part_2_mars_weather_SDT.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 12 Challenge\n",
    "## Deliverable 2: Scrape and Analyze Mars Weather Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import relevant libraries\n",
    "from splinter import Browser\n",
    "from bs4 import BeautifulSoup as soup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "import json\n",
    "import pprint as pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup webdriver\n",
    "from webdriver_manager.chrome import ChromeDriverManager\n",
    "executable_path = {'executable_path': ChromeDriverManager().install()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup automated browser\n",
    "browser = Browser('chrome', **executable_path, headless=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Visit the Website\n",
    "\n",
    "Use automated browsing to visit the [Mars Temperature Data Site](https://static.bc-edx.com/data/web/mars_facts/temperature.html). Inspect the page to identify which elements to scrape.\n",
    "\n",
    "   > **Hint** To identify which elements to scrape, you might want to inspect the page by using Chrome DevTools to discover whether the table contains usable classes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visit the website\n",
    "# https://static.bc-edx.com/data/web/mars_facts/temperature.html\n",
    "url = \"https://static.bc-edx.com/data/web/mars_facts/temperature.html\"\n",
    "browser.visit(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Scrape the Table\n",
    "\n",
    "Create a Beautiful Soup object and use it to scrape the data in the HTML table.\n",
    "\n",
    "Note that this can also be achieved by using the Pandas `read_html` function. However, use Beautiful Soup here to continue sharpening your web scraping skills."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Beautiful Soup Object\n",
    "html = browser.html\n",
    "\n",
    "weather_soup =  soup(html, 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tr class=\"data-row\">\n",
       " <td>2</td>\n",
       " <td>2012-08-16</td>\n",
       " <td>10</td>\n",
       " <td>155</td>\n",
       " <td>6</td>\n",
       " <td>-75.0</td>\n",
       " <td>739.0</td>\n",
       " </tr>,\n",
       " <tr class=\"data-row\">\n",
       " <td>13</td>\n",
       " <td>2012-08-17</td>\n",
       " <td>11</td>\n",
       " <td>156</td>\n",
       " <td>6</td>\n",
       " <td>-76.0</td>\n",
       " <td>740.0</td>\n",
       " </tr>,\n",
       " <tr class=\"data-row\">\n",
       " <td>24</td>\n",
       " <td>2012-08-18</td>\n",
       " <td>12</td>\n",
       " <td>156</td>\n",
       " <td>6</td>\n",
       " <td>-76.0</td>\n",
       " <td>741.0</td>\n",
       " </tr>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract all rows of data\n",
    "table_rows = weather_soup.find_all('tr', class_='data-row')\n",
    "\n",
    "# Verification! Pull in first 3 rows from table to ensure accuracy\n",
    "table_rows[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Store the Data\n",
    "\n",
    "Assemble the scraped data into a Pandas DataFrame. The columns should have the same headings as the table on the website. Here’s an explanation of the column headings:\n",
    "\n",
    "* `id`: the identification number of a single transmission from the Curiosity rover\n",
    "* `terrestrial_date`: the date on Earth\n",
    "* `sol`: the number of elapsed sols (Martian days) since Curiosity landed on Mars\n",
    "* `ls`: the solar longitude\n",
    "* `month`: the Martian month\n",
    "* `min_temp`: the minimum temperature, in Celsius, of a single Martian day (sol)\n",
    "* `pressure`: The atmospheric pressure at Curiosity's location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      " ['1759', '2017-10-10', '1841', '72', '3', '-77.0', '870.0'],\n",
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      " ['1864', '2018-01-26', '1946', '120', '5', '-79.0', '753.0'],\n",
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      " ['1876', '2018-02-09', '1960', '127', '5', '-78.0', '740.0'],\n",
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      " ['1884', '2018-02-16', '1966', '130', '5', '-77.0', '735.0'],\n",
      " ['1885', '2018-02-17', '1967', '130', '5', '-76.0', '734.0'],\n",
      " ['1886', '2018-02-18', '1968', '131', '5', '-76.0', '732.0'],\n",
      " ['1887', '2018-02-19', '1969', '131', '5', '-76.0', '732.0'],\n",
      " ['1888', '2018-02-20', '1970', '132', '5', '-77.0', '731.0'],\n",
      " ['1890', '2018-02-21', '1971', '132', '5', '-78.0', '731.0'],\n",
      " ['1891', '2018-02-22', '1972', '133', '5', '-78.0', '730.0'],\n",
      " ['1889', '2018-02-23', '1973', '133', '5', '-78.0', '730.0'],\n",
      " ['1892', '2018-02-24', '1974', '134', '5', '-77.0', '729.0'],\n",
      " ['1894', '2018-02-25', '1975', '134', '5', '-76.0', '729.0'],\n",
      " ['1893', '2018-02-26', '1976', '135', '5', '-77.0', '728.0'],\n",
      " ['1895', '2018-02-27', '1977', '135', '5', '-77.0', '727.0']]\n"
     ]
    }
   ],
   "source": [
    "# Create an empty list\n",
    "mars_temp_list = []\n",
    "\n",
    "# Loop through the scraped data to create a list of rows\n",
    "for entry in table_rows:\n",
    "    # find all entries within the table that are \"<td>\"\n",
    "    table_entry = entry.find_all('td')\n",
    "    # use list comprehension to find all the text from table_entry\n",
    "    entry_data = [column.text for column in table_entry]\n",
    "    # append the entry_data into mars_temp_list\n",
    "    mars_temp_list.append(entry_data)\n",
    "\n",
    "# Pretty Print the list to confirm success\n",
    "pp.pp(mars_temp_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Pandas DataFrame by using the list of rows and a list of the column names\n",
    "mars_temp_df = pd.DataFrame(mars_temp_list, columns=['id', 'terrestrial_date', 'sol', 'ls', 'month', 'min_temp', 'pressure'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>terrestrial_date</th>\n",
       "      <th>sol</th>\n",
       "      <th>ls</th>\n",
       "      <th>month</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2012-08-16</td>\n",
       "      <td>10</td>\n",
       "      <td>155</td>\n",
       "      <td>6</td>\n",
       "      <td>-75.0</td>\n",
       "      <td>739.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>11</td>\n",
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       "      <td>6</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>2012-08-18</td>\n",
       "      <td>12</td>\n",
       "      <td>156</td>\n",
       "      <td>6</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>741.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>2012-08-19</td>\n",
       "      <td>13</td>\n",
       "      <td>157</td>\n",
       "      <td>6</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>732.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>2012-08-20</td>\n",
       "      <td>14</td>\n",
       "      <td>157</td>\n",
       "      <td>6</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>740.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id terrestrial_date sol   ls month min_temp pressure\n",
       "0   2       2012-08-16  10  155     6    -75.0    739.0\n",
       "1  13       2012-08-17  11  156     6    -76.0    740.0\n",
       "2  24       2012-08-18  12  156     6    -76.0    741.0\n",
       "3  35       2012-08-19  13  157     6    -74.0    732.0\n",
       "4  46       2012-08-20  14  157     6    -74.0    740.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm DataFrame was created successfully\n",
    "mars_temp_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Prepare Data for Analysis\n",
    "\n",
    "Examine the data types that are currently associated with each column. If necessary, cast (or convert) the data to the appropriate `datetime`, `int`, or `float` data types.\n",
    "\n",
    "  > **Hint** You can use the Pandas `astype` and `to_datetime` methods to accomplish this task.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                  object\n",
       "terrestrial_date    object\n",
       "sol                 object\n",
       "ls                  object\n",
       "month               object\n",
       "min_temp            object\n",
       "pressure            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine data type of each column\n",
    "mars_temp_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change data types for data analysis\n",
    "# change 'terrestrial_date' to \"datetime\" data type\n",
    "mars_temp_df['terrestrial_date'] = pd.to_datetime(mars_temp_df['terrestrial_date'])\n",
    "\n",
    "# change 'sol', 'ls', 'month' to \"integer\" data type\n",
    "mars_temp_df[['sol', 'ls', 'month']] = mars_temp_df[['sol', 'ls', 'month']].astype('int')\n",
    "\n",
    "# change 'min_temp', 'pressure' to \"float\" data type\n",
    "mars_temp_df[['min_temp', 'pressure']] = mars_temp_df[['min_temp', 'pressure']].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                          object\n",
       "terrestrial_date    datetime64[ns]\n",
       "sol                          int64\n",
       "ls                           int64\n",
       "month                        int64\n",
       "min_temp                   float64\n",
       "pressure                   float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm type changes were successful by examining data types again\n",
    "mars_temp_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Analyze the Data\n",
    "\n",
    "Analyze your dataset by using Pandas functions to answer the following questions:\n",
    "\n",
    "1. How many months exist on Mars?\n",
    "2. How many Martian (and not Earth) days worth of data exist in the scraped dataset?\n",
    "3. What are the coldest and the warmest months on Mars (at the location of Curiosity)? To answer this question:\n",
    "    * Find the average the minimum daily temperature for all of the months.\n",
    "    * Plot the results as a bar chart.\n",
    "4. Which months have the lowest and the highest atmospheric pressure on Mars? To answer this question:\n",
    "    * Find the average the daily atmospheric pressure of all the months.\n",
    "    * Plot the results as a bar chart.\n",
    "5. About how many terrestrial (Earth) days exist in a Martian year? To answer this question:\n",
    "    * Consider how many days elapse on Earth in the time that Mars circles the Sun once.\n",
    "    * Visually estimate the result by plotting the daily minimum temperature.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     174\n",
      "2     178\n",
      "3     192\n",
      "4     194\n",
      "5     149\n",
      "6     147\n",
      "7     142\n",
      "8     141\n",
      "9     134\n",
      "10    112\n",
      "11    138\n",
      "12    166\n",
      "Name: month, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 1. How many months are there on Mars?\n",
    "mars_month_count = mars_temp_df['month'].value_counts().sort_index()\n",
    "\n",
    "# print how many months are on Mars\n",
    "print(mars_month_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1867\n"
     ]
    }
   ],
   "source": [
    "# 2. How many Martian days' worth of data are there?\n",
    "mars_day_count = len(mars_temp_df['sol'])\n",
    "\n",
    "# print Martian days' worth of data\n",
    "print(mars_day_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month\n",
      "1    -77.160920\n",
      "2    -79.932584\n",
      "3    -83.307292\n",
      "4    -82.747423\n",
      "5    -79.308725\n",
      "6    -75.299320\n",
      "7    -72.281690\n",
      "8    -68.382979\n",
      "9    -69.171642\n",
      "10   -71.982143\n",
      "11   -71.985507\n",
      "12   -74.451807\n",
      "Name: min_temp, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 3. What is the average low temperature by month?\n",
    "mars_avg_temp = mars_temp_df.groupby('month')['min_temp'].mean().sort_index()\n",
    "\n",
    "# pint average low temperature by month\n",
    "print(mars_avg_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the average temperature by month\n",
    "mars_avg_temp.plot.bar(x='month', y='min_temp')\n",
    "\n",
    "# add title and 'x' & 'y' axis labels\n",
    "plt.title('Average Temperature by Month on Mars')\n",
    "plt.xlabel('month')\n",
    "plt.ylabel('Temperature in Celsius')\n",
    "\n",
    "# display and save chart\n",
    "plt.tight_layout()\n",
    "plt.savefig('./Images/Mars_avg_temp_month.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Identify the coldest and hottest months in Curiosity's location\n",
    "# using reset_index then sort_values for the 'min_temp' to sort data coldest to hottest\n",
    "mars_temp_diff = mars_avg_temp.reset_index(name='min_temp').sort_values('min_temp')\n",
    "\n",
    "# Plot the sorted data into bar chart\n",
    "mars_temp_diff.plot.bar(x='month', y='min_temp')\n",
    "\n",
    "# add title and 'x' & 'y' axis labels\n",
    "plt.title('Coldest to Hottest Temperature by Month on Mars')\n",
    "plt.xlabel('month')\n",
    "plt.ylabel('Temperature in Celsius')\n",
    "\n",
    "# display and save chart\n",
    "plt.tight_layout()\n",
    "plt.savefig('./Images/Mars_temp_diff_month.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month\n",
      "1     862.488506\n",
      "2     889.455056\n",
      "3     877.322917\n",
      "4     806.329897\n",
      "5     748.557047\n",
      "6     745.054422\n",
      "7     795.105634\n",
      "8     873.829787\n",
      "9     913.305970\n",
      "10    887.312500\n",
      "11    857.014493\n",
      "12    842.156627\n",
      "Name: pressure, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 4. Average pressure by Martian month\n",
    "mars_avg_pressure = mars_temp_df.groupby('month')['pressure'].mean().sort_index()\n",
    "\n",
    "# print average pressure by Martian month\n",
    "print(mars_avg_pressure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the average pressure by month\n",
    "# using reset_index then sort_values for the 'pressure' to sort data least to greatest\n",
    "mars_press_ltog = mars_avg_pressure.reset_index(name='pressure').sort_values('pressure')\n",
    "\n",
    "mars_press_ltog.plot.bar(x='month', y='pressure')\n",
    "\n",
    "# add title and 'x' & 'y' axis labels\n",
    "plt.title('Pressure from Lowest to Highest by Month on Mars')\n",
    "plt.xlabel('month')\n",
    "plt.ylabel('Atmospheric Pressure')\n",
    "\n",
    "# display and save chart\n",
    "plt.tight_layout()\n",
    "plt.savefig('./Images/Mars_press_lowhi_month.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wbOrcuTMUCgXvXgJAu3btEBkZafbbtm3bhg4dOiA8PNxwHT/88EPcu3ePd73lIjIyEu3ateNtk/pbSkNoaCgGDBiAJUuWIDc3FwB3DU6fPo1hw4YZ0v3111+oVq0aOnToICl/qWXFXvQzF1UqFV5++WWsXLkSWVlZAIDi4mL8+OOP6N27N6Kjow12KRQKvPzyyzy7EhISUL9+fbNrLHSfSgNjzGy2pUKhQM+ePXnb6tWrh6tXrxq+r127FhEREejZsyfP7gYNGiAhIUF02XjjjTdQpkwZtG/fHq1btwYA7NmzBxkZGejXrx8vb51Ohy5duuDgwYOGMlJUVIRp06ahVq1a8Pf3h0qlgr+/Py5cuMCrD/U8/fTTouzatm0bgoOD8cwzz/C29+/fHwCwdetW3va2bdsiNDTU8D0+Ph5xcXG8a2aLkJAQPPvss1i0aBGKiorwww8/YMCAARZnw16+fBl9+vRBQkKCoY5o06YNAIj67VWqVEFkZCRGjRqFBQsWuGSWtKehcrUBhPvQpEkTNGnSBAA33X/UqFGYN28ePv74Y3z88ce4desWAOCRRx4RPN5YjPTp0wdbt27F+PHj8cgjjyAsLAwKhQLdunXjibfPPvsM5cuXx/LlyzFz5kwEBASgc+fOmDVrFqpWrWpYnqFMmTJm5ytbtix0Oh0yMzMRFBRk2K5vjPRoNBoAMJw3NTUVjz32GKpXr45PP/0USUlJCAgIwIEDB/Dmm2+aicvScuvWLaxZswZqtVpwv14464mKiuJ99/f3t7o9Pz+ftz0hIcHsHPpt+ut569YtXLx4UbRNQtf/wIED6NSpEx5//HF8++23KF++PPz9/bF69WpMnTpV9utoyQ6pv8WYihUrAgBSUlJE2/DWW2/hiy++wM8//4zBgwfjiy++QPny5dG7d29Dmjt37hjyloLUsmKMmN+i31ehQgXDtoEDB2LOnDlYtmwZXn/9dWzcuBFpaWkYMGAAzy7GGOLj4wXzTU5O5n0Xuk/22gwAV69e5dkMAEFBQQgICOBt02g0vOfh1q1buH//vuFZMcXa9TTF39+fl4++PjQVVcZkZGQgODgYI0aMwJdffolRo0ahTZs2iIyMhFKpxGuvvSb4nIi9fvfu3UNCQoKZqIqLi4NKpTJb3sa0bgS4ayb1WX311Vfx6KOPYurUqbhz545BSJqSk5ODxx57DAEBAfjoo49QrVo1BAUF4dq1a3jqqafMzhsUFGQ2szY8PBw7d+7E1KlT8b///Q+ZmZkoU6YMBg0ahHHjxll8VnwZEnaEIGq1GhMmTMC8efNw8uRJAEBMTAwA4Pfff0diYqLFY7OysrB27VpMmDABo0ePNmwvKChARkYGL21wcDAmTZqESZMm4datWwbvXc+ePXH27FlDRZSWlmZ2nps3b0KpVAp6kqyxevVq5ObmYuXKlbzf8e+//0rKRywxMTGoV68epk6dKri/bNmysp4vPT3d4jb99YyJiUFgYCAWLVokmIf+XusRehtftmwZ1Go11q5dy2tgV69eLdpW/XEFBQW87ZYaXCE7pP4WY5o0aYKoqCj88ccfmD59uqg1uKpUqYKuXbviyy+/RNeuXfHnn39i0qRJ8PPzM6SJjY3F9evXbeYlZKu9ZaVt27ZQqVRYvXo1hgwZIphGf286duxo2FarVi00bdoUixcvxuuvv47FixejbNmy6NSpE88uhUKBXbt2GV6UjDHdJnYtszJlyqB27drYtGkT8vLyeC9oevbu3Ytbt27h2WefFZWnMTExMYiOjsaGDRsE9xt7r+zJGwA+//xzNG/eXDCNXgj/9NNPeOWVVzBt2jTe/rt37yIiIsLsOLHXLzo6Gvv37zfzaN6+fRtFRUVWy35paNWqFapXr47JkyejY8eOZqJbz7Zt23Dz5k3s2LHD4KUDgPv37wumt/S769ati2XLloExhuPHj2PJkiWYPHkyAgMDeW0MwUHCjkBaWprgG6LeTa5vTDp37gyVSoVLly5Z7SpQKBRgjJlV9t999x2Ki4stHhcfH4/+/fvj2LFj+OSTT5CXl4fq1aujXLlyWLp0Kd577z3Dg5+bm4sVK1agRYsWgo2BNfR5GNvHGMO3334rKR+x9OjRA+vXr0flypUli1B7OHXqFI4dO8brjl26dClCQ0PRqFEjg03Tpk1DdHQ0KlWqZNd5FAoFVCoVT9A8fPgQP/74o1laS14B/eLPx48fR/Xq1Q3b//zzT9F2lOa3qNVqjBo1CqNGjcKUKVPw4YcfmqW5ffs2Lly4gFatWhm2vf322+jUqRP69esHPz8/DBo0iHdM165d8eGHH2Lbtm2SuiRLU1YSEhIwcOBAfPPNN1i+fDmef/553v7z589j5syZqF27Np544gnevgEDBuCNN97A7t27sWbNGowYMYJ3X3v06IEZM2bgxo0beO655yTZZYuxY8eiT58+eO+998zWC8zNzcXw4cMRFBSEd999V3LePXr0wLJly1BcXIxmzZrJZTIATtxERESYdcMLoVAozOrDdevW4caNG6hSpYrdNrRv3x6//vorVq9ejSeffNKw/YcffjDsdxTjxo3D77//jjfffNNiGqG6FgC+/vpru86pUChQv359zJs3D0uWLDEMLSH4kLAj0LlzZ5QvXx49e/ZEjRo1oNPp8O+//2LOnDkICQnB22+/DYBrhCdPnoyxY8fi8uXL6NKlCyIjI3Hr1i0cOHDA4H0LCwtD69atMWvWLMTExCApKQk7d+7EwoULzd5OmzVrhh49eqBevXqIjIzEmTNn8OOPP/IE28cff4yXXnoJPXr0wOuvv46CggLMmjUL9+/fx4wZMyT/3o4dO8Lf3x8vvvgiPvjgA+Tn5+Orr75CZmZmqa+lEJMnT8bmzZvRsmVLDB8+HNWrV0d+fj6uXLmC9evXY8GCBShfvrxs5ytbtix69eqFiRMnokyZMvjpp5+wefNmzJw503BN33nnHaxYsQKtW7fGu+++i3r16kGn0yE1NRWbNm3CyJEjbTaE3bt3x9y5c9GnTx8MHjwY9+7dw+zZswU9Ovo37uXLlyM5ORkBAQGoW7cuHnnkEVSvXh3vvfceioqKEBkZiVWrVmH37t2if29pf8v777+PM2fOYMKECThw4AD69OmDChUqICsrC3///Te++eYbTJo0iSfsOnbsiFq1amH79u14+eWXERcXZ2bT8uXL0bt3b4wePRpNmzbFw4cPsXPnTvTo0QNt27YVtKW0ZWXu3Lk4d+4cXn75Zfz999/o2bMnNBoN9u3bh9mzZyM0NBQrVqzgiTYAePHFFzFixAi8+OKLKCgoMOtaa9WqFQYPHowBAwbg0KFDaN26NYKDg5GWlobdu3ejbt26eOONNyzaZY0XX3wRR44cwezZs3HlyhUMHDgQ8fHxOHfuHObNm4dLly5h6dKlZt29YnjhhRfw888/o1u3bnj77bfRtGlTqNVqXL9+Hdu3b0fv3r15gkgKISEh+Pzzz9GvXz9kZGTgmWeeQVxcHO7cuYNjx47hzp07+OqrrwBwAnPJkiWoUaMG6tWrh8OHD2PWrFmlfu5feeUVfPnll+jXrx+uXLmCunXrYvfu3Zg2bRq6desmeYynFF5++WW8/PLLVtO0bNkSkZGRGDJkCCZMmAC1Wo2ff/4Zx44dE32etWvXYv78+XjiiSeQnJwMxhhWrlyJ+/fv8zzPhBEunLhBuAnLly9nffr0YVWrVmUhISFMrVazihUrsr59+wpOJ1+9ejVr27YtCwsLYxqNhiUmJrJnnnmGN73/+vXr7Omnnzasb9alSxd28uRJlpiYyPr162dIN3r0aNakSRMWGRnJNBoNS05OZu+++65hNp7xOZs1a8YCAgJYcHAwa9++Pfvnn394afSzYo2XU2FMeOblmjVrDOtxlStXjr3//vvsr7/+MpuJKdc6dnfu3GHDhw9nlSpVYmq1mkVFRbHGjRuzsWPHGtZzMp55Zox+ptxvv/0m+LuMZ4Pqz/3777+z2rVrM39/f5aUlMTmzp1rZmdOTg4bN26cYX2v8PBwVrduXfbuu+/ylq/Af+tfCbFo0SJWvXp1w72bPn06W7hwodn1vnLlCuvUqRMLDQ3lrWPHGGPnz59nnTp1YmFhYSw2Npa99dZbbN26dRbXsRNC7G+xxh9//GFYK0ulUrHIyEjWtm1btmDBAlZQUGCWfuLEiQwA27dvn2B+mZmZ7O2332YVK1ZkarWaxcXFse7du/OWt4DAOnZiyoo1CgsL2ZdffsmaNWvGQkJCmEajYdWrV2cffPCB2XNlTJ8+fRgA1qpVK4tpFi1axJo1a8aCg4NZYGAgq1y5MnvllVfYoUOHDGms3SdrrF+/nnXr1o1FR0cztVrNypUrx/r27Ss4E1i/5pkp+jrAGK1Wy2bPnm143kNCQliNGjXY66+/zi5cuCDaPksLj+/cuZN1796dRUVFGezu3r0773nNzMxkr776KouLi2NBQUHs0UcfZbt27WJt2rThzRa39Kxb4969e2zIkCGsTJkyTKVSscTERDZmzBiL69gJ/S7jOlkIS3WTKUKzYvfs2cNatGjBgoKCWGxsLHvttdfYkSNHGAC2ePFiQzpL9/Ts2bPsxRdfZJUrV2aBgYEsPDycNW3alC1ZssSqLb6MgjHGnKwlCYJwEElJSahTpw7Wrl3ralO8niZNmkChUODgwYOuNoUgCMIAdcUSBEGIJDs7GydPnsTatWtx+PBhrFq1ytUmEQRB8CBhRxAEIZIjR46gbdu2iI6OxoQJE8wmIRAEQbga6oolCIIgCILwEijyBEEQBEEQhJdAwo4gCIIgCMJLIGFHEARBEAThJdDkCRN0Oh1u3ryJ0NBQ0WFdCIIgCIIgHAVjDA8ePEDZsmV5cdmFIGFnws2bNy3GvSMIgiAIgnAV165dsxmxhISdCfqg0NeuXUNYWJiLrSEIgiAIwtfJzs5GhQoVDBrFGiTsTNB3v4aFhZGwIwiCIAjCbRAzRIwmTxAEQRAEQXgJJOwIgiAIgiC8BBJ2BEEQBEEQXgIJO4IgCIIgCC+BhB1BEB4JYwyD1wxG1MwoKCYp8G/6v642ifB0duwAFArg/v3S5fPNN0CFCoBSCXzyiQyGEYR4SNgRhA9zO/c2Xl/zOirOqwjNRxokzE5A5586Y++1vYY0ikkKrD67WpbzXbl/RTYRtuHiBiz5dwnW9lmLtJFpqBNXh7f/x2M/InhaMC5mXORtv/ngJiJnRuLTfZ+W2gbCRfTvzwkw078uXVxtGZCdDQwbBowaBdy4AQwezN9//jwQFAQsXcrfrtMBLVsCTz7pPFsJr4SWOyEIH+bpX5+GtliL75/4HsmRybiVewtbL29FxsMM2c9VWFwoa36XMi+hTGgZtKzQUnB/3/p9sersKvRb3Q+7BuyCUsG9xw5eMxgNExpieLPhstoDcL/R389f9nwJAbp0ARYv5m/TaFxjizGpqYBWC3TvDpQpY76/WjVgxgzgrbeAtm1L0syZA1y8CKxeLb9NWi2gVsufL+GWkMeOIHyU+/n3sTt1N2Z2mIm2ldoiMSIRTcs1xZjHxqB7te4AgKRPkgAATy5/EopJCsP3SxmX0HtZb8TPjkfItBA88u0j2HJ5Cy//pE+S8NHfH6H/6v4InxGOQWsGodKnlQAADb9uCMUkBR5f8rhF+3Ze2Ymm3zaF5iMNyswpg9FbRqNIVwQA6L+6P9766y2kZqXy7DLl6x5f42LGRczdOxcAsOTfJdiVuguLey+GVqfFB5s/QLm55RA8LRjNvmuGHVd2GI69l3cPL654EeXnlkfQ1CDU/aoufjnxCy//x5c8jmHrh2HExhGI+TgGHX/sKObSE3Kg0QAJCfy/yMiS/QoF8N13nAcsKAioWhX4809+HuvXc0IrMJATWVeu2D5vairQuzcQEgKEhQHPPQfcusXtW7IEqFuX+5yczNkglOdbbwENGgCDBnHfz54FPvyQ68KNi+MEa82aQEAAUKMGMH8+//hRozi7g4K484wfz4k3PRMncvkvWsTt12gAxmz/NsI7YASPrKwsBoBlZWW52hSCcCjaYi0LmRbC3vnrHZavzRdMczvnNsNEsMVHF7O0B2nsds5txhhj/6b9yxYcXMCOpx9n5++eZ2O3jmUBHwWwq/evGo5NnJfIwqaHsVn/zGIX7l1gF+5dYAeuH2CYCLbl0haW9iCN3cu7J3je61nXWdDUIDZ07VB25s4ZturMKhbzcQybsH0CY4yx+w/vs8k7JrPyc8vz7BJi1ZlVLOCjALbp4iYWMSOCLT66mDHGWJ8VfVjLhS3Z31f+ZhfvXWSz/pnFNFM07Pzd8wYbZv0zix1NO8ouZVxin+37jPlN8mP7ru0z5N1mcRsWMi2Evb/pfXb2zll25s4Z0defKAX9+jHWu7f1NABj5csztnQpYxcuMDZ8OGMhIYzd+6/MpaYyptEw9vbbjJ09y9hPPzEWH88dl5kpnKdOx1jDhow9+ihjhw4xtm8fY40aMdamDbc/L4+xLVu4PA4cYCwtjbGiIuG8rl5lLCyMsW++YaxZM+43McZ9L1OGsRUrGLt8mfsfFcXYkiUlx06Zwtg//zCWksLYn39yds+cWbJ/wgTGgoMZ69yZsSNHGDt2jLOd8FikaBMSdiaQsCN8id9P/c4iZ0SygI8CWMuFLdmYLWPYsfRjvDSYCLbqzCqbedX6shb7fP/nhu+J8xLZE8ue4KVJyUxhmAh2NO2o1bz+t+V/rPrn1ZnOqDH68sCXLGRaCCvWFTPGGJu3dx5LnJdo0y7GGHtl1StMOUnJev3SizHG2MV7F5liooLdyL7BS9f++/ZszJYxFvPp9nM3NnLjSMP3NovbsAYLGoiygZCRfv0Y8/PjxIvx3+TJJWkAxsaNK/mek8OYQsHYX39x38eMYaxmTb7gGTXKurDbtIk7b2pqybZTp0qEHGOMHT3KfU9Jsf07Fi1iTKlkrEIFxu7f57ZVqMCJUWOmTGGsRQvL+Xz8MWONG5d8nzCBMbWasduWX3gIz0KKNqExdgThwzxd62l0r9Ydu67uwt7re7Hh4gZ8/M/H+K7Xd+jfoL/F43ILczFp5ySsPb8WNx/cRJGuCA+LHiI1K5WXrkmZJnbZdebuGbSo0IIXPqdVhVbIKczB9ezrqBheUVJ+41uPxw/HfsD41uMBAEfSjoCBodrn1XjpCooLEB0UDQAo1hVjxu4ZWH5qOW48uIGCogIUFBcgWB3MO8be30iUkrZtga++4m+LiuJ/r1ev5HNwMBAaCty+zX0/cwZo3pzrLtXTooX1c545w812rVChZFutWkBEBLfvkUek/YYBA7hu1OHDgfBw4M4d4No14NVXS7ppAaCoiNuv5/ffudm2Fy8COTncftMQmImJQGysNHsIr4CEHUH4OAGqAHSs3BEdK3fEh20+xGt/voYJOyZYFXbvb34fGy9txOyOs1ElqgoC1YF45tdnzCZIBPsHW8jBOgwMCijMtgEw2y4GlVLF+69jOvgp/HB48GH4Kf14aUP8QwAAc/bOwbx98/BJl09QN64ugv2D8c6Gd2T7jUQpCQ4GqlSxnsZ0woBCwc0+Bewbc8YYXwja2i4GlYr7A0ps+/ZboFkzfjq//8rpvn3ACy8AkyYBnTtzgm/ZMm7yhTHBVC59FRJ2BEHwqBVbi7e8iVqpRrGumJdmV+ou9K/fH0/W5JZmyCnMwZX7V2zmrZ8xapqfmQ0xtbDizAowxgxeuz3X9iDUPxTlwspJ+DXCNCzTEMWsGLdzb+OxxMcE0+xK3YXe1Xvj5XovA+DE4IWMC6gZU7PU5yfcgFq1zGeg7ttn+5jUVM6rpvfanT4NZGVxkx1KS3w8UK4ccPky8NJLwmn++Yfzxo0dW7Lt6tXSn5vwGmhWLEH4KPfy7qHd9+3w0/GfcPzWcaRkpuC3U7/h438+Ru/qvQ3pkiKSsDVlK9Jz0pH5MBMAUCWqClaeXYl/0//FsfRj6LOiD3RMZ/OcccFxCFQFYsPFDbiVcwtZ+VmC6YY+MhTXsq/hrb/ewtm7Z/HH2T8wYccEjGgxwrBsSWmoFl0NL9V9Ca+sfgUrz6xESmYKDt44iJm7Z2L9hfXcb4ysgs2XN2PPtT04c+cMXl/zOtJz0kt9bkImCgqA9HT+39274o8fMgS4dAkYMQI4d45bV27JEuvHdOjAde++9BJw5Ahw4ADwyitAmzZAE5m65CdOBKZPBz79lFvz7sQJbpbsXG5mN6pU4cTlsmWc/Z99BqxaJc+5/yOnoEjW/AjnQsKOIHyUEP8QNCvXDPP2zUPrxa1R56s6GL99PAY1GoQvun1hSDen0xxsvrwZFeZVQMOvGwIA5nWeh8iASLRc2BI9f+mJzpU7o1GZRjbPqVKq8FnXz/D14a9Rdm5Z9F7WWzBdubByWN9nPQ7cOID6C+pjyLoheLXhqxjXepw8Px7A4t6L8Uq9VzBy00hU/6I6ei3rhf039qNCGOeJGd9mPBqVaYTOP3XG498/joSQBDxR4wnZzk+Ukg0buDXgjP8efVT88RUrAitWAGvWAPXrAwsWANOmWT9GoeC8fJGRQOvWnNBLTgaWLy/VT+Hx2mvcMi36pVPatOE+V+KWCkLv3sC773KLIDdoAOzZw43Tk4k9l+6izoSNmPDHSdnyJJyLgjFa3MaY7OxshIeHIysrC2Gmg1EJgiAIwot5cv4/OJp6HwBwZUZ31xpDGJCiTchjRxAEQRAE4SWQsCMIgiAIgvASSNgRBEEQBEF4CSTsCIIgCIIAQCFlvQESdgRBEARBEF4CCTuCIAiCIADYH0CDcB9I2BEEQRAEQXgJJOwIgiAIgiC8BBJ2BEEQBEEQXgIJO4IgCIIgCC+BhJ0Pc/H2A3yy5Tyy87X4asclHLqS4WqTCMLjuZaRh3mbz+NeToGrTSEIwgdRudoAwnV0mPs3AOCbvy8jr7AYAMUGJIjS8uyCvUjPzseR1Ez8+GozV5tDEISPQR47wiDqCIIoPenZ+QCA/ZfJA04QhPMhYUcQBOEAGGgJf4IgnA8JO4IgCIIgCC+BhB1BEIQDUICW8CcIwvmQsCMIgnAA1BVLEIQrIGFHEARBEAThJZCwIwiCIAiC8BJI2BEEQRAEQXgJJOwIgiAcAKMhdgRBuAASdgRBEARBAKAXEm+AhJ0Xo9NZfkIt7dPpGBjj/giCsB8FrXZCEIQLIGHnpUxZexpNpm7B7f/CGxmTlvUQ1cb9JXhc8v/Wo9tnu/HE/D1WhSFBENahdyPCE6EXEs+HhJ2XsnB3CjJyC/Hd7hSzffO3X0KRFdF2Ji0bx67dx93cAkeaSBAEQRCEzJCw83KEulRp4VSCIAiC8E5I2BEWoa4kgiAI34Lqfc+HhB1hEW2xztUmEARBEAQhARJ2hEW0xfTqRhAE4UvQ5AnPh4Sdl6MoxVNaRB47giAIgvAoSNgRFikkYUcQdkP+boIgXAEJO8IiRdQVSxAE4VPQ5AnPx6OE3bp169CsWTMEBgYiJiYGTz31FG+/QqEw+1uwYIGLrHUdckWNoMkTBEEQBOFZqFxtgFhWrFiBQYMGYdq0aWjXrh0YYzhx4oRZusWLF6NLly6G7+Hh4c400y0oliliBE2eIAiC8C1o8oTn4xHCrqioCG+//TZmzZqFV1991bC9evXqZmkjIiKQkJDgTPPcDrkEGXnsCIIgfAvqivV8PKIr9siRI7hx4waUSiUaNmyIMmXKoGvXrjh16pRZ2mHDhiEmJgaPPPIIFixYAJ3Ot8TJwSsZWHv8puH7hpPp+HHvFczddA6nbmZh4e4U/LQvVVRex6/fd5CVhC+z59Jd/Lj3Cr04EARBOACP8NhdvnwZADBx4kTMnTsXSUlJmDNnDtq0aYPz588jKioKADBlyhS0b98egYGB2Lp1K0aOHIm7d+9i3LhxFvMuKChAQUFJTNTs7GzH/hgH8+yCvbzvqRl5GP8HJ4A/23ZRUl5L9lzFsHZVZbONIBhj6PPtfgBATIgGXeuWcbFFBEEYQ12xno9LPXYTJ04UnPBg/Hfo0CGD123s2LF4+umn0bhxYyxevBgKhQK//fabIb9x48ahRYsWaNCgAUaOHInJkydj1qxZVm2YPn06wsPDDX8VKlRw6G92R3rUE25cA9Qe4dAlPAjj4Z/p2fmuM4QgCMJLcanHbtiwYXjhhRespklKSsKDBw8AALVq1TJs12g0SE5ORmqq5W7F5s2bIzs7G7du3UJ8fLxgmjFjxmDEiBGG79nZ2R4r7uyZDVsjIRRf9GmEL/oASaPX8fZRVxkhNzoawEMQBOFQXCrsYmJiEBMTYzNd48aNodFocO7cOTz66KMAAK1WiytXriAxMdHicUePHkVAQAAiIiIsptFoNNBoNJJtd0fsmTRhraGldewIuTEub96u8eRadoggnAkVW8/HI8bYhYWFYciQIZgwYQIqVKiAxMREQxfrs88+CwBYs2YN0tPT0aJFCwQGBmL79u0YO3YsBg8e7DXCzRb2eNisrYxCkScIuTFuNKj9IAiCkB+PEHYAMGvWLKhUKvTt2xcPHz5Es2bNsG3bNkRGRgIA1Go15s+fjxEjRkCn0yE5ORmTJ0/Gm2++6WLLnYc9HjadFWVHHjtCbvgeO+8uX6WJ00wQroKKrefjMcJOrVZj9uzZmD17tuD+Ll268BYm9kXs8bAVW2lcaYwdITcyrZ3tEXi7cCUIwj2haY9eRJEda/ZZi1JRpGPUOBGywnxojB1BEIQrIGHnRWiL5O2KBSisGCEvOt4YOypbBOFu0AuX50PCzovQ2uOxs/EU2+MFJAhLkMeOIAjCsZCw8yLknhUL2OcFJAhL6HxoVqy3/z7CO6HJE54PCTsvQu5ZsYB9XkCCsIQvrWNHEAThCjxmVixhG3tmxar9rGv7Eb8ew8kbWahZJhQFWh0OXc007Nvx3uNIigmWfE7CdzEWdvZEoTibno3XfzyMER2roXeDcnKaJjvk+HAtGbmFeO7rvXiqUTkMfbyKbPnuvXQPo1Ycx9Qn66BIxzDhj1OY81x9PJIUJds5CKI0kMfOi7A2w9USw9tXNXxuXS3WbP/f5+8gI7cQ/1y8xxN1ADBpzSnpRhK+jVERtae8vv3Lv7h6Lw9vL/tXPpscBDkkXcv87Rdx8XYOPt5wTtZ8X/x2H1Iz8tB34QEMWHwQqRl56PPtPlnP4UrIk+75kLDzIqQ2lBvfaY0+zSoavg9vV/JW+7aR4LNEkS8tSkbIgnGRscdjl1NQJKM1hDfjzHU4afUAwp0gYedF2BovZ0pkkJr33XjQbJC/n83jVUrqbCKkweuKtePFwB4vH0EQ4qHJE54PCTsvQmqbpzQTZiXfNSrbRcNPScWHkIaxsLO11I4Q9hxDEIR46BHzfKhl9iKkNnp+Vl7NAtS2PXZqP3q1I6TBeGPspB9PHjuCIAjrkLDzIqR2bZl77EqO16jFeOxI2BHS4C93Il2kFVH8YoIgCKuQsPMipHozrOkyjUqMx46KDyENVspZseSxIwiCsA61zF6E1FmG5h43qWPsyGNHSMOXxth5kKlEKfGmCQfe9Ft8FRJ2XoSpsLM1Bk5JY+wIJ8Nb7oQ8dgThdtALiedDws6LMB1+FGCjO9Wax408doQjYLzIE9KPJ2FHuCNUExLuBAk7L8K0m8qWS92ax07M+DkVLXdCSMRYl9nTrUq6jnBHFNR/SbgRFCvWizCdZWirDbTmcBMn7GxXZoVFOmw/dxv+fkqcTsvGkw3LoWxEoM3jCO/EeLjA3kv3kJ6Vj3+v3QcAtEiOhkatxMZT6WhQIQKJ0RSHmHAPTt7IQpGOobBIeFY2yTrCnSBh50VI7aYyfcuMMIpEEayxPcbOT8QYu8+3XcDn2y4avs/aeA5XZnSXYCXhTRi/e6TczUXz6VsN32uVCUPvBmUx/a+zqBgVhL8/aOsCCwmCj7ZYhx6f77aaxpscdt70W3wVEnZehLGwm/5UXUxbf0Yw3bsdqqFcpLnXrHJsCMZ2q4mYUH9UjApCjYRQnE1/AACIC9Xg9oMCXnoxHrs/j92U8hMIL8fazO3TadkGb25qRp6zTCIIq+Rri22mUXiRz44mT3g+NEjKi9A3mu1rxOHFphUtpnu7Q1U807i84L5BrZPxZMPyUCgUeKdDNcP2+S81MksrZoydtXF8hO9hq9GgpREJd0MnZk1squYIN4KqUS9C77DTR5QobV3jryrJQWjMnRiPHek6whhbay3SiwDhbmhFKTvvgR5Bz4eEnReh74qVaxUSY4+cSmA8nUqEe4UaasIYm8KOltAh3IyiYtt9k95Uaqkr1vMhYedF6BtN/fpypX0+jb10/gIiTky3mTdVeETpsTW/x49eBAg3QysiPjG9wBLuBAk7L6LEYydPJWMcWULlp7TLE0gVHsHHurIzXvTadPkegnAFYoQdVXOEO0HCzovQe0Pkighh7LFT+ynM8hXT7lKFRxhjy2NnXF4oygThDmhFdMUShDtBws6L0MnssTMeV6f2U5rlK6a6oxXZCWNsxYc17oqlBpVwB0R57Jxgh7OgKtvzIWHnRehDNOkFWGmfT+PJE2o/pX0eO4Ft1MXmu9gcY2dUxnxtNiLhnojrivUeNUTVs+dDws6L0HddOWItMJWfQsBjZ7sGEFrqjrrYfBdbot64gRQzG5EgHE2RiPrKe2Qd4Q2QsPMimMyzYo3xF5g8IebNTqhbWExFSXgntm99SQIhT4kXOUYID0FrIT4sDyqXhBtBws5LOJCSgdmbzgNwTLeASqmAv0p6cRGypcb4DTbHWhHeRbGOYdAPh/Dywv1W0/1y4Jrhc7NpW5E0eh2yHmrx5tIjePm7/byXCVtdZBtPpeOZr/bgGoUnI2xwNDUTdSduROX/rceuC3cM2xljGLr0iM3jH+QXofO8v7Hl9C1HmkkQoiBh5yU89/Vew+dyEeZxYMd1rwkAGN6uiug8K0YFGT77KRUY3DqZt1+MOLMkMfdcuifaDsLzuXIvF5vtbPTe+uUo1h1Pw+6Ld3nb/zqZbvW41388jENXMzFm5Qm7zkv4Ds8u2IsH+UUo1jH0XXjAsP3OgwLcz9OKyuPcrQcYvfK4o0x0GuQV93xUrjaAkJ8BrZIA8EXVa48lo0e9sogP04jOJ9DfD8cmdIJKqYBCocCgx5LRvmY8pq07g61nb4vq6rW08kpBke3A2oT3UJoJM7ez8wW3F4kY1A4A9x8W2n1uwjewNDykQEw3rBF3czy/rNHkCc+HhJ0XYmm5k4TwAMl5hQeqDZ8VCgUqx4YgLozLx94xdmKPJbyH0vS8W5psIyakHUGUBjEzYgnC3aCa0QtxdLQHffaiZsWSX59A6YS8JWGnFrkQN71EEPZCE70IT4SEnRdSIrwclP9//0U1mKTrCIh7CbCEpfXs5IqwQhCWKJTYFUsQ7gAJOy/E0V4yff6lGWNH+Bal8thZWM9OLbIrlpzGhL34oseOnhfPh4SdF6IXU456PhUSXHYKC1b4XnXp25RG2GktjrGjFohwLL44xo6GLng+JOy8EEeHtzHoOhFphSJPABRWzNcoTVespdmvKkuFiyBkwheFHeH5UM1ISEYvHMXFiiWvClE6L4Cl7jCxHjt6hyDshcLaEZ4ICTvCbsR4YSw5D6m6JMRiqXGlGdeEoyGPHeGJkLAjJGOYdVuKdewI38IRy50QhKPRkseO8EBI2BGS0Xevnr+Vg2UHUrH59C0UFBUjK0+Lf6/dx+GrGUjPysfJG1kWZ8UeuZopOnIA4dkUFevMwoFJodBCOTl5I0vU8ZbeLe48KMCZtGzetqyHWhy/fl+KeYQHk1dYhJ3n75htX7o/FdvP3sYvB1Il57nhZBp5+giXQpEnvJjKcSE4mnpf9nxT/wuqvuXMLWw5w8X/fKtdFSzdn4p7ufyQOjEhwiHMvv77MpRKBUZ1qSG7fYR78cPeq5i54azs+U748xSqJ4SieXK0Xcc/MnULAGDLiDaoEhcCAOgwdyfuPCjA9wObok21WNlsJdyT8atPYcWR62bb/7fK/vjCQ346gvc7V8ebbcXH5SYIOSGPnRfz+YsN0at+Wfw5rJWs+Qp5NC7fyTUTdQBwN6fAYj7f/H1ZTrMIN2Xb2dsOy3vrfy8W1rDVDXzkaqbh850HXHndeCq9VHYRnoGQqJODDSep/BCugzx2Xkz5yCB89mJD2fMV6trS2TGIipY88Q1Ks9RJ3XLhOGGly1WOBWSFyi6VTaI00MLshCshjx0hGaEJETTAnbCEhYhgovBXWa+i5Ch3xQIirjQ2E4SSlB3hQkjYEZIRqrLs8tiV3hTCAyiNx05jQ9jJ47ET2kalk7AfP1oNgHAhJOwIyQhFtrDHc0Jtp29Qmvtsy2OnE1HubLaxQh47KptEKSCPHeFKSNgRsuCLwbIJcZSmZLjKY0dj7IjS4Mm6jpyNng8JO0IyQg8+hd4hHIG/ys/qfjEeO1saTajblbpiidLgyQuzU9H3fEjYEZIRqrNoQU7CIqXpivVzkceu1LkSvoyfJ7vsCI/HI4Tdjh07oFAoBP8OHjxoSJeamoqePXsiODgYMTExGD58OAoLzddWI0qHQmD6hJa6YgkLlMb7pVE7flasULcrFWeiNHiyx47wfDxiHbuWLVsiLS2Nt238+PHYsmULmjRpAgAoLi5G9+7dERsbi927d+PevXvo168fGGP4/PPPXWG21yLosSsijx0hTGk0km2PXenLnZDupK5YojSQx45wJXYLu8LCQqSkpKBy5cpQqRyrD/39/ZGQkGD4rtVq8eeff2LYsGGGGZqbNm3C6dOnce3aNZQtWxYAMGfOHPTv3x9Tp05FWFiYQ230JYSqLDkaWMI7Kc1EBNseO9t52HKe0ALFhNyQriNciWRFlpeXh7feegvff/89AOD8+fNITk7G8OHDUbZsWYwePVp2I035888/cffuXfTv39+wbe/evahTp45B1AFA586dUVBQgMOHD6Nt27aCeRUUFKCgoCTsVXZ2tmA6d+HK3VxMW38Gj1aNwZJ/ruDy3VxEB/s71Qah5U7O38qxK6/cgiIAwAcrjqNbnTLoXq9MqWwjnMNfJ9Kw9kQaZj5dDyEarhrZc+kuvt9zBcH+Kvxx7CaKdQwNK0aUKl6xxobHbsuZWxiz8gTytcV4p0NVTPjzFJonR+PYtZJznryRjaTR6zDjqbqoUy4cn229gFFdS2IU6xiwcHcKpqw9bdhGus470ekYRq88jrrlI9C3eaLDzrPlzG0s3Z+KPs0q2kx750EBnvrqH9x9UIhRXapj4hquHAb5++HI+I4IUPMnEJ26mYVPt1zA+52ro2p8KADgWkYepqw9jdfbVEbjxEj5fxDhUUgWdmPGjMGxY8ewY8cOdOnSxbC9Q4cOmDBhglOE3cKFC9G5c2dUqFDBsC09PR3x8fG8dJGRkfD390d6uuW4fdOnT8ekSZMcZqvcDPnpMM6mP8Cm0yUxMoVitDoSOYePfPP3ZSgUwLrjaVh3PA3d63WXL3PCYbzx8xEAQFJ0EN7vzImkPt/uN0tXGlEHAGobwg4AfjmQCgDYce42MvO02HHujmC60StPQKnghNzZ9AeG7QyMJ+oA6or1VraevY1fD13Hr4euO1TYAcDsTedECbuP1p3GtYyHAGAQdQCQV1iMn/ZdxWuPJfPSPzV/DwqKdDh+PQv7/tceADBs6REcu56FTadv4coMqkN9HcmTJ1avXo0vvvgCjz76KM9zU6tWLVy6dElSXhMnTrQ4KUL/d+jQId4x169fx8aNG/Hqq6+a5SfkSWKMCW7XM2bMGGRlZRn+rl27Juk3OJsr93JdbYJgV6wYgvzNl664k1OA2w8KBFITnsDtbPvundhGVcpCr5l5Wptp9JMiUjPySrYJzJSgyRPeSfZD22WkNAQaedfErhSQnpVvcV+WgL0F/41nTs8uOe6qUXkmCMkeuzt37iAuLs5se25urlUBJcSwYcPwwgsvWE2TlJTE+7548WJER0ejV69evO0JCQnYv5/vMcjMzIRWqzXz5Bmj0Wig0Wgk2e3rSL3PeoL8VcgrLJbZGsKV2Kt/apcVN+bVGYPQaYFi38HRd/WxqjGG3hQx3mZb6VRKj1i4gnAzJAu7Rx55BOvWrcNbb70FoKSR//bbb9GiRQtJecXExCAmJkZ0esYYFi9ejFdeeQVqtZq3r0WLFpg6dSrS0tJQpgw3TmvTpk3QaDRo3LixJLvcGXeYRm+vBSLrOcKDsLfLUqwnTuUUYUceO1/B0V3sxuVG7EuJys9yOmv7CMISkoXd9OnT0aVLF5w+fRpFRUX49NNPcerUKezduxc7d+50hI0Gtm3bhpSUFMFu2E6dOqFWrVro27cvZs2ahYyMDLz33nsYNGiQV82I9eTHnAJjex/2tpNiX1Cc8SJDy534EEa3VUzUktKcQOw7iTWvnJqEHWEHkn0oLVu2xJ49e5CXl4fKlStj06ZNiI+Px969ex3uGVu4cCFatmyJmjVrmu3z8/PDunXrEBAQgFatWuG5557DE088gdmzZzvUJmdjbzeonDjaS0N4DvaWBbGeOEcVd+N8aYFi30Qu8a7klSXhz9aw9ixQVyxhD5I8dlqtFoMHD8b48eMNy504k6VLl1rdX7FiRaxdu9ZJ1vgucnppFKDwTZ6M3WVBpLBzhseOxtj5DsyotimW6R6r/JQo/G9Cg7FYFJu7te5W8tgR9iDpdUCtVmPVqlWOsoUQgTs85va+6dJq7N6HvY2j2G55h3nsjD4LL1DsmPMSroXxumLlydM4OopxsRFbhqxOnqCByYQdSC41Tz75JFavXu0AUwhRuIE2srfNI2Hnfdjr2RJbFhw19MA4X6FfQGPsvBNj76x8HruSsmRPF771rliqMwnpSJ48UaVKFUyZMgV79uxB48aNERwczNs/fPhw2YwjzHGHx9zuMXbuYDwhK/Z6PUQLO/uyl5Sv8Dp2JOy8HbnusbHHjf+iIy5/612x5LEjpCNZ2H333XeIiIjA4cOHcfjwYd4+hUJBws7BuMXkCTsbczHjpXQ6RpMsPAhmp/9WbHvljMkTxQLCjnSdd2JcXuWaFau2UF+JnzxhrSuW6kJCOpKFXUpKiiPsIATI1xYjM68QZcIDDd+FViL3FIS8NCdvZvMqxgNXMlCnXLgh/ijh3uQWFONaRp7kbnZXL3eiLS5pdfUr+RuTlpWPs+nZCFT74X6eFvXKh7vFSxVROozF1uk0eeKCq3geu5LtOsZw9V4u/JQK+KuUyMrTIlijglKhQESQGnmFxfBTKJBy13I0IVPRdzubH6VCp2NIzcjjRdQ4kJKBBhUi4K9S4lpGHsqEB9BYPR+DWk83psPcnbie+RBbRrRBlbgQdP10l6tNAgDEhGpw4/5DyccJiTXjYO0A8MI3+wCA4h16CLsv3sVjH2+XfJyru2KNWXYw1WxbakYeunxS8ry937k63mxbxQnWEI7E2IkmFNvYHiKD1EjN4D6HBZbUcZl5WrSZtUPwmPBAtaiXdOMxdulZ+Wg+fStv/5Nf7TGrQ5/7ei/iwzSY8XQ9DFh8EI9WicFPrzUT92MIr0CysBs4cKDV/YsWLbLbGILP9UxOPG06nY4qcVWsvtnpGdWlhqPNwqfPN8Djs3dYTRMRpMZ9k9idXeskYH9KhgMtIzyBx6rGoFHFSIv7mydHoVjH8GiVWId1xRqTEB5gCMJuie/3XCFh5w3Y0cdeNjwANwXiub72aCX8e+0+Xm6eiHeW/wsAGNiqEo5dy7L54iu258XYYbc/5Z7ZflNRp+dWdgG+33MFAPfyRfgWkoVdZmYm77tWq8XJkydx//59tGvXTjbDiBKkjAV5pnF5B1rCkRQTbPCo3c8rRIPJm83SbBv5OBpNKdnevkYc/Kg7wOd5JCkSP75q2XsQG6rBssEloQl/PXTN4TYVF9OAOl9B6p1ukRyNXwY3x6LdKZi89jRv39C2VRAV7I+TN7IM24I1Knw/8BF0mPu3DNbydajU4Q4U6cd3kSzshNax0+l0GDp0KJKTk2UxiuBTJEHYOXvegaWJDqaVEM0yJABAIbFz1RnFWcyyF7RUj3cgZzWkLxLGExy4MaGOKStShRpNQvNdZHGhKJVKvPvuu5g3b54c2REmCM3as4SzGyBLlY25sHOGNYTbI7F4OiPyRLGIWd7OsINwPHJGFNG/pBgvSaJQyDuT29hcqUKNPHa+i2x9Y5cuXUJRUZFc2RFGSPLYOVvYWfLYKQQ8duS183mklk5ntE3FItbvoZCdno29gs7qcj7/lU21UeFwZHGVulgxlVnfRXJX7IgRI3jfGWNIS0vDunXr0K9fP9kMI0qQMsbO2W9plk5HlQohhNTi6YziLObFibwfng1jXFmS89VSr7PUqpKyoVBIHWxgHWN7pb60k5fZd5Es7I4ePcr7rlQqERsbizlz5ticMUvYh7Qxdm7SFWuynTE4p5Um3BqpzZ5zumJtP1/USHoHUoeEWHP06dc1NF5rjuuKdUxZkeqxo3GhvotkYbd9u/Q1q4jSIWWMnbM9ZZYqD9OGkCZPEIB7antRwo4aSY9Gf4eldslaS60vEv4mIcXkFHbG9kr1GpOX2XeRLAPatWuH+/fvm23Pzs6m5U4chKTJE07virUg7GhWLCGAreJputsZ0R7EPF/USHo2ck6a0KP3PhvPii3SMce9vEgexkBl1leRLOx27NiBwsJCs+35+fnYtcs9IiN4G1K6Yt3V/U66jgDs6Yp1kCFGiHm+qI30bBxR/ejLhPGs2CKZ10Q0zk1qHUrLhvouortijx8/bvh8+vRppKenG74XFxdjw4YNKFeunLzWEQDEzdrT465vaSTsCHuQdyg64avo6x/J9ZCI9GpTj52DyqzUXg93fcknHI9oYdegQQNuxo9CIdjlGhgYiM8//1xW43yZE9dLVjM/dCUTXT6RZyVzVyG1Upr45ym82bYKPt16Hk81Km81BBXhPZiWEndpm86mPzDb9s/Fu9h0Kh1jutVEgNrPBVb5JjfvP8QX2y8iISwAuYVFGNW5hqgxkF/tuISZG87Kbo/xy3SxTierd/f1Hw8jNECFNtVisfZ4mqRjfzlQErUl9V4eKkYHyWcY4daIFnYpKSlgjCE5ORkHDhxAbGysYZ+/vz/i4uLg50eVm1z0/GK34fNlETFi3ZEQjQo5Bdzaht3rlUHDChGij12y5wqW/Bfr8Kd9qYYQZoRv4U4O6JM3slCnXLjh+0vfcUHkY0I0eKt9VVeZ5XMM/fkI/jWKkdq4YiQ61U6wekxOQZFFURcT4o+7OebDi4CSdewaJ5q/WArNUi0fGYTCIvE9LGJ4kF8kWdSZ8uuha3ivc3WZLCLcHdHCLjExEQAXPoxwHxQK13dzju9RC1NM4igCwPb3HseBlAwEa/zwWNVY+CkV+PX1FlAquAHrRTqGWRvP8SppguBjXdm90iIRP+y96hRL7ucJB26/lpnnlPMTHMeu3+d9v5NTYPMYS2JreLsqeKFpRWTkFqLH57sF0wBA/QoR+PX1FogMUmPT6VtoUy0WKqNBbNtGtsGD/CLEhwXgWob7lYeComJXm0A4EcnLneg5ffo0UlNTzSZS9OrVq9RGEeJ5tEoMdl2461IbKsUIu/hjQzXoXq8Mb1vTSlG871kPtRj68xGH2UZ4NrZ62Iw9aARhiSIBh0TvBmUxohPnxSobEWgzD33dVTU+1GxfcmyI4bM7eZn1iAmbR3gPkoXd5cuX8eSTT+LEiRNQKBSGaeT6cQbFxfRm4Exc7a0rLVIX3SR8C1uTgZy5cLClU3n6M+gLCC1pI6bk2HNv3XECGy035VtInhD99ttvo1KlSrh16xaCgoJw6tQp/P3332jSpAl27NjhABMJa3j6A0sztwhr2CodVHoIMUhZC7S0uGOZ9PR2gpCGZI/d3r17sW3bNsTGxkKpVEKpVOLRRx/F9OnTMXz4cLOQY4Rj8fQHlkI1EdawFUnFHYqPO9hAWEeonhTjWbOndnXH8uBMYUu4Hskeu+LiYoSEcOMJYmJicPPmTQDc5Ipz587Jax1hEw/XdRSqibCKrTXBnNmIevqz5ssILUItrivWO266pzsACGlI9tjVqVMHx48fR3JyMpo1a4aPP/4Y/v7++Oabb5CcnOwIGwkrePrjSqGaCGPMQ4pZT+9Mj6/WwooA1Ga6P4IRIRxUdNxxUW1azMK3kCzsxo0bh9xcbl21jz76CD169MBjjz2G6OhoLF++XHYDCevoPNzFbqurjfBt3GkgutzhogjnIdgV6yAB5kZF1kAxvX34FJKFXefOnQ2fk5OTcfr0aWRkZCAyMtKtKmFfwdMfWBpj51swGz5m0702J08402NnYc0IKsLORQHpPRWCs2Id5rFzPzzdAUBIQ5K/pKioCCqVCidPnuRtj4qKIlHnIjxc19GsWMIqtoS/M4uPJWHn6c+gL2D3cifym+ISaIydbyFJ2KlUKiQmJtJadQ7gQb4Wl+/kGEJwicXTH1fy2LknD/K1YIzhQb4WeYVFuJWdb1i9/3Z2vtPscKcxdrey85H1kIs+8bCwpA4kZ4hzsXS9H+Rrka8tRkFRMbLytMg1ukf2zgq1Sw+5YZWWnV+EvEJpbQvhudg1xm7MmDH46aefEBUVZfsAwiZ3HhSgzaztyCssRohGhV0ftBV9bKDa9YPUVKUYKKdRud5+gs/+y/fw/Df7BPe916kaZm867zRb3Gkdu2nrz2La+rPYMqI1Xvhmv2H7iiPXMee5+k60xHex1KU4c8NZfLXjksXjvt97xWxboL/t2Ob26Tr3U3bbzt5Gqxnb8M/odgjytzvgFOEhSL7Dn332GS5evIiyZcsiMTERwcHBvP1HjlB4KKmk3M1F3n9vlzkFRbgqMtZgWIAKX/dtgvqTNjnSPJu0rByNFsnRKBsRiHO3stGjXlnRx9YqE4bHq8ciPFCNP/696UArCbHM3WxZuEkVdVHB/sjILQk7aOxhe7dDNWw7dxvHrMQKtjXEwxUO390X7uKuiPikhPwUWugOtybqAODynVze98ToILzULNHm+ezp6ndlJ0TjxEgcvpopuC8zT4ub9/NRJS5EcD/hPUgWdk888YQDzPBtTMfuWBsP8e0rTdCxVjxvm59S4dIFKFV+SvwyuLldxyqVCiwZ0BQAcCAlA2lZzuvmIxzH+52r4822VXDpTg7az9lp2K42Cpz+doeqeLtDVSSNXmcxH9NGsmpcCC7czjHaz0/w8TP18MHvx0tpvXW0NDvWZdg7Vsx0Hbud74vrFfGUkIfb33sclWKCcT0zD4/O3G7Y3qlWPDadvmX4bmmcKOFdSBZ2EyZMcIQdPo2ZsLMi0vwEei69ZWCsZ1ShhBRM1ymUOlnGdAydaQNtmpszxtxZ8hoRjsfeqq7Izntmz+QuV9Rj+nOaln+1SYNBS/b4BnYNcLp//z6+++47jBkzBhkZGQC4LtgbN27IapyvYOoBsOZ98+bJBjSz2j2Qo+rX30rThlHtJ+0emxaJIpOVVk3LjDMcLAVFJOxchb0vsfZ6We0ZP+yKekx/StP2QWXyvNFLiW8g2WN3/PhxdOjQAeHh4bhy5QoGDRqEqKgorFq1ClevXsUPP/zgCDu9GtO3SWtr0wnt8RKHHeEuyFCe9APITds4P4kNpWkTaepxMBVyTvHYkbBzGfaOOLFX0NgT8tA1HjtLz5uJx5uEnU8g+XVkxIgR6N+/Py5cuICAgADD9q5du+Lvv/+W1ThfwbTSsSbUvHmhSXLYeQ+WPHZSxyyZej/MumJNsnNGGSooouWeXIW9sVvtFTT2jLFzRT2mP6fpuU3tp/GhvoFkYXfw4EG8/vrrZtvLlSuH9PR0WYzyNUy9ENa6Yl05ScLRkLBzD2xFhxCD/laWdoydaZkwLf/mXbHksfNmhKo/MVWivWPLPG3oi3lXLL+JtxTvmPAuJAu7gIAAZGdnm20/d+4cYmNjZTHK1zCdPGGtK9ZbJkoI4Y7rPxH2YRjzU1qPncl3U8+L6X5ntMNCws6bX7jcCaH6T4wXz96uWLs8di6oxyyNsVObeuzopcQnkCzsevfujcmTJ0Or5VZgVygUSE1NxejRo/H000/LbqAvoDVpFKx1t9IQCcIT0DcwtgZzi81Hj3lXrAs8dgIPIS0j4RyENJwYUW3v/fGTWF4BuGSQncLwvPG3m45pNX1+CO9EsrCbPXs27ty5g7i4ODx8+BBt2rRBlSpVEBoaiqlTpzrCRq/H9C3K2rPn1R47cti5BVKLmL/QGjz/YXpLpc4yNJ8Va2vyhKTs7aJAS8LOVQh558QIO3v1jOlQAjG4ZIyd4dzWX6SonPoGkmfFhoWFYffu3di2bRuOHDkCnU6HRo0aoUOHDo6wzycwXcLBWkXl1cLO1QYQdqH2U6DQZD6BvoGxNZjbFmYeO7OuWP5+Zyw1IeSxo/XBnIPwGDvHXXtPWaBYaeF5M/1Okyd8A7uDxrVr1w7t2rWT0xaf4XZ2Pr7bnYJrGXl4JCkKR67e5+0f8tNhi8d681gea43yI1O34IsXG6JZcjQycguxZM8VPNu4PCpEBTnRQu/lWkYefjt8Hf1aJEqeOiHUWFi6k3Z1bRlhWvxNi4wzumK3nb1ttu2L7RcRFeyPFpWjedt/PXQNNzIfYmCrSggPUjvcNm8ip6AI3+26jO51y6BqfChS7ubiqx0XzdJNW3/WYTZ4zHInFsbYmXoctcU6nEnLxthVJ1CrbBgeFupwN6cASdFBWH7oGmY/SzGPvQG7hN3WrVsxb948nDlzBgqFAjVq1MA777xDXjuRzNtyAb8cSAUA/HUyHYFq28Go9QjF+etaJwF/nUxHS5NGxdPoWCse3/x9WXDfnQcFeP6bfbgyozve/+0Ytp69jaX7U3FoHJU5OXhmwR7cyi7AsWv3JS8pIeTB0rcnASZlu3Ks9TiVnWvzw+WZNlTd65XBuuNphu8VIvnC3lX+lYW7UwS368ObpdzNxWcvNnSmSR7PtPVnsHR/Kj7ZcgFXZnRHj892IdfUNSwjSoX5i0OzSlGS83HJAsX//TfVoREmLxNFxTp0/XQXAOBI6n3Ddn3Qv2FLj6J+hQiH2Eg4D8lj7L744gt06dIFoaGhePvttzF8+HCEhYWhW7du+OKLLxxho9dx+mYW77vYcQ/D21VBvfIRZtsn966DiT1refzb1rsdqolKtz+Fi3ZCgdjl41Y2dy33p9yz6/iv+zbmfde3LwFqPywb3Byju9bA9KfqoneDslbzGdutFu+7caSKl5tXxMyn6/H2V4wOwrC2VQzf7QkBZYsJPWvZTmQDS4HZCcscNRIeABwq6gDg7w/aYu5z9fHdK03w6QsN8MnzDfB0o/KS83GGrCsXESh4UtMXoYYVIzH3uZJ2oVBEV2wxLYni8Uj22E2fPh3z5s3DsGHDDNuGDx+OVq1aYerUqbzthDCmj5bYmUqd6yQIbo8N1aB/q0qltMr1aFTi3jMc0XgTHPaEUAKApkl8z4ax16J5cjSaJ9v2Jj9WNQaB/nwPn3Gsy2cbV0CIxrzKalczDl9s57ro7Ok6s0VkkH+p86CJQe5P+cgglI8s/dAOue91jYRQnE1/YPhes0wYapcNw++Hr5ec00LkCQWApxqVx+6Ld7HyyA1RCzXT/ArPR3Itnp2djS5dupht79Spk+D6doQ5lnq6rM0uBDxvsUypiP15njKg2RPxUyrsWp5YjggQQs+F8aw+S3kaPxf2zGK0hWkgdXvw8kfXIdgbZcLbECp/ltZvtLSGnvq/FzYxvUN03T0fyTVWr169sGrVKrPtf/zxB3r27CmLUd6OpVlcGrWvCztxv88RXhmCw15vqNnsVDvyEIp4YdyoWWq0jE12RNFQl3LCB+FbyL1AsWn5U0DYMwdYLv9qFbdDzKxYb155wVeQ3BVbs2ZNTJ06FTt27ECLFi0AAPv27cM///yDkSNH4rPPPjOkHT58uHyWehGWnhuNyg8PUGTxONIzHOSxcxx+SoXkdewAOCwEhLGwsxTqzPiFxxGiXxaPHS3m4zPI/f5tGhZMobC8zI+ll3+VBI+dN6+84CtIFnYLFy5EZGQkTp8+jdOnTxu2R0REYOHChYbvCoWChJ0FLD02tsaYuWK2lTvi7Z5LV+KnkKcr1h59Zasr1tJYVONzO2L8pdRoGUJQkSXsxXSIjkJh2WNnqZz5/9e2iBnPTcLO85Es7FJShKf1E+KxNIbBVlcsNQ4ccjS0hDD2d8Wafpeej9BjYdyoWWpwjM/lCNEvj8eOIOzDtL5TQGFxTKull399L4dQnGNTSNd5PqWvsZzAjh07oFAoBP8OHjxoSCe0f8GCBS60XBhrXbHWIE8VB82KdRz2imbTBkWuomrc7W6pG8l4Iq9jxtjJMXmCyqxUPHWol9y32rT8cflLi7iiz8M0ypEQ5LHzfCR77Bhj+P3337F9+3bcvn0bOpOCsnLlStmM09OyZUukpaXxto0fPx5btmxBkyZNeNsXL17Mm7UbHh4uuz2lxdJYIVtdsaRnOGiMnePwUyrsalHNPXbSEXoujEW8pQaHNyvWIWPsZOiKlcEOwjNw9OQJQPosdH0e2iKaPOELSBZ2b7/9Nr755hu0bdsW8fHxTnkT9ff3R0JCyRpuWq0Wf/75J4YNG2Z2/oiICF5ad8Syx863Z8WKha6D47B3uRBHLXdi/HxbisfKnxXrgDF2dq7tx4OKrGQsvQC7Ow732EH6i5Q+Dy157HwCycLup59+wsqVK9GtWzdH2COKP//8E3fv3kX//v3N9g0bNgyvvfYaKlWqhFdffRWDBw+GUo6KWSaKinXIeqgV3GcafskU0jMcxt2F+dpiaFRKMGZ5RmSxjlH3Lcyvg07H/huIzV8rrkDEOBxTzJc7kf96mw781v8UhYd57ISuO2EdTxIbct9VsxcLhdAYO+tn1c+szSuwHb0jX8tPo6839PdA/4wxxqzWu4TrkCzswsPDkZyc7AhbRLNw4UJ07twZFSpU4G2fMmUK2rdvj8DAQGzduhUjR47E3bt3MW7cOIt5FRQUoKCgJDSVoxdZXn8yHbcfCIfColmxtvlp31WeV6nG+A0AgMqxwdj4TmuzpQH2XrqHAUsOYFKv2nj+kYpOtdWdOHkjC89/vRfvdKiGQa2TUVSsQ7fPdiEyyB/jupeEzDp/K8eu/M2Kph1F1VbxNg11JLTEgyM8dnI2XMU6hh6f70aIxg+/vt6CnmmRVP7feleb4DL8VQLr2ElcN9L/v5eTDafSbZ4vO79kya2qY9cjQOWHz15siAFLuPHs20a2QXJsCPouPIDbD/KxfvhjZvUu4Vok342JEydi0qRJePjwYalPPnHiRIuTIvR/hw4d4h1z/fp1bNy4Ea+++qpZfuPGjUOLFi3QoEEDjBw5EpMnT8asWbOs2jB9+nSEh4cb/kzFotz4W3n771a3jNVjfWlsWWyoRnD7uNUnBb0yl+7k8sLu6Hn9x0PI1+owasUJ2W30JMauOoHcwmJMXX8GAHDxTg7O38rB/pQMjFpxXPbzSSmpH/aohZgQf3z0RB3B/U81Kofk2GA8Xj1O8BzG5/JTAoNbcy+eYjVTQlgAooOFw4Z1qBmP8pGBgvukoBecqRl5OJOWjYNXMkWHEvRV5Bjq9Xb7qqXPRCJyi3XT+k5wuRMLp2xQIeK/POwTXtpihgcFRQZRBwCzN50DYwy7L97F+Vs5gvUu4Voke+yeffZZ/PLLL4iLi0NSUhLUajVv/5EjR0TnNWzYMLzwwgtW0yQlJfG+L168GNHR0ejVq5fN/Js3b47s7GzcunUL8fHxgmnGjBmDESNGGL5nZ2c7VNxZGq/zToeqeKJhOfSoVwZKhQJ9F+3HPxf5AdnlmJ3nKQx+LBmvPVYJAFBpDP9t3dI1FPLWUNPJYbrivPG1csRgaSmN28BHK2FAqySLx8x9rgEYYxZn3pp67P7XrSbGdK0BwLzsmLLhncdQPT4Uneb9jXu5hbx9FaOC8F0/bnJWx1rx2Hz6lujfZIreRGNvc1Exg43RF4RIlg1ujmaVovDCN/uwPyXDsP3djtUQF6bB2FUnnWaL3K/fYrzQQkMfaiSEGjxpcvsExESwIFyHZGHXv39/HD58GC+//HKpJ0/ExMQgJiZGdHrGGBYvXoxXXnnFTFAKcfToUQQEBCAiIsJiGo1GA41G2DvkCNQ2ulv1D6LQg+pr67dZKluWxlFRr5ZlTJc5ML5UcoxfsrRgqvjjrR8htF8o8Lm+bIitl7g1wczHLJmnKx16W/2MnuHCYh0CQcrOElJKpdpPCYVCYViIl7fPyWOs5a6HTIWdUmHeOtg6p9zj4MREsCBch2Rht27dOmzcuBGPPvqoI+yxyrZt25CSkiLYDbtmzRqkp6ejRYsWCAwMxPbt2zF27FgMHjzYqcLNFmpLosTkURWaEWa6Ark3Y62ikiTs6MUSgPmMUuNrVSyDx860/DqlLdV77IzKg9Qxds4e3cD32FHjKBf6CS5Cw1U8fXC/0EuTmBcX48fa3tnulrA0Q51wDyQLuwoVKiAsLMwRtthk4cKFaNmyJWrWrGm2T61WY/78+RgxYgR0Oh2Sk5MxefJkvPnmmy6w1DK2PHZ6hGal+9IYO2tYEnbUFWsZ0/Fcxg2DziEeO8eXVX0x4C13IvEZsdbeybnchv48xnlSd5Z86IepCA1XcXa1KfcYOzHPktApjcua3LPFxSybQrgOycJuzpw5+OCDD7BgwQKz8W+OZunSpRb3denShbcwsbsiVpwJjXuiJTs4LHrsnGyHJ2HqHeItvyGDvpDaNSQH+gaPt0CxxBM7e1aq8bWm7izrWAq9KITeYyck7Dy93hSaKGEem9nWUAZ5bTIuu1SO3Q/Jwu7ll19GXl4eKleujKCgILOxbhkZGRaOJADxEyCEqjRaGoGDxthJR2vFYydHl6AryqYhPqbRNqldwCUzax1rv/76GHtHqUG0jtQxdtx/gbGYHl4xCFkvZrkTXleszOLWuCuWZne7H5KF3SeffOIAM3wH0TNb6VmxiGWvp2dX4I7EmsfOngWJTTH32Dn+XijMPkgfY6e309HmCmVPDaJ86CedCa2nJvf4MmdjOrxAaLKPredN7mtQaOyxk6H+IORFsrDr16+fI+zwGcSuYk/x+ixjeYyd+TYp3TnejLXJE7IIOxe0nUKNmdQGzFm9dHqzjJ/rQmoQZcN6V6yzrZEXs5cmC9tMMX7i5Z5AYlyfmPYGEK7HriJ/6dIljBs3Di+++CJu374NANiwYQNOnTolq3HeSGm6Yn0Ja2+gUroVfP066jH1Dhnr3YIi22GGbGF6v5whqA1nNDqVVIFprQtWzp9QIuxKtpHHTj70S5p4ZVes0FI/AuPuTDF+BuWOyKIlj51bI1nY7dy5E3Xr1sX+/fuxcuVK5ORwIYiOHz+OCRMmyG6gtyFW2JHHTjp0xSxjuo6d8bXy1NmZQm2VJaFm6V3AWW2+3i7j55rG2NlAQrHUrzYg6LHzeGFn/t18sW7rKwLI7bU0LrumdQvheiTf7tGjR+Ojjz7C5s2b4e9fEoanbdu22Lt3r6zGeSNiFxkmXWeZP/69Kbj9aOp9ZOdr8ePeK7ibU4D7eYXIKyy9N8qTYYzh14PXzMTbmmPC11C+8zo0ewAljZmYU1mKZemsNv/EjSx8+MdJ/HUizbDtu12XcezafQDA/sv3sNEkjmdRsQ5L96fi8h374vd6ElkPS55bPZfv5oo+Xj/uVqh+9fhZsWbfRU71MXow5PTYrT+Rjifn7zF8v55ZEl70+PX7eOuXo5i18Sy2nimJ1HItIw8/778qS++AO3DnQQHe++0Y3ll2FDvO3caP+64ir7DI9oFOQvIYuxMnTgguOxIbG4t79+4JHEEYE+QvvNJ81fgQ3vdHkiLx73+Vvi9iTzX03m/H0P1cGaw7noZfDlxDRJDt6CTezvoT6fjAJBbsyRtZmLv5vGznCA0wr0bkXAPOlPgwDW5lF+DRqlzUmkCjZ0qj5gu4ClGBuJbxEI9Xi8U/F+8it7AYtcuG4dTNbO5YKzG95BanP+y9yvu+8dQtbDx1C1dmdMfz3+wDAOz6oC0qRAUBAH7cdxWT1pwGAFyZ0V1eY9yM9387hk2nb+G3w9fx5zDpi9/rF28vFvA+l40ofaxfOWlaKQoHUsSvHmGqyRolRmDn+Ts2j+N77BwnbvddvofXHuPiM/f64h/evkPjOiAmRIN2c3ZAW8xw50EB3ulQzWG2OItp689g1dEbAIDV/zkazqVn46Mn6rrSLAOShV1ERATS0tJQqVIl3vajR4+iXLlyshnmrQT5qzC2W01DMPaqcSHo06wiutZJ4KUb0bE6YkI06FArHrvO30G9/4I5+wrGldm64Y+i+2e7RR236T+vx+m0bEeY5XGcvJlltk2KJ0RP/5ZJyH6oRUyoBiuP3DB4Vt7rVA29Gzj3uV/xRkusOZaGPs0qAgDCAtT4sk8j+CkVCDARassHt8Af/95En6YVkfVQi/Un0/BSs4r4/fB1hAWoER1iOSqNkyNRAQDSsvINwk5K4+/pbPovDu/x6+bl1ZR65cPRtU4ZRASpkZFbiGrxoYbJAc2To/Hd7hQAwNhu3EL21RNCMeuZenj/9+MW83QWo7rUwHNNyuPy3VycTctG2YhAvPr9IavHGA/FHN21Bvq3TMI3f1+WdF5bHrv3OlWDn1KJmRvOSsoXAAL9LcuIjNxCxIRoDD0Gey/dwzsdJJ/C7dh+7rbZtm1nbgNPON8WIUQLu7///hstWrRAnz59MGrUKPz2229QKBTQ6XT4559/8N577+GVV15xpK1eQ/d6ZQzC7pFKURjQqpJZmkB/P7zepjIAoHJsiNl+X6J22XDMf6kRhv58xGZaP6XCY8eMOQKh8UXFdoyJiQ3VYGKv2gC4Aepfbr8EAHizbRWnD04vHxmENx6vzNvWvV4ZwbRlIwINacOD1Bjy3zMl9MyZ4ozoGaYYj1eSI4avN/Lqo5UsvkzEhwUYPresEm34/GyTCi4XdmEBKkNZjA7R4JGkKMF0CgXfW2y89qG+/HJl03r5EDt5omx4AIa1q4ob9x/aJeysTZ4w9Xp7+HBHj0H0O2nbtm2RmZmJqVOnomLFiihXrhxycnJQq1YttG7dGi1btsS4ceMcaavXYPyQ+VL8VymImc4vhKcPlJYboWUO7Bmzb1xmdbxZqMLX29PGiIqZeegMjDW3L02gMr3W1sLcWXuRMB5jJ/dM0NJiaZynKaa3XVDgS/xp1rpi9bGi7a07rU2ecOSQDMIyoj12evWvVqvx888/Y/LkyTh69Ch0Oh0aNmyIqlWrOsxIb8P4+aH4r+IQ6xXy9IHSciN0Oezx2BnnIya2rKdpEqFS4yxhYOxZMW4kfclhp1QoDAIDsB6L1NpdMZ4V63bCzs66yV5dJ3ZWrP5Fz96hB4VWekhMb6MrvOC+iKQxdsaNa+XKlVG5cmUrqQlLGNc3+mn6BB9TISe2TiRhx0e4K1Z6PsaNpJguQm/QJM4qScaX09hL50tdsabX2nRBbV5aKzdGzfPYldIombFf2JlfCzGalYnwrAMlL3r2CmFrIQlNPXZuprW9FknCbvz48QgKCrKaZu7cuaUyyBcwfmtRu1vt46aIrXT8XDHi3Y0R7oqVruyML3+xCHecV0T8cNKjyfPYGQkaX+6KtbbGnzWvj7HHzt1EhNiuWFOEBL5Uz5e1blb9Qtn2dsVau1c0xs41SBJ2J06c4K1dZ4qnr/DtLHhdsTTGThRi9Rp1bfMR8mCWNuKBr+gNZ3XlGd8O40bcpzx2Cv5kAGsToKzdFv46du5VF9hbNwm9SIny2BldT2slSV/O7A07Zu1emQk7N7sn3ookYbdq1SrExcU5yhafwbjBELtgsa9httq6yAqBumL5CI+xky4YiiQKDm+QJM7rijUeY+ebws60nFr32FmGPxnNva6fvXWT0JhWUWPsLLwwmKLfZ699Vj121BXrEkS7i8gbJx/GV5I8TOIQW/xI2PEx9Tr5KRV2CQaeJ0mMy8692lSbCJUv502eKPlsLPJ8qSvW9FrbO8bOuAfE3SK2ydoVK6JsWipXlvK3t+q0dq98qAi7FaJLmleMmXETjJ9JGhMmDrEvFiSU+ZgKXZVSYVdXrHHjIqYu8IZlDpz1LquzOMbOOed3B0wvdaFVVWZtjF3JPneLYSrnrFjJeVjz2DG9sHOEx46PtziIxKwM4EpEq4rFixcjPDzckbb4DMaFm4SIMKZXReyLhb3jRLwVU2Gn9lPaVSn56tgvZ2DsAS320VmxZh47a8udWJsVa/Si7Ga6zu5hN0LeNjHeXGahXJmn4/7b3RX734UWqqO91etc2nHKjkb0GLt+/fo50g6fgu+xIyEiBrGP0cXb3h8wXSzT1p8xCz3kp1RgnVEgerHwhZ3t9N5QnzurK7bexE2Gzx/8fhwf/H4c4YFqZD3UGrYnjV6HeuXD8duQFtCoLMe39VSML3XS6HXW01rZZ/xi524eO7WdvTNCAt9a96cef5U0kWvvrNhrGQ8t3rOn5u/hfVcqgEW7UzB57Wn4+ykx/am6eLpxeUnnm/jnKfx1Mg0L+z2COuXkcTaN+v04lh+6BgBYNbQlGlaMBGMMr31/CBdu5+C3IS3Qf/FBnLESqvJmVr4stsgB9QO6ACV57CzyWNUYKBRAt7omIaK8QCg4G6F4kmo/BTQCayc+16Skcp30X+gwPf5+SrSvWTJp6o3HuYDfLzxSgZeurkyVrCv4sEctAMCbbUvW5nRlr5GxqNNz/HoWtp4xj1HpDcjpaa+REIqwABVqlgmTLU85GN2thuD2kR2rASh5nj56og5v/5ttqwCAITYyAHz2YkPD5xoJobz0/VsmAQCG/nccADRKjECoRoX4MA1CNCqUjwzE5N61eXkpFEClmGBEBftjXPeakn+fGBQAJq89DYDrbh/52zHJeSzZcwW3sgvw8/5U2ezSizoAhti9OQVF2Hr2NlIz8vDj3qtWRZ27IWlWLCEPxlUYeez4/DCwKQqKdGbB3I3HbP39flvEhZUEb68xfoPT7PN0VEqlWbfMyUmdEaJRYXJvrkEJUPvhxaYVUaTTIUDlh9zCIoQGqA3pq8SF4uyULmb36I83WyH5f+sBeJ4Ob5YcbfhN+ji4cgm75JhgXL6bK0te7t4F5AxsjdNaN/wxFOsYz2Plavb/rz0vjq0xb7WvikGtkxGg9sPEXrURoPbDuNUnDfurJ5g/b13qJODM5C4oZgxBJs/hxF618W7HaggPLHlmg/xVODS+A9RKJfKLiqFSKuGvUuK5JhUM+SoUCmwZ0cZw7V5oWhEKcF3IDwuLEaJRITu/CCo/Bc/LLAU5x9gVaItly8uY3IIiAPxlXMQ8d0sHNXOIPfZAws4F8CJP0Dp2PBQKhZlgAPhde3FhGsE0hG1UfgqzrtQQDVcNGF9Tf5US/v859I1FnR6h62/sdfHErljT3yRXVyyVVdtIGU9o6674KRVu98IcYKP7XF9GLJUVoe2B/pbzNBZ1evRd+EH+Jc2+ab7G105fLxgfGxVseR1bZ+OoKkY/LtA4ooaYRd0rRlkP3uBMSiXscnJyoDP5wWFh7uX+dkeMGwx3q4DcFWOhQNfMftR+SrsiT0jFK2bFypUPFVebSBJ2nng9PdFmB+AJl0FfFrVGZdLaIsx63MlJI9mSlJQUdO/eHcHBwQgPD0dkZCQiIyMRERGByMhIR9jo1dAYO3HwAlp7ZM3uHqjsXMdOKp7osTPFW5Zm8ASkdDF74m3xRJsdgZzXwVFLsOmLorao5AXY+vI7HO4k7CR77F566SUAwKJFixAfH0+Vnx3QrNjSQUua2I/KT4mHhUWuNsMjoKrNeUjrivW8G+N5FjsK+a6Eo99PjWdVFxbZFnbuFEVKsrA7fvw4Dh8+jOrVqzvCHp+AQopJhxbIlge1nwIPnOGxc/gZHA89mc7Dl9bs82Vk9djJl5UghUXM6LOwsFP7KQzdtP5u5LGTbMkjjzyCa9eu2U5IWIQ/K9Z9CoM7Q/W+PNgbUkwyXiDE5Zo8QZ4/efHE8ZvUs8XhjlfBktPA2GNnKbqGcferOw2rkuyx++677zBkyBDcuHEDderUgVrNn31Tr1492YzzVmgdO3vwvMrcXSHviDjkaovl7Dokz7VnQrU8h5x1j1zPgiWTjMWcJWFn3H6707AqycLuzp07uHTpEgYMGGDYplAowBiDQqFAcbFj1pbxJmiMHeEqGHOOsPMG+eGOY7nEzM7zdjxR25LDjkMrp7CTKR9LYc+Mn7VCC8+d8VqJ7uSVlSzsBg4ciIYNG+KXX36hyRN2YnzN1DTGThSeWJm7K85Y5NYb7pdsHjsZH3FrAdd9BW8oW75KkYzlVz6PnYWuWGNhVyTssHJW2EGpSBZ2V69exZ9//okqVarYTkzYhMbYiUOOR/iTLecxpE1ln1gw1lKl9++1+841xINxxzr7yNVM3H1QgN4NyqFitPssiGov59IfYNlB+UJDuSs0+oFjz6V7gtsv3s7BkauZuH7/Ie7nFaJ73TJolhyN29n5+PfafVSLD0VqRh5aV4s1O/bK3Vxcy8zDY1VjcT+vEPsuZ6BdjTj4q5TIeqjFltO38Fi1GMSFBuBASgbCA9XQMYbcgiI0SYrCietZZnnuPH8Hm06lG77vu5whaLc71hGAHcKuXbt2OHbsGAk7mYgMMl8hnDAnJkRjO5ENPtlyAWn38zHzGe8fB+rqcXSWwid5EjUS5FlsPUwgcoe9/Hb4OgBgzubzuDKju2z5uorOn/wtKl2dcmE4eYOL1Wlvya6REIqz6Q9QPjLQzhzsh9betE6HuTt533/YexV7RrdD50/+xoP8kuWZfhvSwvBZ/+76+OwdAIBVQ1tizMoTOJv+AG+2rYz3O9fA1HWn8euh63isagymP1UXz329l3eefWPa45kF/G0A0G/RAVF2lwkPxK3sAlFpnYlkYdezZ0+8++67OHHiBOrWrWs2eaJXr16yGefNzHm2Pu4/1CIxOtjVpngEjyRF4r1O1VA5NqRU+fx6+JpPCDtX6bpF/Zvg39T76Fw73jUGyMAfb7bC2uM38XaHaoL7B7dOxo3Mh1h3Is1qPk83Ko+aZUJx6Y48cWJ9jcToIMSFanD1Xh6+eLGRoQG3l4X9H8HCXSkY0CpJFvussX74Y1hx5Doig9SIDtFYDf9FCHP1Xh5P1AHA0dRMw2fTTokTN7JwNv0BAGDNsTS837kGVh65AQDYdeEurtzNMzvHlXv2P5tTnqiDuuXC8f2eK+hcO8HufByBZGE3ZMgQAMDkyZPN9tHkCfE83bi8q03wKBQKBYa1q1r6fGSwxRMwHTcyuXdtfPjHKYeft12NeLSr4bmiDgDqV4hA/QoRFvf/r1tNvPfbMZv5PNWoHFpVicGYlScspqkeH4pztx7YY6bXs/P9tobPDwtL366UiwjEhz1rlTofMdQqG4ZaZZ1zLldj7DmuPu4vFBit+bZkwCPov/igXfkKjX0z3iTHsjf2jlntUjsBfZsnAgAaPN+g1HbIjWRhZxobliA8CXcd7Co3pl2xvvK73QkxV5xuiziMrxMt+eI5lGbRXiFhZ1ytyVEMirx0ljmN3CcIL8S0UqRldeRFLm3hy/fF2jhQU8FLAtgzUYkQdpaEulD5MK7X5HgExcSA9UQke+yEumCN+fDDD+02hiAcjXe+n5lj6lj3ZQHhzvjyfbHWDWZ6VdxxTUHCNmKW87K0NqNwV6yRsJOhMhcTA9YTkSzsVq1axfuu1WqRkpIClUqFypUrk7Aj3BpLaxZ5G8WmHjtyeTgfEZfcl7vIra2naLo+qrH+9Y0n2DtQi/DYFVkY3iWk+/nVWulLwkOtd84JkCzsjh49arYtOzsb/fv3x5NPPimLUQThKHxE15l1Y/iyZ8id8eXborXiLTHz2BkJPV95hr0BMcJOWySlK7bks9RyICQg5ZiU447IMsYuLCwMkydPxvjx4+XIjiCIUmI6boWEnXviy/dFa2Uinqkjk3+ZSNl5CioxXbEWyoHw5AlpY+yMy5HQRIk8EnbWuX//PrKyzFdwJgjC+Zh1xfqwgHAVYsaF+XJIRmtxb02vnS9fJ09GzBAQS2MtbY+xkybwhc7zsLBIIKXnI7kr9rPPPuN9Z4whLS0NP/74I7p06SKbYQRB2A91xXoGvrx0h5xxQwn3REy9Y2nJEZtdsRJt0Qrk560eO8nCbt68ebzvSqUSsbGx6NevH8aMGSObYQRB2I+pXqDJE/Iix+KogOtDv7kSq7NirRRXH9bCHodShLCztOSIra5YqQiN6cyjyRMcKSkpjrCDIJzGuNUn8NM+LvD4J883wA97r+CT5xuisLgYH/x+HMPbV8Xj1eNcbKV43v/tGNQqJaY9WRcA8OT8f3A09T4vDXns3JPSrI+aNHqd2bazU7ogQO1e4as+3XIB/17LxLevNDGsa/bTvqsYt/qkxWPoPcQzUSkVMI6cKuaFsv2cnYLb1x4zD9l36U6O4TNjfI+3cWSd1Iw8s+djpEC0mKX7U23a54nQAsWEx9O1jnmcvqhgf4vp9aIOAN5Z/i+OpN7HqBXH8dYv3Gd7Q+C4gtvZ+fjt8HUs3Z+K3IIi5BYUmYk6wLKwq18+3MEWegcDWlUCADxePRYA8FKzRJvHiBEno7pUL5Vdpvx26Jqs+cnBvC3nsf3cHWw5c9uwbeKf1sPb0bp17s3/utUAALxrEk95aNsqvO/RIZbrYVtsPXvbbNvGU7cMn3WMuczj3a9lkkvOKxbJHrv8/Hx8/vnn2L59O27fvm0WYuzIkSOyGUcQYviyTyPcuP8QADe9noEhOliD9Kx8BPr7gTGGptO2Ws0jM68QWQ+1zjBXVozXArNWxQmtl7bxndaoGhfiAKu8jxaVo3FgbHvEBGsAAI0TI3FgbHtEBfkjI7cQGrUf6k/aJDq/ExM7QceA8EA1Doxtj07z/sb9PPPy90Wfhhi21HyJKUvkuvGYoYKiEtusrWFnC+qJdT2DW1dG7wblEBeq4W0f+nhl7Dx/BwdSMgBw9fGJiZ1w+0EBNp26hZkbzspmg7ZYZzZJzJTHq8diQs/aaDt7h2zn7V6vDFpUjpYtP0cgWdgNHDgQmzdvxjPPPIOmTZvSbCXC5SiVClSICjLbXjHafJs1PL27UupA/OoJoQ6yxDuJCw0Q/B4XFiCU3CohGpWh7owLDUBMiEZQ2IlZB8wboWbF/YkXKPcKhQKVY0MMwg4AQgPUCA1Qo1JMjln60qAtZjbHW8aEaFApJljW88aGaGwncjGShd26deuwfv16tGrVyhH2EITL8HRhp2Ow6M6ghtL5WLvkpi/ElkS5mJBMnoIUJ4C1lDR5wr2xdJv9VfKW5aJinUu6Yj2hLpUs7MqVK4fQUHrTJ7wPjxd2OstzNT37l3k/lu6bSumrHjsqsZ6KpTsnd1kuLGY2u2IdgSeM/5R8pefMmYNRo0bh6tWrjrCHIFyGygOFnXG1VsyYz8TC9QQk3QkLiaWWSXcuwVJss+qxo1F2bo2l+MdyDysoKtaBuWApRE9455DssWvSpAny8/ORnJyMoKAgqNVq3v6MjAwLRxKEe+Pngd4RnVFXhI4xy2rCAyojb0OKyLZ42zyhFXEEtI6dx2KpyMo9rKBI5yqPnfsjWdi9+OKLuHHjBqZNm4b4+HjfrXgIr8MTx6kbiwedjrwZ7oSVUKhm+FoECoXCukCjVsVzcZbHrrCIxthZQrKw27NnD/bu3Yv69es7wh6LnD9/Hu+//z7++ecfFBYWom7duvjoo4/Qtm1bQ5rU1FS8+eab2LZtGwIDA9GnTx/Mnj0b/v72r6VD+A6e6LEzrtisvb16wrgQb0OObnFvEurGDaJSobBeXj2h9SQkIfcY5iKdziUvRJ5QNiW3ZDVq1MDDhw8dYYtVunfvjqKiImzbtg2HDx9GgwYN0KNHD6SnpwMAiouL0b17d+Tm5mL37t1YtmwZVqxYgZEjRzrdVsIz8cQJiHyPHeN1zepRKDzjLdPbkNJNZDGl9+g6HqUJceell8RrcFZdo3XR5AlPQLKwmzFjBkaOHIkdO3bg3r17yM7O5v05grt37+LixYsYPXo06tWrh6pVq2LGjBnIy8vDqVPcCuabNm3C6dOn8dNPP6Fhw4bo0KED5syZg2+//dZhdhHehSfOQDTWcZZWYvfESSHegBRvgqWkUpstT2nmbD1qVGQ9F0tdsXKjpeVOLCK5K7ZLly4AgPbt2/O2M8agUChQXCz/yufR0dGoWbMmfvjhBzRq1AgajQZff/014uPj0bhxYwDA3r17UadOHZQtW9ZwXOfOnVFQUIDDhw/zumwJwpRLd3KgNQrcmVtQhGCN5MfD6RhXbBtOpqNAINC1n5I6Yl2BlRj3ZljqtpXqkDhxPQvn0h9AxxjKRwbi0NVMaFRKNKgQgUu3c1E+MhCRVsLtyY2xuFVAgfSsfBy+mol8rfWLY627y9fGI3oazqprHuQX4bdD1510thI8oTaV3HJt377dEXZYRaFQYPPmzejduzdCQ0OhVCoRHx+PDRs2ICIiAgCQnp6O+Ph43nGRkZHw9/c3dNcKUVBQgIKCkrDF5N3zTbQm0dg7zt2JPWPaW0jtPhgLu+l/CYfrqRZP6066Ar1YSxARmaJSTDCuZ5oPcQnS+Ek657oTaVh3ggueXiUuBBdv81f7D1T74cyULpLyLA2mHpUXv92HlLu5No+ztrp/eKDa4j7C9SSEC5f3IH9pZVkMn269YHV/GQu2lAbTMGruiGRh16ZNG9lOPnHiREyaNMlqmoMHD6Jx48YYOnQo4uLisGvXLgQGBuK7775Djx49cPDgQZQpUwaA8Fue3pNoienTp9u0gfB86pUPx/HrWaLT38zKd6A18mFrgH65iEB82acR0ox+T2yoBuN71HK0aT6Pfrzj4NbJSM3IQ5c6Cdhz6S4qx5rH5539bH00M4ln/OqjldCwQgTGda+J02nZWHnkhqTzm4o6AHiodW4sWWNdp1AAN+9bH5/dqko0lAoFBrdONts38+m6OH0zG22qxcptJiEjfVsk4lz6A7SvGcfbnixQ7k1pnhyFfZflWzJtSJvKAIBRXWpYjVPbPDkKLzxSEQFqP2w5cwsjO1XDYzO3o3xkIF5unog7DwrQoEIEtp29jZeaV5TNPkchStgdP34cderUgVKpxPHjx62mrVevnuiTDxs2DC+88ILVNElJSdi2bRvWrl2LzMxMhIWFAQDmz5+PzZs34/vvv8fo0aORkJCA/fv3847NzMyEVqs18+QZM2bMGIwYMcLwPTs7GxUqVBD9GwjPoFf9spKEnadgbYjJlRndDZ/Ts0uE3U+vNqM4sU5Af28C/f0w5zluFYGOtYTrItO4m6+0SDSI79ceS8bt7HzJws4dMH3xMP2+64O2uHw3F/0WHQAAPNu4Ap5oWE4wr+cfcf8GlQA0Kj/MelZ41YxnGpfH74e57tNudROw/kRJb9pXLzVC17qckyZp9DrecbaEmRAfdKluGE7Tvmac2fGda8fj675NzI7rUicBAHBxWjezfXr73B1Rwq5BgwZIT09HXFwcGjRoAIVCITjOQeoYu5iYGMTExNhMl5eXBwBQmoy4VSqV0P23WFSLFi0wdepUpKWlGTx4mzZtgkajMYzDE0Kj0UCjcX/XKlE6nDWg19nYM3jYSy+F21Ga5U7MbpGH3jPTS2BaXJVKBW/hWiqb3o2122u8zp2/SolCo/HC9iyBpzbSC0ITyLy1TQBECruUlBTExsYaPjubFi1aIDIyEv369cOHH36IwMBAfPvtt0hJSUH37pxXolOnTqhVqxb69u2LWbNmISMjA++99x4GDRpk8PIRvou3PsNixYPCwmfCcZRK2JkUWE8YsC2E6TUwfRHxUyhkX7iWcF+s1cMqI4GvMRF29ogw4xcGoTKm9OKp16KEXWJiouBnZxETE4MNGzZg7NixaNeuHbRaLWrXro0//vjDsFCyn58f1q1bh6FDh6JVq1a8BYoJwlsfYfLYuS+lWYrB9B55ahtkLOyEdK5SyW90vdmLQlh/QfH3s+xhs2dxY5VRfkLCrjRrKbo7dq3ncP78eezYsQO3b982dIXq+fDDD2UxzJQmTZpg48aNVtNUrFgRa9eudcj5Cc/GW9/ORHvseD/fO6+Fu1GaJbZMG0BPWO1eCOPmQais+ikUvEbcQ38mIRLrHjvLAt8ewe/PE3bmx8sdCcOdkCzsvv32W7zxxhuIiYlBQkICr8JRKBQOE3YEURq89REWH4+UGk9nU5quWNM2x1NvGS8yisD1UCoU8FeVNMCe2uVMiMNa3WMsvkxfxO2ps4y7dlUCHjtvrgclC7uPPvoIU6dOxahRoxxhD0E4Bi99iu0JqeOdV8L9EArvJhbzrljPvGs2hZ2SPHYEh3F3qWk3qT3FgjcZw8e6YiWPWs3MzMSzzz7rCFsIwmF4q9ddrHgwDcBOOJ7SdMWa3SMPvWXG18B0EXCA6w4zboA99GcSorF8h43LvBzdpGqex863umIlC7tnn30WmzZtcoQtBOEwvLWLx65Zsd55KdyO0nTFmhZXT71nxstiCU0mMZ0V66m/kxCH2PsrR9hutZXJGNw5vLewSe6KrVKlCsaPH499+/ahbt26UKv54V2GDx8um3EEIRfe+gzbNSvWS0Wuu1G6dexKP3jcHTAeKlAkUFYVCuGB7YR3IvZOm3WT2lH+VbwXBqF17CRn6TFIFnbffPMNQkJCsHPnTuzcuZO3T6FQkLAj3BIPbRex7/I9LNqdggm9aqNcRCBv33e7LuOjdWdE5cOf5CSriYQFSrPciSdPnmCMYdzqk/h5fyqeaFDWsP3irQdmaf2UCl4DLCT+CO9BtMdOhkrK1gsDjbEzIiUlxeLf5cuXHWEjQZSavELnxsiUixe+2YdNp29h5K//mu2zJura1YizuI9wDLXL8hdCb1XFdlQdS5iGHhNqg7r+F/oIAAY9Vsnuc8nNjfsP8fP+VADA6n9vGrZ/v/cqL11kkBoqpQLB/n4I/i9AfMWoIOcZ6sb0b5kEgIsX7E30qMcJ/XIRgXiqYXnevvJRJS+upt2kZUxC7omhfIT1slQuMtDqfk/GrnXsCMLT0BaLXhcEgPu56VPv5YlK9+0rTRAeqDYTGTTGzvH8PqQlrmbkokx4IO7mFKCyiKDnlmhYMZL3XciD0bRSFIY+XgWFxTrUKReGb3c5PyqQEEKTJExZ+9ajiAvVQKFQQOWnwJaRbZCRW4jaZcOdYKH7M75HLTzTuDxqlvGuqEnNk6Ox6d3WKBcRiGCNChvfaY0gfz8oFEBYQMmwLlNvmj7mKwC826EaBj6ahL/P38WbS48AAN7vXB2zNp4zpNn8bmtUjOYLuyPjO+JBvhYP8ovwIL8ITStFOeInugWihN2IESMwZcoUBAcHY8SIEVbTzp07VxbDCEJOpHbxuFuHkNhlTZonRyE0QG223bie9NTFbt2dQH8/1EjgGuLwQPN7IJa4UHGxq1VKBeqW54SQUOxuVyGmC7pOOb6AKxMeiDLh3utBkYqfUmF2jbyFavGhhs/VE0IF0xh77MID1bwX7foVwhEaoEaVuJIXp3IRgYgK9kdGbiEAoGq8eb5Rwf6ICvYvrfkegShhd/ToUWi1WsNnS1CDQbgrxSK8CMa4UTsJwLyxtLTMiZi4m/SUeh5CHjtbg8NdRalmAxME+D0mxTpmFgiBsI4oYbd9+3bBzwThKdgzKJsx5jaViKn9WgshJywJOwVFnvAYhEqq0D1z13W4SjNphCAAftku0ulM1uHk/vN7IZxkmIcgw2oxBOH+2NPYuFP7ZGq/pXFMYhp7Wu7E8xC6Y0Jrc7kD5LEjSouxh1qn43vw9Pvcs/S7B6InTwwcOFBUukWLFtltDEE4Cns8djrG4Ocm1YepsCuSOBlE6I2XcE+EdJFQV6y7euzExy8mCGGMyzY3vti8x4HGDVtGtLBbsmQJEhMT0bBhQ7caqEsQYii2o7VxJ8+DqbArlCjseFAd6HEItVsqOZbndwD2xC8mCGPMx9gZ76MKzBaihd2QIUOwbNkyXL58GQMHDsTLL7+MqCjvnS5MeBf2jbFzgCF2Yu6xky+qAeH+CHkk3NVjR2PsiNJiKt6Mv5d8NvLiOcMoD0L0K9/8+fORlpaGUaNGYc2aNahQoQKee+45bNy4kTx4hNtj3xg79ynXpl4Qqevy0UBjT0JcuXPXMXbUHhClxfSlRWgdTqrTLCPJl6/RaPDiiy9i8+bNOH36NGrXro2hQ4ciMTEROTk5jrKRIEqNp3vsTG0RswisJagO9A783DTGKnnsiNJiKuz4HjvuP0/sUa3Gw+7IEwqFAgqFAowx6Gi0LOHmSF3HDnCuxy71Xh4YGBgD7uYUICLIHxFB5ovc5hUW4eSNbFy4bR53Uyw00NjdEXd/3NVjR2PsiNJi2hUrNFGC4l9bRpKwKygowMqVK7Fo0SLs3r0bPXr0wBdffIEuXbpA6aYDeQkCAMICpb/DOMvxsOlUOgb/eFhU2lofbrTrHMZtLdWB7o3YyBNB/va9lzt6fUZ6zydKS6zRMxCi4ZdzS5Mn4sMCDJEnfB3Ramzo0KEoU6YMZs6ciR49euD69ev47bff0K1bNxJ1hNszrG1VtK4Wi251E3jbY0L4IWZaVo42fHbWWKEle67Iks/UJ+uISkdvt+7J0kHN0KpKNL58qZHNtO1rxKFRxQjetrnP1ed9rxwbLHiso4u1LU93eS8Ovk7Iw+iuNRD5X4/FL4Oai+iKBb7s0xCtqkRj6WvNnGeomyL6lW/BggWoWLEiKlWqhJ07d2Lnzp2C6VauXCmbcQQhF+FBavwwsKngvs7z/sa5W1zX5k+vNkPy/9YDcJ7HTuz0fUthxADgjccr46VmiaLyofEo7knLyjFoWTnGZrr+LZMwsVdts+1PNSqPpxqV5227lpGHxz7mRwvSMQalA8uAra7Y/i2THHZuwjuICdHg6IedDN/PpGUbPhsWKDaZPJEcG4KfX2vuNBvdGdHC7pVXXqGxOYRXYjxQ17iIO2uMndjHylIYMUBi9yo9xh6NlHW8hJZEcfQLi7UXEMB9l2kh3BehIq8w89kReiQtUEwQ3ojKz1jYKaBQcN1V7jYG3NradbbaeuPfQu2qZyPl/VpIRDGRy6nYi61ZsSTsCKkIr2NHWIIGxxE+j6Wp9e62Hpe1tetsda8aN+bkefcdhG6148fYWd9PDTMhFeMSox/ST+vYWYaEHeHz+JlOrf/vv7PG2IkVWtbWrpNSsVEd6NlIuX+mZRtw/BADW/mTsCOkorDhsaMSxYeEHeHzKC147Jw2xk5kOuseOwnno1rQZ3DFGDvbXbGOPT/hffBjxZpvo14IPvSIET6P6UKv+jrC3SZPlCY+LH8dO6oEPRnTFxGpacljR3ga/JBiAgsUO9ked4eEHeHzWB5j55zzi62UCq157CQ0ltSuejZSbp+QiGIOXkDYlrCjyROEVJQk4iRBwo7weUw9dvqvThN2osfYWRN21o91r2kghLMQGmPn+Fmx1veTx46QCr8r9j+PnYX9BAk7gjDzICjcdIxdYZH9s2J5aakS9Gwk3D+hoECuXsdOSlcyQQD8+s3SAsVECSTsCJ/HVNjlFBQBAFIz8qwel52vxbjVJ3DwSobg/s2nb+Hd5f/ieqb1fMTS+8t/ZMmHxth5NlLuX2lnxV6+k4MxK0/gmo1nwZjfD1+XbBNBWENIxBk/B1Sn8SFhR/g8Xepw8WNN48a+9sMhq8fN3ngOP+1LxbML9gruH/TDIaw6egOfbrlgNR85ZnTZXqDYeB27Up+OcCFS7p9Qt6ctj5oxz3+zD78cSEX/xQdEH3PAwouOHpoVS0iF1xVLHl+biI48QRDeyhMNyiE2JAA1y4Tytlvr+gSAS3dyROV/9Z51b4ccQstWFsZNOY1x8mwkTZ4QaAS1EoTdnQcFAIBLd3IlnNU6KqH+YYKwAj/yBPefV41RlcaDhB3h8ygUCjxa1XbwdVPE9mjZ6vqSo06SElKM6kDfRmvjhcXRqFUk7Ahp8LpiITB5wrnmuD30hBGEnYgdq+SMKRi03InvUNr7V6RznLAT082rpq40QiL8yROGjSX7qVLjQcKOIByMrZizzq6TqBL0bEo7ULywyHGvGloRopE8doRUlCTiJEFPGEHYifiuWOv75RjzZjsLWsmO4HCkx05MdBTTdSMJwiZCIcV4s2IJY0jYEYSdiJVKzvDY0XR/36G0usjaQtelRUzeapoWS0jFqAqldexsQ08YQdiLTB47gpBEKVsxbSliDsuRNwk7ojSUrGNntI1ebHnQE0YQdiI2NJOtdHJUSra8OM4Kj0a4P6722Kn8qBEm7McwK9boBcfRYfI8DRJ2BGEnosfY2Wrr5OiKpbbSZyjtrRYzDs6RefuTx44gHAo9YQRhJ2KbR6esY2cjF3qfJfQUOtBjJyZvihxASEWo/qJSZBkSdgRhhQf5WqRlPcSDfC10OobsfC0YY3jw338x3Lj/EHmFXPzZrDwt8rXFhn2MMdzKzneI7cZQV6z3UNpZ1BduPUBhkQ7aYh3u5XCRJe7lFCBfW4x7OQXQFuuQna9Fdr5Wct76cm4NapAJqfDqL4HIEzTGjg9FniAIK9SduMnwOUCtRL62xCMR5O8nKo8H+UWo9eFGbHq3NXp8thsxIf7Y+UFbqP2UmLnhHA5eySy1nRq19Xc0P/KSeA227rUtZm86j42nbkGpVODkjSx0r1sGfx67afM4bbHO6sQHxhie+Uo4brIxNGyAkIpSYAassZijMXZ8SNgRhEiMRR0A5BUWW0gpzOqjN1BYrMPNrHzcz9MiNlSDBTsvWT0mMTrIYqzZmmXC0KBCOM6kPcCzjStYzadhhQi0rhaLilGBkmwm3If3O1fHplPpeLl5oqTjhreviqX7U3H3P+8cAJy4kWX4LEbUAUB6Vj4qRAVZ3K8tZmZdsX2bJ+JASgYC1Eocu56Fx6rGICEsQJL9BBEbqkH3umWg8lMgLEDtanPcHhJ2BGHCG49Xxlc7rAsuezD2VAjNHiwbHoDP+zTC01/tAQBoVErsfL8tACBp9Dpe2iszuks6t1KpwA8Dm0q0mHAn3mxbBW+2rSL5uBEdq2FEx2oYsPgAtp+7Y/f5bY0VNS7TZyZ3QaBIjzZB2EKhUODLlxqZbHSNLZ4AjbEjCBcgNHtQoVDQopuEwyjt2LxiGwsyGpdpWtKEcDRUP1qGhB1BmOCMiQZCMTUVCn7jK0eoMYLQU9rZqLYW2jbuhqWwYYSjoQWKLUPCjiBMcMZAXKGuWKVCwRskTMKOkBO/UpYnW12x+hi0aj8FBWonHA4tUGwZEnYE4SSM20WhrlilwiSwNbWNhIyUdma0ra5YbRG3n0KGEYRroSeQIExx0MufccMotJCrUqHgeUXIY0fISWmLk62oEvrhBdQNSzgDKmWWIWFHECY4yqlvLOaEJ08ARUbij3QdISel9djZiiqhH17gr6JmhXA8VD9axmOewPPnz6N3796IiYlBWFgYWrVqhe3bt/PScLMK+X8LFixwkcWEp6KzNUrcTozH1QmNsWPge/XIY0fISeljzFoXdvqXFZXSY5oVwoOhCROW8ZgnsHv37igqKsK2bdtw+PBhNGjQAD169EB6ejov3eLFi5GWlmb469evn4ssJgg+xl46IWGn0zGesKNqi3AntDa6YvUePbWKSi5BuBKPEHZ3797FxYsXMXr0aNSrVw9Vq1bFjBkzkJeXh1OnTvHSRkREICEhwfAXGEgr7RPScFRXrHHDKNQVW8wYb4yd8Wdy3hGuRmiJHmP0ZVpNHjvCCVCdaBmPiDwRHR2NmjVr4ocffkCjRo2g0Wjw9ddfIz4+Ho0bN+alHTZsGF577TVUqlQJr776KgYPHgyllYqmoKAABQUloXays7Md9jsIz8Cedez+uXgXKw5fh0KhQJvqsehVv6xZmhVHrhs+v/bDIZQJ54dWKi5mvDF2xp+dsbYeQVgj5U4u2lY3334u/QF6f7nbEHKPZsUShGvxCGGnUCiwefNm9O7dG6GhoVAqlYiPj8eGDRsQERFhSDdlyhS0b98egYGB2Lp1K0aOHIm7d+9i3LhxFvOePn06Jk2a5IRfQXgK9qyJ9NJ3+w2fVxy5jm51Emwek5aVz/veqXYCKkSWeJi71y1j+Ny7QVn88S8X05NmHRKu4Nj1+4LbO3/yN+97VLC/E6whfB1elB4auMLDpcJu4sSJNkXVwYMH0bhxYwwdOhRxcXHYtWsXAgMD8d1336FHjx44ePAgypThGkBjAdegQQMAwOTJk60KuzFjxmDEiBGG79nZ2ahQwXpAdcK7kcM7VqRj0KiUKCiy3n2l54kGZTG6aw0EqP0w59n6OHEjCx90KXGPTH2yLjQqJdKzCzD72XqlN5AgJCLWEzftqboOtoQg+GKOFijm41JhN2zYMLzwwgtW0yQlJWHbtm1Yu3YtMjMzERYWBgCYP38+Nm/ejO+//x6jR48WPLZ58+bIzs7GrVu3EB8fL5hGo9FAo9GU7ocQhAmMSRsDMql3HQSouaDpTzcuj6cbl+ftD9Go8PEz9eU0kSAkYWtWLABEBqlRKSbYCdYQBGEJlwq7mJgYxMTE2EyXl5cHAGZj5ZRKJXRWBvQePXoUAQEBvO5agrAFk8FlV8yYJM+fP41LItwcW7NiCcKZ0OQJy3jEGLsWLVogMjIS/fr1w4cffojAwEB8++23SElJQffu3QEAa9asQXp6Olq0aIHAwEBs374dY8eOxeDBg8kjR0hCjuarWCdN2Kn8qJYi3BuhJXoIwlVQjWkZjxB2MTEx2LBhA8aOHYt27dpBq9Widu3a+OOPP1C/Ptc9pVarMX/+fIwYMQI6nQ7JycmYPHky3nzzTRdbT/giOh2TNO6DJkQQ7g4JO8KdUJDLziIeIewAoEmTJti4caPF/V26dEGXLl2caBHhrcgxeUInsSuWKinC3SlyUEQWgiDkhQb2EIQJcsywKmY0T4vwLgpFzvAmCGdAr8KWIWFHECbI4ZjQ6eSZhEEQ7gJ57Ah3gjo5LEPCjiAcgI48doSXQWPsCHeChq9YhoQdQZggh6NN6qxYgnB3aLkTgvAMSNgRhBmlb8Co24rwNgqKil1tAkEQIiBhRxAmyOHibzt7R+kNIQgZERsSzBKX7+TiuIV4sXpUtNA2QbgcegoJwoTh7aoiPkyDdzpUxacvNEB0KYOav9OhKu/76K41eN8HPVapVPkThBhebFYRoRrxK1yFaFRoXS2Wt230ihNWj1nwciO7bCMIQj5I2BGECQnhAdg3pj3e6VANvRuUw6FxHXj7P3+xoeHzpy80wOVp3fD3+20t5tevRRKqxYcYvg9pU5m3f2z3WjJZThCWaVQxEscmdOJt+7BHLVye1g1RRi8v05+qi8vTuuHExE74YWBT/DmslWGfzmTgqPHM74NjO6BxYpSDrCcIQiwes0AxQTgT4+5Y067ZECOvh7+fEkqlAoH+flbyApQ0g4twA5QmEU78lAoolQremmAalZKXzrjsmgo74wkVFO+YINwDehIJQiLGIk4/bkltJdarAgr4Ucgwwg3RF0vj9w7TcXLGZbfYZFJQka5kCRS1iso4QbgDJOwIQiJBRsJO9Z+gszowXQESdoRbUuKZKymf/iYvKXyPHf94bVHJBpWSmhOCcAfoSSQIiRgLO333k8qKx06poMU0CffE779yyfPYKU09diWfTT12WmOPnZVngCAI50HCjiAkEqA29tj91xVrxVuhUChAbR7hjug9dsbF0/QlxfoYO07YqZQKenkhCDeBhB1BSIQv7LjGzHRQujEKUFcs4Z4oBTx2ppMgeMLOdIzdf5MnSrtGHkEQ8kFPI0FIxLgREyPXaFYs4a7oi7LxvFirkydMPHaFeo8duaQJwm0gYUcQEpE6lkgBBQk7wi0R8tiZlm8lb1Ys/3i9x46WOiEI94GeRoKQiNRuJwXNiiXcFD+BMXam5dtPzBg78tgRhNtAwo4gJKKyQ6RZGoNHjjzCleg9drmFxYZtpsLOuOhm5BZizqZzyMgtxInrWejx+W7BYwiCcB0UeYIgJGI8+y8mRCMiPdC4YiT+Pn/HsK1ybDAu3clFu+pxDrGRIMSgj6KS9VBr2BYawG8WTF9KPt92EZl5hfhpX6phW77WpI+WIJxIfFiAq01wK0jYEYQINr3bGq//eBgfPVEHALB4wCO4n1eIClFBhjS/D2mB3w9fx+GrmbhwO8ewXQEFhjyejCB/PzxenQuq/vNrzbHq6A282LSCc38IQfyHRqVEi8rRZtvLRgTyvvsJuJX3Xc7gfX/+kfLyGkcQIvjx1aa4lV2AavGhrjbFrSBhRxAiqBYfiu3vPW743lbA09YkKQpNkqKQcjcXbWfvMGxXKAB/Pz8Map1s2JYQHoA3Hq/sSJMJwiqvt04260KtkWDeQIqZ+NMkMUo2uwhCLI9VjXW1CW4JDYwgCJkxHYNHM2IJT0FokWExkcKoiBOE+0DCjiBkxl/Ff6yozSPcEgE1JlRWxczoplnfBOE+kLAjCJkx9diRN4PwFIS8c2I8zkLj8AiCcA0k7AhCZkxX7qcYmoQ7IlQqFQJbxQg7ayH1CIJwLiTsCEJmaBV+wpsQ081K40gJwn2gFoggZIZW4Sc8FSF9JqTrinX8CBT0LkMQ7gM9jgQhM/ZEpiAIZyMk4gS7ZwUSak2CxpLHjiDcBxJ2BCEzNKaO8FhEll1TYUezYgnCfSBhRxAEQQAQvzRPUTG/K5Y8dgThPpCwIwiCIACIX5rnXm4h7zsJO4JwH0jYEYQDaFgxAgBQPjLQekKC8ADqlw+3up+6YgnCfaBYsQThAFYMaYmH2mIEqv1cbQpBCCK0Zp0lebZqaCsU6Ri0xTowAA/ytbh4Owd9Fx4AQLNiCcKdIGFHEA5AqVQgWEOPF+FZWOpSVSoV8FcqDOHyQjQq5BUWG/bThCGCcB/oPYsgCIIAIC38nXEYMQopRhDuAwk7giAIH0R4HTvxAs14XB2NsSMI94GEHUEQBMEhQZ8Zx4elWLEE4T6QsCMIgiAkY6zlSNcRhPtAwo4gCMIHEQwfJuF4GmNHEO4JCTuCIAgCgLTJE9QVSxDuCQk7giAIAoC0yRMKC58JgnAtJOwIgiAIANI8dgRBuCck7AiCIAgAJOwIwhsgYUcQBOGDlI8qXRxjfRQK088EQbgWinlEEAThQ/wwsCkOXslAr/rlzPZZCikmRGiAGtOerAsGhtAAtZwmEgRRCkjYEQRB+BCtq8WidbVYWfLq06yiLPkQBCEf5D8nCIIgAAAKGmRHEB4PCTuCIAgCAC1bQhDeAAk7giAIAgDNiiUIb4CEHUEQBEEQhJdAwo4gCIIAQF2xBOENkLAjCIIgANDkCYLwBjxG2B05cgQdO3ZEREQEoqOjMXjwYOTk5PDSpKamomfPnggODkZMTAyGDx+OwsJCF1lMEAThWZCsIwjPxyOE3c2bN9GhQwdUqVIF+/fvx4YNG3Dq1Cn079/fkKa4uBjdu3dHbm4udu/ejWXLlmHFihUYOXKk6wwnCILwIMhhRxCej0csULx27Vqo1Wp8+eWXUCo5Lfrll1+iYcOGuHjxIqpUqYJNmzbh9OnTuHbtGsqWLQsAmDNnDvr374+pU6ciLCzMlT+BIAjCAyBlRxCejkd47AoKCuDv728QdQAQGMjFOdy9ezcAYO/evahTp45B1AFA586dUVBQgMOHD1vNOzs7m/dHEAThi5DHjiA8H48Qdu3atUN6ejpmzZqFwsJCZGZm4n//+x8AIC0tDQCQnp6O+Ph43nGRkZHw9/dHenq6xbynT5+O8PBww1+FChUc90MIgiDckA414wAAA1oludYQgiBKjUuF3cSJE6FQKKz+HTp0CLVr18b333+POXPmICgoCAkJCUhOTkZ8fDz8/PwM+QnN6GKMWZ3pNWbMGGRlZRn+rl275pDfShAE4a5807cJDo7tgJaVY1xtCkEQpcSlwm7YsGE4c+aM1b86deoAAPr06YP09HTcuHED9+7dw8SJE3Hnzh1UqlQJAJCQkGDmmcvMzIRWqzXz5Bmj0WgQFhbG+yMIb2fiRKBBA1db4VyWLAEiIlxthW2SkoBPPpEvvytXuC7Wf/+1nEapVCA2VCPfSQm3RmoZs+fZEVPuCMfgUmEXExODGjVqWP0LCAjgHRMfH4+QkBAsX74cAQEB6NixIwCgRYsWOHnypKFrFgA2bdoEjUaDxo0bO/V3+TL9+3MP84wZ/O2rV0sfvyN3A2fKqVPAc88BsbGARgNUrQqMHw/k5TnunHp27OCuR2QkkJ/P33fgALdPrvFOCgV3/Y157z1g61Z58vcUnn8eOH/e1VbY5uBBYPBgV1thG6FyZYrcjfuNG8DLLwPR0UBQEPdyYjyEOicHGDYMKF8eCAwEatYEvvrK3B6hv99+s3zexx8H3nlHnt/gCKReZ08pY4R9eMQYOwD44osvcOTIEZw/fx5ffvklhg0bhunTpyPiv9eITp06oVatWujbty+OHj2KrVu34r333sOgQYPIC+dkAgKAmTOBzExXW2KZffuAZs2AwkJg3TquwZ82Dfj+e6BjR267MwgNBVat4m9btAioWLH0eVv7DSEhXOPojWi1wtsDA4G4OOfaYg+xsZxoIfhkZgKtWgFqNfDXX8Dp08CcOXxP0rvvAhs2AD/9BJw5w31/6y3gjz+4/RUqAGlp/L9Jk4DgYKBrV8f/BqFnkjGgqMjx5zY+P5UxL4d5CH379mVRUVHM39+f1atXj/3www9maa5evcq6d+/OAgMDWVRUFBs2bBjLz8+XdJ6srCwGgGVlZclluk/Rrx9jPXowVqMGY++/X7J91SrGTEvb778zVqsWY/7+jCUmMjZ7dsm+Nm249MZ/ev75h7HHHmMsIICx8uUZe+stxnJyxNuo03HnbdKEseJi/r5//2VMoWBsxoySbQBj337L2BNPMBYYyFiVKoz98Qf/uFOnGOvalbHgYMbi4hh7+WXG7tyxbMP27Vy+48Yx1qFDyfa8PMbCwxkbP57/m+/eZeyFFxgrV46zoU4dxpYu5efZpg1jb77J2LvvMhYdzVjr1tx1Nb6GiYlc2gkTGKtfv+TYAwc4O6KjGQsL4449fJifv5jrYMykSZydpjRqxP0+PYsWceVFo2GsenXGvvySn/6DDxirWpU7Z6VK3DUrLCzZr/8tCxdy+xUK7h6bsngxd231/PsvY48/zlhICGOhoZxdBw9a/j337zM2aBBjsbFc+rZtuTyMmT6du/8hIYwNHMjYqFH869ymDWNvv80/pndv7rnRk5jI2Lx53OcXXmDs+ef56QsLufu0aBH3/a+/GGvVivttUVGMde/O2MWLJelTUrh7d/RoyTZb5bVNG+65ev99xiIjGYuP566zsY1C5coU02e4TRtue3ExVz7KleOe//r1ud9hjVGjGHv0UetpatdmbPJk/rZGjbgyY4kGDbh7ZYl+/cx/R0oKt0/MdTR9JvXP/oYNjDVuzJhazdi2bVyZnTmTK8MBAYzVq8fYb7+V5JWRwVifPozFxHD7q1QpKQOWrnO/flz5mjaNsTJlSu6TcRljjLE5c7hnNSiIq1PfeIOxBw9K9ps+O0Ls389dS42G+10rV/LLXVERd52Tkjj7q1Vj7JNPSo7fuZMxlYqxtDR+viNGcPU9Y4xducK1LxERnK21ajG2bp11u7wFKdrEY4SdsyBhVzr0FcnKldzDe+0at91U2B06xJhSyVXC585xFUdgIPefMcbu3eMqmMmTuQdd/7AfP841mvPmMXb+PCfyGjZkrH//krwnTLDc0DDG2JEjnC2mwkhPx478xhjgbFm6lLELFxgbPpyz4d49bv/Nm1xlO2YMY2fOcPl37Mg1/JbQV+7nznEV4dWr3PYff+TObXq9rl9nbNYsrpK8dImxzz5jzM+PsX37StK0acPZ9f77jJ09y9ly+zaXz+LF3DW8fbvkGhn/xq1buXOfPs39vfoq15hnZ4u/DqZcu8bd4wMHSrYdO8YJr0uXuO/ffMM1OCtWMHb5Mvc/KoqxJUtKjpkyhbvPKSmM/fknZ9fMmSX7J0zgGtbOnblrf+yYOGFXuzbXEJ85w5WlX381F2p6dDpOPPXsyYm/8+cZGzmSa6z1v3/5ck6kfPstd/3HjuUEYGmE3Zo13HNh3MiuWcM9W/oq6vffuet2/jxXPnr2ZKxu3ZKXFlNhJ6a8tmnDCfyJE7l8v/+eu2+bNnH7LZUrUw4c4NJt2cKl01+ruXO5/H/5hbtWH3zACZzz54XzYYyxmjUZe+cdxp55hhPXDRpw5ceY11/nXtiuX+fu2bZtXBndtUs4z0OHOPv++cfyee/fZ6xFC07U6+uioiLx19H0mdQ/+/Xqcdfz4kXuxe1//+NecDZs4J6PxYu5umHHDi6vN9/kfvPBg9w93byZex6sXed+/bjz9+3L2MmTjJ04wW03FXbz5nHX6vJlri6oXp0Td3psCbucHO6ePP88d541axhLTuaXu8JCxj78kLP18mXGfvqJE2fLl5fkU60aYx9/XPJdq+UEs17Adu/OXePjx7lrtGYNJwh9ARJ2pYCEXenQCzvGGGvevORN2FSo9OnDPaDGvP8+9wamx7TyYYyroAYP5m/btYsTEA8fct8//5yxdu0s27hsmbkHw5jhw7nGVI/es6YnJ4dr5PQehvHjGevUiZ/HtWslwk0IfeWemcl5wCZN4ra3bcvYp58KezhN6daNExd62rThKn5TAC4/Y0yFnSlFRZwoWbOGn4+16yBE1678BuKddzgvmZ4KFcwF9pQpXENqiY8/5jwCxr9FrbYsLvSYNk6hoXwBaY2tWzkhYtoBULkyY19/zX1u0YKxIUP4+5s1K52wKyzkxINxB8WLLzL27LOWbdWLLn0jbirsxJTXNm3MvWOPPMJ5zfQIlStThLyFjDFWtixjU6ea5z90qOW8NBrub8wYTkQtWMAJ3O+/L0lTUMDYK69w51SpOKEt0Llj4I03OMFoC6H7JvY6mj6T+md/9eqSbTk53G/Zs4ef9tVXufvNGCfYBwwQts/Sde7Xj3sRKijgbxeqW4359VfupUWPLWH39dfcC1lubsm2r76yXs8yxt3vp58u+T5zJv9+rF7NCVN9j0zdutzLhi8iRZt4zBg7wvOYOZMbs3b6tPm+M2e48TLGtGoFXLgAFBdbzvPwYW6GVkhIyV/nzoBOB6SkcGmGDSvdxADGzCcu1KtX8jk4mBsbd/t2iU3bt/NtqlGD23fpku3zDRzI/abLl4G9e4GXXjJPU1wMTJ3K2REdzZ1j0yYgNZWfrkkT0T+Tx+3bwJAhQLVqQHg495eTY56/tesgxKBBwC+/cBNEtFrg55+53wsAd+4A164Br77Kv3YffcS/br//Djz6KJCQwO0fP/7/7Z17XFTV2sd/A3ILYUruEwaE4g0CwUow0Qw1DAE1rxyFFE94SU0i7aJgmqKG2uvlaLyCl+M55pva8YRhqJAoiIR4RUkLxQJECQGvILPeP1azmc0MMOAIMTzfz2c+sNdee+1nPftZa5691nrWqMrl4MDXDTWH+fOB8HDAz48H+zT2rHJyuD4Uuld8Cgrqrrt0CfD2Fl9X/7i5GBgAY8dyvQHAvXt8vZiyjfzyCzBpEvDii4C5OfDnRgEqOlKuiyb2qvysAcDOrvFnrSmVlUBRkfr2f+lSw9fJ5YCnJ18L27cv8O673L6UgyP+53/4+tkDB3g94+KAmTOBw4dVy3vwAPjXv7j9tQRN9dhQm1ROz8vjbWToUHF5O3bUlTVjBrB7Nw8Y+fBDICNDMznd3ABDw8bzpKbyez//PG/TU6YAZWXc3jTh0iXA3V28bk+d7W/ezOttZcXrFx8vttOwMODqVf4MAb7eeNw43tcAwJw5vH8YMACIjgbOndNMvo5Gu/hJMaJ94uvLna6PP+YNVhl1zhNjTZcpl/MOfc4c1XOaBhy4uPC/eXnqt/y4fJlHyCpjYCA+lki4LAqZRo7kjmx97OyalmfECF6nadN4OeqCGuLigLVreZSwmxvv6ObNU12MregAm0tYGHe01q3jTpKREe+Y65ffmB7UMXIkL2v/fv730SNgzBh+TnFdfDwPZFFGsT3lyZPAhAl8gfvw4dzh3L2b60OZltQ7JoY7RElJfDF+dDQve9Qo1bxyOX+WaWmq55qzDYSenqqdNxTsoSAkBBg0iDtVKSk8OEl5of/IkTwoID4ekMm4rK6uDQfPaGqvzX3WzUVd+28sEtzODujdW5zWqxewdy///8ED3tfs3w+89RZPe+klHin6xRfcgVfmm294BPyUKS2TX1M9NmSbyukKvSYlcedKGaM/d6Hx9weuX+d5Dh8G3ngDmDWL160xmmob16/zPigiAli6FOjSBTh+nPdHTdmmAk367j17eDBLXBzvW8zMgNWrgaysujzW1lyniYn8ReXgQXGbCw/n/UBSEn+xXbGCl/fee5rJ2VEgx454qsTGcudJ4Uwp6N2bdx7KZGTwfIovdUND1dE7T0++TUm3bi2XycODv1mvXcudBqVfqsPZs7zTXLFC8/I8PfmXi6Mj0KkFLUpfH5g8GVi1ijsY6khPB4KC+FYPAP8iuHKFf7E1hYFB46OgivI3beIdPMBH0m7f1rwODdGpExAayjtqIyOub8VbvY0N/xL79Vf1o5QAcOIEdzQ/+aQu7fr1J5dLgYsL/7z/PjBxIpdTnWPn6QmUlPD6ODqqL6tXL+6IKjsKipEHBVZWPBJTQW0tcOEC8PrrDcvo48Mdt6+/5vYxdmzdCExZGR8t2bIFGDiQp9VvV+rq8iT2qkATu1LIqZzP3Jw7oMeP85c/BRkZwCuvNFzWgAFAfr447eefuX0A3AmpqRG3Z4C3L3UO6datQGCgZiO9DfVF2tAjwPtDIyM+ejVoUMP5rKz4S1hYGH/eUVHcsVOnZ0356ScelRsXV6e7PXuaL//Ondy5/vPXPlVsPz2d2/LMmXVp6kbJw8N5P2FvDzg7q47sdu3KndCICOCjj/gLDTl2YmgqlniquLnxL+3168XpkZF8unTpUt45b98ObNjA91dT4OgIHDvG965SOBkLFvDpylmz+Jv4lSt82kW5YW/YwN9mG0IiAf73f/mI3ZgxfN+4wkK+j9XIkfxtsjl7Vs2aBfzxB3cMTp3ijsoPP/ApR0072qVL+YjZ8OHqz3frxkdrMjL4F/m773JHQxMcHbmuS0oa3oKmWzfeMV+6xN+gQ0LqOugnJTwcOHqUOyWKaVgFMTHcif7yS24H589z52rNmjq5Cgv5SNovv/Cptvrbw7SEBw/4lH1aGncUT5zge3s15Cj7+XG7CA4GDh3i+4ZlZACffsq/GAFg7lw+dZSQwOsSHc1fQpQZMoSPNiQl8ZHhmTOBO3cal1Ui4SOLmzdzG1A49wDfB9HCAvjqKz6FdfQon2JuDG3YK6CZXVlbcztKTgZu3gQqKnh6VBQf6fr6a+6sLVzI2/PcuQ3f7/33ubOwfDmv67/+xes9axY/b27OnaKoKP5cCwr4EocdO1Sd9atXed8SHq55XbOy+HO/fZs7itrSI8BHrz74gNdx+3Zu67m5wMaN/BgAFi/m0/BXr3K7+u67OnttSM+a4OzMHbv163kddu7kttYcJk3iTuG0abxfPXhQdSSxWzfeVg4d4u1j0SLe5uqjGJlftgx45x3xuXnz+PUFBcDp09zeNXm57XC0wpq/dgUFTzwZysETCq5d44ueG9ruxMCAsRde4FGfymRm8six+teeOsUDLzp35tGQL70kXojdVFSsgnPn+MJdCwsug7MzDw5QXgDMmPpF4lJpXQQvYzyab9QoHoZvYsKj2+bNUx+dyZg4eEId9YMnysq4Xjt35lFin37KF4kr61rdAm/GeORct258MXlD252cPs2jCY2M+NYi//d/qgusNdFDQwwcKA6MUWbXLr7A3NCQb63h68ujqhVERfFn1Lkzj7pbu1a8kLupQBAFygvAHz3iW4l07crvK5MxNnt2XQCOOior+RYgMhm3l65dGQsJYaywsC7P55/zYIfOnXlb+PBDsWzV1XzBfpcu/DmuWNF48ISCixfrthWpb1MpKXzBuZERbwtpaeJnpW5hfVP2qkmQhzq7Ukd8PNeVnp767U4MDDTb7oQxHszj6srr2rOnalRscTGPkJfJeDBCjx58K4/6OvvoIx7hXX+7o4bIz+fBYCYm4u1OWqLHhtq+XM4Dp3r04DqxsuKR3oqoz6VL+XM2MeH2ExTEo0sVqNOzuv6YMVUbW7OGR6ebmPB77tghllGT7U4yM/lzNDTk7XnvXrHdPXzIn41UyvU1YwZjCxeqb7uLFvGo/6Iicfrs2byfNjLi+pk8mUcUdwSa45tIGNNkdrzjUFlZCalUioqKCtrYmCC0AGN86vvdd5seTdI1YmL4rzPQzyoRhOZMn85HHg8caGtJ/jo0xzehNXYEQTw1Skv51M7vv6tOqxAEQShTUcGnZ3ftqvu1EKL5kGNHEMRTw8YGsLTka6Gee66tpSEI4q9MUBBfr/juu3z7FaJl0FRsPWgqliAIgiCIvxI0FfsEKPzcysrKNpaEIAiCIAiizifRZCyOHLt6VFVVAQC6du3axpIQBEEQBEHUUVVVBalU2mgemoqth1wuR1FREczMzCBpbBv0J6CyshJdu3bFjRs3Ovx0L+miDtJFHaSLOkgXdZAuxJA+6tB1XTDGUFVVBZlMBr36u3DXg0bs6qGnpwd7e/tWuZe5ublOGmBLIF3UQbqog3RRB+miDtKFGNJHHbqsi6ZG6hTQL08QBEEQBEHoCOTYEQRBEARB6Ajk2LUBRkZGiI6OhpGRUVuL0uaQLuogXdRBuqiDdFEH6UIM6aMO0kUdFDxBEARBEAShI9CIHUEQBEEQhI5Ajh1BEARBEISOQI4dQRAEQRCEjkCOXSuzadMmODk5wdjYGF5eXkhPT29rkbTOihUr8PLLL8PMzAzW1tYIDg5Gfn6+KE9YWBgkEono079/f1GeR48e4b333oOlpSVMTU0RGBiI3377rTWr8sTExMSo1NPW1lY4zxhDTEwMZDIZTExMMHjwYFy8eFFUhi7oAQAcHR1VdCGRSDBr1iwAum0Tx44dw8iRIyGTySCRSPDtt9+KzmvLDsrLyzF58mRIpVJIpVJMnjwZd+7cecq1ax6N6aKmpgYLFiyAm5sbTE1NIZPJMGXKFBQVFYnKGDx4sIqtTJgwQZSnvesC0F6baA+6AJrWh7r+QyKRYPXq1UIeXbGNJ4Ecu1bk66+/xrx58/DJJ58gNzcXAwcOhL+/PwoLC9taNK3y448/YtasWTh58iRSUlLw+PFjDBs2DPfu3RPle/PNN1FcXCx8Dh48KDo/b9487N+/H7t378bx48dx9+5dBAQEoLa2tjWr88T06dNHVM/z588L51atWoU1a9Zgw4YNyM7Ohq2tLYYOHSr8tB2gO3rIzs4W6SElJQUAMHbsWCGPrtrEvXv34O7ujg0bNqg9ry07mDRpEs6cOYPk5GQkJyfjzJkzmDx58lOvX3NoTBf379/H6dOnsWjRIpw+fRr79u3Dzz//jMDAQJW806dPF9nKli1bROfbuy4UaKNNtAddAE3rQ1kPxcXFSEhIgEQiwZgxY0T5dME2nghGtBqvvPIKi4iIEKX17NmTLVy4sI0kah1KS0sZAPbjjz8KaaGhoSwoKKjBa+7cucMMDAzY7t27hbTff/+d6enpseTk5KcprlaJjo5m7u7uas/J5XJma2vLYmNjhbSHDx8yqVTKNm/ezBjTHT2oY+7cuczZ2ZnJ5XLGWMexCQBs//79wrG27CAvL48BYCdPnhTyZGZmMgDs8uXLT7lWLaO+LtRx6tQpBoBdv35dSBs0aBCbO3dug9foii600Sbaoy4Y08w2goKC2JAhQ0RpumgbzYVG7FqJ6upq5OTkYNiwYaL0YcOGISMjo42kah0qKioAAF26dBGlp6WlwdraGi4uLpg+fTpKS0uFczk5OaipqRHpSyaTwdXVtd3p68qVK5DJZHBycsKECRPw66+/AgAKCgpQUlIiqqORkREGDRok1FGX9KBMdXU1/vnPf2Lq1Kmi32TuKDahjLbsIDMzE1KpFK+++qqQp3///pBKpe1aPxUVFZBIJHj22WdF6bt27YKlpSX69OmDDz74QDS6qUu6eNI2oUu6UObmzZtISkrCtGnTVM51FNtoCPqt2Fbi9u3bqK2thY2NjSjdxsYGJSUlbSTV04cxhvnz5+O1116Dq6urkO7v74+xY8fCwcEBBQUFWLRoEYYMGYKcnBwYGRmhpKQEhoaGeO6550TltTd9vfrqq9ixYwdcXFxw8+ZNLFu2DD4+Prh48aJQD3U2cf36dQDQGT3U59tvv8WdO3cQFhYmpHUUm6iPtuygpKQE1tbWKuVbW1u3W/08fPgQCxcuxKRJk0S//xkSEgInJyfY2triwoUL+Oijj3D27Flhel9XdKGNNqEruqjP9u3bYWZmhtGjR4vSO4ptNAY5dq2M8ugEwB2f+mm6xOzZs3Hu3DkcP35clD5+/Hjhf1dXV/Tr1w8ODg5ISkpSaajKtDd9+fv7C/+7ubnB29sbzs7O2L59u7AIuiU20d70UJ+tW7fC398fMplMSOsoNtEQ2rADdfnbq35qamowYcIEyOVybNq0SXRu+vTpwv+urq7o3r07+vXrh9OnT8PT0xOAbuhCW21CF3RRn4SEBISEhMDY2FiU3lFsozFoKraVsLS0hL6+vsobQWlpqcqbuq7w3nvv4cCBA0hNTYW9vX2jee3s7ODg4IArV64AAGxtbVFdXY3y8nJRvvauL1NTU7i5ueHKlStCdGxjNqGLerh+/ToOHz6M8PDwRvN1FJvQlh3Y2tri5s2bKuXfunWr3emnpqYG48aNQ0FBAVJSUkSjderw9PSEgYGByFZ0RRfKtKRN6KIu0tPTkZ+f32QfAnQc21CGHLtWwtDQEF5eXsJwsIKUlBT4+Pi0kVRPB8YYZs+ejX379uHo0aNwcnJq8pqysjLcuHEDdnZ2AAAvLy8YGBiI9FVcXIwLFy60a309evQIly5dgp2dnTBdoFzH6upq/Pjjj0IddVEPiYmJsLa2xltvvdVovo5iE9qyA29vb1RUVODUqVNCnqysLFRUVLQr/SicuitXruDw4cOwsLBo8pqLFy+ipqZGsBVd0UV9WtImdFEXW7duhZeXF9zd3ZvM21FsQ0RbRGx0VHbv3s0MDAzY1q1bWV5eHps3bx4zNTVl165da2vRtMqMGTOYVCplaWlprLi4WPjcv3+fMcZYVVUVi4yMZBkZGaygoIClpqYyb29v9vzzz7PKykqhnIiICGZvb88OHz7MTp8+zYYMGcLc3d3Z48eP26pqzSYyMpKlpaWxX3/9lZ08eZIFBAQwMzMz4ZnHxsYyqVTK9u3bx86fP88mTpzI7OzsdE4PCmpra9kLL7zAFixYIErXdZuoqqpiubm5LDc3lwFga9asYbm5uUKkp7bs4M0332QvvfQSy8zMZJmZmczNzY0FBAS0en0bozFd1NTUsMDAQGZvb8/OnDkj6j8ePXrEGGPs6tWrbMmSJSw7O5sVFBSwpKQk1rNnT9a3b1+d0oU220R70AVjTbcTxhirqKhgzzzzDPvHP/6hcr0u2caTQI5dK7Nx40bm4ODADA0Nmaenp2gLEF0BgNpPYmIiY4yx+/fvs2HDhjErKytmYGDAXnjhBRYaGsoKCwtF5Tx48IDNnj2bdenShZmYmLCAgACVPH91xo8fz+zs7JiBgQGTyWRs9OjR7OLFi8J5uVzOoqOjma2tLTMyMmK+vr7s/PnzojJ0QQ8KDh06xACw/Px8Ubqu20RqaqraNhEaGsoY054dlJWVsZCQEGZmZsbMzMxYSEgIKy8vb6VaakZjuigoKGiw/0hNTWWMMVZYWMh8fX1Zly5dmKGhIXN2dmZz5sxhZWVlovu0d11os020B10w1nQ7YYyxLVu2MBMTE3bnzh2V63XJNp4ECWOMPdUhQYIgCIIgCKJVoDV2BEEQBEEQOgI5dgRBEARBEDoCOXYEQRAEQRA6Ajl2BEEQBEEQOgI5dgRBEARBEDoCOXYEQRAEQRA6Ajl2BEEQBEEQOgI5dgRBEARBEDoCOXYEQbQa165dg0QiwZkzZ9paFIHLly+jf//+MDY2hoeHR1uL0yq05DmEhYUhODj4qclEEIR2IMeOIDoQYWFhkEgkiI2NFaV/++23kEgkbSRV2xIdHQ1TU1Pk5+fjyJEjKuclEkmjn7CwsNYXWg3btm3Ds88+q1Herl27ori4GK6urlq7f1pamqATPT09SKVS9O3bFx9++CGKi4u1dh+CIBqHHDuC6GAYGxtj5cqVKC8vb2tRtEZ1dXWLr/3ll1/w2muvwcHBARYWFirni4uLhc+6detgbm4uSvvyyy+bdb+amhqN0p4W1dXV0NfXh62tLTp16qT18vPz81FUVITs7GwsWLAAhw8fhqurK86fP6/1exEEoQo5dgTRwfDz84OtrS1WrFjRYJ6YmBiVacl169bB0dFROFZMzS1fvhw2NjZ49tlnsWTJEjx+/BhRUVHo0qUL7O3tkZCQoFL+5cuX4ePjA2NjY/Tp0wdpaWmi83l5eRgxYgQ6d+4MGxsbTJ48Gbdv3xbODx48GLNnz8b8+fNhaWmJoUOHqq2HXC7HZ599Bnt7exgZGcHDwwPJycnCeYlEgpycHHz22WeQSCSIiYlRKcPW1lb4SKVSSCQSUdqxY8fg5eUFY2NjvPjii4IOlO+xefNmBAUFwdTUFMuWLRP0m5CQgBdffBFGRkZgjKGiogJ///vfYW1tDXNzcwwZMgRnz54Vyjp79ixef/11mJmZwdzcHF5eXvjpp5+QlpaGd955BxUVFcKomaIujo6OWLZsGcLCwiCVSjF9+nSVqdja2lpMmzYNTk5OMDExQY8ePZrtsCqwtraGra0tXFxcMGHCBJw4cQJWVlaYMWOGkCc7OxtDhw6FpaUlpFIpBg0ahNOnTwvnp06dioCAAFG5jx8/hq2trWBP33zzDdzc3GBiYgILCwv4+fnh3r17LZKZIHQJcuwIooOhr6+P5cuXY/369fjtt9+eqKyjR4+iqKgIx44dw5o1axATE4OAgAA899xzyMrKQkREBCIiInDjxg3RdVFRUYiMjERubi58fHwQGBiIsrIyAHyEbNCgQfDw8MBPP/2E5ORk3Lx5E+PGjROVsX37dnTq1AknTpzAli1b1Mr35ZdfIi4uDl988QXOnTuH4cOHIzAwEFeuXBHu1adPH0RGRqK4uBgffPBBs+p/6NAh/O1vf8OcOXOQl5eHLVu2YNu2bfj8889F+aKjoxEUFITz589j6tSpAICrV69iz5492Lt3r+BgvfXWWygpKcHBgweRk5MDT09PvPHGG/jjjz8AACEhIbC3t0d2djZycnKwcOFCGBgYwMfHR2U0Ubkuq1evhqurK3JycrBo0SKVesjlctjb22PPnj3Iy8vD4sWL8fHHH2PPnj3N0oc6TExMEBERgRMnTqC0tBQAUFVVhdDQUKSnp+PkyZPo3r07RowYgaqqKgBAeHg4kpOTRVO4Bw8exN27dzFu3DgUFxdj4sSJmDp1Ki5duoS0tDSMHj0ajLEnlpcg2j2MIIgOQ2hoKAsKCmKMMda/f382depUxhhj+/fvZ8rdQXR0NHN3dxddu3btWubg4CAqy8HBgdXW1gppPXr0YAMHDhSOHz9+zExNTdm///1vxhhjBQUFDACLjY0V8tTU1DB7e3u2cuVKxhhjixYtYsOGDRPd+8aNGwwAy8/PZ4wxNmjQIObh4dFkfWUyGfv8889FaS+//DKbOXOmcOzu7s6io6ObLIsxxhITE5lUKhWOBw4cyJYvXy7Ks3PnTmZnZyccA2Dz5s0T5YmOjmYGBgastLRUSDty5AgzNzdnDx8+FOV1dnZmW7ZsYYwxZmZmxrZt26aRbAocHBxYcHCwKE3xHHJzcxus68yZM9mYMWOEY2XbUUdqaioDwMrLy1XOff/99wwAy8rKUnvt48ePmZmZGfvvf/8rpPXu3VuwCcYYCw4OZmFhYYwxxnJychgAdu3atQblIYiOCo3YEUQHZeXKldi+fTvy8vJaXEafPn2gp1fXjdjY2MDNzU041tfXh4WFhTBSo8Db21v4v1OnTujXrx8uXboEAMjJyUFqaio6d+4sfHr27AmAr4dT0K9fv0Zlq6ysRFFREQYMGCBKHzBggHCvJ0Uxjass6/Tp01FcXIz79+83KquDgwOsrKxEZd29excWFhai8goKCoR6z58/H+Hh4fDz80NsbKxIH43RlK4AYPPmzejXrx+srKzQuXNnxMfHo7CwUKPym4L9OZKmCNApLS1FREQEXFxcIJVKIZVKcffuXdH9wsPDkZiYKORPSkoSRjvd3d3xxhtvwM3NDWPHjkV8fLxOrRkliCdB+ytnCYJoF/j6+mL48OH4+OOPVSI79fT0VKa11C3wNzAwEB1LJBK1aXK5vEl5FF/6crkcI0eOxMqVK1Xy2NnZCf+bmpo2WaZyuQoYY1qLAJbL5ViyZAlGjx6tcs7Y2Fj4X52s9dPkcjns7OxU1hsCEKJdY2JiMGnSJCQlJeH7779HdHQ0du/ejVGjRjUqZ1O62rNnD95//33ExcXB29sbZmZmWL16NbKyshq9TlMUjrRijWZYWBhu3bqFdevWwcHBAUZGRvD29hYFwUyZMgULFy5EZmYmMjMz4ejoiIEDBwLgLwwpKSnIyMjADz/8gPXr1+OTTz5BVlYWnJyctCIzQbRXyLEjiA5MbGwsPDw84OLiIkq3srJCSUmJyAnS5t5zJ0+ehK+vLwC+KD4nJwezZ88GAHh6emLv3r1wdHR8oqhNc3NzyGQyHD9+XLgXAGRkZOCVV155sgr8iaenJ/Lz89GtWzetlFVSUoJOnTqJglTq4+LiAhcXF7z//vuYOHEiEhMTMWrUKBgaGqK2trZF905PT4ePjw9mzpwppGk6GtgUDx48wFdffQVfX19hhDI9PR2bNm3CiBEjAAA3btwQBccAgIWFBYKDg5GYmIjMzEy88847ovMSiQQDBgzAgAEDsHjxYjg4OGD//v2YP3++VuQmiPYKOXYE0YFxc3NDSEgI1q9fL0ofPHgwbt26hVWrVuHtt99GcnIyvv/+e5ibm2vlvhs3bkT37t3Rq1cvrF27FuXl5cI026xZsxAfH4+JEyciKioKlpaWuHr1Knbv3o34+Hjo6+trfJ+oqChER0fD2dkZHh4eSExMxJkzZ7Br1y6t1GPx4sUICAhA165dMXbsWOjp6eHcuXM4f/48li1b1qyy/Pz84O3tjeDgYKxcuRI9evRAUVERDh48iODgYPTp0wdRUVF4++234eTkhN9++w3Z2dkYM2YMAD4advfuXRw5cgTu7u545pln8Mwzz2h0727dumHHjh04dOgQnJycsHPnTmRnZ7do9Ku0tBQPHz5EVVUVcnJysGrVKty+fRv79u0T3W/nzp3o168fKisrERUVBRMTE5WywsPDERAQgNraWoSGhgrpWVlZOHLkCIYNGwZra2tkZWXh1q1b6NWrV7PlJQhdg9bYEUQHZ+nSpSrTrr169cKmTZuwceNGuLu749SpU82OGG2M2NhYrFy5Eu7u7khPT8d//vMfWFpaAgBkMhlOnDiB2tpaDB8+HK6urpg7dy6kUqloPZ8mzJkzB5GRkYiMjISbmxuSk5Nx4MABdO/eXSv1GD58OL777jukpKTg5ZdfRv/+/bFmzRo4ODg0uyyJRIKDBw/C19cXU6dOFbYLuXbtGmxsbKCvr4+ysjJMmTIFLi4uGDduHPz9/bFkyRIAgI+PDyIiIjB+/HhYWVlh1apVGt87IiICo0ePxvjx4/Hqq6+irKxMNHrXHHr06AGZTAYvLy/ExsbCz88PFy5cQO/evYU8CQkJKC8vR9++fTF58mTMmTMH1tbWKmX5+fnBzs4Ow4cPh0wmE9LNzc1x7NgxjBgxAi4uLvj0008RFxcHf3//FslMELqEhNXv0QmCIAjiL8D9+/chk8mQkJCgdh0jQRCq0FQsQRAE8ZdCLpejpKQEcXFxkEqlCAwMbGuRCKLdQI4dQRAE8ZeisLAQTk5OsLe3x7Zt257KT58RhK5CU7EEQRAEQRA6AgVPEARBEARB6Ajk2BEEQRAEQegI5NgRBEEQBEHoCOTYEQRBEARB6Ajk2BEEQRAEQegI5NgRBEEQBEHoCOTYEQRBEARB6Ajk2BEEQRAEQegI5NgRBEEQBEHoCP8Pcu5BFd0++fwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5. How many terrestrial (earth) days are there in a Martian year?\n",
    "mars_temp_df['min_temp'].plot()\n",
    "\n",
    "# add title and 'x' & 'y' axis labels\n",
    "plt.title('Seasonal Temperature Cycle Over One Year on Mars')\n",
    "plt.xlabel('Number of Terrestrial Days')\n",
    "plt.ylabel('Minimum Temperature')\n",
    "\n",
    "# use annotations to highlight key data points on the chart\n",
    "# various internet searches (Google) confirm count of Earth days for Martian year\n",
    "plt.annotate('Start of Year', (600, -62), color='green')\n",
    "plt.annotate('End of Year', (1250, -62), color='red')\n",
    "plt.annotate('Note: One Martian year is equivalent to 687 terrestrial days', (200, -91), color='blue')\n",
    "\n",
    "# display and save chart\n",
    "plt.tight_layout()\n",
    "plt.savefig('./Images/Mars_seasonal_cycle_temp_year.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average, the third month has the coldest minimum temperature on Mars, and the eighth month is the warmest. But it is always very cold there in human terms!\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Atmospheric pressure is, on average, lowest in the sixth month and highest in the ninth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distance from peak to peak is roughly 1425-750, or 675 days. A year on Mars appears to be about 675 days from the plot. Internet search confirms that a Mars year is equivalent to 687 earth days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: Save the Data\n",
    "\n",
    "Export the DataFrame to a CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the data to a CSV\n",
    "mars_temp_df.to_csv('./Output/mars_temperature_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "browser.quit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
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}