Stats of stock data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics from Stock Data\n",
    "\n",
    "In this lab we will load stock data into a Pandas Dataframe and calculate some statistics on it. We will be working with stock data from Google, Apple, and Amazon. All the stock data was downloaded from yahoo finance in CSV format. In your workspace you should have a file named GOOG.csv containing the Google stock data, a file named AAPL.csv containing the Apple stock data, and a file  named AMZN.csv containing the Amazon stock data. All the files contain 7 columns of data:\n",
    "\n",
    "**Date Open High Low Close Adj_Close Volume**\n",
    "\n",
    "We will start by reading in any of the above CSV files into a DataFrame and see what the data looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>3.745536</td>\n",
       "      <td>4.017857</td>\n",
       "      <td>3.631696</td>\n",
       "      <td>3.997768</td>\n",
       "      <td>3.596616</td>\n",
       "      <td>133949200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>3.866071</td>\n",
       "      <td>3.950893</td>\n",
       "      <td>3.613839</td>\n",
       "      <td>3.660714</td>\n",
       "      <td>3.293384</td>\n",
       "      <td>128094400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>3.705357</td>\n",
       "      <td>3.948661</td>\n",
       "      <td>3.678571</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.341579</td>\n",
       "      <td>194580400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-06</td>\n",
       "      <td>3.790179</td>\n",
       "      <td>3.821429</td>\n",
       "      <td>3.392857</td>\n",
       "      <td>3.392857</td>\n",
       "      <td>3.052405</td>\n",
       "      <td>191993200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-07</td>\n",
       "      <td>3.446429</td>\n",
       "      <td>3.607143</td>\n",
       "      <td>3.410714</td>\n",
       "      <td>3.553571</td>\n",
       "      <td>3.196992</td>\n",
       "      <td>115183600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Open      High       Low     Close  Adj Close     Volume\n",
       "0  2000-01-03  3.745536  4.017857  3.631696  3.997768   3.596616  133949200\n",
       "1  2000-01-04  3.866071  3.950893  3.613839  3.660714   3.293384  128094400\n",
       "2  2000-01-05  3.705357  3.948661  3.678571  3.714286   3.341579  194580400\n",
       "3  2000-01-06  3.790179  3.821429  3.392857  3.392857   3.052405  191993200\n",
       "4  2000-01-07  3.446429  3.607143  3.410714  3.553571   3.196992  115183600"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We import pandas into Python\n",
    "import pandas as pd\n",
    "\n",
    "# We read in a stock data data file into a data frame and see what it looks like\n",
    "Apple_file = pd.read_csv('AAPL.csv')\n",
    "# We display the first 5 rows of the DataFrame\n",
    "Apple_file.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We clearly see that the Dataframe is has automatically labeled the row indices using integers and has labeled the columns of the DataFrame using the names of the columns in the CSV files.\n",
    "\n",
    "# To Do\n",
    "\n",
    "You will now load the stock data from Google, Apple, and Amazon into separte DataFrames. However, for each stock data you will only be interested in loading the `Date` and `Adj Close` columns into the Dataframe. In addtion, you want to use the `Date` column as your row index. Finally, you want the DataFrame to recognize the dates as actual dates (year/month/day) and not as strings. For each stock, you can accomplish all theses things in just one line of code by using the appropiate keywords in the `pd.read_csv()` function. Here are a few hints:\n",
    "\n",
    "* Use the `index_col` keyword to indicate which column you want to use as an index. For example `index_col = ['Open']`\n",
    "\n",
    "* Set the `parse_dates` keyword equal to `True` to convert the Dates into real dates of the form year/month/day\n",
    "\n",
    "* Use the `usecols` keyword to select which columns you want to load into the DataFrame. For example `usecols = ['Open', 'High']`\n",
    "\n",
    "Fill in the code below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We load the Google stock data into a DataFrame\n",
    "google_stock = pd.read_csv('GOOG.csv', index_col = ['Date'], parse_dates = True, usecols = ['Date', 'Adj Close'])\n",
    "\n",
    "# We load the Apple stock data into a DataFrame\n",
    "apple_stock = pd.read_csv('AAPL.csv', index_col = ['Date'], parse_dates =True, usecols = ['Date', 'Adj Close'])\n",
    "                       \n",
    "# We load the Amazon stock data into a DataFrame\n",
    "amazon_stock = pd.read_csv('AMZN.csv', index_col = ['Date'], parse_dates = True, usecols = ['Date', 'Adj Close'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can check that you have loaded the data correctly by displaying the head of the DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "  <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>2004-08-19</th>\n",
       "      <td>49.845802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>53.805050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>54.346527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>52.096165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>52.657513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            Adj Close\n",
       "Date                 \n",
       "2004-08-19  49.845802\n",
       "2004-08-20  53.805050\n",
       "2004-08-23  54.346527\n",
       "2004-08-24  52.096165\n",
       "2004-08-25  52.657513"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We display the google_stock DataFrame\n",
    "google_stock.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will now join the three DataFrames above to create a single new DataFrame that contains all the `Adj Close` for all the stocks. Let's start by creating an empty DataFrame that has as row indices calendar days between `2000-01-01`  and `2016-12-31`. We will use the `pd.date_range()` function to create the calendar dates first and then we will create a DataFrame that uses those dates as row indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create calendar dates between '2000-01-01' and  '2016-12-31'\n",
    "dates = pd.date_range('2000-01-01', '2016-12-31')\n",
    "\n",
    "# We create and empty DataFrame that uses the above dates as indices\n",
    "all_stocks = pd.DataFrame(index = dates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To Do\n",
    "\n",
    "You will now join the the individual DataFrames, `google_stock`, `apple_stock`, and `amazon_stock`, to the `all_stocks` DataFrame. However, before you do this, it is necessary that you change the name of the columns in each of the three dataframes. This is because the column labels in the `all_stocks` dataframe must be unique. Since all the columns in the individual dataframes have the same name, `Adj Close`, we must change them to the stock name before joining them. In the space below change the column label `Adj Close` of each individual dataframe to the name of the corresponding stock. You can do this by using the `pd.DataFrame.rename()` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the Adj Close column label to Google\n",
    "google_stock = google_stock.rename(columns = {'Adj Close' : 'Google'})\n",
    "\n",
    "# Change the Adj Close column label to Apple\n",
    "apple_stock = apple_stock.rename(columns = {'Adj Close' : 'Apple'})\n",
    "\n",
    "# Change the Adj Close column label to Amazon\n",
    "amazon_stock = amazon_stock.rename(columns = {'Adj Close' : 'Amazon'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can check that the column labels have been changed correctly by displaying the datadrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Google</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>49.845802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>53.805050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>54.346527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>52.096165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>52.657513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               Google\n",
       "Date                 \n",
       "2004-08-19  49.845802\n",
       "2004-08-20  53.805050\n",
       "2004-08-23  54.346527\n",
       "2004-08-24  52.096165\n",
       "2004-08-25  52.657513"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We display the google_stock DataFrame\n",
    "google_stock.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apple</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>3.596616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>3.293384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>3.341579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>3.052405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>3.196992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               Apple\n",
       "Date                \n",
       "2000-01-03  3.596616\n",
       "2000-01-04  3.293384\n",
       "2000-01-05  3.341579\n",
       "2000-01-06  3.052405\n",
       "2000-01-07  3.196992"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We display the apple_stock DataFrame\n",
    "apple_stock.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amazon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>89.3750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>81.9375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>69.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>65.5625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>69.5625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             Amazon\n",
       "Date               \n",
       "2000-01-03  89.3750\n",
       "2000-01-04  81.9375\n",
       "2000-01-05  69.7500\n",
       "2000-01-06  65.5625\n",
       "2000-01-07  69.5625"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We display the amazon_stock DataFrame\n",
    "amazon_stock.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have unique column labels, we can join the individual DataFrames to the `all_stocks` DataFrame. For this we will use the `dataframe.join()` function. The function `dataframe1.join(dataframe2)` joins `dataframe1` with `dataframe2`. We will join each dataframe one by one to the `all_stocks` dataframe. Fill in the code below to join the dataframes, the first join has been made for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We join the Google stock to all_stocks\n",
    "all_stocks = all_stocks.join(google_stock)\n",
    "\n",
    "# We join the Apple stock to all_stocks\n",
    "all_stocks = all_stocks.join(apple_stock)\n",
    "\n",
    "# We join the Amazon stock to all_stocks\n",
    "all_stocks = all_stocks.join(amazon_stock)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can check that the dataframes have been joined correctly by displaying the `all_stocks`  dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Google</th>\n",
       "      <th>Apple</th>\n",
       "      <th>Amazon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.596616</td>\n",
       "      <td>89.3750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.293384</td>\n",
       "      <td>81.9375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.341579</td>\n",
       "      <td>69.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            Google     Apple   Amazon\n",
       "2000-01-01     NaN       NaN      NaN\n",
       "2000-01-02     NaN       NaN      NaN\n",
       "2000-01-03     NaN  3.596616  89.3750\n",
       "2000-01-04     NaN  3.293384  81.9375\n",
       "2000-01-05     NaN  3.341579  69.7500"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We display the all_stocks DataFrame\n",
    "all_stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Google</th>\n",
       "      <th>Apple</th>\n",
       "      <th>Amazon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3115.000000</td>\n",
       "      <td>3115.000000</td>\n",
       "      <td>3115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>347.420229</td>\n",
       "      <td>47.736018</td>\n",
       "      <td>216.598177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>187.671596</td>\n",
       "      <td>37.421555</td>\n",
       "      <td>199.129792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>49.681866</td>\n",
       "      <td>1.973460</td>\n",
       "      <td>26.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>220.088539</td>\n",
       "      <td>12.833595</td>\n",
       "      <td>62.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>286.397247</td>\n",
       "      <td>39.461483</td>\n",
       "      <td>161.820007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>508.255340</td>\n",
       "      <td>76.906205</td>\n",
       "      <td>306.154999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>813.109985</td>\n",
       "      <td>126.941574</td>\n",
       "      <td>844.359985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Google        Apple       Amazon\n",
       "count  3115.000000  3115.000000  3115.000000\n",
       "mean    347.420229    47.736018   216.598177\n",
       "std     187.671596    37.421555   199.129792\n",
       "min      49.681866     1.973460    26.070000\n",
       "25%     220.088539    12.833595    62.670000\n",
       "50%     286.397247    39.461483   161.820007\n",
       "75%     508.255340    76.906205   306.154999\n",
       "max     813.109985   126.941574   844.359985"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the all_stocks DataFrame\n",
    "all_stocks.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To Do\n",
    "\n",
    "Before we proceed to get some statistics on the stock data, let's first check that we don't have any *NaN* values. In the space below check if there are any *NaN* values in the `all_stocks`  dataframe. If there are any, remove any rows that have *NaN* values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Google    3095\n",
       "Apple     1933\n",
       "Amazon    1933\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there are any NaN values in the all_stocks dataframe\n",
    "all_stocks.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove any rows that contain NaN values\n",
    "all_stocks.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can check that the *NaN* values have been eliminated by displaying the `all_stocks`  dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Google    0\n",
       "Apple     0\n",
       "Amazon    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there are any NaN values in the all_stocks dataframe\n",
    "all_stocks.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the `all_stocks`  dataframe and verify that there are no *NaN* values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Google</th>\n",
       "      <th>Apple</th>\n",
       "      <th>Amazon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>49.845802</td>\n",
       "      <td>1.973460</td>\n",
       "      <td>38.630001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>53.805050</td>\n",
       "      <td>1.979244</td>\n",
       "      <td>39.509998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>54.346527</td>\n",
       "      <td>1.997236</td>\n",
       "      <td>39.450001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>52.096165</td>\n",
       "      <td>2.053144</td>\n",
       "      <td>39.049999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>52.657513</td>\n",
       "      <td>2.123831</td>\n",
       "      <td>40.299999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Google     Apple     Amazon\n",
       "2004-08-19  49.845802  1.973460  38.630001\n",
       "2004-08-20  53.805050  1.979244  39.509998\n",
       "2004-08-23  54.346527  1.997236  39.450001\n",
       "2004-08-24  52.096165  2.053144  39.049999\n",
       "2004-08-25  52.657513  2.123831  40.299999"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We display the all_stocks DataFrame\n",
    "all_stocks.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have eliminated any *NaN* values we can now calculate some basic statistics on the stock prices. Fill in the code below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean: \n",
      " Google    347.420229\n",
      "Apple      47.736018\n",
      "Amazon    216.598177\n",
      "dtype: float64 \n",
      "\n",
      "median: \n",
      " Google    286.397247\n",
      "Apple      39.461483\n",
      "Amazon    161.820007\n",
      "dtype: float64 \n",
      "\n",
      "standard deviation: \n",
      " Google    187.671596\n",
      "Apple      37.421555\n",
      "Amazon    199.129792\n",
      "dtype: float64 \n",
      "\n",
      "correlation: \n",
      "           Google     Apple    Amazon\n",
      "Google  1.000000  0.900242  0.952444\n",
      "Apple   0.900242  1.000000  0.886321\n",
      "Amazon  0.952444  0.886321  1.000000 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the average stock price for each stock\n",
    "print('mean: \\n', all_stocks.mean(), '\\n')\n",
    "\n",
    "# Print the median stock price for each stock\n",
    "print('median: \\n', all_stocks.median(), '\\n')\n",
    "\n",
    "# Print the standard deviation of the stock price for each stock  \n",
    "print('standard deviation: \\n', all_stocks.std(), '\\n')\n",
    "\n",
    "# Print the correlation between stocks\n",
    "print('correlation: \\n', all_stocks.corr(), '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now look at how we can compute some rolling statistics, also known as moving statistics. We can calculate for example the rolling mean (moving average) of the Google stock price by using the Pandas `dataframe.rolling().mean()` method. The `dataframe.rolling(N).mean()` calculates the rolling mean over an `N`-day window. In other words, we can take a look at the average stock price every `N`  days using the above method. Fill in the code below to calculate the average stock price every 150 days for Google stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2004-08-19           NaN\n",
       "2004-08-20           NaN\n",
       "2004-08-23           NaN\n",
       "2004-08-24           NaN\n",
       "2004-08-25           NaN\n",
       "                 ...    \n",
       "2016-12-23    758.236666\n",
       "2016-12-27    758.713066\n",
       "2016-12-28    759.111599\n",
       "2016-12-29    759.502732\n",
       "2016-12-30    759.763799\n",
       "Name: Google, Length: 3115, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We compute the rolling mean using a 150-Day window for Google stock\n",
    "rollingMean = all_stocks['Google'].rolling(150).mean()\n",
    "rollingMean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize the rolling mean by plotting the data in our dataframe. In the following lessons you will learn how to use **Matplotlib** to visualize data. For now I will just import matplotlib and plot the Google stock data on top of the rolling mean. You can play around by changing the rolling mean window and see how the plot changes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eHh7akkxRdJYvX46vry/169fn5MmTPP3003Tu3FmCEyGEqOCGd6jJR3+pKwczzGVziqdAOSgpKSkOW7+7ublpy0Vr165NSEgIa9eu1a6bTCY2btyoBR9t2rTBaDTatYmOjubgwYMuAxRRPBITExk3bhyNGjVi1KhRtGvXjl9++aW0uyWEEKKY6W1yDb/ZeZ6TsUkObVb8e6kku+SgQCMoAwYM4M0336RmzZo0bdqUvXv3MmvWLB599FFATa6cOHEi06dPp379+tSvX5/p06fj7e3Ngw8+CEBAQACjR49m0qRJBAUFUblyZSZPnkyzZs20VT2iZIwYMcJulY8QQoiKLdVkRq+HjBx1qPq+v4nfn+5id27+ligGtggrye7ZKVCAMnfuXF566SXGjRtHbGwsYWFhjB07lpdffllrM2XKFFJTUxk3bhxxcXF06NCBNWvWaDVQAGbPno3BYGDo0KGkpqbSo0cPFi5cWCw1UIQQQohbVXJ6Jt7ubuh0OlJNZrq8/Sfe7gbOXU+xa5dhVjiUo+R9o2A/SlOBkmTLitySbCShUFQ08jsthCiMfedvcO/H23igfU1eHxzJydgkes7a6LL9O/c259kf9muPh7Sqzqz7WxZpnwqSJHtLbBYohBBC3GoGf7iVTIvC4h1nSU7PJM6mOJszmRb78Yr0Uq6PUrbLqwohhBDipg37bAcHLsbn2ibnap60DOcFOEuKjKAIIYQQpejgxXhW7i/6FTO2RcHzCk4gu+S99b60zNINUGQERQghhChF/eduASDY35N2EZWL7HmNej2mAtQ4yTCrUzy+HgYS0zJJyyjdKR4ZQalgzpw5g06nY9++fQD89ddf6HQ6bty4AcDChQtl11whhCiDDl8qun3mYhPS8gxOHrqtpt1j6xSPn4c6diFTPEIzatQodDodOp0Og8FAzZo1eeKJJ4iLiyuy17j//vu1zR6L08KFC9HpdNruyba+++47dDqd7OYrhBA2irKi65NL9+bZZnLvhnaPZ61VPxv8PI2ABCgih7vuuovo6GjOnDnDF198wa+//sq4ceOK7Pm9vLyoVq1akT1fbnx8fIiNjWX79u1257/88ktq1qzp4i4hhKj4TJkWNp+4gsVm5YzZkr+qH+n5yA2JqOKd6/XVE7vgnxWI5BQa6MmdjarRqW6VfPWnuEiAUsZ4eHgQEhJCjRo16N27N/fffz9r1qzRrlssFl577TVq1KiBh4cHLVu2ZPXq1fl+/pxTPNOmTaNly5YsXryYiIgIAgICGDZsGImJiVqbxMREhg8fjo+PD6GhocyePZtu3boxceLEXF/LYDDw4IMP8uWXX2rnLly4wF9//aVVFrb166+/0qZNGzw9PalTpw6vvvoqmZmZ2vVZs2bRrFkzfHx8CA8PZ9y4cSQlZZdntr63P/74g8aNG+Pr66sFfEIIUZa0eX0tD8/fSZ0XftPOWcOT89dTXAYrU386QJvX13HxRmquz28djHm+byOq+DruZdcoxB+9XudwHqBxqD9fjmrH64Mj834jxejWCFAUBUzJpfN1E3XwTp8+zerVqzEas6Pc999/n/fee493332X/fv306dPHwYOHMiJEycK/TqnTp3i559/ZuXKlaxcuZKNGzfy1ltvadefeeYZtm7dyooVK1i7di2bN2/mn3/+yddzjx49mm+//ZaUFLVq4cKFC7nrrru0zSOt/vjjDx566CGeeuopDh8+zKeffsrChQt58803tTZ6vZ4PPviAgwcPsmjRIv7880+mTJli9zwpKSm8++67LF68mE2bNnHu3DkmT55c2B+NEEIUi8T0TIdzigK/HYimy9sbeGLJHqf3fbPzHEnpmSzZcTbX57dkffa46XSsn9TV7lqNSl653mt0EbiUtFtjFU9GCkwvpf0EXrgE7j75br5y5Up8fX0xm82kpaUB6siB1bvvvstzzz2n7Sg9c+ZMNmzYwJw5c/jwww8L1UWLxcLChQu17Qgefvhh1q9fz5tvvkliYiKLFi1i6dKl9OjRA4AFCxYQFpa/n2fLli2pW7cuP/zwAw8//DALFy5k1qxZnD592q7dm2++yfPPP8/IkSMBqFOnDq+//jpTpkzhlVdeAbAbsalduzavv/46TzzxBB999JF2PiMjg08++UTbkfnJJ5/ktddeK9TPRQghStonG9UdhtccvpxrO1+P3D++rQGKTgcBXkYaBPty/LI64vzufS1yvfev41d4puE1qN4WDO757XqRuzVGUMqR7t27s2/fPv7++28mTJhAnz59mDBhAqCWCL506RKdO3e2u6dz584cOXKk0K8ZERFht1dSaGgosbGxgDqKk5GRQfv27bXrAQEBNGzY0OF5XHn00UdZsGABGzduJCkpiX79+jm02bNnD6+99hq+vr7a15gxY4iOjtZGXzZs2ECvXr2oXr06fn5+jBgxgmvXrpGcnKw9j7e3txac5HwvQghRlnkY9Fpg4UyqKTv35JST3YdtWaeI3LJGQ2xnjKr5ZU/5NAl1LDfvdXEHfDUIvrgTUotukUZB3RojKEZvdSSjtF67AHx8fKhXrx4AH3zwAd27d+fVV1/l9ddf19rodPbDb4qiOJwrUBdtppCsz2/J2ulS0aJwx9fMr+HDhzNlyhSmTZvGiBEjMBgcf+0sFguvvvoqQ4YMcbjm6enJ2bNn6devH48//jivv/46lStXZsuWLYwePZqMjIxc30s53G5KCFGBbTlx1el5o5uO3Bby3EjNLlX/096Lue6TY/1nT5/1b/d797Vg0IdbAYgIyh7Vf39YS3rN3qQ99iSd+V7vg9kEATXBMzCPd1N8bo0ARacr0DRLWfLKK6/Qt29fnnjiCcLCwggLC2PLli3ccccdWptt27bZjXAUpbp162I0Gtm5cyfh4eGAOpJz4sQJunbtmsfdqsqVKzNw4EC+++47PvnkE6dtWrduzbFjx7TgLKfdu3eTmZnJe++9h16vDvx99913hXhHQghRut7546jT88kmc65/UKWY8r/s1zqCYk2EbREeyOnp/dDp7P/gdMuRbzLBsBxfSwL4hcKQz+zL0ZawWyNAKce6detG06ZNmT59OvPmzePZZ5/llVdeoW7durRs2ZIFCxawb98+vv7662J5fT8/P0aOHMmzzz5L5cqVqVatGq+88gp6vb5AozYLFy7ko48+IigoyOn1l19+mf79+xMeHs59992HXq9n//79HDhwgDfeeIO6deuSmZnJ3LlzGTBgAFu3bnUZ7AghRFn27wXnZeeT0jLzPcWTF+vz2MYfzlbtGPTZmR51dRcZ67ZSfdDjFfDwzffrFQfJQSkHnnnmGT7//HPOnz/PU089xaRJk5g0aRLNmjVj9erVrFixgvr16xfb68+aNYuOHTvSv39/evbsSefOnWncuDGenp75fg4vLy+XwQlAnz59WLlyJWvXrqVdu3bcdtttzJo1i1q1agFqsu2sWbOYOXMmkZGRfP3118yYMeOm35sQQpQVSemZWrn5nKatOKSVxAcI8sk9edWac+KWxx+SnkY1DDCSyQfGeRh0FjJD20Lz+wvQ8+KhU8rhBH1CQgIBAQHEx8fj72+f4JOWlkZUVBS1a9cu0AeoyL/k5GSqV6/Oe++9x+jRo0u7OxWe/E4LUbFEPL/K6fl729Tgz6OxXE9Wc03OvHW3y3uGtQvnrXuau3yNRxfu4s+jsbx9T3OGtgt32S7xegwrZo2ji34/NfVXuK744jdhM8YqdQrylvItt8/vnGSKR+Rp7969HD16lPbt2xMfH68t2x00aFAp90wIISqO5PRM4lMz8m5I3iW2tCme3GqanNmC77IHGW5Qp5ySFE+m6Z7gg2IKTgpKAhSRL++++y7Hjh3D3d2dNm3asHnzZqpUKd0yyEIIUR4F+3twOSHd4fzvB2OctndW2j63XBVTpoW/jl0BwN3gIpMjMQZ+eBRdWjwnLNX5zHw39boMY+adLfN+AyVEAhSRp1atWrFnj/OqhkIIIQom00WeiSuv/nrY4Vxuz/DKikPasb+nk495cyYsexCSLkPVRvzn/GSS8OZl38p4ubsVqG/FSZJkhRBCiBJkKuCuxUv/PudwLrcRlG92Zrf3MDgJODa9Axf3gIc/3LuAJNR6XUW5m3JRkABFCCGEKCGJaRkkpqn78HRvWDXP9i7XsWSdTssw883Oc8SnZOeu2NY2Ca+cY9+d87tg09vqcb93IbiJdikzn7spl5QKO8VTDhcnCeGU/C4LUTEoikKzadm7008f0gw3vQ5/TyONXnK+K31qhvPaJ9YRlEnf/cuqA9HsjLrO7KzKsnc3C2XFv5e4s1E1alSyqWZuMcPKiaBYoEFfaHaf3XO2Cg8s9HsrDhVuBMVa6ty6f4sQ5Z31dzlnGX8hRPmyM+q63WN3Nz3V/DwdqrnaSk63D1Cs++hY/2xZdSAagOV7L2ptUkzqCE3vJva7xvP3J3D5oLoFS9+ZkFWkbfOU7ix8pB2d6pWthQ8VbgTFzc2NwMBAbYM4b2/vm9qnRojSoigKKSkpxMbGEhgYiJtb2UleE0IUXExCmt1jg5saIORWTG33meyg5v1hLbmaZOL1lYdxNhuTYbZgdNNrU0i+tgmySbGwYbp63P1FqFRLuxRe2ZvwygXbN64kVLgABSAkJARAdrEVFUJgYKD2Oy2EqDjcswIUvV6HTue8tsmHf53Ujge1rM6CrVFA9tRvC91JBrttpbYuhh0LN9NlxCskpWcFKB5ZH/FXjsOnd0BmKoS2hNvGFd+bKkIVMkDR6XSEhoZSrVo1u51uhShvjEajjJwIUUHk3OzP6JY9cuIq1SwyLICDFxPwMqr/Duhs2lvizrPYfQb+ulT15Pl/Yf4WlLSpQFaAknoDltyjBiduHnD3e9rUTllXIQMUKzc3N/nHXQghRJlwI8X+D+bcck+srCtrJvRQd3q3llD580AUh489QqQ+FZPixsfmQYwxrMI75gBTde/yKBPx9XCDX5+C+HPgUw0e2wABNYr2TRWjCh2gCCGEEGVFzjL2+cmPTMtaxWOdDvr79DUAxhlWEKk/Q5ziyxDTq0QpoZxyb8QHlul0Uf5hvfskqq2qDxe2gd4A9y0sV8EJVMBVPEIIIURZdCGuYKtL/zoWy8r96iodQ9ZoS1yKiSrE87jbrwBMyxhBlBIKwIqUSN6v/D/SFSM19VfwvLBNfaIeL0NE5yJ6FyVHRlCEEEKIEmANNvJr7OLsLUasK34m9W7ImS9nYtSZOWypxS8W+8BjeXpbfjLNpJP+EDPurg01O0KNtjff+VIgAYoQQghRzHKWkf/+8Y553uNu0JOeqd5nneJppTtOe7eNAMzMHEZ22qyqY90qfHMthbPmEGZ0ursIel56ZIpHCCGEKGarbEZPptzVkHYRlfO8x9OYvcjDkLXix3jgG/Q6hZXmDmy0tADUQMa6pDgxTc1zGdUpoqi6XmokQBFCCCGKmbXiK0Dz6oH5uudKYrp2bHDTQ/JV9P9+A8C35u7aNVOmBXeD+nF+NUm9x8/ZLsblTIEClIiICHQ6ncPX+PHjAbVwzLRp0wgLC8PLy4tu3bpx6NAhu+dIT09nwoQJVKlSBR8fHwYOHMiFCxeK7h0JIYQQZUygV/ZWFbfXL3hJeXc3HRz7DcwmDltqsdnSzO66dcmydSmzv2f53xqjQAHKrl27iI6O1r7Wrl0LwH33qRsOvf3228yaNYt58+axa9cuQkJC6NWrF4mJidpzTJw4keXLl7Ns2TK2bNlCUlIS/fv3x2x2viGSEEIIUd7VreYLQPVArzxaOmfQ6+HISgBWmTuQM/fEWi7fGqDcciMoVatWJSQkRPtauXIldevWpWvXriiKwpw5c3jxxRcZMmQIkZGRLFq0iJSUFJYuXQpAfHw88+fP57333qNnz560atWKJUuWcODAAdatW1csb1AIIYQobQlZNVB65dzAL5+8Mq7D6Q0ArLE4rsqxjqBY9/uxzV8prwqdg2IymViyZAmPPvooOp2OqKgoYmJi6N27t9bGw8ODrl27sm2buhZ7z549ZGRk2LUJCwsjMjJSa+NMeno6CQkJdl9CCCFEeZGctT9OYUc2apz8GswmqNqIE0p1u2s1K3tz8Uaq3TmjW/lPMS30O/j555+5ceMGo0aNAiAmJgaA4GD76DA4OFi7FhMTg7u7O5UqVXLZxpkZM2YQEBCgfYWHhxe220IIIUSJs+4+7Kp67IwhzZyeB9BjoWrUL+qD9o+Rc3pn5j3NHe6pAPFJ4QOU+fPn07dvX8LCwuzO5/zhK4qSZznfvNpMnTqV+Ph47ev8+fOF7bYQQghR4hTUCMXVJ12tIG+X9z7i9jveSefAzR3q9bS79u/LvelYN8jhnu2nrhW6r2VFoQKUs2fPsm7dOv773/9q56zbweccCYmNjdVGVUJCQjCZTMTFxbls44yHhwf+/v52X0IIIUR5YR1B0bv4Y9zVbsaBJPJ/hh/VB12nQKVadtcDvJ2v1unWsFqh+lmWFCpAWbBgAdWqVePuu7Or1NWuXZuQkBBtZQ+oeSobN26kU6dOALRp0waj0WjXJjo6moMHD2pthBBCiIpG0aZ4nF/vWCeIh2+rxVM96tud/6Tacnx1aViC6sPtz+T79bo3ugUDFIvFwoIFCxg5ciQGQ3ayj06nY+LEiUyfPp3ly5dz8OBBRo0ahbe3Nw8++CAAAQEBjB49mkmTJrF+/Xr27t3LQw89RLNmzejZs6erlxRCCCHKudynePR6Ha8PjuT+dtk5lh31h7gtYbV6/e53Qa+uzGkRHgjAc3c10toObZu9U/GYLrWLsN+lp8DpxOvWrePcuXM8+uijDtemTJlCamoq48aNIy4ujg4dOrBmzRr8/Py0NrNnz8ZgMDB06FBSU1Pp0aMHCxcuxM2t/C+JEkIIIZzJawTFKmu1MFW5wXTjl+qDlg9BnW5am28fu43LCWnUCvLRzj12Rx2+260WPbVWlS3vChyg9O7dG8XFZJlOp2PatGlMmzbN5f2enp7MnTuXuXPnFvSlhRBCiHJJyWMVj5U1R+U14wJq66LBpxr0fMWujafRzS44yfm87hXkD/6KEWYJIYQQZZh1FU9evNzdCNddpof+H/XEgDngm3c+iW3yrYexYny0V4x3IYQQQpRh+Z3i8fc0MlC/HXedmYOWCGjQN1/Pr7d5XveKUAQFCVCEEEKIYmcdP9G5TJPN1kF/BIB/K/cFff4+pm1HUCpKDkrFeBdCCCFEGWbJGkLR5xWfJMVyh9sBALr1HZrv59fbPLGHBChCCCHEre1yQhpDP93Oin8v5d4wn1M8XD6ofg+qR/WGrfPdD0+boMRVMbjyRgIUIYQQopDmrDvBzqjrPPXN3lzb5XuK59I+9Xu1JgXqRyVvd+04MS2jQPeWVRKgCCGEEIWUabbkq521PEeegxuHszYFtKl7kh+2UzzxqZkFureskgBFCCGEKKTKvu55NwJ+3pfHFBCAOQNiD6vH9XoUuC91qqi1UXo0Lv9l7qEQhdqEEEIIoQryyQ5QMswWjE6W+CalZ49onL+e4vrJzmwBswk8/CGwlut2Lqx86nYuJ6RTu4pP3o3LARlBEUIIIQop0Cs7QFl7+DIRz69i5uqjdm2OX07UjnOtJHvsd/V744H5mAty5O1uqDDBCUiAIoQQQhSabRwx7mu1+uvHf50iwyY35VqSSTs2W1xUlM00wXF1Y0BqdynyfpZHEqAIIYQQheSqgL1tIJKaYc4+72IvO87/DTfOqtM79XsXYQ/LLwlQhBBCiCKWaRugmLJzUCyuRlCi96nfa98B3pWLsWflhwQoQgghRGG5iDfM5uwLO6PitONMVwGKdXlx9TZF1bNyTwIUIYQQoohlWLJzUH7854J27DQHJTMdLmbtXhx5T3F3rdyQAEUIIYQoJMXFEIptINKmViXt2OnanMsHQTGDZwAE1iziHpZfEqAIIYQQheQq59W6iictw8yes9lTPE6XGe9ZpH4P71Co5cUVlRRqE0IIIYqYosCHG06y6fgVu/NO4w9rgmyrh4q9X+WJBChCCCFEIblaZrz99DXe+eOYw/nmNQLsT6TFw5WsdlUbFW3nyjmZ4hFCCCEKydUUz+FLCQ7nnu3TkAfb58gxOfQzZKZB5ToQVK/oO1iOyQiKEEIIUcQWbjvjcG5ct7qOOSixR9TvDfqC3q34O1aOyAiKEEIIUUiuVvHkNLRtDcfgRFHgxB/qcUizIu5Z+ScBihBCCFHMfD2MjidTrsH104AOGt1d4n0q6yRAEUIIIQrJVQ5KTr4eTqZvrMmxgTXB07/oOlVBSIAihBBCFFI+4xO8PZykfEZtUr9Xa1xk/alIJEARQgghipm3u5MRlDOb1e8yveOUBChCCCFEYeVzjsfdzcnH7dXj6ndJkHVKAhQhhBCiCKye2MXlNUPOAOXGOUjOqjIr9U+ckgBFCCGEKCTr+Em/ZiE0CnGd6Gp0y7HE+HjW8uKancDDr3g6V85JgCKEEEIUknWGR+d8n2KNMecIytUT6vcabYuhVxWDBChCCCFEMXMIUM5tV79Xa1LynSknJEARQgghCknJHkLJlcF2iufaKYjZDzo3qN+7+DpXzkmAIoQQQtykPOIT+1U85/9Wv4d3AJ+gYutTeVfgAOXixYs89NBDBAUF4e3tTcuWLdmzZ492XVEUpk2bRlhYGF5eXnTr1o1Dhw7ZPUd6ejoTJkygSpUq+Pj4MHDgQC5cuHDz70YIIUS5cPxyIl9sPs356yml3ZWbkt9CbQa9TQhzdJX6XfJPclWgACUuLo7OnTtjNBr5/fffOXz4MO+99x6BgYFam7fffptZs2Yxb948du3aRUhICL169SIxMVFrM3HiRJYvX86yZcvYsmULSUlJ9O/fH7PZXGRvTAghRNnVf+4W3lh1hC5vb8Biye/HfNmjzfDk3AgwB6PB5uP2wm71e+MBxdSrisFJ7V3XZs6cSXh4OAsWLNDORUREaMeKojBnzhxefPFFhgwZAsCiRYsIDg5m6dKljB07lvj4eObPn8/ixYvp2bMnAEuWLCE8PJx169bRp0+fInhbQgghyqqY+DRMmRbt8Qd/nmBizwal2KPiZ9RnBSipNyApRj2u2rDU+lMeFGgEZcWKFbRt25b77ruPatWq0apVKz7//HPtelRUFDExMfTunZ304+HhQdeuXdm2bRsAe/bsISMjw65NWFgYkZGRWpuc0tPTSUhIsPsSQghRPt02Y73d4znrTrDj9LVS6s3NsY795JWDYjRktbDWPwmoCZ4BxdWtCqFAAcrp06f5+OOPqV+/Pn/88QePP/44Tz31FF999RUAMTFqVBgcHGx3X3BwsHYtJiYGd3d3KlWq5LJNTjNmzCAgIED7Cg8PL0i3hRBClBGKi9Lwx2ISnZ4v66zvJ48ZHgzWEZRzWX+INx1cfJ2qIAoUoFgsFlq3bs306dNp1aoVY8eOZcyYMXz88cd27XLOxSmKkuf8XG5tpk6dSnx8vPZ1/vz5gnRbCCFEGXEt2eT0fIbZ4vR8eTbznuw9drRVPDEH1e+hLUqhR+VLgQKU0NBQmjSxLyrTuHFjzp07B0BISAiAw0hIbGysNqoSEhKCyWQiLi7OZZucPDw88Pf3t/sSQghR/iSkZjg9b8pngKIoCh+sP8HOqOv2z5uWway1xzkZWzojMc7+vO7XLFQ79vFwg7izcDErQbZ665LpWDlWoAClc+fOHDt2zO7c8ePHqVWrFgC1a9cmJCSEtWvXatdNJhMbN26kU6dOALRp0waj0WjXJjo6moMHD2pthBBCVExHorMDiDcGR2rHGZn5W8nT9/3NzFp7nKGfbrc7/87qY3yw/gQ9Z20qmo4WkLMZAD9PI+8Pa8n7w1oS5OsBl/aqF0JbQuU6JdvBcqhAq3j+7//+j06dOjF9+nSGDh3Kzp07+eyzz/jss88A9T/QxIkTmT59OvXr16d+/fpMnz4db29vHnzwQQACAgIYPXo0kyZNIigoiMqVKzN58mSaNWumreoRQghRMc1am/1HbqbNqIk+ryzTLEdd5Kr8e+HGzXSr0Fyk1GgGtaye/cAaoIREOm8s7BQoQGnXrh3Lly9n6tSpvPbaa9SuXZs5c+YwfPhwrc2UKVNITU1l3LhxxMXF0aFDB9asWYOfX/ZujbNnz8ZgMDB06FBSU1Pp0aMHCxcuxM3NrejemRBCiDLl0KV4Tl1J1h6nZGTXvipMKZRdZ67TLqIyhy8lsP9CfFF0scCUrHU81vhKp8slaDnwg/o9okux96siKFCAAtC/f3/69+/v8rpOp2PatGlMmzbNZRtPT0/mzp3L3LlzC/ryQgghyqnHl2RXHW8Y7EeqKTtAScnILPDzTf3pAINbhvHumuN251NMmXi7F/jjrUgY9Xrn+TTXT0PCBdDpoZHrz1CRTfbiEUIIUSLiU7ITZI9dtp+qsQ1WnFEUhc83nbY7dzI2ySE4Adhw9MpN9LJglByFUIxuLuaqDv6kfq99B3j4Fnu/KgIJUIQQQhQ7s0UhIS17lGRUpwhGdYrQHien5x6gbDgWy5u/HSmu7t00XVaE8lJ/daXr6NtrZ1/MTId9X6vHkfeWdNfKrdIZAxNCCHFLWbz9jN3jfs1CCfL14LVBTXn5l0Ok5jHFs+/cjXy/Vqal5GqqmLOGUKxJvsPa16Rrw6qE+HtmNzq6Up3i8akKTQaVWN/KOxlBEUIIUeym/XrY7rF71uZ5XkZ1cURKHlM8H/x5Mt+v9efRWMwltAFhpll9HYNb9sdpaIBX9rJjiwU2vacetxwOnlLHK78kQBFCCFGsnJW3t1ZW9fFQB/JTcpniKehux7/su8Taw5cLdA/A6StJLN5x1m4jw7xYl0q7zD3Z/B7EHgJ3P2g/psB9upXJFI8QQohilZDqOH2jjaC4Z42g5DLFk1mI0ZC95+K4KzKkQPfc+d5GQA2oRnSMyNc9pqwRFKObk7/3ozbDhjfU4+4vQECNAvXnVicjKEIIIYpV1LVkh3MeWQGKT9ZyYGdBjFVhcko+zbHipyC2nLhK1FXHPjtjHUEx5BxBsVhg3TT1uMlguO2JQvfnViUBihBCiGL130W7Hc5ZAxTvrBGUc9dTePXXQ07vz20EJYAkWuhOMtRtAxND9hNE4Qq22W5WuObwZbq/+xeHLyXkeZ+1b0Z9jo/TqL/UfXfc3KHnK3lvdywcyBSPEEKIYnU1Kd3hXCUfdyB7igdgwdYzvDKgqfb41JUkYuLTaBKanVh6X5saBGdeouqh+fR120U13Y3sJ70B//Xw5O3M+1ls7lWgPiamOY7grD18mSZh/iiK4nSvHcgObOxGUCwW+ON/6nHz+2XfnUKSAEUIIUSJs+Zs+OSo+JqYloGfpxGAHlk5IZ8+3Ea7/k43L0yfjsHdkF3ozeLuj656S0i8jO/VY7xmXEQN3VVgQL77k5jmuMuyWVH4cMNJ5m+J4qcnOhFRxcehTYaWJGszgrLzUzUxVm+Abs/nuw/CnkzxCCGEKFF3NKiqHXsa7T+GZq4+6tB+7GK1RH4d3SVYPBj3zETOWaoywvQcTdK+RP/CeXQjf0U3bgevZTwMwGOGVXD0N8cXd7FRjrMcGItF4Z0/jnE92cRrKw87uSt7mbG2iictHja9qx53fV4SY2+CjKAIIYQoNv+ci3M4523MntbxNNpvEnvwopr3kZZhv+y4nu4CP3m+DgmJULkuR9p9zqZfYuyCHfR6TO3G8tnu6zxmWIX5+1GYqnfCyzcA4qLgynG1zHyzodD6YajWWLvV2QiKxSaYuZZscvr+MrJyUAzWHJSdn0PKVQgIh47jnd4j8kcCFCGEEMUiPdPMkI+2OZxffShGO7Ymy1pdiEsFICE1O2CoRAKfG9/DX0lU8zlGraSPfxgn21tw09vnhtSr6suMzPvo7n6Y+uYovM5tsH/xzFTY8SHs+Aj6z4a2j6iv5yQHxXasJc5FgGJXB8Vige0fqhc6Pw3u3k7vEfkjAYoQQohi8cbKvPfOyZl8ejUpnfjUDG5kBSihXGOJ+3Rq6y+T7OaPz4hfwD8MsK/eauXmpicdd/qlvEo7/VHCdNd4865wPCqFQdVGEHcW9iyAE2vgt8mQeh06PukwYgMQ7OehHceluBhBsc1BiT2kPp/BC1qPzPO9i9xJDooQQohikeBk2sSZ94e1tHt8MS6V+NQMmurOsNzjZerqo7mq+PNT5EcQWDPX5zJmjajoDe5ss0Tyg7krh2o9BJH3QHBTaNQPHvxO3RPHkgnrX4PvRmAypTk8V0xC9uojZ6t8ADJsS93vzdoQsE43MLjn670L1yRAEUIIUSwq+zj/kLZdNgxQo5LjVIjpymm+cX+dEF0cpyyh/Mf0KulVmjq0y8k65ZNuU65+xb5L9o10OhjyBfScBujg+Gra7HsJH1Ltmn2y8VSer2ctImd008H5HerJZrJjcVGQAEUIIUSxuJKYPQIx5/6W2vGUuxratdM7KTFS48gX+OtSiVUCGWp6mfNKsENCrTMOFV2BhdvOOO7nY3CH2/8PhnyGgo660av4xf0lmurOYJ99kjvrCErlhGNwaW9W59vl+37hmuSgCCGEKBZJ6eq0yNv3Nmdwq+oE+bpz+FICXW1X3gD6HHkohuRoap7+FoCpGaO5RgBAvjbxc8tZ0TXLtWQTVW1ySjTNhzJi2SneNX5CPf0lVnm8wEUliFXm2/g4cwBxZI/2bD91Db0OOtQJ0s5Zc1cizv2gnmjQFyrVyrOfIm8ygiKEEKJYpJjUD29rMbYu9asytmtdh8TYnAFKtb9noMPCPktd1luyi7TFp+ad02JwNhwDXLqR6vQ8wGZLc+4xTWOHpTGZip7qums8ZljFBo9J3K3fobV74PMd3P/ZDj7fdJqDF+P5ZOMpElIz8CeZ6qe/Uxu1eijPPor8kREUIYQQRe7Po5fZGXUdyN5vxxXbpcId9YcIPLkcgDczhtu1C/LNO/E0Z+E3q0EfbmXBqHZ0b1TN7rx16ueCUo1hppd4snMoY8JOc/WX/1FXH81c41y8M9P43txNu+fN3+xXJ41zW4fekgFB9aFx/zz7KPJHRlCEEEIUuUcXZm8Q6JVHgBIS4AmAG2ZeNiwG4Gjof9ilNOI/raqz4JF2jOhYi/vbhef5uv5ZZfKdeWThLrvH01YcosOM9Xbn9J6+GCIHc5dpJl9n9kCvU3jH+BlvGubjgfOlxv3c/lYP2o/Js38i/2QERQghRLHKud9OTpW81aDiMbdVNNafw6J3Z2eNkRCVgodBT/eG1ejesFquz2Hll0uAApCcnomPh9qfhdvOOFzPNFvwNLqRgYEXMx8lHh/GGVYw3LCeO932clGpgg+p7LA0YZelIZ6YiNRnPU/j/O/9I/ImAYoQQogipeTY78bDxbSLlU6noxpxTDSoiaZ/1xrDFWMYcBJ3Q8EG+v29cv9Y++DPEzQK8aNvZKjT60npmTZTTjrezhzGXks9Zhk/JlR3nVCdOm3VWH+eR/hDu89UswvuWQXkRNGQAEUIIUSRuppkPxUS6JX7qAbAjJC/8LiRySlLKA8c6QRHTgKOpfDz4uth/7H2Qr9GTP8tewPCTzeeBuDAhQSn91tL7D/QPpxvdp4HYK2lLb3T36aP2y6uK/6YMNBV/y8N9BcI013DjJ6gnq8XqJ8ibxKgCCGEKFL95262e1zN3zP3G9ISaJ+gjkbMyrwPyE6aLegISs4A5bE76tKseiAPfL7D7vyXW6Oc3t80TF3SPGNIcy1AAYgmiIXmu+hUN4htp66x2tLe7r6T1VsUqJ8ib5IkK4QQoshYLAqXbUrEV/F1Unskp1Pr8bMkEK1UZp2ltd0lr3wUZ7OVcwkzQMe6QU5aOnqxX2NGdMq9hsmk3g2dnne2L5C4OfITFUIIUWSSTfZ71qyccHveN/2jrtw5H9yDdOyXEucrwCkiY+6og4chOyCKCLIvwd8g2Jc2tSrx6sCmdKyTv6BHFJ4EKEIIIYqMtXoswFN31tOWELuUegNObwDA67ZHHS4H5zU9lE/B/gUPdGxHRUZ1imD+SLWE/chOEbx9b/Mi6ZdwTQIUIYQQRSYpa9dff08Dz7iYDrHz7zJQLBBUD6VaE4fLTsvTF8LCR9rnen1Iq+oO5967rwXV/DyYNbQF0wY2Jbxy9ohKUfVLuCYBihBCiJuWnJ7J+espxGZtEJjvD/ATa9TvrR52mm8SmtcITD7VrOy4Y7KtaYMcd0puER7I3y/0YEjrGg7XPI1ujOgoe+4UJ1nFI4QQ4qa1en2t3WZ+8amZubTOkpYA53eqx7XvcNit2MfdjaAiykHJa7myqwq0zpJurZ7t05B/L8TTNzLkpvomnJMARQghxE356Z8LDjsNX01Kd9HaxvHVYEqEgJoQ0pzADAWjm44Ms1robVz3ekXWx+JYZePnaeSX8Z2L/HmFSqZ4hBBC3BTbQmhWrnYVtnNKTY6l8QBwM+DnaeSbMbcVWb8KWkNFlC0F+q83bdo0dDqd3VdISPbQlqIoTJs2jbCwMLy8vOjWrRuHDh2ye4709HQmTJhAlSpV8PHxYeDAgVy4cKFo3o0QQogS5+Xu+FHSpX6V3G/KSIWDaml7GvTWTreNqKwd63OZXslNZR91qXLnfNY/EWVTgcPLpk2bEh0drX0dOHBAu/b2228za9Ys5s2bx65duwgJCaFXr14kJiZqbSZOnMjy5ctZtmwZW7ZsISkpif79+2M2m4vmHQkhhChRngbH5NZpAx2TTu1E7wezCXyqQu2udpe61K+Cm17HoJaF29vm+8c7MvaOOrx7X/6quz7QPu9dkkXJK3AOisFgsBs1sVIUhTlz5vDiiy8yZMgQABYtWkRwcDBLly5l7NixxMfHM3/+fBYvXkzPnj0BWLJkCeHh4axbt44+ffrc5NsRQghR0k7EJjmcqxXkk/tN+79Vv9doDzlGSuaPbEdaptll4mpe6lb1ZWq/xvlu/8bgZoV6HVG8CjyCcuLECcLCwqhduzbDhg3j9Gl146WoqChiYmLo3Tt7qM7Dw4OuXbuybds2APbs2UNGRoZdm7CwMCIjI7U2zqSnp5OQkGD3JYQQomxqEuqfd6Oojer3VsMdLrkb9IUOTgqqV5Ngm92LRVlSoAClQ4cOfPXVV/zxxx98/vnnxMTE0KlTJ65du0ZMTAwAwcHBdvcEBwdr12JiYnB3d6dSpUou2zgzY8YMAgICtK/wcBmOE0KIsuqHJzrm3uDKMbim7lZMzTzaFrOuDaqW6usL1wo0xdO3b1/tuFmzZnTs2JG6deuyaNEibrtNzbzOuWZcUZRc15Hnp83UqVN55plntMcJCQkSpAghRBnzyUOt6dM0JM9/8znwvfq9bg/wrpx722Lm5ynVNsqqm1qD5ePjQ7NmzThx4oSWl5JzJCQ2NlYbVQkJCcFkMhEXF+eyjTMeHh74+/vbfQkhhCgbjG5qQNIiPDDv4CQ9Cf7+TD1uPKCYe5a3kppKEgV3UwFKeno6R44cITQ0lNq1axMSEsLatWu16yaTiY0bN9KpUycA2rRpg9FotGsTHR3NwYMHtTZCCCHKF0Wtq4aOfORynN8B6fHgGwwtHyzejuVQPdALgEYhfto5XxlBKbMK9F9m8uTJDBgwgJo1axIbG8sbb7xBQkICI0eORKfTMXHiRKZPn079+vWpX78+06dPx9vbmwcfVH8JAwICGD16NJMmTSIoKIjKlSszefJkmjVrpq3qEUIIUb5kxSc5F+M4t2eh+r1eTzCU7IZ73z/ekd8ORDOkdQ1av67+oWwshgqzomgUKEC5cOECDzzwAFevXqVq1arcdttt7Nixg1q11A2TpkyZQmpqKuPGjSMuLo4OHTqwZs0a/Pyyo9XZs2djMBgYOnQoqamp9OjRg4ULF+Lm5riOXgghRNmnZA2h5BmfZKTByfXqcdtHi7VPzoQFevHfLnUA+N/djTl+OZHm1QNKvB8if3SK9TerHElISCAgIID4+HjJRxFCiFJWe+oqFAV2vtiDan657D68+0tY+X/gUw0mHQO9jF7cagry+S2/HUIIIW5KvnNQDq9Qv7cfI8GJyJP8hgghhCgSueagXDkOp7M2B2x0d4n0R5RvEqAIIYQoNNssgVzHT7bPVb9HdIHgPPbpEQIJUIQQQtwE2yxGlzVQMtNh/3fq8W3jir9TokKQAEUIIUSh5WuVxaGfITNNTY5t2DfP5kKABChCCCFuQr6meKyl7Vvcn89iKUJIgCKEEOIm2I6gOI09bpyD03+px83uK4EeiYpCAhQhhBCFZpeD4mwMZftHYMmAGu0hpHnJdUyUexKgCCGEKDQFuwjFXsIl2PW5enz7/8n0jigQCVCEEEIUmv0qnhwX/3wDLJlQox006lei/RLlnwQoQgghnNp3/gb3fbKNv47F5qu9XXwScwD+XaYe93i5yPsmKj4JUIQQQjh13yfb2HUmjtnrTrhs47IOyo5PQDFD/T5qcTYhCkgCFCGEuMVtP3WNO9/9i20nr9qdzzCr0ce/52+4vNc2B0ULT+IvwL6v1eNOT0ruiSgUCVCEEOIW98DnOzh9NZkHv/jbZZsbKSan553moOyaDyhq7kntO4quo+KWIgGKEEIITVJ6ptPzLV9by+4z17XHqSYz56+n2NdBQQdpCVkBCtD+sWLsqajoDKXdASGEEGVH9I1U/r0Qz9lryQ7X7v1kO2feUncibvzyagDmj2yrXdfpUJcVp8dDYE2IvLdE+iwqJglQhBDiFte9YVU2HLsCQGxiOpO//zfX9sdiErXj0Yt2Z1+wmLM3Bez8NOhlkF4Unvz2CCHELa6an6d2vC+XhFhQ994Z9OEW7bHRLTsBVn9uK1w5CkYfaDyoyPspbi0SoAghxC0u05KdSfLOH8dybXspPo20DIv22LrSB8Bt9xfqQdPB4Fu1SPsobj0SoAghxC0uLdOc77YHLtxwer4SCeiP/64+aPtoEfRK3OokQBFCiFuY2aKwan+0y+vDO9S0e/z7wRin7Z4N+BOdYoaqjaFGW6dthCgICVCEEOIWdj3ZeX0Tq9cGRfLtY7fRsU4QAL/su+TQxgMTfTL+VB90fqrI+yhuTRKgCCHELWz9kcu5XnfT6+hQJ4jujexzStrWqqQdD3DbTpDlKngGQqP+xdFNcQuSAEUIIW5hwQGeeTcCfD2M9o89Dfw6vjNd9Pt5wZBV1r7DWPD0L+ouiluU1EERQohb2N+nrzs9X9XPg+fuaqQ9tl1ODOBvVGi08zkWu6s7Fqd6BuPV4fHi66i45cgIihBC3MI+2XhKO36yez0AmtcIYNeLPbm3TQ3tmm0JfDfMjI19HeOBZVgUHd9ldmVzt2/Bu3LJdVxUeBKgCCHELWxwyzAA9Dp4pHMEL/ZrzPyR7RzaDWgRph1PMSyjacJm0LnxeMZEpmSOxa9qTYd7hLgZMsUjhBC3MH8vNbfkye71CPL1YMwddZy2q+Lrwc4Xe7D4rfGMNaxSTw54n06pt1M7Po3b6sjoiShaEqAIIcQtwmJR0Ovtc0kS09SpG1/PvD8Oqp1dxSTjDwDcaDWOwNYPM6rIeymESqZ4hBDiFvDd7vPUeeE3/jxqv6zYGqD4eRqd3ZYt+RqsngpASsvRBA6cXiz9FMJKAhQhhLgFTPlhPwCPLtxNhjl7L52k9AwAfD3yGEFZ9X+QdBn8q+N91zTQ6XJvL8RNkgBFCCFuMTHxadqxdXVOrlM853bA4V/U43u+kFonokRIgCKEELeApmHZQUVsYjoAiqJw8GICAD7uuQQo66ap35vdB7U6FVcXhbBzUwHKjBkz0Ol0TJw4UTunKArTpk0jLCwMLy8vunXrxqFDh+zuS09PZ8KECVSpUgUfHx8GDhzIhQsXbqYrQgghclHZx107vngjFYC4lAztXLqrHY2PrYZz2wEd3Pm/4uyiEHYKHaDs2rWLzz77jObNm9udf/vtt5k1axbz5s1j165dhISE0KtXLxITE7U2EydOZPny5SxbtowtW7aQlJRE//79MZvzv+W3EEKI/LPdFPCpb/ay4t9LHLoUr51rF+FkmbDFAhveUI/bPgKVIoq5l0JkK1SAkpSUxPDhw/n888+pVCl7wyhFUZgzZw4vvvgiQ4YMITIykkWLFpGSksLSpUsBiI+PZ/78+bz33nv07NmTVq1asWTJEg4cOMC6deuK5l0JIYTQmC2KXd4JqEHKw/N3ao89jW6ON57/G2IOgMETukwu7m4KYadQAcr48eO5++676dmzp935qKgoYmJi6N27t3bOw8ODrl27sm3bNgD27NlDRkaGXZuwsDAiIyO1Njmlp6eTkJBg9yWEECJvFotC3/c3cc1mBCXftsxWvzceAAHVi7ZjQuShwIXali1bxj///MOuXbscrsXExAAQHBxsdz44OJizZ89qbdzd3e1GXqxtrPfnNGPGDF599dWCdlUIIW55l+JTOX45qeA3Xo+Ck1mj2p0nFmmfhMiPAo2gnD9/nqeffpolS5bg6el6i25djvXxiqI4nMsptzZTp04lPj5e+zp//nxBui2EELesi3Gpdo+n9m3k0ObORtUcb/xrBihmqNEeQiKLq3tCuFSgAGXPnj3ExsbSpk0bDAYDBoOBjRs38sEHH2AwGLSRk5wjIbGxsdq1kJAQTCYTcXFxLtvk5OHhgb+/v92XEEKIvJ3PEaC46R3/EJw/sq39idijsP9b9bj3G8XVNSFyVaAApUePHhw4cIB9+/ZpX23btmX48OHs27ePOnXqEBISwtq1a7V7TCYTGzdupFMnde18mzZtMBqNdm2io6M5ePCg1kYIIUTRWHvY/g/Goe3CHdo4jF7//bH6vW4PqNmhuLomRK4KlIPi5+dHZKT9UJ+Pjw9BQUHa+YkTJzJ9+nTq169P/fr1mT59Ot7e3jz44IMABAQEMHr0aCZNmkRQUBCVK1dm8uTJNGvWzCHpVgghROHFJZv441D23js/PtEJf08jIzvWYtH2s85vMiXDv1mjJx0eL4FeCuFcke9mPGXKFFJTUxk3bhxxcXF06NCBNWvW4Ofnp7WZPXs2BoOBoUOHkpqaSo8ePVi4cCFubk6WuQkhhCiUk1eyk2Of7dOQNrXUxQk5dzS2s+sLyEyFgHCo36u4uyiESzpFUZTS7kRBJSQkEBAQQHx8vOSjCCGECxHPr9KOj7x2F17u6h+BX2w+zRurjmjXzrx1t3pgSoZ57SHhAvR7F9qPKdH+ioqvIJ/fshePEEKUEzujrjN77XHSMgpeddsanADc18YxDwWAfUvV4MS7CjS/v7DdFKJISIAihBDlxNjFu3l//Qnmb4m6qecJ8DbyYr/G9ieTr8Lal9Xjzk/JjsWi1EmAIoQQ5cCNFJO2ud+O09du+vke7FCTFuGBTOrVABQFfhkPGSngFwrtx9708wtxs4o8SVYIIUTRm732uHa8+cRV/j59jQ51gly2P3ctRTuuW9XH4bqPh4FfxndWH/y7DI6vVo+HfAZG14U4hSgpMoIihBDlQM5lwfd/tiPX9rPWHtOOvxjZznXD6P2w8v/U404ToPYdhe6jEEVJAhQhhCgHAryMBWp/IjZ7iXHtKo4jKABc2ANfDVKndsI7QPf/3UwXhShSEqCICu/dP47x30W7MFvK3Yp6ITS5lS7Jac664xy6pO76/myfhs4bXfwHvr4HUq9DaAt4YJlM7YgyRXJQRIU3b8NJADYdv0J3Z5uiCVHGWSwKCWmZdueCfNydtl28/Qxz1p3QHvs7G3m5fAgW/wfSbkD1NjBiBXj4FmWXhbhpMoIibhlJ6Zl5NxKiDIpPzdBGAB/vWheArg2qOm370i+H7B4bcw69JMbA10PV4CQ4Eh5eLsGJKJMkQBEVmm2hZEv5K5osBADXkk0A+HkaqOKrjpyYXfw+Nw61r19icLP5Z15R4IdHs4qxBcHw78EzoHg6LcRNkgBFVGjpmRbt2KCXX3dRPt1IUQOUyj7uuGWNiDjLqXp62V6ORCfYnTPYjqAc+AHObgWDJ4xcCf5hxddpIW6S/IstKrTrWX95Ani7u3EyNoknluzhpM0KByHKOuvvcaB3doCSc0Rwxb+X+GXfJYd7DW5ZAYo5Aza+pR63+y8ENym+DgtRBCRJVlRoVxLTtWOLotBz1kYAUkxmPhvRBg+D7KAtyr4bWRVkK3sb0eucj6CcuZrs9F5tBOXAD3DtJLj7wh3PFl9nhSgiMoIiKrRYmwAl0+Yf9I3Hr3D7zA2kmCRxVpR93+0+D0AlH3ct4MgZoLjKsTLo9WAxw9Y56omO48ErsLi6KkSRkQBFVGgzfs/eUt6S4x/0K4np7DkbV9JdEqJA1h+5zO6s31M/DwN6FwHK8cuJTu83uOng+B9w5ag6etJ2dPF2WIgiIgGKqNBOX8ke9jaZLQ7XrcWshCir3l6dXbJ+UKvquFmneGzik8OXEvjtQIzT+z0NetgwXX3Q8kHwCy62vgpRlCRAERVa/+ah2vFnm047XI+64nzeXoiyIsg3uyCbj7shO0k2awQlPjWDfh9strunblUfZg1twYiOtWibsBYuHwC9AW5/puQ6LsRNkiRZUaF5GrOTYJ2NlrgbJEYXZZvRpo6Jh0FvN8VjsShcSUyza//BA63oWr8qAd5GhrQMhU/HqhduewL8QxGivJB/nUWFZlsHxZkV/zouyxSirNhz9jobj1/RHgf5umtTPNtPX6PzzD+5EJdqd0+PRtUI8M4qb398NVw+qNY96fBEifVbiKIgAYqo0NIyzLlej0/NKKGeCFFwD8/fqR0PaVUdP0+jNsUDEB2fxqcb7acuvd2zRg0VJTv3pPVICKhe7P0VoihJgCIqtLWHL+d63a0gW8QKUYIOXYonxZQdYN+RtfdOJW/7zf9OX80uOvjx8NboskZYOPyzOnri5g5dJhV7f4UoahKgiApr26mrebaR+ESUVdNW2G/616+Zmj/SNqKy3fnLCWqtn871guib1YaEaPhlgnrc5hFZuSPKJQlQRIW17eS1PNtkOtnPRIj8MlsUnvl2H59tOlXkz73rTHaNnuqBXlpCt5tex9L/dnBob5tMy6pnwJQIAeHQfWqR902IkiABiqiw6lT1cXq+UYifdqwojgXchMivv47F8tPei0z/7WiRP3fNyt7a8YwhzeyudapXhZf72++l89exrGTao7/Bsd/U43u/BK9KRd43IUqCBCiiwnK1hPj3p7uw7+Ve2mMZRRGFZZ1eAVBclJoHyHRSJDAv1vyTFU921vJPbNnmngCEBniqwckPj6gnWj0E4e0L/LpClBUSoIgKK9XkuILnP62qo9Pp7DYJnLPueEl2S1QgSenZq8DSMpwHITN+O0Kr19Zy9lr+iwJaLArXk9XgJ9jf02mbjEz7gOirLvHw3cOQmQa1OkPft/P9ekKURRKgiAorzUkNFGPW1vOexuxf/Y/+OkVyumwaKAouMS379ybZxcaTn246TWJ6Jl3f+ctub6jcxKWYsA7sVfZxd9qmmr+Hdnyv20bq/zkGLJnQqD889CO4O5/iFKK8kABFVFjOtp+3TvtoSzGz5FUvRQhnrtjslu0syD0SbV+9OGfNElesU0eB3kb75Fcbj3euzp36f5ht/JB3jZ+qwUnjAXDfQjB65fMdCFF2SYAiKqxDl+Idzrm7uTlp6Xy0RZRdy3aeKxM7US/bdV47/nDDSVJyjKK8uPxAoZ7XurfOjRQnhQSTr8G3D+Mzux5fur/Lf9y2qudvGw/3LQI3o+M9QpRDshePqJD2notjx+nrDueNBueFT9JlBKXc2Hj8Cs//pH7wn3nr7lLrR1KOEZPvdl+gko87U/s21s41DQvgn3M3CvS8566laMe2K84AsJjh63vg0l71sV8oNOwLze+HmrcV6HWEKOskQBEV0v9+Pqgduxv0mLJGSK4nmZy2d5XgKMqevefsR042n7hCgJeR5jUCS7QfK/Y57uO054x9337ed9HpvemZZpLSMgny9XC4NvmHf7Xjn8Z1sr+4ZZYanHj4w7CvIaIL6KTaoKiYZIpHVEipNiMiU/o01I63n84u3jagRZh2nJYpIyjOfLH5NF9uiSrx141LNvH8j/udblVgO+3xzc5zPDx/JwPnbc11mW9Re3LpP7zgZPom0Ns+odXXw/nfgIPmbaXNG+u4EJficG1nVPbIn6fNajNiDsKmd9Xj3q9D7TskOBEVmgQookI6fSU7QbZn4+wy332ahmjHs4e20I4lSdbR8C928MaqI7y28jDXk52PPBWX11ceZtmu8zz3436Hawk2GzxO/Sk7SEgsxEqsGykm/rtoF6sPRuf7nuOXE1m533l765TMxRupPLFkD9HxaQDUrmK/ouZoTCIAqw/GODzHbXXUUvbe7m7orXsxmFLg58fVJcR1ukGrEfnurxDlVYEClI8//pjmzZvj7++Pv78/HTt25Pfff9euK4rCtGnTCAsLw8vLi27dunHokP1+Eunp6UyYMIEqVarg4+PDwIEDuXDhQtG8GyGc8DS6seW57rzYrzETe9bXzhvc9ERW9wcgXZJk7aRlmNlqs1XAjZSSCVCuJqUT8fwqftqrTo1cTzZp03Og/htjveZwr82Kmvwa9tkO1h2J5fEl/+Sr/e8Hovn4L/uy9lX9sqdpArzUBNUHP9/B7zbBx8cPtdaObUdNTE4KuMVkBTXv3pcVQGea4OcnIOYAeFeBQR+CXv62FBVfgX7La9SowVtvvcXu3bvZvXs3d955J4MGDdKCkLfffptZs2Yxb948du3aRUhICL169SIxMVF7jokTJ7J8+XKWLVvGli1bSEpKon///pjN8hesKDq2f7F6GvXUqOTNmDvq4Odpv8LBOoQuSbL2zl23n3ooqQDulRwb5AHE24yYbD/len+l11ceLvDrWUcy8uOr7Wd44ut/WJ4jQNryXHeaVQ8A1MTZez/extlr9j+/6oHZy35vn7mBlrqTvGv8hNabH4Ofx8PBHyE9katJ6ZzJuteg10FGGnxzv7ozsd4AQ7+CgBoFfp9ClEcFSpIdMGCA3eM333yTjz/+mB07dtCkSRPmzJnDiy++yJAhQwBYtGgRwcHBLF26lLFjxxIfH8/8+fNZvHgxPXv2BGDJkiWEh4ezbt06+vTpU0RvS9zqrCPj3RtWdcgLsOVpVAMUSZK1Z1uADEouQPnHydLhdJv8oGOXXQcUG45dIS7ZRCUXhc3yoiiKQ30cWy//4hg8HXy1Dx4GNxqH+nHgYjzvrz/h9F7bXJS79Tt43zgPg84CZmDfHti3BAB/gx9vGtqzw9KY4IvXYMfXcH6HGpwM+RwiOhfqvQlRHhV6nNBsNrNs2TKSk5Pp2LEjUVFRxMTE0Lt3b62Nh4cHXbt2Zdu2bQDs2bOHjIwMuzZhYWFERkZqbZxJT08nISHB7kuI3FgTKZ/r2yjXdh5ZhdskB8VeziW0KSVUadfPM/uD3Pqhbg0eJ3yzl1d/zX2U5PPN+SuE5oxtcmpOzjaUHNetrtZHfR7JqjqdDj0WRrut4gPjXAw6C39bGvFcxhi4bRz4qrlR7pmJDDesZ677PFpsm6AGJ0YfGLYUIocU+r0JUR4VOEA5cOAAvr6+eHh48Pjjj7N8+XKaNGlCTIw63xocHGzXPjg4WLsWExODu7s7lSpVctnGmRkzZhAQEKB9hYeHF7Tb4haiKAo3sqYFAr1y/2va06Cnte449U58CTs+VldKlOBqkLJq//kbdo9f+uWg84ZFrF41XwDubVMD/6xgxTrF8+u/jst6Ab7+bwftOCHNSWEzF3IWVXO2Ksdq7JI9Dueso2/gWJnYQXoSC40zecn4NW46hZ/NnRhhep5vzd3hrhkw6Sg8d4bfWn3CT+bb2WFpAlUbQ5PBMG47NJDRZXHrKXAdlIYNG7Jv3z5u3LjBjz/+yMiRI9m4caN2Pef/qHkNm+anzdSpU3nmmWe0xwkJCRKkCJcS0zMxZ/3FG+ide1XNofGf09XjGziB+gVQrQn0nObwoTD1pwNcupHKFyPbuiw/XhGkZZh5b639BoqnruS+0d07fxzlQlwqc+5vmfeHdS7Ss0ZL2kdUZkfWkvB7Pt7G8zlGwl4fHMknf50iKT2TyLAA7XyVrLoi+fl35/vd9sn5rt7jwYvxTpc72+7npLd5Kb0O9r7Um693nuXt1cf4fkQjWNSfO9wOkK4YeDtzGPPNfQH1pks3UgkL9OKlPy6yeIc/MI7724Zz273Nc+2/EBVdgf+VdXd3p169erRt25YZM2bQokUL3n//fUJC1CHKnCMhsbGx2qhKSEgIJpOJuLg4l22c8fDw0FYOWb+EcCU+a3rHw6C3+yvXwYEf6HrlGwCO+XaAer3A4Amxh2HpUPj9echUV4bsjLrONzvPsfH4FX47kP8lqeVNQloGjV5aXaB7LBaFDzec4pd9lzgSnf+kU6fPlTV6pdPBhbhU7fxbvx+1a9enaTAbn+3Gjqk9CPA2MvaOOgDMWXeCJ5bsocvbG4i/dBIO/wJ/vgm/TYFTG+ye49SVpDz7czI2kf5ztzi95mXzu+VmE6FU8fUgwNvIE13rcvS1PrTb+zxc2kum0Y+xylTmm/sxomOE1v6TjeqqoMU7zmrnPIwVNwAWIr9u+v8CRVFIT0+ndu3ahISEsHbtWu2ayWRi48aNdOqkVkNs06YNRqPRrk10dDQHDx7U2ghxs6zD/LkmdiZEw69PA/BJZn/eqPQ6PPQDTDqm7mkC8PfH8H5LOPADm49lB97f7jpPxPOriHh+FdeSCr60tSxrPm2N3eMZQ5ppx7YfoLZs649YN2MsLGuqh16nw89FkbPPR7Slmp8nBjc9Xu5qkNCqZqB2/feD0dyXuJiAz9rAdyNg09uw81NYPFgNVizq70V4JW+757XWH7E1P5cidWdsVurY5qD4GHUQexTdmS14rn4GTqwBN3cMj65i/rRJnHnrbl4bFKm1T0jNsEsEBnCvwCN0QuRXgaZ4XnjhBfr27Ut4eDiJiYksW7aMv/76i9WrV6PT6Zg4cSLTp0+nfv361K9fn+nTp+Pt7c2DDz4IQEBAAKNHj2bSpEkEBQVRuXJlJk+eTLNmzbRVPULcrGU7z+fd6PcpYEoiObAh78UMpXaCWnsCr0C4azpUbw2rnoHES/DjaIZ71iXCGIoOOHMmhL30IxVPPlh/gldtPmzKs8vWn0GWlRNup05VH60Y2ks/H+Th22oBcP56Cm/9fpRHb69NsH92HRCD/uYqm1pHUPR66FCnMuuOxNpdP/FmX6fTa01tpnmGu63nacNP6oOqjaBGW8hIVZfybnobki7DgPe117Kq4qTsfEAuOUz7L9zQjq2BWV3dRT5P/wg+yhHY3Pk/CG2B7Xjec3c1Yubqo/y87xI/5yibLyMoQhQwQLl8+TIPP/ww0dHRBAQE0Lx5c1avXk2vXr0AmDJlCqmpqYwbN464uDg6dOjAmjVr8PPL3vBq9uzZGAwGhg4dSmpqKj169GDhwoW4udhlVoiC+uOQ64RrAM7vgiO/AnCu81tk/JjKmas5So43u1fNQdk2F7Z+QEjaKe5xyyrQ5QZD3DbzTMYTJKZXL4Z3UDqesEkE7d88lMjqAS7bzl53nFUHoll1IJrVE7to5803mWCs2IygvPmfZqw7sl67NrRtDZe5P/5ZBdKa6s7wsmExAB9kDqZW5+kMalldfeIa7WD1VPhnEVSujcJAu+dYuT+aeQ/aP2/NyvajLLam3JWdF3Nvmxrs2fw7X7m/hY85HXRuULkO+FSFto+qv085+Hq6/ufXK7epSSFuEQUKUObPn5/rdZ1Ox7Rp05g2bZrLNp6ensydO5e5c+cW5KWFyLc2tSrx+8EY6lb1cd5g52eAAk2HkFilJbAdk9lCeqYZD9u9Tzz8oPsL0OYR/l37FSv/OYMbFh42rCVCf5mv3aczYK83x7vWpUGwn/PXKidiE9Psdt19f1gr7fju5qGs2h9NHZuf51GbXJPfDmQHhDe7H052DoqOYH9Pfn+6C33f3wxAnaq+Lu/z8zBwm/4wHxvn4KHLYLM5klmZ98GyfWqAotPBbU+AJRPW/A/WvUqdJh5ALbvnScsw2+UtKWS/nza1KlGrsrdWybauTX+Cr25nofvb+OjSuejfgupjvgO/7G0VnEnMZcVReC6BkRC3ChlHFBVKcnqmVmL8qR71HRvEnYWDP6jH7f5rN/d/zcVOx/iHcrTmA3xu7s8n5oHclT6Tfyz18NKZeNf4Kcv/Kf9bNYz/OrvUe41KXnZJnwOahwJQ2abgnTX3A+ADm+JkTiq3F4g2xZP18ka37H40z2VER3/kZ5a4z6CSLom9lnqMy5gI6BxHQDo+Cc2HAQq9D09lvfskVnpN4xf3//GM4Tt2Hj1j35+spJh+zUL48YlONKuR3Qcfj6yfwbkd+P/0EH66VHZYGnNl8LI8gxOAiCAXATRwV2Te9wtR0UmAIiqUfTb1OxqGOBnV2P8tKBZ1uD+iMzqyPwBzK5VuMqsfVB4GPYl4MzFjPBmKGy31p2h/dKaWeFlepZiyA7UvRra1u+aWte9LZtaHtdmisMdJxVfICjCSr0K88/1y8mJNkrX+d3Gz2XMmyEmOCADn/oblT2DAzGpzOx42PU8iamAypHWOKTidDgbNg9bqZnt19dFEKsdpoT/NU4afafL7UEjPXt1jfc/WJFgf9+xBZw+DG1w9CUvuRWdO57CuLmMyJlO/erV8vdc+TUOcJgJ/+nAb+5E8IW5REqCICuXAxXgAwgI8aRSSYzm6xZI1vQO0egiwH8L/3cnOslbWvXqsdVXOKcG8nqk+R/f45bDlvSLpf2mxjjT87+7GDj83a9qHtbbMtWTHlUvViGOe8X3qL2oB79SF2U3gh0chxXV1VmeUHCMotjm3lXyc1LRJilX3qslMZYfSlPEZT5GEN8M71HT9Im5GGDiXz25bx/+ZnmCm/wtMMj3ONcWPKiknYen9kHjZ7j1bk387168CqFNKXD8NC+4CUyIE1afupPVse2UQPi5WHzl0Q69j45TududGdYqw23FbiFuZBCii3LmRYqLP7E089tVuh2uxCeqH58CWTpJXD/0EyVfA3Q9aqNmQTiqYO0jLMPPGqiNA9m61AF+Z+/BSxij1wZ9vsGf9dwV7I2XAv+dvEPH8Ki04c5YUah3FsH5Y59ynJ5BEvnV/jf5uf2NIswlIDv4IH7SEfUvzXZ1XG0HJGrHIsJkzquRsT6WV/wepceAXyrJar2HGjQbBvtoUVW7/fVMMASy3dGG/f1d+tNzBGNMk0hQjnN0CH3eCs9u0LRCsIxrVA73YMLkbm8fUhIUD1N8n3xAY/h0evpUcNqPMSyVvo1Y9t1+zEP53d+MC3S9ERSYBiih35m+J4tjlRNYcvuyQaJicVZPDL+cKCUWBvz9Rj9uMBIN71um8PzitFU0Bgv097a4tNvdmjbkNAFU3vmA3PVDWKYrCoA+32p0Ls9l118otK1hwFqBUIoFF7jOprb/MFSWA4aap8EI0jFqlVuRNi4efn4DvHoZMFzk+OfoE2SMndav60q9ZCKM6RTiu4Dm5Do6uBHQw7Gv+N7QLk3o14KtHO2hTMs720LG6kqgGs9Ydrf9RGjDE9CoE1YeUqyiLBhB2fhUAgdbRG0Whdux6Ar8ZCAkXILAWjP5DXbFTCDqdjt+e6sLh1/rw0fA2GKT+iRAa+b9BlDsx8dn1OqLj7Wt3WDe58805zP7vMriwS13+2X6Mdrp2FdeJila2qzqcJTZOznicK4o/NfVX1ByXcsI278QqZwAG2cHescuJxKdmMPn7f9XzpLDU/U1a6E+TgicjTc+x1dIM3L0h4nYYu1ndMkBvUJd1r3gyz5GUozHq6iBrgKHT6fhoeBumDWxq3zAjVa0OC9BqOFRvQxVfDyb0qE9IgKd2/0d/nWT22uOcjLUPHKOuJvP13+cA+5ojh5UIEkauZbPSEp0lk8FR03jSbTkdbqyG9a/Bp13UYCs5Vq2x8sjvUCki1/eUF3eDHm/3Au86IkSFJwGKKFeOX07k+z3Zq2YyciwbsQYodnkAaQmw+jn1+PaJdh8otYJ8eKC9mq8QGuD44QwQm5idc3FHg6oO1xPw4YvMu9UHG98Gc8ns/Huzcv7sQJ1yyKlWUPa0T4tX13AyNgkPTHzj9wGN9eexeFbixv0rOKxE4GnUZ49KuRng9v+DIZ+rj/d/C1vfd9mf+NQMLWg6HptHyfzVz8P1U+p0Xff/OVy2jsBYFHh//Ql6ztqoja4BrNqfXRitso/91FHz6dt4NP0Zvsvsih6FycbvufPYNNj8HsQcAHdf9X39dz0EVJw6OEKUNRKgiHLlxz32S3rNOYbwk7URFJtVEJvfVaca/GtA1+ccnrNXE3XVRXR8Giv3O+6Yu8KmymfPxtXY9Gx3tjzXnX9f6a2dX2juw3XFF5JiYN/XBX9jpcDZCIqzKYZAJ7kfrxi+IjJjPxi80A//jsr11JU/aRkWbqTkqO8ROUT9QAdY9woc/8Npf9q+kb0FRs/GrvfmYvuHsGehejzwffAPdWji5qSi7cUb2Xv72P7aLNlxju8f72jXNgMDz2WOYVbGvey31OZ6UBto8wjcNRMmHlBHhjxc12URQtw8CVBEuaEoCp9uOm13LtPmkybDbGH/BXUVj69H1kiAORMO/Kged3seDI5LVW33UXly6V6u5thfxzrFEeLviU6no2aQNzUqeRPgZdSKl6XjzlfmrIBlz4LCv8kS9FOO+i1NQvO3CeeTbst50PCn+uA/n0B4e7tpsPs/2+54U49X1A94gO9HwbkddpdjE9PIMGf/t3RZ+O7c32qhNYBOT0HkPU6bOdvJuPfsTew+oybxJpvsR7mc7X2joOcD8xAGmt7kWL/vYcAcuO1x8Hbcs0cIUfQkQBHlxh+HHLe8X7U/mg83nCTVZKb+i79jypq28LaOoBxZoSYzuvtB0/84fV59jg+znPkK1pU797RxHM63zYf5OrMHZnRwaa/Dzrllkd5mlOGpHvXtNgbM6e6sYm0T3H5isvF7AH6uPBqaDnZoe/yyk0RhnQ76zoSILpCRAt8+pNZLyTLssx2O9+R0/bS6pFixQN07oeerLps6JElnufeT7fxxKIaE1BwBSh6bHDpd4iyEKFYSoIhyIcWUyfojjgHK/C1RvPPHMd63qWYKUNVa1Ovfb9TvbUa6HJLPGaBcSbQfQbHmVNgWdbN6Y3D2RoFXqMS3md3UB5veKfPF26zLdr2MbjzTqwEtwgNdtn1zcCSD9FuYZFSr8H6UOZDqA+1zP2xzVWw30tMYPGDoV2oOUPIVWDZcTXYFTl9J1pq90K+R4703zsGiQeqS4ioN4d4F6o6CLrTM5b18tum03caIyx67Lc8AJTCXTQOFEMVDAhRR5p25mkyTl/+wS46tnmM57KebTtk9Dq/srVb5PLFGPdF8qMvnz/k5l3NXX+vEg7ONeoe0rsGqp27XHn9p7gsGTzi7FXbnvndVabPuUpxrUbMsgXEHmOOtTl2Zmg6l94QPaRdhP9Xx5ah22vHAefbLlzXeleH+r8HoA+d3wK9PO6zsuaupTU6JxayORn3eA+LPgU81eOAbddfpXNQPzg5Gc+6wnJCawbnr6uaQS0Z34LY6QU6neGwFOkkeFkIULwlQRLEwWxStyNXN2nrqqt3jJ7vXc0iOtf2M01babHpb/V6nG4S2cPn8bjlGUBwCFOtzO8lrAGgalr0/y2WPCCzdXlQf/P4cHFru8nXLipS8/jul3oDvH0GXmQo1O+L+nw+pF+yYr5LvHXhDIuG+BYAO9n+L5den8UMNGGbf34KaQd7q9M9vz8I79WDxYHVZb1A9GLMegurm+RLV/DxZ9dTtbHq2u0MC7InYJG1qzjp952EzglLVz4Mtz9lXePWU3YWFKHESoIhi8cSSPbR7Yx3R8dkrJxRFsVvqmV85/7o1uOly3ar+lQFN4MpxOJC1KeAdz+b6/Dmrf36+OcrusbbDbi7P0aORuhLontY1eD76DtaaW4NiVhNC/5qZ70qqpeHh22rl3uCX8XDjLHhVgvuXaEXucjK42f+ELt1IJT7nih7UvKEH/gogtdcMAPT/LGKTx0TeNnxKn6QV6vTYnGbqtgSp18EzQE2wfXQNBOY92mPVNCyAmkHetKpZiagZ/Tg9vZ92zboc3ZqrZDvFc3ezUGpU8qaqn4u9f4QQJUICFFEs1hy+TGJ6pt1Otx+sP0nTV/5g2opD7Dkbl68qrqDWx7D1SOfazH2gldO2S8d0oG4VH1j7khog1OwItTrn+vwBeQzfZ0/xuA5RGoWqq050Ovhuz0XGZzzNWmPWX+F/TVc/5M2OH9alJdOmBkqIk+JsmmO/Z1VrBYZ8AT5VXDbNGUh2eutPWry2hmMx9jVNxi/9h+2nr9H415ow7BvS/GtTSZfEUMNGvNdPhT/fUBNpQ1vAQz/Cs6fVFTQ+QQV+n1Y6nc4uKdjKuvmfbYDibImyEKLkSYAiipwpM/vD73JCdsLpsl1q5c6F285wz8fb2HTiqsO9zsxcfVQ7bhTiR4CXkcah/lqBNStvdzc61gmCA9/D8dWADvpMdzk1Y2W7v46VbREzaxyV29NYg5eUdHW6xISRSRmPq68Pam2UBX0hwbHOSkk7fz2F+v/7XXtsW0nVTkYqrJ6qHrcZBfV75vq8zuqlAPSZs4nley84vUajfvzddxX/NU3iG8/7oX5vCL8NBn0Ej22Eej3Vgm9FpH2OvBkf6wiKTXAl8YkQZYPUVxZFzrbYWYpNvYmcuR3rDl+mq5PKrDlZ62N0qV+FWUNbaudfG9QUH3c3vtiiTslMHxyJ7sD36mgFQMfxUL11ns/v4+6YX5CQmkGQdSUQeU/xWOtu/GBTW6RZjUC1DwE14JcJaqn9z3vAsCVQvU2e/Souc9adsJtxcpkguvV9iItSl2j3eOWmXnPenycx6PVsOBbrcO1GOqyztCG5cm8eGH7bTb1OXh69vTY7z2RvaGgtMW9boM56nFfirBCieMn/gaJIZZgtdoXOdpy+ztlr6hLSnPu25Xdbeqs3Bkfa5QUY3fQMqufGQ25redMwn0E7H4SfxoDZBA36Qq/X8vW8Op2OHo2qUbOyt7biI8FmQzzramFnUwRWOTfUA9h68po6EtNkEIz5EwLCIfESfHkXRG3O9/suaj/mKNDmdIO6xBjYMls9vvPFmy5OFp+ayYRv9vLTPxftzu85e52ErCk8ZyNZRc0jx3JiZ9M5EVnLpecMa4mfh4G3cqkPI4QoPhKgiCLz0z8XaPzSahbvOGt3vus7f/HPuTiH9sH+zpMQM80WErJ2KbZNqvWyHem4dgp+nUjkd515w7iA4Yb16GL+BTd36DIZ7l8M+vyvvPhiZFs2TO5GtawAyDbvRSHvXBlXscvuM1nvu0o9eOwvqH2HGkB9MwzO78p3/4pKfvN+2Po+ZKZBlQbQ7r/5fv7fnuri9Py15HSn50ct2MX15NILUGz1ahJMRJA3A1qEAdAuojL/vtKbYe3zn5grhCg6MsUjiswz36m73J6/nupwbchH2xzOOdsLBmDwR1s5eDGBRzvXJrK6upw1vLIXVXw8IO4s/PMVbJsL5nR12qV6G/WDPzhSTYotxAZuOp0ON112EJRq07f85KBk5hweynIhLgXISu70qQIPfgdL7lHrpHw/Csb/XaJ7uqTmWFLcKMRJSfnUG/DPYvW4+wvglv/AoUmYP0/dWY8P/jxpd95VXFTNz4PZ644Drn+GRalaLgnBnz3cBkWxHynLbdRMCFG8JEARpWb7qWsci0lkcu+Gau0L1L/wD15MAODLrVH0aFSNqsQx2vNv9AvegvN/Zz9BRBd187/azv9qLwxrFdnz11PoWDeI5XsvaAXinFWSzUt0vH3eDUYvtZrqRx3VEvx/zYA+b950v/PLdmTo4+Gtub2+k1U52+aCKVFd0tt4UIFfIz9Tdw+0D+ebnecJr+zNqawqss1rBORx182rV811MKjT6fLKpxZClCCZ4qmg0jLMPLFkj8N0S0n6bmxHejauZnfONil2y8mrrPj3Eq/+egiA73efp/bU37TrNXSx9Dn5Ojs8nmTU9fezghMd1LpdLXU+8tciDU4gO/dkyo/7Afi/b//VruX2x7S/i+mJWWuPO570qQK931CPdy+ATFPhOlsAmWYL01Yc4qWf1Z91RJA3fZuFOtSAISkWdnykHnf/X67l5F25lpz3+2kUoo6M/XXsinZuRMc86rEUkbuahgDQqmZgibyeEKJwJECpoD7ZeIrfD8bw0s8HOXXFyeZtJaB1zUAGt7KfbmkS5s+wduF25y7EpaIoCs/+sD/rjMIot9WscX+OoYaNuOkUrgY0UzeHe+YIPLIKIofkuXz4ZuXM18jt5Vzl07jU7D7wDoKMZDj4YyF6VzA/7b3Iwm1nWJe1n1HPxsHOG27/UK1BUrWRy52C8zIwK4dDr4NBLcMcrlf188DbycopZzsQF4c3/hPJc3c14rOH25bI6wkhCkcClArqB5t9a9Yedtxkr6gpimI3wrB5SncMbnoS0+wrx4YGeDK8g/1fyumZZp7LGrHwwMQ84wdMM36Fty6dXZYGDEmfRuWnN8PtE8E/lJLy5dYzdo9zm+Lp0zSEzvWyC4nZLlHd6yRBGL0e2j+mHu/6olgrzV66kcoULfhT2W7sp7l2CnZ8rB53mVzo+iOR1QPYP603J9/sx/vDWjH2jjp21z2N+lItHV/F14MnutWVSrFClHESoFRQVXyz//HNyCz+XXWTTWZtGfHfL/RQN+vD8S/1no2DaZYj1+DMtRS+232BasTxjfsb9Hf7G0Vn4K2MYQw1vcw/SoMSS1a8t00NQN1g7vWVh+2u5fYHvtFNz9f/vY1BLcMwuun4YmT2X+cX4hyThgFoPUJddXRxN1z856b77srtM/90OOf0w3nDdDCnQ81O0Ozem3pNf0+j9t8sZzDSKrwStav42J1bYLPRoBBCgAQoFZbt9ERGCayOSLdZHVLN5sOvqp+HXSG0sKxdiKfc1dDu/ga68/zk8Qqt9ScxG7zQDf8Oc6enUdBrQUNJeLyruhGdsxUl/16Iz/P+2UNbsvt/vbijQVW6ZCWg2hars+MfBnV7qMdnXez+e5Mu3Uh1qD8D0KpmJfsTlw/Bway9i3q9VqTTZzkDlFcGNCGyun2Qmp+CfUKIW4sEKBVUWkb2qIntvivFxVqgTK9zzCV4974WDGgRxo6pPbRzT3Sty8/j1T1y+uh3stz9ZWrorpLqE47bmD+hXg9e6NeYPyd1LdFCWdbS5878+m/eZer1ep1Wz8P6/bkfD/DemmPOb4i4Xf2+Z2GB+pkfZovCfZ9sdzjfrWFVgnMut936gfq9bg8IL9rRDNvaI8/3bWRToTebLOcVQuQkAUoFdSM1eyXFNzvPFfvrmbNGbJxV5uzbLJS5D7QiJCD7Q1Gn09HIL533jB/zqfscfHTp7LY04PoDqyC4idamTlVf55VOi4mHwXWAMqpTRIGeyzYRdG6OuiCa1g+D3gDXT8H10wV6fltxySYenv+3Xe7R/327j4s37KeXhneoyUfDc5T/v3oS9n+rHt8+sdB9cMV2BMV2NM1aEM26E7QQQtiSAKUCUhSFOJtt7uNSMjh0yfn0hKIoPLn0H/7z0Va7Tf4KyjqCkq+dYNMSYN00PD9qwz1um7EoOr7MvIv7TS8RHBqe9/3FyFU101GdInipf5MCPZevh/1z7b9ww7GRZ4C6OR7Azi8K9Py2vv77LJtPXGXy9+qy6G2n1CXcOf23Sx1t/xnNhjcABWp3VQveFTF/r+zX87J57bkPtOLMW3czX/JPhBBOSIBSAd1IyXAINvacdbKSBDh+OYmV+6PZe+4Gp68WfjmyFqDklbuQfA0W9lP3eTElcsFQk/tML/Na5gie69e0REdLnHEWYLWqGci0gU3zF3zZCPS2D1CGfuo43QJAu9Hq9+OrC72axzbPJD4lg33nb9hdn31/C1ZP7OKQnMqVY3D4F/X4zpcK9dp5CbGZTnK2vFgIIZyRSrIVxJmrycSlmNhx+jozVx91uP7yL4cY0THC4bztxn5JaS6SOfPBupQ511yCpFhYeDdcPQ6egTBgDuZqPfhvTDI/RIaUWB2Mglr0aPtC3ZczQLHNC7JTtzsYPNVpnuOroWHfAr+WbVxzNCYBS47M2L6Roc6X9u5eAIpFrcpbo3jqgjQNC6BxqD9Xk9JpU6tS3jcIIQQSoFQYfeZsIr0QUzQZNgm01g36bN1IMfHxX6fo1SSYthHOd7Q9GZvEG6uOADjUPdHEnYGlw9TgxKuSWgU2pBm1gFpV/Qvc75LSIjwQ/5zVVvMp35vfeVVSN+TbPg+2zIEGdxV4FY2654/q0YW7qF01e6RkSOvqzoOTlOuwe7563GFssRW+83J347en1GTgshqECiHKHpniqQBupJgKFZwAZJqz/9K27iprZbYotHxtLZ9uOs29TlaDWLnKb9Ec+AE+7gxXjqg5Fw//DCFldwt720GghsGF38gv0Nvd7rE1KdSpDmPVmijnd8ClvQV+rZM21YKTTWZtPyOAp+6s7/ym3V+qOytXaQiN+hf4NQtC3edGghMhRP4VKECZMWMG7dq1w8/Pj2rVqjF48GCOHbNfPqkoCtOmTSMsLAwvLy+6devGoUOH7Nqkp6czYcIEqlSpgo+PDwMHDuTChQuIwolNdL6Vva3KPu4O5xLTMnh6WfaH4bUk++fJmdSZc9oA4O/T11i+96LrF975Ofw4GkxJULkuPPI7hLXMs7+lyXa0oUGwk91+88knR75Frsu9A2tmT+1smV2g11EUhVOxzvOHJvasT0TOvBOAjLTsPXc6PFbs2wYIIURBFShA2bhxI+PHj2fHjh2sXbuWzMxMevfuTXJystbm7bffZtasWcybN49du3YREhJCr169SExM1NpMnDiR5cuXs2zZMrZs2UJSUhL9+/fHbDY7e1mRB9sdal1xtkJnzFe7STZl/8wvJ9gHKDkfJ+UoOLb+yGXu/2yH3YZvWr6GORM2vgO/TVYfd3gCxv8NwU3z7Gtps62y+vBNbGCXcwTl94Mxud/Q6Sn1+8n1kJZ3UTirFJNZ2+Qwp0YhLgKsrXMg5Zo6vdTq4Xy/lhBClJQCBSirV69m1KhRNG3alBYtWrBgwQLOnTvHnj17APUvuTlz5vDiiy8yZMgQIiMjWbRoESkpKSxduhSA+Ph45s+fz3vvvUfPnj1p1aoVS5Ys4cCBA6xbt67o3+Et4EaKY4AyomMtIqtn53aYnPz1vuP0dbvHX26N4qvtZ7THtnkNANtPXbN7/Plm+7od1fw81IqgiZfhy95Zy1eBO6bAXTPArXC5HCXtwwdbU7uKDx8Nb51rXZS81K3qQ9/IELtzJ2MTXbQGwlqrIykZybBvaZ7PrygKz/+4n+7v/gWA0c1xFMRaudfOtVOw+T31uOc0MMieNEKIsuemclDi49W/8ipXVpMno6KiiImJoXfv3lobDw8PunbtyrZt2wDYs2cPGRkZdm3CwsKIjIzU2uSUnp5OQkKC3ZfINmfdcbvH7WtX5rVBkXbTExlmi8PuvM68/Ev2dJw18dVq/Nf2+8Ukpdv/1T61XyOI2gyf3gEX94BHANz9Htz5YrmaQoisHsCGyd3o1+zmNibU6XR8/FAb3h/WUjvnaqQDUDcQbPdf9Xjf12DJfURxx+nrLNt1XpviqxXkQ5NQ+4TjSjlGcbCY4cf/qrkn4R1k9EQIUWYVehWPoig888wz3H777URGRgIQE6MOYQcH228QFxwczNmzZ7U27u7uVKpUyaGN9f6cZsyYwauvvlrYrlZoiqJw6JJ9wDb9P2oCqm3OiKKo+8s4+yvbljdp8O+3XD/5N28ZTmHQWdBhIUXxJN6/AaR3AQ818Ek12X+ANlDOwDfD1HyToHpw/9dQrVERvMvyzTYuzDNGbDpE3bQv5gCc3gD1erpsOnbxbrvHdar48FL/Joz7+h8OXFT/eKhkm3tkzoQfHoFL/4DBCwZ/DHqpSyKEKJsKPYLy5JNPsn//fr755huHazmz9RVFyTODP7c2U6dOJT4+Xvs6f/58Ybtd4bxrs8fLE93qEjWjH/WqqStP/q9XAzyN2f+JTZkWLWhZcyg7GBzbtQ4ALXQn+d39eVj+GJUPzGeY4S/uddvEPW5beNiwjidTPoI5zeFftSy67YftW50Ummz4rxqcRHSBsZslOMmiYBso5hGhBIZn7yS8ba7LZmsPX3YYjTGZLYRX9mb2/S20c3aJuutegSMr1OO734Oguvl7A0IIUQoKNYIyYcIEVqxYwaZNm6hRI3un2ZAQdb49JiaG0NDs4fHY2FhtVCUkJASTyURcXJzdKEpsbCydOnVy+noeHh54eMg8eU6KovDhhlPa44EtwuyCvFpBPhyY1of6L/4OwKr90by28jDv3teCx5fs0do9fFstrm/5kumG+Rh1ZhIUb340d+GKEkgmega1qsGGfccZqN9GzdQrsPwx0q9G4aFrS03dZWbW2EbHA6sgIwWqNoL7l4C7d8n9IMqRfG0n0Olp2LsETv+lLjkOa2V3ee+5OMZ8tdvhtodvUxN661Xz47m7GhES4JH9+3BpH2z/UD2++z1oNfwm3oUQQhS/Ao2gKIrCk08+yU8//cSff/5J7dq17a7Xrl2bkJAQ1q5dq50zmUxs3LhRCz7atGmD0Wi0axMdHc3BgwddBijCuZzJsY1DHQueGW1Kx0/5cT9J6Zl2wQlA2NFFvGP8DKPOzCZLC7qlz+LVzJF8ZB7EZ+YBHK09incz76eH6T0WZqq5Qx6bZ7A8YRgb3J+h45Xv1eAkoguMXAlegUX/Zssx20GT1Ix8rFSr2gDq91GP17/uMC90/LLzRNsejbOnVp/oVpf/tMr64yHTBL8+DSjQ8O7sPBchhCjDChSgjB8/niVLlrB06VL8/PyIiYkhJiaG1FR1x1SdTsfEiROZPn06y5cv5+DBg4waNQpvb28efPBBAAICAhg9ejSTJk1i/fr17N27l4ceeohmzZrRs6fr+XbhKD/1T1y5t4364fVmZDT6tf8DYH5mX0aanuU62YHOf2+vTf8W6mhYBgamZY7k/0xPEKNUwlOXgZtOIT3iTnjwOxixAnyr3sQ7qvhGL9rNx3+dyrthz1dQ0MGp9eqXDTe9/f+2ozpF8Nfkbq6fa+NMiN4HRh/oO7PgnRZCiFJQoCmejz/+GIBu3brZnV+wYAGjRo0CYMqUKaSmpjJu3Dji4uLo0KEDa9aswc8ve0XJ7NmzMRgMDB06lNTUVHr06MHChQtxc5OEvYKITUzTjrvUr1Kgey0WhY76Qww7NROUTBJq9+P1I8MB+zygF+9ujE6nY8noDjw0/29Ax3JLF1amd6SO7hKpeLBx5CPlapVOSevVxD5pfObqo4y9o06u+xYp1Zrwk/l27nHbjOmXibhP3Kst07buWAxqQvSDHWq6fvGL/2QvKe73jprjIoQQ5UCBp3icfVmDE1BHUaZNm0Z0dDRpaWls3LhRW+Vj5enpydy5c7l27RopKSn8+uuvhIfLP5wFFWtTSG3uA61cthvppNiY+dop5hk/wE3JhPq9Se43j5zByfvDWmo5DG0j7FddZWDgmFKTSff3kRLmefDzNDKqU4TdubxGv64kpvNWxjCSFE/cE8/DpncBiI5PtWuXa3BiSoGfxwEKNOgreSdCiHJF9uIpx6wfckNaV3eoWmprq12BNYX++u1MvTyJIF0iCf714b6FhFYNcrjPtg6Ih8HxV2XH1B4Malm98G/gFmJbnRbgthnr2Xf+hsv2F2+kcoVKvJo5Qj2xZTZcPszstdk1b3JdMq4osGpS9v5H/QtWPl8IIUqbBCjlmHWKp5qfZ67t6mTtxRJEPEuNbzLPfS4hujhOWsIwDfsB3NXrB1/tw8Se9dkwuRtRM/rZJdjqdDo61VWDmP+0qs6p6f0ICcj9dUU2dzfH/9VGfrnTZXvr5o/fm7ty3Lc9mNNh7ct8tzt7if2vE253/YIHvod/lwI6uGc++N9c0TkhhChphS7UJkqftVR9Nb/cl2Cnpmcwxm0lTxmW46dLJUXx4AtzXz7JHMi+atlTBL4eBib2bODyeZaM7oBZUewCF5E/7k5GoHLbQyl7d2odT10bwmrvfXByLf30jfnNchsAjUIcV20BcGFP1qodoMszUL/XTfRcCCFKhwQo5diRaLWC7I0Uk+tG5kzmGmYTaPwDgGOWGjyV8STHFDUwMeSSqJmTXq9Dj+SbFIazACU36TbLkY8qNVHaPoru70+YaviG9abW1Aqu7PxGU4paLTYjRa2f0u2Fm+m2EEKUGvlTuJzKtNn8r0Mdx/wRzYY3CDz7BybFjVczHqafaYYWnAC5riQRRcfZFI8rFovCY4vta9WcbvY08Yo34forjDOs4J17WzjemGmCbx+CG2fBvzo8vBzc5G8QIUT5JAFKOWW7/07zGgHOG13YA1vmADAlYywLzH0xI0u5S0NBRlBOX012ONdj3l5ezVATZp90W05ItH1tFFKuw+LBas0UN3cY/BF4VXJ4HiGEKC/kz6ty6sy17A8xP0+jY4PMdPh+FKBAne78fLgzAFV8PbiaVPgCb6Jw9E6WYvdsXM1pW1fTbj9ZutDVvJ9BbtsI/u1ROP4NtH0EovfD7i8hOVbdBPC+BVCnW1F2XwghSpwEKOWUtcz93c1crM7YNR/iz4FHAAz+mM39A9h4/ApXEtN5f/0JAOpnbSooip+zUjE5R1XSMsy8/MtB6lR19d9Fx5SMx2hYPYhGl1fBybXql1VATbj/K4e9e4QQojySAKWcyszaldhpDsmV47D2ZfW4yzPgH0o48NBttYhPyeBCXCphgZ5M6t2w5Dp8i3M2grLlxFW7x59tOs13uy/k+jzpuOMz9DNQotXaKGe2QEANaDMKGvUHoyz9FkJUDBKglFOWrADFYTogMx2+exgsGerOwh3H210O8Dby3lAnCZaiWNWp6uNwLshXXR5+OSGN53/cz64zcXbXq/l5MLZrXV5fedj+vL8HGOrCoHnF12EhhChlkiRbDu07f4OLN9SS5w5/me//Fq4cVRMlhy3V9m8RpatBsB/zR7bl96e7MGNIM0AtZw/w2srDbDh2haT0TLt7rial82jnCGpU8rI772GQRGchRMUnIyhlUFqGmX/P36BNrUoYbJanWiwKA+ZtsVvBY7d61WKGDTPU4y6TIKhuCfVY5EePxuqmgTUqeTH1pwMkpWeSajKzan+00/b9m4eh0+loH1GZC3EXAdmTUQhx65ARlDLo/77dx/2f7WDxjrPaufRMM3Ve+M0uOAFws53i2TUfEi+pibGdniqp7ooC8vXI/rvg76hrLtu1y9qg0dczu/2nD7Upvo4JIUQZIgFKGZKWYSbFlMnvB2MA+GJzlHbtl72XnN6z0vrXd1oCbJmlHnf5P3D3Lta+isKz3f151IJdLttZp3L2nM3OTXG9wkcIISoWmeIpIywWhUHzttrlIdgO58ckpDm9LzEtq/3eJZAYDb7B0O6/xdlVUUI8jOrfD7ajZl7ukn8ihLg1yAhKGXHmWjLHLidqya8AF+JS6fzWnwAkuNhY7u7moeroycaZ6onOE8HDr7i7K0qAdQTl3fuyV115GyVAEULcGiRAKQPmrj/ByAU7nV67eCOVDLOFc9dTALW415r/u0O7Puf+lrDpbUi7AZVqQ/sxJdBjURKsIyhhAdm1TWQERQhxq5ApnlJ26FI87609nmub+i/+rh1/PLw1DYL9eLl/E6r4eWDMSIS9X6sXe7wky4rLqRqVvBh9e20ahfjzwOc7APDIWqIV6O2utfMo4K7IQghRXkmAUsoe+2qPwzm/rFUeiTnqYgDUqKQmvz56e231xNb3IfW6untto/7F11FRpF7s15g3fzuiPW5eI4BHOtfGlJm9S3WKyQxA41A/RnWKoKqfh12CrRBCVGQSoJQy25wTgIWPtKNbw2qcvZbMqAW7iLLZ2bZ5jQAaBNus4jClwOaslTu3/x8YPEqiy6II3N081C5AsQYmtvvzBPmqIyc6nY5pA5uWbAeFEKKUyXhxKavi6273uEWNQABqBfmw7pmudtdWPHm7/V/QexaouSf+1aH1iGLuqShKRjf7//XWHYnVjpc9dhtv/ieSVjUrlXS3hBCizJAApZRVsskvCPJxp5JP9mM3vY4nu9cDoFXNQPsb0+Jh83vqcYfHZfSknHHPEaDYjpzcVieI4R1qlXSXhBCiTJEpnlKUYbZoUziLHm1PZJi/Q5unetSnZmVv7mhQNfukosBvz0LKNfALUwMUUa6450h2ff6uRqXUEyGEKJskQClFl26kkpm1K/Ed9as4TYB0N+gZ2i7c/uSeBeqmgOhg8IdgcHe4T5RtRjf7/9Z9IkNKqSdCCFE2yRRPKVq+96J2nO/VGRf/gdVT1eNuU6HuncXQM1Hc7PZQAqoHerloKYQQtyYJUEqRj7s6gKXP78rR5Gvw3UjITFMDk65Tiq9zolhZdykG+GJE21LujRBClD0yxVOKLsWrS4zHdKmTd+PMdFj2AMSfg4CaMORz+816RLnz3eMdSc80ayXthRBCZJMRlFJ0MU4NUMLyGt5PjYOvBsH5v8HdF4Z9DT5VSqCHorhJcCKEEM7JCEopOp8VoOSaf5B8DZb8B6L/BYMnDF0Eoc1LqIdCCCFE6ZAApZSkZZg5Ep0AQL1qvs4bxV+ArwbDtRPgVQke+hGqtym5TgohhBClRAKUUpBqMtP45dXa4/DK3o6NEi7Bl33VnBPfYBj+PYS2KMFeCiGEEKVHApQSZrYodsGJn4fBYckpqXGweIganPjXgFEroXLtEu6pEEIIUXokSbaEfbPznN3jLc/nqGOiKPDr03DlCHgHwcgVEpwIIYS45RQ4QNm0aRMDBgwgLCwMnU7Hzz//bHddURSmTZtGWFgYXl5edOvWjUOHDtm1SU9PZ8KECVSpUgUfHx8GDhzIhQsXbuqNlBc7Tl/Tjlc82ZkAL6N9g78/gcO/qMf3LYKguiXYOyGEEKJsKHCAkpycTIsWLZg3b57T62+//TazZs1i3rx57Nq1i5CQEHr16kViYqLWZuLEiSxfvpxly5axZcsWkpKS6N+/P2azufDvpIy4kpjOfxft5s+jl51e9/NUZ9WCfNxpnrVzsWbfUlj9vHp8xxSo3aUYeyqEEEKUXQXOQenbty99+/Z1ek1RFObMmcOLL77IkCFDAFi0aBHBwcEsXbqUsWPHEh8fz/z581m8eDE9e/YEYMmSJYSHh7Nu3Tr69OlzE2+n9CiKwvVkE898t4/NJ66y7shlzrx1t0O7VJMahD3RLcfIyKW98OtE9bjVQ9D9hWLusRBCCFF2FWkOSlRUFDExMfTu3Vs75+HhQdeuXdm2bRsAe/bsISMjw65NWFgYkZGRWpuc0tPTSUhIsPsqS/4+fY1GL62mzRvr2HziqnbenLURoK2UrADF02hToOt6FCy5B8zpULMT9H9fqsQKIYS4pRVpgBITEwNAcHCw3fng4GDtWkxMDO7u7lSqVMllm5xmzJhBQECA9hUeHu60XXE7fjmRr7afcQg87v9sB+mZFof2dV/4jfjUDLtzlxPSAAj0zso9iT0KiwdDyjUIaQb3LwY3WVwlhBDi1lYsq3hy7syrKEqeu/Xm1mbq1KnEx8drX+fPny+yvuZXeqaZ3rM38fIvh1i07Yx2XlEcR0ls3f3BZgAyzRYinl/FvxfiAajs464GJ1/2gbgzEBAOD34vJeyFEEIIijhACQkJAXAYCYmNjdVGVUJCQjCZTMTFxblsk5OHhwf+/v52XyVt9cHs97T3/A3teO1h58mwVhfiUhn39R7OXk+xOx9iOqeOnKTdUDf/e+Q38A8twh4LIYQQ5VeRBii1a9cmJCSEtWvXaudMJhMbN26kU6dOALRp0waj0WjXJjo6moMHD2ptyqLJ3/+rHf/67yWGf7GD73af57HFe/K897cDMbz1+1HtcV3dRSJ+vQ8So6FyXRizHgJrFku/hRBCiPKowMkOSUlJnDx5UnscFRXFvn37qFy5MjVr1mTixIlMnz6d+vXrU79+faZPn463tzcPPvggAAEBAYwePZpJkyYRFBRE5cqVmTx5Ms2aNdNW9ZRFGWb7qZytJ6+x9WR2TZP5I9uyaPtZNh2/AsDHw1vzxNf/aNetIy21dDH84jsTfcpVqNoIRqwA32ol8A6EEEKI8qPAAcru3bvp3r279viZZ54BYOTIkSxcuJApU6aQmprKuHHjiIuLo0OHDqxZswY/Pz/tntmzZ2MwGBg6dCipqan06NGDhQsX4uZWdreev7NRNf48Guvyeo/GwURdTdYClL7NQjnz1t1EPL9Ka9NUd4ZfAt7DkHYNguqpwYmf82ktIYQQ4lamU/LK8iyDEhISCAgIID4+vsTyUYZ9tp0dp6+7vH7mrbvJMFt4d80xutSryu311WTXxdvP8NIvh+iq/5eFfh+hS0+EoPow8lfJORFCCHFLKcjnt6xnzae0DMdlxFZzH2gFgNFNz9S+je2uPdwxgmEZyzH+ORPSgept1Z2JvSsXZ3eFEEKIck0ClHzKtKgBSv1qvpyITQLg7Xub061BVar5e7q+UVEwJkWrxy0egLtngbt3cXdXCCGEKNckQMmnzKwk2VcGNKVd7UpEXU2mUUg+ppd0Oug7ExoPkL11hBBCiHwqlkJtFdHJrFETg5sOD4Nb/oITK51OghMhhBCiACRAyYfo+FQys8rbG/SyR44QQghR3CRAyYcNR69ox072/xNCCCFEEZMAJQ9/HYvlheUHtMcBXsZS7I0QQghxa5AAJQ9PfbNXO25eI4CGIX65tBZCCCFEUZAAJRdpGWYS0jK1x9+N7ViKvRFCCCFuHRKg5OJqUrp2fPT1u/A0lt1S/EIIIURFIgGKC4qicPvMDdpjCU6EEEKIkiMBigv7zt8o7S4IIYQQtyypJJvD9lPXmL8lija1KmnnVjzZuRR7JIQQQtx6JECxkZ5p5oHPdwCw7shlAMZ1q0vzGoGl2CshhBDi1iNTPDZupGQ4nOvZJLgUeiKEEELc2iRAsZGYZh+g+HoYaCmjJ0IIIUSJkykeG5V9PJg2oAmbTlzFlGnhw+Gt0cveO0IIIUSJ0ymKUu52l0lISCAgIID4+Hj8/Quwq7AQQgghSk1BPr9likcIIYQQZY4EKEIIIYQocyRAEUIIIUSZIwGKEEIIIcocCVCEEEIIUeZIgCKEEEKIMkcCFCGEEEKUORKgCCGEEKLMkQBFCCGEEGWOBChCCCGEKHMkQBFCCCFEmSMBihBCCCHKHAlQhBBCCFHmGEq7A4Vh3YA5ISGhlHsihBBCiPyyfm5bP8dzUy4DlMTERADCw8NLuSdCCCGEKKjExEQCAgJybaNT8hPGlDEWi4VLly7h5+eHTqcr7e6USQkJCYSHh3P+/Hn8/f1LuztlkvyM8iY/o7zJzyhv8jPK3a3081EUhcTERMLCwtDrc88yKZcjKHq9nho1apR2N8oFf3//Cv8Lf7PkZ5Q3+RnlTX5GeZOfUe5ulZ9PXiMnVpIkK4QQQogyRwIUIYQQQpQ5EqBUUB4eHrzyyit4eHiUdlfKLPkZ5U1+RnmTn1He5GeUO/n5OFcuk2SFEEIIUbHJCIoQQgghyhwJUIQQQghR5kiAIoQQQogyRwIUIYQQQpQ5EqCUUTNmzPj/9u41pKn/jwP4e5KtFWs/L8x5gbB8UGFlaqgZaVJiNDKMHqSMCguslKIi6AL6JBAfJGSEWjCqBxmRQZgslJndZopupoYGpYhzU/FKWl7a5/eoQ/tZ/7zsHLf+nxf4wO8+nHM+b45fPm4exLZt26BUKqFWq3HgwAF0dHQ41RAR8vLyEBQUBIVCgcTERLS1tTnVTE5OIicnB/7+/li1ahX279+Pnp6eWed79uwZYmJioFAo4O/vj7S0NFH7cwUpM/r48SNSU1Ph7++P1atXIz4+HjU1NaL3uFiuyqi0tBSJiYlYvXo1ZDIZRkZGZp1reHgYOp0OKpUKKpUKOp3ul3XuRqqMurq6kJmZidDQUCgUCqxbtw65ubmYmpoSu8VFk/I++mFychIRERGQyWSwWCwidOVaUmfkiXv2fPGA4qZqa2tx+vRp1NXVoaqqCjMzM0hOTsb4+LhQU1BQgOvXr+PmzZtoaGiARqPBnj17hP9VBABnz57FkydPUFZWhtevX+PLly/QarX4/v27UPP48WPodDocO3YMzc3NePPmDdLT0yXtdyGkzGjfvn2YmZmB0WhEY2MjIiIioNVqYbfbJe15vlyV0cTEBFJSUnD58uXfnis9PR0WiwUGgwEGgwEWiwU6nU7U/lxBqoza29vhcDhQUlKCtrY2FBYWori4+H9m6i6kvI9+uHjxIoKCgkTpRwxSZuSpe/a8EfMI/f39BIBqa2uJiMjhcJBGo6H8/Hyh5tu3b6RSqai4uJiIiEZGRsjb25vKysqEGqvVSl5eXmQwGIiIaHp6moKDg+nOnTsSdiMOsTIaGBggAPTy5UuhZmxsjABQdXW1FK25zEIy+llNTQ0BoOHhYaf1Dx8+EACqq6sT1kwmEwGg9vZ2cZoRiVgZ/UpBQQGFhoa67NqlInZGlZWVtH79emprayMAZDabxWhDVGJl9Dft2X/C76B4iNHRUQCAr68vAKCzsxN2ux3JyclCjVwuR0JCAt6+fQsAaGxsxPT0tFNNUFAQwsPDhZqmpiZYrVZ4eXlh69atCAwMxN69e2e97egJxMrIz88PGzZswL179zA+Po6ZmRmUlJQgICAAUVFRUrXnEgvJaC5MJhNUKhViYmKEtdjYWKhUqnkdxx2IldHvzvXjPJ5EzIz6+vpw4sQJ3L9/HytXrnTdRUtMrIz+pj37T3hA8QBEhHPnzmHHjh0IDw8HAOGjhYCAAKfagIAA4TW73Y7ly5fDx8fntzWfP38GAOTl5eHq1auoqKiAj48PEhISMDQ0JGpfriRmRjKZDFVVVTCbzVAqlVixYgUKCwthMBjwzz//iNyZ6yw0o7mw2+1Qq9Wz1tVqtdt/DPYzMTP6r0+fPqGoqAhZWVkLv+AlIGZGRISjR48iKysL0dHRrrtoiYmZ0d+yZ88FDygeIDs7G+/fv8eDBw9mvSaTyZy+J6JZa//1c43D4QAAXLlyBQcPHkRUVBT0ej1kMhkePXrkog7EJ2ZGRIRTp05BrVbj1atXqK+vR2pqKrRaLWw2m+uaEJmrM/rTMRZ6nKUkdkY/9Pb2IiUlBYcOHcLx48cXdIylImZGRUVFGBsbw6VLlxZ9nUtJzIz+lj17LnhAcXM5OTl4+vQpampqEBISIqxrNBoAmDV59/f3CxO6RqPB1NQUhoeHf1sTGBgIANi4caPwulwux9q1a9Hd3e36hkQgdkZGoxEVFRUoKytDfHw8IiMjcevWLSgUCty9e1fM1lxmMRnNhUajQV9f36z1gYGBeR1nKYmd0Q+9vb3YtWsX4uLiUFpauriLlpjYGRmNRtTV1UEul2PZsmUICwsDAERHR+PIkSMu6EB8Ymf0N+zZc8UDipsiImRnZ6O8vBxGoxGhoaFOr4eGhkKj0aCqqkpYm5qaQm1tLbZv3w4AiIqKgre3t1ONzWZDa2urU41cLnd6HG56ehpdXV1Ys2aNmC0umlQZTUxMAAC8vJx/XLy8vITfZtyVKzKai7i4OIyOjqK+vl5Ye/fuHUZHR+d1nKUgVUYAYLVakZiYiMjISOj1+ln3lLuSKqMbN26gubkZFosFFosFlZWVAICHDx/i2rVrrmlGJFJl5Ml79rxJ+Ae5bB5OnjxJKpWKXrx4QTabTfiamJgQavLz80mlUlF5eTm1tLTQ4cOHKTAwkMbGxoSarKwsCgkJoerqampqaqKkpCTasmULzczMCDVnzpyh4OBgev78ObW3t1NmZiap1WoaGhqStOf5kiqjgYEB8vPzo7S0NLJYLNTR0UEXLlwgb29vslgskvc9H67KyGazkdlsptu3bwtPNJnNZhocHBRqUlJSaPPmzWQymchkMtGmTZtIq9VK2u9CSJWR1WqlsLAwSkpKop6eHqdzuTsp76OfdXZ2esxTPFJm5Kl79nzxgOKmAPzyS6/XCzUOh4Nyc3NJo9GQXC6nnTt3UktLi9Nxvn79StnZ2eTr60sKhYK0Wi11d3c71UxNTdH58+dJrVaTUqmk3bt3U2trqxRtLoqUGTU0NFBycjL5+vqSUqmk2NhYqqyslKLNRXFVRrm5uX88zuDgIGVkZJBSqSSlUkkZGRlzetR2qUmVkV6v/+253J2U99HPPGlAkTIjT92z50tGRLS492AYY4wxxlzLMz4AZYwxxtj/FR5QGGOMMeZ2eEBhjDHGmNvhAYUxxhhjbocHFMYYY4y5HR5QGGOMMeZ2eEBhjDHGmNvhAYUxxhhjbocHFMYYY4y5HR5QGGOMMeZ2eEBhjDHGmNvhAYUxxhhjbudfsrO1iBnHNTQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# We import matplotlib into Python\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# We plot the Google stock data\n",
    "plt.plot(all_stocks['Google'])\n",
    "\n",
    "# We plot the rolling mean ontop of our Google stock data\n",
    "plt.plot(rollingMean)\n",
    "plt.legend(['Google Stock Price', 'Rolling Mean'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}