5GOrKDX+01jzT+H0Crpxxdy8DZA5v8GcXZWCr+dmhdj2lOps7E0vZapziBG0pjXUpKPBn0aYLrpHDvILo0aM4IclhFkLMLcHoPC4wOzb5DW/RV5KMx+iNGMRsg6LM6e9gzj7UQOWP3iRcVUjXezaCSu+mfqPx4Wapk89+1Zjy2LirEMvIJNs8j7WkGs/bb0xqLR6XwUPry9nR0MOuBuliFkIsXFE7iteYRx3MLonIEELMHVJgFgvGvvNBBqIWG0eJxzjRv48zA0e5peg+XIZr3HNmnm1CuTShvIXXvXy5exO15Dl+/sV8hwR2SmNvDoR5y14H3QdAjxzrXbkSbJvwgYPpWq4QQkyL3mjvvC0wZ7Udxh0J0luxEYC2weRrZ9E0R2QETjZR9f1XiZXm0vHgZrTLnLZ7r7SKaFJBOhlMbaBShL2LCISOY6/diHnuDEbT+Umt5d5VyS7mb71cJ1nMQogFK2JF8Jjzq4NZIjKEEHOFFJjFgvFGXScuQ7GuIueK41prnj2f7F5em0L3MkBWQwO+3DgDnuJ0LnXO8WDyofgaWlU/z6nDKY3dlBFmu7MGlx3CGBj5eK+3tnY4h1liMoQQc8t8LjDn12/FNr30lyRjjtqGkoXdQv/0FZizjp1n8fdeJJEXoP0Dt6A94/+AOB1W2snX/gMq9ZiMsHcRvngnrFyMVgr3JLuYvS6TD2+sZO/5IFvruiY1lxBCzEVaa6JWdP5lMEsHsxBijpACs1gw3jndTW1JgIyr3oCe6N/HmcFjKXcv4zj4G9vw5SXodxelebVzzwq7mLVWKT8zDtHL+OMs8lw2HZ4aAMzukTEZhs+HZ+kSwnukwCyEmDu01smIDPf8KzAr2yKv4W36SlfjDGddtg+ZuAxNjs+aljXk7D19sbjc9qEtOL7p71gr0gEKncwJxWSEPEsAyDJasJcsw/PW65Nezz0riijJ9vKtl+twHOliFkIsLDE7hkbPqw5mn+mTDGYhxJwhBWaxIARDcY63DrC2fPTu5Sx3bsrdy772Loy4hTfPXpAb/I3mgfhKHBx+YuxJaVxNhouTTiVO18iN/gB8K1YSOXwEJyyf4Ash5oZQIkTCSczLDuas1gO4YwP0Vmy4eKxt0EWBP4Ex9fHH5L91lKr/9xrRsnxaP3wbTsbMdauttIs4plqJklrndtRTiq08BEInsNZtxGw6h3G+YVJrcZkGH7mxkuNtA7xwNLU9EYQQlyil3qeUqlNK1Sul/nSU816l1OPD53cppZZcdu5/DB+vU0rdf9nxf1NKdSqljl4115eVUi1KqYPDvx6Yyq9tPrtQiJ1PHcwe0yMFZiHEnCEFZrEg7GjoQQNrr4rHuNC9vLkwxe5lrtzgz1HT+1jubJWvM7gjsZS3jXpO0znucTdnhtnurMPVdwzs+Ijz3lWrhnOYR98IUAghZptgLAgwLwvM+fVbsVw++otXXjzWPmhSMMXxGMqyKX/8LSqefJvw4hLaH74V7Z3ZDXZr7SIs5XBajf81DwBlEvFWkRU6jr32BrRSeN6aXEwGwO3LCqnK9/O3L9cRt5xJzyfEQqOUMoF/BN4PrAYeVUqtvuqy3wCCWusa4NvAN4bHrgY+DqwB3gf80/B8AP8+fGw039Zabxj+9UI6v56FZD4WmH2uZAezo+XvcyHE7CcFZrEgbK/vxu82qC7KvHhMa80zF7uXb0l5zoyzTShDEyrIT+dS57x7EsvI0l5+YOzAYXyP6C7zxjjMSlw6jtF3bMR5b00NGAaRfaN3OAshxGzTF+0D5l+BWdlx8s6+Q1/ZWrSZLO46GtqHXBRO4QZ/roEwS//hGQreOUbfjcvoeOjmad3QbyyL7VyUhjo6Uh4b9izCH23CyDCxl9bgfvt1mOQGfYah+PjNizjXE+Y/dk5u40AhFqjNQL3WukFrHQceAx6+6pqHgR8M//4/gXuVUmr4+GNa65jW+ixQPzwfWuu3gN7p+AIWqqgVBZhXERkXiuUXvjYhhJjNpMAsFoTt9d2sKsvGZVz6lj/Rt4+GCXYvA2Q3NODNTTDgLUnnUuc8Ly7uj6+g3ujiF+rMuMYYCmz/YhLaRI0Sk2H4/Xiqqgjv35fu5QohxJS40MEccAdmeCXpld20D1c8dEU8Rm/EIOGoKetgDhxvpOYbT+Jv7KLjfTfRe8caMGbHj7A+3JQ6WdSp1AvMIe9iFJpA6CTW+hsxW5owzo7c7DZVG6tyWV+Zw9+/dopgaORTQUKIa6oALt+5s3n42KjXaK0toB8oGOfY0XxeKXV4OEYjb7QLlFKfVUrtVUrt7eqSjTxHMx87mC98LbLRnxBiLpgdP50LMYVa+iKc6wmz5rL8Za01zzR+nyx33oS6l9GajHMt+PIS9Llkg7+r3WhVUG5n87ixFwt7XGNuCNgc0suId44eg+FZvpzIocPoxNQ+gi2EEOnQF0t2MAc886vAXHBmK5Y7g4Gi5RePtQ8mP6RNdweziiUof3wbS//5ebTLpPWROwgtH0+tZnotdvI4rTpxSO0R5oinEgeTrKETWGvWJ2My3nlz0utRSvGpWxYzFLP4+9dOTXo+IRaY0ZLkr360YKxrxjP2av8MLAM2AG3A/xrtIq3197TWm7TWm4qK5L3HaC4UYedjgTmSkBxmIcTsJwVmMe9tr+8GrsxfvtC9fEvRvRPqXvZ29WBEE3jybIZcEpFxNQPF/YkVdKkh3lB14xqzISPMTmc1gdApSIRGnPfW1qIjEaInxzefEELMpGB0/nUwKytG7rlfECxfh77stbNtKBlVUZjGDuasY+ep/ZvHyX/nOH0bq2n5+F3Ei3KuP3AGLLbziKoEjQRTGqcND1FPOYHQCQhkJWMytm9Ny5qq8jN498oS/mNnI/Wdg2mZU4gFohmouuzfK4HWsa5RSrmAHJLxF+MZewWtdYfW2tZaO8C/MBypIVI3nyMypINZCDEXSIFZzHu/qO8mx++mKs8PXNm9vCZ3At3LQMa5ZuDCBn8znwE5Gy23C1lq5/OUcYAY1nWvzzA07e4aDDRm7+ER5721tQBEJCZDCDEH9MX6MJWJ3+Wf6aWkTW7jbsxEhN7yjVccbxt04TI0Ob7r/11/Pe6eARZ970WWfOcFlKNp+6Xb6L1z7azIWx7LYif5RPtEYjLC3sVkRupRTgx77Q2YzY0YjWfTsq5HbqrE5zb4q+dPpGU+IRaIPUCtUmqpUspDctO+Z6665hngM8O//yjwhtZaDx//uFLKq5RaCtQCu691M6VU2WX/+mHgaBq+hgVpPkZk+Fw+4NLXJoQQs5kUmMW8prVme30Pa8qzSe69MfnuZYDMs02gNOHCgnQud15RKO6PL6dPRXhZjdy4bzSBjFKi2k24Y2QOsys/H1dREWHZ6E8IMQf0xfrI8mRdfO2ZD/Lr3yThDTBQuOyK4+1DJgX+BMYkvlQzFKX0qV+w/K8eI+tkEz23raL5E3cTrSyc5KqnXp72k+34JpzDbGibQLgea80NALi3b0vLurL9bj68sZKtdV1sretMy5xCzHfDmcqfB14GTgBPaK2PKaW+qpT64PBl/woUKKXqgS8Afzo89hjwBHAceAn4La21DaCU+gmwA1ihlGpWSv3G8FzfVEodUUodBu4Bfn9avtB56EIRdj51MF/4WsIJ6WAWQsx+E6uuCTFH1HcO0TUU48Mbk5mNye7lf5tU9zJAzpl6vDkWA77idC11Xlri5LPSKuLn5iHutVeSybU7Cm4KJNgzsILVXWPkMNfWEt63D631vCraCCHmn75o37yKxzASEXLO76SnahMYV3YTtw2aFPgntpmcEY5R+NYRCl8/iBGzGFpVSe8tK7Gz5k7nt0Kx2MmlzmxPeWzYuwiNIhA6wWDJGuzF1Xi2byX26K+mZW33rynhtZMd/NVzJ7ijphCXKb0lQlyP1voF4IWrjn3pst9HgUfGGPs14GujHH90jOs/PanFiovmZQezKR3MQoi5Q37KFPPapfzlbACO9+2lYfD4pLqXkxv8NePLSxB0l6RrqfPWexMrCKk4LxjXf+Kv3JPgmFpJQfw8xEZmWXqXL8fu6SHR1DTKaCGEmD2CsSCZ7syZXkba5J7fiWnH6a3YcMVxR0PHkCvlDf5c/SFKf76DlV/6ISXP7yFaXkDzJ95F130b51Rx+YIldj49KkQPI/cQuBbH8BNzF5M1lIyxsNasxzxbj9HWkpZ1uUyDT25eRH3XED/e3ZiWOYUQYjaakQxmrTFOn8PzvSfw/5c/x/s338U8cAKc1DZ9HcuFryViS4FZCDH7SYFZzGu7z/VSlOWlKMuH1ppnh7OX106ie9kd7McIxXHna4bMvDSudn4qd7JZbZXwkjpGhOt3uEV8SwGwug6OOHchh1liMoQQs10wGiTgmT8dzPn1W4n7chgsqL7ieG/EIOGocW/w5+4eoPyxbaz48o8ofP0QkcXFND/6Ljoe2kyiIHsqlj4tLuUwT6SLeTGBcB1oG2vthZiMrWlb202L81hbns3/euUU/eH0bcQohBCzyXR3MKuObvy/8edk/OaXcD/2Ajga17a9+H//b8j4xB8mC82TdKHAfKF4LoQQs5kUmMW8pbVmz9kgK0qygMu7l+/DnGj3MpB5Ltk9GyvKBolpGJd7EssIqTivqZPXvbY8kM+A9tPbMnIzP3d5OUZmpmz0J4SY9fpifWS5s2Z6GWlhxobIadpNb/l6UFf+6Ng2mHw9LbhOB7OvpYeqf3+VFV/9MXk7TzK0spKmT99D5/tuIl6UM2Vrny5lThYebXJqgjnMphMlI3IWnVeAXbEorQVmpRSfunUxg9EEf/dqXdrmFUKI2SRiRXAbbgw19SUO1R3E//tfx2jrJP7I+4h+5beJ//ankv/81AfAcfB98dsYp85N6j4XiuUSkSGEmAukwCzmrcbeMF1DMVaUZl3Vvbx5UvNm19cDmsHi0vQsdAGocnKpsQt5zjhCHOua167KSLBPryLQd2jEOWUYyRzmvVJgFkLMXlpr+mP986aDOe/MNgw7QW/FjSPOtQ8l85jHisjIaGhj8XdfoPbrT5B9+Bz9G6tp+sx9dL/7Bqzc+fHnA2BiUOXkcorUN9MLexYDkBUajslYux7XqROortSL1WNZXJDJfatK+OHO8xxt6U/bvEIIMVtErMi0dC+r4AD+L3wd1dNH/LMfw75tIwQykic9buyb1hL7759A+7z4/vhbqOaJ/10uHcxCiLlECsxi3tpzLpnhu6Iki+N9e9LSvQyQW38KT7ZFMKM8HctcMO6JL6NfRdiqTl3zOlNBu2sZRU47Vmjko8be2lriZ89i9fZO1VKFEGJSBhOD2NqeN5v8FZ18iXBWKaG8RSPOtQ26cBmaHO9lHx5qTeDYear//mmWfftpMs+00XvrChp/7T5671iDHfBN4+qnT4WdTZPqxSK17E3LlU3clX9ZDnMy59r9i21pXd/HNlWR5XPz508fxXF0WucWQoiZNi0F5lgc3x9+A9XWTfw3H8FZUjn6dblZxP/rL6MSFv4/+iaqd2If7LmUC4WSDmYhxJwgBWYxb+0520vA66I818czjf9Odhq6lwF8jR148yz63UVpWOXCUe3ks8jO5Vnj8HXffLsDyW6u1saRncre5csBiBw4kP5FCiFEGvRF+wDmRQezr/c8gc4TdC/aPGosVPuQSYE/gaEAxyFnXz0133iSpd95AU9HH913raXxV++jb/MKHN80brw0A8qdbBLKoY2+lMeGvEvICh0D7aCLirFLy/GkMSYDINPr4hObF3GwqY8n9spmuUKI+SViRaZ8gz/3Ey9hnmki/isP4ywb+aHr5XRJAbHf/BiqK4jnO49N6H5KKbyml6gtHcxCiNlPCsxi3tpzrpflJQFO9u/lbJq6l139g6jBBLrQi6MmN9dCo1DcnVhGlxpip2q45rVLA9n06GxiHSOLyN4lS8DlIrxfNvoTQsxOwVjyCZr50MFcVPcSjjLoqdo06vm2QZMST4S87cdZ/pc/YdG/v4oZjtF53waaPnMvAxuq0e6F8XpZ5iQ3KTyrelIeG/IuxWWHyIicA8Besx7z+BFUb+pzXcudtYWsLM3i6y+dJBi6/sa7QggxV0Tt6JQWmFVnD57/eAZr/QqctbXjGqMXl2O962bcr2yfcB6z1/RKB7MQYk4YV4FZKfU+pVSdUqpeKfWno5z3KqUeHz6/Sym1ZPh4gVLqTaXUkFLq/1w1ZuvwnAeHfxWn4wsSAqBnKEZDd4jlxQGePv+vZLvzWJOG7uWshrMARIvzJj3XQrTSLqbYCfCCOopm7MdzfSacNFZQETmEdq7sdlYeD96lS4nskxxmIcTsdKGDOcsztzf5U3aCgrpX6Ctdg+UdWSx3InG2HNjOnz/xbSof24Y2FB0PbKL5k/cwtHoRmAurj6FIB3Brk/MTKjAvAUh2MQPW2g0orXHvejudS0Qpxa/dvpSBSIJvvnz9jXeFEGKumOqIDM93HgfHwXr43SmNs+69FR3IwPNPPwadejyRx/RIBrMQYk647k/+SikT+Efg/cBq4FGl1OqrLvsNIKi1rgG+DXxj+HgU+AvgD8eY/pNa6w3Dv1LfFUWIMew9n+wec2Uf5/xQHVuK75909zJA4cnDAPSVjJG3Ja7JQHFbYjENRjenuPaGF2FvNUX0cb7j/Ihz3uXLiRw9hhOVH7aEELPPfOlgzmnchTvaT/fiW648MRjB8+R2Mj//XX79yHMMZWfT9qEttP7yXYRqyknmZSw8BopSJ4uzpF5gtlw5xFwFZA0lC8xOSRlOYTHu7enNYQZYlJ/B+9aW8djuJg40BtM+vxBCzIRwIozHmJoOZuPgSdxv7MR6963o/NzUBvt9JO6/A9fBk5g7DqZ8bykwCyHmivG0lmwG6rXWDVrrOPAY8PBV1zwM/GD49/8J3KuUUlrrkNb6HZKFZiGmzd5zvbhNze6+H5PvLWZ17uiP9qYqu6EBd8AiGJAN/ibqRqsCv3bzonHsmtflZVUA0Na0d8Q5T20tWBbRI0emZI1CCDEZ/bHkZj5zPYO56MSLxH059BetAEB19eP5wRtkfv67eJ7aQX9ZKb93129z8AP3EVlUNGpG80JT7mRxTvVc8ymdsYS8S8gaOg7aBqWw1qzHdXg/anAg7ev86I2V5GV6+POnj2LLhn9CiHkgakWnpoPZcfD+7x/i5OdgvfvWCU1hb9mAU5yP9zuPgWWnNFYiMoQQc8V4CswVwOU7gTQPHxv1Gq21BfQDBeOY+/vD8Rh/odTo70qUUp9VSu1VSu3t6uoax5RCwJ5zQcoqTtIWOceW4vdhKDMt87qbe3DlQ8Lwp2W+hciDi82JKnarc3QzNOZ1fm8OHRTgC478pN9bm8w9C++THGYhxOwTjAYxlYnP9M30UibM29dMTuNuuhZtxjjfjfcfniPj9/4v7lcOYK+oIPpf7+et+x+iLn8xRS5543tBmZNNWMWv+fo2lpB3KS4nfDGH2Vq7AWXbuHe+k+ZVgt9j8qlbFnGsdYAf7xr5pJAQQsw1ESuCx5X+DmZz12HMhiasB+4Cj3uCk5gkHroHo7EN16vbUxrqMTxSYBZCzAnjKTCPVvi9utVhPNdc7ZNa63XAncO/Pj3aRVrr72mtN2mtNxUVFV13sUKE4xZHW3qJZL5Ika+CFdk3pGVeV18/esDBLs5My3wL2RZrMQAvG8fHvkgpWl01rLWP0Tx05UZEZiCAu6JCNvoTQsxKfbE+sjxZjPHZ+ZxQcuhJBtv9BJ8MkvHFH+LadwbrluXEPv8giQ/dgi7OoT3uw6Uccs3YTC931ih3cgA4N4Ec5rBvKQDZQ0cBcCqqcPLycW/fmrb1Xe7W6gLWVeTwjZfqaO+Xhw2FEHNbxIrgNdLfwez+6Ss4OVnYG1ZNah5nbS1OWRHup19PaZxEZAgh5orxFJibgarL/r0SaB3rGqWUC8gBeq81qda6Zfifg8CPSUZxCDFpB5v6UNl7iNLJ7cXvR6n0bDJUdnQ3AENlJWmZbyHL1X7W2CW8rk4SJTHmde7MxeSqEAfP1o04562tJbJ//4hNAIUQYqYFo8G5m7+csPA+8xJDf7eHlq25qJYgifvWE/2dB7HuuwGdk3Hx0ra4jyJXZKFGLo+q1MlC6YkVmC0zi5ir8GIOM0phrV6P68AeCIfSvNLkhn+/fvtSErbDnz99FD2BzaeEEGK2iNpRPGZ6O5jV+VZce49i37YRzAk+Eas1WX0nyBqow7r1Bsy6sxgnG8Y93GN6iNjSwSyEmP3GU3nbA9QqpZYqpTzAx4FnrrrmGeAzw7//KPCGvsZPqUopl1KqcPj3buAh4GiqixdiNDsaWvAUvUqZv5rqrKv3o5y4ouOHQGm6ypalbc6F7PbEEsIqznZ1Zsxr3JmLAIi0j+xU9i5fjjM0ROx0/ZStUQghJiIYC869/OWhMO6fPE/Gx7+A++9+jELjPLiG2OcfwNqyEnwj37S3xbwSj3EVDyZFOsC5CWz0B8mYjKzQcA4zwzEZVgL37tQeqR6v0hwfH72pktdOdPD8kbYpuYcQQkw1rTVRK/0FZvfPXkO7TKwtGyY03rRCLK3/PrV1/0TtyX9kuecZcBt4nn513HNIBrMQYq64boF5OFP588DLwAngCa31MaXUV5VSHxy+7F+BAqVUPfAF4E8vjFdKnQP+DvhVpVSzUmo14AVeVkodBg4CLcC/pO/LEgvZyy0/wnAN8e7yh9P6eHLG2RZcORDzZaVtzoVssZNHqZPFa+rkmNdYZjadqpQlsSO0R67cEONCDnNk/74pXacQQqSqL9Y3ZzqYVWcvnn/+CZmP/B7e7z6Ozs+h/N0h8j4SIHbjmjE7thwN7cMdzOJKZcMb/U1EyLcU04mSGU5++OosWoKTk4vn7TfTucQrvH9tGcuKMvnSz48RDMWvP0AIIWaZuBNHo9O7yd9QGPdLbyejMbJSj0jMHDzLqsN/TU7vIVoKbqeh9AHimXnkVA3iev0XMDi+J1MkIkMIMVeMKztAa/2C1nq51nqZ1vprw8e+pLV+Zvj3Ua31I1rrGq31Zq11w2Vjl2it87XWAa11pdb6uNY6pLW+SWu9Xmu9Rmv9u1rr1LZTFWIUTQNNtPEKAesmSv2L0javJzaI0x7HLs64/sViXBSKzYkqzhrdNNA95nVR3xI2Gyd5s+XKH8LMoiLMvFzZ6E8IMev0RftmfQezcbYZ79f/hYxP/AHuJ1/GXlVN9A9+jZxfqiCnuJ/O/E3XHN+T8JDQBkUuedN7tXInhx4VYpDU/2xC3qVoFDmDh5IHDANr7QZce3dOSUwGgGkoPnvXMvojCf7yuWvsjSCEELPUhQJsOjuY3S+9jYrGsO669uvhaJQdZ0n999Eo6qo+Tkf+zfRlLedMxcPYNy6FhCbr8R+Pay4pMAsh5or0hNMKMUt8bee30NpgTeCBtM5beWondtxgqLQ4rfMudButCtza5HVj7C5mMpaQoWKcazp2xWGlFN6aWtnoTwgxqzjaoT/eP2s7mI3T5/F98dtk/NoXcb2xE3vLRmJ/9l9JfPphjNJsSlteZNBfQchXfs152uPJLrFit3QwX63cTj7pNJEuZtvMJOKpIGfwwMVj1rqNyZiMnW+nbY1XW5SfwcMbynnqQAtv1nVO2X2EEGIqXIiQ8BhpKjA7Du6fvYq9tBJdVZby8NLWV/DGg5wveQ8R35XvH7tX3omnwMH13DbM6MB15/KaXqJ2VHLyhRCznhSYxbyxp30P29veJN5zN9UFuWmdu/jYWiWZIgAAIABJREFUXgD6ytPXFS3Aj5v1VhnbVT0RRn8sN+xdgoMif/Aw3dErN/TzLF+O1dpKok1yI4UQs8NgfBBHO2R5ZleckjrfivfL/4eM3/wLzAMnSLzvTqJf+hyJX3oPOj/5mlna8hIuK0Rz4bvgOhFTrTEfgERkjKLMyQag8dr7XY9pyFdDZvg0pjUIDMdk5ObjeeuNtK1xNB/aUEFlnp8vPnWEoZg1pfcSQoh0itkxIH0dzMaxeoyWTuwJZC97I52UtL1GT9ZKhvyVI85rZRK/eRWJPoPSn3zruvNdKJpHbeliFkLMblJgFvNCzI7xlR1fwafycfrupCQnjVvaa02goRFMiBXkpG9eAcAtVhVRZY252Z9tZjDgKud28yivt8WuOOcbzmGWLmYhxGwRjAYBZk8HcziC559/QsavfxHXjoMk3ns70b/471j33wGZl2KfvJF2itu30ZO9dkS31Wja4z7cyibXjF332oUmgJeA9tKsghMaP+SrRaHJGTycPKAU1roNuPbvRg0NpnGlV3KbBp+9s5r2/ijffOkaTxYJIcQsk+6IDNebu9BuF/a65akN1Jqq80+ilUlL4Z1jXha5YQ24FGr7cfzdY294Dpe+JonJEELMdlJgFvPCdw99l/MD58kY/AjluV5MI30F5tzBJqwOG6fQB6b8L5NuVU4uZXbWNWMyEr4l3GicZmvzlY+RuRctQvl8RPYfGGOkEEJMr75YHzA7CszmW3vI+PSf4Hn8RexNa4n+2X/Dev9d4PeNuLay8Wc4hovWgtvGNXdb3EehK0oaX27nlWInk2b6JjQ24qnAMvxkDx68eMxatxFlW7h3TF1MBkBtSRbvW1vK/9txnp0NE9uoUAghpltaIzJsB9fW3TirloEvtU0Ds/uPk91/ktaCLViua2wM6HFh15Qy2Oyn8PgL15xTCsxCiLlCqmVizqvrrePfjv4bW8puo7V1GRX56f22rmjfRzToJlxSmNZ5RZJCsdlaxFnVM+ZmfyFfNW5s6D1K12UxGco08VRXSwezEGLWuFhgnslN/qIxvH/7b/i/9A/g9xL93V8h8fEHIWv0N7v5XTvJ6TtOW/4tWK7xbWbbGvNKPMY1FDsBmlUQzQQyM5VByLssmcOsk695TuUinLwC3G9PbUwGwMc2VVGa7eMLjx+kP5KY8vsJIcRkXYiPSEcHs3n4JEZvP9bGVSmPLWrfRtwVoCtn/XWvtdYswY4aZLzzJsoa+2kgr5kscl8oogshxGwlBWYxp1mOxZe2f4lMdyZbCj5KwoaK/PS2U5XW7UPbBqEy2eBvqmywynFpg23GqVHPh72LcTDZYhzj5ZYrP7331tYSq6vDHgpNx1KFEOKaLkRkzFQGs9HQRMZn/yeu57eRuG8Lsd/7DHpJxZjXZwyeZdHZxxjwV9GZu3Fc93A0dMR9UmC+hhInQEQlCBKe0PhBfy0eqw9/9HzywIWYjIN7UAP9aVzpSD63yW/ds4z2gShf+vnRKb2XEEKkQ8xKXwaz641daK8HZ3VNSuM80W6y+0/Sk70GlHnd652aMnAZhBsUeQ3vjD3v8NcUseU1Vwgxu0mBWcxp3zv8PY73HucTqz5BS3fyxbcyjR3Mvlg/mWcaAYiVpHfjQHGJHzdr7BK2qzMksEec14aHiLeKe1zHeKHpyk/4vbW14DhEDx+aruUKIcSYZjIiw9x/HP9v/SWqb4D4f/s41oN3gzn2m1x3vI9lp/+FhCvA2bIHQI3v9bMn4SGhDYpc8rjuWIqd5AcME81hDvmShY2cgctiMtbfiLJt3Nu3Tnp911NTnMVHbqzk5wdb+fnBlim/nxBCTMaF4uukC8yWhWvbHuw1NeBxpzS0sHM7AN0568Y3wOPCriljoCWDwmPPjX2ZRGQIIeYIKTCLOWt7y3a+c+g73F5+OzeX3MzpdotML+SM7+necVnSup1YjwvH68LKuUaOlpi0m6xKhlSM/apx1PMh7xKWc5azwT6aQpeK0N5ly0ApwpLDLISYBYKxIG7DffGR1ulibt2N74//Fp2bRfT3fxVn+ZJrXu+JdrHs5D9j2FHOlH0A2/SP+15t8WSGc7FbuqnGUuIkP2CYaA6zZWYRcZeRM3gpAsopr8QuLsHz5stpWeP1PLyhghUlWfzZz47SHJxYJ7YQQkyHi5v8TTKD2dx3HDUwhL0htXgM5SQo6NpBf2Y1Cdf4P2C2V1dhRxTuI3V4+5pHvcZrJH+ekAKzEGK2kwKzmJPaQ+38ydt/QkWggk+t/hRKKU63JajIN1AqfREZ1S1vM9SbQbQsH9I4rxipxi4k2/GxTY0ekxHyVWOgudU4wYvNl37AMjIycFdVEZEcZiHELNAf6yfgDqT1tehyyo6T2X6cgpMvU7Hr3yjf+0Ny//Uf8X/l/6Ari4l9/pOQe+14jtye/aw6+k08sR4aSh8i6k1tj4G2WPLNbrFEZIwpEw8Z2jPhDmaAIX8tWaE6XNbwBrdKYW28GdexwxjtrWla6dhMQ/G5u5dhO5rff/wgtjOBPGkhhJgGFwvMk+xgdr25C+334qyqTmlcbu9B3FZoXNnLl7Nry9Aug/4mP0UnXxz1mosRGZLBLISY5aTALOachJ3gD7b+AXE7zuc2fA6v6WUo6tDaZ1NZkL439FmhdgraT2P1qWSBWUwpA8WNVjkHVTN9o2RWRjyVOMrDg77jPNcUQ+tLb3S9NTVEDh1C2yPjNYQQYjoFo8Gp2eBPa3LPvsPax36d1U//DtVbv0XZwcfI/vljWP+xk8zSKCs37WNV/d+xqOFHFHRuxx9qxpUYxJUYxBPtpqh9G7XH/z+q679PxJ3HyUWfZDBzccpLaYv7cCubHHPsTYkWOoWixAnQMsEOZoAB/xoUDrn9uy8eszZsAsD95iuTXuN4FGf7+NXblrDnXJDvbDszLfcUQohUpaXAnLBwvb0Xe+1ycLlSGlrU8TZRdy6DGYtSu6fHjbOsjIHWAPmnXgc98oM82eRPCDFXpPY3pxAzzHIs/uitP+Jw92E+d8PnKM0sBeBMR3KX84o05i8vbXmbSM9w5pUUmKfFjVYlWz0NvKPqeUhf2QGglYuQdzG3JY7xu4M2pwZsVuQk/wrz1tYy9MYbxE6dwrcq9R2fhRAiXYLRYNrzl12RINWv/w05zfsJZ5VSv+lXCOdUkGiP4//pk1CSQeyjNXTY3WRG28ntOUhh185R54p48mkpuI2OvJvGtQnRaNrjPgpdUQx5sOeaip1Mjro60GgUqf9hRd2lxF355PftpLvgPgB0bj5WdS2e118k9vHPTMvTVXfWFnKgKci3Xz3FXbVFrKvMmfJ7CiFEKqL25CMyzKOnUKEI9rrlKY3zRjoJDJ2lufCOCf2dbK+uwqxrwT4/gL/3LJGCK7unL2Yw2xKRIYSY3aTALOYM27H5s3f+jNcbX+fRlY+yqXTTxXNnOiwAyvPS9EZLa5Y1v0X3QBFaadngb5oU6wBVdi7bjNM8aK8b8YY85F1CafRVSlQfLzT7WZGTLOJ4a2sBCO/fLwVmIcSMCsaCFGcUp20+ZcWpfel/ktF1mvPrPkznktvAMFFd/fj/9kl0hpfYo+8iGvAzyPCO91rjTfSTEW3HdGIoNBqDwYxKYp7Jf2DaGvNS5JJM3uspdrIIqSb6iZDLBDaIUIp+/2oKB3dgWoPYrmT0ibVxE76f/gSz7hj2yrVpXvVoy1D8xh3VnO44zO88doDnf+cOMjzyFkIIMXtErSimMjGNiX1wCmDuPIQ2zevuYXC1vN7kPjDBwIoJ3deuLUObiqEWLzmNu0cUmC92MCekg1kIMbtJRIa4QsJJ8NK5l/ibXX/Do88/yuYfbeb2n9zO/f95P7/87C/zlzv+kmfPPEvr0NRn/13Odmy+uvOrvHD2BT5S+xHes/g9V5xv6LQoCCj8nvQUmPP7z5ITamWoN5NYUQ7aLW+kpstNVgVNKsg5ekacC/mWAfDLgeO8cFlMhllYiJmXR0Q2+hNCzLC+WF/6Opi1ZunWbxHoOE7DTZ+gs/pOMEywbHx/93NULEH843dA4KoN+pQi5sklmL2S7twb6MrdQHfu+rQUlx0NHXEfRZK/fF0XNvprmkQO80DGGhQ2eQN7Lh6z1m5Au9143piezf4AAl4X//3uZZzrDvGXz52YtvsKIcR4RO3opDfXNXcdxllWBd7UuqBzew8w5Csj4b72/gdj8rpxqooY6Mgip3HXiNPSwSyEmCukwCwu2tW2i0eeeYQ/2vZH/PT0T4nbcW4vv51NJZtYnLMYFDzb8CxffOeL3P/T+3n0uUf54fEf0hXumtJ1BaNBPvfa53jq9FN8oPoDPFj94IhrznQkKE1X9zKwrGUbljZRbWFi5RKPMZ3WWWUYWvELY2TWY9RdimX4eY/7GK0Rh4O9yc51pRTemhrCstGfEGIG2Y7NQGwgbQXm8n0/pKD+TZpXPUCw/IaLxz0/24l5rpP4B25GF01vXEFPwkNCGxS7pcB8PcU6+X0wmRzmqLucuJlHXt+OSwd9fqxV63Bvew0Sickuc9zWlOfw0PoyfrK7kZePtU/bfYUQ4nqiVnRS+cuqvRvzXAvOqmUpjfNGO8kIt9AXqJ3wvQGcmlISQfCdOYEZG7rinKlMDGVczJkWQojZStoyBeFEmL/Y/he8cv4VivxF/NaG32JD0YZRHzFytEPLUAtHu4+yq20X39zzTb6151tsKt3Eg0sf5L7F95HjTd+b3SNdR/jC1i/QHe3mM6s/w12Vd424ZjDi0D3osHHpxB+Jupw7Eaa28Q2anRqMxCDR0ry0zCvGJxMPy+1CtptneJTNGJfHZCiDkHcptYmjeA14vjnKxgI3AJ7lywnv2UOivR13aekMrV4IsZANxgfR6LRs8ucLnqd833/QXbmJttp7Lx43zrThfnon1rrFOCsrJ32fVLXFfQAUueSN7vVkaS9+7aZZBWHkvk3joxQDGavJH9qJaQ1hu5LfW9bGm3Ef3o979y9I3P6u9C36Oj62qYqjrQP8yU8Ps6Eql5Js37TdWwghxhK1J1dgNncdBsBOscCc23MhHmNyBWa7pgz3a4cJtXrIbt5HcNmlv9eVUnhNr2zyJ4SY9aSDeYGL23F+783f47Xzr/Ghmg/xV7f/FTeV3DRmfpWhDKqyqnj/0vfz5du+zNdu/xofWPYBGgca+fKOL3P3E3fz22/8Ni+dfWlSL4LtoXa+suMrfPrFT2Npi/+x+X/wrqp3oUbZOKGhM9nFWpabnm/n5Y2v4rHCtIUXA7LB30zYYJXTq8LU0THiXNi7FF+ii/uzu3mpOYblJN+1X8hhjkgXsxBihgRjySiErIk+JnuZij0/wDHdNK394KVNg+IJfP/0IjrgJ/G+jZO+x0S0xZKPIBdLRMZ1KRTFToDmSXQwAwz412Bom9yBvReP2bUrcXLy8Lz49GSXmRKXafD5e2oIx23+5D8PX4yqEkKImRS1opPa4M+1+xBOQS66OLX3fXmTjccYpguzcXIyGOzIIKdx94jzUmAWQswF0sG8gFmOxR+/9cfsaNvBr639Ne6suDPlOcoCZXyo5kM8vOxhzg+cZ1f7Lna37WZr01Y8podbSm/hrsq7uLXsVhZlL8JQYxeBE06CAx0HePX8qzx1+ilsbXN31d08vOzha3aDNXQmHw8tS0NEhmEnWHPmWTpyqqEuQiLLj53lv/5AkVar7BLc2mS7Uc8q58pu5CFfcuOLD/uP80zfXezsSnBHiQdPVRXK6yW8/wDZDzwwE8sWQixwfbFkIXGyHcwZ3afJb3iLluXvwfJemsvz5HaM1l5in7wLfBN/Iz0ZbXEfbmWTY8Zm5P5zTYkT4LirE40esXHteEU8FcTNXAqCb9GTf3fyoGmSuPlWvK+9iNHajFM+fd3s5bl+Hr25ih/sOM9P97fw0Zumv5NeCCEuN6mIjHgCc+8x7E1rL32gOw7eaBcZ4RaaC0c+YZsypXBqyggfjlB6djfc7cBl75s9hkcymIUQs54UmBcorTVf2fEVXm98nUdXPjqh4vLllFIsyVnCkpwlPLL8Eep66zjQeYDD3Yd5u+VtADJcGawqWMWirEXkeHPI9mQTs2N0R7rpCHdwoPMAoUQIU5lsKd/CB5d9kEJ/4XXv3dBpkZeZng3+qlveIiMWZE/1B8hr2EFMupdnhBcXq+1idppn+VVuw3XZwxZxVyEJM5u1zhEyzbt4vinKHSUelMuFp7pacpiFEDMmGB3uYPZMrpOpYve/Y7n9dNTcffGY6ujD/eJ+rA1LcapnLgaoLeaj0BXFSN+2B/NakRNgSDUxQJQcJviBtVL0Zd5I8cAbeGPtxLzJ//7Wpi143ngZz0vPEP31z6Vx1df33jWl7Drby1efPcadtYUSlSGEmFERKzLhDmbzcB0qFk89HqP3QjxGzYTuezV7WSmufWdINIXI6K4nXLT84jmP6ZEMZiHErCcF5gXq1fOv8nT90zxU/RDvWfyetM5tKINVBatYVbCKR/WjtIfbqQ/Wc27gHI0DjTT0NxCKh4g7cRSKbE82Od4cbiq+ifVF61ldsBqfa/xvVBo6EmnpXkY7rKt/mmBmGb26hOL+EP0bqic/r5iQG6xyDrnaOKJa2KirLp1QipB3Cbmho9ySp3i9LUbU1vhMhbe2loHnn8cJhTAyM2du8UKIBelCB3Ome+J//2S2Hye3cRfNqx7Adl8qSHoefxsMReLutZNe52S0xb0Uu8Izuoa5pEgnvxfa6Z94gRkIZt5I0cCbFPW+TnPZJwHQObnYq9bheeU5op/+L+Cevq52Qyk+e1c1f/rTI/zZz47wL7+yadQYMyGEmA6T6WA2dx1Cu1w4tYtTGpfbe5CQr5SEO3tC972as7QYbRoMtfrIadw9osAsERlCiNlOCswLUCgR4ht7vsGirEU8vOzhKb2XUoqyzDLKMsu4kyu7pBN2AkMZY+Y9j8dQ1KFzwGHd4slv8LeofQ85oVZ2LP8YGWeT2b+SvzxzlttF+LWb7erMlQVmIOStJjd8mIeyW3mju4xt7XHur/Amc5htm8jhw2Ru2TJDKxdCLFQXCsyTyWAuO/QECW+AjupLr5lGfRvuHXUk7lwNMxjb5GjoiPtYEeiZsTXMNUVOssDcqvpZoSfeeW65shn0r6Cw93VaSj6GNpIb3CZuuR3/sUO4t28lcfd707Lm8SrL8fPIpkp+tKuRF4+288C6smm9vxBCXBC1o+S7J/a+zbXzEE7NIvC4xz3GHe8nM9RES8HtE7rnqDxunMVFDHY6FLccpO2mT106JQVmIcQcIJv8LUDfOfQdOsOdfGr1pyZV3J0st+me9P3PDm/wV543uW9l046x6fgPGPAX0ly4mswzrTgeF/HCyW/UJCbGhcFaq5Q96hwxrCvOhYZzmDdyjDw3PN+UfGTMW1MDSklMhhBiRvRF+3Ab7ol3UcUGyTm/k57Km3Bcyc300Brvj7ehM31YW1akcbWp60l4SGiDYre8yR2vXO3H1Io21T/puYKZm3BbA+QO7Lt4zF62HKegCO/z07vZ3wUPrC1jcX4GX3v+BNGEPSNrEEKIqBXFbY6/QHyB6ujGaGrHXrE0pXHZfccA6M9Mbdz12MtKSQTBc+Ykyk5cPC4RGUKIuUAKzAvM6eBpfnj8h9xVcRc1uenJi5pJFzf4y53cY5nrTz9FdriD/dUfQCuTzFMtRMoLwJD/RWbSBqucmLLYrxqvOJ5w5RJz5ZM7dITbCwy2tcfpizsYGRm4KyuJ7D8wQysWQixkwViQLE/WhKMC8hrexnAseipvvHjMPNCAeaKZxF2rwZv6m+d0aosn46uKXPImd7xMDAp0Jm1MvsA85KshbuZS1PPKpYOGQWLzbbiOH8Y4d2bS90iVYSg+vWUxLX0R/u/bDdN+fyGEgGQHs9fwpjzOPFQHkHI8Rk7fUWKubKKegpTveS1OTfJJkEiLQUbX6YvHPYZ0MAshZj+pni0gWmv+etdf43f5+cjyj8z0ctKiodMiNwMyvBMvMOcMNrO2/mecK7qBztxqXP0hfJ39RCvT+wODSN1SJ59sx8t2NfJNc8i7lKyhY7yrACwNL7fEgGQXc+TgQbQtnVRCiOnVF+0j4A5MeHzB6deJBIoJ51QmD2iN58ntOPlZ2Btnfk+AtljyzXuxS97kpqLAyUhLBzPKoC/zRnKGDuONtV88nLjpVrTbg++pxyZ/jwlYU57D5iX5/OPWM7T3y4cPQojpN9EMZvPgCXSGD11WPO4xykmQ1X+SgcwlkObseV2Qhc72EerwktV2+OJxr+mVArMQYtaTAvMCcqjrEHs79vLBZR+c9A73s8WZjgRlk4nH0JotR76LZXo4tOR9AAROtQAQqSxMxxLFJBgo1ttlHFRNhIhdcS7kq8blhFmtzlLlVzzTOByTUVuLEwoRO316tCmFEGLKBGPBCW/w5x7qIqv1ML2VGy++YTVONGOe68TashzMmf+RrS3uw61scszY9S8WFxU5mXQwgIMz6bmCgRtxMCntvCwSIzOTxM1bcG99BdXZPvbgKfSJWxZh2Q7ffOnkjNxfCLFwaa2J2bGJFZj3H8dZtgiM8ReKswZOYzqJtMdjAKAU9pJSQp0+Aq2HLh72mB6itnyAJ4SY3Wb+3YqYNj868SMyXBncWXHn9S+eA0Ixh45+h7K8iX9yvPLci5T2HOfI4vcS8yS7zjJPt2B73cQLc9K1VDEJN1jlWMphtzp3xfGQN/lDXXboKHcVGhzstWgcsvEuT+64LDnMQojp1hftm/AHuAX1b6DQ9FRcisfwvLAXneHFXpfao7tTpS3mo9AVTeV9uAAKdSYJ5dBNaNJzWWY2wcAminrfxBPruHg8cec9oMH39OOTvsdElGT7eGBdGU8daKGufXBG1iCEWJgsx8LWNm4jtRgp1d6N0d6NXbMopXHZfUexlZtBf9X1L54AZ2kxTkzhPn4SnOQTmV7TKxnMQohZTwrMC0R7qJ1Xz7/KnZV34nWlnk81G50b3uBvoh3Mpd1H2Xzs+7TmraCh5NIb+sCpFqIVBSl9ki2mTqWTQ4GTMSImwzYDRN0lZA8e4c4CAwU81xTFLCzEzMuTHGYhxLQLxoITjsgoOP06Q3mLiQWKAFBtvZj7z2DdtAzcrnQuc8La4l6Jx5iAQifZ1Z6WmAygO+tONFDe+dTFYzo3H2vDTXheegbV35eW+6TqoXXl+N0G/7S1fkbuL4RYmCJ28nXJa6b2Htc8lHziwqlJ4UNcrckJHmUwowptTM1rs720BIBYi0NGbzLb3mN6iNkxHD35J2GEEGKqSIF5gXi87nG01ty76N6ZXkraNAwXmMsn0MEcCHdy995vMegrYOfyj4JK/q/g7h3E0zMo8RiziEJxg1XOMdVGkPAV50LepQRCJyl0W6zJvhST4ampkQ5mIcS0shyLwfggAU/qBWZf73kyehroqdh48Zj7xf1gGFibZseGvI6GjriPQikwp6xQDxeY07DRH4DlSnYxF/ZuvaKLOX7XvahYDO+zP03LfVIV8Lm4d1UJzx5q5Vz35Lu1hRBiPC509qYakWEeOIHO9KNLi8Y9xhdpwxsPTk08xgVZfnRBgFC7l6zWI8Cl4rl0MQshZjMpMC8AUSvKk6ee5IbiGyj0z5/C6ZnOBDkZkJniBn8uK8K793wdQ9tsX/VJLJfv4rlMyV+elTZY5Wil2aGu3KE+5KvG1HEC4VPcVWjQFHY4HLTw1tZitbaS6OgYY0YhhEivgfgAGk2WO/WIjNzGXQAEy9cnDwxFcG87ir12EQR81xg5fboTHhLaoNgtBeZUZWkvXm3SnqYOZhjuYlbqyi7mkjISq9fhefY/IRK+xuip8+C6MkxD8Z1tIzfnFUKIqTDhAvPBEynnL+f0HQVgYCoLzIBdXUq420tmczKH2WMkvzbJYRZCzGZSYF4AXjj7Av2xft6z+D0zvZS0auhIUJqb2rewOxHivTu/Su5gEzuXP8KQv+CK84HTLVh+L4mC+bEJ4nxRrAOU29kjYjJC3iVoFFlDR7klz8BrwDONUby1tQBEpItZCDFN+qLJWIJMT+qb/GU37yeSVULCnwuA+7XDqLiFdcvytK5xMtrjyUJ3kUve3KZKoSh0ArQykLY5LVc2wcxNFPa+QUb40oeviXe9B2NoEO/Pn0jbvVKRm+Hh7hXF/Oe+Zlr75MMIIcTUu1B0vVCEHQ/V1pXMX16Wav7yCcKeIhKuicVhjZe9pARtKdyHj4HWF4vn0sEshJjNpMC8APz4xI+pyqpiRd6KmV5K2kTiDu19TkrxGJ7h4nJhXz07Vvwy7Xm1V16gNZkX8peV5C/PNjfYZZwxuujk0uZBjuEj4qkge/AIGS7FpjyDF5tjUFmF8noJSw6zEGKa9MWSBeZUO5iVnSDQfoSBwuHXJEfjfuMQ9pJidEluupc5Ya2xZIFZMpgnplBn0KbSm43cmXMPlhlgSdM/oXQyNsxZtITEmvX4nvwPVG9PWu83Xh9YX44GvvdWw3WvFUKIyZpIB7N5cDh/uXb8+cuGHSUw2MBA5tRvvOssKQIFsSYbX7DxYkRGxJLXYCHE7CUF5nmuob+BumAdd1TcgZpHRdOznRYaKBtngdkTH+S9O75Mfv9ZfrHi47QUrB55TfcAnr4QkcqCUWYQM22dVQYwMibDu5RA+DSGHeGuQoP+hGZ7t4Onulo6mIUQ0yYYCwKknMGc2XEC04oxUJTsVjbqmjG6BrBvWJLuJU5KS8yHV9nkmrGZXsqcVPj/s3ffgZFd9d3/3+dO7+pdK2mlbdri3nC3g73YGBvHBpuahEB+SUzyhOT3ewhJILQnIfCjhRIIBgwB3MBmXcAYF+z1Fm+vWmm1WnWt6mh6v+f5Y2aLd7XSSDvSqJzXP2iv7j33aFlr5n7nnM9XdzBCkDjJnI2pazYGCu7AEe2kfPiZU8fjG98F8QTW//lBzu41HaUuC9etziqSAAAgAElEQVQsL+aJXT2EYrn7eRVFUSYyswJzC9JpR5ZnH4vo8rUh0PHbZ7/AjNWMrHATGrTgPHFQrWBWFGVBUAXmRe7FzhcBuLz88jzPJLdONvirLJz6n7Al5mfj1n+lMNDNG6vfR3/xmgnPc7b2AhCtzb7RgzJ3iqSd2lQB27Rzc5gFKZyhI1zkEXhMsKkniqWpieiRI+gh1WhIUZTZdzIiY7ormN19u5FCEChpBMD02iGk2UhqdU3O53gh+mI2yk1htcFnhkp1B1LA4Bm7cHIhYF+L37aG6hOPY4kNACBLykhcfR3m3z2H1pmfVcR/tKacUCzFc/sH8nJ/RVGWjplEZBj2tqAvr51W/rLb10JKmAjZqqY9x5lINlQRGTVh7z5wqsCsVjArijKfqQLzIve7rt/RVNBEobUw31PJqY6hBC4buKyTvymwxnzcvvUzuIO9bF79Pk4UnT/P0nWoi4TbTqJg+vmZytzYkKzguBjlBKcbJYXNtegYcAcPYBCCa4s1Xj0RJ9nQBKkUkQMH8jhjRVGWipMrmKebwezu3U2oYBkpkw1iCYzbWkmtqQGzcTamOWM9MSvlxvw0jlsMSmT638VADhv9nTRQeCcSA41dX0fo6RXm8Vs3gtWK7eFv5fx+2VhZ7qS60MbP3+zOy/0VRVk6pruCWQyNog2OkmqsndZ93L4WgvYapDBMe44zoS8vBykw7jtyegWzavKnKMo8pgrMi1iXv4s2b9uiW70McGwwSeUUDf5sUS8bt3wad2iAzWs+wODZmctnEIkkztY+wvXlKn95HlufOhmTcfzUMamZCVuW4QmkuyzfUKKR0OEPtloQgrCKyVAUZQ6MR8cxa+ZTOYnZ0OIhnENH8JemX5+MO44ioglSG+pnaZYzE0lpjCYsVJhUgXmmSvRMgZncF5iTBjd9xfdij3TQ0PMdkBLsDuK33I5p95sYt76e83tORQjBLavK2NszTstA7pobKoqinO3UCuYsC8yGg0cB0Buy3ylkiQ5jiY3is9dPe34zpdcUg0GQ7PBjS6Z376oVzIqizGeqwLyIvdi1OOMxInGdAW9q0vxlW3SMjVs+jSM8xOvNH2SooHHSMR3t/WiJJOH6slxPV8mhAmmjLlU4QUxGE/ZoF6bEGMvtghqb4OkRDVNNNRHV6E9RlDngjXlxmacXj+EaOICQ+qkGf8bXDqEXONDr5ldUU3883eCv3KQebGfKigmXbpmVFcwAAdtqhjx/RPH4G1QOPQlA4urrSVVWY//WfyD8s3PfyVy/ogSjJnhUrWJWFGUWTXcFs3aoHWk2Iauyf+5zjbcAEJiL/OWTTEaodBEeMlPoTf8eVRnMiqLMZ6rAvIj9rvN3NHoaKbIV5XsqOdU1km7wV3WeArM9MsrGLZ/GHh3l9eYPMuxpmHJM16FudKOBaE32jR6U/FifrKBLjNHP+KljAWu6OOMO7EMIwfXFGrtHkyTrmojs3YNMpfI1XUVRlghfzDftBn/u3t2kDCaCRfWIsQCGg12k1tfNu500fTEbgIrIuEDF0j4rK5hPGnFdx7j9YmpOPEbJ6O/BaCR2/wcQAT+273511u57Pi6riasainhqTx+RuHodVhRldpxc1ZttBrPh4FH0ZZVgyD7qwuM7TMzkIWYumNEcZyrZUEXUa6Kopw1QK5gVRZnfVIF5keoJ9NAy1sJlFZfleyo51zF4/gZ/lpif27f+K/boGK81f4gRT/3UA0qJ+2AnkZoSpHFuMrWUmVufqkTIt8ZkxEzlJAwuPIG9AFxfkv63saegDj0YItbenpe5KoqydHijXpym6ReYg0UNSIMJ4+bDCAmpDXO4OipLvVErAkmpWsF8QUp156ytYAZACPqL7iJgXUFD739RPvwMemU18Vvfgfm1lzC99tLs3fs8blldhj+a5PkDqtmfoiizY1ormKMxtPYu9Prs4zGEnsTpP4p/LlcvZ6QaKgGBe296BbUqMCuKMp+pAvMitVjjMQA6hpI4reCyvvW4IRnj1h3/hjMTizHqXpbVeOahccyjARWPsUB4pJV6vYit2rHTB4UgaGlM5zDLFKUWQbNL8KSWfvMYUTnMiqLMMm9segVmQyyA3dtJoKQJAOOWI6Sqi5FF04vZmAt9MRulxggmIfM9lQWtRDrwiyghYrN2DylM9JQ8iM/WzLL+R6g68RiJ628hVVuH7dtfQYwMz9q9J7Km0k2Zy8Iz+/rn9L6KoiwdsVQMgzBg1KZujqu1HkekdPT66qzHdwQ6MOjxvBSY9eoiMICxLf27W0VkKIoyn6kC8yL1+67f0+BuoMS2+CIfjg0mqCzQEGdsIRZ6iht3f41S71G2rbqPEXf2bwDch9KZVpH68pzPVZkdG5IV9IpxevCeOha0rcCYCuIIpwvPN5Ro7BIF6J4CwiqHWVGUWTYeHZ9WRIZjKL3dNVSwDDE0jqFrGH1N9g+8c6k3ZlX5yzkwm43+ziSFkd7i+/HaL6F68Akae79O/N77EYk4js9/EqJzV6AQQnBlQxGb20fwhRNzdl9FUZaOSDIy/QZ/9VVZj+/2tSDRCNhrZzS/C2I0oFU6iZ0wYBDaqYaGiqIo85EqMC9CvpiPgyMH2VC6Id9TyblYQtI3QYO/Kw4/wrLBHexZfid9xWunNabrUBfxYhdJtz2XU1Vm0bpMTMaZzf5ClkYkIr2KGbi6SMNsEHSXLye8YwdSqpV3iqLMjqSeJJAITGsFs2O4FYBQYS3GnekYn9Sq7LfszpWUhIG4VeUv50DpyQLzbMZknCQM9BfdwwnPbRT6trNm/Kvo734nhmNt2L/6BdD12Z9DxlUNxSR1yYstg3N2T0VRlo5oKpp9/vKhdvTyYnBk/9znHj9M0FaFnuU9ci3ZUEFs3ERRWFMrmBVFmddUgXkR2nFiBxJJc3FzvqeSc53DSaR8a4O/+r43aD7+HG2V19BeedW0xtMicezHBgir1csLiktaWK4Xs1V0IEkXjlMGOxFzNZ5AerWywyi4rEDjZUcdyRMnSPSp7bnK3BNCbBRCtAoh2oUQn5zg+xYhxGOZ728XQtSf8b1/zBxvFULcPtWYQoiGzBhHM2OaM8dvEELsFkIkhRD3nXX/D2fOPyqE+PBs/B0sBb5YumA4vRXMrUScZaRMNoxvHkUv8yCLppfhPBeG4haSUqPCpArMF6pI2hASBoR/bm4oBKPu6+gq/RCmxBhrtO9jvHEd5jdexfqzh+dmDkBjqYNSl4Xn96vXYUVRci+ajGa3glnKdIO/acRjGON+7JF+/Pbsohcn0x+z8rnjK/l463o+dWwN/97VxO6AZ8rrEsvTK6c3dKdUBrOiKPOaKjAvQtsGtmE1WGnwNOR7KjnXMZTeXnmywZ8n0Mu1+77DiGsZ++pvn+zSCbkOd6GldFVgXoDWJyvoFz66GTt1LGhtwhE+iiEZBODGEo0dBcsBCO/ckZd5KkuXEMIAfBt4B9AMPCiEOPuTv48AXillE/A14EuZa5uBB4C1wEbgO0IIwxRjfgn4mpRyBeDNjA3QDfwJ8POz5lcEfAa4CrgS+IwQojA3P/3SMh4bB8Blyj4/2TF0hFBBLWI8hNbWR2r1/IzH6IvZAChXBeYLZsRAobTPekTG2ULWRo6V/yVRUyUryl/AutqN9dFHMG96ck7uL4TgyvoiXjs6gj+qYjIURcmtWCqWVYFZ9J5A+IPTavDn9qWb6/nt9TOdHikJTw9X8HdH19EWdlJqCKKnUhwNOfhC5yp+MlBDUorzXq9XFyGM0NyZVAVmRVHmNVVgXoS29m9lVdGqrBodLDTHh5I4LOC2gTEZ4aadXyalGdi66r1IzTDt8Ty7j5F0WIlWFc3CbJXZtC5ZkYnJOH7qWNDahEDiDu4H4CKPwFtUTsTqILxDFZiVOXcl0C6l7JBSxoFHgbvPOudu4JHM108Ct4p0wPzdwKNSypiU8jjQnhlvwjEz19ySGYPMmPcASCk7pZT7gbP3xN8OvCilHJNSeoEXSRezlWnyRtN58NmuYDYFRzCHRwkV1GLYdQwh52c8BqTzlwHKjeqhNhdKdMfcRGScJWn00Fn6Jwx7bqZufSv2Won9e1/H8uTP5uT+VzUUkdQlvz+sYjIURcmtSDKCSTNNeZ7hYDqOSm/I/gNdt+8ICYOdiKV0RnOTEr7S3cRPTixjjXWMf67cyUdLW/jb8v18unIH1zn7eXqkin/pWI0veZ5nd4MBrdLGim6daGzuXz8URVGypQrMi0x/sJ/uQPeijMeATIO/QoEQgisOPYIn2Me2lfcTsbinPZYWieM63E1oRRWI839qrMxPTiw06iVsOSMmI2KuJqnZKPDvBsCoCa4pMbKvsJ7gm6rArMy5aqDnjD/3Zo5NeI6UMgn4gOJJrj3f8WJgPDPG+e41k/khhPiYEGKnEGLn8PDwFEMuTSdXMGebwXw6f3kZxh1H0QsdyPKpt8nmQ1/MhkuL4zAkpz5ZmVKJdDCA79Tr1pwSGsOem+mq+FMqrwvhWhbF9qPvYv2fh9NVkFnUVOakxGnm+QMnZvU+iqIsPdlGZBgOHUXarcjS4uwGljouX0s6HmOGz4q/GS1ju7+Iuz3H+VjJYTyG+KnvmTWdB4va+bPiFo5FHHy9p5HUeX4V6/VlVIwIxKCKGlIUZf5SBeZFZvvAdoBFWWCOJSW9YykqCzSqhvawqvtF2qqvZaigcUbjuQ8cR0umCK7IvouwMr9sSFYyKPx0Mpo+IAwErSvw+HeBTAFwQ4nG/uLlpHp6SAwO5XG2yhI00dPI2Y8O5zsnV8cnk9U1UsrvSykvl1JeXlo6sxU8i503Nr0VzI6hVnShETYVYzjURWpV9bz9oLM3ZlX5yzlUqtuJiSRe8vd3GrbW01H5V7huLsXTEMb6ix9h/9oXIB6btXsKIbiivojX2oYJqJgMRVFyKJKMZFVg1g4eRa+rBi2711t7qBdTMkTAXjejeXVGbDxyYhlrraO83d1z3pf5yxzDvKewnX1BD08OTfxcGm1MR1+WHlHPMoqizF+qwLzIbB3Yisfiocqx+Iqm3cNJdAkNrjDX7vsOPnsZB5fdMuPxPLvaSbhsxCpU5OhCtTZZjiYFW7WOU8cCtlWYUgGc4aMANDkEJ6rSH0KoHGZljvUCtWf8uQY4e+nJqXOEEEbAA4xNcu35jo8ABZkxznevmcxPycJ4dHoZzI7hI0TcVRj29yKSOvqq+Zm/LCX0Rm0qfzmHSvT0hxD5iMk4U8pgp7f0fWhvv4zi5gDml17A/Q8fRYzM3i6FKxuKiKd0/tCmdkIoipI70WQUi2aZ/KRgGK2rH70++2fk0/nL0y8wx3SNr/Y0YhNJPljcNuVnyG9znOAqxyCPD1WzN3Duzly9qoS4CaqPBac9F0VRlLmiCsyLiC51tvVvo7moGTFPV0JdiJMN/h4YfgRbbJw3m+5FzyJvayKGUBTXkR4Vj7HAOTDTlCph6xkxGUFrExKNAv8uIL1qqr6phrDRwvAb2/I5XWXp2QGsEEI0CCHMpJv2bTrrnE3AhzNf3we8LKWUmeMPCCEsQogGYAXw5vnGzFzzSmYMMmP+eor5vQDcJoQozDT3uy1zTJmmsegYVoMVkyGL1yQpcQy1EiqowbCrHemwotdkuV13jvlSRkK6UeUv51CpdAAwgD/PMwGEYNRzI+Fb303l9SEMXcdwf/yDGPfMzoexK8pcOCwGXlMFZkVRciiaik75+mto60RIib4s+wKzy9dC2FJG0mif9pyeGKqiN2bnQ8WtuAxT79oQAt5beJQKU5iv9zSem8ds0DhRrVHflYKU2gWiKMr8pArMi8hR71G8Me+ijMcA6BhK8nbzPppPvMqR6uvxuma+4su9/zhClwRXzs9VY0r2NqQqGBZBOhgBQNdshCx1eHynH5CvKzNxqKge79Y38zVNZQnK5CE/RLpo2wI8LqU8JIT4nBDiXZnTHgaKhRDtwCeAT2auPQQ8DhwGfgv8tZQydb4xM2P9b+ATmbGKM2MjhLhCCNEL3A98TwhxKHOPMeDzpIvWO4DPZY4p0zQeG886HsPi68MYDxH01GLc30mqsQK0+fl2rDuafqiuMofyPJPFwy2tmKSW9xXMZwpZmxi6/E+p3pjEwjjOf/47bN/7OsRyG5lh0ATrqjy82jqMnOXMZ0VRlo5sMpi1lmMA6LWVWY2pJSM4A8dntHo5kDTy/Gg5l9mHWGPzZn2dRdP5s+IWgikjjw6e+4w6sMxG+RgkD78+7TkpiqLMhfn5RKPMyLaB9OrMNcVr8jyT2dF7IsRnDT/CbyvhcO1NFzSWZ1c7CY+DeOn8bKqkZK85WYHhrJiMoG0V9lgv5li6W32ZRTBU1YhzoJvEmKqfKXNHSvm8lHKllLJRSvnFzLFPSyk3Zb6OSinvl1I2SSmvlFJ2nHHtFzPXrZJS/mayMTPHOzJjNGXGjGWO75BS1kgpHVLKYinl2jOu+WHm/CYp5Y/m4u9kMfJGvdnHYwylG/yFfQ5EKIbeWDGbU7sgXVEbANUmVWDOFQ1BsXQwwHi+p/IWCWMRfSs/QtldNgpXBrFsehLX3/4ZhvbWnN7n4toChgIxjpwI5HRcRVGWrmgqilmbvMBsONKBXloEDltWY7r8bQj0GRWYnx0tJ6ob2Ojunva1VeYwN7r6+N1YGZ2Rt851sCH93Bp6+dlpj6soijIXVIF5Edk+sJ1KRyVF1qJ8TyXnEknJXf5fUSWH2L38LnTNOPVF52H0h3Ee7Us391PxGAueHRMrUqVsOyMmI2BdBUCBf+ep85wr0znMrb9Tn/oripJb3qgXlznb/OVWUgYTyfYAUkBqefksz27muqJ23Fo8q+29SvZKdMe8WsF8Uspgp6fyw9iuXUbtTaMYx/pxfuJjWB7/KaRSObnHhpoCAF5tVTEZiqLkRjQZxWKYPINZa+lAr83+A123r4WUZiJky27F80mhlIHnRsq52DZMlXlm/Qvu8HRj15L8cKCOMzd7+CsLCFsgvGPXjMZVFEWZbarAvEjoUmfv0F5WFq7M91Rmhbezk49pz7DXeSlDBcsvaKzCrS3peIw1tVOfrCwI61MVjIgQ7aQfWOOmYqLG0lM5zACr1tQTM5g49ntVYFYUJbfGYmNZR2TYR9qJuKsw7u9ErywC+xSNifKoM2JX8RizoER3MESAJHq+p3IOKYz0Ft9HvHE9K27rxtLgwPbI93D+74fQBvouePwih5m6Yjt/aBvKwWwVZXYIITYKIVqFEO1CiE9O8H2LEOKxzPe3CyHqz/jeP2aOtwohbj/j+A+FEENCiINnjVUkhHhRCHE087+q+/g0JPQEKZmaNCJDjHjRRrzZ5y9LiXu8hYCtFikM05rP86PlhHUjGz3TX718kl1L8k5PJwdDbrb7T/9zMBuMtNQIQkfV709FUeYnVWBeJI77jhNIBGgsaMz3VHJPSpp3fIsYZvY2bLywsXSdojcOE64tIVGYXTFAmf/WJssxSI0tb4nJWIkrdAgtlV494LSa6Curx3JwD8nU/HuoVxRl4cp6BbOU2MeOEzSXo7WfQJ/Hq5dTEnpiNqpUPEbOlUgHKSEZZp7GRAiNgcK7GCu5juWXHcF9SwmG4+24HvoTzL97Fi4wP3lDtYednV6CsWSOJqwouSOEMADfBt4BNAMPCiHObnDzEcArpWwCvgZ8KXNtM+nmu2uBjcB3MuMB/Dhz7GyfBF6SUq4AXsr8WclSLJnOip+syZ92JP18IJdltxrZEh3GEh+bdjxGJKXxzEg566yj1F7gh7PXOgeoMoX48UAtCT2949aCkUN1goQPEh0HpxhBURRl7qkC8yKxd2gvAE0FTXmeSe4VHt9Mg28f39Tvw+bKLjfrfFwtPZi9QQLr6nMzOWVesGJiVaqU7aID/WRMhm0VmkzhCew9fWLjCurG+9m8sz1PM1UUZbGJJCPEUjGcpqk/tDSFxzDGAoSGrOlu9vM4f/lE3EpCaip/eRaU6g4A+udhTMYpQjBU8HYGC95Oddl+yt/tQK+swv6Nf8f+hU8hAv4ZD31xbQFJXfJG+0gOJ6woOXMl0J7paxAHHgXuPuucu4FHMl8/CdwqhBCZ449KKWNSyuNAe2Y8pJSvARM1AjlzrEeAe3L5wyx20VQUAIt2/t1AhiPHkZqGXp3dh7puXwsAfkf9tObyireEYMp0QauXTzIIuKegg6GElVfHS4DTBWaA8Au/vOB7KIqi5JoqMC8Se4f34jQ5KbfP39VQMyGScWq3/BfHRA2vOa+94Mjkos2HSNothJbP34d6ZWY2JCsYE2HaSDf2C5uXkdScFI1vO3VOydp0hMzeZ1/OyxwVRVl8vNF0h/hsVjDbxo4DEO9MIK0m9JriWZ3bhTjZ4E9FZOReSabAfIJ5XGDOGHVfT3/huyiSB1h28zDxjXdi2rEF599+BMOxozMac2W5C5tJ4w9tKodZmZeqgZ4z/tybOTbhOVLKJOADirO89mzlUsqBzFgDQNlEJwkhPiaE2CmE2Dk8rP7bOSmSjABMGpGhHTmGrCoF8/lXOZ/J7TtM1FRA3DS9ZvAveUupNQdpsORmd0qz1Uu92c+TQ1UkdIEFA51lgFkntH1rTu6hKIqSS6rAvEjsHdpLY0EjYpE1ravY/ySW4CCfiX+ISteFbaU0jQVwHeom0LwMDOqf/mKzJlWOSWpsOxmTITT8tjV4/DvR9PT2Oa22jrjRjL57p9qaqyhKTnhj0yswSwnyyAh6fRlo8/e1qDNiRyCpVCuYc86BGbs0zctGfxPxOi+nt/g+XOE2GqteJfbnH0NEIzj//i8wvfSbaY9nNGisrfLw6pEh5AXGbSjKLJjoYersf6jnOyeba2dESvl9KeXlUsrLS0tLczHkonAyIuO8BWZdx3DkOHptdvEYQk/g9B+ddjzG8Yid41EHVztOTOu6Seci4A5PF8MJC6+Ml2DBiNQEeqWBcEtvzu6jKIqSK/P3yUbJ2nh0nE5/56KLxzAFR6jc/XN6izewObWOanfsgsYr3NoCUhJYN703DMrCYMHIqlQZ28Rx9EzjJL99LQYZx+Pfkz7JYCCyrJG1g0d54WDu3gAqirJ0nVrBbJq6wGwfPU4o5kF4Q6TmcTwGQHfMTrkpgkmoAuBsKNEd9C+AFcwn+e3r6Sl+D/ZwByvjPyL2//wlqdo6HF/9ItZHvjftXOb11R76fVG6x8KzNGNFmbFe4MxO4DVA//nOEUIYAQ/p+Itsrj3boBCiMjNWJaA6uE3DyYgMkzbx6mTRN4gIhrNu8OcMdGDQEwSmWWB+2VuCEZ0r7Ln9v+/MVcwGaQQgVucm4UuR6OnM6b0URVEulCowLwL7R/YDiy9/uebNHyD0JJuK7wOg1jXzArNIpCja0kKkvpyk256rKSrzzPpkBeMiwpFMTEbIUkdSc1Do23LqHOvKFSwLDvG711VzDEVRLtz0IjI68I2kO8LP5/xlgM6IjWpTMN/TWLRKdMeCWcF8UsDeTE/JA9ii3awe/ArJD72fxBVvw/r4T7F9498gmf3OoOYqNwDbOkZna7qKMlM7gBVCiAYhhJl0075NZ52zCfhw5uv7gJdlejn+JuABIYRFCNEArADenOJ+Z471YeDXOfgZloyTERkWw8QZzIYj6WgqPcsGf25fCzoaAXtN1nNI6II/jBezwT6Cw5DbHZInVzGPJCwcDKSzmP1N6fcPoRefyum9FEVRLpQqMC8Ce4f2ogmNend9vqeSM47BFkrafs+JxhvZFazCbkxRZJv5C3bh1hZM/jC+i5fncJbKfLMmVYZJGth6KibDgN+2hgL/LkQmJiPVmM5hju3cyaA/mq+pKoqySGRdYNZT2LzdhAeM6MUupMcxB7ObmXBKYyhhpUrFY8yaEunAK8JESeR7KtMStK2iu+R9WGN9rD7+OVLvvI34rRuxvPg8js9/EmLZLQaoLrDhsZnY1jFRzzNFyZ9MpvJDwAtAC/C4lPKQEOJzQoh3ZU57GCgWQrQDnwA+mbn2EPA4cBj4LfDXUsoUgBDiF8BWYJUQolcI8ZHMWP8OvF0IcRR4e+bPSpaiyfR7+fNFZGhHOpAWM7KiJKvxXL4jhGxV6Nr5M53PtiNQQDBl4hrHYNbXTMfJVcwvj6VXYftrajCYdcJbXpuV+ymKosyUKjAvAnuH97LMtQyL8fzdcxcUqbPsjW8Tt7oZWHEr7WMmqt2xGTf4E4kUZS/uJlJZRKQ2uzcXysJkxsjqVBnbxXFSZ8Zk6DE8gb0A6FU1pKw2Ngy3s2nvVLsWFUVRJueNeTEIAzajbdLzLP4BRCJOsieazl+ex7qj6Z0+1arAPGtKTzX68+d5JtMXsjbRVfJBzPEhVh/7DPLGq4je816Mu7bj+MI/QiI+5RhCCNZUuth6bFTlMCvzjpTyeSnlSillo5Tyi5ljn5ZSbsp8HZVS3i+lbJJSXiml7Djj2i9mrlslpfzNGccflFJWSilNUsoaKeXDmeOjUspbpZQrMv+rPnWZhpMRGecrMBtajqFXl2fV88CYCGAP9+G3L5vWHF4aK6XQEGO11Tut67IlBLzD04U3nn7diBhN2CokoYMdU1ypKIoyt1SBeYFL6kkODB+gsaAx31PJmeKjL+EcOkLvmjuJCCs9PiO1F5C/XPBmK6bxEONXrmTGVWplwdiQrMAvorSIdMZyyFJPUnNQNJ7ptqxpyIZGLvd28NSevjzOVFGUxcAb9eIyu6Zssmsf6yDqNUFcR6+b3wXmrmi6WF5lVgXm2VKSKTD3i/E8z2RmwtYGuko/iDkxxur2f0G7ZCWxdz+AafebOP7t01nFZTRXujnhj9I1qnKYFUWZmVMrmCdacZxMorV3Zx2P4fK1AhCYRoF5LGFib9DDVY5BtFl8zFxr9VJlSH94F5JJbMsKSI7HifeqZxlFUeYPVWBe4Nq8bURT0UVTYNYSEWq2/TfBgmWM1l5G57gJXQpq3DOLMhDJFD5NhxgAACAASURBVGUv7CJaUUhkmeq4vBSsSpVhlga2ijNjMlZT4N95OiZj+QpK/cMMHeum9UQgj7NVFGWh80a9OE3OKc+zjR4nNJTeaZSqn9+vR11RO1aRpMhwYc11lfMrlukC88ACXMF8UsRSR1fphzEl/axu/xcMGxqIvut+TNs3Y//yZyE1eZG5udIDqBxmRVFmbrIVzFpXPyKeQNZm1/PA5W8lqVkIW7L/EHirrwiJ4IpZisc4SQi4zZm+x96QxNKYbkIYfvU3k12mKIoyp7IqMAshNgohWoUQ7UKIT07wfYsQ4rHM97cLIeozx4uFEK8IIYJCiG+ddc1lQogDmWu+KaZa+qNMaN/wPmDxNPir3P0LzOExutffA0Lj2Fi6I/BMG/wV7GjD7A3iVauXlwwzBppT5WfFZKzDoEcp8O8CTucwXzJyVK1iVhTlgnijXpzmLArMY8cJjjjRS93gsM7BzGauK2qn2hxSL5uzyIyBAt224Br9nS1iqaGz9E8wpMKsOvavaJetIXbHPZg3v4LtB9+a9NqqAisFNpMqMCuKMmORRLrJ34QF5rZOAPSaLArMUuL2HSFgqwWR/Rq8rb5CqkwhKkyRrK+ZqUts4yBhmx/EsjUYLCnCr7006/dVFEXJ1pS/PYUQBuDbwDuAZuBBIUTzWad9BPBKKZuArwFfyhyPAv8C/MMEQ38X+Bjp7rorgI0z+QGWun3D+yiwFFBsLc73VC6YZbyXin2PM1JzGaGiegDax0y4zEncltS0xxOxBGW/2Um0zENknm9HVnJrfbKCoIhxUKQzlkOWBhIGNyVjrwKgl1eiO13cHOri6b196LrKf1QUZWa8Me/UDf4A63AH0SFt3sdjpCQcj9qpNgXzPZVFr0TaGWBhF5gBouYquko/jDEZYGXH55DXXEb8upuxbHoS8zO/PO916RxmN1s7VA6zoigzE0mmC7sWw7m9iLS2znSDv5KiKcexxIYxx8cJ2Guzvvd4wkhL2MXFtpHsJ3wBDEJglEZ8eooXo43Yy5KE9h1Rvz8VRZk3svl47kqgXUrZIaWMA48Cd591zt3AI5mvnwRuFUIIKWVISrmZdKH5FCFEJeCWUm6V6d+IPwHuuZAfZKk6NHKIBk/DlNmP856U1G3+T3TNSM/au04dPjZmomaGDf7KfrsLszfI6A3r1OrlJWZlqhSLNLLtVEyGxrh9A57AXoyJcdA0UstX0Nx/hBPjEbYfV/1UFEWZmbHo2JQRGSIZg65RZAJSdfM7HqM3ZiOqG6i3qPig2VaiOxgQPiQLvzgQNVfSXfp+LPEhVnZ8geRtbyexZj22730D446t571uTaWbQX+MTpXDrCjKDESSEQzCgFEznvM9Q1tnpsHf1M+BM8lf3u4vRCK4xD6c/YQvkBUDTmOU7/Z5sNU4SPqiJHp65uz++XRsOMjDm4/zD0/s485vvs7bv/oHPvjwdv7hiX08sbOHSHz6C9IURcmtbArM1cCZv7V6M8cmPEdKmQR8wGRLaqsz40w2JgBCiI8JIXYKIXYOD8/dL++FIBgP0unvpM5dl++pXLDC46/j6d1F3+qNJK1uAMIJQX9gZg3+LANjlL68l0BzLbGqhb+6W5keEwaaU2W8KTpJkn6zMe64BIFO8fjrAKRWrMYSGGd1eIinVUyGoigzkNSTBOKBKVcw27xdhAfTkU/6PC8wt4fT2cD1ZlVgnm0luoOwiONnZn0m5puwpZ6e4gewR7po7PkGsfe8H72yCse/fxqtu3PCa5qr0u/5VEyGoigzEUlGJly9TEpHa+9G1pRnNY7bd4SY0U3MVJD1vbf4iig3hqk0zd0HZBYM1FqDtIVNjNbUAxDasnnO7j/XdF3yypEhPvTwdm79///A5589zO8PD6IJgcdmYsAX5aWWQf7fJ/dz5f/5PZ/59UFO+BbHa6qiLETZFJgn+sjv7KUW2Zwzo/OllN+XUl4upby8tHR+P5TNtZaxFgDq3fX5ncgF0hIRlr3xHcLuKoYarj11vONk/vJ0G/xJSdXjr6GbjYy+7ew0F2Wp2JCsJCTiHMjEZMRNpYTN1adiMlIrVgFwV6KH5w4MEE2oT70VRZkeXyy9+nTKAvNYJ+FBM7LUOe/zl49GnNi0JKXG2c+TXOpKTzX6W/gxGScFbSsZKLyTgsAeasaeJPqhjyKNRhz/558heu6/qSqPlQK7ymFWFGVmzldgFj0DiFg8y/xlHae/LR2PkeWuV1/SyKGQm0vsI3O6UdYsDRSYIlSYEjxqvBiDNUX4Dy/O3QTmUPtQgPd8byt/+uMdHOjzcf9lNXzrwUv47gcu41N3rOHvb1vF5+9ex7ffdymffmczG6o9/Gx7N7d//TWe2def7+krypKUTYG5FzgzjKgGOPu/2FPnCCGMgAeYbM95b2acycZUpnB49DDAgl/BXLXzJ5hDI3Rt+GPQDKeOn2zwVzPNBn8Fb7bhbB9g7G3N6PYJPtFWloQVqRKs0sjWkzEZgM9+MfZoF7bIcaSnkFRZOZcMtRGMJXmpZSiPs1UUZSHyRr0AuEyTF5gtI92ER8yk6rNbSZVPR8MO6syBbHYUKxeoRM8UmBd4o7+zeZ1XMOq8ksrhTRSl9hF774fQeruwf/srcFZWqBCCVeUudqioKkVRZuB8BWbDNBr82UPdGFPRacVjvOkvREdw8RzGY0B6l2acBPcXenk60oyjLE5o9/5FlcMcT+p87cU2Nn79dY6cCPDn1zfwzQcv4d5Layh2TvBhQibP/6FbVvAf922g1GXh47/Yw9/8Yg+hWDIPP4GiLF3ZFJh3ACuEEA1CCDPwALDprHM2AR/OfH0f8LKc5LeclHIACAghrhbp8OAPAb+e9uyXuMOjhymyFuGxePI9lRlznDhMxf5fMlR3NcHihrd8r33MRJE1gcOsZz2eeWicqideJ1JZRGBt9m8SlMXHiIG1yXJ2ii4SmZgMn309OobTq5gbV1HQfohyq+ApFZOhKMo0eWOZAvMUK5gNR9qRKY1U3fwuMMd0QVfUruIx5kiBtGGQYtEVmAFOFLyDoKWB+t7/wlxtIX7LRswvv4D5hWfOOXdluYt+X5QBn1o1ryjK9ESSEcwG8znHtbZOpMmILJs6KvFU/rIt+wZ/23yFlBoj1JhC2U82B8wYiJLkVrcfzWgnVmYj5QsT7+yc03nMlgFfhAe+v5VvvHSUq5YX85X7L+LW1eUYtWzKVlDpsfGvd63l/stqeHZ/Px/64Zv4o4lZnrWiKCdN+V9qJlP5IeAFoAV4XEp5SAjxOSHEuzKnPQwUCyHagU8Anzx5vRCiE/gq8CdCiF4hxMnMgr8EfgC0A8eA3+TmR1o6Do8eXtCrl7VElIZXvkTc6qFn7bvO+f6xMSM108hfFrEEdT94AYRgaOOlqrGfwoZUJWERZ79IR76nDHYCtlUUe19H6AlSK1Yh4jHuMY3yausQY6F4nmesKMpCcnIFs9M8eZM/eeQEMP/zlzsiDnQEdWZ/vqeyJBjQKJaORRWRcYow0Fv8HlLCyvLub5C86WaSK1Zj+6+voXUee8upqyrSH9Ds7hrPx0wVRVnAzldgNrR1oleVg2HqwqTb10rYUkrSaM/qnqGUgf1BNxfb5jYeA8AsjcREApOA+wq9bC9ekZ7TGws/h3lL+wh3fnMzhwf8/M0tK3jo5iY8NtO0xzFognsvreFvbl3B3p5x3v/f2xkPq2c8RZkLWX0UJKV8Xkq5UkrZKKX8YubYp6WUmzJfR6WU90spm6SUV0opO864tl5KWSSldEopa6SUhzPHd0op12XGfGiyFc/KuRZDg7/qHT/C5uvj+CUPoJvemknpjwmGw9MoMEtJ9WOvYRkYY+j2S0m5snuDoCxuTakSbNLEVnH81LFxx6WYUn4KfW+SWr4CqRm4xnuMpC557sBAHmerKMpCMx5LF8QmXcGsp4j3RtCKjDDPY5vaI+nIhjpLMM8zWTpKdAf9i3AFM0DK4KC/6B7s0R5qBh8l9p4PIi1WHF/+HCROP+zXFdsxGzV2dqmYDEVRpiecDJ8bkaHraEe7kLVT7xoSqTiOYAcBW/Y7X/cGPKTQWG+f++z4kyuYAW53+9nl2oDJkST08sJeq/ez7V184OHt2MwGvnD3eq5pnHrl+VSuaijmE29fyZETfh74/ja1kllR5kB2ew2UeWehN/hz9e2lfP+vGGy4lkDpinO+f3Q0/Ul0tg3+il/ZT+GONrxXryJSV5bTuSoLlwHtVExGPPNmLGhtImYsomzkN2CxklpWT/GRvSwrsvPU7t48z1hRlIVkLJouiDlN51/BbBnvIzpiQlRNHqMxHxwNOyk0RPEY1EqfuVKiOxjEj072cWALSdC2klHnlVSMPItTHid274MYOo9h/ekPTp1j1DQaSx3s6vLmcaaKoixE0WT0nAKz6B9CRKJZ5S87A8fQZAq/Pft4jJ2BAhxagoY87PYxSwMx0oVSsyZZXliOvTxGcNcBZGLhFVB1XfKl3x7hn546yEU1BXz+XeuoLrTlbPxLlxXyD7et4uhQkId+tptkanG+1irKfKEKzAvUQm7wZw4M0vji54k6S+ltfueE57SOmNCEpDaLFczFfzhA1VNbCDZWMn7FylxPV1ngLkpWEhUJ9mViMhAaXscVuMJHsEU6STWtwnCsjVuqLOzuHqdrdG6z1BRFWbi8US92ox2jZjzvOZaWA+gJjVTN/I7HgNMN/pS5UyodJIXOEIv3733QcxtRYykNPd9BrlpB4sq3YfnVLzAc3HvqnJXlLg73+4nEU3mcqaIoC81ETf4MrZ1Adg3+3P5WdGEgZKvO6n4pCbsCHtZax/LSDNeMgRinG9dd59HxVXggliSwZ+8kV84/sWSK//XYHr776jFuXV3G39+2CpvZkPP7bKgp4CPXNvDa0RG+8FxLzsdXFOU0VWBeoBZqgz8tEaXpt59GJGO0X/ln6MaJtwu3jZqocsYxGyZPTil67SBVT24mtLyCoY2Xqdxl5RzL9WIc0swb4nTm47jjEnRhomzkt6RWrEZIyU3+DgTw9J7+/E1WUZQFxRvzTtngz7Q//YFwvL5mLqY0Y76kkcGElXrL4i10zkdlenr1e98ijckAkJqZgcK7sCRGqBh6itgd70YWl+D4yuchnP5Qd2WZi6Qu2dercpgVRcneRAVm7Wgn0mhAlpdMeb3Ld4SQtRJdyy7rty3sJJgysc429/EYkMlgJokk/YxsEJCsqwMh2fn4uU1U5ytfOMGHHn6TTfsGePCKWj5yXQOGWazY37y6jDvWV/LjLZ38dFvXrN1HUZY6VWBeoBZkgz8pqX/1K9hHO+i4/ANEXRNHWaR0aB81UeeZpJu4Lin77U6qn3id0PIKBt9xeVZNHJSlx4DG+mQFu0U3EdLbvlMGOz77eoq9ryEqitAdDgoP7aK5ys1Te3pRkfCKomTDG/VO2eCP1l40i06q5MLzBGdTezidv1yvVjDPqdJMgbmfxV1YDVvrGbdvoHLo11jwEr3vA4iRIWyPfB+AFeXpvwcVk6EoynRM1ORPa+tErywD4+SrYY2JAPZwH3579vnLOwMFGNBptuXnd5UZA1JAgtO7PVzF9diKEoitrxJYADnDPWNh7v3uG+zq8vLQzU286+JqxBwsEnv/lcu4pLaAz246xIHexfuhrqLkk6rILUALssGfnqLu9W9QfOxVepvvxFfefN5Tu31GYimNZZ6J4zEMoSh133ue8ud2EFhVo4rLypQuTlYRFyl2iNOfWI85r8Ig45T4/kCqaTXGHdu4dnkRnaNh9vYs7gd9RVFywxv14jJNvoI51enDXKbN+x02RyNOBJJaVWCeU3ZMuKSFPrH4X3cGC25DIljW9yP0ugYS19yA+blfYWg5iMtqorrApgrMiqJkTUp5bgazlBjaOpE1Uzf4c/nbAAhMp8DsL6DJ4sOm5SfOxyzTRfMopwvJYUsd9oo45aPDfOvXu/Iyr2zt7x3n3d95gxP+KJ+6Yw3XNk29yjxXNE3wVzc14baZ+PgvdhOKJae+SFGUaVFVuQVooTX4E6k4jb//ImWHn6V/xa2caLp50vNbR9KfQtd7zm3w52jtpek/nsR1pIfhm9YzfNslqrisTGmZXkihbntLTEbUXEnIvCwdk7FqDZp/nOv0EcwGjaf39OVxtoqiLBRTrmAeD5Ac19Eqc9ewZrYcCrmoNoWwaqoBzlwr1R30sfgLq0mDm2H3TRQEduHx7yZ+251ITwH2b/47JBKsKHOyq8uLrqtdRIqiTC2aiiKRbykwixMjiGA4q/xll6+VpGYhbMmuQfxg3ExPzM4629iM53yhzJwsMJ8ujkrNjHGZBwEcevZl9nTPz9eTl1oGee/3tiGE4LN3rWNNpXvO5+C0GvnrmxrpGg3z2WcOzfn9FWWxU5W5Behkg79ZKTBLicXXj3WsE4uvH1NoBOTMHzbNgUFWPvcpijpeo3vd3fQ13znlKq62URNuS5IC6+kXTqM3SO0Pf8fybz2DSKXov+86Ahsa5v2KMGV+0BBclKzigOjDx+nolVHXNVjjgzjLQ0ghcO7bwaV1BWza109CdRlWFGUSUsp0BvMkK5jNe/enz60umqtpzUhM12gNO1llnZ8PpYtdqe6kT4yfytRczMZcVxMzFlEz8DMwm4nd/R4M3Z1YnvwZK8td+CIJOkZUs11FUaYWSabf059ZYNbaOoEsGvxJict3hKCtBkR2JZGd/kKAvOUvQzqDGXhLoz+ARPVyNJPOjWMtfPKXB4gn589zjJSSR7Z08tGf7KTSY+Vz71pLdWH+PnhvrvJw98XVPL6zl2f3q947ipJL5297rsxbLWMtFFoLcVty9KmfnqL46EsUdm7BOXAAU/StmURJs4NwSROh0pUEKjcQrFxPyjJ55qSWiFKx73Eq9jyKkJKOSx5kdNkVWU2ndcREnTuKEGAcD1L68j6KNh8GqTN29Sp8lzYhp8jUUpSzXZys4lXzMbaKDjbKtQAEbGuIGUuoCjzP8Zo6TDu3ct3f3sO2jjH+0DrMHzVPvb1OUZSlKZwMk9ATkzb5M+/ZB0KSyGKrbj61hJwkpcYq6+KPaZiPynQnYZHAR4QC7PmezqySwsiw+2Zqxn5JoW8b3tVvI7HhUqyP/ph1G64GYHeXl6ayKbLNFUVZ8k4WmM3G0xnMhrZOpKYhK0snvdYSG8ES9zLouTjr++0MFFBuDFNmOneX7Vw5uYI5xluzlsP2Jhzl+7l65AhfPOHn+68d46FbVuRjim8RT+p8ZtNBfvFmD5ctK+ShW5qwmvL/HP/Hl1VzsN/HPz11kKuXF1PitEx9kaIoU1IF5gWobayNWldtTsZynjjEss3fwjFylJi9CF/pCoJF9SRNdjQ9iZaMYfcPYPf1Un7gKSr3PYEUGuGi5YRLVxAuaSTmqkRqRqSmYfV24+7fh6tvL6aYn9Hqi+ltvou4vTCr+XgjGkMhI3eITqp/vp2CN1sRuiS4shrvNatJuhf3g5cyeyqki8qUizfEsVMFZoTGiOs6qr1PY6xvRt+8l4vdEo/NxBO7elSBWVGU8xqLpLfITlZgFoePYSlMEHJM/qCbb/uDHgzoNFlU05t8KNPTDRb7xDgFcvG/z/HZ11MSeI3qE4/h9VxF/J33Yjx6hPoffwPXuj9lZ9cY77kiN+9zFUVZvCKJiVcwy8oSME1e5nD6jwIQsGf3uyaqaxwKubjBmd8Vr6ZMBnNMJDlz00vYXENFpSTQG+TOwjjfeOkoN60qY121J08zheFAjL/8n13s7PJyz8VV3H95Ldo82X1s1DT+4obl/OOvDvDZZw7znw9eku8pKcqioArMC0w8FafD18HG+o0XNpCU1G77PhX7niBuLeDYZR9grPqSSSMnRCqB09uFa6Qd51gnBR2vU3rkN+ecF7MV4itbxXDdVQSLl2c/p3CM0HPtfPOV/azw9aEbDQSbaxm/tImkxzGTn1JR3uKiVBW/NbcyqPspJ70DwOfYQJn/ZUoK2xmWEuvenVzXtIoXDp1gNBijWH2irSjKBEaj6S2yHst5Ht6SSWTnKNaGJF7j5I0A821f0E2DxY9F5S/nRZlMr9btY5y1VOV5NnNAaAy7b6F29DGKx99gtPAGYnfcg/WXP+d95Xt4sWv+Z5YripJ/50RkSInW1om+umHKa13+oyQMDmKm7BZBHQq6SEqNZmv+8pcBLJzb5A8AYUCvr4Udo7xf72a7bS0f/clOnvn4dXlZnfv60WH+7rG9+KNJ/uaWJq5pnLtmftmqKbRzzyXVPLmrl7svqlILixQlB1SBeYHp8HWQkqkLW8EsJbVbvkvFgV8xVP82etbehW6c+oVHGkwESpoIlDSdGscU9WGOjCOkRMgUcVsBMXvxtLKRtWMDmF7aj3HLEVbHEhx3VzJ443oiq2vQLaaZ/pSKco6LkukC8xZxjHfL9CfVUhgZdV1DefIFhhxNmHZu5caPXMdzBwZ4em8/H7lu6jepiqIsPaORdIH5fCuYtWM9kNAxlpuzznfMh0DSSGfUzh2ernxPZclySysWaaBPjLMEYpgB8NvWEDFVUnXiMcYK3kbysqtI7t3JHVue5ElzPd5QnEKHeeqBFEVZsk5FZGjp3xVieAzNFyCZRf6y099O0FaV9TPrnqAHk0jRZM3vTp/zZTADRMrWYfH8noLtL/KJT93Lv246xF//bDf/8+dXYTLMzfuQeFLn679v47uvHqO60Mb/d/tqaovm786cuy+qYvvxUf7p6QNcubwIt1XVHhTlQszfJx5lQq1jrQAzLzBLSc32H1Bx4FcMLr+erg1/nFVxeUJCkLAVECqqJ1jcQKCkiZijJLsX6lgC4ysHsH3qp9j/+WcY32gh1VzD1277c758+0cIXdSgistKzhVKGw2pIjZrx97STMnruJyUwYatSmLcuY1aj4XGUgeP7+xByiXytK8oyrScXMHsNk/cD8FwKL39VlTP79XLB0IuJILVKn85bwQi3eiPJfT/gdAY9tyMNT5I4fgWEILYPe/FmEryl/ufYne3ajipKMrkzl7BfLLBn5yi74E5Noo5MU7AVpP1vfYEClhpGcck8vtcYD61gvncAnPQ2oijKo44epzlVp2P3rCc7cfH+Nwzh+fkeWZvzzh3/edmvvPqMW5aVcYX7lk3r4vLAEaDxseub2Q4EOMrL7TmezqKsuCpAvMC0+Ztw6SZKHfMbAtH+YGnqNz7GEP1b6N73T3TWmmcC2JgDPNPX8HxV/+F9fsvIIJR4u+4lOj/uovwHVfyimMlDdbgnM5JWVouSlbSJ8bp5vQWN12zMOq8huLSPrRgAEPrIW5cWUbriQAH+/x5nK2iKPPVyQLz+VYwGw60YbSliBcWzeW0pu1A0I1FJKkzB/I9lSWtVHemVzAvIQHrSqKmMiqHfg1SIktKidyykWsHDtL3zG/zPT1FUea5UwXmzGIpw9EupBDoVZM/JzsD7QAEsywwn4hZGIhbabbl/4OvUxnMZ0dkAFIzozWUgp5eMHNdUwnv3FDJT7d18U9PHySlz06R2RdJ8PlnD3Pvd95gOBjj729bycduWI7FmP9mftloKnPyR2vK+Z9tXRzuV899inIhVIF5gWkda6XaWY02g+22Fl8fNdt/gLdiLV0b7p3T4rLWM4z1q0/j+MQPMb2wh1RDGbEP30zsL24jdXkTWEy0RxwkpEajajKkzKL1yUo0Kdistb/l+JjrGixVBtDAtG0zb2ssxmzQeGJXT55mqijKfDYaGcVpcmLUJk4bMxxoxVYSJ26e3wXm/UEPKyw+DHlelbXUlUkHXhEmTDzfU5k7QmPUdS32aBfuwD4A5A230F9QwYrHv48eCuV5goqizGfnrGBuPY6sKAHz5Ltg0/nLNqJZvj7vCaZ7LeQ7fxlOZzBPFJEBEKttxmBJYd2c7pP0viuXcffFVfx8ezd//fPdxJKpnM0llkzxg9c7uOE/XuGHm49z86oyvnzfBi6vm9/veyZy/2W1OCxGPrPpoNq9qigXQBWYFxApJa3e1pnFY0hJ/atfRRcaXRv+eM7yIMWAF8s3n8X2vx/BsL+LxA3NRP/mThL3XoO+rPQtRe6DITcCqbrYK7PKgZlVqVLeEMfQz4jJ0DUL3pLrcZRGsWz+HQ6LkcvrC/n13n6iidy9GVMUZXEYi46dNx5DjHgRIz5sJXGi5oI5nln2huNmBuJWVql4jLwr09ON/vqXUkwG4LOvJ2FwUzn8dPqAwcCWG++nIDjG4Le+nd/JKYoyr4WTYQDMhnQGs9bWiV499S5fp7+doLU6+/zlgIcSY4RSY3Tmk80RAxqaFETFuSuYAYKO1TirYhj27oFUEiEED1yxjA9eXcdvD57gvd/bdsGrdP3RBP/9Wgc3fflVvvBcC8uK7Hzx3ev58+uXYzcvzBZfTquR915ey45OL5v29ed7OoqyYKkC8wIyHBlmPDY+owJzScvzuAf20bP2LhK22X/YFcM+LN/7LfZ/+CHGnUdJvm010Y/fQfLGdeCcuDv4waCLalMIp2HiT2QVJVcuSlYxJsIc4cRbjo85r8BWKxCDI2jdndy4shRfJMGLhwfzNFNFUearkcjIeQvM2qH0Dgl7STzrDvX5sDuQXpW12pr/bb9L3ckC81KLyZDCyKjzatzBg9jD6f9uPCsa+U3dVXgfeYRoq8rEVBRlYtFkuuBrMVgQo+NoYz70KfKXTbExLPExgvbqrO6R0AUHgm6ard65TpackPi/7N13eFxnlfjx73vv9Bn1XixZcpd7T3USmwCpQCAECFlY2GXZwLK7sHTyY6m7tKXsLmTpEAgQQnpvTsV24m7LtpqtaquOyvRy7/v7YyTbstVsS5qR9X6eJ4/te++8c8axPTPnnvccBDb0USuYDd2DtcwDoRj6oQMnj1+/vIh/3rKAo11+bvrvV/nKo9X0BSe+Y8YwJX9t6OYLDx7gkm8+zzeeOEyWy8rn24zUtgAAIABJREFUr1vMF65fQkWu+4JfW7Jdsyifilw333j8MIGIykcoyvmYmbeYZqnzHfBnDXQzZ9v/MZA7n+7yS6YitJNErx/rg9uxvrAfBBjr5xO7bAl4HGM+LmoKjgTTuNKj7hgqU6/KyMcmdV7TGqgyi04el8JKZMl62Lmf9Gd+w7IP/T9yPTb+vKuVm1YWJzFiRVFSTU+oh0L3yJPq9eo60AWWbB1DH/v9L5le7c+hwBKkyBpMdiizXrZ0oUuRSDDPst25vZ515A28TFHnIzTM/SQLPYJPL72eLV3VtH/53ym/9/cITdXEKIoyXCgeSiRcNRta7WEAZGnRmI9JGzi3/suHg2lEpE6VM/ntMYbY5OgJZoD4/CWIVw7ifPlR/MtXnzx+SWUOy4oz+NPOZn79WiP3bGviygW5XL+8iKXFGRRlOMh0WYkaJv3BGMf7w+xr6WNvSx+v1HXR7Y9it2isn5vN9cuLLoqk8uk0TfDBy+by5UequfulBj715kXJDklRZhyVYJ5BantrAShNm/jEW4DCvfehxcM0rrx16vouDwSxPfo61qf3gGFirKogfkUVMmNik2Nrgx5iUmOhfXZV7ijJYcNClVHAdv0of8ulWDg1hKK/YCOO7L3w1xfQPnwXmxbk8eCeNo73hSjOHLn6XlGU2ccb9rIwa+GI5/QDtdhyBdFp2DF0vrwxK4cCaVyX3pwSVVmznY5GrnTTNstaZACYmoNez1py+rdhi3aTY8/F4XGz9dK385bnf0ff/feT9e53JztMRVFSTCgewqbbEEKg1TUCYJbkj/kYj6+euOYgZMud0HPs8WVgwWRBCn1HtWEhPEaCuT9jDc783Ygdr8LHhp/zOCx8+IpKrq0q5JW6LnYc87K1puvkeYsmiJ8xDDDLZWVhQRrv25DD6rJMHNaZMbzvfCwsSOPSyhx+/sox3n9JOQXpqVskoCipSCWYZ5Ca3hpyHDm4rRO/W6iH+8k7/Dje0rVEPHmTH1Qwgu3xN7A+vguicYzlZcSvXIrM9pzTMif7LztU/2VleqyKF7PXcpy9opV1svzUCaHDvDKib7SQfeB+Ni28mQf2tPHA7lY+vnlB8gJWFCVlRIwI/pifNFvaCCejaHXNuBZFCFtzpj+4CdrWn41EsNbdmexQlEEFZhrNeupUyU0nr2cjOb6/kt/9FK3F72dhmsbvxSpuXryTzu9+j7QtW7DkpO7fJ0VRpl8oHjo54E+vbcLMzwaHfczHeAbq8DuLJ1x0tduXwTx7Pw7NvOB4J4tN6kRG6cEMYOhurHMzCW4PojfXY5TNP+uasmwXt28s530bymjyBunoD9MTiNIfiuGw6njsOplOG5V5brLdiST+bHHb+jm80ejlB8/V8h+3rEh2OIoyo6j9ZjNIjffcB/wVHHwYPR7hxPxrJjeYcBTrQztwf+Kn2B7YjlFZQOQf3kLsbRvPObkMcMCfxhybH6emhqkp02OBkYtb2nhF1J91LrxkIyDIeuLHFHgsVBWl8+ddrWqqsKIoAHhDiSRghj3jrHNaXRMiHseT5Uvp/suv9mdTYvVTaA0lOxRlUJGZRpfwE2TifTEvFjFLJgPOJeR5n0UzIyzyCLoiEH/3+zGDQTq//Z1kh6goSoo5PcGs1RzDLB25bdUQS2wAR6Qbv3Ni/Ze7Y1ZaIi6qnKk1p8CGPmYFM0B48VoA0p/81ZjXCSGYm+NmY2UO1y8v4r0bynjH6hKurSpkfUU2OR77rEouAxSkO3jTkgL+9EYL9Z2+ZIejKDOKSjDPEBEjQtNA0zm1x9BiIfIPPERv4VLC6WO/4U5YNI71iV24/vln2P/0CmZxNuG/u5bYuy5D5o087Gg8EVOjNuhhUQptPVIufjoaq+LF7BJN+IkMP5mfi0hzEGnwk3f0Qa5amEdTT5A3GlPrA6aiKMnRE+4BGHHIn36wDgBnbpSILTVbZHRFbdQE01jj6hr/YmXaFJiJivgWZud7jTftEixGgJzel1mUlkho7LXlkX7ddfQ//DCB7TuSHKGiKKnkZIK5bwCty4ssGXvAn8d3FAC/Y2JzVfYODsKtcqTWzhKr1IkwegUzQCi3Ckeugf7XbdMU1cXlHatLcFh1vvWUGjSrKOdCJZhniIa+BgxpnFMFc+6Rp7BGBmifv/nCA4gbWJ7bh+tffo79nq3InHQif7uZ6HuuRBZdWIXW4YAHA40FDpVgVqbXmngJcWGyXRwdfkIIzAXzCXQ4KNn5QzaWuXFade7b2ZKcQBVFSSnecOLL5ogJ5uo6yHJhcZiEramZYH6tPxuAtSrBnFKKzMSfp2aRWsmM6RK0lROyFpHf9TjlTrBrsM8bJ/3mm7Hk59P+la9gRmdfdbeiKCMb6sGs1zUBYM4Zu6DK7TuKKXRCjrH7NA/Z48skU4+k3CBcGzphMXYFM0JDW1hEvDuK48j26QnsIpLutHLTimKePdTBrqbZ+Z6sKOdDJZhniBpv4u7ZRBPMwohTuO/P+LIr8OdUnP8TS4m+owbXv/0Kxy+eRbrtRO64muj7r8IsndhwhPEcDKSjYzLPrvovK9Or2Eyn0EzjJVF31jm5sBJpQKzBS3njn7mkMpvHD5zAHxnnA52iKBe9ntBgBbP9jASzlGgH67AWJYbCRFI4wVxm85FnDSc7FOU0mdKBQ1pomaUJZoTAm3YJrkgrmYGDzPcI9nhjaDYbWXfcQfTYMby//GWyo1QUJUWEYokKZq22EWDcFhke31GC9gKkGH9InSFhnz+dKoc35Qbh2qRl3ApmgNDSSwFJ+uM/n/qgLkJvXVZIutPC9589+3uioigjUwnmGaK2txa7biffNbE7rhktr2P3d9I+/+rzfk7tSCvOu36P8wePgpRE3nMF0Q9uxpw7sRgm6qA/nTKbL6WGJyizg0CwOl5CndbJCc64wVFSiHS78LYXULr/f3jTPA+hqMGj+44nJ1hFUVLGaC0yRHs3Wu8A9nyI6S5MfexhQ8lwLOSkIeRhnUsN90s1AkGhmUYTszTBDPS7lhHX3BR0PcEij6CmP04wLnGuWIFrw3q6f3I30ebmZIepKEoKCMaD2HU7em0jZm4WOB2jXivMKM5ga2LA3wTUBj0ETQtLUqz/MkysBzOAkVmErdCC3FmNHlGFXOfKYdW5aUUxr9Z3s7Nx9r4vK8q5UAnmGeKI9wjFnmI0MbH/ZTm1zxGzeegvqDrn5xJtPTi++yCur/wR0dlP9Kb1RP7+WswFE5+4O1EBQ6c+5GahQ73pKcmxOl6MkPCKdsawP02DhRWEmk10v5cre+5jTraT3+9oSk6giqKkjJ5QD06LE5tuG3Zcr05UubiyAylbvfxQVzEOEedST3uyQ1FGUGim0SK8SGbnUFkprHg968j07WK9oxNDwsHeRKVe5nvfB5pG+9e+robuKopyskWGVtuIOU7/Zbe/GU0aBCbYf3mPLwMNyWJHaiaYIxNIMAMYS5YSG9Ap2PpfUxzVxelNSwpId1r4wXOqillRJkIlmGcAKSW1vbUTbo+hRQNkNm3HW7ISqY2/BeikSAzb717E9Zlfox9sInbNMiIfuw5jVUUi2TYF9voyMBEpNzxBmT3SpYP5Ri4vizrMM77Qy0XzIG7Q6a+i+NDPuHG+g4NtAxxoVTdEFGU26wn3kGZLO+u4drAeabfhcfYQTsEBfx1RG6/2Z3O55wQuzUh2OMoICs00giJGD4Fkh5I0vZ4NSDSuCD8FwF5vIsFsyc4m85ZbCLzyCr6nn05miIqipIBQPER6REM70YUcpz2G2z844M9ZNKG19/gzqLAPpOR7pU3qREX8rO8tI4ktWQ0CHFsfxzK4+0qZOFXFrCjnRiWYZ4COYAcD0YEJJ5izjr6KZkTpKV074efQDzXj+syvsT2+E2NlBeGPXU/8iiqwWs437Al5w5eJW4tRaR+Y0udRlLGsjZfQLfwc4YyKvsE2GX3tOeixALdF7sdu0bj3dbU9V1FmM2/IO/KAv4O1yLJC7MZASlYwP9JVhIZkc1pbskNRRlFoJm5cNM3WPsxAXE9jwLWU4r6tLHSE2NtzqlLPs2ULtrlz6fjGNzH8/iRGqShKsoXiIYrbIgCYpWNXMHt8xwjbsjF057jr9sUtNIQ8LEnB6mVIVDADRCdSxex2Iubk42u0UHzgJ1Mc2cVJVTErysSpBPMMUNtbC0x8wF9O3XOE3bkEssrHv9g0sf3pVZxfuw9iBpE7riZ24zpwj97DarIYEnYNZLLM6UVLseEJyuxSZRRilxZe1s744DDYJsM81IQ393LK6u7hujKTh/e2qWF/ijKLdYe6z04wB8NoR1vRShLHI9asJEQ2uv64hed7c9ng7iTTEk12OMoohhLMzbO4DzNAj+dSdDPEB+0vs9cbwxxsiSF0nawPfIB4dzddP/xRkqNUFCWZQvEQha2J3R5jDviTJm7fUfyOiVUv7/NlAKTsDlubTBSATbxNxhJifguZ2/6ILXBiKkO7KDmsOjcuT1Qx723pS3Y4ipLSVIJ5Bqjx1gBQ6ikd91proJv0tr30lKwev1+yP4zj2w9ie2g78VUVRP7hzZM+wG8sR4JpBEwLy5xqu46SXDZ0lsUL2S6OnfVhTS6ah4jF6AouA0zuFH8hGDV4ZK8a9qcos1VPuId0+/AEs15zFGGaWAoTfZkjKdYi44meAmJS403pLckORRmDAytZppOWWVzBDBC2lxC0zeGt8acZiBk0+k9tU7dXVuLZvJne3/+e0MHqJEapKEqyxIwYhjTIa/FhZmeAxzXqtY5wJxYjOOH+y3v9GXi0GHNsqblLYqiCOUxsYg9YWAkWnf4GO5U7vgSqh/0527IkH5dN52cvH012KIqS0lSCeQao7a0l15mLyzr6G+eQ7PqtCCTecdpjiK5+XF/6HfrBJqI3rCV20/opb4dxpp0DmVgwqUrR7UfK7LImXkJYxHhDNA4/Mdgmw9x3jN6SLSw4/hCXZ/aqYX+KMkvFzTj9kf6zKpi1g4lBoa7cxE2qVGqR0R6180hXIauc3RRaQ8kORxlHoZk2q1tkDOlJu4Rso52rtX3s7RmeSMl817vQ09Np//cvI43U65GqKMrUCsaDAGQ39SHHG/Dnm3j/ZVMmBvwtcaTuDlubTCSYJ1rBjMOOXDiP/mYPGU0vUnjk11MX3EXKZbOwZXE+Tx48QXNPMNnhKErKUgnmGeCI98jE22PUPk8gcw7htNErkUVXP86v/gkxECR6x9UYa+ZNVqjn5PWBTBY4+nCk4PAEZfapMLPJMp28LOqHnxhsk8H+w3QWXYepWfmC4y9UHx9gn9ompSizTl+kD4k8K8GsV9dhFuTgwEvU4sHUrEmKcDhTwv+2ViCQvDOrIdnhKBNQaKZxgn5izO7PRwPOKqJ6On9veYq93uGJFM3lIvO97yV8sJreP/wxSREqipIsoXgIZ0SS1uEbuz0G4PYfI647J9S66ljYxYBhpcqZugVQdhJFYSEm3u5KrlwCUYOO3sWU7/5PXF61++NcvXVZEZoQ/PxVVcWsKKNRCeYUF46HafY1TyjBbBs4gbunPtEeYxSiewDn1/6E8IeI3L4Jc07uZIY7YccjDk5EnSxzqgodJTVoCFbHSzgg2vASGHZOVi1ExOKYBxrpKb+epX0vsNbayG+3qSpmRZltekKJtk7DEsymiV5djzm3FHu4M6Wql5/25lMdSOedWUfJUr2XZ4QiMx1TSNqY5TcxhU6vZz2XaQfwdh8767Rr40Ycy5bS9YPvE2tvH2EBRVEuVuF4mLkdiZ+PP+DvKAFH4fjtI0lULwMpO+APwCETN7CDYoItMiCxIzMni76jHgxrGgtf/icskdR9jako223j8vm53LezBW9AfZ5SlJGoBHOKq++rx5TmhPovZza/DkBf4dKRL/CHcX79PsRAiMjtVyGLsicz1HPyxkDiy/dy1X9ZSSGr48VIIXlNnFHlV5SPzExHbNtNT/n1xK1pfNXzFx7dd1x9wFCUWeZkgvm0HsyipR3hC2BWlGAPd6VMgrkzauO3J+awxOHlUrdKwM0URYOD/hqF+ozU615HDCtvijxBX8Qcdk4IQdbffAAZi3PirruQqq+ooswaoXiIyvbE33lzzuitL/R4EEe4M5FgnoDdvkzKbD7S9HNI3k4zx+CQv+A5VDAjBHLFYjjWRnPu+7AH2lj69G3Yguqzwbm4cUUR4ZjJParISFFGpBLMKa62txZgQhXMGc07CLvziHjyzj5pShw/fgLRPUD0vVcii5OXXIZEgrnE6ifHEklqHIpyujzpoczI5CWtFslpX1SFgKoFUNOAORCle+7NLA3tYpVZzX071cAsRZlNesJnVzDrhwZb65TmYI0HCKfAgL++uIWvNy5EIHlfdt1ECreUFJEj3TikhQbRlexQks7Q3bTYV3GL/goHOs7e9WYtKCDz1lsJvPIq/Q88kIQIFUVJhlA8REW7JJruhDT3qNe5As0ABOzjJ5gDhk5t0JPy84EcnEeCGaBqIVLXieztpGn1ZxNJ5qduxTHQOPlBXqRKs1ysLsvkN9saicRndxsrRRmJSjCnuBpvDQ7dQZ5rhKTxaUQ8QnrbXvoLFo943vrwDix7jhK7dmXS2mIM6Y1ZORxMU9XLSkpaEy+hVfTRyPA/n7JqIUJK2LEH75xridkz+aL7Ye7Z1oRhqqopRZktvOFEkmtYgvlgHdLlwJY2NOBv/D6PU2kgbuHfjy6mM2rno3nVZKubuTOKhqDEzKAelWAGiGRswCmi6K1Pjnjes2UL9sWL6fiP/yR24sQ0R6coSjKE4iEqOiShkpwxr3P7GwEIOsZuowGw35+OiWCJI7VbOJ5skXGuCWaXMzFX5q87CXoW0Ljmi1iiAyx/4u0U1P4epDn+GgpvXVqINxDlqYOq+ltRzqQSzCmupreGkrQSNDH2/6r04/vQjCh9BUvOOqfvb8T251eJLyvDWDd/qkKdsJf7cpAI1rs7kx2KopxlRbwYi9R4UasdfiIrA1lcgNi2C6nb6C6/iZXx/ZT272brEfVnWVFmi55QD1bNitPiPHlMO1iHWV6CI9oNkNQWGd6YlX8/togTUTv/kFfNAkd/0mJRzl+pkUGz8M76QX8Apr2AfSxmdf9jYJ69bV1oGjkf/jAyHufEl1SrDEWZDcL+Pkp6IFYydhGW299E2JaNoTvGXXO3LwOnFqfCPjBZYU4JKxqaFATFubfpkyuWIEJh2L6bcEYlxzZ8hbCnlModd7HsyVtIb98G6t/QMS0ryaAow8Fv/6raZCjKmVSCOYVJKanx1jDHM4H2GE07MHQrvpx5w08Ewth//AQyN4PYDesmNNxgKkkJW3tzmWsboNAaSmosijISF1aWGgW8KuqJMnxqvVyyANHWDi3H6S3dQsyexaftf+G32xqTEquiKNOvJ9xDui0dMfR+6gugNx3HrCjBEe5EAhFrRlJie60vm3+pW0ZbxMFHcg+x2DHLh8TNYKVmBoYwaULt9gJ43baFXLyYzc+OeN6Sn59olfHaa/T9+c/THJ2iKNPNrD2KJsEYa8CflLj8jQTs41cvSwl7fJkssveip3hLKYHAiZXQuVYwA8wpRhbmIZ7cCqZJ1FVI09ov0rrsYzh8zSx99nZWPbyZ4gM/xtV7WFU1j0ATgjctKWBXcy/Vx9VNfEU5nUowp7ATgRP4Y/7x+y9LSWbTdny5C5C6ddgp+x9fQQyEiL5tA9gsUxjtxBwLu2iOuNjo7kh2KIoyqvWxOQRElDfEGXemF89DatqpKua5N7GOQ0QbXqahy5+cYBVFmVY9oR7SbGknf60fSgwFNecmBvxFLelIbXrfb9sjdr7XPI/vtcwnVw/x+cLdVDlTu4ekMrY5ZqIKvkF0JzmS1GBzz2WfWYne8AcwR67q9mzejH3JEjq/9W1ibW3THKGiKNNJq2tM/GSMAX+2SA/WuH9CA/6aI068cduMee90SMu5t8iAxLC/DasRnd2wa//JY/1Fl1N3xQ9oXXYnpsVF+d7vsvKxG1h/3xoWv/Bhynf9B/l1fyL9xGu4vIexBdsRxuxtv7VpYR42i6aG/SnKGZKfcVRGNdEBf46+Fuz+Dk5UXjnsuFZ/Asvz+zA2LEAWJbcf5JCtvblYMFnrUn0FldRVaeaQZTrZKmq4XJ62K8DlhMoy2LYLbrme3pLNZB97lE+a9/OrV9/C19+xInlBK4oyLTpDnWTaT7XA0KvrkEJglhVjr+skMo0D/tojdu7vKubF3lx0YXJjRiNvTm9O+eorZXwZ0kGatCcG/andyixxRvjf+Nv4afT7hE+8iFGy5axrhlplnPjSlzj+pbso++UvTu00UBTlomKta6HPBdaM0d9z3YFE8i/gGD0JPWSPL7HzKNUH/A2xSwuB82iRAcDCCmR2JuKx55HrVp7c4Sx1G/1FV9BfdAXWUDeu3kO4e4/g7K8j48QraGb8rKUMi5O4LRPD6sa0uDAsTkyLE1N3YlhciZ9bnIPHXRgWx+CPLkw98fNh508ec8A4LUKTyWO3cPm8XB7a28bnr19ChtM6/oMUZRZQCeYUVuOtAaAkrWTM6zKaXwcYPuDPMLH//BlkmpPYVcumLMZzEZeCV/pyWO7swa2f/QalKKlCQ7A2Xspztjo6TR/5nKpWlMsXoz34FHL/IeSa5XgrbmZDzW/48e5n6HvLYjJdtiRGrijKVOsMdrImf83JX2vV9ciSfLBZsYe76PUsmPIYzkwsX5XWxrXprWTo5/llU0k5AkGJkUG9rm7IA7h1kwbrMlpECSUN9xIqvmbE5IMlL4/M226j9ze/oe9P95H1ntuSEK2iKFPN2XCc2kLBIm30z90ufyOmsBCyjz0IEGCXL5MSa4CsGTIU18F5VjDDYBXzKrSnXkQePALLz57hFHPm0u/cRH/xpsQBaWINdWENe9FjPiwxH3rMhx71o8f8aEYYzYhgifQjQl1oRmTwv2jinHnusSaSzU5M3XFGsto1PJl9WuLa1J3EbelE3UVEnQVEXYWJZPUUuLaqgK01ndy/q5UPX1ExJc+hKDONSjCnsJreGvKd+cMGCY0ko3kHwbRCoq7sk8esT+9Bb+oi8q7LwJ4ad9R2+zIYMKxs9Kj2GErqWxcv5XlrHS9ptdxqrj11orIM6XEjXtqBXLOc3pJryDz2KHeG7+PeHW/jzmumPrmkKEpyRI0o/ZH+UxXMhol+qB5jzVIscT8WI0R4Cgf8+eIW/tBRwjPefJVYngVKzQye1+sIEsWFunlZ5Yjwg8Db+F7gx+gdr2EUXjnidZ5rriH4xht0fOtbuC+9BFt5+TRHqijKVDLDYVytXpov0VgyRpWr299I0J4PQh9zvaChcSTgYXN662SHOmUc0opPu4D3/qoFyL/uRDz+AnKEBPNZhEbMVUDMNX4/6xFJE2FGz0o8C+O0Y2biR3HymqFfR08msPWYH2u4Z8THjSZmyyDqKiSUuZBA1mIC2Uvx5a3FtLrP77UMqsh1syDfw707mvjQ5XPVjhlFQSWYU1qNt4bStNIxrxHxKGntB+mce9mpg8EItgf+ijGvEHPx2NXP02lrby5pWnTGbD1SZrdM6WS+kcuLei3vZDXaUMt6TYPli2D7HujpReZk0Vd5MxuO/JrfvvYIsU3/ilVP3S1diqKcv85gJ8DJBLN2rBURigwO+EtUmkZsk9+SypTwjDefeztKCBoWrvAc560ZLSqxfJGbY2YiBRwT3SyVxckOJ+mWOkN8u/8yvub8C46aXxDKvxRG6HcuhCDn7/6O9rvuou1Tn2LuvfcibCpBrygXi0htLZopaS0cvYhKmHFcgVa6MsZvX7ffn4GBxtIZ9B3VIS10cAHzX3QduW4l2guvIY/Uw+L5kxfcSISG1B0YuoORu+hfoJMJ7Ch6zIc13Isl4sUa8WINe7GGu0lv30Zu46MAmMKCP3cFfSXX0F3xNiKesXMuo7lmUT4/feUoe1r6WFOWGi1JFSWZVBYkRQVjQVp8LeP2X/Z0HkYzYgzknnpTsD61GxGIELtm2cmeSsnWFbWxcyCTDe4OdKGaCSozw/r4HHpEgAPi+LDjcvliQCJeTbSn6S25hoA1hw9E/8gT+4+PsJKiKBeDrlAiiZzlSHyJ0KrrATDnlmIfSjBPcgVzwND5VtMCfnp8LsWWAJ8r3MVt2Q0quTwLlBqJnqANqDYZAEudYUw0XnC/Ey3YhqXlsVGvteTkkP2hDxE+WE3n938wjVEqijLVwocOAXCi2D7qNc7gcTQZn9CAv12+DJxanEr7wKTFONUcWAmdb4uMISuWINPTEH94GExzcgJLlqEEti2dqLuEQM4y+os30V3xdk4s+RDNqz9D3ZU/4vDVP6NxzefpKb8eS6Sfsr3fY82Dm1j69G1kNz0B8tx+Hy6pzMFu0fjzzpYpemGKMrOoBHOKqu+rRyLHTTCnHd+HRODPqUwcCISxPfYG8YUlyKLsMR87nR7uTry5X52mkm/KzFFlFOCWNp4Th4efyEiH8jnwyutgmkjNSt+8m1mv1fLGCw8gpbqJoigXo45gosXTUAWzXl2HTHMjszOwh7uQCCLW9El7vtawg8/WV7HLl8m7sur5RP5+SmzBSVtfSW1ubGSbLhpEd7JDSQnZFoMSa5Q/h9dieOZjq7sHYoFRr3etW4dn82a8v/oV/pdfnsZIFUWZSuHqasIuC4GM0RPMLn8jAMFxEsxSwm5fJosdvTOqCMohLYSIYV7IFFirBXn1JYjWE/Dy9skLLoWZVjeBnOV0LngPxzZ+jdorfkjH/Hdj97ew6OWPs+KxG8lqfibxB2MCnDadjRXZPLLvOKHolNRmK8qMohLMKaqmNzHgb7wWGWnH9xHMKMGwJvo0257YhQhFiV9VNeUxTlR/3MJz3jzWuzvJniGDExQFwILG2lgpu0QzvQxP6sgVixG9/VCd+LvaX3I1A5Yc3tH/W7bVq2SAolyMuoKJStKTCeZSoq93AAAgAElEQVSDdZhzS0AIHOFOItaMcXs9TlRT2MnnG6rwxXU+kb+fa9KOp8qmJGUalZoZ1IvOZIeRMlY4Q+wcsBMsvgliPqwN9455feZ734t1zhyOf/ZzxDrV76OiXAxC1YfoKHJi00ZvkeEONBHTXUQtaaNeA9AYdtEbt7HU4Z3sMKeUQ1qQAsLELmyhhZXIOcWIB56CwOy7gR1z5tFd8XbqL/surcvuxBLtZ/FLH2XhS/+IJTyxPxNXL8onEDF48uCJKY5WUVKfSjCnqBpvDU6Lk1xn7qjXiHgUT8chfLnzEgf8IaxP7sJYXIosTJ0eQI93FxCTGtemq60jysyzIT4HU0heFDXDT8yfi3Q5EVu3ASA1K/2VN7NWq+Olp+9PQqSKoky1rmAXVs2K2+pG9A6gHe/ErEjcCLaHOyetPUZn1MZXjy3CIgz+rXAvCxz9k7KuMvOUGZn0iADdF9Jr8yKy0hUkYGrsNyuIZ6/F2vgAIjD6YC7NZiP3zjsxAwGOf+YzSENVmCnKTGZGo0RqazleZMcuRk8wu/zNBO0F47aL3OVLtCKqcs6wBDOJ1x680DYZQiA3XwbBEOLhZyYhshlKaPQXXUH9pd+mfcF7yWp9gZWPXUfG8VfGfejiwjQK0+3cp9pkKIpKMKeq2t5aSjwlaGNNxu08Mqz/su3JRPVybFPqVC8HDY0negpY6eym0BpKdjiKcs5ypZv5Rg7PazWYnNaXS9dh5RLYfxg6ExXLvjlX06/nsKXzVxxo6UtSxIqiTJWOYAdZ9iyEEGjVdQCY5SUgJfZwFxHbhSeY++MWvnJsERFT4+N5B8i1hC94TWXmmmfkAFAtVIsxgBWuEALJtn47seIbQLNh3/dtkKMnjq3FxWTdfjvB7Tvo+dnPpzFaRVEmW6SuDuJxWor0URPMmhHGEe4g4CgYd73dvkzm2Hxk6BdYCTzNHDIx4PSC+zAD5OfCiiWw9a/Q1Hbh681kmk7P3Js4tvGrmJqdJS/8LXkNfxnzIUIINi3MZ/tRL809s68KXFFOpxLMKUhKSW1v7bj9l9NP778cjWF9Zi/GwmJkweQOGLoQT3kLCJoW3pKh7ugpM9eGWBndws8+MfxDl1y1FIRAPP9a4tealb7Km9ig1fD8U39ORqiKokyhrmAXGfZEtZNeXY/Udcw5hVhiA+hm9IIrmA0J32maT0/MxkfzDlKs+i3PegUyDZe0US3U1luADN2k0h5le78NaU0nUvoO9P7DWI+O/Z7rvuoqXBs30vXf/01w955pilZRlMk2NODvaKE2aoLZFWhFIAmOk2D2xXVqgp4Z1x4DwEEiwRyYjAQzIK/cAC4n4v9+B2HV0jKcNpejG79KIHsp8//6aQpq7hnz+k0LchHA/btUzkOZ3VSCOQW1+dsIxAITGvA31H/Z8toRhD9MfOPCaYpyfL64zsNdhSx29FJmU1s7lZmryijAI208L44MP+Fxw6J58OrrEEpUGQbmXE2/nsWlLT+noUv9uVeUi0lHsIMsR6IFlX6wDnNOIVgtOMKJ3sxh64W1p3q0u5BDwXRuy6pn3gyaZq9MHQ1BpZFNtTiOvJBhTheRVa4ge302gobAyFpNPHMF1rrfIHzHRn2MEILsD34QS3Y2bZ/6FPHe3mmMWFGUyRKurka4XLRmxLAL24jXuAJNAIkWGWPY589AIlg6w9pjADjkYIsMMTkJZpwO5A2bobMLce+Dk7PmDCd1B80rP8VA3loqX/8yRYd+Meq1OR47y0oyeHBvmxr2rsxqKsGcgoYG/I2VYB7Wf1lKrE/txszPwCzPm64wx/W7jjkEDAvvyDya7FAU5YIMDfvbLZrpYfjEerl2OSIcgdfeSPxat9Ez9yY2akd49omxt1QpijJzSCnpCnUlBvzF4mg1x5DlxUCi/zJwQS0ymsJO7u0oZaWzm43ujkmJWbk4zDOy6REBulQfZgBWOkPEpWC3zwpCECl9J+hOHHu/CbHAqI/TXC5y/vEfiXd1cfzTqh+zosxE4epD2MrLCMgwLmEf8RqXv5mIJY24xTXmWrt8mbi1GHNtvqkIdUo5B1tkXHAP5tOVlcAlaxGv7YTtuydv3RlM6jZaVvwz/fkbKN/1TTLbto567eXzc2jxhtij2iQqs5hKMKeg2t5aBIJST+mo1yT6L0fx5cxDO9yK3txFfMOCcQcZTJeaoJtnvflcndZGqW30D/uKMlNsjJchkTynHR5+oigfWVyAeP5VMBM9msPlm+nXMlnZcDctXrXFfTYTQrxVCFEjhKgXQnxuhPN2IcSfBs/vEELMPe3c5weP1wgh3jLemkKIisE16gbXtI31HEKIuUKIkBBi7+B/d0/d78TM54/5CcVDZNoz0eqbENEYxtzE+7Qj3Ikp9HGn1Y8mZgp+1FKJU8R5b3ZdqryVKymiUvVhHmapM4RVSLb3DyaXrB7C5e9DBJpx7PkKmKP3UrXPm0f2+99P4NVX6frv/56miBVFmQwyFiNSU4O1vJygGcE5SgWzO9A0bnsMU8IeXwZLHL1oM/A9d9KG/J1BXrYWWVqI+O390KpaMwGgWWhb9o+E08pZ8Mq/4BgYebfM+rnZ2HSNh/fM8j7WyqymEswpqNZbS4GrALtl5LuyAGkn9iMR+HIqsT29G+m0YSwrm8YoR2dIuLttLpl6hBsympIdjqJMimzpYrGRz/PiCDGGVz3JtcsRnT2JgX8k7nZ3ld/EpdohHntUVTHPVkIIHfhf4DqgCnivEOLMKawfBnqllPOB7wPfGnxsFfAeYCnwVuDHQgh9nDW/BXxfSrkA6B1ce9TnGNQgpVw1+N9HJ/HlX3S6gok2GJmOTPSDgwP+KkoAsIc6iFizYIzBvGN5pLuQY2E3782uI22GDRpSpl6+9OBRfZhPcmiSxY4w2/tOJZfM9IVE57wbvWcPtoPfhzG2KLuvvhr3pk303P1/+J57bjpCVhRlEoRrapHRKHLuHCQSl3b2d2U9FsAe6Rm3PUZDyM2AYZ2R7THg9CF/k/yZQdOQN7wJrFbE9/4POromd/0ZSup2Wlb+KwCLtn4EPXp21bvLZmF1WSaP7j9BzDDPOq8os4FKMKegI94jlKSVjHlNov9yMWZ/DP2NeuKrK8FqmaYIx/ZETwFNYTfvymrAoanth8rF49J4OQMizHZxRtuXBRXI9DTEEy+c/FIbrdjMgJbB8oa7aepRVfyz1AagXkp5VEoZBf4IvO2Ma94G/Gbw5/cDW4QQYvD4H6WUESnlMaB+cL0R1xx8zObBNRhc8+3jPIdyDjpDiTYYWfYstOp6zKwMyEhULDvCnYTPc8DfQNzCA11FrHB2s9LVM2nxKhcPgaDSyFF9mE+zyhXkSNBKb+zUP2XxnPVEi96Kte1ZbNU/BDM+4mOFEGTfcQe2ygqOf/ZzRI6O3rtZUZTUET6wH4Do3EIAnCO0yHAFmgEIOArHXGu3LwOBpGoGDviDRPs+XYrJ68F8unQP8t03QtxAfOdu6J6Zv0eTLebMo2XFJ3D6jlG+6xsjXnP5/Fy8gSiv1ndPc3SKkhpUgjnFBGIBWv2tYw/4Mw08HUfwZ1dgfW4fCDDWzZ++IMfQEHLxu/Y5LHX0sMqp/mFVLi7zjVzyTDdPiUPDT+g6cv1KREMT1DQAiTvd3eU3coV2kIcfVcMyZqkS4PRx0q2Dx0a8RkoZB/qBnDEeO9rxHKBvcI0zn2u05wCoEELsEUK8JIS4cqQXIYT4iBBipxBiZ1fX7K1k6QwmEsyZtgz0/TUnq5cxDezhbiK28xvw9+fOYsKmzs2ZjZMUqXIxqjSy6RVB2lHDHyHRh1ki2NE/PMEUK3gT0YItWFsex/HG5yA68u+XsNnI/fg/gabR+k//hOFXN4IVJdWF9h9AS08nkOkAGLEHs3swwRyyjz2XaJcvk7k2Hx595BtRqU4gcGCd9BYZJ+VkId91A4TCiO/+H3Sq7/UAweyl9JTfQEH9faS3bzvr/Ko5mbjtumqTocxaKsGcYup6E9tux0owu7xH0eMhfBllWF6uxpxXhMwYe4jBdBiIW/h20wI8WpQ7cmpVD0nloqMhuDRWToPWRT2dw08uX4x0uxCPndpuG67Ygk9LZ9XRu2noUsOZZqGR/hU8s/xwtGsm6/hYz3ECKJNSrgY+CdwrhEg/60IpfyqlXCelXJeXlzqDZKfbUII5uy+O5u3HrEy8T9sjPQhMwueRYG6P2nmqJ59L3e0UWVW/dmV08072YVZtMgAWOsKk6QYv9Z2RYBKCWPH1hMvfh9ZbjfOvH0Pv2jniGpacHHLvvJPosWOc+OIXkWO01VAUJflC+/djq6jAL8MAOEdokeEKNBG2ZmHojlHX6YtbaAi5qZqh7TGGOKRl6hLMAAW5yHdeDz4/4qs/gH2Hxn/MLNBZeQsRZwGV27+AFg8PO2fVNTZW5PD0oQ6C0Zl580JRLoRKMKeYGm8NMHaC2dOe+Mc9eMKK1hcgvmrudIQ2JkPCf7XMozdu5e9zD6kekspFa028BLvUeVo740OW1YJctwJxuB4aEr3Hpe6gp/wGNmkHePjRh5IQrZJkrcDp/5iXAmdO6Tp5jRDCAmQA3jEeO9rxbiBzcI0zn2vE5xhsv9EDIKXcBTQAC8/ztV70OoOduC1unIcSf7/NiqEBfx0AhK3nnmD+Q3sJupBqXoEyrlzpJsN0sF+0JjuUlKALWO8K8HKvnfgIeWEjey3hBXeCGcex8/PYd34J4Ws86zpHVRWZt96K7+mn8f7yl1MfuDIjTPOA3l8LIY6dNnB31VS/vpnI8PuJHj2KvbKSgJm4ITtiiwz/+AP+9voykAiWztD2GEMccgormIcUFyDveCeke9B+9EvEg09BfHYnTqVu50TV3+H0NVG6/0dnnb98fi6hqMGzhzqSEJ2iJJdKMKeYw97DuK1uchw5o17jaa8m6shEbGtEuu2YC4qmL8ARSAm/bZ/Dfn8Gt2XVUW5XlZrKxcuBlTXxUraJo/RxRsXhqqVIp2NYFXOw4lr8Wjrrj/2Eg2390xytkmRvAAuEEBVCCBuJoX2PnHHNI8AHBn/+LuAFmSijewR4z+CX2ApgAfD6aGsOPmbr4BoMrvnwWM8hhMgbHBqIEKJy8DnOaDCuDOkKdiUG/B2oRTrtyMJENbc9nKhsPtcWGU1hJ6/053JNWhuZlin+gqjMeALBEiOfvaKVCLP7y/2QDe4AfXGdfT7riOdNdzmhxZ8mUnwjuncfrlf/Hsf2f8HS+jTETrXESLvuOpzr19P5vf/C/9pr0xW+kqKSMKAX4NOnDdzdO4Uvb8YKH6wGKbFVVuIzQsDZLTKs0X5ssQEC4wz42+3LJE2LMsc2s7+z2pniCuYhmenI970duXwx4rHnEHd9B3buH3OY6sUukL2U3uKrKT70M5x9dcPOLS5MI9tt47H9aseRMvuoBHOKOdxzmLK0Msaav+Rpr2bAMQd9VwPx5eWg69MY4dn+1FnCo91FbPK0cZlH3alTLn6Xx+ZiYJ5dxWyzItcsR+w/DE2J3ltSd9BTcTNX6gd54IE/qC24s8hgv+OPA08Dh4H7pJTVQoivCiFuHrzsF0COEKKeRJuKzw0+thq4DzgEPAV8TEppjLbm4FqfBT45uFbO4NqjPgewCdgvhNhHYvjfR6WUM7ucZwp1BDvItGeiHajFnFsCWuJ92hHqJKY7x9yOO5JHugqxCYMtaaoiVZmYpfFCoiKuqpgHrXUFsSDZ2jvG3z3NQrzgGoJLPk+0+AZEsAv7ge/iev4WHNv+GWvdPeh9h8n50N9iLS6m7ZOfItrSMvp6ymwwbQN6p+G1XDRCgwP+bJWV+E9WMNuGXePyJ3YDjVXBbEjY48ugyukdehufsRzSQmAqhvyNxGJBvvVqzFuuA9NE+8lvEf/xP7B9N0Qi0xNDiulY8B5M3U7Z3u8OO64JwYaKbF6s6cQXVru6ldlFJZhTSMyMUddXR1l62ajXWAPd2P0d9De7EIaJsbJiGiM82/2dRdzXWcIl7nZuzWpIaiyKMl1ypZsqo4BnxCHCnPHBYc0ypMOOeODJk4cC5W9iwJLDDV0/54XD6ibMbCKlfEJKuVBKOU9K+Y3BY/9PSvnI4M/DUspbpZTzpZQbpJRHT3vsNwYft0hK+eRYaw4ePzq4xvzBNSNjPYeU8i9SyqVSypVSyjVSyken6/dlJuoMdlIQd6M3tp1sjwFgD3cQOcf2GN6YlZf7crjU3Y57hg4YUqZfpZmNS1p5XTQmO5SU4NIlK1whtnrP3iZ/FquHWMFmQks+Q2jhPxHL3wxRP9b6e3Bu/2c8r91O8RYBkSCtH/0IZkAN/ZvFpnNA75BvCCH2CyG+L8QIfR8UwvsPYCkoQPd4CJiDFcxn9GB2BZqRCIJjDPg7EkgjYFpYNsP7LwM4pYXQdFQwn25eOfIDt2K+eRN0e9F+di/iX7+C+Pkf4NXX4UQnmOb0xpQkhi2d7vIbyG55Fk/X7mHnLq3MIWZI1SZDmXUs41+iTJejfUeJmTHK08pHvcbTfggpIbJ3ALMkG5mfMY0RniIl/KWriHs75rDe1cHt2bUz/i6wopyLTbFKqi3beFHU8la59NQJhx25YRXayzuQR+ph8XykbqN//ttZe+QXfPHRe9i06FNYdXV/T1FmClOadIe6md+RSCybFafaYDtCnfS7Rr8xPJInegowEVyTpqaMKxOno7Ekns8uSzNxDCwkdwdbKtjoDvCTrjyOhXQqnMb4DxAC0z0X0z2XGNdBPIDuq0P31eAYqKFkY4iWl6Mcf89llNz1ccSKd4MjOZ+1laSZigG9I33oG1rz80A7YAN+SmI30lfPCkqIjwAfASgrO7f3nItBaP9+bJWVAPiNEDoa1jNSGW5/EyF7LlIbuW0OwOu+TCyYVDl6pzTe6eBgGnowj0TTYGUVcsUSZOsJRHUt7DmItm0XANJhh9xsyMmEzAyw28BqRVotYLWA1QqWwR9tgz9arWCzJq7NSAOPO/E8Ka6n/DqyW56lfPe3qH7zH2FwF/qCfA+5nkSbjFvWlI6ziqJcPFSCOYUc8R4BGLOC2dNRTbDfiTjej3H92ukKbZi4FPysrZxne/NZ5+rkjpwalVxWZp1yM4tyI4vHtQNcayxBP/27w5rlyD0HEfc/jvziJ0AIBkquov/oY9weuIc/vv4u7rg0ubsPFEWZOG/YiyENyhoDSF3HLEvMPtDiIaxx3zlVMIcMjad78lnp7CbPGh7/AYpymqVGIbusbVSLE6yU6kvrhsEE84u9Diqc51F1bHFjZK3CyFoFUqLN7yRbvoj35VZ6vvvv5K68Czb8A1zxL+A890Geyox0LgN6Wyc4oJfRjksphxq1RoQQvwL+baSgpJQ/JZGAZt26dbOq31qso5N4RweeN70JAL8ZwqU5hreUlBJXoIk+97xR15ESXh/IYqGjD4c2gRtSKc4hLYSJYSLRRry3McWEgDnFyDnF8JarkN4+ON6BaO8Cnx+Od0JdY2IoYNxAO4fKZqlpkJkOxQVQXIgsLYT5FZCfczKJmwqk7qCr8h0UH/kVmcdfoq/kagCEEGysyOHp6nb6gzEyXKPf9FCUi8mEEsxCiLcCPwR04OdSyv8847wd+C2wFugBbpNSNg6e+zyJQQgG8Akp5dODxxsB3+DxuJRy3SS8nhntsPcwdt1Oobtw1Gs8Jw7ScyIXqUuMpXNGvW6qBAyd7zbPZ58/g7ekN3NjRqNKLiuz1pWxCn7n2M3ropFLZeWpE1YL8vL1aE+9iNx1ANatAM3CwIJ3UlX9Y379zG/oW/kFMl220RdXFCVldAW7ACio82KWFiSqbADH4IC/sC1zwmu90JtHwLSwJV310VXO3QIjF5vUeV00qgQzkG+NU2mPsNVr52+LL7CthRBIRwHu699NuP9xuvbX4FhUjOe1H8KuX8GmT8Mld4KmKscvcieH6QJtJIb2ve+Ma4aG525j+PDcR4B7hRD/BRRzakCvGG1NIUSRlPLEYA/ntwMHp/oFzjThgwcAsA9WMAfM0Fn9l+2RbixGiMAY/ZfbIg46og6uzro4+qw7pAUpIEwMF0n+TiEE5GRBThZy+eIRL5GmCXEDDONk0vmsH6NRCIQQ/gAM+KCzGw7XoxmJGwIyPQ0WVSaeY8USSPNM56scUV/JNeQ2PUHZnu/QV3zVyQT4pfNyePzACZ451M6t66Y/b6MoyTBugvm0qbfXkrgr+4YQ4hEp5enTrU5O0hVCvIfEJN3bzpikWww8J4RYKKUcumV4jZSyexJfz4x2uOcwpZ5SNDHydhARj+DsrCdYX4y5oAgc0/tG0hm18Y3GhRyPOLg9u0YN9FNmvSqjgFzTzaNiP5fICsTp1QNLFyJ37kM88ARy1VKw6AwUXcbA0Uf4aOCPfPept/H1W1YnL3hFUSasM9iJJS5JO9qBcfmp3UNDCeaILXtC65gSHuspoMLWT6XdNyWxKhc3KzqLjXze0Bv5MJehqXEqbHQH+JM3C29MI9t64b0/hRBkv+stxLp6aXu8l7lf/TL29sfgmS/B4cfglp9C1ujt7JSZTUoZF0IMDdPVgV8ODegFdg7OUPgFcM/g8Fwvie+7DF43NKA3zuCAXoCR1hx8yt8LIfJIJKH3Ah+drtc6U4T2HwBdxzrYGsRvBHGd0ar61IC/0Qu1Xh9I7EJY7uyZokinl4PEze4g0eQnmCdC08CmAeNX8w4r0TdNZE8ftLUjWk/A4Tq0N/YhhYDKMuTG1bB+JaSnTVXkY8eqWeisfAel1XcPq2KuzHWTn2bnsf0nVIJZmTUm8ql0KibpKmcwpckR75Ex22O4u2oJd+jIgEF86fT23qoLuvlsQxXdMRt35h9UyWVFATQEV8YqOKp1s1+c0UtV05BXbkR0dMPW1xLHhEbfwlup1E4Q2XUv+1r6pj9oRVHOWUewg8p20GIGZuWpLwn2UAcSQcQ6sR6tBwLpdEQdXJV25m5rRZm4ZfFCBkSYanFi/ItngSs8fkwET3Y7Jm1NzWYl72/eBgJaf/gIxiWfgSs+Be0H4CeXwaGHJ+25lNQzzQN6N0spl0spl0kp3y+l9E/na50JQvv3Y5szB82WSKL6zRDOMxPMgSZMYSFkyxl1nTd8mcyx+ciyJKFv8RRwyEStYIBIkiOZYpoGedmwqgp54xbkR+/AvOOdyEvXwoAf7d6HEJ/6GuL7P4c9BxMV0tNsoPAyoo5cSg7+5OQxIQSXVObwWn03vYGL48+cooxnIgnmqZikC4kbU88IIXYNDi0YkRDiI0KInUKInV1dXRMId2Zq9bUSjAcpTx9rwF81/U1OpM2SqGCeJtv7s7jr6GIsmPxbwR4WO1RSTFGGrI2Xkmk6uF/sRp45A2ZeObJiDuKhp6FvAABf3jr8afP4pPUBvvzgbgxzVrXRU5QZqc3fRlVbYoeCOffURyBHuJOINQMpJrZl/jlvHm4txiqX2rylnL8lRj4uaeMZcWj8i2eBufYo8+wRHu5yTuq6lqx0cm+/kWhbB8e/+ytkxVVw848goxTu+wDs+OmkPp+iKGeThkH4wH5sFadml/iN4FktMtz+ZoL2fBhlJ3BfzEJt0HPRVC/DqQRzkFiSI5lmQkBhHly+DvnBWzE/+O5EBXNTC9r//Brx2W/Co89B/8C0hSQ1Cz3l15He+Qaert0nj19SmUPclDxd3T5tsShKMk0kwTwVk3QBLpdSrgGuAz4mhNg00pNLKX8qpVwnpVyXl5c3gXBnpkPexJeEsrQxKpjbDjLQ5sJYXJqYwDrFpISHugr5TvN8SqwBPl2wh0JraMqfV1FmEgsaV8fmUad1clCcUZUoBHLLFRCLI+579OSx7gXvpohuVnQ8xL2vN09/0IqinJM2fxsr2iyY+dmQ5j553B7qJGKdWP/lgbiFHQNZbHB3YBXqxpJy/qzobIiVslM004VqtQKwOW2A6oCN+uDkfj52zCsj68ar8W/fR/cfHgdPAbz5GzBnAzz5aXj+q4kPzIqiTIlIfQOmP4B9wYKTxxJD/k6rYJYGrmDLmP2Xd/kykQhWXEwJ5qEWGWKWV8fmZSM3bUR+5HbMt70F0tPQHnoK8W9fR9x9D9Q0TMu/070l1xC3eig5ePfJY3NzXBSmO3hsv9pxpMwOE0kwn8skXSY6SVdKOfRjJ/Ags7x1xpGeI+hCpyTtzOLwQVIidtUgowJj2dS3x4hLwd3H5/Lb9jJWu7r5RP5+0vRZdndUUSZoXbyUjNGqmLMyYMMqxI49cKQegED2MvxZS/hX28P86Mm9tPeHkxC1oigTdXyglfmNUcx5p+0ykiaOcCdhW9aE1nipL4e41LjMrapYlAt3SbwckDyrHU52KCnhqjQ/GpJHJ7mKGcBz2Wrca5fS/btH8W3bCxY7XP0FWPAWeOV7sPWbk/6ciqL8f/buOz6u8kr8/+e5d3rTaNQly7Jky70XcMEGDJjQQyAbQhohm2RJ8t1fyiabzm6yu2kECElIAqGEEmoAYwOmBBv33iTj3tR7m9H0uc/vj3FBWHLBHtXn/Xrpxet157lzj+LYM/fc85yTFNqWrAa1jBp14ljnh1pk2IO1aEaMoLXnBPMmfzrpephh5vMcBtqPHK9gDjHEE8zHaRqMLkb+y/UYX7oNpk2Esj1ov/4T4v9+DzveT2miWeo2WgoX4at6B3vbfuB4mwwf6w420xwY5K1MFIWzSzCfmKQrhLCQHGLw6ofWHJ+kCx+YpHvs+G1CCOuxqbmlwEYhhFMI4QYQQjiBRQzxibl7WvaQ78rHrHXf9N7aXk3nAcChYxRnpzSWzoTO/x4Zzdst2SzyVPDFjN1YtPMfmqIog5UJnUtjJezV6rvtiSkvnopMcyOeeik5JVkIGkZ9inTZxh3yFX70ShlSVUApSr+l7T+KLWJgjDr5gNcSaUGTMcJnMeBPSni7JYtiSwf5lmAqQ1WGCK+0Mz6Rw7tiL1HifR1On0s3JWvID1sAACAASURBVJjpDPJqo53EBf44FULgu/lKLMNyqf71I0SO1oCmw5xvQOkiWPlr2Pzohb2ooigABLduRUtLw5SdvP81pEHQiHQZ8ufoTO4GDPZQwRwxBNv9HibZmxHd7a8eoGzy5JA/5UN8XuTCucl+zVfOh6ZWtAceRfzXvbBpOxipyW20FC7C0K3kv//wiWOzSzJISMky1SZDGQLOmGA+1lP5+NTb3cDzxyfpCiFuPLbsESDj2CTdbwPfP3buLuD4JN1lnJykmwOsFkLsADYCr0kpl13YX23gkFLyfvP7p2+PcWQ7gWobcmxe8ulcijRELfzw4DjKA24+49vLTd4jaIPog1hRUmVWvBCPYeNFseXUKmazGXnFJYjaBsTSdwAIeUfTnjOHr+hL2b37fZaorVOK0i8FY0EKDyT7+CVGnvyctoWSNwrh0wwUOm5f0EVVxMFcl7q5UC6cubERBESENeJgX4fSLyx0d9AQ09nYbjnz4nMkzCYyP3cjwqRT+bMHSQSCyT6gs78Ow2bCa9+BPa9f8OsqylAX2rIVa2kp4lhmOGiEkUjsH2iR4QwcJa5Ze2xZtTOQRlTqTLa39ErMvcXG8R7MKsHcI7MJpk1A/uttGNdcBsEQ2p+fQvz4N7B28wVPNCcsHtry5pN5+FVM4WQ7luE+B/leG0t3qHs9ZfA7q0zlhZ6kK6U8JKWccuxnwgcn6Q5FDcEGWiOtDPf0nGC2rNyANATRyaNTFsfBkIPvHxxPU8zC17LLmeuqT9m1FGWwMaNzeayEPVo920XVqQtGFiEnjIbX3oWjydfrS29DF/Bz14vcvbhcbZ1SlH6oJlDDhApJZ5YL0lwnjttPJJjPXMH8TmsmVhFnuqMhZXEqQ0+J4SPXcPO6Vo6B2ml2sTOIS0vwagraZACYvG4yP3sDsbomqn/1V2TCSFYyL/g+ZIyCf9wJjftScm1FGYpiDQ3EqqtP6b8MdGmR4eg8mqxe7qE8eVOHF5uIUzrIBtWb0dGlpnownw1dh4ljkV/8F4wbrgRpoD3ybLKiuXzvBb1US+EiNCNKzv5ngeNtMjLYcLiZBr9qi6gMbqkrhVXO2u6WZP+8Ik9Rj2sSWyrRnWAMO3Ol1EdRHnDz00Pj0KTBd3K2M3aQfQArSm+YFR9OpuHgaW1Dtzf7cuE8cNgRf30WYnFi9iyai67livhKRkZ2c/eru/ogakVRTqemo4pxlZJwSX6X47ZQLVGTi4RuO+35UUOwtt3HNEcTNtVuSrmABILLoiOpFK0sFyqxadEkC9wB3my20xxLzS2OrXgY6TctpHNzOY1PLk4eNNuSPZk1M7z4RYipBIKiXAihbdsBuiaYE8kE8/EWGcKIYg/W9th/2ZCwye9lvL0F0yAcsGvHpCqYz4WmwdhRyM/fmkw0dwbR7nsYce9DUNd4QS4RcQ0jkDGJnL1PIYzkDKvZxRkYEpaVq51syuCmEsz9QHlTORoaRe7uE8xacwOhygT6KHePT2bPx8YOLz8/MgavHuY7OdvJM6v+kIryUZjQuDo6hirRxnti/6kLbFbk1QsQNXWIJW8D0DTiRmLWdH7reY6lO2tYuvPDM1QVRelLLWVbcERAG1Xc5bgtWHtW1cub/V5CholZTlW9rFx4UxJ5FCd8PKNtIoBKbH7c20ZUwlO1jpRdw3XxZJwXTaL5uTfwb9yZPOjMhHnfhPpyePunKbu2ogwloa1bERYLlqKT98gnK5iTrXAcnVUIDDptud2+x4GQk/a4hUmDrD3GcXZpJoDaAXnOhEgmmr/4KYzL58KBI4i7fwuvvgWx859r0Fx4NdZQPb6KtwAo9DkoTLezZIe6z1MGN5Vg7gfKGssocBdgNVm7fd257E0wBIkJPbfQ+KiWt2bw66OlFJgDfDN7B16TegKqKOdjYiKXwoSX57UtRLobvFRShJw4Bl5/F/YdwjDZaBj5SYpCu/hX71Z+9HI5DR0qSaAo/UViSzKB9MEKKqSBLVxH6Cz6L69syyRNjzDaqnYGKReeQHBDdDydRHhe29rX4fS5AkuMua5Onqlz0plIzRARIQS+Gxdizs+m5jePEmtsTb5QeBGMuwk2/kX1Y1aUCyC4dSuW4hEIk+nEsU4jWQjlONaD+UwD/jZ1pKMhmWAbnAlml7TSRqivwxi4TDrMnIy881Mwqght8VuI//otHOmm3eE5CGROJWLPIXfP4yeOzS7JYPORVura1X2eMnipBHMfk1JS1lRGcVpxj2v0VVsxO+MEi0Ze0Gsvbcrh91UjKbW28f+yd+LS1RRyRTlfAsG10bG0iiCvi/Ju18gr5oHXg3joaQgEactfQMg9gm/zNDIa5PsvlSHl4NvGpygDkbP8MA0+HZHmPnHMEmlBN2JnHPDnj+ts6UhjpqNBDcxVUibf8DA7XsTbYjeHaerrcPrcremt+BMaL9SnropZmE1kfuZ6ZCxO9S8fRiYSyRdm3AG+EnjtWxDuSNn1FWWwM0Ihwu+/j3VUaZfjx1tkHO/B7AwcJWpyETO5TnkPSO7UHWVtxzlI73Pd0kqr6OzrMAY+lxN5w1UYt14LnUHE//0eli3/6EMAhUZL4SI8jVtwNpcByQSzBF4vU8P+lMFLJZj7WIW/An/MT0laSfcL2v3E9zVjHyEwTKfv83i2pIRn6gt4tLaIqfZG7souV30hFeUCKjZ8TIjnsFjbTjPdfOmzWJDXXwntfsTjzwGCujGfwxGu477CVby7p4EXNp/fk3NFUc6fTCTI2ddM5Qhnl+P2UPLm4EwtMta2+0igqfYYSspdFR2NEzP36/+kfYhXs422RZhsD/K3GifRFH69NWem4/vElYTeP0Djk68mD+pmmP118NfD8iE9w1xRzku4vBwSCayjuw64P94i43gPZkfgaI/9l+siViojDibZm1MbbB9yqwrmC6t4OPILn4SS4WgvvIa492EIfLQEflv+pSR0G7l7ngAg32unKMPBaztVglkZvFSCuY+VNSWfaPVUwWxatRkM0Macuc/j2ZASnqwr5IWGAuY467gzczfmQTjwQFH62nXRcSQweEJb3/2C3CzkgosR23bB8rUE08fRnn0RlzU+xSU5Mf576S4qW1Q/dEXpS+Hde7BFDFpLulYq20LJIS1napHxXlsmueYgw8yqukhJLQdmPh+eSQtBfq29SZhYX4fUp25Nb6MhprO0yZ7S6zinjsM5K9mPObDl2KDerDEw5lrY+BDUbEvp9RVlsApuTf7dsYwa1eV45/EezJoVPR7EFmmks6f2GH4vwKBOMHuklYiIE1KD/i4cuw150yKMRQtg3yHEz38H1ec+nM8wO2jLn0/mkVcxhZK7i2YXZ7ClopWaNvVQQBmcVIK5j5U1lmHVrRS4Crp93fL2SsyuOOFhwy7I9Z6pL+CVpjwWuGr4jG8futqyqygp4ZMOLo+NYoN2mO2isvtFMycjS4YjnnsVDldQX/pphIzzC89LGAZ894UdGIZ6AKQofaV13SoAwiX5XY7bQrVETW4MvfvZCQANUQt7gm5mOepTMZ9XUU4x3PDy6chUDokmfqe9S7S7OQBDxHRHkFJbmAcq3CnrxXxc+o2XY87NpObXjxBrPtZrffrnweaFpd8CI5HS6yvKYBTauhVTfj66q2vri0AihI6GBdPJ/ss9VDBvaE8nz9xJlnnw9rx1y+QO51ZVxXxhCQFTxiNvuxHCEcT/PgDbd53z27QULkIzYuTsfxZItskA1SZDGbxUgrmPlTWVUeQpQhPd/FG0+dHKDuEpDBG055/6+jl6vj6fFxsLmOus5ZPpB9QNr6Kk2IJYMVmGk8e0td3f6AuBvHYhOByIB58gZrhoHn4thZWL+eGEFtYfbuHxtUd6PW5FUZLa16yi2gfu9K43r/ZgLaEztMdY3Za8iVDtMZTeND6Rw03RCWzTKvm+/jIHOL///xkYxEhgMLAedgoB/5bVSFNM4w+V3fdmvVA0i5nM26/HCEeo+dVfkQkDLE6Y9aVkBfO2p1J6fUUZbGQiQXDbNqwfql4GCBhB7MKKEAJH4CjQ/YC/triJ3UE3U+2Duy+920g+6G5D7XpMifwc5GdvhvQ0xB8eh1Ubzun0qLMAf8Zkcvc9iTBi5KbZKM50smRHTWriVZQ+phLMfSiWiLGnZU+P/ZdNKzeBIXEUSyJm73ld67WmHJ5tGMbFzjo+7duvhg0pSi8woXNjZAL1ws9ibUf3i+w25I1XJfsxP/Q0jSNuImrP4paae5g1zMmvlu3hQEOgdwNXFAUjEsHYWsaOEkGm7jn5gjSwherPOOBvVbuPYksHGaZIiiNVlK5mx4v4UmgWnTLCT/UlPKqtZR/1PSaJDSQN+NkijvKK2M7vteX8UHuFu/S/81n9MT5neozbTY/wGf1Rvq2/wH3aP3lFbKeGtl7+zc7NWFuEj3k6eLrWyZ5OU0qvZc7JIP3jVxAs20fT35cmD45YAFljYcUvIKaqCxXlbIX37MHo6MA2btwprwWMEA7t2IC/zgrC5nQS+qlzijZ2pCMRTHMM7gSzRx5LMAuVYE4ZtytZyVxciPb4C/Dme+d0ekvh1VhCjfiOLgOSVcw7qtpVK0RlUErtty3ltPa17iNmxHpOMC/fgDlNEsvN4nzKjde2p/No7XCm2Jv4rG+fSi4rSi8qNTKZGs/nFX07MymimMxTF+VlI6+Yh/bWSuTrK6mdewdF23/D/0xYwS2Nc/jO89v5x11zMenqmaCi9JbQli1o0Rg7ijVm6GknjlsizWgydtoBf9URG0fDTm7xHuyNUBXlFKVGFt8Mzec1y27+adrNW6b3SZcOCqQXJ1Ys6HQQplV0Uo+fiDi5y8Zr2Mk2nIyUPtwyuTaBJEaCRq2Tg1oDG/TDPMtmiqSPhcZYFsoxmNH78Dfu3hcym1nb6eJnh9J4amJzSr8Du2ZMIHKwkqZnXsM5fTyOCaNg+h3w5vdhw1/gkm+m7uKKMogE1yerRK3dJJg7EyHswgIkB/wFbLndvse6dh9ZphD5g3wGgvtYgrlVVTCnltmM/PjV8No/0Z5fggyFkDddfVY5mkDmFCKOXPL2PE5z8Q3MLvbxzMYKXiur5d8uHdkLwStK71EJ5j50ugF/orkNffse0sYHCNq7T0CfjV2dbn5XOZISawd3ZOxRyWVF6QM3RMZzyN7MH/UV/F/i41i6+6d38jhkdR0seZtAyZdoz76I0j0P8q0ZC/j52nb+/N5BvrGwtPeDV5QhKrB6DQld42CRBbc4OSjMfhYD/ta0+RBIpjsaUx6novTEjplbo5O5LjqOPXoDu00NtIkgDaKDKAlc0oLbsDJDFpBruMkx3OQYLmyYz/je7SJMmV7LdlMNj+lrWSy38wljGpfLMej9aIOkWze4M7OJ++pzeLzGyZ0FqU02pd+0kMjhKmrueZSSB3+KljsRhs2E1ffCjC+APT2l11eUwaBz4wZM+fmY0k/9+xIwQtiFFXO0HUusnc60qaes8cd1ygNuFnqqBn1LSDtmTFJLVjAPrE5GA4+uI6+/EiwrEUveQeo63HDVmc8TGi2Fi8jb+wTOph1kZ05hVLaLJTtqVIJZGXT6zzfAIaisqYw0Sxo+26lVUKb3NiKkxDM8RKc97yO9f3XExi+PlOLTw3wlcxcWzTjfkBVF+QicWLglOokq0cYL2pbuFwmBvGo+ZGUgHvo7tRk3INH4RN39zCn2cf87+9lV0967gSvKENa5ejU1RU7cNi/iA3eotmByMEtPFcxSwqr2DEZa2/Ga1FR3pe/ZMTMtUcDtkWl8LTyXb4cW8P3Q5XwjPI8vRGZyY3QCF8WHU2Skn1VyGSBN2rgkXszXw3P5UugiXIaFv+pr+In2KlW0pvg3OjdXuP1c4vJzb4Wb91p7Hsx5IWhWC75/+Rix+ibqH34heXDaFyDcAavvS+m1FWUwkLEYwU2bu22PARBIBHEI62n7L2/yp5NAY9og778MIBC4pVUN+estmoa8+lLkhNFor7x51u0y2vIXkNDt5O19AoA5JRnsqungQIM/ldEqSq9TCeY+tLNxJ8VpxV1uXI8z/XM9WpYNS5pBZw+TcU8nmND45ZFSNAy+nl2GSx+6k8QVpT8Yk8jm4lghS0UZu6nrfpHZjLxxESQSGI+8Sn3RzaTXvMf3R+zFZTPxree2E4mrafSKkmqxhgYi+/axq8RE1gfaYwDYQ7VETW4MvftEVUXETnXEzgxVvawMAQJBqZHJXeE53B6eRr3o4Pv6yywRO5H9pJxOCPhWTgMjrVG+u8/LgWBqN3DaiofhXjCLtjdW4d+4E3zFUHIZbPgzBNS/C4pyOqHycmQw2HOC2Qhh16w4OiuQCILWrFPWrG9Px6eHGW4ZGjNM3NKqhvz1JiGQH7sMOboE7fklsGLdGU8xTA7a8heQcWQJ5lAjc0dmoAl4aWt1LwSsKL1HJZj7iD/q50jHke7bY9Q3oe86gKsoQdCahdTOrprkOEPCA1Ul1EZt3Jm5Ww0YUpR+4troOHzSwe/1d+noqdIgPQ157UJERTVtqzoJuUcwbvv/8rU52eyrD3Df2/t7N2hFGYI6164FYF1RpOuAP8AerCZk6aaX+jHH22MM9sFCivJBAsHkRB7fCs5nbCKbp/WN/E57lwj9o8DBpkl+kleDVRh8bU861eHU9ov2LpqLOS+L2vv+RrzdD5Nvg3gU1v8xpddVlIEuuKHn/ssAnUYIh7DiDBwm1M19cjChsT2QxhRH06Bvj3GcW1ppVUP+epemIa+/AllShHjqJdhafsZTWgoXoRlxcvY/g9dhYfIwL69sq8Yw+sfDWEW5EFSCuY+UNSb7L5d4T+2vbFq+EQBfXt1Hao/xUmMeGzt83Ow9xGib2lKvKP2FFRO3R6bRQZjfaysw6KFtzagRyNnTEKs2UdUxF3OokRuaH+PyMVk8tPIgW4629G7gijLEdK5eg0jzsCcrQpbuPXFcJKLYQnUEbdndniclrG7PYIytDbce661wFaXfcGHlM5FpXBsZywZxmLv1JTTRP6oIM80JfpJfS3tM8OnyDHYFUlfJLEwmMj51DYlAkLrfP4305MOIS2DjwxDqXy1EFKU/6Vy/HnNREbrLdcprhjToNMLYhRln4AgB26n3yVv8XuJSG1IPeVUFcx/RdeSNV0JuFuKhp+Dg0dMujzrz8GdOJWfvU4hElEtGZVLTHmbDYXVfpwweKsHcRzbXb0YXOqPSRp3ymmn5BmRBBnZnhM5uPjhPpyzg5pn6Ycx0NHC5W225UJT+psBI46boBMq0al7UtvW4Ts6bhSwqIPbSahrM88jd+wR3jfaT4bTy7ed3EIz2j6owRRlspGHQuWYN4THDkUJQoJ8c5mcP1SCQ3W7JBTgUdlAXtanhfsqQJhAsiJfwhchMamnnbn0JdXT0dVgAjLZF+E1hFbo0+MKuDJa3pK4nsyUvC+9Vc/Gv2UrH8g0w6ZMQDcDGv6bsmooykBmRCKGt23psjxEyIkgknngY3YjSac8/Zc2adh9peoRiS//4N6c3uKWNThEl2k92jAwpZjPyE9eA04F44FFoOP2DjZbCq7GEm8ioeIOZI9Kxm3Ve3lbVS8EqSuqpBHMf2Vy/mSJPEVZT1y+2oqoefe9hrKXJp7bdPZntiT+u87vKkWSbQtzu2zdktgUpykAzK17IjNgwXtK2sVVUdL9I05KTim02Wl5vISI9TNj8Q+6aX8jR5iC/fGNP7watKENEePduEq2tVI9ODvHLN51MMDs6KwEIWruvYF7VloGOMaQqpxSlJ2MT2XwlfDEhYvy3vpRq2vo6JACGW2L8trCKAnOMb+z18ZMDaXTEU/Ol2b1gJtYRBdT98RliCS8MuwjWPwiR/lHVrSj9SWjbdmQ0etr+ywC+cHKHbsDWNcHcmdDZ5vcy3dGINoTugz0ymU9oU4P++obDjrzlWkgkEPf9FQI9V5MHMiYRceSRt/sxrLrGRcU+Xi+rIxxTM3aUwUElmPtAOB6mvKmc0emjT3nNtCLZd8ozPETU5CJmcp/Ve0oJf64upj1u4o7MPVi1HrbeK4rSL3w8OoH8hIcHtOUcpbn7RQ478sarkK0dVGwbiaNlD1e2PMs1E3N5Yt1RVu9XSSxFudA6V68BoLxYwyYs+LSTn8OOzkriur3bz2ZDJvsvj7e34tBUFZGiQHLXzldCFxMj3q+SzD5Tgt8Mq+KT6a280mjnxu1ZvNZk40K3whSaRsa/fAyZSFDz28eQE26FUAtsefzCXkhRBoHODetB07COGdPt6wEjmbjzhZuImNzEzF0/izd2eIlJbcgN2XWfSDCrNhl9xudFfvxqaG5F/PkJ6Gkou9BoLroWV/NOPHXruGRUJoFInLffr+/deBUlRVSCuQ+UNZURM2KM8Z364Wl6dwOJ4mF4qEi2xzjLMuTlbZms6/BxvffIkJmYqygDmRmdL0RmYpU6v9TfpIXO7hfm5yAvn0tiby3VFeMYVvYAd44Ok++18Z//2EkgohJZinIhBZYvx1JczB5LM/l6BuIDn8OOzspke4xuPpt3B900x63McDT0ZriK0u/lSjdfCc1GIvk//Q2ae/q862UWTXJHZjP3Flbh1uJ8b386t+zMZEWrFXkBE82mDC/p119KcMde2jY3QM4kWP8nSKjPb0X5oOD69ViKi9Hs9m5fDySSFboZnXV02k5tj7G6LYMMU5gRFn9K4+xvTiSY1aC/vjUsD7loPmL3AcSzi3tc1pY3n5g1nYLyBxmf7yHDaeHFLapNhjI4qARzH9hcvxmBoNRb2uW4OFqNfqgSJo3AGm056/7LDVELf60potTaxpVu9Y+TogwUadLGFyIzCRLhV/qbhIh2v3DaBOS4Uvzr2/HXuxm34fv82yVF1LSF+JVqlaEoF0ysoYHQ9u3Yp0/ncKSmS3sMYcSxhWp7bI+xus2HRSSYZO9hR4KiDGHZ0sUd4ZkEiPB/+hsECPd1SCeU2iLcX1jJd3Pq8Mfg63t8fLIsg7eaL1xFs3PWJGylRdQ/8iLR7Muhowr2vnZh3lxRBoFEWxuhnWXYxo/vcU3nsRYZ6bEAgQ/1X+6Im9gZ8DDD0TDk2kR6pA2AVtUio+9NHIucNQWxfC28u6bbJVK30Dz8Wrx1a/E07eDS0Vms3NdIZYt6QKAMfCrB3Ac2122m0F2Iw+zoctz87gakENiLk1OtP/zB2Z1ka4wRSCn5fMbeIdVvSlEGg3zDw+2R6VTSym+1d7of0CEEctECZIaPmnVpWCvLWdDyIh+bmMuT64+y7qBKaCnKhRB4dzkAxtRxNCfauwz4s4Vq0WSi2wRzXArWtvuYZG/GplpUKUq3Cow0Ph+eQR0d/Fp7q18NpNIFXOYJ8Oeio/x7dj3tUfjWvnRu3J7J4kY7sfP8ay2EwHfLIpBQ+1wZ0pkL6x68MMEryiAQWL0GEgnsU6f2uKYlnhzc50sk6LR3LcRa35FOgqHXHgPAKS0IqSqY+wu54GJkSRHimcXw/v5u17QOu4K42UVB+YNcPjYbIeDZTT3M5VGUAUQlmHtZLBFjR+OOU/svS4np3fUYIwvxyAoSmqXHKqkPWtmWwfaAlxu9R/CZIimKWlGUVBqTyOKW6CTKtRru194lTjd3shYz8qZFyJikYstwCrfdyx1j4uR6rHzvHzsIRvvPjbqiDFT+f76DKSeHymN55a4D/pI7hELdfDbvDHjwJ8xD8sZWUc7FSCOD2yJT2Kc18JC2GskFbnp8nkwCrk7z8+eiCv4ztw6kwQ8PeLl2WxbP1zvOK9FsSveQft2lBHfupa1tMlSuh+qtFy54RRnAAitWoHk8WEpKelzTFE8O9/NJnZAls8trq9syyDEFKTD3jxY8vUlD4JZWWlUP5v5B05DXXwE+L+JPT0D9qd8NDZONlsJF+KreoTB2hKmFXp7bVEksoYoUlIFNJZh72a7mXUQSkVP6L2sHK9Eq60hMG4+7Y19yKq44/R9Pe9zEo7XDKbZ0sMBVk8qwFUVJsRnxYdwUmcBWrYIHtRUY3SWZfV7kFfOIVkdp3uNg3MYf8uX5xVS2hLjnzX29H7SiDCKJQIDOdeuT7TFitQAUdEkwV5LQLETMaaecu7otA4cWZ7y9pdfiVZSBalIij6uio1mtHeBVsbOvw+mWLmCBO8DvCyu5O78Gjxbnvw+lcdOOLN5utn3kHs3Oi461ynhtH9GICzb8+cIGrigDkIzHCaxciX3yZITW8/1vU7wNlyFJWPO63Ce3xMzs6nQzw9E45NpjHOeWNjXkrz+xWpA3fwykRDzwKARPbV/SUng1Cd1G4c4HuGJcDk2BqBr2pwx4KsHcyzbXbwagNL1r/2XTu+uRmoY2Lg9buAG/o/CM7/V47XCCCZ3bfftUawxFGQTmxIu4JjqWtdohHtTeI9FdknniGOSYEpp3ODDv3cHCjle5anwOj605zOYjKrmlKB9V58qVEI/jONZ/2SJMZGgnk8n2Hgb8RQzBho50ptibMIv+VY2pKP3VwthIpsTzeEbfxGZxtK/D6ZEQcJEzyD3DqvhJXg2GYfDNfenc+b6PoyH9I7zfB1pl7ChAlr0EHbUpiFxRBo7Qtm0YHR2nbY8B0BxrJjseJ/ChOUVr2n1IBDOcQ3fIrltaaFUtMvoXrwd50yJoaEb85SlIJLq8nLC4aS66loyKN5hvPUSmy8LTG/rv56GinA2VYO5lW+q3kO/Kx2PxnDxoGMn2GKVFuBLJLbh++7DTvk95wM17bZlc5akk36I+TBRlsLg0VsLV0dGs1g5yv/ZPYnT9MoIQyKsWIJ0OqjbmMmzTr7hzPGS6rXz3xZ2EY4nu31hRlNPyv/NPtLQ0LKNGcSRaS57uQzueTJYJHMHqbltXbfN7CRk6M4fwja2inCuB4NbIZIYl0viDtoJqWvs6pNMSAma7gvxxeAVfy2qgPGDm5h1ZPFLtJHGOz5VOtMo45KdtvwU2P5KaoBVlgPCvWAEmACk1SgAAIABJREFUE7aJE0+7rilSR1YiQeeH5hS925pJkcVPrnnoDrlLVjAP3d+/3yrMR141H1G+F/H80lNebi66npjVy4htv+Dy0VmsOdDMkaah1+ZFGTxUgrkXJYwEW+u3ntJ/Wd+xF62uicSsSbg79hHXrISsWT2/j4RHaofj08Nc7alMddiKovSyy2OjuCEynk3aUX6jvU3kw4OQ7DbktQuJtyVo3Oxgwob/5CuXFHG4qZP73lGtMhTlXBnRKIH33sM+dSpC0zgcqSG/y4C/BjQZ67b/8qq2DNxalFJrW2+GrCgDnhmdz0amY0Jwj/4OQaJ9HdIZ6QKu83bwp6KjTHd0cm+Fh39930dj9NxuqY63ymjY4SW2/BGIhVMUsaL0f4HlK7CNGYNmt592XXOslYxEgs4PVDAfDtk5GnZysbMu1WH2a25ppYNw9y32lL41eRxyxiTEO6vgvfVdXjJMNhpLbsXTuJXbPDvQBDy5XlUxKwOXSjD3ovLmcoLxIGPSu/ZfNi1bhbRZSEwajatjPwF7wWn7L/+zNYujYScf9x7CoqbVK8qgNC8+glsikygTVfxCW0bowzfewwuQF02l/YAVtpWzqP0FFo7N5uGVh9heqRJdinIughs2YnR24pg+nc5EiPp4CwWmkwOEHJ3Jyd4frmAOJjQ2+71MdzSiq1ZVinLOvNLO7eHp1NPBH7UVGP1s6F9PMk0Jfpxfx7dy6tnhN3Przkw2tVvO+vzjrTKk0KldKZA7n09htIrSf0UrKogeOoTtDO0xpJQ0EcUrHBia+cTxFa1Z6BhDfsiuV9qQQtKEqn7tj+Rlc5DFhYinXoK9B7u81pp/KWHnMMbv+i3zS9L4+8YKWjv7/wNXRemOSjD3opVVK9HQmJAx4eTBYBjTe5tITB2HWQawRZoInKY9RmdC5+m6YYyytjPd0dQLUSuK0ldmxQu5LTKVfaKe/9HeIEDXCid5ySxkThY1mzLIXXs/Xx0TJN1p4bsv7CASV60yFOVsdby5DGG1Yhs/nqPRZBVU/gcG/Ln8B4lrVsIWX5fzNnakE5MaM51D+8ZWUc5HieHjuug4tmgVvCy29XU45+RKj597C6uwkuBL7/tY3Hj6CswPMqV78F53GZ31VtoeuZ+PPDlQUQawwIr3ALBPmXLadf5wHVEBLv3kbIS4FLzXlsEkezMuPX6aswe/PCPZfvOIaO7jSJRuaRryhishPQ3xx79B4wf+nDSd+tJPY/cf5T+cywhFEzyxTlUxKwOTSjD3olVVqxjpHYnL4jpxzLRyEyIcITFrMu6O/QD4HT0nmJ9vyCeQMHFL+sEhOyVXUYaSKYl8PhOZzhHRzM/017tOiNZ15PVXIA2d2vVeJqz/Ll+eU8D+hgB/ePdA3wWtKAOIEQ7jX7YMx8yZCIuFw9EaAAr0DyaYDyR7Pn7og3dVWwY+PUyxpaNXY1aUwWZuvIjpsQJe0LeypR8P/evOCGuU+wsrmeQI8cMDXh6udp51rth18WRsRV4aVnYSW/9yagNVlH7Iv2IFpvx8zDk5p13X2rwRALvp5Lpt/jQ6EmYudtanNMaBINdwo0nBYaEK0PotqxV589WQSCB+9yiEThYOBbKm0Z4zmwkH/sTN+W08tvYwwejQfmiiDEwqwdxLGoON7G7ZzeSsyV2Om5atwshKxyguwOXfT1yzEbJ033+5OmLj9aYc5jjrGG4J9EbYiqL0AxMSOdwRnkEd7fxUX0Id7Sdf9HkxLp9LqE4nurGKG1r+xoLSTB5ccZDy6vae31RRFAACy5djBDpxzpsHwOFIDSZ0snQvAKZYB7ZwIwFbQZfzOuImdgY8zHA2qAe+inKeBIKboxMpODb0r4aB1erJoUv+K7+Gy9x+7q/w8Kuj7rNKMgsh8H3q44Cg5me/QKoqZmUIibe2EtywAfsZ2mMAtLTvBMBuOdmqanlrJm4tygR7/x4S2hvM6GRLF4dRCeZ+Ld2LvHER1DciHnoajJPtTmvH3kHC5OTHsQcIBEM8s1HN2lIGHpVg7iWrqlcBdEkwi9pGTNv3kJg5CYTA3b7vWP/l7u9UH6sdjlkY3OA90hshK4rSj5QaWfxr+GICRPipvoSDfGBL/uRxyJFF1O/0krHuEb4xqhmPzcR3X9xBNK76tCvK6bS9shjd58M6bhwAByJV5Jl86MdmIbg6kr3y/PauCeZ17ekk0Jg5xPs+KsqFcnzon4bgHv3tATH074PMAr6TU89N3jaerHXxm7NMMpsyMvAtyCd4OEDb4w+mPlBF6Sf8y5ZBIoFzzpwzrm3xJ3fmpQtH8ty4zma/l5nOBnShHswA5Cc8HBJNyAHSy37IKipAXjEPsXM34oXXThxOWDzUjruTDP8e7vYu4+GVh9R9nDLgqARzL1lZtRKfzccw18n2F6a31iCFIDFrItZwI9ZoC35HYbfnb/OnsdXv5WNpFXj0WG+FrShKPzLc8HJXaDa6FPxMf40doir5ghDIRZeCxULNhgwmbfgeX52dw+5aP39+7+Dp31RRhrB4czOdq1fjmD0boWkY0qA8dJAS08kJ9S7/QQxhImTrOuBveWsmeeZOCsxqoI6iXCjp0s7t4WnU0cGftPcGzNC/4zQBX85s4vq0Nv5W6+J3lWeXZHZcdh2OnAgN9/+ZWE1N6gNVlH6gfckSzAUFmAu7v/89TgRraTb8AKRhA2B5axZxqTFHtcc4ocBIo0OEaf1gOz2lf5o6ATltIuKt92DZ8hOH/dmzaMudx2fCz1Ea2Mjzm1UVszKwqARzL4glYqyrWcfkzMmI49XJ8QTmpSswSouQ6Wl4WssBaHcWn3J+XAoerR1OlinEZe7q3gxdUZR+Jku6uCs8B5/h4Nfam6wWx3otuxwYiy4l0qzh39DGzY1/Yu7IDB54dz976lR/WEXpTsdrryWrp461xzgarSNghBhlzj+xxuU/SMCWhxT6iWNVYRv7Qm5mO+tUewxFucBGGhlcEx3LJu0oi8X2vg7nnAkBX81q4mpPOw9Xu/hrjfPMJ1nTyL6yAJmIUfPDH6hWGcqgF62qJrR1G445c07eH/dAb95Go65jkzp2YcaQsKwlm5HWdgos6iHvcQXHBv0dVoP+BgS5cC5y7Ei0F16D99afOF477k4irgL+Yvkdi998i/aQKi5UBg6VYO4FWxq2EIwHmZQ16cQxfd12tMYW4vOmA5DWVk7IkkHUnHbK+W82Z1MdsXOz9xBmtQVIUYY8j7Tx1fDFFBnp/EFfwVKR7EvH6BLkhNE0v+/Gs/5Fvjd8D06Lif94YQfxhNpipSgf1r54MZaiIizDkruLykLJiv/jCWYtHsIerE4O+PuAd1qz0DG42NnQuwEryhBxSXwE0+L5PK9tYZsYeBVcmoBvZDdyqSvZk3lpo+3MJ41aSPaUdoLrN9L2wgupD1JR+lDHa8nWAM7Zs8+4Vm/aSoPZRpqwA7Az4KEuauMSV21KYxxo8gwPQsIR1Yd5YNA05LULkSXDEU/+AzZsA8Aw2amY9l2E2cb9xi947I21fRyoopw9lWDuBSurVmLWzIz3jT9xzPzKOxjpHowJpWjxEO6OA7Q7R5xyrj9u4tn6AsZYW5lsV08jFUVJsmHmzvAsJsVzeUrfyJPaegwk8op5SLeLqo3ZjF/3I/59upny6g4eXnW4r0NWlH4lcuAA4V3v4zhWvQzJBLNT2MjVfQC4AocQSPz2k+2tYoZgRWsmk+zNuFXLKkVJCYHg5sgk8gwPv9eWdx1uO0BoAr6VU88ke4gfHfSyod1y2vWGoxDPlCwceZKGX/1atcpQBrWOpUuwlpZiyup+uP0J0kBv3kqjxUkaViBZvezSYkxTMxC6sGIiUzpVBfNAouvIG6+CglzEX5+BtZsBiNsyqJn+XTK0ANdtv4ujB/f0caCKcnZUgrkXrKxayZj0MVhNyQ9FUVGLacsuEnOmgq7had+DwKDdWXLKuc/WFxA0dG5NP6i24SqK0oUJnU9HpjEnVsRrWjl/1FYQt5qQ11xOvF3StNXKvxy5mzkj3Nz39j721fv7OmRF6TfaXn4ZNK1L9VRZ6AAjzXloxz5wXR0HkWh02nJPrNni99KRMDPHpfo+KkoqWY4N/QPJPfrbhBl4D3TMGvwor5Z8c4x/35vOoZB+2vXx7AXkzWhAxqLU/PgnSEPtPlIGn/DevUT2H8BxFtXLWtseRMxPo67jFTaaYmY2daQzx1WrdvZ2I9/wcFioCuYBxWxG3nItDMtDe+RZeHcNAGHPCPZP+i45opW0p6+Bmm19HKiinJlKMKfYgdYDHO04ypSsKSeOmRf/E6nrxGdPBZLtMeKajU5bXpdzj4btvNmSzXxXLfkW1axfUZRTaQhujI7n6uho1mgH+ZX2FqHhWcgZk2jfZ0WU7+F/3S9jt+h87emthKKJvg5ZUfqcEQzS9vwL2GfMQE9LtqbyJ4IcidYyylxwYp3Lf4BOWw5SM5849k5rFml6hHG2ll6PW1GGGp908OnwNKpp40/aygE39A/ArRv8d34NOpL/tycdf7znipGEdxImn4es2RaCa9fS+tTTvRipovSOjqVLQdNwXHTRGdfq9auRQqdJxPFi4+2W5MDdS1x1qQ5zQCpIpNEsOukg1NehKOfCYkbecg1y1Ai0p1+Gpe+AlGg543lq2H8RSGjEH/kY7HiOs5ocqyh9RCWYU2zJoSVoQmNW7qzkgWAY8xurSEwZA24nSANP2y46nCNAnPzjkBIeqxmOTYtzXdqRPoldUZSBQSC4PDaKWyOT2SVq+Jn2Oq3zJyEz0qnenENR+WP8cmINBxsC/PeSXX0drqL0ufbFizH8fjyLFp04tit0CPhA/+VEBEdnRZf+y80xM9v8acx21qOrXUWK0itKjUyuiY5lg3aYJ7X1yAGYZM42x/lBbi2VYRP/ecCL0dOvIHRiWfPx5e3HPmEUDffcQ3jvvl6NVVFSScZitC9+FdvEiegezxkWS0x1q/C7RxEijhs7b7dkMd7WQqYp3DsBDzD5atDfwGUyIW+8Cjm+FO3lZYiH/w6RKPPGDuN79p9RFh8OL38Fnr4VWo/2dbSK0i2VYE6hhJFg6aGlTMyciMea/Mfe9PZaRDBE4pIZADgDRzHHO2l3Fnc5d5Pfy87ONK5NO4pLj/d67IqiDDwz48P4fGQGVaKVu21v0HTdxSRCBtU78rly3918boKZZzdVsnh7dV+Hqih9RhoGLX97AktJCZbS0hPHy0IHEQhKTMndRO72PWgyQbtjxIk1bx2rnJrjVJVTitKb5seLmRcbwRvaLl7UtvZ1OB/JJEeYL2c18V6rjQerXD2ui2dcDCY7ufPiCLudmv/4DkYk0ouRKkrq+N95h3hDA67LLz/jWs1/EC1UR33aaABqg1m0xS1c7lb9yXuSbyR3ZR1A9acekHQdee1CjPkXwcZtiF/8AVNLC1+e4eUO46f82XIH8sgaePBiePNH0KH+Lij9i0owp9Cm+k00BBuYmz83eSCewPLsaySG52GMSG7BTWsrQyLocBSdOC9mCB6vHU6uOcgCNR1XUZRzMDaRzZfDF+EnzA/yV9MydxydhySBAwbf8f+acdkOfvBSGQcaAn0dqqL0ic7Vq4keOYJ70SLEB4YblIcPMsyUiV1LDuLytu4krtkIHKtgjhiCZc3ZTLS3kGVWlVOK0psEguuj45gZG8Y/tG0sFjv6OqSP5Pq0dq50d/CnKjfvNFu7X6TbiGXOwdqxnszP3kxk/wEa7vlt7waqKCnS8sQTmLKzsU+desa1et1qJIJ6V/K+eWvrMAotfsbaWlMd5oDlwExxwsdybS8JVA/3AUkImD0d+YlroaEJ8V/3kr1tE/9vsuCXHYu4r+A+KJwN6/8E90+GV74OR9eq1hlKv2Dq6wAGsyUHl2A32ZmalfwANS3fgFbbSOTOWzg+sS+ttYyAvYCEbjtx3uKmXOqiNr6RVYauhhcoinKOhhvp/FtoDo/ZNvGduQf446F06raYGJWxhQemvs4t/iv50t82sfjr8/A6Tj/VXlEGm5a/PYGeno5j1qwTxwxpsCt0iFmWZJUUMkFaa3lyd5FIDuVa2ZaJP2FmobuqL8JWlCFPIPhEdBIxkeAZ0yYCRoTbjVkIBk6/GiHg69mNVEQt/OCAlxH2ZkY5Tt2pGM+aj7lhJR5nGaFFi2h98kkcs2Z2aeujKANNqKyc0LbteG+/HaGduc7NVLcKw1VCk5a8H24OZfJFT6UafH8Gl8RG8KRtKxvFEebIkr4OR/moSoYjP3cLYtlytEefY+7EHXx69q08sNeL7aJv8LWbPwO7XobyF2H7U+Atggk3Q+lVUHgx6OYzX6O3SAmhVmg9DP466GyEYDPEQhCPgBFPxmuygcUF7tzkjysX3Dlg9aD+4g8MKsGcIsFYkLePvs3MnJlYdAsYBpYnX8XIz8aYkNySa++swh6qoyLr5BahxqiFFxvymWpvZJxdPZ1VFOWjyZYu7grP4VHbJn58fYB7HpNUbC9hlO0hHpgykn/dMpyvP72Vx++8CLOuNrMoQ0Nk/34616wh7dZbEaaTX4GORGsJGKET/Zdd/kOYEkHanckbMylhaVMOw8wBSq3tfRK7oijJwbafikzFLs0sMe+knRBfMeZjGkCbMi2a5Ed5tXyzspB/35vOs5Oa8Ji6FpRIs4e4bwamqjfx3vQ4kYMHqP3hD7GNGYOlqKiHd1aU/q3lyScQNhuu+fPPuFYEKtA6K4gMu5kmoxOADExMtTelOswBb1wih0zDwVJRxmxZPKAewikfkp6GvO0m5LZyxMoNfG7vrxg96RLujV6GlD6+PvsumHEHVKyDQ8th3e9hzf3JJG3JZclk88grwFvYezHHQtCwG+p2Ql0Z1O6Epr0Q7u77swDdApqeTDInot2/p9kBvhLIGgOZY5L/zRoDvpFgUsVS/YlKMKfIu5XvEoqHTrTH0FdtQauoIfr5m0BL/iOf0bQBQ+i0usecOO/x2uFI4BPph/oibEVRBhGPtPHV0Gye9G7hoauauOv1ILU5I5hvvpsfTn+In21u5udL3+dnN03s61AVpVc0PfwwwmLBddllXY5v6nwfgNGWYQCkte7EEDodzmQiZ0fAQ2XEwed8e1UBhaL0MQ3BTdEJuKWVty37qaOdfzcWkknPfY37m0xzgh/k1fGDqgK+t9/LH8e2njI4NJZ9GabmjViqXiHza1+n7qc/per/+yYjnn0GzWbr/o0VpZ+KNTTQ8fobuC6/HM3hOON6U/1qABJpE9kRfB9pmLjK1XT8Nlo5DQ3BvFgxi6272GvUM5bcvg5JOR9CwPRJyJIixOpNXLx1OU9a1vLSgUt4sHk+X70iD33kQhi5EKJBqN0ONVuhciPsWZp8D3ceFMyAYTOhYCbkTwWr+/ziMgzoqE4mj+vfTyaT63ZC036QieQaswN8xVA0LxmDOw+cmWBLS1Yl65aulclSJhPUoRYItpz8b7AZOqrgyGoo/8fJ9ZoZssdB3pSTPzkTwOI8v99N+chUgjlFlhxYQqY9k9L0UpASy5OLMbJ9JKaMBUAYcdKbNtHuLDnRHmNHwMO6Dh/Xpx0hw6SGeSiKcv7smPlieBbPjd/OqqM1zNscxZ7h4jOHvs/ecX/giXVHyU2z8bXLRvV1qIqSUuE9e+hYshTPtdeiu7t+qV7u30qhKYts3QtS4m3Zid8xHONYP+YlTbl4tCgznA19EbqiKB8iEFwRKyXTcPKStYz/1F/iLuNSZsqBU907wR7mruxG/tCQze8q3Hy7yN/ldWnLJpE+DfPRxcSKbyXjK1+h8b77qPuf/yHv5z/v0kNeUfq7tmefhUQC91VXnXmxlOi175FwDMcwe9nSGUFYXFysPoPP2oz4MN627GOJtpOxhkowp4qBQQtBmgmQQCIQeLCRRxraha4c93qQ118Bs6dhXr2J2/a9i7z3XV57fjQTb55H0fxJ6G4nFM1N/kgJbRVQtwMa90L11pMJZ6ElW2pkjoaMUeDJT7akcGYmk8ImW3JNIgLxaLK9RWcD+OuheT807Uv+xEIn43NmQXoxTPpkstrYVwKu7OT7nC0hwOJI/qQN635NPAzt1dBeCa1HoPkgvL8Ytj158nfLm5JMao+4BIbPAbv3I/1Prpw7lWBOgSPtR1hXu47rS65HExr6mq3oByqIfvo6ONZvytO2C3O8k2bPeACihuDh6iKyTCGu9FT2ZfiKogwyZnRuj07njSvMjKw9QnCNg/FX1fNj2900Fv+cXy/bi9Ni4gtzR/R1qIqSMg2//S2aw4Hnuuu6HG+Ot7MjtJ+bHHMAsAersUZbqPNOB+BwyMG2gJfr045gVnMRFKVfmZLIpyCUxt+t27hHf5uLjBF81riYbM6zMquXXJPWwaGIlUdq0hjjjHFdZtcBotHcRdhbt2M59CxMvQvPDTfQ/uI/sI0eg+/zn+ujqBXl3CT8flr//gy2KVMw5+Sccb3Wthvdf4hI4S0sb7XSSoBMacGkPoPPmgWdebERvG3Zz4tyK7fK6X0d0ikkkv00sE47TAMddBBGIsnHS6FMZ5IsoAhfv2rxESLKdlHFblHHPlFPJa0kxKnDFO3SzCiZzaWylNmy5MK2ccr0wcevxmjzc2TrYbzvv0/k94+y9w8Cy+hiPNPHYR81HFtpEabM4Yj0Ihh37Nxwx8nkcFtFMll8aEUykXwWKk06y9NzCNg9hIomMNpVwKWZ0/FklILNc+F+x9Mx2SBjZPLnOCkh2JRMNjfvh/pdsOEvsO4PgICciTBqIYy7CQqmq37OKaQSzCnw2K7HMGkmFg5fCLE41j89k6xenjHhxJqMxvXEdCcdjmSlxXMNBdRE7Xwja6e6gVUU5YLTEFzLJDZcp3Hd3w/x3pZcLp6zm3vy7uOu4f/B3a/uwmHR+eTMXuzRpSi9pHP9ejpXrcZ7221ozq7b5lYFtiORzLAl5yOkte5EQnLAH/B0/TAcWoxL3dW9HbaiKGchUzr5WngOq8yHWW4+yDa9kmvkBK4xJpLOmbfi97WvZCWH/v3kgJcRtiYmuE4O/ZO2rGQv5oolxEbcStonPkGsppr6X/4SS/GIs+plqyh9rfmhh0m0tZF1881ntd5UsQSp2QilTec35W7Mw2sZZWSmOMrB5/LYKJpFkBfNW5GG5JPGjL4OCYAEBsvFXl7VdtIg/JilRqbhxEly19h2UclKfT8AOdLDXFnCFcbYPmuDZCDZJipZLvayQ1QSEwZWqVNoeLkkMQKfdOCVdkxSQyJp08JUam0c1Jv4g76Cv8uN3GhM4So5Dv0CJpqF103xwsk0zpvKX/ZEcR09ykUNexj57OtoMplP0px2zLlZWHIzMWWmo3uc6G4nunsUunsKer4TzW5FEzFEvBOR6ERoCQRxhJDJFha6mQZN8JeWrfyjeSsJaQBBTEaEeHsDpo6dzGsdz5cKFzEtbeTpg04VIZIV1M4sGD47eSweSbbvqCuH+rJksnnN78AzjP+/vfsOj6O4Hz/+nr1+0ql3ybIld2NjsI1tTLPp3RAgoSQBQiCNEEiBEEgPCaQRCL8kfClJSIGAqSEEQjcdbONeZVuyeq930pXd+f1xZyNjyZaErDtZn9fz7HN3u3t7M7Nz99md251h9mdgzuWQPnruehotpIF5mNX563hm+zOcUHQCqa5UHI88h1FVT/CaT4MtOhK9PdxBattGGtKPBGWwLZDEU435LEqqZbqnLc45EEIcqhSK2ekzWXuyxbwXynksu5BTHW9xV1EWVxdcyU2Pr6UnYvG5hRJsxaFDWxYNv/o1tqwsfCedtM/y1zpXkWNLY5wtG7RFRtMKutyFROxJbPIns6ozjaVpO/AaZhxSL4QYCDs2loQnMSdSyH+dW/i3bS3P2dZzrJ7EidZUJpOTUFfB9eZQcHNeHddXFnHdlnQePbyZTMdHV8SF807B3roSx46HCR12HZnXfIn6226j+oYbmPDII7gmSRdXInGFa2tpeeghvIsW4Zww4cBvCLVjr32NSOYCHm3KoNLsJNnWzbhI6kFP66HGQHFh6HAUiscdH1JJK5dZ88llhK40/RiNZrWq4u/Ge1SrNorNNC6MHM7MSC5uHHut20mQTfZ61trreMpYzVO2NczR4zhZT2e2Lhr+7if60EOY19RW/musp151kmK5mR8pZmYkj/FWev9psGAeRVhottoaWe7YwV9s7/Cy3syV5iJmkD+s6cx2WVw92872adN5pvVo3mlzMa69juntlRzRU0tBoIX0jRW4/euxhfoZRK8P2magHA5Mu6JDBTne0JzscOF1+bA5HGi7jR7DpEP30KzXsU6tptydTGlKIdneDJTDjrLbUXZbbIo97z3fYd+zjN7r9rGO4XJieD0YHjdqIAPU212Qd3h0Agh2QeV7UPEGvHknvPHb6CCIx94Q7VJEDAtpYB5mf93wV7TWnDbhNFRzG86/PoU5YxLW9I/+zcloWoHCotk3g7CluKeqhFRbSAb2E0KMiIyph1PZGOaEFdX8MbeAC23P8qciF9fZLuX7T62nvr2Hb506Rfp3FIeEjuf+S8+GDWRcfTXKufdI051mgBX+TZzinYNSipS2jbiDjdTmnY7W8Pe6IlJtQRYn18Qp9UKIwUjVHi4OHsEpajJvOHbyln07r9m3kqN9HK1LOdIax2RyhvUqsuGQZje5Nb+OG6sKuWFLGvfPaMEZS6J2ZRLJXIC98jnC45diJI8n+/rrqfvxj6m85kuMf/ifA+p2QIh4aLzrbrAs0i64YEDrO6qeR+kI7enH8IdNyZSmfkgDUGhJA/NQGCguCM0iU3t5zbGdVbZdnKynsdiaygQy+3xPBItdtFCj2mikiy4VJF17yCCJyTqH7CF0QVRBM3833medUU2m5eWzwTkcZub2+8efDxfzI8XMjxTTogK8b69khaOSlcYusnUyJ1vTWaKnkIJn0Gk5kDraecnYzCtqMwEVZpy2FSS7AAAgAElEQVSZxiXhI5hp5g0qdhgoppk5TDWz2WCr5z/OTfzE/h8WWRP5rDWfDIZ3ILqJrhA35DXwxWyDdd0e1nXP5O/d86gLO+i0Yhc6WhGSQ934wgF8oejkjQRxWJHoZEY+em5FcNtqcHu2Yg95UJ2luEwbbh3GE4rgCoZx6QhObaPAcmHqABHtp6VyMwHLRrJlx2kqtGmiTRPMfbsSGaqIw4npcmG6PUSSkyEjHVtGGp6cdDIL0knJS8dZkIMtzffR+awrGSadFJ26GqDsRdj6PPz5DJhwHCy+GSYcM2xpHKukgXkYtfS0sGzrMhbmLyTLk4Xz7vsgFCZ8Xq8rprRJVv0b+F159LgyWVZfQGXQy1ey1+ORq6OEECMkvOhI2pu6+dzzLdx2cQ6fqXuae/I0t3iu4J5Xy6jr6OFn583E7bDFO6lCDFmkqYn6236Gs6SEpEX7Xp3wVtdaIpjMc0W7x8iuW07YlkRb8mQ+7EplUyCFz6Rvw2kM30GxEOLgy9RJnBeayemhqay317PaXsMzxhqetq/Bqx1M1XlM0tlMJJuJOhsf7ngnmUnuINflNvCrujx+UZ7CD0s79iwL5Z2GvXU1ro3/j56j7sCemUnODTdQf8cd7LryC4z/+9+wZ2TEMfVC7Ktn82ban34a3xlnYM8aQBcX2sS+61nM5In8vmkS7RGDY9K206wVeVZ8ukc4FBgoTgxPYm6kiP85tvCifRPP2zdSpNMo0Vnk4MPQBs2qi1ra2a6aCKmPuupxaINwr+OgIp3GHF3MkVYxU/bzh51Gs1HV8rzawApVgRsHZwenszAyflB9EmdoL6eHp3JyeDIbbHW869jFw7YPeEyvZL6ewGxdxHSdP+S+9zWaGtrYpOp4W+1go1GLoRUzzTyODU+g2Eof0nZ3U0S3NaU7m9cc21nu2MEqWwVn6VmcZs0YUCO5RlNPBxtULdtUAwFChIjgw80kncMUncMEMlEofDaLRcl+FiX797w/YCmaI3bCWmFqFXt0ENFpmH008pto3nZ+wHueMjKChRzWchqm5aXbsuG3HHSZDjotJ12x59bubagIjtSVODNfx3C2QDCP5PYTyA5Ox47CsEwMy8RmRh+1aRIMayIRk1DYwjQt7Ja5Z3JYEWzawmFFcEVCeCNBvJEevOGePc/TurrIaqwgs2cthhmmFWiN5aPHk0QoLw/vpGIKZ5eSPL0ER342KjkHjrgMZl4AW1+A9Y/DX86EGUvh1NsgTbqMHCppYB5G/9j0D4JmkDNKzsBYtxXHf98gvGQBOvujA76Mpg9wB5vYnn8267p8LGsoYEFSPTM9LXFMuRBizDEMWk+fj/uxN7jp8QC3XpbB9pb/8rP0CHce+WUeW1nFptoO/nDZHMZnDu8/7EKMBK01tT/8IVaXn+xvfwdl7Hsy81rnStKMZErtBbh6Gkht30hNxkJC2s5DtePItHezKLkuDqkXQgwHNw7mRYqYFykiQJjttia22BrZZWtltVGJjp0T52ofJTqLQtLI16kU6jTySd3ntu2DbbGvi53BVh6tT6fYbXJlQayBwOEjlH86rqonsdW9jpm/GGdJCdnXX0/jb37Dri9ezfi//gWbb3QMbigOfdqyqL/9doykJFLPPntA77E1rsDormNT7hU8VJHEaSkdtNjrybNSsCMXPHxSqdrNRaHZnBmazlp7LevtdaxT1bSrbrQBPu0izfIwzyxkvJlOnuUjXXtxYNBDhFajm+1GE5vtjTxrrOMZ+1q82slEnU0x6WTqZDSaMCY7VTPbVD0tKoBXO1kcnshx4RK8OA+c0H7YMZhtFjDbLKBedfKuYxer7ZW8bUTvAndogyRceHAQwSKESQSTMCYacGHHjQM3dlzagQL8KkQ73QRUtOuITMvLaaEpzIkUkaqH949HJzZODU9hbqSQ55ybedz+Ic+otRyjJzJLFzJV55KBFwODHsJU08Yu1cImVccGVUOzisaDJO0kWbtwaIPtRiNvGGUAZOtkFumJHG2V7jM4otfQeJ3hAaUzgsl9xpu8Z2zjyHAhF0RmYU9p6Hd9S0PAstNlOeg0nXRZPjpalrLDVUFF8od05fyLYDiT1LZj8flnYhg2MGwowIbGa1h4DQuPofEYFp7dr1Wv5x9b5lQaC4hoRdBSdFo2yiIGnf4wnR09hNoD6NZ2PC3NFLTXU/rKm9S/+Cr1QNCXgmv2dPIXTCd57kzsM5bClNNhwxOwblm0wfmEG2HRN8AmzaWDpbQePQPKzZs3T69YsSLeyehTnb+OpU8tZXrmdL428Qq8V90Kpknw218Atyu6kmVy2NqfYmLj7bzP862yWXiMMN/JXYVbro4SQsSBvSNA/rI3CaoIN31WM8EV5BbbVF4c/zN+/1YdCsUvLzycM2cNb39ho5FSaqXWel680xFPiRyHP67tiSep/d73SLvkElJOP32f5a2RTpZu/w7HuQ/j875TKKp4nKy65awvuYp/Nk/mkYYirsnawGxvcxxSL4Q42HoIU210UGlro9Joo87opEUF9jQ6Q/TKuWhjcxoFOpUCUinQaWSSdND6dDY1/Koulze6fPx8UhtLs7ujC7SFe8vvUFYP3cc/CPboAIbda9bQeNddeA6fxbh778WWEp/+VUeKxOLREYtb/vpX6n9xO+lXXIFvyZIDv0Fr3O9cB4FaTg7dSbdl4+7iCr7ueIiZkVw+FZp18BM9RkWI3kU9mEb8HsJsszWx1dZEjdFBg9FJWH3UnpFueRhnpTHFzGZ2JB/HQfqDwEJTrzrZaWuhTfUQUCFCysSuDezEJm0AipCKEMIkpExCRNAKPNqBVzsotFIpMTPI1N4R66+/QXXxpmMna+w1BNVHd7IrzV5xKEk7KTUzmGhmUmpmkq0/ij8aTZvqYbutmbW2WspsTVhKk69TWahLmKpzmaSzSR7gXTo7aOLPxltsMxo5OTSZk8KTPlF5WGjW2+p41bGdWlsH6drLYj2FJdbUIV9xPhhaQ7NpY5PfSW1tG5H6nRQ37GB2XR2pPSE0ECjNZ9zxC0g/YT7OZAtW3A8Vb0PhXDj/XsiafNDTORr1F4ulgXkYaK259pVrea/2PX589I8Y96vHsL+xguDXP4seX7hnvcyGdxi/859szT+X6+vPpKw7iRvzPiTfEYhj6oUQY52juYOCZW/hT7bzvUvD4Db5abeXrrn38st3uihr6OKsWfn88NwZ5PjifxtxvMhJbeLG4Y8L19Sw49xzcRQWkfPd7/Z59fIfG5/goebn+HnGlRSpJGatupX2pBJeTzufb287jCO9TVyZtTkOqRdCxEsYk2YVoNHoosnw06D8NBl+Go0uenrdMu7SdvKJXulcqrOYqLMpIQvXMN0cGrbgR7UFrAt4uGdaK8enBwEw/BV4tt5NePz5hGZ8dc/6gQ8+oOlPf8JVWkrxA/djz84elnQkIonFiR+LezZvpvyiT+OeNYus664b0JgetppXca/5OY95ruDG1lO4vaiaLE8D19sf5fzgTBZEikcg5WKoLDQ9RFBEu+QYrt/CscDEos7opMJoJaDCWGjsGORYyeRayWTqpAEPaugnxHp7HWvsNew0WvY0VGdoLwU6jYLYn6X5pJKmPfhw00OEctXEalXFG2obSTg5JziD2WbBsOVRo9lia+RdewVbbI1oBcU6gyN0EZN0Nrk6hXR25zNal/wE8RMioEJ7nvtVEAuNWzvw4CAVD2naSxoe0vHijNU7C4sGOtmlWtmpmthELWWqkUjsTxClNePrYW6Z5qitFqX1sYROKyH/9OPwFQexrbkfzDCc/GOYfw30cS4xlvUXi+WbPwyeL3+e5VXLuXjqxRS8thnHa+8TPnvxXo3LWCZ51c/jd+VyT8fxbAykcHnmZmlcFkLEXTgzhbpzF5D/1Dv88p9ufvEZ+EpKD1evvJRfz/kZ/2yaypMfVvHGtkZuPnM6n543DpshAwCKxGR2dVH5la+iTYvMq6/us3G5w/TzWOvLzHNNocCeSXbN/7BZQepSZ3NPVQleI8JF6WVxSL0QIp4c2MjTPvJMH/QaGkWj6VRBGlW0sbnR8NOo/Kw3qnnLth0AQyuKSGOSzmG6zmOWLiQN79DSYcAtebXcXF3E9VvSuXtaC8emhbCSxhPOOgZHxZOYWXMxcxYA4D3qKLLdbpp+/3vKL72M4gcfwDlO+pAUI8/q7qb6m9/CSEoi4wtfGNiA0WYI55b76XAWcVPryZyX1sZMTw/vqCYAimSAv4RnoPCOcJdChwobBoVW6rAMZJmEkwWRYhZEigkSocpop9LWRoOKxq0yo4EeI9Lne+3a4LhwKSeGJw5791AqNuDhNDOHNtXNGnsNW2zRrlYsY3AXvCoNup+2Xqe2Y2Jh9rqa3tCKAiuFo83xjDPTYl2/eMAHHbMVj88MU9tVzVHlmzlhfTn8bic1DgepC48jNWcXSc/dhNr8LJz3B0iTP7oORBqYP6HWnlZ+/t7PKU0t5dRAKa67f4E5ZQKRJQv3Wi+r8R1coRYeTbqCZ5oLOCG5mvlJ/fdlI4QQIymYn0HteUeT98x7fP8fDv5yWS73pjbw/pbv8Y20U1l43re47+0qbn5iHX95q5zvnTWdE6YculdIidFJh0JUXft1gmVlZN9wQ79X8S1rfYWA1cO5SQtxBpvJr3qetqRJPNQxl+3dyXwhcyPJtr4PwIUQY49CkaLdpGg3E63MvZZ1qiCVRhtVRjtVRhvv2HbwirEFgPE6gwVWCQt1KQUMrvHAa9P8pLCaW6sLuXZzBr+b2sri9CChwnMw/Dtxrb2D7mPuRXuiv3OeWbPIufFGGn/7W8o//RkK77yTpIULhqcAhBgArTV1t91GaMcOcm68ccB9gjvKn8DoaeD68M2Md4X5XGZ0bKIdqhGbNsi1pG9xIQbLhZ2JVuZeMUuj6VIhGpUfvwoSUGFsGBRYKeRYSSPS13ma9nBCeCInhCcSJEKT4adFBehQQTR6T9rd2o4ndqWyWzvw6Gj/2QoIYxFUETpVsNfUE81PrHuUVO0mz/KRayXvubL54zINuMBwQXopG30T+MGcTeS2lHP8Kh/HvbuZjnAP9vSJpBevJ7VsEY4Lbo8ODjiQP87GKOki4xPQWvOd5d/hpYqX+Hn+l5hw8/1ot5Pg1z8HKR+NdOvqaWDaul9SZRRwQtfPmOtt5IrMzcgFgEKIRONsaifvqXdBKZZfOo2HMjegtck1QRfzZt/Oq10TeOSDXdR3BDlmYibfOHkK80vGxsj1cltu4sXh3rRlUfOdG+n4z3/IuPpqko89ts/1/GY352+/iYn2fK5PPY+JW+8luX0rf02/jp/UzOUobz2XZ26RY0chxJBYaGqMDspsTWy2NVBui45nX6wzONoqZaEuIX8Qjc2dpsH3awrY0ePijsltnJHVg+ppxLPlTqzUSfTM/w0YHzUKhGtqaPz974nU1ZHzrW8O/CrSUUJicWLGYq01jb+9k+b77iPlnHNIu/DCAb1PBVtwv34Fb0am8U3zBn5TVEmWI3r7wE+N52jXfq7tOeZgJl0IIfZYbqvgOddGPP58pr83n7N3rmZmYxko8BV2k3bCYSR94wFUSl68kxpX/cVi6UjkE/jtyt/yQvkLfNZ3EuN/8Be03Uboy5fs1bisrAgTtv2FsDa4pOsGDnO38vnMLdK4LIRISKGsVGouPAbLaWfJA2v4wabDmWTL4C5PmDvWXcu8+p9w5znj+dzC8ayv6eDT977DZ+59h+VbGxlNf1iKQ4sOhai95VY6/vMfUi+6qN/GZYB/tb5Eh+XnnKSFpLauJbVtA2t9J/CL2iOZ6Grnssyt0rgshBgyA0WRlcri8ES+3HM0NweWcHZwOlgW/7Kt4Ab7Y3zfeJqX1WYChA64PZ/N4raCGqa6e/j2tnT+X2Uypiub4LgLsbVuwLn+N6A/uh3YUVBA3g9+gGfuXBp+9Wuqrr2WSGPjwcyyEDT94Q8033cfyUuWkHrBBQN7kxXGvvInhM0It4cv4YcFNXsalzWanaqRQuvQHrRSCJFYjjfH8/ngHCJJ9ZQvfo1Hz1jKF0++kScmHk9rYwqV/9jB9hNPoOlnNxBpaop3chOOXME8RH9e/2d+u/K3fFodxafu24Lq7iF47WXo3Ky91iuoeIq8upf5Uuh6tjum8LXs9TgNq5+tCiFEYlDBMDkvrSZpey1tR5Ty+ln5/Nv8gE5Mzg6EuDD3U/infokXt/v595oaWgNhSrOSuHzRBC6YW0Sy69DrgUmumkqsOLxbpKWFqq9fR/fKlaScdx6p553X79V6awLb+OquX3KkaxI3eJcwbd2v6MbJ4sAvsCv4du6H0jWGEOKgifY9WctKexUNRhdObWehLmGJNZVp5KL2M5BT2ILfN+TwcmcKp2R0c9ukdtIaXsBZ9wLhcWcSOuz6vW7b1VrT+cILtC9bhnK7yf3uTaR+6lOj/mpmicWJFYu11jTfey+Nv7uLpGOPJeOqq/oc+6Av4dW/I632P1wf+hpz8kqYm/TR2ESVtPId++N8KjiT+TLAnxBihJUbLfzZ/QEZJPGl7qW83l7Iq61ezqxezpW7XkA3aDAUvhMXk37pZ/EuXDjg375DQX+xWBqYh+DRLY/y03d/yuWVJZz5aDna6yH0xQvRhbl7rZfcuJIpO/7C3yMn8YzzbC7N3IpDjZ7yFkKMcVqTuqqMjHc2Y7qd7PrUAp4vreet0Hbs2uLCQIRT8y8gMPkK3qy2eGFDHdsb/SS5bFw4p4jPL5rAxOzkA3/OKCEntYkTh3frXree6uuvJ9zQQOZVV5F09NH9rtsS6eDzO3+EgeKnvgs5Ysu9OLobuCR0C1uZwPU5a8h1dI9g6oUQY5VGU2m08YG9irX2GoLKJE+nsMSaynF6Ehkk9f0+DU+1pfFgUyZ5TpOfTmzjuMDTOOtfJlx8DqEZXwO1dx+a4dpaWv78Z4JbtuCZN4+cb34T75wjRyKbB4XE4sSJxWZbGzW33ELXy6/gPfpoMq+5ZsANLPWbn6F05+95wDwLT85iZnv3jr9/MF7nHbWdG7uX4NOug5F8IYTYr3KjhQfdH5BJEreaZ+EyfbzQkcJzbUlc1bGMsyvepmOnFyukcBQXk/7pi0hdurTfMWAOJdLAPAwC4QC3vXcbL69/mq+9l868d5owJxUT+vx54Nv7QDC8802OaniUVdYkHvFcwuKUBrnlVggxKjmaO8h+ZQ3u2la6JuWz/fTD+G/6FlaHa7FrzXn+bk5LPxbXlC+yNlLMCxvqeHdHMxFLc3RpJufPKeSMmXn43KN7dGk5qY1/HN7N7Oig8Xd30frww9hSU8m67jpcEyf2u35ER/hG5Z2sDZTxw9SLOGnbv3D7K/lC6Ntst03iy9nrybAHRzAHQggRFSLCOnsdK+xV7LS1oLTiSF3EYj2VOXpcn4Mubex287v6HKrDTj6d08WtzodJaXoVM+NwgrO/h3bvPRChtiz8r79O25NPYrW3k3T88WRf+zU8hx8+UtkcNhKLEyMWB1atovpb3ybS2EjaRRfhO+20AV0dHzEt1qx6jEWND/K+Poz23Ispdpt7rVNLO9+0LeOYyHjODs04WFkQQogDil7JvIJkXNxinkkBqZga3upKZn1bO18M/53S6hrqdmRhNZhgGHgXLCD17LPxnXwSttTBDfA7WkgD8ye0vmk9t752M5Nf38Flb9twBiJETjiKyNmLwfbRgV9Lj6Zn8/OcHXyO16wj+DDlZKZ4u+KSZiGEGDZa41tXTvr7W7EHgnRNLmDHkim8mFvBynAlEQVzeno4x0xh+rgLqc89g+d3GSzf2khdRw9uu8FJ03M5bWYeS6Zmj8rGZjmpjf9JrdnRQdsTT9D8f/dhtrWRfNJJpJ1/PkZS31f7ATSGW/l+zb2s7t7Gl92LuKT8XVK7K/hq6BtUOifxhaxNeAyz3/cLIcRIaVRdrLRXscpeTYcRJEW7OU5PZok1hSLS91o3aCn+1pzB021puAzNL9Nf4qzAP1COJIIzb8DMOXqfke6tYJCul16i4z//wfL7cR82g7SLLyb1zDP3+zuaSCQWxzcW92zdSuNdd9P18svYsrPJ+spX9vsHb29rm/x0rryTU63XeZ8jMHPPJcW5b5dq0auXd3BjYDE+5OplIUR8VRvtPOj+ABsGN5qnMomcPcs2BZw0t27iAvNJfB0Btu4aj6524mhrB5sN71Hz8J14EsnHH4dj/PhR303VbtLAPEQr61fy8Ov3kPzi+5y8FjLbLczJ4wkvPWmvLjG2ttvYtauMc7seY5pRyXJjPi3pc3Dse9GBEEKMWiocwbe+grRVZdj9QcKpSdQfVcoHkyO8mlpOnQrj0JqF3T0sMjKZnL2Ees9xPNuQxfvl7bR1h3HYFAtKMjluchbHTs5iel4KxigY+VROauMTh7Vp0r1mLR3P/pu2J59Cd3fjmjqV9EsvxTlhQv/v05r3Axv5cc39+K1uruop4qqatwloJz+MXIk7JZtjk2tl0F0hRMIxsdhma+IDeyWbbA1YSjNeZzBXj2euVUwJmRixsdqrQg7+0ZzB8i4fhxmV3Oe+iwKrBjN1GqEpV2JlHrlvQ3N3N/633qLr1VcJV1WhXC6SjjkG30knknTccThycvpKVkKQWDzysdgKhfAvX07708/Q+dJLKLeblNNPx3faaRgez37fa2rNK9U9bN/6Cud2P8Zko5qV7lNxZy0CtW93GjW08y3bMo6NTOCs0PSDlSUhhBiURtXFg+4P6FA9XGDNYamejY2PfsPaQyb+1jXMCb5CBh1saS5kV90EMur8JDU1AGDPzydp0dF4587Dc8RsnBMmjNp+mz9RA7NS6nTgLsAG3K+1vv1jy13AQ8BcoBn4jNa6PLbsZuAqwASu01q/MJBt9mUkgmnYDLOhehWr33ySjrffpHBLM9OqwNAQmlKMPn4+1oxJ+E2DrR12ypoCpLSuY4n1LguNTTSQwaakBXR5i/c5mBNCiENGxCSpvJ7kjZV4KxpQWhNJclM/JYtt+SFWZLaxOtck5FDkRSIcGbKY4CrAZ59MTc903mwpZl2bC1D43HZmF6UxpziNGQUpTM71MT7Di92WWAF3KCe1Ixk/lVIlwCNABrAK+JzWOjSUz+jPSMRhq6eH4Nat9GzcSGDlKvxvvIHZ1gZ2O0kLF+I75ZT9Niz3WEFe6/yQf7X8j03BCnIjTu6oa2JuuI3/mXN52nk2x6c1y2B+QohRoYsgH9qr2WCvp8JoRSvwagdTdR7TdB4TyKREZ9IcTOXZtlTe7vJwpnqT6+1PkKda6HTm05N7PN6i4yBlEhgfXf2itSa0bRv+99+n+8MPMZuaAHAUFuKZOwfPrMNxTZ6Ea+JEbFlZCXHl1Ug0MCdC7N5f+g52LNamSbCsjO5VqwisXEXXa69hdXVhpKSQfNxx+M48E1ty32NsaK1pClqsbAqzsboCd9P7nMsrTDWqaDTyaE0/mbB3Sp/v3UkTf7Qtp5Z2bgosJlmuXhZCJJAAIZ52bWCNvZYSK4ul+nDm6QnYezU0KyuE2bGRNP8qxlnlWFqxpqOE8voCkls0OXX1OHuiA5oaPh/u6dNxTZmCa8pknMXFOAoKcOTloZzOeGVzQIbcwKyUsgFbgVOAKuAD4BKt9cZe63wVOFxr/WWl1MXA+VrrzyilZgAPA/OBAuAlYHdE2e82+zIcwVSHwwS3baOxcRcVNRvpam0gVF9HuL4B6hvJqPOT06oxAAtoykulq2QqtZNL8LttmD1+nMFm8q0apqlKpqtdGErTotKo9M6k2jsdreSyZSHE2GEL9OCpaMSzqwFPZRP2QLQvW60gkOKkOVVTlWLSlKTpciu6PBByabxOjcvhwFAuIpaLnoiHHpLptlLpIQWvM4lkdzLO9EySM3NJ96WR5UvC7TBwOcBpVzjtCpdDkZWcQ2ZSNoZSGAbRx9hzp80YlpPiwZ7UjnT8VEo9CjyhtX5EKfUnYI3W+o+D/Qytdb/9RQzXSW1wxw66V63CbGvDbGsj0txCuLo6OtXVgRlNguHz4Z41C88RR+CZOXPPLdz+iKY1aOGPaAIRTW3nJpb7X2AHtVQZ7VgKikMmn+to56zObj7Us9jinEWKL4Vku3SHIYQYnfyE2GprZKethZ22FhoN/55lPu0iT6eQpVPpCqXQGkwmK9jEfGsnC3QFXm1hYqPZVkjAUYDlyiTsTCfsdGM57OTbMyluiUBZBeauWqzyKnRH+57tG0lJ2PPzcOQXYM/KwvAlY0v2YaT4sPl8GMk+DLcreoGNUkD0URkKW3o67unDczXqwW5gTpTYvb80Dlcs9r/7HuGqSiJNzURamglX1xAqLydcWYkORdu4bWlpuGbMgKMWEpw8nW5tEIjoPfE3HGiix99IsLuNYHcb9u4aCq1qZqgKJhj1ADTbCuhKPYYu72F9XrUcIsLjxof8W60lCScXBGcxzUzcq+iFEGPbGlsNzzu30Gp0k6Y9HKHHMVFnU6zT8eEhGRcGYA814ApsIzuwk2yzEgONaSl2duRR35RKuMWOpz1CUrsfW+Sj8xOtFDo1FZ2dg5Gbgy0tFXtKCo7UFGwZudhSUjB8yRhuF4bDgeFw4va4UE4HyuEAy0Kb5j6PjoIC7Onp/WdsEPqLxft2erSv+UCZ1npHbEOPAEuB3o3BS4EfxZ4vA+5R0bP5pcAjWusgsFMpVRbbHgPY5kFhdnWx81MXAJAZmwD8bkWz10lZWjYvj8tnu3cG6zKm0+X08kv7vXwx9BTs/i9ZQZs9lQ5HDtuTT6DFNwW/MweUwn2wMyCEEIkmDcyCcXQdDV1aY3QGcNQ24ahrxtbaSV5rB4W7/KjuHozwxxv3wrGp/77q/77EYNlCI7pKbd/rHNZUwruNX+pz2eofnEKaNy7/Ao9Y/FRKbQJOBC6NrfPX2Hb/OITPeGe4CqA//nffpf4nP42+cNixpaRiz8rCWcdI0OwAAAywSURBVFKCd+FCnCUlOEtLsWdn9/nnwL82tPObD1v3vL7I/Rbvjt/IzGCIk3rCTOzxkBYeR8CzgOXjSvF4PAysh0ghhEhcHiCLQhbFXvt1kGrdQrVuoYEOGnUHW3UdXZ5ygp4IVcBq4P/o3VgXAspjU0wYvtrQxpK2DkiGt6YexmUlt5Ie7GR8Zz3H6iYuTuvGbGoiXFNDcPNmTL8fHQgMKN1Jxx5L8f33ffICGBmJErsPusa776Z71SoAlNeLPTMTR34+7pkzcRYX45o2DXtODkopjni4gmBF+z7buM9xNxfYVu15bSqDNmc2QVcedanHE0idScSZBUB/o2+0Wx284N/EUWoi59nm4XXKlctCiMS1kGzm61ls1NW8Y23jA13Ba2zdd0U7jEvO4ndJN9EZ6cITKMfwV+JNrmRqVjO+SCMugmgNYb/toylgI+zvItxRT6TOhhlUhMIGIf3JLpjKv/0XpJ133ifaxoEMpIG5EKjs9boKWNDfOlrriFKqnWjbbSHw7sfeWxh7fqBtAqCUuga4JvaySym1ZQBpHlafiU176yCahZUjnZy+ZAFN8U5EApPy6Z+Uzf5J+fRv5MpmC/Cn/a+ynvXAv/tcln7HsKVk/CDXH8n4mQm0aa0jfaw/lM/YIxHi8IH8Ova49/Vcm2HsfYclv4c2ye+hLa75/WpsinoHOIcKog3UTwPf+SQb37IZHri/ryVDyfNgY/FgJUrs3kuixuJT+5zbBmwD3hjUtlawigd46pMkZ6z9ZnxSUl6DJ2U2eGO6zNYD/+W1wb7t4JTZ+ecP59b6jMUDaWDuq5n84/1q9LdOf/P76lizz746tNb/B/zf/hI41imlVoz1wS72R8qnf1I2+yfl0z8pmwEZyfi5v88a7GfsPWMUx+GxVk8lv4c2ye+hbazlFxI2z4kSu/eeOYpj8UhJ0PqUsKS8Bk/KbPCkzAZvNJfZQEZQqgLG9XpdBNT0t45Syg6kAi37ee9AtimEEEKMZiMZP5uAtNg2Pv5Zg/0MIYQQYqxKlNgthBBCjCoDaWD+AJislCpRSjmBi4FnPrbOM8DlsecXAq/o6OiBzwAXK6VcsRFyJwPvD3CbQgghxGg2YvEz9p5XY9sgts2nh/gZQgghxFiVKLFbCCGEGFUO2EVGrF+pa4EXABvwoNZ6g1LqJ8AKrfUzwAPA32IDGbQQDZrE1nuU6KAIEeBru0en72ubw5+9MUNul9o/KZ/+Sdnsn5RP/6RsDiAO8fMm4BGl1M+AD2PbZiifcQgZa/VU8ntok/we2sZafiEB85xAsVsMXsLVpwQn5TV4UmaDJ2U2eKO2zFT0j1MhhBBCCCGEEEIIIYQQYnAG0kWGEEIIIYQQQgghhBBCCLEPaWAWQgghhBBCCCGEEEIIMSTSwDzKKKXGKaVeVUptUkptUEp9IzY/Qyn1olJqW+wxPd5pjRellE0p9aFS6tnY6xKl1HuxsvlXbHCNMUkplaaUWqaU2hyrQ0dL3YlSSt0Q+06tV0o9rJRyj+W6o5R6UCnVoJRa32ten3VFRd2tlCpTSq1VSs2JX8rFoWawcW9/9VEpdXls/W1Kqct7zZ+rlFoXe8/dSik18jnd20BjWWwwqX/F0v6eUmpCr23cHJu/RSl1Wq/5p8fmlSmlvjvSefu4wcSmQ2H/DibejNb9O1wxZLD7tL/PiFN+fxWr02uVUk8qpdJ6LRvUvhtK/Rjp/PZa9m2llFZKZcVej/r9KxJbIvzmJSqlVHnsu7RaKbUiNk+O53s52PHqUNRPmf1IKVUdq2urlVJn9lqWsMcrI0GNwLlMwtBayzSKJiAfmBN77gO2AjOAXwLfjc3/LnBHvNMaxzL6JvBP4NnY60eBi2PP/wR8Jd5pjGPZ/BX4Yuy5E0iTuqMBCoGdgKdXnbliLNcd4HhgDrC+17w+6wpwJvBfQAELgffinX6ZDp1psHGvv/oIZAA7Yo/psefpsWXvA0fH3vNf4IwEyPeAYhnwVeBPsecXA/+KPZ8BrAFcQAmwnejgUrbY89JYHFgDzIhzXgccm0b7/h1svBmt+5dhiCFD2af9fUac8nsqYI89v6NXfge97wZbP+KR39j8cUQHsqsAsg6V/StT4k6J8puXqBNQvvu72GueHM/vXR4HNV4dilM/ZfYj4Nt9rJvQxysjVF4H/VwmUSa5gnmU0VrXaq1XxZ53ApuInqwsJXqCRuzxvPikML6UUkXAWcD9sdcKOBFYFltlLJdNCtFg8ACA1jqktW5D6s5udsCjlLIDXqCWMVx3tNbLiY6M3lt/dWUp8JCOehdIU0rlj0xKxaFuCHGvv/p4GvCi1rpFa90KvAicHluWorV+R0eP3h4izt/1Qcay3uWwDDgptv5S4BGtdVBrvRMoA+bHpjKt9Q6tdQh4JLZuXAwhNo36/cvg4s2o3L/DFEOGsk/jckzTV3611v/TWkdiL98FinqlccD7bojf/4Oqn/0LcCdwI9B7FPlRv39FQkuI37xRRo7nezmY8ergpz4+9hMD+pLQxysj4WCfy4xgVg5IGphHMRW9De5I4D0gV2tdC9EKDOTEL2Vx9TuiB7ZW7HUm0NbrAL+K6Jd5LCoFGoE/q+ht1/crpZKQuoPWuhr4NbCL6Il+O7ASqTsf119dKQQqe60nZSUOigHGvf7q4/7mV/UxP54GE8v25Cu2vD22/mDLIV4GG5tG9f4dQrwZ7fu3t5HYp4l6TPMFolcjweDzO5Tv/4hTSp0LVGut13xs0VjYvyJ+Evk3LxFo4H9KqZVKqWti8+R4/sCGK16NNdfGunR4sFcXRlJmvRykc5mEIQ3Mo5RSKhl4HLhea90R7/QkAqXU2UCD1npl79l9rKr7mDcW2IneyvJHrfWRgJ/orRhjXiwALiV6204BkASc0ceqY7XuHIh8z8RBN4i41199HOz8uBhCLBvV+WXwsWlU53cI8WZU53eADuk8KqVuASLAP3bP6mO1oeY3IcpCKeUFbgF+0NfiPuYdMvtXxJ3Ul/07Rms9h2ic+ZpS6vj9rCtleWDyu9W/PwITgSOI/oH+m9h8KbOYg3gukzCkgXkUUko5iFbMf2itn4jNrt99C0vssSFe6YujY4BzlVLlRG+xOJHoVWBpsdtQIXp7Yk18khd3VUCV1vq92OtlRE/qpe7AycBOrXWj1joMPAEsQurOx/VXV6qI9ru4m5SVGFaDjHv91cf9zS/qY368DDaW7clXbHkq0dsWB1sO8TLY2DTa9+9g481o37+9jcQ+TahjmtgAPGcDl8W6e4DB57eJwdePkTaR6J8ma2K/XUXAKqVUHofw/hUJIZF/8+JOa10Te2wAniTaLYEczx/YcMWrMUNrXa+1NrXWFnAf0boGUmbAQT+XSRjSwDzKxPpVewDYpLX+ba9FzwCXx55fDjw90mmLN631zVrrIq31BKIDnbyitb4MeBW4MLbamCwbAK11HVCplJoam3USsBGpOxC9VXmhUsob+47tLhupO3vrr648A3w+NuLtQqB99+0+QnxSQ4h7/dXHF4BTlVLpsatITwVeiC3rVEotjH3W54njd30Isax3OVwYW1/H5l+slHIppUqAyUQHzvoAmKyUKlFKOWOf8cwIZK1PQ4hNo3r/Mvh4M6r378eMxD5NmGMapdTpwE3AuVrrQK9Fg9p3sf092PoxorTW67TWOVrrCbHfriqiAxrVcYjuX5EwEvk3L66UUklKKd/u50S/Y+uR4/mBGJZ4NdKJjie1d3/d5xOtazA6j1eG1cE+lxmRTAyUToCRBmUa+AQcS/Qy+LXA6th0JtH+1l4GtsUeM+Kd1jiX02Lg2djzUqI/YmXAY4Ar3umLY7kcAayI1Z+niI4+KnUnWjY/BjYTDYZ/IzrS7ZitO8DDRG9vChM9Ubyqv7pC9Had/0d0JOB1wLx4p1+mQ2cabNzbX30k2g9qWWy6stf8ebHv/nbgHkDFO9+xdB0wlgHu2Ouy2PLSXu+/JZanLcAZveafSXQE6+3ALQmQzwHHpkNh/w4m3ozW/TtcMWSw+7S/z4hTfsuI9pW4+3frT0Pdd0OpHyOd348tLweyDpX9K1NiT4nwm5eIU+x3Y01s2rC7bIbyW3woTwc7Xh2KUz9l9rdYmawl2kCa32v9hD1eGaHyOujnMoky7Q7WQgghhBBCCCGEEEIIIcSgSBcZQgghhBBCCCGEEEIIIYZEGpiFEEIIIYQQQgghhBBCDIk0MAshhBBCCCGEEEIIIYQYEmlgFkIIIYQQQgghhBBCCDEk0sAshBBCCCGEEEIIIYQQYkikgVkIIYQQQgghhBBCCCHEkEgDsxBCCCGEEEIIIYQQQogh+f/6Uen0wnC5uAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "\n",
    "for i, feature in enumerate(['age','income','member_days']):\n",
    "    plt.subplot(1,3,i+1)\n",
    "    sns.kdeplot(temp[temp[\"type\"] == \"always_respond\"][feature], shade=True, label='always_respond')\n",
    "    sns.kdeplot(temp[temp[\"type\"] == \"has_response\"][feature], shade=True, label='has_response')\n",
    "    sns.kdeplot(temp[temp[\"type\"] == \"never_respond\"][feature], shade=True, label='never_respond')\n",
    "    sns.kdeplot(temp[temp[\"type\"] == \"dont_care\"][feature], shade=True, label='dont_care')\n",
    "    plt.legend(); plt.title(f'{feature} distribution')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Findings:__\n",
    "* Customers who have __never respond__ (green curve) are mostly young in age, have low income, and have a short length of membership. It may be because new members don’t know how to navigate around the app and respond to the offers.\n",
    "* Customers who __always respond__ (blue curve) are average in income, and average in length of membership\n",
    "* Customers who __do not care__ about campain (red_curve) has higher income and longer membership (but the differences are not significant)\n",
    "\n",
    "__Others:__\n",
    "* Customer segmentation are recommended to get insight from data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4. BOGO vs discount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fk_customer_id</th>\n",
       "      <th>fk_offer</th>\n",
       "      <th>n_received</th>\n",
       "      <th>n_responded</th>\n",
       "      <th>n_not_responded</th>\n",
       "      <th>n_invalid_responded</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0020c2b971eb4e9188eac86d93036a77</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>always_respond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00715b6e55c3431cb56ff7307eb19675</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>dont_care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00857b24b13f4fe0ad17b605f00357f5</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>never_respond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>008d7088107b468893889da0ede0df5c</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>never_respond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0091d2b6a5ea4defaa8393e4e816db60</td>\n",
       "      <td>bogo_10_5_10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>has_response</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     fk_customer_id      fk_offer n_received n_responded  \\\n",
       "0  0020c2b971eb4e9188eac86d93036a77  bogo_10_5_10          1           1   \n",
       "1  00715b6e55c3431cb56ff7307eb19675  bogo_10_5_10          1           0   \n",
       "2  00857b24b13f4fe0ad17b605f00357f5  bogo_10_5_10          1           0   \n",
       "3  008d7088107b468893889da0ede0df5c  bogo_10_5_10          1           0   \n",
       "4  0091d2b6a5ea4defaa8393e4e816db60  bogo_10_5_10          2           1   \n",
       "\n",
       "  n_not_responded n_invalid_responded            type  \n",
       "0               0                   0  always_respond  \n",
       "1               0                   1       dont_care  \n",
       "2               1                   0   never_respond  \n",
       "3               1                   0   never_respond  \n",
       "4               1                   0    has_response  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = number.copy()\n",
    "df[\"type\"] = df.apply(lambda row: get_type(row), axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fk_customer_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fk_offer</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bogo_10_5_10</th>\n",
       "      <td>6329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bogo_10_7_10</th>\n",
       "      <td>6373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bogo_5_5_5</th>\n",
       "      <td>6261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bogo_5_7_5</th>\n",
       "      <td>6354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discount_10_10_2</th>\n",
       "      <td>6331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discount_10_7_2</th>\n",
       "      <td>6284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discount_20_10_5</th>\n",
       "      <td>6373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discount_7_7_3</th>\n",
       "      <td>6324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  fk_customer_id\n",
       "fk_offer                        \n",
       "bogo_10_5_10                6329\n",
       "bogo_10_7_10                6373\n",
       "bogo_5_5_5                  6261\n",
       "bogo_5_7_5                  6354\n",
       "discount_10_10_2            6331\n",
       "discount_10_7_2             6284\n",
       "discount_20_10_5            6373\n",
       "discount_7_7_3              6324"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of offers\n",
    "df = number.groupby([\"fk_offer\"])[[\"fk_customer_id\"]].count()\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 fk_customer_id                                              \n",
      "type             always_respond dont_care has_response never_respond no_offer\n",
      "fk_offer                                                                     \n",
      "bogo_10_5_10             2137.0     402.0        346.0        3444.0      0.0\n",
      "bogo_10_7_10             2035.0     784.0        358.0        2763.0    433.0\n",
      "bogo_5_5_5               2719.0     536.0        485.0        2521.0      0.0\n",
      "bogo_5_7_5               1621.0    1751.0        411.0        2165.0    406.0\n",
      "discount_10_10_2         3777.0     429.0        323.0        1177.0    625.0\n",
      "discount_10_7_2          1611.0    1481.0        387.0        2411.0    394.0\n",
      "discount_20_10_5         1042.0    1715.0        220.0        2362.0   1034.0\n",
      "discount_7_7_3           3482.0     524.0        414.0        1596.0    308.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ca95120b8>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = number.groupby([\"fk_offer\",\"type\"])[[\"fk_customer_id\"]].count().unstack()\n",
    "df = df.fillna(0)\n",
    "print(df.head(10))\n",
    "\n",
    "df.rename(columns = {\"fk_customer_id\": \"n_customers\"}, inplace = True)\n",
    "df.plot.bar(figsize=(15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Findings:__\n",
    "* The number of an offer is about 6300\n",
    "* Customers respond to discount better than bogo\n",
    "* Among all types of offer, customers responded to discount_10_10_2 and discount_7_7_3 the most. On the other words, people prefer discount with lower minimum spend and longer expiration days\n",
    "\n",
    "__Others:__\n",
    "* The offer approaches are not considered in this investigation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Future work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Possible tasks\n",
    "1. Customer segmentation\n",
    "2. Customer type predictation: to predict whether customer respond to offer or not\n",
    "\n",
    "However, we do not cover them here because:\n",
    "* This task has smaller datasets compared to the Arvato project. So, we leave this for future work\n",
    "* Starbucks has many products, so 1 product is not enough to clusify customers\n",
    "\n",
    "### Customer features for the two tasks:\n",
    "\n",
    "Each customers have following 36 attributes:\n",
    "* number of transactions per week\n",
    "* amount of money per transaction\n",
    "* age\n",
    "* income\n",
    "* n_received (8 types of offers)\n",
    "* n_responded (8 types of offers)\n",
    "* n_not_responded (8 types of offers)\n",
    "* n_invalid_responded (8 types of offers)\n",
    "\n",
    "One problem of seperating train/test sets is that if we use 36 features as explained before, a feature (e.g. number of transaction per week) may contain the informaiton of the whole month. On the other words, there is relationship between the feature and the customer type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions\n",
    "In this project, we perform data processing and data analytic based on Starbucks dataset. The most important thing is to understand the business flow. A lot of solutions are failed in this task because they simply evalutate the number of offer_received, offer_viewed and offer_complete while ingoring their orders and the time instant those event happens. .\n",
    "\n",
    "After analysing the data, we have some findings:\n",
    "* 40% of offerred was not responded. Starbucks and use this number to evaluate its campaign success\n",
    "* 7.1% customers are loyal users, they just bought products regardless of offers. Starbucks may send offer infrequently to save its cost\n",
    "* 27.7% customers have never responded to offers. They are mostly young in age, have low income, and have a short length of membership. One possible reason is that new members don’t know how to navigate around the app and respond to the offers. Also, Starbucks may create another offer which focuses on this group\n",
    "* Customers respond to discount offer better than BOGO offer. Among all types of discount, people prefer discount with lower minimum spend and longer expiration days\n",
    "\n",
    "Since customer segmentation and prediction tasks are somehow similar to that in Arvato project but in a smaller scale (1 month duration + small numbers of features), we decided to leave them for future work. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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