05_jupyter-labs-eda-dataviz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Assignment: Exploring and Preparing Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **70** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage. \n",
    "\n",
    "In this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Falcon 9 first stage will land successfully\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  ## Objectives\n",
    "Perform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n",
    "\n",
    "- Exploratory Data Analysis\n",
    "- Preparing Data  Feature Engineering \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following libraries the lab \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# andas is a software library written for the Python programming language for data manipulation and analysis.\n",
    "import pandas as pd\n",
    "#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
    "import numpy as np\n",
    "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
    "import matplotlib.pyplot as plt\n",
    "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>FlightNumber</th>\n",
       "      <th>Date</th>\n",
       "      <th>BoosterVersion</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-06-04</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-09-29</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>False Ocean</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "      <td>-120.610829</td>\n",
       "      <td>34.632093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013-12-03</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
       "\n",
       "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
       "0    None None        1     False   False  False        NaN    1.0   \n",
       "1    None None        1     False   False  False        NaN    1.0   \n",
       "2    None None        1     False   False  False        NaN    1.0   \n",
       "3  False Ocean        1     False   False  False        NaN    1.0   \n",
       "4    None None        1     False   False  False        NaN    1.0   \n",
       "\n",
       "   ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0            0  B0003  -80.577366  28.561857      0  \n",
       "1            0  B0005  -80.577366  28.561857      0  \n",
       "2            0  B0007  -80.577366  28.561857      0  \n",
       "3            0  B1003 -120.610829  34.632093      0  \n",
       "4            0  B1004  -80.577366  28.561857      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n",
    "\n",
    "# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n",
    "\n",
    "# df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n",
    "\n",
    "We can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's drill down to each site visualize its detailed launch records.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACgcAAAH3CAYAAACVGB36AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAACTgUlEQVR4nOzdd3jV5d0/8PdJWLKCCjJUwIEo7rpn3Xu1bqtWa4et1lr7PLXb0fq0v1qrXdraotZWW2sVd93WvVBQKm4RREFEJCA74fz+SEUxCQZOyIHD63VdueR87u/3vj8nakhO3ue+C8VisRgAAAAAAAAAAACgYlSVuwEAAAAAAAAAAACgdQkHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgSw1xWIx06ZNS7FYLHcrAAAAAAAAAAAAKxThQJaa6dOnp6amJtOnTy93KwAAAAAAAAAAACsU4UAAAAAAAAAAAACoMMKBAAAAAAAAAAAAUGGEAwEAAAAAAAAAAKDCCAcCAAAAAAAAAABAhREOBAAAAAAAAAAAgAojHAgAAAAAAAAAAAAVRjgQAAAAAAAAAAAAKoxwIAAAAAAAAAAAAFQY4UAAAAAAAAAAAACoMMKBAAAAAAAAAAAAUGGEAwEAAAAAAAAAAKDCCAcCAAAAAAAAAABAhREOBAAAAAAAAAAAgAojHAgAAAAAAAAAAAAVRjgQAAAAAAAAAAAAKoxwIAAAAAAAAAAAAFQY4UAAAAAAAAAAAACoMMKBAAAAAAAAAAAAUGGEAwEAAAAAAAAAAKDCCAcCAAAAAAAAAABAhREOBAAAAAAAAAAAgAojHAgAAAAAAAAAAAAVRjgQAAAAAAAAAAAAKoxwIAAAAAAAAAAAAFQY4UAAAAAAAAAAAACoMMKBAAAAAAAAAAAAUGGEAwEAAAAAAAAAAKDCCAcCAAAAAAAAAABAhREOBAAAAAAAAAAAgAojHAgAAAAAAAAAAAAVRjgQAAAAAAAAAAAAKoxwIAAAAAAAAAAAAFQY4UAAAAAAAAAAAACoMMKBAAAAAAAAAAAAUGGEAwEAAAAAAAAAAKDCCAcCAAAAAAAAAABAhREOBAAAAAAAAAAAgAojHAgAAAAAAAAAAAAVRjgQAAAAAAAAAAAAKoxwIAAAAAAAAAAAAFQY4UAAAAAAAAAAAACoMMKBAAAAAAAAAAAAUGGEAwEAAAAAAAAAAKDCCAcCAAAAAAAAAABAhREOBAAAAAAAAAAAgAojHAgAAAAAAAAAAAAVRjgQAAAAAAAAAAAAKoxwIAAAAAAAAAAAAFQY4UAAAAAAAAAAAACoMMKBAAAAAAAAAAAAUGGEAwEAAAAAAAAAAKDCCAcCAAAAAAAAAABAhWlX7gYAAAAAAABYcsNfn5LhY99L35pO2XvDPunUvrrcLQEAALAMEA4EAAAAAABYDs2rn5+vXfV07hr99oJan+6d8tcvbpN1V+taxs4AAABYFjhWGACAFdfMKcljv0/uPid55e6kWCx3RwAAANBiVz8+bqFgYJJMnDY737t+VJk6AgAAYFli50BYgRWLxcytn5+O7RwxAcAKaNzjyVWHJXOmNTx+6JfJoL2So65OqtuXtzcAAIDlzZgHkgcvSN4enfQclOz4zWTQnuXuquLdNmpCk/UnXp+SSdNnZ7Vundq4IwCA5cy82cmLtybvT0oGbJ/03bTcHQG0KuFAWAEVi8Vccv+rueyh1zP5/TlZv0+3/M9eg7PHkN7lbg2oVDOnJI/+Lnnt38lKKydbfD7Z4MByd8WK7qavfxgM/MDLdyYj/ppseWJ5egIAAFgevXZ/8pfPJMX6hsczJiVjH0mOuipZf//y9rYCK6RQ7hYAVkj184t5f05dundql0LB12JYpk16vuH72OkfecPFJkclh1ySVDmIE6gMvprBCujX97ySn9/+Yia/PydJ8sLE6fnKX5/KE2OmlLkzoCLNnpZctnfy4C+SN4cnr9yVXHNsw24CUC6TX04mv9j02Au3tm0vAACwuOrmJC/dmbz4r2TuzHJ3A8kD538YDFygmNz//8rSzorkgE36NlnfZq1V0qtbxzbuBtre7Hn1mVP38a8/0AJ1c5JisdWnvfSBV7PN/92TTc+5Mzv9/L5cO/yNVl8DVkhzpidvP9fwz9Z0w1cXDgYmybN/T569pnXXASgjOwfCCmZe/fxc/siYRvX6+cX86cHXsvVaq5ShK6CijfhLMvmlxvUHLki2+mLSqabte4LqDs2PtfPLEwAAlmGv3Z/88wvJzMkNjzvVJAdfnGxwQHn7YsX29nNN1yf+p237WAEdvXX/PPzKu7n9uYkLav1qOuWnn9241daYUDsrVz8+Lq9NnpEN+nTL0Vv3z6pd/exMeb0+eUbOufm53P/SO6kqFLL3hn1y1kFDHKXdFubPT566PBl1bVI3Oxm8X7Lt15KOXcvdWcu8fHdyzznJxGeTLr2Sbb6S7PitVtkhbOhDY/J/t72w4PH492blf//5bLp1apd9Nmo6zA18gmIxuefc5PE/JPNmJO27JNt8Odn9rKTUnTnfez15a0TTY88NSzY7urT5AZYRwoGwgpk2a16mzpzX5NjYd73THFgK3nii6fq8GQ2/PBiwfdv2A0my8oBkja2T8U3897nxYW3fDwBAa6ubkzzwi+SZvyfzZiaD90l2/UHS3S8l20KxWMxjr03JlBlzs9XAlbNa91YKKsyZnlxzXDKn9sPa7NqGsODpo5JuvVtnHVhcq67b9M9Xq67b9r2sYNpVV+X3x22REePey1Nj30ufmk7Zc0jvdGxX3Srzj35rWo669NFMm12XJLn12Qm58tGxue6r22fNVTq3yhrLvXmzkvHDk5V6JH1aL5RJ82bNrc8xf3wsb9XOTpLMLxZz66gJeW3yjNx22o6OcV3abvp6MvKvHz5+a0Ty8l3Jif9KqpfxXz2/8WTytyOT+Q1f0zLjneTenzR877zbD0qefuiDrzVZ/9ODY4QDYUk9dkny0C8/fDxvRvLQhUnnnsn2p5Y296J2Dy3OL21ugGWIY4VhBbNy5w7pV9P0C9Ibrt69jbsBVgjd+zU/1s0LIpTRZ36frLL2RwqFZJuvJht+pmwtAQC0mutOSh74eVI7rmGHuRF/TS7fN5k7o9ydVbyx787IHr+8P0f/8bGccvXT2f5n9+bCu5rYTX1JvPivhYOBH6ifkzx3feusAUtix9ObqX+zTdtYkW3ef+V8cae1c8Am/VotGJgkP/3X8wuCgR+YNH1OLry7lb6uLe9GXp1cMDj58wHJ73dM/vDp5L2x5e5q2TFzSvLk0OShi5rfYXQJ3DpqwoJg4Ec9P2FaHnplcqutQxMmv7xwMPAD459IXryt7ftZXI9d/GEw8KMevzSZ1/i/qcVRP7/Y5H+XSfLGezbngCX25J8Wr744Vlkr6bNJ02MbHlL6/ADLCOFAWMFUVRXyjT0GNap36VCdkz+9Thk6AireFicm1U0cNbPePg0/eEG5rLpOcurw5HPXNRzDdtqIZN+flburxTNjcjLqn8mLtyd1c8vdDQCwrJj0fPL8zY3r741p+N6Bpeobfx+ZV9/5MIRZN7+YX93zcu57cVLpky8q3LmcBD+feWNqvvjn4dnm/+7OYZc8ktv/M6HcLdEa1t8/OezyZLUhDY9XHdTwc5aj2JZrxWIxDzcTtHroZQGsTHgmufGUhh1cF9RGJv84rmwtLVNeuSe5cKPk1jOSu89KLtk+uf17rTL1uCnNB60WNUYrGD+8+bE3FzG2rJjS9M5+mVObzHy3pKmrqwrZsF/Tm3BsskaPkuaGFdr7zfwc1Vx9cR1yccMR4x+14WeSTX0fC1SOZXxvZ2BpOHKr/qlZqUMue3hM3nxvVjZbs0dO2XXdrNe7W7lbg6Vuxpy6vDzp/fTp3il9mtlFk1bWa73k6KuT27+bTH4pqWqfDDk4OeCXn3wvLG1V1cmgPcrdxZJ57JLkrh8l9f8NBXbtkxz9t2T1T5W3LwCg/CaNXrIxSjZm8oyMfGNqk2PDnn4zuw5erbQF1t0jKVQnxfrGY4P3LW3uNvCfN2tz5KWPZva8hiO63p42J8PHvpfzD9skh2+5Zpm7o2Qbfbbho1hMHOlZEQqFQmpWap/3Zs5rNFazUvsydLSMGXFV00cOTngmmfBs0reZnYhWBHVzk2FfaTj68aMe+12y3t7J2p8uafqNV69pdmyT1XuUNDefoMci/r6uWQ7+Lu+7SUOI9+O69km69i55+jP2XC9funJ45n/kpNKO7ary9d3WLXluWGEN2D55+Y7G9YE7tM78fTZOvvFMMvqm5P2JyYAdkzW3ap25AZYRwoGwgtpnoz7ZZ6M+5W4D2tQf7n81v7n3lbw/py5VhWTfjfvm/MM2SecO/jpc6tbdIzl1j6R2fNKxW9Kp+RfwgBZ4a0Ry+3cWrr0/MfnH8Q0vZFS13hFSAMByqOd6ixhrfJoArWfW3CZCe/81cxFjLdZjzWT3HyZ3n71wffvTkt4blj7/UnbJ/a8uCAZ+1K/vfTmHbbFGCq0QKJs5ty63PDsh46fMzCZr9Mhu66+WqipBtTYlGFhRjtyqf35//6uN6kdt3b8M3SxjZk9dsrEVwbhHkxnvND02+saSw4G7rb9aNu/fIyPGTV2ovteQ3tl4Da87LlUDdmg4gnPiswvXO6+abHx4eXpaHNufljx3QzJn2sL1nf8nqS799wS7b9A7V31x2/zpwdcy5t0Z2aBv93z10+tko0UEWlmO1M9LXro9mfpGssaWyZpbl7ujFcNuP0jGPpLMnf5hrUPXZNfvt94aHbrY8RqoaNIQAKwQbv/PhPz0Xy8seDy/mNz67IR06VCdnx+2aRk7W8HUrFHuDqAyPHtt0/XaNxpeKFlrp7btZxkzr35+fnvvK7nmyTdSO2tedhzUM9/ee3AG2SUZgBVFn42TdfdMXrlr4Xr3NZJNjixPTyuI9ft0yxorr5Tx781qNLbnkBJ3DfzAjt9M1t41ee76ZH59ssFBSf9tWmfupez5CdOarL8xZVamz6lL906l7UT22jvv55g/Pp6J02YvqH2qf4/85aRt0qWjl8JhSXxzz0GZNG12bhj5ZuYXk/bVhXxumwE5cfuB5W6t/NbZLXn2msb1TjXJ6lu2fT/LkkWFhAtVJU9fXVXIX07aJpfe/2r+9Z+JaVddlYM27ZeTdlyr5Ln5BIVC8rl/NhwX/eK/GnYz7r9dst8vkpV6lLu7T9ZzUPKFO5IHzk/GP5l0Xz3Z9uSGI0RbyXbrrJrt1lm11eZjGfHe2OTKg5P3xnxYG7xfcvifk3YdytfXiqDvJslX7k+euDSZ9HzSa/1km68kq65T7s4AlhuFYrFY/OTLYPFNmzYtNTU1qa2tTffu3cvdDrCCO/6yJ/LAS43frdqhXVVG/mhPuwcCy5dbzkiGD2167HP/TAbt2bb9LGPO/OezuWb4GwvVVu7cPnecvnNW6+5IeQBWEHNnJPeelzz792TuzIYjZ/c4O1l5QLk7q3gPvPROvvyX4QvtkLfzer0y9PNbpn116YGI5dkXrngy974wqVG9Z9eOeeJ7u5e8w19zP/uftvugnLHnInbUBD7RhNpZGfvuzKzTq2t6detY7naWDfXzkr8emoy5/8NaoSo58NfJp44rX1/Lgvq65KKNkukTGo+d+K+GIyJZ/s2elsyvSzqvUu5OVjwzpzTsfthjgB1728pfD01eubtxfa/zku1Pbft+AGAxSEIAsGyZ8W7y2n0NW4Kvs1urveNqyow5Tdbn1s3P+7PrlptwYLFYTP38Ytqt4L9QghXe4P2aDgd2qmk43mUFNrF2dv759PhG9fdmzsvVT4zL6Xv4pTAAK4gOXZJ9/q/hgza183q9ct//7JLrnhqfd2fMzXZrr5rdN+idakfb5os7rZV/vzgp8z/2dvUv7bRWycHAmXPr8uDLTR9heedzE4UDP1A3N5k0OunS0+7+LJa+NSulb81K5W5j2VLdPjn2uuQ/1zcERlbqkWz2uaTfZq27Tu2bydv/SVYemPQa3LpzLy3V7ZJD/5T87ZhkTu1/i4Vkp28JBlaSTjYGaXOzpia3fDN5/qaGYOYqayd7/zQZvE+5O6tss2uTV+5peuy5YcKBACzzlttkwcSJE/P1r389a6+9djp27Jg111wzBx54YO65Z+G/mEeMGJHDDz88vXv3TqdOnbLeeuvlS1/6Ul566aVGc+61116prq7OY4891mjshBNOSKFQaPTxyiuvLFjngAMOyGqrrZZOnTpl4MCBOfLIIzN58uRmn8Nrr72Wo48+Ov369UunTp2yxhpr5OCDD16ot0KhkBtuuKHZOYrFYi699NJss8026dq1a3r06JEtt9wyF110UWbOnPlJn8a8++67WWONNVIoFDJ16tSFxkaNGpVPf/rTWWmllbL66qvn3HPPjY0mWRy1s+bl7Y8c4wKf6PFLk19ukFx3UvK3IxveXTp+eKtMvf06PZusD1qt63Kxi9Scuvr8323PZ9Nz7sy63/9Xjrr00Tw7fmq52wLKZd3dk82OXbhW1S454MKkQ+fy9LSMeG3y+6n/+G+b/+uVSe+3cTcAwIqqb81KOXW3QTnrwA2z14Z9BAP/a/t1eubS47bMkL4NYYK+NZ3ywwOG5CufLv1IsKpCIdXN7JzTrtrnP0ky8m8Nr7tc+unkwo2Sq45o2HkIWHLV7ZNNj0wO/WOy3/mtGwycPz+59X+SizZOrj4i+d3WyVWHJ3Omt94aS9PAHZMznksO+X2y7/nJ159Kdv9hubuC5duwryTPXd8QDEySKa8l1xzbcNQqS8+ifj9enN/82LJk/vzkuRuS676U3HBK8up95e4IgDa0fGyT9DGvv/56dthhh/To0SM///nPs8kmm2TevHm54447csopp+SFF15Iktxyyy059NBDs/fee+eqq67KOuusk0mTJuXaa6/ND3/4w1xzzTUL5hw3blweffTRnHrqqRk6dGi23XbbRuvus88+ufzyyxeq9erVK5MmTcoee+yRAw88MHfccUd69OiRMWPG5Kabbmo2oDd37tzsueeeWX/99XP99denb9++GT9+fG677bbU1tY2eU9TjjvuuFx//fX5wQ9+kN/+9rfp1atXnnnmmVx00UUZOHBgDjnkkEXef9JJJ2WTTTbJm2++uVB92rRp2XPPPbPrrrvmySefzEsvvZQTTjghXbp0ybe+9a0W98eKacqMufn+sFG5c/TbqZ9fzJC+3XPOwRtmq4G2lm8Lo8bX5uonxmbStDnZaq1Vcsw2/dO9U/tyt/XJJo5K/vXtJB/5Iev9t5N/HJ9849mGd5uW4Es7rZ1//WdC3pgya0GtQ3VVfnDAkJLmbSvfuW5Uho348Gv1Y69Nyef++Hj+dfpOWWPlFTsIBCukQiE55HfJZkcnL9+ZdOiWbHJ4wy4CK7h1e3VNu6pC6poICA7u3a0MHQEA8FF7DOmdPYb0Tv38YquGJju1r85eG/bObaMmNho7aNN+rbbOcuuNJ5MbvpoPX3cpJi/fkQw7OfncP8rZGdCc4UOTJ/+4cO3lO5M7vp8c9Ovy9LS4OnZreO0CKN17Y5OX7mhcnz8vGX55st/P276nFcVKPZK1P5289u/GYxse0nrrzJ2ZvPSvZO6MhpO1WnOX52FfTkZd++HjkX9NdvofoW2AFUShuBxuBbfffvvl2WefzYsvvpguXbosNDZ16tT06NEjM2fOzIABA7Ljjjtm2LBhjeb44LoPnHPOOXnhhRdy1llnZeutt86ECRMWmvuEE07I1KlTm9zF74Ybbsjhhx+eWbNmpV27loVXRo4cmc033zyvv/56BgwY0Ox1hUIhw4YNazLk949//CNHHnlkbrjhhhx88MELjRWLxUybNi01NTXNzn3JJZfkmmuuyY9+9KPsvvvuee+99xZ8Ti655JJ897vfzdtvv52OHTsmSX72s5/lN7/5TcaPH59CE+/CnTNnTubM+fDYzmnTpmXNNddMbW1tune3tfiK5IjfP5onXl/4XcddOlTn7m99ulWPnnhl0vS0r67KgFW7fPLFK4hbn52Q0/4+YqEdk9ZdrWuuO3n71HRexgOCd/4weaSZF7WOvzFZe5eSl5g6c26uenxcRoybmn49OuXYbQdkveUgKDKxdna2/9k9jY5dSpKv7rJOztxn/bZvCmAZ9r1ho3L14+MWqvXs2iH/+sbO6dWtY5m6AmC5Neu95N3XkpUHNBzDSZspFot5e9qc1KzUPit1qC53OywHJk2fnc9f9mSenzBtQW3fjfrk10dvnvbVy+0hOq3jhlMafgncSCH55n8cMQzLokt3Sd4a0bjebqXku2807FoIrDjGPZ5ctlfTYxscmBzZ1N/ztJp3X02uPDipfePD2jq7JUf/PWnXCq83jn00+fsxyaz//n61UJ3s9oNkpzNKn/v1h5Mr9mtcL1Ql33gm6dG/9DUAWKYtdzsHTpkyJbfffnvOO++8RsHAJAvCbXfccUcmT56cb3/7203O89FgYLFYzOWXX57f/e53WX/99bPeeuvlH//4R0488cQW9dSnT5/U1dVl2LBhOeyww5oMzn1cr169UlVVlX/+8585/fTTU129+C9wXnXVVRk8eHCjYGDSECpcVDBw9OjROffcc/P444/ntddeazT+6KOP5tOf/vSCYGCS7L333vnud7+b119/PWuttVaje37605/mnHPOWeznQWV57q3aRsHAJJkxtz7/eHJ8vrHHoJLXeGrse/n2P5/Jq+/MSJJsukZNfnnkZlmnV9eS516e1c8v5rxbRzc6SvGVSe/nL4+9nlN3K/1zv1TVzWl+bF7rHE/do3OHnLLruq0yV1t6472ZTQYDk+T1yTPathmA5cCPD94oq/dYKdc8+UamzpybndfrlW/tNVgwEIDFUywmd5+dPP6HpG5WUtU+2fzYZL9flLyzOZ/slmffys9vfzHjpszMSu2rc/iWa+T7+2+Qju2EBGneat065bbTdsxDr0zOG1NmZZM1arLR6s2/RrpCmfFOMwPFZMZk4UBYFjV3fHDdrKR+nnAgrGh6b9hwesjcJr42rNn4RDxa2arrJKcOT164pSEguPoWyVo7t87c9XXJP0/8MBiYJMX65J5zkrU+nayxRWnzv9bMEcLF+cmYBxp+zoVyee6Ghs1jpryW9Nkk+fS3k4E7lrsrqDjL3dslX3nllRSLxay//qJ3SXr55ZeT5BOvS5K77747M2fOzN57750kOfbYYzN06NBG191yyy3p2rXrgo/DDz88SbLtttvme9/7Xo455pj07Nkz++67b84///y8/fbbza65+uqr59e//nV+9KMfZeWVV85uu+2WH//4x00G9Rb1HAcPHtzi6z8wZ86cHH300Tn//PPTv3/T7wSYOHFievfuvVDtg8cTJzY+miNJvvvd76a2tnbBxxtvvNHkdVS2ibXNh7gm1M5qdqylps6cmxMuf2JBMDBJnhlfm89f9kTq6ueXPP/ybMzkGXmrmc//w6+828bdLIHB+zZd79g9WWuntu1lCQ1/fUq+8pfh2eeiB3LGNSPz4sRmXsBbTOv06poO7Zr+K3vDfnZmBfi46qpCTtl13Tzw7V3z7Nl757fHfCpr9bTTMACL6Yk/Jg9f1PAL+KThuK6nLk/u/39lbWtFMPz1KTntbyMybsrMJMmsefW58tGx+fEto8vcGcuDQqGQnQb1yjHb9F86wcD59cmr9zX8Emtm4zfILrOae22lS69ktSFt2wvQMuvu2XR94E5Jh85t2wtQfh27Jrt8p3G95+DkU8e3fT8rovadko0PS3b8ZusFA5Nk7EPJ9AlNj/3nn6XPv9LKSza2OMY/lfz5oOTcnskvN0we/GUyf8X+vS0t8Mzfk2s/n7z5VMOpDWPub9ihc+yj5e4MKs5yFw784BTkT9qdb3FOSx46dGiOPPLIBUcCH3300Xn88cfz4osvLnTdrrvumpEjRy74+PWvPzz+8rzzzsvEiRPz+9//PkOGDMnvf//7rL/++hk1alSz655yyimZOHFi/vrXv2a77bbLtddemw033DB33XVXi/ouFost2qXw47773e9mgw02yLHHLvpdAB+f+5M+9x07dkz37t0X+mDFs/HqNWlf3fR/I5/qX/o3mDeOfCvTZ9c1qo9/b1buf6m5d0CvGHp0bp/qqqY/96t27dDG3SyBdXZt/ANsVbtk/18mHZb9QMe9L7ydIy99LHc893ZemDg91494M5+5+OE891ZtyXOv0qVDPr9d4yPoe3fvmKO3tt17ktTOnJc/PvBaTv/7iFx410uLDCoDAECLPHX54tVpNX9+dGyTu6dfO3x8ps+e1/YNwQfefi759WbJXw5p+CXWLzdIHruk3F21zBYnJL03+lixkOx5btJuOXjdCFZEO30rWWWdhWudapK9flKefoDy2/7U5Jh/JIP3a9gt8NPfSb5we9LJ72SXa/WNf+/54Vgr/Pyz8eFJ+yZC5d36NR9EXxyTX07+fGBDsGv+vGTa+IZdD+/+UelzU9nu/3nj2vy65KFftn0vUOGWu3DgoEGDUigU8vzzzy/yuvXWWy9J8sILLyzyuilTpuSGG27IxRdfnHbt2qVdu3ZZffXVU1dXl8suu2yha7t06ZJ11113wUffvn0XGl911VVz+OGH54ILLsjzzz+ffv365Re/+MUi1+/WrVsOOuignHfeeXnmmWey00475Sc/adkPduutt94nfh6acu+99+baa69d8Hx33333JEnPnj1z1llnJWk4KvnjOwROmjQpSRrtKAgftVr3TvnCjo2PnR7St3sO2qxfyfNPfr/5o2cXNbYi6Nm1Y/besOn/P49ZXgJkB/0mOfFfyQ7fSHb9fsMW7ZscXu6uWuQXd7zU6EjnmXPr89t7X2mV+b+33wb58cEbZkjf7ulX0ylHbLlG/nny9lm1qyMyJ9TOyn6/fjDn3fZ8bhj5Vn51z8vZ68L78583Sw9mVoT6umTk1cnfP5dce2Lywm3l7ojlxcwpyQO/SP52TPKvM5N3XvzkewCgksxsZgf25uq0mremNn3ywJy6+XlvhnAgZVIsJv84Ppk67sNa3ezk9u807JSyrOvYrSE8sNd5yaC9k82OTU66M9nsmHJ3BjSna6/kK/cn+/2i4cjH3X6QnPJE0m+zcncGlNN6eydH/y056Y5k1+8mnVcpd0eUauAOze/gt8EBpc/fdbXkqKuS7qt/WOu5XvK5f7TOm0Qe/0Myb0bj+pNDk9nTSp+fylQ3J5nyatNjkxY/AwMsWrtyN7C4Vlllley999753e9+l9NOOy1duiy8m9TUqVPTo0eP7LXXXunZs2d+/vOfZ9iwYY3m+eC6q666KmussUZuuOGGhcbvueee/PSnP8155523YEfBxdGhQ4ess846mTGjib8Im1EoFLL++uvnkUceadH1xxxzTI466qjceOONOfjggxcaKxaLmTZtWmpqGh+dcd1112XWrA9fZH3yySfzhS98IQ8++GDWWafhXWjbbbddvve972Xu3Lnp0KHhm4I777wz/fr1y8CBA1v8nFgxfXffDbJBn+7551Pj8/6cuuw6eLWcuOPAdGpfXfLc26y1an6TxmGrQiHZeq1VS55/effTz26SefXF3P382ykWG3YT/J+9Bmf7dXuWu7WWG7B9w8dyZF79/Iye0PQPOM+8MbVV1igUCjluu4E5bruBrTJfJfn1Pa/kzY/98nDa7Lr8323P5+ovbVumrpYRxWLDbhYv3PJh7bnrk+1PS/b6cfn6WtHMmtpw9FiX5ejvqelvJ0P3WPgXn8MvT465pmGnVwBYEQzcqekjnAY2czTniqZ2fHL3OclLtyftOiabHNnwJq9WOObwU/175Kmx7zWq9+7eMf16dCp5flgi459M3m3mDYDPXpOssUXb9rMkOnZr2HFo+1PL3QnQUh27JVt/qdxdALA0tV8pOfh3DW/ur//IRihbfzlZe5fWWWOd3ZLTRyVvPp1Ut2/doPnkl5quz5vZ8HNjpyGttxaVo13HpEf/hX8H8YFV1237fqDCLXfhwCS5+OKLs/3222frrbfOueeem0022SR1dXW56667cskll+T5559Ply5d8qc//SmHH354DjrooJx22mlZd911M3ny5PzjH//IuHHj8ve//z1Dhw7NYYcdlo02WvhIhQEDBuTMM8/Mrbfe2ih493G33HJL/v73v+eoo47Keuutl2KxmJtvvjm33XZbLr+86aNmRo4cmbPOOivHHXdchgwZkg4dOuT+++/PZZddljPPPHOha8eMGZORI0cuVFt33XVzxBFHZNiwYTn66KPzwx/+MHvuuWd69eqVUaNG5cILL8zXv/71HHLIIY3W/iAA+IHJkycnSTbYYIP06NEjSUPw8JxzzskJJ5yQ733ve3n55Zfzf//3f/nRj360REcZs+I5ZPPVc8jmq3/yhYtpx0E9s+eQ3rlr9NsL1T+/3cCs1XPZP3p2aatZqX3+ePyWeWvqrEx+f07W692tVUKZLFr76qqs1q1jJk1vvHvl6iuvVIaOViwPNHOk+COvvpt59fPTvnq52yi59bz274WDgR949LfJVl9MVm58XDWtqPbN5JZvJq/clRTnNxz1ccCFSe/l4MWQh3/V+Ify+jnJnT9IvvpweXoCgLa26/cavp+aOfnDWoduyR5nl6ujZcfcGcnl+yVTxzY8npOG7zHfeSE59rqSpz9px7Vzw8i38s7Hfsb61l6D025F/v6e8po3cxFjLX+DOAAANLL+/snpzyb/uS6Z836y3l5Jv81bd42q6mTNrVp3ziTpvWHDkcIf16FrQ/gLmrPjGcktpy9cK1QlO57e1NVACZbLcOBaa62Vp59+Ouedd16+9a1vZcKECenVq1e22GKLXHLJJQuuO/jgg/PII4/kpz/9aY455phMmzYta665Znbbbbf85Cc/yVNPPZVnnnkmf/zjHxut0a1bt+y1114ZOnToJ4YDhwwZks6dO+db3/pW3njjjXTs2DGDBg3Kn/70pxx33HFN3rPGGmtk4MCBOeecc/L666+nUCgsePzNb35zoWvPOOOMRvffd9992WWXXXL11Vfn0ksvzWWXXZaf/OQnadeuXQYNGpTjjz8+e++9d0s+nU2qqanJXXfdlVNOOSVbbrllVl555ZxxxhlN9gJt7ZLPfSrXP/1m7nhuYtpXV+Wgzfplv437fvKNy4iZc+ty48i38tLb07Pual1zyGarp0vH1v1y3K/HSunXQyitLZ24w1r5f7c3Psr+Czs0Pmab1tV9pfaNdg5Mkq4d26V6RQ+0j3mg6XpxfvL6Q8tFOPC9GXPzj+Fv5KW338/gPl1zxJZrpkfnVjjqYGmbPz/566HJOx/Z/v6Nx5IrD0pOG9Hwzv9lWXP/7bz9n2TG5KRLK+1IO3dG8v7bSfc1WucICwBoTauuk5z8UPLkn5K3n2t4vNUXk1V8j59R134YDPyoV+5OJjyT9N20pOn71HTKjafskEsfeC1PjX0vvbt3zPHbDczO6/UqaV4oyZrbNhz3NqvxrpZZb9+27wcAgMrSrU+y3Snl7mLxbfOVZMRVyZzahevbfi3p2LU8PbF82PLEhp0sH/5VMuW1htcSPn1m6+2YCSxQKBaLxXI3QWX64Fjj2tradO/evdztwDJhQu2sHPmHxzJuyofvNl9j5ZVyzVe2y+rCfMu1YrGY3977SoY+PCZTZ85L35pOOW33QTl66+XjXVH184u55sk3ctuoCUmS/TbumyO2XGO52JXjiofH5OybRzeqn7D9wJx90IZl6GjJvPv+nMycW581Vyn9GLYFHr04ueO7TY8dc23Duw+XYWMmz8gRf3h0oR1jenfvmH98ZbsMWHUZ3y32lXuSv3626bEDLmr4oXdZ9ueDmn63Z7tOyZmvNxx1UYr59cndZyVPXtawy0rnnsnO/5tse3Jp8wIAbeP27yaPXdz02Gf/lGxyeNv2A23luWHJdV9K5s/7sLbhZ5NDhyZVy/7PzwAsQ957PXn80mTS6KTX4IbjQ1dd5xNvA1gmvf1c8u+fJq8/nHTtnWx1UrL1l8rdFTR4b2zDm7x6b9gQRoQV0HK5cyDA8uoXd7y0UDAwSca/Nyvn3/5CLjqqlbcHp00VCoV8ffdBOXmXdVI7a15W7twh1VXLz651p/1tRG79bzAwSR56ZXIeeuWdXPy5LcrYVcscv93AvP7uzPz1sbGpm19MoZDsu1GfnLnP+uVurUUmTZ+d71w3Kve9OCnFYrJe76758cEbZZu1Vy198k2OSO47L5n7/sL1HgOSdXcvff6l7Bd3vNjoKLm3p83JL+58Kb85ehn/mjntzebHase3XR9LaosTmg4HbnJE6cHAJLn//yWP/ObDxzMnJ7efmXTtlWx0aOnzAwBL12obLGJs+fg+HJbIhp9p2M3i2X8ks2sbfq5aZ/dkRd+1HqASTXktuf/8htM3uvZKtvxCsvmxrTP3288ll+374S5br92XjPhrcsItrXeM6Fsjk6cuT6a/nfTftuG1npV6tM7cAB/Xe8PkyL+WuwtY2PuTkuu/lLz274bHXXsne/9fsvFhZW0LysHOgSw1dg6ExjY9587UzprXqN61Y7v855wlPwocSvH0uPfy2YsfaXLs+q9tn0/1X7mNO1oyk6bNzktvv5/+q3RO/1Vbcfe9/xr37sxMqJ2V9ft0T03n1ntn0SG/ezgj35i6UK1zh+rc861Pp29NK4SwXn8oufHU5L0xDY/7btqwm0uv9Uqfeynb6Kw78v6cukb1bp3aZdTZy/jXzLdHJ5ds1/TY0dckg/dp236WxIMXJA/+siFcWqhKhhySHPzbpEOJuzbOn5+cv04ya0rjsf7bJV+4vbT5AYClb+7M5JLtP/we8wOD9k4+94/y9AQA0Fpq30z+sHPDmxk/atcfJJ/+39Ln/9sxyYu3Nq6vs3ty3PWlzz/6xuTaE5Ni/Ye1nuslX7gj6bxK6fMDwPLg8v2TsQ8tXCtUJ1/+d9J3k7K0BOVi50CANrRS++omw4Gd2leXoZslNH54wzvk6+cm6++frLuHd8gv5556/b1mx4a/PmW5CQeu1r1TVuveqdXnnT57Xr55zTO5+/m3kySd2lflq59eN9/YY1DJcz/zxtRGwcAkmTm3Pv8cPj5f3730NTJwx+S0EQ3vSG7XMenZCnO2ka4d2zUZDuzeaTnY9r33kGSTI5Nnr1m4PmCHZNCyfZzzAjt9q+FIm3deTLr3a/hoDfVzmg4GJsm0t1pnDQBg6erQOTnxtuTenyQv3pa0WynZ9Mjk02eWuzMAgNI9cWnjYGCSPPyrZNuvJh27ljb/x4MKH3i9mfrimD8/ueP7CwcDk2TyS8kTf0x28f0aACuAyS83/fdtsT55+spk/1+0fU9QRsKBAG3oM59aPZf8+9XG9c1bKXCxtD38q+SuH334+KnLG45SOPh35euJkq3WvWOzY72XQthueXPWTc8tCAYmyex583Ph3S9l7V5dcuCmpf2/O3Ha7CUaW2yFQtJno9abr40cvuUa+c29rzSqH7bFGmXoZgkcckmy5tbJqH/+N1B9QLLNyUlVVbk7a7mO3ZI1tmzdOduv1LCD5YRnGo/137Z11wIAlp7u/ZJDLi53FwAArW/is03X505v2Dm5z8alzd9ltYbj6T+ua+/S5k0a+qt9o+mxMQ8IBwKwYpjRRMj/A029AQAq3HL0m0mA5d83dh+UPTZY+Af8XQf3yhl7Di5TR4th+sTknh83ro/4azLusbbvh1az94Z90qeJEGDv7h2z94Z9ytDRsmPGnLrc8syEJseuebKZF9kWw+Zr9ki7qqZ33txy4PKxY+PS9PXdBuWQzfot2Jy0qpB8dvPVc+pu65a3sZaqqk62+mLDMblfujfZ6YyGXXZIdj8rqfrYDpCdeiQ7/U9Z2gEAAABYYJW1m65Xd0i6r176/Ft9sZn6F0qfu1OPhiMTm9Jl1dLnB4DlQd9Nko41TY+ttXPb9gLLADsHArShTu2r86fPb5nnJ0zLy5Pez7q9umZIv+7lbqtlXvt3Mr/xkchJkpfvtNvTcqxT++r85aStc+Z1z+bpcVOTJJv375GfH7rJ8nXk9VIwc2595tbPb3Js6qy5Jc+/WvdO+dLOazfaUXTTNXtk/42Xkx1Fl6IO7apy0VGb51t7Dc4r7zR8zVxzFeG6irDu7skX70oe+/1/33G/SbLdKckqa5W7MwAAAGBFt/WXkxFXJXWzFq5vflzSeZXS59/mK8mMSQ2vi8ybkbRbKdnqpGT7b5Q+d5dVkyEHJc8Nazy2xYmlzw8Ay4MOXZI9zkpuPWPher9PJZseXZ6eoIwKxWKxWO4mqEzTpk1LTU1Namtr0737chJ+Apr3wq3J349pemyPs5Mdv9mm7bB0TKhteMGrb81KZe5k2bHPRQ/khYnTG9W/uss6OXOf9VtljVuefSvXPTU+M+bWZ7f1V8tx2w5Il47ewwEAAAAAZTH2keSus5LxTyQrrZxscUKy6/eT6vafeGuLzZmevDc26bFm0qmZ3Y2WxOza5IavNbymn2JD/7v9oPkdCwGgUo19JHn6L8msKcnauyafOq4hOAgrGOFAlhrhQKgwdXOSizZO3n974Xp1h+S0EUnNGuXpC5ayR16dnBMvfzJz6j7cQXCtnl3yz5O3y6pdO5axMwAAAABgqaqb2xAILBTK3cniq32z4fX81TZI2nszOADAiko4kKVGOBAq0JtPJf84Iakd1/B4pVWSg36dbHBgWduCpW3M5Bn52xPj8ubUWdl8zR45Yqs1071TK75LGAAAAAAAAABamXAgS41wIFSo+fXJuMeS+jlJ/+2T9p3K3REAAAAAAAAAAB/TrtwNALCcqapOBu5Q7i4AAAAAAAAAAFiEqnI3AAAAAAAAAAAAALQu4UAAAAAAAAAAAACoMMKBAAAAAAAAAAAAUGGEAwEAAAAAAAAAAKDCCAcCAAAAAAAAAABAhREOBAAAAAAAAAAAgAojHAgAAAAAAAAAAAAVRjgQAAAAAAAAAAAAKoxwIAAAAAAAAAAAAFQY4UAAAAAAAAAAAACoMMKBAAAAAAAAAAAAUGGEAwEAAAAAAAAAAKDCCAcCAAAAAAAAAABAhREOBAAAAAAAAAAAgAojHAgAAAAAAAAAAAAVRjgQAAAAAAAAAAAAKoxwIAAAAAAAAAAAAFQY4UAAAAAAAAAAAACoMMKBAAAAAAAAAAAAUGGEAwEAAAAAAAAAAKDCCAcCAAAAAAAAAABAhREOBAAAAAAAAAAAgAojHAgAAAAAAAAAAAAVRjgQAAAAAAAAAAAAKoxwIAAAAAAAAAAAAFQY4UAAAAAAAAAAAACoMMKBAAAAAAAAAAAAUGGEAwEAAAAAAAAAAKDCCAcCAAAAAAAAAABAhREOBAAAAAAAAAAAgAojHAgAAAAAAAAAAAAVRjgQAAAAAAAAAAAAKoxwIAAAAAAAAAAAAFQY4UAAAAAAAAAAAACoMMKBAAAAAAAAAAAAUGGEAwEAAAAAAAAAAKDCCAcCAAAAAAAAAABAhREOBAAAAAAAAAAAgAojHAgAAAAAAAAAAAAVRjgQAAAAAAAAAAAAKoxwIAAAAAAAAAAAAFQY4UAAAAAAAAAAAACoMMKBAAAAAAAAAAAAUGGEAwEAAAAAAAAAAKDCCAcCAAAAAAAAAABAhWnXmpPNnj07Tz31VCZOnJiZM2fm4IMPTvfu3VtzCQAAAAAAAAAAAOATtEo48I033sgPfvCDXHPNNZk3b96C+qhRozJkyJAFj4cOHZo//OEPqampyZ133plCodAaywMAAAAAAAAAAAAfUfKxwk888UQ233zz/PWvf83cuXNTLBZTLBabvPaggw7Ks88+m3vvvTd33nlnqUsDAAAAAAAAAAAATSgpHFhbW5uDDz44U6ZMSZ8+fXLxxRdn1KhRzV7fq1ev7LvvvkmSW2+9tZSlAQAAAAAAAAAAgGaUdKzwb37zm7z99tvp2bNnHn300fTv3/8T79lzzz1z44035oknnihlaQAAAAAAAAAAAKAZJe0cePPNN6dQKOSMM85oUTAwSTbccMMkyauvvlrK0gAAAAAAAAAAAEAzSgoHvvzyy0mSnXfeucX39OjRI0kybdq0UpYGAAAAAAAAAAAAmlFSOHDWrFlJki5durT4nvfffz9J0qlTp1KWBgAAAAAAAAAAAJpRUjiwV69eSZI33nijxfc89dRTSZK+ffuWsjQAAAAAAAAAAADQjJLCgVtvvXWS5F//+leLrq+vr8+ll16aQqGQHXfcsZSlAQAAAAAAAAAAgGaUFA48+uijUywWc9lll2XEiBGLvHb+/Pk5+eSTM3r06CTJscceW8rSAAAAAAAAAAAAQDNKCgceeuih2X777TNnzpzsvvvu+d3vfpdJkyYtGC8UCnn77bfzl7/8JVtuuWUuu+yyFAqF7LPPPtlll11K7R0AAAAAAAAAAABoQqFYLBZLmWDy5MnZeeed88ILL6RQKCw01qFDh8ydO3fB42KxmI033jj3339/evToUcqyLAemTZuWmpqa1NbWpnv37uVuBwAAAAAAAAAAYIVR0s6BSdKzZ88MHz48p5xySjp27JhisbjgY86cOQv+3K5du3z5y1/OI488IhgIAAAAAAAAAAAAS1HJOwd+1Lvvvps77rgjw4cPz6RJk1JfX59VV101m2++efbdd9/069evtZZiOWDnQAAAAAAAAAAAgPJo1XAgfJRwIAAAAAAAAAAAQHm0K+XmcePGJUlWX331VFdXt+ie+fPnZ/z48UmS/v37l7I8AAAAAAAAAAAA0ISSwoEDBw5MVVVVnn322QwZMqRF94wZMyaDBg1KVVVV6urqSlkeAAAAAAAAAAAAaEJVqRMs6anETjMGAAAAAAAAAACApaPkcODi+iAUWFXV5ksDAAAAAAAAAADACqHNE3oTJkxIknTr1q2tlwYAAAAAAAAAAIAVQrvWmKRQKHziNfPmzcurr76a8847L0kyePDg1lgaAAAAAAAAAAAA+JjFCgdWV1c3qhWLxWy00UaLtWihUMhhhx22WPcAAAAAAAAAAAAALbNY4cBisbhY9eYcccQROf300xfrHgAAAAAAAAAAAKBlFisceNZZZy30+JxzzkmhUMjJJ5+c1VZbrdn7CoVCOnXqlL59+2b77bfPOuuss2TdAgAAAAAAAAAAAJ+oUFzcbf8+oqqqKoVCIaNGjcqQIUNasy8qwLRp01JTU5Pa2tp079693O0AAAAAAAAAAACsMBZr58CPu/zyy5Mka6yxRqs0AwAAAAAAAAAAAJSupJ0DYVHsHAgAAAAAAAAAAFAeVeVuAAAAAAAAAAAAAGhdLTpW+IEHHljw55133rnJ+pL46FwAAAAAAAAAAABA62jRscJVVVUpFAopFAqpq6trVF+ihT82F5XHscIAAAAAAAAAAADl0aKdA5OkuQxhC7KFAAAAAAAAAAAAQBtqUTjwvvvuW6w6AAAAAAAAAAAAUD4tOlYYloRjhQEAAAAAAAAAAMqjqtwNAAAAAAAAAAAAAK1LOBAAAAAAAAAAAAAqTLulNfG4ceMybNiwvPLKK6mqqspaa62VAw88MOuss87SWhIAAAAAAAAAAABIUigWi8WWXFhXV5fLLrssSbLxxhtnu+22a/bac889N+edd17q6uoWqldVVeW0007LBRdcUELLLC+mTZuWmpqa1NbWpnv37uVuBwAAAAAAAAAAYIXR4p0Dhw8fnpNPPjmFQiF33nlns9edf/75Ofvss5scq6+vz0UXXZSqqqqcf/75i90sAAAAAAAAAAAA8MmqWnrh/fffnyTp379/dt999yaveeutt3LWWWcteLzDDjtk6NCh+de//pVzzz03NTU1KRaLueiii/Lyyy+X2DoAAAAAAAAAAADQlBbvHPjggw+mUCjk4IMPbvaayy67LLNnz15w3XXXXZdCoZAk2XvvvXPggQdm2223zdy5c3PllVfmxz/+cenPAAAAAAAAAAAAAFhIi3cOHDduXJJku+22a/aam2++ecGff/7zny8IBn5g0003zfHHH59isZiHHnpocXsFAAAAAAAAAAAAWqDF4cBJkyYlSQYOHNjk+MyZMzNixIgUCoVsvPHGWXfddZu8bp999kmSvPjii4vZKgAAAAAAAAAAANASLQ4Hvvfee0mSlVZaqcnx4cOHp66uLkmyww47NDvPgAEDkiRTp05t6dIAAAAAAAAAAADAYmhxOLBz585JknfeeafJ8ccff3zBnzfbbLNm5/ngqOH6+vqWLg0AAAAAAAAAAAAshhaHAz84TvjRRx9tcvzf//73gj9vt912zc7zQbiwpqampUsDAAAAAAAAAAAAi6HF4cAdd9wxxWIxv//97zN9+vSFxsaOHZu77rorhUIh/fr1y0YbbdTsPCNHjkySrLXWWkvWMQAAAAAAAAAAALBILQ4HnnTSSSkUCpkwYUJ22WWX3H777Xn55Zdz0003ZZ999kldXV2S5POf//wi57nnnntSKBSy6aabltY5AAAAAAAAAAAA0KRCsVgstvTiU089NRdffHEKhUKjsWKxmN69e2f06NFZeeWVm7x/woQJ6d+/f+bPn58///nPOfbYY5e8c5Z506ZNS01NTWpra9O9e/dytwMAAAAAAAAAALDCaLc4F//6179ecLTwxzOFffr0yY033thsMDBJLrrootTX16ddu3bZd999l6xjAAAAAAAAAAAAYJEWa+fAD4wePTo33XRTxo4dmw4dOmTzzTfP4Ycfni5duizyvnPOOSfvv/9++vbtmzPOOGOJm2b5YOdAAAAAAAAAAACA8liicCC0hHAgAAAAAAAAAABAeVSVuwEAAAAAAAAAAACgdQkHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAK0641J5s+fXrGjBmT6dOnp76+/hOv33nnnVtzeQAAAAAAAAAAACCtFA784x//mIsvvjjPPvtsi+8pFAqpq6trjeUBAAAAYPk0ZUwyfULSe8OkU025uwEAAAAAKkhJ4cD6+voceuihufnmm5MkxWKxVZoCAAAAgIo2a2py/ZeTl+9oeNy+S7Lz/yQ7nVHWtgAAAACAylFSOPD3v/99brrppiRJ7969c+KJJ2aLLbbIKquskqqqqlZpEAAAAAAqzq1nfBgMTJJ5M5J7zkl6DU7W3798fQEAAAAAFaOkcOCVV16ZJBkyZEgefPDBrLzyyq3SFAAAAABUrFlTk9E3Nj321J+FA2FF9vpDyfM3J4XqZKNDkzW2KHdHAAAAwHKspHDg888/n0KhkB/+8IeCgQAAAADQEnOmJ/Prmh6bNaVtewGWHbd/L3nsdx8+fux3yW4/bDhyHAAAAGAJtMrZv4MHD26NaQAAAACg8tWskaw6qOmxtXdt216AZcOEZxcOBn7gvvOSqW+0fT8AAABARSgpHDhoUMOLmFOmeEczAAAAALRIoZDs+7OkuuPC9Z6Dk22/Wp6egPJ6+Y6m68X5ySt3t20vAAAAQMUo6Vjho446KiNGjMgtt9yS3XbbrbV6AgBgOfGfN2tzwZ0v5snX30uvbh1z3LYDcuIOA1MoFMrdGgDAsm3dPZKTH0qeuiKZ9may5jbJ5scmnbqXuzOgHDp0W8RY17brAwAAAKgohWKxWFzSm+fMmZNtttkmL774Yu68887stNNOrdkby7lp06alpqYmtbW16d7dC9sAUGnGTJ6RA379YGbMrV+o/vXd1s239hpcpq4AgMU28T/JyKuT2bXJOrsmQw5Jqkt6PykAi+v9SclFGyd1sxeud6pJzng+6dClPH0BAAAAy7WSjhXu2LFj7rzzzmyxxRbZc8898+1vfzsjR47M7NmzP/lmAACWa5c/PKZRMLCh/npmzKkrQ0cAwGIb+bfkDzslj/0uGfnX5LqTkr8dmdT7uxygTXVdLTn8imSllT9S650cdbVgIAAAALDEWrRzYHV19SdOVCwWF+v4uEKhkLo6LzRXMjsHAkBlO+rSR/PYa1OaHLvrmztnUO9FHIsFAJTf3JnJL9dv2DHw4z77p2STw9u+J4AV3bzZyesPJlXVycCdkur25e4IAAAAWI61aOfAYrH4iR8tve7j9ywNBx54YPbYY48mxx599NEUCoU8/fTTC2pf/vKXU11dnb///e+Nrj/77LNTKBQafdx9991NjtfU1GSnnXbK/fffv8geZ8yYkTPPPDNrr712OnXqlF69emWXXXbJLbfcsuCaXXbZJaeffvoi57nvvvuy3377ZdVVV03nzp0zZMiQfOtb38qbb765yPuShn9f++67bwqFQm644YaFxpp6zoVCocnPEQCwYhq0WtPhvy4dqtOvx0pt3A0AsNjefKrpYGCSvHJ32/YCQIP2nZJBeybr7CYYCAAAAJSsXUsuOuuss5Z2H63qpJNOymc/+9mMHTs2AwYMWGjssssuy2abbZZPfepTSZKZM2fmmmuuyf/+7/9m6NChOeqooxrNt+GGGy4IA35glVVWaXJ8ypQp+cUvfpEDDjgg48ePT01NTZM9nnzyyXniiSfy29/+NkOGDMm7776bRx55JO+++26Ln+cf/vCHfO1rX8vnP//5XHfddRk4cGDGjRuXK6+8MhdccEF++ctfLvL+iy66aJG7PV5++eXZZ599Fqr16NGjxf0BAJXtxB0G5vqnxzc6WviEHQamS8cWfZsJAJRTp0Xs8r+oMQAAAAAAlgsVGQ484IADstpqq+WKK65YqPcPgoD/93//t6B27bXXZsiQIfnud7+bvn375vXXX8/AgQMXmq9du3bp06dPs+t9dLxPnz4555xzcvnll+ell17KVltt1eQ9N998c371q19lv/32S5IMHDgwW2yxRYuf4/jx43PaaafltNNOy4UXXrigPnDgwOy8886ZOnXqIu9/5pln8stf/jJPPvlk+vbt2+Q1PXr0WOTz/rg5c+Zkzpw5Cx5PmzatxfcCAMuftXt1zd+/vF0uuOvFPDFmSlbr1jHHbjsgJ+24VrlbAwBaou+mSe+Nk7dHfWygkGx2TFlaAgAAAACg9bToWOHlTbt27XL88cfniiuuWOj44muvvTZz587N5z73uQW1oUOH5thjj01NTU3222+/XH755SWtPWfOnFxxxRXp0aNHBg8e3Ox1ffr0yW233Zbp06cv0TofPJdvf/vbTY4vaoe/mTNn5uijj85vf/vbxQr/fZKf/vSnqampWfCx5pprttrcAMCyaeM1anLFiVtn9Ln75N//u2u+uNPai9yZGABYxhx5ZdJ7ow8fd+yeHPirpN/m5esJAAAAAIBWUZHhwCT5whe+kNdffz3//ve/F9Quu+yyfPazn83KK6+cJHn55Zfz2GOP5cgjj0ySHHvssbn88sszf/78heYaNWpUunbtuuBj6623bnZ8pZVWyi9+8Yv87W9/S/fuzR/Bc+mll+aRRx7Jqquumq222irf/OY38/DDD7f4+b388svp3r17s7v+Lco3v/nNbL/99jn44IMXed3RRx+90PPu2rVrXnvttWav/+53v5va2toFH2+88cZi9wYAAAC0oVXWTr76cPKle5PjhiVnPJ9s8flydwUAAAAAQCto0bHCzamvr18QaNt0001TU1OzyOunTp2aZ599Nkmy8847l7L0J1p//fWz/fbb57LLLsuuu+6aV199NQ8++GDuvPPOBdcMHTo0e++9d3r27Jkk2W+//XLSSSfl7rvvzl577bXgusGDB+emm25a8Lhjx44LrfXR8enTp+eaa67J4Ycfnvvuuy9bbrllk/3tvPPOee211/LYY4/l4Ycfzr333ptf/epXOeecc/LDH/7wE59fsVhcol15brrpptx7770ZMWLEJ1574YUXZo899liotqjdADt27NjocwMAAAAsB1bfotwdAAAAAAAsFwqFQoYNG5ZDDjmk3K18opJ2Drz77ruzyy675NBDD02HDh0+8foOHTrk0EMPza677pr77ruvlKVb5KSTTsp1112XadOm5fLLL8+AAQOy++67J2kINl555ZW59dZb065du7Rr1y6dO3fOlClTMnTo0EZ9r7vuugs+Ph6Q++j45ptvnp/97GdZffXVc9FFFy2yv/bt22ennXbKd77zndx5550599xz8+Mf/zhz5879xOe23nrrpba2NhMmTFisz8m9996bV199NT169FjwvJPk0EMPzS677LLQtX369Fnoea+77rpp3779Yq0HAAAAAAAAAACwvJg4cWK+/vWvZ+21107Hjh2z5ppr5sADD8w999xT7tYWW0nhwH/84x9JksMPPzwrrbTSJ17fuXPnHHnkkSkWi7nmmmtKWbpFjjjiiFRXV+fqq6/On//855x44okLdtu77bbbMn369IwYMSIjR45c8HHttdfmhhtuyLvvvlvS2tXV1Zk1a9Zi3TNkyJDU1dVl9uzZn3jtYYcdlg4dOuTnP/95k+NTp05tsv6d73wnzz777ELPOWnYJfDyyy9frH4BAAAAAAAAAAAqxeuvv54tttgi9957b37+859n1KhRuf3227PrrrvmlFNOKXd7i62kY4WfeuqpFAqF7Lbbbi2+Z9ddd83FF1+cJ554opSlW6Rr16458sgj873vfS+1tbU54YQTFowNHTo0+++/fzbddNOF7tlwww1z+umn569//Wu+8Y1vtGidurq6TJw4McmHxwqPHj06Z555ZrP37LLLLjn66KOz5ZZbZtVVV83o0aPzve99L7vuumu6d+++4Lp33nlnQYDvA3369Mmaa66ZCy+8MKeeemqmTZuW448/PgMHDsz48eNz5ZVXpmvXrrngggsardunT5/06dOnUb1///5Za621FqpNnTp1wfP6QLdu3dKlS5dP/JwAAAAAAAAAAAAsT772ta+lUCjkiSeeWCgjteGGG+YLX/hCk/eceeaZGTZsWMaPH58+ffrkc5/7XH70ox8tOKH1mWeeyemnn57hw4enUChk0KBB+cMf/pAtt9wyY8eOzamnnpqHHnooc+fOzcCBA3P++ednv/32a5XnU1I4cPz48UnSKFS2KAMHDkySvPnmm6Us3WInnXRShg4dmr322iv9+/dPkrz99tu59dZbc/XVVze6vlAo5LOf/WyGDh3a4nDgc889l759+yZp2B1xnXXWySWXXJLjjz++2Xv23nvv/PnPf873vve9zJw5M/369csBBxyQH/3oRwtdd/XVVzfq86yzzsrZZ5+dr33ta1lvvfXyi1/8Ip/5zGcya9asDBw4MAcccEDOOOOMFvW+KCeeeGKj2k9/+tN85zvfKXluAABYrk1/O3nxtqRQSAbvn3TtVe6OgHIb80Dy5lNJj/7J+gck7TqWuyMAqDzjHk+mv5WssXVSs3q5u2m56W8n//5p8tIdSftOyaZHJzucnrTrUO7OAAAA+IgpU6bk9ttvz3nnndfk5mk9evRo8r5u3brliiuuSL9+/TJq1Kh86UtfSrdu3fLtb387SfK5z30um2++eS655JJUV1dn5MiRC4KDp5xySubOnZsHHnggXbp0yejRo9O1a9dWe06FYrFYXNKbO3funDlz5uTxxx/Plltu2aJ7hg8fnq233jodO3Zc7GN3Wb5MmzYtNTU1qa2tXWg3RAAAWK6NuCq55fSkfm7D4+qOycG/TTY5oqxtAWUyb3byt6OS1+77sNZjQPL5m5KVB5atLQCoKNMmJH87MpnwTMPjQnWy7VeTvc8rb18tMXdm8oedkndfWbi+4WeSw68oS0vLnPnzk7EPJzMnJ/23T7r1LndHAADACuqJJ57INttsk+uvvz6f+cxnmr2uUChk2LBhOeSQQ5ocP//883PNNddk+PDhSZLu3bvnN7/5TT7/+c83unaTTTbJoYcemrPOOqtVnsPHVZVyc69eDbtjvPDCCy2+54NrV1111VKWBgAAaHvT3kpuPu3DYGCS1M9JbjwleX9S+foCyuexixcOBibJ1LHJrf9Tnn4AoBLddOqHwcAkKdYnj/42GfXP8vXUUqOubRwMTJLnhiXvvNj2/Sxr3n01uXib5M8HJNeekFy4YXL/+eXuasUy5/1kypikfl65OwEAgLL7YI+9QqGwWPf985//zI477pg+ffqka9eu+eEPf5hx48YtGD/jjDPyxS9+MXvssUd+9rOf5dVXX10wdtppp+UnP/lJdthhh5x11ll59tlnW+fJ/FdJ4cCtttoqxWIxV155ZYvvueKKK1IoFPKpT32qlKUBAADa3vM3J/PrGtfr5yYv3NL2/QDlN/qGpuuv3pPMmd6mrQBARXr/neSVe5oee+bvbdvLknj7uUWM/aft+lhWXXdSMvmlDx/Pn5fc95Pm/53TeurnJbd9Ozl/3eTXmzUEM4dfVu6uAACgrAYNGpRCoZDnn3++xfc89thjOeqoo7LvvvvmlltuyYgRI/L9738/c+d+uNHE2Wefneeeey77779/7r333gwZMiTDhg1Lknzxi1/Ma6+9luOOOy6jRo3Klltumd/85jet9pxKCgcedthhSZJ77rknF1xwwSdef8EFF+Tee+9Nkhx++OGlLA2w1Ix/b2a+N2xUdr/g3znyD4/mlmffKndLAMCyYn5982PF+W3XB7AMWdQ7SBfv3aUAQBPqZiUpNj02b2abtrJEVl13EWOD2q6PZdE7LyZvjWh67Nlr2raXFdHdZydP/OG//48lef/t5JZvJi/+q6xtAQBAOa2yyirZe++987vf/S4zZsxoND516tRGtYcffjgDBgzI97///Wy55ZYZNGhQxo4d2+i69dZbL9/85jdz55135rOf/Wwuv/zyBWNrrrlmTj755Fx//fX51re+lT/+8Y+t9pxKCgceeeSR2XTTTVMsFvPtb387hx56aB588MHMm/fh1uN1dXV58MEH89nPfjbf/va3UygUstFGG+XYY48tuXmA1vb2tNn5zMWP5OrHx+XVd2bk8TFTcurVI/LHB14rd2sAwLJggwOSQhM/RlW1Swbv3/b9QFuZPz956KLkok2S8/omVx2eTGjdow2WWxt+pun6oL2Sjl3bthegxf41akIO+u1D2eisO3LoJY/kvhcmlbsloDk9+ierDWl6bL192raXJbHpkUm3fo3r6+yW9N2k7ftZliwq3Lk8BD+XZ3Vzk6f+3PTYE5e2bS8AALCMufjii1NfX5+tt9461113XV5++eU8//zz+fWvf53tttuu0fXrrrtuxo0bl7///e959dVX8+tf/3rBroBJMmvWrJx66qn597//nbFjx+bhhx/Ok08+mQ022CBJcvrpp+eOO+7ImDFj8vTTT+fee+9dMNYaSgoHFgqFDBs2LH379k2xWMwNN9yQXXbZJd26dUu/fv2y+uqrp2vXrtlll11y4403plgspm/fvrnxxhsX+2xmgLbw50dezzvT5zSq/+belzNr7iJ2CgIAVgw9+if7/nzhgGChOtnvF0n3vuXrC5a2u36Y3H1WMnVswy9qX74zuWL/ZMqYcndWftt+tSEI+FGrrJ3sd355+gE+0c3PvJWvXvV0nh1fm/fn1OWpse/lpD8/mftfeqfcrQHN2f+XSYduC9fW3CbZ6ovl6WdxdKpJTrglWf+AhjcVdeyebPWl5Ii/lLuz8uuzSdJ9jabH1tu3bXtZ0cx9P5k7vemxaRPathcAAFjGrLXWWnn66aez66675lvf+lY22mij7LnnnrnnnntyySWXNLr+4IMPzje/+c2ceuqp2WyzzfLII4/khz/84YLx6urqvPvuuzn++OOz3nrr5Ygjjsi+++6bc845J0lSX1+fU045JRtssEH22WefDB48OBdffHGrPZ9CsVhsZj/+lps0aVK+8pWv5Kabbkpz0xUKhXzmM5/JxRdfnNVWW63UJVkOTJs2LTU1NamtrU337t3L3Q60yLF/ejwPvTK5ybHbT98p6/fx3zIAkOS915Pnb24ICW5wYENoECrVrKnJBet/eNzYR217SrLP/7V5S8ukcY8lbz7V8PVgvX2T6nbl7ghoxj4XPZAXJjYORGy91ir5x1cav/sbWEZMn5iMvLrhn/23STY4KKluX+6uFk+xmNg4YWEv35Vcc2xSN/vD2rp7Jkf/bfn797u8+e1WyeSXGte3OCE58Fdt3g4AALB0tMor1auttlqGDRuWF198MbfddltGjBiRyZMbwjU9e/bMpz71qey///4ZNGhQaywHsNSsucpKTdY7VFeld7dObdwNALDMWnlgsv3Xy90FtI2pY5sOBibJOy+0bS/Lsv7bNnwAy7yXJ73fdP3tZnZQApYN3fokO51R7i5KIxjY2KA9k1OHJ8/8PZk5OVnr0w3HRVeVdPAVLbHH2ck1xyXFj5yY07lnssPp5eoIAABYClr1beyDBw/O4MGDW3NKgDZ1/HYDc91Tb2Zu/fyF6odusXpW7tKhTF0BAEAZrTwwad+54Tjhj1ttgzZvB6BU6/bqmhebCAIOWq1bE1cDsNT1WDP59P+Wu4sVz/r7J1+4I3n898nUccnqWyTbfc3O+AAAUGG89QrgIzbo2z1/+vyWGdK34fjgrh3b5aQd18rZB21Y5s4AAKBMOtUkW3+pcb1jM3WAZdzXdl2nUa1QSL7aRB0AKtqaWyWHDU2+eFey788EAwEAoAIVisVisdxNUJmmTZuWmpqa1NbWpnv37uVuBxbbtNnzslL76rSvlqMGAGAFVywmj/4uGT40mTE5Gbhjsuv3kz4blbszgCVyy7Nv5ZJ/v5pX33k/g/t0z2m7rZvdN+hd7rYAAAAAoFW1Wjhw/vz5GT16dF577bVMnz499fX1n3jP8ccf3xpLs4wSDgQAAAAAAAAAACiPksOBs2bNyk9+8pP88Y9/zLvvvtvyhQuF1NXVlbI0yzjhQAAAAAAAAAAAgPJoV8rNs2bNym677ZYnnngiTicGAAAAAAAAAACAZUNJ4cALL7wwjz/+eJJko402yqmnnpotttgiq6yySqqqqlqlQQAAAAAAAAAAAGDxlBQOvOaaa5Ik22+/fe6999506NChVZoCAAAAAAAAAAAAllxJ2/u9+uqrKRQK+fa3vy0YCAAAAAAAAAAAAMuIksKBHwQC+/fv3yrNAAAAAAAAAAAAwLKgfn4xj776bm4c+WYeffXd1M8vtsm6F198cdZaa6106tQpW2yxRR588MElmqekY4XXX3/9PP7445k4cWIp0wAAAAAAAAAAAMAy4/b/TMg5N4/OhNrZC2p9azrlrAOHZJ+N+i61da+55pqcfvrpufjii7PDDjvkD3/4Q/bdd9+MHj16sTfxK2nnwBNOOCHFYjHXXnttKdMAAAAAAAAAAADAMuH2/0zIV//69ELBwCSZWDs7X/3r07n9PxOW2tq//OUvc9JJJ+WLX/xiNthgg1x00UVZc801c8kllyz2XCWFA7/0pS9l1113zZVXXpm//e1vpUwFAAAAAAAAAAAAZVU/v5hzbh6dpg4Q/qB2zs2jl8oRw3Pnzs1TTz2Vvfbaa6H6XnvtlUceeWSx5yvpWOE33ngjv/nNb/LlL385xx57bIYNG5Zjjjkm66+/fjp37vyJ9y/uNocAAAAAAAAAAACwtDwxZkqjHQM/qphkQu3sPDFmSrZbZ9VWXXvy5Mmpr69P7969F6r37t07EydOXOz5SgoHDhw4MIVCIUlSLBZz3XXX5brrrmvRvYVCIXV1daUsDwAAAAAAAAAAAK1m0vTmg4FLct2S+CCT94Fisdio1hIlhQM/WLipPwMAAAAAAAAAAMDyZLVunVr1usXRs2fPVFdXN9olcNKkSY12E2yJksKBl19+eSm3AwAAAAAAAAAAwDJj67VWSd+aTplYOztNbZVXSNKnplO2XmuVVl+7Q4cO2WKLLXLXXXflM5/5zIL6XXfdlYMPPnix5yspHPj5z3++lNsBAAAAAAAAAABgmVFdVchZBw7JV//6dArJQgHBDw72PevAIamuWvxjflvijDPOyHHHHZctt9wy2223XS699NKMGzcuJ5988mLPVfKxwgAAAAAAAAAAAFAp9tmoby459lM55+bRmVA7e0G9T02nnHXgkOyzUd+ltvaRRx6Zd999N+eee24mTJiQjTbaKLfddlsGDBiw2HMVisViU7sfQsmmTZuWmpqa1NbWpnv37uVuBwAAAAAAAAAAoMXq5xfzxJgpmTR9dlbr1nCU8NLaMXBpsHMgAAAAAAAAAAAAfEx1VSHbrbNqudtYYiWFA7/whS8s8b2FQiFDhw4tZXkAAAAAAAAAAACgCSUdK1xVVZVCYfG3SSwWiykUCqmvr1/SpVkOOFYYAAAAAAAAAACgPEraObB///6fGA6cMWNGJk+enKRht8CePXumc+fOpSwLAAAAAAAAAAAALEJJ4cDXX3+9RddNmTIlV199dc4666z06NEjN954Y9Zff/1SlgYAAAAAAAAAAACaUdKxwovr+eefz3bbbZeVV145Tz/9dFZeeeW2WpoycKwwAAAAAAAAAABAeVS15WIbbLBBTjvttIwdOzYXXHBBWy4NAAAAAAAAAAAAK4w2DQcmyZ577pkkuf7669t6aQAAAAAAAAAAAFghtHk4sGvXrkmScePGtfXSAAAAAAAAAAAAsEJo83DgiBEjkiTt27dv66UBAAAAAAAAAABghdCm4cAxY8bk7LPPTqFQyGabbdaWSwMAAAAAAAAAAEDLza9PxjyYjPpnwz/n1y/1JR944IEceOCB6devXwqFQm644YYlnqtdKY1ceeWVn3jN/Pnz895772X48OG58cYbM3PmzBQKhZx88smlLA0AAAAAAAAAAABLx+ibktvPTKa99WGte79kn/+XDDloqS07Y8aMbLrppjnxxBNz6KGHljRXoVgsFpf05qqqqhQKhRZf/8FS3/jGN3LhhRcu6bIsJ6ZNm5aamprU1tame/fu5W4HAAAAAAAAAADgk42+KfnH8Uk+Hq37b1buiCuXakBwwWqFQoYNG5ZDDjlkie4v+VjhYrHYoo+ampocdNBBuf322wUDAQAAAAAAAAAAWPbMr2/YMbBRMDAf1m7/TpscMVyqko4VHjNmzCdeU1VVlW7duqVHjx6lLAUAAAAAAAAAAABL19hHFj5KuJFiMu3NhuvW2qnN2loSJYUDBwwY0Fp9AAAAAAAAAAAAQHm9/3brXldGJR8rDAAAAAAAAAAAABWha+/Wva6MhAMBAAAAAAAAAAAgSQZsn3Tvl6TQzAWFpPvqDdct44QDAQAAAAAAAAAAIEmqqpN9/t9/H3w8IPjfx/v8rOG6peD999/PyJEjM3LkyCTJmDFjMnLkyIwbN26x5yoUi8ViqQ3NnTs3V111VW644YY888wzmTx5cmbNmrXohQuF1NXVlbo0y7Bp06alpqYmtbW16d69e7nbAQAAAAAAAAAAaJnRNyW3n5lMe+vDWvfVG4KBQw5aasv++9//zq677tqo/vnPfz5XXHHFYs1VcjjwpZdeyiGHHJIXX3wxizNVoVBIfX19KUuzjBMOBAAAAAAAAAAAllvz65OxjyTvv5107d1wlPBS2jFwaWhXys0zZszIvvvumzFjxqSqqioHH3xwevXqlT/+8Y8pFAr5wQ9+kPfeey/Dhw/PY489lkKhkO222y577rlna/UPAAAAAAAAAAAAra+qOllrp3J3scRK2jnwggsuyP/+7/+muro6d9xxR3bbbbc899xz2XjjjRvtDDhy5Mgce+yxeeGFF3LRRRfl1FNPbZUnwLLLzoEAAAAAAAAAAADlUVXKzTfffHMKhUKOOOKI7Lbbbou8drPNNst9992X1VZbLWeccUaeeuqpUpYGAAAAAAAAAAAAmlFSOHD06NFJks985jNNjn98U8JevXrljDPOSF1dXX7729+WsjQAAAAAAAAAAADQjJLCgVOnTk2SDBgwYEGtY8eOC/78/vvvN7pnhx12SJLcf//9pSwNAAAAAAAAAAAANKOkcGDnzp2TJIVCYUGtR48eC/48bty4Rvd8cO3EiRNLWRoAAAAAAAAAAABoRknhwLXWWitJ8tZbby2o9ezZM6usskqS5OGHH250z1NPPZUk6dChQylLAwAAAAAAAAAAAM0oKRy45ZZbJkmGDx++UH333XdPsVjM+eefn3fffXdB/fXXX8//+3//L4VCIZtttlkpSwMAAAAAAAAAAADNKCkcuOeee6ZYLOamm25aqH7aaaclSV577bWst956Ofzww7P//vtn0003zfjx45MkX/7yl0tZGgAAAAAAAAAAAGhGoVgsFpf05pkzZ2a//fZLfX19rrjiiqyzzjoLxs4+++yce+65DYsUCkmSD5b6whe+kD/96U+l9M1yYNq0aampqUltbW26d+9e7nYAAAAAAAAAAABWGCWFAz/JPffckz/96U957rnnUldXl0GDBuX444/PoYceurSWZBkiHAgAAAAAAAAAAFAeSzUcyIpNOBAAAAAAAAAAAKA82rX1gq+99loeeuihJMnxxx/f1ssDAAAAAAAAAABAxWvzcOCDDz6YE088MVVVVcKBAAAAAAAAAAAAsBRUlWthpxkDAAAAAAAAAADA0lG2cCAAAAAAAAAAAACwdAgHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDtWnrhueee2yoLjhw5slXmAQAAAAAAAAAAAJpWKBaLxZZcWFVVlUKh0CqLFovFFAqF1NfXt8p8LJumTZuWmpqa1NbWpnv37uVuBwAAAAAAAAAAYIXR4p0Dk4ZQHwAAAAAAAAAAALBsa3E48L777luafQAAAAAAAAAAAACtpMXHCsPicqwwAAAAAAAAAABAeVSVuwEAAAAAAAAAAACgdQkHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIVpV+4GAAAAYFkxe159nhgzJR3bVWXLgaukuqpQ7pYAAAAAAACWiHAgAAAAJPnXqAn57rBRmTpzXpJkjZVXysWf+1Q2WaNHeRsDAAAAAABYAo4VBgAAYIU3/r2ZOe3vIxYEAxtqs/LFPw/P3Lr5ZewMAAAAAABgydg5EAAAYAXyzvQ5+etjY/P8hGlZq2eXHLvtgKy5Sudyt1V2N458K/Pqi43qk6bPyYMvv5PdN+hdhq4AAAAAAACWnHAgAADACmLcuzNz6O8fyTvT5yyoXfX4uFz9pW1W+KNzp8+uW6IxAAAAAACAZZVjhQEAAFYQv7rn5YWCgUny/py6/OxfL5SpoyXw1sjk9u8mN52WvHBrMr91jvzdZXCvJusdqquyw7o9W2UNAAAAAACAtmTnQAAAgBXEw69MbrL+6Gvvpn5+MdVVhTbuaDE98cfktv9N8t/jf5/+c7LhZ5PDLksKpfW+7dqr5rAt1sg/nxq/UP1/9x6cXt06ljQ3AAAAALCCGvtoMurapH5usv7+yXr7lPxaJsDiEA4EAABYQazcpUMmTpvdqF6zUvtlPxg4c0py5w+zIBj4geeuTzb7XDJoj5KXOP+wTbL/Jn1z1+i307FdVQ7ZbPVsumaPkucFAAAAAFZAD5yf3PuTDx+P+EvDa5mHXNx6a8ybnYx/IunQNVn9U603L1AxhAMBAABWEEdvvWZ+dONzjepHbdW/DN0sprEPJ3Wzmh57+c5WCQcWCoXsOni17Dp4tZLnAgAAAABWYNPeSu77aeP6yKuSzY9LBmxX+hqj/pnc9j/JrPcaHvfaIDniyqTXeqXPDVQM4UAAAIAVxHHbDsib783KFY+8njl189OuqpDPbL56zthzOXixqGO3JRtb1hSLyYzJDT2371TubpYpM+fW5f4X38m8+cV8er1eqVmpfblbAgAAoI29N2NuLv73K7n3hUnp3KFdPvup1XP8dgOX/RMPAD7utX8nxfqmx165u/Rw4DsvJdd/eeE13nk++fvRyanDW+/o4rdGJuOfTGrWTAbtmVRVt868QJsRDgQAAFhBFAqFfHe/DfLVXdbJq++8nzVX7pzVui8nAbWBOyU9BiRTxy5cL1Qnmx5dnp4W1wu3Jnedlbz7ctK+S/Kp45I9z03adSx3Z2V3/0vv5OtXP51ps+uSJCu1r85PP7txDtl89TJ3BgAAQFuZPa8+R176aF56+/0FtVFv1uaFCdPz/w7bpIydASyBTjVLNtZSz/yt6fDhu68k4x5NBmxf2vz1dcl1JyWjb/iwtuqg5LhhSY81S5sbaFNV5W4AAACAttWjc4dsMWCV5ScYmDS8I/Woq5NV1vmw1qkm+cwfkp7rlq+vlho/PLnmuIZgYJLMm5E8/vvkX2eWt69lwPtz6nLqVR8GA5Nk1rz6/M+1z+Stqc0cJQ0AAC0xvz4ZfllyxQHJ5fslj/8hqZ9X7q6AZtw08q2FgoEfuPapNzLu3Zll6AigBOvukXTt3bhe3THZ+PDS559d2/zYrKmlz//knxYOBiYNr23e8s3S5wbalJ0DAQAAliVzZyYjr2o4dqLzKsmnPp+ssWW5u1o29Nko+fpTyRuPJ3NnJP23Szp0LndXLfPEpU2/k3fk1cme57TOu4VnTml4x/A7LyarDUk2O7p15l3K7nn+7UyfU9eoXje/mFuefStf3nmdJu4CAIAWuO6LyXPXf/h47MPJK/ckn/tH+XoCmjXqzaaDLvOLyXNv1ab/qsvJawAAScNpIcdck1x7QvLe6w21Lr2Sg36bdO9b+vzr7p4MH9q43r5z6bsGJsl/rmu6/srdDeHDlXqUvgbQJoQDAQAAlhVzZyZ/PiB586kPa0//JTn4t8nmx5avr2VJoZD037bcXSy+2vFN1+vnJO+/U3qI791XG3ZCeX/ih7VHf5uceFvSo39pcy9lc+bNX6IxAABYpLdGLBwM/MDLdySvP5QM3LHtewIWqf8qzYf/1lzEGMAyq9/myddHJG8OT+rmJGtuk7Tr0Dpzr7dvMnj/5MVbP1IsJHue2zrBvabe6NwwkBS9ZgfLkxX2WOETTjghhxxyyEK1f/7zn+nUqVN+/vOfJ0lmzJiRM8888/+3d+dxVlf1/8BfF0YGhk12QVAwJQxwA5JQFJfILXMJTXFtURMzccktl5+mppZlEirFF82trL6KG4lKkiggGLgvuJCmIi4ICCop8/uDr2MjAyIMc+HyfD4e9/GYOed8zuc9l3lw7sy87jnZZJNN0rBhw7Rp0yYDBgzIHXfcUe26559/PkceeWQ6duyY8vLydOnSJQcddFCmTp36he7/WdOmTcugQYPSrl27NGzYMF27ds0PfvCDPPfcc8u85txzz023bt3SuHHjtGjRIrvuumsmT55cbcwLL7yQfffdN23atEmzZs1ywAEH5I033qhxvqOOOir169fPH//4x+XWCgAA1ILpN1QPBiZJKpOxZyX/+aAoJX1hz45J/vCt5Ireya1DkreeL3ZFa4YNt6m5vXGb2gnv3Xtu9WBgksx9JRl3warPvZoN6NYm69Uv1Nj39e41HL0CAAAr4uXJy+57ZTl9QNHs36tjWjZeOjTztU1apceGa/7O+KXi6dfn5anX5qWysrLYpUBpqFcv6fTVpEv/2gsGfjLvgdclg65Jtjw46fOD5Af3JV/9Qe3Mv/neNbd37r/kxBtgrbHOhgM/6/e//30GDx6cYcOG5Sc/+UmS5Jhjjsmtt96aYcOG5Zlnnsnf/va37L///nn77berrps6dWp69eqV5557LldffXWeeuqp3HLLLenWrVtOOumkla7njjvuSN++ffPhhx/mhhtuyNNPP53rrrsuzZs3z1lnnbXM67p27Zphw4bl8ccfz4QJE9K5c+cMHDgwb775ZpIlgceBAwemUChk3LhxefDBB7No0aJ885vfzOLF1dPdCxcuzJ/+9KeccsopGTmyhu1oAQCA2vXS+Jrb338nmfV43dayMv75h+Sm7yw5EvntGcn065Pf75K882KxKyu+vscmTWoIug04vXZ+KTjjnmW0j131uVeztk0b5uy9vpLCZ/KBx+20abpt0Kw4RQEAsPZr1mHZfU2X0wcUTcvGDXLD97dNvy+1SpKUl9XLt3t1zFWH9ipyZeuGx/79bnb55f3Z/fIHssdvHsguvxyfaS/Pqb0bLFqQTLoyuWFQ8r9HJS89UHtzw7qqXv2k+77Jvlcme/4i2bAW/7/c9piky47V25q2T/a8rPbuAdSJQuU6Gvk/4ogj8u677+bWW2/NJZdckrPPPjs33HBD9t9//6ox66+/fi6//PIcfvjhNc5RWVmZnj17pmHDhnn44YdTr171rOW7776b9ddf/3Pv/1kLFy7MxhtvnO233z633HLLUv3Lm/ez5s2bl+bNm+fee+/NLrvskrFjx2b33XfPnDlz0qzZkj+yzJkzJy1btsw999yTXXfdteraa6+9NldddVX+9re/pX379nnqqafSuXPnFbrvf9977ty5VfcCAACW4/YTkkdG1dz3o38mrb5Up+V8IYs/Tn7VI5n/2tJ9vb+b7PWruq9pTfPuK8lDv0n+NTFpusGSd/F2/UbtzH3ppsmCN5duX3+j5IS1IFia5PnZ7+WOx17LRx9XZrceG9TurhBzZibjfpY8f29S3jTZanDS/6Sk/nq1dw8AANYsH/8nuWKb5N2Xq7c32SA5flrSwBGlsCZ7f9HHqV+vkAZl9rqpC+8v+jjbXzwuby9YVK19/Yr1MuHUndOkvGzVbvCf95NRuy858v2/7X5psu1RqzY3sPpUViYvjFty2k3zjslX9vEaCtZC6/yrqdNOOy3nn39+7rjjjmrBwCTZYIMNctddd2X+/Pk1Xjt9+vQ8+eSTOemkk5YKBiZZ4QDfZ91999156623qnYwXNl5Fy1alBEjRqR58+bZcsstkyQffvhhCoVCysvLq8Y1bNgw9erVy4QJE6pdP3LkyBxyyCFp3rx59thjj4watYw/Uv6fDz/8MPPmzav2AAAAvoBtDk0KNfyY1rn/mh0MTJL5s2oOBibJq/+s21rWVOt3Sva4NPnhhOSQv9ReMDBJtjq45vYtl9G+Btq0bZOcsGvXnPyNL9duMPD9Ocn/7J48/uclH7/7cnL/Rcno42rvHgClZuE7ydO3Jy+OTz5z2gjAWqP+esmhty75eeoTnfomh93qj9qwFmjUoL5gYB26+8lZSwUDk+Tdhf/JXY+/vuo3mH7j0sHAJBl3fvLhe6s+P7B6FArJprskO/5kye8fvYaCtdI6/YpqzJgxufjiizN69OhqO+Z9YsSIEXnooYfSqlWr9OnTJ0OHDs2DDz5Y1T9jxowkSbdu3Wq1rlWd94477kiTJk3SsGHD/OpXv8o999yT1q1bJ0n69u2bxo0b59RTT83ChQuzYMGCnHLKKVm8eHFef/3TF3YzZszIpEmTcuCBByZJDjnkkIwaNWqpo4f/20UXXZTmzZtXPTp16rRS9QMAwDprw17Jt36bVLT6tK1z/2T/kcWraUVVtEwaNK25r8XGdVvLumjAGcnme/9XQyHp8e0lu+Ot66bdUHNw9fGbkzn/qvt6ANZ0k69OLts8+dMhyR/2Tq7YOnnjqWJXBbByWn0pOeKO5OQZyUnPJt+7O2m7ebGrAljj1BQM/MQ7y+lbYTMn1Nz+4bzk9emrPj8AsEzrdDhwiy22SOfOnXP22WfXuDvgDjvskBdffDH33Xdf9t9//zz55JPp379/zj///CRLjhVOkkKhUKt1repJzzvttFOmT5+ehx56KLvttlsOOOCAzJ49O0nSpk2b/PnPf87tt9+eJk2aVB37u80226R+/fpVc4wcOTLf+MY3qkKFe+yxRxYsWJB77713mfc9/fTTM3fu3KrHK6+8skpfBwAArJO2Ojg58enk+/ctOUr4iDuSpu2KXdXnW69R0ue7S7cX6id9j637etY16zVMDrwuOW5q8p2bkh89knx7ZFLWoNiVFd/sp2tur1ycvPls3dYCsKb79yPJmJ8kH33waducmcnNhy05TgpgbdWkbdJ0g2JXAbDG6velVsvs237T1qt+gyZtl93XeDl9AMAqW6fDgRtuuGHGjx+f119/PbvttluNAcH11lsv/fv3z2mnnZaxY8fmvPPOy/nnn59Fixala9euSZKnn17GHxpW0ifzPvPMMyt1fePGjbPpppumb9++GTlyZMrKyjJy5Kc7jQwcODAvvPBCZs+enbfeeivXXXddXn311XTp0iVJ8vHHH+cPf/hD7rzzzpSVlaWsrCwVFRV55513qs3zWeXl5WnWrFm1BwAAsBLKypOOvdf8o4Q/a5dzkv4nJw3XX/J56y8nB/wh2ahvUctap7TeLOm2x9r3vbM6tem6jI7CkucLgE899sea29+ekbz6SN3WAgBAndm8fbMcvO1GS7Uf0LtjemzYfNVvsM1hSb2ypds33n45P7cDALWhhhV43bLRRhtl/Pjx2WmnnTJw4MDcfffdyw21feUrX8lHH32UDz74IFtttVW+8pWv5Je//GUOPPDA1KtXPWv57rvvZv311//CNQ0cODCtW7fOJZdckltuuWWp/i86b2VlZT788MOl2j/ZFXDcuHGZPXt29t57yRFUd911V+bPn59p06ZV203wmWeeyeDBg/P222+nVatlv3sEAABYR9Wrn+xyVjLg9GTRe0mj9YtdESRbH5pM/G3y3hvV23vsl7TsUpyaqD2zn0n+/rPkpX8kFa2T3kcmfYck9dbp98PCylu0cDl979VdHQAA1LkL9umRHTZrnTseez2VSfbo0T579KylXVfbdU/2H5n87fRk/mtL2r60c7Lv1bUzPwCwTOt8ODBJOnbsmPvvv79aQLB58+YZMGBADjrooPTu3TutWrXKU089lTPOOCM77bRTVYBw1KhR2XXXXbPDDjvkjDPOSLdu3fLee+/l9ttvz9ixYzN+/Phl3nfu3LmZPn16tbaWLVtmo402yu9///sMGjQoe++9d44//vhsuummeeutt3LzzTfn5Zdfzh//uPS7eBcsWJALLrgge++9d9q3b5+33347w4cPz7///e8MGjSoatyoUaOy+eabp02bNpk4cWJ+/OMfZ+jQofnyl7+cZMmRwnvuuWe23HLLavN37949J5xwQq6//vr8+Mc/XtmnGwAAKHX1ywQDWXNUtEyOHJPc9/+SGfck5U2TrQ9Jdjy12JWxqua+mozaLXl/zpLPP5ibjP1pMu/1ZLcLi1sbrK26DkymX790e6MWSadt674eqEsL30mevi356MOk6zeSFp2LXREA1KlCoZDderTPbj3ar54bdN8n6bZX8ubTS06dWL/T6rkPAFBNobKysrLYRRTDEUcckXfffTe33nprVdvrr79eFfwbO3Zsrrzyytx+++159tlns3DhwnTo0CF77bVXzj777Go75z333HO54IILcu+99+att95K+/bt069fv5xyyinZeuutl3n/a6+9dqn2ww8/PNdcc02SZOrUqbnooovywAMPZN68eenUqVN23nnnnHLKKdl0002XuvaDDz7IwQcfnMmTJ+ett95Kq1at0qdPn/z0pz9Nnz59qsaddtppueaaa/LOO++kc+fOOeaYYzJ06NAUCoW88cYb6dixY2688cZqgcJPHH/88bn//vvz2GOPfe5zPG/evDRv3jxz5851xDAAAACsrEULkydvSebMTDpslXTdbclOneu6e/9fMuGypdvrlycnPbMkGAp8MYsXJ385Inlq9Kdt9cqS/UYkPfYvWlmw2j07JvnLd5P//N/umYV6yc5nJf1PLG5dAMCK+/g/ydO3J7MeS1p0WfL6tbxJsasCgKJbZ8OBrH7CgQAAALCK3n4hufabybxXP23bsHdy2K1LdkFcl934neS5MTX3fX9c0rFX3dYDpaKycslOq8/fmzRsnmz5naTVl4pdFaw+ixYkv9w8+XDu0n3HTEg26Fn3NQEAX8z77y752XnWf21w07RDcsQdXssCsM6rV+wCAAAAAFiGv51WPRiYJK9OTSb8uijlrFHadK25vX4DR0HCqigUlhwvvMclyc5n+mMqpe+FcTUHA5MlO/cCAGu+B35RPRiYJPNfS+4+ozj1AMAaRDgQAAAAYE30nw+W7NxVk6dvr9ta1kR9vp+UN1+6fZvDk8at6r4eANZOyztcycFLALB2eOaumttnjE0+WlS3tQDAGqas2AUAAAAAUINCISnUTyoXL91Xf726r2dlvTAueeq2pFAv6bFf0nn72pl3/Y2SI+9M7js/eWl8UtE66XVE0v/E2pkfgHXDl3ZOypslH85buq/7PnVeDgCwEuo3qLm93npJvfp1WwsArGGEAwEAAADWRGXlyebfTJ7836X7en677utZGWNOTSZf9ennU0cm/U9OdjmrdubfoGcy+ObamQuAdVN5k2SfK5O/fi/56IP/aywkO52ZtN+yqKUBACtoi0HJfect3d59X+FAANZ5hcpK++KzesybNy/NmzfP3Llz06xZs2KXAwAAAGuf92Yn1++fzHrs07ZueyXfHpWULWNnhDXF648lV/evoaOQHP/PpOUmdV4SACzTgreSp0YvCQh23S1p9aViVwQArKiPFiV//W7y9O2ftm3YOxn856SiZfHqAoA1gJ0DAQAAANZUTdomR/9jybG577yUdNgq6bB1sataMc/fu4yOyuT5+5KvCgcCsAZp3Drp871iVwEArIyyBsmB1yezHk9ef3TJm9E27lfsqgBgjSAcCAAAALAmKxSSTQYseaxNGi7nFIFyJwwAAABQyzboueQBAFSpV+wCAAAAAChB3fdL1mu8dHvD9ZNue9Z5OQAAAAAA6xrhQAAAAABqX0XL5MDrksZtP21r2iE56KakvEnx6gIAAAAAWEcUKisrK4tdBKVp3rx5ad68eebOnZtmzRwXBAAAAOukj/+TvDwxKdRLOvVN6pcVuyIAAAAAgHWC38YCAAAAsPrUXy/pskOxqwAAAAAAWOc4VhgAAAAAAAAAAABKjHAgAAAAAAAAAAAAlBjhQAAAAAAAAAAAACgxwoEAAAAAAAAAAABQYoQDAQAAAAAAAAAAoMQIBwIAAAAAAAAAAECJEQ4EAAAAAAAAAACAEiMcCAAAAAAAAAAAACVGOBAAAAAAAAAAAABKjHAgAAAAAAAAAAAAlBjhQAAAAAAAAAAAACgxwoEAAAAAAAAAAABQYoQDAQAAAAAAAAAAoMQIBwIAAAAAAAAAAECJEQ4EAAAAAAAAAACAEiMcCAAAAAAAAAAAACVGOBAAAAAAAAAAAABKjHAgAAAAAAAAAAAAlBjhQAAAAAAAAAAAACgxwoEAAAAAAAAAAABQYoQDAQAAAAAAAAAAoMQIBwIAAAAAAAAAAECJEQ4EAAAAAAAAAACAEiMcCAAAAAAAAAAAACVGOBAAAAAAAAAAAABKjHAgAAAAAAAAAAAAlBjhQAAAAAAAAAAAACgxwoEAAAAAAAAAAABQYoQDAQAAAAAAAAAAoMQIBwIAAAAAAAAAAECJEQ4EAAAAAAAAAACAEiMcCAAAAAAAAAAAACVGOBAAAAAAAAAAAABKjHAgAAAAAAAAAAAAlBjhQAAAAAAAAAAAACgxwoEAAAAAAAAAAABQYoQDAQAAAAAAAAAAoMQIBwIAAAAAAAAAAECJEQ4EAAAAAAAAAACAEiMcCAAAAAAAAAAAACVGOBAAAAAAAAAAAABKjHAgAAAAAAAAAAAAlBjhQAAAAAAAAAAAACgxwoEAAAAAAAAAAABQYoQDAQAAAAAAAAAAoMQIBwIAAAAAAAAAAECJEQ4EAAAAAAAAAACAEiMcCAAAAAAAAAAAACVGOBAAAAAAAAAAAABKjHAgAAAAAAAAAAAAlBjhQAAAAAAAAAAAACgxwoEAAAAAAAAAAABQYsqKXQClq7KyMkkyb968IlcCAAAAAAAAAACsqZo2bZpCoVDsMkqOcCCrzfz585MknTp1KnIlAAAAAAAAAADAmmru3Llp1qxZscsoOYXKT7Z3g1q2ePHivPbaa5K9q2jevHnp1KlTXnnlFf8JAsBqYK0FgNXPegsAq5/1FgBWL2stwOolX7R62DmQ1aZevXrp2LFjscsoGc2aNfMiEwBWI2stAKx+1lsAWP2stwCwellrAVib1Ct2AQAAAAAAAAAAAEDtEg4EAAAAAAAAAACAEiMcCGu48vLynHPOOSkvLy92KQBQkqy1ALD6WW8BYPWz3gLA6mWtBWBtVKisrKwsdhEAAAAAAAAAAABA7bFzIAAAAAAAAAAAAJQY4UAAAAAAAAAAAAAoMcKBAAAAAAAAAAAAUGKEAwEAAAAAAAAAAKDECAfCGmz48OHp0qVLGjZsmF69euWBBx4odkkAsFa66KKL0qdPnzRt2jRt27bNPvvsk2effbbamMrKypx77rnp0KFDGjVqlAEDBuTJJ58sUsUAsPa76KKLUigUcsIJJ1S1WW8BYNW9+uqrOeSQQ9KqVatUVFRkq622yiOPPFLVb70FgJX30Ucf5ac//Wm6dOmSRo0aZZNNNsl5552XxYsXV42x1gKwNhEOhDXUn/70p5xwwgk588wzM23atPTv3z+77757Xn755WKXBgBrnfHjx2fIkCGZNGlS7rnnnnz00UcZOHBgFixYUDXmkksuyWWXXZZhw4ZlypQp2WCDDfL1r3898+fPL2LlALB2mjJlSkaMGJEtttiiWrv1FgBWzZw5c7LddttlvfXWy5gxY/LUU0/ll7/8ZdZff/2qMdZbAFh5F198ca666qoMGzYsTz/9dC655JJceumlueKKK6rGWGsBWJsUKisrK4tdBLC0bbfdNttss02uvPLKqrbNN988++yzTy666KIiVgYAa78333wzbdu2zfjx47PDDjuksrIyHTp0yAknnJBTTz01SfLhhx+mXbt2ufjii3P00UcXuWIAWHu899572WabbTJ8+PD87Gc/y1ZbbZVf//rX1lsAqAWnnXZaHnzwwWWeMmO9BYBVs9dee6Vdu3YZOXJkVdv++++fioqKXHfdddZaANY6dg6ENdCiRYvyyCOPZODAgdXaBw4cmIceeqhIVQFA6Zg7d26SpGXLlkmSl156KbNmzaq29paXl2fHHXe09gLAFzRkyJDsueee2XXXXau1W28BYNXddttt6d27dwYNGpS2bdtm6623zu9+97uqfustAKya7bffPvfdd1+ee+65JMmjjz6aCRMmZI899khirQVg7VNW7AKApb311lv5+OOP065du2rt7dq1y6xZs4pUFQCUhsrKypx44onZfvvt06NHjySpWl9rWnv/9a9/1XmNALC2+uMf/5h//vOfmTJlylJ91lsAWHUvvvhirrzyypx44ok544wz8vDDD+f4449PeXl5DjvsMOstAKyiU089NXPnzk23bt1Sv379fPzxx7ngggty0EEHJfGzLQBrH+FAWIMVCoVqn1dWVi7VBgB8Mccdd1wee+yxTJgwYak+ay8ArLxXXnklP/7xjzN27Ng0bNhwmeOstwCw8hYvXpzevXvnwgsvTJJsvfXWefLJJ3PllVfmsMMOqxpnvQWAlfOnP/0p119/fW688cZ0794906dPzwknnJAOHTrk8MMPrxpnrQVgbeFYYVgDtW7dOvXr119ql8DZs2cv9S4UAGDF/ehHP8ptt92Wv//97+nYsWNV+wYbbJAk1l4AWAWPPPJIZs+enV69eqWsrCxlZWUZP358fvOb36SsrKxqTbXeAsDKa9++fb7yla9Ua9t8883z8ssvJ/HzLQCsqlNOOSWnnXZavvOd76Rnz5459NBDM3To0Fx00UVJrLUArH2EA2EN1KBBg/Tq1Sv33HNPtfZ77rkn/fr1K1JVALD2qqyszHHHHZf//d//zbhx49KlS5dq/V26dMkGG2xQbe1dtGhRxo8fb+0FgBW0yy675PHHH8/06dOrHr17987gwYMzffr0bLLJJtZbAFhF2223XZ599tlqbc8991w23njjJH6+BYBVtXDhwtSrVz1GUb9+/SxevDiJtRaAtY9jhWENdeKJJ+bQQw9N796987WvfS0jRozIyy+/nGOOOabYpQHAWmfIkCG58cYbM3r06DRt2rTqXZ3NmzdPo0aNUigUcsIJJ+TCCy/MZpttls022ywXXnhhKioqcvDBBxe5egBYOzRt2jQ9evSo1ta4ceO0atWqqt16CwCrZujQoenXr18uvPDCHHDAAXn44YczYsSIjBgxIkn8fAsAq+ib3/xmLrjggmy00Ubp3r17pk2blssuuyzf/e53k1hrAVj7CAfCGurAAw/M22+/nfPOOy+vv/56evTokbvuuqvqHaAAwIq78sorkyQDBgyo1j5q1KgcccQRSZKf/OQnef/993Psscdmzpw52XbbbTN27Ng0bdq0jqsFgNJlvQWAVdOnT5/ccsstOf3003PeeeelS5cu+fWvf53BgwdXjbHeAsDKu+KKK3LWWWfl2GOPzezZs9OhQ4ccffTROfvss6vGWGsBWJsUKisrK4tdBAAAAAAAAAAAAFB76hW7AAAAAAAAAAAAAKB2CQcCAAAAAAAAAABAiREOBAAAAAAAAAAAgBIjHAgAAAAAAAAAAAAlRjgQAAAAAAAAAAAASoxwIAAAAAAAAAAAAJQY4UAAAAAAAAAAAAAoMcKBAAAAAAAAAAAAUGKEAwEAAABgBV1zzTUpFAopFAqZOXPmUv1HHHFECoVCOnfuXLQaKJ5zzz236t8GAAAAAIpNOBAAAACAdcb9999fFd5akcc111xT7JLXGgMGDKh63urXr58nn3xyueNnzpxZNf7cc8+tmyIBAAAAYB0iHAgAAAAA66DVucvh4sWLBf4AAAAAoMjKil0AAAAAABTDD3/4wxx77LHLHdOxY8cvNOc111xjt8H/89e//jWPPvpottxyy2KXAgAAAADrJOFAAAAAANZJbdu2TY8ePYpdRslp1qxZPvjggyxatChnn312Ro8eXeySAAAAAGCd5FhhAAAAAKDWtGjRIt///veTJLfddlumTp1a5IoAAAAAYN0kHAgAAAAAteSII45IoVBI586dlzvutttuyze+8Y20bt06FRUV6dq1a0455ZTMmjUrSdK5c+cUCoUcccQRn3vPxYsXZ8SIEenXr19atGiRxo0bZ4sttsgFF1yQhQsXLjX+3HPPTaFQyLXXXpsk+de//pVCobDUY1WceeaZadiwYZLk7LPPXqk5rrnmmqpaZs6cucxxM2fOrBpX05HOn/03mTVrVk4++eR07do1FRUV2XDDDXPAAQfkySefXGre448/Pl27dk2jRo3Srl27DB48OC+88MIKfw3vvvtuzjnnnHTv3j1NmjRJy5YtM2DAgNxwww0rdP1HH32UkSNHZo899kiHDh1SXl6e1q1bZ4cddsivf/3rfPDBB8u8dsCAASkUChkwYECSZMaMGTnuuOOy2WabpaKi4nOfVwAAAADWfo4VBgAAAIA6UllZmR/+8Ie5+uqrq7XPmDEjv/jFL3L99dfnrrvuWuH5FixYkK9//esZN25ctfbHH388jz/+eG677baMGzcujRs3rpX6V1SHDh1y9NFH5/LLL8+YMWMyceLEfO1rX6vTGmry6KOPZrfddqsKYSbJ+++/nz//+c+58847c/fdd2f77bfPuHHjst9++2Xu3LlV4z744IPceOONGTNmTB544IF07959ufd66aWX8vWvf71amHDBggUZP358xo8fn1tvvTU33XRTyspq/hXtCy+8kL333jtPPfVUtfa33347DzzwQB544IEMHz48d955ZzbbbLPl1jJ69OgMHjw4CxYsWO44AAAAAEqLnQMBAAAAoI78/Oc/rwoGduzYMcOGDcvkyZPzj3/8I2eeeWbmzp2bb3/72zXu+FeTo446Kvfff38OP/zw3HnnnXnkkUdyyy23VAXxHn744fzsZz+rds2xxx6bxx9/PN/61reSLAnyfRIm/O/Hqjr99NNTUVGRJDnrrLNWeb5VtXDhwuy7775ZtGhRLrzwwjz44IOZNGlSzj333DRo0CALFy7MoYcemueffz777rtvmjZtmssvvzyTJk3KhAkTMnTo0BQKhcyZMyff+973Pvd+Bx54YF566aUcc8wxuffeezNlypSMHDkyXbt2TZL85S9/yYknnljjta+//nq22267PPXUU2natGlOOumkjBkzJv/85z/z97//veq5nTFjRnbbbbdqIcbPevnll3PIIYekoqIiP//5z6u+7iuuuCJNmjRZuScTAAAAgLWCnQMBAAAAWCfNnj07TzzxxDL727Ztm7Zt29ba/V5//fWcd955SZJNNtkkEydOrDZ///79s8cee2SnnXbKokWLVmjOhx56KNddd10OOeSQqrZtttkmu+++e3r37p0nnngiv/vd73L++edX7VD3yde1/vrrJ0nWW2+99OjRo5a+yk+1a9cuQ4YMyaWXXpr77rsv//jHP7LDDjvU+n1W1JtvvpnKyso8/PDD+dKXvlTVvu2226ZNmzYZMmRIZs6cmX79+qVdu3Z58MEH06ZNm6px2223XcrKynLppZdm8uTJmTZtWrbeeutl3m/KlCm58cYbc9BBB1W19e7dO4MGDUr//v3z6KOP5re//W1+8IMfpGfPntWuPeqoo/LGG2+kU6dOuf/++7PJJptU6x8wYEDVPC+++GJ+8Ytf5Pzzz6+xjpdeeikdOnTIxIkTs9FGG1X7ugEAAAAobXYOBAAAAGCddOWVV6Znz57LfAwfPrxW73fttdfmgw8+SJL86le/qjF42K9fvwwZMmSF59xvv/2qBQM/UV5enuOOOy7JkmNoP3s0bV35yU9+UrU73Zqwe+D5559fLRj4iSOPPDINGzZMsiREeMUVV1QLBn7ihz/8YdXHDzzwwHLvtddee1ULBn6iadOmGTFiRJJk8eLFueqqq6r1P/HEE7njjjuSJMOGDVsqGPiJrbfeuup75X/+53+WW8vPf/7zasFAAAAAANYNwoEAAAAAUAfuu+++JEmrVq2y5557LnPcYYcdtsJzDh48eJl9vXr1qvr4xRdfXOE5a1Pr1q1z/PHHJ0n+8Y9/5N577y1KHUlSKBRywAEH1NjXqFGjbLbZZkmSFi1aZODAgTWO69KlS5o2bZrk85/TI488cpl9X/3qV9O9e/ckWeo5GT16dJKkoqJiud8nSap2Ynzttdfyyiuv1DimQYMGGTRo0HLnAQAAAKA0CQcCAAAAsE4655xzUllZuczHueeeW6v3++QI46222ir169df5riePXumvLx8hebs1q3bMvtatmxZ9fH8+fNXsMrad/LJJ6d58+ZJirt7YOvWras9J5/1yTHLm266aQqFwueO+7zntE+fPsvt/+pXv5okmTFjRrVjpKdOnZokWbhwYcrKylIoFJb52GuvvaqumzVrVo332Wyzzap2RQQAAABg3SIcCAAAAAB1YM6cOUlS43HC/61+/fpp0aLFCs1ZUVGxzL569T791d/HH3+8QvOtDi1atMjQoUOTJJMmTcqYMWOKUsfynqvk0+drRcd93nP6ef/O7dq1S5JUVlZWfW8kyezZs5d73bIsXLiwxvYV/V4CAAAAoPSUFbsAAAAAAKC0DR06NJdffnnmzJmTs88+O7vvvnuxS1rtlrf7YLIkFFiTT0KHXbp0yW233bbC9+vSpUuN7cvbpRIAAACA0iYcCAAAAAB1oEWLFpk1a9bn7gz38ccfV9tJrhQ0a9YsJ598cs4888xMnTo1o0ePzpZbbrnca/5758PFixcvc9yCBQtqrc7a9MYbb6RTp07L7P/k+6BQKFTb3a9Vq1ZV13fr1i1lZX6FCwAAAMDKcawwAAAAANSB7t27J0mmT5++3CNpH3/88Xz44YervZ7P29muth1//PFp3bp1kuScc85Z5s55n2jatGnVx8sLSz777LO1U2AtmzJlygr1b7bZZmnQoEFV+9Zbb51kyTHBDz744OorEAAAAICSJxwIAAAAAHVgl112SZK8/fbbufPOO5c57g9/+EOd1NOwYcMkqZMgYpI0adIkp556apLk0UcfzV//+tfljv/vY3KnTp26zHE33nhj7RRYy6699tpl9k2dOjVPPPFEkmTXXXet1vetb32r6uNLLrlk9RQHAAAAwDpBOBAAAAAA6sDhhx+e8vLyJMnQoUPz5ptvLjVm4sSJ+e1vf1sn9bRv3z7JkuNt58+fXyf3HDJkSDbYYIMkycUXX7zcsT169EjLli2TJMOGDasxxHjTTTd9bsiwWG677bbcfPPNS7W/9957Oeqoo5IsOTr56KOPrtbfp0+fDBw4MEly11135ZxzzlnufWbOnJmbbrqplqoGAAAAoJQIBwIAAABAHejQoUNV0OvFF19Mr169Mnz48EyZMiUTJkzIWWedlV122SUdOnRImzZtkqzeo3/79euXJFm8eHGOOeaYTJo0KTNmzMjzzz+f559/frXcs1GjRjnttNOSJG+99dZyx5aVlVWF6J544onsvPPOGT16dKZNm5YxY8bku9/9bg455JB87WtfWy21rqrevXvn4IMPzpAhQ/L3v/89jzzySEaNGpXevXtn2rRpSZaEJbfYYoulrh01alRVePO8885L3759M2LEiEycODHTpk3Lvffem8suuywDBw7MpptuusYGJAEAAAAorrJiFwAAAAAA64rTTjst//rXv3L11VfnlVdeyZAhQ6r1t27dOn/+85+z3377Jfn06N/VYeedd07fvn0zadKk3HjjjUsdz1tZWbla7nv00Ufn0ksvzauvvvq5Y88666zcf//9mTRpUh566KHss88+1fp33HHHDBs2LD179lwtta6Km2++ObvsskuGDx+e4cOHL9W///7757LLLqvx2g4dOmTixIkZNGhQpkyZksmTJ2fy5MnLvFezZs1qrW4AAAAASoedAwEAAACgjhQKhVx11VUZPXp0Bg4cmJYtW6Zhw4bZdNNNc/zxx2fatGnp3bt35s2blyRp3rz5aqulXr16GTt2bH76059myy23TJMmTVbrToWfaNiwYc4888wVGltRUZFx48blggsuSM+ePdOoUaM0a9Ysffr0ybBhw3LfffelSZMmq7nildOlS5c88sgjOeOMM7L55punoqIizZs3zw477JDrr78+f/nLX1JWtuz3bm+88caZPHlybrnllnznO99Jly5dUlFRkfXWWy9t2rRJv379ctJJJ2X8+PEZOXJkHX5lAAAAAKwtCpWr6y3AAAAAAMAX9u9//zudOnVKkvz+97/P9773vSJXBAAAAACsjewcCAAAAABrkJtuuqnq4759+xaxEgAAAABgbWbnQAAAAACoIwsWLMi8efPSvn37GvunTZuWHXfcMfPnz0+vXr0yderUOq4QAAAAACgVZcUuAAAAAADWFW+++WY233zz7LPPPtltt93y5S9/OeXl5Xnttdfyt7/9LSNHjsz777+fQqGQyy67rNjlAgAAAABrMTsHAgAAAEAdmTlzZrp06bLcMQ0aNMjvfve7HHbYYXVUFQAAAABQioQDAQAAAKCO/Oc//8ktt9ySMWPGZOrUqZk9e3bmzJmTioqKdO7cObvuumt+9KMfZeONNy52qQAAAADAWk44EAAAAAAAAAAAAEpMvWIXAAAAAAAAAAAAANQu4UAAAAAAAAAAAAAoMcKBAAAAAAAAAAAAUGKEAwEAAAAAAAAAAKDECAcCAAAAAAAAAABAiREOBAAAAAAAAAAAgBIjHAgAAAAAAAAAAAAlRjgQAAAAAAAAAAAASsz/Bxu4fh/9HBaYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\n",
    "sns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to observe if there is any relationship between launch sites and their payload mass.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\n",
    "sns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a `bar chart` for the sucess rate of each orbit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Success Rate')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column\n",
    "df_bar = df[['Class', 'Orbit']].groupby(by='Orbit').mean().transpose()\n",
    "\n",
    "sns.barplot(data = df_bar)\n",
    "plt.xlabel('Orbit')\n",
    "plt.ylabel('Success Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACggAAAH4CAYAAACVRfSiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAACIJUlEQVR4nOzdeZyVZd0/8M8ZlkGWGUQ2NVDZFNxx39PMJTUtc18isyzNNM1Kq1/5PI/ZY5mmplamkqbWo0luuZT7gvsOiKIiLoCCzIDAAMP5/TGJjjMo4MwcOLzfr9d56fle931d3xuEQeZzrqtQLBaLAQAAAAAAAAAAAMpKRakbAAAAAAAAAAAAAFqegCAAAAAAAAAAAACUIQFBAAAAAAAAAAAAKEMCggAAAAAAAAAAAFCGBAQBAAAAAAAAAACgDAkIAgAAAAAAAAAAQBkSEAQAAAAAAAAAAIAyJCAIAAAAAAAAAAAAZUhAsA0Vi8XU1tamWCyWuhUAAAAAAAAAAADKnIBgG5o5c2aqq6szc+bMUrcCAAAAAAAAAABAmRMQBAAAAAAAAAAAgDIkIAgAAAAAAAAAAABlSEAQAAAAAAAAAAAAypCAIAAAAAAAAAAAAJQhAUEAAAAAAAAAAAAoQwKCAAAAAAAAAAAAUIYEBAEAAAAAAAAAAKAMCQgCAAAAAAAAAABAGRIQBAAAAAAAAAAAgDIkIAgAAAAAAAAAAABlSEAQAAAAAAAAAAAAylD7UjcAK7MHX3on9730TqpX6ZD9Nlkzfas7lbolKA8LFya1bySdqpNOVaXuBgCgbdUvSF64JZk6Nuk5OFlv76R9x1J3BQAAAAAAlEChWCwWS93EyqK2tjbV1dWpqalJVZXAysps4cJiTvzrU7nh6TcX1SrbV+Siw4dnl/X6lLAzKANjbkju+Gny7qtJu8pkowOTPc9KOnYudWcAAK1v9vRk5BeTKc9+UOs5JPnqjUm3vqXrCwAAAAAAKAlHDEMJ3Pb85EbhwCSpW7AwP7ru2cyvX1iirqAMvPF48n8jGsKBSVJflzx5RXLT90rZFQBA27nrF43DgUnyzvjkXz8vSTsAAAAAAEBprRQBwREjRmS//fZrdmzttddOoVBo8vrlL3/Z6LqRI0dmyy23TJcuXdKtW7fsuOOOuemmm9qge8rRHWOmNFufOrMuT02a0bbNQDl55JKkWN+0/ty1yXvT2r4fAIC2NvaGxdRvbNs+AAAAAACA5cJKERD8JP/1X/+Vt956q9Hr+OOPXzT+/e9/P8ccc0wOPPDAPP3003nkkUeyww47ZN99980FF1xQws5ZUVV2WPwvvcr2flnCMqt9o/n6wgXJe1PbthcAeF/9/FJ3wMqk0K75esVi6gAAAAAAQFlrX+oGlgfdunVL3759mx0bPXp0zj777Jx33nmNQoNnnHFG5s6dm5NOOin77rtv+vXr11btUgb23WTNXP3IpCb1gb26ZMM1q0vQEZSJflslr9zTtN65Z9JjQNv3A8DKbeJDyb9+lkx6OFmlR7LF15OdfpS0879htJ5XV98ja8+8tEn9lb67Z50S9AMAAAAAAJSWrco+wdVXX52uXbvmmGOOaTJ28sknZ/78+bnuuuuavbeuri61tbWNXpAkWw9YLSd/fkjaVxQW1dao7pTzDxmeQqHwMXeyXFlYn4y7JXngvOSlfyfFYqk7YstvJtX9m9Z3Pi1pX9n2/QCw8nr7heSKLzWEA5NkzvTk3l8lt51a2r4oe6dN3ysP1Q9rVHti4aCcMv1LJeoIAAAAAAAoJVtXJPnhD3+Yn/zkJ41qN910Uz772c9m/PjxGThwYDp27NjkvjXWWCPV1dUZP358s/OeeeaZOf3001ulZ1Z8x39ucA7col8eeOmddO/cITsM7pUO7WR2Vxiz3k7+vG8y9fkPav22Sg67NulUVbq+VnZdeyVH/ysZ/bvk1QeSrn2SLY5KBu1a6s4AWNk8/PtkwZym9cdHNgTXV1m17XtipfD8tIX5U/2e6VWYkbUKU/JGsWf+tOALeXpaqTsDAAAAAABKQUAwySmnnJIRI0Y0qq255ppLdG+xWFzsjm+nnnpqTjrppEXva2trHUVMI32qOuXLwz9T6jZYFnf8tHE4MGnYIejes5Ld/qc0PdGgW5/k8/9V6i4AWNlNf7n5en1dUvOGgCCt5pDuY/KD6b9JRaFhd+u1C1Pyu47n5X+7dkryhdI2BwAAAAAAtDnblSXp2bNnBg0a1Oi1yiqrJEmGDBmSCRMmZN68eU3ue/PNN1NbW5vBgwc3O29lZWWqqqoavYAy8fyopasDACuXvhs2X6+sSnqs07a9sFL5drt/LAoHfrQOAAAAAACsfAQEP8HBBx+cWbNm5fe//32TsV//+tfp0KFD9t9//xJ0BgAALLe2OibpvFrT+rbfTTp2aft+WGlUz5rQbL1q5mJ2tQQAAAAAAMraSnPEcE1NTZ566qlGtR49eiRJZs6cmcmTJzca69y5c6qqqrLNNtvkhBNOyCmnnJJ58+Zlv/32y/z583PllVfmt7/9bc4991zHBsPKaP39kqevbr4OAFD9meSo25N7f5W8en/StVey+deT4UeUujPKXe9hyWsPNVNfr+17AQAAAAAASq5QLBabnj1UZkaMGJGRI0c2qX/1q1/N3XffnYkTJzYZO+aYY3LxxRcven/ppZfmoosuyvPPP59CoZDhw4fnBz/4QfbZZ58l7qO2tjbV1dWpqalx3DCs6Ga9nfz5i8nUMR/UPrNlcvh1SSe/vgEAKJEX70iuOjApLmxcP2CkD7MAAAAAAMBKaKUICC4vBAShzCysT168PXnnxaTP+snAXZJCodRdAQCwsht/e3Lfr5OpY5PVBiU7nJQMXfIPtwEAAAAAAOVDQLANCQgCAAAAAAAAAADQVipK3QAAAAAAAAAAAADQ8gQEAQAAAAAAAAAAoAwJCAIAAAAAAAAAAEAZEhAEAAAAAAAAAACAMiQgCAAAAAAAAAAAAGVIQBAAAAAAAAAAAADKkIAgAAAAAAAAAAAAlCEBQQAAAAAAAAAAAChDAoIAAAAAAAAAAABQhgQEAQAAAAAAAAAAoAwJCAIAAAAAAAAAAEAZEhAEAAAAAAAAAACAMiQgCAAAAAAAAAAAAGVIQBAAAAAAAAAAAADKkIAgAAAAAAAAAAAAlCEBQQAAAAAAAAAAAChDAoIAAAAAAAAAAABQhgQEAQAAAAAAAAAAoAwJCAIAAAAAAAAAAEAZEhAEAAAAAAAAAACAMiQgCAAAAAAAAAAAAGVIQBAAAAAAAAAAAADKkIAgAAAAAAAAAAAAlCEBQQAAAAAAAAAAAChD7UvdAAAsk/r5SbsOpe4iqX0zeeovyczJSb+tk/X3K2lfc+fX58nXZqRqlfZZf43qJbpnzJu1+dP9r+SlqTMzpE+3fGPHARnSp1ur9Df65Wm5/ok3Mnt+fT63Xu/svdHqad/O5xUAAAAAgJXE7OnJC/9MUkyG7Jl0Wa3UHQEAUOYKxWKxWOomVha1tbWprq5OTU1NqqqqSt0OwIpp7E3JXb9Ipj6fVK2ZbHNcw6sUXn0g+csByfz3Pqj12yo5YlTSsXObt3Pt46/nv28ak5o585MkG65ZnQsPG55+PRbfy+MTp+fQPz6cugULF9VW6dAu13xz62zcr3uL9nfR3RPyv7eOa1TbdWif/OGIzVJRUWjRtQAAAAAAljvPXpv847hkwdyG9+0qky+en2x8UGn7AgCgrNmyB8pM/cJi7hgzJX+89+Xc/+I7kQGmrLx8T/K3IxrCgUlS+0Zy22nJg+eXpp+bvtc4HJgkkx5OHvtTiy0xb8HC/GvMlNzw9JuZ/t68xV73/Js1+cG1Ty8KBybJs2/U5FtXPv6x8599+/hG4cAkmTO/Puf8a/yna/wj3plVl3PuaDrnv8ZOyV0vTG3RtQAAAAAAljuz3k5GHftBODBJ6usaAoMzJ5euLwAAyt5KGxAcMWJE9ttvvyTJ1KlTc8wxx6R///6prKxM3759s/vuu+ehhx5adP2TTz6ZvffeO717906nTp2y9tpr56CDDso777xToieApt6eWZcv/Pa+fOPPj+WMW8bm8D89nAMufiiz6haUujVoGQ+enxQXNl9f2Ey9tbzwz+QvBybvvND8+PjbWmSZJ197N9v97505+s+P5btXP5nh/31HBp12S4689JE890ZNo2v/77HXs7CZPPDzb9Y2ufbDHp/47lLVl9Ujr0zPvPrmf47ue9HXUmAl8NYzycQHkwV1pe4EAAAAKIVxNzYEAj9q4fxkzA1t3w8AACuN9qVuYHmw//77Z/78+Rk5cmQGDBiQKVOm5N///nemT5+epCFAuOuuu2afffbJbbfdlu7du+eVV17JDTfckNmzZ5e4e/jAL24ZmxemzGxUe2ziu7ngzpfyoz3XK1FX0IKmv9x8fdaUZN6spFMbHN9++0+TB8/7+Gs6dvnUyyyoX5hvX/lE3p7Z+C+MFiws5t7xb+eJie/mpuO3z9o9G9aq/dDOgR/1cWN9qztl4rSmX8tWr+60jJ03r/sqHRY7tmrnji26FsByZdqE5G9fTaY82/C+82rJF36VbLB/afsCAAAA2tbC+o8Zs9EDAACtZ6UPCM6YMSP3339/7r777uy0005JkrXWWitbbrnlomsefPDB1NbW5pJLLkn79g0/ZOuss0522WWXkvQMzSkWi7nl2beaHbv52TcFBCkPfTdMpk9oWl91naSyW+uv/+7E5KELPvm6jQ/+1Es99PK0TK6du9jxWXULcvmDr+bnX1w/SbLDkJ75+5NvNLmuW6f22bT/qoudZ8S2a+f0G8c0U19nGbpevK0HrJZ1enbJK+80PpK5Y7uKfHn4mi26FsByo1hMrjk0eXvcB7XZ05K/fzPps2HSa0jpegMAAADa1rpfSP75w6T4kaBgoSJZb6/S9AQAwEphpT1i+H1du3ZN165dM2rUqNTVNX/cV9++fbNgwYJcf/31KRabOb9xMerq6lJbW9voBcCnsMNJSftVmtY/e2pSKLT++q/e3/wRx/9RrGifbHBA8tRVyW83Tq4+JJn0yAcXvD0+mTpusfd/WN38Tz4yecLbsxb9+94brZHtB/VMkhze7vbc1fHETKg8NKM7fTerPPq7hpBKM7623To5+fNDUv2fHf5W7dwhP9hj3Ry6Vf8l6nNJVVQU8scjN896fT8IcvbqVpkLDt00/Xp0btG1SmLahOSpq5OX71nsjzWwEpr0cONw4PsWLkievqrt+wEAoHTqFyQzJiXznEgDsNKqXjPZ838bAoGLFJLdf5GsulbJ2gIAoPyt9DsItm/fPpdffnm+8Y1v5OKLL87w4cOz00475eCDD85GG22UJNl6661z2mmn5dBDD823vvWtbLnlltlll11y5JFHpk+fPoud+8wzz8zpp5/eVo/CSq5QKOQLG66e65vZQewLG65ego5gyd05bkoue+DVvDljTob3XzXf/uzADOjVtemFq2+cfO2W5L6zkzefTFZdO9nmuLb7dGWXnosd+tuCnTK977b51vNnffAJ0HdfTV68I9nn3GT0xR8cL9lzSLLfRclnNl/sfNsMXC1dK9tnVt3ij5YY0ueDsF2HdhW57LBhmfn7PdNjxrMftDx3cnLHT5N57yU7n9rsPMd/bnC+udOAvD2zLr26VaayfbvFrvmJ6ucnz49KJj6QdO2TbHpY0r0hbDiod9fceuKOGftWbWbPq89Gn6lOh3Yt8FmFuTXJ039N3nkh6T002eigttlRMmkIA970veTxy5P8JxjYe/3ksP9r+As/YOU2Z8ayjQEAUF6euCK56xfJzDeTDl2Szb+W7Hp60m6l/+t5gJXPlt9IBn0uGXNDkmIy9IvJagNL3RUAAGWuUFyaLfHKyIgRIzJjxoyMGjUqSTJ37tzcd999eeihh3LrrbfmkUceySWXXJIRI0YsumfatGm58847M3r06IwaNSrTp0/Pvffemw033LDZNerq6hrtSlhbW5t+/fqlpqYmVVVVrfl4rKSmzpybwy95OOOnfLCr2GZrrZqRR22ZrpX+wpHl098em5QfXPtMo1r1Kh3yj+O2y9o9u5Soq8WoX5Ccv2ky47VG5VnFTtmx7txc0vHXGV7xUtP72nVM6uc1rnXqnpz4TNKperHL/eOpN3Ly357OgoVNv1R369Q+t3x3h8a7793x/5IHftv8ZJ2qk5PHJx06LXa9T23e7OSKLyWTRn9Qa79KcsjVycCdW2fN6a8kl++V1H4oHN29f/K1fybVn2mdNT/syb8k/zi2aX3Qrsnh17X++sDybc6M5DdDk/nN7BJz4BXJsC+2eUsAALSx8bclVx3YtL7dCcnn/6vt+wEAAABWOgKC/wkIftTRRx+dO+64IxMnTmx2fN68edl0002z+eabZ+TIkUu0Zm1tbaqrqwUEaVX1C4v599gpeeWd9zJsjapsP6hnCm1x9Cosg/qFxWz/v3fmrZq5TcYO2bJ/zvxy8wHsknr7hUy4+JAMrJ+QJJm4sHd+uOCbGb1wWF6oPDKVhcXv+NfEPr9NNhvxsZe8Nm12Rj35eka/Mj0Tp83OzLnzs/WA1XLSbkOyXt+PfC35zbDGQbmPOvG5pHu/Je9vaY2+KLn1R03rPQYmxz/eOsdA/9+I5Pnrm9Y3PjT50kUtv95H/Xnf5OW7mxkoJKdMSLqs1vo9AMu3h/+Q/POUxrXBuyWHXJNUfIodWwEAWDFc8aVkwp1N65XVyQ8mJO06tH1PAAAAwErFlmKLMWzYsMWGB5OkY8eOGThwYN577722awqWQLuKQnZbv2+p24Al8vbMumbDgUny7Bsz2raZJdVr3dy2w99y3W13pmMWZGyxf5KG4Ns77VfPmvWTlnyuWW9/4iX9V+uc7+46JN9dkvkWfkw4sXPPhiN/W9P425qvT5+QTHsp6Tm4Fda8fTH1W1t+reYsqFvMQDGpX9wYsFLZ6pvJGpsmT1+V1M1MBu+erP8l4UAAgJVF7ZvN1+tqGv582LlH2/YDAAAArHRW+oDgtGnTcsABB+Soo47KRhttlG7duuWxxx7LWWedlX333TdJctNNN+Waa67JwQcfnCFDhqRYLObGG2/MLbfckssuu6zETwCw4ureuUO6VrbPrLqmwbbPdO/czB3Lh6O2Wyf3vLBhHn5l+qJar26Vab/t8ck9P1jyidbZsWUbW2/v5LE/NT+2/feS9h1bdr2Pquy6+LGOrXRcdMfOyfxmwvqttd5Hrbtn8tpDTetrbJpUrdE2PQDLv35bNLwAAFj59NsyeXtc0/pqg4UDAQAAgDax0gcEu3btmq222irnnHNOJkyYkPnz56dfv375xje+kdNOOy1Jw26CnTt3zsknn5xJkyalsrIygwcPziWXXJIjjjiixE8AsOLq1KFdDt96rVx8z4RG9YpCctT265Soq0/WqUO7XP2NrXPP+Lfz5KQZWbN7p+y90RrpUrlr0qUiuf/cpPb1pHv/ZMdTkjefTB67tPEkG+yf9N+qZRvb+bRk4oPJ22M/qLXrmOx6erLNsS27VnM2PjQZe2PT+oDPtl5YbuODkwfPb6Z+SOus91FbfrNh58SJD3xQ69Q92fuctlkfAACA5dv230vG3pTM+eBDhim0S3b9Wel6AgAAAFYqhWKxWCx1EyuL2traVFdXp6amJlVVVaVuB2C5UL+wmN/c8UKueGhiaucuyMBeXXLK7utmjw1WL3Vry65YTObPTjp0TgqFhvdjRiXP/T0pLkyGfjHZ8ICkoqLl114wLxl3YzJ1bMNuBOvvl7SvbPl1FueuM5P7f5PUz2t4v/omySFXt15AcP6c5Nqjkhdu+aA2bN/ky39su+deWN9wpPGkh5OqNRt+bu0CAQAAwPvefTV58ILkjccbPky49beT/luXuisAAABgJSEg2IYEBAEWb0H9wrw3rz7Vq3QodSt8WrPeTl5/NOnWJ1lzs7ZZc+rY5J3xSa+hSa8hbbMmAAAAAAAAACznBATbkIAgAAAAAAAAAAAAbaUVzjYEAAAAAAAAAAAASk1AEAAAAAAAAAAAAMqQgCAAAAAAAAAAAACUIQFBAAAAAAAAAAAAKEMCggAAAAAAAAAAAFCGBAQBAAAAAAAAAACgDAkIAgAAAAAAAAAAQBkSEAQAAAAAAAAAAIAyJCAIAAAAAAAAAAAAZUhAEAAAAAAAAAAAAMqQgCAAAAAAAAAAAACUIQFBAAAAAAAAAAAAKEMCggAAAAAAAAAAAFCGBAQBAAAAAAAAAACgDAkIAgAAAAAAAAAAQBkSEAQAAAAAAAAAAIAyJCAIAAAAAAAAAAAAZUhAEAAAAAAAAAAAAMqQgCAAAAAAAAAAAACUIQFBAAAAAAAAAAAAKEMCggAAAAAAAAAAAFCGBAQBAAAAAAAAAACgDAkIQht7r25BptTOLXUbAAAAAAAAAABAmWtf6gZgZfFe3YL87Ibnc8NTb2Ze/cIM6t01P9lraD67bu9StwYAAAAAAAAAAJQhOwhCG/nBtc/k2sdfz7z6hUmSl6bOyjf//HjGT5lZ4s4AAAAAAAAAAIByJCCYZMSIESkUCikUCunQoUMGDBiQ73//+3nvvfcWXTNy5MhsueWW6dKlS7p165Ydd9wxN910Uwm7ZkXyVs2c/PO5t5rU59UvzJWjJ5agIwAAAABguTRzcvLWM8mCulJ3AgAAAJQBAcH/2GOPPfLWW2/l5Zdfzv/8z//kwgsvzPe///0kyfe///0cc8wxOfDAA/P000/nkUceyQ477JB99903F1xwQYk7Z0UwpbYuC4vNj705Y27bNgMAAAAALH/m1iZ/PSI5e73k9zskvx6SPHZZqbsCAAAAVnDtS93A8qKysjJ9+/ZNkhx66KG56667MmrUqHz1q1/N2WefnfPOOy/HH3/8ouvPOOOMzJ07NyeddFL23Xff9OvXr1StswIY3Ltrula2z6y6BU3GNu3fve0bAgAAAACWL38/Ohl/2wfv585IbjoxWXXtZODOJWoKAAAAWNHZQXAxVllllcyfPz9XX311unbtmmOOOabJNSeffHLmz5+f6667rgQdsiLpUtk+x+8yqEm9f4/OOWyr/iXoCAAAAABYbsyevigcWF8sZHax8oOxO/+7RE0BAAAA5cAOgs145JFHctVVV+Vzn/tcxo8fn4EDB6Zjx45NrltjjTVSXV2d8ePHNztPXV1d6urqFr2vra1ttZ5Z/h2z08CstVqXXP3Ia5n+3rxsO2i1HL39gHTv3PS/LQAAAABgJfL2C1lQrMjvFuyb2alMj8LMvLGwZ3Zt93h2fKf5v38GAAAAWBICgv9x0003pWvXrlmwYEHmz5+ffffdN+eff36++tWvfux9xWIxhUKh2bEzzzwzp59+emu0ywpqjw36Zo8N+jY7Nrlmbi6468U88NK0VK/SIQdv0S8Hb2l3QQAAAAAoex275PL63XJM+5vSqTB/UfnZhWvn+XmrZ/0StgYAAACs2AQE/2PnnXfORRddlA4dOmSNNdZIhw4dkiRDhgzJ/fffn3nz5jXZRfDNN99MbW1tBg8e3Oycp556ak466aRF72tra9OvX7/WewhWWO++Ny/7X/Rg3pgxZ1HtqUkzMnH67Pxwj/VK2BkAAAAA0NpmzU++3O7+RuHAJNmw4tXcWb+pgCAAAACwzCpK3cDyokuXLhk0aFDWWmutReHAJDn44IMza9as/P73v29yz69//et06NAh+++/f7NzVlZWpqqqqtELmnP1o681Cge+79L7X8m7780rQUcAAAAAQFt5980J6VGY1ezYpoUX27gbAAAAoJzYQfATbLPNNjnhhBNyyimnZN68edlvv/0yf/78XHnllfntb3+bc889166AfGrPv1HbbL1uwcKMnzIzWw1YrY07AgAAAADaSp9Vuy12rFNhQRt2AgAAAJQbAcElcO6552ajjTbKRRddlJ/+9KcpFAoZPnx4Ro0alX322afU7VEG+vXo3Gy9opB8ZjFjAAAAAEB56Dhwh9SnIu2ysOng6hu3fUMAAABA2SgUi8ViqZtYWdTW1qa6ujo1NTWOG6aRSdNnZ49z78178+ob1ffeaPVccOjwEnUFAAAAALSZf/9Pct+vGpUWVnRMxfGPJ6v2L1FTAAAAwIquotQNAA07CF5x9FbZfK1VkyRdOrbLV7dZK78+wKeDAQAAAGCl8LmfJF+5POm9ftKldzJsv1R85xHhQAAAAOBTsYNgG7KDIEtizrz6dGhXSPt28rsAAAAAAAAAAMCya1/qBoDGVunYrtQtAAAAAAAAAAAAZcAWZQAAAAAAAAAAAFCGBAQBAAAAAAAAAACgDAkIAgAAAAAAAAAAQBkSEAQAAAAAAAAAAIAyJCAIAAAAAAAAAAAAZUhAEAAAAAAAAAAAAMqQgCAAAAAAAAAAAACUIQFBAAAAAAAAAAAAKEMCggAAAAAAAAAAAFCGBAQBAAAAAAAAAACgDAkIAgAAAAAAAAAAQBkSEAQAAAAAAAAAAIAyJCAIAAAAAAAAAAAAZUhAEAAAAAAAAAAAAMqQgCAAAAAAAAAAAACUIQFBAAAAAAAAAAAAKEMCggAAAAAAAAAAAFCGBAQBAAAAAAAAAACgDAkIAgAAAAAAAAAAQBkSEAQAAAAAAAAAAIAyJCAIAAAAAAAAAAAAZUhAEAAAAAAAAAAAAMqQgCAAAEC5mVubvPZwUvN6qy5TM3t+bnt+ch6aMC0LFxZbdS0AAAAAAACWXvtSNwAAAIs17ubk6auTebOTIXskw49MOnQqdVewfLv3V8l95yTz30sKFcnQfZJ9L0wqu378fQvmJU/9JRl/W9Kxc7LxIcngzy/28pEPvpoz/zk2c+cvTJKsvVrn/OHIzTOkT7eWfBpYsdUvSMb/M5k6Luk1JFn3C0m7DqXuCgAAAACAlUihWCza5qGN1NbWprq6OjU1Namqqip1OwCw8nr7heTxkcmsyUn/bZJNDk06dil1V3zUv/8rue/sxrW1d0iOGJW08zmXslMsJoXCkl07Y1Jy1xnJi3c0/Nrd5LBkh5OEbpLkueuSa49qWt/0iGTfCxZ/38L65Mr9k5fvalz/7KnJZ3/U5PKnJ83Ivr97oEl9UO+uueN7O6awpD+XUM7em5b8ed9kyrMf1Hqtl3z1pqRrr9L1BQAAAADASqXsjxiePHlyTjjhhAwaNCidOnVKnz59sv322+fiiy/OlltumUKhsNjX2muvvWie559/PgceeGB69eqVysrKDB48OD/96U8ze/bs0j0cALD0xt+WXLx9Mvp3DUGaW76f/Gm3ZG5NqTtbeUx8KBl9ccPPxcL65q+pfSt54LdN66/el7xwc+v2R9uZPze57cfJL9dK/mu15MqvJFPGfPw9c2uSy/Zs2Fly9jvJjInJ3b9IRn27bXpe3j0+svn6s//XsBPn4rxwS9NwYJLc++tk1tQm5b8/0fzRxS9NnZWnX/f7KSRpCDJ/OByYJG+PS/7985K008SCeaXuAAAAAACANlDWW6+8/PLL2W677dK9e/f84he/yIYbbpgFCxZk/PjxufTSS/Od73wnu+22W5Jk0qRJ2XLLLfOvf/0r66+/fpKkXbt2SZLRo0dn1113za677pqbb745ffr0ySOPPJKTTz45d955Z+6666507NixZM8JACyhhQuTf/4gqf/IN8SnPJc8ekmyw8ml6WsJvVe3INc/+Uaef7Mm/Xp0zgGb9UuvbpWlbmvJzZ+TXHNoMuHOD2q9hyVfuTR588kkhWTI7knnHsmkh5OFC5qf59UHkmH7tknLfHpvzJiTDu0K6d2tmaOhR307ef7vH7x/6Y7kjceTY0cn3fo0P+HT1yQ1k5rWn7022fm0pMeAlml8RTV7evP1BXOT+bMbjg7+j2KxmPtefCcPvTwte79+U9Zv7r6F85OJDybr79d4mXmLCfcmmV23mF+7sLIZe0Pz9TE3JPv+rm17+bBnr03u/mUy7cWkun+y/YnJFl8vXT8AAAAAALSqsg4IHnvssWnfvn0ee+yxdOnywbGBG264Yfbff/8Ui8VFR1/NnTs3SbLaaqulb9++i64tFov5+te/nqFDh+bvf/97KioaNl1ca621MmTIkGy66aY555xz8sMf/rANnwwAWCbvvpK8+2rzYxPuWq4Dgu/MqsuBFz+Ul995b1HtD/e+nKuO3jrD1qgqYWdL4YHzGocDk2TqmOSibZPiwob37Ts1hCaq1lj8PF17t16PNDFp+uw88dq76VPVKVut02OJj459etKMnHb9s3n+zdokybYDV8tZX9kon1n1PwG16a8kz1/f9MY505Mn/pzsdErzE095fjErFpOp4wQEB+zUdMeyJOmzYdKl56K3C+oX5tt/eSJ3jJmSJGnXrj7rL+6E5i5Nj0LdZb3e+b/Hm+4i2L1zhwxfa9Vlan25Vj8/efD85Jm/NYQt19ur4WtG5x6l7mylVTt3fh6f+G66r9Ihm/Yv/X9zU2vn5pe3jsttz01ORUUh+2y8Rv4nFc0f21Ao4WEOY29KrvtQGLDmteTmkxp62vxrpesLAAAAAIBWU7ZHDE+bNi233357jjvuuEbhwA9bkm9uPvXUUxkzZkxOOumkReHA92288cbZddddc/XVV7dIzwBAK6usWvw35Vfp3qatLK3f3fVSo3BgksyYPT//c/MnHMe6PPnwTnEf9n44MGkI3oz6dtJjYNK7mf3MOnRONjm0dfqjiZ/f8Hx2+tVdOeGap3LwH0Znz9/elzdnzPnE+2bMnpcjL31kUTgwSR6cMC0jLns0CxcWGwrTX05SbH6CaS8tfvKeQxY7dMbD87OgfuFix1cK252QrLp241r7Tsnu/9OodP2TbywKBybJ3+t3yNxiMwnBXkOTtbZtUt59/b7ZY/2+jWrtKwr5r303SKcO7Za5/aWxoH5hXn93duZ8zG6GLea6o5N/n568PbYhbP7QBcnley83R7ROrpmbe8e/nUdemZafjnoue/72vhx1+aO564Wmx0OXg8sfeCVbnfHvfO2yR/OlCx/MHufem9emfcwR2q1s3oKFOeSPo/P3J97Ie/PqM3Puglz18Gu5aeE2zd+wwf5t2+CHPXhe8/UHftu2fQAAAAAA0GbKdgfBl156KcViMeuuu26jes+ePRftFnjcccflf//3fz92nvHjxydJhg4d2uz40KFDc//99zc7VldXl7q6ukXva2trm70OAGgjXXslQ/ZMXri56dimR7boUmPfqs34KTMzqHfXrL9G9aee7+4X3m62/uCEaZk7v77NAjmfSnEJg1v185JxNyaHXpOMOjZ59b6G+mqDkr3P+fjdBcvd6481hDveHp/0Hpps991kjU1bZalRT76Ryx98tVFt3OSZ+eF1z+SKr2/1iffWzJnfpP7S1Fl5YMI72WFwr4b+C+2SYjPhrj7NHnbbYJNDG3ZymzW5UfnW+i3yx7Ht0/P+V3LMTgM/tr+y1rV38s27k8cvbziuubp/w65gPQc3uuzD4cAkeTM98835J+WM9pemX8V/fr/pt1Xy5T8kzXywqqKikAsPG567x0/NPS+8napVOuRLm66ZAb26LlW7Y96szRWjJ+bNGXOycb/uOXKbtdKz6ycfnX7Vw6/l3H+Nz9SZdenSsV0O23qt/GD3ddO+XSt8Bm7KmGTMqKb1qc8nY/6RbHRAy6+5hOoXFvOTUc/lb49NSv3CxoHbsW/V5s5xU3PW/hvlwC36lajDlvf4xOn5+Y2Nw/HjJs/McVc9kRuP374kPd0+ZnImvP1ek/pp7+6dz/Z7PVVvP/ZB8TNbJJ/7f23Y3UdMf7n5+ruvJHXvJZXNf8ASAAAAAIAVV9kGBN/30V0CH3nkkSxcuDCHHXZYo/DesvrwMcUfdeaZZ+b000//1GsAAC1o3wuSa2cnL9/V8L5j12SnHyZDdvvke199ILnv1w1HnK42KNnuxCb3zZ1fn+9c9WT+NfaD8M1n1+2Viw7bLKt0XPYQX5fK5u/t1KEi7SuW7MjXkhv6xYYfvyWxsD7p3j8ZcVMyY1Iyf05DwGkJj7ctS6/cm1zx5WThf4J3b49Nxt2cfPXGpP/HB/aWxd+ffKPZ+v0vvZO3Z9alV7fFh7gm1y7+z9mTaxo+rJOqNZLhRyaPX9b4gqo1k00PX3xjnXtkwZE35d8XHJudCk9mVlbJdfU75DcLGkJa1z/5xsodEEySVVZNtv/ex17SoX3TIN29CzfOjvPOyb1f7ZN+fXp+4nHNFRWF7LJen+yyXp9lavOuF6bmm39+LPPrG4Jt94x/O9c+NinXH7dd+lR1Wux9tz8/Oadd/8Exyu/Nq88f7n05HdoVcsru6y1TLx9r8jMfM/Z0SQOCf7r/5Vz9yGsfe83Zd7yQLw9fs3XCkyVwbTNHWyfJs2/UZNzk2qzXt6qNO0omTG0aDkySWemcfwz/U47oOymZOjbptW6yzo6l/VrWd8Nkwp3Nj527QXLoX5N+W7ZtTwAAAAAAtKry+A5BMwYNGpRCoZBx48Y1qg8YMCCDBg3KKqusskTzDBnScITZmDHNH983bty4DB48uNmxU089NTU1NYtekyZNWoonAABaReceyZGjku88noy4OTlpbMMubJ/k1fuTP3+x4Zvqs6YkEx9IrjowGXNDo8vO+/eLjcKBScPuf2ff/sKnavsrwz/TbP1Lm65AoY/tv5f02/qTryu0S9b9wgfvu/dLeg1ZucOBSXLXmR+EA99XX5fc88tWWa5ufvPHthaLybxPOMZ3i7VXbbZeKCSbr93jg8JeZyef/++k57pJ177JJocnR93W8Ov0YyzsMTDfmn9i1qsbmc3rLs6ZCw5LXTomaQjp8sn222TNZusbrLlq+g3d8hPDgS3hFzePXRQOfN+bNXPz+3sWs8PZf3x0Z8v3XTn6tdY5Yvrjfiza4Mfp4/z10U/+f8wptXV56/1gbhmYOXfBYsdmfcxYa1q37+J3zly3b1Wyzg7JVt9MBuxU+q9lO/4gqWjmOPEkmTM9ufaohpA+AAAAAABlYwX5bvLSW2211fL5z38+F1xwQd57r/lP8y+JTTbZJOutt17OOeecLFzY+JtNTz/9dP71r3/lkEMOafbeysrKVFVVNXoBAMuJnoOStbdPOi3h1+d7f50s/GjwoJjcc1ajyvWL2XVtcfUldeQ2a+fQrfrnw5sF7jSkV36817BPNW+bquyaHHVrcth1yS4/Tfb/U7LbGUk+HJYoJHuc2RAKpLE3Hm++/vpi6p/SrkOb3xVu6OpVWbP7x3/YZud1e2e7Qas1qR+6Zf+s0/NDx1dWtGsI6H7nkeT7LyT7/W6Jfu47tq/IjoN7LVXfNPb5YX1yzI4DGv2e0q/HKjnnoE3aZP13ZtXlxamzmh176OVpH3vv5MWE3WrmzM/s1giI9tuy+XBzt9WTDUu3e2Dy8WG593XqUJEeXTq2QTdt47Pr9m62vlqXjtnoM93btpn/2HVonwxdvemfJ7Ye0CNbrvPxgec2t9Y2DR+QqF7M77U1k5JJj7RtTwAAAAAAtKqyPmL4wgsvzHbbbZfNN988P//5z7PRRhuloqIijz76aMaNG5fNNtvsE+coFAq55JJLsttuu2X//ffPqaeemr59++bhhx/OySefnG222SYnnnhi6z8MAFBaU55bovridi/7tLuaVVQU8osvbZjjdh6UMW/WZq3VOmdIn26fas6SKBSSwbs2vN637p7JmH80/PuwfZPVVvLjYRene79k2kvN1Pu3ynJHbLNW7hg7JY+8Mn1RrVun9jnjSxt84r0VFYX86atb5KqHX8vtYyanY/t22W+TNfKlTZvftW5Z/HTvYRnzx9F5e+YHxxmv26dbjtt5UIutUe5O/cLQHL71Wnn4lelZrWvH7DCoZ5vtSNq1sn06dajI3PlNd/zr2fXjw2zD11o1L7/T9ENgQ/p0TVWnxeyM9mkdek1y20+S565N6uclg3dPdj8jqSzt78M7Dum12CN333fQ5v3SpbJ8/td/303WyD+eeiP3vfjOolr7ikJO33f9dGzm6Oy20L5dRa46equc86/x+edzk9O+opB9Nl4jJ3yu+dMGSq7/VsnAXZInRjY/3uQDEQAAAAAArMgKxWKx+MmXrbjeeuut/OIXv8jNN9+c119/PZWVlRk2bFgOOOCAHHvssencuXOS5NVXX80666yTJ598MptsskmTeZ599tmcfvrpufvuu1NbW5v+/fvnkEMOyamnnrpojk9SW1ub6urq1NTU2E0QAFY0l+6RvPZQ0/pqg5LjP9jB7fv/93SzYY19N1kjvz1409bskHL36J+Sm09qWt/vomSTQ1tlyfqFxdwxZnIee/Xd9KnqlC8PXzOrda1slbWWRe3c+Rn15Bt59Z3Z2WDNquy10eqpbN+u1G2xhH58/bP5y8OvNalffPhm2WODvou97+W3Z+VLFz6YmjkfHLndrqKQiw/fLJ8f1so7SC5cmBTrk3atFERcSq+/OzsHXPxQoyOEKwrJwmLSpWO7HLRF//xoz/VKFpxrLQvqF+a256fkvhffTnXnDjlgs34Z1Hvxx/zSjJf+lVy5f9N61z7J955fbv4bBwAAAADg0yv7gODyREAQAFZgL/wzufqQJB/5o9MXz0+GH7no7eSauTnw9w/ltemzF9XW7L5K/vatbT7xWFb4RA9ekDx4XjJrSsPxptuflGz1zVJ3Bctk7vz6nPb3Z3PD029mwcJiqjq1z3c/NzhH7zDgE++dOO29/PG+l/PsG7Xpt+oq+dp262SztVZtg66XPzNmz8vfHpuUsW/NzICeXXLgFp9JRaEi3Tq1T6cOArN8jJu+lzx26QfvO3RODroiGbTr4u8BAAAAAGCFIyDYhgQEAWAF9/z1yT1nJVPHJD0GJtuf2Cgc+L7Z8xbkhqfezPgpszKwd5fst8maZXW8IyW2sD6ZMyNZpXtSIfzDiu+dWXWZUjs3A3p2zSod/TcNbeqNJ5KX/p10qko22D/p0rPUHQEAAAAA0MIEBNuQgCAAlImFC5OK8jquEQAAAAAAAIDy4zvbAABLSzgQAAAAAAAAgBWA724DAAAAAAAAAABAGRIQBAAAAAAAAAAAgDIkIAgAAAAAAAAAAABlSEAQAAAAAAAAAAAAypCAIAAAAAAAAAAAAJQhAUEAAAAAAAAAAAAoQwKCAAAAAAAAAAAAUIYEBAEAAAAAAAAAAKAMCQgCAAAAAAAAAABAGRIQBAAAAAAAAAAAgDIkIAgAAAAAAAAAAABlSEAQAAAAAAAAAAAAypCAIAAAAAAAAAAAAJQhAUEAAAAAAAAAAAAoQwKCAAAAAAAAAAAAUIYEBAEAAAAAAAAAAKAMCQgCAAAAAAAAAABAGRIQBAAAAAAAAAAAgDIkIAgAAAAAAAAAAABlSEAQAAAAAAAAAAAAypCAIAAAAAAAAAAAAJQhAUEAAAAAAAAAAAAoQwKCAAAAAAAAAAAAUIYEBAEAAAAAAAAAAKAMCQgCAAAAAAAAAABAGVohA4IjRoxIoVBo8tpjjz2SJE8++WT23nvv9O7dO506dcraa6+dgw46KO+8885i57z77rtTKBQyY8aMZseff/757L///ll77bVTKBRy7rnntsKTAQAAAAAAAAAAQMtoX+oGltUee+yRyy67rFGtsrIyU6dOza677pp99tknt912W7p3755XXnklN9xwQ2bPnr3M682ePTsDBgzIAQcckO9973uftn0AAAAAAAAAAABoVStsQLCysjJ9+/ZtUh81alRqa2tzySWXpH37hsdbZ511sssuu3yq9bbYYotsscUWSZIf/ehHn2ou+ERjb0ruOzt5e1zSa71kh5OToXuXuisAAAAAAAAAAGAFskIeMfxx+vbtmwULFuT6669PsVgsdTuw9MbelPz1sOTNJ5L5sxv++dfDG+oAAAAAAAAAAABLaIUNCN50003p2rVro9d///d/Z+utt85pp52WQw89ND179syee+6ZX/3qV5kyZUqb91hXV5fa2tpGL/hE953dTLGY3P+bNm8FAAAAAAAAAABYca2wAcGdd945Tz31VKPXcccdlyQ544wzMnny5Fx88cUZNmxYLr744qy33np59tlnkyTrr7/+olDhnnvu2Wo9nnnmmamurl706tevX6utRRl5e1zz9amLqQMAAAAAAAAAADSjfakbWFZdunTJoEGDFju+2mqr5YADDsgBBxyQM888M5tuuml+/etfZ+TIkbnlllsyf/78JMkqq6zSaj2eeuqpOemkkxa9r62tFRLkk/Var+FY4Y/qvV7b9wIAAAAAAAAAAKywVtiA4NLo2LFjBg4cmPfeey9JstZaa7XJupWVlamsrGyTtSgjO5yc/PXwJMUPFQsNdQAAAAAAAAAAgCW0wgYE6+rqMnny5Ea19u3bZ/To0bnmmmty8MEHZ8iQISkWi7nxxhtzyy235LLLLvvEeZ999tl069atUW2TTTbJvHnzMmbMmCTJvHnz8sYbb+Spp55K165dP3YnQ1hqQ/dODroyuf83DccK916vIRy43l6l7gwAAAAAAAAAAFiBFIrFYvGTL1u+jBgxIiNHjmxSX3fddXPLLbfkl7/8Ze65555MmjQplZWVGTx4cI499tiMGDFisXPefffd2XnnnZsdKxaLefXVV7POOus0Gdtpp51y9913L1HftbW1qa6uTk1NTaqqqpboHgAAAAAAAAAAAFgWK2RAcEUlIAgAAAAAAAAAAEBbqSh1AwAAAAAAAAAAAEDLa5GA4DrrrJOBAwfmpZdeWuJ7XnvttQwYMCADBw5siRYAAAAAAAAAAACAD2nfEpNMnDgxhUIh8+bNW+J75s+fn1dffTWFQqElWgAAAAAAAAAAAAA+xBHDAAAAAAAAAAAAUIZKFhCsqalJknTu3LlULQAAAAAAAAAAAEDZKllA8Morr0ySrLXWWqVqAQAAAAAAAAAAAMpW+2W5aZdddmm2/rWvfS1dunT52Hvr6ury8ssvZ+rUqSkUCtltt92WpQUAAAAAAAAAAADgYxSKxWJxaW+qqKhIoVDIMtzayIABA/LQQw+lV69en2qeFUVtbW2qq6tTU1OTqqqqUrcDAAAAAAAAAABAGVumHQR33HHHFAqFRe/vueeeFAqFbLbZZh+7g2ChUEinTp2y+uqrZ9ttt83BBx/8iTsOAgAAAAAAAAAAAEtvmXYQ/Kj3dxR89tlnM2zYsJboqyzZQRAAAAAAAAAAAIC2skw7CH7UkUcemUKhkFVXXbUlpgMAAAAAAAAAAAA+pRbZQZAlYwdBAAAAAAAAAAAA2kpFqRsAAAAAAAAAAAAAWp6AIAAAAAAAAAAAAJSh9ktzcbt27ZIkhUIhCxYsaFJfFh+dCwAAAAAAAAAAAPj0liogWCwWl6oOAAAAAAAAAAAAlMZSBQR/9rOfLVUdAAAAAAAAAAAAKI1C0fZ/baa2tjbV1dWpqalJVVVVqdsBAAAAAAAAAACgjFWUugEAAAAAAAAAAACg5QkIAgAAAAAAAAAAQBlq31oTT5kyJc8991ymT5+eJOnRo0c22GCD9OnTp7WWBAAAAAAAAAAAAP6jRQOCCxcuzO9///tceOGFGTNmTLPXDBs2LMcee2yOOeaYVFTYwBAAAAAAAAAAAABaQ6FYLBZbYqIpU6Zkr732ypNPPpkkWdy0hUIhSTJ8+PDceOON6du3b0ssv0Kora1NdXV1ampqUlVVVep2AAAAAAAAAAAAKGMtEhCcO3duNttss4wbNy7FYjG9evXKgQcemC233DJ9+vRJsVjM1KlT8+ijj+Zvf/tbpk6dmqRhN8HHH388lZWVn/pBVgQCggAAAAAAAAAAALSVFgkInnnmmfnxj3+cQqGQo446Kueee266dOnS7LWzZ8/O9773vfzxj39MoVDIGWeckR/96EeftoUVgoAgAAAAAAAAAAAAbaVFAoKbbLJJnn322Xz+85/PrbfeukT37LHHHrn99tuz0UYb5amnnvq0LawQBASBRhYuTCY/naSQrL5x8p8j2AEAAAAAAAAAoCW0b4lJXnrppSTJscceu8T3HHvssbn99tszYcKElmgBYMXy2ujk+mOSd19teN9jYLL/H5M1NytpWwB8giljkokPJF16JevumbSvLHVHAAAAAAAAAIvVIgHBysrKzJkzJ/369Vvie96/tmPHji3RAsCKY25NctWBDf983/QJyV8OSE58LunYuXS9AdC8YjG58bvJE3/+oNZtjeSIvye9h5auLwAAAAAAAICPUdESk6y33npJkkmTJi3xPe9f+/69rWXq1Kk55phj0r9//1RWVqZv377Zfffd89BDDyVJnnzyyey9997p3bt3OnXqlLXXXjsHHXRQ3nnnnUbzjBw5MltuuWW6dOmSbt26Zccdd8xNN93Uqr0DZer5UY3Dge+bPS0Z5/cVgOXSc9c1Dgcmycw3k+u/VZp+AAAAAAAAAJZAiwQER4wYkWKxmIsvvniJ77n44otTKBRy5JFHtkQLi7X//vvn6aefzsiRIzN+/PjccMMN+exnP5vp06dn6tSp2XXXXdOzZ8/cdtttGTt2bC699NKsvvrqmT179qI5vv/97+eYY47JgQcemKeffjqPPPJIdthhh+y777654IILWrV/ykDdzIbjZN8/ShbmTF/82OyPGYNyNvnZ5P5zk8cuTea8W+puoKnn/t58/a2nkmkT2rQVAAAAAAAAgCVVKBaLxU87SbFYzBe+8IXcfvvtOeaYY/Kb3/wmnTp1avbaurq6nHzyybnwwguz++6755///OenXX6xZsyYkVVXXTV33313dtpppybjo0aNygEHHJA5c+akffvmT1sePXp0ttlmm5x33nk5/vjjG42dfPLJOf/88zNhwoQlOl65trY21dXVqampSVVV1bI9FCuWB85L7vnfZN6shveDd0++/Idkle4lbYsSe/PJ5A+fbX7s2IeT3q27syosd275QfLI7z9437FbcvBfkgFNv3ZDyVx1UDL+1ubHvvNY0nNw2/YDAAAAAAAAsASWKiB47733Lnasrq4uP/nJT/LYY4+lT58+OfDAA7PFFlukd+/eKRQKmTJlSh599NH83//9XyZPnpzNN988Z5xxRjp27Jgdd9yxRR7moxYsWJBVV101Rx99dH75y1+msrKy0fj74b+//e1v+cpXvpJCodBkjhNOOCGXXnpppk2blo4dOzYae/PNN7PmmmvmnHPOyYknntjk3rq6utTV1S16X1tbm379+gkIrixe+Gdy9cFN6+t/OTngsrbvh+XLP76TPHlF49qWxyRfOKs0/UCpvPSv5Mr9m9a7rZ6c+FzSrvkAP7S5J/+S/OPYpvXe6yfHPtj2/QAAAAAAAAAsgaUKCFZUVDQbovtUDRQKWbBgQYvO+WHXXXddvvGNb2TOnDkZPnx4dtpppxx88MHZaKONkiQ//vGPc9ZZZ6Wqqipbbrlldtlllxx55JHp06dPkmTPPffMW2+9laeeeqrZ+bt3755DDz00F154YZOxn//85zn99NOb1AUEVxJXHZyMb2aHzIr2ySkvJaus2vY9sfwoFpNxNydjRiWFimT9LyXr7tny68yYlLx4W9K+U7Le3navZPlzw/HJE39ufuxr/0zW2rZt+4HFWVifXHtUw+/b71ulR3L4dcmaw0vWFgAAAAAAAMDHWeqAYIs3UCikvr6+xef9sLlz5+a+++7LQw89lFtvvTWPPPJILrnkkowYMSJJMm3atNx5550ZPXp0Ro0alenTp+fee+/Nhhtu+IkBwerq6hx++OH53e9+12TMDoIruT/tlkx6uPmxE55JVl2rbfth5fPgBckd/y8p/uf32A5dGnavHLJ7afuCD7vhu8kTI5sfO+q2pP/WbdsPfJLXRiev3pd07dMQ7q7sVuqOAAAAAAAAgDZWKBRy/fXXZ7/99it1K59oqQKC99xzT6s0sdNOO7XKvItz9NFH54477sjEiRObjM2bNy+bbrppNt9884wcOfJTHTH8UbW1tamurhYQXFncdWZyzy+b1nsMTI5/PGnh3TihkSljkou2aVqvrE5OHpt07NL2PUFzXr47+fO+TetVn0lOfCapaNfmLQEAAAAAAACwcps8eXLOOOOM3HzzzXnjjTfSu3fvbLLJJjnxxBPzuc99boUKCLZfmovbOsjXWoYNG5ZRo0Y1O9axY8cMHDgw7733XpLk4IMPznnnnZff//73Of744xtd++tf/zodOnTI/vvv39otsyLa6pjk+euTd174oNauY7LHmcKBtL7nr2++XleTvPTvZNgX27YfWJwBn022OyF54Lwk//nMQqfuyf6XCAcCAAAAAAAA0OZeffXVbLfddunevXvOOuusbLTRRpk/f35uu+22HHfccRk3blypW1wqSxUQXJzXXnstSdK1a9f06NGjJaZsEdOmTcsBBxyQo446KhtttFG6deuWxx57LGeddVb23Xff3HTTTbnmmmty8MEHZ8iQISkWi7nxxhtzyy235LLLLkuSbLPNNjnhhBNyyimnZN68edlvv/0yf/78XHnllfntb3+bc889N/369Svxk7Jc6twjOfpfyZNXNBxHWLVGstnXkt7rlbozVgbFjzm6vbiw7fqAJfH5/0o2PSKZcGfSqTpZb++ksmupuwIAAAAAAABgJXTsscemUCjkkUceSZcuH5zQuP766+eoo45q9p4f/vCHuf766/P666+nb9++Oeyww/L//t//S4cOHZIkTz/9dE488cQ89thjKRQKGTx4cH7/+99n8803z8SJE/Od73wn999/f+bNm5e11147v/rVr/KFL3yhRZ6nRQKCa6+9dgqFQs4///wce+yxLTFli+jatWu22mqrnHPOOZkwYULmz5+ffv365Rvf+EZOO+20vPXWW+ncuXNOPvnkTJo0KZWVlRk8eHAuueSSHHHEEYvmOffcc7PRRhvloosuyk9/+tMUCoUMHz48o0aNyj777FPCJ2S516kq2ea4hhe0pWH7Jved3bTesWsy6HNt3w98kp6DG14AAAAAAAAAUCLTp0/PrbfemjPOOKNROPB93bt3b/a+bt265fLLL88aa6yRZ599Nt/4xjfSrVu3/OAHP0iSHHbYYdl0001z0UUXpV27dnnqqacWhQePO+64zJs3L/fee2+6dOmSMWPGpGvXlttUp1AsFoufdpIuXbpk7ty5GT16dLbYYouW6Kss1dbWprq6OjU1Namqqip1O0C5u/uXyd1nfvC+Xcfky39I1v9S6XoCAAAAAAAAAFhOPfLII9lqq63y97//PV/60uLzFYVCIddff33222+/Zsd/9atf5a9//Wsee+yxJElVVVXOP//8fPWrX21y7UYbbZT9998/P/vZz1rkGT6qRXYQXHPNNTNhwoTU13/MkZYAtK3P/ihZ/8vJC7ck7Ts1BAO79Sl1VwAAAAAAAAAAy6X399orFApLdd+1116bc889Ny+99FJmzZqVBQsWNNpA7qSTTsrRRx+dK664IrvuumsOOOCADBw4MEny3e9+N9/+9rdz++23Z9ddd83++++fjTbaqMWeqaIlJtltt92SJPfff39LTAdAS+k1JNn+xGTrbwkHAgAAAAAAAAB8jMGDB6dQKGTs2LFLfM/o0aNz8MEHZ88998xNN92UJ598Mj/+8Y8zb968Rdf8/Oc/z/PPP5+99tord955Z4YNG5brr78+SXL00Ufn5ZdfzhFHHJFnn302m2++ec4///wWe6YWOWL4xRdfzKabbpquXbvm8ccfz5prrtkSvZUdRwwDAAAAAAAAAAAsv/bcc888++yzeeGFF9KlS5dGYzNmzEj37t0bHTF89tln58ILL8yECRMWXXf00Ufn2muvzYwZM5pd45BDDsl7772XG264ocnYqaeemptvvjnPPPNMizxPi+wgOHjw4Fx11VWZPXt2tt5661x11VWNEpAAAAAAAAAAAACwvLvwwgtTX1+fLbfcMtddd11efPHFjB07Nuedd1622WabJtcPGjQor732Wq655ppMmDAh55133qLdAZNkzpw5+c53vpO77747EydOzAMPPJBHH300Q4cOTZKceOKJue222/LKK6/kiSeeyJ133rlorCW0b4lJdtlllyRJr1698sorr+SII47I17/+9QwePDirrrpq2rVrt9h7C4VC/v3vf7dEGwAAAAAAAAAAALDM1llnnTzxxBM544wzcvLJJ+ett95Kr169stlmm+Wiiy5qcv2+++6b733ve/nOd76Turq67LXXXvnpT3+an//850mSdu3aZdq0aTnyyCMzZcqU9OzZM1/+8pdz+umnJ0nq6+tz3HHH5fXXX09VVVX22GOPnHPOOS32PC1yxHBFRUUKhUKSZEmnKxQKKRaLKRQKqa+v/7QtrBAcMQwAAAAAAAAAAEBbaZEdBHfcccdFAUEAAAAAAAAAAACg9FpkB0GWjB0EAQAAAAAAAAAAaCsVpW4AAAAAAAAAAAAAaHktcsTwh02ePDnTpk1LbW1tqqqqstpqq6Vv374tvQwAAAAAAAAAAADwMVokIHjzzTfnsssuy/3335+33367yXivXr2y/fbb52tf+1r22muvllgSAAAAAAAAAAAA+BiFYrFYXNabn3nmmRxxxBF57rnnkiQfN1WhUEiSrL/++rniiiuy8cYbL+uyK6za2tpUV1enpqYmVVVVpW4HAAAAAAAAAACAMrbMAcHrrrsuhx9+eObNm7coGNi5c+dsvPHG6dOnT7p06ZJZs2ZlypQpeeaZZzJ79uxF91ZWVuaKK67IV77ylZZ5ihWEgCAAAAAAAAAAAABtZZkCgg888EB23XXX1NXVJUn22muvfPe7383nPve5VFRUNLl+4cKFueOOO3L++efnlltuSdIQErzjjjuy/fbbf8pHWHEICAIAAAAAAAAAANBWljoguHDhwmy00UYZM2ZMKisrc/nll+eggw5a4vuvueaajBgxIvPmzcuwYcPyzDPPNBsqLEcCggAAAAAAAAAAALSVpU7mXXvttRkzZkwKhUL+/Oc/L1U4MEkOPvjg/PnPf06SjB07Ntdee+3StgAAAAAAAAAAAAB8gqUOCN5www1Jks997nM54IADlmnRAw88MLvsskuS5B//+McyzQEAAAAAAAAAAADl6MILL8w666yTTp06ZbPNNst99923TPMsdUDw0UcfTaFQyCGHHLJMC77vsMMOS7FYzKOPPvqp5gEAAAAAAAAAAIDWUr+wmIcmTMs/nnojD02YlvqFxVZd769//WtOPPHE/PjHP86TTz6ZHXbYIXvuuWdee+21pZ6rUCwWl6rb7t27Z+bMmbnvvvuy7bbbLvWC73vooYey3XbbpVu3bqmpqVnmeVYktbW1qa6uTk1NTaqqqkrdDgAAAAAAAAAAAB/j1ufeyuk3jslbNXMX1Vav7pSf7TMse2ywequsudVWW2X48OG56KKLFtWGDh2a/fbbL2eeeeZSzbXUOwjOnz8/SdKxY8elvbWRDh06JEkWLFjwqeYBAAAAAAAAAACAlnbrc2/l21c+0SgcmCSTa+bm21c+kVufe6vF15w3b14ef/zx7Lbbbo3qu+22Wx588MGlnm+pA4K9e/dOkkycOHGpF/uw9+/v1avXp5oHAAAAAAAAAAAAWlL9wmJOv3FMmjue9/3a6TeOafHjht95553U19enT58+jep9+vTJ5MmTl3q+pQ4IDhs2LEly4403LvViH3bDDTckSdZff/1PNQ8AAAAAAAAAAAC0pEdemd5k58APKyZ5q2ZuHnllequsXygUGq9XLDapLYmlDgjutddeKRaLufrqq/Pcc88t9YJJ8swzz+Tqq69OoVDIXnvttUxzAAAAAAAAAAAAQGuYOnPx4cBluW5J9ezZM+3atWuyW+DUqVOb7Cq4JJY6IHj44YdntdVWy4IFC7LPPvvklVdeWar7X3755Xzxi1/MggUL0qNHjxx++OFL2wIAAAAAAAAAAAC0mt7dOrXodUuqY8eO2WyzzXLHHXc0qt9xxx3Zdtttl3q+pQ4IVlVV5cwzz0yxWMxrr72WTTbZJOecc05mzpz5sffNnDkzv/nNb7LpppvmtddeS6FQyBlnnJGqqqqlbhoAAAAAAAAAAABay5br9Mjq1Z2yuEN9C0lWr+6ULdfp0eJrn3TSSbnkkkty6aWXZuzYsfne976X1157Ld/61reWeq5CsVgsLksTp5xySs4+++xF5xqvssoq2XHHHTN8+PD07ds3Xbp0yaxZszJ58uQ8+eSTuffeezNnzpy8v9yJJ56Y3/zmN8uy9AqrtrY21dXVqampEYwEAAAAAAAAAABYjt363Fv59pVPJEk+HLJ7PzR40eHDs8cGq7fK2hdeeGHOOuusvPXWW9lggw1yzjnnZMcdd1zqeZY5IJgkv/vd73LKKadk7tyGc5TfDws25/1lKisrc9ZZZ+X4449f1mVXWAKCAAAAAAAAAAAAK45bn3srp984Jm/VzF1UW726U362z7BWCwe2pE8VEEyS1157Leecc06uuOKKTJ8+fbHX9ejRI0cccUROOOGErL322p9myRWWgCAAAAAAAAAAAMCKpX5hMY+8Mj1TZ85N724Nxwq3q1j8ZnrLk08dEPyw559/Pk8//XTeeeedzJw5M926dctqq62WjTfeOBtssEFLLbNERowYkRkzZmTUqFGN6nfffXd23nnnvPvuu3nqqaey8847N3v/W2+9lb59+yZJpk+fnv/6r//KqFGj8uabb2a11VbLHnvskdNPPz39+/df4p4EBAEAAAAAAAAAAGgr7VtysvXXXz/rr79+S07ZJl544YUmgb3evXsnaQgHbr311unYsWMuvPDCbLDBBnn11Vfzk5/8JFtssUUeeuihDBgwoBRt09rG3pTcd3by9rik13rJDicnQ/cudVcAAAAAAAAAAABLpEUDgiuq3r17p3v37s2O/fjHP86bb76Zl156adGOgv37989tt92WwYMH57jjjss///nPNuyWNjH2puSvh33w/s0nkr8enhx0pZAgAAAAAAAAAACwQqgodQPLs4ULF+aaa67JYYcdtigc+L5VVlklxx57bG677bZMnz692fvr6upSW1vb6MUK4r6zmykWk/t/0+atAAAAAAAAAAAALIuy3kHwpptuSteuXRvV6uvrm1z3mc98ptH7NddcMy+88ELefvvtzJgxI0OHDm12/qFDh6ZYLOall17Klltu2WT8zDPPzOmnn/4pnoCSeXtc8/Wpi6kDAAAAAAAAAAAsZ8o6ILjzzjvnoosualR7+OGHc/jhhzeq3XfffenWrdui9+3bL9kPS7FYTJIUCoVmx0899dScdNJJi97X1tamX79+SzQ3JdZrvYZjhT+q93pt3wsAAAAAAAAAAMAyKOuAYJcuXTJo0KBGtddff73Jdeuss066d+/epN6rV6907949Y8aMaXb+cePGpVAoZODAgc2OV1ZWprKycukbp/R2ODn56+FJih8qFhrqAAAAAAAAAAAAK4CKUjewPKuoqMiBBx6Yq666KpMnT240NmfOnFx44YXZfffd06NHjxJ1SKsZundy0JXJmpslHbo0/PPgvyTr7VXqzgAAAAAAAAAAAJZIWe8guKSmTp2auXPnNqqtttpq6dChQ84444z8+9//zuc///mcddZZ2WCDDfLKK6/kJz/5SebPn5/f/e53JeqaVjd074YXAAAAAAAAAADACsgOgknWXXfdrL766o1ejz/+eJKkZ8+eGT16dHbeeeccc8wxGTBgQA488MAMGDAgjz76aAYMGFDi7gEAAAAAAAAAACgX9957b/bZZ5+sscYaKRQKGTVq1DLPVSgWi8WWa42PU1tbm+rq6tTU1KSqqqrU7QAAAAAAAAAAAPBJFtYnEx9MZk1JuvZJ1to2qWjXasv985//zAMPPJDhw4dn//33z/XXX5/99ttvmeZyxDAAAAAAAAAAAAA0Z8wNya0/TGrf/KBWtUayx/8mw77YKkvuueee2XPPPVtkLkcMAwAAAAAAAAAAwEeNuSH525GNw4FJUvtWQ33MDaXpaykICAIAAAAAAAAAAMCHLaxv2DkwxWYG/1O79UcN1y3HBAQBAAAAAAAAAADgwyY+2HTnwEaKSe0bDdctxwQEAQAAAAAAAAAA4MNmTWnZ60pEQBAAAAAAAAAAAAA+rGuflr2uRNqXugEAAAAAAAAAAABYrqy1bVK1RlL7VpJiMxcUGsbX2rbFl541a1ZeeumlRe9feeWVPPXUU+nRo0f69++/VHPZQRAAAAAAAAAAAAA+rKJdssf//udN4SOD/3m/xy8brmthjz32WDbddNNsuummSZKTTjopm266af7f//t/Sz1XoVgsNhdvpBXU1tamuro6NTU1qaqqKnU7AAAAAAAAAAAAfJwxNyS3/jCpffODWtWaDeHAYV8sXV9LSECwDQkIAgAAAAAAAAAArGAW1icTH0xmTUm69mk4VrgVdg5sDe1L3QAAAAAAAAAAAAAstyraJevsUOoulklFqRsAAAAAAAAAAAAAWp6AIAAAAAAAAAAAAJQhAUEAAAAAAAAAAAAoQwKCAAAAAAAAAAAAUIYEBAEAAAAAAAAAAKAMCQgCAAAAAAAAAABAGRIQBAAAAAAAAAAAgDIkIAgAAAAAAAAAAABlSEAQAAAAAAAAAAAAypCAIAAAAAAAAAAAAJQhAUEAAAAAAAAAAAAoQwKCAAAAAAAAAAAAUIYEBAEAAAAAAAAAAKAMCQgCAAAAAAAAAABAGRIQBAAAAAAAAAAAgDIkIAgAAAAAAAAAAABlqOwCgiNGjEihUMi3vvWtJmPHHntsCoVCRowY0ejaj7722GOPRvc9+OCD+cIXvpBVV101nTp1yoYbbpizzz479fX1bfFIAAAAAAAAAAAAsNTKLiCYJP369cs111yTOXPmLKrNnTs3V199dfr379/o2j322CNvvfVWo9fVV1+9aPz666/PTjvtlM985jO56667Mm7cuJxwwgk544wzcvDBB6dYLLbZcwEAAAAAAAAAAMCSal/qBlrD8OHD8/LLL+fvf/97DjvssCTJ3//+9/Tr1y8DBgxodG1lZWX69u3b7DzvvfdevvGNb+SLX/xi/vCHPyyqH3300enTp0+++MUv5m9/+1sOOuig1nsYAIByNGNS8tRfkllTkv7bJuvvl7TrUOquVnrPvVGTc+4Yn0dfnZ7eVZ3y1W3WyhHbrF3qtgAAAAAAAIBlVJY7CCbJ1772tVx22WWL3l966aU56qijlmqO22+/PdOmTcv3v//9JmP77LNPhgwZ0mi3QQAAlsDL9yS/2zK5+8zksUuTvx+dXL53Mm92qTtbqb389qwc9PuH8u9xU1M7d0FemjorP/3H8/ntv14sdWsAAAAAAADAMirbgOARRxyR+++/P6+++momTpyYBx54IIcffniT62666aZ07dq10eu///u/kyTjx49PkgwdOrTZNdZbb71F1zSnrq4utbW1jV4AACu1YjG5+aRk/kfCgJNGJ49f3nrrvnJfcumeyX/3Ts4bnjzyx9ZbawV16QOv5L159U3ql9z/cubOb1oHAAAAAAAAln9lecRwkvTs2TN77bVXRo4cmWKxmL322is9e/Zsct3OO++ciy66qFGtR48ejd4Xi8Vm1ygWiykUCovt4cwzz8zpp5++DN0DAJSpaROSaS81P/bibck2x7b8mq8/nlz55aR+XsP76ROSW76fzJ+TbPfdll9vBTV+8qxm6zPnLsibM+ZkQK+ubdwRAAAAAAAA8GmV7Q6CSXLUUUfl8ssvz8iRIxd7vHCXLl0yaNCgRq/3A4JDhgxJkowdO7bZe8eNG5fBgwcvdv1TTz01NTU1i16TJk36lE8EALCC69jlY8ZaKYD20PkfhAM/7MHzkvoFrbPmCmhg7+Z//LtWtk/f6k5t3A0AAAAAAADQEso6ILjHHntk3rx5mTdvXnbfffelvn+33XZLjx49cvbZZzcZu+GGG/Liiy/mkEMOWez9lZWVqaqqavQCAFipVa2eDNi5+bGNF//nqk/l7fHN1997O5kzvXXWXAEdtd3aWaVDuyb1Eduunc4dy3bjcQAAAAAAAChrZR0QbNeuXcaOHZuxY8emXbum3+xMkrq6ukyePLnR65133knSsLvg73//+/zjH//IN7/5zTzzzDN59dVX86c//SkjRozIV77ylRx44IFt+UgAACu+/S5MVt/kg/cVHZIdf5AM3bt11us9tPl61z7JKj1aZ80V0OA+3fKXb2yVHQb3TGX7ivTv0Tk/2WtoTt5tSKlbAwAAAAAAAJZR2W8F8km79t16661ZffXVG9XWXXfdjBs3Lknyla98JXfddVd+8YtfZMcdd8ycOXMyaNCg/PjHP86JJ56YQqHQar0DAJSlqjWSY+5JXn8smTk56bdl0rV366233XeTcTclC+Y2rm9/UtKu7P84vFSG9181V3x9q1K3AQAAAAAAALSQQrFYLJa6iZVFbW1tqqurU1NT47hhAIC2NOmR5O4zkzceT6r7J1t/O9n0sFJ3BQAAAAAAANCqBATbkIAgAAAAAAAAAAAAbaWi1A0AAAAAAAAAAAAALU9AEAAAAAAAAAAAAMqQgCAAAAAAAAAAAACUIQFBAAAAAAAAAAAAKEMCggAAAAAAAAAAAFCGBAQBAAAAAAAAAACgDAkIAgAAAAAAAAAAQBkSEAQAAAAAAAAAAIAyJCAIAAAAAAAAAAAAZUhAEAAAAAAAAAAAAMqQgCAAAAAAAAAAAACUIQFBAAAAAAAAAAAAKEMCggAAAAAAAAAAAFCGBAQBAAAAAAAAAACgDAkIAgAAAAAAAAAAQBkSEAQAAAAAAAAAAIAyJCAIAAAAAAAAAAAAZUhAEAAAAAAAAAAAAMqQgCAAAAAAAAAAAACUIQFBAAAAAAAAAAAAKEMCggAAAAAAAAAAAFCGBAQBAAAAAAAAAACgDAkIAgAAAAAAAAAAQBkSEAQAAAAAAAAAAIAyJCAIAAAAAAAAAAAAZah9qRsAAAAAAAAAAADKUM3rWfDYyMyf/loq19oyFZscnHTs0rprFovJu68kHbok3fq07lqwAigUi8ViqZtYWdTW1qa6ujo1NTWpqqoqdTsAAAAAAAAAANAqFrz6UIoj902HYt2i2qzO/dP1O/cknXu0zqIv35PcfHIy7cUkhWTQ55J9LxQUZKW2wh4xvM8++2TXXXdtduyhhx5KoVDIE088kUKhkKeeeqrZ6y6//PIUCoUmr06dOjW6btKkSfn617+eNdZYIx07dsxaa62VE044IdOmTWvpxwIAAAAAAACA8lU3K3nxjmTiQw27fAFl672/HNkoHJgkXWe/ljev+2HrLDjjteSqg/4TDkySYvLSv5JrDm2d9WAFscIeMfz1r389X/7ylzNx4sSstdZajcYuvfTSbLLJJunR45PTxlVVVXnhhRca1QqFwqJ/f/nll7PNNttkyJAhufrqq7POOuvk+eefzymnnJJ//vOfGT169BKtAwAAAAAAAAArtSevTP75o2TezIb3PQYmB12Z9BlW2r6AFjevZmqq509tdqznhOuT/LHlF33yL8mCOXlp4Rq5e+Em6Zy5+UK7h9P9jceSN59M1ti05deEFcAKGxDce++907t371x++eX52c9+tqg+e/bs/PWvf80vfvGLJZqnUCikb9++ix0/7rjj0rFjx9x+++1ZZZVVkiT9+/fPpptumoEDB+bHP/5xLrrook/3MAAAAAAAAABQzqaMSW44Piku/KA2fULy18OS7zyeVKywByACzZj1zmtZ3HZbHTK/lRadnP+df1Auqt93Uel/Fhye33f4TXaYObl11oQVwAr7FbZ9+/Y58sgjc/nll6f4oW2H/+///i/z5s3LYYcd9qnXmD59em677bYce+yxi8KB7+vbt28OO+yw/PWvf220PgAAAAAAAADwEU9f3Tgc+L7pLyeTRrd9P0CrWnWVjosda62UzSNz+zUKBybJ7HTK9+Yfl3k912+lVWH5t8IGBJPkqKOOyquvvpq77757Ue3SSy/Nl7/85ay66qpLNEdNTU26du3a6LXbbrslSV588cUUi8UMHTq02XuHDh2ad999N2+//Xaz43V1damtrW30AgAAAAAAAICVzrxZix+rm9l2fQBtotCtTxYuJgm4oFDZKmve8maXZuvvpDqjnxnXKmvCimCFPWI4SdZbb71su+22ufTSS7PzzjtnwoQJue+++3L77bcv8RzdunXLE0880aj20d0CF+f9nQMLhUKz42eeeWZOP/30Je4FAAAAAAAAAMrSoM8nj13atN6xW7LWtm3fD9C6uvRKRfNxmnTs1HyQ71OrWHwMqtCuXeusCSuAFXoHwST5+te/nuuuuy61tbW57LLLstZaa+Vzn/vcEt9fUVGRQYMGNXqtueaaSZJBgwalUChkzJgxzd47bty4rLrqqunZs2ez46eeempqamoWvSZNmrT0DwgAAAAAAAAAK7oheyTDGh/9mUJFsscvkspupekJaD21by1+bM67rbLkXltt0Gy9Z2Fmttpqh1ZZE1YEK3xA8MADD0y7du1y1VVXZeTIkfna17622B39ltZqq62Wz3/+87nwwgszZ86cRmOTJ0/OX/7ylxx00EGLXa+ysjJVVVWNXgAAAAAAAACw0qmoSL5yeXLw1clmI5Jtj0++dX8y/MhSdwa0hsnPfszgYs4e/pS22HKbfGfQtBSycFGta+bk3L3XSMfKjq2yJqwIVugjhpOka9euOeigg3LaaaelpqYmI0aMaHLNCy+80KQ2bNiwJA3HBE+ePLnJeO/evVNRUZELLrgg2267bXbffff8z//8T9ZZZ508//zzOeWUU7LmmmvmjDPOaPFnAgAAAAAAAICyU1GRrPeFhhdQ3gbtsvixitYL633/6CPz5Zcn5O5HHk/XVSqzxy67p6qbXUpZua3wAcGk4ZjhP/3pT9ltt93Sv3//JuMHH3xwk9orr7ySJKmtrc3qq6/eZPytt95K3759M3jw4Dz22GP5+c9/noMOOijTpk1L3759s99+++VnP/tZevTo0fIPBAAAAAAAAAAAK6r2HZPe6ydTn2869rmftOrSAwYMzIABA1t1DViRFIrFYuvs20kTtbW1qa6uTk1NjeOGAQAAAAAAAAAob1d+JXnpX0mKSUWHZOfTkh1OKnVXsFIpix0EAQAAAAAAAACA5czh15a6A1jpVZS6AQAAAAAAAAAAAKDlCQgCAAAAAAAAAABAGRIQBAAAAAAAAAAAgDIkIAgAAAAAAAAAAABlSEAQAAAAAAAAAAAAypCAIAAAAAAAAAAAAJQhAUEAAAAAAAAAAAAoQwKCAAAAAAAAAAAAUIYEBAEAAAAAAAAAAKAMCQgCAAAAAAAAAABAGRIQBAAAAAAAAAAAgDIkIAgAAAAAAAAAAABlSEAQAAAAAAAAAAAAypCAIAAAAAAAAAAAAJQhAUEAAAAAAAAAAAAoQwKCAAAAAAAAAAAAUIYEBAEAAAAAAAAAAKAMCQgCAAAAAAAAAABAGRIQBAAAAAAAAAAAgDIkIAgAAAAAAAAAAABlSEAQAAAAAAAAAAAAypCAIAAAAAAAAAAAAJQhAUEAAAAAAAAAAAAoQwKCAAAAAAAAAAAAUIYEBAEAAAAAAAAAAKAMCQgCAAAAAAAAAABAGRIQBAAAAAAAAAAAgDIkIJhk6tSpOeaYY9K/f/9UVlamb9++2X333fPQQw8tuubBBx/MF77whay66qrp1KlTNtxww5x99tmpr68vYecAAAAAAAAAAADQvPalbmB5sP/++2f+/PkZOXJkBgwYkClTpuTf//53pk+fniS5/vrrc+CBB+ZrX/ta7rrrrnTv3j3/+te/8oMf/CCjR4/O3/72txQKhRI/BQAAAAAAAAAAAHygUCwWi6VuopRmzJiRVVddNXfffXd22mmnJuPvvfde1lprrey000657rrrGo3deOON+eIXv5hrrrkmBx100CeuVVtbm+rq6tTU1KSqqqrFngEAAAA+7LbnJ+fCu17K+CmzMqRP1xy786Dsvn7fUrcFAAAAAAC0sZX+iOGuXbuma9euGTVqVOrq6pqM33777Zk2bVq+//3vNxnbZ599MmTIkFx99dVt0SoAAAB8otuen5xjrng8T79ekznz6/P06zX51pWP57bnJ5e6NQAAAAAAoI2t9AHB9u3b5/LLL8/IkSPTvXv3bLfddjnttNPyzDPPJEnGjx///9u79yity3J//O+Rw4AcBpAQR0gkbGuhqJhoWFqGhyQPlCeUQLOkoqTQNDtI7lI7mGnkoZ2nwi3uBJU0E0xQ2SIYguIRtwLKSQoRVJDTfH5/9PP5Ns6ASMOMDq/XWp/l89z39bmf63YtrvWsh4v7kyTZY489ar1/9913L8W83Zo1a7Jy5cpqFwAAAGxNV076vxpjRZFcOfn5BsgGAAAAAABoSNt8g2CSfP7zn8+iRYsyfvz4HH744Zk8eXL23Xff3HDDDaWYjT2JuSiKlJWV1Tp38cUXp6KionR17dp1a6QPAAAAJXNefr3W8edefq2eMwEAAAAAABqaBsH/X4sWLdKvX7/88Ic/zEMPPZQhQ4bkggsuyIc//OEkydNPP13rfc8880x22223Wue++93vZsWKFaXrpZde2mr5AwAAQJJ8eMfWtY7vtmObes4EAAAAAABoaBoEN+IjH/lI3njjjRx22GHp0KFDLr300hox48ePz3PPPZeTTz651jXKy8vTtm3bahcAAABsTV/7VI+8/aD7srLk64d8qGESAgAAAAAAGsw23yC4bNmyfPrTn87o0aPz+OOPZ+7cufnjH/+Yn/3sZznmmGPSqlWrXHPNNbnjjjvyla98JY8//njmzZuXa6+9NkOGDMkXvvCFnHDCCQ29DQAAAEiSHP7Rzrn61N7p1bVdtm/eJL26tss1p/bOYR/t3NCpAQAAAAAA9aysKIqioZNoSGvWrMnIkSMzYcKEPP/881m3bl26du2a448/Pueff35atmyZJHnwwQdz0UUXZerUqVm9enV69OiR008/PcOHD0+TJk0267NWrlyZioqKrFixwmmCAAAAAAAAAAAAbFXbfINgfdIgCAAAAAAAAAAAQH3Z5h8xDAAAAAAAAAAAAI2RBkEAAAAAAAAAAABohDQIAgAAAAAAAAAAQCOkQRAAAAAAAAAAAAAaIQ2CAAAAAAAAAAAA0AhpEAQAAAAAAAAAAIBGSIMgAAAAAAAAAAAANEIaBAEAAAAAAAAAAKAR0iAIAAAAAAAAAAAAjZAGQQAAAAAAAAAAAGiENAgCAAAAAAAAAABAI6RBEAAAAAAAAAAAABohDYIAAAAAAAAAAADQCGkQBAAAAAAAAAAAgEZIgyAAAAAAAAAAAAA0QhoEAQAAAAAAAAAAoBHSIAgAAAAAAAAAAACNkAZBAAAAAAAAAAAAaIQ0CAIAAAAAAAAAAEAjpEEQAAAAAAAAAAAAGiENggAAAAAAAAAAANAIaRAEAAAAAAAAAACARkiDIAAAAAAAAAAAADRCGgQBAAAAAAAAAACgEdIgCAAAAAAAAAAAAI2QBkEAAAAAAAAAAABohDQIAgAAAAAAAAAAQCOkQRAAAAAAAAAAAAAaoUbdILhkyZKcddZZ6dGjR1q0aJEdd9wxBx10UK6++uqsWrUqSdKtW7eUlZXVuC655JJqa914443Zf//906pVq7Rp0yaf/OQnc+eddzbEtgAAAAAAAAAAAOAdNW3oBLaWF154IX379k27du1y0UUXZc8998z69eszZ86cXHfddamsrMzRRx+dJLnwwgvz5S9/udr9bdq0Kb0+++yzM2rUqPz4xz/Osccem3Xr1mX06NE55phjcvnll2fYsGH1ujcAAADYpKfvTB68NPn7M8kHdk8+MSLZo39DZwUAAAAAANSzsqIoioZOYms44ogj8uSTT+aZZ55Jq1ataswXRZGysrJ069Ytw4cPz/Dhw2td5+GHH86BBx6YK664It/4xjeqzY0YMSK//vWv8/zzz6dr167vmNPKlStTUVGRFStWpG3btlu0LwAAANikp+9MbjnlbYNlyYmjNQkCAAAAAMA2plE+YnjZsmWZMGFCvv71r9faHJgkZWVlm7XWzTffnNatW+fMM8+sMTdixIisW7cuY8eOrfXeNWvWZOXKldUuAAAA2KoevLSWwSKZ8st6TwUAAAAAAGhYjbJB8P/+7/9SFEX+4z/+o9p4x44d07p167Ru3Trnnntuafzcc88tjb91TZ48OUkyZ86cfOhDH0rz5s1rfE5lZWUqKioyZ86cWvO4+OKLU1FRUbo255RBAAAA+Lf8/Znax5duZBwAAAAAAGi0mjZ0AlvT208JnD59eqqqqnLKKadkzZo1pfFzzjknQ4YMqRa78847b9ZnvPWo4tp897vfzbe//e3S+5UrV2oSBAAAYOv6wO7Jokdrjnfavf5zAQAAAAAAGlSjbBDs0aNHysrK8swz1U9H6N69e5KkZcuW1cY7duyYHj161LrWhz/84UyZMiVr166tcYrgokWLsnLlyuy222613lteXp7y8vIt3QYAAAC8e58YkdxyapLiXwbL/jkOAAAAAABsUxrlI4Z32GGH9OvXL6NGjcobb7zxb6110kkn5fXXX88111xTY+4Xv/hFmjVrls9//vP/1mcAAABAndmjf3Li6GTn3kmzVv/870k3Jbsf1dCZAQAAAAAA9axRniCYJFdeeWX69u2b/fbbLyNHjsxee+2V7bbbLo888kieeeaZ9O7duxT72muvZcmSJdXu33777dO2bdsceOCBOeuss3LOOedk7dq1OfbYY7Nu3bqMHj06l19+eX71q195bDAAAADvLXv0/+cFAAAAAABs08qKoijeOez9afHixbnoooty1113ZcGCBSkvL89HPvKRHH/88fna176W7bffPt26dcv8+fNr3HvmmWfm6quvLr2/7rrrctVVV+XJJ59MWVlZ9t1333znO9/J5z73uc3OZ+XKlamoqMiKFSvStm3bOtkjAAAAAAAAAAAA1KZRNwi+12gQBAAAAAAAAAAAoL5s19AJAAAAAAAAAAAAAHVPgyAAAAAAAAAAAAA0QhoEAQAAAAAAAAAAoBHSIAgAAAAAAAAAAACNkAZBAAAAAAAAAAAAaIQ0CAIAAAAAAAAAAEAjpEEQAAAAAAAAAAAAGiENggAAAAAAAAAAANAIaRAEAAAAAAAAAACARkiDIAAAAAAAAAAAADRCGgQBAAAAAAAAAACgEdIgCAAAAAAAAAAAAI1Q04ZOYFtSFEWSZOXKlQ2cCQAAAAAAAAAAwHtHmzZtUlZW1tBpNDoaBOvRa6+9liTp2rVrA2cCAAAAAAAAAADw3rFixYq0bdu2odNodMqKt461Y6urqqrKokWLtolu15UrV6Zr16556aWX/MEFtjo1B6hPag5Qn9QcoL6oN0B9UnOA+qTmAPVJzQHqU2OsOdtCT1VDcIJgPdpuu+3SpUuXhk6jXrVt27bRFCHgvU/NAeqTmgPUJzUHqC/qDVCf1BygPqk5QH1Sc4D6pObwTrZr6AQAAAAAAAAAAACAuqdBEAAAAAAAAAAAABohDYJsFeXl5bngggtSXl7e0KkA2wA1B6hPag5Qn9QcoL6oN0B9UnOA+qTmAPVJzQHqk5rD5ioriqJo6CQAAAAAAAAAAACAuuUEQQAAAAAAAAAAAGiENAgCAAAAAAAAAABAI6RBEAAAAAAAAAAAABohDYLUuSuvvDK77rprWrRokd69e+fBBx9s6JSA97iLL744H/vYx9KmTZt06tQpxx57bJ599tlqMUVRZOTIkamsrEzLli1zyCGH5Mknn6wWs2bNmnzjG99Ix44d06pVqxx99NFZsGBBtZjly5dn0KBBqaioSEVFRQYNGpRXX311a28ReA+7+OKLU1ZWluHDh5fG1BygLi1cuDCnnnpqdthhh2y//fbZe++9M2PGjNK8mgPUlfXr1+f73/9+dt1117Rs2TLdu3fPhRdemKqqqlKMmgNsqQceeCCf+9znUllZmbKystx+++3V5uuzvrz44ov53Oc+l1atWqVjx4755je/mbVr126NbQMNYFP1Zt26dTn33HOz5557plWrVqmsrMwXv/jFLFq0qNoa6g2wud7pO86/OvPMM1NWVpZf/epX1cbVHGBzbU7Nefrpp3P00UenoqIibdq0yQEHHJAXX3yxNK/msCU0CFKnbrnllgwfPjzf+973MnPmzHziE5/IkUceWa1YAbzd/fffn69//et5+OGHM3HixKxfvz6HHXZY3njjjVLMz372s/zyl7/MqFGj8sgjj6Rz587p169fXnvttVLM8OHDc9ttt2XMmDGZMmVKXn/99fTv3z8bNmwoxQwcODCzZs3KX/7yl/zlL3/JrFmzMmjQoHrdL/De8cgjj+S3v/1t9tprr2rjag5QV5YvX56+ffumWbNmufvuu/PUU0/l0ksvTbt27Uoxag5QV37605/m6quvzqhRo/L000/nZz/7WX7+85/n17/+dSlGzQG21BtvvJFevXpl1KhRtc7XV33ZsGFDjjrqqLzxxhuZMmVKxowZk7Fjx2bEiBFbb/NAvdpUvVm1alUeffTR/OAHP8ijjz6acePGZc6cOTn66KOrxak3wOZ6p+84b7n99tszbdq0VFZW1phTc4DN9U415/nnn89BBx2U3XffPZMnT85jjz2WH/zgB2nRokUpRs1hixRQh/bff/9i6NCh1cZ233334rzzzmugjID3o6VLlxZJivvvv78oiqKoqqoqOnfuXFxyySWlmDfffLOoqKgorr766qIoiuLVV18tmjVrVowZM6YUs3DhwmK77bYr/vKXvxRFURRPPfVUkaR4+OGHSzFTp04tkhTPPPNMfWwNeA957bXXit12262YOHFicfDBBxdnnXVWURRqDlC3zj333OKggw7a6LyaA9Slo446qjj99NOrjQ0YMKA49dRTi6JQc4C6k6S47bbbSu/rs778+c9/Lrbbbrti4cKFpZibb765KC8vL1asWLFV9gs0nLfXm9pMnz69SFLMnz+/KAr1BthyG6s5CxYsKHbeeefiiSeeKHbZZZfisssuK82pOcCWqq3mnHjiiaXfcWqj5rClnCBInVm7dm1mzJiRww47rNr4YYcdloceeqiBsgLej1asWJEk6dChQ5Jk7ty5WbJkSbX6Ul5enoMPPrhUX2bMmJF169ZVi6msrEzPnj1LMVOnTk1FRUX69OlTijnggANSUVGhTsE26Otf/3qOOuqofOYzn6k2ruYAdWn8+PHZb7/9cvzxx6dTp07ZZ5998l//9V+leTUHqEsHHXRQ/vrXv2bOnDlJksceeyxTpkzJZz/72SRqDrD11Gd9mTp1anr27Fnt9J7DDz88a9asyYwZM7bqPoH3phUrVqSsrKx0Urt6A9SlqqqqDBo0KOecc04++tGP1phXc4C6UlVVlbvuuisf/vCHc/jhh6dTp07p06dPtccQqzlsKQ2C1Jl//OMf2bBhQ3bcccdq4zvuuGOWLFnSQFkB7zdFUeTb3/52DjrooPTs2TNJSjVkU/VlyZIlad68edq3b7/JmE6dOtX4zE6dOqlTsI0ZM2ZMHn300Vx88cU15tQcoC698MILueqqq7LbbrvlnnvuydChQ/PNb34zv//975OoOUDdOvfcc3PyySdn9913T7NmzbLPPvtk+PDhOfnkk5OoOcDWU5/1ZcmSJTU+p3379mnevLkaBNugN998M+edd14GDhyYtm3bJlFvgLr105/+NE2bNs03v/nNWufVHKCuLF26NK+//nouueSSHHHEEZkwYUKOO+64DBgwIPfff38SNYct17ShE6DxKSsrq/a+KIoaYwAbM2zYsDz++OOZMmVKjbktqS9vj6ktXp2CbctLL72Us846KxMmTEiLFi02GqfmAHWhqqoq++23Xy666KIkyT777JMnn3wyV111Vb74xS+W4tQcoC7ccsstGT16dP77v/87H/3oRzNr1qwMHz48lZWVGTx4cClOzQG2lvqqL2oQkCTr1q3LSSedlKqqqlx55ZXvGK/eAO/WjBkzcvnll+fRRx9913/u1Rzg3aqqqkqSHHPMMfnWt76VJNl7773z0EMP5eqrr87BBx+80XvVHN6JEwSpMx07dkyTJk1qdBMvXbq0RucxQG2+8Y1vZPz48Zk0aVK6dOlSGu/cuXOSbLK+dO7cOWvXrs3y5cs3GfPyyy/X+Ny///3v6hRsQ2bMmJGlS5emd+/eadq0aZo2bZr7778/V1xxRZo2bVqqB2oOUBd22mmnfOQjH6k2tscee+TFF19M4nsOULfOOeecnHfeeTnppJOy5557ZtCgQfnWt75VOjVZzQG2lvqsL507d67xOcuXL8+6devUINiGrFu3LieccELmzp2biRMnlk4PTNQboO48+OCDWbp0aT74wQ+WfkueP39+RowYkW7duiVRc4C607FjxzRt2vQdf09Wc9gSGgSpM82bN0/v3r0zceLEauMTJ07Mxz/+8QbKCng/KIoiw4YNy7hx43Lfffdl1113rTa/6667pnPnztXqy9q1a3P//feX6kvv3r3TrFmzajGLFy/OE088UYo58MADs2LFikyfPr0UM23atKxYsUKdgm3IoYcemtmzZ2fWrFmla7/99sspp5ySWbNmpXv37moOUGf69u2bZ599ttrYnDlzsssuuyTxPQeoW6tWrcp221X/ua9Jkyalf4Gu5gBbS33WlwMPPDBPPPFEFi9eXIqZMGFCysvL07t37626T+C94a3mwOeeey733ntvdthhh2rz6g1QVwYNGpTHH3+82m/JlZWVOeecc3LPPfckUXOAutO8efN87GMf2+TvyWoOW6yAOjRmzJiiWbNmxbXXXls89dRTxfDhw4tWrVoV8+bNa+jUgPewr371q0VFRUUxefLkYvHixaVr1apVpZhLLrmkqKioKMaNG1fMnj27OPnkk4uddtqpWLlyZSlm6NChRZcuXYp77723ePTRR4tPf/rTRa9evYr169eXYo444ohir732KqZOnVpMnTq12HPPPYv+/fvX636B956DDz64OOuss0rv1RygrkyfPr1o2rRp8ZOf/KR47rnniptuuqnYfvvti9GjR5di1BygrgwePLjYeeedizvvvLOYO3duMW7cuKJjx47Fd77znVKMmgNsqddee62YOXNmMXPmzCJJ8ctf/rKYOXNmMX/+/KIo6q++rF+/vujZs2dx6KGHFo8++mhx7733Fl26dCmGDRtWf/8zgK1qU/Vm3bp1xdFHH1106dKlmDVrVrXfk9esWVNaQ70BNtc7fcd5u1122aW47LLLqo2pOcDmeqeaM27cuKJZs2bFb3/72+K5554rfv3rXxdNmjQpHnzwwdIaag5bQoMgde43v/lNscsuuxTNmzcv9t133+L+++9v6JSA97gktV7XX399Kaaqqqq44IILis6dOxfl5eXFJz/5yWL27NnV1lm9enUxbNiwokOHDkXLli2L/v37Fy+++GK1mGXLlhWnnHJK0aZNm6JNmzbFKaecUixfvrwedgm8l729QVDNAerSn/70p6Jnz55FeXl5sfvuuxe//e1vq82rOUBdWblyZXHWWWcVH/zgB4sWLVoU3bt3L773ve9V+8tyNQfYUpMmTar195vBgwcXRVG/9WX+/PnFUUcdVbRs2bLo0KFDMWzYsOLNN9/cmtsH6tGm6s3cuXM3+nvypEmTSmuoN8DmeqfvOG9XW4OgmgNsrs2pOddee23Ro0ePokWLFkWvXr2K22+/vdoaag5boqwoimLrnlEIAAAAAAAAAAAA1LftGjoBAAAAAAAAAAAAoO5pEAQAAAAAAAAAAIBGSIMgAAAAAAAAAAAANEIaBAEAAAAAAAAAAKAR0iAIAAAAAAAAAAAAjZAGQQAAAAAAAAAAAGiENAgCAAAAAAAAAABAI6RBEAAAAAAAAAAAABohDYIAAAAAsI2YPHlyysrKUlZWlsmTJzd0Ov+2IUOGpKysLN26dWvoVAAAAADgPUmDIAAAAABspn9tsHv71bJly3Tt2jX9+/fP7373u7z55psNnS4AAAAAsI3TIAgAAAAAdeDNN9/MggULctddd+XLX/5y9t5778yZM6eh0wIAAAAAtmEaBAEAAABgC3z1q1/N7NmzS9e0adNyzTXXZI899kiSPPvsszniiCOyevXqBs4UAAAAANhWaRAEAAAAgC3QqVOn9OzZs3Ttv//++cpXvpIZM2Zk//33T5LMnTs31157bQNnCgAAAABsqzQIAgAAAEAdatmyZX7yk5+U3t99990NmA0AAAAAsC3TIAgAAAAAdeyAAw4ovZ4/f36SZO3atfnTn/6UYcOG5WMf+1jat2+fZs2aZYcddkifPn0ycuTI/OMf/6h1vfHjx6esrCxlZWW55ZZb3vHzR4wYkbKysjRp0iQvvfTSu87/9ddfzyWXXJIDDzwwHTp0SHl5ebp06ZIvfOELufPOOzd5b1VVVe67776cffbZ6du3bzp27JhmzZqlXbt22XvvvXP22WfnxRdf3Kw8nnrqqQwePDhdu3ZNixYt0rVr1wwcODCPPPLIu94TAAAAAGyLmjZ0AgAAAADQ2DRt+v9+dtuwYUOS5Ctf+UpuvPHGGrGvvPJKpk+fnunTp2fUqFG544470rdv32oxRx11VCorK7No0aJcf/31OfHEEzf62evXr8/o0aOTJP369UvXrl3fVe4zZ85M//79s2jRomrjCxcuzNixYzN27NgMGDAgN910U1q0aFHj/gsvvDA/+tGPaoyvWLEijz32WB577LFcddVVGT16dI477riN5jFmzJgMHjw4a9euLY0tWLAgN998c/74xz/mmmuueVf7AgAAAIBtkRMEAQAAAKCOPf7446XXlZWVSf7ZuNe9e/eMGDEit9xyS6ZOnZpHHnkkt956a4YOHZrmzZtn2bJlOe6447J06dJq6zVp0iSDBw9OkkycODELFizY6GffddddpftPP/30d5X3woULc+ihh2bRokUpKyvLaaedlnvuuSd/+9vf8vvf/z69evVKkowbN66Uz9utX78+O+20U772ta/lD3/4Q/73f/83M2bMyO23357vfOc7ad26dVatWpWBAwfm6aefrnWNadOmZdCgQVm7dm3Ky8tz3nnn5YEHHsi0adNyxRVXpGPHjhk6dGhmzZr1rvYHAAAAANuasqIoioZOAgAAAADeDyZPnpxPfepTSZILLrggI0eOrDXu2GOPzR133JHknyfq/eAHP8jzzz+f7t27p6ysrNZ7Zs+enY9//ON5/fXX8/3vfz//+Z//WW3+hRdeSI8ePVIURX7yk5/k/PPPr3WdY445JuPHj88OO+yQhQsXpry8vNb8J02alEMOOaTavccff3xuvfXWJMnvfve7fOlLX6o2v2bNmhx55JGZNGlSkuTPf/5zjjzyyGox8+bNy84775xmzZrVmt+CBQtywAEHZOHChTn11FPzhz/8oUbMfvvtlxkzZqRZs2a5995788lPfrLa/MKFC3PAAQeUGiV32WWXzJs3r9bPAwAAAIBtmRMEAQAAAKAOrF69OlOnTs3RRx9dag5s27Zthg4dmiT50Ic+tNHmwCTZc889c8YZZyRJbr/99hrz3bt3LzX33XDDDbWusXTp0vz5z39OkpxyyinVmgPfyeLFi3PbbbclSQ4//PAazYFJUl5enuuuu670COVRo0bViOnWrdtGmwOTpEuXLjnnnHOSJOPHj8/b//3y9OnTM2PGjCTJmWeeWaM5MEl23nnnXHrppZu5MwAAAADYdjVt6AQAAAAA4P3oRz/6UX70ox9tdL5t27YZO3ZsPvCBD9Q6v3z58rzyyit58803S01y7dq1S5I89dRTWbduXY1GuzPOOCP33XdfnnvuuUyZMiUHHXRQtfk//OEPWb9+fZJ3/3jhSZMmZcOGDUlSa3PgW7p165Z+/frl7rvvzuTJk7Nhw4Y0adJko/ErV67MsmXLsmrVqtI+t99++9Lc3Llz071791L8vffeW3p92mmnbXTd4447Lu3atcurr766WfsDAAAAgG2RBkEAAAAAqENdu3bNsccem7PPPjsf/OAHq83Nnj07l112We6+++4sWbJko2tUVVVl+fLl6dSpU7XxAQMGpEOHDnnllVdy/fXX12gQvP7665Mk++67b3r16vWu8n7iiSdKr/v06bPJ2D59+uTuu+/OqlWr8sILL2S33XarNj9//vz84he/yJ/+9KfMnz9/k2v94x//qNYgOHv27CRJ8+bNs9dee230vmbNmmWfffYpPe4YAAAAAKhJgyAAAAAAbIGvfvWr+drXvlZ636JFi+ywww5p3759rfHXXntthg4dWjrh752sXr26xlh5eXkGDRqUyy+/PP/zP/+TK664Iq1atUryz0fzPvnkk0k2fQLgxrzyyiul1zvuuOMmYzt37lzrfUly99135wtf+EJWrVq1WZ/79n0uX748SdKhQ4fSo4w35p3yBAAAAIBt3XYNnQAAAAAAvB916tQpPXv2LF09evTYaHPgM888U2oO7NSpU37+859nxowZWbZsWdauXZuiKFIURa699trSPW89jvftzjjjjCTJ66+/nltvvbU0/tbpgS1atMjAgQPrapu12lhuy5Yty8CBA7Nq1aq0bt06I0eOzNSpU7N06dKsWbOmtM+//vWvG13rrfdlZWVbnAcAAAAA8E9OEAQAAACAreyGG27I+vXr06RJk0yePDl77LFHrXFvnZ63KT179kyfPn0ybdq0XH/99Rk8eHDefPPNjBkzJkly3HHHpV27du86xw4dOpRev/zyyzUej/yvXn755Vrv++Mf/5hXX301STJu3Lj069ev1vs3tc+31lu2bFk2bNiQJk2abDR26dKlG50DAAAAAJwgCAAAAABb3VuP/u3Vq9dGmwOT5G9/+9tmrffWKYIPPPBAXnjhhYwbN67UmHf66advUY49e/YsvZ42bdomY6dPn54k2X777bPrrruWxt/aZ4cOHTbaHJhsep977rlnkmTt2rV57LHHNhq3fv36zJo1a5N5AgAAAMC2ToMgAAAAAGxl69evT5KsWrVqozFLlizJHXfcsVnrnXTSSWndunWKosgNN9xQerxwt27dcuihh25RjoccckjptL5/fdTx27344ouZOHFi6Z6mTf/fQ0re2ueaNWtSVVVV6/2rVq3K73//+42u/5nPfKb0+sYbb9xo3G233bZZJy4CAAAAwLZMgyAAAAAAbGW77bZbkmTOnDl5+OGHa8yvWrUqAwcOzOrVqzdrvdatW+ekk05KklxzzTW57777kiRDhgxJWVnZFuVYWVmZ4447Lklyzz335LrrrqsRs3bt2px++ulZt25dkmTYsGHV5t/a5xtvvJFbb721xv0bNmzIGWeckUWLFm00j/333z/77rtvkuSqq67KlClTasQsXrw4Z5999mbuDAAAAAC2XRoEAQAAAGArGzRoUJKkqqoqn/3sZ3PJJZfkgQceyPTp03PVVVdl7733zqRJk9K3b9/NXvOtxwwvXbo0VVVVKSsry5AhQ/6tPC+77LK0b9++tP6XvvSlTJw4MTNmzMhNN92UPn365K9//WuS5IQTTsiRRx5Z7f4TTjgh5eXlSf7ZrHj++efnvvvuy9/+9rfceOON6dOnT26++eZ33OeVV16Zpk2bZt26denXr1/OP//8TJkyJY888khGjRqV3r17Z/HixenVq9e/tV8AAAAAaOzKiqIoGjoJAAAAAHg/mDx5cj71qU8lSS644IKMHDlys++98MILc8EFF2x0fsSIEenZs2dOO+20JMncuXPTrVu3Ta651157Zfbs2UmSfv36ZcKECZud/6RJk3LIIYfUiJk5c2b69++/yVP+BgwYkJtuuiktWrSoMXf99dfnjDPO2Ogjhk888cR8+ctfLj1KeGN53HzzzRkyZEjWrl1bY65p06al0wVvvPHG7LLLLpk3b95G8wUAAACAbZUTBAEAAACgHvzwhz/MXXfdlcMOOyzt27dP8+bN06VLlwwYMCATJkzIL37xi3e95qmnnlp6ffrpp9dJnvvss0+effbZXHzxxenTp0/atWuX5s2bp7KyMgMGDMj48eMzduzYWpsDk+S0007Lgw8+mGOPPTYf+MAH0qxZs+y000454ogjcsstt2TMmDFp0qTJO+Zx8sknZ+bMmRk0aFAqKyvTvHnz7LzzzjnhhBMyZcqU0gmKAAAAAMDGOUEQAAAAAN6nBg0alNGjR6d9+/ZZvHhx6fG+AAAAAACJEwQBAAAA4H1pxYoVGTt2bJLklFNO0RwIAAAAANSgQRAAAAAA3od+85vfZPXq1UmSM888s4GzAQAAAADei5o2dAIAAAAAwDtbv3595s2blzVr1uSBBx7Ij3/84yRJ//7907NnzwbODgAAAAB4LyoriqJo6CQAAAAAgE2bN29edt1112pjbdu2zYwZM9KjR48GygoAAAAAeC/ziGEAAAAAeJ/p1KlTjjnmmDz00EOaAwEAAACAjXKCIAAAAAAAAAAAADRCThAEAAAAAAAAAACARkiDIAAAAAAAAAAAADRCGgQBAAAAAAAAAACgEdIgCAAAAAAAAAAAAI2QBkEAAAAAAAAAAABohDQIAgAAAAAAAAAAQCOkQRAAAAAAAAAAAAAaIQ2CAAAAAAAAAAAA0AhpEAQAAAAAAAAAAIBG6P8DIUbvla8T2KoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Payload\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.   \n",
    "\n",
    "However for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  6: Visualize the launch success yearly trend\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function will help you get the year from the date:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function to Extract years from the date \n",
    "year=[]\n",
    "def Extract_year(date):\n",
    "    for i in df[\"Date\"]:\n",
    "        year.append(i.split(\"-\")[0])\n",
    "    return year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Success Rate')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n",
    "#df_bar = df[['Class', 'Orbit']].groupby(by='Orbit').mean().transpose()\n",
    "year=[]\n",
    "year = Extract_year(df['Date'])\n",
    "df['Year'] = year\n",
    "\n",
    "df_sr = df[['Year', 'Class']].groupby(by='Year').mean()\n",
    "sns.lineplot(x='Year', y='Class', data=df_sr)\n",
    "plt.xlabel('Year', fontsize=20)\n",
    "plt.ylabel('Success Rate', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features Engineering \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n",
       "0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n",
       "1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n",
       "2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n",
       "3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n",
       "4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n",
       "\n",
       "    Legs LandingPad  Block  ReusedCount Serial  \n",
       "0  False        NaN    1.0            0  B0003  \n",
       "1  False        NaN    1.0            0  B0005  \n",
       "2  False        NaN    1.0            0  B0007  \n",
       "3  False        NaN    1.0            0  B1003  \n",
       "4  False        NaN    1.0            0  B1004  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### TASK  7: Create dummy variables to categorical columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>get_dummies</code> and <code>features</code> dataframe to apply OneHotEncoder to the column <code>Orbits</code>, <code>LaunchSite</code>, <code>LandingPad</code>, and <code>Serial</code>. Assign the value to the variable <code>features_one_hot</code>, display the results using the method head. Your result dataframe must include all features including the encoded ones.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Orbit_ES-L1</th>\n",
       "      <th>Orbit_GEO</th>\n",
       "      <th>...</th>\n",
       "      <th>Serial_B1048</th>\n",
       "      <th>Serial_B1049</th>\n",
       "      <th>Serial_B1050</th>\n",
       "      <th>Serial_B1051</th>\n",
       "      <th>Serial_B1054</th>\n",
       "      <th>Serial_B1056</th>\n",
       "      <th>Serial_B1058</th>\n",
       "      <th>Serial_B1059</th>\n",
       "      <th>Serial_B1060</th>\n",
       "      <th>Serial_B1062</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n",
       "0             1  6104.959412        1     False   False  False    1.0   \n",
       "1             2   525.000000        1     False   False  False    1.0   \n",
       "2             3   677.000000        1     False   False  False    1.0   \n",
       "3             4   500.000000        1     False   False  False    1.0   \n",
       "4             5  3170.000000        1     False   False  False    1.0   \n",
       "\n",
       "   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
       "0            0            0          0  ...             0             0   \n",
       "1            0            0          0  ...             0             0   \n",
       "2            0            0          0  ...             0             0   \n",
       "3            0            0          0  ...             0             0   \n",
       "4            0            0          0  ...             0             0   \n",
       "\n",
       "   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0             0             0             0  \n",
       "1             0             0             0  \n",
       "2             0             0             0  \n",
       "3             0             0             0  \n",
       "4             0             0             0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "\n",
    "features_one_hot = pd.get_dummies(features, columns = ['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])\n",
    "features_one_hot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  8: Cast all numeric columns to `float64`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot = features_one_hot.astype(dtype = 'float64')\n",
    "features_one_hot.dtypes\n",
    "\n",
    "features_one_hot.to_csv('dataset_part_3.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>features_one_hot.to_csv('dataset_part_3.csv', index=False)</code>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description      |\n",
    "| ----------------- | ------- | ---------- | ----------------------- |\n",
    "| 2021-10-12        | 1.1     | Lakshmi Holla     | Modified markdown |\n",
    "| 2020-09-20        | 1.0     | Joseph     | Modified Multiple Areas |\n",
    "| 2020-11-10       | 1.1    | Nayef      | updating the input data |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}