Portfolio Optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2d9df0ff",
   "metadata": {
    "id": "2d9df0ff"
   },
   "source": [
    "# Case Study - Portfolio Optimization\n",
    "In this case study, we will build two $10,000 investment portfolios containing four stocks. The first portfolio will have an equal weighting between the stocks. The second portfolio will be optimized with a weighting allocation that provides the best return, adjusted for risk. To build these two portfolios, we will:\n",
    "1. Import two years of data for four stocks\n",
    "2. Build the initial portfolio with equal weighting to each of the stocks\n",
    "3. Analyze and visualize the equal-weighted portfolio\n",
    "4. Generate 10,000 portfolio scenarios with random weighting to each of the stocks\n",
    "5. Identify the optimal portfolio from the scenarios and visualize the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "720791ef",
   "metadata": {
    "id": "720791ef"
   },
   "source": [
    "## Import Packages & Connect to Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9187de70",
   "metadata": {
    "id": "9187de70"
   },
   "outputs": [],
   "source": [
    "# Import packages needed for case study\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas_datareader.data as pdr\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5792b948",
   "metadata": {
    "id": "5792b948"
   },
   "outputs": [],
   "source": [
    "# Set start and end date parameters\n",
    "startdate = '2019-01-01'\n",
    "enddate = '2021-12-31'\n",
    "\n",
    "# List the four stock ticker symbols for our portfolio\n",
    "stock_list = ['AMD', 'AAPL', 'MSFT', 'ORCL']\n",
    "\n",
    "# Create an empty dictionary to store our stock info\n",
    "stocks = {}\n",
    "\n",
    "# Loop through each stock in the stock_list and return the Adj Close\n",
    "for i_stock in stock_list:\n",
    "    stocks[i_stock] = pdr.DataReader(i_stock, 'yahoo', startdate, enddate)[['Adj Close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3619470",
   "metadata": {
    "id": "f3619470",
    "outputId": "814a1fe0-520c-4358-f757-395d3cbef238"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>18.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>17.049999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>20.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>20.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close\n",
       "Date                 \n",
       "2019-01-02  18.830000\n",
       "2019-01-03  17.049999\n",
       "2019-01-04  19.000000\n",
       "2019-01-07  20.570000\n",
       "2019-01-08  20.750000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine the 'AMD' Adj Close from the stocks dictionary\n",
    "stocks['AMD'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2733fc05",
   "metadata": {
    "id": "2733fc05"
   },
   "source": [
    "## Create the Equal-Weighted Portfolio\n",
    "To create the equal-weighted portfolio, we need to add some additional columns to the DataFrames in the `stocks` dictionary. The three columns that we will build are:\n",
    "* Normalized Return = Adjusted Close / Adjusted Close on the `startdate` of the portfolio\n",
    "* Allocation = Normalized Return * 0.25 (equal weighting for each of the four stocks)\n",
    "* Position Value = Allocation * 10,000 (value of the portfolio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f5388a1",
   "metadata": {
    "id": "3f5388a1"
   },
   "outputs": [],
   "source": [
    "# Create 'Normalized Return' column for each stock\n",
    "for stock_name, stock_data in stocks.items():\n",
    "    first_adj_close = stock_data.iloc[0]['Adj Close'] # Select the first row from the Adj Close column\n",
    "    stock_data['Normalized Return'] = stock_data['Adj Close'] / first_adj_close # Divide each row by the first Adj Close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0ae4ce2",
   "metadata": {
    "id": "a0ae4ce2",
    "outputId": "2500583f-0feb-439d-aafe-561bdc1ad185"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Normalized Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>38.221359</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>34.414238</td>\n",
       "      <td>0.900393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>35.883354</td>\n",
       "      <td>0.938830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>35.803493</td>\n",
       "      <td>0.936740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>36.486012</td>\n",
       "      <td>0.954597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close  Normalized Return\n",
       "Date                                    \n",
       "2019-01-02  38.221359           1.000000\n",
       "2019-01-03  34.414238           0.900393\n",
       "2019-01-04  35.883354           0.938830\n",
       "2019-01-07  35.803493           0.936740\n",
       "2019-01-08  36.486012           0.954597"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks['AAPL'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a605d85",
   "metadata": {
    "id": "8a605d85"
   },
   "outputs": [],
   "source": [
    "# Create allocation for each stock - equally weighted in our initial portfolio\n",
    "for stock_name, stock_data in stocks.items():\n",
    "    stock_data['Allocation'] = stock_data['Normalized Return'] * 0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c89341de",
   "metadata": {
    "id": "c89341de",
    "outputId": "2451866f-929b-4239-abe8-25cd4a7ac2b2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Normalized Return</th>\n",
       "      <th>Allocation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>97.353905</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>93.772446</td>\n",
       "      <td>0.963212</td>\n",
       "      <td>0.240803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>98.133743</td>\n",
       "      <td>1.008010</td>\n",
       "      <td>0.252003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>98.258888</td>\n",
       "      <td>1.009296</td>\n",
       "      <td>0.252324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>98.971336</td>\n",
       "      <td>1.016614</td>\n",
       "      <td>0.254153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close  Normalized Return  Allocation\n",
       "Date                                                \n",
       "2019-01-02  97.353905           1.000000    0.250000\n",
       "2019-01-03  93.772446           0.963212    0.240803\n",
       "2019-01-04  98.133743           1.008010    0.252003\n",
       "2019-01-07  98.258888           1.009296    0.252324\n",
       "2019-01-08  98.971336           1.016614    0.254153"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks['MSFT'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ad751db",
   "metadata": {
    "id": "4ad751db"
   },
   "outputs": [],
   "source": [
    "# Set the value of the portfolio to $10k\n",
    "for stock_name, stock_data in stocks.items():\n",
    "    stock_data['Position Value'] = stock_data['Allocation'] * 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53c36f9d",
   "metadata": {
    "id": "53c36f9d",
    "outputId": "d58f9d6c-0b94-4993-bcb1-6c03a3785409"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Normalized Return</th>\n",
       "      <th>Allocation</th>\n",
       "      <th>Position Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>42.703712</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>2500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>42.288193</td>\n",
       "      <td>0.990270</td>\n",
       "      <td>0.247567</td>\n",
       "      <td>2475.674263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>44.110802</td>\n",
       "      <td>1.032950</td>\n",
       "      <td>0.258238</td>\n",
       "      <td>2582.375112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>44.809624</td>\n",
       "      <td>1.049314</td>\n",
       "      <td>0.262329</td>\n",
       "      <td>2623.286193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>45.215694</td>\n",
       "      <td>1.058824</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>2647.058758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close  Normalized Return  Allocation  Position Value\n",
       "Date                                                                \n",
       "2019-01-02  42.703712           1.000000    0.250000     2500.000000\n",
       "2019-01-03  42.288193           0.990270    0.247567     2475.674263\n",
       "2019-01-04  44.110802           1.032950    0.258238     2582.375112\n",
       "2019-01-07  44.809624           1.049314    0.262329     2623.286193\n",
       "2019-01-08  45.215694           1.058824    0.264706     2647.058758"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks['ORCL'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d042b6",
   "metadata": {
    "id": "26d042b6"
   },
   "source": [
    "## Visualize the Portfolio Performance\n",
    "To visualize the performance of the portfolio, we can create two line charts that show the return of the portfolio, and the return of the individual stocks, over time. Let's build a new DataFrame that contains just the `position value` for each stock, as well as the total value for the portfolio. We can use this DataFrame to create the two visuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc9cadf3",
   "metadata": {
    "id": "fc9cadf3"
   },
   "outputs": [],
   "source": [
    "# Create position_values dictionary\n",
    "position_values = {}\n",
    "\n",
    "for stock_name, stock_data in stocks.items():\n",
    "    position_values[stock_name] = stock_data['Position Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a7ffce6",
   "metadata": {
    "id": "3a7ffce6",
    "outputId": "458b167b-348a-40c9-9cc3-4e87e5f44b87"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMD</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>ORCL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>2500.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>2263.674895</td>\n",
       "      <td>2250.982085</td>\n",
       "      <td>2408.029908</td>\n",
       "      <td>2475.674263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>2522.570377</td>\n",
       "      <td>2347.074704</td>\n",
       "      <td>2520.025868</td>\n",
       "      <td>2582.375112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>2731.014309</td>\n",
       "      <td>2341.851140</td>\n",
       "      <td>2523.239528</td>\n",
       "      <td>2623.286193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>2754.912385</td>\n",
       "      <td>2386.493588</td>\n",
       "      <td>2541.534843</td>\n",
       "      <td>2647.058758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    AMD         AAPL         MSFT         ORCL\n",
       "Date                                                          \n",
       "2019-01-02  2500.000000  2500.000000  2500.000000  2500.000000\n",
       "2019-01-03  2263.674895  2250.982085  2408.029908  2475.674263\n",
       "2019-01-04  2522.570377  2347.074704  2520.025868  2582.375112\n",
       "2019-01-07  2731.014309  2341.851140  2523.239528  2623.286193\n",
       "2019-01-08  2754.912385  2386.493588  2541.534843  2647.058758"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the position_values dictionary to a DataFrame\n",
    "position_values = pd.DataFrame(data=position_values)\n",
    "\n",
    "position_values.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c778dba8",
   "metadata": {
    "id": "c778dba8"
   },
   "outputs": [],
   "source": [
    "# Add 'Total' column to position values, summing the other columns\n",
    "position_values['Total'] = position_values.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b2d20f8",
   "metadata": {
    "id": "9b2d20f8",
    "outputId": "cbc7dd9d-c5a2-4fd6-f26b-c42154ba284e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMD</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>ORCL</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>2500.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>2263.674895</td>\n",
       "      <td>2250.982085</td>\n",
       "      <td>2408.029908</td>\n",
       "      <td>2475.674263</td>\n",
       "      <td>9398.361150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>2522.570377</td>\n",
       "      <td>2347.074704</td>\n",
       "      <td>2520.025868</td>\n",
       "      <td>2582.375112</td>\n",
       "      <td>9972.046060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>2731.014309</td>\n",
       "      <td>2341.851140</td>\n",
       "      <td>2523.239528</td>\n",
       "      <td>2623.286193</td>\n",
       "      <td>10219.391171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>2754.912385</td>\n",
       "      <td>2386.493588</td>\n",
       "      <td>2541.534843</td>\n",
       "      <td>2647.058758</td>\n",
       "      <td>10329.999573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    AMD         AAPL         MSFT         ORCL         Total\n",
       "Date                                                                        \n",
       "2019-01-02  2500.000000  2500.000000  2500.000000  2500.000000  10000.000000\n",
       "2019-01-03  2263.674895  2250.982085  2408.029908  2475.674263   9398.361150\n",
       "2019-01-04  2522.570377  2347.074704  2520.025868  2582.375112   9972.046060\n",
       "2019-01-07  2731.014309  2341.851140  2523.239528  2623.286193  10219.391171\n",
       "2019-01-08  2754.912385  2386.493588  2541.534843  2647.058758  10329.999573"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position_values.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbee56dd",
   "metadata": {
    "id": "dbee56dd",
    "outputId": "7bd0ac1e-90aa-433d-c465-84a2534244bd"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View the total portfolio\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.plot(position_values['Total'])\n",
    "\n",
    "plt.title('Equal-Weighted Portfolio Performance')\n",
    "plt.ylabel('Total Value');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a4c4f02",
   "metadata": {
    "id": "8a4c4f02",
    "outputId": "e52a0d04-3cc9-4be1-e426-998db922a569"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View the four stocks in the portfolio\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.plot(position_values.iloc[:,0:4])\n",
    "\n",
    "plt.title('Equal-Weighted Portfolio Stock Performance')\n",
    "plt.ylabel('Total Value');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e79bf233",
   "metadata": {
    "id": "e79bf233"
   },
   "source": [
    "## Calculate Performance Metrics for the Portfolio\n",
    "Now that we have created and visualized the equal-weighted portfolio, we can calculate a few metrics to further measure the performance of the portfolio. We will create five performances metrics:\n",
    " * Cumulative Return\n",
    " * Mean Daily Return\n",
    " * Standard Deviation Daily Return\n",
    " * Sharpe Ratio\n",
    " * Annualized Sharpe Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1005b5e3",
   "metadata": {
    "id": "1005b5e3",
    "outputId": "2a40bda5-75e8-47bd-cb42-bddd3fde1fda"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "343.53 %\n"
     ]
    }
   ],
   "source": [
    "# Define the end and start value of the portfolio\n",
    "end_value = position_values['Total'][-1]\n",
    "start_value = position_values['Total'][0]\n",
    "\n",
    "# Calculate the cumulative portfolio return as a percentage\n",
    "cumulative_return = end_value / start_value - 1\n",
    "\n",
    "print(str(round(cumulative_return*100,2)), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aafd2d14",
   "metadata": {
    "id": "aafd2d14",
    "outputId": "833abe73-39d5-4a42-e335-d9340d339498"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMD</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>ORCL</th>\n",
       "      <th>Total</th>\n",
       "      <th>Daily Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>2500.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>2263.674895</td>\n",
       "      <td>2250.982085</td>\n",
       "      <td>2408.029908</td>\n",
       "      <td>2475.674263</td>\n",
       "      <td>9398.361150</td>\n",
       "      <td>-0.060164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>2522.570377</td>\n",
       "      <td>2347.074704</td>\n",
       "      <td>2520.025868</td>\n",
       "      <td>2582.375112</td>\n",
       "      <td>9972.046060</td>\n",
       "      <td>0.061041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>2731.014309</td>\n",
       "      <td>2341.851140</td>\n",
       "      <td>2523.239528</td>\n",
       "      <td>2623.286193</td>\n",
       "      <td>10219.391171</td>\n",
       "      <td>0.024804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>2754.912385</td>\n",
       "      <td>2386.493588</td>\n",
       "      <td>2541.534843</td>\n",
       "      <td>2647.058758</td>\n",
       "      <td>10329.999573</td>\n",
       "      <td>0.010823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    AMD         AAPL         MSFT         ORCL         Total  \\\n",
       "Date                                                                           \n",
       "2019-01-02  2500.000000  2500.000000  2500.000000  2500.000000  10000.000000   \n",
       "2019-01-03  2263.674895  2250.982085  2408.029908  2475.674263   9398.361150   \n",
       "2019-01-04  2522.570377  2347.074704  2520.025868  2582.375112   9972.046060   \n",
       "2019-01-07  2731.014309  2341.851140  2523.239528  2623.286193  10219.391171   \n",
       "2019-01-08  2754.912385  2386.493588  2541.534843  2647.058758  10329.999573   \n",
       "\n",
       "            Daily Return  \n",
       "Date                      \n",
       "2019-01-02           NaN  \n",
       "2019-01-03     -0.060164  \n",
       "2019-01-04      0.061041  \n",
       "2019-01-07      0.024804  \n",
       "2019-01-08      0.010823  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a 'Daily Returns' column\n",
    "position_values['Daily Return'] = position_values['Total'].pct_change()\n",
    "\n",
    "position_values.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5879e065",
   "metadata": {
    "id": "5879e065",
    "outputId": "d988cdd2-fcdc-49ca-9fc1-8f73f931c2cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean daily return is: 0.0022\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean Daily Return \n",
    "mean_daily_return = position_values['Daily Return'].mean()\n",
    "\n",
    "print('The mean daily return is:', str(round(mean_daily_return, 4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1138fc75",
   "metadata": {
    "id": "1138fc75",
    "outputId": "cf82fc69-b2cc-47e1-864e-07d6365c992c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The std daily return is: 0.0207\n"
     ]
    }
   ],
   "source": [
    "# Calculate the standard deviation of Daily Return \n",
    "std_daily_return = position_values['Daily Return'].std()\n",
    "\n",
    "print('The std daily return is:', str(round(std_daily_return, 4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5ac9439",
   "metadata": {
    "id": "f5ac9439"
   },
   "source": [
    "### Sharpe Ratio\n",
    "Now, let's explore a risk-adjusted return metric called the sharpe ratio. The sharpe ratio helps us quantify how much return we are getting by a given level of risk. When comparing two different investments against the same benchmark, the asset with the higher Sharpe ratio provides a higher return for the same amount of risk or the same return for a lower risk than the other asset. \n",
    "\n",
    "It is calculated by the average return of the portfolio minus a risk free rate (such as government bonds), divided by the standard deviation of the return. In this case, we assume the risk-free rate is close to zero, so we won't add it to the formula."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3693e3dd",
   "metadata": {
    "id": "3693e3dd",
    "outputId": "d7d696ab-ef62-402d-d33f-7a955712be22"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10575282273400717"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the sharpe ratio\n",
    "sharpe_ratio = mean_daily_return / std_daily_return\n",
    "\n",
    "sharpe_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae5bb80b",
   "metadata": {
    "id": "ae5bb80b",
    "outputId": "773f40e0-3ecb-4a70-d60f-72ed8cb2e220"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6787740163836844"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the annualized sharpe ratio\n",
    "sharpe_ratio_annualized = sharpe_ratio * 252**0.5\n",
    "\n",
    "sharpe_ratio_annualized"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de803160",
   "metadata": {
    "id": "de803160"
   },
   "source": [
    "## Prepare Scenarios to Optimize Portfolio Weighting\n",
    "We need to prepare our data ahead of generating our scenarios to optimize the portfolio weighting. We will:\n",
    " * Create a dictionary containing the adjusted close for each of our stocks: stock_adj_close\n",
    " * Create another dictionary that transforms the adjusted close for each day to a percent change from the previous day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "434b0dcd",
   "metadata": {
    "id": "434b0dcd"
   },
   "outputs": [],
   "source": [
    "# Create stock_adj_close dictionary\n",
    "stock_adj_close = {}\n",
    "\n",
    "for stock_name, stock_data in stocks.items():\n",
    "    stock_adj_close[stock_name] = stock_data['Adj Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ffa9762",
   "metadata": {
    "id": "0ffa9762",
    "outputId": "796324f2-4fad-4398-dc51-a8c26b263218"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMD</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>ORCL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>18.830000</td>\n",
       "      <td>38.221359</td>\n",
       "      <td>97.353905</td>\n",
       "      <td>42.703712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>17.049999</td>\n",
       "      <td>34.414238</td>\n",
       "      <td>93.772446</td>\n",
       "      <td>42.288193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>35.883354</td>\n",
       "      <td>98.133743</td>\n",
       "      <td>44.110802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>20.570000</td>\n",
       "      <td>35.803493</td>\n",
       "      <td>98.258888</td>\n",
       "      <td>44.809624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>20.750000</td>\n",
       "      <td>36.486012</td>\n",
       "      <td>98.971336</td>\n",
       "      <td>45.215694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AMD       AAPL       MSFT       ORCL\n",
       "Date                                                  \n",
       "2019-01-02  18.830000  38.221359  97.353905  42.703712\n",
       "2019-01-03  17.049999  34.414238  93.772446  42.288193\n",
       "2019-01-04  19.000000  35.883354  98.133743  44.110802\n",
       "2019-01-07  20.570000  35.803493  98.258888  44.809624\n",
       "2019-01-08  20.750000  36.486012  98.971336  45.215694"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the stock_adj_close dictionary to a DataFrame\n",
    "stock_adj_close = pd.DataFrame(data=stock_adj_close)\n",
    "\n",
    "stock_adj_close.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95bf6803",
   "metadata": {
    "id": "95bf6803",
    "outputId": "cb0f59b9-ee7b-4dc4-b43c-ebb103621d7b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMD</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>ORCL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.094530</td>\n",
       "      <td>-0.099607</td>\n",
       "      <td>-0.036788</td>\n",
       "      <td>-0.009730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>0.114370</td>\n",
       "      <td>0.042689</td>\n",
       "      <td>0.046509</td>\n",
       "      <td>0.043100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>0.082632</td>\n",
       "      <td>-0.002226</td>\n",
       "      <td>0.001275</td>\n",
       "      <td>0.015842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>0.008751</td>\n",
       "      <td>0.019063</td>\n",
       "      <td>0.007251</td>\n",
       "      <td>0.009062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AMD      AAPL      MSFT      ORCL\n",
       "Date                                              \n",
       "2019-01-02       NaN       NaN       NaN       NaN\n",
       "2019-01-03 -0.094530 -0.099607 -0.036788 -0.009730\n",
       "2019-01-04  0.114370  0.042689  0.046509  0.043100\n",
       "2019-01-07  0.082632 -0.002226  0.001275  0.015842\n",
       "2019-01-08  0.008751  0.019063  0.007251  0.009062"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create stock_returns DataFrames to see the day over day change in stock value\n",
    "stock_returns = stock_adj_close.pct_change()\n",
    "\n",
    "stock_returns.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce3c7f1a",
   "metadata": {
    "id": "ce3c7f1a"
   },
   "source": [
    "## Build & Run 10,000 Portfolio Scenarios\n",
    "Now that we've prepared our data, we're almost ready to run our scenarios. First, we need to build the structures required to generate these scenarios and store the output. To do this, we will use the `numpy.zeros()` function. \n",
    "\n",
    "This function creates arrays that are filled with zeros. After we run the scenarios, we replace these zeros with the corresponding output. The reason we create the arrays with zeros first is to give our arrays the correct shape before we replace them with the correct values.\n",
    "\n",
    "We will create four different arrays:\n",
    " * weights_array - this array will have 10,000 rows and 4 columns and hold the weighting allocation for each stock\n",
    " * returns_array - this array will contain the portfolio return for each scenario\n",
    " * volatility_array - this array will contain the portfolio volatility for each scenario\n",
    " * sharpe_array - this array will contain the sharpe ratio for each scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26aae2af",
   "metadata": {
    "id": "26aae2af",
    "outputId": "a44c349d-9f79-42c9-f9e5-f03e748f1371"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the number of scenarios and create a blank array to populate stock weightings for each scenario\n",
    "scenarios = 10000\n",
    "\n",
    "weights_array = np.zeros((scenarios, len(stock_returns.columns)))\n",
    "\n",
    "weights_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5245589e",
   "metadata": {
    "id": "5245589e"
   },
   "outputs": [],
   "source": [
    "# Create additional blank arrays for scenario output\n",
    "returns_array = np.zeros(scenarios)\n",
    "volatility_array = np.zeros(scenarios)\n",
    "sharpe_array = np.zeros(scenarios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1815480a",
   "metadata": {
    "id": "1815480a"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "random.seed(3)\n",
    "np.random.seed(3)\n",
    "\n",
    "for index in range(scenarios): \n",
    "    # Generate four random numbers for each index\n",
    "    numbers = np.array(np.random.random(4))\n",
    "    \n",
    "    # Divide each number by the sum of the numbers to generate the random weight\n",
    "    weights = numbers / np.sum(numbers)\n",
    "    \n",
    "    # Save the weights in weights_array\n",
    "    weights_array[index,:] = weights\n",
    "    \n",
    "    # Calculate the return for each scenario\n",
    "    returns_array[index] = np.sum(stock_returns.mean()*252*weights)\n",
    "    \n",
    "    # Calculate the expected volatility for each scenario\n",
    "    volatility_array[index] = np.sqrt(np.dot(weights.T,np.dot(stock_returns.cov()*252, weights)))\n",
    "\n",
    "    # Calculate the Sharpe Ratio for each scenario \n",
    "    sharpe_array[index] = returns_array[index] / volatility_array[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "119d847f",
   "metadata": {
    "id": "119d847f",
    "outputId": "f976b6c0-6bf7-4ee8-fafb-8820edf2c393"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first combination: [0.26728964 0.34364796 0.14116943 0.24789297]\n"
     ]
    }
   ],
   "source": [
    "print(\"The first combination:\", weights_array[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccd573c2",
   "metadata": {
    "id": "ccd573c2",
    "outputId": "a2986283-eedf-4fd8-ac24-ba95ed6ec82a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sharpe ratio of the first portfolio: 1.7654791450911809\n"
     ]
    }
   ],
   "source": [
    "print(\"The sharpe ratio of the first portfolio:\", sharpe_array[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47e6455a",
   "metadata": {
    "id": "47e6455a"
   },
   "source": [
    "## Identify the Optimal Portfolio\n",
    "Now that we have the output for all 10,000 scenarios, we can identify the optimal portfolio. The optimal portfolio in this case study is the portfolio that has the highest sharpe ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54a088bd",
   "metadata": {
    "id": "54a088bd",
    "outputId": "d690ff2b-ba5f-4794-a1d5-928b2c3ca5e7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.825267235177286"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the highest sharpe ratio in sharpe_array\n",
    "sharpe_array.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5d73de5",
   "metadata": {
    "id": "f5d73de5",
    "outputId": "b44db50f-ef98-44ca-ac5c-8ca33c1bb675"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8746"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the index of the optimal portfolio\n",
    "index_max_sharpe = sharpe_array.argmax()\n",
    "index_max_sharpe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cb85e68",
   "metadata": {
    "id": "0cb85e68",
    "outputId": "a7720d83-9930-4166-d436-4620e2c13959"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AMD', 'AAPL', 'MSFT', 'ORCL']\n",
      "[0.29761151 0.55853186 0.13323304 0.0106236 ]\n"
     ]
    }
   ],
   "source": [
    "# Print the optimal weights for each stock\n",
    "print(stock_list)\n",
    "print(weights_array[index_max_sharpe,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a26b16aa",
   "metadata": {
    "id": "a26b16aa"
   },
   "source": [
    "## Visualize the Optimal Portfolio & Portfolio Scenarios\n",
    "Let's visualize our portfolio scenarios by using a scatter chart. We can use the volatility and returns arrays on each axis to see the relationship between risk and reward. As a final step, we can visualize where the optimal portfolio appears among all of the scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d02af92e",
   "metadata": {
    "id": "d02af92e",
    "outputId": "939baabb-3d5d-4ae2-c29b-a019783cb6fa"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize volatility vs returns for each scenario\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "plt.scatter(volatility_array, returns_array, c=sharpe_array, cmap='viridis')\n",
    "\n",
    "plt.colorbar(label='Sharpe Ratio')\n",
    "plt.xlabel('Volatility')\n",
    "plt.ylabel('Return');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "401ebb80",
   "metadata": {
    "id": "401ebb80",
    "outputId": "0bc75398-4437-4dfb-cdec-39eccede9f77"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqcAAAHgCAYAAACYUMRNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAD/AklEQVR4nOydd7wcV3m/nzNld29T78WWZVV3uTeMwdgYMBhw6C1gQs8PCBBaCCSQkARCSSChmhoIGAjV2BR344Lcq2xZtrp0da9065aZOef9/TGz7e5eSbYlq70Pn0Xa3ZkzZ9b23u99y/c1IoKiKIqiKIqi7A94+3oDiqIoiqIoilJFxamiKIqiKIqy36DiVFEURVEURdlvUHGqKIqiKIqi7DeoOFUURVEURVH2G1ScKoqiKIqiKPsNwb7ewBNl2rRpsmDBgn29DUVRFEVRDiJuv/32PhGZvi/38NxndUn/drvH1739nspVInLhHl94L3HAidMFCxawcuXKfb0NRVEURVEOIowxa/f1Hvq3W2676rA9vq4/+5Fpe3zRvcgBJ04VRVEURVEORgRwuH29jX2OilNFURRFUZT9AsGKilNtiFIURVEURVH2GzRyqiiKoiiKsh+QpvVlX29jn6ORU0VRFEVRFGW/QSOniqIoiqIo+wnaEKXiVFEURVEUZb9AEKxoWl/T+oqiKIqiKMp+g0ZOFUVRFEVR9hO0IUojp4qiKIqiKMp+hEZOFUVRFEVR9gMEsBo51cipoiiKoijKoYwx5jJjTK8x5r5x3p9ojPmVMeZuY8z9xpg37s39qDhVFEVRFEXZT3DIHn/sBt8GLtzJ++8EHhCR44FzgX83xuSe8s2Og6b1FUVRFEVR9gME9omVlIhcb4xZsLNDgB5jjAG6ge1Asrf2o+JUURRFUZR9johw9da7+dG6GxiKS5w9/Shes+BcQhOwsbSdWYVJTMx17uttHqp8CfglsAnoAV4hInttWoCKU0VRFEVR9jlfXX0lP1l/I2UXA3D5upv42fpbSRyEnk/sLBfOXsHfHnUxgefv493uPfaS4ptmjFnZ8PxrIvK1J3D+c4G7gGcDRwK/N8bcICJDe3CPNVScKoqiKIqyT9kRjfDj9TcQuXqmOHIOkST7e/rnVZvvYlKuk3cs2Vl5pNKGPhE5+Smc/0bgX0REgNXGmMeAZcBte2R3Y9CGKEVRFEVR9ikPD28kNM3xMhHTclzFxVy+7mbkIB3xKQh2Lzz2AOuA8wCMMTOBpcCaPbFwOzRyqiiKoijKPmVqbgJW7G4dW7YRVhyBOQhT+wJ2H+huY8wPSbvwpxljNgAfB0IAEfkK8Eng28aYewEDfFBE+vbWflScKoqiKIqyT0icpbcyxJyOKRzWNYNHhzdjd1F1uaBrxkFdc7ovEJFX7eL9TcAFT9N2VJwqiqIoivL083/rbuXLj1xJ4iwO4dwZR5P3QlYNb8A3Hsb3KCUO6xwOwWDIewHvX/6ifb31vYaw1xqiDihUnCqKoiiK8rRyfe8DfHHVb2qd+QDX9T7A8+acwD8e+xqGkxKHdU5nXbGPbz16DauGNnJkz0zesPBZLJswdx/uXHk6UHGqKIqiKMrTyrcevbpJmELa7HTFpjt599KLmF6YCMDC7pl88vhX7ost7iMMltZGsEMNFaeKoiiKojytbC0Ptn3dwzAYF+kI9tpkzP0aAdzBaUTwhFArKUVRFEVRnlaOmXgYpk2EMPQCpuV79sGOlP0JFaeKoiiKojytvG3JBRT8sEmgFryQdy258JDvxLdZan9PPg40NK2vKIqiKAoA9w6s5ndbbyVxCc+ccSKnTjkaz+z5ONbC7pl88/R38NVHfs/9g+uYWZjEGxc+i7NnLN/j11IOPFScKoqiKIrCdx77Nf+38ToiFyHALf33cfKU5Xxk+RsxZs9H3xZ2z+RfV7x2j697ICNwQEY69zSa1lcURVGUQ5wtpX5+tvEaKpkwBSi7iJXbH+TugUf26d4ONZyYPf440FBxqiiKoiiHOHfseKhtg1LZRdzSf98+2JFyKKNpfUVRFEU5xCn4+ba1pb7x6QoK+2BHhyaa1k/RyKmiKIqiHOKcPvUY2tlr+sbj2TNPftr3oxzaqDhVFEVRlEOczqDAJ47+Kzr9QvbIk/NC/nrxy5nbMWNfb++QQTBYvD3+ONDQtL6iKIqiKBw/aTE/PONT3LXjYWJJOGHSErqCjn29LeUQRMWpoiiKoigA5LyQU6ceva+3cUhzIHbX72lUnCqKoiiKouwHaENUyoFXiKAoiqIoiqIctGjkVFEURVEOIio24srNt3Ldtrvo9Au8cO5ZnDb1qH29LWW3MFjRuKGKU0VRFEU5SIhcwnvv/E/WFbdScTEAdw+s5iXzzuFNC1+wj3enKLuHynNFURRFOUi4rvdO1hd7a8IU0ilPP91wLX2VwX24M2V3EMDh7fHHgcaBt2NFURRFUdpyS//9lF3U8npgfO4fXLMPdqQ8USxmjz8ONFScKoqiKMpBwpTcBLw2P9oF6Am7nv4NKcqTQMWpoiiKohwkPH/2GYSe3/SaATr8PMdPWrRvNqXsNiJpQ9SefhxoaEOUoiiKomTcO/Anrt76IwbjPmZ3HMFzZ72Ow7qW7utt7TZHdM/mvUtfzhcevhwPgxNhYq6bfzr2LfjmwBMpyqGJilNFURRFAW7tv5LfbvoOsVQAeHz0AS5b8wkuPfIfmN+5ZB/vbvc5b+bJnD3tOFYNr6fDz7Goex7GHHh1h4cq7gCsEd3T6K9RiqIoyiGPE8vvt/ygJkyrxFLhd5u/v4929eTJ+zmOm3Qki3vmqzA9gEgnRHl7/HGgoZFTRVEU5ZCnmAwTu0rb9zaXH396N/MESJzlB2uv4xcbb6FiY86efhRvOfJCpuR79vXWFOVJo+JUURRFOeQp+F2YcSJMk8LpT/Nudp+P3ft9but/uOZr+tvNt3Nr/yr+54z30xkU9vHulCeOTogCTesriqIoCoEXcvq05xOafNProclz3qxX7qNd7ZzHRrY2CVMAK47huMQVm27fhztTlKeGRk4VRVEUBbhg1qvxjMfNfb/BSkKH38VzZ72e5RNOeVLrWbH8dMOVXLnlOkq2wvKeI3njES9jfufsPbLfR4Y34rWpJy27mHsHH+cvOGuPXEd5+qhOiDrUUXGqKIqiKIBnfC6Y9RrOm/lKIlsi73fiPQX7pS+v/j43999BlEU27xl8iA/f+xk+f8LfMT0/pelY6yzb42EmBl3k/JCyjbh/cC05L+CoiYe3tYGa1TGl5TWA0As4rHP/LUVQlF2h4lRRFEVRGvCNT0fQ/ZTW6K8M8Ke+24klqb0mQOxifr3pj7zxiJfVXv/5hhu47LHfkjgLwPETF3HnjsdqUdGCn+Nfjr+UJRPmNV3j2ImHM7swmXXFbSTiaq8HxuNFc097SvtX9h1W1F1BY8eKoiiKsofZUNpC6IUtrydieWRkbe35NVvv5GuP/prRpEzFxZRtzM39qyi7iKKtULQVtkfDvO/OrxK5pGktYwxfPOmtnDxlMYHxCY3Pgq4ZfPHEtzC9MHGv36Oy5xGMWkmhkVNFURRF2ePMLkwnHiMmATw8Du+cW3v+3cd/19TQ5MSQxlibo2dWHLf1P8TZ049pen1yrpvPrriUYlIhdgkTc1179D4UZV+g4lRRFEVRGrhzx138ZvNvGYgHWD5hORfPuYhp+WlPaI0ZhakcP2kZdw882JTaD72AF845r/a8vzLY5uzWtK4TYTgujXu9ziAP5Md9XzlwcGolpeJUURRFUar8bsvvuXzDz4hcBED/tptYuf12PnXMJ5ian7pba4gIf9x6G48MbSNy1GpHD++cw1uPfDVzOmbUjl3UM5e7Bx6tPfeM4ATGClSHY8XkI5/azSnKAYLKc0VRFEUBIhfxkw3/VxOmkIrCsi3zq01XjHve+uIWfrflZlZuvx8rlp9suJovrf4Jmyv9lKxhNPFJXIG3L3oDS3qOaDr3LUdeRH5MbaqHITB+7XneC3npvLPH7c5XDh50fGmKRk4VRVGUQ4LtUR9D8RBzOuaS81pT4FvKWzHtUuo4Hhx+qPV1cXzh4e9zU99dGAyeMRS8PMNJpamOFAyRS/juY7/hH499a9MayyYczudXvIvL1lzB6pGNzCxMZlo4k2t678Mzae1pOfE5YdKSp3r7ygGAYLRbHxWniqIoyj5kS+khrt36FXrLj1DwJ3DK1JdzwuQXY9qYyz8RhuJBru79DQ8O3UtPMJGheJjN5c34JsDhuHjOX3DezAubzpkQTMBKaxOTCEwOJ7e8fvXW2/hT3901H1OAsq0gGMam5QVYM7qx7V6XTpjPv56QitZ7B9bxztu+ScVB/Ue044N3/YCrnv0RCn5udz8CRTlgOfBivYqiKMoBiYgwHPdStkMAbCuv4fK1H2Bz6QGsxIwm/dzYexk39V72lK4zFA/w6Qc/xLW9V7GptI5Vw/eyqfw4iVQouxKRq/CLTZdz7+BdTedNyk3k6IlHEZhUFFoxFJOQUZvj9h2P8Yn7v8hgNFQ7/orNN1BpKAGAVISm/y8t+5pd2HXN6q833EGlbZe/4ea+R3Z5vnLg4/D2+ONAQyOniqIoyl5le+Vxbu37FmtHbsOJBWOY03Eshg4SaRZ3iVS4c8fPOXX6q8l5HU/qer/f8itKySgWW3vNAD6QZDZNkYv43ZbfcOzEE5rOfduRb+Frj36DuwbuY9T6NYnpEO4dXMWbV36Efz7m/SyesGBM6r6Obzw8fCKpXz/vhbz68AvbHt9ILAnSRtgK1Ez6FeVg58CT04qiKMoBw6rB33H542/l0eEbSKSCI8FJzMbi3Wwo3oaIw4ohEZN1qYMxHkPx1id9zQeH7mkSpuMxFLfaOHX4Hbx7yV9z4ayL8E1r/MaK4+/v/yIlW+ac6SeRM61G+z1BFy+a80wKXg7feEzNTeR9S1/LislLd7mn82cdR0eb1H0illOnLtrl+cqBjQhY8fb440BDI6eKoijKXiF2Ja7b+jniWnS0XocpWKxAZcyPIU8EzyX0BE9+NvyEcBJbK5t2eoyPz1ETjhn3/d7KdpI29acAViy39N/NxXPP5ca+O9hS6qNkIwwBnvF4z5LXctKUo/jLhRdRtjGdfn63a2hPn7aYc2Ys5/reBynbCN94+Mbj/Ue9kIm5zt1aQzmQMbg2TXmHGgeenFYURVEOCDaX7sOrRR/HNAgJlCTMXq8/HB5zOo8n7z/5SUfnzXxB2258yfbhm4COoJMLZ79o3DUWdR+OP86PSIswGA9T8PN87oQP8MI5zyGynZSTHJHN8/f3/oDb+lfhG5+uoPCEmruMMfzjcS/n8ye9nlccfiavP+KZfP+s/8fF807Z7TUU5UBHI6eKoijKXiE0hYb6yeaRnA6Tvdcq3J6qL+PRE0/gotkv49ebf4yHj5WE6YXZdPgTGEqGOKrnWM6f9XwmhpPGXePcGafzv+t+zYgt1l4Tqd5XwNETFgNQcTE/XHsDUVYPGtv0z4/d821+cOaHmZqf8IT3b4zhxCkLOXHKwid8rnJgI3BApuH3NCpOFUVRlL3CzI6jCE2BiGJDF3sqRj0T4EmAy2pDE+dRcQGC4eHh1Tw0tJJlE05+UtfdUNwIpoOXz38b03KTmJybwpT8EysT6PALfPb4j/C39/wrQ8lwgzDNsWLSUSzuORyAG3rvbdeYj4hw9da7eNlh5zype1CUQxkVp4qiKMpuIyJsKz9Ab/l+OoPpHN51Fr7X3nvTMz4XzftXfrn+fSQSk7gYhyX0eljYfT53DV6LkyKx86i4aoofKi7if9d+llcc/j6WT9j9dLYTx1fXfIM7dtwBYvCMR87L8eHlf/uk7nV6YQpfP/mf+cPWP3FN7y14xuP8mWfyzBmn1Y4ZiovE0tp8VXEJv910Oy+Yc1o2915Rdo8DcaLTnkbFqaIoirJbWIn5/cYPsaV0D04svgn5k5fjovlfZlLusLbnTCss4g2LfsL60ZVU7DAPDN3HfUN/YvvATSQiiIRErtW0PpaIKzd/5wmJ0xv7buKOHXfWTfEFyq7MfzzyJT597KeelLF/4PlcOPsZXDj7GW3fP3HKYvw1V5G0EaiPDvfy/ju/zX+d8tY2ZyqKMh4qzxVFUZTd4t7t/8um4h0kUsYRE0uRsh3kj5s+ttPzfBOyoPsMBpKIB4ZuwUpMxRWxkmQd7m3y4sD2ypba3+8aWMm/PPhxPnzPu7nssf9mW6XVaurq3muJxpjiA2yr9LG5vKXl9T3B4p65PGvG8QTGr70mAk4MkTgeHNzA6uHNe+XaysGHYHCy5x8HGipOFUVRlF0yGm/j9v5v4hhrryQMxRsY2Q1f0lv6fkUslabXLAmeSdcZy4Qwnaj0h62/5bLH/pvHi2vYEW/nz9tv5p8f/Bh9ld6m4+M2k5UAEpfw5dWXkYzz/lPlg0e9ggVdc7Eu9WpNxCMRD0hLC9aObtsr11UOTizeHn8caBx4O1YURVGedv7U+6V0ulNbDLIbpvdlN9r2dQ+P0DTXZYYmz3kzX8loMsovNl5OxdYjooJQsRWu2PyLpnPOnHo6YRtTfAHWFTfzu63X7XKPTwZjDGdNOxrP5IldgMuEKaSm/Qu7Z+6V6yrKwYqKU0VRFGWXrB29OZ0Y3yYD3xlMpTuYvcs1FnadgGljHTUhnM4Fs15Hp9+DwaPLn8jz57yRWPJ88J73UnG2lu6sXt/hWD2yqmmd58x8NtPz02rHiKSP2PlELuG63lue6G3vNi+efxp5L2y6v5wXcMLkBRyh4lTZTQRw4u3xx4GGNkQpiqIou8QzPlZM6k4qYEx11KIh9I/iK6svpSuYwhnT/oLFPae3XeM5s17HmtE7iVw5qzf1CEzIi+a+k4Xdx3PGtOcTS0RocqwrruXfHvqnMdOlBCG9sDEwJTetaf28n+dtR76Nj933zyQuQjCZZ2TVvmrv1d5NznXzzdPeyece+iW3b3+UvB/ywrkn85ZFz91r11SUgxUVp4qiKAobR1eysu/rDMcb6cnNZcWU13NY91m19xf1PIdVQ7/FZWLRiODEZ8j1MDCyEsEyEG/hFxv+lXOmv55Tp72k5RqTczN5x6IvcWv/r1hXfICpubmcMe1iZhTSTn9jDDsqA9w7eA/XbbuaSKIxcdY0clpxPp7xOW5ivZN/MBrmm49dzr0DjxA5DycBjVo05+V41oyz2JvM75rG50960169hnKwY7A6vlTFqaIoyqHEptFbuaPvSwzGa+n0p3HclL9iY+luHh76Te2YcnmQP2z6GKdMeyvHTnkFAKfPeDvbyqsYiNYhODw8SnTgiBFc7dxYKly/7XusmPI8Qq/Qcv2ecDLPmfX6tnv7+cafcdWW3+LEYbMaVqG1/kzwiMTwvXU/I+d1sWzCUt628uO1c6pnFUyaZA88n6MmLOG8mWc/yU9NUZ4eqmn9Qx0Vp4qiKIcIm4sruXbz32KzjvmRZBO3bPs0ZddaSOqIWdn/dZZPupjAK5DzOnnp4V9lS+ketlfWMDF3GJev/zeESsu5xhi2RxuZWTiy6XURGddr9NGR1fxu65XEEjeuBG2GnFYjS5GL+Pba/2FaOL9BmNbPqzh49WHP49hJy1nSvfBJ+ZwqivL0o+JUURTlEOHOvi/XhGkVJxFCQLsZ9wAD0VqmFZYCqeic3Xk8szuPZ+3ofZTtaNvTrCR0+ZNrzzcUH+fyDZexdnQ1oZfjjKnP4oVzXkXYMFnqlv6biV3culiDMhWBuKGGtPrao6Nr2+zcIMARXUewtOfINu8/MXZURvnKI3/kmt4HKHgBlxx2Kq9ecBah5+/6ZEV5AmhaX8WpoijKfo+IZahyD+DoyR+H18YuaXcYjNqJuFTGSZsfiCKWjgaR2cg9A9eSiMEHxgYkp+UOpzucAsD2aBv/8cg/UHFlACJX4U99f6S/0stfHfmB2jlWkixGOmYP2f85IHZBi2ejFUua+G9v5L+2uI3HR69nYfdsTpi06ElFT4tJhdf+6b/oqwyRSFrC8LVHrubegfV89sTXPOH1FGV/wxhzGXAR0Csix7R5/wNA9V/2AFgOTBeR7XtjPypOFUVR9mMGy3dwf+87cFnE05iAo6Z/gckdT7y5pzuczUD0KCKQ4BFL+iPAN7Ztyn1mx3F0hTParuXEZt3wBr9BGBrjc9KUi2vPr+u9ssX8PpaYVcP30lfZyrR8arN06pTTuWX7LURubJmAwQKGEGMCkPpaHh6Hdc6jy5/GLdvvbj4tm9J02ZqrSFxC4PnM7ZjO51a8k66guRb2oaGN3NT7EHk/5DmzjmNWx6Sm96/YeBcD8WhNmAKUXcyftj3MmuFeFva0/4wU5YkiYvZVzem3gS8B3233poh8BvgMgDHmhcB795Ywhb3sc2qMudAYs8oYs9oY86E273/AGHNX9rjPGGONMVP25p4URVEOFBI3wr1bLyV227EyipVREjfIfb3vILL9T3i9E6a+Fd8UKEtIWXJYfCw+seRxeE3Bxym5xZw/55/GXeuYSecQmjyCTyL1h0iepRPqVlIbSo9jW6ZKpSNNe8v1sZ5Le5ZxwsQVNW/S6sNJ+gN7Xsd8zphyOtWOfTBMDCfynsVv552LX8ukcMKYKxjKSY6SrRCLpWQj1o5u5euP/rp2hIjw7w/+krfe+lW++egf+eojv+MVN/47v910R9NKd+x4nLJtLTnwjMcDQxvH/YwU5clgxdvjj10hItcDuys2XwX88Knc467Ya+LUGOMDXwaeBxwFvMoYc1TjMSLyGRE5QUROAD4MXLc3lbiiKMqBgnVFto1c0T7VLY7e0V+3OWvnzO8+h6MnX0oypsZUEDCdHNnzPI6b/GpefNi3eOmCy8j53eOutbDrBI6ZeE422cnDJyQweV44968p+F31a3YcgU9rXWYiMTMLc2rPjTE8Y/qzMaaLRAyJeFScTyQBsfisGt7Eb7f8mZE4pGxDinFAbznhum230x108vWTP8XfLHkjz55+Bq857EWMJvkWSRxLwtW9deF5147H+dWGlVRcjEOIxVJxCf9y//8xGBVrxx3eNY1cm9pSA8wuTBr3M1KUgw1jTCdwIfDTvXmdvZnWPxVYLSJrAIwx/wtcDDwwzvF7XYkriqLs7wxX7ufB/o8yGj2cikYRInwEDw9HDodnKsR217/HO0nYVn4Yz3hMyy/BGA9MAY8QR3Mk0EqFQjidU6f/1W7t0xjDC+f9NSdOeS4PD/+ZnFfgmInnMDE3vem4Z854Hn/q/yPW1bvpQ5Nj+YTjmRhO4eHhRwm9kAWd85nXMYfECYk0/2gSgYqt1pWmpQQAFRfxo/VXctGccwm9gGdMP5lnTD+ZxFm+uvp343wmaWr+4aFNfPLen1DMIqKGeu2sbzxu7lvFhXNWAPCS+Sfz/cduJGpwBPDxmF6YwIlTFuzW56Uou4MAbu80RE0zxqxseP41Efnak1jnhcBNezuQuDfF6VxgfcPzDcBp7Q5sUOLvGuf9twBvATjssMP27C4VRVH2E8rJFu7Y8jqs1GfQC+kXdYTB4lHCo5OAyYUzd7rWxuId/H7Tx3EuAYTQ6+S5c/+JvN+DbwJcZtkkkl7DNyEd/sTWPdlRtpbXMyGcwuRca23l3M4lzO1cMu4+Juem8p4l/8BPN3ybNSOryHkFzpz2bGYXlvHW29+HIDgReoJu/nbZuzhvxjlcs+0GKi6q7S/9HAztrAGcOAbiYabn641bgedz3KQjuXvg0abIs4/HWdOO5fb+Nbz39u9QdjGNdlVIXaA2TpOaUZjIf536Jj5+z0/YXNqBCKyYsoBPHv8ytadSDhT6ROTkPbDOK3kaAol7U5y2+y+2fTvlLpR4pu6/BnDyySePt4aiKMoBiYhjOHqADUPfr01gqmIM6bjOho56a7qZWDh13PWKyXau3PBhEinXXottiV9veB/PnPnRWg1oIh5FF9bWfWDoTpZMeC6dwUREhD9u/RE3bPs5vgmwEnN411G8+vAPUPA7d3lPm0ub+f667/PQ0EOEXsjZ087mX477IHk/z9ZyL397zz8Sufq9VqIKH7znk6yYuIILZp7Pn3f8ma3lPhIRYjf+jyorlqhNPej7lr2Cd93+BSo2puwiOvwcPUEnb130It5+6zczYVr7lIHacFSsCGdOW9a03rGT5vPTZ7yH/miEvBfQE3bs8jNQlCeO2a0a0X2BMWYi8EzgtXv7WntTnG4A5jc8nwdsGufYp0WJK4qi7G8Mlu/h3t53YmUEK2VomLbUSJM4lWSnEbvVQ39smtpUJXYlrtj0KRIxBHiMSo7GOML64n38aN3HeOPC/+CegRu5cdsviF1EhRjB8OjI/Vy+7j943REt/a1NDMVDfOrBT1GyJQSh4ipcv+16Npc384GlH+Ca3pua0vxVnDhu23EXdwzm+NCyt/P7rX/iT3131O58rB2/CETicemfP8tL5z6DvzryotrnMqdjKv9z+t9xde8drBvtZVHPXJ45/Xg84/H46LZxdm7Iez6fOPZldIet062MMUzL9+z03hXlQMQY80PgXNL0/wbg40AIICJfyQ57CfA7kYbUzl5ib4rTPwOLjTFHABtJBeirxx70dCpxRVGU/YXEjbCq/x/YOvrLWurakUovr43ubPQhzflTd7p2ye7AjonAQhqhdRJhCahIq1eqw9Jf2cCW0qPcsO3nRK5C1PBjwgrcO3QnA5U+JuWnjXv9a3qvIXZxU0o9lpjVw6vZUNzAQDw4ZqJT851GLubra/6X9y25lJXb76HiYoypp/jTe4HIecQOIOEXG2/i2EkLOWPa0bVjOoI8L5hzxpjPQCj4ISXb+vl0+Xl+cs7fMFUFqLKPSMeXPv2lIiLyqt045tukllN7nb0WOxaRhLSG9CrgQeDHInK/MeZtxpi3NRz6tClxRVGU/YW7t/4VW0fTefa1OkeqP5zqx4mkDRJ1cepz5KS37HTtuZ0nEpj2aedmE/vWH4LGeAzFWxm1QyS1LntTezjgqq0/bjmvv9JPb7kXEWFtce2YMaTZ/RmPzeXNHD/pGPJevu3+qs0gW8t9zO6YwYeWv5WZhWn4xiPvBZw++Tis62Q0CbN0f3p82UX8atPN43wijfdnuGT+aeS9ZnFe8ELedOSzVJgq+xyLt8cfBxp71YRfRK4Arhjz2lfGPP82T5MSVxRFeTqIki1sG/kB5eQxuvOnMa3rJfhe3V5puPIAw9GD0BA9bIwMOgwme5LgkWQ1aMbAtMLZzOuum9y3Y07HCmYUlrOpdCfVUv+q8b7LflC1n1oPVmJmdixiYdexbItubHkf4P6h22t/31LewpdXf5mt5a0YY+gJejhqwlEEJiCRZjMnh2NWYRazC7O5ovP3rCturNWdVkV49XqB5xN6ISdMWs5/n/gJSrZMzstx/+Bj/Kn/MWjjnVpKyi2vteMdSy5gMB7lqs33EHo+sbO8aN7JvOaIs3frfEVR9i46IUpRFGUPMlK5g1W9r0UkQYgYKP2BLUP/xVGzfkWYpeOLyeOYtlFLMJKm8CvZ13NdsPqETGLFzH8Zt95URPhz/49Y2f9jym4EH5/ApAI4loCkIYLiIS2WNQafkvX4pwfew4LOxbTva63bMSUu4dMPfprhZDizvYL+qJ9b+29tEaeBCVjYtZD5nWkrwseOej/X9N7Arzf9nr5oB4nUSxdyJuS8GWcyEI3w+y0r6asMc+KURZw2dTnLJxzetrM274U8a8aKtvsdS+D5fOzYv+Cvlz6PzaUB5nZOYYI2OCn7AYLZJ2n9/Q0Vp4qiKHsIEWFN//twUjdwd1IitgkbBz/PgimfAqArXIy0qbmUNqor1aEdzO58JkdPfT+hN37a+ba+H3Bb/49IpIwAMX5tROlYQi9PiMe8zhPZVFpF0RYZtR4l5wOWx4qrMmfV5sYqH59jJp4CwD2D9xC5qGVQgCCcO/1cHi8+zqrhVYReyFlTz+IV819ROybnhTx31rM5f+a5fPvxn/K7LTcQegGxSzh16vEcP/F4Xnvzp3HiiCThis23sLBrDp878e28f+kr+LeHfkjiLBZHwc+xoGsWF84e38GgHZNyXUzKde36QEVRnlZUnCqKouwhEtdHlLSOsxRiBopXQSZOu3OLmZQ/he3lm2hMu6fH+uT8WQT4TMofx+LJ76And+Qur+3EsnL75U32Ue0ITI6F3adxWNfxLJtwLgW/h8dHH+Zraz7VNNdeEHwCfJPWZsZSwcPHYdhcHuGR4YcZiAew0iqyY4mJJeaDyz6IiOzUWcAzHm864mW8fP4L2FzqZXp+Cj1hN39x4z9QbrCaKtmI1SMb+dXGP3HJ/HNY2D2b32y+hR2VYU6fdjTnTD+OoM0UJ0U50HAHYI3onkbFqaIoyh7CmBzj2Tl7XnPa+NgZ/8Wq/n9ky+hPERHAw5g8k3JHc+Ksb+B7TyzNXLEj2IYmJFPbSb2u1MOnK5jKRXM/jGfqQm5reUPbbSfErJh4FtNy8/jt1p8TW6EiHjviB3hkZDUXzbo4FZ5jzs17eZb2LM0+k91LUXYHnSzuWQDA6uGNNRP+pnt0Mb/bcjuXzD+Hw7pm8vZFO6+9VZQDDRFqE9AOZVScKoqi7CECbyI9+VMZqtxKY8OOZwpM72p20htNNvHIyM1YmYqRUQITkPfnsnzaZ/G9DqzErB76I2uGryXndXPUpBcxu/O4ca+d97sJTL4mUA1p9381Ke+bgEXdZ/LsWe9sEqYAMwtzG9VsjdDkmN+5iIeG1zKa+E3p+8hFXLHlVxRMJwlJLf3vG58Ov4s5HfN5svjGb1viABAYjY4qysGOilNFUZQnQGx7KcePkw8XkPPTcZ4ijoHyLZTix5na/QYqyWZitzV9D8vEwrnMmvDmpnVu2fphIjcECE4Chl2A2G38bO3LmJpfRsXBjmh9lqY3PD5yPSdNfSMnTG1vR+gZnzOmv4Ebe79BIml63gB5k+cl8/+JeV3jC9vDO5cwIz+XzeV12KyJyWAIvRynTHkmv9j0kZa6UoCSLVOyCb4B35i0ztX69JXLvOP2TzE57OEDyy7l6ImLntBnvKBrJpNy3WwpNw8NLHg5Xjj39Ce0lqIcaGhDlIpTRVGUnTJSvpmtQ1+iEq/FGo9KsgVj8ohETO68kHmT/o57tqaCVMSC8egMF3Lk1I+TSD9duePoCJvFWTnpYzhaCwgiUG4wxBccfZUHU+sn8amGNBOpcEvfZdw1eC39lXUU/B5OnvJSTp36cu4ZuI5re/+H4bgfz+QJTZ7QlJmWn885M9/SIkyLyQhri4/SHUxgXscCjDG87ciP8YuN3+HOgZtwYlnUfQwvnXcpHX4XPcEEBuPBtp+PYIidR1F8rGv0Y4Ud8TAfufcLXDL3QlaPbmb96FYWdc/jNQuey4Ku2eN+5sYYPnXcG3nvHf+NFUsiFoPhzGlHccGsPTEeXFGU/Rkj4+VO9lNOPvlkWbly5b7ehqIoBzHWDTNY/BXD5WsZLP0RkUqTR2gVjwLiHUYx2YA0pPENOWb1XMKSqf/QsraIY/3INdzW+2EESyIekdTN5OvHQSx+7ZpWDEVpNq4PTJ6p+WWsKz5aS+dXK0zzXg/vWPyf9IRTms75/ZZfcOWWnxKYEIdlUjiVty/6MFNy9YlPY5uYbu2/he+s/VZzw1RWG1dxIU5SgZrOBB/rm2qInV+rGjAYcl7IZ45/F0snHNbu468R2Zg/9T/AjmiYYycuZFHPnJ0eryhPBWPM7SKyT3/7mXHUVHnF9y/c4+t+6aQf7PN7eyJo5FRRlEMWEcvAyHcYHP02IkW6ChfS1fF8Hu97MyIxQgkkE2INoquq26yUKSWPt65LRO/IL1vE6Wi8lT9sfBsVO0C1wNM6k05xEQ8BAuPwsvdMQyq90sYSKnYVHht9kEZBWBWBFTfC9dsu5zkz38AjI/fjG5/IRly55WfELiYxqZjdVtnC1x79Nz60/N8a7q9ZYJ465TS2VXq5YvOv8YxPxZVJBCpu1z9CEudln0n1sxEqLuKrj/4fn1vx7p2em/NDzp1x/C6voSgHE3Ycf+FDCRWniqIcsmzd/h5GylcgUgJgcPR79I58r8mD1IohysZ4VqOSIuDVfn60TlkCcNI6weimLR+lmGxBcIAhACoSZmNC0zViMXgIAbbJJH+sYX71ym1HkJKKwPsGb+S6bTfhm3QKUsXZ2n5FMvFrHP2VXraUNzKrMLft52SM4aI5L+I5My9ga3kLW8rb+O9HvwNE2fXaZ+BExvMugEdG1o/zjqIohzoqThVFOSSJkscYKf0aoZ6qjiXBialFRkXIhGn6Ql0GprWixoBPnkSiJomYOkOFTenxUtJPf+UhYgc2E7sRfja1qfFsg8NQ8KdQToqICQgk7bxPxNEY1DTZXsab5DSSjBBLnsjRkHKvHitpfagIzjjuHriT6TNn4e+kG77gFzi8awGHdy1gfXEzv9x0Jb7x0+YpIwwlu18m1hOo+b2ijEXQhihQcaooyiFCHK9hYOS/qUT3kA+PIQgXIwTEEmGpzp43TV+K46XXqk0/nsnjmIG4DUAqVqvRworAaPwY3bmFACSuTMVVDbarslLwEewYgWnFsDXxEHqwNsaRIAQk4vBECIzFmHTK06Tc4Wwpr0MywZyIhzGCj6FkczgxuLaase4dFbuYn2/8NX/Yeh0fXf5RJuUm7fLz/Iv5L+K8mefwwNDDdPkdHDNxOfcPreYf7/9vksyY3xjo9NKxqZGre7DmvRwvm/+sXV5DUZRDExWniqIc9FSiu9m07RJEyoAliu/HEBBLkiXwTYMQrUdFZRxxmtJNXxIQy3YgR4jFz6KRCR4+AVbqUdnBaFPmBNocJa3GMms1mQJFV8AxZtKTJJisKSt20BV0cuLkF3Hm9Fdx5eZvcFPf7yk1dv2Lycy8Te1a7UjrUwNiqRBFMd987Ju8b+n7dnLfdSbnJnHWtPrI0OMnLePHZ36e27bfw+rhdRw7cQnHTlrMlx75KX/YehuB8UnE8aI5Z/Piuc/crWsoyqGFwYlOiFJxqijKAY24UaLKdYAQ5s/By2bPW9uHdf2EwQL6Bj6GyGjDWRbBElJPsacYEiDIBKonDsYZJThoDXGtNtUjxpCI1GpRPZNjQm5J7fg7+r8+7j2YaoqdtLbUtTvGpPupSEjZ5SnGIVf2XsH6ch/HTXoG1/XdTLUGtIpvJEvnUxPcjYgYyi6ofQYOx4PDDxK7mNBrFLpCb2U7gQmYmp847n2k1/Q4Y+oJnDH1hNpr717yci5deBHbygPMLEyhMyjsdA1FUQ5tVJwqinLAEpWuYnjgXVAVmJLQOfFfGC5dQbF8NcakFk1Wim3Pr/eRm0wgQlWgGuqC0I6pC7UCpaYOeclWAIOPZwKOn/5pYqkQJyN0+FPpqzw8bnVorXHIpKI2MGkdaztKLkz3mFlHPTD0Zx4Zvo94zPHVEoPGmtRGgZpaVZkx4jwVoo2G+w8NPcZnH/oOO+IhRITDumbzoeVvYlZhGrtDxcZcvu5PXLn5TjxjuHjuqVw871QCTyc9KUo72jU/HmqoOFUU5YAjiVdRKV5OefTr0NBZDzA6+DeUnAEipCGtPh5lF2RNSUIOl3Wwm6wxIU3tu1r6PfUd3eFyNLdHpf/vEbBw4l8yq+sFrOz/HmtHP575enbjSMb9kRPhgxiseEwOF1OWLTBGbIpA5FoFXSIxiSTQ0ljVjDGpqDbZPSXitaQPDYYju48k5+UA2BEN8bF7v0y5wd90zcgGPnT3F/jmqf+w0+YpACuOd638Oo8Mb6aS1Zx+6eEruLl/FZ9d8Zc7PVdRDkWq/sGHOipOFUU5oCgOfYHSyJeACm2NisQR4Ihb3mgeHi8CRfGJqXexl7PwZYhDMJQlIBKf6YVTyAdzWDtyFSWXHlONrIqkTU4GYUbnqeTC4/i/de8gyeypAEp2O+BlFlKNe02brhIJGHWpwX6xsgHPgIdP2jLlaj+wRlweb2xunsw0v/2n0XS/SYOpP0Do5QjwqLgKOS9HzuR40xFvqr3/h623YqVZ/AtC0Za5Y8eDnDLlmJ1cEW7pe5hHR7bUhClA2cXcvv1R7h9cz9ET5+/0fEVRDk1UnCqKcsCQxA9nwrS8k6NSCRhlSs2n6knagaNYU3AVMQy5NO1fM9UnSM3ux0Qu+sp3M+wezPxI6wTiKLqwJvgeL27ggeFPtpXGkEZe0yKAJNspRC6g6PJUo54OixMITY5F3SvoizaxobiFkvg1j9U2+jSNilIXzOmxqfCuDhFoTBfmvTyvO/wvEWDt6FpmFmZy+tTT6fA7asf0lvuJ2/i1VmzMDx//I4aQkyYvaTHtr3LXjsco2dbyBOsc9+x4XMWporRBG6LGq/RXFEXZD4nKV0Em/JwIFbGUs0dJHLEIIkJMdcqRIcFQcj4SLCfX8XKKLs9WW6DfdeDwcaRd7VUbpnap8UhSM6e6T2h6TCIegUkN9Z0YhpOhcYUpCBPD+Syd8CLwDmfQTmO7nURR8k01nlViiUgE1pdGKEuANIxPbZw6LQKReMQSYDPbKMFjac9JHDvxdBIXUnIBkfMRqdeezumYx3ETT2CgkvDw8A7uH9zM5lJ/0x6OnriIgtc8MhXSdP3dA4/zsXu/zacf+CHjjcGeUZhAvqGxqkro+UzN94zzOSmKcqijkVNFUQ4YDB4ihkRsS1LfIJSr04+qr2W5bsExGD2EiR+mRCfp2U22+Q1ntrYtpdHUsS83z0WK24wXbT46oCuczz3D9zIcD+FqtbI+nhGctF63ZItUXKVpFYuHh6vdvBVDlEWALT42e70nnIxhAiM2qO3WN+l5HiHHTzyVD9z972yrbKfiIjwM12/7M+9Y9CrOnZHaQ5017Xh+vP4qtpT6ahHUaomBw6NsI27Ydi/PHziVEyYvarnn82edwFceuarl9dALOGfG0Tv9vBTlUESyX3QPdTRyqijKAUGSbGSw+DNKVCjTWl8pkFVpthI5j5KzDFtLImOFKVT77f22Jk6Z3+k4qWsrHtZJ1lQ1PoJw/9B9DMZbG4RpdeeNf6aEpsDswrIxr6cC1uGnQhQfwWvjx2pIvVdDnPOJnE/FBRRtjsgFGBOyamgdveV+Ki5NuzuEiov5yqM/qhnmh17IZ4//G14879n0BN2IGCLnUbZ1IV5xETduu6/tPU/KdfHFk97MjPxECn5IwQuZ3zmVL5/8Fgp+a0RVUZSqndyefRxoaORUUZT9hrhyA5WRbyMySFh4HvnOV2O8DqLoLrZuewFkzUECtalOQNZNL4TQtmF9hGp3/S6+pE0aFWyUmdVIodBenwqGiDRyKWmYtuk4EegK57KlUiSmtX6zimc8EB+HJfQKzOtYzsT84VhJI6XGVL1Qsxhv9d4lIDQ54sxaKt0vXN17M/M7F7R4BCTikYhjY2k7kbQrQTCsGVnPsgnpZKvOoIPXL3ghswtz+PyqnxLZCk5SUW6AwECuIXXvxPHYyDZ843F41zSOmXQYPz/nQ6wtbsPHY17n1HFrVBVFUUDFqaIo+wmloS9SGfkPqkbyNrqTqPi/dE/7Bb19L4MsqimQSrwGtVi1RyoBwRgvz2LLTPn2ODGUSS2U0vXTqVEjNo/B4JmGyVENojgVpVXH1FodQY1EDBvKo9gxlleNGDxWTH4BBa+Hkh1iUc+pHNl9Ig8N3UvkfAIjBA0lB5KZ9nsE/L/Ff8931v6AjaXHMAix80nEB2IeHXkEsiasxqtNy82me5zZ9k4cnQ1NUVXOmHYUn77/x8SNtaukY1qv732E1x8R89DQJj505w8ZSSqICNMLE/jsia9hUc9MFnTN2OnnrygKmYWd/vKm4lRRlH2Otf1URj4HTWn1iCR+iG29z8e6kdrkpdoRDdG3am0ppMIyaPh7tBudryJk0c9sP+IxZDuzlHm6mCcOH6lZ7bsxotRhMrfUBkN/gUhyWZPU+Ptw4thW3sFrj3h70+vDyRAGj0RCEkmvaEx6/dn5Bbx36SfI+wX6KsOUba7tfbVjIB7iFYe9hAeG1tTS+pAaZM3IT2F+56yWc1YPb8GJD9gm8W+ADcV+vv/YdXx7zU1N3fnri/285dZv8Ntn/S15TeMrirKbaM2poij7FGf7GNp2ATImshg5x7AklJJVWCAWSKQqTttHFgykghCo4JGIV4vyjUWELD1tqBDU/T8Fhm0nDq82McqJx6jLExOQSDru04mXTVnyCWoVq9WWKpM2bmV7sM5rKxSrJQMlm+PhkbvZUelter/T767tMREfKwFOAqzzmZibTt5Px4DOzD+xqOTcjlmcMuVYLp77bEIT0OkXKHh5ZuSn8HdHv61t2v3H624kHuN5Wj2s4iJ+vuFWnLTW7MYu4breh57Q/hTlUMZlAzL25ONAQyOniqLsM5JkDYO95yNUag6iIkIiQhFXry1tOXO8QaDUYptSi556LWdU3ytJ2CTEhNRQv9GoXgTKkkb9ii6HjyMwDkc6m94Yg2c8uv35jCTrawJNgIoLGbQdCJBLJW1tzaILKbl8lp4XQhK+8Mjf8ZdHvJ8jupYAEHgFKuI33b8nQohlezRYe+2l8y7mcw//J1FDFDS1xjLZHhs/IcMr5r8IgNccfhEvmP1MVg0/xoSwm6XdC1i5YzWXPXo1gedx4eyTOW7SAgD6K0NtP+/G61Vca01tIpa+yvBOz1UUJUO0Wx80cqooytOMs5spj3yV4uBnGOx9AUIFA1gRRiVhBMtobSpSVUiaMY9m6s1BqZh1mdVRLnc6ER0MSz6LolIzpB+RfBodlfpse0PApMI5+Kazvt/a12R6bYtPRUJiCXEEiBiGbcDmylZKrptYOoiz90suzOpQPSIJKbuQWAIGkg6KrpCVBaR7rRAwGA/x36s/zVA8AMDPN/40O7/+cBgifDaVtnJT3204cRw9cTlvP/LNTMtNxcNQ8AokElBxQc3DtRopNuQ4auKS2v1NyvVw2tTjWNZzBJ9+8HI+cs93uWLzSn618c/8zR1f52urrwTgrGnLyXnt4xl5E/LsmcfS6beWFnjGY8Xkw3fr3w1FURTQyKmiKE8jUem3FHf8P9LkfIzBgaT1maWGelPJUvPpxKN2YtQ0R0JJU/6CIRYPQ0jgT2RKzzt4vPwBoJI1O0lN7LqG1Hsq3jyK0sk2u4bIRRg8/Cx6O+79SMCoC2s7SSTCNzkWdZ/K6uGbM+/TxsisR+Qgofn12k0AViy39l/HOdMv5LHiY22umorNYRvz9TXf5bbtd/CexW/l5CknctLkFcSS4ONx6cqPUnHDOFJRKiJY8ZhTmEYpqdARNJvr3ze4lmu23ks5i74KQtnF/O+663nBnFN46fwz+MXG2+ivDDWl933j8YyZR/H/lj2fewc38fDQ5loEteCHnDFtMcsnzt3Jp6goSpU0e6ORU42cKorytCCumAnTMqn0rLpzGuIxErCxRaftWsCo8xiwAdttjkEXMuTy7HB5EvEZkB4mdv81G0evxEnj+ExTE7tWDEOug1GXpyh5ipLDkRC54ewaHgn+GKv9hj1kNbCtIlOY03k8L5j7cdp9xbZ6kla3VhW4MdujbbhxPFera8TiU3ERdw/cxyMja7IlDDkvxPd8Xnf4xbXpTNYZRuI8pSRkQ3GES276e/6wZWXTmjdte6CpOaqRm/seojvs4Nunv5s3LDyPpT1zWdozl0vmncm3Tn83nzzu1YRewFdPvZS3LzmfJT2zWD5hDn+z7Pn864pXjXsfiqIo7dDIqaIoTwtx5ZpaCr+KwaT+nQ3dQq7ejl/reG9nixmJT1yNQDbox4p4WCqsHvhPPG8GbStWBUZcodbwBOk1PKFm01TbT8PI0MYu9TT6GjatCeB5Pnmvk2UTz+Rl89/PTzZ8EdvgJypO2tSB1ksL8l6BI7uX0+F3MLdjHuuKa1uOS6Re3hC5mPuHHmJJz5FNx5038ww6/QLfX/srVg0N1D6Fcmaw/7lVl7N0wmHM70ybqQp+Dt/4JGOannzjUcjS9T1hB29ceB5vXHhey2cKkPdDXnfE2bzuiLPbvq8oyq7RmlONnCqKshexyQaKw19kdODjlIs/Q9pEA42MHSRK1gVvqIhHJAbrqNeGCoxaD4vffhwU4CE4KVNONrTtkre1qUrNPwSMIR0NWn8lGw/qYcnm1kuadkvwEHI4gVGbY8gWGLIFBhOYXlgMwPTCAiaFh6d1rg5Gk5Ah15EK6zYd/E48JuemcsKk0wB404I30+F3EJpUBFcFbDquNCX0Qrr99p6lZ0xbwSsPezEdfr7lvcRZrtx0W+35+bNW4LctoRAdNaooytOKRk4VRdmjuGQdzq4nSTYyOvQREAvEkIlJMakTaCNV0ZU+gQp+VlNqsGSz3MVQkYBK49eWSVuKYvEoS4jL5s77WAxprWVZ0qak0KQDP42BiuzeV59Itfs/vb40GPAnBFww6z38aP1/NtWIJWL41mOf5NWHfZgvP/rPVGyMo1Czl6qGTJ1AhE8oFkwqTLv8Ht675B8JssajeZ3z+edj/o0/9d/I+uJ6buq7nbJzNIpqA5w+9eRx72E0KePaKHSL46frb+LwrjlcMPtE5nZO5f3LLuEzD/2UwPjZHh2fPO51TAg7W85XFGXPoyb8KSpOFUV5yogbIBr5Kknxuzg3iiMkoTTmKNtg8ZTaRZUkdTf1jMGJkAiMZFb3VTzSFLjJRGtTtDOLsI6Sr71uSSckRZLOnk8x2bjNtLs+naDU5j6ktSbU4iPGIxaPUFLRG5GuNWpjfJNvqmsVhNhWuHzDV4hcpVY60ChM05fSxqYEDw9DzuS4aM7LKfjNQrAn7OG5s54HwJlTV/G5R76CEwuSlhC8d/Fb6Qm7x/1ns2Ly4rb1qyIw6hL+5YGfEXoBz5p5HM+bcxJnTV/On/sfJvB8TpmyhM6gNeqqKMreQ8WpilNFUZ4i4nZQ3HYB4vpqc5N8KlgREhpMkEwqMq0IZScUmxZJO/CLDWKy+oajXn+UzidqoFYE2vplbtuM7axGP6v1pY56/Wc1XW8zw+pYPEqSw7q0MUqyy6STqgKM6eKXm74xdkcARFKmVNlUu35V9I7dZXpdj8AEnDzlLM6c9qz2H3LGUROX8tWTPsvqkccQERb3LMQ37YV2lZmFyfzFvGfysw3X1+pNU1upajQ65qurr+JZM48DYELYyXmzTtjpmoqiKHsTFaeKojxp4tJVlHe8HUNS64Kv/pnDJ5K6KXs68z6NkBbbrJXUpFuzoCQbBWpIm52qHfQBgm+kqXA+9Tcd38TamPSYBINxwrDk0zVEiCUgxgeEyAVEEqZitmE/sQQUjMH3hL4oTY17jK1chZxXwEoHJVekYbJq6z07A3Tw7Gkv4FkzzsEzu24D8I3P0p5FuzyukUuPfAHHTlrI3971DSAdz+oamqq2lHY8ofUURdk7yE6+vw4ltCFKUZQnRRI/QmnHXzUJ07F4DbLNIsROGBCDrdaSMr5wq2OIJfUvTbJoqOAR45FI1a8UKi6gLCGRBNlxraTG/h5Fm2dECsQElCVPMft7Nf0fSZh5qY41/zdE4rMj6cBmnqnVR/2efTr9Hs6b+TJyJp+WJGR32XhcMQ4ZTgoMJ/DzTVfy3rv+jhu23bLLT+PJcsqUZUwIJhO7IBtnWP9nM7dz6l67rqIoyhNFI6eKojwhRITS4MeJit8CUjt9X9K60ZZjG6SnFRhsbHzKYoqJVEeG0nYqqQhUMtHZTNoxTzbxybbEMKXJ/qlKRYL0NYEQRzzO7+jjiWZBCIwQuXQ8KQg5Ywmzus5FE1Zwybx30BVMZDgZ4NreKygEAcNJVKu5TZyXNWVVPVctFsvX13yPEyYds9Ma0pXbH+Lrj/6K9cVepuUn8pdHPI9nzzxp3ONrn5YxvGXRc/n8Q7+opfcB8l7I2xc/b5fnK4ry9KAm/CpOFUXZCSIRrnId2G2Y3El44VLKQ5/JhGldSaZRRME3VU9QIRJHKRsn6pFNKRqjPivOJ6pFOSVthRrjJ+owWbo9s4ga2+kPWYNTS0VnbY0qZQlrTVJp3akwVhHvKp1kMAhC5Kr79ojEI8quU7Qe3eEkAC6a80qeM/Ni+qNeApPje49/g8eLayiO668v3DVwL8+Yfkbbd2/fvopP3HcZlUxcbi7387lVP6KUVHjB3DN3sXO4aO4phF7A11dfRW9lkLkdU3nH4udx9vSjdnmuoihPA6INUaDiVFGUcXDJo8R9LwcZRrAYPEz+mVTK12Y29c1foA4w4jAYyuLYIa6m+2w7r1ExmTCt15paBA+HyY6P8ClLOh407dhvXmjcaUsNlCVNYydjXADaUbW0EqnGdVvxjM9ZU17Kb7b8qu0aj4zc3/S84HcwtyOdLf/+ZR/jz9tX8sWHvw5tOuittDZXNfKNNb+uCdMqFRfzrcd/y/PnnDFueUUjz529gufOXrHL4xRFUfYVKk4VRakhdhtu5MvY0tU42YQ0daI7pPxH0tal9in8KIuNDlZFZHZYtRu+sds+lnYxSoMTjzJBgw3UOHulHhUdPwUPCX5qut9uKlObc2Lnk4ghMM1HGDx8E3LujNdwxrQXc+XW32IbGr6q5LwcViz9le10B110BmOsoYKJBF6IbTMqVIAVk49req1iI/7Udz874mHWFbe2vc+RpEjJVugMCkBqsP8/j93Ij9fdQimpcPq0xbxr6YXM6Zzc9nxFUfYP1Oc0RcWpoihAagll+18EbgdOKjgRHOkDUp0ZZC1O44lBR5rSb5e1rtZ5tqsDbdpH1uDkBJzxsOLhGYcvDmu82rWrYzwtAdWe/eraVdFalpBEfEZdjk4T1Uah1t53Yb2EgLQ8QPCIJcSK4BuXiWBD3u/i7Ys/hyHPTzb8gNB04GSkqa42MCHzO5byztv/hthFWHGsmHwCbzvyTeSzKU2Luo8Y1/7p2dPPpjuoT3t6bGQzf3Pnl4nFkohtGoPaSMHP10aMAvzDPT/hmq0P1KKsf9xyH7f1P8rlz3gPk/Pj17MqiqLsD6g4VRQFADf6PXBDiMQIqU1SowgVICZN2zvSlH1CGjH1BeLMw9SnPn7UVus+gUg8JJvglBdLsJNmpJLkiCSoeY4iacd7KAmeV59tH4lPQoAIjLgcYMiZJIuYBjgxjLo8gmFU8qQVp6klfcUGOILmFipJ62ChaklV399oUuYT93+UyMWku7EERvAM5EwqDGcXDuf2HWsoO1v77G7ZfifrS5/iM8f9I8YYAi/grxe9mc8/8lWsSxuhfOOzpHshb1z46oa9CH9/72UMJc3GW94YYZ/3Ql592Pk1G6pNxR1cvfV+IleP6jqEko24fN0tvGXxc9p+5oqi7B9o5FTFqaIoGRLdDFSQrLkpbRVKo6DVGtO0sUmoiKPUFDOsNjwBAmXxG+Kt9ZR+nHXVj4pPhEeQ+YQ2NiWNVoXpmO57J4YSefzEEnqWUSnULJqGXd0KKpKAMCseSMRr2KMhJiDOXrB4xOIT1iR0KqCr5QZVe6pEMl9Vz1JxldpaYEgkwIjgewF/u+wTfOfxH1N2m5vuHWBjaSu/2fx7LppzAQAnTD6Wzx3/SW7su4WhZITjJh7FsROXN/mcbihtoz8aGvNPKfWJDT0fJ47uoINXH34+l8x7Zu2I1cNbCD2/SZwCRC7hnh3rUBRl/0V9TlNUnCrKIYxIgqv8EVu5CXEjeJkxe1WYRk0J+lRyBUCxTWK/mu5PxLQI0ygTo4IhjZ+mnfkxISYzkrLiUSLEZTWiY7vvq931A9IFSRrZTNdJasK0upMYH5wQ7ewrzghWfKz4tXurruEctYhrzZHAeZlbQHO0V0gjxL7Jsa3c1yJMq1y55Y81cQowNT+Zi+eOb+GUONu2thcM8ztm8uWT30to/JYmqLmdU7CutbAiMB5HdM8Y93qKoij7CypOFeUQRMThKtcSD34ccdtII6YGh6Sz7IGkbeVomsoff2Eo4Te1MpUkIMbDZeI0tYxyBNn6gkciHkkmW3dty5/OHzWZzVRErk2zk0GMIXJBNjp17P1X7aca1sxweBRdrkmYVo/xICt2aF4wchXee9cncDv5dIq2vIv7aubwrpl0BQXKUXPjVN4LuWDWKeS89l/fR/bMZOnEOTwwsIG4ofs/8HxesWDXdlOKouxbRCOnOiFKUQ41xPYRbbuAaMfbEbcBSFPV1XinlWpd6TjnjyMeDakZftU5NF3LpJOc8NNJTtkjwU8Fa8NSAnjiEDGZJf8Y2yihKaKaTl5KfUfNOHsSSetSyzYgcV5tmpPDkEirEE6jvK0lBY0kzjTvWyCyHonITtNxy57gyFHPeHzs6NdT8HM1Idrh5ziyew4Xzz1rp+d+4aQ3cO7MowiNn0ZMu2bwpVPexLzOKU9oD4qiKPsCjZwqyiFGMvgRsOuAGCeSNT6lNaVB9vtqmXinbfXt3kqFXRoh9TNpW5/cBI1pdyBNqePhO8eIy1MhbSqKxcMCHSapRT2r/qNFl2/ZS60IoSF6Wi0lKEm9gz0RHyOpj2o1PlzzNc12ldaYViO87eVp2s1vyGefWyIeo7aQvevhxOGZenTVYMh7OV512CXp/bmEH6+7lt9svpnYWZ4x/TjecMRzmRh2tVxrXucM3n7kJTw8vI6C73PC5MWcNnV5bdjBeHSHBf55xauo2JjYWbrDwk6PVxRl/0EnRKk4VZSDGhEHGGzp59jhz4PbDqTd34lYbEPkMK0xtYR4mWGUQUSaahpFhErWpR9UrUwbxGMlizjGkjY7IdImPZ5122cTo/pdd1bHmR5TlV0lyZHGMIVEDJHkapZRTWtlZQix+PhZc1PiDIOus/W6gMOnamFadj6JhBgj2AZR2m6OahpxTdP+BsO8zsWsHt5Iydkxx3o4Sbv453bMYlH3Ebx47vOZVUjrPf/+3m9x98Dqms3TbzbdzG39D/LNUz9AvsEO6jtrruGbj15NaNLmsg4/5MLZZ+xSmDaS90PyfrjbxyuKouwPqDhVlIMQFz+ODPw1kjyEbTDSl8zgU5AmYdqIzWyiXG1MUvWc9DEiJqtPdWltqUDJ+QxJSN4IFo+yBNTPqiMCJRfWGpUM6ThSEZPVnKYpej+LHSQEqcDNrtkoGuvTnAyJ+Dg8YuczYvPZPKt2gV9T+wzKLqCSTZ+q+q/GmcuAh8M6g99wvhVDyWVCzwie8TCmcaxA83Vm5mfw2eP/oenVR4Y3NAlTSH9J2BENc03vXVw4+1QAbt++hm89eg2RS4iyOtairfCelZfx82d+sKmrX1GUg4fU41kjp/oNpygHGW74P3D95yP2QSxJZgclWRI6O2bcytHM/B4YEqHfCUMOigLDzrDd1as7BY9IDNttniHJAz4l51NyQUO0NKvrzAThqMtnwjR932UR1ijr0Hd4WLzsmHoHvTHgY7FianWjltQqKhaPSHycgyHbUZsGNR4Ww7DNUZYcVaGbiGHE5qm4gMgFlF0ORw/DNs+ozTFicxSz7n3BkPNynDhpRRbpbKyyTQlNyCvmX9xy7YeH17ftwC+7iHsH19Se/2zdLZRdq+H+SFLhvoH1496boigHPiJmjz8ONDRyqigHEa70a2T0S5B5lTYZQWUCMcbhGH/KfIKj5BxxdkSEIRpHyZZcmNo2Zd58cZYyb147FaiJg2RMo1G1JCA0CVFWH5qIT+TS2KmPI0+E70GMz6DtysYASM3WqRqhjWuepgYL+G3kt0g6ntSJl9bZmlTslm1dqFaPK0tqtt9c/5VGbwtegXOmn8MxE4/j3x76T/qjQRJJP+2eoJs3LHgZZ0w7qeX6MwtTWqyfAHJeyLyO6bXnw0mp7edtSCOoiqIoBzMqThXlIEGS9bjBD0PNoqmZqigKxKNEnHma0lJTGok0JKrH/407doZSNQoqEOGnifk2pwiGsgvbLmcM+JlwjpxPRfzadRN8EjowzkHWw08WvRybTLcNBlbVqG5uzFGR8xiyBaqRW09SD4Fxev3b3r+Hz8eP/jidQSedQSdfXPHPrC9tJHIxh3XMJddQNzqWFZMXMynsomKjpvlbgfFqKX2A82Ydxz071rZETxNxHDvp8HHXVxTlQEdN+EHT+opyUCC2D9v/YqAecXPjmkGBh0dislS/1B8VHDYbyTme36gVw3ZbYEA6svhlPbqY1oC2v2Yp8yNt2buQNiNlHfbUYq/1hzRESNsRmAKdfn1mvMn8ByoSEItPIh4jNs+g7YSG6K3LbPy9MV+FqU1V67UCE3DO9GcwJTel4VjDYZ3zWNR9xE6FKYBvPD5/4rs4duJCAuMTGp/DO2fy7yveweRcT+24581ZwcLumRS8tMbVw5D3Qt677CK6glbHAkVRlIMJjZwqykGAK34PpH0quB3VWGElk7AerW09IhBlNaAeQphJuQFXyMRo01T6miA0SJPVlAiMuhyJBHgmwRNpeg9gxOVIpF0ivhkf1xQhrXLMxPM4Z8al/NODr8mOE5JGAZo1YrWLhBb8bkIvZDgZbr6WASc+tsHIPgHOmf5MngrT85P43InvZDgukohtEqVVcl7AV097K3/Ycg/Xbr2fSbluXjL/VJZNmPuUrq0oyv7PgVgjuqdRcaooBzgiCVK5GmieJOThpZ33bah26vsYLNKaIhcoUo0Cpil0S+oT2ipMq61PkjU1pX+vCs+y89nhuiAzvs+ZhFwm+GLxGLBdJFQjpoLLalNt5pcaGpfWpgKegSS7pWq9qjGGs2a8jrIrEbkOQlPK7s2lHqsCgakL06oJf7UEYTQZocufQ86LsGJJspn0iTOMWA/feKmrQOYY8NlV3+C/TvzHtrWjT4SesHOn74dewPPmnMjz5pz4lK6jKMqBg6Dd+qBpfUU5IHHJZip9r6K8eTHlLYux8QO1hqcqnjGZE1Q9bV92CX2uQtE5Iufa5uBFYFiqv7c2GucbYmmNWqZvm+zLRLI0fyouY/Hodz2k3p+p5VPR5RlyHWxPOuizE1K7qGqUUwxF8lQISfCpEDIieawz2Oz8UZcnEp/YeSQScPb0v6QrmMTtO27C4THqcrV0fux8xHRx8dy3MjM/h4r1KdocZRumfyY+iTNsqwxwWMciYpsjcgFFm2PU5hDxqCQ+pSQksj5ODEPxCGtGtWNeUZSDB2PMZcaYXmPMfTs55lxjzF3GmPuNMdftzf1o5FRRDiAkuhs78hWk8nvA4bIOcWdcS92kSJq83yEVOvApiaPSkDi3QCT1L4HqO4lARby206G8cRLvIlASHyseQRbpdGKoSNoZb6n6lAJZtFaqtqUNlKTdTHuhRI48CWWb2lRFEtbWOqwzjSyW7CiJJKQR2rqIdmLZVNpCIp3Z6/W1LT6RDYjFcOfAajw8XPaJWDEkrj7dSgDnDKHn+OqjP6eYWE6cvISXznsmk3L1eldFUZQnjYxft7+X+TbwJeC77d40xkwC/gu4UETWGWNm7M3NqDhVlP0ccduR0i+hciMu+hNClKW0U5P8qm+mxeFLXaCWxDLoKjhgBJtZuTfjgMhBBcMoYa0pKTSubVrFmHSOfGia60YdhpKkjTqJOMKsnKBoc1nKHpoFp2kRpk5oWzKQGufDsMs3rFW9tvD1Nf/I+5d9kWUTjueGviuJXLPVUjEJ+PXma4gz4Tp2bc8AmX9qgpA4U6tTbdbP6bNSYrlrxxoEw5qRTfx28y189eQPMCU/oc0npiiKsv8jItcbYxbs5JBXAz8TkXXZ8b17cz+a1leU/RSRCDfyFaT3bGToX7GVa3BSRsRlUVFpEngCxGIZchU22yLbXQWbvW6rB4zBGLAGRklrMuNshGeSdc837wcq4lMmT0lCYvFIxFCUkEHXQTX1n4hH0YXsSDooUn+9uoYVw7AtUJR8zVA/3d74dVbVaGq7L6xIIq7f9huO7FrOsp7jyXn1bnaPPEUbZMJ0fJwIFVf1V/Wyzyaz2mr+xGq1pwCxJIwkJX647g87XV9RFGV3cZkDyp587AGWAJONMdcaY243xrx+Tyw6Hho5VZT9EHHbkf6X88PL7+fTn+/jwTUJyxb6fPC9k3nFS3rS8aPiWsZYlsVSbGiDqk5SSkdytsYNq9HB6t9DI1Qk7XBPRAgaFG0kfjavPvUfLbuAsbPuyy7IJi9lUdAxVqHGgCdVCyuPKDvfFyFsOwa0Ll6deJRsSIKHjxAaixhD4iz3DtzBEV0n8Ir5b+PhkXtYuf0GfONjmMQNfbfidiJOnUDkxtbY1vebXr/5Rqoi2zqPCnD11nt4x6KX1AStiDzlhilFUQ49qiOZ9wLTjDErG55/TUS+9gTOD4CTgPOADuBmY8wtIvLwntxk48UURdmPEDeEbH8LP7z8Pj72yV6++Sbh7KVw4yrLpZ/sB+AVL+khwKfs4tS8yUCIT4mkQcxBo4W7R7W7vf5+0fkkGBx16eUjWAwWHytpjNAKDElXu93WzkzEaxoJCrStW021nskmS0G1BjUiQBo2UheGEDuPwaSQRTUNCUJFgkxYhozYHXzl0S/hsLzqsNfx5oUfAOD3W/6I4bbxP2uByjjCdLzjE2fS5qiGiPC2colPP3A5s/LT+MHamxiKSyzsnsH7lr+QU6Yeuct1FUVR9jJ9InLyUzh/Q7bGKDBqjLkeOB7YK+JU0/qKsp8gIrji/yG9ZyLxPXz68/18803Cs46GMIBnHQ3ffJPwr5/fUTvHAqNYimIZlKgW56wL07qRfVK7DlScxzZXYISQMiGWtGkJwJjGfH6axo7EJxaPigRUxMeKaWmOqriQVlprCQw0CdOmP40hEZPWn2bRydh5OLyaMK3tyxkqLm2gsuIyK6mIH6z9HuuLawE4acr4NkyLuxdj6BinWKBhvwYKXp6cFzAh7MFK2FIb6xCu2nIH31pzDUNxamW1ZqSXv7n9u9w/oJ39iqLsLul37p5+7AF+ATzDGBMYYzqB04AH98TC7dDIqaLsB4gIMvBepHwFCQkBPg+uSTh7afNxZy+Fh9Y0DxcNskhiug5ZWr4d6XHiYIBwTI1naqFvAesMcc183xGIY1g6U8/QqqUUBg+XeolmnfHV0aKNe5Pqpmrpbii5oE1Hfv3c2AUUXb6pTqo5tpt9ZlXz0zGvW0m4rvcaXrvgL5mSm8wbFryW7zz+fYwxNbutV85/Gc+ZdR6bSlu5fP2vuab3zrY+BL7xWNZzBC+ddwHzOmfS6Xfwxlv+nZIdaTnWOkciMY2/81dczNdX/5EvnPyXbVZXFEXZPzDG/BA4lzT9vwH4OBACiMhXRORBY8yVwD2kX/ffEJFxbaeeKipOFWU/QCo3QOVqLAkOweJYvjDgxlUJzzq6ftyNq2DZQh8RqdkxeVIXpXFNEo73m7IhabJ1an5PBAZcJzF+0zGxeKRDNKtd+mlHeywBVjxKNiAmJGdsLZNfq2Wl3olfkTAbUTrO5wBjhGl6P2ltq9T26Eh9TxvLFKo4XNO0p2dMP5tjJx7LnQN34kRYMfkEpuQmAzCnYybvXnIpR3Zdy3fX/oLIxTWRajCcN+MM3rzwEvINY0lfMv8ZfOPR35FI+xrZsawZ2atNrYqiHGTsCyspEXnVbhzzGeAzT8N2VJwqyr7EVW7DjXwW4jsh8yVNZ9zHfPC9U7j0k9saak7h0ssMH/+7yTiESoM5VCQyJo3fntQuySBi2pWDUhGfiLE1mEJghESyrvls/Khk6aIh25FFVVNh6NXeT88edSGj2TEGCDzb0InV3GQUuQCX7a+aijLG1OR0IoaizTVFOX2R1A4qI+flOWFyczp/Um4iz5px7rify0Vzz2Vxz+H8dssNDMdFzph6POdMP4lcgyit8txZJ/LtNX9oK07Hps8MsKhn5rjXVRRFUVpRcaoo+wAXP4DrfxNIXxoPzDRl5JKa8Lr4xXlimcw7Pz/IqscsS4/w+cjfTeBFLylQGuNa6kj/Y07aRE0bfwt3UKstHRt1TL1EO7JnzSl/g+Cl0hPJnkN9zGj1+IoE+Fm6X0gbh4ZcVy2NL0DkDD4Wz0gmyDOfVjGUbIh1psEFwOBcGjNNnMmE85g0PmAyQZzzcswpzOGUyaft8p/BWJZOOIKlE47Y5XHTCxP51HGv5xP3/Q8u+3ALfo4TJy3l6q33U7b1NrS8F/LmRec94b0oinLospe69Q8oVJwqytOMS9Yh/S8Gcemwz0whWufSyClCgsMBF704z/NePL1msuSRmtx7DZZF5YZIpQ+1YxvFZ1F8YvGpEOBlM+cDXJNwjfEaZtyPJW2ActU0e3Ze3YKpflza5Z+m7ksS0lpfakgkIErSVX2Tzq13WWRVxphemVqlQqtxf5XJuWl0+h0MJ46tZY8frvs1L577HHrCvTO56fRpS/nVOX/PA0PrCYzPsgnzMMCRPbP4/mM3MBgXWdw9i/cuv4ijJs7bK3tQFOXgI/V+VnGq4lRRnkbc8BexI/+ZSc8UEYelKkwdMfX0fpT9vUoi6SSoXJboLjuXRg5Ng4hrvJ6kkdIyYRp9xCASYpCsrjQVvLF4RIQ4SedNtUv5y5i/l2yOUtYt3+4cEYilNdJZXyOta22O7LY/tuqP2h5DdzCdh4a3UHERAFs293FD35/5/AkfpSvoHOe8p0boBRw/qTnS+rojzuF1R5yzV66nKIpyqKDiVFGeBlyyFtnxbmxyL5L9rzp2FEm7wq1x6RhNBBGpidRGqhHSIRHKYqh2hoci5BosoKrm+zE+Ran/Zx7gSIyX1nQCCVlDk8s3XcsThz9mvepIz1GXw+Iz6gpNewuk3gwl2fAqqd5gG9EpeDhJfVUxO6+W3VmDQN7L89DweiquflAiCcPxCFduvp5L5l84/smKoij7GXvI+umARn1OFWUvIiK4wX9H+s7HJfc1CdNELGWJKZNQlgQndTG6s2ZNY9LfKqv+dSKptVMsqVm+ZFZSFfysy77Z3qm6hk+aPqoL07onqsuEozSs58QQi2HYdWbC1DQ9EvFr51jS+lPPtN5JtdteMBiTjkyt2IDEGZb3nM14ErWa9ncCkfVTI3zxmJGfhW9aG5ciiblz4P6dfJKKoijK/ohGThVlL+HsMNJ3AUgfUDViypp7xBKPcSO1YySpjOMIVRud2fCmJxAbLxtV6uFIhaeMOa8oOWLx8YwQkmBrVvpjm6gMFQIqEpKIR8HEgKFUNaBv0+0vGCLxsvy+V3u13kgFiMkspRq/egyRBEQSUHZljpt4MvcMrmyKlibZPPvEepRcXYhGzrBhtEjsWp1dDYZp+SmtH6CiKMp+zL6wktrfUHGqKHsJ6TsfpL+evjfUuruTcWzyIbOSEvBJTeNr3fwNFMdERB1pPaobkwyp1nAmzrDdddcnG4lQJsSM8y1oDIjziCUEhLLk8HEM27Sb3x8n6+Twa2NSXdbjb0gjuhWXa2sfldTuRVhXeox/PvYrDCWD3LH9Fu4fuo9Vw48gUmFuYT4PDPUzNq48lAwjxmQjA+qfa+gFXDT7We03qiiKsp+iDVEqThVlj+OiO3A7PoBx/bVO/Np7We/6uL8YC4wKJGTWUAKBAb/hhJK0bxxyWfq9+k4kXk2sDrvCmJGbJvM89dp6norQ5HfqxFCUXBppFfBwbZum0lopwYqHzURnLB7b4y4MQsFLLbCsVC2pHEmDKf9gNMJbVv41F846n5fMeyHPmlmvF71+262sHvkhZVdp87kJhSBP7BICz8dg+KuFr2BRz4K2H7OiKIqy/6LiVFH2EM6VcYMfwJV/k3XBe3hCk0D1MNidRE0dUvMqjTPD/FjqJvTVEaEBjojmSUtlCbD4GFJbKMGj7AJKksNlAtOKh8tsoUJj8REiDEEWQa02XFk84rGTnLJRoVXB7DVYVUnmCmDxGIrzmGyqU9n6xOkEPASP0cQjHvO14+MIvLSDqiIBkStzxZarqLgKrz785bXjTLbqeAQm5HMnfpSRZJR5HbMJPf16UxTlwEKyISSHOvrtrShPEZEyduCjUPkFIvUufCsOz1RHjTocgic+1qTxzLESVUQYkdYZ8pB9YTU8H3uEZJHNhIBq5DIWPxOCqTCtjw012OyYPDE2s8z3cYgzRPhtLaBGJY+IIcGnJIa0Z1+y9dJxotZ52Qx7Q9kFteuntlJew0jU+toWD+NcNoQg/UqKXMQfe6/lknkXk/fzAKyYfPS44tRhmJGfyvT8FKZrnamiKMoBjYpTRXkKiES4bReC25AK0yyUWE2gj0qlpfHJy+pJq+n9al1pRRzlNl1Q1fR7ImlLVQ6LmOai+VEJqUiI4KUz55GaMARauvZrE50IIYvLJlmHfnUKk6F+jRGXh1rzFFgJiI3BNmwi7eavR1tTI/769Kfq+NJ2MYFEPMo2bHrXAIPxEDP86QB0B12848jX8R+PfDuV+tmlhdRO6hWHPb/NyrtPMYn44oO/55cb7iJ2lrNmLOKDRz+fOZ2TntK6iqIoTwTth1JxqihPGhHBDnwSsesBWupLAQLjEUuzOHVA7ByDInXZKun0pSibjmSAnHEYgSEXEjek8EcJ8USy8Z9QFp+i5IklqNWijv1yk7aSsPq6kIhPyYXYrGY0NA7PpKn6EVvIIrI01a0mElCN6VY78a149VR//daa9t9KGnlt3aNhcm5S0ytnTT+FZRMW8Z+PfJcHhlYjQE/QzRuPeCknTj56J9fYOSLC2275Lg8MbiRy6Yyt67au4u7t6/nVs99NT1jYxQqKoih7AJ0QBag4VZQnhavcRLL9nQhDAFlMsBljDL60txKWxrAk6XgnI5DLakDT6U8eFQkyYZeu72od+YLJ2vjT+lYZ0/C0mwiUXMCIdNT3gpAI5CQmkiAbadoYqW2M7pralKeiDSm7HAGWnBfjG0eSjSUdDyeGsvWzqHDqThBJKpJ7gk6u613JeTNPbxL+U/OT+cQx7yZxlpIt0xV04JnmayTOcn3vg6zsX830wgReMPckZhQmjruP+wc3sWpoc02YpnsTijbil+vv5DULz9i9z1NRFEV5yqg4VZQngLUVZMdrkPjOnTbnALVa03ZUnUANkIgQ4ZNQLwlIu/ZzTXPpI/FqQrFKTlIx5dFcq5qeJZlXatpAlYyZWS8CFfEpS3M6Pa1RhRI5Yhdk9aqpSDYIOWPbeJxC2eWAtCbVOo+QBLIyg+r6jZ+ZdR7DSb5hx4JIWrsqYhiKS3zl0R8zFI/w0vnnA/DI8Ab+sOVOrFieOeN4jh0zPrRsIx4d2sq/Pvh/rC/2U7IRoefznceu5TMrXs8pUxe1/eexemhr29fLNuaBwU1t31MURdkraF5fxami7C6udCVu8F0gaVd903s4PPFaUvtlSVrWqY4mNVnRaYyXCdO6gKs3QNVrNpOGCGp1nUEpNHW/e5kgtZmQNJKaV1XFa9WY35B2xpclxCcdANCYVjcmjWom4lMRPxtdmkZBI0jrXhvuaTjpoDGa6sQwKvmGqGn1qqZ2pdEkx1hRnN6DywQzxNbyzcd+zTGTlnJb38N8f+0fiF3qZ/CbTbdy4exTePfSlwLw3TXXctmjV2PFEUs9Aho7S4zl7+/5X3597kfwTWskd0H3NNpFnQteyJIJs1peVxRFUfYeKk4VZTdwrkwy8C5cJspSmVVNtQsJCRiPQjb5yCKUXEyCIxYhxOB7Xq35KWpoJAoQbC0tD1WRVI92pgJ2rHgqSb4lGmrFMOpySHaeQfAlFXODrgcRwTdSn9iUHePhsGOisk4MFfFTz9KG95wExOLj44itTyRB23rbuhjNkKr3lMG69l6txlDbee16wIfv+RIjMU1p97KL+O3m23ju7JPZWBzkskevpuziNvtIqdiYR4e3sGTCnJb3jp88nwVdU1k93FsTtgZDzvd58fwV466pKIqyp9GaU3ZSDLYHMMZcaIxZZYxZbYz50DjHnGuMucsYc78x5rq9uR9FeTKIOOIdb8dmwlREsOKIJCEWS4mYCEdZEgakzICUGZYKiUlT+g4oIpSdpSzCsDTHXdPpRtCYyzEGPNO4hzTdnUhqgu/EtAjTKjljqQtDQ0JaC5qm+L3MAGrn9akiUHFhZqbvZ1+WWeRV0qKERHyKEpLgpROgrM9IkmMkyVFxfmbIXy9TqNam1qdC7T6pKG0tkYhcwo3b7uM7a67dqTCF9JeI8bxPjTF8/cy/5II5RxN6Ph6Gk6cezvfOfgsTc51PaK+KoihPher35J58HGjstcipMcYHvgycD2wA/myM+aWIPNBwzCTgv4ALRWSdMWbG3tqPojwZXOUmKtvfBQzUXjPGQGYHlYjDx1DBjRM9bJjYRCqQGo3rBbANxzV2uFe/UJIsRd6YdvfHq2U1meFT7csoXTUmaJ2B2kAi9S5/A5RdSOT8NGJqTD2maxr3ZvBNVnLgDCVXt66q2l6FxtZEdiIePgFDiZ9Nh2osXKjfd+xahasTh9em498zHjkvYHs0PO69Va8xvTCBBV3Txz1mQtjBp0/8C/5ZLkl9X9uk/xVFUZS9z9789j0VWC0ia0QkAv4XuHjMMa8GfiYi6wBEpHcv7kdRnhCSbCDafikw0FKgXvMzNaSpewQZ59dT2/D3NPJatVcy9Lk8Iy6X+ZBWD0rXibPu9UHX2RDpzNLiWcd+y56FrAFpzH4Z57fnLAo7nBQYtXmKNsdg0pFGWrNRpS1rjW2GyqKsNO0xjZRGEhBnAwEMHod3Hc6swozaZ1H7TLK/zy7MbBpnWiX0/FqpQiO+8Xj2zBWsmLywrWMCQKefZ2LYxb+teN24v0A0359RYaooyj4h/Rlh9vjjQGNvfgPPBdY3PN+QvdbIEmCyMeZaY8ztxpjX78X9KMpuYUu/pdJ7AZVtzwLazHFvRKCIw1E31K+9JUJCs4QUYEg8tto8210hq/00VPCJxNTGgJbFz0adeuNaRJmq0q1dL/0zluaEiIhhxIVNx1T/LhhifEJPsPgkEtQitPXWqba3XRPCTkybPdb/7hFS8DqYnJvEOxa9lTcsuIScVzX/pxY/nZk/nNcteCXLeo7I3k/JeyHHT1rCh5a/hrwX0uHnKHg5cl7Auxa9iHmd03nLoufQEeTwG77Scibgojkn8YnjXs6vzv0QR3TPbHsviqIoyv7F3myIavdTbWwcJgBOAs4DOoCbjTG3iMjDTQsZ8xbgLQCHHXbYXtiqoqRiMtr+FiT6Y01q1aN79bGkjTT2rEcIPuBnWfUkE6c0rFN2PiOSr71YtZ43xhBLwHbJI5IKUpd1vbfXiOn7iTMEmR5zGMourKf/Jd31kMvjCCmLEJoEP1OoQiouIwmzyGo64Upc6n0aS0Do2XSfTfWvqd9qkk2dsuP+jpt+YudMfwYrJp/AMROPwjc+0/PT+OCyt/I/a3/BY6MbsQ6Kic+OSi//cN+3een8Z3DW9OP549Y/4xuP5846g+fNPgPf+Jw0ZTG39D+IFctpU5czOdcDwPyuaXzvjP/Ht9Zcw907Hmd2x2TesPBcTpyycJy9KYqi7IcImRH2oc3eFKcbgPkNz+cBYw0DNwB9IjIKjBpjrgeOB5rEqYh8DfgawMknn3wAlvYqBwLJ6P9AJkyrVI3hBaBhPKmI4BCGJHMnzU6ypKK0WhHa6FtfFI8BydOoNKsysS6GvZohvxNDUfLkaw6odapNRYOui8BZcl7VjCqNRabi0WfY1ac7pWn2MBWhmXX/2IinczBiqxZQhtgZQk+afq20Ala8bEIUmHGr7QWDz3Nnnc/sjmY7phMmHcW2cpEvrPoJZRfVXi+7iMvXXccPzvg7Lpn37JYVe8JOzp91UturzemcwkePuWScvSiKoigHCnszrf9nYLEx5ghjTA54JfDLMcf8AniGMSYwxnQCpwEP7sU9KUoLYrcRDXycZPhjbetGYyxlktQLVNJHWSx9LmpJ20PdDqme+obt1qffFWgNgdYnLMmYNXyTCshI/KYGKZE0SjpkOxAMFp+y5ChJjrLkiCUkIWDIddSE6dhrOvyaAK0KXcEQi1d7PcUjcR6xS+2fEkkFdMXmcS1doGMLGAxHT1zeIkyr/KnvviZhWiX0fO4dXNP2HEVRlIMd7dbfi5FTEUmMMe8CrgJ84DIRud8Y87bs/a+IyIPGmCuBe0h9Yr4hIvftrT0pylhcsoZo24uxDAI01SzWjslEV5S1No06S6O1viVN5Tefk6W+gUGbp0TY6PjZggCVMZOaqob6MSGJBORMAiJY8YnEzyyhWn1BawuSfSu1bQKSWpG8FUMsqcG/bWlGqhYeCD3hZI6acByz8kv4wfqfUrbl2lGJyyZemeyBx9nTzuQvj3jtuPc8KdeDh2kZaADQE6h9k6IohygHoJjc0+xVE34RuQK4YsxrXxnz/DPAZ/bmPhRlLCJCMvI17MhnEYmpjtV0WU+4aRKJhkTSdHlFpNYiVY0vCmSDOlNiScVpIoZhl6dCQDpdqTq2VFr0YlkCojH/OdabocDhUZZc0/uBscQSYrF1cZxtyGLqafem+87M712e5RPP5M6BW7DSYP9vXNsvRoPPWdMu4MLZF7OptBm3vu5BEFmPSKo2UoJvfLr8Tv5i/kubGpuqDESjGAMXzTmDqzbfRqXBn9QABT/P8ZPbjxlVFEVRDn50QpRySBLt+CuSyu/T4GJNmKZjSAPxUm/OLNVeFgfiGCFNa4NJx3tmMcWqKLUCkYA02BBF+CS10aMpBshLjJd5hlbEIyLX0nRUdq3CrpmqZZNPLI5EAiqSCmFLNkpVmv1QBUPFhoy4kJUDD5Izc3H0E7tK9YDa9Rv9WB2On2+6itOmnsOcjtkcO/EY7h24j4qLG4RpuicrjlFb4v82/I5LF76sdu21o7188v4fsGZkCwZY1DOH1y94Lt97/Hd4xkMQeoJOPn38X6mVk6IohygHpvXTnkbFqXLIUR7+Bbby+1p9abXJycuanBIc1R6gIZdQRtImoIbEvJHUhB8jtWZ6AYYlT+zSiKeHyyY5+TQ3QQllQgqSRgwrFFIxWEugQ+L8+hptzOeBmh+oCAzban1p3WPAiOAZDyuSWUSl9aLV/ZbsKCVGsRLgCAmMQwQi5+FBVjSQpv1LLsQQc9ma7zOr4zCWdB/FtNx0frP5D233ZsVy18BDtefFpMI7V36Z4aSY1qoC9w9s4LGRbXzv9A+yqdxHh59nac/83fIiVRRFUQ5eVJwqhwzlHR/Blv8HyCY1QZMQMsbgCbU59waoZMI0aYgMQiowEyBoaGQalgBjIMRRER+Hn40YHUsqIC0eoy5HRIAIeM41zSz1SUjIYaRVoFYa/EiBMQK4Pu0pdRqoNjnVEerTnqwIDr9Wb5paUjV+NVTXc9wxcDfl7Q8AXlovKgVEbNuy1qm5SbW/X9N7N7EkuMyFoLruaBLxups/z3fOeA+zO6a0+awURVEOMbTmVMWpcmhQ6v0LxK7EiKml8ceL0ElmxbQ9a3yqf0+MNZlPV7IYRiVo8vv0MoErLefVGbIFBqWr6bWCiwiMIyGt40zEY4dNjymYGB8hwa8J07QswEfEZE1S6R4CHKFJEAOJ88DU/UnjsZFcqYvVdLeNBaw0HefEZHfusvtLK2nH9l35xuPFc59Te761vIOSjZqEaZWirfCvD/yML5z05rafk6IoyiGDoGl9VJwqBzkiMeX+N6bCFAMmjZpK1gnfTqAmTtguLjvG1MaPth6avjfsQioSkmQDNEMsVUnrI1k3+lihZxiSjqbXnYNBOrBSXSdhSDpr1k5F8QhqPfopkfiUbEhJck0KM8HDSUCIJcbHFyERj5INauUAoedqa/hGamb7nkkFaqOwrlqRJOJDJnIrLgAEzzgCUz/GGHj+rHM5YfLy2j6X9synw88x4uLWzwK4fftqEmcJvPYlDIqiKMqhg4pT5aDF2u2Ue8/GSDFLw7uabZGI4JnG6KFQFkdZ0o78NCVuqOBhkHGqPoWy8xmtTnzKYo4RPgYhQPBxxPi1yGJVwJVcQOOseBEoSq4pVR9LgMXgxMOKl7kC+PgmlctRZj1VbYJqFs8Gh0dF0mskAiWXA5MK5rILSGxAaBISCUgEApMQ1ISvo5h0ZFOg0khsumx2HanNtsJKOqgg8Bw5E3Li5KN54xEvbfqkTp+2jLkd01gVb2n7SRpjtNZUURQFNK2PilPlIMXG91La9iIEiwGSBlFaJUHSmlERBsRRyaKF1ThnNaWfNibVI4vpOmnn+6hUO+qbU/5CNUJrsNmxXu25YVQKTfstu4D6nKg6znlNaXiL4MTgN6TTY/Hb1nyKpNfycJmHaiPpHouu7q2aSEDk0massmv2XK3WzlZHn1ZFa/3ePU6ctJzzZ53B6VOPbxGavvH40klv5+1//m9WjzQLVB+PM6ct0w59RVEUBVBxqhyEiJQpbXsZLkvIOwQnQtxg926AUAwVDAlCubGyNNNVIVDKJiclCDkcQSbOLIaIzAhfDEnqXoqXRUsNaW1nUfKQyU6XicmqWG1kbEc/pMI4JqCd8K3Wlkq1x38cr/2KS9P4Imka3zPStJYPtbKF2KUlAu1rZNO9V5whMDJGnKbepC+ZdwHHTRrfn7QzKPDlk9/GO1Z+hU2l7STOEnoBk3Jd/O3yl457nqIoyqHFwZFFMsbMBE7Jnt4mIr27e66KU+WgQlyJYv9LcRSzxqa05jMekydJ0+/pBKXhMQ1BjYQIUVbzGeERZXKwmpIXEYpjzPE9hDwxRfLZcdnYUwfbXRcxISAEOHzTashfxY5TTNDYstSY/xnrTZqukZYDVFxA0foYHDnPUvVqlYZzxxem9WMqLqCYCVPPCL5JI7n9sePv7/kRS3rm8qYjn8OyCfPartEddvDt09/Nyu2rWTOyhfmd0zl92lKNmiqKolQ5CNL6xpiXkw5Yupb0B8t/GmM+ICI/2Z3zVZwqBwUiQnn4i1RGPk8aK61GS8fGKBtPgiFJ0/cG8KTJyQlIG39sZitlkKxutD7tqVhLzzc0NkE6FaomYA3DNj/mWEMshkjAiUeCl/bgN1x//HGnUttb2j2fCsSqqX/1iJINqCRBZpJfXdGn7Dx8HKHnsFlXaOJ2Lg5FoGjDrNveNF2/nASAx7bKEH2VIVZuX81nV7yREyYfgddGdHrG49SpSzh16pKdXlNRFEU5YPkocEo1WmqMmQ78AVBxqhw6DG8+EUdf7XlVmFb/3q7ZxgpUxkhXT9JoqTEw6gKGas1OqbTLk2AFRqWDsvhI1qHfTDqrftTlKHgxsfNrNaaG5hR9JD4VyaXi2ES19LwIeNl0p7E2TSKpD6kTj1h8ImcIvOzA7J7TLv7cmOlN9etaPHL4OFx2zdZ610YSZ5qEae0TyQS9y0SxExiJLG+99Zv4xuOcGcv50NEXMyXfPe7aiqIoSgMHQeQU8Mak8fuB3U6RqThVDmhEhJFtL2oSpgAehnSmkRu3C3xojGk9kE2IShuRUmHaONkpNb8viV+bcz9+dNNgxWNb0tNigO9VW58MxFnjkQBlyRFkc6hEoOjyODy87HmVSHwS8Rm2hWzakqFi03U9I1jxMqun8dP0Hj7nTL+ApROOYEt5K9Ny0/jcw98nEdt0XPW6sYz/nVL9DEQgtvXjrDhu6H2QR4Y385Nz/kZT94qiKIcOVxpjrgJ+mD1/BXDF7p6s4lQ5YBGJGe1/Mza5q+W9dNqT4GOyDneISCN7gUkjpEl65NgzM+/S3NglqcqwWBpFX318aX1fWeRScjWP0oZ3m453YyKRCUFmwkztXFc/lbLzAY8Rm2/yIU2PM7UI5q5+9bbi+O2W6/nzjgf49LEfoDPo4J1O+OIj320SwlagYv0m26umT8TUm7vaGewn4theGeFP2x7mGTOW7XRPiqIohzwCHAQm/CLyAWPMJcBZpD8UviYi/7e756s4VQ5IKqO/pjz4boRo3GOMMfgClYamp+qoJENrurxKarw/VlTW36sJS4Gyy2WNS2nKP2cSnPEou3CcNWpxxjQljowRqE1XyyaFpO9XJHUHQKrn7Kx5aeeNTQ5DxSVsLffxy01/4JWHvZBzZ5xGwSvwxUe+S+RihmNDZH0Cr/0ODRB6IbiAsiSZqG09MnKWtaPbeAYqThVFUQ4VROSnwE+fzLmaZ1MOOKLy3RQH307dmXR8BBipWTc1RhnbS7tUuEGyk4S9JU2bj7p8bZSo4FEmZFgKjLjmcoC2+8rEcuS8pkhl9b2KS6OjcZaiL7qQRFIvVLsb/9kak0Yy263tgMilTgCxJNzUd3vt/dOnHc//nP4ZXjr3hSAdgEfiAhLngxgMhtD4hMbnyO65/NdJ7+HF884g7wUU/Pa/6+Y8nyO7Z+5yz4qiKEr282EPP54ujDE3Zn8OG2OGGh7Dxpih3V1HI6fKAYOIUBr6LOXRLzYJx7SGs1UMOhFGpZoUb06+m+yMahq82oTkgB2us3Z045/Ujk8bmZrT6unfq6ltkeoI0LFjS1OhPGwLlCQEPDyx5LJhAY6ss9+mjVgVQgwOEZ/QS9LVGm6+PnWqfn8iqWdpIl7mu1qN1ULs0lcaCb1mg34B/rDlXkrW1iW9SQ39O708f3f0q1k28TCm5HoA+OulF3HpkeezodjHe2//Pv2VYWz2uYfGZ1bHJE6bNr7/qaIoitLAAdwQJSJnZ3/2PJV1NHKqHBCICEPbntsiTCGdmlSNoIpI7REhVEj/JW/3L3pVnJbEp+J8RiTMhGkqyRxVuyRq5vkVfDwsVsZJ+wMjLs+gdGbd9vUO+mq0dEfSSUnytV05fEouZGvcQ2/cQ8nla+UGqTD2iAUGkk52JB0M2E5i59d+IzZAzgvp9DqxLmA0yTNq0/UdPrEExM5jNMkRubBuNyUgkmNp91Jil1bglm3EW2/7EqtHtiJZHasVQ+LqUdgJYVdNmFbpDPIsmTCX75z5Dp496xjyXkiHn+PCOSfw9dPf2tZSSlEURTk4McZ8b3deGw+NnCr7PSIJ2zefgpFtbevEDekEJ+cclSwiGiO1yUfGgMnEpd9gUm8zn85RxkvDp6n9IZcnloBIfELjyJnU2t62tEKlzyx+lubPkZMkS8UbEvEou6CtuX4iPmPHl9ajotm+G+pPBZNaSBmh259M4E2gw5/CxvIaEkmFZqNvqtQ8AtLorE0ChpKAwPj8cuMd/GbTXXzquDdx5/bHeHh4Y0O5hKmW6VJKfEJjWD7xsHH/WU3L9/DPJ7xy3PcVRVGUXXAQNEQBRzc+McYEwEm7e7KKU2W/xrkhdmw5HWRwZ3b6mCwqWRknH5IKVg8Rh09aUzoiASXJE2X/Gfg4AlzTlKXIeZSy2lKDEGfeoq7NNUTSGfR16yhDWUIqLiBnXBZDrY4wrU5ZcqlP6Dj3ZrKagqokHVtGMJKEDMYJsfSR2shV71awGHypp/Sr504KJ7M+igBLIo7EVgD40N3foNPvaRjy2voplq3HqqFNHDWx/QQoRVEU5dDFGPNh4CNAR0ONqSE1zPna7q6juTZlv8XGj9G/+ThEBrPwZ/q6c47IOUrOEolDREAMIyJYkZbib5Hq/PjUHH/I5Ri2AUPSSUSAFS8dy+lyjLpcQxrf0OcmEKWSFUs6yUlqFaumqeA8Ep8R19FyH5GEjLg8ifNInEfJ5YjEJxKfsguJnIfXVu7WSwFa61vJXhEScW1qlNJjbfZn7OrRWt+EYw+uMZqUx30vXcfyk3W37PSYsTwytI3fb1zF2pHtT+g8RVGUQxEje/7xdCEin87qTT8jIhOyR4+ITBWRD+/uOho5VfZLysX/Y3jgXVlLUYMRvgijSIP3pzAiQkn8pkafnDgCr54Wr0j9vQSfSiYyE+eTNFg+JRiGXYECFQZcd1ufUoepGeOXnc+Q6yQw7c3+XUNt6ojLU3L52nImiwUn4uNjSQ1OqflbNdaGxuLjskYnKx6+cQTGkoiPdaZRuzeQiueKeIgYEucRWY/1SZmyTf0KPNM4ECBhUfdc7h1c27RKVSBXW7y2V0bb/jMby2gS8bYbf8xd2zcQGJ9YLM+YuZAvnnEJOa+1tEFRFOWQpx6NOKARkQ8bYyYDi4FCw+vX7875Kk6V/Y7+3mdj44fqVY8Noq8srinG6BwU8RkrzSI8xDnEGCKp11vWuu7JBFuL+DRZeUCOiHYTlkxm0WSzetRUwCaSEDTUeAKZeE2jlLF4lCTXZKxaS7iLSUsLJC2OrfqFCnVhGjmf0SRf24MVL436urRxazypl65hKCVBFj01JDbGy96z4mEQAiMkzlKKYwIT1BqkqiQ2vULBDzl35lHjXK2ZT915FXf0rydylurIgxu2rOE/7r+O9x/77N1aQ1EURTnwMMa8GXg3MA+4CzgduBnYrS9/Tesr+w0iQt/mk2vCNO2WTzvvq8QNx1uB0XFkmWAoi0+l1mhUrSH1icXDiocdt+jctIwcHcuAdFGUfK1W1OFjxdRKAhJnGLX5WvNTue0o0TR2WtWrozZHf6Wb/qiLoThH2Qapmb8ElG2udk7rn3Uh2/QZSDrdqWL9mjCtXbkh0ioYnINEDGtL/fzVwucyMz8FEQ/rPGKbnlvwQuZ3TuN5c07Y6WcD6T+3X667LxOmdSou4Udr7tzl+YqiKIcmJg1U7OnH08+7gVOAtSLyLGAFsG13T97tyKkxZi5weOM5uxueVZRdISL0bz0b5zZhTFbRmY1wsiJETihDzTzfA4rSLLiq6edRCfGQmndolZLz6Xc9uzSxr48Obf9eIqk5VdT0H7zB4uNEcGKoNIw4rXqb7ux6FRdQsmFtslMkOSInBMYRemClnQg3mKwTzEmaom9MByUuPS/ZyRdT1cM1ER8nhoSYewbW8rNzPsS60W3cvv0xbt72MENJifNmHsML551M3h+/ZrWKFUfi2tfRlpK47euKoijKQUNZRMrGGIwxeRF5yBizdHdP3i1xaoz5V+AVwANQc+gRQMWpskfYvu3FOPtYS92mk3TCUz3+ltY9Jk0W+dk72Us+QoJHCY9Akmyik8eA62wzUlRa1gEoulxTVBPq4rcoudr1PHFpatxUI5j1eKRg0qz9zuqHMrEbu3QsaTXSKgJOfGKEWBwmu+9251f3ng4AqHuqplOg2g8oaFpCqsMD0kav7jAtDzqsazqHdU3nJfNP3en57Qg9n6Mnz+beHZuaXjfAGTMXPOH1FEVRDhkOgppTYIMxZhLwc+D3xpgdwKadntHA7kZOXwwsFZHKE92douyK/m2XEsd/bopVVqqRUmn336nJRGijaK3jNcx1svhUJGDQdVBNgY9da6x4LEmOhCDtrJeQgknwjSUh9TqtR0QNZRekJv8iWf1qisPDOlPzL3XVpqUxYjdyHpELqNiAhAAwWFcXuYKhYj0EwW9zfiIekTXkPCH0AkIvIHIxxoQYoOySncaJx362eS/gxfNO28kZu88nT3o+r772u8TOEjtLzgso+D4fPf6CPbK+oijKQclBIE5F5CXZXz9hjLkGmAj8dnfP311xugYIARWnyh7D2lG2blkGJFhJU/WxpOb59YTw+P6fnkhbcVpP26fTjba7blIb+varOQyjroBBapFVAwQmHRtakjSqWX0/cgEChCYhljCNfI5Ju+dMwnDSkdWkpkLZx9VGiQKMJiGjNlfbJzSWALRGhB2C1/ClFTvDUJyeHzlhaq6D9y19DcsmLKTTL7CuuJW/Xvmt/9/ee8dLUlb5/+9T1d03TE4MMwwzDAw5w5BBgqCACGLEnFHXsK6r66q7q6u7/tSvrlkRXQyrK65kFTEgCJIzDDkOYXK+qbur6jm/P56q7uo0c2fmXiad9+vV3NsVnnqquy/z6fOc8zmsivqAhELQuMyeza0oJcIgJNaE8+e9jAMnzmn7mm8s+0/amWte/j5+/sSdPLxmKQdP3oU3zzucqd1jR2R8wzAMY+tHVf8qIi8DrgZOG845wxWng8C9InItOYGqqh/Z6FkaBr7r09IlRxFrXFtYjyEzVKIenfQRyTgTjDhCafT/rC2kq7eCymeaRg2V/K1fR2s2TamgVfXnRC5MF9KzZXFhyBXSXFJPVb1IrbqApOZ/6nvZe2/U+n14ARoSO8Up9CXd5CO52vSzHYnLXgelnIRUXaFh/FXVCgNxzNhCLwDP9K9kMK6kpvoBVQeBKIFAKCEnTTuAj+9zLo/2LWIwrnDopLlMKI3peP1NYUbvBD5x0EtHdEzDMIztmm04cioipwAXADPxS/pfBH6G/8fqP4c7znDF6VXpwzA2m/7+n7Fm7Sd93/qmfb57k0cVyoSp2PThw5iQojqKoj7a6JQ47dseadAgRlWhP+nKne980VBtNP+oagFQVL3Had1eyh8b4Jfwm4ucYg2IVdLleD+qF9lCqI4g78eaHSFCJclEZV2IBql/6obQ1MqqUZhm++AXC//MiTsdAsDzgyupunzxUeBfc4U3zz2Z9+/5cgCOnLLnBq9rGIZhGMPga8D5eNuoM4BbgX9V1W9uzCAbFKciEgJvVdVTN2WWhpFn9arP0T/4A28TBTXLKEh9zdRLrggvCJMGEed/RgQMJUKIY0C76HddCEqXJLV4JcCqpIc+rXdsiggpEROKSyOmATEFRJUqvltTo+9pZhOlDRXvToUhV0p9QttZO/nUgnZSM+s81Sws62d3di3NxOv6vlSvqqyr/b7HuJ0pBgXipNpwTE9YYs9xM9YzimEYhrFFULaU9dNIoap6ffr7FSKyfGOFKQxDnKpqIiKDIjJBVddu7AUMI2PFindRLv++tnTfvISfpCb2QbpkH7UtYMpEa0Cf62KQrtoxsRYoEhOIF5592ttyflULlNIKfm/9BKvcmLRKfnj/Qyi7YlM70fbnVZwQprskl4rQ7ugsh7axLr9erBWnglbX0z1EEPafOLf2/Kgpe7JLz2QWDiwnUp+dW5CAqV3jOXGn/Yd1r4ZhGIaxEUwUkVfnnkv+uapeNpxBhrusXwYeEJE/AbXehZZzagyXoaE/M1T+fS2uWddYjV6hdbukfJ5o6/J1VQuoBOk3zPqxMSFFHEM10/pWIry5vAgMuK4NC1P1ua+ZB6nrIJobTlEhoYhTCMQhqRdpkqULqL/XrPo+E66xC4ACVS2hGqOqtehxIP7VS9L+Tqp1q6sAoSss8e65Z9bmEEjABUe+nwse/wN/XHwvDuWU6Qfyd3ueTsHahxqGYWyVyBbIORWRi4CzgGWqekCb/ScBVwJPp5suU9XPtxnqr8ArOzxXYETF6e/Sh2FsNOv6LmL12s/UlvFJfUszQ/0Ga9Ocp2mI8+bzbXRgte1H1+d8+lzWzlHXAddNpAVCSXyV/gYCpr6a30dog6awpZeajQLa56MKDiGgbqAfq9Afd5Gkve4DcZTER3pjFdZWu3Pm/w7S1IBsftk4quBcQDUJiV0BRRhX6OJ9e57J7DHTG+Y3ptDNP+57Dv+47znrv0nDMAxj62DLFET9BPgOvnipEzeq6lnrG0RV3zkSkxmWOFXVn47ExYwdC9Uyzy8+k9g9XPtbc+qX6/N/e4FSW/4GUoN9ACEioKCN9vN9zle6q6YCtilHMyFIOye1X0T3bUcD0M4f/7z3aSUponi7KO9AWl92z9ne1yOrKqkBfv3/MepgXdxTP09SD1MtIqr0RfX0BHJnalrA1ChShWpSaGhJ2hdX+MpDv2VmzxQOn7J7x/syDMMwjGZU9QYR2W1LzyNjuB2inqaNlldV+1fQaIuqsvCFw3Csrn9wGoRprsAIRRSCVLApdb/PAe1C1VtIKdJSje/S473Y8+MmGvpiJw0I06XzWncn14XLiVkFnCPd5tuFZtsjV8hZRHkpGqWm+nlj+wAf56y6MJ1/Nj9Bnb/+YNKcp+p/V4XItbPJ9/vKSYjTIL2Ow2lAlARtXVsrLuJf7vsVldi7qZ42Y18+uPdLmVDq3cC7ZRiGYRgb5BgRuQ/f6enjqvrgaF1ouMv683O/dwOvAyaP/HSM7YVmYeqNnNrlmXocPs8m1iyiGLJOS7Vjq9Stk1RhwJUY0O7a+F1EdEmMiu/atDbxrUqL4iimBVCDrotBV8Kly+WhJFSTkCqNveJ7g0raaz6g6ryfaIIQipeEZRdScQVKkhCIL2LKV+CremE6FBepNERn2+cPuI7tAbKtacGYhmmb0c49n1ZU+qjE/poXP3MHv39hAVed/BEmdY2sf6lhGIaxTTFVRO7MPb9QVS/ciPPvBuaoar+InIn3MB01H8LhLuuvbNr0DRH5G/BvIz8lY1tn4eKX4XQJKu0WqtshJArrXEhZSyBSi4iCj4pKbml/0HXRr5mJvadCkaGk0FShL0QaUKVA2RWo1gz0JTXPLzSIXnLjhyT0x90N4jJSb/VUdr41aKQF0Lo7aRahBeiPS6l/6oYdAFzONkTTwim/TUlcUBtCxPuhJiiqQdtc2eZWrGujIX78xI18bP/TNzgPwzAMY8szSgVRK1R1/oYPa4+qrsv9frWIfE9EpqrqinbHi0gv8I/AbFV9r4jsCeytqr8dzvXW13Y7f5HDco/5IvJ+YNxwzjV2LJ5bcgZJ/EBby6ROHzZVGNQiZbpqrTtbzk2FGSj92pyfiX8uQXqVxmIo70dab2maUd/WSiUppMJUGh5O/fJ/3W0gyC3j+0fVSSqE2xVkNf7u80fr55fjgo+4JoU011VaBGeUhCTaKkRVs2r/Rv685OGO92kYhmFsZaiM/GMzEZGdRXxIRESOxP9j2xy4zPNjfEfRY9LnzwP/MdzrDXdZ/2u532O8lcDrh3sRY/sncWtZsfrzVKJ7gQ7xQoFQNa3Gz4qHfN5oORVzqlDRsBaxLEpCkQQEylpkTbpc385hNJV4tXGqWqgVMbVbOu+UYqAq3qoqZ9WUJ8TbO9W1odSKoSIVKm79Nk0uPTFyAUOxF8BONRWdzfNsVKCZKFb1dlJhrRwL4iSo5afmmTzCLUkNwzCM7QsR+SVwEn75/3ngs+Bz3lT1AuC1wAdEJAaGgPNUm0MkDeyhqm8QkTemYwxl4nY4DFecvltVn2q6kbnDvYixfVOuPsDCpa/A93Xy0iovz3y/+gAHFNURI95UXqCiBcparEUfB7TUsNQea0gZx1DSRdTQJrRWB1+Tcpl9lHMwoN05O6n6sXnhF4riOlT0D7li50hv7S6pzcNHZ9NobIeIbBbZHEqKuVEkbUkKsWvnt5qaVaW3UIlDshh04nzUNbu7QLRFTAvCe/Z8SYc7MQzDMLYq1tNoZVQvq/rGDez/Dt5qarhURaSH9G5EZA98JHVYDFecXgIc1mbb4cO9kLF9Uo0WsnDpyxvMlTQVU1UNqKgwlCs48j6kgiNIo411NRWn+aHN8dBEw1SYNsdJ63/BqjDkSpRdgUout7TxZ5NHqfraepdGSGsC0AUkWiCQhHa49HrpAkeDgX5f1E2sIaE4CkGrWMxHVRMH1SSNEAdx22tl899tzDT6o4hnq74HhnM+Upp/LRIVwtw1BXjHHsdy0s77rGdswzAMwxhxPgtcA+wqIr8AjgPeMdyT1ytORWQfYH9gQlM7qvH4qn1jB2ZN/yUsW/0Rv8Cdq35SFdZRIFZJe9VDXlh6Saq5mKcn6hBxTDrGMKVmeD+kJYZcvjq+/XJ987hxZtOUCszECWviMUhagR82CdpIAwYjnxtbCpJUlIZUE6HsunJ2VgE458UiPre0nBRqy+5DUbEh5zVJSgRAGAQk6hquuee4mfzk6L8H4KsPXs2vFt7OQNIc8fUCVTTgg3ufxMRSL6fvcoDZSBmGYWxrbBkT/hFFVf8kIncDR+P/gfr7TsVT7dhQ5HRvfDuriTS2o+oD3rtxUzW2J/oG/8Sy1b57rYgQq498OrwQaxSUrUJRGtYupMNRnc72+PzUAmXtAqAgSlXJLbS35piujbvQtIjJ+5U6SkFCkua+ro57IW0z6ghJ0qM1vS+n4qv0EeI06pmot69qnKuP+MaxF8+aez1iJ21yS73fa29QRFGGkipdQZFCEPIv+9fTuz+496ncvPwpHqm2+xsXpnSN4317n9ThFTMMwzC2drZE+9JR4kTgePw/v0Xg8uGeuF5xqqpXAleKyDGqestmTdHYbnBukBdWvt17l6ovAspHSBP8Mrnr8Acm6Yq8pOciaTJAh9zqgKRjcVKsjcVHobiWbVA37E+a0gMcAXFqlJ9oQOyCVHxmdwNduSX3gbjUMu5Q0ixMG+9VVH2Xp/SAaty5YGp8oZd3zTuVB9c8y+wxUzljxuFMyBU09RRKfPvIN3Pmn75DpK1pB9O6zUTDMAzD2LKIyPeAecAv003vE5FTVfWDwzl/uDmnK0XkWmC6qh4gIgcBZ6vqsG0BjO0DVeXxxafg8BHBpNZtvl59H2mIalq20+YrYJaPuiIZn+sl76Opoo4KRSItICjdEhFKwqAr0hNEDQKwrIX0/HyUtLkIqi5M18WN3qh+n1DWAqH4eUdN3qSKUnEhpSBhMCkiaSuqLOc0Xo8dVXbtRAOc1q20mtMZ8nNR4BUz5/OKmZ3t6HbpncR+E2ewYPULJLn7DAl4aNVKDr3sq5wz5wA+ftDJjC12rXd+hmEYxlbG9hE5PRE4IKvoF5GfAg8M9+Rh+ZwCPwQ+RVqOrar3A+dt3DyNbZ1qvIgFz+9KNXnOL3HT2Io0UWFIi+n2wC+Na13MQV0ornU9RBRqeZ9VDam4Iuu0l7KWSAiJKdCv3Qy4LhwBA66LIVdkyBXpd13E2vrdKhOLWX6pqu/otCbuaRDCeRwBg0mJ/qjUZq9f/vftR4PaFkRrhVB5tOFMGFPooSjdJBowFBUZioo1Od+OZUMDuKac03Z848jXM2fsFHrDEmMKXoBGCQxFCeuiCr966l7edN3PO0ajDcMwDGMUeRSYnXu+K3D/cE8erjjtVdXbm7atr7zY2M4YLD/Mo4uPQFXTfM0sAliPAlY1s0KqRzGzVqRlLVBxIf3axSo3Ju1hL8QuoKI+UlohbBnTd3kqkqj3Ek0ISfDXyUzvVdM+9K7R+D5ywrqkmwHXGjHNk7gQTcV2p0Kq5mhnVohVV6h1SydN98/uncmF8z/DnN5dGYpKRC4kStuhOs3Pv/4oO8ea6uAG34/pPeP5zUs/yH8f9zbOnXUoYdJDFNetqKou4em+ldyybOEGxzIMwzC2InQUHi8+U4CHReR6EbkeeAiYJiJXichVGzp5uMv6K1KPqiw8+1pg8SZO2NjG6C/fxtPLX5MuZeeEZ+4Dn0VEW/EV7+s0bwSvvtjIQUyzt2cbcZj+dPiq+oxqatbvq+GD2tyyeQ26YrpMn4VtpeVvNHIhjqDeGjSXG9oyD83yaIW+qESiIapKIfAxVRGpCVNV6A4mkCjcu2pRy305J4AjSLMSkqxoSmFsodEIo5rEfP3B6/m/p+9mKI44Ytoc/vWQlzNv/DQOnjyL3y58mMG49bti7ByPrlnGsdN3a39DhmEYhjE6bFZ7++GK0w8CFwL7iMgL+A5Rb96cCxvbBqoRTy8/D1Wtib9EhXKaV1oS54t+GtM8G8do2eKX+2PaFQa1VtkL3vbJaVDr+BRpgNNCKiTTinqtj1BOikhDBFeJnTDkipSCJL2PoLZUn2bF1PxO876nifoosTfQLxCKI8lFiSOnhOIfiLejSjQg0oS/LnsY1+GFUQJil8tvTb/hPtW/nH0mzKht/8itl3LT0qeoOC9Ab132NK//y4/5/cs/wPSecewxfio9YZGhJGoYvxiE7DZuUttrG4ZhGFsfott+tb6IhMC/quqpmzrGsJb1VfWp9CLTgH2Ak/D2AMZ2jKpjwfNHoxrVhamDQS3UDPMjwlpEMfT29E1jQCVnwp/RLsraLmCpSs3WySEMui4GXDdVLdbM8MGb+icqqa1TMe26lBVL+WOy6GSkBSJXaBCm2jQnp769aF9Uoj/upj/qYigpoRpQdc3f6bxtVNUVqCYFEg0pBSVetvPhhMPs1pYJYecCfvPcvbXtz/StbBCmpHdUdTE/f/IOAM6avR89hWKuqSsUJGBKVy8v2XmPYV3fMAzD2EpQGfnHizl91QQYFJEJmzrGesWpiIwXkU+JyHdE5DRgEHg78ATw+vWda2zbDFWe5u7n5lJ2ywCfX9nvCgw2CE2hokXWum76tIshLdYq9Z3WfUgHXav9UrYcnzfGl9z+7JEQpDmjTTb+aZepJJd76hAqroCq/zmYFH1BVjom4lMDXCZG03nGGlBxvkrfjwPVJKQ/9kVXiQtq40it10Dz87oVlncqCDh9xhGcOH2/XAS3EZe7T4Akye6hLkSf7FtBMWiNMFddwoJVPrNmbLGLS059B0dM25VQhIIEvGTGHvzfS99OGAw3rdwwDMMwRowy8ICI/LeIfCt7DPfkDS3r/w+wGrgFb7r/T0AJeJWq3ruJEza2cpwb4uGlJ3rxRkBVXUtbUcFHShNCqtQ7H/l9jrIrUtUiCQHdEiFOidPYXqJCFb8kX0kCAhFCnO8ND6x1XalBlDRU2DfEZEVqgjPSoNbdqexKVDVMK/mVKIGuIPKpAEmYOgkIofi2pXUrqHoOrdN67mjFhTVhGqAUg4TmGG+WAuDSb6hOhSFVBuIKk0pj+fu9z+Abj17dcM6YQom+KsSpV2kWAe4Ji5w2c//acXPHTakdk6cYhOw3cefa8zljJ/G/p7yVapIgQltBaxiGYWwDbOPL+im/Sx+bxIbE6e6qeiCAiPwIWAHMVtW+Tb2gsXUTuzXc/9zxJGTL6UHb9qE+x7S1DDDrcV+hRCbi+l0XZVdqOLJIzDrX4/NO0x0FTSiQEKIthVJeKDYK5KoLGXLeuipEiVyYzlXSZXlvVTWY+GhvkM5bgSjn1pQ4SQWpT5wtuwJJElJOCg3uAbEK1ciLvjBQglxikHOQaAGnkLiARAKWDK1lYmkMb5p7PMfvtA/fefQanu5bxRGTd+fD+76My569i28+/Ccil+BQusMSp87YlyOnzK2Nu/u4qRwxdQ63L1/YEFEtBSFvmdfqhVoKTZQahmEYWxZV/enmnL8hcVqrsFDVRESeNmG6ffPgC68kYR1x2uLT0zlfRYACjmoq4vpdF1Ut1ISgU2HIlZrGUKpaTLtK1bdngjTA+a5KuX1lV6Cq9ZSCwaTQMG6sWvPe9wI5O7Zuyu+AMC2cKidhrcCqHvX10dTIhcROGoWpk9xxgnPeUKsgCbGGPpc1CagmYe2Yt9/8Qz6016mcNesQrn3hMf743EKKQciTa+/n+iXP8MPj3sKRU3fnt8/fSzmJOXXGfsyfslvOEcHz3WNfx5fu+xOXLbyPShJz6JRZfPbQM5jRu8npPIZhGMZWyrZeEAUgInsC/x+wH1CzoFHV3Yd1/vpMukUkAQayp0APPu/Ux81Ux2/atDed+fPn65133vliX3aH4MHFb2Rd9RYvypQGT6Wsgj2PpvmaDqFCkaoLWet6gXqUspz4oqnWzkwwpMXUs7SOpOKxL+mmGMSEosSJUFFvNJ8toa+Ke9uOmVXqR9p6TdCaOF0T9bTZ7/NAB3Jm/Nn1Itd+vGosqaVUo5hVhSTxgjZII7n5v7QAYe64qfzmpR9oEaOd8B6zEAzzeMMwDGP4iMhdqtq5Pd+LQPesXXXXD35sxMd94tMfe1HvTUT+BnwW+DrwSuCdeM352eGcv95qCVUNVXV8+hinqoXc7y+6MDVGh8QNcsvCA1lbvbVmaN+sRDORln/EBFQJiBGcKpVax6ac32f6PI8qqRVUvRioNhcV1iZjcIRUXCn1A/XtTrPq/E4tQ7PiJJ8j2kbAqbeFKicbWjBo8iTtWOkoiHgBqk1/SnEitfMSVbQp/cGhLBpcw+Prlm1gLrmriZgwNQzDMLYFelT1WrwgXaiqnwNOGe7Jw/U5NbZT4qTMHc8f4CvNBZD2teWxBqxJeqhSQIECCd1UcYSsSsYSUUgNnzTN6/TeoqE4v+Re61wU0ud6WvJPA8mEZ2MkNVLfVSnAEbkCIpoa4beiqZl9Vj3fIopT8/xYQx/LbRMJTlyrkO7k4aqp/2m+I1Zte4tAbn1VQwlYEw21vxnDMAxjx2M78DlNKYtIADwuIh8CXgB2Gu7JJk53YFSVO547lAif6Rmotu2OpArL02imR4gR+gmpupAoreT30cN8Zbmkmas+bphowDrXvJyuRFqgqDFI1oK0fn5VfYmUS/NEQSgECUGaQ9os+gbibmIHYdAoPjPh6qOuAYnm7kbqEdxqUo/+1hTpelJf4mTDBUiq1CLAAEHgX+dYHQdMnLnB8w3DMAxjG+OjQC/wEeAL+Kjp24d7sonTHZgHFr+WKhGaGtx30doCEzIP0TZtRjXtcpQus4dAIiFhk/VRgCMhoNxiXp+Og08JiFLz/Px1vZm+sCbqSQuoAOcjt8XAm/5rmkYwGBeJ1bcCiB2UgqQ2WuQCBuKu+tgEVBKhELi0bangnKRFWFnUtTWKCj7n06lSTYKG+bbDOW+snx/LOSiFAZ884GX0FkqdTzYMwzB2PLaDyKmq3pH+2o/PN90oTJzuoKwr38ua6j2ohrWq+ESEsCl6qgp92tV+EBEKJDWBFiMEqr4ZRWpm703yfXQxaVuk5JfAE/V+otm1s0jn6qiXWIOaRVRGTEichMQapHIyy/FUJO0OVU58Bb2rRXUbcUhTtyclEOdzSHP/c3Bazx9VhTgOKSdBba4tEdoku4lWYZqeQU/YzRvmHt7+dTUMwzB2XLYDcSoiewGfAOaQ05qqOqy8UxOnOyDrhh7k3qWvwRGSpBFCES/4hIQgp8wiDRjoJE4B1aaoICAqROpzS8Gb9RdIGNRSLSe1GZdGP2MnDMRdVFLbKFWpzbERSf9+g1yD0rTaP3dsgu/ulDh/XCg5f9Isz7YpZ7QSBzgNEdH0PB9hhcwNIL1SKuRrwwBRHBDHXoyL5MxUm+iPygwlkUVODcMwjO2RXwMXAD+kMd9vWJg43cFYNnANC5Z9xEcXvRQlzMm7QS0x6Eo1/07vPdq5AmlIWz9CCgy6bjLJlhAg6qhZ9udsqbJ2oU6F2AWsice0rfDvROx8t6e84BWBIo5IA+JEckVWfjk+QCkEjkhDwgY3VajEYRpNDXK3rcROCNKuUnWTi6x1qhJSZLDamA6g2jkfdUyhi+6w2HG/YRiGsWOynRRExar6/U092cTpDkSUrOO+ZR+h3nzUNwnNUKVWsBTnRKeqtEYB1S/jR9oosFSFftfd1FVKKGuJ2IWoOC8m0+hsogGJ+shpf9I1bGGqCpUkZDDxkceiJBQlRnOWV9U4q/5vzJV1+BxUCIjTiCppGoEviGofpXUqRHE7wektnqQWzV0/PWGR9+9znNlCGYZhGNsVIjI5/fU3IvJ3wOVAJduvqquGM46J0x2ESryKG547BgjqFkgCtXIlhWqbKGhG4gICgaxYKFKhv6Xy3hO1+VhpWnnfJXFD9bvDd3OquBKJtuZnikCgvmApe575pw4l9Q5RkYapJ0CAqtAXdXW0nAKpjZef3/pFsTClMJElcT/Ngj4bIZCApE1lf4gwoauHtdUhxhS6eP8+x/HuvY5Zz7UMwzAMY5vkLhq9HD+R26fAsDpEmTjdAYiSQW547ri6ABMvCpWAAIevV8+smdqjIlQ0oIBjeTIWJSQkpkg94VIVBlwX7arcK2kEs+KK1GvivUisuHzeZas/aQBUs2IpBxUXUklKTZXyPtobomlUdD330jFLQdJ5tc6hOyhy4THv5J03X8SqygAu7TQlQCko8v69TmRtJeaHj96My4nXAGG/STtzySnvZiiJ6A6LFjE1DMMwtktUde5IjGPidDunGvdx/XNHApC31w/xnYuceIEKwXrbhbk0qjnoSunZQkIBR0JRHbEKq+JxOAJ6g8hfT3w70ESl5l+aqFBOukg0JBBHUep50iHqs6abclIVfAqCCs5BORcxbZ2n1DtAaZaS0Hpcs2VV1lUqFCXOcgNEakL28El7MWfsVP5w6sf48+KHuW7JIywaXMMuPZN4/dwjOHzKHKou4aHVi7ljxbM4hWIYMKZQ4htHvwYRseInwzAMY8NswzmnInIE8JyqLkmfvw14DbAQ+Jwt6xtU4lX85bnjCHNiL0OAQJRYAzTtCpUVJtVq11NxWdECq5MxNR/UkIS4ZrwfUtWQNXEvVS3gNGDIlegOIgKUqoZUtUCIF7B9cXdtBk59sVJRHEFa1BQlQoDUirQiFzCUFAmDetFTc0U+pBZPLmAgLhEGvuipHBe8f2vYWCjoW6um9leJ1IR3/bXx4zuXiuukwN+WPsXC/pXMGTuFM3Y5kDN2ObDl9b596bM8uXodg2Wff3r4jDl89/hzGVvs7HZgGIZhGDW2/Q5RPwBOBRCRlwBfAj4MHAJcCLx2OIOYON1OiZMy1z57/AayKDMBKqxLuujTXsCXShUlToVmkbL6SGWFopejmjDkfOemQB2OLF9V0txPYSDJ7JQgM8ofjEu5K9d/RhpQImHIhQzFXWmhVn6WCupbozrq0dRs/Cy6OZSUfFMAlxVb+TmUY19pn4lOSf/yk8R3uEocqaF//ZqJC2r+ppoWPN298lnmjJ3S9rV8aNVSzv/rJQwlWSMD5balz/HJW6/muyecu553wTAMwzC2G8JcdPQNwIWqeilwqYjcO9xBTJxuh6gqf1w4HyXAqa+Obzkme6T97L0wzayfQvqSrtS4vjGvUxVWJWNJCFEVIg1INEhN8DPT/Xrxkqj6jkq0GunX5wur4m4vCNse46ObgkuP8VHNWL24VJX0fhotnrLfSedWb0ea/ZDacn6UthetNy3N7KD8z0CEiV29HV/z7z90C5WkMUJbSWKufeFxlg31s1PP2I7nGoZhGEaNbTtyGopIQVVj4KXA+bl9w9acJk63Q2594XySNLoYU0I1poukIY8T8UvmDiFyQZZiWaNVmGbbhYCEqitQ0WJa2iSpX2nd0D8j69rUrh1oHe9xKh33e2IXpkVbAUNJoY1NVCN1X9JWVOvCNJuDn2/9mCTX3akUFDhupz06XuuJtSsbCqEySkGBFwbWmjg1DMMwdgR+CfxVRFYAQ8CNACIyD1g73EFMnG5nrBi8myWV21Hqwi2iiGpAiRjB55muTsZSznmUxoSI+iV9kSzvEtqJvwHXk0Yys+OC9X7R87mlncqtfAFSpAW6gqTtN8ZM+A7FRRIHYdDOv3TDaCqUgzRfNkqVqAgUCOgOigwmEQEBsQrdhQJOlcldY/jeUW+iFHT+czl06kweX7u8xUqqmsTMHTe5w1mGYRiG0cQ2HDlV1f8UkWuBGcAfVWv/KAb43NNhYeJ0O6ISr+GGxe+kORoIaS/6NAdzXdKddn6q7y/g6HM+37NIQoGYiEJTFBQqroAjzBypwEHVZUv29TKlQLSWTuC0uYc9ZH99ToU11R6UgKoTCmmuaxaBdWnF/bqoKx1diZLhidJ2UdNyVKAUdHPGLgfweN8iHl23hDAIeNmMA/jU/mcxtugLtiKX8NCaRXSFRfYePx3ZgP3T+/c7mqueeYjBuFr7/0pPWOS8eQczsatnWPM1DMMwdmyEbb4gClW9tc22xzZmDBOn2wnVpI8/LjwTkHQVvb2YSlRahGlGl8QMaRcRITG+c1MxJzgrrsDaZEzteKcw6Lpq1e9xWgyVEeLoDiJvzdRg3ZQVLAlro+5UuKZRXsn6LPlF/qrzeaUNgrvjH66m40M19mPmUxkSFRChnERc/fwDXHnKh5jSNZaCBBSCxs5PxSDk4Mm7ArB4cB0PrlrKzDHj2W/S9LZXnj1uEpe9/G188e6/cOfy55lQ6uY9+x7J2/ee32myhmEYhmG0wcTpdkCcDHDNM2dS1SGE9r6eGUmH5XUR7zPq9Z3PIY1cgUEKBCiJBlS04HM18QKy6sLa0n9WFJUXoQkB5ZzwzIL7sQZUkgKRCyknpZoUVXyFfaY94yRoqtxfP6pC7IQoDog1JBBq0duklmOa3a9w07IneP1uR3Qcz6nyL7dfw6VPPUBXGBI7Zd6EKfz0lDcwqU1x1F4Tp/GTU94w7PkahmEYRgvbeOR0JBj+v/zGVstVT5/OkA7UrKE64Tsate8DpeotnWqIt1/yZ/iCqWoSUnYFqi6k4gpEubxP1yRM00FICImdsLZaYmW1hxXVXtZEYxiIuxhMzfSzQqiWZATJPAWaRhXA+Q5N+UfiAjRNCVANcBoQuZDIhS1epoEIvYX1+4/+7+P3cMXTD1J1CX1RlaEk4pHVy/jYzb9Z73mGYRiGYWw6FjndxvnDwjcSMZgWQMUdj1OFvqSLfu2hIAkh2rDkrUA1VyBVr773VDXsYAXV2uqz8brCqiizqcpHLmn77TC/OUkECRqvkYnkwaiE72fv47VB4LxllfqoqHMQBI2dpgCc8/sGq44e6cKlVlft+MmjdzKURA3bInXcvGQhaytDTLBcUsMwDGMk2fZN+EcEi5xuwyweuI1V0eMovnI+SSOcebKo4pArMqg+UphoQJJ6f2YR0z7X1ZAvqsBgUqyNV3FFWnve1y+WOY02X9tX6Xf2N22LZucWqMRFXGp1pQpREqTCVIAAp6FPBXBdnDBlf4pSYkyhi4CgJkSzc+NYqFQLVKoFhqrwvpt+zSGXf4VH1yxrO43+qNJ2u4gw2CRaDcMwDGNE0FF4bGOYON1GiV2F6xZ9kIZCI3w+aH6pO9KAlckYBrSn9mYrQqIhkRaoqvcLjV1YF3HOtxmtaonYBV7EtplDkFlOpRZU2ehQj8YOxJ37yTuVtmI6SguokrQ7U9UVqCT+UY6LHcZSTt/lMK466RP8+0Gv5917nkB3WKIah5SrRSpRgWpcwLksgutft8E44jXXXkTiWtMdTtllHgVp/ROZ0tXLzj3jOt6XYRiGYRibjonTbZBEIy5+8qUk7TopEdZsowZcF2uSsQy5LtYl3axzPZRdMS1e8tX2fUk3fa6XSIsMJkXWxr30Jb0kGvpqeQpErpCKzzb5n8Dqajd9STeDcYFK4nNMqy5kbbW7YwEWeOHsK/1zeaNp4VLsGtMAMkHZXHRVf02U//fgNUzpGseJ0/flw/u8lPft+RICDXz01AnaMqannMT84YVHWrZ/9KATmNjVQ3fos19CEXrCIl855hUbtJYyDMMwjE3CIqeWc7ot8vPHT8KRAEVEIwotmssvsle0SNkVqVKvmHeEVFxAl0RUKJDgLZRE/DeVfOvRQB0xBaJ0AV9TG6d8P/t1UTciQa2zUqwQOSjHYa23fRhoY/cpzc73UVEvObVeVKWaRoDTOv7cuYG4ps5OdRau6+ODt/6Srx3xWlD4ywvPgCuiLqm1FOjEE2tXwK6N23bqGcufznovv3j8Hm5b9ixzxk7iHfvMZ4/xU9Y7lmEYhmEYm46J022M/3nsFHwHdy/8ylpCFMYE1YbjBtMIaV6YZijCkJZqwhTSAqic1VIWxVS8GBWBSuJtpQJ1OA3oi0sknT5CQlohrzgnFIJ6eVXkfMvUrAuV1iKiaX5sXESdj6yGAoWgLizjJMCp1OaUnRMnfowbljzBP995OTO7p3DfykVUXVKfUO0rZOPrIcDBU2a2vY2JXT188IBj+SDHtr9PwzAMwxhBrCDKxOk2xZ+f/yQRWZGOF1hZ//jVrpuetFp/nXb7fA2nqLb6nnaqlI+00Yi+L+4GhJ6wmqYDhCTU80ljLdTHa0Jry/n+YrELEFESV/curST+/IK4tBuUUEkKJC6sRU0TFZLET7YaeUsoVQgDRxh4cRsnYZpLCrE6rlvyGCXtoeKa3QvaL8XP7J3A8dN3b7vPMAzDMIwXl1EVpyJyOvBNfJjvR6r6pab9JwFXAk+nmy5T1c+P5py2VcpRP88O3kizJROkfqQaMkiJahISS0gBxXVYys7EZc1eCZ8j6rS+fN8Xd/s2pSj9SXdDJb8IqMuW2IOWsSGr0q/PsV6578VqljuauKCtEwACgZKWWUka2a1X/ScuJE5oPQ8oSMhg3L7SPkDYa8I0Hl27jADh1Fl78Z+Hn0UYtObGluOYPzzzGE+sXsm8SVM5fe6edIX2fc4wDMMYRSxyOnriVERC4LvAacDzwB0icpWqPtR06I2qetZozWN7IHZVfv7UGakfZztLJl9ElKgwRBcoBBIjeHEXaGt0M1GfJ1p2IZEWURX6ky5Esi5SzZb49eVwVW8zVXEFipI0VNUpUI5bUwka5wvOQSguFcCN+2InoD7SirSPzDZesfGAwbhKHPtOU5qkObShEggcMHkGl536rvUNCMDSgX5edcXPWVspMxhHjCkW+fJtf+WKc9/CTr1jN3i+YRiGYWw022gB00gzmtX6RwJPqOpTqloFLgbOGcXrbbf8+PGX4Ve2O39q/ZJ7odaXvqJFKhR9VT6NFfGxBj5aqgUiLeBUKDtfHJVovo99K1kuqo92BlRdgcE4pJyEDMYF+qpdRK7Qco6fdWpzhXD81EO44oR/Z9/xM2vXcw4qcUjiAhINiF3gc0zdejxRyV6X+rXiOCCOBBcFtSp9F4WEWuBLRwzve9C/3fRnlg32Mxh7P9OBKGLpYD+fu+naYZ1vGIZhGMamMZridBfgudzz59NtzRwjIveJyO9FZP9RnM82yU8fP5eKOiIKDLliuvzeWLjkELL4ZZhrT6qI75aEECNEGhBpSKQhVfXRzcGki/6ki7Irpue0krUGrbqAgbjI2qiHTFCKCIEEDMQlhpIuIg1bxLAjs4iCRCGgi7fv/nImlsbylt1exsTimFrnp8a0BUFViOKQSlSo5ZUG6e7spyAUJUBUiKOAOApy4+TGcyGyHmur+v0q1z77JEmTIk5U+dPCJzd4vmEYhmFsKqIj/9jWGM0Eunbht+aX6G5gjqr2i8iZwBXAni0DiZwPnA8we/bsEZ7m1stz6+6mL1kNeIN8JCRGiRWKmlAQR0zAkOuiViCVnqsKZVdkMCnhEAo4xoQVYg1xaTf71XEPA0m9M5SoUhSHSq6NqMJQUiTWAlXXvtNTI5JGcf2oXpwGac6o33/K9EPYfewM/vfpW/jmI3+inETQQTSKd6/CJX6/c0opCDlmp904cupuvGLWgUztGsuCVUt4w59/jtNWM/2M2CX84blH2XPi1A2+9tLhPs3e1DAMwzBGl9EUp8/T6Bw5C1iUP0BV1+V+v1pEviciU1V1RdNxFwIXAsyfP38b/A6w8URJhasWfYwselgXRf6XqhYYrLUG9fgld1/U1BeXGHTdtX0JQjXurXmEVl3IkCuRF5sKVDWgiMvsRnFA5MKWHvWQs3JqewdS6yyV5ZGW4yJOA65+4WHetNsyvpUJU3xkNm8P1YBCkuWOCjiBHx771oZDHlu7nFAkTX9ojyBp3u76ERFevts8rnnmceJc56hiEHDGbntt8HzDMAzD2GR2CJWzfkZTnN4B7Ckic4EXgPOAN+UPEJGdgaWqqiJyJF5prRzFOW0z/OCxV9Yq2jvpqXaOUFUtELkgJ0wbxWesAUOu1CGHM62E13rccF3kuzwVcTjno6jZqUVxFIOEobhAooFvZ5oTsaowGBUJUIYSX5GvCgOJ4yN3/Lxh8llZkzYVb6lCFAc4Vxfh40q9LTPfffwUCkGQ8zVtJQwCzpi9T8f9ef79uFN5YMVSVgwNUIkTugohO/WO5bPHnTKs8w3DMAxjU9gWl+FHmlETp6oai8iHgD/graQuUtUHReT96f4LgNcCHxCRGBgCzlNdf+nLjsBFj72VCAUKhCS1rkzN1AuNfGS0khRxEjDosn72ze0/lWLgGHJSK1BqHVOoaIHYhanvqT+vnAQMJY2R1kgDojgkct5yyvef8jZRg1GBSlJsuH7iwLkApwFPrVtb2551kHLq80jzn4DECXHcWKS1bGiI7z94M6rQUyhy1px9OWLaruw2bjKPr11O5Fw6hpe8pcBHfv/pkJOYO37y+l/8lCk9vVz7+ndz3bNP8cSalew5aQon7bo7hTaWU4ZhGIZhjByjatqoqlcDVzdtuyD3+3eA74zmHLY1Fq67lzXJklrOoyNoKHLK4xDWxD1U1IvAAKUniJqsoBpR9eovdgVC0bait5IUcXirqSgJSZAOLUMzketFoNOA2AmVJCR2jYJSBMLAd3JqHidxgnPe8xSUMFCCQHEOqtVWD1QFvvrAdUgSUghCvnLP9XzjuLP5xUvfxOfu+CNXP/cwCcohU2dyzE5zmdzdw2mz9mLW2AltX5NOFIKA03abx2nM26jzDMMwDGOT2eFDdNYhaqtC1XHJ85/0wrSWzxngUAKty0CAQdeFoKkwzYQslJOQWH1j0Hb5lXFa7eTw4ez8Mrqm1fSOwHumxtnHo0NLqZb5e4GaX4Jvxne0ym+RmhtAVlmfOMW59Zu9KeBUSRLfBeofbvoNd7z2I3z9uHP4Lz0bhWHll+ZZPjDAYBwxe/wExCqfDMMwDGOLYOJ0K+I3z32ZRKHQpIscIZFTVIREA5K0en8gqVfpA5STImVXz8cMUEpBnIpP38pUCWpCMFIhFEfWuj5Roeq8WKwkzX6nwxNrdZurTomy0qI5Jdus9S1OhU7po9pGt4aB8LfFT/Py2Xsj0qnWvj1L+/v54DW/4YFlSwlFGN/VzddOO53jdp2zEaMYhmEYxmZiJvzA6PqcGhvBFc99mQf6biBpektUYU3cw4pkPCvjcaxJxlB1rR2YqkmYepXWvT2ztqTexsn7nCYqvvMSkNk+Zd6nDqGaFHBKg5dq83zyv2fL/Vn+q1PS8dv/dbk2GQpZRX8zEtT2tp7jRiayqaq88fL/494li6kmCUNxzNKBft772ytYuHbNiFzDMAzDMIZDs0P3SD22NUycbgWsHFrMg+uuRxAUIUnFHsDapIeKZmLUPypaINKAigsZjIusjboYcI1RVI8356+4gKoWqLgCVRemo2hboRlrwbcPXQ9ZJX6UCJU4SH+GDEVFYucFcHpkw/FJkua8NovNtvmspIVgQndYYLexk5jWPYaDJ82kpMX0nDqJU/abNJ3/vv9O/uOWv/CHJhuoTty1eBHLBvpbDPdj5/jFA/dt8HzDMAzDMEYWW9bfCvjuk+8iTHMcFWFt3E1JEkoS54RpHmHIlRhIun03JoLO34xUGHA9OJW0UMp7jg4mRUpBQilIEHwuqreECtNOTkFLwVQWHVWFclSkXKvGrwvOeomU1M5xTqjGBeLUSD8IHGGgjC128bbdj+EHj95KTJy7UP3XQIQJpW6uPv18usICqsq/3f4HLnnqASKXUAi82P7Qfsdx+iU/IXZKJYn55cP3s8fEyfzq7DfSU8i7BjSyZKC/reF+5BzP1RwFDMMwDONFwpb1TZxuaS587GOgAQlCiGPAlXAExFpgULs6nqep/6nk7KQyr9DWYz2C4jRgMLWEqrqAata2NM0zTTSoFSxJ4AhyfyWJejsoVFJh2mpV5Y9Lr6YwVEk9VXORTpeEuEQ4Y/YhfGjfkzlk8mw+eedV9EcVYnWUJGQgiglEOGLarnz5iFfSFfqPqojwhaNO5417Hcr1LzxJb6HEmbP35lWX/4KBKKpdYyCOeGz1Sn6y4G4+cMhRHV/Hg3famahNcmtPocAxs3Ztc4ZhGIZhGKOJidMtSDUps7jyOAkhzgUoiqPRBqrZlD7bFjdVxNdM7HM/UahoY2FT0iGXVAQKgSNJwmwLVRf4RAPJiqm8wI2STtkg4n1Jk4BiACdM25c/vvBETt7mr6388sm7+cSBL+X46Xtw45kfZWH/KnoLJab3jKMce6HZ3SHqud+k6ew3aToAT65ZxepKueWYchJz2WMPrlec7jphAmfvtQ+/ffxRhmIfvS0GIVN6e3nNPvt3PM8wDMMwRoMtYcIvIhcBZwHLVPWA9Rx3BHAr8AZVvWS05mM5p1uQ//fgO6lqkLYcFRJal/Abq9jrv1dcGkmsH9kQQVWFqgtJNKwd4VTaFh41kyUY+P96eyjFt1EdqhYZjEpp7mgj2RJ+nIQMRQXuXbnUWzJp40zz8719+ULAL9/PHTeF6T3jAC9KOwnTZopBQKfeDcMxzf/SS1/Ov51wMvtMmcqu4yfwjoMP5arXv4UxpdIGzzUMwzCM7YCfAKev7wARCYEv45srjSoWOd1C/OTJzzNIGU2Fn6/+6Xx84nyVfawB5dQkH0iX9rUWKVWRenETQU2MJuqLozpdw0djw+at1KSq+kr8alLvGtUuqhvnLKiWlte1FC41M6aw+QJw9viJzBo3nifXrGpI1ekpFDhv34M2eH4gwnkHHMR5B2z4WMMwDMMYVbZA5FRVbxCR3TZw2IeBS4EjRns+FjndAqyprOCR/nuINSAmJCHzLm3qhJR+QBWh7EoMJV1Uk2KtsCkjwJfDO4TIefuoqtbHq7iAyGXPfSh2MCrQXy0yGPnq/NhJbclfpCGxAFASB4OVIqRpB4mri9Za9X7sW5NmNNblt/61FYOQw6aOTF7nBae9ikndPYwplugKQnoKBY7bZQ5v3veQERnfMAzDMF4UdBQem4mI7AKcC1ywoWNHAoucbgEuePzTKFnENC8DFXV+yb7sSr7LEUoxiFvGyK9iu7RAKdJCamjvEDJrKEE1pJIEBAKVOKwVRAEkqgzFWW6pn0WG4AugBqql9FtMXhSnxVFAnGRdodrbQbUjlID/OvJVFIPmaO2mMW/SFG558/v588InWTbYz+HTd+GgaTuPyNiGYRiGsY0zVUTuzD2/UFUv3IjzvwF8UlWTF6ODoonTF5m11ZWsilem6+GteZiDSYFqQ0tSoeKKFMVbPmXOnZW0d71Luz5l5yuQuJByXCByASJQChKcQjkuUHH1sbO2ob4Yy7cWLYYJIpqOKwxUSr4lqT8jNdhv6gKl3r+02XYqf0xRAj56wEnct+oFZvZO4I17HM7u46YA0F+t8J37buWqJx8mDALesNeBvPfAI2oV+sOlKyzwit333qhzDMMwDGOrQUetIGqFqs7fjPPnAxenwnQqcKaIxKp6xUhMrhkTpy8iicZ84aEPAkGDRVOGKg3CNE+sAQVxPndUg7TQKROZfklf1ds4rat246gv4VeTkMgFuDQHNRORiZNcT3ufU1qJC5BaTlXj/DWyY5Ug8EI2K9bKukk11yS53NL/2FI35+9zbMt9RS7h1b/9Bc+sXU0ltXT69r23cNOihfzvGW+wHveGYRiGsYVR1bnZ7yLyE+C3oyVMwcTpi8o3HvkXYk3jnCoEQaMzqetYESU+8qm++t7nnOYFobCm0k2sYS3i2RDdFCgEymAkhGmQNYuaNnuVerEZELuwJj6TRHJ5rgEkEAYBsUbEcTYfB07SlqP18TIOnDiz7Z39ceETPN+3tiZMwVtA3bt8MXcvW8Th03fp9HIahmEYxvbHlrGS+iVwEn75/3ngs0ARQFVflDzTPCZOXyTuXnkzzww9gxICvqtRmDgKkhAEPvrZSZqqpsv7SbEW9cy6NZWTkIG4VIukZn2a2uNFrndU7XCE1Fub1iv9G0WsAyqJI0kKPtfUAfjcUc3yDoJ6d6mihJw7t30l/N3LXvDm+S6dt3jXgtg57lu+2MSpYRiGsUOxJXxOVfWNG3HsO0ZxKoCJ0xeFalLlooXfBg2Q1Oop0oBK+vIXXYzP6vQ5pL6oqXGMyIUMxQVKoY8welHaRSdJmy23iyhBTdCm3Z8Cl3NFbXeuEMVBx7EBryMDhSS3oT4COIHQbz186ixOn7VP+2s5kMTLZaHuw1osBswYO77z9Q3DMAzD2C4xcfoi8O8L/gHVwBc0OaimUcasp3tVC6BCVUNiB8VAKdZKkHw3qFhDqlqkGhe8eHRBrhCqjgDVRKgmxdw2pRTGNbEZJyFRIgSpcG0uZKpEhRYngWZq+aUdDPYB9h4/lfftewyvmL1/gxl+X7XCLx+6n+ufe4q7ly5ueC0kM6Bywimzdu94fcMwDMPYLtkCkdOtDROno8y9K+9gVbyGAG+EH1G3TsoyTiWNY1aTEEdAnEAZKOUspAbjzKzeV8UXA0fFtQrIxGXCtNGptBwXa1d0KiQuJAEKQUIY1P8SqnGQtkbdcCGSus42uQUJ+NWpb2dcsath+5ryEK+49H9YOTRIOWmyyMqmIYJLYL8LvkUpCDln7335lxNOYqx1bDIMwzCM7R4Tp6OIqvL9p79JIEKiENPY517Tzk6+Bh7C1MIJvOdp1YWo0tARKk+jyb0naunylB1Zu2pDF6fYFYhdfZSswr79/ZA7jlzTgMbCLoC9Jk5rEaYAF95/J8uHBqgm9QIoHLn0AD9UOYkRFcpJzOWPPMTjq1ZwyWvfaNX7hmEYxnbNlsg53dowcTqK/PKZn/loqabWUVKvsK8kBSLNlveV7iBqqtb3Ec51USntbQ8hSiiu1u60trKudXnotPNyfBQHLbms2bUytI1naX17OttUmGqqrMUJhSAkcgldYUgxCPnK0We1ncOfnn6iUZgqkNSX9f21tEF1V13CwytWcP+ypRw83Yz1DcMwjO2UEerotK1j4nSUiF3Mn1f8Ja3OJy338dSFqdT2DbnWJWulsVK+6oRq7jhBiZPM8zTzHnW0i2SCt47SWry19RhvL+XFJwENzgD+l0z4+g2a+ANOnrknp+yyO3eveIE9Jkzl9XsczNTuMW1fl4nd3U2Taj3GF0Y1/nUGAk+sWmni1DAMwzC2c0ycjhIfu+djftlbBOcL8WvG+3lh2khjBLOahN78Xn0ve0eTpZMKQQBJkprkK0CY5pDWxacqREm7AqrGYwDiyM9NVL0Pq4A6wSXe5klqz+tz6SkUePNeh/PmvQ7f4OvyzgMP48EVyxiMo9oUpM1r0bzNKew5ecoGxzcMwzCMbRqLnJo4HQ1uWHoDa5N1SCqxktTCqRQm6zXab8jpVGEwbUGqSNvzah2anO8glaEkhJIt9wtREpK05JJqwzWdg2pUgFTAqgtIXO5o1TRS2pg20BsWee3u7T1M23HG3L1YsHwp//3AXZSCkCEXkyRt/hJzCbWlMGTfqdM4cKfpw76OYRiGYRjbJiZOR4ELn/kxgaSmSAoJQogvVgqlzTp2dpxS6/JUdQEJXpjWC486CFshXXL3xC5EgoRyVKBZTPpr+UhsEge5LlHSNGDTJcR3fxINKIUFYpcQSsC5cw/gxJne8mnV0CD/dcfNXPP0YxSDkPP2PYgPHHokpTBsGOefjnoJ7z5oPvcvX4I6+Ps//o6BarX2ZbErDJnY1c2KwUFKYYFz99mXTx93ohVDGYZhGNs1ghVEgYnTEeeB1Q+iGqIoiiNO8zQdCiooBQJcyxI9+FzUJN0u+LahsQtzZvratqApaVOhHyXtqvbry/fVanvhmh7Vdvv4Uje/POXN3LtyMX1RhZfM2J29J04DoBxHnH3Zz1k60E/kfMj1+/fcxl1LnudnZ72uZawpPb2cPNuL2stf+ya+8LfruX3R84wtlXj7QYfygcOOJAw6W1UZhmEYhrF9YuJ0hPnSo1+vRT5FvW8p4suQCuLwmZ9euLq02ClR8fmlBLXVbBHf4z6rvlcFxLcVzRcqxa5dJyepjZEkrZX35Up9+b4zdYEqwLHTd+NrR5/NtJ6x7DOpdXn9qiceYdXQUE2YgreDumPJCyxYvpQDpnVekp83eQo/Pfs1G5iPYRiGYewAWOTUxOlIcseKe4md4EgLmUjr4l2IiBJrSCmIa1JyKCmmArWRWnFSQ+GUkLiAQDLxpzgnVOLWCKmvuveFSwpEUYAEfow4loYUgBZSdVwIAo6cNpt546fyut0PYb82gjTPXUsW1YucmliwYv3idGNwqtz69LMsWLSUGRPGc9o+8+gu2sfYMAzD2D4QNXVq/6qPEKrKlx77PpIKShHIGi8lkkYhVam6AqgSuUJLxFNJC5xSD1FtEZGC0xBUUVWGqiXf415osX1K0mX9xOGr9LU+Rrsl++w8Ad67z9F8cP/j25rod2LuhEl0h4WWrk+BCLuMHT/scdbHUBTxjp9dwmPLVlKJY7qKBf7zD9dz8TvfwG5TJo3INQzDMAzD2LJYUt8I8YE7PoNo4AuHUu2X/cwspBBf3FRtEKZa/2+6TO9UGqrvm9FaGoAfL0kyMes7PEWpHZSqr7r3pvmkKQGdHX41AVzAZY8+zL3LF23U/b9unwMoNOWIhiJM7RnDcbPmbNRYnbjwb3fw8NLlDEYRiSqD1Yg1g0P842W/H5HxDcMwDGOLoqP02MYwcToClJMyS6ur2+4TWX+XeqUuLIeSAhVXpL/axVDUBbl9UBeYlTjILc37TlLVKKQaFYhT0ZrtUxVcHOCSoLHLU9OY6gAVkiosGxrg/L9cxjPr2t9TO6b09HLxOW9g78lTKQYBxSDgqJm78n/nnEcwQlX2V9z/EJW40e1AgUeXrWDVwOCIXMMwDMMwjC2LLeuPAJ9d8G0cQriBryf1rpzS8DzWoFYQBRDgal2fylFIIXQEoqgKkQtxzhv7Z8v5gUA7q1AAF6cFVUmW+wrgL6zpNyp1uaKrdG5x4vj5I/fwL0ee0jDeYBTRFYZtK+kPmDqdP7z+HawuD1EIAsaVhp8WMBw6peHkLFENwzAMY5vGrKRMnG42kYt4eN0zgOBECXLV9LETynEBp6kRPwGCoxgmCELkAqoupDmAHQRKkviuSN5Ev/FtEoEggDgRwvRUQdOc1ZzfaTVoHLtWgJ+K46T+e8t9qeO5/rW15zc+9wz/+tdreW7dWophyBv2PYBPH3ciXWHrR2hSd88GXrVGnCoLFi8lThwHzJze4Iua55UH7sNPbr2balKPngqwx9TJTBnTu1HXNAzDMIytEhOnJk43l/9+4jKyAqgk9SNFYSguMBCXUhP+TCD6GF/FFX1ZUi2KqbV2T/WOTlCNvU1Uu1XxLMqZOAhEiZ0QVwtkAU1X6+bUdF7aSjXrHiXkIpKufk5PocDxM3yu6ILlS3nv1VdSjn2xUxLH/OqhBawpl/nmy16xWa/fg4uX8v5fXUl/pYogBCJ87dVncOK8uS3Hvv+EI/nbk8/wzKo1DFYjeotFioWQr776jM2ag2EYhmEYWw8mTjeT3y65kSDVgE4D+uKAAKWclKCh7Wjupypay0UVXFrIpOo7NjkVhqoFnAqFsNHbFOoV/dlSfKyCSzs9OScb/NblC6QEXO7QTJgqBElItQxfv+UWFixZzoqBASpxYxV+OYn5/VOP86+Dg0ztrUctnSqrBgcZ19VFV2H9H69yFPOOn1/KunKlYftHfv1brvm7dzBjwriG7WNKJS55z5u44YlneGDREmZMGM+Z++/FmFJp/TdsGIZhGNsItqxv4nSzuOCxS6m6gO7Qe486FZSAisv3uYeWCKakojJRIhdSjf3bUMyNk/mfxolQCF1DvqVLbaayYqbEiY+IDgPvkS94E9amdAKEUlIgipXEKavKQ1z6yINpsV9r16hqkvD/bv4bX3rpaYgIly14iP/vuhsYrFYBOH63Obz9sEM4cs6uLZX8AH957EkS1zpxp44r7n+ID5xwVMu+MAg4ea/dOXmv3Yd3w4ZhGIZhbFOYON1EYhdz6aIbQQOqCZRCl/ZlGn6BzlBcInFClhdaTfwSfWaeL7XipKCWAqDauFwfVUNUg1o+aafCeAFOmL4HN73wbEMXpxpVL4oriWuYe6yOMLMcaL4phd888jBzJ0xk3ynT+Owfr2UojsH5fX957ClueOIZxnd38cPXvYqDZu7ccPqaoXJbcVpNHCus+t4wDMPYEbHIqVlJbSrfePj//C8iRBoyGPtleEER6oJL2piM1aKdSC1H1G8LqUQFKnFYi4zWRlG/7F/3MyX1M02FayoeMweA2iPblgjfPP4cpveOpStIC46yg2IgNepv9zeRqLbaQSngoBwn/ODOO/jWTbd6YZqOmR0dO8eqwSHeefFlLakBR82ZRbu82N5SkRP2GBlvVMMwDMPYZlC/rD/Sj20NE6ebQKKOq5fcgXNCNQ6oJCHVpJCa6wcEgdREac2AH60LS/VV/OClmXNKNQ6JkwCnIU7DBh/SjMxkv1oJiapF1LVpfZrgI5fOC1L/CAgImdDVze9e+U4+fPCxhAT+uFjA1Ur+21IKQ16/zwHU/PtTYZp1Ul1bqfD82nX+SYf0gkSVG556pmHbHtOmcNYB+9CTaz/aUyxw4IzpvKRNQZRhGIZhGNs/tqy/CXzy7h+RaEBSW2L3sdJqohQDR6y+KEoVIheSuIBYBUkN8/PFTKBESWadlJZIpXZPTnMNThWSWEiSzhX8/rhGv9Ls3Ng5PvqX3/GF40/lQwcfS1854icL7qKidVumMBC6wyKVKCHOJbEWgoCPzD+GBxYt4+Hly1suOa2rl3X95drzdlNTVfqaCp8A/uOsUzl+jzn8390PECUJZx+4L686eL8RM+43DMMwjG2KbTDSOdKYON0Ebl/9WCpMGyOXlaTIQFZklOaABviWpNohLJk4v2QvbcWYUK0E9chmNnAgOc/Sxl0df1e4+qlHeaF/Hb8++438w+HHcu+yRdy/fCngl+13HjOOC047h/+86a/c9PxCBGHW+PF89ZQzmDluPJ95yYm858orapZSAF0SMrCuSpw48F1TvbtA09Ri5zh6zq6tdyjCGfvtxRn77dX29TEMwzAMY8fCxOlGcuVzt+IcaTeneqQzcb7TU02WpQVMPv6YKcVWAeraLM1nqAIuSMVr3duUBDRokrtpBT8CJM35od42quoSFqxYwiOrlrPP5GlcfNZ53LfcP589bgJHz5xNIMJPznoNfdUK1SRhSk/dJurYXWfzs1e/hq/ddBOPr1rJbhMnMVFK/O3JZ/1cklxX1Rw9xQLvmH8YMyeM73ivhmEYhrGjI2ybOaIjjYnTjeQrD1+BI6wXGqXL8y7Xr74eBPVVSoFoGmmt43NPvXE/aYFUEGjtXFXfejQTpvlxVYGqoKHWRauk0dS8+X6+wj4VrKEEPLN2NftMnoaIcMhOM9h9/CSeXbuWdeUyE3t8d6dOrUfnz9yFX77u9bXnb//FJTituxSkPQjoLoTsudNUZk2YwHmHHsixu83eqNfZMAzDMHZIOvXq3oEwcboR3LfqaRJVRHy5U6xNrUFJzfHVW0J54SjEsZCobzWaCUynQqXqX35V398+cUIQuvoxNQupNohA0pouoGmJfpDmpTqnDZ2fIufYZ/K0dA7Kf1x3HRc/8ADFMCRKEl613358/qUvbetL2o5jd5/D3S8sohI3thRVhQtfew5Tx44Z1jiGYRiGYRhg1fobxSfv/R9qXZlctoSfPiRzJfUqNfviU40DKnGROClQrqaPSpFKtUgmGJ2K9zZNhCQKcIl/nh+nM+0OEHrCEr3Shbiwdp1SEHLyrnPZbcIkAC64/XZ+9cADVOKE/kqVSpxw5cMP842bbx72a/LGQw9kYk8PxZyY7SkWeMv8g02YGoZhGMZGYlZSJk6HTeRiVlW9Mfz63ucgrbRXhCgOiOICmYAVSf1Kc8erCi4KveWT87ZPSTX0bUihJU11WNF+9dZNR02b5e2e0oKoJFLmjZtaO+yC226nHCUNxqjlKOZ/7r13GBfxjO/u5sp3vZm3zj+E2ZMmcOCM6fzHmafxyVNeMuwxDMMwDMMwMmxZf5j8x/2X47JC+XZVP1DP+0ypRvWoZcMhqYUU+EgpzdZPKC7KcgC09Xr5vNLMRD9Iu5FGvsXn7HETueGZhaiTWqcpp/D9O27ndfsdQLkaMVCNWu9Bob9SxbUz3u/A5DG9/POpJ/LPp54IwLMr1/Chn1/FrU8+R2+xyHlHHcT5Jx9JMQw3MJJhGIZh7MBkwaIdHBOnw+SPi+8ndiGqjkLQzscJUPVL9GnlfCf7qNzhaF5o1pC6HZOAVoGg3s605Xjnq/ElFbmloMDBE2fw5AurMtOA2kNR3vzrXzOp2N1xXrMmjK8J02qS8N2/3srFd93PYDXiiDm78JmXn8Qe06a0PXdF3wCv/97/0l/2AneoGvHfN9zJE8tW8vU3nbXe18MwDMMwDMOW9YdJ2SWoE2IXUokLWeVRfZ1dNa3cz+eKKtphHV6dr8ZfH6qgWQcnDdKxcxFTSFuP5nJf8YJy8bp1tU5RWa97L2+Fxev6eKiNmX7Gq/fbr/b7xy/7PT+59W7WDJWpJgk3P/Usr7/oYpau62977sW33Uc5imsV/ADlOOb6R57i+VVr13u/hmEYhrGjI27kH9saJk6HwWPrlhAnvpo+yyeNXYE4STs+OW8JlRnqx2knJ5f4oqnmFqRJJMSVAi4OfYS1Sb/WnrustWjgl/Iz+yoFEqAi4FqXyid2dfPI0hVpm1Hx5qMqDVraX6j1XotBwBsPOgiA51av5frHn2ow3VegEsf8z+33tH2t7n12CdVc5X5t3DDksaUr2p5jGIZhGEaKjsJjG8PE6TC48KHrcS7EaYBr+AYS+G0a4FxAuVxkYLCLcqVEpVIkSUKiiq++dw5cIsTVEJe0ZlPkArAel6YFxOKjo7FAFEA5QMohxCHt3j4BXrnH3gzFcc0Rv13iQG1j7kNbCALee8QR7DR2LABPLl/ZNk80ShwPLFra9rXac/oUCmHrvGLnmD15YttzDMMwDMMwMkycboDblj/F1YsfTvNHfXTUNUVCVWFwqECcZAVQknPiF5IoJK4UiasFNO0IpXll6AAVNP1J1tI0K3ZSEM3ySiW3TJ8j/XY0rWcMJ+26Oy5x6dXrSP7Y3DkoBAivO+AA/vGE42rH7zZlElHSuh5QDAL22Xla29frLcceQqlJ0JYKIQfO2pl509vnqRqGYRiG4TErKROnG+R9N/+8KewY+IhoHBDHQqUaMjhUQrV9ZX6nmihf2J8rcFJ8ub3W81YZEkSDWiGUqCAJ/hFRF6nqf+8KCnz0iGM5dMYMYuc2UI7lzxMEUWFModQgTMGL0yPnzKKr0Cg2i4WQtx55SNshd5k0gR+/57XsO3MagQjFMOSMA/fie287Z0OzMQzDMAzDsGr99VFNYirpOr602Cpl+aaZGX+u+l5rh3T8ytJYT6U+pzT7qpDg25Oq1KvtAYmzYQVFkWzlXiBQYe6ESbxij72Z0N3NEbvswu3Pv9ByXQHm77ILfUMVnlm1BhFhWk8vx87clc//9i8ctdsszj54P3pLRQC+/fqz+NIfb+Dy+x6kGiccOHNnPnvmKcyaOKHj63bgrJ259ENvoRzFFIKg7TK/YRiGYRhNKNa+FJBO1eRbK/Pnz9c777zzRbnWzUuf4J1/+wWIEuRX6lNUIYp8wZJqWn2fr6jPq9SsyN7h80dVQNQfnkBNmUbp8n1zFDYnTBvmkA5fiAN6ggIiwk9f9xp2GjOW037844ZiplIYctDOO/Or894AwLL+fu5+djGfuvwaYqdESUJPscDkMb1ccv6bmNTbk7tXJVEddltTwzAMw9iWEJG7VHX+lpzD2Em76iGn/P2Ij3vTZZ/Y4ve2MZjSWA8/ffz2NvZQ1H5PkqZ9DcIUmtf0NRYoh2gcQBL4n1GQVuRTW55vFqD10Tos1EfgEmUgiuivVnnf5VcyfewYfnXeGzhw+nS/vB4EvHT33fnvV59bO23amDH8vz/ewFAUEyW+wn4oilm2rp/v//W2xmuLmDA1DMMwDGPUMbXRgShJuG7Jk2QFTjXLqBiiakBlyBc4uSTAJazHqsG3K1UHVH0KQC2HlDSC6kAqglSCJl/SDSPa6mE2FEU8sHQpB0yfzqv23JfeaoGeaoG/Lniaf7jkd/RXKgAsWdfPiv6B1nt3jj8+/PjwJmAYhmEYxshhVlImTjvxuXuuxmlqlp8IcRQQVQpE1SJJXCBvfK8apJ2eWtE0GqpR0Pbz4YubAnC+MElSP9KaSAWfmyq+u1Pj4LRPARAhThx/eexJvnHdTZSjmMFqRDVJuPWZ5/j45dcA0F0odExt6SkWh/MyGYZhGIZhjCgmTjtw5dMLcHFAEge4OECTrPCpSQjWfgnSFID6vpp3aSSpiX5z0iqQCJKPpkrueSZSs2OzKGz+21Cr3z2BCAfP2Jkf3XwnQ1HcsK+aJNz01EJWDQwyaUwPB8/ambApmbanWOBNRxy8wdfIMAzDMIyRI6ujNispoy2DtS5H0vTojCZAkuvilFXvJ95MP195T/PveST7ITUBKghhIIwpFgldui2RhqhpKQzpKRT45lmvoBiGLOtrXbIHKIQBqweHAPjqa85k18kTGFMq0lsq0lUIOXmv3XnTkSZODcMwDONFJRMQI/3YxjArqTZc8+zDNIjRpk5K7XBpByff1SnbqhAoJKlXaaIQ1sdSNiR3U2/T2B87dUwvV775Lbzl179maX8/IkI1jjl29mzmTZ7ClN5eztlvX6anHZ6O3X02l967gNg1Tj4QYde0W9P08WP5/YfewZ0LX2DJuj72nzmd3adOHsarZBiGYRiGMfKYOG3DP932O1pkYweBmtmUEtX9ThtOck3PE61rXpeNoY15o5r7qfUSqmqSMH3sWP74jndwz+LFLO3v5+Cdd2bm+PFt7+MDxx/FNQ89zkC1SuwcKJQk4B3zD6WYq7wXEY7YbVanl8MwDMMwjBeJbXEZfqSxZf0mKnFEX7W6weMyX1NfMLW+A9sIVs35oSr1Iqh89D39KbmxD9hput8mwmEzZ3LGXnt1FKYAMyaM4ydvfjV7TphEsQyFIShUhZ/+9W5e862fs7zDsr9hGIZhGMaWwsRpE1+5+68dUzSyYiRfgZ/mkkYBuLD14NxJ4ryIbRmsXaW95vbF9f2lMOTjxx+/UfeyfF0/5//4cp5cvIq00RXVOGGoGvHEspX8wy9+u1HjGYZhGIYxypiVlInTZn7+2N21pfiWynsHmgRoFKJxiH/58tZP2lrwlHqgSnZMTnw2f2AmdnUzNikRlIUgrttKCfD++Udw0M47b9S9XPjXO1hXrpA4bUk4SJzy4AtLWbq2f6PGNAzDMAzDGE1MnDZRcc7bQiU+2qnqvU41kVpnp/xSvUYCQ4FvSRoLRNSFZ5yvqM+J2LSqPy9OD9t5Bq/aY1+GotjnmDqpHVdwIT2FjfcdvenxhcRJc8i2TiEI6CuXN3pcwzAMwzBGB7OSMnHawGDkc019xFRAvb+puiCXJ5qioBUgCmrm+ZLtj3zHpyCr0ofafyURgjjw+2KhKwj55stfwaWvexPzZ+7CmJz5feZ32hWG7D1tWsNcV/YPcuNjz/DokuUd72fa2N78dFsoFkJ2s8p8wzAMw9g6UPwq7Eg/tjGsWj/HgpVL2/o71Zf3Nc0FDdA414I02wd1gdqEAjgIKlJTigEB+03diUN28sv1p+01j6/deBOVdX216voCwtSeXo7fbTa3PPks//23O3n4haWsHarQUyzi1DF32mR+8LZzmZITowDvesl8HnhhKeVqXJtDNrvuYoF/P/dUCmHn7yeJc4SBfX8xDMMwDOPFw5RHjhXlAS9Ec0b6tW5MVYE4QKpBaqgvTcVMbbo/5X4XhWCInDWUP/7+xUt5zc9+STmKKYUhv37Lebxy333ocgGFIQirsGrVACd//Ud84OdXcNPjC1k1WMapMlCtMhTFPLZkBR+7+Hct93PiPrvz96cdS3epwJiuImEgjO/u4qxD9uEXHziP0w7Ys+3r8LtbH+b0T/2QIz74TU775A+45Ib70W3QxNcwDMMwtjmsIMoip3luX/Scr8DP8j0bgqI+4qmuWZTmyZmhOurSX4Gqj5Q2oN7rdE1/me/87Vb+8aTjmNLby3G7zOJ3dz4MCkmsDMUxQ2n0s12fqtg57ntuMcv7Bpg2bkzDvrcffzivO/Ignli6kqnjepk5sW49papIU+vSP9z5KP/5yz/Xoq0r1w3yX5f+FYDXvuSgjq+dYRiGYRjGSGDiNMU5x08X3AtIqv6k9dvGBr99pEv7EQQu8BHYdI84vGDN1KWDINf2/sc338WC55dw/rFH8KnL/9h6qXylfzq9PGEY0FeutIhTgN5SkYN29akD5WrMN664gStvfYhKFHPoHjP51BtOYd6MqQB898qbasI0o1yNueC3t5g4NQzDMIxRZlssYBppbFk/5Vv33ILTrJ+9tDc63VCvUQWpihem+dMyIar144K4rlMFH/2867lFvPsXl7UK0yT1PE0r/LVNmL67UGDOlIkbvM+P/fAqLrt5AUPVCKfK3U+8wNu/9iuWrfGWUktW97U9b3Xf4Hor/w3DMAzDGAHyeYUj9djGMHGacvEj9zdtyQnUXN6G1DbkyPZFQtBUECXqRWVDMkCHz0k1SVqL6tKl/7yQFXICNT3+lQfug9vAB/DpJau464kXqMb1tlMKVOOYX91wHwCzpk5oe+60iWPXWzxlGIZhGIYxEpjaSClIWBOP4oAY71ua+ZG6LNSeCdbsGwlI1Rc7Ba59aDVrQSrZ2BvxJUY6tEatmfonEJThspsX8K7v/Zoo7txL9emlq9oKzCh2PPL8MgA+cu4JdBcbsz26iwU+eM5xw5+0YRiGYRibxJbwORWRi0RkmYgs6LD/HBG5X0TuFZE7RWTjWlZuJCZOU8aHXV40RgKRIHH6qApS9b6lDAlSDQgqAVIR72U6REM3p/zSfc2IP5cPIEDQ6YPSoaqu0wdLYggjv3+oGvHoouX8/t5HO97j7jtPbrs0XyqE7LvrTgCcdPAefPHdZ7Lb9EkUwoBZUyfw2beexiuP3q/juIZhGIZhbNP8BDh9PfuvBQ5W1UOAdwE/Gs3JWEEU0Fet8NCK5bW2pXkxqSjiAiRpWq6XtLVoWqAvCOo0t+7uC6NEBc1qrGonp37+SuNSfxYNzbkESEzHrxDSpDOHqjF/vPcxzp7fXkjuNn0y8/ecxZ2PP0clSrKpUCyEvOElB9eOO+ngPTjp4D3aX9QwDMMwjNFhC1k/qeoNIrLbevbne52PYZRnaZFT4MFly8ClXZ6aqp6klmWq+Y2Nvwu+aCkbwwlBFYJEfL5pU7tSzULt2RjZ0EkaJc0eCY1V+vlH3FqfJcC4nq713uvX3vNKXnPsgfSmvqfz95zFz/7xPKZNGLve8wzDMAzDGF0EENURf4zI3ETOFZFHgN/ho6ejholT4O4lizbvO4CkYjPNAfVFTFIvhorTRzWNplbxOa154ZlkH8q0AMpB4PwbVBO4qR2VxH5fu7qsE/fbfb1T7S4VOGrerszsGkNxEJa9sJbHnlm2GTdvGIZhGMZWztQ0VzR7nL+xA6jq5aq6D/Aq4AsjPsMctqwPTO8d66OQWaX9hiyj8uSX4vMCNRcpzQ+nDoIsQiqgQf1cUlFKAGEgFMMAp+rzRPNJzXlRmg/oOrj+vic5/dC9O073r/c8yWd+cDWV1Mt00Yp1fPGnfyKKE84+4YCNuHHDMAzDMEac0XFtXKGq80dioDQFYA8RmaqqK0ZizGYscgqcPm/PevV7u6KmxOeO1mgSiZn/KAkEkX9IG2Fae57loOavmQlT/O+3fOoD3P5vH+TAXXemVAjbXzs7L04jsgnc8cTz673X715yY02YZpSrMd+77Kb1nmcYhmEYxo6JiMyTtKWkiBwGlICVo3U9i5wCY0olxpSKDAxFPuIJrUnJSrrGTr1wymUV842V+g0dQdeTLlCzlmr6lnTI7BmMTXNHf/DeV/ONq2/kktsWeH/SvN+qQlhprOYvbCDs+/zytW23r1o3QBQnFPNC2DAMwzCMF5WRyhHdqGuK/BI4Cb/8/zzwWaAIoKoXAK8B3iYiETAEvEF19CZq4jTl8ye+lH+85hofIY3bHyN5H9M04hlW0321/zSfRKvIzT0PBApBSDVJKIYBpTDkX88+pba/t1Tk0686hU+/6hR+f8+j/PDPt7Fi3SDFMGD1sgFfXJUbfs2aIW575FmO2md223uYMWU8zy5d3bJ94tgeM9k3DMMwjB0QVX3jBvZ/GfjyizQdE6cZDy1ZXquCh1Y7qQZSi6e8uf6wyCKr6c9CEPDeE48gih0LXljCvjN24i3HHsqMiePann7qgfOoDsX86c5HCcOAvy19uuWYKE646Pe3dxSnH3j1sfz7f/+Bcm5pv7tU4PxXHYvIxiTbGoZhGIYxomwhK6mtjVEVpyJyOvBNIAR+pKpf6nDcEcCt+DDxJaM5p05cfN8CJPbirK2dVFrAhPNL6dAaFG1LdkBcX4rPvFGT2PHDq29jzrSJfPzsEzlx/8ZK+3WDZQbKVaZPHEeijvf+1695/PnlDFU7hHZTFq9a13HfqUfsTZwo37nkRpau6mPK+F7ee84xvPqkgzZ0J4ZhGIZhjCpab52+AzNq4lREQuC7wGnA88AdInKVqj7U5rgvA38YrblsCFWlHEWpCNX1hkKD/DI+HQ7NhGwWKR306lwVNE3pDJJ6AdTC5Wv4xM9+xzffdTbH7D2HdYNl/uUn13DrI88SiDC+t4sz5u8zLGEaBsJh83ZZ7zGnH70Ppx+9D84pQWDRUsMwDMMwth5GM8nwSOAJVX1KVavAxcA5bY77MHApsMXMNn/30KNozTdUWsOhWRFSpck8vx15A30FGYAw8ydVCNKWo4FrHKccxXzral8x/5HvXcGtDy8kihMqUczytQP84tq7NyhMAxF6uoq89xVHD+u+g0CIooS/3fEkV/9lAS8sWTOs8wzDMAzDGB1ER/6xrTGay/q7AM/lnj8PHJU/QER2Ac4FTgGOGMW5rJdv33grkqRRzYQ0zElj9DPZgJLPBGxm7ZRFRpvErK5H2S5cvpqnl6zi0eeWEyWNJfyuQ5i/q1hg58njKFcjDps3i/effQy7TJ2w/htOeXLhcj7y2f8jihOcU5xzvPLUg/jou0+x/FPDMAzDMLYIoylO26mbZoX1DeCTqpqsTwylnQzOB5g9u32hz+awsn/QTzbTgwk+gJp2fsqinhLTaCmVkUZKw6R+vmSRUckdQ/0bjNL6As2ZNomlq/sIg5w1VdoeVfGV/c0adUx3iV//29s2utJeVfnEFy9jbd9Qw/bf/WUBhx84m5cctedGjWcYhmEYxghgOaejuqz/PLBr7vksYFHTMfOBi0XkGeC1wPdE5FXNA6nqhao6X1XnT5s2bcQnusfUyXUz+7jeQjRwdZEZVBqjoQ2PtOtThuSX7DtU3oVNYry7WOAjZx7H2O4SQwNRPfoaAwmUiiHHHTCXrmKBsd0leruKTJ0whu9/9DWbZAH16FNL6esvt2wvVyKu+MN9Gz2eYRiGYRibSa6F+Ug+tjVGU5zeAewpInNFpAScB1yVP0BV56rqbqq6G3AJ8HeqesUozqktX3zFaQ397KXixWg4BGEZgsG64BTSYqZMyEYQxk1iNCN/XFyPvHYXC3z87JcwbfwYAhF2mzaJr779FRy152w+/t3f1FIBatdzUFRh6fNriNdFaDnh1IP25Hf/+W723GXqJt1zNUo6Lt2XK9EmjWkYhmEYhrG5jNqyvqrGIvIhfBV+CFykqg+KyPvT/ReM1rU3lkm9PRQQElUfBW1qPdpOw9Xaj3boc9+wtJ/bLQl88IxjeMuJh/GWEw+r7esfrPCGz/2U5WsH2s6xOhTz9LpVCFAZivnLrY/SUwj55NtP3ci79eyzx/S2eRfdXQVOO2HfTRrTMAzDMIzNxJb1RzVyiqperap7qeoeqvqf6bYL2glTVX3HlvI4fWDRUh8xTeo5oQ3CrUP2rCTeWkqqQAxBGYKhuk1U82lZJPT0g/duGe6LP/8zzy5Z3fFD2VwQVa7G/ObGB+kbaF2aHw6lYoF/+ciZdJUKtbSAnu4i8+bsxCteesAmjWkYhmEYhrG5WIco4LYnniVJdMOdnnIV/EHOVkpJl+3T3Vlr03YDisCqvkF2nlTvAhXHCdfd9Tixay9MG4q1chQLIYtX9jFuTPeGZt6WE46cx8++/g5+e+0DrFozwNGHzeWEI/e0NqaGYRiGsaWwwKmJU4BrH35yeAfmbKXyfqf5qvwGD/82AlUV5u48uWFb7BxOte5cpdqQS9DbVaIcVWn+xEZxwsyp44c39w7ssvNE3vfmEzZrDMMwDMMwRgaxZf3RXdbfVqjGyfoPyHmY1oqcOh3bvKM5J9X5gqg83aUie86a1nh6+uHcc5cpfPejr6a7GDadU+Dckw5ibG/X+uduGIZhGIaxDWHiFDhp77ntdzTZRan6HNMg7f7U8Zx2AjV9dApV/8s7XkZvd5FiIUDwAnbSmC6+/uFXceC8GXznk69l/913JgyESeN6eNc5R/MPbzpp427UMAzDMIytG9WRf2xj2LI+MDhQrYvNfFeoKLV/SjeF+e6hjYHMjtTahimEAiccsHvNwklVue+xRTz6zFJmTJ3AxZ97O1fe+ABPvLCCA3efwbkvOZAJY3sAOGjeTH782Tdtxl0ahmEYhmFs/Zg4Bf768NNIBARpe9HMBDfnN6ppQVKt4ZOjMe6cdZLC/yTniwp+GX7CmG7+/uzjeer5FUyeMIZ//sZVPPrMMmLnKIYBY3u7+eFnz2P6lHEYhmEYhrGDkWqJHR0TpwBSF5WdcknzBVDZc01TVcO4HmHNo2mb01Ix5NNvOIV7FzzP2/715xTCkEolQhSStEI/ihLK1ZjPff/3fP9fXj9y92YYhmEYhrENYeIUePlBe3Hp7QsaN2ZFUEl9pb85nVSAohM0bs3nUHz0tbunwKfOO4Wnn1nBn257lGqUUI2StkLYOeWBxxfRP1hpKHTqH6jwo/+9kT/f8AiKcuoJ+/KeNx3PuLGbZiFlGIZhGMbWh6BWrY8VRAHwD2cc31j8lH4uwjIE+fajzZ8XBY06e5P2dhX59ofO5cyj9uXSv9xHpdomvNqGxNVj+kni+OCn/5er/nA/a/uGWNdX5jd/vJ+/+9T/EicW+zcMwzCM7QoriLLIKcDEMT2MC4oMVKNazmlQqbcxBW876pryTKW5fWkT+82ZzuF7zmKoEhFFrXZV7SKxe+w6tVYEBXDbPU+zeOlaopzdVRQnLFu+jlvveorjj5zHQw+/wF+uexiAU07el/323WVjbt8wDMMwDGOrwcRpyt67TOOepxb53NJqYxtTb4zvxaomoGmlfhDTsRMUqowtlQBfDDV9yngWLV3jfVLJ8lGFQASnSnepQLEQ8m/vO71hmCeeXkal0hpxHSpHPPH0Mh687zkuv+puqlUvXn/7+/s495zDed97Ttrcl8QwDMMwjBebbTDSOdLYsn7KO06Z783xtbFKn/SnpPmnQZLmoabV/CRtQuaqkMAdCxaiqogIR+87m7Dii6eCGMIqFGLHmcfswzH7zubNpx/OZV9/N3vsOrVhqJnTJ9LV1fodoru7SCEIuPyqu6mUIzRxqHNUKjGXX3kXTz+zfDReJsMwDMMwjFHFxGnKifvvzquPOYCwY7k+NbUapCIVp15sRv53VMEpUoXQQbUakzjl7gef46o/3N8wjKgiFfjL9Y/w4IIXuPjSO/j6hdfiXKPQPeHoPenpKREE9YkFgdDTXSQeiomGIiRRxKn/GTuiKOGWW4fZktUwDMMwjK2DzEpqpB/bGCZOU0SET776ZCYWunPG+a3JxLVoqqbFUkDgoFCFQsX/DNJc1N1mTiYMhE999crW/NI0hTSKEobKEdVqzI23Pc5v/3x/w7y6SgUu+NKbOXi/WYRhQBgGHLTvLL7/5TfzxGOLvRimKdKbOEqlYXYJMAzDMAxjq0FUR/yxrWE5p03EcfoVI1YfHU1RUV8sJYLgvUtd1Ln6vlgM+cRbT+GBRxcxMFCmQZqmH5TmIG25EnPZ1fdy9ssObtg+Y/oEvvUf51GuRKgqPd0+l/Xxx5a0XFcATZRjjp43vBs2DMMwDMPYirDIaRMH7z6zJkzzEcnApWb7idLbVeQT553MEfvObjtGAHz6rS/lp7+8hQ995mJIzxtOkvPiZWs67uvuKtaEKUC5HLW/fiBMGN/Tdp9hGIZhGFsxZiVl4rSZfzrvZIJ2naJSlSoJvPcVR/HqlxzIgbvPaLWSUoVE+c6PruOeBc/h8h8KB7q+D4kq5b4K73n/RXz169fwwgur1zvXww+f25CLmrHLLpMYawb9hmEYhmFsg9iyfhO77jSRQiC1tqLtePK5FVSqERf/5k4k0Zq1FOA7P1WVvqTScE7Nkgq8aar44qnac1XvFFBRnnxqOU8/s4K/XPcQ3/jam9hrz50BL2z/9tdHueLSOxjsr3DokXPp7e2ikvqohmFAsRjysX88c8ReD8MwDMMwXiy2zUjnSGPitA1jekqsG6h03L9yzQDX3vwYUZwgjoboaWY71Q4Bxo/p5uzTDmLZorVcd8MjOFVU1EdrY61FbF3iGBqq8q3v/onvfOOtAPzgO9fyuyvvri3nP/vsSnbaaTzHvvIQHnl4MbPnTOE1rzmCWbMmb/ZrYBiGYRjGi4xi4hQTp2057Yi9ufS6+xvX9tMKfAEO2WsXHn16KUmsEHRIAehgzv/a0w/l3W88judeWMX1f3mIsE2XKYmdz1EFHrnnOa647E6OPW5Prrr8TqJqvUqrWolZsbyPWTMm8d73nry5t20YhmEYhrHFsZzTNpx5zL6Q+phm32KkqhQHlXBIIVLmzppCT1fR+4dp/qE1m6gG0al+9X7+wXMA2GnqOCRTrzkRmwnTvDXUj35wHT//6U0UC632UOVyxO03PzFyN28YhmEYxpbDfE4tctqOQIQARR2gSqHst2eC8X8uu41jDplLMhgRJg4XgIaCOAgrzqeMFCXNJaV28sxpEzgo7XtfLBboKhUoVxor7jNhmqdcjvjbjY+21F4BhKEwdadxI3fzhmEYhmEYWxCLnLZhrzk70R0WCSoQpqmnecEYx44b73ySJHHeZipRCmVHYcgROG/CH0Te+DYAuksF9pq7E9/6whsQ8SMFgXD2WYe0bU3ajnXrhhgzplQ7P6NQCDnrVYdv/k0bhmEYhrHFMRN+E6dtKRULnP+aYwjwxU1tO5pqfUeQeIspye2TRJGKo0sC/uHdp3DR197G9GnjG4Z477tO5KUn7UepFNI7pkSpq8DYsV1t5yRA1F9l+vTxdHUX6e0tMWZsF5/8t3OYs9vU2nHlwSoDfeXNfAUMwzAMwzC2DLas34Hzzjyc/7n8dtatK3eqbapX1eV9UZ36Cv70nLg/YuHClW2vUSiEfOJjZ/C+95zEsuV9zJwxgXvueoYvfuFKKpVc9ylVtOpYNxQTBMK3L3wH1WrMHvOmU0jzUFct7+Nrn76E+297CkWZM286//jF17L7PjNG5PUwDMMwDONFYBuMdI40FjntQBgE/Mc/vJJSKWwVppknKTQmGqsXps297q+66m7612NNNdhfIS5HBEHAcSfszb/++6sZN7YrFb6KVB1B6olarcSsXtHP3vvMrAlT5xyfeNuF3Hfrk8RxQhI7nnpkMf/0th+ydvXAiLwehmEYhmGMMoovxh7pxzaGidP1MP+A2fz62+9h/z3T6GPWBswp4WBCUM4sn9I3vkNFXFRN+P0197VsX7d2kI+978e857zv8c8f+TmvO/2rXP5/t3H0MfM49IDZBOWEoJIg+Q+WwEB/o9C979anWL2inyRpnEAUxfzx8rs29fYNwzAMwzBedEycboBpk8fy5U++ii68SX5QTigMOoI0ciouNd3X1ir7DOccF3zvWu664+mG7f/+z//HIw++QLUaMzhQoVKOuOh7f+HO257k+JP3obun2DJWHDkOOmxOw7YlL6zGOeeFc5xAFEEUUx2q8vzTy0fkdTAMwzAMY7TReiBsJB/bGCZOh8GkCb28980n0F0KCeLG/FMBJPLL/NpJnQKaKP/66V8zNFgFYOniNTz60CLiuDHaWSlHXPqLW3jJqfsxb68ZNYEqAl3dRd7+/pOYMLG34Zw99p2BOoUoBufqubBxwvLnVo3AK2AYhmEYhvHiYAVRw+SNrzqSnSaN44tf+W3L8rloKlAFam77meVTljeqft9ttz7BSafsx9o1gxQKIdV84VPKyhV9FAohX/n+W7n+Tw9y47UPMWZsN6949eHsf9CuLcfvdcAsps+YwHN9Qy37Ftz+FMsXrWHazImb/RoYhmEYhjHKbIORzpHGxOlGMG/uThAlBLH/4Ghqvo9kS/v4CKaQbsDni6afMxGhmrYfnTN3ml+Kb6JQDDni2D3974WQU884iFPPOGiDc9t1zlSee2xJ2/EeuucZTpx5yMbfsGEYhmEYLy4mTm1Zf7gMDVX55Cd+2dBaVBwEsSLFgFKpQBAIIkLgvM9pkPiIaapTUVWOOHIu4Jfoz//waXR11/NKC8WQceO6ee2bjtno+e20y0TCsP3bOXHy2I0ezzAMwzAMY0tgkdNh8uc/P0hfX7nhC43gv+CEsWP+EbsxdkIvjz22hOk7jefRh15g7dohMpPUrmLIe953CpNyQvGsV89nl9lTuOQXt7ByeR/zj96D17zpGESVK392E4P9ZQ47bi/2Prh1Kb+ZM847mqsvvq0h5UBEGDu+hwOP2n0EXwnDMAzDMEaFzEpqB8fE6TB58MHnKZejlu2iiisn3H7dowjQ1V3g2EN34/OffzUP3P8cN/71Ubq7i5z28gPZfY+dWs4/dP5cDp0/t/b8zhse5T8+9D+oKlGU8KsfXMexpx3Ax7/yeoKgc6B79rzp/NN/vZGv//P/4RLFOcdOMyfxuR+8c73nGYZhGIZhbE2YOB0ms3edQqkU1nJGazgliHy0UoHyUMSVv76dvnVD/MOnX8lhh89tHawD1UrE//fRX1DJieDKUMQtf36QW699mGNP23+95x/3sgM56uT9ePLhRfT0lth1j50QWY+FgGEYhmEYWxEK2sE0fQfCQmrD5PQzDq51ZKqhikStH6JKJeaPv7uPz330F1z0zT+ydNGaYV1jwZ1Pt91eHqzy52Ga6ReKIXsftCuz5003YWoYhmEY2xrmc2ridLhMnjyGr/3Xm5k1a7LfkL7Z0sF8P0kct1z/CJf9z82879Xf4oG7ngFgxdK1fP6DP+M1h3+W1x/573zvC1cy0F/e4PVNZxqGYRiGsSNgy/obwV577cxPf/Y+7r1nIRf96Hoee3gxBIJL2nwrUW8hFUcJcZTw/z5zKW94x3F893NXNHyJ+f3Ft7Hgjqf5zhUf4YAjdoc2Ure7p8Sp5x4+ejdmGIZhGMaWxwqiAIucbhKHHDqHd7/zJZSGYu952hwyV4XYNcjM1Sv6uOA/f9NyaBw7Fj+7krtufJxSqcBnvv1munqKdHUXCcOAru4ix59+IEe/dL9Rvy/DMAzDMIwtjUVONwHnHF/85K/rhUsCFAIIxNtLRQ5p6iIVxw6tJm1TACqViKcfXcwRJ+7NYcftxU+v+xQ3XnM/g31lDjt+L+btv8to35JhGIZhGFsD22CO6Ehj4nQTePrxZQ0V9aJAWhg1dadx9K8ZpJITp0Eo9HSXGByqth2vWCwyY9fJtecTJo/hrE0w4jcMwzAMw9jWsWX9TaBQCNAO32ym7DSeY07el2KpQE9viZ7eEjN3ncK5bzqKUlex7Tljxnfbsr1hGIZhGFatj0VON4nZu09j0pSxLH5+dcP27p4iBx4ym5t/ew/aP0Q1EA48Ynf+9dtvIQgCrv/NfSxbtIaoGtfO2XnXyXz5Z+dTLBVQVR66/Ulu+8N9dI/p4uTXHMWM3aa92LdnGIZhGMYWYdsUkyONdIoAbq3Mnz9f77zzzi09DZ55YimfeM+PieKEJHJIIBx02BweuOFhqkP1Jf9iqcABR8zliz89n4H+Mr/731u56U8LGDu+h1e+6ZhaxFRV+a8P/4Qbr7qL8lCVQiEkCAM+/NU3c9p5x26p2zQMwzCMHQIRuUtV52/JOUwo7aTHTnvDiI97zaLvbPF72xgscrqJ7DZvOr/4wz9y2w2PsXpVPwccOocrL7qBuBI3HBdVYx6882kWLVzBzDlTef35J/H6809qGe/u6x/ixqvupjzo81LjKIEo4dsf/wVHn34w4yaOWe98VJVVS9ZQ6i4ybtLYEbtPwzAMwzBeJBRw1iHKxOlmUOoqckKupehzTy3DtfEnK5QKLFu0hplzpnYc66+X30F5sNKyPSyE3H3dQ5x47hEdz33olkf5ytu/w7LnVqKqHHD8Pnzq5x9h8s6TNvKODMMwDMMwtixWEDWC7Hf4bhRLYcv2qBIzZ8/p6z03LIRt240KEBZbx8xY/vxKPvmyL/DCE0uIKhFxNeaBGx7i46d8rmPRlmEYhmEYWylWEGXidCQ59x0n0NVdIgjqIrOrp8jLXnsEk6aOW++5p513LKXu1mp+55T5J+/f5gzP7374Z5I4adiWxI4Vz69iwd8e2cg7MAzDMAxji7IFxKmIXCQiy0RkQYf9bxaR+9PHzSJy8Ijfdw4TpyPIlOkT+NYVf89xpx/IuAm97LzrZN75iTP5u8+9aoPn7nfkHrz6A6dS6i5S6i7S3Vuiq6fEZy46Hwng3usW8NAtj+KaclFeeGwRUVOea8bShctH4rYMwzAMw9i++Qlw+nr2Pw2cqKoHAV8ALhzNyVjO6QgzY/YUPv2ttzZsKw9WuOV397BqyRr2PWoe+x6xR9sl/Ld/+lWc9sZjufPaBXT3dnHMmYdw9x/v43XT34MEgqrSM7ab//ztp5l36FwADnrJftz6m7ta8lWTxLHX/D1G70YNwzAMwxhhFNrUroz6VVVvEJHd1rP/5tzTW4FZozkfE6ejzDMPPc8nzvwycRQTVWIKxQIHHLcXn/vlhykUW1/+mXN34uz3nALA848t4v+987tUcp2lhvrK/NNpn+dXiy6kWCpy6ltfwsVfvoJ4cewr/IGu3hJHnnkYs/extqeGYRiGYYwo7wZ+P5oXsGX9UURV+cJbv0vf6gGG+ivEUUJ5sMIDf3uU3110/QbPv+aivxA35ZMCJFHCHdfcC0DP2B6+e8eXOPO9pzJ5xiRm7jGdd3z+PD7zvx8d2ZsxDMMwDGN0UVB1I/4AporInbnH+ZsyPRE5GS9OPzmSt92MRU5HkcVPL2fFC6tbtleGqlzzsxs4532nrvf8tcvXkUSt4tSp0reqv/Z84rQJfPg77+HD33nP5k/aMAzDMIwtx+gs66/YXBN+ETkI+BFwhqquHJlptccip6OIc857QbXbl2z4w3fkKw6ne2x3y/YkTjjk5AM2d3qGYRiGYRgbRERmA5cBb1XVx0b7ehY5HUV22WM6E6aMY9lg4xeMrp4ip75xwy1Jjz17PnseOpfH7nqKSlrw1D2mi7Pe/zKmz5k2KnM2DMMwDGMLsgV8SUXkl8BJ+OX/54HPAkU/Hb0A+DdgCvC9tKA7Hs12qCZORxER4TM//QD/fM5XcbGjMlSle0wXc/eftcElffDG/F/+079y7c9v5C+//BvdY7p4xfmnceQZh74IszcMwzAMY0dAVd+4gf3vAV603EETp6PM3ofvzs/u/wrXXXobKxevYf+j92T+qQcQBMPLqCiWipz+rlM4/V2njPJMDcMwDMPYoqhCk5/5joiJ0xeBcZPHcvZ7X7qlp2EYhmEYhrHVY+LUMAzDMAxja2EL5JxubZg4NQzDMAzD2EpQW9Y3KynDMAzDMAxj68Eip4ZhGIZhGFsFasv6WOTUMAzDMAzD2IqwyKlhGIZhGMbWgDJa7Uu3KUycGoZhGIZhbC2oFUTZsr5hGIZhGIax1WCRU8MwDMMwjK0ABdSW9S1yahiGYRiGYWw9WOTUMAzDMAxja0DVck4xcWoYhmEYhrHVYMv6tqxvGIZhGIZhbEVY5NQwDMMwDGNrwZb1LXJqGIZhGIZhbD2IbmM9XEVkObBwS89jO2cqsGJLT8IYcex93f6w93T7xN7XLcMcVZ22JScgItfg3/+RZoWqnj4K444K25w4NUYfEblTVedv6XkYI4u9r9sf9p5un9j7auzo2LK+YRiGYRiGsdVg4tQwDMMwDMPYajBxarTjwi09AWNUsPd1+8Pe0+0Te1+NHRrLOTUMwzAMwzC2GixyahiGYRiGYWw1mDjdgRCR00XkURF5QkT+uc3+c0TkfhG5V0TuFJHjh3uuseXYzPf1GRF5INv34s7cWB/D/ZsTkSNEJBGR127sucaLz2a+r/b3auwQ2LL+DoKIhMBjwGnA88AdwBtV9aHcMWOBAVVVETkI+D9V3Wc45xpbhs15X9N9zwDzVdU8Fbcihvs3lx73J6AMXKSql9jf69bL5ryv6fZnsL9XYwfAIqc7DkcCT6jqU6paBS4GzskfoKr9Wv+2MgbQ4Z5rbDE25301tl6G+zf3YeBSYNkmnGu8+GzO+2oYOwwmTnccdgGeyz1/Pt3WgIicKyKPAL8D3rUx5xpbhM15X8EL1T+KyF0icv6oztTYGDb4vorILsC5wAUbe66xxdic9xXs79XYQTBxuuMgbba1RNBU9fJ0yfdVwBc25lxji7A57yvAcap6GHAG8EERecmozNLYWIbzvn4D+KSqJptwrrFl2Jz3Fezv1dhBKGzpCRgvGs8Du+aezwIWdTpYVW8QkT1EZOrGnmu8qGzy+6qqK1R1Ubp9mYhcjl92vGFUZ2wMh+G8r/OBi0UEfC/uM0UkHua5xpZhk99XVb3C/l6NHQWLnO443AHsKSJzRaQEnAdclT9AROZJ+n9EETkMKAErh3OuscXY5PdVRMaIyLh0+xjgZcCCF3X2Ric2+L6q6lxV3U1VdwMuAf5OVa8YzrnGFmOT31f7ezV2JCxyuoOgqrGIfAj4AxDiK0AfFJH3p/svAF4DvE1EImAIeENaSNP23C1yI0YDm/O+ish04PJUtxaA/1XVa7bIjRgNDPN93ahzX4x5G+tnc95XwP5ejR0Gs5IyDMMwDMMwthpsWd8wDMMwDMPYajBxahiGYRiGYWw1mDg1DMMwDMMwthpMnBqGYRiGYRhbDSZODcMwDMMwjK0GE6eGYWxxROR6EXl507aPisj3Ohz/TNogYn1jfrrp+c3pz91EZEH6+3wR+Vb6+0kicuzm3IdhGIax+Zg4NQxja+CXeEPyPOel2zeVBnGqqi3CU1XvVNWPpE9PAkycGoZhbGFMnBqGsTVwCXCWiHSBj24CM4FZIvKAiCwQkS+3O1FErhCRu0TkQRE5P932JaBHRO4VkV+k2/rbnHuSiPw2vd77gX9IzzlBRJ4WkWJ63Pg0Wlsc+Vs3DMMw8pg4NQxji6OqK4HbgdPTTefhu+h8GTgFOAQ4QkRe1eb0d6nq4fie5B8RkSmq+s/AkKoeoqpvHsb1nwEuAL6ennMjcD3witx8LlXVaNPu0DAMwxguJk4Nw9hayC/tnwc8D1yvqstVNQZ+AbykzXkfEZH7gFuBXYE9R2g+PwLemf7+TuDHIzSuYRiGsR5MnBqGsbVwBfBSETkM6AHu29AJInIScCpwjKoeDNwDdI/EZFT1JmA3ETkRCFV1wUiMaxiGYawfE6eGYWwVqGo/fin9InwU9TbgRBGZKiIh8Ebgr02nTQBWq+qgiOwDHJ3bF21kjmgfMK5p28/SuVjU1DAM40XCxKlhGFsTvwQOBi5W1cXAp4Dr8FHUu1X1yqbjrwEKInI/8AX80n7GhcD9WUHUMPgNcG5WEJVu+wUwic1zDTAMwzA2AlHVLT0HwzCMrRIReS1wjqq+dUvPxTAMY0ehsKUnYBiGsTUiIt8GzgDO3NJzMQzD2JGwyKlhGIZhGIax1WA5p4ZhGIZhGMZWg4lTwzAMwzAMY6vBxKlhGIZhGIax1WDi1DAMwzAMw9hqMHFqGIZhGIZhbDWYODUMwzAMwzC2Gv5/lF7ow9EzyM0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Identify the optimal portfolio in the returns and volatility arrays\n",
    "max_sharpe_return = returns_array[index_max_sharpe]\n",
    "max_sharpe_volatility = volatility_array[index_max_sharpe]\n",
    "\n",
    "# Visualize volatility vs returns for each scenario\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "plt.scatter(volatility_array, returns_array, c=sharpe_array, cmap='viridis')\n",
    "\n",
    "plt.colorbar(label='Sharpe Ratio')\n",
    "plt.xlabel('Volatility')\n",
    "plt.ylabel('Return')\n",
    "\n",
    "# Add the optimal portfolio to the visual\n",
    "plt.scatter(max_sharpe_volatility, max_sharpe_return, c='orange', edgecolors='black');"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "e79bf233",
    "f5ac9439",
    "de803160",
    "ce3c7f1a",
    "47e6455a",
    "a26b16aa"
   ],
   "name": "5 - Case Study - Portfolio Optimization.ipynb",
   "provenance": []
  },
  "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}