Quantium Virtual Internship Task 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 1 : Data preparation and customer analytics\n",
    "Conduct analysis on your client's transaction dataset and identify customer purchasing behaviours to generate insights and provide commercial recommendations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment Set Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Complete\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "print(\"Setup Complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Examine and clean transaction data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ready\n"
     ]
    }
   ],
   "source": [
    "# Read data\n",
    "filepath_t = \"/Users/victoriafy/Desktop/Quantium/QVI_transaction_data.xlsx\"\n",
    "transaction = pd.read_excel(filepath_t)\n",
    "print(\"Ready\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1）Summary original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43390</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43599</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>CCs Nacho Cheese    175g</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43605</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43329</td>\n",
       "      <td>2</td>\n",
       "      <td>2373</td>\n",
       "      <td>974</td>\n",
       "      <td>69</td>\n",
       "      <td>Smiths Chip Thinly  S/Cream&amp;Onion 175g</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43330</td>\n",
       "      <td>2</td>\n",
       "      <td>2426</td>\n",
       "      <td>1038</td>\n",
       "      <td>108</td>\n",
       "      <td>Kettle Tortilla ChpsHny&amp;Jlpno Chili 150g</td>\n",
       "      <td>3</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>43604</td>\n",
       "      <td>4</td>\n",
       "      <td>4074</td>\n",
       "      <td>2982</td>\n",
       "      <td>57</td>\n",
       "      <td>Old El Paso Salsa   Dip Tomato Mild 300g</td>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>43601</td>\n",
       "      <td>4</td>\n",
       "      <td>4149</td>\n",
       "      <td>3333</td>\n",
       "      <td>16</td>\n",
       "      <td>Smiths Crinkle Chips Salt &amp; Vinegar 330g</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>43601</td>\n",
       "      <td>4</td>\n",
       "      <td>4196</td>\n",
       "      <td>3539</td>\n",
       "      <td>24</td>\n",
       "      <td>Grain Waves         Sweet Chilli 210g</td>\n",
       "      <td>1</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>43332</td>\n",
       "      <td>5</td>\n",
       "      <td>5026</td>\n",
       "      <td>4525</td>\n",
       "      <td>42</td>\n",
       "      <td>Doritos Corn Chip Mexican Jalapeno 150g</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>43330</td>\n",
       "      <td>7</td>\n",
       "      <td>7150</td>\n",
       "      <td>6900</td>\n",
       "      <td>52</td>\n",
       "      <td>Grain Waves Sour    Cream&amp;Chives 210G</td>\n",
       "      <td>2</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0  43390          1            1000       1         5   \n",
       "1  43599          1            1307     348        66   \n",
       "2  43605          1            1343     383        61   \n",
       "3  43329          2            2373     974        69   \n",
       "4  43330          2            2426    1038       108   \n",
       "5  43604          4            4074    2982        57   \n",
       "6  43601          4            4149    3333        16   \n",
       "7  43601          4            4196    3539        24   \n",
       "8  43332          5            5026    4525        42   \n",
       "9  43330          7            7150    6900        52   \n",
       "\n",
       "                                  PROD_NAME  PROD_QTY  TOT_SALES  \n",
       "0    Natural Chip        Compny SeaSalt175g         2        6.0  \n",
       "1                  CCs Nacho Cheese    175g         3        6.3  \n",
       "2    Smiths Crinkle Cut  Chips Chicken 170g         2        2.9  \n",
       "3    Smiths Chip Thinly  S/Cream&Onion 175g         5       15.0  \n",
       "4  Kettle Tortilla ChpsHny&Jlpno Chili 150g         3       13.8  \n",
       "5  Old El Paso Salsa   Dip Tomato Mild 300g         1        5.1  \n",
       "6  Smiths Crinkle Chips Salt & Vinegar 330g         1        5.7  \n",
       "7     Grain Waves         Sweet Chilli 210g         1        3.6  \n",
       "8   Doritos Corn Chip Mexican Jalapeno 150g         1        3.9  \n",
       "9     Grain Waves Sour    Cream&Chives 210G         2        7.2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print first 10 rows\n",
    "transaction.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 264836 entries, 0 to 264835\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   DATE            264836 non-null  int64  \n",
      " 1   STORE_NBR       264836 non-null  int64  \n",
      " 2   LYLTY_CARD_NBR  264836 non-null  int64  \n",
      " 3   TXN_ID          264836 non-null  int64  \n",
      " 4   PROD_NBR        264836 non-null  int64  \n",
      " 5   PROD_NAME       264836 non-null  object \n",
      " 6   PROD_QTY        264836 non-null  int64  \n",
      " 7   TOT_SALES       264836 non-null  float64\n",
      "dtypes: float64(1), int64(6), object(1)\n",
      "memory usage: 16.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# Data.info\n",
    "transaction.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2）Correcting the data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2018-10-17\n",
       "1   2019-05-14\n",
       "2   2019-05-20\n",
       "3   2018-08-17\n",
       "4   2018-08-18\n",
       "Name: DATE, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seeing that 'DATE' is not in the correct format\n",
    "transaction[\"DATE\"] = pd.to_datetime(transaction[\"DATE\"], origin=\"1899-12-30\",unit=\"D\")\n",
    "transaction['DATE'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3）Removing irrelevant values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should check that we are looking at the right products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                                     264836\n",
       "unique                                       114\n",
       "top       Kettle Mozzarella   Basil & Pesto 175g\n",
       "freq                                        3304\n",
       "Name: PROD_NAME, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a summary of the PROD_NAME column\n",
    "transaction[\"PROD_NAME\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like we are definitely looking at potato chips but how can we check that these are all chips? <br>\n",
    "We can do some basic text analysis by summarising the individual words in the product name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chips     49770\n",
       "Kettle    41288\n",
       "Smiths    28860\n",
       "Salt      27976\n",
       "Cheese    27890\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split product names as well as remove all digits and special characters such as '&'\n",
    "import re\n",
    "PROD_word_only = transaction[\"PROD_NAME\"].str.replace('([0-9]+[gG])','').str.replace('[^\\w]', ' ').str.split()\n",
    "\n",
    "# Calculate the frequency grouped by words and sort them\n",
    "PROD_freq = pd.value_counts([word for name in PROD_word_only\n",
    "                                for word in name]).sort_values(ascending=False)  \n",
    "PROD_freq.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are salsa products in the dataset but we are only interested in the chips category, so let's remove these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove salsa\n",
    "transaction = transaction[~transaction[\"PROD_NAME\"].str.contains(\"[Ss]alsa\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4）Handling missing values and outlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 246742 entries, 0 to 264835\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count   Dtype         \n",
      "---  ------          --------------   -----         \n",
      " 0   DATE            246742 non-null  datetime64[ns]\n",
      " 1   STORE_NBR       246742 non-null  int64         \n",
      " 2   LYLTY_CARD_NBR  246742 non-null  int64         \n",
      " 3   TXN_ID          246742 non-null  int64         \n",
      " 4   PROD_NBR        246742 non-null  int64         \n",
      " 5   PROD_NAME       246742 non-null  object        \n",
      " 6   PROD_QTY        246742 non-null  int64         \n",
      " 7   TOT_SALES       246742 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(1), int64(5), object(1)\n",
      "memory usage: 16.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# Summarise the data to check for nulls and possible outliers\n",
    "transaction.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>246742.000000</td>\n",
       "      <td>2.467420e+05</td>\n",
       "      <td>2.467420e+05</td>\n",
       "      <td>246742.000000</td>\n",
       "      <td>246742.000000</td>\n",
       "      <td>246742.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>135.051098</td>\n",
       "      <td>1.355310e+05</td>\n",
       "      <td>1.351311e+05</td>\n",
       "      <td>56.351789</td>\n",
       "      <td>1.908062</td>\n",
       "      <td>7.321322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>76.787096</td>\n",
       "      <td>8.071528e+04</td>\n",
       "      <td>7.814772e+04</td>\n",
       "      <td>33.695428</td>\n",
       "      <td>0.659831</td>\n",
       "      <td>3.077828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>7.001500e+04</td>\n",
       "      <td>6.756925e+04</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>130.000000</td>\n",
       "      <td>1.303670e+05</td>\n",
       "      <td>1.351830e+05</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>203.000000</td>\n",
       "      <td>2.030840e+05</td>\n",
       "      <td>2.026538e+05</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>272.000000</td>\n",
       "      <td>2.373711e+06</td>\n",
       "      <td>2.415841e+06</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>650.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           STORE_NBR  LYLTY_CARD_NBR        TXN_ID       PROD_NBR  \\\n",
       "count  246742.000000    2.467420e+05  2.467420e+05  246742.000000   \n",
       "mean      135.051098    1.355310e+05  1.351311e+05      56.351789   \n",
       "std        76.787096    8.071528e+04  7.814772e+04      33.695428   \n",
       "min         1.000000    1.000000e+03  1.000000e+00       1.000000   \n",
       "25%        70.000000    7.001500e+04  6.756925e+04      26.000000   \n",
       "50%       130.000000    1.303670e+05  1.351830e+05      53.000000   \n",
       "75%       203.000000    2.030840e+05  2.026538e+05      87.000000   \n",
       "max       272.000000    2.373711e+06  2.415841e+06     114.000000   \n",
       "\n",
       "            PROD_QTY      TOT_SALES  \n",
       "count  246742.000000  246742.000000  \n",
       "mean        1.908062       7.321322  \n",
       "std         0.659831       3.077828  \n",
       "min         1.000000       1.700000  \n",
       "25%         2.000000       5.800000  \n",
       "50%         2.000000       7.400000  \n",
       "75%         2.000000       8.800000  \n",
       "max       200.000000     650.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE              0\n",
       "STORE_NBR         0\n",
       "LYLTY_CARD_NBR    0\n",
       "TXN_ID            0\n",
       "PROD_NBR          0\n",
       "PROD_NAME         0\n",
       "PROD_QTY          0\n",
       "TOT_SALES         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot\n",
    "sns.boxplot(data=transaction.loc[:,[\"PROD_QTY\",\"TOT_SALES\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no nulls in the columns but product quantity appears to have an outlier which we should investigate further. Let's investigate further the case where 200 packets of chips are bought in one transaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69762</th>\n",
       "      <td>2018-08-19</td>\n",
       "      <td>226</td>\n",
       "      <td>226000</td>\n",
       "      <td>226201</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>200</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69763</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>226</td>\n",
       "      <td>226000</td>\n",
       "      <td>226210</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>200</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "69762 2018-08-19        226          226000  226201         4   \n",
       "69763 2019-05-20        226          226000  226210         4   \n",
       "\n",
       "                              PROD_NAME  PROD_QTY  TOT_SALES  \n",
       "69762  Dorito Corn Chp     Supreme 380g       200      650.0  \n",
       "69763  Dorito Corn Chp     Supreme 380g       200      650.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the outlier\n",
    "transaction.loc[transaction[\"PROD_QTY\"]==200]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two transactions where 200 packets of chips are bought in one transaction and both of these transactions were by the same customer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69762</th>\n",
       "      <td>2018-08-19</td>\n",
       "      <td>226</td>\n",
       "      <td>226000</td>\n",
       "      <td>226201</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>200</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69763</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>226</td>\n",
       "      <td>226000</td>\n",
       "      <td>226210</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>200</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "69762 2018-08-19        226          226000  226201         4   \n",
       "69763 2019-05-20        226          226000  226210         4   \n",
       "\n",
       "                              PROD_NAME  PROD_QTY  TOT_SALES  \n",
       "69762  Dorito Corn Chp     Supreme 380g       200      650.0  \n",
       "69763  Dorito Corn Chp     Supreme 380g       200      650.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See what other transactions that customer made\n",
    "transaction.loc[transaction[\"LYLTY_CARD_NBR\"]==226000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like this customer has only had the two transactions over the year and is not an ordinary retail customer. The customer might be buying chips for commercial purposes instead. We'll remove this loyalty card number from further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [DATE, STORE_NBR, LYLTY_CARD_NBR, TXN_ID, PROD_NBR, PROD_NAME, PROD_QTY, TOT_SALES]\n",
       "Index: []"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter out the customer based on the loyalty card number\n",
    "transaction = transaction.drop(index=[69762,69763])\n",
    "\n",
    "# Re-examine transaction data\n",
    "transaction.loc[transaction[\"LYLTY_CARD_NBR\"]==226000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's better. Now, let's look at the number of transaction lines over time to see if there are any obvious data issues such as missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></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>2018-07-01</th>\n",
       "      <td>663</td>\n",
       "      <td>663</td>\n",
       "      <td>663</td>\n",
       "      <td>663</td>\n",
       "      <td>663</td>\n",
       "      <td>663</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-02</th>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-03</th>\n",
       "      <td>674</td>\n",
       "      <td>674</td>\n",
       "      <td>674</td>\n",
       "      <td>674</td>\n",
       "      <td>674</td>\n",
       "      <td>674</td>\n",
       "      <td>674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-04</th>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-05</th>\n",
       "      <td>660</td>\n",
       "      <td>660</td>\n",
       "      <td>660</td>\n",
       "      <td>660</td>\n",
       "      <td>660</td>\n",
       "      <td>660</td>\n",
       "      <td>660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  PROD_NAME  PROD_QTY  \\\n",
       "DATE                                                                           \n",
       "2018-07-01        663             663     663       663        663       663   \n",
       "2018-07-02        650             650     650       650        650       650   \n",
       "2018-07-03        674             674     674       674        674       674   \n",
       "2018-07-04        669             669     669       669        669       669   \n",
       "2018-07-05        660             660     660       660        660       660   \n",
       "\n",
       "            TOT_SALES  \n",
       "DATE                   \n",
       "2018-07-01        663  \n",
       "2018-07-02        650  \n",
       "2018-07-03        674  \n",
       "2018-07-04        669  \n",
       "2018-07-05        660  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of transactions by date\n",
    "trans_by_date = transaction.groupby([\"DATE\"]).count()\n",
    "trans_by_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "        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>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>677.857143</td>\n",
       "      <td>677.857143</td>\n",
       "      <td>677.857143</td>\n",
       "      <td>677.857143</td>\n",
       "      <td>677.857143</td>\n",
       "      <td>677.857143</td>\n",
       "      <td>677.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>33.687536</td>\n",
       "      <td>33.687536</td>\n",
       "      <td>33.687536</td>\n",
       "      <td>33.687536</td>\n",
       "      <td>33.687536</td>\n",
       "      <td>33.687536</td>\n",
       "      <td>33.687536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>607.000000</td>\n",
       "      <td>607.000000</td>\n",
       "      <td>607.000000</td>\n",
       "      <td>607.000000</td>\n",
       "      <td>607.000000</td>\n",
       "      <td>607.000000</td>\n",
       "      <td>607.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>658.000000</td>\n",
       "      <td>658.000000</td>\n",
       "      <td>658.000000</td>\n",
       "      <td>658.000000</td>\n",
       "      <td>658.000000</td>\n",
       "      <td>658.000000</td>\n",
       "      <td>658.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>674.000000</td>\n",
       "      <td>674.000000</td>\n",
       "      <td>674.000000</td>\n",
       "      <td>674.000000</td>\n",
       "      <td>674.000000</td>\n",
       "      <td>674.000000</td>\n",
       "      <td>674.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>694.250000</td>\n",
       "      <td>694.250000</td>\n",
       "      <td>694.250000</td>\n",
       "      <td>694.250000</td>\n",
       "      <td>694.250000</td>\n",
       "      <td>694.250000</td>\n",
       "      <td>694.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>865.000000</td>\n",
       "      <td>865.000000</td>\n",
       "      <td>865.000000</td>\n",
       "      <td>865.000000</td>\n",
       "      <td>865.000000</td>\n",
       "      <td>865.000000</td>\n",
       "      <td>865.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        STORE_NBR  LYLTY_CARD_NBR      TXN_ID    PROD_NBR   PROD_NAME  \\\n",
       "count  364.000000      364.000000  364.000000  364.000000  364.000000   \n",
       "mean   677.857143      677.857143  677.857143  677.857143  677.857143   \n",
       "std     33.687536       33.687536   33.687536   33.687536   33.687536   \n",
       "min    607.000000      607.000000  607.000000  607.000000  607.000000   \n",
       "25%    658.000000      658.000000  658.000000  658.000000  658.000000   \n",
       "50%    674.000000      674.000000  674.000000  674.000000  674.000000   \n",
       "75%    694.250000      694.250000  694.250000  694.250000  694.250000   \n",
       "max    865.000000      865.000000  865.000000  865.000000  865.000000   \n",
       "\n",
       "         PROD_QTY   TOT_SALES  \n",
       "count  364.000000  364.000000  \n",
       "mean   677.857143  677.857143  \n",
       "std     33.687536   33.687536  \n",
       "min    607.000000  607.000000  \n",
       "25%    658.000000  658.000000  \n",
       "50%    674.000000  674.000000  \n",
       "75%    694.250000  694.250000  \n",
       "max    865.000000  865.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a summary of transaction count by date\n",
    "trans_by_date.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's only 364 rows, meaning only 364 dates which indicates a missing date. Let's create a sequence of dates from \n",
    "1 Jul 2018 to 30 Jun 2019 and use this to create a chart of number of transactions over time to find the missing date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\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>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>663.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>663.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-02</th>\n",
       "      <td>650.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-03</th>\n",
       "      <td>674.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>674.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-04</th>\n",
       "      <td>669.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-05</th>\n",
       "      <td>660.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-26</th>\n",
       "      <td>657.0</td>\n",
       "      <td>657.0</td>\n",
       "      <td>657.0</td>\n",
       "      <td>657.0</td>\n",
       "      <td>657.0</td>\n",
       "      <td>657.0</td>\n",
       "      <td>657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-27</th>\n",
       "      <td>669.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-28</th>\n",
       "      <td>673.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>673.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-29</th>\n",
       "      <td>703.0</td>\n",
       "      <td>703.0</td>\n",
       "      <td>703.0</td>\n",
       "      <td>703.0</td>\n",
       "      <td>703.0</td>\n",
       "      <td>703.0</td>\n",
       "      <td>703.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>704.0</td>\n",
       "      <td>704.0</td>\n",
       "      <td>704.0</td>\n",
       "      <td>704.0</td>\n",
       "      <td>704.0</td>\n",
       "      <td>704.0</td>\n",
       "      <td>704.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  PROD_NAME  PROD_QTY  \\\n",
       "2018-07-01      663.0           663.0   663.0     663.0      663.0     663.0   \n",
       "2018-07-02      650.0           650.0   650.0     650.0      650.0     650.0   \n",
       "2018-07-03      674.0           674.0   674.0     674.0      674.0     674.0   \n",
       "2018-07-04      669.0           669.0   669.0     669.0      669.0     669.0   \n",
       "2018-07-05      660.0           660.0   660.0     660.0      660.0     660.0   \n",
       "...               ...             ...     ...       ...        ...       ...   \n",
       "2019-06-26      657.0           657.0   657.0     657.0      657.0     657.0   \n",
       "2019-06-27      669.0           669.0   669.0     669.0      669.0     669.0   \n",
       "2019-06-28      673.0           673.0   673.0     673.0      673.0     673.0   \n",
       "2019-06-29      703.0           703.0   703.0     703.0      703.0     703.0   \n",
       "2019-06-30      704.0           704.0   704.0     704.0      704.0     704.0   \n",
       "\n",
       "            TOT_SALES  \n",
       "2018-07-01      663.0  \n",
       "2018-07-02      650.0  \n",
       "2018-07-03      674.0  \n",
       "2018-07-04      669.0  \n",
       "2018-07-05      660.0  \n",
       "...               ...  \n",
       "2019-06-26      657.0  \n",
       "2019-06-27      669.0  \n",
       "2019-06-28      673.0  \n",
       "2019-06-29      703.0  \n",
       "2019-06-30      704.0  \n",
       "\n",
       "[365 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a sequence of dates and join this the count of transactions by date\n",
    "# Create a column of dates that includes every day from 1 Jul 2018 to30 Jun 2019\n",
    "dates_seq = pd.date_range(\"2018-07-01\",\"2019-06-30\")\n",
    "\n",
    "# Join it onto the data to fill in the missing day.\n",
    "fill_dates = trans_by_date.reindex(dates_seq)\n",
    "fill_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-12-25'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out the missing day\n",
    "fill_dates.index.difference(transaction[\"DATE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STORE_NBR        NaN\n",
       "LYLTY_CARD_NBR   NaN\n",
       "TXN_ID           NaN\n",
       "PROD_NBR         NaN\n",
       "PROD_NAME        NaN\n",
       "PROD_QTY         NaN\n",
       "TOT_SALES        NaN\n",
       "Name: 2018-12-25 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It seems like 2018-12-25 is the missing day\n",
    "fill_dates.loc['2018-12-25',:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Transaction')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yt7/9jbPOOovjjz8ewzBYvHgxHo+HBx98MHz8Dh06sH37dm655RaOPfZYzj777BoxHnHEEUybNo1//OMfTJgwgeOPPx6ATz/9lIKCAq666qrwLGn1cdJJJzF8+HAWLlzIxo0bGTZsGE6nk//973/h9+ZArrrqKr788kveffddfvrpJ0aPHk1RUREfffQRhmHw2GOP1agjdfzxx5OQkMCGDRs48sgjaxT/Hj16NFOnTmXBggX87ne/Y9y4cdjtdj766CN2797NhRdeyKhRo+p9rr/IyMgA4LbbbuOYY46pNhNeY2rq8xAREWnvlFASERGROnnggQfo2LEjH330ES+99BLp6ekcdthh/P73v+e9997jX//6FytWrOCoo46qV7tDhgzhpZde4tlnn2X58uUUFxfToUMHLrnkEq699tpwPaQpU6ZgGAYLFy7k9ddfJz4+np49ezJ37lwcDgd/+MMfWLp0KcOHDwfgD3/4Az179uRf//oXb7/9NiaTieHDh3PDDTcwdOjQ8PFvvfVWbrvtNt5//32KiopqTSgBzJw5k4EDB/Lyyy/z7rvvYrVaGTBgAHfddRennHLKIb2ndrudefPm8cILL/DRRx/x8ssv43A4GDZsGFdffXWts8r9msPh4J///Cfz58/n3XffZeHChSQkJHD88cdz9dVXM3DgwFqPeeqpp/Laa6/V6J30izvuuIPDDjuMhQsX8s4772CxWOjZsyfXX399uOfPobrmmmvYsmULy5YtY/v27U2WUIKmPQ8REZH2zmQcasEDERERERERERFpl1RDSURERERERERE6kUJJRERERERERERqRcllEREREREREREpF6UUBIRERERERERkXpRQklEREREREREROrFGukAGsP333+Pw+GIdBiNwuPxtJlzkfZB16y0BrpOpTXQdSotia5HaQ10nUpr0NqvU4/Hw7Bhw2pd1yYSSg6HgwEDBkQ6jEaRnZ3dZs5F2gdds9Ia6DqV1kDXqbQkuh6lNdB1Kq1Ba79Os7Oz97tOQ95ERERERERERKRelFASEREREREREZF6UUJJRERERERERETqpU3UUBIRERERERGRxuPz+cjNzcXtdkc6lFbN5/MdsA5RSxEVFUVmZiY2m63O+yihJCIiIiIiIiLV5ObmEh8fT48ePTCZTJEOp9VyuVxER0dHOowDMgyDoqIicnNz6dmzZ53305A3EREREREREanG7XaTmpqqZFI7YDKZSE1NrXdvNCWURERERERERKQGJZPaj0P5t1ZCSURERERERERE6kU1lERERERERESkxXn++ef56quvMJvNmEwmbrjhBh555BEAsrOz6dGjB9HR0UyYMIHzzjuPJUuW8NJLL2E2m/H7/VxwwQWcffbZAJxwwgl06tQJs9lMIBCgqqqKe++9l8MOO4ypU6fWqHV0xRVXcNxxx9Ua15NPPsnSpUtZtGgRVmsorXL++eczd+5cdu3axY033kifPn0wDAOPx8Ps2bPp3bs3M2fOZN26dSQlJWEYBqWlpUybNo1zzjmnSd/HpqKEkoiIiIiIiIjs15src3ntu5xGbfP8kV055/DM/a7fvHkzn3zyCQsXLsRkMpGdnc2MGTN45513AJg6dSqzZs2id+/eAHz55ZcsWrSI5557jvj4eNxuNzfccAMOh4PTTjsNgBdffBGHwwHAF198wVNPPcW8efMAmDNnTrituti1axfz5s3jD3/4Q411o0eP5rHHHgPg448/5qGHHgof59Zbb2Xs2LEAlJaWcsYZZzBp0qRWObxQQ95EREREREREpEVJSUkhLy+PN954g7179zJgwADeeOON/W6/YMECbrnlFuLj4wGIiopixowZvPzyy7Vun5eXR0JCwiHHd+WVV/Luu++yfv36A25XUVFBly5dal1XWFiI3W5vlckkUA8lERERkToJBg22FVXSOz0u0qGIiIg0q3MOzzxgb6KmkJKSwrPPPstLL73E008/TVRUFNOnT2f8+PG1bp+Tk0O3bt2qLevatSt5eXnh15dffjkej4f8/HzGjBnDjBkzwutmzJhRbcjbE088QUpKyn7ji4mJ4b777mPmzJk1El3Lly9n6tSpeL1eNmzYwPPPPx9e9/DDD/Pcc8+Rl5dH7969eeKJJ+r2hrRASiiJiIiI1MFHdzyLqdxE2j2TSUxNinQ4IiIibdqOHTuIi4vjwQcfBGDNmjX8/ve/Z9SoUSQlJdXYPiMjg127dpGYmBhetn37djp16hR+/cuQt7lz55Kbm0tqamp4XX2HvAGMHDmSo48+ukZS6NdD3rKzs7n00kv5/PPPgX1D3pYuXcojjzxSIwnWmmjIm4iIiEgdJFZm0jO6PzHxMZEORUREpM376aefmDVrFh6PB4CePXsSHx+PxWKpdfupU6fy0EMP4XQ6AaisrOShhx7ioosuqrHtjTfeSH5+Pq+88kqD45w+fTqff/45O3bsqHX9/no5jRs3jhNPPJE777yzwTFEinooiYiIiNRBvDWGcl8VNrs90qGIiIi0eaeccgpbtmzhvPPOIyYmBsMw+POf/xyukfRbJ5xwAk6nkyuvvBKTyUQwGOTcc8/l9NNPr7Gt2Wzm/vvv56KLLuKkk04Cag55O+2005gyZcpB43Q4HDzwwANceOGF4WW/DHkzm81UVFQwc+ZMoqKiaux73XXXMWnSJD777LP9zijXkpkMwzAiHURDZWdnM2DAgEiH0Sja0rlI+6BrVloDXafSUGVFe1l8y4MEOnTg4jm3NckxdJ1KS6LrUVoDXadNS+9v43C5XNUSVS1Zbf/mB7oO1ENJRERE5CC+efVVKnwldO/VOdKhiIiISDPwer1cccUVNZb37NmTv/71rxGIqOVRQklERETkIAJr/ZyeeSW704sjHYqIiIg0A7vdzoIFCyIdRoumotwiIiIiB1FWvIJv9r7LEZPOinQoIiIiIi2CeiiJiIiIHEQHfzrukuxIhyEiIiLSYiihJCIiInIAOeuy6dP7fLa4tkQ6FBEREZEWQ0PeRERERA5gw5c7MZlMuDrbIx2KiIiISIuhHkoiIiIiB+Delg/0oPf4YyIdioiISLuyYsUKFi1axGOPPYbT6WTixInMnj2bww8/HID169dz880388Ybb3DmmWeyZMkSHA4HAH//+99ZunQp5eXl5Ofn06dPHwBOOukkvvjiC/7+97+Hj3P99ddz9NFHM3ny5FrjCAaDPP/883z++edYLBYA7rjjDvr16xfe5qyzzmLEiBHcfffd4WWDBw9myJAhWCwWfD4fwWCQRx99lK5du3LCCSfQqVMnzGYzHo+HQYMGMXPmzHD8v5Wbm8v48eN59dVXGTx4MAALFy6ksLCQ66+/PtyeyWSiqqqKc845h4suuogVK1Zw4403hs+/srKSzMxMHnnkEez2hj0sU0JJRERE5AAyquJwOXxkDe8R6VBEREQi5x+/q335tP+G/r9kJuxZU3P9qQ9CpyGw+mX4/pWa+9VRXFwc999/P3fccQdvvfUWZrOZO+64g9mzZxMbG1tj+yuvvJIrr7yyWlIKwDAMPvvsM15//XXOO+88/vvf/+Lz+fabTIJQcqqkpISXXnoJs9nMjz/+yHXXXcf777+PzWZj5cqVZGVlsXz5cpxOJ3FxcQAkJiYyf/58oqOjAVi0aBH/+Mc/uOuuuwB48cUXwwmkZ599lscee4yZM2ce8D34y1/+wptvvllrMuiX9rxeL6effjqnnnoqAKNHjw6fP8DNN9/MJ598El5/qDTkTUREROQAEuzxOL3OSIchIiLS7h155JGMGzeOp59+mhdeeIETTzyRoUOH1qsNk8nEgw8+yLPPPsvmzZt57rnneOCBBw64z6uvvsp1112H2RxKoQwZMoQ33ngDm80GwOuvv8748eM5+eST+c9//rPfdvLy8khISKh13bRp0/jggw8OGEf37t0ZM2ZMteRQbdxuNw6Hg/j4+BrrvF4v+fn5JCYmHrCNulAPJREREZH92PLTevJdP1Fp9kY6FBERkcg6WI+i02YfeP3wi0L/NdD06dO54IILSEpKYv78+YfURseOHbnhhhu44IILmDt3LikpKQfc3u1210jAJCcnA+B0Olm5ciX33Xcfffv25brrruPiiy8GoKysjCuuuAKXy0VpaSmnnHIKN9xwQ63HiIqKwuPxHDT2G2+8kXPPPZfvvvuuxrrLL78ck8nE1q1bOemkk8IJr+XLlzN16lSKioowm82cf/75HHXUUQc91sGoh5KIiIjIfny59EO+K/mBnd0rIh2KiIiIAA6HgxNPPJHx48eH6xkdirPPPpuoqCjGjRt30G0TEhJwOqv3Vv7www9xOp288847BINBrr76av76179SUFDA119/Dewb8vbGG28wcuRIbDZbrcPzIJSY2t+6X7Pb7Tz44IPccccduFyuautefPFFXnrpJT777DN27tzJO++8A4SGvC1YsICXX34Zm81GZmbmQY9TF82WUPL5fNx8881ceOGFTJkyhS1btrBu3TrGjBnD1KlTmTp1KosXLwbgtddeY9KkSZx//vl8+umnzRWiiIiISDVHbHAxKf00Tj7hjEiHIiIiIhEyceJEnnrqKQzDAGDVqlU8+OCD2O123njjDZ577jnmz5/P/PnzueOOO3j55Zer7W+xWLj33nv58MMP+eyzz2o9xgsvvMBpp51Wp3gGDRrEGWecwQsvvFDrervdTmpqKj6fr9ry5ORkHn74Ye644w7y8/PrdKwDabYhb0uXLsXv97No0SKWLVvG448/ztixY5k2bRqXX355eLuCggIWLFjAm2++icfjYcqUKRxzzDENrj4uIiIiUl+VnhSio6LpNWJEpEMRERFpl5YtW8akSZPCrx999NH9bvvrwtpnnnkm06ZNa5QYrrjiCp544gkuuOACrFYrVqs1XIPJMAz69u0b3nb8+PE8+OCD7N69u1obUVFR3H///cyYMYMjjzwSCA1RM5vNBINBBgwYwJ///Oc6x3TNNdfU6IDz6/Y6duzIhAkTWL16dbVt+vTpw9SpU7nvvvv429/+Vt+3oppmSyj17NmTQCBAMBjE6XRitVpZu3Yt27Zt4+OPP6Z79+7cdttt/PjjjwwfPhy73Y7dbqdbt25s2LCBIUOGNFeoIiIiIgBE2dMo8pfQM9KBiIiItEOjRo3im2++qbH8+uuvr7Hsk08+2W8bo0aNqnXdsmXL6hSHxWLhpptuqnXdW2+9Ve21w+EID3lbtmxZtWFpI0eO5OOPPz5gvPuTmZnJa6+9Fn5ttVp58803w6/rc/7XXnttvY69P82WUIqJiWHXrl2cdtpplJSU8Nxzz7Ft2zbOO+88Bg8ezLPPPsvTTz9N//79q1Uij42NrTFW8bc8Hg/Z2dlNfQrNwu12t5lzkfZB16y0BrpO5VB4KipJtiezrWJts1w/uk6lJdH1KK2BrtOm5fP5atToacvWrFnD448/XmP5+PHjOf/88w+5XcMw6vU+zps3r9Yk2l//+le6dOlyyHHUhc/nq9fvVLMllP75z39y7LHHcvPNN7N7924uvfRSXn75ZdLT0wE4+eSTuffeexk5ciSVlZXh/SorK2ud6u7XHA4HAwYMaNL4m0t2dnabORdpH3TNSmug61QOxcf/WESaqQveVGuzXD+6TqUl0fUorYGu06aVnZ1NdHR0pMNoNkceeSSvvPJKo7frcrnq9T7eeOONjR5DXdlsthq/UwdKMDVbUe6EhIRwYigxMRG/388111zDjz/+CMDXX3/NoEGDGDJkCCtXrsTj8VBRUcGWLVvIyspqrjBFREREACjdvAcAU7/kCEciIiIi0vI0Ww+lyy67jNtuu40pU6bg8/mYPn06vXr14t5778Vms5GWlsa9995LXFwcU6dOZcqUKRiGwfTp03E4HM0VpoiIiAgApiIfS4JFTDlbM7yJiIiI/FazJZRiY2N54oknaixftGhRjWXnn39+g8YoioiIiDRUPB2xGy5i4+IiHYqIiIhIi9NsQ95EREREWouA309mbBIJUZUH31hERESkHWq2HkoiIiIircWWNet4f++HpGemRjoUERGRdmn27NmsW7eOgoIC3G43Xbt2JSEhgQ0bNvD666+TkpJCZWUlF198MQ8++CAffvghS5cuZdGiRVitoVTH+eefz9y5c8nMzKz1GE8++SRpaWlMnjyZwYMHM3z4cCA0g+Cxxx7L9ddfj9msfjj7o3dGRERE5Dc2fLiO4WmT6Dx0ZKRDERERaRGmvT+N/2z+T6P+fCAzZ85kwYIF/P73v+eMM85gwYIFPP3001x++eXMmDEDwzC47bbbuOiii+jfvz8Au3btYt68eYd0fomJiSxYsIAFCxbw2muvUVRUxMsvv3xIbbUXSiiJiIiI/EZ0rpPeDjN9Rg6KdCgiIiLyKxdddBF2u52rr76amJgYzj333PC6K6+8knfffZf169c36Bgmk4lp06axePHihobbpmnIm4iIiMhvpARMVPpK6N93bKRDERERaRH+ceo/Gv3nQ3XRRRcxbdo0Fi5cWG15TEwM9913HzNnzuSNN95o0DHS0tIoKSlpUBttnXooiYiIiPxGvCMTl7c40mGIiIjIb5SXl3P//fdzzz33cMcdd1BZWX0CjZEjR3L00UfXOst8fezatYuOHTs2qI22TgklERERaVfKi0r43z1P73d9MBAg2hZLhaUZgxIREZE6+ctf/sJFF13EhRdeyMknn8w999xTY5vp06fz+eefs2PHjkM6RjAY5MUXX+R3v/tdQ8Nt05RQEhERkXbl+3v+wyDXEJY+9Gyt66vKyjCZTEQlVda6XkRERCLjxRdfxGw2M2XKFACuv/56duzYwX/+859q2zkcDh544AGcTmed2y4rK2Pq1KlccsklXHzxxXTr1q1afSapSTWUREREpF3xOHdCTB9sBUW1rt+9cxtrChbjSg5yZDPHJiIiItVNmjQp/PPll19ebZ3VauXVV1+tdb/DDjuMdevWHbDt66+/Pvzz2rVrGxBl+6SEkoiIiLQrpdZQB+1Se1yt6wu2bmOvewfWsuTmDEtERESaQF5eHjNmzKix/IgjjuCGG26IQERthxJKIiIi0q6kJg0AIBiTUOt6W6mdM7tey0+eH5ozLBEREWkCnTt3ZsGCBZEOo01SDSURERFpVwr9pQBkFLtqXV8cGw9AeZdOzRWSiIiISKujHkoiIiLSrgSLctgYU0GuexdH1LI+4AsAVkzJtQ+JExERERH1UBIREZF2pndSPzrH9KEcf63rEzdtB6BzSX4zRiUiIiLSuiihJCIiIu2K3WIizpbEUYmja11fXlEKQKWp9iFxIiIiIqIhbyIiItLO2EwOAOwme63rDSNAld+FNSWmOcMSERGR31ixYgU33ngjffr0AcDj8XDmmWeybt061q1bR1JSEoZhUFpayrRp0zjnnHMAWL58Oc888wyGYeDz+Rg/fjyXXXYZJpOJqVOn4nK5iI6OxufzkZmZye23305y8v5nd+3Xrx9PP/00J510EgCff/45ixcvZvbs2dXac7lcHHPMMUyfPp3c3FwmTJhA//79sVgseDweYmJieOKJJ0hMTGz6N68ZKKEkIiIi7YrVFEoURVlrv3HsaNtE4J1/0veSl5ozLBERkRZtx9RLqr1OnDiRpEkTKXz+BSq/+ILYMWNI+/1VlP77Lcreeqvatt0X/B/+ggJ23XQzAF3mPoo1Pb1Oxx09ejSPPfYYAF6vl1NPPZX+/ftz6623MnbsWABKS0s544wzmDRpEps3b2bOnDnMmzePDh064Pf7mTVrFvPnz+fKK68EYM6cOfTu3RuAd955h7vuuosnn3xyvzFER0cze/ZsRowYQUpKSo31v7RnGAZTpkxhzZo1JCcn06dPH+bPn090dDQAjz76KG+88QZXXHFFnc69pdOQNxEREWlXokw2AGxmS63rt9OTopNvwWePbc6wRERE5CCcTidmsxmrtXrfmMLCQux2OyaTiYULF3L11VfToUMHAKxWKzNnzuTVV1+ttc0JEyawbt06PB7Pfo8bGxvLtGnTmDVr1gHj83q9+P3+8LF/zTAMdu/eTUJCwkHOsvVQDyURERFpN8rLyqly7sEUnUSsPYFdP22kS7+s6hsFoung6Eh8WlpkghQREWmBui/4v1qXp/3+KtJ+f1X4ddKkUM+l37Kmp++3jQNZvnw5U6dOxWQyYbPZuPPOO1myZAkPP/wwzz33HHl5efTu3ZsnnngCgJycHM4999xqbcTFxeFyuQgGg7UeIyEhgfLyctIP0GtqypQpfPzxx7z77rs1hqzNmDGD6OhocnJy6N+/P8nJyeTn57N582auuOIKKioqwsP1Jk6s+d60VuqhJCIiIu3GjnU/8t/ihawr+44KXxm5GzbV2MZl8/LF9/NIUkJJREQk4kaPHs2CBQv4v//7P+bPn8+4ceMAuPXWW3nllVe45557yM/Pp1u3bgBkZGSwa9euam04nU7sdjtmc80UiGEYFBYWkpqaesA4TCYTDzzwAI8//jj5+dVngp0zZw4LFizgk08+ISkpib///e8A4SFvr7/+Op07dyY1NbVG76rWTAklERERaTf8pT6O73wF3qR4TrOZiB15bI1tUsoTGdLrgghEJyIiIvU1btw4TjzxRO68804AJk+ezLPPPktBQQEAPp+P+++/nwsvvLDW/d944w1Gjx5da7Lptzp27Mj111/Po48+Wut6s9lMRkYGPp+v2vKoqCgeeeQRnnnmGTZs2FCf02vR2k5qTEREROQg/FV+OjnS2Gv38c+KKjYu/pTBV51dbZtYaxRJMeqdJCIi0lpcd911TJo0ic8++4zjjjuO6dOnM336dAKBAH6/n5NPPjlckBv2DVGDUI+mu+++u87HOvvss/nwww+rLft1e1FRUTz88MM4nc5q26SlpfHnP/+Zu+66i0WLFtUpgdXSKaEkIiIi7UbBmnV04nCMsl10tPahfO1q4Oxq29hNFgKGr9b9RUREpPmMGjWKUaNG1Vg+e/bsaq/tdjvvvfde+PWYMWMYM2ZMrW0uWLCg3nEsW7as2uunn376oO0lJSXx2muv4XK5wssmTJjAhAkT6n38lkoJJREREWk37E4DbOBI9PP91w/ToV9mjW3Mhhm/EkoiIiLtyquvvlotKfWLm266ieHDh0cgopZPCSURERFpN8wxSeCD9JHDsXm7UxhTWGMbi8lKIBho/uBEREQkYi644AIuuEA1FOuj9Q/aExEREamjEnssALH9e9E5mEpKaVyNbWzmaAxMzR2aiIiISKuihJKIiIi0G8787/ki9w06ZnbG668kupaRbdsrveRUljd/cCIiIiKtiIa8iYiISLthKyvFi5/YmCi8QQ82s6PGNoWutfjM/ghEJyIiItJ6KKEkIiIi7Ub3hJGkRXUEwBP0EGutOeTtmIzR7LKWNXdoIiIiIq2KEkoiIiLSbuQUfsROSxz9OB1f0I/DHFVjG5vZDniaPzgRERGpZsWKFdx444306dMHAI/Hw5lnnsm6detYt24dSUlJGIZBaWkp06ZN45xzzgFg+fLlPPPMMxiGgc/nY/z48Vx22WWYTCamTp2Ky+UiOjoan89HZmYmt99+O8nJyfuNo1+/fjz99NOcdNJJAHz++ecsXryY2bNnh+M64YQTmDZtGldeeSUAubm5nHjiifzpT3/iuuuuC7d1zTXXUFlZyYIFC6rF8osrrriC4447rlHfx6aihJKIiIi0Gw57HMGAG4DtMX72VFnp5fdjse67JbKabNirgpEKUUREpEV669FV+13X/6hODDi6E289uqrazwfbvi5Gjx7NY489BoDX6+XUU0+lf//+3HrrrYwdOxaA0tJSzjjjDCZNmsTmzZuZM2cO8+bNo0OHDvj9fmbNmsX8+fPDyZ45c+bQu3dvAN555x3uuusunnzyyf3GEB0dzezZsxkxYgQpKSk11v/vf//j9NNP56233uLyyy/HbA6Vq+7WrRsffvhhOKFUWlrKjh07SEtLC+/761haGxXlFhERkXajV8fz6ZwZmhK4KGk3heZt7N6VG17vKi3DbDIRHdgTqRBFRERkP5xOJ2azGau1et+YwsJC7HY7JpOJhQsXcvXVV9OhQwcArFYrM2fO5NVXX621zQkTJrBu3To8nv33To6NjWXatGnMmjWr1vWvv/4655xzDv3792fp0qXh5cnJyaSkpLBlyxYAFi9ezKmnnlqfU27R1ENJRERE2o2oYBBPsAqAI8psdIvpj3PVGujeA4A9u/OwAflx0ftvREREpB2aePOIem1Tl+3rYvny5UydOhWTyYTNZuPOO+9kyZIlPPzwwzz33HPk5eXRu3dvnnjiCQBycnI499xzq7URFxeHy+UiGKy9B3JCQgLl5eWkp6fvN44pU6bw8ccf8+6775KYmBhevn37dlwuF/379+ecc87hxRdf5Pjjjw+vP+200/jvf//LDTfcwMcff8xNN93Ed999F14/Y8aMakPennjiiVp7QbVESiiJiIhIu2E1gccTKrhdZbFAEPI27aT/z+srS8uxBgK4Y2MiF6SIiIiE/XrI2y+WLFkSHvK2dOlSHnnkEbp16wZARkYGu3btYuDAgeHtnU4ndrs9PBTt1wzDoLCwkNTU1APGYTKZeOCBB7jooou49tprw8tff/11XC4XV1xxBQCrVq1ix44dWCwWAI4//niuuOIKJk2aRHp6OlFR1es3asibiIiISAvn9/uxmaPw/HyDZ+oWmu3Nb933VNBcVMZH5X5iqjTLm4iISGswbtw4TjzxRO68804AJk+ezLPPPktBQQEAPp+P+++/nwsvvLDW/d944w1Gjx5da7Lptzp27Mj111/Po48+CoTuLRYvXszLL7/M/PnzmT9/Pr///e955ZVXwvvExMTQs2dPHn74Yc4444yGnm6Loh5KIiIi0i6UF7swm0x4O4WeAvYdMQi2V5JiNsLb5O7YwzBbFXsDKsotIiLSWlx33XVMmjSJzz77jOOOO47p06czffp0AoEAfr+fk08+OVyQG6oPM8vIyODuu++u87HOPvtsPvzwQwA++eQTBg0aRFJSUnj9pEmTOOusszjvvPPCy84880zuuusu5s6dy/bt26u199shb6eddhpTpkypz+lHjBJKIiIi0i7krv0er3svZksBMBZTQhxQScH2qvA2bqePbo7OFET5IxaniIiIhIwaNYpRo0bVWD579uxqr+12O++991749ZgxYxgzZkytbS5YsKDecSxbtqza66effjr88ymnnFJtXUZGBsuXLwfgtddew+VyccIJJ3DCCScA0Lt373AMhxJLS6KEkoiIiLQLO39axU+7PyQhvQcAGb16sMm3FcO3r5aB4a3kv7lv0W/kmRGKUkRERCLh1VdfrZaU+sVNN93E8OHDIxBRy6eEkoiIiLQLmcEYslIvprDzLiD0NHNZiZs4wx3eJuDzMir9dzhjVWZSRESkPbngggu44IILIh1Gq6KEkoiIiLQLvjIPMQn9iUv1hJf1jSomaNjCr61R8fSIG8imDsWRCFFERESk1dDjNxEREWkX9u4N1UoqDO6rj2QzG1jNpvDrPm4XAFmdOzVvcCIiIiKtjBJKIiIi0i7Exod6ImUO7h9e9oNzOatLloZf7ykJJZvcaJY3ERERkQPRkDcRERFpFyrN6QBkDBwcXtYlpg8Z8enh115L6NYoulOH5g1OREREpJVRQklERETahagiJ4G4ADEJMeFlCaYYMmP64CovJzohgaQuXTAKDTK6dotgpCIiIgKwYsUKbrzxRvr06QOAx+PhzDPPZN26daxbt46kpCQMw6C0tJRp06ZxzjnnALB8+XKeeeYZDMPA5/Mxfvx4LrvsMkwmE1OnTsXlchEdHY3P5yMzM5Pbb7+d5OTk/cZRVFTE7Nmz2bFjB1arlU6dOjFz5kzS09O5+eabyc/PZ9euXdhsNjp06EBWVhYFBQUMHjyYqVOnAlBZWcmkSZN44okn6N+//36P1ZoooSQiIiLtQr4zj92uEqbajgsvS0swYTJMlOYUED0ogew9+ex2VjDROjZygYqIiLRAr94zs9rrQeNOYvBxJ7HiP6+z/YeVHH/p7+nQoxfvPT6HyrISLrh7NpWlJbz3xJxq+x151nn0HHZ4nY87evRoHnvsMQC8Xi+nnnoq/fv359Zbb2Xs2NDndWlpKWeccQaTJk1i8+bNzJkzh3nz5tGhQwf8fj+zZs1i/vz5XHnllQDMmTOH3r17A/DOO+9w11138eSTT9Z6fMMwuOaaa7jmmms48cQTAfjqq6+4+uqref3113n00UcBePLJJ0lLS2Py5MkAFBcXc84553D00UczePBg5syZwwUXXNBmkkmgGkoiIiLSTjiSuxMVnVVt2dZefQFYuzkPAF/BFvIrVzZ7bCIiInJwTqcTs9mM1Vq9b0xhYSF2ux2TycTChQu5+uqr6dAhNHzdarUyc+ZMXn311VrbnDBhAuvWrcPj8dS6fvXq1aSmpoaTSQBHH3003bt359tvv91vrCkpKdx5553cc889fPPNN+Tk5DBt2rT6nnKLph5KIiIi0i5kWpKIsjqqLTO8JUAMVRs3A2MYkjCEJEdaROITERFpyS64e3aty0edfR6jzj4v/PqMG2eEf45NSt7vfnW1fPlypk6dislkwmazceedd7JkyRIefvhhnnvuOfLy8ujduzdPPPEEADk5OZx77rnV2oiLi8PlchEM1j7pRkJCAuXl5aSnp9dYl5eXR2ZmZo3lXbp0YdeuXQeM/YQTTuD9999n5syZLFy4EJPJdMDtWxsllERERKRdWJP/KVaTjaGcEV42MCMKcqBbUT4AdksyBtGRClFERER+49dD3n6xZMmS8JC3pUuX8sgjj9CtW6j+YUZGBrt27WLgwIHh7Z1OJ3a7HbO55iAtwzAoLCwkNTW11uN37tyZ9957r8by7du3M3r06IPGf8YZZ+Dz+cjIyDjotq2NhryJiIhIu5BsH0asuVO1ZQmHDWDd7q1sKS8NLTCb8Rv+5g9OREREDsm4ceM48cQTufPOOwGYPHkyzz77LAUFBQD4fD7uv/9+Lrzwwlr3f+ONNxg9enStySaA4cOHU1hYyMcffwzAzJkzmTNnDjt27GDUqFFNcEatR7P1UPL5fMycOZNdu3ZhNpu59957w2MZTSYTffv25e6778ZsNvPaa6+xaNEirFYr1157Lccff3xzhSkiIiJt1NCUPhR6CqotS+/Vi92W/2BzhW4iTbjwG1WRCE9EREQO0XXXXcekSZP47LPPOO6445g+fTrTp08nEAjg9/s5+eSTwwW5AWbMmEF0dKhHckZGBnffffd+2zaZTMybN4/Zs2fz/PPPEwgESExMJD09na1bt9KvX78mP7+WqtkSSkuXLsXv97No0SKWLVvG448/js/n48Ybb2TUqFHcddddfPzxxwwbNowFCxbw5ptv4vF4mDJlCscccwx2u725QhUREZE2yGoKEAhUTyhZbTaOShlMsTs05M1isuIOuiIRnoiIiPzGqFGjau0FNHt29bpMdru92rC0MWPGMGbMmFrbXLBgQb3jSE1N5eGHH662rKCgAJdr3z3D9ddfX+u+RxxxRHg2uram2RJKPXv2JBAIEAwGcTqdWK1Wvv/+e4488kgAxo4dy7JlyzCbzQwfPhy73Y7dbqdbt25s2LCBIUOGNFeoIiIi0saUlpRjNdtx2Wt2Z/dgxhYVKsTt9jtx+9VDSUREpL159dVXa62VdNNNNzF8+PAay2sr4N3eNFtCKSYmhl27dnHaaadRUlLCc889x7fffhuuch4bG0tFRQVOp5P4+PjwfrGxsTidzgO27fF4yM7ObtL4m4vb7W4z5yLtg65ZaQ10ncqu7bn0J4rihNga10KVxSDWiCE7O5vluxZhN8eQnj2u2WPUdSotia5HaQ10nTYtn89XrQdOWzdhwgQmTJhQ67qGvA+GYbSa99Hn89Xrd6rZEkr//Oc/OfbYY7n55pvZvXs3l156KT6fL7y+srKShIQE4uLiqKysrLb81wmm2jgcDgYMGNBksTen7OzsNnMu0j7ompXWQNepOPKLgSDdoytrXAtfulYRZU+j74AB7Ox5KXmxnohcL7pOpSXR9Sitga7TppWdnR2uMySHzuVytZr30Waz1fidOlCCqdlmeUtISAgnhhITE/H7/QwcOJAVK1YA8PnnnzNy5EiGDBnCypUr8Xg8VFRUsGXLFrKysporTBEREWmD8jdtYFflJsoLdtVY5wk6cViicFdW0cmcSromeRMRERE5qGbroXTZZZdx2223MWXKFHw+H9OnT2fw4MHceeedzJ07l169ejF+/HgsFgtTp05lypQpGIbB9OnTcTgczRWmiIiItEG5FQXsyP+SpG59a6yrsJaQ796Jc62Vb7Jn0ykpGrik+YMUERERaUWaLaEUGxvLE088UWP5Sy+9VGPZ+eefz/nnn98cYYmIiEg7kGLtTL/Ol5HXobTGOkcfN0u/eJWRW85mYPcLKPBubf4ARURERFqZZksoiYiIiESKv7SKZEdPCswVNdYZVYmc2Gkaed9vZ0jckVS5NeZNREQk0mbPns26desoKCjA7XbTtWtXkpOTufXWW3nooYcoLS3F5/PRv39/brnlFuLi4rj00ksJBoNs3bqVlJQUkpKSOProo7n22mtrPcaOHTu4//77CQQC+P1+Bg8ezM0334zZHKoOtHjxYm677Tb+97//kZGRAcCTTz5JWloakydPrtbW4MGDa8wG98gjjxAXF8eDDz7Ixo0bMZvN2Gw2br/9drp27doE71rzUkJJRERE2jyv2wDAZzHVWJfcqRPWXbHEVJkhDuKsGmovIiLyW/nzfqx1eYerh1D53V4qV+6tsS728AxiR2aQP+/Haj//st+BzJw5E4B///vfbN26lVtuuQW32815553Hfffdx9ChQwF46623uPnmm5k3bx7/+te/wvuefvrpjB079oDHmDt3LhdffDFjx47FMAz++Mc/8vHHH3PyyScD8Prrr3PxxRfz2muvcf311x+wrcTERBYsWFBj+QcffEB+fj7/+Mc/APjoo4944IEHePbZZw/YXmvQbEW5RURERCIl1tYZgLQu/Wqs6zt6KFu+/yvlFWsBKLTV7MUkIiIikffZZ59xxBFHhJNJABMnTqSkpIScnJx6t9e5c2feeustVq5cid/v5/HHH+ekk04CICcnh7KyMq6++mrefvvtarPU10fHjh1Zu3Ytixcvpri4mBNPPLHWckCtkXooiYiISJvnK98L9hRMZk+Ndck9+9J56DX4jAAArpTU5g5PRESkxTtQj6LYkaHeR3XZ92A9kw4kJyeHbt261ViemZlJXl5evYeRTZ8+nVdeeYW5c+eyceNGxo0bx1133UVCQgJvvPEG55xzDvHx8QwbNowPP/yQ008/fb9tlZWVMXXq1PDrDh068Oijj9K3b1/uvfdeXnvtNe677z46duzIzJkzOfLII+sVa0ukhJKIiIi0eQFPOdjBsFXWWGexWkl0dMYgNByuc0L35g5PRERE6iAjI4Mff6w59G779u107ty53u0tX76cyy67jMsuu4zKykrmzJnDM888w6233sq7775Lly5d+OSTTygrK+Oll146YEJpf0PeNm7cSM+ePZk7dy6GYbBs2TJuvPFGli1bhslUcyh+a6IhbyIiItLmmfqUsK3ySToPHVTr+iqTDa9hZkV5HpV+zfImIiLSEp144ol89dVX1ZJKr7/+OikpKYdU5Prhhx9m2bJlQGhm+p49e2K321m6dCmDBw9mwYIFzJ8/nzfeeIOioiI2bNhQ72OsWLGCuXPnEggEMJlM9O3bl+jo6FafTAL1UBIREZF2wGuJJjqxI4npabWu9/nyiTI7KCpfjCPm6GaOTkREROoiNjaW5557jgceeIDS0lICgQD9+vVj7ty5h9Te448/zn333cejjz6K3W4nMzOTWbNmccstt3DeeedV2/bcc8/l5ZdfpkOHDjz//PO8/vrr4ZgWLFhQY8gbwE033cTkyZP529/+xtlnn01cXBxms5mHHnro0N6AFkYJJREREWnzYn9KplfcWKwWW63rvb4K4uI6cnrmlbh6t/4nhiIiIm3FpEmTqr3u1q0bzz333AH3mT17dp3a7t27d3j2tV+rrf2rrroq/HNtM76tXbu21mO4XC5uv/32OsXT2iihJCIiIm1eaennfFq6kksctU8fbDU7wj/7gkZzhSUiIiJN7Mcff+Thhx+usfy0005jypQpEYio7VBCSURERNq8YJ+hJBdX7Hf92oxC/PkOukdnEJua1HyBiYiISJMaMmRIrcWypeGUUBIREZE2L8kzkG7RCftdb3LtIcHcBYBOvfs0V1giIiItmmEYbaJ4tBycYdS/h7ZmeRMREZE2L66ygmCgcr/rBzvSSHZkAGCPiW6usERERFqsqKgoioqKDinRIK2LYRgUFRURFRVVr/3UQ0lERETaPKvJRMDw73d9ZYfOsK0ZAxIREWnhMjMzyc3NpaCgINKhtGo+nw+brfZJQVqSqKgoMjMz67WPEkoiIiLS5plNFgJGYL/rUwf0gW1uVhV9RCZjmjEyERGRlslms9GzZ89Ih9HqZWdnM2DAgEiH0SQ05E1ERETaPIvJQtAI7nf9gCOHAjAi9aTmCklERESkVVMPJREREWnzyvxBDPY/5M0WZW/GaERERERaPyWUREREpM372urH7yjmuANss6l8FU5fiYa8iYiIiNSBEkoiIiLS5p1i+Kh0eQ64TV5yCSazqgGIiIiI1IUSSiIiItLmxdsycLl8B9zmovtnNU8wIiIiIm2AHsOJiIhIm2cxx+KzJUU6DBEREZE2QwklERERafMs5gCOdHekwxARERFpM5RQEhERkTbN6/FgwYa3qCjSoYiIiIi0GUooiYiISJtWkpcHgNdjiXAkIiIiIm2HEkoiIiLSpgVcXj4p9+ExuSIdioiIiEiboYSSiIiItGmlebuJMfYQMB14ljcRERERqTsllERERKRNK9ixk9GJXbFij3QoIiIiIm2GEkoiIiLSpgXTu/N+7osUd0yOdCgiIiIibYYSSiIiItKmOV0+4m3JmBISIx2KiIiISJuhhJKIiIi0aUb2Oo7JmEhC/u5IhyIiIiLSZiihJCIiIm1a1ygLAJnpMRGORERERKTtUEJJRERE2jRXfBwA1q4dIxyJiIiISNuhhJKIiIi0aXv2lAGQX1UV4UhERERE2g4llERERKRN6/BzIqljXGyEIxERERFpO6x13TAYDPLf//6X77//Hp/Ph2EY1dbfe++9jR6ciIiISEOZDDfOoq0kdh4Y6VBERERE2ow6J5QeeOABXn75Zfr160d8fHy1dSaTqdEDExEREWkMa11BvIadCYnxB99YREREROqkzgmlDz/8kDvuuIOLLrqoKeMRERERaVRdzR3pFm8nKVVFuUVEREQaS51rKDmdTo499timjEVERESk0UXZ4kmwpxGXpB5KIiIiIo2lzgmlE088kffff78pYxERERFpdL5YO0FMWCyWSIciIiIi0mbUechbx44defrpp/nkk0/o0aMHdru92noV5RYREZGWKKEyQMDuj3QYIiIi7cIjD8zBWLeGW19+KdKhSBOrc0Jp9erVDB06FIC8vLxq61SUW0RERFoqw+/Bb3VHOgwREZF2IWt7kB4dJlBeVkpCYlKkw5EmVOeE0oIFC5oyDhEREZEmYTJbCBjqoSQiItIcRqSEai+7d+9RQqmNq3NCCUI9k15++WU2bdqE1Wqlb9++nH/++XTp0qWp4hMRERFpkE2e9zGcFgYyIdKhiIiItBt5W7fRoX//SIchTajORbmzs7M588wzWbx4MdHR0VgsFt5++20mTJjAhg0bmjJGERERkUNmCWRgITnSYYiIiLQrnoBK47R1de6hNGfOHMaOHctDDz2EzWYDwOfzMXPmTB5++GHmz5/fZEGKiIiIHKrD4k8jSDDSYYiIiLQrHVJSIh2CNLE691D6/vvvue6668LJJACbzcbVV1/N6tWrmyQ4ERERkYba/dPL5O54O9JhiIiItCtbv1GeoK2rc0IpISGBysrKGsudTidWa71KMYmIiIg0m/LUZFzRgUiHISIi0i6sKVkGQIUzwoFIk6tzQum4447jr3/9Kzt37gwv2759O/fffz/jxo1rkuBEREREGuqwDuPJTBkT6TBERETahd1l63k75x/YEjIiHYo0sTp3LbrpppuYNm0a48ePJzk5VNiypKSEoUOH8pe//KXJAhQRERFpCKslBkt0fKTDEBERaRdO6H4FVpOZH/PXRjoUaWJ1TiglJSXx5ptv8sUXX7Bp0yYcDgd9+vThqKOOasr4RERERBrEjIEXT6TDEBERaReWBrZworUvnsqSSIciTaxexY/MZjPjxo3TEDcRERFpNSyAt7I40mGIiIi0C769n/KWfwkpPQdFOhRpYgdMKA0ePJjPP/+clJQUBg0ahMlk2u+2a9ceuDvbv//9b9566y0APB4P2dnZLFq0iGuuuYYePXoAMHnyZE4//XRee+01Fi1ahNVq5dprr+X444+v52mJiIiIgM/lxWK2YjPZIx2KiIhIu3Bql98DsNKcHeFIpKkdMKF07733EhcXB8B9993XoANNmjSJSZMmAXDPPfdwzjnnsH79eqZNm8bll18e3q6goIAFCxbw5ptv4vF4mDJlCscccwx2u24ERUREpH4qXB52eYPkd8iMdCgiIiJtnvtXM8MnlfkjGIk0hwMmlCZOnBj+2WQycfrpp9dI7FRVVfHaa6/V+YBr1qxh8+bN3H333dx9991s27aNjz/+mO7du3Pbbbfx448/Mnz4cOx2O3a7nW7durFhwwaGDBlSz1MTERGR9m5vaSFfF/yP0uQ0zuHUSIcjIiLSphXvzA3/7AhURDASaQ51rqH0l7/8hbFjx5KSklJt+datW3n00Ue57LLL6tTOvHnz+MMf/gDAkCFDOO+88xg8eDDPPvssTz/9NP379yc+ft9MLLGxsTidzgO2+csQurbA7Xa3mXOR9kHXrLQGuk7br4LvVnNK+pHsLFjd4q8BXafSkuh6lNZA12nLk7vqBwbQBYDSqBT9+9C2r9MDJpT+9a9/MXv2bAAMw+CYY46pdbuRI0fW6WDl5eVs3bqV0aNHA3DyySeTkJAQ/vnee+9l5MiRVP6qm1xlZWW1BFNtHA4HAwYMqFMMLV12dnabORdpH3TNSmug67T9qlz5Awn2VByGvcVfA7pOpSXR9Sitga7TlsfYXQ5rvADYDKv+fWj91+mBkmEHTChdfPHFpKSkEAwGmTFjBnfccUe15I7JZCI2NpZRo0bVKZBvv/2Wo48+Ovz6iiuu4M4772TIkCF8/fXXDBo0iCFDhvD444/j8Xjwer1s2bKFrKysOrUvIiIi8mvF+cV0pAuVhiXSoYiIiLR5ZX4H+EpIsMWS4HVHOhxpYgdMKFksFs4880wAOnXqxPDhwykvLyc1NRWAVatWcdhhh2Gz2ep0sG3btpGZua8o5qxZs7j33nux2WykpaWFi4BPnTqVKVOmYBgG06dPx+FwHOr5iYiISDuWYjEDkBgb4UBERETagZ1bviM39z9kxh6Lze/kiEgHJE2qzjWU0tPTOfXUUznllFOYMWMGAH/84x9JSEjghRdeoGvXrgdt48orr6z2etCgQSxatKjGdueffz7nn39+XUMTkVbmvTse4aS/XEtUrL7hiUjTCkaF/s5Ed+oQ4UhERETavq4+g/6JE9lKEl6jONLhSBMz13XD++67j0GDBnH11VeHl33wwQf07duXBx54oEmCE5G256OZcxnmH8UXM56PdCgi0g7kuXwA7I2OjnAkIiIibZ/DEUVqShYjklPpl5gc6XCkidU5obR69WpuuukmkpKSwsvi4uK48cYb+e6775oiNhFpgyxVoT871oDqmYhI06t0elmS+3esUXGRDkVERKTN27G3FABPwIXJZIpsMNLk6pxQio6OJj8/v8bykpISzOY6NyMi7VxFamikbYmjMMKRiEh7YK3wkxQ9hBirPdKhiIiItHlJzioAvCYTFltig9sL+P08esEZPH2zRkW1RHXOBJ1yyinMmjWL7777Do/Hg8fj4bvvvuOee+7hpJNOasoYRaQNcXhCw04cUT0jHImItAdx5miOSj0SR1zdJhARERGRQ5dkhFIMhj+ALdjwjicej4cLes7gSH/fBrclja/ORblvueUW/vSnP3HxxRdX67p2wgkn8Je//KVJghORtsde4YJYsOONdCgi0g44HaV8kLOa43ro4ZeIiEhTK/bG0yEaKv0lRFkaXuLCWRQa1dApumOD25LGV+eEUmxsLH//+9/ZunUrGzduxGaz0bt3b3r06NGE4YlIW/NTYA+rdiwjKyoAXH3Q7UVEGiJQVEKc4cYR5Yh0KCIiIm3enspciiu2EBvXE5u/4b2DfVVuSr0FxFmTGh6cNLo6J5R+0atXL3r16hV+7fV6WbNmDYcffnijBiYibVN0vxGcsXs8P5Z+EelQRKQd6Gh0JavDQOKTNdOMiIhIU3OZoNKzly5HuBiy5xXggga15/F4MGPGarZRXlREQmpq4wQqjaLOgxrXrFnDxIkTGTRoEAMGDAj/N3ToUC6++OKmjFFE2pA+hQUAOOI6RziSQ7NixXrWZW+PdBgiUkcZRjlGMEBCSkqkQxERaXP8Ph+fPPZUpMOQFqRbXHdO6XIJJRu78GPpC5QWFDWovSqfnwR7KIm0+evljRGiNKI6J5QeeOABHA4Hf/3rX7HZbMyaNYsrr7wSu93OY4891pQxikgb4i0OABBvT4hwJIemy1tFJP4rJ9JhiEgdVRh2AoY/0mGIiLRJ/771DratWsXi+x6KdCjSQuQ7N/NN4YfgDmIzByjJ3dGg9uLdvvDP/j0lDQ1PGlmdE0rZ2dnccccdnHPOOQwYMICePXty8803c8stt7Bw4cKmjFFE2pBEI1TU31WhDwQRaXpecyx+ApEOQ0SkTYqjB+O7TCPg1LBiCbF5Y3EFUjGMtXyY93+YvZUNaq+kzAPA2pJlfPejeii1NHVOKBmGQcrP3cW7d+/Oxo0bATj++OPZsGFD00QnIm1OfNANgMmIcCAi0i4Eo6Lwq4eSiEiTcERFA5BGdIQjkZaiT/JQRqUdTtDaizEZ55K7qWE9+4uLigFIi+5LF1//xghRGlGdE0p9+/Zl6dKlAPTp04dVq1YBUFRURDAYbJroRKTNKbKFEtMOS3qEIzk0250b2FqxNtJhiEgdFfl2k+veGukwRETapL1sA8BTWRzhSKSlMFvL8JuLsThS6BzTG3e57+A7HUCyEXoY3TGqAx1i+jZGiNKI6jzL21VXXcX06dOxWCz87ne/46mnnuK6665jw4YNjBo1qiljFJE2JNe9Hb8RILeyitb4l2O7ay8ubz5jIx2IiNTJ7oId5BLkpEgHIiLSBllcJogGSzAm0qFIC2EOmAniIXrIcfBTFbFdRzeoPZfhJrfyJzrH9MERrGicIKXR1DmhNH78eF599VVsNhtdunTh+eefZ+HChYwbN44bbrihKWMUkTZkh2c35e4cLOaoSIdySI5LHxfpEESkHkannQJYIh2GiEiblBAI9TxPqfvXSmnjLKY0fEEvXsoBK8UFOUC/Q25vT5yZnIpSRrCNDEfHRotTGke9fvMPO+yw8M+jR48mKysrXFdJRKQuTko9iVRHBoXeLZEORaRV8nm9vDRzBmfechNpnbtGOpwWz24zE0RD80VEmkK0OVRBJRAsjWwg0mJEBTwEApWY83YAw2DD99CAfsKZziT6p/fA568i2hJDxd4C4jNaZ+mMtqjONZTKysq4/fbb+emnnwgGg1xzzTUcc8wxjB8/nh07GjYVoIi0Hzk/j7EPBB0RjuTQ+A2DjVWaoU7276Mn5jVp+y/fegvO3Tn8+/YHmvQ4bYbhxRMoj3QUIiJtkvnn2Xv/51R9SQkpCxRT7s8nKSHU8STaaNg9v2tXHgkWE55AGSaTiR/e/7AxwpRGUueE0v3338/q1aux2WwsWbKEr7/+mrlz55KVlcWDDz7YlDGKSBtSYCSw170HaysdgmI1mciK0dS4UrsP//wo/XcP5KPbHmmyYwxzDGRi9z/RK6ZPkx2jLbGZorEYtkiHISLSJplMCQCMSTo2wpG0Xj6vl6KduZEOo9F8XvghP5SvwTy0G8udfnK6NOyev9xbCUCRbwc/lX1L+WbNMN+S1DmhtHTpUmbPnk2vXr345JNPOPbYYznttNO48cYb+fbbb5syRhFpQ45NSiUjqiMxRiDSodSb1+MJ/+yuckUwEmmpUr2xAMS5mm6aeguhYW4BU5Mdok2xmKLAsEc6DBGRNikmLtQDtHNMT9yVVRGOpnX6evrfcT2zjeIt2yMdSqM4uvOVZHUegz3ORrmxkWBFYYPai+11TOiH3pnkbXsXR9nORohSGkudE0per5f09NBYxa+++opjjgn9w5pMJszmOjcjIu2cxRT6FlwVnRbhSOrP5/WGf/a79t00bVjwCrkzvyD7Xy9HIixpQazWUOLCaWuaovOVZRUkm0NPg03m1jlstLmZTX7chjPSYYiItElbK3JwB6owm8xsWL0+0uG0Sg5rEgAbvvsmsoE0Ap/XS2eblVhbNB2ssZwSP5B+exuWKwh4fADEdO7NUUNm4U44rTFClUZS53/d/v378+abb7Jw4UJKSko47rjj8Hq9/P3vf6d///5NGaOItCG2n+cCKK8qiHAk9WcE/aws/ACA7WvXhZf/uCrU9Xbz6s0RiUtajhWF6/EGPFRZm2b65OyF7+170XlkkxyjLXGVl/NB7ny2VbX+m3QRkZaotMzO9+XbAXBXaKa3Q1EWCD2kLKwqjWwgjaC8sAizyURlxV6iO2YA4Ca+QW26NoVGQ1Xu+oEfAtvY5c1vcJzSeOqcUJoxYwavvPIKf/3rX7niiivo3LkzDzzwAB9//DG33nprU8Yozej7Z+az/DrVxJKmUVZQiMlsYW35T3yb/59Ih1NvRTtyODztFAC8e/clxOym0AcmwYRIhCUtSKI5is/2LGRnE5XsKSqoDP/sK93eNAdpQzauWsmAmAF0yFBvLhGRpjAyaQSjkwcC4N+2KcLRtE6pljgAOpS0/rHs5Tl7AbAYQWKTkggaBiZrw2aFt1t28OHO+SQn+8nf9m8Cmz5ojFClkdQ5jTxs2DC+/PJLKioqSExMBOCKK67glltuIS4urskClOblyYbMhGNxlZcTnaAvx9K4ynYXYAZ6xXQl3TEt0uHUW9DnC5cST4iODS/v4o8HO3QwqU5Lexbw+zki7ThsZgcryr5vkmNEladSbvJjN7zYnHua5BhtSc7G3QxLP4FVXTUbrYhIU/CYXRiGgclkonyzatscCu/PfTxKd7ojHEnDVTqrSAKMKBs2ux0j4CEq0LDzinYkclhlLI7UDgzoeBpdYno2SqzSOOo1oNHr9ZKTk8Pq1atZtWoVBQUFbNy4kVWrVjVVfNLMnObQL2j+7tY3HElaPk+Fm6ARJBjwYpiapsZMU4qNtrHzi7/x37JiNpj31WTxBkMFuqPMKtTdnm1auxbbz3WNEryNn1z0udx8t/Mllu9+m11f/IXYksWNfoy2Jrj9Sz7KeZquvTUzo4jU7r07HqFgpxIhDVHmK+az3a/iqVgb6VBaJa+3CICYYMN68rQEe12heqPF8aH7fJ8pgLuBEzu7nGl0HHAaZeU+vI4g0dZYCnbuamio0kjq3EPpo48+4i9/+QtOpxPDMKqtM5lMZGdnN3pw0rzW/fsD+kVHA+Cr8B5ka5H6y7X5WV8WIOBahd+zgZEcH+mQ6iVn03ZSRl/KcF8Bu7fuW+61hXLzOfZYhkYotgP58qVFHHvxhZEOo80r/m4zcWSwvKKcgLvxk/LL/rmQ4aknUUgOvuP+QHFUeaMfo60J7LUT6+hK7+GHRzoUkTarcM9udv60kRHjxjXL8SryS3CXuUjv27nBbS19+gWG+UeR/dCHpD91RSNE1/5Ek4JhmOhQ4oI09dQ+FEUFb9IpqhPp/tY/S16cw8o3eR9hH50FQLGnAJfRsAeurpRdvJ+9miNOnYJnsxN8sO2LVaRf1KUxQpYGqnNC6amnnmLkyJH86U9/Ij6+YYW1pGXaufQHDoseDUBgdw4wILIBSZtj27GH/nYbQWsm6cn9qHQ6iW1FQ2Y9gTgybDbibImUlex7mmm3hf4mlgQqIhXafv1v1mMMco9kyU2PcNrcWyIdTptmbCwCcwYDHH52mhMbvX33T04GxA8l9vBOxK2wgDvQ6Mdoa9JiDqOjozMJ6R0jHYpIm/Xdva8xOH4E3tEe7I6mr1dW9MBSrFGpMLvhCSVH50zIATP6e3qozCaDoOGnd/9LyHfvjXQ4rZKj43hiHKkUGZ5Ih9Jg1tz19NyxEd+I0OQkX+Uvwd7AUQm+gJV4WzLW6CiMRKAQSjZqKHtLUeeE0vbt23nkkUfo06dPU8YjERRjpIZ/zqkopV8EY5G2KfhTHv1iBlLoSyHNloa7srJVJZTiO/UFtgNQ5SwKLw+6AhgxBnhaXt0xmznUozTOG3uQLaWhqpwuSACr2UqSufGv69zCfLabs7ni7Gn8fdGZJJoCwO8b/ThtSawljqpA5cE3FJFD5vYUUO4owr57Dx16dG/y41mjUg++UR1VxHcAqjCb9Rl5qAyq8AbdfF/yLl6rm6M4P9IhtTqmhA4QhACtvyj37s3lDDvqWn4oDs3MNiT5GAyCDWozpiCdYRmHs6fYQ1KndCgER6VmFGwp6lxDqVevXuzdq6xzW5ZuS6fKX8VmdwCbo/XVt5GWr9xZBoDhLQUgWNa6ag6V5awJ/+zw7hsWWur8EZPJRJcWeENqzvAB4I9romnHJCzgymdH2Qb8vgqiA8bBd6inAcndSYwqxGa3MyzpTDKTxjf6MdqaaGsSzoNvJiINEGPriitQgd/lj3Qo9eb7cgUAsZaYCEfSen29fR4bcv9JjiWWsmBGpMNplTpueI4ft/6bFe5EgoHW3VvOHMzny71vEZsSug+yR2dije7aoDZtplBJlsSkjqR164xhGARcrb+AeVtR59TeNddcw6xZs7jyyivp3r07dnv1MbIjRoxo9OCk+az88EsybEmsKVvLJo+JoepFKE3AHB0PLqggSDqQsz2X9KwekQ6rzjw5O4FBAEQb+5KuBZUb+bF4KWWOlncT4FxnhRiIDmjIT1Oz71wJ/mV4h10DjfzlpMpZSffYfhT/PJokIbkf7gbOmtLWearcxFii2esriXQoIm1aj7g+xFjsFAYsNHwQ2sGVunIJWGLp5PdjsTasl4LJvQE4jEp/YeME1w51TjsKL076RB1NijUp0uG0SiXp59ElphtJRoAN33zLwKNGRzqkQxafGEP6D+uJ6zsGgO2BXQT8FhpUYS0hFfwQnZZIcqcePP/sLVij4jixUSKWhqrzX+EbbrgBgLvvvrvGOhXlbv2Kl35JvP9ITBYvZ6YNYm3+ykiHJG2Q3R+a5sFjCSVevD5fJMOptxjzviRBecq+IU2DOl5KjMVPbtUXkQjrgDzW0BNjm1f9NJpax+E3so1iorxWoiyN28uzcM1azEBqMPSlxx8oA6P19QZoTjs+/4YYkwkTKl4u0pRiLKGHzMWbN8LgHk1+vCJzJ3rbLZQVFpDSsVOD2vImdcBVWEGBVwmlQ5WVeCR7/HswBf3YzQ2czqudSojpRqojA4evlJ3LWndCqSDQlSOHnEhuVGiYW4rXS5y5YQ/ZvJYq8IM91kpsXBy9U47CmaJrraWoc0Lp448/bso4JMLiN36Af90/Cf7hJrau+QF3xzqPhhSpM5/TBw6wO0I1TeItLa9Hz4EUdnWwc7OXbvYyPFF7wsv3sJ3DHYdzWODoCEZXOztufixajjvoQvNcNZ3yogJynWspjzfTNZBGNI17o7P9+3X0oi9OI3SD5ieA2aSbqQPZuGo5MWU23PGtv8ipSGuQnJ/XLMdxVS2nwjwQ186cBieUDF8m0dZ4dlfubqTo2p/NWx4k0LErcfZjsZk7sTc3l4zMzEiH1brYOlDgc/NJ7jx6xIyNdDQNElfsAhsk2UNdqpOtyaTY0xrUZnxxJcSC3RK67xkQm0yFv+gge0lzqXPWoEuXLvv9z2LRTW2r1+Fito+eQlLWEL4v/pTK6JRIRyRtwLeffcH3X60Iv/Y6DPbkrWeXZy8f573E9i3rIhhd/ZnKS/Eba/l2z8uYt3wVXl7kfJ8qXzlRgZbXEyLO04lBKSPZUbk+0qG0afkrl2NfuYiulk18zTo+cS5r1PZ9ptDzn0BGqBhtgCBmsxL/B1JUXMJaTzSmvi2vWL5IW1Re1Ty9JrvE9KHcW4i3uLTBbaUWhBLOxydGtkfId2+/y9L5/xfRGA5VWvxkTMZwyqJDtSU3f/9jhCNqfez+CiyGjyPTTsdlNP4ssc3J7AvVO0roECq1kBCsIupXdSXX/28Fe7fsqlebFe5icit/IiY+NDqgyusjUNn0M0pK3dS5h1JOTg5z5sxh48aNBH4uFmYYBl6vl+LiYtav15eV1mr39u1YLVGYHXEEKsqZ1P1G1uzSv6c0XKf3wWu44eeOO87yzfxYvI6ETsdgjj4KizdyCZiSvfkAJGd0qPM+HfIqGZZwBCSMYGPFvt+REYlXEmNLoMK1s9HjbCiLxYzFZGVS96vxudzYolVwvylsWbaTfsc/xtb0dVC2mehgw2Y0+a0OsaEbtKxRAwEwBQNYNLTggEZ07E2ytTNRpx0W6VBE2gVvRfNMtJHsyCDZkcGm0t0/VzU8dH5/aOi9ERvZGWdTvoyhoyUJn9eL7Td1alu6jp0GUu7ZimFyggGB7N1wRqSjal1Mhhtf0EuPuMFUuFp3GRnDErpfScoMFWgvN1mIsYWGvHk9HhI+9VL04VoyHulS5zbzgy52eDoxOibUjsnvJs6shFJLUefHm7NmzWLz5s2ceeaZ7N27lwkTJjBs2DCKioq45557mjJGaWIF275jZPK1ZF7Ulw6dUrGZHSRaNSOUNFyZt4C9lZvCrzvSh1MG30W3xN4cm9ybNF/DusA2xLu33sV/bvlLvfbZ6/m5ToTPyfbO+3rx2U2h3xe/Nb7xAmwktp9js5isbFnbunqEtSaGy4TVbCJ1+ECG2YYzJmMi/kasEfbDd1+xvOA9St2hJOyuygK2VBQ0WvttUUmeH2/QSlpmw2aXEZED+7Y41CMl1lPVrMcN+BqeVDd+7v1JMLIJevvPdah+fO39iMZRX36PD4vJgtXsxxZvZXfVNsoLNCt4fZlNZoLBAEFfBb0rKyMdToNYHTYCRpCYnx+EufCEe1RnL/0cgARr/WoqHW6zc5q1Ivw6xlJCjFW9tFuKOv9LrF69mvvuu4/rr7+erKwsxo0bx9y5c/nDH/6g+kqt3I738lhd/n8kdsiiU5dQNjkjUBbhqKQtSLSn0zWuX/i1K3YPWyq+pywYur4KSvbsb9cmd0T6aRyeVs9p143Qn8zPi98nx7pv5iiLOXRDajKbGi2+xmI29t0k795Zvy7GLcHLpx/He2c1aG6QZhHjseMP+hg05mjceDCbTJTsbbyaHP5KK0XecjJ69wBgd5SbvY1c+LutifbHU+lr3TfmIq1CMI/s0uVsczRPD6VfxLsaNsMbgM0U6uVgr/ugjUbnrgq9b55AFV//2PIm9ziQ8qJQHRuTxYGlbzJfle1ir0sFzutrVdEXbCj/HncwgMfePdLhNIjXmUeJZ9/9fUK6GYvJgqeyEve60HJ3oH5/K8rtKZjj0sOvt1vj8VsOfWjgpzP/Ru7ML/C6NFtuY6hzQsnv99OlS6hrWs+ePdmwYQMAZ555JmvWrGma6KRZJHniSLBE07VvP8wJoVoTOd7GnfJaBCCuUy59Ux4hGBP6IhyI4A1cwPCTbO9Yr32STaH6EGekn8XxvxrdZjWHnsIEzJE7n/2xmxwYRmjsevymHRGOpv5GD7qdjj2ujXQYBxVlRFHpr8Bqs1FpCc2oV5TTeAnTvnHHcmrnyXTsFUrQjo5P5OSE1EZrvy1KNqLA0/Lqmom0NYennkxiVBYVCfX7TG2oktJ9PaJKCwtZd/Pb/OeBp+rVRqEvlPywmh34vN5Gja+ufvzgEwA2lq8kqbh1zd5Zmh/qjRTweRhw1DjOiO5Bv6h+B9lLfisQdOENVlIerMBibd3D2TcW/8Ane97c99pUypeVWyjYu4uqQi+V/kq+2fFyvdqssidgsK8OU3HlNtaXfIH7EHtz9WU4AD63EkqNoc4Jpe7du/PDDz8AoYTS2rVrAXC5XFRVNW8XV2lccbYMnP7Qh2hcYgIBI4BNI96kEfiDPrYVr8HjDHVTtW4bS4XrGTIOPwkAR89jIhabw5qKyVS/HkXlPyfCrGYbsdZ9T0qsJisbq0r4tmRro8bYGCzWBCr9oQ/Mwj2NNwSruTgsUXSMrvs4+0iJscZR6Q8lkqJiYgEIOhvvi4Ep4MEb9GO1hpKWpS4zBq2rzkZz8vl8mGJS2ZWhpJs0nt25O1l644us+rx19SJpSl63B7PJQueoFHrnNc/vmzcYumc1Gft6029Z/i2JthRS9tav5+a6pCjWVVZiNlso2R2Zmd4CuzayedsHdLV1ZUzSaXhb0ZAnf34oaW/yu0jP7IwlIQNvQF8i6uukLqdydM9h4Msn+ucaRE3pvZlz+OC6J5qk7UExPTg8ad+sx4HdZfiq9lK0dTtr96xlfcnnJKYNqVebMVEJ/HpeaGfAzdaKH8ndsL1BseZt2nTwjeSg6pxQmjJlCjNnzmTx4sWMHz+e//znP9x///3cdtttDB06tCljlCYWZ4ulNGrfr6nF5CYjXYXOpGG8Hh9Ws42eKYeR/d0qINRbJhAM4gmEhotVFUaux0z0IRQ09tht5PuCuINBbKbQl3lPlRuLyUQnewyDogc0dpgN9l5VNouC6zEMA5+jeZ8eNwZ/0MuG0hUH3zCCKquqiLbE4/65ELfbEerhWVhUcaDd6sVq8uL59VBkIw6LSTft+7N5zRpsmPA4W16hfGm9Vv7f6/SO6kv+W99HOpQWozAvP/yzr6p56rp9lPcvAOKMffeuVlcokWSqKK1XW+e6rXQ0lfDvHY9TlhuZhJIvZxdJO7+n2JyHxRrNJ/9YHJE4DkVZeRHrSr+lLFgMgDtYhTXC9ahaI3cwnjxfKjtTK1jp2UV+YdPWoRrG0QxMGNEkbSdFdSYtKjP8ulNUT45PP5pAkYnMhN4ckX4ane2ZB2ihpvjiXVj8+x6KpsZ24pwe09mxduMhxVjhC92fleVFrvRGW1LnhNLkyZOZM2cOHTp0oG/fvtx///2sXr2atLQ07r333qaMUZpQ7pr1RJstuPz7/nC9U7iDj3Lzqm330adfs3V766u/IpFTXFzEirLQl7mSnaHry2FNBlMU5rJt7HFtx7RrVSRDBKhXd1nftmw+3fUMFd69+Anttzt3B69te5gyz046Rbe8IW8jDC/93OX8u8LNzuikSIdTLx6XG6vZTu+E4ZEO5YDeuPEB3q8wsb1jqLduwOqlsKoQd0XjDbeKsiXg/1V37yRLJRaTmYC/dQ2PaC57l33LG9sfxR/Q55Y0ng7OUgAcTvXM/0XFzqLwzyaTcYAtG0dVZSUjM85jY8UGtu7eFl5eUhnqiZuWXL9537wk47DF0StpPIWVkamD6KvoSerYW0iYNA7DMLD9lH/wnVqIKqOKzQzDGfPLZ5GTDpamvw7aGosRwFJezJ6UeFyeRDava9qeM+tKvyJoBMMztzcmkzWDoCk2/LosLVROpcAEfWOSqHTnsnn9k/Vqs9ISjc+8b/bcQHSAH0pW4CuqfxK7pLiIxbnP8Oq2OeDx1Ht/qanOCaWZM2eSlZXFyJEjATjrrLN44403eP755+naVTOoRNL6ha9Tlpt7SPvufH85APbgvpujjuymk6/6pWF9/Vu+uffxQ45R2h/nzlyyfu6pEbc3dKPhsRhUmn107t2db3MWYDVvj0xs5ft6jhRuq3sPhigjkRM7TsRqsmIxhX5HAkVFHBV9GrG2VMz1HELXHPrHDaOfKZ7u/tV03b020uHUS+nu0JMjm7nlDu36cvbTjEkaRxofcckdVwMQl1DGx3vnY63YdpC9685mjsNj2ZewLPWEruHiXUqY1KZ0214GJI0mvlNGpEORNqTQlQxAVFTnCEfScuzJ3R7+OaqR/lZ/8ux88jZvrnVdZVkZVnMMMY4sYgP7htg5naEhxzkVP9brWGZLLHG2JIYlDqDUHTz4Dk1gj28lKwrfY9hxoynz7CHdlBSROA5Feu9jOTPRStdRkwAwVVVi9SVEOKrWx2wy4zC5yfJ04YTkDrhWNG3Pmf6JozGbzOQXljZ620EHeOz7EjUmc2iIqr+wGKunEw5HF3r3vL5eNcs2uH7k873vhV937pZGjmkEdk/9H6rteO8LLug5gwt6zqB0b9HBd5CDqnNC6aOPPsKmwjotTll+AQk/dGTbnA8Paf/121axJPfvBHrvG3PeL/EIusYPDr8O+P30SRhOz5iWN5xHWi5fXh7JUWkAlJWEhrg5/G4Mbxnm1AxG9bkZd8cxEYmtqGRfzxF3Zd3rChlpaQQtnQgQxPLzVMOWMhddOw4mwZqI1Rx7kBaal8/rxWKJwmlNITO6G5lRrWvmEH9h4w0ZawqfPPUPMksGUeEv4MyHZoSX+9xwUqepOPMar8Crx1uCq3LfcAyXyYQ7UEVZfvMMMWlt7O5oDkseQ8ZQfW5J46lMCv1N8hl6qv2LQEkuO5zrQy/8De+ZUppfQNaOLHY/+V2t6/0uH19vfpRMu5kOpISXx/zca6zMX78ZxuIdfkr9m3l796dYtvwQXu4sK2fXT4c2nKa+UjuZSO66BYACexlJ0V3J3d46JtHIzV6H2WSiIi/0ACXfbMKwa2Kf+jKZrFSmJmLrGKqf5CosOcgeDfPLQ9HyvMYf5mnxB/AE9t3/pP48k1r8niKiLWasJhMdkjtTXI/OEFFGFDHW+PBrX5d0zkiyUeav/8iA8u2hvxGegAuXLane+0tNdU4onXnmmfztb39jx44d+NXFvsXYuzX0AZQW2+eQ9k92W4mzJjP87N+Fl3ktDgKOfV+MK4sKKHTvwmE69OkZpf3Zs6c4/LPLF3rqZwKMoI+Y2FiMgAfzz08Um5tzS6g7+cqS1Wzy1X3oQkVyBsvKC1la9Dmf7HkLgA15oeGhXiOAhZbVQ6ksLw+TyYQpWMjW4hxWVbS8IXkHsqvw0HpeNpduG+z4A0E4I5OYhH1/Hx0pSXiDHgI0zmel3+vl490vUlj2bnhZoHcK/6uwEZWsotO1qags5+3d3zBg3NhIhyJtiG/YsfiCAWJMLevhQSQ5jAqWF7xLsbsEc7DOXyv2q3hT6PO5yF/77Eu7t+1kRNcLAfAE9/2NLXSGPou71bMujM8fi8tnoavdT1XuvvIPy/48H9/85vkM6uuaiqNsCgBVWbHs9Ab4/q0PmuXYDeXd9BMAxt5QAmyrsYnP9r6l4dj14PV4KPP7qPJ46X5k6Puc3dq0tWyXFy4DILAhu9HbtmPB5N33+2tODnVa8LlCD2PLPKEHYWWb6t7Dum/MIIYl75vIJ6NLqCZofEX9v0e4i1wAfFS0HluPgfXeX2qq81/+r7/+mnfeeYdTTz2VIUOGMHjw4Gr/SWREuRs2a1OWoy8jU44nrfO+YYsmfxUxvn1P3/bszSctqgtpMR0adCxpX+Li4sI/Wy2hGdE+2v06Kwo/Iy4uns9y5hHY+WlEYivds5MKXzH943th31r3bsUxW7YxJtZLF0sKfe2haXEdgVCyzBMox0xkusvvT9nOHAAspipiYm30jYumsqz1TKNe6Cljh3M9nmALnZ3OEY/Tu56hJ1RPWnQfNYTP976GuUPj9LDaun49x2RMItD1iPAyX6Wbwxxu9u5U0enaxCf3o2d8H+wOTTAhjSd1aR42s4VcQzWUflFszuC0bjfizl5E/M4XGtze1j2hyQfiHD1Z//mXNdYHy8rpFNMTAJNj32yrFlfo763JXL/eMVaTFcOwMCL1ZMzGvkSh32QhYPjZ9sOaep9DfeRt2InDbKHKFxp6M3bieFbkPcWe775t0uM2lg6WUA2ejgmhB1YJaV1JTbqQ/JyW/UCoJfFUOPkw93GK81bTrV9f/MEAcd6mK2y+NyePLtFpfJ2/mE0FOY3e/oaKIEWVv7r/iTNR6q7A7wk9dC0llNApriyrbXf+fdtUnppyarVl28uL+bFkX22x7v2yMIwgKfUYZfCLKF9oxNWxKcMJbFhZ7/2lpgM+rj7xxBN54403SE5O5uqrr26umKQe9jidxHhdbCpfSUfvaKz2+g1LDMT3o8Dkoe+vl/md2Cz7bsLLd5cQg2ZskHryhpKSP5V9y05vPnAhgxOG4/IVYLFaGdfpQgrdkan/Uubaw86qNMYkppCwp+51heKxkxaViZkKkqyh4WMdnaHzLCrfTC5eMgNjsVhaxu/LnvIquhPD3vQORO2toHdMPJu+/pphp46PdGh1kuWLIiquS6TD2C+TOYoic80pqk22GE7KvI6dxVsa5Th5OSWk2ZIw7PtunGwFZXR2mMnZtAVOGNcox2lLOpijMDlaVoJXIuODP72AJWDixKeubHBbNk8l2GGL82vgioYH1xbYgxSby7H0PZF8b8NnfS5dvQoYSje7mR/e/IqBY4+ttt7h2vezKXZfLatMbyzYIM6oex0nv8fH/wpXE2f20DmmGykJ++6hY+1d2Vm5HltxOj0P+WwObuOKNfQhgUByKBEWn5rKKZ2n4vS37CHfvyix2ekMVKWEeulGOy0MjrWSu34rnXr2iGhsrUVpfj7jOl3Gbs9mLFYrVYFyzL7GL5b9i905e+ga2494Rw9yYxt/2HyVqQJ79L5f1LTO8Sz+6BmSTQmURG/G1mUo3f1QSlKt+6eWjWZIl97Vlllt6Zh+NbOtIzYWnwFuS7d6x+ewhR54J1rN2PeqbEBjOGBCadeuXQR/ngZ54sSJzRKQ1M8Gp5Oy3a+SHtWdNV+sYviJo+q8r9/rI9FkIddevRaAnwAO074vxMam3UD9pncU2b0pj050o9SbT4w31Cumf+LhOCtDM7sl2ztS6Y9MHYouHitDEkM97orK6/6h3dUUusHLt9tIMIWSrtsKfAwBjMqtdFn/DVWlvyc+Nf0ArTQjb4C1rgDWzg7iEuywBwxPy0h21cXuHbvpSSq7KreQSWTqbR3IZk+AIkfNp+Ep6R2w2PIpr4qvZa/6M5cV8PHOf9L7mNPDy9x2F2/vfIqRWZMb5RhtjcMwURWIzJBaaVkGRvdvtLZsRugJ+2mJxx5ky/bDsn4Z3+7KZ3TGuUTZGz4Et1tpBcTBJ7sXUVVL0d49ziKSCRV9tlVsA0L/Fs7Az8NqDFeNffanIGcHmXGHgzfUG8jzq/uBzvYoYmIOZ9nKNXB80800WrRtM30YQaDLvgcGeYDH1jo+q12mn9/3+NDnXbHFyfu5L9KzsGV/b/y/p9/E7azk9zMuiXQo+Kpc2K3p2I1Qj/kS9158waa7P074+d47yeqgML/xE1fHJnZmo2/f528gIZ1xnS6mwFPJHl8VqQlJUAy2TbXPZtg7fkiNZT1iLVhN1QdWuanEH13/um0xljgqfGXE2xKJNevBU2No+GBniaiMHJjY/U8cm3E2u1fXbxzstk+WYjNZSaysPhNRQeVP5JTv67VRXLwve+uubB/dvN3lpfx39nzem7Ug0qG0XpYE1ldVMTxpHCPjhuD3+TCZ7WxPCfU4CRoBzKbI1PQp/lVR7lh33b905gd/nrnEW47VZMFTVUVBTBTrS7/G3eUokk97nPy99SsI2pRKtu5hY+FCgk4f7ozQ+77jp9ANy+L7nuDdW+ZEMryDKnCHro/NFasoL25ZM3FUlFSwyWPH8NT8whOfnEzA8BMINs6NmmvrXo7reA3x8cnhZf7OWRyeejKlUUmNcoy2JsYUi7UJpkOW1mvv1oZPw221JgFg2c+T9fbI8PdnfNerWZTo4snBDa9H4nZVUZa/HsOIontCX7y/SSr5/aHf6yp/BVWuvH0rfu5Zb5jq/nu/Z+MmhsZYsNpsGIaBzb8vIRZjDfViSN1d+7CcxpJcFPoq1m/kvtpP63xFbHK3juHpMSVlvFPqIyMm9HAlOtZGnC2ZyryWPeRtwE+VHJbbMobTB/0mPi38mtJg6KHl9t2vkr3nnSY73qbP9w2ndO7JO8CW9edze/i6+FuKXfuKfSd0ycRiT6F/fF/GJA+B2BK+K3yf/D0/1NpGuTdUg/WXTi0AFosfzNWTbFX+Kox69uQK+P18lvcSy6u+AqAoQXUoG8NBv82tWbOGhISDT/84YkT9iuBJ4zDl54ClIytLfsTlqKzXvpuXr+cwhlMVXz2vWFG2giJj35MRkxH6kHYH3GzdtoeBg3s1PPAWatuGn9g47xMCgb3E7g3QtZuGkhwqZ1JXcgotdDb5MDk6U1aYj8lkwuYNJVyCJhtE196D4/ulO4lLjaLP4Kap21Xs+9WTP3/dn0748RM0AnQIhBKrnpJSXAasKfmcjKgh/FC+nV6VJzd2uIfMX1zFpK4Xs9qykdjo0JN1W24RwUAAx64AXWNbdv275F4DYU2QcR3Pw1daASkt54N/5/crOTvZzpqK2hNd/qAPn71xEqaWUhOpccm44/bdONkw6JMwgm/KWvZNe6SYrNE4DdVPau92bw4VCv6h6DPSP4oj4/fTG9Se2Rr6nQ7aohscW1sRTB9MgiuBU10F2H7aCBzdoPayPRYwuemc3J3DYobz5cLFnHDp2eH1scWhBNOKgv8SrNwIhGbYNOLTIAjBesy26jNZeHvnU6T0GEjQ4yba2JdgCBhBLCYzmZ763VvXV9Bno9BTxrCR+3rhjjZZiHMkNelxG4uzooKerMbjC303cFgNjsiYyOqiryIc2YGZLMmkWhv/97is2MmPH61mzPl171Wd7/RwdEInNsWGrrWk1DH0jqr/UK4diz8EI0D33516wO0CRVXw88djjCWt3sc5kLL8fIKOI0kIrggvy+jchc9X/R5vxyzibN3pevIgtv1gwZ64rdY2vEE3QSOIu7yMmKTQgzRblJWgqfr9+rf52WCKpj7f1LZ88w2Hm4/EGQwlvKK96qHUGA56t3vttddiGAfuTmYymcjObvwq8XJw7kAALGAEdmPsrt8MUy5bNNvLgmScOrLa8qS+l3NEYN+4dIcl1LNh6Z5XSF99SptOKG2c9z8GOYbzk9vM3uTtOMu+pmfgeMwtpCZOaxJVtJOzkgdR6CnDbrJTtaccE2D3hv6eBEwmLKbaa36lLfl5utzZTZNQSvPve+JZaqn7sKToGDCMIIXWRFKAPTty6LGnjAE9bmZVwYtsqqqgZ2Hda0j4vF5WvvUeoy+YVJ/w66wyKsD3OZ8QM3IkQ44dQeny9fQIuvjvE0/TL7YfVnPL/lIU2L4JCI2j31vhoeWkk8C5t4ickk1UWWrvtek32XFYGydiiycIduiStW82zwE/D1Xu6yre327tltftxm42Y3PU3p2+tcpe9T0YAQYcfnikQ2k1tr/7GV3pxdDU41iRs4ojG9ie6eeHCVZT/YdZtFUOQp8jKT478baGzwY8On0w5d4SYs8eBh+4sRZVny1sT1UuOXvW0it+KP7EfZ+3Fk+AKpOL7wqLqOsj7rKCQrrE9MEXE8vGou9x+so5AijZWxyeVn2vLYXGGzRZ044qP9tdBQz71TKT4cdurnstqEiym5M5LP5wdseFknGdBveBryEhoWZ9wZakwu8h1db4szWue+S/9Ax2Zl3X7xl01LA67RPwuukW04sd0RsBMMV1I5pO+Hw+bLa618a1fP7ze/67A2/X9VdfF62mxr2z2rM9l3HxVta59nVGcUQ5yBpwMV7DzcaydQwYdCldP9lESVLtMwGmRYW+g1YUl4YTSkF3Av5g9e17xXQhyt6xXvFtW7WRQd2OIttYDUBsftPdJ+zduo0KUxR9enZqsmO0FAdNKL322mukpKQ0RyxyCJJTuoETBiePodRTvyEhJbs2kuPcxPVjHqy2fGeyjfKC3fzy1WWTezNGuYnj0s6lwtm2btB/y+41gQOG/ek0fnj/KXps+QfOij+QkNTw3wFnaSmOmBhs9tZxk9BQ3rJdrAkWkxqbTJylB3uLyugIVNpD72XQX4HNH5kaJ4GKIETDupJllFnq3q18TclP/OTNJjm+M5u82STs7UiFp5iNge+wxx7GuR2O4qetP1HXTv+f/XUBA/xZfFb4L477w6WHdjIHYFQVsaViDSdknERcWipfGKspSA/Sc0sUMfEH73kaab78En7+roKzvGUVKPWUlbKu9Eu6d6y9l5cR8BPva5zu9EFHLEEjSKfB/cLLnBYriUBJpYZ1/Vbuhmw2FH+BO65lXTMNFf/az+ejfFKdrd/6LSsL/8uozpdhjq85PLW+lhV8zJDEIfSOb7qaOq2NLX8j/tgsTJix1jJJQX3FWxMp95cw4oQj2PjBe0TvqZ68c/Xoj1G8BzeAse/vn9VtJSY+mr7RdR927t2ym5Fpp/K982s2VORhNoW+aRetz+aXM4mmcWrh7c/w+Ggq3NVn67QGyrGZuu5nj5al3J7M2yVuJmaFPgvT+/aGrwsw7C33gVVpcTF9YzMAcJeXE1WHkTh1VuGDWCjM3gR1TCglF+8ButDZFSoxkjvhaG797/d85Q2QWMeEkqeq7rXDtlaWkVL0Ez06ZuFz7z74DvVguEK/kyZb9Z4/HWJ6YDVZKI+PwZEUjxPI33Lg+2+30x3+2RyswPjNd4Z4G3SMrt93Kp9vM+/nvku/SRfwwVsL8NtjOapeLRzc5y/+C/sq6BDTk1VVK+nxxJ+wWtp2laEDnp3JZKJz58506dLloP9JZAQqQzdIFe7dxFvjDrJ1dUOtwzk6aWiN3jeOjR+Su/MlvJ7QE3C3sYc97r04HAlUGm27p45hhKaJTejSEf+m7tit/2L71w2fUjLvp40UPvADW2/7hJev+hMFW2rv5tmWVDkLyS79mkqrBbs5Bp/JYG1lOZWdQv1svyv8jDUl39XYz/dzvYQtZbWPrW4MLmuoi++Oqh14Kuv+pNnhjcaMDac1yKrCd/D4rBAwke1NhpTQky6Lve6/hwmuUHItkNvwLzq16eBM45weN2H/+YYke+9mynfsZfvWV8mp/AmAssKWU/PptwortrA8/10AMspbVpwlrgDn9/gz5g61f/59nf8uq4o/aZRjWR1JeA2qJaOdPxfVrCir+01ke5Gfs4Od5tE4jbZ9AycHlxiVRLe4/vi+vYu0r99qcHtZ0T3oGN0XTBbKCltWXbdIsZuiCBhBXHFRWMwNG2a6fUsOdksUxY7Ql9KCyjLSScP5q/e6T9cjOSnlSNJ3vkOXFX8LL99UtpYt5evoGlf3/kQpPw9hTEuLY0znUzm28ykArN31Ex/mvURlVS4Zvvr1/q8Pp7OStLjeWH4z/L/C5MBqaR0PH602P30dQaLiQvcZcemh+ysjr+V+X8hduSr88+6NWxu1bWd5qE5lmr3uQ6n27g31NC73he5HEz98i3/5vKz78IM6t7Hifx/Wedt8t4s1gVVsKl9FeWDnwXeoB095KOljjq7eZ8X682RPCUluomNj8QW9WIO1X+PZpaHhcu5fJcmKArmUmvdW267KqMRmtuMsq/uDYc9WPymOLIaMO56ExMnER/U7+E718PHL/2TVB/8hNbojpZ5iUrIS23wyCQ7SQ+lgQ93q49///jdvvRX6MPd4PGRnZ/PKK6/wwAMPYDKZ6Nu3L3fffTdms5nXXnuNRYsWYbVaufbaazn++OMbLY62xldWAgk9qPQXkBbTi73bt5PRo0ed9i30RWEJ1hwu0cnRn+NTJ7N362a6DhjEib504juEOorvzSlpzPBbnGhzPP5gEJvdjkHoC1v5loZPbe/fvhOr2YEnGGSvs5Adz+xgU2AtX++ez5FpZzDqsUuwt7GeS70ShtI99lzW+3IptkDxTxvYVPA2mZ1DM7KYkzOxmmp+4JbuDj2hSY3u3mSx7czsTclmHyd0vJB8b92/FPRNGUcHazzfdXcy0X4cP0VZ6RzfnxFRmeRkWmCLgS25b53bM3xOsKVhCtbe7bcuFt/8NN2MLgyee3aNdSk/v7+ZA0IfmGMTRtMxpisbU3tR5QoN3SjJ2UtiWuOOoW8ssdY0BsaHumPk7Myj6a6I+kuwWjGZTCR1SKp1fRQ2rObGqaEUVxXEZ62eOOqcngq7IMVcey+ob294GU/QxbGNMFV6a2Pb4eaMpFR2WtrW8Ow85zbi7EmRDqNV6RrsTFpSL3IHZZDoaPjsmz0Sh1Hhy+eD/E+ZtC2WxLSWNBA3MmLMiQSw4PNWYLE07P3Y8/UmMrFh/TnBYsTuxEo3NvxzISNv+SMAZZ8vBtMIqrpdiL/Hvl4w3RI70jt+EL5g3b+7lGYOpetPYBs5jsC7m7AZob/ZlrIgsfEXsLXqe8xemuyzZ+2abWSaLORmVj+CJRYsJisVxcXEt/BRInFeNwNjYoiNCz1US0pPozToJcZXWuv2eT9kU/qvzax3rqb0yEymTbsUSzOXlfD9tA1+HsiYv3U7PUcOa7S2O6eEhulXmOueRLCmpEABkBG6F4v3WOkY3YONW+teTqZwQwnu0g1U+csPOivugJRRpJjj+HzvU8QmNu7QxD1FeXSkL2XW2h+Umj2hnrb+oIfYYO33L/G20N+RisI94WVfWTrijyrghF9tF7QbmEwm8jdtJa6O/4aZppGkpKSRmJrOEbFm8qoaPkz318rzysiM6cvK2BVc+PBdjdp2S3bAq33ixIk4HI1T1HLSpEksWLCABQsWMGjQIO644w6efvppbrzxRl555RUMw+Djjz+moKCABQsWsGjRIubPn8/cuXNrzPAg+5SVfclH2+ZRnpCAyWRi/ec/1mm/8pJyNgfiKTbXnJbSkpaOxWTB9/PbXly1749Nehuf5c1mScb/81NtR1ToaUuwxH2gXepkc0EoY78pbieHH30c+VF57IoqoFvscDrHdOPrN95u8DFamlgMjKCfQkshn+yah3dTPuf2uJnon0dtHBObwjHRNccV524IzTCY1IRfnJK2LMXi+ZbV7jy2Wupeg6Yy2kLABI54C7neACZ3FZgqMIIuKn8ujly6ve4zCZV5iwgYAdZZDr3H2hDbEJL2M1Wz1e8jGPASmxj6wAwaoWs6gb7EdMkMLfO03FogcdYk4mxJ+IIeyopKIx1ONR2KQxdy98Tah0OMju3FkfGHHVLbK15/n7enXh+e4WRNydesKvqs2jbB7hmsq1zH7rTab8QdFjuJtqRDOn5rl18QevBRYW8ZM/g0ls5xPUmwJx98Qwmz+TtQ6a/C54gmyZ6Gz92wa8JqiSPW1oHBcZ3J/WlzI0X5/+y9d2Bc13nm/bttesegV/ZOiuqiZHVZxXIvcpx4k03bTZxiJ7Gd3eTbzWZ3U+00O06cxHHciyxbsiRLVhdFSqTYK9jR62Awvd72/XGHGAxnAAxAULa0fv4hcefcfu4573nf533eNzcOxQ7y6uTrKLkssijNsIxnY7R/iNd/5+u8+LVH5z1W5oLFlnAHrQCbeeNa9ude54KrnNomZSznuiQK+OxlRrDDZqUwSTUCVXMhMWkxX3NCEbWYRDatfRv6TXZ4FSLaNH36AQbPLi+L5SLEC5aMROvqSqZrQ5flnMnFrmyFueVAk57HMDXsrrJzbyh7mnGpNnt2aPdhfLYAoqHie7WXE7/3PRLjbyzbb2io/Fzt4vIy0ATRsrMmxuu3LZsarHG9a4WlB9RUqsrr1etPt9THDCKazGRhfMG2DlOkYBS4LvyLrJLeW/c56kGgVHUt6K4dUNM6LW1U1Siiy7VTDTvcluiKo1C2b+7GzS3pyqCA5LHWp9OD9cuxOCUnec3qm7pRJCcur/SG27WGG5veyca7row26k8r5nUo/fmf/zkez+LSqBbCsWPHOHfuHA899BAnTpzg+ust5sutt97Kq6++ytGjR9m+fTs2mw2v10tXVxenTp1a1mt4K8Huuoo16goa325V1RBd9eVc9+7az7uCNpxKNTPCIVsfsJ60nE1FUUQ1ihimAWr94nBvRjxVeJXHEi8CoIWtyTFbXDp75CLSujWwZr1ebvvtX+OB//sRPvgXv0zL7WuxS07E4beeNlXQUJGLGRwhB7c1PYBg2jiR03E1Ws6NaFohnXdV7Tdy4RQ/GPh7fjDw91fs2trMZm4IXs1U/Hmy0UN17+fMTGIaGZyDx9g39jdox/dilwV0CthLYq3SIsrb+0cf5eDUMzTlrgylPiUGKVLuv1KJMTNljtKbOcSjYy9w6syeK3Lu5UC/fp7v9P0lj0+dx+X46RL4vBC3FjzjUnUfBhj1+Mm6lhZZzn/reVpd13Fkp5USmne1EHVUiocKfi8DxU6GhdpGW8jeQtDevKTzv9lRyFmLQj20/IKrc+HxI6O8cOhnDoafNvhEN0VNpeD1IosKp/dd3juyCeCQ7KzxXU1q4qcrDfcnBZeSxS5OMFQcYffED5iORKraHH70Gdpc3Wj75rd18jnL7vSutdYept9LtLidVK7sJGq/GOc2RWShHPQ+E9M4Gz+OKEjks/UFPztGLRHkVjGPaMookjVmpLJWwKDNv4IHO/8rUweXt7T6RaT3vAZAk1LZl3b1H+bhvr/h6P5dV+S8ywrdhXkJK+xl0ctr3tpCxLFJq3/IbWH8vh5Czjb2/cd3rvhlzobNsOz7/tRxzi9z2n+ypJ0oxqq/g7lw/MhrvDz+Xc6ctUgB2W7LkTSu1V9hsNO0c1v4Ou5r+08LtnWINjS9QCI9Rja9vH3bkctSSI7R4KpNSNn+/ncDkNUNNKG2/XQRccpOSo8kowiVAYG0ZI0XE331V7t1SCJJ1Uqd001tJhVvuZDvs5zi0k+vhNgVwfLw8ReBL37xi3zsYx8DrJQ6oSSA53a7SaVSpNNpvN6yR9btdpNOz+89vJhC91ZAPp9f1L2EnWsIrQwRd+ToTx/n/GsTNG1aeOEVHR2mXViB7jCqzjcdGaDH1cHJV19GCztxywa6WUA3NUzR/5Z51rWwCTt5SaW3txctEIBJUAlc9j0H4knAT7PDVnEs38Y16CdSMFb9Ht4smKvPTjub8Yt2RJeCrHSipyc5mz/MKl2it7cX0zSRBaVq3+C0zlXdv8t44swVeyaRdIzh/Bg3hO5HFuWa58lMTJJ8+QCtH7p/ZptiSJimSVbVeU/X73A0+Tqa2I4qODDVITRDRcjl6r5utfn9XNdwFVPJnUu+13S2D5toZ+y1V/EEKtkLosOBZmozxx4LJdCjWRrev4nko7002rwULgwv63OeiE7z8tPP8b6H3ocsX94U4/fdxCbXe1BNiah68rKuc7Fj60IoOhQwIGcXax63qOeQ5aVVQG1bdTdOycW+R17A0ezlrsBVjLqzFceKj6R5IOjj+Gjt8eniLPpmHVcuB5LkARPkxpY37P4D/7obvxKk13F5DJj5+ulTfX+FickDvT+dKao/bcgl4jTanIiFkxQ1E2jh7GPfRwovPXLsRmYktZ+9sZfoaLzrLf991TNubgpcjYHOq8mDKGqcowcO05Gs1DTRtQG+2/cYG7w2envnTseZLr7IkxNPc0PbH1nnHUxxr7+J3n77zHVEVRcBBTIOgVZR4vjRo0iKwnqvTtBmBVX373yFhu6Fy64nczGG88eRCmspuGSUkk2imHYM0yDb7oBpGBg8j7O3vnS+C68fIDk6xlXveXDBtpppBdjSDZXjuE8L0BVoI3khsaQ+ZhoGI69/n6bt78Bmv7Ir24zsJShUzjV3CiHEbG27VogVwQk2RURdAYxBNqa/ofO7W7ODBAeizyL1rlvW71gupaHnkvXbgvmEho6bdM6y11L5OODHjKTqPsax2CvI0p20OFo48PoeXN65U7nsooeMkSYc/zY2PUdv7/KxlA5OjFDU1tDKKLZZ1z6VOk6jo4PUyAiMjLBTkhA1iVCN+7PpWQYzvZxKBGnotVhJkiBizrJnAfQGP+Ov7SMXcNf1nBKRKB2yhzQD9Pb2Ipg6Nq22DbdUNOU1TLeJRrVvYrnt0J8mvKEOpWQyyYULF7jxxhsBEGfll2YyGXw+Hx6Ph0wmU7F9toOpFux2Oxs2bLgyF/0Go7e3d1H38u+5P8FMSPzKzd/k7/7xy9iE+p5F7qjVoVvaPVXthxyWsFuDI8iGDRt4TXsVVdDYFz2Mbpjc+hZ51rXgsG2gqEZYt2EDNs2DeboPTPmy+9eF73yXM2PTiF3Vxzr+zWfxsfpN24fn6rN7zcOoRo517S3sPBmn2xR5T8s1nPOeY8OGDfRqL+MSPHRfsu+rxvMAtPjX0nGFnsk5eSeS5MGuJXHIflbVOM++f9nLWvt1TJ88xdb3W5PtAfEM4KRxzQrsJ5zY3R5c0RyiqCGvDvDkS+Dzx7m3zuvebd8NQFxZye1LvNdhlxVdM71hOjesrfjtydAkWh5uKx17w/8sn8PRe5pg/ypijeeWte899du/x+qUSu/rB/jQr/zSZR0rUThBh8uGahSJauZlXedix9aFkArshWnYcdtNuLzVTJgp7WVkRWbNEs75ROaPuMr3CRqVFlatXMm4OQlTZ9mw4T0zbWIN02SeP4FkUvO+Xs4/zSqHm1UrV2JbptT1NwsGCs+DDTZc1UPn+vo1zS4HzxvPE7CFEQsGHVdtWvJx5uunxdZfxqT4pp0r3mjs/NJXaRJWMCHprLh5K+yCBr1xyc8vOjpGTpxCk7xsDd1Lsqi/5d9FPePmMU6R0zNs8nezMvx+8h6R1Zfsc+5fnXxoxSc5kd4/7/FOZ1bjcWhcdfW1ACgZE3qj6JTHubPaY0TzYxQUi0XUGGygubOdPucoU/kRTsZfYYvnXayt4928ZjroE9bwa7fczI+f/TYrBIGVPSuJiq9RNPKEtzbBSyClcnW/62f/9//GNHKs/v3fXbCq7/GGl9k5VeAjt9xdsX16w91095sMtihL6mO7/+Uf8R1bwdiZb3Pfn//VovdfDFLSixhGsOI695uHsEm11yPDjpcAaN6wirX33sr0/93PytDlreMWO7//mf3b9E3beF/3x+ktnqzY96XPfI9rf/nteEJLq/x2QrRYby7BV/c1RRpuZqXsYXy1yIYNGwgJCmrvGCG1eo1WC8lEgqDnVuLFJC2OFnzC3LaHpqqMSBGyPjspx2/TRIityziOXTDbaLCP0fGOd9PRvWJm+9/HPotp7uXjG/4DgDPF5/BQ+/6GpSnW+K7B546Wv3thAMGstAPNrM6zOz9Lu/f2up7Tyf49gIqrq4kNGzZwwjyDIIjLOo6fkH6MqaXZdu2tVb8ttx36RmM+Z9gbKju+b98+duzYMfP3xo0b2bvXUnLfuXMn1157LVu3buXAgQMUCgVSqRTnz59n7dq1cx3y/3n43F34AlY+6i3hB7i16f4F9rAwdGoAgEiimk4Z8AUAcBkW00nVDTJaGtmxHqe3/uoZy410PM6pV69seo5ubyKuWN7wUJeX7/b/FefGL59yPJ7LckrdTcpRndqk5k/hlyTU3OVrNf00wVvQsBXyCEE3NzqTtLms5xpcaQkWJiU7ao2KaLOlcmppMSwHWtweVrtFDCMFYm26q1eyWABGocw4yORHSObGaO608tv9igySSpEkDa3tXCWfptFXf5pwUvgyeT2DrNdPa74Uh7NWSlu6RrWvM6d+wMGBx2vuN95lCXWfFJc3LepqYwW3tXyIpt76q27MBdmIkyhG+P7A35Iu6AvvsEREh4eJjSyO9n38dB9PRDM1nUkABUNHFmwz1TIXAyW4haJh4JPaGO89hSiIFNXK8cET9GKaJqKjNmW8Rbb6RXJsoubvbyUMHjhc8be9aEM3DRo737gKtFIIREHk9LefuyLHV4tFGhxhwo42Ctm31lxxpZBOFziZ6kdvd7L53rvQDBOnuvTy4JmJSU5ms+QNkQ3+q3BmysdKRaNMXCGdnZ92KIKEqRcphq2xMJ6vziqw65ZjxbOAaHePYwerfeVCPMEOax6WpXJgeUqe5rnx76HkrDkv2zeCVtSQBZl8IUkkP0I2W9+4u1bp5k6vjs3hYDp7nO/1f5bY+ASibCev51m/bSsA/un6dZne1/UbvL/n9zix6/UF25qxNJJerXmTCFm2z0i6/lSe2cgM5Wl3d0O6aUn7Lwb9qRFOJyulA1RTxybWZkYlvJYtGLhqI26vh6yWJFNnRbi+1+rTiV0Ia2IyAXMQQRAwnWUpj+Pffp7VU80c+ZOl65rm9BwFPUfe3VL3PumMNU9nDUvbqWFVN3tSRfrV+lI3D76wixsDHTQ4S3brPNWLJ4cGSGspkmYEWTVwLjO3xGeGaXR0VTiTAH73m//Gx7/1HzN/+w2ZBrnaftK0skyDOFlORxQRMcTK+zKNHO9f/cso/fVpjZ3uPcSp9DDFsHWOSSNKTLl8W3U2EkKIFG8t/cZ68IY6lPr6+ujo6Jj5+9Of/jSf+9zneOihh1BVlXvvvZfGxkY++tGP8pGPfIRf/MVf5BOf+MSyCYO/FbFevIc1olXmtKikcSlKXYvwgGZNjn5P9YB/zpnh+yM/5mjOmuROJfIciE6ywm2ywfmTexcn/ufzeH6okopduUpzsmCQK+WyB/0BNvhvZLUxf45vPeiRV/Kurt+ku7naMx2TJhjIneG1737rss8zG1fKGVMvXs0Y7ElP07l1Ky3OHgI2y4hoWms5Jaf0KJOF6nKlcmN5Ek5OXBltKUU2MYUc06Id5NopokrJf5FSyxNY3+QjHBv7CvmSYn1m0uDpxGs8k9uH3x+k27MZl1o/vdxr+x0ckhuHuXSdrjV26/omBseqflvv6mTVHCK+XsnazzW+vA4Hu1iqzpO7/Lx00+5EFBWuaf04Pn3pC8GFkPt8H7G/O7GofYKCTIdwfM7fk1KK0ew5pifr11EAGDlzhk363dhEEb9oZ/qwdQ63vdKwVBQFHRPXHJVk3LJFd+/vO72o87/ZcOSLX0F8OMWpbz8ysy008U2yT/06Dvflj931Yu3P34Nh6gjJK0P8Tk2U+1FkYOki/v8vQdNFzuntbHrXPciKwtMj3+XVqaeXfLzcdIKzBRHTtOalwCztjf7/+xTqly6/IuybEU4kvLJG1m71/XS62uHpFq3x22nOr2uW2f/HpM5/ZuZvf0MQ0zTx6eVxLuC+hlvbf4OIGOXF2ASR1ARTk+MIgojfv5oHO/8LCaO++ceUBGShNBeisMZ3NeOnTjFu1+iXUoRam8nrWSRz8VV4x04urKfYqvu5xltdfdCTt+xQz+DS+pSYtq7Xz5VPjx1LjXE2WclckLHP6FFdini+j0cGvk17yeGfUjM4xcCC59nzz4+gPJbg9T/76mVf8xrnJlZ4rKIZmjRLhys7BEDBXHoA6/jQtzmZOI7qqF/Dz8xafcVQrfcuKwr55MuY+YUFtgGUg9bzT8pxABKJuQXBU+d6eXr4n5BTe5BMF8ocjr+lwm2GyOsL64JOZEbZVYPUkMnkyGmWU3pkrHwfomgn56nMWOpev55T068gF+qbE6fOHeNI5BuopvW8D3WPc3LF8q6V4sUsI4W3ni7uQnhDU95+9VcryxevWLGCr3/961XtPvShD/GhD33ojbqsNzVEQUcvibZNCVN0iR2cPnCIzTfdMO9+DT43RKFrQ3VZZV+4Eb9zikzJc7w+0E1Rz3FqehcFNc02Fs4LvxKIjL1ILryWtfEevMHlr3STSSSRRQW7VPYsr/FtJ1bnQDUfiuksOEGs4btIb26nf4+O89QE1QTJpeHVf/gKXaMrOd99gdt+4xeX6aiLQ5tziqxQINDUSEw/jL2kUKc4LMNwxBzg1OQudvArFfsJevn5T4yM0NDZwXIj62qhIIJpKyCbtgo9t4vYNf0cjfYwSvC2mW2d3R9BNAVCnR3AJE7JxjbvveSJUDRMnEAmXp+fXlVVumTLeaa525Z8L27Jiq6JU9UT8wrvNrJaquZ+7QEHkCEUX96JLytYiwmbp/7qJHNBkIMYpp3VDhtHhSsb8bFJixP9bnV0EnTMXYZc6gix71wLHerinIVju4/QQgtj2QFaXd1M7Zsg7O0ipVQXRDCMIsV89fu9yGDpzRUp9J6GO+5Z1DW8mRA7q9Igw9j+COs/bG0733IHtrDMG0ksb9uwjqPqacLOK1NgPDI8wcXlSS6xvBHV5cbAp19iIHOOWz//qws3voLw9Onc7xNoX2lFyn12hZCydMZ7/3iEdwebeF3ygQ7xWYK5I7kiQRucPXiENVdvu+xrfzPBkN2MOVw0Z0tOkNHq4EbBsOZX1Zx7HC8UiqQ3PUSyWGaLyoqCZqpkhPKzdiWzyHYVs8uDYzCFobiIDAwQBCYLk/QbnYTU+pzJhsOHhuV8ckoBtoV2cPrcKTbu/BvUZh/wC8SLUVShvkVnIZtFN3XOJg8wUsei+lziGGdTR1jNvRXbW5pa4CS4i0srhFOQrHsaTx4EfmlJx6gXOxrvxi5VBpuLYh5FEFCLxaq0P//IGH67C5vdureBRD8RaTObNA1pHt3F2NkJOpQm9GWIKdslPzkti2poBKfL79aMjAKrkGqw5+tFQ/PVbPdfx/5k/Wwqr78ZctC0Yt3MtmvC28kU67PP9CkBw2dQMKdIa+3kRobmbDt2eozbwx8lEjiLkcwiiyLpeBxPIFD39c4HpySR1haunCb3dHNNaiWpZBKvrxww1DSBV2OHuKvxbXhn+YUnJ/4GsaUV+PDMNo/XS/PBJ8isqY9pv1lqo637gxhrrX52zfkmFGHhb+zJT/4l26QdDG0a5qaP/ty8bSczHpzGG1u18KcBbyhD6WdYfkiCQaE00fpd1qCd3bVw2fJn0gN8b/RJJv3VH1J3SyPbQrfTIlkfnFPKYJPS+MRGWjx3LePVLw5Jl0gkP0RimRkVFzF8wvLwZ3LlibEgSKiO8mI/k8lx8BPf5eG/+Mqijn3RR+VqqBbJu+Mjv8Dd4S2ssN2yhKuuDe9ZaxTOnqu/bOlyY41nI602q8pHwbTo56Zp4AtYjoZmuYu7Wj9ctZ97fNYCPHdlStoHcgbuZIbsmk1MCgapTHVEVRCdgEAuUp4Ypt0NxD0NhNosJ5dgwCq7kzbDQWO7tc0n1BfJnOqbQBRE8nqRqLb4tKiLeHnSirgbyWomjCTKxJ21qfq2cACAjLa8rENRszp7g3j5DqBAJoY9a1H+/WJ91O9L0X/iDKd+/0mOfaN2KlJ0eILRYoGsvrjrVaQQhjm3E0qaLnC7z8XI4WOLOu7EtNUXpp1JziUPkdCKRAsRdF91FDGnZzBqXPd0KX1vg9OGf3h5K5j8tMEsWt9bKl12sjSJ3XQ41s21yxVD3qFhF71Ep5e/1HdiFutDniNN96cBmqoiCRItjvoqzl5JTKb6ORl/HZvDGuO2rt/I5uDNpJdYir0oeDgWf518uAHdNJDM8tjZ4OwBYPi51y77uuvB7o//O7s//u9vyLnmg6aqDKVPkEwMcXEJGZuuDm4YwkWH0twLzdP7TrApuAPXJdIKGS1JYZZD50JiHy+O/gvtvlXc5FqNnHcjj1nzn5bpZzT2LVJ9Z+q6fpcRQDctW9cViPLMyb/Aq6TJ3/CnjDd/EIATua/Tn/lBXcc7uecAkiCx3n89K9WFgyqy5KqoVHcRvtWW3amxxMrKDmuBXgwuLEx+ubCJfgQqHXjTcgJREJgYrnZstPuvZ2v47TN/yz4Xq9w2Tr0+/1zpbrJc6sU62WfzQZEgI6dQjTzmLL9f27hlLzXWeCf1YqX3ZgCajGoZgrmQ0y2713SVL6aACrb6AnN2pZmUGkcK53hy+O9AnDstOpN34XE0IjSvQTWseXPi1PJUKC0WCjglhby28DrNlsvRaRMZOVX5rU6eOs5djZZwv0D5efQLq0mMVQfolNv/P8w6Cy2k7V4kUaZx02YACmqCnLrwOqmYiJHREsR7jyzY9vpwA01zSCG8lfEzh9KbHLKgkC9F7h0dFtshlV54Ed4a87HW4SHkrabDxmNWlCk+bH1kuyYf40RuH0FvK9cG2knG4wD07TrB0Y/9B7HhxaV0LBW3tnyQm5rexehg/xU5fmoqQ282S9Y2i+pqpgnMWrCNnDlLo60F39jiNCzcssVkaFu1uuo3r9dL0UgQStZHbV0I+UwGuz3ESOYscc/CUYIrBVl0g2FNylmxNCnomRmBYFHVUI0C6UtSGE9Hd/PKxCOcywyQiC7PM7kUkpkHdYzzR77HK4Pf5sLRg1Vtbm28j62hWxEGBma2ZY59jXjvN3F4nDw68DmOTB9BFEyyZhJv2CoRX3QHap5z93//O177h3+b+bvv7CkAjk4/x9nRR5d0H7lMhqsarMhmoQYTRkZHytSeLIMd7ZxJ7CeZX16noyhb77eYD1z2sY5mjnM8Yzl6c6mlUdBPvfA6HsWHeyJe8/eDz+ylzWbHschFuiRLqPPkyev5OJnCBZLDi9NmyiQth5J08wb2x3YzkouyKxeg64F7q9q+3P9PjE1UL3Qm+87P/H9aWnyqxpsJQTIYps5UpnzPzkP/hH7kM/PsdWVwuDHCj0a/w+Nf/LeFGy8SslZeWA6fH5in5U8WU+PjZHQTl+xakn7YciKubGBQLy90z+leQGT3Yy8v6Xi2hEYf22kONCOoGValynNXq816P4HxK5eSPxvdjjV0O94Ywfn5kJqMsD9+jFxmAtqaSOomRU91erJZomfbhLkXWsV4iSm0eXvF9leG/pnoZFkLsCuwluvC9xKbigMwduI8/SMX2Dn+PYoCPNjxUfSJ+mwHSYuj69ZxnCvXs7b9XpKGg6wWI2O3+q/P+wAbvR+r63iaEZj5f4iFWVLr/Vu5KrSjantDVxf9U8coZOtzjF0KX2l1F3Zded1TQZC5NGxVUM7x5NC/kJ2snv+MloeJev5+5m/TLbHe6eTczgPznkdOWU6KMxO7L+t6M4k0dkFBNzJopoQilvtrumAFbora0nXqUq//MQD2Yv1JQPr0CLFCFFEvO06LqCh1Bii9tgBRMUO6+1qub/kgE8rczP5ieohdp/+Mtq1diIL15nLLtI4799peZFEhJy489gf1ksPtkjEzPTZBomjZpZHS/Wuaxs3N78Hru7bqOEU9iyLUab9pDlSjiCdkZbk0Dn6HlkP/uuBuxfVhjsd2oTaE5m2n5otM5M4Q139ywfyfFH7mUHqTQxYUBM1arBeC1oeXSdVOcZmNNmk1W0O30dZZTc8PliobeEpMg+tbfo22huuRgpZzIj5g6d5MPrKXkHcVhz73xOXfyCKgxK6MNtD0RIyjE58jYysvzM1cDHHWOnZ03yEEQaCZ+sX2ADTRjm6aOObwWify4wSVAIVs/RGNuXDga9/DJXtpda3CoQYu+3hLhSQAWM/SlYsSK0T4zsiXZ37POjzszYcoFiodIX5fO7IU4oTaRjx+ZRxK44UBYuIk3bqT60PbKV6opuintAwFPYeplaPZqzrvpbv5FkRRpDv0EK2BDciCgmFqSJKEZqjkM7XTUbqNa+gcLbMmkn3Wd7QhcDP3tFUztepBZCyKXxLoS/cidVU6NwqZLIpoA63292Kz2zmWHCBTWHi8WAxSfkt0NaYuXWj8IiY1JxOqhmEaCOrSIrUOp3U9NmftSF+qtx+wBJUXA8EsUDDmTj0SXQK7ozsR1MWNVw3T1ni+5ar13BC8lfe1vYsbnHmc3moK/urwHQSC1SW4PVo5qKAqgUWd/82Gk0Y/oiCxPlB+Dpm1/5nJng+84ddy21072OZbQ8vw8gvIJyPnGMgOM5QdRo/+9Bqrw+f6cFuDP4cff+ondh2qqnJvqJvO1p6ZbS5vadl7dmm6NPrAWd4VUDATFzg7fYTerLUIy2ezGKZ17IRZOx1/eiTKK7/3KImJ5WevLQbP/+mXeOlvq6UmlorxvgEebP8ITf41BNes49nIPnKeQFW7E7kp9qU1TipzL7InD1pFVxzuqYrtq4J3EvKUv++g5CNsb0GRrO9MyqbJaCZ5PYNut95Ds6e+lDdJFFANy3mQsQXo8W8nMa1zbejThK+1juXU3fhkJxNDC9sjsbExskaRVGEcjzz/4hPA4RQQlBqsC5uNA/HHSS4ibWo2zk1bbB9P+o0IKqbRzHjFFlNqIWRvYfTCVFXrqQurGR8rB1cb77qeR4a+QdI2/zs7cP48Tw5/jUbXjZd1tWeP9CIIAllBIL/vj5GOl4MPtpISzGhycZqKsxFZZaWYJ6VA3fsUi4d5ZvTfCLeV0+hlNY6zDoba2MAFIvkRYo40LreHBnsXYnFuYoGWEVjf80HcIS99qwyejKUYcl/+2gPAELOM5L+EvHlheyofslisk4nK+dLMZ3lu9Cu8OvUaqs9KZTNM+B1hhN7bqtOJTdOGU55f7P8iGlU7FMts90zPf0G7ceEqiIGRMDc0vgMzNj9jbGJwkmN5hZRjcRIKbwX8zKH0JkYiFmMgc5K4bhkoXeu7eaT/b8gUKoViT5yOsvO1ykoRpqGimybuQPXH0bHOimi0+B1oqkqnTSGYM9BKwsGjxy1mRUG1Jttuo52pwbnzdZcDsyOdxhxMA4An/vir9J86P+fv8yE3OMpDKz6Na5ZWSVKQEeTyQi6bsq7DPk+edy3kixNk1Picv0sOAUW0c+6xlxZ13FpoiFjGUdEU8KTfUJm0GSQnpxAFAVW3FtyH4oeZyPex0V+OxBU7VnCrz0s0WRnJ6PJfy03hO3jAXaQ5d2Wuf//U8xxWh/H4GunxbKaYqWaaKIKOXXIiGOVr8NvDuG2WkbjRHabL0Y0oiBii1eZYfDeJTLXQeC04x6wJfErLM2EsTRTRuJhKKbmIj1UuVEYvWNpfeWFuDZ+72t9FZ8fypVoCDAtj7Bl7neNax2WLqt/TehtXh27CMDW85tIihvlhazzotG+p+Xu8OMj3+/+WR2OLc6zJSGjG3FE4Z6CJt7f9Mtoixcm1osZQrkBzVys4rSnaJ0FzS6CqbbN7CwHXxqrtGRuMpa1xuu2n1/ewLBhPRzkeP8DkLB2E1kA3HtfCi7nlxrqtW+h0tNPlWrom2lxIJyc5mMnx6sQ3KGo/vRVkMqfPcXTaYgBlDv7kBOFHDx1BEQSkYtk2ueYdlkqhf5G6ZhchOS1HmbsjzLF8L0OlVInx030zDmlFr70oPv3XL7DC1sDxL+xc0rkvRUqtHq/UYpEff+rzDB6vXSxALRZZl13L6onl0/lKT0wyWYihkiWfynC9149+vtphd00ugdMcYVybu0+I45bzw9tSGXgLuVfjdZcrRjk1FYwirUFr3g2pGVqkLt7e/os0B6w0e0Gvner9vS9+iW/+zedn/t43/TVOFCyWZ9hu2XqNEzbOZx4mdda6jqyjlx+NfI6JI3vnfxjA2Vce4fGBv2UgP4JD9nDmtfkrE0uSDErt7/me7t+gpYYsQD1QclYgQ9WvvENJFBQ0Kh0YshzkpqZ3ER+OV7Vf7bmRDnuZaXL1TZtZ3/hhPDV0IGdje+ha3t72Ya4O9pCYqnZU1Yt8PM34+GmMRokzG9/J8Nr3zvzmEhTyaooVR7695ONvDFnjjF2u36bQfFdza/MHwV1ek2k4UaSFi5Hsf+JJjmtrSJsjrLDHGdz9h7QMPjNne7+jiy73BtpXr6apIUi7bwBHenkC9ecPH2O0YCOwpmfBtkW5xI6aqHyX2VSBezt/G69zO0rSapOOx/lA7Byhc9XZBFGnhCDXx+QyRSdFs9xXTRlc8zi5L8JZqvbclJ7fUdR/9gQPhDfjbbtyRWR+WvEzh9KbGNGxYY5q65kSrUEr3NHCKu9VyEJlJHvvZ55g+D9erNgma9k5F0NSwNL5uWD6yUxOIgoCijFBoVS9KT5pDTwXEic4GX8Nu+Rk9LNfW9Z7uxR9p8vC2HmjtjF4+IWDbFJ7mP7C/LTZuaAaKY7HduMKlxcmk7rBhVnrWMk5QqIYIVlc3GQWkZ7n5Yl/nPN39w1WnvvA+ctj5CSiU7x4+iCv5Z/hh8PfYDRx8rKOt1SMDFgGZbZU4jNlN1jlvYqV7q0zbbzTw4RkidRopbOzd/QM+yLHUBQ3kcTCopaLhVoscl/7f2KbsI5kSdIqkqmOGntKzA6bUJ4YREQoVYTRjAx6wTLWEm5rKD2fOkEqU+1EmV0G9SKiapL9aY1TRoxjyQtoxcUvciZOWlG0Zmc3rrFKIzyTTHNg6hni8twpGMP5YRLZ5WWBefuO43bkudOjcuDHl6cnImGQVFTOTx5gMr80SraUt/rQ+YnabIlgMcz2hrtZLUfJZ+rXabqQOMhEqnfO38W2TgKygKYszrHRnxnj6LR13EKb5Yw6nDyHpwZD6X+sDvDPV1UvDgey07yWNSjoOQztjUnB+UnhrsB9NNhCnE6VI/mK6EA06y/zvZzoc5q4xC5ikaUveGqhPSPx3sY1PLTi0yQ8V14XZanITSVI6lbE2Rb7yTm+xkol2wNymUXYsKKDaTNPE9v4/qf+tOZ+kQv9nHii9mLMVepSbStWcGfLB7ml6d0ATF+wxtC90T3smXis5njvKmkIjRqLS4GthamxMQbSRzgUfb5i+2tfe5hN4jYi/3aafA22895ZlRCXC7m0zt7xfyUpF/B4XfhtnTj16sWdz7+Czd5uHhB65jyWGxeaobLulsoUsL9a38bnN5YLyIhSENNUyJXS5xNFhVTSeuZTNmtxOJCrZgkm4jmKr51gbG+50l+DsgmnYTHOPS2WBIRD8OFV7JilynIud4C3NX6Ikf0Lp591mz1sDd6Bq8XaN3tgfhvMyNvIpWrbOWeyJxlQl8amu95hOdYk8cpXZpZFD6JZ6Uhtv2MVJ/l7GjdVn18RbehGeWxwul2sUDTaU/MLYbe5VzCZH+aZse/R++OXlny9uZM7OTb5bcINOdocPaxyl9MCc95uHIoX+13/vOTjS6LV/xNq/XOAYbqxiQ6cnrLO6qRdY1ovUMzNH0zznRG526OzfvN2srqAd/svE03MXd0vlFIomiZ2p4PWySxXSZtxHl8eUoD9tMR1/o/OMMPnQ6vbsrf8sco1gKIoeCSZLW4HjqT1DBPDw9wauI2mwRqOr3wUe52Vkk2xXDgGQJAlJHFhFphYckIVjPklZQpDozhEAYf9pzfoc6XwM4fSmxji5DQPOrNc7bImUE8wxObg2/Arqyravb1hJbcGeyq2SYKEZtb2SHv9Pnrje8lGBxiLWgvmSLiV5tVWhMhfygsXRJ3BdB+DYzs5HF/B5Gh9zIylIJcsMzjsodrR32j/GEemd3Es/jw/+tO/XPQ5CqiciO+iaVN5ckkBg6owUzUpe8LAb2tEqVWubR4EnXeypvWDc/6++r6b0AwVIXJ51Xt2/9WXCCouMmKaq4NbcflXLbzTFUARgSPTLzLtt/rYlqYdKKINbVaFl5aCJXbtiVVOugF7ErtsOTsLheV3KCVGx/HbGnHLPgIlirVvniwEX0N5shEEEcOwVhW6oSEjcUg7S7HDMprua/soG4PVdOx4dJoXx77Fk0NfRC2UIofZGJl0P81ahnc2rKD/5KlF30thzFrIHJl+iVy+khqvFTUKI4exN81N0T01+RxDk0tzwM6FnuAtbAneSrwwSGRs6fQYtVhEEiRypOmTVjMmLY314SxY46Mm1TYaVthXssK7hc3eHmIDwzXb1MJA6gA5fe50hKZVPQDI7sVVi7kxtIqtbquPXfsRq6LmjtD2mm1/5fgrfHD/i1XbAxeKPBhehV1yWk7QtzDskoNW1wreHboJgNjUFLIoo7l/QpRzeQRZEDj7te8s62Gn4tZcPF7MIas/vaLcQl7hlsY72TnxJAfiS2MLLwemBqxAW6a5MgVN/GCQQ/H99A28zmO//n+qdJ72/8VeRp+1oddwCmUj1nepCToFWUZULLtk+PBhAOyiwHXhOzj8RKXzWldVjg58EwBfdmnsqNk4f/QYm4O3YJN8qMWyHSePW3ZKs6OJH336H6r2S5yy5tpjJ7902ddwEXHJxYOdv4vUuY6O7hZezCgIhXhVO0G2FsqCMPd3OaiPcCKxb0Zn8SI+cuR1funwoZm/JcGGiYi3q4Nx1UC1OTET1vdh95ZYS1K17XDu89/m1pb38tCKT6Opli2yNXgnrbarAGhcZ9lLLsXqM4EVVkXAwJr1tHlWIBeqi6pcigZnK53udXTfuAlNyxEbnp+lqwCCXpvJMjr+CtmxhVlRNfd1WQt6p3jlmRKTr36SzOAXKrZ5HXYcmXWkT1U7xBTBVlXtL18YwS3NPVedem0PNtFOohjDK8kMDCw9dTQz6uCuLX9MXnBhV7+D5+Tvz/yWKqRJqEnOJV8nNr34YIyqqkjAqWyCocTC/eUisvHj7Bz8Mh5POTA4Kk/y0sjnmRif344yVQGHpLDxwbtxBpvo9mzEKcwdyNKNHAUtDkDMbbUbzyyP1mJ8ei8vjX6L1VdfvWDbfHuAvZEnOWNUvktbqRucTRwgrlj3LpWcu85a1XjzCVQjX5fcy6uR3eyf2DXztyrbkOqo8maWHLOaPj+Tyz1g2eSt9re23VUL/+/d8VsIE0NjyI4Acbn8Mewa+GsysUcr2hWNAnk9OzOBAujuBtR5nCInU6MkcjqptLVPwRdGdOkMpnuZHDxGX+8J7mr9IC3NmxjcLONQLvD6335/eW9wFoYO7SZatAaLwqyqW7ORGD3P9uBNKKIdZcxBfHpxi9mGXBMf6vkUzlnlKwPuDA+GvAyetyrnhUXLwIgVF8eWaMg00KjNrbvk9fl48fxnGR98fM429SCYa+b68AO89/c/RZtzJeFF5HAvK4pRziYP4W6x+s/F0vUC5cE4a7ccHdFkJc15c/B6WpyWoGBn1/IvCqdGLAOnIBuEG6wojrNQOaEYuk66lKKYTpcjvbLgRbrIABRcKIKHlU/8NetilrHbn80Rz1dHJlKTk7T77uKe9l+j94zleO02gtzWshq7zcAmOUgPLT5y7claz84jB5gOV6Z0TY6Ns2PbpxA8c0eqbm5+N7e2vnfO35eCROseTvAwr6eSmHVQiedCaiKCIAi4iwVuDrrY4F9amsZA6hymaXJV0/01fw8I5cjq6PmFK2SCVdnoXSs+RaD1/XO20Uuiktrw4gSUPfZGpIsCv63zO9HabT102auF/rOJHDbRzktj3+HM8DcXdf43G+yS9f6cshtNVRk+Z7FZ4/alVQW8XKy96zoAin3Lm2qim5bz4mj0xxjnXl/WYy8n/CV9Cq+tnXUN97zh5z9wcA//9Cf/h/XyFjRDZcP9d1b8vvnaa7njTz7MSs/1XBO6jR//t7+r+N1tL7LWAUPHqzVUHFisl+ZVa8gYcaSL1UsTI/TG9yDaDdrdW4ge7q/Yb9djT7Ci0UotduYu3+zWR88zlr3ApsB1qLPmm0TGmqsOx/cxkl/J9/7of1fem2rZILa2rSwXnJKIW3YQaAvjDAW5xjaJYFYv0JyllEBxHjbARH6UE/lq1merFKLT0V7eYGYw9GnCq1ayK/ICKTmJrWg9h9aWMHp2Gu8lDK2n//ofaUqVWU6DB6xqTdkX/wQl/iQA/pAf3dRxS9bi0bvOel4b77wDAMc8VT0vwia5yBpZmt+2g0eGvsCuBYqt5BhB89R2jtzY9V+4pee3FzxnLUiq9bwn1CtfNCex5f30dd9esS0fy7PSfR/GhUrHXi6TRRHtXBorTMgZnLZQhYN0NqbHrB2KAR83N78XObl0BmpWnORUYi/d120nVdzERNsfzjiWz4w9xtPD/8Sx2E4i0cU7rRLTUQRBYL3LzyZP/UGwTvtmbm37zYptfiHMXW2/xsCR+VOHZcmLbpo4vR7aVlv2gM2c20GkSA4KpQyVYMmJKngDdV/rfEj7V5EVzCqncC04OtqZzA+S1irZhMMT1rWlxQ6cLktTSs2VipXUSG07nT/Kk8NfpO9YdTrcpVgXvp/mcHn8E/IFJEGguIB+7cXCgk4xMG+76ZTVUGt841Puf9L4mUPpTYxo6QNLeMsMhFXSLTRQyZCQkBAFmdhoecE6bR5lQJi7/OH9be9iY2AbrmFr8dtTiOLs9PNa5IeMxocZP3SaXfER9CaBD3z891jnXcsqo3phs1wYP7mL50a+wHf6/pKzU7UHV58kI4ky3qY1bPBfzd6/+OqiztGgFhEEgY41ZVZPTslxJLaX5JhVXcKNh4xeZJ++OCbRqdF/Z//A383bJuDdQnvj2+dtMx8K2Twtjg6G1WH87a3oeh5v8SdDu5w83M8Hen4POWJFWwqFUpRhViTAFKxJREuXHUqaqqKIdoqlqiuR4eVNGwHIT1kRYkMQZ4Ts44VKSnFB1ditfQ8ANVE2IgVMRMO67ryo4rG58b/nXxhOWMKBE5kUyWL1xCSqMmvdTdhEkbHj1oI37WqjaBTx2a32YmThMquXon96mP1TP2aV7yqabJVl0sXsJMeSL+F0z02XLrpERPvy6lRlh4tkYhnuCl9LcGTpRl+k33LEaEYR3cgj60sT+S4KOucyQ0iCQPwS/bWRgQFku5+UahmOWrQ+J0AmPs1g6jiGNLcTsKHRinL783M/gzOv7aH/0zv58R9bmh7RyCR2ycXkLG274dtzDN5a2zki48EmVkdBM6XFW5trNU3Ndyx8Q29SpKMJFFFB1fNIgszkqTMUe605y5O7MsUbFkLPbW9DNUxEYXnT0po2WvPr21vfg6O4NM21NwIJ0+qPrsZNbHR3EKkhQv3KJ/6Nvk89X7X9crHvsdeJ//tZtLHrSWZ6mQwcpX1tdSW0UFsb1/zue3lm5D+IxSsLMkxmjiEJEud376/ar8HUMLQ8Hr8PUS1gE0rsR6Y4GnuZFQ9cxVMJjcJEJbMhdnCUnuB1PDXyTY7E9132faYGx1BLC8ILp/tnthcK1lgjbGxgU+EC1xVupP9g2UFzIZpjtJBilW87E4OXn3oHEOq1WJpt8SGcHjetjmbccnX/VExroSWLc7OO3++7n/c4r6/aLuNAEcvMjT2pfezJHcXhdnJnwyY8CQW5JKYsr2zh0dQAe7PlMVMrqnQOBSgYeY7lrPd6Zuce0okk0Y0PEp/lrDqV2E+ypI3VvsZKG/M1BSnoOWx5mT/7rU/yw1/7X7z6if9T8x48ciO6oOBwOVnV9H6us9065/0CPJ9x87xYe6xSbSbiPKyd+eDUTOKFSY6kdy3c+DKxyX8DrUplwKd78yYAXFT2hcRUDkEQMFrXVmyPyzo20cGZ/bW/j0Tc+jf4th2oRp72yyhgo0rjnIy/TPeWzWiONtob1zE5aM0bd7V9hNtbf5EP9Pw+Z59avN5ZftZ4ZxPrt38cUhCfq5JN5nY5McUAxUh8/n2dPnRK+m4NQXRTQ1fmZrHaRRtqKbjbtdJyegWM5RHlvt59I1tCt9XVdmXnat7Z+RtszFb2HU8peLLd20wgZtnosWnLPk7VYPM1rejk+vADFJIL24irHU7srrKzSy1VuUvH5t9XLBUYSgnzr6kUh3W9HdcvzNB6q+FnDqU3MbpKwtCrWsueUHfrdtzBShV8SZSxiTbGz12Y2RY5+zKRMz+a89j96UGmMiOcP3+IPZOPM5o+T8/6zdzb/sus8V1F4kgfhtRMV087sqIwbei45CtHrd0gNPNgx3/lXt8HWetfW7NN26Tl5Nnxi3cTyZ9lg7mZyJFj9Z9ENND0PK5ZWiVhxc6QcDVGiXJqigXcko3bPNVCuPOhuetBmjrnj9YG3Svp8i7uuLMxdmEMRbSTdFoDmm4aiNLcJXqvJNxChvHhI3g91qTl8FmLa1EqD+SeUmUT5yxnTnR0BEmQKGAZg8bU4tIDXv3DZ3n2716Zt00yZR1btcuEV69gb+RJ4sXK/PELx89yo/TzAASc5Wf4yuQjHE5YukCD6SOcTx7gROI16LScTtvCXlY1NFWdc/C0Fe3eM/k4qUPW/oYjTF4QmC4Jeg8NLl7AOpEtMJw5g2oYtMQrHUd63xnOxY7g1eZmariNSdz68jodu6RbWeN6EJuZJVBY+rHjyTgFPUfOpgMO7CxtfNm85mbWlDRnTr9eKVZ7/Fmrr8RLOkNmvD4nRLz/PKeHfogyXVv8FqCpx0oRdplzG2pn9u9GFgTUPivdMbLPGq+CStlhfeN9b2fHA/fW3D9nUzHF6px+l8NKd2h0raLH+9Y1bE6fsOa06ZLwbO+JU4yOnedg9DlSwk9OjfxAtJeB+IWFGy4Cg31l57qDy0+bulIolBwHSecogiCw58vV4rYr7OtQRFtVutnlYnrnUda4O1m9aZSr//n3uf6/f3zOtm0bN+CyrwH3yort3d6rAHA0V6eL7yrIPFVaeBh6Frl0D275Nt7T9Tusu+cuVgtHkMTKNOP42Ud5dugfCPqb6Wq5fEFsM9NIl2cDANOny6zK+AqFR/r/hpV33UDwgzuQZAcHH3kVgNjIMB1GhMnzz5D74W+iRxbHnJwLEwmrL0ZL2pa6qeJyVI9JQilVRRZcVb9dhG7zM61UpwjrmBXBKN1/K8nwVQQaGijgRtScqFKeRDFPx4Z1XO2SCbnL7JAff/4b+F1dJKQjuNdbwR8562Cib5DVbTdjzmJ5XsjKTBenKRoFQq2tM9vzuka7o5N1xQ7WeK9CyVdfZ7FYxCFKmKo15ncJXlYtoKF3t8vJ9kSg9vMoTiObS5tDBclBwN7EbaGlBynrReGJj9E4/o2KbaHOdnTTxC5WppwOnT3K+ewkRbOSDRx2Wf0ns28OlvARyxHoDaXJFWK45qimWA96tPu5t/1jiJKEUkqNzo7FKeQLZPGQN0ESZJTRxQc009OTnMuXqg8upnKsUUA3K8dDjyvNy1M7UcfmTx0uqlnUWQE33RQxXXMz0w9Ov8yplBWY94SsAIA+vTzuAI/kR6C+9Dl/SxO6aSBdMiaExLLNpJRsM1m22ojO6mN3tnSzwrsFc3r+gGBiYozvD3yG0emy0/K4a4Knh7/EZGr+9MbT+QGeH/06hwuH5m0XsinoRoGWruUrfPBmwc8cSm9ixPosKq2WK398qp5CpkwfnJ3mJunl7Tes+BjrO/7TnMc+EXuakfQRsokM0cIYBbuMw+3GKXmwCQ7ErIu3eSQ6bi4tVgSQBIHMIisl1Q8/TtlDoGElzdnag8Z4MYBpmri6OhG3ORElO4e+urvuM2SFAIVLJm9VlngwoDDVZ3nH7SWHSNsinWcd9s2EHZvmbRPjBOOv/E8y8aWJ6GYGrciIR7aMWRM7tiuYP59KJfnnX/4Mp49Xa//k9STHC0/gbLQma7HLMswmCmWNmqEmheemnuOCs/zMJ0atPh3zO3gqlqCgLi5tpQsHGxbQmE5L1vnybS7aVnRiTvYjXpKaFe09hWEm0U2dMTk+s90hBTHt1j2NT+7h7Phz3BX+v6y5eTMARcFEqEHpL5YMkxub3oknZ72TtkQUoTCFp8tyQHl8i39X3e71vKf7t1FEEc2o/C6MQg/v7f5dVFvjHHtDMi0Ay5tWKAkSBjoFLVo7371OtK5tZ5X7g7Td6QVTQBGWluOfmiwbaeOXpJ/ZhywnXMpl/Tvtq4+tNXD0PPdu+iNUuba2EYDN5eTHI19ltz63tlzk+Cs8PvgF8k7LWBo4Yl1fTqmv5LVajCPWKK6g5KyxIJJ5muyRP8I05xeSfLNibHAIzVCZlqy+74qlmdbS9KePk2v8yekMxdXT6NLyzoXyLH095zILju/92nO8+LF/Q1Uv37kc0nKoRoH2lda37xyeewxPjC5vQQBRExAFiW3vuGvBtrIsc3XoJpp9lQ6lVqdVyjqTrx67wnaBNtla3OWkIrqhMj0+zpRQ5ERhBFGSaHC20eHomdlnajzC+oaPEJSbuSbQzVWOy9c1dJjNM//3pcuOU9fwKKv92/E6Jba/821Mq3FWFqyF5flvPsrGluvxtTUwftdvcep4WWB66MIFXvhvj3Bs7+LZU0JpgefpttLUNVOnqFamu8Qj0xyL7WSyEKNgzr3skCSZjFHdX3JmHHFWtdI7ZD935qxAz760yJgcIGZ7lUPT/xefz0O3ZyMtavk8YkTEME22/q+P03mDle6ipfMYQskp0Vpmsd3nDtCgBHhh/AcV15B2TzKc7+fq33wfBiaGXO0oOvnqQSRBIIIlyzBtjOCUJAZO1BbmzmbzNNhbcUq1dfZUXUMRlzbvFXTreQXMKxtULBYKRG76DSYd1cFQ1dC4dKQaPvY6+ye+zGSkMtjrvOkGhsZOcGGOSm+pqVfYOfYw66/eTFqwYXN0LPmaPUIIo5QSVixVjx09fYrJ/gH2TD3LmG4FA9zK3E4ZsHQen/7EP/HDz/3rzLbR2AiHIo8wnutHFOtnf+s2OwaV7D3dLnNrcBX5aesaC7kiz/7Rt+ndX+l0U3QDfZYdoJsm7nxtFquu69iMFvKlN+PyejmfK1JYQlGYSzFw7AQ2UUTX61/DqCaYjkoH/J70KZ4a/jcAFNN6JjlBYChzmnSNTLpUxgoERk/OH5QdPt7L1vDPEZj1XoN6DK/SQH58flFyb9HD9eEHebt987ztDowVeDlSvxbnWwk/cyi9iRHNWwvXmL08cBQFBXkWKyU1WqZzT02WDdIzmWOcZ+7qEbd3fISrOx6kSW3mHZ2/TpffMrpUw8RheLE5gqT1JI0dVuTf1KwJdOrc8kZlL0KSAmS1NLqpMa3XHihlu0HRyOP0etj48+8no2dxE6j7HDbZT/GSAd3ZaRkcUmlOt0vWuSVRIRmrf9BUdAOphrZOxblam1Fu+jjnjs7NepgPw8ctanNWtVKqbIVhHMby6njMxjPffozVdpn8S9VaE7EpN3ev+iOimmUoOdu9TBfGiEyVtT/srStxO26HWZFEz7S1COsJCLTaHKihhStFXEQ+bRmiE/n5o0phJhkf/yzt4TR2p5NrN/4mQXflJOEqjvLi4Jf4Xv9nOB8rpwzc1HwHm9fcAMCmlgd41/o/Ylx9grH91mTkyRXw56ury/hLFeMAgqXojSG5yBpFeq62tI8agnOLZ8+FbEmroqDnkYTKmdYesBYdTRvnZr3ZBA1FWDjXfTEQBQnDNIiLRWzK0qOI/XuO8Wr0H4icSpMnji4sLeXNXSgbSo2xyu97LB5ncOwk2oYQR/QL4K4vlUjBWkzY/XM76wDskhdfsfZiYToS5Vbfb3FX20e5MfxODrz4Etm81TfErrn11iquQ5dqVvHRSyy8nN5NbsWvMtK3PBVcfhJ44fNfnkkJvBRt2TOMnvg4Hps1BtkHx2gN38j7uj/Oppve/UZeZgVWNGzlpvCdc/7+oz/9Wx7/5OIKR5haih/GrODR9DzR51r49y9/gZ3/+C/ERmqnOfmP6KzxruP8s5dXlREgn44ymelj+/vfRdHQ8dk6q9qcTVhaF9N9y6vv4nIGKBoGbauqz1kLhh6lNVt7EaLtrdap6rK3ssJtOYTMlaMcnf7/MMwCa498nY5DliixTxwjJHpRS5WZ9j55lI7AasQNNzORsJEvLu7d1YJaKDsrx9TyfNOUXcm24B2EGq0Ajmj0EpZDDD/3MrEhy8EaC7tZ491KYqxsGxx89CXWmk3YvnVu0dfiK6XWdJRSCw1TRzEqnSCT/UOcTx3mxdH/4Kmhv615nNTEBJIo46NaI9NUBCRBRp8JiubJldLir3UnaJHzeDLX0OV5CADd1BHUsitjdPh1nhr+CordRsfmtahGEUdOJjlVso895aVQSrERcjTjojIwdMP//iVu/LuPsuKqTjS9iKuGBml6MsLh0R9RCFhzTqHBmm8GX622kQAS49b5jRoC4gCyaEcWlBn9mMXgbEpgKD+NUWc59aUiFZ1mZePVSEr1N1cw0nCJX3+F4eV97R9jS1ulY3XljVs4bO8mO1nbZnU0NhGQp7DZ7ej+MdyiQC61NPtWkkRyJUeSZpQK7oxFOfvSLu4JX4tSeh9Ref5AzM5/+xqb7ZtZ019mpYclL+9tfw8tzp5FMZQEuxfjEnHo0MoVNDk6cdutYOOr//5dNujtpL9TmY47deYHDJ1+eObvA1PPcDZWm6mfHBzh5rYdXBcuM2iOTD3JWGp+naZ6cOY5K0UwJtQ/rqtGFtGo7P++tA2/rRHTNCkqVjrq1PQ4r04+yniq2lnT0FnSQp2DbHARU0nY4O1Cau6Z2bZW93Nz83twROcPAK30tOC1BfCK869JmmQnbml5mbdvFvzMofQmRqRrDU9N7qXtxlklVg2wz6qicf7EYU4nrKjT9JmyRsup1FH6fXN7pAW5iEPWyZYmA7PZGtB0U8AheggKHmL28iCQL6UojfUt3iCpB2GPiCAl0E0N+xykh7BTRhKtQUGx2RhSYrRJjUQn6hvc/Jk0Sq7SSdSy0hp07Spk0mmOTr/MaL4fgGKy/slMFHSKRnzeNlkhSLezh5ETS3PK6akIp+J7EUqaOfu10+xNzy3eevT7O7mwZ2nOK4DGaQ/r/dew2b6+6reQYfWXprA10a7avgKfEsbr7ZlpE457uMkj0zDrds+fOMS52H6mUpNscgr48/UPUVPnLSZIQq0t2n4RkWNnOS+sIj9htTua/BGpVKXOQHxS4I723+YDPZ/iJq6d2S6YIrlJqz8l8kOkSloL9lKp16ziQnNUa9pEbGXDxKmVGFLIJAWdiew0Pxj8Z14+MH+qXi04C9bHoBpilVjgxao3gca5J8CAmEAURXKx5UsPEgUZwzQpkMEpecjG40s6TqpvgmZHK1pUo2Dm0c1qR109aNCLFHNTTBSzJEYrF9R6QuOkGCTglDk1+DDF8/VVpTKLJfZFQ/u87a5ruIu1l2hbXcTRr+zFLtkZ1CwH6NRTFzBtWZ5OqGy5+7q6rmNczXM6U/1c8unX+OGFv0FzyKwIbWGgt6+u412KV/76i3M6Id4o+I85WVWoHRWMXihgb/498q3tfK//s7w20Y86ZfV7h+8nVOUNGFb7eXn8O0QHarPTtmavZbu0o+Zvc6EQCrLWbmCaJn7n4nTPzL0+VgyuJ/73J3nlt/6dEy9VjjWSYE30U9OX/67PGV4OZnQUm41p8ig12LxjuQvsmXyc4djidePmg72gkZujYlYt5NUsGGUncnpWlaAGvfq7UqQgumC1FwuNNLs+xtjxM7i3fpLCVb8FwJjiQJDs7Hn0OQBcJ0cxTIMb//P95I0ktjpKVC8EQbbeV1ZLk0vOSg+Z/CaDL//hTNq+8/5rAOj9wSHCNg9ZLUfPFitVrjFRvm//lDX+jxeneP3pHy7qWk6MnOLJ4a9g91nnlIp53JeIco8M9PH+nt/jxqZ3c33T+4nXqJ41NmQ5vfPh6iDEuLvA4axBOmkxLyVBQSvZpl7JRdAAnx7EI1iONN3UZjSVAJxyCJ/NWnQqNhs7+75E//jzTB2y7CNjpGwHXWRubPRXFrmYDW9hAr9R7QQSxk7RPnSMtVdZ19F5rTVu6WO1U6lzI5YtYavBMgWriIkgCEQvLD49cWXATacjhCTUx2bc9xd/z9ihuauWzoXcpNV3FKqfx2AxxsQl1aTjDg+KzYNtTaXmqj/g4w6vziZb7WBKl+2dNIR/AYCEGeNC6hj7n1paERu7AEnTuu5QtyUR4lJWIudF/LYwzpAbwzTwRufXFZrYbzkKE7M0VacHx5BFG7qpoywg4DwbzmwB9Mp+sP4WSw+3pamkRdpvXbOuVo7/5ubNmJvKDqK4kSQv1b723lessX+6GJ/ZdlvTDtaHrqr7WueCI2rZ696W+gWpVT2H7ZL77lCb2NH0bgRBoFCax5tFD/e3/TKrPNU2V/PWEutQm5/Z7bCXqjduLq9ZMjbr2U7F5tfF1S46fW3zV+5b5Wmm27X44PBbAT9zKL2J4ZuapsVzDQF/2XA29SSSUF68TkVVYkXLaPPMcrZ/pPO3uDkxdwUCJR3Hni2CJwCAd7UVfciZSQRRw48XMxmfaZ/3Jjk6/TKmVOmkOnPwNYb76qucNB8KZiMxh4Ik5vD5ajMqdg8bPD9eNlRyrXFeiE7xzBe+ULP9pfhx7BmemX62YptQ0v7JTMLA4YPEVRfTOet5Rgfrj/pLgoyxQC58UyntxTG4tFSJkJBAPvAtVmzrASDi9pGT5xaGDb0uYHt0ael1AJm4xXCzS9UOC7FgGZeBBqvfhJra0EyVvLNsdGRKtNhMupz2MJiKciD+PFMeeHL4K5ydqk0Vr4XhM1bbtd7aC/iLMNQwNze/B63EnpmKnkedrIxSaxmZoMuLJAgos1LCBEAoiX36k7sZjVgGWLjb0vVSxRgC1cZhIpklkh9GNQoUHJaT7UhBYsqTpbmtiy7XGsLFau2lS/HqNx+uqIJi1y2trLHCOJNaJTNLz1rtAq1zH/eVTD8P9/8NZ4/PLdC/WAhyCNUWxinriILI6OHq7//VvTuZmp6fnuxobeK1o3+Gf9NKfGf+Edeu/7mk60n7mph2abw08nnOpivTMzcHVnGtH/I5jQ/0/AHSVH3R3JGkZeBPuOLzttNMDXGOkrT2/giaabD1d96OZqi4cg6MwQhBbR+NTfWxGMaLA0ykqsWDTTNMl28bvpC1mHdmFk9nH9x7gImBtbzy/z256H2XEw63HYdUO30tn3cQbuyh9dobWBN4kDb7VlxRa1zxB5eXebcYOIwsDslN77MvLtsxPVmTVQ4F01CxTdcvOJ6ORGk0xjgyvYux3CArPGsofL8ytSl1sRKUdPmpdFeF1rA5bC2oBzOvcDbyNMVcZRrTNY3v5camd4KxvKl7dhSyav2VmQqCDWWWxs3A6XKU3u6qvjbFFDBLZaONhESLs4Pp04M4JDd6aX5PtVkLjqn+adRikbDhJlqYoKk5gN1hQxFtJCeXPu8CBIUQad3k2bQdX7ZsV4yEr2Fyc5kZt+rtdzGWHUTXdHxCkILcj7/HWrDrubLDzMxYi6pD0y9w+tuLEyJutG2mJfBhAmGLrfmEc4ofuyvvTzZFjmdzaJKPZudKEmNjVccZGRxFM3QiNSqDphNxEulhYiXHlyQ6cLoCADgLGVoLeXJjPyB74jPW/ZhKRYBltXctW4LlYjXx9fdSWPte7BnLHnHaymOFKVsL8bh77sXpgNeL6qxeNE9FvDTe8Gn0Nks/r2fHFgzTRIzUtv3iU1ZQKy/WLpxxSO3lO31/yYXRxafQuLCOKQkLO5+nz5+jNX41U/9xeNHnSaas761YY+ocTqaJxitZPtNj1sI9Y6se0zN6BuccrOZTg19lbOoxAFJukyM5kaFj1RUBF4KaK2CXXBhiqepgh5uTOZ1oIYarxD7pvOpqikYemz7/s/P4brDSyY1y/0lGLfvv/OBzJA78Wd3XdSy+j31Tz1Vsc/p96KbG5JA15sRio5imiXlJ0LTL3E5QLUtq3NdxPW9vqZ2OnymtkfKzpscJVSO2DO6AlK4zphp07phbCuBSFDQDQa/sPFopRX+qMEa+aNmJNoeIz96Iv7k6hbOhpwPDNIH5HVnT+yw9OS3WP7NNLvVDSZ0/YCmXUu8U5m9nl3yob1GJgYXwM4fSmxjhqQTbXTJed3nBMlHo53RiL+mkNWgHcg3c2PggAAXDMhrz6SyyIKLMk9+bEW3osotg0lpMt7daXuGMmcWv+JBFEZX+mfa+1T5OJV4HW9kI0zWNH/3F/yHxueM8/lufYfd3Hl3yvboMhfx0kqzmIBOvrTXTak8TkMtspFs+/B5CNh/h8frSRzb4r6fDVamn0LVuFaZp4kDAPhbj3rbbaSrlzg+8frju65cFGWGBgci/0rpOoVD5XpIT9UVxx5Vt9Nz5txSbrHu4UdzGPcHagrwXxVDTaryuY9eCqlnv2l6DrZ3NWtdsll6VKEms/ut7eeff/v5MG4f3Yhnhcp9ZE7qN9/V8ig1b76HF2U2Drf5Ih16KdmvG/Itn79UyUeXPWXG7xQK5oeNXWNv88xVtlFk6DwWj7OATEQlIJd2dtb/EurbbAXC2Wg40h72ALFYbj47iFK8OfIWJ/BAu0UkyGmWbLYuSzNC+ejXbG+6kJXjDvNf98j98ka6jLez8ZDlfXxUzaEaBfalnOZx8oaK9S25BM00crrnTuJz+Ttb6riabXL4JMKXFyBsTjHeGeCEyzf6TlUbf6Pkpxr6m86W/+fq8x8lPw8auX0N0+hlrfR/T2z6xpOvR8jkyuQy3NL+bbaENFb+5FT8ZI0v75m4uGOcxu+rTkPGmre8n7Jxfp8fEhU2udrgWCwVCjkYmc0M0rWwhrhYI25rpsnezfgGH6GxsdjVyT9vbMC5ZmHsdm7kqfBd4LasxPrD4BcnAZJwbPTLd/vrTTq8E/La5nWsZ20G+3/9XrLr6KtZ6V9Nu68JVTKAbGs1d9aU9XQkE2rq5teWDpCLVC0VNVUmWhPIXI0o9NHmI7w98gYcHv8Ce6cN179e7s5erQzdi94bY8blfxtBVVKFyDj2bOsjx2C7s/svXnfIYOnbVYonZxWHSmVGOPvr9ijbp0sIpeImj6XKRjlwgmamf4WsLJXDL5W/HLOkKvTa9mx/lqiPWAoUZprG7WeX7A5/FLEaxSw5spRSot33kHZimSfNonjN/+BRBeyOTRSv4kikt8s/ueXVJ93cR6WySZDHJNkeeQro8drfat7HWf1NF21OZPVxI7ccu+RkqpmncuAbDNCjMYnyKYiOqYbIteA9va3qAs7vrL2bSZGtmi6tss9gnj+ObqEyf0fvH6CuYDOXP8HTkCIZZbTRoA8M8MvAZRLU6ZX2VI8QdzT0YEatfiZhkSzpxGdlLyt6O2fUgiY2llDfAmGXfHpp6hdcmy4v1DaqTu/VWsiEX53MphNXloEuqpFmUtc3NcIyoUwylT5C6hKWeix/i9ciPWL3FWtz7GoLsG3+M0chztQ5DNGfZFqk5ZI5CPRvYGLofQaydNj0fdk33cipxuELMfC4cesGquKhJ87MvaiGZsVLRszWE2K+xp7nZU2nPNpaY051trVXtYy4RjxJA1yptuIkL5zEdIbRS8YWr73wv9zduob2w+IITY0dOYgDeUv+XPVlORr5JdHw/4xcsJm9WKvDy5DgnU3MHd0/se53t9lYEQSCglftB0G05xLI+B0Or6mMaA2R8jaQd1XNdUc9jV61xOeBoQxAEZKPyul4d/DL944/M/B3LOkjnAjXP09phPfdVt5adPiftHk4r86fv14Mj+bPsmnqBzpu2Ldy4BMeZP8e1/39UbLNLFsPnhbHvcWHUYhGOJa17HlerHf02m43D2QyTyvxrLKNUxVeYVSjFo1g2n5KeP5Bv163zyqKN6UhtWQ3DMLBLNnTl/03Xyv+bd/0WwWDyOI8P/SveYNmjHxVTnNFaGOuzDKNowoqkv5SIcsJtDUrRc/0AGMbcmiRFSUUUJHK5OKZp4mmyFvaj2nkGTnyLydR5lOayi7sQcfOhFZ9k+ng5MhUZ6Ge7/W0UjCLp9DD7f/AVnvrclxZ9n/HxCE+NfJe+/ADHp1+hL15bZX+NbwNtzvIkFWxsoV2eosfbRT6zsP7KWv82ul2VizmHy4leTBHWspwetTzlI+Ioz4x8Az1Xn6ZLNpthf/RpBo25NasAWq+xFruaVPbWH3n4+/zr7/wK3//dTy94nmLyLOfTB2nptphnGTWOJAika+SZR85ZEcLBTC+JyNJ0LPxZy1AQRZmRE5UaAfaS/kBz19zCiY0t1m/OWVoE6XgRRRBwtjSxwX8tPZdU4JkP+VyGSH6Igj4/TTl1ZoqJqY0oUqnksywgX5JHWSxak4dh6sglEYBcNocgiEyU0vhs+fJiMNRsLboPjU/zwnS1o9aRULi163eRdRWn6Gbw8FGcooSsa8iyjGoa5MT5Uyi10ZOcSexHz5Xp+ZokkTGKXNdwG7c3PFDRXtIL5PX5nWsNzVvZFrodo7B4g3Uu7B37LkfGniPc0YjfnsJ+vnLyPf6Pj3ODR2bLAlVFhEicpoYujFQMmyNEwNc8b/u5sHP8EU5N7MIt+3EbZYNtfGgEp+QkFpRo7u6iVftzXHJ9IsH+rIJhmrQ1zn9NulGkltv+xA9345a9TCvWeJnWRvAqNvZPPc+u8afrvre8AqIgkkpUsjLSa4c4U/gfmE6rj+ZHFl9BMDZtLfbPJasZUG8UcpksI1krjVqrIRgdLFzF9vB9KDYbWTMFUh5D9M+UVP9Jofsuq/R52FMdtj/3+h6ihSkM02D09Omq3+dC2NXFev91rAs/SEvTtQvvUEJ/qSpZ4VqL5q+ikb90WJCuYUNgB5PHL485A2ATnWilypGODXfy9pUfY/J4pWNtuJQ+MHxq+cRLk5PjeA5/g6B4sO59IulxhtP9MwG4gdF+jkZ2EhBt3GRWMzsl0U6u9EGbjeu5yf8u0nHL/hoNWfcYCPs5Gj9Eb95BWk3Qm9nPTf/bKoCSdmboTRxkaqSaobMYHIw/zb7IozTJYM6KrPt0P1yiX3TNz3+Au9s+CoDS4cXt9aAaBRSp7FTs04bYkzmH/cbVSILM6cfmr2Q0G6aRQpslpH2j/zZuDlVWpUyNx3hn0IVflNjukhip0e/V6Tzbm34Bd410kXRJ42hiegJd14kWRsjkrGdYMJMgFGhRWmgoOe+PxR7nfKbMNm/23UjQVU5zSUUf54nBf6C/MMTxgoNce/mZ2fIlseLU3OmfBcnJ0bxM5ELlfBGKJLGPHcPtKc+nqfwkRaG2c8pFmh+PfBmbp3bK+aa1K9ni30pwkZXecpkMOxrvJoOHp0a/U3PsnI2J80MU9TxqcfEpqL6SdpLPV63VmHN5UFyVjgpTsr6rlg3VUgnOYgabIDD0amX/O72nl7e1fABX49sA6N6+FhkVIbv48ero4Vf4wcijnMRymq7auJUPdH2UTQ2rsZeec8uaNtqJsKpGBdWLOPzVx3hy+IuMp84Sm8UYMrKW/dni2czVobvrvq4dDRvY0Vid2q3qeZRSmqzTtJEoTnEsXek0b/Zuwm4rZ5wUtXyNBEQLI2cse7/oLPfJa9Qod1ObJbcY7NA3cKO/G8VWv27XYMeDRK/+WMU2RfSjmyZXh25nnctyzgaj1jcSTsVrHmcs+RSZ+PxM+5Bhvd8128p9NeKxvLkjxvxsNHNWgZnB47WlXaaHx5AEAcNWP4P4rYSfOZTexHDLQcL2ynzSa7du4X2NPbhFi66rJSxD6SZvgBvHLWfLyHnL2ZRiHoeSzYkkyORknYQ6PTNAtBanyLfezWtaJ84d5RzzYNDG+OhxPEZ5YTM5MMqq9puJKDGu+Z1f5t3dv4vz7OL1A/pOn6an4SOEVnYwoU4R02svvG2ig4JZOShOieM4ZQ87v/C1ec+hFovIgsCEp9oZ8Ww8w76ciTphebB1nwef/yFEIVDX9cfjMSacm5nqmD8FI9zZRdEoYNPKn+XYkX4eWvFpgsXV8+xZwtAhjk4+Q1OJTZaTrUFtcqR6kTxeyg3fGLiJoR8/X9d9XAp5lpjzyacrNTlcghvVMPGFAnPub5SYPGqmbIyKU5bTzReyoagZ/Hr9C8OpQhONjk7cyvz5y8pkIxv970Ys6TyoxRiiVvneXcVSzrqeh1KkMxNPMJ6fIFtK9ZRz5b7m9lnnDCs2VjtqpFwUsiAYnMyd48ej32HkzBDPjP4HKdn6BlUjgzczv0MpnfUiChIppWxEtZ74ArZdn8RfVAgJlfe9L/oMTw3/66WHqUC2ZEBGztUfkb4ItVjk9B+8yLO/908V2+8M3sU1rh42X7uFa3zdBHOV/T5sWGNTmzj/e7Ir1n4NXV30x5/nmcEvLvoaAa5puJ0mdxc5TcU+6xkN/tDqs0EMRCScyr9inqyvcsyJdC+PTp3HbJ1fpydHDIRqQ23k2AAFw2TzL94PQL65wKnE66z1byZgr1/IPCdbY0VyutJpNzngIZl6Gx1rLQe5Q128MGuHbpmk7b7bF1yQXCn07tyFrSQ6nkhUs1kaWUdTqbpQQUviRMKUwhTMy9epuRx0bliDaZqkL8SrfssePs0KdxfZ2BdwBOoTgQdocXay0ruJNe5u1ijV0f25ELhgGeIrNlvzwsHpVxgsVmpqXWsTkAQRl17NylksFNEFJWdF+7VX09//IgWj8rjXhK1S5t5l9Psd/NHLBB/8B0adNy3cuITz0SFeizxJ/zHLCZXJ6AwpN+GQgnS711a1n8wcIytYz87uDdIV2kBDqYqaPCvV2XGrn+ZrU9zwuf/EPZ/7BL4Gy9HhXtXC0elnyeXmD3r0HzjAwKde5sU//Yeav9/Zdifr2lbz5MQLTOizI/YaxUuqpG288zaGChPsi/dz869ZTNxj8X2MJMtOHTE9QiZ9iB2/+HYukGCT0ENyvL6S6YrsRJu17jZRkMTKdDFH0JpPnYqdFd4t6JF41XF02cNKVxuO9monva5bY2h6YpJCNsuLY98kYlr6ZIKeRUZAEWQoOTLP2aDfXWbbrPX10BgqMxad/ia2OG5g6zm4z6vSnS3bXNM563vJ63PbxwG3hwfCm+k/XBlIa277T3St/+2KbRub72N7S2Ww5yJyQ+O4ZB+2Kg+vheFzlq1+dtfinPpTEzFabSJhJURH6MOk5mBUXES40INNcixY1azmuaLD9GazpNRqZ1RaEFFER0WaPqYH1TCxOavt4bRgOWZOvVRZndks+Z093ZZOkCzLpNUErhoFKRaCfTzJZk8rTQGrf/ibGjFMAxU3XruCZqi0r1tLgzdIa2jugGZQXYnfvond0Z3sTZW1p7KlNdd4ZC9Dez5X93UJiTxmunqOezn1Cnuzh9BUlQNTP+ZQ8jweTyUDaGv4TkKe8nrMpSVw1GABAhRHp4kXp+hYX65sKBsq9hrVBM987zkGP72T3q//qK57aKQBj1n/nAZgt4fx2yu/+bSURjeLNDk7CDgsh6R+sbiKWPu+rg/ewtXeG2v+dhFjuSi906/RsL7szJTWX8to0SDfOn8V7ozu4kJ2ikcHPkdhtLam2cndFvN0IR3Xtyp+5lB6E6PDvZotwZsrtkVSWUDi3GFrgAulrUHCLkozk60sWh+8LTT3wuXE2Ks8NfoEzat20+z7+Mz2nLKSDQ0t3OZR2fq2cq6+d8M6Xs08zZS7vGBT46UcbpfC2qu3klaLhKXFswxGei+wzSWB28b14dvY0Xh7VZvE1DSKaEOTKimPqz98K6Zp4p6eP3UqMjCEKAhIhepFfYttgGanSFcpFc3b1sqNHpliur70ADES412eHu7JLyzUdjR+mMFZmkJGwXpHKgt7vDuafpUHuv9g5u9AMGD9J1G9oB09U54A+48urXSzXQpSMDQOZjVinsqJ93nnab4f651jz9L1dbQQy6YqDHEPdnRTo6WzHU2R0J210xtroSjm62ImKCVR1caScVLUC8jCpe/SjmboaKaKXtJv0bN5Xh77D2Jxy5AtOKxre3TwX8r3pHhZ7anWrcqn9vPM2D+hr1xNR8PPkxrKcEvT+7AFSnoWhoGD+QUF7WYjq33baZTK0d+JjocY3fzLZESzqprLmkAP6/xzV3gDCJacep7k/AucWnjlC9/CLcusUSrPEfB24Ak30dDVQU4vYLrL397gwdM02q3FsG1WRK0WtNL0ZA968ARWsrXx7Yu+xkw6RYujC5fiw9DzeISysRM5Z71HzVe0FnyCgLPO1IKg18k6ZZLmnvlLgEfECJNGtaF97uxOHh9/jI6tlqN466+/l96Cm/X+62lw1V8OuaG0eBRilXTt1dkw20K303HtVk5kc6S0xUfMMgOWQHmH3cmRXZdf/WspmDzeR6PDWggmL1SzOiShSK6kHSboIk4pwCn1FHsz9bNUrgTcgQB5PYdNrZ53hqasd5b2fpCz++rXLnMZApJaQNOy6Pn6U8WciTSqUWDtemu8myqMkb9EuDouF5nKj5Dx1T/e1kIqnkIRBYqC1d9CrQ0ck06RSpXZubOdky3LWIAqaQoci71Csbv+RfHKllt5sOsP0CZKwq/5Dt4RUEAwsUkOxvsrRdXTJ58iF7dWt50rrHHMJcgYpkHAX7Zt7vr5X+LB/1IZdQfwBhv4YM+nsE/N7/A899geJFEkGKlOQVKLRYLyCoKuANeE34GnocyqlkSdolGduhH69XdgvuNGXF5rjhlInSGZLb+TGxrezuZSqpzdPo0iCJz8Wn3aaU7FD7MWo0Ujg0ilgyRUsgtbg5Z91mavdgSMugL8YORRmq+9ueq3lpL9E0qmmBye4PaWh/Aq1jy7N3+IXdPPIQsyWolhcp/zPu6Xb5/Z327mcebLbDilaR1rW27GZVuFXXJhC5dZNEJJy9J0zR0sEAI5RrPnSEUrGXY2ySRDJWvmdPF1DsUrtTkvoiB08Lbm92P4a6c5e0t6Aq4Sw/7soeN88xP/QmxyfgeRoVoevqAist0lExmdv71ut/qZMQeTaj5MRE5wdOJzTBaq9S5dWgJJkEiNlPvaqcgLPD3yzZrHsm9qIavnyV3C681MWM/Z110en4qSiM9Rn5zFbIR1JxsCN7J2k+WUkWQZ1QR70smBieM8O/5tAJI2AZtU2x5IT0XZ6FtNl3cdW1ruZkfrh2Z+iyoxHun/Wwwpi9l5NYVsfbaVCBhmtWNREVyExGYOv7CHO1o+wO3hG1gzqzKypqrIgoIyy7lWkNyIc1z7UG6SZ0cfIdRafnYNSgpXlQ0MR557FVEQmDhYH3PN5mggKS1uUPdc3ccW369izCqCEDMmGMyeQRcd6CU7O6NZfSIr13ZYjaSSDGTmX5MlzDDnjY04feW1WNAw2JscQR6any3bmxjhdCZGl+8O0hO1GYVyOs5o9Bw218+qvP0MbzI47TrmJTTFfNxyiCTPWh+HadQoK647Smlsczt3VjpXsyn8fpKnr6c38scz2w37BRLFKUwzg6yUjaKkbud9a/4Afao8ISUm4gBoJdpoojCA12ZntG9xFYdyp07y2ODn8dl0zid7ORU/UNUm2mdNWLK3clG+9qYbiOlJ/MU5ktQv7n/SWjwpNSLxHa5uOpwrSGtBDMOg6bpNPDvyFcbE+lLeBo5bUayRGhH2SzGd9yLky9cqOVwYpsGJ2MJCmYbDgzrLeZ/XrOubvFBNz5QvvMYzI1+x/l9YYjWkY3/P5Il/YCx1gKmhynS+TRmTDdICUbHWFvIv/yGeTHmxahN9aIY1LKUEEY36r61z7BhDyTMki1EKhbkX0JJpohkq9pLBqJsG8iWVd44Uj/FK5HGej+/kmGYJOcdjSe5u/SgBhxXpzzdaRuh631Uz++UFA0mQSacvcUz6b+C2lodY39DCdpdMOO+m3b0GV8hbugZbRQpCLcg2iypuF8qOYLezEberGdEWRxYqIzdtztW0u1fMe8zGbut39yK0qi5C6beMn2OxSoFfEzsR3Zr0c4JJgMDMb/2P7EMQBOKFBI4FxEJtWet+fGE/LnsHa/3XkFlkmeDEaIIfTe5kRMyjiyqy4iJTysXPCDYyWpKe+28BShUshfocSq1CE+t91+ELzM8m6hx5jI5Xqstk7wjfynqlbAA2NbXxYNB6B9J19Qta6qUxPRmLV2z3Ch4MJFxBPwPpNNPa/PoAD//5n/HV3/vvFdviuTLzIXNs8SlzywGPbH1jOyeeZGysuvS2TXJQNKxvPScUsYsS7bgJ1TDM32iYhSRBs7qPCxnr22hRmkkcrK+qIEDOHSJjd6GZeURh7lSMS+GQ3KTV5AzLeEfj/Wy/JJLrU5oJO9rJTFwes2vgiOUgy2pWkCLQFObBjv9Ko63seE1OleeFfnVx0ez5ENdVeuOvseKeaofEXLCHArglidiYFVFOl3RVMqLVp0YO9s+0jU5MsfWm/4XbeRUA/nZrPI4Wkjye0JG2L8waa1uzgnP5Alp8fiaYuzSW73FVO1HHzg0gCjLRGLQpBmG1/G0rogfBrF5Ubd/SxPveWWYk3Bnawd0lB72uaRzMQKRUbETeHAAg1l9fKryuFynMYvMYRq6qf0b7rbFkOGjNcRdy1UEu3+Agmz2tBHzVC9Kiw7LrkhmNzNQYoiAjC9Y3bigNeOVgKVBaqlY4/hyR4R/P7C8LdtRZNkHnVsuB47ZZ9+ruKDvxhVIZedM29xgSXt3Jvsj3yWfK77FYLOIUHWQuCf65WwqEA7VtP33Hdfwh/QTuvrXm700XqwyX0vOPffUA3UYrrz/8xJzXBjB+wgoYns3188PBL1CQ5mfyuEoOnFe0xVWPBFjT0MlDKz7NVT3VKWx6qWLeaO+ZmW1BVwsNNbSCAHb8ws9xn1yk78Z7KraLholhGqy6tZwSlrEXcQjyjLB5vRiUrHMLm8pOPE2LY9PjtEir8ErWMxd8JjbRhpqvdg6c/McvI4siYuM4boeH0CxWvE0KsDr4DlyuNazuvIPo6PxSFxchmwZiDbmGq53XcH3gZlLnpmhyWk5UXSv3p0QsgyAI5HzlZzqFSdKo3efWtd7Piq5KzdDzrhCm5K5kkgGJkv5V2lx47ZKJxXCIEoZ7cZVCx4daeT35LwydLfeRdRd+QNO+zyEaAm7NmpN0I8czU4eQa4m2AhlbgpQ4v/NuS2g9dwYr1xT56AXu8DmQx+evpLjBb+Mqr5erg5vRUnMUk2jtYfLcN2i9+Zp5j/VWxc8cSm9iqI4G8vZKAzDcYzFFvHaLYp0U48SKcVRDwiVZC5bDA0f4bv9fcTw3t2MnpHjZ4LKhmDYcsyYj/5q1+G1hNKPSYGgvVSVq0MsLslzG+ug0t7VtoiWHJEjs/cEzi7pPpeihw3srbRvXIakuVKpZF0Ml4d+MUu0QGlaHCCg+ju+emzY8Mm55nJO26k9CFRzYpCDFYpqEGqOlI4RdcuHK1mdwpUqpPSnfwov2W1waNwXLkQNXJocoiLyz89c5tW9+XQOXmkDTypNrPjdBohglMdBf1dZISjSbDWTMHMHG+Uufz4WBVXdxtmMzt4U2cFWi8rl10sFK1/wUUsXpQr7jvxFTZqUoCMywjGQ1hm0RC0ND28bKwBaeGf0amfTc+eCiIMxEMgGKWpqcVumocGsJ8kaW62I5rpqyUtgKmTRFowAlcXVn3nr/a/3lyUMrRV4zsUpno1uw0lOLsnWsQEljasvtlqjkVOIwU/HD896fIgUAsM/6HnNH/5nUhf9A03UkQakQ+RVxUdDnyqS3kGmwrmNMXZwWQTqRoFXpZihzmgupSiaaKIAzZ/XDYi6K0yiLD/uzDuLFabL5EyiyjcI82maKpmOaJsGWFvwl9tbFEsX1Yjye4obGd+Dq2kDcSCMIIr0vvgxArzHKdya+QffGknaZUaBYo0IfQN+JXr7x3//njFiolnOh1qFrMRV8D+qtn63cNhWlzb0asaHS2Zcqpa6oufpSTQASTgc5wyCRrTT4CoqOXirXfEvIxsqG+b/FQl8Ljkxlik/IVf6mc+cvv5z8UpAfthxZocAm7P5qtoZDclAwLCNywjvA8cQrtDpX0Wibu7rlG4XJwhjxfPVipyGroRoFvtP3l9iMwRp71oZNK6LrOQTTgU2sn0k0njnNcKrsjOtLjjGUrvzelVJ0OGjMz5JcCLLT5ED0X9Dare/UZrehmkVcsxaq48PlSHC2Th3CetB0ZIoP9PwBDYvIgslh9evUhdIYZvOgmTqO9lLa+FR5HkmPj3B4+kVSulXd1Rvyo5kGXc4WVthOsmbr/GxQgLa1azg0/jkmcvMvXsS4dRPFbLUDPTlQKnjh0ND1DP5cuc1A0SBSR6BLEyRk2XKGDx47hlk8SAFr3Nn67vu4kD1NdoEKlhcxmeplMlVmp6gOO7JYuWhTtBy6oZFzlGyhdLWd1aw7Weu/jkC4+jvX3E6yhkmxIBDqbqHH9XEadliOp622zdwYvg9ZELGVmFITcj/DivVuE7EYkqiQdwRmjrfy1uvRTR237MEwDcJt5eBqy62dPNr/71z78w/Oec+Bxgbe1f1JgtPlY57ZdxRZVMgXK1PeA4mb2Gr/VbI1giHZXU/zjokDmBO1bXGjxbKro7r1PFeZbrqdAYoD8zv41Yg17iiSmzbvzaTj8ztdfHmdgp6jIbt4Zmc6ajnVtBrLyYzHCghMzapKudK7gdW+rTWPZZNF/ikxwIbHKkXM/bqTnJ7GO2sOSCcGEAWB0y8triqhM1vANE26V5VZ9QVBxXD6WR/czqaAZZOFE5MIgsDEkWoWaSbmQTNUNv7Su8mlI0izCu6E0z62+ddiLwV7jHh92kSmaEOtEWTLGUlkwcQ/ZI07qWIcZRaDa2zc6gtxudzvTtqiPDfyRbRitf3sKBaRhMq+KGWjiIJAcrKSidTZfAcAQXXhwO75vVZg8WKAp17IMTsd9lbGZunpZTt/i8INf4GmJzBKEicJyYtbH6Dgre2M9Dtc3BboqHKKzYauFSlcws71tDeT1opo7vmZVYZgI6IleGJiN9kaFUABhl4fZON1n2ZamJ/A8FbFzxxKb2I4snbIVr5CoceiQsZKA1PC2MUr45+naBYxSqk7DVmJrf5baXPPHV1PmdbglOz7DhNn/3lme0Yo6QVckm/b2GIdy5Mpf8yenLVYbQhak8rVH7rPqn4ytjhafUjuZHtgCz3XXUtnoIcd4erqPfpEPxciB1CkeNVvWpvAKxOPcGHX3BOPGvJxdPplCmtqDFZqHkUQOBXbxwtjj+BvaOCW5vfTOkeZ99HjJ+nbvXfm75Y1FuOg/bZ7arafjYgI2MuTZsC0nEsOyUbk9fkXHwoKxVmaQ/6mAk+P/BsOodqYcIub2dbxIFkzjiuxtApf24M30eJawbHpPZxNHK74zZC8qObCJaFVwyBvK0epT2S+zO7o5wHIpUdITs9veFegVC3uxrbfZmJgbmep0xHGmDUhn82f4YXoIdKp8oR8jX07OwK34eh+O84WK7VTKeZ5JfIsRc2adEdTR4nkx0nMouraSofNxyoXbM6iiG5qrLjxKgA8Uoli77HufSzxLKcT85cY9xcvnqe8YMhd8/tEtvwShYKAKIgkZlUEdJoyijZ/xCbUFOBUfC96ahHPGTj6pX8jr+p0utexo6VcnU5VVSRBxpSthUPUGMWjeHj6j7/M4R9+n7OJ17mQOcCY348giBw7cmrOc1xgkIPRF1BsNvQScyAeWRxTJn6hly6biN2MMO1N89TI10hnc6jFIu8wt7HRVY7aq0Zuzvz8Vz7zb6zPrGHn178DgIKAVofhlLKlSapjFPNlo/L0UYsxGFcqzzUUnkI1igRXBOq+v5RH44cDf42hVDoCDKOIVoq0F4w8zhqVUWbj9tAadgQr0/eGpgY5FnsFzVBxXKajYamQS49ts70HT6FyrotNRFBEO2bJgeDrkjgV28uu2DRnazAg3mgcyh7jtfQLVQauSwyh6Spbg7cz6VhTe+casBsqspZAB8Q6KjcBpKajnE0dIJPdM7OtoCjkLynNrYuWbeBPXR5VP5VOE2z2E1hX7kuaoaIYZdZMPmp9y/sT50hkA5d1vtnwmyE0U6drXf1VEtvWW9fpVKwFQGvejmqA1mN9L5FkeZGTiwxzLnkIhzA0s003wKd4We9YR3geCYGLkBWFBzp+jVX+6+dtF1QslsR2ozr9deqcJcgrOyVMA6RZWo7Ho49xVDhetc+lSIogyzaKhQJ9rx3k1sabcZbSy212O/7277DRUV9xAPf4w9hS5bZjITdxU0ObVanLp2XRtDSdXiu4GM5WO+O9ZhFNz2OrIejrDjh5cuRbZNMTDJ/so6/4ZaYHSos2U0USJHZGXuKkZLEcuhrfyw2tvwhAPmvZN9q6sgPf5nKSLQn0GqZacc7rP/wBfuvbX6ZpXbWG1kV0b96MQxDwz7rH+H7LKSnZK+0poRSsmjhePdf5Rg0r5W28dkpRU3cPJ+L7KGQsB61Z0pLK5+cPrLhKFasaDY1rg9sonjk7b3uHZMcuObmp4aa6CtjMRmokbv0bq8G6aw7zdCyKViw/31cnnmLf1Ny2jk904r0k5S1eiDKWvcT+dfbxvf7PIucWx1ByZjQ0s1ghHJ2VvehSE6JokpCsZzdoWPPzqT2Hq44Rtq9g3Ezi7+jAMA1kQUEtZTeMJ07x6NhzGB5rjM5m63ueSS1LSq+et/JGEkWUieftaIZKSkshzapgqJ/vByCQLTtK2g24uem9jA9VPrPx02fYM/ZFpscrn79XL6Vh91XagS2jVtDUIS08/0+esa4jl1uc3qLitGxFc7pso0TNQaJaHzoGglCa9z0N3Nz8HsxQ7cC8IOqIgsTkhaGavwM4Stqus9Gxbh0Hik3IDYF5r/NExstEtkCzlCI/VUMnFTBSg/SnjtPZc/kV896M+JlD6U0MmRyaVsmSsXutgcYoRawbzQfYGPo5ckYcqZQe1+1xsyF0E+vX9Mx5bMFheaRbV/wGofafm9nescpy5kwplfmm7oYguqmRmcX0iE0cpz99HLDo7z3r1hItZCikFzfg+NQUmprE7nJiaGNIVHvd4w43+9PP491cbaDf8vMPklKj5EbndsgEHUkUz9Osv7raWZUVMsiCycbW97Km+T5sdjuqUcBdozz9iZdfwvh6FOnx8kJ+fJeVP58+vaeqffW5dOySk9iE9V69ZoFsSTyzOFZ7ELsIm2RDM8rPNi8F2dH0bpKRagp8fuL7nDj+BeLpKUytSCGzMKV1NgrpFKlz/wPDc5yEGcOkULFwEmQRVVj4Pf96uJFDd98183dQuRafw1oQHHD42G+rv8qbrSTE65dMkmdrV2EAmBaKTMzSWlq9soO7wivJRMvPV5A9FEyDw+nvciz1MABZWeae9l/F0WlRu9sbwjQ6WrAxi+0kWZNiYrIyVUGUAhhIdG3ZgmqYOCQ7hqnT1GktGMId7+Oulb877/3JotXf7LN0gNZIPhqLAlPFQZ4Y+RqZyVnjgeyg4J3fedu5ejXKgW/Rmj4zb7tLcfzUWfqO/C+ixRFEoWxspEqpHPnSNd7xNx/joHGE/ysFGHrsCPFihHXvu46038lA+iQTQ3Mbg+N6iv6sdV2JUgW86GRtw3su+EatsaeNLL61q2kI/BzZtMbJJ5/AQQMeV9k40TBqllgeOTfAFv+1NDu7oc96vm5TQSwu7LQoXNXAc7xKVis7HR19Vt9scFZ+H+/4499ixV/dxXX31U5/qAVRd3Nt43tJXjI22A1xRo9B04o4qE/XYPZiIpZI0RvfS1KbIKgsHKE8sfcIU+NLqxg5F/yzNPfO76lkaJ4/dZJDiX6m3NbYlY8188EVn+SuhkZswZ+8hsHq1m28o/M3mDhTOe8U8ZLQdDYEbsCTrj+SGY0OEUsMcj6/nzOp3QvvABx5dT/vX/FJxDVl3bV2l4dtnkoWiFlKUUqqixPlTqRT7Hq9zPyNvHiKldpDZE6WFze6KSGLgZm/k2MRJnIDbPH2sMZTvwD9QnDaghSNxQVHOrZZ7ESzaO1nGCpFPUNrT5jXJn+INllmJwyNJflAz+9RaChX2Dua2EVBz5JfoLLobKhmERdza32M9l5gZ8YyzaUamm6pQWtuyUs5CpKCabfG+HQkwgc6HuIWFtaplP1pREEkOT4JTitoZVtbtn3Gh9+Bq/gZhnvL7NOTrx2smV7k3fCX5Ff9l5m/i/FD7Dr72QpGzuG8yQuxDHkxw+uRHzEeqzHfSH7UOZz0eqiRawNbyNsbSZ8ZpEn2Ik1YzzwnZ5AEAV0fJeOwvnuHaUcp6QENnDrFhdRRklOVNkHOtJ6xWrMO5/xwej0U9SzirGBSdmySeDGKK1xJkdPslk0wNVI9dwUkq/+3zqHF19AY5oK+EVW33pFHDmGYOpoyf6ro+cgwuyd+QEqz7HS/NrctZhoGhuBgIJfkxfgU0f76WZMAwVIJ9iZfDWqgnCGk7SU2UnZo9XhvpFGptrMvYnDyWxyYeHSGDQxwrmByIlMZJDQ7VrLGdw3j6UWO9YId1ah8HkdHT3Jo9Dh2QZyRitBanRyLvUJGqNRlzcXjBEwbfqzr0Rp6EASBRKkkveEJ0WUTmLZZ89JEvD7G8SHDz9lAtbPELpcC83KYlJ6iIKrIs5hMuVSMC7kUhjirSEzRwKMEmL5EXuToE89xV+sv4AtUjhEntVEe7v8Mp89Xss0dovWNOOWF5yktk+bxoX9CcyzufRQaLMddNFP+PnLii0SNJ5gYP0Jk2JpffFqKF5IqTXOYInKpQEnkzNwOJbvkwTQv6aeGzu1uFdvg/I6/a30iXXYb14XvozHXUPsaxvdxeOo5elbVv255K+FnDqU3Mcb1PqblyvzcnhVd/DCuopR0Cty6G48QoK+4k3OxxwDoS1qD6fQ8QU5PSaSwwdOE7C57WzfdcyvuT6znjr/8zap9NEPDNqvy12Qxwuuxw8idZeP1lcgjHJz+PtMLaAjMRtEeJF2q3iYJxarUHgA108QHez6J0FT9IYc6Orip6X2slObWVsi8MsUG25+TPFmd7xx3iSiijYBkR3FaA7lJAdlTaexNjQ7z7D/9O5pRRJxlMGZHR0ircQrxhRfDztI4HxvsJ5GIcSJ7lJP2frJaGiWtz7uvItgwZqXhyP4QPiWMZlS/6LRnNfFwA1NmPy9M7efc7oWdXbNx/vV9FOwfwJYWaGhczd1tP8ehV16d+V11NJL01B50Z+OLsSQ7Xiwvjta5r6NTsapVrHW28PYaegpzwWb6yehFfjT4WQqXaMrMxqnhpzkw+t2Zv92OAEHbWhLjZXFyh+hFQCHkCNJYeilGsUhQFlFKc3lryRnhmMXWU/Qhhkb2wyXi7rrdg1oabvOlaJCuZbGVUuQmbHn608fR9bnf8fH0KZJqFvsssUVFULAVswS8LbQFfo5MrOxYkAWZaX1+Fo3TH6Dzjr9Bd31g3nazMRWNcbvvXeS3fhBMPzbK40M2EuGFiaeJZiwnqM1u511/9Vv86QdvYGvD7VwTvo91976TkEtnT+RxikfnTkO9VdrEfa2WM9vhCVj3S3U6xHxIxq1xoqgo2EyV690y0skI6Z2Poz3/B2x4TzlN5cXxH7J7srqayaufe5hGh+X4ay6xxExbIwVlYaalfczLhxwf5fgzZcaiOWGx15oaL5/14xHttLvWIFwSFRzNDjKYtox4UZuu6KPzIdJfDhRs9d/Mu7t+mynxPFM1Urcuhf5wjJN/vrhxZCEkNIFY0fpelGLltxE/e4po0Y9qxAFoWNtBb3wPbtmLz5h/rHwj4G5vxSO7GDpZyQ54NfIwx6LPYJgm4UJ9ml0Ax50dHHe6OekuciI/t8N8NlJFnaPmEJ4tZXaGqWWQLykq/cNSYQFbnY5HADVf5Gu/+Qn2fvZPSJcCEmb+PM+OfBVfS3n+0yQBxVHu66OxM7w0/m36U4fIFBe3eJ0PgmCQMOOL2qehs81iUOWt56GTJ6PHWLN1O9n0CLZZ4qtu3boHX0N5vOvLnSWvZyhekjI9Hww1hbuGttZF7PnOjwnp+ygaBnINkeSMXeW50a8T3LwSSZjAieW8Gz55ClEQKaoLO9WyDssmGDh5ivS0tZAKbSxr4ORdGU4l9nLyeSs9uJjLM/m1o4z82S5GRypZopHMGZKz0nR9aYHrOj7A2Kny4rTF46HdmUT22pnIT5GvoUsjy43oc2gm+lauotm1ETPUjdOw5kynzWorlzQ672z9ebaHLLasXswglvR7xs4c5kD8GIlY5TczkjjC4eiLvDSxuJSpi8gbeQylbOOMZqK8nPXhuaEylTnTai3Gp3PVQTtNt5wmrvDcjtV7Ana67X4yiSQOu1Xp1SPML5+QU01S6jSJkt9pWqsOfl7E1OQI690fJJ99Cq8tROTshTnb1kJet/qy7q6ezxyNTq5vfABpVur9Jv9Gwv65tR0dwW3c1PYQvS9bVViz8QTvau1h66ZKaQbN1cm20O2kpxenwzZSHOBkstLuaLClWOMVsYkOZNPqVw2ru+mNv0YhXvltv/ztr7In8iPOq5a9mLJZc+PIUD8AIe86Ngdvx10SNFUi9TlYtisGTflqey3dWLoeuQl3YZiEnGBqVjXtiJngWMFBJFB2aqfNSX488nWMTGXQyxxLEHa0426t7G9edxtrfddizpKuyMViuGQv4+lznB14EsOYn+WsZ1WaPTfivlgMqE4INuuejenyPfVIv0G37b/g4Cw2oVQEZ7gfZ3EfWrT2Oipd0i4bOVEtDn8RkmCQkyuDb75wA37FhUOfX14jrLjBVsA0dPxS7XGqpeF93Nv5q0jy4p3UbwX8zKH0JsYhYw37vJXeVl9TmBXiMKZhdeizw1+md/CLdMRzdMWtFC1vIg5A0D+3MTuVtKJgiiiTFyuN82BzbTqfjoxDLC/4rlm9iQ903MvqbeWIXvjWe7m18z9z/mD9mhxO0UW2pAcSCbdYor7RSsqvPT/Emcx5elbWFsYcU3cj934TfY7y17nUGY5HXyHQXj1RG94Mp6MHeWnoCwyOvwRAVnBRFMtaR5qqMvDnr7E5fD/nE0fQTa1czcZQeXpyF4H2havPXJQWOL//GMcP7OdcLkufVyNppmiS5jYi0vE4j0+d4lS2HPlb9cAD/FVyD/G3VTvSVvhuYk3wNkI338ANjfdz/tziNFIGj56hrft60lIz7karr9lPlgVmAwUBT2LhqK1fdODXyouY5/q/yImopbEl6zkckrvKeTgXFAQcosxNTR8mVaNixUXcHLyeG2fpHkUmrAlm4EA5Gu0winiKLtI5vQAA6HtJREFUw7SLb6fHbmkpOCMW+yIYs4zn4bR1f7PNd9WZ4nX1FTStMtph15LopdztgcwxzqdO8Yqt/K7M1HmOxXaSnq7NQssmkyQcK3glleVH0X4yqRSZVApZVNCMIqbNx9Vumci4dY58JsOh6LPEMgsbhxfi+5nMzJ16dilOfOOrjKoxzJVtOPNDuItlZ0MmGkU04hhi5Tu795oVTN1WwPZuyzkXbPFzf8dv4UjPzXyRRBtGKW0yFghwNFsgzuIYCMVSKpjQ3kJo1Qp+PPLvTEyfQS/eSOq2j9Fx8x0zbVfYmllvrzZ0U9MenhrdhWoUGStYTqRz6f2cTS+sN2HXC3hkhczx8rcRmbDGhZTn8h1KbluSH00dR0lVGll96dOcTVq6ORmfDaWOCCNAIVp+vrIUQEdmIJPlpL6Js/vndhZFJuMU1UHkOsQ7F4OD09/mYMSqNuUxK+ehwGiGuxuDbGqy0rw33bSN6Jgl8B/S5mdzvhHIl6rPTe8r6xedP3GSWxrvwBNsRDVNlHmYKpfiHl+Qqx1hblNX81DzL9S1T2zny0SP/zubtpVLStuRsVMOMqjFIhsC1njokepnDJ3+g8/xnrZf5F2dH+Pc09aiPGtzY5OcBGelnaUL4+TyZSfE6rZOHuh5iMF0H6cT8+sCLgYu0UZRXfx7P9H7BbSJxwE4Evs6pwrfweZ0cXP3r9MUeNdMO3E0DkCgUP5G7gjejd/WiLGI8xawo8xTnt01pXN9aAeGWQCzmgWpuFpweh+i59prOD59jCNRq0jJZImNmbEtnA5ZNNwUDJNofwpbnzV2uLzlvnjVR9+P48DD5EetxfRr33qUScNOojjBd//nZ2bYI6ZpcjTyJJnpw7NuwIlL9pIs2ZkAbbYwrXY/3R3reGfnf2KDs7rwgNeRwGavbYe0h4M8k9QRI1HSAWssK5bSSjSz/D5ShRJb0UzPLG7asj7e0f4RNjZUikZf0HrJ6kkC0uIY8xeRN/LYZlW0WuUJcKPjLJt3VDJMg27LJnVEq8f7gma9X3fT3N/dqcRextKnGTlSrszbwPwVg9sdK7mv41ewBy0mUMqYe4F76DuP8erUn6HJPVzvlhk9Pn963KXIiJZTTQ9US0CsXL+WZ5IqilpiyxY1FAFyTXOvPSR/E22uVYwdsubMl774MJLZjOKv/GZWb1rFoT1/gpvFpehlgAm10jYN2RtZWyquokvW8RyeIB9a8SmcU5VrrLHBJNHiKPo6a94RE6/z3OjXMUosrPZUDFHNkBFMLqSOEM/WZ7/KRhxntprNFA/7GM4Ps3tqL4fy/XSMPk742f8983uT5uQ+r8rKWVqojvVbeVv7x0nLlWOBL2fNoVvuvb1ie0vrdraFbsdllKvHHX7qOQRBpJCewml66e9doF8YYa4PbsfWvDj9wu4SW6oxW3YMOiUHNkHB3vJOwp0PASALbm4K70A1a9uAjc2WLRmeg41n6DoiOhmjkszgcLnQDQ3XPHqthq4jiTLBoEDBVIkrtb8nl+iHRQRl3mr4mUPpTYx3Ol28bZYoIFglMNe6WwiUNBJWd76PxqZb0BreRtMaa2HszlsfTktXz5zHLpLnRNxinKhmfQPiDzJH+bFc9g6nUwICYfyzSrLe8577aZVDpF7aV+sQNeEUHWBaUX2b0xog88lKQ0s48yLDF75OU1NtVozbXSThWsVrX/t6zd/N3BinEq/RvWVz1W+NqeNkxnZzU9ODBOzWvQiFcTy5sk7Oc3/8BZrdLfgEHVdIRhJkEiWWmIKf6xsfwBFYmLEjKgo5Q0cbz2HbH+F9bfdzk3MjvvwF3IKH+ERt73xsbJRr3Haa2ssOPbsi8yv+t2HvrdadcQgOCoZK17a1NEhFtPOL06Zx6pZB4+vuwN9lGRKZC2XHQm9iN4OphRcLJvJMaVbDMAj1/Bzq1vsBkEws5+HE+HyHmEFWj1M08nS5u5G1uYc2h7cdm7/cJ0Neq0+FZkXtdZuLYbsdzUhCKVV0Kl7KMy/pgbiarGc9kS/3g7xtLe/v+QTjU5WT0/HpPRyMWtHelzev5kz2FJnpcuSrxbmO9/d8ioHDtZ/Z1KGjvMPVxjYGWSWPMHH8OJFBi9qbl3VizR6eGPpnCh7L6Th+speofD0eFtax6p16jmjq9ILtEiNjPPk7fwUnJFpe/FPe86vvIGaXyNnLhkAsk+H21g/jDLRV7X/NO+5j0x23A7D61ptImU7ynrkNS0UMYWIdu1AYYSwTIxVZHKPBL1nvoWfDGrzNTbhsa5H8q2hxrUSRvBWRpM3+TjYHK4V1n//7f+WqpvW0+mRSWhxfSUtoNNfHuNq/4PnDYev65Vnvejo1yMvjD9N1dfVYs1jkC3m2OXRSscoF7X2ht3Nn4HYAkjYnhikTm6o2VgHSibKB1Xfy2Mz/RTQ0I0+nv4l3+iXGf3QYgD2f/hrHPvFURfn3Y088QcDWRH9yN1OX6DAsFcV8gXvbf47WgLUQnMxXLsjGS994scdasAVbW7llwycAyMg/edOmbdNqAJzZslE/1jeIw9aF1NlGUU9j1jm3AkiigmboxFInOJ3Yu/AOQJu66v9n770DJU3LMu/fGyvnOjn26ZzDdE/OOcMQRFCCurK4iIquoqzruuq6qy5B1MWwi/ihCMIAQ5oBZobJsSd0ms59cj6nTuX0pu+P5+2uU111Qg8Njsj1T/epeurN7/Pc4bqvmyu3/BYtfbX3sSB5kBTPuUB9en6e7bFreC17jAe9qwssf/PX/5Ro8FIq1Rw+Ncjsq2L+0Kr9XNf+dvztNafihZnv8/zMI+f+duZLhKR+tiWu5Iq22xq2/XqQnV9AlnyozvJNCJpBW99PZb1Ieq0P3kUQ8V4ekb7NlO/Rc+Pm5sU6NOfU1jnDDXKWlNUzJxUng1da+vmc1xQezQxSsgpNNd0SkwpXBFUSrS2kymUybjAr3CWO27e1ueDxYuidcb4+8SVy8wv0OFUc22SdW/4H0LdtE+3X/SmtecH4to6UWecP87IyQaya5aFfFZ0r5ycnuaf3g7T7asEava/Idyf+gUBkEXNX0qjaBt72KAA5vdEpfGXuMK+km69DHp/KDvUYvswEZUPMAxVd2Li2nKViibnIsMUcZyOjykLXJpfK4Jcl8NUHb3es3cKVrW9iV2L1JcaLUdAktEXaMt5KKz4lie6tT5LIusN8eZL0ROO86PFFsBybcGJp23AkP0+2UuLU8UMMlkVQSaku/5wXbXHto2Gxvg0so4tUmHFo9fVj+8X1CYZWz5oEGGuJ8qXxbxDb0NjlLdrWStyZxJoT69/o6SEkScLKLs3WX3fL9eI4HGHLaKNVbMcmcdnOunGx1gQd3W9Gmb+wgNK+wJVcEa2/50P5A3xj9NOM5I5TkkQyfe3eHRwtFik79etOlzHAnd3v54Z3/AIAHlk8a7lxMT9UHIeKZDMXKPDi3ENMWatrJPL8whCzxcYKibaQxf7pr+BY46QjMlPhGzFv+Z/nytNzkyk8ih/TqCVcNNuiU5epztQzi0vBfiq2Qcfu+jmi2Cfu+ZSnxpCaH55mrJwhl0iyZf2bOPr08mvOZDzOQzMvkNi9c9lx50PtjbK/YDJRqfkgElUMO8+E8RwnckIyJBQTz3TLQPOAVdumNZilOWS7eUCpmJpn+In/gifX2BTKckxK8tJz+ILrd2VzCoZdwVttbld71Qi2/a9ve/xr4d/vmf8bwUP/79N86p0/z5nD9Z0GsrNz+BXvuU4+i/Hw5CuMpEXpQrt/DX5PKxOBl3lk8h8oFwuUiGDZJoHE0lmR2L7tHB9+Cduxkaqryzqvs9NsWiycmpIwnCqyUlvIk21BskaBcGbpViz5XJ5nPvRZHvzoZ8mnFxjKHSFVEIuxlJojb6TJLCrNAEgk3swVW/5z3b4Wo7pmF+vWv4nsK82dqmT4Xu5b85t4Y40Le9rsY8+2D9Ed2IjmdpmYKU0wt2gC9OR1LNtk2x+/memAYDGMnBkCwDJtEnIVObBy5rD7yuv4TsYiT4K56SySJKH2tfCCNsOXx/6SR//x75v+Lr1g0R/cTLhr17nPon4vA1qEjonGDl5exYtplli/fTvfHPlbpiZXDigshuWWTUb7O/BtXIdpG1SqtQl5vHiG+fLYUj8/B1NRMdxyiLmZGa7T+ugoi2fI6wZ4yuOrW5BfWTjOC1OCJt02vXSATHL8ZKkxNnw7BE2+nBAGtWEYLFTTzHp0TKxzk6TS4pa+dQoHtmXrLgDmi7VrF01IDGVeIuapDygl7Q7Clgi8XT5e5o62N7PXs6g8MxlBlSSMJUhdJ154VRyDHmB77BpykznsnDAg/OEIvrCf1uBlVLPiWo0ePsntsQTe4MqsuGvWfZhta96z7JjH/+tfMfvJg+z0X0FLsJf0T78dPRCkLHtQFmk6SZUKj0z8E2Urvez2kh2dvDD/XRYyS4unB8x59IowsDaaFW5raadzZvUd0ABOjZ7ghdkHUV025rbEZWzz7sSveHFa66nyI94WSmr9++85bRBXHDa893qcSo6ELZ6b2zvfw47oyiL70V2C8bQ4aebR27GUTXRsWFr0dbXQgkHWhnehK/UMJMMfI+/q5JRmjvD18S8xdaC5WO/4a4s6NKVrATtFMqnaOeI716DIKqXZMrMTU3RL/cQ8QY7urzG0fDM5vEqAfcnbee4z/8LFwJkjh4locfSkj6FyjlK0nhlb1URGNrapMTBXXQVL44eNDdddieWYoNYczPIrx3g8W6CzLUTVLqHJzTOdR7/5KM996B+YPl5jtimShiP50TxnMFMPN/3d+Qj7u6gg1YkOy8YYsiRRmBXvkpnN88BChZIxxdriygzAE1/7FtvUS0iVZxhaK5yvSkHMReGyF9ux6eyrGf1bgtu5wV9jyc4dn3bPRyamrZxkWQ0OHjjMQ1mLg/4L6wIJEJGuosu4hHKxRGdgPSFZzO/zC2Nkphe9uFHxvIXW14LOlqu/M+FfPTNPclJIksT8UPO5L2m34w0apDSHjK/xOR5ceJlHJ/8Fj8/Ltugl3NwhMvjpUeHQyp6Vn/1AZ4hdobUY5TzT3grp6ok6kWKArDFDSFWoZjP0yZ2UrGk+8KcfZUPkCnYEL+XgJ/+O9OAIPjWEKtf2ac36uLbtnUwdrJ2fKunYlkEgFsVxHCyjkXmVqgaZyzdnCfjDIfqCWwn6knjGxHMbzogghSwP8tzsVwHoXCvuXUV2yFbnSc8sUMyLcSVPva3seIUNerhYr3e4WhzJv8zj87WyvqDUTr5JaZncDg9PfYmJbKNuVNoXYNJYvjx3X/dN7Oy+kZmDoxwsbyZv5upYWc3gq4h7aYc9jGYOM5xOLznWq1vY3/wNfFcK1tiCUWM/PfOJz/LMB/8fpSYd6s4iMHmazf4YstU4Jtbexq5QByE3wT19VASspSYlj2fRc+kmHMehMCWOudcOUSrOsn5X/TwfaU3S3r0LvXJhTX7O5E4wdF4CzR9pY3Pkcl6c/zahdvGcxJMxThWqkKufUyIVh5yRJhx3E6FSO9e3v4PslDgnR/ZiYrGlp4d7e36JDcHVBZuvaL2WUG9jN1Zdgjf3/QpXt72FzTtvxqBC3kwx5wpuW3nX51Fr729LSTDVA5P1z7ZPDpKVG699GHHvAuO1gNbCwiRPT/411e4Uj01+EXt66VIyAHXqNDFphmj0AtpsAqGuJLML/0RuUQMEWVKwMOmenqRv2F2nXCF/WWoeTDUH2vnK7Ld5rNAYlAMYOXSEyLYPYYU3N3xnORaSsjSLL+f6E1aliKeao2MJ/UxJtsgoF6ZD+OOEnwSU3uAYffIImyObGfrU/XWfz4y4k4nS+HL1RNcRS27CNAw0WcdRQXUUAmqU8WNHmS9Ncji9vNZFMZXiLTs/hCzJKNLKLAeAtfpa1qk1J9nnGFhW4+SVLZ0k6Unw9C9/hn/8D7/N1z/2f+u+P/ydh+kNrGW7vZaX/+t3OMZuxqJishyyp3lo5ilGZuqNsYCSxFpGl+Cq9/0MFatKksauKQCKpmM69jlNm8XQ4zZjBWEMRC8R3VlOViROVWrCdpoWpWQV8YeCOPkJHp3+Dvmj4jdz8gzfHP1z2jas3H0mvq6N7cpRKsV5lLK47t3X7KBjxxY2hC9DH2p+L864grXzMzU2j8fvxbCrqOfZHvlcDo/iw9DE678ldhc74reueGyLYbm6C4H2EJrXR7G6QGyROPM9PT/P5uTKmT/bLKBWRRQl7Wa5Q/OitCxviczrzPDqGA+72tbSERNG/0Ru6VISzaqgFWvXabIqFt9R18DPzud5rpygKodACpwTRvW1iOBAoE84FDl3UdkU23duWx5flNzRByiX6qfW7ZFudgSFoaZkhRFqL3peNfcdK082Bv8AjLI4hjnXZjg1m2VmZohXiiYZLU94YVbQjQfFopwbG+XFue9QWcXiJjkOfmv50ptqTkGTPZyQX2XgD27i8o/+EQDDeYOn0zVD3ZxbQFXb8azQVQzg0shGNrBuye+LevActbjoMokyhQsrTzDsXuaVvSQ6BUPDqsyScJ0fa0t9sE0pz6IuKqvKz8/T413PrHWMDfv2cZRZ9mezLMzNczK/QLawcplo/yV7sGwTXa4Fr5LRDVya2IKyRPD7QrDuMtHiuHdDfamv4phYrq6L4vGwK7KOsUNHG34PUBif4ouDfyLa2LfWJgtNCWLKClvedAum4+BVOnn6f/7Due8nXq0Z5d6KeAZ8aghP5gc/L4CRh0VGNF8pcqDsRY7Uz8298wUc22TNur6G327ctLTo648Kut9H0SygLZoX89Mm+7wzeDvbGTaKjCyRzTz1tWfpDgzw6l+J4Fy5UEaRJGRJw2l5K3vX/zGloitKnCvy6ff9BseePdiwnQA+DLn+nZHVMvlqipzL/Jw5eYYNHovd8WtYLzVey/Mxt/8Eqqwj7alw2bvfjuM4xP0igOSXIpgOdQGsDlUiFus/93fIbWKgVebwrLJb3UooHTzNZg7QEl5ZPP58HJr5Fs+PfI7CQg77m7+B0Srmrg3eO7k6+dPnxsnBdViOQ8fmXec+C7nsg4XI6jUzngzN8qXhj3Ho1JGG7w5/53ts9SWJFLMMDz3MzNFvNozRgt0oqgiuWpKFJusY1SoFl11oTJ9u+M35aB/ooC+0G6/kZ//sCR6abxR5LyhZwlqM9FefwKP48MUXUD06ne/fg1WeJ3ImRu6MCA56lNp99CtROvy9SLmabarJOlVZPtexM2Q26t7c3nEtOxNXL3nMilSiq0VmvnyGx+eeoeQR7Iu8uoHr2n9KnLvL/DrgG+LB8b9nLjeP1xUt79xc76yrSfF5m/z63KAOp4W14UspptIYFYOg4gGncb3dsG0r94TvZZ/WmMAdHnmBF2YfW35Hzhxeq8A6Zz03BU1ss4jfWT4I5TEtHMfB7mzhmdS3GCosXZKpTFpkr/1tIi6bVhmuJeOS4x30hjZw5OvfWfL3LUWVbbGr8S4x7duUiYfFNU64gaTu2NLreCASomBm8VdVUpOT+II9VKzGIEEgHKJqV/BcoKj6WPElJgqH6j7TW7vZENnLW/s/DP27zn1+fdShO1RfyhfVW8kv6p4baINHJv4R1RF2q8cxcKwyPq8XnxrGLqyOhdqqygQqjYGK+LpacL736u20+4fwP/JxNFcfM+R2Z2xfVwuU5NwO3qly/XsWq0gYhUbGfzIotuHN1Gy51lIPN3e8m32330bbQgXPxPJMsL5yhEuTd5FMRpcddz46BjZwW9e72eKpnaciKdhY2B1vJbzzFwGwiuIBq1aa64Ku6d/IpbE9bCo2suMBJg6fIJHopUzje+hoNsGWpQOTUkWsCbpqk8oOMltotNNN08QrqVStC2PM/TjhJwGlNzg2dt3G5ujl9EWuYvz4ogzHgnipAp5GbYxWj0S74mV+cgJFUikrFoFiF1e3vYXxA8fIOQlGystHzT1WlmOZ/RzOzzHevboJu4qErNQmsKonSrVJRzal16Zs5kl62/DhIXOmXhvIk1A5nPoY08U5BgJdvDmms6l3FwC967ZyWcudxPz12WpN1jGXEWLVPR7SlknQ2970e8eyqFq5pt8l+9uYLg2J/bintyXQzuXx2rZ8kkrRFouMolhI9gJZt6uHz2rl0uSdqxKra+9I0OZfQ6wKAclPySrQ3tPFVT/1LrbGrqRVbu6Ax6tigU7G658HE3AC9fudPS5KpdSEcHRCuo9uX7yuq8ZKUBzRpaxvh9DmyFgpym6HFrNqcKSQYVZpHhxZDNuuoLg6OTPD4rhMt0yp6mobZJYo1Tkf3VovEa+4QbK0tGaMJutoi7ryJLw2z8x8HWdKGOLDLz7Plf4ykfIkklc+V3Vw+uATPDzxOUZOCOM7EXP1dEq1DFZqusT2y/+AzGC9YVnWg8y6TAVPWxHTNjAXZfTKbtlCeongWcJ1SqUOcX7+bIbU2BAz6afJlyZpjYvP46bIlFcLZSZLEwTbokteh7MwqymUFboUaWqEqm1w4x9/CG+gdm095GhVa/e5UnW4rvUm/KGVOw35JT/xRZotowcO8uUP/07tuGSdosvgcNxudbZ8YQ7jJu86boomzmXfq6Y41opVYu/t9a27g9U82iIH99kvfhNF9pJqc8ts7Qks6WUOPfBNTqY+x6S5MqtP93go22X8as1YOTV7gBdSF0e8OpUVz83oYH13NR0Jny3mM0/7GvqCu6gazQ2muXSO69rfxZt7P0T2eM1YUxwPjinh9fvJ2CZeNcLE+KO8NCOci8Bw7ZmZcINIJTNDQl05I/vdz9/Po/d/fdkxgXlxr8Jxh1uCZQKn67u3VIwqZbtcF5hLGeKdyvY3Txz8qPHs5Nc4MVoLCnh9rbT7+tl01T4OBWc4IzcP+J6t2K16xLWcHR4CoGJM43MDSelR4WQ9/ff/iGbOc/zv64Vm0zOz+GSZhUL9nPLSgI9HiiEmbbcLUSrPZr8fWVKQlEZH/3ykacN2HAbeci/BcJiSbSAXxBwuY2Oc1/10RlKR1FqJXUAWgcG87EFWPGRTy68TT33lOR7/o28tO8Y7NcOG6F7W9TVf35dDi5Zgq3YJx184QP76j2K4wtCW4sMj61RcDZSR49/ggbmTqJ6anWB4XP2PZbponY++zq3sav8VlJnGoHv+aZEYCnZAS6Sfrs5LG8ZsiOxhY8uV4g9VQZZkcnPzFHNjfGnwzwhEVw7S9a1bx0PpKs74DDfG7uGS0DUNYzL+PJIk0WHuoWrbbPqVnxe/3b6d2bZxFH8bpRfF85vrrJVJBd0SaE9BBKdN00STPSy49o+FjCTVawDNz8ygSBI539JMmLKlkJ7T8ckmYbOLkGu/6P0DmI5D1baRNHHue1vauaX7XvzlCpYljiM2UC/qfMm9dwLQG1g5iNoMMX+IbT6FsVde49D+15BklUxno85lvLMLX7QHR4k2fLfd38EVkf5l9yMXMmi2gqJ6Me0S1coQpr08E68qlzAdg45YO3f3/FJdovfY/Q/wwh9+7NzfYb2FcCjOuh0i4dm2SLdLdpMv0xNLyw5M5E/wxcE/Id7V3JGvOjZVMwrA6cHjnF54iTl5eXujaBmEtCTPfOYLIEkMe5szQkxHRvVcGMvx9q73siN5Y91nG8PCyMvYBdrW1nQUDbuMd1FZfGpojIDqRVrU2Te2pp8uU0NxmfYjlUGGS8fOlaHnxpr7FYthlMrIkkyo0Hhf+7Zu5kzuIGWrQPdAO2e670S66885nRLzbM5tQON4agFcvV0kmBa/Z8VCgSenv8mRfH0wDSCy0V0vF3WVLJXSLFRTdGzexrpN78bnWbqpEUCHlcGxKnX24WqgahoGNtlYzbdRJAVkMJUyEi771S/WwpYlOiLqXi8dgc20S81L4jSpwP3DH8PX0jjHPJU+xUtTS3c6HltI8cjEP3LKXuCZ6kmeqzRew6/9j8+DpGP9OxXkhp8ElN7wcAxX8Fjxc+SvHjr3+WTKNSqbUKIts4DHgZTbrli1QfFHxZfFAGvbN3J1e2O982IkQgqHFh5joniE/CoCAwCmXEKVa8a95qiYTYTO9v32r7Lp429i4/++i2B0M9cHk3UaIDOjM2gtO7HuVXkt/zIlM09iozDcgqZKjy5jjp+XcZGr5J3lAw8jvnnCisqpY41CxToahtOcVWG19HJJUjB44m3CWLLlHBG1dq4eSaXiCjFroQg3dPw0dlZsL2l66Q1uJhxZHfX1mbk8hVQFr+qlaJ4VCPQzb5eQ9OZCvhFZOIK96+qzKSYy6nmdU0699DxncgfIVATDQpdOoSoqI088tarjA5gvT3Eye+Aco+vx0CDP5oeYn5tmfnaa0/OfJe0s3b7zLF5JPcvL86JMrZx3OwW52gvyBouvDH+SQN/KC1S1UkHGS64kjP92eengoix7qKi1QEZndytF/TpCrjh4OjWPYy1geCrMlk4zURDMDl8hj89/NSFb7KNroJsH0gYoNbZcLCwW8AD1bApF1rDc1txGeD2qrBH01J6HWKf7fC8htpnKiGxsX6+4/53zc0TMVm7rvI6+lq3Ybvnqwlkx8nAP9/b8HKFd25tubzFMu74NbTMEzQiW05iCbNE0dkRqC7i3V8wr+pbl5xcA1ZjHv2gJ+vaf/A3T04MceEB0WjuQrjDlik33bRflYS2dF0Zvx0ph2DUDouBqSqWrebz+eud5wdOFLKtk02Ju0SbENffq4jfrujdzTdtbsU6UeGv/h4m1NTp7zfBMaZZXF3X90808RmXljo+rgVUWhraUqje4FTwolri2rdu38GDWwrCaBwsMFBKebjTZi16oBZQOH/kDZsf+AoCcMU6n6mdz2x2osSzPF0zGF+lgxBydslViVpojrPZjLNH84Cy2HGxlw4vLC0AX3VLKvhuuYKo0SLVUH3xxUCmc110r74rcaq0rl3r+KNChxNmi7zn3d0soSEGp4PX7uKno5c16c2e2KyoCI1pY/LswMYFpVzEpI5fFPFlwu23lTbi9+xe4JL6eVx587Nw2Tj/0FLKkYCj1z8b6cow93jLysLie8a5a6aXShPF8PuYmD/PCwosEXP2zgjGH19UukqlSOa/cVQqYyJJC0XV80ra4N7Ouvsv0ZKMTZZkmD37453nsl/8P/S8YrM2HGXx+6c52aa+YPyOX71rx+M9HJLiJNb1Xkz54lI5oP3JZrLmSM4skSZx+XKyL3WaFzVqKREvt2Uq5AfzoBUxL3XovG30+aMI2zhS9OI7Durffhe4J4dUbbYZwtQBlEUBWXUZWYS6H7kTZnHwrgfbmTVMWQ1FV1sqHURWFhKeFUBMNqL7LBePBr3qYKpzAH6u9r/s++iHGi2fo9fe726vN4zld/H++KJ677OwMxxb2Y9oiYZMzs2SV+vd29Jhw5irm0t0kDbuCY0K35Wd3ex8dIXGe3Zf28eToJ/nq8J/R0i0c6WTKJK5toDw6iVIG27GJt9cnOQLxOLZRIptfvZ7nYqSCwg44PXSawWcEmzLjaXRWFVWl4lTI02jH+L1hAnp02f2U5TCqEiKs+Mnaeb7ld3hEXr6sqKKYLFg5IskAATWMItWYp/LTMp2FS8lNCPvPp/gpWUVCsSjp6lEy0ZrtdHRBPPve8tKJHH+shy2x6/GHmtsuVdNGrYh3fXJuhv3ph0mtkBd6OZjlpapEJl/ga2OfZ/07rm86rizZONqFlVjJkkTxPCbjRF6sVy9Pf53egVpAKRDIEfXWbKMXP/8VADLe2pxltg6wqfcdZDTBXD/qlDhEhkRMzI/hVXSAzKXE9qqLOviehT8YZiC0A69b1q6UDWKKh/xZKQi3q6MnWvMNuttFYDNWrflJp149RDL+LpS+xsZF8c4uThonSMdra/pg4TCH0kLwv2hl8a6wNKS8YSrS8l2Fl0LFsXEWNYp4PJPhKNMYkobs2rSpapbUwikU39IPj2HbOEu8GxPFMDti12O45ITF8JTGCBaWDvyVcwUUScPWdHZGL+O2rnfUfT81PMjUUJonZ0e59g9/eomt/PjjJwGlNzhClRzz5VnS5gTr/Osol8XEN59PA5ALNjrOqmnhk/yUZsSCrnq9+NpFBNo2daYXXuF0dnnB5HxZ5+39/5lbW69jx8odowEIh0z8iwyLUwsZjiws7zjNlc/w1MxXOP1KTQzU3p9mo3Uzdtrk1r/8Vdb/7zvYepvIoOXSYhHMjNZnXT2SpykbajECITHZjf1zI33XJ0dwnObq/P1barXbA263nLwXFFllfmYa27KYe+aPKMx+AYB4XwePTn6eQlFM+H4srCWE4pqh12vREmxFkVTyTo0+mTanSUieps7a4Jz4LB2sn2wtI4unXJ8Nmh49yUvpI+Q9aQAW2oQhcOjF5horzTBROM3BhRfO/b1RinNTYjvHPv9dps+M8pa+X6NFWrnsRHFsvO6CEciKf2PhKACG3saa4HZmZldmTmVGR5ElGclO80LqMV61mnc3K2VzPDz7ElOFGrsk2N7OzdEIfkkYqFogyBPpQbwDrYykj/LC3IMAdNDOVdE1dLkt5L2RCB/86xu59c9/+9y2YjFxLYNOfaBXUTXUbpFJ63KDI1PUSqbircLYVUrNtRGMSoV0dRbvxn4A5soxcjlh0Bp+L7hth4tuhilgieegZ/3SJWXnti1bdRoYzfDy/PM8O/tCw+dzxRG+NfMKRknMNYUJ8b5b+ZU7/E3JEpI3QrUq3ssbu+7jTb0fZPqACNCt8ZY4m3RbyIvFfnL4wjSUFFnDWPTuSa64/5Fwo3O6YKd5LTfEvBtIclwHoG2DuIZtt4gAkqEI79EXXl2AOJI7RW++dtxXtN7FpvgVF3QeS6HLpbl7tfOCRYpOWhPH7wlZbJYOU55t7pAbExm+PvsqY4/+IZ1KbR6ODdxDrkuc81kGXYuvn1v++M9YKDxOcVE5ZUfJRivOksPBK+uceLJe8+/1wKkGMG2Dzu3bOVJdg6PVB9MHcw6vzdc/D447X67t6f+B938x4Ituoqv3CrLz4pnS0wpVV9+iZCvospdysTH7brit0ZVCGoBAS5D7hz+BsWaG8lmH3e2i1ep28nl65rvs/9w/ndtGeTCF6Tgk++uZGd5UgR5viNJJwXAqDIruPZZTJqKtrKHkmBXmi7Ug+qm5xxk6+SUAMmaR+Up9gCjcLd6TQlY8L8ftMR6d/CK620DDKjUGVl74zKc5PZVjNn+CoZLY17GnG8uyzqKlXMJwqvSta569XvZ83ICzvyzel5aN6wHIymkARg4I7ZAurY0NoT0Ew7XoUUEd4yuzJ1h7ZWPXsqVQCIt5e2GmscFA2IyRM9O0bdxAeO0TrIn9t4YxquLFMcTzlC2L53/05DGCSpJtoQFivasr9+z2r6HD14MmS2RIN3y/6+47zv3fHmhMZCV/tpas8GVqzpjW7ia1dHGdUieOcTz3LNqMYNB9d/5zvGbW30t1VDwbSWfp4IBjVfBgMel2Ucrowl6Ye/kAN/R+mHes+QjBThGAHXO7WY2cHiJTOsPg859qKmfQ+7Fb2fKXv77kPpdDyE3i+FIi+fBEepz125uzIwy7RKCp7pEfY4XOpaUWHVVWCCphTCPPZWUftyjLB8x7jv8D/sd/m4ir92h4arat6rY8f+H/fFkcvxKg6HbnfDAzxgvpGts1W5lhrHCCmczSjKhoYA2bI0snV6pmioCrT7Oh7Rre0vdbbNp1w5LjAarOcbTKs7SXNtHXcjubLrus6TirmsJXWdnWOAujWkWTFKzzoiPlhAjA3NDxTvzRWuB0shDENGsB2sLsLPOVCbQNtTEtcTcZOC/sjLujd3BL8Ea0iHj+LWdlxmCpIK6/rSw9drYs9JASFbEf/4iwtcYY5mvDf0Frf+3Zq/hMnp7+GkcqNTu4cGiafQGVTT2NOk2arnNk4kuU0sIvNA2DO3v+A/29gpVUsLL4pBWigLaPqr2yrd4MjpEmnhNzmmWaDJQztJVyUC0iu2GKwdRRvpe+H1lZ+n0pWQvIcvOglpqHzdHL8MQb351tscvPdTpthoipc33HO1gT6QFVwneeZuXQX36D66KdhC5fwOP7SZe3n+ANCq/ipWIVKN2yBZ0Q3/l9IcocLp7ggZG/JBFqdLAKHi+S6uOYmeOJqS8x2KlR1YQjVZgZZiZzitF0I2VvMcLxGtPl/NaTSyGdCeFQM7ainjhBafmXy5tQKdky88O1lpTF9FN8c/Rv2HBl4yISjArj1GPVsgbVahVZkrFXEDfceudlDJdzZKYaI9EOVYxFwZvFiLeJCShvpNFcIceIV5xnYXSG0cFBnOt+meKe6wDo2LUXWWml6hOBByMWx1xF5vcswrpFqy9I7MBvocrfqH1hZtFlL6898L2G31TyKV5L76d3UbtmgIMLz/Ja5qW6z3YS5S3JO9mzQ2j/xPYJp1ROr34xuKHjJq5uv+7c3+v37CDu6cA8XUS1xDXyhaIrbmd7eCt7E2I7o+UJXks/SyUhpiUt72N34iakseXvK0DapWTLAYmZwmny5eYBvJmRESJMYmi1c422t/HszDeZzAlnunB8gutbrkE3Jbb5urij6+cxDYO5nGAEpCpLG1e5mDByp87LwCqOSmpBGD47b7qBwjvD3Pgn/7E2oD/J/UOf4OVco64GwOjcIb438WU6N27maOZVZuwp5JLYh+Yz6XLp5mvzwvHXp1JYtkmybeVstSPZqCtomUS8ZVS9MTjsjXewJrSHzIJbSjYmNJyM+ZWFTstqBUVSmTwhMteKuxpl3cxpuzeG1y2fcGyLilXCzlyYwaIrGqZTexZsT5WKVaSlo9G5KFWHOTT3RQojwoF90l/lgbnHCe4Wmcc1u7di2BYJV/C0Jbe0oPhitES72dAmukhZpolXkZBbLk5be60lwv65hzjirzeqHxj5C05VhGh258Z+NkR2E1qiDNScy7PHr+K79kOM+kXgvlwqsy58KYGgYJolusbIfu39VHueR/d4uCK2ky36rnPbeDA7yMOZ12jdI+a8qWeXFvCslCrYjs1rK+j4Dc68wsOTX0LTda6OegiE60sqemJRwrF6Zt2e//lWeE8v/uAKxu+PCGnzGb4+8pdMvSKCsT7ZQ6GaBqAQjiFJEtOTjQ0EtIpYL+ySuK8TJ0fYGbueaiGIGhQOjEwUAHXYnfscix0tN3HoReG4H548wdfnTqBdVn/dil5hkKeLLtPpjEjOlC2ZqrV8qappmlzXdi+b47XyBy3ZTZd3OwtzKV7U/TwdrheonnRbzw8+J55HTQ1gKwmyucM8NnOC4cOPN+xnfrDEm/t+BV9PF/l+EXCz0kvrwel5hXL19b1Thk8EtKSqmBP8vWK993WLuVNxPy9rvZjnMTnv+rPf5qd/5wZ6L9276v2tuUQ4dLqnntFx5MmX6VTbKFaETTd3eg9D8/+jbkylWEJX/JTc0l/JqVAwi1RnZphQxvnK+EO0rVs5iQAi2RTWogAY5wstuvin0iRfW6hyyS+9peG7tZfu4auZZ/n29PPk+2vB9a4Nwp4IxcRcMHZilPv6fgXVJ4JuewPXsN2uF1hemBNBDDOwtJ10JP0ix3NHwdU88rlBAK9Um8sjLgvJ3ye+C7W1kO/uZ3h3Y0nfD4oOtzObZ66KlPchSXG2XtO8LMg0y+h245rjdSwkq3k517nfevwokowiq1SkIn4pQExf/j2d7Xon05d8CI/fh+U4JOzoue/G/WL9suYhOzOLTwlQdkuFr/T1cR010fkr2t9GVekmXVhaE7F3YRzJXFo3JmOdYrh4nEf+v/9HqVhEkyVCncvbJXukCNclbmGjt40Wa2nB+9H8ICOF1a3DANOnTyNJEvZ53WJ33nRd0/FOZRJdls91NG1fO0XC85tc9Z4aQyXZLoJLmstwUTAxzDKKW+JZdefpZeHKLC1FFLd+Jkrvb4lnWAqIYymVxf58hOgKbMC/qNQs0ttDUemn4qvte+GI8LGcUPOAy40dH2SNdS8AYy8fICAreLzC3irbMgF1eUZxq2Hgqa5c3tcMlm2husnQSi7Hus6NtHoiyDgokoplmmxq3cft3R8kEGzOhAOoYOGVmzOxW90mNmvbGstSDc0HTdigZ5F3r7XhlcnqQjYj7TLZn/jbz9Ep76Qoz/CW//ih1Z3wjyl+ElB6g8OrRSkFvOy6cRPzlWnai15Mw6A4lqHLvx6lid+s6tOoEjiVIkVPL76OIJHWKAC6LXFj17u4sq3RQFiMZHctq2m3ra7GfLp4gkMLT55jK6wL+0n4lxdoDa4f4NaO+8hnawuWGR4g7mkj3tlYkx10jQjZqhkeY6dP8bWRT3G49HLD+MXo2rGZg7kUk01slu9M/n88Unio8QtAdWtis2ZtYbPdLPPs0dOcePYFBujHLImJrn2gm+tabiIUFtdNrqqYq9M1B8BjzBHWYkyv+2/krFrGtegRi/b4a421vlrJy2lrE4nW+oXasuJ4rPqM5XzOi+KLElortr11n8guJbPLGzZ1x6jFwF8LFm57660ULQuv0k615IquJldmcBSDYRxNXLfTYS9H8tN43Vr+7m09LHznt4kkVw4oDbt6IrO6zk0dP80epZY9fegzX2Tst5/k+e8+wuzMNJe33oM/XKP9KqrKXHkeV2oBeSFHUpXRZQcnFCWsJ8jOz2O7XefwLP1Mh3ziXHyLLmWlUGS+PIqZqTGSNu7cWddVp3vdWgYi1xBLNu/8tTGym03hS/D4vBxaeJxsaYawy47u3rKOUDzOWOY1RvLC6VIkHxW7fO7ZXQ4H8kN8c+41zGU0tC6LX8aelsbuGI6usd2vMOt2E+l0A3XdyZUd+u7trvZBPs/04DgaGicy+0lXHWzL4nvpWc64wqnt67fwtZFPMWldmNOoSBrGooDSVX/0W2T7RrjjP/1qw9hA51reseYjzLsZz3WZCJ2+K+hw3ylV08gbKSKyuMeTldWJbc6HA3gUH/PzKUYPHkZCoZJ/fYbX+QgHA4yXxjFzNY2LYqHAhvhdhHWRrU+2d4i24Hbz99sbaGFNaDsxJUS4KB6qUq5A+YEPgCSc2x2/+Ycof/ARdv2KEGMv2BYBp2aY9QbC6LrE5jddy2NTX+LVyf2NO3IxNzKFLMlsiV6+7Lm1+68kpIsWxLpTJliqD9ImZQlNrn9mfQEf3VtenybKDwOB/n7WBbZw9PkTZCYn8KoBFLc0u6vTDQLONyaFHNfD0MoiUGtMZVgX2UewFEFyDCzHojIrvlsoiXctfPVWOrydZL56kG/+5ifZ693NOmWGzZfWs+E861xn3i1VjbkCs6O5Q5zKLr+Gjp46zbMFk/GemumoyRE61l/PgQce5F5MbjivVWW1OsczM1+n4p7nDqeXKxPXE9C8BAIDSDON75HMAgdnHiXQ6mXj1cJJj2uNpRpnoWpxivbq3sfzURkQ81DA7eAYTop3ILllHanKFEX3uVOUHKZd/94qqkrrugEuBN1re7Ech6hcS76NnXiNx/7mm4xMnyb+U2I99toB2nz1gYPs8AiyJBHW0gD0rLGxv/WrtEVsPGWVNV4vPn9zh+p8FGQbvyaOQWtpdLIA7v7AJjpun11ym//xk79G8roebn9vrcyj6kzz9NwzDI0/BkBAMZgcfpFAi1jHo2ofUbmeNZeZnqRilSkoSwcmZo0MGatEwmVztLoOe7JLBK72z33nHAsp6SYBvbLChmyUPVL/ktt9vZD7RdJ1oVikzdfCDl8Z1dM8gVrBQVablLzZHjwraFeePP1dHpkQzEO7Taek6aiytmxZsap40DRXC8yxsBetVcdKp3h54UUOLzzBxHNHkCQJn3vdAkqSiCbs7uzMFKkjX2fA6+cyaWlbLiUHqUpLs0bkazZwZOEphp48iD0v7JNo2/LBCXmDmMOfm/0moyyd/D7l8TMsrS6ACmC60hphtf6aS7b43DivksCJhrBti9RMGgD79K2cMP4QTa8FBwPRIJbjoLgMWhmbqpMnNrCeb00/y4noypINU+U8j01+kdfcqoHz0bd9OwmXbUaLuBemy2bqk9ezI35T3fiWRCs3J3ZySaHmPwSdKLZjs/W2ev2os0ilxylkhB0x+6RI9kRsscZYmoomy0wcXbpBzlx+gfn8yo1KmiGj6BQVYVdNz4jg8pwvgCKpZCybcr6A7okQVgN4lpvfLBmP3DzgpNo+HMehfXMTFqtVQV1Gg9eeFtdBzhfweMTckhkRn0UPaZi2Q+xtSzOc/r3gJwGlNzBmxkYo2xUyehZN0zhdOcALRZljjzyBY7eyN3kbjr+RvjdZmuTwwlNsPTnK7bHLuFTXiQ10M1GaoCAZKGoFw5dedt+BtTWjvKt7aUNuMfLmHGeyB5ifEM79I5Pf5lTm1WV/09ctDKa2yRrNtlvdxdZY84yBnBSTRXVRd5xyJsslnbfRubar6W8WY0PLOHdGLmXytWN1n++OXs5atblRdRadvlqAK+OKqc5MjaMOH+DLQx+jJSwmGN2jYThluvLCyQ4UMuBmpVeDbNSPJMm0RLso+Gr317uthVfmH8EINxrO7cE+boo0GjM7w21cEq8XqDVN0WkluUFMrP5ohGcnvsvp/NLCi+dDd0JU8/X7m7fGSSheRk+JbkOz2aEVt2NJxrn6/mudjbyl8x42DwhWhOFkGb7sNmbnV16kZGLsL5ho7d2YjoEk1YI+oSMiIzP36FHsdIFvjf4tJep1wba3XsNlLYKGHTLF+M6+TnJuFjmXy+F1u2lE25cW/I2EQxh2BXVRN7LMzAyPTX2RVLl5GR5AIBBgR2w72yLNDS2/FsPnai7d3vVLXOK/mYU5ESCwPQq+SISnU9/iVEk4bbouUXVWRwXvDCW40vaTm1+aAWA7cTKFxrKHuOtgqRlhkGXyC6Tyw1jL0JLP4pSV5tupQZ555lFe+MKDSJLMhshetoa3kZmfZ094Ld15cZ/8LTF2x2/CE74w0V3LcqgatffFEwiw+5d/selYLe7jWNnC1YRnbQG2ee060eeCmcOriOvgC6xOfNHvEQ7QzMHDnH5OBFryxQvrVrcUdK+Hu7t/niup6fTk5jLsCK8nJtfmbcuxWag26jMArIlWAYuSVSXgiDGnXjrExPXvp+hqkimaxsafeh+yey0MyyDgCjjPnhxkV+JaunxdeP0eSpFdrJOXZgSM7hdBi/IK3VC2RHvpc9k4mBX8Zm3OT01M8J2JzzCRX76V8b829vz8++mIX82O7C4e//PP8vDEPzIsCabfzIxgOp55vjH45qg+5isT6OXHAOiImZQe+ABdPSVOh0/x5aH/zaAjEgt6QMF2bG742bdRqByi317PLuUSVEXHe+WlaFo9+zDk6rwpeTF/pIwcg3MvM5w7xrHM0mVlAEe+/T2quS/jk2psRX2DybfH/g5DmsGvd+OU6pkYTv960tVp0h4RmB1Mn+CV+SPE1Sj7Aipak5LmaECh58jD9G5dR/vmNTyVrXBqonmXQoCjmRc5VV1aVHU5dMXEcQVlL5Zj0bVZrD/tOwf43uQXGHPFWhVJwnJWn3RZCoqqitKQvNvEwjAY/4v99PslzvRPM3CtCPhJjoMqa1SLtXl86NgRpvInyHjFezma3MTMbb/NkVyYbmUDm6PLC+cuRtZTC/o7A83nsq2bd7Bl084lt6F7dN7yvnfUJS5CoQD5ynGkBbEWVTPTzM49RDDpduod/N+cGfxU3XY0c5Cvjfw5nRuiS+7rio47uLb9TeRt8fyqbsmxr1cEdvYmbzs3dmJE7HvstUlGRv+ZQ2OfW3K7rxdr9mzlVOYV8pVZXp1/nJfnnlhy7Au6xPPVxjnf0CPkFjVsaIYOLUKnfy1H93+CDTdtpxyNI0sSC7NLa/M4r/0T5ZPinE27jLlI26Y0+hjj+f1cnryV5545ygsFk5lLRNLRwEZTxPt78rlnOBzPkDMWsJWlSxFlOYy9jCt5wzvfww0d72Fv+AZ0SXQeDieWt7XX3yLs/8tb7mbjz/zskuO2rdnAXbHlg1OLYVWqOFaBcLL+fCIdbeSMFOlqfXD/yTUhvp2TefJVYdOa5SnsYqOdZDoOXq9YO1XZi+b1EgkHaZHTeLKNDNTzUUlPYTkGsr1yoikaEwFw3S0VnspM89RcPWte1TQsx6Js1JLgYbmFkmURjDR/3mYDIxRnn+P7f/MZZsfEPFdxmWQV+wwTxVMc+17zpDvAs5WXeK6ycqOSZjhjHOJVt5LCtl0GXbyHk8YZHssWyGdSWOk0Nstn5nO5xxk7/bWmDYZMGQzHQvM1PstWNYdiL63/JLvXWlcUNHcNmDt5CrNaJeRNkM6/woYrmpdl/nvCTwJKb2DMvfw8D478OWpGiBav/+nbuSTUw4kXhojutrA9H2LDDY21y7OFFMczzzPlatKUfDrJ/g5erLSQmp9GtRUkY/l24qHQolbH0uqy6R3xnby1/0PMuWKhffG3EIkvnz3o2S7otWa2NgEE8FOxmr/cPbt38XD2NaY21gJeUrrIOs8uNvY2Z3csRmTnALKk8Opf10rJcrNzbIzso0taemEa61hgenPNsJP2beShsf+HtW4NRjrEtYE3E99U01GommUoCUbL6dwpTqSWz/wuxpRfGEwPz/0NPXfuOvf5pqsvY0P4SwS9jYbJWOE4xzON4pJptYqt10f01UQHRada19q5FNpDNdFYW90M+elpNFlCNuuzFTk9j1fxow0KQyeqrVzm58vOotgi+OBMjSEBvpAwdG3Zx67oDSj5lRVPC9NZUtUMPn8I07GRFxlA5ZIISoyf+j7puRJxTzuSXL/NvF4ioIaZGRwnY4rrL3d1EXevnWZqFIvivPLLiCwGOlv5yvAnOV6ola5NT01xY8e78CeXZ05ULJv8VPMsySEjwmzY1QArzDNVyqCWfZQsi1ibCBzc0vketgWF8e9YFcrW8p1gzqJLMmjv2ET2VHOHzDQMVEnCNBq3Z7j1/OkxEfQ7mZnme7NfYIaVg1khVFqsY+hn8njc+5WqzhBR/MyePk2/R0F3AxqhkJ++0G5iq+ggthj+Jz9KcOrvVzVWm5/m4OwXKLjdNCVZpmLV3+tRJUXeSGPaBrH40tTrxWhxxPxZPnQUX1psLxS7eEuv7XY4OotyvsRXJ7/LxCKBW8OpUpWbM+sWxh2qloHlyOguXTxz8iQbo3vxLqErp9uDaIrKzNHjHHxZGILZkLhXO20Pu31tzA2PNf3tqaMv8MXBP+GBkb9c8pzKhQLfGf8sJzPPAmDJARytxtYYOnySS5N3Elyh9ONfG4mWVpR7I1h2le32pUJ7oUWsM1XbpmKVyE82Nr2oyAESnk6sqGB+HM17GbvhFxnydbO2e4C9PXfRGxCORYUABTOL7vGg3bqe0czzHPTvZ8v/vJsb33dzw7bPtrfWXF2UWVPioLqdtbGreXv/r2HbSxvt0mCRmzrfRXRRg4mBq6+mt72N3JzQYsufVzoghwLc2f2LmFkxJ1SVFCXrNFZEBCFKTRztUbYTu+GPyUT68QX9lHNfQS3MNowDSM0tsFAZZXoZ5uhy0MMap3MHOFkY4uXc4DnmaFtnD3cGbuMqTSSqHCtA1V45UL4aHE89zmRGlEF+5w/+ma7AGgJajPf+4e+fG2NK4rrMjNaaWxybGebx2a9yIm65x6TRpiQw5goErTJyZfUMzrgmtvHkxN/Tv+HidUVs7x3gtq6fY29QrHdTC0F2XPJfmVWFc+ptu4VE95vrfrMgr2N3/CakzqXtxYoEkqSRjnQwb1oEXYZSogmT3QmFeXbm6xSqBr36Hnrki9/10ev3kXjmb+lOvUjME8NWlmY4KJqPQJN25U9N38/h1GPL7ifuS7I5ejmejTfRc8llUBKJv8kjx5b8TWXvbzC/6wMAHDA+w0T02XPf3dn9HvbE7yTp7SI2P898+vNoLW5Zp5VFlcR5jJ6e4+au9xFQI3jU+k5qj/7q3/Gd3xXzd8QqoVWWtokAcgGHmBrHq/oxVsEkjHXUkiG7Lt+25Dh1/Ai6pDI/Xks6fvdX/o7vfPSzTce/PHaUfxn5S55w6pOnoXCUkBanxVufPL/j+n30SiNseiTHke8/y+HJB5nUhxq2O1U6RcpNoCqSgoWEqqtckryNtcbywTMAz0SJmzp/lvXl1hXHWn6T745/lqG5w+7fFQLqyYZxplNlcR8VVVUoGEvPD9ve/Ra2b/slPIdkkpaG4zhsuVusH9nWMk9O38/w0NLP3C2db2dbdN+Kx98M0arGDpeNKbs2UlulSFtYZ59nGqNYRnVsbGf5aoUscwwG8swONZZBehyoLpHEstFQlukgnAu5nX7bQ+RlsQ7NjJ7iqc/9C5riZV77wRMNPw74SUDpDYyRQyNclXwbRMUiu/XqKwhKKTyTGVKHFjg28xZUrfElWB/czNvX/CYRl3ETHegnlmzlJmWK7XhRJA+l8vLaRoszTv721XXMSbUmOFTIUpJCzM9MszOgowSbZ8XPIpBIULEqSEptIvepMewlxBljiSjfD1Qphmsz5ZknhJDc6OD4ise44y33kq0u0L5IMHrokKDUZqSlszCX/+q9XPLeW2vHHQgR0uIUBqdQzD46WjfSsbtGeTSQUTSxOFSVzeTl1dPik64w6r7Qu+jeWpugI7FOAtpfoxxpZGJNFkc5nWvMDlSbCC6HjSLOeRo/O31+Nsv1htn3vn0/H/9oo2DlaZfdlY+c18lsszjfSCWIYVeIeFbW3qpIPmRXv8eSdKpO5RwjpKtNHE97ZeWAkj02wq2xBGZxGlsNouu10j/ZdXCu6Hkv1arCFa334k/Un2vADQ4cf+RlJEW8G60D/ZTdIFJqapaq4zBdHEMPLh14DEYibE/eRKynVh5WmJnDdmykFeJrplXAazVmViYGx9jtlZEq4jody3yPU9lHyBdTfDddJubqRniVAF732F9ZsHhpvrlDfz7m3C4hg8eaB5QWzgwiSzJak8ByxmVwzbnB04G2vdzU/at09S5tBJ7FpkuvYl/LHcTldjqKWSBPqXSGgBKBlEtNl93OiZrGdxfKmNON7/jQwRN887f+mOe+9WTDd1PX/g6HNr99xWMBMFF5e/e7qC4IR06XPVSteuO3sKuLb439DfcPf5zJ3uXntrOYd0Uax4cWyKXmmC4N4W/SXvr1wnLKdcyJ+eEhBjwaHqt2v2yq+LXmxphia1TtClUri+w6sZ6cCCqEvc2NrJQbLD70nUexR9yueG4b73l9AlmSeP4zX2z6247QZt6x5iPc1/frLEw1b9pw7LkX6fb3orol05aVR7VrQcpMpsya0HaUyIW1jP7XwLar9zEUPI0me7iq7T523vxWAGI9cb428ikqhcaA0qnZGV7LnaFoiu/MBYnNkX3YUpDuksxadRvxUXHdj48/y4Pjnwdg3Z23csWn/zN3/t6H60pqF6NjwwBTJ7+PbgmR9oHABu6JKMz4FU6USuSXEdSPmWA7Frvvu6u2vf5NbFHuoXNcrKn2jvoOjwmfxLMzX8dxdUu2Ry5ja/wyWrYI8Wuvr/EeBjPiGBIRMb9cGr+W7aEdTY/p6U99g7t7PkBfcGW9uGbo2LeD/XMPMVEpcNxbz9QxY93kY2IuG8wd5XR2aYfqQjCXeoZ04RTf/6+fZke1nzPMc9Un3l83puzaYOm52lrdNubltq6fY52vH4DN69qYf+z3aLfGKHk0ctrqa+unUq9yMvsSKGH6Nq8umbQa6AE/lmMzGRU2nV8V9kpLn7jfMV8PnYH6wJHshOgPbiPet7SdZJUWUJwKad3kiWzqnJ0QCDXOw9GWFrJ0Issasc5r0VsvvoYSgHHrnxJY8yGuaL2bYPvSwbD1mQmuDnqpnlcmHfauA29jQGwxSh5hEwyEdqB6PDhll+F4dOlSsD48tLiB2gX5FvKGKHs1DQNN9lAO6nxv4vt45CB3dL2HKzcLe6Uim2iyTqVUIZEXpVolpol6aoHUaqXCBt8mEm451SlnmJPG8uzAciSFLEl0e7upLsMEWYzRwuOMlB5bdkylKp73EZdBBLDFv4mtdnNx/uBYld3xm+iyG224ieIwE/76QMSe9WsIJQuEtBh8Y45rN/w6scsbn6X9c89yOn2ScqGAIklU3STmYwt5FvIrM5TyBbfkbpnuxGeR6GjFp4YImOKe9Hn7WR9sTKabtoG6KEgyWTzOmHF6ye2uv3Qvw+UzdAfW4JfbMasZkt1iTt9+/U3c3Plu+v1LP6tBNYJ6AVqxi+GRdIJqGNMwGD4kGLvzC6OEg0m6AusxqlUkXxhnhZCFL7CHW7vey+kXG5tOjZXTnCw29xELxgK53NLdzB2vypSZx9MaIbgtwFeGP4knYTE+mGWyeAZj3cUPWP9bxE8CSm9gOAWFmK+PsLvIyrLMs1Pf5kT6CP50K1siN+BvIlDmRMQCG8OPYxl0DIjfq/44hi+JKqvoS4gwLsZXpw/w1bkRehd1D1gOVZ/N8dn/SyaTJj8lJkjPChFl8cMcyUWMJF2WKNnNX25Jkvh9aQtbXqxlJHxlsUhH2lZmDOgeD7PGMElvJ6eeelVs021p6g2vTnsAQJqZ46q2+/APLaCbJmWrSLy9Vo4j+zKE/G7GPtbOlsjq21gXpRkqVomkL0lropbtDSdiHJh9lPkmYrY3dt7DvvabGj4PmllUScco1wwZteynVKw3PA1jAa9Z70iY3z7GNfmdHH6mXjR1IiMWjUxnf93n17zzLh4a/DTPzD7AV4Y/ybR3ZcMhHdSZtSSe/dw/0+vrJ2/VMkfRLhEoMVeRFTYraZ6ff4aW7hiGWUKxalnvkCcKgGPKRKbFgtqzod6J0TYJ4zd3egZ/0IdhVwiGgpTdFtJzw2fwr2kj9cyn6d3eqCV0bju6zkBoD/FEjf7qq9o8WwoSlJc/D8suoSuNWfbhlw/T4Qnhce/PnsTV3Nn9Mzh+jU1qzZAzsbHdFro7YhsYCKxcAgrgaxfzhT/QPEAyclIw0QrexsBeaItwROK9wolU1QBJzYs/uXLApH3rBkzbwJRVHjtxggfHniDjC6DIKkMviuBoTq4tUeuUY8hyo8P00v/6CJVTx+l+Evb/2hd5+m9E95pUKk2LHMVfXl1TgfCaDiRJQnfnAV3xYJxn/HYVcrxjzUe4o+c/09u9flXbbd2+geFyDgyFwZLNU+kRJLdb38WA7YBnUQeW/OQEO+M3EFRq89mh9AscTzc3JlssA72SwbCLqLJ4t7OuPocRax5kNxNepkvDZGazhGbFb9b29QPQfede0aZ7vrmDrxTFM67LGvmp5mW205Nz7E3egm+90FOz7SyyVFtLWlvE3NC5p3mQ4Y2Gm//bB3gxkmbGtllziTgnf1sru+M3oVQbg3ZbIjJ2aYrwKw8A0FkSzl2/v4VRt1va0LSYm7ZHbmVH7K2rPpZIexsH1eMs5ESmtVQ1kSWZaW2OA9N/QXp+aUayJscoGhkiydp65g8GKJp5gi7LMDhVz8bt7G6hoF9H0BDrrAcJySzQtUXMox6lcd0Nu126Wl3BW8NRUD3Njfbs4DyHs1lu+q2fWdX5n49ES5Lbu/4Dd7Vdxl1GvUk8Z87SS4zHP/a3TFWmGK02Z0ldKDZ0vYtb1v8WfZX1pKvzbPyVyxoCgFq/KGPVg7Vkm5mXiOqtRDoE+0dRJfyX/i7mvIbmaBgX0GVp3GcS0hJcmrwT3ddYzvyDwLSrKFlRbuPLuQmiLuGcWoqMcp76sF08yHdPfoKunqXlFRzHQJEUuqbH2Xue1kzVKpNZVK6kSnlua91LpOygSBaGfXGaIJyP8cIr5MuCQRbZsHQwbLp8hsem/pmZ0/Vz8L7EZSTblrYnACKu/X0kLRjoajjNg2P/D91ZmoGsSiqaW+p9Oe3sKgk7cm56CkWSyEpZ2rdsZFtMJEHjveK6Z5N+ZElmbHQUZsX9S8kZHCdC2RXxH3vpVR4Y+SuOLTxIpVjieHGUg4XlA63XffA9lK0io/kTTJz8i2XHnsUVf/G7XPnn/3XZMWGvSKRoC2kAipla5cXgscYEq56BDZG9JJRGX+HST/0sl/7eexo+v/4PP8hr+ZeI6FHisoee/say+1t8W7jZt57iwhxfGvzfpPOPABB0DlPxrWwHS64fZyejK47tGNjANW1vpdsrnreQKaM20dOyHIuzygMzswscXPg+IxuX337XT+/DcWwWzHkeWPjWuc837N1HRGtBKjX3kbKZtGCnaquffxZjvDLCw5OfIzOTwqmaDBdHsVWbYkHYbunJaQq6D1taPmRhtBd5cvp+HLkxKVIqqswVGq8TgJ26n+EzSz+XcjHL2P4/IhlvIRRpZXvsMozpMlPZFE9Mf4Xr/8PbLuBsf3zxk4DSGxjFaorhZz7C5ttvOfdZb98Wbu56D22etViORTjRmN0zElEAJF8bFclBcqkRVSRQ27EtA2cV7JFrY2t5U6IbXzS6quONpsZ5W/+vUzn8KqkxkUWRlhE6O4sZY5ZpxEQ0fewUmqxg2uklxx+Z+h75TM1ojbuPcf+u1TkXoRu3IkkSZ+4XUezxR4S2TXzbpuV+Voe1u/oZ3v9pos5reLQA5fM6daRK48y5mQlJMiixvF7IYug9cSpWkaLZaNhPLLxEvtK4LV3WcaTGbhgFS0KSJCZP1VqGPzb1JV6d+37dONuR8Sr1AYM2tY8O/wBTD9dnwuyhOb4+N0o0VL8o67pOmxrmpq4PcEnHr9HZtbLDPeUxeX72YaKvBjFtg+g7a8wWfzyG7VhUzZXLpzQ1TFbbR/fWLRTKY5SqNebDSOYkr2WPMHjoj8mMCNZOaaE+a7T7tisxnSoxq8zJ4hjPlAWFWPYaPJyeJeTVyacyJK//TTKZeZaDbNsEBofO/Z2anuPqUAxJX95oLwGO0hjU8c+7Lds7XO0TXxWPouOXIyTrspseQkSpFIp0e3W8q8h2AXRsFs5J0NfcWMjZQYYqJrnWRmNf8VY4XTpDMSOM6uK0uK7VysoUYFXTKFtFgo6frZE1BDUHzw4RiFLcn1eV2hLVH9xI0ttozPUk38rWNW/naPEUUS1C/IRw0I35WdKP/T5+eWjFYwFoXe9ql/jEeXpkH8Xzuh50bxeZwLCq0BJZnbbcxmuu5tWSjlUy6Gq9hetarmbz3itW/uEqYUs6jrf2LkZdgd1IV00MdKowRabcvMz5VD7Dmewwr8nTvFBMA1B12RFOZ7Tpb9bffh2PTX2BgpGl4IrwJy8XjtH2m24gVUrT5u9jbj7d8FtrvubcGdPNs7dtE+LztX1ifXs8/yyPpL5z7vvZl4VzZc4tnXF9o+G+37mHPX96HR6fWHvbN2+gP7QHv974TGtykC0tlxO+7CMAVN0Ok46SJjIgAit6WDznbb4IrZ7VJ0NCiSQ39/8iEZ/Q29EqFqZt0F82+an+32Lq8NLl2X5PF4UmLMqClSWqi3uVWahfn3xtSW6ORvBL4ntF1rFtg2AsguWYFHNNAliSjuU4+ILiGc4oJprSqPPz5N//Ex59irnC9wiGV8cYbAbFdVRK1Dt/Pb90LSUjzZqxLu7r+Vl2dl3/uvexGCOOsDkcR8a6XKOts3EuSdkneD5vMjVRW38dRxxnYpOY932JFloirUhGEK8DygpSBouxc+Ba2n39vJZdWvvn9cK2q/irYv0xSmItDrrrV1lWUM5jTbdV+rgk/hZkeWmXxIiEUWQviqOjyPVrqa548S4KEgQ7W3l+fj8LShpN9mA2eXYuBjJT3yedE8y7tZt7lhwXDiXQgz9FZqw+gP7Y3CtkcssHY4Ktgvkd0NyucpEoET1JfnbpwK8iqeeaUbwy9g8cdjWksoeEHRjI5mnf05gojkVOMzX+P2F+DNN2MOwqmWwOWVI4/pTQVztzYJBr2n+G69vfzjOf+wK3Jd7CrcnlHWpPwM8IRXqCG/BFL97aV3H9mOkRtwzweVEGZjs2Rx96oGF8zu2OU4itzHpfjBs//kGGc8fIGWm2XLKn4XvHl0QKdJOdneMKz80EQ2sA2BrZRqdeC4S/+JUHeO2JxvfN63YADPlWnsM8gQCWbWHKImEjyx4cpzHRlq2mqboaSi/9w+e5t+eDdKSWD6xuuGoPQ8VTrAltpzNY86cUTadkVfEbzUvMs6dFgj8oL13lsRzCgU6ubn0LQ0dP4de9vFptJxQP43OZ0LrjY3zyezw1/NlltxNa10fJzLFQaCyrvDTRwY5Qcxu80nE7G7f/xpLbrU5W6N/1y2RTaSRTZ334CqRJH9coW7ik7W34V9kI4ccdPwkovYFgWDa2U2MxOGYcz+73EF3U5nD9fVcTVSTinjCm05xeGF8z4G4vy1y5RvFzjBKBUokvjXycVzIHm/52MRJ6EFmS8fhW14K5NeF2R3FkFmaEEVH1rqxr8IjxKi8siADH8OmTnMoPU/AtLVrbKb1ENFELniyYwhgLrmtOcz0fe+67kalKAdsI8IXf/V9sUno5Wj3Njp+5YVW/B0j0r8UT6MM0wni1OHmt3mB5dfoYz06LhUORfZT11U+0W/deR1hP4Fcbsyj7+n6JPd2NWRSvpBMvpxs+zwc9pI0CWVc02ahWWRPcgCcUrRunONI5McazUN3OHoFiPdukeOYovfIpdK0xsOVr3UXSE2Gd10NLy8oMmUSqyHWJXUyWFzgRPEr/vvpOCZZj4ZFWnqxVX5ybQyqxnm6KU19g+ujfnvtuJH2I1xaeJbLtZ9jVIcqfqufpdnh8XqZKJ8jlMoTSPmQ3MyJHwkS0GBXLRl6Yw6wuIC2h73XumO0Sql0z7KupPElNxlqB6j1RGmG00CjcPT/iBmfdFum+mIUiqZyafYlHJ796bpxplcE2OXPoKLIkk5dWV9d9+rRgIJ18Xug+pSfn6kQN04UCz098DKuj0dhJjR7mQK7K5FGho6O5parx9tUFW6q2RIveyYbIJbSF2unc0Ee2Oo9acRkKbsc/AE+1SHsT/arx4hFO5V7mlk/9HIPONEE1zPiJ44wfPoL/8v+G4qzO0Sy53ZyM0TlMy+aUnGL6PPtp41VXcnjhKQBC3asrW/ME/FztzxAMtBLMThOSTQLBi8cIyNk5sos0qxZSgt1ZXrS6X912H1e33tv096OMccqZppQ0kNyWwh2uAPtAR3/T3wxs38a1He8jWellllMczbxAsrfmUE1ssNFlmUf+4isNv/VRW0/OTDV2OANID1k4jkOyXxjjMb2FLk9Ng6w4LoK6peLqA/VvNLSvX883siaHu3fXfV4tFHm+kuR0YZSgJua+SlWcp+lxSLjvls91vmWpSsFqfh2XwunCw1TiorxDkzRMxyCmi+SDb9HcmJ2dp5QT+7ZtG6/mIxdpZFpmKGLYFR4a+wxavJ5p09LXj+M46FWxBqlKiKoi3p3R/HGKpSZljzp17MBiVMGreCgV6pML1ssFLk3eyYZ7mjfxWC0MV2ykch7DZ93GNXhu8GCr4n3IrqJ8ZTUo3Xsfv2oe5/C2KrvecXfTMfbEYTKZfyZ37MC5z6KBVmzHoWejsPGSXZ1YjoWOxpncEcYKqw+wOgFxH/3q6ysVXA4mNrYb9NFUBdM2CEXdjnKWgirVPyMefx+h+PJM+JyUp2hXyRTKHMo22pVapbbmhtpamCu+TNkoo8oqc/HoD3hGzdHZ9mZ2tt+O5Vh07lja/lR8fq4OaUyfqOlO5uZTBOwRpJVEm4PimSzhdhsMd3Nl65tQqs0bVOSzOSHm7mo69ag99HsEk3h6SCR+DMXkqjuFiHm2Unv/SpMZBrWtDD/7MroWomQVqARKfG36JcouY6k0ViGuRwHQp2RkPOCsHLCbWytsqlDi4nXDMtyS2Kpbcn/o9Cs8N/MNnj35GcKvNDL5/bKYd5K9F1aipHs8XPqJ99L/+zc0LSXOyRKW6iE1Pkt35y4kTQSUHLtKYFFTkI4X4gS+2Zjomx8Rdl82tbouaaZjo2huObgqU5Aa/aXI4Y/RckiI35vjQ0wWzxBtXbnjXOI2cexrztMZVcvTJO3mflnKTdTKvD6NOX+sja7AeqqFKtOlMjcFTbIBDyVXMqVoywQsCZ++fCDQoYNbu96HM9jobymanyLNj7+ilMlUZzGqzW10n10Bx0swINGxqZ+vn/4EhflHKFphHHN1idt/D/hJQOkNhC//yh9z5G9rBngHMdq8/XVdhtZduo90WXQPM+3mzA1NEwvryex+np195NznplXA6wmxI3YdraGVtScWKtOMF0+tOO4sIu3CMJEkm5hHTPDJ/pUnsK2eAa5vvQejUmGoNMMRs5PZbUs7XHrrb6Cobz7397AxwasLzxJOrL7bwzHtOc6kHmBhsocn559k3+82d7aWQiCWoGXTrSCtJ+DIKOeJEm6IXsOt3R/ANE00SSdyAU2dWteIQEzObJzcSnKloSwqPTOLrHiY1hrvqWdNgscLOoorLj0+NMjuxA1EgvXGSM5OoUlCDPccbGFAVIz6RSJih9gVv4GW/kYBwcQ9vRxaEIE0uWfljlxeLYAlBanII9z1+416TWcqNuPelYWYQ5UChlNF93rwJu+ge0Mt23Bz+11cGr0UQ6tdtzWbGtlTh9LTnKlsZCDcxRY34FZ0bPYFVOZSKRKFQ0y9+mnaWpd/dySrSnCRsG3A7cQY71mz7O/yRoRJo0ktfFEYlN4eEd0YmxX3aGtsF5sitWxZ1c4hSQaVqjCynJ6VReoBgkEPR9IvYhfzPPe975D55BGe/ZMv1QYcPMA9Pf8Jf7ax/rxNSnBXoo+2gnj3HPcaR5Orexcrtn1O98ns0endtZPvzTzLyXSV71dHmVvEiiroOqlAfelotVxmrPAa2ZDo9qV6i0iSxODXnmH+5ATJcJKqvbpgbudaERCJKhJzI+N4H/1T/MX6sgJZUdgWuxqAYGDlue0spirjpKoL+KsG5WVE3V8Pnhr9LMfGv3bub8MthTp/eVDl5po617XdwebkZeydkblJ3kh+epqxhdMczJ5EXaLkTfXo4ITQqkFOhMM8r9Y7sjf9wp28knmW+cFnG36rKbWAkt7EOE/N5OkLbmO8NEzHHhFsWasOsDN6+bkxMUe8Ez1rVlfW+UaELMts5CC9w8/UfT43McEVvjIJPYQmeyhlcgQQ62F7dz8VV3MuNyfWWE32Ykqr184BqISPkncDCrKqYToWwZjYrrLIQJ750/2c/L0HATjy2inGKuMstDQG+C3bRpM9RD2tBM6bH3WPB9OuoLltrjXHRpXEM3pg+gHmC43NJOxqiZJVCxCYrhDxge9+r3YOhSLrAp1kGOTSt913Qed/PnwlYVPpdmMZUf89d3BY2s9Lc99lZmH1zTWWw7tv3sX/+b2f5U3vvX3JMesjndza9W7a7UXzTKmI4VjnNC5lRQRrbFVlsFJlzGhkKS8FySOemQ3B3SuMvHCkq/MULXcCsiyKi/TcdKsodGYWBQd1bKordLhKp17hwZFP0hKMszlY77r4f6GT+O/UOtz5/H7u6nk/vYaYH0KVi9NV83yYHnEcRTO9bDtzb4t49nVq71Zqepqr2t5MMry8vuaum29m8tIU1/yv9wHQc+VOpkp/ir62OdskP5sW+wwIOyDSeiOtHUIOIZ0tY9oGJZclqbwjQtsv7zr3W9vfyRWt95KftylXM8xW59CvuJX14V2My+K99qqLup5KSaBMxVmZGXfHfXv4l8E/48DI/11x7GrRfs1lfD1tYPaLYF62mGXWSLO29x2E4+9vGB90L39H5+olKM5C03V8TfS6AMq6gyqrSD19fGHo40xuiwJg2Racx9pR5Mbgm+WLkCov4ERXl6SynTw+xP3XkdGsxus/u/4/kN4m7OA27ww9hz7G5e94y4rb3nbPzdh3+tj23++p+3wsoGM20bsDmB4XAaW0+vpsm/6tIlAU99gYpRLz5UEkDXKyWOfn82l6wnvZ2XLLcpuhd20/AMFy/X0yqlV02bdkt0Jl4SWem/0GpXTz6oM26Rie73yUnvUDtPavozu8FUNayyupb5PxDF3Amf544ycBpTcQtupbWOvUhP38eClbjUGjMfXs5NE8Mhpvj+M4DvuSt7M1UssGlCWbgB5lc/RykpGVDfGYp40u//Jd2hZjxp0nxzImY4MnOJ07QLG6coe4iKPR7uvjzHMvs+ZYmjtDVfYElw5G2GYB76Ka4bRd4kTuyJLjm2Hzbddxb+8HuCqc4Ppfvo9o/MJFcg3bQNN8HFx4hOHc4brvPN4wMc3DzMgMiuRQMldfw69qGqf6zqD9VGM03rDm8EhSXSR9ZkhQrqvBxmtdmMmzzzPL0H5Bq04dPsNXR/+RYrWeep1jlpnyCFODNYaM7mpbJJ16x7lQzPOtVJl1+/Y27G/HNbewMSL0g1paVw4q7P7w3Uz2ZLnjL3636fcvVI5xQGpk7ZyPcFnCcUvjTN2P5q1R6r1qGEly0ILCgDasKlqT4ONAtIVrEwmCeoyqKy6o6SYPjv8jqlHB1K5g4JL/jt3EIFgMQw5R0WtsjdlAFIBK9/LvXGcwzK0tUarnZUlCuoLjOGy6VgQyPLLIePUHN9LhirMCnPZ+lxN8kex0WoxrW7ljCEDXpVdxil1U23Zx6POvIiNhztQMAyubYao0SDjcGOTVIkE8ig/HFkamTRXbsdD9qwvizBWEg2Y5FnveeS8ev59d0WtYH95NIT1JeazWvaRqV7Hs+iXrwEPf476+/0zQEeL1LXvWUraKLEzN4ymIexj0r44NlGgX1ytYXuDww0/Rdf0fYxVfn8jk+Ti2cJCxhYP41QSW/fq6US2FnaFr2B6sOVOtbqljZ6wWrCnbaSSaM9Z02UOFCvNuU4JTr51mlCwnUl8nObB0Z8IXU/t5LX+Mt5av4Fb5trrvPD4PZeMAITQe/au/qd+f4sVwM52tM40O/AtfOY4mq2hvqQkFO7IXdVGZy5xR4MzEcbwD/7bFMHv9a+mV6o30bN6h1RPCccvAzxw/ykzQ5kjJItLXRVtPB4dzQxQcB9Mw0GUPaujCSjh6K++le1KUNDieCKbsYdDVqjs1UdMJ8ip+4p42jj3zHAe/9gX2T36BuZkXGrZX8Yg56/KWuwm0NTprhqRRDPVQLOSZroySNwQ7alfn21mbvKdh/PH0FE/P1uZ928wzXjhJ6rVaguu7f/hpIEJl6+svdTuLnOskZwPNg653/+lvEtzcwq2//oEfeF8AqiLTFl6e+V1a1w/AXDh67jOpWsU4r1OR6VSRZZV7u27nktjqW1cP7BW2Yc5YXRffC8HB+eMMumXQB+ee5zvjNYH+E9oQz818g5n5GjNGl8NYzvLzYkDv4crkW4h4AoS1+nUgvn4twY4anTScSAhWnFXlVPYVypmjF+O0GiC3iiRR9ryuYecjsEawr8p27Z57dGGjRNaszMDe95Y34XUTGKFkktPZDYyONtermR0f5Lm8yYzbzdGQMkguW1nxWtw//HHCroZkx+4dJNbWbPy2y9fz7bG/w0lmOTD/fQ7MPYU/aLPVp2CcEO+eL1ukZOaZNeZJLBSRJbCllbVzQm2dXHf33dz8e3+w4tjVQgvHGeAVUqfFs7Z3XueerneTco5yJv8U+Uy9FmvKbfLh7724rDxVyqFJOnNDE2wJX4LiasBZkoIki/lpsSD7yedeqvt9rCuG9tBH6Nq6uuY9VcfGNMV6OFcZIqs3as5amoykCvulYN3FwuV/RSC5Opuw99q9+GL1idysU8Arexk60ciCVNs0Hhj5S/RNr6/kbWxSzAWnXjqCNDvFc7PfIOTYhCnzfN4kVMozPPMtnh/752W307XelYCo1rP+SukMsiSR6G/uM+veS7ij6/1Mnhpu+v2wtpPyHX9ARVZQdY1tiZvZ23IHd7XfTGDPxqa/+feInwSU3kDIGQtE9Ni5YIFX8TUNKBmd4qWoLNHet3PTRnIPvJ+h/HHkRbXjRxf288LstwHIW+mLfPTQ1iIWqUilyvTEOPvnHqLQ5PjPR0ETE+3o4SOkZww0JYBvmQCP7Shoci3IcU3gMm7t/NkLOtZLbrqBV41nyOyap3vX9gv67VlYkoauJRgvnGTaV+8Y5Q2xwJ1+7lkKRpqstHptA4Drf+m9rLu0sQVnwSmjyhrjr9UMpPLsDLZjEYo0GsOy4+CTFSqu0OrcvMW65DsJ9tfTjuPX+tkY/HXURSWKZwNKYS1OZqYWuZd8PjqV0abUX0XT0N2MjKatrNPV0dvJHR/+qSW/v9Mb574mXd6e/Pg/8fwH/++5tvWGp4+qJBbYdPQVvjv+t5QKOZGZUHyUJZWtP38Xpm1wJn8AX0uj0yOFHMKKQkDxY7oaD2E9SDL603jKEorjIeLxoXqXd2BOFI4zVKk5Ph6X2eNRln8GFpxRjqaf4qUv19f+Hy3P8mz+OTwBcX7hjgi2Y6PJOpFyzZCd91xHwb6Vhf2PAVCZbmSHNIMSD3FPRCWULXNjYheSJKEuYvUUzSwvzj1Iz9ZGjTE5Id5D2xVZNbLP8cro51e1X4By9WUmi2fI2pDsFsEBv1Jlrc/P3a2X0mPXniHHtvE49UuWcVSU+oTj4hnZePf1PDD9IIfsOUru9ZLXrsyUA8G6+ZfBj/Nk5hR4yoxXhlG7Gp/x/E1eJgYuzAnbFt3NbV1vR1O9GMqFsUlWQiAygD/Uf+7vExocSz/PVKxWMmtL5jmdmMWYHx9HlTWkao5yRNzLXM5gy7o3c0/frxFcRj+vIxrhmsRWpuZeYK74WsP3m9/5i1zb/nacY/Xne2z2JZ6fe4KHxr7AM5P1FP8zRw5y5sn/wzOlA1xyfY19Vwp4UBa1SUxZHk7624j1rK5hxBsVgXKKNrNe78E/I5IPRVsEdbMjcwwxzrHcfvzhENHWBGfMDtRqmclTJ5EliVxpeV2382HZDppbSiy/8gfk9n+EkC6eD39ROJ/lYu24Rj//VfrSHdzW82F2b9nZsD27p8K3MgbfzxkMXHZpw/dVCngsi+mpcZ6cfYRXfO45KgZVqVHvYo+ismeRBlx4TZKnZr5CKZs+91lXsZ28kWXrW++8oHNvhrLL+sovI4p702/8Cn1N9FN+WLDchKG1UAvuZ8a/wqnBf6of50gECAuxZc/qyy/aNqzhCKeQ7lpdefKFYG37Pja1X41RrbLNt4ZtgZrwdDZoMq9EyC/UnllN8mHbywfvg5qfhG8NuubFXsG00D0eLMfAkG1emv8uU54LKwldLcpVcQ7DmeW7Jls+kcAqT9bY3jMnRFC1lL6wtSQcS3BF6710p5snqFLjo1Syj1GuCKfacAwU19UzHJ3drW8j0NX8nifaO4jqbVTHStwVu5fdwU42rmvn/qFPUBoUgeSAE6Vg5ilUR4lrMRRJxWyi4dMMl7z7/XTtunglb20tIbZFL6UtI577+awPx3FwNlUYzx/iua9/rW58vnKcV+YfIdZEe/YHQaWEKDM8cJgd8esIjohrr1VL+F35gMFXXsV2bA5mB3nykafrfp86+BqlW/8bqZOr6yK5UJEpGyI4+VxF5hlvIwOvGHuMvP1/AIgbMlyAvloz6JvifC+d4bW/+peG77KFGPHINXi6Xt/8eLYZiLpQQnKS3Nn9i1jeKE4pQ674IqWZGfyhVqLe5RP//miIqlXGPk+j7eAjQlJlPNucQSW3rSWsx7DV5qxznx2gxdNOMC78hqpjo8squuLn8nt/8PXnxwU/CSi9gVB2cngVPwe+LcRHvWqUst6YtbnxvW/DtA1Sxcmm29G9XoZueA9jhWOMSrUxiqbT4hUGeCy88oQ63ZdjsnX1hmprTzembeApV+hjDff0/Cd6kivrGsV2CmPDr7di6Z04jkPn3qUnJgutTpRxPDvDaHZ1LdIX4+6PfYRr3vv6OsMAGGaVpBTgzX0fYs/G+uPV3OCOlS7y4Pj/pRS/OO2GAx6x3ezR2vmeHjzD19MWw9VGFlSir4PHciY+VUyw5eEptvsUVH99GVtmOsjpyuc4/oQQKs9nsjw2dT9HsseQJYmXvlgLcrRrrfT7lmYfTXWMM1a5OGK5Eiqy1Mgw6Zvuoiu0kYlXxX4M3SSniMVClTZwded7mDwzTG5uHlmSifTJtKzpQ5U1Nkb2EWttzNRErqqViEllUTpQ1gPsC6gULC+ym8EPrSDoOFrYz0yx1mEkOiWMi1Z1eWZTy6YEx4sZZg+erPs8nO3FKtUYT2NdQb409GfkjDTTi7rzbF9QuFrvR6uM8erEt0kOrI7W3RKPcDTzLN1lHU3xYtoWQbl2f6Oxbdzd86u0bG4soYu7bWV1r3BOo1WVrvQFsHo23kGHfwDDrpVq5MxadkldWzOa51KnmZ8frft5riCCJu37RNmGputsjq7jxuo6qoYwcpW2lUsmz2JT4m66YztItGtcF/9NNt/UaPxuumUfl77/Ao2IgERIjeCRHBbsi9Mp6izGrU8zWvrEub+HVI1D+XGyLbU53vIEUOVGYylzWqwPHkUl0S3G+4wSoakxnCX0Es7CH9Bo8XZxuLKfcqUxeLntput5rvp1gtrfUSoIp8moVlk3OUGgMkZY9+Ho9evQ0b/4FhHNj9NdX7pj2QayJFF0W9r3evq5KRwgkrjw0oU3EmbDXrLh+rmoNCkC0I7riPhmilxZ7ODe5C4UVUVRFO6MKMT1GAuTs7ww+yBZc+jCdix70TUxj6W672S292aSPhFE9ufFtZ85LOahIzOPc2b6AJYaIqxobL29sZvotn2XcVdEY7OWJtokWF8oTGBlp3GmC9zS9QvsCu4CoOPI39Lz3F82jPeEB9DDNae39bJ93Nr9YaSAsF8MwyLpaSdbncAX+MH1yM6Yszww/SJ+6+KzdV4v2lwnPTpTmy8i+joScrRu3IlcgdGSeFbSamNwbjnc9r9+js03XfwgWSGcRjFT/NMHf5N+3xrW6rV7ebMZ467EtYQyNcakKpkY9vIOb8h/ksFnPkLFUqg4K+t6WpJKOLCBe3s+yKa1W1cc/3rgzYtj7vQurx3T4zIpw0YtwTpzSjDw8gsrJ10XQ9d1TAcCSnO2ib8S5rrOm+mOiwSQbRrnuupZBZu1/gH0UHPHOdHVz5Wt99JR3UEg0ks4FKZ9TR99we2EFcEACyoBjOoMtjJPqjKFR/bgNBHq/1Eg0ZbkG3MZSkXx3lqKl7JVYOP1b+ee3t+Al+oDfV3TJ0m89vBFP46sucDj6Tl8LqM54hHP5whTDLvstfRMmS8N/RlBKUd8vn7OsiwDWQZWqR1/OnuaocIY1WKJN8U3clm+CePKvpag96MABNUolVU0t1kOt773XmJmgaC1ro5tBaCcmOeayE7kCwyOnsXGnVsA6PVqEIoS0uIEujsxDImbW6+gnCnTkryDNW1vWnFbBioxp34NWjg8SMUqUa02nx/lWD8AjhNt+r2vImE7FkG3EZFhCUaYlBsiFFm9ffnjjp8ElN5AKCXEpDz13DHmhifRJAlPsJFCGEwmeT79IEP64JLbGgjs5Oq2N9Gu1DK4idha1oREJ6NYd+dSPz2HS37pTvb9+ptXffyhri7uH/44LxUmkBwVvxoi3LPyfiIdIupcHpvH29JKTrIIhhsFqc/CsPPIi8TVRouPM1R8ZsnxPyxU7TT6Wf2XaP2kokbFglGYWOCKlnuR0xeHYqskfBStCqnpWsZNy8bZ7DFIdGxpGK9rVS6PdOAsCMMnmRWLW29f/fW1SjKWMUd2RDCtJgaHiUTeQdZf4EjqabLH9p8bG0TGalKzfRZ7f/WnufwT73vd57gYBg6S3Gg8ypIItO5/WQRuzEqKoiGOPVSM0uXpIDcyzfAhUQqZccUCT22NcUJrHvDYccsNVNygUcnVc4isEQFRPdFGIdKG7ThNmVmLcWXLm7kyWVv4BguzPD35ME50ec2dXe98G/d23o2u1NOeN0SSbAzVLI2BDZeyq/1XCGlRStQMuYnUN3l49O/wtPpYY7/E2itX100lFAlyMvMqUT3BaWmOdDWNLNcM5IQtoSsqfn/j8esR4ZQWq+I4lOj1RLa+fVX7BfB6C5TMPJO52vtrh2vn6mupBe+OaSVOaPUGgV3MYDkmfdfWWIZxx0NHcADftHBYWtySw9VgXXCAHv8AU9+a4VThftKj6VX/djm09vmRJJnnpu4nXVgdc2y10MpXEHHuOvf3vukE93Xdx3qrFhS0i1nMJh03Z/IuU6Q7QiUj5oa5I8eRK1adKHIz9CjiHXlr/4fxxa9tOqZj90Yq1Xfy/D8LTa5TL75I296fJxi/nCtb38S6RSXV4ydOsjG0h02RK3nHb/5W3XZst+HA9KBY8xxJx3T+7YthlsoldKs+aeS42doJZZIHF+Y4MfsapuXBWnS+h9PPMGWOUtKDjObnUXesrsPpWRSk4rmSuv7oPjrCe5iIBPmXwT/l1aJ4Jk4f28/jU/9CVi5yY9+v0mXFKJjZhlIIgN69gk3b7m2+zr2YPsGB/CQLp4eJqTIetwvPzMB7KV/RWAIjqV4qixh167ZtoYKCkhVBlsdeeoVvOLMcu2x5XbrVwq9GeFPbPkrGyqzaHxUqEbdrnlO73uGuG9G6bqwbN+ocoWCLBFNUv7jMi9eLOz/6s7wy9xiyGUIP9TOv1+aiCVeTcehkLel0cOFxzhSXL22fVtoJ7vl5IooHtbKyVotllQnZMj41iLRCmfrrxdb33chkcZjIdc27X51Fa08bM6UR8kZNhzDgdQP4kQsrVwXBOpKU5s7xQtrVvvQKu8lGqen2jD3Ml4f+lJ4l9C0jiQSG7RBShU0+5rJCN0YuodO/hvnULMPGSWZD82TWR3hs6gvcP/wJFqaXL0X6YaLPb0HC7XgW7CCjOPRvXk/OrJCg/trqbT9Hy5V/dNGPQW6P4dMSlM92yu0S9/ZA8TCHMoLZNZXKsi16DT2BTXSfd1wdl8fo9f0aA9evjr3VFfGyKZJkbnwKXfac6/64GMGcn041xpkjp/AoHvL6D/YOKKqK1z9Opy/Mwx/7q7rv7PlHePDMp+joeH0lb/R380RpP097c3R3i2DQwIYBFJ/Mk9P3Y1klvNUqsrVywDxbyVKq1pf3F9OzfG3kU7Rsas7qK4+7Hb8PNrfNHEeqW39Nt3nDUBMx9H/P+ElA6Q2Ene8UwtBJfYBXvv8gT0x9ieFc825s7/jbP+N9n/qfS26r8NTvA+BZVHJW8RicKIha1czS8ZrXDU3T2NX6QQYClyO5C1Jy48o1weEuMbmacxmCk+M4RqOuxmKUzRQlq9a17M7ud7O+pbE87IeNaSt/bmJJbKjXmjIQi7q8UCXmaUdfZaeplTDTZ/KNkU9yMF0redPm82z0B4j6G/ehBHQ6NBnHzShU3S4JnTvry/y6dg/w2NQXMHUxbuyp/VwWVAlqCscWnqSSqTHVVCVIxfrRTKSSXERX6rN/i/WjjJNCu2r/9MMcnHoSAEcX16G8AFMviYBSwdUVuv7d27jxD69uui9N00g74n0pSuJf2RL30ZmbwNEDq+phMV4oMlaoLWh2sg9C1xPrW748JxyPkbfn6CjX08c9ioecXNveQHcnG13DpXWRGGbMm2S7tJNqZhOhrt8l0rZ6weI39/0nZEmm7eoARSuNvqh9czBXwF5CCy3klohoVeGIjc49xAvjX1j1fjfu28m3hv6CjitqBnlwfS2b3RKsUZz3Bddxfaz+uU3aMQpWBZ+vZsjMyAUUWSNVmOG5mW8QW7d0O+fzYVhFzGoB3VTxKh46NqxO2HwljE2IjNaAvIN49OKyapJ6H92BWh2/lZtAlSSkRQLDU7mH+Mbox6mW6w2t9NgwGTNHWc7ijUeYMWwoOXgdA9ta3mmbWMSe9XY3DyTIcoyB4OUUD4rzz4+c4ompL5O1xfxlTtUYZ69+/ht4FT/TaiPbVFXmmEmNYeXTAAStANYqOgu90RGszOOlPkDd0RIFILA+SdJ8EUsLgOLBcmrB40knjhWIMHV6kreu+Vkc+QIX9GoOzTWQrSf/C/bgX9PRM8C2tg/S4RXPklEpIUm9GC0yM+UxvIqfgtl8HlBXKG/e0LmXXW1XMzMtShzTnO345iGsN7JdVUnBXNSlUvd6eHLiM0yOC+ds5mvfxDP9KEG9SYe41wG9w8Z2bPqv3nZRtncxEOrqImM5mNTW9Ufnv8JzRr3Tc1Woh+sSosxQu8Bufz8saLrO2//od9kTE933JKXmjJXcTsaFlFvSmcmSczaTbcLGXwzJE6UvuBWv4kNagc0EUKhMw1kn0LO6TsUXio5tm9j3qZ9l+08179R3FprPw/env8KhUq1iIJvPMVo4jhG48CBm1crBEgxSpZgGQPcJW246qDFcnKBUqaIba9kZuY5Yx9Lac1W7SEgVc1JonQiMWNU0YeDAK/s5Xt3B6S4/N7zrfVzd/m7eseYjhCI/evv7LHo1aDfF+p8sg2QKmy1VHiKmB0gvYvgNmk9xOPe9ptv5QWCZFpcHVUxD2E12UiSUrw7dwG1togrCHB9lc/RySrZNVG2r10E96jBofIbMzOoYPhUkirbNzIxYV4tNHiFTAlmSGfye6BJpBH/wUvtt77+db49/g9Rr9ZpNlhUjGdpAtHf1ttZixFtbKc4fxJwokHlN2ATF/Dx6yE/JzGEhOuZZq5BQeWb2q7yY+mbdZ5KeZHf8Jrqvurzpb7yOWGvUbHOtW8sbquvA7nGT3DnPD8b6+nHDTwJKbyD0bN7A0UqOE7ks2fkqhiVjxV9f5Ke8WUxiPnnRC1IsY1sLvJZ+gfb+1Ym/XSj6vD7a1QBOuBPTcQjFoiv/Zvt2Xh76Eun8SXQpSKlJd7PFeN56he9PiTresZdfQZN1nAvUKLoYeCmUYbBSxrEMNu6td3S7t/Ty8onPQuZl+NZHCGy+OFofmzfsZG/ydvpyi4zwqlhAvU0Y0H27hN5FNCIW3KreTtVxiHTW19DryTg3dLwLsq6I39Q43x3/ByxPgWu638u6lrcCcPL4OH41RI4fDcXZkWRUqV608vlnv8/9kw/x1eFPkZkUjIW9ietZExSd23JxsbrOGGVkR+WB6f34OleXBSwUhzHsKq2OYMxUUsIAtGfThOcnsJ2VA2mD5QMMFp489/caM8mVAYVIbOVAQiU/TFhpPddtb+zVI/iUAPlFOiORtojo1GJVOGXUnC4tuIe+vmvJzzzPC7PfWNHJW4y8kSFVnmTL3bdznGFemq/RwstanNISDQCi8RizD/8e1YoI7Pl8MSKsXnR64Nqb+bUvf5s9P/ehc59JsZrhE+yoXTPTMVGk+m3rUpCqUX9PYgPiefHLMnOVKRIdqxOiBNE5U3UcglIM07bo2tDYDfD1QHV9wq72rRR8m5cffIFwHLvuulTOPhP+2v0vDtzE7tZ7mZmvnyc908d4aPT/0OrJkOzt5Im5xylmZ9HlGJK1fOmif6CW5U5sbC7evfveOzFtg6Al1jGrVGJ9ao72ze04joO0yKj26iJ46OtvzPaHvOPkXvnf57ppaliYdnOR8X9LmLVTjMyMUFrUXfPk0CAvzZ+hfdMO9rXcQSKyk4Bq4Di1a3VNdBNrGUAf28+huYeJqxcWSCgbeYpucGhk3y9ycu09tEWibPEHaHODwz3Rbq5tu5y9117P0Yzo3LmceI3pTOM06ZIGELEcWiWVoNtsI9QjHA9VjaFJOka5/h1WZQ3lvPKtvfHr2JG4Rhxbto1bOt/FzdfVi8G/Xtz1O/+B3j+5jl03X3NRtncx0NLZysOzL5DN14Ku1bYrKA3UN8MwXJF/x3HIaqvv8vbDRsvAGqr3CsdrLlqbS9rWCSZ1a5sI1ldTC9zc2s/VW5YX2E8gbJiDqSd4sXJgxf0/nTrCqQVRttm7dunmAj8qXJa8nb1KjUk4lRri2flnSHFh+mcAtiPhk5onKf1eca3b1orreSqe5pDRQTabIhzZyZropSjLlN9XrCK624mz/2rBcjY1C9WbRP7+OHdHZPaYbQRjMQJKFADJ+8PxJ1YDuZolYgqboWSVyZWFz5PVF1BljRc+++VzY0ewOW5enCD0YthGiu+O/yPeOTGnJqrindTx4VMEszuYzvP1tMlMcRivLHPqoZqNqE9VaVNDWKnV+TGzRY1TxSiUhQuvRBq1hfJu6bE1JGzYWOfqu9Iuhd7t2+hLbmdXx02cPlaTdUj49nBJ8jYibStXpDSD3x/gps73sFXZjVwRc1gwEqUaS3Jr1/uw/G1Ish9HWpkBtcPfx/Wxesa0R+2mO7iN7rXNJVjiUZEQC0vN/W3F34sl1RI/ebfJ0sYbLowZ/OOOnwSU3mA4MvVlstPfp8dq4aaut3LZlbe+ru0k4sIRKnoXTSK6j03hTQRPH6C7/4fTbrlq56jIBdTcaJ0uykoIZcr4rRiPTn6ZV+eeWHbsJm8rlyeFITn1nGBwBTw/eurhrekS631BinYRRal3dLt27ybR4aea2Ebxxv9GPn1h2gZLYe2WbawJbidSqd1Xn09Mhme7tixGpE04Z0VDjO/Mp7HNRueju6+XVm8PkZIwJIq+BD59DdHNmzhtHuRU5jks0yT7lSdwJInEu6+7KOezEgwjiHzeJJ968jgDus7uxE3sabkBgN7gBqKuQ6rYaTFweoS5xCYGwrsJXnbDqvY3Xz1AxSoyrgnGWZvbsSgsVTmZPcBjk/evuI3dsY3c3FbTGTGKBUzHWNaIO7d/D2iyh5e+JsTzT/zD95AlhVCk/vk27Cp5Y4ZBZRETShdBPqtqkcs371axFDo/egUD/120sC52RSiZGWamRAmUDz+OvTQ3a/SSqym4Yu7R1rvp7X7HBe37fOy89UZmyqNYtlnXitm2rHNaEACVYpFHp7/Kcwv1mgh7fvZtWI7FrsT13NL5LiRp9ZpOlhREUROg+DFWQ0dbJdbuEhoeJzL70dourii3hYwi1Rz9sCxKZFq6ag6az9tGj28j2YV6g7WcjXF98t3IkW68rXGuj21At0IYaoCivLzxtueemo5UfAkqeSAcomCaBDXx/eSsRseWD1Bo34DpGOiL7qdXE+95x9ZGvZPZUg9d1/8PTh0QZTGObK6qVfUbHYOKyjElzciJRR3NMhVSlWdoXSuCLtLpCWRC2IvMNctcQKumMSbHGSweJxG7sMTToexLPD71FQC2BjfRKkXQWsUakXKTUGdeFY0FUoUC624R7aYzSqHJ1gS6//BNdP6Pu5p+V65kUSWJsiSCu2qXCKpOx3UkSWJiun5NUiUPRaOeuRVQoiQ0sdb51QjZ6gLhJh3lflzgjwS4LBTDr9few7dLG7m0XkYOx1KpWgb/MvSnJHa9MUrezmLP9dcQ+Z093PJfau3Kc24DhQl3TTn+vCinH51t7FS1GIYq1kBN1nGsldfSHdEr2JQUCTWt81+/G2RESxDRao7/muQ2bu96H+v7d13wtjKVMulq83UtNSWCGnk3MXvzdI47AmWMwTH8koq5QlLsbGt1x3GIrxGBuBldRZP9lLJVFEkhtlvM0ePqHKZdZSr09JLb+2HDcSRUJUY5m+Wxyf/HYE48T2vuvRIAf6F2ze/w3sXtobde9GPwd+8mHPlp8kaFo+nnICjsoSI5ZEkk48ySzEaPiZUUvsDZzssAurt+x+KrS361exyuiOpUZ8Rk4JMbkyuaywI9ouV4YHo/a+66OCwy3xVJoorCic/V/DSP14/p2Oie11nyBpiOhCr7CGPiOA7Rjna61omATbRjA44WxtBXXue8yXUkw5vIZGr2wfxslcdnl658GZcn+O7sy5zOHG/6/dDUE7yQqnXyflge5YHZM/Tc1NiA4t8zfhJQeoNhW2wrb+r5BUqjIoru73p9pVKSe2urrbWFNLRbsGh6L/lFPL4fDgXYcmw8kgevpWEt0YWuGeKb7qY7uY8N4W2E9OVFzlqNIH3BLeTnU8zNCsMiN3BxmAQXgqqbMcs1KQMIJVrpCb6NHmUzbeEupMLyxtJqoXl1ylYRlVpASXXAsK2mHZk8fh+WbeHN2hjVKicLwwymG7N70UQcwzYJW+J5k5ROrmm5ltiOXUTtIfyTQzz86U9z4OgpThlPsvuyi8uyWAoH0s/y7bG/q/ssORNmd+JGio5B2pgmNTEhOo24HQ2TrpMamK/iGRtlu0/BH15dUMG7rhOfGsL2CMqyv7WFhdRJLKcMXhoCh82g2OU6xoiCVleushxCN4iSi7IjMvl+q52KVWHvr7yrbpwse2nx9rApVCt1qqrCcNnV/iZ2db5tVfs7i3AiQdDVcuidMrm9++c5/h3RGSNkV/FWl2ZAbAlfTRLx/vkqBRxzZX2L5eANBHht8iFeHv5i3ecS9jntLIDBxx/nkthWWvz19ySQSJA10gCr7j5zFgXdQVIUJMmitIxO2IUiunc3j6VOMlMeoa3zBxcRXgxLDSAvytxZrkB6pKPGIOpLPcTwE/8Z31y9TokpJQl4W/D3b6Rj/XpsKQx6gEcnP8MLuW8vu19vNMJcZR7Hcehev/T8m7dKBDVxzsEpQRFvb2/HtKuwKKCUnhCZVMvX6PBICZXJ4ghKQIyv2iXybyAB5deLfl8fd7ZdRu5kzdhdF9zKde1vp7WrDdu2CFay2FYQY1HnRRMTVB2Ps5l7ez+IHFtew+V8rPMmuaLlNmzbRpU8BB2VaCKJ7dj4bRHI0TJut9mgl+vfcx8jl5vs+/03L7lNVddQtOaOvuOYqLJO6YzoBljJDgEgufPK7KJ2zdVSmcNlwaJaDM2cwuuW4kb0+Ll3/McVuteLTwmd07/JzcyJTodmfVmGZo2jKxqXJu/Go0f/FY50eYQigbp1s1wYJ29kKI6KstfCoChxtUrLJ92U9UJkenP0crb7Vu7MG9ElNFkVgZGu1bNUf1iwHB2PWgtuVItlwoqEErhwJ/xI9kVeTT/V9DulopE2LSIJsa+iKaFrIeZHx7GNCpUV1rVZOc9gYYTHpr5CMOh2SqWCJilEiGPaFuuuEnaKoYj3Olx+fZ2SLwZysgqaj8mXD3B161uIBMW93njN5WSMHPl8zfZWJLBW0AZ8PZCcMpcHVRwpylEjjrfPTbDIJjLC9vTZXjb6vGx8z63Yjo2Uq7nfWbe5itKxunncS4GIFiA7KZJ+VbMx0B9w2U6XGhG62mTaL1JH1Fvf+VZS5Ul6CjU/zawUqNpLJxtWA8EOL3OikueZmXFUTcPnss28I2dYKJwgnW8e8FmM4ZgHSZJ4+dlD5z7r89l06ksHlLSATtAZQ1mCia9VpnAqNZmRX/wvv8Dlv7wBr//i2nL/1vGTgNIbDNKAEG+Nam0YdpVk/+vMrLhR8djmGsUvrqQBmC5eGHvhgnbrePESwXPybym/+vFV/+61/Dc5kv42O+LXEg0s38a26DJR5sZGiVbFI7z1hh99pLjoOkPHaQwWqZrGI+P/yHBWBG8S/St3u1stpGqJ5CIRPrtcWXYyN+wyilPl2e8/yaS0jaFQ87GGI6HIIqDUMTOL4zis6e/A2nIfu3d+mMjJJDJjmNt+dFnhkB5hXajeWJGlKCXLYiFZ5mjFy5kXhU4SAXFNzKTKs3PPMFOdITfyPF8Z/iRrelfXOv7mD/8mL4UOcctHPwiAEg3yhNzPoGGzPbSZK5K3r7gN1SjVBZRUST2ntbUS9t1+A3mrQObAQYxSETmzwGRhFP95QrgWIlCiyjUj3HQ7nfjVINUVOnQth3CrxdSx7+B3HZcH0t/h2/bSJQaPDv01UxNfFcdjmzj2D87Gu6L3Z9i3pr4Do6OEUBfRjounJhgI7WT9wLrzf84JZ5a85WDZqy+/A5hPP83IwhPokkrFuXglVfG2FnYHY1zd9hYiGy6uTosT8KFINTpVwSliOza+SC2bN2PE0S55L8NH6xs5ZPKHeer4H7Nm+wa8gQDPlGXGk31si15OzNe/4r6r1gJla3lDsqDM4pMVZofHUc0gtmMzsGs748VTpI2Jc+OyhTFeTX2f7g0bG7bRf+MWrmn5Tdq2CEr9y6OfZ+wCtLreqDACYl6Ymx4595miJLAcFU3XqdolVNvk6el/4YnJmvBtWY9ie1rxOMIZXbNr1wXtN5LYQm9wM3NjE8iShGPbaLqO5UiETfHc+F2G2oZLRYnVlW++gXB0+WTPUnAkC1mSMTOv8vDE50i0ibUzWnE1KE7WbJLZ8THGcy+T1+sDShMeC0ULcOTrj+BTA1jSv/2Sx5XwyNQXmZoXTIapEcFGqHrqnZ6MW2K8JrQV2XnjiIovhbhX41tjf43tlpNHDPEOtLcs75x1r6sxFyV5ZUeu4hRJV1M8nM3j9ftWHP/DhuNYaIvamVcmRDCglLvwrp9b/DFubG1u85arGR7LFmh3y3sqqrhWmUwejxzAspdPrh0b/x6D2Sex5Voyu6AUOTP7EkFZ2F6625gk7pZRtdoXRxz/9cC0UmgSFGcWCKgR1KC414qq8p3xz3J86sVzY2WqVK3lNVpfDxJ+EaTa5otyX3wNnd2C2WXrIVRZwzAM4o6BaRXpXreeZ6a+yMzEI7XjOiv1FV7dc+qRCsiSjJw9zdfPfBxvcKZhzAyTPDn1ZWJSGxvyF7cTWcpYIKAnznV70xwV8wewOQGqVgFFAkUPo3rE9Uy7c0Q2neLQ/BO8llq5oUkyLI5JPVQLKPUEOoku00xn/dY9XNl6L2s6myfGLglt4/LYVef+bmlp45Ldq2t68+8JPwkovcFQ7hXR9YTeik3mdVMIp8oi61Ms1hyIzIIIfExkXvsBj3JpVO0SkuQwsvEaRrauvh2tt0snGovzjbRBZfPy2SSlQyyItuXg5McpVlN0r7s40fcLgVcWBm9siZKajpKHVlUsLE54ZYr2qmHNo6o1htls5iAnF55fcvjTCw/zgjNG+bUQdye6uPQd/7HpuKqVQ3IDkWq5RNUu4fd52Hnf1YKF4O9ma+Q6bvu5D1y8c1kBHf4+dsavopirORcBBRbMOSKBCHcktzH7zBAgsmUA4b52sNagSg5t3i0MxH6G4CodIU3XecvvfuhcK9BgLMn1/jxd/i5Mw19XdrIUSnIQZVHgQ0FeNUNJ96g8NPp5RkZf4Km/+RQnpr6I0naoYdxIQXy2WKRQC4hyB1lSkFl68VwJ8V1rMSJHyLpZr37fABFl6evXEtiA3yeCAIoSwZF+8KzNkR0GT8fqgx9FxaBq2xTctubDGfHelfc2lkip5WmCioRkNRdZXAqluecZSb3IY9P383zq+6/z6JsjootAbM+6i8umHMs8z/65h87pbs2nH+FrQ/XBfFmJsja0k0qq3gkPqB1s6nsrwahgp+3RUvTPztAf2ESSld+Z/K5+htYuP7flwgWOZPbzwiPfRVPCVB3wBv28EDnNsUXTdqGzkzOzQ0QSjSLNYwdmOVW4n9MPC4bVtvY3EYr+2zfobPdUK/ka20pSZKpuFt1wHBytnfWRvfQFa108VTODzyygyV6qdgVf8ML0McZcNuvxwycAyMpu223HouoGq/N6BMOu4o//4GVUpbPdcKoaqqwTbRVrfDpS4uGswWS5JlY8MTzKXW2XonvqyxuMsJh7Bw+I4Ju5/se/XfMliVvodQX3J8dF8DXnqV9Lqp4AWaPAV4Y+Qfv6H70ddKGQNZnLWu5CqwrneQYRZCn1LJ9ItCsTPDcrxHZNZ2WWiSx7iOpxotLkimN/FMjbaZRFXaFCbsCmrePChYyNQAthvYVKuTF5Y+te9nqmCLoMpc6oeG/adB1dDuGwvE+xM9jNjR0/w/rWy2rH3iZzSNuBqpjkzFrwYt9/vJej1cN4/v/2/jxAjrpO/P+fVdXV9zX3nTmSTDJJyA0kQCCAiCCnCCqCuq6yIurKx/X4uR67yq73sauuq79VdxcPdGXXk0MRJRwhhBAICZNAjpnJ3PfRd3d1ff+oYSbD3JmZ7pnk9fiH0F1d9a6e6jpe7/f79bp+fA6fTGmw9fPSwNNEUikebv0JgS2jHUwb/VvZeErBCpuqk5zhvdhseIutc6Ti8IFqw+W37oFSw8HSgZ5+Ut4iksO5R2OpOB73chLDOQQDw3nrgp6ZTV3uGS7KMjSgscy/gbQ+Pkeo4TSIGiE89lx65znnadQVRlftNB2yniVtip3kHANKSZJoqk6Jo4T1w6PM8ldagUp3oZurAzu4zDP+Xu+1KiqsYzHVZOUmS8XiuFQ3gfTkgdvB4cBYR9vEnRQJvZCkenqzhc4mElBaZJbVLicx/JA4OIc/j021enJzTglIlVRar20quWoOLZzaseRujoR2Ua1tpkjdMOPP+Qc3scFxNSts7Rj5U4/KimnWybOvL8HT0WP8uvOXUy6/UJI267v1JicOKJVXXkdl0Lq4mbG5TQM6VauujbmA9CeP0RmaPKAUS8Wwx1N4Thwmno6y7ryJEygmjDD6cHBMsxWTGM5xUFJaxFCq31poU2Z7QHtyBvhN90nCQ9YDz1BvH35bgAhDFG5cg0tV8IetHtqwZv1eKsqq2V5UwnLTS76zjFr3+AfUmXJ5PPj1IAoOHKaOOoMRLx4lhnZKyes4SQaTM+9NX+ZZzgWFl5NqyGHruo9Qeesd45aJ9lq91o5TThHJZcWcCFsPWz2+07+4R20BKkr+HwPHNGKDIbbk7aROm3yKY23uRZQFrJtPTdPANvOcRZO57vZreMvH3zHmtcaSNn7d9FWMlLVv/r4Uhpli3Xlbx31eybcu/i7H7EbTlRTdxNXL/x8rPKsoVE4/KDcVfYqestORjA7Rl3Qz2GPdMDnSNVR5x557fU7rRtUZG/v7zXWvoMhTN5Jnyq+5yMXOb05+h4bo1GW8AS5796VccecVUy7jWRHguLmeofpjOGwaMdO6eTs/cSnbezeOLLciVs6VK98x4Tp0xYdTc6CFbKTTaQpcK/G4sp8XZa5KhqsY+npGb/htxEmkrUBoWzJOn6lQ4V1DseeUioOxbjDiJHwFJJh9Z4U/MlxJr8kaJWF3WsfkkaEnaTGGqwIlEnTFZz96YiKhLav5v9BBAs5V7Cx+C64Ca2qHozIPv/I8pm30vGpENfYNNpJ0je2tXx6w2qj2RmlLdFBx5dIPKE6n0F1D0DecwLzRGlnhT409v7ZvvogXBl+mNnAuOeXZGykyU87CQvId5Si69fdNJnWiRhz7BIHkU/ny/fTGrU7RpDn9KFjTsI6XWufiyLM15EhjV52kUsO/dZ8VEA2eRmfokK6iKiotze3j3lNMFw7NM5Kzsd9m3SS09/bx8mA9J0P14z4zRqV1f9jef3TkpbryOq4L2PDrAQZOKYCj2+1c8fU7OeeyzOTUnEiL3sXLg89iS5Swo+xvKV4zOqLd6y4l6B2dHWBTdAx1fnMYAujDuaYcmgfDTGEb/u67E8/x++P/Qjw0SNr0MjT8TFdYfBHnF13D849ao6c64m08NZTA4Z3ZCCVzOPG6W6lgY/7l2Pzjg7Er8yp4fdm70BQbYef8Tg/PrQoCEDnRAkBLrImmeMuc1tmX7iWU6kNRnBjD35MvL5dkugWHPY3NWYQ2QUXQ1yq7eAeGaZA2rWVfOXAARdXoKZi8A8KmD6f6GLICfL/62Df57Xf+Y+T9tJoiwZk/InauJKC0yNgcDiJxK7I6GDr9oZmuihD98TB1O0az3eevWvg8QyeK83jeZtBW/2/0tv58+g8MU4anbNV5K/AMTt2jFO/pB6Dr5QYuyLmJ1YVTP9AslIGA9UDTaZv44T1h9I/8Ozdv/ubwtxqtPN//AslYnEg0zsXlH6S65NZJlz8/5xLO955Lgb2AnljHpHmA9tDH7uGKcXmpCHp8tPrIyegxjg7Vc+67b563/ZiJRKSfSvUo7Sesh9tnfnY/mmojbHSx/rJtJNNxHKkE7YcfJlBpPZw5hqtbdKkeHOkBksnTr+ph03XSgG54sRlhmEHeFlUJYRgxIgPWQ6E77z9x5n97xtsMeAKUuqtJpdK8rLVTOpw74lQ1JdcBkDolFVqoNMWhhNVTpkX7Z7y916rdsgmAnJibxuFhwyF1qmlNThyqFeDsjhyjJ3LstLc9lZy+KFsLrqbjhLV+f1wnkujHPcHojDW33UginaApPruqR/2xoxzuf4aNeZdSHZzfwgVHYi/RsADTjbcHirmydCe2mPWgUuStZXXO2PK4yRwr+B1Sx/ZU+qNWgOZV7mQ/+aqdLYXvIjhPiWy3v/F6rvHD2rCXcLSNvqj1HSSHGogNjR4r3V27ONL3pwnXESy2/hY21U00HMV44G+JeOd/6kKm+SutqbhKevSHbFfdJLGC+UejT3NsaA8PDDby6CnPGk/17uUP7f+HO2kjMYPKk6/li/UDED7ZwIt9j9OXts71z9i7OJJr3VyH+xLUz1MxCXd1EU3OCK3Kfv7Uei+5JdYDUEVeOef5ziUvMnpNSsUUus1SnL6x18y2IDzS+jgNjQ/zVOOPWLlq8QdP5srUY9jd1sNpONRPU+QkacfYagH6iT3syNvEupyLFsXUrumUra7loc4nCA9PE4qF3PxhSGXFOVOPaC8oreDq8tsBiMwgB2g83UvciLK368DcGz0PUvkdHO+5l9iAFRQbMKzfrS9v9qN7grp1PTZONox7r6Wnnj8PJ9wH6PZYD9KdKQdNoSdojk49Q6FzOFdb8pTRvcHKfBRF4Wj7U7iKps9jk0lbzBpurvo7lkddFOsKVSuqRt4LK2lcqnV/EB4cImUmSOvzH1AqHM59pCkq2ilT/l1GgmJ/Hf0njuE2UhhJ65qVs8FB2+5vkX7mVwAk420YQ1MXIzpVzXC/kN3XwMvxz1C+ffzInXBecOTf9pqq2e3QNKJ5On/s7uPAM1ZH9kuKjRecc8s52dv9W15qvg9X2ok5PHU3jcFvOk9y8JVjJGwaA9r0HdregJ9dHb+lIWYFRE++YAVQe/SJ8yMBFJRZ12G7bqe5oZGt6hbcL40GkGxKCmXKe2ABnEbX1hx873vf49FHHyWZTPK2t72NNWvW8L73vY+qqioA3va2t3H11Vfzi1/8gvvuuw+bzcadd97JpZfOrELTmaLfTGPGO3h5YA+X8O7TWsfmj44PMATLyzkYfw7DTLNQfbvbe6op855PZ7CAIcfMy6GauQ4YfvZbc+7UCZ+dQTvPNqfwJ2JU2IuxR+Yn4fVsVV13AY/8xzHO+avrJnw/Plzlbu9AE5tXzV+OJ5+p0Ws/j5aDh+jpjdDcW0/snMlzBJno+O1+HJpOc/TlSZfrLvHj67d6PlrMbtRUklcHD1/5b3fPW/tnI5jwsD73PJpONMOFED1+kBeG2nCs9mGz2Qgn47jd1bzs20VFrjUqw5eXS69pEPcUo4RVYunoNFuZWto0sZt2YjYnqjp9DP73ngjuE9/iirYq1gcCRHtuIz6LEufmSg+0QKA/wkDJxMPTW5LHCNg3YHhG27N2MEbw1cTsqdPf52BePr3pNC4zh772Xjz4iTonf7CME+fVAVnP9TyGprh43WlvfXKBeB7lrrWEhitCuew5DE2QjBJgxapKvhE9hnt1KRdPuMTE3OZRhlpVyN1Be0HBPLR61OXfnHiq6Vw1+n2si0Fbbw+5rKBN+y1N/VFqGa3CZltWBJ2Q8o3t4UsTI2aMBpk6fH6K03msUDT6PfOTlNyTk0NjOkTYUcxzbQ+iKx528F7aB35H2FC4nA8DEO87RmSSqSxKrnUzGDP7OXbkOEOX/y39ytx+14tB3tpq+h85SkQfDQolY3GSaWtE6wpHDQXeAtqiDXQNjQbQytzL8OhejFgnEWX2/YIhZwBSMDiYpLn/WYIeqxf/zbY3kgpb33V5oIBUevKb8NlI/+kv/K3tPA7qaZyVL49M5Y8Pr3/g5OjvuL/+IFcG1rE/NHb0ReGatfQ88SsuWfFhTg6+Mi/tWuzikRjmcKLYdsVgMFFM6bKxx70v1LOkuoZ9gSDnF1xNb9QaZeuODVBq0wgWTj3KRbPZSJtpVEUlHpj+4e6odgglmiCQtzgCj972ZtSeAM0HD7L6kkvp6t7LQ7G/8B7XjtmvS0uDAeGW8blzVnpX4tBG8/Tl1azhoZfayK0q4cbK99PEc1OuO7enHRyllDlGE0SXr1tN5/88w4riCzhO9iq6TUR36ZACv+YglhrC4Rq9Z3LqCZw4MVIp2ptP8EBvCzbvCS6fYn2nw5eTy5HwUYpcVRinxKt0vYINwQs40XKSWO9JEpr1TLDuqst54M/7UNtTbANWuJeR7515CfqjaphoewMRj4OCxCWk9PGjb/Ta9fCyFRRMaPNbBXv5lq0MPr4PBqwA9lvtywjHJn+2mIkC/+WszqvDIIphWs8h3kAu23x5dCdSqAootpndR+u+NXhNK5AYONkJVFNVNH5a4KtsfjtJIJKK0/LY7xjsGiRySvGDtBokwtK/31hoGQso7dmzh/379/Ozn/2MaDTKD3/4QwD+6q/+ine/ezRo0tXVxb333sv9999PPB7n1ltv5cILLxxJAnc2aDaP4TFX4AtUzet6bbpOvmNhq130Rp8n3u9kRcVFRKMNM/5cojoXhnMrl6ybOqCkaxFOhvdiHouguavpSWdnjvzmzZVs/rfKSd8PeaxetDabk2DRzJJCz4Tq9nOZqnN4/xGMZx+n/IVDVHzyR5MunzYVHMORfb128pPq5a3dVLg2kozG2Nu3m5Qvh4vmrdWnpyKoQhyKhitX6T1d9LceYMfnfgxAmF7K7FVsLbuTY+nRB650KkFxKo1d8zBgzq1nwTBjGEofYcNJdAbz76vKNrEsvIG+l/phtZUfXzNmPg1s+zvfSt8/P8+y8vNo2TDxjUByeTs/3/0Qb3nXPaPt1FRURePZgSaiBXMb4hw3YqBAMmrlArDnlU66bNIcwjlcZeyi/CsIx1snXXYufK56Gv70X1Rd+Q+k4gkO9D4GngCThWrv/tZls95GqOomzi+sAsBhLo0biGjcCsL0NbbANgimN2FzjX3CLC6oBTrwRcYGyXRVJ37K9BEj0o/usq4RFdH5GwE0YITwazlcUngN3RGrhzyn5lbWpkaH6m+u/jCRSQKEwZwg0I8zrjBwspFq7xo6bZMnil8q8svK+XHTHRSXBwCrEEDb0a/gyvUB72Yg0U8qFaLWvxlHZHQKYqGzlFJ3NQ+0PopiRtnGxFMFJ5OuzmXPsy14/LncUv0xGous6QptfXsx0yngKkxzEHOe8o0Ei2zs3/1rynK34YuNTlEtqKmCJ3uwn1Jkwhu1Apl+59htr95yHr6CJJqiMRScn0DXYmea5kghgvJUkHLnIKGisUUI7AGNprZ6chxLYwqo3emgVDeJha3HD7eZpsgRmFEF1UQ6RnP4MDnRZ4CpA/R6up1K7zWYiblNxZkvYaWCTasuoP7gEVZfcikFaS/u2OnlSO2y2SlJQHubdY5ufPFFjJRBzaaNFDpKsGujeQztkR6K7W5Sx+pB28DQNNWXg+U26AIlbzRNw6m5XFMFk98LZEMszwYd4LXn02OMnaLrTHSjOlfR8vwB0km4NG3S6p5dRcyZUFWVxzt/x7aC15PjGL2mGZUhfrXvW1xw+e0ccm7EsFsjenwFRWwtuIoThpVoP+rNJz1JLtaJhHKWYVOep0pZS23eSswJPlvh8wADRIwUG6+aTbfa9Cpq60h4TtIeGQ7wOHPoT53esfyq/pwichMaZjpNcvjey+PzUeJeQdJUAY2wbWa5AqsULwVO6z7mRDRNrPMIpTdMHrAL5uaxL/QgcQZp/nMj5+TfQCQ1OhI0lIC4cXZcc+YiY/0aTzzxBLW1tdx11128733vY+fOnRw8eJC//OUvvP3tb+eTn/wkoVCIAwcOsGnTJux2Oz6fj2XLlnH48OFMNXNR8J9TR4GzAt1/+rlfphIzZjcNZDZSsZc4nHgRm2ojPYuh+NWrRntUXp37PZlEOs0bCrcTZPjCVr44k3O6h3MQXevOJ+CbWbK9mYisruCpzt/Qg4cC5yXYd3yYwuUT50UCSA5PvRtKhjj3PbdMulzYSKAoKif2v8BVpbdxjmPqwF4m9OZZ31trxDpm8wNvYcP6T+Evtv72hscaBfdy5yNULa8a+VySFEY8il11YihzuxCkTRND0+m0RTipTB+cWue0s9xThO0568G58KWvk9cz84pUHr+P9HC5+/OvmTgoctUH/5G/+tb3KV+9ceQ1ZTjx+NbAMio2rJnwczMVTQ1gV6HnlcPD/z/5aBXN1NGHE3HnOEux2RcmZ0WruwJt8zto6B7i8LP7iNtqUHOnn/owG6o2mngxoC6NOfNVw1OUgkPWMVPAOor0sdNHUuogz3b/keb2sbnWdNWOcUrCTnuif+TffXNPhTUikeonoOdS5K7Cbbd+017DTfCUak02VSE9yUi+kuGSxznpIL4e6/dc6liY62Omrc25Ek/UCosmEwlK6j5EX7E1Krs13ceLfX9BU1SKzdGHpZDXgabaOb+ynJpVUycznoiWjtM79BBGZPjhcvjc0d7/KE2D1giEFwY7aBqaPo/WTJRsPody+3EU1SR5yrTh0lW1pM00dmX0d+x3Wv8uqRkbILHpOjEjRMPQQRy185sTZLHKifcQTFkB33hoCJczD7t/7LQPpayYZd46fPr83WMsNCOdwD4cyG4Nq+xun9m0NF3RWOHfzFBy+qq5xalzAVAT8zPScq4cG/08O/gfeCqs83WBdwvLqq89rXWpZV5+f/J7RIcrHfb/8Ajaz/oY7OgmJxnCExs9V6TiPWz02PFErftRIzr1ffnmj9yF/Z0FXPi5j455vSfWSke0gXPfcuNptXmh5BQOTxtWFKLa2EfaluGKdq88vY+h/gQ5RauwBRcm8Hph0XWEUwM80n7/yGuqO4/qvLX0NfdyTY6bdZ7RfFlJUyXPtO6VTLsdYxYBpcrCVVxYdAOFDmsqeFHZ+A5rY3jKx9GhAxTNsMrxbESMBC49n66mJjTVRmiO4YSOcoM/NX+HzuhxOuKjBVlMIKU5URUVPTqzKdivRF7kz23/TU9LJ51qgFec+ZRvmDyg5HC72NP7DKGBdmqCr8dt82I7ZXf2dP2c+oHHTnfXzhoZG6HU19dHa2sr//7v/05zczN33nknd9xxBzfffDPr1q3ju9/9Lt/5zndYvXo1vlMevj0eD6HQ1AmN4/E49fXTJJpbImKxGNUXr+Mvxn7OuWjbvO/XyS0GroCf5AJ9X27vRdzgt26ODSU24/YbztGL3HSfSecHeOZEE26HNfdcq169KP/+PQMNVOaUkyY9r+2zBTz0UI7W2Em+cyUnhg5Puf6oGieRjhHO+zonmqomXS6e64YkHNn7EpvsK1ASbTNqdyw287/zbDWFDJYDDS2D5NfX0xR9FD0ZxKy3HqIOmm0cbP1flvcYDKaNkXYMJnsImTESPQfp1UNzal/45e9hd5uUF1wHmNOuqy8QpYAAQ3391NfX07/lCzTp/eizaEPn1kEwYWiaz3T0jA7LTemjDxqhuHNO+xwxYuial/hAC68kEhj52qTr64+GGLQ5UerrcagOUop7QY6HtOFlhX8jT7e9ROTA01yRv5X95t4Zb2smx6mtuxOGE3m3OIKL8rzyWu3eHAp64PjxAbT6ehQSpNKpMW3vjnTTEm0Ee2DM6/t6/kxKcxEYfq0+/Ao96Rjn5FxM2G6bt/0fPGVK16B9+PgI9aD5ijn4wgtodjtOxSRm9E26zSd6OtCS9RQdLyCfXHpj3Uvi7zOdPMcKkqko9fX19LU2YzcNUKzrYFGwmC25r0dRVJr8HtKv7q/Nh2aoFBlrSEcPzvp7iPYMcFX5X9MSsUZvdHefoL6+npXltxOw53LwwAEuLNzBSfXovHzHTe0DrC2ypk33JMb+jZ3pNAo5I68dx00hcJQ0xmu27bMF8PgCPJ966Yz420/ncKQFxewnUl9PTHNAGrpTkTH7njglpdJCfCcLcX3X0yY2xcWLL7xAXcE62tMzO850RUUDkn7vtMsPxQ6CfwPJdHJxHCu6nQKbl+7jjdTX19Pcdh+NOEnVz37ETDKdJt+zlXhXmH17dlNgL0BVVPZ97ufkuUtImHGiw/uc8Hn4ddN3KA5aOVTzg/qMvo+e1yyjKhpBvYjm5uZZt3chDRphSocrkvoGjo3Zt7grzq6up3CpKdoTBylkFa2xngU5Hry2IOXuGgZTpzyPNqfZ4L2M5198Fuw1hOKj575EMow2fJ739A+gODwzbtdg72GgGodqI2UaHGs8MW6ZWDxCAbA+uHlB9jeESlAP0PDcAUoIkGub23123vFmziu+nefb7kdP9lBfbwVbHWaSlGryRPcL2I2ZbSPl9uFjNbt+9Qib4m6KfA4aGsZ/R6d6Xcmt9MVbyXVYo7g1ZfQe6sKCy+g0e+fle1zI56Vsy1hAKRgMUlNTg91up6amBofDwc6dO8nLs5LIXnHFFXz+859n69athMOjowDC4fCYANNEHA4HdXXZH00xH+rr66mrq1uw/Vno76nB9ceRf4c95qy295vvP4wz3s3r66aeU+5MKzz2bCs56X4ANuxYOWGEPtv+6PhPAoPPU+peMa/fu8cwUPfa6Y6EUO0qrjL3lOtvdO3FrjhpjFzMTVMsl1p1CA6CI2aF5uOumf39Xj1mF0Lf44dhEJaZdvLzi+gId5Nalcc1w9sbPHcbZUY+lEL5ptFRGT9K/QHDtBPX16MEnuJNc2jfo44KDC2NmrJ6fKbb1+U1NbQ8+wRO3UtdXR3HtRZCsb5ZfUen831Gy8roecSaBlReWTqnv8k9ub/B07OOba42orsfYsttX6NykvX9MfHvGGGFy+quoUlpw5GXXpDjYch5ECJQSSHPN/yBfV4H2z95E4VVk087PdVMjtMTrocgDT1Jg1UXbFkS15VkV4jIsR7SsX7q6uo4oL6CYabGtH3l8uWs/LOHgbzDI68bhsEL6SKi2uDIa3t0DwHdGiq+ftN6qudp/5ur9sDwzGTfptXU1dXRrPwBVVHJcbnwFBUTUrvpdavsmGSb9ca9mEQZmT2a71wSf5/p/KbtcyjtTZxf9yRHB/r5dfvPqS4qp66ujtbALvSwFSiOJUfPx61De8BdgKrYGBhMc/4sv4dEcx97fxMnYHYBZdRuWUtdXR1PKruxKTaCeYUk9AFatPn5LftUDZ6xiiPEdQcbTlnnrkQDISUx8ndvDT0AKtSurGbFa7bdzOMAXHHbO3AsgQTUc/WEMoiG9btOmta91fYdO7A7R6eX5EYTJI8M0hPvYMM0906nYyGu70eUFlTNQ7KjizK7g3Y1MaNt7I3VkzDCVL7eP+3yvcN5/1Is/H3vTHQ/s59K91sZaNxLXV0dL3qLMdOn17bkyTZyj+nUxw8yMBDmLw3f5OKi26j2noNpJmlLNrN1eL2uNAy5l6Or1nl94JzzOO80tvmnjYeJdDRzbd3rZ/3ZhZTr8hE/dJzWyCucSDTwtlP2Lbp1I4f/4sPpOESZ3ZpSWVPoWZDj4UWsCrs13tqR9fd4n4ZBcCfsYIe0Gh957yljDzn2fOrq6tit7iGFyZoZtit2tBEawK5qJNPxSffnlz/6KaYJNy/AeeGP9lcoNRXiw1NVE/mBkWPudDQ78ijSS9lZ+V5awsdG9umo2YTdjJLj3YDhODGjv11TzT7WtlfyYtdLuM0AUSMy5pozkScS9dhUO5qiEDGi2FQ7K1/9W5lPYprzcy1cyOelTJgqGJaxKW9btmzh8ccfxzRNOjo6iEaj3HHHHRw4YA113b17N2vXrmX9+vXs27ePeDzO0NAQx44do7a2dpq1i8XC7h2dwuBndnmvhgJPElk7fUWuVDzG6/w6aqqFPR2/XZTBJIDKtdtY7t+Iy+adfuFZKKmtxUgnybd7Mcw01e+ZuspdrON5OqNNOL1T589qPWnlvtE7rNEEeasmn0aXKSl7jMMDz9Db18D+7/2Oayr+hlJGAwgbr3jDhJ9bZitjo2s51wY0VvtmX5b3VAXlOwnmbac5aqMtMv30ObvDQcxM47dZx6VN0XEaCx+7d/lH82PVzHHK26ZEjJ16D7H4eVRe8jVseZP/xjbnVvHG4teRTCRQsGEYC1MNY0CzuuK7WjrJdxfSHXlxxsGkmXJVWiW6X+x/gqq67B//M1F37jn89uS/0dNvJcXUVDfma869NrsdwzTobR3NnxHp6OKi0gvYmlsx8lpF3haWeVdzeOAZIkXzl5R8801X8PSAdcOtFw2PLHVYHUXh7hAdR61ok2KbvIrLxpyLWO6qJDA8gqx85dyO8cUiHSijYP27eemJJzlx8CSXldyK6bTy5BSkR6en558ydSdujo5MdNtnX4nNX1xAb/hZjOHqclrAukaZqomm2Bg4Zl0LXOb8zHssqq7CNK3fry0+9vywv/unnBh4fOT/bUODRFMhCovHn3M6S9toVo6eFcEkgHX529hZ/CYAwgMmKSMxJpgEoOVaozNa41OP5F9cerHrQxi7rQ6QskmKT7zW/p4HcGkqrbunL/gS16xcXV7mp1LhXBWusB4ibSnrHrk6eBGrc0+v4FBJ3Voe7txLa3SQ0KOd7Ci7g6EtCmCiq3bykv0jy3r8Xs7NfwOltkISRoxIuGvS9U7l8nfeyLUfO70iQQspt6yIV566h6PRAVJu95j3XMsruNhnIxG3ofdZ589c18Lk4zXTKXqTYfZ1HR15LTqcO1VTrfNVIBAcfc8dwDk8xX7AHqSXmecgyiu0RuhrioYxRZ4719oUq2+bfnro6YjHj6MpGkefs0b+NGtzy6HkHa7cGEsZpFOj+5Q20yiGyUa3E68ysxQHy7dblZGDthROzTWjNC/PdP2cgfgrVowi3oxdGd2f3Z2/o6Pv7Eq9czoyNkLp0ksvZe/evbz5zW/GNE0+85nPkJuby+c//3l0XSc/P5/Pf/7zeL1ebr/9dm699VZM0+Tuu+/G4ZjbgSoyJ243IA59qTTqLOMob/+nz81ouaJlyxjkELrqJHxK2enFJifRDcx/PhndbidmhPCoOXTH29hSNHV1lJIbLmffL37CO788eeJuAN3vorXtGBgm2MHhmJ+ErHOR1OO80PtnKpPnskqtJJI2WHvNaFJXh2fiBwvd6MNnd6IqrpGKSafr+db/AdNgeeU7iSkzy6sTSyZwqE4GunpQFRVzjnmcZqo11kORI4jb655+4Sn4oxoFrmra+h+l2TDYXrVt0mU1mxMHHsL9AyiKQqRvYfY1oVg3BWVGKXn5a3ilZn7yu5zKpVs3mzsLLsETXBo5SXSXk3NyLiWhDgeLlBRRbXzi1VQ6iXnK/JiXn3mGInLoPSVQMVQahHZojTSyunT+zl155WWc4wkCYBvO7aC4gmBArMck2vUCmhHAnZw830lMc9ITXEHPUD39sXLWlgTnrX3ZZM8tpZxaXn72FVTFQ4Gzgg6f9XdqTqbIBVrCr9BO/8hnkjUGJ5uPUaEvx++f/QOSw2vnysJtxAzrYdvjsy7WCdWBTXVy/HgLKwmS1OfnFlG320mZoCvQE20a897m3MtwqKMPCgdCbTzb+10+lPfLcevZ/KHJcwCeieI2DQ0ryJrSvCQmqILoLykhxknO8S/Mg+NC2Nf/DCkMqpKrwAWll82s/Me6nO0UupbRzMC0yyYd1vcW9k6f7DsTqjetoeXXT+AYLoluM7WRCn6zlVOSh482kikP/s5ONI+Hy975Fh7+6H9TYfg5EI6xanjZQGEhHeZxYkOt/Dr+FDu3nVlVs3Vdx7vxbez0rOCkf2yBnprVq7m//ROEXDloIRcFbmiPRViI8SExo5dSZxXlntGk5el8D7wCDs3q7FNPuab2e1209IKjuZ2u5Ek6DMeMi+D4K0sIMchAopem8Cus4o0TLvfGD8yuWMNs6MEErY2HMTw9/KrvXsouvHJO6wvr1r31ydZHCNlG7wP2d/weJR2hvu9xHBNUs5tI9ZaNnLx/F/G+NC7NTV98fDXE17qw5C2UOQroT3QTTfQxqPnobW0lt7SUy0vfTVt0/u83zzQZCygBfOxjHxv32n33jU9We8stt3DLLWfXjcOZwjWcGPLFwRPYyhemx8yfn0ufabDMuxqlZPIe7WxLziLJ3mzF0yk8QHd6+kpU59/wRs6/YeILzqnKN2zgt7s+x3LfNvCtJqVnv8pVTcUaVlV/HIDueDs5N9VQuXnsyIQXki9jT6uUMzqs91Dv07Q4c9lRdB2qZ44DMV3FKKpG0EgQSs9s9E1T/yPETRNXQz52IDHDz81ViSMXZR6Ou/7hfEy98WaOx3dzieP9ky6biLrQ3DZiA4M81t+Gh4VJglowXA44zxHkJGEuveOd876NnngjDR1/Jse9aczxtNgFHYWEh4+x40MvEJmosIERJ3hK0uvBti6KyCFmGw0y6el2wMdlJTdTUjB/I5TASuKpKXHWbLOS5R72NnPk4P9SEL0RbfAke3oHySuefIRqghg2M8HQYD+R6OMEyu6Y1/ZlS83rLoZHorgSAZRwH1BMgdf6+3kLA9AKLZFX6PSPPhgXVTiIHN0D+nJO4GbmBact+aXFdJiNnBh8gYORDt5f/o8AaAZodgUz0g8E0fLn7xqWTEdpjpzkWKSZU8fUJlN2DHU0eFuUu568xFr0s6iy72TS4QE0bxnxcJSTtiiN0TZeO17f7nDQGznJoC+8ZM5ZRrIKTbGTduQSMRLUrp3ZI35At0Y3ulzTP1hG3V5IQKNncQSU7G4H/Ykegjar7briIj5JEYLp2Gw2tuRdRVuslefaf8KyiMEWrmfr31/Hf7///+EsGQ1q2B0OzLRBnr+OzWwiWBWcj91ZVMo81ojOSHXumNcdbhcpZQXehEmcLp7rfZrKi89ZkDakhgupBB2j195ywwBsuPFhmCb5G1aPvNfb/WeaelLEn2imNKWiMPMO3GBxIUejT3EifISW0EGumre9mLnaSzehff69dC27FX/urfjcc7vHdZUXQD+sq76e/bEXR143lT5Ut0ae7Wq05MyCOprNRtRI4DcqsOtOUurUlQ0BbA6rU6Ut3MgJvZcjQ+XsbGvFEwiS78ghbMxvAZgzUUYDSuLMpwZzoBcuzl1O27KFq8JjpJMMGb00mfvZzrsWbDtzUb22FprhYM8r836jF0tH6E/0Ejt//ircFa2p5XWlt9M51Mj+l77H9g98Yd7WfboKVtfQ8btDdBtdrPzkpeQWjB818cav/fW418p8q9icux2AiuVzmzK7Kud87IqOZoKamlnlwkE9TCQSpfdwA8Xko83iZmEuFEUhPg9VHANry+AIbC94K2Fj6rabWBWXBvsHsNsKIPf0htRPJ1XkpvmlI5R7VtFfOH0P9emoqF5O1+FaOuNLq0TsC73PkTKdXAqcjHnRzLZxy6RNlbRt9PdTXFYKjbDs3NHfR8AYDSBMV21zthoTx2kNr+P9wxXFct1xNGcZ9v52gkWlFLUPktyycdLP2+KDBNIxVuqlVAbXkFcw9RTepWLZeavp/uM+wq1dhF1RyoFwjvV3qFlfB61wXsHVnAiOdtB0PznERt+tHB54BmVz1ay3aXc4SJkJyr0rSJqJkSlkMTVF2kyj91kPB8XL5u877kt0UO1dhTcxNqlvV+I43ek8dg7/f7FeQbF9ZuWhz3SGanWGDHT2oxv5mOrEv8n1/3prJps1Z+uDVeiag6QRpkfpnfHn9vc+zs7iG0mlp/9M7pABDqgcWDwzHAbT/VQ6VxINR1E1z5zuClR1kEK3jzdUfpADAesBPC8Y5O6f/nDcsknSeBwelqVT5FbNLQXAYlQfe4I650UUrS4d997W3Doi6RhtqRjtbGHbquoFacNQ/l6I1aDaRzvUDHuSY0PHaIse5nG/wtc3/PPIe7WBAKX6RvqajhJBR5llBpq+5D2stb+fNcHL520fZsO3aj2hTT+kJh3DZ9Nocs2tKm7V5i2YB60iEW7b6MyDtcXvZkiJskwJcDQy83tbLdpGnrOIwWScuHv6aa/OSB/tSgdN9qPkXvx2nPv60NylxPqt+1tdXRxTZxezjOVQEmeHUI2NPV1/AsCWt3AXLkUBm6KRv3X8BWSxCFZb+78iOP9t1G0xgvZcrr7lunlbZyA3SNx043GuoFgpJb+8YvoPLTBvYT7L/nEb53/tzRMGkyb9nH000Oatnn0VlVPZFA921Yei2kgpMztl+n3reGPFHbxy5BiPtN7LCU/3nNowU79u+g4PtPzXnNeTv3YTAA7VjmZOvc8JZXh6Tv0xtnttkFiYYIy9NAeH5sZIp9h684ULso3S8ipWudzsWCLT3V5V6a5jU741leGa3Cp2use3f8hMEmK0t/7ky1aenEFzdISSrWh+872d6obvfIT3f390WPxyw8O2gmsobIlgNnRTwRryphh5YCgOFC0H1dRIpROo6plx++L2ewmnBvAZTmzDOSL81VY56PTyKnb3PAdA3DXaA6x6YtT3P01L+GUKy4Ontd1UOoVPz6U2ODqddZ+vhfubf85LTQf5c9vPiNpmFkCfiV29j5A208Sc/jGvOwJersyvoafTCkS7kibpac45Z4uwxxop2jUwSK1qY7ma/Wno8yGk9GMqUfZ1/p5joWdm/Lna4GUAaO7pg92my+p0cOQsns4BozJIa0plzx//hKoYxOYwwD5spHArubhtPmpft3PKZZNmnBNDL/K/jf9CIC93ymWXokrbRgBWrV0x7r240oZTC6N4i7kqoOMrWZicq5HhIHjCNRrAVHPsPNv9KzQUronl4rCP/sGdxYW4bD4GO0O80LubA727Z7W9HP3D+P0rCXvnr1N5NvKLijgyuJfnuh/k/xr/hYLa8d/9bLhLcnm8axcAvsTo6NS2zv8l1PF7ABRmPgqqxdHKsehh/tD5L7gvLp92+YC7lGJXFTtuv41gdyeb8NG7/wXa+qzzSJtzbmkkzgZy1RbzakXAz7n51kXf4Vq4AXA2dLx6Lom24IJtY668JVbiPKc2/72ttZ+6Cdu7i9HnKcfFqw4lopiagn9ldno9JqK7Zt/DqJVYo+PSZprC1XO70ClGGhWb1YM0RQLEU9lWeHi663ckEq3oqgPFn5nBoDd/8Svc9o1vzHk9haeMTIgZU/cGJ4dHMIXbe3mk9ScMJec+Qmoiq6pWUeCsgOhJCiqmv0E4HSm/f/qFFiHFY1LmsBMeCmHa3bR7x59z/tTze546JflxtLGVvngnqcjoyJfctZlLRD44HBDqHjQIt1i9f9Hk+Bwxr2pRE7QpaVTVTjI9f4GOxSCUDOHV/RRErJvWmpVWBpSS4mIqnNbfMmyO/g7Lq3JwxFQuL70Np/30pqVpqSTtkTCPdo2ud4Nb5/ycDQSTfkzSFFXPX2/+5sA5qIpKl/01N+a+PJyah/Zj7Va7SJLK0BThxS5ldtITbyXceownux7iuZ7ZPXQuVinSeDQPdtVOchZJs3N069gpWT39JM9L/vkDRF+ncOGn33fa7ZxvynKTfYMnOf7Io7SHXyYUOnLa60qkrEBZOBVn/fbzp1y2QzuC17GMc/LffNrbW8xsinV/NdE02YQRRsdD51ALz3Y/gmuBEvq7W4c7YE8ZEVdeuYI3l76f7YXXUekee8zallmdBsmonY15F1IX3DKr7Q0l+qzPp7KXjL+544+YmCzzrKGgvGxO68rJCRJPWvn1Qumhkddbo0N0DndSxuwzj8A+rfbSFj3BtuD1mPr00/dTprWN2vO2EW99BYD+gwdJJ63XjSXWyZgNElAS8yrHl4M6nMOlrGJ+c3Cc6tl+q9c2WDG30ScLye5w8Eh/Fw/0DU2/8Cx5cnIorl057+tdaYuhpnv4Y+//zfu6MymVsHoV7taasM8xqX803QdmBLtp4jdmdvFed+lWvK6DFCXdXFJ8C6vUzIykK1xWQU7R3H8T+cUF7O49QSqdIqpO3cM74B/ifxu+QVp34fa/BW9gfiuvvcpXXERkZRP5n716QdYPkFtsBdKiC1SpbqEYpQUoikLr8Q40xUYkPf7SvsqzknM8o9Pb+lJx/tD2UyrOWTfyWvmqlbRHG+iOtSx4m3PqrECvPS9I4XBwqXrThkmXbwk9x7HeXbjtkCb7+d3m034a2dv9EIcHj7Cr92V8RXmAlXC23GMFl4odoz3R7YNeaorPoyl8jKr1p5cTJE4rOQ6NEttoXoryiMoy7xpWetdySfFbKV2xeoo1zE6hZnWw2F7TQVCeZwX/1U7r3KooJskZ5AY8G/hTR9nV+CNcsX4KnMV4tDMjj4eWSmFT7ewseSurVm6e8ee6wlalpX5mNuJ35etmmuY4My54/bVc4tVZZ5TzYjhGc3j81OQZGw4kdMcap130lfY8kvgo9SzOishz1X9Zmn35+yd8L5IwcaoekgMdNEcWLrHyoM3K/6clR6+d3oJCNIeP/hO7UJ75+Jjl6y65CNNMk+O3U+AoI+CY3fPS4a4HCKcGcJrz/3wxU6vKbmVn8VvYmHspwfy5F/HYWfwWAOL6aIfRhpI3san4BgDCs+jwq3PUcUnxLZR7VlFTPf1smf7rXbS8zrrXLV1udViWOHT8fda5ZnWkdcbbPltJDiUxrxwFeSSwTqye/LwF287WHKvS19o37FywbcyH5Vc4yC9fOhdxt2Kj0F3NXwqW9qkh3R8CF7wrPvcHE1NTUBUbf0q1YAbCrJv+IxRXrULxfJzBpLX9iHfhfgsLZTD6NLbcatRpysE6vDksz7maZO8QF3htvBBfmKTcALV//fYFWzeA3W7n5dVxKrfO34N0JqQVa2RPw5PPUUcxgXj/uGXybHnY1dG/pV62jR2uKyg9JSDh9vopdlUtdHMBqKpbTfjPJyjxO4l02jCNJHkVk0+zXanXkO8tJKkpJDAnXW4pCuUo5IWDoMZJxPehKOPzwtm8o387W34pdEJ/2kPwNJOn/7bnKS7NcVLpHR2V1hQ1yNPAYfOTTCfnNTG2qVkBMXt0bP6z/lgrxQQ5secp6q7fjq76iRtzy8dxplA9K7mk/Hr6ujrZknsJJxfwgTiTjOFqdaZpUnvTzKuOJYennJvh7D1Ez4XD7cZUUzi9lVyjmQzMoeMiMjyya8g2fSfX+oCNUrtKT6z/tLe3mG2+6kpck+SG0hhAU21sztmKVw8uXCNqVzNwtIeGXDcXDL9kc9gxzBTB6ot5tiw9prqc2+ejTYlian5CLj/93tlNRVxTdhMeW4BodOGK/0wnqVhT6A1zfkYMp7GSZ3v9o9+FigNdta5Dy5h5vrVggQc64JWjv+XSlV+edvmNF4yO8guUFsBhMEIJXm46QktrPXoOrJni80ICSmKeBfLyePVSb9MXrgLbq5Ws/PnBBdvGfNhx0/TV1RYTf6oXqODSnqU59edVHps17Wq1Mvc8UBFNpcvQcEW6iMwwjabD5SRqRPDYhh/IcpZez/LWvNcDoMWmvun1RlTWBlZxdHAPuMBn9mWieQvmsne9LttNmDXncEVG7UQzaMXEJxh8fKDnSWJmhHOxgnLugQHsigOnc+x5umOwhVjvsYWvGKVbN6NtLw/ixEvMnHrqy2C8m4QRImhfSXqC8ulL2RuLivDELySejjPExBVpjODo7Zq/qBReGmS97/Q7K87xXUiJu4be8ImR1wI5ThgEl+Ylbs5v0C5tiwBBoq95+HAVeaAX9LD19+9I/YWETQNumNftL0VpzY6mqcQHBtFVO/bAmTHVMzn8+w0bKSqqZj6itdhjBQ2Kcuc2vSabmj1O1icKULwKkaHTDxB2GHYqTZOqa6a/XmnDydzPxpF/pjNGd6gBh91OioULVKc7hwjYl9Gtjq1UapgGmmIjNz1+mn4soaBHA3j0ENFoH4yEoqbn0a0Rn3169nL7vJrWxKbOT+L7cCpMTEnRlG4YeS1lhjGBptAxhoaaJ/3saxVsLIWHYfny2T+Dxd1OHKTpjdgwDAVVTZLwLVyRqTOFTHkT885IG7TGF/aGf2/gKPsdJ6ZfUMxKU9A6aaZSPVluydy0rPDyeMf9PNn4vTmvq7HnMM/3N3FBzrmsTpXM+HPh1AC6aj2slwQXR+ni2XjZ6OaR1h/zQmiavB16jF+1PUo40W/9v3vpBc+Wuuo662bVGbEeOKO28fP9i5ftYFPpu0b+v635T/y5ZXxFoDbHEZKXLXwupUBREQ2hQwwmIjQl23lx8MUpl2+Nt/Ni3+M82flT9nc/uODtyyTbemtam0N1oKXGTjFtj56gO9ZC+ZrRXHDpxMxvrCdTaLeOmbg6GjgqKbWmy9o1G1Hm9xreb1qBafu6sSMJaredB4A3ZbWjKdRADyfnddtLlScY5qHW70DoJIqiYgTOjHNrrvEYQ8l+hlLts/qc/Y1BYo7DLN+5MEUZMsHtSY10iA6ap9+n3/OGZfzI3sCKnaumXTbhGE4YPUmw+kzWWujnyVQZqpIe6XhZCMX91nn7/IGx+QvTaasTUjfGd7A/3vFbdnf+Bg2wxWc3Wk1NxAmnBjieOnB6DZ4HXcXW96nOsFjNdNzxCH7dg3JKNcu4EiWZjrK76zccN2c++r32wu2n3bZl55zDk91P0WsLUeKu4pLim6ktmfnU3LOVjFAS805TNQr1hb1w3fj/+6sFXf/ZygwPgRNi+tLuCTV0J1XejbQ45z7SqtIVoNSVg6roMIuemAG7wquprUPRpdczmEw1szZ4AQ2J56ZeLuih0h4iOTycvE8CShmXv3EDD/7sS8SSCZ43khQWjB8+71V0lumjx+/q4FbSEySZv+Ybn13Qtr5KVVVejJTg1nTsbpWoOfXQ/Sr/ala6r6ct3kGr1p+RNmZKxblbaP71Luyak5Qx9qY5116KXXOg6qMPKjl5ecDcztFJYwDwEbeNTmtrcDlHpmWkkvPcqRCzgxfM8NiRaDk11Xy/6XNEAh62A5fk/TUNsYb53fYSFfeXsaPkVtpiLaBCqGVp5XabTJN/C1u1IK2pjukXPkX1JRfAJTMfxbEY5a7OhaetfwcqT7+q5h3Xvx6un9myjlgKXGCzZy+Bc7aUrdlBXUc3Qb2crtjCTcdPpa0AvPGakbbmcPAwqo+fPuzKWU6p4cWZNhmd2zEzcXOQQttyfLa55y46XQXVJdA5f+tLO6z79VLX6D7ZVDc+PY+ryt5Dg3vmuR11u53e2BDxdB+zLeHiDfhJODagBjrpbT5BIasIDXXNci1nHxmhJBaETV246W5i4TiHH+pi+uIptXs6ynq9VHiWs9o1914Fu+4nYC9ABUK+2cxzt+L13ck0vsLsXfRPV7VtGSXuGkpdU+doUQqr2JR3OUG7tY8FXgkoZZqvIJ+odi5213pq1BbMCfJq+AeaOXWcXKGziIA9u8O4Lwi6WO5wcY5WzCo1OOWytoAPh+Ygx1GOzbt0p7xMxO5wkIpZd+ZmauyU0UjKmr7rzxs9h+RXzb362quV1IrSozfK3qIyXux7nJ54L5EJ8nDNRTI1/KDVPvYJxKbr5LkqqaCcdDpN9/HdDC7xabPzRa/cQKmzAoduTd1262dGQElPWYGUmOvsGzFTd9XFI//uK5m+Wt18iBr9ADRGp0/gfaYpKgmSb7eOt1RqDknQp6GtsrbR7x4biH+y1xp5G0mPD0gUBorY6F/FH9VWdhXNvNohgKZYnUMBLXuFiTbssEYBDSUHpllyZvqHr32h6GiupFQ8SSSdxm/PQzNmNw17/Tev5tx/Pb28m9vcNir7dRJ91jk3OQ/5WM90ElAS8+6XDV/j/xr/NdvNEKehN249vGiJmeUKWqySw2MvTXPupzhDsQIkiqKgziJZbMzopTfexlNdD5FftvQegBWbFX4YmiaheE2uNQ7LPzjIwEADLn3pTe87E5wfzGNzoJJ1/ipUc/zNT1hzoykaAz39ANhVB0qqP7ONfI3u6BF6kyc51Pc4J/r3Tblsfq51w/1Y97NEug5nonkZ1eOyzlVRxo7UerHpP/j5iS/h9oyWu/YX5hFORWgKn/7UN3V4WsGRodGKWfk+D/X9u3np5V8SiPz6tNc9kcH0Uf7Q8l/krR5/LjwnsJM6x2ZC/b0knc/gzVu4B7+lpACrY06LwMF4M6EN52a5RfOjOK+Jk3/4IGuuWjH9wmcYu8PBEz3WlM7Ei3szss1Xkx1HUvOT62YpKV1ZiDmcDy6dWrgRSjvvei+F/3geV3/5Y2NeL/dYYz7d2vh76nBvPb9p/xO2zn2YrbMbEZpID3dYapHTbPHcefJz+XV/kid75qcD4MTQMQB0z+iAhOcGHuPJzt8AkJyH+/mZ2tv1R472P4dNtTrdPDlzz8d6ppMpb2Le3fCpr2KzywilpShlG6IhfBLFt7SnvPnK8qATwum574crJ8SruRyVyMzH93b42og1n+CC3A2485ZeQr9UdQm0gnvN1HXtYtEefLhoTSdpVMtYlbM0K/AsdaFkF7pi4+HuFlbWjb+0p4bLzg/2DBDIC2K3eQmdEqTIhsORI2iajhnvRdc9Uy7b3DpIvh0uClZwLPpShlqYOW3qABWU02eM/f288T/+m57GsYEjzWbDY3PTGz39pL59QZ1lSThi9nHN8Gsuj8ot1dYD0R7zaTad9trHu/47n+Pgbx9k45uuG/deLB3DrtppeuUYlRUfZ19k4hLgZxtTs46FWLyXpvRqXLlnRnXDjR//Am2vu47ybRdPv/AZyEzV0xk1MfTM5Krs7PkL1d6VrHCdfQ/FvkCATtOkLZHmYMrg/Ok/ctrsjvEBuzLd6iAI5U/w29XTVNptbMp7OycGD85qW4fTJzCGOtDs8zM66HRd8k47ucXb5mVdZQVrAShYMXqclhTXUmdaOR09vswdv5F0GE3VicUGwQZx/cwqBLIQJKAk5l3VurOv1+lM8cavfoxffe2rvOljH892U+bk3Le8gd+fvI/t77xizusKp6yg1GAsjMLMe4M2b91IbiwIgDdn6QWUtr//dTz3eBMXXTJxOd5XGUoccFHkKaVc7cMoqJtyebEw4qkYxd7lrPLnY+PIuPeD9iiYoPT2MdCXg03RCPuy22O9Ke9KdGwU6x4OJqe+oXYr1jTcXEcJbWdg0uawGuPnx76E0zW26pU/J4g/Jzhu+a6yeirOO/0RK4nUIWAZG4svH3ktf9kyHu9/gOW+c2CeSkG/yqbrEwaTAIbMPkrUfIYauoAAjllObThTBSuLMGimwlFKrRals+HMmAqo6fpZG0wCcAZ8FDqXcULNzBQ03WM9iIdmkdT4TBIzQlQ5/DQ7pr6XWQh/af8xCiolm8ZX4/PkFnJObDgtg212nTsBM85y/3YGE0/ORzNP27rtF83bulRDAxX6BkZHWPvVPPyqlWoiGctcHqP1uRfgUB0c5wBtkRM43FPneBQy5U0IcQq708ktf/8pbPrSH2H2xr97K7kFU0/XmonEcPWOrv3fwm/OvOe8dsfFmKZJfWRpJsLUbCrnXlqFqk59mVhWuxIAv81LvqMQB0t7uuRSlcA6Tte4NKKMz4F2wmzkma4HaGg/Rtcha2SLO5ndXGmu5CA5yvB0UnXqthSXBkb+XZG/9M9Pr1VUXMhbqj/OuuD0ZcABNn3wDsrP33ra21N7rDwVtujoKAmn281RczUH6h9hmWt2FbjmwtQ1HJqbRKfVJt2UgBJAYZkVCHBpHjw2P44sB4DF/Ljk79/Ns8Z+Lv+792Vke0UB6zyR8I2v/nk2SKStjsCCZOZHT5+feyPXVPwNmyvXjHtv+frlI/+OqbNLFVDnsHLqleZNPbJ3KWnsPUDD0EHMntHAkRYezRsXT2QwMbYRwoGN4BqddPpLVG3bkLltL1ESUBJCiCkMGR38sfW/yd38V3Qaq2f8OWcggKIo1LlPv5LLUuAqyKN16CVCKSuQZFezN6f/bBZ2jwbySmrG56kZyPfSbeYymDZ4qf4F/tJ2H63J1kw2cZy0OYBTt34fvmkS3u91hXmy+3EAWpzjK+YsdRf91XWcCDcQW5OZKQxOuzWVNR0fGwC+NsfH1nXX0OXdmJF2ABjpKJqi0dNiVf2K2GaXoPZMpdlsPN71OG1xa2SSu7I4yy0S8yG3IJ8bvvIhgrmzKfJx+pIO63dVcM7pB6CXsu7+JwCIpzOf1L7QY/1m81atHPeeu7Zm9H/SM8/PCRBRrEDS4NLOTjHG+lt3cLh/Pxd+4K9HXnOZ1t+sJfoF8s/P3PnPk4riwkGoIYjL+Q2iEekonY4ElIQQYirBALn2EgwzhXHmDYyYM1cgyOPdv6UjaT30BAqyV3XkbJbwj87xV7zjp1huNKq5unAbxe0apNOonhtxVWbmgWYyffbRioB96alzFHjjbpYPT91Iec68kRpun5cd37qdZTsWMsvHqIjXCtoklIl77U0lc8n1E2oLTb0v4YpY0wr8S3CK8EKJJhqwmdbDZtmK5dMsLcR48X7rtx5pn12enjOFO2FdL7yaO3ttmOCcVnRKkCmuzy6Inj7XRV8iStUNZ87U0TVXXMZ7fvJ9XAH/yGsezRqtGvZuxHRmboRdry0HdDdqdzepRC96BhOCL1XyDQkhxBTU4lVsyb+CfT1/QFs2ux6ujsEX6Is0LVDLFgebrnNdxV2s91s3R55ceRjMhtUVKm3HrBE8yQku7UPDoxx62lrIH/Jzsd9BbcU5GW3ja71afQjArU2dH6bMt5JiVxUAy4pLFrJZZwUv1lTcyYoyFhXlZ6wtrpwYuwd+SzyeJJI2cS+b+UjQM91FhddR5a3GMA28hZn7m4gzR8xjBSRLNlZltyFZouStByCtZz4PzlF7G0OTVJfTdZ1k2gokxRyzu7fcduvNnPP111O98cyeitWJNYK51riKvmczk3MMwDTa0BSFWOQAf+74MYXLSjO27aVKknILIcQUHIqDJ/rbOL/gjbzUN7sy3Vv+7QML1KrFRR0ezZA207gCZ2eehmwLOfMoXW7dOOcHx+dVSHpViEDUNEg1NoKnEMOe3alFnvgA2CGWCpFTOnW+s5QnCdg5GT6CJz+7gbAzgb2wFDrBzJu41969InPBC8NfxY2Vr+eZoef5fctPWXPNOzK27cUuZVq5xVLpBLr9zJvqKRbeG7+ytIuszFV78ijLqKTP7M74tnd+7pYp3z/ct5uwMYQrmMH8QEtIp9JCb+cxhhLdrLpgPuuOTi3i9dAbbcVe/AYuM8qx+wLTf+gsJyOUhBBiCkZvA0HzOEF7Id60XFQmYpgG7bFWWuL/gaZlbqqMGFW0xiq5O5Tsw+YZf2nPq6wFwGkvwJm2KsqUr8tuRb7GdAcP9fXx0JCd5KbxOSZO5VStXvZIaoD8KuktnKtL/t970e8o5/LPvH/C9yvWZW6UUOnmdXREW8jTkmwNrqfcJYGTVxmmSWe0iYdb7812U4RYkpyBfvZ2PYgnJ7tFKCbSnIhT4z8PTZPq2BPR8nNR9LUU567BuSpzHUn764p5PF6AZpSSq3qxO868afbzTQJKQggxBTsp1uVcCEDAJaNvJmIoNpw2H416YbabctaqPOccHjz8HR5o/j7u/PGjfdJO64YoNhjGp+aSTJvklWZ36pjbkcMGj5utrgRlRRVTLptbaCXkXBU4jyKZ8jYvimqqJ33PF8hc8Hz5uVt5OPwQeiJCte8cSjKUrHgpMLw2Cl3LKNAKst0UIZakK+/5KlWXL+f1H//7bDdlnC2F2yl0FpHKk1QBE/FFAmzPWcEG93b6Eq6MbXfDQIptrhhFiSiGOXV+R2GRKW9CCDGFyvJCOGr9u0eTYckTUwnafGwxrsh2Q85qV62+CwB/7viHT7dfJ5zqhngYgwCJdPYraflxUWJ3AA4KCqdO5m6vLAKsKaeaTW5dFsoDPQ04VYV3sCNj27TbHbwl5y349CAAniJJ7P8qIxoFW4CV+ZdluylCLEmazcamW/96+gWzYCjRRIFzLY6czFT3XGoGUiEaY4eo9K7F1xfK2HZd/b0UOcoYShqkzcU3sm0xkhFKQggxhYRzdKirkjyDarTOo2QqxGCih90tv8x2UwRMODy7pq6K9O8+QnnqafpT7XTFGjLfsNcYyhkNDE2XH6agWEqmZ0LJxl70oqMZ365pWtV8UukEDu/4HGBnq8FINy2RBvryfpztpggh5ltKRVEUIi9LqoCJJN0qibQ13V1LZS7oZuRZ9yNOzY+JTHebCenmE0KIKfQoJq9O5CrU/FMue7Z6rvH/D95SdLv0USxWzkAO4Z2fosc8wVPJQygONxdluU2e0hI4NrNlHW4XzeGX8dpyKV/YZp3Vrr3rb7Oy3fjwQ4NMLxirYfAEPjuQlFFbQpxp7DbrnjJXkavaRHw+FysTqwBI6pnbbummjdASRVdVEim5Js2EBJSEEGIK5cOlmo9HhxgI9ma5NYtTlfscVpReTX+iJ9tNOau1Jo7hMPImDLhouo7H46Un4uYqz4V0G5mvePNa/vTEFcYmU+6pXaCWiGyLKNboT8N0Zrkli0t+xQY2mcs4ETqS7aYIIeZZwXXrCT/YSemNW7PdlEWpTB2dbpbjC2Zsu/78XKCFVwb3czx8lFVcnrFtL1USUBJCiCk4vX76QydIpBoxUpFsN2dR8uVdAICCPAxm03lff9eU7x8PfYF0UTVm7+XoqeznBQjpMcA7q8/EjeznfhLzz9SsKQb1A01I2HCUFguBA+xK5hLSCiEyY8Xla+HytdluxqKVHBwEIGLEsednrlhDXnkh3WYzXlsARSIlMyLzE4QQYgpRt5MD4edZHTyf8jwp7TqRF1I/pTvWhomS7aaIKbh870ONbcf/7FcJsCfbzSG4qoxoKsSh3idntPzPT3yJ+/t/usCtEtmgxa2Eq+2Rp7PcksWluetFAAxSWW6JEEJkVl/Kyq337FAPaknmKo+6vF4eb/sffHoO53rPz9h2lzKJuwkhxBSCOUVcXPQGAIbKJi+zfTYz7OvJd5TQGWvLdlPEFErMYmLJIImL/4W2ghkmL1pAK9Yu55ff/lfykh3AJ6Zd/q+//zPs9gwmUhAZczD6DB6bhy35masutxRU+DcCMJCWKlBCiLNLwOeAfrg4WIpDyewYmGQyhK46iSAzE2ZCAkpCCDEFj1thaPjfip79aUKLUV16JQDptHw/i1kyHcGlOvCqbpr6s1+xUNedvL7yb+hP9M1o+WDAt8AtEtmSX1iKETE4GTmOZBMZ5Xdax7ySI0m5hRBnFy3ghX7r33ZnZovibC25AYfmIqFLlbeZkClvQggxBZdv9CE2v09G4ExEH85rM6DMLh+OyKyE5sRjs47nHDX7I31cHg/7jaeIXSJTJc92y9ftoMBZjqLKbempbCnrmqP45XsRQpxd2muqOTRojaZ2eD0Z3XYrfwRA98n9yUzICCUhhJiC3THaOzGQkITAEwnZhkibaf7kbJJaGItYWgVFGb45qlocZYqv/crHs90EsQj05erkkmZDYHu2m7KodBhxyoCejheAm7PdHCGEyJgKVzElfqvzy+nObNGXmCcCSUj2yHTjmZAuDyGEmCEtN3NJAZeSmGqgKiqXh6PZboqYghobvTHyLCvMYkuEGKu8IgiAluE8GYtdvs265pTaZbqnEOLsorc1ZG3bef0XA9Af78laG5YSuXILIcQ0Ykaco4P7ySktzXZTFiV33MqdlGNmdo67mJ2jgy/SHWsBoGq1JJgXi0dlbS2ReA8d6ZlV/Dtb+Dd6SKUTrHjjhdluihBCZFRCy+KsABPiRpzBaCx7bVhCZMqbEEJM4+WBPdT4N5FOJLLdlEWp3+gFIG7LzXJLxFQGU9280PtnVL2a2yukmpZYPGy6Tu03bgBuyHJLFpd1f3V7tpsghBBZkVuzEp7t43gsSqYn6SsuFw7NQb63OMNbXppkhJIQQkzD4yjDqbpx+mTK20T69BCpdArTIUnLF7ONvlouL72NaH4w200RQgghhJiUO2CNevep6Yxvu9NujUyKmjOrQnu2k4CSEEJMo8BeQlv0BN6gjMCZyJryc7GpNhxJqfK2mIVcVlLLS2PB7DZECCGEEGIKipIEoMCe2QpvAHrM2nYroYxveymSgJIQQkyjdegguY5CVF1OmRPpM6x57ilDZlEvZv251qDxsCJlcIUQQgixeAUKCtnb9SCPtf8i49vWdA2ACte6jG97KZKnIyGEmEa+uxyPzY/XL5V2JhJODQFwMtqY5ZaIqRSkhgN/pLLcEiGEEEKIybl9PnQ1D4+e+aq0/Vj3s/ZIb8a3vRRJd7IQQkwj32lVd3O4XVluyeJU7XdBBLyqI9tNEVOI9DaBuxRTNbLdFCGEEEKIKW3MOy8r2y2365CAgCYdcDMhI5SEEELMSXwoCkCxsybLLRFTibvt1n9TmU9wKYQQQggxG6l0grSZ+XsWwz08I8Huz/i2lyIJKAkhhJiTfs0a8dJq9GS5JWIqemGQtkSaPkUGJwshhBBicYunoxhmMuPbLbziAgCaC6XK20zIXaUQQog5qbp4E7/89tcpr92c7aaIKSSbW0kk0iTjHdluihBCCCHElDy2QFa2u3rLVtgC5ezIyvaXGgkoCSHENLpirRQM51ES4625aAcVa9fhy8nJdlPEFGwOlUr7Cvq01mw3RQghhBBCnAFkypsQQkyjMXSQIwN7s92MRU2CSYvf1V/4OEcqjnDNv/x9tpsihBBCCCHOADJCSQghppF/x/U0nGjIdjOEmLPL73pPtpsghBBCCDGtgVSSsGFQnu2GiClJQEkIIaZx0Xlbuei8rdluhhBCCCGEEGeFgE0nYNOz3QwxDZnyJoQQQgghhBBCiEXjUOIIhzia7WaIacgIJSGEEEIIIYQQQiwaV35dpukvBTJCSQghhBBCCCGEEELMigSUhBBCCCGEEEIIIcSsSEBJCCGEEEIIIYQQQsxKRnMofe973+PRRx8lmUzytre9jfPOO49PfOITKIrCypUr+exnP4uqqvziF7/gvvvuw2azceedd3LppZdmsplCCCGEEEIIIYQQYgoZG6G0Z88e9u/fz89+9jPuvfde2tvb+cIXvsCHP/xhfvrTn2KaJn/605/o6uri3nvv5b777uMHP/gBX//610kkEplqphBCCCGEEEIIIYSYRsYCSk888QS1tbXcddddvO9972Pnzp0cOnSI8847D4CLL76Yp556igMHDrBp0ybsdjs+n49ly5Zx+PDhTDVTCCGEEEIIIYQQQkwjY1Pe+vr6aG1t5d///d9pbm7mzjvvxDRNFEUBwOPxMDQ0RCgUwufzjXzO4/EQCoWmXHc8Hqe+vn5B258psVjsjNkXcXaQY1YsBXKciqVAjlOxmMjxKJYCOU7FUnAmH6cZCygFg0Fqamqw2+3U1NTgcDhob28feT8cDuP3+/F6vYTD4TGvnxpgmojD4aCurm7B2p5J9fX1Z8y+iLODHLNiKZDjVCwFcpyKxUSOR7EUyHEqloKlfpxOFQzL2JS3LVu28Pjjj2OaJh0dHUSjUbZv386ePXsA2LVrF1u3bmX9+vXs27ePeDzO0NAQx44do7a2NlPNFEIIIYQQQgghhBDTyNgIpUsvvZS9e/fy5je/GdM0+cxnPkN5eTmf/vSn+frXv05NTQ1XXnklmqZx++23c+utt2KaJnfffTcOhyNTzRRCCCGEEEIIIYQQ08hYQAngYx/72LjXfvzjH4977ZZbbuGWW27JRJOEEEIIIYQQQgghxCxlbMqbEEIIIYQQQgghhDgzSEBJCCGEEEIIIYQQQsyKYpqmme1GzNXzzz8veZaEEEIIIYQQQggh5lE8Hmfjxo0TvndGBJSEEEIIIYQQQgghRObIlDchhBBCCCGEEEIIMSsSUBJCCCGEEEIIIYQQsyIBJSGEEEIIIYQQQggxKxJQEkIIIYQQQgghhBCzIgElIYQQQgghhBBCCDErtmw34EyRTCb55Cc/SUtLC4lEgjvvvJMVK1bwiU98AkVRWLlyJZ/97GdRVSuG19vby1vf+lZ++9vf4nA4GBoa4u677yYajaLrOl/5ylcoKCgYs41YLMZHP/pRenp68Hg8fOlLXyI3N5fbb799ZJnjx49z44038nd/93cZ3X+xtGTzeH3iiSf46le/isvlYseOHbz//e/PxlcglohMHKuv+uMf/8hDDz3E1772tZHX/vM//5Pu7m45p4pJzfUY7e/v56Mf/SihUIhgMMg999xDXl7emG1Mdj4FMAyDu+++mze/+c1cfPHFGd9/sfhk85h89tln+dKXvoSiKFx88cV84AMfyMZXIJaAbB6nf/jDH/jyl79MSUkJAB/84Ac577zzMv4diMUvm8fpknnGN8W8+OUvf2nec889pmmaZm9vr3nJJZeYf/M3f2M+/fTTpmma5qc//WnzD3/4g2maprlr1y7z+uuvNzdt2mTGYjHTNE3zP//zP80vfelLpmma5s9//nPzC1/4wrht/PCHPzT/9V//1TRN0/zd735nfv7znx/zflNTk3njjTeaoVBoYXZSnDGydbwahmFecsklZlNTk2mapvmRj3zE3Lt378LurFjSMnGsmqZpfv7znzevvPJK88Mf/rBpmqYZjUbNj3zkI+YVV1xhfuUrX1nQfRRL21yP0S9+8Yvmd7/7XdM0TfPJJ580P/nJT47bxmTX/8bGRvOtb32ruXPnTvOxxx5b2B0VS0Y2j8kbb7xx5Bp/2223mYcOHVrAPRVLWTaP069//evmQw89tLA7KM4I2TxOX7XYn/Flyts8ecMb3sDf/u3fjvy/pmkcOnRoJNp98cUX89RTTwGgqio/+tGPCAaDI8vX1tYSDocBCIVC2GzjB4/t27ePHTt2jKxv9+7dY97/p3/6Jz760Y/i8Xjmdd/EmSdbx2tfXx9+v5+KigoANm/ezHPPPbcg+yjODJk4VsE6Fv/hH/5h5P/j8Tg33HAD73vf++Z5j8SZZq7H6NGjR0dGFm3evJl9+/aN28Zk1/9IJMI999zD+eefvyD7JpambB6Tv/jFL6ioqCAcDo/0yAsxkWwep4cOHeL+++/n1ltv5Ytf/CKpVGpB9lEsfdk8Tl+12J/xJaA0TzweD16vl1AoxIc+9CE+/OEPY5omiqKMvD80NATAhRdeSE5OzpjP5+Tk8OSTT3L11Vfzgx/8gDe/+c3jthEKhfD5fOPWB3D48GHC4TDbt29fqF0UZ5BsHa+5ubnEYjGOHTuGYRjs2rWLSCSywHsrlrJMHKsAV1999cg6AQKBABdddNEC7ZU4k8z1GK2rq+PRRx8F4NFHHyUWi43bxmTX/9WrV7N8+fIF2zexNGXzmLTZbDz//PNce+215Ofnj0zNFOK1snmcXnjhhXz605/mJz/5CZFIhPvuu2/B9lMsbdk8TmFpPONLQGketbW18Y53vIPrr7+ea6+9dmQuJUA4HMbv90/62W9/+9u85z3v4YEHHuAHP/gBH/zgB2lsbOT222/n9ttv53/+53/wer0jPe2vXd9vfvMbbr755oXbOXHGycbxqigKX/7yl/mHf/gHPvShD1FdXT3uxCvEay30sSrEXM3lGL3jjjtoaWnhXe96F21tbRQXF8/q+i/ERLJ5TG7cuJFHH32UNWvW8P3vf3/hdlIsedk6Tm+66SYqKipQFIXLL7+cl156aWF3VCxp2TyfLoVnfEnKPU+6u7t597vfzWc+85mRCOKaNWvYs2cP559/Prt27WLbtm2Tft7v949EJvPy8giHw1RWVnLvvfeOLDM0NMRjjz3G+vXr2bVrF1u2bBl57+mnn+a9733vAu2dONNk83jdtWsX3/ve93C5XHzgAx/gTW960wLuqVjqMnGsCjEXcz1Gn332Wa6//nq2bdvGww8/zObNm2d1/RfitbJ1TJqmydvf/na++93vEggE8Hg8JBKJBd9fsTRl8zi97rrruO+++yguLmb37t2sXbt2wfdXLE3ZvsYvhWd8xTRNM9uNOBPcc889PPjgg9TU1Iy89vd///fcc889JJNJampquOeee9A0beT9yy67jAcffBCHw0FHRwef+tSniEQipFIpPvShD3HhhReO2UY0GuXjH/84XV1d6LrO1772tZFqRTt27ODxxx/PzM6KJS+bx+svfvELfvKTn+B0Orn22mu57bbbMrbfYunJxLH6qj179nDffffxjW98Y+S1//3f/+X48eOLs6qGWBTmeow2Njby8Y9/HIDCwkL++Z//Ga/XO2YbU13/AT7xiU9w9dVXS5U3AWT3mHzkkUf4/ve/j91up6CggHvuuWfR5v0Q2ZXN4/SJJ57gm9/8Jk6nk+XLl/OpT30KXdczs+NiScn2NX4pPONLQEkIIYQQQgghhBBCzIrkUBJCCCGEEEIIIYQQsyIBJSGEEEIIIYQQQggxKxJQEkIIIYQQQgghhBCzIgElIYQQQgghhBBCCDErElASQgghhBBCCCGEELNiy3YDhBBCCCGWmssuu4yWlpaR/9d1naKiIl7/+tdz1113jSsLPJmjR4/S3NzMzp07F6ilQgghhBALQ0YoCSGEEEKchve+97088cQTPPHEEzzwwAN85CMf4fe//z3vec97SCQSM1rH+9//fl588cUFbqkQQgghxPyTEUpCCCGEEKfB7XZTUFAw8v/Lli2jsrKSm266ifvvv5+3ve1t067DNM2FbKIQQgghxIKREUpCCCGEEPNk7dq1bNmyhQceeACAhx9+mJtuuon169ezYcMG3vrWt3LgwAEAbr/9dpqamvj2t7/NZZddBkAikeCLX/wiF110EZs3b+a2227j+eefz9buCCGEEEJMSgJKQgghhBDzqLa2lpdffpkDBw7w4Q9/mDe96U088MAD3HvvvQB8+tOfBuBb3/oWZWVlvPvd7+aXv/wlAB/72MfYu3cv3/zmN7n//vvZtm0b73jHOzhx4kTW9kcIIYQQYiISUBJCCCGEmEd+v59QKISu63z2s5/l7W9/O+Xl5axfv56bb76Zl19+GYBgMIimabjdbnJzc2lsbOTBBx/ki1/8Ilu3bqW6upoPfOADbNmyhR/96EdZ3ishhBBCiLEkh5IQQgghxDwKh8P4fD7q6urw+Xx873vf4+jRozQ2NlJfX086nZ7wcy+99BIAt9xyy5jXE4nEjJN8CyGEEEJkigSUhBBCCCHm0aFDh1i7di1PP/00733ve7n88svZvHkzN910Ew0NDXz2s5+d8HO6rgNw33334XQ6x7xnt9sXvN1CCCGEELMhASUhhBBCiHly+PBh9u/fzxe+8AV++tOfcuGFF/LNb35z5P0nn3wSsKq7KYqCoigj761cuRKAnp4eLrjggpHX//Ef/5Hly5dz2223ZWYnhBBCCCFmQHIoCSGEEEKchkgkQldXF11dXZw8eZJf/epXvPe97+Xcc8/luuuuIzc3lyNHjvD8889z8uRJ7r33Xv7rv/4LYGQKm8fjoaGhgY6ODiorK7n66qv59Kc/zWOPPUZTUxPf+MY3uO+++1i+fHk2d1UIIYQQYhzFNE0z240QQgghhFhKLrvsMlpaWkb+3+PxUFZWxtVXX8273vUuXC4Xvb29fOpTn2LPnj1omsaqVat429vext13381PfvITtm7dyq9//WvuueceVFVl9+7dRKNRvva1r/Hwww8zNDTE8uXLueuuu3jd616Xxb0VQgghhBhPAkpCCCGEEEIIIYQQYlZkypsQQgghhBBCCCGEmBUJKAkhhBBCCCGEEEKIWZGAkhBCCCGEEEIIIYSYFQkoCSGEEEIIIYQQQohZkYCSEEIIIYQQQgghhJgVCSgJIYQQQgghhBBCiFmRgJIQQgghhBBCCCGEmBUJKAkhhBBCCCGEEEKIWZGAkhBCCCGEEEIIIYSYlf8PuH+5v5UZLZcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot transactions over time\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(20,6))\n",
    "plt.title(\"Transactions over time\",fontsize=20)\n",
    "sns.lineplot(data=fill_dates)\n",
    "plt.xlabel(\"Date\",fontsize=15)\n",
    "plt.ylabel(\"Transaction\",fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there is an increase in purchases in December and a break in late December. Let's zoom in on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Transaction')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fT3YuLi6OK1eu4OrqqoKTiIjIM1LRSURERID7+/ZYWVkRGhqaZO+gqKgoJk+eDDx8b53U2L17N0uXLk22dCswMBC4vzcS3F/yFx8fz507d5Jct3jx4sRr4+LiAGjbti1WVlYsXbqU4ODgxGsvXbrEpk2bKFy4cOLyMVtb28TXpaRz584AzJ07N0n/d+7cYdasWQBJnnKWFQUHBzNy5EhiY2N57bXXEvdMKly4MDVr1mTXrl388ssvSV6zYcMGFi1axO+//47ZbMbOzo6WLVty9uxZ1q5dm+TapUuXAjzyqX3Xr1/Hz88Pi8XCvHnzaNWqVYrXOjo60qJFCw4fPszWrVsTj9+4cQN/f3+8vLxo3Ljxk74NVK9eHS8vL37++edkxbNFixYREhJCmzZtnrhdERERSUrL60RERAQABwcHWrRowa+//kq3bt2oX78+ERERbN++nVu3buHm5sa9e/dISEh4omVrAEOHDmX//v289NJLtGrVirx583LmzBm2b99O8eLFad++PXD/6XlHjhyhV69etG7dGltbW/bv38+JEyfIlSsXt2/fJiQkBLi/NOqNN97gww8/pEOHDjRp0gSLxcLGjRuJjo5m+vTpif17eXlx4cIF3nrrLRo0aEDHjh2TZaxZsyb9+vXj888/p3379olPcdu+fTtBQUG8+uqr1KxZ8+ne3AwWGBiYZEPwqKgoAgMD+f333wkLC6NTp07069cvyWvef/99+vTpw5tvvknDhg0pWbIk58+fZ8eOHbi7uyfOSoP7T/g7ePAgY8eO5ddff6VkyZIcO3aMAwcO0Lx580cWbD799FPCwsIoXLgwp06dSrY5O0DlypVp2LAhACNHjuSPP/5g2LBhvPDCC3h4ePDzzz9z+/ZtFi5c+NA9sB7HbDYzffp0XnvtNfr06cPzzz+Pl5cXR44c4dChQ5QoUYKRI0c+cbsiIiKSlIpOIiIikmjatGnky5ePLVu28PXXX5MnTx4qVqzIwIED+emnn/jyyy/Zv3//I2eyPEylSpX4+uuvWbJkCfv27ePOnTt4eXnx0ksv8dprryXuAdS7d28sFgvffPMNa9euxcXFBR8fH+bOnYudnR2vv/46O3fuTFxS9frrr+Pj48OXX37J999/j8lkomrVqgwbNozKlSsn9j969GjeffddfvnlF27fvv3QohOAn58f5cqVY8WKFfz444/Y2NhQtmxZxo8fn+SpapldYGAgH330UeL3tra2eHh4UKtWLbp06ULz5s2TvaZYsWJ89913LF68mJ07d7J37168vLzo0KEDr7/+euKsMYC8efOydu1aFi5cyPbt29m7dy958+bltddeY8iQIY/M9mCp3OXLl5Nk/K+XXnopsehUoEABVq9ezQcffMD27duJj4+nTJkyzJw5k/r16z/xe/NAgwYNWL16NYsXL2b37t2Eh4eTN29e+vfvz5AhQ5Lt6yUiIiJPzmRJq8egiIiIiIiIiIiI/H/a00lERERERERERNKcik4iIiIiIiIiIpLmVHQSEREREREREZE0p6KTiIiIiIiIiIikORWdREREREREREQkzdkYHSAtHDlyBDs7O6NjpIno6Ohscy+StjQ2JCUaG/IoGh+SEo0NSYnGhqREY0MeReMj80mLzyQ6OpoqVao89euzRdHJzs6OsmXLGh0jTQQEBGSbe5G0pbEhKdHYkEfR+JCUaGxISjQ2JCUaG/IoGh+ZT1p8JgEBAc/0ei2vExERERERERGRNKeik4iIiIiIiIiIpDkVnUREREREREREJM1liz2dRERERERERCTtxMbGcuXKFaKiolJ9/bPu/yNp60k+E3t7ewoVKoStrW2aZlDRSURERERERESSuHLlCi4uLhQtWhSTyfTY6yMjI3FwcMiAZJJaqf1MLBYLt2/f5sqVK/j4+KRpBi2vExEREREREZEkoqKiyJUrV6oKTpK1mUwmcuXKlepZbU9CRScRERERERERSUYFp5wjvT5rFZ1ERERERERERCTNaU8nEREREREREcl0Pv74Y/bs2YOVlRUmk4lhw4bxwQcfABAQEEDRokVxcHCgffv2dOvWjU2bNvH1119jZWVFXFwcPXr0oGPHjgA0bdqU/PnzY2VlRXx8PBEREUyePJmKFSvi6+ubbP+jV155hcaNGz8018KFC9m5cyerVq3CxuZ+WaV79+7MnTuXwMBAhg8fTokSJbBYLMTFxTF16lSKFy+On58fJ06cwN3dHYvFQkhICP369aNLly7p+j4aSUUnEREREREREUnRtwevsOavy4+8JiEhASur1C+m6l6jMF2qF0rx/JkzZ9i2bRvffPMNJpOJgIAAxowZww8//ACAr68vEydOpHjx4gDs3r2bVatWsXTpUlxcXIiKimLYsGHY2dnRunVrAD777DPs7OwA+P333/noo49YtmwZADNnzkxsKzUCAwNZtmwZr7/+erJzderUYd68eYm5Zs2aldjP6NGjadiwIQAhISG0bduWzp07Z9uljFpeJyIiIiIiIiKZiqenJ1evXmXdunXcuHGDsmXLsm7duhSv9/f356233sLFxQUAe3t7xowZw4oVKx56/dWrV3F1dX3qfAMGDODHH3/k5MmTj7wuNDSUggULPvTcrVu3MJvN2bbgBJrpJCIiIiIikmlF3Avj5JGTVKlXAxtrzRkQY3SpXuiRs5KAZMvTnpWnpydLlizh66+/ZtGiRdjb2zNixAhatmz50OsvX76Mt7d3kmOFCxfm6tWrid/379+f6Ohobt68yXPPPceYMWMSz40ZMyZJ/gULFuDp6ZliPkdHR6ZMmYKfn1+yYti+ffvw9fUlJiaGf/75J3GWE8Ds2bNZunQpV69epXjx4ixYsCB1b0gWpaKTiIiIiIhIJnP2n/PsXrwE7xv2OHpV5m/vElQvkvIvwCLZzcWLF3F2dmb69OkAHDt2jIEDB1K7dm3c3d2TXZ83b14CAwNxc3NLPHbhwgXy58+f+P2D5XVz587lypUr5MqVK/Hcky6vA6hRowb16tVLVjj67/K6c+fO0bNnT3bt2gX83/K6nTt38sEHHyQrlGU3KpWLiIiIiIhkEqc2bWCb36tYLb+IZ4Qjl8P/Jtj5GtW8PYyOJpKh/vnnHyZOnEh0dDQAPj4+uLi4YG1t/dDrfX19mTVrFmFhYQCEh4cza9Ys+vTpk+za4cOHc/PmTVauXPnMOUeMGMGuXbu4ePHiQ8/nzp37occbNWpEs2bNGDdu3DNnyMw000lERERERMRAcbGx/D5vJU6BNkTG3iH/kZ2caFIPS72qvNRvotHxRAzx/PPPc/bsWbp164ajoyMWi4W33347cc+m/9W0aVPCwsIYMGAAJpOJhIQEunbtSps2bZJda2VlxdSpU+nTpw/NmzcHki+va926Nb17935sTjs7O6ZNm0bPnj0Tjz1YXmdlZUV4eDh+fn7Y29sne+2QIUPo3LkzO3bsSPFJeVmdyWKxWIwO8awCAgIoW7as0THSRHa6F0lbGhuSEo0NeRSND0mJxoakRGMj49wJvM3+OevxJg9uZk8i48M5HxZAuQF1KFS9rtHxktHYyFme9PNO6z2d5Nk96WfysM/8Wf/ca6aTiIiIiIhIBvp93SqOb9hJiwI9qWguzZ3oIAJMx6n9dndK5mlldDwRAWJiYnjllVeSHffx8eH99983IFHWpKKTiIiIiIhIOouJjmbrgi/wvmfG+Z6JiNgrnA09g1uVYlR/pR2VbG2Njigi/2E2m/H39zc6RpanopOIiIiIiEg6uR0cyvFl68h93YWK9uWIscQQ53Ga2o1epEGnHkbHExFJVyo6iYiIiIiIpLFjm35jz6pNNPBqR3HrkoSZbnAy/A8qDe9OseLNjI4nIpIhVHQSERERERFJI1vnfEj8vxYKOhYmLOpfTkacwap2MTr16USZFB71LiKSXanoJCIiIiIi8gwi7gSz+4vv8LxWkBOB+/CyK4jZ2o4G7X2p3UdL6EQk51LRSURERERE5CmcPX2RMx/9RjFzQcrZlCLUFEfBvJWoN7AT+cuWNjqeSJa3f/9+Vq1axbx58wgLC6NTp07MmDGD6tWrA3Dy5ElGjRrFunXraNeuHZs2bcLOzg6ATz/9lJ07dxIaGsrNmzcpUaIEAM2bN+f333/n008/Texn6NCh1KtXj169ej00R0JCAh9//DG7du3C+v/PWBw7diylS//fn/MOHTpQrVo1JkyYkHisQoUKVK1aFYDY2FgSEhKYM2cOhQsXpmnTpuTPnx8rKyuio6MpX748fn5+ifn/15UrV2jZsiWrV6+mQoUKAHzzzTfcunWLoUOHJrZnMpmIiIigS5cudO7cmf379zN8+PDE+w8PD6dQoUJ88MEHmM3mJ/9QnpCKTiIiIiIiIk9gw4IPsD8TTnmn5yjvWIYbEZe4lesKNUb2pZxdE6PjiaSPz194+PF+PwNgu2Uc3ApIfr7VdMhfCQ6vgCMrk70utZydnZk6dSpjx45l/fr1WFlZMXbsWGbMmIGTk1Oy6wcMGMCAAQOSFK4ALBYLO3bsYO3atXTr1o2ff/6Z2NjYFAtOcL+AFRwczNdff42VlRVHjx5lyJAh/PLLL9ja2nLw4EFKlSrFvn37CAsLw9nZGQA3N7ckT8BbtWoVn3/+OePHjwfgs88+SywyLVmyhHnz5uHn5/fI9+Cdd97h22+/fWjB6EF7MTExtGnThsaNGwNQp06dxPsHGDVqFNu2baNVq1Yp9pVWVHQSERERERF5jLC7oRyYuYLcET4E3T6PjSkO71yniWxUlepN+hgdTyRHqFWrFo0aNWLRokXY29vTrFkzKleu/ERtmEwmpk+fTu/evalatSpLly7lyy+/fORrVq9ezXfffYeVlRUAlSpVYt26ddja2gKwdu1aWrZsSf78+dmwYQMvvvjiQ9u5evUqrq6uDz3Xr18/2rRp88iiU5EiRahRowbz5s1jzJgxKV4XFRWFnZ0dLi4uyc7FxMRw8+ZN3NzcUnx9WlLRSUREREREJAVnT13g9Jf78YlxpqRtBSJt4yngUgSfl+tRrnYDo+OJZJzHzEyKbT4ZGweHlC+o2uf+f89oxIgR9OjRA3d3d5YvX/5UbeTLl49hw4bRo0cP5s6di6en5yOvj4qKSlak8fDwACAsLIyDBw8yZcoUSpYsyZAhQxKLTnfv3sXX15ewsDBCQkJ4/vnnGTZs2EP7sLe3Jzo6+rHZhw8fTteuXfnrr7+Snevfvz8mk4lz587RvHlzbGzul3z27duHr68vt2/fxsrKiu7du1O3bt3H9pUWVHQSERERERH5H1f27eLwx5uokLs5Fa0KcDPhOmeij9NkwmBKujc2Op5IjmVnZ0ezZs3InTt34v5KT6Njx47Mnj2bRo0aPfZaV1fXJMvmADZv3kzdunX54YcfSEhIYNCgQQAEBQWxd+9e6tatm7i8Lj4+Hj8/P2xtbR+6FBDuF69SOvdfZrOZ6dOnM2rUKLp3757k3H+X1w0cOJCNGzdSuHDhxOV1wcHB9O/fn0KFCj22n7RilWE9iYiIiIiIZHK7vvicnW+t5pK/P6bAk1yNuMjx8hFUm9eNtvPG4OSeMUtSRCTz6NSpEx999BEWiwWAQ4cOMX36dMxmM+vWrWPp0qUsX76c5cuXM3bsWFasWJHk9dbW1kyePJnNmzezY8eOh/bxySef0Lp161TlKV++PG3btuWTTz556Hmz2UyuXLmIjY1NctzDw4PZs2czduxYbt68maq+nlWGzXSKjY3Fz8+PwMBArKysmDx5MlFRUQwePJiiRYsC0KtXL9q0acOaNWtYtWoVNjY2vPbaazRpos34REREREQk/fzywVfYHT7OkbhTtCz0KqdyV6HewNrkq1zD6GgiOdoff/xB586dE7+fM2dOitf+dzPwdu3a0a9fvzTJ8Morr7BgwQJ69OiBjY0NNjY2LFmyhDNnzmCxWChZsmTitS1btmT69Olcu3YtSRv29vZMnTqVMWPGUKtWLeD+cjgrKysSEhIoW7Ysb7/9dqozDR48mO3btyc59t/28uXLR5s2bTh16lSSa0qUKIGvry9Tpkzhww8/fNK34omZLA9Kdelsy5Yt/PjjjyxYsIA//viDVatW0bBhQ+7du0f//v0TrwsKCqJ///58++23REdH07t37xR3Zn8gICCAsmXLZsRtpLvsdC+StjQ2JCUaG/IoGh+SEo0NSUlOGhsxUdH8NvNrCgW7kts+H3djorh05yMqjJxA4UrVjI6X6eSksSFP/nlHRkbi8Kg9nSTDPeln8rDP/Fn/3GfYTCcfHx/i4+NJSEggLCwMGxsbjh8/zvnz59m6dStFihTh3Xff5ejRo1StWhWz2YzZbMbb25tTp05RqVKljIoqIiIiIiLZ2N1bQRydthZPm9JUsSpFuE0oJ6PPUHl4K8oX+d7oeCJigKNHjzJ79uxkx1u3bk3v3r0zLMdHH33E/v37kx2fNm0ahQsXzrAcaSXDZjpdu3aNIUOGEBERQXBwMEuXLuX8+fOULl2aChUqsGTJEkJDQylTpgynT59m9OjRALz99tt07NiRevXqpdj2kSNHsLOzy4jbSHdRUVHY29sbHUMyIY0NSYnGhjyKxoekRGNDUpKdx0ZEcDD/fP8T1y+epUX+bthYu3Pc/RxFn6+Ms8vDH2Mu/yc7jw1JLjY2NsmyscexWCyYTKZ0TCRP6kk/k3///RdbW9tkx7PETKcvvviCBg0aMGrUKK5du8bLL7/MihUryJMnDwAtWrRg8uTJ1KhRg/Dw8MTXhYeH4+Li8si27ezsss00T01ZlZRobEhKNDbkUTQ+JCUaG5KS7Dg2Ak9f4sRHmylmV4i6pqb8aH2NAOvDdHx/LGUdmxsdL8vIjmNDUhYQEPBES7O0vC7zedLPxNbW9qHL655FhhWdXF1dEytmbm5uxMXFMXjwYCZMmEClSpXYu3cv5cuXp1KlSsyfP5/o6GhiYmI4e/YspUqVyqiYIiIiIiKSTfy57ntitl3A27Ec5RxLcS3yEqZqJobN+tzoaCIiOUKGFZ369u3Lu+++S+/evYmNjWXEiBEUK1aMyZMnY2trS+7cuZk8eTLOzs74+vrSu3dvLBYLI0aMyDZL50REREREJP3t+2ULF7f/i3ekO94uVQgNPUH88xWp2amP0dFERHKUDCs6OTk5sWDBgmTHV61alexY9+7d6d69e0bEEhERERGRbGKX/8/c2XmYarme4/S9YlwynyC2TAIN+w0xOpqISI6UYUUnERERERGRtBYXG8vOuZ/hdNubG+EOBEVewTXsGNX716V8jZFGxxORpzRjxgxOnDhBUFAQUVFRFC5cGFdXV06dOsXatWvx9PQkPDycF198kenTp7N582Z27tzJqlWrsLG5X+ro3r07c+fOpVChQg/tY+HCheTOnZtevXpRoUIFqlatCtzfNL9BgwYMHToUKyurDLvn7EjvnoiIiIiIZDmR9+7x04gZBIzeQOngcnhabIgqHEHX6e/TdNEblK9R3eiIItlKv1/6seHMhjT9+lH8/Pzw9/dn4MCBtG3bFn9/fxYtWkT//v0ZM2YMFouFd999lz59+lCmTBkAAgMDWbZs2VPdn5ubG/7+/vj7+7NmzRpu377NihUrnqot+T8qOomIiIiISJYRcvcO302bzqXxW6hiVx8ba3suRe7Ea3g5fP3a4uXtZXREEUlHffr0wWw2M2jQIBwdHenatWviuQEDBvDjjz9y8uTJZ+rDZDLRr18/Nm7c+KxxczwtrxMRERERkUzvwol/+XfZDu7G2XDdUo0yVlc4Yx1AjfFvUNatndHxRLK9z1t9/sivIyMjH3vN/379tPr06UO/fv345ptvkhx3dHRkypQp+Pn5sW7dumfqI3fu3AQHBz9TG6Kik4iIiIiIZGIXDp/kwrpjeMfmpaxjGc6HXyC81nlK9xpAORv9OiOS04SGhjJ16lQmTZrE2LFjWbt2LU5OTonna9SoQb169R76ILMnERgYSL58+Z41bo6nn9IiIiIiIpLp/LtjK9fWnsPbqTjFTfm5Gn+XO/nu0XqkL88ZHU5EDPPOO+/Qp08fevbsybVr15g0aRKzZs1Kcs2IESPo2rUrN2/efKo+EhIS+Oyzz3jhhRfSInKOpqKTiIiIiIhkKnsHdMPtj2N4tvqAKxEXcGzsTe2uWkInktN99tlnWFlZ0bt3bwCGDh1Knz592LBhQ5Lr7OzsmDZtGj179kx123fv3sXX1xeTyURcXBz16tVLsl+UPB0VnUREREQkx7py8gQnFv2Kd7calG3c0Og4AvyxbhP5PQZytuY2SnV3o0LdtkZHEhEDde7cOfHr/v37JzlnY2PD6tWrH/q6ihUrcuLEiUe2PXTo0MSvjx8//gwpJSUqOomIiIhIjnXmo52Ud61JyPf3CCkVjHsBD6Mj5XgH9+6jalReHNs3oEhdLaQTkWdz9epVxowZk+x4zZo1GTZsmAGJchYVnUREREQkRzoydxKu+zZzvtaLFHUrx4l5f1BpfBNcXJwe/2JJF8f37sc+KJAd+SyM6zrE6Dgikg0UKFAAf39/o2PkWFZGBxARERERyWh/79mDJbAKkaVLUnNWT/a63KCItRtH31tNbEyM0fFyrOv+f9I6/0vUr1HZ6CgiIpIGVHQSERERkRwlPj6e48uXY7YkENaqOfau7nQf242zYSfIZS7M2jdmGh0xR7p15hLFnCtxI/IGTbt3MTqOiIikARWdRERERCRHWf/WB9yOCOF47A+0fLlP4vH6817hwK0j3LOUYcvUuQYmzJn+/HwzZisrrErrVxQRkexCP9FFREREJMc4uOl3qttWp2autnT6YGmScza2tnT88FUqOV+jVGh1Nr4726CUOc/dq9cpEV2AG9HXqfa6ZjmJiGQX2khcRERERHKEqIhIzNvCwWQmpkVhzHZ2ya5xcXWnzFtdufzBHsrZVmfH/M9pPLyfAWlzlhMf/Ii3uQwXHc4aHUVEMpH9+/czfPhwSpQoAUB0dDTt2rXjxIkTnDhxAnd3dywWCyEhIfTr148uXe4Xrfft28fixYuxWCzExsbSsmVL+vbti8lkwtfXl8jISBwcHIiNjaVQoUK89957eHik/PTS0qVLs2jRIpo3bw7Arl272LhxIzNmzEjSXmRkJPXr12fEiBFcuXKF9u3bU758+cTsjo6OLFiwADc3t3R+5zIPFZ1EREREJEf4fcxnlHWqxAXX6zTq0C3F6zwKFuD88y5EbQ6jcGAB/vx2K7W6NMvApDlLZGQUp4NdCbe/TZMPBxkdR0RScNH3pSTfu3XqhHvnTtz6+BPCf/8dc506OLw+hJDv1nN3/fok1xbx/4q4oCACR44CoODcOdjkyZOqfuvUqcO8efMAiImJoVWrVpQpU4bRo0fTsGFDAEJCQmjbti2dO3fmzJkzzJw5k2XLluHl5UVcXBwTJ05k+fLlDBgwAICZM2dSvHhxAH744QfGjx/PwoULU8zg4ODAjBkzqFatGp6ensnOP2jPYrHQu3dvjh07hoeHByVKlEjy5Lw5c+awbt06XnnllVTde3ag5XUiIiIiku3tX+hPKcdyXIu4Qu23Ojz2+mqtWxLf3BOTxQr3fVac3Lk7A1LmTDumfYqz5TgXPP/FxtbW6DgikomFhYVhZWWFjU3S+TO3bt3CbDZjMpn45ptvGDRoEF5eXgDY2Njg5+fH6tWrH9pm+/btOXHiBNHR0Sn26+TkRL9+/Zg4ceIj88XExBAXF5fY939ZLBauXbuGq6vrY+4ye9FMJxERERHJ1oKDrnPvpBlP51jcO3pjazan6nXl2zRgy9mvsfo3FyeXf4ubdyEK+hRN37A5TFxsLN738mDtWRifsS2NjiMij1DE/6uHHs898FVyD3yVyMhIANw7358B9b9s8uRJsY1H2bdvH76+vphMJmxtbRk3bhybNm1i9uzZLF26lKtXr1K8eHEWLFgAwOXLl+natWuSNpydnYmMjCQhIeGhfbi6uhIaGkqeR8y+6t27N1u3buXHH39MtjxuzJgxODg4cPnyZcqUKYOHhwc3b97kzJkz+Pr6EhISkrg0sFOn5O9NdqaZTiIiIiKSrf08aTH/WPJw3OEQJZvWf6LXNh/6ImHFzpHXxpGrC/4k5GZQOqXMmTZ9uho3+wJccr6CnYO90XFEJBOqU6cO/v7+fPXVVyxfvpxGjRoBMHr0aFauXMmkSZO4efMm3t7eAOTNm5fAwMAkbYSFhWE2m7GySl4CsVgs3Lp1i1y5cj0yh8lkYtq0acyfP5+bN28mOTdz5kz8/f3Ztm0b7u7ufPrppwCJy+vWrl1LgQIFyJUrV7JZWtmdik4iIiIikm3tXLuLhk7NyO9wiE7v+z1VG+2Hv46jWz5srL3YPOprYqNSXoIhT6bY6XgiLZHUfLOH0VFEJItq1KgRzZo1Y9y4cQD06tWLJUuWEBR0/x8JYmNjmTp1Kj179nzo69etW0edOnUeWpD6X/ny5WPo0KHMmTPnoeetrKzImzcvsbGxSY7b29vzwQcfsHjxYk6dOvUkt5fl5awSm4iIiIjkGJdPBJBvfzThxNDIb8AztfXCvJF8P2gK92zgN7+PeWH+0DRKmXP9On0B5e2qcTl0JyVzPW90HBHJwoYMGULnzp3ZsWMHjRs3ZsSIEYwYMYL4+Hji4uJo0aJF4ibi8H/L4eD+zKgJEyakuq+OHTuyefPmJMf+2569vT2zZ88mLCwsyTW5c+fm7bffZvz48axatSpVRa7swGSxWCxGh3hWAQEBlC1b1ugYaSI73YukLY0NSYnGhjyKxoekJCeMjYDXFuDsWpWTXudoOarvM7cXHxfH1rc+pZx9ec7aXqbR5N7PHjITyqixsWvopxSyL4KlZy6KV6+W7v3Js8sJPzfk/zzp5x0ZGZlYeJHM4Uk/k4d95s/6514znUREREQk21k/eSE13apxJ2wvLUe9nSZtWtvYUH/ay/zjt55iDgX5ze9Dnp8xLE3azmn27DjMwZs/cClPZV6sPtnoOCIirF69mp9++inZ8ZEjR1K1alUDEmUPKjqJiIiISLZyetduit3JR7DpFsWmDEzTtp0cHSg9sR3n3t9CabuK7Jw0h0YTRqVpHzlBxLq/8XGpQYWhfY2OIiICQI8ePejRQ/vLpbWcsYhQRERERHKEmOhoti35kh3XVnO9YhjO7u5p3oeTpws2XbwIjwmhcFgltn7xWZr3kZ2d2bWHXHZFyeVYlhKlvY2OIyIi6UhFJxERERHJNra89yVNCnbCy6koLfr5pls/ZZ+rj7mFIwfvbOXwr7/w55Yt6dZXdrP742/4/fYhPF/wMjqKiIikMxWdRERERCRbOPnHSULCvLkTHUunDyele38l2rTEtkUpyrvVwfUXC4GHT6Z7n1ldwO7faZC/LUVsIinfspHRcUREJJ2p6CQiIiIiWV54WBiXV2whNOokHn3KYO/klCH9duzZn9zF8uFgbcetr85zNfBmhvSbVUVsP4G9tSOOFXIbHUVERDKANhIXERERkSxv/6yvKO9alXC3vyhTp3SG9l3z7d788O6nVDWX4tLsbeSa3gk7B7sMzZAVXDl9Gs+wosSYT9Ni+CCj44hIFrB//36GDx9OiRIlAIiOjqZdu3acOHGCEydO4O7ujsViISQkhH79+tGlSxcA9u3bx+LFi7FYLMTGxtKyZUv69u2LyWTC19eXyMhIHBwciI2NpVChQrz33nt4eHikmKN06dIsWrSI5s2bA7Br1y42btzIjBkzEnM1bdqUfv36MWDAAACuXLlCs2bNGDVqFAMH/t9DLQYPHkx4eDj+/v5Jsjzwyiuv0Lhx4zR9H42kopOIiIiIZGmHv1hFycgyhCScpt301w3J0H7aAH55dwUV7L056LeBmrM7YWs2G5Ilszq+4EcquNTieJ4EihkdRkSe2Po5h1I8V6ZufopWdWf9nEOUqZufsvXyP/b6svXyp6rfOnXqMG/ePABiYmJo1aoVZcqUYfTo0TRs2BCAkJAQ2rZtS+fOnTlz5gwzZ85k2bJleHl5ERcXx8SJE1m+fHliQWjmzJkUL14cgB9++IHx48ezcOHCFDM4ODgwY8YMqlWrhqenZ7Lzv/76K23atGH9+vX0798fK6v7i8q8vb359ddfE4tOISEhXLx4kdy5/2+253+zZEdaXiciIiIiWdbt69exPepAjCWefC/XMbTQ02paH85FXCC3bW42DnzZsByZUXREFCXNpbgdGUirYa8aHUdEsqiwsDCsrKywsUk6f+bWrVuYzWZMJhPffPMNgwYNwsvr/sMKbGxs8PPzY/Xq1Q9ts3379pw4cYLo6OgU+3VycqJfv35MnDjxoefXrl1Lly5dKFOmDDt37kw87uHhQa5cuTh79iwAGzdupFWrVk9yy1meZjqJiIiISJZ14aNvyWuuxEHTPjpUamF0HOrM6sYG3+7csLHjp2FTafvhe0ZHyhQWr/mVUlbx5Kmc1+goIvKUOo2q9sjzkZGRSa553PWptW/fPnx9fTGZTNja2jJu3Dg2bdrE7NmzWbp0KVevXqV48eIsWLAAgMuXL9O1a9ckbTg7OxMZGUlCQsJD+3B1dSU0NJQ8efKkmKN3795s3bqVH3/8ETc3t8TjFy5cIDIykjJlytClSxc+++wzmjRpknj+hRde4Oeff2bYsGFs3bqVkSNH8tdffyWeHzNmTJLldQsWLHjobKqsSkUnEREREcmSVi36krx3HbB1O0GHKaONjgOA2d6etp98yeH3NlDQvji/r/iU5/oMMDqWoWIiInHevo1jdu6M9h1vdBwRyWL+u7zugU2bNiUur9u5cycffPAB3t7eAOTNm5fAwEDKlSuXeH1YWBhmszlx2dt/WSwWbt26Ra5cuR6Zw2QyMW3aNPr06cNrr72WeHzt2rVERkbyyiuvAHDo0CEuXryItbU1AM2bN6dPnz507tyZPHnyYG9vn6RdLa8TEREREclkrpy8iNWhQP4JtZDr1bZGx0nC0d0D7yF1OB33Ndu2/84PK382OpKhdo//mNb5u1LB2THxlzARkbTSqFEjmjVrxrhx4wDo1asXS5YsISgoCIDY2FimTp1Kz549H/r6devWUadOnYcWpP5Xvnz5GDp0KHPmzAEgLi6OjRs3smLFCpYvX87y5csZOHAgK1euTHyNk5MTPj4+zJ49m7ZtM9f/rzKCZjqJiIiISJZzafFW6uSuz/E8lyhYpJDRcZIpXL4sCSPfx3r6WooctuGPmO+o37ez0bEyXEx0NLniCxJmuUvTcYONjiMi2dSQIUPo3LkzO3bsoHHjxowYMYIRI0YQHx9PXFwcLVq0SNxEHJIuacubNy8TJkxIdV8dO3Zk8+bNAGzbto3y5cvj7u6eeL5z58506NCBbt26JR5r164d48ePZ+7cuVy4cCFJe/+7vK5169b07t37SW4/UzNZLBaL0SGeVUBAAGXLljU6RprITvciaUtjQ1KisSGPovEhKcnKY+PHUTOpaluPM6FHabzYmKfVpdaWL9dT9LgjcQmxWDrno2z9GkZHeqy0HBvbx31EydjKHLu3n9aL3kqTNsU4Wfnnhjy5J/28IyMjkxRPxHhP+pk87DN/1j/3mukkIiIiIlnG+b9OUNaqCsHRQVQcm/lnDjV/uRO7Z3xCoTvFCV1/jSsFL1KoaBGjY2UYz3tFibKNodZb3Y2OIiLySKtXr+ann35KdnzkyJFUrVrVgETZg4pOIiIiIpIlxMbEcOubc3jZuBJSK45cBfIbHSlVGvi9yvoZS6keXI4780/g9q4TLl65jY6V7n5evZnK9m6csrpACZ9mRscREXmkHj160KNHD6NjZDvaSFxEREREsoTfRg3DFUdOx52mRq8XjI7zRDr5DeZq3BniTJF8Pfw9YmNijI6U7s79+h1/hx6gxKtNjY4iIiIGUdFJRERERDK937fs41zQbfadn0vDyS8aHeep1JnTn323f8FsZcOfb31qdJx0tWflOopEOXA9/BhFfQoaHUdERAyiopOIiIiIZGpx0dGYf7tL00L9qTl5HnaOWXej2lc/XUQlt8oUcazId++vMjpOuok8GEXlwh2p8dJLRkcREREDqegkIiIiIpna9iG9SDjpz/HCkZQrX8roOM/E1mymypx+XIy7Sc3wAmyfusDoSGnuUsBR7oYs5S/7n6ncrInRcURExEDaSFxEREREMq1fZ3+Em2NbLtv/QLc3OhgdJ03Y2ttS7M0a3Jq7h+J3K7Jr9jIajh5kdKw0E/DhFmytWlCyrQpOIvJs9u/fz/DhwylRogQA0dHRtGvXjhMnTnDixAnc3d2xWCyEhITQr18/unTpAsC+fftYvHgxFouF2NhYWrZsSd++fTGZTPj6+hIZGYmDgwOxsbEUKlSI9957Dw8PjxRz3L59mxkzZnDx4kVsbGzInz8/fn5+5MmTh1GjRnHz5k0CAwOxtbXFy8uLUqVKERQURIUKFRg4cCAA4eHhdO7cmQULFlCmTJn0f/MyCRWdRERERCRTunvtJt7Xy2Bjb8Hl9eFYWVsbHSnN5C9cBNtXwrnz2SUKBZVm2+q1NO3RzehYz+za3/9QyqUGN6OuU6ZmQ6PjiEgaWj3JL8n35Rs1p0Lj5uzfsJYLfx+kXo+XKFymHD/Nn0n43WB6TJhBeEgwPy2YmeR1tTp0w6dK9VT3W6dOHebNmwdATEwMrVq1okyZMowePZqGDe//nAkJCaFt27Z07tyZM2fOMHPmTJYtW4aXlxdxcXFMnDiR5cuXM2DAAABmzpxJ8eLFAfjhhx8YP348CxcufGj/FouFwYMHM3jwYJo1u/8kzj179jBo0CDWrl3LnDlzAFi4cCG5c+emV69eANy5c4cuXbrQtGlTSpQowcyZM+nRo0eOKjiBlteJiIiISCZ1dOYmXGztuO0cSPkmjY2Ok+ZyVyhHSINYDt3ZwZENazn421ajIz2zM5/swtbKhG1F/du2iKS9sLAwrKyssLFJ+jPm1q1bmM1mTCYT33zzDYMGDcLLywsAGxsb/Pz8WL169UPbbN++PSdOnCA6Ovqh5w8fPkyuXLkSC04A9erVo0iRIhw4cCDFrJ6enowbN46xY8fy559/cvnyZfr16/ekt5zl6f8GIiIiIpLp/Dj/C6rYFeNs9FkazehrdJx0U6tzO6Li43DcbIf9L2EctT9ApYY1jY71VK5euEx+B2+uR16hxqu9jI4jImmsx4QZDz1eu2M3anfsRmRkJABth49JPOfk7pHi61Jr3759+Pr6YjKZsLW1Zdy4cWzatInZs2ezdOlSrl69SvHixVmw4P4eeZcvX6Zr165J2nB2diYyMpKEhISH9uHq6kpoaCh58uRJdu7q1asUKlQo2fGCBQsSGBj4yOxNmzZl8+bN+Pn58c0332AymVJ729mGZjqJiIiISKZy5cQFSl3JQ1jcXYoPfc7oOOmuYbdOxOV1JCjqElsXf8SZQ6eMjvRUDi/bib21I4G57xkdRUSykTp16uDv789XX33F8uXLadSoEQCjR49m5cqVTJo0iZs3b+Lt7Q1A3rx5kxWDwsLCMJvNWFklL4FYLBZu3bpFrly5Htp/gQIFuHLlSrLjFy5cIG/evI/N37FjRypXrpyqa7MjFZ1EREREJFP5buo0LoQd50bBGAoVK250nAzRfvKbhBR1oqDb88SsCOTIpt+MjvRE7gbfxj0silvRN3jhnf5GxxGRHKRRo0Y0a9aMcePGAdCrVy+WLFlCUFAQALGxsUydOpWePXs+9PXr1q2jTp06Dy1IAVStWpVbt26xdev9JdB+fn7MnDmTixcvUrt27XS4o+xFy+tEREREJNP4+8vVFHAtzKmoc7w5cszjX5CNdHy7P7/NWIrjnYJYbw3jrNt+itfLGr/QfDthKsE3L5CrTEWq2HR9/AtERNLQkCFD6Ny5Mzt27KBx48aMGDGCESNGEB8fT1xcHC1atEjcRBxgzJgxODg4APdnRk2YMCHFtk0mE8uWLWPGjBl8/PHHxMfH4+bmRp48eTh37hylS5dO9/vLykwWi8VidIhnFRAQQNmyZY2OkSay071I2tLYkJRobMijaHxISjLj2Diy6Vfcd1iwsr5D7nfa4ejiYnQkQ/zlvwbPYx7EJiRwt3YUtbp1yND+n3RsxMfGcfnNb7gRfoMqS4Zg5+iYjunESJnx54aknyf9vCMjIxMLOTlBUFAQkZGRicv6MqMn/Uwe9pk/6597zXQSEREREcPFxcYStykIk31B7tR2xzuHFpwAavh2Z9Oizyh9sThee2IJsP6Bsp3bGx0rRT998DHVXStiVyBEBScRybJWr17NTz/9lOz4yJEjqVq1arLjD9t0XJJT0UlEREREDHfynTnkc6zPqXt7aN4hZy2re5jWr/fnnxXrsD5kz58/buNfZyfaP9/s8S80gPfN3IRbh1J0SB+jo4iIPLUePXrQo0cPo2NkO9pIXEREREQMtXvdZlyt6hJ+7wKNPnjT6DiZRuk+XQlobM2tuDhcf47jb//NRkdK5pj/FvLY5eeW5STunp5GxxERkUwmw2Y6xcbG4ufnR2BgIFZWVkyePBkbGxv8/PwwmUyULFmSCRMmYGVlxZo1a1i1ahU2Nja89tprNGnSJKNiioiIiEgGioqOIeCnX3D2aER8Gy9sHeyNjpSptGvXmgO2efHYGorpaDy/zPqIVm+/YXSsRDEHw4i1gcrvDDI6ioiIZEIZNtNp586dxMXFsWrVKl5//XXmz5/P9OnTGT58OCtXrsRisbB161aCgoLw9/dn1apVLF++nLlz5xITE5NRMUVEREQkA/008QuiY4I4mD+U6i+0NDpOplSzVTWCK94izhJNyaDS/DFrudGRANixZDmRcdc4HXUG59yuRscREZFMKMOKTj4+PsTHx5OQkEBYWBg2NjacOHGCWrVqAdCwYUP27NnD0aNHqVq1KmazGRcXF7y9vTl16lRGxRQRERGRDLLnkxVUTyhD5bzt6Tuql9FxMrWaL3XlTqUw4hJiyH+rCD98+InRkbi2/xp/3t5L2VfqGh1FREQyqQxbXufo6EhgYCCtW7cmODiYpUuXcuDAAUwmEwBOTk7cu3ePsLAwXP7ztBInJyfCwsIe2XZ0dDQBAQHpmj+jREVFZZt7kbSlsSEp0diQR9H4kJQYPTZioiI5sO1XPOxr4N2+CKdPnzYsS1bhXrMigVEHyf+POwXOu7BtyVzyN26d5v2kZmycPXKc+nkbciQ+P+H2Zv2cySGM/rkhGSs2NpbIyMhUX2+xWJ7o+seZM2cOJ0+e5Pbt20RFRVGwYEE8PDwYMWIEc+fOJSQkhLi4OEqVKsXw4cNxcnJi4MCBxMfHc+HCBTw9PXF1daVOnTq8+uqrD+3j0qVLzJo1i/j4eOLj4ylXrhzDhg3Dyur+/Jxff/2VCRMm8MMPP+Dl5QXAkiVLyJ07N926dUvSVo0aNahcuXKSY9OmTSNPnjzMnTuXf//9FysrK2xtbXn77bcpVKhQmr1XKXnSzyQ2NjbN/4xnWNHpiy++oEGDBowaNYpr167x8ssvExsbm3g+PDwcV1dXnJ2dCQ8PT3Lc5TGPzLWzs6Ns2bLplj0jBQQEZJt7kbSlsSEp0diQR9H4kJQYPTa+nfwh9b2ac87hIo07dTAsR1ZTtmxZDmzZRsTyhTj/5cK/wR60f7dfmvaRmrGxd9YyAhyrUeTFCvoZk4MY/XNDMlZAQAAODg6J399cdvSh13kNqkT4Xze4d+AqVlbWSc45Vc+LU4283Fx2NMnXD173KGPHjgXgu+++49y5c7z11ltERUXRrVs3pkyZkljgWb9+Pe+99x7Lli3D398fAD8/P9q0aUPDhg0f2cfixYt5+eWXadiwIRaLhTfeeIM9e/bQokULAL7//nt8fX354YcfGDp0KAC2trbY2tomeW8A3N3dWblyZbI+du7cyZ07d/jqq68A2LJlC3PnzmXJkiWPzJYWIiMjk+V8FFtb22R/xp+1CJVhy+tcXV0Ti0dubm7ExcVRrlw59u/fD8CuXbuoUaMGlSpV4uDBg0RHR3Pv3j3Onj1LqVKlMiqmiIiIiGQA71uuuJm9eP7t142OkuXUbN6UMtM/x6ZaL0rf8eLXLz7K0P4P/rQJJ2y5cOd3KtetnaF9i0jOtmPHDmrWrJlkRlGnTp0IDg7m8uXLT9xegQIFWL9+PQcPHiQuLo758+fTvHlzAC5fvszdu3cZNGgQ33//fZJJM08iX758HD9+nI0bN3Lnzh2aNWvGggULnqqtrCjDZjr17duXd999l969exMbG8uIESOoUKEC48aNY+7cuRQrVoyWLVtibW2Nr68vvXv3xmKxMGLECOzs7DIqpoiIiIiksxO/bCWvvQ/XQvdR0rOV0XGypLxFC3Cthw97PvuIOsf288sUC63GDs2Qvu/8do56Xu04W+xshvQnIpnDo2YmOdXIi1V51xRn1fz3tY+b4fQoly9fxtvbO9nxQoUKcfXqVQoXLvxE7Y0YMYKVK1cyd+5cTp8+TaNGjRg/fjyurq6sW7eOLl264OLiQpUqVdi8eTNt2rRJsa27d+/i6+ub+L2Xlxdz5syhdOnSTJ48mTVr1jBlyhTy5cuHn59f4v7W2V2GFZ2cnJweWs37+uuvkx3r3r073bt3z4hYIiIiIpLBrm4KpKyDDzaNSxsdJUurUq8aRUtN4+SkrZS7V5jNo+bRYs6IdO0z6NptituX5lLcRRoPSttlfSIij5M3b16OHk2+zO/ChQsUKFDgidvbt28fffv2pW/fvoSHhzNz5kwWL17M6NGj+fHHHylYsCDbtm3j7t27fP31148sOrm5uSUu7/uvU6dO4ePjw9y5c7FYLPzxxx8MHz6cP/74I3GP6+wsw5bXiYiIiIjERkZR3CoXdyLvUL1HR6PjZHnuufORb0BFgqODKG1TjS2jF6Zrf4c++AmzlR3WTVUwFJGM16xZs8Sn3j+wdu1aPD09n3iWE8Ds2bP5448/gPsTZXx8fDCbzezcuZMKFSrg7+/P8uXLWbduHbdv3+bUqVNP3MfevXuZO3cu8fHxmEwmSpYsiYODQ44oOEEGznQSEREREfl17jzKWlclyv2K0VGyjWKVK3FtgJmbn56mtF1lfp24kpYTe6d5P6E3rlPSVIhbUfeo88Jzad6+iMjjODk5sXTpUqZNm0ZISAjx8fGULl2auXPnPlV78+fPZ8qUKcyZMwez2UyhQoWYOHEib731VrKn03Xt2pUVK1bg5eXFxx9/zNq1axMz+fv7J1teBzBy5Eh8fX2ZOXMmHTt2xNnZGSsrK2bNmvV0b0AWpKKTiIiIiGSY4xevcTr0CP3m5ZxNVDNC/gpl4A1rri85Q7nIQqya+CU9J76cpn38PXUZPo5NsXg9+b/0i4g8rc6dOyf53tvbm6VLlz7yNTNmzEhV28WLF+fzzz9Pdvxh7b/66quJXz94kt1/HT9+PMV+3nvvvVTlyY5UdBIRERGRDHFp9yHau3XhuMcJPPLnMzpOtpO/REns3vZiz6SvuHphM18NPMBLH6fNk+3CQ+/y960Qbtj+QMfJ09KkTRGRjHD06FFmz56d7Hjr1q3p3TvtZ4VKUio6iYiIiEiGOLNqH6WcK1KhcjGjo2RbnnndqDmuK3dG/8Nz7h3Z+fYHNJr11jO3+9fHv1AiTwduO23HxtY2DZKKiGSMSpUqPXSDb8kY2khcRERERNJdXHQs+e0KExR1jXK9WhkdJ1vLmz8/XRZM4nb0BYpb1ebnCV88U3sx0dHkuuZEUXMsPSaMTZuQIpIlWCwWoyNIBkmvz1pFJxERERFJd3+uO4KLrTs33UKMjpIjOOXKRalJXblAEJWji7P/9elP3damSZ/gbuvGv5YT2NhplpNITmFvb8/t27dVeMoBLBYLt2/fxt7ePs3b1vI6EREREUl3pr+uE2PrRr3hvo+/WNKEq7s7tca15ORbn1PQrQGb31hG0wUDsLa2fqJ2fCJLEWkTTb3RL6ZTUhHJjAoVKsSVK1cICgpK1fWxsbHYavltpvIkn4m9vT2FChVK8wwqOomIiIhIurp48ACOlkguR96hmEdDo+PkKGYnJ0rMeInD760nlOJ8/foX9P7QF1uzOVWv3/jx11SyK8JpmzBK5s2dzmlFJDOxtbXFx8cn1dcHBARQtmzZdEwkTyozfCZaXiciIiIi6erwohlsufYV5hrxRkfJkZzdXKk7tw93Iy5QxNaOHW9/TmxMTKpea3v0DtHxUZQaoF8kRUTkyanoJCIiIiLpJiY6mqKuPSjrUou6L/c3Ok6OZWO24cUlvsRZR0B0JN8NnkVc9KMLT0dWfc7RoN84dOdrChUtmjFBRUQkW1HRSURERETSze6vlxMed4cEh9Qt55L0Y7azo9GsvgTH3cPeqT7731pLdERkiteH/h5LJY9G1H/zzQxMKSIi2YmKTiIiIiKSbmKv7weW8NzEfkZHEcDWbKbLkndw5RxFnLz5e9wXxERFJbvu3M7NWCwx2Fg5UaBSNQOSiohIdqCik4iIiIiki3/276d06CuEOg7BLVdeo+PI/2dtY0PjD/tzwXSQArYV+Gv0KkKD7ya55uKKVbjvWox395IGpRQRkexARScRERERSRcXVxzGxmTCtoD+ypnZmEwmGkwfzvGwI3g7FeefseuIDr0HwPkzV/Dx8OVcu/co3riFwUlFRCQr098ARERERCTNxcXFUcC2MHeig2g+/FWj40gKWn00lNNRR8hrX5ifX/+Q4KDr2P52EWuscKrhZXQ8ERHJ4lR0EhEREZE0t+Wb73G3deVUXpPRUeQxms4fyp+x27lqzseB8Wso7lCcG5HXeL6LZjmJiMizUdFJRERERNKc+/5w4hISqNu9stFRJBW6fjgdb49gyrpWwWxlQ2jRe0ZHEhGRbEBFJxERERFJU2ePHiI4NpAL905QuFgxo+NIKrX/4C1OeJzjsM15mo4cYHQcERHJBmyMDiAiIiIi2cvhT9dyNbYI+cvcMTqKPKFWfn0JCAgwOoaIiGQTKjqJiIiISJqqEV+eWJtwfEaMNjqKiIiIGEjL60REREQkzfz0iT82LkW44RKKja2t0XFERETEQCo6iYiIiEiasTuVQFxCLHleet7oKCIiImIwFZ1EREREJE0EXQikhHVBAqMvUrqynlonIiKS06noJCIiIiJp4s85K7C1siOmUJzRUURERCQTUNFJRERERNJEtFVBLkfdpP6wl4yOIiIiIpmAik4iIiIi8sx+X7mWG1G5uVkgGLOdndFxREREJBOwMTqAiIiIiGR9CX/coYWrK9at6hgdRURERDKJVBedEhIS+Pnnnzly5AixsbFYLJYk5ydPnpzm4UREREQk8wu9cZNLkcGExYTyQqWWRscRERGRTCLVRadp06axYsUKSpcujYuLS5JzJpMpzYOJiIiISNawc/56Euxr4VrirNFRREREJBNJddFp8+bNjB07lj59+qRnHhERERHJYgpH5qGkYzzF33jZ6CgiIiKSiaR6I/GwsDAaNGiQnllEREREJIs5vnk3ue3zcM7qArZms9FxREREJBNJddGpWbNm/PLLL+mZRURERESymFtbz5BgiafSyw2NjiIiIiKZTKqX1+XLl49Fixaxbds2ihYtivl//iVLG4mLiIiI5Cyh167hHV+EG7F3qVm+sdFxREREJJNJddHp8OHDVK5cGYCrV68mOaeNxEVERERynhMffUVh63pEO50xOoqIiIhkQqkuOvn7+6dnDhERERHJYg5ePUewXRwt5o0yOoqIiIhkQqkuOsH9GU4rVqzg33//xcbGhpIlS9K9e3cKFiyYXvlEREREJBP66+v1uFtsOW4OpI2DvdFxREREJBNK9UbiAQEBtGvXjo0bN+Lg4IC1tTXff/897du359SpU+mZUUREREQymbB9t2mYtxvtWnQxOoqIiIhkUqme6TRz5kwaNmzIrFmzsLW1BSA2NhY/Pz9mz57N8uXL0y2kiIiIiGQeoSEhFHYoyc2oO9Ro18noOCIiIpJJpXqm05EjRxgyZEhiwQnA1taWQYMGcfjw4XQJJyIiIiKZz6EZq7GztuJmnnCjo4iIiEgmluqik6urK+Hhyf9iERYWho3NE20NJSIiIiJZmEukKxFxYTQd3tnoKCIiIpKJpbro1LhxY95//30uXbqUeOzChQtMnTqVRo0apUs4EREREclc9m3diZd9Qc4nXMDe2dHoOCIiIpKJpXqK0siRI+nXrx8tW7bEw8MDgODgYCpXrsw777yTbgFFREREJPOI/+1zzsQVJ3enJkZHERERkUwu1UUnd3d3vv32W37//Xf+/fdf7OzsKFGiBHXr1k3PfCIiIiKSSYTevk3IjYLYB+2g+nPjjI4jIiIimdwTbcZkZWVFo0aNtJxOREREJAfauvArauZrxRFPV6OjiIiISBbwyKJThQoV2LVrF56enpQvXx6TyZTitcePH0/zcCIiIiKSefxUuDYBp/cw5L0BRkcRERGRLOCRRafJkyfj7OwMwJQpUzIkkIiIiIhkPvt+2saLh69xvFJZ3HPlMjqOiIiIZAGPLDp16tQp8WuTyUSbNm0wm81JromIiGDNmjXpk05EREREMoXwjSco7VoFK6c7RkcRERGRLMIqtRe+8847hIWFJTt+7tw55syZk6ahRERERCTziLwXRiHHYtyMDKRRnw5GxxEREZEs4pEznb788ktmzJgBgMVioX79+g+9rkaNGmmfTEREREQyhS3vL6KqTT0uxp8yOoqIiIhkIY8sOr344ot4enqSkJDAmDFjGDt2LC4uLonnTSYTTk5O1K5dO92DioiIiIgxvGIKEWWOoNqoHkZHERERkSzkkUUna2tr2rVrB0D+/PmpWrUqoaGh5Pr/m0ceOnSIihUrYmtrm/5JRURERCTDnT12knz2hbnGNUoUKmR0HBEREclCUr2nU548eWjVqhWffvpp4rE33niDdu3acfny5XQJJyIiIiLG2rToS7bcvYu5eQGjo4iIiEgWk+qi05QpUyhfvjyDBg1KPPbbb79RsmRJpk2b9tjXf/fdd/j6+uLr60v37t2pWLEiJ06c4Lnnnks8vnHjRgDWrFlD586d6d69O9u3b3+K2xIRERGRZxUbFUNlU2HsIrZSrcVzRscRERGRLOaRy+v+6/Dhw3z33Xe4u7snHnN2dmb48OH07Nnzsa/v3LkznTt3BmDSpEl06dKFkydP0q9fP/r37594XVBQEP7+/nz77bdER0fTu3dv6tevj9lsfoLbEhEREZFntWvNb5R2q0x8Piejo4iIiEgWlOqZTg4ODty8eTPZ8eDgYKysUt0Mx44d48yZM/To0YPjx4+zY8cO+vTpw7vvvktYWBhHjx6latWqmM1mXFxc8Pb25tQpPSlFREREJKPZnown2pJA1Te7GB1FREREsqBUz3R6/vnnmThxIu+//z4VK1YE7heQJk2aRPPmzVPd4bJly3j99dcBqFSpEt26daNChQosWbKERYsWUaZMmSRPyHNyciIsLOyRbUZHRxMQEJDqDJlZVFRUtrkXSVsaG5ISjQ15FI0PScnjxkbIiQCKJHhwKS6AmGteXL0WmIHpxEj6uSEp0diQR9H4yHwyw2eS6qLTW2+9xZtvvsmLL76IyWRKPN60aVPeeeedVLURGhrKuXPnqFOnDgAtWrTA1dU18evJkydTo0YNwsPDE18THh6epAj1MHZ2dpQtWza1t5KpBQQEZJt7kbSlsSEp0diQR9H4kJQ8bmxsW/Qz1s61ca/oojGUw+jnhqREY0MeReMj80mLz+RZi1apLjo5OTnx6aefcu7cOU6fPo2trS3FixenaNGiqe7swIED1KtXL/H7V155hXHjxlGpUiX27t1L+fLlqVSpEvPnzyc6OpqYmBjOnj1LqVKlnuimREREROTpxcfF4WWuQnB0GBX79TE6joiIiGRRqS46PVCsWDGKFSuW+H1MTAzHjh2jevXqj33t+fPnKVSoUOL3EydOZPLkydja2pI7d24mT56Ms7Mzvr6+9O7dG4vFwogRI7Czs3vSmCIiIiLylHZ88BH/hOahiOtVKtLa6DgiIiKSRaW66HTs2DHGjx/P6dOnSUhISHY+NVOuBgwYkOT78uXLs2rVqmTXde/ene7du6c2moiIiIikoegrtuS1BFB76IDHXywiIiKSglQ/dm7atGnY2dnx/vvvY2try8SJExkwYABms5l58+alZ0YRERERySAXzgZSwCYXrq7Fye1T1Og4IiIikoWleqZTQEAAX3/9NRUqVGDNmjX4+PjQo0cPvLy8+Oabb2jVqlV65hQRERGRDLBr+gcE3fuXil2HGx1FREREsrhUz3SyWCx4enoCUKRIEU6fPg1AkyZNOHXqVPqkExEREZEMExsTQ0XnBpRxe47nuzU3Oo6IiIhkcakuOpUsWZKdO3cCUKJECQ4dOgTA7du3H7rHk4iIiIhkLQG/biWPvRdWHi5GRxEREZFsINXL61599VVGjBiBtbU1L7zwAh999BFDhgzh1KlT1K5dOz0zioiIiEgGiPv9IlgVp8FbeqCLiIiIPLtUz3Rq2bIlq1evplKlShQsWJCPP/4YW1tbGjVqxOTJk9Mzo4iIiIikszN/HcCTUlwIO49LntxGxxEREZFsINUznQAqVqyY+HWdOnUoVapU4j5PIiIiIpJ1ha7agZdNHSIKRhkdRURERLKJVM90unv3Lu+99x7//PMPCQkJDB48mPr169OyZUsuXryYnhlFREREJJ3F3XYg+l4gzUYPMTqKiIiIZBOpLjpNnTqVw4cPY2try6ZNm9i7dy9z586lVKlSTJ8+PT0zioiIiEg6Wr1wGb9H/s5vphNY2zzRRHgRERGRFKX6bxU7d+7kk08+oVixYixatIgGDRrQunVrSpUqRffu2mxSREREJKsqdM6BwrlbkHtQHaOjiIiISDaS6plOMTEx5MmTB4A9e/ZQv359AEwmE1ZWqW5GRERERDKRwDOnKWguio1jLkpVrGB0HBEREclGUj3TqUyZMnz77bfkypWL4OBgGjduTExMDJ9++illypRJz4wiIiIikk6Of7iR8o7VCc5/x+goIiIiks2kuug0ZswYhgwZQnBwMK+88goFChRg4sSJbN26lU8++SQ9M4qIiIhIOslvXZi7MXdo8tqLRkcRERGRbCbVRacqVaqwe/du7t27h5ubGwCvvPIKb731Fs7OzukWUERERETSx6bl/lS0K0pA9FHK29kZHUdERESymSd6PElMTAyXL1/m3LlzWCwWAIKCggCoVq1a2qcTERERkXQTdugskQ65KfiiNhAXERGRtJfqotOWLVt45513CAsLSyw4PWAymQgICEjzcCIiIiKSPsLv3uPe+d1scz7BoOprjY4jIiIi2VCqi04fffQRNWrU4M0338TFxSU9M4mIiIhIOru27jD1yr7DlXJ3jY4iIiIi2VSqi04XLlzggw8+oESJEumZR0REREQygPW1fwj3yEWjlzoYHUVERESyKavUXlisWDFu3LiRnllEREREJAP8sfJr/o0/z+XwFdja2hodR0RERLKpVM90Gjx4MBMnTmTAgAEUKVIEs9mc5Lw2EhcRERHJGsKOWdPWeyjxnfXEOhEREUk/qS46DRs2DIAJEyYkO6eNxEVEREQyv9Dbtzk0bjXlXCpw2RRMvTpNjY4kIiIi2Viqi05bt25NzxwiIiIiko5OH75OhP8BSrhW5ErYeex7ljI6koiIiGRzqS46FSxYMMVz169fT5MwIiIiIpL2PntzJM/ZdcDV1o2/4o/Q8aOhmqUuIiIi6S7VRafLly8zc+ZMTp8+TXx8PAAWi4WYmBju3LnDyZMn0y2kiIiIiDy5oH9Oc+yDVYSE/ssR14NUfr03Has0NDqWiIiI5BCpfnrdxIkTOXPmDO3atePGjRu0b9+eKlWqcPv2bSZNmpSeGUVERETkCW2atoDby05RwrMRPk4laDWrL6Wq5DM6loiIiOQgqZ7pdPjwYT7++GNq1KjB9u3badSoEVWqVKFYsWJs3bqVbt26pWdOEREREUmFmKgY/lnsT/m7VYi0CueCy0E6zZxndCwRERHJgVI90ykuLi5xXycfHx9OnToFQLt27Th27Fj6pBMRERGRVDu05Q+O+X2Px81SRNv+S3xHNxqOHWl0LBEREcmhUj3TqUiRIvz999/kz58fHx8fjh8/DkBkZCQRERHpFlBEREREHm/jsq/5d9ch6uaqzEHLKdpN6o+VtbXRsURERCQHS3XRqXfv3vj5+ZGQkEDLli3p1KkTDg4OHDx4kMqVK6dnRhERERFJwZ2rV9k9fS2VHKsS4hxPTL18dOjZ0uhYIiIiIqkvOvXq1QtPT09y5cpFyZIlmTp1Kv7+/uTOnZtx48alZ0YREREReYhdn67g3K6jhMddwGzvSutp7fDIldvoWCIiIiLAExSd/Pz8GDRoED4+PgB06NCBDh06pFswEREREXm4uLg4Dr89hyK2dXHJlZtLdntpNaOf0bFEREREkkj1RuJbtmzB1tY2PbOIiIiIyGOc+PsUf45eTX5zPcLDL2DTzkyHGRONjiUiIiKSTKqLTu3atePDDz/k4sWLxMXFpWcmEREREXmI/VPmYvY/R0FzQQ4nBFDyg55UbNrE6FgiIiIiD5Xq5XV79+7lwoUL/Pjjj5hMJqysktarHjzNTkRERETSVlRYOF+M+YD8kRbc3KM5Vy2Odr0HGh1LRERE5JEeWXRq1qwZ69atw8PDg0GDBmVUJhERERH5//asWIfLITtK2DbiXNgOar9WnjIlSxkdS0REROSxHll0CgwMJCEhAYBOnTplSCARERERuW/rO19QLL4wFqsEHPIFMXD+RKMjiYiIiKRaqpfXiYiIiEjGOHvkOEGfBVDasTh3Yu9yr2Y49Xv3MDqWiIiIyBN5bNHp2LFjuLq6PrahatWqpUkgERERkZzs5L7fuO1/jiLOZTkbdYPaU9pi72xvdCwRERGRJ/bYotNrr72GxWJ55DUmk4mAgIA0CyUiIiKS04TdDWXPpFXkcfyeMCwcs4/ghZkjjY4lIiIi8tQeW3Ras2YNnp6eGZFFREREJEc6e+AP/pg5i3o+Q7gY0Y16U17AzSOP0bFEREREnskji04mk4kCBQqQK1eujMojIiIikqOsmfoFJYPsuGsNJyI20HrufMx2dkbHEhEREXlmjyw6PW5ZnYiIiIg8nZuXLnLmg4PUcyzOHasgKvV4mUadOxsdS0RERCTNPLLo1KlTJ+z0L20iIiIiaWrvwq9wu5APb8c8nIsIpOJ7LaiUVzPLRUREJHt5ZNFp+vTpGZVDREREJNuLj4tj1fg5NIirRaxVDMft/qbVjDeMjiUiIiKSLqyMDiAiIiKSU3w+eSYhd2oScO8UlpZOtJqkgpOIiIhkX499ep2IiIiIPLvfR02nlW1D/vLaSYvxfljb6K9hIiIikr1pppOIiIhIOvv9t30Usq7N7cgrtHprmApOIiIikiOo6CQiIiKSjqLCwjDvCiPeZCK8qRf2rq5GRxIRERHJECo6iYiIiKSjvX5fUjDOjju1nanXoZnRcUREREQyjIpOIiIiIulkg98kCtoX5VzYKWp1rml0HBEREZEMpQ0FRERERNLBoZ83cenCKa5aXaLj++8YHUdEREQkw6noJCIiIpLG4mPjSPjtHk0LdCO47F0KlChhdCQRERGRDKeik4iIiEga+3rRFsxxuXAzHaXNqyOMjiMiIiJiiAwrOn333XesX78egOjoaAICAli5ciXTpk3DZDJRsmRJJkyYgJWVFWvWrGHVqlXY2Njw2muv0aRJk4yKKSIiIvJMfvzgc0pcsuOAszM9pg0zOo6IiIiIYTKs6NS5c2c6d+4MwKRJk+jSpQuLFi1i+PDh1K5dm/Hjx7N161aqVKmCv78/3377LdHR0fTu3Zv69etjNpszKqqIiIjIUwk5f5FSV3OD2YqWvqWwsrY2OpKIiIiIYTL86XXHjh3jzJkz9OjRgxMnTlCrVi0AGjZsyJ49ezh69ChVq1bFbDbj4uKCt7c3p06dyuiYIiIiIk8kLjaWMx/vwNHWhdACVyhboaTRkUREsp3t/9xk++tL2PH6EvbM+YLYmBijI4nII2T4nk7Lli3j9ddfB8BisWAymQBwcnLi3r17hIWF4eLikni9k5MTYWFhj2zzwXK97CAqKirb3IukLY0NSYnGhjyKxkfG+efzn6hhVYej9qfwad0w07/vGhuSEo0NSUlmGBs24XFYm+wp6OSDdZA1p/02ERh7GaempfEsU8jQbDldZhgfklRm+EwytOgUGhrKuXPnqFOnDgBWVv830So8PBxXV1ecnZ0JDw9Pcvy/RaiHsbOzo2zZsukTOoMFBARkm3uRtKWxISnR2JBH0fjIGKe/+54qVCMo6hKNxvbCydnZ6EiPpbEhKdHYkJRkmrFRoyIX9hzkn5W/k8umIOWcKhO3z8KG71eS282K51/vh1epokanzHEyzfiQRGnxmTxr0SpDl9cdOHCAevXqJX5frlw59u/fD8CuXbuoUaMGlSpV4uDBg0RHR3Pv3j3Onj1LqVKlMjKmiIiIpJGIe2HsmZe9lz/cCbrKng23uB0UgOsL+bJEwUlEJKsrWq86zT4aTpX53Qhr6cRRpxtYoq9QjpJELT/H72O+Z8v4OYTfvm10VJEcLUNnOp0/f55Chf5vyuOYMWMYN24cc+fOpVixYrRs2RJra2t8fX3p3bs3FouFESNGYGdnl5ExRUREJA1ER0RycNw6fOyLs2+EPzVn98be0cHoWGnu8ISfcHRy4bTXFXq1aGZ0HBGRHKdMk2qUaVKN2NiOHP3yRy5fuED+mKLYx9TixvS/uB4dTEzxuzR44xVsbDJ8hxmRHC1D/8QNGDAgyfc+Pj58/fXXya7r3r073bt3z6hYIiIiksbiYmM5NGYtPk7FuRV1HR+XUhwas4FKU9ri7PboZfNZydrpX1HXtSxnw/6l0aQJRscREcnRbG1tqT7g/hPT792+w8n5n2ATVoBcDt5s/GsvIa/NwNo1L/meK0jNjm0MTiuSM2T40+tEREQke4uLjcV/4Azszd5cCD9NuVkdOBl+CvtYG053a0bw9atGR0wTJzf/QbU7+QiOuUXlsR2MjiMiIv/hksuT2pPHUP0jX9wGFSK3pwfu9vnJH2fPrm8Ws+W1Zfz67sfcvnjZ6Kgi2ZqKTiIiIpKmfh/3ERZTFIdv76LO3Jcwm808v/BV4s0/cCFfbU7P2M7FM6eNjvlMIu6FEf1TIFYma27VjMc9Xy6jI4mISArylSiO74cTabzwFey6FCO3gzd5HQpTPqEsEYvPcmCYP7+NWUD4nXtGRxXJdlR0EhERkTTz61tfUZIalPTIz4tL38bG1jbxXM0P/aFgMeyt7LkxfiDBF84amPTZ7J2wljwO+TkffZwmvboaHUdERFKpYsP6vPzFYirObUN4S7gUfxR3cx7KmaoR9P4OPuz9Kt9NmU1cdKzRUUWyBRWdREREJE38OXIO5W18uBJ5g9rTB2G2t092TefpI4gtcwbzRWfOzzvMwV9/NSDps9m74Svi2cmZmEs8N/s1o+OIiMhTKt3kOep/MIwC4+tzo9hdToefIjb+FsXvlOL8uD/YPuwzdn34mdExRbI0bd0vIiIiz+zXYTMo71ifkNDTlJvWFbPZNsVrawwcw6YrZspGeRL5WwSn816nVJV8GZj26f3z199E/RrChXt36D6/ObZms9GRRETkGTm5ulB9YFsY2JbyZ05z7tvDxN6yobhDcayuWnFs5HrOWl+ndPe6lK1Zxei4IlmKZjqJiIjIM/lp5seUdajHzYhLFJ3QFld3t8e+pvX7IzhbJh47a2dMK06x6/NVGZD02SQkJHB+0a/4uFamUNmmuOfOa3QkERFJYwVLlOK5MT2oMfsFQpvGcTrsMAkkUNZSmL/GjWRn39n8MnI+dy6dMzqqSJagopOIiIg8tXVT53LhjAeHI65Q4M26OOdNfSGmWf8WXKoUixUWCpz0YM+cz9Mx6bNbP3wcHoe+5m/+pM17w42OIyIi6axCy2Y0/WgYled24VZTRxyK1cDTvSJ54rz4atQbfD9sHDevXjM6pkimpqKTiIiIPJUN782iZmhVCltdoOG458lXvNgTt9H4xRbcKnODeEsct09d4ZtJY9Mh6bPbsfgLqts3426tF2k7daTRcUREJIPVb9OUrotnUWZWU0IKX6SwvQdVHVoQNO8EBz9ZY3Q8kUxLRScRERF5Yt999gPnzuzlZMghSvrWw8Mr91O3VfuVnoTWjeXMvZM4Xjaxb3HmmvF0LyiEfOcLE2exkKtDeaxtrI2OJCIiBrE1m2k2+h26+K/gqPdl7AHPf3Px/ejFRkcTyZRUdBIREZEn8uvn31ApwA4vp9IUH9CKcg3qP3ObNbt0pPmQgZR0q0CBiz78PO7jNEiaNg5P/h5nazN3SkRTve0LRscREZFMou3rfYiqb+FuTAjVrSuy871thNy4ZXQskUxFRScRERFJtX9XfkvZ457EASVf7kypmuXTrO2KDZuQd8RzBMdFUymmDNvfXpJmbT+tbSPnU8yxGP+GnaDWoDZGxxERkUymfMcWFJvUgpN28RSMsfDNyHfZ4r/c6FgimYaKTiIiki38e+Q4f7zxORuHfGh0lGxr66JPMR12ISEhFudWDtRuVCvN+8hVqABeb1bkVsRFSlpV4Iexxs142rlnH27xXtyJvknl9zoalkNERDI3VzdXnp/UmH1W24iODyH3Xw784TfN6Fg5zg9jZvP38HVsmb3U6CjyHyo6iYhIlnfuu58wfX6WIs4luHT3AKf2HzA6UrZzefMOip4rACRwrvQNCjVrmG595fcuQMkpnbgQf5VyMWVYP3g6cbGx6dbfw0SFRXDk4y/YdfM7Ipt74Jk/X4b2LyIiWc+LM6fStEdvbLCiCM+x841PiA6PMDpWtncz8BIn319KNVMdPO288L5eiMtnrhodS/4/FZ1ERCRL+3HkDKz2OWG2suGY4ynyORTlrP/vRsfKVv44cIBdPx3FNj6OyPK3aTr4lXTv09nNmaoTXuDvO4e4fO88W974kKjw8HTv94Ez7yyhfe5OOJWvT+0XmmZYvyIikrVV6tSRAmMacfbeKYo7l+H49J3s3f6n0bGyrZ0Ll7P27Y8JDjIR7HiCS0VvYGfjxNVFO4mJijY6nqCik4iIZFFRUVEcfn8RVc31uRt7h7stzbQe/yr5HEriZV2AO5euGB0xW/hn51HCP9vDjTsXONownIp9+2RY307OzrRfOJjiTiUo516D34Z+RnRY+heefh43BzfHmkRGnGXg2BHp3p+IiGQvngXy02jRq9yoFodHrB1eG0NY/+4so2NlK7GxsWwf9RVFrxTDy97CpVzXqDh+MPVf68ZB639IwINNb84zOqagopOIiGRBZ/bs48uBC7l1z4Y7VsfxHlOfiq1aABBTMpo/o734Y8k6g1NmfWf2/0n8Dzco5VKR0o2a07ZzrwzPYOtgT4uFb/J3yAEu2ZZj0xh/bt26k279Bf51kFKRZQiLu4vb8Gbp1o+IiGR/1bs34ULFe5wJPcmVoGJ8++4nhN27Z3SsLO/8sQscfnsjJW19uBMVSpVerek1cWLi+faTXuHK7ROEm4ryk5+KfUZT0UlERLKU7ev3EfftLfLZxXMx4TKVpr2GR77/22+n4bBXKWp/naIxJQgPCzMwadZ29fQFItYG4WRjx1n3QF4YYNyT28x2drRb9ha53QOo4VKes1N/5c7ltN+rIS42lmurzmBn5UhU8ZsULFU6zfsQEZGcpemLHagzqRf5XYKoFV+a30d/wsnN24yOlWVtmrKYuC9PkdfsyrG4C5T/oDUlmzdIco2NrS1NJnejooMdhaKKcGLTFoPSCqjoJCIiWchPM9bhsy8GKysznj55GDBnykOvi3YNwsPWjb0frMzghNnDlZMnuLnkAO7WTgTkOkezd32NjgRAz5lv8KfTefKbC3Dlg12c2rsvTdv/btJCvKzzc9TpPFWHpP++VSIikjO45ctL17mvccD6IKdD9nJszQV+e3+x0bGylIiwe+x+sQ0+waWJBa6VvUfrD3wx25ofen3uIt7cKhvM3pCrHF0TyN3g4IwNLIlUdBIRkUwv9PZtdr2+lCohebmdEE3Uy8V57t2UiwIv+A0iKuoOBW+5ZWDK7CE06DpnFm7D0+xFgPUJWr3d1+hISXQe9xLnEv7AzT4fceuuE3TuYpq0+9vHa7j870523N5Mq0xSZBMRkeyl87QRNGjXjdLOhSgbXoGdQz8iOjLK6FiZ3vY16zk2dgP/xNsTFPgZnq+VpE7f9o99XZOB/SnmY0ONPEXYN/XHDEgqD6Oik4iIZGpXDu7lyLg1FHMpz4V7AZR+uxqVKhR/5GvsHO05bxOMi3MBfp21JIOSZn2hIXf5Zcib/B28ncMR22k57XWjIz1U49nvctTtOG62zgQt+YXd3699pvZu3LhN4dOeVM/dnrqDe2Jja5tGSUVERJKq3qc7eV/34WrYPxR3qszRdzdw+Pc/jI6VKcXExLB6zDjOfb8BD5vcuJary3OrVpCv+KP/HvhfLScM5WzCecrb+LBz4mfpmFZSoqKTiIhkWpuW+/PLrLn8E/IHR8L/oMGigTjnyZWq15buW5OY+ChyXzClc8rsIS4+gaPjN1Ot6Gv4eHjSYeFEoyM9Urt33yCsfhy7r1/l+JpfOPjJmqdu69fpH3Mp1pqwEm6UrFE+DVOKiIgkl79ESaov6McBm1PksfXC+fu7/DljgdGxMpUbJ89w+K0fKR5ViriIm8S0NtFt3OinaqvhhO7cDb9C0Yhi7P78mzROKo+jopOIiGRK61b+TKl/vCmXvwNVmtWm7UK/J3q9d4Xy3Aw7RJzJxL69O9InZDYRHRHJFyP6sjn6KKctl2i/5FOjI6VK2XatyFPVkdp5WuJ+2outn/30xG38+OYMXMPCuWG9nbYjuqZDShERkeRsbGzoNOVVblS9Q3xCNNcC4OORk4iLjjU6muF+nv8Jdz+7QH6HPARbYunz1VoqNGv11O05ujgR2zoXsUCukw4EB6b9w0gkZSo6Sab00/BZ/D1iHf4vj+Dg6u+MjiMiGej21WtsHDSbcP/57LA+TK5XKlPzlaFP1ZZV/3r8mVCMfRsPpXHK7CMuNpYDY9ZR3ao1JTyDaPPBS0ZHeiKdRo7BulFREoBip5zZviD1m8f/s/5HytvVwMe5Cu3feboxJiIi8ixq9+qK08BSXLeOp3xCHY6MWUfAzt+NjmWI6PBIdoz5korXSmEhhlv5rtHiw/44uzg/c9tV2jxPUIkLOFm5s33Sh8TGxKRBYkkNFZ0kUwkNC+fQpE+oYl8XWyszN6POcGf7WQ4N/57Va48SHhFtdEQRSUeXD58mcN7fVPKog1Ohurw4bhClK1Z86vZq1XyOcuZA2kdX5sqRE2mYNHuIjYnh4FtrKOpUlHvx0fSclDWn9lfqVIvgpjZExd/DJzAfv8z76rGvCbkZRMyOcLBYiKtjwj1PngxIKiIiklzhMuV5ZdlQou0vk8cuP9Y/RvH7x78aHStD/fH9WraPXEcJUzGuxF7Fum9hqo3okaZ91B3Yl23B6zh/9zhr33gvTduWlKnolIkcuHCHM7v2Gh3DMOcPHOPcuO14RZbhtvk4PhOb0qrfMOJM1oRZ7An67RyH3v6SHa8v5uLPOeuHsEhO8PPEuUStvISrlSMn3M/T9ZP3sXN0eOZ27QrGY7Yyc+KTzWmQMnv5ze8TCjp4cy0ykHpz+2Brfvhjh7OCWq0bYNutEGEJ0ZS7XpRt499/5PV/T/kJD4eCnE/4k+o9u2RQShERkYezsbWl6YwBBHieAmwoctaB395YREx09v9H9x0r93H1h4OcCtvHufiT1JrZlRIVnv4fHR+l1/xZlHJtQH2PF9g1Y3m69CFJqeiUiZyftYSKZ4vy2xtzc9x0v1++XUnsqkt42Dpy2Pocld9/DQcXZ8q3akHrJSOpO6cF+ZrlxsbKgWLO5bH+3ZG/3lrPj+/NJfS61uSKZGVxsbHsHbmcCpHViEuI4lbVe7T0S7slXg3efoWbUdco6lCKyHA9lviBPSNWUtlcibNRZ6g0vUO2eGJbiTpVceiVl4vX93H66G7WTBj70Ov2bTqBj0NxLkVfpNkc/UuniIhkHq3GDMZ1QGluRF2nnHMltg6fT2jIbaNjpYvAf07zx7A1lDgai7dnbcp0rEvD2YPS9e8kdo6OVB7Qiqj4GPLfLs6pv46nW19yn4pOmUixlpW4HX2Dcs41OTxqNSd37TI6Uob48b2POL0lN+djj3Or1C3aTX052TW2NlZ07dmAuh/6YtPJkWv2x3DGjqrxNbk9bR+f9Huf3/y195NIVnP96lU+fWUSZlMJgqNCsHTyoEbPF9K8nxuFHHCycWHb/CffaDo7WvS6H1am/NyMCKHaxM5pMqMssyhevRoVpnbDI09Z6kS1YO1b05OcP7jtN/5eMZdT8VepMrm7QSlFRERSlq9MASrP6sDRe3up5PYJ+2a/y69zlhgdK039teQzQpcep4hjfv6NvkKVye1o1e3FDOnbu3YVLpWOxGxlxb1vzhIVEZkh/eZUKjplIjU6tMPcrzSnQw+T17EwNj9E8tcX2XcZ2e3Lgex+/VOqxlcmr801fIa2pfqAx/8CUKBOTWpOHILP+0245nyQa6HHiDIVoeBhODx8Ld+9u4AbV65kwB2IyLM4ffhfLn+wG7PlJkdDtlJueivKNaifLn01f6MV92LvUeS2U46bSfq//hg5GZewuxy4voFSk5rg4u5mdKQ051HIh2aT/TgRdpArV/bh/+pwYmNjiY2MJmLdLSzYcK9CNI7OWXc5oYiIZG9mOzvaLHqbux0Wky+yLQWu5OGHwb1IiI83OtoziY+L4/vXZ5DnvA+21g5ctN9Pk3m9sLO3ydAcLV7twCm7QPLberLrnc8ztO+cRkWnTMbGzoGmi4dx0u1vLCYbnE7YM/e1dwm9G2p0tDR1fvtfXJt7nKIupTl37zjtp3SmVMnyT9SGnaMDNccOp94XE3julbJcjb2NvbU9tRKqEb4ggH1vrGDnp1+n0x2IUQJOHWPb0BUc/CD1T6iSzGfje7OxWnmO3DaeFKjfnn7L38fsaJ9u/dna23I54QzRwdc48fWydOsns/tu/HwK2zakgkt1us/xw9nj2Z8Gk1nlzpefprNfw8M6D/U9OvDnqLVsm7YaH9fCVM5dmy59k8+qFRERyWzK1X0e2jhy7Op3XA5NYOuoz7l4/rLRsZ5KwP4D+L8xjaC7p7gWeZnw522oP/Etw/I0G9eVixEX8LYpzV/zpj/+BfJUVHTKpFq/+yZeIyqxI+oQ5pB/Of3uWi7v2W90rDSxbska4jYG42x24KTlbxoueg0HN5dnarNK/Vq0WPgqJaa35Gjuf7gVdZ38ToXJcyovy/u2ZM17M7l2SXs/ZXUX9+1nx4JviDflwuGaG3dvBhkdSZ5QXGwsh8cvoEJcLWLiozhXIYhWg9tmSN/13+vFucAv+HfrkQzpL7PZP38Z1aOrcDvmBkVHNiFv/vxGR0p3do4O9PzkI65FXaeIY2HKx/pwOe4GNWf7Gh1NREQk1So1b0b3rz7Hs1BdStuVJPyj4xz9/AejYz2R76Z+jNXaMCo4PodDYR9qzutN5edbGJrJxtaWEsMbEPPnWG7v387eTb8Ymie7UtEpE/MokIfXFo6giE998jh6E/t9JBsmLzU61lOLj4tj/VuvUO6MHXEJUQRVSOD5mW+kaR+2ZjNt3hpArYW+RHYwc9J5N/Hx1lSPqcm1GXvZ0LMzW+YtTNM+JWPs/3EbEWuu0cSxLvfiDuFq68qeSY9/LLpkHrcvXOL3d9aRJ6YaEZEBeA4oSbOXemVY/0658+BarD11Cr3MH1+tzrB+M4Nt0xZTILAkkbHXcXmlLJ7FihsdKcPYO9pTe05Xzt07zu3oG7j38skWm6aLiEjOYmNnR69ZwzmZ+wZmKwdcT7mzcdhso2M91t3gYNaPWsKFoz9yPORvQsvfoc/0CVjbZOxyupTkLeaD9dT3KVRgCObfQgm7c8voSNmOik5ZQLtpwwnu4ERIQgQ1wsvz15CPuHwiwOhYT+TahXPsHLqEK4F3+Sv4e6wGlKemb+N07bNMvTp0nTiJvss38E+Bm1y+d5DgBDOlr1fm+Fu/8cfUdZw7mbXex5zq3z/247ojAnsbF257R9B23ihuRd2mlFMNbpy6aHQ8SYW9P2xnhd9Ygu4c4bDNWYrN6ke+cuUyPIdX+wqcDdtCwu3su1/e/zqw5QBFg0sRHh9Dnv6V0+0RxJmZja0tDRe9RuV5XSlbvbrRcURERJ5aq9HdiW9jQ3D0bSo51mHfG18ScOoo8XFxRkdL5tTa77g+eRs1bSuQL3cDGk/qTdNXuhgdK5nyDVtwyu08EWe/Z8+bo4yOk+1kjvKiPFaVBvWIqHiXo35LKOhal/DPr7G72H4aDOlrdLTHun7pX44tn05p15eIiL9Nq4XvYGNnl2H9m81m2ozoCfTk9N69nFy5H2/7ihS5l5fYL67yR8QeYirk5bkBrbDJJBV3+T8HN/yM2x9x2Fk7cjRvIB3efOn+iTIm7C5YcfSTfbSYU8TYkPJIP81ZR7kbeSifqxVhrqdpN6WvYVmqtmnFxmPrib5QkZMH/qZczcqGZckIW9bsJGjfXvaYXSjbtTFlKpQ1OpKIiIg8o9JNGhBe/R4H/b4mxqYYf80Lwuy6ldj4aCJMdpgdL3ErNILIuFgi7e/gnD8fXpXKUa5hQ8z26f8Qjfi4OH4ZOZ9y9lUxmWI5lec0vWeMSfd+n0WXCSP4ceAoCts/x/cjF9Jh7lCjI2Ub+g07C3F0c6POEj9+m74E0+UCHN31C5f+DaHLtNewc8i4Is6T+PHdWXiEelLD7RcOVapP2z4TDc1Tqm5dStWtS3xcHJsXf4XLaQsFHX04c+QQc19eTbPCFSn2ajc8ivsYmlPu+3PlOtwP58balEBo7TA6dHkp8VyVwR3YOeJrStoW4MDufdRsUMfApPIwsTExrB8xjSt3/sXOvQtOLfPTrGNvo2NBruepGezFiS93Z+ui06Gff6XYX/aQqx15+5emYpWcs6ROREQku3NydaHh4tdY993XxPxzkZvXXLGyuGJnlx+XGC9c7d1IsMTz7cV5VAr3wCPIzNLvfsfHcoTCDt64RF0jMLcjIVER5LMNJ76QO5ZSJalUvRbOnq5PnevKv2eJmfMzld1rczPyKs6t3Gje+pU0vPP003DmOM5P2kxlm/L8uXgltYZkgr+3ZgMqOmVBz7/zGpcOH+XawpI0tKvOT8PnUq5bHco2b2J0tESxsbHseecnKlvXJswcwunqH9K8c1ejYyWytrGh1bD+AFwJuMixFQfwiC9MXuoSuuwCJyO24lAijspvDMg0641zmp2ffM7BrRsp4doUlyqONOmS/ElT1u1Ksu/rL/FZeBwa7DIgpaTk7sVLBCz9g3ouzdgZEUnld+ri5Z05Nq5uM6ALh4avp5hDOcLv3sPpGR9kkFnt9V9DEfc6WCraUrFKG6PjiIiISDro2vnFhx6PCg/n5I5dVD3ditDLwQTEHCeuRHVizllhsSRgay6Ad2wuitnYgAUu/PkPx/76m8JbzFyLjyAiIZyYuEjiLTaE2IcSX+giCaaieHoXp3KjGjh7JC9M7fptH2H7N9PKszVREXsoM/V1HF2yzpNy3TzcsWuWi9hdMTifdeb8qX/wKVPa6FhZnn6bzqK8q1ai17KyrJu0nGshfxK/MoyLO47Taorx0wAD/znN5UWHKe5YgOuRIeR+pQTlKpU3OlaKCpUtwogpY4iPi+P8inXcOBBFAUcfbK+bCXh3C8dtzuJW0JPa3Z/HM08uo+PmCLtX/UjB00UJda2BR00HGrz68EebN2ham31fzMXWfQhb5n1C8xGv/r/27jugqrr/A/j7Xu5g76GyBBQZigoO3KNyr9RcqZmPq+xxlCtnJqUNR+XIirQws2FljhzlxIGmEooTZQvKkHEv4wL3/P6w+D3mQrj3nnvh/fqPe8853/c59wMcPpxzvgZOSg9zaOOXqH/ZAe7KBvjL6jqGbVwGuUL/l3I/jRJPDVyzZDiw9nsMWmAa/317GnvmroanTUNkl8SibXfdTthARERExs/cygqhfXsDff//tV4AgLaVX5dpNEi6eAnl8ZdRaHMHitsVuKa+BrmZGSwk5rCU2cBCZg87rTP2nPgV7VwawTlFghPvPwt58xfhatkYFVIJ8stzINGUoIdVQxyQ2ePPcDleGm3ct9M9SmD/7th1cT1C8oKR9ulpeH7gywlIaohNJxMml8sxMmIKTm6yhmWcPZzK7RC3eAO8/jsE9i6uomS6cCQGkh0ZcLeoj8tF19Ft1ViTeU6SmUyGRi+NQKOXgKyr15C2eSckCIF11l1cvXkQgSnuOFmagbSiY3C38IRzK2+0GjGEP4R0bP/OaFw8kIEwm4awb++NjuNGPHb5erMWQvP1LaRevQJtRQWkZmYGSkoPc/yTr+Cd6gVBrkWWQxz6zhO/Ef4wbaYNxtV5++CX44gyjcbommI1UXAnC/7aZtDYCmgwtwUycjgLCxERET1IrlCgcWgLILQFHvfUx9tpKeidOhMJx5ORk5cISfPm0FYUobiiGFZSO9SXuUKhUCJdnYi+ixfAvZG7oXZBL/q9+SoOvLERgeZBOD4rEl0+miJ2JJNmGt0Aeqx2L49G7p3bSNqwA67FTZG2/AQuBKnRacKLBs2xY+nbuJUeitbWMmQ53MRz74036Pi65NLEHy7L781cIDnhDPVuOySr0lBYcRfSoiK0cGwPxANJb/6B/LISZDsJUDZ2hHtzezQOqr3PiNG3P1ZvRMN0L2RWXIfjxIFoFuz2xHUahjTDdrtIJKUmYPdHK9D/9QUGSEr/VlpSis+mzEIP514oEu4iv50MHYYaZ8MJuDeb2W3NDfhbheLIsg14dtl0sSPpzMUPvoentCnyQ3Ng7+rCphMRERHViJuHF9w8vBDU7p9XHvw7r0SthjrF1eQbTv/o9u44HJr2A65qJEj/6AOMmj5b7Egmi02nWsLR1Q2OSyZh95vvI8AsFHZXbXFoQSQ6vjUWcj1fiaMp1eDk/E/QTBqO4qI4lAwJQLee4/Q6piE1b98Szdu3rPy6ICcHJyK3wio1H4oKR9ha+CO4SIk7Man4dcc6tHMZB5m8CLdkaXD0cUGbFwfC3NZ07mUWy+XvfkHjNB+UQIB3v+5Vajj9o+8b7yBm1ma4pHmiICcHtk68DVKfMhNvIjHmCsqv56EiswD2cjfYKBwh1driaM4BjFgdARt746/55rOHYPei93FXU4hnxQ6jIzvfj0QLaTPc1FxEl5Gvih2HiIiI6ghzKyuxI+iUTKmE96sByP74O4SkdEJmWi7qeTiKHcskselUy/RdPgfpF27i7vpjSFL9hasvT0O/uTPh0cxfL+PduZmGhI8Pw8+8DTKL4tFj2RA4+tTumd9snZzQa879V3DkZhUi6acEWOW5QCkpg73MEQ2kbkAykPnOOeSV3kVc3l44etjCrWtPtH62u0jpjdOOVZ8iJLMxyiUFsBrgii6dOzzV+uaWViiwUMNP7oCYd3bguVWme5WdMdGUluLS/r3I+fMIVNogZMkdUHo+Et18psBT4QDABuVWZSgsK0R+cRacvetj8OLJem9064qTlwfKJRo0sw/BxW9+QNMXXxA7Uo0UFajhm2GPYjMVPMZ2EjsOERERkUnzb94K+cOksDpYgIyVJ2D7dleTejC6sWDTqRZyb+YL51UNcGTqSbSy6YKSqFvY43ECfaaN0+k4x77aBudYJTyUnjijvYR+H46vVc9FeRqOLjYYPPllYPLLAO49lO/4r7shi7kDaakjrMxkyFWnw+lOA9T/XY6vI/vBR9oERa7+UPraoemQnnBu4CDyXogj5t1VaJ7XHEUVaqh726H1Uzac/tFrxUxcmrMfvnI/pFxLhJd/7W5+6tqtG8lI2BuPO8USuGTehW25DHZyB2g1KsRmXkMXj85wlClxUWmPBNV5yD3c4dM+BF7hoWhofe8/W6Ei70N19Jg8HtqfVEj6Kwcw7B3JOhf59hJ0EsKQZV+Iri05Wx0RERFRTbXuGYrffv8SzZSNETd3PcLXzxE7kslh06mWUlqYY+qXn+Kvn46i7GQ5mqX74vCr69Fq6XBY62AGtt8WfAD/sjaQyMqQUu8Wnp85WQepaw+5QoGuQ58Hhv7/az5Z7XHsu59wOvk0nFAMpVUIfJS+QDpQ9NEFxJdrUFiajlvCDdi180XX4aNhJq/d36JfLnwLytS7sHbwgOWg+gjqWv2rM+RyOW430cApSYprnxyA1yeTdJi09tCUlOBO9CnE7z8NsyIFvOyscValhir3Enq7j4YvgDIzSxSUZ+FWURLykI3w9u1Rv3cTuPsHohN6i70LOuXRrg327dqJgHI3xJ2LR0io8c60+TiXfz0Ec3UH7BWiMW/hSrHjEBEREdUaPd8bh3OvrYOHbTvsffMD9FrO5zs9jdr9Fy2h+eDOSA++isRPotHIthluvHsIuc86oFvfZ6q1vdKSUuyetRqJWTGosHVF/QGu6Nh/pI5T1062Lm7o+9orf3917yHl8dGnkH48FdLb5XAWHOBm6YGkrFhkHDRDxsUDyCzJQb4mFzIve5h3fNycEqbn2Pt70aigGc6W/ozyQY7wqUHD6R89pjyPk9O/gq9FI1w6dApB3cJ1kNR0Zd1Mwukt21F+V0CKOh1+Uk8E2raEXCpDsLQdBCsB5eXZsLLKQn6eDJekN+DStz2CwxrCx7zuXLUoC7eB2XEJ1Jt2ASbYdNKUlqL0j3x0s3GEavBgseMQERER1SpSqRSN5g3CrTV/wc+sNS6dOoOg8NZixzIZbDrVAe5NmsB9bRPsXbge/hWNkfHrn9h9IR595017qu3cvpKAkxtj0Ma6AypKS9B+6XNwbNBAT6nrhuCO4Qju+P+NkRKVCrZbLqEsIwkFUMNc4wZXG09I86TQ/JqPX3/9FF1mDIJdg3oipq657Us/RuuiFiiUyTFkwdvwauars21b9/TFlV2JuPldRp1pOpUXFeP0j3txJy4LTnfzYCa1gbXCA4cLpfARgDCHtkjKXIcCK3ukFCXD0Sobxe52cOsWDp9mnVHXb0R8pn9X/LXvS7iZt0R2WiqcPTzFjvRU9s9ZgxYW7XFVdR7PhD/dz3UiIiIiejJ7L09cbnUaN6MrkL9xF9x8vODkVvWJj+oyNp3qkF4Rr+LY78eR+fFJBGe1RMy0H+H2n0Zo2LzFE9fdu+FblJxNRrokCBXOVzHo7fl19vlN+mRubY0+U+5/SPmdm6mI/+00zBNKIa/wxQ8LolHP6gY6L51gkrO0HZmzHq0lIcgty4HTpJbwDGio0+0369MJZ7b/jsCyBoj7/GuETByr0+0bm++XLkGYqj0czKzQAFYQLLVQld1FtpCL+naZKJeUILl5Jl59+2t+zz5GplsJHFXmiN60F4MWTRQ7TpVlnL+EQEUb5JZmo+0748SOQ0RERFRrtRs1BOmpq9A6vRnil+1E57UTxI5kEth0qmM6PdsB4Z1aI3rWZthLtPht6Tw07TMAnSY8emrtmO0xaJJYDxp7O1i43kDPf83cRvrl6usJ16meuHz5MoQ/TkETW4wW1u2xd/ZGOHpbo+v8VyAzkdnC9s1aiyCzEORqcuEwJQieTRrqZZz+s19E4ReJyI+/gzKNptY2W84cPIr0jJYo0R6Bj40dSgICENavAzzrO4sdzeQ8M28ioqauQmH6NVSUvwwzmWn8erz+zVU0NHOC0FQKaztbseMQERER1WpD576OA6+uh0qTjq/fWIGxK+eJHcnoScUOQIYnVyrQ7ZNJUDW8Bnu5A9yvB2Pvoi0oVqnuW64gJweHX/sM7mc0uKMoQ35/ZzacRNb+tZfRe91/cE5zDPnltpDeDUHcvD04umW/2NGeaMcHc1BP645cjRoe08Lg3aSJ3sZyCfDHZdlluJg3xC9f7tPbOGLKupkEy12FaGNhBfuBrdBp5Vw8N3EgHNlwqhaZXA4LxxK0deyIvYvXiB2nSn7a+iu8pfa4IKSi+X8Gih2HiIiIqE7o+N44JKkz4Fyoxf6obWLHMXpsOtVhnWYuRPjbb+OWJB9Ny7xxecFuxOw8CABIi/kTiW/tQiPrQCSpr6PFmx3RpksbkRMTACiUSgxYNR9j17+MPKebsJYo4HvRArH//Q7RX38ndryH2jnnS+RcSMW5gig4TwuEk4/+n5nTYd5/UFBRjBaXylGqLtb7eIZ2ZdUB2ClsUW5+DY19a9dD5sXy3LRJsJZ7QprjLnaUJ8rPugPLyPewJ3MrWk6v3sQQRERERPT0LGws0eWVl9HYri284uyQl5gsdiSjxqZTHefTpDHaRfTBtaJzcFS6wPmYFL/M+wK7P14PS6ULEkuOoOMn46G0shQ7Kv2L0toKgxeNh/PrYUgujYedRX14x9fD6TfWIWbPDrHjVfppTgSC4Ad/+0F4YWUk3H0aGmRce3sbFLjmw8LSGQfnrTXImIay+52f4GMdgKuqC+iyfKrYcWoNpwb1cb4sFdfLS/D7V8bzPfQw5yJ2w73pQrQO80EDd9OeWICIiIjI1IR27oTcRrdgJbFA0of7oSktFTuS0WLTiWAmk6H7x9ORFFYGFYCgioYo0RYj3u0vdFqzUOx49ASODVzRYfUUlA63QbpwAWWlMkR/tQm/ToxA2tkLombbOWUpUpLPIDb5ezRd0hPWdg4GHT902gBkl2TCRdoAt68nGHRsffn9q58QlG+HrNI7aPvOS2LHqXU6TOyAbrY2sDiTK3aUR7pwbB/yiq7jLrLRavp8seMQERER1UltJ4/BTfVpONsE4PAb68SOY7TYdKJKXYf3RKMFYUgzT0afaXMwaPabYkeipxAQ1hLh778GTXsL2Mo90MKxE0q33cG+16NQkK82eJ7fZ6xHS/vuCLLriD6fvQN7J3uDZ1Aolbjtloqjmduw5+2VBh9f14rycmF3uhxaAajo7QFrOyuxI9U6Ps38cbc8Gx5WPkg9Fyt2nAeUqNVI+PoI0lXn4fvfMLHjEBEREdVp7VZNx211EgItQ3Fl49dixzFKbDrRfazsbNB92X/QuF1LsaNQNXUbNRYTt6xFmutNFJSVIFjREBnLjmL3m5+gpKhI7+OXaTT4/fWNCDBvhpSiGwhfPh4WdvZ6H/dRer75OjxsQ9HBrT9O/3FItBy6cGTyeJgfXo5k23No1aOt2HFqLddBAZBCimtbj4od5QGH521Aa6fe8HLpCI9GQWLHISIiIqrTFEolfKa1x93Cq9h3NhFnL8aLHcnosOlEVEu1f+NltFzTF5f9BGi0JWgutEDsf79G7MYPoNVq9TJmeVkZzsz6DgGKICSVJCJ0xXDYOjnpZaynUa9PGPIqgJQdl8WOUm07Z6xAE6+puNm6Nbovmi12nFot4JmuSC9JRkNJELKSjefBkDdPXYO/eQtklWSg1wezxI5DRERERAAcG/vhcm8PWBQlovDTmFo5iVFNGLTptHHjRgwfPhyDBw/GDz/8gPj4eHTq1AljxozBmDFjsGfPHgDA999/j8GDB2PYsGE4dMi0r0wgEttzEzujcURPXCw/D/PrP+Dm6Rs498b3OPL1Vzodp7y8HL+8sRFelg1xS52B1u++AHNLC52OUV2dnx+A9MILKCx1w74NW8WO89RSEy7AVXIQaUVJ6PL+O2LHqRNKPVRQmimRtc54psHN/vYyzKQKlLdygkKpFDsOEREREf1t+IC+8PZ4Bo2sG+PMmz+IHceoGKzpFBMTg/Pnz+Pbb79FVFQUMjMzcenSJbz88suIiopCVFQU+vTpg6ysLERFRWHbtm2IjIzEqlWroNFoDBWTqFYyt7RErw+nocnOH6F18oZCooDdyjXY9dLbOPTNzhpvv7xUg3ULPkLq7d9xWh2H0FXPQ2kkDad/tHytHzrZWsPxikTsKE8lMyUNSWvjoLCpgP+8brB3dRE7Up3QceZE5Jb8hb/UamhKSsSOg4MrNqOBhSOul6Si9ehnxI5DRERERP/Sc8UYpBdloqGlD/a985nYcYyGwZpO0dHR8Pf3x9SpUzFlyhR07doVFy9exOHDh/Hiiy9i/vz5UKlUiIuLQ8uWLaFQKGBjYwMvLy9cuXLFUDGJajULOwcMff9NNJjREpntuqCRc2v4xtkgeupnuHr4RLW2WaJW48qMTzG4IgTw64gBqydDJpfrOHnNNWwRjHjzTNS38EDcDtO5gvLa+/vhY+6FVMkg1PNsJHacOsNMJsMfTndwJ+cifvhQ3IdC3kq5CUVaGQrK7qLV/B6iZiEiIiKiR/Oe3QZ3Ndmwvq2AIAhixzEKEsFAR2LhwoW4desWPv30U6SlpeGVV17BpEmT0KRJEzRt2hQbNmxAQUEBAgICcO3aNcyefe+ZJXPmzMGgQYPQvn37R247NjYWylpyq0FJSQnMzc3FjkFGSB+1cSPmAuzPFMLLuhEqhHJcLbsKt37NYN7AuUrrlxcVo3DTeXhZ+yM7/wQUr/aBRCbTaUZd0pRWwHrrTWgqFCgd4QSltbXYkR7r8i9/oU2+O64XXke919o9cjn+3NCP4uIiKL+5g3KtFIpJXqLlOPvBFtyVBsPBLw1hg/o/9fqsD3oU1gY9CmuDHoW1QY/D+rjnzvU0/FlmiV6BDpBKxL3LQlefSWBgYLXXNdhfh/b29vD19YVCoYCvry+USiW6du0Kp78fMvzcc89h2bJlaNWqFdTq/5/eXa1Ww8bG5rHbViqVNToIxuTy5cu1Zl9It/RRG4GBgcA44FDU9zA/q0FT82bI2qXBcU0kXnh3AezsHB65rlqlwq05m+Bg2wJXCs7h2Q1zdZpNX3775gCayVshc8t+dPtogdhxHunkNz+hZV59ZAklCF8xAhY2lo9clj839OfIlm/hp/TA5d1H8NysKQYfPy5qGzo7dsWNnKPo9uayam2D9UGPwtqgR2Ft0KOwNuhxWB/3BAYGoovYIf6mi8/k8uWaTcZksNvrwsLCcOzYMQiCgNu3b6O4uBiTJk1CXFwcAODkyZMIDg5GSEgIzp49i9LSUhQWFuLGjRvw9/c3VEyiOqnbmGFot2Y0brul42ZBOkry7uLm0j9w4IvfUVZW9sDyRUUlOLd4ByxsW6Cg+DieXT9dhNTV80zEq8grToOnJBiZyTfEjvNQ2SnpsP5TAq1Wg/Jeisc2nEi/vMYGoUyrgXWy4f9rp1apIJyXQyOpQPAbIw0+PhERERFRTRnsSqdu3brhzJkzGDp0KARBwOLFi+Ho6Ihly5ZBLpfD2dkZy5Ytg7W1NcaMGYNRo0ZBEATMnDmz1tw6R2TswmaOQIhGg50byiBJs0RgghJXZ+9FSf1EtJo7DQCQk3kHW15fiPrmtsh1zcfAj+aJnPrpKJRKlDQpwLmzFiiP2Izxn1fv6hF92jZvMbytPGFt74hu3aeJHadO82nRHNGffwYv6ya4+McxNH2mk8HGPvf+L/BReCNGfg5DmvYy2LhERERERLpi0IevzJkz54HXtm17cDrqYcOGYdiwYYaIRET/IlcoMHj6f6BWFSH6rW/gZ+YO+7stcXb6D7gboIbdFQWa2gUgUVqACSteFTtutbR6dTKuTFiJ5lbNcPXUZTQJN57LgE/MfgdeVh5ILk7Fa5+/JXYcAuDYLwCSw0DawTQ0NdDEcad2HYR3UT3kFyVj4Eem+X1GRERERGSw2+uIyLRYWVui54cT4TStKa4XnoKD0hW3Ym1QoLmNYrkEE9a9JXbEGgke2BrOSlfc2nZG7CiVoo/+hQaScDSyaIJxa94XOw79Lah3Z8QKcSi8dRDZiQl6H0+r1QL70qCVSiEdGmiUs0ESEREREVUFm05E9FgO3l7otm42pGOcYWZ3BbkeZej38UyxY9VYy/6dkVB8HX5KbxxZ963YcVCQkwfL3RmokMggDA+AtbOT2JHof7j4liJbo8aRDzbrfayfZy1EbO4+xOWfQGD3ML2PR0RERESkL8Y7tzkRGZWGzYLR8P1gsWPoVL3hLVH+y11YXRPEjoLLizbD3TYMZ/1UGNjBWOa7oH+ET3kDyivfQ2lmjpLSUpjr6VmDCRcvoEFZI9yV5uOZ5bytjoiIiIhMG690IqI6K7hDKyRV3EQ9C0/8umGLaDl2v7UR7rZhSM7/EwMn9hYtBz1efMNiHM74Fvu3btbbGGcjD8DF3ANu4aGwcnTU2zhERERERIbAphMR1WktFwxGtrYElucuQ1NcZPDxz/9xCFm3XZCoyUeziJcMPj5V3fCpo+BiaQv7P/XzjKVDH0UipygE54ri0H/qf/QyBhERERGRIbHpRER1moOTC/KkPyMuPw57Zs836NhZyclQ/ZyAUtUB1B/rDVsnXtlizORyORxsQuFr0xjHV36t020XFKjhmuyMthZFeGY2Z28lIiIiotqBTSciqvM6RXyMdq6DEWzRD7eTkww27sm3f4G7pR/cXRuiUYtmBhuXqi/o1e7QaDWQJ0l0ut2TkSdhp3BEsn0mnBp66XTbRERERERiYdOJiOo8M5kMKu8y5F/Zh1MfRRlkzO0LPkWqLAixOefQb+Vcg4xJNdegcWOkqK6hnqUXTn27XSfbPPXdTwjIlCFBocbgtybpZJtERERERMaATSciIgDPzZ6EK9b5sCi2xtlT5/Q61ulf96NlWSOEyNLRaw1nKDM1Vj08AEigPaet8bbKy8pgfrwYWgGwD+evZCIiIiKqXXiGS0T0N7cRwxFg2xKqH+L1NkZhTg6sDt8FBAHo6gpLG0u9jUX60XpQf2RADSfY4/qNpBptK3reMjhbeiGt+Dha9Omhm4BEREREREaCTScior89N/AZ3Cq/DR+ZN+K+2qmXMc4u2Q47RQNkNkhEpxf66GUM0r+sUCl+S12HPctXVXsb187G4MrdUMTdOog2K2boLhwRERERkZFg04mI6H94Dg9ChaBF6Tm1zrcd/9YqNLIOREJhPNrP5LN7TFm/4T3RUNkMvWShKC0orNY2srdcg5+1EmbdG0BpZaXjhERERERE4mPTiYjof7i3aYYrxX+hvoU7Dq/U3UPFD+7fCU2hJSrUiWi9bJjOtkvi8W/uASt7Pxz69LunXvfnd7+El7IhFGZp6Dluoh7SERERERGJj00nIqJ/CV8wCuryApQm5KC0qKjG28u/nYvErX/g4O1zKBzbBFaOjjpISWJrPPVlFJQXoEGmI8rLyqq8Xs6tbATkuEBVlo8mswfqMSERERERkbjYdCIi+heHei44ofkDF3N/x1fz3q7x9i6+cxjPuQ1CA78maNq6rQ4SkjGQKxW4JcTDUeGC8yvXV3m9uBXbYSO3x02bO6jXwFWPCYmIiIiIxMWmExHRQ4x67100t+mMZ6Xtoc6t3jN7AGDP3I/gbemClJK7GLn0DR0mJGPQesYolFYUAekOVVr+yI7d8LMIQEpJIvq8NV7P6YiIiIiIxMWmExHRQygtzOHUIRQKcwecfmtrtbYRu2UHArRBuFt6F00Xd9NxQjIGdp4eSFJfh4XcGie+2/PYZctLSyHftAFp5dlwHNfcQAmJiIiIiMTDphMR0SO0fPlZZBRnoKG5H9IuXnqqdfNzcyGNVQKQQNsasHey10tGEp/bsDAcysvD1QO3HrvcnteX46qVM7TSvQhowaYTEREREdV+bDoRET2GWVdzmEnkyNx29KnWO7zyXVibWeKv+qloPmqAntKRMWjatR1cHOzQwcEPl86efegy6X+dgb95W4Q490K75RsMnJCIiIiISBxsOhERPUbo8wOQLE2Ea1kQjn+9rUrr7HhjIRKvX8Wl4s/w/Osv6zkhGQPHVmbIKbmBs5E/PPT9uKWroDm8HGXhEpjJZAZOR0REREQkDjadiIiewLGnH26oM3BpX/QTl405cR7NpF0Q5twX3d/+xADpyBj0GDkI53P+RHmRG3Iz7r/NbtsHUfD2GYULPQYhfORwkRISERERERkem05ERE8Q3LUTkouOwUIuwfYvfnnkcoWFKuyJXI+TOfug7d4QlvZVm9GMaodGAU3xrFsYYj75tfK13KQ0tLztAgFa9JrN2eqIiIiIqG5h04mIqAp6RyxCa9fn4XbVEuWa8ocuEzv/J3RRNoNZjy7oMrSXgROS2LrNm4QC7V001DijovxejVxddRRWMiuoPIvg5GgvbkAiIiIiIgNj04mIqArcGrgh1rkQsbm38POcpQ+8f2LBF/Cx8oHSzAkjRvQXISGJTa5Q4K5tCmxkbjjw7joc/Hwr3C3ckapOQ9sZI8WOR0RERERkcGw6ERFV0eBpveEqJMBfEoLMxMTK149t/hb1NN7I0+QjZAmvcKrLmrw0ECUV5aiX6wfny0qUaothN7qR2LGIiIiIiETBphMRURXJzRWwqGcLJ2U9XP/w3kx2RQUq2MfZARIJ1M2LYOPsJHJKEpOLlxcytMlwNrdDuvoqbprfRFDb1mLHIiIiIiISBZtORERPof+7c5CtugZ3y1a4eCoGfy7/DQ4KG9y0TkLbl4aJHY+MgM/YDriSF4OUwpvovmiC2HGIiIiIiETDphMR0VMqbW0BmVSBrK0J+Cv5Z5zTJuLZxWwu0D0ezRtD2dMTL6yOgFyhEDsOEREREZFo2HQiInpKbV8ahWR5ChpbesHezAXtZ/E5TnS/Ts8Ph6NbPbFjEBERERGJik0nIqJqCBjWClnq6+gweSKcXV3FjkNERERERGR0ZGIHICIyRS4hgXD5JFDsGEREREREREaLVzoREREREREREZHOselEREREREREREQ6x6YTERERERERERHpHJtORERERERERESkc2w6ERERERERERGRzrHpREREREREREREOsemExERERERERER6RybTkREREREREREpHNsOhERERERERERkc6x6URERERERERERDrHphMREREREREREekcm05ERERERERERKRzbDoREREREREREZHOSQRBEMQOUVOxsbFQKpVixyAiIiIiIiIiqjVKS0vRokWLaq9fK5pORERERERERERkXHh7HRERERERERER6RybTkREREREREREpHNsOhERERERERERkc6x6URERERERERERDrHphMREREREREREemcTOwApqKsrAzz589Heno6NBoNXnnlFTRq1Ajz5s2DRCJB48aNsWTJEkil9/p4ubm5GDFiBHbu3AmlUonCwkLMnDkTxcXFkMvl+OCDD+Di4vLQsQ4cOIC9e/di5cqVAICTJ09izZo1kMlkcHJywnvvvQcLC4v71omNjcU777wDMzMzdOzYEa+99lrle8nJyZg6dSp27dqlp6NTt5lqbUyZMgV5eXmQy+VQKpX44osv9HiU6i5TrY+IiAicO3cOVlZWmDVrFpo3b67Ho1Q3GXttAEBFRQVmzpyJoUOHonPnzgCA1atX48SJE5BIJFi4cCFCQkL0dITqLlOsjaNHj+Lzzz8HAAiCgLNnz2LXrl3w8/PT01Gqu4y9Ph63DM9J9ctUa4PnpPpnqrVR289Ha/q55OXlYfbs2VCpVLC3t0dERAScnJzuG6OkpASzZ89GTk4OrKys8N5778HR0RHAw8/z/pfOegwCVcmPP/4oRERECIIgCLm5uUKXLl2EyZMnC6dOnRIEQRAWLVok7N+/XxAEQTh69KgwcOBAoWXLlkJJSYkgCIKwefNm4b333hMEQRC+++47Yfny5Q8dZ9myZULPnj2FGTNmVL7Wo0cPISsrSxAEQfjwww+Fr7766oH1BgwYICQnJwtarVaYMGGCcPHiRUEQBOHnn38Wnn/+eaF9+/a6OAz0EKZaG7179xa0Wq0uDgE9hinWx8GDB4Xx48cLFRUVQk5OjvD888/r6GjQ/zL22khOThZGjBghdO3aVThy5IggCIIQHx8vjB07VtBqtUJqaqrQv39/XRwK+hdTrI3/9fnnnwsrV66s7u7TExh7fTxqGZ6T6p+p1gbPSfXPFGujLpyP1vRzWbFihbBhwwZBEATh+PHjwvz58x8Y48svvxQ+/vhjQRAEYdeuXcKyZcsEQXjy73JB0F2PgbfXVVGvXr0wffr0yq/NzMwQHx+PNm3aAAA6d+6MEydOAACkUik2bdoEe3v7yuX9/f2hVqsBACqVCjLZwy8yCw0NxVtvvXXfa1FRUXB2dgYAlJeXQ6lU3ve+SqWCRqOBl5cXJBIJOnbsiJMnTwIA7OzssGXLlurvOD2RKdZGdnY2CgoKMGXKFIwcORKHDh2q0TGgRzPF+khISECnTp0glUrh6OgIMzMzZGVl1eg40IOMuTYAoKioCBEREWjbtm3la0FBQYiMjIREIsGtW7cqt0G6ZYq18Y/MzEzs2LHjvv+Gkm4Ze308ahmek+qfKdYGz0kNwxRroy6cj9b0c0lISKi8Qik0NBRnz559YIyzZ8+iU6dOldv7p0/wuN/lgG57DGw6VZGVlRWsra2hUqkwbdo0zJgxA4IgQCKRVL5fWFgIAOjQoQMcHBzuW9/BwQHHjx9Hnz59EBkZiaFDhz50nD59+lRu8x+urq4A7l2qGBMTg0GDBt33vkqlgrW19X1Z/8nSrVs3WFpaVn/H6YlMsTbKysowfvx4rFu3DmvXrsXy5cuRk5NTo+NAD2eK9REYGIhjx46hrKwMqampSEhIQHFxcY2OAz3ImGsDAAICAh56a5RMJsPq1asxefJk9OvX76n3m57MVGsDADZt2oRx48ZBoVA81T5T1Rl7fTxqGZ6T6p8p1gbPSQ3DFGujLpyP1vRzCQwMxMGDBwEABw8eRElJyQNjqFQq2NjYPLC9x/0u/2c9XfUY2HR6ChkZGRg7diwGDhyI/v37V95bCQBqtRq2traPXHft2rWYMGEC9uzZg8jISPz3v/9FcnIyxowZgzFjxuCHH3547NibN29GZGQkvvjiCyiVSmzZsqVy3YqKisrOc1WykO6ZWm04OztjxIgRlfdNBwYGIjExseYHgh7K1OqjY8eOaNWqFV566SVs2rQJwcHB9/1XhXTHWGvj9u3bj1135syZOHbsGCIjI5GSkvJ0O01VYoq1odVqcfjwYfTt2/fpd5ieirHXx7+XIcMxtdrgOanhmFpt1JXz0Zp8LpMmTUJ6ejrGjRuHjIwM1KtX74HPxdrauvJ8/0nb01ePgQ8Sr6Ls7GyMHz8eixcvRrt27QDcu80gJiYGbdu2xdGjRxEeHv7I9W1tbSs7jE5OTlCr1fD29kZUVNQTx96wYQPi4+OxefNmmJubAwBGjx6N0aNHVy4jl8uRkpICT09PREdH87J2AzLF2jhx4gS++eYbfPbZZ1Cr1bh+/Tp8fX1rchjoEUyxPhITE+Hk5IStW7ciIyMDc+bMYSNbD4y9Nh7m5MmT2L9/P5YsWQKlUgmZTPbAfzSp5kyxNgDg2rVr8PHxqVyP9MPY6+Nhy5BhmGJt8JzUMEyxNurC+WhNP5c///wTAwcORHh4OPbt24fQ0NAHPpfCwkIcOXIEISEhOHr0KMLCwh65PX31GNh0qqJPP/0UBQUFWL9+PdavXw8AWLBgASIiIrBq1Sr4+vqiZ8+ej1x/+vTpWLhwIbZu3Yry8nIsW7asSuNmZ2dj3bp1CAoKwsSJEwEAvXv3xqhRo+5bbunSpZg1axYqKirQsWPHWvdkf2NmqrURHR2NYcOGQSqV4vXXX6+cxYB0yxTro7S0FMeOHcOPP/4IpVKJxYsXV3Pv6XGMvTYepk2bNti7dy9GjBgBrVaLF198EZ6enlUal6rOFGsDuPcHAutB/4y5PmpaQ1QzplobPCfVP1OsjSFDhtT689Gafi4+Pj6YO3cugHu3KL777rsPLDNy5EjMnTsXI0eOhFwur5xVsCp01WOQCIIgVGtNIiIiIiIiIiKiR+AznYiIiIiIiIiISOfYdCIiIiIiIiIiIp1j04mIiIiIiIiIiHSOTSciIiIiIiIiItI5Np2IiIiIiIiIiEjnZGIHICIiIjI13bt3R3p6euXXcrkcbm5u6NGjB6ZOnQpra+sqbSchIQFpaWno2rWrnpISERERiYdXOhERERFVw8SJExEdHY3o6Gjs2bMHb7zxBnbv3o0JEyZAo9FUaRuvvvoqLly4oOekREREROLglU5ERERE1WBpaQkXF5fKr728vODt7Y0hQ4Zg+/btGDly5BO3IQiCPiMSERERiYpXOhERERHpSHBwMMLCwrBnzx4AwL59+zBkyBCEhISgefPmGDFiBOLi4gAAY8aMQUpKCtauXYvu3bsDADQaDVasWIGOHTsiNDQUo0ePRmxsrFi7Q0RERFQjbDoRERER6ZC/vz+uXbuGuLg4zJgxA4MHD8aePXsQFRUFAFi0aBEA4JNPPoG7uzvGjx+PH3/8EQAwZ84cnDlzBmvWrMH27dsRHh6OsWPHIjExUbT9ISIiIqouNp2IiIiIdMjW1hYqlQpyuRxLlizBiy++CA8PD4SEhOCFF17AtWvXAAD29vYwMzODpaUlHB0dkZycjN9++w0rVqxAq1at4OPjg9deew1hYWHYtGmTyHtFRERE9PT4TCciIiIiHVKr1bCxsUFgYCBsbGywceNGJCQkIDk5GZcvX4ZWq33oepcuXQIADBs27L7XNRpNlR9MTkRERGRM2HQiIiIi0qH4+HgEBwfj1KlTmDhxIp555hmEhoZiyJAhSEpKwpIlSx66nlwuBwBs27YN5ubm972nUCj0npuIiIhI19h0IiIiItKRK1eu4Pz581i+fDm2bt2KDh06YM2aNZXvHz9+HMC9WeskEgkkEknle40bNwYA5OTkoH379pWvL126FH5+fhg9erRhdoKIiIhIR/hMJyIiIqJqKCoqQlZWFrKyspCamopffvkFEydOROvWrTFgwAA4Ojri6tWriI2NRWpqKqKiovDVV18BQOXtclZWVkhKSsLt27fh7e2NPn36YNGiRThy5AhSUlKwevVqbNu2DX5+fmLuKhEREVG1SARBEMQOQURERGRKunfvjvT09Mqvrays4O7ujj59+mDcuHGwsLBAbm4uFi5ciJiYGJiZmaFJkyYYOXIkZs6ciW+++QatWrXCjh07EBERAalUipMnT6K4uBgrV67Evn37UFhYCD8/P0ydOhXPPvusiHtLREREVD1sOhERERERERERkc7x9joiIiIiIiIiItI5Np2IiIiIiIiIiEjn2HQiIiIiIiIiIiKdY9OJiIiIiIiIiIh0jk0nIiIiIiIiIiLSOTadiIiIiIiIiIhI59h0IiIiIiIiIiIinWPTiYiIiIiIiIiIdI5NJyIiIiIiIiIi0rn/A/WdXOUMwaqMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Zoom in to December and look at individual days\n",
    "plt.figure(figsize=(20,6))\n",
    "plt.title(\"Transaction in Dec 2018\",fontsize=20)\n",
    "sns.lineplot(data=fill_dates.loc['2018-12-01':'2018-12-31',:])\n",
    "plt.xlabel(\"Date\",fontsize=15)\n",
    "plt.ylabel(\"Transaction\",fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the increase in sales occurs in the lead-up to Christmas and that there are zero sales on Christmas day itself. This is due to shops being closed on Christmas day. Thus, we will not treat this point as an outlier.\n",
    "Now we can move on to create other features such as brand of chips or pack size from PROD_NAME. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5）Creating other features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    175.0\n",
       "1    175.0\n",
       "2    170.0\n",
       "3    175.0\n",
       "4    150.0\n",
       "Name: PACK_SIZE, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Start with pack size\n",
    "# Taking the digits that are in PROD_NAME\n",
    "transaction[\"PACK_SIZE\"] = transaction[\"PROD_NAME\"].str.extract(\"([0-9]+)\").astype(\"float\")\n",
    "transaction[\"PACK_SIZE\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check if the pack sizes look sensible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    246740.000000\n",
       "mean        175.583521\n",
       "std          59.432118\n",
       "min          70.000000\n",
       "25%         150.000000\n",
       "50%         170.000000\n",
       "75%         175.000000\n",
       "max         380.000000\n",
       "Name: PACK_SIZE, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics of PACK_SIZE\n",
    "transaction[\"PACK_SIZE\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The largest size is 380g and the smallest size is 70g - seems sensible!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Transactions')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram showing the number of transactions by pack size\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.histplot(x=transaction[\"PACK_SIZE\"], bins=10, kde=False)\n",
    "plt.title(\"Histogram of Transactions, by Pack Size\",fontsize=20)\n",
    "plt.xlabel(\"Pack Size\",fontsize=15)\n",
    "plt.ylabel(\"Number of Transactions\",fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pack sizes created look reasonable, since they are mostly concentrated between 150 and 200, which is a common size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Natural\n",
       "1        CCs\n",
       "2     Smiths\n",
       "3     Smiths\n",
       "4     Kettle\n",
       "Name: BRAND, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now to create brands, we can use the first word in PROD_NAME \n",
    "transaction[\"BRAND\"] = transaction[\"PROD_NAME\"].str.split().str[0]\n",
    "transaction[\"BRAND\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Burger', 'CCs', 'Cheetos', 'Cheezels', 'Cobs', 'Dorito',\n",
       "       'Doritos', 'French', 'Grain', 'GrnWves', 'Infuzions', 'Infzns',\n",
       "       'Kettle', 'NCC', 'Natural', 'Pringles', 'RRD', 'Red', 'Smith',\n",
       "       'Smiths', 'Snbts', 'Sunbites', 'Thins', 'Tostitos', 'Twisties',\n",
       "       'Tyrrells', 'WW', 'Woolworths'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check their names\n",
    "bd = transaction[\"BRAND\"].unique()\n",
    "bd.sort()\n",
    "bd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the brand names look like they are of the same brands <br>\n",
    "*  Dorito    and Doritos<br>\n",
    "*  Grain     and GrnWves    (not sure)<br>\n",
    "*  Infuzions and Infzns<br>\n",
    "*  NCC       and Natural    (not sure)<br>\n",
    "*  Red       and RRD        (not sure)<br>\n",
    "*  Smith     and Smiths<br>\n",
    "*  Snbts     and Sunbites<br>\n",
    "*  WW        and Woolworths<br>\n",
    "Let's combine these together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7      Grain Waves         Sweet Chilli 210g\n",
       "9      Grain Waves Sour    Cream&Chives 210G\n",
       "85     Grain Waves         Sweet Chilli 210g\n",
       "181    Grain Waves         Sweet Chilli 210g\n",
       "225    Grain Waves         Sweet Chilli 210g\n",
       "Name: PROD_NAME, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check brands which are not sure\n",
    "transaction[\"PROD_NAME\"].loc[transaction[\"BRAND\"]==\"Grain\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Natural Chip        Compny SeaSalt175g\n",
       "40      Natural ChipCo      Hony Soy Chckn175g\n",
       "75     Natural Chip Co     Tmato Hrb&Spce 175g\n",
       "214    Natural Chip Co     Tmato Hrb&Spce 175g\n",
       "234     Natural ChipCo      Hony Soy Chckn175g\n",
       "Name: PROD_NAME, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction[\"PROD_NAME\"].loc[transaction[\"BRAND\"]==\"Natural\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28        Red Rock Deli Thai  Chilli&Lime 150g\n",
       "34     Red Rock Deli Sp    Salt & Truffle 150G\n",
       "212    Red Rock Deli Sp    Salt & Truffle 150G\n",
       "297       Red Rock Deli Thai  Chilli&Lime 150g\n",
       "331    Red Rock Deli Sp    Salt & Truffle 150G\n",
       "Name: PROD_NAME, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction[\"PROD_NAME\"].loc[transaction[\"BRAND\"]==\"Red\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like they are all the same brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Burger', 'CCs', 'Cheetos', 'Cheezels', 'Cobs', 'Doritos',\n",
       "       'French', 'Grain Waves', 'Infuzions', 'Kettle', 'Natural Chip Co',\n",
       "       'Pringles', 'Red Rock Deli', 'Smiths', 'Sunbites', 'Thins',\n",
       "       'Tostitos', 'Twisties', 'Tyrrells', 'Woolworths'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean brand names\n",
    "def rename_brand(new,old):\n",
    "    transaction[\"BRAND\"].loc[transaction[\"BRAND\"]==old] = new\n",
    "\n",
    "rename_brand(\"Doritos\",\"Dorito\")\n",
    "rename_brand(\"Grain Waves\",\"Grain\")\n",
    "rename_brand(\"Grain Waves\",\"GrnWves\")\n",
    "rename_brand(\"Infuzions\",\"Infzns\")\n",
    "rename_brand(\"Natural Chip Co\",\"NCC\")\n",
    "rename_brand(\"Natural Chip Co\",\"Natural\")\n",
    "rename_brand(\"Red Rock Deli\",\"Red\")\n",
    "rename_brand(\"Red Rock Deli\",\"RRD\")\n",
    "rename_brand(\"Smiths\",\"Smith\")\n",
    "rename_brand(\"Sunbites\",\"Snbts\")\n",
    "rename_brand(\"Woolworths\",\"WW\")\n",
    "\n",
    "# Check results\n",
    "nbd = transaction[\"BRAND\"].unique()\n",
    "nbd.sort()\n",
    "nbd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Transaction')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a barchart to show the total sales of each brand\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.barplot(x = transaction[\"BRAND\"].value_counts(),y=transaction[\"BRAND\"].value_counts().index)\n",
    "plt.title(\"Transaction of Chips, by Brands\",fontsize=20)\n",
    "plt.ylabel(\"Brand\",fontsize=15)\n",
    "plt.xlabel(\"Transaction\",fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bar chart clearly indicates that Kettle products the most popular chips."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6）Check entire transaction table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Natural Chip Co</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>CCs Nacho Cheese    175g</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>175.0</td>\n",
       "      <td>CCs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>170.0</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>2</td>\n",
       "      <td>2373</td>\n",
       "      <td>974</td>\n",
       "      <td>69</td>\n",
       "      <td>Smiths Chip Thinly  S/Cream&amp;Onion 175g</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>2</td>\n",
       "      <td>2426</td>\n",
       "      <td>1038</td>\n",
       "      <td>108</td>\n",
       "      <td>Kettle Tortilla ChpsHny&amp;Jlpno Chili 150g</td>\n",
       "      <td>3</td>\n",
       "      <td>13.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Kettle</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0 2018-10-17          1            1000       1         5   \n",
       "1 2019-05-14          1            1307     348        66   \n",
       "2 2019-05-20          1            1343     383        61   \n",
       "3 2018-08-17          2            2373     974        69   \n",
       "4 2018-08-18          2            2426    1038       108   \n",
       "\n",
       "                                  PROD_NAME  PROD_QTY  TOT_SALES  PACK_SIZE  \\\n",
       "0    Natural Chip        Compny SeaSalt175g         2        6.0      175.0   \n",
       "1                  CCs Nacho Cheese    175g         3        6.3      175.0   \n",
       "2    Smiths Crinkle Cut  Chips Chicken 170g         2        2.9      170.0   \n",
       "3    Smiths Chip Thinly  S/Cream&Onion 175g         5       15.0      175.0   \n",
       "4  Kettle Tortilla ChpsHny&Jlpno Chili 150g         3       13.8      150.0   \n",
       "\n",
       "             BRAND  \n",
       "0  Natural Chip Co  \n",
       "1              CCs  \n",
       "2           Smiths  \n",
       "3           Smiths  \n",
       "4           Kettle  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Examine and clean customer data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ready\n"
     ]
    }
   ],
   "source": [
    "# Read data\n",
    "filepath_c = \"/Users/victoriafy/Desktop/Quantium/QVI_purchase_behaviour.csv\"\n",
    "customer = pd.read_csv(filepath_c)\n",
    "print(\"Ready\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1）Summary original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>LYLTY_CARD_NBR</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>OLDER SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LYLTY_CARD_NBR               LIFESTAGE PREMIUM_CUSTOMER\n",
       "0            1000   YOUNG SINGLES/COUPLES          Premium\n",
       "1            1002   YOUNG SINGLES/COUPLES       Mainstream\n",
       "2            1003          YOUNG FAMILIES           Budget\n",
       "3            1004   OLDER SINGLES/COUPLES       Mainstream\n",
       "4            1005  MIDAGE SINGLES/COUPLES       Mainstream"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print first 5 rows\n",
    "customer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 72637 entries, 0 to 72636\n",
      "Data columns (total 3 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   LYLTY_CARD_NBR    72637 non-null  int64 \n",
      " 1   LIFESTAGE         72637 non-null  object\n",
      " 2   PREMIUM_CUSTOMER  72637 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# Data.info\n",
    "customer.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>LYLTY_CARD_NBR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.263700e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.361859e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.989293e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.620200e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.340400e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.033750e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.373711e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LYLTY_CARD_NBR\n",
       "count    7.263700e+04\n",
       "mean     1.361859e+05\n",
       "std      8.989293e+04\n",
       "min      1.000000e+03\n",
       "25%      6.620200e+04\n",
       "50%      1.340400e+05\n",
       "75%      2.033750e+05\n",
       "max      2.373711e+06"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary numerical variable LYLTY_CARD_NBR\n",
    "customer.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the distribution of Lifestage & Customer type by using pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.15, 'Distribution of Lifestage')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a pie chart of LIFESTAGE\n",
    "plt.figure(figsize=(6,6))\n",
    "colors = ['salmon', 'peachpuff', 'lemonchiffon', 'lightgreen', 'lightskyblue','steelblue','mediumpurple']\n",
    "plt.pie(customer[\"LIFESTAGE\"].value_counts(), colors=colors, shadow=True, explode=(0.1,0,0,0,0,0,0),\n",
    "                                              textprops= {'fontsize':15,'color':'midnightblue'},\n",
    "                                              radius=1.5, autopct='%.2f%%', pctdistance=0.7, startangle=-30)\n",
    "plt.legend(customer[\"LIFESTAGE\"].value_counts().index.str.capitalize(), fontsize=15,\n",
    "                                                                        loc=2, \n",
    "                                                                        bbox_to_anchor=(1.2,0.7),borderaxespad = 0.)\n",
    "plt.title(\"Distribution of Lifestage\", y=1.15, fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.15, 'Distribution of Customer Type')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jH//4R/iCFkLUKFnTU80++OAD/H4/kyZNOmM66aSjR4/yr3/9i0WLFgHQokULrr32Wl577TUGDx5MdnZ2cN3F+T4wL8TWrVvPuOzkupFu3boFL7PZbGcd+Tl69OgZl4US28kRq/Xr159xnWmarF+/HofDUW1THienyNatW3fGdYcPHyY3N/e862LO52Si88orrwTLEfxQXl4eX375JZqmMWTIEIBg8npy2vMkpdQZj+snn3zCI488glKKmJgYBg0axL333hucYjnb43jSycf6bPcdAtu3NU27oCnU8zlZpmDbtm1nvf7ZZ5/l5ZdfrnJKsrpMnjyZmJgYFi1axKJFi0hMTGTMmDFnHFdWVnbWdjGbN28mJSWF1q1b065dO2JiYti+fftZvwS8+eabzJkzR2oxCRFlJOmpRitXrgxuI7/jjjvOeVxcXByvvPIKzzzzTKUPBK/XS25uLjExMcEFylZrYDDuh/V0LtSePXv46quvgj+Xlpby9NNPo2lapeq37du359ixY+zduzd4WWZmJp988skZ5zwZ2/k+1Pr160daWhrz58+vtMgUAh+Kx48fZ9KkSVVOpYRq2rRpWK1WXnzxxUoJhcvl4uGHHw4eczH69+/P5MmT2b9/P7/85S/PqDGUk5PD3XffTVFRETNnzgxOb57czr906VIMwwge/+6771JUVFTpHJs2beI///lPpf8rIJgEt2jRIniZzWarlHy1aNGCQYMGsX37dt57771Kv//hhx+yYcMGBg0aRLNmzS7q/v9Q69atGTBgAEuWLOHrr7+udN0nn3zC888/z9KlS6vt/7YqSUlJjB49mqVLl7J8+XImTpx4zingJ554otLz9vXXX+fo0aPMmDEDi8VCbGwskydPZt++fbzxxhuVfnf16tX84x//4KOPPjpjlFQIUbvJ9NZFWLBgQXDdjlIKp9PJjh07WLduHXFxcTz11FPnHblo3LgxN998M2+88QZXXnklI0eORNd1li5dyv79+7nzzjuDa2BOroV47733KC4u5sYbb7yomNu0acPvf/97FixYQEpKCosWLeLYsWP87Gc/C+7KAbjuuut45JFHuPHGG7nyyivxer189dVXpKennzGCcDK2F154gZ07d3L33Xefcbu6rvPYY4/xk5/8hJ///OeMHj2aNm3asHHjRjZt2kSHDh34wx/+cFH36Wxat27Nfffdx1//+ldmzJjB2LFjcTgcLFmyhKNHj3LFFVcwffr0iz7/3/72N5xOJ999911w11KzZs3IzMxkyZIllJWVMW7cuEr3qWvXrnTr1o2NGzcyc+ZMBgwYwJ49e1i5ciW9evVi8+bNwWN/+tOf8tVXX/H73/+er7/+mrS0NDIzM5k/fz6NGzdm1qxZwWObNGnCoUOH+P3vf8+wYcOYPn06Dz/8MDfccAMPPvgg8+fPJyMjgz179rB8+XKaNGnCI488ctH3/WxO3t4999zDiBEj6NSpEwcPHuT7778nOTmZBx54oFpvryozZswIFrE8X3K7YcMGrr76aoYMGcL+/ftZtmwZnTp14q677goec99997Fx40Yef/xxFi5cSM+ePcnOzmb+/PlYrVb+9re/hb2/mhCieknScxEWLlzIwoULgz/b7XZatmzJrFmzuPnmm8/YDXM29957L2lpaXz44Yf897//xTAMOnbsyGOPPVZpvciAAQO44YYb+PTTT3nnnXcYOnToOafNzmfUqFF07dqVF198kczMTNq2bcujjz56xtbmWbNmYRgG7777Lu+//z7NmzfnjjvuYMiQIWf0Q5o8eTKLFy/m+++/59133z3nOpe+ffsyb9485syZw4oVK1i6dCktWrTgF7/4Bbfffnu1bwO/6aabaNu2La+99hrz589HKUWHDh244447uOaaay7p3Ha7nZdeeolvv/2WefPmsXHjRnJycrDb7fTq1Ytrr72WyZMnn/F7L730Ek8++SSLFi1i9+7ddO/enbfeeouvvvqqUtLTqlUr3nvvPebMmcOGDRv47rvvSElJYerUqdx9992VFgTfe++9/N///R9ff/01+fn5TJ8+nbZt2/LRRx/x/PPP8/3337N27VqaNGnCjTfeyC9+8YsqG6peqPbt2/Pxxx8zZ84cFi9ezMqVK2nSpAnTpk07o2xATRg+fDjx8fEkJyeft4jlW2+9xT//+U8++OADGjRowE033cQvf/nLSgvuU1NTmTt3bvD/+9///jepqamMGTOGO++8s9JuQyFEdNBUVdthhBAiShw4cIBJkybxi1/8gl//+tdnXH/jjTeyZs0a1q5dW6k2kRCifpCxWSFEnaCUYs6cOei6ztVXXx3pcIQQtZBMbwkhoprX6+Wqq66ipKSE7Oxsrr766hqfVhNCRAcZ6RFCRLWYmBhsNhslJSVMnjyZ//f//l+kQxJC1FKypkcIIYQQ9YKM9AghhBCiXpCkRwghhBD1giQ9QgghhKgXJOkRQgghRL0gSY8QQggh6gVJeoQQQghRL0jSI4QQQoh6QZIeIYQQQtQLkvQIIYQQol6QpEcIIYQQ9YIkPUIIIYSoFyTpEUIIIUS9IEmPEEIIIeoFSXqEEEIIUS9YIx2AECI0yusBpcBiRbPKS1cIIS6UvHMKES00HePzD9ASk6BBClpKQ7SkBpCQCPZ4UCb4/YGkyGaLdLRCCFHrSNIjxNkoBaiTP5z2b+0cv6BV/ls713EXxjAMvOXlxDkc4C5DbdsYjOQMjni0xs3QmjSDFq3Rm7eElEaBWAw/WK1oVkmGhBD1lyQ9om5TCjAJJC0ap5ITo+KPGRghwQz8rIzK12FWXPbDc5z2RzvLZegVf1tAswFW0KyBn7EEzqVVvaRu1YJvWbXwW9qmZ3DFuLFYznewqwx1eD/q8H6oiBiAhMRgMqS1aIPWvAWkNAw8NoYBVptMlwkh6gV5pxPRLZiwnJ7Q+EF5AS8oT8W/facuO5UOVFMMF/E71u5ATJWH5Z04TnxiEk1atoRS50XcEFDqRJU6UQf3Vr48KTmQCDVuhta6LVpae7DZQCm0mNiLuy0hhKjFJOkR0SE4YgOBURQvKDeosoq/w5TQhM15x2yCykpKsFit2OMT0MtKqzeEkiJUSRFq3y5YWXFZcipa247o6V3R2nYAixWQJEgIUTdI0iNqH2UQGD7RCUwvlVckN65AgoOHixteqU1CqxZR5izBFhtLnN2OXpof/ntdVIDatAZj05rAzykNKydBeiBuSYKEENFIkh4RecogMDVlgnKCKq0YvXETWFtT1wRGT869KDpAKUVZaSkN4xOIt9vhRDWP9ISiMB9VmI+xcXXg59RGp5KgtPaBJEjT0GxVT9UJIUSkSdIjal4wyTECSY5ZEkh08EY4sJpiI5SRKk95OcpU6LqOw2GH6p7euhgFeaiCPIwNqwI/N2yC3iEdvfdAaNwksB5IEiAhRC0lSY8Iv7MmOU7AF+HAIkQ7OdJzfh63C71iOinO7oCyi1zIHE75OZj5OZhrlkFiEnrXXuh9BkHDRpIACSFqHUl6RPU7fZu4Kj5tJKeeJjlnsFHV1BaAu8zFyeQo1uFAXezurZriLMFcvRRz9VJISkbvVpEAJacCSMFEIUTESdIjqoc6WcfGBLMQVGFg8bE4k2YllKSn3O0KjgfFOOIxasP0VqhKijBXLsZcuRgapKB3643eZyA0SAY0SYCEEBEhSY+4eOrk9nAfmAVgFgHuCAYULWwhFSb0uN2gFNaK2jn4onTNU3Eh5opFmCsWQUrDUwlQYhJouhRGFELUGHm3ERcmuD7Hc1qi44lsTNFGC22di6u0FAXEORzRNcpzPoX5mMsWYi5bCKmN0PsPRe87OFDYWrbBCyHCTJIeUbXgGh0vysxDM4uQ9TmXIrSpHWdRERaLlTi7A9NVGmJlnyhSkIc5/zPM775C69EXy7DLIT4BYmxoIYyECSHEhZKkR5ybCtTIUWYBmpkHuENYiSKqpIX2sistLsYWYws0Gy2tIyM9Z+P3oTauxr9xNVpae/Rhl0Nah0D9H5n6EkJUI3lHEZWdXKej3GDmgCpCi/rqx7VNaC87Z3ERFqsNu8OBVtt3blUTdfgAxuEDgcXPg4YHpr4ALVamvoQQl06SHhEQHNXJqxjVkXU64RNa361SpxOrzUas3Y5eVlK/Us/iwoqpry9Pm/pKlKkvIcQlkaSnPguO6pRVjOoUy/RV2FkItQWF2+kkqWFD4u12tMIT9SvpOcnvR21cg3/jGrQ27dGHn5z6As0q296FEBdGkp76qNKoTg6yKLkmhdaCwu/z4vN5sVgsOKKhMGENUEcOYLxzIFD4cPhY9F79QdfRLKGNnAkhhCQ99YkyUCgwTqCpPDTMqn9HVK8QW1CUu9xoFS0o7PZa0nertigpwvxiHuayhVjGXgEZ3cGio+mS/Aghzk+SnvpAGSj8aMZxNFVIKB+6IlxCr8asVRwX63CgamPfrUgrLsT46D/QsAmWCVOhbQewWIPJohBC/JAkPXWZMlDKi2ZmoaniSEcjALTQ+m6Vu1zBf8c44mV663zyczDefRWat8IyYRo0bymFDoUQZyVJT11TUUhQKQ+amYmm5MOydglxpMflQimFrlvQbTaM8vLwhxbtjh/DePN5tDbtsUyaDimNZKu7EKISSXrqEmWglLtiZEfWgNRKWgxoIXRYd7lQpiLOYcdwlSFTkqFTRw7gf+lfaB07Y5k0A+ITJfkRQgDUvcr29ZIyUKYHjINoxh6QhKcWC63vlrOoCN1qIc7hwJRFzBdF7duF/7nHMD6fiyopRnmk9pQQ9Z2M9ESz4G6sTDSVH+loRChCbkFRhNVmI87ukJ1bl0Shtm/Cv2MLWp+BWMZNAatV2lsIUU/JKz8aKbMi2clBU9kgW8+jSIhJT0lJIOlxOKC0JMwx1QPKRG1YhX/nViyTr4L0rmgxoY26CSHqDkl6oolSKBTKKERXWUhRwWgUWi0ZZ3ExVpuVOIcDXXZuVR93GcZH/0Zr1wnLjB9DnB3NJsmPEPWFrOmJFsrANEvR/LvR1WEk4YlGOqHs3AJwlTqxWm047HHoMr1V7dTBvfif/RvmmmUonxdlymipEPWBJD21nTJQZjkYB9HNvYA70hGJi2YllKlIw+/H4y7HYrXisNulMGG4+P2YC77A/8rTqJwTKK8sdBairpOkp7ZSJkoZmEYWmrEDlKzriHohtqDwlLvRNA1N07DbHSDTW+GVm43x0r8wv/0fyutBGf5IRySECBNZ01MLKdOPabqwqCNoeCMdjqg2oXUFd5e5gqV84hwOlExv1QCFuW4F5q6tWKZcB207SFVnIeogGempTZSJMv0o4xgWtQ8k4aljQu+7dTLriXE4QKa3ak6pE+O91zDm/RtVVoryyWtQiLpEkp5aQikDw1+MZuxApyDS4Yhw0GyE8pLzuN0oU6FpGja7A8rKwh+bqETt3Yn/mb9iblqL8kriI0RdIUlPpCkT0/ShGYexcBCQ9QR1V4gtKMpcKGUSExeHUe4GJTuLIsLnxfzyY4y5b6LK3SjDiHREQohLJElPBAVGd/LRjR2giiIdjgg3LbQ1PWXOEjTdgl1aUNQKav9u/HP+gcrOkh1eQkQ5SXoiQCkD0/SgGQewcBSQb5D1QohJj7OoKFiYUBYx1xLOEoxXn8Vcs1zW+QgRxSTpqWGm6cf0F6AbO0HJAtX6JbTNks7iIqxW6btV6ygTc+EXGO+/IdNdQkQpSXpqiDJNTNMPxtGK0R1Zp1H/hNaC4vS+W7r03ap11IE9+J9/HHX8mEx3CRFlJOmpAYbhwzDc6MZudAojHY6ICI1QX26uiqTHbrdL363aqtSJ8fpzmKuXynSXEFFEkp4wMwwfyijAyh5AvhXWX6G1oDBNE1dZ2WktKGR6q9ZSCvO7rzDefQ3ldkklZyGigCQ9YWKaJqbhQ1dHsWqZhNJ+QNRloa3n8ZaXAwpd13HY7VKYMAqoQ/sC011ZMt0lRG0nSU8YGH4fpuFCN/egyVZ0ARU7t6pOfMvdLrSKWj5xDum7FTXKSjHemI254nuZ7hKiFpOkp5oZhg9l5mNlLzKdJU4JsQWFyxU8Lla2rEcXpTAXz8eY+1agcamS0V0hahtJeqqJaZoYhg+LOopVy0Kms0Qlmo3Qkh538N82R7xMb0UhtW8X/teeA1eZbGsXopaRpKcaeL0eTNODxdwrlZXFOdhAq/rlVu52YSoTW0xM4APTL4tjo1LOcfwvPgEFeSifL9LRCCEqSNJzidzuMjTKsaq9QHmkwxG1lRYT0mFlTieowHoewyVTW1Gt1In/ladRh/fLAmchaglJei5BeXkZVosLm3YAaRQqzi+0FhSlxUXBwoSqVJKeqOfzYrz7KubGNdKtXYhaQJKei2CaJl5vOTa9CJt2FFm/I6qkhdiCoqg4kPTYHbKep65QCvPrTzAXfimJjxARJknPBfL7fZimH6t2HIuWHelwRNQIrQWFs+RUs1FNtqvXKeaapRiffiBb2oWIIEl6LoDHUw6YWDkk7STEBQot6XE5nVhtMcTZ7egy0lPnqB2bMN59Vba0CxEhkvSEyO0uQ9cNrGofKFlrIS6EhVCmQJVSlJU4sVqtxNvtaFKjp05Sh/bjf302lLtRpjQeFqImSdITAre7FKvFh419SMFBceFCq8bs83oxDAPdYgn03ZLprborOwv/y09BaYn07BKiBknScx5KKcpKi7FayrFpBwEpNCYugmYlpBYULhe6HnhJ2h2ykLnOKyrA/9JTUFIsRQyFqCGS9JyDYRg4SwqJifFi044gO7TExQuxGrP7VDVmaUFRT7hKA9WbS52S+AhRAyTpOQvD8FNaWozd7q/Yki7EJdBC7btVFvx3jCNemo3WF2VO/K89E2hbYUriI0Q4SdLzA36/n7JSJ/F2HzbtWKTDEXVC6H23lFJYLBY0iwU8UuG73nCW4H/tWXC7ZHGzEGEkSc9pfD4fblcpDrsHq5YZ6XBEXaHFgBbaSI9SqqIFRVmVx4s6prgwMNUlu7qECBtJeir4fF485S7sce6KLulCVJfQWlCUFBVhsVqIczgwZT1P/VSYj//158BTjlKS+AhR3STpIdAl3espxx5bhlU7HulwRF2jXWDfLbtD1vPUZ/m5+N+YDR4pYChEdav3SY/H48Hv8xIXU4JFOxHpcESdFFrfrdLiYqzWQLNR2a5ez+Vm43/zeZDu7EJUq3qd9JSXuzEMH7ExRVi0nEiHI+qs0FpQlJaUBDusW2SkR2Qfx3jrBZQsaBei2tTbpMftduH1lBNrLcZCbqTDEXWWTqg1nsqcTqw2G444O5qM9AhAHT+G8Z+XUTLiI0S1qJdJj8tVRmlpMQ67VzqlizALrQWF4ffj9ZQHWlA44qQwoQhSxw5jvPOqJD5CVIN6l/R4ysspyM8luUGMbEsX4RdiCwq3y4Wm6Wiahl0WMosfUEcOYHzwJsrnjXQoQkS1epX0+Hw+MjOP0LRpckVrCSHCLbRFzOVuF5oeqOUT53CgZHpL/IA6sAdzwZcoryQ+QlysepP0GIbBwQN7ad26BTbtMNJLS9QILbRqzB6XGyq2J8c4HCDTW+IszDVLMXdulsRHiItUL5IepRT79u6mXft20i1d1DArobzM3K4yFKBpGtbYOJCKzOIczM8+ROWeQPn9kQ5FiKhTL5Kefft2075De2wcBHyRDkfUKyG2oHAHWg/E2u0Y5adGfYQ4g2lgvPNqoE+XVG0W4oLU+aTn4IH9tE1rWzGlJfUuRA3TYkI6rLS4GF2XFhQiRO4y/P9+EXzyJU6IC1Gnk57MY0dp0bI5Nj0TlEwXiAjQQlvI7CwqxFLRgkK2q4uQ5GZjzPu37OgS4gLU2aQnO/sEDZKTsOk5oIojHY6ot0JtQVGCzWYjzmGX7eoiZGrvTszF30oNHyFCVCeTnsLCfHy+cuJi3OgURDocUa+FmPSUFAdGehwOdNmuLi6Aufw71N6dsqNLiBDUuaSntNTJoUP7adokRTqmiwjTCGW7OlS0oLBacdgd6DLSIy6Q8d/3UAV5KEN2dAlxPnUq6fH7/WzauI7u3XtILR5RC1iBqnfXmKaJ21UW6Ltlj5PChOLCGX6M/7wM5eUo2fknxDnVmaRHKcWGDWsYOGhIRbVl+cYjIkyzhXSYx+1GQ0PTNBx2KUwoLlKZE/9/XpYdXUKcR51Jevbt3U2nTulYtWzZqSVqidD6bpW7XMFSPnEO6bslLsGJTIz/zZOFzUKcQ51IenJzc9A0RWK8Qic/0uEIUcFKKGt6yt2u4HExDtmyLi6N2roedXAvyi8jPkL8UNQnPW63i/379pCW1gqrdizS4QhximYjlJdYucuNqhgRsjniZXpLXDLjk/dBdnMJcYaoTnoMw2D9utX0699fFi6LWsgWWgsKVxnKVMTExqJ8PpAdOOJSlbsxPnxbChcK8QNRnfRs2byB/gMGYtOOAfLiFrVMiC0oXKWloAXW8xgyyiOqiTq0D3PDaqnfI8RpojbpOXhwPy1atMBmKQJVEulwhDiL0HZvlRQVYZUWFCIMzG8/h9ISaUwqRIWoTHoKCwvIyT5Oamo8FrIjHY4QZxdq363iIqzWQDVmpEaPqE6Ggf+DN8AvU6ZCQBQmPV6vh5UrFtO33wBs2tFIhyPEeYSW9JQVlwRGehx2NNmuLqpbzgnM776WbexCEGVJj1KKFSuWMGz4KKxaJiBbMkVtFtrLq9QZSHrsdum7JcLDXLUElX0cZRiRDkWIiIqqpOfQwf2kpqbiiDPQVFGkwxHiPEIrTKiUwlXqxGqzEW+XkR4RLgrjw7dAaveIei5qkp7SUifbd2yha5euFaM8QtRmoSU9Xo8H0zDRdR273S59t0T4OEswPvtAprlEvRYVSY9pmixd8h2XXz6hogChDNGKWk6zEXILCj1QyyewkFl2b4nwUTu2oPbuDNSDEqIeioqkZ8eOLbRr1x6btRSUfBMW0SC0FhQnm40CxDocKJneEmFmfDYXPOWRDkOIiKj1SU9hYQEH9u+jY8eOWDke6XCECI1mI5Skx+061Rw3RlpQiJrg9WB8/qFMc4l6qVYnPYZhsPj7hYyfMAmrdhSQAlsiWthAC6HvltuNaZpYrFY0TQP5IBI1QO3Zjso6hjJlqYCoX2p10rNt6ya6dOmCTS8FJd+ARRTRQqvG7C4LjPRICwpR04zP54JsYRf1TK1NegoL8zmwfy8dOnTAosm0log2ofXdchYWYrFaibM7MCXpETWpIA9z3UppSirqlVqZ9BiGwcJvv+LycROwalnItJaIOiG3oCgOFCaUnVsiAsxFX0vtHlGv1MqkZ9vWTTRp2hR7HKCKIx2OEBchtKSntCSQ9MRKCwoRCT4vxpf/lUXNot6odUlPaamT9etXM2ToCGxaVqTDEeIiWUI6qqykBKvVWtGCoiTMMQlxJrVtIyovB2XKiLqo+2pd0rNi+WKGDR+FTgEg3z5ENLIQSmFCgDKnE2uMDYfdjibTWyJCjE8/AEM6sYu6r1YlPVlZxygoyKN9u/ZYyI50OEJcpNBaUPi8XnxeD7puweGwS2FCETk5xzG3rJdKzaLOqzVJj2H4+f67+UyceGXFbi0ZahVRSgst6fG43Wi6jqZp2O2ykFlElrngC9nCLuq8WpP07NixlSZNmxEfb0NThZEOR4hLEFo15nK3K1CQkIoWFNJsVERSuRvj289kUbOo02pF0uNylbFm9QpGjBwji5dF9Atxu3q5yw0qMCIU43BAqYz0iMhSG9ZAcRFKhbYmTYhoUyuSntWrljF48GVYKAbckQ5HiEtkI5SXVrmrDKVA03WsMbHgdoU/NCHOS2F8+r7U7hF1VsSTnpycE2RlHiWjcxcs2olIhyNENYgBLZTpLTdKmcTZ7RhuF6Hu+BIinFTmEdSh/bKFXdRJEU16TNNk0XffMHb8JHR1ApBFdKIOCLHvlrO4GN1ikRYUotYxFn4hW9hFnRTRpGfPnp3omk5qago6+ZEMRYjqE2rSU1iI1WojzuFASdIjapPs46ijh2S0R9Q5EUt6ysvdfL/oW0ZfPg6blhOpMIQIg1BbUJRgjQkkPZRKNWZRuxgLvgC/jPaIuiViSc/WrRtp3LgpDRokgSqIVBhChEFoLShKS4oDIz12O7oUJhS1zfFjqONHUUpGe0TdEZGkx+12sWb1CkaNvlxGeUQdoxFKjR6AMmcJVpsNh8OOLtNbohYyF3wBPhntEXVHRJKeLZs30KJFK5KSEmSUR9QxNkKpJm4YBuUuNxarlXi7FCYUtZM6dhiVnSV1e0SdUeNJj8vlYu3alYwadTk2TfpriTrmQlpQaBqaphFnt4NMb4laKjDa4410GEJUixpPerZsXk+rlm1ISHCAtJsQdU6ILShcruBhsntL1GbqyAFUXm6kwxCiWtRo0uNylbF23arAWh5dRnlEXRRiCwq3C42TfbfiQaa3RC1mLvif9OQSdUKNJj2bNq2nTeu2xMfHgSqqyZsWomZoVkJqQeF2B9dJ2Ox2KCsLc2BCXDx1cC8USi01Ef1qLOkpKytl/bpVjBo1BpsuO7ZEXRViC4qyMpRSxMbFYXo9YEo1clG7GQu+kNEeEfVqLOnZvHEdaWntcThia/0oT9bxMn72i8V07fkBnXu8z8/vXsKJ7EAzSKUUz83ZxqDLPqZ9+rtMuOILFiw8dt7zKaV47vmtDBj6MR06v8uMa79h2/ZTu9ZMU/HSKzsYNuoTOnZ5j2uvn8+WrZW/Vf3pgTV07vE+w0Z/yrLlxytd9+cH1/L0s1uq6d6LS6LFhHRYmdOJpmvEORwYMsojooDatwuKiyIdhhCXpEaSntJSJ+vWr+ayYcOx6bV7QZxSiptuXURRsZcP3x/HRx+MJyfHzc23LQLgpVd28vwL23jogQEsWjCFyRPbcOvt35+RpJzuX89s4fkXt/PwA/355osraNbMwY23fEdpaaCT8ew523j8n5v4+c+68s0XVzBoUFNmXPsN+/YXA7Bg4TG+nn+Uj+dO4Gc/6cLd9ywPTo0cPVrKV98c4fafdAnzIyNCE2ILiqKiisKEsl1dRA9jwRcoT3mkwxDiotVI0rNp41oaNmpEgwYNav2Ordzccjp2TOLJx4fQrWsq3bqm8rOfdmXrtgKKij243X7+8v/6MXF8a9LaJHLPL3uQEG9j1eqzL8wuK/Mx58XtPPCn/kya0IaOHRrwj78NIiZGZ+u2wGjPCy/t4I7buzBrZjod2ifx+9/0on+/xsx+YTsAu/cWM6B/E7p2SeG6azqQk+umoCAwzPyPJwPJUnx8aB+2Isy00BYyO4uLsNoqWlDIzi0RJdSeHeD3RToMIS5aaO/Ql6CsrJQNG9ZyxZXT0VXtXwjXpImdF2ePCP6cdbyM/7yzh969GpLcIJbf3NMzeF15ucH7c/fhLvczZHCzs55vzdocPB6TKye3CV6WmBjD6uVXAZCfX05xiZdBA5tW+r3u3VL54ssjALRpncDcD/dTWupj3YZcEhJspKTEsmNnIWvX5fLkP4ZU2/0Xlyq0FhRlJYFqzHF2O5r03RJRQ2GuXY5+2eVoNvmiJaJP2JOenTu3ERMTS1pae3S1M9w3V61uvX0R38w/RnKDGOZ9ML7SdV9+dYTbf7EYpeAPv+tFj+6pZz3H/oMlNGwYy4ZNefzzyc0cOVpK926pPPinfqSnJ5OcHENsrE5mVuV1HceOlZGXHxhGnjyxNR9+tJ8uPT/AZtN54rHB6LrG3x/fyG/u6UlMTGgftKImhDZ4WuYswRYbS5zDge7MD6GcoRC1g7lhNfpll0c6DCEuSlint/x+PxvWr6Zf/4FgFgHR1cPl3t/25otPJzFgQBN+dMO3HD/hCl7Xp08j5n95BQ/+uR9PPbuVd97be9ZzlDp9lJb6+NMDa/nV3d15+/XROOxWZlw3n/z8ciwWnelT2/HUs1vYsjUfwzD53xeH+XbBMXy+QDsDi0Xn7dfHsGndNezc8iOumtGeVauzOXK0lGuuasczz21l4GUfc+318zmWKYtiIye0asxKKcpKS7FYbSTY7TK9JaKLswR17FCkoxDiooQ16Tl8+ABut5se3Xth1Wv/1NYPde2SQp/ejXhh9nBMQ/HhvP3B65o3cwTX+/z4Rx154aXtZz2H1abjdhs89uggxo9tTe9ejZj9zDA0DeZ9fACAh/7Sn/79GjN56lekdXyX197cxU9u60xiYuXh44apccTGBkZ1/vrYRu77fW+2bS/k7Xf2MP/LKxg1qiV/emBNmB4NUTUbIbWgKC8HpdB1HbvdLoUJRdQxVy6WBc0iKoUt6VFKsW7dajIyumKxGKBcVf9SLZCb6+aTzw5Wusxht5KWlsjxEy4WLDzGwUOV12B0zkiuNAp0uuZNHYFjOicHL4uLs9CmdQJHjga+4SclxfDi7BHs2XE9m9ddw38/nIDPa9I2LfGs5/zqmyP4/SaTJ7Vh9Zps+vdrTHKDWMZf3oo1a6QGUsSE3HfLhVZRyyfO4ZC+WyLqqH27way6sa4QtU3Ykp683ByOZx2jX/8B2PTo6aR+LLOMO3+5jM1bTo1MlZR42X+ghPROyTzytw288tquSr+zaXMe6Z2Sz3q+gQOaALB586nzlZcbHD7sDCY1v713BXPn7cdht9KwYRyGYfLtwmOMGN78jPMZhsnj/9zEH+/rA4CmaSgz8EHr85uY0g05gkLru+UuO5Ugx0rfLRGNlIm5biXKH11LFoQIW9KzdetGGjduTEpKaq3fpn66Xj0bMmhgE35330o2bspj67YC7rhrCQ1T47jumvb87KddeOe9vXzw4X4OHiphzovb+fiTg/z+N72C58jPL6ekJNCVuHXrBK6e0Y77/7SaJcuOs3dfMb+5dwW6ReOqGe0AaNrEzj+e3MTqNdns21/Mnb9aRlmZn5/edmbtnbnzDtC4URwjhgUSot69GrJ8ZTZbtubzznt76d+3cQ08SuKsNCshNRt1u4JVm22OeFnTI6KSuX4lyJcsEWXCsnvL5XKxbdtmJk2eWrFNPXpeGLqu8epLI3nkr+u56bbv8HhMRo1ozkdzxxMfb+OGH3fCMAJVmTMzS+nQvgGvvDiSy8e0DJ5j0tQvGTq4KU8/eRkATzw+hMf+uYlf/noZTqeP/n0bM+/98TRMjQPgN/f0pMzl52d3LqG83GDwoKZ8/OF4UlNiK8VWXm7w5NObeXnOqS31A/o3YdbMTvxo5gLatUvk+WeH1cCjJM7OBlrV3yM8bjfKMLHabIEPDZ+3BmITopoVF6KOH0Nr0y7SkQgRMk2p6k/VN21cy8oVS7n9jruxqt2AFLMS9YClHegpVR62YdlSFn/xOe27dGXylKnw7F9rIDghqp/WpQeWadejxcZFOhQhQlLt01uGYbBu3Sp69x2AMkuQhEfUH6EVaystLg62oDBlaktEMbV7h0xxiahS7UnPkSOHcDqddOvaDZsePWt5hLhkWoh9t4qLsNqsFS0oZOeWiGKmgblhtSxoFlGj2pOeDetX06xZcxzx8aDkDV3UJ6FVxi4tLsZitWF3ONBku7qIcua6FTLaI6JGtSY9hYX5HD16mG7de6FF0Y4tIapHiEmP04nNZiPWbkeXkR4R7QrzUTnHIx2FECGp1qRn/769aJpGxw6dsGjF1XlqIWo5C6G2oHA5S7DYbMTb7TLSI+oEc90KlMcT6TCEqFK1JT1KKbZs2UBaWntiYqygpAeUqE9Ca0Hh93nx+3xYLBYcDrsUJhR1gtqzAyzS+FjUftVWpyc3N5uSkmKGXjYCnaLqOq0Q0SHEFhTlLjeaHviuYbfLQmZRR7jKID8HmraIWAglJSXk5OTg88mO4frKZrPRpEkTkpKSznlMtSU9+/bsQtd12rVrj86h6jqtEFEi9GrMWsVxsQ4HqlRGekTdYG7ZgD6qMZottF2M1amkpITs7GxatmyJ3W4P9rYT9YdSCrfbTWZmJsA5E59qmd4yDIOt2zbTsVMGumYC0n1X1DNaaH23yl3u4L9jHPEy0iPqDHP3tojt4srJyaFly5Y4HA5JeOopTdNwOBy0bNmSnJxzN96ulqQn+8Rx3G4XnTO6YNFlAbOoj0Ic6XGVoVDougXdZoNyd5W/I0RUyM+N2PPZ5/Nht9sjctuidrHb7eed4qyWpGfPnh3YbDG0btMWXbaqi/pIiw02ET0ft8uFMhRxDjuGLGIWdYy5fRPKNCNy2zLCI6Dq58ElJz1+v58dO7aRkdEVZZYD0jxR1Eeht6DQrRbiHA5Ml+xwFHWL2rlVGuiKWu2Sk56szKP4fF7SMzKwWWRqS9RTWmh7ApxFhdhsgb5bSI0eUceoY4ciHYIQ53XJSc/OnVux2Wy0aN4KTUnSI+qrEJOe4kBhwjiHA00WMYu6RinU3p2oWtKWQvkjs339Ym53zJgxZGRk8O677571+p/85CdkZGTw6aefVnmu1atXk5GRwYkTJy44jrPZt28f33//fbWcK9Iuacu61+th755ddOiYgVJe0KQ+gqivQivMVuYsCTYblWrMoi4yt23C0rEzxEV+YbFmteF76Hc1fru2B568uN+z2fjmm2+YOXNmpcuLiopYvXp1yOfp06cPy5Yto2HDhhcVxw/deeedTJkyhVGjRlXL+SLpkkZ6jh07gmGYtGrdBl2TN3BRX+mEsnMLTiY9MTjscdJ3S9RJ6sAesNZ8rZ66YPDgwaxdu5aCgoJKl3/77bf06tUr5PPExMTQuHFjdL16mi7UlpG76nBJj8iB/XuxWq20bt0aiyY7UUR9ZQWq3rFi+P143O5ACwq7tKAQdZTPizp2ONJRRKU+ffrQqFEjFixYUOnyr776ismTJwd/Nk2TOXPmMH78eLp3707//v355S9/GUyWfji9NWbMGF5//XV+/vOf06tXLy6//HJmz54dPF9eXh533303gwYNonfv3txyyy3s3LkTgBtvvJEjR44we/ZsxowZEzzf448/zoQJExg8eDDbt2/H6/Xy2GOPMWzYMPr27cusWbPYtGnTBcXco0cPlixZwoQJE+jZsye33HILJ06c4OGHH6Zfv34MHTqUl19++ZIe44tOekzTZN++PaQ2bEhiYjIoeQMX9ZQWWt8tT7kbTdPRNC3QgkKmt0QdZW5dj/JKA9ILpWka48eP55tvvgleVlBQwNq1a5kwYULwsjfeeIO3336bP/3pT3zzzTc8+eSTrF+/nhdeeOGc537mmWcYPXo0n3zyCVOmTOG5555j3bp1ADz00EP4/X7effddPv74Y+Lj4/nlL38JwHPPPUfLli257bbbmDdvXvB87733Ho888ggvvfQSXbp04Q9/+ANr167l6aef5qOPPmLw4MHcdNNNHDx4MOSYfT4fzz77LE888QRvvfUWO3bsYOrUqTgcDubNm8ePfvQjnnzySfbt23fRj/FFJz0FBXl4POW0apWG4S8hlDd9Ieqm0JbGuctcaHpgGizO4UDJ9Jaoo9SeHVBNUyv1zcSJE1m9ejXFxYGNQfPnz6dv3740atQoeEy7du14/PHHGTFiBC1btmTkyJEMHz6cPXv2nPO8o0eP5kc/+hHt2rXj17/+NUlJScGRmMOHD5OYmEirVq1o3749Dz/8MH/9618xTZPk5OSKBskOUlNTg+cbM2YMAwcOpFevXhw9epSvvvqKxx57jP79+9OuXTvuvvtu+vXrxxtvvBFyzEopfvOb39CjRw/69OnD4MGDSUhI4He/+x3t2rXjjjvuAGDv3r0X/fhe9ELmzMxjALRq1RqbpUxyHlGPhd536+TrJMbhAJneEnVVqTPwJzm16mNFJf369SMlJYWFCxdy1VVXnTG1BYGEY+PGjTz11FMcPHiQAwcOsH//fvr373/O87Zt27bSz4mJicHKxXfeeSf33Xcf8+fPZ8CAAYwYMYLp06efd01Q69atg//esWMHANddd12lY7xeL16v94JibtOmTfDfDoeDVq1aBQsOxsXFBc97sS466dm7dydxcXZatGiJpg5edABCRD3NRiiDph63G6UUmqZhi7PjL5PihKLuMg/uRe89UColXyBN05gwYQLffPMNo0aNYsOGDTz11FOVjnnhhRd4+eWXueqqqxg+fDh33HEHb7/9NllZWec8b0xMzBmXnVygPHHiRIYOHcrixYtZsWIFc+bM4c0332Tu3LmVRphOFxsbG/y3raLJ7Pvvvx9MTH54u6HGbPtBw9rqWox90kUlPV6vh6zMY7Rt2wGLRQNk7lbUZzGhtaAoc6GUIiYuDsNTDioy5fqFqAnqwF7o2gti46o+WFQyceJEbr31Vj755BMGDhxYaVoJ4K233uJXv/oVt956a/Cyw4cPY7Ve+Ee63+/nySefZOrUqUyZMoUpU6aQn5/P0KFDWbNmDZMnT64yce3UqRNA8PdOeuihh+jQoQOzZs2q1pgvxUWlUCdOHEcpRcuWrUHJugRRz2mhbc8tcxaj6Tp2hwNTprZEHaeOHAQ9tPpVorK+ffvSoEEDZs+efcbUFkBqairLli1j//797N27l4cffpiNGzde1LSP1Wpl+/bt/OUvf2Hz5s0cPXqUDz74AJvNRrdu3QCIj4/n0KFDZGdnn/UcaWlpTJ48mT//+c8sXryYI0eO8NRTT/H+++/ToUOHao/5UlxUinXkyEF0TadNm9ZY9VJZzyPqtxCTHmdRMVablVi7A1VWGmJlHyGiVElRoA+XLXI1e5Tfd9GFAi/1drVLqFWk6zoTJkzggw8+YOzYsWdc//jjj/Pwww8zY8YMkpKSGDhwIL/73e948cUXcbsvvNP9k08+yd/+9jfuuOMOysrK6NSpE88//zxpaWkA3HLLLTz66KMsW7aMlStXnvUcjz76KE8++ST/93//h9PppEOHDjz33HMMGTIkLDFfLE1dYNUhpRRvvP4iFouFWTfehsXcDhhhCk+IKGDtDtqZ8+U/9Mmbr3Pi2FF6DBhE/5bN0D/6dw0EJ0TkWH50K3rn7mG/nZ07d9KlS5ew346IDud7Plzw9JazpBhnSREtW7bC8LuQhEeI0IbwS0uKsVoDfbekGrOoD9T+XSjpui5qkQtOerKyjoEGjRo1waK7whGTEFFEI9SXkavEidVmw263o0thQlEPmEcOgSnrH0TtccFJz/79e4iJiaVp0yZYtJqbhxOidgqtBYVpmrjKyrDabBUtKCTpEfVAbjZYZDGzqD0uKOlRSnH48EHi4xNJTW0ESkZ6RH0X2l4Ab3k5EKjR47DbpTChqB+UCfk5kY5CiKALSnpKSorx+fwkJCRgtdmQ+jyi3gux71a523WqqqhD+m6J+sM8fAAlNalELXFBSU9Bfh4aitSGjTD88k1ViJBbULhcoAVebrEOh3RYF/WGOnoIargWixDnckFJT3b2cdA0GjZshNVSHq6YhIgeIbagKHe5gxWYbY54kDU9op5QmUeCCb8QkXZBz8SjRw8TF2enadOm6Mh6HiHAFlILinK3C1MpbDExKMMAv78GYhOiFijMj3QEQgSFnPQYhkH2iSzsdgcpKQ1lEbMQEFJRQoAyZ2BkJ87hwJCprai0IVvR/mWTlVmn1nAtOaqYNM8k/VWTCR+aLDpy/vVdh4oVt39j0vstkz5vmfziW5NMZ+XfWXtCMf2/gXMOe9fkjW2Vr39guUmPN0xGv2+yPLPydQ8uN3l2Q+3bIq5yjkc6BCGAC0h6iouLMEyTpKQGFV9sfeGLSoioEWoLiqJgYUJZzxN9XD7Fb75TGKflE3sKFT/9RjG5vcaXV2uMaws/+0axp+DsSYfLp7jxS4VhwntXarw9WaOgHG7+SuGpOPG+QsWsLxR9msD8azXu6afx15WKLw4Erl94WDH/EMydqvGTnhr3fKeCnbKPOhXfHIKf9AjjA3GR1LHDEbrhCC2gvojbHTNmDBkZGcE/PXr04Morr2TevHmXFMott9zC/ffff0nnOJ3f7+fNN9+stvPVtJB7bxUU5IEKLGI2jbKLbFUqRB2jhfYSKi0uxmqzEWd3yHqeKPTISkWzBDhUcuqyN7YGkpNf9g1Mb/5+gMbaEyavb1U8NvLMKc8lxyCrFL68WiMxJnD9U6NhyDuKTdkwqAXM2aTo1RgeuCzwBtu2Aaw/oVh7XHFFe429hdC/KXRpqNGugeL/LYWCcmhohyfXKn7WUyPeVgu7uuVlo7xetJjQRkarjaaDb0PN3iaAre9F/drtt9/OzTffDIDb7WbZsmX85S9/oVGjRowaNaoaA7x4X375JX//+9+55ZZbIh3KRQk5dTmelYlusdCoUSOsFilKKERAaEmPs6QokPQ4HGiyXT2qfHdE8d0ReGho5WRizXEY3KLysUOaa6w5cfbz9G4Cb046lfAA6BX/LK7Y3LTkKFzRvvLtPDZS58GKJKh1IuzIh1KvYs1xSLBBShzszFesOwE3dL34+xlOqiAfTGlZVBWHw0Hjxo1p3Lgxbdq0YebMmQwZMoRPPvkk0qEFXWC7zlon5KTn6LHD2O0OmjZpgo4kPUIEhPYSKqtoQRFnt0vfrShS4Fbct1jx+AiNBrGVrztRBs3iKycoTePh+DlmL5vFawxvVfn4ORsVdisMaAZOryLXDfE2+PV3Jv3eDqwTem/nqQ+Zie0gLQl6vqm4fb7i0eEauqbx+BrFPf00Yiy1cJQHUAV5oMv0wMWw2+3BGl8ZGRl8+umnla4//TLTNHn22WcZNmwYffr04e9//zuGUTnZ3Lx5M9dffz09e/Zk0qRJfPjhh2RkZHDs2DEAvF4vjz32GMOGDaNv377MmjWLTZs2AbB69Wr+8Ic/BG/3448/DuddD4uQnoV+v5+83Bzi4uwkJiXLImYhgMAoT9XfepRSuJxOrFYr8Xa7jPREkT8uVYxNg1Ftzkwm3H6I/UGHhRgLeEIc0Pj3dsVb2+H+QRopcRqlFaM9j6xUdEoJrPn5cReNvyxTzN0VeJ5ZdI3XJ+msu1Fjyy0aMzpprM5SHC2BqzrBcxsUl71jcv3nZy6QjqiSIrCEvJpCEHjfWLFiBcuXL+eaa64J6XdeeOEF3n77bf70pz8xb948iouLWbNmTfD67Oxsbr31Vjp27Mh///tf7rnnHp544olK5/jDH/7A2rVrefrpp/noo48YPHgwN910EwcPHqRPnz785S9/AWDZsmVMnjy5+u5wDQnpWVhYWIBSitjYOHTdAqZstxUi1KTH5/VimAa6xSJ9t6LIvN2K7XnwzbVnHz2Js56Z4HgNcISwtv25DYon1iru6g23dA+c31rxFfTyNLirT+Cybo1gf5Hita2K6zqfiiPVfurfj61R/H6AxvZ8eGeH4ptrNd7bCQ8sV7w6sZaM/CgFrlJIbBDpSGq1OXPm8MorrwCBERe/38+4ceMYMGBAlb+rlOLdd9/l1ltvZeLEiQA8/PDDrFixInjMBx98QEpKCg899BAWi4UOHTqQl5fHI488AsDhw4f56quv+N///kenTp0AuPvuu1m/fj1vvPEGDz/8MAkJCQA0bty4Wu97TQkp6SkoyEMpRUJiIobhppaOoApRs7TQkp5y16kWFHaHQ/puRYkP9yhOlEH/txWggv/TN3+puCZd0Twecn4w6J1dBk0d5z6nqRR/Wqp4Zyf8cZDGz3ufejNNiQuMHGWkVn6D7ZSi8dGesz/Pvjmo8Jswqb3Gq1sU/ZpCg1iNy9MUczZd+H0OJ1WYjyZJz3ndcMMNzJw5EwgkPXv37uWf//wnd911VzAZOpfCwkLy8vLo3r178LKYmBi6dj210GvHjh306NEDy2lNYPv161fpeoDrrruu0rm9Xi/eOlJVO6SkJz8vB4tuITEhEZQ3lKr7QtQDNkJqQeF2o1UcF+twoGR6Kyo8M0aj/LRB7VwXXPOZ4vGRGsNbwRNrFKuPK05/DqzMUgxqfu5z/nmZ4v1d8MQojWszKj93rLpGn6aKLTmVz7m7QJGWdOa5DFPxz7WKBysWWGvAyY3SfjMwuFKbqJwT0KZ9pMOo1Ro0aEBaWlrw506dOuH3+7n33nvZu3fvGcf7z1Lk9IcLjWNO2zFnsVgwzXNvp7fZAsOU77//PnFxcec8TzQLaU1PXl4uMbGxJCQmYdXrRrYnxCXTQu27VRb8d4wjXkZ6okSzeI22DU79aZV48nJoZNe4pbvGmuPwr7Um+woVT6412ZgDt/Y49ZzIdytKPIEPoe+OKP6zA37ZF0a2hhyXCv4p9weOubuPxlcHAwucj5QoPtilmLsLftrzzOfZvD3QyA7DKhZH92oCK7Nga67ivZ2Kvk3D/ABdqNxslE/qu12ok0mMaZrYbDZKS0+9fxw+fKr+UWpqKk2bNmXjxo3By0zTDI7eQGDx8fbt2ystbt68eXPw3yentPLz80lLSwv+efPNN1m4cCFAcNQ6WoWU9OQX5BMTE0NKcgN0TZIeIQJsIfUUKne5UcrEYrGgWSzgkb51dUHnhhovjdf48iBM/kix4DC8PlGjU8qpD4WpHyseWhH40Prv3sDfT6+HAf9Wlf58eSBw/PBWgXN+tk9x+QeKORsVD12mcXV65Q+acr/i6fWK+waeurx/M42ZnWHm/xSbcuChy2rXh5MqyAND1oOej8vlIjc3l9zcXLKzs1mxYgXPPfccXbp0IT09nd69ezN37lx27drF9u3beeCBByqNwNx22228/fbbfPLJJxw4cIBHHnmErKys4PUzZ86koKCAhx56iP3797Nw4UKeeeYZIJDMpKWlMXnyZP785z+zePFijhw5wlNPPcX7779Phw4dAIiPjwdg69atlJWVEW2qnN4yDIOS4iIaNmxMUlISUBT+qISIBiG2oCh3laGUtKCIds0TNA7fUTmRuDxN4/K0cycXy284lRQ/d7nOc5dXfTvj2mqMa3v+hCXOqrHyhjOP+cMgnT8Mqvo2IkEV5IXUp64+e+WVV4JrdywWC6mpqYwZM4a77roLTdN48MEHefDBB7n22mtp0qQJ99xzD9nZ2cHfv+WWW1BK8fTTT1NYWMiECRMYO3Zs8PpGjRrx8ssv87e//Y1p06aRlpbGzJkzmT17dnBq69FHH+XJJ5/k//7v/3A6nXTo0IHnnnuOIUOGADBo0CAGDhzIj3/8Y373u99x66231uAjdOk0VUWlIaezhNdfm0Pjxk255prridH3IS0ohAAsnUBPrPKwZV9/xYZlS0jv2YvRw4ahv/JUDQQnRC2jW7D+v8fQwlCvZ+fOnXTp0uXMK5QZmQ7vkbrdKuzbtw+n00mfPn2Cl33xxRfcf//9bNy4Eau1bpQVOOfzgRCmt5zOEjRNw2KxYLXFIAmPEBW0C+i7FWxBISM9op4yDXDXcI23SCUetTDhATh+/Dg33XQTX375JVlZWaxZs4Znn32WyZMn15mEpypV3stSpzOwXT0hEcNfHiybLoQI7U2irKQ42GxU+m6J+kwVF6LFJ0Q6jHpr+PDh3HfffTzzzDNkZWWRnJzMpEmT+M1vfhPp0GpMle/aRUUFaGgkJCahlEe2qwsRZKn6EKC0pCTYd8tSWhJCZR8h6iaVcwJatI50GPXarFmzmDVrVqTDiJgqx+Dy8nKxxcSQmJCIRbarC1FBJ5TChABlzkDfLUdcHJpMb4n6LC8bZUjjURE5VSY9+QV5xMbEkpzcAIsu63mECLARStJj+P14PeWBFhQOuxQmFPWacrtBkh4RQedNepRSFBUWYIuJCfTbUDLSIwQQcgsKt8uFpulomoZdFjKL+s5TDuepCCxEuJ036XG5yjDNQFG12Lg4QApLCREQ2s6tcvepvltxDoc0GxX1W7mbUKeFhQiH8yY9paXO4Bt2bEwsKEl6hABCbkHhcZ16k49xOKBURnpEPSbVyEWEnTfpKXe7g0m5LSYWqdEjxElWQuni4naVoQiUeLfGxoE7+sq2C1FdVHm5VGUWEXXed22Px4NSJrpuQdMsnOrhK0R9FxPSm3e5240yTWLtdoxyd+1rfS1ETSp319rCfaJ+OO+zz13uRgFxcXGYhixiFiIoxL5bpcXF6LqFOIcDUxYxi/rOUw6W0OpbVQe/GZkvGRdzu2PGjCEjIyP4p0uXLvTv35+f/vSn7Nq1KwxRnltGRgaffvppjd5mTTlvccJSZ0lgEXNsLKbyS2FCIU4KuQVFIZaKFhSq1CkvIVG/+bw1Or1l1TUe21jzyzLu7xPa+8MP3X777dx8880AmKZJXl4ejzzyCLfddhvz588P7KKuAcuWLatoMF73VLmQ2Wq1YouJkUXMQlQSWguK0uISbDYbcQ67bFcXAsAva0PPxeFw0LhxYxo3bkzTpk3p1q0b9913H/n5+axatarG4mjcuDGxsbE1dns16bxJT1lZKRaLlRhbDCAFpYQ4JdQWFMWBkR6HA720JMwxCREFfJL0XAhLxXRgTEwMGRkZPPPMM4wYMYIRI0aQm5tLcXExf/zjHxk0aBADBw7k9ttv58CBA8Hfv//++/nTn/7E448/Tv/+/Rk0aBDPP/88e/fu5frrr6dnz55MmzaNrVu3Bn/n9Omt+++/n1tuuaVSTKdftnr1anr06MGSJUuYMGECPXv25JZbbuHEiRM8/PDD9OvXj6FDh/Lyyy+H94EKURV1elxYLYGRHk2SHiEqaIQ611vmDIyWOuwOdBnpEQK8nkhHEDWOHj3Kk08+SePGjenbty8AH374IS+99BKzZ8+mUaNG/OxnPyMnJ4dXX32Vd999lxYtWjBz5kwKCwuD5/nkk08A+Pjjj7n55pt59tlnueuuu7jjjjv48MMPsdlsPPzwwxcdp8/n49lnn+WJJ57grbfeYseOHUydOhWHw8G8efP40Y9+xJNPPsm+ffsu6fGoDuffvVXuRrdYiLHFoOuyc0uIACuh7GQ0TRO3qyzQd8seJ4UJhQCp1XMec+bMoU+fPvTp04fu3bszbtw4nE4ns2fPDq7nmTFjBl26dKFnz56sXLmSrVu38swzz9CjRw86duzIQw89RIMGDZg7d27wvKmpqfzhD3+gTZs2wRGaK6+8ktGjR5ORkcFVV13F3r17LzpupRS/+c1v6NGjB3369GHw4MEkJCTwu9/9jnbt2nHHHXcAXNJtVJfzLkzweD0kxsZhi4lB10wppCkEhLyI2eN2o6GhaRoOuwOk75YQKLdbFvSfww033MDMmTOBwLRWcnLyGYuXW7c+1aV+x44dGIbB8OHDKx3j8XjYv39/8Oc2bdoECw07HI7gZSfFxcXh9V7aDu3Tz+dwOGjVqtWpavRxcQCXfBvV4ZxJj2maeL0edF0nNjYGTZIeISqE1ner3O0OblSJc0jfLSEAcLsiHUGt1aBBA9LS0s57zOkLjG02G8nJyZVGdU46mdwAWK1nftRrl7CLzu8/c2OTzVb5y6Cu1856TOeMyufzBb+l2qyhDecLUT/YCGVNT7mrLLg9N8bhQEnSIwTKJVXJq0unTp0oKioCIC0tjbS0NFq1asXTTz/N2rVrq+U2bDYbpT9on3P48OFqOXcknCfp8aIFMzUZjBQiSAutBUW5y42qqMBsc8SDrOkRApR8ga4uQ4YMoXfv3vz6179m3bp1HDx4kD/96U8sWrSI9PT0armN3r17s2PHDr744guOHj3K7Nmz2bNnT7WcOxLOOb3l852ae5NWKUKczhZiCwoXylTExMaifF4wZAekEJxlqiVc/Ka66EKBl3q7Vj38H5yapvH888/z+OOPc+edd+L1eunSpQuvvvoqHTt2rJbbmDp1Kjt37uShhx7C7/czadIkbr75ZrZs2VIt569pmlJnbwaUn5/HO/95jUaNmjBk6DDatY4BlV/T8QlR+1jag55c5WHrlyxm6Vdf0qFbNyZMmow2++/hj02IWs4yYyZ6z37Ves6dO3fSpUuXaj2niF7nez6cd4z+ZJ4qIz1CnC60b44lRUVYbFZiYmPBkIrmQgBgqbmRHiF+6NxJj1Kn7U+RrEeIoFD7bhUXYbXayM/JwWexonWSb6JC1OT0lhA/dM6kp1LKIzmPEKcJrQVFWXEJVpsNZZqsXrYMNWE66DXXYVqIWqkGu6wL8UMhbaTX0JAiPULY8Wtd8Jmh1Z+Itcfh9QRK7h8/cpj84mIYNCycAQpR+0nSIyLo3CM9p+c4MtIj6jUHXjrjs3RmU0EMm/NNvEbVXwIumzAJj9uFWbFra83yZWjDLgdHQhW/KUQdFqbRznPsyRH1TFXPg/Ou6QkuZJasR9RL8Xi1Lvj0DDbkxzBnu58FmSbfZ5n4Q3h/bdqqFT0HDaEgJwcAZ1ER+3ftQo2ZFOa4hajFwjDSY7PZcLvd1X5eEX3cbvcZ1aFPF9r0lizqEfVKAl6tC149nXW5Np7f7uf74wp3xQYsv4L5R42QRnsGjx2HbtHxVXSW3rx2DXTuDs1ahvMOCFF7hSHpadKkCZmZmbhcLhnxqaeUUrhcLjIzM2nSpMk5jzvnMnpVaSmzEPVBIl5agx7Lmmw/a/P8eM5RT3BXkWJwU0VT+/m/FCQkJTF0/ESWfPE5TVq2wuf1smHVKvpOmoH2xuww3Q8hai8tDNNbSUlJAGRlZeHz+ar9/CI62Gw2mjZtGnw+nM25kx6lTm3b0mQhs6jLkvBorUGLYXW2wfpcP54QKuV/dcRgVroVWxUDoT0HDWbTiuWUOZ3EJyZyYOcOuvbogaNrL9SOzdVzF4SIFpbwNKJMSko674edEFDV9FbFMKFhGFUeKkT0aYCHbnj0DqzMtvD8Nj8rslVICQ9Athv2FCn85vm/ENhiYhg9bTqlRUUopVBKsXrpEtT4qVKzRNQ/UrZBRFBIu7c85R7Q5Ikq6opkPFp3yrX2rMi2MHubn1U5Cu9F9EFcmGlQRc4DQLuMzqRlZFCcH2jlkpOVRXZ2NgwdfeE3KkQ00+ULtIickNpQeDwelJInqohyWgoeuuPW2rH0uM7sbX5W5yp8l9D02eWH5Seq3sKuaRojr5yC1+PBqGhJsW7FcrRBIyAp+eIDECLayEiPiKBzZjK204bdvV4vpiQ9IlppqZRrPXDTlsUnNJ7f5mddngpp23ko1uaa51zwfLpGTZvR57JhFGQHtrCXOZ3s2roFNfbK6glEiGggU7oigs6d9MTEBJcue31ezBCr0ApRa2gNKdd64FJpfJ+lMXubnw15VFuyc5Kp4OsQt7APHD0GW0wMnvJyALZtWI9K64DWum31BiVEbRUXF+kIRD12zkwmJiYmWO/A5/WiQuw3JETEaY3xaD0pVa35LlNj9nY/m/IVIeQkF21/iSLHrTCrqBHiSEhgxOQrKMrNBcDw+1m3cgXmpBlI6XNR59njwbyE+WQhLtG5R3psMSjTRCmF1+cl1CaLQkSMHkh2nGZLFhyDOdsMthSokBYaV4evjhohJVZd+/WnYdOmlBYXA3Boz27KlIbWq3+YIxQiwhKTwB/CXLAQYXLOpMdisWC12TBNE59Xkh5Ri+lN8Wg9KTFaMv8YzNlusLVQUdPfJ/PLYVuBWeUWdovVypjpV1HmLAmOpq5auhQunwwxsTURqhARoSUmITXfRCSdd6FOXFwcpmni9XnRdFl8JmoZvRkerRfF/uZ8fQxe2G6wvTCylcS/zzJDGu1p1b49Hbv3CE5zFeRkc+zoERg+NswRChFBiUmyZV1E1HmffbExcRiGgc/rRZekR9QWenM8Wi8Kfc348qjihR0GOyOc7JzkMQKJTyhb2IdPugK/34/fH9jCvn7lSrS+gyGlYU2EKkSN0xKSwHruZpBChNv5R3rs9kDS4/OhS20FEWl6CzxaLwp8TfniiOKlnQa7i2pDqlPZpjwTl7/q41IaNWLAqNEUVnRhL3e52LZxA2rCtDBHKESEJKeiyUiPiKAQprcMlFKYplnV4UKEh94Sj9abPG8TPj+seHmnwZ7i2pfsnKQI9OUKZQt7vxEjiXPYKXe5ANi5aRNG0xZo7TqFOUohap6WnBrpEEQ9d94sxh5nr+i7BX6/l/P0JxWi+umt8Gi9yfE05tPDJq/uMthXUnuTndMdLlUcK1MYVSxqjrPbGXnlVIoL8iu+XBisWb4MNXEGaPIlQ9QtgYXMQkTO+Ud6HA7MiqTHU14OmszFihqgt8aj9ybb05D/HjJ5fbfBgShJdk73zVEjpB1k6T170axVa5xFRQAcO3CAovJy6D8krPEJUePiEyIdgajnzj/SY7djmIGkx1nqBGJqIiZRT5laG7x6b7LKU/n4gMkbu00OOaMv2Tmp2Asbck18VW1ht1gYNXUa7tLSimlkWL10KdrI8WB31ESoQtSMOHukIxD1XBXTW45gHZHi4hKUJD2i2umnJTspfHjA5O09JodLozfZOd3yEyZGCMM9LdLa0rVfPwoqFjUXF+RzaN8+1KgJYY5QiBoSZ4cqKpYLEW7nTXoc8fHoFesKSp1ODFOmt0R10TG1NLx6L465U/hgv8l/9pocrSPJzkleExZkhraoeej4iQAVxUBh45rVaN36QOOmYY1RiBqR2ACMELY1ChFG5016EhISg+2AysqcmEqSHnGpdAytLV69F0ddyby3z+DdfSaZZXUr2TndtgJFiZfgqOm5JKWkMHTceApzA6M93vJyNq9dg5o4vQaiFCK8tMQkGekREXfepCc+PiH4Rl1aWoqmSYl8cbF0DL0dXr0XR8oa8O5eg/f2mxx31Y83wS+PGCF1d+89dCjxiUm4SksB2LN9G96kFLT0bmGOUIgwS2ogOxJFxFVRp8eOxWLBMAzKykqxWONqKi5RZ1gxtPb49F4ccibxzl4/HxwwOeGuH8nOSVkuxYGSqrew22JiGT1tOiWFBSilUKbJ6mXLAgULLVIgVEQvrWkLiJHZAhFZ5016NE2jQXIKPp8X0zTx+zyAPGlFKKwYWgd8eg8OlCbw9h4/Hx40yXZHOq7I+fZYaFvYO3TtRuv2HSkuKADgxNEj5BUWwqAR4Q1QiDDSWqWhyUiPiLAqn4Epyal4KxZWulxlIFNc4rys+LWO+Cw92FsSz5t7/Hx0UJFbHum4Iq/UB6uzq+7Lpes6o6ZOw+N2B+tkrV2+HO2yMRCfWBOhClHttEZNIh2CEFUnPampDYO7SaRWjzi3GPxaJ3yWHuwpcfDGLj+fHFbkS7JTyapsE18Iwz1NWrSg1+AhwS3szuIi9u7cgbp8cpgjFCIMHAlglYr+IvKqHulJbYhZUaCwuLhYavWIHziZ7HRjV7Gd13b6+eywosAT6bhqJ7+C+cdC28I+eOw4dIsFryfwYG5ZuxY6dUFr3ircYQpRrbSmzcEv29VF5FWZ9MTHJwS74pY6S/FLrR4BQCw+LR2fpRs7iuy8utPP/44oiryRjqv2212kyPeoKrewxycmMmziJIrycgHw+7ysX7USc9KMmghTiGqjNW0uIz2iVqgy6UlIOLWGoLTMiVIy0lO/xeHTMvBZurK9MI5Xdvj58qiiWJKdC/JViFvYewwcRIPUhpSVlABwYOdO3LY4tO59whyhENVHa5mGZpUvzCLyQhrpMU0TpRTFRYVYrNILqH6yVyQ7XdhaEMvLO/x8fUxR4ot0XNEpxw27Ck38VWxht9psjJk+A2dxcXBkaPWypaixV4J8iIgooTVvGekQhABCSHpiYmKIiYnBMAw8Hg9+nw9ZzFyf2PFpnfFZOrO5IJYXt/uZn6lwSrJzyb7LMqki5wEgrVM67bt0oSgvD4Dc41mcOHECLhsT5giFqAaaDsmpkY5CCCCEpEfTNFJTG+H1BhZTFhYVgCajPXVfPN6KZGdjfgwvbPezIFNRJmsRq43bD8tOVL2FXdM0Rky+Ep/Xg1HRu2jdihVoA4dBUnINRCrEJUhtBH75liRqh5AqRbVo2Rq3O1BVLicnB1PZwxqUiKR4vFoXfHo6G/JimLPdz3dZCpckO2GxLtek3Kj6uIZNm9Jv+MjgFnZXqZOdWzajxk8Jc4RCXBqtaXOoXwXYRS0WUtLTvFlzTDPwqVdQkI/flHYUdU8iXrrg1dNZl2vj+e1+vj+ucEuyE1amgq+PhraFfcCo0cTExOCp+AKyfeMGzNbt0Nq0C3eYQlw0rXlLiJUlEaJ2CCnpSUltiFbRbr2wIB/dEh/WoERNSsRLV7x6R9bkWnl+u58lJ1RIow+iehwoUZxwKcwqtrDb4+MZccUUivIDa3sMv591K1YEtrBrWk2EKsQF01q1lfYTotYI6ZmYnJyKQsM0TdxuN6ZpIj24ol0SHq0bHr0jq3KtPL/Nz7Js8EiyExFfHzUIYbCHLn360qhZc5xFRQAc3ruHUkNB7wHhDVCIi6Q1bhrpEIQICinpsVqtNGrUCI8n0FOgSBYzR7FkPHTDo3dgZbaF57f5WZGt8ITSCVOETYEHtuSb+KrIfCxWK2OmzcDldFZ8+YDVS5eijZkMsTLtLGqZ2DiIk+elqD1CHnNs0aJVcDFzbk4uJrKYObok49G6U661Y3m2hdnb/KzKUXgl2ak1lhw3Q+rC3rJdO9J79aIoN1CpuSA3h6OHDqFGjAtvgEJcIC2tPfhk55aoPUJOepq3aIVRse0wPz8PvyHZe1TQUvFo3XFr7Vh6XGf2Nj9rclVITS9FzfIYsCgztC3swyZOwjCNirpZsGHVKrTeAwLbg4WoJbSOnSEmNtJhCBEUctKTmtoQrWKxZEFhProu01u1mpaKR+uBizQWH9eYvc3PujwVUusDETmb882QaiElN2zEoDFjg1vYy90utm3YgJowLcwRChE6vVOXYO9GIWqDkJ+NKSmpmCrQJNFVVlbxq9JArtbRGgWSHZXGoiyN57f52ZBHSItkReQpAn25qlrbA9B32HDs8fG4XS4Adm3ejL9xM7QOGWGOUogQxCfCab0bhagNQk56bLYYUlManraYOR802bpea2iNKdd6UqpasTBTY/Z2P5vylSQ7UehIqeJIqcKookdFbFwco6ZMpTg/H6UUpmmwZtky1ITpIN+uRYRp7TqCIdtBRe1yQe+MLVqeWsx89FgmhpKkJ+L0Jni0njjNliw8BnO2GWwpUCH1dBK11/xjRkiLmtN79qJFmzY4iwoByDx0kEJXGVr/oeENUIgq6Old0WRHoahlLizpadEKn88LwIkTWZhKhi4jRm+KR+tFidGC+cdgznaDrYUqpA9KUfsVe2F9btVb2HVdZ/S06bjLyk7bwr4MRowDu3wpEZGjtU+PdAhCnOGCkp7GjZui6YHFzEWFBWiaDSlSWMP0Zni0XhT5m/P1UcUL2w22FyppbVMHLT9hhrTwvFnrNnTrN4DCikXNJYUFHNy7FzV6YpgjFOIcUhpCjLSeELXPBSU9qQ0bYbPa8Pv9KKXIyT0Bmoz21Ai9OR6tF4W+Znx5VPHiDoOdRZLs1GU+ExYcC60v19DxE9A0DZ83MBK7ac1qtK49oUmzcIcpxBm0dp2girYqQkTCBSU9FouFtm3bU1ZWCsCxo8fwmTKEHlZ6CzxaLwp8TfniiOKlnQa7i+TNpL7YXqgo8oKq4gMkMTmZIePGU1hRsNDr8bBpzRrUpKtqIkwhKtEzuqFJfR5RC13wFo/27Tvh9XqAwLoeTZeRnrDQW+LRepPnbcLnhxUv7zTYUyzJTn301REjpGmuXkOGkNigAa7SwJeSvdu34UlIQuvcPcwRCnE6DS2tQ6SDEOKsLjjpadqsRfDfpaVO/H4DkBX61UZvhUfvTY6nMZ8eNnl1l8G+Ekl26rPjLsW+4qq3sNtiYhk9bTolhQWoippaq5cuRY2fBhapqSVqSFOZUhW11wUnPcnJKTgc8adGe45ngYz2XDq9NR69NyfKG/Lfgyav7zY4IMmOqLDwmBFSGYL2XbrSpmMnivLzAcg+dpTc/HwYPCLMEQoRoLdLlzpRota64Gempml06NCJ0ooh9MzMTHx+aUlxsUytDV69N1nlqXx0wOTNPSaHnJLsiMpK/bAyO7S+XKOmTMVXXo5RURhu7fLlaENHQ0JSTYQq6jmtc3c0m+zqFbXTRaXjaW3bY/gDDYJOnDiObpU30wujY2ppFclOCh8eMHl7j8mRUkl2xLmtzjFDahTbuHkLeg0dGtzCXlpSzJ4d21CXXxHmCEW9Z7GgtWgd6SiEOKeLSnqaNWsBBNYMeDzluF1loMloT9VOJju9OOZO5oP9Jv/Za3JUkh0RAkPBN0dD28I++PKxWKxWvJ7ANPTWdetQHTLkA0mEldaxC5jSekLUXheV9MTHJ5CS2pDy8kBLiszMTKnOfF46htYWr96LI65k3ttn8O4+k8wySXbEhdlTrMgrV5hVbGF3JCQybOIkiiq2sPt9PtavXIEpW9hFGOl9B0nrCVGrXfRqs44dM4L1eo4ePYyhZIrrTDqG3g6f3ovDZQ14Z6+f9/ebHHdJsiMu3ldHjJAayXYfMJDkxo0oLSkB4ODuXbisNrQefcMcoaiXbDFo7TtFOgohzuuik57WbdKCBdNyc3NQ2AApRhVgxdDa49N7cdCZxL/3+pl7wCTbHem4RF2QWw47C0z8VWznstpsjJk2g9Li4uBrdfXSJaixV4JNWgSI6qV17i5d1UWtd9FJT5MmzdE0DdM0UUpx+PAhTJKrMbRoZMXQOuDTe7DfmcDbe/zMO2iSI8mOqGaLssyQtrC36diJjl27UZQXmObKO3GC41mZMGxMmCMU9Y3ed7BMbYla76KTntjYWNq164DTWQzAoYMHMFSDagssutjwax3xWXqwtySeN3f7+fiQIrc80nGJusptwJLjoW1hHz75Cvw+f3DH5boVK9H6XwYNUmoiVFEf2B1ordIiHYUQVbqkClJdu/bEU7E7JDc3G6Us1K/qzDH4tU74LN3ZU+LgjV1+PjmsyPdEOi5RH2zINXGHMJuQ2qQJ/UeMpCA3sIXdXVbKjs2bUOOnhjlCUV/oXXvKri0RFS4p6WnVqg26rgeLoB06fBCT+jDaczLZ6cauYjuv7fTz2WFFgSQ7ogaZwNdHQtvC3n/kKGJiYvG4A3OtOzZuwGzZBi2tfZijFPWB3m+INBgVUeGSkp7YuDjad+iE0xnYHXLo4EEMlVwdcdVSsfi0dHyWbuwosvPqTj//OxLogi1EJBx0Ko67FGYVC3ziHA5GTplKUV4eAIZhsHbFcsxJM0DTaiJUUVclJkHjppGOQoiQXHKDlC5duuOr6MOVl5eDqTTq3hRXHD4tA5+lK9sL43hlh58vjyqKJdkRtcDXRw1CmVjo3LsPjVu0wFlUBMCRfftw+gzoMyis8Ym6Te/eF6qoGyVEbXHJSU+rVmloFsupKa5DB+vQLi57RbLTha0Fsby0w8/XxxQlvkjHJcQphR7YnGfiq2K0x2KxMGbaDFxOJ6YZ6GexeukStNETQXbdiIuk9x2EJiUQRJS45KQnNjaWjh3ScZactosr6tf1OPDSGZ+lM5sLYnlxu5/5mYpSSXZELbX0uBlSwcIWbduS0bt3sFJzYV4eRw4ehFHjwxyhqJNSG0GD5EhHIUTILjnpgYopLl9gric/Pw/D0AB7dZy6hsXjpQs+SwabCmJ4YbufBZmKMn+k4xLi/DwmLMqselGzpmkMmzgJ0zTx+wJZ/IbVq6Bnf2jYuCZCFXWI3rMfaNXyMSJEjaiWZ2vLVq2xWCwYRiA7OHToQJRNccXj1brg09PZkG9jznY/32UpXJLsiCiyJT+00cgGqQ0ZdPlYCnKyAfC43Wxdvw41YVqYIxR1jd57IJrVGukwhAhZtSQ9MTGxdOzUmZKKHj8HDuzHJBoKnyXipStePZ11uTae3+7n++MKtyQ7Igop4MsjBr4Q5rn6XDYMR0IC7rIyAHZv2YKvYRO0jp3DHKWoK7QWrcEejSP6oj6rtnHJjM7d8FdMcRUVFlBaVgpabV3bk4hH64pX78iaXCvPb/ez5ISiXGpriSh3rExxyKkwqljUHBsXx6ip0ykpKEAphWmarFm2DDVxOuiWmglWRDV92Biw2iIdhhAXpNqSnlatWmOxWvFXlLrfsWMHPjO1uk5fTRrg0brh0TuyOsfK89v8LMtWeCTZEXXIt8cMzBCO69S9B83T2lJSWABA1uFDFJQ4YeBl4Q1QRL/4BLSOXdB0Wc8joku1PWNtthi6d+9FcXHgDfTI4UOgOagdndeT8Wjd8ejtWZlt4fltflZkKzyhfDIIEWVKfLAux6xymkvXdUZPnUa5y4VZUXJizfJlaMPHgiO+JkIVUUrrP4TAhKoQ0aVa0/Ru3XtjGGbFcLnB/n17MWhYnTdxgVLwaN0p19qx/ITO7G1+VuUovJLsiDpuRbaJL4TPpGatW9NjwKBgX66SwkIO7N6NGj0pzBGKqKXrWAaNkNo8IipVa9LTqFFjmjdvQWmpE4A9e3aBlgrUcJl7LRWP1h231palxwPJzppchU+SHVFP+MzANFcofbmGjBuPpmnByuqb166BLj2gaYtwhymikNa5O8i0lohS1frM1TSNvv0G4Xa7ACgtdZJfkAdaDe3k0hri0XrgIo3FWRqzt/lZl6fwyyisqId2FioKPaCqaBGQ0KABl42fSEFOoGCh1+Nh4+rVqEkzaiJMEWX0YZejSQVvEaWqPV1v27YDsbGxeL2BnVw7a2JBs9YIj9aDMtWGRVkaz2/zsyGfkCrUClGXfXnEH1LS33PwEJJSU3BVjNLu37EdjyMBrUvPMEcookqzlmgNm0Q6CiEuWrUnPTabjd69B1BSXARAVtYxDNNKWCo0643xaD0pVa1YmKnx/HY/m/KVJDtCVMh2w94ihb+KLey2mBjGTJ1OSWERSimUUqxaugQ1fipI8TlRwTJinDwfRFQLy8Rsl67dMZWJaQYWNe/ZvRO/qsYFzXoTPFpPnEZLFhyDOdsMthQoqnhfF6JeWphphPTaaNe5C23T0ynOzwcgJzOTnNxcGDIqvAGK6NAgBa1jZ9mmLqJaWJ69yckptGvbAacz0IR03749aHoKcIlFz/RmeLRelBgtmH8M5mw32FqoQqpJIkR9VeaHldlmSH25Rl45Ba+nPNhSZu2K5WiDR0JiUk2EKmoxfego0Gp4U4oQ1SxsKXufvgPweMoBKC8v5/jxTEztItf2VCQ7Rf5mfH1U8cJ2g+2FSqpECBGiNTlmSKUaGjVrTp+hwyjMCWxhLyspYfe2raixV4Y5QlGrxdnR+0ifLRH9wpb0tGzVhoSERMrLA4nPjh3bMFWjCzuJ3hyP1otCXzO+PKp4cYfBziJJdoS4UIaCr4+EtoV94JgxWG0xeCteu9s2rEe164TWsk24wxS1lDZAqnSLuiFsSY/FYqFfv8HBKa68vFyKS0oq6vZUFVVLPFov8n1N+d8RxUs7DXYXSaojxKXYV6LIdSvMKrawOxISGT75CgrzAlvY/T4f61euwJx0FTVec0tEnsWCZchIKUYo6oSwrkhLz+iChoZRUeJ+86ZN+FTj80TTCo/WmzxvYz47rHhlp8HeYkl2hKguXx01Qtrd2K1ff1IbN6G0JPCl5eDu3bh0C1rPvmGOUNQ2Wq8B0oRW1BlhTXri4xPo3rM3hYWB3SAnTmThKisHLfkHUbTCo/cmx9OITw+bvLrLYH+JJDtCVLe8ctheYFa5hd1itTJm+gzKSkqCxQ1XLV0CY6+EmNrQT0/UCKsNy9gr0GLl/1zUDWHfe9inzwBMM7B9HWDz5k34zIrRHr01Hr03J8ob8t+DJq/vNjggyY4QYfV9lhnSFvbWHTrSoWt3inID01z52dlkHj0Kwy4Pc4SittAGjwCLLF4WdUfYk56UlFQ6d+lGUVGg+/qxY0fw+cGr9yHLncpHB0ze3GNyyCnJjhA1odwIJD6hbGEfMfkK/H4/fn9gC/v6VSvR+g2B5DBXWReRZ3dgGX45Woys5RF1R41UmRrQfwh+ny842rNh40aK3D7e3mtypFSSHSFq2sY8E5e/6uNSGjem/6hRwS3s7rIytm/aGKjULOo0bcQ40KQQoahbauQZ3bBRYzp0yuD4ieOs2bSdZ1/9D0Z5Ge0SZSeIEJGgCCxqDmULe//hI4mNi6O8opHwjk0bMZq3QmvbMcxRiohJSsbSbzCazRbpSISoVjWWxvfrP5iNe4/jMm0M6D+QzVu3Mbyp1FIWIlIOOxWZZQqzigU+cQ4HI6dMpTg/H6UUpmGwdsVyzInTZSSgjtLGXiE7tkSdVGPvWM2btWD85WNp0aIldoedfYeOEGN6aZ8koz1CRMo3Rw2MEI7L6NWbJi1b4SwqAuDo/v2UeLzQb1BY4xMR0KQZls7d0SyS9Ii6p8aSHk3TGDtiCB6PN7gFdu36DYyU0R4hIqbIG1jf46tqC7vFwphp03GXlQbX5q1etgxt1ESIs9dApKKm6BOmyY4tUWfV6Nh0q+bN6NKpA/kFRQDsP3wU0+Oie4qM9ggRKcuOmxghfPdokdaWLn36UZgbWNRclJ/H4f37YeT4MEcoaorWpj16qzTppC7qrBp/Zl8+bDAerzf4bXH5qtWMaKawSt4jRER4TViYGdqi5qHjJ6BMhc/rBWDj6tXQsx80ahruMEUN0CfNQJPik6IOq/Gkp2XzpnTv3Ck42nMiJ4/s7BwGnKc7hRAivLYWKJy+qo9rkJrK4LHjgqM9nnI3W9auRU2cHt4ARdhpnbujpTaMdBhChFVExjDHjxyGz+/H7w8soVy5dh0DG2vEyzSyEBHz5REDXwijPX0uG0Z8YhLusjIAdm/bii85Fa1Tl3CHKMJF09EnTpdRHlHnRSTpadwwhWED+5FXEKjSXOIsZdfe/QxvKoUKhYiUzDLFQafCqKILe0xsLKOnTg9uYVemyeplSwOjPbLNOSppfQag2R2RDkOIsIvYarWRQ/pjtVjxeAJrA9Zt2kJ6A41GcZGKSAjx7TEjpL5cHbp1o1X7DpRUfHE5fuQI+UXFMGh4mCMU1c7uwDJuiozyiHohYklPvMPB+FGXkVdQCIDH62Xdpi2MaSajPUJEitMHa7Kr7sul6zqjp06j3O3CNALT1GuWL0MbNgbiE2oiVFFNtEkzwCqVl0X9ENF9if17dSe5QRKlZYHy9tt27SHF5qettKcQImJWZpv4Q/ju0aRlS3oOGhJc1OwsKmL/zp2oMZPDHKGoLlqHDCwZ3dCssqBS1A8RTXpibDauHDuSouKSQHl702TF6rWMa6GwSN4jRET4FcwPsS/X4LHj0HQdr8cDwOZ1ayG9GzRrGe4wxaWyxaBPu16mtUS9EvEKVF06daBtq5YUFpcAcPDIMYry8xjcJMKBCVGP7SpSFHhUsHr6uSQkJTFs4iQKc3MB8Hm9bFi9EjVpRk2EKS7F5VegxckiSlG/RDzp0XWdyWNH4naXBwsWLl25iv6NNFLlC4gQEfPVESOkaa7uAwbRIDWVMqcTgAM7d1Ie50Dr1ju8AYqLprVKw9pnAJotJtKhCFGjIp70ALRp2Zz+vbqTnRfYCVJa5mLtxs1MbCmLmoWIlGw37C5S+KvYzmWLiWHMtBmUFhUFtrArxeqlS1DjpsgC2drIYkGbcYNMa4l6qVYkPQATRg0j1mbDXR5YG7B1525iDTc9pC+XEBHzXWZoW9jbZmTQNqMzRfl5AORkZZGdnQ1DR4c5QnHBho9FT5AddqJ+qjVJT0K8g6njR5NfUBj8tvj9suWMag522VggRES4/LD8RNVb2DVNY8SVV+LzeDEMPwDrVixHGzQckpJrIFIRkibNsA4ZJaM8ot6qNUkPQM+uGXRsl0Z+YREAufmF7Nm3nzHNZZpLiEhZm2viMao+rlHTZvQdNpyCnMAW9jKnk11bt6DGXRnmCEVINA19xkyQ7emiHqtVSY+u60ybcDk+vx+fL/Btcc2GTaQ5TNISZJpLiEgwFXwd4hb2AaNGY7PF4CkvB2DbhvWoNh3QWrcNc5SiKtqg4WgpDdH0WvW2L0SNqnXP/sYNUxg3/DJy8vIB8Pn9LFm+iomtFLZaF60Q9cP+EkW2W2FWsYXdkZDAiMlXUJQX2MJu+P2sW7kCc9IMQL64RExyKpbRE9FjZYu6qN9qZRoxdEBvGjVMpbgksAX20LFMTmRlMaZ5hAMToh77+ohBCIM9dO3Xn4ZNmuEsLgLg0J7dlCrQevcPb4DinLRp18u0lhDU0qQnxmbj6ivGUVrmwgjW7llNh3iDDknybVGISMj3wNZ8E18V27ksVitjps+grKQkWNxw9dKlMGYyyALamjd4BHrzlmi6JdKRCBFxtTLpAWjbqiWD+vYiJzcwzeX1+ViweCmTWoFDvrAIERGLj5shbWFv1b496T17UZgTmOYqyMnh2JEjMGJcmCMUp9NatcUySqa1hDip1iY9ABNGXUaDpESKnaUAZGXnsHvPXiZJ0UIhIsJjwPdZoW1hHz5pMobhx+/zAbB+1Uq0PoMgpWFNhCoc8WjX3YweK6NrQpxUq5Mee1wc10+bTGlpGX5/YDfX6g2baKCV0ys1wsEJUU9tyjMp81d9XHLDRgwcPYbCii3s5S4X2zZuQE2YFuYIBZqGumqWTCcK8QO1OumBQIuKscOHciInP9iJ/dtFixnZXCNZ2sYIUeMUgUXNoWxh7zt8BHHxDspdLgB2btqEv0kLtPbpYY6yflPDx6E3b4lFRnmEqKTWJz0AI4b0p03LZhQUFQNQWFzC2g2bmNpGySZYISLgcKniWJnCqGKBT5zdzsgrp1Kcf/JLi8Ha5ctQE6eDFhVvP1FHa5+OZcgIrI74SIciRK0TFe86NquVa6dMxOfz4/F4gUBvLl9pEZc1jXBwQtRT3xw1MEM4Lr1nL5q1bo2zqBCAYwcPUORyQ/8h4Q2wPkpsgHbVDVji7JGORIhaKSqSHoDGDVOZOn4MufkFwW2wCxcvpXeKSbtEGe8RoqYVe2FDbghb2C0WRk+bjrusDLOiBMXqZcvQRo4Hu6MmQq0fdB2uvQliZN5fiHOJmqQHoH+vbnRN70hufgEALnc53yxazJWtoYG8zoWocctOmPhDGO5p3iaNrn37B/tyFRfkc2jfPtSoCWGOsP5Ql1+B1rAxFpu8GQpxLlGV9Oi6zvSJlxNjs1FaFlgYeTw7lw2btzAjDSwy4CNEjfKZsDAztEXNQ8dPQNPA5w1MUW9csxqtWx9o3CzcYdZ5WuceWPoMlHU8QlQhqpIegKTEBGbOuJLiEmewKenm7TspyctmfAup3yNETdtWoCj2Epx2PpeklBSGjB1PYW5gtMdbXs7mtWtQk6bXQJR1WEpDtKnXYZGpQiGqFHVJD0D7tNZMvnwkJ3Lzgm+0i5Yup1Wcl55Sv0eIGvfVEQN/CN85eg8dSkJSA1ylgYKje7ZtxZuYjJbRLcwR1lFWK/zoVlnHI0SIojLpAbhsQB96dckgOzcPCHRj/2rBIkY1g6aycUGIGpXlUuwvrnoLuy0mltHTpuMsDGxIUEqxetky1PhpYJHeUBdGQ035EVpiAywW6c0jRCiiNunRdZ3pk8aS0qABhUUlABQVl7B4+UpmpEGcvH8KUaMWZIa2hb1D1260at+R4vxAX70TR4+QV1gIg0aEN8A6Ro29Atp3wuqQaS0hQhW1SQ+Awx7HDVdNwevzUl7uAWD/4aMc2L+faW1UdN85IaJMqQ9WZYfWl2vU1Gl4yt0YhgHAmuXL0C4bA/GJNRFq9Bs0AtWzHzEJ8ngJcSE0VdXqwyiwZedu3vnoc1o0a4LFYkHTNCaPG0OxLZWvM2VLVzi4Cp0se+NLjmzYg9/ro1lGG4b/5Eoatg3sxMk/ks2Slz/j+I5DxCbY6TpuAINvGIemnz0V9ZV7WfLyZ+xfuQ3TMOk0rCfDb59CjP1UGf1XZj6Eu7is0u8NvnECA6+/HIDFL37Kzu/W40hOYPRdV9G6V8fgcYtf+hR7g4TgsSI8rBr8opuVeFvVr7uF//2YbWtX06h5CwD6DhlKB4tC++yDcIcZ1bRuvTEnTsfqiEc/x+tJCHF2deIV06NzOsMH9ed4Tl5wncD87xbTwlbOoMaRjq7uUabJ/x59m6LMXK78881c+8RdxDji+Pj/vYy7pAx3cRkf3/8icYkOfvzsPYz6xQw2f76cjf9des5zfjf7Y7J2HGLKA7cy5S+3cmzrfhbN/ih4vavQibu4jKsf/wU/+fefg3/6TB8OwME1O9m/ajvXPP4L+kwfzjf/fC+4yL0ku4D9K7fTe9qw8D4wAr+C+UdD28I+eOw4dIsVrycwSrtl3Tro1AWtRetwhxm1tHadUJOvQouNk4RHiItQJ141mqYxYfQw2qe1IjcvULjQ5/fzxfwF9Es16NxARnuqU+7B45zYdZix91xLs4w2NGzTlPG/vx6f28OhtbvY/PlyYhxxjP/d9aS0akKHId3oM304x3cePuv5SvOK2bN4I6PvnEHzzmm07N6Oy391DbsXb6Y0L9BvLf/wCXSLTrOM1sSnJgb/2OICu1YKjmTToksajdo1p8vY/oEkqSQwKrTy3/Ppe9WISqNGInx2FyvyyxVmFYPI8YmJDJs4icLcXAD8Pi/rV63EnDi9BqKMQk1bwNWzMHULNpst0tEIEZXqRNIDgf5cM6dfSVJSYrAxaZnLzRfzFzK+paJlvCQ+1SWxcTJTHriVlFanhtE0LfD4ekrdHN6whw5DumGxnlpNPmjmOK74001nPV/WzkNomkbzrm2Dl7Xo2hZd18jacRCA/MPZNGjeEIvt7LtUkpqmknvwOF5XOZnbDmKzx2JPdJB38DjHdxyix6TBl3q3xQX46qhBCIM99Bg4iOSGDSkrCWxGOLBzJ25bHFr3PmGOMMokp8LMn+JTEBsXF+lohIhadSbpAUiId3DztdNBgbMs8C0/v7CIBd8vZUYaJEspi2phT4qn3cAuldbnbP5sGX6vnzZ9OlGUmYsjOZHvX/iE1256lH/f8U/Wzv0O0zj73p7SvGLsyQmVkiTdYsGenEBp7qmRHs2i89mDr/PqDQ/z3q+eYed364PHdxjanQbNG/LSjx7ki0ffYvRdM9B0nRVvfsXAH489Z7IkwiPHDbsKTfxVbGG32myMmT4DZ3FxcDpy9bIlMG4KSDuFAHs86obb8SqwJyREOhoholqd+yRo3DCFm6+bzsv/mYvNaiUuNpYjWcdZs2491/Xpy7/3gduIdJR1y4FV21nx1tf0mT6c1DZN8bo8rP3gO7qM7ceUv9xC/pFsFr/4KYbXz+BZ48/4fb/Hd9akxGK14q+oup1/OJvyEhdDZk1gyE0TObxuNwue+hBlmHQdNwDdojP1gVtxF5dhc8RitVnJ3HaA4uwCOo/py9r3F7LtmzU0aJbKuN9cR2KTlLA/LvXdd1kmGclVf69K65RO+y5dyDx4kJTGjck9fpzjWVk0u2w0fP9NDURai9liUD/+CR6LlcQGDSIdjRBRr06N9JyU1qoF10+bTG5+IT5/4ENzx559HNy/j6vbKunRVY12fLuOL//2bzoN78Ww2yYDoFt1GrZrxoifTaVJx1Z0GdOPgT+6nE2fLjvrOawxVgzfmZmo4fdjiw2sXbjqsTu46eV76TC0O43bt6D/daPpPnHgGYuj7Q3isVYkUMvf+IohN04g90AmW79axY+fvYe0fhl8/+Kn1fkQiHNw+2HpidC2sI+YfCV+rxej4vW6buUKtAHDoEE9Tk41HXX1jXgdCSSmSKl5IapDnUx6AHp0SeeKy0dyIjsP0wxMq6xct4GygmymSw2farH2/YUseHou3ScNZvzvfhSc7kpo2IBGbZtXOja1TRO8rvLg4uLTJTROxl1cWmn6yzQM3EWlxDcKfLu12qzEOCqvZWiY1gxnXtFZYwtsfTfoeFkPsrYdpHmXNOISHbQb2IWs7Qcv5W6LC7A+x6Q8hJHVhk2b0m/EyGAXdldpKTu3bEaNmxLmCGsvdeU1eBs1IaFhw0iHIkSdUac/+4cP6seQ/r05np0bXC+wcPEyrO4CprRRyIDPxVs/73tW/vsbBs8az6hfTA8uZAZo0a0d2XuOVjo+/3A2sQl24hLO7BHSoktbTMPkxK5Tu7uyth9CKVVxncHrN/+Vjf9dUun3cvYdo2GbpmeczzRMVr79DUNvnhS4QNNQFWtLTMOssjGmqD4m8HWIW9j7jxxFTGwMHrcbgO0bN2C2bovWpl2Yo6x91OhJ+Nt1Ij5VEh4hqlOdTno0TeOKsSPJ6NAu2KPLNE2+XriIeG8xk1tFOMAolXfwOCve+pqu4wbQbcIgygqcwT++ci99rxpB3qHjLHn5M4qy8ti3fCvr5i6i97RhwdEgV3EpnrLAh1tCowZ0Gt6TBc98SNaOQ2RtP8jC5+bReXRfEho1QLdYaDewK2s/+I4Dq7ZTlJXHho8Ws+u7DQycOe6M+HYuDBQobNOnEwDN0ltzbOt+cvYeY9vXq2neJa3mHizBgRLFCVfVW9jt8fGMuGIKRfmB16rh97NuxXLMSTNAqz9fUdTYK/H36Ic9OaXSlwkhxKWrExWZq+IuL+eN9z/meG4eTRsFvjlZrRamTBhHNknMz4xwgFFmxVtfsW7uorNed7JCctaOQyx77Qty92dibxBPj0mD6X/d6GDS88atf6dVj/aM++2PAPC6PSx+8VP2r9iGbtHpeFkPRvxsKtaKNT1+n5817y5g9/cbKSsoIbV1EwbNHEeHod0r3b7f6+Ptn/2TyffPolnnNpVi3vrFKpJbNmLCvT8muUWjcDw04hxSY+HWzlZs+vk/xA2/n3eeewZXaSmJyckATLrqahLXr4CNq2sg0kjSUJNnUN62E/EpqVisdW6fiRARVy+SHoAyl4vX3v2I/MIiGjcKLAq02axMmzSBw754Fh2PcIBC1HHjWun0aqhjrSLxOXbgAHNfnEPjli3RdZ2Uxo0Zf+UU1OzHwFNeQ9HWME1HTb2O0iYtSGjYiJhYKaQpRDjU6emt08U7HNzyoxk0SEokL78QAJ/Pz+dff0v7ODfDzlwaIoSoRkuOmyEVLGzZrh3pvXpRVFGpuTA3l6OHDsHIM6cy6wTdgrp6FsWpTXAkp0jCI0QY1ZukByApMYHbfnw18Q47+YVFwP9v786fo77zO48/P9++W32qW/eFhCQOcZ8WGLBscPAFnoT4nLHHM55MzWQ2+TeSSvanTVK7tVVb2a0kO+NJNrGxscEYxgZjQNyXESAEEkKtq1tH38f3uz+0kK2MPZIxSEh6P6pES3RLekvdUr/0ud6QSqd578MDLHWlpE+XEA9RKgeHuydf1KyUYsszz5LL5chmMgCcOX4cVq6Hwjk2LWk2o7/8YyJOD57iEuxO50xXJMScNq9CD4DP4+anr+3BarEQGc4ffZ9Ipnj3wwOs9mUk+AjxEJ0fNIhmJ7+dtzDAxqe2j29hTybiXDp9GuOPdj/kCqeR1Yb+6tsMmqwUVpRLewkhpsG8Cz0AhT4vb7/2pygUQ2PBJxZP8O8ffMQKT4qtpTNcoBBzlAF82JkjM4V5rjWPb8FRUEAiHgfg6oXzZIOlqIWLHnKV08DuwPjhnzGQg+LqGixWmdISYjrMy9AD+XYVb7+2h5yuMzwaBcaCz/sfsdAWZ0f5vFjfLcS064oa3I4a5Cbpy2Wz22nZtZuRwUEMw0DXdU5+fhRj54ugzeJfXc4CjDd+QW8iRWltLWbpmC7EtJnFvzm+v9LiIG+/9qfksrnxqa5kKsW7+/ZTqkZ5rkoOMBTiYfj4To5vbj87UcPyFZRV1zASyW8+6L7VQSQWQ63f/HALfFjcHowf/zk9I6NU1DfItnQhptm8Dj0AFaXF/PxHL2FSGuFIvqN3OpNh70cf405HeLFGenUJ8aANp+FUvz7pNJemabTs3k0yHhtvJ3PiyFHYsh0cBdNR6oPj9ecDTzhMdeMitNk8WiXELCU/dUBJUZCfv/Eydpt1fDt7Npdj38eH0Eb72LPAwCLfKSEeqGMhnewUZpFLq6pZtm4Dkb5eAEYiYW5euw4tOx9yhQ+OKquEt35FT/8A1Y2L5aRlIWaIPJWPCfh9/NmPXsbrdtE/EAbyLSsOHP6M+EA3L9ca2EwzXKQQc0hGz09zTaUvV/OOp1FKI5NOA3C+9QTG0hVQXDbJe848tXwN+us/405PiOrGRRJ4hJhBEnq+xudx87MfvkRRMECobwDDMDAMg8NHjjFw5xY/XAgeWXMoxANzJWIwlGbSJrBun4/mHU8T7s9vYU+nUpw/eRLjmR9MR5n3RymM7c+TemIntzs6qF0sIzxCzDQJPf+Jq8DJT175Y6oryyZ0Zz96opWrly/yo3pFye83ChdC3Kd9nbkpTXOtbG7G4/URj44CcP3yJVIuD2rx8odc4X2wOzBefZvB0ip6QiEalj2CNQoxD0no+QZOh50397xI48JaukN94wsoz1/+kiPHjvFynaLeI3+xCfEghOIGN4YNspNsYbdYbbTsfpGRSGR8FPbEkSMYT+8C0yO0CypYjPHTv+RGeAhltVK/tGmmKxJCjJHQ8y1sNiuv/eA51ixbSndPH9lsDoCbt7v4YP9BdlborJtjJ+ILMVMO3skxldbHdUuWUlPfwHB4EIDeO130Dw5C89aHXOHUqMal8OYvOXv+PNWLl1BaVT3TJQkhvkZCzx9gtVjY8/zTbN/aTE9vH6mxRZS9A4P82959rHIn2VlhMEnTaCHEJGJZONarT6kv17YXdpFOpsjl8n+ItH5+FNXcAi7PdJT67R5/iuyzezjb2sqaLVtxeb0zW48Q4vdI6JmEpmls39LMS7ueYSA8RGzsSPzRaIx/27sPbzrMK7UGdtnZJcT3crJPJz2FEwuLyspZtWkTkbG+XNGREa5duYSx/bmHXOG3sFgx9rzBcGMT7devsf6JFixW68zUIoT4gyT0TIFSirUrmnj71T0kEinCQ/lDDDPZLPsOHqK/q503GyAo/QKFuG85A/Z3TW0L+8Ynn8JkNpNOJgG4eOoURt0iVMU0Tyd5/Rg/+S/czugkMjma1q2XHVpCPMIk9HwHCxdU8edvvYbTbqO3f3B8MeUXrWdobW3ltTpY5p/pKoWYva4PGwwkDfRJFvg4XW62PPMskYEBALKZDKe/OIa+8wcwTc1jVE0d/PQvuPBlG0VVVVTV1U3L5xVC3D8JPd9RUaCQX7z5Kgsqy+kO9ZEb29l1rb2Ddz88wKZAmp0V0rpCiPv1YWeOKQz20LRuPf6iINGRfN+8jrarxM0W1PLVD7dAzQQtO8n9yRucPnGC5c3N+AKyq0GI2UBCz30ocDp546UXaV67irs9vaRS+QXOg5Ehfvsf7+OM9/FGPfhkWl+I76w/CVfC+qRb2M0WC0/u/gHR4aHx87ROHPkMtj8Plof0wxcswfjpX9BTVMHl8+fY8ORT2Owyry3EbCGh5z5ZzGZ2Pd3CS7ueITw8zNBYl/ZMNsuBw59y9dJ53qhXNHhlyEeI7+rwXZ1JMg8A1fUN1C9dRqS/H4CBUIi7d7vh8acecEUKHtsKP/4lR0+eJAuseXyLNA0VYpaRn9jv4d4C51+99Tp2m41Q31cnOF/8so0PDhxkR0mWljJDvtFCfAfJHHx2d2pb2Lc+9zzZTIZsNgvAqWNfoNZvAl/hgynG68d48xdEFq3g/Xd+w4rmTdQtWfpgPrYQYlopY7KmN2JK4okk7350kPOX2ygpDmCx5Jt02W1Wtm/bisntZ2+nYiQzw4UKMUtowM+bzHitk4+WHv1wH6c++5Si8nIAlq9bz2KvC/Xb//39ili5HnY8z4lPf4cymdnQ8qRsRxdiFpMBiAfE6bDz8u5n2bXzSfoHI4yMRgFIptK8f+AgXdev8OMGJbu7hJgiHfioc2pb2NdtewKr3U4qkQDgytkz6BXVqJqF9/fJnS6Ml98itnEre3/za+qalrH5j3ZK4BFilpORnoegs7uHf/5/e0kkkhQFC8fP7Qj4fex4Yith3cZH3Yp4doYLFWIWeKXeRHWBQpvk6PPLp0+x/51fU1JZBUDVwnqa166B//FfmVKPizFqURPGc3s439rKaHSUTU/vlMXKQswRMtLzEFRXlPGrt16nrqaK7p5e0pn8nNZgZIh33n2f0e52ftIoi5yFmIr9XTlyU7jd4lWrKSovZ3RoCICu9huMZLKwZuPUPpHNjrH7FZI7dvHBv/4rwYpKWna9KIFHiDlERnoeolwux4mzF/jgk0+xWa0U+r7qxVNWXMRT27bQlTBx8K4iNYXj94WYr7ZXaKwMalgmGe3pvtXBr//h7ymuqEDTNPzBIE+/sAvj7/4KUslvfT+1oB5j9yu0Xf2S7s4utjz7HE6X60F/GUKIGSahZxr09g/w27376Q71UlwUwGI2A/lt75s2rKO6ppp9XYrbUbkrhPgmVg1+2WTGbp58dPSDf/kn2q9cJlBSCkDzEy1UxSKw/73fv7Hbg7FjF7mqWg7ufY/GlStpXL5CWkkIMUdJ6JkmmWyWI8dPcfDIFzidDnwe9/h11RXltGzZxPVRjU9DkJrKWL4Q88zKQsVTlSaskxx3Phwe5B//9m/wBYOYLRZsdge7X38d/tffwWC+SSmaCR7bitrcwpkvjhHq7qZl14u4fb6H/4UIIWaMhJ5pdqcnxG/e+4jBcISSogAmU749u9Vq4bG1a6irXcDhHrgckbtFiK9TwNtLzATsk4/CHP/kIF98fIDiigoAFq9cxfKyEtS//E9UXSPGM39MbzjMJ+/vZfXmx1m2foMcNCjEPCChZwakUmk+OXqcz46fwutx4XYVjF9XHAywbXMzKZOD/d2KwdQMFirEI6ayQPFyvWnStT2pZJJ//Nu/xmS24CgoQNM0dr36GtbRYTIeHwfe/Q8Mw+CJF3bjD0rfLCHmCwk9M6ij8w7v7P2IoZFRioOF42t9lFIsW9zIhjUrOTsIx3ohK/eSEAD8Sa2JOq/CNMm6m2sXLrD3n/4PJZWVKKXwFhbi8Xpp//JLtr2wi9pFi2XtjhDzjISeGZZKpTl68jSHPj+BxWIm4PeN/yIucDjY/Nh6iktK+fiuon1E7iohPBb42VLzpKM9uq7zzn//BwZ7e9FzOXRdp3nH06zevBmL1TZN1QohHiUSeh4R/YNh3t1/iOsdtwn4fTgdX50NUlVeytbNzfSnzRwOKSIy5SXmue0VGisC2qSLmkNdXfzfv/9vLFzaxLbnn8dbGJimCoUQjyIJPY8QXde5ePU67+0/RDKVojhYOL7Q2WTSWNm0lNXLl3IlYvB5n5zoLOYfmwm2lWksD2iYYNJTmgHi0VEcBS6ZyhJCSOh5FMXiCQ4dPc6xU2dxOhz4fZ7x6+w2G+tWrWBRfR2t/QatA5CRgw3FHKcBqwLweKmGxTz5IYVCCPFNJPQ8wu70hPj3Dw/SHeqj0OvB6XSMX+dxu3hs3VrKSks52mtwIQxyR4q5RgFL/YrHiw3CgwO8v/9jfvb6SxOmf4UQYqok9Dzicrkcl9pusO+TTxkZjRIs9GOzfdXpuThYSPOG9ThcXn4XUtyQxc5ijljsU2wpNkhER9h38BCpVIoXdrSwoKpipksTQsxSEnpmiXQ6Q+v5S3z86edkslmKAn7MY1vcAWoqy2nesJ6ksnKsX+OmhB8xSzV6FVtKdLKJGB9+fIievn6e3/4EyxY3jK9xE0KI+yGhZ5aJxeMcOXGGIydOYdI0ggH/+EmySinqF1SzdvUqslo+/FwblrtXzA4LPYqtJTqkE+z/5He03+qkZdNGHlu3ErtNtpgLIb4/CT2zVHhomE+OHufMhSvYbJYJ5/sA1FZXsnbVKsx2B1/0a1yJGLLmRzySFnoUm4t0LHqKA4d+x9UbHWxrXkfz2lUUOJ0zXZ4QYg6R0DPL3e3tY//ho1y7eRubzUKhzzuhh1BVeSlrV6/C5fbwRZ/iUsQgJ/e4mGFmBU1+xYagQTYV5/CRz7ly9TqbN65l07rVE1qzCCHEgyKhZw4wDIO7oT4OHzvJ5bbr+ZOdC/2YvhZ+yoqLWLt6FYFCPycHFBfChnRzF9POaYY1AVgdUPT293P4s8/p6Opm07rVbN6wBq/bNdMlCiHmMAk9c0yob4DPjp/i3OUvMZk0goX+CYs/iwKFrFq+jJrKMq5GdM6EFX2JGSxYzAtBO6wPGCzya7S1d3DoyDEGwhEeW72SLRvX4vN6Jv8gQgjxPUnomaMGwhGOnjhN6/lLKAXBwom7vZwOO0saG1i2ZBHDacXpsEbbsIEujwbxgChggVuxPqBT4oAzFy7x2bGTZLJZNq1fzYZVyyXsCCGmlYSeOW5oeIRjp87xxamz5AwDv9eNw/7VwW6aUtRWV7KsqQm/z8u5QYNzYYhmZrBoMat5rbDcDysKIRGP03rmLCdOX8DpdNCyeSMrlzZOeAwKIcR0kdAzT0Rjcc5ducqR46cYicZw2G34vZ4JO74KfV6WLVlMY90Cbo/qXBzKn/cjXS7EZEwKGryKlT6d0gJFW3sHx0+eobO7h4ryElo2baSxbgFms5yzI4SYORJ65plsNsfN210cOXma9ludaJoi4PdjsXw19WWxmGmsq2VRQwM+r5svIzqXhjR64vJQERMV2fMjOk1+xUB4iAuXLtF69iKZXI6mxnq2bFxLVXmpNPsUQjwSJPTMY/2DYVrPX+LEmQtk0hncngJcTueEJyiPy0XjwloaG+rBZOHyEFyOwFB65uoWM8trhUVexRKvjstscKXtOsdPnaG3f5ACp4PmtatY2bSYgN8306UKIcQEEnoEyVSKy203+Oz4KfoHw2iaht/nwWa1TrhdcbCQRfX1NNQtIJIyuDSUX/wcz85Q4WLaeK35XliLPTpeq+JmZxeXr7Rx4eo1MAyWNi5kw+oV1FZVyhSWEOKRJaFHjDMMg56+fi5cuUbruYskkkksFjN+r3fCE5mmFFUVZTQ21FNTUUZ/PMf1URPXhg0ZAZpDfF8LOm4L3LzdxbUbN7j45XXSmQyFPi+b16+haVE9HjlfRwgxC0joEd8om81x+85dzl66wvkrbeRyORwOO163a8KJz5qmUVlWSl1tDbVVlcSzcH1U0T6quBuT1heziQLKCxS1Lmhw67gs0H67k7br7Vxuu046ncFiNrNy2RLWrWiisqxkwmNBCCEedRJ6xKSSqRTtt7o4ee4C12/eBqDA6cDtKpjwpKeUoqQowIKqKmqqqyhwOugY0WmPatweNYjJNNgjx2uFWrdGnStLtdvMSDRO150u2m91crntBplsFovJxIqli1ixpJGaqgqsFstMly2EEPdFQo/4TkZGo1y7eYtzl67S0XUHwwC7zYrX45pw8jOAq8BJTWU51dU1VBQHiWV0umJwO67RNWoQlRA07awa1LgVtS6DWhdYNYPO7h667tyho/MO3aFeslkdq9XCiqWLWL64gZrKcgk6Qog5QUKPuG+xeJxbXXc5d/kqbTduktN1NE3hdbux220TbquUIuD3UV5aTHl5BRUlQRJZg84odMY1uqIGo3Ig4gPnt0FFgaLcYVDhMPA7TPQMROjq6uR2Vzed3T1EY3EUYLFYWLl0EcuWNFJTWY7layd4CyHEXCChRzwQ6UyGO3dDXG3v4OKVawyPRgEDu82G21Uw4RygewJ+HxXjIaiItA6huE4oaaI3CaG47Az7LqwalBUoKpxQYc9R7jKRyWYJ9Q/SG+qhp2+A7rshwsMj6DkdlEFleRnLFzdQW1VJSVFQdl4JIeY0CT3igTMMg4FwhK67Ia7e6ODGrdukUmkwDKw2Kx5XAZZvmC7xeT0UFfopCgYoKiqmyO8loxv0/qcgNN9HhDTAb4egXRG0QZE1R9Ch8NhM9EdGCPWG6O3rJ9TbT2RkhFgsQTKVAsDjdrFsUQONdQuoLC/F6ZB2EEKI+UNCj3jodF1nMDLEnZ5e2to7uHbzFqlUGgOwWS0UOB3YrNZvPLXX7SqgKFCYfykqpqjQh9lkIpLMEklBOGMikoZICiKpuTUyZNXAY4XAWLgJWnME7Qqfw0w0kSI8NEx4cJDI0BCDkSEGwhGisTixeALDMDAMA5fTSd2CKhrrFlBTWU6hzyunIwsh5i0JPWLa5UPQMN09Ia533OZWVzeR4RE0TUPX84toC5wO7DbbNz5B26xWvB43Po8br8eN1+fH6/HgcznRTBpDiXwgGslqRHMa8axBLAOxsct4lhnfSm9WUGABt0XhtoDbqnCbdTxmHa8VvDYTZpNiJJ5kaHiUSHiQcGSI8NAwkeERstksyVSaeCJBKpVGUwqUoqKshIbaaqrKyygpCuJ1uyTkCCHEGAk94pGQSCbpH4zQNzBI55273Lpzl8HIEArQDQOz2YzdZsVut2Exm7/1iTwfiFz4PB4KnE6cTgcOZwFOhwOHw47TbsVusZDK5oildWIZg5QOGR0yhiKjKzKGImuo/P/pBtmx6wGUyp9no339UoGGGrsEiwlsmoFD07FpBnYT2ExgNylsZg2bRQMDYqk0sXiSWCxGNBYlHosxEo0xGo0yGo2RSKYwDINkKk0imSSZSqHQAAMDg6DPT1VFKXU1VZQWF1EcLJTFx0II8QdI6BGPrFQqzUA4Mr4+KNQ/QF//INF4HE1TYCh0Q8diMWOzWrHbbFgs3x6I7lFK4bDbcNjtOB12rBYLZrMZs9mExWwee92M2WzBPH6dBaXy65UMXUcfmz7SdX18KknXdTAMMpk0qVSKVDpDOp0mde8llc7/XyZNLvdV73pd10mnM6Qy+dtn0lmUlg9RumFQ6PNRUVpCVVkJwYCfgN+Hz+uRgCOEEN+RhB4x66TTGYZHRxkaGWV4ZJRQ3wCh/gH6B8OMRmMoTeNe7LkXSDRNw2IxYzGbsVjMmE1mNJOGptQDn/7RdZ1cLkc2l7/Mv54bez0fdpRSYOTHbJRS+LweAn4fRYV+ggE/Po+bQp8Xv88r4UYIIR4QCT1iTslksyQSSeKJJIlk/jKeSIyFpCjDIyOMjEaJxRNkMlky2fzKZzW2JuZe/FFj/9z76VBjryvy64HuXd57wxh7wzAMNKVwOOzjI0lOhwOn00GBw4HHXYDble9mX+B04Cpw4rDbpZ2DEEJMAwk9Yl4zDINsLkc2myWbHbvM5ciMvZ3L5fJTTSg0Tcuv6VH5S01paFp+pEjTNGxWK1ar5Q+uORJCCDFzJPQIIYQQYl6QMXUhhBBCzAsSeoQQQggxL0joEUIIIcS8IKFHCCGEEPOChB4hhBBCzAv/H+XW7foWqnQDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a pie chart of PREMIUM_CUSTOMER\n",
    "plt.figure(figsize=(6,6))\n",
    "colors = ['salmon', 'lemonchiffon', 'lightskyblue']\n",
    "plt.pie(customer[\"PREMIUM_CUSTOMER\"].value_counts(), colors=colors, shadow=True, explode=(0.1,0,0),\n",
    "                                                     textprops= {'fontsize':15,'color':'midnightblue'},\n",
    "                                                     radius=1.5, autopct='%.2f%%', pctdistance=0.7, startangle=-60)\n",
    "plt.legend(customer[\"PREMIUM_CUSTOMER\"].value_counts().index, fontsize=15,\n",
    "                                                       loc=2, bbox_to_anchor=(1.2,0.7),borderaxespad = 0.)\n",
    "plt.title(\"Distribution of Customer Type\", y=1.15, fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 72637 customer sample in this table. Among these customers, the majority are retirees, 20.38%, while the number of new families is the least, only 3.51%. For customer type, Mainstream customers account for the largest proportion，followed by Budget customers, and finally by the Premium customers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2）Merge dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Natural Chip Co</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>CCs Nacho Cheese    175g</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>175.0</td>\n",
       "      <td>CCs</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-11-10</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>346</td>\n",
       "      <td>96</td>\n",
       "      <td>WW Original Stacked Chips 160g</td>\n",
       "      <td>2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>160.0</td>\n",
       "      <td>Woolworths</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>347</td>\n",
       "      <td>54</td>\n",
       "      <td>CCs Original 175g</td>\n",
       "      <td>1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>175.0</td>\n",
       "      <td>CCs</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>170.0</td>\n",
       "      <td>Smiths</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0 2018-10-17          1            1000       1         5   \n",
       "1 2019-05-14          1            1307     348        66   \n",
       "2 2018-11-10          1            1307     346        96   \n",
       "3 2019-03-09          1            1307     347        54   \n",
       "4 2019-05-20          1            1343     383        61   \n",
       "\n",
       "                                PROD_NAME  PROD_QTY  TOT_SALES  PACK_SIZE  \\\n",
       "0  Natural Chip        Compny SeaSalt175g         2        6.0      175.0   \n",
       "1                CCs Nacho Cheese    175g         3        6.3      175.0   \n",
       "2          WW Original Stacked Chips 160g         2        3.8      160.0   \n",
       "3                       CCs Original 175g         1        2.1      175.0   \n",
       "4  Smiths Crinkle Cut  Chips Chicken 170g         2        2.9      170.0   \n",
       "\n",
       "             BRAND               LIFESTAGE PREMIUM_CUSTOMER  \n",
       "0  Natural Chip Co   YOUNG SINGLES/COUPLES          Premium  \n",
       "1              CCs  MIDAGE SINGLES/COUPLES           Budget  \n",
       "2       Woolworths  MIDAGE SINGLES/COUPLES           Budget  \n",
       "3              CCs  MIDAGE SINGLES/COUPLES           Budget  \n",
       "4           Smiths  MIDAGE SINGLES/COUPLES           Budget  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge transaction data to customer data\n",
    "merge_data = pd.merge(transaction,customer,on=\"LYLTY_CARD_NBR\")\n",
    "merge_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE                246740\n",
       "STORE_NBR           246740\n",
       "LYLTY_CARD_NBR      246740\n",
       "TXN_ID              246740\n",
       "PROD_NBR            246740\n",
       "PROD_NAME           246740\n",
       "PROD_QTY            246740\n",
       "TOT_SALES           246740\n",
       "PACK_SIZE           246740\n",
       "BRAND               246740\n",
       "LIFESTAGE           246740\n",
       "PREMIUM_CUSTOMER    246740\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE                0\n",
       "STORE_NBR           0\n",
       "LYLTY_CARD_NBR      0\n",
       "TXN_ID              0\n",
       "PROD_NBR            0\n",
       "PROD_NAME           0\n",
       "PROD_QTY            0\n",
       "TOT_SALES           0\n",
       "PACK_SIZE           0\n",
       "BRAND               0\n",
       "LIFESTAGE           0\n",
       "PREMIUM_CUSTOMER    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if some customers were not matched on by checking for nulls\n",
    "merge_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, there are no nulls! So all our customers in the transaction data has been accounted for in the customer dataset.<br>\n",
    "For next step(Task 2), we will save this dataset as csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3）Saving as csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save as csv\n",
    "merge_data.to_csv(\"merge_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data analysis on customer segments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the data is ready for analysis, we can define some metrics of interest to the client:<br>\n",
    "* Who spends the most on chips (total sales), describing customers by lifestage and how premium their general purchasing behaviour is<br>\n",
    "* How many customers are in each segment<br>\n",
    "* How many chips are bought per customer by segment<br>\n",
    "* What's the average chip price by customer segment<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also ask our data team for more information. Examples are:<br>\n",
    "* The customer's total spend over the period and total spend for each transaction to understand what proportion of their grocery spend is on chips<br>\n",
    "* Proportion of customers in each customer segment overall to compare against the mix of customers who purchase chips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1）Total sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with calculating total sales by LIFESTAGE and PREMIUM_CUSTOMER and plotting the split by these segments to describe which customer segment contribute most to chip sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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></th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">MIDAGE SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>33345.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>84734.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>54443.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">NEW FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>20607.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>15979.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>10760.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OLDER FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>156863.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>96413.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>75242.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OLDER SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>127833.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>124648.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>123537.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">RETIREES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>105916.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>145168.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>91296.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">YOUNG FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>129717.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>86338.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>78571.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">YOUNG SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>57122.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>147582.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>39052.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         TOT_SALES\n",
       "LIFESTAGE              PREMIUM_CUSTOMER           \n",
       "MIDAGE SINGLES/COUPLES Budget             33345.70\n",
       "                       Mainstream         84734.25\n",
       "                       Premium            54443.85\n",
       "NEW FAMILIES           Budget             20607.45\n",
       "                       Mainstream         15979.70\n",
       "                       Premium            10760.80\n",
       "OLDER FAMILIES         Budget            156863.75\n",
       "                       Mainstream         96413.55\n",
       "                       Premium            75242.60\n",
       "OLDER SINGLES/COUPLES  Budget            127833.60\n",
       "                       Mainstream        124648.50\n",
       "                       Premium           123537.55\n",
       "RETIREES               Budget            105916.30\n",
       "                       Mainstream        145168.95\n",
       "                       Premium            91296.65\n",
       "YOUNG FAMILIES         Budget            129717.95\n",
       "                       Mainstream         86338.25\n",
       "                       Premium            78571.70\n",
       "YOUNG SINGLES/COUPLES  Budget             57122.10\n",
       "                       Mainstream        147582.20\n",
       "                       Premium            39052.30"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Calculate the total sales by those dimensions\n",
    "totsales_l_p = pd.DataFrame(merge_data.groupby([\"LIFESTAGE\",\"PREMIUM_CUSTOMER\"])[\"TOT_SALES\"].sum())\n",
    "totsales_l_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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atWeeGxkZyYYNG3Bzc6NXr14Wx6pWrUrt2rW5cOECv/32m8Wx2rVrW+wKX6JECQDKly9vHj0Icc8A4qZHQ9wU/9WrV5vXrH1ajRo1KFGiBL/88gsPHz60OFanTp3n3oNX0bhxY2JjY9m4caO57OTJk5w9e5b69esn+NnarVs3XFxcLGIqWbIkgYGBXL9+HYibxp4mTRp69+5tMaqzcOHC1K1bl2PHjiU6/VtERORto6ncIiIiyeTkyZMAlCpVKtHjJUqU4NixY5w6deqZO3mnT5+eBg0aEBsby+nTpzl37hwhISEEBweb10CMT1a8LHt7e9q2bUvbtm25desW+/fvZ9++fWzbto27d+/St29fnJyczMmO+Cmqt27d4tSpU1y6dInz58/z+++/AyRYs+9px48fB+LWSHx6zcR4tra2nDp1CoAKFSqQI0cOtm/fTsWKFalQoQJVqlShWrVqZM2a9ZX6Gs/d3Z3UqVNblGXKlIksWbKYrw9xiYzp06ezbt06atWqBcRN+7Szs+O99977RzG8Cmvev4IFC1KwYEEePXrEkSNHuHjxIhcuXODYsWNcvHjR4jkfP36cdOnSkTNnTos2bGxsKF68uMW6hfF9+PXXXxMkbB4/fgxg8QwSU6pUKUqVKsW9e/cICgoyvwcPHz5MREREou/Bd955J0FZfLIxMjKSFClScOrUKbJly0aGDBks6jk4OFCoUCH279//3LgA0qVLx61bt3jw4IFFUisp1alTh2nTprFs2TI2b95MpUqVqFKlClWrVsXV1fW5554/f57w8HBKlChhkXyNV7JkSTZt2sSpU6fMHzYACZ5t/NT2v/7MSpEiBfDn5k3nz5/n8ePHxMTEJPqejX9ewcHBlCxZ0lwev4RFUqpbty6jRo1i3bp1dOzYEcD8QURiHzTEf+jztCJFivD7778THBxMmjRpOH/+PK6ursycOTNB3du3bwMQFBREvnz5krAnIiIi/z1KTIqIiCST+JFAzxpxFb+22tOj2hKzefNmvv76ay5cuACAo6Mjnp6e5M+fn3379v3jzSEAXF1dadCgAQ0aNODJkyeMGTOGZcuWMWfOHHNi8tq1a4waNYpt27ZhGAY2Nja88847lCxZ0pyEfZawsDCABKMwn3b//n0gLvGxfPlyZsyYwcaNG9m8eTObN2/GxsaGWrVqMXLkyFdeYzJjxoyJljs5OVlsHpIrVy6KFy/Ojh07ePjwIYZhsH37dipXrvzakk7PY837FxERwYQJE1i2bBlPnjwBIHPmzJQuXZr06dNb3Ke7d+8mGBkZ769rB8b3YenSpX/bh+cdHzNmDOvXrycqKgqTyYSbmxvlypV75nvQwcEhQVn8CLf4750HDx4kSErGc3Z2fm5M8XLkyMGtW7e4ePHic98jYWFhPHnyJMH9eRGZM2fmhx9+YMaMGWzdupV169axbt067O3tadq0KYMHDzYnCP/qVX8ePWuNzcTu69PiR5D+8ccfTJ069Zn1/vrMX3a93BeROnVqatasyfr167lw4QI5c+Zk/fr1uLu7U6BAgQT1/7qjOvz5syMsLMx8L2/duvVSfRMREXkbKTEpIiKSTOKndD69+/bT4v/j/rwk0ZEjR/jkk0/IkiULEyZMoHDhwuTIkQOTycSsWbMS3Tn67/z6668MGjSIli1bJrrZSKpUqRgyZAjr1q0zJ0MNw+DDDz/k7NmzdO3alZo1a5IvXz5SpkzJ7du3E93x9mmOjo4AzJ8/P8FO34lxcXFh8ODBDBo0iODgYHbv3s2aNWvYtGkTNjY2TJo06aX7DX/e87+6efNmggRU48aNOXToENu3bycmJobIyMhkmcYN1r1/Y8eO5fvvv6dOnTq0bdsWDw8P83u0Xr16FonJ1KlTJ5iKG++v5fF92LJlS6Ib47yI/v37s3PnTlq1akWjRo0sRsDGj4B7FWnTpjUnTv8qfjTn36lcuTIHDx5k7969FC9e/Jn1li1bxvjx4+nWrZvFNODEPmCITww/LUeOHIwePZqYmBiOHz/O7t27+fHHH1m2bBlp0qShf//+iV43KX4evYz46zVq1Ihx48YlSZv/ROPGjVm/fj0///wzJUuW5ObNm3To0CHRuuHh4QkSsvHvj/Tp05vfy6VKlWLJkiWvN3AREZH/OK0xKSIikkziR+L8dc22eAcOHMBkMpE3b16ARHef3bBhA7GxsQwbNoz33nuPnDlzmuv98ccfQOIJjedxdXXl6tWrbNq06Zl14q8RP4oqODjYvMtznz59KFy4sHlk07lz5/42Dg8PD+DP6bxPu3fvHl9++SVr1qwB4u7LF198waVLlzCZTOTPn58uXbqwYsUKHB0dn3k/X8SpU6eIioqyKDt37hwPHjwwr7UZz9vbGwcHB7Zv38727dtJkyYNXl5er3ztf+J13b/E3nPr168nQ4YMTJ48mbJly5oTVeHh4Vy9ehX481kXKlSI69evJ5rsOnLkSKJ9OHbsWIK6Fy5cwM/Pj23btj3zHjx48ICdO3fi6enJiBEjKFGihDkpefnyZSIiIl559HChQoW4du2auX/xYmJiCAoKeqE2GjRogL29PYsXL35mkvPJkyfmJH7FihUBzGtzJpYADQkJsXi9detWhg8fzsOHD7G1taVo0aL06NHDnByLX1YhMe+++y4pUqTg6NGj5unWTztw4ACA+efRP5U7d24cHBw4ceJEos9l/vz5TJ8+nbt37ybJ9f5OhQoVyJQpk/n72cbGhgYNGiRaN7H36KFDh7Czs6NQoUKkSZOGbNmycfbs2URHvK9evZopU6Y8d/1gERGRt4USkyIiIskkW7ZslC1blhMnTuDv729xbMWKFRw8eJCyZcuSJUsWAOzs4iY6PJ04i5+WGb9mWbxff/2V9evXA3GbTLyMvHnzUrZsWU6ePMnIkSPNG4DEi42NZdKkSTx+/JimTZsCf07b/OvmIvfu3TOPhnpeHLVq1SJ16tTMmTPHYt1BgPHjx7Nw4UIuXboExE2PXLRoEXPnzrWod/v2bSIiIv7RGnT37t1jwYIF5teRkZHm+Js1a2ZRN23atFSvXp3du3ezd+9e6tat+8xpsq/b67p/z3rPRUREWIwujYmJ4csvvzQnYeLrN23aFMMwGDdunMX6jmvWrEmQ3GnYsCG2trZMmjTJYtRldHQ0o0aNYu7cudy7d++Z98De3h4bGxsePHhgkVgLDw9n1KhRCfrxMpo0aQLEjRZ9uo3vvvsuwffes+TIkYOOHTty9+5dPvjggwTJ2rCwMPr168eFCxeoXr26eR3DXLlyYWtry/79+y1GSO7YsYMTJ05YtPHHH3/g7++f4OdJ/IYzT29SE5/wjO+Pg4MD7733Hjdv3uSbb76xOH/Xrl1s3LiRXLlymTe3+adSpEiBt7c3Z8+eZd68eRbHAgICGDduHCtXrnzhqfL/lK2tLfXr1+fo0aP89NNPlCtXLtEp2wBTpkyxGPG7adMmAgICqFGjhjlR36RJE+7du8dXX31lsc7v2bNnGTlyJPPmzUuy0aciIiL/ZZrKLSIikoxGjhxJ27ZtGT58OJs3b8bDw4PTp0+zd+9eMmXKZE6owJ/rmm3cuBFHR0eaNGmCt7c38+bNY8SIERw4cABXV1eCg4PZs2cP6dOn586dO89N5jzL119/Tfv27VmyZAmbNm2icuXKZM6cmfv37/Prr79y4cIF6tata97B+Z133qFIkSL89ttvtGnThhIlSnD37l22bNlCZGQkqVKleu7Ip7Rp0/LFF1/Qr18/mjRpQs2aNcmUKROBgYEcO3aMwoUL07lzZwBq1qxJ8eLF8ff35/Tp0xQrVoyHDx+aR3g+vat2QEAAgYGBlClTxmLDjmdxc3Nj4sSJ/P777+TKlYu9e/dy+vRp3nvvPerWrZugfpMmTczXfdlp3EFBQfj4+CR6LGfOnHz55Zcv3Nbrun/x77mxY8dSoUIFevToQYMGDZg7dy7NmjWjZs2aREdHs2fPHs6fP4+LiwuhoaHcu3ePTJky8d5777FmzRrWrVvH2bNnKVu2LBcvXmTHjh2kT5+eu3fvmnc8fuedd+jfvz9jx46lfv36eHl54ezszK5duzh37hzVq1enYcOGz7wHqVKlolatWmzatIn333+fihUr8vjxY7Zv387t27dxdnYmLCyM2NjYRDd3eR5vb282bdrEzz//zPnz5ylfvjxnz55l//79uLm5mRN/f6dPnz7cuXOHH3/8kRo1alCtWjVy5szJjRs32Lt3L6GhoZQoUcJiarOLiws1a9Y096tq1aqEhISwbds2SpYsaTEKskWLFixfvpyvvvqKwMBAPDw8uHPnDj///DOOjo58+OGHCZ7toEGDqFixIu3bt6d///4cPHiQ2bNnc+DAAYoXL26+lpOTE+PHj090FO2rGjBgAIcOHcLPz4+tW7dSpEgRbty4webNm7Gzs2P06NEv/az+iSZNmjB37lyuXbtG7969n1nv0qVLNG7cmGrVqnHjxg22bNlC5syZ8fX1Ndf58MMP2bNnD4sWLeL333+nTJkyPHjwgJ9//pknT54wfvz4BBttiYiIvI00YlJERCQZvfPOO6xcuZIWLVpw9uxZFi9ezIULF/Dx8WH16tUWO966ubmZ15xbsmQJR48epUCBAsyaNYtChQqxZcsWli9fzu3bt+nVqxdr1qzBxsaGnTt3vnRcrq6urFmzhsGDB/Puu++ya9cuvvvuO37++WeyZcvGV199xeTJk81JAxsbG6ZPn07Tpk25fPkyixYt4rfffqNKlSqsXLmSihUrcuHCBfOovcTUq1ePxYsXU65cOXbv3s3ixYt59OgR3bt3Z/78+eY16RwcHPj222/p0qULoaGhLFmyhJ9//pmiRYuyaNEiKlWqZG4zMDCQqVOnEhgY+EL9zp8/P9OnT+fKlSssXryYJ0+e0K9fP8aPH59o/cqVK+Pk5ISbm9szd1d/lrCwMAIDAxP9SmxK9t95HfevTZs2VKxYkePHj7No0SIePXpEnz596NmzJzY2Nnz//fds2bIFNzc3vvvuO/OapPHvOZPJxJQpU/joo4+4f/8+S5YsISQkhHHjxlGuXDnAcjOTTp06MWvWLPLnz8/mzZtZtmwZdnZ2+Pr68s0335hHcD7L6NGj6dChA2FhYSxevJjdu3dTuHBh/P39ady4MeHh4QQEBLz0vQWYMGEC/fr1IzIyEn9/f/PGJvnz53/hNmxtbRkzZgzfffcdVatW5dSpUyxatIht27bxzjvvMGLECBYvXkzatGkT9MvHx4d79+6xaNEirly5wjfffEPt2rUt6jk7O7N48WJat27NhQsXWLBgATt27KBKlSosX77cItaPPvqIokWLsnfvXvNUbxcXF5YvX07nzp25desWixcv5tixYzRu3Jgff/yRokWLvtK9e5anr3fjxg3zzw0vLy+WL1/+Qh8mJCV3d3fy5MljTnI/y+TJk/H09GTlypX8/vvvNG7cmBUrVliMSE2ZMiULFy6kZ8+eRERE8P3337Nz505KlCjBwoULnzlNXERE5G1jMpJiq04RERGRf6nRo0eTIUMGunbtmuRt//HHH9SrV8+8UYlYunbtGmnSpEl0ZFi7du04fvw4hw4dStJReCKvKiwsjIoVK1KnTp1EP4zw9fVl1apVrF69OtHdukVEROTlacSkiIiIvLEePXrEjh07XmpU24syDIPp06djY2OTYP1JiTN79mxKliyZYMTq4cOHzdNblZSUf4vZs2cTERFBixYtkjsUERGRt4bWmBQREZE31tatW6lZsyZVq1ZNsjYjIyNp2rQpDx484MaNGzRr1owcOXIkWftvkmbNmrF8+XK6du1K7dq1yZw5M5cvX2bLli04OTkxYMCA5A5RhLZt23Ljxg1CQkIoV66ceeMhERERef2UmBQREZE3VsOGDZ+7YcqrcHBwwN7engcPHuDt7c3gwYOTtP03SaFChVi+fDnffvst+/fv586dO7i4uODt7U337t0t1lAVSS7Ozs4cP36cihUrWmw8JCIiIq+f1pgUERERERERERERq9OISfnXOnjwIKlSpUruMCQZREREkCJFiuQOQ5KJnv/bS8/+7aVn//bSs3976dm/3fT83wwREREUK1YsucOQ/zglJuVfy2QyacfDt1RQUJCe/VtMz//tpWf/9tKzf3vp2b+99Ozfbnr+b4agoKDkDkHeANqVW0RERERERERERKxOiUkRERERERERERGxOiUmRURERERERERExOqUmBQRERERERERERGrU2JSRERERERERERErE6JSREREREREREREbE6JSZFRERERERERETE6pSYFBEREREREREREatTYlJERERERERERESsTolJERERERERERERsTolJkVERERERERERMTqlJgUERERERERERERq1NiUkRERERERERERKxOiUkRERERERERERGxOiUmRURERERERERExOqUmBQRERERERERERGrs0vuAESexTAMjh8/ntxhSDJJqmdvionBsLVNkrbEevS9//bSs397vcyzjzVM2JiM1xiNvCiTrT2FCngkdxgiIiLyH6XEpPyreXpEJncI8h93PNgBj5XzkjsMERFJQsHNOrE+Ssmwf4P6BCd3CCIiIvIfpqncIiIiIiIiIiIiYnVKTIqIiIiIiIiIiIjVKTEpIiIiIiIiIiIiVqfEpIiIiIiIiIiIiFidEpMiIiIiIiIiIiJidUpMioiIiIiIiIiIiNUpMSkiIiIiIiIiIiJWp8SkiIiIiIiIiIiIWJ0SkyIiIiIiIiIiImJ1SkyKiIiIiIiIiIiI1SkxKSIiIiIiIiIiIlanxKSIiIiIiIiIiIhYnRKTIiIiIiIiIiIiYnVKTIqIiIiIiIiIiIjVKTEpIiIiIiIiIiIiVqfEpIiIiIiIiIiIiFidEpMiIiIiIiIiIiJidXbJHYCIxFm7/gJfTTjC1WuPye7mxID+xahXJ2eCekv8zzB95glu3Q4nz7tpGT60JGXLZAZgyLBAVqz8g2xZHZn2TWUKFkgPwPSZJ4g1DHp087Rqn0Tk1fx23WD4XoPz9yGTI3xS0kTjfKYE9fyDDGYeNrj9BN5NB0PLmyiTNa7esL2xrDwNWZ3gmxomCmSIK5952MAAuhVL2J6IvJqrJ86ze8567l6+Rcq0jpRsXo3C9colqHf85wB+X7mDx3cfkj67K5U/aICbZ24Ads5cQ9DW30nt6kzd/m3ImDsrAL+v3IERa1Dq/epW7ZOIiIiINWjEpMi/wLk/HtCn3z6+Hlees0GtGTm8NN167OZOaLhFvb37rjNm3CG+nV6FU8da0qmDBx3+t53QuxEEn77Hlq1XCNzblLZt8jF5yjEA7oSG8+Oa83zQqUBydE1EXlJMrMGHmwy6FTdxorMNflVN9N1hEBJmWNTbd8VgXKDB9FomjnUy0aGQif/9bHA33OB0qMHWi7C3jYk2BUxMORh3bugTgzVnDTrpMwqRJBMe9ph1I+dTtGFFui4bjvfAduybv5FLh85Y1As5cpZ9C37G29eHj5aPoGj9CqwbOY8nDx5x5+J1zgcG0WneQDzrliVw6VYAntx/RPD2QxRrVCk5uiYiIiLy2ikxKfIvkOfdtBz9/X1Kl8rEo0dR3Lz5BCcnexzsLb9Fr11/TLeuhfAs5IKNjYkWzfNga2Pi9Ol72NrGjX6KGwuF+fVXE47Qo1shUqa0tW6nROSVPIiEO+EQEwuGYWAygb0N2P5lgOP1R9C1qIlCGU3YmEw09zBhY4LToWD7l9/u8a8n/GbQrZiJlHYaLSmSVMJu3uOd0gXIX70EJhsbMuXNTvYiebgWdMGi3sM79ynZrCquebJhsrGhQM1S2NjYEHrpBjb//01qGHG/w+Nf71+ymVLvV8fOwd6qfRIRERGxlmSZyt2rVy88PT358MMPAXj06BFNmzZl8uTJnD9/nsWLF2NjY0N0dDQtW7akcePGAHh5ebFx40ZSpEgBwLlz5xg+fDiLFi3Cx8eHggULMnDgQAAiIiKoV68e27ZtA2Dfvn18++23REZGYmdnh5ubG4MHDyZNmjQWse3cuZO5c+diY2NDTEwMzZs3p2HDhgQEBLB06VImTpz4j67l4+PD8OHDyZMnj/maAQEB9O7dm7x585rL0qdPzzfffENoaCjDhg3j8ePHGIZBtmzZGDJkCClTpgSga9eufPvtt5w5c4bx48fz5MkTHj9+TNWqVenZsycmk4nQ0FD8/Py4evUqMTExZM2aFV9fX1xdXfnxxx/5448/6Nevn/naffr0oVWrVgAWcUVERNCgQQN8fHzw9fXF29ubKlWqmM+7fPkyDRs2pFChQhb3dP78+URFRTF8+HBu3ryJyWQiderUDB8+nPTp07/0++dN5eRkz6VLYVSougbDMBj7ZVnSpHGwqNO86bsWrwMP3OTho2jy5XMmg0tKGtTPRaVqa8iZIzVTJlXizJn7nAy6y+hRZazZFRH5B9KnNOFT0KDnVoPe2yDWgHFVTWRLbZlMbOpu+frAdYNHUZAvPbikMlE/j0G1pQY50sAkLxNn7hoEhcIoDbwSSVKuebJRp18r8+vwsMdcPXGe/F4lLeoV+MvrqycvEPkkApccmUnl7ES+ykVY+OF4nLO4ULtfK0Iv3eD2+WtU69bYGt0QERERSRbJkpgcPnw4zZo1w8vLi7x58+Ln50fLli25ffs2S5cuZebMmaRJk4bw8HB69epFihQpqFev3t+2u379emrUqEGZMpZJmFOnTjF+/HhmzpxJ5sxxa/HNnz+fOXPm0KdPnwSxrVmzhrRp0/Lw4UMaNWpExYoVX8u1nlauXDkmTpyYoHzOnDlUqFCB1q1bA/Dll1+ydOlSOnbsyNWrV3Fzc+PBgwd8+umnTJkyhXfeeYeYmBg++eQTli5dSqtWrejRowedO3emZs2aQFzitGvXrqxYseLvbqlFXJGRkdStW5dGjRo9s37evHlZtGhRgvKlS5eSMWNGxo4da74n06ZNY8iQIX8bw9skWzYnzp9uQ0DgDTp+sIPc76ShUsWsidY9ffoeXbrtpN+nRcngEpeoHuxbgsG+Jcx1fDpuY8jAEny/9CwLFp3Gzc2Rr/zKm+uLyL9PrGGQyg6m1zJRKxfsvgyfbDPwdIWCGRIf6Xj6rkG3zQafljLhkiqujm9ZG3zL/lmn48ZYBpY1sfQULDoZi1tq8KvyZ30R+eciHj1h3cj5ZMqbnXfLPnsJlTuXbvDTlwsp17Y2qZydAKjYyZuKnbzNddYOm0ulTt6c2BTIsZ9+JY1remr0am6uLyIiIvImSJbEpIuLC0OHDmXIkCF8+umnhISEMGLECD766CP69etnHsWYMmVKBgwYwLBhw14oMTl48GCGDh3Kjz/+iJ3dn13z9/enW7du5kQhQMeOHRNtI0OGDCxcuJA6deqQN29eNm7ciIODA2fPnk3ya70INzc3Nm3aRK5cuShRogQDBgzAZIr7T+T27dupWrUqW7dupWzZsrzzzjsA2Nra4ufnh729PcePHydNmjTmpCRAhQoVyJkzJwcOHHipWB4+fIiNjQ22ti8/JdjNzY0ffviBEiVKUKZMGXx8fMzTleRPdnZxU7cqVczKe/Vy8vPmkEQTkzt2XaXbx7vp2qUAPbsnvljcjl1XcXSy493cafmox252bW3IvIXBTJ12nGFDS73WfojIq9t4Hg7fhMHl437W18gF1XMarDxtULB8wiTirhCDj7cYdClqonvxxJOMu0IMnOwgtzP02GKwtaWJhSdg2mGDoYm0KSIv7/71UNaNmIdzFhfq+bbDZJP4ikkXD57m57FLKN60CqVaJL6hzcWDp7FP5YCzW0Y2+i2h3cx+HF2/j99WbKPyBw1eZzdeSVBQ0CufGx4e/o/Ol/8uPfu3m56/iMRLtl25vby8+OWXX/D19cXf3x+TyURISAg5c1ruQpwjRw6uXr36Qm16eHjQuHFjxo4dazES7/Lly+Z2Q0JCGDRoEIZhEBMTg7+/v0UbM2bMYP78+Xz66aeEhoaaRxy+jms9bf/+/fj4+JhfV61alQ8++IDWrVuTIkUKvvvuOz755BNKlizJsGHDyJo1KwEBATRv3pz58+eTI0cOi/acnJzMMfz1GPz9fTWZTBiGYY7LZDJhb2/P0KFDzW0n5uzZsxb9KFSoEL6+vlSrVo3IyEh++OEHBg4ciLu7O0OGDMHDw+OZbb1Ntm67wrdzTrL8+1rmssioWNKmdUhQd+nys3w+4jf8RpelSaPcibYXExPL2HGHmDW9ChcuhuGWzQknJ3s8C7kw5zv9ASDyb3b1IUTGWpbZ28R9/dXyUwYj9hmMrmKiUd7EE4wxsX9uknPxAWRLDU72JgplMPju2GvogMhb6ObZy6z5/Ds8qpeg8v/ee2ZS8uQvB9j57Vq8ejTFo1rxROvExsTy64KN1Bvow/1rd0jjmg6HVClwzePG4TW7X2c3XlmBAq++wV5QUNA/Ol/+u/Ts3256/m8GJZclKSRbYhKgcePGhIeHm0cXZs6cmStXruDs7Gyuc+HCBbJmjRsxliJFCiIjI81rTD5+/Ni81mK8Dz/8kNatW7Nr1y5zWdasWbl8+TL58+cnR44cLFq0yLwu5NPu37/P1atX6d+/P/379+fGjRv07NmTQoUKJZqM+yfX+qtnTeUOCAigcePGNG/enMjISGbPns3o0aMZN24ctra2pEiRgmzZsnHy5EmL80JCQrh+/br5nv7VxYsXqVChAnfv3iUyMtLiWPx9ffLkyTPjepZnTeU+dOgQ5cuXp3bt2sTExLBmzRoGDhzIjz/++MJtv8kKe7pw9NgdfvjxD5o2zs32HVfZtv0K61dbvm9277nGoCGB+C+uQdkymZ/RGizxP0uF8lnImTMNKVLYcvFSGPfuR3Do0G1y5kzzzPNEJPlVdoNxAXFJx/c9IOAabDoP/g0sE497LhsM2WOw+D0TZbI+e9Sj/ykonw1ypjWRwtbg0gO4H2Fw6CbkTPu6eyPy5nt8N4w1n39H8SZVKPV+4iMgAUIOn2H79FU0HtUFN8/EP1gEOLEpgOxF8uCcxQU7BzvuX7tDeNhjbgRfwjlLhtfRBREREZFk86/aldvHx4dx48bx8OFDIG5TnHHjxtG2bVsAChYsyKZNm8z1d+3aReHChS3asLW1ZezYsYwZM8Zc1qpVK2bMmMHNmzfNZfv3709w/cjISHr37s21a9cAcHV1JWPGjDg4JBy19k+v9aIWLFhgTt45ODiQL18+HBwc2Lt3L+XLlwegevXq7N69m0uXLgEQFRXF2LFjOX36NCVKlOD27dvmjXkg7r5dvHiRMmXKkD9/fvbt28ejR48AuHfvHmfOnLHYnCcpbNiwgTlz5gBx983Dw+OZ9/VtlClTKhZ8V505c4PIX3gZ4yccZu6sauTL68yAQfsZMCjuPTRt5gkio2Jp22EbeQv4m7+27/gz+RwWFsnc+afo3TPueyNzZkc+6FyACpVXs3X7FXp+nPjUbxH5d8ifwcSMWibmHTfwnGcwdI/B19VNFHE1MWhXLIN2xQ2nnHnEICoWOvxkUOC7WPPXjkt/LpMRFmkw/7hBzxJxicvMTiY6FzZR2d9g+yWDj58x9VtEXtyJzQd4cv8RB5ZuZUazIeavfQt+ZtvUlWybuhKA337YQWx0DGuHfWdR78Jvwea2Ih6Hc2TdPkq3qgGAk0taijWqzIIP/Dh/4NRzE58iIiIi/0UmIxkX+nt6p+t4a9eu5fvvv8dkMhEbG0vz5s15//33Abhx4wZDhw7lzp072NnZkSNHDkaMGIGTk1OC3a4XLFjAggULzAm5gIAAvv32W6Kionjy5AnZsmWjV69eFjthA2zdupXp06djZ2dHTEwM1apVo0ePHgl25X7Va/n4+HD37l1zUq5s2bJUq1Ytwa7cALNnz+b+/fuMGDGCq1evkjJlStKnT8/w4cOZNm0aH3/8sXm06fHjxxk3bhyGYfDo0SOqV69Ojx49MJlM3Llzh9GjR3P58mUAsmTJwqBBg8znfv/99yxbtgwnJyeio6Pp2rUrNWrUSPT5xPP19eXQoUPmkaS5c+emT58+ie7KPXr0aNKnT8+oUaM4deoUqVKlwtHRkcGDBz83AXrw4EFKFH7mYZEXcjzYAY+V85I7DBERSULBzTqxPkrLwfwb1LcPxtPz1T/01HTOt5ee/dtNz//NoOcoSSFZE5Miz6PEpCQFJSZFRN48Skz+eygxKa9Kz/7tpuf/ZtBzlKTwr5rKLSIiIiIiIiIiIm8HJSZFRERERERERETE6pSYFBEREREREREREatTYlJERERERERERESsTolJERERERERERERsTolJkVERERERERERMTqlJgUERERERERERERq1NiUkRERERERERERKxOiUkRERERERERERGxOiUmRURERERERERExOqUmBQRERERERERERGrU2JSRERERERERERErE6JSREREREREREREbE6JSZFRERERERERETE6pSYFBEREREREREREatTYlJERERERERERESsTolJERERERERERERsTolJkVERERERERERMTqlJgUERERERERERERq7NL7gBEnud4sENyhyD/caaYaIKbdUruMEREJAnFGFDfPji5wxAgxjBx/Pjxf9TGPz1fkp/J1p5CBTySOwwREfkPUmJS/tU8PSKTOwR5I+h9JG+m48EOeKycl9xhiLwVgpt1Yn2UEi8iiamPPigQEZFXo6ncIiIiIiIiIiIiYnVKTIqIiIiIiIiIiIjVKTEpIiIiIiIiIiIiVqfEpIiIiIiIiIiIiFidEpMiIiIiIiIiIiJidUpMioiIiIiIiIiIiNUpMSkiIiIiIiIiIiJWp8SkiIiIiIiIiIiIWJ0SkyIiIiIiIiIiImJ1SkyKiIiIiIiIiIiI1SkxKSIiIiIiIiIiIlanxKSIiIiIiIiIiIhYnRKTIiIiIiIiIiIiYnVKTIqIiIiIiIiIiIjVKTEpIiIiIiIiIiIiVqfEpIiIiIiIiIiIiFidEpMiIiIiIiIiIiJidUpMioiIiIiIiIiIiNUpMSkib50fV/1B3gL+Fl9u7yyiv++vCeou8T9DxaqrcS+0lHoNfiIg8Ib52JBhgXh4LqV6rbWcDLprLp8+8wRTZxy3Sl9EJPndemxQYkEsWy8aiR73DzKo9H0snvNiabo6lmO3/qw3bG9cea3lsQTd+bN85mGDGYcTb0/kTfT4bhiz24zgfODJ59YLOXyGb+oPIPJJhLls58w1zHz/cxZ3/5rb56+Zy39fuYPfVmx/bTGLiIjIP6fEpIi8dZo2eZezQa3NX/NmVyOTayr6fFLEot7efdcZM+4Q306vwqljLenUwYMO/9tO6N0Igk/fY8vWKwTubUrbNvmYPOUYAHdCw/lxzXk+6FQgObomIsngs50GdyMSPxZ0x8Av0GCht4mjHU3UzGWi2y9xCcfToQZbL8LeNibaFDAx5WBceegTgzVnDTp5WqsHIslvy+QVhIc9fm6d8LDHbJm0Aow/k/Z3Ll7nfGAQneYNxLNuWQKXbgXgyf1HBG8/RLFGlV5r3CIiIvLPKDH5Cnr16sWsWbPMrx89ekSdOnU4deoUGzdupG3btvj4+NC6dWtWr15trufl5UVExJ//czl37hw+Pj4A+Pj4MGbMGPOxiIgIvLy8zK/37dtHhw4daN26NT4+Pvj6+hIWFpYgNi8vL/P1fXx86NGjh/nYsGHDaNy4sUV9Hx8f6tWrZ1G2efNmPDw8uHz5MgEBAfTp08dc99y5cxZ1L1++TIsWLQDw9fWlQYMG5mv7+PiwYsUKAHbu3EmHDh3o1KkT7du3Z+3atc++wSJW9OhRFL377mP0F2XIltXJ4ti164/p1rUQnoVcsLEx0aJ5HmxtTJw+fQ9bWxMABnH/OYp//dWEI/ToVoiUKW2t2xERSRaLTxo42kE2p8SPn78PMbEQbcTlUmxMkNIu7pjtX/4Ki3894TeDbsVMpLQzvb7ARf5Fjv30K/YpHUidMd1z6+2Yvgr3KsUsymz+/xvH+P9kZfzr/Us2U+r96tg52Cd5vCIiIpJ07JI7gP+i4cOH06xZM7y8vMibNy9+fn60bNmS27dvs3TpUmbOnEmaNGkIDw+nV69epEiRIkHyLzHr16+nRo0alClTxqL81KlTjB8/npkzZ5I5c2YA5s+fz5w5c8xJw6fNnTuXFClSWJQ9efKEgwcP4u7uTkBAAGXLlrU4HhQURIECcSO8NmzYgJub20vdk3j9+/enSpUqCcqHDx/OmjVrSJs2LQ8fPqRRo0ZUrFiRDBkyvNJ1RJLKtJknyO+Rjnp1ciY41rzpuxavAw/c5OGjaPLlcyaDS0oa1M9FpWpryJkjNVMmVeLMmfucDLrL6FFlErQlIm+e8/cNZh8xWNPExHsrE592XTUHvJsOai03sDWBkz0sbRCXcMyTzkT9PAbVlhrkSAOTvEycuWsQFAqjNMhL3hL3rtzi4KpdtJzQE/9ek59Z79T2g4Q/fEKFjvX4feUOc3n67JnIV7kICz8cj3MWF2r3a0XopRvcPn+Nat0av/4OiIiIyD+iEZOvwMXFhaFDhzJkyBACAwMJCQmhU6dOLFq0iH79+pEmTRoAUqZMyYABA1iyZMkLtTt48GCGDh3Ko0ePLMr9/f3p1q2bOSkJ0LFjx0STks+yceNGypcvT5MmTRLE895777F+/XoAHjx4QEREBBkzZnzhtl9EhgwZWLhwIWfOnMHJyYmNGzcqKSnJ7tGjKObND+bT3kX+tu7p0/fo0m0n/T4tSgaXlAAM9i3B8UMt+GmtN3neTcvIL39nyMASfL/0LLW9N9Cpy3buhIa/7m6ISDKIjjXovc1gWEUT6VI+e2RjRDTkSw/rmpo42dnE/wqb6LrZIDw6LpHpW9aGQx1sWNvUhnfTmfhyv8HAsiaWngLvlbF02RRL6BOtNSlvptiYGDZ9tZSqHzYkZRrHZ9YLu3WP/Ys2U7P3+4ker9jJmw/9h9FyYk/Su7my57sNVOrkzYlNgfj3msT6UQt4cv9RoueKiIhI8tKIyVfk5eXFL7/8gq+vL/7+/phMJkJCQsiZ03LUVY4cObh69eoLtenh4UHjxo0ZO3YsQ4YMMZdfvnzZ3G5ISAiDBg3CMAxiYmLw9/dP0E7nzp2xsYnLOf/vf/+jWrVqrFixgpEjR5InTx6GDx/OjRs3zIlOLy8vBgwYQL9+/di0aRN169bl+++/f6X7Mn78eGbPnm1+PWTIEDw8PJgxYwbz58/n008/JTQ0lFatWtGjRw9MJk1Tk+Tz86YQ3LI7UbKE63Pr7dh1lW4f76ZrlwL07J74om87dl3F0cmOd3On5aMeu9m1tSHzFgYzddpxhg0t9TrCF5Fk9M3vBgUzgFfO5/8em/i7QVYnKOIaV++Tkgb+p2DPZaj5jmXdXSEGTnaQ2xl6bDHY2tLEwhMw7bDB0PL6fSlvnkD/rbi+m413Sj97XWbDMPhlwjLKt69D6gzOPLgR+tw2Lx48jX0qB5zdMrLRbwntZvbj6Pp9/LZiG5U/aJDUXZCnBAUFvVT98PDwlz5H3hx6/iIST4nJf6Bx48aEh4ebE3yZM2fmypUrODs7m+tcuHCBrFmzApAiRQoiIyPN06wfP35MypQpLdr88MMPad26Nbt27TKXZc2alcuXL5M/f35y5MjBokWLiIiIeOb08L9O5T537hxnzpxh7NixAJhMJvz9/endu7c5rgIFCnDo0CF++eUXJk6c+MqJycSmct+/f5+rV6/Sv39/+vfvz40bN+jZsyeFChWyWEdTxNo2b71Mw/dyPbfO0uVn+XzEb/iNLkuTRrkTrRMTE8vYcYeYNb0KFy6G4ZbNCScnezwLuTDnO/3BJfImWv8H3HwM68/FAhAWGZdM7FkCuhf/M4l49SE4PvXXlslkwtZkYPeXOSsxsQbjAg2m1zJx8QFkSw1O9iYKZTD47pg1eiRifWd2H+FRaBindx8BIPJxBD/7fU/pljUo1aI6AA9v3ePaqYvcPHeF7dNXYcTGjSCe2+FLGg7rRLZCf/5ujo2J5dcFG6k30If71+6QxjUdDqlS4JrHjcNrdlu/g2+Z+GWhXtTTS0nJ20fP/82g5LIkBSUmk5CPjw/jxo1j6tSppE6dmkePHjFu3Djatm0LQMGCBdm0aRPNmzcHYNeuXRQuXNiiDVtbW8aOHcsHH3xgLmvVqhXDhg2jSJEiZMqUCYD9+/e/cFwrVqygT58+5jiuXr1Ky5Yt6d69u7lO/fr1mT9/Ps7Ozjg5PWMF/1cUGRlJ7969+f7778maNSuurq5kzJgRBweHJL2OyMs6eOg27du6P/P47j3XGDQkEP/FNShbJvMz6y3xP0uF8lnImTMNKVLYcvFSGPfuR3Do0G1y5kzzOkIXkWS2raVlZrHiklhGVjJRI5flyEavnCbGBRrUz2NQIAPMOw4xBpTKYtme/ykonw1ypjWRwtbg0gO4H2Fw6CbkTPu6eyOSPHy+7W/xel6nMVTr1ojcZQqay9JkSs/Hq0abXz+4Ecr8zmPpvGAwDqks11Q/sSmA7EXy4JzFBTsHO+5fu0N42GNuBF/COYuWEBIREfk3UmIyCXl5efHw4UM++OADTCYTsbGxNG/eHG9vbwA+++wzhg4dir+/P3Z2duTIkYMRI0YkaOfdd9+lQ4cOLFiwAABPT08+++wzfH19iYqK4smTJ2TLls1iZ/BniYyMZMOGDaxZs8Zcli1bNvLnz8+mTZvMZRUrVsTX19diZ/DEfPLJJ+aEYtmyZc3Jznh/ncpdunRpevXqxZAhQ+jRowd2dnbExMRQrVo1KlXSyv6SfGJiYrl27TGZMqWyKB8wKC7p7ze6HNNmniAyKpa2HbZZ1Jk9owrVq8VtEBUWFsnc+adY+2NdADJnduSDzgWoUHk1776bltkzq1qhNyLybzJoV9woytFVbGhTAO5HmPjoF4MHEVAwIyz0NpHa4c8EZlikwfzjBj82iivL7GSic2Go7G/wrjPMrK1p3PL22TZ1JQBePZq9UP2Ix+EcWbeP97+K++DdySUtxRpVZsEHfqRzc+W9QT6vLVYRERF5dSbDMLSiuvwrHTx4kBKF/76eiMjb6niwAx4r5yV3GCJvheBmnVgf5ZHcYYj8K9W3D8bTM/F1uJ9FU3nfbnr+bwY9R0kK2pVbRERERERERERErE6JSREREREREREREbE6JSZFRERERERERETE6pSYFBEREREREREREatTYlJERERERERERESsTolJERERERERERERsTolJkVERERERERERMTqlJgUERERERERERERq1NiUkRERERERERERKxOiUkRERERERERERGxOiUmRURERERERERExOqUmBQRERERERERERGrU2JSRERERERERERErE6JSREREREREREREbE6JSZFRERERERERETE6pSYFBEREREREREREatTYlJERERERERERESsTolJERERERERERERsTolJkVERERERERERMTqTIZhGMkdhEhiDh48iIODQ3KHISL/IaaYaAxbu+QOw2retv6KJKcYA2xNyR2FyL9TjGHC1qT/Vr7JTLb2FCrgkWTtBQUFUaBAgSRrT5KHnqMkBf1vRv7VPD0ikzsEEfkPOR7sgMfKeckdhoiIvILgZp1YH5V0iQ8RSTr1CU7uEETkDaWp3CIiIiIiIiIiImJ1SkyKiIiIiIiIiIiI1SkxKSIiIiIiIiIiIlanxKSIiIiIiIiIiIhYnRKTIiIiIiIiIiIiYnVKTIqIiIiIiIiIiIjVKTEpIiIiIiIiIiIiVqfEpIiIiIiIiIiIiFidEpMiIiIiIiIiIiJidUpMioiIiIiIiIiIiNUpMSkiIiIiIiIiIiJWp8SkiIiIiIiIiIiIWJ0SkyIiIiIiIiIiImJ1SkyKiIiIiIiIiIiI1SkxKSIiIiIiIiIiIlanxKSIiIiIiIiIiIhYnRKTIiIiIiIiIiIiYnVKTIqIiIiIiIiIiIjV2SV3ACLydps+8wR+Xx3G3v7Pz0mWLPCibJnMFvXmzA1i9ndB3H8QSaUKWRnzRRlcXVMBMGRYICtW/kG2rI5M+6YyBQukN7cdaxj06OZpvQ6JiLyA364bDN9rcP4+ZHKET0qaaJzPlKDemrMG4wMN7jyBctlgXFUTro5x9YbtjWXlacjqBN/UMFEgQ1z5zMMGBtCtWML2RERexe8rd/Drwk3Y2tmayxqO+B9unrkTrX9o1S6uBl3kvUE+5rKdM9cQtPV3Urs6U7d/GzLmzmpu24g1KPV+9dfbCRER+VfSiEkRSVYnTobi278YZ4Nam7/+mpRcu/4CEyYdZdo3lTl2sAXu7s50/GA7AMGn77Fl6xUC9zalbZt8TJ5yDIA7oeH8uOY8H3QqYPU+iYg8T0yswYebDLoVN3Gisw1+VU303WEQEmZY1Au6YzB4t8GUGiYOdTDh6ghD9sTVOR1qsPUi7G1jok0BE1MOxpWHPjFYc9agkz6PEZEkdPuPq1RoX5duK78wfyWWlIwKj2TP3A3s/m6DRfmdi9c5HxhEp3kD8axblsClWwF4cv8RwdsPUaxRJav0Q0RE/n2UmBSRZHX8xF0KFXJ5bp0NGy/Rtk0+SpV0xd7ehn59inL6zH2CTt3F1jZuRFDc+CDMr7+acIQe3QqRMqXtM9sVEUkODyLhTjjExIJhGJhMYG8Dtn8Z4Lj6jEGtXFA8s4mUdiYGljXxywW4/cTA9i9/wcW/nvCbQbdicfVFRJLKzXNXyfhutr+tt/6LBdy7epvC9cpalNv8/w8pwzAsXu9fsplS71fHzsE+iSMWEZH/Ck3l/g8LCAigd+/e5M2bF4BHjx6RPXt2+vTpQ7NmzShUqJBF/fnz59O5c2diY2P5448/cHFxIV26dFSoUIESJUqwdOlSJk6ciI+PD0+ePCFVqlTExsby4MED+vXrR9WqVfH19eXEiROkS5fO3G7Dhg15//338fT0pHjx4hbX/Oqrr3B1dcXPz4/Tp09jY2ODvb09gwcPJkeOHK/9Hsm/2+Mn0fxx/gFz5gbRs/cenNM60K1rIVq3zGtRLzbGIFWqP39cmUxgMpk4fz4M73o5aVA/F5WqrSFnjtRMmVSJM2fuczLoLqNHlbF2l0RE/lb6lCZ8Chr03GrQexvEGnFTtLOltkwmnrsHJTKbLM5Lm8Lg3F0om81E/TwG1ZYa5EgDk7xMnLlrEBQKozTwSESSUFR4JPeu3Obw2j1s/sqfFKkdKdGsKoVql05Qt1afFqTO4Mz+JZt5fP+RuTx99kzkq1yEhR+OxzmLC7X7tSL00g1un79GtW6NrdgbERH5t1Fi8j+uXLlyTJw40fy6b9++bNu2jbx587Jo0aIE9RcsWACAr68v3t7eVKlSBYhLcj7Nz8+PPHnyAPDHH3/Qq1cvqlatCkD//v3N5z3N2dk50Wvu3LmTmzdvMm/ePAC2bNnC6NGjmTFjxqt0Wd4gt289oXQpVzq086BypSwcOnybDv/bTuZMqfCq7mauV7tWdsaOO0ydWtnJl9eZqTNOEB4eTXhEDACDfUsw2LeEub5Px20MGViC75eeZcGi07i5OfKVX3kyuKS0eh9FRP4q1jBIZQfTa5molQt2X4ZPthl4ukLBDH8mIh9HQ6q//KWWyg6eRMf927esDb5PDUrquDGWgWVNLD0Fi07G4pYa/KqYcEml0ZMi8uoe33tI1oK5KOJdjhyDfLgeHMK6kfNwcknDO6XyW9RNncH5me1U7ORNxU7e5tdrh82lUidvTmwK5NhPv5LGNT01ejUnlbPTa+uLiIj8+ygx+QaJjIzk5s2blCtXLknbvXr1KmnTpn3l87NkycLx48f56aefKFeuHDVq1Eg0sSlvn5w50/Dj8jrm12XLZKZZk3f5eXOIRWLy/WZ5uH7jCZ267CA6KpbOnfLjni8dzmkdErS5Y9dVHJ3seDd3Wj7qsZtdWxsyb2EwU6cdZ9jQUlbpl4jI82w8D4dvwuDycQnDGrmgek6DlacNCpb/M4mYyg7CYyzPfRINTonMeNwVYuBkB7mdoccWg60tTSw8AdMOGwwtr8SkiLw65ywuNPfrZn7t5pmb/NVL8MevJxIkJl/UxYOnsU/lgLNbRjb6LaHdzH4cXb+P31Zso/IHDZIqdEliQUFBSdZWeHh4krYnIv9dSkz+x+3fvx8fHx/u3LmDjY0NLVq0oHz58owZMwYfnz93wStUqBC+vr4v3O6AAQOws7Pj6tWrFCtWjDFjxpiPjR8/ntmzZ5tfDxkyBA8PD+7fv29xzUyZMvH111/j4eHBqFGjWL58OV988QVZsmTB19eXMmU0zfZtd/TYHXbuvkbP7n/u0hAREWMxbRvgxo3HNG7wjrne/fuRTJh0FM9C6S3qxcTEMnbcIWZNr8KFi2G4ZXPCyckez0IuzPlOf/iIyL/D1YcQGWtZZm8T9/W0vOng3D0DiEsshj4xuBcOeS1/9BETazAu0GB6LRMXH0C21OBkb6JQBoPvjr22bojIW+Lm2ctcOniGUi3+3DU7JioauxSvti5kbEwsvy7YSL2BPty/doc0rulwSJUC1zxuHF6zO6nCltegQIGk21QyKCgoSduT5KHksiQFJSb/4+Knct+9e5fOnTuTPXt2gGdO5X5R8VO5ly5dyvr168maNav52MtO5T516hS5c+dmwoQJGIbB3r176d27N3v37sVk0iiOt5mTkz0TJx0ld640eNfLyd5911mz7gIrl9W2qLd773WmTj/OyuW1cbC3YejwA1SpnJXMmR0t6i3xP0uF8lnImTMNKVLYcvFSGPfuR3Do0G1y5kxjza6JiDxTZTcYFwDLTxm87wEB12DTefBvYPk7sWFeEy3XGbT0MCjsCn6BBtVyxq01+TT/U1A+G+RMayKFrcGlB3A/wuDQTcj56hMeREQAsE+VggD/X3DOloG8FTy5fPQcp3cdptnYj16pvRObAsheJA/OWVywc7Dj/rU7hIc95kbwJZyzZEji6EVE5N9Ou3K/IdKnT8/48eMZMmQIt27dSrJ2W7VqRdasWS3WsXxZv/76KxMmTCAmJgaTyUS+fPlIlSqVkpJCnnfTMnN6ZSZ+c5R8Bf0ZODSQieMrUKRwBgYM2s+AQfsBaNYkN9WruVG1xlrKVlyFYRh8M6GiRVthYZHMnX+K3j0LA5A5syMfdC5Ahcqr2br9Cj0/9kxwfRGR5JA/g4kZtUzMO27gOc9g6B6Dr6ubKOJqYtCuWAbtihtOWSijibFVTPTfaVByocGNx/BVNcvfnWGRBvOPG/QsEVee2clE58ImKvsbbL9k8HFx/a4VkX8mvZsr9XzbEei/hRnNh7J9+ipq9m5BprzZ2TZ1JdumrnzhtiIeh3Nk3T5Kt6oBgJNLWoo1qsyCD/w4f+AUpd6v/jctiIjIm8ZkGIaR3EHIqwkICDDvpB1vxowZBAUFsWfPngS7co8ePdq8E3Zim988vSv38OHDzZvf3Lt3j4YNGzJr1izmz5+fYFfu0qVL06tXr0R35f70008pXLgwfn5+7N+/n9SpU2NjY8Onn35KyZIln9u/gwcPUqLwK98eEXkLHQ92wGPlvOQOQ0REXkFws06sj/JI7jBEJBH17YPx9Ey6D/o1lfvNoOcoSUGJSfnXUmJSRF6WEpMiIv9dSkyK/HspMSmJ0XOUpKCp3CIiIiIiIiIiImJ1SkyKiIiIiIiIiIiI1SkxKSIiIiIiIiIiIlanxKSIiIiIiIiIiIhYnRKTIiIiIiIiIiIiYnVKTIqIiIiIiIiIiIjVKTEpIiIiIiIiIiIiVqfEpIiIiIiIiIiIiFidEpMiIiIiIiIiIiJidXbJHYCIiIiIiIiIiLxZHjx4wM2bN4mKikruUCSZ2NvbkylTJtKmTfvMOkpMioiIiIiIiIhIknnw4AE3btzAzc2NVKlSYTKZkjsksTLDMHjy5AlXrlwBeGZyUlO5RUREREREREQkydy8eRM3NzccHR2VlHxLmUwmHB0dcXNz4+bNm8+sp8SkiIiIiIiIiIgkmaioKFKlSpXcYci/QKpUqZ47nV+JSRERERERERERSVIaKSnw9+8DJSZFRERERERERETE6pSYFBEREREREREREavTrtwiIiIiIiIiIvLaBQcHERUVY/Xr2tvb4uFR4KXO8fLyMu8oDeDg4ECuXLno2LEjzZs3f+VYOnbsSJYsWRg7duwrt/G06OhoFi9eTMeOHZOkPWt7pcTk5cuXyZIlC4ZhYG9vn9QxiYiIiIiIiIjIGyYqKgZPj0irX/d4sMMrndelSxc6dOgAwJMnT9izZw+ff/45GTNmpFq1akkY4av76aefGDNmzH82MfnCU7kNw2DatGkUK1aM2rVrc+3aNT777DMGDRr03N11RERERERERERE/mscHR1xdXXF1dWVnDlz0qZNG8qXL8/q1auTOzQzwzCSO4R/5IVHTM6bN48ffviBUaNGMXToUAC8vb0ZNmwYkyZNon///q8tSHl7veqnGiLydjLFRBPcrFNyhyEiIq8gxoD69sHJHYaIJCLGMHH8+PEkbTOp2/svMtnaU6iAR3KHIS8pVapU5p2mPTw8GDduHI0aNTIff7osNjaWqVOnsnz5ch49ekSLFi2IibGcyn7kyBHGjBnDyZMncXNzo3PnzgwZMoStW7eSPXt2IiMjmTBhAuvXr+fx48cULFiQfv36UaxYMQICAvjss8/M1x0zZgxNmza13s1IAi+cmPzhhx/4/PPPqV69Op9//jkAtWrVwt7enuHDhysxKa9FcgzxFpH/ujfz58bxYAc8Vs5L7jBERKwiuFkn1kfpP+si8marjz6M+S8xDINff/2VvXv3MnXq1Bc6Z8aMGSxcuJAvvviCfPnyMXv2bAIDA2nSpAkAN27coFOnTnh7e/Pll19y5swZhg0bZtHGZ599RkhICJMmTSJDhgxs2LCB9u3bs2bNGooXL87nn3/OyJEj2bNnD2nSpEnyfr9uL5yYvHz5Mnnz5k1Qnjt3bkJDQ5M0KBERERERERERkeQ0ffp0Zs+eDUBkZCTR0dHUqlWL0qVL/+25hmHw/fff06lTJ+rWrQvAyJEj2bdvn7nOsmXLSJ8+PSNGjMDW1pY8efJw+/ZtRo0aBcDFixfZuHEj69evJ1++fAD06NGD33//nXnz5jFy5EhSp04NgKura5L23VpeODGZO3dufvvtN3LkyGFRvmnTJnLnzp3kgYmIiIiIiIiIiCSXtm3b0qZNGyAuMXnmzBnGjx/Pxx9/bE5YPsvdu3e5ffs2np6e5jIHBwcKFixofn3y5EkKFy6Mra2tuaxkyZIWxwFatGhh0XZkZCSRkW/GTLEXTkz27NmT/v37c/bsWWJiYli7di0XL15kw4YNjBs37nXGKCIiIiIiIiIiYlXOzs7kypXL/DpfvnxER0fTv39/zpw5k6B+dHR0grK/bk7j4PDnXhq2trbExsY+8/r29vYALF26lJQpUz6znf+yF96Vu2bNmkyaNIlDhw5ha2vLggULuHz5MjNnzuS99957nTGKiIiIiIiIiIgku/hEY2xsLPb29jx8+NB87OLFi+Z/u7i4kDlzZg4dOmQui42NNY+ChLgNa06cOGGxIc6RI0fM/46fvn3nzh1y5cpl/po/fz5bt24FMG/E81/1wiMmAapWrUrVqlVfVywiIiIiIiIiIiL/Co8fP+bWrVtAXFLx3LlzTJkyhQIFCuDu7k6xYsVYvnw5JUuWJCYmhjFjxliMZOzcuTOTJ08md+7cFClShEWLFnH16lXz8TZt2jBv3jxGjBhBhw4duHDhApMnTwbiEo65cuXC29uboUOH8vnnn5M7d25WrlzJ0qVLmTt3LgBOTk4AHDt2jHfffdf8+r/ihROTz9pxyGQyYW9vT5YsWahSpQrp0qVLqthEREREREREROQNYW9vy/Fg609Btre3/ftKiZg9e7Z5LUlbW1tcXFzw8vLi448/xmQyMXz4cIYPH877779PpkyZ+OSTT7hx44b5/I4dO2IYBpMmTeLu3bvUqVOHmjVrmo9nzJiRWbNmMXr0aBo1akSuXLlo06YNU6dONU/j/uKLL/j6668ZNGgQYWFh5MmThylTplC+fHkAypYtS5kyZWjdujV9+/alU6dOr3qbkoXJ+Otk92do3749v/32Gw4ODrzzzjtA3BDV8PBwsmbNyv3797G3t2f+/Pnkz5//dcYsb4mDBw9SonByRyEi8u9wPNgBj5XzkjsMERGrCG7WifVRHskdhojIa1XfPthiY5T/mqCgIAoUKPDSx+RPZ8+eJSwsjOLFi5vLNmzYgK+vL4cOHcLO7qUmOv9rPe/98MJrTHp6elKmTBm2bdvG6tWrWb16Ndu3b6dy5co0bNiQgIAAatasyfjx45MscBERERERERERkTfRtWvXaN++PT/99BNXr14lMDCQb775Bm9v7zcmKfl3XriXK1euZMGCBbi4uJjL0qVLR9++fWnfvj29e/emU6dOCbYwFxEREREREREREUuVK1dmwIABTJ48matXr5IuXTrq1atHnz59kjs0q3mp9OvTOw3FCwsLM+8eZGtri43NCw/CFBEREREREREReWu1a9eOdu3aJXcYyeaFs4g1a9Zk6NChBAYGEhERQXh4OAEBAQwbNgwvLy/Cw8OZPXv2f3p9BBEREREREREREbGOFx4xOXjwYAYMGED79u0xmUxA3I7ctWrVYujQoezZs4ddu3Yxa9as1xasiIiIiIiIiIiIvBleODHp6OjIlClTuHz5MidPnsTW1hZ3d3dy5MgBQJUqVdi9e/drC1RERERERERERETeHC+1xmR0dDT29vbm6dqGYXD+/HmOHTtGw4YNX0uAIiIiIiIiIiIi8uZ54cTk7t278fX1JTQ0NMGxVKlSKTEpIiIiIiIiIiIiL+yFN7/5+uuvKVKkCPPmzSNlypTMmDGDYcOGkTZtWsaOHfs6YxQREREREREREZE3zAuPmDx37hzjxo3D3d2dggULYm9vT6tWrUiVKhVz586ldu3arzNOERERERERERH5Dws+cZwow/rXtTeBRyHPlzrHy8uLK1euMGzYMNq0aZPg+P/+9z/27NnDuHHjaNSo0XPbCggIoH379uzcuZMsWbK8VByJOXv2LJcvX6ZatWr/uK3k9sKJSTs7O5ycnADIlSsXp0+fpmLFipQuXZovvvjitQUoIpJUflz1B58NCrAoe/Ikmjat8jJ+bPlEz5k15yQHfrvF7JlVzWVDhgWyYuUfZMvqyLRvKlOwQHoAps88Qaxh0KPby/3CExGxlmsPDQbtNgi8Bqkd4KOiJjoVNiWoN/eYwXfHDB5EQAU3+KKSCVfHuHrD9say8jRkdYJvapgokCGufOZhAwPoVixheyIi1hR2+x7bp/7IlePncXBMScnmVSnWsFKCesc3BfDbsu2Ehz0mQ67MVO3aiEz5sgOwc+Yagrb+TmpXZ+r2b0PG3FkB+H3lDoxYg1LvV7dqn0TeFFEGeKycZ/XrBjfr9Ern2dvbs2nTpgSJyXv37hEQEPCMsxIqXrw4e/bsIUOGDK8Ux191796dBg0avBGJyReeyu3p6cnKlSsBcHd359dffwXgwoUL2Ni8cDMiIsmmaZN3ORvU2vw1b3Y1Mrmmos8nRRLUffw4ilGjf2fEF79blAefvseWrVcI3NuUtm3yMXnKMQDuhIbz45rzfNCpgFX6IiLysgzDoMsmg7zp4HAHE4u8TUz83eC365bDFtafM5j0u8E3XiYOtjfhnh4+2BRX53SowdaLsLeNiTYFTEw5GFce+sRgzVmDTvpcRkSSmWEYbBi1AJccmfhw6XAaj/ofAUt+4drJCxb1bp+/xr75G2k06n90XTac3GUK8tOYxQDcuXid84FBdJo3EM+6ZQlcuhWAJ/cfEbz9EMUaJUxyisibqVy5chw4cCDBfiu//PILRYsWfeF2HBwccHV1TbL8mWEkw7DT1+SF70iPHj347rvvmDdvHg0bNuTw4cM0btyYTz75BC8vr9cZo4hIknv0KIreffcx+osyZMvqlOB45w93cv5CGD5t3S3KbW3jRgLFjQv68/VXE47Qo1shUqa0fc2Ri4i8mkM34cZj8C1rwt7WhLuLiVWNTeRJZ1lv4x8GbQpAySxx9fqUMnHmLpy6Y2D7l78c419P+M2gWzETKe00WlJEktf14Es8Cn1AhY7e2NrZkiFXFlp8/THpsrta1Lt39TZGjEFsTCyGASZbE3Yp7AGw+f8fbvH/8Y9/vX/JZkq9Xx07B3sr9khEklPx4sXJmDEjW7ZssSjfuHEj3t7e5texsbFMnz6d2rVr4+npSalSpejZs6c5oRkQEICHhwfXr18H4qaJz507l48++oiiRYtSo0YNpk6dam7v9u3b9OjRg7Jly1KsWDE6duxIUFAQAD4+Ply6dImpU6ea83FeXl74+flRp04dypUrx4kTJ4iMjGTs2LFUqlSJEiVK0K5dOw4fPvxSMRcuXJhdu3ZRp04dihQpQseOHbl+/TojR46kZMmSVKhQgVmzZv2je/zCU7nLli3Lpk2biIqKwsXFhSVLlrBq1SpcXFxo3779K108JCSEcePGce/ePaKiosifPz/9+vUjderUTJkyhYwZM9K6dWuLczw9PSlevDgA4eHhVKpUiZ49e2JjY4OXlxdZs2a1yEAPGDCAR48e0bt3b/LmzQvAo0ePyJ49O1999RUODg7murGxsfj5+XH69GlsbGywt7dn8ODB5MiRA19fX7y9vXn33XepU6cOy5Ytw9MzbliAv78/t2/fpmfPnkRHRzNz5kx27txJihQpAGjQoAEtW7bk8uXLfPrppyxfvtyiT76+vpw4cYJ06dKZyxo2bMj777/Pzp07mTt3LjY2NsTExNC8eXPzDujHjx9n27Zt9OrVi2XLlrF27VpsbGyIioqiT58+lC1bFoD9+/czffp0DMMgKiqKOnXq0LFjR0wmEz4+PgwfPpw8efIAEBERQb169di2bVuCuGJiYhgxYgT58uWjYsWK7N2716IfU6ZMYf369WTKlMlcVqFCBbp168bRo0eZNGkShmEQGxtL1apV6dy580u8W0SS1rSZJ8jvkY56dXImenzS1xXIktmRryYe4c6dcHN53jzONKifi0rV1pAzR2qmTKrEmTP3ORl0l9GjylgrfBGRl3b8Nrinh9EBBmvOGKR2gB7FTTT3sEwmxhiQ6qkEo+n/v87fh3rvmqifx6DaUoMcaWCSl4kzdw2CQmGUBhCJyL/ArbNXcMmZhb3zNhC8/RAOjikp3dKLAjVLWdTLWcKddG4ZWdLta0w2NjikSkHTMV0BSJ89E/kqF2Hhh+NxzuJC7X6tCL10g9vnr1GtW+Nk6JWIJBeTyUTt2rXZtGkTLVq0ACA0NJQDBw4wbtw4Ro4cCcC8efNYuHAh48aNI0+ePJw9e5aBAwcyY8YMBg8enGjbkydPZtCgQQwYMIA1a9YwZcoUypUrR6lSpRgxYgTR0dF8//33mEwmvv76a3r27MmWLVuYMmUKTZs2pU6dOnTp0sXcnr+/P7NmzSJFihQUKFCATz/9lJCQECZNmkSGDBnYsGED7du3Z82aNeTOnfuFYo6KiuKbb77hq6++Ijo6mq5du9KwYUNatGjBDz/8wNq1a/n666/x8vIy59xe1gsnJgcOHMjgwYNJnTo1APny5eOzzz7j3r179O3blylTprzUhcPDw+nevTtffPGFefjrqlWr6Nu3L99+++0zz3N2dmbRokVA3CdYw4YNY8mSJfj4+AAwd+5cc0IwXkBAAOXKlWPixInmsr59+7Jt2zbq1q1rLtu9ezc3b95k3ry49Q62bNnC6NGjmTFjhkV7qVOnZuDAgaxcudIisQkwceJEYmNjWbp0Kba2tjx69IiuXbtSqlSpBHE9rX///lSpUiVB+fDhw1mzZg1p06bl4cOHNGrUiIoVK5IhQwZ27NhBtWrV2LBhA3v37mX+/PnY29sTEhJCu3btWLVqFXfu3MHPz49vv/2WTJkyER0dzfDhw/nuu+/44IMPnhlPYnHt3LmTyZMnW2Tx/6pjx44JkskAI0eOxM/Pjzx58hAVFUWrVq0oV64cBQsW/NsYRJLao0dRzJsfzOIFzx7tnSWz4zOPDfYtwWDfEubXPh23MWRgCb5fepYFi07j5ubIV37lyeCSMknjFhH5J+5FwK9XoYKbiX1t4egt6PCTQc60UCbrn4nIWu+YGBdoUOsdyJsOZhyG8BiIiIk77lvWBt+yf7bbcWMsA8uaWHoKFp2MxS01+FUx4ZJKoydFxPrCHz7h8tFzZC+ah07zB3HzzGVWf/4dabNkwM0zt7leTGQ0GXJlplr3xmTIlYXff9jBhi8X0m5GX+xS2FOxkzcVO/05GmrtsLlU6uTNiU2BHPvpV9K4pqdGr+akck4480ZE3ix169alY8eO3L9/H2dnZzZv3kyJEiXImDGjuU7u3Lnx8/Mz50/c3NyoXLkyp0+ffma71atXp2XLlgD07t2bJUuWcPjwYUqVKsXFixfx8PAge/bspEiRgpEjR3L27FliY2NJly4dtra2ODo64uLiYm7Py8uLMmXiBstcvHiRjRs3sn79evLlywfEzYT+/fffmTdvHiNHjnyhmA3DoE+fPhQuXBiIm9p+/Phx+vbti8lkomvXrkyfPp0zZ868cmLyuVO5z507x4EDBzhw4ACrV69m//795tfxX+vXr2f37t0vfeEdO3ZQunRpizn5TZo04e7du4SEhLxQGyaTiU6dOvHTTz+91LUjIyO5efMmzs7OFuVZsmTh+PHj/PTTT4SGhlKjRg0mT56c4PxcuXJRuXJli0QnQHR0NBs3bqRv377Y2sZN53RycmLRokXmEYkvK0OGDCxcuJAzZ87g5OTExo0bzYulHj9+nMKFC7N06VI++ugj7O3jphTkyJGD1atX4+Ligr+/P127djWPYrSzs8PX15dly5a9dCz379/H0fHZyZrnyZYtG0uWLOH48ePY2Njg7++vpKQkm583heCW3YmSJVz/vvLf2LHrKo5OdrybOy0TJh9l1YralCzhytRpx5MgUhGRpONgA+lSwMfFTTjYmiiVxUS93LD5guUaRc3cTXQoZKLLzwaVvjdwsIV86SBtIp+v7goxcLKD3M4w+XeDFQ1NlMhsYtrhN2fdIxH5b7G1tyVlmlSUbuGFrb0dWQu+Q96Khflj/wmLegHf/0LqDM5kzpcDOwd7yrSuSUx0DJcOn0nQ5sWDp7FP5YCzW0YC/bfQzK8bWfLn5LcV26zVLRFJRiVLliR9+vRs3Rq33uxfp3FDXFIwbdq0TJw4kV69elG/fn3Wrl1LbGzsM9t95513LF6nSZOGqKgoIG5zm82bN1OmTBk++OADNmzYQIECBZ67RmWOHDnM/z558iQALVq0oHjx4uavwMBAzp0791Ix58z55yxDR0dHsmfPjskU9wF0ypRxg3EiIyOfGdffee6IycuXL9O1a9xwdpPJRI8ePRKt165du5e+cEhIiEXn4mXPnp2rV6++cDsZM2bk7t275tedO3c2PygbGxsWLFgAxE1n9vHx4c6dO9jY2NCiRQvKl7fchdfDw4NRo0axfPlyvvjiC7JkyYKvr6854/y03r1707x5c3777Tdz2d27d3F2dsbOLu62fv/992zcuJFHjx7RsGFDatas+cx+jB8/ntmzZ5tfDxkyBA8PD2bMmMH8+fP59NNPCQ0NpVWrVvTo0YPbt2+TMWNGTCYTN2/etHgDAqRPH7dLcEhICM2bN7c4ljp1ap48efLcb5C/xmVjY0OmTJno37//c+vPnz/fIlH80UcfUbFiRUaPHs2CBQsYPnw4ISEh1K9fnwEDBiQYcSpiDZu3Xqbhe7n+cTsxMbGMHXeIWdOrcOFiGG7ZnHByssezkAtzvgtKgkhFRJJOnnTwOBqiYw3sbOL+mIwx4K8pxBuPDBrkhe7F4/6euh9hMOl3KPSXTSRjYg3GBRpMr2Xi4gPIlhqc7E0UymDw3bHX3x8RkcSkd3MlKjyS2JgYbP5/sIgRG8tff9qF3byLfYo//y9iMpmwsTGZz4kXGxPLrws2Um+gD/ev3SGNazocUqXANY8bh9e8/AAdkb+KXzdQ/r1MJhN16tRh06ZNVKtWjYMHDyYYqDZjxgxmzZpF06ZNqVy5Ml27dmXhwoXPzW8llg+JX9u2bt26VKhQgZ07d7Jv3z6mT5/O/PnzWb58ucVIzac9PUs3fuDa0qVLzcnDv173RWOObyteUm+A/dzEZNWqVdm5cyeGYVCtWjXzmpJPc3JyMk/vfhmZM2fm6NGjCcovXLhAtmzZXridK1eukCVLFvPrxKZyA+ap3Hfv3qVz585kz549QZ1Tp06RO3duJkyYgGEY7N27l969eydYSxHiHuSYMWPo27eveZ2BdOnSce/ePWJiYrC1taVNmza0adPGvAbl8yQ2lfv+/ftcvXqV/v37079/f27cuEHPnj0pVKgQd+7coWrVqkDccNtr166RJk0a87l79uzBw8ODzJkzc+XKFYvRiQ8fPsTBwQEbGxtSpEhhzshD3PqbT79pnzXF/FkSm8odERHBiRMn+Pjjj/n444+5e/cugwYNYtmyZeYp+CLWdPDQbdr/ZVObV7HE/ywVymchZ840pEhhy8VLYdy7H8GhQ7fJmTPN3zcgImJFlbODcwoYG2DgWxYO34RNF2Dxe5ZTrvdegemHDZY3AHtbGL7XoLIbZHayrOd/Cspng5xpTaSwNbj0IC6Jeegm5ExrxY6JiDwlZ3F3UqR2ZO+8jVTsVI8bwSGc+/U4jb/oYlHvnTIF+HXBz+SrUoSMubNyeO1eYmMNshW0/PD6xKYAshfJg3MWF+wc7Lh/7Q7hYY+5EXwJ5yx/+cRG5BUUKFAguUN4ZW9TUrVu3bp06tSJ1atXU6ZMmQS5sQULFtCrVy86depkLrt48aJ54NrLiI6O5uuvv6Zhw4Y0aNCABg0acOfOHSpUqEBgYCDe3t7mEYvPEj99O/68eCNGjCBPnjy0a9cuSWP+J/42zZk5c2ayZMnCqVOnKFCgAJkzZzZ/2dvbv1JSEqBGjRrs27fPIjm5YsUKXFxcEoz+e5bY2Fjmzp3Le++998LXTZ8+PePHj2fIkCHcvHnT4tivv/7KhAkTiImJwWQykS9fPlKlSvXMB16oUCHq169vHulob29P7dq1mTRpknk0YkREBEeOHPnbN01iIiMj6d27N9euXQPA1dWVjBkz4uDgwL59+8xvrmbNmjF9+nSio6MBOH/+PIMHD8bGxobWrVszY8YMbt26BcQtXPrll1/SqlUrcx82bdpkvuauXbvMawckFZPJRP/+/c3rFKRPnx43NzeNlpRkERMTy7Vrj8mUKZVF+YBB+xkwaP8LtxMWFsnc+afo3TPu+yVzZkc+6FyACpVXs3X7FXp+7JmkcYuI/FMp7Uwsa2DidCiUWGjwyVaD4RXipl4P2hXLoF1xf7s0yQfVckCN5QYVvzcwgAnVLf+OCYs0mH/coGeJuPLMTiY6FzZR2d9g+yWDj4trfUkRSR52KexpNrYrdy5eZ3abkfw83p+qXRuRNX8utk1dybapKwHwrFuWEk2r8tPoRcxqPYLzASdpPPJ/ODj+OUgj4nE4R9bto3SrGgA4uaSlWKPKLPjAj/MHTlHq/erJ0kcRsb4SJUrg7OzM1KlTE0zjBnBxcWHPnj2cO3eOM2fOMHLkSA4dOvRKU5zt7Ow4ceIEn3/+OUeOHCEkJIRly5Zhb29PoUKFgLhBghcuXODGjRuJtpErVy68vb0ZOnQoO3fu5NKlS0ycOJGlS5ealxpMypj/iRdOgz548AA/Pz/at29Pvnz56N69Ozt37iRnzpzMmjWLXLleblqkk5MTM2fOZPTo0eZRhh4eHkyYMMFcZ9asWaxYscJcf9GiRdy/fx8fHx9MJhPR0dFUqFDBYqry01O5Adq3b0/atJYf2+fNmxcfHx+++OILvvnmG3O5j48Pfn5+NG7cmNSpU2NjY8O4ceOe24+PPvqI7du3m1/379+fOXPm0LZtW+zs7Hj48CE1a9akU6dOhIaGcubMGZo2bWqu7+vrCyScyl26dGl69erFkCFD6NGjB3Z2dsTExFCtWjXKlCnD8uXLcXKKW2j5vffe49atW7Rp0wZ7e3tiYmIYP348GTJkIEOGDPTp04c+ffoQExNDdHQ0tWrVMm9806VLFz7//HOaNm2Kg4MD6dKlY9SoUX/z9ODevXsW/YjfYfuvU7lz587NyJEjmTRpEp9//rk56Vu4cGGaNWv2t9cRSWq2tjZcPp9w+Qm/0eUSrd+vT9FEy9OkcWDHloYWZT27e9KzuxKSIvLv9Y6ziYXvJUwajq7y599OJpOJIeVNDCmfoJpZGgcTW1pYttO9uInuSkiKyL9AumwZaTwq4UafXj3+/P+HyWSiVIvqlGrx7ORiCseUtJvR16Ls784RkTeTjY0NderUYdmyZYku0+fn58fIkSNp0qQJadOmpUyZMvTt25eZM2fy5MmTl77e119/zejRo+natSuPHj0iX758TJs2zZx769ixI1988QV79uzh119/TbSNL774gq+//ppBgwYRFhZGnjx5mDJlinlZw6SO+VWZjPgJ7H/js88+4/jx40ydOpWgoCAGDRrE2LFj+emnn4iKimLmzJmvO1Z5yxw8eJASSTt4U0TkP+t4sAMeK+cldxgiIlYR3KwT66M8kjsMEZHXqr59MJ6e/92BDUFBQc+civ6sY8EnjhOVDHvk2ZvAo9B/917/1z3vvfLCIyZ37tzJ7Nmzeffdd5k2bRqVKlWiXr16uLu7m9dYFBERERERERERSYySg/JXL7yVTmRkJK6urgDs27ePihUrAvG7lyXtjjwiIiIiIiIiIiLyZnvhEZP58+dn5cqVZMiQgbt371KtWjUiIyOZM2cO+fPnf50xioiIiIiIiIiIyBvmhROTAwYMoHv37ty9e5f//e9/ZMuWjeHDh7N161aLTVtERERERERERERE/s4LJyaLFSvGnj17CAsLw9nZGYD//e9/9OvXj9SpU7+2AEVEREREREREROTN81KLQ8bGxrJ3716mTJnCvXv3uHbtGpGRka8rNhEREREREREREXlDvfCIyZs3b9KhQwdu3LhBeHg4jRs3Zt68eRw9epQFCxaQN2/e1xmniIiIiIiIiIiIvEFeeMTk2LFjyZcvH/v37ydFihQAjB8/Hk9PT/z8/F5bgCIiIiIiIiIiIvLmeeHEZEBAAN27d8fBwcFcljp1avr27cvhw4dfR2wiIiIiIiIiIiLyhnrhxGR4eDj29vYJyiMjIzEMI0mDEhERERERERERkTfbC68xWbFiRWbPns2YMWPMZWFhYUyYMIGyZcu+luBEREREREREROTNcCIoGCMmyurXNdnaU6iAx0ud4+XlxZUrV8yvbWxscHJyolixYvTr14/8+fMndZjP5OHhwbhx42jUqJHVrmktL5yYHDhwIO3bt6dy5cpERETQo0cPLl++TPr06Zk3b97rjFFERERERERERP7jjJgo1ke9XIIwKdQn+JXO69KlCx06dAAgNjaW27dvM2rUKDp37szmzZtJnTp1Uob5THv27CFt2rRWuZa1PTcxefLkSTw8PLC1tSVr1qysXbuW9evXExQUhL29PXnz5qVhw4bmzXBERERERERERETeBI6Ojri6uppfZ86cmQEDBtCqVSv2799PzZo1rRLH0zG8aZ67xmS7du24desWAO3btycqKor333+fzz//nIEDB/L+++8rKSkiIiIiIiIiIm8FW1tbABwcHPDw8GDy5MlUqVKFKlWqcOvWLe7fv8/AgQMpW7YsZcqUoUuXLvzxxx/m8319fRkyZAh+fn6UKlWKsmXLMm3aNM6cOUOrVq0oUqQIjRo14tixY+ZzPDw8WLNmjfn8jh07WsT0dFlAQACFCxdm165d1KlThyJFitCxY0euX7/OyJEjKVmyJBUqVGDWrFmv90a9oOeOmLS3t2fFihWULVuWwMBAAgMDcXZ2TrRu6dKlX0uAIiIiIiIiIiIiyS0kJISvv/4aV1dXSpQoAcCKFSuYPXs2UVFRZMyYkVatWpE6dWrmzJlDqlSpWLRoEW3atGHjxo2kT58egNWrV+Pj48OPP/7I+vXrmTx5MmvWrGHgwIFky5aNwYMHM3LkSFasWPFKcUZFRfHNN9/w1VdfER0dTdeuXWnYsCEtWrTghx9+YO3atXz99dd4eXmRN2/eJLs/r+K5icn//e9/TJw4kWnTpmEymejRo0ei9UwmE0FBQa8lQBEREREREREREWubPn06s2fPBuKSfdHR0RQsWJCpU6ea15ds0qQJBQoUAGDfvn0cO3aMwMBA8/ERI0awf/9+li9fTteuXQFwcXHhs88+w2Qy0bFjRyZPnkz9+vWpXr06AE2bNmXcuHGvHLdhGPTp04fChQsDUK5cOY4fP07fvn0xmUx07dqV6dOnc+bMmX93YvLDDz+kXbt2hIWFUbVqVVatWoWLi4u1YhMREREREREREUkWbdu2pU2bNkDcFO506dIl2PAmR44c5n+fPHmSmJgYKleubFEnIiKCc+fOmV/nzJkTk8kExK1jGV8WL2XKlERGRv6j2J9uz9HRkezZs5uvmTJlSoB/fI2k8Le7cjs6OuLo6MjChQvJly8fdnYvvJG3iIiIiIiIiIjIf5KzszO5cuV6bp2n916xt7cnXbp0LF++PEG9+AQkkGhuLT5p+Cqio6MTlNnb21u8trF57jYzyea5WcahQ4fi6+uLk5MT69atY926dc+sO2rUqCQPTkRERERERERE5L8gX7583Lt3D8Cc0IyJiaFfv37UqlULb2/vf3wNe3t7Hj58aFF28eJFnJyc/nHbyeG5ickLFy4QExNj/vez/JOsrsjzHA92SO4QRET+FUwx0QQ365TcYYiIWEWMAfXtg5M7DBGR18pka//3leQ/pXz58hQrVozevXszePBgMmTIwKxZs9i+fTsff/xxklyjWLFirFy5kg0bNlCkSBHWrFnD6dOnKV68eJK0b23PTUwuWrQo0X8/7ejRo/j5+SVtVCL/z9Mj+dc7EBH599DPxL9zPNgBj5XzkjsMEUlCwc06sT7KI7nDELG6+vbBeHp6JncYr0VQUJB5sxB5u5hs7amP9T94slYS2GQyMW3aNPz8/OjevTuRkZEUKFCAOXPmJNkmMw0bNiQoKIgRI0YQHR1NvXr16NChA0ePHk2S9q3NZBiG8U8a2L17Nx9++KF25ZYkd/DgQUoUTu4oRETkv0SJSZE3jxKT8rZSYlL+7Z73HPWM5WnPez/8O1e+FBERERERERERkTeaEpMiIiIiIiIiIiJidUpMioiIiIiIiIiIiNU9d/ObmTNn/m0Dz9utW0RERERERERERCQxz01MLl++/IUayZo1a5IEIyIiIiIiIiIi/32GYWAymZI7DElmf7fn9nMTk9u2bUvSYERERERERERE5M1mb2/PkydPcHR0TO5QJJk9efIEe3v7Zx7XGpMiIiIiIiIiIpJkMmXKxJUrV3j8+PHfjpiTN5NhGDx+/JgrV66QKVOmZ9Z77ohJERERERERERGRl5E2bVoArl69SlRUVDJHI8nF3t6ezJkzm98PiVFiUkREREREREREklTatGmfm5ASAU3lFhERERERERERkWSgxKSIiIiIiIiIiIhYnRKTIiIiIiIiIiIiYnVKTIqIiIiIiIiIiIjVKTEpIiIiIiIiIiIiVqfEpIiIiIiIiIiIiFidEpMiIiIiIiIiIiJidUpMioiIiIiIiIiIiNXZJXcAIiJvswO/3WTo8AP8cT6MTJlS8eknRWjaOLdFnQGD9rNy1Xnza8MwePIkhqmTK9G0cW6GDAtkxco/yJbVkWnfVKZggfQATJ95gljDoEc3T6v2SUTkZc08bPDVAQP7pz4yX+BtokxWk/m1YRhM+M1g6Sl4FAWFXWFURRPuLnF1hu2NZeVpyOoE39QwUSCDydy2AXQrZkJE4PHdMJZ8PIGavd8nd5mCCY4f/zmA31fu4PHdh6TP7krlDxrg5hn3t8nOmWsI2vo7qV2dqdu/DRlzZwXg95U7MGINSr1f3ap9ERGR/z6NmBQRSSYxMbF0/nAHPbp5cvpEK772K0fvvnsJCXloUc9vdDnOBrU2f3XtUpDy5TLT4L1cBJ++x5atVwjc25S2bfIxecoxAO6EhvPjmvN80KlAcnRNROSlnLxj0L+MiaD/2Zi/nk5KAiw9BT/9AeuamjjeyUSZLNB7uwHA6VCDrRdhbxsTbQqYmHIwrjz0icGaswad9PmMiNmWySsID3uc6LGQI2fZt+BnvH19+Gj5CIrWr8C6kfN48uARdy5e53xgEJ3mDcSzblkCl24F4Mn9RwRvP0SxRpWs2Q0REXlDaMTkPxQSEsK4ceO4d+8eUVFR5M+fn379+pE6dWqmTJlCxowZad26tcU5np6eFC9eHIDw8HAqVapEz549sbGxwcvLi6xZs2Jj82fOeMCAATx69IjevXuTN29eAB49ekT27Nn56quvcHBwMNcNCAiwqAdQv359WrZsSUREBF5eXnTq1IkPPvgAgMuXL1OjRg369u3Lhx9+aD7no48+4tGjRyxatAhfX1+8vb159913+fTTT1m+fLlFf57u59N9i/fVV1/h6uqKn58fp0+fxsbGBnt7ewYPHkyOHDn+ye0X+U+7/yCKO3ciiI6JxTAMTCYT9vY22Ng+e1TP0WN3mDv/FFs3NcDe3gbb/68bNx4I8+uvJhyhR7dCpExp+/o7IiLyD524DS08nl+nVX5olNeEo72JW48NHkSCS8q4Y7Z/+ag9/vWE3wy6FTOR0k6jJUUAjv30K/YpHUidMV2ixx/euU/JZlVxzZMNgAI1S7F7znpCL93AMV1qIG70MoDN/3+j7V+ymVLvV8fOwf71d0BERN44Skz+A+Hh4XTv3p0vvviCokWLArBq1Sr69u3Lt99++8zznJ2dWbRoERD3i33YsGEsWbIEHx8fAObOnUuKFCkszgkICKBcuXJMnDjRXNa3b1+2bdtG3bp1Ler+tV68TZs24e3tzapVq+jcubM5+ZkzZ042bdpkTkzeu3ePixcvkjFjxpe9JRZ9e9rOnTu5efMm8+bNA2DLli2MHj2aGTNmvPQ1RN4ULulT0MHHne4999Cz915iYw2+Hlcet2xOzzxn2Mjf6NHd01wnbx5nGtTPRaVqa8iZIzVTJlXizJn7nAy6y+hRZazVFRGRV/YkyuD8fZh7zKD3NoO0KaBrURMt81smE00mE472sCLYoP8OgzQOsPi9uDp50pmon8eg2lKDHGlgkpeJM3cNgkJhlAZxiQBw78otDq7aRcsJPfHvNTnROgW8Slq8vnryApFPInDJkZlUzk7kq1yEhR+OxzmLC7X7tSL00g1un79GtW6NrdADERF5E2kq9z+wY8cOSpcubU5KAjRp0oS7d+8SEhLyQm2YTCY6derET//X3p3HVVE1fhz/DpsLIIi7KIaoZOKaaa65PiZupWKG4s81tTQ1M3HN3Io00zQzy/1x39LMpXKB3HMPQ1wy9zRRCTeWe+f3B083r6BZ6YXk8369eNWce+7MOXMYge89Z2bt2r907KSkJF26dEleXl4P/J6lS5eqVatWevLJJxUZGWkrz507t/LkyaMTJ05IktauXZsm7PynChYsqOjoaK1du1ZXrlxR/fr1NWlS+r8QAVmF1WoqRw4XTZ9aWyeOvKw5M+rq7ZF7dPjHK+nW3/39JR09Fq9OHeynFQ0Jr6To/W20dnWwAorn0sgxezV0UCUtWHRc/wn+Sp26bVbclduO6BIA/GW/3pIqF5TalzG0vZ2h92obGr3D1ObTZrr1m5eQjnY11KuSobC1pq7dTq0XXtVJ+//PSatbOqm4t6ExO00Nqmpo0REpeLlV3TZYdeVW+vsEHndWi0Ubxi/Sc680V3bPnA/0nrjTF7V2zFw92+4/yuGV+oFojU7BemXh23rpw97K7ZtPW2d8pZqdgnV4w24tfH2i1oyao1vxNx5lVwAAjxlmTP4DZ86ckZ+fX5ryIkWK6Pz58w+8n7x58+rq1au27TtnMzo5OWnOnDmSpJ07dyosLExxcXFycnJSmzZtVK1atTT7+73e72bPnq0zZ87o1q1bevLJJ9WqVSvNnDlTdev+cXPqJk2a6KuvvtLrr7+ujRs36o033tCePXseuA+/i4+Ptzt2/vz59cEHHygwMFCjRo3SkiVLNHr0aBUsWFDh4eGqUoUZXci61q47rf0HLmv4kNTZCQ3qF1H9ur5atvwnlXnKJ039xUtPqNWL/nJ3v/dSqS1R55XT3UXF/XOpR6/vFLWxuWbNjdWUj6P19rDKj6wvAPB3+eUytKT5H7MjqxSSXixp6uufTdX1S7sEO9v/blnRvbw0N9rUjvNS4+L2daLOmHJ3kfy9pF7fmtr4kqG5h6WPD5gaVo1l3ch6di/cqHzFC+uJZx7s3tOn9h3V+vfmq2LL2qrcJv0H2pzad1SuOdzk5ZtX6yLmq/20N3VozXbtWbpJtbo2e5jNz9JiYmIyugmPxO3btx/bvgH4awgm/4ECBQro0KFDacp//vlnFS5c+IH3c+7cORUsWNC2nd5SbumPJdpXr15V586dVaRIkXT3l95S7qVLl+rWrVvq0qWLJGnfvn06deqUnJ1T7z/XoEEDtWvXTi1btlS+fPmUPXv2B27/ne61lPvIkSPy9/fXhAkTZJqmtm3bpr59+2rbtm0yDP5AQNZ07vwNJSVZ7MpcXZ3k4pr+ZPZvNp7VjE+fu+f+LBar3nt/v6ZPra2fTyXIt7C73N1dFVTGR5/P4Bc/AJnTD7+a+u6s9GrFP34fSLRIOe76LXXC91almNJbVVL/jTRNU0lWKdddvzJZrKbe321qakNDp36TCntI7q6GyuQxNeOHR90bIHM69t1B3biSoKPfHZQkJd1M1PqIBXrmpfppgscfv/lekZ+uVr1eLRVYp2J6u5PVYtWOOevUeFCY4i/EyTOft9xyZFO+AF8dWPXdI+9PVlK69OP5IMOYmJjHtm9ZCeEyHgaWcv8D9evX1/bt2+3CyaVLl8rHx+eBH+pitVo1c+ZMNWnS5IGPmzt3bo0bN05Dhw7VpUuX/rR+SkqK1q5dq/nz52vGjBmaMWOGXnnlFS1YsMBWx93dXf7+/ho3bpyaNm36wG15UDt27NCECRNksVhkGIZKliypHDlyEEoiS6tdq5AO/3hVi5Ycl2ma2rHzotZtOKOmwcXS1D19OkHx8UkqXy7PPfc3f+FxVa9WUH5+niri665TpxN0LT5R+/dflp+f56PsCgD8be6u0sS9pr76yZTVNLX1rKkvT0itStn/jlCxgKH//pj6BO8ki6mJe1PvM/l0Afv9LTwiVSucOhPT10M6/ZsUn2hq/yXJL5cDOwZkImGfDlCPpSPVY0nql2c+bz0/MDRNKHnmwDFtnrpSzUd0vmcoKUmHN+xSkXIB8iroo1z5cyv+QpxuJ9zUxdjT8ip4799VAAC4GzMm/wF3d3dNmzZNY8eO1bVr12SxWBQYGKgJEybY6kyfPl1Lly611Z83b55tubNhGEpJSVH16tXVunVr23vuXMotSR06dFCuXPa/SZcoUUJhYWEaPXq0Pvroo/u2c9OmTSpTpoy8vb1tZS1btlSLFi0UEhJiK2vWrJmGDx+uCRMm6Oeff053X8eOHVPLli1t2+Hh4Xav372UW5LeeOMNhYWFKSIiQi+88II8PDzk5OSk999//77tBh53pZ/Mremf1Nb7HxzU8Hf2yLdwTk36oLrKl8ujgYN3SpIixj4rSTpz9oa8vd3k5pb+U7YTEpI0c/YRrV6Ren/YAgVyqmvn0qpe6wsVL55Ln02790xLAMhIxb0NTW0gjfveVP/NUiF3aVwdQ2XzGRocZZUkja3tpLp+hgZWkV7ZkPpE7qcLSHOC7Z+4nZBkana0qRUtUssKuBvqXFaqtdBUcS9p2n/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      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot to show total sales in each customer segement\n",
    "# from https://github.com/kevwij/insidesherpa_quantium_virtual-experience/blob/master/Quantium%20-%20Module%201.ipynb\n",
    "# Values of each group\n",
    "bars1 = totsales_l_p[totsales_l_p.index.get_level_values(\"PREMIUM_CUSTOMER\") == \"Budget\"][\"TOT_SALES\"]\n",
    "bars2 = totsales_l_p[totsales_l_p.index.get_level_values(\"PREMIUM_CUSTOMER\") == \"Mainstream\"][\"TOT_SALES\"]\n",
    "bars3 = totsales_l_p[totsales_l_p.index.get_level_values(\"PREMIUM_CUSTOMER\") == \"Premium\"][\"TOT_SALES\"]\n",
    " \n",
    "bars1_text = (bars1 / sum(totsales_l_p[\"TOT_SALES\"])).apply(\"{:.1%}\".format)\n",
    "bars2_text = (bars2 / sum(totsales_l_p[\"TOT_SALES\"])).apply(\"{:.1%}\".format)\n",
    "bars3_text = (bars3 / sum(totsales_l_p[\"TOT_SALES\"])).apply(\"{:.1%}\".format)\n",
    " \n",
    "# Names of group and bar width\n",
    "names = totsales_l_p.index.get_level_values(\"LIFESTAGE\").unique()\n",
    "\n",
    "# The position of the bars on the x-axis\n",
    "r = np.arange(len(names))\n",
    "\n",
    "plt.figure(figsize=(18,7)) \n",
    "\n",
    "colors = ['salmon', 'lemonchiffon', 'lightskyblue']\n",
    "# Create yellow bars\n",
    "budget_bar = plt.barh(r, bars1, color=colors[1], edgecolor=\"lightgrey\", height=1, label=\"Budget\")\n",
    "# Create red bars (middle)\n",
    "mains_bar = plt.barh(r, bars2, left=bars1, color=colors[0], edgecolor=\"lightgrey\", height=1, label=\"Mainstream\")\n",
    "# Create blue bars (top)\n",
    "prem_bar = plt.barh(r, bars3, left=bars1.append(bars2).groupby([\"LIFESTAGE\"]).sum(), color=colors[2], edgecolor=\"lightgrey\", height=1, label=\"Premium\")\n",
    "    \n",
    "for i in range(7):\n",
    "    budget_width = budget_bar[i].get_width()\n",
    "    budget_main_width = budget_width + mains_bar[i].get_width()\n",
    "    plt.text(budget_width/2, i, bars1_text[i], va='center', ha='center', size=13, color='midnightblue')\n",
    "    plt.text(budget_width + mains_bar[i].get_width()/2, i, bars2_text[i], va='center', ha='center', size=13, color='midnightblue')\n",
    "    plt.text(budget_main_width + prem_bar[i].get_width()/2, i, bars3_text[i], va='center', ha='center', size=13, color='midnightblue')\n",
    "    \n",
    "# Custom X axis\n",
    "plt.yticks(r, names)\n",
    "plt.ylabel(\"Lifestage\",fontsize=15)\n",
    "plt.xlabel(\"Total Sales\",fontsize=15)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5),fontsize=15)\n",
    "\n",
    "plt.title(\"Total Sales, by Lifestage and Customer Type\",fontsize=20)\n",
    "\n",
    "# Show graphic\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot to show total sales in each customer segement\n",
    "#plt.figure(figsize=(18,7))\n",
    "#plt.tick_params(labelsize=12)\n",
    "#ax=sns.barplot(x=totsales_l_p.reset_index()[\"LIFESTAGE\"], y=totsales_l_p.reset_index()[\"TOT_SALES\"], \n",
    "#            hue=totsales_l_p.reset_index()[\"PREMIUM_CUSTOMER\"], \n",
    "#            palette=sns.color_palette(\"Paired\", 3))\n",
    "#plt.title(\"Total Sales, by Lifestage and Customer Type\",fontsize=20)\n",
    "#plt.legend(fontsize=15)\n",
    "#plt.xlabel(\"Lifestage\",fontsize=15)\n",
    "#plt.ylabel(\"Total Sales\",fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sales are coming mainly from <br>\n",
    "* Budget - Older Families<br>\n",
    "* Mainstream - Young Singles/Couples<br>\n",
    "* Mainstream - Retirees<br>\n",
    "Let's see if the higher sales are due to there being more customers who buy chips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2）Customer counts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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></th>\n",
       "      <th>CUSTOMER_COUNTS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">MIDAGE SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>1504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>3340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>2431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">NEW FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>1112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OLDER FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>4675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>2831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>2274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OLDER SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>4929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>4930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>4750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">RETIREES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>4454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>6479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>3872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">YOUNG FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>4017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>2728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>2433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">YOUNG SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>3779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>8088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>2574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         CUSTOMER_COUNTS\n",
       "LIFESTAGE              PREMIUM_CUSTOMER                 \n",
       "MIDAGE SINGLES/COUPLES Budget                       1504\n",
       "                       Mainstream                   3340\n",
       "                       Premium                      2431\n",
       "NEW FAMILIES           Budget                       1112\n",
       "                       Mainstream                    849\n",
       "                       Premium                       588\n",
       "OLDER FAMILIES         Budget                       4675\n",
       "                       Mainstream                   2831\n",
       "                       Premium                      2274\n",
       "OLDER SINGLES/COUPLES  Budget                       4929\n",
       "                       Mainstream                   4930\n",
       "                       Premium                      4750\n",
       "RETIREES               Budget                       4454\n",
       "                       Mainstream                   6479\n",
       "                       Premium                      3872\n",
       "YOUNG FAMILIES         Budget                       4017\n",
       "                       Mainstream                   2728\n",
       "                       Premium                      2433\n",
       "YOUNG SINGLES/COUPLES  Budget                       3779\n",
       "                       Mainstream                   8088\n",
       "                       Premium                      2574"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Calculate the customer count in each segment\n",
    "count_l_p = pd.DataFrame(customer.groupby([\"LIFESTAGE\",\"PREMIUM_CUSTOMER\"]).count())\n",
    "count_l_p.columns=[\"CUSTOMER_COUNTS\"]\n",
    "count_l_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t2rXcuXOHFi1akC9fPku5g4MDPXv2JHHixCxatOiF2nzSJ598YjVqqnz58gCcOXPmP7UblyxZslhd38SJE9O5c2cAli9fDkRfoypVqvDPP/9YNqIBWLlyJZGRkf9pxNvLWrZsGWFhYXTs2NFq7cnEiRPTsWNHoqKi+O2334D/dt9D9Oix//3vf7FGlxUoUIDEiRNbTVP97bffcHJyolOnTlajdf39/XFxcbE6fuHChRiGwTfffEOSJEks5SlTpuTzzz8nODiYVatWPTO2Vq1aMWLEiFgj6WKWHohrCm2DBg1wdXW1vPby8iJ58uRW99fKlStJnjw5bdu2tZQ5ODjQvXv3p45Cfpwtnmfw6LPds2ePVfmQIUPYsWNHrNHOj/v333/ZuXMnRYsWjbWpUrNmzcifP79lyYfH1a9f32rEaMwz7aOPPrJaG/jJZ9rVq1fZvHkz5cuXjzWKtXnz5mTIkCHOtWzj+0yLL5PJRJ06dbh37x6bNm2ylG/atIk7d+7E+f389ddfkzhxYsvrli1b4ubmxurVqwkLC3vp57WIiMibSFO5RUTkvZQiRQquXbvG3bt3rZIGr1K1atX48ccfmTdvHmvWrKFMmTKUK1eO8uXLkyZNmmcee/r0aUJCQvDy8sLOLvbfEYsUKcLq1as5evSo1XqMWbJksaoXk4DJlCmTVXnMjtcxazSePn2ahw8fEhkZydixY2OdLzQ0lMjISI4dO2a1RmR8pzTHbMRx586deNWPcfToUSB6DcInubq6ki1bNoKCgrh37x7JkiV7obZjZMuWzep1TDtPrl/5KhQsWBB7e3ursnz58mFnZ2fpK0RP/1y6dCnLly+3TLVftmwZKVOmfO7yA6/DoUOHgOg1Jk+cOGH13sOHD4FHn9V/ue8BihYtStGiRbl9+zZBQUGcO3eO06dPs2/fPst9CNH35PHjx8mXL1+sz97Z2dlqDUuAw4cPA9FrfG7cuNGq/r///gtgtT5sXGKu/bVr1zh69Kgltr///hvAKhEb48n7C6I3oolJGoWEhHDmzBmKFy9uWfcxRtq0acmUKZMlIfg0Md9fj68F+TrUq1ePOXPmMGbMGObNm0e5cuUoV66cZSmHZ4lJsse1BixEJxwPHjzI0aNHrZ5XL/tMO3LkCIZhcPv27TifaWazmcuXL3PlyhWrBOfLLtPwLHXr1mX8+PEsX76cKlWqANF/iHBwcOCjjz6yqmsymWJdI3t7e/Lly8eaNWs4d+4chmG81PNaRETkTaTEpIiIvJcyZ87MtWvXOHv27DMTk/fu3SM4OPil1upKly4dCxcuZMKECaxbt47ly5ezfPlyzGYz9evXp1evXpZfpp8Uk7R4WrItJp6QkBCr8sdHgj3ueWswxiQ0/vnnH8vmJ3F5MrH4+KieZ4lJIpw7d+65dU+dOmUZkRZzHZImTRpn3bRp0xIUFERwcPBLJyafvDYxI9SMp2zk81+kTp06VpnZbCZRokSWBB/Ahx9+SPr06fn999/55ptvOH36NIcPH6Z58+axkle2EDPide7cuU+tE3Nv/Jf7PqadIUOGsGLFCsLDwzGZTLi5ufHhhx9ajSC9ffs2wFOTnU9+z8b0YdKkSc/tw9NcvnyZgQMHsn79egzDwM7Ojg8++IAiRYpYxfa4uL73TCaT5f6KOefTRju6uLg8dwdtR0dH0qVLx5UrV7h69eozn1c3btzA3t4+1q7d8ZE7d27mz5/PxIkT2bRpE/Pnz2f+/Pk4OTnRokWLWCNXH/eyz7SnJTzj+0zbt28f+/bte2q927dvWyUm4/tMexFZs2alcOHCbNy4kfv372MYBhs2bKBs2bKx/v9JmTJlnH2LeXbcu3fPkqh+0ee1iIjIm0iJSREReS+VLVuWPXv2sG3bNgoXLvzUevPmzWPEiBG0b9/e6pfuuJJWwcHBscoyZ87M4MGDiYyM5NChQ2zZsoXFixczb948kiVLRrdu3eI8b0yS4smdamPE/NL9MsmFZ52vTp06DB8+/JW0+biyZcsyceJEtm7dajVd9UkHDhygUaNGFClShF9//fWlrkNcn82TyY6EEteItvv37xMcHGw19djOzo7atWszadIk9u7da9nRNyGmccOj5NDatWtjbU4Sl5e97yF6s5hNmzbRpEkT6tSpg7u7uyUxHTPdHR7ds0+brvrgwYNYfbC3t2f//v0vldw1DIM2bdpw8uRJ2rZtS+XKlcmVKxeJEyfm+vXrL71TesznHpM4fdLjCetnKVu2LAsXLmTbtm3Uq1fvqfXGjRvH3Llz6d+/P40bN37mVPG4nmm5c+dm9OjRhIWFsXfvXjZv3szixYuZOHEi6dKlo1mzZnG2ZetnWsw9+8UXX7yS3bX/q7p167J37142bNhAZGQkYWFhcX4/h4aGxnl8zPVJmTKl5Xn2up7XIiIitqQ1JkVE5L1Uq1YtzGYzs2bNempCIDg42JJsKF26NIAloRFXsuD8+fNWr9etW0e/fv24f/8+9vb2FCxYEH9/f2bPng1gmf4Zl+zZs5MoUSIOHDgQ55Ti3bt3A5AzZ87ndTVesmXLhqOjI4cPH44zsTdt2jTGjx//3JFbT1O4cGE++OADAgMDn9nvGTNmAFjWF4zZzTeuY+7fv09QUBBZs2a1jDAym82EhITE6kN8Rmo+S3zW+YuPgwcPxiqLWa/v8TU0ITqRAbBhwwY2btxItmzZLOvo2ZqHhwcQd/xnzpxh2LBhrF+/Hvhv9/3du3fZtGkTnp6e9O/fHy8vL0tS8sKFC4SGhlo+26RJk/LBBx9w9OjRWN8jMQnRJ/sQGRkZ53TtvXv3MnLkyFhrtj7u2LFjHD9+nCpVqtC5c2fy589vGV136tQp4OVG2SZOnJgcOXJw5MiRWAn0u3fvxvvejUlGTp48mYiIiDjrXL9+nd9//x2TyUTJkiWBR8+0JxO5hmHEeqYtWbKEgQMHYhgGjo6OlChRgm7dulmmEz/rs435Xn7aNd69ezcmk+mVPdNi7tkn74MYP/zwA5MmTXotSzbExcfHB0dHRzZs2MCGDRtIliwZ3t7eseo9ePDAcj89bv/+/aRMmZLMmTO/9ue1iIiILSkxKSIi76XMmTPTqlUrbt26xWeffRZrFM+9e/fo2rUrZ86coWLFipY1DrNmzYq9vT07d+60Gk20ceNGyxp2Mf755x/mzJnDnDlzrMpjNmd4fJOamORAeHg4ED1N8aOPPuLq1av88MMPVsdv3ryZVatWkTVrVstGEP9VokSJ8PHx4eTJk0ydOtXqvV27djF8+HAWLVoUa0OR+LK3t6dXr14AdOzYMdbUysjISCZMmMDy5cvJkCEDLVu2BKBy5cokS5aMX3/91er6RkRE8L///Y+QkBCrUUfZs2cnIiKCzZs3W8pu375tSYq9LAeH6EkmMZ/Pyzp+/LjVBiv3799n9OjRmEwm6tevb1U3R44ceHp6snz5cg4dOpRgoyUhekMae3t7Ro8ezbVr1yzlERERDBw4kClTplimVv+X+95sNmNnZ8fdu3etEkYhISEMHDjQqi5Eb4xy//79WOvs/fTTT1ZxwqPE3eDBg61GWd6/f59+/foxefLkONeIjBGT/H5yg5vbt29bRq09LSH4PPXq1ePhw4eMHDnSkmgyDIPvv/8+3m0WLVoUHx8fTp06RYcOHWL9weXq1av4+/tz+/ZtmjVrZhn5mj17dgC2bNli1f9ff/3V8pnG2LdvH7NmzYq1SVDMhjVPfraPx54xY0ZKlCjB4cOHY90bCxYsYM+ePZQoUYL06dPHq7/PkzlzZooVK8bmzZv5448/rN5bsmQJP/74I1u2bHnulPBXJXny5FSsWJEtW7awbds2qlev/tQlDUaOHGl1/0+ZMoXz589Tr1497O3tX/vzWkRExJY0lVtERN5bnTt35saNGyxevJhKlSpRoUIFsmTJwpUrV9i2bRs3b97Ey8vLaqqcq6srlStXZvXq1TRq1Ijy5ctz/vx51q9fT5EiRaxGDDVu3Jj58+czcuRIAgMD8fDw4MaNG/zxxx84OTnRpk0bS92YNc569uxJ6dKladGiBd26dWPPnj1MnjyZ3bt3U7hwYcu5nJ2dGTFixCsbyQfQvXt39u7dy7Bhw1i3bh0FChTgypUrrFmzBgcHBwYPHhznRjzxVa5cOQYNGkTfvn35+OOPKV68OPny5ePhw4fs3r2bf/75hzRp0jBx4kTLOnRJkyZl8ODBdO7cmSZNmlClShVSpUrFzp07OX78OEWLFuXzzz+3nKNx48asX7+eTp06UatWLRwdHfnjjz/ImjUrp0+ffunYYz6fVatW4eTkRL169ciVKxdr164lKCiIypUrW0aEPUuWLFno2rUra9euJWXKlGzYsIELFy7Qpk2bOEdD1qtXj4EDB2IymahVq9YLxbxlyxZ8fX3jfM/Ly8uyG3h8fPDBB3Tr1o2hQ4dSs2ZNvL29cXFxYfPmzZw6dYqKFStSu3Zt4L/f91WqVLF8f5UuXZqHDx+yYcMGrl+/jouLi2WNPTs7O1q1asUff/zBpEmT+PvvvylQoABHjhzhr7/+Inny5FYJyA8//BBfX19mzpzJRx99RPny5XF0dGTt2rVcvnyZJk2aWG0kFdc1KFCgAH/99RfNmjXDy8uLW7dusXbtWsLCwkiSJMlLj1Br2bIl69evZ+bMmRw6dIiCBQuyb98+jh8/TqpUqeLdzuDBg7l37x7r16+37EadPn16Ll68yObNm3nw4AFVqlThm2++sRyTN29e8uXLx969e2nWrBnFihXj+PHj7Nixg4IFC7J//35L3c8++4xVq1bRtWtXy/fVxYsXWbNmDWnSpKF58+aWumnTpuXMmTN07dqVMmXKULduXQYMGMAnn3xCv379WLNmDR4eHhw/fpxt27aRNm1aS/L5VYk531dffUW5cuXIlSsXp0+fZuPGjaRIkYK+ffu+0vM9T7169Vi9ejXw7GUZ9uzZQ4MGDShZsiSnTp1i69at5MqViy+//NJS53U/r0VERGxF/1uJiMh7y97eniFDhvDLL79Qvnx5jh49ysyZM1m/fj0ffPAB/fv3Z9asWSRPntzquMGDB+Pr68vt27eZOXMmFy9e5IcffqBq1apW9VxcXJg1axZNmzblzJkzTJ8+nY0bN1KuXDnmz59P7ty5LXXbtWtHwYIF2bZtm2V0n6urK/Pnz6d169Zcu3aNWbNmcfDgQerWrcvixYspWLDgK70ej5/vypUrzJw5k7/++gtvb2/mz5//zKRNfDVq1IilS5fSpEkTbt26xYIFC1iyZAn29va0a9eOlStXWl0XgKpVq/Lrr79SunRptmzZwvz58wH45ptvmDZtmtWIp4oVKzJy5EiyZMnCb7/9xp9//km9evUYPXr0f4rbzc3Nssbo7NmzOXDgABC95uK4ceOeu5tzjAoVKjBo0CAOHTrE3LlzSZIkCYMGDaJLly5x1q9RowYQPRruyV2In+f69esEBgbG+fXkztrx4efnx6RJk8idOzdr1qxh3rx5ODg4EBAQwA8//GAZVfpf7/vBgwfTsmVL7t27x6xZs9iyZQv58+dnzpw51K1bl5CQEHbt2gVEj/SdNm0azZo149y5c8yaNYv79+8zadIkPvjgg1gbmfTu3Zvhw4eTIUMGli1bxm+//Ubq1KkZPHjwc5NUdnZ2jB8/nvr163PhwgXL90e5cuVYtGgRpUuX5syZMy+1bICjoyNTpkyhTZs2XLlyxTKicMqUKfHayTxGkiRJ+OmnnxgzZgxeXl7s3buX6dOns23bNgoWLMioUaMYN25crFGCP/30E/Xq1ePMmTPMmjWLhw8fMn369FjPmEyZMjFnzhx8fHw4dOgQU6dOZffu3dSuXZv58+dbbSLTrVs3cuXKxR9//MHSpUuB6OTuokWLaNy4MSdPnmTWrFmcOXMGX19flixZEmsH7v8qe/bsLF68mMaNG3Ps2DFmzJjBsWPHqFOnDgsXLnxl08bjq2zZsjg7O+Pm5vbU3ckBpk+fTtq0aZk3bx4nTpygRYsW/Prrr1abgNnieS0iImILJuN1bDkpIiIi8p748ssvqVGjBjVr1nzlbW/dupVPP/2UQYMG0ahRo1fe/tvuwoULuLq6xrlzc8WKFUmSJAm///57AkQmEts///xDjRo1LJupPcnX15fAwEB2794d6w9iIiIi7yqNmBQRERF5SVevXmX37t2xRnm+CmFhYfz0008kTZoUHx+fV97+u2DgwIEUKVIk1iYtv//+O5cuXdKoMXljGIbB+PHjsbOzo0GDBgkdjoiIyBtDa0yKiIiIvKSlS5fyxRdfvNIpoefOncPf359r165x8+ZNOnbsiLOz8ytr/13y8ccfs2nTJho2bEjVqlVJkSIFp06dYuPGjaRPnx5/f/+EDlHec2FhYdSvX5+7d+9y5coVGjRoYNl4SERERJSYFBEREXlpj2+886qkSpWKe/fuERoaSvPmzWnbtu0rP8e7wtvbm2nTpjFlyhQ2bNjAnTt3SJMmDU2bNuWLL754oY1jRF4HR0dHzGYzd+/excfHh169eiV0SCIiIm8UrTEpIiIiIiIiIiIiNqcRk/LG2rNnD0mSJEnoMCQBhYaGkihRooQOQxKY7gPRPSC6B0T3gIDuA9E98KYJDQ2lUKFCCR2GvOWUmJQ3lslkIk+ePAkdhiSgoKAg3QOi+0B0D4juAdE9IIDuA9E98KYJCgpK6BDkHaBduUVERERERERERMTmlJgUERERERERERERm1NiUkRERERERERERGxOiUkRERERERERERGxOSUmRURERERERERExOaUmBQRERERERERERGbU2JSREREREREREREbE6JSREREREREREREbE5JSZFRERERERERETE5pSYFBEREREREREREZtTYlJERERERERERERsTolJERERERERERERsTklJkVERERERERERMTmlJgUERERERERERERm1NiUkRERERERERERGxOiUkRERERERERERGxOZNhGEZCByESlz179uDo6JjQYYjIK2CKjMSwt0/oMEREROQNFWWYsDPpV1N5s5jszeTL45HQYbyxgoKCyJMnT0KHIW85h4QOQORZPD3CEjoEEXkFDh1zxGPR1IQOQ0RERN5Qxxr4sSJcCSB5s9TkWEKHIPLO01RuERERERERERERsTklJkVERERERERERMTmlJgUERERERERERERm1NiUkRERERERERERGxOiUkRERERERERERGxOSUmRURERERERERExOaUmBQRERERERERERGbU2JSREREREREREREbE6JSREREREREREREbE5JSZFRERERERERETE5pSYFBEREREREREREZtTYlJERERERERERERsTolJERERERERERERsTklJkVERERERERERMTmlJgUERERERERERERm1NiUkRERERERERERGxOiUkRERERERERERGxOYeEDkBE3h3jJx5m2Mh9mM2P/uYxe7o3JYqns6o3e84Jxk88zLXrIeTInpx+fYpY6vTuG8iCRf+QMYMTP/5Qlrx5UlrajjIM/Nt72q5DIiLyTPuuGny+2mC376Pn/q0Qg1qLDX6pbsLD1fTCxy8+bjBoh4G9HfQuaaJOzug29l81+P4vg+k++ru6iLw/jm/Zz65Zf3L/+m2SpU1JyRbVyFEy9s/Dy/pO4fyBk9jZPXpGtl80CIBNE5cStO5vkqZxoXq3ZqTOlgGAvxdtxIgyKNqoom06IyISByUmReSVOXzkJgHdCtG+bb6n1tm2/V+GDN/L3FmVyZsnJQsX/0PLTzewfXM9rl0LZu26iwRuq8+CxacYM/YgP40vx42bISxeepoVv9WwYW9ERORpDMNg/jEsCcQYuy8bdN9scP7eyx0PMHCHwa81o5ORn6wwLInJwTsNBpZ5dqJTRORdcuviNdaOmk+9QZ+TIe8HnNt7gmX9pvDpjN4kcXG2qnvt9CUaDm9PulyZrcpvnP2X04FB+E3tQdD6vwmcuw6fHs0JvvOAYxv20vh7f1t2SUQkFv3JWURemUOHb5Evn+sz61z+9yHt2+bDM58rdnYmGjfMgb2diePHb2NvH/0Lp4EBYHk98vv9+LfPR+LE9q+3AyIiEi/j9sLUgwZfFn6UKNx92aD9nwb+hZ+fPIzr+BgxiUrjsX+vPGWQIwW4P2cEpojIuySlWxo+m/0tGfJ+QFhwKA9u3cXRKRF2ZuufiR/evk/w7fukypo+Vht2//8gNQzD6vXO2Wso2qgiDo7m19wLEZFnS5ARkx07dsTT05M2bdoA8ODBA+rXr8+YMWM4ffo0s2bNws7OjoiICD7++GPq1q0LgLe3N6tWrSJRokQAnDp1in79+jFz5kx8fX3JmzcvPXr0ACA0NJQaNWqwfv16ALZv385PP/1EWFgYDg4OuLm50atXL5IlS2YV26ZNm5gyZQp2dnZERkbSsGFDateuza5du5g7dy6jRo36T+fy9fWlX79+5MiRw3LOXbt20alTJ3LmzGkpS5kyJT/88AM3b96kb9++PHz4EMMwyJgxI7179yZx4sQAtG3blp9++okTJ04wYsQIgoODefjwIeXLl6dDhw6YTCZu3rzJsGHDuHTpEpGRkWTIkIGAgADSpEnD4sWL+eeff+jatavl3J07d6ZJkyYAVnGFhoZSq1YtfH19CQgIwMfHh3LlylmOu3DhArVr1yZfPuvRctOmTSM8PJx+/fpx9epVTCYTSZMmpV+/fqRMmfKF7x95Mz0MjuCf03f5eUoQHTptxSW5I+3b5qPpxzmt6jWsn93qdeDuq9x/EEGuXC6kck1MrZpZKVNhKVkyJ2Xs6DKcOHGHI0G3GDywuC27IyIiz9DYA/wLm9h5+VGZuytsbWYisYOJzhuMFz4+Rv9SJj79w8DOBIPLmgiNNBi312CGj5KSIvL+cUySiDv/3mTG58MwDKj4ZT0SOSW2qnPt1EXMSRKxvN9Urp2+TEq31JT5tCYZ8mQlZaa05CpbgBltRuCS3pWqXZtw89wVrp++TIX2dROmUyIij0mQxGS/fv1o0KAB3t7e5MyZk2HDhvHxxx9z/fp15s6dy8SJE0mWLBkhISF07NiRRIkSUaPG86dwrlixgkqVKlG8uHUC4+jRo4wYMYKJEyeSLl30OnbTpk3j559/pnPnzrFiW7p0KcmTJ+f+/fvUqVOH0qVLv5ZzPe7DDz9k1KhRscp//vlnSpUqRdOmTQH43//+x9y5c2nVqhWXLl3Czc2Nu3fv8vXXXzN27Fg++OADIiMj+eqrr5g7dy5NmjTB39+f1q1bU7lyZSA6cdq2bVsWLFjwvEtqFVdYWBjVq1enTp06T62fM2dOZs6cGat87ty5pE6dmqFDh1quyY8//kjv3r2fG4O8Ha5fC6ZY0TS0bO5B2TLp2bvvOi0/3UC6tEnwrugW5zHHj9/m8/ab6Pp1QVK5Rv+A1SvAi14BXpY6vq3W07uHF7/OPcn0mcdxc3Ni5LCSlvoiImJ76ZxjkoSPEpAuieKfOIzr+Bgf5TDxUY5HbU3cZ1Azh4kL9+Cz1VE42EHfUiYKpFGiUkTeD8nSuPDFb4O5dPg0KwZMI0XG1GQu+OiP/xFhEWTInZXSrX1wyZiaI2t2s7TvL/hO7IazazJK+/lQ2s/HUn9Z3ymU8fPh8OpADv6+g2RpUlKpY8NY08NFRGwhQRKTrq6u9OnTh969e/P1119z/vx5+vfvT7t27ejatatlFGPixInp3r07ffv2jVdislevXvTp04fFixfj4PCoa3PmzKF9+/aWRCFAq1at4mwjVapUzJgxg2rVqpEzZ05WrVqFo6MjJ0+efOXnig83NzdWr15N1qxZ8fLyonv37phM0T+Ib9iwgfLly7Nu3TpKlCjBBx98AIC9vT3Dhg3DbDZz6NAhkiVLZklKApQqVYosWbKwe/fuF4rl/v372NnZYW//4tNp3dzcWLhwIV5eXhQvXhxfX1/LdAJ5N2TJkozF86tZXpcono4G9bLzx5rzcSYmN26+RPsvt9D28zx0+CLuDW02br6Ek7MD2bMlp53/Fjavq83UGccY9+Mh+vYp+tr6IiIib4YbwQa/nTBYWs9EvSUGQ8uZiDKgx2aDlQ2UmBSR94Pd///+lblgTnKUzs8/Ow5bJSZzlMxHjpKPZq0V+KgkB1fu4MKBk3hUKGzV1tk9xzEnccTFLTWrhs2m+cSuHFixnb8WrKfsZ7Vs06G3TFBQUEKHIPJOS7DNb7y9vfnzzz8JCAhgzpw5mEwmzp8/T5YsWazqZc6cmUuXLsWrTQ8PD+rWrcvQoUOtRuJduHDB0u758+fp2bMnhmEQGRnJnDlzrNqYMGEC06ZN4+uvv+bmzZuWEYev41yP27lzJ76+vpbX5cuX57PPPqNp06YkSpSIX375ha+++ooiRYrQt29fMmTIwK5du2jYsCHTpk0jc2brRY6dnZ0tMTz5Hjz/uppMJgzDsMRlMpkwm8306dPH0nZcTp48adWPfPnyERAQQIUKFQgLC2PhwoX06NEDd3d3evfujYeHx1PbkrfLgYM32LTlslWSMTQ0kiRJYj9m5s4/ybf9/2LY4BLUq5MtzvYiI6MYOnwvk8aX48zZe7hldMbZ2YxnPld+/kU/HIiIvA++/8ugfaHo6eGnbhvkTRU9xvLk7YSOTETk9TuzO4i9v22h3uA2lrKo8AgSOVvPHDqx9QCGYeBetqClLCI8PNb6kVGRUeyYvooaPXy5c/kGydKkwDFJItLkcGPf0i2vtzNvsTx58iR0CG8sJW3lVUjQXbnr1q1LSEiIZXRhunTpuHjxIi4uLpY6Z86cIUOGDAAkSpSIsLAwyxqTDx8+tKy1GKNNmzY0bdqUzZs3W8oyZMjAhQsXyJ07N5kzZ2bmzJmWdSEfd+fOHS5dukS3bt3o1q0bV65coUOHDuTLly/OZNx/OdeTnjaVe9euXdStW5eGDRsSFhbG5MmTGTx4MMOHD8fe3p5EiRKRMWNGjhw5YnXc+fPn+ffffy3X9Elnz56lVKlS3Lp1i7CwMKv3Yq5rcHDwU+N6mqdN5d67dy8lS5akatWqREZGsnTpUnr06MHixYvj3ba82ZydzYwafYBsWZPhUyML27b/y9LlZ1g0r6pVvS1bL9OzdyBzZlWiRPF0T2kNZs85SamS6cmSJRmJEtlz9tw9bt8JZe/e62TJkuypx4mIyLvh+E2DIzdgUJno15mTwb6r0YlJ/TcgIu+DNDkyceXkBYLW/03uCoU5+/cxzvx1jMbfV7GqFx4cxvZpv5Mqa3pSZEzNvqVbiAiNIIuXu1W9w6t3kalADlzSu+Lg6MCdyzcIufeQK8fO4ZI+lS27JiJi8Ubtyu3r68vw4cO5f/8+EL0pzvDhw/nkk08AyJs3L6tXr7bU37x5M/nz57dqw97enqFDhzJkyBBLWZMmTZgwYQJXr161lO3cuTPW+cPCwujUqROXL0evxJ4mTRpSp06No6NjnPH+l3PF1/Tp0y3JO0dHR3LlyoWjoyPbtm2jZMmSAFSsWJEtW7Zw7tw5AMLDwxk6dCjHjx/Hy8uL69evWzbmgejrdvbsWYoXL07u3LnZvn07Dx48AOD27ducOHHCanOeV2HlypX8/PPPQPR18/DweOp1lbdTjuzJmTi+LKN+OECuvHPo0SeQUSNKUSB/Krr33En3ntHfBz9OPExYeBSftFxPzjxzLF8bNj5KoN+7F8aUaUfp1CH6+ztdOic+a52HUmWXsG7DRTp8GffUbxEReTP9dsKg8vyoFzpm0E6DniVMliVs+pU20WGdQef1BgPKaBq3iLz7nF2TUetbP/Yv3crExn3ZOWsNH/VugWvmtKwft4j14xYBkLdKUQrWKcPSb3/hp8bfcnrXEeoMaI058aPft0IfhrB/+XaKNan0/20np1Cdskz/bBindx+laKOKCdJHERGTkYAL/T2+03WMZcuW8euvv2IymYiKiqJhw4Y0atQIgCtXrtCnTx9u3LiBg4MDmTNnpn///jg7O8fa7Xr69OlMnz7dkpDbtWsXP/30E+Hh4QQHB5MxY0Y6duxotRM2wLp16xg/fjwODg5ERkZSoUIF/P39Y+3K/bLn8vX15datW5akXIkSJahQoUKsXbkBJk+ezJ07d+jfvz+XLl0iceLEpEyZkn79+vHjjz/y5ZdfWkabHjp0iOHDh2MYBg8ePKBixYr4+/tjMpm4ceMGgwcP5sKFCwCkT5+enj17Wo799ddfmTdvHs7OzkRERNC2bVsqVaoU5+cTIyAggL1791pGkmbLlo3OnTvHuSv34MGDSZkyJQMHDuTo0aMkSZIEJycnevXq9cwE6J49e/DK/9S3ReQtcuiYIx6LpiZ0GCIiIvKGOtbAjxXhWuZJ3iw1zcfw9NSgiKcJCgrSVHf5zxI0MSnyLEpMirw7lJgUERGRZ1FiUt5ESkw+mxKT8iq8UVO5RURERERERERE5P2gxKSIiIiIiIiIiIjYnBKTIiIiIiIiIiIiYnNKTIqIiIiIiIiIiIjNKTEpIiIiIiIiIiIiNqfEpIiIiIiIiIiIiNicEpMiIiIiIiIiIiJic0pMioiIiIiIiIiIiM0pMSkiIiIiIiIiIiI2p8SkiIiIiIiIiIiI2JwSkyIiIiIiIiIiImJzSkyKiIiIiIiIiIiIzSkxKSIiIiIiIiIiIjanxKSIiIiIiIiIiIjYnBKTIiIiIiIiIiIiYnNKTIqIiIiIiIiIiIjNKTEpIiIiIiIiIiIiNqfEpIiIiIiIiIiIiNicEpMiIiIiIiIiIiJicw4JHYDIsxw65pjQIYjIK2CKjOBYA7+EDkMkQUQaJuxNRkKHIe8R3XPyNooyTNQ0H0voMESsmOzNCR2CyDtPiUl5o3l6hCV0CCLyyrz89/OhY454LJr6CmMRsZ1jDfxYEe6R0GHIe6Sm+Rienp4JHcYrFRQURJ48eRI6DElgug9E94DIu0dTuUVERERERERERMTmlJgUERERERERERERm1NiUkRERERERERERGxOiUkRERERERERERGxOSUmRURERERERERExOaUmBQRERERERERERGbU2JSREREREREREREbE6JSREREREREREREbE5JSZFRERERERERETE5pSYFBEREREREREREZtTYlJERERERERERERsTolJERERERERERERsTklJkVERERERERERMTmlJgUERERERERERERm1NiUkRERERERERERGxOiUkRERERERERERGxOSUmRURERERERERExOaUmBQRERERERERERGbc0joAETk/TF+4mGGjdyH2fzobyKzp3tTong6q3q+rdazbfu/2NmbLGUng5oC0LtvIAsW/UPGDE78+ENZ8uZJaWk7yjDwb+9pg56IvLlWnDL4/i+Dy/fBLRl0K2aiWjZTrHpzggx+3GtwOxTcU0L/0ibyp4mu13dbFIuOQwZn+KGSiTypossn7jMwgPaFYrcn8izHt+xn16w/uX/9NsnSpqRki2rkKGn9vDYMg12z1nB4TSBhwaGkzZmJCu3rkipregA2TVxK0Lq/SZrGherdmpE6WwYA/l60ESPKoGijijbvl4iIiIj8NxoxKSI2c/jITQK6FeJkUFPL15NJyZh6vy2sZlUP4Njx26xdd5HAbfX5pFkuxow9CMCNmyEsXnqaz/zy2LQ/Im+af24bdN1oMLy8iaBP7ehXyoT/WoObwYZVvaAbBsMCDWb4mDjQykTlrCba/xld5/hNg3VnYVszE83ymBi7J7r8ZrDB0pMGfsr9ywu6dfEaa0fNp/JXDWm/aBDl2tRm1dDZBN95YFXv8JpATmw7yMejOtJu/gDc8mVjzXdzAbhx9l9OBwbhN7UHntVLEDh3HQDBdx5wbMNeCtUpY/N+iYiIiMh/p8TkS+jYsSOTJk2yvH7w4AHVqlXj6NGjrFq1ik8++QRfX1+aNm3KkiVLLPW8vb0JDQ21vD516hS+vr4A+Pr6MmTIEMt7oaGheHt7W15v376dli1b0rRpU3x9fQkICODevXuxYvP29rac39fXF39/f8t7ffv2pW7dulb1fX19qVGjhlXZmjVr8PDw4MKFC+zatYvOnTtb6p46dcqq7oULF2jcuDEAAQEB1KpVy3JuX19fFixYAMCmTZto2bIlfn5+tGjRgmXLlj39Ass769DhW+TL5/rMOtevB3P9Rgge7ilivWf//yMoo8dsPXo98vv9+LfPR+LE9q82YJG3TPYUJv5uYaJoehMPwg2uPgRnRzA/8a1x+g5ERkGEAYYBdiZI/P9zKOyf+Mkg5vX3fxm0L2QisYNGS8qLSemWhs9mf0uGvB8QFhzKg1t3cXRKhN0TN2a+qsVpMrojSVO7EHznAaEPQkic3BkAu/+/EQ3DsHq9c/YaijaqiIOj2YY9EhEREZFXRVO5X0K/fv1o0KAB3t7e5MyZk2HDhvHxxx9z/fp15s6dy8SJE0mWLBkhISF07NiRRIkSxUr+xWXFihVUqlSJ4sWLW5UfPXqUESNGMHHiRNKlix5dNm3aNH7++WdL0vBxU6ZMIVGiRFZlwcHB7NmzB3d3d3bt2kWJEiWs3g8KCiJPnujRZitXrsTNze2FrkmMbt26Ua5cuVjl/fr1Y+nSpSRPnpz79+9Tp04dSpcuTapUqV7qPPL2eRgcwT+n7/LzlCA6dNqKS3JH2rfNR9OPc1rVO3T4FkmdzbRovZ4jQbfIni053/YqQtEiaciZw4VaNbNSpsJSsmROytjRZThx4g5Hgm4xeGDxp5xZ5P3ibDZx7q5B+bkGhgH/K2simaN1MrF8ZsieAqrMN7A3gbMZ5taKrpMjhYmaOQwqzDXInAxGe5s4ccsg6CYM1KA0eUmOSRJx59+bzPh8GIYBFb+sRyKnxFZ1TCYT5sSOHPnzL9aOWYCjUyLqDfocgJSZ0pKrbAFmtBmBS3pXqnZtws1zV7h++jIV2tdNgB6JiIiIyKugEZMvwdXVlT59+tC7d28CAwM5f/48fn5+zJw5k65du5IsWTIAEidOTPfu3Zk9e3a82u3Vqxd9+vThwQPrqU1z5syhffv2lqQkQKtWreJMSj7NqlWrKFmyJPXq1YsVz0cffcSKFSsAuHv3LqGhoaROnTrebcdHqlSpmDFjBidOnMDZ2ZlVq1YpKfmeuX4tmGJF09CyuQe7t9dnxNAP6T/oL9ZvuGhVLyQ0kiJeaRjYrxh/72xAg3rZaN5qHVevBgPQK8CLQ3sb8/syH3JkT86A//1N7x5e/Dr3JFV9VuL3+QZu3AxJiC6KvDEyJoXjn5qYXdPEoB0G2y5aT+UOjYBcKWF5fRNHWpv4NL+JtmsMQiKi6wWUsGNvSzuW1bcjewoT/9tp0KOEiblHwWdRFJ+vjoo1PVzkeZKlceGL3wZT73+fs/XnFZzffzLOeu4VCvHlb/+j+MeVWNLnZ0LuPQSgtJ8Pbeb05eNRHUjploatv6ykjJ8Ph1cHMqfjaFYMnB5reriIiIiIvNk0YvIleXt78+effxIQEMCcOXMwmUycP3+eLFmyWNXLnDkzly5dilebHh4e1K1bl6FDh9K7d29L+YULFyztnj9/np49e2IYBpGRkcyZMydWO61bt8bOLjrn/Omnn1KhQgUWLFjAgAEDyJEjB/369ePKlSuWRKe3tzfdu3ena9eurF69murVq/Prr7++1HUZMWIEkydPtrzu3bs3Hh4eTJgwgWnTpvH1119z8+ZNmjRpgr+/PyaTpgS+L7JkScbi+dUsr0sUT0eDetn5Y815vCs+GqFbvWpmqlfNbHnd0teD6TOPs23Hv9Srk82qzY2bL+Hk7ED2bMlp57+FzetqM3XGMcb9eIi+fYq+/k6JvKEc7KKfraXdoEZ2gzVnDEq7PXrejvrbIIMzFPj/zW6+KmIw5yhsvQCVP7Bua/N5A2cHyOYC/msN1n1sYsZh+HGfQZ+SeoZL/NnZR0/dzlwwJzlK5+efHYfJXDBnrHoO5ugfT70alGf/iu1cOHCKnKXzW9U5u+c45iSOuLilZtWw2TSf2JUDK7bz14L1lP2s1uvvzFsgKCgooUN4pUJCQt65PsmL030gugdE3j1KTP4HdevWJSQkxJLgS5cuHRcvXsTFxcVS58yZM2TIEL1rZKJEiQgLC7NMs3748CGJE1tPY2rTpg1NmzZl8+bNlrIMGTJw4cIFcufOTebMmZk5cyahoaFPnR7+5FTuU6dOceLECYYOHQpET5WaM2cOnTp1ssSVJ08e9u7dy59//smoUaNeOjEZ11TuO3fucOnSJbp160a3bt24cuUKHTp0IF++fFbraMq77cDBG2zacpkOXzzaOSM0NJIkSawfQytWniXKMKhd8wOreokTWa9FFhkZxdDhe5k0vhxnzt7DLaMzzs5mPPO58vMv+mFF3k/rzxn8fMDg15qPJkSER0JyR+t6l+6D02PfeiaTCXuTgcMT8ygiowyGBxqMr2Li7N3okZjOZhP5Uhn8cvA1dkTeKWd2B7H3ty3UG9zGUhYVHkEiZ+ufgXbOWkNUZBSlWlYHoteTjIqIIFHSJFb1oiKj2DF9FTV6+HLn8g2SpUmBY5JEpMnhxr6lW15/h94SMUv0vCseX3ZI3l+6D0T3wJtFSWJ5FTSV+xXy9fVl+PDh3L9/H4jeFGf48OF88sknAOTNm5fVq1db6m/evJn8+a1HANjb2zN06FCrjXCaNGnChAkTuHr1qqVs586d8Y5rwYIFdO7cmV9++YVffvmF6dOns2jRIsLCwix1atasybRp03BxccHZ2fnFOv4cYWFhdOrUicuXLwOQJk0aUqdOjaOj43OOlHeJs7OZUaMPRCceowy2bL3M0uVnaNQgu1W9Bw8j6NNvN8eP3yY8PIrxEw8TEhJJ+XIZrOrNnnOSUiXTkyVLMjK5OXP23D1u3wll797rZMmSzJZdE3ljeKaGg9dg8XGDKMNg/TmDDeehTk7rkY3eWaKnZR+8ZhARZTD5gEGkAUXTW7c35yiUzAhZkptwSwrn7sKdUIO9VyFLcht2TN5qaXJk4srJCwSt/xsjKoozu4M489cx3CsUtqqXPncWDv6+g2v/XCIyPIJdv/6Jo1NiMuTJalXv8OpdZCqQA5f0riRPm5I7l28Qcu8hV46dwyW9lokREREReZtoxOQr5O3tzf379/nss88wmUxERUXRsGFDfHx8APjmm2/o06cPc+bMwcHBgcyZM9O/f/9Y7WTPnp2WLVsyffp0ADw9Pfnmm28ICAggPDyc4OBgMmbMaLUz+NOEhYWxcuVKli5dainLmDEjuXPntkqSli5dmoCAAKuEaFy++uorS0KxRIkSlqRrjCenchcrVoyOHTvSu3dv/P39cXBwIDIykgoVKlCmjHZReJ/kyJ6ciePLMmzEPr7qso0MGZwZNaIUBfKnonvP6ET7sMEf8nGjHFy9+pBmLddx61Yo+T1TMWu6N05Oj3ZcvXcvjCnTjrJscfSomnTpnPisdR5KlV1C9uzJmTyxfIL0USShpXUy8Ut1GLDdoM/W6OnXk6qayJnSRM/NUQAMLmdHszxwJ9REuz8N7oZC3tQww8dE0sc2ybkXZjDtkMHiOtFl6ZxNtM4PZecYZHeBiVU1jVvix9k1GbW+9WPL5GVsHL+ElG6p+ah3C1wzp2X9uEUAePs34IOiuSnVqgYrB80g9EEwGfJkpc6AT6123A59GML+5dtpNPKL/287OYXqlGX6Z8NI4ZaGj3r6JkgfRUREROTlmAzD0Or18kbas2cPXvmfX09E3n2HjjnisWhqQoch8lKONfBjRbhHQoch75Ga5mN4eno+v+JbRNM3BXQfiO6BN40+D3kVNJVbREREREREREREbE6JSREREREREREREbE5JSZFRERERERERETE5pSYFBEREREREREREZtTYlJERERERERERERsTolJERERERERERERsTklJkVERERERERERMTmlJgUERERERERERERm1NiUkRERERERERERGxOiUkRERERERERERGxOSUmRURERERERERExOaUmBQRERERERERERGbU2JSREREREREREREbE6JSREREREREREREbE5JSZFRERERERERETE5pSYFBEREREREREREZtTYlJERERERERERERsTolJERERERERERERsTklJkVERERERERERMTmTIZhGAkdhEhc9uzZg6OjY0KHISJvAFNkBIa9Q0KHIfJSIg2wNyV0FPI+iTRM2Jv0I/7bLsowYafPUeSdZrI3ky+PR0KH8dKCgoLIkydPQochbzn9lidvNE+PsIQOQUTeGLZ7Hhw65ojHoqk2O5+IiMiTjjXwY0X425uwEJHnq8mxhA5BJMFpKreIiIiIiIiIiIjYnBKTIiIiIiIiIiIiYnNKTIqIiIiIiIiIiIjNKTEpIiIiIiIiIiIiNqfEpIiIiIiIiIiIiNicEpMiIiIiIiIiIiJic0pMioiIiIiIiIiIiM0pMSkiIiIiIiIiIiI2p8SkiIiIiIiIiIiI2JwSkyIiIiIiIiIiImJzSkyKiIiIiIiIiIiIzSkxKSIiIiIiIiIiIjanxKSIiIiIiIiIiIjYnBKTIiIiIiIiIiIiYnNKTIqIiIiIiIiIiIjNKTEpIiIiIiIiIiIiNqfEpIiIiIiIiIiIiNicEpMiIiIiIiIiIiJicw4JHYCIyPNcuvyAgJ672Bl4lWRJzbRvl5fP/PI8tf6kn4+w+69rTJ5Y3lLWu28gCxb9Q8YMTvz4Q1ny5kkJwPiJh4kyDPzbe772foiIvI3++teg3zaD03cgrRN8VcRE3VymWPWmHDT45aDB3VAo5QaDyphI4xRdr++2KBYdhwzO8EMlE3lSRZdP3GdgAO0LxW5PRORt9feijeyYsRp7B3tLWe3+n+Lmmc3y2jAMds1aw+E1gYQFh5I2ZyYqtK9LqqzpAdg0cSlB6/4maRoXqndrRupsGSxtG1EGRRtVtG2nREReE42YFJE3mmEYtP58IzlzunB4X2N+nVmJ70cdYPdfV2PVffgwnIGD/6b/oL+tyo8dv83adRcJ3FafT5rlYszYgwDcuBnC4qWnn5nkFBF5n0VGGbRZbdC+sInDre0YVt5El40G5+8ZVvVWnDIY/bfBD94m9rQw4Z4SPlsdXef4TYN1Z2FbMxPN8pgYuye6/GawwdKTBn76u5CIvGOu/3OJUi2q037RIMvX40lJgMNrAjmx7SAfj+pIu/kDcMuXjTXfzQXgxtl/OR0YhN/UHnhWL0Hg3HUABN95wLENeylUp4zN+yQi8rpoxKSIvNH27L3OlSvB9AoojL29HR7uKVj2W3VcXRPHqtu6zSacnBzw/cSdGzdCLOX29tEjcaLH5Tx6PfL7/fi3z0fixPax2hIREbgbBjdCIDIq+g9FJhOY7cD+iQGOq/4xaJYHiqSPfqNzUfjloMHRGwbmJx6x9v//Z/Hv/zJoX8hEYgeNlhSRd8vVU5fIU7nYM+vkq1ocj/KFMSd25OGte4Q+CCFxcmcA7P7/QWkYhtXrnbPXULRRRRwcza8xehER21Ji8i22a9cuOnXqRM6cOQF48OABmTJlonPnzjRo0IB8+fJZ1Z82bRqtW7cmKiqKf/75B1dXV1KkSEGpUqXw8vJi7ty5jBo1Cl9fX4KDg0mSJAlRUVHcvXuXrl27Ur58eQICAjh8+DApUqSwtFu7dm0aNWqEp6cnhQsXtjrnyJEjSZMmDcOGDeP48ePY2dlhNpvp1asXmTNnfu3XSN5+Bw/dxN3dhUGD97B46WmSJTXT0T8/jRvmiFV39HelSJ/OiZGj9lslJnPmcKFWzayUqbCULJmTMnZ0GU6cuMORoFsMHljclt0REXmrpExswjevQYd1Bp3WQ5QBw8ubyJjUOpkYaUCSxxKMpv//On0HamQ3UTOHQYW5BpmTwWhvEyduGQTdhIEa9CMi75jwkDBuX7zOvmVbWTNyDomSOuHVoDz5qlonKk0mE+bEjhz58y/WjlmAo1Mi6g36HICUmdKSq2wBZrQZgUt6V6p2bcLNc1e4fvoyFdrXTYBeiYi8PkpMvuU+/PBDRo0aZXndpUsX1q9fT86cOZk5c2as+tOnTwcgICAAHx8fypUrB0QnOR83bNgwcuSITvz8888/dOzYkfLlo9fr69atm+W4x7m4uMR5zk2bNnH16lWmTp0KwNq1axk8eDATJkx4mS7Le+b27VC277hCmVLp2b29PvsP3OCTluvJmiUpJYqns6qbPp3TU9vpFeBFrwAvy2vfVuvp3cOLX+eeZPrM47i5OTFyWElSxTESU0TkfRVlGCRxgPFVTFTJClsuwFfrDTzTQN5UjxKRVT4wMTzQoMoHkDMFTNgHIZEQGhn9fkAJOwJKPGq31aooepQwMfcozDwShVtSGFbOhGsSjZ4Ukbfbw9v3yZA3KwV8PiRzT1/+PXae5QOm4uyajA+K5o5V371CITwqFGL/sm0s6fMzLX/uTuJkTpT286G0n4+l3rK+Uyjj58Ph1YEc/H0HydKkpFLHhiRxcbZl90REXjklJt8hYWFhXL16lQ8//PCVtnvp0iWSJ0/+0senT5+eQ4cO8fvvv/Phhx9SqVKlOBObInFxdLQnRQpHOnyZH4BiRdPiUyMLf6y5ECsxGV8bN1/CydmB7NmS085/C5vX1WbqjGOM+/EQffsUfZXhi4i81Vadhn1XoVfJ6IRhpaxQMYvBouMGeUs+SiI2cDdx5QF8/odBeBT45TeRKwUkTxS7zc3nDZwdIJsL+K81WPexiRmH4cd9Bn1KKjEpIm83l/SuNBzW3vLazTMbuSt68c+Ow3EmJh3M0b+SezUoz/4V27lw4BQ5S+e3qnN2z3HMSRxxcUvNqmGzaT6xKwdWbOevBesp+1mt19shee2CgoISOgSRBKXE5Ftu586d+Pr6cuPGDezs7GjcuDElS5ZkyJAh+Pr6Wurly5ePgICAeLfbvXt3HBwcuHTpEoUKFWLIkCGW90aMGMHkyZMtr3v37o2Hhwd37tyxOmfatGn57rvv8PDwYODAgcyfP59BgwaRPn16AgICKF5cU2jl+XLkSM7DhxFERETh4BC9vk5UpAGG8Zwj4xYZGcXQ4XuZNL4cZ87ewy2jM87OZjzzufLzL/qhQETkcZfuQ1iUdZnZLvrrcVceGNTKCV8Ujn7jTqjB6L8hXyrrepFRBsMDDcZXMXH2LmRMCs5mE/lSGfxy8DV2RETERq6evMC5PSco2vjRrtmR4RE4JLJeF3LnrDVERUZRqmV1IHo9yaiICBIlTWJVLyoyih3TV1Gjhy93Lt8gWZoUOCZJRJocbuxbuuX1d0heuzx53t6NOJVUlVdBicm3XMxU7lu3btG6dWsyZcoE8NSp3PEVM5V77ty5rFixggwZMljee9Gp3EePHiVbtmx8//33GIbBtm3b6NSpE9u2bcNk0sgIebZyZTOQwiUR/xu6l14Bhdm77zqrVp9j7qzKL9Xe7DknKVUyPVmyJCNRInvOnrvH7Tuh7N17nSxZkr3i6EVE3m5l3WD4Lph/1KCRB+y6DKtPw5xa1v9/b7sI4/cZzK8FZnvot82grBukc7auN+colMwIWZKbSGRvcO5udBJz71XI8vKTM0RE3hjmJInYNedPXDKmImcpTy4cOMXxzftoMLSdVb30ubOwesQccpUtgGvmtOyevx5Hp8RkyJPVqt7h1bvIVCAHLuldcXB04M7lG4Tce8iVY+dwSf/EX39ERN5Cds+vIm+DlClTMmLECHr37s21a9deWbtNmjQhQ4YMVutYvqgdO3bw/fffExkZiclkIleuXCRJkkRJSYmXJIkdWDivCseO36aA1wK+/GorA/sVo4hXGrr33En3njvj3da9e2FMmXaUTh2ip8ekS+fEZ63zUKrsEtZtuEiHLz1fVzdERN5KuVOZmFDFxNRDBp5TDfpsNfiuookCaUz03BxFz83Rwynr5YIKmaHSfIPSvxoYwPcVrf+fvxdmMO2QQQev6PJ0ziZa5zdRdo7BhnMGXxbWzwUi8vZL6ZaGGgHNCZyzlgkN+7Bh/G9U7tSYtDkzsX7cItaPWwTAB0VzU6pVDVYOmsHPzQdy9cQF6gz41GrH7dCHIexfvp1iTSoB4OyanEJ1yjL9s2Gc3n2Uoo0qxhmDiMjbxGQYLzkfUhLcrl27LDtpx5gwYQJBQUFs3bo11q7cgwcPtuyEHdfmN4/vyt2vXz/L5je3b9+mdu3aTJo0iWnTpsXalbtYsWJ07Ngxzl25v/76a/Lnz8+wYcPYuXMnSZMmxc7Ojq+//poiRYo8s3979uzBK/8zq4iIvBaHjjnisWhqQochIiLvsWMN/FgR7pHQYYjIa1TTfAxPz7d3cERQUNBbPRVd3gxKTMobS4lJEUkoSkyKiEhCU2JS5N2nxKSIpnKLiIiIiIiIiIhIAlBiUkRERERERERERGxOiUkRERERERERERGxOSUmRURERERERERExOaUmBQRERERERERERGbU2JSREREREREREREbE6JSREREREREREREbE5JSZFRERERERERETE5pSYFBEREREREREREZtzSOgARERERERERETk3XL37l2uXr1KeHh4QociCcRsNpM2bVqSJ0/+1DpKTIqIiIiIiIiIyCtz9+5drly5gpubG0mSJMFkMiV0SGJjhmEQHBzMxYsXAZ6anNRUbhEREREREREReWWuXr2Km5sbTk5OSkq+p0wmE05OTri5uXH16tWn1lNiUkREREREREREXpnw8HCSJEmS0GHIGyBJkiTPnM6vxKSIiIiIiIiIiLxSGikp8Pz7QIlJERERERERERERsTklJkVERERERERERMTmtCu3iIiIiIiIiIi8dseOBREeHmnz85rN9nh45HmhY7y9vS07SgM4OjqSNWtWWrVqRcOGDV86llatWpE+fXqGDh360m08LiIiglmzZtGqVatX0p6tvVRi8sKFC6RPnx7DMDCbza86JhEREREREREReceEh0fi6RFm8/MeOub4Usd9/vnntGzZEoDg4GC2bt3Kt99+S+rUqalQocIrjPDl/f777wwZMuStTUzGeyq3YRj8+OOPFCpUiKpVq3L58mW++eYbevbs+czddURERERERERERN42Tk5OpEmThjRp0pAlSxaaNWtGyZIlWbJkSUKHZmEYRkKH8J/Ee8Tk1KlTWbhwIQMHDqRPnz4A+Pj40LdvX0aPHk23bt1eW5Dy/nrZv2qIiPwXpsgIjjXwS+gwRETeC5GGCXvT2/1L1esQaZioaT6W0GGIyGtkstcM1LdRkiRJLDtNe3h4MHz4cOrUqWN5//GyqKgoxo0bx/z583nw4AGNGzcmMtJ6Kvv+/fsZMmQIR44cwc3NjdatW9O7d2/WrVtHpkyZCAsL4/vvv2fFihU8fPiQvHnz0rVrVwoVKsSuXbv45ptvLOcdMmQI9evXt93FeAXinZhcuHAh3377LRUrVuTbb78FoEqVKpjNZvr166fEpLwWCTHEW0QkWsI8fw4dc8Rj0dQEObeISEI41sCPFeEeCR2GiMh/UtN8DE9Pz4QOQ14jwzDYsWMH27ZtY9y4cfE6ZsKECcyYMYNBgwaRK1cuJk+eTGBgIPXq1QPgypUr+Pn54ePjw//+9z9OnDhB3759rdr45ptvOH/+PKNHjyZVqlSsXLmSFi1asHTpUgoXLsy3337LgAED2Lp1K8mSJXvl/X7d4p2YvHDhAjlz5oxVni1bNm7evPlKgxIREREREREREUlI48ePZ/LkyQCEhYURERFBlSpVKFas2HOPNQyDX3/9FT8/P6pXrw7AgAED2L59u6XOvHnzSJkyJf3798fe3p4cOXJw/fp1Bg4cCMDZs2dZtWoVK1asIFeuXAD4+/vz999/M3XqVAYMGEDSpEkBSJMmzSvtu63EOzGZLVs2/vrrLzJnzmxVvnr1arJly/bKAxMREREREREREUkon3zyCc2aNQOiE5MnTpxgxIgRfPnll5aE5dPcunWL69evW42kdXR0JG/evJbXR44cIX/+/Njb21vKihQpYvU+QOPGja3aDgsLIyzs3ZhhGu/EZIcOHejWrRsnT54kMjKSZcuWcfbsWVauXMnw4cNfZ4wiIiIiIiIiIiI25eLiQtasWS2vc+XKRUREBN26dePEiROx6kdERMQqe3JzGkfHR3tp2NvbExUV9dTzm83R65DOnTuXxIkTP7Wdt1m8d+WuXLkyo0ePZu/evdjb2zN9+nQuXLjAxIkT+eijj15njCIiIiIiIiIiIgkuJtEYFRWF2Wzm/v37lvfOnj1r+berqyvp0qVj7969lrKoqCjLKEiI3rDm8OHDVhvi7N+/3/LvmOnbN27cIGvWrJavadOmsW7dOgDLRjxvq3iPmAQoX7485cuXf12xiIiIiIiIiIiIvBEePnzItWvXgOik4qlTpxg7dix58uTB3d2dQoUKMX/+fIoUKUJkZCRDhgyxGsnYunVrxowZQ7Zs2ShQoAAzZ87k0qVLlvebNWvG1KlT6d+/Py1btuTMmTOMGTMGiE44Zs2aFR8fH/r06cO3335LtmzZWLRoEXPnzmXKlCkAODs7A3Dw4EGyZ89uef22iHdi8mk7DplMJsxmM+nTp6dcuXKkSJHiVcUmIiIiIiIiIiLvCLPZnkPHbD8F2Wy2f36lOEyePNmylqS9vT2urq54e3vz5ZdfYjKZ6NevH/369aNRo0akTZuWr776iitXrliOb9WqFYZhMHr0aG7dukW1atWoXLmy5f3UqVMzadIkBg8eTJ06dciaNSvNmjVj3LhxlmncgwYN4rvvvqNnz57cu3ePHDlyMHbsWEqWLAlAiRIlKF68OE2bNqVLly74+fm97GVKECbjycnuT9GiRQv++usvHB0d+eCDD4DoIaohISFkyJCBO3fuYDabmTZtGrlz536dMct7Ys+ePXjlT+goRERs69AxRzwWTU3oMEREbOZYAz9WhHskdBgiIv9JTfMxq01O3gdBQUHkyZPnhd+TR06ePMm9e/coXLiwpWzlypUEBASwd+9eHBxeaKLzG+tZ90O815j09PSkePHirF+/niVLlrBkyRI2bNhA2bJlqV27Nrt27aJy5cqMGDHilQUuIiIiIiIiIiLyLrp8+TItWrTg999/59KlSwQGBvLDDz/g4+PzziQlnyfevVy0aBHTp0/H1dXVUpYiRQq6dOlCixYt6NSpE35+frG2MBcRERERERERERFrZcuWpXv37owZM4ZLly6RIkUKatSoQefOnRM6NJt5ofTr4zsNxbh3755l9yB7e3vs7OI9CFNEREREREREROS91bx5c5o3b57QYSSYeGcRK1euTJ8+fQgMDCQ0NJSQkBB27dpF37598fb2JiQkhMmTJ793ayqIiIiIiIiIiIjIi4v3iMlevXrRvXt3WrRogclkAqJ35K5SpQp9+vRh69atbN68mUmTJr22YEVEREREREREROTdEO/EpJOTE2PHjuXChQscOXIEe3t73N3dyZw5MwDlypVjy5Ytry1QEREREREREREReXe80BqTERERmM1my3RtwzA4ffo0Bw8epHbt2q8lQBEREREREREREXn3xDsxuWXLFgICArh582as95IkSaLEpIiIiIiIiIiIiMRbvDe/+e677yhQoABTp04lceLETJgwgb59+5I8eXKGDh36OmMUERERERERERGRd0y8R0yeOnWK4cOH4+7uTt68eTGbzTRp0oQkSZIwZcoUqlat+jrjFBERERERERGRt9ixw4cIN2x/XrMJPPJ5vtAx3t7eXLx4kb59+9KsWbNY73/66ads3bqV4cOHU6dOnWe2tWvXLlq0aMGmTZtInz79C8URl5MnT3LhwgUqVKjwn9tKaPFOTDo4OODs7AxA1qxZOX78OKVLl6ZYsWIMGjTotQUoIvJfXLr8gICeu9gZeJVkSc20b5eXz/zyxKo3e84Jxv54iFu3Q/FwT8Gg/sUokD8VAL37BrJg0T9kzODEjz+UJW+elACMn3iYKMPAv/2L/QcnIvK6Xb5v0HOLQeBlSOoI7Qqa8MtvilVvTpDBj3sNboeCe0roX9pE/jTR9fpui2LRccjgDD9UMpEnVXT5xH0GBtC+UOz2RERet3vXb7Nh3GIuHjqNo1NiijQsT6HaZWLVW9Z3CucPnMTO7tEkwfaLon9v3TRxKUHr/iZpGheqd2tG6mwZAPh70UaMKIOijSrapjMi76FwAzwWTbX5eY818Hup48xmM6tXr46VmLx9+za7du2KdzuFCxdm69atpEqV6qXieNIXX3xBrVq13onEZLyncnt6erJo0SIA3N3d2bFjBwBnzpyxetiLiLwpDMOg9ecbyZnThcP7GvPrzEp8P+oAu/+6alXvSNAtBg/by+wZlQg68DFVKmeiTfvNABw7fpu16y4SuK0+nzTLxZixBwG4cTOExUtPx5nkFBFJSIZh8Plqg5wpYF9LEzN9TIz62+Cvf62HJwTdMBgWaDDDx8SBViYqZzXR/s/oOsdvGqw7C9uamWiWx8TYPdHlN4MNlp408NPfY0QkARiGwcqB03HNnJY2c/tRd+Cn7Jr9J5ePnIlV99rpSzQc3p72iwZZvgBunP2X04FB+E3tgWf1EgTOXQdA8J0HHNuwl0J1Yic5ReT99eGHH7J79+5Y+638+eefFCxYMN7tODo6kiZNmleWPzOMBBh2+prE+4r4+/vzyy+/MHXqVGrXrs2+ffuoW7cuX331Fd7e3q8zRhGRl7Jn73WuXAmmV0BhzGY7PNxTsOy36uTI4WJV7/Tpu0RFGkRGRGEYBvZ2JhIntgfA3j56RFD0+KBHr0d+vx//9vks9URE3hR7r8KVhxBQwoTZ3oS7q4nf6prIkcK63uk7EBkFEQYYBtiZIPH/z6Wxf+InxJjX3/9l0L6QicQOGi0pIrb377FzPLh5l1KtfLB3sCdV1vQ0/u5LUmRKY1Xv4e37BN++T6qssadL2v3/Ay3ml/qY1ztnr6Foo4o4OJpfcy9E5G1SuHBhUqdOzdq1a63KV61ahY+Pj+V1VFQU48ePp2rVqnh6elK0aFE6dOhgSWju2rULDw8P/v33XyB6mviUKVNo164dBQsWpFKlSowbN87S3vXr1/H396dEiRIUKlSIVq1aERQUBICvry/nzp1j3Lhxlnyct7c3w4YNo1q1anz44YccPnyYsLAwhg4dSpkyZfDy8qJ58+bs27fvhWLOnz8/mzdvplq1ahQoUIBWrVrx77//MmDAAIoUKUKpUqWYNGnSf7rG8Z7KXaJECVavXk14eDiurq7Mnj2b3377DVdXV1q0aPFSJz9//jzDhw/n9u3bhIeHkzt3brp27UrSpEkZO3YsqVOnpmnTplbHeHp6UrhwYQBCQkIoU6YMHTp0wM7ODm9vbzJkyGCVge7evTsPHjygU6dO5MyZE4AHDx6QKVMmRo4ciaOjo6VuVFQUw4YN4/jx49jZ2WE2m+nVqxeZM2cmICAAHx8fsmfPTrVq1Zg3bx6entHDBebMmcP169fp0KEDERERTJw4kU2bNpEoUSIAatWqxccff8yFCxf4+uuvmT9/vlWfAgICOHz4MClSpLCU1a5dm0aNGrFp0yamTJmCnZ0dkZGRNGzY0LID+qFDh1i/fj0dO3Zk3rx5LFu2DDs7O8LDw+ncuTMlSpQAYOfOnYwfPx7DMAgPD6datWq0atUKk8mEr68v/fr1I0eOHACEhoZSo0YN1q9fHyuuyMhI+vfvT65cuShdujTbtm2z6sfYsWNZsWIFadOmtZSVKlWK9u3bc+DAAUaPHo1hGERFRVG+fHlat279AneLyIs7eOgm7u4uDBq8h8VLT5MsqZmO/vlp3DCHVb0K5TOSPXsyKlRZjr29iaTOZhbMrQJAzhwu1KqZlTIVlpIlc1LGji7DiRN3okdZDiyeEN0SEXmmQ9ejp2UP3mWw9IRBUkfwL2yioYd1MrF8ZsieAqrMN7A3gbMZ5taKrpMjhYmaOQwqzDXInAxGe5s4ccsg6CYM1GAiEUkg105exDVLerZNXcmxDXtxdEpMsY+9yVO5qHW9UxcxJ0nE8n5TuXb6MindUlPm05pkyJOVlJnSkqtsAWa0GYFLeleqdm3CzXNXuH76MhXa102YjonIG8tkMlG1alVWr15N48aNAbh58ya7d+9m+PDhDBgwAICpU6cyY8YMhg8fTo4cOTh58iQ9evRgwoQJ9OrVK862x4wZQ8+ePenevTtLly5l7NixfPjhhxQtWpT+/fsTERHBr7/+islk4rvvvqNDhw6sXbuWsWPHUr9+fapVq8bnn39uaW/OnDlMmjSJRIkSkSdPHr7++mvOnz/P6NGjSZUqFStXrqRFixYsXbqUbNmyxSvm8PBwfvjhB0aOHElERARt27aldu3aNG7cmIULF7Js2TK+++47vL29LTm3FxXvxGSPHj3o1asXSZMmBSBXrlx888033L59my5dujB27NgXOnFISAhffPEFgwYNsgx//e233+jSpQs//fTTU49zcXFh5syZQPRfufr27cvs2bPx9fUFYMqUKZaEYIxdu3bx4YcfMmrUKEtZly5dWL9+PdWrV7eUbdmyhatXrzJ1avR6B2vXrmXw4MFMmDDBqr2kSZPSo0cPFi1aZJXYBBg1ahRRUVHMnTsXe3t7Hjx4QNu2bSlatGisuB7XrVs3ypUrF6u8X79+LF26lOTJk3P//n3q1KlD6dKlSZUqFRs3bqRChQqsXLmSbdu2MW3aNMxmM+fPn6d58+b89ttv3Lhxg2HDhvHTTz+RNm1aIiIi6NevH7/88gufffbZU+OJK65NmzYxZswYqyz+k1q1ahUrmQwwYMAAhg0bRo4cOQgPD6dJkyZ8+OGH5M2b97kxiLys27dD2b7jCmVKpWf39vrsP3CDT1quJ2uWpJQons5SLyQ0EvdcKRg8sAQe7in4ceIhPmu7iQ1ra5EksQO9ArzoFeBlqe/baj29e3jx69yTTJ95HDc3J0YOK0kq18QJ0U0RESu3Q2HHJSjlZmL7J3DgGrT83SBLciie4VFyMjQCcqWEgWVMuKeEifug7RqDtY0hsYOJgBJ2BJR41G6rVVH0KGFi7lGYeSQKt6QwrJwJ1yQaPSkithFyP5gLB06RqWAO/Kb15OqJCyz59heSp0+Fm2c2S72IsAgy5M5K6dY+uGRMzZE1u1na9xd8J3bD2TUZpf18KO33aKTTsr5TKOPnw+HVgRz8fQfJ0qSkUseGJHFxTohuisgbpnr16rRq1Yo7d+7g4uLCmjVr8PLyInXq1JY62bJlY9iwYZb8iZubG2XLluX48eNPbbdixYp8/PHHAHTq1InZs2ezb98+ihYtytmzZ/Hw8CBTpkwkSpSIAQMGcPLkSaKiokiRIgX29vY4OTnh6upqac/b25vixaMHz5w9e5ZVq1axYsUKcuXKBUTPhP7777+ZOnUqAwYMiFfMhmHQuXNn8ufPD0RPbT906BBdunTBZDLRtm1bxo8fz4kTJ146MfnMqdynTp1i9+7d7N69myVLlrBz507L65ivFStWsGXLlhc+8caNGylWrJjVnPx69epx69Ytzp8/H682TCYTfn5+/P777y907rCwMK5evYqLi/V0zvTp03Po0CF+//13bt68SaVKlRgzZkys47NmzUrZsmWtEp0AERERrFq1ii5dumBvHz2909nZmZkzZ1pGJL6oVKlSMWPGDE6cOIGzszOrVq2yLJZ66NAh8ufPz9y5c2nXrh1mc/S0g8yZM7NkyRJcXV2ZM2cObdu2tYxidHBwICAggHnz5r1wLHfu3MHJyeml+pExY0Zmz57NoUOHsLOzY86cOUpKymvn6GhPihSOdPgyP46O9hQrmhafGln4Y80Fq3rfjTpAhgxOFCyQisSJ7fn6qwKEh0exZevlWG1u3HwJJ2cHsmdLzvdjDvDbgqoU8UrDuB8P2apbIiLP5GgHKRLBl4VNONqbKJreRI1ssOaM9VpEo/42yOAMBdJET83+qgiER8HWC7Hb3HzewNkBsrnAmL8NFtQ24ZXOxI/73p31jUTkzWdvtidxsiQUa+yNvdmBDHk/IGfp/Pyz87BVvRwl81G7f2tSZU2Pg9mBAh+VJFnqFFw4cDJWm2f3HMecxBEXt9QEzllLg2HtSZ87C38tWG+rbonIG65IkSKkTJmSdeui16R9cho3RCcFkydPzqhRo+jYsSM1a9Zk2bJlREVFPbXdDz74wOp1smTJCA8PB6I3t1mzZg3Fixfns88+Y+XKleTJk+eZa1RmzpzZ8u8jR44A0LhxYwoXLmz5CgwM5NSpUy8Uc5YsWSz/dnJyIlOmTJhM0X+YTpw4enBOWFjYU+N6nmeOmLxw4QJt27YFopOA/v7+cdZr3rz5C5/4/PnzVp2LkSlTJi5duhTvdlKnTs2tW7csr1u3bm35oOzs7Jg+fToQPZ3Z19eXGzduYGdnR+PGjSlZsqRVWx4eHgwcOJD58+czaNAg0qdPT0BAgCXj/LhOnTrRsGFD/vrrL0vZrVu3cHFxwcEh+rL++uuvrFq1igcPHlC7dm0qV6781H6MGDGCyZMnW1737t0bDw8PJkyYwLRp0/j666+5efMmTZo0wd/fn+vXr5M6dWpMJhNXr161ugEBUqaM3jX4/PnzNGzY0Oq9pEmTEhwc/MxvkCfjsrOzI23atHTr1u2Z9adNm2aVKG7Xrh2lS5dm8ODBTJ8+nX79+nH+/Hlq1qxJ9+7dY404FXmVcuRIzsOHEUREROHgEP1ciIr8/8XUHnPx0gOcnB49Dk0mE3b2JswO1g/9yMgohg7fy6Tx5Thz9h5uGZ1xdjbjmc+Vn38Jev0dEhGJhxwp4GEEREQZONhF/9AYacCTKcRL9+GxRx8mkwl7k8ETjz4iowyGBxqMr2Li7F3ImBSczSbypTL45eBr7YqIiJWUbmkIDwkjKjISu/8fCGJERfHkE+7E1gMYhoF72UeDYCLCw2OtHxkVGcWO6auo0cOXO5dvkCxNChyTJCJNDjf2LX3xwTciMWsAyrvFZDJRrVo1Vq9eTYUKFdizZ0+sgWoTJkxg0qRJ1K9fn7Jly9K2bVtmzJjxzPxWXPmQmPVvq1evTqlSpdi0aRPbt29n/PjxTJs2jfnz51uN1Hzc47N0YwauzZ0715I8fPK88Y05pq0Yr3oD7GcmJsuXL8+mTZswDIMKFSpY1pR8nLOzs2V694tIly4dBw4ciFV+5swZMmbMGO92Ll68SPr0jxY1jmsqN2CZyn3r1i1at25NpkyZYtU5evQo2bJl4/vvv8cwDLZt20anTp1iraUI0R/kkCFD6NKli2WdgRQpUnD79m0iIyOxt7enWbNmNGvWzLIG5bPENZX7zp07XLp0iW7dutGtWzeuXLlChw4dyJcvHzdu3KB8+fJA9HDby5cvkyxZMsuxW7duxcPDg3Tp0nHx4kWr0Yn379/H0dEROzs7EiVKZMnIQ/T6m4/ftE+bYv40cU3lDg0N5fDhw3z55Zd8+eWX3Lp1i549ezJv3jzLFHyR16Fc2QykcEnE/4bupVdAYfbuu86q1eeYO8v6jwSVvd0YOnwvtWtmJW+elPwy9ShRkQbFiqa1qjd7zklKlUxPlizJSJTInrPn7nH7Tih7914nS5ZkiIi8CcpmApdEMHSXQUAJ2HcVVp+BWR9ZT7n2zmJieKBBzRwGeVLB1EPRCcyiT+wVMecolMwIWZKbSGRvcO4u3Ak12HsVsiS3Xb9ERLIUdidRUie2TV1Fab8aXDl2nlM7DlF30OdW9cKDw9g+7XdSZU1Pioyp2bd0CxGhEWTxcreqd3j1LjIVyIFLelccHB24c/kGIfcecuXYOVzSp7Jl1+QdkSdPnoQOwabep0Rs9erV8fPzY8mSJRQvXjxWbmz69Ol07NgRPz8/S9nZs2ctA9deREREBN999x21a9emVq1a1KpVixs3blCqVCkCAwPx8fGxjFh8mpjp2zHHxejfvz85cuSgefPmrzTm/+K5ac506dKRPn16jh49Sp48eUiXLp3ly2w2v1RSEqBSpUps377dKjm5YMECXF1dY43+e5qoqCimTJnCRx99FO/zpkyZkhEjRtC7d2+uXr1q9d6OHTv4/vvviYyMxGQykStXLpIkSfLUDzxfvnzUrFnTMtLRbDZTtWpVRo8ebRmNGBoayv79+59708QlLCyMTp06cfly9HTSNGnSkDp1ahwdHdm+fbvl5mrQoAHjx48nIiICgNOnT9OrVy/s7Oxo2rQpEyZM4Nq1a0D0wqX/+9//aNKkiaUPq1evtpxz8+bNlrUDXhWTyUS3bt0s6xSkTJkSNzc3jZaU1y5JYgcWzqvCseO3KeC1gC+/2srAfsUo4pWG7j130r3nTgCaN8tFu7b5+LzdJjwLz+fPtReYPcObpEkf/WXo3r0wpkw7SqcO0d8f6dI58VnrPJQqu4R1Gy7S4UvPBOmjiMiTEjuYmFfLxPGb4DXD4Kt1Bv1KRU+97rk5ip6bo39GaZYH2hY00e5Pg8LTDdaeNZjhYyKp46OfWe6FGUw7ZNDBK7osnbOJ1vlNlJ1jsOGcwZeFtb6kiNiOQyIzDYa25cbZf5ncbAB/jJhD+bZ1yJA7K+vHLWL9uEUA5K1SlIJ1yrD021/4qfG3nN51hDoDWmNO/Oj3j9CHIexfvp1iTSoB4OyanEJ1yjL9s2Gc3n2Uoo0qJkgfReTN5OXlhYuLC+PGjYs1jRvA1dWVrVu3curUKU6cOMGAAQPYu3fvS01xdnBw4PDhw3z77bfs37+f8+fPM2/ePMxmM/ny5QOiBwmeOXOGK1euxNlG1qxZ8fHxoU+fPmzatIlz584xatQo5s6da1lq8FXG/F/EOw169+5dhg0bRosWLciVKxdffPEFmzZtIkuWLEyaNImsWbO+0ImdnZ2ZOHEigwcPtowy9PDw4Pvvv7fUmTRpEgsWLLDUnzlzJnfu3MHX1xeTyURERASlSpWymqr8+FRugBYtWpA8ufWf83PmzImvry+DBg3ihx9+sJT7+voybNgw6tatS9KkSbGzs2P48OHP7Ee7du3YsGGD5XW3bt34+eef+eSTT3BwcOD+/ftUrlwZPz8/bt68yYkTJ6hfv76lfkBAABB7KnexYsXo2LEjvXv3xt/fHwcHByIjI6lQoQLFixdn/vz5ODtHL8b80Ucfce3aNZo1a4bZbCYyMpIRI0aQKlUqUqVKRefOnencuTORkZFERERQpUoVy8Y3n3/+Od9++y3169fH0dGRFClSMHDgwOd8enD79m2rfsTssP3kVO5s2bIxYMAARo8ezbfffmtJ+ubPn58GDRo89zwi/1W2D5Lz64xKscqHDf7Q8m+TyUSHLzzp8MXTk4vJkjmycW1tq7LnHSMiklA+cDEx46PYScPB5R79jGQymfiiMHzxjORiMkcTaxtbv/9FYdMzjxEReZ1SZExN3YGxN/H09rf+3aJYY2+KNfZ+ajuJnBLTfEIXq7KijStStLESkiISm52dHdWqVWPevHlxLtM3bNgwBgwYQL169UiePDnFixenS5cuTJw4keDg4Bc+33fffcfgwYNp27YtDx48IFeuXPz444+W3FurVq0YNGgQW7duZceOHXG2MWjQIL777jt69uzJvXv3yJEjB2PHjrUsa/iqY35ZJsMw4rVq+TfffMOhQ4cYN24cQUFB9OzZk6FDh/L7778THh7OxIkTX3es8p7Zs2cPXq928KaIyBvv0DFHPBZNTegwRERs5lgDP1aEeyR0GCIi/0lN8zE8Pd+vQQtBQUFPnb7+tPeOHT5EeALsnWc2gUe+9+vzeZM8616J94jJTZs2MXnyZLJnz86PP/5ImTJlqFGjBu7u7pY1FkVEREREREREROKi5KA8Kd5b6YSFhZEmTRoAtm/fTunSpYH/3732Fe/IIyIiIiIiIiIiIu+2eI+YzJ07N4sWLSJVqlTcunWLChUqEBYWxs8//0zu3LlfZ4wiIiIiIiIiIiLyjol3YrJ79+588cUX3Lp1i08//ZSMGTPSr18/1q1bZ7Vpi4iIiIiIiIiIiMjzxDsxWahQIbZu3cq9e/dwcXEB4NNPP6Vr164kTZr0tQUoIiIiIiIiIiIi754XWhwyKiqKbdu2MXbsWG7fvs3ly5cJCwt7XbGJiIiIiIiIiIjIOyreIyavXr1Ky5YtuXLlCiEhIdStW5epU6dy4MABpk+fTs6cOV9nnCIiIiIiIiIiIvIOifeIyaFDh5IrVy527txJokSJABgxYgSenp4MGzbstQUoIiIiIiIiIiIi7554JyZ37drFF198gaOjo6UsadKkdOnShX379r2O2EREREREREREROQdFe/EZEhICGazOVZ5WFgYhmG80qBERERERERERETk3RbvNSZLly7N5MmTGTJkiKXs3r17fP/995QoUeK1BCciIiIiIiIiIu+Gw0HHMCLDbX5ek72ZfHk8XugYb29vLl68aHltZ2eHs7MzhQoVomvXruTOnftVh/lUHh4eDB8+nDp16tjsnLYS78Rkjx49aNGiBWXLliU0NBR/f38uXLhAypQpmTp16uuMUURERERERERE3nJGZDgrwl8sQfgq1OTYSx33+eef07JlSwCioqK4fv06AwcOpHXr1qxZs4akSZO+yjCfauvWrSRPntwm57K1ZyYmjxw5goeHB/b29mTIkIFly5axYsUKgoKCMJvN5MyZk9q1a1s2wxEREREREREREXkXODk5kSZNGsvrdOnS0b17d5o0acLOnTupXLmyTeJ4PIZ3zTPXmGzevDnXrl0DoEWLFoSHh9OoUSO+/fZbevToQaNGjZSUFBERERERERGR94K9vT0Ajo6OeHh4MGbMGMqVK0e5cuW4du0ad+7coUePHpQoUYLixYvz+eef888//1iODwgIoHfv3gwbNoyiRYtSokQJfvzxR06cOEGTJk0oUKAAderU4eDBg5ZjPDw8WLp0qeX4Vq1aWcX0eNmuXbvInz8/mzdvplq1ahQoUIBWrVrx77//MmDAAIoUKUKpUqWYNGnS671Q8fTMEZNms5kFCxZQokQJAgMDCQwMxMXFJc66xYoVey0BioiIiIiIiIiIJLTz58/z3XffkSZNGry8vABYsGABkydPJjw8nNSpU9OkSROSJk3Kzz//TJIkSZg5cybNmjVj1apVpEyZEoAlS5bg6+vL4sWLWbFiBWPGjGHp0qX06NGDjBkz0qtXLwYMGMCCBQteKs7w8HB++OEHRo4cSUREBG3btqV27do0btyYhQsXsmzZMr777ju8vb3JmTPnK7s+L+OZiclPP/2UUaNG8eOPP2IymfD394+znslkIigo6LUEKCIiIiIiIiIiYmvjx49n8uTJQHSyLyIigrx58zJu3DjL+pL16tUjT548AGzfvp2DBw8SGBhoeb9///7s3LmT+fPn07ZtWwBcXV355ptvMJlMtGrVijFjxlCzZk0qVqwIQP369Rk+fPhLx20YBp07dyZ//vwAfPjhhxw6dIguXbpgMplo27Yt48eP58SJE292YrJNmzY0b96ce/fuUb58eX777TdcXV1tFZuIiIiIiIiIiEiC+OSTT2jWrBkQPYU7RYoUsTa8yZw5s+XfR44cITIykrJly1rVCQ0N5dSpU5bXWbJkwWQyAdHrWMaUxUicODFhYWH/KfbH23NyciJTpkyWcyZOnBjgP5/jVXjurtxOTk44OTkxY8YMcuXKhYNDvDfyFhEREREREREReSu5uLiQNWvWZ9Z5fO8Vs9lMihQpmD9/fqx6MQlIIM7cWkzS8GVERETEKjObzVav7eyeuc1MgnlmlrFPnz4EBATg7OzM8uXLWb58+VPrDhw48JUHJyIiIiIiIiIi8jbIlSsXt2/fBrAkNCMjI+natStVqlTBx8fnP5/DbDZz//59q7KzZ8/i7Oz8n9tOCM9MTJ45c4bIyEjLv5/mv2R1RZ7l0DHHhA5BRCTeTJERGPb/bWaBKTKCYw38XlFEIq9GpGHC3mQkdBjyDojrXooyTNQ0H0ugiEREXg2Tvfn5leSdV7JkSQoVKkSnTp3o1asXqVKlYtKkSWzYsIEvv/zylZyjUKFCLFq0iJUrV1KgQAGWLl3K8ePHKVy48Ctp39ae+dvTzJkz4/z34w4cOMCwYcNebVQi/8/TI+HXOxARia9DxxzxWDQ1ocMQeeWONfBjRbhHQoch74Ca5mN4enq+8HFBQUGWjQXk/aX7QHQPvP1M9mZqYvs/RtkqcWwymfjxxx8ZNmwYX3zxBWFhYeTJk4eff/75lW0yU7t2bYKCgujfvz8RERHUqFGDli1bcuDAgVfSvq2ZDMP4T3/+3rJlC23atNGu3PLK7dmzB6/8CR2FiEj8KTEp7yolJuVVUWJS/gvdB6J74M3yrM9Dn5U87ln3w5u58qWIiIiIiIiIiIi805SYFBEREREREREREZtTYlJERERERERERERs7pmb30ycOPG5DTxrt24RERERERERERGRuDwzMTl//vx4NZIhQ4ZXEoyIiIiIiIiIiLz9DMPAZDIldBiSwJ635/YzE5Pr169/pcGIiIiIiIiIiMi7zWw2ExwcjJOTU0KHIgksODgYs9n81Pe1xqSIiIiIiIiIiLwyadOm5eLFizx8+PC5I+bk3WQYBg8fPuTixYukTZv2qfWeOWJSRERERERERETkRSRPnhyAS5cuER4ensDRSEIxm82kS5fOcj/ERYlJERERERERERF5pZInT/7MhJQIaCq3iIiIiIiIiIiIJAAlJkVERERERERERMTmlJgUERERERERERERm1NiUkRERERERERERGxOiUkRERERERERERGxOSUmRURERERERERExOaUmBQRERERERERERGbU2JSREREREREREREbE6JSRF5q126/IAWfutxzzeXIiUW8fPUoGfW37L1Mm4fzOTBg3BLWe++gXh4zqVilWUcCbplKR8/8TDjJhx6bbGLyCMrThl4z4sizy9RVJ4fxerTRpz1phw0KP1rFPmnRtF2TRTXHj6q13dbFJ5To6gyP4qgG4/KJ+4zmLAv7vZE4nJ8y35mth3JhAa9mdX+O07tePb/BXt/28zKwTOtyjZNXMrERt8y64vvuH76sqX870Ub+WvBhtcSt4iIiMjbRolJEXlrGYZB6883kjOnC4f3NebXmZX4ftQBdv91Nc76t++E8vU3OzAey08cO36btesuEritPp80y8WYsQcBuHEzhMVLT/OZXx5bdEXkvfbPbYOuGw2GlzcR9Kkd/UqZ8F9rcDPYOpm44pTB6L8NfvA2saeFCfeU8Nnq6DrHbxqsOwvbmplolsfE2D3R5TeDDZaeNPDztHm35C116+I11o6aT+WvGtJ+0SDKtanNqqGzCb7zIFbd8JAwtk5ZyZZfVlqV3zj7L6cDg/Cb2gPP6iUInLsOgOA7Dzi2YS+F6pSxSV9ERERE3nQOCR3A2+78+fMMHz6c27dvEx4eTu7cuenatStJkyZl7NixpE6dmqZNm1od4+npSeHChQEICQmhTJkydOjQATs7O7y9vcmQIQN2do9yxt27d+fBgwd06tSJnDlzAvDgwQMyZcrEyJEjcXR0tNTdtWuXVT2AmjVr8vHHHxMaGoq3tzd+fn589tlnAFy4cIFKlSrRpUsX2rRpYzmmXbt2PHjwgJkzZxIQEICPjw/Zs2fn66+/Zv78+Vb9ebyfj/ctxsiRI0mTJg3Dhg3j+PHj2NnZYTab6dWrF5kzZ/4vl1/ec3v2XufKlWB6BRTG3t4OD/cULPutOq6uieOs36N3IHVqfcD4iYctZfb2JgAMDKvXI7/fj3/7fCRObP+aeyEi2VOY+LsFOJtNPAg3uPoQnB3B/MS336p/DJrlgSLpo79POxeFXw4aHL1hxKr7f+3de3zO9f/H8ee1I3a0Oc/W1+aYKaecT+lADOUQjckxZ1+H/IiSwr5JOVWoHENzFjmkoijHnBszlGZKDhtjzGzX9fn9sVy6bA6Fz7DH/XZzy+d9vT+f6/2+rlfbPPd+Xx/nv76NjtthqEd5i3K5WEyYCR4GeQPyq8u84XLL7a4rKam6ePa83PK4y+n6IpO0ctRsueZyU7nnqurS34JLp78K0PjrN2FXj7fO+1qVWz0pFzdXE2YCAABw/yOYvAOXL19Wz549NWrUKD3++OOSpGXLlmngwIH6+OOPb3iej4+P5szJ2O5jGIbefPNNzZs3TxEREZKkGTNmyN3d3eGcbdu2qVq1aho/fry9beDAgVq/fr0aNmzo0Pf6fletXbtWjRo10rJly9SpUyd7+BkUFKS1a9fag8lz584pLi5O+fLl+6cvicPc/m7Dhg06deqUZs6cKUn69ttvFRkZqSlTpvzj5wCu+jk6USVL+mhU5C4tXX5UXp6u6tu7nF5sGZKp79Jlvyop6YqG/l8Fh2CyeIiPmoQ9olr1liso0FMfTKilw4eTdCDmrCJHVjFzOkCO5uFq0bHzhurON2QY0ujaFnm5OYaJVkPK/beA0fLXn6NJ0nPBFoWFGKo331CglzShvkWHzxqKSZRGsjgN/5Bbbncl/Zmoz7qOkWFIT/Z6Qe55Mv/S65n+L8rT30db533tEEzmLVpAJWo/ps9eGSufQn569tU2Sjx2UmeOnlC9Hs+bOBMAAID7G1u578D333+vJ554wh5KStILL7ygs2fPKj4+/rauYbFY1LFjR61evfofPfeVK1d06tQp+fj43PY5ixYtUosWLVS6dGlt2LDB3p43b175+/vrl19+kSStXr06U9h5pwoVKqTo6GitXr1aiYmJeuqppzRx4sS7+hzIec6dS9XmLSfl5+eunzY31/j3auj1N3/Stu0nHfr9/sdFvfv+Xo0bWz3L6wwbUlHRu1/U6hWNFBLsrbdH79Trr1XU5/OP6NlGq9Sx63dKSLxsxpSAHK2Ip3Sos0XzwiwatcXQpt8dt3I/8x+LPo8xdCDB0BWroUm7pMtWKdWa8fiQqk7a/bKTVjR3UrCvRaO3GnqtqkXzD0qNltjUda0t0/Zw4Ea88vuo57JIvTC6q36ctlLxe49k6uPpf+Ofw2p2bKRXot5U6/F9lDcgv36cvkq1OjbS/rXbFdV3glaOnJ3l9nAAAICchBWTdyA+Pl5BQUGZ2osWLao//vjjtq+TL18+nT177YYbf1/N6OTkpNmzZ0uStm7dqoiICCUkJMjJyUkvvviiqlfPHLRc7XfVrFmzFB8fr5SUFJUuXVotWrTQjBkz9OSTT9r7NG7cWKtWrVLfvn21bt06DRgwQDt27LjtOVyVlJTk8NwFChTQ+++/r1KlSmnkyJFauHChRo0apUKFCmnIkCGqUoUVafj33Nyc5evrpj69ykmSnqhcQI2eC9JXXx9X1SoFJWWsSu43YJMGDyqvQgXzKD4++abX/H7jH8rj4aLgYt7q3vsHbVzXVDM/i9WHH0XrzTcq3/M5ATmZi1PGasiaAdJzwYa+/s1QzYBrKyRblLTo5EWp61eG0mxSx3IWlfCVvN0zX2tjvCEPF6mYj9T7W0PrWlv02X7poz2G3qjOtm7cmpNzxtbtwMeLK6RmOf26Zb8CHy9+i7OyFrfrkFxzu8knIJ/WjJmndlNf1b6Vm7Vj0XrV7tLkbg77gRATc/Mb1WXl8uXL/+o8PFyoA1ADwMOHYPIOFCxYUPv27cvU/ttvv6lIkSK3fZ3ff/9dhQoVsh9ntZVburZF++zZs+rUqZOKFi2a5fWy2sq9aNEipaSkqHPnzpKkXbt2KS4uTs5//dD99NNPq23btmrevLny58+vXLmy/oy+W7nRVu6DBw+qWLFiGjdunAzD0KZNm9SvXz9t2rRJFgv/QMS/ExLirUuX0pWebpOLS0aYb7Ma+vvdbX7/45J27jqjn/cn6rXXt8lmy2ivVG2JZs+or6pPFLD3tVpteufd3fpkch39FndBAUU85OHhqtCyfpo2nR+AgHtl/TFD0/YZ+jzs2kaONKvk7ebY7+RFQ02KSz0rZPRLSjU0YadU1t+xn9Vm6N3thiY/Y1Hc+YyVmB6uFpX1NzT953s9GzzofvspRru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      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot to show total sales in each customer segement\n",
    "# from https://github.com/kevwij/insidesherpa_quantium_virtual-experience/blob/master/Quantium%20-%20Module%201.ipynb\n",
    "# Values of each group\n",
    "bars1 = count_l_p[count_l_p.index.get_level_values(\"PREMIUM_CUSTOMER\") == \"Budget\"][\"CUSTOMER_COUNTS\"]\n",
    "bars2 = count_l_p[count_l_p.index.get_level_values(\"PREMIUM_CUSTOMER\") == \"Mainstream\"][\"CUSTOMER_COUNTS\"]\n",
    "bars3 = count_l_p[count_l_p.index.get_level_values(\"PREMIUM_CUSTOMER\") == \"Premium\"][\"CUSTOMER_COUNTS\"]\n",
    " \n",
    "bars1_text = (bars1 / sum(count_l_p[\"CUSTOMER_COUNTS\"])).apply(\"{:.1%}\".format)\n",
    "bars2_text = (bars2 / sum(count_l_p[\"CUSTOMER_COUNTS\"])).apply(\"{:.1%}\".format)\n",
    "bars3_text = (bars3 / sum(count_l_p[\"CUSTOMER_COUNTS\"])).apply(\"{:.1%}\".format)\n",
    " \n",
    "# Names of group and bar width\n",
    "names = count_l_p.index.get_level_values(\"LIFESTAGE\").unique()\n",
    "\n",
    "# The position of the bars on the x-axis\n",
    "r = np.arange(len(names))\n",
    "\n",
    "plt.figure(figsize=(18,7)) \n",
    "\n",
    "colors = ['salmon', 'lemonchiffon', 'lightskyblue']\n",
    "# Create yellow bars\n",
    "budget_bar = plt.barh(r, bars1, color=colors[1], edgecolor=\"lightgrey\", height=1, label=\"Budget\")\n",
    "# Create red bars (middle)\n",
    "mains_bar = plt.barh(r, bars2, left=bars1, color=colors[0], edgecolor=\"lightgrey\", height=1, label=\"Mainstream\")\n",
    "# Create blue bars (top)\n",
    "prem_bar = plt.barh(r, bars3, left=bars1.append(bars2).groupby([\"LIFESTAGE\"]).sum(), color=colors[2], edgecolor=\"lightgrey\", height=1, label=\"Premium\")\n",
    "    \n",
    "for i in range(7):\n",
    "    budget_width = budget_bar[i].get_width()\n",
    "    budget_main_width = budget_width + mains_bar[i].get_width()\n",
    "    plt.text(budget_width/2, i, bars1_text[i], va='center', ha='center', size=13, color='midnightblue')\n",
    "    plt.text(budget_width + mains_bar[i].get_width()/2, i, bars2_text[i], va='center', ha='center', size=13, color='midnightblue')\n",
    "    plt.text(budget_main_width + prem_bar[i].get_width()/2, i, bars3_text[i], va='center', ha='center', size=13, color='midnightblue')\n",
    "    \n",
    "# Custom X axis\n",
    "plt.yticks(r, names)\n",
    "plt.ylabel(\"Lifestage\",fontsize=15)\n",
    "plt.xlabel(\"Count\",fontsize=15)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5),fontsize=15)\n",
    "\n",
    "plt.title(\"Customer Count, by Lifestage and Customer Type\",fontsize=20)\n",
    "\n",
    "# Show graphic\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot to show customer count in each segment\n",
    "#plt.figure(figsize=(18,7))\n",
    "#plt.tick_params(labelsize=12)\n",
    "#sns.barplot(x=count_l_p.reset_index()[\"LIFESTAGE\"], y=count_l_p.reset_index()[\"CUSTOMER_COUNTS\"], \n",
    "#            hue=count_l_p.reset_index()[\"PREMIUM_CUSTOMER\"], \n",
    "#            palette=sns.color_palette(\"Paired\", 3))\n",
    "#plt.title(\"Customer Count, by Lifestage and Customer Type\",fontsize=20)\n",
    "#plt.legend(loc=0, fontsize=15, bbox_to_anchor=(0.14,0.98),borderaxespad = 0.)\n",
    "#plt.xlabel(\"Lifestage\",fontsize=15)\n",
    "#plt.ylabel(\"Count\",fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly find out that there are more Mainstream - young singles/couples and Mainstream - retirees who buy\n",
    "chips. This contributes to there being more sales to these customer segments but this is not a major driver for the Budget - Older families segment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3）Average Sales Quantity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Higher sales may also be driven by more units of chips being bought per customer. Let's have a look at this next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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></th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>CUSTOMER_COUNTS</th>\n",
       "      <th>SALES_QTY</th>\n",
       "      <th>AVG_SALES_QTY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">MIDAGE SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>33345.70</td>\n",
       "      <td>1504</td>\n",
       "      <td>8883</td>\n",
       "      <td>5.906250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>84734.25</td>\n",
       "      <td>3340</td>\n",
       "      <td>21213</td>\n",
       "      <td>6.351198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>54443.85</td>\n",
       "      <td>2431</td>\n",
       "      <td>14400</td>\n",
       "      <td>5.923488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">NEW FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>20607.45</td>\n",
       "      <td>1112</td>\n",
       "      <td>5241</td>\n",
       "      <td>4.713129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>15979.70</td>\n",
       "      <td>849</td>\n",
       "      <td>4060</td>\n",
       "      <td>4.782097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>10760.80</td>\n",
       "      <td>588</td>\n",
       "      <td>2769</td>\n",
       "      <td>4.709184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OLDER FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>156863.75</td>\n",
       "      <td>4675</td>\n",
       "      <td>41853</td>\n",
       "      <td>8.952513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>96413.55</td>\n",
       "      <td>2831</td>\n",
       "      <td>25804</td>\n",
       "      <td>9.114800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>75242.60</td>\n",
       "      <td>2274</td>\n",
       "      <td>20239</td>\n",
       "      <td>8.900176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OLDER SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>127833.60</td>\n",
       "      <td>4929</td>\n",
       "      <td>32883</td>\n",
       "      <td>6.671333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>124648.50</td>\n",
       "      <td>4930</td>\n",
       "      <td>32607</td>\n",
       "      <td>6.613996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>123537.55</td>\n",
       "      <td>4750</td>\n",
       "      <td>31695</td>\n",
       "      <td>6.672632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">RETIREES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>105916.30</td>\n",
       "      <td>4454</td>\n",
       "      <td>26932</td>\n",
       "      <td>6.046700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>145168.95</td>\n",
       "      <td>6479</td>\n",
       "      <td>37677</td>\n",
       "      <td>5.815249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>91296.65</td>\n",
       "      <td>3872</td>\n",
       "      <td>23266</td>\n",
       "      <td>6.008781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">YOUNG FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>129717.95</td>\n",
       "      <td>4017</td>\n",
       "      <td>34482</td>\n",
       "      <td>8.584018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>86338.25</td>\n",
       "      <td>2728</td>\n",
       "      <td>23194</td>\n",
       "      <td>8.502199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>78571.70</td>\n",
       "      <td>2433</td>\n",
       "      <td>20901</td>\n",
       "      <td>8.590629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">YOUNG SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>57122.10</td>\n",
       "      <td>3779</td>\n",
       "      <td>15500</td>\n",
       "      <td>4.101614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>147582.20</td>\n",
       "      <td>8088</td>\n",
       "      <td>36225</td>\n",
       "      <td>4.478858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>39052.30</td>\n",
       "      <td>2574</td>\n",
       "      <td>10575</td>\n",
       "      <td>4.108392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         TOT_SALES  CUSTOMER_COUNTS  \\\n",
       "LIFESTAGE              PREMIUM_CUSTOMER                               \n",
       "MIDAGE SINGLES/COUPLES Budget             33345.70             1504   \n",
       "                       Mainstream         84734.25             3340   \n",
       "                       Premium            54443.85             2431   \n",
       "NEW FAMILIES           Budget             20607.45             1112   \n",
       "                       Mainstream         15979.70              849   \n",
       "                       Premium            10760.80              588   \n",
       "OLDER FAMILIES         Budget            156863.75             4675   \n",
       "                       Mainstream         96413.55             2831   \n",
       "                       Premium            75242.60             2274   \n",
       "OLDER SINGLES/COUPLES  Budget            127833.60             4929   \n",
       "                       Mainstream        124648.50             4930   \n",
       "                       Premium           123537.55             4750   \n",
       "RETIREES               Budget            105916.30             4454   \n",
       "                       Mainstream        145168.95             6479   \n",
       "                       Premium            91296.65             3872   \n",
       "YOUNG FAMILIES         Budget            129717.95             4017   \n",
       "                       Mainstream         86338.25             2728   \n",
       "                       Premium            78571.70             2433   \n",
       "YOUNG SINGLES/COUPLES  Budget             57122.10             3779   \n",
       "                       Mainstream        147582.20             8088   \n",
       "                       Premium            39052.30             2574   \n",
       "\n",
       "                                         SALES_QTY  AVG_SALES_QTY  \n",
       "LIFESTAGE              PREMIUM_CUSTOMER                            \n",
       "MIDAGE SINGLES/COUPLES Budget                 8883       5.906250  \n",
       "                       Mainstream            21213       6.351198  \n",
       "                       Premium               14400       5.923488  \n",
       "NEW FAMILIES           Budget                 5241       4.713129  \n",
       "                       Mainstream             4060       4.782097  \n",
       "                       Premium                2769       4.709184  \n",
       "OLDER FAMILIES         Budget                41853       8.952513  \n",
       "                       Mainstream            25804       9.114800  \n",
       "                       Premium               20239       8.900176  \n",
       "OLDER SINGLES/COUPLES  Budget                32883       6.671333  \n",
       "                       Mainstream            32607       6.613996  \n",
       "                       Premium               31695       6.672632  \n",
       "RETIREES               Budget                26932       6.046700  \n",
       "                       Mainstream            37677       5.815249  \n",
       "                       Premium               23266       6.008781  \n",
       "YOUNG FAMILIES         Budget                34482       8.584018  \n",
       "                       Mainstream            23194       8.502199  \n",
       "                       Premium               20901       8.590629  \n",
       "YOUNG SINGLES/COUPLES  Budget                15500       4.101614  \n",
       "                       Mainstream            36225       4.478858  \n",
       "                       Premium               10575       4.108392  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average number of units per customer by those two dimensions\n",
    "merge_l_p = pd.merge(totsales_l_p,count_l_p,on=[\"LIFESTAGE\",\"PREMIUM_CUSTOMER\"])\n",
    "merge_l_p['SALES_QTY'] = merge_data.groupby([\"LIFESTAGE\",\"PREMIUM_CUSTOMER\"])[\"PROD_QTY\"].sum()\n",
    "merge_l_p['AVG_SALES_QTY'] = (merge_l_p[\"SALES_QTY\"]/merge_l_p[\"CUSTOMER_COUNTS\"])\n",
    "merge_l_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Sales Quantity')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the average number of units per customer by those two dimensions\n",
    "plt.figure(figsize=(18,7))\n",
    "plt.tick_params(labelsize=12)\n",
    "sns.barplot(x=merge_l_p.reset_index()[\"LIFESTAGE\"], y=merge_l_p.reset_index()[\"AVG_SALES_QTY\"], \n",
    "            hue=merge_l_p.reset_index()[\"PREMIUM_CUSTOMER\"], \n",
    "            palette=sns.color_palette(\"Paired\", 3))\n",
    "plt.title(\"Average Sales Quantity per Customer, by Lifestage and Customer Type\",fontsize=20)\n",
    "plt.legend(fontsize=15, bbox_to_anchor=(0.14,0.98),borderaxespad = 0.)\n",
    "plt.xlabel(\"Lifestage\",fontsize=15)\n",
    "plt.ylabel(\"Average Sales Quantity\",fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Older families and young families in general buy more chips per customer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4）Average sales price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also investigate the average price per unit chips bought for each customer segment as this is also a driver of total sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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></th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>CUSTOMER_COUNTS</th>\n",
       "      <th>SALES_QTY</th>\n",
       "      <th>AVG_SALES_QTY</th>\n",
       "      <th>AVG_SALES_PRICE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</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 rowspan=\"3\" valign=\"top\">MIDAGE SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>33345.70</td>\n",
       "      <td>1504</td>\n",
       "      <td>8883</td>\n",
       "      <td>5.906250</td>\n",
       "      <td>3.753878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>84734.25</td>\n",
       "      <td>3340</td>\n",
       "      <td>21213</td>\n",
       "      <td>6.351198</td>\n",
       "      <td>3.994449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>54443.85</td>\n",
       "      <td>2431</td>\n",
       "      <td>14400</td>\n",
       "      <td>5.923488</td>\n",
       "      <td>3.780823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">NEW FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>20607.45</td>\n",
       "      <td>1112</td>\n",
       "      <td>5241</td>\n",
       "      <td>4.713129</td>\n",
       "      <td>3.931969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>15979.70</td>\n",
       "      <td>849</td>\n",
       "      <td>4060</td>\n",
       "      <td>4.782097</td>\n",
       "      <td>3.935887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>10760.80</td>\n",
       "      <td>588</td>\n",
       "      <td>2769</td>\n",
       "      <td>4.709184</td>\n",
       "      <td>3.886168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OLDER FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>156863.75</td>\n",
       "      <td>4675</td>\n",
       "      <td>41853</td>\n",
       "      <td>8.952513</td>\n",
       "      <td>3.747969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>96413.55</td>\n",
       "      <td>2831</td>\n",
       "      <td>25804</td>\n",
       "      <td>9.114800</td>\n",
       "      <td>3.736380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>75242.60</td>\n",
       "      <td>2274</td>\n",
       "      <td>20239</td>\n",
       "      <td>8.900176</td>\n",
       "      <td>3.717703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OLDER SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>127833.60</td>\n",
       "      <td>4929</td>\n",
       "      <td>32883</td>\n",
       "      <td>6.671333</td>\n",
       "      <td>3.887529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>124648.50</td>\n",
       "      <td>4930</td>\n",
       "      <td>32607</td>\n",
       "      <td>6.613996</td>\n",
       "      <td>3.822753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>123537.55</td>\n",
       "      <td>4750</td>\n",
       "      <td>31695</td>\n",
       "      <td>6.672632</td>\n",
       "      <td>3.897698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">RETIREES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>105916.30</td>\n",
       "      <td>4454</td>\n",
       "      <td>26932</td>\n",
       "      <td>6.046700</td>\n",
       "      <td>3.932731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>145168.95</td>\n",
       "      <td>6479</td>\n",
       "      <td>37677</td>\n",
       "      <td>5.815249</td>\n",
       "      <td>3.852986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>91296.65</td>\n",
       "      <td>3872</td>\n",
       "      <td>23266</td>\n",
       "      <td>6.008781</td>\n",
       "      <td>3.924037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">YOUNG FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>129717.95</td>\n",
       "      <td>4017</td>\n",
       "      <td>34482</td>\n",
       "      <td>8.584018</td>\n",
       "      <td>3.761903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>86338.25</td>\n",
       "      <td>2728</td>\n",
       "      <td>23194</td>\n",
       "      <td>8.502199</td>\n",
       "      <td>3.722439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>78571.70</td>\n",
       "      <td>2433</td>\n",
       "      <td>20901</td>\n",
       "      <td>8.590629</td>\n",
       "      <td>3.759232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">YOUNG SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>57122.10</td>\n",
       "      <td>3779</td>\n",
       "      <td>15500</td>\n",
       "      <td>4.101614</td>\n",
       "      <td>3.685297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>147582.20</td>\n",
       "      <td>8088</td>\n",
       "      <td>36225</td>\n",
       "      <td>4.478858</td>\n",
       "      <td>4.074043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>39052.30</td>\n",
       "      <td>2574</td>\n",
       "      <td>10575</td>\n",
       "      <td>4.108392</td>\n",
       "      <td>3.692889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         TOT_SALES  CUSTOMER_COUNTS  \\\n",
       "LIFESTAGE              PREMIUM_CUSTOMER                               \n",
       "MIDAGE SINGLES/COUPLES Budget             33345.70             1504   \n",
       "                       Mainstream         84734.25             3340   \n",
       "                       Premium            54443.85             2431   \n",
       "NEW FAMILIES           Budget             20607.45             1112   \n",
       "                       Mainstream         15979.70              849   \n",
       "                       Premium            10760.80              588   \n",
       "OLDER FAMILIES         Budget            156863.75             4675   \n",
       "                       Mainstream         96413.55             2831   \n",
       "                       Premium            75242.60             2274   \n",
       "OLDER SINGLES/COUPLES  Budget            127833.60             4929   \n",
       "                       Mainstream        124648.50             4930   \n",
       "                       Premium           123537.55             4750   \n",
       "RETIREES               Budget            105916.30             4454   \n",
       "                       Mainstream        145168.95             6479   \n",
       "                       Premium            91296.65             3872   \n",
       "YOUNG FAMILIES         Budget            129717.95             4017   \n",
       "                       Mainstream         86338.25             2728   \n",
       "                       Premium            78571.70             2433   \n",
       "YOUNG SINGLES/COUPLES  Budget             57122.10             3779   \n",
       "                       Mainstream        147582.20             8088   \n",
       "                       Premium            39052.30             2574   \n",
       "\n",
       "                                         SALES_QTY  AVG_SALES_QTY  \\\n",
       "LIFESTAGE              PREMIUM_CUSTOMER                             \n",
       "MIDAGE SINGLES/COUPLES Budget                 8883       5.906250   \n",
       "                       Mainstream            21213       6.351198   \n",
       "                       Premium               14400       5.923488   \n",
       "NEW FAMILIES           Budget                 5241       4.713129   \n",
       "                       Mainstream             4060       4.782097   \n",
       "                       Premium                2769       4.709184   \n",
       "OLDER FAMILIES         Budget                41853       8.952513   \n",
       "                       Mainstream            25804       9.114800   \n",
       "                       Premium               20239       8.900176   \n",
       "OLDER SINGLES/COUPLES  Budget                32883       6.671333   \n",
       "                       Mainstream            32607       6.613996   \n",
       "                       Premium               31695       6.672632   \n",
       "RETIREES               Budget                26932       6.046700   \n",
       "                       Mainstream            37677       5.815249   \n",
       "                       Premium               23266       6.008781   \n",
       "YOUNG FAMILIES         Budget                34482       8.584018   \n",
       "                       Mainstream            23194       8.502199   \n",
       "                       Premium               20901       8.590629   \n",
       "YOUNG SINGLES/COUPLES  Budget                15500       4.101614   \n",
       "                       Mainstream            36225       4.478858   \n",
       "                       Premium               10575       4.108392   \n",
       "\n",
       "                                         AVG_SALES_PRICE  \n",
       "LIFESTAGE              PREMIUM_CUSTOMER                   \n",
       "MIDAGE SINGLES/COUPLES Budget                   3.753878  \n",
       "                       Mainstream               3.994449  \n",
       "                       Premium                  3.780823  \n",
       "NEW FAMILIES           Budget                   3.931969  \n",
       "                       Mainstream               3.935887  \n",
       "                       Premium                  3.886168  \n",
       "OLDER FAMILIES         Budget                   3.747969  \n",
       "                       Mainstream               3.736380  \n",
       "                       Premium                  3.717703  \n",
       "OLDER SINGLES/COUPLES  Budget                   3.887529  \n",
       "                       Mainstream               3.822753  \n",
       "                       Premium                  3.897698  \n",
       "RETIREES               Budget                   3.932731  \n",
       "                       Mainstream               3.852986  \n",
       "                       Premium                  3.924037  \n",
       "YOUNG FAMILIES         Budget                   3.761903  \n",
       "                       Mainstream               3.722439  \n",
       "                       Premium                  3.759232  \n",
       "YOUNG SINGLES/COUPLES  Budget                   3.685297  \n",
       "                       Mainstream               4.074043  \n",
       "                       Premium                  3.692889  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average price per unit sold (average sale price) by those two customer dimensions\n",
    "merge_l_p['AVG_SALES_PRICE'] = (merge_l_p[\"TOT_SALES\"]/merge_l_p[\"SALES_QTY\"])\n",
    "merge_l_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Sales Price')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the average price per unit sold (average sale price) by those two customer dimensions.\n",
    "plt.figure(figsize=(18,7))\n",
    "plt.tick_params(labelsize=12)\n",
    "sns.barplot(x=merge_l_p.reset_index()[\"LIFESTAGE\"], y=merge_l_p.reset_index()[\"AVG_SALES_PRICE\"], \n",
    "            hue=merge_l_p.reset_index()[\"PREMIUM_CUSTOMER\"], \n",
    "            palette=sns.color_palette(\"Paired\", 3))\n",
    "plt.title(\"Average Sales Price per Customer, by Lifestage and Customer Type\",fontsize=20)\n",
    "plt.legend(fontsize=15, bbox_to_anchor=(1.02,0.98),borderaxespad = 0.)\n",
    "plt.xlabel(\"Lifestage\",fontsize=15)\n",
    "plt.ylabel(\"Average Sales Price\",fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mainstream midage and young singles/couples are more willing to pay more per packet of chips compared to their budget and premium counterparts. This may be due to premium shoppers being more likely to buy healthy snacks and when they buy chips, this is mainly for entertainment purposes rather than their own consumption. This is also supported by there being fewer premium midage and young singles and couples buying chips compared to their mainstream counterparts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the difference in average price per unit isn't large, we can check if this difference is statistically different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "# Perform an independent t-test between mainstream vs premium and budget midage and young singles and couples\n",
    "new_merge=merge_l_p.reset_index()\n",
    "mainstream = new_merge.loc[new_merge[\"PREMIUM_CUSTOMER\"]==\"Mainstream\"]\n",
    "others = new_merge.loc[(new_merge[\"PREMIUM_CUSTOMER\"]==\"Premium\")|(new_merge[\"PREMIUM_CUSTOMER\"]==\"Budget\")]\n",
    "main_mid_young = mainstream[\"AVG_SALES_PRICE\"][(new_merge[\"LIFESTAGE\"]==\"MIDAGE SINGLES/COUPLES\")|(new_merge[\"LIFESTAGE\"]==\"YOUNG SINGLES/COUPLES\")]\n",
    "others_mid_young = others[\"AVG_SALES_PRICE\"][(new_merge[\"LIFESTAGE\"]==\"MIDAGE SINGLES/COUPLES\")|(new_merge[\"LIFESTAGE\"]==\"YOUNG SINGLES/COUPLES\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=0.00490158871604531, pvalue=0.9475450406275435)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Homogeneity of variance test\n",
    "stats.levene(main_mid_young,others_mid_young)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "p > 0.05 indicates that the variance of them are different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=6.635796901881934, pvalue=0.031128004009134885)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Independent T-test\n",
    "stats.ttest_ind(main_mid_young,others_mid_young, equal_var=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The t-test results in a p-value of 0.03 (< 0.05) show the statistical significant (95% confidence), i.e. the unit price for mainstream, young and mid-age singles and couples are significantly higher than that of budget or premium, young and midage singles and couples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Deep dive into specific customer segments for insights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have found quite a few interesting insights that we can dive deeper into.<br>\n",
    "We might want to target customer segments that contribute the most to sales to retain them or further increase sales. Let's look at Mainstream - young singles/couples. For instance, let's find out if they tend to buy a particular brand of chips. Besides, let's also find out if our target segment tends to buy larger packs of chips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>LYLTY_CARD_NBR</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>PROD_QTY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>221345</th>\n",
       "      <td>1020</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>Smiths</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221346</th>\n",
       "      <td>1020</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>Smiths</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221347</th>\n",
       "      <td>1020</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>Grain Waves</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221348</th>\n",
       "      <td>1163</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>Kettle</td>\n",
       "      <td>175.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221349</th>\n",
       "      <td>1163</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>Natural Chip Co</td>\n",
       "      <td>175.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        LYLTY_CARD_NBR              LIFESTAGE PREMIUM_CUSTOMER  \\\n",
       "221345            1020  YOUNG SINGLES/COUPLES       Mainstream   \n",
       "221346            1020  YOUNG SINGLES/COUPLES       Mainstream   \n",
       "221347            1020  YOUNG SINGLES/COUPLES       Mainstream   \n",
       "221348            1163  YOUNG SINGLES/COUPLES       Mainstream   \n",
       "221349            1163  YOUNG SINGLES/COUPLES       Mainstream   \n",
       "\n",
       "                  BRAND  PACK_SIZE  PROD_QTY  \n",
       "221345           Smiths      150.0         1  \n",
       "221346           Smiths      330.0         1  \n",
       "221347      Grain Waves      180.0         1  \n",
       "221348           Kettle      175.0         1  \n",
       "221349  Natural Chip Co      175.0         1  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Brands and package sizes of 'Mainstream - young singles/couples' Segment and other segments\n",
    "s = pd.DataFrame(merge_data.loc[:,[\"LYLTY_CARD_NBR\",\"LIFESTAGE\",\"PREMIUM_CUSTOMER\",\"BRAND\",\"PACK_SIZE\",\"PROD_QTY\"]])\n",
    "m_ysc = s[(s[\"LIFESTAGE\"]==\"YOUNG SINGLES/COUPLES\")&(s[\"PREMIUM_CUSTOMER\"]==\"Mainstream\")]\n",
    "m_ysc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kettle      3844\n",
       "Doritos     2379\n",
       "Pringles    2315\n",
       "Name: BRAND, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most popular Brands and Package Sizes in this segment\n",
    "# Most popular brands\n",
    "m_ysc[\"BRAND\"].value_counts().head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "175.0    4997\n",
       "150.0    3080\n",
       "134.0    2315\n",
       "Name: PACK_SIZE, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most popular package sizes\n",
    "m_ysc[\"PACK_SIZE\"].value_counts().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best-selling brands in 'Mainstream - young singles/couples' Segment are Kettle, Doritos, and Pringles.<br>\n",
    "For package sizes, 175g, 150g, and 134g are the most popular choices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>BRAND</th>\n",
       "      <th>Burger</th>\n",
       "      <th>CCs</th>\n",
       "      <th>Cheetos</th>\n",
       "      <th>Cheezels</th>\n",
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       "</table>\n",
       "<p>7917 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "BRAND           Burger  CCs  Cheetos  Cheezels  Cobs  Doritos  French  \\\n",
       "LYLTY_CARD_NBR                                                          \n",
       "1002                 0    0        0         0     0        0       0   \n",
       "1010                 0    0        0         0     0        1       0   \n",
       "1018                 0    0        0         0     0        0       0   \n",
       "1020                 0    0        0         0     0        0       0   \n",
       "1060                 0    0        0         0     0        1       0   \n",
       "...                ...  ...      ...       ...   ...      ...     ...   \n",
       "272391               0    0        0         0     0        0       0   \n",
       "2330041              0    0        0         0     0        0       0   \n",
       "2330321              0    0        0         0     0        0       0   \n",
       "2370181              0    0        0         0     0        0       0   \n",
       "2373711              0    0        0         0     0        0       0   \n",
       "\n",
       "BRAND           Grain Waves  Infuzions  Kettle  Natural Chip Co  Pringles  \\\n",
       "LYLTY_CARD_NBR                                                              \n",
       "1002                      0          0       0                0         0   \n",
       "1010                      0          0       0                0         0   \n",
       "1018                      0          1       1                0         0   \n",
       "1020                      1          0       0                0         0   \n",
       "1060                      0          0       0                0         0   \n",
       "...                     ...        ...     ...              ...       ...   \n",
       "272391                    0          0       1                0         0   \n",
       "2330041                   1          0       0                0         0   \n",
       "2330321                   0          0       0                0         0   \n",
       "2370181                   0          0       1                0         0   \n",
       "2373711                   0          0       0                0         0   \n",
       "\n",
       "BRAND           Red Rock Deli  Smiths  Sunbites  Thins  Tostitos  Twisties  \\\n",
       "LYLTY_CARD_NBR                                                               \n",
       "1002                        1       0         0      0         0         0   \n",
       "1010                        0       0         0      0         0         0   \n",
       "1018                        1       0         0      0         0         0   \n",
       "1020                        0       1         0      0         0         0   \n",
       "1060                        0       0         0      0         0         1   \n",
       "...                       ...     ...       ...    ...       ...       ...   \n",
       "272391                      0       0         0      0         0         0   \n",
       "2330041                     0       0         0      0         0         0   \n",
       "2330321                     0       0         0      0         0         1   \n",
       "2370181                     0       0         0      0         0         0   \n",
       "2373711                     0       1         0      0         0         0   \n",
       "\n",
       "BRAND           Tyrrells  Woolworths  \n",
       "LYLTY_CARD_NBR                        \n",
       "1002                   0           0  \n",
       "1010                   0           0  \n",
       "1018                   0           0  \n",
       "1020                   0           0  \n",
       "1060                   1           0  \n",
       "...                  ...         ...  \n",
       "272391                 0           0  \n",
       "2330041                0           0  \n",
       "2330321                0           0  \n",
       "2370181                0           0  \n",
       "2373711                0           0  \n",
       "\n",
       "[7917 rows x 20 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apriori analysis on brands\n",
    "# Calculate sales quantity per brand per custermor in target segment\n",
    "brand_s = m_ysc.groupby([\"LYLTY_CARD_NBR\",\"BRAND\"])[\"PROD_QTY\"].sum().unstack()\n",
    "# Turn the shopping quantity into a 0/1 variable, i.e., whether to buy the item\n",
    "basket_b = brand_s.applymap(lambda x : 1 if x>0 else 0)\n",
    "basket_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5</th>\n",
       "      <td>0.387142</td>\n",
       "      <td>(Kettle)</td>\n",
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       "      <th>10</th>\n",
       "      <td>0.136036</td>\n",
       "      <td>(Thins)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.107364</td>\n",
       "      <td>(Twisties)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.105848</td>\n",
       "      <td>(Tostitos)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.103827</td>\n",
       "      <td>(Cobs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.093975</td>\n",
       "      <td>(Red Rock Deli)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.091449</td>\n",
       "      <td>(Pringles, Kettle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.089933</td>\n",
       "      <td>(Doritos, Kettle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.078818</td>\n",
       "      <td>(Grain Waves)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.076165</td>\n",
       "      <td>(Tyrrells)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.075660</td>\n",
       "      <td>(Kettle, Smiths)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.062650</td>\n",
       "      <td>(Doritos, Pringles)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.053808</td>\n",
       "      <td>(Woolworths)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.051282</td>\n",
       "      <td>(Thins, Kettle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.051156</td>\n",
       "      <td>(Doritos, Smiths)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.049766</td>\n",
       "      <td>(Kettle, Infuzions)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.047114</td>\n",
       "      <td>(Pringles, Smiths)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.046482</td>\n",
       "      <td>(Natural Chip Co)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.042440</td>\n",
       "      <td>(Cheezels)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.041051</td>\n",
       "      <td>(Pringles, Infuzions)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     support               itemsets\n",
       "5   0.387142               (Kettle)\n",
       "2   0.260326              (Doritos)\n",
       "7   0.256158             (Pringles)\n",
       "9   0.202476               (Smiths)\n",
       "4   0.143110            (Infuzions)\n",
       "10  0.136036                (Thins)\n",
       "12  0.107364             (Twisties)\n",
       "11  0.105848             (Tostitos)\n",
       "1   0.103827                 (Cobs)\n",
       "8   0.093975        (Red Rock Deli)\n",
       "20  0.091449     (Pringles, Kettle)\n",
       "15  0.089933      (Doritos, Kettle)\n",
       "3   0.078818          (Grain Waves)\n",
       "13  0.076165             (Tyrrells)\n",
       "21  0.075660       (Kettle, Smiths)\n",
       "16  0.062650    (Doritos, Pringles)\n",
       "14  0.053808           (Woolworths)\n",
       "22  0.051282        (Thins, Kettle)\n",
       "17  0.051156      (Doritos, Smiths)\n",
       "18  0.049766    (Kettle, Infuzions)\n",
       "23  0.047114     (Pringles, Smiths)\n",
       "6   0.046482      (Natural Chip Co)\n",
       "0   0.042440             (Cheezels)\n",
       "19  0.041051  (Pringles, Infuzions)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules\n",
    "\n",
    "# Frequent itemsets of our target segment\n",
    "frequent_itemsets = apriori(basket_b, min_support=0.04, use_colnames=True)\n",
    "frequent_itemsets.sort_values(by=\"support\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Infuzions)</td>\n",
       "      <td>(Pringles)</td>\n",
       "      <td>0.143110</td>\n",
       "      <td>0.256158</td>\n",
       "      <td>0.041051</td>\n",
       "      <td>0.286849</td>\n",
       "      <td>1.119815</td>\n",
       "      <td>0.004392</td>\n",
       "      <td>1.043036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Pringles)</td>\n",
       "      <td>(Infuzions)</td>\n",
       "      <td>0.256158</td>\n",
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      ],
      "text/plain": [
       "   antecedents  consequents  antecedent support  consequent support   support  \\\n",
       "1  (Infuzions)   (Pringles)            0.143110            0.256158  0.041051   \n",
       "0   (Pringles)  (Infuzions)            0.256158            0.143110  0.041051   \n",
       "\n",
       "   confidence      lift  leverage  conviction  \n",
       "1    0.286849  1.119815  0.004392    1.043036  \n",
       "0    0.160256  1.119815  0.004392    1.020419  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output of apriori analysis\n",
    "association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1).sort_values(by=[\"lift\",\"confidence\",\"support\"],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the Frequent itemsets, we clearly see that customers in our target segement(Mainstream - young singles/couples) prefer Kettle more than other brands.<br>\n",
    "We can also prove an association among chips of different brands from the output of apriori analysis. More specifically, since lift > 1, which means that buying chips of one brand will boost sales of another brand chips, we can put chips of Infuzions and Pringles together for sale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
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       "PACK_SIZE       70.0   90.0   110.0  125.0  134.0  135.0  150.0  160.0  165.0  \\\n",
       "LYLTY_CARD_NBR                                                                  \n",
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       "PACK_SIZE       170.0  175.0  180.0  190.0  200.0  210.0  220.0  250.0  270.0  \\\n",
       "LYLTY_CARD_NBR                                                                  \n",
       "1002                0      0      0      0      0      0      0      0      0   \n",
       "1010                1      0      0      0      0      0      0      0      0   \n",
       "1018                0      0      0      0      0      0      0      0      0   \n",
       "1020                0      0      1      0      0      0      0      0      0   \n",
       "1060                1      0      0      0      0      0      0      0      1   \n",
       "...               ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "272391              0      0      0      0      0      0      0      0      0   \n",
       "2330041             0      0      0      0      0      1      0      0      0   \n",
       "2330321             0      0      0      0      0      0      0      1      0   \n",
       "2370181             0      1      0      0      0      0      0      0      0   \n",
       "2373711             0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "PACK_SIZE       330.0  380.0  \n",
       "LYLTY_CARD_NBR                \n",
       "1002                0      0  \n",
       "1010                0      0  \n",
       "1018                0      0  \n",
       "1020                1      0  \n",
       "1060                0      0  \n",
       "...               ...    ...  \n",
       "272391              0      0  \n",
       "2330041             0      0  \n",
       "2330321             0      0  \n",
       "2370181             0      0  \n",
       "2373711             1      0  \n",
       "\n",
       "[7917 rows x 20 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apriori analysis on package size\n",
    "# Calculate sales quantity per package size per custermor in target segment\n",
    "brand_s = m_ysc.groupby([\"LYLTY_CARD_NBR\",\"PACK_SIZE\"])[\"PROD_QTY\"].sum().unstack()\n",
    "# Turn the shopping quantity into a 0/1 variable, i.e., whether to buy the item\n",
    "basket_ps = brand_s.applymap(lambda x : 1 if x>0 else 0)\n",
    "basket_ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.458254</td>\n",
       "      <td>(175.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.313755</td>\n",
       "      <td>(150.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.256158</td>\n",
       "      <td>(134.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.224454</td>\n",
       "      <td>(110.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.176835</td>\n",
       "      <td>(170.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.139320</td>\n",
       "      <td>(330.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.137931</td>\n",
       "      <td>(150.0, 175.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.127700</td>\n",
       "      <td>(165.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.111153</td>\n",
       "      <td>(134.0, 175.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.094480</td>\n",
       "      <td>(110.0, 175.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.080586</td>\n",
       "      <td>(170.0, 175.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.076418</td>\n",
       "      <td>(380.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.075028</td>\n",
       "      <td>(150.0, 134.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.074902</td>\n",
       "      <td>(270.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.070102</td>\n",
       "      <td>(210.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.065303</td>\n",
       "      <td>(150.0, 110.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.060124</td>\n",
       "      <td>(165.0, 175.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     support        itemsets\n",
       "5   0.458254         (175.0)\n",
       "2   0.313755         (150.0)\n",
       "1   0.256158         (134.0)\n",
       "0   0.224454         (110.0)\n",
       "4   0.176835         (170.0)\n",
       "8   0.139320         (330.0)\n",
       "14  0.137931  (150.0, 175.0)\n",
       "3   0.127700         (165.0)\n",
       "13  0.111153  (134.0, 175.0)\n",
       "11  0.094480  (110.0, 175.0)\n",
       "16  0.080586  (170.0, 175.0)\n",
       "9   0.076418         (380.0)\n",
       "12  0.075028  (150.0, 134.0)\n",
       "7   0.074902         (270.0)\n",
       "6   0.070102         (210.0)\n",
       "10  0.065303  (150.0, 110.0)\n",
       "15  0.060124  (165.0, 175.0)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Frequent itemsets of our target segment\n",
    "frequent_itemsets_ps = apriori(basket_ps, min_support=0.06, use_colnames=True)\n",
    "frequent_itemsets_ps.sort_values(by=\"support\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(165.0)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.127700</td>\n",
       "      <td>0.458254</td>\n",
       "      <td>0.060124</td>\n",
       "      <td>0.470821</td>\n",
       "      <td>1.027423</td>\n",
       "      <td>0.001605</td>\n",
       "      <td>1.023747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(175.0)</td>\n",
       "      <td>(165.0)</td>\n",
       "      <td>0.458254</td>\n",
       "      <td>0.127700</td>\n",
       "      <td>0.060124</td>\n",
       "      <td>0.131202</td>\n",
       "      <td>1.027423</td>\n",
       "      <td>0.001605</td>\n",
       "      <td>1.004031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  antecedents consequents  antecedent support  consequent support   support  \\\n",
       "0     (165.0)     (175.0)            0.127700            0.458254  0.060124   \n",
       "1     (175.0)     (165.0)            0.458254            0.127700  0.060124   \n",
       "\n",
       "   confidence      lift  leverage  conviction  \n",
       "0    0.470821  1.027423  0.001605    1.023747  \n",
       "1    0.131202  1.027423  0.001605    1.004031  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output of apriori analysis\n",
    "association_rules(frequent_itemsets_ps, metric=\"lift\", min_threshold=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our target segement(Mainstream - young singles/couples) prefer chips with 175g package size, which is a regular size, more than others. Similarly, there is an association among chips of different size, which recommands us to put chips of 175g and 165g together for sales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Initial findings & Recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1）Finding Summary<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Brands : <br>\n",
    "#####  Top 3 best-selling brands :<br>\n",
    "* Kettle<br>\n",
    "* Smiths<br>\n",
    "* Doritos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  Best-selling package sizes :<br>\n",
    "* 175g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Customers : <br>\n",
    "#####  Top 3 Lifestage :<br>\n",
    "* Retirees 20.38%<br>\n",
    "* Older Singles/Couples 20.11%<br>\n",
    "* Young Singles/Couples 19.88%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  Customer Type :<br>\n",
    "* Mainstream 40.26%<br>\n",
    "* Budget 33.69%<br>\n",
    "* Premium 26.05%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  Total Sales - Top 3 sales driver segments :<br>\n",
    "* Budget - Older Families (higher quantity per customer in older families)<br>\n",
    "* Mainstream - Young Singles/Couples (higher customer counts and higher average purchase price)<br>\n",
    "* Mainstream - Retirees (higher customer counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  Other facts :<br>\n",
    "* Customer counts : There are more Mainstream - young singles/couples and Mainstream - retirees who buy chips.<br>\n",
    "* Average purchase quantity : Older families and young families in general buy more chips per customer.<br>\n",
    "* Average purchase price : Mainstream midage and young singles/couples are more willing to pay more per packet of chips compared to their budget and premium counterparts (statistical significant)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Target segment : Mainstream - Young Singles/Couples<br>\n",
    "#####  Top 3 best-selling brands :<br>\n",
    "* Kettle<br>\n",
    "* Doritos<br>\n",
    "* Pringles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  Top 3 best-selling package sizes :<br>\n",
    "* 175g<br>\n",
    "* 150g<br>\n",
    "* 134g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2）Recommendations<br>\n",
    "#### General :<br>\n",
    "* Increase sales promotion of Kettle (all size) and 175g (all brands) chips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Target segments : <br>\n",
    "##### Budget - Older Families :<br>\n",
    "* Promotion like 'Buy Two Get One Free' : <br>  Because of their higher average purchase quantity, promotions like this can boost sales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Mainstream - Young Singles/Couples :<br>\n",
    "* Target advertisements : <br>  This segment has the majority of customers and thus should be the focus of our marketing. Targeted advertising around areas such as colleges and universities is probably a good strategy.<br>\n",
    "* Change the display area : <br>  Placing some chips next to other young people’s daily necessities will also attract their attention, thereby increasing sales.<br>\n",
    "* Promotion and repackaging on popular brands and sizes : <br>  In addition to Kettle and 175g chips, which is popular among all segments, Doritos and Pringles are popular in this segement. Therefore, we can increase promotion of these chips as well as advice their productors and Suppliers to repackage their products. For instance, they can use more vibrant colors and fonts to gain love from young people. Meanwhile, similar strategy is also useful for 150g and 134g chips.<br>\n",
    "* Strategies metioned above can be used in combination to maximize the effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Mainstream - Retirees :<br>\n",
    "* Target advertisements : <br>  Similar to last segment, we can put targeted advertising because of the large number of customers. \n",
    "* Limited time sales promotions during daytime : <br>  Retired customers are more inclined to shop during daytime, so we can choose products that are popular in this segment for promotion when these customers visit."
   ]
  }
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