5. Space-X EDA DataViz Using Pandas and Matplotlib - SpaceX.ipynb

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    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n\n    \n    \n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Assignment: Exploring and Preparing Data\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Estimated time needed: **70** minutes\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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\nIn this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Falcon 9 first stage will land successfully\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Several examples of an unsuccessful landing are shown here:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Objectives\n\nPerform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n\n*   Exploratory Data Analysis\n*   Preparing Data  Feature Engineering\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Import Libraries and Define Auxiliary Functions\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We will import the following libraries the lab\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['numpy'])\nawait piplite.install(['pandas'])\nawait piplite.install(['seaborn'])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "# pandas is a software library written for the Python programming language for data manipulation and analysis.\nimport 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\nimport 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.\nimport 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\nimport seaborn as sns",
      "metadata": {
        "trusted": true
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      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Exploratory Data Analysis\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from js import fetch\nimport io\n\nURL = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\nresp = await fetch(URL)\ndataset_part_2_csv = io.BytesIO((await resp.arrayBuffer()).to_py())\ndf=pd.read_csv(dataset_part_2_csv)\ndf.head(5)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "execution_count": 8,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010-06-04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
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    {
      "cell_type": "markdown",
      "source": "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n\nWe 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",
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    },
    {
      "cell_type": "code",
      "source": "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5, height=5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.show()",
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      "execution_count": 14,
      "outputs": [
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Next, let's drill down to each site visualize its detailed launch records.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## TASK 1: Visualize the relationship between Flight Number and Launch Site",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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\nsns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 1.5 ,height=5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5ce1130>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can deduce that, as the flight number increases in each of the 3 launcg sites, so does the success rate. For instance, the success rate for the VAFB SLC 4E launch site is 100% after the Flight number 50. Both KSC LC 39A and CCAFS SLC 40 have a 100% success rates after 80th flight.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## TASK 2: Visualize the relationship between Payload and Launch Site",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We also want to observe if there is any relationship between launch sites and their payload mass.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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\nsns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 1.5 ,height=6)\nplt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x4eba320>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  3: Visualize the relationship between success rate of each orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Next, we want to visually check if there are any relationship between success rate and orbit type.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's create a `bar chart` for the sucess rate of each orbit\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# HINT use groupby method on Orbit column and get the mean of Class column\nsr_df = df.groupby('Orbit')['Class'].mean().reset_index().sort_values(by='Class', ascending=False)\nsr_df['Class'] = sr_df['Class'] * 100\n\n\nsns.barplot(data=sr_df, x='Orbit', y='Class')\nplt.xlabel('Orbit')\nplt.ylabel('Class (Success Rate (%))')\nplt.show()\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x47ae500>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "#### Analyze the ploted bar chart try to find which orbits have high sucess rate.\nOrbits ES-L1, GEO, HEO & SSO have the highest success rates at 100%, with SO orbit having the lowest success rate at ~50%. Orbit SO has 0% success rate.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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\nsns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 1.5 ,height=6)\n\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x7597370>",
            "image/png": 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X0pFDJzTaZFx2nTvcupZ+KHP+v6TK6nCuhGTZpj8gI61dvY9jrlsp14qlUkW5jN6DZIy4sH6963a7FBldvfDWyVr5Zn7tsPRI/WvtYfeH7jEhgYaGeOlD9/V1Oe7Qe0xxpUvzNubqnmGpXjkGzt7z32bryz2F7scF5VWasypbieF2hvT5iLl1vWS6PG/bso7g62NmRYXMPdtVWVoohUX57Lilla4an50n2nPUexfy395wRHM3HHHPYY0OCdBD56V5bTj1psOlenTpAZU5q8/p+ZtydW5ahH47orUCbE0//AbYDN0/Mk1//OqAO/wGBxi6aWCS0mO8c9H2vxtza33+uszq6UYEX7REBF9JISEhCgkJ0erVq9WpUyfZ7fZaz4mJiVFVVZVWr16twYMH1/uKosPhkMPhcD82DEOhoc1/Do352ULP7Z9/IPkg+P6QXeLxg9vpkr78sVBX+GDhJCMqRrYHnpS59AOZO7fIiImVcd5FMjpUD4039+6S+Z/nJfOED52cbLn+OVu2R/9Rr3PIteBNmR/Ocz82N30vc+0K2e7+vYyAU8/JM2w2GaMvlvn+W7W22cZcUs93eXaiQgJ13/DWembFQRVXVn85iQiy6Z6hqYoMPvs5hY4qUztyyz1u23y49Kz3D+8orqzSVyeE3hN9vONoswm+pmlq8c4CLdmVr5JKl/qlhGlqz3jFhjaPj1IjMtrj1ANJ1RfJfMR0uaSN38ncvU2KTaieYxwS5rPjNwWu5Z/KnPeyVFqiQ5LUtqNst/1WxgmjhRpLqN2mmJAA5ZdX1dqWGuWdi8abDpfqrfU1e5ULyqv0568P6vmftZftLHtlTdPU31dmuUPvMasOFOuLHws0pkPMWe3fVzrGheiFn3XQhkOlKne61CspTOFB3ptvvzPP8+fjnvwKOV2mApvBBQLAm5rHp3UjCwgI0PTp0/X8889ryZIlat++vbp166Zhw4apbdu2kqTOnTtr8uTJevbZZ/Xiiy+qY8eO6tmzp0aOHKmYmJg6971gwQLNnz/f/TgjI0OzZ89u7LfU+I7WXrBIkpRfR7uXlVR67rWQqr9k+4oRGSVj0lUet5krPq8Zeo85lCnt3ib9FJDrYubnyvzkf7U3bNsgrVspDRh2+vouvkKqcsr8fJFUViLFxFUPR+8/5LSv9ZZBaRH61+SOWn+oVLGxsWoTVHHKIdANYQ8wFBUcoMKK2v/mrXwYRhxVLi3celRf7yuSZGpoepQmdW3l9WF9zVVxRZWcdfzKHi3z3e/r2Xpl7WG9t/Wo+/HBokqtOViipy5qpwgvflltND0HSLHxUt5Jw1yDQ2Wcc55PSjArKuR6dlb19I9jbe/9W7Z7HpPRuvEX3GsKzL27ZL7295q973t3yvXcnxTw8NNeO46jyqXVmcUqLK9Sr+QwpUVV9yIG2Axd2i1Wr36fU+P5NkO61EvrLiyr40LX4RKHtuWUqVvi2V3o2FdQqYNFDo/bVh0objbBV6r+92isi3/JEXblltbuJIgPCyT0okUi+P5k8ODB6t+/v7Zu3art27dr3bp1ev/993Xbbbdp1KhRkqRp06Zp4sSJ2rhxo3bs2KElS5ZowYIFmjVrltLTPX9gT548WRMnTnQ/bi5zT06rYzdp/be12zt088nheyWHKdBmyHnyfQAk9U/1/6JSkqTKUwwZqyg7/et3bZWqPIcCc9tGGfUJvjabjEuvlnnxlVJxoRQd45fVW4MDbTonLVIpKfHKysqS6emCwBma0DnmhNslndjeymvHOJ0/fpWp7w6WuB/vysvRhuwSzRrTdL7I7yuo0H/WH9HGQ6WKDgnQ+E4xurhzK5/8TUqMsCsxPNDjKI2eSc2jpy+/3KlF24/Waj9U7NBnuwr0s27eCQyNyQgMlO1XM+V66UnpwJ7qxoRk2X75KxmRvhlqa36+sEbolSQV5sv11nMKuPePPqnB38zln3oecr5vl8x9u2Wktz/rY/x4tFyzlh7Q0bLjv3MTu7TSzQOre5Qnd49ToM3Q+1vzdLjEqQ6xIbqqd7x6eOn30dNn8zEnT005E6cKbQS6437WLVabDmd6bAdaIrojThAUFKTevXtr6tSpeuyxxzRq1CjNmzevxnMiIyM1ZMgQXXvttXr66afVqlUrvf/++3Xu0263KywszP2fFYY5S5Jt0jQp+KSVH+1BstXR++ltMSGBurpP7eHMw9tGqk9y0/gibfQa6HlDWLjUsfvpdxAZU/e26IaFOsNul9EqzpK3LLmiZ7wmdW2l4J96kSODbPplvwSNbu+boZubD5fWCL3HrMsu1frs2u3+kFPi0P2L9+qbfUUqrKjS/oJKvbjmsN78ofYCN43BZhj6Zb9Enfx9NDHcrku6+u4CxdnYm19RZ691XcMJmyKjdVsFzHxWtkf+LtvDT8v22D99OrfX/H6l5w3bN8ks9txLaDmlxXVvK/PO34ynlh+sEXol6YNtR7Vqf5H78SVdY/XipR214KoueuqidhrY2nsXjeuaPxodEqBuCWf/Gd06KqjO1adHtvPdfOmm7ty0SN09NEUpkdVT+FqFBur6/gma5KWefaC5ocf3FNLS0vTttx56NX8SGBiopKSkFrmqs9G2o2wP/EXmkvdkZu6VkdxaxgWXymiT4bMaJnePU7eEMH3xY4Eqqkydkxahc9Mimk6vep9zZAwYJvO75cfbbDYZV90mI6geC1d06i61blt7ZeagYBlDzvdurc1YgM3QjQOSNK13vPLKnEoMt/t0iHFdc4yPbevdBG4bsWjbUfcc6xMt3JqnKd1jvTqnrC7D20YpIdyuj3ccVW6pU90SQjWhcytFhzSPj6GkcLsMyeMc2eSI2utCNHV+G1Zc19oEhk2y4IU5j3r0l1Z9Wbs9LFxq1/msd783v0L7Cio9blu2t1DnnhRKz3a+rScDUsM1tkO0Pt11fHHFoABDvxqc4rXbmN0zNEWzlh7Q4ZLqIc82Q7qkSysWbTrJqIxondcuSmVOl0ICbY3y7w00F83jG0cjKyoq0lNPPaXRo0erbdu2Cg0N1a5du/Tee+9p4MDqXrvvvvtOy5cv17Bhw5SSkiJJWrNmjb7//ntNnz7dn+X7jZGaLuOXd/q1hq4Joeqa0DR70Q2bTbr1Phkbv5O54TspNFzGkFEyktPq93rDkO3O38n1r6ePDw1MTJXt6ttlxPpmVebmJMweoDC77784J54i9CSGN41AtDff88W5iipTh4odah/rm59bl/hQdfHSiq6+lhwZpHPbRGjl/pq9dWF2G7dkagDjnJEyd26pvaFnfxlh/r9I5AvGoBHVa0Bs+eGERpuMK26SEXz2q/m6TjGVpMp7s0xOyTAM3Tk4RRd0iNHarGKF2wM0sl2UV9deSIsO1j8ntdf3WSXKL3eqZ2KYkiO5f7snhmH45fMRaGoIvqpe1blTp05atGiRDh06pKqqKsXFxWnMmDGaMmWKpOre3+DgYL3++uvKzc2V3W5XcnKybrvtNo0cOdLP7wBNlWEYUq+BdQ97Pt3r4xIUcO8fZObmVM8ZTm7ddHq0IUka1DpCKZF2ZZ200EpShL3J9Dy0jgrS2qzaQyiDAowmE86bg7uHpuqVtYe1dHf1KJPuCaG6vn+iEvgZ1ptx3nhp11aZJ/Z4pqbLdnXLuYB8bK61uWaZtOl7hSckqazfEKnN2c/tlaR2McEe/yZJ8tpt5OqrsS9OB9gMrw7RBmBthunNVWZQbzk5OTVuc9RYDMNQSkqK1xcUAhrKyufi4WKHXliT7Z7r2y8lXLcOSvLJ/aTrI6uoUr/+cI/KT5qkekmXVrppYOPfPqWpOdtzscplyukyFRzIMhlnyjywR+aP22XEJkjd+rjvfd7SNNbfxS2HS/XoFwdU4jj+O39euyj9emgKQ13hkZU/o1uy1NRUf5fQpNDjCwBnKTHCrodGtVGZwyVTZpMbUpYSGaTHxrbRm+tytOFQqWJCAjW+c4wu6x7n79KapQCboQBWjj0rRlo7GWnt/F2GZXVLDNMLl3bQsj2FKqyoUp/k8CY7LQgAfIXgCwBeEmpvur1WneJCm9TtlQA0roigAF3kw9u6AUBT13S/pQEAAAAA4AUEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApQX6u4CmYM6cOfryyy8lSQEBAYqPj9d5552nyZMnKyAgQC6XSx9++KGWLl2q7OxsBQUFqVOnTpoyZYq6du3q5+oBwDcOFzv0zf5CuUxpSJtIpUQG+bskoEUrLHfK4TIVF2b3dykA0OQRfH/St29fTZ8+XQ6HQ99//71efvllBQQE6NJLL9UzzzyjDRs26Oqrr1avXr1UWlqqTz75RLNmzdLdd9+tc845x9/lA0CjWrwzX8+tzpbLrH78+vc5ur5/on7WLda/hcEjs+CoFBgoIzzS36WgEeSWOjRnVbbWHiyRKalDbIhuPydJneJC/V0aADRZBN+fBAYGKiYmRpJ04YUXavXq1VqzZo2SkpK0cuVK3XfffRo4cKD7+bfeequKior0/PPPq3fv3goJCfFT5UDjMrMPyPzgbZk7NkvRrWSMvli2IaP9XRZ86GiZU89/ezz0SpIp6ZW1h3VOWgQ9v02IuXeXXP9+Tvpxu2QYUo/+sl0zQ0ZsvL9Lg5eYpqnfLz2gPfkV7rZdeeWa+fl+/fOS9ooK4asdAHjCHN86BAUFyel06uuvv1ZKSkqN0HvMJZdcoqKiIq1fv94PFTYPpsslM++IzIpyf5eCM2AeOSTXn34rc9WXUl6O9ON2mf96Wq6P/+fv0uBDqw8Uy+mq3W5KWrm/yOf1wDOzpEiup39XHXolyTSljd/J9ewsmaZ56hej2dh0uKxG6D2mpNKlL/YU+qEiAGgeuCx4EtM0tWHDBv3www8aP3681q5dq9atW3t87rH2rKysOvfncDjkcDjcjw3DUGhoyxiK5Fr1pcwFb0i5h6WgYBnDxsq44gYZgcxFai7MzxZKJbWDjfnR/2SOuUSGvWX19BVVVMkeYCgksGVdM7QZp9p2io1+8OPRcq3PLlVUcICGpEe2qH8rc+WXHn9flblX2rpe6tbH90U1AqfL1JKd+Vp1oFiBNkMj20VpRNtIGU3sXGwsR0oddW7LLXX6sBIAaF4Ivj9Zu3atrrnmGlVVVck0TQ0bNkyXX3651q5de1b7XbBggebPn+9+nJGRodmzZ59tuU2euW2jzJefqu5xkKTKCplLF0kyZVx1m19rQ/2Z+3Z73lBaLB05LKWk+bYgP9mRW6YX1xzStiPlCrRJg9tE6tZByYoKDvB3aT5xblqEXlhjqLKqZq+hzZCGpjeNOaSmaWrOqmwt2VXgbntl7WE9PDrNp/MeNx0q1Qfbjyq31Kmu8SH6WbdY3y08dPRInZvMo0dkhVhomqb++OUBrTlY4m77NrNYW3JidOugZD9W5jtd40NlqHrEhadtAADPCL4/6dGjh26++WYFBgaqVatWCgio/kKbkpKizMxMj6851p6SklLnfidPnqyJEye6H7eUK9Kuzz84HnpPYC7/VOaUX8oI4cO5OTASU2Ru31h7Q1CQFNMyFjXKL3Nq5uf7VVJZPdbX6ZK+3luko2VO/eGCtn6uzjeiQgJ115AU/XVFljv8BtoM3X5OkhLCm8YIjm/2FdUIvZJUUFGlZ77J0pxL2vukhq/2FOqp5QfdgWTbkTJ9tbdIfxnfVvE+CL9Gh64ew5AMQ0Z7a9yB4Puskhqh95iPtufrki6xSo2y/iiU5Mggje8Uo4925Ndo754QqnPSIvxTFAA0AwTfnwQHBys5ufbV4qFDh+rZZ5/VmjVras3zXbhwoSIjI9W7d+8692u322W3N40vhj5VV89DZWX1UDyCb7NgjJkoc+UXkrPm0Dpj5HgZoWH+KcrHPttd4A69J9p0uEy78srVIbZlLGw3vG2UeieHa/WBIrlM6ZzWEYoJbTofIcv3eZ5rfKCwUnvzK9Q2JrhRj+8yTb2x7nCt4Hm0zKn3tuTpxgFJjXp8SVKfQVKn7tKOzTWajeEXyEj2PGWnudl8uMxjuylpc05piwi+knTroCR1jg/Vlz8WqLLK1DlpEZrQuZUCTjUvAQBauKbzraWJGjZsmFauXKk5c+bommuuUc+ePVVWVqZPPvlE3333ne6++25WdPbA6NBV5rEFVk4UGy+1ivN9QTgjRlqGbHfNlGvBG9LubVJElIxRE2RMvNLfpflMTknd8+kOlzhaTPCVpKjgAI3tEOPvMjw61dJNvljYKbfUqcMlnudXbsnxHNa8zbAFyHbXLJlLP5C5bpUUaJdx7nkyhl/gk+P7QqtTXGxp1YJWMzYMQ+e3j9b57aP9XQoANBst51PiDBmGobvvvluLFi3SokWL9NJLL8lut6tz586aOXOmuna1xvAxbzMu+JnM1V9JhfknNBoyLr1Ghq1lzIu0CqNrbwXc/2eZTqeMwJb3J6NTXIg+2lG73WZIHVtQ6G3qhqVH6hsPvb5pUUFq16rx/50iggIUFFB7HrQkxYX57vfGCA6WMf4yafxlPjumL41sF6V/r8+pNQojOcKuvinhfqoKANActLxvsR7MmDHjlNsDAgI0adIkTZo0yUcVNX9GbIJsDzwpc8m7MndukVrFy3b+xTIssqpoS9QSQ68kjWgXpYXbjurHozVvHzK+U0yTmd+K6uD7fYdofXrCPN/I4AD9emjdazB4U6jdpjHto2vNuzQkXdy5lU9qaAkigwM0c3QbzVmZrb0F1b+T3RJCddeQFIb5AgBOyTC5uZ9f5OTk1LjNUWMxDEMpKSnKysriPo7wq+Z8LhZXVum9LXn6NrNYoYE2jW4frQs6RLeYxeqak9155fohu0TRIYEa0iZSofbatzNqrHPRUeXSS98d1ue7q+ddxoUF6uo+CQxHbSRZRZUKtBnN+gJUc/67CGvhXLSm1NRUf5fQpLTMLhwAaICIoAD9ok+CftEnwd+l4DTax4aovZ+GoNsDbLr9nGT9sl+CiiqqFB9mpxeyEaVEtoyFrAAA3kHwBQDAi8LsAQqzs5YBAABNSe0xYAAAAAAAWAjBFwAAAABgaQRfAAAAAIClEXwBAAAAAJZG8AUAAAAAWBrBFwAAAABgaQRfAAAAAIClEXwBAAAAAJZG8AUAAAAAWBrBFwAAAABgaQRfAAAAAIClEXwBAAAAAJZG8AUAAAAAWBrBFwAAAABgaQRfAAAAAIClEXwBAAAAAJZG8AUAAAAAWBrBFwAAAABgaQRfAAAAAIClEXwBAAAAAJZG8AUAAAAAWBrBFwAAAABgaQRfAAAAAIClEXwBAAAAAJZG8AUAAAAAWBrBFwAAAABgaQRfAAAAAIClEXwBAAAAAJZG8AUAAAAAWBrBFwAAAABgaQRfAM3CwcJKbT5cqjKH64xeX1hRpc2HS5Vb6vByZbCarKJKHSis8HcZAADAiwL9XQAAnEphuVN/WX5QP2SXSpLC7DZd1Ttel3SNrdfrTdPUa9/n6INtR+VwmbIZ0oi2UbpjcLKCAqx17c88uE/m3l0yEpJkdOzu22O7qmR+8q7Mbz6VykpldO8n42e/kBGX4NM66qPKZcplmrXa9xdU6K8rsrQjt1ySlB4dpDsGp6hLfKivS6yTefigzPf/I3Preik8UsbIcTLOnyjDMPxdGnBGTIdDMiQj0O7vUgBYHMEXwFkxnU6Zq7+SNn4nhYTKGHq+V0PXsyuz3KFXkkodLr303WG1iQ5W35Tw077+k535WrAlz/3YZUpf7ilUdEiAbhyQ5LU6/cmsqpL5r6er/x0kmZLUoatsdzwkIyKqXvtwVLm0eGeBvs0sVnCgodEZ0RrcJrL+Nbz5nMxli48/XvG5zG3rZfvdszLCIxrydhpNZmGlXv7ukL7PKpE9YLtGtI3U9f0TFREUIEeVqVmf71dOqdP9/H0Flfr90v164WcdFB4U4MfKq5kFR+X602+looLqhoKjMue+KOXlyLj8Bv8W1wzllDhUVFGl9JhgBdq4cOBr5pFDcs19UdqwRjJsMvoNlvHzm2VEt/J3aQAsiuAL4IyZVVVy/f1RadP3x9uWLZZx5U2yjZ101vvPK3NqTWaJx21LduXXK/gu3pnvsf2zXQW6oX9ik+kp+3jHUX24LV955U51TwjVtN7xymgVUq/Xmp++7w69bru2ynz7JRk33nPa11e5TP1+6QGtP3T8AsPK/cWa2iNO1/Q9fY+tmZcj8+tPa2/IOyJz+RIZF04+7T4aW0lllR78dJ+OllUH2wqnS5/uKlBWUaX+cEFbrTlYXCP0HlNc6dKyvYUa38n/X8bNLz46HnpPbP98kcyLptb7IkdzsO1ImdZllSgiKEAj2kUpKth7Fx7yy5165pssfZ9V/belVUiAbhiQpJHtrPPza+pMh0OuJx+Sjhz6qcUlc83XMrP2y/a7v8qweW80jqPKVElllaJCAmRrIn/vAfiH5YNvfn6+FixYoLVr1yovL09hYWFKTk7WiBEjtHTpUu3atavO13bv3l2PPPKIJGnbtm165513tH37dlVWVio5OVmjR4/WhAkTZPPiH2j4lrl3l8zPP5B55JCMdh1ljLlERmzTG5rZZH2/okboPcZc8LrMoefLCDu7nr6SyirVHpBaraiiql77KKrwPCe4xOGS0yXZ/d+Rp3kbjujf64+4H686UKz12aV66qJ2So0KOu3rzZVfeG5fs1zmL+887RDCVQeKaoTeYxZsztWEzjGKCzvNEMSD+ySzjrnXB/ae+rU+8uWeQnfoPdGmw2XadqRM+R62HZNfVr9zzRu25pTp7Q1HtCO3TAnhdk3qGqvR7aMlSeaBPZ5f5HRIhw5KFgi+pmnq2ZXZ+nz38YD/+rocPXhea/VOPv2Frvp4cvlBrT9hFMnR8io9/c1BtY4KUofY+l1swtkx135zQug9Qebe6s+UXgPO+hgu09R/1h/Rou1HVVLpUkJYoH7eO15jO8Sc9b4BNE+WDr6HDh3Sww8/rPDwcE2bNk3p6emy2+3at2+fPv30U40fP159+vSRJB05ckQPPPCAHn74YbVp00aSFBhY/eNZvXq1nn76aY0aNUozZ85UWFiYNmzYoDfffFPbt2/X3Xff3WR6jZoT82iuzA/nydy4VgoLlzH8AhmjJvjsZ2luXCvX3x+Tqqq/8JrbN8pcsVS2+/8sIyG5uq2iXOaqL6QDe6SkNBlDRp11mLMSc8sPnjdUVko7tkh9Bp3V/lMjgxQfFqgjHnri+tbzS3Cf5DAt2VW7l6xnYqjsAf7/va1wuvTuCUOxjylzurRwW55uHZR8+p04Kz23Vzkl1+kXA9t0uMzzy83qIDas7WmCb1JryTAkD/NmlZR62uP7QlZRHT8jSdlFleqZFFbn9lNt86ZdeeV6+LN9qqyq/jkWVVbomRVZKnO6NKFzKxlJqZ4vBAUESPFNZ9i+6aqS1n8rM3OfjNbpUu9BMmz1u8K06kBxjdArSeVOl55dkaUXLu1w1j12WUWVNULvMS5TWrIzXx3OqcfvG85eTnadm8ycLHnjL/O8jbmatzH3+CFLnfrbymxFBQfonLT6T+MAYB2WDr4vvfSSAgIC9Mc//lEhIcev4iYlJWnQoEEyTdMdsiorq78URUZGKiYmxv3c8vJyPf/88xowYIBuvfVWd/uYMWMUHR2tJ554QitWrNDQoUN986Yswiwtlmv2b6Xcw8fb3npeOnRQxs9v9kkNrv+95g69bkUFMj+aL+PaO2Tm58n15/ulw1nHa/x4vmz3/kFGYtP4Mu93p5q7GXH2XywCbIZuGpikPy/LVNUJ3/gzWgVrfOeYeu3jyl7xWnuwRLkn9OiFBtr0y36JZ12fN+SUOlRSx0rVe/Prt7Kw0edcmdnv1N7Qo5+MoODTvj4mpO5QEhN6+o8JIyFZxoBhMtd8XXNDRJSMERee9vW+0P4Uw8YzYkPUJjpYF3WK0Uc78mtsG9ImwmfBd8HmXHfoPdF/N+ZqXMcY2UZdJPOrj6WymsHNGHJ+k5kXaRYVyvXUw9KBH6sfS1KbDNnuebReQ7FX7Cvy2J5T6tSO3PKzXmisoLzu3vv8U2yri8s0GT57Boy2HeoczWO07XjW+69ymVq07ajHbQu3HiX4Ai2UZcfoFhUVaf369Ro3blyN0Hui+vQsrl+/XkVFRZo0qfZ8xYEDByolJUVff/21h1dWczgcKi0tdf9XVua5Z6WlMb9eUiP0utu/+EhmgecPK68ev6zU/cWs1rbtm6r/9/23aoReSVJ+nlzzX23k6hrGNE2ZOzfLXPuNT352JzKGjqnubTpZ67YyOnT1yjGGtInU0xMydHGXVhqWHqlbBibpTxe2VVg9xygnhNv1zIR2urpPvIa0idSU7rH668Xt1LmJrNQbF2pXSKDnP8VpUacPrZJkTJgqtcmo2RjdSrYrbqrX689vH62QwNp/D9vGBKt7Qv1+TsYNv66eyxseWX1O9BpYfZEoKqZer29sw9tGqm107Z/n8LaRSv+p/dZBSbp3eKrOTYvQoNbhunNwsu4d3tpnNdZ1oSOvzKmSyioZ8Umy/eZxqXtfybBJkdEyJlwu4xe3+6zG0zHffaP239b9P8pc8Ga9Xn+qmUMBXgiYGa2CFR7k+SC9GnCB48sfCzRj4W5Nfmubbn9/d61eapxGj/6Sp8+IXgO98tlRWWWqsI7pMDnc0g5osSzb45udnS3TNJWaWrNn7sYbb3T37o4bN05XX331Kfdz8OBBSVLr1p6//LRu3VpZWVket0nSggULNH/+fPfjjIwMzZ49u17vwdL27fbcXuWUMvdIP/VemKZZPczYFlA9ZM5bgoKl0LBaPSeSjh97/RrPr62r3Q/M3MNy/e3R6nlRkhQQKGPC5bJNmuaT4xvJabLd/H9y/fufxxfdSe8g2633NWg/aw8Wa+nuQpU5XRrYOlxj2kfLfsKthtrGBOuWgWc+lDMqJFCX94yvc7vpdFT3Vm7fJEXFyBg21j3cvbGF2m2a2KWV5m/KrdEeFGBoYpf69eIZYRGyPfAXmWtXSHt3SQnJMs49T0Zo/b7Ix4XZ9eB5aXpudbYOFlV/KeyRGKpfD0mt99QDwx4k4/Lrpcuvr9fzfc0eYNNjF6Rr3sYj+vZAscJCgjQ8LUw/63b8tliGYWh42ygNb+ufubKto4K1r6D2kOzokAD3qtJG2w4KuPv3vi6thn35Ffp4x1HllDrVJS5U4zrFKPKnxafM777x+Bpz7XLpmumn3feItlH6fHdhrfbUSLs6xNbvQtCpBAfa9Mu+iXpudXaNHscOsSEa0yG6XvtYvq9QT31z/HP/YFGl/roiSzZDGpVRv320dIbNJtuvZ8n8+H/Vf7dsNhkDh8sY552F8ELtNrWJDtJ+D79PneOaxkVPAL5n2eBblz/84Q/Vi2c8+6yczroXMzmZ6WnuWj1MnjxZEydOdD9mLvBPElI8txuGe5u5c7Ncr/z1eK9r67ay3XSPjLQMz69tACMgQMaIcTIXL6i1zTZ6QvX/Ca7jS1Y9ho76iuulp46HXkmqcspc+B+Z7TrK6H1282vryxgwTLY+50h7dkqhYTJat23Q6+dtPKJ//3B8YadvM4u1fG+RHjm/jQJ8cIsRs6JCrqcflnZtPd62+F3Zpj8go2d/rx3HtepLmd98JpWXyeg5oHohtbDqecq/6BOvcLtNH24/qrwyp7onhunqPglKj6n/uWYE2mWcM1I6Z+QZ1dc7OVz/uKS9MgsrFRxoU0K49e6pGRUcoJsGJOnmgclKSUlRVlbWGf9tbwyTu8fq28wiOU8a+T65W6xPfhfqY+3BYj3+Zaacruqf2+oDxVq8K19PXNi2elh8XZ9xRv0GmPVPjdDkbrF6d0ueO5jGhAToN8Na1/vzs9RRpbd+OKIv9xTKUWXq3LQIXdsvwb1I27hOMWoTHaTFO/NVVFGlPinhuqBDTJ0jL072zqbac/Il6Z3NeQTfBjBCQmVcerV06ak7IM7U1X0SNHtZplwn/IqH2W2a2jOuUY4HoOmzbPBNTk6WYRjuHttjkpKqe42Cgk6/Uqokd49xZmamunTpUmt7ZmZmnb3BkmS322W3W+8L5NkyRo6T+dlCqeykW9X0GyIjIVlmSbFczz5ac3vmXrn+Oku2P7wowws/U2PyNZKjovo2LI5KKSJSxsVXyBg4vHr7kNEy33ur9uuGjD7rY3uDmZMt7dzseduKpT4LvlJ16FLHbg1+XUG5U29vyK3Vvv5QqVbuL9IwH/S8mcs+qRF6JUmOSrne+qdsjz/vlYtVrndek/nR/44fc/c2md+vkO3/PSEjKFg2w9CUHnGa0sO/X8gMw1Cah+HA8I0u8aH63eg2+s/6I9p+pEyJEdWrOk/o3DTm70rSv9YedofeYw4VO/Tuljxd1z9RxsDhMr/4sNbrjv1drY/r+idqXKcYrcsqUWRwgM5Ji1BQQP1nZv3xq8waC1h9sadQW4+U6dmLMxT8U7jtnhim7olnNne7roXSDhbWvYAafG9wm0g9NiZd72/LU3aRQx3jQjS5Wyx/44AWzLLBNzIyUr1799bHH3+s8ePH1znP93R69+6tiIgILVy4sFbwXbNmjbKysnTllVd6o+QWxWgVJ9tvHpNr/ivS1vVSSKiMoWNkTPmlJMn8dlntUCxJ+XnS+m+lAWe/mJgRGCjjqttkXnqNVHBUik+UYT9+QcQYf5l0cH91Lcf0HCBjyrVnfWyvqKx74SOzotyHhZy5rTlltb5EH7P+UKlvgu/G7zxvyMmWsg9IKW3Obv8FR2Uufq/2hv0/ylz9lYzhF5zV/mEtfZLD1cdLt+3xtrwyp8eho5K0/lD132tj8tUy9+6Uftx+fGNGZxmX/qJBx0qJDFJKZP0uUJ9o25Eyj6s2Zxc7tHxfkc5vf/Y9su1aBXtcCT2jFYGqqemRFKYePlqcDkDTZ9ngK1XP53344Yd1//336/LLL1d6erpsNpt27typzMxMtW/f/rT7CAkJ0S233KJnnnlGzz//vMaPH6/Q0FBt3LhRb7zxhgYPHqwhQ4b44N1Yj9G2gwJ+85hMp6N6Du+Jq5oU171QiFmU75VbHbjrCAuXwmp/0TQC7TJuuVfmJdOkg3ulpFSvDLP2mpQ21bcw8XAvRKP3QD8U1HBRp1hNOPoU27wq5BTzvYK9cE/PPTtrrx5+zK6tEsEXzUSY3Sa7zZDDw8WqyODqrxNGWIRs9/9Z2rJO5sH9MlLTpW59fDbNZ39B3RcET7WtIa7oGa9ZS/fXGEJrM6pXkAcANF2WDr7Jycl64okntGDBAr311lvKzc2V3W5XWlqaLrnkEo0bN65e+xk8eLBmzpypd955R7/73e/kcDiUkpKiKVOm6OKLL2be7lkyAmsPWza69vY4zFiGIaNrHx9UdcIhU9KklDSfHrM+DJtNtqunyzXn8eqh2sd06SVjWPMIU90SwtQuJlh7TlrN1m4zNMYLPTP1YRs6Ri5PC/J06yMjNuHsDxB7ii/DrfiijOYjJNCmke2i9JmHFYzHd4xx/3/DMKTu/WR07+fD6qq1PcW8+HQvDXHtmxKuWee30f825WpvQaXaRAVpSo849Utpmj31AIBqhtmUVvZoQXJycuRwNP6S+oZhNMlFXOrD9eJfZK7+qkabMeYS2Xx0n9/mwszNkbniM6kwX0aXXlLfwTI83WLIz+o6Fw8XO/T0Nwe1Oad66GBCWKBuPydZA1qf4h7BXub64G2Zi+ZJzp9+J9t1ql7cqpV35txW/fn+6hWjTxQcItvv53gnXKNBmvPfRX8rd7r07IosrdhfJJdZ3Qt8ec84TenedBYMeuTz/fo+q+ZUmdTIIP314nYNmivsC5yLaCo4F63p5LvbtHQEXz8h+J6e6XLJ/HaZzLUrqldhHjRCRr/B/i4LZ+h052JWUaXKnS61jale7MnXzKJCafc2KTpGRrtOXt+3681/SOtWSi6XlJYh27RbZHTu4dXjoH6a89/FpiK31KHcUqfaRAcr1N60wmSF06W5G47oyx8LVekyNTgtQr/ok6BWoU1vkBvnIpoKzkVrIvjWRPD1E4IvWhrORcksKZYqK7zWk4wzw7mIpoJzEU0F56I1EXxranqXPwHAoozwCCncd0O4AQAAUK1pjU8CAAAAAMDLCL4AAAAAAEsj+AIAAAAALI3gCwAAAACwNIIvAAAAAMDSCL4AAAAAAEsj+AIAAAAALI3gCwAAAACwNIIvAAAAAMDSCL4AAAAAAEsj+AIAAAAALI3gCwAAAACwNIIvAAAAAMDSCL4AAAAAAEsj+AIAAAAALI3gCwAAAACwNIIvAAAAAMDSCL4AAAAAAEsj+AIAAAAALI3gCwAAAACwNIIvAAAAAMDSCL4AAAAAAEsj+AIAAAAALI3gCwAAAACwNIIvAAAAAMDSCL4AAAAAAEsj+AIAAAAALI3gCwAAAACwNIIvAAAAAMDSCL4AAAAAAEsj+AIAAAAALI3gCwAAAACwNIIvAAAAAMDSAv1dwJmaM2eOvvzyy1rtffr00YMPPqg9e/bo7bff1o4dO1RWVqaYmBh17NhRN9xwg6Kjoz3uc9OmTZo1a5ZeeeUVhYeH19q+f/9+vf322/rxxx+Vk5OjX/7yl7r44ou9/t4AAAAAAN7TbIOvJPXt21fTp0+v0RYYGKjCwkI9+uij6t+/vx588EGFh4fr8OHDWrNmjSoqKs74eBUVFUpKStKQIUP02muvnW35AAAAAAAfaNbBNzAwUDExMbXaV69erdLSUt12220KCAiQJCUmJqpnz55ndbyOHTuqY8eOkqS33nrrrPYFHLNyf5Hmb8rVvvwKpccEa2qPOA1uE+nvsgAAAADLsOQc35iYGFVVVWn16tUyTdPf5QB1Wrm/SH/8KlM7cstVUWVqR265/vRVplbuL/J3aQAAAIBlNOse37Vr1+qaa66p0TZ58mRNmTJFkydP1rPPPqsXX3xRHTt2VM+ePTVy5EiPPcSNyeFwyOFwuB8bhqHQ0FCf1oCma/6m3Fpt5k/t9PoCAAAA3tGsg2+PHj10880312iLiIiQJE2bNk0TJ07Uxo0btWPHDi1ZskQLFizQrFmzlJ6ernvuuUc5OTmSpG7duumBBx5olBoXLFig+fPnux9nZGRo9uzZjXIsND/78j3POd9fcOZz0QEAAADU1KyDb3BwsJKTk+vcHhkZqSFDhmjIkCG66qqrdN999+n999/XHXfcofvvv19VVVWSpKCgoEarcfLkyZo4caL7sWEYjXYsND/pMcHakVteq71NdLAfqgEAAACsqVkH34YIDAxUUlKSe1XnhIQEnxzXbrfLbrf75Fhofqb2iNOfvsrUiTPRDUmX94jzV0kAAACA5TTr4Ot0OpWfn1+jzWazaceOHVq+fLmGDRumlJQUSdKaNWv0/fff17r9kSf79u2rNQ+3Xbt2cjqdOnDggPvYeXl52rNnj0JCQk7Z8wzUZXCbSP2/ka01f1Ou9hdUqE10sC7vEadzmd8LAAAAeE2zDr7r1q3TLbfcUqMtNTVV999/v4KDg/X6668rNzdXdrtdycnJuu222zRy5MjT7nfmzJk1HttsNs2dO1d5eXm677773O0LFy7UwoUL1b17dz3yyCNeeU9oeQa3iWQhKwAAAKARGSb3+/GLnJycGqs9NxbDMJSSkqKsrCxu7QS/4lxEU8G5iKaCcxFNBeeiNaWmpvq7hCbFkvfxBQAAAADgGIIvAAAAAMDSCL4AAAAAAEsj+AIAAAAALI3gCwAAAACwNIIvAAAAAMDSCL4AAAAAAEsj+AIAAAAALI3gCwAAAACwNIIvAAAAAMDSvBJ8Z8yYoTvvvFPZ2dn1fs2RI0d0xx136M477/RGCQAAAAAAeBTojZ0cOXJEkuR0Ouv9GqfTqZycHG8cHgAAAACAOjHUGQAAAABgaX4LvqWlpZKk4OBgf5UAAAAAAGgB/BZ8ly1bJklKSEjwVwkAAAAAgBbgjOb4zpo1y2P7P/7xj9P24DqdTh06dEgFBQWSpN69e59JCQAAAAAA1MsZBd/Nmzd7bN+1a1eD9pOYmKjJkyefSQkAAAAAANTLGQXfbt26yTAM9+NjQbh9+/YKCQk55WuDgoIUExOjLl26aOjQoad9PgAAAAAAZ+OMgu8jjzxS4/GVV14pqfp+vmlpaWddFAAAAAAA3uKV+/iOHDlShmEoPDzcG7sDAAAAAMBrvBJ8Z8yY4Y3dAAAAAADgdX67nREAAAAAAL5A8AUAAAAAWFqDhjofW8TKMAzNnTu3VvuZOHlfAAAAAAB40xn1+Jqm6bUCvLkvAAAAAABO1qAe36lTpzaoHQAAAAAAf2tQ8L388ssb1A4AAAAAgL+xuBUAAAAAwNIIvgAAAAAAS2vQUOeGKCgo0L59+1RcXCxJioiIUJs2bRQTE9NYhwQAAAAAoBavBl/TNLVkyRItXrxY+/fv9/ictLQ0jRs3TmPHjpXNRoczAAAAAKBxeS34FhQUaPbs2dq1a9cpn3fgwAG9/PLLWrp0qX7729/SAwwAAAAAaFReCb4Oh0O///3vdeDAAUlSVFSUhgwZoo4dOyo6OlpSdTDeuXOnVqxYocLCQu3evVuPPvqo/vSnP8lut3ujDAAAAAAAavFK8F20aJE79I4ePVrXXXedQkJCaj1v5MiR+sUvfqHXXntNn332mQ4cOKBFixbp0ksv9UYZAAAAAADU4pVJtsuXL5ck9e7dW7fddpvH0HtMcHCwbrnlFvXu3bvGawEAAKzgQGGFnvnmoG5/f5ce+nSfVu4v8ndJANDieaXHNzs7W5I0bty4er9m3LhxWr9+vQ4dOuSNEgAAAPwuq6hS932yVyWVLknSwSKHNhwq1fRzkjWuU4x/iwOaoS9+LNCCzXk6WFSpjFYh+nmvOPVPjfB3WWiGvNLje2yOblxcXL1fc+y5gYGNdkclAAAAn3p3S5479J5o7oYjqnKZfqgIaL4+25Wvp7/J0p78ClVWmdp2pEyPfnFA67JK/F0amiGvpM7WrVtr+/btys3NVUZGRr1ek5ub635tYyssLNTbb7+ttWvXqqCgQOHh4WrXrp0uu+wyde3aVXv27NHbb7+tHTt2qKysTDExMerYsaNuuOEG9+JckvTFF1/ok08+0f79+2Wz2ZSRkaFJkyZpwIABjf4eAMDqSh1V+vLHQmUWVapdTLBGtI1ScCC3vUPzsjO33GN7XplTeWVOJYRbZ0HPModLe/MrFBsaqMQI67wvNB3/3ZRbq81lSu9szlXflHA/VISTGYahBQsWNIs1m7wSfM877zxt375dS5Ys0cCBA+v1miVLlkiqXvCqsT355JNyOp2aMWOGkpKSVFBQoA0bNqi4uFiFhYV69NFH1b9/fz344IMKDw/X4cOHtWbNGlVUVLj38frrr+uTTz7RlVdeqbvuuktOp1PLli3TE088oeuvv17jx49v9PcBAFZ1qLhSDyzZpyOlTnfb/zbl6Q8XpKtVKCOD0HwkRdi1M692+A0NtCkqOMAPFTWO97bk6T/rj6jM6ZIhaVBahH49JEXhQdZ5j/Avp8tUVpHD47YDBZU+rqblys7O1uOPP65FixYpMzNTiYmJ6tu3r379619rzJgx/i6vQbzybWLMmDFavXq11q1bp5deeknXXnutgoKCPD7X4XDo9ddf17p169SnTx+NHTvWGyXUqaSkRFu2bNEjjzyi7t27S5ISEhLUsWNHSdLq1atVWlqq2267TQEB1X+sExMT1bNnT/c+tm/frg8++EDXX3+9LrroInf7tGnT5HA49Nprr2ngwIGKj49v1PcCAFb16vc5NUKvJB0sqtR/1h/R9HOT/VQV0HCXdG2lFfuLdPKo5os6x1hmBMO3B4r1r7WH3Y9NSasPFOufqw/pN8NT/VcYLCXQZig1MkgHi2qH3DYxwX6oqOXZs2ePhg0bppiYGP35z39Wr1695HA49Mknn2jGjBnaunWrv0tskAb9Bd68ebPH/7Zs2aKJEyeqQ4cOWrJkie688069+uqrWrZsmdavX6/169dr2bJlevXVV3XHHXdo8eLF6tChgyZOnKgtW7Y01nuTJIWEhCgkJESrV6+Ww1H7qlFMTIyqqqq0evVqmabnuTfLly9XSEiILrjgglrbJk6cqKqqKq1atcrjax0Oh0pLS93/lZWVnd0bAgAL+vZAscf21Zme2wFfcJ7BnNxuCWG6f2RrtY2u/mIeGWTTFT3jdHWfBG+X5zef7Mz32L58X6GKK6p8Wwws7YqetdcPshnSZd1j/VBNyzN9+nQZhqHVq1frsssuU+fOndWjRw/dc889WrlypcfX/Pa3v1Xnzp0VFham9u3b6+GHH66RwX744QeNHj1akZGRioqK0oABA7RmzRpJ0t69e3XJJZeoVatWCg8PV48ePfThhx967f00qMd31qxZ9Xpefn6+Pvroo1M+Z9euXXr88cdlGIbmzp3bkDIaJCAgQNOnT9fzzz+vJUuWqH379urWrZuGDRumtm3bqnPnzpo8ebKeffZZvfjii+rYsaN69uypkSNHKiYmRpKUlZWlpKQkjwtxxcbGKjQ0VFlZWR6Pv2DBAs2fP9/9OCMjQ7Nnz26U9woAzVVQoCFHZe2QERxg+KEatGSmaWrB5jy9v+2ojpY5ldEqWFf3SdDA1vVfRfactEidkxapUkeVggNsCrBZ6zwuqiPcVplSiaNKERYa0g3/Gt0+WoE2Qwu25OpgoUMZrYJ1Za949U5mfm9jy8vL08cff6zHH39c4eG1f97HctLJIiMj9eqrryo1NVUbNmzQzTffrMjISN13332SpF/84hfq16+fnnvuOQUEBGjdunXuhZJnzJihyspKffXVVwoPD9fmzZsVEeG9Fbz9PnGqrl5Wbxo8eLD69++vrVu3avv27Vq3bp3ef/993XbbbRo1apSmTZumiRMnauPGjdqxY4eWLFmiBQsWaNasWUpPTz+rY0+ePFkTJ050PzYMa334AYA3jGoXpUXb82u3Z0T5vhi0aP/dmKt/rz/ifvzj0Qo9/uUBPT42Xd0Twxq0rzC7NQNg7+QwbT1SewRbaqRdiRZavAtNw4h2URrRjs8CX9u5c6dM01TXrl0b9LqHHnrI/f/btWun//u//9PcuXPdwXffvn2699573fvt1KmT+/n79u3TZZddpl69ekmS2rdvf7Zvo4YGBd+ZM2d69eC+FBQUpN69e6t3796aOnWq/vnPf2revHkaNWqUpOqrE0OGDNGQIUN01VVX6b777tP777+vO+64QykpKdq6daucTmetXt+8vDyVlZUpJSXF43Htdrv7KgYAwLOr+yYos7BS67JL3W2D20Roao/63yYPOFtOl6n3tx2t1e4ypfe25jU4+FrVJV1jtXxfkTILj8+9DLRJNw5I4gI/YBFn2jn59ttv69lnn9WuXbtUXFwsp9OpqKjjFy7uuece3XTTTXrjjTc0duxYXX755erQoYMk6Ve/+pVuv/12LV68WGPHjtVll12m3r17e+X9SA0MvscWh7KCtLQ0ffvttx63BQYGKikpyb2q87Bhw/TRRx9pyZIlNRa3kqSFCxcqICBA5557bqPXDABWFWYP0Kwx6dqVV67Mwkq1jQlWWxYvgY+VVFbVOYw3q9Dz6rItUVRwgP48rq2W7MrXlpwyxYUGalynVvzOAhbSqVMnGYbRoAWsVqxYoV/84heaNWuWxo0bp+joaM2dO1dPPvmk+zmPPPKIrrrqKi1atEgfffSRZs6cqblz52ry5Mm66aabNG7cOC1atEiLFy/WH//4Rz355JO68847vfKevDLU+ciR6iFBISEhXh2H7Q1FRUV66qmnNHr0aLVt21ahoaHatWuX3nvvPQ0cOFDfffedli9frmHDhrl7bdesWaPvv/9e06dPlyR17txZEyZM0Jtvvimn06lBgwapqqpKy5Yt04cffqjrrruOFZ0BwAs6xIaoQ2yIv8tACxUZHKD4sMBaK4xLUkYsoe5E4UEBurRbnC7t5u9KADSG2NhYjRs3TnPmzNGvfvWrWvN88/Pza83z/eabb9S2bVs9+OCD7ra9e/fW2nfnzp3VuXNn3X333Zo2bZpeeeUVTZ48WZLUpk0b3Xbbbbrtttt0//3368UXX2xawXfGjBmSpBtuuEHjxo3zxi69JiQkRJ06ddKiRYt06NAhVVVVKS4uTmPGjNGUKVN09OhRBQcH6/XXX1dubq7sdruSk5N122231bjH8HXXXaf09HQtXrxYc+fOlc1mU/v27XXvvffW+97FAACg6bIZhq7sFa85q7JrtIcEGprSnWH3AFqWOXPmaNiwYTrnnHP0+9//Xr1795bT6dSSJUv03HPP1bo7T6dOnbRv3z7NnTtXgwYN0qJFi7RgwQL39rKyMt17772aOnWqMjIydODAAX377be67LLLJEm//vWvddFFF6lz5846evSoli5dqm7dvHd1zSvBNygoSJWVle7x2U2J3W7XVVddpauuusrj9qSkJN1666312tf555+v888/35vlAQCAJuTCjjGKCLJp4dajOlziUKe4UF3RM45hvABanPbt22vt2rV6/PHH9Zvf/EZZWVlKSEjQgAED9Nxzz9V6/qRJk3T33XfrjjvuUEVFhS6++GI9/PDDeuSRRyRV320nNzdX1157rQ4dOqT4+HhNmTLFfeegqqoqzZgxQwcOHFBUVJTGjx+vp59+2mvvxzC9sKzyXXfdpezsbD366KPq3LmzN+qyvJycHI/3FfY2wzCUkpKirKwsn6ygDdSFcxFNBecimgrORTQVnIvWlJqa6u8SmhSbN3ZybLWthkx+BgAAAADAF7wSfCdMmKCgoCAtXLhQeXl53tglAAAAAABe4ZXgm5KSorvuukuVlZV68MEH9fXXX8vprL0iIgAAAAAAvuaVxa2OTUiOiorS4cOH9be//U3PPfecUlJSFB4eLput7nxtGIZ+97vfeaMMAAAAAABq8Urw3bx5c602p9Op/fv3e2P3AAAAAACcMa8E327duskwDG/sCgAAAAAAr/JK8D12byYAAAAAAJoaryxuBQAAAABAU+WVHt8T5efnq6ioSGVlZQoNDVVkZKRiYmK8fRgAAAAAAOrFK8F37dq1Wrp0qbZu3arCwsJa26OiotStWzeNGjVK/fv398YhAQAAAACol7MKvnv37tXf//537du375TPKyws1KpVq7Rq1Sqlp6frzjvvVHp6+tkcGgAAAABgcXPmzNGf//xnZWdnq0+fPvrb3/6mc845p8H7OeM5vitXrtSDDz5YI/QGBQWpU6dOGjhwoIYPH66BAweqU6dOCgoKcj9n3759uv/++7V69eozPTQAAAAAwA9M0/TZsd5++23dc889mjlzptauXas+ffpo3LhxOnz4cIP3ZZhnUPnWrVv16KOPyul0SpL69u2rCRMmqFevXrLZamdpl8ul9evX6+OPP9b3338vSbLb7Xr44YfVpUuXBhdtBTk5OXI4HI1+HMMwlJKSoqysLJ+epMDJOBfRVHAuoqngXERTwbloTampqV7bl6u0RAWv/0Nlq76SqpxSQKBCzx2p6GunyxYW7rXjnOzcc8/VoEGD9Pe//726DpdLbdq00Z133qn/9//+X4P21eChzi6XSy+++KKcTqcCAwN1++23a/jw4ad8jc1mU9++fdW3b18tX75c//jHP+RwOPTCCy/oL3/5C/cABgAAAIAmyFVaokO/uV7O/Xsk0+VuL170X5X/8K2SnnylUcJvZWWlvvvuO91///3uNpvNprFjx2rFihUN3l+DhzqvXLlSBw4ckCRNnz79tKH3ZMOGDdOMGTMkSQcOHDijogEAAAAAja/g9X/UCr2SJJdLzgN7VPDGc41y3CNHjqiqqkpJSUk12pOSkpSdnd3g/TU4+K5Zs0aS1LNnTw0bNqzBB5SkoUOHqmfPnjX2BwAAAABoWspWfVU79B7jclVvbwYaHHx37dolSQ3u6T3Zsdcf2x8AAAAAoOkwTbN6Tu+pOB2NMjc8Pj5eAQEBOnToUI32Q4cOKTk5ucH7a3DwLSgokHT2k6Vbt24tScrPzz+r/QAAAAAAvM8wDCngNMtCBQQ2yppNQUFBGjBggD777DN3m8vl0meffaYhQ4Y0eH8NDr5VVVWSpMDAs7oFsAICAiRVFw8AAAAAaHpCzx0pebhzjyTJZlPo4PMa7dj33HOPXnzxRb322mvasmWLbr/9dpWUlOj6669v8L4anF6joqJ05MgR5eTkqEOHDg0+4DE5OTnu/QEAAAAAmp7oa6er/Idv5TywRzqx09JmU2Cbdoq+5vZGO/aVV16pnJwc/e53v1N2drb69u2rjz/+uNaCV/XR4B7ftLQ0SdJ3333X4IOd6NiiVsf2BwAAAABoWmxh4Up68hVFTLxCAUmpCohLUEBSqiImXqGkvzTOrYxOdMcdd2jv3r2qqKjQqlWrdO65557Rfhrc49u/f3+tW7dOy5cv1yWXXKL09PQGH3Tv3r1avny5e38AAAAAgKbJFhauVrf+n1rd+n8yTbNR5vQ2tgb3+I4YMUIRERGqqqrS7NmzG3wPpUOHDumJJ56Qy+VSRESERowY0dASAAAAAAB+0BxDr3QGwTcsLEzTpk2TVH1T4d/+9rdauHChysrKTvm6srIyffDBB7rvvvt05MgRSdK0adMUFhZ2BmUDAAAAAFA/Z7Q089ixY3Xw4EEtWrRI5eXlevPNNzVv3jx169ZNGRkZiomJUUhIiMrLy5Wfn689e/Zo8+bNqqysdO9jwoQJGjt2rNfeCAAAAAAAnpzxPYmuvfZaJSUl6Y033pDD4VBlZaV++OEH/fDDD6c+YGCgrrnmGo0fP/5MDw0AAAAAQL2d1c14x40bpwEDBuiDDz7QsmXLVFxcXOdzIyIiNHLkSE2YMEEJCQlnc1gAAAAAAOrtrIKvJMXHx+u6667Tddddp/3792vv3r0qLCxUeXm5QkJCFBUVpfT09DNa/RkAAAAAgLN11sH3RG3atFGbNm28uUsAAAAAAM5Kg1d1BgAAAACgOSH4AgAAAAAsjeALAAAAAGhyvvrqK11yySVKTU2VYRh69913z3hfBF8AAAAAQL2YpumzY5WUlKhPnz6aM2fOWe/Lq4tbAQAAAACspaTSqeeW7dZXO4/I6XIp0GbTyI7xun1Ee4UHNV6kvOiii3TRRRd5ZV8EXwAAAACARyWVTt3w5nfak1si1wnt//3+gL7de1T/unpAo4Zfb2n6FZ6FOXPmqKSkRPfdd1+N9k2bNmnWrFl65ZVXtGfPHs2aNcvj61944QXFxMRIkoqLizV//nytXr1aR48eVVRUlPr06aMrrrhC8fHxjf1WAAAAAMDnnlu2u1bolSSXKe3JK9Fzy3br/8Z09kttDWHp4NsQzzzzjMLCwmq0RUVFSaoOvQ8++KACAwN18803q02bNjp8+LDefvtt3X///XrssceUlJTkj7IBAAAAoNF8tfNIrdB7jMuUlu06QvBtTqKjoxUeHu5x23/+8x/l5eXpb3/7m7sHOD4+Xg8++KB+9atf6eWXX9YDDzzgw2oBAGgcK/cXaf6mXO3Lr1B6TLCm9ojT4DaR/i4LAOAHpmnK6aor9lZzVJkyTVOGYfioqjPDqs6n4XK59M0332jEiBHu0HtMUFCQLrzwQv3www8qLi72T4EAAHjJyv1F+uNXmdqRW66KKlM7csv1p68ytXJ/kb9LAwD4gWEYCrSdOjIG2owmH3qlFtDju3btWl1zzTU12lwerlrcdtttNR4nJCToqaeeUmFhoUpKStS6dWuP+09LS5NpmsrOzlbHjh1rbXc4HHI4HO7HhmEoNDT0TN4KAACNav6m3Fpt5k/t9PoCQMs0smO8/vv9Abk83MXIZlRvbyzFxcXauXOn+/GPP/6odevWKTY2Vunp6Q3al+WDb48ePXTzzTfXaNuxY4f+9re/1Wj7/e9/XyOQBgQE1Nh+pverWrBggebPn+9+nJGRodmzZ5/RvgAAaEz78is8tu8v8NwOALC+20e017d7j2pPXkmN8GszpHax4bp9RPtGO/aaNWs0evRo9+N77rlHkvTLX/5Sr776aoP2ZfngGxwcrOTk5Bptubm1r2gnJiZ6nOMbFRWl8PBwZWZmetz/gQMHZBhGrWMcM3nyZE2cONH9uDkMAwAAtEzpMcHakVteq71NdLAfqgEANAXhQYH619UD9Nyy3Vq264gcVabsAYZGdGj8+/iOGjXqjDsgT2b54Hu2bDabhgwZomXLlunKK6+sMc+3srJSixcvVp8+fRQREeHx9Xa7XXa73UfVAgBw5qb2iNOfvsrUiV8xDEmX94jzV0kAgCYgPChQ/zems/5vTOdmsZCVJwTfnxQUFNSYiytJERERCgwM1LRp07RhwwY9+uijuvrqq2vczqiqqko33nijn6oGAMB7BreJ1P8b2VrzN+Vqf0GF2kQH6/IecTqX+b0AgJ80x9ArEXzdfv3rX9dqe+yxx9S5c2dFRkbq8ccf1/z58/XCCy8oPz9fERER6tevn+68807FxzfehG4AAHxpcJtIFrICAFiOYXpr0DQaJCcnp1YPc2MwDEMpKSnKysry2vh44ExwLqKp4FxEU8G5iKaCc9GaUlNT/V1Ck8J9fAEAAAAAlkbwBQAAAABYGsEXAAAAAGBpBF8AAAAAgKURfAEAAAAAlkbwBQAAAABYGsEXAAAAAGBpBF8AAAAAgKURfAEAAAAAlkbwBQAAAABYGsEXAAAAAGBpBF8AAAAAgKURfAEAAAAAlkbwBQAAAABYGsEXAAAAAGBpBF8AAAAAgKURfAEAAAAAlkbwBQAAAABYGsEXAAAAAGBpBF8AAAAAgKURfAEAAAAAlkbwBQAAAABYGsEXAAAAAGBpBF8AAAAAgKURfAEAAAAAlkbwBQAAAABYGsEXAAAAAGBpBF8AAAAAgKURfAEAAAAAlkbwBQAAAABYGsEXAAAAAGBpBF8AAAAAgKURfAEAAAAAlkbwBQAAAABYGsEXAAAAAGBpgf4uAAAAAEDztC+/Qsv3FUqShqZHqW1MsJ8rAjwj+AIAAABosHc25+q173Pcj+duyNUv+yZoSo84P1YFeGbJ4Dtnzhx9+eWXGjt2rG655ZYa21566SUtXrxY5513nmbMmOF+7sn69OmjBx980P1427Zteuedd7R9+3ZVVlYqOTlZo0eP1oQJE2SzMWIcAAAALUd2UaXeWJdTq/2NH3I0ND1SyZFBfqgKqJslg68kxcXF6ZtvvtF1112noKDqX7zKykotX75c8fHxNZ7bt29fTZ8+vUZbYODxH83q1av19NNPa9SoUZo5c6bCwsK0YcMGvfnmm9q+fbvuvvtuGYbR+G8KAAAAaAK+zSyWy6zd7jKrt13SNdb3RQGnYNngm5GRoUOHDmnVqlUaMWKEpOoAGx8fr4SEhBrPDQwMVExMjMf9lJeX6/nnn9eAAQN06623utvHjBmj6OhoPfHEE1qxYoWGDh3aaO8FAAC0PGZFuWQYMoKYM4mmxx5Qd6dPoK3ldQgVlDtV7nQpKYKe7qbKssFXkkaPHq0vvvjCHXyXLl2qUaNGadOmTfXex/r161VUVKRJkybV2jZw4EClpKTo66+/rjP4OhwOORwO92PDMBQaGtrAdwIAAFoK89BBuea+IG36XrLZpD7nyjbtFhkx9KCh6RjSJlIvf3dYlVU1u33tNkND0yP9VJXv5Zc79feV2fruYHUPeFpUkG4ZlKQ+yeH+Lg0nsXTwHTlypP7zn/8oJ6d6/sHWrVt111131Qq+a9eu1TXXXFOjbfLkyZoyZYoOHjwoSWrdurXHY7Ru3VpZWVl11rBgwQLNnz/f/TgjI0OzZ88+o/cDAACszayokOvJh6SjR6obqqqktd/IdShTtt/9VQbriqCJiA4J1D1DU/XMiiyVO12SpJBAm349JEXRIZaOGDXM/ipTm3PK3I8PFFbqsS8O6O8TM+j9bWIsfVZGRUWpX79++uKLL2Sapvr376+oqKhaz+vRo4duvvnmGm0RERE1Hpumh0kM9TB58mRNnDjR/Zi5wAAAoC7mmq+Ph94TZe6VNq+Tevb3eU1AXYakR6pPSpi+yyyRJA1oHa4we4Cfq/KdH4+W1wi9x1RWmfp0V4F+0SfBw6vgL5YOvpJ0/vnn6+WXX5Yk3XjjjR6fExwcrOTkZI/bUlNTJUmZmZnq0qVLre2ZmZl19gZLkt1ul91ub2jZAACgJTpyqM5NZu5hcfkcTU2YPUAj2tXuWGoJckuddW7LK6t7G/zD8uNl+vbtK6fTqaqqKvXt27fBr+/du7ciIiK0cOHCWtvWrFmjrKwsDR8+3AuVAgCAls5o1+kU2zr6sBIAp9MpLkT2Ohby6pbAmj5NjeV7fG02m55++mn3//fE6XQqPz+/1uuioqIUEhKiW265Rc8884yef/55jR8/XqGhodq4caPeeOMNDR48WEOGDGnstwEAAFqCXgOkjt2knVtqtvcbLKMtwRdoSqJDAnVZj1jN3ZBbo71DbIhGttBe8KbM8sFXksLCwk65fd26dbrllltqtKWmpuqZZ56RJA0ePFgzZ87UO++8o9/97ndyOBxKSUnRlClTdPHFFzNvFwAAeIVhs8l21yMyFy+Q+f1KyRYg45wRMsbUvrsEAP+b1jtB7WJC9NnufJU6XOqfGqEJnWMUFGD5gbXNjmGe6apNOCs5OTk1bnPUWAzDUEpKirKyss54gS7AGzgX0VRwLqKp4FxEU8G5aE3H1ipCNS5FAAAAAAAsjeALAAAAALA0gi8AAAAAwNIIvgAAAAAASyP4AgAAAAAsjeALAAAAALA0gi8AAAAAwNIIvgAAAAAASyP4AgAAAAAsjeALAAAAALA0gi8AAAAAwNIIvgAAAAAASyP4AgAAAAAsjeALAAAAALA0gi8AAAAAwNIIvgAAAAAASyP4AgAAAAAsjeALAAAAALA0gi8AAAAAwNIIvgAAAAAASyP4AgAAAAAsjeALAAAAALA0gi8AAAAAwNIIvgAAAAAASyP4AgAAAAAsjeALAAAAALA0gi8AAAAAwNIIvgAAAAAASyP4AgAAAAAsjeALAAAAALA0gi8AAAAAwNIIvgAAAAAASyP4AgAAAKhTcUWVKpwuf5cBnJVAfxcAAAAAoOnZkVumF9cc1rYjZQq0SUPTo3TLwCRFBgf4u7R6K6yo0ofbj2rToVLFhARqXKcY9UwKq/frTdPUl3sK9fXeIkmmhqZH6bx2UQqwGY1XNBoFwRcAAABADUfLnJr5+X6VVFb39Dpd0ld7CnW0zKnHxqb7ubr6Kaqo0m8/2aODRQ5327K9hbpjcLLGdoip1z7+tjJbn+0ucD/+NrNEaw8W6/+Gt/Z2uWhkDHUGAABALXllTq3PLlFOieP0T4blfLarwB16T7ThUKl255X7oaKG+2j70RqhV5JMSa+vy5Gjyjzt6388Wl4j9B6zbG+Rth0p81aZ8JFm2+P7pz/9SVVVVXrwwQdrbduyZYtmzpypP//5z7r33nv1xBNPqF27drWe98UXX+gf//hHrXa73a5///vf7sdHjhzRvHnz9MMPP6iwsFCtWrXSoEGDNHXqVEVGRnr1fQEAAPhTlcvUC2sOacnOfFWZks2QRraL0h3npsgewPDOhjKzDkiGISO5efUQHj7FBY+cEofax4b4sJozsynHczgtKK9SZmGF2rU69XvYeKi0zm0bDpWqS3zoWdUH32q2wff888/Xk08+qdzcXMXFxdXYtnTpUnXo0EGhoac/GUNDQ/XXv/61zu2HDh3SQw89pJSUFN11111KTEzU/v379eabb2rdunV6/PHHFRERcdbvBwAAoCl4b2uePt6R737sMqUvfixUfJhd1/RN8F9hzYy5d6dcr/xVytxb3ZCWIduNv5aRluHfwuqpU1yIPtlZu91mqFmEXklqFeJ5LrLNkKJDTh+DYk7xnLr2jaar2Q51HjBggKKiovTFF1/UaC8vL9fKlSs1evToeu3HMAzFxMTU+u+Yl19+WYGBgXrooYfUvXt3xcfHq1+/fnr44YeVl5en//znP158VwAAAP61ZGftoZ2StGRXvk/r2JVXrpe+O6R/rMrWtweKZZqnH5raVJjlZXI988jx0CtJB36U65lHZDoq/VbXyUzT1NqDxXpj3WG9vXa/iiqq3NtGtotS25jgWq+5qFOMEsLtvizzjF3UuZU8rUE1pE2kWoVWh9riiiq9/v1h/eqDH3Xvx3v04fajcv10rp3bJkKxobXDb3RwgIamRzVq7fC+ZtvjGxAQoPPOO09ffPGFpkyZIsOoPqtXrFghl8ul4cOHq7i4+KyOUVxcrB9++EE///nPFRQUVGNbTEyMhg8frm+++UY33XST+/gAAADNWUllVYPaG8OibUf14ppDOhZ1P9mZr/PaRenuoSnN4juX+d03UnFh7Q0FR2V+v1LGOSN9X9RJnC5Tf/jygL47WFLdsDFX4XabZp7fRl3iQxUcaNPjY9P17pY8fXewWCGBNp3fPloXdIj2b+EN0CU+VL8ekqLXvs9RbplTNkMamh6pGecmS5Iqq1x64NN92ptf4X7N9txy7TlaoennJisowKZHzm+jZ745qN1Hq5/TNiZYdw1JUai92fYftljNNvhK0ujRo/X+++9r8+bN6tGjh6TqebvnnnuuwsLC6hV8S0tLdc0119Ro69atmx544AFlZWXJNE21bu15Tkbr1q1VUlKiwsJCRUd7/iPgcDjkcByfI2EYRr2GYAMAAPhD35RwfbmndmjrlxLuk+MXVlTp1e8P6+T+3S/3FOr89tHq66U6nC5TK/cXac/RCqVGBWlYeqSCA70UZgrzz2xbAxVVVOnz3QXKLq5U+1YhGtkuqt7vYcnO/OOh9yclDpfmrMzWsxOrh2NHBgfomr4JjTrEvcLp0sr9Rcovr1L3xFB1ivPu9+TzMqI1vG2UDhZVKio4oMYQ52V7CmuE3mOW7MrXZT1ilRQRpLYxwXp6QoayiirlMqXWUUG1no/moVkH39atW6tLly5aunSpevTooezsbPfCVvUVGhqq2bNn12g7uXf3bCxYsEDz5893P87IyKh1PAAAgKbiqt7xWn+oVEfLnO62yCCbrumb6JPjbzhUoso6Vtz97mCxV4JvYUWVHv50n/acEHrmbjiix8emN2gYb5XL1O6j5QqzB9QIREaXnrWC+/FtvRpUq5mTLRUVSGntZAQdH3q8r6BCD326TwXlx3vi392Spz9ckF6v+aurDnjuINpbUKGsokqlRDZ+wNubX6GZn++vca6N/Kln31bPnv312SV6e2OudueVKznCrsnd4zSyXc1hyAE2Q22iaw/b3lnH6tQus3pbUsTxn4Evfh5oXM06+ErVvb6vvPKKbrzxRi1dulRJSUnq3r17vV9vGIaSk5M9bktOTpZhGDpw4IDOOeecWtszMzMVHh6uqKi6x/hPnjxZEydOrHE8AACApio5Mkh/ndBOS3YWaG9+hVpHB+nCjjEe5zo2hjB73YsGhXlpeOnbG47UCL2SdKjYoVfWHtZ9I+q3+vLK/UX657eH3KGta3yofjMsVYkRdhntu8g49zyZq76s8Rpj2FgZbeq3uJVZXCjXy09LG7+rbgiLkDHlWtnOGy9Jevm7wzVCryQdKKzUfzfl6qYBSafdf6Cnya/12OZNz67IqhF6pep7BfdNDtOYetxnd/PhUj3y+X4du06y+2iFnlx+UJVVrnrdpzfxFBc5ksIJulbT7AenDx06VIZh6Ouvv9ZXX32l0aNHey1cRkZGqnfv3lq8eLEqK2suRJCfn6+vv/7affy62O12hYWFuf9jmDMAAGjqokMCNbVnnH4zPFU/7xXvs9ArSb2TwpQQVvt4AYY0KsM780tX7i/y2L7qQFG9FtHKLKzUn7/OrBHath4p0x+/OuB+bNxwt4wb75b6nCP1HSzjpt/I+OWd9a7R9eqzx0OvJJUWy3zzHzK3bZCjytQPWSUeX/dtHT25Jzu5V/SYbgmhPlm86lBxZZ09rsv3ef73Odn/NuXK0+CAeRtz6/X689tHKzKodhzqmRiqjnHNY+Vq1F+z7/ENCQnR0KFD9dZbb6msrEyjRo2q9ZyDBw/WaktLS5NUvZpdfn5+re1RUVGy2Wy64YYb9NBDD+nxxx/XlVdeqcTERB04cEBvvPGGYmNjNW3aNG+/JQAAgBYrwGbogfPS9KdlmTpUXL1OSniQTbcNSvbacNO6ejQDbUa9OlA+25Uvp6t2++6jFdqRW6ZOcaEybDYZg0dLg+t3p5ETmfm50vpvPW/7arFsnXrKHmB4HBIeUs85viPbRWlLTqk+2p7vHpadEmnXXUNSGlyvt9V3Ae99BbXn50rVvfcVTtdp5ztHhwTq0bHpemnNIW08XKZAm6Hh6ZG6aeDpe8zR/DT74CtV39P3888/V79+/RQbG1tr+zPPPFOr7bnnnpMklZWV6ZZbbqm1/YUXXlBMTIxSUlL0pz/9SfPmzdPTTz+t4uJixcTEaNCgQbr88su5hy8AAICXtY8N0T8ntdeWw2WqqHKpR2KY9xaeUnXo89QrOKJt/W5RU1BR9wrXJw8/PiMlxXWmP7O4QAE2QyPbRenTXbVvPTW6ff1vs3ProGRN6hqrzTll6tA6UelBFR5v/9MYkiKC1CE2RLs89PoOaxtZr320iQ7W4RJnrfbEcHu9z5eMViF6/IK2KnVUKdBmKCig2Q+IRR0sEXw7d+6sefPm1WpPTEz02H7MqFGjPPYQnywhIUEzZsw4mxIBAADQADbDUI+ksEbZ99QecdqVV15jVeMu8aG6rl/9FvDqkxzuMXQGBxjqmuCFaW3JaVJMrJSfV2uT0a2PJOmG/ok6XOLQ+uzS6nZVh95LutTuBDqVlMggpUYFKyUlzn1HE1/51eBkPfL5fh094WLB8LaRGl3PIe1Tusfp+6wSuU4qeWqPuAbXcqq55bAGSwRfAAAAoL6CA2363eg22pFbph+PVqh1ZFCDQvbQ9Egt3hmmDYdKa7T/ok+CIoLOPkAZAQEyrrhR5ktPSq4TxlS3bivjvIskSeFBAXp0TLp255Ur66fbGTW3lYfbtQrR8z/roBX7i3S0zKkeiWHqHF//Cwc9k8L0u9Ft9PaGI9p1wqrO57dvPvcahu8QfAEAANAidYo7s/vGBtoMzRydpqU/Fuq7g8UKs9t0fvto9Ury3r2ObYNGyExMlbnsE5kF+TK69JQxfKyMkJoBvX1siNrHNt+FmIIDbWe1aFm/lHCf3WMazRvBFwAAAGgge4BNF3aM0YUdYxrtGEbbDjLaTm+0/QMtCbO3AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFgawRcAAAAAYGkEXwAAAACApRF8AQAAAACWRvAFAAAAAFhaoL8LaCoKCwv19ttva+3atSooKFB4eLjatWunyy67TF27dpUkbdu2Te+88462b9+uyspKJScna/To0ZowYYJsNq4hAAAAAEBTRPD9yZNPPimn06kZM2YoKSlJBQUF2rBhg4qLiyVJq1ev1tNPP61Ro0Zp5syZCgsL04YNG/Tmm29q+/btuvvuu2UYhp/fBQAAALzF/H6lXB/+Vzq4T0pNl23C5TL6DfZ3WQDOAMFXUklJibZs2aJHHnlE3bt3lyQlJCSoY8eOkqTy8nI9//zzGjBggG699Vb368aMGaPo6Gg98cQTWrFihYYOHeqX+gEAAOBd5vcr5frHH4437Nkh13N/lO32+wm/QDPE+FxJISEhCgkJ0erVq+VwOGptX79+vYqKijRp0qRa2wYOHKiUlBR9/fXXHvftcDhUWlrq/q+srMzr9QMAAMC7XB/+t3ajacr10XzfFwPgrNHjKykgIEDTp0/X888/ryVLlqh9+/bq1q2bhg0bprZt2+rgwYOSpNatW3t8fevWrZWVleVx24IFCzR//vE/kBkZGZo9e7b33wQAAAC85+C+hrUDaNIIvj8ZPHiw+vfvr61bt2r79u1at26d3n//fd12223u55im2eD9Tp48WRMnTnQ/Zh4wAABAM5CaLu3Z4bkdQLPDUOcTBAUFqXfv3po6daoee+wxjRo1SvPmzVNqaqokKTMz0+PrMjMzlZKS4nGb3W5XWFiY+7/Q0NBGqx8AAADeYZtwuXRyh4VhyDZhqn8KAnBWCL6nkJaWpoqKCvXu3VsRERFauHBhreesWbNGWVlZGj58uB8qBAAAQGMw+g2W7fb7pYzOUnCIlNFZtun3y+jLwlZAc8RQZ0lFRUV66qmnNHr0aLVt21ahoaHatWuX3nvvPQ0cOFAhISG65ZZb9Mwzz+j555/X+PHjFRoaqo0bN+qNN97Q4MGDNWTIEH+/DQAAAHiR0W+wAljBGbAEgq+qV3Xu1KmTFi1apEOHDqmqqkpxcXEaM2aMpkyZIql6DvDMmTP1zjvv6He/+50cDodSUlI0ZcoUXXzxxczdBQAAAIAmyjDPZMUmnLWcnByPt07yNsMwlJKSoqysrDNanAvwFs5FNBWci2gqOBfRVHAuWtOxdYpQjTm+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gi+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gi+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gi+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gi+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gi+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gi+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gi+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gi+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gL9XUBjy8/P14IFC7R27Vrl5eUpLCxMycnJGjFihM477zwFBwdrxowZysnJqfXaq666Spdeeqn78RdffKFPPvlE+/fvl81mU0ZGhiZNmqQBAwb48B0BAAAAABrC0sH30KFDevjhhxUeHq5p06YpPT1ddrtd+/bt06effqrY2FgNHDhQknTFFVdo7NixNV4fEhLi/v+vv/66PvnkE1155ZW666675HQ6tWzZMj3xxBO6/vrrNX78eJ++NwAAAABA/Vg6+L700ksKCAjQH//4xxohNikpSYMGDZJpmu620NBQxcTEeNzP9u3b9cEHH+j666/XRRdd5G6fNm2aHA6HXnvtNQ0cOFDx8fGN9l4AAACA5mbl/iLN35SrffkVSo8J1tQecRrcJtLfZaEFsuwc36KiIq1fv17jxo2rEXpPZBhGvfa1fPlyhYSE6IILLqi1beLEiaqqqtKqVavOql4AAADASlbuL9Ifv8rUjtxyVVSZ2pFbrj99lamV+4v8XRpaIMv2+GZnZ8s0TaWmptZov/HGG1VZWSlJGjdunK6++mpJ0r///W/NnTu3xnMfeOABdevWTVlZWUpKSlJgYO0fV2xsrEJDQ5WVleWxDofDIYfD4X5sGIZCQ0PP6r0BAAAATd38Tbm12syf2un1ha9ZNvjW5Q9/+INM09Szzz4rp9Ppbp80aZJGjRpV47mxsbFnfbwFCxZo/vz57scZGRmaPXv2We8XAAAAaMr25Vd4bN9f4LkdaEyWDb7JyckyDEMHDx6s0Z6UlCRJCgoKqtEeGRmp5ORkj/tKSUnR1q1b5XQ6a/X65uXlqaysTCkpKR5fO3nyZE2cONH9uL7DqwEAAIDmLD0mWDtyy2u1t4kO9kM1aOksO8c3MjJSvXv31scff6zy8tq/cA0xbNgwlZeXa8mSJbW2LVy4UAEBATr33HM9vtZutyssLMz9H8OcAQAA0BJM7RGnk7t8DEmX94jzRzlo4SwbfKXq+bxVVVW6//779c033+jAgQM6ePCgvvrqK2VmZspmO/72y8rKlJ+fX+O/0tJSSVLnzp01YcIEvfnmm1q4cKGys7OVmZmpuXPn6sMPP9S1117Lis4AAADACQa3idT/G9laneJCFBJoqFNciO4f2VrnMr8XfmCYJ97Tx4KOHj2qBQsWaO3atcrNzZXdbldaWpoGDx6scePGKTg4WDNmzFBOTk6t144dO1a33HKL+/Hnn3+uxYsXa//+/bLZbGrfvr0uueQS972AGyInJ6fGoleNxTAMpaSkKCsrSxb/p0YTx7mIpoJzEU0F5yKaCs5Fazp5kd+WzvLBt6ki+KKl4VxEU8G5iKaCcxFNBeeiNRF8a7L0UGcAAAAAAAi+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gi+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gi+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gi+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gL9XUBLFRjo2x+9r48H1IVzEU0F5yKaCs5FNBWci7AywzRN099FAAAAAADQWBjqbHFlZWX67W9/q7KyMn+XghaOcxFNBecimgrORTQVnItoCQi+Fmeapn788UfRsQ9/41xEU8G5iKaCcxFNBeciWgKCLwAAAADA0gi+AAAAAABLI/hanN1u19SpU2W32/1dClo4zkU0FZyLaCo4F9FUcC6iJWBVZwAAAACApdHjCwAAAACwNIIvAAAAAMDSCL4AAAAAAEsL9HcBaFwff/yxFi5cqPz8fLVt21Y33HCDOnbs6O+yYFELFizQ6tWrlZmZqaCgIHXu3FlXX321UlNT3c8xTVPz5s3TZ599ppKSEnXt2lU33XSTUlJS/Fg5rO7dd9/VW2+9pQkTJui6666TxLkI38nLy9Obb76pdevWqaKiQsnJyZo+fbo6dOggiXMRvuFyuTRv3jwtW7ZM+fn5io2N1XnnnafLLrtMhmFI4lyEtdHja2HffPONXn/9dU2dOlWzZ89W27Zt9fjjj6ugoMDfpcGiNm/erHHjxunxxx/XQw89pKqqKj322GMqLy93P+e9997TRx99pJtvvll/+MMfFBwcrMcff1yVlZV+rBxWtnPnTi1ZskRt27at0c65CF8oLi7Www8/rMDAQD3wwAN6+umnde211yo8PNz9HM5F+MK7776rJUuW6MYbb9TTTz+tX/ziF3r//ff10UcfuZ/DuQgrI/ha2AcffKAxY8Zo9OjRSktL080336ygoCAtXbrU36XBoh588EGNGjVKbdq0Ubt27TRjxgwdOXJEu3fvllR9JfnDDz/UlClTNGjQILVt21Z33HGHjh49qm+//dbP1cOKysvL9be//U233nprjaDBuQhfee+99xQXF6fp06erY8eOSkxMVJ8+fZScnCyJcxG+s337dg0cOFD9+/dXYmKiBg8erN69e2vnzp2SOBdhfQRfi3I6ndq9e7d69erlbrPZbOrVq5e2b9/ux8rQkpSWlkqSIiIiJEmHDx9Wfn6+evfu7X5OWFiYOnbsyHmJRvHSSy+pX79+Nc45iXMRvrNmzRq1b99eTz31lG666Sbdd999+vTTT93bORfhK507d9bGjRt18OBBSdKePXu0bds29evXTxLnIqyPOb4WVVhYKJfLpZiYmBrtMTEx7j94QGNyuVx69dVX1aVLF6Wnp0uS8vPzJUnR0dE1nhsdHe3eBnjL8uXL9eOPP+qPf/xjrW2ci/CVw4cPa8mSJbr44os1efJk7dq1S6+88ooCAwM1atQozkX4zKWXXqqysjLdfffdstlscrlc+vnPf64RI0ZI4u8irI/gC6BRvPzyy9q/f79+//vf+7sUtEBHjhzRq6++qoceekhBQUH+LgctmMvlUocOHXTVVVdJkjIyMrRv3z4tWbJEo0aN8m9xaFFWrFihr7/+Wr/61a/Upk0b7dmzR6+++qpatWrFuYgWgeBrUVFRUbLZbLWu0OXn59fqBQa87eWXX9batWs1a9YsxcXFuduPnXsFBQVq1aqVu72goEDt2rXzcZWwst27d6ugoEC//e1v3W0ul0tbtmzRxx9/rGeeeUYS5yIaX6tWrZSWllajLS0tTatWrZLE30X4zptvvqmf/exnGjZsmCQpPT1dOTk5evfddzVq1CjORVgewdeiAgMD1b59e23cuFHnnHOOpOovfRs3btT48eP9XB2syjRN/etf/9Lq1av1yCOPKDExscb2xMRExcTEaMOGDe4P0dLSUu3cuVMXXnihHyqGVfXq1Ut/+ctfarQ999xzSk1N1c9+9jMlJSVxLsInunTpUmuK0cGDB5WQkCCJv4vwnYqKCtlsNZf3sdlsMk1TEucirI/ga2ETJ07UnDlz1L59e3Xs2FEffvihKioqGM6CRvPyyy/r66+/1n333afQ0FD3iIOwsDAFBQXJMAxNmDBB77zzjlJSUpSYmKi5c+eqVatWGjRokH+Lh6WEhoa655YfExwcrMjISHc75yJ84eKLL9bDDz+sd955R0OHDtXOnTv12Wef6ZZbbpEk/i7CZwYMGKB33nlH8fHxSktL0549e/TBBx9o9OjRkjgXYX2GeewyDyzp448/1vvvv6/8/Hy1a9dO119/vTp16uTvsmBRV1xxhcf26dOnuy+4mKapefPm6dNPP1Vpaam6du2qG2+8UampqT6sFC3RI488onbt2um6666TxLkI3/nuu+/01ltvKTs7W4mJibr44os1duxY93bORfhCWVmZ3n77ba1evVoFBQWKjY3VsGHDNHXqVAUGVveFcS7Cygi+AAAAAABL4z6+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gi+AAAAAABLI/gCAAAAACyN4AsAAAAAsDSCLwAAAADA0gi+AIBG98UXX+iKK67QFVdcocOHD9faPmfOHF1xxRWaMWOG32qA/8ybN8/9bwMAQGMI9HcBAICmb9OmTZo1a1a9nz99+nSNGjWq8QqykEceeUSbN2+WJBmGob/85S9q06ZNnc8/fPiw7rjjDknS1KlTCYsAANQDPb4AADRAY/ZOm6ap//73v17fLwAALR09vgCABrnwwgt14YUXnvI5cXFxDdrnjBkzGnWYc3OyatUq7dmzR+3atfN3KQAAWAbBFwDQIFFRUUpPT/d3GZYTGhoqh8Mhp9OpefPm6b777vN3SQAAWAZDnQEAaAIiIiJ0/vnnS5LWrFmjXbt2+bkiAACsgx5fAIDfzZkz5/+3d69BUVZhHMD/3GVxdWGBVpoEuYiSBJgZUrYTQWgEkZlOfSirmaypmLGayUIt8FIzS5epbGp0ym8p042VMFAJB0LEZcAIjMtyWxN0MWC5yMICfWD2jdXdBXSBhfn/ZphZ3nPe933eXWb02XPOc3DmzBn4+Pjg4MGDFvupVCrk5eWhsbERer0eUqkUa9asQVJSEiQSCV577TVotVrI5fIJp06PjIygoKAAhYWF+Oeff2AwGHDHHXcgJiYGiYmJcHNzM+mflZWFH374Qfhdq9WaLSyVlZU1xaf/36ZNm/D7779jaGgIWVlZePfdd6d8jcLCQnz11VcAgC+//BK+vr5m+40vkmWuGNmNn0lXVxeUSiXKy8tx7do1eHh4IDQ0FE8//bRJMa6rV68iJycHFy5cQEdHB0QiEcLDw7FlyxbIZLJJPUNfXx9+/fVXlJaWQqvVwtnZGQEBAXjkkUfw4IMPTnj+4OAgCgoKUFZWBo1Gg97eXnh4eMDf3x8PPPAA5HI5nJyczJ57499QY2MjTpw4gZqaGnR2dgoj8kRENLcw8SUiojnh8OHDyM/PNznW1taG48ePo6ioaEpJ4uDgIPbv34+qqiqT462trWhtbYVKpcKePXuwYMECm8Q+WV5eXoiPj0dubi4qKipQV1eH5cuXz2gM5jQ3N+PAgQPo6uoSjg0ODqK0tBQVFRVIS0vDihUr8NdffyEzMxP9/f1Cv+7ubhQXF6OiogIZGRlWK1YDY4nz3r17ceXKFeGYXq9HdXU1qqurcf78eaSmplpMXJubm6FQKKDVak2O63Q6VFVVoaqqCidPnsQ777wDiURiNZb8/Hx89913GB4ettqPiIjsHxNfIiKye9nZ2ULSK5VKkZKSgqCgIAwNDeHChQvIycnBJ598Ar1eP6nrffPNN6irq4NcLse6desgkUjQ0dEBpVKJuro6NDQ04KeffsKzzz4rnJOQkIDo6GgcPXoUKpUKnp6eSEtLs/mzpqSk4PTp09Dr9Th27Bh2795t83tMhV6vR2ZmJgwGA5555hmEhYXB0dERlZWV+Pnnn6HX6/HFF19g9+7dUCgUEIlE2LJlC0JCQjA8PIxz584hNzcXfX19+Prrr7F//36r9/v0009x9epVxMfHIzo6GiKRCC0tLcjOzkZbWxvOnj0LT09PbNu27aZz29vb8cEHH6C/vx/u7u5ISEhAcHAwvL290dPTA5VKhVOnTkGtVkOhUCA9PR3Ozub/K6RWq1FUVASpVIqkpCQEBQVheHgYf//9ty3eViIimmFMfImIaEp0Oh1aW1stti9evBiLFy+22f26urqEqaUymQz79u3DokWLhPaVK1ciKioK6enpMBgMk7pmbW0tXn/9dTz00EPCscDAQERFRWHnzp3QaDQ4ffo0tm7dKowsGp/Lw8MDAODs7DwtRb4kEgkSEhKgVCpRVVWFmpoahIWF2fw+k6XT6TA6OooDBw6YTFUOCQmBWCzGt99+C61Wi127dkEikWDv3r0mn8+KFSvg5OQEpVKJ+vp6NDU1YdmyZRbvp1arkZqaajKlOSgoCOvWrcOePXvQ0tKCEydOIDY29qb3/+DBg+jv78eyZcuQlpZmEgcARERE4N5778WHH36I+vp6FBYWIi4uzmwcly5dwtKlS5Geni585sbnISKiuYfFrYiIaEry8/Px9ttvW/zJy8uz6f0KCwsxNDQEANi2bdtNyQwAhIaGIiEhYdLXXLt2rUnSa+Ti4oINGzYAAHp6enDp0qVbjPr2JCcnC9Osjx07NisxjLd161az63NjY2Ph4uICYCxBfuGFF8x+PuO3v7p48aLVe61evdrsOl53d3ds374dwNh+xydPnjRpv3jxImprawGMrdM1FwcAREZGIjo6GgBw5swZq7G89NJLJkkvERHNXUx8iYjIrhnX4YrFYkRGRlrsJ5fLJ33N9evXW2wLDAwUXo9fZzqTFi1ahI0bNwIYS+j+/PPPWYkDABwcHBATE2O2zdXVFUuWLAEAeHh4ICIiwmw/X19fuLu7Axhbw2vNww8/bLEtODhYWCN84/pslUoFAPDz85twJH7lypUAgIaGBovrd6VSqdCPiIjmPk51JiKiKdm8ebPZasbTRaPRAAACAgLg6Gj5+9qlS5fC2dl5UtOd77zzTottCxcuFF4PDAxMIVLbSkpKQl5eHvr7+5GVlYV77rlnVuIQi8Um78mNjCOiMpkMDg4OFvuJRCJcv34d169ft3q/oKCgCds1Gg3a2tpgMBiENbqNjY0AgMuXL0/673N4eBi9vb1mp+b7+/tP6hpERDQ3cMSXiIjsWl9fHwBYnLpq5OjoaDVBG8/V1dVi2/jkbWRkZFLXmw4LFy5EYmIiAKCurg4VFRWzEseN2zrdyPh+TdTP+KXFRO/pROvDjZWYR0dH0dvbKxzv7u62ep4llgqicYozEdH8whFfIiIiO5WYmChURM7KykJUVNRshzTtrI0aW2NMqP39/fHGG29M+jwvLy+zx63NLiAiormHiS8REdk1Dw8PdHV1QafTWe03MjIijA7PFyKRCElJSTh69CjUajXOnz8/4RTc8Ynj6OioxX6T3fpppnV1dcHb29tqOzD2nONH+MViMYCx6enTUW2biIjmNn6dSUREds1YzKi5udnqNNnW1lah+vN0utURyVv12GOPCUmdcVsna4xFpABY/SKgra3t9oObBmq1elLtMpnMZA/egIAAAGPFs4zJMRERkRETXyIismurVq0CMLa9UGVlpcV+E21NYyvG7XtmIskGgAULFuCJJ54AALS0tKC0tNRqf19fX+G1tSSyuLjYNgHamLXPsaGhQSh2Fh4ebtK2Zs0aAGOj3Lm5udMXIBERzUlMfImIyK7J5XIh2Txy5IjZKc91dXU23z/YEk9PTwBjxZQmqlBsKwkJCULRp+zsbKt977rrLmEK8G+//WY2QS8pKZkwgZ4tKpUKJSUlNx0fGBjAoUOHAIyNusfHx5u0R0REIDg4GACgVCrNXmO81tZWYQskIiKa/7jGl4iI7JqXlxc2b96M77//Hu3t7di5cydSUlIQGBgIg8GAyspK5OTkwNPTE3q9HjqdblqnIy9fvhzA2MjioUOHsGHDBpOK0zKZzOb3dHNzw5NPPokjR46gp6fHal8nJyfExcXhl19+gUajQUZGBpKTk+Ht7Y3u7m6cPXsWhYWFCA0NRW1trc1jvV1BQUH4/PPPUVNTg+joaIhEIrS0tCA7OxuXL18GMPZFgLm1zqmpqXjvvffQ29uLzz77DEVFRYiJicGSJUvg6OiI7u5uNDU1oby8HPX19Xj88ceFkWIiIprfmPgSEZHdS0lJgVarxalTp9DR0YHDhw+btIvFYrz55pvIzMwE8P905OmwatUqhISEoL6+HsXFxTdNGZ7MOtxbERcXB6VSiX///XfCvk899RSqq6tRX1+P2tpaKBQKk/a7774bL774It56661pifV27NixAxkZGcjPz0d+fv5N7ffffz+ef/55s+fKZDLs27cPH3/8MTQaDcrLy1FeXm7xXiKRyGZxExGRfWPiS0REds/BwQEvv/wyVq9ejby8PKjVauj1ekilUkRFRSE5ORlSqVSYejydCY2joyN27dqF7OxslJeX48qVK9Dr9VYrKNuCq6srNm3adFPSb46bmxvef/995OTkoKSkBO3t7XBycoKfnx/kcjkeffRRdHR0TGu8t8rX1xcfffQRjh8/jrKyMnR0dMDJyQn+/v6Ii4vD+vXrrZ7v5+cHhUKBkpISnDt3Dmq1GjqdDiMjIxCLxfDz80NoaCjWrl2LwMDAGXoqIiKabQ6j0/0vNRER0Qy4du0aXn31VQDAK6+8gtjY2FmOiIiIiOwFi1sREdG88McffwivQ0JCZjESIiIisjdMfImIyO4NDAygs7PTYntTUxN+/PFHAEBgYKCw9y8RERERwDW+REQ0B+h0OuzYsQP33XcfIiMj4efnBxcXF3R2dqKyshIFBQUYHByEg4MDnnvuudkOl4iIiOwME18iIpoThoaGUFJSYnF/VmdnZ2zfvh1hYWEzHBkRERHZOxa3IiIiu2cwGFBWVobKykqhSm9vby/c3Nzg4+OD8PBwbNy4ET4+PrMdKhEREdkhJr5EREREREQ0r7G4FREREREREc1rTHyJiIiIiIhoXmPiS0RERERERPMaE18iIiIiIiKa1/4DS8und74VMcoAAAAASUVORK5CYII="
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  5: Visualize the relationship between Payload and Orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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\nsns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 1.5 ,height=6)\n\nplt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x7e788e8>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n\nHowever for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  6: Visualize the launch success yearly trend\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The function will help you get the year from the date:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# A function to Extract years from the date \nyear=[]\ndef Extract_year():\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year\nExtract_year()\ndf['Date'] = year\ndf.head()\n    ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 49,
      "outputs": [
        {
          "execution_count": 49,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber  Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010</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</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</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</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</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\nsns.lineplot(data=df, x=\"Date\", y=\"Class\")\nplt.xlabel(\"Year\")\nplt.ylabel(\"Success Rate\")\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x65a1258>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "you can observe that the sucess rate since 2013 kept increasing till 2020\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Features Engineering\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\nfeatures.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 56,
      "outputs": [
        {
          "execution_count": 56,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n\n    Legs LandingPad  Block  ReusedCount Serial  \n0  False        NaN    1.0            0  B0003  \n1  False        NaN    1.0            0  B0005  \n2  False        NaN    1.0            0  B0007  \n3  False        NaN    1.0            0  B1003  \n4  False        NaN    1.0            0  B1004  ",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  7: Create dummy variables to categorical columns\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Use get_dummies() function on the categorical columns\nfeatures_one_hot = pd.get_dummies(features, columns=['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])\nfeatures_one_hot.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 57,
      "outputs": [
        {
          "execution_count": 57,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n0             1  6104.959412        1     False   False  False    1.0   \n1             2   525.000000        1     False   False  False    1.0   \n2             3   677.000000        1     False   False  False    1.0   \n3             4   500.000000        1     False   False  False    1.0   \n4             5  3170.000000        1     False   False  False    1.0   \n\n   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n0            0            0          0  ...             0             0   \n1            0            0          0  ...             0             0   \n2            0            0          0  ...             0             0   \n3            0            0          0  ...             0             0   \n4            0            0          0  ...             0             0   \n\n   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0             0             0             0             0             0   \n1             0             0             0             0             0   \n2             0             0             0             0             0   \n3             0             0             0             0             0   \n4             0             0             0             0             0   \n\n   Serial_B1059  Serial_B1060  Serial_B1062  \n0             0             0             0  \n1             0             0             0  \n2             0             0             0  \n3             0             0             0  \n4             0             0             0  \n\n[5 rows x 80 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 80 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  8: Cast all numeric columns to `float64`\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# HINT: use astype function\nfeatures_one_hot.astype('float64')",
      "metadata": {
        "trusted": true
      },
      "execution_count": 58,
      "outputs": [
        {
          "execution_count": 58,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    FlightNumber   PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n0            1.0   6104.959412      1.0       0.0     0.0   0.0    1.0   \n1            2.0    525.000000      1.0       0.0     0.0   0.0    1.0   \n2            3.0    677.000000      1.0       0.0     0.0   0.0    1.0   \n3            4.0    500.000000      1.0       0.0     0.0   0.0    1.0   \n4            5.0   3170.000000      1.0       0.0     0.0   0.0    1.0   \n..           ...           ...      ...       ...     ...   ...    ...   \n85          86.0  15400.000000      2.0       1.0     1.0   1.0    5.0   \n86          87.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n87          88.0  15400.000000      6.0       1.0     1.0   1.0    5.0   \n88          89.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n89          90.0   3681.000000      1.0       1.0     0.0   1.0    5.0   \n\n    ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n0           0.0          0.0        0.0  ...           0.0           0.0   \n1           0.0          0.0        0.0  ...           0.0           0.0   \n2           0.0          0.0        0.0  ...           0.0           0.0   \n3           0.0          0.0        0.0  ...           0.0           0.0   \n4           0.0          0.0        0.0  ...           0.0           0.0   \n..          ...          ...        ...  ...           ...           ...   \n85          2.0          0.0        0.0  ...           0.0           0.0   \n86          2.0          0.0        0.0  ...           0.0           0.0   \n87          5.0          0.0        0.0  ...           0.0           0.0   \n88          2.0          0.0        0.0  ...           0.0           0.0   \n89          0.0          0.0        0.0  ...           0.0           0.0   \n\n    Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0            0.0           0.0           0.0           0.0           0.0   \n1            0.0           0.0           0.0           0.0           0.0   \n2            0.0           0.0           0.0           0.0           0.0   \n3            0.0           0.0           0.0           0.0           0.0   \n4            0.0           0.0           0.0           0.0           0.0   \n..           ...           ...           ...           ...           ...   \n85           0.0           0.0           0.0           0.0           0.0   \n86           0.0           0.0           0.0           0.0           1.0   \n87           0.0           1.0           0.0           0.0           0.0   \n88           0.0           0.0           0.0           0.0           0.0   \n89           0.0           0.0           0.0           0.0           0.0   \n\n    Serial_B1059  Serial_B1060  Serial_B1062  \n0            0.0           0.0           0.0  \n1            0.0           0.0           0.0  \n2            0.0           0.0           0.0  \n3            0.0           0.0           0.0  \n4            0.0           0.0           0.0  \n..           ...           ...           ...  \n85           0.0           1.0           0.0  \n86           0.0           0.0           0.0  \n87           0.0           0.0           0.0  \n88           0.0           1.0           0.0  \n89           0.0           0.0           1.0  \n\n[90 rows x 80 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>6104.959412</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>525.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>677.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>500.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3170.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>86.0</td>\n      <td>15400.000000</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>87.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>88.0</td>\n      <td>15400.000000</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90.0</td>\n      <td>3681.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows × 80 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Change Log\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "| Date (YYYY-MM-DD) | Version | Changed By      | Change Description      |\n| ----------------- | ------- | -------------   | ----------------------- |\n| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}