EDA_Data_Vizualization.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "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",
      "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
      },
      "outputs": [],
      "execution_count": 1
    },
    {
      "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
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-2-cde6ab162d36>:2: DeprecationWarning: \nPyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\nbut was not found to be installed on your system.\nIf this would cause problems for you,\nplease provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n        \n  import pandas as pd\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 2
    },
    {
      "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
      },
      "outputs": [
        {
          "execution_count": 3,
          "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>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3
    },
    {
      "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 also appears to be a factor; even with more massive payloads, the first stage often returns successfully.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 4)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ],
      "execution_count": 4
    },
    {
      "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\n",
      "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 = 3)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1558.88x500 with 1 Axes>",
            "image/png": 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GtHGRtenzusd/ldZ+Yt1bKiVZqv6o9PAoyY2vna7qva41VaiAp37dEqao64lqWK6QXu1QTWUKM9MhJ5jNJrWoWkwtqho7cxfZ5Px+aeOX6dvXjpPq9pUKV8j5mpzpwgHp3G7bx3bPtB1E57SzO63LKt3Jkmz9ftP+w5yvCcgnTBZLLv9mAZcVExOjwMBARUdHKyAgD114M8Ds7eEaOcv2P85/DntItVjD0yWduhyr4TN2affpKEnWjcY+fKyWHq5cNNO+/5yJ1vqjl1SogKc61CohP6/8++X7dGSc3vzjH60+fFGS1LRSUb3XpYbKFnat5TxOXIrVon8i5G42qUOtYJUumEe+fMdflXbPsG5wXaiCdF//zDePXf6e9SLS7YLKSv9abZ2eDCB3Obdb+rZZ+navAGnkwVy9vJIkhV2O048bT+rIhWuqXMxPAxuHZnk5RSA/S06xyM2cv2+KATK0/jNpqZ1ZzB3GWG9Ayk/ObJe+b2n7WPUuUq9pOVuPLcdWSj91tX2sTl+p28QcLQfIT/LvVS/AiQ6ft78x26HzVwkdXFTZwgX0x9AmOn7xmuISklW9RIDMWfxiVrNUoGqWcu0/1/ikZB29cE1F/LxUPMA7W8ZITE5R/0mbdepyXGrbmsMX1e+HzVo+8uFMN/TOSeWKFNBzzXPwbqX4q9aHf4ns24/jaoQ0ub105cSttg2fS4P+korauQs4LtL2HV1Rp6QdP0lNhmdPrQCyT4k6Uss3pBUfKHUjaXdvqds3uT5wkKQyhX31Zqfqzi4j11t56IImrzuh05FxqlkqUENbVFS1EtxIlJcROACZ8PSzf8wrHy6dW6Ku5F9Supp+Q3RV6Zhx32sXpTVjpMOLJI8CUp3HpQeHGT8zrnQD600V8THpj1VsZexYANIgdAAMsP1UpKZvDtOlawlqVL6Q+jUsq0Af+0vGVCxm/4eVShkcg2soXzTv/RnN3Bqm/y46pMjYBJlNUtvqwRrTs7YCsrL00V1YfuB8msDhpvAr17Vk33l1rlPS0PFyhYRY6e9R0t7fpOQEqXBFqe0HUpX2xo+1+r9pAwdJir0oLXlT6veb7T4X9ktJN2wfO7vD2PoA5Jxm/yfVeMy6bJ2Ht1Sti1TA9n4IyH/+2HVGL83clbra1snLcVpx8ILmPN9YVYMJHpBLpKRYL2geXWq9IFynj1SsmrOrQm5Wo5v15+bEtEvuyitQqtbZOTU5k9lN6vyZdc+E5Phb7ZXbSzV72O+XECdN7ShdOnyrbdlo6fw+qfv3xtbo5Se1fU/68yWl3mghWZeLrd7F2LEApJFrN5KOiIjQCy+8oPLly8vLy0shISHq3Lmzli9fnua8nTt3qmfPnipevLi8vb1VqVIlDRkyRIcPH073mu3atZObm5u2bt2a7tigQYNkMpnSPY4etW6Ys3v3bj366KMqVqyYvL29FRoaqt69e+vChQt238OJEyfUt29flSxZUt7e3ipdurS6dOmigwcPpp5jMpk0b948u69hsVj03XffqWHDhvLz81NQUJDq16+vCRMmKC4u/YW9O12+fFmlS5eWyWRSVFRUmmN79uxR06ZN5e3trZCQEI0ZMybT18uPZm8PV49vNmrOjjNac/iixiw6pB4TNyjmf+vV29K5TkmVtbFO6kMVi6hOSFA2Vgukt+n4Zb06Z68iYxMkSSkWadG+CL0228balw4Kv3L9no7lafOHS7t+tgYOknUjtpn9pXN7jB/r8BLb7UeXWr+Y2xIYIsnOnY9BZbM07Nmo67ryv79fAFxI4QrSg89L9QcTOCCVxWLR+KWH023vEZeQrImrjjmnKOBupaRIswZKM/pI2yZbl8WZ2Ni6hjtwr3wLSb1/kgrctryuX7D0+PT8OdNBkiq3lV7Ybp1B2eh5qc9M6/4+Gc1Y+Of3tIHDTXtnSZeOpm93VL1B0pDlUoOnpVq9pG7fSf1mSW7G3mCXq8RFShu+kBa8LG2bYr0RDjBYrpzpcPLkSTVp0kRBQUEaO3asatWqpcTERC1evFhDhw5NvWi/YMECde/eXe3atdP06dNVoUIFXbhwQbNmzdKbb76pmTNnpr5mWFiYNmzYoGHDhmny5Mlq0KBBunHbt2+vKVOmpGkrWrSoLl68qFatWqlTp05avHixgoKCdPLkSc2fP1+xsbb/x01MTFSbNm1UpUoVzZkzRyVKlFB4eLgWLlyY7uJ/Rp544gnNmTNHb7zxhr788ksVLVpUu3fv1oQJExQaGqquXbtm2P+pp55S7dq1debMmTTtMTExatu2rVq3bq1vvvlGe/fu1eDBgxUUFKRnnnkmy/XldQlJKfpo4YF0X8qOXLim6ZvC7C7N4u3hphnPNNLYRYe0ZP95ebmb1aVuKY1sWzkHqkZusut0lE5djlXNUoGqkE0zLKZvDrO5b+iifRG6fC1ehTPbPPku1M0gVMvoWJ519by0b2769pREadsk651DRvKy83fIo4BktnMfQsGyUrVO0oE/0/epNyjD4baejNSb8/7RwYirMpukVtWK66PHamW+ITcAwGmuxifppI1ZiZK0Nzw6h6sB7tGhv6QD89O2WVKkha9Y727OA0vJwUkqtpJe3i+FbZBMZqnMg/n74rUkBYVYZ1Bmla2NnSVJFun8XqlIRUPKSqNUPesD1g3Ap3aS4i7datv4pfTkQsmPTe1hnFwZOjz//PMymUzasmWLChS49cNCjRo1NHjwYElSXFycnnzySXXs2FFz5966oFOuXDk1bNgw3YX9KVOmqFOnTnruuefUqFEjffrpp/Lx8UlzjpeXl4KDg9PVs379ekVHR+uHH36Qu7t76jgtWrSw+x727dunY8eOafny5Spb1nqnaNmyZdWkSZMs/z789ttvmj59uubNm6cuXW5NCwsNDdWjjz6qmBgba9bdZuLEiYqKitJbb72lhQsXpjk2ffp0JSQkaPLkyfL09FSNGjW0a9cuffrpp3ZDh/j4eMXH35pSl9n4ecHRC9d06Zrtu3c3Hr+c4XrwJQJ99GnvutlUmXHCLsdp5aEL8vV0U7uawXe13E5CUoo83XPthCqnio5L1JBp27TlZGRqW9e6JfVJzzpydzP29/TytXib7ckpFl2JSzA0dKgfWkgtqxbTioNpZ4E1q1xUD1bIh3fZXj0nWZJtH4s+Y7vdEXX6WKcu36lun4z7dftW8i0s7Z4pJV2XSt4vtftQKlTObpdz0dc1aPIWxSZY31+KRVq6/7wuX4vXnOez/m8dACBnFfB0V+ECnrpsY4ZaGRszdQGXdGSp7fb4GClso1Sxdc7Wg7zF3VMq39zZVeRehcrf2zEYY/F/0gYOknW2/ZpPpI6scALj5LqrgZGRkVq0aJGGDh2aJnC4KSgoSJK0ePFiXbp0SaNGjbL5OjfPk6xTiKdMmaL+/furatWqqlixon7//fcs1xQcHKykpCTNnTtXFlu3C9tQtGhRmc1m/f7770pOtnPBKRPTp09XlSpV0gQON5lMJgUG2t+0dv/+/Xr33Xc1bdo0mW3c3bpx40Y1a9ZMnp6eqW3t2rXToUOHdOXKFZuv+dFHHykwMDD1ERIScg/vKncp7Odpd7/XIn6etg/kIp8tO6KHP1mp0fP36f9+36PGH63QuiOXMu03e3u4WnyySpXfWKiHx67UzK1h2Vvo1fPSnlnWpWOS7S9rlZu8u2B/msBBkubtOqsp608aPlaj8rYv9gcHeKtcEeNnV3zTv57+07Ga6pQOVO3SgXq1Q1V9PyCf3nVStIrkHWT7WEhD48d7cJhUp6/SLJdUqZ3U+u2M+3kWsM66ePWU9Mop6ZmVUtkHM+wya1t4auBwux1hUdwpCwAuzM1s0uCH0ofKJpP0lI12wCVltOGvZz5dBgdwFXUelwrYuKO+fHOpRJ0cLydfSU6Ujq2wfezwopytBXlergsdjh49KovFoqpVq2Z43pEjRyQp0/MkadmyZYqLi1O7du0kSf3799ekSZPSnbdgwQL5+fmlPnr27ClJatSokV5//XX17dtXRYoUUYcOHTR27FidP3/e7pilSpXS559/rrfeeksFCxZUy5Yt9d577+n48eOZ1nv7e6xSpUqWz78pPj5effr00dixY1WmTBmb50RERKh48eJp2m4+j4iIsNnntddeU3R0dOrj9OnTd11bblM8wFutqxVP124ySf0a2v69zS12nY7S+GVp1/O9Fp+kF2fsVHyS/aBswZ6zGjlrt05csi4tdupynF6ZvVezt4dnT6Hrxkvja0hznpZ+6Sl9Vid71sLPQUnJKfpzz1mbx+bsNP7u94EPhqp80bQhrtkkvdaxqtzMdlI1B3i6mzWkWXn9MewhzR/2kJ59uIK83N0MHydX8PCRmr+Wvj2ojHWNdaO5uUvdJkrDd0qP/yI9v8m6gXRWlxhw95J8grJ06rloO5tPyzoLAgCQMyJjE/TJ4kPqPnGDnpm2TasO2d9z7qbnm1fQqPZVUm+iKV+0gL7sc7+aViqaSU/ARdR5XDb3pCpcUQp5IMfLAXAbn4LSoL+sNz+ZzJKHr3XZ1l4/Zd43/pq0dZL010hp87fS9ajsrjZvMblZv4PaklFYC9yDXLe8UlZnEmT1PEmaPHmyevfunbo0Up8+ffR///d/OnbsmCpUuLU8TosWLTRx4sTU57fPtPjggw80YsQIrVixQps3b9Y333yjDz/8UGvWrFGtWrVsjjt06FANGDBAq1at0qZNmzRr1ix9+OGHmj9/vtq0aWPoe7zda6+9pmrVqql///731N8eLy8veXnlv3W6P+lZR/83a7eWHTivFItUxM9Lr3WoqnplCzm7NIcs2G37ovfl2ARtPHZZzavYXuvv29W2g7Pv1hxX93ql7Q+YkiJt+VbaOd067blSG+u6kP7plzRLFbZJWvZ22raYM9KsQdbNrOxNQ3FxyRaLEpNtb+obn3hvM6MyEujrobnPNdHPm09py4lIFfP3Ur9GZfPnHgvO0OhZ674J26ZIsRelck2tMxKyc1P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          },
          "metadata": {}
        }
      ],
      "execution_count": 11
    },
    {
      "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": "### TASK 2: Visualize the relationship between Payload Mass and Launch Site\n",
      "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(x=\"PayloadMass\", y=\"LaunchSite\", hue=\"Class\", data=df, aspect = 4)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.xlabel(\"Payload Mass (kg)\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2058.88x500 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "execution_count": 17
    },
    {
      "cell_type": "markdown",
      "source": "Now if you observe Payload Mass 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\ndfv = df.groupby('Orbit')[['Class']].mean()\n\nsns.set(style=\"whitegrid\")\nsns.barplot(x=\"Orbit\", y=\"Class\", data=dfv);",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 8
    },
    {
      "cell_type": "markdown",
      "source": "Analyze the plotted bar chart to identify which orbits have the highest success rates.\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 = 3)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.xlabel(\"Flight Number \",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1558.88x500 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "execution_count": 18
    },
    {
      "cell_type": "markdown",
      "source": "You can observe that in the LEO orbit, success seems to be related to the number of flights. Conversely, in the GTO orbit, there appears to be no relationship between flight number and success.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  5: Visualize the relationship between Payload Mass and Orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Similarly, we can plot the Payload Mass vs. Orbit scatter point charts to reveal the relationship between Payload Mass and Orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be Payload Mass and y axis to be the Orbit, and hue to be the class value\nsns.catplot(x=\"PayloadMass\", y=\"Orbit\", hue=\"Class\", data=df, aspect = 4)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.xlabel(\"Payload Mass (kg)\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2058.88x500 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 19
    },
    {
      "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, it's difficult to distinguish between successful and unsuccessful landings as both outcomes are present.\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
      },
      "outputs": [
        {
          "execution_count": 11,
          "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": {}
        }
      ],
      "execution_count": 11
    },
    {
      "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')",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 12,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='Date', ylabel='Class'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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7U1RV5vdDtJiFQ176Ymma2qPMnFxFRYHq8HT3p2jZHyfo8+Dz5u+5S6V1nn29FYALFk/G6Ywddp+aiJ9Vyxu5eOk0tu3tY8Pmdt7Z1UPL/hgt+2P8+YVdLDyhnqUnjWfGxIgsvR4gSZAQQuRBPKnS3HFgA3TYb/uj33ZTXemneyBFc/sAMyZV5X1Woy+Wprk9isftJOjPbxL23JstpBSdCbUhTp5VS3Pz4UnQQU6ng3mNNcxrrCGeVHlt635eeaeD/b1J/rZlP3/bsp/66gBLTxzP4nnjy76ukSRBQgiRY73RNHs7oiiaQW0kPyeJSlFN2E/PQIqmtgFmTq7KW2+ugyf4LKAymN+kIZ7SeP6NfQBcsrxxRD87FUEv55w6hbMXTaa5Pcormzt4c3snXX0pHnlpD4+9vIcTZ9Sy9MQJzG2sKcuTiZIECSFEjpiWxf7eJF1RHZfTQW248Bt7i5nTkfk37I6m2NsRpXFiJOcbk5PpTDVoVTWpieS/GOIzr+1F0QwmN1SwYGYtiqKM+BoOh4PGiREaJ0b4h7NnsnF7F6+8005zR4y3d/Xw9q4eIiEvi+ePZ+mJ46mrKp+fU0mChBAiB0zTontAI+mIUR2uyPsSSqlyOh3UVvrp7EvhdjmZMj6csxmMtKrT3B4lkVQLcjJtIKHw0lttALxveWNW9vH4vW6WnTSBZSdNoL0nwaubO/jblv0MJFSe/ttenv7bXmZNrmLZSeNZMKuu5PetSRIkhBA50BtN0xvTGTfBKwlQlrlcTqor/bR1J3C5nEyqr8h6rR5NN9nbEaMvplBXoBpOT7+6F003aZwQZm5jTdavP6E2xKUrZ/L+FdN5Z3cPG95pZ1tzHztb+9nZ2k/A5+a0uQ0sPXE8kxsqs/797UCSICGEyLJESqOtO4nH48zbvpVy43E7iYR87OuM43Y5mFCXvXo4hmHSsj9ziq827C/IHq7eaJr1b7cD8L4zsjMLdDRul5OFJ9Sz8IR6+qJpXn23gw2bO+iLZWaiXnqrjckNFSw9cTynzRlXUmUdSmckQghhA7ph0toZJ60ZBLxyAiyXfF4Xpulh7/44bpeL+uqxL1mZpkVrV5yOngQ1lYU7xffUq80YpsUJU6o4YUp13r5vddjPxcsauXDpNHbs7eOVdzp4e3c3rZ1xWjt38pcXdnPyCXUsO3ECMycX/1F7SYKEECKLOroT9A6kqK7wEestdDSlL+B3Y5iZPl5ul4PqMXRytyyL9p44bV0JIhU+3AVqPdHVn+LVzR1AZi9QITgdDuZMq2HOtBoSKY3Xtu5nwzsdtPckeH1rJ69v7aSuKsDS+eNZPH9cwYtYjpYkQUIIkSX9MYW2nkTmGLWlFzqcslER9DIQV2hqj+JyOUdd+6arL0VrZ5yKgAdfAZcxn3ilCdOC+dNraJwYKVgcB4UCHs5eNJmVCyexd3+MDe908Mb2Trr7Uzy6bg+Pr9/DvOm1LD1xPPMba4qqBpYkQUIIkQVpVae1M4YTB36fm3RakqB8ilT46B1I09yeqSE00s3ovdE0zR0x/B53QdtLdPQkeGNrJ5CpC2QnDoeDaePDTBsf5tKzZ/LW9i5e2dzOnrYom3f3sHl3D5VBL4vnZ/qW1Vfbp+nt0UgSJIQQY2SaFm1dCaJJlXobNPksV9VhH93RTHuNGZMi+L3D+4jLzCIN4HI6CBWoJcdBa19pwgJOnlXHFBufyPJ5XCw5cTxLThzP/t4kGza387ct+4klVZ55rYVnXmth5qQIS08czykn1Nv2gIAkQUIIMUbdAyk6e5NUV/iLfqNoMXM4HNQeqCrd3B5jxqQwnuPUuUmkMsUQDd2iOlzYfS2tnTHe2tGNA/vNAh3LuJogHzprJu8/Yzqb9/TyyjvtbG3uZde+AXbtG+B/ntvJqXMaWHrSBKY0VNjqNSJJkBBCjEE8pdHaGcPvdeEp0EZa8XdOh4OacICegRQul4PpE8JH3aOSVnSa2gdIKTq1Y9hQnS2Pr28C4NS5DUyoDRU2mFFwuZycPKuOk2fV0R9T+NuWDl55p4PeaJp1b7ez7u12JtaFWHrSBE6b21DocAFJgoQQYtQyx+FjqJpJnSyD2YbL6aCm0k9XbxKP23nEZSVVM2jqiBJNaNRGCj+D19Q2wLt7enE64OKl0woaSzZUVfq4cMk0zl88lV2t/bzyTgebdnbR1p3gT8/t5H9f3MUJkyMsnlnYZTJJgoQQYpQyx+HTBWmpII7N7XYSqfDR1hXH7XRQFfr7bJBuZI7U90bT1IXt0dD2sQOzQIvnF8eG4uFyOhycMKWaE6ZUk0jP4o2tnbzyTjtt3Qm2NPfjdwV5fwHjkyRICCFGoS+Wpq0nQTjoLcvu28XA63FRGfDS0hlHr8kcmzdNi9b9MTr7UtSE/VlvtzEaO1r62NHSj8vp4KISmAU6mpDfw1kLJ3HmKRNp7Yzz+pZ2ptWoBY1JkiAhhBihzHH4+OBx+FK2d3+MN3YlmDzZpPC7ZkbO73NjWBYt++NoSYP27gRdUZ2qSl/OO9APh2VZg3uBli+YQI0N9iblmsPhYMq4StxOk1hfW0FjKfxPgBBCFJGDx+HjSZVIxeiK8hWLvlia/3p0G6/tSLB2Q0uhwxm1kN+Dy+mgs1+jrSdJJOSzTXf0rc197GmL4nE5uWDJ1EKHU3YkCRJCiBHo7s8ch68q8ePwpmnx+7VbSasGAOvf7mBXa39hgxqDyqAXhwNCfjc+rz0SIMuyeGzdHgDOXDiRSKg4W08UM0mChBBimOIpjdauGH6fu+SPw//1tb3s2jeA1+1kWoMXC/jvp7ahaEahQxu1gM857AKK+fD2rh5aO+P4PC7OO21KocMpS6X9KhZCiCw59Dh8RYGrCudac0eUta80A/ChMxs59+QwVRVeegbSPPLS7gJHVxpM0+Lx9ZlZoJWLJlERLO2lVbuSJEgIIYah/cBx+OrK0t64mlZ1fvf4FkzTYuEJ9Zw6pw6v28lHzpkBwEtvtbGjpa/AURa/jdu76OhJEvC5OfdUmQUqFEmChBDiOPqiadq7E4RDpX8c/k/P7aR7IE11pY8rzj9hcN/TrMkRzlgwAYD7ntqOohbvslihGabF2leaADj3tMkE/PZZois3tkuCdu3axac//WkWLlzIihUruO2221DV49cRSKVS3H777Zx//vmccsopXHzxxfziF79A16WTsxBi9NKqTmtXHKfTYav9JLnw5rZOXn13Pw4HfPySuYd1Yv/gWTOoCfvpjab5X1kWG7XXtuynqz9FKJCpmyMKx1ZJ0MDAAFdffTWaprFmzRpuuukmHnjgAVavXn3cx/6///f/+MMf/sBnPvMZfvnLX3LZZZfx05/+lDVr1uQhciFEKRpyHD5U2ns2eqNpHvjrdgAuWDyVmZOqDruP3+vmqgtnA/Dypja27ZVlsZHSDZMnDswCXbB4Sskn1nZnq3/9++67j0QiwR133EFVVRUAhmFw6623cu211zJu3LgjPs40TR5//HE++9nP8vGPfxyAZcuWsWfPHh599FFuuummfA1BCFFCuvtT7C+X4/BPZI7DTxtfeczeVSdMqebMUyby0ltt3P/UNv7ln04v+YKR2bThnQ76YgrhkJczTp5Y6HDKnq1mgl544QWWL18+mAABrFq1CtM0efnll4/6OMuy0HWdysqhTfIqKyuxLCtX4QohSlg8qdLaGSNQBsfhn/7bXnbvG8DndfFPq+Ydtev6QR9YMYPaiJ++mMLDL8qy2HCpusFTr2ZO3V24ZKptCjaWM1u9snfv3s2MGTOG3BYOh6mvr2f37qO/0FwuFx/+8If53e9+x6ZNm0gkEqxbt46HH36Yf/qnf8p12EKIEqPpJi2dMVS99I/DN7VHB5dnPnLuCdQNoxmsz+viYxfOAeCVd9rZ2tybyxBLxrpNbQwkVKorfSw7cUKhwxHYbDksGo0SDocPuz0SiTAwMHDMx37rW9/iW9/6FldcccXgbddeey2f/vSnRx2PZVkkk8lRP/5oUqnUkD+LlYzDXmQc2WFZmX1AHV0JasJ+0un0qK6jKMqQP+0orer85rF3MS04ZVYtJzWGDxvv0cYxqc7PGQvGs+7tDu57chs3XrnA1stihX4+FM3g6Vf3AnDeaRPRdZXRnNsp9Diy6eChp2y/1i3LGvbytX1/Ykfo3//933nuuef47ne/S2NjIxs3buTOO+8kHA7zuc99blTX1DSNLVu2ZDnSv2tqasrZtfNJxmEvMo6xiaUM2npVfB4H8f6xT5a3tRe2QeSxPLspSl9MocLvZNE0B03NTUe975HGMXe8xTu7XAwkVO57cjNnLzj8l1i7KdTz8eauBIm0TjjootobZ09TYkzXs/PP1XDFUgZVIVdOXute7/AOMtgqCQqHw8RiscNuHxgYIBKJHPVx27dv5z//8z/5+c9/znnnnQfA4sWL0XWdn/zkJ1x11VVUVFSMOB6Px8OsWbNG/LjjSaVSNDU10djYSCBw/Klnu5Jx2IuMY+zSqsGufQNMC5ljbo6qKApt7W1MnDARn89+PaE27uhmR1tn5jj8xXNpnFB5xPsdbxwfC9Zz18Pvsm1fmmUnT2PutKocRz46hXw+UorO289sBGDV8kZmzqgb9bXs/nM1Evt7YiiJ7qy/1nfu3Dns+9oqCZoxY8Zhe39isRhdXV2H7RU61MEBz5s3b8jt8+fPR1VV9u/fP6okyOFwEAwGR/y44QoEAjm9fr7IOOxFxjE6pmnR1juAZjhpqA1l7TSYz+fD77dXlenegTQPv9gEwEVLpjF3ev1xH3O0ccyd7ufsUyfz3But/PmFPfzLJ04/rL6QnRTi+Xj2zT2kVYPxtUGWnDgJZxYKbtrx52qkvF4FJZH91/pIXru22hi9cuVK1q1bRzQaHbxt7dq1OJ1OVqxYcdTHTZqUKTa1efPmIbe/8847OBwOJk6UY4hCiGPr6k/R1ZeiprK0j8MbpsVv124hrRo0Tghz4TGOww/XqjMaqa8OMJBQ+fPzu7IQZemIpzSef2MfAKuWT89KAiSyx1ZJ0FVXXUUoFOL666/npZde4qGHHuK2227jqquuGlIj6JZbbmH+/PmDfz/ppJM46aST+Na3vsX999/P+vXr+eUvf8ldd93FRz7ykaJeGhBC5F48qbKvM0bQ58Zd4sfhn3q1mab2KH6vi3+6ZG5W2oB43S7+8cI5OBzwty37eWd3dxYiLQ3PvLYXRTOY3FDBgpm1hQ5HvIetlsMikQj33nsv3/nOd7j++usJhUJcfvnlhxU7NE0Tw/h73xqXy8UvfvELfvKTn/DLX/6Snp4exo8fz+c+9zmuueaafA9DCFFENN2kZX/mOHxdpLj3WBzPnrYBntyQqVNz+XknUDuM4/DD1TgxwjmnTubZ11t54OkdTP9khJCNl8XyYSCh8NJbmQ3M71veWNIzjMXKVkkQwMyZM7nnnnuOeZ/Vq1cf1kqjvr6e7373uzmMTAhRaizLor07QV9MGVZ9nGKWUnR+t3YrlgWnzW3gtLlHrsA/FquWT2fz7l46+5L86bmd/NMl847/oBL29Kt70XSTxglh5jbWFDoccQSlPe8rhBDH0BdTaO9JEAn5Sn6vxkPP7qA3mqYm7Ofyc0/IyffwuJ3848WZZbHXt3by9s7yXRbrjaZZ/3Y7AO87Q2aB7EqSICFEWUorOvs6Y7idDnze0m5f8NqW/by+tROnAz5xydycFjWcNj7MeadNAeCBZ7YTT2k5+1529tSrzRimxQlTqjhhSnWhwxFHIUmQEKLsGKZFa1eceEojXOLd4bsHUvzx2R0AXLR0Go0Tj15zLVsuWdbI+Nog8aTG/xz43uWkqz/Fq5s7gExdIGFfkgQJIcpOV1+Srr4k1SXeHd4wLX6/diuKajB9YpgLloz9OPxwuN1OPnbRXJwOeHN7Fxt3dOXl+9rFE680YVowr7GG6XlIOsXoSRIkhCgrsaTKvq44QZ+n5I/DP7nh0OPw87JyHH64po6r5PzFUwF46JkdxJNq3r53IXX0JHhjayeQqZ8k7K203wGEEOIQmm7Suj+GbpiESrw7/K59/Tz1auY4/BXnz6YmnP/qwhctncbEuhDxlMYfnx1+K4NitvaVJizg5Fl1TGk4cisSYR+SBAkhysKhx+GrK4q73cDxpNI6vz9wHP70eeM4dU5DQeJwu5x87KI5OJ0O3trRxZvbOwsSR760dsZ4a0c3DuAS2QtUFCQJEkKUhXI5Dm9ZFg8+s52+mEJtxM9Hzs1+E+iRmNxQyYWHLIvFEqW7LPb4+iYATp3bwITaUGGDEcMiSZAQouSlFZ3WMjoO/+b2rgPH4efh9xa+Ju4FS6Yyqb6CRFrnwWd2YFlWoUPKuqa2Ad7d04vTARdnoR+byA9JgoQQJe3gcfhEGRyH7+pP8dCBvTeXLG9k2oRwgSPKOLgs5nI6eHtXN29sK71lsccOzAItnj+e+ursdUQXuSVJkBCipHX2JsrjOLxh8rvHt6BoBjMnRTj/9KmFDmmISfUVXHRghuR/nt3JQEIpcETZs6Oljx0t/bicjsExiuIgSZAQomTFkipt3QmC/tI/Dv/Ehmb27o8R8Ln5+MVzbbnv6fzTpzC5oYKkovPgX0tjWcyyrMG9QMsXTCjIKTwxeqX9riCEKFuabvz9OHyJdzPf1drP06/uBeDK82dTbdMPYpfLyT9eNBeX08Hm3T28trX4l8W2Nvexpy2Kx+XkgiX2mn0TxydJkBCi5FiWRVuZHIdPpjV+98RWLGDJ/PEsnF1f6JCOaUJdiEuWNQLwp+d2MhAv3mUxy7J4bN0eAM5cOJFIyFfgiMRISRIkhCg5vdE0Hd3lcRz+gb/uoD+mUFcV4MPnFPY4/HCde/oUpo6rJKXoPPDX7UW7LPb2rh5aO+P4PK7BprGiuEgSJIQoKSlFp7UzjtvtLPnj8K++u5+3dnThdDr4xCVzi2a8Lqcjc1rM5eDdPb387d39hQ5pxEzT4vH1mVmglYsmUREs7ZOHpUqSICFEyTBMi32dMZJpnXCJfyh19SX5n+cyHdpXLW9k6nh7HIcfrvG1ocEO6396fif9seJaFtu4vYuOniQBn5tzT5VZoGIlSZAQomR09ibo7EtRXekr6ePwumHy27VbUTWTWZMjRbsUc+6pU5g2vpK0anD/09uKZlnMMC3WvtIEwLmnTSbgL3xBSjE6kgQJIUpCNKHS1pUgFPDgdpX2W9va9U207I8R9Ln5+MXzinbfk9Pp4B8vmovH5WRrcx8bNncUOqRheW3Lfrr6U4QCHs5aOKnQ4YgxKO13CiFEWdB0g32dMQzTKvnj8Dta+njmtRYArrxgNlWVxX0iqaEmyKozGgH48wu76IumCxvQceiGyRMHZoHOP32KLdqSiNGTJEgIUdQOPQ5fVVHcCcHxJNIavz9wHH7ZSeM55QR7H4cfrrMXTaZxQhhFNbjP5stiG97poC+mEA55WXHKxEKHI8ZIkiAhRFHrjabp6EkQqSiD4/BPb2cgrlJfHeAfzi6O4/DD4TxwWszjdrJ9bz/r324vdEhHpOoGT73aDMCFS6bidRfHaTxxdJIECSGK1uBxeJcTn6e0P5A2bO5g085uXE4Hn7hkXsmNt6E6yPtXTAfgLy/upnfAfsti6za1MZBQqa70sezECYUOR2SBJEFCiKJUTsfhO3uT/Om5THf4VWc0MmVcZYEjyo2zFk5ixqQIipZZFjNttCymqAZP/y2zF+viZdNKvhdduZBnUQhRlMrrOPwWVN3khClVnFukx+GHw+lwcNWFc/C6nexo6WfdprZChzTohY2tJFIadVUBTp83vtDhiCyRJEgIUXQOHoevKIPj8I+va6K1M07Q7+YfL56Ls4QTPoD6qgAfOHMGAP/74m66B1IFjghSaZ1nX28F4JJl03CV8N6zclPa7x5CiJKj6QatnTEMwyJY4sfht+/t49nXM0swV10wp+RPvx204pSJzJocQdVN7nuy8Mtiz73ZQkrRGV8bZNHshoLGAhBLGXQPpOjqT9Ldn6IvmiaaUEmkNNKqjqabmKZ9lhLtTAocCCGKxsHj8P0xhbpIoNDh5FQipfGHA8fhl580gQWz6godUt4cXBa77XevsWvfAC9t3MfKRZMLEks8pfH8G/uATHuSQp9A1HQTy4LGCWGCgQCabqBoJopmoGkGmm6SNg0Mw8S0LBw4AAunw4nT5cDtdOByOnG5HLicDpxOR0kvJx+PJEFCiKJRTsfh7396GwMJlYbqIJeePbPQIeVdbSTAh86cyR+f3cEjL+9h3vRa6qvyn/g+89peFM1gckMFC2YWPhGNJzUqAy7qq/yEQsEhXzNNC8M00XQTw7TQDRNdz9ymqDqKbqKqBrphoqoWhmFhWpmkygHgyCRGhyZJbmcmeSrVZVhJgoQQRSGZ1mjtjONxuUruePh7rX+nnbd39WSOw6+aW/LjPZrlJ0/grZ1d7Gjp57+f3MoNly/Ma/I7kFB46a3M5uz3LW8s+IyJrptYWERCriPG4nQ6cDpdeI5Tv0g3zAP/WRiH/L+uG6RVA1UzUHUTVTeGzCpxYFbJ5XQeSJYcuFzOIX8WG0mChBC2Z5gWbV1xkmmt5JfB9vcm+fPzuwB4/4rpTG4ozePwwzG4LPbb19jTFuWFjfs459T8LYs9/epeNN2kcUKYuY01efu+RxNLqkQqvKjW2Lbzul3O4x4oMA/OJA1JljK3qZp+IFkyMUwTVbMwTAvTNMnsRHLgcID7QLKUWYY7mCzZa/lNkiAhhO0dPA5fE/bb6g0023Td5LePb0HTTWZPrebsPH7g21VN2M+lK2fywF+389jLe5jfWENDTfD4Dxyj3mh6sHL1+84o/CyQYZgYlkl9VYD2eO5jcTodeJ0uvMeYhbSsTPKj6yb6gT8NM5MsqVpmRimt6uiGhaLpGCaY5t9nldKakfNxHI8kQUIIWxuIK2VzHP7RdXvY1xUn5HfzjxfNKdl9GCO17KTxvLWji217+/jvp7bxxStyvyz21KvNGKbFCVOqOGFKdU6/13DEkhqRkI9wyItdmoo4HA7cLsdxX5eG+Z5ltwP/H094aGnuzlO0R1ba7yhCiKI2eBzeLP3j8Nuae3nujUwtmqsunEOkTI7DD4fD4eCjF87G73XR1B7l+Tdbc/r9uvpTvLq5A8icCCs0w7TQDJOGmmBRHghwOR14PS6Cfg/hkJeasJ+G6iDja4JU+Au7302SICGELVmWxb7OOANxlarK0k4I4imNPzy5DYAVJ0/kJBucQrKb6ko//3DglNxj6/awvzeRs+/1xCtNmBbMa6xh+sRIzr7PcMWTKuGQp2zqROWTJEFCCFvqiyns70tmjsOX8LKQZVnc/9Q2ogmVcTVBPnTWjEKHZFtL5o9nXmMNumHxhye2YeSgIGBHT4I3tnYCmT5thWaaFopmMK4mhKvEl4MLQf5FhRC2o2gm+7oSZXEcft2mdt7Z3YPL5eATq+YdcyNquXM4HFx5QWZZbO/+2GA17Wxa+0oTFnDyrDqm2OBkXjylURnylvxsaKHIxmghSoRhmPRENfZ1xvEFCn/qYrSUVIquAZ2IW6cmUlHocHKqoyfBwy9kjsN/cMUMJtXbc7zxtIGiGvj9hY4Eqip8XHbOLP77yW2sfaWJE2fUMqE2lJVrt3bGeGtHNw7gEhvsBTIti7SqM7mhquQPBRSKJEFClIikotMV1fH3JPH5ijgJUhQSikFjRYl3hz94HN4wmTutmrMWTSp0SEd0sE1DNKkSrgza4jlZPG8cb+3o4t09vfzhia3c+NFFWVkqenx9EwCnzm3IWmI1FomURkXQQ3VYZoFyRZIgIUqEohpYVqauit8Ov7KPUjrtYKDXVfL7Hx55eTdt3Zmj/x+7yL7d4ZNpnYDXSdDvJpHWqQgU/pSew+HgyvNnc9tvX6O1M84zr7dw4ZJpY7pmU9sA7+7pxemAi5eO7VrZYFkWKUVnxqTIcStAi9Er7XcZIcpIPKVRhKdny9KWpl6efzPTlPOqC+cQDnkLHNHRqbpBOOiioTpAMq0VvKP7QZEKHx8+dxYAT7zSTFtXfEzXe+zALNDi+eOpr859McbjSaR1gn431eHi/YWmGEgSJEQJMEyLRErH45YsyO5iSZX/fnIrAGeeMpETZ9QWOKKjU1QDr8dJwOukJuynMuQlntQKHdagU+c0cNKMWgzT4g9PbsMwzFFdZ0dLHzta+nE5HVxkk1mgZFqjoTpY8gcDCk2SICFKgKLqqJqB2yVJkJ1ZlsV9T20jltSYUBvigzY/Dp9MZ6oU+zxOPG4n42tCpFU9J0fTR8PhcHDF+bMJ+t3s64rz9N/2jvgalmUN7gVavmACNTaYeUkqOgGfm5pI4WMpdZIECVEC0qqBbliSBNncy5vaeHdPL26Xg39aNRevjfd6WFamSnFV5d+X6qrDPqoqfcQSagEjGyoc8vKRc08A4MlX97Kvc2TLYlub+9jTFsXjcnLB4qm5CHHEEmmN+qoAfq9s2801SYKEKAEpRcMev5uLo2nvPuQ4/JkzmFhnz+PwB6UVg4DPPWQjtNvlZFxNEN000Ee59JQLi2bXc/KsOkzT4g9Pbh12bJZl8di6PQCcuXCiLVqVpNI6fo+L2qpAoUMpC5IECVECYgkNr1teznalHTgOrxsW8xprOGuhPY/DHyqhaFRX+g4r3lhV6ae6MkA0bp/ZIIfDweXnnUAo4KGtO8FTrzYP63Fv7+qhtTOOz+PivNOm5DjK4YmnVeqqggR8MguUD/KuKUSRUzWDpKLh9cjL2a7+96XdtPckqAh6+NhFc2xRa+dYTNMCyyJyhCrFLqeDcTVBLEcmubOLyqCXyw8siz396l5aOmPHvL9pWjy+PjMLtHLRJCqChT+hl1Z1vG4XtbIXKG/kXVOIIpdWDTTNtPX+knK2bW8/L27MHIf/xwvnUGmDD9vjSSo6Ab/nqLFGKrzURvwMJJQ8R3ZsC2fXs/CEekwL/vuJbejHSNI2bu+ioydJwOfm3FPtMQsUS6rURvyEbFCLqVxIEiREkUsrOqYFTikSZDtJxeSPz2b2AZ21cBLzptv3OPyhUopGTdh/1FYNDkdmNsjldKKo9qpO/pHzTqAi6KG9J8ETG468LGaYFmtfaQLg3NMmE/AXfulJ0Qw8bhd1shcoryQJEqLIxVMaLkmAbMeyLJ5/O0o8pTOhLsQHz7T3cfiDdMPE6XAcd5NwZdBLfZWfaNI+e4MAKgIerjgvsyz219f2srcjeth9Xtuyn67+FKGAxzb7s6JJlZqw3xbLcuVEkiAhiphhWsSTqhRUs6H17+ynpVvF7XLwiUvm4SmSjeuptE5F0DOsJZmGmiBej5OUouchsuE7eVY9p85pwLLgD09uG7J3STdMnjgwC3T+6VNscQxd1Q1cTofMAhVAcbwqhRBHpKg6imYedoJHFFY8pfHEqy0AvG/5VCbUFb4Z53ClNJ2asH9Ys4tBv4eG6iCxlIplk3YaB334nFlUBj3s700OLn0BvLa1i76YQjjkZcUpEwsX4CGiCZWaSh+VQdkLlG+SBAlRxDJFEo2imWUoF8++1oKqmdSG3Sw7cVyhwxk2VTfwuFxUhoZfL6e+KkDQ5yaZttdsUCjg4YrzZwPw7Ost7N0fQzcsnn09s0n9wiVTbXGYQNNNHEB9ddD2pwZLkbxzClHEkikN5I3TVmIJlZfeynzQnj4rVFQfbMm0TmXQQ2gEG4X9PjfjaoIkbNRc9aAFM+s4fd44LAv++OxuNjUliSYz9Y+WnTih0OEBmZ+X6rC/KE4NliLbJUG7du3i05/+NAsXLmTFihXcdtttqOrwNt5Fo1G++93vcuaZZ7JgwQIuuOAC/vM//zPHEQtROLGkKkUSbebp1/ai6iZTGkJMrS+uDzZVN6iJ+EecuNVGAlQGPSRs1Fz1oMvOnkk45KWrP81rOxIAXLxsGm4bvG50w8TEor46IKc7C6TwO8IOMTAwwNVXX01jYyNr1qxh//79rF69mnQ6zTe/+c1jPjaZTPKJT3wCl8vFLbfcQm1tLU1NTcTjI+sjI0SxUDSDlKrLpmgb6Y8rrNvUBsAFiyfjMPoKHNHwpQ/8LI1mRsLrcTGuNsSu1n5CpsdWH+hBv4crz5/Nr/7yDgC1YR+nzxtf4KgyYkmVqkofkREsP4rsslUSdN9995FIJLjjjjuoqqoCwDAMbr31Vq699lrGjTv62vpdd91FIpHgL3/5C8FgEIClS5fmI2whCiKt6KiqSYW/uGYbStnTr+5FNyymTwxzwuQITc3FkwQl0zo1Yd+o2zXUhP10hbxEkypVNujBdagTZ9SydH4DG97tZNXyqbYoKWEYJoZh0VAdtFXSWG4KPx94iBdeeIHly5cPJkAAq1atwjRNXn755WM+9o9//CMf+chHBhMgIUqdohqYWPIGahO90TSvvNMOwPuWTy+qvUCmZaEbJtXh0bdrcLucjK8Noesmho2aqx506VmNfPK8Ok6cXlPoUACIpTQiFV5bNG0tZ7ZKgnbv3s2MGUMLioXDYerr69m9e/dRH9fa2kpXVxfV1dV8/vOf56STTmLJkiV8/etfJ5FI5DpsIQoiltTwOG31Ei5rT73ajGFanDClillTqgodzogoBzrGj3VzblWln6qwj2jCXgUUIVPl2u+1x+vFNC003aChJmiLWalyZqvlsGg0SjgcPuz2SCTCwMDAUR/X3d0NwA9/+EMuuugi7r77bpqamrj99ttJJpP86Ec/GlU8lmWRTCZH9dhjSaVSQ/4sVjKOwjEMk56+GBYW6XTmNkVRhvxZrIpxHD0DaV7d3AHAeadOJJ1OF9U4eqNpxlUH0DUF/T17m0f6+ggHHHR2p3E7TVuVbrDT8xFNqPi8LjxOY8SfMcX4fnU0uRqLZVnDnom1VRI0WqaZmXqdPn06P/zhDwFYvnw5brebr3/969x0001MmTLyBnmaprFly5asxnqopqamnF07n2Qc+ZdWTfZ2KQR8zsN+k2xrbytQVNlVTON4blMU04LJdV4spZs9Td2DX7P7OEzLIpY0IeUl0Xf0TfbDfX2YlsVAr0ZLq0E4aL9N+4V+PizLIpo0mFjjZUeqY9TXKab3q+PJxVi83uHNatoqCQqHw8RiscNuHxgYIBKJHPVxB7/23o3Qy5YtA2DHjh2jSoI8Hg+zZs0a8eOOJ5VK0dTURGNjI4FA8ZZJl3EUTm80jeYeGFJmX1EU2trbmDhhIj5f8e4zKLZxdPWn2NHeCcCHzjqBKeMqgOIZRyKlMcHhYG5j1REbpo7m9TE1qbKjZYCgz22bauZ2eT5iSY2Jbgezp1SN6ph+Mb5fHU2uxrJz585h39dWSdCMGTMO2/sTi8Xo6uo6bK/QoaZMmXLMrG+0058OhyOnG60DgUBJbOSWceRfb8zAF/Dj9x++kdXn8x3x9mJTLON47s09WFbmBNIJ0+oO+7rdxxFXLCbVVxCurDjm/Uby+ggEAiRVBx09CcKV9hp7IZ8P07KwUiZTJ1QRDo/tvaaY3q+OJ9tjGcmhBPss2AIrV65k3bp1RKN/7/q7du1anE4nK1asOOrjvF4vK1asYP369UNuX7duHQAnnnhibgIWogAy0+kqPhuU/C937d0J3tyWmQVatayxsMGMgm6YOBwOwlk+oeRwOGioDuJ1u0jbrLlqISVTGqGAh6pK+84MlhtbJUFXXXUVoVCI66+/npdeeomHHnqI2267jauuumpIjaBbbrmF+fPnD3nsDTfcwM6dO/nqV7/KSy+9xO9//3t++MMf8sEPfpCpU6fmeyhC5Iyqm6QVHa/HVi/fsrT2lSYs4ORZdUxqOPZMih0l0zoVAfewOsaPVCjgob46SDRlv5NihWBZFklFp6E6YJslQmGzJCgSiXDvvfficrm4/vrruf3227n88su5+eabh9zPNE0Mwxhy20knnTR4KuwLX/gCP//5z/noRz/K97///XwOQYicSys6qi6d4wttX2ecTTu7cQCXLG8sdDijkh5Bx/jRqK8OEPC6SaTt104j35JpnaDfTU2kuPfxlBpb7QkCmDlzJvfcc88x77N69WpWr1592O3Lly/noYceylFkQthDWjWwLAtnERXjK0VrX2kCYNGcBibUhgobzCgc7BgfzmHLhsCB5qpN7VGCPndRFZDMtkRaY9qESmlzYzO2mgkSQhxfLKngliKJBbW3I8o7u3twOODipdMKHc6oJFM6lSEPwRF0jB+N2qoAoYCHeKp8Z4OSaQ2/z0VtWGaB7EbeSYUoIoZhkkjpeL3y22QhPb6+CYDT5o6joab4TuhYlpXpGB8eecf4kfJ5XIyvDZFSdEzTyun3sqtEWqO+Koh/lH3ZRO5IEiREEUmrBopmyMmwAtrTNsDW5j6cRTwLpGgGPq+L8BjbZAxXTdhPOOQlliy/TdIpRcfrcVEbsVepAJEhSZAQRSSt6hiGNaoiayI7Ds4CLTlx/JBilcUkmdaJVHjzNjPhcWeaq6o2ba6aS/GURl1VgKA/+yfwxNjJO6kQRSSZ1injvaUFt7Olnx0t/bicDi5cUpyzQKZlYZgm1XkuYlhd6aOqwku0jGaDFNXA63ZSJyfCbEuSICGKhGVZRBOKLIUViGVZPLZ+DwDLTppATbg4lzfSip6VjvEj5XJlZoMM00LXy2M2KJZUqI34c1KHSWSHJEFCFAlFM0irhtQHKpBte/vY0xbF7XJwwZLiLcCaVHSqK30F+TmKVPioCfsZSBa+k3uuKZqBy+WitkiXTMuFJEFCFAlFNdB0A49Uis47y7JYe2Av0BknT6Qqy20m8sUwLcAiUlGYWSyn08G4miAOHKiacfwHFLFYUqU67KNCZoFsbdTvplu2bOGRRx4ZctuLL77Ixz/+ca644gruvffeMQcnhPi7lKKDhRRJLIB3m3pp7ojhcTs5//TinQVKpTVCfg8VwcJ9MIdDXuqqAkQTpTsbpOoGToeD+qpAWReILAajToL+7d/+jccee2zw7y0tLdxwww20trYCmarO999//9gjFEIAEE+quFwyC5Rvh84CnXnKRMKh/O6lyaaUalBd6cddwJ+jTHPVAB63i7Rams1VY0mVmrCvqH9WysWoXwlbt27ltNNOG/z7ww8/jNPp5E9/+hMPPvggF198Mffdd19WghSi3BmGSSKtS8n9Anh7Vw+tnXF8HhfnFfEskG6YOJ0OIjboYF4R9FJXHSCaKL2TYrpuggV1VUGZBSoCo06CYrEYVVVVg39//vnnWbFiBTU1NQCsWLGC5ubmMQcohMj8Bq/Ipui8Mw+ZBVq5aFJR7+8Y7Bhvk3o19VUB/D4XyRJrrhpNqlRV+mUWqEiMOgmqr69n165dAHR2drJ582ZWrFgx+PVEIoFT+hsJkRWKqmMYZkGXMcrRW9u7aO9J4Pe6OOfUyYUOZ0zSqk5tJIAzRx3jRyro9zCuOkg8pWFZpdFOwzBMTMukodo+/87i2EZdLvT888/nd7/7Haqq8tZbb+H1ernwwgsHv75t2zamTJmSlSCFKHeJlI5D3lTzyjQt1r6Smc0+59TJRV3xV9Uys4j5rg10PHXVQbr6UyRSGhU2i200YkmNSMhHuEhPD5ajUf9aeeONN3LhhRfy8MMP09PTww9+8APq6uoAiMfjrF27dsjMkBBidCzLIpaUIon59sa2Tjr7kgR9bs5eVNyzQMm0TkUw9x3jR8rncTGuJkhS0TGLfDbIMC00w6ShJohLfmEpGqN+RYRCIW6//fYjfi0YDPLCCy/g9xdnRVUh7ERRM0US/V57fYCVMsO0eGJDZhbo3NOmFHX3b8uy0HSD2nDYlht166oCdA+kiSe1ot5HE0+qhEOeoq0hVa6yvsFAVVXS6TSVlZV4PMU7fSyEXaRVA03L9CAS+fHalg66+1NUBDyctXBSocMZE0XNdIyvLGBtoGPxuF2MrwmhaPqBYo7FxzQtFM1gXE1IylgUmVE/W48++ijf//73h9x2xx13cOqpp7J48WKuv/56EonEmAMUotylVR0LbPlbfCnSDZMnDuwFOu/0Kfi8xb0MmUzrhCt8tp7Nqgn7qKrwESvSI/PxVGYWq8oG5QfEyIw6CfrP//xPUqnU4N/feOMN7rjjDs4880yuvvpqXnzxRX7xi19kJUghylksqeJxFfcHcTHZsLmDvphCZdDLipMnFjqcMTEtC8Mybd/s1eVyMq42hG4a6EZxNVc1LYu0qtNQE5TTm0Vo1L8atLS0cNlllw3+/ZFHHqGuro477rgDt9uNZVk8+eSTfPWrX81KoEKUI90wSaQ0vNIvLC803eSpVzOzQBcsmVr0dZn+3jHenkthh6qq8FFdGWAgplATsXfSdqhESqMy6KFaZoGK0qjfWVVVxef7+5P+8ssvs3LlStzuTF41c+ZMOjo6xh6hEGUsreiomimVovNk/dttDMRVqip8LD9pQqHDGbOEolMd9uMpgpOFTqeD8bVBLEem91YxsCyLlKrTUBMqin9jcbhRJ0GTJ09m3bp1ALz99ts0Nzdz1llnDX69p6eHYDA49giFKGNp1UA3TdlsmQeqZvD03/YCcOGSqXiKfCO6YVo4gEioeGYowiEvtRE/A0WyNyiR1gn63DILVMRGvRz20Y9+lO9973vs3LmT/fv3M378eM4999zBr7/xxhvMmjUrK0EKUa6SaV26xufJy5vaiCU1asJ+lpw4vtDhjFmmY3xxLIUd5HA4GFcTpD+mDJ5qsyvLskimNaZPDBf9smk5G3US9IlPfAKfz8fzzz/PSSedxOc+97nBukD9/f10dXXxsY99LGuBClFuLMsimlBkKSwPFNXgr6+1AHDR0qklscE1pRpMHV9RdLOIlUEvdRE/7T0J6r32XU1IHthvVW3zTefi2MZ0ZvLKK6/kyiuvPOz2qqoq/ud//mcslxai7CkHmqba+WhzqXhx4z4SKY26qgCnzyv+WSBdN3E5HYSLaCnsUA01QXqjaVJpnYDNqlwflEhrTG2olCKmRa64fkUQooykVB1NN6VIYo6lFJ1nX8/MAl28dFpJtDxIKBohG3WMH6mg30N9dZBYSrVlc9VUWsfvcVFbFSh0KGKMxpTCdnV18cc//pF3332XWCyGaQ6t7+BwOLj33nvHFKAQ5UpRDSmSmAfPv9lKUtEZVxPk1DkNhQ4nK1TNZHJDZVF3Mq+vDtAzkCKR1qkI2CuZi6dVJtVXEpBZ2qI36mdw69atfPKTnySdTjN9+nS2b9/OrFmziEaj7N+/n6lTpzJ+fPFPKwtRKAMJtehPKNldIq3x/ButAFy8bFpRJw0HqQdarBTThugj8XvdjKsJsqc9StDvts0BgbSi43W7qKuSvUClYNTvsLfffjvBYJC1a9fyX//1X1iWxS233MLzzz/Pj3/8YwYGBvjnf/7nbMYqRNnQdJNUWpPO8Tn23OutpFWDiXUhTjmhvtDhZEUirVER8pbELEVdVYDKgIdEUit0KINiKZXaiJ9gkS41iqFGnQS98cYbfPSjH2XixIk4nZnLHFy7XbVqFR/84Ae57bbbshOlEGVGUXUUzZRK0TkUT6q8sDEzC3TJskbbzDSMhWVZ6LpJTaW/JJZRPW4X42pDpFQd0wbNVRXVwON2UVdt31NrYmRG/Q5rmiZ1dXUAhMNhXC4X/f39g1+fM2cOmzdvHnOAQpSjlGJgSpHEnHrm9ZYDe2cqOGlmbaHDyYrBjvGh0pmlqAn7iVR4iSYLX0AxmlKpCfttt0dJjN6YKka3tmZ+i3I6nUyePJn169cPfv2NN96gsrJy7BEKUYaSabUkZibsKppQeemtNgAuWd5YErMmkCmuGanwl9SxbbfLyfjaELpuYhSwuaqqGbicDuqr5URYKRn1K+XMM89k7dq13HTTTQB87GMfY/Xq1bS0tGBZFq+++iqf/vSnsxaoEOXCNC1iSU2KJObQX/+2F003mTa+kvmNNYUOJysyHeMtqsPFWRvoWKoq/VSFfQzEMzMxhRBNqtRVBWQWqMSMOgn6/Oc/z/vf/340TcPj8XD11VeTTCZ58skncTqdXHfddVx77bXZjFWIsqBomSKJpbCx1Y76Ywrr3s7MAq0qoVmglKIT8LmK/lTYkbicDsbXhBiIKWi6mfdTk5pu4nA4qK8KlMzPi8gY9btsJBIhEokM/t3hcHDddddx3XXXZSUwIcpVWtVRdZNwSPYD5cLTf9uLbljMmBRh9tTqQoeTNUlFZ1JdRcl2M49UZJqrdg+kqYvkd0kqllCpDvsIh7x5/b4i9+RdVgibSaV1wJLfOHOgN5rmlXfagdKaBRrsGF9Ruh/SDoeDhpoQLqcTRTPy9n11w8RyWNRXyyxQKRr2TNDXvva1EV/c4XDw/e9/f8SPE6KcxZJqyf42X2hPbmjGMC1OmFLFrMlVhQ4na5JpjVDAU/L7VSqDHuqr/LR1J2jI0zH1WEIlUuEjUqR92MSxDTsJ2rBhw4gvLlmzECOj6SYpRZdN0TnQ3Z/ib+92AJlZoFKSVnWm1lSWfEkFh8NBfXWQnoH0gT1Qud03ZxgmhmnSUB0siWri4nDD/gl65plnchmHEILMh5miGlRVlvZv9IXwxIZmTAvmTqtm+sTI8R9QJDId451lM1MRCmSaq7Z2xfB7XTn9ZTuW1DKzQBXl8W9bjkb0a4OiKHzzm9/kt7/97THv95vf/IZvf/vbaJp9Sp0LUQzSioFpWiXRydxO9vcmeX3rfqD0ZoESikZl0FNWbRwaqgMEvG6SaT1n38MwLTTDoKEmKK/HEjaiJOj+++/nT3/6E+ecc84x73fOOefw0EMP8eCDD44lNiHKTiKl4nKW9pJGITzxShOWBSfNqGXq+HChw8kqRTWoiQTKarnG78s0V02ktcF2TdkWT6mEQ16qZBaopI3o3fbxxx/noosuYsqUKce839SpU7nkkkt49NFHxxScEOXENC1iKU36hWVZe3eCjdu7gEx16FKiaAY+T2nWBjqeuqoAoYCHeCr7Kw6maaGoBg3VoZLfZ1XuRvTsbt++ndNOO21Y9120aBHbtm0bVVBClKO0qqOqBl6vbIrOprWvNGEBp5xQx6T6ikKHk1XJEuoYP1Jej4sJtSHSOWiumkhpVIa8VFXKLFCpG1ESdLA69HB4PB5UtfAN74QoFopqoOoGHvnNM2taO2Ns2tmNA7h4WWOhw8kqy7LQdJPacGl0jB+N6rCfcMhLLIvNVU3LIq3qjKsO5r0ytci/ET3DDQ0N7NixY1j33bFjBw0NDaMKSohylFIymzzL9QMtFx5f3wTAojkNTKgNFTaYLEurBn6fi4oyXAo7yOPONFdVs9hcNZnSCAY8JdmDTRxuREnQGWecwcMPP0xPT88x79fT08PDDz/MGWecMabghCgn0aSKV4okZk1zR5R39/TicMDFS6cVOpysS6Y1qkqsY/xoVFX6qarwEk2MfTbIsiySik5DdUAKlpaJESVB11xzDYqicPXVV/PWW28d8T5vvfUWn/rUp1AUhc997nNZCVKIUqfpBqm0jleKJGbN2gOzQKfPHUdDTX6qC+eLaVmYFlTLnpVMc9XaECYWuj622aBkWifod1OT595konBG9CvElClT+I//+A++8pWvcNVVVzFlyhRmz55NKBQikUiwY8cO9u7di9/v50c/+hFTp07NVdxClJSUYqBqRlnVesml3W0DbG3uw+l0cNGy0psFSimZD+uKYOn2ChuJSIWP6ko/vdHRN1e1LItEWqNxQlgqtpeREc+jnnPOOfzlL3/h7rvv5rnnnuPpp58e/FpDQwNXXHEF11xzzXGP0Qsh/i6t6pgWUpQtSx5f1wTAkvnj895xPB9SaY2J9ZWycfcAp9PBuJog/XEFVTNGNaOaUnT8Phc1YX8OIhR2NarF5MmTJ3PrrbcCEI/HSSQShEIhKipK6/ipEPmSSGm4ZEN0Vuxo6WNnaz8ul4OLlpTebLRhmDgcjpLuGD8a4ZCXukiAzt4EdVUjX/5MpDWmNFTiL8NyA+VszM92RUWFJD9CjIFhWsSSmtQHygLLsgZPhC0/aQLVJfhbfVLRCZZBx/iRcjgcNNQE6YumSSv6iJKZlJLZj1cTKb2fF3FsMpcqRIEpqn5gCl9ejmO1rbmPPW1R3C4HFywuvVkgyCyd1ob9Usn4CCoCHuqqA8RSIzspFk9p1FUFZE9eGZJXkRAFllYNNN2Q4/FjdOgs0BknTyzJzt+abuJyOQmHZCnsaBqqg/i8LpLp4bXTSKs6XrezJPeOieOTJEiIAksp2e99VI7e3dPL3v0xvG4nF5xemrNAybRGZaC8OsaPVOBAc9V4anjNVeNJldqIn5AsL5Yl2yVBu3bt4tOf/jQLFy5kxYoV3HbbbSNuv/H0008zZ84cPvCBD+QoSiGyJ5bQpD7QGB06C3TmKZOoLNGZElUzqC2zjvGjUVcVJBTwkDhOc1VFM3C5XNRVySxQubJVEjQwMMDVV1+NpmmsWbOGm266iQceeIDVq1cP+xrpdJrvf//71NXV5TBSIbJD1QxSio5PlsLG5O1d3ezriuPzuDj39NIsz6EcOPpdzm0yhsvncTG+NkRK0TGPMRsUS6rUhGUWqJzZ6izgfffdRyKR4I477qCqqgoAwzC49dZbufbaaxk3btxxr/HLX/6SiRMnMnnyZN55550cRyzE2KTVTJHEkCxvjJppWaxd3wzAykWTSvbUVDKlURX2yVLYMNWE/XQdaK4aCR2+P0zVDZwOB/XVAenXV8ZsNRP0wgsvsHz58sEECGDVqlWYpsnLL7983Mfv3buX//qv/+LrX/96DqMUInvSSqZIoixvjN7G7V209yTwe12cc+rkQoeTE5ZloZsm1ZVyhHu4BpuragaGefhsUGYWyEelzKyVNVslQbt372bGjBlDbguHw9TX17N79+7jPv573/sel156KXPnzs1ViEJkVTylSZXoMTBMiydeaQLg3NOmlOwsSVo18HldJbvXKVeqK31UVfiIJpQht+u6CRbUVwdlFqjM2Wo5LBqNEg6HD7s9EokwMDBwzMc+88wzvPnmm6xduzZr8ViWRTKZzNr1DkqlUkP+LFYyjrExTYvuvhiWZZFOj/16iqIM+bNYjWQcb2zrorMvRcDnZsm8WtLZ+IfMkmw+H32xNHURP4amkMzzYcJif52Hg046e9OYWmY2SFEU+mIKkQovLnSSSaPAEY5MsT8fh8rVWCzLGnZya6skaLQUReH73/8+X/ziF6mpqcnadTVNY8uWLVm73ns1NTXl7Nr5JOMYnbRmsrdTwe914nZl77fRtva2rF2rkI43DtO0eGJDLwALpvlob2vJR1gjNtbnw7Qs4kkDs85Hqr9wG+iL9XVuWhb9vRrxlEE46KJl3z4SKZNJdV60WPEeSCjW5+NIcjEWr3d4s6a2SoLC4TCxWOyw2wcGBohEIkd93L333ovT6eT9738/0WgUyCQwpmkSjUbx+/3D/gc5lMfjYdasWSN+3PGkUimamppobGwkECjeo5kyjrHpiylo7gHqslSqX1EU2trbmDhhIj5f8RYKHO44/ralk2iyi5DfzfvOmme7zt/Zej4SB4r+zZtWjbsADVNL4XU+JaHyzs5OYgPdRKrqmTYtxAmTI0W5F68Uno+DcjWWnTt3Dvu+tkqCZsyYcdjen1gsRldX12F7hQ61e/dumpubWb58+WFfW7x4Md/+9rf52Mc+NuJ4HA4HweDIG/ENVyAQyOn180XGMTp9cQOf34ffn93Nrj5f9q9ZCMcah26YPPtGZoblgiVTiVSG8hnaiIz1+YinTSY2VBIOF7ZHYzG/zgOBAD0DaTo69lNT72bqhGoqKoo7gSjm5+O9sj2WkezzslUStHLlSn7xi18M2Ru0du1anE4nK1asOOrjrrnmGi677LIht911113s2bOHH/zgBzQ2NuYybCFGJZpQ8RbgN/tSsOGdDvpiCuGQlzNOnljocHJmsGO8bIgeE8eBo/Aut4PKoJeqEmypIkbHVu/AV111FaFQiOuvv56XXnqJhx56iNtuu42rrrpqSI2gW265hfnz5w/+febMmSxdunTIf/X19QSDQZYuXTqs+kJC5JOqGaRU3XZLOMVA002e+lumLtAFi6eWdM+1ZFonFPBQEZQkaKwqAh5qKtyMrw1K81kxyFY/CZFIhHvvvReXy8X111/P7bffzuWXX87NN9885H6maWIYxbWjX4hDpRQdVTVL+gM8V9a93cZAXKWq0sfykyYUOpycSms6NWG/lFHIkrqwR2aBxBC2Wg6DzKzOPffcc8z7rF69+ritNEbSakOIfFNUAxOrKDdmFpKqGfz1b3sBuHDJ1IJsFM6XwY7x8qEtRM6U7juIEDYWS2p4nPLyG6mX3mojltSoCftZMn98ocPJqYMd40N+2/2uKkTJkHdhIfLMMC3iKVU6x49QWtV55rXMLNBFS6fhLvF9HcqBjvFS0ViI3CntdxEhbCit6KiaKZuiR+jFjftIpHXqqwKcPq+0DzsoqoHPI20yhMg1SYKEyLO0qqMbRknvZ8m2lKLz3OutAFy8bFrJbxROpjXCFV4CPlkKEyKX5F1YiDxLpXWQJY4Ref6NVpKKzriaIItmNxQ6nJw62DG+Jlz8BS+FsDtJgoTII8uyiCVVfHI0ftgSaY3n38zMAl2yrLHkT9SlFQO/1y21gYTIA0mChMgjVTdJKTpej7z0huu511tIqwYT60KcfEJdocPJuYSiUVXpkz1jQuSBvBMLkUdpRUfVTTkZNkzxpMoLG/cBB2aBSnwZ0TQtsCyqKqU2kBD5IEmQEHmUVg0syyr5D/Nseeb1FlTNZHJDBSfNrC10ODmXUnQCfg+VshQmRF5IEiREHsWTKm4pkjgssaTKS29lOsWvWt5YFvVykkqmEGSp10ASwi7klSZEnhiGSTyl4fXKUthwPPdGG5puMm18JfMaawodTs4ZhonT4SAstYGEyBtJgoTIk7RqoGiGnAwbhnjaYMO7nQCsOmN6ecwCScd4IfJOkiAh8iSt6hiGJUUSh+HNXUkM02LmpAizp1QVOpy8SGk6tRHpGC9EPsm7sRB5kkzrUiNxGPqiCttaUwBcUiZ7gVTdwONyURmSU2FC5JPUZBciDyzLIppQcroU9trWTt7dFeOtvXtwuYp3ya1lfxTTgpmTwsyaXFXocPIimdapDErHeCHyTV5xQuSBohkHmmLm5iXX0hnjoef2HPhbKiffI98uXDy50CHkjaoZ1IyvLItZLyHsRJIgIfJAUQ1U3cjZptfn38i0lRhX5WHBrAbc7uJ9aeu6jsOIM218ZaFDyQtFNfB5XVIbSIgCKN53SiGKSErRwSInRRL7Ywpvbu8C4Ix5FSw5ZTJ+f/E230yn0+xp2nP8O5aIRFqjJuyTjvFCFIBsjBYiD+JJFVeOCuC9uHEfpmkxfUIl9RFPTr6HyA3LstANk2rpGC9EQUgSJESOGYZJIq3npCGmohqseztTVfmsUyZk/foit9KKQcDnlqUwIQpEkiAhciylZjZF56Jp6obN7aRVg/rqAHOmVWX9+iK3EopGdaVPGuoKUSCSBAmRY4qqYxhm1vtBmabF829mOqyfvWiyNGUtMqZpARYR6RgvRMFIEiREjiVSOo4cVAF+e1c3vdE0Ib+bxfPGZf36IreSik7AJx3jhSgkSYKEyCHLsoglc1Mk8bkDx+LPOHmiLKcUoZSiUSsd44UoKHn1CZFDimaQVo2sd45vao/S1B7F5XJw5imTsnptkXu6YeJ0OglXyFKYEIUkSZAQOZRWDDTNwJvlpqkHZ4FOmzOOcEiWU4pNMq1TEXATCkhJAyEKSZIgIXIorepYkNV2CD0DKTbtzBRHPPtUmQUqRmlNpyYsHeOFKDRJgoTIoVhSxZPlZqYvbNyHZcGcqdVMrKvI6rVF7h3sGB+WjvFCFJwkQULkiG6YJFIaXk/2XmaptM6GdzoAOOfU8mkwWkqSKZ3KkIegdIwXouAkCRIiR9KqgaqZWa0U/crmdhTNYHxtkDnTqrN2XZE/qm5QE/ZLx3ghbECSICFyJK3o6KaZtZ5hhmHywoHiiOecOlk+RItQWtXxeV2EpTaQELYgSZAQOZJM61mt4rxxRxf9cYXKoIfT5khxxGKUTOtEKrz4pWO8ELYgSZAQOWBZFtGEkrWlMMuyBo/Fn3nKJNxZPnIvcs+0LAzTpLpSOsYLYRfyTipEDihZbpq6a98ArZ1xPC4nZ5w8MSvXFPmVVnTpGC+EzUgSJEQOpFQdTTezViTx+QOzQKfPH0eFFNgrSklFp6pCOsYLYSeSBAmRA4pqZK1IYmdfks27ewA4Z5Eciy9GxoGO8VWyFCaErUgSJEQORJMqniydCnvhzX1YwPzpNTTUBLNyTZFfKUUn6PNQEZRZPCHsRJIgIbJM002SKS0rm6ITKY1X380URzz31Cljvp4ojJSSaZMhHeOFsBd5RQqRZYqqo2hmVipFr3u7DU03mVRfwczJkSxEJ/It0zHeQaRS2mQIYTeSBAmRZSnFwMxCkURdN3lxYxsA554mxRGLVUo50DHeL0thQtiNJEFCZFkyrWalSOIb2zqJJVUiFV4WnlCfhchEIaRVg9pIAKd0jBfCdiQJEiKLTNMilhz7fiDLsnjuzcyx+LMWTspa6w2RX7ph4XU7pTaQEDYl76xCZJGiZadI4va9fbR3J/B6nJxxkhRHLFZpzaQiIB3jhbArSYKEyKK0qqPqJp4xFkk82CJj6YkTCMgHaFGyLAtdt6iOSMd4IexKkiAhsiiV1gFrTB967d0Jtjb34XDAykWTshecyKt4SsPvdRKW2kBC2JYkQUJkUSyp4nGPbSns+QN7gRbMrKMuEshGWCLPdMNE0QxqK93SJkMIG5N5diGyRNNNUoo+pk3RsYTKa1v3A3DuqdIio1j1xxVqwn4USxIgIexMZoKEyJK0qo95U/RLm/ZhGBbTxlfSOFGKIxajtKrjdjkZVyPH4oWwO0mChMiStGJgmhauUX7wqbrBy29liiOec5q0yChGlmURTag0VAflWLwQRUCSICGyJJFScTlH/5J67d39JNKZHlMLZtZlMTKRL/GkRijgoaFG9nIJUQwkCRIiC0zTIpbSRt0vzLSswQ3RKxdNGvVskigcwzBJazoT6kL4vbLdUohiIEmQEFmgaAaqauD1jm4/0JY9vXT2pfB7XSw9cXyWoxP50J9QqAkHqJUTfUIUDUmChMiCtKKj6gaeUba3OFgccfmCCTKLUIQU1cDpcDK+NiizeEIUEUmChMiClKIDjKpIYmtnjJ2t/TidDs5aKMURi41lWQwkFRpqAkQqfIUORwgxApIECZEF0aSKd5RFEg/OAi08oZ7qSn82wxJ5EE9phPwextUECx2KEGKEbJcE7dq1i09/+tMsXLiQFStWcNttt6Gq6jEfE4/HWbNmDZdffjmnn346Z5xxBp///OfZtm1bnqIW5UzTDVJpfVT1gfpjCm9u7wLgHCmOWHQMI1MgUzZDC1GcbJUEDQwMcPXVV6NpGmvWrOGmm27igQceYPXq1cd8XFtbG/fffz8rVqzgP/7jP/jOd75DLBbjox/9KLt27cpT9KJcpVUDVRtdkcQX39qHaVrMnBRhyrjKHEQncmkgrlIT8VMblhk8IYqRrX51ue+++0gkEtxxxx1UVVUBYBgGt956K9deey3jxo074uMmT57MU089RSDw91MZy5Yt47zzzuMPf/gD3/jGN/IRvihTKUXHtBjxhlhFNVj/djsgs0DFSFENcMKE2hCuUW6IF0IUlq1euS+88ALLly8fTIAAVq1ahWmavPzyy0d9XDAYHJIAAYRCIaZOnUpnZ2euwhUCgERKwzWKDdEb3u0gpejUVwWYP6M2B5GJXLEsi2hSoaEqQDgklaGFKFa2mgnavXs3H/nIR4bcFg6Hqa+vZ/fu3SO6VjQaZceOHZxxxhmjjseyLJLJ5KgffzSpVGrIn8VKxpEpktjdG8MyLdLpkT3u+TdaADhjwThURRnx934v5cA1lCxcq5CKYRzxlIbTAeGg86g/N/L6sBcZh/3kaiyWZQ37pK6tkqBoNEo4HD7s9kgkwsDAwIiu9W//9m84HA4+9rGPjToeTdPYsmXLqB9/PE1NTTm7dj6V8zgUzaS5S8HvceJ2DX82aE9Hmt6ogs/joNafYE/TnhF/76Npa2/L2rUKya7jME2LaNJgYq2XPerxZ5rL+fVhRzIO+8nFWLze4c3Q2ioJypaHHnpocEP1+PGjr77r8XiYNWtWFiPLSKVSNDU10djYeNgyXjGRcUBfTEFzD1AbGdnG2Cfe3AzAGQsmcMLM7DRLVRSFtvY2Jk6YiM9XvPVq7D6OvpjC1KCHWZPCx9wLJK8Pe5Fx2E+uxrJz585h39dWSVA4HCYWix12+8DAAJFIZFjXeP755/nmN7/Jddddx2WXXTameBwOB8Fg7mp/BAKBnF4/X8p5HH0JA5/Pi98//CSoqT1K8/44LpeDc06bht+f3T0lPp9vRPHYlR3HoWgGPh9Mn1RNZeXwErRyfn3YkYzDfrI9lpEUrbXVxugZM2YctvcnFovR1dXFjBkzjvv4jRs38uUvf5l/+Id/4Mtf/nKuwhRiUCyh4RlhkcSDxRFPm9Mgm2qLzEBcoaE6QKRCnjchSoGtkqCVK1eybt06otHo4G1r167F6XSyYsWKYz52586dXHvttSxbtoxbb70116EKgaoZpBQd3wjqA/UOpNm0M1Mc8Ww5Fl9U4imNoN/NuNrQqNqjCCHsx1ZJ0FVXXUUoFOL666/npZde4qGHHuK2227jqquuGlIj6JZbbmH+/PmDf+/p6eGzn/0sPp+Pq6++mnfeeYeNGzeycePGEa0NCjESoymS+PzGViwLZk+tZmJdRQ6jE9lkmBYpRWNCXQUBn612EQghxsBWr+ZIJMK9997Ld77zHa6//npCoRCXX345N91005D7maaJYRiDf9+5cycdHR0AfOpTnxpy3yVLlvDb3/4257GL8pM+UCTROcwiiSlFZ8M7mZ/Tc2UWqKhE4wpVFb4Rb4AXQtibrZIggJkzZ3LPPfcc8z6rV68e0kpj6dKl0idM5F08pY2oSvQr77SjaAbja4PMmVadw8hENqmagQlMqKvALZWhhSgp8ooWYhQM0yKeVIe9H8gwTF7YuA/ItMiQPSXFYyChME42QwtRkiQJEmIUFFVH1c1h7wd6a0c3/TGFiqCHU+ccuQeesJ9ESsPvddNQE5TEVYgSJEmQEKNwcFO0x338l5BlWTz3ZuZY/FmnTBrWY0ThmaZFIq0xvjZI0O8pdDhCiByQd2MhRiGV1nAMcz/Q7n0DtOyP4XE5OWPBhBxHJrJlIKFQVemjvro0CtIJIQ4nSZAQoxBNqHiHOaNzsDji6fPHURGUfSXFQNUNTNNiQl1INkMLUcLk1S3ECKmaQUodXpHErr4km3f3AHD2IjkWXywG4ir11UGqKuzXu0wIkT2SBAkxQilFR1VNvMNol/H8m/uwgPnTaxhXI8sqxSCR1vB5nYyvlc3QQpQ6SYKEGCFFNTCxjlskMZHSePXdg8URs9MpXuSWaVokUhoTakOyGVqIMiBJkBAjFEtqeJzHf+mse7sNTTeZVF/BzMmRPEQmxiqaUIlUeKmrklk7IcqBJEFCjIBhWsRT6nHrA+m6yYsb2wA49zQpjlgMVN3AsEwm1lVIGQMhyoS80oUYAUXVUTXzuJui39jWSSyZmVVYeEJ9nqITYzEQV6iNBKiqlM3QQpQLSYKEGIGUoqMbBu5jzBQMKY64cBIuOWJte8m0hs/jYmJdSGbthCgj8u4sxAik0joc50Ny+94+2rsTeD1Olp8kxRHtzrQs4imN8bIZWoiyI0mQEMNkWRaxpIrvOEfjDxZHXHriBPlQLQIHN0NLZWghyo8kQUIMk6qbpBQdr+foL5v2ngRbm/twOGDlokl5jE6Mhqab6IbJBNkMLURZkle9EMOUVo7fOf75A7NAC2bWURcJ5Cs0MUoD8TR1VQGpDC1EmZIkSIhhSqsGlmXhPMqeoFhC5bWt+wE451RpkWF3ybSGx+NiQm3ouIUvhRClSZIgIYYpnlRxH6NI4kub2jAMi2njK5k+UYoj2tnfN0MHCQVk35YQ5UqSICGGwTBM4ikNr/fIS2GqbvDypkxxxHOkRYbtxRIq4ZCXBtkMLURZkyRIiGFIqwaKZhz1ZNhrW/aTSGnUhP0smFWX5+jESOi6iWYYTKgL4RlGE1whROmSJEiIYUirOoZhHbFIomlZgxuiVy6chEv2l9haf0KhLhKgutJf6FCEEAUmSZAQw5BM60etkbilqZfOvhR+r4ulJ43Pb2BiRFJpHY/Lyfi6CtkMLYSQJEiI47Esi2hCOepS2HOvZ2aBli+YgN/rzmdoYgRMyyKWUhlXE6RCNkMLIZAkSIjjUjQDRTWOWB+otTPGztZ+nI5MnzBhX7GESmXIS0ONbIYWQmRIEiTEcSiqgaobeI5QKfpgi4yFsxtkj4mN6bqJphtMrAsds9ilEKK8SBIkxHGkFB0sDiuS2B9XeHN7FwBnS3FEW+tPKNRVyWZoIcRQkgQJcRzxpIrLdfhL5cWN+zBNi5mTIkwdV1mAyMRwpJQDm6GlMrQQ4j0kCRLiGAzDJJHW8b1nCUVRDda/3Q5Iiww7My2LWFKloSZIRdBb6HCEEDYjSZAQx5A6UCTxvftINrzbQUrRqa8KMH9GbYGiE8cTS6pUBj2Mk83QQogjkCRIiGNQVB1DN3EfshxmmhYvvJnZEH32oslHbagqCks3TDTNZEJdhWyGFkIckSRBQhxDIqXjeM8+knd2d9MzkCbod7N4/rgCRSaOpz+uUBPxUx2WzdBCiCOTJEiIo7Asi1jy8CKJB4/Frzh5osww2FRa0XG7nEyoC0kbEyHEUUkSJMRRKJpBWjWGdI5vbo+ypy2Ky+VgxSkTCxidOBrLsogmVRqqg1TKZmghxDFIEiTEUaQVA00z8B7SNPXgLNBpcxqIhHyFCk0cQyypURH0MK5WNkMLIY5NkiAhjiKt6liA48DG596BNG/tPFAccZEci7cj3TBRNIMJtRWHlTUQQoj3kiRIiKOIJVU8rr9/kL6wsRXLgtlTq5lYX1HAyMTR9McVasJ+aiKyGVoIcXySBAlxBLphkkhpeA/0C0spOq9s7gCkOKJdpVUdl1M2Qwshhk+SICGOIK0aqJo5uKTyyjvtKKrBuJogc6dVFzg68V6WZRFNqIyrCRIOyWZoIcTwSBIkxBGkFR3dNHG5nBiGyQsb9wGZWSCHFEe0nXhSIxTw0FATKHQoQogiIkmQEEeQTOuDlaDf2tlNf0yhIujhtLlSHNFuDMMkrelMqAvh97oLHY4QoohIEiTEe2SWVhR8HheWZQ0eiz/rlEl43PKSsZv+hEJNOEBtRGaBhBAjI+/oQryHohooaqZp6u62AVr2x/C4nJyxYEKhQxPvoagGToeT8bVB2QwthBgxSYKEeI+0aqDpJl63k+dez8wCnT5/HBVSfdhWLMtiIKnQUBMgUiGFK4UQIydJkBDvcbBIYnd/is27ewApjmhH8ZRG0OdmXI1UhhZCjI4kQUK8RzSp4nE5ef7NfVjA/Ok18kFrM4ZhklJ0JtZVyGZoIcSoSRIkxCE03SSZ0jAMk1ffleKIdjUQV6mJ+KmVytBCiDGQJEiIQyiqjqKZvL51P5puMqm+glmTqwodljiEohnghAm1IVwueQsTQoyevIMIcYiUYqBqOi9tagekOKLdHCxf0FAVkMrQQogxkyRIiEMk0ypb9vQSS6pEKrwsnF1f6JDEIRIpjYDPzfjakCSnQogxkyRIiANMM9N/6tV39wOZ4ohuWW6xDcO0SCo6E+oq8PtkM7QQYuzkHV6IA1TNYEtTH/t7k3g9TpZLcURbGYgrVFf6qJPN0EKILJEkSIgD0prBuk1tACw9cQJBv6fAEYmDFM0AYEJdhWyGFkJkjbybCHHAnrYou/YN4ABWLppU6HDEIaIJhYbqAJEK2QwthMgeSYKEOOCpv+0DYMGsOuqkGadtxA9shh4nm6GFEFlmuyRo165dfPrTn2bhwoWsWLGC2267DVVVh/XYBx98kIsvvpgFCxbwoQ99iGeffTbH0YpS0Z/Q2bi9G5DiiHZimBYpRWN8bYiAbIYWQmSZrZKggYEBrr76ajRNY82aNdx000088MADrF69+riPffTRR/nGN77BqlWruPvuu1m4cCE33HADGzduzH3goui9uj2OYVpMHVdJ44RwocMRB0TjClUVPuqqZGZOCJF9tvrV6r777iORSHDHHXdQVVUFgGEY3HrrrVx77bWMGzfuqI/96U9/yvvf/35uvPFGAJYtW8b27du58847ufvuu/MQvShWqmbw5q4EAOeeNkWWXGxC1QxMXIyvC0mpAiFETtjqneWFF15g+fLlgwkQwKpVqzBNk5dffvmoj2tpaaGpqYlVq1YNuf1973sf69evH/ZymihPL2xsJ6VahEMeFsyqK3Q44oBoUqOhOkBVha/QoQghSpStZoJ2797NRz7ykSG3hcNh6uvr2b179zEfBzB9+vQht8+cORNN02hpaWHmzJkjjseyLJLJ5IgfdyyKavDQszvY19HH81vexu221VMwIrquE4vFin4cr2/rBOC02bVoqoJW4HhGS1GUIX8WK0VRSKsmYUsnHHCQSqUKHdKoHIy7WOM/SMZhL6UyDsjdWCzLGvaMvq0+uaLRKOHw4fsxIpEIAwMDR33cwa+997EH/36sxx6Lpmls2bJlVI89ml3taR5+qfvA3xJZvXbhFP84PC4HVT6FjZt3FDqUMRvYubfQIYyZZYGa6KZp9+heu3bS1NRU6BCyQsZhL6UyDsjNWLze4ZXTsFUSZDcej4dZs2Zl9ZonnGDi9DXRvK+L6qoq3J7ifQp0Taevv7/ox6GqGn5HgtMXzMDvL95qxOl0mtbWViZPnlz049i3r5WT5s6gsiJU6HBGLZVK0dTURGNjI4FA8W7slnHYS6mMA3I3lp07dw77vrb65AqHw8RiscNuHxgYIBKJHPVxB78Wi8Wor/97w8toNDrk6yPlcDgIBoOjeuyxvG/FDLZsUZg3b05Orp8vyWSSLVu2lMw4pk6oLvpxJPo7SmYclRWhoh7HQYFAQMZhIzIO+8n2WEZyuMVWG6NnzJhx2N6fWCxGV1cXM2bMOObjgMMeu3v3bjweD1OmTMl+sEIIIYQoarZKglauXMm6desGZ3AA1q5di9PpZMWKFUd93JQpU2hsbGTt2rVDbn/sscdYvnz5sNcGhRBCCFE+bJUEXXXVVYRCIa6//npeeuklHnroIW677TauuuqqITWCbrnlFubPnz/ksV/84hd55JFH+OlPf8qGDRv41re+xaZNm7juuuvyPQwhhBBCFAFb7QmKRCLce++9fOc73+H6668nFApx+eWXc9NNNw25n2maGIYx5LYPfOADpFIp7r77bu666y6mT5/OHXfcwaJFi/I5BCGEEEIUCVslQZCp7XPPPfcc8z6rV68+YiuNK664giuuuCJHkQkhhBCilNhqOUwIIYQQIl8kCRJCCCFEWZIkSAghhBBlSZIgIYQQQpQlSYKEEEIIUZYkCRJCCCFEWZIkSAghhBBlSZIgIYQQQpQlh2VZVqGDsKM33ngDy7Jy0nfMsiw0TcPj8Yyo263dyDjsRcZhLzIOe5Fx2E+uxqKqKg6Hg1NPPfW497VdxWi7yOUPl8PhKImmrjIOe5Fx2IuMw15kHPaTq7E4HI5hf4bLTJAQQgghypLsCRJCCCFEWZIkSAghhBBlSZIgIYQQQpQlSYKEEEIIUZYkCRJCCCFEWZIkSAghhBBlSZIgIYQQQpQlSYKEEEIIUZYkCRJCCCFEWZIkSAghhBBlSZIgIYQQQpQlSYKEEEIIUZYkCRqBxx9/nC984QusXLmShQsXcumll/LHP/6R9/agfeaZZ/jQhz7EggULuPjii3nooYcOu9bvf/97rr32WpYtW8acOXNYu3btEb9nLBbjlltuYcmSJSxatIgvfelLdHZ2FtU4Nm3axNe+9jUuvPBCTjnlFC666CJuv/12kslkUY2jpaWFa6+9lpUrV7JgwQLOPPNMvvSlL7Fnz54xjaMQY3mv6667jjlz5vDrX/+6qMbR2trKnDlzDvvvyiuvLKpxHLRx40Y+9alPsWjRIk499VSuvPJKtmzZUjTjWLNmzRGfjzlz5vDNb36zaMYBsH379sH7nX766Xz84x/nlVdeGfUYCjWOXbt2cc0117Bw4UIWL17M//2//5fe3l5bjKOzs5PbbruNSy+9lEWLFrFy5Uq++tWvsm/fvsO+Zy4+C0GSoBG55557CAQC3Hzzzfz85z9n5cqVfOMb3+DOO+8cvM9rr73GDTfcwMKFC7n77rtZtWoV/9//9/8d9gP68MMP09fXx9lnn33M73njjTfy8ssv8+1vf5t///d/Z8+ePVxzzTXoul4043j88cdpbm7mc5/7HHfddRdXX301DzzwAJ///OdHPYZCjCORSFBXV8dXvvIVfvWrX3HzzTezZ88ePvnJT475TaUQP1sHPf/887z11ltjir/Q4/jKV77C/fffP/jf9773vaIbx/r16/nEJz5BY2Mjd9xxBz/+8Y8566yzSKVSRTOOK664YsjzcP/99/PP//zPAKxcubJoxtHb28unPvUp+vv7+d73vsePfvQjgsEg11xzDdu2bSuaccTjca6++mp6e3v593//d771rW/x+uuvc+2112KaZsHHsXnzZp566ilWrVrFz372M26++Wa2b9/OFVdccdh7ai4+CwGwxLD19PQcdtvXv/5169RTT7UMw7Asy7I+85nPWB/96EeH3OcrX/mKtWrVqiG3Hbx/S0uLNXv2bOvxxx8/7NpvvPGGNXv2bOvFF18cvG3Xrl3WnDlzrEcffbRoxnGk7/eXv/zFmj17tvX2228XzTiOZM+ePdbs2bOtv/zlL6MZwqBCjUVRFOvCCy+0/vjHP1qzZ8+2fvWrXxXVOEb6fA1XvsehaZp17rnnWrfddlu2hmBZlj1eI//6r/9qLV682FIUZTRDsCwr/+N45JFHrNmzZ1stLS2Dt6VSKWvBggXWHXfcUTTj+OUvf2mdfPLJVldX1+BtmzZtsmbPnm09+eSTBR/HwMCApWnakPu0t7dbc+bMsX79618P3parz0LLsiyZCRqBmpqaw26bN28e8XicZDKJqqps2LCBSy65ZMh93ve+97Fr1y5aW1sHb3M6j/9P/8ILLxAOh1mxYsXgbTNmzGDevHm88MILRTOOI32/+fPnA4xpOjPf4ziSqqoqADRNG9XjDyrUWH79618TDof58Ic/PPrgD2GH5yQb8j2OdevWsW/fPj75yU+OPfhDFPr5UBSFp556iosvvhiv1zvyARyQ73EcfD1XVlYO3ubz+fB4PIct+YxEvsfx7rvvMnfuXOrq6gZvW7BgAVVVVTzzzDMFH0c4HMbtdg+5z/jx46mpqRny2ZCrz0KQ5bAxe/311xk3bhwVFRXs3bsXTdOYMWPGkPvMnDkTgN27d4/o2rt372b69Ok4HI4ht8+YMWPE1zqeXI7jaN8POOx7ZOO6uR6HaZpomkZrayvf+c53mDBhAhdeeOGYY3+vXI+lra2Nu+66i69//euH/YxlUz6ek29/+9vMmzeP5cuX8/Wvf53+/v6xhn2YXI7jrbfeoqqqirfffpuLL76Y+fPnc/HFF/PnP/85W+EPyudr/dlnnyUej/OBD3xgTNc5klyO49xzz6Wuro7Vq1fT2dlJb28vt99+Ow6Hg0svvTRrY4DcjkNRlCMmn16v17afIXv27KGnp2fwvgfvn6vPQkmCxuC1117jscce4zOf+QwAAwMDQCa7PdTBvx/8+nBFo9Ehv4kcFIlERnytY8n1ON6rt7eXNWvWcP7559PY2Dimax0qX+P4l3/5F0466STOP/98Nm3axH/9138d8Xkai3yM5Qc/+AEXXnghCxcuHFuwx5DrcXi9Xj72sY/x3e9+l3vvvZfPfOYzPP7443zqU58a8+zcoXI9jq6uLlKpFLfccguf+MQn+PWvf83pp5/Ov/7rv/Liiy9mYQQZ+X6tP/LII4wbN47FixeP6TrvletxRCIRfv/73/PGG29w1llnsXz5ch588EHuvvtupkyZkoURZOR6HI2NjWzfvp10Oj14W1tbG11dXVn9RSFb47Asi+9+97s0NDTw/ve/f/D2XH4WShI0Sh0dHdx0000sXbo061PY+ZTvcWiaxle+8hUg89t7tuRzHF/+8pd58MEH+elPf0p9fT2f/vSnaWtry9r18zGWl156iZdeeomvfvWrObk+5GccDQ0NfPvb3+aCCy5gyZIlXHPNNdx+++1s2bKFp556KivfIx/jsCwLRVG44YYb+Kd/+ieWL1/O9773PU499VR+8YtfZOV75Pu1Ho1Gef7553n/+9+f1aXNfIyjp6eHG264galTp3LXXXfx61//mqVLl/KFL3yBXbt2ZeV75GMcV1xxBfF4nG9+85vs37+f5uZmbr75ZpxOZ9Zmf7M5jjVr1vDKK69w2223EQwGsxLf8UgSNArRaJRrrrmGqqoq1qxZM/gCj0QiQOYo33vvf+jXhyscDhOPxw+7fWBgYMTXOpJ8jeMgy7K45ZZb2LRpE3fffTcNDQ1jiH5oXPkcx5QpUzj55JO5+OKL+fWvf41hGPzqV78awwiGxpaPsXz3u9/lk5/8JIFAgGg0OngdRVEG/38s8v2cHOrss88mGAyyefPmMV8rn691gGXLlg25ffny5ezcuXNUsb83rnw/H0888QSqqvLBD35w1Nd4r3yN41e/+hUDAwPceeednH322Zx55pn8+Mc/pqqqip/97GdFM44ZM2bwve99j2effZaVK1dy0UUXEYlEWLlyJfX19bYaxwMPPMCdd97JrbfeyvLly4d8LZefhZIEjVA6nebaa68lFovxq1/9asgU3dSpU/F4PIetUR78+0j3v8yYMYM9e/YcthFvz549Y95Lk89xHPTDH/6Qxx9/nDvvvJO5c+eOPvhDFGIchwoEAsycOZPm5uYxXyufY9mzZw+/+MUvWLx48eB/AD/5yU9YvHgxiqIUxThyKZ/jOOGEE476tbE8F1C45+ORRx5hxowZg4cgxiqf49i5cyczZswYsp/G5XIxZ84c9u7dO4ZR5P/5+Id/+Adefvll/vd//5cXXniBNWvW0NLSMuZl8GyO46mnnuLb3/42X/rSl7j88ssP+165/CyUJGgEdF3nxhtvZPfu3fzqV79i3LhxQ77u9XpZunQpTzzxxJDbH3vsMWbOnMnkyZNH9P1WrlzJwMAA69evH7xtz549vPvuu2OquZHvcQDcdddd3HPPPaxevfqwLH+0CjGO94rH42zbtm3M+wTyPZbf/OY3h/0HcNVVV/Gb3/wGj8dTFOM4kmeffZZkMsmCBQtGfY18j+PMM8/E4/Gwbt26IbevW7eOE088cXSDoHDPR2dnJ6+++mrWNkTnexwTJ05k165dQxJQwzDYunUrkyZNKppxHHrd2bNnM27cONavX09TUxOXXXaZLcaxYcMGvvKVr3DFFVdw/fXXH/H75eqzEMB9/LuIg2699VaeffZZbr75ZuLxOBs3bhz82vz58/F6vXzhC1/gk5/8JN/+9rdZtWoVGzZs4JFHHuHHP/7xkGu9/fbb7Nu3b7Ag1MFidTU1NSxZsgSARYsWceaZZ3LLLbfwr//6r/h8Pn784x8zZ84cLrrooqIZx//+7/9y++2386EPfYjJkycP+X5Tp0494nFLO45jzZo1xGIxTj31VGpqati3bx+//e1vUVWVq6++elRjKNRYli5desQ4pk6detSv2XEcq1evxuFwsHDhQsLhMJs2beKXv/wlJ510EhdccEHRjKOuro5PfOIT/OQnP8HhcDBz5kweffRRNm7cOKal1nyP46DHHnsM0zSzthSW73FcccUV/PGPf+S6667j4x//OC6Xi/vvv5/m5ma++93vFs04kskka9asYfHixfh8PjZu3Mhdd93FDTfcMKYZlGyNY9euXVx//fU0NjZy6aWXDrlOTU0NU6dOBXL3WQjgsMZS9KDMnHfeeUcs5w3w17/+dTC7/etf/8p//Md/sGfPHiZOnMj/+T//57Apvptvvpk//elPh11nyZIl/Pa3vx38eywW4wc/+AFPPfUUuq5z5pln8vWvf/2wzNvO4zjafSBzQmm0NWryPY6//vWv3HPPPWzfvp1kMjl46uW6664b80xQIX623mvOnDn8y7/8C5/97GeLZhwPPvgg//3f/01zczPpdJpx48ZxwQUX8KUvfYmKioqiGQdkfrv++c9/zoMPPkhvby8zZ87kS1/6Eueff35RjQPgIx/5CE6nkwcffHDUsRd6HOvXr+dnP/sZ27dvxzRNZs2aNdgqoljGkU6nuf7663nnnXdIJpPMmDGDq6++esx1wbI1jv/5n//ha1/72hGvc9lll7F69erBv+fisxAkCRJCCCFEmZI9QUIIIYQoS5IECSGEEKIsSRIkhBBCiLIkSZAQQgghypIkQUIIIYQoS5IECSGEEKIsSRIkhBBCiLIkSZAQQgghypK0zRBCFJX3Vpn1er1EIhHmzJnD2WefzYc//OFRVYt+4403ePnll7n66qsHu7oLIUqbJEFCiKL0pS99icmTJ6PrOt3d3bz66qt8//vf55577uFnP/sZc+fOHdH13nzzTe644w4uu+wySYKEKBOSBAkhitLKlSuHdIq/9tprWb9+PZ///Oe57rrreOyxx/D7/QWMUAhhd7InSAhRMpYvX851113Hvn37+Mtf/gLA1q1bufnmmzn//PNZsGABK1as4Gtf+xp9fX2Dj1uzZg233XYbAOeffz5z5sxhzpw5tLa2Dt7n4Ycf5sMf/jAnn3wyS5Ys4aabbqK9vT2/AxRCZJXMBAkhSsqll17Kj370I1566SWuvPJK1q1bR0tLCx/+8Iepr69nx44dPPDAA+zcuZMHHngAh8PBhRdeSFNTE4888ghf+9rXqK6uBqCmpgaAn//85/zkJz9h1apVXH755fT29vK73/2Oj3/84/z5z3+W5TMhipQkQUKIkjJ+/HgqKytpaWkB4B//8R/5zGc+M+Q+Cxcu5Ctf+Qqvv/46p59+OnPnzmX+/Pk88sgjXHDBBUyePHnwvvv27WPNmjXceOONfP7znx+8/aKLLuKyyy7jD3/4w5DbhRDFQ5bDhBAlJxgMkkgkAIbsC1IUhd7eXk455RQANm/efNxrPfXUU5imyapVq+jt7R38r66ujmnTprFhw4bcDEIIkXMyEySEKDnJZJLa2loA+vv7ueOOO3jsscfo6ekZcr9YLHbcazU1NWFZFhdddNERv+52y9uoEMVKXr1CiJLS0dFBLBZj6tSpANx44428+eabfPazn2XevHkEg0FM0+Rzn/sclmUd93qmaeJwOLj77rtxuVyHfT0YDGZ9DEKI/JAkSAhRUh5++GEAzjzzTAYGBli/fj1f/OIXueGGGwbv09TUdNjjHA7HEa83depULMti8uTJTJ8+PScxCyEKQ/YECSFKxvr16/nZz37G5MmT+dCHPnTEmRuAe++997DbAoEAcPgS2UUXXYTL5eKOO+44bObIsqwhR+2FEMVFZoKEEEXphRdeYPfu3RiGQXd3Nxs2bODll19m4sSJ/PznP8fn8+Hz+Vi8eDG/+tWv0DSNcePG8fLLLw+p/3PQiSeeCMCPf/xj3ve+9+HxeDj33HOZOnUqN954I7fffjv79u3jggsuIBQK0draytNPP82VV17JZz/72XwPXwiRBZIECSGK0k9/+lMAPB4PVVVVzJ49m1tuueWw3mG333473/nOd/jDH/6AZVmsWLGCu+++m7POOmvI9U4++WS+/OUvc9999/Hiiy9imiZ//etfCQaD/J//839obGzknnvu4c477wQyR/FXrFjBeeedl79BCyGyymENZ2egEEIIIUSJkT1BQgghhChLkgQJIYQQoixJEiSEEEKIsiRJkBBCCCHKkiRBQgghhChLkgQJIYQQoixJEiSEEEKIsiRJkBBCCCHKkiRBQgghhChLkgQJIYQQoixJEiSEEEKIsiRJkBBCCCHKkiRBQgghhChL/z/blyc4TJaoeQAAAABJRU5ErkJggg=="
          },
          "metadata": {}
        }
      ],
      "execution_count": 12
    },
    {
      "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
      },
      "outputs": [
        {
          "execution_count": 13,
          "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": {}
        }
      ],
      "execution_count": 13
    },
    {
      "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": "# HINT: Use get_dummies() function on the categorical columns\ncategoricals = ['Orbit', 'LaunchSite', 'LandingPad', 'Serial']\nfor c in categoricals:\n    temp_df = pd.get_dummies(features[c])\n\n    #Merge temp_df with features\n    features = pd.concat([features, temp_df], axis=1)\n\n    #Drop the original column\n    features.drop(c, axis=1, inplace=True)\n    \nfeatures.head()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 14,
          "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  ES-L1    GEO  ...  B1048  B1049  B1050  B1051  B1054  B1056  \\\n0            0  False  False  ...  False  False  False  False  False  False   \n1            0  False  False  ...  False  False  False  False  False  False   \n2            0  False  False  ...  False  False  False  False  False  False   \n3            0  False  False  ...  False  False  False  False  False  False   \n4            0  False  False  ...  False  False  False  False  False  False   \n\n   B1058  B1059  B1060  B1062  \n0  False  False  False  False  \n1  False  False  False  False  \n2  False  False  False  False  \n3  False  False  False  False  \n4  False  False  False  False  \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>ES-L1</th>\n      <th>GEO</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>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>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</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>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</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>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</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>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</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>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 80 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 14
    },
    {
      "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 = features.astype('float64')",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 15
    },
    {
      "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": "code",
      "source": "features_one_hot.to_csv('dataset_part_3.csv', index=False)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 16
    },
    {
      "cell_type": "markdown",
      "source": "## Authors\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<!--## Change Log-->\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<!--\n| Date (YYYY-MM-DD) | Version | Changed By      | Change Description      |\n| ----------------- | ------- | -------------   | ----------------------- |\n| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n| 2024-07-18        | 1.2     | Anita Verma | Clarified instructions|\n-->\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}