lab 1 assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Basic Descriptive Analytics with Python\r\n",
    "This notbook is a submission for CSDA1010 - Basic Methods of Data Analytics course asignement individual asignement one."
   ],
   "metadata": {
    "tags": []
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "source": [
    "# import the necessary packages to manipulalate data\r\n",
    "import os\r\n",
    "import numpy as np\r\n",
    "import pandas as pd\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import seaborn as sns\r\n",
    "%matplotlib inline"
   ],
   "outputs": [],
   "metadata": {
    "tags": []
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "source": [
    "#set working directory as the current working directory\r\n",
    "os.chdir(os.getcwd()) "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "source": [
    "# import data as a pandas dataframe\r\n",
    "# IBMStock.csv, GEStock.csv, ProcterGambleStock.csv, CocaColaStock.csv, BoeingStock.csv\r\n",
    "\r\n",
    "IBM = pd.read_csv('data/IBMStock.csv')\r\n",
    "GE = pd.read_csv('data/GEStock.csv')\r\n",
    "ProcterGamble = pd.read_csv('data/ProcterGambleStock.csv')\r\n",
    "CocaCola = pd.read_csv('data/CocaColaStock.csv')\r\n",
    "Boeing = pd.read_csv('data/BoeingStock.csv')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "source": [
    "GE.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     Date  StockPrice\n",
       "0  1/1/70   74.253333\n",
       "1  2/1/70   69.976842\n",
       "2  3/1/70   72.158571\n",
       "3  4/1/70   74.252727\n",
       "4  5/1/70   66.665238"
      ],
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>StockPrice</th>\n",
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     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Change datatypes for all stock dataframes\r\n",
    "- Change Date datatype as a native datetime rather than the default object "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "source": [
    "IBM['Date'] = pd.to_datetime(IBM['Date'], format='%m/%d/%y')\r\n",
    "GE['Date'] = pd.to_datetime(GE['Date'], format='%m/%d/%y')\r\n",
    "ProcterGamble['Date'] = pd.to_datetime(ProcterGamble['Date'], format='%m/%d/%y')\r\n",
    "CocaCola['Date'] = pd.to_datetime(CocaCola['Date'], format='%m/%d/%y')\r\n",
    "Boeing['Date'] = pd.to_datetime(Boeing['Date'], format='%m/%d/%y')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "source": [
    "# Check the data after data type adjstement\r\n",
    "IBM.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        Date  StockPrice\n",
       "0 1970-01-01  360.319048\n",
       "1 1970-02-01  346.723684\n",
       "2 1970-03-01  327.345714\n",
       "3 1970-04-01  319.852727\n",
       "4 1970-05-01  270.375238"
      ],
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       "      <th>3</th>\n",
       "      <td>1970-04-01</td>\n",
       "      <td>319.852727</td>\n",
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       "      <th>4</th>\n",
       "      <td>1970-05-01</td>\n",
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     "execution_count": 37
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  {
   "cell_type": "code",
   "execution_count": 38,
   "source": [
    "GE.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        Date  StockPrice\n",
       "0 1970-01-01   74.253333\n",
       "1 1970-02-01   69.976842\n",
       "2 1970-03-01   72.158571\n",
       "3 1970-04-01   74.252727\n",
       "4 1970-05-01   66.665238"
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     "metadata": {},
     "execution_count": 38
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  {
   "cell_type": "code",
   "execution_count": 39,
   "source": [
    "ProcterGamble.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        Date  StockPrice\n",
       "0 1970-01-01  111.874286\n",
       "1 1970-02-01  111.453684\n",
       "2 1970-03-01  108.451429\n",
       "3 1970-04-01  106.288636\n",
       "4 1970-05-01   73.332857"
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       "      <td>1970-04-01</td>\n",
       "      <td>106.288636</td>\n",
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     "metadata": {},
     "execution_count": 39
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  {
   "cell_type": "code",
   "execution_count": 40,
   "source": [
    "CocaCola.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        Date  StockPrice\n",
       "0 1970-01-01   83.368095\n",
       "1 1970-02-01   81.591053\n",
       "2 1970-03-01   81.338095\n",
       "3 1970-04-01   76.805909\n",
       "4 1970-05-01   69.278571"
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       "      <td>1970-05-01</td>\n",
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     "metadata": {},
     "execution_count": 40
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  {
   "cell_type": "code",
   "execution_count": 41,
   "source": [
    "Boeing.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        Date  StockPrice\n",
       "0 1970-01-01   27.853810\n",
       "1 1970-02-01   22.381053\n",
       "2 1970-03-01   23.105238\n",
       "3 1970-04-01   21.571364\n",
       "4 1970-05-01   18.932857"
      ],
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       "      <th></th>\n",
       "      <th>Date</th>\n",
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       "      <th>1</th>\n",
       "      <td>1970-02-01</td>\n",
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       "      <td>1970-03-01</td>\n",
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       "      <th>3</th>\n",
       "      <td>1970-04-01</td>\n",
       "      <td>21.571364</td>\n",
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       "      <td>1970-05-01</td>\n",
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      ]
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Warm-up/Basic statistics Questions\r\n",
    "#### How many rows of data are in each dataset?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "source": [
    "print(\"There are {} rows and {} columns in IBMStock dataset\". format(IBM.shape[0], IBM.shape[1]))\r\n",
    "print(\"There are {} rows and {} columns in GEStock dataset\". format(GE.shape[0], GE.shape[1]))\r\n",
    "print(\"There are {} rows and {} columns in ProcterGambleStock dataset\". format(ProcterGamble.shape[0], ProcterGamble.shape[1]))\r\n",
    "print(\"There are {} rows and {} columns in CocaColaStock dataset\". format(CocaCola.shape[0], CocaCola.shape[1]))\r\n",
    "print(\"There are {} rows and {} columns in BoeingStock dataset\". format(Boeing.shape[0], Boeing.shape[1]))\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "There are 480 rows and 2 columns in IBMStock dataset\n",
      "There are 480 rows and 2 columns in GEStock dataset\n",
      "There are 480 rows and 2 columns in ProcterGambleStock dataset\n",
      "There are 480 rows and 2 columns in CocaColaStock dataset\n",
      "There are 480 rows and 2 columns in BoeingStock dataset\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### What is the earliest/latest year in our datasets?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "source": [
    "# Finding the earliest and latest year in our dataset\r\n",
    "print(\"The  earliest year is IBMStock dataset {}\".format(IBM['Date'].dt.year.min()))\r\n",
    "print(\"The  latest year is IBMStock dataset {}\".format(IBM['Date'].dt.year.max()))\r\n",
    "print(\"\\n\")\r\n",
    "print(\"The  earliest year is GEStock dataset {}\".format(GE['Date'].dt.year.min()))\r\n",
    "print(\"The  latest year is GEStock  dataset {}\".format(IBM['Date'].dt.year.max()))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The  earliest year is IBMStock dataset 1970\n",
      "The  latest year is IBMStock dataset 2009\n",
      "\n",
      "\n",
      "The  earliest year is GEStock dataset 1970\n",
      "The  latest year is GEStock  dataset 2009\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### For the period above what is the average stock price of Coca Cola?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "# The avaregae stock price for Coca Cola from Year 1970 to 2009\r\n",
    "print(\"The average Coca Cola is {}\".format(CocaCola.StockPrice.mean()))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The average Coca Cola is 60.02972973327079\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### What is the maximum price of IBM during this period?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "source": [
    "print(\"The maximum price of IBM is {}\".format(IBM.StockPrice.max()))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The maximum price of IBM is 438.9015789\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### What is the standard deviation of P&G stock price over this period?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "source": [
    "print(\"The standard deviation of P&G stock is {}\".format(ProcterGamble.StockPrice.std()))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The standard deviation of P&G stock is 18.19414030797156\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### What is the median price of Boeing in the last 5 years for which we have data?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "source": [
    "# To get the last five years, subtract 5 from the max year\r\n",
    "\r\n",
    "last_five_year_data = Boeing[Boeing.Date.dt.year > Boeing.Date.dt.year.max() - 5]\r\n",
    "\r\n",
    "print(\"The median price of Boeing Stock price in the last 5 years is {}\".format(last_five_year_data.StockPrice.median()))\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The median price of Boeing Stock price in the last 5 years is 69.675666665\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Basic Plotting Questions"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Part 1:\r\n",
    "Plot the StockPrice of Coca-Cola on the Y-axis across Date on the x-axis using the basic plot() fuction. What do you see when you use the default plot function what do you see? Scatter-plot, eh?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "source": [
    "plt.xlabel('Date')\r\n",
    "plt.ylabel(\"Stock Price\")\r\n",
    "plt.title(\"Stock to Year plot\")\r\n",
    "plt.plot(CocaCola.Date, CocaCola.StockPrice)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19033002c40>]"
      ]
     },
     "metadata": {},
     "execution_count": 48
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Based on the above line plot, we can answer the following two questions\r\n",
    "\r\n",
    "#### Identify the year during which Coca-Cola had the highest/lowest stock price?\r\n",
    "\r\n",
    "    **Answer** - Coca-Cola Highest/Lowest stock price = **1973/1980**\r\n",
    "    \r\n",
    "\r\n",
    "#### What calendar year did it look to have the biggest (Year-over-Year) percentage increase?\r\n",
    "\r\n",
    "    **Answer** - Biggest Year over Year percentage increase = **1971 through 1973**\r\n",
    "\r\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "source": [
    "# We can also draw a scatter draw to see which plot represent our data more\r\n",
    "plt.xlabel('Date')\r\n",
    "plt.ylabel(\"Stock Price\")\r\n",
    "plt.title(\"Square Roots\")\r\n",
    "plt.scatter(CocaCola.Date, CocaCola.StockPrice)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x190330bfc10>"
      ]
     },
     "metadata": {},
     "execution_count": 49
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 2. Basic Plotting Questions\\\r\n",
    "Part two:\r\n",
    "Next we want to add P&G stock price onto the same graph.\r\n",
    "Go back to the plot function and add argument col=”red” and col=”blue” argument for CocaCola and P&G respectively.\r\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "source": [
    "plt.xlabel('Date')\r\n",
    "plt.ylabel(\"Stock Price\")\r\n",
    "plt.title(\"Stock to Year plot of Cocacola and ProcterGamble Stocks\")\r\n",
    "plt.plot(CocaCola.Date, CocaCola.StockPrice, 'red', label='CocaCola' )\r\n",
    "plt.plot(ProcterGamble.Date, ProcterGamble.StockPrice, 'blue', label='ProcterGamble' )\r\n",
    "plt.legend(loc='upper right')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19033148130>"
      ]
     },
     "metadata": {},
     "execution_count": 50
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Based on the above line plot of Cocacola and P&G stock, we can answer the following two questions\r\n",
    "\r\n",
    "#### In March of 2000 the stock market plummeted as the tech bubble burst. Using the plot above, which company’s stock dropped more (relatively – i.e. percentage-wise)?\r\n",
    "\r\n",
    "    **Answer** -  **ProcterGamble** droped more.\r\n",
    "    \r\n",
    "\r\n",
    "#### In the year 1983 which company stock was going up? Which was going down?\r\n",
    "\r\n",
    "    **Answer** - **Cocacola** was going **up** and P&G was going down in 1983\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "#### Across the entire time period shown in your plot which stock had a generally lower price?\r\n",
    "\r\n",
    "    **Answer** - **Cocacola** stock had a generally low price\r\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Data Visualization from 1995-2005\r\n",
    "\r\n",
    "Instead of looking at the plot across the entire date range, we want to see what’s happening between 1995-2005. Remember, you can use the matrix notation [rows, columns] to subset data."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "First stock price of the year 1995 sits in row position: **300**"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "source": [
    "IBM.loc[IBM.Date.dt.year == 1995].head(1)\r\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          Date  StockPrice\n",
       "300 1995-01-01   74.849048"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>StockPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>74.849048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 51
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Last stock price of the year 2005 sits in row position: **431**"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "source": [
    "IBM.loc[IBM.Date == \"2005-12-01\"].tail()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          Date  StockPrice\n",
       "431 2005-12-01   85.137619"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>StockPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>2005-12-01</td>\n",
       "      <td>85.137619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 52
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Answer the following questions:\r\n",
    "#### Which stock price fell the most right after the tech bubble of March 2000?\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "**Answer:** ProcterGamble fell the most after the tech bubble of March 2000"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "source": [
    "# To Answer the question first we need to plot all stock on a line plot\r\n",
    "\r\n",
    "sns.lineplot(data=IBM, x=IBM.Date, y=IBM.StockPrice, label = \"IBM\", color=\"green\")\r\n",
    "sns.lineplot(data=GE, x=GE.Date, y=GE.StockPrice, label = \"GEStock\", color=\"purple\")\r\n",
    "sns.lineplot(data=ProcterGamble, x=ProcterGamble.Date, y=ProcterGamble.StockPrice, label = \"ProcterGamble\", color=\"blue\")\r\n",
    "sns.lineplot(data=CocaCola, x=CocaCola.Date, y=CocaCola.StockPrice, label = \"CocaCola\", color=\"black\")\r\n",
    "sns.lineplot(data=Boeing, x=Boeing.Date, y=Boeing.StockPrice, label = \"Boeing\", color=\"red\")\r\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date', ylabel='StockPrice'>"
      ]
     },
     "metadata": {},
     "execution_count": 53
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
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w2NjYkJGhxowb+3rjtogwYsQIPvnkkzzf081SIjWFSzGXGPr3UPZf339L50cmR1LGOW/TEejmIx2du0GnTp1YsGABAGfOnOHKlSvUrl2bnj17MmvWLPOgayqBnbV8dps2bdi5cyfnzp0DVFnuM2fO5HifiRMn8vzzz5tn9iJiHvjj4uJwdXXF09OT0NBQVq9efdPvY9++fVy8eBGj0cjixYvp0KGD1fFu3brx119/ERYWZn4/WbWaW6VEagrl3csDFodxbny1+yvi0+L5oPMHVvsjkyPz9SeAbj7S0bkbvPDCC4wdO5aGDRtiZ2fHvHnzcHR05Nlnn+XMmTM0atQIe3t7nnvuOV566aVs5bPnzZvH4MGDSU1NBWDq1KnUqlUr232ef/55kpKSaN26NY6Ojri5udG+fXuaNm2Kp6cnTZs2pX79+lSrVo327dvf9Pto27Ytb7/9NsePH6dTp04MGDDA6ni9evWYOnUqPXv2xGg0Ym9vz/fff29eK+JWKZGlsw1GAw5THXinwztMeWBKjm1EBJvJSpGSD62fUfPZzSnnVo6VQ1bmeZ9JWybx0daPMH5gzFdt1dEp7uils+9N9NLZBcDWxhY/Nz+uxeeuKQSEB5hf3yg4I5MizUtu5oW9jaqWqvsVdHR07hVKpFAAZUK6Hp/78nkHrls0kPi0eKtjUclRBfYpALpfQUdH556hxAqFCu4V8tQUIpIsaeTB8cHm1xnGDOLT4vFy9sr3HqZ1FXS/go6Ozr1CiRUK+WkKkUmR5tdZ28WlxgHg6eiZ7z1085GOjs69RokVCmWcyxCdHI1RsqeOgyVrGSA4waIpxKTEAODpVAChYNIUdPORjo7OPUKJFQpezl4IYp7530hEUgTl3FRiW1himHl/bEosUDBNwexT0M1HOjo69wglVyg4KZ9AdHJ0jscjkyPNi+iYBAFAbKp6nV+JC9DNRzo6RUFISAiDBg2ievXq1KtXj759++aaYHYr/PrrrzRo0ID69etTr149Pv/88zzbjxw5kr/++qvQ7n+3KblCIdNRbDIH3UhEUgS+br642rtaaRO6+UhH5+4hIgwYMIAuXbpw/vx5Tp48yccff0xoaGihXH/16tV8/fXXrFu3joCAAA4dOoSnZ/6/9fuJkisUTJpCSi6aQpIqZeHp5GnWDkA3H+no3E02b96Mvb29uZIoQJMmTejQoQNvvPEGDRo0oGHDhixevNh8fMaMGeZFcN5++20AfvrpJ1q2bEnjxo157LHHzOspfPLJJ3z++eeUL6+qHjg5OfHcc88BcOTIEdq0aUOjRo0YMGAA0dHZx47JkyfTsmVLGjRowOjRo3Otw1ScKZFlLsBi/snJfCQiRCZH4u3ijYejh7VQyHxdIE3BRtcUdO5Pxo0bl2NhutuhSZMmfJ1Ppb0TJ07QvHnzbPv//vtvjhw5wtGjR4mIiKBly5Z06tSJI0eOsHz5cvbu3YuLi4u53tGjjz5qHuzfe+895s6dy8svv5zr9QGGDx/Ot99+S+fOnfnggw/46KOPsvX3pZdeMpfZHjZsGCtXruThhx++ySdxdym5moJz7ppCXGocGcYMpSk4elr7FG5CUzCZj3Sfgo5O0bJjxw4GDx6Mra0tvr6+dO7cmf3797NhwwaefvppXFxcAEu57BMnTtCxY0caNmzIggULCAgIyOvyxMbGEhMTQ+fOnQEYMWIE27Zty9Zu8+bNtG7dmoYNG7Jp06Z8r1scKbGagsl8lJNPIesiOp5O1kIhJiUGF3sX84CfF2ZNQTcf6dxn5DejLyrq16+fo1P3Zstljxw5kuXLl9O4cWPmzZvHli1bzNc/ePAgDzzwwE33LSUlhRdeeIEDBw5QqVIlJk2aZFUu+16hxGoKbg5u2NvYW4WbmjAlrnm7eCtN4QbzUUG0BNDLXOjoFDYPPPAAqamp/PTTT+Z9pjULFi9ejMFgIDw8nG3bttGqVSt69uzJzz//bPYZmMxH8fHxlCtXjvT0dHOZbVDlsN98801CQkIASE1N5ZtvvsHT0xMvLy+2b98OwG+//WbWGkyYBIC3tzcJCQn3bERSidUUNE2jrk9dToSdyHbMVOKijHMZ5VPIoikkpCXg7uheoHvo5iMdncJF0zSWLVvGuHHjmD59Ok5OTlStWpWvv/6ahIQEGjdujKZpzJgxAz8/P3r37s2RI0do0aIFDg4O9O3bl48//pgpU6bQunVrqlSpQsOGDc3rKfTt25fQ0FC6d+9u1jKeeeYZAObPn8/YsWNJSkqiWrVq/PLLL1Z9K1WqFM899xwNGzakatWqtGzZ8o4/n0JBRO7Zv+bNm8vtMHzZcCn3ebls+387+pswCTkdcVpeW/OauExzMR976I+HpOmspgW6/q4ru4RJyJqza26rnzo6xYGTJ0/e7S7o3AI5fW7AAcllXC2x5iOApn5NCU4IJjTBOsbZpCl4u3jj6eRJUnqSebafkJaAm4Nbga6v5yno6Ojca5RooVDFU61QlLW2ESifgo1mQymnUubQVZNDOjEtEVcHVwqCnqego6Nzr1GihYKjnVowOzUj1bxPRNgVtIuyrmWx0Wzwc/MDICRBOZ5uRlMw1U46G3W2MLuto6OjU2SUbKFgmykUDBahsO/aPjZd3MQb7d4AyCYUEtMTcbUvmKbg6+ZLPZ96bLy4sTC7XaTM2DmDiRsm3u1u6Ojo3CVKtlDIQVMwrZ3wgL+KU74dTQHggaoPsOPKjlxLdBcnUjJSeGvDW0zfOT3X6rE6Ojr3NyVbKOSgKZgT1zKX2zSZgEyrryWmJd6UUGjs15ik9CQux1wulD4XFXuC9vDgHw+at9eeW3sXe6Ojo3O3KNlCIQdNwZyj4KKEgpuDGy72LoQkhJBhzCDVkFpg8xFAPZ96AASEF+909/6L+rPp4ibztkkz0tEpTtja2tKkSRMaN25Ms2bN2LVr1y1fq127doXYs/uHIhMKmqY5aZq2T9O0o5qmBWia9lHm/tKapq3XNO1s5n+vLOdM1DTtnKZppzVN61VUfTORo6aQFImTnRMu9i6mPuHn5seqc6uwn6JCTG9GUzAJhZPhJwur20VCBfcKVtvCvVfdUef+x9nZ2Vz47pNPPmHixFv3f92OQLmfKUpNIRV4QEQaA02A3pqmtQHeBjaKSE1gY+Y2mqbVAwYB9YHewA+aptkWYf9y1BRM1VGz0rlKZ05FnDJvFzQkFVQ11rKuZTkXde42e1u0mHwnfz/xN8A94QPRKdnExcXh5aXmlCKSa+nszz77jJYtW9KoUSM+/PBD8343NzW527JlC126dOHxxx+nTp06DB061FxLadWqVdSpU4cOHTrwyiuv8NBDD93Bd3h3KLIyF5lZcwmZm/aZfwL0B7pk7p8PbAHeyty/SERSgYuapp0DWgG7i6qPOWkKEUkRZn+CiendpxOfFs+ac2tISEu4KfMRqOJ7WesnFUfi0+J5wP8Bs4Pd9KPQ0cmRceOgkEtn06QJ5FNoLzk5mSZNmpCSkkJwcDCbNimTZ26ls48fP87Zs2fZt28fIkK/fv3Ytm0bnTp1srru4cOHCQgIoHz58rRv356dO3fSokULxowZw7Zt2/D392fw4MGF+36LKUXqU9A0zVbTtCNAGLBeRPYCviISDJD5v2xm8wrA1SynB2Xuu/GaozVNO6Bp2oHw8PDb6l9umoLJn2CirGtZ/hz4J4/WfRSApPSkm7qPh6NHsY/miUuNw93B3VxRUtcUdIojJvPRqVOnWLNmDcOHD0dEci2dvW7dOtatW0fTpk1p1qwZp06d4uzZ7HlDrVq1omLFitjY2NCkSRMuXbrEqVOnqFatGv7+/gAlRigUaUE8ETEATTRNKwUs0zStQR7Ns9e3JbthW0RmA7MBWrRocVvT2Rs1hTRDGpdjLtO+cvsc2z9Q9QF+PfqreS2GgnJj+e3iSHxqPO6O7thoap6g+xR08uQulc7OStu2bYmIiCA8PDzP0tkTJ05kzJgxeV7L0dHR/NrW1paMjIwSqy3fkegjEYlBmYl6A6GappUDyPxvql0dBFTKclpF4HpR9utGTWH2wdlci7/G0IZDc2w/vPFwdj2zi8fqPnZT97kXNIX4tHilKaBrCjr3BqdOncJgMFCmTBk6deqUY+nsXr168fPPP5OQoCzZ165dIywse7n8nKhTpw4XLlzg0qVLAFZ+ivuZItMUNE3zAdJFJEbTNGegO/Ap8C8wApie+f+fzFP+Bf7QNO1LoDxQE9hXVP0DVZvIRrMxawpnI8/i6ehJv9r9cmyvaRptK7W96fsUV6Gw6+ouopOj6Vuzr9IUHLJoCiV0lqRTvDH5FEB9R+fPn4+trS0DBgxg9+7d2Upn+/n5ERgYSNu26nfr5ubG77//TtmyZfO4i8LZ2ZkffviB3r174+3tTatWrYryrRUbitJ8VA6YnxlBZAMsEZGVmqbtBpZomjYKuAIMBBCRAE3TlgAngQzgxUzzU5HiaOto1hSiUqIo7Vy60O/h4VA8hcLjSx4nOCGYfwf9S6ohFXdH3aegU7wxGHIeEjRN47PPPuOzzz7LduzVV1/l1VdfzbbfpD106dKFLl26mPd/99135tddu3bl1KlTiAgvvvgiLVq0uM13UPwpyuijY0DTHPZHAt1yOWcaMK2o+pQTjnaOZk0hOjm6SISCp5MncalxGMVonokXBwyZMnf7FbWalIejh7l/ulDQ0YGffvqJ+fPnk5aWRtOmTfP1TdwPlNiV10xYaQrJUTftRC4IHo4eCEJiWmKBV227E5j8B4ERgQBWPgXd0ayjA+PHj2f8+PF3uxt3lOIzbb1LZNUUopKLyHzk6AFQ7ExIpv6Ysq2zRh/pmoJOTui+pnuLW/m8dKFge4NQcCoC85GjJ0CRJLAdDTlK7997m3Mn/j39L+9sfCff89IN6SRnJANwIfoCgFWegv7j17kRJycnIiMj9e/GPYKIEBkZiZOT002dp5uP7JT5SESKXFMITwxHvMU88BYGg5cOJjAikEPBh+hQuQP9F/UHYErXKdja5F4lJD4tPts+bxdvPSRVJ1cqVqxIUFAQt5s0qnPncHJyomLFijd1ji4UMjWF+LR4DGIoEqFQq0wtALrM78JnPT5jQrsJhXbtK7FXgOxVTa/FX6OyZ+VczzOZjqp4VuFyrCrr7ePqg6ZpaGi6T0EnG/b29ubsXp37F918lKkpRCdHAxSJo7l66erm1/+d/a/Qrjth3QQS0xMBuBRzCYPREq53MfpinueahEL9svXN+3xcfAAV3qdrCjo6JZMCCwVN06pomtY987WzpmnFJ4zmNjBpCiZ7v8n+X9hsGq4Kd1UtVbXQrrn32l7z6/UX1tN4VmPztslPkBsmoVDPu555n7O9MwA2mo1uN9bRKaEUSChomvYc8BfwY+auisDyIurTHcXJzonYlFjiU5WNvahCRrv6d6W+T/1CjUC6GnuVpxo9RWPfxqw7v46A8ABGNR0F3IRQ8KmX7ZiGrino6JRUCqopvAi0B+IAROQsluqm9zRdq3bleNhxcwKXu0PRKUAejh5m4XO7GIwGrsVfo5JHJTpW7ghAh8odmNNvDn5ufgQnBLPw+EL6LOhDYloi6YZ0fjv6GykZKUDeQsFGs9F9Cjo6JZSCCoVUEUkzbWiaZkcOFUzvRUY1UzPrP0/+CRSdpgCFWwMpLDGMDGMGFT0q8laHtyjrWpa32r8FKN/AhegLDPl7CGvOrWHX1V08u+JZhi8fzrLAZYgIvxz5BYCKHtkjE3Sfgo5OyaWg0UdbNU17B3DWNK0H8AKwoui6deco7VwaD0cPzkaqGutFqSm4O7qbo4VuBxExRwxV8qhERY+KhE4INR/3cfVh91XL2kQ7ruxg0YlFAJyJPMN3+75j3fl1tKrQirKuZelStQvtKlrWq9V9Cjo6JZeCCoW3gVHAcWAMsAqYU1SdutP4uvpyNipTKBSlplBIhfHGrRnHN/u+Acgx7NTHxcecmAYw5/Ac0gxK0TsVeYrQhFCa+DVhz6g9aJrG5hGbrc7XfQo6OiWXgpqPnIGfRWSgiDwO/Jy5777AtD4xFL1P4XaFwn9n/jMLBLAOdzVhCi0FGNd6HNfj1bIUFdwrEBgeSFhiGNW8quWaRKf7FHR0Si4FFQobsRYCzsCGwu/O3cEkFBxtHbG3tS+y+3g4ehCfFn9bs/APt3yIg62DedvNwS1bGx9XJRTcHdx5qdVL2NvYU8mjEo/UeYRzUecITQylrEvucQK6T0FHp+RSUKHgJCIJpo3M1y5F06U7j0koFHUFU9P1E9MSb/kaQXFBtK2Y90I/plyLyp6VqV66Otdfv87FVy9S0aMiiemJRCRF4Ovmm+v5NpqNLhR0dEooBRUKiZqmNTNtaJrWHEjOo/09hUko2NkUbdWP262WahQjEUkRtK7QGgBnu5wteBU8KgDwfqf3AVXTyNbGFm8Xb3Obsq55aApouqNZR6eEUtBRcBzwp6ZppjWTywFPFkmP7gKmWP0b6wcVNqa6SuFJ4eaB+2aITo7GIAbKu5dn0WOLaOzXOMd2j9Z9lJMvnKSuT12r/Vl9DXkJBV1T0NEpuRRIKIjIfk3T6gC1AQ04JSLpRdqzO0iXql3uyH38S6liYhejL9LEr8lNnx+epKpT+rj68GSD3GWyjWaTTSCYzjPh65q7+UjT9IJ4OjollTyFgqZpD4jIJk3THr3hUE1N0xCRv4uwb3eMUk6lcHNwo2HZhkV6H3+vTKEQk3exutwIT8wUCllm/DdDVvNRlVJVcm2nawo6OiWX/DSFzsAm4OEcjglwXwgFgMg3I7HVcl9/oDDwcvLCw9GDr/Z8xaITi9g9aneeax7cSFhiGJC36ScvsgqTvMpq68lrOjollzyFgoh8qGmaDbBaRJbcoT7dFbKGeRYVmqbhX8qfo6FHCYoLIjQxlPLu5Qt8flbz0a1QyqkUQL4akZ68pqNTcsk3+khEjMBLd6AvJQLTgjsA1+Ku3dS5EUkRgLUZ6GbQNI1jY4+x45kdebbTk9d0dEouBQ1JXa9p2gRN0yppmlba9FekPbtPaVOxjfl1UFzQTZ0bkxKDi73LbWk1DX0bmkNjc0NPXtPRKbkUNCT1mcz/L2bZJ0C1wu3O/U9WoXAt/uY0hejkaLycCn9luBvRNQUdnZJLgTQFEfHP4U8XCLdAy/It6V+7P3ALmkJqjNkvUJToPgUdnZJLnkJB07TWmqYd1TQtQdO03ZqmZQ9+17kp7G3tWT5oOVU8q9y0UIhOji6SNaRvRI8+0tEpueSnKXwPTADKAF8CXxd1h0oK/l7+N52vEJNyhzQF3aego1NiyU8o2IjIehFJFZE/gVuLhdTJRs3SNTkTeeamzolO0X0KOjo6RUt+juZSN2QzW23fLxnNd4OapWsSkRRxU7P/O6Yp6D4FHZ0SS35CYSvW2cxZt++rjOY7jSlf4XTEaVpXbJ1ve6MYiU2JvWOagi4UdHRKJvllND8NoGmao4ikZj2m5yncHi3Kt8DZzpl3Nr3DxuEb822/+MRiBLljPgXd0ayjUzIpaPLa35qmmQWIpml+wPqi6VLJoIJHBV5v+zqbLm4q0KI703dOB6BB2QZF3TVdU9DRKcEUVCgsB/7SNM1W07SqwDpgYlF1qqTQ0FfVIDoXdS7bsYikCALCAszb0cnRDG88nB7VexR5vzT00tk6OiWVgiav/YTSDJYDK4CxIrKuCPtVIqhdpjYApyNPW+3v8HMHfD7zocHMBsSmxGIUI8EJwVR0r3hH+qVrCjpFyYR1E/D6tOh9Yzq3Rn7rKbyWdROoBBwB2mia1kZEvizCvt331ChdA8AqNDXdkM7OqzvN2wtPLGRAnQFkGDNuqqLq7aAnr+kUJV/s/uJud0EnD/KLPrpxJftluezXuQVcHVwp717eynx0NuqsVZudV3ea12S+U0JBT17T0Sm55Bd99NGd6khJxb+UP5djL5u3T4SdsDp+NvKsuXDerazrfCvoyWs6OiWXAvkUNE1br2laqSzbXpqmrc3nnEqapm3WNC1Q07QATdNezdxfOvN6ZzP/e2U5Z6Kmaec0TTutaVqvW3xP9xRVS1XlUswl8/aV2Cvm1y3Kt+Bc1DlCEkIAKOdW7o70SU9e09EpuRQ0+shHRGJMGyISDeS3JmQG8LqI1AXaAC9qmlYPeBvYKCI1gY2Z22QeGwTUB3oDP2haEa+PWQyoWqoqV2OvkmHMACA5PRmA9PfTebL+k0QmR3Ih+gIApZ3vTGqI7lPQuRPo37HiSUGFgkHTNPOivpqmVYG87QsiEiwihzJfxwOBQAWgPzA/s9l84JHM1/2BRZl1li4C54BWBezfPUvVUlUxiMG8CltSehL2NvbY2dhR3as6AIdDDmNnY4eLvcsd6ZPuU9C5E+jfseJJQRfZeRfYoWna1sztTsDogt4kM7ehKbAX8BWRYFCCQ9M0k8ZRAdiT5bSgzH03Xmu06d6VK+e++Py9QkUPFWZ6Lf4aVUpVITkj2Tz4m9ZiPhd1jlJOpdA07Y70Sfcp6NwJDGLAlvveGHDPUdA8hTVAM2Bx5l9zEcnTp2BC0zQ3YCkwTkTi8mqa061z6MtsEWkhIi18fO79oq3uDiqQKyEtAVCagrO9M2AxF52POn9Hah6Z0H0KOkVFVpORyWSqU7woqKYA0A6lIZhYmd8JmqbZowTCgiwVVUM1TSuXqSWUA8Iy9weh8iBMVASu30T/7kncHNwAi1BIzkjG2U4JhTLOZQDuWM0jE7pPQaeoSDVYSqjpQqF4UtDoo+nAq8DJzL9XNU37JJ9zNGAuEHhDktu/wIjM1yOAf7LsH6RpmqOmaf5ATWBfQd/IvUo2oZBuMR9ldSzfSaGg+xR0ior41Hjza10oFE8Kqin0BZqIqJFC07T5wGHyrn/UHhgGHNc07UjmvneA6cASTdNGAVeAgQAiEqBp2hKU0MkAXhQRw829nXuPG4VCVvORva09Ho4exKXG3XFNQRcKOkVBXKrFgqwLheLJzZiPSgFRma8982ssIjvI2U8A0C2Xc6YB026iT/c8eZmPQGkLcalxd9ynoDuadQqbwPBA8/ccwGC87+d89yQFFQqfAIc1TduMGug7oWb9OreJs70zGpq5fHZSepKVACjjXIZLMZfuuKaQbky/Y/fTuf8JSQih3g/1qOZVzbxP1xSKJwUSCiKyUNO0LUBLlFB4S0RCirJjJQUbzQZXB1cS0hLIMGaQnJ5sVePoYPBBAKqXrn7H+qQvsqNT2IQmhAKYEzFBFwrFlYI6mjdmJqP9KyL/iEiIpmn5LxemUyDcHNxYdmoZntM9uRx72SpJ7fW2r+Pl5MXIJiPvWH90n4JOYWMq1ZIVXSgUT/IUCpqmOWUuu+mdWe+odOZfVeDOlOwsAbg5uHEx5iJJ6UnEpcZZ+RQ+7/k54W+E42DrcMf6oyev6RQ2ulC4d8jPfDQGGIcSAAez7I8Hvi+iPpU4XO1drbazCgUAW5s7m/WpJ6/pFDY5CQXD/R9ceE+Sn/loFyppbYKIVAM+Ak4AW4E/irhvJQZTBJKJO1XjKDf05DWdwiarUDB933VNoXiSn1D4EUgVkW81TeuEikKaD8QCs4u6cyWFG4WCKU/hbqEnr+kUNiGJFqHwWhu1oKMuFIon+QkFWxEx5SY8CcwWkaUi8j5Qo2i7VnIwCQWThlAsNAXdp6BTiIQkhNC6QmsuvXqJNhXbALpQKK7kKxQ0TTP5HboBm7Icu5nEN508aFWhFQ/VeohaZWoBd18olFSfQnB8MM1nN+doyNG73ZX7jpCEECp5VqJKqSrY2aihQxcKxZP8hMJCYKumaf8AycB2AE3TaqBMSDqFwJvt32TF4BXMeXgOH3T6gMfqPnZX+1NSfQpf7v6SQ8GHmH90fv6NdW6KkIQQ/Fz9AMxCQc9oLp7kt0bztMx8hHLAOrGMFDbAy0XduZJG8/LNaV6++d3uRon0KVyLu8YfJ1TsRJoh7S735v4iJSOFmJQY/NyshYKuKRRP8jUBicieHPadKZru6BQHSppPISYlhopfVTRvn4s6dxd7c/9hymYu567WGNeFQvGmoMtx6pQgSppPwVR6oZt/NwbWG6gLhVzYemkrdb6rQ0RSBKkZqQUe1E3hqCZNwZR3owuF4okuFHSycT+Vubgae5UNFzbk2eZyzGUAZvSYQXWv6lyOvXzfvP/CpPtv3TkdeZpDwYdwmuZEsx+bFei8G4VCXprC5e2X2f7x9kLqsc6toAsFnWzcTwXxms9uTo/fehCeGJ5rmyuxVwCo7FmZMi5lyDBmWJV41lEDuGkQNw3yx8OOF+jciKQIwLKSoNnRnENG87xO89j07qZs+3XuHLpQ0MnG/aIpiAjhSUoYlP28bK6hpqYihGWcy5jLlsekxNypbt4ThCWGmV8HxQXd1LmmZ2kq/677FIo3ulDQycb9ssjOjis7rLZ/Pfprju0uxVyismdlNE0zD1y6ULAma5mKs1Fn1YtEb/buzf/c2NRYNDTcHd0BXSgUd3ShoJON+0VTeG/ze2Y7NsDXe79m4wXriu8iwq6ru2hWTtnHTUIhOjn6jvXzXiCrUDgWeky9+H0NbdpAWj4RvDEpMXg6eWKjqeHGVrN2NItR2DZ1G5FnI83n3C/my3sRXSgUIkajkf79++Pt7c2ZM/du1O79krx2LPQYj9V9jLT30oh4IwKjGFl7fq1Vm5PhJwlNDKWbv1oh1stZNx/lhEko1POpx+Hgw2pnsMqpCQzM+9zY1Fg8HS0r+N6oKUSdi2Lz+5v5rtZ35jZiuPe/f/cqulAoBI4ePcqXX37Jhg0b+Pfff4mMjKR27dqsWbMGEeHUqVN88803bN269W53tUDcD8lrSelJxKTEUMG9Ava29pRxKUMd7zqcjz5v1W5PkErD6Vi5I4BuPsqF4PhgAB6s+aDFtGiXBMDRfKqCxKTEWC0ne2NGc1xQXLZzjBn39vfvXkavX1QIdOjQgYSEBDp37oy3tzfvvfce48aN45NPPmHGjBls3rwZAA8PD8LDw3FwuHML5twK90Py2rW4awBU8Khg3lfNq5rVcpCg7OP2Nvb4e/kDulDIjZCEEDwdPXmw5oN8tusztdMuFTJcCiQUPJ1y1xR0oVC80DWFQiAhQYUvbt26lZEjR/Lqq68yadIktm3bxubNm3n11VeZOnUqcXFxbNu27S73Nn/uh+S1a/GZQsE9i1AoVY3zUee5HGPJQzgbdZZqXtXMA5XJzBGdovsUshKSGIKfmx9tK7VVO9KcIUWZ2kJD8z43NiU2R03BLBSuZRcKhnS9LtLdQhcKt0lkZKTV9pgxYwB49NFHzfumT5/O+PHjsbOzM2sNxZn7wadgCpvMqinULFOT+LR4qv6vKr8c/gVQJS1qlqlpbmNrY4uHo4euKdxASEII5dzL4WDrwIZhGyCmqvlYfHze58akxFj5FG7MaI4LikOz1azO0TWFu4cuFG6TvVli8oYNG0aNGmqZiQYNGgBQunRpnJyccHFxwdfXl+Dg4LvSz5vhXtMUzkedzybEzEIhi6bwaF2LoD4deRoR4XzUeap7Vbc618vJi6jkKHQsBMcHmyO5ulXrRnvtDQDKlMlfKMSm5q0pxAfF41PPx+ocXSjcPXShcJvs3r0bW1tbEhISmD/fUnJZ0zQuXrzIyZMnzfvKli1LWFhYTpcpVtxLPoUjIUeo8W0Nvtn7jdX+05Gn8XPzM8fGA1T0qGhe9SslI4XY1FgS0xOp5FHJ6lxvF29zFm5RkZqRysnwk/k3LCZkLX0NEB5YG801ghYtICGP5O90Q3qu5iNTRnPctTg8KnhYnWdM14XC3UIXCreBiLBhwwYaNWqEq6srmmatAletWhVfX1/z9r0iFIpD9FFSehIrz6zMt50pPHL2IevVYQPDA6nrXTdb+y96fUEd7zoEJwTn6IyGOyMUXlv7GvV/qG+O6inOJKQlkJieaJXzEX7OH7vK+/HwyFtTOBN5BkHMC0iBRShM3DiRyKRI4oLicK/ozounXqTrlK6ArinkR1Em/ulC4Ra5fPky7du3Z8+ePTzzzDMFOsfX15fQ/LxyxYDi4FN4be1rPLzwYUtMfC6cCDsBqHyDWt/WYv+1/UzbNo291/bmKBQAyrmVIzg+2OyMruhR0eq4j6uPuTzGzZKRAenp+bfbFbQLuDfKdJtyFEylr0UgIdwLSp/H3T13oZCUnsSI5SMAaOTbyLzflLwGUOPLGiSGJeJRwQPv2t54VVPOa10o5E3v33vzyupXiuTaulC4RYYPH05AQACfffYZL774YoHOMWkKd3vAzY/i4FM4GHwQgAPXD+TZ7ljYMcq7lwdUJFGrOa14b/N7gEq0yoly7uW4Fn+NJQFLAGu/A4CPi0+eBfRMBIYHsjfIus5Djx7g6ZnLCVko7VwaUDPp4o7JP1POTQmFmBhIT3bC6HE5T6Gw9ORS8+dYx7uOeX9WjVoiBQQ8KirzkY2dGpIM6QaMYrynTGy3Q3J6MknpSQSEBZCcnpxn2xNhJ9h4cWO2yUxhoQuFW+D8+fNs27aNiRMnMmHChGxmo9woW7YsKSkp5hDW4kpxKHNhKjOxO2h3tmPHQ4/z2JLH+Hj7x2y4sIGHaz3M9deum4/PfHAmMx+cybDGw3K8dnm38lyKucTcw3PVdqZQMeHt4k1ieqLVj/NC9AW0jzT+O/MfAPGp8fT8vad5JgywYwds2QLJef+mAcs63KcjT+ff+C5jKmvRoKwKnriiispi9LiEm5vyKeQ0zzGd91Sjp3CwzTk3xyNOCYMbhYIxw8iEdROo/0N9c2nz+xWjGKn3Qz1cP3alwcwGfLH7izzbf7/vexxtHXmmacEsFDeLLhTyQYzZv+3r1q0D4Iknnripa5UvrwafI0eO3Ha/ihJNu3sF8S5GX6TF7BbmzONlp5YRmWQd9vvS6pf4O/Bv3t30LgBdqnahnHs5JnWexMB6AxnbYixjW4zFw9Ej2/VBDVImu7aznTOOdo5Wx31cVCRMVr/C+vPrAfjp0E8AvLfpPYLigrgYcxGjGBGBjh0t18hPGTS9pxszrIsjh4IP4efmRzn3cvzwAzRpovaLxyXc3IwYjZCUZH1OakYqh0MO07J8S34b8Fuu1zYJBfcKKiDAxt4iFL7a8xVAvqY8ESn22ndenAw/yaWYS1TxrALAvmv7cm0bmxLLb8d+Y3DDwXi7eBdJf3ShkAcRpyKYbDuZMyutVfz9+/fj7e2Nv7//TV2vX79++Pn5MXXq1MLsZqFzNzWFT3d+ajY5/DvoXxLSEhj450Dzjz4+NZ5tl7fxRrs3zGafzlU6A/Bhlw9ZMnBJvvdo7NeY4NeDiX07luuvX8923PRjyzoYHQk5AkBieiIfbfmIb/Z9Q1nXsqQZ0ghNCM2WwJVVGbwSe8WsYZgwXdvk7C7OHA45TFO/pgAsWpTlgOcVXFxVBFFWE9LJ8JM4TXNi48WN5kKDudEspBkGRwNlaqq1FkyaQlqqpcpefjkj9X+oz2NLHivguyl+bL2kyt9sGrGJR+o8YqlCmwPf7/+exPREXmxZMJP1raALhTxY+5oqnnZl5xXzPhFh3759tGzZssBmIxPu7u4MHDiQnTt3YjQWX0eaxp1bZGfjRlizRr3OMGbwz+l/AHii/hM8XPthpnSdwuZLm812bZPTs5FvI44/f5z1w9abHaD5ceXKFdIyS3p6u3jj4ehhFSppwmROuhh90bxv33U1e9twYQOTtk4C4OMHPgbUegynTql2Aweq/7GxluuNXjGahxY+ZBVNZVqfwOTsLi4YjAa2XNpCSkaKeftUxCkalm0IQFAQdO4Mgz5YCW5hOLtlFwpZgwPyEgq9qvWi0vFKhDUI42LiRabvmG5OYguLtUTp5SUUktOTCYwIZNmpZeY+F3eikqNIM1iE3j+n/8G/lD/+pfyp71Ofs5FnSc1IzXbe+vPreXfTu7Sr1I4W5VsUWf90oZAHYcfVF9PRw2Je+OqrrwgICKB79+45niMCeRVIbdq0KYmJiQwcOLDYqrx3UlPo3h369FGvN17YSEhCCH8N/IvFjy8GoHWF1oDF9p51aUcvZy+6V8v5c7iRkJAQqlSpwnvvvZdv26blmqKh8fifj7Pw+EIMRgMnw09amaP2PruX1hVV3y7FXCIwUH2WJx1VprRJKIiI2Rzw18m/AGVaiUtVpR2C44PNheGKA2vPr6Xr/K60m9sOUO8tzZBGHe86GI1KKLRpA236KLOXk4sKtcoqFLKa3XITChHPR/DMimdwiHXgas2rvLDqBSZunMjxSLWa25Uoy0QsaxnzyzGX6bugr7mQoSn6DGDb5eJfQiY+NZ5q/6uG///8SUhLIDg+mI0XN/JUo6fQNI1aZWphEIN5NcCsbLqoVqT7d9C/RdrHEikURCC/dIH05HRzoS5DquVHu2LFCpo2bcq4ceNyPO+HH6B2bdiXi1mwWTP1I/n777/ZuHEj//5btB/wrXCnkteymlyuXYPFAYtV0bVaD5r3m+LbTVE6oYnqJF9XS/5HQdiX+YEUxJ/jYOtgLpA35O8hPLTwIVIyUniz3ZvmNq0qtKK6V3Vc7F3YdnkbS7eeAvsEAkQJs5gY1S4gPMBcRyk4QeUkzDsyD1BmL4MYrFY1u9uY8iYOhxzmUswlTkUoFaiOdx1CQ1W4beXKYG9rD4Cza2ZWchahcCnmkvm1yTmdlYSQBE7OPUngn6rm9mXfy8SnqguMWz8OgLfWvmVun1VTWH9hPavPrabbr6rUucmsB9aaXXFl7fm1xKbGcj3+OkdDjrL/+n6MYqRPDTUzMq38F5sam+3cU5GnqOtdlzIuZYq0jyWySurx49C4MTz+OCxZAjlZgSJPW5ybGSmZi4GIcOzYMR5//HFsbHKWpytWqP87d0KrVtmP169fn2rVqnHhwgV69OgBqHUYbtYUVZTcqeS1jVnWu9m23cjqa6vpXaM3TnZO5v0mU86Lq15kccBi848mayJVQTAJg0aNGuXdMJOlTyzlxVUvsuvqLtacU/at7tW6E5wQbLavO9s781Cth1gcsJiEXWNwrHyCVGclAEyawq6rKh+hnk89guODOR91nvFrx9OjWg+eb/E8Wy9v5Xr89QKbwIqarAPwohOLsLdRg39t79qczVxbp3JlCM7cb++kNIWULJabS7GXcHNwY8njS6w+S4Cru6/yc7uf8W1kEernPc/jcLkC2ILRRn3vbIyW31dMSgzJ6cmMXzverF0npSeRlJ7E2aiz2Gq2GMRQ7ExxN3I9/jovrXrJvH068rQ59NkUPm3SRk2aZFZORZzKNfemMCmRmoK3NwwdCn/9BbnVpwsLsMzeMlIySElJoV69ekRFReU6sKxdq/4A5s+3/qGYsLOz4/Tp0zg7O5v3RUcXr4qcdyp5bf58KFtWvd51NBSXoy5007pZtdE0jR7VemCj2bDt8jb+Of0PtprtTc2WkpOTWb58OQCurq4FOqeJXxO2jdzGhmEbeLnVy7jYu1C/bH2+6/sdo5qNMrd7s92buGk+pF2rR68uHji7KVuxSSjsCdqDt4s3HSt35Hr8dRYcX0BKRgo/9/+ZKqVUtMmN5bwLg5v5/LZf3k66QQ3uMSkx2Gg2tKnYhoUnFnIp5hKejp6Udi7NsUyh4O9v0RRs7bMLhQvRF+hcpTN9avbJdq/za5XZKfSY0vi0xhopF9sTN+0UjXYEUNdPDY4moVDGuQzRKdH8dfIvfjz4o1XmemB4IFdir+Dv5U85t3LF3mn/d+DfhCaGsvSJpdjb2PPZrs/Yf30/5dzKmUuL5yYUktKTOBd1jtplahd5P0ukUChfHubMAV9fePNNMORg0r1+4Dp2TnY4l3YmIzWDL774glOZ3sQuXbrkeN2fVLQi9eqphUd++QWiorIvQmJnZ0fr1q3N28Uty/lOJK8lJ8P69fDMM1CqFBzfEcnw34YTMTp7eYnVQ1eT/n46TzV6CoCyrmXNSzsWhMWLF3P4sHJ+ZmRYlwdIikxiy0dbSInJLsFtbWzpVq0b3/T5hui3onFzcMvWpnn55izpFAhGe0b0rUulsupHHROjkr5WnFlBu0rtKO9ensjkSJYELKFpuaZU9KhIfZ/6ONg6sP/6/gK/l/wwZhhZOmQpMxvOLFBW8Lrz6+g0r5O5dpSpoumT9Z/kWOgxdgftpoJHBRISYMXnpznl0JC6UwabNQhbB4tQEBEMRgNnIs9YJatl5erOq+bXFdtUxHeOL2x/F8SOYxvrMaPrz4BFKHg5exGTEmNVoNCkLR4OOcyV2CtU9qxMBY8Kt60pHDkCv+UePXvb7Lu2Dz83PwbUGUC6MZ1TEadYGriUuj6W2b9JOMSmWJuP5h+ZT4Yxg741+xZdBzMpMqGgadrPmqaFaZp2Isu+0pqmrdc07Wzmf68sxyZqmnZO07TTmqb1Kqp+mXBygkmT4ODBnJcTDD4QjF9TP+xd7TGkGPjjjz/o3LkzRqOR+vXr53jN8+eV0/TECXBxgWPH4KmnVFy3KTrFhMm3AMoJWpy4Ez6FCxeUb6dRIyhfXgg9qr6KGXHZa7rY2thio9nQvJxa/rFG6Ro3da8dO3bg5eWFo6Mj6TfUoDg89zBbJ23lr0F/5XmN3JKvAM6cUaa/evU0qpVTGkxsLMzYOYP41Himd5tuzgYOCA+gRzVlNnS0c6RZuWZmp2lO5DRhyYuTf53kxMIThAeEc35d/jkQpvyL6/EqNDcmVa2SZlqJ7mDwQSq4V+CDD6DmmZXUTjuBzeJFlN2nynN8d0gtuLP74mGcpzmz6eImUjJSqO+T/TciRiFob5B5Oz05XWlLkTXNq7jFxitz05ROUzg4+iClnEoRnRJtNeA/4P8AtcvUZuLGiQRGBFLZszIejh6sPb/W7K+5WY4fh6ZNYfjw/NeHuBXWnFvDb8d+o1WFVmiaZo5cA+hSpYv5dU6aglFUzkbL8i3pULlD4XfuBopSU5gH9L5h39vARhGpCWzM3EbTtHrAIKB+5jk/aFqWAilFRNWq6v+NafpGg5HgQ8GUb1meONs4vtz8JSdPnmTgwIG52v5FlFCoXl35KJo1U87m1avV8c8+s27/8MMPm18XN6FwJ3wK5zJL/tSoAW7GeBLi3Un2zDsV+In6TzC4wWAWPb4oz3Y3snPnTtq1a4eDg0M2oXB9vxoMwwNurdYRwOnTYGsL1apB/XLVwTaNyKh0/jr5Fw/WepC6PnWp7FkZgMfrPc67Hd81n9upcif2BO3hSuwVpmydYhXOGRMDdnbwRd4JrlYc/PEgnpU9cXBz4PSK/LOlt1/ZDlgGIdPSmY18G+Fsp0yc5d3Ls2+3gW6ue0nCmXTs8HpjHTYGG1Zd/BuAn/b9RqohldfXvQ5A/bLZhULU+SjS4tN4cOaD1BtYjw4TO+DvVhfiK0Il5XuJiFZuztqlatOsXDMqelTkdMRpq9j9GqVr8HG3j4lIiiAmJYbKHpXNGeKm5MKbISgI+vWzbIff+lchV+YenoudAT6MbAQGAxM7TqRVBeV0fLi2ZSwwCYWsjuYVp1dwNuosr7d9/Y74HotMKIjINuDGovT9AVN96fnAI1n2LxKRVBG5CJwDcnDTFi4u6nuULRsz4lQE6UnplG9RnuVRy9l2eRt9+vTJs/BdRIQSLtUzS/PXqweHDqnXfn7w+++weDHEZU4AunTpQo8eKvzuwoXiJRTuhE/hfOYktnp1cEmPIU5zI6FXAsYMY45Z5KAGpz8e+yNbWYq8yMjI4PTp0zRt2hR7e/ts5iPTzDX+evwtF2E7dUoJBAcHaF+5HdglsevSEYITgnminsp671G9B7tH7WbJ40usynm/2EolIXX/tTsfbPmAkf+MxGA0MGwYeGXq0X/+WbB+GNIMXN11lfpP1qdCqwpmgZcbIkJgRGYEUKwqJWESCva29uZBq4J7BY4dSqde4l4uU4WoCg1pEbOLnqcdwE6Z3dJS1VByPOw4jraO2RyiGakZ/Nj0R0CZjQYuGUiDJxuwamFm2fIbhILps+hRrQcXYy6y6uwq87ValG9hZVuvUqoKP/T9AbCYlm6GGTNU9Nv06Wq7KIRCQFgAP1xpQLOXpsLo0RARwcLHFvJlzy9p7NvY3M7B1gEnOycrTeHLPV9SxbMKj9W7Mwl6d9qn4CsiwQCZ/zPdjFQArmZpF5S5Lxuapo3WNO2ApmkHwm/z0zP5HBMTrfebfkz7wvdxJO4I/av3Z9WqVVbOYRPh4fDff/BY5udlEgomt8PUqfDyy5CWBoMGwbRpar/RCOvX1wccWL/+1sonX7miTFWFjUbRl7k4f14NeqVLgzHmGgniRh0/NZBkpBZeWeCIiAhEhHLlymFnZ2elKcQHxxN3NQ7vut6IUTh3NIEqVeDBB/MvU5GVkyehTqYJvV2ldmCfzK7zh3G2czaH15qctzfO9Cp7Vubznp+bZ8LHQo8xa/d8fv/d0sb0ncqN3V/uZl6XeQT8GYAhzUD5luUp37I8ocdC83yW4Unh5sHncuxl4lLjrNY+aFdJ5SpERIBbWhRVuYJ7/wdI761mtm/uNpiFwocdPiF+Yjx7Ru3h6NijVoIPIPhQMOmJ6biXdyfY4IMIXL8Or72WOQRlCoXwSEuZC4DeNZSxIWuyV6sKrajmVc283axcMyp5VqJPjT7mPJaCsncvzP7JiFfLNbTpouawWYeVhLSEW9aaM4wZPPfvc7Sb247AiEA6BWbOPn/+GWrXplq6G+Pbjs/2nfBw9DB/LikZKWy/vN2qNEtRU1wczTnpRDn+LEVktoi0EJEWPj4+OTUpMCZNIZtQOHAdBzcHvv/1eyq7VubBCg9mPxk162/eHB56CLZvV6+7ZQbPDB6sBv5331WDjOlz37lT/VcRkhrgw5Ur1s7VCxfg44/VNVatIlc6dYKGDeHff29uEMsPkxO3KLWFc+fUYHf06FGcIuMwYk/VMmrGlDUv5HYxrV9RtmxZ7O3trYTCtb3KTl3v8Xpcoxy1W3hw5Yp65gVd9iI2VmkKLVuqbV83X5ycjZDhzIjGI3J0Tt/Iy61eZkjDIXTz70bzcs35dpmqvLpihQqdvhAayvwj83M89/qB66x7fR2Xt15m2VPLAKjQsgJ+TfwwphuJOpv7CnKm3I8GZRtwJvIMntM9OR523CwUxrYYi6+rL90vNOd65hyt4htDSHpkCMH44Zihga3KvDWk2ePm4Ebriq2p7Z09QubaPvWsK30ylmYtbJkzB1ZmWS5j5pincXaGsEhlNTat0exfKnspmUoelXC2t0zQTLkQfm5+5lyQgrBokUqexC2EsHYj2BOt4skjMn+OiWmJVP6qMqNXjC7wNbOy/NRy5hyew+6g3bQKglr7L8LYscoeGBUFGzbkeJ6Ho4fZfGRajyKnfI+i4k4LhVBN08oBZP43/fSCgKzLX1UE8tZ9CwGTpnCj+ej6get4Nvbk2LFjNPdtjk16zo9p5ky4ehV691aD/I4dFkEDFkHQuLFK+hk/Xjm209Iwh/fZ25cmOtryww0LgxYtlDBZtEgJHNPibWFhsGyZ5fqXM4tH9u+vNJWLhZS7Y5q5FKVfweR/WTZyGQ6ogdrWXg2ghakpmCK7fH19sbOzszIfXdt3DRs7G2r3q81u2lqdd7qAxUv37VMCuU0by77KZXyo5dGUGT1mFOgamqbx+4Df2TB8A20rtuXcceWsbtsWnFwy2HPhOCP/GZlNSIsI26Zsw97VnlF7RvHAxw/Q6YNOeFbxxLmMGjRTYnMu/ZCcnkzHX5Qz+dPun1qtoWwq613ZszIhE0LwWrzTcmLLljh6OpGAG65pGpqNMpvlFH4deyWWtAQ1w7+25xruFdxZ/K/6gSxeDOvWQZUqavI0tuOT+PlBaLi1ppB1Fr32qbUkvZNk3le1VFW8XbzNM+hybuUITQgt0Pc2MRFGjYKq1dLhmY7gFsbeKDUDM2kKfxz/g+iUaOYenpurUM4Noxj5dOen5jpaH2wFKesDn34Kr76qQu5yiYf3dPQ0awqB4cq8dyfyE0zcaaHwL2CqNTwC+CfL/kGapjlqmuYP1ARyLxVYSORkPjKkGwg5EkK0bzQiQh2fOrnOXFeuVBELq1ergd/JKcdmgHJEduigfjx//AGvvaZ+TGXKlCEx0ZIoN38+REfDV1/B/v2CCBw4AK+8okJoH31UDf6mmaypMueyZcq5XRj2UJOmUFRCIT1dCTT/ygbkuBDdUM3uMrTMZRrvkKYQejQUl1oV+HmFN6GoZCqTU/fGaLGcSEtTgtve3jpR0dPdgeruDbKZUPLCNNDVKlMLQ3g1cA0hQk4TGLsf0pSwzFr6QET4pcMvnP73NJ3e60TF1hXpOLEjXT/qiqZpOLqr0iypcdlr6ADmMNgmfk3oXaM3Qa8F8fEDH9OifAueb/G85T5Xg6h94m9CKUvcr8vAwQFHD0fScMA1XajgUQEnp+zlwkWEr6t8zbzO80hPTufsqrNU6lLDrPlu2aJqXrVpY5k8lS4NsfGZE5Is/h2T5lLHu46VhhD4YiBXxlmeiZ+bH4bjjzF0RCpnz5rCZK37dfUqzJuntMGkJKg5ZCYZrpfpX7s/y84uwdUjzawp/HvmX/zc/HjA/wFGrxx9U2tfrDq7igPXD/BZj894r8pw+pzXsBn1LHh4qMGgWzc1gGRknwCVcipFVHIURjHyw4Ef0NCsVq4raooyJHUhsBuorWlakKZpo4DpQA9N084CPTK3EZEAYAlwElgDvCgiRV4QJidHc+jRUAypBi4ZLmFnZ0cdvzrmjOasREbC7t2Wuj0FoU8fcHeHp59WA39amhIK6elRZsF06pRyTA8ceI2RIxtia/s5+/bBt98CXAM+ZMSI7WYb9kcfqRC65cuVKeObb3K+942IqJnar79C6g3jhpZpzSsqv8KVK+q34GmIwMZgg0vtzHBUTcW+5/S8bxWTUPD19c3maA49HspB+9a8P9mBcMryRPtrjBsHzs45hynfyGOPKfPw4MHWC+s4O2fXPhMScvz9Z6NWmVoQVRPKnOW5Fc8RJ9fxtFGO9UPBhyx9PxrK1V1XaTa6Ge3fbJ/tOg7uKoQ2LT4t2zHAvDjQuqfWYaPZ4ObgxsSOE9n/3H6ql1ZOjEcegb8rv4qXIZwf7F/FfWh/AJw8nUjHHpd0oYK7Ego3agohJ6OZyRj+OVSR1bMuMzNuMJsNHUlOVn42g0FNxpo3t5zj6gpJydmFwtqn1jK62ehsi8o42TlZCYmYMw3hr8Us+s2ZWrXU9dq3V78zULlJlSur398TT4CNRzDLksYzvPFw/hz4JxXcK5Bod4lD568iIuwJ2kOv6r34fcDvpBnSWHpyaY7PMidWn13NjM32jGz6NFOe/hUbG1vlYDYxdCiEhFin9WdStVRVLkZfZPkpFeTSs3pPq/dZ1BRl9NFgESknIvYiUlFE5opIpIh0E5Gamf+jsrSfJiLVRaS2iKwuqn5lxclJzVKyagrHFx7Hxt6GkyEnadasGW5ubjkOUosXqy+2qSpmQXB2VqU1TLz4IpQtWxqI5NIlte/0aahVCz799FMCAgIwGN7g++9TgDR69ZoKTGb79ieJjk6kWzchOHgxP/00jYceMvDAA/DPP9nvmxMTJkCvXjBiBIwZo97HS5kZ+EWtKRxUlbFxilazPN/6KgzPYBIKhWg+CgkJwd7eHk9PTytHc0pMCnFX4zgZpwaaOgTSvf51bGyU2ea///L202zZoiZ61atbggdMuLhYz5xN+RiTJ+ff37o+dSGqBpQ+p8JF7RNxxRd7G3u2XNpibnfqn1OgQdfJXdFssrvkzJpCfM6awr7r+/Av5Y+Pq7VfLjhYDaKnTsHlfw4zgGX8ZPs8VVqXM9/H0cORdBxwyRD61uybTSiICN9+nkIofmyhM3/MiucKVZi5yAtXV6X1msLBs6TrKKGQKUyN6ZbvXqsKrfjx4R/zTVjcurAZuITjV0XZ421t1cTt5ZdVZOAnnwi29hm4e6j5prHFt3St1pmPunyEva09Hat0BOdodp0OJCA8gIikCNpVakc593L4ufnlWdL6Rvac3sAbW7OEP8+erWxlJnr3VoPPnuw5KjVK1yA0MZSv9nxFRY+K/Dfkv2xtipLi4mi+48THx6Np1l/EsBNh7P9xPwf8D7Br7y569uyJnZNdtkHqyhWV+Na4sfq7GYZlLgY2frya/VeuXAaI5PPPBU1TjujateGMVanV/djY9Gft2lnY2TkCwVSo0If27acwdOgg3nvvPTZu3EjXrioJJyKL3/rTT9VfcLClxv/SpfDll/DcczBggDJZ/fUXfP89XLpkMWUUpqNZRM3OGjRQgsjfH5xCjxLlFUXl8ioILZ1MJ2MhmY9WrFjBTz/9RL169dA0zcp8FBYQRjxunLjswTvvwFNOf1PVMwZQk7izZ6FrVxWdkhNffgk+Pir6q+INqyLeqCmcO6dMfvszE5ffeQfGjVOCpVkzS+gyQGnbyhBfgRd798JGs6GUpz3pKQ70r9OfBccXEBcVx7oJ69g6aSvVe1THzTdnR3Z+msKB6wdoWaGleTstDd54AypVAnfHVBrXTeULXiccHy4ZKlK7n8V84eDuQBr2uInGe53esxIKSUnwRM0jfDKvPDAWOxZy6oyGg5bGyy/Dpk1KWz50SH3fOne29Ck3TaEgXLkCm9e6Y9P8Fww26uHPnw/t2qnx2MMDLlzQMHQfh81bftR55XXaPLmTTSM2UclTuTM/7PwhLi4CGc60mN0CZztnc6G6WmVqWQkFEeHpf55myNIhyv4fFKRmE4cPcz3+OnV2Zv5+n3tOaQRPP23dYWdnJRlNdsp9+1Q7LMmZO67sYGC9gdjaFHnKlhUlUijs3r2bqlWrsmHDBlxcLJrCjuk7OGo8yr9n/uXxxx/nvffew9bRNpumsHy5st3/+mvOxfTyonNn+PBDeP55dW6NGqWBDObNs6zKUqsWXL58mcZmiXMOo1EVZXvnnXcYNuxbrl3bzuTJH9KpUydcXFz4888/6dpVtTaV27h4Ed5+W/1VrqwmJ0YjfP451K8P330Hc+eqwW/IEHXO338XvqYQFKR+A3/+CQEBagCZOhWiAsII8QuhqrcqbpchhacpiAj9+vUjOjraXFIkq/ko7HgYR2mMwagxciTYOduRnqwExqBBykewdasSmjeWpjp6VEUGjR2bsx/pRk3BNBk8d04Nvp98Av/7n3ruhw/DCy8ogVG9usWc8kSf8kS8FsGwpv1ISNB4rO5jRCZHsvLDlez+Yjela5Sm69Suub7/vDSFyKRILsVcokU5S03+RYvU98JggH9oSwIuPMBmTlKPmrVtaTve4oi3tbfFYO+EXUY6NkaxEgrPPmPgr/NNgRPAj8SzmHB8qO4RwTffWHwvXl7qfdtlibJ0dbX8Fm9WKMydC6BRq9cm/Hr8Aaj1srduhe/+OIeNrRFb+wyo9R+x6RGcKv0lzSpYR/TU8a5D11qtKG1XES9nL2Y9NMssMGqVrmX2KRwPPc7CEwuZd2QeC08sZPLWyQS89Yz6oJ95hs0XNjH0GKRW8INZs5QzMCfq1FEPfuZMaN1adRjrjH3TAlJ3khIpFOrXr0/ZsmXp0aMHRuOvJCWpgejMijMEuAdQq1YtlixZgqOjo9IUbhAKBw4ou38BC25aYWOjtIyaNdV2+fKmwm4WZ/PDDwuXLl2iU6dO2NjY0Lq1EghPPfUUb7/9Bj/+aCnINmjQIB599FH+/PNPGjdOon9/mDJF+TxMq4W6ucEDDygtZN48NUvr21c5ur281OxtwQLlCP/4Y7h4WKm5efkUoqMtM9/8ePlli99izRrlmH/0wRTSrqSRUCWB+uVVzHm6FJ6mcMW0kDBQvXp1DGkGYi/EEh+h0tdDj4cSaFOfNm2EmjXB3tmejGT1Obu4qMFk9Wrl0H/1Vct1d++2LEf5vMUfa8WNmoJJKFy8mD3gZOpU9RxbtVI+obQ0FcLs7R1IaffSLPi9CcnJUMVDPaOgzUFU7liZl8++TIWWOabyAGoFMzsnuxw1BdPKds3LWwz6y5ZBhQqQvGETvTmMPWpQDvFvy9DVQ80ropkwOlkcciahMGcOLFxsS1MOUaGMWldC2M91yuHvl/8COEooaGi2mjkktaCsXKm0guZ1vYmu8zVGoyp8Ofq/Z3jpTE2MYxpQakIbWtb34aWWyk7ayDf7D9jV1YayDv4Evx7M8MbDzftrlalFWGIY84/Mp9GsRgz9eyjl3MrxeL3H+WL3F3iuUOVCOHKEqD/m0OMC2A8eqn7wueGeGYjwwgvqf2bSUR3vOubM5jtR1uJGSqRQ8PDwYOPGjZmRP9+RmAgXN13kXNw5AsMCGTt2rNmEkpNQOHjQ2kF2O5QuXTrzlRIKAweCo+NlUlJSqFmzJpUrV2bvXrXE5JtvvomzszPOzs5s2bKFbt26MWDAAEaPHk1sbCxz587hmWdSSE4ONZsl/vlH2VNXr1bawahRauDJGkJpYs4c9T2d9fKTsGAlv86zZdOmnPs9dKgayEy+kNyYOVNpVmPHKg2mVy+lsQRuDUQTjVbdW+HumqkhiJo2FoamcDDTcfH4448z5tkxbHh7AynhKQSdjOLCBQg8kMQ1YzkeeUR9zvYu9qQnWWzATk6qn+++q4qkrVihHMUmR/6SJVDOVO36m2/UjkwcHdOIi1MOxMDAQLZuvYKmqVn4+++rwW/8eCUM3n1X7QNlkrp4Ef7918iro18BICrqHCCUta+KW7wbySeT8e9WsGVgHdwdctQUDlw/AFgWwDEalb/zwQchcsIEwoEmwEc2DXjk4Pt4+WfPEhbnzNC9hAScnFR14Oeegxo+sTyk/Yed61Ls7R2BWOA09Rrmr1KbNAUbO5sCawrp6apExaFDKpCjYdmGBMUFEZO5hsX8o5mhpGUDiXQ8yOttX+ebPt+wdeRWnm76dLbrubhkDxIAzCudjfrXMiF7p+M7zHpwFgM92lIxHiY95EawtxMvz9iKvRFsHuiW/UJZmTDBejszOdbJzonrr13n/Cvni3zthBwxLXp9L/41b95cbodp06YJIF26XJd/Rv0jrexbiZubm8THx5vbbPloi0xikhjSDSIi8sMPIiDyxRe3dWszu3fvFkCaNVspGzeqfbVq1RJA1qxZI926dRNAXF1dJSMjI8drGI1G6dGjh7i4uEj58hUE7AUWypgx1u1++031HUTCw2+4yJEjIn/8IYmJIj2f2W1uByKzZlmaHT0qMmiQ5dibb+b+3oKCRGxtRfr0EUlLsz4295W58oH2gaw+slrOnFHX+nZKtExikgQuCyzYw8uDd999V2xtbSUuOk5mNZklE5kmjnQQaCuVK2RIW5s9YqMZ5OJF1f6Hhj/IokcWZbtOWpqIj4/qX/v2Ik5OIi++mKWB0Wh5GJk0b/6KALJjxw5p5+snidjK690Om5u9/LL1PTIyjHLggLqUiMiE4RMEEBdcBBCIl7ecvpJJTJJJTJKI0xEFegb/q/Y/WTpkabb9jy5+VGp8U8O8feqU6tfbfSeKgHyArdhiK0/3fzrXa2+pMUqddPas9OypXtrYGOV9++nSvVZ3AeSpp8Zk9n+tbNxozLe/H36orjPV9RNZ89qagr3H/6lzmjQRuXxZ5L8z/wmTkG2XtklyerIwCZm8ZbI0ntlYnvzzSckw5PwbMvHCCyLe3tn3x6fGC5MQJiGj/x0tx0OPi9H0gf35pwjIrB9GySu9s/xwIiPzfwMLFojVjy0pqUDv+3YBDkgu42qJ1BRCj4Uyr8s8qtkrlTwkeC8n/zxJkHMQnTt3xs3N4ryzc7LMXtPTlbrfpYu1SeF2qFBBmQBGjw7igQcgODiYM2fOMGLECHr27EmbzCl9uXLlsLXN2eGkaRrz58+nWbNmmYlw6cA4Ro2yDh5/8kmV6Pbbb0q1tqJJExgyBBctmQefPWje3aGDmuW//74Kff39d+vF2/+7ITAiKkppAz/8oMI1DQblu7C3t2537cA1IrwjaFmzpWmCRLpRvb/C0BQOHTpEvXr1OPnbSXYdceIT3iEVdyCdK9ds2W1szSN90sxRMPbO9mafQlbs7ZWPB5T5LSXFOoKMYOsMWhHh5ElVf3ny5Cn0DQ3BBQPPey7AtILru5Z6eKQnpTOnxWwuf/k3CSHxJCUlMWvxLGrZ1uK5Xs9ltoojKUX9VKN6RVGmVsFmjzlpCtO2TePvwL/NFWfBFA2WQq1VnxAPzMWRMmXKYOuTu4PTzjszBjcx0exXqV0qjGSJYOPZjQwdOpR33hmX2TqCevUKpikAGB2crEyIqanKbDdmjPJPgfpeHTumogDr11e+mcqVLdnNvRf05vn/lH2vgkcFDo85zKLHF+XrtM1NU3BzcDPXgnqw1oM0KNvAkli3ciW4u/PUsM/Y91ATTrStodRMsxUgOwaDykUKbdRDORrfeUcduFD4a2vcLCVSKLh4u3B562UC3g0A4NTpASyJW8L1uOt07WrtvLPPNG38uciAg4OpXosKd8uJuKA4Vr+ymrTEnKM+bsTPzw8bGxuuXVNlALZu3QrASy+9hKZpDB+u7Jo1TU6IXChXrhzbt28nKSmJxYsXA6HY2loH29vbK1POU0/dcHLWVeYPHKBD1bYwvhL/27gI05LGU6eqfJv9+1UE0dmzqoBYQICK/BBRpqIxY1T+w4svqqinTp1UsbgbSTmbQkLFBMq4lDEPKmmGTKFwG3kKf/31F1OmTGHfvn00a9aM4wuOs8u1JwDVq9vjX8kySD79vMVLbOdsZ/Yp3Mi336qIqcGDVYBJh6xm3hsWyzhy5AjJmWsKr1u3FlPBh/jD55j25FEiIy1+x7hrcXxV6StCjoRw/I/jzG07l/1795OQmkDP6j1pM9Jk44vl0WXD2DNnDzsf3UlBcXR3NPsUlp5cSvkvyvPe5vew1WyZ0G4Cp08rH9K2beBl9xlPAH9iz/Bur1LapzSxWb8XN2DvUwoAQ0wcVzOrlvlEnSa1byoiwocffoivr6m02QVWrJiD0Zi3ScgkFDQ3V3PS3fTpypRniiIyFQecN09F/u3aZSktA6oEhreLN0npSeYy2uXdyxe4uqhJKOQUeLdp+CYuvnqRh2tZqpqSkKBC9558Elc3L3a9cIgGu85ayiPngIjKL3rtNfjoBx8VhjZggDp4tuBhr0VFiRQK7uXdqdS+EnbpltCHw6iSxYMGDbJq6+rjSgY2jHtTRXP07atKT+TG5g83s+/bfRz55UiB+mJvb4+vry9BmVOgwMBANE0zRx7VqlWLlStX8ssvvxT07eGVWV4z+cY00xv58EOYONE6VnrnTpr6NaV8RSMTdg7Hs/4eLl1SM7KAAPX9bdJElbx+5BFwcDTSuUccU6cqf9lfWZYlSEy0fNezkpaQhkO4A861Mm2oZqGgvo634mjetWsX33zzDQMHDuSDDz4gMjISN2qxb6+Rc4nl+PhjaNzYDhcPIw89pGaVppk75K4pgMpanzdPZaLv2mUdMWNOushklblY1bt4A6bcRvuLZ1j93HKryWPA4gCSo5Lp/ml3nvj7CWIvx7Lg7QUANG7cGA8Pj8yWcdiW8aJOhToERgQWOCrMpCmICFO3TyU4IZiyrmXZPWo3dTxa0LKl0lrm/xjPaOMnuAJ2DiMY/+14PD098xQKtmWVpvBul1cp5an681CDS5xJPUPdunWpWbMmpUqVyly29n1Gj36OlVmLHeWASSiIi4tZKMyapfZlKtSY4gdMj7lPH3j2Wcs1NE2jgru1A/5mquqaElpzKtvh6uBK1VJVLQJmxw7lhEtMhJEjzffPjzlzVDAIqEjU+Hi4bJ8ZhjrvLC1aWNb4vhuUSKEA0On9TrQe1xpv+qFRk4GNBvLNN9+YzTkmXH1d+Y1hRETZMGOGmmlrmprN/t77d5YNX0bwoWASwxKJuRzDsV9VUaN93+7LtQT0jVSsWNGsKVy7dg0/Pz/ss9hbHnzwQXxzC2vLAZfMb3ZSTnpwViZPVlOx//1Pbfv6wpo1aJrG0ieWUta1LEOWDsGvQiqbbC2L1psiZZ18L5PW/QUunfHggw/Uvod7JPPDD0prePRRS6hrVk6uV8WcKjRSz9okFNIzhcLNmo+uX79O+/btefXVV2nVqhVDhgyhV6++fD//RX5CZZF26YI5T2HFCuXQzRpOaudsZ+VoLjBZMlLDr1/nv//+o1KlJsBoRqLhClxBoxQxOHk5WdYYjkhi//f78WviR/s321PnkTq0ndCWg/sO4oQTdZrXwdOcJh1LbKxaoyApPYlLMZcK1DWnUk6kxqZyMPggR0KO8EPfHwidEErplJZ06GDJW/mOQUw3qgmEY68u+NT1oVSpUsTkMTLtC1da9hkO0/bMN7zId7QdW41t27aZ1x63sbGhTBmLqWtt5lq1//33H2+88Ua2PBjzSqkurqTGpmI0qtD9N8enE3QkwrxYlcGgJidPP62EQ8OG1n2b/8h88yJBcGtCIc+fTlCQqrVtUrkrVVKqTAEQga+/Vr+hKVPUnKJZM6jfvhQZXt4E/HOOgweVuexuUWKFQo1eNej9VW/a1piOxglm/G8GL7/8crZ26Y5uXKYK3VrGMmGCxTZ+cfNFzq89z8k/TzK7+Ww+9/2c/1X9H2jQ/dPuRJ6JtFr5au+3e1n0yKIcQ+0qVKhg1hSCgoKoeGM21E1iKvGdp1AQsUx5V69WBbqefVaVe42Opk3FNvz08E9cjLlI/0X9+fGEZZWgIUPg4+0fU/V/VaGBxcFQTztJ8/UzcFkyn2GPJbJ0qWUNZlAaQnpyOitGrSDKK4rGvZV0sbNTfynpt6Yp/PyzWsKxfPnyLFiwgAULFvDgA38CalB1clI/vKx5CjdGCtq72OdqPrIiIUFNr2Ni1Otdu8xRIw0qVGD37t20atURqEwLunOBUqxC8LaPJSU6mbggVejs0NxDRF+IpsvkLoCaYZYZVIYzzmeoW74uDQY1sNIUYmMt9vKAsIACPRdHT0dSYlL46eBPuNi7MKShktDPPgtnzqjkrlPb9jIKNe2OwpPKXZStLz9NYVmAMmP5AuHRJxk2vyXTVk4jOTmZwYMHm9t5Z3Fe/fLLL7z77rs89NBDfP7552y6IbTNJBSuGX1JjUvl2jWwSU1i0pwK4O9Po2oJXD8SxsbGr5EalUDvG5fwyqSxX2O2Pb2N8W3GU6N0DXOBv4KQr1DYuVOpmRUrqgJezz+vHBo5aAgiyjRnKn4JKiT55EkVfTZ2rEqqu3ZNKRsnUmrQgR3YYLhRAb2jlFihYKL5oBoYcaBc26o5Ht9/yh3QGNDkItf2BpEYlsjGdzay+f3N2LvaM3jlYKv2rV9pTZtxbXDzc2PvN5Z02LXj1nL6n9Nsem8TCSEJVudUrVqVS5cuISIEBQVl01ZulgJpClFR1sV4atZUcapGoxoxULXs6/nUY+35tbSIcmKLtw/Pj1+Je5l4ZuycQQX3ChwZt5W/dhymbZduPCHK4HtpyyUOzDpgdbu09DQ+bfwpH7t8jDHayKWBl+jZqKf5uJMTpKUXXFOYN28etWrV4vVBQxj8/vvML1uOa0FBeNt5k56czqb/UnAhkdULIti5ExwdybaeQlZMyWvn153P26cxf74yxL/yikruSEsz2y9Mc+IeHeoztd4fNOUSgfhxAnBMT8SdOIIPKsd01NkoXH1dqf2w8jrMnDmTFi1akJCcwAfff0CpKqWsNIWYGKjnoxa1PxFWsEU0nEo5kRKTwppza3iw5oMc3OXJZ5+pWfYnn8AjLQLx6qJmuBv9GvGH7UhqPqh8V6VKlcpVKBiNRnYEnSMNjSqA/wR/9sXuY82aNQwaNMgcHAGQkKmOvPbaa9jb2/Pxx5ZlKF988UUrE6dpQP7xaFsOXC/H+fPQhS04x4dDQgLdyh7n4ZDZ9Az4ineZlqcZF+DLXl9y5qUzN7Wed55C4epV68U2/vc/Ne0vk93xv3q1KqfeubPSCpo1Uz6R115TQR6PPprOP//MZfHiUPbtU37mlcndqM9JPveaxvvv51gBw0x0tBImRUGJFwplfNXU37QimonkZPjgAxj6tCMuJBL60wrmtp3L576fs+OTHQQfDKb2w7Wp1q0ab8W8xcSEifT7uR9dp3TF1sGWFs+34Nzqc0ScjiD4cLBZVd41YxezGs+yuleNGjVISkoiJCSkUDSFAgkFk3dwRGbR2vbtLYbbzG+bpmnMfmg2FdwrsGWNH50jImjj8AMbL24kNjWW3x/9ncZ+jWma7EnPbR25WPUie2ftxbeRLyf/PGl+z4FBgUwsMxHjBYst/OlRT+NkZ7HfJCTA/76xIQbPPDWFtLQ0unfvztNPP83Zs2eJXLyQ6sDwsGDSPLz5qdp0vqn+DUcOGalgH07PJ0ur+joi9DtxgjeionI02No725MQnMDvvX7ntx65rN5uNKqUb1AhXGPHWhbUAEoDw4YNo0Z8DbxO7qAG50h2cMAkHitowQQfUkIh+nw0patbZrBz5szBx8eHr776yrxU642agoejB5U8KhEQXjBNIcUxBWOGkeCIYDL2jKFbN3jzTaUUPvMMHHrpJbyNRtY+M5EdIY/S8IMBeNdWM3tPT88czUfh4eH8/fffJKemkuhVmrouLmzbv42NGzdSuXJlFi5caNX+aub37KWXXmL//v1Mnz6dBQsWsGLFCk6fPs0vv/zCkSNH6NatGz/9NAY7O/Wd2RZVn8WLU+jBOvO1WjseoQZqHdeXyy+1KlOfGze7fGVuC28BKus0JUXVfU9PVxMDBwcuXrT2Qbz9tvI9xserYItq1ZQyMWaMikuYOhVeeulZnn32WcaP74KLywUGDTpOg7+nENKwO2MMP2BPGm++mbPDe8cOlf2eg2GjUCjxQsH0uzNNioKC1MTv8cctzqDWzsewxTKg2TnZodlqtH9bVad08nTCwdWBpk83xd5ZCZnmY5pj62DL93W+Z3az2SDQ5JkmACSGJZIUYRmwq2curXXkyBFiY2MLTSgkJyXlvvK7Kbbv+eeVBPziC0sRH9MxoH3l9lx99TKu55SHzy7gJBsvbMTF3oV2ldqREJLA6ldW41XZi/qz67M6ZDVH2x4l7HgY51afIy4ojgWNFuAR70Fo2VBmjp3J4icW07Npzxt7BMA+2zZkpGYgYpFbWVm0aBEbN27k+eef5++/rzLUsTpx2HEVPxwSoqhWMZXQELiS4EW9WhnY2GZ+xQ8dYsCBA7ycmHhDTKnCztniPb6y4wopMTd4GtPSVFnUTZvU4DBqFJQvr+orZM4U/Wxtmf3FF9iu2cxI298xeJbB5fOXOQqIrS01vGPMmkL0hWi8qquAgISEBI4ePcqYMWMYN26cOfTYPTPj1dY21izHGpRtkK9Q2LgzhgMHYG2osuE7RFZizawutG+vSrMcOABxcddI3rmTK47O7PnZEe+63nSYaAmr8vT0JCUlhTRTidFMnn76aQZmVoHUqlWlWenSrFmzhn/++YdOnTpl68tPP/3EU089hb+/P7Vq1eKtt95iyJAhPPjgg7Ru3ZoffviBV155hU2bNrFgwWwCAs7xUJOrnE4LYO8sZ17kW5YBqZpG7f+9wMjM1Xzdrp9V2m4hk6umEBCgog1eeEGN8nZ2GI3w448q6KJxYzWxXLNGCYLRo5XZ6M03lbkoI0M991WroGvXs/z22280aNCAU6dOUb16dVq1akXbdmH4vf00LnGh/PrOabZvt5Ssycq0aWA0BvDmmwnZDxYCJV4omDR0k6YwbJj6na9apT7/3bthYD3lGDXFh3ed0pV3Et7Br7Ffrtd183Wj+4zuOLg70GVyF8YeHUu/Of14bKFat/PipovmtjVqqMiDvn37AsqcVBCO/nqUzR9uzrbfxcWFusDwd95VVdvi47OfnLlCT4ZPOWW7sbFReq2DQza9VAsNVbNkwO9UEP+d/Y9uHt24sv4Ka19bS0RgBL2+7sWoDirbc3bZ2cR4xrB88nL+HvE39tH2xFWIw/ZnW059dYq/vvvLvNC6CZULIJyX6qTEpvLRR8p0W9oumukP70SMwj///MPzzz9P5VJVcN3zCIce/YUeqec569uDbS+qsKctEfX5TF7HiC0PPVVKXfz8ebVyEfC5o6NyDt+gm5uEuYmwEzcsvTZnjooy+PxzpULOmaOeU+PG/JsZlvqgfQWSKzWiy65p2Gvp2O/dgU+bxqQACeXLU945mqC9QZz+9zSxV2LxqqaEwtq1azEYDLS7wVlpa2uLu7s7jo4x5klLfZ/6BIYHYjBmF/Yi0P6xI3TvUIo2bY1sDwrhOn54HP+A5CRb5s5V328bm4u0bNGSBqmphKYqJ2zHdztia2+Jszb5AsKyLEGXlJTE+vXrzdvOdepQKSiIP7p25bXXXuONN97I1qdnn32W337LrnlpmsZjjz1GQEAA27dvp21bVVvp0KGDtK4XB7xLd8ABIxOcXXjfzo5EIMPGjtABY9VF9mVfcuX06dPsL2j9lRzIuhqjiBrM06IToU8fxMsLeXsiKSnKivjEE0pZtLNTFte331YBFmXLqtwcRxWwiKOjCmFv3lxFS3311Zc4ODiwfv16xo8fz5NPPklqaio//PCDuZLqEx2u06yZkkNZOXoU1q6NBmNvpkx58pbfZ57kltV2L/zdbkaziMimTSqRcPNmkZ071etRo0R+/lnElEAcdz1Ogg8HS0psiqx7Y50kxyQX+PrmrMdMMtIy5OuqX8tXVb6S0OOhIiKSlpaWmfmp/g4fPpzr9dIS0yQjLUMSwhLMGa4L+y2U1IRUSYxIlNT4VIm8ECmbsmRJZhw7kf1CI0ZIkp2b/Nx+rhgNWfpYtarI0KEiImLIUFncsmuXCEiqu4ucL4VoH2gyxW+K+f7Ln1luPv2ngz9J1a+rSvf23c3H2/RqI8v2L8vzOZ08eVqcnT0Flkk9l4sCGQJfC7iIN1/L+6PeFztbO6lbrZHUZKtsprP5/T3C3+JslyYGzUYm856MHCny+++WDGHp1UsEZF/9+lLWxUXEy0ukf3+r+699fa1MYpLMbDxTJjFJ9s/cb93BevVE2rbNclFFSkqKeHl5SZRma+5PjGNZCZ3xi4iIXL16VQC50KSJJJf3Nz+TKY5T5PSW0/Lcc88JIHXr1pXk5OzfK39/f3F3HyJPPqm2Z/y7VHihnqw9t1Y2X9xs1fb3P9JVF+otFjBYJcr262dp9/LzL8tINBGQEx3GSNy1uGz3XbVqlQCyc+dO876VK1eaM+2vXLkisnq1unjp0iJZqgAUlL1795q/819//bU4OjrKhAkTZMl7fwogP4KEgXQr20127twpDiCzp0wRiY5WqeX+/iJZnll6erp4enoKIBtN5QFuksOH1VtyIEU6d8iQOpUTZYTLEhGQ56qskaZNRTp0MGVwi7z/vojBIDJunOVZv/9+7tf/66+/xN7eXkaPHm21v1+/flKmTBn515Si/fPP8sorIq6ulnFo5UqRMmVEnJ1HyiZNk5CBA2/pPYrkndF81wf22/krDKFw4IB6Ci++KOLnp0oa3ML3+6YI2hckn3h+Ij93+NmyLyjI/ANJSEjI8bz4kHj5qoql3MEkJsnslrNlEpNkTts58rHbxzK3/VxZM2S+GEBOOZYSAdnYbZqVcAr4K0Bi3cpJILVlEpPk4JyDlpt06CDSubOEBYTJp2U+lQOzD4jh199FQE5RW1JtkJpDqln14cayFEajUQ7tPiTjSo+TRgMaiecnnpKUlnP6vqlfH374oQBia1NK4IrAt1aCEpBylJMGbJcFDBKDZiPxbbtLUscecvxQmoDIZSrJap9h1je4fl19wJMny1tvvSUODg4iH3yg9vXtq6T/uHFyauFBmcz7Ev/WZPnO7S1Z+cJKyzXCw1X7Tz7J1v/FixcLIGfxFgExtmpldTw9PV1sbGxka/v2YrSzk8m8L5OYJGc2nZGmTZuKpmkyZswYuXTpUo7Pp02bNuLh0U169VKVSNTAYxCHQUPE7slBEp4YLsnpamBs3j5KKH1G2v7UXpye7S3e/kekJfukWqVUuXJFJD4+Xg7uOSjutu5yCkdJqFQn17IKAQEBAsiCBQvk6tWrcvXqVenevbu4uLhISkqKpeHu3apTX36pts+fF+nUSdWsyIf09HTzZ7tt2zZp2bKldO3aVV594lUBZC+ushNkGMPkyu4r4uHhIS+99JI6eepUdd+TJ83XW7Zsmfl6PXr0yDYhyw+DwSBXriRIJb8YuUBVNaHCRgQkDG+xIUPKlFEycPp0kZgYy7lGo8hPP4lMnCiSnp792itWrJC2bdsKIO3atZOoqCir49u2bRNAHDMlS+qHH8ovv1je4iefqNdurJIWIAYQmTTppt5fVnShkAdnz5p+aCKVKomcyGFSXRRsnrRZJjFJQo6GmPeZvtAmEkITZMPEDZKerL5lGyZusBqM/3n2HxERWTRgkUxiknxVWQmMpTwqArKwc2cRkN8ZKlOdpkrc9TgxpBvk+1LviIDs9O4nP7X+ST4t86kkhCYoDejJJyWtYlX5uePP5vtcf+IVEZC19BABWfrQNzLda7okRSbJ+fXnc/3xLT25VFynucrUrVNzfQ4jR46Uzp07S/ny5aVu3bri5uomdriKHW7i7+EvX3+90vxcpjx9UjyJFgOaGF9/3eo6334rcsitgyS27GTZ+corIuXKqQ93/3557733RNM0yyCf9W/yZMlY8IcISJKts/zS0SKwZdky1WbHDqt7Go1Gadasmfg4+kiQUzXVZmn2WkPly5eX2R07ioBc/GGVXNp6Sb779ltxB1lqam80qinmv/+K7N8vMmaMSHq69O/fX9zcGkrr1iIDBmTvNgMfFxf/YzJzpohmYxA6TZGY5BgxGo0SHhguk5gkh385LKu+XSV+vn4CiAeIUdNEPvoo188lISFBAOnbt6/Y2tpaDbbZ6NpVdWbECJGHHhLzNDqr8MiFdevWSbt27SQ+Pl7Gjh0rHh4eUr9afSlPeYl38JIL7TrKdK/pMsNnhjRt0lS6d+0uq8etlvgFy9V9tmwxX2vYsGFSunRpefPNNwWQBx98UGKyjtw5YDAYZNasWVK3bl0BRAOZVaVKtge9sf37cuFCvm8nR4KCgsTV1VUAeeihhyQxMTFbG6PRaNZyIkCuPPyweRLQrFmmQHDbLxttHSz9Onr01jokulDIk9BQyzOOjr7tyxWYiNMRMtVpqkz3mm5W30ePHi3PPPOMiKgvybwu82QSk+TYH8dk3RvrZBKTZG77uSIikhydbDbvGA1GiTgTIRlpGXJwzkG51mWwJIO8nzmK7Gz6okxikiwZuEROLj0pq+gtAmIMCJCQYyHyke1HMolJMsNnhpyt30/SsJPJth/JwaGfy06b9nKQZpKIs/xhN0wEZK7ts7LqlVUFep/phvRchcaVK1esNIFFixbJRx99JKCKwf3505+SkW6Uco6fSJkyy6R0aZFBZdaqD2tNDgXTXlHCS+rXF1m/3vLBenuLpKebr20wGNQAv22byPLlIq1aiZQvL5ENGpjP+cH9VdXvPXtEGjUS8fDINsgtWbxEAOlPfznw4lxVnS2H99qqVSt5rl07de05c+Ts2bPyUalSarb32msiP/4o8vXXlv7WqWNuO3r0aHFyKms+NGmSSNmhb4hT9X3ZBYRmkGqTupvvG38tVtbSQ35grFSikrhoLvICzWRDhUbqhPXr8/zsvLy8rD6f2rVry+7du7M3XLfOuiP16qn///6b5/Vv5KeffjLf651uz6lrTJ0qp1eelklMku7NuouPp488z/Pyq+8EEZAhDg5Sv359OXPmjLzh7CxT+vSR5ORkee211wSQ8uXLS2xsrNV9sn4fX3rpJav3uDzzPUTXri3y5JMihw6JTJsmksNAnhdGo1HeffddadWqlXTo0EFsbGzk1KlTeWovV65ckZ07d8oxkFN16ojBoL66IPJUuSHyLEgaSIqnp1JVblITyoouFPLAaBQZP/62hO4tE3I0RKY4TpH/XvrPav+6N9bJ9/W/t9IKTH9rxudRPdL0JenaVY44OcmYRx5RH/F338nWqVvN17joWk+MtevIlStX5MSJE3Jg9gF5j/dkAhNkrY2yv8fvPmr1Qw+u3FI2PfadCMhy50E52qFvlkmTJommafLHH3/Il19+KQaDQVJPn5Y/n3tT9v60V72fPn3EYGMrPoRK1aoiJ5/5TPUpLCz7Bc+fl2wj5YoVyoQklqq4Wc0fmzdvlvdMvzyQLzP//0wnifhuoeU6mcLaxKm1p8TH0UfKUlY+4AMJPhKc6/vs16+fAJLu4yMGX195xdFRIrKN6IiUKiVSubJl29lZfhwxQmxsbGTSpAzp31+ZLA5cOyCBYadkyNAM6fLkYaHH6+LsmibuXWbK4L8Gq+qcLVqIsXkLEZA4G0fpC/Jd1V6Wa1eubGWPz4l27doJIPPmzVOCNDdMWo7p2mfOKCGa12w2PV2VHP71V/Ouc+fOiZOTkwwcOFAMpnLER49KRmqGTGKSPMIj5sG7Hl4iIGMzt8tnfY6nTomIyMyZMwWQadOmme+xePFiqVSpkgQEBMjmzZtF0zR54YUX5Pz58xK4f78Y7e1lo62tPH/D530zpKSkyHvvvSeAVKpUSWxsbGTgTdj/N7i7S7CdnZwJDJShQ0Xs7ZOsvydr195y30zoQqEY83vv3+W7ut+Zt1MTUs2D955v9si8rkpbmP/AfNk6davEBlnPeiQlRanQU6aoGeaiRSIgi8uUkUdNqvyUKZKRliFbJm+RRQMWSVrlapLWv7+5RPfYsWOlRYsW4uToJFenfaHOefddqy+i4dvvxBAVIwKS8u7k237fGRkZUrFiRendu7dlp8Eg4uio7rlvn9nBLSCJ079RMq9vX2Xny42QEJFVq0ScndXMPQszZswQwKo0ert27aRm5j0OgXTt3FmiQFZRSfajBtWYZ1+TkDUH5dQ/pyT4VLC80vQVKUMZAWQoQ2XJwCV5zgBfffVVAWRp6dLm93PB2Vkur16tPrtOndT+efNEvlOC11yvG+QFkOXLlsnvv/9uvo/BYJCEhARJTk0W9wfchR4ITyHD33xMouvXN59rLF1aQmxsLJ+lr68ajAPzL09+/fr1PIMesvHdd8pfIyLy/PPqfo8/nr3dqVNK+zL16d13Rf77T+SRRyw11vv2FalRwzzROb7ouHzAB1KPegKIXea5B/v1k5kzZ8qwrINm8+Zmg3/Pnj0FkBEjRojBYJCKFSsKIJqmCSB16tSR+O3blZaZWVv+o44dxdfXV5Iy/S3p6ely7do16dOnjzRv3lwGDhwor7/+upw9e9b8eaSkpMiPP/4oY8aMMWtYQ4cOFaPRKFFRUTkGEeTG2ueUlvQyyCcvvSn96ja3Fgo3qbXkhC4UijHbp2+XSUyS+GA1UJ1ff145IledERGRsJNhsun9TRJ+6sYFEEQJhD59rL8wIFK9ujzcvLl0795dxMVFmSgyMWZkSJqmyfTMGZa7u7uV+jzKZLoAETs7kWPHlCplsq15ed2woICohRPeeCPfmWdWTJEsf//9t2WnKZrF9MMeOFDE3l5FRLVqJXLwoJhtKPmRgz37q6++EkCiM99LYGCgAOLs7Cx9QTxBEhMTZZeDgxx38ZAY29ISSG2Z4qgirT7kQ6lKVfOz+nn2zxJ3Lc46eisHwsPDZdiwYeIIUg9k2qOPKgFowmgUOX1avY6PF3n0UeVVDgiQkEyTVr/Me7700ksyZMgQ8fX1lUqVKsmECROsPr+fUE7IaY0by9Y33pDvn3lGSoMEjhmjQpC2b8//2RUGCQki1asrg/ipUyrayxRAMXCg+hwHDBB57DHr7+66dSrcxsND5IYIHZP2HHYyTBaPWixJOEvGmOdFRCS1b18xli0r8s8/6ntbvbpIfLykpaXJyy+/LIBMnDhRAJk8ebKMHTtWevToIUEW773yg1SuLJtXrxZAXnvtNVm+fLnUr1/f/HzLlSsnmqaZhUrLli2lT58+5uOOjo4yZMgQWbNmTa7aVdz1OAn4KyDXiYQxJUUybFU025VMbcjqGRUCulAoxlzZdcUqgmfjexvlI9uPJCU2fyedvPSS+giHDhWZM0f98EDkl1/Mi/OEOznJrjp1zAPhhnnzREA+9PWVHj16SHx8vIwaNUo+++wz+ffff6WGvb35y7fY01MWLbph4ZnGjZUzMTxc2TVnz1aDNiiHbAHp16+f+Pn5SZppZrh8eXbhBsqu+1mmyahJEyXk8nEe5saiRYsEkOXLl4uIyODBg8XBwUGOHj0qdnZ2Mnv2bBER2dm4sfn+F198Q0YyUt7iLXnG/hkBpF+3frIli4OzIKSnp0vTpk3Fzc1NIguy+EomIUFBcs3ZWcKcnKRe5sDj7e0tgFQGmQ/yXceOkvDEE3K6e3fJsLGRtXXqiJ1KDRZABg4ceNOROIXCuHHq8+reXT3PFSuUAPTzE+nRQyQ1VbWbPt3yeY8ZY4kN/+03q8tFnouUc2vPiYjImf/OSDhl5GrldjKvxhQx2tjK6foD5Ny6c5bv0o8/iohIWFiY2VnesmVLy2CdlqYEsI2N6pOvr1k4P/PMM+bn5+HhIVOnTpVvvvlGDAaDpKeny5EjR+Sdd96RKlWqSNmyZaVDhw4yY8aMPDWCyHOR8nuf382WgMO/HM792Y0aZX4mgb6+YvTxUUJ9w4Zb+yxuQBcKxZi0pDSZbDdZlo1YJgdmH5C57efK7Jaz8z8xJkb94J7OsjqW0SgSECBiNIq/v78AcsLGRv4G8fT0lC+//FIGV6woApK2cmWOl12+bJm8CVIty+wzJMQSISUPPywCYsxqksj8O9O7t2zdulW++eabPKM+9u3bJzY2NvLOO++oHaGhKubcdK2ff1ahsU8/LRIVpUxCJnPKbdh609LSpFatWlK9enUZP368ADIpU+tIzxJHGD5pkrkv1bM8B0DcXN3MAvZmSUlJkYiIgq2aZsXOnSKlSkmKi4v88sorYhg+XOTTT+V406Y5C9LDhyUxMVH+/PNP2bBhg9V7u6PMmqX6U6GC+j9zpvp+ZhmwRUSFxY4aJdKwoTpWurQaoLMtD2ghJS5FLlJFrlFODtFE0rGVrxgnX1f9Wv0OGjdW5qdMjfGff/6RUaNGWb7LRqPFRPrZZ0pDy2JWjImJkXfffVcWzlwowSeDJTkmWUKOhsi1/dcKLGDDAsJkxdgVcnnHZTm37px85vuZCujwniGflvlUPvH4RCLP5jJBSEw0m4IFlPAqRHShUMyZ1WSWlTN57YQCOJKmTVMf3/79OR7+5Zdf5OWXXxZjx44S37SptGnTRgCZbmurBvRr13K99L59+2TZsmXmmXXfvn3ll19+kWvXrknGs88qoQLyFMjFN96QtFOn5GS1ahIN8mLm4Pnmm2/Ktm3bpE+fPjJ8+HCJjo6WkJAQ2b9/v5QqVUp8fHxUAlRSkkiLFsoHMH++si3nRECAGmRuM4nk228t+Q9dunTJeYnTvXtFQCLLlpW6derIgAEDxMPDQx588EHZt2/fbd3/ljElNd349+KLanDbuVPk889FZsy4raiUQiWraQZEMickAiLBOTjmTYOgn5/6vPNhbzXLurDhDz8tk5gkU52nqhDuNWvUsblzrU9KSlImKpNjfNCgHK9tNBrNS/He+Ldk4BIJOxkmmz/cLN/W/lYubrkooSdC5diCYxIfEi/Bh4PluzrfZTvvuzrfyZVdVyQpMkmiL0XLp6U/lW9qfCPr3lwnl7dfzqkTluf1/fcFeeIFJi+hoKnj9yYtWrSQAwcO5N+wmLP9k+0ELg0018UZc3gMfk1yL6GB0ahKUnTsCP/8k/fFhw2D7dtJP3uW9atX02vkSGw7d4ZlywrUt2HDhvH7778DqiT3rGrVGB4QwDDgd6Bly5Y8/vjj/PjWW/wH1AGeBeZmnm9aw6BJkyaEhIQQEhKCjY0NR44coWHDhjBunKo2+c8/agX2IiY2NpaRI0cyYMAAhgwZgp3VijlZOHUKqlY1L7ogIjddXK1QEVFluufNUxVtZ85UNXhWrjSX7i6WDB2qVifKykcfYV6AIysZGap+xIABeS5laSLlcigOTz6CTWQE7N/PhQNR/NbjN/r/0p8mIxqrdQ4yMlStq4oVVR2KqVPV2rIAAwfCggXs+mY/lzZd4uE5D5ORksGBWQc4tewUUWejqN6rOtV7VkeMglMpJ+Kvx7P1o63mtVKcyziTHGmp9KrZamiahpOXE42eakTz0c3Z8uEWUmJSeGj2Q5SqUsrc9sLGC/z1xF8kRyVjY2fD88efx7vODevk2tioz/76dShXrkCPvCBomnZQRFrkeEwXCsWHKzuukJGSQbXumetXHjyoBv/MeihmTp5UC9POm2epcpob77+v6iSnpKhBpXNntTzaY48VqE/JycksXLiQqlWr0q1bN5yBhnZ2TF21ipCQEPNyoWXKlGHbli3Ue/llZPt2pnXqxPubN7NixQquXbvG2LGqXs1XX31FrVq1VJ2n9HRVKKZvX1WGWuf+IyMDjh9XS6bFxqpJShEJVxFhdvPZJEUk4dvIlw5n5lL5bJY1G06cwDhmLDY7dxD61Gtsj6xP/WfbsuSxJYBaejc9UZVWd/Nzo/3b7WkxtgV2jtYTh5CjIYSfDMezsic+dX3Y8PYGyjYsi28jXw78cAAbOxt6fNYD9/Lu+fbZkGYgPjieWY1nUa1bNZ5Y+oR1g1274NAheOml23s4N6ALhRuJiFDFzfr3VwXjiiNHj6p1L0HVyr18GQYNUrOGX39VtY8DA6FOnbyvM3euKvv61VeqyPumTRAZaSkPexMMGjSIXbt2sXXrVvz9/RER5syZw549e3jnnXdUtdf4eGjRAklLI2TDBsplVoDduHEjXl5eNGvWzHLBLVuga1dYulQt06ajc5tc23eNn9v/jDHDiAcxvPh5NRyeeARq1SKlbRfsN69nD63ZgKVKr1d1L7pO7sqhOYeIC4pj8L+DKVO7zB3VDDe+u5EdH++gVNVS9PqqF3Ueyed3fZvkJRTuul/gdv5u2aewf7+y0/3xx62dL6IcoDlx4cItR8dYMWxYdvvxgw+KZIaqia+vdVhjbmzYYH2N23BYpaen553EZGLzZnWv8eNFLudgKzUxfryIg0PRF5vSKVHs/nq32Y6/dOhSObbgmKQ/NUIEVcvoO16Uue3myv5Z++WPh/6Q0BOhd7vLkhiRKLOaKt/iZ2U/M5e2KSrQHc03kJEh4umpImnyiswwGkU2bsyeLPLtt2J2UmWNzDl1SiVfubndfhGlypVVPPczz6iKkKZB3d9fZPDgXB3M2bhyRZ1XvrxImzYq7+BOYIpFd3TMOQMzOlr1qU+fO9MfnRKF0WCUP5/80ywcvuZVOUIjuTqpAJF9dwlDhkFOLDkhk5gkFzbdYqGlAqILhZzo21e9/d69cxYMn32mBlFTHoCJlBQ1uzUN0vXqqbj9ypWtslClQQORWwk/FFECBZTwMRhU/9auVVEZK1bc2vVyirIpSoKCLPHpOWllQ4aIaNpN18fR0SkoGWkZcn7Dedk/c7+5kvBdyde4CZKikmQSk2Tr1K1Feh9dKOTEvn2W8oNz5lgfu3ZN7a9TR2VHgqWGy8aNavvXX1VpCTs7NeN98kll3tm/X5lsHBxEunRRg6PBoMqx5hbOaDLJpKQo7aRFC5U5fPWqdbti/oXOkcREVcbBwUGFnK5aJRIXp3IsbshY1dEpKsICwgqWEFoM+L7e9/Jrj1/zb3gb6EIhN4xGkZYtVUp8erpKrgkKsiTdnDihiou5uIg88YRqP2GCKr1gsoOnpeVs2//+e8ss2SRYwDr+eskSFbttZ6c0DVdXkcmTVbsffri991aciIwUqVXL8gxMrzdvvts909Epdmx8d6OqcrA8//pUt4ouFPJiwQL1GF5+Wf13dlZlG+rUsczMP/rIMqCBMhcVhF271AD48MPKNwAiw4erTM3XX7e+ZtY/X9/CcVYXJ65cUULXtL5B9+73puajo1PExAbFyicen8gkbZIE7QuyOrbzs50yu8Vsmdt+rqx/K+/S53mRl1AomSGpWUlNVWGdly5Z758zRy3ODiqe/oMP4Lff1Lq8mzdDly43f6+nn1a5BT4+EB4OjRrBokUqD+HyZRXTvWuXCpX1yyN57V7m2DG1uvmzzxYoQUlHpyQSdy2OH5v8SLnm5ej9v95semcTmo3Gyb9OUrZhWRw9HKnzSB3aTWiX/8VyQM9TyI/jx1W2Y8+eKos1KUnlBOQUp5ycfOsZpAcOQPfuSih8+qmK0ffyuq2u6+jo3J9s/nAz2yZvw9XXlcTQRAAqtK7AMzufwcbW5raurQuF4kRGBtjaFllWp46Ozv1BzOUY/lf1fwA88usjpCemU/PBmnhW8rzta+clFHIp/KJTZORWa0dHR0cnC6WqlOKpdU8RciSERk81umMZ1rengxQBmqb11jTttKZp5zRNe/tu90dHR0fnblG9R3Xav9H+jpbcKFZCQdM0W+B7oA9QDxisaVq9u9srHR0dnZJDsRIKQCvgnIhcEJE0YBHQ/y73SUdHR6fEUNyEQgXgapbtoMx9ZjRNG61p2gFN0w6Eh4ff0c7p6Ojo3O8UN6GQk+HMKjxKRGaLSAsRaeFTXMte6+jo6NyjFDehEARUyrJdEbh+l/qio6OjU+IobkJhP1BT0zR/TdMcgEHAv3e5Tzo6OjolhmIVNC8iGZqmvQSsBWyBn0Uk4C53S0dHR6fEUKyEAoCIrAJW3e1+6Ojo6JRE7ukyF5qmhQOXb+MS3kBEIXWnMNH7dXPo/bo59H7dHMW1X3DrfasiIjlG6tzTQuF20TTtQG71P+4mer9uDr1fN4fer5ujuPYLiqZvxc3RrKOjo6NzF9GFgo6Ojo6OmZIuFGbf7Q7kgt6vm0Pv182h9+vmKK79giLoW4n2Kejo6OjoWFPSNQUdHR0dnSzoQkFHR0dHx8x9JRQ0TftZ07QwTdNOZNnXWNO03ZqmHdc0bYWmaR6Z+4dqmnYky59R07T/t3e2IVJVYRz//dVNfKlIzbCIVkErwzJdzKAXCTQqyaAkM0owiMLCXkGp8EN9qIgo2aI+lGmUQhnklxILxdLMUtzw/R0pFg00Wi3TXZ8+nDPjuO2szsyZucv2/GCYM+fec+c//7lznznn3vuc0XHZ2Lj+bknzVeEMFwl1rYoTEOWWDa6hrjpJC2P9NklzC9pk6VdnurL06zxJC2J9k6QJBW2S+pVYWzLPJF0uaWX8XrZImh3rB0haIWlXfL6ooM3c6MsOSbcX1CfzLLGu1PtYSdokDYzrH5XU2G5b5XlmZt3mAdwCjAE2F9T9BNwayzOBlztoNwrYW/B6PXAjIWvrV8AdXUTXKqAhC7+A6cCSWO4L7Afqs/brLLqy9GsWsCCWBwMbgB7V8CuxtmSeAUOAMbF8PrCTMHnW68CcWD8HeC2WRwJNQG9gKLAH6Jnas8S6Uu9jpWrrB9wEPAY0tttWWZ51q56Cma0GDrervhJYHcsrgHs7aPoAsBhA0hDgAjP7wYKzi4B7stZVDUrUZUA/Sb2APsAJ4M8u4FeHuip5/0S6RgLfxnaHgD+Ahmr4lUpbpRo60NRsZhtjuQXYRpgfZQqwMK62kNOffwohwP9jZvuA3cC41J6l0lXu+6fUZmbHzOx74HjhdirxrFsFhSJsBu6O5amcmZo7x/2cPvheRkjhneM/E/1kpCvHgthNfSnFsEMJuj4HjgHNwAHgDTM7TPZ+FdOVIyu/moApknpJGgqMjctq5Vc52nIk90xSPXA98CNwiZk1QzgIEnorUHySrap5VqGuHFXZx85RWzHK9uz/EBRmArMkbSB0x04ULpR0A/CXmeXGYs860U9GugAeNLNRwM3x8VANdY0D2oBLCV3oZyUNI3u/iumCbP36kPBD/Bl4C1gLtFI7v8rRBlXwTFJ/YCnwlJl11osr5k1VPEugC6q0j5WgregmOqg7J8+6XJbU1JjZdmASgKQRwF3tVpnGmf/GfyVM7pOjKhP9lKELM/stPrdI+pRwQFxUI13Tga/N7CRwSNIawpDDd2TrVzFde7P0y8xagadz60laC+wCjlADv8rUlnwfk1RHOLh9YmZfxOqDkoaYWXMc5jgU64tNspX8N5lIV1V+kyVqK0bZnnX7nkLuagBJPYAXgfcKlvUgdKuX5Opi16xF0vjYFXwY+DJrXbGrPyiW64DJhOGBWuk6ANymQD9gPLC9C/jVoa6s/ZLUN+pB0kSg1cy21sqvcrSl9ix+vg+AbWb2ZsGiZcCMWJ7B6c+/DJgmqXcc1hoOrE/tWSpd1djHytDWIRV5luqseVd4EP5ZNwMnCZHyEWA24Qz+TuBV4l3ccf0JwLoOttNA+HL3AI2FbbLSRbjKYAPwC7AFeJt4BUQtdAH9gc/ie28Fnu8KfhXT1QX8qgd2EE4UfkNIVVwVv1JpS+0Z4aoYi9vbFB93AgMJJ7p3xecBBW1eiL7soOBqmZSepdJVpX2sHG37CRcZHI3f/chKPPM0F47jOE6ebj985DiO45w7HhQcx3GcPB4UHMdxnDweFBzHcZw8HhQcx3GcPB4UHKcEJLXFlAZbFDKMPhPvA+isTb2k6bXS6DiV4EHBcUrjbzMbbWbXABMJ15DPO0ubesKd147T5fH7FBynBCQdNbP+Ba+HEVJUDwKuAD4m3NQE8ISZrZW0Drga2EfIcDmfcDPZBEI65nfM7P2afQjH6QQPCo5TAu2DQqw7AlwFtACnzOy4pOHAYjNrUJjE5jkzmxzXfxQYbGavSOoNrAGmWkjL7DiZ0u0T4jlODchlpKwDGhVmymsDRhRZfxJwraT74usLCfl0PCg4meNBwXEqIA4ftRGyVs4DDgLXEc7XHS/WDHjSzJbXRKTjlICfaHacMpF0MSHzaKOFcdgLgWYzO0XIq98zrtpCmM8gx3Lg8ZhZE0kjcllLHSdrvKfgOKXRR9ImwlBRK+HEci7F8bvAUklTgZWEGeEgZLxsldQEfETIplkPbIxpjX8nwZScjpMCP9HsOI7j5PHhI8dxHCePBwXHcRwnjwcFx3EcJ48HBcdxHCePBwXHcRwnjwcFx3EcJ48HBcdxHCfPv9eqpyMzORyoAAAAAElFTkSuQmCC"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "As anyone can see by the blue line in the plot, **Answer:** **ProcterGamble** has fell the most after the tech bubble of March 2000. "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### What stock had the highest maximum price between 1995-2005?\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "**Answer: <u>IBM</u>**  has the highest maximum price between 1995 - 2005, you can see the **Green** line in the bove diagram."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### A few years before the tech bubble of 1997, there was another stock market crash trigged by economic crisis in Asia in October of 1997. If you compare stock prices from September 1997 to November 1997, which companies saw a decrease in price? Which company experienced the biggest decrease?\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "**Answer:  <u>ProcteeGamble and Boeing</u> ,** have both decresesd in the specified time peried between September - and Novemeber but **<u>Procter Gamble</u> experienced the greatest decrease**  as show by the blue line and red line consectivily on the diagram below."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "source": [
    "# To answer the question we need to sample out the exact data and draw it in a more specific grap\r\n",
    "aisia_crisis_IBM = IBM[IBM.Date.dt.year == 1997]\r\n",
    "aisia_crisis_GE = GE[GE.Date.dt.year == 1997]\r\n",
    "aisia_crisis_ProcterGamble = ProcterGamble[ProcterGamble.Date.dt.year == 1997]\r\n",
    "aisia_crisis_CocaCola = CocaCola[CocaCola.Date.dt.year == 1997]\r\n",
    "aisia_crisis_Boeing = Boeing[Boeing.Date.dt.year == 1997]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "source": [
    "sns.lineplot(data=aisia_crisis_IBM, x=aisia_crisis_IBM.Date.dt.month, y=aisia_crisis_IBM.StockPrice, label = \"IBM\", color=\"green\")\r\n",
    "sns.lineplot(data=aisia_crisis_GE, x=aisia_crisis_GE.Date.dt.month, y=aisia_crisis_GE.StockPrice, label = \"GEStock\", color=\"purple\")\r\n",
    "sns.lineplot(data=aisia_crisis_ProcterGamble, x=aisia_crisis_ProcterGamble.Date.dt.month, y=aisia_crisis_ProcterGamble.StockPrice, label = \"ProcterGamble\", color=\"blue\")\r\n",
    "sns.lineplot(data=aisia_crisis_CocaCola, x=aisia_crisis_CocaCola.Date.dt.month, y=aisia_crisis_CocaCola.StockPrice, label = \"CocaCola\", color=\"black\")\r\n",
    "sns.lineplot(data=aisia_crisis_Boeing, x=aisia_crisis_Boeing.Date.dt.month, y=aisia_crisis_Boeing.StockPrice, label = \"Boeing\", color=\"red\")\r\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date', ylabel='StockPrice'>"
      ]
     },
     "metadata": {},
     "execution_count": 55
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Which stock seemed to provide the best return (i.e. increase in price) between 2004-2005?\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "**Answer:  <u> Boeing</u> ,** you can verify boing retunr from 2004 too the end of 2005 by the red line in the diagram below."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "new_ibm_data = IBM[(IBM.Date.dt.year >= 2004) & (IBM.Date.dt.year <= 2005)]\r\n",
    "new_ge_data = GE[(GE.Date.dt.year >= 2004) & (GE.Date.dt.year <= 2005)]\r\n",
    "new_pg =  ProcterGamble[(ProcterGamble.Date.dt.year >= 2004) & (ProcterGamble.Date.dt.year <= 2005)]\r\n",
    "new_coca = CocaCola[(CocaCola.Date.dt.year >= 2004) & (CocaCola.Date.dt.year <= 2005)]\r\n",
    "new_boeing = Boeing[(Boeing.Date.dt.year >= 2004) & (Boeing.Date.dt.year <= 2005)]\r\n",
    "\r\n",
    "\r\n",
    "sns.lineplot(data=new_ibm_data, x=new_ibm_data.Date, y=new_ibm_data.StockPrice, label = \"IBM\", color=\"green\")\r\n",
    "sns.lineplot(data=new_ge_data, x=new_ge_data.Date, y=new_ge_data.StockPrice, label = \"GEStock\", color=\"purple\")\r\n",
    "sns.lineplot(data=new_pg, x=new_pg.Date, y=new_pg.StockPrice, label = \"ProcterGamble\", color=\"blue\")\r\n",
    "sns.lineplot(data=new_coca, x=new_coca.Date, y=new_coca.StockPrice, label = \"CocaCola\", color=\"black\")\r\n",
    "sns.lineplot(data=new_boeing, x=new_boeing.Date, y=new_boeing.StockPrice, label = \"Boeing\", color=\"red\")\r\n",
    "\r\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date', ylabel='StockPrice'>"
      ]
     },
     "metadata": {},
     "execution_count": 25
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"265.362818pt\" version=\"1.1\" viewBox=\"0 0 408.11781 265.362818\" width=\"408.11781pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-08-11T14:43:00.422608</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.4.2, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n  <style 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Between 1995-2005, which company had the biggest delta between the maximum and minimum stock price?\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "**Answer:  <u> IBM</u> ,** has the biggest delta vale of **146.763**. Check the calculation of delta for each stock"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "IBM_delta = IBM[(IBM.Date.dt.year >= 1995) & (IBM.Date.dt.year <= 2005)]['StockPrice'].max() - IBM[(IBM.Date.dt.year >= 1995) & (IBM.Date.dt.year <= 2005)]['StockPrice'].min()\r\n",
    "GE_delta = GE[(GE.Date.dt.year >= 1995) & (GE.Date.dt.year <= 2005)]['StockPrice'].max() - GE[(GE.Date.dt.year >= 1995) & (GE.Date.dt.year <= 2005)]['StockPrice'].min()\r\n",
    "ProcterGamble_delta = ProcterGamble[(ProcterGamble.Date.dt.year >= 1995) & (ProcterGamble.Date.dt.year <= 2005)]['StockPrice'].max() - ProcterGamble[(ProcterGamble.Date.dt.year >= 1995) & (ProcterGamble.Date.dt.year <= 2005)]['StockPrice'].min()\r\n",
    "CocaCola_delta = CocaCola[(CocaCola.Date.dt.year >= 1995) & (CocaCola.Date.dt.year <= 2005)]['StockPrice'].max() - CocaCola[(CocaCola.Date.dt.year >= 1995) & (CocaCola.Date.dt.year <= 2005)]['StockPrice'].min()\r\n",
    "Boeing_delta = Boeing[(Boeing.Date.dt.year >= 1995) & (Boeing.Date.dt.year <= 2005)]['StockPrice'].max() - Boeing[(Boeing.Date.dt.year >= 1995) & (Boeing.Date.dt.year <= 2005)]['StockPrice'].min()\r\n",
    "\r\n",
    "print(\"IBM Delta: {}\".format(IBM_delta))\r\n",
    "print(\"GE Delta: {}\".format(GE_delta))\r\n",
    "print(\"ProcterGamble Delta: {}\".format(ProcterGamble_delta))\r\n",
    "print(\"Cocacola Delta: {}\".format(CocaCola_delta))\r\n",
    "print(\"Boeing Delta: {}\".format(Boeing_delta))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "IBM Delta: 146.76306522000002\n",
      "GE Delta: 133.77789473\n",
      "ProcterGamble Delta: 96.62526316\n",
      "Cocacola Delta: 45.67551948\n",
      "Boeing Delta: 80.66904762\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Which two companies’ stock price seem to be the most correlated (i.e. move up/down together)?\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "**Answer:  <u> Cocacola and ProcterGamble </u> ,** have the biggest coorlation to move together. Check the plot drawn above in question one for more detail."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Monthly Trend Analysis\r\n",
    "\r\n",
    "We want to see if there are any monthly patterns (i.e. consistently higher/lower prices at various months of the year). To do for each company, we essentially want to compare “mean” by month vs the overall mean across the entire date range."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Questions:\r\n",
    "\r\n",
    "\r\n",
    "#### For IBM, compare the average stock price for each month to the its overall average stock price and identify all the months for which IBM historically had a higher stock price (we call this over- indexing)? Which month over-indexed the most?\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "**Answer:  <u>  January, February, March, April, May</u> ,** and **<u>February</u>**.  overindexed the most within these months."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "total_ibm_average = IBM.StockPrice.mean()\r\n",
    "print(\"The total Average of IBM stock is {}\".format(total_ibm_average))\r\n",
    "print(\"The follwing months have an average greator than {}\". format(total_ibm_average))\r\n",
    "ibm_month_group = IBM.groupby(IBM.Date.dt.month)\r\n",
    "ibm_month_group.StockPrice.agg('mean').loc[ibm_month_group.StockPrice.mean() > total_ibm_average]"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The total Average of IBM stock is 144.3750303076664\n",
      "The follwing months have an average greator than 144.3750303076664\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Date\n",
       "1    150.238423\n",
       "2    152.693993\n",
       "3    152.432690\n",
       "4    152.116824\n",
       "5    151.502194\n",
       "Name: StockPrice, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Repeat the  function you used to solve the last question for each of the 4 remaining companies. Do any of two or more companies have their highest stock price in the same months as each other? Which companies and months does this happen for?\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "**Answer:  For <u>IBM</u> and <u>GE</u> ,**  **<u>January, February, March, April, May</u>**.\r\n",
    "\r\n",
    "\r\n",
    "**Answer:  <u>ProcterGamble</u> and <u>CocaCola</u> ,** have increased together in  **<u>January, February</u>**."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "ge_month_group = GE.groupby(GE.Date.dt.month)\r\n",
    "ge_month_group.StockPrice.agg('mean').loc[ge_month_group.StockPrice.mean() > GE.StockPrice.mean()]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Date\n",
       "1    62.045106\n",
       "2    62.520805\n",
       "3    63.150548\n",
       "4    64.480092\n",
       "5    60.871351\n",
       "Name: StockPrice, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "source": [
    "pandg_month_group = ProcterGamble.groupby(ProcterGamble.Date.dt.month)\r\n",
    "pandg_month_group.StockPrice.agg('mean').loc[pandg_month_group.StockPrice.mean() > ProcterGamble.StockPrice.mean()]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Date\n",
       "1     79.617984\n",
       "2     79.025755\n",
       "5     77.859578\n",
       "11    78.456104\n",
       "12    78.296608\n",
       "Name: StockPrice, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "source": [
    "coca_month_group = CocaCola.groupby(ProcterGamble.Date.dt.month)\r\n",
    "coca_month_group.StockPrice.agg('mean').loc[coca_month_group.StockPrice.mean() > CocaCola.StockPrice.mean()]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Date\n",
       "1    60.368487\n",
       "2    60.734754\n",
       "3    62.071354\n",
       "4    62.688882\n",
       "5    61.443581\n",
       "6    60.812084\n",
       "Name: StockPrice, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "source": [
    "boeing_month_group = Boeing.groupby(ProcterGamble.Date.dt.month)\r\n",
    "boeing_month_group.StockPrice.agg('mean').loc[boeing_month_group.StockPrice.mean() > Boeing.StockPrice.mean()]\r\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Date\n",
       "2    46.892233\n",
       "3    46.882076\n",
       "4    47.046860\n",
       "5    48.137160\n",
       "6    47.385255\n",
       "8    46.863107\n",
       "Name: StockPrice, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### What trend do you see for the months of December vs January for each company? Is there an over-arching trend that applies to all companies when comparing all historical December vs January stock prices?\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "**Answer:  For <u>All Stock Prices </u> ,** increased between **<u>December and January</u>**."
   ],
   "metadata": {}
  }
 ],
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