jupyter-labs-eda-dataviz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\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",
    "\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [
    {
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       "<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",
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       "      <th>...</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>VLEO</td>\n",
       "      <td>KSC LC 39A</td>\n",
       "      <td>True ASDS</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5e9e3032383ecb6bb234e7ca</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>B1060</td>\n",
       "      <td>-80.603956</td>\n",
       "      <td>28.608058</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>87</td>\n",
       "      <td>2020-10-06</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>VLEO</td>\n",
       "      <td>KSC LC 39A</td>\n",
       "      <td>True ASDS</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5e9e3032383ecb6bb234e7ca</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>B1058</td>\n",
       "      <td>-80.603956</td>\n",
       "      <td>28.608058</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88</td>\n",
       "      <td>2020-10-18</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>VLEO</td>\n",
       "      <td>KSC LC 39A</td>\n",
       "      <td>True ASDS</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5e9e3032383ecb6bb234e7ca</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>B1051</td>\n",
       "      <td>-80.603956</td>\n",
       "      <td>28.608058</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>2020-10-24</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>VLEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>True ASDS</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5e9e3033383ecbb9e534e7cc</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>B1060</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>90</td>\n",
       "      <td>2020-11-05</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3681.000000</td>\n",
       "      <td>MEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>True ASDS</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5e9e3032383ecb6bb234e7ca</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1062</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 18 columns</p>\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",
       "85            86  2020-09-03       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n",
       "86            87  2020-10-06       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n",
       "87            88  2020-10-18       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n",
       "88            89  2020-10-24       Falcon 9  15400.000000  VLEO  CCAFS SLC 40   \n",
       "89            90  2020-11-05       Falcon 9   3681.000000   MEO  CCAFS SLC 40   \n",
       "\n",
       "        Outcome  Flights  GridFins  Reused   Legs                LandingPad  \\\n",
       "0     None None        1     False   False  False                       NaN   \n",
       "1     None None        1     False   False  False                       NaN   \n",
       "2     None None        1     False   False  False                       NaN   \n",
       "3   False Ocean        1     False   False  False                       NaN   \n",
       "4     None None        1     False   False  False                       NaN   \n",
       "..          ...      ...       ...     ...    ...                       ...   \n",
       "85    True ASDS        2      True    True   True  5e9e3032383ecb6bb234e7ca   \n",
       "86    True ASDS        3      True    True   True  5e9e3032383ecb6bb234e7ca   \n",
       "87    True ASDS        6      True    True   True  5e9e3032383ecb6bb234e7ca   \n",
       "88    True ASDS        3      True    True   True  5e9e3033383ecbb9e534e7cc   \n",
       "89    True ASDS        1      True   False   True  5e9e3032383ecb6bb234e7ca   \n",
       "\n",
       "    Block  ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0     1.0            0  B0003  -80.577366  28.561857      0  \n",
       "1     1.0            0  B0005  -80.577366  28.561857      0  \n",
       "2     1.0            0  B0007  -80.577366  28.561857      0  \n",
       "3     1.0            0  B1003 -120.610829  34.632093      0  \n",
       "4     1.0            0  B1004  -80.577366  28.561857      0  \n",
       "..    ...          ...    ...         ...        ...    ...  \n",
       "85    5.0            2  B1060  -80.603956  28.608058      1  \n",
       "86    5.0            2  B1058  -80.603956  28.608058      1  \n",
       "87    5.0            5  B1051  -80.603956  28.608058      1  \n",
       "88    5.0            2  B1060  -80.577366  28.561857      1  \n",
       "89    5.0            0  B1062  -80.577366  28.561857      1  \n",
       "\n",
       "[90 rows x 18 columns]"
      ]
     },
     "execution_count": 3,
     "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)\n",
    "df"
   ]
  },
  {
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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JrM7Mn5WjM18EXAXcBFxW7gvwCuDlEbGd4pqc763weiVJkiRJkiRJkiTNMNFw2cz2O0Z8BlgOPCMzPzVh22HAh4DfBf4pM1/S60Sn08jISI6OjvY7DUmSJEmSJEmSJKmV6HcC/VJlBOdvA98CPhIRjz6wMiICeD9F5+a7B71zU5IkSZIkSZIkSdLM1XEHZ2Z+DzgD+CGwKSKOKze9F3g28IHMfEHPM5QkSZIkSZIkSZKkUpURnGTmTuAZwNHAlRHxHuB5wL8B5/Q8O0mSJEmSJEmSJElqMFT1AZm5LSJWAR8FHgVsBP4gM/f3OjlJkiRJkiRJkiRJatSygzMinjvJYz8OPA7YBDynuBRnITPf35PsJEmSJEmSJEmSJKlBuxGcFwHZZH2U6w/0aL5zwroE7OCUJEmSJEmSJEmS1HPtOji9pqYkSZIkSZIkSZKkGaVlB2dmXjydiUiSJEmSJEmSJEnSZOb1OwFJkiRJkiRJkiRJ6pQdnJIkSZIkSZIkSZIGRssOzojYFhEr6wSNiPtHxFsj4hX1U5MkSZIkSZIkSZKkg7Ubwfk94CMRcVNEvCIihtsFiogjIuIZEXEJcAtwNnBjzzKVJEmSJEmSJEmSNOcNtdqQmU+KiFXA64G/A94QEd8GRoHbgT3AvYGFwCOAXwHuBfwUuAh4bWbeMaXZS5IkSZIkSZIkSZpTWnZwAmTmBmBDRDwdOBd4CvD/Ndn1Z8D1wIeB92bmnT3OU5IkSZIkSZIkSZLad3AekJlXA1cDRMTDgcUUIzd/CNwB3JiZd09VkpIkSZIkSZIkSZIEHXZwNsrMm4GbpyAXSZIkSZIkSZIkSWprXr8TkCRJkiRJkiRJkqRO2cEpSZIkSZIkSZIkaWDYwSlJkiRJkiRJkiRpYNjBKUmSJEmSJEmSJGlg2MEpSZIkSZIkSZIkaWDYwSlJkiRJkiRJkiRpYNjBKUmSJEmSJEmSJGlgDHUbICIeATwD2Atcmpl3dZ2VJEmSJEmSJEmSJDXR8QjOiHhtRNweEQsa1v0G8CVgLfB2YGtELOx9mpIkSZIkSZIkSZJUbYraZwBfy8zxhnV/ByTwOuAdwDDwkt6lJ0mSJEmSJEmSJEn3qNLBeQJw04GFiDgOeCzw9sz828x8EfBJ4Jm9TFCSJEmSJEmSJEmSDqjSwTkfaBy9eSrF6M2PNqz7IrC4B3lJkiRJkiRJkiRJ0iGqdHDeCRzXsPwU4KfA5oZ1h1eJGRHvi4g7IuKGhnULIuLqiPhGeTu/XB8R8baI2B4RX46IZQ2PObvc/xsRcXbD+sdGxFfKx7wtIqLC65UkSZIkSZIkSZI0w1Tp4Lwe+O2IeHREnAj8PvC5zPxhwz4nALdXiHkRcMaEda8EPpGZJwGfKJehuAboSeXPeRTX/CQiFlBcA/RkYDnwugOdouU+/6fhcROfS5IkSZIkSZIkSdIAqdLB+Q/A0cA24Oby/v87sDEiDqOYtna004CZ+RkOnvYWYCVwcXn/Yu65pudK4P1Z+DxwTEQ8CDgduDozxzNzD3A1cEa57Rcz8/OZmcD78fqgkiRJkiRJkiRJ0kAb6nTHzPxsRPwWxYjIBP41Mz/WsMvjgduAf+8ypwdk5oFRoN8GHlDePw7Y1bDfreW6dutvbbK+qYg4j2JkKIsXexlRSZIkSZIkSZIkaSbquIMTIDOvBK5sse2zwGN6kVRDzIyI7GXMNs/1LuBdACMjI9PynJIkSZIkSZIkSZKqqTJFbUsRMT8ijupFLOA75fSylLd3lOtvAxY17Hd8ua7d+uObrJckSZIkSZIkSZI0oDru4IyIp0XEP0TE/IZ194+ITwPfBcYj4h97kNMVwNnl/bOBjQ3rnxuFxwF3lVPZXgWcVnayzgdOA64qt90dEY+LiACe2xBLkiRJkiRJkiRJ0gCqMoLzT4Hfzcw9DevWAk8EvgnsBl4SEWd2GjAiLgGuAx4eEbdGxLnAG4GnR8Q3gN8olwE2Ad8CtgPvBl4IkJnjwN8AXyh/LijXUe7znvIx3wQarxkqSZIkSZIkSZIkacBEZmeXm4yIMeDTmfm8cvk+FJ2an83M0yPiF4CvAN/KzKdOUb7TYmRkJEdHR/udhiRJc9v4GGxcDbs2w6KTYeU6WDDc76w021hn089jXo3Hq5adu/eyZsM2tu7Yw7Il81m7aimLFx7Z77TUb/49aapYW5IkqX+i3wn0S5URnPcH/qdh+WTg3sBFAJn5v8BHgYf3KjlJkjSHbVwNO66F/fuK242r+52RZiPrbPp5zKvxeNWyZsM2toyNs29/smVsnDUbtvU7Jc0E/j1pqlhbkiRJ065KB+ePgfs0LD8RSOAzDevuBhb0IC9JkjTX7drcflnqBets+nnMq/F41bJ1x562y5qj/HvSVLG2JOke42OwfgVcsLC4HR/rd0aSZqkqHZxjQOPUs88CvpGZtzWsWwR8txeJSZKkOW7Rye2XpV6wzqafx7waj1cty5bMb7usOcq/J00Va0uS7uGodknTpEoH58XAL0fE5oj4LPDLwAcn7PMrwM29Sk6SJM1hK9fBklNh3lBxu3JdvzPSbGSdTT+PeTUer1rWrlrK8uEFDM0Llg8vYO2qpf1OSTOBf0+aKtaWJN3DUe2SpklkZmc7RtyLopPz9ykuWvofwJmZ+eNy+6OBLwOvzcy/nZp0p8fIyEiOjo72Ow1JkiRJkiRJkgbH+hXFyM0DlpwK52zqXz7S7Bf9TqBfOh7BmZk/zcw/AOYDR2fmygOdm6VvA48B/qnHOUqSJEmSJEmSpJnOUe2SpslQ1Qdk5t0t1n8Xr78pSZIkSZIkSdLctGDYEZuSpkWVa3BKkiRJkiRJkiRJUl9VGsEZEUcBLwROB44DjmiyW2bmQ3uQmyRJkiRJkiRJkiQdpOMOzog4Bvgc8CjgbuAXgbuAw4H7lLv9D/DT3qYoSZIkSZIkSZIkSYUqU9S+hqJz81xgfrnuzcB9gccDW4FvAo/sZYKSJEmSJEmSJEmSdECVDs7fBj6TmeszMw+szMLngRXAI4C/7HGOkiRJkiRJkiRJkgRU6+BcBHyxYXk/DdfgzMw7gI8BZ/UmNUmSJEmSJEmSJEk6WJUOzr0UnZoH3AU8cMI+3wGO6zYpSZIkSZIkSZIkSWqmSgfnLopRnAd8Ffj1iGiM8QTg271ITJIkSZIkSZIkSZImqtLB+WngSRER5fKHgIcCmyJidUT8G/A4YFOPc5QkSZIkSZIkSZIkAIYq7HsxcDhwPMVozncCTwWeCZxW7nMt8Joe5idJkiRJkiRJkiRJP9dxB2dmbgX+pGF5H/C7EfFY4ETgFuALmbm/eQRJkiRJkiRJkiRJ6k6VEZxNZeYXgS/2IBdJkiRJkiRJkiRJaqvKNTglSZIkSZIkSZIkqa/ajuCMiOfWCZqZ76+XjiRJkiRJkiRJkiS1NtkUtRcBWSFelPvbwSlJkiRJkiRJkiSp5zq5Buc+4D+Am6Y4F0mSJEmSJEmSJElqa7IOzk8DTwJ+B3gA8G7gssz80VQnJkmSJEmSJEmSJEkTzWu3MTOfAjwMWAucBKwHbo+If4qIX5mG/CRJkiRJkiRJkiTp59p2cAJk5vbMfAVwPHAmsBn4E+BLEbElIs6NiKOmOE9JkiRJkiRJkiRJmryD84DM3JeZH87MM4CHAm8AHgS8C/ifiDhlinKUJEmSJEmSJEmSJKBCB2ejzNyRmX8FvAC4DbgvcGwvE4uIl0XEjRFxQ0RcEhH3jojhiNgcEdsj4kMRcXi57xHl8vZy+wkNcV5Vrr85Ik7vZY6SJEmSJEmSJEmSplflDs6IeHBEvCYivgX8B7AQ+Bdga6+SiojjgBcDI5n5aOAw4Czg74E3Z+aJwB7g3PIh5wJ7yvVvLvcjIh5VPu6XgDOAt0fEYb3KU5IkSZIkSZIkSdL06qiDMyLmRcRvR8QVwC3ABcD/Ai8BHpyZZ2fmrT3ObQi4T0QMAUcCtwNPBTaU2y8GnlneX1kuU25/WkREuf7SzPxxZo4B24HlPc5TkiRJkiRJkiRJ0jQZarcxIoYpRkeeQ3G9zR9QdCS+OzO3TFVSmXlbRKwFdgI/BD4OfBH4XmbuK3e7FTiuvH8csKt87L6IuItiZOlxwOcbQjc+RpIkSZIkSZIkSdKAadvBSTHiEWAUeB1wSWb+YGpTgoiYTzH6chj4HvBvFFPMTuVzngecB7B48eKpfCpJkiRJkiRJkiRJNU02RW0A+yhGb74WuCkidk7ys6MHef0GMJaZd2bmT4GPAKcCx5RT1gIcD9xW3r8NWARQbj8a2N24vsljDpKZ78rMkcwcOfbYY3vwEiRJkiRJkiRJkqTBFhEPjIhLI+KbEfHFiNgUEQ+LiBv6ldNkIzgB7kXRMTiddgKPi4gjKaaofRrFKNJPAauAS4GzgY3l/leUy9eV2z+ZmVleM/SDEfGPwIOBk4Apm1pXkiRJkiRJkiRJmi0iIoB/By7OzLPKdUuBB/Qzr7YjODNzXp2fbpPKzM3ABmAr8JUyz3cBrwBeHhHbKa6x+d7yIe8FFpbrXw68soxzI3AZ8FXgSmB1Zv6s2/wkSZIkSZIkSZKkOeApwE8z850HVmTmNmDXgeWIOCEiPhsRW8ufx5frHxQRn4mI6yPihoh4YkQcFhEXlctfiYiX1UkqMrPbFzbrjIyM5OjoaL/TkCRJkiRJkiRJklqJKX+CiBcDw5n5sgnrTwA+mpmPLmdk3Z+ZP4qIk4BLMnMkIv4MuHdmvj4iDgOOBB4GvDEzn17GOSYzv1c1r06mqJUkSZIkSZIkSZKkZu4F/HNE/CrwM4pOTIAvAO+LiHsBl2fm9RHxLeAhEfFPwH8CH6/zhF1PJytJkiRJkiRJkiRpVroReOwk+7wM+A6wFBgBDgfIzM8Avw7cBlwUEc/NzD3lftcA/xd4T52k7OCUJEmSJEmSJEmS1MwngSMi4rwDKyLiV4BFDfscDdyemfuBPwIOK/dbAnwnM99N0ZG5LCLuB8zLzA8DrwGW1UnKKWolSZIkSZIkSZIkHSIzMyJ+B3hLRLwC+BFwC/DSht3eDnw4Ip4LXAn8oFz/ZODPI+KnwPeB5wLHAesj4sAgzFfVySsys87jZrWRkZEcHR3tdxqSJEmSJEmSJElSK9HvBPrFKWolSZIkSZIkSZIkDQw7OCVJkiRJkiRJkiQNDDs4JUmSJEmSJEmSJA2MoVYbIuKTNWNmZj6t5mMlSZIkSZIkSZIkqaWWHZzAk1usT5pftPTA+uwyJ0mSJEmSJEmSJElqquUUtZk5r/EHuDdwBTAGnAMMA/cpb58PfAvYWO4nSZIkSZJU3/gYrF8BFywsbsfH+p1Rb8321ydJkiRNocjsbMBlRPwNRcfmozPze022LwC+Arw3M1/byySn28jISI6OjvY7DUmSJEmS5q71K2DHtfcsLzkVztnUv3x6bba/PkmSJE2HZjOuTokTXvmf84BnAy8FFgG7gLcAl9zyxt/c303siDgDeCtwGPCezHzjZI9pOYKziecAH27WuQmQmePABuAPK8SUJEmSJEk61K7N7ZcH3Wx/fZIkSZo1ys7NDwMXAiPAA8rbC4EN5fZaIuIwYB3wDOBRwLMj4lGTPa7KEz4Y+Mkk+/wUeFCFmJIkSZIkSYdadHL75UE321+fJEmSZpNnA08Hjpqw/ijgNOCsLmIvB7Zn5rcy8yfApcDKyR5UpYPzVmBlRBzebGNEHFE+4W0VYkqSJEmSJB1q5bpi2tZ5Q8XtynX9zqi3ZvvrkyRJ0mzyUg7t3DzgKOBlXcQ+jmK62wNuLde1NVThCS4G/hr4ZES8Grg2M39WDh19AvB64CHA6yrElCRJkmaFnbv3smbDNrbu2MOyJfNZu2opixce2e+0JGlwLRie3deknO2vT9LMNj4GG1cX02MvOrn4ksWC4X5nJUmauRZ1ub3nqozgfCNwBfB44FPAjyLiO8CPgE+W6/+j3E+SJEmaU9Zs2MaWsXH27U+2jI2zZsO2fqckSZIkNbdxNey4FvbvK243ru53RpKkmW1Xl9vbuY2DO0iPp4PZYjvu4MzMn2bmM4E/pOjQvAtYUN5+AnhOZj4zM/dVSFqSJEmaFbbu2NN2WZIkSZoxdm1uvyxJ0sHeAvygxbYfAG/uIvYXgJMiYri8TOZZFAMu26oyghOAzPxgZj49M++Xmfcqb0/LzEtqJC1JkiTNCsuWzG+7LEmSJM0Yi05uvyxJ0sEuAa7m0E7OHwAfBy6tG7gcOPki4CrgJuCyzLxxssdV7uCUJEmSdKi1q5ayfHgBQ/OC5cMLWLtqab9TkiRJkppbuQ6WnArzhorblev6nZEkaQa75Y2/uR94FnAeMAp8p7w9D1hVbq8tMzdl5sMy86GZ+fpOHhOZ2c1zzkojIyM5Ojra7zQkSZIkSZIkTZOdu/eyZsM2tu7Yw7Il81m7aimLFx7Z77QkSWon+p1Av1QawRkRD4qIdRGxPSJ+GBE/a/LjNTglSZIkSZIkDZQ1G7axZWycffuTLWPjrNmwrd8pSZKkFoY63TEijgO2AA8AbgSOAHYAPwYeUsa6Hrir51lKkiRJkiRJ0hTaumNP22VJkjRzVBnB+VrggcAZmXnggkLrM/MRFB2cVwH3AX63tylKkiRJkiRJ0tRatmR+22VJkjRzVOngPB24MjP/a+KGzLwV+D2KDs6/7lFukiRJkiRJkjQt1q5ayvLhBQzNC5YPL2DtqqWTP0iSJPVFx1PUUozevKxh+WcUHZoAZOb3I+JqYCXw4t6kJ0mSJEmSJElTb/HCI7nsBaf0Ow1JktSBKh2cdwOHNyzvAY6bsM9dwLHdJiVJkiRJkiRJkiRphjj/6HnAs4GXAouAXcBbgEs4/679dcNGxPuA3wLuyMxHd/q4KlPU7qBI+IBtwFMj4sgygXnAacCtFWIOnJ2793Lmhddx4qs3ceaF17Fz995+pyRJkgTjY7B+BVywsLgdH+t3RpJmMt8zJEnSDOS5V805tss1KIrOzQ8DFwIjwAPK2wuBDeX2ui4Czqj6oCpP+AngKRFxr3L5YuDBwH9HxJuAa4FfAj5UNYlmIuKYiNgQEV+LiJsi4pSIWBARV0fEN8rb+eW+ERFvi4jtEfHliFjWEOfscv9vRMTZ3ea1ZsM2toyNs29/smVsnDUbtnUbUpIkqXsbV8OOa2H/vuJ24+quwnliQZrlevye0S3fcyRJEnjuVXPQDGuXS208G3g6cNSE9UdRDH48q27gzPwMMF71cVU6ON8L/D1wv/IJ/wV4K/Bo4M+Akyk6N19fNYkW3gpcmZmPAJYCNwGvBD6RmSdRdLi+stz3GcBJ5c95wDsAImIB8Loyt+XA6w50ita1dceetsuSJEl9sWtz++WKPLEgzXI9fs/olu85kiQJPPeqOWiGtculNl7KoZ2bBxwFvGz6Uil03MGZmd/IzL/PzNsb1r0MeBBwCvCgzPyDzPxRt0lFxNHAr1N0qpKZP8nM7wErKUaOUt4+s7y/Enh/Fj4PHBMRDwJOB67OzPHM3ANcTY1hro2WLZnfdlmSJKkvFp3cfrkiTyxIs1yP3zO65XuOJEkCz71qDpph7XKpjUVdbu+5bubEBSAz78zMzZn5nV4kVBoG7gTWR8SXIuI9EXEU8ICGDtZvU8zxC3AcxcVMD7i1XNdq/SEi4ryIGI2I0TvvvLNlYmtXLWX58AKG5gXLhxewdtXSOq9PkiSpt1augyWnwryh4nbluq7CeWJBmuV6/J7RLd9zJEkSeO5Vc9AMa5dLbezqcnvPRWZWf1DE8cBjgGOAu4CtmXlrz5KKGAE+D5yamZsj4q3A3cCfZuYxDfvtycz5EfFR4I2Z+bly/SeAVwBPBu6dmX9brv8r4IeZubbd84+MjOTo6GivXo4kSdLA2bl7L2s2bGPrjj0sWzKftauWsnjhkf1OS9Is5XuOJEmSJNUS0/Is5x/9HOBCmk9T+wPgPM6/64N1w0fECcBHM/PRnT5mqOITLKF4AU9vsu1q4P9m5i1VYrZwK3BrZh6YcHoDxfU2vxMRD8rM28spaO8ot9/GwcNfjy/X3UbRydm4/poe5CdJkjSrLV54JJe94JR+pyFpjvA9RwNpfAw2ri6ulbXo5GLExYLhfmclSZIkTYVLgFUU/YONnZw/AD4OXFo3cERcQtGXd7+IuBV4XWa+d7LHdTxFbUQ8EPgccBqwA/gA8A/l7S3l+s+V+3UlM78N7IqIh5erngZ8FbgCOLtcdzawsbx/BfDcKDwOuKucyvYq4LSImB8R88scr+o2P0mSJEmSNMdtXA07roX9+4rbjav7nZEkSZI0Nc6/az/wLOA8YBT4Tnl7HrCq3F5LZj47Mx+UmffKzOM76dyEaiM4/4ri+pWvAP4xM392YENEHAa8jKLD8zXAiyrEbeVPgX+NiMOBbwHnUHTIXhYR51J0sp5Z7rsJWAFsB/aW+5KZ4xHxN8AXyv0uyMzxHuQmSZIkSZLmsl2b2y9LkiRJs0nRifnB8qfvOr4GZ0TcAnwtM89os8+VwCMy84SeZNcnXoNTkiRJkiS1tX5FMXLzgCWnwjmb+pePJEmS5qLpuQbnDNTxFLXAA4EvTrLPF8v9JEmSZqWdu/dy5oXXceKrN3Hmhdexc/fefqckSZL6YeW6olNz3lBxu3JdvzOSJEmS5owqU9TeBSyZZJ/F5X6SJGmO2bl7L2s2bGPrjj0sWzKftauWsnjhkf1Oq+fWbNjGlrFixvstY+Os2bCNy15wSp+zkiRJ027BsCM2JUmSpD6pMoLzc8CqiHh8s40RcTLwe+V+kiRpjjnQ8bdvf/6842822rpjT9tlSZIkSZIkSVOrSgfn68vbT0fEByLi+RHxjIg4JyIuBj5bbn9Db1OUJEmDYK50/C1bMr/tsiRJkiRJkqSp1XEHZ2ZuBVYBdwPPAd4NfBR4D/BH5fozM3Oy63RKkqRZaK50/K1dtZTlwwsYmhcsH17A2lVL+52SJEld8xrTkiRJkgZJZGa1B0QcBawElgFHU1xz80vA5Zn5g55n2AcjIyM5Ojra7zQkSRooc+UanFNifAw2roZdm2HRybByXXFdL0mSpsmZF17382tMAywfXuA1piVJkqSZL/qdQL9U7uCcC+zglKTpZceY5rz1K2DHtfcsLzkVztnUv3wkSXPOia/exL7995wfGJoXbH/Dij5mJEmSJKkDc7aDs8o1OCVJmhJrNmxjy9g4+/YnW8bGWbNhW79TkqbXrs3tlyW1Nj5WfEnggoXF7fhYvzOSBtJcmWpekiRJ0uww1GpDRDy3btDMfH/dx0qS5p6tO/a0XZZmvUUnHzyCc9HJ/ctFGjQbV9/z97Pj2mLZEdBSZWtXLT1kRg1JkiRJmqladnACFwFV56+N8jF2cEqSOrZsyfyDrvnkiAHNOSvXHXoNTkmdcQS01BOLFx7pNTclSZIkDYx2HZznTFsWkqQ5zREDmvMWDDviTKrLEdCSJEmSJM05kVl1kObsNzIykqOjo/1OQ5IkSZpZxscOHW28YNicJEmSppLtHUlSa9HvBPrFDs4m7OCUJEmSmli/4uDRkktOdfSxJEnSVLMNJklqbc52cM7rdwKSJEmSBoTXu5QkSZp+tsEkSTqEHZySJEmSOjPx+pZe71KSJGnq2QaTJOkQdnBKkiRJ6szKdcWUaPOGituV6/qdkSRJ0uxnG0ySpEN4Dc4mvAanpG7s3L2XNRu2sXXHHpYtmc/aVUtZvPDIfqelboyPwcbVxTRAi04uPkwuGO53VpIkSZIkSZLmNq/BKUnqjTUbtrFlbJx9+5MtY+Os2bCt3ympWxtXw45rYf++4nbj6n5nJEmSJEmSJElzlh2cktRjW3fsabusAbRrc/vl2WJ8DNavgAsWFrfjY/3OSJIkSZIkSZIO0XEHZ0S8LSIeOZXJSHPZzt17OfPC6zjx1Zs488Lr2Ll7b79TUk3Llsxvu6wBtOjk9suzhSNVJUmSJEmSJA2AKiM4XwTcEBGfiYjnRMThU5WUNBc5renssXbVUpYPL2BoXrB8eAFrVy3td0rq1sp1sORUmDdU3K5c1++MpsZcGakqSZIkSZIkaaANVdj394AXAE8DTgXeGhEXAe/OzJunIDdpTun1tKY7d+9lzYZtbN2xh2VL5rN21VIWLzyyq5jqzOKFR3LZC07pdxrqpQXDcM6mfmcx9RadXIzcbFyWJEmSJEmSpBmm4xGcmfnhzDwNOBH4B+AnwMuBr0bEJyPizIi41xTlKc16vZ7W1BGhkiqbKyNVJUmSJLxUjCRJ0iCLzKz3wIghYCX3jOoE+C6wnmJU5zd7kmEfjIyM5OjoaL/T0BzT6xGXJ756E/v23/P3PTQv2P6GFb1IVZIkSZKkgXfmhdexZWz858vLhxc4G48kSRo00e8E+qXKFLUHycx9wIeBD0fE8vL+ccBfAGsi4krgdZn5xZ5kKs1yvZ7WdNmS+Qd9UOt2RKgkSZIkSbNJry8VI0mSpOnT8RS1zUTEkyLig8CnKTo37wTeAnwOWAF8PiJ+v9skJVW3dtVSlg8vYGhesHx4AWtXLe13SpIkSZptxsdg/Qq4YGFxOz7W74wkqWO9vlSMJEmSpk/lKWojYj7wPOA84GEUw1+vBd4B/Ftm/rTcbznwEeB/M/ORPcx5yjlFrSRJkiR1YP0K2HHtPctLToVzNvUvH0mqoNeXipEkSeoDp6idTEQ8kaJT81nAvYHvAxcC78jMr0zcPzO3RMR64BU9ylWSJEmSNJPs2tx+WZJmsF5fKkaSJEnTp8oUtZ8GngN8E3gR8ODMfGGzzs0Gt5U/tUTEYRHxpYj4aLk8HBGbI2J7RHwoIg4v1x9RLm8vt5/QEONV5fqbI+L0urlIkiRJkiZYdHL7ZUmSJEmSpkCVDs5LgSdl5i9n5tsz8/uTPSAz35mZw/XT4yXATQ3Lfw+8OTNPBPYA55brzwX2lOvfXO5HRDwKOAv4JeAM4O0RcVgX+UiSJEmSDli5rpiWdt5QcbtyXb8zkjTTeK1eSZIkTYHK1+CcLhFxPHAx8Hrg5cD/B9wJPDAz90XEKcD5mXl6RFxV3r8uIoaAbwPHAq8EyMy/K2P+fL92z+01OCVJkiRJknrAa/VKkiRNpTl7Dc4qIzin21uAvwD2l8sLge9l5r5y+VbguPL+ccAugHL7XeX+P1/f5DEHiYjzImI0IkbvvPPOHr4MSZIkSZKkOcpr9UqSZitnKZD6aqjqAyLi14DTKToKj2iyS2bmuU3WV3mO3wLuyMwvRsSTu4nVqcx8F/AuKEZwTsdzSpIkSZIkzWqLTj54BKfX6pUkzRYbV9/zP27HtcWysxRI06bjDs6ICOAi4A8phrwmBw99zYb1XXVwAqcCvx0RK4B7A78IvBU4JiKGylGaxwO3lfvfBiwCbi2nqD0a2N2w/oDGx0iSJEmSJGkqrVxXnPDdtbno3PRavZKk2cJZCqS+qjJF7YuAPwI+AIxQdGa+BXg88Grgf4FLgYd0m1Rmviozj8/ME4CzgE9m5nOATwGryt3OBjaW968olym3fzKLi4teAZwVEUdExDBwErCl2/wkSZIkzTJOL6VBYr1qkCwYLkazvHZ3cbtguN8ZSZLUGxNnJXCWAmlaVengPBu4OTOfl5lby3Xfy8zPZ+YbgacAzwKe2uskG7wCeHlEbKe4xuZ7y/XvBRaW618OvBIgM28ELgO+ClwJrM7Mn01hfpIkSZoKnszXVDswvdT+ffdMLyXNVNarJElS/61cB0tOhXlDxa2zFKgXPP/RsSgGOnawY8T3gYszc3W5vB/428x8bcM+HwYWZ+avTUWy02VkZCRHR0f7nYYkSZIOWL/i4Ot3LTnVa5uoty5YWHQWHTBvqBhtJM1E1qskSZI0O1U//xHtNs5mVUZwAtzVcP8HwIIJ278BPKKrjCRJkqSJvLaJpprTS2mQWK+SJEnS7OT5j45V6eC8DTiuYflbwGMn7HMSRcenJEmS1DuezNdUc3opDRLrVZIkSZqdPP/RsSpT1H4AeExmPrpcfiPw58D5wEeAJwNvBT6amc+cglynjVPUSpIkzTDjY8U15nZtLhr3K9fBguF+ZyVJkiRJktQ71c9/zNkpaqt0cD4T+DtgRWaORcQCYBQ4AUiKgzgOPCEzvzYl2U4TOzglSZIkSZIkSZI0w83ZDs6hTnfMzMuByxuWxyPiMcD/AR4K3AK8PzNv722K0j127t7Lmg3b2LpjD8uWzGftqqUsXnhkv9OSJEmSJEmSJEnSNOloBGdELAZ+jWKk5hcyc9dUJ9ZPjuCcuc688Dq2jI3/fHn58AIue8EpfcxIkiRJkiRJkiSpLxzB2UpErAVeyj0HKSPizZn551OZmNTM1h172i5LkiRJkiRJkiRpdpvXbmNEPBt4OUXn5teAm8v7Ly+3SZPauXsvZ154HSe+ehNnXngdO3fvrR1r2ZL5bZclSZIkSZIkSZI0u7Xt4AT+GNgH/EZm/lJmPgo4HdgPnDvVyWl2WLNhG1vGxtm3P9kyNs6aDdtqx1q7ainLhxcwNC9YPryAtauW9jBTSbPC+BisXwEXLCxux8f6ndHBZnp+kiRJkiRJkjTDtb0GZ0TcCVyTmb83Yf0G4MmZeb8pzq8vvAZnb5346k3s239PnQ3NC7a/YUUfM5I0q61fATuuvWd5yalwzqb+5TPRTM9PkiRJkiRJ0qCYs9fgnGwE53yKqWkn+hpwTM+z0azktLKSptWuze2X+22m5ydJkiQ564gkSZJmuMk6OOcBP22y/qfM4V5hVeO0spKm1aKT2y/320zPT5IkSdq4uph1ZP++4nbj6n5nJEmSJB1kqIN9Ws9hK3Vg8cIjuewFp/Q7DUlzxcp1xQmYXZuLzsOV6/qd0cFmen6SZo7xsUPfLxYM9zsrSdJc4KwjkiRJmuEmuwbnfqp3cGZmdtJxOmN5DU5JkiT1ndfslST1i/+DJEmSBsWcnW11silqoTg4VX46iSlJkiSpHUfPSJKq6tW1M1euKzo15w0Vt846IkmSpBmm7UjLzLSzUpIkSeqHRScfPHrGa/ZKfbNz917WbNjG1h17WLZkPmtXLWXxwiP7nZZ0qAPXzoR7rp1ZZ+TlgmFHbEqSJGlGswNTkiRJU6dXI0nmIkfPSDPGmg3b2DI2zr79yZaxcdZs2NbvlKTmHP0vSZKkOWKgr5UpSZKkGa5XI0nmIkfPSDPG1h172i5LM4aj/yVJkjRHOIJTkiRJU8eRJJJmgWVL5rddlmaMOTL6f+fuvZx54XWc+OpNnHnhdezcvbffKUmSJGma2cEpSZKkqTNx5IgjSSQNoLWrlrJ8eAFD84LlwwtYu2ppv1OSmjsw+v+1u4vbBcP9zmhKOG20JEmSnKJWkmawnbv3smbDNrbu2MOyJfNZu2opixce2e+0pME1PlZMkbprc9HRtnLdrD3xN2OsXHfoMZekAbN44ZFc9oJT+p2GpJLTRnfItq8kSZrFHMEpaVo5lVA1fjNZ6rED14Pcv++e60Fqas2RkSSSJNv6mj5OG90h276SJGkWs4NT0rSyw64av5ks9ZjXg5QkacrY1td0cdroDtn2lSRJs5hT1GrucqqWvrDDrpplS+azZWz8oGVJXVh0cvHt9cZlSZLUE7b1NV2cNrpDtn0lSdIs5ghOzV1O1dIXTiVUjd9MVlPjY7B+BVywsLgdH+t3RoNj5TpYcirMGypuvR6kJEk9Y1tfmmHmQtvXz0aSJM1ZkZn9zmHGGRkZydHR0X6noal2wcKic/OAeUPF9cF0iJ2797Jmwza27tjDsiXzWbtqKYsXHtn3WNKctX7Fwd/EXnJqcW1DSdLM4owhmmNs60uadn42kiQp+p1Av9jB2YQdnHOEjeCOnXnhdQdNk7p8eIHTAUn95Bc0JGkw2N6UJGlq+dlIkqQ528E5I6eojYhFEfGpiPhqRNwYES8p1y+IiKsj4hvl7fxyfUTE2yJie0R8OSKWNcQ6u9z/GxFxdr9ek2aguTBVS494LR1phpl47RyvpSNJM9Ouze2XJUlSd/xsJEnSnDUjOziBfcCfZeajgMcBqyPiUcArgU9k5knAJ8plgGcAJ5U/5wHvgKJDFHgdcDKwHHjdgU5RiQXDxTfoX7u7uHW6sJa8ls4s4zVKBp9f0JCkweBJV0mSppafjSRJmrMGYoraiNgI/HP58+TMvD0iHgRck5kPj4gLy/uXlPvfDDz5wE9mvqBcf9B+rThFrXQwr6UzyzhdniRJ08NrcEqSJOkA24aSpsacnaJ2qN8JTCYiTgAeA2wGHpCZt5ebvg08oLx/HLCr4WG3lutarZdUweKFR86Ja27OmY5cp8uTJGl6HJgxRJIkSdq4+p4vnO+4tli2rShJtc3UKWoBiIj7Ah8GXpqZdzduy2Loac+Gn0bEeRExGhGjd955Z6/CShogazZsY8vYOPv2J1vGxlmzYVu/U5oaTpcnSZIkSZI0vfzCuST11Izt4IyIe1F0bv5rZn6kXP2dcmpayts7yvW3AYsaHn58ua7V+kNk5rsycyQzR4499tjevRBJA2Prjj1tl2cNr1EiSZIkSZI0vfzCuST11Izs4IyIAN4L3JSZ/9iw6Qrg7PL+2cDGhvXPjcLjgLvKqWyvAk6LiPkRMR84rVwnSYdYtmR+2+VZ48B0ea/dXdx6vQdJkiRJkqSp5RfOJamnopjpdWaJiCcAnwW+AuwvV7+a4jqclwGLgR3AmZk5XnaI/jNwBrAXOCczR8tYzy8fC/D6zFw/2fOPjIzk6OhoD1+RpEEwZ67BKUmSJEmSJEmaDaLfCfTLjOzg7Dc7OCVJkiRJkiRJkjTDzdkOzhk5Ra0kSZIkSZIkSZL6YHwM1q+ACxYWt+Nj/c5IOoQdnJIkSZIkSZIkSSpsXA07roX9+4rbjav7nZF0CDs4JUmSJEmSJEmSVNi1uf2yNAPYwSlJkiRJkiRpejn9oSTNXItObr8szQB2cEqSJEmSJEmaXk5/KEkz18p1sORUmDdU3K5c1++MpEMM9TsBaeCNjxWN8F2bi2+yrFwHC4b7nZUkSZIkSdLM5fSHkjRzLRiGczb1OwupLUdwSt3yG4eSJEmSJEnVOP2hJEnqgh2cUrf8xqEkSZIkSVI1Tn8oSZK64BS1UrcWnVyM3GxcliRJkiRJUmtOfyhJkrrgCE6pW37jUJIkSZIkSZIkado4glPqlt84lCRJkiRJkiRJmjaO4JQkSZIkSZIkSZI0MOzglCRJkiRJkiRJkjQw7OCUJEmSJEmSJEmSNDDs4JQkSZIkSZIkSZI0MOzglCRJkiRJkiRJkjQw7OCUJEmSJEmSJEmSNDCG+p2Aurdz917WbNjG1h17WLZkPmtXLWXxwiP7nZYkSZIkSZIkSZLUc47gnAXWbNjGlrFx9u1PtoyNs2bDtn6nJEmSJEmSJEmSJE0JOzhnga079rRdlma18TFYvwIuWFjcjo/1O6M5YefuvZx54XWc+OpNnHnhdezcvbffKUmSJEmSJEmS5gg7OPukl50Dy5bMb7tclx0YGggbV8OOa2H/vuJ24+p+ZzQnOHJckiRJkiRJktQvdnD2SS87B9auWsry4QUMzQuWDy9g7aqlMy5Hacrs2tx+WVPCkeOSNCCc6UCSJEmSJM1CQ/1OYK7qZefA4oVHctkLTuk2pUPYgaGBsOjkYuRm47Km3LIl89kyNn7QsiRpBjow0wHcM9PBOZv6m5MkSZIkSVKXHMHZJ1M1rWwvDUKOEivXwZJTYd5QcbtyXb8zmhOmauS4JKnHnOlAmnkcWS1JkiRJXYvM7HcOM87IyEiOjo5O6XPs3L2XNRu2sXXHHpYtmc/aVUtZvPDIKX3OqgYhR0mSJLWxfsXBMx0sOdURnFK/+XcpSZIkqXei3wn0ix2cTUxHB6ckSZI05cbHimlpd20upnFfuQ4WDPc7K2luu2Ah7N93z/K8IXjt7v7lI0mSJGmQzdkOTq/BKUmSJM1WC4YdGSbNNF5DXpIkSZK65jU4JUmSJEmaLl5DXpIkSZK6NidGcEbEGcBbgcOA92TmG/uckiRJkiRpLnJktSRJkiR1bdaP4IyIw4B1wDOARwHPjohH9TcrSZIkSZIkSZIkSXXM+g5OYDmwPTO/lZk/AS4FVvY5J0mSJEmSJEmSJEk1zIUOzuOAXQ3Lt5brDhIR50XEaESM3nnnndOWnCRJkiRJkiRJkqTOzYUOzo5k5rsycyQzR4499th+pyNJkiRJkiRJkiSpibnQwXkbsKhh+fhynSRJkiRJkiRJkqQBMxc6OL8AnBQRwxFxOHAWcEWfc5IkSZIkSZIkSZJUw1C/E5hqmbkvIl4EXAUcBrwvM2/sc1qSJEmSJEmSJEmSapj1HZwAmbkJ2NTvPCRJkiRJkiRJkiR1Zy5MUStJkiRJkiRJkiRplrCDU5IkSZIkSZIkSdLAiMzsdw4zTkTcCeyYZLf7Ad/t0VPOhVi9jmcsYxlr5sTqdTxjGctYszNWr+MZy1jGmjmxeh3PWMYy1syJ1et4xjKWsWZOrF7HM5axjDU7Y/U6nrF6H+u7mXlGj55voNjBWVNEjGbmiLH6E89YxjLWzInV63jGMpaxZmesXsczlrGMNXNi9TqesYxlrJkTq9fxjGUsY82cWL2OZyxjGWt2xup1PGP1L9Zs5BS1kiRJkiRJkiRJkgaGHZySJEmSJEmSJEmSBoYdnPW9y1h9jWcsYxlr5sTqdTxjGctYszNWr+MZy1jGmjmxeh3PWMYy1syJ1et4xjKWsWZOrF7HM5axjDU7Y/U6nrH6F2vW8RqckiRJkiRJkiRJkgaGIzglSZIkSZIkSZIkDQw7OCVJkiRJkiRJkiQNDDs4K4qI90XEHRFxQw9iLYqIT0XEVyPixoh4SRex7h0RWyJiWxnrr3uQ32ER8aWI+GiXcW6JiK9ExPURMdplrGMiYkNEfC0iboqIU2rGeXiZz4GfuyPipV3k9bLyuN8QEZdExL27iPWSMs6NdXJqVqMRsSAiro6Ib5S387uI9XtlbvsjYqTLvN5U/i6/HBH/HhHHdBHrb8o410fExyPiwXVjNWz7s4jIiLhfF3mdHxG3NdTaim7yiog/LY/ZjRHxD13k9aGGnG6JiOu7iPWrEfH5A3/jEbG8i1hLI+K68j3jPyLiFzuM1fT9tE7tt4lVufbbxKpc+21iVa79VrEatndc+23yqlz77fKqWfutcqtc/21iVa7/NrEq13+0+P8fEcMRsTkitpev9/AuYr2ojFPl/bBVrH+NiJuj+D/3voi4Vxex3luu+3IUbYP71o3VsP1tEfH9Ll/jRREx1lBjv9pFrIiI10fE16No97y4i1ifbcjpfyLi8i5f59MiYmsZ73MRcWIXsZ5axrohIi6OiKEOczuorVqn7tvEqlz3bWJVrvtJ4lWu/VaxGtZ3XPtt8qpc+21iVa79NrFq1X6LWJXrvk2sunV/yGerqN/WbxarVlu/Tby67f1mseq29w+J1bCtanu/WV512/tN84p6bZ5medVt7zeLVbe93yxW3fb+MTHhXEAXtd8sVt3Puc1i1a37ZrHq1v0hsRq2Va37ZnnVqvt2udWs/Wa51a39ZrHq1n6zWHXa+k3PXdWp/Tax6nzObRWrzufcVrHqfM5te66vSu23yaty7bfLq2rdt8mrbt23ile59tvEqvu+/7KYcK41arb3W8Sq1d5vEatWe79FrFpt/WaxGrZVauu3yOuiqNHWbxErokZbv0Wsup9zm8Wq1dZvEatuW/+QfoGo2d6ZMzLTnwo/wK8Dy4AbehDrQcCy8v4vAF8HHlUzVgD3Le/fC9gMPK7L/F4OfBD4aJdxbgHu16PjfzHwx+X9w4FjehDzMODbwJKajz8OGAPuUy5fBjyvZqxHAzcARwJDwH8BJ1aMcUiNAv8AvLK8/0rg77uI9Ujg4cA1wEiXeZ0GDJX3/77LvH6x4f6LgXfWjVWuXwRcBezotH5b5HU+sKZGLTSL9ZSyJo4ol+/fzWts2P7/gNd2kdfHgWeU91cA13QR6wvAk8r7zwf+psNYTd9P69R+m1iVa79NrMq13yZW5dpvFatO7bfJq3Ltt4lVt/Yn/T/baf23ya1y/beJVbn+afH/n+J/0Vnl+ncCf9JFrMcAJ1Dh/3mbWCvKbQFc0mVejbX/j5R/63VilcsjwAeA73f5Gi8CVlWs/VaxzgHeD8zrtPbbvcaGfT4MPLfL3L4OPLJc/0LgopqxHg/sAh5Wrr8AOLfD3A5qq9ap+zaxKtd9m1iV636SeJVrv1WsOrXfJq/Ktd8mVuXab/ca69R+i7wq132zWBRfNK5b94fUJPXb+s1i1Wrrt4lXt73fLFbd9n7Tv2Pqtfeb5XU+9dr7zWLVbfM0fY0N26u095vlVbe93yxW3fb+IecCuqj9ZrHqfs5tFqtu3TeLVbfum547qVn3zfKqVfdt4tWt/bbniCrWfrO86tZ+s1i1ar8h5s/PXdWt/Raxar/vN4lVq/ZbxKpV+81i1a39FnnVrv0msWrVfavXWKfu2+RWq/ZbxKrzObfpuVbqfc5tFavO59xWsep8zm0Vq87n3Jbnpqn+ObdVXhdR/XNuq1h1PudOev6dDtv6bfKq8xm3WaznU6OtT4t+Abp8z5/tP47grCgzPwOM9yjW7Zm5tbz/v8BNFH8UdWJlZh74Jsa9yp+sm1tEHA/8JvCeujF6LSKOpugQeS9AZv4kM7/Xg9BPA76ZmTu6iDEE3Kf8NsaRwP/UjPNIYHNm7s3MfcCngd+tEqBFja6kaGBT3j6zbqzMvCkzb66SU5tYHy9fJ8DngeO7iHV3w+JRdFj/bf6m3wz8RadxJolVWYtYfwK8MTN/XO5zR7d5RUQAZ1I0wOrGSuDAN/COpsP6bxHrYcBnyvtXA8/qMFar99PKtd8qVp3abxOrcu23iVW59if5/1Op9nv8v6xVrLq13za3KvXfJlbl+m8Tq3L9t/n//1RgQ7m+09pvGiszv5SZt0z2+A5jbSq3JbCFzmq/Vay74ee/x/vQWe03jRURhwFvoqj9rl5jp4/vMNafABdk5v5yv0lrf7K8ovjG9FOBy7vMrU7tN4v1M+Anmfn1cn1HtT+xrVrWQeW6bxarzLVy3beJVbnuJ4lXufZbxapT+61i1dUiVuXanyyvqrXfIlatNk+TWAupUfdt1GrrN1OnvTNJvFrt/RaxarX326jc3p8Gtdo87VRt77dQq/ZbqNzeaXMuoHLtt4pVp/bbxKpc921iVa77Sc6dVKr7Xp+HaROvcu1PlluV2m8Tq3Ltt4lV67Nug8ZzV92+7/88Vg/e9xtjdfue3xir2/f8ief6unnP78V5w2axun3PPySvLt/zG+N1+77fGKtu7U8813o7Ndv7TWL9T932fotYddv7zWLVaus3i1W3rd8sVsXHTxarVlu/XV5V2/otYtWt+4mxfkC9tn6rfoGetfVnIzs4Z4iIOIHimyObu4hxWBRTENwBXJ2ZtWMBb6F489vfRYwDEvh4RHwxIs7rIs4wcCewPorpnd4TEUf1IL+z6OLDXmbeBqwFdlL8s70rMz9eM9wNwBMjYmFEHEnxDaBFdXNr8IDMvL28/23gAT2I2WvPBz7WTYByeoNdwHOA13YRZyVwW2Zu6yafBi+KYmqJ93U5jcDDKOpjc0R8OiJ+rQe5PRH4TmZ+o4sYLwXeVB77tcCruoh1I8U/boDfo0b9T3g/7ar2e/He3EGsyrU/MVY3td8Yq9vab/Iaa9f+hFhd136L41+r/ifEeild1P+EWLXqf+L/f+CbwPcaTi7cSoedzr1sS7SLFcWUPX8EXNlNrIhYT/G3/Qjgn7qI9SLgiob3i460eY2vL2v/zRFxRBexHgr8fhTTQn0sIk7qMi8oPgx9YsJJozrx/hjYFBG3Uvwu31gnFsUJgKG4Z0q0VXRW+2/h4LbqQmrWfZNY3WgZq2rdt4tXp/ZbxKpV+63yokbtt4hVq/bb5AXVa79ZrFp13yTWd6lX99D8s1Xd9k6vPqd1Gq9Km6dprJptnkNiddHmafUa67R5msWq2+Zpd+yrtneaxXop9do7zWLVae+0OhdQp/Z7eV6hk1id1n3LWDXqvmmsmnXf7jXWqftW8erU/mTHv0rtt4r1UqrXfqtY3X7WbTx31e05nq7Og3UYq845noNi1XzPPyRWt59zJ+ZFd+d4GmN1+zm32bHv5hxPY7yX0t15nsZYlWu/2blW4IvUaO/38rztZLGqtPfbxara1m8Tq3Jbf5LXWKmt3yZW5bZ+B7/HZ9JhW79NrMpt/Ra1ehn12vqt+gUG4bx+/+QMGEY6aD8Uw9e7nqK2Id59Kd6kf7dH8Y4BPgU8uubjfwt4e3n/yXQ/Re1x5e39gW3Ar9eMMwLsA04ul99KxSk9msQ8nOIEwwO6iDEf+CRwLMVIhMuBP+wi3rllPXwGeAfwlhoxDqpRigZA4/Y9dWM1rL+G6tNWtYr1l8C/A9FtrHLbq4C/rhOL4ps2m4Gjy+VbqDB9SZNj/wCKqTnmAa8H3tdFrBsoGjYBLKeYAqGjY9bm2L8D+LMu6+ttwLPK+2cC/9VFrEdQTIXyReB1wO6KuR30ftpl7Td9b65Z+61i1an9lv8zatT+z2P1oPYnHvtuan9irNq1P8nxr1P/E3Prpv4nxuq2/o+h+P//BGB7w/pFzf7+O4z16IZ1lWpikljvpt7/t2axDgPeDpxTM9avA5/jnum0Kk3TOTEviimIAziC4tuVlaaHmhDr+wdqtPw7/WwPjtfHDtRsl6/zI9zTJvtz4D1dxDoF+CxFZ+ffAtdP8thD2qrA/erUfbNYE7Z3XPcdxKpU9x3E67j2WxyzB9ep/VZ51an9NrEq134Hx6vj2m+TV+W6bxOrUt03xDvksxU12zvNYjVsu4bq7Z128Sq1edrFKtd33OZpccxqtXlaxKrV5mkRq1abZ5JjX6m90yKvWu2dFrEqt3docS6gTu23ilWn9juI1XHdTxarSt23iPWmOnXf5tjXrftW8SrXfgfHv+Pab5NX5dpvE6t2W58J567q1H6rWHVqv4NYdT7ntjw/12ntN4tF959zJx77bj7nTozVzTmeVse+8mfcFrl18zl3Yqw67/tNz7VSr73f9rxtlZroIFbH7f0OYlVp6zeL9VzqtfVbHfs6bf1Wseq09Sc7XlXa+q3yqtPWbxWrblv/kH4BunjPnws/fU9gEH/oYQdnWfhXAS/vcY6vpf71EP6O4lswt1B8K2Av8C89yuv8LvJ6IHBLw/ITgf/sMp+VwMe7jPF7wHsblp9LeRKjB8frDcALazzuoBoFbgYeVN5/EHBz3VgN66+hBx2cFHOcXwcc2W2shm2Lq/yNcnAH5y9TjCq5pfzZR/EtnAf2IK9K7x1Nfo9XAk9pWP4mcGwXx34I+A5wfJf1dRdlI5yisXN3j36PDwO2VIh1yPtp3dpvFqthW6XabxWrTu23y6vc3nHtT4zVTe13kFfHtd/i99hN7bc6/pXrv0Vuteq/g2NWqf4bHvdaiob4d7nng8wpwFU1Y61pWL6FmtfUboxF8aH2csrrbXSbV7nu16nxhawy1uso2jsHan8/DR+cu8zryV3ktQb4GjDcUF93dXns7wfsBu5d59hPqLFvNqxbDHy1R8fsNOCySR7XrK36r3XqvkWsf2nY3nHdt4tVp+4ny61K7beItadO7XeYV0e13ypWndqf5PhXqv0Wsf6zTt13eLwmrfsWsc+neK+o3dafGKth+RpqXIutWTxqtvdb5dZw/Ct/Ji9j/RVdtPcnyeuELvJaQxdtnhbHvlZ7v0letdv7kxyvjto7tDgXUKf2W8VqWO649tvFqlr3k+VVruuo7lvE+kSduu8wr47rvs3vsnLtT3L8K9V+m7wq136Hx6zqZ92VNJy7qlP7rWLVqf12sarW/mR5Van9ZrHo/hxPu7w6rv0Wv8duPuc2O/a13/Ob5NbNeZ52x6zT9/1m51rfQb32ftvztlRr77eMRcX2/mR5les6bes3izVGvbZ+J3k9uYu83k69tn67Y1+1rd+qvuq09Ts5XnXb+m+guBZo12392fzjFLV9FBFBMSf/TZn5j13GOjYijinv3wd4OsWbRWWZ+arMPD4zT6CYUuCTmfmHNfM6KiJ+4cB9ij/oG2rm9W1gV0Q8vFz1NOCrdWI1eDbdT8uxE3hcRBxZ/k6fRnE9tVoi4v7l7WKKb7F8sMv8AK4Azi7vnw1s7EHMrkXEGRTTdf12Zu7tMlbjdAYrqV//X8nM+2fmCeXfwK3AsrL+6uT1oIbF36Fm/Zcup7gIPRHxMO75VlxdvwF8LTNv7SIGFHPSP6m8/1Sg9nS3DfU/D3gNxUXjO3lcq/fTyrXf4/fmprHq1H6bWJVrv1msurXfJq/Ktd/m2F9Ojdqf5HdZqf7bxKpc/22OWeX6b/H//yaKkXGryt06rf2etSVaxYqIPwZOB56d5fU2asa6OSJOLNcF8Nud5Noi1hcz84ENtb83M0/s4jU+qCGvZ9JZ7bc69pdT1j5FnX292eM7jAVFTXw0M380WZxJ4t0EHF3+PdKwrlZuDbV/BPAKJqn9Fm3V51Cj7nvZ7m0Vq07dt4oH/FGd2m+R2/w6td/mdVau/TbH/3Iq1v4kv8tKtd/i2K+kRt23OV6V6r7ct9VnqzrtnZ59TmsXr2abp1WsOm2eZrG+ULPN0yqvOm2eVsf/ciq2eSb5XVZt77SKVae90+p4VW7vtDkXULn2e3leoVWsOnXfJlblum8Ra2udum+TV63PuW2O/+VUrP1JfpeVar9NrMq13+aY1fqsW5p47qqbczy9OA/WNFad2m8Tq5tzPD+P1YNzPBPz6uYcz8Rjfzn1z/E0+z12c45nYrxuzvNMPGZ1ar/ZudavUqO93yJW3fO2TWPVbO+3ilW5rd8i1j/Waeu3yatyW79VLGq09dvEguqfc1vVV+W2fqu86rT1y/2b9QvMyPP6M0adXtG5/EPxBn078FOKf4rndhHrCRTXpPgycH35s6JmrF8BvlTGuoGK06G1iftkupiiFngIxVQ02yjmXP/LLvP5VWC0fJ2XA/O7iHUUxbc7ju7Bcfprin84NwAfAI7oItZnKd5UtwFPq/H4Q2qU4rpUn6BokPwXsKCLWL9T3v8xxTfDOhoV1CLWdmBXQ/2/s4tYHy6P/5eB/6CcDqlOrAnbb6Hzb3I1y+sDwFfKvK6g/MZNzViHU4xquAHYCjy1m9cIXAT83x7U1xMopk/YRjH1y2O7iPUSiobN1ynmuu90epam76d1ar9NrMq13yZW5dpvE6ty7beKVaf22+RVufbbxKpb+y1fJxXrv01uleu/TazK9U+L//8U/3+3lLX2b3Twf6lNrBdT1P4+ig+6nUzT0irWPopvJh943Z1Ma3NILIopoa4ta+wGitF7v1g3rwn7dDp1T6vX+MmGvP4FuG8XsY6hGD3wFYpvwi/t5jVSfDP/jE7rfpLcfqfMa1sZ9yFdxHoTxYfHm4GXVszvydwz5Wflum8Tq3Ldt4lVue5bxatb+61yq1P7bV5n5dpvE6ty7bd7jXVqv0Veleu+TazKdU+Lz1bUa++0ilW3rd8qXp02T6tYddo8k34epfM2T6u86rR5WsWq3OZp9xqp3t5plVed9k6rWHXb+7/KhHMBdWq/Tay6td8sVt3Puc1i1f2ce0isOnXfJq9an3PbxKvb3m/6OqvWfpu86n7WbRarbu0fcu6qi9pvFqtu7TeLVbf2m8WqW/ttz/VVrP1medU9x9MsVt26b/oa69R9m9zq1n6zWHVr/5BzrdRs77eIVau93yJWrfZ+i1i12vrNYk3Y3nFbv0Vetdr6LWIdQ422fqvXSL3Puc3yqtXWbxGr1mdcmvQLUPM9f678HBhqLkmSJEmSJEmSJEkznlPUSpIkSZIkSZIkSRoYdnBKkiRJkiRJkiRJGhh2cEqSJEmSJEmSJEkaGHZwSpIkSZIkSZIkSRoYdnBKkiRJkiRJkiRJGhh2cEqSJElSH0XERRGREXFCw7oTynUX9SD+LRFxS7dxZruIOL885k/udy6SJEmSpPbs4JQkSZKkHis7ytr9PK/fObbSTedqRFxTPvZnEfHLLfY50KH7G10nK0mSJEmak4b6nYAkSZIkzWJ/3WL99ZM87jbgkcBdPc1m+swD3gSc0e9EJEmSJEmzjx2ckiRJkjRFMvP8mo/7KfC13mYzrbYDp0fE0zPz6n4nI0mSJEmaXZyiVpIkSZJmmHbTxEbEwyLiwxGxJyJ+EBH/HRG/GRHPazf9bUQcFRFvioidEfHjiNgeEa+IiGjY53xgrFw8u4tpdV8NJPCmiOjoc2f5HNe02Nb2OqUR8dCI2BARuyPifyPi4xHx6HK/YyPiXRFxe0T8KCK+EBFPmSSXsyPiSxHxw4i4IyLeFxEPbLHvgoj4u4i4qdz/roj4RESc1mTfn/+OIuKMckrfuyIiOzlGkiRJkqSCIzglSZIkaUBExCOA/wbmA/8JfBl4CPDvwKY2D70XcBXwYOBjwD7gmcAbgXtzz1S61wDHAC8BtgGXN8S4vkKqXwL+Bfgj4GxgfYXHVnUCsBm4CbioXP4d4JqIOAW4Ergb+BCwADgL+FhEPCwzdzaJ9zLgtHL/K4EnAOcAT46IkzPzzgM7RsQSimN2AvDZcv+jgN8CroyIF2Tmu5s8xyqK6Xs/BrwTWFL3xUuSJEnSXGQHpyRJkiRNkXJE5ES3ZOZFNUOuo+jcfGFmvqPheZ5B+w7OB1N0WD49M39YPuavga8DL4uIN2TmTzPzmoi4haKD8/q6U+yW/hL4PeBvIuLSA887BZ4EvCYzX39gRUT8FXABRcfnZRTHa3+57Wrg/RQdmS9rEu8ZwMmZ+aWGeG8GXkrRIXxuw74XU3ROPjszL23Y/xiKjs+3RcQVmfmdCc+xAliRmVfWeL2SJEmSNOc5Ra0kSZIkTZ3XNfl5Xp1AEbEIeCrF9S0vbNyWmR8D/muSEC9u7GTMzDuAjcDRwMPr5NROZu4C3gIcB/xZr+M3uIWi47HRxeXtEcCfH+jcLH2QYgTrr7aI94HGzs3S+cBdwB9ExBEAEbGUonP1w42dmwCZ+T2K3/W9gWc1eY6Ndm5KkiRJUn2O4JQkSZKkKZKZMfleHfvV8va6CR12B3wO+I0Wj70rM7c3Wb+rvJ3fZW6t/B3FiMe/iIh3NxnJ2AvXZ+bPJqz7n/L265n5v40bMvNnEfEd4PgW8T49cUVm3hUR11N0aD6SYrreU8rNR7cYqXtsefvIJtu2tHhuSZIkSVIH7OCUJEmSpMFwdHnbqpOwXefh91qs31feHlYnoclk5t3lVLj/TDEK8k+m4GnuavK8+yKi6bbSPorrkjbT6jh+u7w98HtYWN4+vfxp5b5tYkmSJEmSanCKWkmSJEkaDHeXtw9osb3V+n67kOJan38cEY9os1/S+ku4x/Q6qTZaHccHlrd3Tbh9SWZGm59zmsTKnmYsSZIkSXOMHZySJEmSNBiuL29PiYhmn+We0KPnOTDda09GdWbmPuAVFJ2Xb2qz6x5g0cSVEXEYra+XORWe1CSHo8scfgTcVK7+fHn7xOlJS5IkSZJ0gB2ckiRJkjQAMnMncA1wIvCCxm0RcQatr79Z1R6KEYaLexSPzLwc+CzwW8CpLXbbAiyOiNMmrH8NsKRXuXTgjyLiMRPWnU8xNe0lmfljgMwcpXhNvxsRz28WKCJ+OSLuP5XJSpIkSdJc5DU4JUmSJGlwrAauBd4eESuALwMPAZ4FbARWAvu7eYLM/H5EbAaeGBH/SjG97M+AKzLzy12EXkMx6vHEFtvXAqcDGyPiQ8A48HhgmKJj98ldPHcVHwOujYjLgNspRsY+AbgFeOWEff8A+CTw3oh4MbCZ4nqnxwO/AjwaOAW4YzoSlyRJkqS5whGckiRJkjQgMvOrFB1m/04xNepLgROA3wE+V+52d7PHVvRHwH8CZwCvA/4GWNZNwMzcAnyozfZPAM8EbgTOAs6m6FRcDuzo5rkrejPwQoopaV8KPAK4CHh8Zh7UUZmZtwKPBf6SohP4OcCLKTpmd1KMtP3K9KQtSZIkSXNHZGa/c5AkSZIkdakcbfkHwCMy8+Z+5yNJkiRJ0lRxBKckSZIkDYiImBcRD2yy/mnA7wNftXNTkiRJkjTbeQ1OSZIkSRochwO7IuJTwNeAfcAvAU8HfkJxjU5JkiRJkmY1p6iVJEmSpAEREYcBbwGeChwPHAl8F/gM8MbM/FL/spMkSZIkaXrYwSlJkiRJkiRJkiRpYHgNTkmSJEmSJEmSJEkDww5OSZIkSZIkSZIkSQPDDk5JkiRJkiRJkiRJA8MOTkmSJEmSJEmSJEkDww5OSZIkSZIkSZIkSQPj/wffjagcIOq8uAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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(\"Payload Mass\",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": 7,
   "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>Orbit</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ES-L1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GEO</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GTO</td>\n",
       "      <td>0.518519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HEO</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ISS</td>\n",
       "      <td>0.619048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LEO</td>\n",
       "      <td>0.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MEO</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PO</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SO</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SSO</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>VLEO</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Orbit     Class\n",
       "0   ES-L1  1.000000\n",
       "1     GEO  1.000000\n",
       "2     GTO  0.518519\n",
       "3     HEO  1.000000\n",
       "4     ISS  0.619048\n",
       "5     LEO  0.714286\n",
       "6     MEO  0.666667\n",
       "7      PO  0.666667\n",
       "8      SO  0.000000\n",
       "9     SSO  1.000000\n",
       "10   VLEO  0.857143"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=df.groupby(\"Orbit\").mean()[\"Class\"].reset_index(name='Class')\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column\n",
    "sns.barplot(x=\"Orbit\", y=\"Class\", hue=\"Class\", data=a)\n",
    "plt.xlabel(\"Orbit\",fontsize=20)\n",
    "plt.ylabel(\"Success Rate\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZtQM96bDuE9egc4dyLj1xJy4tkr9vG3frxKf3aMYniAvsybdmfFTcBKhN8J+3Z1jgBDjjjlzBYc54GH5c85a0Kibj/tNI35MWOFfqNzx3U2F16d1In2+yU1YOHbvm3hhZqWM2qwpoLQbunpt5/JGAAZWZxdHqBm/clY036sisRav2xdt1UK/sAhWZrTftxtab+tyi9XPRkcM4c+/BzFq4nO0375HZBzCLSgRsexSMfWBV33bHZJenEAbvnfugzkeF24BdPp1ppA3W8PecVAPjn7bQpJY19sFVxU3IbWvz6q1w0A/WfI0kCSjhAmdE7Ap8mFKaGhFlwE7A6IjYHNgspfRK/tRdgAkZxVQL27RHZ/78ed9Qb1RZWe7NNKmVfKzf6m96NdbXLnXqDvucu+7zSkX/XRrp+/jqfVIhDdoDhh2z6o3BngNhj69km6kYlJXDgd+Hh7+ba0c5HNgGlulqa3b+NEwbAy/fkPs/4eAf+mZpkelUUc7vP70r37/3NSbMWsyh2/Xj24dvm3UsqWT179mF/j27ZB2juBx3FXTvD5NfyhUHD/x+1ok2THkHOOth+O/VsHB6bhWTrQ7MOtWG6b/z+vVJhdShkefKDiW+tLcktZJIqbRWZ42IhSmlbvllaH8GdMofehH4OtCP3IzNzYGlwAzgqymlNa79UVlZmaqqqlo2uCS1cbW1iYvveY07qiYREZy220D+9/gd/MR2W/XYj+CFPwIBe34VDrkk60RqLya+AEtmw5YHQofmLTXeJk0bAx+Myr1h2ntI1mkkSZJKT21Nbm/UV/4KFZ3hExfCvudnnUptXfUy+PPhMHVkrt1zEHz5idzqbJK0/trlG7AlV+BsCRY4Jalw5ixaTgT06tox6yhqadX5vcoqOq39PEmSJEkqFSuW5lbJKG/+1jZSk1Qvh3f+mfu7t+2R0MnVsCQ1WbsscJbsErWSpOLUeyMLm+2GhU1JkiRJbY2rhKi1VXSE7Y7NOoUklZyyrANIkiRJkiRJkiRJ0vqywClJkiRJkiRJkiSpZFjglCRJkiRJkiRJklQyLHBKkiRJkiRJkiRJKhkWOCVJkiRJkiRJkiSVDAuckiRJkiRJkiRJkkqGBU5JkiRJkiRJkiRJJcMCpyRJkiRJkiRJkqSSYYFTkiRJkiRJkiRJUsmwwClJkiRJkiRJkiSpZFjglCRJkiRJkiRJklQyLHBKkiRJkiRJkiRJKhkWOCVJkiRJkiRJkiSVDAuckiRJkiRJkiRJkkqGBU5JkiRJkiRJkiRJJcMCpyRJkiRJkiRJkqSSYYFTkiRJaqqF0+GR/we3nAYjb846jSRgytwlLF5enXUMSZIkSVIrqMg6gCRJklRy/nYifPha7uu3H4bqpbDbF7PN1JZUL4exD8DiWbDdJ6F7v6wTqYjNXLiML/+lipET57JRx3J+cMxwTt99UNaxJEmSJEktqCRncEZETUSMiojXI+LvEdE13z8gIu6LiHci4r2I+G1EdMw6r4rfvSOnsM9lT7Dzjx/ll4+MJaWUdSRJklSspo9dVdxc6bW/Z5OlLaqthb8cB3eeBQ9dCFftBjPfyTqVitjvn3iXkRPnArBoeQ0/uu8NZi1clm0oSZIkSVKLKskCJ7AkpbRLSmkHYDnw1YgI4G7g3pTSNsDHgG7AzzLMqRIwfuYivnXHKKbMXcK8JSu4+sn3uHfUlKxjSZJa2+t3wx1nwmM/gkWzsk6jYrZRXyjrUL+vx+bZZGmLJjwLE59b1V46D168Lrs8KnrvTF9Qr728ppYJsxdnlEaSJEmS1BpKtcBZ19PA1sBBwNKU0g0AKaUa4ALgCytneEqNeXnCHGobTNh8cdycbMJIkrIx6pbcbLEx98KzV8LfTsg6kYrZRhvDgRdD5F9Kd+8P+1+Ubaa2pHbF+vVJeQcNq7+Ecb8endhh854ZpZEkSZIktYaS3oMzIiqAI4FHgO2Bl+seTynNj4iJ5AqgoxtcezZwNsCgQe7P0p59fFAvIqDuqrS7DuqVWR5JhbFoWTX3jprC/CXVHLtzfwb09rMuWotRt9Rvf/AqTBsD/YZnk0fFb79vwY6nwNwJMGA3qOiUdaK2Y+j+sNmOq5YBrugClV/INlOxmD0OHvwWTHkFhuwLx1wJ3fpmnSpzZ+09hKUravjHq1MZ0LsLFx6+LR0r2sJnedWo1+7MrbrQcwvY9wJn0GvtprySW+585juw7ZFw9K+hU/esU0mSJKkAohT3GoyIGmDlxkdPA98GvgoMTSld0ODckcCZKaXRrEFlZWWqqqpqqbgqAbe/NJFf/fNtFi2r5tN7DOL7R21HWVlkHUtSM62oqeXY3z3D2A9zS9Z161TBvefsw9abdss4mYrWnV+A1+9a1Y5y+NYY6L5Zdpmk9mzZAnj1Nlg8G3Y8GTbeKutExeH6Q2DyS6vaw46B027OLo/U2kbfAXd/eVV7463hnBehrDy7TCpeNdXw251gfp0taPb4Khz5i+wySZIktYx2Wcwo1RmcS1JKu9TtiIgxwMkN+noAg4B3Wy+aStGpuw3i1N2cySu1Fc+8M/Oj4ibAwmXV3PLCRH54rLPxtAaf+C6MewoWzci19/uWxU0pS526w+5fXvd57Un1svrFTYDxT2eTRcrK6Dvqt2e9C1NHwoDKbPKouM0ZX7+4CTD+2UyiSJIkqfBKtcDZmMeByyLicymlv0REOfBr4MaU0uKMs0mSWlE08pklJ2VrrTYdBueNhonPQa8hsMnWWSeSpPoqOsGm28P0N1b1bb5rdnmkLPToX78dZdCtX+PnSr0GwUabwqLpq/q28HlTkiSprWgzG5Ok3Fq7JwCnRMQ7wNvAUuDiTINJklrdvltvwk4Den7U7tG5gjP2HJxhIpWEjl1h60MsbkoqXideA5vmVyMYsBscc0W2eaTWtt+3oefKlXcC9v0W9BqYaSQVsYqOcPKfoc9WQMA2h8Mhl2SdSpIkSQVSkntwFpp7cEpS27N0RQ0Pjv6A+UtXcNSO/enXo3PWkSRJKowVS6GD/6+pnapZAZNehJ5bQO8hWadRqaiphvK2tIiZJElSPe1y7Tpf3UmS2qTOHco5acSArGNIklR4FjfVnpV3gCH7ZJ1CpcbipiRJUpvTZpaolSRJkiRJkiRJktT2WeCUJEmSJEmSJEmSVDIscEqSJEmSJEmSJEkqGRY4JUmSJEmSJEmSJJUMC5ySJEmSJEmSJEmSSoYFTkmSJEmSJEmSJEklwwKnJEmSJEmSJEmSpJJhgVOSJEmSJEmSJElSybDAKUmSJEmSJEmSJKlkWOCUJEmSJEmSJEmSVDIscEqSJEmSJEmSJEkqGRY4JUmSJEmSJEmSJJUMC5ySJEmSJEmSJEmSSoYFTkmSJEmSJEmSJEklwwKnJEmSJEmSJEmSpJJhgVMqsIXLqqmtTVnHkCRJklTqli/OOoEkSZIkFaWKrANIbcXsRcv55q2v8Oy7s+jXoxOXnrgjBw3rl3WsonD90+9z3dPvUxbB1w/Yis/uNaRZ40yYtYgXx81mpwG92Haz7oUNKbUnKcHSedClV9ZJCialREpQVhZZR5HU1nz4GvzzYpg9DoYdA4f+GCo6ZZ1Kbd0Ho+Hus2HGm7D5x+GkP8HGW2WdSsXs5RvhqcuhZgXs9XXY57ysE0mSJEktKlJyplllZWWqqqrKOoZK3MX3vMYtL0z8qN2jcwUvXHwIXTqWZ5gqe8++O5Mzrn+hXt9dX9ubEYN7N2mcB0d/wLm3jaQmPzv2R8cO56x9hhYsp1QSamthysvQpTdssnXzxpj0Itz9ZZgzHvrvDKfcCH22LGTKpqutgbLmP1feUTWJXz4ylvlLqjmlcgA//uT2VJS7SEWxmbVwGVUT5jC8fw8G9umadRy1F7Peg7cegl6DYNujobyJn++sqYbf7gzzJ6/q2+9COPh/CptTaugP+8C011e1h+wHn38guzxt0bIFMH8qbPIxiBL/gNTUkXDtAfX7zrgLtjkkkziSJElqdSX+grZ5ivrdv4joFxG3RMT7EfFyRDwfEW9ExKiIGBMRS/Jfj4qIkyPnBxHxTkS8HRH/jojts/4+1D68PmVevfb8pdVMmuOSUi+Mm71a30vjV+9bl9889tZHxU2AKx57u15bavMWz4Zr9oM/HQK/HwH3n9v0MVLKzQaZMz7X/uBVeOg7BY3ZJPMmww1Hw0/6wB/2zc1WaaKJsxZz0V2jmblwOctrarn5hYnc+tKkFgirDfHU2zPY+7In+MpfX2b/X/2bm1+YkHUktQcTnoer94JHfwB3fA7u/HzTx5j5dv3iJsD7/y5IPGmNalbUL25C7v/sZhg/cxE/fWAMl9z/Bm9PW1CAcBugphrGPgSv/AUWzcw2y6hb4PJt4ardc7fZ47LNs6EmPNdI37Otn0NqhpraxC8fGcs+lz3BSX94jqpmvF9QlKaOgheva9bvOJIkaf0UbYEzIgK4F3gqpbRlSmkEcBrwx5TSLsBRwHsppV3ytzuBc4C9gZ1TSh8DLgXuj4jOmXwTalf22XqTeu3+PTuz5SYbZZSmeHx8UK/V+nYZuHrfuixaVlOvvXRFrQXOAvtw3lKefGs685asyDqKGvPCNfXf7HzlptxszqZYtgDmNHgDL8tfuB+8ECY8k/t62mtw15eaPMSrk+fS8Klg1MS5G55NBXX5o2+xrLoWgNoEv3h4LCtqajNOVRxenjCHi+4azc8fepOpc5dkHadtef73ULNsVfvNf8DMd5s2Ru/B0KlH/b5+O2x4NmltyjvAwD3r9w39RJOHmb5gKcdd9Sx/emYcNz43nuOvepYJsxYVKGQz3HIK3HY63P9N+N0ImPlONjmWLci9BlmR/1nMfBue+GnzxyuGVbG2qGykb0Tr55Ca4YZnx3H1k+8xZe4SXp4why/c+BKLllVnHWvDvHAtXLs/PHRh7kOqVTdknUiSpDapaAucwEHA8pTSH1d2pJQmpJR+t5Zrvgd8I6W0OH/+o8BzwBktmlQCzjt4Gz6752A27d6J3Yf24brPVTZ7icTqmloWLG0bRaYDt92U8w7ehm6dKujeuYLvHrEte265cZPH+cyeg+q1P7XbADpWFPNTWGm5/aWJ7POLJ/j8DS+x96WP8/x7s7KOpIYaziCC3LJqTdG5B2y+a/2+rQ5sfqYNNaXB8vAz38q96dgEIwb3pqLBvpt7bNlnQ5Nl7u1pC/jFI2P5w5PvMWfR8uYPVFsD8z/I/M3XWQvrfw8Ll1VnW+BMCaa/CUvmZJcBGDVpLqde8zy3vTSJa596nxOvfo7Fy0v8Db1ikhr5O5ZqVu9bm44bwfF/gI02zbUH7wsHuTwtkJtl+NSv4Iaj4KHv5lYaUOGcdB1sfSh06QPbHQvHXNnkIR55/cN6H1xbvLyG+0c18bVDoUx6Ed57YlV76Vx44Y9rPL1FLZi2qri50qz3mj5ObS388/vw883hV1tnW8AYtAcc+hPo3BM6bAT7fRu2Oya7PFITPPNu/Rnd85dWM3ryvDWcXSL+c1mD9i+zySFJUhvXxE1oWtX2wCvre3JE9AA2Sim93+BQVX6shuefDZwNMGjQoIaHpSbr3KGcnx6/Az89fsM+1f/A6Kn86L43mLVoOft/rC//d/rH6dmlQ4FSZuOCQz/G+YdsA0A0c3+bbxy0DUM36cbz789kpy16cdKIAYWM2K6tqKnl5w+N/WhG7KLlNfzikbHce84+GSfLXkqJ658exyNvfMjgPl254NCPZbd34A4nw8ibgXyhqusmsGUzipOn3JB7I/rD0TB0fzjyFwWN2SSD94Yx961qb7YjdOrepCE279WF353+cX75z7eYt2QFp+42kFNK/PlhzNT5nHD1sx/NePx71SQeOf8TTf9Qx/hnc7NiF+T3Fzv1b9B32xZIvG6nVA7gyn+tmqlz9E6b07VjRi9D502Bv50IM8ZCeSc47H9hj7MziXLXy5OprjMF+cP5S3nq7RkcsUP/TPK0OXt8Fd55FGrzReNtDmvev4HtjoGPHQHLF+T2QFbO4z+G5/KfPZ3wbO7f1Jn3N3mYhcuquevlycxatJxP7rw5W2/arcBBS1SvQfCZOzdoiMZ+h+jVNaPfK1Y0MkN9xdLWzwGw8VbQdzuY8eaqvmHNKAa+dkdupjjAisXwwAUwaC/YdFhhcjbVPufB3ufmPsRT5gdBVTq237wHT74146N2x/IytulX4v8XVDf4gGK1q3RIktQSirnAWU9EXAXsS25W524bOl5K6VrgWoDKysoiWFNGgnlLVnDh319l6Yrcm9r/eXsGv3v8HX5wzPCMk2245hY26zp6p/4cvZNv+hbasupa5jeYMTxjwbI1nN2+/OmZcfzsodybXy9PmMOoSXP517f2p6wsg327tzoQTr8NRv4VuvSCfc6HTs34xb/3EDjjjgKHa6ajfp3bj2vcf6D/znDsb5s1zJE79ufIHdvOc8PtL038qLgJ8P7MRTz19gwOGd5v/QdJCe77eq64Cbnl9x76TrOKD4Vw3sHbsHnPLjzz7ky237wHZ+49JJMcQO4T9TPG5r6uWQaPfh92OAk2avrqAhuqdyOFhl5dO7Z6jjZry/3hK0/D2AdyxaLtT2z+WOUVFjcbqvsBFcg9ly+Z06SfU01t4lN/fJ4xH8wH4Jr/vMddX9ubHbboWcik7dYRO2zGxwf1YmR+6fZhm3Xn+I9vkU2YIftCvx1zS9IDlHeEyi9kkyUi91roif/NLZM77CjY91tNH2fSiw06Um51iqwKnJD73grwe5fUmr52wNa89eECHh87nV5dOvA/xwxnk26dso61YfY4G57+dZ3217LLIklSG1bMBc43gJNWNlJK50TEJuRmZK4mpTQ/IhZFxJYNZnGOAP7TslGlwnhvxsKPipsrvTF1fkZp1F5061TB4cM345E3Pvyo76RdM3rzq8j8s87PBHKFpremLWC7/j3WcEUL2/aI3K2t6NYXTr8l6xRFp3OH8tX6unRcvW+tViyGOePr900f0/xQGygi+NRuA/nUbgMzy/CR2Q32oa1ZDvMmZVLg/OxeQ7h31FQmzl4MwCHb9WOPoaW/xHJR6Tc8d1Ph9RoMcyeuanfdGDo27YM3/31/1kfFTch96OrmFyZw6Yk7FSplu9apopw7v7o3z703k+raxL5bb0KHZm6hscHKyuGsB3OrUSyaATueku2/zV6D4MRrN2yMwXtD1Z9WtaNs9b1TJa1Tt04VXH/mbixYuoLOHcqze54qpIN/CP13gckv5mZ2Dzs660SSJLVJxVzgfAL4eUR8LaX0h3zfutYF/BXwfxFxSkppSUQcQm7W51daMqhUKMP796DPRh2ZXWe/tX232STDRGovrjh1F4Y//T5jps5nn2024YzdXbobYFCfjXhp/Ko9+jpWlNG/Z+cME6k9+Oxeg7nrlcnMzO9bueeWfdirqXsXd9wo92bKxOdX9W19aAFTlrBhx8D4p1e1ew/JLY+cgb7dO/HYtz7Bs+/OpEfnDlQOsbipEnLY/8LNp8Ci6dChKxz1Kyhv2vKnjb2J3Sbe2C4i5WXBftv0zTpGTueesNfXs05RODuclFsR4KXroWN3OOj7sMnWWaeSSlb3zqW9Nc9qhn8yd5MkSS0mUire1Vkjoj9wBbAHMANYBPwxpXR7RAwBHkgp7VDn/AB+CHwWqAE+BL6RUnptbY9TWVmZqqoanRgqtbqRE+fw84feZPKcJRyzU3++e8Qw3+iRMjJ5zmLO/POLvDdjEZ0qyvjBMcP57J6Ds46ldmDe4hU89uY0enbpwIHb9qWiOf8PzJ8K/7wYpo6CoZ/IFSM6ZzT7uJikBP/9A4y5NzcD7cD/B322zDqVVJqql8O013N7GnZu+rKyKSU+fd0LPP/+LAB6dK7gnnP2Yau+Jb73miRJkiS1rna5T0FRFzhbiwVOSdKapJR4e9pCNuvRmZ6N7JcnSZKab0VNLf8aM41Zi5Zz+Pab0bd7ie+7JkmSJEmtr10WOIt5iVpJkjIXEWy7WfesY0iS1CZ1KC/jyB37Zx1DkiRJklRiXPdSkiRJkiRJkiRJUsmwwClJkiRJkiRJkiSpZFjglCRJkiRJkiRJklQyLHBKkiRJkiRJkiRJKhkWOCVJkiRJkiRJkiSVDAuckiRJkiRJkiRJkkqGBU5JkiRJkiRJkiRJJcMCpyRJkiRJkiRJkqSSYYFTkiRJkiRJkiRJUsmwwClJkiRJkiRJkiSpZFjglCRJkiRJkiRJklQyLHBKkiRJkiRJkiRJKhkWOCVJkiRJkiRJkiSVDAuckiRJkiRJkiRJkkqGBU5JkiRJkiRJkiRJJcMCpyRJkiRJkiRJkqSSYYFTkiRJkiRJkiRJUsmwwClJkiRJkiRJkiSpZGRa4IyImogYVed2Ub7/mIgYGRGvRsSYiPjKGq4fHxGbNOgbFhHPR8SyiLiwNb4PSWqW2ePghqPgJxvn7mePyzqRJEmSJEmSJElFryLjx1+SUtqlbkdEdACuBXZPKU2OiE7AkCaMORs4Fzi+QBklqWXcdw5MeDb39YRnc+2zHso2kyRJkiRJkiRJRa4Yl6jtTq7wOgsgpbQspfTW+l6cUpqeUnoJWNFC+SSpMCa9sPa2JEmSJEmSJElaTdYFzi4Nlqg9NaU0G7gfmBARt0bEGRFR8JwRcXZEVEVE1YwZMwo9vCSt28A91t6WJEmSJEmSJEmrybrAuSSltEud2+0AKaUvAQcDLwIXAn8u9AOnlK5NKVWmlCr79u1b6OElad2OuwoG7wNlFbn7467KOpEkSZIkSZIkSUUv6z041yil9BrwWkT8FRgXEV8EXs4fvj+l9MPs0klSAfQZ6p6bkiRJkiRJkiQ1UdEVOCOiG1CZUnoy37ULMCGlVJP/WpIkSZIkSZIkSVI7lXWBs0tEjKrTfgT4GfDdiLgGWAIsAj6/ljFGR0Rt/us7gF8CVUAPoDYizgeGp5TmFza6JEmSJEmSJEmSpNaWaYEzpVS+hkNHref1Q9ZwaECzAkmSJEmSJEmSJEkqamVZB5AkSZIkSZIkSZKk9dWkAmdEPBERn1vHOZ+JiCc2LJYkSZIkSZIkSZIkra6pMzgPAIas45zBwP7NCSNJkiRJkiRJkiRJa9MSS9R2AapbYFxJkiRJkiRJkiRJ7VxFM65JjXVGRACDgKOASRsSSpIkSZIkSZIkSZIas84ZnBFRGxE1EVGT77pkZbvujdyszfeBXYDbWi6yJEmSJEmSJEmSpPZqfWZwPsWqWZufACYC4xs5rwaYBTwOXF+IcJIkSZIkSZIkSZJU1zoLnCmlA1Z+HRG1wA0ppZ+0ZChJkiRJkiRJkiRJakxT9+AcCsxtgRySJEmSJEmSJEmStE5NKnCmlCa0VBBJkiRJkiRJkiRJWpe1Fjgj4ofk9t+8KqU0O99eHyml9NMNTidJkiRJkiRJkiRJdURKac0Hc3tuJmC7lNLb+fb6SCml8kIEbA2VlZWpqqoq6xiSJEmSJEmSJElSU0TWAbKwriVqD8zfT2zQliRJkiRJkiRJkqRWt9YCZ0rpP2trS5IkSZIkSZIkSVJrKss6gCRJkiRJkiRJkiStr3UtUduoiBgCfBb4ONATmAeMBP6WUhpXsHSSJEmSJEmSJEmSVEeTC5wR8W3gZ0AH6m9cejzwg4j4fyml3xQmniRJkiRJkiRJkiSt0qQlaiPidOBXwCLgJ8CBwHb5+5/k+38VEacWOKckSZIkSZIkSVLrm/YGVN0AH76edRJJeZFSWv+TI6qAocCuKaUJjRwfCrwMvJdS2q1gKVtYZWVlqqqqyjqGJEmSJElqT5Yvgqd/DZNfgsH7wL4XQEWnrFNJkqS6Xr4J/nHuqvYxV0DlF7LLI60u1n1K29OkGZzAcOCOxoqbAPn9N/8ObL+hwVaKiO9HxBsRMToiRkXEHhFxTESMjIhXI2JMRHylzvlnR8TY/O3FiNi3UFkkSZIkSZIK5r5v5Aqc456CJy+Fh7+XdSJJktTQk5c2aF+WTQ5J9TR1D84FwNx1nDMHmN+sNA1ExF7AMeRmjC6LiE2AjYB7gN1TSpMjohMwJH/+McBXgH1TSjMjYlfg3ojYPaX0YSEySZIkSZIkbbDaGhhzX/2+1++GY6/MJA7Vy+E/v4D3HodNt4eD/we6b5ZNFkmSismKxfXbyxc3fp6kVtXUGZyPAoev6WBEBHBY/rxC6A/MTCktA0gpzSRXZK0AZuX7lqWU3sqf/z3gO/nzSCm9AtwEnFOgPJIkSZIkSRuurBx6bFG/r9fAbLIAPPETePpymDoSRv0Nbv9sdlnUslKCl66HW06Df/0Yls7LOpEkFbfdz27Q/nI2OSTV09QC53eB3hFxa0QMrnsgIgYBtwC98ucVwqPAwIh4OyKujoj9U0qzgfuBCfkcZ0TEyu9je3J7gNZVRSNL5uaXsq2KiKoZM2YUKK4kSZJUot78B1x/CFx3UG4GkSSp5R31S+jYLfd1p55wxKVrP78ljX2wfnvyi7BwejZZ1LKevhwe/Da8/TA88xu443NZJ5Kk4nbQD+CUG2GPr8HJf4aDf5h1IklApJTWfDDiiUa6ewM7ATXARGAa0A8YBJQDo4HZKaWDCxIwohzYDziQ3PKzF6WUboyIHYFDgM8Br6aUPh8Rs4GhKaV5da4/DjgzpXTimh6jsrIyVVVVFSKuJEmSVHo+fB2u2Q9Sbb4j4MuPwxYjMo0lSe3C0vkwYyz02x46bpRdjr+dDO8+tqrddWP41lio6JhdJrWM3+8GM9+u33fhO9Bt02zySJKkDRUt/gARmwFXAruR28pyGnA+cHdKaYeWfvzGrGsPzgPWce2W+VtdOwNrrpo2UUqpBngSeDIiXgPOBG5MKb0GvBYRfwXGAZ8HxgAjgLqF2RHAG4XKI0mSJLU57/6rTnETIME7/7LAKUmtoXMPGLh71ingsP/NFb3mToBOPeCYKyxutlXdN6tf4OzYHTp1zy6PJEkqavntKe8BbkopnZbv25nc5MfMrHWJ2pRSWTNv5YUIFxHbRsQ2dbp2AaZFxAEN+ibkv/4l8IuI2Dh//S7kCp9XFyKPJEmS1CZtul0jfcNaP4ckKTubDoNzR8I5L8K3x8Lw47JOpJZy8CXQpXfu67IKOPTH0KFLppEkSVJROxBYkVL648qOlNKrwKSV7YgYEhFPR8Qr+dve+f7+EfFURIyKiNcjYr+IKI+IG/Pt1yLiguaEWtcMzqx1A34XEb2AauBd4Dzgmoi4BlgCLCJXxCSldH9EbAE8FxEJWAB8JqX0QQbZJUmSpNKwzWGw25eg6oZc++NnwLBjs80kSWp9ZeXQd9usU6ilDRgBF7wBk6tgk49Bj/5ZJ5IkScVtB+DldZwzHTg0pbQ0P3HxVqAS+DTwz5TSz/JbUnYlN3Fxi5VL2+ZrgE221j04Vzs5oga4LaV0RnMerFi5B6ckSZIELJkDKUHXPlknkSRJkiRJ66dF9+CMiHOBoSmlCxr0DwEeSCntEBE9gd+TK17WAB9LKXWNiE8Afwb+BtybUhoVEb2BKuAh4EHg0ZTq7ZuzXta6RG0jFgATm/ogkiRJkkpAl94WNyVJkiRJUl1vACPWcc4FwDRgZ3IzNzsCpJSeAj4BTAFujIjPpZTm5M97EvgqcH1zQjW1wDkSGN6cB5IkSZIkSZIkSZJUUp4AOkXE2Ss7ImInYGCdc3oCH+RnYn4WKM+fNxiYllK6jlwhc9eI2AQoSyndBfwA2LU5oZpa4PwFcFREHNqcB5MkSZIkSZIkSZJUGlJur8sTgEMi4r2IeAO4FPiwzmlXA2dGxKvAMGBRvv8A4NWIGAmcCvwW2AJ4MiJGkVu69v81J1dT9+D8HHAKcCRwL/BS/htYbZCU0l+aEygL7sEpSZIkSZIkSZKkEtSie3AWq6YWOGvJFTMb/rDqDhLkCrrlGx6vdVjglCRJkiRJkiRJUglqlwXOiiaef1aLpJAkSZIkSZIkSZKk9dCkAmdK6aaWCiJJkiRJkiRJkiRJ69LUGZxExP7APsDm+a6pwLMppf8UMpgkSZIkSZIkSZIkNbTeBc58YfMPwLYru/L3KX98LPC1lNJTBU0oSZIkSZIkSZIkSXnrVeCMiJOAW/PnfwD8G5iUPzwQOADYDvhXRJyWUrq78FElSZIkSZIkSZIktXfrLHBGxObATUA18E3g+pRSTYNzyoAvAlcCf4mI/6aUphY+riRJkiRJkiRJkqTWNOSiB8uA04HzyU1+nESuLnjr+MuOrt2QsSPiCOC3QDm5OuRl67qmbD3GPR/oCpyRUrqmYXETIKVUm1K6Djgjf+55TQkuSZIkSZIkSZIkqfjki5t3AdcAlUC//P01wJ35480SEeXAVcCRwHDg9IgYvq7r1ucBjwBeSCnds64TU0r3Ai/kQ0iSJEmSJEmSJEkqbacDhwIbNejfCDgMOG0Dxt4deDel9H5KaTlwG3Dcui5anwLnYOC5JgR5DhjShPMlSZIkSZIkSZIkFafzWb24udJGwAUbMPYW5Ja7XWlyvm+t1qfA2QFY3oQgK8itkStJkiRJkiRJkiSptA3cwOMFtz4Fzg+AHZsw5vbAh82LI0mSJEmSJEmSJKmITNrA42szhfoF0gH5vrVanwLnU8ChETFsXSdGxHbA4flrJEmSJEmSJEmSJJW2K4FFazi2CLhiA8Z+CdgmIoZGREdy+3nev66L1qfA+Xtyy9Q+EBHD13RSvrj5D3LL0161XpElSZIkSWpLZo+DG46Cn2ycu589LutEkiRJkrShbgUeY/Ui5yLgUeC25g6cUqoGvgH8E3gTuCOl9Ma6rouU0joHj4hfAN8htxfn3cDjrJpuOhA4BDgB6Aj8OqX0nWZ8D5mprKxMVVVVWceQJEmSJJW6G46CCc+uag/eB856KLs8kiRJktq6aI0HGXLRg2XkZldeQK42OInczM3bxl92dG1rZKhrvQqcABHxQ+AHQAXQ8KIAaoCfA5ek9R107Y+3MKXUrU7780BlSukbEXEJ8GVgRp1LDkgpzY2IfYHfAD3y/b9JKV27tseywClJkiRJKoifbAy11avaZRXww1nZ5ZEkSZLU1rVKgbPYVKzviSmln0TETcAXgH2A/vlDHwLPADemlFpz7Z0rUkqX1+2IiM2AW4DjU0qvRMQmwD8jYkpK6cFWzCZJkiRJao8G7lF/BufAPbLLIkmSJElt1HoXOAFSShOAH7VQlkI4h1yh9RWAlNLMiPgucAlggVOSJEmS1LKOuwruOwcmvZArbh53VdaJJEmSJKnNaVKBs5V1iYhRddp9gPvrtC+IiM/kv56TUjoQ2B64qcE4Vfn+eiLibOBsgEGDBhUqsyRJkiSpPesz1D03JUmSJKmFFXOBc0lKaZeVjZV7cNY5vtoStU2R35fzWsjtwdnccSRJkiRJkiRJkiS1nrKsAxTYGGBEg74RwBsZZJEkSZIkSZIkSZJUYMU8g7M5rgJeiIi7U0qjImJj4BfATzLOJUmSJEmSJEmSJJWmS3qWAacD5wMDgUnAlcCtXDKvtrnDRsSfgWOA6SmlHdb3ulKewXlBRIyqcxuSUvoA+AxwXUSMBZ4D/pxS+ke2USVJkiRJkiRJkqQSlCtu3gVcQ247yX75+2uAO/PHm+tG4IimXlS0MzhTSt0atG8k902SUroEuGQN1z0F7Nai4SRJkiRJkiRJkqT24XTgUGCjBv0bAYcBpwG3NGfglNJTETGkqdeV8gxOSZIkSZIkSZIkSS3rfFYvbq60EXBB60XJscApSZIkSZIkSZIkaU0GbuDxgrPAKUmSJEmSJEmSJGlNJm3g8YKzwClJkiRJkiRJkiRpTa4EFq3h2CLgitaLkmOBU5IkSZIkSZIkSdKa3Ao8xupFzkXAo8BtzR04Im4Fnge2jYjJEfHF9bnOAqckSZIkSZIkSZKkxl0yrxY4CTgbqAKm5e/PBk7OH2+WlNLpKaX+KaUOKaUBKaU/rc91kVJq7mO2GZWVlamqqirrGJIkSZIkSZIkSVJTRNYBsuAMTkmSJEmSJEmSJEklwwKnJEmSJEmSJEmSpJJhgVOSJEmSJEmSJElSybDAKUmSJEmSJEmSJKlkWOCUJEmSJEmSJEmSVDIscEqSJEmSJEmSJEkqGRY4JUmSJEmSJEmSJJUMC5ySJEmSJEmSJEmSSoYFTkmSJEmSJEmSJEklwwKnJEmSJEmSJEmSpJJRkXUASZIkSZIkaaXqmloue3gs9786lf49O/P9o4ez+9A+WceSJElSEXEGpyRJkiRJkorG9c+M4/pnxjF9wTJenTyPL930EouXV2cdS5IkSUWkKAucEZEi4m912hURMSMiHsi3P59vj6pzG54/tn1EPBERb0XEOxHxPxERWX0vkiRJkiRJWn/PvTerXnv+0mpemzwvozSSJEkqRkVZ4AQWATtERJd8+1BgSoNzbk8p7VLnNiZ//v3AZSmlbYGdgb2Br7dackmSJEmSJDXbjlv0qNfuVFHGx/p1zyiNJKnJqpfDuKdgxttZJ1GpWDIXpo6CmhVZJ1EJKdYCJ8BDwNH5r08Hbl2Paz4NPJtSehQgpbQY+AZwUYsklCRJkiRJUkF97YCtOXz7fkTAJt06cfkpO9N7o45Zx5IkrY+5k+D3I+CmY+Gq3eDh72WdSMXu1dvh18Pg2v3htzvDtDFZJ1KJqMg6wFrcBvwwvyztTsCfgf3qHD81Ivat094L2B54ue4gKaX3IqJbRPRIKc1f2R8RZwNnAwwaNKiFvgVJkiRJkiQ1RbdOFVzz2UqWrqihY3kZZWXuPCRJJePZ38LciavaL/wRdvsSbLJNdplUvFYshYe+A9VLcu35U+BfP4Iz/p5tLpWEop3BmVIaDQwhN3vzoUZOabhE7ZImjn9tSqkypVTZt2/fAiSWJEmSJElSoXTuUG5xU5JKzcJp69cnASyZA8sa7LM9Z3wmUVR6irbAmXc/cDnrtzwtwBhgRN2OiNgSWFh39qYkSZIkSZIkSSqwnU+r3+49BAbukUkUlYAe/WHA7vX7hh+XTRaVnGJeohZyy9LOTSm9FhEHrMf5NwMXR8QhKaV/RUQX4P+AX7ZgRkmSJEmSJEmSNOxoOO0WGH07dO8Pe58L5R2yTqVidtrN8OSlMH0sbHNo7u+MtB6KusCZUppMrkDZmIZ7cH49pfRcRBwH/C4irgLKgb8Cv2/hqJIkSZIkSZIkadjRuZu0PrptCsdckXUKlaBIKWWdIXOVlZWpqqoq6xiSJEmSJEmSJElSU7TLTcuLfQ9OSZIkSZIkSZIkSfqIBU5JkiRJkiRJkiRJJcMCpyRJkiRJkiRJkqSSYYFTkiRJkiRJkiRJUsmwwClJkiRJkiRJkiSpZFjglCRJkiRJkiRJklQyLHBKkiRJkiRJkiRJKhkWOCVJkiRJkiRJkiSVDAuckiRJkiRJkiRJkkqGBU5JkiRJkiRJkiRJJcMCpyRJkiRJkiRJkqSSYYFTkiRJkiRJkiRJUsmwwClJkiRJkiRJkiSpZFjglCRJkiRJkiRJklQyLHBKkiRJkiRJkiRJKhkWOCVJkiRJkiRJkiSVDAuckiRJkiRJkiRJkkpGRdYBJEmSJEmSJEkNfPAqVC+HAZUQkXUarUlNNZS3kbfZly2AkX+DBR/A9ifC5rtknagwli+CKIcOnbNOUlxqVsCKJdC5x4aNU70cyiqgzPl0al1t5JlXkiRJkiRJauNqa2Hck7k3pbc8ECo6Zp1ILaG2Bm47A95+ONfeohI+dx906pZtLtU38x24+2yY+gpsviuceB1ssnXWqZovJbjpk7nvB+D5q+Bz98OQfbLNtSFqa+DBb8PIv0J5R9jnPDjgoqxTFYdRt8A/L4Ylc+FjR8BJ10Gn7k0bY8USuP+b8Prd0HVjOPznsNMpLRJXakzmJfWI+HdEHN6g7/yIeDgiXm/k/BsjYlxEjMrfnqtz7PiIGB0Rb0bEaxFxfCt8C5IkSZIkSVLLql4ONxwJfz0BbvkUXLMfLJ2XdSq1hHceXVXcBJhSBa/eml0eNe6+c1YVA6e+kmuXsskvrfp+AGqroepP2eUphNfuhJdvyH0vKxbDk5fCxBeyy/PBaLjry7kPMLz37+xyLJwO958LS+YAKfd888yVTR/n+avgtb9DqoFF0+Her8GCaYVOK61R5gVO4FbgtAZ9pwGXruWa76SUdsnf9gaIiJ2By4HjUkrbAZ8ELo+InVoitCRJkiRJktRq3noQJv13VXvG2NwMHLU9Cz5YvW/+1NbP0QKmL1jKkuU1WccojMlV9dtTXs4mR6FUNLJ8a0WX1s9RSB+OXr++1rBgGtxwFLx2B4x9AP52UnZ/Z6a/CbUr6vd9+FrTx5k6sn67dgVMa8Y4UjMVQ4HzTuDoiOgIEBFDgM2BSU0c50Lg5ymlcQD5+0uB7xQuqiRJkiRJkpSBJXPXr0+lb9ujoGOdpSLLOsAOJ2aXB6B6WX62V/PMW7KCM67/L7v/7HEq//cx/vbfCQUMl5HBe6+93ZqWzs8tl3vZYPjT4c0rVvXfCYYds6rdqSfsVeKzUrc6sH47ymHLAzKJwjv/hOULVrVTDbxxTzZZttg19+dbV8Of1foYsl/9doeusMWI5ueSmijzAmdKaTbwInBkvus04A4greWyX9VZovbmfN/2QMOPPFTl+1cTEWdHRFVEVM2YMaP534AkSZIkSZLU0rY7Frr0WdXu0BV2+lR2eYrJzHfg5lPgtzvDwxfBiqVZJ9ow3TeDLzwCu5wBO5wMZ/4DNtsxuzxVN8CvtoFfDIGbPwXLFqzzkob++J/3ePbdWQAsWl7DJfe/wbT52f053fbiRI648ilOuPpZnhjbzCU1j/8DbHVwrhi91cFw/NWFDdkU//oRjL4dls7NzfS+7Yzcnr1N9am/whl3wid/B998GfoNL3jUVrX1IXDU5bDx1tBvRzj5z7DJNtlk6bFFI30Dmj7OymL2pYPg+kPhg1ebPkan7vDp22HgHtBzEOz3bdj97KaPs/uXYe9vwkZ9cz/fU/8GXXo3fRypmSKltdURWylExBnAMSml0yNiFPBFYBbwQEpphwbn3pjvv7NB/yvAWSmlV+v07QzckFLadW2PX1lZmaqqqtZ2iiRJkiRJkpSt2ePgpeuhZgWMOBP6Nfq5/vYlJfh9Jcx6d1Xf3ufCYT/NLlNbMn8qXLFDbrbZSvt/Dw68uEnDfPHGl3h87PR6fbd8eQ/23mqTQqRskqffmcFn//TiR+2KsuDxb+/P4I03avUsBfP73WHmW/X7zhsNvQdnk0erSwnu+iK8fleuPXAP+Mzd0Klb08Z54Fv190btOQjOexXKMp/LpmxF1gGyUCx/6+8DDo6IXYGuKaXmLD49Bmg4/3kE8MaGhpMkSZIkSZIy12coHP4zOOqXFjdXmjOufnET4N3Hs8nSFk1/s35xE2Ba099uPWDYpvXavbt24OMDs5np9eRb9Vfzq65NPPPuzEyyFMwWDeb3dNus8RmDyk5EbgbpOS/C2f+BLz7a9OImwITn6rfnTYS5bWDJZ6kZKrIOAJBSWhgR/wb+DNzazGEuB/4eEU+klMbn9/K8GDi5QDElSZIkSZIkFZPum+eWRKy7P6TF38IZsBt06gHL5q/q2+qgJg/zmT0GMX/JCu4dOYXNenbmwsO2pUvH8gIGXX/bbtZ9tb5hjfSVlEN/Cgs+hPf/DX22hE/+HsqL4q1/NdR32w27fotdYcabq9rd+kHPZix1K7UBRbFELUBEHA/cA2yXUhqbL1C+A9RdBP0C4Ghgf2Benf7dU0rLI+JE4MdAB2AF8KOU0t3remyXqJUkSZIkSZJK1FsPw/3fhEUzYIsRuX0Eezp7rWAm/hf+9WNY+CHsdBrs/93cbLQSVV1Ty3fvHM29o6ZQUV7Gl/cbyncOH5Z1rMKoXg4VHbNOoZa0aCbc85XcTPU+W8In/w+G7Jt1KmWvdJ+UN0DRFDizZIFTkiRJkiRJKmE1K2DJXOjWN+skKhFzFy+noryMbp2c6agSVLMCyjtknULFo10WOH32liRJkiRJklTayjtY3FST9OrqTEeVMIubEmVZB5AkSZIkSZIkSZKk9WWBU5IkSZIkSZIkSVLJsMApSZIkSZIkSZIkqWRY4JQkSZIkSZIkSZJUMixwSpIkSZIkSZIkSSoZFjglSZIkSZIkSZIklQwLnJIkSZIkSZIkSZJKhgVOSZIkSZIkSZIkSSXDAqckSZIkSZIkSZKkkmGBU5IkSZIkSZIkSVLJsMApSZIkSZIkSZIkqWRY4JQkSZIkSZIkSZJUMixwSpIkSZIkSZIkSSoZFjglSZIkSZIkSZIklQwLnJIkSZIkSZIkSZJKhgVOSZIkSZIkSZIkSSXDAqckSZIkSZIkSZKkkmGBU5IkSZIkSZIkSVLJKNkCZ0R8PyLeiIjRETEqIvaIiI4RcWVEvBsR70TEfRExIOuskiRJkiRJkiRJkgqjIusAzRERewHHALumlJZFxCZAR+DnQHdg25RSTUScBdwdEXuklFKGkSVJkiRJkiSpVU2ctZgL73yVVybMYdfBvbn85J0ZtHHXrGNJkrTBSnUGZ39gZkppGUBKaSYwFzgLuCClVJPvvwFYBhyUUU5JkiRJkiRJysSFd77Ki+NmU12beHHcbC6889WsI0mSVBClWuB8FBgYEW9HxNURsT+wNTAxpTS/wblVwPYNB4iIsyOiKiKqZsyY0QqRJUmSJEmSJKn1vDJhzlrbkiSVqpIscKaUFgIjgLOBGcDtwAFNHOPalFJlSqmyb9++hQ8pSZIkSZIkSRnadXDvtbYlSSpVJVngBEgp1aSUnkwp/Qj4BnAsMCgiujc4dQTwRqsHlCRJkiRJkqQMXX7yzuw+tA8VZcHuQ/tw+ck7Zx1JkqSCqMg6QHNExLZAbUrpnXzXLsBbwGvAbyLiqymlmoj4HNAVeCKbpJIkSZIkSZKUjUEbd+WOr+yVdQxJkgquJAucQDfgdxHRC6gG3iW3XO0C4HLg7YioBcYCJ6SUUlZBJUmSJEmSJEmSJBVOWPuDysrKVFVVlXUMSZIkSZIkSZIkqSki6wBZKNk9OCVJkiRJkiRJkiS1PxY4JUmSJEmSJEmSJJUMC5ySJEmSJEmSJEmSSoYFTkmSJEmSJEmSJEklwwKnJEmSJEmSJEmSpJJhgVOSJEmSJEmSJElSybDAKUmSJEmSJEmSJKlkWOCUJEmSJEmSJEmSVDIscEqSJEmSJEmSJEkqGRY4JUmSJEmSJEmSJJUMC5ySJEmSJEmSJEmSSoYFTkmSJEmSJEmSJEklwwKnJEmSJEmSJEmSpJJhgVOSJEmSJEmSJElSybDAKUmSJEmSJEmSJKlkWOCUJEmSJEmSJEmSVDIscEqSJEmSJEmSJEkqGRY4JUmSJEmSJEmSJJUMC5ySJEmSJEmSJEmSSkbRFjgjol9E3BIR70fEyxHxfEScEBEHRMS8iBhV53ZI/poBEXFfRLwTEe9FxG8jomPW34skSZIkSZIkSVpPs8fBDUfBTzbO3c8el3UiSUWmKAucERHAvcBTKaUtU0ojgNOAAflTnk4p7VLn9q/8NXcD96aUtgE+BnQDfpbBtyBJkiRJkiRJkprjvnNgwrNQW527v++crBNJKjJFWeAEDgKWp5T+uLIjpTQhpfS7dVyzNKV0Q/78GuAC4AsR0bVF00qSJEmSJEmSpMKY9MLa25LavWItcG4PvLKW4/s1WKJ2q/w1L9c9KaU0H5gIbN1wgIg4OyKqIqJqxowZhcwuSZIkSZIkSZKaa+Aea29LaveKtcBZT0RcFRGvRsRL+a6GS9S+19QxU0rXppQqU0qVffv2LXBiSZIkSZIkSZLULMddBYP3gbKK3P1xV2WdSFKRqcg6wBq8AZy0spFSOiciNgGq1nLNGODkuh0R0QMYBLzbEiElSZIkSZIkSVKB9RkKZz2UdQpJRaxYZ3A+AXSOiK/V6VvXPpqPA10j4nMAEVEO/Bq4MaW0uGViSpIkSZIkSZIkSWpNRVngTCkl4Hhg/4gYFxEvAjcB38uf0nAPzpPz15wAnBIR7wBvA0uBizP4FiRJkiRJkiRJkiS1gGJdopaU0gfAaWs43HMN10wCjm2xUJIkSZIkSZIkSZIyVZQzOCVJkiRJkiRJkiSpMRY4JUmSJEmSJEmSJJUMC5ySJEmSJEmSJEmSSoYFTkmSJEmSJEmSJEklwwKnJEmSJEmSJEmSpJJhgVOSJEmSJEmSJElSybDAKUmSJEmSJEmSJKlkWOCUJEmSJEmSJEmSVDIipZR1hsxFxAxgQtY5WtkmwMysQ0hSO+NzryRlw+dfSWp9PvdKUjZ8/pXan5kppSOyDtHaLHC2UxFRlVKqzDqHJLUnPvdKUjZ8/pWk1udzryRlw+dfSe2FS9RKkiRJkiRJkiRJKhkWOCVJkiRJkiRJkiSVDAuc7de1WQeQpHbI515JyobPv5LU+nzulaRs+PwrqV1wD05JkiRJkiRJkiRJJcMZnJIkSZIkSZIkSZJKhgVOSZIkSZIkSZIkSSXDAmc7ExFHRMRbEfFuRFyUdR5JaqsiYmBE/DsixkTEGxFxXr6/T0Q8FhHv5O97Z51VktqaiCiPiJER8UC+PTQiXsi/Br49IjpmnVGS2pqI6BURd0bE2Ih4MyL28rWvJLWsiLgg/57D6xFxa0R09rWvpPbCAmc7EhHlwFXAkcBw4PSIGJ5tKklqs6qBb6eUhgN7Aufkn3MvAh5PKW0DPJ5vS5IK6zzgzTrtXwBXpJS2BuYAX8wklSS1bb8FHkkpDQN2Jvc87GtfSWohEbEFcC5QmVLaASgHTsPXvpLaCQuc7cvuwLsppfdTSsuB24DjMs4kSW1SSumDlNIr+a8XkHuDZwtyz7s35U+7CTg+k4CS1EZFxADgaOD6fDuAg4A786f43CtJBRYRPYFPAH8CSCktTynNxde+ktTSKoAuEVEBdAU+wNe+ktoJC5ztyxbApDrtyfk+SVILioghwMeBF4B+KaUP8oc+BPpllUuS2qgrge8Ctfn2xsDclFJ1vu1rYEkqvKHADOCG/BLh10fERvjaV5JaTEppCnA5MJFcYXMe8DK+9pXUTljglCSpBUVEN+Au4PyU0vy6x1JKCUiZBJOkNigijgGmp5RezjqLJLUzFcCuwB9SSh8HFtFgOVpf+0pSYeX3NT6O3IdMNgc2Ao7INJQktSILnO3LFGBgnfaAfJ8kqQVERAdyxc2bU0p357unRUT//PH+wPSs8klSG7QP8MmIGE9uO4aDyO0J1yu/bBf4GliSWsJkYHJK6YV8+05yBU9f+0pSyzkEGJdSmpFSWgHcTe71sK99JbULFjjbl5eAbSJiaER0JLfp9P0ZZ5KkNim/59ufgDdTSr+pc+h+4Mz812cC97V2Nklqq1JK/y+lNCClNITca90nUkpnAP8GTs6f5nOvJBVYSulDYFJEbJvvOhgYg699JaklTQT2jIiu+fcgVj73+tpXUrsQuRVC1F5ExFHk9iUqB/6cUvpZtokkqW2KiH2Bp4HXWLUP3MXk9uG8AxgETAA+lVKanUlISWrDIuIA4MKU0jERsSW5GZ19gJHAZ1JKyzKMJ0ltTkTsAlwPdATeB84i98F6X/tKUguJiB8DpwLV5F7nfoncnpu+9pXU5lnglCRJkiRJkiRJklQyXKJWkiRJkiRJkiRJUsmwwClJkiRJkiRJkiSpZFjglCRJkiRJkiRJklQyLHBKkiRJkiRJkiRJKhkWOCVJkiRJkiRJkiSVDAuckiRJkpSBiLgxIlJEDKnTNyTfd2MBxh8fEeM3dJy2LiIuyf/MD8g6iyRJkiRp/VjglCRJkqQCyRfK1nb7fNYZ12RDiqsR8WT+2pqI2HEN56ws6B6ywWElSZIkSe1aRdYBJEmSJKkN+vEa+ket47opwHbAvIKmaT1lwK+AI7IOIkmSJElquyxwSpIkSVKBpZQuaeZ1K4CxhU3Tqt4FDo+IQ1NKj2UdRpIkSZLUNrlErSRJkiQVibUtExsRH4uIuyJiTkQsiojnIuLoiPj82pa/jYiNIuJXETExIpZFxLsR8b2IiDrnXAKMyzfP3IBldS8GEvCriFiv3zfzj/HkGo6tdZ/SiNgqIu6MiFkRsSAiHo2IHfLn9Y2IayPig4hYGhEvRcSB68hyZkSMjIglETE9Iv4cEZut4dw+EXFpRLyZP39eRDweEYc1cu5Hf0YRcUR+Sd95EZHW52ckSZIkSarPGZySJEmSVOQiYhjwHNAbeBAYDWwJ3AM8tJZLOwD/BDYHHgaqgeOBy4DOrFpK90mgF3Ae8Cpwb50xRjUh6kjgb8BngTOBG5pwbVMNAV4A3gRuzLdPAJ6MiL2AR4D5wO1AH+A04OGI+FhKaWIj410AHJY//xFgX+As4ICI2COlNGPliRExmNzPbAjwdP78jYBjgEci4isppesaeYyTyS3f+zDwR2Bwc795SZIkSWrPLHBKkiRJUoHlZ0Q2ND6ldGMzh7yKXHHz6ymlP9R5nCNZe4Fzc3IFy0NTSkvy1/wYeBu4ICJ+nlJakVJ6MiLGkytwjmruErt53wdOAX4aEbetfNwWsD/wg5TSz1Z2RMT/AD8hV/i8g9zPqzZ/7DHgL+QKmRc0Mt6RwB4ppZF1xrsCOJ9cQfiLdc69iVxx8vSU0m11zu9FrvD5fxFxf0ppWoPHOAo4KqX0SDO+X0mSJElSnkvUSpIkSVLh/aiR2+ebM1BEDAQOIre/5TV1j6WUHgb+tY4hzq1bZEwpTQfuA3oC2zYn09qklCYBVwJbAN8u9Ph1jCdXeKzrpvx9J+A7K4ubebeQm8G6yxrG+2vd4mbeJcA84NMR0QkgInYmV1y9q25xEyClNJfcn3Vn4KRGHuM+i5uSJEmStOGcwSlJkiRJBZZSinWftd52yd8/36Bgt9IzwCFruHZeSundRvon5e97b2C2NbmU3IzH70bEdY3MZCyEUSmlmgZ9U/P3b6eUFtQ9kFKqiYhpwIA1jPefhh0ppXkRMYpcQXM7csv17pU/3HMNM3X75u+3a+TYi2t4bEmSJElSE1jglCRJkqTi1jN/v6Yi4dqKh3PX0F+dvy9vTqB1SSnNzy+F+3tysyC/1gIPM6+Rx62OiEaP5VWT25e0MWv6OX6Yv1/557Bx/v7Q/G1Nuq1lLEmSJEnSBnCJWkmSJEkqbvPz9/3WcHxN/Vm7htxen1+KiGFrOS+x5g/f9ip0qLVY089xs/z9vAb356WUYi23sxoZKxU0sSRJkiS1UxY4JUmSJKm4jcrf7xURjf0Ot2+BHmflcq8FmdWZUqoGvkeuePmrtZw6BxjYsDMiylnzfpktYf9GMvTMZ1gKvJnv/m/+fr/WiSVJkiRJasgCpyRJkiQVsZTSROBJYGvgK3WPRcQRrHn/zaaaQ26G4aACjUdK6V7gaeAYYJ81nPYiMCgiDmvQ/wNgcKGyrIfPRsTHG/RdQm5p2ltTSssAUkpV5L6nEyPiC40NFBE7RsSmLRlWkiRJktoz9+CUJEmSpOJ3DvAscHVEHAWMBrYETgLuA44DajfkAVJKCyPiBWC/iLiZ3PKyNcD9KaXRGzD0heRmPW69huOXA4cD90XE7cBsYG9gKLnC7gEb8NhN8TDwbETcAXxAbmbsvsB44KIG534aeAL4U0ScC7xAbr/TAcBOwA7AXsD01gguSZIkSe2NMzglSZIkqcillMaQK5jdQ25p1POBIcAJwDP50+Y3dm0TfRZ4EDgC+BHwU2DXDRkwpfQicPtajj8OHA+8AZwGnEmuqLg7MGFDHruJrgC+Tm5J2vOBYcCNwN4ppXqFypTSZGAE8H1yReAzgHPJFWYnkptp+1rrxJYkSZKk9idSSllnkCRJkiQ1U3625aeBYSmlt7LOI0mSJElSS3MGpyRJkiQVuYgoi4jNGuk/GDgVGGNxU5IkSZLUXrgHpyRJkiQVv47ApIj4NzAWqAa2Bw4FlpPbo1OSJEmSpHbBJWolSZIkqchFRDlwJXAQMADoCswEngIuSymNzC6dJEmSJEmtywKnJEmSJEmSJEmSpJLhHpySJEmSJEmSJEmSSoYFTkmSJEmSJEmSJEklwwKnJEmSJEmSJEmSpJJhgVOSJEmSJEmSJElSybDAKUmSJEmSJEmSJKlk/H902hazknaqrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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 Mass\",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"
   ]
  },
  {
   "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": 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>index</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.611111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020</td>\n",
       "      <td>0.842105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  index     Class\n",
       "0  2010  0.000000\n",
       "1  2012  0.000000\n",
       "2  2013  0.000000\n",
       "3  2014  0.333333\n",
       "4  2015  0.333333\n",
       "5  2016  0.625000\n",
       "6  2017  0.833333\n",
       "7  2018  0.611111\n",
       "8  2019  0.900000\n",
       "9  2020  0.842105"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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\n",
    "ey=Extract_year(df[\"Date\"])\n",
    "ydf=df.groupby(ey).mean()[\"Class\"].reset_index(name='Class')\n",
    "ydf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "sns.lineplot(y=\"Class\", x=\"index\",  data=ydf)\n",
    "plt.xlabel(\"Year\",fontsize=20)\n",
    "plt.ylabel(\"Rate\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 12)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\n",
    "features.head()\n",
    "features.shape"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights',\n",
       "       'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount',\n",
       "       'Serial', 'Orbit_ES-L1', 'Orbit_GEO', 'Orbit_GTO', 'Orbit_HEO',\n",
       "       'Orbit_ISS', 'Orbit_LEO', 'Orbit_MEO', 'Orbit_PO', 'Orbit_SO',\n",
       "       'Orbit_SSO', 'Orbit_VLEO', 'LaunchSite_CCAFS SLC 40',\n",
       "       'LaunchSite_KSC LC 39A', 'LaunchSite_VAFB SLC 4E',\n",
       "       'LandingPad_5e9e3032383ecb267a34e7c7',\n",
       "       'LandingPad_5e9e3032383ecb554034e7c9',\n",
       "       'LandingPad_5e9e3032383ecb6bb234e7ca',\n",
       "       'LandingPad_5e9e3032383ecb761634e7cb',\n",
       "       'LandingPad_5e9e3033383ecbb9e534e7cc', 'Serial_B0003', 'Serial_B0005',\n",
       "       'Serial_B0007', 'Serial_B1003', 'Serial_B1004', 'Serial_B1005',\n",
       "       'Serial_B1006', 'Serial_B1007', 'Serial_B1008', 'Serial_B1010',\n",
       "       'Serial_B1011', 'Serial_B1012', 'Serial_B1013', 'Serial_B1015',\n",
       "       'Serial_B1016', 'Serial_B1017', 'Serial_B1018', 'Serial_B1019',\n",
       "       'Serial_B1020', 'Serial_B1021', 'Serial_B1022', 'Serial_B1023',\n",
       "       'Serial_B1025', 'Serial_B1026', 'Serial_B1028', 'Serial_B1029',\n",
       "       'Serial_B1030', 'Serial_B1031', 'Serial_B1032', 'Serial_B1034',\n",
       "       'Serial_B1035', 'Serial_B1036', 'Serial_B1037', 'Serial_B1038',\n",
       "       'Serial_B1039', 'Serial_B1040', 'Serial_B1041', 'Serial_B1042',\n",
       "       'Serial_B1043', 'Serial_B1044', 'Serial_B1045', 'Serial_B1046',\n",
       "       'Serial_B1047', 'Serial_B1048', 'Serial_B1049', 'Serial_B1050',\n",
       "       'Serial_B1051', 'Serial_B1054', 'Serial_B1056', 'Serial_B1058',\n",
       "       'Serial_B1059', 'Serial_B1060', 'Serial_B1062'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "#features = pd.concat([features,pd.get_dummies(df['Orbit'])], axis=1)\n",
    "features_one_hot =pd.get_dummies(features[['Orbit', 'LaunchSite', 'LandingPad', 'Serial']])\n",
    "features_one_hot.head()\n",
    "features = pd.concat([features, features_one_hot], axis=1)\n",
    "features.columns"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Orbit_ES-L1     float64\n",
       "Orbit_GEO       float64\n",
       "Orbit_GTO       float64\n",
       "Orbit_HEO       float64\n",
       "Orbit_ISS       float64\n",
       "                 ...   \n",
       "Serial_B1056    float64\n",
       "Serial_B1058    float64\n",
       "Serial_B1059    float64\n",
       "Serial_B1060    float64\n",
       "Serial_B1062    float64\n",
       "Length: 72, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot.dtypes\n",
    "features_one_hot = features_one_hot.astype('float64', copy=False)\n",
    "features_one_hot.dtypes"
   ]
  },
  {
   "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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">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"
   ]
  }
 ],
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