Cohort Analysis with Python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0918deef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f5b2f7",
   "metadata": {},
   "source": [
    "#### Loading Transaction Data into a Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3961248f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   InvoiceNo    541909 non-null  object        \n",
      " 1   StockCode    541909 non-null  object        \n",
      " 2   Description  540455 non-null  object        \n",
      " 3   Quantity     541909 non-null  int64         \n",
      " 4   InvoiceDate  541909 non-null  datetime64[ns]\n",
      " 5   UnitPrice    541909 non-null  float64       \n",
      " 6   CustomerID   406829 non-null  float64       \n",
      " 7   Country      541909 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_excel(\"Online Retail.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c02f2691",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "079cc504",
   "metadata": {},
   "source": [
    "#### Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee54ab3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 406829 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   InvoiceNo    406829 non-null  object        \n",
      " 1   StockCode    406829 non-null  object        \n",
      " 2   Description  406829 non-null  object        \n",
      " 3   Quantity     406829 non-null  int64         \n",
      " 4   InvoiceDate  406829 non-null  datetime64[ns]\n",
      " 5   UnitPrice    406829 non-null  float64       \n",
      " 6   CustomerID   406829 non-null  float64       \n",
      " 7   Country      406829 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 27.9+ MB\n"
     ]
    }
   ],
   "source": [
    "#Cleaning Data\n",
    "#Droping rows with no CustomerID\n",
    "\n",
    "df = df.dropna(subset=['CustomerID'])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5830bf78",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>InvoiceMonth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country InvoiceMonth  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom   2010-12-01  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom   2010-12-01  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom   2010-12-01  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom   2010-12-01  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom   2010-12-01  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting only the month and year of the Invoice Date\n",
    "\n",
    "#Function for month\n",
    "def get_month(x):\n",
    "    return datetime.datetime(x.year, x.month,1)\n",
    "\n",
    "df['InvoiceMonth'] = df['InvoiceDate'].apply(get_month)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04eb34b1",
   "metadata": {},
   "source": [
    "#### Separating months into Cohort indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "498cfe67",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>InvoiceMonth</th>\n",
       "      <th>Cohort Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>541899</th>\n",
       "      <td>581587</td>\n",
       "      <td>22726</td>\n",
       "      <td>ALARM CLOCK BAKELIKE GREEN</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541900</th>\n",
       "      <td>581587</td>\n",
       "      <td>22730</td>\n",
       "      <td>ALARM CLOCK BAKELIKE IVORY</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541901</th>\n",
       "      <td>581587</td>\n",
       "      <td>22367</td>\n",
       "      <td>CHILDRENS APRON SPACEBOY DESIGN</td>\n",
       "      <td>8</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>1.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541902</th>\n",
       "      <td>581587</td>\n",
       "      <td>22629</td>\n",
       "      <td>SPACEBOY LUNCH BOX</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>1.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541903</th>\n",
       "      <td>581587</td>\n",
       "      <td>23256</td>\n",
       "      <td>CHILDRENS CUTLERY SPACEBOY</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>581587</td>\n",
       "      <td>22613</td>\n",
       "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541905</th>\n",
       "      <td>581587</td>\n",
       "      <td>22899</td>\n",
       "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541906</th>\n",
       "      <td>581587</td>\n",
       "      <td>23254</td>\n",
       "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541907</th>\n",
       "      <td>581587</td>\n",
       "      <td>23255</td>\n",
       "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541908</th>\n",
       "      <td>581587</td>\n",
       "      <td>22138</td>\n",
       "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                      Description  Quantity  \\\n",
       "541899    581587     22726       ALARM CLOCK BAKELIKE GREEN         4   \n",
       "541900    581587     22730       ALARM CLOCK BAKELIKE IVORY         4   \n",
       "541901    581587     22367  CHILDRENS APRON SPACEBOY DESIGN         8   \n",
       "541902    581587     22629              SPACEBOY LUNCH BOX         12   \n",
       "541903    581587     23256      CHILDRENS CUTLERY SPACEBOY          4   \n",
       "541904    581587     22613      PACK OF 20 SPACEBOY NAPKINS        12   \n",
       "541905    581587     22899     CHILDREN'S APRON DOLLY GIRL          6   \n",
       "541906    581587     23254    CHILDRENS CUTLERY DOLLY GIRL          4   \n",
       "541907    581587     23255  CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
       "541908    581587     22138    BAKING SET 9 PIECE RETROSPOT          3   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID Country InvoiceMonth  \\\n",
       "541899 2011-12-09 12:50:00       3.75     12680.0  France   2011-12-01   \n",
       "541900 2011-12-09 12:50:00       3.75     12680.0  France   2011-12-01   \n",
       "541901 2011-12-09 12:50:00       1.95     12680.0  France   2011-12-01   \n",
       "541902 2011-12-09 12:50:00       1.95     12680.0  France   2011-12-01   \n",
       "541903 2011-12-09 12:50:00       4.15     12680.0  France   2011-12-01   \n",
       "541904 2011-12-09 12:50:00       0.85     12680.0  France   2011-12-01   \n",
       "541905 2011-12-09 12:50:00       2.10     12680.0  France   2011-12-01   \n",
       "541906 2011-12-09 12:50:00       4.15     12680.0  France   2011-12-01   \n",
       "541907 2011-12-09 12:50:00       4.15     12680.0  France   2011-12-01   \n",
       "541908 2011-12-09 12:50:00       4.95     12680.0  France   2011-12-01   \n",
       "\n",
       "       Cohort Month  \n",
       "541899   2011-08-01  \n",
       "541900   2011-08-01  \n",
       "541901   2011-08-01  \n",
       "541902   2011-08-01  \n",
       "541903   2011-08-01  \n",
       "541904   2011-08-01  \n",
       "541905   2011-08-01  \n",
       "541906   2011-08-01  \n",
       "541907   2011-08-01  \n",
       "541908   2011-08-01  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cohort Month is the first time customer was acquired (Customer Birth)\n",
    "\n",
    "df['Cohort Month'] =  df.groupby('CustomerID')['InvoiceMonth'].transform('min')\n",
    "\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "54981527",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a date element function to get a series for cohort calculation\n",
    "\n",
    "def get_date_elements(df, column):\n",
    "    day = df[column].dt.day\n",
    "    month = df[column].dt.month\n",
    "    year = df[column].dt.year\n",
    "    return day, month, year "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f0570357",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get date elements for our cohort and invoice columns\n",
    "\n",
    "Invoice_day,Invoice_month,Invoice_year =  get_date_elements(df,'InvoiceMonth')\n",
    "Cohort_day,Cohort_month,Cohort_year =  get_date_elements(df,'Cohort Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "acb6edb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>InvoiceMonth</th>\n",
       "      <th>Cohort Month</th>\n",
       "      <th>CohortIndex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>581587</td>\n",
       "      <td>22613</td>\n",
       "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541905</th>\n",
       "      <td>581587</td>\n",
       "      <td>22899</td>\n",
       "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541906</th>\n",
       "      <td>581587</td>\n",
       "      <td>23254</td>\n",
       "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541907</th>\n",
       "      <td>581587</td>\n",
       "      <td>23255</td>\n",
       "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541908</th>\n",
       "      <td>581587</td>\n",
       "      <td>22138</td>\n",
       "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2011-08-01</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                      Description  Quantity  \\\n",
       "541904    581587     22613      PACK OF 20 SPACEBOY NAPKINS        12   \n",
       "541905    581587     22899     CHILDREN'S APRON DOLLY GIRL          6   \n",
       "541906    581587     23254    CHILDRENS CUTLERY DOLLY GIRL          4   \n",
       "541907    581587     23255  CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
       "541908    581587     22138    BAKING SET 9 PIECE RETROSPOT          3   \n",
       "\n",
       "               InvoiceDate  UnitPrice  CustomerID Country InvoiceMonth  \\\n",
       "541904 2011-12-09 12:50:00       0.85     12680.0  France   2011-12-01   \n",
       "541905 2011-12-09 12:50:00       2.10     12680.0  France   2011-12-01   \n",
       "541906 2011-12-09 12:50:00       4.15     12680.0  France   2011-12-01   \n",
       "541907 2011-12-09 12:50:00       4.15     12680.0  France   2011-12-01   \n",
       "541908 2011-12-09 12:50:00       4.95     12680.0  France   2011-12-01   \n",
       "\n",
       "       Cohort Month  CohortIndex  \n",
       "541904   2011-08-01            5  \n",
       "541905   2011-08-01            5  \n",
       "541906   2011-08-01            5  \n",
       "541907   2011-08-01            5  \n",
       "541908   2011-08-01            5  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a Cohort Index\n",
    "#Cohort index is the month difference between invoice month and cohort month for each row.\n",
    "\n",
    "year_diff = Invoice_year - Cohort_year\n",
    "month_diff = Invoice_month - Cohort_month\n",
    "df['CohortIndex'] = year_diff*12+month_diff +1\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "65fef310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cohort Month</th>\n",
       "      <th>CohortIndex</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>1</td>\n",
       "      <td>948</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>3</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>4</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>5</td>\n",
       "      <td>341</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2011-10-01</td>\n",
       "      <td>2</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2011-10-01</td>\n",
       "      <td>3</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2011-11-01</td>\n",
       "      <td>1</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2011-11-01</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cohort Month  CohortIndex  CustomerID\n",
       "0    2010-12-01            1         948\n",
       "1    2010-12-01            2         362\n",
       "2    2010-12-01            3         317\n",
       "3    2010-12-01            4         367\n",
       "4    2010-12-01            5         341\n",
       "..          ...          ...         ...\n",
       "86   2011-10-01            2          93\n",
       "87   2011-10-01            3          46\n",
       "88   2011-11-01            1         321\n",
       "89   2011-11-01            2          43\n",
       "90   2011-12-01            1          41\n",
       "\n",
       "[91 rows x 3 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count the customer ID by grouping by Cohort Month  and Cohort Index\n",
    "\n",
    "cohort_data = df.groupby(['Cohort Month','CohortIndex'])['CustomerID'].apply(pd.Series.nunique).reset_index()\n",
    "cohort_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4f21dd11",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>CohortIndex</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
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       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
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       "    <tr>\n",
       "      <th>Cohort Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-12-01</th>\n",
       "      <td>948.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>376.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>421.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-01</th>\n",
       "      <td>380.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2011-03-01</th>\n",
       "      <td>440.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-01</th>\n",
       "      <td>299.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-01</th>\n",
       "      <td>279.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-01</th>\n",
       "      <td>235.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>191.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01</th>\n",
       "      <td>167.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2011-09-01</th>\n",
       "      <td>298.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-01</th>\n",
       "      <td>352.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-01</th>\n",
       "      <td>321.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-01</th>\n",
       "      <td>41.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "CohortIndex      1      2      3      4      5      6      7      8      9   \\\n",
       "Cohort Month                                                                  \n",
       "2010-12-01    948.0  362.0  317.0  367.0  341.0  376.0  360.0  336.0  336.0   \n",
       "2011-01-01    421.0  101.0  119.0  102.0  138.0  126.0  110.0  108.0  131.0   \n",
       "2011-02-01    380.0   94.0   73.0  106.0  102.0   94.0   97.0  107.0   98.0   \n",
       "2011-03-01    440.0   84.0  112.0   96.0  102.0   78.0  116.0  105.0  127.0   \n",
       "2011-04-01    299.0   68.0   66.0   63.0   62.0   71.0   69.0   78.0   25.0   \n",
       "2011-05-01    279.0   66.0   48.0   48.0   60.0   68.0   74.0   29.0    NaN   \n",
       "2011-06-01    235.0   49.0   44.0   64.0   58.0   79.0   24.0    NaN    NaN   \n",
       "2011-07-01    191.0   40.0   39.0   44.0   52.0   22.0    NaN    NaN    NaN   \n",
       "2011-08-01    167.0   42.0   42.0   42.0   23.0    NaN    NaN    NaN    NaN   \n",
       "2011-09-01    298.0   89.0   97.0   36.0    NaN    NaN    NaN    NaN    NaN   \n",
       "2011-10-01    352.0   93.0   46.0    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2011-11-01    321.0   43.0    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2011-12-01     41.0    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "CohortIndex      10     11     12     13  \n",
       "Cohort Month                              \n",
       "2010-12-01    374.0  354.0  474.0  260.0  \n",
       "2011-01-01    146.0  155.0   63.0    NaN  \n",
       "2011-02-01    119.0   35.0    NaN    NaN  \n",
       "2011-03-01     39.0    NaN    NaN    NaN  \n",
       "2011-04-01      NaN    NaN    NaN    NaN  \n",
       "2011-05-01      NaN    NaN    NaN    NaN  \n",
       "2011-06-01      NaN    NaN    NaN    NaN  \n",
       "2011-07-01      NaN    NaN    NaN    NaN  \n",
       "2011-08-01      NaN    NaN    NaN    NaN  \n",
       "2011-09-01      NaN    NaN    NaN    NaN  \n",
       "2011-10-01      NaN    NaN    NaN    NaN  \n",
       "2011-11-01      NaN    NaN    NaN    NaN  \n",
       "2011-12-01      NaN    NaN    NaN    NaN  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pivot Table for Cohort Table\n",
    "\n",
    "cohort_table = cohort_data.pivot(index='Cohort Month', columns=['CohortIndex'],values='CustomerID')\n",
    "cohort_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57d51c62",
   "metadata": {},
   "source": [
    "#### Cohort Retention Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c6a93cd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Changing Date Format\n",
    "\n",
    "cohort_table.index = cohort_table.index.strftime('%B %Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "13670553",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1656x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(23,12))\n",
    "sns.heatmap(cohort_table,annot=True,cmap='Blues')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c78d630a",
   "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>CohortIndex</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cohort Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>December 2010</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.381857</td>\n",
       "      <td>0.334388</td>\n",
       "      <td>0.387131</td>\n",
       "      <td>0.359705</td>\n",
       "      <td>0.396624</td>\n",
       "      <td>0.379747</td>\n",
       "      <td>0.354430</td>\n",
       "      <td>0.354430</td>\n",
       "      <td>0.394515</td>\n",
       "      <td>0.373418</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.274262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January 2011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.239905</td>\n",
       "      <td>0.282660</td>\n",
       "      <td>0.242280</td>\n",
       "      <td>0.327791</td>\n",
       "      <td>0.299287</td>\n",
       "      <td>0.261283</td>\n",
       "      <td>0.256532</td>\n",
       "      <td>0.311164</td>\n",
       "      <td>0.346793</td>\n",
       "      <td>0.368171</td>\n",
       "      <td>0.149644</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February 2011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.247368</td>\n",
       "      <td>0.192105</td>\n",
       "      <td>0.278947</td>\n",
       "      <td>0.268421</td>\n",
       "      <td>0.247368</td>\n",
       "      <td>0.255263</td>\n",
       "      <td>0.281579</td>\n",
       "      <td>0.257895</td>\n",
       "      <td>0.313158</td>\n",
       "      <td>0.092105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March 2011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.190909</td>\n",
       "      <td>0.254545</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>0.231818</td>\n",
       "      <td>0.177273</td>\n",
       "      <td>0.263636</td>\n",
       "      <td>0.238636</td>\n",
       "      <td>0.288636</td>\n",
       "      <td>0.088636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April 2011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.227425</td>\n",
       "      <td>0.220736</td>\n",
       "      <td>0.210702</td>\n",
       "      <td>0.207358</td>\n",
       "      <td>0.237458</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.083612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May 2011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.236559</td>\n",
       "      <td>0.172043</td>\n",
       "      <td>0.172043</td>\n",
       "      <td>0.215054</td>\n",
       "      <td>0.243728</td>\n",
       "      <td>0.265233</td>\n",
       "      <td>0.103943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June 2011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.208511</td>\n",
       "      <td>0.187234</td>\n",
       "      <td>0.272340</td>\n",
       "      <td>0.246809</td>\n",
       "      <td>0.336170</td>\n",
       "      <td>0.102128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July 2011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.209424</td>\n",
       "      <td>0.204188</td>\n",
       "      <td>0.230366</td>\n",
       "      <td>0.272251</td>\n",
       "      <td>0.115183</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August 2011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.251497</td>\n",
       "      <td>0.251497</td>\n",
       "      <td>0.251497</td>\n",
       "      <td>0.137725</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September 2011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.298658</td>\n",
       "      <td>0.325503</td>\n",
       "      <td>0.120805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October 2011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.264205</td>\n",
       "      <td>0.130682</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November 2011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.133956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December 2011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "CohortIndex      1         2         3         4         5         6   \\\n",
       "Cohort Month                                                            \n",
       "December 2010   1.0  0.381857  0.334388  0.387131  0.359705  0.396624   \n",
       "January 2011    1.0  0.239905  0.282660  0.242280  0.327791  0.299287   \n",
       "February 2011   1.0  0.247368  0.192105  0.278947  0.268421  0.247368   \n",
       "March 2011      1.0  0.190909  0.254545  0.218182  0.231818  0.177273   \n",
       "April 2011      1.0  0.227425  0.220736  0.210702  0.207358  0.237458   \n",
       "May 2011        1.0  0.236559  0.172043  0.172043  0.215054  0.243728   \n",
       "June 2011       1.0  0.208511  0.187234  0.272340  0.246809  0.336170   \n",
       "July 2011       1.0  0.209424  0.204188  0.230366  0.272251  0.115183   \n",
       "August 2011     1.0  0.251497  0.251497  0.251497  0.137725       NaN   \n",
       "September 2011  1.0  0.298658  0.325503  0.120805       NaN       NaN   \n",
       "October 2011    1.0  0.264205  0.130682       NaN       NaN       NaN   \n",
       "November 2011   1.0  0.133956       NaN       NaN       NaN       NaN   \n",
       "December 2011   1.0       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "CohortIndex           7         8         9         10        11        12  \\\n",
       "Cohort Month                                                                 \n",
       "December 2010   0.379747  0.354430  0.354430  0.394515  0.373418  0.500000   \n",
       "January 2011    0.261283  0.256532  0.311164  0.346793  0.368171  0.149644   \n",
       "February 2011   0.255263  0.281579  0.257895  0.313158  0.092105       NaN   \n",
       "March 2011      0.263636  0.238636  0.288636  0.088636       NaN       NaN   \n",
       "April 2011      0.230769  0.260870  0.083612       NaN       NaN       NaN   \n",
       "May 2011        0.265233  0.103943       NaN       NaN       NaN       NaN   \n",
       "June 2011       0.102128       NaN       NaN       NaN       NaN       NaN   \n",
       "July 2011            NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "August 2011          NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "September 2011       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "October 2011         NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "November 2011        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "December 2011        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "CohortIndex           13  \n",
       "Cohort Month              \n",
       "December 2010   0.274262  \n",
       "January 2011         NaN  \n",
       "February 2011        NaN  \n",
       "March 2011           NaN  \n",
       "April 2011           NaN  \n",
       "May 2011             NaN  \n",
       "June 2011            NaN  \n",
       "July 2011            NaN  \n",
       "August 2011          NaN  \n",
       "September 2011       NaN  \n",
       "October 2011         NaN  \n",
       "November 2011        NaN  \n",
       "December 2011        NaN  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cohort Percentage Table\n",
    "\n",
    "Percent_Cohort_Table = cohort_table.divide(cohort_table.iloc[:,0],axis=0)\n",
    "Percent_Cohort_Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b6ebe46f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J9TxPZc7OiW+/4PkXS3H0691cu+z6APNqtfqsnTkOX78E1A7sw5ZF0yhfx7vzbnz35lwKOkept6qRNXc+Lp4/w88nDrNl6Ux8ff14t3E7suR6jjdq+LNi2lh8EySgQYd+bFgwhYr1mnv6FDxiy4LJVGjQivCQkJhlt69fIZm74ylZqjTcvuEabF6magPWzxyPr18CagT2YeuiabxRJ8Aj5Y4roqOieP8P2orN7rairbuteK1aA1Y/0Fa86eVtxdhuzbgYdI7SFauRLXc+vjvwNZ9tWcM3u7eR+dk8VGsaSOKkySlfoyHLpo3B1y8BDTv2Z/2CKbztpdcsKHb/H9HRUUzt1YorQeco/mZVMud6nvSZs3Py2y947sVSHP96N9fd7WzZqg1Y567zarrrvHJeXOdFR0UxqWcLLgWd45U3q5I19/Mx634+cZhkKVKT7unMALxezZ9VM8bi65eAeh36smnBVN6s5731ncGwe+oADPBsyYo8W/ItQm9eI1EK13+dE6VITejNawA8V74W3yybjNPXj+INu3Jo/RwKvO3vucLHAcFB5zn94yly5s0fs+zk0YOkTJWapzNmAaBq3SbMmjQcvwQJaNdjCItnTqJ2kzaeKrKIZzrx3O5+xVIBKGiMqen+PQWQC3gDmGetvQNgrb1ijEkKvAKsjPXNYIJYx9zovu/cUVzTXI8CGGOOA9mAQ7hGwC13b78YWPM3jrvyzzrwjDH5cI3Sq/DAucX2V8M9H2efuOkR39paC1FR0TTpMx8Ap9OHjVPaUbPTDEZ3rU7mDKlYsmkfm/ccfcKFffJ8HA46jpvjun/H2H4E/fYz+z7eyDtNAinwclmOfPkJq6eNofmACTyTPRftRkwD4OfvDpM8VRqstSydMAgfp5NKjdqSLGXqv3jH/w4fh4N+7y3gzq2bTB/Zm3O//kTGrM9StWFrqjZszdaVC9m9eTWV6zcnc47c9Bw3C4Afjh0kReq0YC2zxvTH4XBQM6ADyVN5Uex8HHQY68q7xeP6E/Tbz9Ro04ON8z5g56qFPFfsFRxO1zTpZ7Llou1wV9798t1hkqVK68q7iYNxOBy87YV5133CPEJu32Tu6L5c+PVnoqOiCLl1k06jZvDbjydYMH4g/aYtJ2P2XHQaPQOAn44fInnqtFgsC8YNxOF0UKVJoFfE7tT+r0iaPCUZc+Thl+OH/nL7p7PlpNXwqQCc/u4wyVO76rrlkwbj43BSsWEbknpB3GLzcTjo5G4rFrrbir0fb6Syu604/OUnrJo2hhbutiLwEW3FkgmDcHhpW9Fz4nzu3L7J7FF9OP/rz5R6qxpv1WoCxrBl6SzWzptMg/Z9yJQ9F11HzwTgx+OHSJ7K1VbMGzcAh8NJ1aaBJFfsFLu/wcfHQeCY2YTcvsXScf35/bdfqN66B5vmf8Cu1QvJW7RkTDv7dLactHbXeb98d5hkqdOAtSybNBiHF9Z5Pg4HXcbNJeT2TeaP6ceF337m6SyuqY2HPt9JIfcoPICM2XPRYeR0AH76ztXOYmHRhIE4HE4qN27nVfVduc5jSJQiDaE3r7F7Sj+SPZXpD7dNlSkH5buOByD4x2MkSu6K05fzRuPjcFCoajMSJk/1RModF4SG3GHikB40btOVxEnu3W3ri93bYkbhAWR7Ng/D3p8PwIkjB0iVJh3WWiYN743T4cS/VSdSur+gFHkSPDIG0BiTA4gCgnF1XrW31hZy/2S31m53L3+wE8sHuBZr20LW2udirQ9z/xsd6/Xd3/+ow9L+jePe/pNzyQSsBRpZa39yLz4LxK5BMwHnH9z3AWeBzH9nH2NMS2PMt8aYbyMvHf+Lw/7vBF++SYa0yQHIkDY5F6/cBOBc8DUyZbjXAGRMn+qh0XatapVh8aZ9FC+Yg/CISPx7zqVX8zfxJomSJCNHvsJ8f2gf+3dvI3/xMgAUKPEaZ348cd+21lp2rV7I6zUbs3PlfN6o05TCpcvz5ZbVnii6xyVOmozc+Qtz/MDe+5a/WLY8B7/cdd8yay1bVszn7TpN2bRsLpXrN6P4q2/xyaYVT7LIcUaiJMnI/nwhvj+0j/QZs9Ks3zjaj57JCyXLkeapZ+7b1lrLJ2sWUa5GI3auWsAbtZtQqEx5vvxojYdK71mJkiTj2XyFOXlwLynTpKPgy2UxxpA11/MYY7h941rMttZatq9aSIVaTdi2fD5v1Q2gaJk3+XTzqj9+g/+QX08d4+T+LxkfWJcV7w3hl2MHWfnBcJKkSM3Nq5cBuHn1Mkke+M+CtZbdaxfzavVG7Fq1gNdrNaVQ6fJ8tdU7cw7utRWnHmgrCv5BW/HJ6oWUq9mYj1fOp7y7rfjCW9uKJMnIlb8wJw5+TfKUqfFxOPDx8aFEhXf57YeHY7dt5QLeqt2Yj1bM4+26zShWtgKfblrpodJ7lmL3+BIlSUr25wvxw+F9pMuYhaZ9x9J21EwKlnyd1I9oZ3evXcxr1RvxyaoFlKvVlBe8uM5ztbOFOHXQ9fkuKiqSo3s/pVDJ1x/a1lrLzlULeaNmY7avmMebdQIoUqYCn3tZfZcohavzKGGylGQqWIIrv35PwmQpCbl+BYCQ61dImCzlfftYa/lu23LyvVWPYx8tJf/b9cla7DW+/3Tjky6+x0RGRjJhSA9Kvf4WL5W6l19RUZF88/kuSpQt/9A+1lrWLJ1D9QbNWb1oFrUatqJUuYpsXbfsSRZd7nLfZsSjPx7yxDvx3Pegmw5Mdt8vbhvQxhjj616f2xiTBNgOBBhjEruXp7bW3gB+McbUci8zxpgX/mERfIC7o/7qA58/7nGNMSmBzUBva+0Xd5dbay8AN40xLxvX0L5GwPq/ONw2oIIxJpUxJhWuUX3bHrWhtXamtbaYtbaYM22+R23yRGzecxT/ysUB8K9cnE27j7iW7z5CrTeL4OfrJOszaciZJR3fHDsds1/KZImoWCY/SzbuJXEiX6KjLdZCAr8n/7CEJ+3W9WuE3HZ1dkaEhfHjkW9JlzELyVOn4efvDgHw07EDpM1w/7do+3dvJU+REiROmozwsFCM8cH4+BAeHvbgW/xn3bx+lTu3XLELDwvj5OFvyZApK7+fvzcL/ci+z3kqU9b79vvqky3kL/YKSZImjxU7Q3iY98Tu1o1YeRcexk9H95MuYxZuXXdNZYyOjmbXmkUUL//uffsd2LOVvEVeJlHSZETcjZ3xISIs9Imfg6fcun41JnbhYWF8f+Rb0mfKQv7ipfnh6H4Ags//RlRkJEncT24E+GbXRzxf1HXNRoSFYnyMO++8I3YV6reg+7SVdJ28jNodB5A9f2Fqte9L3mKvcHCPq2k7uGcbzxV75b79Du7ZRp7CxV05Fx6GMQZjDBFedL3Co9uK9H+zrchbJFbeuduKCG9rK2Jds6cOf8tTGbNy/cqlmG2OfP0pT8e6gTnAvl0fka9oCRInTe6OnfHOdlaxeyy3b1wj5PYtwN3OHttP2mfub2d3r1nES+Ur37ef6ryH67sfjuwnfUbXZznX6yykTJP+of2+3b2V5+62s+Fh7s8o3tPOAkSGhRIReifmddDJg6R4OivP5C/O6X07ATi9bycZCxS/b7/T+3byTL5i+CVOSlTEvdhFeck1a61lxoQhZMySnUo1759OfPTAPp7JnI006Z56aL89OzZRuHgpkiZz/5/Cx2CMD2Gh3pNzEjc8qem0iYwxhwBfIBJYBExwr5uNa6rrAXeH10WgqrV2qzGmEPCtMSYc1xNe+wANgGnGmH7u4y0DDv+DstwG8hlj9gPXgTru5Y9z3EAgJ9DfGNPfvayC++EdbYD5QCLgI/cPxpgXcY3cSwVUNsYMttbmc08XHgp84z7OEGvtlX9wXv9TC0Y2oXTRXKRNmZQftw5l6PQtjJu3g8WjA2hctQRnLlylQY85AJz4OYjV2w9ycHVfIqOi6TRqBdHR9wZV9mlZkVGztwKw48sTtKpdhm9X9mH2qs89cm5P0s1rl1kxeQQ2OhprLQVKvMpzRV8hUeKkbJz3AVHRUfj6+lGtVbeYfcLDQjmwZyvN+rmGv5euXJvF4/rjdPpSt9MAT53KE3f9ymUWTBpKdHQ01kZTtFQ5Cr5Ykhkj+/D7uV8xxofU6TNQv22PmH3Cw0L5+pOP6Dh4EgBvVKnLjFF9cDp9adZtsIfO5Mm7efUyK6eMdOddNAVKvMZzRV/hiy2r+GrbOgDyv1Saoq9VjNnHlXfbCOg7DoBS79RmyfgBOJy+1O3Y/1Fv85904+plln4wgujoKGy0pVDJ18hXrCSREREsmzKS0R0b4XA6qd+hT8wN4MPDQvlm91ZaD3A1c2XfrcP8Mf1xOJ007PLEH8oep5SpUo/lkwazf9cWUqZNT53Og2LWhYeFcmjPNhr3HQvAK5Vq8eGEgTidTmp18J6cg3ttRbS7rSjobisSutuK6OgonL5+VH+grdi/ZyvNH2grHE5f6nlRW3Hj6mUWvz/cVd9FR1Oo5Ovkf7EkCycN5dwvP2CMIXX6DNRp3T1mn/CwUPbu+oh2AycC8Nq7dZkzph8Op5MmXQZ55kQ8QLF7fDevXmb11FGuazY6mvwlXiVv0RJ8uWUVe7e7vsd//qXSFHn1/nb24J5tNHHXeSXddZ7D6aS2F9V5N65eZtnkEdjoKKKt5YVXXuN59xc8h77YSaGSbzy0T3hYKN/u3krL/q76rsw7tVkwrh8Opy/+nbynnQ29eY3PZw8DwEZHk7VoWZ5+viips+biy3mj+Pnr7SROlY5XmvaO2ScyPJTTe3dStt1QAPK8VpUv5ozAx+GkRJMej3yf/5pTxw/z2cdbyJI9Jz1bu566XTegLYVfKsWXu7fzymsVHtonLDSUT3dsos9I171o367RgIlDeuB0+tK+z/AnWn4R4xoMJ/FVosKB+gM+pqUL+nm6CPFWygT//VGT/ytXQ8M9XYR4KaHT4ekixFu3IiI9XYR4y8/Hc08ei+8S6ZoVD7gRHuHpIsRbCR26Zh/XN+dveLoI8Va1vBk8XYR4q3DWZJ6bzxkHJCrV3+P9ICGfD/XI30CfTkVEREREREREROI4Tz6dVkRERERERERE5O/z4IMlPE0j8UREREREREREROI4deKJiIiIiIiIiIjEcZpOKyIiIiIiIiIi8YPx3vFo3nvmIiIiIiIiIiIi8YRG4omIiIiIiIiISPygkXgiIiIiIiIiIiISV6kTT0REREREREREJI7TdFoREREREREREYkffIynS+AxGoknIiIiIiIiIiISx2kknoiIiIiIiIiIxA96sIWIiIiIiIiIiIjEVerEExERERERERERieM0nVZEREREREREROIHowdbiIiIiIiIiIiISBylkXgiIiIiIiIiIhI/6MEWIiIiIiIiIiIiElepE09ERERERERERCSO03RaERERERERERGJH/RgCxEREREREREREYmrNBJPvFbetMk8XYR4KyQ8ytNFiLeeSpbQ00WIly7dCvN0EeKttIkSeLoI8VZCX4enixBvefEX5P9vt0MjPV2EeMtHiffYwqL02e5xBb6SzdNFiLfSJ/P1dBEkvtKDLURERERERERERCSuUieeiIiIiIiIiIhIHKfptCIiIiIiIiIiEj948e0TNBJPREREREREREQkjlMnnoiIiIiIiIiISByn6bQiIiIiIiIiIhI/6Om0IiIiIiIiIiIiEldpJJ6IiIiIiIiIiMQPerCFiIiIiIiIiIiIxFXqxBMREREREREREYnjNJ1WRERERERERETiBz3YQkREREREREREROIqjcQTEREREREREZH4QQ+2EBERERERERERkbhKnXgiIiIiIiIiIiJxnKbTioiIiIiIiIhI/KAHW4iIiIiIiIiIiEhcpZF4IiIiIiIiIiISP2gknoiIiIiIiIiIiMRVcX4knjHmlrU2qafL8SjGmMzAQiADEA3MtNa+516XGlgOZANOA7WttVeNMWmAVcCLwHxrbWCs4w0HGgGp4to5Tx/YgIpl8nPxyk2K1RoBQKrkiVk0OoCsz6Tm1/NX8O8xh2s3QwDoFlCBJlVKEBUdTdcxq/j4qxP4+TpZObElGZ9KycwVnzFz5WcATO5Xj1krP+PwqbMeO78n5WJwEO+NHMC1K5cwxocK71Sncs36MevXLV/I/OmTWLhuJ8lTpOLE0UNMnzQCX18/uvYfwdMZs3Dr1k3GDe7JwDFTMF70aO3LwUFMHTuIa1cvY4yh3NvVqFitHqd/OsWc90cRER6Gj8NJQGBPcubNx6njh5nzwSh8fX1p32s4GTJm5vatm7w/og+9hr/vVbG75M67q1cu4WN8KP+IvFswfRILYuXdDHfedXHn3W133g3wsryLCA9jTK82REZEEBUVRdGSr1GlQQvWLZ7Bob2fYYwPyVOkommnfqRMk44fvzvM4mljcTr9aNl9MOmfycydWzeZMaY/nQZP9KrYhYeHMbx7KyIiwomOiuLFUuWo0bAlt25eZ/LIvlz6/QJpn3qa9r1HkCRZcr4/fpj5k0fj9PWlXa9hPPWM65qdMrIP3Yd53zU7efQAd33nwxuVqlGpuuua/WjtMj5avwKHw0GR4qVo2LIjJ48dYtZ7I/H19aNj3xE87a7vJg7tRd9Rk70udh+Muhe78pWqUamGK3Zb1i5j67oV+DgcFC1eioatXLGbOWkkvn5+dIoVuwlDe9HPy2IXHh7GiJ6tiYwIJyoqihdLvk51/5bs+2wna5fO4sKZ0wycOI/suZ4D4PvvDrNgyhh8fX1p02NozDU7dXRfug15z6tiFxEexqS+7dxtRSSFX3mNSvWas/nDOXy5YwNJk6cE4F3/VuQr9go/nTjC8unjcPr60rTrYNI9nYk7t24yd9wA2g2c4HWxm9K/PZEREURHRVGwxKu8VTeAheMHcvH8GQBCbt8iUZKkdB0/l19OHmX1jPE4ff3w7zyAtE9nIuT2TRaOH0TL/uO8KnYjB/fjy88/JVWq1CxcsQ6AuTOmsHHdalKmSgVAy7YdKVGqDEcOHWD8qKH4+fkxcPhYMmXOws2bNxjYuxvjP5jhVXETie/ifCeeJxhjHNbaqL+xaSTQ1Vp7wBiTDNhvjNlhrf0O6AXstNaOMsb0cv/eEwgF+gP53T+xbQQmAz/8W+fyb1m08WumL9/D7KGNYpZ1a1qe3ftOMW7eDro1LU+3phXo9/568ubIQK03i1Ck5nCeTpeCLdMDKVB1COVfeY6DJ85Qtf00vvqwJzNXfkaB3Bnx8TFe0YEH4HA4aNqmM8/mfo6QO7fp2qoBhYq9TOZsObgYHMShb78m3VMZYrZfv3IRPQePIzjoPB+tX0VA2y6sWDiLmg2aeV1j6+Nw4t+yE9lz5SXkzm36BDaiQJHiLJ39ATX8m1PoxZIc3PcFS+e8z4CxM9i8ejGd+4/mYtAFdmxaRcNWnVmzZDZV6jbxwtg5aPIHeXcpOIjDj8i7Hu6827p+FU3deVfDC/PO6etH1+GTSZgoMZGRkYzp2Yr8RUvwZnV/qvq3AmDnhhVsXDaXhu16sn3dh7TpPZLLv19g90drqd2sA5uWz+PtWo28Lna+vn70HjU1JnZDu7XghWIl+ObLXeQr9CKVazdm44oFbFyxgLrN2vPRmiV06DeKi79fYOfm1dRv0Yn1H86hcp2mXhc7h8NBo9adyZHLdc32bONPwaIvc/3qZb75cg/jZy7D18+P61evALBx1WK6DRxL8O/n2b5xJY1bd2HV4llUqx/glbFr3LozOdz1XY/WD8Ru1v2x27ByMd0HjSU46DzbN6ykcZsurFo0i+peGDtfXz96jZgSc80O796SgsVKkClrDjr0Hc38yaPu237rmqW07zOSS8EX+GTLGuo178iGZXOpXNv72lmnrx8dhrxPgkSJiYqMZELvNjxf5GUAXnu3Dm9UrX/f9p+s/5DmPYdzOfgCn320luoB7dm6Yj5v1vS+tsLp60ebQZNiYje5XzueK1KcRl0Hx2yzYf5kEiZ2jW/Ys2EZjbsP5crFIL7cto53mwSyY+UC3qjR0OtiV7FyVarXqc/wAX3uW167fkPqNWx637LlSxYwbMwkgs6fY92q5QR27s6C2TNo2LSF18VN/iO8OG/jxXRaY0xSY8xOY8wBY8xRY0wV9/JsxpgTxphZxpjjxpjtxphE7nW7jTHF3K/TGmNOx9rnM/exDhhjXnEvf9UYs8sYsxQ4aowZaozpGKsMw40xHWKXy1p7wVp7wP36JnACyOheXQVY4H69AKjq3u62tfZzXJ1597HWfm2tvfBvxOzf9sWBn7hy/c59y955tSCLN+4FYPHGvVR+rWDM8pXbDhAeEcmv5y/z05lLvJg/GxGRUSRK6IvTcS/tBrR9h6HTNj+5E/Gw1GnS8Wxu1zfYiRInIVOW7Fy+FAzA3CnjadyqE3CvQnI4nISHhRIWGorT6eTCuTNcuRRM/kJFPVB6z0qVJi3Zc+UFXLHLmDkbVy5dxBhDyO3bANy5fYtUqdMBd2MXRniYK3a/nz/L1csXeb6g98Xur/Ku0QN554yVdw4vzztjDAkTJQYgKjKSqMhIjDEkSpwkZpuwsJCYD8AOh5MId945HE6CL5zl2uWL5ClQxCPl96RHxQ5jOPDVp5R+oxIApd+oxP6v9gDgcDoJD78Xu9/Pn+XKpYs8V9D7YpcqTTpy5Lp3zWbMkp0rl4LZvmEVVes2wdfPD4AUqVID7vouPIzwUFfsgs6f4cqli+R7wfuu2VRp0pEjVn2XMasrdts2rqLaI2LndDoJCwsjLMxV3wWdP8NlL43dQ9dsVCQGwzNZsvN0pqwPbR9zzbrz7vcLZ7l6OZi8XlrfJbgbuyh37P7kP5gOh5OI8DAiwsJwOJ1cvHCWa1cukit/4SdV5DjjodhFRhL7M4m1lkNf7qJwqXKA60vdiPBwIsJC8XE4uRR0jutXLvFsvkIeKL1nFSpSjOTJU/ytbV11XSih7v9TnDv7GxeDf6dw0Rf/x6UUkX9bfBmJFwpUs9beMMakBb42xmxwr8sF1LPWtjDGrABqAIv/5FjBQHlrbagxJhfwIVDMve4lIL+19hdjTDZgDfCeMcYHqOte/0ju7QsDe92LnrrbIWetvWCMSf+PzzqOS58mGUGXbgAQdOkG6VInAyBjuhTsPXo6ZrtzwVd5Jn0KNuw6Qv1KL/Hpom5MnP8xlcoW4OB3v3Hh4nVPFN/jfg86z88/niL3c/nZ98Ue0qRNT/acue/bpkaDAKaOH4ZfgoR06jOU+dMmUj+grYdKHHdcDDrP6Z9OkTNvPhq17sLIPu1ZPOs9rLUMnjgHgCp1mzD7vRH4+SWgbY/BLJn1HrUat/ZwyT0vOOg8v8TKu9R/kHfT3HnXsc9QFkybSD0vzrvoqCiGdm7KxQtnebVSDXLkyQfA2oXT+WrXRyRKnJRuIyYDULFWIxZOGYWfXwICugxk1dwPqOLf0pPF96joqCj6d2jE7+fP8sY7NcmZNz83rl0hZeq0AKRMnZYb168CULl2E+a+NxK/BAlo3W0QS2e/T81GrTxZ/DjBdc2eJFfe/Cya+R4njh3kw3lT8PVLQKOWnciZNx/V6jVlxoRh+CVIQPteQ1k4YxJ1m7TxdNE9LjjoPKd/PEmu59yxO3qQpXOn4OeXgEatYsVu4jD8/BLQofdQFkyfRL2m3hu76KgoBnZszO8XzlKuUk2ezfvgpJF73qnVmPkfjMTXLwGtug1i2Zz3qe7vvddsdFQUo7sGcDHoHGUqVidb7nwc3/81n25ezb5dW8mSMy/VmwaSOGlyKtRoyIdTR7uu404DWDt/Mu/Ub+HpU/CY6KgoJvZowaWgc5R8qypZcz8fs+7n7w6TLGVq0j2TGYBy1f1ZOX0svn4JqN+xLxsXTOWtus08VfQ4ac2KD9m6eQN5n8tHYOfuJEueAv8mLRg7fDAJEiSg35CRTJk0juZt2nu6qCKPz4sfbBFfOvEMMMIYUwbXvecyAk+51/1irT3kfr0f1z3o/owvMNkYUwiIAmL/73WftfYXAGvtaWPMZWNMYfd7HbTWXn5k4YxJCqwGOllrb/zDc/vvecQ3j9ZCVFQ0TfrMB8Dp9GHjlHbU7DSD0V2rkzlDKpZs2sfmPUefcGE9IyTkDqMHdKNZu644HA5WLp7DoLFTHtouR848jJm6EIDjh/eTOk06rLWMHdwTp9NJ0zZdSJk6zZMuvkeFhtxh4tCeNGrdhcRJkrJiwXQatupC8dKv89WeHcycMJS+o6eS7dk8DH1vHgAnjh4gVZp0YC3vDe+Nw+mampsylXfF7m7eBbjzbtXiOQx8RN5lz5mH0bHyLpU778YN7onDC/POx+Fg4PsLuXPrJlNH9OLcrz+RMeuzVGvUmmqNWrNl5QI+2bSKKg1akCVHbvqMmw3A98cOkiJ1Wqy1zBjdD4fTSe2ADiR3jwDyBj4OB8OnLOH2rZu8N7QHZ07/9IfbZn02N4MmzQXg5NEDpErjit3kkX1wOJzUb9GRFF54zY4b3J2mbbuROElSoqOiuH3zBiM+WMCPp44zYVgvpizaQPaceRgx2TX4/7sjrvrOYpkwtBdOp5NGrTt7ZX03blB3mrhjFxUVxa1bNxg52R27ob2YstgVu5GxYne3nZ0wtBcOh5PGrTt7XX03dPJi1z1kh/Xg7OmfyJTt2Udum/XZ3AyY4L5mjx0kpXsk/JRRfXE4nNRr3sGrrlkfh4PekxZw59ZNZo3qzflff6Z0xWpUrN0EjGHT0lmsmTcZ//Z9yJQjN93GzALgx+OHYtqKuWP743A6qda0PclTeldb0XX8XEJu32Te6H5c+O1nns6SA4CDn++MGYUHkDF7LjqOmg7AT8cPkTyV60uhheMH4nA6ebdxO5J5UeweVLVmHRo3b40xhtnTPmDyxLH0HjiMXHnyMmP+UgAOHfiWtOnSY61lYO+uOJxOAjt1J3WatB4uvYj8HfGl+7IBkA4oaq0tBPwOJHSvC4u1XRT3OiYjuXd+CWNt09m9/wu4RuD5xVp3+4H3nQ00AZoCcx9VMGOML64OvCXW2jWxVv1ujHnavc3TuEYA/iuMMS2NMd8aY76NvHT83zrsPxZ8+SYZ0iYHIEPa5Fy8chOAc8HXyJQhVcx2GdOnemi0XataZVi8aR/FC+YgPCIS/55z6dX8zSdXeA+KjIxg9IBulH3jbUqUKceF82cJDjpHp+Z1aVG3EpcvBtOlZQOuXrkUs4+1lhWL51C7UQuWL5hJvaatKVv+bTat+dCDZ/LkRUZGMnFoT0q+/hYvlXodgE93bOKlUq8B8HKZN/jp++/u28day9qlc6lWvxmrF8+iZsNWlHq9ItvWLX/i5fekyMgIxgzoRhl33gWdP8vvQefo3LwuLd151/URebfSnXcrFsykrpfm3V2JkyYjd4EiHNv/9X3Li5etwIEvd9+3zFrL5uXzeaduUzZ+OId36zfn5VffZOfGFU+wxHFHkqTJyFuwCEe+/YrkKVNzzZ1n165cInmKVPdta61l/YfzqFqvGWuXzKa6f0tKvl6R7eu975odP6g7pctVpHhpV32XOm16ipd6HWMMufLmx8cYbly/FrOPtZbVS2ZT0785KxfOpE7jVpQu9zZb1i7z0Fl4RmRkBOPcsXvZHbs06e6PnXlE7FYtnk3Nhs1ZscgVuzJveF/s7nJds0U5sv+rv9zWWsuGZXOpUjeAdUtnU61BC1557S12bPDO+i5x0mTkyl+E7w5+TfKUqfFxOPDx8aFk+Xf59YeHP6NsXTGfirWb8NHyubxdrzkvln2T3ZtWeqj0npUoSTKezV+IkwddE5uioiI5uvdTCpV8/aFtrbV8vHoh5Ws1ZtuKebxZJ4CiZSrw2ebVT7rYcUrqNGlxuHOucrWanDh+7L711loWzplBk+atmD9rGgGt2vFmxcqsWrbEQyUWkX8qvnTipQCCrbURxpjXgIdvzPGw08DdG5rUfOBYF6y10UBDwPEnx1gLvIXrSbLbHlxpXDe7mAOcsNZOeGD1BqCx+3VjYP3fKPPfYq2daa0tZq0t5kyb79867D+2ec9R/CsXB8C/cnE27T7iWr77CLXeLIKfr5Osz6QhZ5Z0fHPsdMx+KZMlomKZ/CzZuJfEiXyJjrZYCwn8fD1xGk+UtZbJY4aQKWt2qtT2ByBbjlwsWLuTWcs2M2vZZtKkS8+EmUtIlfret2GfbNtIseKlSJosOWFhoRjjgzE+hIU9dGvF/yxrLTMnDOWZzNmoVKNBzPJUadJx4sgBAI4f+oYM7ukWd326YxOFXyrpjl0YPj4GHx/vi92UB/IuqzvvZi7bzEx33o1/IO92bdtI0Qfyzsf4EO5Fsbt5/Sp3brm+oAgPC+XEoW/IkCkrv7ufmAdwaO/nZHjgflFf7txCgRdfIUnS5ISHhWJ8XNesN8XuxrWr3I4Vu+MH9/FM5qwUebkMn33suhfqZx9vpkiJMvft99nHmyn0UkmSJHPHzhiMMV53zU4bN5SMWbNTuaZ/zPKXSr7K0UPfAHD+7K9ERkaSPEXKmPW7t2+kSOxr1scHHx9DeKh3xW7quKFkypKdyrXuxe7Fkq9y7KA7dmceEbtY9V14qLud9fGuvLtx/f5r9rtD+3gmc7a/3O/zjzdT6EXXNRsWFoqPj/fF7v62IoxTh7/hqYxZuR7ri7HDe/fEjC67a+8nW8hX7BUSJ01OeFhYTH0X4UWxu3X9GiG3XbGLCAvjhyP7eSqjq0394ch+0mfMQso0D9+V6JtdW3muSAkSJ01GRFiYK++MISLce2L3KJcuXYx5/emunWR/Nud96z/atJ4SpcqQLHkKQkNDYuq6UC9qJ+Q/whjP/3hInJ5Oa4xx4hpptwTYaIz5FjgEnPwbu48DVhhjGgKfxFo+FVhtjKkF7OLh0XcxrLXhxphdwLU/eFptSVwdgUeNMYfcy/pYa7cAo9zv3wz4DagV67xOA8kBP2NMVaCCtfY7Y8wYoD6Q2BhzFphtrR30N871f27ByCaULpqLtCmT8uPWoQydvoVx83aweHQAjauW4MyFqzTo4boX2Ymfg1i9/SAHV/clMiqaTqNWEB1tY47Vp2VFRs3eCsCOL0/QqnYZvl3Zh9mrPvfIuT1JJ44dYveOzWTNkZNOzesC4N88kGIvl/rDfcJCQ9i1bVPMdNt3azVg9MDuOJ1OuvYf+UTKHRecOn6Yz3ZuIXP2nPRq43rKW52m7WjRqS8Lp40nKioKXz8/mne694SusNBQPt2xmd4jXfcrq1S9PhOH9sTp9KV972EeOQ9PiJ13nWPlXdG/kXcDY+XdGHfedfGivLt+5TJzJw0hOjoaG20pVup1XnipFNNG9Cbo3G8YH0OadBnwb9cjZp+w0FC++mQLnYa8B0D5qvWYPrI3DqcvLboP8dSpPHHXrl5i5rjBREdHE22jKV76DQoXL03O5woweUQf9mzbQJp0T9G+7718CgsN5fOPN9Nj+AcAVKxen/eH98Lp9KVtz6GeOpUn7uSxQ3z68WayZM9Jt1b1AKgf0I7X3qrCtHGD6dK8Nk6nk3Y9BsXcPD8sNIQ92zfRb7Trmq1c059xg7rj9PWlU98RHjuXJ+3ksUN8usMdu5bu2DVrx+tvVWHq2MF0buaKXWDP+2O3e/sm+o9xxe6dmv6MG9wdp9O7YnftyiVmTXDXdzaal0qVo9BLpfj2y90snj6Om9evMWFQZ7LkyE33oe8D7mt252a6D3Nds29VrccHw3vhcDpp29N72tkbVy+z6L1hMbErUvJ1CrxYkgUTh3D2lx8wxpA6fQbqtbnXVoSHhbJ310cEDpoEwOvv1mH26L44nb406TrIMyfiATeuXubDySOwUVFYa3nhldd4vtgrwN2ptG88tE94WCjf7t5KqwHjAShbuTbzx/bD6fTFv/PAJ1p+TxrUpzsH93/D9WvXqP52OQJatuXg/m/48ftTYODppzPSre+9eISGhrB103omTJkJQJ0GjenfozNOX18GDh/jqdMQkX/IWGv/eisPMca8AMyy1v7hAyX+x+/vAxwAallrf/BEGf5KosKBcfcPGMcd2Dza00WIt0LCH9WnLX9HQr8/G/wrf+TSrbC/3kgeKaFTOfe4Evoqdo/Lg19Qx3u3QyM9XYR462aEYve4wqL02e5xvZTVe+/B9/+VPtl/fybW/5BXt7SJqs32eD9IyNrmHvkbxNnptMaY1rieHNvPQ+//PPAjsDOuduCJiIiIiIiIiIh3iLPTaa2104HpHnz/74Acf7mhiIiIiIiIiIjI/1ic7cQTERERERERERG5jxfftyPOTqcVERERERERERERF3XiiYiIiIiIiIiIxHGaTisiIiIiIiIiIvGC0XRaERERERERERERias0Ek9EREREREREROIFjcQTERERERERERGROEudeCIiIiIiIiIiInGcptOKiIiIiIiIiEj84L2zaTUST0REREREREREJK7TSDwREREREREREYkX9GALERERERERERERibPUiSciIiIiIiIiIhLHaTqtiIiIiIiIiIjEC5pOKyIiIiIiIiIiInGWRuKJiIiIiIiIiEi8oJF4IiIiIiIiIiIiEmepE09ERERERERERCSO03RaERERERERERGJF7x5Oq068cRrRVtPlyD+Sp3Mz9NFiLeu3Ar3dBHipRQJfT1dhHjL6dCg+8dlrRqKxxUeGe3pIsRbSRLq4/nj0hX7+ErmTOPpIsRbiX29tzNBRJ48fUoQEREREREREZH4wYv7zvX1vIiIiIiIiIiISBynTjwREREREREREZE4TtNpRUREREREREQkXvDmB1toJJ6IiIiIiIiIiEgcp048ERERERERERGROE7TaUVEREREREREJF7QdFoRERERERERERGJszQST0RERERERERE4gWNxBMREREREREREZE4S514IiIiIiIiIiIicZym04qIiIiIiIiISLyg6bQiIiIiIiIiIiISZ2kknoiIiIiIiIiIxA/eOxBPI/FERERERERERETiOnXiiYiIiIiIiIiIxHGaTisiIiIiIiIiIvGCHmwhIiIiIiIiIiIicZZG4omIiIiIiIiISLygkXgiIiIiIiIiIiISZ8WJTjxjTJQx5lCsn2x/su1uY0yxJ1i8P2WMGWuMOWmMOWKMWWuMSRlrXW9jzI/GmFPGmDdjLR9ujDljjLn1wLHKGGMOGGMijTE1n+Bp/KXpAxvw686RfLuyT8yyVMkTs2laIEfXD2DTtEBSJksUs65bQAWOrR/I4bX9eaPEcwD4+TpZP7kt367sQ8tapWO2ndyvHi/kyfTkTsaDLgUHMaBLS9o3qU7HpjXZtHopAMvmT6d5rTfp0qIuXVrUZf/XnwNw4tghOjevTfc2/lw49xsAt2/dZEiPtlhrPXYenjB++ABqv/0qLRtUj1n20w+n6NSiIa38azCge3tu33ZdUsePHKR1w5q0D6jPubOuuN26eYM+nVp7XdwALgcHMbR7a7o2q0W3FrX5aO2HAJz+6RT9OzSlV+v69GnXiB9PHgfg1PHD9GhVj76BjQg6dwZw5d3I3u29Ln6XgoMY3K0VnQNq0qV5bbasccVu4rDedG9Vn+6t6tPOvzLdW9UH4OSxQ3RrWZfe7e6P3fBegV4ZO9V3j+du7Do0rUHHgFoxsQPYsnYZ7RtXp2NALRbOeA9w5V3n5nXo0bYhF2Ll3ZCe7bwudjH1XfNH1Hcdm9KrTX36BD5Q37WuR9/2D9R3fbyzvhvYpSUdm9agU0AtNj+Qdx0aV6dTQC0Wxcq7Ls3r0POBvBvqhXkXHh7G4M5N6R/YgD5t6rJ28UwA9n22kz5t6tL0nZf55YcTMdv/8N1h+rVrwOBOTfj9/L3Yjevfweti96ClixZSs2plalR5hyWLFgDw3oRx1K72Lv1694zZbtOG9SxdtNBTxRQR8Zi4Mp02xFpb6N86mDHGaa2N/H8ew2Gtjfobm+4AeltrI40xo4HeQE9jzPNAXSAf8AzwsTEmt/uYG4HJwA8PHOs3oAnQ7f9T9v+FRRu/ZvryPcwe2ihmWbem5dm97xTj5u2gW9PydGtagX7vrydvjgzUerMIRWoO5+l0KdgyPZACVYdQ/pXnOHjiDFXbT+OrD3syc+VnFMidER8fw+FTZz14dk+Oj8NB49adeTb3c4TcuU231g14oejLALxTswFV6zS6b/sNKxbRfdA4goPOs3XDKpq26cKKRbOo0aCZ1w0hrvB2Fd6tWY+xQ/rGLJs0cjAt2nehYOFibNu0llVL5tO4ZSCrP1xI/xHjCbpwnk1rVtCqQzeWzJtJ3cbNvS5uAD4OJ/4tO5E9V15C7tymT7tGFChSnKWzPqCGf3MKvVSSg/u+YOns9xkwbgabVy2m84DRXAy6wI5Nq2jYqjNrlsymSr0mXhc/h8NJw1adyeGOXa+2DSlYtDid+42M2Wbh9IkkTpIUgE2rltB1wBgu/n6e7RtX0ah1Z1Yvnk21ek29Lnaq7x6fw+GgSevO5HDHrntrf14o+jLXrl5m35d7mDBrGb5+fly/egWADSsX033QWC4GnWfbhpU0adOFlYtmUaN+gNfF7qH6LtBd381213cvuuu7Oe8zYOwMNq9eTOf+j6jv6npjfee6Zu/mXY/W/hQs+jLXr17mmy/3MP4P8i446DzbN6ykcZsurFo0i+pemHe+vn70HDGFhIkSExkZyYjuLSlQrASZsuagfd/RzJ886r7tt65ZSmCfkVwKvsAnW9ZQr3lHNiybyzu1vS/vYvvxh+9Zs3oliz5cga+vL+1at6DYi8U5fOggK9ZuoE/Pbvzw/SkyZ8nKxvVrmTx9lqeLLCIe4s11ZZwYifcoxpiixpg9xpj9xphtxpinY632N8Z8aYw5Zox5yb39IGPMTGPMdmChMaaJMWZyrONtMsa86n49zRjzrTHmuDFmcKxtThtjBhhjPgd6GWMOxFqXyxiz/8FyWmu3x+ow/Bq4O6SsCrDMWhtmrf0F+BF4yb3P19baC4841mlr7REg+p9H7H/riwM/ceX6nfuWvfNqQRZv3AvA4o17qfxawZjlK7cdIDwikl/PX+anM5d4MX82IiKjSJTQF6fjXtoNaPsOQ6dtfnIn4mGp06Tj2dyukYmJEichU5bsXL4U/IfbO5xOwsNCCQ8LxelwEnTuDFcuBZPvhaJPqshxRoHCRUmWPPl9y87+dpoChVyxKPxiCT7fvRNwxS0sLIyw0FCcTifnz57h8sVgChaOM4N4n6hUadKSPVdewJV3GbNk48qlixhjCLlzG4A7t2+RKk064G7ehbnyzunk9/NnuXrpIs8X9L68S5UmLTkeit29a9Zay1effkzJ11yDrR1OJ+HhYYSFheJwOgk6f5Yrl4N53guvWdV3jy9VmnTkiB27rNm5cimYbRtXUa1uE3z9/ABIkSo1cO+avZd3Z7hy6aKXxu6B+i5zrPrudqz6LrW7vnM8or677K313f15l/Ev8s55t62NlXeXvTTvjDEkTJQYgKjISKKiIjEYnsmSnaczZX1oe4fTSUR4GOGhoTgcToIvnOXq5WDyFijypIsep/zy888UKPgCiRIlwul0UrTYi+z+5GMiIiKw1hIWGobT6cuCeXOo26Ahvr6+ni6yiMgTF1c68RLFmkq71hjjC3wA1LTWFgXmAsNjbZ/EWvsK0Na97q6iQBVrbf2/eL++1tpiQEGgrDGmYKx1odbaUtba4cB1Y0wh9/KmwPy/OG4A8JH7dUbgTKx1Z93L/jPSp0lG0KUbAARdukG61MkAyJguBWeDrsZsdy74Ks+kT8HOr0/yVJrkfLqoGxPnf0ylsgU4+N1vXLh43SPl97TgoPP88uMpcj+XH4CP1i2nc/PaTB4ziFs3XXGtUT+AaROGsWn1UipWq8OSuVOo17StJ4sdp2TNkZOvPtsNwGefbOdicBAAdRs2473RQ1i7YjHv1qzH/Bkf0LhlO88VNA65GHSe0z+eImfefDRq04Uls96nXf1KLJn5HnUDXDGqUrcJsyeN4KO1H1Lh3dosnzeVWk1ae7jknnf3ms2ZN3/MshNHD5IiZWqezpQFgGp1mzBz4nC2rPmQt6rUZtm8KdRp3MZTRY4zVN89PlfsTpLrufxcOPsbJ44epFe7RvTv3CJmSmj1ek2ZPtEVu7er1mHpnKnUbaq8uxh0ntM/ueu71l1YMvt92jWoxJJZD9R378Wq7+ZPpVZj1XfBQec5/Yi8GxAr76rVa8qMicPYvHopFd15V8+L8y46Kor+gf50aPAW+Qq9xLOx2ooHVarVmHkfjGT7+mW8UbkmqxZOo7p/qydY2rjp2Zy5OLD/G65du0pISAiff7aHq9euUq58BerWrMYzmTKSNFlSvjt2lNdeL+fp4oqIJ5k48OMhcXI6rTEmP5Af2OEeJukAYo9c+xDAWvupMSZ5rPvQbbDWhvyN96ttjGmJ6/yfBp4HjrjXLY+13WygqTGmC1AH90i6RzHG9AUigSV3Fz1is3/lJhfusrcEcGZ6FWfafP/GYf89jxjaai1ERUXTpM98AJxOHzZOaUfNTjMY3bU6mTOkYsmmfWzec/QJF9YzQkLuMGZgNwLadiVxkqS89W4tajVsgTGGD+dNZf60CQT2GET2nHkYPcV1v4/jh/eTOk06sJZxQ3ridDpp0roLKVOn8fDZeE6XPoOZNnEUS+bNoESpV3E6Xd/IPps7L+/NWgzA0YP7SZM2HdZahvfvjtPhpGWHbqTywriFhtxh4pCeNGrThcRJkrJi/nQatu5C8dKv89WeHcycMJS+o6eS7dk8DH1/HgAnjhxwjdCzlveG98bhcOLfqhMpU3lX/EJD7jB+SA+atOkaM3UW4Itd22JG4QFky5mH4R/MB+A7d+wslonDeuNwOmnkhbFTfff4QkLuMHZQd5q27UbiJEmJiori9q0bjJy8gB9PHWf80F5MXbyB7DnzMGqy695Rx2Nds+OH9sLpcNK4dWevi11oyB0mDu1Jo9bu+m7BdBq2+oP67j13fXf0gfrO6Zqa643X7LhB3WkSK+9uxcq7CUN7McWddyPdeffdkQOkTuNqaycM7YXDC/POx+Fg6OTF3L51kw+G9eDs6Z/IlO3ZR26b9dncDJjgGodw6tjBmJGhU0f1xeFwUrd5B1J4Wd4B5Hj2WZoEtKBNi2YkSpyY3Lnz4nQ4aRLQnCYBzQEYPKAfbQI7sGbVSr7+6gty5c5Di1be23ksIt4nrozEe5ABjltrC7l/ClhrK8Ra/2Bn2N3fb8daFsn955cQwBiTHdc958pZawsCm++ue8QxVgMVgXeA/dbay48srDGN3ds0sPfuRnsWyBxrs0zA+Uft/09Za2daa4tZa4t5sgMv+PJNMqR1TW/MkDY5F6/cBOBc8DUyZUgVs13G9KkeGm3XqlYZFm/aR/GCOQiPiMS/51x6NX8TbxAZGcHYgd0o88bbvFzG9S1iytRpcDgc+Pj4UL5SdX5wf8t9l7WWVYvnUKthC5YvnEndJq0p88bbbHbfsNtbZcmWnZHvzWDKvGW8Wv4tns54/wNSrLUsnT+T+k1bsXjuDBo2a8vrb73DuhVL/+CI/12RkZFMHNKTkq+/xUulXgfg0x2beKnUawC8XOYNfjr13X37WGtZu3Qu1Ro0Y/WiWdRs2IpS5Sqybd3yh47/XxYZGcn4wT0o/fpbFC/9eszyqKhI9n2+i1deLf/QPtZa1iyZQ40GzVm1aBa1G7WiTLmKfLR22ZMsusepvnt8kZERjB3UndLlKvKyO+/SpEtP8VKvY4whV978GGO4cf1azD7WWlYvnk2ths1ZsWgmdRq3csfO2/IukolD/6K++/4P6rv6zVi92F3fve6N9V0E4x4j71Ytnk3NB/Jui5fl3V1JkiYjb8GiHN3/1V9ua61lw7K5vFs3gHVLZ1O1QQtKvPYWOzaseAIljZuq1ajJhyvXMHfBYlKkSEGWrPemI5884bpus2bNxqaN6xkzfhI//vADv/562kOlFRF58uJqJ94pIJ0xpgSAMcbXGBO7t6qOe3kp4Lq19lHzMU8DhYwxPsaYzNwbRZccV0fddWPMU7g66R7JWhsKbAOmAfMetY0x5i2gJ/CutTb2TeM2AHWNMQncHYe5gH1/ftrxy+Y9R/GvXBwA/8rF2bTbNZhx8+4j1HqzCH6+TrI+k4acWdLxzbHTMfulTJaIimXys2TjXhIn8iU62mItJPD779/XwlrLlLFDyJglO+/W8o9ZfuXyxZjXez/7hCzZ7//mdte2jRR9uRRJkyUnLDQUH+ODj/EhLDT0iZU9Lrp2xdWvHh0dzdL5s3inWq371u/YsoGXXilDsuTJCQsNwcfH4GMMYWHeFTdrLTMnDOWZLNmoVLNBzPJUadJx4ojr1p/HD31Dhmcy37ffpzs2Ubh4SVfehYW54+ddeWetZfp41zX7Tk3/+9YdPbCPZzJnI026px7ab8/2TRQp7r5mw0IxPgZjfLwq91TfPT5rLVPHDSXTA7F7qeSrHD34DQDnz/xKZGQkyVOkjFm/a9vGe3nnjp3xMYR7Wd7NnDCUZzJno1KNf1jfvfRAfefjfdfs3byrHCvvXiz5Ksf+JO92b9tIUXfehYeGYtx5502xu3H9Krdvub7MDg8L5btD+3g6c7a/3O/zjzfzwoslSZIsOeFhofj4+ODjZdfsg65cdn22u3DhPJ/s3MFbFSvFrJv6wXu0CWxPZGQk0VGu5w/6+BhCQ7w3XiLeyhjj8R9PiSvTae9jrQ03xtQE3jfGpMBVzknA3a/rrxpjvsTVIRfwB4f5AvgFOAocAw64j33YGHPQfayf3dv9mSVAdWD7H6yfDCTg3tTfr621ra21x40xK4DvcI0KbHf3abfGmDFAfSCxMeYsMNtaO8gY8yKwFkgFVDbGDLbWxom5sgtGNqF00VykTZmUH7cOZej0LYybt4PFowNoXLUEZy5cpUGPOQCc+DmI1dsPcnB1XyKjouk0agXR0fcGT/ZpWZFRs7cCsOPLE7SqXYZvV/Zh9qrPPXJuT9LJY4fYs2MzWXPkpEuLugA0aBbI559s5ZefvscYSPfUM7Tucu8JrGGhIezevokBY6YA8G6tBowZ1B2n00mXWE/I/K8bOaAnRw5+y/Vr12hQpTwNm7ch5E4IG9e4vukvWbYcFSpVjdk+NDSEHR9tYOSk6QBUr9uIoX264vT1pffgUY96i/+sU8cP89nHW8icPSe9WrtuGVonoB0tOvdl4dTxREVH4evrR/NOfWL2CQsN5dMdm+k90vV8oEo16jNxSE+cTl/a9xnmkfPwhFPHD/Ppx1vIkj0n3Vu5YlcvoC1Fipfii13bKflahYf2CQsNZc+OTfQd5bpm36nRgPGDe+D09aVjn+EPbf9fpfru8d2NXZbsOenash4A9Zu14/W3qjB17GA6NauN0+mkfc9BMR8iH4xd5Zr+jB3cHafTl859R3jsXJ60U8cP89lOd33Xxl3fNW1Hi059WThtPFFRUfj6/UV9V70+E4e667ve3lPfnTx2iE/dedftEXnX2Z13gY/Iu/7uvHunpj/j3HnXyYvy7vqVS8yaMITo6GisjealUuUo9FIp9n+5m8XTx3Hz+jUmDupMlhy56Tb0fcCVd1/s3Ey3YR8A8GbVekwe3guH00mbnt6Tdw/q1rkD165dw+l00qvvAJKnSAHArp0fky9/AdKnd31xVvCFQtSqVplcufOQJ29eTxZZROSJMvdmf8qjGGO6ASmstf09XZZHSVQ4UH/Ax/TtptGeLkK8lSShw9NFiLeu3Ar3dBHiJYcXP0b+/yv2E8Hln9FnpMcXHhnt6SLEW75OXbOP61ZopKeLEG+9kDWFp4sQbyX21WcU8QivTrwMLVZ5/ENa0KyaHvkbxMmReHGFMWYt8Czw+l9tKyIiIiIiIiIi8r+iTrw/Ya2t5ukyiIiIiIiIiIiIqBNPRERERERERETiBU8+WMLTdNMNERERERERERGROE6deCIiIiIiIiIiInGcptOKiIiIiIiIiEi8oOm0IiIiIiIiIiIiEmdpJJ6IiIiIiIiIiMQP3jsQTyPxRERERERERERE4jp14omIiIiIiIiIiPyLjDFvGWNOGWN+NMb0esT6FMaYjcaYw8aY48aYpn91TE2nFRERERERERGReCE+PNjCGOMApgDlgbPAN8aYDdba72Jt1g74zlpb2RiTDjhljFlirQ3/o+NqJJ6IiIiIiIiIiMi/5yXgR2vtz+5OuWVAlQe2sUAy4+qVTApcASL/7KAaiSciIiIiIiIiIvFCXBiJZ4xpCbSMtWimtXZmrN8zAmdi/X4WKP7AYSYDG4DzQDKgjrU2+s/eV514IiIiIiIiIiIif5O7w27mn2zyqJ5G+8DvbwKHgNeBZ4EdxpjPrLU3/uigmk4rIiIiIiIiIiLy7zkLZI71eyZcI+5iawqssS4/Ar8Aef/soBqJJyIiIiIiIiIi8UJcmE77N3wD5DLGZAfOAXWB+g9s8xtQDvjMGPMUkAf4+c8Oqk48ERERERERERGRf4m1NtIYEwhsAxzAXGvtcWNMa/f66cBQYL4x5iiu6bc9rbWX/uy46sQTEREREREREZH4IV4MxANr7RZgywPLpsd6fR6o8E+OqXviiYiIiIiIiIiIxHHqxBMREREREREREYnjNJ1WvFbShEr/x3UzJMLTRYi3kiX09XQR4qWo6Aefxi5/V9KEDk8XId66fCvc00WItxL5Ke8e1+2wKE8XId4qlDWlp4sQbyXSxxMRiUfiyYMt/ic0Ek9ERERERERERCSO01AkERERERERERGJFzQST0REREREREREROIsdeKJiIiIiIiIiIjEcZpOKyIiIiIiIiIi8YKm04qIiIiIiIiIiEicpZF4IiIiIiIiIiISL2gknoiIiIiIiIiIiMRZ6sQTERERERERERGJ4zSdVkRERERERERE4gfvnU2rkXgiIiIiIiIiIiJxnTrxRERERERERERE4jhNpxURERERERERkXhBT6cVERERERERERGROEsj8UREREREREREJF7QSDwRERERERERERGJs9SJJyIiIiIiIiIiEsdpOq2IiIiIiIiIiMQLXjybViPxRERERERERERE4jqNxBMRERERERERkXhBD7aIh4wx1hizKNbvTmPMRWPMpn/p+Lf+xjYNjDFH3D9fGmNeiLXuLWPMKWPMj8aYXrGW1zLGHDfGRBtjisVansYYs8sYc8sYM/nfOId/0/SBDfh150i+XdknZlmq5InZNC2Qo+sHsGlaICmTJYpZ1y2gAsfWD+Tw2v68UeI5APx8nayf3JZvV/ahZa3SMdtO7lePF/JkenIn40Hjhw+g1ttladGgWsyyn344RccW/rT0r07/7oHcvu1KveNHDtKqYQ0CA+px7uxvANy6eYPenVpjrfVI+T3pUnAQA7q0pEPTGnQMqMWm1UsBWL5gBi1qv0XXlvXo2rIe+/d+DsDJY4fo3LwOPdo25MK5MwDcvnWTIT3beV38Lv4eRO+OzWntX422jaqzfuUSAOZOnUBr/6oENqnFsL6duXXzBgDfHT1IYJNadG5Zn/Oxcq9/1zbeF7vgIPp2akG7RtUJbFKDjatcebdkzhQ6BNSmU7M6DOzWhsuXggE4cfQQHQJq07VVAy7ExO4mA7u39brYjR02gBoVy9Ks/r367sfvTxLYrAEtG9aiTZO6nDx+FIBjhw/SvEEN2jatx7kz93KuZ0fvre8Gd21F54CadGlWmy1rPoxZ99HaZXRsUp0uzWqzeOZ7gKu+69aiLr3bNiIoVn03vGeg18XvYnAQ/Tq3JLBxddo3qRlzzd61bvlCqr5WhBvXrwKua7Zjs9p0a+3PhXPu3Lt1k0FeeM1evhjEsB6t6d6iFj1a1mbrOlfe/frz9wzsFEDP1nUZN7Azd9yfU04dP0yv1vXo374RQefv5d2oPu29LnYPWrJoATWqvkP1KpVYvGg+AJMmjKVWtcr0690jZrtNG9axZNECD5VSRETio3jbiQfcBvIbY+72HJUHzv2TAxhj/r8jEX8BylprCwJDgZnu4zqAKUBF4HmgnjHmefc+x4DqwKcPHCsU6A90+3+W6X9i0cavqdJuyn3LujUtz+59pyhQZQi7952iW9MKAOTNkYFabxahSM3hvNtuKu/1ro2Pj6H8K89x8MQZXqw9koAaJQEokDsjPj6Gw6fOPvFz8oTyb7/LiInT7ls2ceQgmrXtxMzFayhZthwrl8wHYNWHCxkwYgJNW3dg05oVACyZN5N6jZt75TcPDoeDJq078/681YyaPJ+t61dy5vTPALxTsz7jZ37I+JkfUrR4KQA2rFxM90FjaRDQjm0bVgKwctEsatQP8Lr4ORwOmrXtyvTFaxk3fRGb1y7nt9M/UajYy0yZv4rJ81eSMVNWVi6eC8DaZYvoPXQcjVq0Z8s6V+yWLZhF7YbNvDJ2AW27MGXhGsZMXciWda7YVavbmPfnrmDSnOUUK1Ga5QtmArBuxSJ6DRlLwxbt+ciddysWzaRWA+/LuzcrvcvIB+q7mZMn0rBZa2YuWkmTlu2YOXkiACuXLmTQyAkEtO7ABnd9t2juTOp7bX3npGHrzkycu4rhH8xj2/qVnP31Z44d+pZvv/yUcTOXMWHOCirXagjAplVL6DpwDPWatWX7xlUArF48m2r1m3pd/BwOB03bdGbygjWMmbqAj9aviGkrLgYHcejbr0n3VIaY7devXETPwePwbx7IR+tdsVuxcBY1G3hffefj46RBi06MnbWSwZPmsWPjKs7++jOzJw6jbkA7Rk9fRrFXXmPzKtd36FtWL6ZT/9HUbtqOjze5Yrd26Wyq1G3idbGL7ccfvmfN6pUs/nAlK1av57M9uzl18iSHDx1k5dqNREVH8cP3pwgNDWXD+rXUrlvf00UWEZF4JD534gF8BFRyv64HxHxVbYx5yT067qD73zzu5U2MMSuNMRuB7caYpMaYecaYo+4RdTViHWO4MeawMeZrY8xTD765tfZLa+1V969fA3eHk70E/Git/dlaGw4sA6q49zlhrT31iGPdttZ+jqszL8754sBPXLl+575l77xakMUb9wKweONeKr9WMGb5ym0HCI+I5Nfzl/npzCVezJ+NiMgoEiX0xem4l3YD2r7D0Gmbn9yJeFjBwsVIljzFfcvO/naaAoWKAlDkxRJ8vvtjAJxOJ2FhYYSFhuJ0Ojl/9gyXLgZTsHCxh47rDVKlSUeO3K5RnYkSJyFT1uxccY9+ehSH00l4WBhhYaE4nE6Czp/hyqWL5Huh6JMqcpyROm06cuZxxS5x4iRkzpqDyxeDKfLSKzicru8y8uQryKWLvwOu3AsPv5d7F86d4fKlYAoU8r7cS50mHc/mvhc7V95dJHGSpDHbhIWGxPyHNSbvQkNwONyxuxhMfi+MXcHCxUj+QH1njOHO7duAa8ROmnTpgFj1Xdj99d0LRbwvbgCp0qQlR668gKu+y5glG1cuBbN9wyqq1G2Mr58fAClSpQZcnX53r1mHw0nQ+bNcuRTM895Y38W6ZhMlTkKmLNljRsrOnTKexq06Afc6mBwOJ+FhoffVd1cuBZO/kPfFLlWatGSPlXfPZM7G1csXOX/uN/IWKAJAgSIvse+LXcD97azT6eT382e5evkizxX0vtjF9vPPP1Gw4AskSpQIp9NJ0WIvsuuTj4mIiMBaS1hoGE6nkwXzZlOvQUN8fX09XWQRkXjHGM//eEp878RbBtQ1xiQECgJ7Y607CZSx1hYGBgAjYq0rATS21r6Oa/TbdWttAfeIuk/c2yQBvrbWvoBr1FyLvyhLM1ydigAZgTOx1p11L/tPSZ8mGUGXXNPvgi7dIF3qZABkTJeCs0FXY7Y7F3yVZ9KnYOfXJ3kqTXI+XdSNifM/plLZAhz87jcuXLzukfLHFdly5OSrz3YD8Okn27kYHARA3YbNmDR6MGtXLObdmnWZN+MDmrRs57mCxiHBQef55ceT5HouPwAfrVtB5+Z1mDJ2cMyU0Or1mjJ94jA2rV7K21XrsHTOVOo2bePJYscJv184x88/nCTP8wXuW75jyzqKvewaxVjLP4DJY4eyftUS3qlel4WzJuPfrK0nihun/H7hPD//cIrc7rxbNHsyAbXeYs+Oj6gf4MqtmvUDmDJuGBtWLaVStbosnj2ZBopdjLadejBz8gTqvlue6R9MoHmbjgDUa9yMiaMGs3rZYqrWqsuc6R/QVPUdcLe+O0XOvPm5cO43Th47RJ/Axgzs0pIfTx4HoFq9JsycMJwtaz7kraq1WTZ3CnWaqL77Peg8P//oumb3fbGHNGnTkz1n7vu2qdEggKnjh7Fx9VLerlaHJXOmUD9A1+zFoPP8+tMpns2Tj8xZc7D/a9cEkr2f7uSK+wufd+s0Yfb7I9i69kPKV67NigVTqdWotSeLHSfkzJmb/fu/5dq1q4SEhPD5Z59y7dpVypWvQJ2aVcmYKRNJkyXj+LFjvPb6G54uroiIxDPx+sEW1tojxphsuEbhbXlgdQpggTEmF2CB2F9z7bDWXnG/fgOoG+uYd3ufwoG799fbj2u67iMZY17D1YlX6u6iRxX3r87n7zLGtARaAjgzvYozbb5/69D/jkd0S1sLUVHRNOkzHwCn04eNU9pRs9MMRnetTuYMqViyaR+b9xx9woX1vC59hjB14igWz5tOiVKv4nS6UvXZ3Hl5f5br3mVHDn5LmrTpsNYyvH93HA4nrTp0I1XqNJ4sukeEhNxh7KDuNG3bjcRJkvJm5ZrU9HdNuftw3jQWTJ9Iu+4DyZ4zD6Mmu+4zc/zIAVKlSQfWMn5oL5wOJ41bdyall8Uv5M4dRvTvRov23e8bSbZ84SwcDgevln8bgBy58jJ+umu61LFD+0md1jVaavTAHjicTpq16+p1uRdy5w6jB3ajeWC3mNg1bB5Iw+aBrFoyh81rl1O/aRty5MrD2GkLATh+2BU7a2HM4J44HU4C2nbxuryLbeOaFbTp2J0yr5dn98fbGDd8IGMnzyJn7rxMnvNAfYdlaN/uOJxOWnfoRuo03he30JA7jB/cgyZtu5I4SVKioyK5dfMGwz+Yz0+njjNxWG8mL1pPtpx5GD55PgDfues7i2Xi0N44nE4ate5EylTeFb+QkDuMHtCNZu264nA4WLl4DoPGTnlouxw58zBmaqxrNo2rrR07uCdOp5Ombbzvmg0NucOkYT1p2KoLiZMkpWWXASyYNo61S2ZT5OUyMZ9Tsj2bhyGT5gFw4ugBUqV2xe79Eb1xOpw0aNmJFF6WdwA5nn2WpgHNad0igMSJE5M7dx7XNO+AFjQNcI0JGDygL20DO7Bm1Uq++upzcufOQ4tW6jwWEfm7vPm2DfF9JB7ABmAcsabSug0Fdllr8wOVgYSx1t2O9drw6A62CHvvrrxR/EGHpzGmIDAbqGKtvexefBbIHGuzTMD5vz6Vv8daO9NaW8xaW8yTHXjBl2+SIW1yADKkTc7FKzcBOBd8jUwZUsVslzF9qodG27WqVYbFm/ZRvGAOwiMi8e85l17N33xyhY9DsmTLzqj3ZjB13nJeK1+RZzJmvm+9tZal82fRoGkrFs2dTsNmbSn3ViXWrVjioRJ7TmRkBGMHdad0uYq8XPp1AFKmToPD4cDHx4fylarxg3tkyl3WWlYvnk2ths1ZsWgmdRq3oswbb7N57TJPnILHREZGMKJ/V14t/zavlC0Xs3znRxvY99VndOs/4qHG0FrL8oWzqNe4JUvnTad+QBteq1CJjauXPnj4/7TIyAhGDexG2TcqUqJMuYfWlylXka/27LxvmbWWFYtmU6dRS5YtmEH9Jq15tfzbbFzzYFPlXbZv2UDp11wjT8qWq8DJ747dt95ay+J5s2gY0IpFs6fTuEVb3nirEmu9sr6LZPygHpQu9xbF3fVd6rRPUbzUaxhjyJk3Pz7GcPP6tZh9rLWsWTKHGv7NWbVwFrUbt6JMuYp85IX13egB3Sj7xtuUKFOOC+fPEhx0jk7N69KibiUuXwymS8sGXL1yKWYfay0rFs+hdqMWLF8wk3pNW1O2/Nts8rJrNjIykklDe1Lytbd4sZQr757JnI3eIyYzfPIiXnm1Aumfvn9yibWWdR/OpVr9ZqxZMouaDVtRslxFtq1f7olTiBOq1ajFspVrmbtgCclTpCRL1qwx606e+A6ArFmzsWnjOsaOf48ff/iBX3897aHSiohIfPJf6MSbCwyx1j44hCsF9x500eRP9t8OBN79xRiT6k+2vY8xJguwBmhorf0+1qpvgFzGmOzGGD9cI/02/N3jxheb9xzFv3JxAPwrF2fT7iOu5buPUOvNIvj5Osn6TBpyZknHN8dOx+yXMlkiKpbJz5KNe0mcyJfoaIu1kMDPO+8JcvWKq+83OjqapfNnUqlarfvW79iygeKvlCZZ8uSEhYbi42MwxofQsDh5+8T/GWstU8cNJVOW7Lxbyz9m+dXLF2Ne7/18F1myPXvffru2baRI8VIkTeaOn/HB+BjCvSh+1lreGz2YzFmzU61Ow5jl+/d+waql8xkwchIJEyZ6aL+dWzdQrERpV+zCQvHx8cEYQ1iod8XugzGDyZwlO1Vq34vd+bO/xrze9+UeMmbJdt9+n2zdSLGXS8fknfHxwfj4eFXsHiVN2nQcPvAtAAe/3UvGzFnuW79t8waKl3TVd6FhoRgfg4+PD2FedL2CK++mjxtCxqzZeafmvfruxZJlOXbIFb/zZ38lMjKSZClSxqzfs33TvfrOHT9vyztrLZPHDCFT1uxUqe2KXbYcuViwdiezlm1m1rLNpEmXngkzl5AqddqY/T7ZtpFisWNnfDDGu3LPWsusiUPJmCUbb9doELP8+jXX5JXo6GjWfTiXcpVq3Lffpzs2UfilkiRJlpzwsDCMMV4Xuwdduez6bHfhwnk+2bmdihXfiVk35YP3aBPYgYjISKKiogAwPj6EhnhvvERE5O+L19NpAay1Z4H3HrFqDK7ptF24d5+7RxkGTDHGHMM14m4wro65v2MAkAaY6h7BEukeIRdpjAkEtgEOYK619jiAMaYa8AGQDthsjDlkrX3Tve40kBzwM8ZUBSpYa7/7m2X5n1owsgmli+Yibcqk/Lh1KEOnb2HcvB0sHh1A46olOHPhKg16zAHgxM9BrN5+kIOr+xIZFU2nUSuIjr432LFPy4qMmr0VgB1fnqBV7TJ8u7IPs1d97pFze5JGDOjBkYPfcv3aNepXeYOGzdsSeucOG9a4vq0uVbYcb1aqGrN9aGgIOz7awMhJ0wGoUbchQ/p0wenrS5/Boz1xCh5z8tgh9uzYTJbsOenash4A9Zu14/NPtnH6p1OAIX2GZ2jduU/MPmGhIezevokBY1xTqCrX9Gfs4O44nb507jviUW/zn/Td0UPs2raJbDly0T6gNgCNWrRn5vtjiAgPp18X1z2M8jxfkMBu/QBX7u3cupGh411PF61auyEj+nXF6etLj4GjPHMiHnDi6CF2b99M1hy56NSsDgD+LQL5eMs6zv32K8bHh/RPPU2bLn1j9gkLDeGTbRsZPG4qAFVq+zN6QDecvr507T/SI+fhCcP69+DwAVd9V6fyGzRu0ZYuvQcyZeJooqKi8PPzo0vvgTHbh4aGsH3LBsa876rvatZryODeXXA6fek71Lvqu1PHDvPpx1vIkj0n3Vu5nlxZL6Atr79VhanjhtC1eW2cTl/a9RgUM4I2LDSUPds30Xe0q757p2YDxg/qgdPXl459h3vsXJ60E8cOsXvHZrLmyEmn5q67pfg3D4y55+ejhIWGsGvbppjptu/WasDogd1xOp1edc1+f/wwn+/cQuZsOend1pV3dZq0I+j8b+xwP/X4xZKvUrZC5Zh9wkJD+ezjzfQaMRmAitXrM2lYT5xOXwJ7DXvyJxFHdO3cnuvXruF0OunddyDJU7ge8vPJzo/Jl78A6dO7npf3wguFqVmtMrly5yZP3ryeLLKISLzixbNpMfdmjEp8lKhwoP6Aj+nkx+M9XYR462ZIhKeLEG8l8HV4ugjxUlS0qrrHlTShcu5xXb4V7ukixFt+jv/CZA/PuB0W5ekixFv5MiX3dBHirUTeOSFGJD7z4m4syNtrm8f/c3By1Jse+RvE+5F4IiIiIiIiIiLiHXx8vLcPU1+TioiIiIiIiIiIxHHqxBMREREREREREYnjNJ1WRERERERERETiBW9+sIVG4omIiIiIiIiIiMRxGoknIiIiIiIiIiLxgvHioXgaiSciIiIiIiIiIhLHqRNPREREREREREQkjtN0WhERERERERERiRe8eDatRuKJiIiIiIiIiIjEderEExERERERERERieM0nVZEREREREREROIFPZ1WRERERERERERE4iyNxBMRERERERERkXhBI/FEREREREREREQkzlInnoiIiIiIiIiISByn6bQiIiIiIiIiIhIvePFsWo3EExERERERERERies0Ek9EREREREREROIFPdhCRERERERERERE4iyNxBOvFRoR5ekixFt+vur/f1w+3vul0f+Lj1OBe1w3QyM9XYR4K4HT4ekixFvhkdGeLkK8lT9zck8XId5KqP/ZiIjIf5yaOhERERERERERiRe8eDatptOKiIiIiIiIiIjEdRqJJyIiIiIiIiIi8YIebCEiIiIiIiIiIiJxljrxRERERERERERE4jhNpxURERERERERkXjBi2fTaiSeiIiIiIiIiIhIXKeReCIiIiIiIiIiEi/owRYiIiIiIiIiIiISZ6kTT0REREREREREJI7TdFoREREREREREYkXvHg2rUbiiYiIiIiIiIiIxHXqxBMREREREREREYnjNJ1WRERERERERETiBT2dVkREREREREREROIsjcQTEREREREREZF4wYsH4mkknoiIiIiIiIiISFynTjwREREREREREZE4TtNpRUREREREREQkXtCDLURERERERERERCTO+k934hljqhljrDEm72PuP9sY87z79WljTNoH1ic2xmw2xpw0xhw3xoyKtS6BMWa5MeZHY8xeY0y2WOu2GmOuGWM2PXC8QPf29sH38rTpAxvw686RfLuyT8yyVMkTs2laIEfXD2DTtEBSJksUs65bQAWOrR/I4bX9eaPEcwD4+TpZP7kt367sQ8tapWO2ndyvHi/kyfTkTsaDLgYH0bdjC9o2rE67xjXYsGopAIvnTKF909p0bFaHAV3bcPlSMADfHT1E+6a16dKyAefP/gbArZs3GditLdZaj52HJ1z8PYg+HVvQxr86bRvVYMNKV+zmTp1Ia/9qtG9Sm+F9u3Dr5k3AHbsmten8QOwGdPXO2PXq0JxW/tVo3bA661YuAeCzXdtp3bA6lcoU5vuTx2O2P37kIG0b16Jji/qxYneDfl3aeGfs2jenVYNqtPavzroV7th9sp3W/tWpVPoPYtdcsbsYHES/Ti0JbFSd9k1qstFd3921btlCqr5ahBvXrgJw4ughOgbUplsrfy7EumYHdffCazY4iL6dWtCuUXUCm9SIid2SOVPoEFCbTs3qMLDbvbbixNFDdAioTddWDe6L3UAvjN2l4CAGdW1Jp4AadG5Wi81r7uXdR2uX0aFJdTo3q8Wime8BcPLYIbq2qEOvtg25cO4MALdv3WRYz3ZeF7sHLVown2rvVqJ6lXfo2a0LYWFhTBw/lprVKtO3d4+Y7TZuWMeSRQs8WFIREfE2xnj+x1P+0514QD3gc6DuP93RGOOw1ja31n73F5uOs9bmBQoDJY0xFd3LmwFXrbU5gYnA6Fj7jAUaPuJYXwBvAL/+0/L+ry3a+DVV2k25b1m3puXZve8UBaoMYfe+U3RrWgGAvDkyUOvNIhSpOZx3203lvd618fExlH/lOQ6eOMOLtUcSUKMkAAVyZ8THx3D41Nknfk6e4HA4CGjXhamL1jB22kK2rF3Ob6d/onrdxnwwbwXvzVnOiyVKs3zBTADWLV9E76FjadiiPR+tXwnA8oUzqeUf4HVDiB0OBwFtuzBt8RrGTV/IZnfsChV7mSnzV/LB/BVkzJSVVYvnArBumSt2jVq056N17tgtmEmtht4Zu+btujJj8VomzFjEpjXL+e2Xn8iaPSf9hk8g/wtF7tt+7fJF9B02jsYt27PZHbsPF8yiTsNm3hm7wK7MWLKWCTNjxS5HTvqNeETslrlj16o9m9e6Yzffe2PXtG1nJi9cw5ipC/ho3QrOnP4ZcHVSHdr/NemeyhCz/foVi+g5ZBz+LQL5aMMqAFYsmkXNBt4Zu4C2XZiycA1jpi5kyzpXfVetbmPen7uCSXOWUyx2W7FiEb2GuNuKDa68W7FoJrUaeGd916h1ZybNXc2ID+azbf1Kzvz6M8cOfcM3X+5h/MxlTJyzkndruT6GbVy1mG4Dx1KvWTu2b3TFbtXiWVSr732xi+33339n6ZKFfLhiNWvWbyI6OopVK5Zx+NBBVq3dSHRUFD98f4rQ0FA2rFtL7br1PV1kERERr/Cf7cQzxiQFSuLqTKsba/mrxphPjTFrjTHfGWOmG2N83OtuGWOGGGP2AiWMMbuNMcX+6D2stXestbvcr8OBA8DdIWVVgLtfS64Cyhn3p0Fr7U7g5iOOd9Bae/r/d+b/G18c+Ikr1+/ct+ydVwuyeONeABZv3Evl1wrGLF+57QDhEZH8ev4yP525xIv5sxERGUWihL44HffSbkDbdxg6bfOTOxEPS50mHc/mdo1MTJw4CZmyZufyxYskTpI0ZpvQ0BDA9R8Hp9NJWFgYYWEhOJ1OLpw7w5VLweQv9Idp+Z+VOm06cua5F7vM7tgVeakEDqfr9p558hXg0sXfAXA4nYSFhxEWGoLDHbvLl4IpoNiRJVsOLl0KJku2HGTKku2h7R1OJ+FhYYSFhsbk3eWLwRQorNg9VuwueWnsYtV3ie7Wd+6RY3Mnj6dxq07crevgbuxCXbFzuOu7i8HkL1TUA6X3rEe1FVcu3d9WhIWGxHQy3cu7EByOe9esN7YVqdKkI0eue3mXMUt2rlwKZvuGVVSt2wRfPz8AUqRKDYDD4SQ8PIzw0FAcDidB589w5dJF8r3gfXn3oKioKMJCQ4mMjCQkNJRnMmYkIiICay2hYWE4nU7mz51Nff+G+Pr6erq4IiIiXuG//GCLqsBWa+33xpgrxpgi1toD7nUvAc/jGvG2FaiOq6MtCXDMWjsA/tnNEo0xKYHKwHvuRRmBMwDW2khjzHUgDXDp/3dacUf6NMkIunQDgKBLN0iXOhkAGdOlYO/R0zHbnQu+yjPpU7Bh1xHqV3qJTxd1Y+L8j6lUtgAHv/uNCxeve6L4Hvf7hfP8/MMp8jyfH4BFsyaza9smEidNyvBJrtEVNRsEMGXcMBL4JaBz32HMmzaBBs3aerLYccLvF87zU6zY3bVjy3pKv+4aEVrLP4ApY4fhlyABXfoOY+7UCfgrdvx+4Rw/fX+SvM8X+MNtavsH8P7YoSRIkIBu/YYze8oEGjZX7P5W7BoG8P4Yd+z6K3Z33a3vcj+Xn31f7CFNuvRkz5n7vm1q1A9g6rhh+CVISKc+Q5k/bSL1dc3eFzuARbNdbUWSJEkZdretqO9qK/wSJKBzH7UVdwUHneeXH0+SK29+Fs18jxPHDvLhvCn4+iWgUctO5Mybj2r1mjJjgit27XsNZeGMSdRt0sbTRfe4p556isZNAnjzjddImDABJV4pyWuvv8HpX36hTo2qvPRyCZImS8bxY8do3TbQ08UVEREv482j5f/LnXj1gEnu18vcv9/txNtnrf0ZwBjzIVAKVydeFLD6n76RMcYJfAi8f/e4xB5ecM+/cnMVY0xLoCWAM9OrONPm+zcO++95xAVlLURFRdOkz3wAnE4fNk5pR81OMxjdtTqZM6RiyaZ9bN5z9AkX1jNC7txh1IBuNG/fLWZkRcMWgTRsEcjKxXPYvGY59QPakCNXHsZNWwjAscP7SZ0mHdbCmEE9cTidBLTtQqrUaTx5Kk9cyJ07jOzfjRaxYgewfOFsHA4Hr5Z/G8AVu+nu2B3aT+q06bDA6IE9cTqdBLTzztgN79eNlh263xe7Bz2bKy8TZywC4Ojd2FkYObAHToeT5oFdvTN2fbvRsuPfiN3MR8RuQA+cTu+N3eiB3WgW2BWHw8HKxXMYNHbKQ9vlyJWHMe767vjhu7GzjB3cE6fDSdO2XUjppbFrHhirrWgeSMPmgaxaMofNa5dTv6mrrRj7UOxgjDt2Ad4Yu5A7jBvcnaZtXbGLjori9s0bjPhgAT+eOs6EYb2YsmgD2XPmYcRk18SJ744cIFWadFgsE4b2wul00qh1Z1Km8q7YAdy4fp1dn+xky/adJEuWjO5dOrJp43qaNmtB02YtABg0oC9t23dgzaqVfPXl5+TKnYeWrdV5LCIi8r/0n5xOa4xJA7wOzDbGnAa6A3XMve7aBzvT7v4eaq2Neoy3nAn8YK2dFGvZWSCzuzxOIAVw5TGO/RBr7UxrbTFrbTFPduAFX75JhrTJAciQNjkXr7hmCJ8LvkamDKlitsuYPtVDo+1a1SrD4k37KF4wB+ERkfj3nEuv5m8+ucJ7UGRkBKMGdKPsGxV5pUy5h9aXfaMiX366875l1lpWLJxNncYtWTZ/BvWbtubV8m+zafWHT6rYcUJkZAQj+3fj1fIVeaXsvdjt/GgD33z1KV37D3/oWxlrLcsXzqZu45Z8OG8G9QNa82qFt9nohbEb3q8rr5Z/m5JlH867R7HWsmzhLOo1acnS+dPxD2jDa29Winkgi7eIiV2Ffxi7BbOo17glS+dNx79ZG16rUCnmgSzeIjIygtEDu1H2jbcpUaYcF86fJfjCOTo1q0uLOpW4fDGYLi0bcPXyvUHq1lpWLJpD7UYtWL5gJvWatKZs+bfZtMb7rtlRA11tRYlHtBVlylXkqz2PaCsWzaZOo5YsWzCD+k1cbcVGL4zd+EHdKV2uIsVLvw5A6rTpKV7qdYwx5MqbHx9juHH9Wsw+1lpWL5lNTf/mrFw4kzqNW1G63NtsWbvMQ2fhWV9//SUZM2UiderU+Pr6Uu6NChw+eDBm/YkTrltGZ82ajY0b1jF2wnv8+OMP/PrraQ+VWEREvImnH2qhB1v8+2oCC621Wa212ay1mYFfcI24A3jJGJPdfS+8OrgefvFYjDHDcHXQdXpg1QagcazyfGL/Y48527znKP6ViwPgX7k4m3YfcS3ffYRabxbBz9dJ1mfSkDNLOr45djpmv5TJElGxTH6WbNxL4kS+REdbrIUEfv/9+6lYa/lg9GAyZc1O1Tr3nm1y/uy9Z5ns+2LPQ/fa+mTrRoqVKE3SZMkJCw3F+Pjg4+NDWFjokyq6x1lreX/0YDI/ELv9e79g9dL59B85iYQJEz20386tG3nxbuzCQvHx8cEYH8JCvSt2k0YNJnO27FSv+6hn6jzaxx9t4MUSpUl2N++MDz7GeF/sRrry7rFilzxW7HyM112zk8cMIVOW7FSp7Q9Athy5WLBuJ7OWb2bW8s2kSZeeCTOXkCrNvQeyf7J1I8VeLnVffWd8vO+a/WDMYDJnyU6V2n/QVny5h4yPaiteLu31sZs2bigZs2anck3/mOUvlXyVo4e+AVxxjIyMJHmKlDHrd2/fSJHipWLaClc7awj3otjFluHpZzhy+DAhISFYa9n79Vdkf/bZmPVTPniPtoEdiIyMJDrK9f23j/EhNMQ74yUiIvKk/Fen09YDRj2wbDVQH1gOfOVeXwD4FFj7OG9ijMkE9AVOAgfcI4AmW2tnA3OARcaYH3GNwIv9cI3PgLxAUmPMWaCZtXabMaYD0APIABwxxmyx1jZ/nLL92xaMbELporlImzIpP24dytDpWxg3bweLRwfQuGoJzly4SoMecwA48XMQq7cf5ODqvkRGRdNp1Aqio+/1X/ZpWZFRs7cCsOPLE7SqXYZvV/Zh9qrH7kuNN04cPcSu7ZvJmiMXHZvVAVzTaHdsXse5M79ijA/pn3qatl37xuwTFhrCJ1s3Mnj8VACq1PZnVP9uOH196TZgpEfOwxO+O3qIXds2ky1HLjoEuGLXqEUgM98fS0R4OP27uO5hlOf5ArTr1g9wPSTkk60bGeKOXdXa/ozs54pd94HeFbtPtm0iW45cBDatDUDjlu2JiIhg2qRRXL92lUE92pMjZx6GTZgGuGK3c+vGmN+r1WnI8P5d8XX60mPgg9Xrf9d3R9yxezYXgU3csWvVnojwWLHr3p4cuR6I3UcbGTbRHbu6DRnezx27Qd4TuxNHD7F7+2ay5shJp2auJtC/RSDFXi71h/uEhYawa9smBo1zTbd9t3YDRg/ojtPXSdf+3nPN3otdLjq52wr/FoF8vGUd5377FePjaivadHmgrdi2kcHj7rUVowe46jtvit3JY4f49OPNZMmek26t6gFQP6Adr71VhWnjBtOleW2cTiftegyKGbkdFhrCnu2b6DfalXeVa/ozblB3nL6+dOo7wmPn4kkFC75A+QpvUrdWNRwOJ3mfe46atVy5+MnOj8mfvwDp0z/l2rZQYWpUrUzu3LnJkzevJ4stIiLyn2f+Y4PD/pIx5lWgm7X2HQ8X5V+RqHCgd/0B/0WHPhrj6SLEW158H9H/N4eC93gUtscWHhnt6SLEW0aJ99iUd48v99N/fN9N+XMJ/6vDE0REHubVH1JKj//c4/0gn3Ut5ZG/wX91Oq2IiIiIiIiIiMh/htd9X2Wt3Q3s9nAxRERERERERETkH3rwYYbeRCPxRERERERERERE4jh14omIiIiIiIiIiMRxXjedVkRERERERERE4icvnk2rkXgiIiIiIiIiIiJxnUbiiYiIiIiIiIhIvKAHW4iIiIiIiIiIiEicpU48ERERERERERGROE7TaUVEREREROT/2LvzeKvn/IHjr/e950qW9kxDiLGObYQxtixjJ2tFKqVSVAgJ2Zcm+zZSskVly5bKvjNmkKIYzA+DQlNJZHRvqs/vj3O6bglp6p5zndfT4zzuPZ/v5/s978/nnnvKu/fn+5GkGqGIV9NaiSdJkiRJkiQVOpN4kiRJkiRJUoFzOa0kSZIkSZJqBHenlSRJkiRJklSwrMSTJEmSJElSjVDEhXhW4kmSJEmSJEmFziSeJEmSJEmSVOBcTitJkiRJkqQaoaSI19NaiSdJkiRJkiQVOCvxJEmSJEmSVCMUcSGeSTwVrwUp5TuEGqvOymX5DkFFZs7c+fkOocbyo27ZzZ3v+25Z/W6N1fIdQo21sn87lyRJP8LltJIkSZIkSVKB89/6JEmSJEmSVCNEEa+ntRJPkiRJkiRJKnBW4kmSJEmSJKlGKCneQjwr8SRJkiRJkqRCZxJPkiRJkiRJKnAup5UkSZIkSVKN4MYWkiRJkiRJkgqWlXiSJEmSJEmqEYq4EM9KPEmSJEmSJKnQmcSTJEmSJEmSCpzLaSVJkiRJklQjBMW7ntZKPEmSJEmSJKnAWYknSZIkSZKkGqGkeAvxrMSTJEmSJEmSCp1JPEmSJEmSJKnAuZxWkiRJkiRJNUJE8a6ntRJPkiRJkiRJKnAm8SRJkiRJkqQC53JaSZIkSZIk1QhFvJr211+JFxEpIoZVeZ6JiOkRMWY5XHuviHg9Iiblvu5R5dg2ufb3I+K6yC3ajogWETE+IuZFRKvFrvdYRMxaHrEtb4PPa8fHTw9g3Mh+lW3166zCmEG9mDTqXMYM6kW91WtXHuvTeW/eGnUebz54DnvusCkAK5VlGHV9D8aN7Ee31rtU9r3+7LZstXHT6htMHk2fNpWze3ej19GHcUKnVoy+785Fjj909x0csltzvp71JQDvTHqDkzq3oU/39nw+5RMAvpk9m/NP60FKqdrjz6fLLjqHw/bdlc5tD61su/CsPhzbvhXHtm9F20P24dj22V+pt96cQNd2h3F8pyP5dPLCefuavid2L7p5A+fufzH9P1M548SudG9/KMd3OIxRI0cAcMvAq+je7hB6dmzNxf1O5pvZXwPwz4kT6NmxNb2PPYrPpnw/f+eccnzRzd+MaVM55+Ru9Op4GCcu6fPunjs4dPfmfP3V9593vbu04bTj2vP5p9m5++83s7mgCD/vZkybygWndufkzq04pUsbHnngLgCuvuhMTut+FKd1P4qe7VpyWvejAHj3rTfoc+yRnNnjaKZ+OhnIzl3/03sV3dxdcG4/9tx1R9oc2rKy7auvZtGjW2cOOXAfenTrzNdffwXAGxPGc8ThB9GhbSsmf/IxALO//pqex3UpunmTJEk1w68+iQf8F9g8IhZmmPYCPl1O154BtEwpbQF0BIZVOTYI6AZsmHvsm2v/BOgELPp/M1mXAx2WU2zL1bDR/+DgngMXaetzzF489+p7bHHwhTz36nv0OWZvADZZvwmt92lO81b9OajnDVx7ZhtKSoK9dtyUCe9MZrs2A+h8+E4AbLHRWpSUBG++N6Xax5QPpaWlHNPjZK6/4wEuu+F2Hn3oXiZ/9CGQTfC98fo/aPybJpX9R907jNMvvIL2x/bi0YfvA+DeYTfRql2XoruZ5z4HHswl1wxapO3c/ldw0/D7uGn4fbTYfU922e3PANx75+2cP+Bquhx/Ig8/cA8Aw269kXaduhbdvIFz978oLS2la89TuXH4g1x54zDGPHAPn/z7A7be7k/ccPt9DLx9JGuuvS73Dr8VgAfuGUa/i6+gY7cTeOShkQDcfftNtOlQfL+zJaWldDr+ZK6//QEuveF2Hh31/efdjGlTeXPcYp93I4fR94IraNe1F4+Nyn3e3XEThxfh511paYYOx53M1bfeR/+/3sbjo0Yy5eMPOfmcAVx+451cfuOdbL/LHmy/8+4AjLlvBKeedxltu/TgidHZubt/+M0cetQxRTd3LQ86lL8OummRtqG33MR22/+Jh8Y8znbb/4mht2SPD7/jNi6/6jp6nngyI+/NJkpvGnIDnbt2L7p5kySpJimJyPsjb2PP2ytXr0eBA3LftwXuWnggIv4YES9HxITc141z7S9GxB+q9PtbRGxZ9aIppQkppc9yT98GVo6IWhHxW6BOSunvKftPuXcAh+TO+SilNBFYsHiQKaWngdnLZ8jL19/Gf8DMr75dpO3A3bZk+OhXABg++hVa7r5lZfvIx8cz97t5fPzZF3wweQbbbd6M7+bNp/bKZWRKv3/bndvjQC4aNLb6BpJnDRo25ncbZSsTa6+yKk3XXY8vZkwD4Nbrr6Rj997A9x8IpZkMcyvKqSgvJ1Oa4fNPJzNz+jQ2/8M2eYg+v7baelvq1Km7xGMpJZ576nH22Ht/ADKZDBW5eSvNZPh0ymRmTJvGVs23q86QC4Zzt+waNGrMBhtnf2dXWWVV1m62Pl/MmEbzP+5IaSZ7R4pNNtuSL6b/B8jO39yKCspz8/f5p5P5Yvo0tth627yNIV9+8Hm3TpXPu4FXcvRin3eZ0u8/7xbO3cwZxfl5V79hI9bfcBMgO3drrdOMmbm5g+zv7d+ff4qddt8HyCb95s6tyM5daYapn01h5oxp/H6r4pu75ttuR926i37ePf/s0xx40CEAHHjQITz3zFPAws+77O9rJlPG5MmfMH3aNLbZ9o/VHbYkSdJSKZZ74t0NnJtbprolcCuwcD3nu0CLlNK8iNgT+AtwOHAz2Yq53hGxEVArl3z7MYcDE1JKFRGxFlC1tGwKsNbyHFAhWKPh6kydkV1CNnXG1zRusDoAazWuyyuTPqrs9+m0L1lzjbo8/OxEjjrgj7wwrA9XD32KA3bdggn//ITPp3+Vj/Dz7j+ff8aH//ceG226Oa/+7XkaNl6D9TbYaJE+hx/VmRuuuJiVaq1M734XMXTQ1RzVpUeeIi5cE994nfoNGtJ0nXUBOKpjV64acCG1atXizPP/wuDrruSY7r3yHGVhcu6W3n8+/5QP//UuG/9+i0Xanxz7ELvskU2mtG7fmb9efhEr1apFn7P7c8vAq2jf1d/ZaVM/49/vf/9516DREj7v2nVm0JXZz7uT+l3E7YOupm1n527h3G2wyeaVbe9MmkDd+g34bdN1ADi0bSeGXNWflWrVotcZFzLsxms4otPx+Qq54Hwx8wsaN14DgMaN12DmzJkAHNOlGxdfcC61Vq7FRf0v45qrLuP4nifmM1RJkqSfVBRJvJTSxIhoRrYK75HFDtcFbo+IDYEElOXaRwLnRMRpQGdg6I9dPyI2Ay4F9l7YtKQwljX+JbxeN7JLdck03Y1Mo82W16WXjyWUlqYE8+cvoFO/oQBkMiWMHtiTVr1v5NJTD2PtJvUZMeZVxj4/qZqDzY85337Lpef1oUuvUyktLWXk8Fs4//KBP+i3/oYbc9mgOwB4+83XadCoMSklLr/gdDKlGY7pcQr1GjSs7vALzjNPPFpZSQawwUabMPDW7L3L3pwwjoaNG5NIXHhWHzKZDMed2IcGDRvlK9yC4twtnTnffkv/s/tw7Imnscqqq1W2333HTZSWlrJ7bg5/t+EmXHVj9s4Kb72R/Z0lwSXn9aW0NEPXXqdSv8h+Z+fM+ZZLz+1D557Zz7v7ht/CeUv4vFtvg4259IbvP+/qN8x+3l1xwemUZjIcc3zxfd6Vz/mWKy/oS6cepy7yvvvbM49XVuEBNNtgY/pfPxSAf04cn507EldfdCalmQxHH9ebevWLa+6WxsabbMrtI7K3DRg/7jUaN86+58447WQymQwn9zmdhkX4eSdJUqEr5rteFMtyWoCHgSuospQ25yLg2ZTS5kBLYGWAlNK3wJPAwUAblnwPOyKiKfAgcHRK6YNc8xSg6k4NTYHPFj93WaWUhqSUtk0pbZvPBN60L2bTpFEdAJo0qsP0mdmVwJ9Om0XTJvUr+621Rv0fVNt1b92C4WNeZfst12fud/Nof/qtnNF1H4rBvHnfcel5fdh1z/3ZocWf+fyzKUz7/FN6dzmSY484gC+mT+OUbu348osZleeklLh32C20OfpY7rl9CG07Hceue+3PmAcWfzsXn/nz5vHSs0+x+54/fP+klBh+6xA6dO7OHTcPotOxPdhz3wN58N4l/joXHedu6cyb9x1/OftUdt9rf3ba9c+V7U89+jCvvfwifc79yw/un5VS4u47buLITt24c+hg2nU+nt33OYCH7yuu+Zs37zsuO7cPLXKfd1M/m8J/pn7KyV2PpNuR2c+7U7u148uZi37ejRye/by79/YhHHlMcX7ezZs3jyvP78suf96X7Xep3DeL+fPn8epLz7Ljbnv94JyUEg+MuIXD23flvjtuok3H7rT48348+uDd1Rl6wWnYoCHTp2eXI0+fPo0GDRoscjylxM03DaJr9x4MGTyQ7j1OYP8DDuLuEcOWdDlJkqS8KaYk3q3AhSmlxUu96vL9RhedFjt2M3Ad8FpKaebiF4yIesBY4MyU0t8WtqeUPgdmR8SfcrvSHg2MWh6DKCRjn59E+5bbA9C+5faMeS672njscxNpvU9zVirLsO6aDdlgnca89tZHlefVW702+7XYnBGjX2GV2mUsWJBICWqtVLakl/lVSSlx/WUX0nSd9Ti4TXsAmq2/Ibc/9DQ33TOWm+4ZS8PGa3DVkBHUr/Kv/888Nppt/7Qzq61eh4rycqKkhCgpoaK8PF9DKRivv/YP1m623iI3yF/o8bGj+NNOLVi9Tl3KF85blFBePicPkRYe5+7npZS49pILWLvZehx65Pf7Do175W/cN2Io5w64hpVXrv2D85569GG222EXVs/9zpZECSURRfU7m1Ji4GUX0nTd7z/v1l1/Q25/8GmG3D2WIXdnP++uHDKC+g2+/7x79vHRbLN97vOuopyIEkqihLkVxTV3g6+4kLXWXY8DW7Vf5Nik119lzXWa0bDxb35w3vNPjKF51bkrCf+sAFrstgdjHn4IgDEPP8Suu/95keOjH36QnXfZjTq5z7uSCKIkKC/yeZMkqVBFRN4f+VIUy2kBUkpTgGuXcOgysstpTwGeWeyc1yPia+C2H7lsL2ADsstuz8m17Z1SmgYcT3YJbm2yG2s8ChAR25Gt3KsPtIyIC1JKm+WOvQhsAqwWEVOALimlx5dxyMvV7QM6scs2G9Ko3mq8/9hFXDT4Ea647UmGX9qZjofswOTPv6Rd31sAeOfDqdz/xAQm3H8W8+YvoPcl97Jgwferift1249Lbn4MgCdffofubVowbmQ/br7vpbyMrTq9M+kNnntiLOuuvwG9uxwJQPtje7Htn3b+0XMqyufw7ONjOP+K7PKzg9q049JzTyNTluHUcwZUS9yF4KKz+/Lm+Nf4atYs2hz4Zzp168n+Bx3Gs08uuhx0ofLyOTwx9mEu++uNALRuezTnn3EymUwZZ198WXWHn1fO3bL756Q3eObxMTRbf0N6HdMGgI7dTuDGay/ju+/mctYpxwHZzS169TkbyM7f04+N5uKrsjsCH3JEB/qfcyqZTBmnn3dJfgaSB++89QbPPZn9vDu5a+7zrmsvtlmKz7uFy20Pat2Oy847jUwmwylF9Hn33ltv8sJTj7DOehtwWvejAGjbuQfNt9+Zvz33BDvtvvcPzqkoL+f5J8Zw1qXZuTuwVTuuPL8vmbIyTjqrf7XGn0/9+p7CuHGvMWvWl+y3565073ECnbocyxl9TmbUg/fTpMlvufTKayr7z5kzhzEPP8TAwdm/w7Tv0InTTjmRsrIy/nLplXkahSRJ0pJFdvNULUlErAk8B2ySUvrBbrKFoPbWvfwBLqPxj1ya7xBqrDq1f/1Vkyosc+bOz3cINdbceQX5x1eN8N18525Z/W6N1X6+k5ZotVpFfKMfSdLSKuo/LFrdNj7veZD7jmmel59B0VTi/VIRcTTQHzilUBN4kiRJkiRJxcSNLfQDKaU7Ukprp5RG5jsWSZIkSZIk1RwRsW9EvBcR70fEGT/SZ7eIeCMi3o6I53/umlbiSZIkSZIkqUYoqQGleBFRCgwE9gKmAK9FxMMppX9W6VMPuAHYN6X0SUSs8XPXtRJPkiRJkiRJWn7+CLyfUvowpTQXuBs4eLE+RwEPpJQ+AchtkvqTTOJJkiRJkiRJSykiukXEuCqPbot1WQuYXOX5lFxbVRsB9SPiuYh4Pbc3w09yOa0kSZIkSZJqhEJYTJtSGgIM+YkuSwpz8V11M8A2wJ+B2sDfI+IfKaV//dhFTeJJkiRJkiRJy88UYO0qz5sCny2hz4yU0n+B/0bEC8BWwI8m8VxOK0mSJEmSpBohIvL+WAqvARtGxHoRsRJwJPDwYn1GAbtERCYiVgG2B975qYtaiSdJkiRJkiQtJymleRHRC3gcKAVuTSm9HRHH5Y4PTim9ExGPAROBBcDNKaW3fuq6JvEkSZIkSZKk5Sil9AjwyGJtgxd7fjlw+dJe0ySeJEmSJEmSaoSSQtjZIk+8J54kSZIkSZJU4EziSZIkSZIkSQXO5bSSJEmSJEmqEZZyd9hfpZ9N4kXERsBpwLpV+6eU9liBcUmSJEmSJEnKWZpKvJHAYOAmYP6KDUeSJEmSJElasiIuxFuqJN68lNKgFR6JJEmSJEmSpCX60SReRDTIfTs6InoADwIVC4+nlGau4NgkSZIkSZIk8dOVeK8DCVhYqHhalWMJWH9FBSVJkiRJkiQtzo0tliCltB5ARKycUiqveiwiVl7RgUmSJEmSJEnKKlmKPi8vZZskSZIkSZK0wpRE/h/58lP3xGsCrAXUjoit+X5ZbR1glWqITVqhVsosTQ5bSzJ/Qcp3CDXWzG/m5juEGqms1N/XZTXtm/Kf76Ql2q5Zg5/vpCVarVbxLnORJElaUX7qnnj7AJ2ApsBVVdpnA/1WYEySJEmSJEmSqvipe+LdDtweEYenlO6vxpgkSZIkSZKkH3Bji582JiKOAppV7Z9SunBFBSVJkiRJkiTpe0uTxBsFfAW8DlSs2HAkSZIkSZKkJSveOrylS+I1TSntu8IjkSRJkiRJkrRES7Pd38sRscUKj0SSJEmSJEnSEi1NJd7OQKeI+DfZ5bQBpJTSlis0MkmSJEmSJKmKEje2+En7rfAoJEmSJEmSJP2on03ipZQ+joitgF1yTS+mlN5csWFJkiRJkiRJiyriQryfvydeRJwEjADWyD2GR8QJKzowSZIkSZIkSVlLs5y2C7B9Sum/ABFxKfB34K8rMjBJkiRJkiRJWUuTxAtgfpXn83NtkiRJkiRJUrWJIl5PuzRJvNuAVyLiwdzzQ4BbVlhEkiRJkiRJkhaxNBtbXBURzwE7k63AOyalNGFFByZJkiRJkiRVVcSFeD+exIuIBlWefpR7VB5LKc1ccWFJkiRJkiRJWuinKvFmAFOAebnnVXOdCVh/RQUlSZIkSZIk6Xs/lcT7K7Ab8DfgLuCllFKqjqAkSZIkSZKkxZUU8Xrakh87kFI6CfgDMBLoAEyIiMsiYr1qik2SJEmSJEkSP5HEA0hZzwJ9gcHAMcCe1RGYJEmSJEmSpKwfTeJFxKoRcVREjAIeAVYDmqeUbqq26JaDiPhmBV77DxHx94h4OyImRsQRVY6tFxGvRMT/RcQ9EbFSrn2T3DkVEdFnsevdGhHTIuKtFRXzshp8Xjs+fnoA40b2q2yrX2cVxgzqxaRR5zJmUC/qrV678lifznvz1qjzePPBc9hzh00BWKksw6jrezBuZD+6td6lsu/1Z7dlq42bVt9g8mj6f6Zyxgld6d7uUI5rfxgP3TsCgBefeYLj2h/GAbtszb/efbuy/9sTJ9CjY2tO6noUn035BIBvZn/N2accT7Gtbr/i4nNpvf+uHNvu0Mq2D/7vPU48tj3HtjuMc/r04r//zf66v/XmBLq1P5yendvy6eTv5+2M3scV3bwBzJg2lQv6dOfkzq04tWsbHnngLgCuufhM+nY/ir7dj6JX+5b07X4UAO++9QandTuSM3sezdRPJwPw329m0/+MXkU3fzOmTeXcU7pxQqfDOOmYVoy5/04A7h46mK6t9+GUY4/klGOP5PV/vATAO2+9wcld23Da8e35/NPse++/38zmwr49im7uvptbwYBTu3DRiUdzQc92jL7z5kWOP/HgnRx30I588/UsAN7/50QuOqEDA07pzLTPpgDw7Tezue683kU3dxec2489d92RNoe2rGz76qtZ9OjWmUMO3Ice3Trz9ddfAfDGhPEccfhBdGjbismffAzA7K+/pudxXYpu3iRJUnGIyP8jX36qEm8a2Qq8l4ErgQ+B7SLisIg4rDqCqwG+BY5OKW0G7AtcExH1cscuBa5OKW0IfAl0ybXPBE4ErljC9YbmrlNwho3+Bwf3HLhIW59j9uK5V99ji4Mv5LlX36PPMXsDsMn6TWi9T3Oat+rPQT1v4Noz21BSEuy146ZMeGcy27UZQOfDdwJgi43WoqQkePO9KdU+pnwoLS2la69TuXHEg1w1ZBhjHriHT/79AeuuvwFn/+UqNt+q+SL9H7x7GGddfAUdu5/A2AdHAnDX0Js4okMXosjuA7D3AQfxl6sHLdJ21YDz6XJ8b24a8QA77fpnRg4fCsB9d93BuQOuovNxJzL6wXsBGH7bENoe3bXo5g2gtDRDh+4nc/Wt93HxdbfxxMMjmfLxh/Q+ewCX3Xgnl914J3/ceQ/+uPPuAIy5bwSnnHsZbTv34InR9wFw//CbObTtMUU3fyWlpXQ87mT+OvQBLhl4O4+OupfJH30IwIGt2nHVTXdz1U13s82fdgbg4XuHcdr5V9CuSy8eezg7d/cOu4nD2xXf72ymbCVOvvivnHPdHZx97e28Pf4ffPhu9t+oZk7/D+++8SoNGv+msv9TD91FtzP/wsEdjuOFRx8A4JF7bmPf1h2Lbu5aHnQofx206L+ZDr3lJrbb/k88NOZxttv+Twy9JXt8+B23cflV19HzxJMZeW82QX/TkBvo3LV70c2bJEnSr91PJfFGAhOATYADgZZVHgeu+NCWn4jYLSLGVHl+fUR0yn3/UURcEBHjI2JSRGySa181Vxn3WkRMiIiDF79uSulfKaX/y33/GdnEZ+PI/q15D+C+XNfbgUNy/aallF4DvlvC9V4gm+QrOH8b/wEzv/p2kbYDd9uS4aNfAWD46FdoufuWle0jHx/P3O/m8fFnX/DB5Blst3kzvps3n9orl5Ep/f5td26PA7lo0NjqG0ieNWjUmA02zlYmrrLKqqzTbH1mzJjGOs3Wp+k6zX7QvzSTYW5FBRXl5WQyGT7/dDJfzJjGFltvW82R59+WW2/L6nXqLtI25eOP2HLrbQBo/scdePG5pwDILDZvn02ZzBfTp7FV8+KbN4D6DRux/oabAFB7lVVZa51mzJwxrfJ4Sol/vPAUO+2+D5Cbv7kVVFRk52/qZ1OY+cU0fr/VNnmJP58aNGzM7zbK/s7WXmVVmq6zHl9UmbvFZX9ny5lbUU6mNMPUTyczc8Y0NivCuYsIVq69CgDz589j/rx5lUmlkbdcy2Gdei7yz5ilmQzfVVQwt6Kc0kyG6Z9PYdbM6Wy0+dZ5iT+fmm+7HXXrLvp59/yzT3PgQYcAcOBBh/DcM99/3lVUVFBeXk4mU8bkyZ8wfdo0ttn2j9UdtiRJUrWIiLw/8uVHd6dNKXWqxjjybUZKqXlE9AD6AF2Bs4BnUkqdc9V1r0bEUyml/y7pAhHxR2Al4AOgITArpTQvd3gKsNaKHkR1W6Ph6kyd8TUAU2d8TeMGqwOwVuO6vDLpo8p+n077kjXXqMvDz07kqAP+yAvD+nD10Kc4YNctmPDPT/h8+lf5CD/v/vP5p3zwr3fZ5Pdb/GifNh06c91lF1GrVi36nNOfmwdeRYeuPaoxysLWbP0N+PuLz7Fji9154ZknmD5tKgBHHt2Fqy+5gFq1Vub08/oz5K9X0bFbz/wGWyCmTf2Mf7//Hhtssnll2zuTJlC3XgN+23QdAA45shNDru7PSrVq0ev0Cxk25BqO6Hh8vkIuGAvnbqNNN+fdt97g0Yfu4fknx/C7jX5Pp+NPYbXV63D4UZ0ZdNXF1Kq1MieeeRG3D76atscU7+/sgvnz+cspnZn++RR23f8w1tt4M9585UXqNWxM0/U2XKTvvq06MGLgpZTVqsUxJ5/L/bf9lZbtuuUp8sLzxcwvaNx4DQAaN16DmTOz/+Z3TJduXHzBudRauRYX9b+Ma666jON7npjPUCVJkrSC/GgSr8g8kPv6OrBwqfDewEFV7lu3MrAO8M7iJ0fEb4FhQMeU0oJYclq2eG5Ms4ThpwTz5y+gU7+hAGQyJYwe2JNWvW/k0lMPY+0m9Rkx5lXGPj+pmoPNjznffkv/s/rQ7aTTWGXV1X603+823ISrhwwDYNIbr9OgUWNSggHn9iWTydC116nUb9CwusIuOKeedSEDr76E4bcOZodddiOTKQNgg4024a83Z+83OHHCOBo2agwpcfHZp5HJZOh+Yp+inLfyOd9y1YV96Xj8qYu8715+9nF2zFXhATTbYGP6/3UoAP+cOJ4GDRuTSFxz8ZmUZjJ06N6bevWLa/7mzPmWy87rQ+ce2bnb96DWtO5wLBHBXbfdwNBBV9Gr7/mst8HGXDrwDgDefvN1GjTMvveuuPB0MpkMnY47hXpF9N4rKS3l7Gtv59tvZjN4wJlM+ff7PDrydk664Jof9F17/Y04/YrsEtH/e2sCdRs0gpS46bJzKC0tpVXnE6lTv0E1j6DwbbzJptw+4h4Axo97jcaNG5NS4ozTTiaTyXByn9Np2LBRnqOUJEnS8vCTu9P+isxj0bGuvNjxitzX+Xyf2Azg8JTSH3KPdVJKS0rg1QHGAmenlP6Ra54B1IuIhddqCny2HMax8DW7RcS4iBg3b8bbP3/CCjLti9k0aVQHgCaN6jB95mwAPp02i6ZN6lf2W2uN+j+otuveugXDx7zK9luuz9zv5tH+9Fs5o+s+FIN5876j/9mnstve+7PTrn9eqnNSStx9+0207diNO28bTPsux7P73gfw8Mg7V3C0hW2dZutx6bU3csPQe9h9r/1Yc621FzmeUmLE0Jtod0x3ht0ymKO79uDP+xzAg7kNRYrJvHnzuPKCvuy8x75sv8sele3z58/j1ZeeZcfd9vrBOSklHhxxC4e168p9w26i9dHd2eXP+/HYg3dXZ+h5N2/ed1x+Xh9a7Lk/f2qR/Z2t16AhpaWllJSUsNcBh/F/7y76WZxS4r7ht9C6w7Hcc8cQjux0HC323J+xD96VjyHk3Sqrrc5Gm2/Nm6+8yBf/+YyLTjqafl0PY9aM6fTvfQxffflFZd+UEo/cO5T9jziGMXffSsujurD9bvvyzJh78ziC/GvYoCHTp2eXck+fPo0GDRZNaKaUuPmmQXTt3oMhgwfSvccJ7H/AQdw9Ylg+wpUkSVphSgrgkS8/+9oRUWtp2grcx8DvI6JWRNQFliZz8jhwwsKquoj4wU15cjvOPgjckVIaubA9ZbeDexZolWvqCIz634bwvZTSkJTStimlbTONNltel/3Fxj4/ifYttwegfcvtGfPcxGz7cxNpvU9zVirLsO6aDdlgnca89tZHlefVW702+7XYnBGjX2GV2mUsWJBICWqtVJaPYVSrlBLXDLiAtdddj8OO7LDU5z316MNst8MurF6nDhXl5USUUFISVFSUr8BoC9+XM7P/479gwQJG3DaEAw9tvcjxJx55mO13/H7eSkqCKCmhory45i2lxOArL2StddbjwFbtFzk2afyrrLl2MxpW2WBgoeefGMPW2+/MaqvXYW5FOVESRJQU1fsupcTAy7Nzd1Dr7+du5hfTK79/5cVnWGe93y1y3rOPj2abP2XnrqK8nJIooSSK6703+6sv+fab7D/uzK2o4N03x7H27zbi8mGP8JebH+AvNz9AvUaNOeua26hbpbLz7888wubb7siqq+Xed1FClARzKyp+7KWKQovd9mDMww8BMObhh9h190X/KjP64QfZeZfdqFOnLuXl5ZREECVBeRG95yRJkn7tlmY57d+B5kvRVnBylXAVKaXJEXEvMBH4P7Ibdvyci4BrgIm5RN5H/HBDjzZAC6Dhwo0ygE4ppTeA04G7I+Li3OvdkoupCTAOqAMsiIjewO9TSl9HxF3AbkCjiJgCnJdSuuWXj3z5u31AJ3bZZkMa1VuN9x+7iIsGP8IVtz3J8Es70/GQHZj8+Ze065sN9Z0Pp3L/ExOYcP9ZzJu/gN6X3MuCBd+vJu7XbT8uufkxAJ58+R26t2nBuJH9uPm+l/Iytur0z4lv8MzjY2j2uw3p1akNAB27n8B3c79j0DWX8NWsLzn/tBNYf8ONufiq7E6s5eVzePrR0Vyc25n10CM70P/sUynLlNH3/EvyNpbq1v/cvkwcP46vZs2i7UF7cnTXHsyZ8y0P359dRrbzbn9mnwMPqexfXj6HJx95mEuuHQzA4W07cMGZp1BWVka/Cy/NxxDy5r233+TFpx5hnfU2oG/3owBo27kHW2+/My8/+wQ77b73D86pKC/n+SfHcNYl2V2pDzi8HVdd0JdMWRkn9utfrfHn07tvvcHzT45l3fU34JRjjwSgXZdevPTMY/z7g38RAY1/sybHnXJW5TkV5XN47okxnHtZdu4Oat2Oy87PLuU+5ewBeRlHPnw18wtuv+YiFixYQEoL2GbnP7Pldjv95DlzK8r5xzOPVi633fPgI7nxkn5kMmV06XNBNURdGPr1PYVx415j1qwv2W/PXene4wQ6dTmWM/qczKgH76dJk99y6ZXXVPafM2cOYx5+iIGDs38Ot+/QidNOOZGysjL+cumVeRqFJEnSipHPjSXyLbJFY0s4kE02rQUMB44iu7wUssmnwSmlTaolwv9BRGwF3JRS+tVu0VZ7617Fc6+95eytJy7Pdwg1VllpPguIa7aZ38zNdwg1ku+5ZTftGyuxltV2zbwH37JarVbx/uVakqRqUNR/0J740Lt5z4Ncd8gmefkZ/FQl3j5AJ7L3c7uS798kXwP9VmxY/7uIOA44Eeid51AkSZIkSZKk/8mPJvFSSrdHxDCgbUqpxt0FPqU0GBic7zgkSZIkSZK0fJQUcR3iT65PSiktALpXUyySJEmSJEmSlmBpNrZ4MiL6APcA/13YmFKaucKikiRJkiRJkhZTzJV4S5PE65z72rNKWwLWX/7hSJIkSZIkSVrczybxUkrrVUcgkiRJkiRJkpbsZ5N4EVEGHA+0yDU9B9yYUvpuBcYlSZIkSZIkLSKieNfTLs1y2kFAGXBD7nmHXFvXFRWUJEmSJEmSpO8tTRJvu5TSVlWePxMRb66ogCRJkiRJkqQlKeaNLUqWos/8iPjdwicRsT4wf8WFJEmSJEmSJKmqpanEOw14NiI+BAJYFzhmhUYlSZIkSZIkqdLS7E77dERsCGxMNon3bkqpYoVHJkmSJEmSJFVRxPtaLFUlHsA2QLNc/60igpTSHSssKkmSJEmSJEmVfjaJFxHDgN8Bb/D9vfASYBJPkiRJkiRJ1aakiEvxlqYSb1vg9ymltKKDkSRJkiRJkvRDS7M77VtAkxUdiCRJkiRJkqQl+9FKvIgYTXbZ7OrAPyPiVaByQ4uU0kErPjxJkiRJkiQpa2mq0X6tfmo57RXVFoUkSZIkSZKkH/WjSbyU0vMLv4+I3wDb5Z6+mlKatqIDkyRJkiRJkpT1s1WIEdEGeBVoDbQBXomIVis6MEmSJEmSJKmqiPw/8mVpdqc9C9huYfVdRDQGngLuW5GBSZIkSZIkScpamiReyWLLZ7+guO8jqF+LlO8Aaq558528ZVV7pdJ8h1AjzZk7P98h1FjbrFs/3yHUWKvVyuM/s0qSJGmJSvJZCpdnS5PEeywiHgfuyj0/Anh0xYUkSZIkSZIkqaqfTeKllE6LiMOAnYEAhqSUHlzhkUmSJEmSJEkCfiKJFxEbAL9JKf0tpfQA8ECuvUVE/C6l9EF1BSlJkiRJkiQV8Wran7y33TXA7CW0f5s7JkmSJEmSJKka/NRy2mYppYmLN6aUxkVEsxUXkiRJkiRJkvRDJVbiLdHKP3Gs9vIORJIkSZIkSdKS/VQS77WIOHbxxojoAry+4kKSJEmSJEmSVNVPLaftDTwYEe34Pmm3LbAScOgKjkuSJEmSJElaREkR72zxo0m8lNJ/gB0jYndg81zz2JTSM9USmSRJkiRJkiTgpyvxAEgpPQs8Ww2xSJIkSZIkST+qiAvxfvKeeJIkSZIkSZIKgEk8SZIkSZIkqcD97HJaSZIkSZIkqRCUuJxWkiRJkiRJUqGyEk+SJEmSJEk1QlC8pXhW4kmSJEmSJEkFziSeJEmSJEmSVOBcTitJkiRJkqQawY0tJEmSJEmSJBUsk3iSJEmSJElSgSuqJF5EfPMzx5+LiG2X8lqrRMTYiHg3It6OiEuqHKsVEfdExPsR8UpENKty7LGImBURYxa7Xq9c/xQRjX7h0Fa4wee14+OnBzBuZL/Ktvp1VmHMoF5MGnUuYwb1ot7qtSuP9em8N2+NOo83HzyHPXfYFICVyjKMur4H40b2o1vrXSr7Xn92W7bauGn1DSaPpv9nKmec2JXu7Q/luA6H8dDIEQC8+OwTHNfhMA5osTX/evftyv5vT5xAj46tOenYo/hsyicAfDP7a84+5XhSSnkZQ75M/89UTj+hC93aHUL39ofy0L3ZuZv99Vf0692dLke2pF/v7sz++msgO3fHd2zFiV0XnbuzTjmu+OZu2lTOOulYenQ4jJ4dD+fh++4EYPgtAznhmDac1OUIzj31eL6YMQ2Af056gxOOacMp3dpVmbvZnNenR9HN3YxpU7mgT3dO7tyKU7u24ZEH7gLgmovPpG/3o+jb/Sh6tW9J3+5HAfDuW29wWrcjObPn0Uz9dDIA//1mNv3P6FV0c3fhuWex9247ccRhLSvbrr3qclodvD9tWx3Mab17Vf6+vjlhPG1bHczRR7Vm8icfAzD766854biuRTdvkiRJ+mklkf9H3saev5f+VbgipbQJsDWwU0Tsl2vvAnyZUtoAuBq4tMo5lwMdlnCtvwF7Ah+vwHiX2bDR/+DgngMXaetzzF489+p7bHHwhTz36nv0OWZvADZZvwmt92lO81b9OajnDVx7ZhtKSoK9dtyUCe9MZrs2A+h8+E4AbLHRWpSUBG++N6Xax5QPpaWldO15KjcOf5CrbhzGmAfu4ZN/f8C6623A2f2vYvOtmi/S/8F7hnHWxVfQsdsJjH1oJAB33X4TR3ToQkRx3QigtLSUY3v1YciIh7h6yHDGPHA3H//7A+4dfit/2OaP3HL3aP6wzR+5d/gtADxw9x2cffGVdOp+AmMfvBeAu4YO4YgOXYty7jr3PIUbhj3A5YPu4JEH7+GTjz7gsCM78tfb7uXaW+5hux124Z7bhwDw0D3DOPOiy+lw7Ak8Oir7vrvnjiG0bt+5COcuQ4fuJ3P1rfdx8XW38cTDI5ny8Yf0PnsAl914J5fdeCd/3HkP/rjz7gCMuW8Ep5x7GW079+CJ0fcBcP/wmzm07TFFN3cHHnwI1w0askjb9n/akbvvf5i77hvFOus2Y+gt2ePD77iNS6+8lp4n9Oa+e+8G4OYhg+jUtVvRzZskSZL0Y4ouiRcRu1WtgouI6yOi02J9ukTE1VWeHxsRV1Xtk1L6NqX0bO77ucB4YGE52cHA7bnv7wP+HLn/C0kpPQ3MXjyulNKElNJH/+PwVpi/jf+AmV99u0jbgbttyfDRrwAwfPQrtNx9y8r2kY+PZ+538/j4sy/4YPIMttu8Gd/Nm0/tlcvIlH7/tju3x4FcNGhs9Q0kzxo0aswGG2crE1dZZVXWabY+M2ZMY51m69N0nWY/6F+ayTC3ooKK8nIymQyffzqZL6ZPY4utl6pg9Fdl8blbu9n6fDFjGn9/8Vn23O8gAPbc7yD+/uKzAGSqzF1pJsNnn05mxoxpbFmMc9ewMb/b6Pu5a7ruenwxfTqrrLpaZZ/y8jlANlmSyWSoqKigomJO5ftu5oxpbP6H4pu7+g0bsf6GmwBQe5VVWWudZszMVSwCpJT4xwtPsdPu+wC5993cCioqsr+zUz+bwswvpvH7rbbJS/z51Hyb7ahTp94ibX/acScymeyeWptvuRX/mfYf4Pv3XHnus27K5E+YPu0/bLPtH6s7bEmSJBW4iMj7I1/cnXbJ7gYmRkTflNJ3wDFA9x/rHBH1gJbAtbmmtYDJACmleRHxFdAQmLEig65uazRcnakzskuhps74msYNVgdgrcZ1eWXSR5X9Pp32JWuuUZeHn53IUQf8kReG9eHqoU9xwK5bMOGfn/D59K/yEX7e/efzT/ngX++yye+3+NE+bdp35rrLL6JWrVr0Obs/Nw+8ig5de1RjlIVp4dxt/PstmPXlTBo0agxkE31ffTkTgDYdunDtZRdm5+6cv3DzwCs5umvPfIZdEP7z+Wd8+H/vsfHvNwdg2E3X8+zjY1hltdXof022KqpVu84MvOJiaq1Ui5PPupjbBl1Fuy6+76ZN/Yx/v/8eG2yyeWXbO5MmULdeA37bdB0ADjmyE0Ou7s9KtWrR6/QLGTbkGo7oeHy+Qi5oDz/0AHvtky1g79SlG3+58Fxq1VqZC/5yKddeeRnH9TwxzxFKkiRJhcUk3hKklP4bEc8AB0bEO0BZSmnSkvpGRAa4C7gupfThwuYlXXZ5xRcR3YBuAJmmu5FptNnyuvTysYSsdEowf/4COvUbCkAmU8LogT1p1ftGLj31MNZuUp8RY15l7PNLnOZfnTnffkv/s/vQ7cTTFqmGWtzvNtyEq28cBsCkN16nQaPGpAQDzutLpjRD116nUr9Bw+oKuyDM+fZbLj7rVLqfdBqr/szcXTNkOJCdu4aNGpNSYsC5p1GayXBsrz5FOXeXnNuHrif0qXzfdTi2Fx2O7cXI4bcw9oF7OKrz8ay/4cZcMegOAN5683UaNMy+7y47/3RKMxk69zil6OaufM63XHVhXzoef+oiv7MvP/s4O+aq8ACabbAx/f86FIB/ThyfnTsS11x8JqWZDB2696Ze/eKauyW59abBZEpL2e+A7P3yNt5kU24bfg8A419/jcaN1yClxJmnnUwmU0bvPn1p2LDgbhcrSZIkVauiW04LzGPRca/8I/1uBjqRrcK77SeuNwT4v5TSNVXapgBrQ2WSry4wc9nC/aGU0pCU0rYppW3zmcCb9sVsmjSqA0CTRnWYPjO7SvjTabNo2qR+Zb+11qj/g2q77q1bMHzMq2y/5frM/W4e7U+/lTO67kMxmDfvO/qffSq77bU/O+3656U6J6XE3XfcRNtO3bhz6GDadz6e3fc5oHKDgmIxb953XHz2Key+9/7stOueANSr34CZM6YDMHPGdOrWb7DIOSkl7rp9CG07dmfEbTfSvksP9tj7QEaNLL65u+TcPuy6537s2OKH77td99yPl194epG2lBL33nEzR3Tsxt1Db+SoY45jt732Z8z9d1VX2AVh3rx5XHlBX3beY1+232WPyvb58+fx6kvPsuNue/3gnJQSD464hcPadeW+YTfR+uju7PLn/XjswburM/SCNObhh3jphee4aMDlP1iKkFLi1iGD6dL9eG668Qa69ziB/Q5syT13Ds9TtJIkSSo0+d7Uwo0tqtfHwO9zO8jWBZaYRUkpvUI2EXcU2Uq7H4iIi8km6HovduhhoGPu+1bAM+lXuL3e2Ocn0b7l9gC0b7k9Y56bmG1/biKt92nOSmUZ1l2zIRus05jX3vqo8rx6q9dmvxabM2L0K6xSu4wFCxIpQa2VyvIxjGqVUuKaSy5g7WbrcdiRS9rfZMmeevRhttthF1ZfvQ4V5eVElFASQUV5+QqMtrCklLhmwPmsve76HHbk0ZXtf9p5N5569GEgO0877LL7Iuc99ejD/HGHFqxepw4V5XOy9zAoCSoqimvu/nrpBTRddz0OOeL7991nU77fR+fVvz3/g/syPvPYaLbdYRdWW/i+KymhpKSk6OZu8JUXstY663Fgq/aLHJs0/lXWXLsZDRv/5gfnPf/EGLbefmdWW70OcyvKiZIgorjmbkle/tuL3HHbzVx57Q2sXLv2D46PefghdmqxK3Xq1KViTvb3tSRKcvdslCRJkopb0SynzVXEVaSUJkfEvcBE4P+ACT9x2r3AH1JKXy7hek2Bs4B3gfG5aoLrU0o3A7cAwyLifbIVeEdWOe9FYBNgtYiYAnRJKT0eEScCfYEmZO/H90hKqev/PPDl5PYBndhlmw1pVG813n/sIi4a/AhX3PYkwy/tTMdDdmDy51/Srm92V9B3PpzK/U9MYML9ZzFv/gJ6X3IvCxZ8n8Ps120/Lrn5MQCefPkdurdpwbiR/bj5vpfyMrbq9M9Jb/DM42Notv6G9DqmDQAdu53Ad999x6BrLuGrWV9yft8TWH+Djbn4qkFAdsOBpx8bXfn80CM60P+cUynLlNH3vEvyNpbq9vbECTz9+Bia/W5DenbKzV33E2jTvjN/Ofc0Hh/7EI1/04SzLrqi8pzy8jk89ejD9L96MACHHXk0/c8+lUymjNPPL565e2fSGzz7xFjWXX9DTupyBJBdRvvk2If4dPLHRJSwxm9+S49Tz6o8p6J8Ds88NpoLrrwBgIPbtOeSc/qQKSujz7kD8jKOfHjv7Td58alHWGe9Dejb/SgA2nbuwdbb78zLzz7BTrvv/YNzKsrLef7JMZx1SXZH7wMOb8dVF/QlU1bGif36V2v8+XTW6afy+rhXmTVrFgfstRvdju/F0FtvYu7cufQ8rgsAW2yxFWeecz4A5XPmMPbhh7h+8M0AHHV0J04/9STKysq4+JIrfuxlJEmSVGTyuK9E3sWvsEBsiSJiK+CmlNJSb3WX28X26tyOsgWp9ta9iuMHuAK89fjl+Q6hxsrnbjw13XfzF+Q7hBppztz5+Q6hxlp/jVXzHUKNVWflYlywIEmSaoCi/h+yq174MO95kFNarJ+Xn0FR/O00Io4juyT27KXsXy8i/gXMKeQEniRJkiRJkopDUSynTSkNBgb/gv6zgI1WWECSJEmSJEn6xUqKeGVYUVTiSZIkSZIkSTVZUVTiSZIkSZIkqeYrKd5CPCvxJEmSJEmSpEJnEk+SJEmSJEkqcC6nlSRJkiRJUo1QxPtaWIknSZIkSZIkFTor8SRJkiRJklQjlFC8pXhW4kmSJEmSJEkFziSeJEmSJEmSVOBcTitJkiRJkqQawY0tJEmSJEmSJBUsK/EkSZIkSZJUI5RYiSdJkiRJkiSpUJnEkyRJkiRJkgqcy2klSZIkSZJUI5QU8c4WVuJJkiRJkiRJBc4kniRJkiRJklTgXE4rSZIkSZKkGqGIV9OaxFPxmvPd/HyHIGkpNW1QO98h1Fh1VrboXpIkSfo1MIknSZIkSZKkGsGNLSRJkiRJkiQVLJN4kiRJkiRJUoFzOa0kSZIkSZJqhCJeTWslniRJkiRJklTorMSTJEmSJElSjVDM1WjFPHZJkiRJkiSpRjCJJ0mSJEmSJBU4l9NKkiRJkiSpRogi3tnCSjxJkiRJkiSpwFmJJ0mSJEmSpBqheOvwrMSTJEmSJEmSCp5JPEmSJEmSJKnAuZxWkiRJkiRJNUKJG1tIkiRJkiRJKlRW4kmSJEmSJKlGKN46PCvxJEmSJEmSpIJnEk+SJEmSJEkqcC6nlSRJkiRJUo1QxPtaWIknSZIkSZIkFboalcSLiEMjIkXEJtX8ur0jYpUfOTYiIt6LiLci4taIKMu1R0RcFxHvR8TEiGhe5ZxbI2JaRLy12LVaR8TbEbEgIrZdsaP6ZQaf146Pnx7AuJH9Ktvq11mFMYN6MWnUuYwZ1It6q9euPNan8968Neo83nzwHPbcYVMAVirLMOr6Howb2Y9urXep7Hv92W3ZauOm1TeYPJoxbSrnntKNEzsdzknHtGbM/XcCcM/QGzm29b6cemxbTj22La//4yUA3n3rDU7uegR9j+/A559OBuC/38zmwr49SSnlbRz54NwtO+du2f3lgrM5YM9daN/m4B8cu/OO29hpm82Y9eWXAEx8YzxHH3EoXTq0YcrkjwGYPftrTu55bNHNmyRJkrSiRETeH/lSo5J4QFvgJeDIan7d3sASk3jACGATYAugNtA1174fsGHu0Q0YVOWcocC+S7jWW8BhwAv/a8DL27DR/+DgngMXaetzzF489+p7bHHwhTz36nv0OWZvADZZvwmt92lO81b9OajnDVx7ZhtKSoK9dtyUCe9MZrs2A+h8+E4AbLHRWpSUBG++N6Xax5QPpaWldDruZK4bej+XDBzKY6NGMvmjDwE4sNVRXHnTXVx5011s86edAXj43uGcdv7ltOvSk8cfHgnAyGE3cXi7znn94MgH527ZOXfLbv+Wh3DVX2/8Qft/pn7Oa6+8zG+a/Lay7a7ht9P/8mvo3rM3D468B4ChNw3m6M7dim7eJEmSpGIXEfvmir7ej4gzfqLfdhExPyJa/dw1a0wSLyJWA3YCulAliRcRu0XEmCrPr4+ITrnv94+IdyPipVxV3Jhc+/kR0afKOW9FRLOIWDUixkbEm7m2IyLiRGBN4NmIeHbxuFJKj6Qc4FVgYUnZwcAduUP/AOpFxG9z57wAzFzCtd5JKb33v83UivG38R8w86tvF2k7cLctGT76FQCGj36FlrtvWdk+8vHxzP1uHh9/9gUfTJ7Bdps347t586m9chmZ0u/fduf2OJCLBo2tvoHkWf2GjVl/o2xlYu1VVqXpOusxc8a0H+1fmskwt6KCiopySkszTP10MjNnTGezrbaprpALhnO37Jy7ZfeH5ttSp27dH7Rfd9Wl9Djp1EWSc5lMhoqKcirKy8lkMkyZ/AnTp09j6222q86QJUmSJOVZRJQCA8kWeP0eaBsRv/+RfpcCjy/NdWtMEg84BHgspfQvYGbV5alLEhErAzcC+6WUdgYaL8Vr7At8llLaKqW0ee71rgM+A3ZPKe3+E69XBnQAHss1rQVMrtJlSq7tV2ONhqszdcbXAEyd8TWNG6wOwFqN6zJl6peV/T6d9iVrrlGXp//xLr9pWIcXhvXh6qFPccCuWzDhn5/w+fSv8hJ/vk2b+hn/fv9dNtx0cwAefeheTu56BAMvu4BvZmfn9bCjjmHwVRcz5v472f/QI7jz1hs48pjj8xl2QXDulp1z97978flnaNz4N2y40aJ3duhwTFcuvfh87rnzDg4/4iiG3HAdxx5/Qp6ilCRJkn6dSgrgsRT+CLyfUvowpTQXuJtssdfiTgDuB368yqKKmrQ7bVvgmtz3d+eej/+J/psAH6aU/p17fhfZZa0/ZRJwRURcCoxJKb34C+K7AXihyjlLWju1XG6KFBHdyI0l03Q3Mo02Wx6XXX6WsGwsJZg/fwGd+g0FIJMpYfTAnrTqfSOXnnoYazepz4gxrzL2+UnVHGx+zJnzLZefdxrH9OjDKquuxj4HtaJVh65EBHfdNojbB11Nz77nsd4GG3PJwNsBePvN8dRv2BhS4soLzyCTydDxuJOp16BhnkdTvZy7Zefc/e/K58zhjluGcPXAm35wbKONN+Wm2+8C4I3x42jUuDEpJc4541QymQwnnHwaDRo2qu6QJUmSJFW/JRV2bV+1Q0SsBRwK7AEs1fKdGlGJFxENyQ7q5oj4CDgNOCKy65jmseg4Vl542k9cconn5Kr8tiGbzBsQEecuZXznka30O6VK8xRg7SrPm5Kt6PufpZSGpJS2TSltm88E3rQvZtOkUR0AmjSqw/SZswH4dNosmjapX9lvrTXq/6DarnvrFgwf8yrbb7k+c7+bR/vTb+WMrvtUX/B5NG/ed1x+3mnssud+/KnFHgDUa9CQ0tJSSkpK2OuAQ/m/d99e5JyUEvcPv5nWHbpy7x1DOKJTd1rsuT9jH7w7H0PIG+du2Tl3y8enUybz2Wef0rHtYRx+4F5Mn/YfOrdrxRczplf2SSkx9OYb6dT1OG4dcgNdu/dkn/0PZOTdI/IYuSRJkqTlJSK6RcS4Ko/Fi8aWprDrGuD0lNL8pX3dGpHEA1qRvb/cuimlZimltYF/AzsDHwO/j4haEVEX+HPunHeB9SOiWe75EVWu9xHQHCC3LHe93PdrAt+mlIYDVyzsA8wGVl9SYBHRFdgHaJtSWlDl0MPA0bldav8EfJVS+nxZJ6AQjX1+Eu1bZhPJ7Vtuz5jnJmbbn5tI632as1JZhnXXbMgG6zTmtbc+qjyv3uq12a/F5owY/Qqr1C5jwYJESlBrpbJ8DKNapZS44fKLaLrOehzUun1l+5dffJ8AeOXFZ1lnvd8tct6zj4+m+Z92ZrXV61BRXk5JlBARzC0vr7bY8825W3bO3fLzuw03YuxTL3L/mCe5f8yTNF7jN9w64j4aNvr+jg2PjH6IHXduQZ06dakoLydKSogoobx8Th4jlyRJkn4d8r0zbUQsUlyVewxZLMylKezaFrg7V6zWCrghIg75qbHXlOW0bYFLFmu7HzgqpXR8RNwLTAT+D5gAkFKaExE9gMciYgbZTSeqnnt0RLwBvAb8K9e+BXB5RCwAvgMW3gRqCPBoRHy+hPviDSabSPx77gbnD6SULgQeAfYH3ge+BY5ZeEJE3AXsBjSKiCnAeSmlWyLiUOCvZKv6xkbEGymlgihPu31AJ3bZZkMa1VuN9x+7iIsGP8IVtz3J8Es70/GQHZj8+Ze063sLAO98OJX7n5jAhPvPYt78BfS+5F4WLPg+4dyv235ccnP21oFPvvwO3du0YNzIftx830t5GVt1evetN3j+ybGss/4GnHpsWwCO6tKTl555nI8+eA8iWOM3a3LcKf0qz6kon8NzT4zh3MuyuwO3bN2ey88/jUymjJPP/ktexpEPzt2yc+6W3Xn9+jBh3GvMmjWLQ/bbgy7de9LykMN/tH/5nDk8OmYU1+SW2x7RviNnndabsrIyzv/L5dUVtiRJkqT8eg3YMCLWAz4lu0HrUVU7pJTWW/h9RAwle1u3h37qopHdVPXXKSJWSyl9k1t2OxD4v5TS1fmOa3mqvXWvX+8PcAV7bczieWFJhapJ3ZV/vpOWqNFqNeXf6yRJkrSUfur2Yb96I9/4LO95kNZ/WPNnfwYRsT/ZJbOlwK0ppf4RcRxASmnwYn2Hkk3i3fdT1/y1/83+2IjoCKxEtkLvxjzHI0mSJEmSpF+5lNIjZFdpVm0b/CN9Oy3NNX/VSbxc1d2vqvJOkiRJkiRJxedXncSTJEmSJEnSr0duP4KiVFN2p5UkSZIkSZKKlpV4kiRJkiRJqhGKuRqtmMcuSZIkSZIk1Qgm8SRJkiRJkqQC53JaSZIkSZIk1QhubCFJkiRJkiSpYFmJJ0mSJEmSpBqheOvwrMSTJEmSJEmSCp5JPEmSJEmSJKnAuZxWkiRJkiRJNUIR72thJZ4kSZIkSZJU6KzEkyRJkiRJUo1QUsRbW1iJJ0mSJEmSJBU4k3iSJEmSJElSgXM5rSRJkiRJkmoEN7aQJEmSJEmSVLCsxJMkSZIkSVKNEEW8sYVJPBWtbyvm5zuEGqti3oJ8h1BjbbF2nXyHUCPVq12a7xAkSZIkKa9cTitJkiRJkiQVOCvxJEmSJEmSVCO4sYUkSZIkSZKkgmUST5IkSZIkSSpwLqeVJEmSJElSjVBSxLvTWoknSZIkSZIkFTgr8SRJkiRJklQjuLGFJEmSJEmSpIJlEk+SJEmSJEkqcC6nlSRJkiRJUo3gclpJkiRJkiRJBctKPEmSJEmSJNUIQfGW4lmJJ0mSJEmSJBU4k3iSJEmSJElSgXM5rSRJkiRJkmqEkuJdTWslniRJkiRJklTorMSTJEmSJElSjeDGFpIkSZIkSZIKlkk8SZIkSZIkqcC5nFaSJEmSJEk1QhTvatoVV4kXEWdFxNsRMTEi3oiI7ZfxOrtFxI7LMa6PIqLRcrrWHyLi71XGeUSVY+tFxCsR8X8RcU9ErJRr3yR3TkVE9FnserdGxLSIeGt5xLc8DT6vHR8/PYBxI/tVttWvswpjBvVi0qhzGTOoF/VWr115rE/nvXlr1Hm8+eA57LnDpgCsVJZh1PU9GDeyH91a71LZ9/qz27LVxk2rbzB5NHduBeed1Il+PY7ijO5HcP+wIQB8M/srLunXiz5dDueSfr347+yvAfjX22/S7/ijOPfEjvzns8kA/Peb2Vx21gmklPI2jnz4bm4FF5/SmfNPaM+5PdoyasRNADw0/EbOO6EdF5zYgavOOZFZX0wH4P/++SbnndCOi08+pnLuvv1mNlefe1LRzd1F553FvrvvTNvDD6psu+6qy2lzyAG0a30IfU8+gdlfZ99zb04YT7vWh9DpqDZM/uRjAGZ//TUnHn9s0c2bJEmSJBWSFZLEi4gdgAOB5imlLYE9gcnLeLndgOWWxPtfRMTilYvfAkenlDYD9gWuiYh6uWOXAlenlDYEvgS65NpnAicCVyzhJYbmrlNwho3+Bwf3HLhIW59j9uK5V99ji4Mv5LlX36PPMXsDsMn6TWi9T3Oat+rPQT1v4Noz21BSEuy146ZMeGcy27UZQOfDdwJgi43WoqQkePO9KdU+pnwoK1uJMy+5gb/ccCcXDxzBxNf/zvvvTGL0vbez2R+244pb7mezP2zH6HtvB+DRB0Zw4tmX0LpTD54eez8Ao+66hZZHHEMU2T8/ZMpWok//6zn/r8M597phvDX+73zw7lvsc1h7LvjrCM67bhhbbrcTo+++FYAnHrqTHmcO4NCjj+O5Rx8AYPQ9t7J/645FN3cHHnQo19wwZJG2P/5pR+68bxQjRj7EOus24/Zbs0nRO4cNZcAV13D8Cb15YOTdANx60yA6delWdPMmSZIkqfBEAfyXLyuqEu+3wIyUUgVASmlGSukzgIjYJiKej4jXI+LxiPhtrv25iLgmIl6OiLci4o8R0Qw4Djg5V823S0Q0joj7I+K13GOn3PnnR8TtEfFErtrusIi4LCImRcRjEVFWJb7TIuLV3GOD3Pk/dd0hEfEEcEfVQaaU/pVS+r/c958B04DGkf0/3T2A+3JdbwcOyfWbllJ6Dfhu8UlLKb1ANslXcP42/gNmfvXtIm0H7rYlw0e/AsDw0a/QcvctK9tHPj6eud/N4+PPvuCDyTPYbvNmfDdvPrVXLiNT+v3b7tweB3LRoLHVN5A8iwhWrr0KAPPnzWP+vHkQwfi/v8Auex4AwC57HsDrf38egNJMhrlzK5hbUU5paYb/fDaFmTOms+mWzfM2hnxZ0txFQO1VVq3sM7einIWfp6WlGeZWVDC3ooLS0gzTPp/CrC+ms/EWxTd3W2+zLXXq1F2k7U877kQmk/13ic233Ipp/5kKQCaToaKigvLyOWQyZUyZ/AnTpk2j+bbbVXvckiRJkqTvrah74j0BnBsR/wKeAu5JKT2fS6T9FTg4pTQ9t/y0P9A5d96qKaUdI6IFcGtKafOIGAx8k1K6AiAi7iRb4fZSRKwDPA5smjv/d8DuwO+BvwOHp5T6RsSDwAHAQ7l+X6eU/hgRRwPXkK0avPYnrrsNsHNKac6PDTgi/gisBHwANARmpZTm5Q5PAdb6xbNY4NZouDpTZ2SX4E2d8TWNG6wOwFqN6/LKpI8q+3067UvWXKMuDz87kaMO+CMvDOvD1UOf4oBdt2DCPz/h8+lf5SP8vFkwfz7nnHg0//lsCnse2IoNNtmcr2fNpF6D7Crveg0a8fVXXwLQsk0nbr12ACvVqsVxfc7nzpuvo9XR3fMZfl4tmD+fi07uxLTPp7D7AYez/sabA/DAHYP4+7OPUnuV1TjtL9mK0f1bd2TYwEsoW6kWXU45n5G3Xsch7bvlMfrCNfqhB9hzn2wRcMfOxzLgovOoVWtlzu9/CddddTnde5yQ5wglSZIkSSskiZdS+iYitgF2IZtUuycizgDGAZsDT+aWZZUCn1c59a7c+S9ERJ0qS1Or2hP4fZVlXXUiYvXc94+mlL6LiEm5az+Wa58ENFv8dXJfr16K6z78Mwm83wLDgI4ppQWx5DVny+1mUhHRDegGkGm6G5lGmy2vSy8fSxh+SjB//gI69RsKQCZTwuiBPWnV+0YuPfUw1m5SnxFjXmXs85OqOdjqV1JaSv+BI/jvN7O59qK+TP7ogx/tu+7vNuL8a7LLQ9+dNJ76DRuRUuL6Af0oLc1w1LEnUbd+w+oKPe9KSks577phfPvNbAb+5XQ+/fgD1lr3dxx29PEcdvTxPDLydp4Zcx8HtzuWddbfiH5X3ALAv96aQL0GjUgJBl96FqWZDG06n1hUc/djbrtpMKWlpey7f0sANtpkU24dll1GO+H1cTRuvAYAZ/U9hUwmw4mn9qVhw+VyW1FJkiRJ+sVKivguPytsY4uU0vyU0nMppfOAXsDhZBe6vZ1S+kPusUVKae+qpy1+mSVcugTYoco11kopzc4dW7h8dwHwXfr+LuwLWDRhmZbw/U9d978/Ns6IqAOMBc5OKf0j1zwDqFflHnpNgc9+7Bq/VEppSEpp25TStvlM4E37YjZNGtUBoEmjOkyfmZ2uT6fNommT+pX91lqj/g+q7bq3bsHwMa+y/ZbrM/e7ebQ//VbO6LpP9QVfAFZdbXU22bI5E8f9nTr1GjBr5gwAZs2cQZ269Rfpm1Ji1F23cUjbLjw44mYOa9+NnfbYjydG3ZOP0PNuldVWZ+MtmvPW6/9YpH37Xffm9ZefXaQtpcSYe27jwCM7M/qumzn4qGPZYbd9eXr0vdUZckEa+/BDvPTi81z4l8t+cL+7lBK33jSYzt2O4+bBAzn2+F7se0BL7r1zeJ6ilSRJkqTitqI2ttg4Ijas0vQH4GPgPbL3jNsh168sIqpmoY7Ite8MfJVS+gqYDaxepc8TZJOCC1/rD8sQ4hFVvv59Wa+b23H2QeCOlNLIhe255OGzQKtcU0dg1DLEWdDGPj+J9i2zmw63b7k9Y56bmG1/biKt92nOSmUZ1l2zIRus05jX3vqo8rx6q9dmvxabM2L0K6xSu4wFCxIpQa2Vypb0Mr8qX8/6kv9+k012zq0o5+0Jr7Lm2uvS/E8tePGp7L0BX3xqLM13aLHIeS8+NZY//HEnVl29DnMryokIIoKKivJqH0O+zP7qS76tMnfvvPEaTZquy38++6SyzxuvvMhvm667yHkvPz2WLbfbiVVXq8PcigqipISIEuZWVFRr/IXm7397kTuG3swV1wxk5dq1f3B87MMPsdMuu1KnTl3Ky8spyc1beXnxvOckSZIkqZCsqHvirQb8Nbccdh7wPtAtpTQ3IloB10VE3dzrXwO8nTvvy4h4GajD9/fJGw3cFxEHAyeQ3dl1YERMzJ3/AtnNL36JWhHxCtkkZttc27Jctw3QAmgYEZ1ybZ1SSm8ApwN3R8TFwATgFoCIaEJ2WXEdYEFE9AZ+n1L6OiLuIrsbb6OImAKcl1K65ReObYW4fUAndtlmQxrVW433H7uIiwY/whW3PcnwSzvT8ZAdmPz5l7Trmw31nQ+ncv8TE5hw/1nMm7+A3pfcy4IF3xc/9uu2H5fcnF3p/OTL79C9TQvGjezHzfe9lJexVadZX85gyBUXsGDBAhakBWy/y55svf0ubLDpFlz/l348//jDNGz8G044a0DlORXl5bz01Fj69v8rAPsddhTX9T+DTKaMHqdflK+hVLtZM2dw6zUXsWDBfNKCxHY7/5mt/rgzN/zlDKZ++glREjRs3IQOPU+vPKeivJyXn3mEky+8DoC9DmnLoAFnUprJ0O204pm7s8/ow/hxrzJr1iwO3Ht3uh3fi9tvHcLcud9xwnHZjbM333Irzjj7fADK58xh7OhR/HVQdsfath06csapJ5EpK+PiS5a0sbYkSZIkVY987g6bb/H9itP8iojngD4ppXH5jqUmqb11r8L4AdZAz9/fP98h1FgV8xbkO4Qaa4u16+Q7hBqpXu3SfIcgSZIkFYrizWIBL/7ry7znQXbZqH5efgYrqhJPkiRJkiRJWq6WuJVokSiYJF5Kabd8xyBJkiRJkiQVohW2O60kSZIkSZKk5aNgKvEkSZIkSZKkn1LEq2mtxJMkSZIkSZIKnZV4kiRJkiRJqhFKinhnCyvxJEmSJEmSpAJnEk+SJEmSJEkqcC6nlSRJkiRJUo1QvItprcSTJEmSJEmSCp6VeJIkSZIkSaoZirgUz0o8SZIkSZIkqcCZxJMkSZIkSZIKnMtpJUmSJEmSVCNEEa+ntRJPkiRJkiRJKnBW4kmSJEmSJKlGiOItxLMST5IkSZIkSSp0JvEkSZIkSZKkAudyWkmSJEmSJNUIRbya1ko8SZIkSZIkqdBZiaeiVavMHPay2uA3q+U7hBqrXu3SfIcgSZIkSTVXEZfimcWQJEmSJEmSCpxJPEmSJEmSJKnAuZxWkiRJkiRJNUIU8XpaK/EkSZIkSZKkAmcST5IkSZIkSSpwLqeVJEmSJElSjRDFu5rWSjxJkiRJkiSp0FmJJ0mSJEmSpBqhiAvxrMSTJEmSJEmSCp1JPEmSJEmSJKnAuZxWkiRJkiRJNUMRr6e1Ek+SJEmSJEkqcFbiSZIkSZIkqUaIIi7FsxJPkiRJkiRJKnAm8SRJkiRJkqQC53JaSZIkSZIk1QhRvKtprcSTJEmSJEmSCp2VeJIkSZIkSaoRirgQz0o8SZIkSZIkqdCZxJMkSZIkSZIKXEEm8SKiaUSMioj/i4gPIuLaiFjpZ87pHRGrLMW1v1mOce4VEa9HxKTc1z2qHNsm1/5+RFwXkb31YkS0iIjxETEvIlotdr3HImJWRIxZXjEuL4PPa8fHTw9g3Mh+lW3166zCmEG9mDTqXMYM6kW91WtXHuvTeW/eGnUebz54DnvusCkAK5VlGHV9D8aN7Ee31rtU9r3+7LZstXHT6htMHs2YNpULTu3OyZ1bcUqXNjzywF2Vxx598G5O6nQYp3Rpw/Ah1wLw7ltv0OfYIzmzx9FM/XQyAP/9Zjb9T+9FSikvY8iXi88/i/3/vDPtWh9U2XbjDdfRvs0hHH3koZzUoyvTp08D4M03xtO+zSF0bt+GyZ98DMDs2V/Tu8exRTdvkiRJkvSrEgXwyJOCS+Llkl0PAA+llDYENgJWA/r/zKm9gZ9N4v2PsS1+D8EZQMuU0hZAR2BYlWODgG7AhrnHvrn2T4BOwJ1LeInLgQ7LMeTlZtjof3Bwz4GLtPU5Zi+ee/U9tjj4Qp579T36HLM3AJus34TW+zSneav+HNTzBq49sw0lJcFeO27KhHcms12bAXQ+fCcAtthoLUpKgjffm1LtY8qH0tIMHY47matvvY/+f72Nx0eNZMrHH/LWG+MY9/ILXDHkbq665V5ats6+DcbcN4JTz7uMtl168MTo+wC4f/jNHHrUMUSRbclzQMtDufr6IYu0tT+6M8PvfYg77n6QnXbZlVuH3ADAXcOGMuCKaziuV28evO9uAG67aRBHd+lWdPMmSZIkSfp1KLgkHrAHUJ5Sug0gpTQfOBnoHBGrRERpRFyRq3KbGBEnRMSJwJrAsxHxLEBEtM31eSsiLq36AhFxZa4a7umIaJxr+12uEu71iHgxIjbJtQ+NiKty113kOimlCSmlz3JP3wZWjohaEfFboE5K6e8pW/ZzB3BI7pyPUkoTgQWLDzyl9DQwe3lM4vL2t/EfMPOrbxdpO3C3LRk++hUAho9+hZa7b1nZPvLx8cz9bh4ff/YFH0yewXabN+O7efOpvXIZmdLv33bn9jiQiwaNrb6B5Fn9ho1Yf8NNAKi9yqqstU4zZs6YxhMP38fBR3akbKVswWnd+g2AbNJv7twKKsrLKS3NMPWzKcycMY3fb7VN3saQL1tvsy116tZdpG3V1Var/H7OnDmVCbpMJkNFeQXl5XPIZMqYMvkTpk+bRvNttqvWmCVJkiRJy1cUwH/5UohJvM2A16s2pJS+JlvBtgHZ6rb1gK1TSlsCI1JK1wGfAbunlHaPiDXJJtz2AP4AbBcRh+QutyowPqXUHHgeOC/XPgQ4IaW0DdAHuKFKCBsBe6aUTv2JuA8HJqSUKoC1gKqlZVNybb8qazRcnakzvgZg6oyvadxgdQDWalyXKVO/rOz36bQvWXONujz9j3f5TcM6vDCsD1cPfYoDdt2CCf/8hM+nf5WX+PNt2tTP+Pf777HBJpvz+aef8O5bb9CvV0fOO6Ub77/7NgCHtu3EkKv688gDd7HvIW24+9aBHNHp+DxHXlgGX38NB++3B088OoZjjz8BgKM7H8slF5/HPXcOo9URR3HjwGvp1uOEPEcqSZIkSdKyW3x5aCEIYEk3rVrYvicwOKU0DyClNHMJfbcDnkspTQeIiBFAC+AhshVw9+T6DQceiIjVgB2BkVWW2tWqcr2RuYrAJQccsRnZpOHeVWJd3HK7EVdEdCObzCTTdDcyjTZbXpdePpawXDElmD9/AZ36DQUgkylh9MCetOp9I5eeehhrN6nPiDGvMvb5SdUcbH6Uz/mWKy/oS6cep7LKqquxYP48vpn9Nf3/OpQP3nubqy8+k+uHjaLZBhvT//qhAPxz4njqN2xMInH1RWdSmslw9HG9qVe/YX4Hk2fH9erNcb16c/utQ7jv7hEce/wJbLTxptx8R3YZ7YTXx9Go8RqkBGeffgqZTIYTT+lLg4aN8hy5JEmSJElLrxAr8d4Gtq3aEBF1gLWBD/jxJN8ip/yC10tk52FWSukPVR6bVunz3x99oYimwIPA0SmlD3LNU4CqOzU0JVspuFyklIaklLZNKW2bzwTetC9m06RRHQCaNKrD9JnZlcCfTptF0yb1K/uttUb9H1TbdW/dguFjXmX7Lddn7nfzaH/6rZzRdZ/qCz6P5s2bx5Xn92WXP+/L9rtk90Jp0Og3bL/z7kQEG2yyOSURzP5qVuU5KSUeGHELh7fvyn133ESbjt1p8ef9ePTBu/M0isKz974H8NwzTy7SllJi6C2DOebY47hlyEC6HteLffZvyb13Dc9TlJIkSZKk/0VE/h/5UohJvKeBVSLiaICIKAWuBIamlL4FngCOW7jJREQ0yJ03G1g99/0rwK4R0Sh3fluyS2chO+aFu8IeBbyUW67774honbtmRMRWPxdoRNQDxgJnppT+trA9pfQ5MDsi/pTbqONoYNQvn4rCNvb5SbRvuT0A7Vtuz5jnJmbbn5tI632as1JZhnXXbMgG6zTmtbc+qjyv3uq12a/F5owY/Qqr1C5jwYJESlBrpbJ8DKNapZQYfMWFrLXuehzYqn1l+3Y77cpbb4wD4LMpHzNv3jxWr1uv8vjzT4yh+fY7s9rqdaioKCdKgigpoaK8vLqHUFAmf/JR5fcvvfAs6zZbf5Hjj4x+iB133pU6depSXl5OSUkJJSUllBf5vEmSJEmSap6CW06bUkoRcShwQ0ScQzbp9gjQL9flZrL3qJsYEd8BNwHXk72n3aMR8XnuvnhnAs+Srcp7JKW0MIn2X2CziHgd+Ao4ItfeDhgUEWcDZcDdwJs/E24vsvfpOycXK8DeKaVpwPHAUKA28GjuQURsR7Zyrz7QMiIuSCltljv2IrAJsFpETAG6pJQe/wXTt8LcPqATu2yzIY3qrcb7j13ERYMf4YrbnmT4pZ3peMgOTP78S9r1vQWAdz6cyv1PTGDC/Wcxb/4Cel9yLwsWfF882a/bflxy82MAPPnyO3Rv04JxI/tx830v5WVs1em9t97khaceYZ31NuC07kcB0LZzD/bY92BuuOJCTu3ahkymjJ59z6/cpKGivJznnxjDWZdmdwc+sFU7rjy/L5myMk466+c2bf71OPfMPox//VVmzZrFQfvuTtfjevH3l17gk4//TUQJTX67Jn3POq+yf/mcOTwyZhTXDrwJgLbtOnLmaSdRlinjwgFX5GsYkiRJkqT/QR4L4fIuspunqqaqvXUvf4DL6B8PD8h3CDXW2g1WyXcINVaDVUvzHYIkSZKkmq2Y81i8NeWbvOdBNm+6Wl5+BoW4nFaSJEmSJElSFQW3nFaSJEmSJElaoiKuQ7QST5IkSZIkSSpwJvEkSZIkSZKkAudyWkmSJEmSJNUIUcTraa3EkyRJkiRJkgqclXiSJEmSJEmqEaJ4C/GsxJMkSZIkSZIKnUk8SZIkSZIkqcC5nFaSJEmSJEk1QhGvprUST5IkSZIkSSp0VuJJkiRJkiSpZijiUjwr8SRJkiRJkqQCZxJPkiRJkiRJKnAup5UkSZIkSVKNEEW8ntZKPEmSJEmSJKnAWYknSZIkSZKkGiGKtxDPSjxJkiRJkiSp0JnEkyRJkiRJkgqcy2klSZIkSZJUIxTxalqTeCpeTRvUzncINVaDVUvzHYIkSZIkSUXFJJ4kSZIkSZJqhiIuxfOeeJIkSZIkSVKBM4knSZIkSZIkFTiX00qSJEmSJKlGiCJeT2slniRJkiRJklTgTOJJkiRJkiRJBc7ltJIkSZIkSaoRonhX01qJJ0mSJEmSJBU6K/EkSZIkSZJUIxRxIZ6VeJIkSZIkSVKhM4knSZIkSZIkFTiX00qSJEmSJKlmKOL1tFbiSZIkSZIkSQXOSjxJkiRJkiTVCFHEpXhW4kmSJEmSJEnLUUTsGxHvRcT7EXHGEo63i4iJucfLEbHVz13TJJ4kSZIkSZK0nEREKTAQ2A/4PdA2In6/WLd/A7umlLYELgKG/Nx1XU4rSZIkSZKkGiFqxmraPwLvp5Q+BIiIu4GDgX8u7JBSerlK/38ATX/uolbiSZIkSZIkSUspIrpFxLgqj26LdVkLmFzl+ZRc24/pAjz6c69rJZ4kSZIkSZJqhEIoxEspDeGnl78uKcy0xI4Ru5NN4u38c69rEk+SJEmSJElafqYAa1d53hT4bPFOEbElcDOwX0rpi5+7aLUsp42IFBFXVnneJyLOr47XXkIs3yzHa+0VEa9HxKTc1z2qHNsm1/5+RFwXkV21HREtImJ8RMyLiFaLXe+xiJgVEWOWV4zLy+Dz2vHx0wMYN7JfZVv9OqswZlAvJo06lzGDelFv9dqVx/p03pu3Rp3Hmw+ew547bArASmUZRl3fg3Ej+9Gt9S6Vfa8/uy1bbfyzS79/Ffqffzb7/3kX2rU+uLJtyA3X0aHNoXQ88jBO6nEs06dPA2DiG+Pp0OZQOrdvw5RPPgZg9uyv6d3jWFJaYgJfkiRJkiTl32vAhhGxXkSsBBwJPFy1Q0SsAzwAdEgp/WtpLlpd98SrAA6LiEbV9HorREQsXrk4A2iZUtoC6AgMq3JsENAN2DD32DfX/gnQCbhzCS9xOdBhOYa83Awb/Q8O7jlwkbY+x+zFc6++xxYHX8hzr75Hn2P2BmCT9ZvQep/mNG/Vn4N63sC1Z7ahpCTYa8dNmfDOZLZrM4DOh+8EwBYbrUVJSfDme1OqfUz5sH/LQ7j6+hsXaWt3dGeG3fsgt9/9ADvtsiu3DRkEwF3DbucvV1zDcb1688B99wBw202D6dilG1FD7uQpSZIkSdJyFQXw+BkppXlAL+Bx4B3g3pTS2xFxXEQcl+t2LtAQuCEi3oiIcT933epK4s0ju1b45MUPRMS6EfF0REzMfV0nIupGxEcRUZLrs0pETI6Isoj4Xa5i7fWIeDEiNsn1GRoRgyLi2Yj4MCJ2jYhbI+KdiBi62GtemauGezoiGufafuq6V0XEs8ClVa+TUpqQUlpYDvk2sHJE1IqI3wJ1Ukp/T9mSqTuAQ3LnfJRSmggsWHwuUkpPA7OXdZJXpL+N/4CZX327SNuBu23J8NGvADB89Cu03H3LyvaRj49n7nfz+PizL/hg8gy227wZ382bT+2Vy8iUfv+2O7fHgVw0aGz1DSTPtt5mW+rUrbtI26qrrVb5ffmcOZUJukwmQ0V5OeXl5WQyGaZM/oTp06ax9TbbVWvMkiRJkiTpl0kpPZJS2iil9LuUUv9c2+CU0uDc911TSvVTSn/IPbb9uWtW5+60A4F2EVF3sfbrgTtSSlsCI4DrUkpfAW8Cu+b6tAQeTyl9RzYZeEJKaRugD3BDlWvVB/YgmywcDVwNbAZsERF/yPVZFRifUmoOPA+cl2v/qetuBOyZUjr1J8Z3ODAhpVRBdseRqqVlP7cLSY20RsPVmTrjawCmzviaxg1WB2CtxnWZMvXLyn6fTvuSNdeoy9P/eJffNKzDC8P6cPXQpzhg1y2Y8M9P+Hz6V3mJv5AMvv5aDtnvzzz+6Bi6Ht8LgA6du3Lpxedz75130OqIo7hx4HV063FCniOVJEmSJCl/ogD+y5dq29gipfR1RNwBnAjMqXJoB+Cw3PfDgMty398DHAE8S3bt8A0RsRqwIzCyynLCWlWuNTqllCJiEvCflNIkgIh4G2gGvEG2Au6eXP/hwANLcd2RKaX5Pza2iNiMbJXe3gubljQFP3b+r84SlnqmBPPnL6BTv6EAZDIljB7Yk1a9b+TSUw9j7Sb1GTHmVcY+P6magy0Mx/U6ieN6ncQdt97E/XffSdfje7HRxpty0x13ATDh9XE0atyYlBLnnH4qpZkMJ55yGg0a1ugV6pIkSZIkaSlVZyUewDVkt81d9Sf6LEx2PQzsFxENgG2AZ8jGO6tKqeEfUkqbVjm3Ivd1QZXvFz7/sYRlWorr/vfHgo2IpsCDwNEppQ9yzVPI7jyy0BJ3IVlWEdEtIsZFxLh5M95eXpf9xaZ9MZsmjeoA0KRRHabPzK4E/nTaLJo2qV/Zb6016v+g2q576xYMH/Mq22+5PnO/m0f702/ljK77VF/wBWqvfQ/g2WeeXKQtpcTQW27kmGOP49YhN9DluJ7su/+B3HvXiDxFKUmSJEmSqlu1JvFSSjOBe8km8hZ6mWylHUA74KVc32+AV4FrgTEppfkppa+Bf0dEa4DI2uoXhlECLNwV9ijgpWW9bkTUA8YCZ6aU/lZlnJ8DsyPiT7ldaY8GRv3COH9USmlISmnblNK2mUabLa/L/mJjn59E+5bbA9C+5faMeW5itv25ibTepzkrlWVYd82GbLBOY15766PK8+qtXpv9WmzOiNGvsErtMhYsSKQEtVYqy8cw8m5ybudZgJdeeJZ1m623yPFHRj/Ejju3oE6dupSXl1NSUkKUlFBRPmfxS0mSJEmS9KsWkf9HvlTbctoqriS7Q8dCJwK3RsRpwHTgmCrH7gFGArtVaWsHDIqIs4Ey4G6y989bWv8FNouI14GvyC7ZXdbr9gI2AM6JiHNybXunlKYBxwNDgdrAo7kHEbEd2cq9+kDLiLggpbRZ7tiLwCbAahExBeiSUnr8F4xthbl9QCd22WZDGtVbjfcfu4iLBj/CFbc9yfBLO9PxkB2Y/PmXtOt7CwDvfDiV+5+YwIT7z2Le/AX0vuReFiz4fjVxv277ccnNjwHw5Mvv0L1NC8aN7MfN972Ul7FVp3PP7MOE119j1qxZHLzvHnQ9rid/f+kFPv74I0qihCa//S19zzqvsn/5nDk8OmYU1wy8CYAj23Wk32m9KcuUccGAy/M1DEmSJEmSVM0iu3mqaqraW/fyB7iMprx0Tb5DqLEarpqP/L8kSZIkiSXfh79oTJ5Zkfc8yNoNauXlZ1Dd98STJEmSJEmS9AuZxJMkSZIkSZIKnGviJEmSJEmSVCPkc2OJfLMST5IkSZIkSSpwJvEkSZIkSZKkAudyWkmSJEmSJNUQxbue1ko8SZIkSZIkqcBZiSdJkiRJkqQawY0tJEmSJEmSJBUsk3iSJEmSJElSgXM5rSRJkiRJkmqEIl5NayWeJEmSJEmSVOisxJMkSZIkSVKN4MYWkiRJkiRJkgqWSTxJkiRJkiSpwLmcVpIkSZIkSTVCFPHWFlbiSZIkSZIkSQXOSjxJkiRJkiTVDMVbiGclniRJkiRJklToTOJJkiRJkiRJBc7ltJIkSZIkSaoRing1rUk8Fa+Gq/r2lyRJkiRJNYNZDEmSJEmSJNUIUcSleN4TT5IkSZIkSSpwJvEkSZIkSZKkAudyWkmSJEmSJNUIUcRbW1iJJ0mSJEmSJBU4K/EkSZIkSZJUMxRvIZ6VeJIkSZIkSVKhM4knSZIkSZIkFTiX00qSJEmSJKlGKOLVtFbiSZIkSZIkSYXOJJ4kSZIkSZJU4FxOK0mSJEmSpBohing9rZV4kiRJkiRJUoGzEk+SJEmSJEk1QhTx1hZW4kmSJEmSJEkFziSeJEmSJEmSVOBcTitJkiRJkqQawY0tJEmSJEmSJBUsk3iSJEmSJElSgauWJF5EzI+INyLi7Yh4MyJOiYi8JBAj4pvleK29IuL1iJiU+7pHlWPb5Nrfj4jrIrIFnxHRIiLGR8S8iGi12PUei4hZETFmecW4vAw+rx0fPz2AcSP7VbbVr7MKYwb1YtKocxkzqBf1Vq9deaxP5715a9R5vPngOey5w6YArFSWYdT1PRg3sh/dWu9S2ff6s9uy1cZNq28wkiRJkiRJNUx1JdLmpJT+kFLaDNgL2B84r5pee7mJiMXvITgDaJlS2gLoCAyrcmwQ0A3YMPfYN9f+CdAJuHMJL3E50GE5hrzcDBv9Dw7uOXCRtj7H7MVzr77HFgdfyHOvvkefY/YGYJP1m9B6n+Y0b9Wfg3rewLVntqGkJNhrx02Z8M5ktmszgM6H7wTAFhutRUlJ8OZ7U6p9TJIkSZIkSTVFtVfDpZSmkU1u9Yqs0oi4PCJei4iJEdF9Yd+I6JurZnszIi7Jtf0uV7H2ekS8GBGb5NqHRsSgiHg2Ij6MiF0j4taIeCcihlaNISKuzFXDPR0RjZfiuldFxLPApYuNZUJK6bPc07eBlSOiVkT8FqiTUvp7SikBdwCH5M75KKU0EViwhLl5Gpj9v87xivC38R8w86tvF2k7cLctGT76FQCGj36FlrtvWdk+8vHxzP1uHh9/9gUfTJ7Bdps347t586m9chmZ0u/fduf2OJCLBo2tvoFIkiRJkqQaKyL/j3zJy5LWlNKHuddeA+gCfJVS2g7YDjg2ItaLiP3IJr62TyltBVyWO30IcEJKaRugD3BDlUvXB/YATgZGA1cDmwFbRMQfcn1WBcanlJoDz/N9ReBPXXcjYM+U0qk/MazDgQkppQpgLaBqadmUXNuvyhoNV2fqjK8BmDrjaxo3WB2AtRrXZcrULyv7fTrtS9Zcoy5P/+NdftOwDi8M68PVQ5/igF23YMI/P+Hz6V/lJX5JkiRJkqSaYvHlodVpYe5yb2DLKveHq0t2+emewG0ppW8BUkozI2I1YEdgZHyf+qxV5ZqjU0opIiYB/0kpTQKIiLeBZsAbZCvg7sn1Hw48sBTXHZlSmv+jA4nYjGyV3t6Lja2q9GPn/+osIS2dEsyfv4BO/YYCkMmUMHpgT1r1vpFLTz2MtZvUZ8SYVxn7/KRqDlaSJEmSJNUUscSUS3HISxIvItYH5gPTyCa8TkgpPb5Yn335YeKrBJiVUvrDj1y6Ivd1QZXvFz7/sbGmpbjuf3+knYhoCjwIHJ1S+iDXPAWoulNDU+Czxc9dVhHRjeySZDJNdyPTaLPldelfZNoXs2nSqA5TZ3xNk0Z1mD4zuxL402mzaNqkfmW/tdao/4Nqu+6tWzB8zKtsv+X6zP1uHu1Pv5Xnbz/VJJ4kSZIkSdISVPty2tw96AYD1+fuF/c4cHxElOWObxQRqwJPAJ0jYpVce4OU0tfAvyOida4tImKrXxhCCbCw6u8o4KVlvW5E1APGAmemlP62sD2l9DkwOyL+lNuV9mhg1C+M80ellIaklLZNKW2brwQewNjnJ9G+5fYAtG+5PWOem5htf24irfdpzkplGdZdsyEbrNOY1976qPK8eqvXZr8WmzNi9CusUruMBQsSKUGtlcryMQxJkiRJkqSCV12VeLUj4g2gDJhHdhfXq3LHbia71HV8LuE1HTgkpfRY7j524yJiLvAI0A9oBwyKiLNz17sbePMXxPJfYLOIeB34Cjgi174s1+0FbACcExHn5Nr2zm3ecTwwFKgNPJp7EBHbka3cqw+0jIgLcrv2EhEvApsAq0XEFKDL4hWK+XL7gE7sss2GNKq3Gu8/dhEXDX6EK257kuGXdqbjITsw+fMvadf3FgDe+XAq9z8xgQn3n8W8+Qvofcm9LFjwfVFlv277ccnNjwHw5Mvv0L1NC8aN7MfN972Ul7FJkiRJkqSaIZ8bS+RbZIvhVFPV3rqXP8BlNGfC9fkOQZIkSZKkX6qI01jwdfmCvOdB6qxckpefQT43tpAkSZIkSZKWWjFnMKv9nniSJEmSJEmSfhmTeJIkSZIkSVKBczmtJEmSJEmSaoYiXk9rJZ4kSZIkSZJU4EziSZIkSZIkSQXO5bSSJEmSJEmqEaKI19NaiSdJkiRJkiQVOCvxJEmSJEmSVCNE8RbiWYknSZIkSZIkFTqTeJIkSZIkSVKBczmtJEmSJEmSaoQiXk1rJZ4kSZIkSZJU6KzEkyRJkiRJUs1QxKV4VuJJkiRJkiRJBc4kniRJkiRJklTgXE4rSZIkSZKkGiGKeD2tlXiSJEmSJElSgbMST5IkSZIkSTVCFG8hnpV4kiRJkiRJUqEziSdJkiRJkiQVuEgp5TsG/UpFRLeU0pB8x1ETOXfLzrlbds7dsnPulp1zt+ycu2Xn3C07527ZOXfLzrlbds7dsnPuVIisxNOK1C3fAdRgzt2yc+6WnXO37Jy7ZefcLTvnbtk5d8vOuVt2zt2yc+6WnXO37Jw7FRyTeJIkSZIkSVKBM4knSZIkSZIkFTiTeFqRvH/AsnPulp1zt+ycu2Xn3C07527ZOXfLzrlbds7dsnPulp1zt+ycu2Xn3KnguLGFJEmSJEmSVOCsxJMkSZIkSZIKnEk8SZIkSZIkqcCZxNNyFxG3RsT/t3fvMXaUdRjHv49d5NJiUITSC6agyCWVFigNWNIARQQkRVACWAgXg2IQixqRQgIhhKipMRD/EBWkjZQiFBpNQWwFuRnKpe0WCi0QpEDlUhJQKNdCH/+YWd20u2Xrlr4z9PkkJ2fO7HtmnryZs2fmd+adWSlpSeksbSNpZ0l/k7RU0qOSJpfO1BaStpL0gKTFdd9dUjpTm0gaIGmRpDmls7SNpOWSHpHUKemh0nnaRNJ2kmZJWlb/3zuwdKY2kLR7vb11PV6TdG7pXG0g6fv1d8QSSTMlbVU6U1tImlz326PZ3j5YT/vDkj4laZ6kJ+vnT5bM2FS99N3x9ba3RtKYkvmarJe+m1p/zz4sabak7QpGbKxe+u7Sut86Jc2VNLRkxghIES8+HNOAI0qHaKn3gB/a3hM4ADhb0l6FM7XFO8ChtkcBo4EjJB1QNlKrTAaWlg7RYofYHm07BxYb5grgNtt7AKPINtgnth+vt7fRwH7Am8DssqmaT9Iw4HvAGNsjgQHAiWVTtYOkkcCZwFiqz+rRknYrm6rxprHu/vD5wO22dwNur1/Huqaxbt8tAY4D7t7kadplGuv23TxgpO29gSeAKZs6VEtMY92+m2p77/r7dg5w0aYOFbG2FPFio7N9N/BK6RxtZPsF2wvr6depDmiHlU3VDq6sql9uUT9y554+kDQc+ApwVekssfmQ9AlgPHA1gO13bf+raKh2mgA8ZfuZ0kFaogPYWlIHsA3wfOE8bbEnMN/2m7bfA+4Cji2cqdF62R8+BpheT08HvropM7VFT31ne6ntxwtFao1e+m5u/bkFmA8M3+TBWqCXvnut28uB5NgiGiBFvIiGkjQC2Ae4v3CU1qiHhHYCK4F5ttN3fXM5cB6wpnCOtjIwV9ICSd8qHaZFdgVeBq6ph3JfJWlg6VAtdCIws3SINrD9T+DnwLPAC8C/bc8tm6o1lgDjJW0vaRvgKGDnwpnaaLDtF6D64RbYsXCe2PycAfy5dIg2kXSZpOeASeRMvGiAFPEiGkjSIOAm4Ny1fgGK9bD9fn26+3BgbD38J9ZD0tHAStsLSmdpsXG29wWOpBoCP750oJboAPYFfmV7H+ANMrRsg0j6ODARuLF0ljaorz92DLALMBQYKOnksqnawfZS4GdUw/JuAxZTXQIkIlpC0oVUn9sZpbO0ie0Lbe9M1W/fLZ0nIkW8iIaRtAVVAW+G7ZtL52mjekjeneTajH0xDpgoaTlwPXCopGvLRmoX28/Xzyuprks2tmyi1lgBrOh2xuwsqqJe9N2RwELbL5UO0hKHAU/bftn2auBm4IuFM7WG7att72t7PNWQsydLZ2qhlyQNAaifVxbOE5sJSacCRwOTbGdI6P/nOuBrpUNEpIgX0SCSRHV9qKW2f1E6T5tI2qHrbluStqY6WFtWNFQL2J5ie7jtEVTD8u6wnTNT+kjSQEnbdk0Dh1MNO4sPYPtF4DlJu9ezJgCPFYzURieRobQb4lngAEnb1N+3E8jNVPpM0o7182eobjCQbW/D/Qk4tZ4+FfhjwSyxmZB0BPBjYKLtN0vnaZO1buAzkRxbRAN0lA4QHz2SZgIHA5+WtAK42PbVZVO1xjjgFOCR+tpuABfYvrVcpNYYAkyXNIDqB4obbM8pnCk++gYDs6t6AB3AdbZvKxupVc4BZtTDQv8BnF44T2vU1yX7EvDt0lnawvb9kmYBC6mGlC0CflM2VavcJGl7YDVwtu1XSwdqsp72h4GfAjdI+iZVUfn4cgmbq5e+ewX4JbADcIukTttfLpeymXrpuynAlsC8en9lvu2zioVsqF767qj6x8Y1wDNA+i2KU86mjYiIiIiIiIiIaLYMp42IiIiIiIiIiGi4FPEiIiIiIiIiIiIaLkW8iIiIiIiIiIiIhksRLyIiIiIiIiIiouFSxIuIiIiIiIiIiGi4FPEiIiIi+knSTpKul/SUpMck3Srp8720PVjSnH6u7zRJQ7u9vlPSmA1cxqr+ZIiIiIiITStFvIiIiIh+kCRgNnCn7c/a3gu4ABj8Ia1vAHAaMPQDmkZERETER0iKeBERERH9cwiw2vaVXTNsdwL3SpoqaYmkRySd0O09gyTNkrRM0oy6EIikCZIW1e1/J2nLev5ySRdJuhc4CRgDzJDUKWnr7mEkrZJ0maTFkuZLGlzP30XSfZIelHTpWu/5UT3/YUmX1POOlfRXVYZIekLSThu99yIiIiKiT1LEi4iIiOifkcCCHuYfB4wGRgGHAVMlDan/tg9wLrAXsCswTtJWwDTgBNtfADqA73Rb3tu2D7J9LfAQMMn2aNtvrbXegcB826OAu4Ez6/lXAL+yvT/wYldjSYcDuwFj67z7SRpve3bd7mzgt8DFtv/7voiIiIjYtFLEi4iIiPhwHATMtP2+7ZeAu4D96789YHuF7TVAJzAC2B142vYTdZvpwPhuy/tDH9f7LtB1zb0F9bIBxgEz6+nfd2t/eP1YBCwE9qAq6gGcA0wB3rE9k4iIiIgopqN0gIiIiIiWexT4eg/ztZ73vNNt+n2qfbL1tQd4o495Vtv2Wsvu4h7aC/iJ7V/38LdhwBpgsKSP1UXHiIiIiCggZ+JFRERE9M8dwJaSuoatIml/4FXgBEkDJO1AdVbdA+tZzjJghKTP1a9PoTp7ryevA9tuYM6/AyfW05O6zf8LcIakQXX2YZJ2lNQBXAN8A1gK/GAD1xcRERERG1HOxIuIiIjoB9uWdCxwuaTzgbeB5VTXvBsELKY6A+482y9K2qOX5bwt6XTgxrqA9iBwZU9tqa6dd6Wkt4AD+xh1MnCdpMnATd3WO1fSnsB99f01VgEnA2cB99i+R1In8KCkW2wv7eP6IiIiImIj0v9GW0REREREREREREQTZThtREREREREREREw6WIFxERERERERER0XAp4kVERERERERERDRcingRERERERERERENlyJeREREREREREREw6WIFxERERERERER0XAp4kVERERERERERDTcfwCWF0zqPWlJmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1656x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(23,12))\n",
    "sns.heatmap(Percent_Cohort_Table,annot=True,cmap='Blues', fmt='.0%')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}