jupyter-labs-eda-dataviz (1).ipynb

{"cells": [{"metadata": {}, "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\u00a0Data\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\u00a0Data  Feature Engineering\n"}, {"metadata": {}, "cell_type": "markdown", "source": "***\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": "# andas 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", "execution_count": 1, "outputs": []}, {"metadata": {}, "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": "df=pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n\n# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n\n# df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n\ndf.head(5)", "execution_count": 2, "outputs": [{"output_type": "execute_result", "execution_count": 2, "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": {}}]}, {"metadata": {}, "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"}, {"metadata": {}, "cell_type": "code", "source": "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.show()", "execution_count": 3, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 2558.88x500 with 1 Axes>", "image/png": "iVBORw0KGgoAAAANSUhEUgAACggAAAH3CAYAAABkE0Z3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAACweUlEQVR4nOzde5zWZZ038M8tR0XmBhRmxMVDnkAgJTyhuUkaaCCalfqgmJWH0nRJtHJbd60nNU3toIuHrXRDe7TWxRIV1BSVFFFcLAhEzETEAVScEQ8chvv5Y3LW4Swzw8Dwfr9e87qZ7+/6/a7v7+aecbj9zHUVSqVSKQAAAAAAAAAAAECLsk1zNwAAAAAAAAAAAAA0PgFBAAAAAAAAAAAAaIEEBAEAAAAAAAAAAKAFEhAEAAAAAAAAAACAFkhAEAAAAAAAAAAAAFogAUEAAAAAAAAAAABogQQEAQAAAAAAAAAAoAUSENwMlEqlVFdXp1QqNXcrAAAAAAAAAAAAtBACgpuBt99+O8ViMW+//XZztwIAAAAAAAAAAEALISAIAAAAAAAAAAAALZCAIAAAAAAAAAAAALRAAoIAAAAAAAAAAADQAgkIAgAAAAAAAAAAQAskIAgAAAAAAAAAAAAt0BYZEHzsscdy7LHHpnv37ikUCrn77rtXGzNz5swMGzYsxWIxHTt2zCGHHJK5c+fWHV+6dGnOO++87LjjjunQoUOGDRuWefPm1bvG4sWLM2LEiBSLxRSLxYwYMSJvvfVWvTFz587Nsccemw4dOmTHHXfM+eefn2XLljXFbQMAAAAAAAAAAMAG2yIDgu+8807222+/XH/99Ws8/uKLL+aTn/xkevbsmYkTJ+a5557LJZdckvbt29eNGTlyZMaOHZs77rgjkyZNypIlSzJ06NDU1NTUjRk+fHimTZuW8ePHZ/z48Zk2bVpGjBhRd7ympiZDhgzJO++8k0mTJuWOO+7IXXfdlVGjRjXdzQMAAAAAAAAAAMAGKJRKpVJzN9EQhUIhY8eOzfHHH19XO/nkk9OmTZuMGTNmjedUVVWla9euGTNmTE466aQkyfz589OjR4/cd999GTx4cGbOnJl99903kydPzsEHH5wkmTx5cgYMGJBZs2Zln332yf3335+hQ4fmlVdeSffu3ZMkd9xxR04//fQsXLgwZWVla5x/6dKlWbp0ad3n1dXV6dGjR6qqqtZ6DgAAAAAAAAAAAHwUW+QKguuycuXK3Hvvvdl7770zePDgdOvWLQcffHC9bYinTp2a5cuXZ9CgQXW17t27p0+fPnniiSeSJE8++WSKxWJdODBJDjnkkBSLxXpj+vTpUxcOTJLBgwdn6dKlmTp16lp7vOKKK+q2LS4Wi+nRo0dj3T4AAAAAAAAAAAAkaYEBwYULF2bJkiX54Q9/mKOPPjoPPPBAPve5z+WEE07Io48+miSprKxM27Zt07lz53rnlpeXp7Kysm5Mt27dVrt+t27d6o0pLy+vd7xz585p27Zt3Zg1ufjii1NVVVX38corrzTongEAAAAAAAAAAGBVrZu7gca2cuXKJMlxxx2Xb37zm0mS/fffP0888URuvPHGfOpTn1rruaVSKYVCoe7zD/+5IWNW1a5du7Rr1279NwMAAAAAAAAAAAAbqcWtILjjjjumdevW2XfffevVe/Xqlblz5yZJKioqsmzZsixevLjemIULF9atCFhRUZEFCxasdv1FixbVG7PqSoGLFy/O8uXLV1tZEAAAAAAAAAAAADalFhcQbNu2bQ488MA8//zz9eqzZ8/OrrvumiTp379/2rRpkwcffLDu+GuvvZbp06fn0EMPTZIMGDAgVVVVmTJlSt2Yp556KlVVVfXGTJ8+Pa+99lrdmAceeCDt2rVL//79m+weAQAAAAAAAAAAYH22yC2GlyxZkjlz5tR9/tJLL2XatGnp0qVLdtlll1x00UU56aST8o//+I8ZOHBgxo8fn3vuuScTJ05MkhSLxXz1q1/NqFGjssMOO6RLly658MIL07dv3xx11FFJalccPProo3PmmWfmpptuSpKcddZZGTp0aPbZZ58kyaBBg7LvvvtmxIgR+dGPfpQ333wzF154Yc4888yUlZVt2icFAAAAAAAAAAAAPqRQKpVKzd3ERzVx4sQMHDhwtfqXvvSl3HrrrUmSX/7yl7niiisyb9687LPPPvne976X4447rm7s+++/n4suuii//vWv89577+XII4/M6NGj06NHj7oxb775Zs4///z8/ve/T5IMGzYs119/fTp16lQ3Zu7cuTnnnHPy8MMPZ9ttt83w4cNz9dVXp127dht8P9XV1SkWi6mqqhIsBAAAAAAAAAAAoFFskQHBlkZAEAAAAAAAAFinmeOSx69JFs1KuvZMDh+V9Bra3F3R3LwuNsqEGZUZ/ciczF6wJHuXb59zBu6Zwb0rmrstAGgSAoKbAQFBAAAAAAAAYK1mjkvuPGWVYiE56TZhsK2Z18VGmTCjMmePmVqvVigkN57aX0gQgBZpm+ZuAAAAAAAAAIB1ePyaNRRLyaRrN3krbEa8LjbK6EfmrFYrlZLRE19shm4AoOkJCAIAAAAAAABszhbNWnN94VrqbB28LjbK7AVL1lh/YcHbm7gTANg0BAQBAAAAAAAANmdde6653m0tdbYOXhcbZe/y7ddY36u84ybuBAA2DQFBAAAAAAAAgM3Z4aOSFFYpFv5eZ71mjktuHphctlPt48xxzd1R42jC18WEGZU57vpJ6XXJ+Bx3/aRMmFHZ4GtuLs4ZuGcKqzxthUJy7hF7NP5kLfW194Gmvr+W/vw1pZb83G3Ke2tpr/GW/LpgnQqlUqnU3E1s7aqrq1MsFlNVVZWysrLmbgcAAAAAAADY3Mwcl0y6tnb72G49a0NgPYc0d1ebv5njkjtPWaVYSE66Lek1tFlaalRN8LqYMKMyZ4+ZWq9WKCQ3nto/g3tXNOjam4sJMyozeuKLeWHB29mrvGPOPWKPDGrse9saXntNeX8t/flrSi35uduU99bSXuMt+XXBegkIbgYEBAEAAAAAAACawM0Dk/nPrl7fuX9y5sObvp8twHHXT8pz86pWq+/Xo1N+d+5hzdDRFqqlv/aa+v5a+vPXlFryc7cp762lvcZb8uuC9bLFMAAAAAAAAAAt06JZa64vXEudzF6wZI31Fxa8vYk72cK19NdeU99fS3/+mlJLfu425b21tNd4S35dsF4CggAAAAAAAAC0TF17rrnebS11snf59mus71XecRN3soVr6a+9pr6/lv78NaWW/Nxtyntraa/xlvy6YL0EBAEAAAAAAABomQ4flaSwSrHw9zprcs7APVNY5SkrFJJzj9ijeRraUrX0115T319Lf/6aUkt+7jblvbW013hLfl2wXoVSqVRq7ia2dtXV1SkWi6mqqkpZWVlztwMAAAAAAADQcswcl0y6tnYbxW49a8MQPYc0d1ebtQkzKjN64ot5YcHb2au8Y849Yo8M6l3R3G1teVr6a6+p76+lP39NqSU/d5vy3lraa7wlvy5YJwHBzYCAIAAAAAAAAAAAAI3NFsMAAAAAAAAAAADQAgkIAgAAAAAAAAAAQAskIAgAAAAAAAAAAAAtkIAgAAAAAAAAAAAAtEACggAAAAAAAAAAANACCQgCAAAAAAAAAABACyQgCAAAAAAAAAAAAC2QgCAAAAAAAAAAAAC0QAKCAAAAAAAAAAAA0AIJCAIAAAAAAAAAAEALJCAIAAAAAAAAAAAALZCAIAAAAAAAAAAAALRAAoIAAAAAAAAAAADQAgkIAgAAAAAAAAAAQAskIAgAAAAAAAAAAAAtkIAgAAAAAAAAAAAAtEACggAAAAAAAAAAANACCQgCAAAAAAAAAABACyQgCAAAAAAAAAAAAC2QgCAAAAAAAAAAAAC0QAKCAAAAAAAAAAAA0AIJCAIAAAAAAAAAAEALJCAIAAAAAAAAAAAALZCAIAAAAAAAAAAAALRAAoIAAAAAAAAAAADQAgkIAgAAAAAAAAAAQAskIAgAAAAAAAAAAAAtkIAgAAAAAAAAAAAAtEBbZEDwsccey7HHHpvu3bunUCjk7rvvXuvYs88+O4VCIT/5yU/q1ZcuXZrzzjsvO+64Yzp06JBhw4Zl3rx59cYsXrw4I0aMSLFYTLFYzIgRI/LWW2/VGzN37twce+yx6dChQ3bcccecf/75WbZsWSPdKQAAAAAAAAAAAGycLTIg+M4772S//fbL9ddfv85xd999d5566ql07959tWMjR47M2LFjc8cdd2TSpElZsmRJhg4dmpqamroxw4cPz7Rp0zJ+/PiMHz8+06ZNy4gRI+qO19TUZMiQIXnnnXcyadKk3HHHHbnrrrsyatSoxrtZAAAAAAAAAAAA2AiFUqlUau4mGqJQKGTs2LE5/vjj69VfffXVHHzwwZkwYUKGDBmSkSNHZuTIkUmSqqqqdO3aNWPGjMlJJ52UJJk/f3569OiR++67L4MHD87MmTOz7777ZvLkyTn44IOTJJMnT86AAQMya9as7LPPPrn//vszdOjQvPLKK3UhxDvuuCOnn356Fi5cmLKysjX2vHTp0ixdurTu8+rq6vTo0SNVVVVrPQcAAAAAAAAAAAA+ii1yBcH1WblyZUaMGJGLLroovXv3Xu341KlTs3z58gwaNKiu1r179/Tp0ydPPPFEkuTJJ59MsVisCwcmySGHHJJisVhvTJ8+feqtUDh48OAsXbo0U6dOXWt/V1xxRd22xcViMT169GjwPQMAAAAAAAAAAMCHtciA4JVXXpnWrVvn/PPPX+PxysrKtG3bNp07d65XLy8vT2VlZd2Ybt26rXZut27d6o0pLy+vd7xz585p27Zt3Zg1ufjii1NVVVX38corr3yk+wMAAAAAAAAAAID1ad3cDTS2qVOn5qc//WmeffbZFAqFj3RuqVSqd86azt+YMatq165d2rVr95F6AwAAAAAAAAAAgI+ixa0g+Pjjj2fhwoXZZZdd0rp167Ru3Tovv/xyRo0ald122y1JUlFRkWXLlmXx4sX1zl24cGHdioAVFRVZsGDBatdftGhRvTGrrhS4ePHiLF++fLWVBQEAAAAAAAAAAGBTanEBwREjRuRPf/pTpk2bVvfRvXv3XHTRRZkwYUKSpH///mnTpk0efPDBuvNee+21TJ8+PYceemiSZMCAAamqqsqUKVPqxjz11FOpqqqqN2b69Ol57bXX6sY88MADadeuXfr3778pbhcAAAAAAAAAAADWaIvcYnjJkiWZM2dO3ecvvfRSpk2bli5dumSXXXbJDjvsUG98mzZtUlFRkX322SdJUiwW89WvfjWjRo3KDjvskC5duuTCCy9M3759c9RRRyVJevXqlaOPPjpnnnlmbrrppiTJWWedlaFDh9ZdZ9CgQdl3330zYsSI/OhHP8qbb76ZCy+8MGeeeWbKyso2xVMBAAAAAAAAAAAAa7RFriD4zDPPpF+/funXr1+S5IILLki/fv3yr//6rxt8jR//+Mc5/vjjc+KJJ+awww7Ldtttl3vuuSetWrWqG3P77benb9++GTRoUAYNGpSPf/zjGTNmTN3xVq1a5d5770379u1z2GGH5cQTT8zxxx+fq6++uvFuFgAAAAAAAAAAADZCoVQqlZq7ia1ddXV1isViqqqqrDwIAAAAAADApjFzXPL4NcmiWUnXnsnho5JeQ5u7KwAAoBFtkSsIAgAAAAAAAA0wc1xy5ynJ/GeT5e/WPt55am0dAABoMQQEAQAAAAAAYGvz+DVrKJaSSddu8lYAAICmIyAIAAAAAAAAW5tFs9ZcX7iWOgAAsEUSEAQAAAAAAICtTdeea653W0sdAADYIgkIAgAAAAAAwNbm8FFJCqsUC3+vAwAALYWAIAAAAAAAAGxteg1NTrot2bl/0qZD7ePJtyc9hzR3ZwAAQCMqlEqlUnM3sbWrrq5OsVhMVVVVysrKmrsdAAAAAAAAAAAAWgArCAIAAAAAAAAAAEALJCAIAAAAAAAAAAAALZCAIAAAAAAAAAAAALRAAoIAAAAAAAAAAADQAgkIAgAAAAAAAAAAQAskIAgAAAAAAAAAAAAtkIAgAAAAAAAAAAAAtEACggAAAAAAAAAAANACCQgCAAAAAAAAAABACyQgCAAAAAAAAAAAAC2QgCAAAAAAAAAAAAC0QAKCAAAAAAAAAAAA0AK1bugF5s2bl8cffzxPPPFE5s2bl0WLFuWNN97Itttum65du6Zr167p27dvDj/88Bx44IFp06ZNY/QNAAAAAAAAAAAArEOhVCqVPupJc+bMyS233JI777wzL7300mrHP7hkoVCoV2/Xrl2OOuqojBgxIp/73OfSunWD84ktQnV1dYrFYqqqqlJWVtbc7QAAAAAAAAAAANACfKSA4FNPPZUf/OAHuf/++1MqlfLhU9u0aZMuXbqkS5cu6dy5c9577728+eabWbx4caqrq/93wr+HBsvLy/NP//RP+cY3vpEOHTo04i1teQQEAQAAAAAAAAAAaGwbFBCcM2dORo0alXHjxtWFAnfYYYcce+yxOeSQQ3LQQQelb9++adWq1RrPX7RoUaZMmZIpU6bkD3/4Q5588smUSqUUCoXssMMO+d73vpevfe1rq604uLUQEAQAAAAAAAAAAKCxbVBAsH379lm2bFnatGmTz3/+8xk+fHiOPvrojd4ieO7cubn99tvzn//5n5k9e3YKhUIuu+yyfOc739mo623pBAQBAAAAAAAAAABobNtsyKBSqZSzzz47L7zwQn79619n6NChGx0OTJJddtklF198cWbOnJnbb789++67b5YtW7bR1wMAAAAAAAAAAADq26AVBF999dXsvPPOTdrIa6+9lp122qlJ59hcWUEQAAAAAAAAAACAxrZBKwg2dTgwyVYbDgQAAAAAAAAAAICmsEEBQQAAAAAAAAAAAGDLIiAIAAAAAAAAAAAALZCAIAAAAAAAAAAAALRArRtycqtWrTbqvPbt26dYLGavvfbKIYccktNOOy29e/duSCsAAAAAAAAAAADAhxRKpVJpY0/eZpuGL0BYKBSSJGeccUZ+9rOfpV27dg2+5pamuro6xWIxVVVVKSsra+52AAAAAAAAAAAAaAEatILgv/3bvyVJ7r///kyZMiVJst9+++WAAw5I165dkySLFi3KM888k+eeey6FQiEHHnhgBg8enOrq6kyfPj2PPfZYli9fnp///Od5880389vf/raBtwQAAAAAAAAAAAA0aAXBJLniiivy3e9+NwcddFBuvvnmfPzjH1/juOeeey5nnXVWnnnmmVx66aW55JJLkiTz58/P6aefnoceeiiFQiH33ntvjj766Ia0tMWxgiAAAAAAAMAWZOa45PFrkkWzkq49k8NHJb2GNndXAAAAq2lQQHDixIk58sgjs+++++bpp59O+/bt1zn+/fffT//+/TNr1qxMmDAhRx11VF394x//eF588cWceOKJ+X//7/9tbEtbJAFBAAAAAACALcTMccmdp6xSLCQn3SYkCAAAbHa2acjJP/3pT5MkF1100XrDgUnSvn37fOtb30qpVMp1111Xr37OOeekVCpl8uTJDWkJAAAAAAAAms7j16yhWEomXbvJWwEAAFifBgUEp0yZkiTp06fPBp/Tt2/fJMnTTz9dr37AAQckSRYuXNiQlgAAAAAAAKDpLJq15vrCtdQBAACaUYMCgm+++WaS2i1yN9QHYxcvXlyv3rFjxyRJoVBoSEsAAAAAAADQdLr2XHO921rqAAAAzahBAcGKiookydixYzf4nP/+7/9Okuy000716h+EDbt27dqQlgAAAAAAAKDpHD4qyaoLXhT+XgcAANi8NCggOHjw4JRKpdxwww0bFBL87//+79xwww0pFAo5+uij6x2bOnVqkuQf/uEf1nudxx57LMcee2y6d++eQqGQu+++u+7Y8uXL8+1vfzt9+/ZNhw4d0r1795x22mmZP39+vWssXbo05513Xnbcccd06NAhw4YNy7x58+qNWbx4cUaMGJFisZhisZgRI0bkrbfeqjdm7ty5OfbYY9OhQ4fsuOOOOf/887Ns2bL13gMAAAAAAABboF5Dk5NuS3bun7TpUPt48u1JzyHN3RkAAMBqGhQQ/Od//ud06NAhNTU1+cIXvpAvfvGLueeeezJ//vysWLEiK1asyPz58/P73/++7nhNTU06dOiQ73znO/Wu9Zvf/CaFQiGf/vSn1zvvO++8k/322y/XX3/9asfefffdPPvss7nkkkvy7LPP5r//+78ze/bsDBs2rN64kSNHZuzYsbnjjjsyadKkLFmyJEOHDk1NTU3dmOHDh2fatGkZP358xo8fn2nTpmXEiBF1x2tqajJkyJC88847mTRpUu64447cddddGTXKb4gBAAAAAAC0WL2GJmc+nHx3fu2jcCAAALCZKpRKpVJDLvDggw/mc5/7XN59990UCqsup15fqVTKdtttl7vvvjtHHXVUXf3FF1/MGWeckST58Y9/nP3333+D5y8UChk7dmyOP/74tY55+umnc9BBB+Xll1/OLrvskqqqqnTt2jVjxozJSSedlCSZP39+evTokfvuuy+DBw/OzJkzs++++2by5Mk5+OCDkySTJ0/OgAEDMmvWrOyzzz65//77M3To0Lzyyivp3r17kuSOO+7I6aefnoULF6asrGyD7qG6ujrFYjFVVVUbfA4AAAAAAAAAAACsS4NWEEySz3zmM3nuuedy7LHHplAopFQqrfGjUCjk2GOPzXPPPVcvHJgke+yxRx555JE88sgjHykcuKGqqqpSKBTSqVOnJLXbGS9fvjyDBg2qG9O9e/f06dMnTzzxRJLkySefTLFYrAsHJskhhxySYrFYb0yfPn3qwoFJ7bbLS5curdsyeU2WLl2a6urqeh8AAAAAAAAAAADQmFo3xkX22GOP/O53v8v8+fMzceLETJ8+PYsXL06SdO7cOb17987AgQPrBek2lffffz/f+c53Mnz48LrV+SorK9O2bdt07ty53tjy8vJUVlbWjenWrdtq1+vWrVu9MeXl5fWOd+7cOW3btq0bsyZXXHFFvve97zXovgAAAAAAAAAAAGBdGiUg+IHu3btn+PDhjXnJBlm+fHlOPvnkrFy5MqNHj17v+A9WOvzAmrZM3pgxq7r44otzwQUX1H1eXV2dHj16rLc/AAAAAAAAAAAA2FAN2mL4lVde2ehzf/3rXzdk6vVavnx5TjzxxLz00kt58MEH61YPTJKKioosW7asbpXDDyxcuLBuRcCKioosWLBgtesuWrSo3phVVwpcvHhxli9fvtrKgh/Wrl27lJWV1fsAAAAAAAAAAACAxtSggOBnPvOZvPHGGx/5vF/96lf50pe+1JCp1+mDcOALL7yQhx56KDvssEO94/3790+bNm3y4IMP1tVee+21TJ8+PYceemiSZMCAAamqqsqUKVPqxjz11FOpqqqqN2b69Ol57bXX6sY88MADadeuXfr3799k9wcAAAAAAAAAAADrUyiVSqWNPXmbbbbJJz7xiTzyyCPp2LHjBp1z66235owzzkipVEpNTc1GzbtkyZLMmTMnSdKvX79ce+21GThwYLp06ZLu3bvn85//fJ599tmMGzeu3kp+Xbp0Sdu2bZMkX//61zNu3Ljceuut6dKlSy688MK88cYbmTp1alq1apUkOeaYYzJ//vzcdNNNSZKzzjoru+66a+65554kSU1NTfbff/+Ul5fnRz/6Ud58882cfvrpOf7443Pddddt8P1UV1enWCymqqrKaoIAAAAAAAAAAAA0igYFBDt27Jh33303//iP/5jx48enXbt26xz/85//PF/72teycuXKfPzjH8+0adM2at6JEydm4MCBq9W/9KUv5dJLL83uu+++xvMeeeSRHHHEEUmS999/PxdddFF+/etf57333suRRx6Z0aNHp0ePHnXj33zzzZx//vn5/e9/nyQZNmxYrr/++nTq1KluzNy5c3POOefk4Ycfzrbbbpvhw4fn6quvXu9z8WECggAAAAAAAAAAADS2BgUE//CHP2To0KFZtmxZhgwZkrFjx9atvreqm266Keeee25WrlyZ/fffPw8++OBqW/9urQQEAQAAAAAAAAAAaGzbNOTkI488Mr/+9a+zzTbb5N57783pp5++xnGjR4/OOeeck5UrV+YTn/hEHnroIeFAAAAAAAAAAAAAaEINCggmyec+97ncfPPNKZVK+fWvf53zzz+/3vGf/exnOe+881IqlXLAAQfkoYceSpcuXRo6LQAAAAAAAAAAALAODQ4IJsmXv/zlXHXVVSmVSvn3f//3/Nu//VuS5Nprr803v/nNlEqlHHTQQXnwwQfTqVOnxpgSAAAAAAAAAAAAWIdCqVQqNdbF/vmf/zk//OEPUygU8tnPfjb33XdfSqVSDjnkkIwfPz5lZWWNNVWLUl1dnWKxmKqqKs8RAAAAAAAAAAAAjaJRA4JJ8rWvfS0333xzCoVCSqVSBgwYkPHjx6djx46NOU2LIiAIAAAAAAAAAABAY2uULYY/7IYbbsiJJ56YUqmUT37yk3nggQeEAwEAAAAAAAAAAGATa70hgz72sY99pIuuWLEihUIhc+bMSd++fdc4plAo5MUXX/xI1wUAAAAAAAAAAAA2zAYFBP/2t79t1MUrKyvXeqxQKGzUNQEAAAAAAAAAAID126CA4Je+9KWm7gMAAAAAAAAAAABoRIVSqVRq7ia2dtXV1SkWi6mqqkpZWVlztwMAAAAAAAAAAEALsE1zNwAAAAAAAAAAAAA0PgFBAAAAAAAAAAAAaIEEBAEAAAAAAAAAAKAF2qCA4G9/+9smbWLevHl54oknmnQOAAAAAAAAAAAA2JpsUEDwpJNOSt++fRs9KDh37tx8/etfz5577pmHHnqoUa8NAAAAAAAAAAAAW7MNCgjutddemTFjRk4++eTstttu+ed//ufMmDFjoyZ85513ctttt+WYY47JnnvumZtvvjk1NTXZc889N+p6AAAAAAAAAAAAwOoKpVKptL5BK1asyM9+9rP88Ic/zOuvv55CoZCkNjh4yCGH5MADD0y/fv3SrVu3dO7cOZ07d857772XN998M4sXL87s2bPz9NNPZ8qUKZkyZUref//9fDDtMccckyuvvDJ9+vRp2jvdjFVXV6dYLKaqqiplZWXN3Q4AAAAAAAAAAAAtwAYFBD/wzjvvZPTo0fn3f//3zJ07t/YCfw8LbogPpmrVqlWOO+64XHTRRTn44IM/Ysstj4AgAAAAAAAAAAAAje0jBQQ/sHLlyjz44IP5zW9+k0ceeSR/+9vf1nvOtttum4MOOihDhgzJ8OHD0717943pt0USEAQAAAAAAAAAAKCxbVRAcFWvvvpqnnjiicybNy+LFi3Km2++mfbt26dr167p2rVr+vbtmwMOOCBt2rRpjJ5bHAFBAAAAAAAAAAAAGlujBARpGAFBAAAAAAAAAAAAGts2zd0AAAAAAAAAAAAA0PgEBAEAAAAAAAAAAKAFEhAEAAAAAAAAAACAFkhAEAAAAAAAAAAAAFogAUEAAAAAAAAAAABogQQEAQAAAAAAAAAAoAUSEAQAAAAAAAAAAIAWSEAQAAAAAAAAAAAAWiABQQAAAAAAAAAAAGiBBAQBAAAAAAAAAACgBRIQBAAAAAAAAAAAgBaodVNPcM899+Q3v/lNXn/99ey+++4588wz069fv6aeFgAAAAAAAAAAALZqhVKpVNrYkx955JGcdNJJad++ff70pz+lU6dO9Y5fcsklufzyy+vVWrVqlVtuuSWnnHLKxk7b4lRXV6dYLKaqqiplZWXN3Q4AAAAAAAAAAAAtQIO2GL7vvvvy+uuv55BDDlktHPinP/0pl19+eUqlUkqlUjp16pRSqZQVK1bkrLPOyssvv9yQqQEAAAAAAAAAAIB1aFBAcNKkSSkUCvnMZz6z2rEbbrghpVIpnTt3ztSpU/PGG29kypQp6dKlS95///3ceOONDZkaAAAAAAAAAAAAWIcGBQQrKyuTJD179lzt2Lhx41IoFHLuueemX79+SZIDDjgg3/jGN1IqlfLQQw81ZGoAAAAAAAAAAABgHRoUEFy4cGGSpFgs1qu/+OKLefXVV5MkJ5xwQr1jhx9+eJJkzpw5DZkaAAAAAAAAAAAAWIcGBQRLpVKSpKqqql798ccfT1IbHNx///3rHdthhx2SJO+++25DpgYAAAAAAAAAAADWoUEBwYqKiiTJzJkz69UnTJiQJDnssMNWO+edd95JknTu3LkhUwMAAAAAAAAAAADr0KCA4CGHHJJSqZQbbrihbkXAv/71r/nd736XQqGQz3zmM6udM3v27CT/Gy7cGI899liOPfbYdO/ePYVCIXfffXe946VSKZdeemm6d++ebbfdNkcccURmzJhRb8zSpUtz3nnnZccdd0yHDh0ybNiwzJs3r96YxYsXZ8SIESkWiykWixkxYkTeeuutemPmzp2bY489Nh06dMiOO+6Y888/P8uWLdvoewMAAAAAAAAAAIDG0KCA4BlnnJEk+dOf/pQ+ffrkC1/4Qg455JC8//772XbbbTN8+PDVznnssceSJPvuu+9Gz/vOO+9kv/32y/XXX7/G41dddVWuvfbaXH/99Xn66adTUVGRz3zmM3n77bfrxowcOTJjx47NHXfckUmTJmXJkiUZOnRoampq6sYMHz4806ZNy/jx4zN+/PhMmzYtI0aMqDteU1OTIUOG5J133smkSZNyxx135K677sqoUaM2+t4AAAAAAAAAAACgMRRKpVKpIRe44IIL8pOf/KT2YoVCPrjcv//7v+frX/96vbHvv/9+unfvnqqqqvz85z/Pl7/85YZMXTfn2LFjc/zxxyepXT2we/fuGTlyZL797W8nqV0tsLy8PFdeeWXOPvvsVFVVpWvXrhkzZkxOOumkJMn8+fPTo0eP3HfffRk8eHBmzpyZfffdN5MnT87BBx+cJJk8eXIGDBiQWbNmZZ999sn999+foUOH5pVXXkn37t2TJHfccUdOP/30LFy4MGVlZRt0D9XV1SkWi6mqqtrgcwAAAAAAAAAAAGBdGrSCYJJce+21+f3vf58RI0bkqKOOymmnnZaHHnpotXBgkvz+979PWVlZdtlllzVuP9wYXnrppVRWVmbQoEF1tXbt2uVTn/pUnnjiiSTJ1KlTs3z58npjunfvnj59+tSNefLJJ1MsFuvCgUntlsrFYrHemD59+tSFA5Nk8ODBWbp0aaZOnbrWHpcuXZrq6up6HwAAAAAAAAAAANCYWjfGRYYOHZqhQ4eud9yJJ56YE088sTGmXKvKysokSXl5eb16eXl5Xn755boxbdu2TefOnVcb88H5lZWV6dat22rX79atW70xq87TuXPntG3btm7MmlxxxRX53ve+9xHvDAAAAAAAAAAAADZcg1cQ3FwVCoV6n5dKpdVqq1p1zJrGb8yYVV188cWpqqqq+3jllVfW2RcAAAAAAAAAAAB8VJskIPjiiy/mqaeeyoIFC5p8roqKiiRZbQW/hQsX1q32V1FRkWXLlmXx4sXrHLOmfhctWlRvzKrzLF68OMuXL19tZcEPa9euXcrKyup9AAAAAAAAAAAAQGNqUEBw0aJFGT16dEaPHp2qqqrVjs+ZMyf9+/fP3nvvnUMPPTQ777xzvvCFL+Stt95qyLTrtPvuu6eioiIPPvhgXW3ZsmV59NFHc+ihhyZJ+vfvnzZt2tQb89prr2X69Ol1YwYMGJCqqqpMmTKlbsxTTz2VqqqqemOmT5+e1157rW7MAw88kHbt2qV///5Ndo8AAAAAAAAAAACwPoVSqVTa2JNvvPHGnHPOOdlnn30yc+bMeseWLl2aPn365K9//Ws+PEWhUMjhhx+eiRMnbnTTS5YsyZw5c5Ik/fr1y7XXXpuBAwemS5cu2WWXXXLllVfmiiuuyC233JK99torl19+eSZOnJjnn38+HTt2TJJ8/etfz7hx43LrrbemS5cuufDCC/PGG29k6tSpadWqVZLkmGOOyfz583PTTTclSc4666zsuuuuueeee5IkNTU12X///VNeXp4f/ehHefPNN3P66afn+OOPz3XXXbfB91NdXZ1isZiqqiqrCQIAAAAAAAAAANAoGrSC4AMPPJBCoZDPf/7zqx279dZb8+KLLyZJhg0blp/+9Kc59thjUyqV8vjjj+c3v/nNRs/7zDPPpF+/funXr1+S5IILLki/fv3yr//6r0mSb33rWxk5cmTOOeecHHDAAXn11VfzwAMP1IUDk+THP/5xjj/++Jx44ok57LDDst122+Wee+6pCwcmye23356+fftm0KBBGTRoUD7+8Y9nzJgxdcdbtWqVe++9N+3bt89hhx2WE088Mccff3yuvvrqjb43AAAAAAAAAAAAaAwNWkGwd+/emTVrVsaOHZthw4bVO3bEEUfksccey5FHHllvK99BgwbloYceynHHHZexY8dufOctiBUEAQAAAAAAAAAAaGytG3LyokWLkiTdu3evV3/vvffy5JNPplAo5Kyzzqp37Ctf+UoeeuihPPvssw2ZGgAAaAwzxyWPX5MsmpV07ZkcPirpNbS5u4Itj68lPuC1sGXx97XZmjCjMqMfmZPZC5Zk7/Ltc87APTO4d0VztwVNw/ciaDy+ngAAAFbToBUE27Ztm5qamjz99NP5xCc+UVd/5JFHcuSRR2abbbbJokWL0rlz57pjTz31VAYMGJB27drlvffea1j3LYQVBAEAaBYzxyV3nrJKsZCcdJv/gQIfha8lPuC1sGXx97XZmjCjMmePmVqvVigkN57aX0iQlsf3Img8vp4AAADWaJuGnLz99tsnSSorK+vVJ06cmCTZd99964UDk6RNmzZJktatG7R4IQAA0FCPX7OGYimZdO0mbwW2aL6W+IDXwpbF39dma/Qjc1arlUrJ6IkvNkM30MR8L4LG4+sJgK3VzHHJzQOTy3aqfZw5rrk7AmAz06CAYM+ePZMk48ePr1e/6667UigU8qlPfWq1cz4IE5aXlzdkagAAoKEWzVpzfeFa6sCa+VriA14LWxZ/X5ut2QuWrLH+woK3N3EnsAn4XgSNx9cTAFujD1bQnf9ssvzd2sc7TxUSBKCeBgUEhwwZklKplJtvvjk33HBDpk+fngsvvDB/+ctfkiQnnHDCauc8++yzSZJ/+Id/aMjUAABAQ3XtueZ6t7XUgTXztcQHvBa2LP6+Nlt7l2+/xvpe5R03cSewCfheBI3H1xMAWyMr6AKwARoUEPzGN76RnXbaKcuWLcs3vvGN7Lfffvnxj3+cJBkwYEAGDhy42jn33HNPCoVCDj/88IZMDQAANNTho5IUVikW/l4HNpivJT7gtbBl8fe12Tpn4J4prPJXUygk5x6xR/M0BE3J9yJoPL6eANgaWUEXgA3QoIBgsVjMQw89lE984hMplUp1H4cffnh+85vfrDb+ueeey9NPP50k+cxnPtOQqQEAgIbqNTQ56bZk5/5Jmw61jyffnvQc0tydwZbF1xIf8FrYsvj72mwN7l2RG0/tn/16dMp2bVtlvx6dctOp/TOod0VztwaNz/ciaDy+ngDYGllBF4ANUCiVSqXGuNBLL72UysrK7LTTTtltt93WOOa5557LtGnTkiSnnHJKWrdu3RhTb/Gqq6tTLBZTVVWVsrKy5m4HAAAAAAAAANjczRyX3Hlqkg/HPgpC8gDU02gBQTaegCAAAAAAAAAA8JHNHJdMurZ2W+FuPZPDRwkHAlCPgOBmQEAQAAAAAAAAAACAxrZNczcAAAAAAAAAAAAANL7WjXWht99+Ow899FCee+65vP7663nvvfeyrsUJC4VCfvGLXzTW9AAAAAAAAAAAAMCHNHiL4ZUrV+b//t//m2uuuSbvvPPOBp1TKpVSKBRSU1PTkKlbDFsMAwAAAAAAAAAA0NgavILg6aefnttvvz2lUimtWrXKDjvskIULF6ZQKOQf/uEfsnjx4ixZsiRJ7aqBO+64Y7bbbrsGNw4AAAAAAAAAAACs3TYNOXnChAm57bbbktQGBRcuXJiHHnqo7vjLL7+c6urqzJo1K//0T/+UbbbZJp07d87999+fl156qWGdAwAAAAAAAAAAAGvVoIDgLbfckiTp3bt3fvnLX6Zz584pFAqrjdt7773z4x//OGPHjs2LL76Yz372s6mqqmrI1AAAAAAAAAAAAMA6NCggOHny5BQKhZx77rkbNH7o0KH50pe+lJdffjk/+9nPGjI1AAAAAAAAAAAAsA4NCgguXLgwSe0KgR9o1apV3Z+XLl262jlf+MIXUiqVMnbs2IZMDQAAAAAAAAAAAKxDgwKCH+jSpUvdnzt27Fj35w8ChB/WrVu3JMnf/va3xpgaAAAAAAAAAAAAWIMGBQTLy8uTJG+++Wa9Wtu2bZMkf/rTn1Y7Z+7cuUmS999/vyFTAwAAAAAAAAAAAOvQoIBg3759kyR/+ctf6mqtW7dOv379kiS33HLLaufcdNNNSZJdd921IVMDAAAAAAAAAAAA69CggOARRxyRUqmUhx56qF791FNPTalUytixY3Paaafl3nvvzW9/+9sce+yxmTBhQgqFQo477rgGNQ4AAAAAAAAAAACsXaFUKpU29uSXXnope+yxR9q1a5e//e1vdVsOr1ixIoccckieffbZFAqFeueUSqXsuuuuefbZZ9O5c+eGdd9CVFdXp1gspqqqKmVlZc3dDgAAAAAAAAAAAC1Ag1YQ3H333fPXv/4106dPrxdsa926dR588MGccsopad26dUqlUj7IIQ4ZMiSPP/64cCAAAAAAAAAAAAA0oQatILgh3n777bzwwgtZsWJF9txzz3Tp0qUpp9siWUEQAAAAAAAAAACAxtbkAUHWT0AQAAAAAAAAAACAxtagLYYBAAAAAAAAAACAzZOAIAAAAAAAAAAAALRArTd04K9+9atGn/y0005r9GsCAAAAAAAAAAAASaFUKpU2ZOA222yTQqHQeBMXClmxYkWjXW9LVl1dnWKxmKqqqpSVlTV3OwAAAAAAAAAAALQAG7yC4Ac2ME8IAAAAAAAAAAAANKOPHBDcdtttc9xxx+XEE09M586dm6InAAAAAAAAAAAAoIE2eIvhvffeO3PmzKk9qVBI27ZtM2TIkIwYMSJDhgxJ69YfOWvI39liGAAAAAAAAAAAgMa2wQHBJHnqqafyq1/9Kr/5zW/yxhtvpFAoJEk6d+6ck08+OaeeemoOOeSQJmu2pRIQBAAAAAAAAAAAoLF9pIDgB1asWJH77rsv//mf/5n77rsvS5curQsL7rHHHhkxYkROOeWUfOxjH2v0hlsiAUEAAAAAAAAAAAAa20YFBD/srbfeym9+85uMGTMmf/zjH2sv+vew4IABA3LaaaflxBNPTKdOnRrcbEslIAgAAAAAAAAAAEBja3BA8MNeeumljBkzJrfffnteeOGF2gkKhbRt2zbHHXdc7rjjjsaaqkUREAQAAAAAAAAAAKCxNWpA8MMmT56cMWPG5JZbbsn777+f9u3b5913322KqbZ4AoIAAAAAAAAAAAA0tm2a4qKvvvpqHnvssTz22GNZunRpU0wBAAAAAAAAAAAArEPrxrrQkiVLctddd2XMmDGZOHFiSqVSPlic8KCDDsqXvvSlxpoKAAAAAAAAAAAAWI8GrSC4cuXK3H///Rk+fHgqKiryla98JQ8//HBWrlyZXXfdNd/97nfz/PPPZ/Lkyfn617/eWD2v14oVK/Iv//Iv2X333bPtttvmYx/7WL7//e9n5cqVdWNKpVIuvfTSdO/ePdtuu22OOOKIzJgxo951li5dmvPOOy877rhjOnTokGHDhmXevHn1xixevDgjRoxIsVhMsVjMiBEj8tZbb22K2wQAAAAAAAAAAIC12qgVBJ999tmMGTMm/+///b8sWrQoSW3grlgs5gtf+EJOO+20HH744Y3a6Edx5ZVX5sYbb8x//ud/pnfv3nnmmWfy5S9/OcViMf/0T/+UJLnqqqty7bXX5tZbb83ee++dH/zgB/nMZz6T559/Ph07dkySjBw5Mvfcc0/uuOOO7LDDDhk1alSGDh2aqVOnplWrVkmS4cOHZ968eRk/fnyS5KyzzsqIESNyzz33NM/NAwAAAAAAAAAAQJJC6YN9gNfjlVdeye23354xY8Zk1qxZSWpDga1bt87gwYNz2mmnZdiwYWnXrl2TNrwhhg4dmvLy8vziF7+oq33+85/PdtttlzFjxqRUKqV79+4ZOXJkvv3tbyepXS2wvLw8V155Zc4+++xUVVWla9euGTNmTE466aQkyfz589OjR4/cd999GTx4cGbOnJl99903kydPzsEHH5wkmTx5cgYMGJBZs2Zln3322aB+q6urUywWU1VVlbKyskZ+NgAAAAAAAAAAANgabfAWw7vttlu++93vZubMmSmVSunfv39+9rOfZf78+bnnnnvyxS9+cbMIBybJJz/5yfzhD3/I7NmzkyTPPfdcJk2alM9+9rNJkpdeeimVlZUZNGhQ3Tnt2rXLpz71qTzxxBNJkqlTp2b58uX1xnTv3j19+vSpG/Pkk0+mWCzWhQOT5JBDDkmxWKwbsyZLly5NdXV1vQ8AAAAAAAAAAABoTBu8xXCpVEqhUMi2226bYcOGpVevXnnzzTczevTojZ78X//1Xzf63HX59re/naqqqvTs2TOtWrVKTU1NLrvssvyf//N/kiSVlZVJkvLy8nrnlZeX5+WXX64b07Zt23Tu3Hm1MR+cX1lZmW7duq02f7du3erGrMkVV1yR733vext/gwAAAAAAAAAAALAeGxwQ/MD777+f3/zmN40yeVMFBO+8887cdttt+fWvf53evXtn2rRpGTlyZLp3754vfelLdeMKhUK98z4IQa7LqmPWNH5917n44otzwQUX1H1eXV2dHj16rPe+AAAAAAAAAAAAYENt8BbDSW3wrbE+mtJFF12U73znOzn55JPTt2/fjBgxIt/85jdzxRVXJEkqKiqSZLVV/hYuXFi3qmBFRUWWLVuWxYsXr3PMggULVpt/0aJFq61O+GHt2rVLWVlZvQ8AAAAAAAAAAAA2f4VCIXfffXdzt7FBNngFwZdeeqkp+2hU7777brbZpn72sVWrVlm5cmWSZPfdd09FRUUefPDB9OvXL0mybNmyPProo7nyyiuTJP3790+bNm3y4IMP5sQTT0ySvPbaa5k+fXquuuqqJMmAAQNSVVWVKVOm5KCDDkqSPPXUU6mqqsqhhx66Se4VAAAAAAAAAACAxlNZWZnLLrss9957b1599dV069Yt+++/f0aOHJkjjzyyudv7SDY4ILjrrrs2ZR+N6thjj81ll12WXXbZJb17987//M//5Nprr81XvvKVJLUJzpEjR+byyy/PXnvtlb322iuXX355tttuuwwfPjxJUiwW89WvfjWjRo3KDjvskC5duuTCCy9M3759c9RRRyVJevXqlaOPPjpnnnlmbrrppiTJWWedlaFDh2afffZpnpsHAAAAAAAAAABgo/ztb3/LYYcdlk6dOuWqq67Kxz/+8SxfvjwTJkzIueeem1mzZjV3ix/JR9pieEtx3XXX5Qtf+ELOOeec9OrVKxdeeGHOPvvs/N//+3/rxnzrW9/KyJEjc8455+SAAw7Iq6++mgceeCAdO3asG/PjH/84xx9/fE488cQcdthh2W677XLPPfekVatWdWNuv/329O3bN4MGDcqgQYPy8Y9/PGPGjNmk9wsAAAAAAAAAAEDDnXPOOSkUCpkyZUq+8IUvZO+9907v3r1zwQUXZPLkyWs859vf/nb23nvvbLfddvnYxz6WSy65JMuXL687/txzz2XgwIHp2LFjysrK0r9//zzzzDNJkpdffjnHHntsOnfunA4dOqR379657777Gu1+CqVSqdRoV2OjVFdXp1gspqqqKmVlZc3dDgAAAAAAAAAAwFbnzTffzI477pjLLrssF1988VrHFQqFjB07Nscff3yS5Ac/+EE+/elPp3v37vnzn/+cM888MxdccEG+9a1vJUn69OmTfv365bvf/W5atWqVadOmZe+9985+++2XoUOHZtmyZbnmmmvSoUOH/OUvf0lZWVn+8R//sVHuaYO3GAYAAAAAAAAAAICWas6cOSmVSunZs+dHOu9f/uVf6v682267ZdSoUbnzzjvrAoJz587NRRddVHfdvfbaq2783Llz8/nPfz59+/ZNknzsYx9r6G3U0yK3GAYAAAAAAAAAAICP4oPNeAuFwkc677/+67/yyU9+MhUVFdl+++1zySWXZO7cuXXHL7jggpxxxhk56qij8sMf/jAvvvhi3bHzzz8/P/jBD3LYYYfl3/7t3/KnP/2pcW7m7wQEAQAAAAAAAAAA2OrttddeKRQKmTlz5gafM3ny5Jx88sk55phjMm7cuPzP//xPvvvd72bZsmV1Yy699NLMmDEjQ4YMycMPP5x99903Y8eOTZKcccYZ+etf/5oRI0bkz3/+cw444IBcd911jXZPhdIHsUeaTXV1dYrFYqqqqlJWVtbc7QAAAAAAAAAAAGyVjjnmmPz5z3/O888/nw4dOtQ79tZbb6VTp04pFAoZO3Zsjj/++FxzzTUZPXp0vVUBzzjjjPzXf/1X3nrrrTXO8X/+z//JO++8k9///verHbv44otz7733NtpKglYQBAAAAAAAAAAAgCSjR49OTU1NDjrooNx111154YUXMnPmzPzsZz/LgAEDVhu/5557Zu7cubnjjjvy4osv5mc/+1nd6oBJ8t577+Ub3/hGJk6cmJdffjl//OMf8/TTT6dXr15JkpEjR2bChAl56aWX8uyzz+bhhx+uO9YYWjfalQAAAAAAAAAAAGALtvvuu+fZZ5/NZZddllGjRuW1115L165d079//9xwww2rjT/uuOPyzW9+M9/4xjeydOnSDBkyJJdcckkuvfTSJEmrVq3yxhtv5LTTTsuCBQuy44475oQTTsj3vve9JElNTU3OPffczJs3L2VlZTn66KPz4x//uNHuxxbDmwFbDAMAAAAAAAAAANDYbDEMAAAAAAAAAAAALZCAIAAAAAAAAAAAALRAAoIAAAAAAAAAAADQArXekEGf/vSnG33iQqGQP/zhD41+XQAAAAAAAAAAAGADA4ITJ05MoVBIqVRa65hCoVDv8w/GbmgdAAAAAAAAAAAAaDwbFBD8x3/8x3UG+ubPn58XXnghSW3wb7fddkt5eXlKpVIWLlyYv/3tbymVSikUCtl7772z0047NU73AAAAAAAAAAAAwBoVSutaFnADjB8/PsOHD8/KlSvz3e9+N1/+8pez44471hvz+uuv55Zbbsnll1+eQqGQX//61zn66KMb1HhLUl1dnWKxmKqqqpSVlTV3OwAAAAAAW6eZ45LHr0kWzUq69kwOH5X0GtrcXQEAAABstAYFBGfPnp3+/fundevWmTRpUnr37r3O8X/5y19y2GGHpaamJs8880z23nvvjZ26RREQBAAAAABoZjPHJXeeskqxkJx0m5DgRyFkCQAAAJuVbRpy8jXXXJN33nkn3/rWt9YbDkySfffdN9/61reyZMmSXH311Q2ZGgAAAAAAGs/j16yhWEomXbvJW9lifRCynP9ssvzd2sc7T62tAwAAAM2idUNOfvDBB1MoFPLpT396g88ZOHBgkuShhx5qyNQAAAAAANB4Fs1ac33hWuqsbl0hS6sIAgAAsIWaVVmd8dMrU/3eipRt2zpH96lIz4otZ5fYBq0g+Nprr33kcwqFQpKksrKyIVMDAAAAAEDj6dpzzfVua6mzOiFLAAAAWpC/vf5OThj9xxz9k8dz3cNzMmby33Ldw3Ny9E8ez+dveCJ/e/2dJp1/9OjR2X333dO+ffv0798/jz/++EZdp0EBwU6dOiVJHn300Q0+Z+LEiUmSYrHYkKkBAAAAAKDxHD4qSWGVYuHvdTaIkCUAAAAtxN9efyfH/fsf89y8qiRJzcpSlteUUrOylCSZ9spbOe7f/9hkIcE777wzI0eOzHe/+938z//8Tw4//PAcc8wxmTt37ke+VoMCgp/85CdTKpXywx/+MLNnz17v+NmzZ+fKK69MoVDIJz/5yYZMDQAAAAAAjafX0OSk25Kd+ydtOtQ+nnx70nNIc3e25RCyBAAAoIW44DfTsmTpirpA4KpqVpayZOmKXPjb55pk/muvvTZf/epXc8YZZ6RXr175yU9+kh49euSGG274yNdqUEDwggsuyDbbbJOqqqoccsgh+clPfpI333xztXGLFy/OT3/60xx66KF56623ss0222TUKG8IAAAAAACwGek1NDnz4eS782sfhQM/GiFLADbWzHHJzQOTy3aqfZw5rrk7AgC2YrMqq/Ps3LfWGg78QM3KUp55eXFmVVY36vzLli3L1KlTM2jQoHr1QYMG5YknnvjI12vdkGYGDBiQK6+8MhdddFGqqqoyatSoXHjhhdl9993TrVu3FAqFLFiwIC+99FJKpVJKpdon7corr8whhxzSkKkBAABgqzJhRmVGPzInsxcsyd7l2+ecgXtmcO+K5m4LAKC+XkNrPwBgQ80cl9x5yv9+Pv/Z5M5Ta0Pn/psCADSD8dMr02qbwnoDgknSaptCJkxfkJ4VZY02/+uvv56ampqUl5fXq5eXl6eysvIjX69BKwgmyahRo/Lb3/42FRUVKZVKWblyZV588cVMnjw5Tz75ZF588cWsXLkypVIpO+20U/7rv/4rF1xwQUOnBQAAgK3GhBmVOXvM1Dw3ryrvLa/Jc/Oq8rXbpmbCjI/+RgAAAABsVh6/Zg3FUjLp2k3eCgBAklS/tyLbFDZs7DaFpOq95U3SR6FQv4lSqbRabUM0aAXBD3z+85/PsGHDcvfdd+ehhx7Kn//85yxevDilUildunRJ3759c9RRR+X4449PmzZtGmNKAAAA2GqMfmTOarVSKRk98UWrCAIAALBlWzRrzfWFa6kDADSxsm1bZwMWD0ySrCwlxW0bNw+34447plWrVqutFrhw4cLVVhXcEI0SEEySNm3a5Itf/GK++MUvNtYlAQAAgCSzFyxZY/2FBW9v4k4AAACgkXXtWbut8Kq69dz0vQAAJDm6T0V+8tALGzS2ZmUpR/dp3F/kb9u2bfr3758HH3wwn/vc5+rqDz74YI477riPfL0GbzEMAAAANK29y7dfY32v8o6buBMAAABoZIePSrLqVnmFv9cBADa9nhVl+cQundJqPfsMt9qmkAN27Zx9Khr/vfoLLrggP//5z/PLX/4yM2fOzDe/+c3MnTs3X/va1z7ytQQEAQAAYDN3zsA9U1jlfYhCITn3iD2apyEAAABoLL2GJifdluzcP2nTofbx5NuTnkOauzMAYCt27Yn7Z/t2rdcaEmy1TSHbt2udq7+4X5PMf9JJJ+UnP/lJvv/972f//ffPY489lvvuuy+77rrrR75WoVQqbeCOyRumpqYmixcvznvvvZf1XXqXXXZpzKm3WNXV1SkWi6mqqkpZWVlztwMAAMBmaMKMyoye+GJeWPB29irvmHOP2CODejfutgUAAAAAAECtv73+Ti787XN55uXFabVNIdsUkpWl2m2FD9i1c67+4n7ZbccOzd3mejVKQPD111/Pddddl7vvvjt/+ctfsnLlyvVPXChkxYoVDZ26RRAQBAAAAAAAgC3LhBmVGf3InMxesCR7l2+fcwbumcF+kQsAoMWZVVmdCdMXpOq95Slu2yZH96lokm2Fm0qDA4JPPPFETjjhhCxatGi9KwbWm7hQSE1NTUOmbjEEBAEAAAAAAGDLMWFGZc4eM7VerVBIbjy1v5AgAACbldYNOfmNN97IcccdlzfeeCPbb799zjjjjHTq1CmXXnppCoVCfv7zn2fx4sV55pln8rvf/S7vv/9+DjvssHz1q19trP4BAAAAAAAANqnRj8xZrVYqJaMnviggCADAZqVBAcHrr78+b7zxRtq1a5cnn3wyvXv3zowZM3LppZcmSb785S/Xja2srMzw4cPz6KOPZsCAAbnyyisb1DgAAAAAAABAc5i9YMka6y8seHsTdwIAAOu2TUNOvv/++1MoFPKVr3wlvXv3XufYioqK3Hvvvdljjz1y9dVX5+GHH27I1AAAAAAAAADNYu/y7ddY36u84ybuBAAA1q1BAcE5c2qXzj7qqKPqaoVCoe7PNTU19cZvu+22+eY3v5lSqZQbb7yxIVMDAAAAAAAANItzBu6ZD/1v0SRJoZCce8QezdMQAACsRYMCgtXV1UmSXXfdta7Wvn37uj+//fbqS2gfcMABSZKnnnqqIVMDAAAAAAAANIvBvSty46n9s1+PTtmubavs16NTbjq1fwb1rmju1gAAoJ7WDTl5++23T1VVVVasWFFX69KlS92f//a3v2X//fevd87777+fJFm4cGFDpgYAAAAAAABoNoN7V2SwQCAAAJu5Bq0guOeeeyZJ5s6dW1fr1KlTKipqfxB+5JFHVjvniSeeSJJ06NChIVMDAAAAAAAAAAAA69CggODBBx+cJHn66afr1Y8++uiUSqVcddVVmT17dl19ypQpueqqq1IoFHLggQc2ZGoAAAAAAAAAAABoWgtmJBN/mIy/uPZxwYzm7ugjKZRKpdLGnjxu3LgMGzYse+yxR1544YW6+vTp0/OJT3wiNTU1adWqVfbbb7+8++67mT17dmpqalIoFHLvvffm6KOPbpSb2NJVV1enWCymqqoqZWVl6xw7YUZlRj8yJ7MXLMne5dvnnIF7WrocAAC2BjPHJY9fkyyalXTtmRw+Kuk1tLm7AmB9Wtj3b+9NAQDA+vm5GVivFvZ+AbRYb7yYjP1aMm9KUmiVFLZJSiuTUk3S4+Dk+BuSHfZokqkfe+yx/OhHP8rUqVPz2muvZezYsTn++OM36loNCgguX748Z555ZmpqavL9738/u+++e92xX/ziF/n617+eFStWrHbe9773vVxyySUbO22Ls6EBwQkzKnP2mKn1aoVCcuOp/f1ACQAALdnMccmdp6xSLCQn3dbgN428YQ3QhJrw+3dz8N4UAACsn5+bgfVqYe8XQIv1xovJfxyZLK2uDQSuqtAqaVeWnPmHJgkJ3n///fnjH/+YT3ziE/n85z/foIBgg7YYbtOmTW699daMGTOmXjgwSb761a/mz3/+c7797W9n6NChOfroo3PeeedlypQpmyQc+Oqrr+bUU0/NDjvskO222y77779/pk793x/ESqVSLr300nTv3j3bbrttjjjiiMyYUX/5x6VLl+a8887LjjvumA4dOmTYsGGZN29evTGLFy/OiBEjUiwWUywWM2LEiLz11ltNck+jH5mzWq1USkZPfLFJ5gMAADYTj1+zhmIpmXRtgy77wRvWz82rynvLa/LcvKp87bapmTCjskHXBeDvmuj7d3Px3hQAAKyfn5uB9Wph7xdAizX2a2sPBya19aXVyd1fb5LpjznmmPzgBz/ICSec0OBrtW6EftZqn332yRVXXNGUU6zR4sWLc9hhh2XgwIG5//77061bt7z44ovp1KlT3Zirrroq1157bW699dbsvffe+cEPfpDPfOYzef7559OxY8ckyciRI3PPPffkjjvuyA477JBRo0Zl6NChmTp1alq1apUkGT58eObNm5fx48cnSc4666yMGDEi99xzT6Pf1+wFS9ZYf2HB240+FwAAsBlZNGvN9YVrqW+gdb1h7TfaARpBE33/bi7emwIAgPXzczOwXi3s/QJokRbMqN1WeH1KNckrT9WOL+/d9H1tpAatILi5uvLKK9OjR4/ccsstOeigg7LbbrvlyCOPzB571C7nWCqV8pOf/CTf/e53c8IJJ6RPnz75z//8z7z77rv59a9/nSSpqqrKL37xi1xzzTU56qij0q9fv9x2223585//nIceeihJMnPmzIwfPz4///nPM2DAgAwYMCD/8R//kXHjxuX5559v9Pvau3z7Ndb3Ku/Y6HMBAACbka4911zvtpb6BvKGNUATa6Lv383Fe1MAALB+fm4G1quFvV8ALdLMe2q3EN4QhVa1W4dvxpokILhixYosWrQoixYtyooVK5piinX6/e9/nwMOOCBf/OIX061bt/Tr1y//8R//UXf8pZdeSmVlZQYNGlRXa9euXT71qU/liSeeSJJMnTo1y5cvrzeme/fu6dOnT92YJ598MsViMQcffHDdmEMOOSTFYrFuzJosXbo01dXV9T42xDkD90yhUL9WKCTnHtH4+1gDAACbkcNHJVnlHwMp/L2+8bxhDdDEmuj7d3Px3hQAAKyfn5uB9Wph7xdAi/R+VVLYwFhdYZvk/beatJ2GarSA4MyZM3PeeeelV69ead++fSoqKlJRUZH27dunV69eOf/88/OXv/ylsaZbp7/+9a+54YYbstdee2XChAn52te+lvPPPz+/+tWvkiSVlZVJkvLy8nrnlZeX1x2rrKxM27Zt07lz53WO6dat22rzd+vWrW7MmlxxxRUpFot1Hz169Nig+xrcuyI3nto/+/XolO3atsp+PTrlplP7Z5CtvwAAoGXrNTQ56bZk5/5Jmw61jyffnvQc0qDLesMaoIk10ffv5uK9KQAAWD8/NwPr1cLeL4AWqX0xKa3csLGllUn7Tk3aTkO1boyLXHzxxbn66quzcuXKlEqlesdKpVKef/75zJ49OzfccEMuuuiiXH755Y0x7VqtXLkyBxxwQN08/fr1y4wZM3LDDTfktNNOqxtXWOX/hJVKpdVqq1p1zJrGr+86F198cS644IK6z6urqz9SSHCwHx4BAGDr02to7Ucj+uAN69ETX8wLC97OXuUdc+4Re3jDGqAxNcH37+bkvSkAAFg/PzcD69XC3i+AFqfXscnEKzZsbKmmdvxmrMEBwfPOOy+jR4+uCwb26tUrBx98cCoqKlIqlbJgwYJMmTIlf/nLX1JTU5Mrr7wy77zzTn760582uPm12WmnnbLvvvvWq/Xq1St33XVXkqSiovaHscrKyuy00051YxYuXFi3qmBFRUWWLVuWxYsX11tFcOHChTn00EPrxixYsGC1+RctWrTa6oQf1q5du7Rr124j7w4AAKDxeMMaAKCFmzkuefyaZNGspGvP2m3L/I9IAAAAWLvy3sk/HJS8OrU2ALg2hVbJPxyQlO+79jEbacmSJZkzZ07d5y+99FKmTZuWLl26ZJdddvlI12rQFsN//OMf8+///u9Jkn333TeTJk3KjBkz8stf/jKXX355rrjiivzyl7/M9OnT88c//jF9+/ZNqVTK9ddfnyeeeKIhU6/TYYcdlueff75ebfbs2dl1112TJLvvvnsqKiry4IMP1h1ftmxZHn300brwX//+/dOmTZt6Y1577bVMnz69bsyAAQNSVVWVKVOm1I156qmnUlVVVTcGAAAAAACaxcxxyZ2nJPOfTZa/W/t456m1dQAAAGDtPndj0q6sNgS4JoVWtcePv6FJpn/mmWfSr1+/9OvXL0lywQUXpF+/fvnXf/3Xj3ytQmnVPYE/gtNOOy233XZbPvaxj2Xq1KkpFovrHF9VVZX+/fvnpZdeyimnnJJf/epXGzv1Oj399NM59NBD873vfS8nnnhipkyZkjPPPDM333xzTjnllCTJlVdemSuuuCK33HJL9tprr1x++eWZOHFinn/++XTs2DFJ8vWvfz3jxo3Lrbfemi5duuTCCy/MG2+8kalTp6ZVq9q//GOOOSbz58/PTTfdlCQ566yzsuuuu+aee+7Z4H6rq6tTLBZTVVWVsrKyRn42AAAAAADYKt08sDYUuKqd+ydnPrzp+wEAAIAtyRsvJnd/PXnlqdpAYGGbpLSydlXBHgfXhgN32KO5u1yvBm0x/Pjjj6dQKOQ73/nOesOBSVIsFvPtb387Z599dh5//PGGTL1OBx54YMaOHZuLL7443//+97P77rvnJz/5SV04MEm+9a1v5b333ss555yTxYsX5+CDD84DDzxQFw5Mkh//+Mdp3bp1TjzxxLz33ns58sgjc+utt9aFA5Pk9ttvz/nnn59BgwYlSYYNG5brr7++ye4NAAAAAAA2yKJZa64vXEsdAAAA+F877JF89YFkwYza1fjffytp3ynpdWyTbCvcVBq0guC2226bZcuWZcqUKenfv/8GnTN16tQceOCBad++fd59992NnbpFsYIgAAAAAACNzgqCAAAAsNXbpiEnt2/fPknyzjvvbPA5S5YsSZK0a9euIVMDAACsZsKMyhx3/aT0umR8jrt+UibMqGzulgAAoPkcPipJYZVi4e91AAAAYGvQoIDg7rvvniT5/e9/v8Hn3HPPPUmSj33sYw2ZGgAAoJ4JMypz9pipeW5eVd5bXpPn5lXla7dNFRIEAGDr1WtoctJttSsGtulQ+3jy7UnPIc3dGQAAALCJtG7IyZ/97Gczbdq0XH/99TnmmGNy5JFHrnP8H/7wh1x33XUpFAr57Gc/25CpAQCgRZowozKjH5mT2QuWZO/y7XPOwD0zuHdFc7e1RRj9yJzVaqVSMnrii55DAAC2Xr2G1n4AAAAAW6UGrSA4cuTIlJWVZfny5TnmmGNy7rnnZurUqVm5cmXdmJUrV2bq1Kk555xzcswxx2T58uUpKyvLyJEjG9o7AAC0KFbAa5jZC5assf7Cgrc3cScAAAAAAACweWhQQHDHHXfMb37zm7Rp0yYrVqzIjTfemIMOOigdOnTIzjvvnH/4h39Ihw4dctBBB+Wmm27KihUr0rZt2/z2t7/NDjvs0Fj3AAAALcK6VsBj/fYu336N9b3KO27iTgAAAAAAAGDz0KCAYJIMGjQokydPzgEHHJBSqZRSqZSlS5fmtddey/z587N06dK6+oEHHpinnnoqRx11VGP0DgAALYoV8BrmnIF7plCoXysUknOP2KN5GgIA2ApNmFGZ466flF6XjM9x10+yGjYAAABAMyuUSqVSY13s6aefzkMPPZTp06fnzTffTJJ06dIlffr0yVFHHZUDDzywsaZqUaqrq1MsFlNVVZWysrLmbgcAgGZy3PWT8ty8qtXq+/XolN+de1gzdLTlmTCjMqMnvpgXFrydvco75twj9sig3hXN3RYAwFZhwozKnD1mar1aoZDceGr/DPYzGQAAAECzaNSAIBtHQBAAgKT2f6h+7bap+fBP6IVCctOp/YXcNjczxyWPX5MsmpV07ZkcPirpNbS5uwIAaFZ+4QUAAABg89PgLYYBAFoqW2OxqQ3uXZEbT+2f/Xp0ynZtW2W/Hp2EAzdHM8cld56SzH82Wf5u7eOdp9bWAQC2YrMXLFlj/YUFb2/iTgAAAAD4QOvmbgAAYHO06tZYz82rytdum2prLJrc4N4VXmObu8evWUOxlEy61iqCAMBWbe/y7de4guBe5R2boRsAAAAAkg0MCM6dO7dJJt9ll12a5LoAAA01+pE5q9VKpWT0xBeFt2Brt2jWmusL11IHaGq2PQc2E+cM3DNfu21qSqX/rRUKyblH7NF8TQEAAABs5TYoILj77rs3+sSFQiErVqxo9OsCADQGW2MBa9W1Z+22wqvq1nPT9wLwwbbnH/hg2/OTbhMSBDa5wb0rcuOp/TN64ot5YcHb2au8Y849Yo8M8ktWAAAAAM1mgwKCpQ//yicAwFbA1ljAWh0+qjZ8kw//O6lQWwfY1Gx7DmxmBveusOo6AAAAwGZkgwKCt9xyS1P3AQCwWbE1FrBWvYbWrsw16drabYW7/X07z55DmrszYGtk23MAAAAAANahULI8YLOrrq5OsVhMVVVVysrKmrsdAODvJsyotDUWALB5u3ngmrc937l/cubDm74fAAAAAAA2KwKCmwEBQQAAAGhCM8fVbsW7aFbS9e+rfraU7Xdnjlvztucn325lUwAAoGFa8r+lAAC2IgKCmwEBQQAAAGgiM8cld56ySrFQu1V4S/kfWzPH2fYcAABoXFvDv6UAALYSAoKbAQFBAAAAaCK24AUAAPjo/FsKAKDF2Ka5GwAAAABoMotmrbm+cC11AAAA/FsKAKAFERAEAAAAWq6uPddc77aWOgAAAP4tBQDQgggIAgAAAC3X4aOSFFYpFv5eBwAAYI38WwoAoMUQEAQAAABarl5Dk5NuS3bun7TpUPt48u1JzyHN3RkAAMDmy7+lAABajEKpVCo1dxNbu+rq6hSLxVRVVaWsrKy52wGgCU2YUZnRj8zJ7AVLsnf59jln4J4Z3LuiudsCAAAAAAAAAFogAcHNgIAgwNZhwozKnD1mar1aoZDceGp/IUGa3sxxyePXJItmJV171m4F0mtoc3cFAAAAAAAAQBOyxTAAbCKjH5mzWq1USkZPfLEZumGrMnNccucpyfxnk+Xv1j7eeWptHQAAAAAAAIAWq0EBwVdffbWx+gCAFm/2giVrrL+w4O1N3AlbncevWUOxlEy6dpO3skWbOS65eWBy2U61jwKWAAAAAAAAwGauQQHB3XbbLUOGDMldd92V5cuXN1ZPANAi7V2+/Rrre5V33MSdsNVZNGvN9YVrqbM6qzACAAAAAAAAW6AGBQRramoyfvz4nHjiidl5551zwQUX5M9//nNj9QbQpCbMqMxx109Kr0vG57jrJ2XCjMrmbokW7pyBe6ZQqF8rFJJzj9ijeRpi69G155rr3dZSZ3VWYQQAAAAAAAC2QA0KCH7zm99M165dUyqV8vrrr+enP/1p9t9//xx44IG56aabUl1d3Vh9AjSqCTMqc/aYqXluXlXeW16T5+ZV5Wu3TRUSpEkN7l2RG0/tn/16dMp2bVtlvx6dctOp/TOod0Vzt0ZLd/ioJKukU1P4e50NYhVGAAAAAAAAYAtUKJVKpYZcoKamJvfee29++ctf5r777suKFStqL1wopH379jnhhBPy5S9/OZ/+9KcbpeGWqLq6OsViMVVVVSkrK2vudmCrcNz1k/LcvKrV6vv16JTfnXtYM3QE0MRmjqtd7W7hrNqVAw8flfQc0txdbTluHli7rfCqdu6fnPnwpu8HAAAAAAAAYAM0OCD4YYsWLcqvfvWr3HrrrZkxY0btBH/fS3G33XbLl7/85XzpS19Kjx49GmvKFkFAEDa9XpeMz3vLa1arb9e2Vf7y/aMbda4JMyoz+pE5mb1gSfYu3z7nDNwzg60YB7BlmTkuufPUJB/+0bmQnHy7oCUAAACsg/dHAQAAmlejBgQ/7Omnn84vf/nL3HnnnXnrrbdqJysUUigUctRRR+WrX/1qjj/++LRp06Yppt+iCAjCprepVhD8YCvjDysUkhtP7e9NMIAtjVUYAQAA4CPx/igAAEDza7KA4AeWLl2au+66K7feemsefvjhrFy5sm5Vwc6dO+fUU0/NWWedlX333bcp29isCQjCpjdhRmW+dtvUfPg7YKGQ3HRq/wxqxDembGUMAAAAAGytvD8KAADQ/LZp6gnatWuXQw89NAMGDMiOO+6YQqGQUqmUUqmUN998M9ddd1369u2b448/Pn/961+buh2AJMng3hW58dT+2a9Hp2zXtlX269Gp0cOBSTJ7wZI11l9Y8HajzgMAAAAAsLnx/igAAEDza91UF37vvffyX//1X7nlllvy2GOP1YUCk6Rv37459dRT8+c//zl33XVX3nvvvdxzzz157LHH8sc//jG9evVqqrYA6gzuXdHk21jsXb79Gn9Ddq/yjk06LwAAAABAc/P+KAAAQPNr9BUEn3jiiZx55pmpqKjI6aefnkcffTQrV65Mhw4dcsYZZ2Ty5Ml57rnnctFFF+VXv/pVXnvttfzgBz9I+/btU1VVlX/5l39p7JYAms05A/fM33dVr1MoJOcesUfzNAQAAEDLN3NccvPA5LKdah9njmvujgDYSnl/FAAAoPkVSh8s69cA8+fPz69+9avceuuteeGFF5KkbrXAgw8+OGeccUZOPvnkdOjQYa3XuOGGG3LuueemvLw8r732WkNb2qJUV1enWCymqqoqZWVlzd0O0MgmzKjM6Ikv5oUFb2ev8o4594g9Gn0rYwAAAEhSGwa885RVioXkpNuSXkObpSUAtm7eHwUAAGheDQoI/va3v80tt9ySBx98MCtXrqwLBXbp0iUjRozIGWeckd69e2/Qtf7yl7+kT58+KRQKqamp2diWtkgCggAAAAA0ipsHJvOfXb2+c//kzIc3fT8AAAAAQLNq3ZCTTzrppBQKhZRKpRQKhXz605/OGWeckRNOOCFt27b9SNdq165dQ1oBAAAAABbNWnN94VrqAAAAAECL1qCAYJLstNNO+dKXvpQzzjgju++++0ZfZ7fddstLL73U0HYAAAAAYOvVteeaVxDs1nPT9wIAAAAANLttGnLy7373u8ydOzeXXXZZg8KBSdKqVavsuuuu2XXXXRt0nTW54oorUigUMnLkyLpaqVTKpZdemu7du2fbbbfNEUcckRkzZtQ7b+nSpTnvvPOy4447pkOHDhk2bFjmzZtXb8zixYszYsSIFIvFFIvFjBgxIm+99Vaj3wMAAAAArNfho5IUVikW/l4HYLM3c1ztdvGX7VT7OHNcc3cEAADAFq5BAcFjjz0222zToEs0uaeffjo333xzPv7xj9erX3XVVbn22mtz/fXX5+mnn05FRUU+85nP5O23364bM3LkyIwdOzZ33HFHJk2alCVLlmTo0KGpqampGzN8+PBMmzYt48ePz/jx4zNt2rSMGDFik90fAAAAANTpNTQ56bZk5/5Jmw61jyffnvQc0tydAbA+M8cld55SuxLs8ndrH+88VUgQAACABimUSqVSczfRVJYsWZJPfOITGT16dH7wgx9k//33z09+8pOUSqV07949I0eOzLe//e0ktasFlpeX58orr8zZZ5+dqqqqdO3aNWPGjMlJJ52UJJk/f3569OiR++67L4MHD87MmTOz7777ZvLkyTn44IOTJJMnT86AAQMya9as7LPPPmvsa+nSpVm6dGnd59XV1enRo0eqqqpSVlbWxM8KAAAAAACw2bl54Jq3id+5f3Lmw5u+HwAAAFqERl/+r6amJq+//npeeeWVzJ07d50fTe3cc8/NkCFDctRRR9Wrv/TSS6msrMygQYPqau3atcunPvWpPPHEE0mSqVOnZvny5fXGdO/ePX369Kkb8+STT6ZYLNaFA5PkkEMOSbFYrBuzJldccUXdlsTFYjE9evRolPsFAAAAAAC2UItmrbm+cC11AAAA2ACtG+Mir7/+eq677rrcfffd+ctf/pKVK1eu95xCoZAVK1Y0xvRrdMcdd+TZZ5/N008/vdqxysrKJEl5eXm9enl5eV5++eW6MW3btk3nzp1XG/PB+ZWVlenWrdtq1+/WrVvdmDW5+OKLc8EFF9R9/sEKggAAAAAAwFaqa881ryDYreem7wUAYGs1c1zy+DW1v7zRtWdy+Kik19Dm7gqgQRocEHziiSdywgknZNGiRdlcdit+5ZVX8k//9E954IEH0r59+7WOKxQK9T4vlUqr1Va16pg1jV/fddq1a5d27dqtcx4AAAAAAGArcvio5M5Tk3z4/7UUausAADS9meOSO0/538/nP1v789lJtwkJAlu0Bm0x/MYbb+S4447LwoUL06FDh4wcOTKXXnppktrg3C9+8YtcffXVOfnkk7PtttumUCjkk5/8ZG655Zb88pe/bIz+12jq1KlZuHBh+vfvn9atW6d169Z59NFH87Of/SytW7euWzlw1VX+Fi5cWHesoqIiy5Yty+LFi9c5ZsGCBavNv2jRotVWJwQAAAAAAFirXkNr/+fzzv2TNh1qH0++Pek5pLk7AwDYOjx+zRqKpWTStZu8FYDG1KAVBK+//vq88cYbadeuXZ588sn07t07M2bMqAsJfvnLX64bW1lZmeHDh+fRRx/NgAEDcuWVVzao8XU58sgj8+c//7le7ctf/nJ69uyZb3/72/nYxz6WioqKPPjgg+nXr1+SZNmyZXn00Ufr+urfv3/atGmTBx98MCeeeGKS5LXXXsv06dNz1VVXJUkGDBiQqqqqTJkyJQcddFCS5KmnnkpVVVUOPfTQJrs/AAAAAIANYnss2LL0GuprFACguSyateb6wrXUAbYQDQoI3n///SkUCvnKV76S3r17r3NsRUVF7r333uy33365+uqrM3jw4Hz6059uyPRr1bFjx/Tp06derUOHDtlhhx3q6iNHjszll1+evfbaK3vttVcuv/zybLfddhk+fHiSpFgs5qtf/WpGjRqVHXbYIV26dMmFF16Yvn375qijjkqS9OrVK0cffXTOPPPM3HTTTUmSs846K0OHDs0+++zTJPcGAAAA0CyEjGDLY3ssAACADde1Z+2/m1bVreem7wWgETVoi+E5c+YkSV1gLqndWvgDNTU19cZvu+22+eY3v5lSqZQbb7yxIVM32Le+9a2MHDky55xzTg444IC8+uqreeCBB9KxY8e6MT/+8Y9z/PHH58QTT8xhhx2W7bbbLvfcc09atWpVN+b2229P3759M2jQoAwaNCgf//jHM2bMmOa4JQAAANZm5rjk5oHJZTvVPs4c19wdwZblg5DR/GeT5e/+b8jI1xJs3myPBQAAsOEOH5WksEqx8Pc6sE7eg9+sFUqlUmljT27btm1qamry9NNP5xOf+ESS5K9//Wv23HPPFAqFvPHGG+nUqVO9c55++ukcfPDB6dGjR15++eUGNd9SVFdXp1gspqqqKmVlZc3dDgAAQMuy6upJSZKC1ZPgo7h54Jp/g37n/smZD2/6foANc9lOtaHeVbXpkHx3/qbvBwAAYHM3c1ztL1UtnFW7cuDho5KeQ5q7K9i8eQ9+s9egFQS33377JMmKFSvqal26dKn789/+9rfVznn//feTJAsXLmzI1AAAALBhrJ4EDbdo1prrC9dSBzYPXdeyDZbtsQAAANas19DaX4b87vzaR+FAWD/vwW/2GhQQ3HPPPZMkc+fOrat16tQpFRUVSZJHHnlktXOeeOKJJEmHDh0aMjUAAABsGMEmaDghI9gy2R4LAAAAaGreg9/sNSggePDBByep3Tb4w44++uiUSqVcddVVmT17dl19ypQpueqqq1IoFHLggQc2ZGoAAADYMIJN0HBCRrBl6jW0djufnfvXbiu8c//k5NutgAEAAAA0Hu/Bb/YKpVKptLEnjxs3LsOGDcsee+yRF154oa4+ffr0fOITn0hNTU1atWqV/fbbL++++25mz56dmpqaFAqF3HvvvTn66KMb5Sa2dNXV1SkWi6mqqkpZWVlztwMAANCyzByX3Hlqkg//87cgIAEf1cxxtduCLJxV++be4aN8DQEAAADA1s578Ju9BgUEly9fnjPPPDM1NTX5/ve/n913373u2C9+8Yt8/etfz4oVK1Y773vf+14uueSSjZ22xREQBAAAaGKCTQAAAAAA0DS8B79Za1BAcH2ef/753HrrrZkxY0ZWrFiRvfbaKyNGjMgBBxzQVFNukQQEAQAAAAAAAAAAaGxNGhBkwwgIAgAAAAAAAAAA0Nhaf9QTli5dml/84he5//778/LLL6empibdu3fPwIEDc/bZZ2eHHXZoij6BDTBhRmVGPzInsxcsyd7l2+ecgXtmcO+K5m4LAAAAAAAAAABoBh9pBcEXXnghxxxzTF566aU1Hi8rK8t///d/Z+DAgY3W4NbACoI0hgkzKnP2mKn1aoVCcuOp/YUEAQAAAAAAAABgK7TNhg5cunRphg0blr/+9a8plUpr/KiqqsoJJ5yQefPmNWXPwBqMfmTOarVSKRk98cVm6AYAAAAAAAAAAGhuGxwQvO222/L888+nUCjkoP/f3r1HR1Ud7B9/hgCBQBJBSCCAgALCC0ptQAVFQTR4oWDtq9gfqFTrDdBq0apFhVerYC20LmlErAUrSulFBYNIUbkqICAiYIRwUxQCaDHhfsv+/TElTcLMnDMz+8xkJt/PWlnRk2Gevbk8OZedc84/X/PmzdO+fft06NAhLV++XAMGDJDkvxve+PHjPRswgMA27tofcHvRrn0xHgkAAAAAAAAAAAAAAACA6sD1AsG33npLktSpUyctXLhQffv2VYMGDZSamqru3bvrrbfeUv/+/WWM0ZtvvunVeIGEMXd9sQZOXKJOj72rgROXaO76Yk/zOmQ3DLi9fXa6p7kAgAgVFkiT+0hPNfd/LiyI94gAAAAAAAAAAAAAAEnG9QLBNWvWyOfz6b777lNqamrA1/z617+WJG3fvl0lJSV2RggkoLnri3Xnq6u05usSHTp2Qmu+LtFd01Z5ukhwWJ928vkqb/P5pOG9z/IsEwAQocICacZgaccn0rGD/s8zhiTnIkEWQgIAAAAAAAAAAABA3LheIPjtt99Kkn7wgx8EfU3Fr3333XcRDwpIdPnzN52yzRgpf8FmzzL7dW6mSUNy1bXVaUqrm6KurU7Ti0Nylde5mWeZAIAILR4fYKORlkyI+VA8VZMWQgIAAAAAAAAAAABANVTb7QsPHz4sn8+ntLS0oK+pV69epdcDNdXGXfsDbi/atc/T3H6dm6kfCwIBoPrb80Xg7buDbE9UoRZCduof8+EAAAAAAAAAAAAAQE3j+g6CANzrkN0w4Pb22ekxHgkAoFpq2jHw9qwg2xNVTVkICQAAAACoGQoLpMl9pKea+z9zh3wAAAAAQAJggSDggWF92snnq7zN55OG9z4rPgMCAFQvvUZKqvKNQr7/bE8iNWUhJAAAAAAg+RUWSDMGSzs+kY4d9H+eMYRFggAAAACAas/1I4ZPys/PV1ZWlpXXPf744+HGAwmhX+dmmjQkV/kLNqto1z61z07X8N5nKY/H/wIAJP/jdQdN8z9qd/cX/gVzvUZKHa+J98js6jXSf7FEpsLGJFwICQDJrrDA/9j4PV/4F3/3Gsmj4gEAQM2zeHyAjcZ/bM++EQAAAACgGvMZY4zzy6RatWrJV/WWaFE6ceKE1fdLVKWlpcrMzFRJSYkyMjLiPRwAAAB7CguSfyEkACSzk3fKqcTnX+jOhXAAAFCTPNXcf+fAquo0kEbtiP14AAAAAABwKaw7CLpcS+iK7cWGAAAAqIY69WcBCQAkMu6UAwAA4Ne0o/+xwlVldYz9WAAAAAAACIPrBYLz58/3chwAAAAAAKC62fNF4O27g2wHAACIhcIC/w8y7PnCv3Cv10jvf3ih10hpxhBJFW+k4PNvBwAAAACgGnP9iGF4h0cMAwAAAACqpcl9At8pp0WudPsHsR8PAHhs7vpi5c/fpI279qtDdkMN69NO/To3i/ewAFRUWCDNGFxlo08aNM37RYKFBf47Ke/+wn/nwF4jpY7XeJsJAAAAAECUWCBYDbBAEAAAAECNFI87vyA8hQWB75Rz42tcDAeQdOauL9adr66qtM3nkyYNyWWRIFCd8AMMAAAAAACEpVa8BwAAAAAAqIFO3vllxyfSsYP+zzOG+Lej+ujU3383nha5Up0G/s8sDgSQpPLnbzplmzFS/oLNcRgNgKD2fBF4++4g2wEAAAAAqOFqx3sAAAAAAIAaaPH4ABuN/5Ft3EWweunUnz8TADXCxl37A24v2rUvxiMBEFLTjoHvIJjVMfZjAQAAAAAgAXAHQQAAAABA7HHnFwBANdMhu2HA7e2z02M8EgAh9RopyVdlo+8/2wEAAAAAQFUsEAQAAAAAxF7TIHd44c4vAIA4GdannXxV1hz5fNLw3mfFZ0AAAuvUXxo0TWqRK9Vp4P9842tSx2viPbKEMXd9sQZOXKJOj72rgROXaO764ngPCQAAAADgIZ8xxsR7EDVdaWmpMjMzVVJSooyMjHgPBwAAAAC8V1ggzRgiqeIhqY+LuwCAuJq7vlj5CzaraNc+tc9O1/DeZymvc7N4DwsArJm7vlh3vrqq0jafT5o0JFf96DsAAAAASEosEKwGWCAIAPDS3PXFyp+/SRt37VeH7IYa1qcdJ3wBxEdhgbR4vP/Rsk07+h8B1ql/vEeFeCoskJZM8D9WOOs/fydYHAgAAAB4ZuDEJVrzdckp27u2Ok0zh18UhxEhII6fAQAAAFjEAsFqgAWCAACv8FPhAKqNwgJpxuAqG33+R4NxkQMAAAAAYqLTY+/q0LETp2xPq5uiz5+4Mg4jwik4fgYAAABgWa14DwAAkLjmri/WwIlL1OmxdzVw4hLNXV8c7yGhivz5m07ZZoyUv2BzHEYDoEZbPD7ARuO/exwAAAAQBs5HAJHrkN0w4Pb22ekxHgmC4vgZAAAAgGUsEAQAROTknenWfF2iQ8dOaM3XJbpr2ipOylczG3ftD7i9aNe+GI8EQI2354vA23cH2Q4AAAAEwPkIIDrD+rSTz1d5m88nDe99VnwGhFNx/AwAAADAMhYIAgAiwp3pEgM/FQ6g2mjaMfD2rCDbAQAAgAA4HwFEp1/nZpo0JFddW52mtLop6trqNL04JFd5nZvFe2g4ieNnAAAAAJbVjvcAACSgwgL/Yw72fOE/WdFrpNSpf7xHhRjjznSJYVifdrpr2ioZ899t/FQ4gLjoNVKaMURShUKSz78dAAAAcInzEUD0+nVupn4sCKy+OH4GAAAAYBl3EAQQnsICacZgaccn0rGD/s8zhvi3o0bhznSJgZ8KR1IrLJAm95Geau7/zPei6q1Tf2nQNKlFrlSngf/zja9JHa+J98gAAACQQDgfASDpcfwMAAAAwDKfMRXvKYR4KC0tVWZmpkpKSpSRkRHv4QChTe7jXxRYVYtc6fYPYj8eVDJ3fbHy52/Sxl371SG7oYb1aefZTwPPXV8c8M50LD4DEBMnF6xX4vOfQOeutgAAJD7uXA8gCM5HAAAcsS8JAAAAVMICwWqABYJIKE819985sKo6DaRRO2I/HpSbu75Yd766qtI2n0+aNCTX00WC+Qs2q2jXPrXPTtfw3mdxMh5AbLBgHQCA5MUPAgBwwPkIAEBQ7EsCAAAAp6gd7wEASDBNOwZekJHVMfZjQSX58zedss0YKX/BZs8WCPbr3Myz9waAkPZ8EXj77iDbAQBA4lg8PsBGIy2ZwEVdAJI4HwEACIF9SQAAAOAUteI9AAAJptdISb4qG33/2Y542rhrf8DtRbv2xXgkABADTYMsTGfBOgAAiY8fBAAAAECk2JcEAAAATsECQQDh6dTffyv+Frn+xwq3yJVufE3qeE28R1bjdchuGHB7++z0GI8ENVJhgf+Rr081938uLIj3iJDsWLAOAEDy4gcBAAAAECn2JQEAAIBTJOUCwbFjx6p79+5KT09XVlaWrr32Wm3YsKHSa4wxGjNmjHJyclS/fn317t1b69evr/SaI0eO6J577lGTJk3UoEEDDRgwQF9//XWl1+zdu1c33XSTMjMzlZmZqZtuuknff/+911ME4qtTf+n2D6RRO/yfWRxYLQzr006+KmtlfD5peO+z4jMg1ByFBdKMwf7Hjx876P88YwiLBOEtFqwDAJC8+EEAAAAARIp9SQAAAOAUPmOMifcgbLvyyit14403qnv37jp+/LhGjRqltWvX6vPPP1eDBg0kSc8884yeeuopTZ06VR06dNBvfvMbLVq0SBs2bFB6uv9uW3fffbfefvttTZ06VaeffrpGjhypf//731q1apVSUlIkSVdddZW+/vprTZ48WZJ0xx13qE2bNnr77bddj7e0tFSZmZkqKSlRRkaG5d8NADXJ3PXFyl+wWUW79ql9drqG9z5LeZ2bxXtYSHaT+/gXBVbVIte/iBgAAAAIV2GBtGSC/1FwWR39F3T5QQAAAAC4wb4kkHgKC6TF4/2PCW/6n3+3nfrHe1QAACSNpFwgWNWePXuUlZWlhQsX6pJLLpExRjk5Obrvvvv00EMPSfLfLTA7O1vPPPOM7rzzTpWUlKhp06Z69dVXNWjQIEnSjh071KpVK73zzjvq16+fCgsL9T//8z9atmyZLrjgAknSsmXL1KNHD33xxRc6++yzA47nyJEjOnLkSPn/l5aWqlWrViwQBAAkpqea++8cWFWdBv47jQIAAAAAAAAAAARy8ilFlfj8T5BhkSAAAFYk5SOGqyopKZEkNW7cWJK0detWFRcXKy8vr/w1qampuvTSS/XRRx9JklatWqVjx45Vek1OTo66dOlS/pqlS5cqMzOzfHGgJF144YXKzMwsf00gY8eOLX8kcWZmplq1amVvsgCAsM1dX6yBE5eo02PvauDEJZq7vjjeQ0osTTsG3p4VZDsAAAAAAAAAAIDkv3PgKYz/TqAAAMCKpF8gaIzRL3/5S1188cXq0qWLJKm42L/wIzs7u9Jrs7Ozy79WXFysunXrqlGjRiFfk5WVdUpmVlZW+WsCeeSRR1RSUlL+sX379sgnCACIytz1xbrz1VVa83WJDh07oTVfl+iuaatYJBiOXiMl+aps9P1nOwAAAAAAAAAAQBB7vgi8fXeQ7QAAIGxJv0BwxIgR+uyzzzR9+vRTvubzVV7MYIw5ZVtVVV8T6PVO75OamqqMjIxKHwCA+Mifv+mUbcZI+Qs2x2E0CapTf/+t/lvk+h8r3CJXuvE1qeM18R4ZAAAAAAAAAACoznhKEQAAnqsd7wF46Z577tGsWbO0aNEitWzZsnx7s2bNJPnvANi8efPy7bt37y6/q2CzZs109OhR7d27t9JdBHfv3q2ePXuWv2bXrl2n5O7Zs+eUuxMCAKqnjbv2B9xetGtfjEeS4Dr1938AAAAAAAAAAAC41WukNGOIJFNhI08pAgDApqS8g6AxRiNGjNAbb7yhDz74QG3btq309bZt26pZs2aaN29e+bajR49q4cKF5Yv/cnNzVadOnUqv2blzp9atW1f+mh49eqikpEQff/xx+WuWL1+ukpKS8tcAAKq3DtkNA25vn50e45EAAAAAAAAAAADUMDylCAAAz/mMMcb5ZYll2LBhev311zVz5kydffbZ5dszMzNVv359SdIzzzyjsWPHasqUKWrfvr2efvppLViwQBs2bFB6un9RyN13362CggJNnTpVjRs31gMPPKDvvvtOq1atUkpKiiTpqquu0o4dO/Tiiy9Kku644w61bt1ab7/9tuvxlpaWKjMzUyUlJTxuGABibO76Yt01bZUqfjf0+aQXh+Qqr3Oz+A0MAAAAAAAAAAAAAAAgSkm5QNDn8wXcPmXKFA0dOlSS/y6D//d//6cXX3xRe/fu1QUXXKA//vGP6tKlS/nrDx8+rAcffFCvv/66Dh06pL59+yo/P1+tWrUqf82///1v3XvvvZo1a5YkacCAAZo4caJOO+001+NlgSAAxNfc9cXKX7BZRbv2qX12uob3PovFgQAAAAAAAAAAAAAAIOEl5QLBRMMCQQAAAAAAAAAAAAAAAACAbbXjPQAAAAAAAAAAAAAAAAAACaKwQFo8XtrzhdS0o9RrpNSpf7xHBSCIWvEeAAAAAAAAAAAAAAAAAIAEUFggzRgs7fhEOnbQ/3nGEP92ANUSCwQBAAAAAAAAAAAAAAAAOFs8PsBGIy2ZEPOhAHCHBYIAAAAAAAAAAAAAAAAAnO35IvD23UG2A4g7FggCAAAAAAAAAAAAiI3CAmlyH+mp5v7PPI4QAIDE0rRj4O1ZQbYDiDsWCAIAAAAAAAAAAADwXmGBNGOwtOMT6dhB/+cZQ1gkCABAIuk1UpKvykbff7YDqI5YIAgAAAAAAAAAAADAe4vHB9hopCUTYj4UAAAQoU79pUHTpBa5Up0G/s83viZ1vCbeIwMQRO14DwAAAios8J8o2POF/xbFvUb6dzQAAAAAAAAAAEBi2vNF4O27g2wHAADVU6f+XL8HEgh3EARQ/fCIAQAAAAAAAAAAkk/TjoG3ZwXZDgAAACBqLBAEUP3wiAEAAAAAAAAAAJJPr5GSfFU2+v6zHQAAAIAXWCAIoPrhEQMAAAAAAAAAACSfTv2lQdOkFrlSnQb+zze+JnW8Jt4jAwAAAJJW7XgPAABO0bSj/7HCVfGIAQAAAAAAAAAAElun/v4PAAAAADHBHQQBVD88YgAAAAAAAAAAAAAAAACIGgsEAVQ/PGIAAAAAAAAAAAAAAAAAiJrPGGPiPYiarrS0VJmZmSopKVFGRka8hwMAAAAAAAAAAAAAAAAASALcQRAAAAAAAAAAAAAAAAAAgCTEAkEAAAAAAAAAAAAAAAAAAJIQCwQBAAAAAAAAAAAAAAAAAEhCLBAEAAAAAAAAAAAAAAAAACAJsUAQAAAAAAAAAAAAAAAAAIAkxAJBAAAAAAAAAAAAAAAAAACSEAsEAQAAAAAAAAAAAAAAAABIQiwQBAAAAAAAAAAAAAAAAAAgCdWO9wAASZq7vlj58zdp46796pDdUMP6tFO/zs3iPSwAAAAAAAAAAAAAAAAASFg+Y4yJ9yBqutLSUmVmZqqkpEQZGRnxHk7MzV1frDtfXVVpm88nTRqSyyJBAAAAAAAAAAAAAAAAAIgQjxhG3OXP33TKNmOk/AWb4zAaAAAAAAAAAAAAAAAAAEgOPGIYcbdx1/6A24t27YvxSABUa4UF0uLx0p4vpKYdpV4jpU794z0qxBGPpwcAAAAAAAAAAAAAIDTuIAhHc9cXa+DEJer02LsaOHGJ5q4vtvr+HbIbBtzePjvdag6ABFZYIM0YLO34RDp20P95xhD/dtRIJx9Pv+brEh06dkJrvi7RXdNWWf8eBQAAAAAAAAAAAABAImOBIEKKxQKMYX3ayeervM3nk4b3PstaRlVeL3oEYNni8QE2GmnJhJgPBdUDj6cHACAJFRZIk/tITzX3f+aHQQAAAAAAAAAAiBoLBBFSLBZg9OvcTJOG5Kprq9OUVjdFXVudpheH5CrPo8dEctcpIAHt+SLw9t1BtiPp8Xh6AACSDHeMBgAAAAAAAADAE7XjPQBUb7FagNGvczP182hBYFWhFj3GagwAwtS0o/8icVVZHWM/FlQLHbIbas3XJads5/H0AAAkqFB3jO7UP+bDAQAAAAAAAAAgWXAHQYTUIbthwO2JvACDu04BCajXSElVnkUu33+2oyaKx+PpAQCAh7hjNICagsepAwAAAAAAIMZYIIiQknEBRjIuegSSXqf+0qBpUotcqU4D/+cbX5M6XhPvkSFOYv14egAA4LGmQe4MzR2jASQTHqcOAAAAAACAOPAZY0y8B1HTlZaWKjMzUyUlJcrIyIj3cE4xd32x8hdsVtGufWqfna7hvc9K6AUYc9cX665pq1Txb77PJxaWAAAAAEC8FBb4F8mo4ikKHz8UAiC5TO7jXxRYVYtc6fYPYj8eAAAAAAAA1AgsEKwGqvsCwWSUbIseAQAAACDhFRZISyb4Hyuc1VHqNZLFgQCSy1PN/XcOrKpOA2nUjtiPBwAAAAAAADUCCwSrARYIAgAAAAAAAEmOOwgCAAAAAAAgDmrFewAAAAAAAAAAkPR6jZTkq7LR95/tAAAAAAAAgDdYIAgAAAAAAAAAXuvUXxo0zX/HwDoN/J9vfI3HqQMAAAAAAMBTPGK4GuARwwAAAAAAAAAAAAAAAAAA27iDIAAAAAAAAAAAAAAAAAAASYgFgpbk5+erbdu2qlevnnJzc7V48eJ4DwkAAAAAAAAAAAAAAAAAUIOxQNCCGTNm6L777tOoUaO0evVq9erVS1dddZW++uqreA8NAAAAAAAAAAAAAAAAAFBD+YwxJt6DSHQXXHCBfvjDH+qFF14o39apUydde+21Gjt27CmvP3LkiI4cOVL+/6WlpWrVqpVKSkqUkZERkzEDAAAAAAAAAAAAAAAAAJIbdxCM0tGjR7Vq1Srl5eVV2p6Xl6ePPvoo4K8ZO3asMjMzyz9atWoVi6ECAAAAAAAAAAAAAAAAAGoQFghG6dtvv9WJEyeUnZ1daXt2draKi4sD/ppHHnlEJSUl5R/bt2+PxVABAAAAAAAAAAAAAAAAADVI7XgPIFn4fL5K/2+MOWXbSampqUpNTY3FsAAAAAAAAAAAAAAAAAAANRR3EIxSkyZNlJKScsrdAnfv3n3KXQUBAAAAAAAAAAAAAAAAAIgVFghGqW7dusrNzdW8efMqbZ83b5569uwZp1EBAAAAAAAAAAAAAAAAAGo6HjFswS9/+UvddNNN6tatm3r06KHJkyfrq6++0l133RXvoQEAAAAAAAAAAAAAAAAAaigWCFowaNAgfffdd3riiSe0c+dOdenSRe+8845at24d76EBAAAAAAAAAAAAAAAAAGoonzHGxHsQNV1paakyMzNVUlKijIyMeA8HAAAAAAAAAAAAAAAAAJAEasV7AAAAAAAAAAAAAAAAAAAAwD4WCAIAAAAAAAAAAAAAAAAAkIRqx3sAkE4+5bm0tDTOIwEAAAAAAAAAAAAAAACA2EtPT5fP54v3MJIOCwSrgX379kmSWrVqFeeRAAAAAAAAAAAAAAAAAEDslZSUKCMjI97DSDo+c/L2dYibsrIy7dixI6xVsKWlpWrVqpW2b9/u6T+MWOXEMos5JUYWc0qMLOaUGFnMKTGymFNiZDGnxMhKtpxYZjGnxMhiTomRxZwSI4s5JUYWc0qMLOaUGFnMKTGymFP1z4llFnNKjCzmlBhZzCkxsphTYmQxp8TIYk6JkcWcKuMOgt7gDoLVQK1atdSyZcuIfm1GRkZMVs7GKieWWcwpMbKYU2JkMafEyGJOiZHFnBIjizklRlay5cQyizklRhZzSows5pQYWcwpMbKYU2JkMafEyGJOiZHFnKp/TiyzmFNiZDGnxMhiTomRxZwSI4s5JUYWc0qMLOYEL9WK9wAAAAAAAAAAAAAAAAAAAIB9LBAEAAAAAAAAAAAAAAAAACAJsUAwQaWmpmr06NFKTU1NipxYZjGnxMhiTomRxZwSI4s5JUYWc0qMLOaUGFnJlhPLLOaUGFnMKTGymFNiZDGnxMhiTomRxZwSI4s5JUYWc6r+ObHMYk6JkcWcEiOLOSVGFnNKjCzmlBhZzCkxspgTYsFnjDHxHgQAAAAAAAAAAAAAAAAAALCLOwgCAAAAAAAAAAAAAAAAAJCEWCAIAAAAAAAAAAAAAAAAAEASYoEgAAAAAAAAAAAAAAAAAABJiAWCAAAAAAAAAAAAAAAAAAAkIRYIJphFixbpRz/6kXJycuTz+fTWW295kjN27Fh1795d6enpysrK0rXXXqsNGzZYz3nhhRd07rnnKiMjQxkZGerRo4fmzJljPSeQsWPHyufz6b777rP6vmPGjJHP56v00axZM6sZFX3zzTcaMmSITj/9dKWlpekHP/iBVq1aZTWjTZs2p8zJ5/Np+PDhVnMk6fjx43r00UfVtm1b1a9fX2eeeaaeeOIJlZWVWc/at2+f7rvvPrVu3Vr169dXz549tWLFiqjf1+nfqTFGY8aMUU5OjurXr6/evXtr/fr11nPeeOMN9evXT02aNJHP59Onn37qyZyOHTumhx56SOecc44aNGignJwc3XzzzdqxY4f1OY0ZM0YdO3ZUgwYN1KhRI11++eVavny59TlVdeedd8rn8+kPf/iD9ZyhQ4ee8m/rwgsvDDvHTZYkFRYWasCAAcrMzFR6erouvPBCffXVV1ZzAvWFz+fTs88+a31O+/fv14gRI9SyZUvVr19fnTp10gsvvGA9Z9euXRo6dKhycnKUlpamK6+8UkVFRWHnuPn+aqsj3GTZ6AmnHJsd4WZONnoi3P2gaDrCTZaNnnA7Jxsd4SbLRk+4ybHVEW6ybPSE076xrX5wk2VrPyJUjs1+cDMnW/sR4RzDRNMPbrJs7Ue4mZONfnCTZWs/winHVj+4ybK1H1FVoGNamz0RKsfmsUaoLNs9ESxHsnus4ZRVUbQ9ESrH5rGGU5ZkrydC5dg81nDKstkToXJsdYTT+Shb/eCUY7MfQmXZ7AenOdnsh3DOG0bTD045NvvBzZxs9INTjs1+cMqy1Q9OObb3IZzOI9vqCaccmz0RKstmTzjNyWZPhHO+P5qecMqx2RNu5mSjJ5xybPaEU5atnnDKsdUTTteAbPWDU47NfgiVZbMfnOZksx/CuVYXTT845djsBzdzstEPTjk2+8Epy1Y/OOXY6gc3121tdYSbLBs94ZRjsyPczMlGT4R7fT2ajnCTZaMn3M7JRke4ybLRE25ybHWEmyxbPeG05sKL85WIDAsEE8yBAwfUtWtXTZw40dOchQsXavjw4Vq2bJnmzZun48ePKy8vTwcOHLCa07JlS40bN04rV67UypUrddlll2ngwIGeF8KKFSs0efJknXvuuZ68f+fOnbVz587yj7Vr13qSs3fvXl100UWqU6eO5syZo88//1zjx4/XaaedZjVnxYoVleYzb948SdL1119vNUeSnnnmGU2aNEkTJ05UYWGhfvvb3+rZZ5/V888/bz3r5z//uebNm6dXX31Va9euVV5eni6//HJ98803Ub2v07/T3/72t5owYYImTpyoFStWqFmzZrriiiu0b98+qzkHDhzQRRddpHHjxoU9h3CyDh48qE8++USPPfaYPvnkE73xxhvauHGjBgwYYDVHkjp06KCJEydq7dq1WrJkidq0aaO8vDzt2bPHetZJb731lpYvX66cnJywM9zmXHnllZX+jb3zzjueZG3evFkXX3yxOnbsqAULFmjNmjV67LHHVK9ePas5Feeyc+dO/fnPf5bP59NPfvIT63O6//779e6772ratGkqLCzU/fffr3vuuUczZ860lmOM0bXXXqstW7Zo5syZWr16tVq3bq3LL7887O+Lbr6/2uoIN1k2esIpx2ZHuJmTjZ4IZz8o2o5wmxVtT7jJsdURbrJs9ISbHFsd4ZRlqyec9o1t9YObLFv7EaFybPaDmznZ2o9wewwTbT+4zbKxH+GUY6sf3GTZ2o9wyrHVD05ZNvcjKgp2TGuzJ0Ll2DzWCJVluyeC5Uh2jzWcsk6y0RNOObaONZyybPZEqBybxxpOWTZ7IliO7Y4IdT7KZj+EyrHdD8GybPdDqDnZ7gc35w1t9INTjs1+CJVlsx9C5djuh1BZNvshWI7tfnBzHtlGT7jJsdUTTlm2esLNnGz1RDjn+6PpCbc5NnrCTZaNnnCTY6sn3GTZ6AmnHJs94XQNyNZ+hFOOzf2IUFk29yOc5mRzP8Lttbpo9yPc5Njaj3DKsrUf4ZRjcz/CKcvWfkSoHJv94Oa6ra2OcJNloyeccmx2hJs52eiJcK6vR9sRbrOi7Qk3ObY6wk2WjZ5wk2OrI5yybPaE05oL2+crEQWDhCXJvPnmmzHJ2r17t5FkFi5c6HlWo0aNzJ/+9CfP3n/fvn2mffv2Zt68eebSSy81v/jFL6y+/+jRo03Xrl2tvmcwDz30kLn44otjklXRL37xC3PWWWeZsrIy6+99zTXXmFtvvbXStuuuu84MGTLEas7BgwdNSkqKKSgoqLS9a9euZtSoUdZyqv47LSsrM82aNTPjxo0r33b48GGTmZlpJk2aZC2noq1btxpJZvXq1RG/v9uskz7++GMjyXz55Zee5pSUlBhJ5r333os4J1TW119/bVq0aGHWrVtnWrdubX7/+99bz7nlllvMwIEDo3pft1mDBg2y/m/JzZ/TwIEDzWWXXeZJVufOnc0TTzxRadsPf/hD8+ijj1rL2bBhg5Fk1q1bV77t+PHjpnHjxuall16KOMeYU7+/etURgbIqstkTbvYZbHSE2ywbPREsx3ZHBMvyoicC5XjREcGyqrLRE4FyvOiIQFle9sTJfWMv+6FqVkW29yOC5Zxkqx/cZNnajwiU40U/BMryaj+iao5X/RAoqypb+xFVc7zqh6pZXvRDsGNa2z3h5tjZVkeEc5weTU+EkxNtRzhl2eqJUDm2OyJUls2eCOfPKdqOCJVlsyeC5djsiFDno2z2g9vzXjb6IdxzbJH2Q7g50fSDmywb/eCUY7MfnLJs9UO4f07R9INTlq1+CJVjex/C6TyyrZ4I53x1tD0RybnxSHoikpxIe8JtVrQ94SbHVk+4ybLRE5H8OUXaE26ybPSEU46X5yIqXgPy8nxEsGtNXpyLcLquZet8hFOOzXMRgbK8OB9RNcfLcxFVs7w6H+H052TzXETVLK/OR1TMsdkPTtdtbXZEONeIo+mJSK5FR9oRkWRF0hNuc2x0hJssGz3hJsdWR0Ty5xRJT7jJsdURTlm2esJpzUUsrmvAPe4gCFdKSkokSY0bN/Ys48SJE/rrX/+qAwcOqEePHp7lDB8+XNdcc40uv/xyzzKKioqUk5Ojtm3b6sYbb9SWLVs8yZk1a5a6deum66+/XllZWTrvvPP00ksveZJ10tGjRzVt2jTdeuut8vl81t//4osv1vvvv6+NGzdKktasWaMlS5bo6quvtppz/PhxnThx4pSfIKhfv76WLFliNauirVu3qri4WHl5eeXbUlNTdemll+qjjz7yLDfWSkpK5PP5rN/NsqKjR49q8uTJyszMVNeuXa2/f1lZmW666SY9+OCD6ty5s/X3r2jBggXKyspShw4ddPvtt2v37t3WM8rKyjR79mx16NBB/fr1U1ZWli644ALPHlV/0q5duzR79mzddtttnrz/xRdfrFmzZumbb76RMUbz58/Xxo0b1a9fP2sZR44ckaRKfZGSkqK6detG3RdVv7962RGx+F7uNsdWRzhl2eqJQDledUSwOdnuiao5XnaE05+TrZ4IlONVR1TN8qInqu4be9kPsdoPd5Njqx+csmz1Q6Acr/oh2Jxs90PVHC/7wenPyVY/BMrxqh+qZnnRD8GOaW33RCyOnSPJiqYn3ObY6IhQWTZ7wmlONjsiWJbtnnD752SjI0Jl2eyJYDm2OyLY+Sjb/RCr817hZkXTD25zbPRDqCyb/eA0J5v9ECzLdj+4/XOy0Q+hsmz2Q7Ac2/3gdB7ZVk/E8nx1JFmR9ES4OdH0hJssGz3hdk42esIpy1ZPhPvnFE1PuMmy0RNOOV6ds6x6Dcir8xFeX2sKN8vG+QinHJvXNAJleXE+IticvLimUTXLq/MRTn9ONq9pBMry4nxE1Ryb/eB03dZmR8TqGnEkOZF2RLhZkfaEmxxbHeF2TtH2hFOOzY4I988p0p5wk2OrI5yybPWE05qLmrI2ImHEb20ioqUY3UGwrKzM/OhHP/LsTnWfffaZadCggUlJSTGZmZlm9uzZnuQYY8z06dNNly5dzKFDh4wxxpM7CL7zzjvmH//4h/nss8/KfzI8OzvbfPvtt1ZzjDEmNTXVpKammkceecR88sknZtKkSaZevXrmlVdesZ510owZM0xKSor55ptvPHn/srIy8/DDDxufz2dq165tfD6fefrppz3J6tGjh7n00kvNN998Y44fP25effVV4/P5TIcOHaxlVP13+uGHHxpJp/z+3X777SYvL89aTkWxvoPgoUOHTG5urhk8eLAnOW+//bZp0KCB8fl8Jicnx3z88cdR5QTLevrpp80VV1xR/lNdXt1B8K9//aspKCgwa9euNbNmzTJdu3Y1nTt3NocPH7aatXPnTiPJpKWlmQkTJpjVq1ebsWPHGp/PZxYsWGAtp6pnnnnGNGrUqLx3oxEo68iRI+bmm282kkzt2rVN3bp1zV/+8herOUePHjWtW7c2119/vfn3v/9tjhw5YsaOHWskRfXvNtD3V686wul7ua2ecLPPYKsjQmXZ7IlgOV50RLAs2z0RKMerjnDzd8JGTwTL8aIjAmXZ7Ilg+8Ze9IOb/XAb/eB2f99GPzhl2eqHUDm2+yFUls1+CJbjRT+4/TsRbT+EyrHdD8GybO9HhDqmtdkTbo+dbXREOMfp0fSEmxxbHeGUZasnnHJsdkSoLJs9Ec7fh2g7winLVk+EyrHZEaHOR9nsB7fnvWz0Qzjn2KLpBzc5tvrBKctWPzjl2OyHUFk2+yGcvw/R9oNTlq1+CJVjex/C6TyyrZ4I53x1tD0R7rnxSHvCbY6NnnCTZaMn3OTY6gmnLFs9Ee7fh2h6wk2WjZ5wyvHqnGXVa0Bena8Mda3J9jUNp+tats5XBsvx4ppGoCwvzlcGyvHqmkbVLK/OVzr9fbB5TSNQlhfnK6vm2OwHp+u2NjsinGvE0fREuNeio+kIt1nR9oSbHFsd4SbLRk845djsiHD/TkTaE25ybHWEU5bNngi15sKr/QhEhgWCCSxWCwSHDRtmWrdubbZv3+7J+x85csQUFRWZFStWmIcfftg0adLErF+/3nrOV199ZbKyssynn35avs2LBYJV7d+/32RnZ5vx48dbf+86deqYHj16VNp2zz33mAsvvNB61kl5eXmmf//+nr3/9OnTTcuWLc306dPNZ599Zv7yl7+Yxo0bm6lTp1rP2rRpk7nkkkuMJJOSkmK6d+9uBg8ebDp16mQtI9gCwR07dlR63c9//nPTr18/azkVxXKB4NGjR83AgQPNeeedZ0pKSjzJ2b9/vykqKjJLly41t956q2nTpo3ZtWuX1ayVK1ea7OzsSjsrXi0QrGrHjh2mTp065p///KfVrG+++cZIMj/96U8rve5HP/qRufHGG63lVHX22WebESNGRPz+TlnPPvus6dChg5k1a5ZZs2aNef75503Dhg3NvHnzrOasXLnSdO3atbwv+vXrZ6666ipz1VVXRZwT6PurVx3h9L3cVk845djsiFBZNnsiUI5XHeF2nyvangiU41VHuJmTjZ4IluNFRwTLstUTwfaNvegHN/vhNvrBTY6tfnDKstUPwXK86Idwjpei6YdgOV70g9s5RdsPoXJs90OoLFv94HRMa6snwjl2jrYjwsmKpifc5tjoCKcsWz0RyTmOSDvCKctWT4Q7p2g6wk2WjZ5wk+PFsYYxlc9HeXWcUTWnIi8eDRgsy+ZxRrAcL85FVM3y6jijak4gts5FVM3y6jijak5VNs9FBMry4jgjUI7NfnA6j2yrJ8I5Xx1tT4STFU1PuM2x0RNOWbZ6IpLrCpH2hFOWrZ4Id07R9ISbLBs94SbHi/2IqteAvNqPCHWtyfZ+RKgsm/sRwXK82I+omuXVfoSba4K29iOqZnm1H+E0J5v7EYGyvNiPCJRjqx+crtva7IhwrhFH0xPh5ETbEW6zou0JpxybHRHJtfxIesIpx2ZHhDunSHvCTY6tjnCTZasnQq258PJ8BMLHAsEEFosFgiNGjDAtW7Y0W7Zs8TSnor59+5o77rjD+vu++eab5aV08kOS8fl8JiUlxRw/ftx65kmXX365ueuuu6y/7xlnnGFuu+22Stvy8/NNTk6O9SxjjNm2bZupVauWeeuttzx5f2OMadmypZk4cWKlbU8++aQ5++yzPcvcv39/+TelG264wVx99dXW3rvqv9PNmzcbSeaTTz6p9LoBAwaYm2++2VpORbFaIHj06FFz7bXXmnPPPdfKHTPddly7du2ivstk1azf//735d1QsS9q1aplWrdubS0nmHbt2plx48ZFnBMo68iRI6Z27drmySefrPS6X/3qV6Znz57WcipatGiRkVTpYlg0qmYdPHjQ1KlTxxQUFFR63W233ebZgtvvv//e7N692xhjzPnnn2+GDRsWUUaw769edISb7+U2esIpx2ZHhLt/EmlPBMvxoiMimVMkPREsx4uOcDMnGz0RLMeLjnAzJ1s9cdLJfWOv9iECZVXkxcX9qjm29yFCZVVlYz+iYo5X+xCBsoKxsR9RMcerfYhAWRXZ3o+omOPVPkSgrIqi7QenY9pNmzZZ6Ylwjp2j7Qi3WdH2RKTnAyLpCKes3/3ud1Z6Ipo5hdsRTlmHDx+20hPhzCnajnDK2r9/v5WeCGdOtvchjPnv+Siv9yMCnffyYh8iUJZX+xFO5/Js7UNUzPJ6P8LNnGzsQ1TM8no/ItCcvNiHqJjl9X5EoDnZ6Aen88i2eiKc89XR9oTbrGh7ItJz8JH0hFOWrZ6IZk7h9oRTlq2eCGdO0faEU5atnghnTrb2IwJdA/JiP8LpWpPN/YhQWTb3I8K5fhbtfkSgLC/2I8KdUzT7EYGyvNiPcJqTzf2IQFle7Ec4zSnafnC6bmuzI8K5RhxNT7jNsdERkV73DrcnnHJsdkQ0cwqnJ5xybHZEOHOKpieccmx2RDhzsrUfEWjNRSyua8C9WgICMMZoxIgReuONN/TBBx+obdu2Mc0++cxzm/r27au1a9fq008/Lf/o1q2bBg8erE8//VQpKSnWMyX/89sLCwvVvHlz6+990UUXacOGDZW2bdy4Ua1bt7aeJUlTpkxRVlaWrrnmGk/eX5IOHjyoWrUqV1NKSorKyso8y2zQoIGaN2+uvXv3au7cuRo4cKBnWW3btlWzZs00b9688m1Hjx7VwoUL1bNnT89yvXbs2DHdcMMNKioq0nvvvafTTz89ZtledMZNN92kzz77rFJf5OTk6MEHH9TcuXOtZlX13Xffafv27dY7o27duurevXtMO+Pll19Wbm6uunbt6sn7Hzt2TMeOHYtpZ2RmZqpp06YqKirSypUrw+4Lp++vNjsiVt/L3eTY6ohI5xRuTzjl2OyISOYUSU845djsiHDmFE1POOXY7Ihw5hRtTwTKPnLkSEz2IbzaDw+V4/U+hNOcbM355PvEYh8i1Jht7keczInFPkSgOXmxH3EyJxb7EIHmFG0/OB3TnnnmmVZ6IpbHzm6ybPREpHOKpCOcsoYOHWqlJyKZU6Qd4ZSVmppqpSfCmVO0HeGUdeLECSs9Ec6cbO9DVDwf5eV+hJfnvZyyvNqPcDMnW/sQFbO83I9wmpPNfYiKWV7uRwSbkxf7EBWzvNyPCDYnG/3gdB7ZVk/E8ny1mywbPRHpnCLpCacsWz0RyZwi7QmnLFs9Ec6cou0JpyxbPRHOnGztRwS6BuTFfkQsrjU5ZdnejwhnTtHuRwTK8mI/wu2cbOxHBMryYj/CaU429yMCZXmxH+E0p2j7wem6rc2OiNU1Yjc5tjoi0jmF2xNOOTY7IpI5RdITTjk2OyKcOUXTE045NjsinDnZ2o8ItOYiWddGJKxYrEKEPfv27TOrV682q1evNpLKn6f+5ZdfWs25++67TWZmplmwYIHZuXNn+cfBgwet5jzyyCNm0aJFZuvWreazzz4zv/71r02tWrXMv/71L6s5wXjxiOGRI0eaBQsWmC1btphly5aZ/v37m/T0dLNt2zarOcYY8/HHH5vatWubp556yhQVFZnXXnvNpKWlmWnTplnPOnHihDnjjDPMQw89ZP29K7rllltMixYtTEFBgdm6dat54403TJMmTcyvfvUr61nvvvuumTNnjtmyZYv517/+Zbp27WrOP/98c/To0aje1+nf6bhx40xmZqZ54403zNq1a81Pf/pT07x5c1NaWmo157vvvjOrV682s2fPNpLMX//6V7N69Wqzc+dOq3M6duyYGTBggGnZsqX59NNPK3XGkSNHrOXs37/fPPLII2bp0qVm27ZtZtWqVea2224zqampZt26dVbnFEikt9oOlbNv3z4zcuRI89FHH5mtW7ea+fPnmx49epgWLVqE/ffBzZzeeOMNU6dOHTN58mRTVFRknn/+eZOSkmIWL15sNccYY0pKSkxaWpp54YUXwp5HOFmXXnqp6dy5s5k/f77ZsmWLmTJliqlXr57Jz8+3mvO3v/3NzJ8/32zevNm89dZbpnXr1ua6664Lez5uvr/a6gg3WTZ6winHZkc4ZdnqiUj2gyLtCKcsWz3hZk62OsLt71+0PeEmx1ZHuMmy0RNO+8a2+sFNlq39iFA5NvvBKcvmfkS4xzDRPNInVJbN/QinOdnqBzdZxtjZj3DKsdUPbrJs7UcEUvWY1mZPhMqxeawRKst2TwTLsX2sESorEFuPEK2YY/tYI1SWMXZ7IlSOMfaONZyybPZEqBxbHeF0PspWPzjl2OyHUFk2+yFUju1+CPe8YaT9ECrHdj84zclWP7j5vbPVD05ZtvrBKcfmPoSb88g2esJNjq2ecMqy1RNOOTZ7IpLz/ZH0hFOOzZ5wMycbPeH2985GT7jJstETbnJs9kSoa0A2jzNC5dg+zgiWZfs4I1iOF8cZ4Vyri+Y4I1iOF8cZoeZk8zjD6ffO5nFGqCybxxmhcmz1g5vrtrY6wk2WjZ5wyrHZEU5ZtnoikuvrkXaEU5atnnAzJ1sd4fb3L9qecJNjqyPcZNnqCac1F16dr0T4WCCYYObPn28knfJxyy23WM0JlCHJTJkyxWrOrbfealq3bm3q1q1rmjZtavr27RuzxYHGeLNAcNCgQaZ58+amTp06Jicnx1x33XVm/fr1VjMqevvtt02XLl1Mamqq6dixo5k8ebInOXPnzjWSzIYNGzx5/5NKS0vNL37xC3PGGWeYevXqmTPPPNOMGjUq6os/gcyYMcOceeaZpm7duqZZs2Zm+PDh5vvvv4/6fZ3+nZaVlZnRo0ebZs2amdTUVHPJJZeYtWvXWs+ZMmVKwK+PHj3aatbJ23gH+pg/f761nEOHDpkf//jHJicnx9StW9c0b97cDBgwwHz88cdhz8cpK5BId5RD5Rw8eNDk5eWZpk2bmjp16pgzzjjD3HLLLearr77ybE4vv/yyadeunalXr57p2rVrRI8Md5Pz4osvmvr160f9b8opa+fOnWbo0KEmJyfH1KtXz5x99tlm/PjxpqyszGrOc889Z1q2bFn+5/Too49G1Etuvr/a6gg3WTZ6winHZkc4ZdnqiUj2gyLtCKcsWz3hdk42OsJtVrQ94SbHVke4ybLRE077xrb6wU2Wrf2IUDk2+8Epy+Z+RLjHMNGckA+VZXM/ws2cbPSD2ywb+xFOObb6wU2Wrf2IQKoe09rsiVA5No81QmXZ7olgObaPNUJlBeLFAkHbxxqhsk6y1RNOObaONZyybPZEqBxbHeF0PspWPzjl2OyHUFk2+yFUju1+CPe8YaT9ECrHdj+4mZONfnCTY6sfnLJs9YNTju19CKfzyLZ6winHZk+EyrLZE6FybPdEuOf7I+2JUDm2e8LNnGz0hJscWz3hlGWrJ5xybPZEqGtANo8zQuXYPs4IlmX7OCNYjhfHGeFcq4vmOCNYjhfHGU5zsnWc4ZRj8zgjVJbN44xQObb6wc11W1sd4SbLRk845djsCKcsWz0RyfX1SDvCKctWT7idk42OcJsVbU+4ybHVEW6ybPWE05oLr85XInw+Y4wRAAAAAAAAAAAAAAAAAABIKrWcXwIAAAAAAAAAAAAAAAAAABINCwQBAAAAAAAAAAAAAAAAAEhCLBAEAAAAAAAAAAAAAAAAACAJsUAQAAAAAAAAAAAAAAAAAIAkxAJBAAAAAAAAAAAAAAAAAACSEAsEAQAAAAAAAAAAAAAAAABIQiwQBAAAAAAAAAAAAAAAAAAgCbFAEAAAAAAAAAAAAAAAAACAJMQCQQAAAAAAACS0qVOnyufzyefzadu2bad8fejQofL5fGrTpk3cxoD4GTNmTPmfDQAAAAAAAFDTsEAQAAAAAAAAcbVgwYLyBVxuPqZOnRrvISeM3r17l/++paSkaP369SFfv23btvLXjxkzJjaDBAAAAAAAAOAZFggCAAAAAAAA1YSXdzssKytj0R8AAAAAAABQw9SO9wAAAAAAAACAk+6++24NGzYs5GtatmwZ1ntOnTqVuw7+xz//+U+tWbNGXbt2jfdQAAAAAAAAAMQACwQBAAAAAABQbWRlZalLly7xHkbSycjI0OHDh3X06FE9/vjjmjlzZryHBAAAAAAAACAGeMQwAAAAAAAAkOQaNWqkn//855KkWbNmaeXKlXEeEQAAAAAAAIBYYIEgAAAAAAAAktrQoUPl8/nUpk2bkK+bNWuW+vXrpyZNmigtLU0dOnTQgw8+qOLiYklSmzZt5PP5NHToUMfMsrIyTZ48WT179lSjRo3UoEEDnXvuuXrqqad08ODBU14/ZswY+Xw+vfLKK5KkL7/8Uj6f75SPaIwaNUr16tWTJD3++OMRvcfUqVPLx7Jt27agr9u2bVv56wI93rnqn0lxcbEeeOABdejQQWlpaWrRooVuuOEGrV+//pT3vffee9WhQwfVr19f2dnZGjx4sDZv3ux6Dt9//71Gjx6tzp07q2HDhmrcuLF69+6t1157zdWvP378uF5++WVdffXVysnJUWpqqpo0aaJLLrlEf/jDH3T48OGgv7Z3797y+Xzq3bu3JKmoqEgjRoxQ+/btlZaW5vj7CgAAAAAAAISLRwwDAAAAAACgRjPG6O6779aLL75YaXtRUZF+97vfadq0aXrnnXdcv9+BAwd0xRVX6IMPPqi0fe3atVq7dq1mzZqlDz74QA0aNLAyfrdycnJ055136rnnntOcOXO0dOlS9ejRI6ZjCGTNmjW68soryxdiStKhQ4f097//XbNnz9bcuXN18cUX64MPPtB1112nkpKS8tcdPnxYr7/+uubMmaPFixerc+fOIbO2bt2qK664otKCwgMHDmjhwoVauHCh3nrrLU2fPl21awc+bbp582YNGDBAn3/+eaXt3333nRYvXqzFixcrPz9fs2fPVvv27UOOZebMmRo8eLAOHDgQ8nUAAAAAAABANLiDIAAAAAAAAGq0cePGlS8ObNmypSZOnKjly5dr0aJFGjVqlEpKSvS///u/Ae/8F8gdd9yhBQsW6JZbbtHs2bO1atUqvfnmm+WL8T7++GP95je/qfRrhg0bprVr12rgwIGS/Iv5Ti4orPgRrUceeURpaWmSpMceeyzq94vWwYMH9eMf/1hHjx7V008/rQ8//FDLli3TmDFjVLduXR08eFA33XSTNm3apB//+MdKT0/Xc889p2XLlmnJkiW6//775fP5tHfvXt12222OeYMGDdLWrVt111136b333tOKFSv08ssvq0OHDpKkf/zjH/rlL38Z8Nfu3LlTF110kT7//HOlp6dr5MiRmjNnjj755BPNnz+//Pe2qKhIV155ZaWFjFV99dVXGjJkiNLS0jRu3LjyeT///PNq2LBhZL+ZAAAAAAAAQADcQRAAAAAAAADVxu7du7Vu3bqgX8/KylJWVpa1vJ07d+qJJ56QJJ155plaunRppffv1auXrr76avXp00dHjx519Z4fffSRXn31VQ0ZMqR82w9/+ENdddVV6tatm9atW6eXXnpJTz75ZPmd6k7O67TTTpMk1alTR126dLE0y//Kzs7W8OHD9eyzz+r999/XokWLdMkll1jPcWvPnj0yxujjjz/WWWedVb79ggsuUNOmTTV8+HBt27ZNPXv2VHZ2tj788EM1bdq0/HUXXXSRateurWeffVbLly/X6tWrdd555wXNW7FihV5//XX99Kc/Ld/WrVs3XX/99erVq5fWrFmjP/7xj7r99tt1zjnnVPq1d9xxh3bt2qVWrVppwYIFOvPMMyt9vXfv3uXvs2XLFv3ud7/Tk08+GXAcW7duVU5OjpYuXaozzjij0rwBAAAAAAAAm7iDIAAAAAAAAKqNF154Qeecc07Qj/z8fKt5r7zyig4fPixJ+v3vfx9w8WHPnj01fPhw1+953XXXVVoceFJqaqpGjBghyf9I2qqPqY2VX/3qV+V3qasOdxF88sknKy0OPOlnP/uZ6tWrJ8m/kPD555+vtDjwpLvvvrv8vxcvXhwyq3///pUWB56Unp6uyZMnS5LKyso0adKkSl9ft26dCgoKJEkTJ048ZXHgSeedd17535U///nPIccybty4SosDAQAAAAAAAC+wQBAAAAAAAAA11vvvvy9JOv3003XNNdcEfd3NN9/s+j0HDx4c9Gu5ubnl/71lyxbX72lTkyZNdO+990qSFi1apPfeey8u45Akn8+nG264IeDX6tevr/bt20uSGjVqpLy8vICva9u2rdLT0yU5/57+7Gc/C/q1888/X507d5akU35PZs6cKUlKS0sL+fdEUvkdGXfs2KHt27cHfE3dunV1/fXXh3wfAAAAAAAAwAYWCAIAAAAAAKDaGD16tIwxQT/GjBljNe/k44x/8IMfKCUlJejrzjnnHKWmprp6z44dOwb9WuPGjcv/e9++fS5Had8DDzygzMxMSfG9i2CTJk0q/Z5UdfKRy+3atZPP53N8ndPvaffu3UN+/fzzz5ckFRUVVXqk9MqVKyVJBw8eVO3ateXz+YJ+9O/fv/zXFRcXB8xp3759+d0RAQAAAAAAAC+xQBAAAAAAAAA11t69eyUp4KOFK0pJSVGjRo1cvWdaWlrQr9Wq9d/TcSdOnHD1fl5o1KiR7r//fknSsmXLNGfOnLiMI9TvlfTf3y+3r3P6PXX6c87OzpYkGWPK/25I0u7du0P+umAOHjwYcLvbv0sAAAAAAABAtGrHewAAAAAAAAAAYu/+++/Xc889p7179+rxxx/XVVddFe8heS7UXQgl/8LAQE4uPGzbtq1mzZrlOq9t27YBt4e6WyUAAAAAAABgEwsEAQAAAAAAUGM1atRIxcXFjneIO3HiRKU7yiWDjIwMPfDAAxo1apRWrlypmTNnqmvXriF/TcU7IJaVlQV93YEDB6yN06Zdu3apVatWQb9+8u+Bz+erdJe/008/vfzXd+zYUbVrc1oVAAAAAAAAiYFHDAMAAAAAAKDG6ty5syTp008/Dfl42rVr1+rIkSOej8fpDne23XvvvWrSpIkkafTo0UHvoHdSenp6+X+HWjC5YcMGOwO0bMWKFa6+3r59e9WtW7d8+3nnnSfJ/8jgDz/80LsBAgAAAAAAAJaxQBAAAAAAAAA1Vt++fSVJ3333nWbPnh30dX/5y19iMp569epJUkwWI0pSw4YN9dBDD0mS1qxZo3/+858hX1/xkbkrV64M+rrXX3/dzgAte+WVV4J+beXKlVq3bp0k6fLLL6/0tYEDB5b/929/+1tvBgcAAAAAAAB4gAWCAAAAAAAAqLFuueUWpaamSpLuv/9+7dmz55TXLF26VH/84x9jMp7mzZtL8j/qdt++fTHJHD58uJo1ayZJeuaZZ0K+tkuXLmrcuLEkaeLEiQEXMk6fPt1xoWG8zJo1S3/7299O2b5//37dcccdkvyPUb7zzjsrfb179+7Ky8uTJL3zzjsaPXp0yJxt27Zp+vTplkYNAAAAAAAARI4FggAAAAAAAKixcnJyyhd7bdmyRbm5ucrPz9eKFSu0ZMkSPfbYY+rbt69ycnLUtGlTSd4+Brhnz56SpLKyMt11111atmyZioqKtGnTJm3atMmTzPr16+vhhx+WJH377bchX1u7du3yhXTr1q3TZZddppkzZ2r16tWaM2eObr31Vg0ZMkQ9evTwZKzR6tatm/7f//t/Gj58uObPn69Vq1ZpypQp6tatm1avXi3Jv2Dy3HPPPeXXTpkypXwB5xNPPKELL7xQkydP1tKlS7V69Wq99957mjBhgvLy8tSuXbtqu0gSAAAAAAAANUvteA8AAAAAAAAAiKeHH35YX375pV588UVt375dw4cPr/T1Jk2a6O9//7uuu+46Sf99DLAXLrvsMl144YVatmyZXn/99VMe1WuM8ST3zjvv1LPPPqtvvvnG8bWPPfaYFixYoGXLlumjjz7StddeW+nrl156qSZOnKhzzjnHk7FG429/+5v69u2r/Px85efnn/L1n/zkJ5owYULAX5uTk6OlS5fq+uuv14oVK7R8+XItX748aFZGRoa1cQMAAAAAAACR4g6CAAAAAAAAqNF8Pp8mTZqkmTNnKi8vT40bN1a9evXUrl073XvvvVq9erW6deum0tJSSVJmZqZnY6lVq5b+9a9/6dFHH1XXrl3VsGFDT+9YeFK9evU0atQoV69NS0vTBx98oKeeekrnnHOO6tevr4yMDHXv3l0TJ07U+++/r4YNG3o84si0bdtWq1at0q9//Wt16tRJaWlpyszM1CWXXKJp06bpH//4h2rXDv4z1a1bt9by5cv15ptv6sYbb1Tbtm2VlpamOnXqqGnTpurZs6dGjhyphQsX6uWXX47hzAAAAAAAAIDAfMarHzsGAAAAAAAAksTXX3+tVq1aSZL+9Kc/6bbbbovziAAAAAAAAADAGXcQBAAAAAAAABxMnz69/L8vvPDCOI4EAAAAAAAAANzjDoIAAAAAAACo0Q4cOKDS0lI1b9484NdXr16tSy+9VPv27VNubq5WrlwZ4xECAAAAAAAAQGRqx3sAAAAAAAAAQDzt2bNHnTp10rXXXqsrr7xSZ599tlJTU7Vjxw69++67evnll3Xo0CH5fD5NmDAh3sMFAAAAAAAAANe4gyAAAAAAAABqtG3btqlt27YhX1O3bl299NJLuvnmm2M0KgAAAAAAAACIHgsEAQAAAAAAUKMdO3ZMb775pubMmaOVK1dq9+7d2rt3r9LS0tSmTRtdfvnluueee9S6det4DxUAAAAAAAAAwsICQQAAAAAAAAAAAAAAAAAAklCteA8AAAAAAAAAAAAAAAAAAADYxwJBAAAAAAAAAAAAAAAAAACSEAsEAQAAAAAAAAAAAAAAAABIQiwQBAAAAAAAAAAAAAAAAAAgCbFAEAAAAAAAAAAAAAAAAACAJMQCQQAAAAAAAAAAAAAAAAAAkhALBAEAAAAAAAAAAAAAAAAASEIsEAQAAAAAAAAAAAAAAAAAIAn9f44s55IujprSAAAAAElFTkSuQmCC\n"}, "metadata": {}}]}, {"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\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\n\nsns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()", "execution_count": 8, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 2558.88x500 with 1 Axes>", "image/png": "iVBORw0KGgoAAAANSUhEUgAACgcAAAH3CAYAAACVGB36AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAACUZUlEQVR4nOzdeZyVZd0/8M8ZVtkGERAXEBUU9w333PctzV0ztSzzUTO1J8t6yrSsfplli9oiapblkvuSmuKWG6LijisIKIigDKvADOf3xySKMwODZ5gDh/f79ZpXnOu67+v6HlJkznzu71UoFovFAAAAAAAAAAAAABWjqtwFAAAAAAAAAAAAAC1LOBAAAAAAAAAAAAAqjHAgAAAAAAAAAAAAVBjhQAAAAAAAAAAAAKgwwoEAAAAAAAAAAABQYYQDAQAAAAAAAAAAoMIIBwIAAAAAAAAAAECFEQ5kiSkWi5k6dWqKxWK5SwEAAAAAAAAAAFiuCAeyxEybNi3V1dWZNm1auUsBAAAAAAAAAABYrggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAA0Drm1SUTRybTJpS7EgAAAAAAAACoeG3LXQAAsBx4+bbkX99Npo5LUkjW2Ss58JKk80rlrgwAAAAAAAAAKlKhWCwWy10ElWnq1Kmprq5OTU1NunXrVu5yAJZpc+vm5XdDX881w8Zkyqy5+dyAnjlr73UzqM8y8Ofruy8mf9wxmVe74PhaOyfH3lKWkgAAAAAAWE699VgybljSbbVkvQOSth3KXREAwBKjcyCwRBSLxTw7riYzZtdm834rZoX2bcpdEizT/u+mF3Lt8LHzXw8dOTFPvfVB7jp9h6xSvUIZK2uGp/7SMBiYJG8+kEx+I1lp7VYvCWiGefOS1+9N3h6eVPdNNjw4ad+53FUBAAAAwGdTNze57tjklTs/Huu2ev1D7D0HlK8uYOGKxfqfKb33StJr3frmE4VCuasCWGYIBwIt7vWJ0/M/f3sqr02cniTp1rFtfvT5DXLw5quXuTJYNk2c+mH++fS4BuM1s+bm6sfH5H/3WrcMVS2G6e8ufE44EJY+c2YmVx+WvPWfj8eG/iQ57tb6D18AAAAAKt3kN5LR/0m69E4G7J60adey68+dlUx9J+m6StK+U8uuTeOevGzBYGCSTB2X3H56cvztZSkJWIQPa5K/HVrf7fMjqw1OjrkhWaF72coCWJZUlbsAoLIUi8Wc+Nfh84OBSTL1w9p8+5/P5dV3p5WxMlh2jZ48M3Xzio3OvfHe9EbHlyprbN/4eIduSZ+NW7cWoHmeuHTBYGCSTJ+Q3PGtlttj7qzk2WuS+3+WjLwzmVfXcmsDAAAAlOLOs5LfbZHcdlryjyOT326evPdqy63/4AXJhesmv9u8/n+H/qS+MxZL1os3Nz4++uFkxuRWLQVopqHnLxgMTOpPu7n//PLUA7AMEg4EWtRTb32QN9+b0WC8bl4xNzTS+QxYtLV6dU67No23R1+3T9dWruYz2OyLSZ+NGo7v8v2kQ5fWrwcqSe2cZMzjyYQXWnbdl29rfHz0w8msD0pf/4O3kou3Sm76evLgz5NrjkqG7Jl8OLX0tQEAAKA1jR1W/+Db45cm098rdzW0hBdvTob9Mcknwno1Y5KbTmyZ9Z+8LLn/J/XdsJJk9tTkoQuSxy9pmfVpmmNIWVoVi8nwy5PL9kgu2Tb59w+Tme+Xu6rmm/VB/Z9jf/1CcuPXkzFPtNzaL97Y+PgLN7TcHgAVzrHCQIua9mFtk3NTZzU9BzStZ5cOOXqrfvnLY299arx9jt66X5mqWgztOyfH31n/je2b9ycr9Eg2PzZZe5dyVwbLthdvru/kN3NS/etVNkkOuzLpsVbpa1c18W1CoSoptCl9/bvOTqaMWXDs7eHJf36d7H5O6esDAMu8mplzc/WwtzJ89AdZuVvHfHHrftlwtepylwUAHysWk1tPTZ7528dj952XHPHX+iNoWXY9f33j4+88U3/U8Eprl7b+sD83Pv7EH5NtTyltbRZu/YOSMY81HO+/Q9J5pVYvB+b711nJsD99/HriS8mrdydfu3/pP3Z81gf1ocbJr3089vx1yUGXJpscWfr6TZ04U5xX+toAywnhQKBFDe6/Yjq1b5OZcxr+RW2ndXqVoaKl0ysTpuXVd6dl4MpdMqhPt3KXs3jeejR57rqkbk4yaL9k3X1b7mm7urn1a796V9K+S7LpUcmaO7bM2su4cw7YIKutuEKuGTY2H8yck88N7JUzdh+Y3l07lru05unYLfnc6fVfy6DJ02fnxqffzttTZmWzft2zz4arpH3blmvA/MLbNbnh6XGZMbs2uw7qnT3X75OqKk+xshCTXktuOCGZ94ng/fhnk38cnZz8WOl/Lm94aDLuyYbjA/es//e5FLVz6v+cb8zLtwkHAgD5YMacHHzpoxk16eOTCa4fPjYXf3Hz7LVBnzJWBv81Z2Yyd5YQASzvXr1rwWBgksydmdx8SnLGi0kbP4JbZjUVREkW/Czms5o2vonxCaWvzcJteUIy6qHklTs+Hqvulxzwm/LVBDXjkieHNBx/b2Ty3LXJ4C+3fk2LY9hlCwYDk/rg3r/Pqf+cudT/Hq5/YPLUFQ3H1/t8aesCLEd8ZwK0qK4d2+X/9ls/37/5+RQ/0XF/9/VWzh7rr9xi+zz6xqTc8+K7ademkAM2WTUbr969xdZekj6cW5dv/OOZ/Puld+eP7Taody7+4ubp2K4FOjEtaQ/9Mhn6449fj7g62fjI5OA/lr72vLrk70ckb9z38dizf0/2OC/Z/pulr7+Mq6oq5MQd186JO5b4VGoFmzmnNm9NnplVq1dIdad2Lbbu8+NqcsyQJ1Iza26S5MpHk8tXH5Wrv7ZNunQo/a9Sf3v8rfzglhfm/5l53fBx2XuDPrnki5sLCNK0EVc3/mH0ey/XH2fUb+vS1t/yq8nYx5MXb/p4rNegZL9flbZuUh9crGqT1DXyQXtTHQsBgOXKFY+MWiAYmCS184o5/46Xs8d6K/t7MuUza0p9V5cXb6p/aHLVzZK9f57026bclQHl8PJtjY9Pn5CMG5assV3r1kPLWW//5NV/NRzvuU7Sa93S1++7TfLa3Q3H/fdkyWvTLjnq7/Wfn419IqlePVl3v6Rt+3JXxvJs/HNJsYlQ8jvPJFnKw4Fv/afx8ekT6kODvdcrbf1df5CMG568+/zHY703SHb7YWnrAixH/PQNaHFHb90vG69enX8+Vd8Fa5dBvbPXBn3SpoU+vD/nlhcWOF71zw+Pyvf2HbRMhKZ+e99rCwQDk+S+kRPz63+/mrP3LfEvx5/w6rvTMnHq7Gy0enWqV2ihkNTUd5IHftZw/Llr6o+I7b99aeuPvGPBYOBH7v9pstmXkk49Slufivb7oa/lDw++memza9O+bVWO3LJvfrj/+mnbpvTufj+45YX5wcCPPDuuJpf/Z1RO221gSWtP/XBuzr/j5QXC1Ely14sTcv8rE7Pbei0XqqbCzPrgs801V5u29UcUf+6M5O2n6p+gXnvXpKoFOma2aVf/tGdjR/RseEjp6wMAy7xH35jc6PiY92dm3Aez0m+lpfxYLSrXDSckr9/78et3nkn+dkhy8uNJ977lqwtawdQP52ZCzYdZfcUV0qm9Hy0lWfgDblUt9+AqZbDxkckr/0pG3v7xWIfq5MCLW2b9Xb6XvPVIMmf6x2NtV6gPwNA6+m5V/wVLgxXXaHque7/Wq+Oz6ty78fFCm6RTzxZYf6XkxAfqO/a+93L9Q+zr7NPyHXpnTEpmTk56rFX/GTZABfEdHLBEbLhadTZcrbrF13127JQFgoEf+cVdr+TATVfLyt2W7iNWb3z67cbHn3m7RcKBk6bPzslXP51ho95PkqzQrk2+sduAnLzzgJLXzpsPNn1kwuv3lh4OfPOBxsdrP0zGPFZ/hDE04vrhY/PLe16d/3pO7bxc9dhb6dqxbb6916CS1p48fXZGjJ3S6Ny9L79bcjhw2JvvZ9bcxp8IfOCV94QDadpauyRPXdlwvF2nln3KfJVN6r9a2l4/qz8WY8InnvYcuFey/WkttsW8ecU8PmpyambOzdZrrZQenT0BDgDLiqb+u922qtByD8BRsa4ZNiaX/WdUxr4/M5us3j2n7z4w2w1ogR9KvvfqgsHAj8yZnjx9VbLr90vfA5ZCdfOK+emdL+fqJ97Kh3PnpWuHtvnqDmvlm7uX9plIRdjosOTpvzQcX7F/stoWrV4OLahN2+TIq+uPnx31cNKld/0DjS31APuqm9YHXR67OJn4ctJrnWSbk0vvrgUsm1beIFlzp2TUgwuOd6yub56xtBv8lf8+CP6pLgjr7Z906dUye7RpW7/eevu3zHqfNHtacts3kxdvru/g2KVPsse5ySZHtvxeAGUiHAgsU4aOnNjoeO28Yh589b0cPnjpfkq7qRDQrDlNtAtfTGf987n5wcCP9vvFXa9kUJ+u2XVQiSGjjgsJe67QvbS1k6TTSp9tjuXe3x5vGBhOkqufGJP/3XPdFAqfvWtpu7ZVaVNVSN28YoO5ljgKvGvHpv8qtrA5yHoHJAN2b/jDyd3OaZk/k5e0Lr2Srz+cvHl/8v6o+gDi6oNbbPnXJ07PV//yZEZPnpkkad+2Kt/ec918bce1WmwPAGDJ+eI2a+SeT3XdT5L9Nl4l1Z2EA2naXx4dnXNufXH+62Gj38+xlw/LtV/fJlusUWKgo2bMQubGlbY2LMUuvv/1DPnPqPmvp82uza/vfTW9u3XIUVstA92MlqQ1d0h2PCt5+JdJcV79WOfeyaGXt0znfZplTu28FApJuxY4QaSBNXes/1oSeg5MDrhoyawNLHsOvyq567vJCzcmdbPrjx/f5+dJ12WggcAa2yYHXZL8+5xkxsSkUFX/+fXnf1fuyprnttOTF274+PX0CclNJyXd16h/bwAVwE+dgWVK5w5Nh3E6LwPHWey2Xu9Guwfutl4TLbcXw8RpH+b+VxoPT14/fFzp4cABuyddV0mmjV9wvE2H+qdkS7Xp0ckjFyV1cxYc77Ve0nfr0tenYk2aPqfR8Skz52ZuXTHt2372cGC3ju2yx3or564XJzSYO3iz1T7zuh/Zsn+PrNmzc0ZNmrHAeJuqQg7efPWS16eCVbVJjrqm/sOi1+5O2nep/3O0JbsGLmmFQv1RxWu3/NKnXP30/GBgUv+DgvPvfDmb9uueLfs7ph4AlnY7rdMr5x24QS6859XUzJqbqkKy1wZ98pODNix3abSAydNn52+Pj8nzb0/J6it2yjHbrJEBvbuUvO68ecVc+sAbDcZr5xXzxwffzJ+OLfHvgX02Sdq0b/i5RZKstnlpa8NS7K9NPJR51WNvCQcm9V1DNzum/lSUjtXJOnsn7VrwdJs5M5Pnr0veGVHfkXCzY5LOLdANtQKMfX9mzr3tpdz/ysS0KRSyz0Z98oP910/PLh3KXRrA4luhe/KFPyT7X1QfDlxYw46l0aZH1/+scNKr9UcJLwuhxqT+KOEXb2pkopgMv1w4EKgYS3+SBuATPr/Javnl3a9mTt28BcZX7NSuRQJ2S9q391o3T45+P2PfnzV/bLXuK+Tbe61b8trTPqxNsWFzsyRJzay5Ja+ftu2To69Nrj8+ef/N+rHOveuf/Om2aunr91gzOezK5I5vfRxAXHWz+idtS+j8RuXbes0eufGZhqHbzft1T/u2pT8x/JMvbJjxUz/Ms/89XriqkBy9db8csWXpnUqrqgr505e2yNf/9lTefK8+INitY9uce+AGLfLDsfnGPJGMvD1p26H+CBRHlFSGNu2STY6o/2K+F96uySvvTmt07san3xYOBGDxvT+q/ri3lQbUH/lGqzh22/45fHDfvPbu9PTq2iF9qlswaEHZjK+ZlYMveTTjaz6cP/aPYWNy5Ze3yrZrl3ZqwLQPazNh6oeNzr3+3vSS1k5S3/16m5PrH2z8pJ7rJpscVfr6sBQqFouZNH12o3PvTWt8fLm04hrJFse1/LozJidX7JNMeuXjsUd+kxx3W9Jn+Q7Mfzi3Lkf+6fG8PaX+c/a6FHPLiHfyxnvTc9upnyvpJBFY6r1+X/2R5jPfT9baKdnya8vGSSI0T7uOLRsyb01t2tUfkbwsmTm5/ijhxkxv2DQCYFm1zPY1nzBhQr7xjW9krbXWSocOHdK3b98ccMABue+++xa47plnnslhhx2WlVdeOR07dsw666yTr33ta3n11VcbrLnnnnumTZs2efzxxxvMHX/88SkUCg2+Xn/99fn77L///undu3c6duyY/v3754gjjsikSZOafA9vvvlmjjrqqKy66qrp2LFjVl999Rx44IEL1FYoFHLzzTc3uUaxWMyf/vSnbL311unSpUu6d++ewYMH56KLLsrMmTObvO8jkydPzuqrr55CoZApU6YsMPf8889np512ygorrJDVVlst5513XopNJY+glfSp7pjfHLlpun3iuM3eXTvkT8cObpEjPpe0VapXyF3f3DE//cJGOX67/vnJQRvm7jN2zOordip57TVX6px+PRpfZ6d1epW8fpL6Yx+/8XTy1fuS4+9IzngxWXfvllk7SQbtl5z+QvK1ockpw5ITH0h6OAKShfvGbgPTo3P7BcY6tK3Kd/Ye1CLr9+zSIbecsn1uPHm7/P7ozfLgt3fJTw7aqMU+ZBy4ctfcd+ZOufHk7fK3E7bOE9/bPV/YrAW7Bt71veTyPZNHf5s8dEFyybbJsD+33PqwlJk1t4kPc5LMmlPbipUAsMyrq01u+p/kt5sl1xyVXLxl8o+j6zv40Co6tmuTjVavFgysIBff//oCwcAkmV07Lz+98+WS1+7asW1WaeKflXV6dy15/STJHucmB/0h6b9D0mej5HNnJF+5K+nQgg93wVKkUCg0+YDV1mt58GqJe/jCBYOBSTLr/eTus8tTz1LkjufGzw8GftILb0/No29MLkNF0Eoe/0Pyt4OTl25JRj+cDP1JfYh4duMPygKL0GOtpEufxufW2L51awFYgpbJzoGjR4/O9ttvn+7du+cXv/hFNt5448ydOzd33313TjnllIwcOTJJcvvtt+eQQw7JXnvtlauvvjprr712Jk6cmOuvvz4/+MEPcu21185fc8yYMXnsscdy6qmnZsiQIdlmm4ZHsu2999654oorFhjr1atXJk6cmN133z0HHHBA7r777nTv3j2jRo3Krbfe2mRAb86cOdljjz0yaNCg3HjjjVlllVUybty43HnnnampqWn278WXvvSl3Hjjjfm///u//P73v0+vXr3y7LPP5qKLLkr//v1z0EEHLfT+E044IRtvvHHefnvBjktTp07NHnvskV122SVPPvlkXn311Rx//PHp3LlzvvWtbzW7PlgS9tloley8bu889uaktGtTlW3WWint2iw7WefOHdrm6K1b/siNqqpCzv38Bvn6357KnNqPOytutFp1vrjNGi23UaGQrD645db7tDZtk9W2WHLrL0G3jHg7f3zwzbw1eUbWX7VbTtttYHYY2ELBTJq0Zs/Ouf0bn8tfHhudl96Zmn49OuX47fpn4Mot9MOf/9q834rZvN+KLbrmRwqFwpJZ++2nkscv/tRgMbn7+8kGX3AMTWuonZ28cX/90WNr7Zx07FbuiireJqt3T88u7Rs9cny39ZaR4ywAWDo8+pvk2b8vOPbKHcnQHyd7/6w8NbFMGTF2St6fMTub91sx3Tu1X/QNy4HHmghsPP92TaZ9ODddO7b7zGtXVRVyyi4D8n83v7DAePs2Vfn6Ti344OGmR9V/wXLiO3uvmy9e9kQ+nPvx543VK7TL6bsNLGNVy4nX7m58fNRDydxZSbsVWreepchb7zf9sMboyTOy/QCfeVGB5sxI7j+/4fjEl5Jn/pZs8z+tXxPLjnnzkpduSkbekbRpn2x4aDJw93JXVX5t2tU/AHTTSUk+0SSpx1rJVieWrSyAllYoLoOt4Pbdd98899xzeeWVV9K5c+cF5qZMmZLu3btn5syZWWONNfK5z30uN93U8Jz4j677yLnnnpuRI0fmnHPOyVZbbZXx48cvsPbxxx+fKVOmNNrF7+abb85hhx2WWbNmpW3b5uUtR4wYkc022yyjR4/OGms0HdopFAq56aabGg35XXfddTniiCNy880358ADD1xgrlgsZurUqamurm5y7UsvvTTXXnttfvjDH2a33XbLBx98MP/35NJLL83ZZ5+dd999Nx06dEiS/PznP8/vfve7jBs3rtFuSbNnz87s2R8fJTB16tT07ds3NTU16dbND8KhtYyaNCPXPDkm702dna3W7JGDNlttmeiquKz751Pj8r/XP7vAWJuqQv52wtYlH40En9nQn9R3C2zMF/7kONolbdTD9Uexz/xvJ+n2XZIDfpNsdGhZy1oe3PXChHzjH09nbt3H3+rssf7K+cMxW6RNlaOFAGim322RTH694XjH6uS7Y1q/HpYZb0+ZlROvGp4X35mapL6z+Rl7rJOTdlq7zJWV32F/eDRPjv6gwXjn9m0y4pw9W+ThzxueGpc/P/xm3v5gVjZavTrf3G1gtl7L9+VQitcnTsuVj47Om+/NyHqrdMvx2/VP3yZOMKEF/XGnZPyIhuNtV0jOHlsfaFhO3f3ihHz9r081OnfLKdtnk77dW7cgaA1jhyVD9mh8br0DkiP+1rr1sGz551eSF25YcOxzZya7n1OeepY2bz2WDL+8/ijhNbavDwZ20iUZqBzLXOfA999/P3fddVfOP//8BsHAJPPDbXfffXcmTZqUs846q9F1PhkMLBaLueKKK3LxxRdn0KBBWWeddXLdddfly1/+crNq6tOnT2pra3PTTTfl0EMPbdYxg7169UpVVVX++c9/5vTTT0+bNosf3Ln66quz7rrrNggGJvWhwoUFA1966aWcd955eeKJJ/Lmm282mH/sscey0047zQ8GJslee+2Vs88+O6NHj86aa67Z4J6f/exnOffccxf7fQAta82enXP2PuuVu4zlzsX3N/yhYd28Yi598A3hQMpnYU+Qt3M02xI1Z2Zy7THJh1M+MTY9uenrSd+tk+59y1ba8mDvDfvkvjN3zg1Pj0vNrLnZcZ2e2Xmd3qkSDARgccyZsXjj8F+nX/PM/GBgUn9s7s//NTIbrlqdzw1cvjsZHb11v0bDgYcN7ttip0IcssXqOWSL1VtkLaDegN5d85ODNip3GcufTY9uPBy40aHLTjBw/LPJiH/UH3k6cPdkvc8nVaU/yL77eitnk9Wr8+y4mk+N9xYMbC3P/zN57rqk9sNk3X2TwV9O2nZY9H18dl16L2SuiWNRIal/iP3TwcAkeeSi+n93u7f8aWfLnDW2rf8CqFDLzjmc//X666+nWCxm0KBBC73utddeS5JFXpck9957b2bOnJm99torSXLMMcdkyJAhDa67/fbb06VLl/lfhx12WJJkm222yfe+970cffTR6dmzZ/bZZ59ccMEFeffdd5vcc7XVVstvf/vb/PCHP8yKK66YXXfdNT/+8Y8bDeot7D2uu+66zb7+I7Nnz85RRx2VCy64IP36Nf4f+wkTJmTllRc8du2j1xMmTGj0nrPPPjs1NTXzv8aOHbvYtQEsi+bUzsuoSY3/gPC1d6e1cjXwCRsemlQ18izICj2SgXu2fj3Lk1fvWjAY+JF5tckL/2z1cpZH/VbqlDP2WCc/+vwG2XXQyoKBACy+AU0csTSgiW4dkOStyTMaDb8lyT+f8lnZFzZbPWfusU46ta8PhrSpKuQLm62W7+6z6M9wgaZNmj47F97zSo7+8+M589oReWZM438OsYzZ8mvJ5sclhU/8KG+tnZO9GjlWdGn01F/qux8+cWky4m/1pytce0wyr67kpdtUFfK3r26d/9l57azVs3PWWblL/nfPdXLxFzcvvW4W7c6zkhtOqD/6etSDyV3fSa4+rP7YUpacFfs3/r1IVdtki+NbuxqWJW8+0Ph4cV79UfVQySY8n/zru8mNX69/YKF2TrkrgrJY5joHfnQK8qK68y3OaclDhgzJEUccMf9I4KOOOirf/va388orrywQvttll11y6aWXzn/9yc6F559/fs4888wMHTo0jz/+eP7whz/kpz/9aR566KFstFHjT9SdcsopOfbYY3P//ffniSeeyPXXX5+f/vSnufXWW7PHHov+oLlYLDarS+GnnX322VlvvfVyzDHHLPS6T6+9qN/7Dh06LNBpEGB50b5tVfqv1CmjJ89sMDegd5cyVAT/teIayYGXJLefkcz9b4C1U8/k8L8svKsgpav9sOm5ubNarw4A4LPb5Xv1P+yc8okjhDv3SvY4r3w1sdSbPrv2M80tT07bbWC+vH3/vPHejKzavWN6d9XVHEoxcdqH+cLFj+btKR9/r3nLs+/kd0dtln03WqWMlVGyqqrk879NdvhW/Q+2V+yf9Nmw3FU1z+xpyd3fT/Kpn9W9cmcy8o5k/c+XvEXXju3ynb0H5Tt7C5i3qvdHJcP+1HB81IPJa/ck6+7d+jUtTw7+U3LrN+r/XSrOS6r7JXv/dNn5s2FZN/zy5PFLkyljk9UHJzufnfTfvtxVLVqnhZxutYKjc6lgz16b3HxS/Z+XSfLcNcmIq5Njbkzati9vbdDKlrnOgQMHDkyhUMjLL7+80OvWWWedJMnIkSMXet3777+fm2++OZdccknatm2btm3bZrXVVkttbW0uv/zyBa7t3LlzBgwYMP9rlVUW/MZ6pZVWymGHHZYLL7wwL7/8clZdddX88pe/XOj+Xbt2zec///mcf/75efbZZ7PDDjvkJz/5yULv+eR7XNTvQ2OGDh2a66+/fv773W233ZIkPXv2zDnnnJOk/qjkT3cInDhxYpI06CgIQHLyLgMajLWpKuR/dlq7DNXAJ2xyRPKtkcnhVyVHXZuc+VLS/3Plrqryrb1bUtXEET/r7tu6tQAAn023VZOTHkn2uaC+a88eP05OfiLptU65K2MpNqhPt6xS3XjYbZdBCzkKbjnTtWO7bNq3u2AgtIAhD49aIBiYJHXzivnZv17OvHnNb6LAUmzFNZL19l+2wj9jn0jmNHGiyuv/bt1aaFljn0iD0OdHxjzaqqUslzr1SI68OvnWq8mpw5NvPpusd0C5q1o+PPq7+ofwJ72a1M5KRj+c/PWg5O2ny13Zom10WNKuc8PxbqslA3XGp0LN/TC567sfBwM/Mvrh5PnrylMTlNEyFw7s0aNH9tprr1x88cWZMaPhEY5TpkxJkuy5557p2bNnfvGLXzS6zkfXXX311Vl99dXz7LPPZsSIEfO/LrroovzlL39Jbe1ne6K3ffv2WXvttRutsSmFQiGDBg1q9j1HH310Xn311dxyyy0N5orFYmpqahq974Ybbljg/V522WVJkocffjinnHJKkmTbbbfNQw89lDlzPm6res8992TVVVdN//79m/2eAJYmj74+KQdf8kjWOvuObP/zobns4eYf5b4ohw/um18fsUkG9emaju2qssUaK+by47fMdgN6ttge8Jl17Jasf2D9k7ttdfltFV1X/u8xP5/quLztqcmqm5ajIgDgs+jYLdn6xPquPduflnReSMcFSP1DYj8+cMO0b7Pgx67brNUjh26xepmqAirZE6Peb3R87Puz8k6NzvWUSYfqhcx1a706aHldFtJApEuf1qtjedelV9JzYH2HUZa8utrkkd80Mj6nPjS4tOvSKznq70l134/Heq2XfPH6pE0TD7hDa5ozM/ngraRubsutOX5EMqvxvyfn9ftabh9YRixzxwonySWXXJLtttsuW221Vc4777xsvPHGqa2tzb///e9ceumlefnll9O5c+dcdtllOeyww/L5z38+p512WgYMGJBJkybluuuuy5gxY3LNNddkyJAhOfTQQ7Phhgs+cbXGGmvkO9/5Tu64444ceOCBC63n9ttvzzXXXJMjjzwy66yzTorFYm677bbceeedueKKKxq9Z8SIETnnnHPypS99Keuvv37at2+fBx98MJdffnm+853vLHDtqFGjMmLEiAXGBgwYkMMPPzw33XRTjjrqqPzgBz/IHnvskV69euX555/Pr3/963zjG9/IQQcd1GDvtddesIvVpEmTkiTrrbdeunfvnqQ+eHjuuefm+OOPz/e+97289tpr+elPf5of/vCHn+koY4ByGzF2So67Yljm1tU/1fj2lFn5yR0v58O5dTl114EtsscXNls9X9hs2f1hz4dz6/LYG5OTJNuuvVI6tmtT5opgGbf115P+OyQv/LP+g6JBByT9ti53VQAALWv2tKSqbdJuhXJXstTYff2Vc88ZO+b6p8Zm8vQ52XbtlbLvRqukXRs/vKXM5s2r79g16qGkc89k4yOTbi187GxdbTJzcn1nIz9sbhW9ujb+EGD7tlWpXsH/B5TJ6oOTnusmk15ZcLxQlWx6dHlqomWsuVPj/992qE42PqI8NcGSNuv9ZMZ7jc9NerV1a/ms1tq5vtPk+BFJm/ZJn43KXRHUf39y37nJk0PqOw537pXs8L/JNieVvnbHhTyosLA5qFCFYrG4TPaVHz9+fM4///zcfvvtGT9+fHr16pUtttgiZ5xxRnbeeef51w0fPjw/+9nP8vDDD2fq1Knp27dvdt1113z7299OTU1NBg8enGHDhmXLLbdssMfnP//5JMmtt96a448/PlOmTMnNN9/c4Lo333wzP//5z/Pggw9m7Nix6dChQwYOHJiTTz45xx9/fKP1T5o0KT/+8Y8zdOjQjB49OoVCIf37989xxx2XM844I1X/fdKjqSDe/fffn5133jnz5s3Ln/70p1x++eV58cUX07Zt2wwcODDHHntsvva1r2WFFRb9wewDDzyQXXbZJR988MH8cGCSPP/88znllFMybNiwrLjiijnppJMWKxw4derUVFdXp6amJt26eRJsaVQza27emzY7/Xp0Svu2PqCmsn3jH8/ktmffaTC+Yqd2eeJ7uy/3/w488MrEnHHtiHwws/6pnBU7tcuvjtg0u6zr2CvKq7ZuXv790rt5feL0DFy5a3Zfr3fa+qEqAED5vftS8q+z6o/kqWqXrP/5+iOYdVeEpVPd3OSao5PX7vl4rF2n5Kh/1P+wuCU88cfk4QuT6e8mnVaq75y+w5ktszZNeujV93Ls5cMajB8xuG/+36Ebl6Ei+K9JryXXHpO8N7L+dYfqZJ+fCwdWgg/eSm45pf7vgUmy8obJ/hclfRv+rBUqQl1t8qv1khkTG85teEhy6OWtXxNUggd/kdx/fsPxQ4YkGx1a+vp/3i15e/inBgvJV+9LVt+i9PVhGbLMhgNZ+gkHLr1m19blR7e+lBueHpc5tfPSo3P7nL77wBy7bf9ylwZLzH6/fTgvvjO10bknvrdbVu7WsZUrWnpMmTkn2/18aGbOqVtgvFP7Nnn0u7ume6f2ZaqM5d3k6bPzxcueyMgJ0+aPDerTNX//2jbp0dk/lwAAi/RhTTLyjmTuzGTgXkn3vou+pzlmfZD8bnAyc9KC46tvmXz13pbZA2hZT/81ufXUhuMr9k++8UzpxxKO+EdycyMdPvb5RX1XdZaofwwbkwvveSWTps9JuzaFHLDJqjn/oI2yQnunQrAUGPdUMrsm6btN0r5TuatZfoz4R/Lkn5Np7yb9tkl2+k7Sa52W3WPq+KT2w6THmi27LiyNHrskufvsBcfadEhOuCdZddOylATLtGIxuWBAw88VkqTv1vX/bpVqytjkui8l7zxT/7pDt2SP85LBXy59bVjGLJPHCgOl+dmdI/OPYWPmv35/xpz88JYXs0r1Ctlj/ZXLWBksOev26dpoOLBX1w5ZaTkPGf3rhQkNgoFJMnNOXf71woQctVW/MlQFyQV3v7JAMDBJRk6Yll/e80p++gXHHgAALNTr9yXXHVd/NE+SFM5Kdvth8rnTS1/7uesa/wB/3JPJ2GFJ361K3wNoWa/8q/HxD0YnE19K+mxY2vpPXNr4+OOXCAe2gqO26pdDNl89b02ekZ5dOmTF5fyzLpYyOvO0vkd+k/z7hx+/fuGfyev3Jl9/sD4U3lJa+mh6WJpte3LSoUvy+KVJzbhktc2Tnc8WDITPqm5u458rJMnUhifBfSbd+yYnPpCMf67+IcfVByftO7fM2rCMcSYbLGdm19bluuFjG5276rHRrVsMtKITd1wrK7Rr+LT0yTuvvdwfUdpYMPAjM2bXtmIlsKB/vTCh8fHnx7fcJm/cX3/EzZA9k3t/lEx/r+XWBgAol7mzkhtO+DgYmCTFuuTec5Lxz5a+/gdvfbY5Wk6xWN/9YNxTybx55a6GZUHbDk3PtVuh9PWnNP55Y2rGlb42zdK+bVUGrtxVMBCWd3M/TB7+VcPxD6fUh5pY4ubNK2boyHcz5D+j8ugbTQRfWDZtfmxy8mPJ2WOTY2+p78oJfDZt2yerbNr4XEs/cLjKxslaOwkGslzTORCWMzNm1zUZBHpv2uxWrgZaz6A+3XLt17fJb+97Lc+MmZJVunfMV7ZfMwdvvnq5Syu7XdbtlZ/cUf+zpU8qFJJdB/UuT1GQpE1VYbHGF9vwK5LbT//49dgnkhduSL52f9K5Z8vsAQBQDm8+UP9UfGNevClZZZPS1l9t8yYmCsmqm5W2Nov29tPJjV9LJr9e/7q6X3LQJcmaO5S3LpZumxyZvHhjw/HVtkhWWrv09VffMnnt7kbWH1z62gA0X83Y+iBgYyY836qlLI/emzY7XxryxAKnoWyzVo9cfvyW6dTej+UBFrDbD5O/H5HMm/vxWIfqZMezylcTVKjlu1USLId6dG6fAb27NDq31Zo9WrkaaF0br949lx23ZZ76wR65/Rs7CAb+11q9uuQbuw5sMP6NXQZkrV6N/3kBrWH/jRs/mmT/jVctffHaOcnQnzQcnzImGfbn0tcHACineU13B1/oXHOt9/nGA4abHJX0HFD6+jRt7qzk74d/HAxMkpoxyT+OSma+X766WPqts1eyw/8mhU+cqrDSgOTgFvr+Z6fvJG0/1YGwql2yy/daZn0Amqdrn6Rdp8bneqzZurUsh35658sLBAOT5PE338/F97/exB0Ay7EBuyUn3JNsdHj9Q0WDT0hOvD/pPajclUHFKRSLn+4TBC1j6tSpqa6uTk1NTbp161bucviEe196Nyf97anUzvv4X/9eXTvk5lO2z2rdW+AYEWCZ9PSYD3Lnc/XHte678SrZvN+KZa6I5V3NrLk5/opheWbMlPljW6yxYq748pbp1rFdaYu/+1Jy6baNz621c/2xEAAAy6o5M5IL10tm1zSc++rQZPUtSt/jw5rkkd8kr/wraduxvivZll9Nqtos+l4+uxduTP755cbn9ruw/v8DWJiat5O3Hq3vlr7mTklVC/YPmPB88ujv6r/f6jkw2fbUlvnzBoDFc/f3k8d+v+BYmw7J14YmfTYsT03LgWKxmHV/cFfm1M5rMNd/pU554Nu7lKEqAADHCsNyaff1V86NJ2+Xvzz6Vt6eMjOb9O2er2y/Zlbu1rHcpQFltHm/FQUCWapUr9AuN/7Pdnn0jcl5feL0DOzdJdsNaKHjfrv0TqraJvNqG851bYHOhAAA5dS+c3LQxck/T0jqZn88vsP/tlxQp2N1/RFAu/2wZdajeZo6LjpJZi5kDj5SvVqy8WFLZu0+GyUH/2nJrA1A8+1xXtKha/3pGDMn1R/9vts5goGtoYmWPPO06gEAykjnQJYYnQMBgKXaDV9Nnr9+wbFCm+SEf+tuAQBUhukTkxdvTubOSNbdN+m1brkrolST30h+t0Ua/cnzV+9LVh/c6iUBAEuxutqkjV4xreUb/3gmtz37ToPxk3ZaO9/dxzGZAEB5tOCZAQAAsAw54DfJpl9M2rSvf929X3LoEMFAAKBydOmdbH1i8rkzBAMrxUprJ9ue0nB80y8KBgIADQkGtqr/22+9rNWr8wJjm/frnlN3HVCmigAAdA5kCdI5EABYJnxYU388W3W/pMqzMwAALANeuzd54YZkXm2y/ueTQfsnhUK5qwIAWO7NrZuXe196N6Mmz8j6q3TLTuv0SsHf0wCAMhIOZIkRDgQAAAAAAAAAACgPrVEAAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUmLYtudiHH36Yp556KhMmTMjMmTNz4IEHplu3bi25BQAAAAAAAAAAALAILRIOHDt2bP7v//4v1157bebOnTt//Pnnn8/6668///WQIUPyxz/+MdXV1bnnnntSKBRaYnsAAAAAAAAAAADgE0o+VnjYsGHZbLPN8re//S1z5sxJsVhMsVhs9NrPf/7zee655zJ06NDcc889pW4NAAAAAAAAAAAANKKkcGBNTU0OPPDAvP/+++nTp08uueSSPP/8801e36tXr+yzzz5JkjvuuKOUrQEAAAAAAAAAAIAmlHSs8O9+97u8++676dmzZx577LH069dvkffsscceueWWWzJs2LBStgYAAAAAAAAAAACaUFLnwNtuuy2FQiFnnnlms4KBSbLBBhskSd54441StgYAAAAAAAAAAACaUFI48LXXXkuS7Ljjjs2+p3v37kmSqVOnlrI1AAAAAAAAAAAA0ISSwoGzZs1KknTu3LnZ90yfPj1J0rFjx1K2BgAAAAAAAAAAAJpQUjiwV69eSZKxY8c2+56nnnoqSbLKKquUsjUAAAAAAAAAAADQhJLCgVtttVWS5F//+lezrq+rq8uf/vSnFAqFfO5znytlawAAAAAAAAAAAKAJJYUDjzrqqBSLxVx++eV55plnFnrtvHnzctJJJ+Wll15KkhxzzDGlbA0AAAAAAAAAAAA0oaRw4CGHHJLtttsus2fPzm677ZaLL744EydOnD9fKBTy7rvv5q9//WsGDx6cyy+/PIVCIXvvvXd23nnnUmsHAAAAAAAAAAAAGlEoFovFUhaYNGlSdtxxx4wcOTKFQmGBufbt22fOnDnzXxeLxWy00UZ58MEH071791K2ZRkwderUVFdXp6amJt26dSt3OQAAAAAAAAAAAMuNkjoHJknPnj0zfPjwnHLKKenQoUOKxeL8r9mzZ8//ddu2bXPiiSfm0UcfFQwEAAAAAAAAAACAJajkzoGfNHny5Nx9990ZPnx4Jk6cmLq6uqy00krZbLPNss8++2TVVVdtqa1YBugcCAAAAAAAAAAAUB4tGg6ETxIOBAAAAAAAAAAAKI+2pdw8ZsyYJMlqq62WNm3aNOueefPmZdy4cUmSfv36lbI9AAAAAAAAAAAA0IiSwoH9+/dPVVVVnnvuuay//vrNumfUqFEZOHBgqqqqUltbW8r2AAAAAAAAAAAAQCOqSl3gs55K7DRjAAAAAAAAAAAAWDJKDgcuro9CgVVVrb41AAAAAAAAAAAALBdaPaE3fvz4JEnXrl1be2sAAAAAAAAAAABYLrRtiUUKhcIir5k7d27eeOONnH/++UmSddddtyW2BgAAAAAAAAAAAD5lscKBbdq0aTBWLBaz4YYbLtamhUIhhx566GLdAwAAAAAAAAAAADTPYoUDi8XiYo035fDDD8/pp5++WPcAAAAAAAAAAAAAzbNY4cBzzjlngdfnnntuCoVCTjrppPTu3bvJ+wqFQjp27JhVVlkl2223XdZee+3PVi0AAAAAAAAAAACwSIXi4rb9+4SqqqoUCoU8//zzWX/99VuyLirA1KlTU11dnZqamnTr1q3c5QAAAAAAAAAAACw3Fqtz4KddccUVSZLVV1+9RYoBAAAAAAAAAAAASldS50BYGJ0DAQAAAAAAAAAAyqOq3AUAAAAAAAAAAAAALatZxwo/9NBD83+94447Njr+WXxyLQAAAAAAAAAAAKBlNOtY4aqqqhQKhRQKhdTW1jYY/0wbf2otKo9jhQEAAAAAAAAAAMqjWZ0Dk6SpDGEzsoUAAAAAAAAAAABAK2pWOPD+++9frHEAAAAAAAAAAACgfJp1rDB8Fo4VBgAAAAAAAAAAKI+qchcAAAAAAAAAAAAAtCzhQAAAAAAAAAAAAKgwbZfUwmPGjMlNN92U119/PVVVVVlzzTVzwAEHZO21115SWwIAAAAAAAAAAABJCsVisdicC2tra3P55ZcnSTbaaKNsu+22TV573nnn5fzzz09tbe0C41VVVTnttNNy4YUXllAyy4qpU6emuro6NTU16datW7nLAQAAAAAAAAAAWG40u3Pg8OHDc9JJJ6VQKOSee+5p8roLLrggP/rRjxqdq6ury0UXXZSqqqpccMEFi10sAAAAAAAAAAAAsGhVzb3wwQcfTJL069cvu+22W6PXvPPOOznnnHPmv95+++0zZMiQ/Otf/8p5552X6urqFIvFXHTRRXnttddKLB0AAAAAAAAAAABoTLM7Bz788MMpFAo58MADm7zm8ssvz4cffjj/uhtuuCGFQiFJstdee+WAAw7INttskzlz5uSqq67Kj3/849LfAQAAAAAAAAAAALCAZncOHDNmTJJk2223bfKa2267bf6vf/GLX8wPBn5kk002ybHHHptisZj//Oc/i1srAAAAAAAAAAAA0AzNDgdOnDgxSdK/f/9G52fOnJlnnnkmhUIhG220UQYMGNDodXvvvXeS5JVXXlnMUgEAAAAAAAAAAIDmaHY48IMPPkiSrLDCCo3ODx8+PLW1tUmS7bffvsl11lhjjSTJlClTmrs1AAAAAAAAAAAAsBiaHQ7s1KlTkuS9995rdP6JJ56Y/+tNN920yXU+Omq4rq6uuVsDAAAAAAAAAAAAi6HZ4cCPjhN+7LHHGp1/4IEH5v962223bXKdj8KF1dXVzd0aAAAAAAAAAAAAWAzNDgd+7nOfS7FYzB/+8IdMmzZtgbm33nor//73v1MoFLLqqqtmww03bHKdESNGJEnWXHPNz1YxAAAAAAAAAAAAsFDNDgeecMIJKRQKGT9+fHbeeefcddddee2113Lrrbdm7733Tm1tbZLkuOOOW+g69913XwqFQjbZZJPSKgcAAAAAAAAAAAAaVSgWi8XmXnzqqafmkksuSaFQaDBXLBaz8sor56WXXsqKK67Y6P3jx49Pv379Mm/evPzlL3/JMccc89krZ6k3derUVFdXp6amJt26dSt3OQAAAAAAAAAAAMuNtotz8W9/+9v5Rwt/OlPYp0+f3HLLLU0GA5PkoosuSl1dXdq2bZt99tnns1UMAAAAAAAAAAAALNRidQ78yEsvvZRbb701b731Vtq3b5/NNtsshx12WDp37rzQ+84999xMnz49q6yySs4888zPXDTLBp0DAQAAAAAAAAAAyuMzhQOhOYQDAQAAAAAAAAAAyqOq3AUAAAAAAAAAAAAALUs4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUmLYtudi0adMyatSoTJs2LXV1dYu8fscdd2zJ7QEAAAAAAAAAAIC0UDjwz3/+cy655JI899xzzb6nUCiktra2JbYHAAAAAAAAAAAAPqGkcGBdXV0OOeSQ3HbbbUmSYrHYIkUBAAAAQMUbfnnyxB+Tqe8kfbdOdjk7WW2LclcFAAAAAFSIksKBf/jDH3LrrbcmSVZeeeV8+ctfzhZbbJEePXqkqqqqRQoEAAAAlhKzpiQfTkmq+yW+74fSPPyr5L5zP379+r+Ttx5JvjY06b1e+eoCymvUQ8lLtyZVbZIND0n6blXuigAAAIBlWEnhwKuuuipJsv766+fhhx/Oiiuu2CJFAQAAAEuR2dOTO76VvHBDMm9u0n2NZM+fJOt/vtyVwbKpdnby6G8bjs+dmTz2++TAi1u/JqD8/vXd5IlLP379xB+SXb6f7HRW+WqCJa2uNnn4l8lTVyYzJiVr7ZTsdk6yysblrgwAAKAilPSY/8svv5xCoZAf/OAHgoEAAABQqW47LXnumvpgYJJMeSu5/vjk7afLWhYss6aNT2Z90PjcxJGtWwuwdBj/7ILBwI888LNkypjWrwday7/Oqv/nfNr4+r9rvn5vcuV+yQdvlbsyAACAitAiZwCtu+66LbEMAAAAsLSZ9m7y4s0Nx4t1yfDLW70cqAhd+iQdqxuf6+VzttYwc05trhk2Jj+98+Xc8NS4fDi3rtwlsbx77Z7Gx4vz6sNSUIlmTEqevqrh+OypyZOXtX49AAAAFaikY4UHDhyYESNG5P3332+pegAAAIClyfR364OAjZk2vnVrgUrRrmOy7TeS+3+y4Hjbjsk2J5enpuXIuA9m5sg/PZ5xH8yaP3bJA6/nmhO3Ta+uHcpYGcu19l2bnuvQrfXqgNb0weiPO1N/2uTXW7UUAACASlVS58AjjzwyxWIxt99+e0vVAwAAACxNeq6TrNCj8bm+27RuLVBJdvp2ss8vkh5rJ21XSNbcKTnutqTPhuWurOL97F8jFwgGJskb783IRfe+WqaKIMmGh9QHhD+tY/dk3X1avRxoFT3Wavyf+yTpvV7r1gIAAFChSgoHnnbaadl4441z6aWX5uGHH26pmgAAAIClRbuOya7fbzi+Yv9kyxNavRyoKFt/PTnt6eT/JiTH3Zr03arcFS0X7n3p3UbH72liHFpFl17J4VctGMjv0ic58u9J+87lqwuWpE49ki2/2nB8hSbGAQAAWGwlHSvcoUOH3HPPPTn44IOzxx575LTTTsvRRx+dQYMGpWPHJp72AgAAAJYtW361Pgw4/IpkxntJ/8/VH33aqYmOgixbZn2QvHhz8uGUZO3dklU2LndFsER1aFuV2bXzGh2Hslpnr+RbI5PR/0mq2iZrbJ+0KekjfFj67fHjpNuqyVNXJjMmJWvtlOz8vfoxAAAASlYoFovFRV3Upk2bRS5ULBZTKBSav3GhkNra2mZfz7Jn6tSpqa6uTk1NTbp161bucgAAAIBPG/VQ8o+jkznTPh7b8qvJfheWryZYwn54ywu56rG3Gox/Y9cB+dae65ahIgAAAABYMpr1OGyxWFzkV3Ov+/Q9S8IBBxyQ3XffvdG5xx57LIVCIU8//fT8sRNPPDFt2rTJNddc0+D6H/3oRykUCg2+7r333kbnq6urs8MOO+TBBx9caI0zZszId77znay11lrp2LFjevXqlZ133jm33377/Gt23nnnnH766Qtd5/7778++++6blVZaKZ06dcr666+fb33rW3n77bcXel9S///XPvvsk0KhkJtvvnmBucbec6FQaPT3CAAAAFgG1dUmN359wWBgkjx5WfLav8tTE7SCb++1brZda6UFxnZfr3dO2WVAmSoCAAAAgCWjWWcSnHPOOUu6jhZ1wgkn5OCDD85bb72VNdZYY4G5yy+/PJtuumk233zzJMnMmTNz7bXX5tvf/naGDBmSI488ssF6G2ywwfww4Ed69OjR6Pz777+fX/7yl9l///0zbty4VFdXN1rjSSedlGHDhuX3v/991l9//UyePDmPPvpoJk+e3Oz3+cc//jEnn3xyjjvuuNxwww3p379/xowZk6uuuioXXnhhfvWrXy30/osuumih3R6vuOKK7L333guMde/evdn1AQAAAEuxt4cn095pfO6lm5OBe7RqOdBaunZsl3+cuE1GjJ2SN9+bnnVW7poNV2v8MzwAqGgTnk+G/SmZMiZZbYtkqxOTrn3KXRUAANCCKjIcuP/++6d379658sorF6j9oyDgT3/60/lj119/fdZff/2cffbZWWWVVTJ69Oj0799/gfXatm2bPn2a/mbok/N9+vTJueeemyuuuCKvvvpqttxyy0bvue222/Kb3/wm++67b5Kkf//+2WKLLZr9HseNG5fTTjstp512Wn7961/PH+/fv3923HHHTJkyZaH3P/vss/nVr36VJ598Mqusskqj13Tv3n2h7/vTZs+endmzZ89/PXXq1GbfCwAsmz6cW5cbn347T45+P726dsgRW/bN2r26lLssAKBZmn5gMIVmHTYBy7RN+3bPpn27l7sMACiP1+5N/nFkMm9u/es3H0hG/D054d9J975lLQ0AAGg5FflJb9u2bXPsscfmyiuvXOD44uuvvz5z5szJF7/4xfljQ4YMyTHHHJPq6ursu+++ueKKK0rae/bs2bnyyivTvXv3rLvuuk1e16dPn9x5552ZNm1ak9cszEfv5ayzzmp0fmEd/mbOnJmjjjoqv//97xcr/LcoP/vZz1JdXT3/q29f3zwCQCWbPrs2h/3hsXzvpudz0zNv508PvZl9Lno4Q0e+W+7SAKB11dUmL96c3PODZNifkw9ryl1R86y+ZVLdr/G5DQ5u3VoAoDnGPpm8dEsytYnOt0Dz/fsHHwcDPzJtfPLIb8pTDwAAsERUZDgwSb7yla9k9OjReeCBB+aPXX755Tn44IOz4oorJklee+21PP744zniiCOSJMccc0yuuOKKzJs3b4G1nn/++XTp0mX+11ZbbdXk/AorrJBf/vKX+cc//pFu3bo1Wd+f/vSnPProo1lppZWy5ZZb5owzzsgjjzzS7Pf32muvpVu3bk12/VuYM844I9ttt10OPPDAhV531FFHLfC+u3TpkjfffLPJ688+++zU1NTM/xo7duxi1wYALDuufvytPP/2guGHOXXz8qNbX8q8ecUm7gKACjN7WnL5nsn1xyWP/ja583+T322RTHy53JUtWlVVcshlyQorfmKwkGx3WrL2LmUrCwAamDYh+dPOyZDdk+uOTS7aKPn3D8tdFSy7Zr6fTHyp8bnR/2ndWgAAgCWqWccKN6Wurm5+oG2TTTZJdXX1Qq+fMmVKnnvuuSTJjjvuWMrWizRo0KBst912ufzyy7PLLrvkjTfeyMMPP5x77rln/jVDhgzJXnvtlZ49eyZJ9t1335xwwgm59957s+eee86/bt11182tt946/3WHDh0W2OuT89OmTcu1116bww47LPfff38GDx7caH077rhj3nzzzTz++ON55JFHMnTo0PzmN7/Jueeemx/84AeLfH/FYjGFwkKO/2nCrbfemqFDh+aZZ55Z5LW//vWvs/vuuy8wtrBugB06dGjwewMAVK6HXnuv0fEx78/Mm5NmZEBvxwsDsBx49HfJ208tODbjveTObyfH316emhZHv62TM15MXr49+XBKsvZuSc8B5a4KABZ0y6nJO5/4THtebX13s1U2STY8pHx1wbKqfeekXedk7oyGc116t349AACwjCkUCrnpppty0EEHlbuURSqpc+C9996bnXfeOYccckjat2+/yOvbt2+fQw45JLvsskvuv//+UrZulhNOOCE33HBDpk6dmiuuuCJrrLFGdttttyT1wcarrroqd9xxR9q2bZu2bdumU6dOef/99zNkyJAGdQ8YMGD+16cDcp+c32yzzfLzn/88q622Wi666KKF1teuXbvssMMO+e53v5t77rkn5513Xn784x9nzpw5i3xv66yzTmpqajJ+/PjF+j0ZOnRo3njjjXTv3n3++06SQw45JDvvvPMC1/bp02eB9z1gwIC0a9dusfYDACpXt46N/72gUEi6dSzpGRQAWHaMvLPx8dEPJx9Obd1aPqv2nZNNjki2/rpgIABLn+nvJa/f2/jcs9e0bi1QKdp2SDY7pvG5LU9o3VoAAGApNGHChHzjG9/IWmutlQ4dOqRv37454IADct9995W7tMVWUjjwuuuuS5IcdthhWWGFFRZ5fadOnXLEEUekWCzm2muvLWXrZjn88MPTpk2b/P3vf89f/vKXfPnLX57fbe/OO+/MtGnT8swzz2TEiBHzv66//vrcfPPNmTx5ckl7t2nTJrNmzVqse9Zff/3U1tbmww8/XOS1hx56aNq3b59f/OIXjc5PmTKl0fHvfve7ee655xZ4z0l9l8ArrrhiseoFAJZvhw9uvKPwLuv2Tu9uHVu5GgAok7ZNdNCvalv/BQCUpnZWkmLjc3Ma6XoGNM+eP042+1JS9d+HPzt2T/b6abL+gWUtCwAAym306NHZYostMnTo0PziF7/I888/n7vuuiu77LJLTjnllHKXt9hK+pT6qaeeSqFQyK677trse3bZZZdccsklGTZsWClbN0uXLl1yxBFH5Hvf+15qampy/PHHz58bMmRI9ttvv2yyySYL3LPBBhvk9NNPz9/+9rd885vfbNY+tbW1mTBhQpKPjxV+6aWX8p3vfKfJe3beeeccddRRGTx4cFZaaaW89NJL+d73vpdddtkl3bp1m3/de++9Nz/A95E+ffqkb9+++fWvf51TTz01U6dOzbHHHpv+/ftn3Lhxueqqq9KlS5dceOGFDfbt06dP+vTp02C8X79+WXPNNRcYmzJlyvz39ZGuXbumc+fOi/w9AQAq3y6Deue7+wzKb+59LbPm1iVJtl6zR35x6MZlrgwAWtHGhydvD284Pmi/pH2n1q8HACpN935J7/WTiS81nFtnr9av57OYPjF58P8lr96dtO2YbHJksv03kzZO6qGM2nZIDvx9ssd5ybTxSY+1knaLbgQCAACV7uSTT06hUMiwYcMWyEhtsMEG+cpXvtLoPd/5zndy0003Zdy4cenTp0+++MUv5oc//OH8E1qfffbZnH766Rk+fHgKhUIGDhyYP/7xjxk8eHDeeuutnHrqqfnPf/6TOXPmpH///rnggguy7777tsj7KSkcOG7cuCRpECpbmP79+ydJ3n777VK2brYTTjghQ4YMyZ577pl+/folSd59993ccccd+fvf/97g+kKhkIMPPjhDhgxpdjjwxRdfzCqrrJKkvjvi2muvnUsvvTTHHntsk/fstdde+ctf/pLvfe97mTlzZlZdddXsv//++eEPf7jAdX//+98b1HnOOefkRz/6UU4++eSss846+eUvf5kvfOELmTVrVvr375/9998/Z555ZrNqX5gvf/nLDcZ+9rOf5bvf/W7JawMAleGkndbOUVv1y/PjatK7W4ess3LXcpcErWPqO8nIO+p/vd4BSdeGD+AAy4ktv5q8/VTy3CdOSFhl02TfX5atJACoOPtdmFx9WDJn+sdjq29V/9/hpd2cmckV+yaTX/t4bOiP68OOh15evrrgI5161H8BAAB5//33c9ddd+X8889vtHla9+7dG72va9euufLKK7Pqqqvm+eefz9e+9rV07do1Z511VpLki1/8YjbbbLNceumladOmTUaMGDE/OHjKKadkzpw5eeihh9K5c+e89NJL6dKlS4u9p0KxWGyiH/+iderUKbNnz84TTzyRwYMHN+ue4cOHZ6uttkqHDh0W+9hdli1Tp05NdXV1ampqFuiGCMuCd6bMypOj30+vLh2y7dorzT+SHAAgT/0luePMZF5t/euqdskBv0k2+2J56wLKa+LI5J2n67sb9f9cuasBgMozdXwy4upk2oSk3zbJep9P2rYvd1WL9vRVya3faHzulCeTXuu0bj0AAAA0adiwYdl6661z44035gtf+EKT1xUKhdx000056KCDGp2/4IILcu2112b48PpTZ7p165bf/e53Oe644xpcu/HGG+eQQw7JOeec0yLv4dNK6hzYq1evjBs3LiNHjmx2OHDkyJFJkpVWWqmUrQGWmJ//a2T+/PCbqZtXn50e0LtLrjh+y/Tt4UgwAFju1YxLbj8jKdZ9PDZvbnLbN5MBuyddVy5fbUB59R5U/wUALBndVkl2/N9yV7H4JrzQ9Ny7zwsHAgAALEU+6rG3uA2k/vnPf+aiiy7K66+/nunTp6e2tnaBRmpnnnlmvvrVr+avf/1rdt999xx22GFZe+21kySnnXZa/ud//if33HNPdt999xxyyCHZeOONW+w9VZVy85ZbbplisZirrrqq2fdceeWVKRQK2XzzzUvZGmCJuPvFCfnDg2/MDwYmyesTp+fM60aUrygAYOnx8u0LBgM/Mm9uMvL21q8HAABYuq209kLmBrReHQAAACzSwIEDUygU8vLLLzf7nscffzxHHnlk9tlnn9x+++155pln8v3vfz9z5syZf82PfvSjvPjii9lvv/0ydOjQrL/++rnpppuSJF/96lfz5ptv5ktf+lKef/75DB48OL/73e9a7D2VFA489NBDkyT33XdfLrzwwkVef+GFF2bo0KFJksMOO6yUrQGWiBufHtfo+JOjP8jY92e2cjUAwNKn+BnnAACA5dLGRyRd+jQcX2vnZJVNWr0cAAAAmtajR4/stddeufjiizNjxowG81OmTGkw9sgjj2SNNdbI97///QwePDgDBw7MW2+91eC6ddZZJ2eccUbuueeeHHzwwbniiivmz/Xt2zcnnXRSbrzxxnzrW9/Kn//85xZ7TyWFA4844ohssskmKRaLOeuss3LIIYfk4Ycfzty5c+dfU1tbm4cffjgHH3xwzjrrrBQKhWy44YY55phjSi4eoKXNrp23kLlGugQBAMuX9Q5ICm0ajle1Swbt3/r1AAAAS7cVuidfvjNZd9/67yXad0kGfyU54m/lrgwAAIBGXHLJJamrq8tWW22VG264Ia+99lpefvnl/Pa3v822227b4PoBAwZkzJgxueaaa/LGG2/kt7/97fyugEkya9asnHrqqXnggQfy1ltv5ZFHHsmTTz6Z9dZbL0ly+umn5+67786oUaPy9NNPZ+jQofPnWkLbUm4uFAq56aabsv3222f8+PG5+eabc/PNN6ddu3bp0aNHCoVCJk+ePD8sWCwWs+qqq+aWW25Z7LOZAVrDbuutnAdeea/B+Fo9O2ftXl3KUBEAsFSpXj3Z78Lkjm99fLxwVdvkgIuSro10AwEAAFhp7eSofyTz5iWFQv0XAAAAS6U111wzTz/9dM4///x861vfyvjx49OrV69sscUWufTSSxtcf+CBB+aMM87IqaeemtmzZ2e//fbLD37wg/zoRz9KkrRp0yaTJ0/Osccem3fffTc9e/bMwQcfnHPPPTdJUldXl1NOOSXjxo1Lt27dsvfee+fXv/51i72fQrFYLPnsq4kTJ+brX/96br311jS1XKFQyBe+8IVccskl6d27d6lbsgyYOnVqqqurU1NTk27dupW7HGiWObXzcsJfnszDr02aP9a5fZtcdtyW2XbtlcpYGQCwVKl5Oxl5e5JCfTfBbquUuyIAAAAAAABYQIuEAz/yyiuv5M4778wzzzyTSZPqgzU9e/bM5ptvnv322y8DBw5sqa1YBggHsqyqm1fM0JET8/ibk9Ora4ccvNlq6d2tY7nLAgAAAAAAAACAZmvRcCB8knAgAAAAAAAAAABAeVSVuwAAAAAAAAAAAACgZQkHAgAAAAAAAAAAQIVp21ILzZs3Ly+99FLefPPNTJs2LXV1dYu859hjj22p7QEAAAAAAAAAAID/KhSLxWIpC8yaNSs/+clP8uc//zmTJ09u/saFQmpra0vZmqXc1KlTU11dnZqamnTr1q3c5QAAAAAAAAAAACw3SuocOGvWrOy6664ZNmxYSswYAgAAAAAAAAAAAC2kpHDgr3/96zzxxBNJkg033DCnnnpqtthii/To0SNVVVUtUiAAAAAAAAAAAACweEoKB1577bVJku222y5Dhw5N+/btW6QoAAAAAAAAAAAA4LMrKRz4xhtvpFAo5KyzzhIMBAAAAAAAAAAAoGKMnDA1d70wIVNn1abbCm2z94Z9MqhPt3KX1WwlhQPbt2+fWbNmpV+/fi1VDwAAAAAAAAAAAJTN6EkzcuZ1I/L0mClpU1VIVSGZV0wuuve1bLHGirnwsE3Sv2fnJbb/JZdckgsuuCDjx4/PBhtskIsuuig77LDDYq9TVUoRgwYNSpJMmDChlGUAAAAAAAAAAACg7EZPmpEDL34kz46rSZLUzStmbl0xdfOKSZIRY6fkwIsfyehJM5bI/tdee21OP/30fP/7388zzzyTHXbYIfvss0/GjBmz2GuVFA48/vjjUywWc/3115eyDAAAAAAAAAAAAJTdmdeNyPTZtfPDgJ9WN6+Y6bNr87/XP7tE9v/Vr36VE044IV/96lez3nrr5aKLLkrfvn1z6aWXLvZaJYUDv/a1r2WXXXbJVVddlX/84x+lLAUAAAAAAAAAAABlM3LC1Dw9ZkqTwcCP1M0rZvhbH2TkhKktuv+cOXPy1FNPZc8991xgfM8998yjjz662Ou1LaWYsWPH5ne/+11OPPHEHHPMMbnpppty9NFHZ9CgQenUqdMi7+/Xr18p2wMAAAAAAAAAAECLuOuFCWlTVVhkODBJ2lQVcvcL72ZQn24ttv+kSZNSV1eXlVdeeYHxlVdeORMmTFjs9UoKB/bv3z+FQiFJUiwWc8MNN+SGG25o1r2FQiG1tbWlbA8AAAAAAAAAAAAtYuqs2lQVkrpmXFtVSGpmzV0idXyUyftIsVhsMNYcJYUDP9q4sV8DAAAAAAAAAADAsqLbCm3TjKaBSZJ5xaR6hXYtun/Pnj3Tpk2bBl0CJ06c2KCbYHOUFA684oorSrkdAAAAAAAAAAAAlgp7b9gnF937WrOurZtXzN4b9mnR/du3b58tttgi//73v/OFL3xh/vi///3vHHjggYu9XknhwOOOO66U2wEAAAAAAAAAAGCpMKhPt2zer3ueHVeTuoW0EGxTVchmfbtn3T5dW7yGM888M1/60pcyePDgbLvttvnTn/6UMWPG5KSTTlrstUo+VhgAAAAAAAAAAAAqwa8O3zQHXvxIps+ubTQg2KaqkC4d2uaXh22yRPY/4ogjMnny5Jx33nkZP358Ntxww9x5551ZY401FnutQrFYbOYpybB4pk6dmurq6tTU1KRbt27lLgcAAAAAAAAAAGCRRk+akf+9/tkMf+uDtKkqpKqQzCvWHyU8eI0V88vDNkn/np3LXeYiCQeyxAgHAgAAAAAAAAAAy6qRE6bm7hfeTc2sualeoV323rDPEjlKeEkp6Vjhr3zlK5/53kKhkCFDhpSyPQAAAAAAAAAAACwRg/p0y6A+y25TtJI6B1ZVVaVQKCz2fcViMYVCIXV1dZ91a5YBOgcCAAAAAAAAAACUR0mdA/v167fIcOCMGTMyadKkJPXdAnv27JlOnTqVsi0AAAAAAAAAAACwECWFA0ePHt2s695///38/e9/zznnnJPu3bvnlltuyaBBg0rZGgAAAAAAAAAAAGhCSccKL66XX3452267bVZcccU8/fTTWXHFFVtra8rAscIAAAAAAAAAAADlUdWam6233no57bTT8tZbb+XCCy9sza0BAAAAAAAAAABgudGq4cAk2WOPPZIkN954Y2tvDQAAAAAAAAAAAMuFVg8HdunSJUkyZsyY1t4aAAAAAAAAAAAAlgttW3vDZ555JknSrl271t4aAAAAAAAAAAAAmufdF5OXb0s+rEk6VifrHZCsvEG5q2q2Vu0cOGrUqPzoRz9KoVDIpptu2ppbAwAAAAAAAAAAwKJNfiO5bI/k0u2SB3+RDPtz/f9eul0yZM/6+SXkoYceygEHHJBVV101hUIhN99882deq6TOgVddddUir5k3b14++OCDDB8+PLfccktmzpyZQqGQk046qZStAQAAAAAAAAAAoGVNfiP5827J7Kn1r4t19V8fGTe8fv5r9yUrrd3i28+YMSObbLJJvvzlL+eQQw4paa1CsVgsftabq6qqUigUmn39R1t985vfzK9//evPui3LiKlTp6a6ujo1NTXp1q1bucsBAAAAAAAAAABYuMv2SN5+asFA4KcV2iSrD05OuGeJllIoFHLTTTfloIMO+kz3l9Q5MPk48Lco3bt3z4477piTTz45e+65Z6nbAgAAAAAAAAAAQMt598Vk3LBFX1esS8Y+UX/9yhss+bo+o5LCgaNGjVrkNVVVVenatWu6d+9eylYAAAAAAAAAAACw5Lx8W31XwIV1DfxIoU3y8u2VGw5cY401WqoOAAAAAAAAAAAAKJ8Pa5JCVTPDgVXJh1OWeEmlqCp3AQAAAAAAAAAAAFB2HauT4rzmXVucl3TsvkTLKZVwIAAAAAAAAAAAAKx3QPO6Bib11613wJKtp0QlHSsMAAAAAAAAAAAAFWHlDZLVt0refmrhIcFCm2T1wcnK67d4CdOnT8/rr78+//WoUaMyYsSI9OjRI/369VustQrFYrFYakFz5szJ1VdfnZtvvjnPPvtsJk2alFmzZi1840IhtbW1pW7NUmzq1Kmprq5OTU1NunXrVu5yAAAAAAAAAAAAFm7yG8mfd0tmT208IFhok3TolnztvmSltVt8+wceeCC77LJLg/HjjjsuV1555WKtVXI48NVXX81BBx2UV155JYuzVKFQSF1dM1swskwSDgQAAAAAAAAAAJY5k99Ibv6fZOwT9WHAQlVSnFcfFuy7dXLQpUskGNjSSjpWeMaMGdlnn30yatSoVFVV5cADD0yvXr3y5z//OYVCIf/3f/+XDz74IMOHD8/jjz+eQqGQbbfdNnvssUdL1Q8AAAAAAAAAAAAtZ6W1kxPuSd59MXn59uTDKUnH7sl6ByyRo4SXlJI6B1544YX59re/nTZt2uTuu+/OrrvumhdffDEbbbRRg86AI0aMyDHHHJORI0fmoosuyqmnntoib4Cll86BAAAAAAAAAAAA5VFVys233XZbCoVCDj/88Oy6664LvXbTTTfN/fffn969e+fMM8/MU089VcrWAAAAAAAAAAAAQBNKCge+9NJLSZIvfOELjc5/uilhr169cuaZZ6a2tja///3vS9kaAAAAAAAAAAAAaEJJ4cApU6YkSdZYY435Yx06dJj/6+nTpze4Z/vtt0+SPPjgg6VsDQAAAAAAAAAAADShpHBgp06dkiSFQmH+WPfu3ef/esyYMQ3u+ejaCRMmlLI1AAAAAAAAAAAA0ISSwoFrrrlmkuSdd96ZP9azZ8/06NEjSfLII480uOepp55KkrRv376UrQEAAAAAAAAAAIAmlBQOHDx4cJJk+PDhC4zvtttuKRaLueCCCzJ58uT546NHj87/+3//L4VCIZtuumkpWwMAAAAAAAAAAABNKCkcuMcee6RYLObWW29dYPy0005Lkrz55ptZZ511cthhh2W//fbLJptsknHjxiVJTjzxxFK2BgAAAAAAAAAAAJpQKBaLxc9688yZM7Pvvvumrq4uV155ZdZee+35cz/60Y9y3nnn1W9SKCRJPtrqK1/5Si677LJS6mYZMHXq1FRXV6empibdunUrdzkAAAAAAAAAAADLjZLCgYty33335bLLLsuLL76Y2traDBw4MMcee2wOOeSQJbUlSxHhQAAAAAAAAAAAgPJYouFAlm/CgQAAAAAAAAAAAOXRtrU3fPPNN/Of//wnSXLssce29vYAAAAAAAAAAABQ8Vo9HPjwww/ny1/+cqqqqoQDAQAAAAAAAAAAYAmoKtfGTjMGAAAAAAAAAACAJaNs4UAAAAAAAAAAAABgyRAOBAAAAAAAAAAAgAojHAgAAAAAAAAAAAAVRjgQAAAAAAAAAAAAKoxwIAAAAAAAAAAAAFQY4UAAAAAAAAAAAACoMMKBAAAAAAAAAAAAUGHaNvfC8847r0U2HDFiRIusAwAAAAAAAAAAADSuUCwWi825sKqqKoVCoUU2LRaLKRQKqaura5H1WDpNnTo11dXVqampSbdu3cpdDgAAAAAAAAAAwHKj2Z0Dk/pQHwAAAAAAAAAAALB0a3Y48P7771+SdQAAAAAAAAAAAAAtpNnHCsPicqwwAAAAAAAAAABAeVSVuwAAAAAAAAAAAACgZQkHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAAIAKIxwIAAAAAAAAAAAAFUY4EAAAAAAAAAAAACqMcCAAAAAAAAAAAABUGOFAAAAAAAAAAAAAqDDCgQAAAAAAAAAAAFBhhAMBAAAAAAAAAACgwggHAgAAAAAAAAAAQIURDgQAAAAAAAAAgGXNvLrknWeSSa+XuxJgKdW23AUAAAAAAAAAAACL4ZW7ktvPSKa9U/969S2TQ4YkK65R3rqApYrOgQAAAAAAAAAAsKx4/83kui99HAxMknFPJv84qnw1AUsl4UAAAAAAAAAAAFhWjPhHUjen4fjEF5Oxw1q/HmCp5VhhAAAAAADqFYvJm/cnr9+XdKxONj48WbF/uasCAADgk2ZOanpuxnutVwew1BMOBAAAAAAgmTcvufFryQv//HjswV8khw5J1j+wfHUBAACwoDV3TIZf3nC8bcek37atXw+w1HKsMAAAAAAAySt3LhgMTJJ5c5PbTk/mfliWkgAAoFnefTG54avJ77dK/n5EMuqhclcES9agA5K1dm44vvN3k049Wr0cYOmlcyAAAMBypGbW3PzzqXF5efzUrNmzcw4f3De9unYod1kAwNLg1X81Pj7r/WTsE8laO7VuPQAtYfb05PV/J8V5yYDd649MB6CyjH8uuXzvZO6M+teTXkleuyc5/K/JevuXtzZYUtq0TY6+PnnumuTVu5P2XZJNj2o8MAgs14QDAQAAlhPja2bl0Esfy9tTZs0fu+zhN3PNidtm3T5dy1gZLGFTxycP/SJ5/d6kQ7dk06OTrf8nqXKgAsAC2q7Q9Fy7hcwBLK1e+Vdy44nJ7Kn1r9t1Tg78fbLhweWtC2hSbd283P7c+AwdOTGd2rfJwZuvnq3W1AGLRXj4wo+DgR8pzkvuP184kMrWtn2y+bH1XwBN8Ck4AADAcuK39722QDAwST6YOTc/vfPlMlUEreDDqckVeyfDL0+mjEnefSG5+3vJHWeWuzKApc/GRzQ+3mOtZPUtW7cWgFLN+iD55wkfBwOT+uDITV9Ppr1bvrqAJs2bV8xJf3sqp187Irc++06ueXJsDv/jY/nzQ2+WuzSWdm8/3fj4xJeSuR+2bi0AsJQRDgQAAFhOPPjKe42OP/zae5k3r9jK1UArGfH35IPRDcefviqpGdfq5XwmH7yV3Htucv2Xk0d+k8x8v9wVAZWq75bJnj9J2rT/eKy6b3L4VUmhUL66oLVMGZtMer3cVdBSRt7RsItUktTNSV66udXLARbtvpETc+/LExuMX3DPK/lgxpwyVMQyo3u/xse7rJy07dC6tQDAUsaxwgAAAMuJLh3bJjUNxzu3b5uqKj/wp0JNeL7x8WJd8u5LSfXqrVvP4hr7ZPLXg5I50+tfv3hjMuyy5IR7km6rlLU0oEJt941k4yOT0Q8lHauTNXdO2vgYmQr3wVvJzf+TvPVI/eue6yQH/CZZY7vy1kVpahfSKWphc0DZ/Oe1xh9qnFM7L0+Mej97b9inlStimbHtKclb/2k4vs3/eMgFgOWezoEAAADLicO26Nvo+KGDl/JwVCsaMXZKfnn3K/ndfa9l9KRGuoyw7Omx5kLm1mq9Oj6re77/cTDwIzVjkv/8qjz1AMuHLr2SDQ9JBuwuGEjlKxaTqw/7OBiYJJNerR+b3rB7FcuQdfZOCm0amSgk6+7X6uUAi1bdqX2Tc907tWvFSljmDNo3OejSpPsa9a879Ux2/UGy/ellLQsAlgY+2QEAAFhOfOVza+bNSTNy3fCxqZtXTKGQ7LV+n3xn70HlLq353n4qefba+uPBBu6VDNo/qWqZ597Ov+Ol/PnhUfNf//reV/PzgzfO4Vs2HqpkGbH5scljFyezPnUU77r7JT0HlKem5pozMxn7RONzr9/XurUA/P/27jxM66ruH/h7YNiGTZBFCZRBIRBcAfcFU9HM3AhzFyuXxFQsc0vl0dTUMn1EVIwHLcPUysCFnxtJKqJAYIqmoKKmImIICAoC8/uDHJ0YEJmBgZvX67ru65o553zP9zM3c3Fm7nnf50Chmv5EMuvl5dsXfZQ894dktzPWfk1Uj6Ztlx2V/tAFSco+b9/7wnX/50DYQPXZ4Wu56fFp+XRJWYX2Di0aZqfS5jVUFeuN7Y5Otj0q+Xh2Uq+JN7kAwH9ssDsH9uvXL4ceemiFtj/+8Y+pX79+rr766iTJ/Pnzc+6556ZDhw6pX79+WrZsmV69euX++++vcN20adNy4oknpm3btqlXr15KS0tz1FFHZcKECV/p/v9t0qRJ6du3b1q3bp369eunU6dOOemkk/LKK6+s8JqBAwemc+fOadiwYZo1a5Z99903zzxT8Q8Jr776ag477LC0bNkyTZo0yRFHHJH33nuv0vlOPvnk1K5dO3/4wx9WWisAALDuq12rKFcevnWe+OneGXZiz/z1x71y83HdU79OZbtprIOeuSW59RvJs7ckk+5I7j4u+WO/Zbu9VNGkN2dXCAYmydKy5OKRL+TDBYuqPP9aM/uNZNqjyYdv1XQl645GrZJ+9ydbfCNJUVK3UbLjyUmfW2u6si9Xu25Sp2HlfQ2ard1aAKBQrWx3wI8qf92c9cgupyWnjUv2Oi/Z86fJqU8me51T01UBK7D5xg1zw1E7pEWjz3cQ7LxJ49x6Qo8UORqWVVFUlJQ0FwwEgC+wKv7Hb37zm/Tv3z833nhjfvCDHyRJTj311Dz77LMZNGhQttpqq3zwwQcZO3ZsPvjgg/LrJkyYkH322SfdunXLLbfcks6dO2fevHkZMWJEfvzjH2fMmDGrVc/999+fPn36ZP/998/vf//7bLHFFpk5c2buueeeXHTRRbnrrrsqva5Tp04ZNGhQOnTokI8//ji//vWv07t370ybNi0tW7bM/Pnz07t372y77bYZPXp0kuSiiy7Kt7/97YwbNy61vrDjxoIFC3LXXXflnHPOydChQ3PkkUeu1tcCAACsW9ps1CBtNmpQ02V8NQv+nTxy8fLtL45Ipj6SdOpdpekffanyP/x+8unS/G3qrBy8bZsqzZ8kefcfyV+vSN58Omm8SbLjSUnPH1R93iRZvDD5y2nJC39KUpYU1Vr2bvlv/68XxJOkddfkuHuTJZ8mtYqX/bFgfVC7ONnuqGT8b5bv2+H4tV8PABSidjst+9mpbOnyfZvvtvbrofq16py0Or+mqwBW0QHdNsk3OrfKP/71YUrqFmerNk1quiQAgPWavxAkufrqq3PxxRdn+PDh6dOnT3n7fffdl+uvvz4HHnhgkqR9+/bp3r17eX9ZWVn69euXjh075oknnqgQrNtuu+1y5plnrlY9CxYsyIknnpgDDzww9957b3l7aWlpdtppp3z44YcrvPboo4+u8Pm1116boUOH5h//+Ef22WefPPXUU5k+fXomTZqUJk2W/TA9bNiwNG/ePKNHj86+++5bfu0999yTrbbaKueff3423XTTTJ8+Pe3bt1+trwkAAKBK3ngqWfxJ5X3THq1yOLBu7RXvnli3djVsuj9rWjLswGTRvGWff/Jh8sCPl4Ue9/pp1ecfc1Xywh8//7xsaTL590mzUjujfFHtOjVdwVe332XJ/FnLgrApS2rXS3Y+Nel+Qk1XtmoWL0omDF1Wf1GtpOthSfcThVYBWHds1C7ZpX8y9oaK7R16JZ32r5GSADZ0dYtrpUd7xwgDAFSHDf6V2PPOOy833nhj7r///grBuCTZZJNN8uCDD+bwww9P48aNl7t28uTJmTJlSoYPH14hGPiZjTbaaLVqeuihhzJr1qz89KeV/4FoVeddtGhRhgwZkqZNm2bbbbdNkixcuDBFRUWpV69e+bj69eunVq1aefLJJys8B0OHDs2xxx6bpk2b5sADD8ywYcPyP//zPyu838KFC7Nw4cLyz+fOnbtKdQIAAAVk6qPJMzcnc99J2vVMdjszad6h6vPWW8lOAfWrvovAwdu1yfWPvZKl/3VCcbOSOun19ZZVnj/jBn8eDPyisYOSXU5P6pZUbf5Jd6yg/XfrRTjwo4WLc+3Dr+S+f7yTxUuW5oBum+THvb+eFo3qffnFha5uSXLE7cmHby47NrrVVknDjWu6qlV31zHJ1Ic///yNp5Y9+t5WYyUBwHJ6/zxp2zN57q5k8cfJ1w9ctktvrRW/gQQAWE3zZyV1GiR1G9Z0JQCwQaiG7Q/WX6NGjcpVV12VESNGLBcMTJIhQ4Zk7Nix2XjjjdOzZ88MGDAgTz31VHn/1KlTkySdO3eu1rqqOu/999+fRo0apX79+vn1r3+dRx55JC1atEiS7LzzzmnYsGHOPffcLFiwIPPnz88555yTpUuX5t13361Qw7hx4/Ld7343SXLsscdm2LBhWbq0kqMV/uPKK69M06ZNyx/t2rVbrfoBAID11OThye/7JNMeSWZOSSbelty6TzJ7etXnbr970qz98u21ipcdn1tFpS0a5orDtk694s9/Td6opE5uPGaH1K9TDX8UnvlS5e0L5ywLUlbVJyt4c9bC9eNNW9+/bXz+76nX8/68hZm94NPc+exbOWrIuHy6ZMW/g25wNtosKd1j/QoGvv63isHAz0y5N3n779V3n8WLknefSz58q/rmBGDDs9UhyVHDk+PuTXY8KSn2JgUAqFbTn0xu3j25ZovkqtLkL6clCyt5IyUAUK026HDgNttsk/bt2+fiiy/OvHnL/+Cx55575rXXXstjjz2WPn36ZMqUKdljjz1y2WWXJVl2rHCSFBUVVWtdn827uvbee+9Mnjw5Y8eOzQEHHJAjjjgiM2fOTJK0bNky99xzT+677740atQoTZs2zZw5c7LDDjuk9heO0Ro6dGj233//8lDhgQcemPnz5+fRRx9d4X3PP//8zJkzp/zx1ltelAcAgA3G0iXJ6J8v3/7xv5c/om111KqdHHlnsnHHz9saNEsOH5JsvEXV509y5I6bZdz5++T6I7fLzcfukHHn75Ndt2hRLXOnRcfK2+s2TppsWvX5t9xnBe3LvxFuXTN++r/zzOv/Xq596syP8tCUGTVQEdXmrWdXr++reO6u5NdbJbfsmVzXLbmjTzL/g+qZGwAAYAPz2Evv5ey7JmfAXZPzyIvvVd/EH7ya3PGdZMbzyz5fsjCZ/Pvkz6dU3z0AgEpt0OHAr33taxkzZkzefffdHHDAAZUGBOvUqZM99tgj5513Xh5++OFceumlueyyy7Jo0aJ06tQpSfLSSyvYAWI1fTbvP//5z9W6vmHDhtlyyy2z8847Z+jQoSkuLs7QoUPL+3v37p1XX301M2fOzKxZs/K73/0ub7/9dkpLS5MkS5YsyW9/+9s88MADKS4uTnFxcUpKSvLvf/+7wjz/rV69emnSpEmFBwAAsIGYNyOZ+3blfW9PrJ57tN4qOX188v1Hk+NHJGe/lHTrUz1z/0ezhnVzyHZfywHdNq2eHQM/s/NpSZ1Kjg7e6eTqOUZn34FJw/86/rjxpsneF1Z97jXs5Rkr3iXglfc+WouVUO2afG3FfU1X0req3p6Y/OXUZP77n7dNezS59+Sqzw0AALCBGThySr5/+4T8edLbuXfS2znptxNy4b3PV8/kE29LFn+8fPvLDyb/fr167gEAVGqDDgcmyWabbZYxY8Zk5syZ6d27d+bOXfmRS1tttVUWL16cTz75JNttt1222mqr/OpXv6r0uN0PP/xwtWrq3bt3WrRokauvvrrS/q86b1lZWRYuXLhce4sWLbLRRhtl9OjRmTlzZg4++OAkyYMPPph58+Zl0qRJmTx5cvnjnnvuyV/+8pd88IF34AMAAP+lpHlSt1HlfU3bVd99ioqSdj2TDr2SOg2qb941rVXn5PiRy+quXW/ZEbH7XZZ846Lqmb9Fx+S0cck+lyw7Znm/S5Mfjk2al1bP/GtQx1Yr+L75kj7WA1sdkjTaZPn2Zu2TTgdUff6JtyVllRw9Pe3RZPYbVZ8fAABgA/HyjHm5bez05dp//8ybmfLOnKrfYM6/VtBRtuI3mwIA1aK4pgtYF7Rt2zaPP/549t577/Tu3TsPPfRQmjZtml69euWoo45Kjx49svHGG+fFF1/MBRdckL333rt8V7xhw4Zl3333zZ577pkLLrggnTt3zkcffZT77rsvDz/8cMaMGbPC+86ZMyeTJ0+u0Na8efNsttlm+c1vfpO+ffvm4IMPzhlnnJEtt9wys2bNyt13350333wzf/jDH5abb/78+bn88stz8MEHZ9NNN80HH3yQwYMH51//+lf69u1bPm7YsGHp0qVLWrZsmaeffjpnnnlmBgwYkK9//etJlh0p/K1vfSvbbrtthfm7du2as846K3fccUfOPPPM1X26AQCAQlSnQdK9X/L0oIrtRbWSnU6tkZLWOe16LtvxcE1p2CLZ4+w1N/8aslOHjbNj++Z5dnrFo4W3aNkw+3etJFjG+qNuSXLCyOS+s5I3xy5rK90z+fb1Se06VZ9/ZccHL5iVNNu86vcAAADYADw1bdYK+56cOitd2zSt2g2+1j2Z8ufl24sbJK22qtrcAMBKbfA7B37msyOGP/zww+y333758MMPs//+++f2229P796906VLl/zoRz/K/vvvn7vvvrv8uh133DETJkzIFltskZNOOildunTJwQcfnClTpuS6665b6T0ff/zxbL/99hUeF198cZLkkEMOydixY1OnTp0cffTR6dy5c4466qjMmTMnP//5zyudr3bt2vnnP/+ZPn36pFOnTjnooIPy/vvv54knnkjXrl3Lx7388ss59NBD06VLl1x66aW58MIL88tf/jJJ8t577+WBBx5Inz7LH81VVFSUww8/fKVHCwMAABuwff8n2fWMpN6yN1Ol+RbJd4Yl7Xer2bpY5w3t1yP9dm2f5g3rpnH94hzRo23+cPIuqVvsZYv1XsuvJ98blfxkanLOq8kJ9yXNO1TP3O13r7y9QfOkdbfquQcAAMAGYKOSFb+Bq1lJ3arfYIfjlr1O9N92O2PZaRQAwBpTVFZWVlbTRVCY5s6dm6ZNm2bOnDnlOy0CAAAbgMULk0/mLtvJrqiopqsBCtXCj5JhByQznv9CY1Fy8A3L/vAEAADAKpm/cHF2u2p0PlzwaYX2JvWL89R530jj+tWx+/usZOz/Jq+OTho0S7Y/Ptmm75dfBwBUiXAga4xwIAAAALBGLZyXTLw9eX1MUtIi6X5CstnONV0VAADAeufvb87OgLsm540PFiRJ2jVvkF8fsV16tLezHwCsz4QDWWOEAwEAoArmz0rqlCR1S2q6EgAAAAA2AGVlZXn+7TkpK0u2/lrT1KrlRAjY4C1ZnMx5a9kpMfUa13Q1wGoorukCAAAA+ILXHk8eujB574WkuH6ydd/kgF8k9RrVdGUAAAAAFLCioqJs03ajmi4DCstHM5MXRyRLFiVf/2bSvENNV7Tq/v7bZPTlyUczlr1Wvf2xyf5XJsV1a7oy4CuwcyBrjJ0DAQDgK5o1Nbl592TxJxXbtzo0OeL2GikJAAAAAIDV8MKfk3tPWRYMTJIUJfsOTHY/qwaLWkVTH0l+/53l23f6YfLNX6z9eoDVVqumCwAAAOA/Jvzf8sHAJHlpZDLnX2u/HgAAAAAAvrqPP0xG9P9CMDBJypJHL0lm/rOmqlp1z95aefvff5t8+vHarQWoEuFAAACAdcWHb1beXrY0mfvO2q0FAAAAAIDVM+3R5NMFlfe9+Je1WspqmbeC16M/nZ98Mnft1gJUiXAgAADAuqJtj8rb6zRMWnZeu7UAAAAAALAGFNV0AV+u3c6VtzfvkDRqtXZrAapEOBAAAGBd0b1fstHmy7fvMSCp32StlwMAAAAAwGro2Dup26iSjqKk62FrvZyvbLczkob/FQIsqpXsOzApWg/CjUC5orKysrKaLoLCNHfu3DRt2jRz5sxJkyb+kAkAAKvko5nJU9cnr41JSpotCwx261PTVQEAAAAA8FW8dF/yp5OSxR8v+7yodtL7smSX/jVb16qa86/k6cHJ2xOSpm2THU9JNtuppqsCviLhQNYY4UAAAAAAAAAAYIO14N/JSyOTJZ8mnQ5INmpX0xUBG5jimi4AAAAAAAAAAAAKTknzZafDANSQWjVdAAAAAAAAAAAAAFC9hAMBAAAAAAAAAACgwAgHAgAAAAAAAAAAQIERDgQAAAAAAAAAAIACIxwIAAAAAAAAAAAABUY4EAAAAAAAAAAAAAqMcCAAAAAAAAAAAAAUGOFAAAAAAAAAAAAAKDDCgQAAAAAAAAAAAFBghAMBAAAAAAAAAACgwAgHAgAAAAAAAAAAQIERDgQAAAAAAAAAAIACIxwIAAAAAAAAAAAABUY4EAAAAAAAAAAAAAqMcCAAAAAAAAAAAAAUGOFAAAAAAAAAAAAAKDDCgQAAAAAAAAAAAFBghAMBAAAAAAAAAACgwAgHAgAAAAAAAAAAQIERDgQAAAAAAAAAAIACIxwIAAAAAAAAAAAABUY4EAAAAAAAAAAAAAqMcCAAAAAAAAAAAAAUGOFAAAAAAAAAAAAAKDDCgQAAAAAAAAAAAFBghAMBAAAAAAAAAACgwAgHAgAAAAAAAAAAQIERDgQAAAAAAAAAAIACIxwIAAAAAAAAAAAABUY4EAAAAAAAAAAAAAqMcCAAAAAAAAAAAAAUGOFAAAAAAAAAAAAAKDDCgQAAAAAAAAAAAFBghAMBAAAAAAAAAACgwAgHAgAAAAAAAAAAQIERDgQAAAAAAAAAAIACIxwIAAAAAAAAAAAABUY4EAAAAAAAAAAAAAqMcCAAAAAAAAAAAAAUGOFAAAAAAAAAAAAAKDDCgQAAAAAAAAAAAFBghAMBAAAAAAAAAACgwAgHAgAAAAAAAAAAQIERDgQAAAAAAAAAAIACIxwIAAAAAAAAAAAABUY4EAAAAAAAAAAAAAqMcCAAAAAAAAAAAAAUGOFAAAAAAAAAAAAAKDDCgQAAAAAAAAAAAFBghAMBAAAAAAAAAACgwAgHAgAAAAAAAAAAQIERDgQAAAAAAAAAAIACU1zTBVC4ysrKkiRz586t4UoAAAAAAAAAAIB1VePGjVNUVFTTZRQc4UDWmHnz5iVJ2rVrV8OVAAAAAAAAAAAA66o5c+akSZMmNV1GwSkq+2x7N6hmS5cuzTvvvCPZW0Vz585Nu3bt8tZbb/lPEADWAGstAKx51lsAWPOstwCwZllrAdYs+aI1w86BrDG1atVK27Zta7qMgtGkSRM/ZALAGmStBYA1z3oLAGue9RYA1ixrLQDrk1o1XQAAAAAAAAAAAABQvYQDAQAAAAAAAAAAoMAIB8I6rl69ernkkktSr169mi4FAAqStRYA1jzrLQCsedZbAFizrLUArI+KysrKymq6CAAAAAAAAAAAAKD62DkQAAAAAAAAAAAACoxwIAAAAAAAAAAAABQY4UAAAAAAAAAAAAAoMMKBAAAAAAAAAAAAUGCEA2EdNnjw4JSWlqZ+/frp3r17nnjiiZouCQDWS1deeWV69uyZxo0bp1WrVjn00EPz8ssvVxhTVlaWgQMHpk2bNmnQoEF69eqVKVOm1FDFALD+u/LKK1NUVJSzzjqrvM16CwBV9/bbb+fYY4/NxhtvnJKSkmy33XaZOHFieb/1FgBW3+LFi/Ozn/0spaWladCgQTp06JBLL700S5cuLR9jrQVgfSIcCOuou+66K2eddVYuvPDCTJo0KXvssUe++c1v5s0336zp0gBgvTNmzJj0798/48aNyyOPPJLFixend+/emT9/fvmYq6++Otdee20GDRqU8ePHZ5NNNsl+++2XefPm1WDlALB+Gj9+fIYMGZJtttmmQrv1FgCqZvbs2dltt91Sp06djBo1Ki+++GJ+9atfZaONNiofY70FgNV31VVX5eabb86gQYPy0ksv5eqrr84111yTG264oXyMtRaA9UlRWVlZWU0XASxvp512yg477JCbbrqpvK1Lly459NBDc+WVV9ZgZQCw/nv//ffTqlWrjBkzJnvuuWfKysrSpk2bnHXWWTn33HOTJAsXLkzr1q1z1VVX5ZRTTqnhigFg/fHRRx9lhx12yODBg/Pzn/882223Xa677jrrLQBUg/POOy9PPfXUCk+Zsd4CQNUcdNBBad26dYYOHVre1qdPn5SUlOR3v/udtRaA9Y6dA2EdtGjRokycODG9e/eu0N67d++MHTu2hqoCgMIxZ86cJEnz5s2TJK+//npmzJhRYe2tV69e9tprL2svAHxF/fv3z7e+9a3su+++FdqttwBQdSNHjkyPHj3St2/ftGrVKttvv31uvfXW8n7rLQBUze67757HHnssr7zySpLkueeey5NPPpkDDzwwibUWgPVPcU0XACxv1qxZWbJkSVq3bl2hvXXr1pkxY0YNVQUAhaGsrCxnn312dt9993Tr1i1JytfXytbeN954Y63XCADrqz/84Q/5+9//nvHjxy/XZ70FgKp77bXXctNNN+Xss8/OBRdckGeffTZnnHFG6tWrl+OPP956CwBVdO6552bOnDnp3LlzateunSVLluTyyy/PUUcdlcTvtgCsf4QDYR1WVFRU4fOysrLl2gCAr+b000/PP/7xjzz55JPL9Vl7AWD1vfXWWznzzDPz8MMPp379+iscZ70FgNW3dOnS9OjRI1dccUWSZPvtt8+UKVNy00035fjjjy8fZ70FgNVz11135Y477sjw4cPTtWvXTJ48OWeddVbatGmTE044oXyctRaA9YVjhWEd1KJFi9SuXXu5XQJnzpy53LtQAIBV96Mf/SgjR47MX//617Rt27a8fZNNNkkSay8AVMHEiRMzc+bMdO/ePcXFxSkuLs6YMWPyv//7vykuLi5fU623ALD6Nt1002y11VYV2rp06ZI333wzid9vAaCqzjnnnJx33nk58sgjs/XWW+e4447LgAEDcuWVVyax1gKw/hEOhHVQ3bp107179zzyyCMV2h955JHsuuuuNVQVAKy/ysrKcvrpp+fPf/5zRo8endLS0gr9paWl2WSTTSqsvYsWLcqYMWOsvQCwivbZZ588//zzmTx5cvmjR48eOeaYYzJ58uR06NDBegsAVbTbbrvl5ZdfrtD2yiuvZPPNN0/i91sAqKoFCxakVq2KMYratWtn6dKlSay1AKx/HCsM66izzz47xx13XHr06JFddtklQ4YMyZtvvplTTz21pksDgPVO//79M3z48IwYMSKNGzcuf1dn06ZN06BBgxQVFeWss87KFVdckY4dO6Zjx4654oorUlJSkqOPPrqGqweA9UPjxo3TrVu3Cm0NGzbMxhtvXN5uvQWAqhkwYEB23XXXXHHFFTniiCPy7LPPZsiQIRkyZEiS+P0WAKro29/+di6//PJsttlm6dq1ayZNmpRrr7023/ve95JYawFY/wgHwjrqu9/9bj744INceumleffdd9OtW7c8+OCD5e8ABQBW3U033ZQk6dWrV4X2YcOGpV+/fkmSn/70p/n4449z2mmnZfbs2dlpp53y8MMPp3Hjxmu5WgAoXNZbAKianj175t57783555+fSy+9NKWlpbnuuutyzDHHlI+x3gLA6rvhhhty0UUX5bTTTsvMmTPTpk2bnHLKKbn44ovLx1hrAVifFJWVlZXVdBEAAAAAAAAAAABA9alV0wUAAAAAAAAAAAAA1Us4EAAAAAAAAAAAAAqMcCAAAAAAAAAAAAAUGOFAAAAAAAAAAAAAKDDCgQAAAAAAAAAAAFBghAMBAAAAAAAAAACgwAgHAgAAAAAAAAAAQIERDgQAAAAAAAAAAIACIxwIAAAAAKvotttuS1FRUYqKijJ9+vTl+vv165eioqK0b9++xmqg5gwcOLD83wYAAAAAappwIAAAAAAbjMcff7w8vLUqj9tuu62mS15v9OrVq/x5q127dqZMmbLS8dOnTy8fP3DgwLVTJAAAAABsQIQDAQAAAGADtCZ3OVy6dKnAHwAAAADUsOKaLgAAAAAAasIPf/jDnHbaaSsd07Zt268052233Wa3wf/405/+lOeeey7bbrttTZcCAAAAABsk4UAAAAAANkitWrVKt27darqMgtOkSZN88sknWbRoUS6++OKMGDGipksCAAAAgA2SY4UBAAAAgGrTrFmz/OAHP0iSjBw5MhMmTKjhigAAAABgwyQcCAAAAADVpF+/fikqKkr79u1XOm7kyJHZf//906JFi5SUlKRTp04555xzMmPGjCRJ+/btU1RUlH79+n3pPZcuXZohQ4Zk1113TbNmzdKwYcNss802ufzyy7NgwYLlxg8cODBFRUW5/fbbkyRvvPFGioqKlntUxYUXXpj69esnSS6++OLVmuO2224rr2X69OkrHDd9+vTycZUd6fzf/yYzZszIT37yk3Tq1CklJSX52te+liOOOCJTpkxZbt4zzjgjnTp1SoMGDdK6descc8wxefXVV1f5a/jwww9zySWXpGvXrmnUqFGaN2+eXr165fe///0qXb948eIMHTo0Bx54YNq0aZN69eqlRYsW2XPPPXPdddflk08+WeG1vXr1SlFRUXr16pUkmTp1ak4//fR07NgxJSUlX/q8AgAAALD+c6wwAAAAAKwlZWVl+eEPf5hbbrmlQvvUqVPzy1/+MnfccUcefPDBVZ5v/vz52W+//TJ69OgK7c8//3yef/75jBw5MqNHj07Dhg2rpf5V1aZNm5xyyim5/vrrM2rUqDz99NPZZZdd1moNlXnuuedywAEHlIcwk+Tjjz/OPffckwceeCAPPfRQdt9994wePTqHH3545syZUz7uk08+yfDhwzNq1Kg88cQT6dq160rv9frrr2e//farECacP39+xowZkzFjxuQvf/lL7rzzzhQXV/4S7auvvpqDDz44L774YoX2Dz74IE888USeeOKJDB48OA888EA6duy40lpGjBiRY445JvPnz1/pOAAAAAAKi50DAQAAAGAt+cUvflEeDGzbtm0GDRqUZ555Jn/7299y4YUXZs6cOfnOd75T6Y5/lTn55JPz+OOP54QTTsgDDzyQiRMn5t577y0P4j377LP5+c9/XuGa0047Lc8//3wOOeSQJMuCfJ+FCb/4qKrzzz8/JSUlSZKLLrqoyvNV1YIFC3LYYYdl0aJFueKKK/LUU09l3LhxGThwYOrWrZsFCxbkuOOOy7Rp03LYYYelcePGuf766zNu3Lg8+eSTGTBgQIqKijJ79ux8//vf/9L7ffe7383rr7+eU089NY8++mjGjx+foUOHplOnTkmSP/7xjzn77LMrvfbdd9/NbrvtlhdffDGNGzfOj3/844waNSp///vf89e//rX8uZ06dWoOOOCACiHG//bmm2/m2GOPTUlJSX7xi1+Uf9033HBDGjVqtHpPJgAAAADrBTsHAgAAALBBmjlzZl544YUV9rdq1SqtWrWqtvu9++67ufTSS5MkHTp0yNNPP11h/j322CMHHnhg9t577yxatGiV5hw7dmx+97vf5dhjjy1v22GHHfLNb34zPXr0yAsvvJBbb701l112WfkOdZ99XRtttFGSpE6dOunWrVs1fZWfa926dfr3759rrrkmjz32WP72t79lzz33rPb7rKr3338/ZWVlefbZZ7PFFluUt++0005p2bJl+vfvn+nTp2fXXXdN69at89RTT6Vly5bl43bbbbcUFxfnmmuuyTPPPJNJkyZl++23X+H9xo8fn+HDh+eoo44qb+vRo0f69u2bPfbYI88991xuvPHGnHTSSdl6660rXHvyySfnvffeS7t27fL444+nQ4cOFfp79epVPs9rr72WX/7yl7nssssqreP1119PmzZt8vTTT2ezzTar8HUDAAAAUNjsHAgAAADABummm27K1ltvvcLH4MGDq/V+t99+ez755JMkya9//etKg4e77rpr+vfvv8pzHn744RWCgZ+pV69eTj/99CTLjqH976Np15af/vSn5bvTrQu7B1522WUVgoGfOfHEE1O/fv0ky0KEN9xwQ4Vg4Gd++MMfln/8xBNPrPReBx10UIVg4GcaN26cIUOGJEmWLl2am2++uUL/Cy+8kPvvvz9JMmjQoOWCgZ/Zfvvty79X/u///m+ltfziF7+oEAwEAAAAYMMgHAgAAAAAa8Fjjz2WJNl4443zrW99a4Xjjj/++FWe85hjjllhX/fu3cs/fu2111Z5zurUokWLnHHGGUmSv/3tb3n00UdrpI4kKSoqyhFHHFFpX4MGDdKxY8ckSbNmzdK7d+9Kx5WWlqZx48ZJvvw5PfHEE1fYt+OOO6Zr165JstxzMmLEiCRJSUnJSr9PkpTvxPjOO+/krbfeqnRM3bp107dv35XOAwAAAEBhEg4EAAAAYIN0ySWXpKysbIWPgQMHVuv9PjvCeLvttkvt2rVXOG7rrbdOvXr1VmnOzp07r7CvefPm5R/PmzdvFausfj/5yU/StGnTJDW7e2CLFi0qPCf/7bNjlrfccssUFRV96bgve0579uy50v4dd9wxSTJ16tQKx0hPmDAhSbJgwYIUFxenqKhohY+DDjqo/LoZM2ZUep+OHTuW74oIAAAAwIZFOBAAAAAA1oLZs2cnSaXHCX9R7dq106xZs1Was6SkZIV9tWp9/tLfkiVLVmm+NaFZs2YZMGBAkmTcuHEZNWpUjdSxsucq+fz5WtVxX/acftm/c+vWrZMkZWVl5d8bSTJz5syVXrciCxYsqLR9Vb+XAAAAACg8xTVdAAAAAABQ2AYMGJDrr78+s2fPzsUXX5xvfvObNV3SGrey3QeTZaHAynwWOiwtLc3IkSNX+X6lpaWVtq9sl0oAAAAACptwIAAAAACsBc2aNcuMGTO+dGe4JUuWVNhJrhA0adIkP/nJT3LhhRdmwoQJGTFiRLbddtuVXvPFnQ+XLl26wnHz58+vtjqr03vvvZd27dqtsP+z74OioqIKu/ttvPHG5dd37tw5xcVewgUAAABg9ThWGAAAAADWgq5duyZJJk+evNIjaZ9//vksXLhwjdfzZTvbVbczzjgjLVq0SJJccsklK9w57zONGzcu/3hlYcmXX365egqsZuPHj1+l/o4dO6Zu3brl7dtvv32SZccEP/XUU2uuQAAAAAAKnnAgAAAAAKwF++yzT5Lkgw8+yAMPPLDCcb/97W/XSj3169dPkrUSREySRo0a5dxzz02SPPfcc/nTn/600vFfPCZ3woQJKxw3fPjw6imwmt1+++0r7JswYUJeeOGFJMm+++5boe+QQw4p//jqq69eM8UBAAAAsEEQDgQAAACAteCEE05IvXr1kiQDBgzI+++/v9yYp59+OjfeeONaqWfTTTdNsux423nz5q2Ve/bv3z+bbLJJkuSqq65a6dhu3bqlefPmSZJBgwZVGmK88847vzRkWFNGjhyZu+++e7n2jz76KCeffHKSZUcnn3LKKRX6e/bsmd69eydJHnzwwVxyySUrvc/06dNz5513VlPVAAAAABQS4UAAAAAAWAvatGlTHvR67bXX0r179wwePDjjx4/Pk08+mYsuuij77LNP2rRpk5YtWyZZs0f/7rrrrkmSpUuX5tRTT824ceMyderUTJs2LdOmTVsj92zQoEHOO++8JMmsWbNWOra4uLg8RPfCCy/kG9/4RkaMGJFJkyZl1KhR+d73vpdjjz02u+yyyxqptap69OiRo48+Ov37989f//rXTJw4McOGDUuPHj0yadKkJMvCkttss81y1w4bNqw8vHnppZdm5513zpAhQ/L0009n0qRJefTRR3Pttdemd+/e2XLLLdfZgCQAAAAANau4pgsAAAAAgA3FeeedlzfeeCO33HJL3nrrrfTv379Cf4sWLXLPPffk8MMPT/L50b9rwje+8Y3svPPOGTduXIYPH77c8bxlZWVr5L6nnHJKrrnmmrz99ttfOvaiiy7K448/nnHjxmXs2LE59NBDK/TvtddeGTRoULbeeus1UmtV3H333dlnn30yePDgDB48eLn+Pn365Nprr6302jZt2uTpp59O3759M378+DzzzDN55plnVnivJk2aVFvdAAAAABQOOwcCAAAAwFpSVFSUm2++OSNGjEjv3r3TvHnz1K9fP1tuuWXOOOOMTJo0KT169MjcuXOTJE2bNl1jtdSqVSsPP/xwfvazn2XbbbdNo0aN1uhOhZ+pX79+LrzwwlUaW1JSktGjR+fyyy/P1ltvnQYNGqRJkybp2bNnBg0alMceeyyNGjVawxWvntLS0kycODEXXHBBunTpkpKSkjRt2jR77rln7rjjjvzxj39McfGK37u9+eab55lnnsm9996bI488MqWlpSkpKUmdOnXSsmXL7Lrrrvnxj3+cMWPGZOjQoWvxKwMAAABgfVFUtqbeAgwAAAAAfGX/+te/0q5duyTJb37zm3z/+9+v4YoAAAAAgPWRnQMBAAAAYB1y5513ln+8884712AlAAAAAMD6zM6BAAAAALCWzJ8/P3Pnzs2mm25aaf+kSZOy1157Zd68eenevXsmTJiwlisEAAAAAApFcU0XAAAAAAAbivfffz9dunTJoYcemgMOOCBf//rXU69evbzzzjv5f//v/2Xo0KH5+OOPU1RUlGuvvbamywUAAAAA1mN2DgQAAACAtWT69OkpLS1d6Zi6devm1ltvzfHHH7+WqgIAAAAACpFwIAAAAACsJZ9++mnuvffejBo1KhMmTMjMmTMze/bslJSUpH379tl3333zox/9KJtvvnlNlwoAAAAArOeEAwEAAAAAAAAAAKDA1KrpAgAAAAAAAAAAAIDqJRwIAAAAAAAAAAAABUY4EAAAAAAAAAAAAAqMcCAAAAAAAAAAAAAUGOFAAAAAAAAAAAAAKDDCgQAAAAAAAAAAAFBghAMBAAAAAAAAAACgwAgHAgAAAAAAAAAAQIH5/1Dbo/KTmFAsAAAAAElFTkSuQmCC\n"}, "metadata": {}}]}, {"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": "### TASK 2: Visualize the relationship between Payload 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(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()\n", "execution_count": 7, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 2558.88x500 with 1 Axes>", "image/png": "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\n"}, "metadata": {}}]}, {"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\nsns.barplot(data = df, x=\"Orbit\" , y=\"Class\")\nplt.xlabel(\"Orbit\",fontsize=20)\nplt.ylabel(\"Success rate\",fontsize=20)\nplt.show()", "execution_count": 9, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 640x480 with 1 Axes>", "image/png": "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\n"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "markdown", "source": "Analyze the ploted bar chart try to find which orbits have high sucess 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=\"FlightNumber\", x=\"Orbit\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Orbit\",fontsize=20)\nplt.ylabel(\"Flight Number\",fontsize=20)\nplt.show()", "execution_count": 10, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 2558.88x500 with 1 Axes>", "image/png": "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\n"}, "metadata": {}}]}, {"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=\"PayloadMass\", x=\"Orbit\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Orbit\",fontsize=20)\nplt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.show()", "execution_count": 11, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 2558.88x500 with 1 Axes>", "image/png": "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\n"}, "metadata": {}}]}, {"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\n    ", "execution_count": 14, "outputs": []}, {"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\nExtract_year()\ndf[\"years\"]=year\nsns.lineplot(data=df, y=\"Class\", x=\"years\")", "execution_count": 16, "outputs": [{"output_type": "error", "ename": "ValueError", "evalue": "Length of values (180) does not match length of index (90)", "traceback": ["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)", "Input \u001b[0;32mIn [16]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\u001b[39;00m\n\u001b[1;32m      2\u001b[0m Extract_year()\n\u001b[0;32m----> 3\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myears\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m=\u001b[39myear\n\u001b[1;32m      4\u001b[0m sns\u001b[38;5;241m.\u001b[39mlineplot(data\u001b[38;5;241m=\u001b[39mdf, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m\"\u001b[39m, x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myears\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/pandas/core/frame.py:3655\u001b[0m, in \u001b[0;36mDataFrame.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3652\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setitem_array([key], value)\n\u001b[1;32m   3653\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   3654\u001b[0m     \u001b[38;5;66;03m# set column\u001b[39;00m\n\u001b[0;32m-> 3655\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_item\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/pandas/core/frame.py:3832\u001b[0m, in \u001b[0;36mDataFrame._set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3822\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_set_item\u001b[39m(\u001b[38;5;28mself\u001b[39m, key, value) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3823\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   3824\u001b[0m \u001b[38;5;124;03m    Add series to DataFrame in specified column.\u001b[39;00m\n\u001b[1;32m   3825\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3830\u001b[0m \u001b[38;5;124;03m    ensure homogeneity.\u001b[39;00m\n\u001b[1;32m   3831\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3832\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sanitize_column\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3834\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   3835\u001b[0m         key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[1;32m   3836\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m value\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   3837\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_extension_array_dtype(value)\n\u001b[1;32m   3838\u001b[0m     ):\n\u001b[1;32m   3839\u001b[0m         \u001b[38;5;66;03m# broadcast across multiple columns if necessary\u001b[39;00m\n\u001b[1;32m   3840\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mis_unique \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns, MultiIndex):\n", "File \u001b[0;32m/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/pandas/core/frame.py:4535\u001b[0m, in \u001b[0;36mDataFrame._sanitize_column\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m   4532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _reindex_for_setitem(value, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex)\n\u001b[1;32m   4534\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_list_like(value):\n\u001b[0;32m-> 4535\u001b[0m     \u001b[43mcom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequire_length_match\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4536\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sanitize_array(value, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, allow_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", "File \u001b[0;32m/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/pandas/core/common.py:557\u001b[0m, in \u001b[0;36mrequire_length_match\u001b[0;34m(data, index)\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    554\u001b[0m \u001b[38;5;124;03mCheck the length of data matches the length of the index.\u001b[39;00m\n\u001b[1;32m    555\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    556\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(data) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(index):\n\u001b[0;32m--> 557\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    558\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength of values \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    559\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(data)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    560\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdoes not match length of index \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    561\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(index)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    562\u001b[0m     )\n", "\u001b[0;31mValueError\u001b[0m: Length of values (180) does not match length of index (90)"]}]}, {"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()", "execution_count": 17, "outputs": [{"output_type": "execute_result", "execution_count": 17, "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": {}}]}, {"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": "# HINT: Use get_dummies() function on the categorical columns\nfeatures_one_hot = pd.get_dummies(features, columns=['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])\nfeatures_one_hot.head()", "execution_count": 19, "outputs": [{"output_type": "execute_result", "execution_count": 19, "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 \u00d7 80 columns</p>\n</div>"}, "metadata": {}}]}, {"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\n", "execution_count": null, "outputs": []}, {"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": "## Authors\n"}, {"metadata": {}, "cell_type": "markdown", "source": "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"}, {"metadata": {}, "cell_type": "markdown", "source": "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"}, {"metadata": {}, "cell_type": "markdown", "source": "## Change Log\n"}, {"metadata": {}, "cell_type": "markdown", "source": "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n| ----------------- | ------- | ------------- | ----------------------- |\n| 2021-10-12        | 1.1     | Lakshmi Holla | Modified markdown       |\n| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n| 2020-11-10        | 1.1     | Nayef         | updating the input data |\n"}, {"metadata": {}, "cell_type": "markdown", "source": "Copyright \u00a9 2020 IBM Corporation. All rights reserved.\n"}], "metadata": {"kernelspec": {"name": "python3", "display_name": "Python 3.10", "language": "python"}, "language_info": {"name": "python", "version": "3.10.6", "mimetype": "text/x-python", "codemirror_mode": {"name": "ipython", "version": 3}, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py"}}, "nbformat": 4, "nbformat_minor": 4}