Mod10_Week2_Lab2_EDA_with_Visualization.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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\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",
    "\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": 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": {
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       "\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",
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       "      <td>B0005</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "      <td>1.0</td>\n",
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       "      <td>B0007</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
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      "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 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": 4,
   "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": 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 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 (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": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 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",
    "df_success_rate_by_orbit_type = df[['Orbit', 'Class']].groupby('Orbit').mean()\n",
    "df_success_rate_by_orbit_type.plot(kind='bar', figsize=(10, 6))\n",
    "plt.xlabel('Orbit Type') # add to x-label to the plot\n",
    "plt.ylabel('Success rate') # add y-label to the plot\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": 7,
   "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 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": 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jOOvgzcpdlnqjCXfBI7+GPlXFTM1Z78DDFxXHu59a/MR4C96dNZ8HX36XDVYdzBZrdcHSupIkSZIkSZKknqxXzuCsuICzKxhwVq73Zi+gqm+w4gpN72koSZIkSZIkSZLUg/XKgLNil6iVAFYe3L/1TpIkSZIkSZIkSeox+pS7AEmSJEmSJEmSJElqKwNOSZIkSZIkSZIkSRXDgFOSJEmSJEmSJElSxTDglCRJkiRJkiRJklQxDDglSZIkSZIkSZIkVQwDTkmSJEmSJEmSJEkVw4BTkiRJkiRJkiRJUsUw4JQkSZIkSZIkSZJUMQw4JUmSJEmSJEmSJFUMA05JkiRJkiRJkiRJFcOAU5IkSZIkSZIkSVLFMOCUJEmSJEmSJEmSVDEMOCVJkiRJkiRJkiRVDANOSZIkSZIkSZIkSRXDgFOSJEmSJEmSJElSxTDglCRJkiRJkiRJklQxqspdgLTYgkV1zF1Yy7CB/cpdiiRJkiSph5r//lv8845bWVSX7Lf/wQwcMbLcJUmSJElqJwNOlUVdXdKnT3x4fMPjr/PDvz3HjLkL2XfT1fj50dsyZIC/PSVJkiRJnWfejKkcd951bJvP0Z9FXP7MPK74+uEMW2O9cpcmSZIkqR0qMkGKiFpgHEX9zwHHZ+aciFgHuAjYnGL53duAMzJzQdmKVQPzFtbynZvGcevTb7LqigP43mGbs/16K3P2zeNYWJsA3PP8O1xy38v854GblLlaSZIkSVJPcuftt3BZ3/9hxZgHwMl5Czf/dRFHffE/y1yZJEmSpPao1D0452bmNpm5JbAA+EpEBHATcHNmbgRsDAwBfljGOtXIb+5/hZueeoNFdcmUGfP4xvVjeeK19z4MNxd7bsrMMlUoSZIkSeqpNnj79g/DTYAVYiHbTf9bGSuSJEmS1BGVGnDW9y9gQ2BfYF5mXgGQmbXAacAJETGojPWpnrGvv9/geMGiOvr16cOKKzScTLzHRqsux6okSZIkSb3BBqsMWKpt5Er9ylCJJEmSpGVR0QFnRFQBB1MsV7sF8ET985k5E3iNIgBtfO2JEVETETVTp05dHuUK2HH08AbHg/r3ZYfRw7ni8zuww6iVGTl8EF/fd0M+t7P7n0iSJEmSOteAtbYk6y0glAkrrDq6fAVJkiRJ6pCK3IMTGBgRY0tf/wu4DDgJyCb6RlPtmXkpcClAdXV1U9epC5yw+2jefH8uf37qDdYYtgLfOWQzhg3sR/Wo4fzxK7uWuzxJkiRJUk828X4ilhxGAJOfLFs5kiRJkjqmUgPOuZm5Tf2GiBgPfLJR21BgXeDl5VeaWtKvbx/+a8yW/NeYLctdiiRJkiSptxk4fOm2FYYu/zokSZIkLZOKXqK2kbuBQRFxHEBE9AX+D7gyM+eUtTJJkiRJklR+u3+jibZvLv86JEmSJC2THhNwZmYCRwCfioiXgBeBecB3ylqYJEmSJEnqHtbdEQ78IaywMvRfEfY6EzY9uNxVSZIkSWqnKHLB3q26ujpramrKXYYkSZIkSZIkSZLUHtF6l56nx8zglCRJkiRJkiRJktTzGXBKkiRJkiRJkiRJqhgGnJIkSZIkSZIkSZIqhgGnJEmSJEmSJEmSpIphwClJkiRJkiRJkiSpYhhwSpIkSZIkSZIkSaoYBpySJEmSJEmSJEmSKoYBpyRJkiRJkiRJkqSKYcApSZIkSZIkSZIkqWIYcEqSJEmSJEmSJEmqGAackiRJkiRJkiRJkiqGAackSZIkSZIkSZKkimHAKUmSJEmSJEmSJKliGHBKkiRJkiRJkiRJqhgGnJIkSZIkSZIkSZIqhgGnJEmSJEmSJEmSpIphwClJkiRJkiRJkiSpYlSVuwBJktSCN56ABy6AhXNhhy/CJgeXu6L2y4QJd8G7L8NGB8AqG5S7ovaZ+x6MvRYWzIKtPg0rjyp3RV3mz09N5sK7XmLewjpO3GElTljxMahdAB/5NAxdc5nvX1eXvPH+XNYYtgL9+rb8c3YLa+uo6hNExDI/V5IkSZIkST1LZGa5ayi76urqrKmpKXcZktQ7TJ8Ic6bDWttCn44tJDDp3dk89PK7bLbmULZZd6XOra87+eAt+Pl2sHB2qSHghNth5M5lLavdbjkFnrq6+Lpvfzjmj7D+3mUtqc0WzoWLd4PpLxfH/VeEE++FERuWtayu8NLbH3DgBfeTCUOYw9/7n8W6faYWJwetAl95AIautdR1s+YvYlC/vvTp03IQ+fxbM/ny1U8w6d05rLriAC48aht23WDEUv3mL6rlrJvGcevYN1lpUH/OPnRTjth2nU75jOqG3n4Wnr4GBgyF7T8PQ1Yrd0WSJEmSJFWaXvnT4d16BmdErA6cD+wMvAcsAIYCC4H+wGjghVL3HwB/As4GjgcSeAM4JTPHL9/KJUlN+tu34LFLiq9X2xyO/wsMXjrgaMldz77NV37/BIvqih/QOW3/jfnG/ht1dqXLzwu3w7gbYMU1of9geOr30KcK9jwdiHrhJkDCs7dWVsA5c0rxmRarXQAPXlg5AeeL/1gSbgIs+ACeugoOOLd8NS2rmivgmethyOqw95mw2mYAPDpxOot/7u3gvo8tCTcB5rwLT18Le/znh03vzJzHKdc+xWMTp7P2SgP5ySe3YveNmh/P37tlPJPenQPA1A/m8+0/PcP9Z+yz1AzNKx98lZuefAOAabPmc8Yfn2GX9UewxrAVOuPTLxevTJ3FHx59jbpMjtlpJBuutmK5S2rVB/MWMn9RHSOGDOiyZ9TVJT/463Nc89gkhgzoxw92SQ56+FhYNK/o8NTV8NVHof+gLqtBkiRJkiT1DN024IziX7tuBn6XmZ8tta0HfDwzfxERo4DbMnObetecAuwKbJ2ZcyLiQODWiNgiM+ct788gSarnrXFLwk2Ad56FR34F+31v6b6Z8PxtMOVpGL0XjN7jw1M/v+elD8NNgIvvm8CJe67PwP59u7L6rvH8X+G6zzZ97tavwWHnL92+8npdW1Nnq1tE8TNH9dQuXG6PnzF3Id+/5d88MGEam605lHPHbMnoEYPbfoO+/ZdqeujVmexUl/RtZcZit/T0dXDbqUuOJz0Epz4D/Qay9Torfdhcm03Mro6GY+x//v48j02cDsAb78/l1Ouf4uGz9mt26dkJ78xqcPz69LnMX1THCv0a3veZyTMaHC+qS8a/OWPZAs450+GB8+HdCbDJIbDd5zp+r1a8PXMeh1/0IDPnLQLgusde54yPbszh267D8MFL/37qDs6/80Uuvu9lFtbW8dHN1+CCo7ZZ6telM9z45GQuf3AiAPMWzueNf14NVfX+iP7+azDhTth8TKc/+0ML58K4G2H2VNjicBi+ftc9S5IkSZIkdZmOrQ24fOwLLMjMXy9uyMxJmfmLFq75NvC1zJxT6n8H8BBwTJdWKklq3Yw3mmib3HTff5wN1x8L9/8v/O4wePyyD0/NWVDboOvC2mRRXV27Shk3eQZXPjiRcY2ClOXu6WtbPr9gDmxz7JLjUXvAtsc23787Wmld2Oxj9RoCdj6pU249f1Ftq33O/cuz3Dz2TabNWsC/XprGyX94smGHue/DtJeav8FGB1C7xtYfHk7NoZz+8rZcUQppKs5zf2l4PPsdeO0RAD6yzjC+e+hmDF2hin/22ZlpK4xc0m/FNWGbhmH8v99oOH6mzVrAWzOa/3myfTdtuPTorhus0mSIttP6wxscD6jqw7YjV272vm1yzafhoZ/DC3+DW0+BRy9p/ZoO+uszUz4MNwHmLqzl3NueY6+f/nOp71l38O83ZnDh3S+xYFEdmXD7+Le4/vHXu+RZT732XoPjOTQxW7R/O34AoT0WLYD7z4PzNi5+D9z9X8Xy01Oe7prnSZIkSZKkLtVtZ3ACWwBPttqrJCKGAoMz8+VGp2pK92rc/0TgRICRI0c2Pi1J6myj94DBqxWBymJbfnLpfgvnweO/bdj28C9hhy8CcPwu6/H/blmy8vjh26zNiiv0a3MZVz/8aoPrzx2zBcftMqrN13eqwau2fH7t7WHXU2CvM4pZR6WlRCvOJy+Hf98I775czJ5bZ/tlut07M+fxjevG8vAr77L+iMH89MitqB41vMm+D708rcHxs1Nm8t7sBaw8uH8Rct35vWJ5zNU/AsfeCCuu0fAGfftRs991/OGKixgcc/l77Y68z4o8MGEa/7FHBc78Wmq2WsDw0R8e/cce63PCbqOpy6Rq0QHw7M2waD5scQQMavg93m3DEbxUb1bmeqsMYp2VBzb76HM+vgWD+vfloZff5SNrD+OsQzZtst8xO63HG+/N5U9PTmaVwQM485BNl23m4/RXYPLjDdueuR52+nLH79mCYQOb/u/RB/MXcfF9L3PRZ7frkud21Itvf9Cmts6ww6jhXPvYkvD0mtr9OWnYo1TNnlI0rLc7rL9vlzybO86Gxy5t2LZwDjz2Gxjzy655piRJkiRJ6jLdOeBsICIuAnanmNW5Q3suZam18SAzLwUuBaiurl7qvCSpk/UfDF/4e7FM5JxpsM0xsPFHl+4XAX36Qv3JeX2WBAaf22UU66w8iPtenMrmaw7lE9ut3a4yLrx7QoPjn989oXwB526nwot3wMzSTNY1ty5mE/apgt1Pg/V2KdpXLlN9naWq/1Kz/5bFubc9y8OvvAvAK9Nm8/Vrn+Jf3963ySVjt1hrGFPqzSpcd/jAIoCa9U4xU7iutFzu2+Pgvp/CYT9b6h4brLkKt8fuLKhdMlN4szWHdtrnWa52OxVe/Re8+VQxrvb+9lK/v/r0CfoQ0HdIizOGv3XQJsxbWMvdz7/DRqsN4fsf22Kp/TTrGzKginPHbNlqiX37BGcdshlnHdJJgf7A4dB3ANTOX9K24pqdc+8mHLrVmlz18Ks83cQM8Vn1ZnZ2F7tuMIL+VX1YsGjJ7+/Gs207yxHbrs2Ed4r9SVdcoYrTD9yXqs0PL/a6HTAUNtwP+nTRAjPjbmy6PbrzgjaSJEmSJKk53TngHA98OLUnM0+OiBEUMzKXkpkzI2J2RKyfma/UO7UdcF/XlipJapMRG8LhF7Xcp2oA7HIK3P/TUkPAHt9s0GWfTVdjnw7+A/yCRsuaNj5erlZeD77+JEx6sAhcVtsM6mqB6Lp/5O8BxjVa5vPNGfOYNms+qw9deo/Gcz6+Oe/Ons9Tr73PeqsM4mef3po+faLY66+u0V6g0xsvAlEYMWQA//OJj/Dff32W9+csZJ9NVuWre2/QaZ9nuRq8Cpx4L0x9sZiROXhEh281qH8VP/7kVp1XW1cZuBLs+1246/uQdTBoBOx9Vpc9boV+fbnpq7vxwIRp/OC2Zz+c5RoBR+/Y/VYNWWPYClz5+R244O6XmDVvEcfsPJL9Nlu9S54VEXzroE351kGNZu9+5MgueV4Dw9aBudMbtvUbDDue2PXPliRJkiRJnS4yu+fkxSimADwCXJmZF5faRgL3Z+aoiBgF3JaZW9a75uvAgcCnMnNuROxPMUtzi8yc29yzqqurs6amydxUklQuE+8v9kYbvWcxs7GTXHDXi1xw15I9F0/dfyNO3X/jTru/ut63b3yG62uWLHO5/qqDufube7U4e3DW/EUM7t93SZ/ahfDzbWFGvb0GDzkPdvxSs/dYWFvHnAW1zS5Bqm7u/deL5WrX3RH6Nb+UbmeaNX8R1zw6idenz+Xgj6zBrht0PFDWMpr4L7juGJg/A/r2hy2PhH2+U+wTLEmSJElSZWv+H8V6sG4bcAJExJrA+cBOwFRgNvDrzLy+mYAzgO8Bn6NY3PAt4JTMHNfScww4Jal3uWP8Wzzx2ntsP3JlDtxijdYvULcyY+5CvnfLv7n3halsssaK/ODwLdl49RXbf6NpL8E/f1TM5tzyE7DzV4tpdpJ6pvmz4K1xsNqmMHDlclcjSZIkSVJn6ZX/oNWtA87lxYBTkiRJkiRJkiRJFahXBpxu8CVJkiRJkiRJkiSpYhhwSpIkSZIkSZIkSaoYBpySJEmSJEmSJEmSKoYBpyRJkiRJkiRJkqSKYcApSZIkSZIkSZIkqWIYcEqSJEmSJEmSJEmqGAackiRJkiRJkiRJkiqGAackSZIkSZIkSZKkimHAKUmSJEmSJEmSJKliGHBKkiRJkiRJkiRJqhgGnJIkSZIkSZIkSZIqhgGnJEmSJEmSJEmSpIphwClJkiRJkiRJkiSpYhhwSpIkSZIkSZIkSaoYBpySJEmSJEmSJEmSKoYBpyRJkiRJkiRJkqSKYcApSZIkSZIkSZIkqWIYcEqSJEmSJEmSJEmqGAackiRJkiRJkiRJkipGWQPOiKiNiLH1XmeW2g+LiKci4umIeDYivtzM9a9GxIhGbZtGxMMRMT8iTl8en0OSJEmSJEmSJEnS8lFV5ufPzcxt6jdERD/gUmDHzJwcEQOAUe2453Tg68DhnVSjJEmSJEmSJEmSpG6iOy5RuyJF8PouQGbOz8wX2npxZr6TmY8DC7uoPkmSJEmSJEmSJEllUu6Ac2CjJWo/k5nTgVuBSRFxbUQcExGdXmdEnBgRNRFRM3Xq1M6+vTpq+kS44hA4d5XiffrEclckSZIkSZIkSZKkbqTcAefczNym3ut6gMz8D2A/4DHgdODyzn5wZl6amdWZWb3qqqt29u3VUbecDJMehLpFxfstJ5e7IkmSJEmSJEmSJHUj5Q44m5WZ4zLzfOAA4JMR0bfeTM9zy12fusjrj7Z8LEmSJEmSJEmSpF6tqtwFNBYRQ4DqzLy31LQNMCkza0tfqydbd6di5mb9Y0mSJEmSJEmSJKmk3AHnwIgYW+/4duCHwLci4hJgLjAb+HwL93gmIupKX98A/BSoAYYCdRFxKrB5Zs7s3NLVJcZcVCxL+/qjRbg55qJyVyRJkiRJkiRJkqRuJDKz3DWUXXV1ddbU1JS7DEmSJEmSJEmSJKk9otwFlEO33YNTkiRJkiRJkiRJkhprV8AZEfdExHGt9Dk2Iu5ZtrIkSZIkSZIkSZIkaWntncG5NzCqlT7rAXt1pBhJkiRJkiRJkiRJaklXLFE7EFjUBfeVJEmSJEmSJEmS1MtVdeCabKoxIgIYCRwCvL4sRUmSJEmSJEmSJElSU1qdwRkRdRFRGxG1paZzFh/Xf1HM2nwF2Aa4rutKliRJkiRJkiRJktRbtWUG5/0smbW5J/Aa8GoT/WqBd4G7gd92RnGSJEmSJEmSJEmSVF+rAWdm7r3464ioA67IzHO7sihJkiRJkiRJkiRJakp79+AcDbzfBXVIkiRJkiRJkiRJUqvaFXBm5qSuKkSSJEmSJEmSJEmSWtNiwBkR36PYf/OizJxeOm6LzMz/XubqJEmSJEmSJEmSJKmeyMzmTxZ7biawWWa+WDpui8zMvp1R4PJQXV2dNTU15S5DkiRJkiRJkiRJao8odwHl0NoStfuU3l9rdCxJkiRJkiRJkiRJy12LAWdm3tfSsSRJkiRJkiRJkiQtT33KXYAkSZIkSZIkSZIktVVrS9Q2KSJGAZ8DtgWGATOAp4DfZ+bETqtOkiRJkiRJkiRJkuppd8AZEf8J/BDoR8ONSw8HvhsRZ2XmzzqnPEmd7v3XYchqUDWg3JVIkiRJkiRJkiS1W7uWqI2Io4H/BWYD5wL7AJuV3s8ttf9vRHymk+uUtKzeexV+tStcsCX83ybw3F86dp9FC+DeH8NlH4W/ng5zpndqmepm5s2AF26Hd18udyXqavNmwLO3wpSny12JJEmSJEmSJLUoMrPtnSNqgNHAdpk5qYnzo4EngJczc4dOq7KLVVdXZ01NTbnLkLrWDcfBs7csOR64Mnzzeei3Qvvu84+z4eFfLjkevRccf2vn1KjuZfITcPURMH8GELDf/4M9/rPcVakrvPVvuPJQmPd+cbzTSXDwj8takiRJkiRJkqQ2ida79DztmsEJbA7c0FS4CVDaf/MGYItlLWyxiDg7IsZHxDMRMTYidoqIwyLiqYh4OiKejYgv1+t/YkQ8X3o9FhG7d1YtUkWb+kLD47nvwex32n+f+iEpwMT7YO77HS5L3di9PyqFmwAJ9/6kmOWnnudf/7ck3AR49NcwY3LZypEkSZIkSZKklrR3D84PgPdb6fM+MLMjxTQWEbsAh1HMGJ0fESOAwcCfgR0zc3JEDABGlfofBnwZ2D0zp0XEdsDNEbFjZr7VGTVpGSycBwtmw+BVyl1J77TRgTD1+SXHq20OK41s/31WHgUzXl9yPHhV6D9kmctTNzR7WsPj2vkw/wNYYVh56lHXmfteo4Yswuxh65SlHEmSJEmSJElqSXtncN4BfLS5kxERwIGlfp1hTWBaZs4HyMxpFCFrFfBuqW1+Zi6emvZt4IxSPzLzSeB3wMmdVI86quYKOG8j+N/14epPOAusHPb9Luz6dVh1U9js43DUNR27z4E/gCFrFF/3GwyHnAd92/uzEqoI2xzT8Hj0ngZePdV2xzU8XrsaVu+0xRgkSZIkSZIkqVO1dw/ONYGHgEeAM+svVRsRI4GfADsBu3bGjMmIGAI8AAwC7gKuz8z7IuK3wMeBu4HbgGszsy4ipgOjM3NGvXuMAY7PzE80uveJwIkAI0eO3H7SpCZX3VVnmDkFLtgS6hYtadvj9GI/P1WmRQvgnWdh+PqwwtByV6OuNO5GeOFvMGIT2Pkkf717spfuhPE3w8rrwY4nwsCVyl2RJEmSJEmSpNb1yj04Www4I+KeJppXBrYCaoHXgLeB1YGRQF/gGWB6Zu7XKQVG9AX2APahWH72zMy8MiI+AuwPHAc8nZmfbybgPBz4XGZ+srlnVFdXZ01NTWeUq6a8ci9cNaZh28YHw2evK0s5kiRJkiRJkiRJPUSXB5wRsQZwAbADMB94FTgVuCkzt+zq5zeltXUl927l2vVLr/q2Bto+LbQVmVkL3AvcGxHjgOOBKzNzHDAuIq4GJgKfB54FtgfqB7PbldpVLmtXwworwbz3l7Rt2Cn5tyRJkiRJkiRJkrpIaXvKPwO/y8yjSm3bUEx+LJsW9+DMzD4dfPXtjOIiYpOI2Khe0zbA2xGxd6O2xevL/hT4SUSsUrp+G4rg81edUY86aMAQOOZGGLUHrLIh7P0dqP5iuauSJEmSJEmSJElSy/YBFmbmrxc3ZOZY4PXFxxExKiL+FRFPll67ltrXjIj7I2JsRPw7IvaIiL4RcWXpeFxEnNaRolqbwVluQ4BfRMRKwCJgAvAN4JKIuASYC8ymCDHJzFsjYm3goYhI4APg2MycUobaVd+6O8Dnbyt3FZIkSZIkSZIkSWq7LYEnWunzDnBAZs4rTVy8FqgGPgv8IzN/WNqSchDFxMW1Fy9tW8oA261dAWdE1ALXZ+ZnO/Kw9srMJ4Bdmzh1SAvXXAxc3GVFSZIkSZIkSZIkSVqsH/DL0sqqtcDGpfbHgcsjoh9wc2aOjYhXgPUj4hfAX4E7OvLAFpeobcIHLFkOVpIkSZIkSZIkSVLPNR7YvpU+pwFvA1tTzNzsD5CZ9wN7Am8AV0fEcZn5XqnfvcDJwG87UlR7A86ngM078iBJkiRJkiRJkiRJFeUeYEBEfGlxQ0TsAKxXr88wYEpm1gGfA/qW+q0HvJOZvwEuA7aLiBFAn8z8E/D/gO06UlR7A86fAIdExAEdeZgkSZIkSZIkSZKkypCZCRwBHBARL0fEeOAc4M163X4FHB8Rj1AsTzu71L43MDYingI+CVwIrA3cGxFjgSuBszpSVxR1tbFzxHHAp4CDgZsp1s59C1jqJpl5VUcKKofq6uqsqakpdxmSJEmSJEmSJElSe0S5CyiH9gacdRRhZuNvVv2bBEWg23fZy1s+DDglSZIkSZIkSZJUgXplwFnVzv5f6JIqJEmSJEmSJEmSJKkN2hVwZubvuqoQSZIkSZIkSZIkSWpNe2dwEhF7AbsBa1EsTTsFeDAz7+vk2iRJkiRJkiRJkiSpgTYHnKVg82Jgk8VNpfcsnX8e+KpBpyRJkiRJkiRJkqSu0qaAMyI+CVxb6v8mcC/wOkXIuQ6wN7AZcGdEHJWZN3VFsZIkSZIkSZIkSZJ6t1YDzohYC/gdsAj4GvDbzKxt1KcP8EXgAuCqiHgkM9/s/HIlSZIkSZIkSZIkLU+jzvxrH+Bo4FRgXYqJkBcA177640PrluXeEXEQcCHQlyKH/HFr1/Rpw31PBQYBx2TmJY3DTYDMrMvM3wDHlPp+oz2FS5IkSZIkSZIkSep+SuHmn4BLgGpg9dL7JcCNpfMdEhF9gYuAg4HNgaMjYvPWrmvLAw8CHs3MP7fWMTNvBh4tFSFJkiRJkiRJkiSpsh0NHAAMbtQ+GDgQOGoZ7r0jMCEzX8nMBcB1wJjWLmpLwLke8FA7CnkIGNWO/pIkSZIkSZIkSZK6p1NZOtxcbDBw2jLce22K5W4Xm1xqa1FbAs5+wIJ2FLKQYo1cSZIkSZIkSZIkSZVt3WU835Jooi1bu6gtAecU4CPtKGQL4K129JckSZIkSZIkSZLUPb2+jOdbMpmGAek6wJutXdSWgPN+4ICI2LS1jhGxGfDR0jWSJEmSJEmSJEmSKtsFwOxmzs0Gzl+Gez8ObBQRoyOiP8V+nre2dlFbAs5fUixTe1tEbN5cp1K4+ReK5WkvalPJkiRJkiRJkiRJkrqza4E7WTrknA3cAVzX0Rtn5iLgFOAfwHPADZk5vrXrIrPVZWyJiJ8AZ1DsxXkTcDfFdNMERgL7A0cA/YH/y8wzOvYxyqO6ujpramrKXYYkSZIkSZIkSZLUHk3tYdnpRp351z4UsytPo1hS9nWKmZvXvfrjQ+uWRw31tSngBIiI7wHfBapYenPPAGqBHwHnZFtv2vLzZmXmkHrHnweqM/OUiDgH+BIwtd4le2fm+xGxO/AzYGip/WeZeWlLzzLglCRJkiRJkiRJUgVaLgFnd1PV1o6ZeW5E/A44AdgNWJPimzYFeAC4MjMndkmVTTs/M8+r3xARawDXAIdn5pMRMQL4R0S8kZl/XY61SZIkSZIkSZIkSeoCbQ44ATJzEvD9LqqlM5xMEbQ+CZCZ0yLiW8A5gAGnJEmSJEmSJEmSVOHaFXAuZwMjYmy94+HArfWOT4uIY0tfv5eZ+wBbAL9rdJ+aUnsDEXEicCLAyJEjO6vm3mH6RLjlZHj9UVh3JxhzEQwfXe6qJEmSJEmSJEmS1Av0KXcBLZibmdssfgHfa3T+/Hrn9ym1BUvvD0pTbZl5aWZWZ2b1qquu2rmV93S3nAyTHoS6RcX7LSeXuyJJkiRJkiRJkiT1Et054OyI8UB1o7btgWfLUEvP9fqjLR9LkiRJkiRJkiRJXaQ7L1HbERcBj0bETZk5NiJWAX4CnFvmunqWdXcqZm7WP5YkSZIkSZIkSVLPdM6wPsDRwKnAusDrwAXAtZwzo66jt42Iy4HDgHcyc8u2XlfJMzhPi4ix9V6jMnMKcCzwm4h4HngIuDwz/1LeUnuYMRfBertBn6rifcxF5a5IkiRJkiRJkiRJXaEIN/8EXEKxkurqpfdLgBtL5zvqSuCg9l4UmU1tWdm7VFdXZ01NTbnLkCRJkiRJkiRJktojuvwJ5ww7hiLMHNzE2dnAiZwz45qO3j4iRgG39ZYZnJIkSZIkSZIkSZK61qk0HW5Saj9t+ZVSMOCUJEmSJEmSJEmS1Jx1l/F8pzPglCRJkiRJkiRJktSc15fxfKcz4JQkSZIkSZIkSZLUnAso9tpsymzg/OVXSsGAU5IkSZIkSZIkSVJzrgXuZOmQczZwB3BdR28cEdcCDwObRMTkiPhiW64z4JQkSZIkSZIkSZLUtHNm1AGfBE4EaoC3S+8nAkeWzndIZh6dmWtmZr/MXCczL2vLdZGZHX1mj1FdXZ01NTXlLkOSJEmSJEmSJElqjyh3AeXgDE5JkiRJkiRJkiRJFcOAU5IkSZIkSZIkSVLFMOCUJEmSJEmSJEmSVDEMOCVJkiRJkiRJkiRVDANOSZIkSZIkSZIkSRXDgFOSJEmSJEmSJElSxTDglCRJkiRJkiRJklQxDDglSZIkSZIkSZIkVQwDTkmSJEmSJEmSJEkVw4BTkiRJkiRJkiRJUsUw4JQkSZIkSZIkSZJUMarKXYAkSVJrMpNL7n+Fvzz9JmutNJAzProJG6++YrnLkiRJkiRJklQG3XIGZ0RkRFxd77gqIqZGxG2l48+XjsfWe21eOrdFRNwTES9GxEsR8f8iIsr1WSRJKosZb8BLd8Lc98pdSae46uFJ/PjvzzP+zZnc+ezbfO6yR1mwqK7cZUmSJEmSJEkqg24ZcAKzgS0jYmDp+ADgjUZ9rs/Mbeq9ni31vxX4cWZuDGwN7Ap8dblVLklSuT15FVzwEfjDkfCzLWDi/eWuaJnd9dzbDY7fnjmfcW/MKFM1kiRJkiRJksqpuwacAH8HDi19fTRwbRuu+SzwYGbeAZCZc4BTgDO7pEJJkrqb2oVwx/+DrC2OF86Gu84pa0mdYYNVhzQ47tc3GDl8UJmqkSRJkiRJklRO3TngvA44KiJWALYCHm10/jONlqgdCGwBPFG/U2a+DAyJiKH12yPixIioiYiaqVOnduHHkCSpFQ9eCD8ZBT8eCf/6v2W716L5MK/RzMZZlf//uVP23ZCt110JgIH9+vK9wzZn1RUHlLcoSZIkSZIkSWVRVe4CmpOZz0TEKIrZm39rosv1mXlK/YbSXpvZ3C0b3f9S4FKA6urq5q6RJKlrvfoA3Pm9Jcd3nwtrbQcb7NOx+w0YApt9DJ67dUnb1kctW43dwIghA7jl5N14ffocVh7cnyEDuu0fYSRJkiRJkiR1se7+r4O3AucBewOrtKH/eGDP+g0RsT4wKzM/6PTqJElaVq83XqAAeP2xjgecAEdcAmtuBVOegfX3hu2/0PF7dTPruiytJEmSJEmS1Ot194DzcmBGZo6LiL3b0P8PwHciYv/MvKu0bO3PgZ92YY2SJHXcyF2aaNt52e7ZfxDsecay3UOSJEmSJEmSuqnuvAcnmTk5My9s5nTjPTh3zcy5wBjguxHxAjAOeBz45fKqWZKkdllvV/joj2DwajBoBBxwLqy/V7mrkiRJkiRJkqRuKzLdfrK6ujpramrKXYYkSZIkSZIkSZLUHlHuAsqhW8/glCRJkiRJkiRJkqT6DDglSZIkSZIkSZIkVQwDTkmSJEmSJEmSJEkVw4BTkiRJkiRJkiRJUsUw4JQkSZIkSZIkSZJUMQw4JUmSJEmSJEmSJFUMA05JkiRJkiRJkiRJFcOAU5IkSZIkSZIkSVLFMOCUJEmSJEmSJEmSVDEMOCVJkiRJkiRJkiRVDANOSZIkSZIkSZIkSRXDgFOSJEmSJEmSJElSxTDglCRJkiRJkiRJklQxDDglSZIkSZIkSZIkVQwDTkmSJEmSJEmSJEkVw4BTkiRJkiRJkiRJUsUw4JQkSZIkSZIkqbPV1cGC2eWuQup08xfVUluXnXfDmVNg4v2wYE7n3VM9XlW5C5AkSZIkSZIkqUd58R9w22kw8w3YYF/45GUwaHi5q5KWycLaOr5z1Z38+cUFDOlby+n7juTYfbdbtps+fhn8/VtQtwgGDodj/wRrL+M91Ss4g1OSJEmSJEmSpM6yYA7c9KUi3AR4+R6457/LW5PUCa75+738+8UJ/F/fi/haXsPP7nieiVOmdvyGC+bAnd8rwk2AudPh7nM7p1j1eGUPOCPi3oj4aKO2UyPibxHx7yb6XxkREyNibOn1UL1zh0fEMxHxfESMi4jDl8NHkCRJkiRJkiSp8N6rMG9Gw7Y3x5ajEqlTzXn27/yt/3cYU/UwX+z3Dx4YcCrPPfmvjt9w/gewYFbDtncnLFuR6jXKHnAC1wJHNWo7CvifFq45IzO3Kb12BYiIrYHzgDGZuSnwceC8iNiqK4qWJEmSJEmSJGkpIzaCFddq2DZ6z/LUInWi4+ZfT8SS40Exn93n3NPxG/YfxMRcky8sOIPd5l3I2QtPYE72X/ZC1St0h4DzRuCwiBgAEBGjgLWAye28z+nAjzJzIkDp/X+AMzqvVEmSJEmSJEmSWtC3Hxx9DYzcBQavBtUnwN5nlrsqaZkNqpu1VNvQd5/u+A0XzefEBafxz7pteYNV+UPt/vxk7seXoUL1JlXlLiAz342Ix4CDgFsoZm9eD2QLl/1vRHy39PX4zDwG2IJiBmd9NcDJTd0gIk4ETgQYOXJkxz+AJEmSJEmSJEn1rbUtnHB7uauQOlVEH8jaho2NZyu3w5RFg3kp12nQ9q/ctsP3U+/SHWZwQsNlao8qHbek/hK1x5TagqVD0abaAMjMSzOzOjOrV1111Y7WLUmSJEmSJEmS1PNFE3Pmom+HbzdiyABGDGm4JO1mo9ft8P3Uu3SXgPNmYL+I2A4YmJlPduAe44HqRm3bAc8uY22SJEmSJEmSJEm92+DhS7eN2KDDt+vXtw8/+/Q2rD50AABbrzOM7xy6WYfvp96l7EvUAmTmrIi4F7ic1mdvNuc84I8RcU9mvlray/M7wJGdU6UkSZIkSZIkSVIvddj5cM2nlxz37Q/7fLf5/m2w58ar8tCZ+zFj7kKGD+7f+gVSSbcIOEuuBW5iyVK1AJtExOR6x6eV3uvvwQmwY2aOjYhvA3+JiH7AQuBbmTm2K4uWJEmSJEmSJEnq8Tb+KPzHPXD/eTBkBOz3feg3YJlv27dPGG6q3SKzyS0qe5Xq6uqsqakpdxmSJEmSJEmSJElSe0S5CyiH7rIHpyRJkiRJkiRJkiS1yoBTkiRJkiRJkiRJUsUw4JQkSZIkSZIkSZJUMQw4JUmSJEmSJEmSJFUMA05JkiRJkiRJkiRJFcOAU5IkSZIkSZIkSVLFMOCUJEmSJEmSJEmSVDEMOCVJkiRJkiRJkiRVDANOSZIkSZIkSZIkSRXDgFOSJEmSJEmSJElSxTDglCRJkiRJkiRJklQxDDglSZIkSZIkSZIkVQwDTkmSJEmSJEmSJEkVw4BTkiRJkiRJkiRJUsUw4JQkSZIkSZIkSZJUMQw4JUmSJEmSJEmSJFUMA05JkiRJkiRJkiRJFcOAU5IkSZIkSZIkSVLFMOCUJEmSJEmSJEmSVDEqNuCMiLMjYnxEPBMRYyNip4joHxEXRMTLEfFSRNwSEeuUu1ZJkiRJkiRJkiRJnaOq3AV0RETsAhwGbJeZ8yNiBNAf+BGwIrBxZtZGxBeAmyJip8zMMpYsSZKkXuq1d+dw+o1P8+Sk99huvZU578itGbnKoHKXJUmSJEmSVLEqdQbnmsC0zJwPkJnTgPeBLwCnZWZtqf0KYD6wb5nqlCRJUi93+o1P89jE6SyqSx6bOJ3Tb3y63CVJkiRJkiRVtEoNOO8A1o2IFyPiVxGxF7Ah8FpmzmzUtwbYovENIuLEiKiJiJqpU6cuh5IlSZLUGz056b0WjyVJkiRJktQ+FRlwZuYsYHvgRGAqcD2wD9DUMrTRVHtmXpqZ1ZlZveqqq3ZluZIkSerFtltv5RaPJUmSJEmS1D4VGXACZGZtZt6bmd8HTqHYk3O9iFixUdftgGeXe4GSJEkScN6RW7Pj6OFU9Ql2HD2c847cutwlSZIkSZIkVbSqchfQERGxCVCXmS+VmrYBXgDGAT+LiK9kZm1EHAcMAu4pT6WSJEnq7UauMogbvrxLucuQJEmSJEnqMSoy4ASGAL+IiJWARcAEiuVqPwDOA16MiDrgeeCIzGxq6VpJkiRJkiRJkiRJFSbM/qC6ujpramrKXYYkSZIkSZIkSZLUHlHuAsqhYvfglCRJkiRJkiRJktT7GHBKkiRJkiRJkiRJqhgGnJIkSZIkSZIkSZIqhgGnJEmSJEmSJEmSpIphwClJkiRJkiRJkiSpYhhwSpIkSZIkSZIkSaoYBpySJEmSJEmSJEmSKoYBpyRJkiRJkiRJkqSKYcApSZIkSZIkSZIkqWIYcEqSJEmSJEmSJEmqGAackiRJkiRJkiRJkiqGAackSZIkSZIkSZKkimHAKUmSJEmSJEmSJKliGHBKkiRJkiRJkiRJqhgGnJIkSZIkSZIkSZIqhgGnJEmSJEmSJEmSpIphwClJkiRJkiRJkiSpYhhwSpIkSZIkSZIkSaoYBpySJElSV5o+Ea44BM5dpXifPrHcFUmSJEmSJFW0bhtwRsTqEXFNRLwSEU9ExMMRcURE7B0RMyJibL3X/qVr1omIWyLipYh4OSIujIj+5f4skiRJ6sVuORkmPQh1i4r3W04ud0WSJEmSJEkVrVsGnBERwM3A/Zm5fmZuDxwFrFPq8q/M3Kbe667SNTcBN2fmRsDGwBDgh2X4CJIkSVLh9UdbPpYkSZIkSVK7dMuAE9gXWJCZv17ckJmTMvMXrVwzLzOvKPWvBU4DToiIQV1arSRJktScdXdq+ViSJEmSJEnt0l0Dzi2AJ1s4v0ejJWo3KF3zRP1OmTkTeA3YsPENIuLEiKiJiJqpU6d2Zu2SJEnSEmMugvV2gz5VxfuYi8pdkSRJkiRJUkWrKncBbRERFwG7AwuAMyiWqD2sUZ8AsqnLm2rPzEuBSwGqq6ubuk6SJEladsNHwxf+Vu4qJEmSJEmSeozuOoNzPLDd4oPMPBnYD1i1lWuq6zdExFBgXeDlLqhRkiRJkiRJkiRJ0nLWXQPOe4AVIuKkem2t7aN5NzAoIo4DiIi+wP8BV2bmnK4pU5IkSZIkSZIkSdLy1C0DzsxM4HBgr4iYGBGPAb8Dvl3q0ngPziNL1xwBfCoiXgJeBOYB3ynDR5AkSZIkSZIkSZLUBbrtHpyZOQU4qpnTw5q55nXgY11WlCRJkiRJkiRJkqSy6pYzOCVJkiRJkiRJkiSpKQackiRJkiRJkiRJkiqGAackSZIkSZIkSZKkimHAKUmSJEmSJEmSJKliGHBKkiRJkiRJkiRJqhgGnJIkSZIkSZIkSZIqhgGnJEmSJEmSJEmSpIphwClJkiRJkiRJkiSpYkRmlruGsouIqcCkctfRRiOAaeUuQqogjhmpfRwzUvs4ZqT2ccxI7eOYkdrHMSO1j2NGap/uOmamZeZB5S5ieTPgrDARUZOZ1eWuQ6oUjhmpfRwzUvs4ZqT2ccxI7eOYkdrHMSO1j2NGah/HTPfiErWSJEmSJEmSJEmSKoYBpyRJkiRJkiRJkqSKYcBZeS4tdwFShXHMSO3jmJHaxzEjtY9jRmofx4zUPo4ZqX0cM1L7OGa6EffglCRJkiRJkiRJklQxnMEpSZIkSZIkSZIkqWIYcEqSJEmSJEmSJEmqGAacFSIiDoqIFyJiQkScWe56pHKJiHUj4p8R8VxEjI+Ib5Tah0fEnRHxUul95XrXnFUaOy9ExEfrtW8fEeNK534eEVGOzyR1tYjoGxFPRcRtpWPHi9SCiFgpIm6MiOdL/7/ZxXEjNS8iTiv9uezfEXFtRKzgmJGWiIjLI+KdiPh3vbZOGyMRMSAiri+1PxoRo5brB5Q6WTNj5n9LfzZ7JiL+HBEr1TvnmFGv1tSYqXfu9IjIiBhRr80xo16vuXETEV8rjY3xEfHTeu2Om27IgLMCRERf4CLgYGBz4OiI2Ly8VUllswj4z8zcDNgZOLk0Hs4E7s7MjYC7S8eUzh0FbAEcBPyqNKYALgZOBDYqvQ5anh9EWo6+ATxX79jxIrXsQuD2zNwU2Jpi/DhupCZExNrA14HqzNwS6EsxJhwz0hJXsvTv584cI18E3svMDYHzgZ902SeRlo8rWXrM3AlsmZlbAS8CZ4FjRiq5kib+3BQR6wIHAK/Va3PMSIUraTRuImIfYAywVWZuAZxXanfcdFMGnJVhR2BCZr6SmQuA6ygGmtTrZOaUzHyy9PUHFP/ovDbFmPhdqdvvgMNLX48BrsvM+Zk5EZgA7BgRawJDM/PhzEzgqnrXSD1GRKwDHAr8tl6z40VqRkQMBfYELgPIzAWZ+T6OG6klVcDAiKgCBgFv4piRPpSZ9wPTGzV35hipf68bgf2cAa1K1tSYycw7MnNR6fARYJ3S144Z9XrN/H8GilDlW0DWa3PMSDQ7bk4CfpyZ80t93im1O266KQPOyrA28Hq948mlNqlXK03t3xZ4FFg9M6dAEYICq5W6NTd+1i593bhd6mkuoPgLTV29NseL1Lz1ganAFVEs7fzbiBiM40ZqUma+QfGTza8BU4AZmXkHjhmpNZ05Rj68phQAzQBW6bLKpfI7Afh76WvHjNSEiPg48EZmPt3olGNGat7GwB6lJWXvi4gdSu2Om27KgLMyNJXsZxNtUq8REUOAPwGnZubMlro20ZYttEs9RkQcBryTmU+09ZIm2hwv6m2qgO2AizNzW2A2pWUDm+G4Ua8Wxb6BY4DRwFrA4Ig4tqVLmmhzzEhLdGSMOH7Ua0TE2RRb1/xhcVMT3Rwz6tUiYhBwNvC9pk430eaYkQpVwMoU26KdAdxQmnXpuOmmDDgrw2Rg3XrH61As+yT1ShHRjyLc/ENm3lRqfru0LACl98VLCDQ3fiazZEmb+u1ST7Ib8PGIeJViefN9I+L3OF6klkwGJmfmo6XjGykCT8eN1LT9gYmZOTUzFwI3AbvimJFa05lj5MNrSktFD6PppQqlihYRxwOHAceUlgIEx4zUlA0ofvjs6dK/B6wDPBkRa+CYkVoyGbgpC49RrIY2AsdNt2XAWRkeBzaKiNER0Z9iQ9tby1yTVBaln5q5DHguM39W79StwPGlr48HbqnXflREDIiI0RSbPT9WWgbqg4jYuXTP4+pdI/UImXlWZq6TmaMo/t9xT2Yei+NFalZmvgW8HhGblJr2A57FcSM15zVg54gYVPq9vh/FHumOGallnTlG6t/rSIo/8zlDQD1KRBwEfBv4eGbOqXfKMSM1kpnjMnO1zBxV+veAycB2pb/rOGak5t0M7AsQERsD/YFpOG66rapyF6DWZeaiiDgF+AfQF7g8M8eXuSypXHYDPgeMi4ixpbbvAD+mWDbgixT/0PYpgMwcHxE3UPzj9CLg5MysLV13EnAlMJBi/47Fe3hIPZ3jRWrZ14A/lH6w7BXgCxQ/GOi4kRrJzEcj4kbgSYox8BRwKTAEx4wEQERcC+wNjIiIycD36dw/j10GXB0REyhmBhy1HD6W1GWaGTNnAQOAO4t/Q+aRzPyKY0Zqesxk5mVN9XXMSIVm/l9zOXB5RPwbWAAcXwolHTfdVBgaS5IkSZIkSZIkSaoULlErSZIkSZIkSZIkqWIYcEqSJEmSJEmSJEmqGAackiRJkiRJkiRJkiqGAackSZIkSZIkSZKkimHAKUmSJEmSJEmSJKliGHBKkiRJUjcREfdGRJa7jvoi4vMRkRHx+XLX0t1ExMYRsSAizmjU3qW/jhHxnxGxMCI27apnSJIkSVJ3ZsApSZIkqVcrhXf1X7URMS0i7omIY8pdX6WpF4hmRNzXQr9REVG3uO/yrLET/Qx4F7hoOT/3V8A7wHnL+bmSJEmS1C1UlbsASZIkSeom/qv03g/YBDgc2Ccits/Mb5atqsq1CNgzIjbJzBeaOP8fQJT6VdzfTSNiV+BQ4OzMnLM8n52ZcyPiQuAnEbFrZj60PJ8vSZIkSeXmDE5JkiRJAjLznNLr7Mw8EvgokMCpETGqvNVVpNtK7//R+ERE9AW+ADwOvL08i+pEJwN1wNVlev7vgVrgq2V6viRJkiSVjQGnJEmSJDUhM+8GnqeYZbgDQEQcHhG/j4gXI2J2RMyKiCci4usR0eDvVxFxXWn51T2bun9EHFk6/4vWaomIPhHxlYh4vPTM2aWvT2r83PbWWe+aDSPijxHxXumahyLi0DZ8q5ozHngYOD4i+jU6dyiwFvCbFj7z5yPiTxHxSkTMjYiZEfFgRBzbTP/1I+LSiJhQ6j89IsZFxK8jYpV6/fqXvg9Plj7rnIh4NSJuiYj92/LBImIocCTwUGa+3pZrStftGxEzIuLNiNimXntExDci4tmImBcRb0TELyNiWKm2VxvfKzPfBP4FHFmqR5IkSZJ6DQNOSZIkSWpelN4X7xH5Y2A74FHgFxSz94YAFwK/a3Ttr0rvX27m3ieW3i9tQx1XAxcDqwO/LV2zaukZTc0gbE+dRMRGwCMUod3DpX6TgZuBT7Shvub8plTnmEbtXwJmAde1cO3FwCjgfuCCUt/1gKsj4r8b1b8mxWzQL1AEqz+n+MwTgc8Ba9brfiXF5+sHXFXqez/wEeCgNn6uPYH+wANt7E9pP9e/A28Cu2Tm2HqnL6L4jMMofm2vBQ4E7izV2ZwHgQGleiRJkiSp16i4fU4kSZIkaXkozebbhCLcfLzUfGhmvtyoXx/gCuC4iPhlZj4KkJn3R8R44JMR8Y3MnFbvmtHA/hQzAMe1UsfRwGeBp4A9M3NWqf27wH3AZyPir5l5Tb3L2lxnyUXAKsCpmXlhvWvGUIScHXU9RXD3JeDG0j3XBg4GrsjMDyKiuWu3bOIz9KcICc+MiF9n5hulU0cCwxvXX7pmMMVSskTEMOAo4Algp8ysbdR3Fdpm99J7TVs6R8S3KELnh4CPZ+b0euf2AE4CXizV9H6p/TvAXRQzXSc1c+vFvy/3ZMmSwJIkSZLU4zmDU5IkSZKAiDin9PphRNwI3E4xg/OCzJwE0DhwK7XVUcwIhGLfzvoupphhd3yj9hNL976kDaWdUHo/c3G4WXrubODbpcMG+1y2p86IWAc4gGK24y8bXXMLRYjaIZk5B7gGOKDePqYnAH1pYXna0rVNfYYFFGFsFbBfE5fNbeKa2Zm5uD0pvu/zKYWejfq+21JN9YwsvU9pqVNpaeFfAj8B/gzsXz/cLFn8e+OHi8PNUi0LgLNaqeOtRvVIkiRJUq9gwClJkiRJhe+XXmcB+1Lsb/i5zPzm4g4RsUpE/Dgininta5kRkRQzAgHWbnTPqyiWYj2x3j36AZ8H3gNuaENd21GEcfc2ce4+oBbYtn5jO+tcfO0DjWc0ljT13Pb4DUWo+MXSLNIvAs9k5mMtXRQRIyPiooh4vrRP5uLP8KdSl/qf4VaK7/NFpX07T4yILaLR9NDMnAn8BdgVGBsR34uIfSJiUDs/0+KZnu+10u9PwMkUywR/KjPnNdHnw+9/E+ceARa1cP/FYemIVuqQJEmSpB7FJWolSZIkCcjMZtdKBYiIlSiWBB0NPEYRXk6nCKBWAr5BMVuz/j0/iIjfA1+JiH0y858U+1GuQTEztKnAq7FhwPTSjL7GNS+KiGnAastQ57DS+9vNPP+tZtrbJDOfjIgnKfbHfIRiH82vtXRNRKxPUfvKFEHzHcAMijB3FMWsxw8/Q2ZOiogdgXMo9tFcvG/o6xFxXmb+vN7tP0Mx8/WzwH+V2uaVZu2enpnNfR/qWzwjdIVW+u1J8X3/S2kGbVOa/f5nZm1EtDSrdGCjeiRJkiSpVzDglCRJkqS2+Q+K0PC/MvOc+iciYheK4LApFwNfAb4M/JMlszkvbeNzZwDDI6JfZi5s9Nwqitl7M5ehzhml99Wbef4abayzJZcCvy695gK/b6X/NylmSX4hM6+sf6K0J2njJX/JzOeAz5S+J1tT7HH6NeDCiJidmZeV+s2lCELPiYh1KULIzwPHUoSne7Th87xTem9tz859KPbR/EtEfDIz/9pEn8W/dqsDr9Q/ERF9S894o/FFjZ7/TjPnJUmSJKlHcolaSZIkSWqbDUvvf2ri3F7NXZSZzwAPAkdExE4Uwdv9pUCuLZ6i+Lvbnk2c25NiP8snl6HOp0rvu5cCtcb2bluZLboGmA2sA/yx/l6TzejQ9xqKWa2Z+URm/gQ4utR8eDN9X8/MP1DsSfoSxfegtdAS4JnS+6at1PJMqd7pwE0R0VQdH37/mzi3My3/YPLi549tqQ5JkiRJ6mkMOCVJkiSpbV4tve9dvzEitqXYt7MlFwP9KQK7oJjJ2FaXl97/p/5ekaWvf1w6vKyjdWbmZOBOilmfpzS6ZgytBIptkZkfUCwdewTw3TZc8mrpfe9G9XyUYoYqjdp3jIimZqAubptT6rdqKWRubDCwIsVyskstBdyEe0vvO7fWsRRk70mxBO0fI+IzjbpcVXo/OyIWL1dLRPQHftTK7Rc//5+t1SFJkiRJPYlL1EqSJElS21wFnAFcEBH7UMz42wg4DLiJYm/H5vwROB9YG5hW6t8mmXlNKWj8NDA+Im4GkmJW4mjghtIsxGWp82Tg4dI1BwJPU8yiPAL4C/Cxttbbwud4oB3df0WxZ+cfI+JPFEu0bkkRkt7A0p/hs8DJEXEfMAF4D9igVPd84IJSv7WBRyLiOYpZr68DQym+N2sAPy+Fsa19ln9HxAvAfhHRNzNrW+k/ISL2AO4B/hARAzLzqtK5+yLiUoqli8eXPu/CUu0zgDeBpfbvjIg+wH7AC5n579ZqliRJkqSexBmckiRJktQGmfkmxf6Mf6VYTvQUYD3gq8CZrVy7AFgcQl6ZmfPb+fijKULIdyn28vwKRYh3CkuWYe1wnZn5EsVswD8Bu1Hs07kuRYja5jC2s5SWdt0HeAg4BDiJIoj8BE3Pfr0W+C2wKkUQfCqwHXAdUJ2ZD5f6vQp8H3irdP9vlu45kSIkPbUdZV5MEYoe2MbPNIliJucE4IqI+FK90yeVaplF8Wv7WYq9Ow+g+NwzWdr+FIFte2YDS5IkSVKPEJlZ7hokSZIkqceLiHspAq5NSoGiKlhEDAVeBh7KzDFd9IyNgBeB6zLz6Ebn/kSxfPAGmTmjK54vSZIkSd2VMzglSZIkqYtFxI4UYdQ/DDd7hsycSTEb9OMRUb0s94qINUpLztZvG8SSpXX/3OjcNhTLB59juClJkiSpN3IPTkmSJEnqIhFxEsUyol+g2Efx++WtSJ3sEmAlYPVlvM+pwNGlWb5TKJa+3Q9YB/g7xR6u9a0J/D9cnlaSJElSL+UStZIkSZLURSLiVYqQ6hWK2XbXlLcidUcRsR9wOrANMBxYRLE07TXABZm5sHzVSZIkSVL3Y8ApSZIkSZIkSZIkqWK4B6ckSZIkSZIkSZKkimHAKUmSJEmSJEmSJKliGHBKkiRJkiRJkiRJqhgGnJIkSZIkSZIkSZIqhgGnJEmSJEmSJEmSpIrx/wENn78rM5tJKgAAAABJRU5ErkJggg==\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 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 (kg)\",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": null,
   "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\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
    "year=[]\n",
    "for i in df[\"Date\"]:\n",
    "   year.append(i.split(\"-\")[0])\n",
    "df['Year'] = year\n",
    "\n",
    "df_mean_by_year = df.groupby('Year').mean()\n",
    "\n",
    "df_year_x_success_rate = df_mean_by_year[['Class']]\n",
    "\n",
    "df_year_x_success_rate.plot(kind='line')\n",
    "plt.xlabel(\"Year\",fontsize=20)\n",
    "plt.ylabel(\"Success 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": 10,
   "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": 10,
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>...</th>\n",
       "      <th>B1048</th>\n",
       "      <th>B1049</th>\n",
       "      <th>B1050</th>\n",
       "      <th>B1051</th>\n",
       "      <th>B1054</th>\n",
       "      <th>B1056</th>\n",
       "      <th>B1058</th>\n",
       "      <th>B1059</th>\n",
       "      <th>B1060</th>\n",
       "      <th>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>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>...</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>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>...</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>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>...</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>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>...</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>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>...</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 × 84 columns</p>\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  ...  B1048 B1049  B1050  B1051  B1054  B1056  \\\n",
       "0  False        NaN    1.0  ...      0     0      0      0      0      0   \n",
       "1  False        NaN    1.0  ...      0     0      0      0      0      0   \n",
       "2  False        NaN    1.0  ...      0     0      0      0      0      0   \n",
       "3  False        NaN    1.0  ...      0     0      0      0      0      0   \n",
       "4  False        NaN    1.0  ...      0     0      0      0      0      0   \n",
       "\n",
       "   B1058  B1059  B1060  B1062  \n",
       "0      0      0      0      0  \n",
       "1      0      0      0      0  \n",
       "2      0      0      0      0  \n",
       "3      0      0      0      0  \n",
       "4      0      0      0      0  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "df_orbit_types_indicators  = pd.get_dummies(features[\"Orbit\"])\n",
    "df_launch_sites_indicators = pd.get_dummies(features[\"LaunchSite\"])\n",
    "df_landing_pads_indicators = pd.get_dummies(features[\"LandingPad\"])\n",
    "df_serials_indicators      = pd.get_dummies(features[\"Serial\"])\n",
    "\n",
    "features_one_hot = pd.concat([df_orbit_types_indicators, df_launch_sites_indicators, df_landing_pads_indicators, df_serials_indicators], axis=1)\n",
    "\n",
    "df_plus_indicators = pd.concat([features, features_one_hot], axis=1)\n",
    "\n",
    "df_plus_indicators.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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ES-L1    float64\n",
       "GEO      float64\n",
       "GTO      float64\n",
       "HEO      float64\n",
       "ISS      float64\n",
       "          ...   \n",
       "B1056    float64\n",
       "B1058    float64\n",
       "B1059    float64\n",
       "B1060    float64\n",
       "B1062    float64\n",
       "Length: 72, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot = features_one_hot.astype(\"float64\")\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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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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