Introduction_to_PySpark.ipynb

{
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   "source": [
    "## Getting to know PySpark\n",
    "\n",
    "### What is Spark, anyway?\n",
    "Spark is a platform for cluster computing. Spark lets you spread data and computations over clusters with multiple nodes (think of each node as a separate computer). Splitting up your data makes it easier to work with very large datasets because each node only works with a small amount of data.\n",
    "\n",
    "As each node works on its own subset of the total data, it also carries out a part of the total calculations required, so that both data processing and computation are performed in parallel over the nodes in the cluster. It is a fact that parallel computation can make certain types of programming tasks much faster.\n",
    "\n",
    "However, with greater computing power comes greater complexity.\n",
    "\n",
    "Deciding whether or not Spark is the best solution for your problem takes some experience, but you can consider questions like:\n",
    "\n",
    "Is my data too big to work with on a single machine?\n",
    "Can my calculations be easily parallelized?\n",
    "\n",
    "### Using Spark in Python\n",
    "The first step in using Spark is connecting to a cluster.\n",
    "\n",
    "In practice, the cluster will be hosted on a remote machine that's connected to all other nodes. There will be one computer, called the master that manages splitting up the data and the computations. The master is connected to the rest of the computers in the cluster, which are called slaves. The master sends the slaves data and calculations to run, and they send their results back to the master.\n",
    "\n",
    "When you're just getting started with Spark it's simpler to just run a cluster locally. Thus, for this course, instead of connecting to another computer, all computations will be run on DataCamp's servers in a simulated cluster.\n",
    "\n",
    "Creating the connection is as simple as creating an instance of the SparkContext class. The class constructor takes a few optional arguments that allow you to specify the attributes of the cluster you're connecting to.\n",
    "\n",
    "An object holding all these attributes can be created with the SparkConf() constructor. Take a look at the documentation for all the details!\n",
    "\n",
    "For the rest of this course you'll have a SparkContext called sc already available in your workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark as sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<SparkContext master=local[*] appName=PySparkShell>\n",
      "2.4.0\n"
     ]
    }
   ],
   "source": [
    "sc = sp.SparkContext.getOrCreate()\n",
    "print(sc)\n",
    "print(sc.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using DataFrames\n",
    "Spark's core data structure is the Resilient Distributed Dataset (RDD). This is a low level object that lets Spark work its magic by splitting data across multiple nodes in the cluster. However, RDDs are hard to work with directly, so in this course you'll be using the Spark DataFrame abstraction built on top of RDDs.\n",
    "\n",
    "The Spark DataFrame was designed to behave a lot like a SQL table (a table with variables in the columns and observations in the rows). Not only are they easier to understand, DataFrames are also more optimized for complicated operations than RDDs.\n",
    "\n",
    "When you start modifying and combining columns and rows of data, there are many ways to arrive at the same result, but some often take much longer than others. When using RDDs, it's up to the data scientist to figure out the right way to optimize the query, but the DataFrame implementation has much of this optimization built in!\n",
    "\n",
    "To start working with Spark DataFrames, you first have to create a SparkSession object from your SparkContext. You can think of the SparkContext as your connection to the cluster and the SparkSession as your interface with that connection.\n",
    "\n",
    "Remember, for the rest of this course you'll have a SparkSession called spark available in your workspace!\n",
    "\n",
    "### Creating a SparkSession\n",
    "We've already created a SparkSession for you called spark, but what if you're not sure there already is one? Creating multiple SparkSessions and SparkContexts can cause issues, so it's best practice to use the SparkSession.builder.getOrCreate() method. This returns an existing SparkSession if there's already one in the environment, or creates a new one if necessary!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pyspark.sql.session.SparkSession object at 0x118ccf860>\n"
     ]
    }
   ],
   "source": [
    "#import SparkSeccion pyspark.sql\n",
    "from pyspark.sql import SparkSession\n",
    "\n",
    "#Create my_spark\n",
    "my_spark = SparkSession.builder.getOrCreate()\n",
    "\n",
    "#print my_spark\n",
    "print(my_spark)"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Viewing tables\n",
    "Once you've created a SparkSession, you can start poking around to see what data is in your cluster!\n",
    "\n",
    "Your SparkSession has an attribute called catalog which lists all the data inside the cluster. This attribute has a few methods for extracting different pieces of information.\n",
    "\n",
    "One of the most useful is the .listTables() method, which returns the names of all the tables in your cluster as a list.\n",
    "```python\n",
    "print(spark.catalog.listTables())\n",
    "```\n",
    "### Are you query-ious?\n",
    "One of the advantages of the DataFrame interface is that you can run SQL queries on the tables in your Spark cluster. If you don't have any experience with SQL, don't worry (you can take our Introduction to SQL course!), we'll provide you with queries!\n",
    "\n",
    "As you saw in the last exercise, one of the tables in your cluster is the flights table. This table contains a row for every flight that left Portland International Airport (PDX) or Seattle-Tacoma International Airport (SEA) in 2014 and 2015.\n",
    "\n",
    "Running a query on this table is as easy as using the .sql() method on your SparkSession. This method takes a string containing the query and returns a DataFrame with the results!\n",
    "\n",
    "If you look closely, you'll notice that the table flights is only mentioned in the query, not as an argument to any of the methods. This is because there isn't a local object in your environment that holds that data, so it wouldn't make sense to pass the table as an argument.\n",
    "\n",
    "Remember, we've already created a SparkSession called spark in your workspace.\n",
    "```python\n",
    "query = \"FROM flights SELECT * LIMIT 10\"\n",
    "\n",
    "# Get the first 10 rows of flights\n",
    "flights10 =sql(query)\n",
    "\n",
    "# Show the results\n",
    "flights10.show()\n",
    "```\n",
    "![Screen%20Shot%202019-01-15%20at%205.11.27%20PM.png](attachment:Screen%20Shot%202019-01-15%20at%205.11.27%20PM.png)"
   ]
  },
  {
   "attachments": {
    "spark_figure.png": {
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AiKGbSVMQQAABBBIKKKz/3//+t1uH/95777m1/iNHjnQh/wrLV7i9tsFLqmhGOVu2bGFfvuqqq6xSpUpu5jj+G7SjQFLLDrQeXYkH44etxz/29ttvj/9j2O8Vxq6Z//POO8/tcqABhZQWLQFQSWqNfkrPk17v04CH8gyMGTMm7CkVRaFOfUrKO++8Yzly5LB77rkn7Nv1mqINwiU0LFasmBtwCHsgTyKAAAIIIBBDAgwAxNDNpCkIIIAAAuEFihcvbjfccIObrd+8ebPNmTPHJXnTenUl5tP673BFM9DJFYWua+Y5frnkkkusbt26cU8ped3kyZNdWLpmtVesWBH3WuJvNKOfXNFsf+fOnV0+AiWti5+3ILnjQq+VKlXKfRsaCAg9n1lfNRCh9gwYMMDtxjBs2DDTNn+hoo658i6kpGgphpL9KaJA5uEeav/evXvda/HPqeMSRyDEf53vEUAAAQQQiBUBBgBi5U7SDgQQQACBFAto+7t//etfLqGc1tBrAECZ/xOXk3U+tZY83Gy+ogo0sKCBB3VitfxAyfRUlFMgXMmXL58LVQ/3mp7ToIUGF7SE4eqrr3ZJA7VjQGqKMvCrrF27NjWHZeh7lchPiffUPg0GaFBAiQ21W4E68SktWo6hpRHyTuqhCACVxEn+kronKb0270MAAQQQQCBaBBgAiJY7RT0RQAABBFIloA6h1vAnVzQj/PTTT5syzSvBXOISfzY68Wv6efv27W6bufivaWnAZZdd5maitYWeBhb0PnVmldguNAsf/xh9f7JQfkUr3HzzzS5HgPIPFC5c2EU1hLLkJz5fuJ9Da/+1RWFKy48//mhdu3Z1Oxqk9Jik3qct/hIXLQF44oknXKJGZfjXkg0tk1A2f0VY/Prrr4kPCfuz1u5r54CVK1ee9FG7du0E5ziZfYI38wMCCCCAAAJRLMAuAFF886g6AggggEDSAuq0alZd4ffJlVDYfbjZ9EWLFiU5Y69zaia5SpUqcacfPny4ffnlly6h3E033RT3fPxvFIJ+KkX1VMdfRbkElCywY8eOcVnyU3JO5T1o2LChffbZZ6adBlKS8E4dcmXo79+/f7KXCO2uoN0MwhV1/pOLPNDxqpse2nZRkRVKpqhBDw3mnKwon4A6/6Eoh5O9n9cRQAABBBAIogARAEG867QZAQQQCICAOpLTpk1z2fWTa25oNwDtRZ+4fPLJJ4mfivtZx02YMMHtOR96UoMN6sgqCiBc2bZtW1wm/nCvJ/dc/G0I9T7NdiuvwX333Rd2GUJS51JiQ0UkfPzxx0m9Je55RUUoQZ8iGpRJP7miWXsVbU8Yruhcx44dS/CStuxTIsUdO3YkeF4/aHnFbbfdZhqEWbJkyQmvJ36iSZMmpl0FQvcz8ev6WcswNJCyf//+cC/zHAIIIIAAAjEvwABAzN9iGogAAggEU6B79+6uw6n18ok7nvFFtP5fYejnnHNO/Kfd98oYH65De+TIEXvggQfcDPqdd94Zd5zC+xWSn1SSPYW16/XUhO3HnTzMN6+88ooVLFjQkoo2CHOIy3av2fK+ffu6aIVw79FzSlaopIXaOi+pXQ3iH6tzKinh0KFD4z/tvpeX1vknLvXr13fXSWowQssetAtDaHAh/vGJDbWDgkL5dV/CRSFo9wdFUChBIyH/8SX5HgEEEEAgSAIMAATpbtNWBBBAIEACl156qZshV1h+q1atTBn0NfOtoq9jx441ZdLXGvcXXnjBataseYKOEgW2bdvW/vOf/7iOqsL3NSPeqVMnt33giy++6BLOhQ7U9nwKz1cEwF9//eUGHnSt0NZ9mqFWpMGsWbPc8oFDhw6FDj2lr1ri8OGHH7o2KKw/JUWDHePGjXPr63v37m233nqrDRo0yOVLUMd5/vz5pnapo6yZ8oEDB1qRIkVOemrV5cYbb7Sff/7Z3nzzTdNuC2qfoiLat2/vfk6cbK9FixYuukC5Eh5++GG33l9LBXTsG2+84QYe+vTp45ZyhCoQqouiOzQIEJrNV04E3UclYNR9UBv37dvnkgvqPl5zzTVuuYbaRkEAAQQQQCCwAt5/PCkIIIAAAgjEpIDXAT3udd6Pex3P495/6N3Dm6WO+75GjRrHvVn+E9r+6quvuvfoBW92+rgXju5+9sL73VdvnfnxIUOGnHCcnvCS1h2vXr26e583e+2+erP0x71O7vHDhw8f9zqp7jnVxwttd+fQa96OAWHPpycvuuii4+eff36Sr3vr5I97HeDj69evT/I9iV/wEhwe9wYAjntZ9+Pq422F575XO70O8/FNmzYlPsz9/P7777v37d69O8HrXof7uBeNEHe+rFmzHte5vHwM7lxeDoLj3oBDgmN0Dm/N/3G9VyahrzLzIiacWfwDvKiA42XKlHHvla83qx//5eO//PLLcW9ZgXs9dL903ssvv/y4l7MhwXv1g+6V/CgIIIAAAggEQSCLGun9h5GCAAIIIIBAzApolliz71pLvnXrVre+XFv8aZbb63Ce0G5tQXf33XcnCNVXwj/NjiszvcLikyuaSde2dmvWrDGvg2lKvud1RuMOUYI7/Vy2bNm45zLrG/0ZsHjx4rhkiYpQ0ONka/6Tq6/W9M+bN8+8zr07V0oS8+3cudMdozwJcpFbUqH6WtKhpRn62rhxY0ucH0F1064L2lVAr3kDPSmKYkiuTbyGAAIIIIBALAgwABALd5E2IIAAAgikq0C4AYB0vQAnQwABBBBAAAEEMkHgxGmPTKgEl0QAAQQQQAABBBBAAAEEEEAAgYwVYAAgY305OwIIIIAAAggggAACCCCAAAK+EGAAwBe3gUoggAACCPhJIG/evCdd5++n+lIXBBBAAAEEEEAgJQLkAEiJEu9BAAEEEEAAAQQQQAABBBBAIMoFiACI8htI9RFAAAEEEEAAAQQQQAABBBBIiQADAClR4j0IIIAAAggggAACCCCAAAIIRLkAAwBRfgOpPgIIIIAAAgikj8Dx48fT50ScBQEEEEAAAZ8KZPdpvagWAggggAACCCBwSgI7duywFStW2Lp16+Ie69evt23btple2759u/u6Z88eO3jwoB04cMB9PXr0qLtelixZLGvWrO5x+umnW548eUyJIfU1f/78VrRoUStWrJj7Wrx4cStbtqyVL1/ePfSzjqcggAACCCDgRwGSAPrxrlAnBBBAAAEEEEhW4NixY7ZkyRKbPXu2zZkzxxYuXGhLly51j61btyZ7bFIvquOe1iiAnDlzWqVKlaxmzZpWo0YN97VWrVpWr149y507d1KX5nkEEEAAAQQiIsAAQESYuQgCCCCAAAIInKqAOvsLFiywyZMnu8eUKVNcp18z9+GKZuorV67stnIsXbp03NciRYpYwYIFrVChQu5rvnz5TB12PTTTny1bNnc6DQLomnrs37/f9u7dG/fYuXOnbd682bZs2eK+btq0yVavXm2rVq1yDz0frujc1atXt8aNG1ujRo2sefPm1rRpU3ftcO/nOQQQQAABBDJCgAGAjFDlnAgggAACCCBwygJHjhyx6dOn29ixY93jjz/+sF27dp1wPoXda2ZdD82yV6lSxapWrWolSpQ44b2RemLfvn0uMkEDFopK0ENRCvPnz7fDhw8nqIYGHpo0aWItW7a0Vq1auYeWGlAQQAABBBDIKAEGADJKlvMigAACCCCAQIoFNIP+448/2ogRI+zXX3+13bt3Jzi2QoUKduaZZ8Y9GjZs6NbjJ3iTj39QrgEtVZgxY4ZNmzbNJkyY4H5WlEGonHbaadaiRQtr166ddejQwUUIkE8gpMNXBBBAAIH0EGAAID0UOQcCCCCAAAIIpFpAs/zffPONDR061ObOnZvgeIXLt27dOu5RqlSpBK/Hwg9aTjBx4kQbP368jR492i1vCCUiVPu0fKFLly528cUX23nnnWc5cuSIhWbTBgQQQACBTBRgACAT8bk0AggggAACQROYOnWqff31167jv3z58rjmK7u+Zr4vuOAC91DnN2hFAwJjxoyxkSNHukiIlStXxhHI55JLLrGePXva+eefH5evIO4NfIMAAggggEAKBBgASAESb0EAAQQQ8L+AEsKpA6W14vpe6621llzJ17Jnz+5mT5X0TR0pfSW0OnL3dM2aNfbll1/ap59+6tbCh66sTn63bt3c4+yzzzaFwFP+J6DlAkOGDLHBgwfbX3/9FfeCchxcfvnlds0117ikgnEv8A0CCCCAAAInEWAA4CRAvIwAAggg4A8BZVcPbfe2bNky00Mdyw0bNrhHUhnhw9VegwLax10dKXVCtW2bHtWqVbM6deq4DPLaB55y6gIKZf/hhx/snXfesVGjRrmM+jqbzK+88kq77LLL7KyzzmIgJoXE2vJwwIAB1r9/f7cjQugw7Spwww03uMgADW5REEAAAQQQSE6AAYDkdHgNAQQQQCBTBHbs2OGSpGnbt0mTJrnEaRs3bky2LtrGTR2gAgUKuC3dNJusmX91RBUJoCRsSiynCIE9e/Yku9+7zqXM8meccYZ7KEu7BgcoJxdYv369vf/+++6hARoVeWote58+fVxyO90XyqkLKHfCF198YZ999plt3brVnUhbH/bq1cvuvvtuq1mz5qmfnCMRQAABBGJagAGAmL69NA4BBBCIDoFDhw7Z77//bj/99JPLAD9z5sy4GeNQCxS2X7duXbfdm/Z410PbwCk5XMmSJS137tyht570qwYEtH+7BhXUSdVadEUUaMs2JaPTvu6Ji66jrdqUnb1jx47umonfE+SfFZ3xyiuvuBlq3U8VdURvvvlmu/rqq61gwYJB5smQtmtQ6/vvv7f33nvPbZd4/PhxF1Fx0UUX2f/93/+5z2uGXJiTIoAAAghErQADAFF766g4AgggEN0CmoVXiLiywP/yyy9uVj7UIu2Pri3fmjdv7r5qJl7bwEWqKEpAs6xTpkxxEQjjxo1zAwah6yt/gEKvL730UvdQxvqglrFjx9qLL77otvCTgZZXdO3a1W699VaXuT6oLpFu96JFi+zVV191eRb279/vLt+0aVO799573XIL3RcKAggggAACDADwGUAAAQQQiJiA9jxXZ//DDz+04cOHW6ijogrUr1/fNHPZtm1btxd6rly5IlavlFxowYIFLju7tqz77bffXJLB0HGNGjWy3r17u3XYWuMehKKO/xNPPOEiN9RehaBfe+21LgRd+RQomSOgXBlvv/22vfXWW3GDVlWqVLGnn37a5V4gt0Xm3BeuigACCPhFgAEAv9wJ6oEAAgjEsIBC6j/66CP3WLVqlWupZtGV+V3ZzDVjXLZs2agR0G4DWq6gwYARI0aYchaoaG271rrfcsstbqu2WNxpQEs1Hn/8cTcIojYXLlzYdfo146/vKf4QUFLMzz//3F5++WVTdICK8lo888wzdvHFF/ujktQCAQQQQCDiAgwARJycCyKAAALBEdBafoWHa993JeNT0Wzkdddd5xLClSlTJuoxQuuwFdUwevTouOSCWv/+4IMPusRssZD0bv78+fbAAw+4ZRu6aers33PPPda3b1+3rWLU38gYbYB+7zQQ8OSTT9rKlStdK5s1a2b/+te/3CBVjDabZiGAAAIIJCHAAEASMDyNAAIIIHDqAr/++qs999xzLmReZ1FG/u7du7vtylq3bh2zW7+tWLHCRTl8/PHHLrmg2q7cBffdd58b9FA2/GgrSpSoGX8NcKgzqWSMas9dd91Fxz+KbqYSMypZoDr+2jpT5cILL7TXX3/dqlatGkUtoaoIIIAAAmkRYAAgLXociwACCCCQQGDatGn20EMPxXX81VnUHuXamiyaQvwTNOoUftAuA9qz/fnnn7d58+a5Myg3gDKz33HHHaYkh34v6uz/5z//cZ1/JUVUFMONN97o1v0XL17c79WnfkkI7Nu3z9544w174YUX3NIVfRY1oPPwww+b3/JuJNEEnkYAAQQQSIMAAwBpwONQBBBAAIG/BbSFnjoQCvXXVmTa8k2d3dtuuy3Q27/JYvDgwfbss8/a1KlTHZa2L3zppZesW7duvv34aNcD3bvZs2e7Onbu3NmtJQ/ybge+vVmnWLHNmze7JR2ffPKJ+51VpMprr73m8nGc4ik5DAEEEEAgCgQYAIiCm0QVEUAAAb8KaP271virg6ukY5pB1Ay31r4XKlTIr9XOlHr9/PPPbqY11Klu06aN63Bp9wO/FCU31LZxCvdX0WCFZou1OwMlNgX+/PNPN9gzY8YM18ALLrjA+vXrZ+XKlYvNBtMqBBBAIOACDAAE/ANA8xFAAIFTFRg1apTrOIQyjPfp08cNBMRCYr9TNTnZcQqrf//99+2xxx4zbdemvdlvuukmF46dN2/ekx2eoa9rRwPtXrBu3Tq3REERHffff79FY96CDIWKwZNre853333XHn30Udu+fbsVKFDA5Qa4+uqrY7C1NAkBBBAItgADAMG+/7QeAQQQSLXA3r17XfZ3JRRTqV27tr3zzjt27rnnpvpcQT1A2wY+9dRTbq/2w4cPW6VKlUyh2JlhqA6ftvAbOHCgux0tW7Z0EQA1atQI6u0JbLs3bdpkN998s33//ffOQFta6vdc+SsoCCCAAAKxIcAAQGzcR1qBAAIIRERg4sSJ1rt3b1uyZImbJVYnVlvBKcs/JfUCc+fOddshTp8+3bJmzeqSJf7zn/+M2Ky7dmtQ5MaaNWssT548bucGrf1XXSjBFfjiiy/cUh4NVBUtWtTefvttu+yyy4ILQssRQACBGBJgACCGbiZNQQABBDJKQMnstH2Y9hJXGHuDBg1MnYS6detm1CUDc17tGPDMM8+45RP6XhEVX375pTVs2DDDDLQlnMK9X375ZZcA7qyzznL3U5EIFAQksHbtWrd1pXJXqFx77bUuYoUlIY6D/0MAAQSiVoABgKi9dVQcAQQQiIyAEsNp1v+HH35wM8PK7q8Oa44cOSJTgYBcRbsEaDZ+/vz5LpniBx98YD179kz31q9cudLN5k6ZMsVt7ad8BI888ojLR5DuF+OEUS+ghICK8tH2gRqU+uabb6xKlSpR3y4agAACCARVgAGAoN552o0AAgikQEAh6pdccoktXrzYChcu7Pa2b9++fQqO5C2nIqCdFPr27esSBep4DbY8//zz6dY5HzFihBvM2bZtm8s7MGDAAGvWrNmpVJVjAiSgnSu6d+9uSvipBIHKV9G1a9cACdBUBBBAIHYEWOQXO/eSliCAAALpKqAs/y1atHCd/0aNGtm0adOMzn+6Ep9wMoVXK+makioqr4JC9C+88EKXmf2EN6fiCWV510x/p06dTJ3/zp07u/tJ5z8ViAF+a7169UwRI5deeqkpIkiDgtohQktWKAgggAAC0SVABEB03S9qiwACCEREQOv7teZXGep79OjhssLnypUrItfmIn8LjB8/3s26bty40apWrWq//PKLm7VPrc+ePXvsqquusiFDhrhIAiUZfOCBByxLliypPRXvR8BeffXVuM6/BgQHDRpk+fPnRwYBBBBAIEoEGACIkhtFNRFAAIFICbz00kuug6jEf/fdd5/bo57OYqT0E15H2fkVaq3oizJlypiiMmrWrJnwTcn8pPX+2spt1qxZbgmHOmvnnXdeMkfwEgInF/jjjz+sW7dupm0DFR2gpSVly5Y9+YG8AwEEEEAg0wUYAMj0W0AFEEAAAf8IaHZYoeLaBk4zfVqPTslcAc3gK2R/7NixVqxYMRcJkJIdArRl48UXX+w6aRo0UBJHRRJQEEgPgRUrVrjlKUpaWbp0aRs2bJhpqRAFAQQQQMDfAuQA8Pf9oXYIIIBAxAS0zZ86/9myZbPPP/+czn/E5JO/UN68ee3HH390na3NmzdbmzZt7M8//0z2oOHDh9v555/vOv8dOnQwDQbQ+U+WjBdTKVCxYkWbMGGC+zyuW7fOzj33XBcJkMrT8HYEEEAAgQgLMAAQYXAuhwACCPhR4LnnnnP7wqvz/9lnn2XI9nN+bHe01EnJAQcPHuy279uxY4e1a9fOderD1f+jjz5yywa0bdv1119vGgxQ5nYKAuktULBgQfvpp5/c9pWKVNFyE/37QUEAAQQQ8K8AAwD+vTfUDAEEEIiIgDqMDz/8sAv7//TTT+n8R0Q99RfRrgDatq9Pnz62d+9el9F/4cKFCU6kLQOvu+46l51d0Rzvv/9+um0hmOBC/IDAfwVy5Mhh+nfj8ccft6NHj9o//vEP++CDD/BBAAEEEPCpADkAfHpjqBYCCCAQCQHN3ml9ubbz6tevn914442RuCzXSIOAOlnahk1r+kNh2KVKlbInn3zSnnrqKTeQ89Zbb9nNN9+chqtwKAKpF1ACUW0PqKSh//nPf+zWW29N/Uk4AgEEEEAgQwUYAMhQXk6OAAII+Fdg+vTp1qpVK1Po7iOPPGJKAEiJDoH9+/e7Nf7KBdCgQQP3/b///W83288Sjui4h7FayzfeeMPuvPNO1zwlEr3rrrtitam0CwEEEIhKAQYAovK2UWkEEEAgbQJbtmyxJk2a2KpVq1xIuUJ4KdElsHXrVmvZsqWFlgFoiUD//v2te/fu0dUQahtzAu+++66b/ddWoi+++KLbTjTmGkmDEEAAgSgVYAAgSm8c1UYAAQROVeDYsWPWsWNHGzlypJ111lluezl1HinRJ7By5Upr0aKFaTDg66+/dtv+RV8rqHEsCii3yA033GD690a5KJSQkoIAAgggkPkCDABk/j2gBggggEBEBRTu/+yzz1qJEiVMywC0hzclegWmTp1qGzZscEkBo7cV1DwWBZRXRLkotLvIN99843aniMV20iYEEEAgmgQYAIimu0VdEUAAgTQKjB492m0hlzVrVhs1apS1bt06jWfkcAQQQCBpgWeeecbtEKCtLJV0VHlHKAgggAACmSfANoCZZ8+VEUAAgYgK7Ny5023RpXW5yhZP5z+i/FwMgUAKaDvK22+/3Q4cOGBdunSxmTNnBtKBRiOAAAJ+ESACwC93gnoggAACGSxw9dVXmzLEN2/e3MaPH8/+8BnszekRQOBvAeUB6NWrlw0cONBKlixpU6ZMsbJly8KDAAIIIJAJAgwAZAI6l0QAAQQiLTB06FCXIC537txuBq5atWqRrgLXQwCBAAscOnTILrzwQtMyJO1AokFILQugIIAAAghEVoAlAJH15moIIIBAxAW0Z3zfvn3ddZ977jmj8x/xW8AFEQi8QI4cOWzQoEFWpUoVmzZtmtshIPAoACCAAAKZIMAAQCagc0kEEEAgkgLq9Gu7uEaNGrm1uJG8NtdCAAEEQgKFChWyIUOGWN68ee2LL76wV155JfQSXxFAAAEEIiTAEoAIQXMZBBBAIDMEli5danXq1DGF3/7xxx9uz/jMqAfXRAABBEICgwcPtm7dupl2IxkxYoS1b98+9BJfEUAAAQQyWIAIgAwG5vQIIIBAZgo8+OCDdvDgQVMCwBYtWmRmVbg2Aggg4AS6du3qtgY8evSo9ejRw9asWYMMAggggECEBIgAiBA0l0EAAQQiLaDttho3buwSbSkSoFSpUpGuAtdDAAEEwgpoO1JtCzhs2DBr1aqVjRkzxkUEhH0zTyKAAAIIpJsAEQDpRsmJEEAAAX8JaP9t/ZF966230vn3162hNggEXiBLliz28ccfu3+bfvvtN3vhhRcCbwIAAgggEAkBIgAiocw1EEAAgQgLTJo0yZo3b+6SbS1fvtyKFi0a4RpwOQQQQODkAiNHjrQOHTpYtmzZbMKECXbGGWec/CDegQACCCBwygJEAJwyHQcigAAC/hV4+eWXXeXuuOMOOv/+vU3UDIHAC7Rr187uvvtuO3LkiPXs2dP27NkTeBMAEEAAgYwUIAIgI3U5NwIIIJAJAtryT3tta0ZN35csWTITasElEUAAgZQJaJeSZs2amfKW3HDDDfbee++l7EDehQACCCCQagEiAFJNxgEIIICAvwXefPNNU3btK664gs6/v28VtUMAAU8gR44c1r9/f8uZM6d98MEHNm7cOFwQQAABBDJIgAGADILltAgggEBmCOzfv9/9Aa1r33XXXZlRBa6JAAIIpFqgVq1a9tBDD7nEpTfddJMpKoCCAAIIIJD+AgwApL8pZ0QAAQQyTeD777+3nTt3ugSA2gKQggACCESLgAYAatasafPnz7fnn38+WqpNPRFAAIGoEmAAIKpuF5VFAAEEkhf4/PPP3Rv69OmT/Bt5FQEEEPCZgJYC9OvXz7RF4LPPPmuLFi3yWQ2pDgIIIBD9AiQBjP57SAsQQAABJ7BhwwYrW7asS/63fv16K1y4MDIIIIBA1Alcf/319uGHH1qbNm1szJgxUVd/KowAAgj4WYAIAD/fHeqGAAIIpELg66+/dsn/LrroIjr/qXDjrQgg4C+Bl156yYoVK2a//vqrDRkyxF+VozYIIIBAlAtkj/L6U30EEEDAlwLKwr9ixQpbvny5rV692j22bNlieuzYscP27dvnHtr7WuGueigDdp48eSxv3rxWpEgR9wdwiRIlrEKFClaxYkWrXLmy5c+fP8n2/vDDD+617t27J/keXkAAAQT8LlCoUCF7/PHH7Y477rAHH3zQNKiZPTt/svr9vlE/BBCIDgGWAETHfaKWCCDgY4E9e/bYlClTbNq0ae4xe/ZsW7JkiR08eDDda12mTBmrXbu2NWjQwM444wz3qFSpkqkOGjTQwMOmTZuIAEh3eU6IAAKRFDh8+LDVqVPHFi9ebO+++65pZwAKAggggEDaBRgASLshZ0AAgYAJqGP/+++/288//2y//fabzZw50zSTH79oRl/r8atUqWLly5e3cuXKWfHixV0nXWvzc+fObbly5bLTTjvNbXt1/PhxO3DggO3du9d15hUpsHnzZtO6fkUSrFy50g0qaJu/xKV06dJWrVo1V5ezzz6bPbQTA/EzAghEpcA333xjl112mZUsWdL9+6cIKQoCCCCAQNoEiKdKmx9HI4BAQAS2b99uCrH/9ttvbdSoUS58P9R0haaeeeaZbjZeW+81bNjQatSo4cL5Q+9Jj6/Hjh1zgwFz58616dOnu6iDyZMn27p169xD11CoLAUBBBCIBQEtZ2revLlNnDjRXn75ZXviiSdioVm0AQEEEMhUASIAMpWfiyOAgJ8FDh06ZMOGDTNtrTd8+HBTSKqKZvfVye/YsaOdf/757g/UzJyZmjdvnpv9V7bsRx991C0P8LMrdUMAAQRSKjB+/Hg755xzXG6UVatWmfIDUBBAAAEETl2AAYBTt+NIBBCIUYGNGze6NafvvPOO6XsVdfo1E3XllVe6kNRSpUrFaOtpFgIIIOAvAQ22asnV008/bY899pi/KkdtEEAAgSgTYAAgym4Y1UUAgYwTmDFjhr3++us2YMAA0+y/Sv369a1Xr152xRVXuGz8GXd1zowAAgggEE5AuVZat25tRYsWdflQlEOFggACCCBwagIMAJyaG0chgEAMCSh7/0MPPWQjR450rcqWLZt16dLF7rzzTmvVqlUMtZSmIIAAAtEp0KJFC5cLQIO0ffv2jc5GUGsEEEDABwIMAPjgJlAFBBDIHAFtL6U184MGDXKZ+AsUKGDXX3+93X777VaxYsXMqRRXRQABBBA4QWDIkCHWtWtXt6uKtlnVDioUBBBAAIHUCzAAkHozjkAAgSgX0PZ6Wkf64Ycfuu37tB3fHXfcYQ8++CAJpqL83lJ9BBCITQFtlVq3bl1T0tNPP/3U+vTpE5sNpVUIIIBABgtkzeDzc3oEEEDAVwLK6F+rVi3r16+fq9cNN9xgigR44YUX6Pz76k5RGQQQQOB/AkrEev/997sn3nzzzf+9wHcIIIAAAqkSIAIgVVy8GQEEolVgxYoVdtNNN9kvv/zimtC+fXvTH5HVq1eP1iZRbwQQQCBQAgcOHLAyZcrYtm3bTElbtR0rBQEEEEAgdQJEAKTOi3cjgEAUCqijr9BRdf6LFCniwke1pRSd/yi8mVQZgUwWWLdunS1dutTVQuHoW7duzeQaBefyp59+uvXu3ds1+L333gtOw2kpAgggkI4CDACkIyanQgABfwns3LnTunXr5jJG792713r06GHz589n7ai/bhO1SWcBbWGpz3tKy+TJk93AmAbH9FAnS9ushX7WVyVdy4yidd+a8Z0wYULYy8+dO9ct3VGbr7nmGhfVE/aN6fTk0aNH7bLLLrODBw+6M27atMmuu+66dDo7p0mJgJZtqXz55Ze2b9++lBzCexBAAAEE4gkwABAPg28RQCB2BGbOnGlNmjSx77//3goWLOi+9u/f34oVKxY7jaQlCIQR0GdeSS1TWs4880w3i62ZbD20E8Z9992X4LmqVaum9HTp+j6t+7700kvt22+/DXtePX/xxRdbjhw53MxwRm/b+cYbb1iFChWsdu3arj7am14DjdpJhBIZgTp16thZZ51lu3btsq+++ioyF+UqCCCAQAwJMAAQQzeTpiCAwN8CH3zwgWnPaIXpahBg+vTpbvsofBBAIPoELr/8cvvuu+/CVlzPa0Ze5fzzz7f69euHfV96PLl//3576aWX7PHHH09wuqefftqefPJJt5Voghf4IcMEbrzxRnfu999/P8OuwYkRQACBWBVgACBW7yztQiCAAgoXVpZohYgqWdTNN99sf/zxh1WqVCmAGjQ5MwV2797tPoeVK1c2zVg+9NBDtmzZsrgqDRw40D766CPTYJUSmelzqqItKnv16uX2Otext99+u/ssxx3offPTTz9Z8+bNrXjx4laiRAlTZ0idUxVtbanO6IgRI+zcc8+15cuXu+ePHDni6lCzZk0rXbq0mzVfvXq1ey0l/6fQ90cffdTtoKHjO3fubKtWrYo79Ouvv3Zteeutt6xBgwYu6ubaa6+1w4cPm3beaNq0qavvlVdemSBsW517ram/8847Xb3KlStnes/GjRvjzt2yZUtTiP+0adPintM3GuBbuXKltWvXzj3/2muvuXaH3nTs2DFT51z+pUqVsgsvvDBu7b5m7UMDB6H36/znnXee2xo09NyCBQvstttucz8q2kDnkmH8cs4557jO/6+//hr/ab7PQAF9bvLmzWt//vlngs9hBl6SUyOAAAIxI8AAQMzcShqCQLAF1MG5+uqr3Qzdaaed5hL9vfPOO5YzZ85gw9D6iAuos9ymTRvX6VSm8pEjR7qOsHaeCHXU16xZY998841bmvLqq6+6SBWtZ1ZnUuHNCxcuNC1j0Vr8K664Iq4Ns2bNcgNb7777rmn9+fjx423Dhg2uo6s3qfN89913u+vr/OXLl3fHXnXVVW5wQWvptSOGOr+qY0rXUOt3a+3ate566nT37NnTFP6+Z88ed3699u9//9t1xnSNKVOmuO01Vfcff/zRJeD866+/3Hu15WaoKP+Akrppa0516JVUTwN2iuAJrbNPahlA/PB/nU95CuIPHGgAUDk/xo4daxrs0Fp9RQls377dChQoYOrchwZIdLzqOWnSpAQDDUOHDnWDLHpdSUQ1ABCuhJKMhnuN59JfIFeuXHbRRRe5Eye1PCT9r8oZEUAAgdgQYAAgNu4jrUAg0AJKeKYZSc00alZo+PDhJPoL9Ccicxuv2X3lmtDsszqamjF/8cUX3WdzzJgxcZXTjLNC2NURV+I9derVqdaMszo4+fPnd8dphjzUeR41apRb3x/a/qxatWp27733xkUQ5MmTx11TxytCIFu2bK4zro61sqYXLlzYrZfXgECXLl1cFEJchZL4RoMYeihaQQkBNcCmhJrdu3d3z4UO0zXVuVcdVC9FMvz+++/28ccfu+tqFl5bceq5+EWdf3XWdXy+fPnsueeec8cryVuoaMY3cUcvfvh/6H2hr+r4a0b+008/dfcie/bsLpeAEgVqYFBFEQHq9IeK7s2tt96aoH76t0ROKlOnTrUaNWqE3p7gq3ICaNCDEjkB5YZQ0UAXBQEEEEAg5QIMAKTcincigIAPBRTKq7BdhUWrw6PZvlBIsA+rS5UCIKDPombi45esWbO6TrQ6naGi7+NHqGhrSnWsExcNEEycONE9fc8997gOv35QiLtm8zUzrSUHSRW9rhl/1SF+0e+NQqhPVnS8OvsaTIhfEh+vUP34RdEHysERv416bseOHfHfdoKVXuzatWuCzP86t6IN5syZ445VBMWiRYuS/F3XQInOoeSA8Uv8OscfAFC0gaIHFI3w22+/uUNUT0U2hAZbFHGROPw/dG4NYuh1SuQEdP80aKTPsAbJKAgggAACKRPInrK38S4EEEDAfwIKX+7UqZMpjFjrpdWByqxs5f7ToUaZJaBOuTLFJy4KZY9fNCMevyhHgGaoNcMev2h5S2gdutbUDx482M3mK4RdW+SFMtLHPyb+9zqvZrIVIRO/aAChcePG8Z8K+72OHzJkiA0YMCDB68q5Ua9evbjnFLGQuIR7TteNX0LLFOI/p6iJYcOGxT0VfxmAwu01+6+Z+cQd/NABqrMiCFTv+EV1Dv0boUEFLWVQ51+dSOVVUHsULaE6auDjggsuiDtcgyzyDldKlizpstKHe43nMkZAkSa6P/os6KF8GRQEEEAAgZMLMABwciPegQACPhRQR0ghoFoDrQ6EZv6VQIyCQGYLaNvJxLPc4eqUuPOq4xSeroR5SRVloR83bpzLdRGamVa+gMQRB/GP13mVHFPRA6dSdLyO1TmSK4kHOPTexM+pA564aOvBxIMAWqdftGjRBG/VMgCF6D/xxBOuw6etCpMqqrPeq4SISRUtC1C+BXnqoeUXqq92EtCSCyVS1FKJUNHyCQ0sVK9ePfRU3FflL9DrlMgK6L8B6vxreQgDAJG152oIIBC9AgnjAaO3HdQcAQQCJKDZOf1hHgr7V5I1Ov8B+gD4vKlKFKcBqcRFnZW333478dNxP+u40G4AcU963zz11FNuoEvPaR2+1tmHOv96TgMAyZWkzqs91HW+k5Wkjh80aJD169fvZIef9HX9/iYuo0ePTtBGva4Zew0WaNBv9uzZSYb/671J1VlJ/d588029xRXNICsPgNb/awBApVWrVu7+KZmhvg8VzfIrOiBcUTSGXqdEVkCJALU0Rb83ygVDQQABBBA4uQADACc34h0IIOAzAc30aNsxJVhT2H+4GTmfVZnqxLCAEs7FD4+/5ZZb7LPPPnNLU0LNVqdVSem6desWeuqEr3fccYeb2V+8eHHcawrd/89//hOXfV7JArds2RL3utadK9lgKGO+XtB7FCETKooOUAdJ5woVZcXXDHlKfnc0864Ed+o8h4rWxqudKTk+dExSXzUIEVrbr/co9F6De//4xz8SHBJaBqBtPrX0J3EERfw3X3zxxS5HgAY5QkVWSkJYpUqV0FMuhFzLBGQaGkTU9okaJFD+gvjLMc4+++xkBwC0gwMlsgL6b4AiZvR51+8YBQEEEEDg5AIMAJzciHcggICPBNRZUJi0kj9pjXD8mVAfVZOqBEhAneNXXnklrsVa/6+dALQFnmaQ9RlVcj91RpObJVaHU+fRVnVKZKn16H379nVbBRYqVMid/4033rBrr73W1MHt0KGDS+6nWXglsFPHWEUdVc2qq+M+d+5ct5WgOtQPPPCAW+euOjVo0MBl21dn92RFAwo6/pFHHnHHa6ZcdXvmmWfcDgYnO/5krys3wfXXX+/arHX4ynegwQqF8ScuSmao2Xa1LbmijrsGB59//nk744wzXKJQDVbIIH4iRuVhUJ4CLQUIFd0vRRqEsv+Hntegg3ZuSFwUkaQtG0Pb0iV+nZ8zVkCJHVXi77CRsVfk7AgggEB0C2Tx1uOduCAvuttE7RFAIEYFtA2XOjea7dQMqzJ2UxDws8DKlSvd7KT2tk+cRT+peus/y1pTrqz9FStWPCF7/4EDB0xRAkqUp235VBT+fPToUdeZ1c/KmK9rK2t9/Otu2LDBtm3b5mbB42fn1zEpKRpo0Gy5ZtE1MJDWovZpAEXbJq5fv94OHToUNoFiWq6zefNmN0CiOmvg8FSL7kujRo3s5ZdftrZt28adRskGtd2gIhcokRfQjg8aMFMkAFsxRt6fKyKAQPQJMAAQffeMGiMQSAF1OjRDumrVKjdDqLBoCgIIRLdA/AGAaGiJogq05CKUq0GDLoqG0ACAIg0okRfYv3+/KUJGywAUuREuciTyteKKCCCAgH8FWALg33tDzRBA4L8CCrFVCLU6/y1atLBXX30VGwQQQCDiAlp2Ua1atbjZfs3+d+7cmc5/xO/E/y6oqA4tHdF/J5TUUbtVaAnOokWLjCDX/znxHQIIIBASIAIgJMFXBBDwrcBrr71md999t5UoUcKmT5/uQp99W1kqhgACKRbQTPqZZ56ZINleig/OpDdq1v/IkSOmJRSafdZXLdegZJ6AojK0PWTiomgA5QjQkg0tE6hatWrit/AzAgggEDgBBgACd8tpMALRJbB8+XKrW7eu7du3zyX9I9FWdN0/aosAAghEQmDdunVugFiDxHooYeOaNWsSXFpLTvTfEO2MoW0ltbMEBQEEEAiaAAMAQbvjtBeBKBPQzI32BO/Vq5d98cUXUVZ7qosAAgggkFkCSoSpJIHaFUP/HYm/hWbZsmXdbhIaDCB/Q2bdIa6LAAKZIcAAQGaoc00EEEiRgLb809ZmyhA+b948K1q0aIqO400IIIAAAgjEF1A+AEUGDBo0yG3JuWLFiriXtbuDtp/s2bNnmnaKiDsh3yCAAAI+FmAAwMc3h6ohEGSBTZs2mfbt3rlzZ9ye6kH2oO0IIIAAAuknMGnSJPfflv79+5v+e6Oi3QSuvfZa69u3r5UvXz79LsaZEEAAAR8JMADgo5tBVRBA4H8Ct99+u7311ltuveawYcP+9wLfIYAAAgggkE4CBw8edFEB+u/NxIkT3Vlz5Mhh//jHP+zhhx9mICCdnDkNAgj4R4ABAP/cC2qCAAL/FWVfvVcAAEAASURBVFiyZInVrl3bbes0e/Zsq1WrFjYIIIAAAghkqIASB2qbWW0jqN0eNBCgiIBHHnnElDOAggACCMSCAPvWxMJdpA0IxJiAZl0OHz7sZmDo/MfYzaU5CCCAgE8FmjRp4pLNKueMEs9qu8d3333XatSoYf/85z/twIEDPq051UIAAQRSLkAEQMqteCcCCERAYObMmaaETLlz57bFixdb6dKlI3BVLoEAAggggEBCgYULF9rjjz9uX3/9tXuhcuXKLkKgS5cuCd/ITwgggEAUCRABEEU3i6oiEASBl19+2TXzpptuovMfhBtOGxFAAAGfCmjm/6uvvrLffvvN6tevb8uWLbOLL77YOnfubOvXr/dprakWAgggkLwAEQDJ+/AqAghEUGD16tWmGRaVpUuXknwpgvZcCgEEEEAgaQHlBHjnnXdcRMD27dutcOHC9vbbb9sVV1yR9EG8ggACCPhQgAgAH94UqoRAUAVef/11t+by8ssvp/Mf1A8B7c5UgWPHjiV7/R9//NF69Ohho0ePTvJ9Wj/95JNP2pw5c5J8Dy8gEG0C2bJlM+1OM3fuXLvwwgtt27ZtduWVV7rfBw0IUBBAAIFoESACIFruFPVEIMYFlFypVKlStmPHDps+fbrLAxDjTaZ5CPhKQHufayu0u+66y1555ZWwdStYsKDt3LnTSpYsmWQIdKVKlWzFihVWrlw5W7VqVdjzKNu6Mq337NmT3/WwQjzpd4H333/f7rnnHtuzZ49VrFjRvvvuOz7Lfr9p1A8BBJwAEQB8EBBAwBcCQ4cOdZ3/pk2b8keUL+4IlYglgUWLFtl9991nv//+e5LN+uCDD9zWm/qaVAntyhH6Gu59SuCpEvoa7j2dOnUy5fvQemoKAtEocMMNN9isWbOsWbNmbsCrZcuW9vnnn0djU6gzAggETIABgIDdcJqLgF8FPv30U1e1q6++2q9VpF4IRK2AZtrV4VboclJbmT300ENWokQJe/DBB5Ns56hRo2zs2LE2fPjwJN/zyy+/WL9+/UzvTapo/bRKkSJFwr7l+PHjbv/1Bg0auOuFfRNPIpDJAop20aDajTfeaPv377c+ffqYImm0fSAFAQQQ8KsASwD8emeoFwIBEti4caOVLVvWsmTJ4sKKk+oUBIiEpiKQYgF1lrU2v1ixYnbGGWeEPa5Dhw6mjrm21VR4/mmnnRb2fZF6Ur/zI0eONNVL9U5cZsyYYY0bN3ZPa9AiuQGHxMfyMwKZIaDIGeUIOHjwoCnCRVsH5sqVKzOqwjURQACBZAWIAEiWhxcRQCASAlo7qRkT/aFP5z8S4lwjlgSUPPOiiy6y5s2bm9bWhyvqjHzxxRc2ceLETO/8q36KNLjqqqvCdv71es2aNa1u3bqmxGuXXHKJnjqhaOBDW7SNGDHihNd4AoFIC1x//fUuWqVo0aI2bNgwa9u2rZEcMNJ3geshgEBKBBgASIkS70EAgQwVCP0Bz3rgDGXm5DEqsGXLFtcyZfDfunVr2FYWKFDAevXq5RLzhX2Dz57UzOnMmTNdB0odq3BFywyUhV2DHz/99FO4t/AcAhEV0CDc+PHjrUKFCjZhwgQ755xzbO3atRGtAxdDAAEETiaQ/WRv4HUEEEAgIwW0HnnMmDEu/P+CCy7IyEtxbgSiUkC7YowbN87NmIeLkHnggQcsa9asbheN9u3bR2Ubw1Vas//58uUL95J7Ln4uA4VdUxDwg0CNGjXsjz/+sI4dO7qtMFu3bu1+f7VzBgUBBBDwgwA5APxwF6gDAgEW0MydOv5NmjSxqVOnBliCpiNwooC23CtTpozt3bvXrSv+4YcfTnxTQJ85fPiwvffee5YnTx5T8lDlEElc1q9f72Zi1RnT+ygIREpA4f/t2rVzy3Lq1KkTtzwgUtfnOggggEBSAkQAJCXD8wggEBEBzf6r6A90CgIInCigte4qCvGn/E9AiQxvu+22/z2R6LujR4+6LdpWr17tBhlDS40SvY0fEcgQgUKFCtnPP/9sigCYM2eOS3ip/95pOQ4FAQQQyEwBcgBkpj7XRgABmzRpklM466yz0EAAgUQC6iz8+uuv9tJLL9knn3yS6FV+TE5AEQKh/AiKBKAgEGkBLdnRdpjVq1c3LeXp3LmzHTp0KNLV4HoIIIBAAgGWACTg4AcEEIikgGbo1MFRePPmzZtN2ZMpCARJQNvhqVOwe/du024YtWrVClLzM7yt6nwNHTrU7dOuXQXSUvRvVLgtC9NyTo4NhoCiUFq2bGn6es0119jHH38cjIbTSgQQ8KUAEQC+vC1UCoFgCMydO9d1/qtUqULnPxi3nFYmEtCa/ilTptiCBQusf//+iV7lx7QKaCu2N954w20pGO5cM2bMcDsJfPrpp+FeTvBc7969uUcJRPghpQLlypVzA1HKQ6FInldeeSWlh/I+BBBAIN0FGABId1JOiAACKRXQNl8qTZs2TekhvA+BmBLo0KGDVaxY0c0sJ7XffUw12GeNufXWW+2rr76ya6+9Nm65QLgqfvvtt2499913323btm0L9xaeQyBZgYYNG9pnn33mklXef//9Rk6KZLl4EQEEMlCAJIAZiMupEUAgeYElS5a4N2h9JCW2BJSw7vDhI3bkyN+Pw97Xo95Dzx/zktodP+Y9vP/pZ21hl0X/y5rFsnqZ3PVztuzZ7TTvkf2/j9NOy+6ejy0lM80MLl++3JToL1wW+1hrr9/ao+zsEydOdPchf/78YaunJUrq+Kts2rTJ7rvvPvvwww/DvtfPTyongn4nDx0+ZIcPHbYj3hKs4/p99H4Xjx3/O8GkfvdCv4P63cuRI4fpd09ftS0jJW0C3bp1s6efftoee+wxU0TJrFmz3C4faTsrRyOAAAKpEyAHQOq8eDcCCKSjQM+ePW3AgAGm8Ns+ffqk45k5VXoKqJOuTtDu3Xts95493vf7bN/+/bZ/3373dd++fXbg4CHTXux/Pw65jn961kHnUockZ84c3iOne5zufZ87d27LlSuX99V7eF/z5MltefPmtXzeI2/ePJk+aKDZYv2hrz3rNfunLf0o/hFQHpI///zTNBCgrO3hyoMPPmgvvPBCgpeUmLG1l93dD2W/97u4Y8dO2+499Pu557+/p3v27DW9tm//AfdVv8dpKfr9y5Xr9L9/37zfNfd7li+P+13Lly+fFSxYwAoVLOgNFpyWlssE4thOnTrZ8OHDrU2bNi5JoAZeKAgggECkBBgAiJQ010EAgRMEmjVrZpMnT7Y//vjD2AXgBJ6IPaHZ5507d3kdiB2uI7HD23teHYod3nNKTqeORGqLZrM1cxg3g+91HjSrH5ph/Hu235v5996n62sGUlEBig5QR0XRAooaiIsg8GYuQ9vhpaYuWnObP7/XOSmQ33VQCnpJJ0MdlQLecxk96/7uu+/aLbfc4qr8zDPP2KOPPpqa6vPeTBYYN26c6+gn7jwrakmztxqMilTZuWuXlyx1i/fYapu3bLUtW7fa9u073KBbSuqgGXx1zrV9Yg7vod/NLF7HM6uLvPm7A6p2ut9Bb2BEv3uHFDXgRQvoa2KDpK6ZxxuUK1S4oBXzkroWLVrEinqZ8EsUL+YG6ZI6JmjPK6FkvXr1TElAn3/+eXvggQeCRkB7EUAgEwUYAMhEfC6NQNAFSpYs6f4AWrt2rZUuXTroHBnefv0Bv83rMGzatNnrQGzx1jJvt61bt9lW76tmQpMr6kjny/f3zLpm2TXb7mbdvT/29X3O0/83K69OkToZ6V0UwqwIg7hogwMH4yIQ9v03GkHRCZoFVbSCohaSK+oQFSlcyAoXLmxFihRyHZbiXkelcKGC6RY5MG/ePDe4pborI32LFi2SqxKv+UxAUQE7vIGxcEVh3Arnzoiimfs1a9fZ2nXrbf2GjbbBe+z3ZvLDFXXqC3oz74W8GXjNxCv6Rb+rioBx0TH/nbVXhz8tRdvXqQ5/RxXsdwODf0cF7badu3b/PWjoDRwm9W+JBuJKlSxhJb1HmdKl3CMj/p1ISxsjeezPP/9sF1xwgRuI0TKUxo0bR/LyXAsBBAIswABAgG8+TUcgswXUUdQflQqPjuRMWma3OxLX1x/hm7zZwvXrN7gOxEav07/FmzXUrF64onBedXw1O+4emin3Hvqj3Q+h9OHqfLLnNOCh6IVd6pwoqiEU2eB1UjQQsscbKAhX1FHSzKVmLdVhKVWqpBUvVvSU10BriYTqImNK9AgMHDjQevTokWSFtS5euwjUrl07yfek9AUtqVm5crWtWLnK2ypujTe7f2KiQYXflyhe3Buo8mbVvYdm2DXTrhl3vxRF6ej3asvW7S6potqhqAX9+6NBsPhF0UAlShS38mXLWMWK5a18ubIZMnAY/5p++/6uu+6y119/3SXCnTRpUroNPPqtndQHAQT8JcAAgL/uB7VBIDACmp1Vh0hruE82UxsYlDQ0dI/nuXr1Wlu9Zq2t8zr9GzduCjsTV8BLdFa8eFEv67wXnuuF5oZmwLW2PmjloJe3QGv0FQGhcGp1VDZt2uLNZu46gULRAuqslPYGA8p5HRY9NDBCiU0BLX2pWbOmrVu3LtkGam93LRNI7VISdZQ1OLdoyVJbtnylm+GPfyENQumzVrZMaW8A6u9Zc/3uRmtRe/V7tn6DNyC5fqOt8f6d0qBA/GU9+h1Te6tUrmjVqlbxonIKR2tzU1xv/bevVq1a3r/dq+2tt94y7UpBQQABBDJagAGAjBbm/AggEFZgzZo1LvO2kqLpe0rqBDSzvXzlSjdrqE6/1gInLurca/Zaj5Je51Wd/tMjuGY5cX2i5ecD3jIDN2vpDaJoMEUdNXVeEpdCXsSEBgIqVChnlSpUYEAgMVAU/3zPPffYq6++mqIWvPPOO3bzzTef9L2KAlnhzfIvWLjIFi9ZlmDgU53fcmVLW6WKFdxMuMLk9VwsF0V/aYmDIh+We5EP+j2LXwp7/35Vr1bFatWs4SJx4r8WS98PHjzYtAWolnEsWLDAG2gsEUvNoy0IIOBDAQYAfHhTqBICQRCYP3++C53V7IfWSVOSF1Do/spV3h/K3myh/lhWBzV+0TrgMl4ehXLlyrhZNHUg6OzHF0rb98o9oLXYWpetSIu13szwIS85Wvyi0Gx14CpVqmAVypdza3vjv8730SEwe/Zstx47qeUyiVtRwFsqo3/PSpUqlfgl97M+M3PnLbD5CxaaclWEipbXVPdmuqtWrewGkoK8Hl4m+72lYCtWrHKDI0uXLnM/h6w02Fa7Vg2rW6e2i1oKPR8rX0O7Amg3HO2KQ0EAAQQyUoABgIzU5dwIIJCkQGgAQOtn586dm+T7gvyCEtot8f4QXuyFCS9fsdLt4R3yUIb98t4e8qG1s1qvzlZSIZ2M/6rZXIUwr/LWa6vTssoL4dUe66Gi+6PBAIUyV6lSyfJ6SRQp0SHQrl07l7AxNbXt3r27DRo0KO4Q/e7OnjvP/po1xyXaDL2gWW11ZGtUr+rW84ee52tCAf1+aeBkwcLFbuBEnqFS1ouUaFi/nhcZUD1mcgYsX77cLQVQngTtLqFtKSkIIIBARgkwAJBRspwXAQSSFVCoo2b/iQBIyKTQfoUIz1+wyK3nj/+q1qBXrVzJzTAri3ashwjHb7vfv1fSRYUzK0JjybLlLgdD/DprqYA6LDVrVCMZYHwYn36vHADaqm2Lt1tGSr7u9BJMDhkyxBo3aWpTp81wv7/qxKpoB416dWtZnVo1XR4JnzbZt9VSngBFP4WiKEKRN0rC2KB+XWvauKEpQiDaS9++fe3NN9+0Sy+91L755ptobw71RwABHwswAODjm0PVEIhlgdAAgBJtKRogyEW7IMybv9A9NKMcKurga4a/WpXK3kxyZbfFV+g1vvpbQB1IrfNe7EVwKEIg/tZoynauWWA9Tj/9dH83hNqdVEAd1Dlz59v4P/607d4OEypKCljV+71t2KCul9SuEtE5J1VM2RvU+ddSCkVWKEIgVGTdvFlTlz8h9Fy0fd3gJUisXLmy2xVn2rRp1qhRo2hrAvVFAIEoEWAAIEpuFNVEINYElixZYtWqVfPCo6uYvg9a0eygwvpnzZ5rixYvjesgqtOvLNhKfKVOv2a5KNEtoGRnGgxQx2XpshUJ7rWSnNWvV8ctF2AJR3TdZ/0Oq+M/YeJkbzeJv5NEakCnYYN6blZaa/wpGSewcdMmmzJ1hosMCA2wKdKmZYtmXke6YsZdOAPPfP/999tLL71kygnwww8/ZOCVODUCCARZgAGAIN992o5AJgpo+7Ui3jZ0hQoVcluxZWJVInpphfjP/Gu2zfhrlu3e/b996CtWKG/16tX2koJVtSBuyRfRm5CJF9PWg4u8Aa85c+Z5A0Cr4mqiLTEbNaxnjbzOo76n+FdAM/4KR/993ATb4YX+qxQokN9aNDvDC/WvHTPr0v17BxLWTIkVp02faVO8pReKplIp7S2RatPqbJeMM+G7/f2TlpxU8HYU2b9/v9sRoHr16v6uMLVDAIGoFGAAICpvG5VGIPoFNHumrNf6Y1qJj2J9PbtC+/VH6sJFSyy0Nriwt261njf7W8/LbM1sYfR/plPbgl27dnszyPNcFMi2/27jqCiA6tWqWtMmDaM6nDm1FtHy/qVLl9uvv423TV5+ABXtVX9W8zOtTu2ahPln8k1UpM30GX/ZpMnTbO++v5MGVq5U0dq0PjuqEi7edNNN9t5779kdd9xhb7zxRiarcnkEEIhFAQYAYvGu0iYEokSgaNGiXobsrS7Jlr6PtaLBjYWLFtufk6bG7XGttcHq4DVp3MA0609BQAIrvK0dp03/y1sOssQNium5UqVKWvMzm7rEgfrcUDJPYNv27TZy9FjTAICKBuzOPfssN+PPvcm8+xLuysoTMGXqdJs4eYop4kalcaP61uqclpYrV65wh/jqOW1DWb9+fZfzZe3ateR+8dXdoTIIxIYAAwCxcR9pBQJRKaAdAJQMcM6cOTG17ZHWo2pt/5+TptiO/yYFy5XrdGvSqKEX5q0/7AjxjsoPbAQqrWUhWh4y3RsM2OeFAasULFjAhZcrV0CsR8pEgDhVlzhy5IiX3G+i15mc6iJ3tDzn7LNauAG87Nmzp+pcvDmyAvr9mTBhklsaoMHYXF5+htbesgDlaPD7oE2bNm1s7NixbleA22+/PbJwXA0BBGJegAGAmL/FNBAB/woo0dHw4cPt+++/t65du/q3oimsmUL71fH/w/ujc+euXe4odd6aebO49VkbnEJF3iYBtx+4lydgshfOvH3HDodSIH9+a3lWM5c0kISBGf85Wb1mrQ0f8bOFlmdoAKZNq3O8bf1yZ/zFuUK6CWzZstV+GfWri7LRSbULx0UXtPf11oHaBvCyyy6zxo0bm3YEoCCAAALpKcAAQHpqci4EEEiVwD333GOvvvqqvfDCC6bsx9FclBTst3F/xM34Fy1axJspbO72fvf7bFM0u8d63TVzuWDhYhvnbTGnjoxKwQIFrNW5Ld02gny20v8ToFn/MWPH2VQvqZyKfpfVYSzjJZajRK/A/AWL7JeRY1x+AEVvKEngGU0b+7JBBw8etBIlSthOL8nk4sWLraqXHJaCAAIIpJdAtie9kl4n4zwIIIBAagSWLVvmIgAqVqxoXbp0Sc2hvnmv9qL+bsgw11k4cOCgFSlcyNq3Pc86tj/fihcr6vtQU99AUpGwAurgF/M6oI0bNfA+W4W9QYAtLiJAuSWWeVsKFvV20iCBZFi6U3py8+YtNnDQd97WpMtcUj9tKXdx5wtdlv9TOiEH+UZAv0cN6tcx7cSyYeMmW7Z8ha3bsMFtwZnDS0jrp6IBivnz59tff/1lJUuWtHPOOcdP1aMuCCAQ5QJEAET5DaT6CESzwJgxY+z888+3Fi1aeGs1J0RVU/bs2WOjxvxm8+YvdPXOkyePtfZmZRUmzKxsVN3KqKqsIgK0zETRJurIqNSuVcPanteK7QPTeCe1PadCxRUBUNgbyOva5SIrWaJ4Gs/K4X4U0G4sI378xfZ72wZqSYfudYXy5XxV1REjRthFF13kEgJqIICCAAIIpJcAAwDpJcl5EEAg1QIKbyxUqJDlyJHDdnlr5vXV70UdMG01Nfb38S7DtGZqlKm9ubcHeI4c/ppF8rsl9Tt1AWU6V5bzid4OE+qwKjld63PPdpECDEClzlVJO0eO/tX7vZ7lDmxQv661O78Nv8+pY4y6d+/evduGDvvJVq5a7QZt257f2s5o0sg37VAeEC0D2O7tQMEyAN/cFiqCQEwIMAAQE7eRRiAQvQJ169a1uXPn2qRJk+zMM8/0dUO2bt1mPwz/ydat3+DqWa1qFevQ7jxCsH1912K7crt27fZmrcd42wcudQ0t7W0d2Omijt7SgMKx3fB0ap0yxX/z3RBbs2ad22Hhgg5tXRRPOp2e0/hcQAO6Gsz9c+IUV1NFcOkz4JfdNnr06GEDBw60d955x26++Wafa1I9BBCIFgFyAETLnaKeCMSowPTp023GjBmmgYDmzZv7spX6I3Ha9Jn23eBhLru/tvHr0qmj2wc8Z86cvqwzlQqGgD5/tWvV9GYKi5vyUWzxBqn+mjXHcngRARoMIBog6c/BDi8C6csBg2zjxs1ua84el19qVatUTvoAXok5Af1+VKpYwSV6XOrlpFm/fqMb4K1RraovBgG2bdtmw4YN85Yp5HG7AsTcDaBBCCCQKQJEAGQKOxdFAIGQwEcffWTXXXed2wZQ2wH6rezdu88LE/3Rlq9Y6aqmGSKFByvkmoKAnwQOHjzk5aUY6wYAVC91bLp0uoBt68LcpE2bN9uAr76zvXv3WvHixezKyy4hh0IYpyA9tX7DRvt60PdulwDlfrjSGxDKnTtXphIsWbLEqlWrZsWKFfMGqjYyoJepd4OLIxA7AgwAxM69pCUIRKXA2rVrrWzZsu6P761bt/oqD4DCgpXhXwn/8uTObRde0M4U9k9BwM8Ci5cs9RKcjXQdmbx581i3izt7v2Ol/VzliNZt46bN1n/gINu//4BVrFDeLr2kCwN6Eb0D/r3Yjh07beDX39q27Tu8TndR63XlZZk+CFC+fHlbvXq1zZo1y+rVq+dfPGqGAAJRI5A1ampKRRFAICYFypQpYw0bNnSd7LFjx/qmjdoD/IsBX7t6lS9X1q67tjedf9/cHSqSnIAGqfR5VVZz7RSgz/GU/+5pn9xxQXjNdf69sH91/hXuf4U38080TxDufMraWLBgAbuq1xUuh4a2hPzSGyjat29/yg7OoHede+657szKk0NBAAEE0kOAAYD0UOQcCCCQJoFOnTq547XWMbOL1vv/MnKM2w7s2LFj1qL5mdbzyu6W11uDSUEgWgT0ee1xxaXu86vP8Uhve7ufvc+1Pt9BLVrzP/Dr79zWbxokufSSzr5Y5x3U++HXdut3p1ePy61Y0SKmQYCvv/nelJE/s0qDBg3cpWfPnp1ZVeC6CCAQYwIMAMTYDaU5CESjQOfOnV21v/32W9OWXJlV9EeeMoJP9RL+KQv0JRd3sjatzrasWfmnMrPuCdc9dQF9bvX51edY21UqkeWgb4dkamfm1FuTtiOV7V+df635r1ihnHXr2onOf9pIY/roPHlyWw9v4LdggQIuKaCWgmkgLTNKKOyfAYDM0OeaCMSmAH/VxuZ9pVUIRJWAtv+rXr26rVu3zkaOHJkpdT906JDrICxessyt+byqx2VWq2b1TKkLF0UgPQX0Oe7lfZ6V0GzJ0mXuc67Pe1CKOm7fezt4bNu23Up4Cf8uveRiOv9BuflpaKciAa68opvlzpXLli5dbqN//T0NZzv1QxkAOHU7jkQAgfACDACEd+FZBBCIsMA111zjrvjJJ59E+Mpmyp6u2cHVa9Za/vz57OqreliZMiRNi/iN4IIZJlCmdCn3udbnW5/zAV6is4MHD2bY9fx04jFjf7eVq1a7rdRY8++nO+P/uhQuVMi6X3qxiwKbMnW6zZk3P+KVVp6cQl49tmzZ4nYCiHgFuCACCMScAAMAMXdLaRAC0SnQu3dv90fW4MGDbfv27RFrxJEjR9waT+2hrgRQvXte4f2xVTBi1+dCCERKQJ/r3l6CM33O165db195a5v1+Y/lsnDREps8Zbr7t0Vr/vPmzRvLzaVtGSBQ1hsM7tDuPHdm7a6xdeu2DLhK8qesXLmye8OqVauSfyOvIoAAAikQYAAgBUi8BQEEMl5AWwG2b9/ezUr269cv4y/oXUEJ0Yb8MCJu5r93z8utQIH8Ebk2F0EgMwQK5M/vDXJ5n3Pvq7a5HDx0RMwmBtzjrfcf8dMvjvn881qZOnIUBE5FoFHD+lavbm03YDZk2I8RzwdQqlQpV+3169efSvU5BgEEEEggwABAAg5+QACBzBS499573eVff/31iIQnjxo91jRDmCvX6V7G9O6WL1++zGw+10YgIgL6nF/p7RCgz/2ixUtspPd7EItFs7Xa7q9ypYp2RpNGsdhE2hRBgfZtz3MDxBs2bLQ//ozslnwMAETwRnMpBAIgwABAAG4yTUQgWgTatm1rjRo1sg0bNtjnn3+eodXWWk7tja5s/1d0v8SKFC6Uodfj5Aj4SUCfd33u9fmf6v0eZMba5oz00MCGEh7mzJnTOl3YPiMvxbkDIpAzZw7rfFFH19oJf062bRFcqsYAQEA+ZDQTgQgJMAAQIWgugwACKRO477773BtffvnlDNsSUHs7//jT37sNdGx/vpX2EqRREAiagD73F3Ro65qt2XL9XsRC0Xaev4z61TWl9bktWfcfCzfVJ20oX66s1a9Xx/23KfQZi0TVihUr5i6jRIAUBBBAIK0CDACkVZDjEUAgXQUuu+wyq1q1qi1cuNA+/PDDdD23TqYtwYYO/8nbC/2IaV1ng/p10/0anBCBaBFQZ0a/B0oGODQT1jZnhNO06TNt167dVrJEcWvcqEFGXIJzBljgvNbn2uleZMmyZStsxcpVEZE4/fTT3XWCsnNHRFC5CAIBFmAAIMA3n6Yj4EeB7Nmz23PPPeeq9sQTT9heL5FXepYJEyd7WyltskIFC1pbLzEYBYGgC+j3QDsEbNy02fT7Ec1FW3r+OXGKa0LrVudYlixZork51N2HArlz57Lmzc9wNfvt9z8iUsPTTjvNXUfRLRQEEEAgrQIMAKRVkOMRQCDdBbp37+79gdXc5QLQUoD0Ktt37LDxf0x0p+t0UQcL/VGVXufnPAhEo4B+Dzpf+PfaZv1+6PckWsv0GX/Z/gMHTKHalStViNZmUG+fCyipZJ7cuW3tuvW2fEXGRwHkyJHDiTAA4PMPBtVDIEoEGACIkhtFNREImsBLL73kmqyvy5cvT5fmj/1tvFsCoLD/cmXLpMs5OQkCsSBQtmxptxxGS2R+HTs+Kpukuk+dPsPV/azmZ0ZlG6h0dAho0Kxp0793lpgydXqGVzo0WH3o0KEMvxYXQACB2BdgACD27zEtRCAqBc4++2zr2bOnWwJw0003pbkN69ZvsPkLFrlZ/1bntEzz+TgBArEmoN8LdTQWLFxk67yZzWgrixYvtd2791iRIoWtErP/0Xb7oq6+yp2hJWvabWLHjp0ZWv9Qxz8UCZChF+PkCCAQ8wIMAMT8LaaBCESvwGuvvWZFixa1kSNH2ieffJKmhkyc9Pe64DO8WZu8efOk6VwcjEAsCuj34symjV3T/pw0NeqaOGfuPFdndcxY+x91ty/qKpw7Vy6rWaOaq3dGb6O5a9cud538+fNHnRMVRgAB/wkwAOC/e0KNEEDgvwLa+iiUEPDee+91OQFOBUezMwsXLXF7njf11m5SEEAgvIB+P7Jly2aLFi+JqlwAB7x1/0u9rOzq+NeuVSN843gWgXQWqFu7ljvj3HkL0vnMCU/HAEBCD35CAIG0CTAAkDY/jkYAgQwS0Hre9957zx555BF3he3bt9vAgQNP6Wp/zZ5jx48ftzq1a1rePMz+nxIiBwVCIE+e3Fa3Ti33+zJr1tyoabM6/0ePHrUK5cvyOx41dy36K1qxYnnL5W3Rt3XrNtvm/TcqowoDABkly3kRCKZA9mA2m1YjgICfBSZMmGC33HKLzZo1y1Xz3HPPNS0HaNTo1Gbv581b6M5Tr25tPzc7Qd20L/vo0aMTPBf6QetO8+XL561zrmSKkvB7WbRoUYoTOZYrV85q146e++R3+1OpnwYA/po1x+bOX2Ctzo2OfBnLV6x0Ta3s/U5EQ9mzZ4/98ssvtnDhQluwYIGpg1e5cmX32e/Ro4fl9jLM+7WsW7fOZs+ebeedd17gd1LJmjWryzcxb/5C79+4lVa4UKEMuW3btm1z5y1QoECGnJ+TIoBAsAQYAAjW/aa1CPhaQJ3ep59+2p599lk3m6cO7osvvmjaFvBUy8ZNm1woc968ed3WYKd6nkgft2/fPuvY8e+t2ZK7dsOGDe2ee+6xq666yrfrnj/88EN3H5NrR+i1G2+80fr16xf6ka+ZIKAt9PLly+sSm23cuMlKlCieCbVI3SVXrlrtDqhUqXzqDsyEdw8ZMsTuuOMOW716talD16xZM9P2bt9//739+9//dlFPzzzzjN1www2ZULuTX3LUqFF29dVXe7PeW61w4cInPyDG31GpYgXTAIA+g00aN8yQ1q5YscKdt0IFtrbMEGBOikDABFgCELAbTnMR8KvAkiVLTJn/9YevwvUfeughmz9/fpo6/2rrypV/dwy0J3g0Jga75pprXEdBnQU9Vq5caXPmzLHff//dWWnQpE+fPnbdddc5t/S4v5s3bzbNxA8dOjQ9Thd3jmnTpiVoS6hN8b8+//zzce/nm8wR0O+JOjX/z95ZwEtRdn/80N3d3Q0i3SAiIB1igIqvgmIBYiH6ov5tAeEFRVFAQRokJKRBkO7uunR3eP/P7+Bc9y57i929O7P7O5/PsrMzzzzxnZnLPOc5ATnwz8Q6MD2JXavXrl2TCxcumtXoxJLV5hYxL730krRs2VIwkdu4caNgZXfOnDmyYMEC2bdvn6xYsUKKFi0qUIR5G/jUouev59mqP9S/c+bMoQjCjp/wGwrcGxAoxSkkQAIk4C0BWgB4S5DnkwAJeE1gxowZArNXmMXixXj06NFSq1Ytr+tFBYcOH9F68uXN45P64rsSWC7kzp3bY7Ng9MYbb6i7xIgRI3TS/sEHH3gsG5ed8KU+cuSIwArBl5IzZ07Jnj27L6tkXX4ikC9fHtm0eatR2ByRKpUr+akV31R74uQprShr1iy2VvJBaTdkyBBVAIwdO1aSG99xd6latar8/vvvUq9ePUH6UzzjhQoVci8Wp9/+ep7j1IkgLpwpYwZNBwglFIJRerqu3gwfCnEofiFUAHhDkueSAAlYBGgBYJHgNwmQQEAIDB48WF+IMfnv2LGjror5avKPAZ08eVrHlSNHcE48kRcaJvawnvjiiy/ULDcgF5KNBhWBnP8oaqzJtZ0Hd+7cee1e5kyZbNtNWOpg9T+D8RH/5Zdfop0kpjKBSpH9BLnfx40bZ9sxsWN3CSAOQEajBICcMxlnfC2IuQDFQtasWQX3BoUESIAEvCVACwBvCfJ8EiCB+yKAKP+9evWSr7/+Ws+H73/fvn3vq66oTsLK1wUTXAsmzRnSB3fwJMRKqF69usASoHfv3vcg+euvv2TMmDECX9IbN26otUDr1q01zoCrawSsL6zVptmzZxsFykkpXLiwPPLII5HqXL16tU5k9u/fry+nefPmVUUOyrnWF+mkOPyAafTPP/8srVq1Uj9jrIouW7ZMmjZtKo0aNYqoCROr8ePHqwk1XAlSmNzcRYoUUZeI4sWLR5TDBiwavv/+e2nSpIlamsAXe9GiRbJ3715dZUXgydKlS+s5YDBz5kx1tcALfuXKlaVr164afDFSpf/8OHr0qLpMgDP6jtgMDz30kCpmPJW3+7705nnBdcSqJp4jpAa0q+AZh6RNm8auXdSAngic9+abb8YqwB8C7OH5xPPnSez6POPv+vTp0wX9g4tDrly55IEHHtDnFttRCZ5dWIKtW7dODh06pM8wXJsefPBBdY3AMbhDpU2bNqoqAro/nbn3ThpLFARzzJE9m0/7Ao6QEiXuphz0aeWsjARIICQJ0AIgJC87B00CgSUAk8ZnnnlGJ/9YwcZEz9eTf4zw4qVL6hef1kTMt/MExhdXA6bDmTNn1gmta31gjRdnHIcPPtwJMKFHzAVM1jt16uRaXM2PLd//lStX6uQaWRlcBRNhvJhDCYCXevgsYxLdrFkz6dChg09iEYSFhckrr7wi69evlwYNGki7du3U0sHVLQGTI0zYEf8Ak25kD8hkVoHRf2xjpdVV8HKOOjE5QT979uwpl8w9gngHUHZgorJ06VKNyo7t4cOH64otJsL9+vWThg0bqpuKa53YnjdvnpQtW1ZjMqB/cHPAJAjZK95++22dQLufY/ffeF6sCfXFi5ds3d0rV+66qqQx7jJ2FUT7h+DvXmwE99ykSZM0+4lreTs/zwgKiL8pbdq00bgGUAoeP35c3n33XSlXrpzucx2LtY1nD8fhzgTXIyjwoCypU6eOvPrqq6pIwHOLZ9yuAlctyOV/7kVf9hN/ZyFQQlJIgARIwCcEzH8mFBIgARKIVwIvvvhiuPkDFm5emsLNCqzf2j5+4kT4R598GT78h1F+a8NfFV+4cEEZGbPhWDdhXr7DjZlopPLGwiLcTObCJ0+eHGk/fhiXAW3DrIRHOmYm37rf+ClH2o8f33zzTbhZEQ+fMGHCPcdMqkY9z0xcIh0zL/a6H/XGVkygQz3HrISGmwlBuFkZDDcr0ZFON37S4WbyHm5WDCPtNxHVw3GesQYINxPyiGPWuHCOUQBEOmZcULSuUqVKhefPnz8cY3GVNWvWaH+MhYXr7nATtE15PPnkk5HqQ6FRo0aFJ0uWLBzXwIkyfMQofX6OHz9h6+5PmTZD+7ll63bb9hPPMZ5DY7HiVR/t8DyPHDlSnwUz4Y8Yi1n5D69UqVK4cXEIN0q0iP3YwDNorBn0OTFKxUjHTKpX3W8UlOHGmijSMfwNwLNoFAHanvvxSIUD/OOPBYv0HlyxcrXPe2KsnnT8xtLJ53WzQhIggdAkQAsAn6hRWAkJkEBsCfTp00cDYSFQElZqscrjL7l585ZWnTRpEn81Yat6s5gI6KdPn460Aj9x4kQ1mYcpvbtgJR8m86tWrXI/FOVv1AfzZE+pGbG6CR/VqOrr3r27PP7441F+kP7RXbBav3z5cqlQoYLAFN8SjNMojzRbBFbwXSVx4sSCthAdHiuJ7mIm5WodgLFbgn7DZH/r1q36jRVHVzGTG7UUgCuCq+B+xur/jz/+qCxdjxmlgLz88svy6aefal9cjzlh23pubpoUdXaWW7dua/eQBcCusnv3brWW8dYSyU7Psytr9AsWRkbpdY/bC54zozDUAHawBnAVPPN49oyCUsxk3/WQGGWcwE1n8eLFkfbb8Qf+5kDgkuRroQWAr4myPhIgAfv+b8lrQwIkEHQEBg4cqPngkyRJouatiHTtT0n0z4QRPsyhIFCqwAcXAaOsyS3M3GFS60mQgzxbtmyams/TcU/7YJJbsGBBT4fUPx7m7/Dn9SQw2YfLR1SSI8fddFqux9u3by9QbLiL0dnLr7/+qmb57sfw2xoz+gJ3BVeBMsTTRAxuEgioCHcDT1KtWjU5ceLfVF8bNmzQGAGY3HiqD3UgrsDnn38uc+fOlRYtWniq1rb77tz5W/vmqnixY2et/uHet6vAFB4BAL0VOz3PrmMZNmyYugIhRocnwTVCEMTXXntNzEq+KgMQiwTPDpRknp591APlGmJ2uCvePLURyH3WvZcwYQKfdmPXrl0aBwIBAN0VJD5tiJWRAAmEFAEqAELqcnOwJBA4AgjghqB/EASag6+ov8WabNp9BdNXHM6fP6/pqKzJP+p1X/nHyjlySuPFEn7qCAoIX/XYCnKYuwr8fq36EKgLsQCqVKniWiRiG6uEcU0DiEm5J4FSAMoBS/ACDv9htI9VfEzko5JixYp5PGTdL9EdR7YKS7Zt26abWKkEV0+C1U3UawVW9FTGrvtumSj0EIuLXftprfxblgB27CcmuIhn4a3Y6Xl2HQtiHMCCJroAoIirAYE1BNLZQTkH5WyZMmVcq7pnG8FN7a4AuP2PFUpio9z2pVjjdg186sv6WRcJkEBoEqACIDSvO0dNAvFKAAHdsKoK80gEekIAtviQFCnvmnhfuXwlPpoLeBuYiCPwlrtMmTJFlS5Qwpw6dTdnOoLlIbheXCfkqBtmuTD1RX1WlHLUB9eAnDlzujfv1e/o+gcTfxOTQGbNmqVuB/gNQVBCrPpjhd6TYFIencR03DrXCuyGgIMxCSKbO02sgGYpXVwl7DiG5MalA2Jdfzv2Ec/lnDlzxMSnEFhAeSN2e56xko9Ude6uOO5jRABSCJSOEMtSKKbznLDyfe3adR2TdS/qDx/8g79tEFhBUEiABEjAVwSoAPAVSdZDAiTgkQBWZjHhhwksJoie/Lw9nuiDnalSphT4e183L6hXr14z6bf+9fn2QfW2qgLR7LHCiNRhrvLEE0+oqXzdunU1BRkm/fny5ZP06dNrsYoVK7oWj3G7c+fO6j+P2A1Q5qA+vKBb9bmb28dYYQwFXK0ZXItCkQFLg3PnzunL8XfffadpEKGAgCsErAGQXcKTRLdK6am8tQ9uB66CdiA7duyIcLlwPe66bUUJd91n5+2rRpkCV5JkyZIaH+2Udu7qv9kKzDNgV8Ezh/sH/txY0Y6NwF8eSj3cx5abgx2fZ1iIpDR/a/E3KDqxjlt/K+CCBMEzHJ24Wt1EVy6Qx5BxBmJlzvBFX5BRBPEPcO0bN27siypZBwmQAAkoASoAeCOQAAn4lcCQIUM0tRrSxcFnOypfaX91ImPGDBIWdlxOGRPtfHkjB4vzV5uBqNdE5lYLC6TgsgR57JEKD/65zz//vLU70veVK7G3jsAKJlb+Bw8eLCaTQ6R6rB9xqc86536+MTmCEgB5wy1/f9d64qMfViwEmDF7srxw7Y/Ttk+duuvSkDFjRtt33coNf/78Rdv2Fc8l7llLURVTR2FZg+CROM+a/Nv1eYZCrXjx4qqsiG5cUGZALBcb6/mJyToG7kp2l/MXLmgX06VN67OuIr0orCug6ESKVwoJkAAJ+IrAvyGVfVUj6yEBEiCBfwjAxBN50CFDhw71GMztn6J++8qd665J+uHDR/3WRqArhnVF//79dWXf1Ucf0fjxco5VQ0+CvNrwx42tWNH9Ed3ek5jUhboa7umYr/ehL3gx9jT5R1vIHuBvgfUE+MIlIiqB0qR8+fLqphBVGTvut56X3DnvDcxot/5myXJ3cnTCTJrtKpj0Nm/eXEwqN7VOiamfsJSCy1SXLl0iitr5ecY9Pn/+fMHflKgElgxY9YeLDgSWAIifAaViVAIrFCiO7SxwPbl48ZJx7UhsxnTXqsEX/bUsmFyVur6ol3WQAAmQABUAvAdIgAT8RgCp2GC+Cf9/vPwGQvLm+cfv9OChQDTv9zZNXm31d0cwPlgBuAZsQ+AxmB1DQeBJYMKP4+6m7VZZ9/1WpG7EdPAkSIkHlw/38zyV9XYf+hLVuMDi//7v/7QJf/YFyge8nH/55ZcR/syu44IVwgsvvKATPrhgOEkOHrqbySFv3rvPj537nslY+SAN24ULF+WamTDaVfC83TSBFRF001oN99RXTPyQMQXuNg8//HBEETs/z++8844G9Hv//fcj+uu6sXTpUkHsApRzdet56623ZNOmTfLTTz+5Fo/Y/vDDD40Fl+e/NxGFArxx/MRdxVPWrFmiDYIYl27CLQJBWmExh9SpFBIgARLwJQEqAHxJk3WRAAlEEEDwIkSFxyrPoEGDIvbH90a+fHn0JerwkaNy5crV+G7e6/YQaf7777+P+MCE+IsvvpDevXsLgs8hNd3Fixf1ZRF++a6CwFFYcYMFwMaNG3VyjhfLP/74Q/3mES2/RIkS+gIOM1xMTiAw+8bKNszrMYGGLyoEkxFcT1gAIN4AJvvIPICVP6T/wj6s6OGF3rU+PdnH/zz22GMa7b9Hjx6qCEA/jx49qtH/wQQfCHyurWBjPu6CVvfRRx9pnAlEOB87dqwGQ0NGAChjwAQcsMIJH2mnCJ6TQ4eP6HOTL9+9QSXtNg6YyOfMkV27degfxYXd+oj+1KhRQ61F4LpSs2ZNwb2zcOFC83fpiipKoczr2LGjPl/IfgH3KVex8/OMqP6YrCMoJyaseO5gvg4LI7gMIUNA5cqVNeWf65gwXmSEeeaZZ6Rv3776dwp/h9auXStdu3ZVyzGkHrWzWMqy3D4MgAqrB/Br2LChzwOr2pkl+0YCJBBPBMxLE4UESIAEfE7ATIgQMS38q6++8nndca1wwqSp4R998mX4qjXr4npqwMobc3rlB4buHxN8LtxExw83L4fhn332WbhZ8Y6yn2aCEW5MbrUOs5qk32YSH/7mm2+Gm4jk4cbPOKL+PXv2RNTTqVMn3W9WVsPNxCVi/5IlS8KNv+899RmFRLh5cQ83q+ER9ZngeHqeWfnUfWYlL6KemDa2bNmi58ydOzfKohi78f+O1BfjVxxuVhr1HPQb7IyiRH+jffw2L9ce6zSTdj0eFU8zsQmvVKnSPecaS4RwoxwJByvUb5Qn+g1OJo3XPeXtvmO1eU7wvIyfONXuXY3o37I/V2qfZ82eF7HPrhtGURZuJvjhJhuA3id4Lq17Bs+mmfiHm7gSHrtvh+c5uufEuLyEG2VApOfArPiHW38fPA3KuDrocetZBguj1AnH/yFGSRluFKBan1F0ejo94PtG/PSz3nt79+73WV9MMFUd85gxY3xWJysiARIgAYtAAmyYFxYKCZAACfiMwG+//SYtWrQQmKwiL7uryafPGolDRTt27pLJU2dIlsyZ5LlnO8fhzOAoiiB1ZkKtpujwvy1cuHAkU1WskGPF30rTZY0aZsoIwIU83QjiaAlW/jdv3qz1wQweH5xvCSLwQ9zrs4778hvWDxgbXE0qVKgQKc4E+on+IzOAFSjOl22714UVO1g/wA8anBH53Qrg5l7Wzr+HjxhlAiyellYtmkmJ4nf9te3cX/Qt7PgJ+XHkL4JsCz26PxfpfrRr33G/wGoG1jn4WwlrHATGiylQqhOeZ1jj4LnE3w2MK6Yx4Rrhed2+fbta9MBawHpmYe307bffqqWT3a4lfP8HDx2uLiivvdzN6xSPGN+KFSs0UwQssfC3NND/f9qNOftDAiTgPQEqALxnyBpIgATcCCA4Gl5sYfoPE+1AC14s//ftDxqoqUPbVlKoUIFAd4ntk4AtCezdt1/GTZii6cy6P/+soxQYQ78dIeeMS0qnjm0lvwNcF2x5A8RzpxDrAMqP9u3bR9kyFJZQjBhroCjLBOrAyr/WyIJFS1RRBoWZLwRxRSZPnqzxEuBWQSEBEiABXxNgDABfE2V9JBDiBJC3GJN/rLr+5z//sQUNrMJWfuBuvvvlK/wfHd4Wg2YnSOA+CFjPR+VKFR01+cdQS5YspiPevGXbfYycpwSCADIbPP3003Lw4EGPzffq1UutyF577TWPxwO9c/OWrdqFUiWL+6QriJkwdepUjStiB+W5TwbFSkiABGxHgAoA210SdogEnE3ghx9+0AE8++yz+hJjl9FULF/WmAenkiNHj8n2Hbvs0i32gwRsQwCuMkeOHJNUqVJJBfO8OE3KlS2tpv/btu90ZMBPp/H2RX8HDBgg2bJl05SeSH04b948DSpqYpNI48aNNcMGggC6ZkPwRbu+qOOAySxz6vQZ/X+lUEHfWJUhowgs1p566inl4ot+sg4SIAEScCdABYA7Ef4mARK4bwLIAz9x4kR9CUdUZzuJCbgldWrV0C4tXLRUTAA8O3WPfSGBgBLA87Bg4VLtQ51a1U06ySQB7c/9NJ7eZLwoUriQpqNbu37D/VTBc+KZQJYsWTSeSLdu3QSR7xE7plGjRjJs2DBVIOP/k6+//tqWMR3+Wr1WaVWqUD5WMQ5iQot4OSNGjFDLm549e8ZUnMdJgARI4L4JUAFw3+h4IgmQgDsBE7FYrl27pqmL8ufP73444L/Llikl2bNllfNGUbFoyfKA94cdIAG7EFhsngc8F9nM84GVdKdKlQcraddNJgO5dv26U4cRUv2GxUm/fv00gKaVEnH//v2CYLLwh7ejHDsWZlwT9mvQP19Zy7z11luqmIZLRLFid91Z7Dh29okESMD5BKgAcP415AhIwDYEJkyYoH3p3NmekfYRqb5Z08a6woIJAnKdU0gg1AngOTApMvW5aG6eD9eMDk5jkyd3LilYIL/JoX5TVqxc7bTuh3x/ce+lTJnS9hwsBXLlByqY/qbwur9//fWX4P9PjP2///2v1/WxAhIgARKIjgAVANHR4TESIIFYEzh37pwsXbpUJxHw3bSrZDUmpzVrVNXuTf1tlly+csWuXWW/SMDvBHD/4zmA4LnA8+F0qVP7rqsPlHxnzd8lCgn4ksCu3XsE/v/JkyWTqg8+4JOqEewQgmCHCKBLIQESIAF/EqACwJ90WTcJhBCBhQsXyu3bt+WBBx6QzJkz23rkNapVkQL582ru+ClTZ2jQJVt3mJ0jAT8QQLCxqdNm6nOAtHl4LoJBcmTPJnD3uXPnjsyZtyAYhsQx2IQAYmXM/WOh9qZOnZqSPHlyr3uGwLnLli2TrFmzSp8+fbyujxWQAAmQQEwEqACIiRCPkwAJxIrAokWLtJydV/+tgcDMtEXzppImTWo5fOSozJo9zzrEbxIIGQK/z/lD3WDwHLR8tKmjTf/dL1r9urV1crZ//0GxUrW5l+FvEogrAQSQvXjxksaSQWYZbyUsLEys1f9BgwaZ/5PSeFslzycBEiCBGAlQARAjIhYgARKIDYGVK1dqsbp168ameMDLwG+zfZuWGu180+atsnDxsoD3iR0ggfgisMjc7xs3bdH7v515DnzhxxxffY9NOxhPo4Z1teiceQs1wGFszmMZEoiKwL79B2TNug3q5tb0kYd8ojDr3r27nD9/Xpo3by4dOnSIqmnuJwESIAGfEqACwKc4WRkJhCYBmEVu2rRJB1+2rPerIvFFERHP27RqoS90K1aukj/Nh0ICwU7AutcTJkxo7v9HdTUzGMdcplRJKVG8qNy8eVPjHMAlgEIC90Pg8uXLMn3mbD0V6WSzGXN9bwVpD6dOnSpp06aVoUOHelsdzycBEiCBWBOgAiDWqFiQBEggKgLIX3zjxg3Jnj277f3/3ceAWACPNmuiqzlYFV2y7E/3IvxNAkFDYOmyFWrtAjcY3PcF8ucLmrF5GsjDjRtKOjPBQto2xgPwRIj7YiIAxdGkKdPlypWrkj9fHqlaxfvAf3v27JHnn39em/7iiy8kV65cMXWDx0mABEjAZwSoAPAZSlZEAqFLYOfOnTr4MmXKOBJCyRLFInygly1fKX8sWCTh4eGOHAs7TQKeCOB+nr9gsSxdvkKVXfD5x30f7JLCBGlr0/pRSZw4sWzYuFnWGhNuCgnEhQBiZRw1CqS0adNIyxbNvDb9v379urRr187EErgo7du3l+eeey4u3WFZEiABEvCaABUAXiNkBSRAAocOHVIIRYsWdSwMmAq3btlcEiVKJKtWr5PJU6cLXBsoJOB0AriPJ5tsF3+tXqv3d+uWzdQ03unjim3/sxtXn0cebqTFYQWwY+fu2J7KciFOYPHS5YIYMUmSJJa2xl0mZYoUXhN55ZVXZMOGDVKkSBH5/vvvva6PFZAACZBAXAlQARBXYixPAiRwD4Fjx47pvhw5ctxzzEk7ihUtLJ06thWsGu7ctUd+HjtBIz47aQzsKwm4Erh06ZLexzt37dao+Liz+ACeAABAAElEQVS/ixUt4lokJLZLlyohdWvX0LFOmz5L87iHxMA5yPsmsGbteln+51+64t/KrPxnN+klvZXhw4fLd999p8/ihAkTGPXfW6A8nwRI4L4IUAFwX9h4EgmQgCuBs2fP6s/MmTO77nbkdp7cueSpJx+TDOnTS1jYcRnx08+yz6QSo5CA0wjsP3BQfvjxZ72P06dPJ53NfY37O1SlerUqUqlieYFP9/iJU6kECNUbIRbjxuR/7h8LtSSsRwoXKhiLs6IvMmvWLOnWrZsWQtC/cuXKRX8Cj5IACZCAnwhQAeAnsKyWBEKJAFYZIcGSwzhTxgzydOdO5qWvgFy9dk1+HT9JgwP+/fffoXRZOVaHEsB9imB/Y8dN0vu3kLmPn+78uOC+DnV5qGE9KV+ujNy+fVuVAPv2HQh1JBy/GwG4yliT/4cfaiDlypZ2KxH3n+vWrdM0f1A+9e3bV7p06RL3SngGCZAACfiIABUAPgLJakgglAkgzRYkWbJkQYMhuXEDQH50pHyCIDjgyJ9/lTNnzwXNGDmQ4COA+3OUuU8R7A9Su1Z1aW/uY7i1UETNuZuYzAAVype9qwSYNFU2b9lKNCSggV/nL1yiwTKBA5P/ihW8X6Xft2+fNG3aVJBKsHPnzvLf//6XtEmABEggoAQSB7R1Nk4CJEACNiaAVGk1qleRPHlyaQ5ouAT88ONoqVunplSuVMHraNA2Hjq75jACiPK/2pgtI5UlVreR+q55s4clb57cDhuJ/7uL5xpKgOTJk8mKlavNsz3HxPq4rM+6/1tnC3YkgGdmxqw5sm37TkmYMKE0a9pYSpcs4XVXkSK3Xr16cvz4cWnYsKEgBgCFBEiABAJNgAqAQF8Btk8CQUAgSZIkOgrLEiAIhhRpCJhEdX36KZk3f6FGhP5j/iLZsmWbNDG+oTl8EBgqUmP8QQJxJHD8+AmZZVKV4RtSpnRJeahhfWORkzSONYVW8Xp1aqnb0lyTGQDR3k+cPCXNHmksSZPe/XsWWjRCd7QXL16SiZOnyfETJ/Xat2nVQgrkz+s1kN27d+vk/+jRo1KrVi2ZMmWKySbAe8trsKyABEjAawJUAHiNkBWQAAlYvv9WLIBgJILJFCYHxYsVkTlzF+jL4k+jxkglYyJaq2Y1SeGD9FDByI1j8h+BayY+BXz9167fqObLyFMOs2VfBCzzX6/tVfMDJihg+nRpBZkBduzcJWfOnNF0oJkyZbRXR9kbvxDYf+CQTPttpsbKQKDMtq1bSNYs3gez3blzp07+w8LCpG7dujJjxgxJlSqVX8bASkmABEggrgSoAIgrMZYnARK4h0DGjHdflvHyHOyCyVW+rnlMUMAVssoEi1qzboNs2bpdatSoKphMJEqUKNgRcHwBJoBAYmvNfYe4FNdv3FBXlCoPPiC1jSKKK4xxvzh4prs89biuAp86fUZGjPxZGjW4Gyww7rXxDCcQwDO0eOmfsvKv1drdAgXySctHm/okVsayZcukVatWcvr0aalfv75Mnz5dUqZM6QQs7CMJkECIEKACIEQuNIdJAv4kkDNnTq0epo6hIJhkNahXW02t5y9YLEi3hm9MyhB0rVSJ4owPEAo3QjyPEX7+8FFeYiYu586f19YL5M8nDerX8cmqZTwPx1bNaeaPpzrJbGPds2XrNpk1e55J/3lAGhuLilScvNnqWnnbmdNGyTN95mwJMy4ziAcBC64aJkUktr2Vn3/+Wbp27So3jGKuWbNmMn78eFqHeQuV55MACficQALzQhHu81pZIQmQQEgRgG9j69atNdIxTB1DTfbu3S/zFy6W02fO6tBhPlyrRjUpUbyoT14qQ40nxxuZAP6b3r5jl1nxXxFxj2U291iDenUEKf4oviWwZdt2mT1nviCmSYoUyaWRSR3oi4Bwvu0la4srAaTHRNDHZX+uFFgAIFBmi0cfkdy57iqw41qfe/l+/fpFRPh/5ZVX5KuvvtKAgu7l+JsESIAEAk2ACoBAXwG2TwJBQGDbtm1SqlQpMxkpJHv27AmCEcV9CHi53GwCA+Ll8sKFi1pB5syZpFqVB6SksQiga0DcmYb6GZikbNu+Q1b8tcaYE991r0ln/NVrVq+q1ieIVk7xDwE8w7ACgHUPpFDBAkYRUFcyZsjgnwZZq18JHD5y1MRumS8nT53WdsqXK6NWXL5KXduzZ0+d8OPv/KBBg6R79+5+HQ8rJwESIAFvCFAB4A09nksCJKAEsFKGQIBIpXThwgVJnTp1yJLBpG3T5q2yfMVfJrXYJeUAHg8+UNHkHi9jIrMnC1k2HHjsCMB8eP2GzSat3zq5dOmynoQAfzBTLlumFJVJscPok1IbN22RP4x7D64JFC6ItYDrwEwBPsHr90rw/CxYtES2btuhbSHQ3yMme0v+fN5H+Xft/IQJE6R9+/ZSoEABVYJTOedKh9skQAJ2I0AFgN2uCPtDAg4lULlyZVmzZo0sWrRI6tSp49BR+K7b1urtSrN6i8BiEEwaSpcqIRUrlKfPtu9QB01NWJ1cZyL6wwf95s1bOq4sxooEk85SJWlFEqgLfeXKVVm4eKkq9tAHRHOvZYJ+YhWZE71AXZXo24XCBn97V61ZK7du3ZbEiRMba6zKUtVYZPkjUCYswIoWLSp79+4VKAPatm0bfQd5lARIgAQCSIAKgADCZ9MkEEwEXnrpJRkyZIh89NFH8vbbbwfT0LweC2IErFy1Rg4eOhxRV57cuaRChbJSrEhhv7yQRjTEDVsTgNXMjl27Zf36TQIzZUvy5c0jVc3Enz7+FpHAfx87FiZz/1gox8KOa2cypE+vQT9LlijGWB+Bvzzag1u3bmlazBUrVsm169d1X7GiRaShCZQJ9xl/yrBhw6Rbt24CZfiqVav82RTrJgESIAGvCFAB4BU+nkwCJGARmDRpkq561KtXTxYsWGDt5rcLAVgCYIUXsQLgNgFJliypCRZYTMqWLiW5c/smGJVLk9y0KYEjR47Jpi1bTXC/nca8/O69kDRpUikDCxGTThIr/xR7Etixc5csXrJczpw9px3MkCG9VK/6oFr3MNZHYK4Z0mEiCwtSs167dnfinzdPbqlXp6bk8lGQv5hGdt0oHPLmzSunTp2ShQsXSt26dWM6hcdJgARIICAEqAAICHY2SgLBR+Ds2bOSNWtW9U/GC1BaE2GZ4pkAJv9btm5Xk2JrNRElsaKIzAElShSVbIYlJbgInDh5Sif827fvikjjhxHmyJFdyhnffriHQAlAsT8BK+gnYn2cP39BO5wmTWp5oGIF4xpQmqnf4ukSnjcxZ9au3SAbNm2OUKTlyplDahoXDQRujG/p37+/vPfee9KuXTtNARjf7bM9EiABEogNASoAYkOJZUiABGJFAKv/iAEwZswYeeyxx2J1TqgXQurAzWYlePOW7XL58t2Ab2CS0awqwjKgaJFCkj17NpoYO/BGQfq+4ydOyq5de3Tif/bc+YhRIDAkJvwI6oeUfhRnEoAiYJux4vjTmJxbmRrgb45rW6lCOcmWjYo8X19ZPFcHDh7SFf9du/dGVA+3mRrVq/g8wF9EA7HYOHbsmOTJk0fdusLCwiQDs0bEghqLkAAJxDcBKgDimzjbI4EgJvDNN9/Iyy+/LM2bN5fffvstiEfq+6HhpfbQ4SM6Udyxc7dcvXotohEEHStSuKB+EL3aH0GsIhrjhlcE4IN84OBh2b1nr+zZu88oda5E1JcyZQopXqyIKnZgnpwgQYKIY9xwNgE8v/v2H5DVa9brtzUaKADKly2tQRyTJ09u7eb3fRC4cPGiWk1t2rRVsA2BywUUpQ8+UEEVpfdRrc9Pady4scydO1eGDh0qL7zwgs/rZ4UkQAIk4C0BKgC8JcjzSYAEIgjA9D9XrlyCl+EjR46Y1a9sEce4EXsCWFWEMmCnUQTsNpNIK50gakDUcQQQLJA/r+TPn09y0Dog9mD9UBL3+vHjJzRf/P4Dh+TI0WOCDBCWIH1f4UIFdeKPST+jxltkgvf7jLHquZvNYXtEIDpcd5ikI2BgkcKFmEYwlpf/ytWrsmPHLtm2fWekIJkI6FfOKFYqlCtrsjKkjGVt8VNs7Nix0qlTJ6lataqsWLEifhplKyRAAiQQBwJUAMQBFouSAAnETKB169YyZcoUgS/ku+++G/MJLBEjgRMnT5oV5X0mv/S+iAjk1knJkyXT4IF5cuc2pqe5VCHAQGQWHd9/Y3IfZib8hw8fNROSI0bRdUwQgMxVchqf/sL/WGwwloMrmdDaRoYHmKhv3LRFTdahLILARaBggfzq3gPlECxDKP8SOHf+vOw23PA3D4pQV25wiSpXtowx889jWwuaa9euGWuE7EZxe9EocXdqesB/R8ctEiABEgg8ASoAAn8N2AMSCCoCyADQoEEDE9gshxw8eJDm6j6+ukhtddD4v2K1ef+BgxEByKxmMLmAVUCOHNkkp7kG+EZwQcr9EUCANwRqDDMf/TaTf0zsXCV9unRSoEA+tcrIZ1w0UtDU2xVP0G7jPujXr595Bs9r+tP00Txnl69c0ZXsrdt3yNGjYZGYIGjd3fsnn2A71KxEbt68ZRRqR/Tv2T7zN82KpQBIYFHQPFslSxaXomo54YwgmZ07d5ZRo0bJ559/Lr169Yp0vfmDBEiABAJNgAqAQF8Btk8CQUigXLlysmnTJvnuu+/kueeeC8IR2mdIcA84ZFai765IH4308mz1EhPSbNmySNYs5pP17geB56AsoNwlgMkcAjKeNJH69WPcWU6cOBVhwu3KKbNJ0Qc3DFhc5DWWFzDzp4QegXHjxknHjh114B988IFGf48NhUuXLmuMiF0mTsRBEy/C1WUkadIkmrZO7y9zj8GaJNhifmCF/IhRghw+ctRY0BxVxRrcnixBalS4S8BVolChAgIrJ6fJ+PHjpUOHDlK/fn2ZP3++07rP/pIACQQ5ASoAgvwCc3gkEAgC1stPvnz5TAT0XUxtFo8XAS/XYWEn9KX6mIlCjW340boLAtBh5TpjpgySOWNGyWQUAshnnj59OkmbJo1tzWvdxxGX3zAlxuQLJsbnTER++GqfMekrz5w5J0gnZpkau9aZKmVKtaK4acz8z507Y6KMV5MyZcoE3aTMdczcjh2B1atXS/Xq1dUiBH7fljIgdmffLYWUoAcP3V39hkUP7klXwXOaxSickAkEn2xGgYffTgkoiCCYp06fNsq0k+o6c/z4yUgpMDFWjBFWSwVMTBNYQsAKwuluTLAKyZw5s1ownDlzRtKYv6kUEiABErALASoA7HIl2A8SCCICWM2BFcCWLVvkk08+kT59+gTR6Jw3FFgJnDQr2ljZRi56fCMlnacJL0YHs9t0adMaZUBas7qdVtKYlHXIca4fs42gWylSpLDVSzpWUaH8uHLlqlwy6RQx0deP2YYv7vnzFzVyuOtKo+uVxCQEqRctCwlMtGAxYa3uX7p0SQoWLGgsLE7r5L9o0aJSurSJ7l6qVMSncOHCtmLiOj5u+4cAfLyvGgVbhQoVfNIAUoEeNnElsDqOD55VT89p6tSpVBGQ0aSZg+tBhgzpIpR38akcQN+QseSieT6gVMPE95xxm4Fi7fTps3LduCy5i+WmZFk55Mqd05Gr/O7jcv9dq1YtWbZsmcbEadmypfth/iYBEiCBgBGgAiBg6NkwCQQ3AZg9NmzY0EwWUxnf1x0mUF3u4B6ww0aHCTOUAGd1Bdy8rGMV/J+X9yvGXzk2AlPdlCmMMsAEMUuePJkkS5pUkhlz3bufpJIkcRJJnCSx+U6s7gaJEydS5QIm2wkSJDTb+E6gE5y//w4333//s/23WVW9oyurt4xp/u1bt+XW7Vty48ZN87lx92NWTq9fvyHXzOTj6rWreiw2fcb9mMFYOWDSlNlYP2T8x/oBk/+YVh2/+OIL6d27d5TNYNzFixePUAhYCoJChQpFeQ4PkEB0BJBWEkqAMLOCjmwTJ0+dNlYCZ+SWeSaiEkywobSDkgCKuhQpkt/9Ns9oEvOMJk2SRJVYKJcQz6JR+OFZhOhzaBS4f5tnEW2g/Zv46PN23SjZ8DHPnHnuoGjDCn9USjXUB2VE5swZJZtRpsGCATFJMmfKpH8HcDyY5eOPP5Z33nlHunXrJv/73/+CeagcGwmQgMMIUAHgsAvG7pKAkwjABxLuAG3btpUJEyY4qesh3Vesck+cOEkaNXpIV/aslXR8Y4USLgWYAHhamQwUOCgSEE0dJvupLYuFf77h0pAOk36TOsybuAeY+MAK4Pjx47EeZvv27QW+4hRnEtiwYYMMGTJEsILbtGlTWwwCz92FCxeN0u6MKvHOG0UeVt3hxoJnFJP1+BTEGIGJO9yHYImAoKMZjGVCFjPxx7MYqoLVf1gBVKxYUdauXRuqGDhuEiABGxKgAsCGF4VdIoFgIXD06FFdEcWkcdasWdKkSZNgGVrQjgOZGx555BFdxYbyJirBJATp76AIwCr89RvXXVbozUr9zRu6cn/7DlYRzSo+VvLNqj5WFsP/We2/u+ofrlYAd60BEprVyARmVTKhmagn0sl6EmNBkDiR+ZjvZElhXWBZGSQ1ZsNmZdNM+jHxR6AwKAH8LYMHD5YePXrEqpkCBQrI+vXrJZ2JtUBxJoHy5cvLxo0b1arl3LlzupJu95FAAaDKOmPJA6WVtWqP5/WWibiPFX2s7OOZtCxv8A3R5xAWAeYZxLOHAIRqMWAsB/42z/Id88mfP79ygIUBLA28UarZnaU3/YMlFZ59uFTBhQgWQhQSIAESsAMBKgDscBXYBxIIYgJffvmlpkHKYkxAsZqWM2fOIB6ts4e2Zs0aad68ua5wI5XjH3/84ewB+aH3cEEoUqSIybpwONraMSnCCmCVKlWiLceD9iaAlf9p06aZ4HQFTG763TG6idh7NN71DmntEOzw999/966iEDobAUMRC2flypX8WxBC151DJQG7E0ho9w6yfyRAAs4m8Prrr0vjxo3llAlC16lTp0gpr5w9suDq/fTp06VOnToR5u3wM6bcSwCreH379r33gNueDz/8kC/8bkyc+BMT3hkzZsiqVatCevKPazdmzBiZPXu2/PDDD068lAHpc+XKlbVdKFcpJEACJGAXAlQA2OVKsB8kEKQEYJY9evRoXflfvHix9OvXL0hH6txhwawdK52IZm4JggNSPBN4+umnTX7yqAP7IfjlG2+84flk7nUUAQTRg+9/ZpPSLZTl5MmTEfnsodSNyQImlFm5jr1SpUr6E24kFBIgARKwCwEqAOxyJdgPEghiAjD/x0oaoqwjMjJMSSmBJ4Do3T179lSfdvdI3rQAiPr6wLw/KkVW1qxZVeEVH/EIou4hj8SWAEyzs2fProHaqPSKmhrigSBmAARpNbt27Rp1YR6JIIDUoJB9+/ZF7OMGCZAACQSaABUAgb4CbJ8EQoRA7dq15euvv9bI8c8++6ya1YbI0G05TAQHa9eunXz11Vce+4cAVvEdTdxjR2y68/HHH5cSJUpE6h0m/SNHjtQJZaQD/GFbArBOOnHihAZrXLhwoW37GeiOwfzfVebOnSvDhw933cVtDwQQOwKyf/9+D0e5iwRIgAQCQ4AKgMBwZ6skEJIEED0d/tNYSUJ6tKVLl4Ykh0APGvEY6tevL5MnT462K7QCiBoPInt/8MEHkQrAmuLhhx+OtM/6gRXUQYMGUaliAbHJ95NPPmly0+cQmGrjmaDcS+DAgQOyYsWKew706tVLDh06dM9+7viXQL58+TQ7CVwm7ty58+8BbpEACZBAAAlQARBA+GyaBEKRwH//+1954YUXND0VfGsZaT5+74Jdu3ZJtWrVNCp1TC3TJDp6Qm3btpVy5cppIQT7gnuLJ0Hsiw4dOsgrr7win3zyiaci3BcgAlWrVpVjx44JgrQhdz3lXgJw3/IkdAXwRCXyPgQNhYIJaReRFpdCAiRAAnYgQAWAHa4C+0ACIUZgyJAhgpU35EZGzvlffvklxAgEZrjr1q3Tyf/evXtj1QEqAKLHBJP//v37S5o0aTTGBXKmexLX/UlNPnVK/BE4cuSIBrj8z3/+Q+uL+8Tubv7vWs28efPku+++c93FbTcCuXPn1j1hYWFuR/iTBEiABAJDgAqAwHBnqyQQ0gRgPg1f6d69e+vKCJQBn332WUgziY/Bly9fXgMwQumCaxCT0AUgJkIizZs317zo0WUFqF69uqZP++mnnwRm05T4IzBgwACZNm2a+qv/9ttv8ddwkLS0efNmzWMf3XBwTx88eDC6IiF9LG3atDp+KLwpJEACJGAHAjG/Adqhl+wDCZBA0BHA6ikm/XhBh/Tp00eD0p0/fz7oxmqXAWHSD7eLmTNnyp49e5R5dOnNaAEQuytXo0aNGAs2btxYOnfuLMgg4Em2b98uf/31l6dD3OcFAVwb3Pew0rDcNbyoLuROjW7134KBiS2zAlg07v3GvQeBywSFBEiABOxAgAoAO1wF9oEEQpgA/KInTpwo6dKl0+8KFSrEyj89hJH5ZOiITg1/dJhIIxI64gK4Cy0A3In45/fatWulTJkyAn90XAtK3Ahcvnw5yhNatWolCGKHT5EiRaIsxwP3EggPD1fXlnuP3LsHsVy+/fbbew9wjyqfgIEWALwZSIAE7EKACgC7XAn2gwRCmEDr1q01DdeDDz6oL+q1atWS9957TwMFhjCWeBk6glQ98cQT8ueff8qGDRsEvtKpUqXStmkBEC+XQFOEWRHCYZlBiT0BpLGEiTVW+q089e5n58mTRzJmzOi+m79jIIC/CbE17Ycl0bhx4/g32wNTugB4gMJdJEACASVABUBA8bNxEiABiwBWpJctW6Y+0pgMIbhayZIl1X/XKsNv3xJARoD169dHVAoTaaziISr6N998I9G5B0ScxA2vCWCVGikFX375ZXn99de9ri+UKpgwYYJgpRqTVUZZ9+2VdzX/R4YEWGfBUiV16tTqSjR9+nRBjACsbCO16IIFCyRFihS+7UQQ1JYoUSIdxd9//x0Eo+EQSIAEgoFAAvMfZ3gwDIRjIAESCB4CeJl/8cUXdUUao2rSpIl89NFH+gIaPKMM7Ehgdg6Tc6yaIs1Xx44dA9shth4tAax0T506Vd5991156KGHoi0bSgcxCX3jjTekYcOGqrQKpbH7e6xLlixR1ywoZ61VbH+3GYz1d+vWTYYNGyZDhw7VFLjBOEaOiQRIwFkEaAHgrOvF3pJASBBA1HTk5R48eLCkT59eo6xXrFhRUwYuX77ctgyctMIDU3PLZHrnzp22ZcqOiZw7d04tY5YuXaqZM0KFydWrV6V27dpqiTJnzhyPw0YWBgRQhMUKxbcEwB5WQZz8e8f11q1bWgFTgHrHkWeTAAn4jgAVAL5jyZpIgAR8SABmk7ACgJk60kzB7PT333+XmjVr6qRg1KhRcuXKFR+2eH9VYdI/e/ZsTQeHGAbXrl27v4ri+aw2bdrI22+/Ld27dxcEYqTYlwACZML8GlK3bl399vQPXGecpITyNAbXfatXrxYoPRCMcsSIEa6HuE0CjiFw48YN7SsVAI65ZOwoCQQ9AboABP0l5gBJIDgIICDdwIEDdaUPK6IQKAXatm0rTz75pCBwYJIkSeJlsPCcgoUCshf88ssvkXyPYer5/PPPx0s/2EjoEMAq4uHDh6VgwYIeB71x40Zp0KCBPgMw3XZCxHv4j//888/Svn17qVSp0j3jgjIN6ROxwo9y2KaQgNMI4P5GrAoEScQ2hQRIgAQCTYAKgEBfAbZPAiQQJwIIOIUXqZEjR2rQQOtkKAPq1aun/tHwBy5WrJgkSJDAOuz1N9LlYWKFQFdY8fcUcKxs2bKyatUqQWR9CgnEJwHEyEB8AAjM4V966SWPzeM+RnDH5MmTezwenzsLFSok+/btk6xZs8qJEyfis2m2RQLxRgD/Ly1atEjmz58v9evXj7d22RAJkAAJREWACoCoyHA/CZCA7QnAjx2uAFOmTJEtW7ZE6i8UAqVLlxZMyvHJmzevZMuWTScb+HaPVg3zaVgWwNwYK62HDh2SHTt2yLZt22TdunUSFhYWqX6kFmvZsqXWiYkXJlSwCihVqlSkcvxBAvFBYP/+/WoNAysYPA85cuS4p9lBgwapuweeBay+R+XbfeDAAcmePXu0SoKLFy9qukgrwrl7Y1CSwb0EbjGwkvFUDqv+eLaKFi0qjEPhTtDev2GRxdSKsbtGxYsX1/sb/5eUKFEidiexFAmQAAn4kQAVAH6Ey6pJgATijwBS182bN0/mzp2rK/VY6YxOEiZMqOnDUCY2yVDwsouo+VjNgYVB+fLlNYgegmThxe7jjz+Wt956K7omeYwEAkoACqtp06ZpH5AFAoE13eWTTz7R+xiuBps2bdJJvnuZ4cOHazRzTGZg8ZIyZUr3IvLwww+LFbhvw4YNGkzOvdDJkyc1rgeyGnhSWLiX5297EIDLFVwyunTpIj/++KM9OmXjXkDRBss1KJgR1JZCAiRAAoEmQAVAoK8A2ycBEvALAaxQYQKDD6wDDh48KFAKnD59Wl/ErMjMVuNQCCDXNSb6uXLlUosB+FGXLFlSLQgKFy5sFY34Rmq2nj17qr812mCQpwg03LAhAUz6e/furcqrL7/80qOLDPzsoUSDwPceq5fu0qxZM5k5c6buxgq+FaDQtdzo0aOla9euqmRYvHgxnw1XOA7fTpMmjVy+fFlTBJ4/f97ho/Fv9xGoFtZosDhDVgsKCZAACdiBABUAdrgK7AMJkEC8E4ACAB+s/mPiHtcAgljRyZ8/v0DRgOwEWPG0uyCoGnJSX7hwQYYMGSI5c+a0e5fZv3gmgBX9N998U5CK88MPP/TYOmJh9OjRQ837v/vuO4+KBJyI5yuuz5XHBrnTVgSQnhU57RFnAn9PKFETgBINSmQokHfv3h11QR4hARIggXgkQAVAPMJmUyRAAsFD4P/+7/80jR5yZWOF0wkCX+wnnnhCu4oUgAgcRyEBEiABEvAPAWSKadeunaaJ/e233/zTCGslARIggTgSSBjH8ixOAiRAAiFPAGadMP+H9OvXzzE84POdKlUqDchWo0YNx/SbHSUBEiABJxJAsE1ImTJlnNh99pkESCBICSQO0nFxWCRAAiTgNwJjxozRWALVqlVzVFonBG1DdoPr16/T/N9vdwcrJgESIIG7BKgA4J1AAiRgRwJUANjxqrBPJEACtiYAv2dIVLnW7dx5pu6y89Vh30iABIKJABUAwXQ1ORYSCB4CjAEQPNeSIyEBEogHAuvXr9fI5phII/VgsmTJ4qFVNkECJEACJOAkAsg4kzVrVkmePLkGXmVATCddPfaVBIKbAGMABPf15ehIgAR8TODXX3/VGhFMj5N/H8NldSRAAo4gcOfOHTlz5owj+hqoTi5atEizzCDeCif/gboKbJcESMATASoAPFHhPhIgARKIgsC0adP0SJs2baIowd0kQAIgsG/fPjl69KjCWLdunSB4JsX5BBBDpFy5cpIlSxb55ptvnD8gP41g4cKFWnO9evX81AKrJQESIIH7I0AFwP1x41kkQAIhSGDHjh2yc+dOyZQpkzgpij7yse/atSsEr1hoDDk8PFzOnz8f58GmTZtWsJIbnezZs0cne9GV8XQMk/1WrVpJ4sR3Qw1t27ZN+vTp46ko9zmMwP79+2Xr1q26uj19+nSH9T7+uksFQPyxZkskQAJxI0AFQNx4sTQJkEAIE5g/f76OvkmTJppKzwko/v77b6levboUK1ZMunXr5oQus49xJIDJf+nSpeN4ln+Lv/fee9K4cWPJli2bNvTYY4/JggUL5M8///Rvw6zd7wSKFy+uAVCRVvSdd97xe3tObADxYbZv3y6pU6eWypUrO3EI7DMJkEAQE2AWgCC+uBwaCZCAbwlYk5eaNWv6tmI/1oaVWJhfQ5YuXerHllg1CdwlcOLECRk9erSuEltMEiVKJH379pV+/frJvHnzrN38diCBBAkS0PQ/hus2efJkLdGwYcMIK5gYTuFhEiABEog3ArQAiDfUbIgESMDpBFasWKFDwIq6UyRNmjQyYMAAqVu3rnz55ZdO6Tb7GUsCSDP2yCOPyKlTp6R27doyfvx4PfP27du6OlugQAHJkCGDlCxZUsaMGXNPrZcvX5bnnntOihQpIij7wgsvyNWrV+8p57pj9erVgolN9uzZpUyZMjJ06FDXw/Ljjz9K06ZN1Ufc9UCHDh1kzZo1dEdxhcLtoCRgPYft27cPyvFxUCRAAs4mQAWAs68fe08CJBBPBGBmDd/XVKlSSalSpeKpVd8006NHD4E/KkyyKcFFAObYI0eOFKSlnDhxojRr1kwH+Pbbb8uBAwdkw4YNqhwYNGiQ4D5AHAtXad68uVSrVk2gSFi5cqUgXgQUClHJ2rVrBZMa1A8z55kzZ8qECRMirQjPnTvX4zOSMGFCKVGihOA4hQSClUBYWJgsX75c0//h+aKQAAmQgN0IUAFgtyvC/pAACdiSAIKhQbBSiokMhQTsQADpxRCNHSb2yDmeMmVK7Rb87T///HNJly6dmiBjxR7KAUzyXQXBLJ955hmdrMBf//vvvxfkL7fiXbiWxTbM+FFv/fr19TnImzevWhbAygTKAwhW+RFzwpPAEgEWBBQSCFYCUMQh9gpixSAGAIUESIAE7EaAb7F2uyLsDwmQgC0J7N69W/tVuHBhW/aPnSIBVwKYhOfMmVN3Xbt2TSfliMR/6dIl12LyxBNPRPoN/26s8EOB4C7IGIA4GC1atIh0CK4AUDQcOnRIkCIObcAywZPAAuDkyZOeDnEfCQQFgZ9++knHAZcXCgmQAAnYkQCDANrxqrBPJEACtiMAc2pIwYIF9Zv/kICdCcA8f8SIEfLzzz8L4gFgQp48efJ7upwvX7579uXKlUsWL158z/4jR45ofABPLjA3b95UywGkFoSgDk8CZcHFixc9HeI+hxE4e/as3lOW1YnDuu+X7iJODIKuwiqnZcuWfmmDlZIACZCAtwSoAPCWIM8nARIICQLnzp3TcWbKlCkkxstBOpdAeHi41KlTR7p3766ZHzAZgfTp0+eeQZ05c+YeM2Ws4KdPn/6estiHyd6uXbvuOWbtgLIBsm/fPo+pCffu3avxCqzy/HYmgVmzZqklCCw/EBfCkyLJmSPzrteDBw/WChBYM1myZN5VxrNJgARIwE8E6ALgJ7CslgRIILgIIAggxNPEyK4jRdR3+HTDH5USOgSweo+J2WuvvRYpEv/OnTvvgYDgkO6CVUxPJvyoE0Ewt27dGukUpJrs0qWL4BlJnDixZM6cWeBu4EkQhBBWABRnE/jjjz/UsgQKpFWrVjl7MD7qPdJfwv8f8TiQTYNCAiRAAnYlQAWAXa8M+0UCJGArApbvtGXibKvOeejM1KlT5fHHH9cUb8OGDfNQgrucSmD79u0yduzYiO4nTZpUJ2PWDpj6436FWb4lv/32m0bfv3HjhrVLv6Eggu++JTNmzJA5c+bovWPtc/1++eWX5dVXXxVM+iGwNujXr59mBLCUYzVr1oxWAVCrVi3XKrntQAIvvvii1KtXT+DnbmWecOAwfNplpMPEM/foo49Knjx5fFo3KyMBEiABXxKgC4AvabIuEiCBoCWA4GgQTHicIK6ZCrAiRQkeAoiij7R+jz32mA4Kq/IITomUYx07dtTJ+0MPPSRlypTRz9GjR6VKlSp6zgcffCD58+eXtm3b6rlffvmltGnTRlftL1y4IMePH5fff/9dLQg8EevZs6emFUQ0f9QP829sw9rEEkwIoYByFygN4BqAvlGcTaBQoUIeA0U6e1T333tYvyATBuT111+//4p4JgmQAAnEA4EE5mXWGW+z8QCDTZAACZBAVASeeuopGT16tIwaNUqefPLJqIrZav+kSZM0aBsivVsKDFt1kJ3xGQFE6IdlACb3VuoxRNvHp2jRogIrAcipU6fUB99VKYTXAJjmp0mTRnLnzh2rPmGlE7EAYO7vbtIPKwMoJGBJAOWAJZ9++qnADQHBCSkkEEwEkB7zww8/lEaNGqmlTTCNjWMhARIIPgJUAATfNeWISIAE/EDg2Wef1YkLTKaxTSEBEoiawA8//KAToXHjxmmhy5cvC7IHLFq0SAoUKBD1iTxCAg4jgDgIuKfhdoP4GVWrVnXYCNhdEiCBUCPAGAChdsU5XhIggfsigJVOCFZQKSRAAtETgJIMZtFbtmzRgnBZ6NWrFyf/0WPjUQcSgGULJv+PPPIIJ/8OvH7sMgmEIgHGAAjFq84xkwAJxJmAZeYMH2kKCZBAzAQQUNDyMkTgwBQpUsR8EkuQgIMIwKVl4MCB6mLVv39/B/WcXSUBEghlArQACOWrz7GTAAnEmoClADh27Fisz2FBEghlAkmSJImIPZAyZUrGoQiym+HWrVsSFhYWZKOK23CQDQHxMLp27SoVK1aM28ksTQIkQAIBIkAFQIDAs1kSIAFnEUAgNQgCrVFIgARIIJQJXL9+XcqVKyc5c+aUzz//PCRR/PrrrzJ//nwNhPnJJ5+EJAMOmgRIwJkEqABw5nVjr0mABOKZAKKZI7UeTD6x8mVXQd/eeOMNee655+Ts2bN27Sb7RQIk4GAChw8fjlCGzp4928Ejub+uX7x4MSLdH2IAZMyY8f4q4lkkQAIkEAACVAAEADqbJAEScB4B+C8j9zUm2Ha2ApgyZYquyCFbAXxTKSRAAiTgawJFihSR3r17a9C7999/39fV276+l156Sd0fqlevLk8//bTt+8sOkgAJkIArAQYBdKXBbRIgARKIhkCVKlVk9+7dsmTJEilbtmw0JQN3CC/m8L2GogJp1ygkQAIk4A8Cn332mT+qtX2dY8eOldGjR0uqVKk0NWyCBAls32d2kARIgARcCdACwJUGt0mABEggGgL169fXowsWLIimVGAPVahQQXbs2CGbN2+W9u3bB7YzbJ0ESIAEgojAgQMHpFu3bjqiAQMGSLFixYJodBwKCZBAqBBIYFL0hIfKYDlOEiABEvCGwMGDByV//vySIUMGOXnypCROTCMqb3jyXBIgARJwCoE7d+5InTp1ZPny5dK6dWuZNGmSU7rOfpIACZBAJAK0AIiEgz9IgARIIGoC+fLlEwQDPHfunEZ/jrokj5AACZAACQQTgVdffVUn/7lz55bhw4cH09A4FhIggRAjQAVAiF1wDpcESMA7Ah07dtQKkAKKQgIkQAIkEPwEhg4dKoMHD5ZkyZLJ+PHjGfU/+C85R0gCQU2ALgBBfXk5OBIgAV8TQBDAokWLSrp06TQKNLIDUEiABEgg1AhcvXpV1q5dK5UrV5bkyZMH7fDnz58vDz/8sNy+fVuD/z3xxBNBO1YOjARIIDQI0AIgNK4zR0kCJOAjAoiyX61aNblw4YK+DPqoWlZDAiRAAo4i0KhRI6ldu7Y0bdrUUf2OS2d37twp7dq108n/22+/LZz8x4Uey5IACdiVABUAdr0y7BcJkIBtCbz88svat4EDB9qqj1999ZWapsJXlUICJEAC/iSwbds2rX7r1q3+bCZgdR86dEig5EDMl1atWsmHH34YsL6wYRIgARLwJQG6APiSJusiARIICQIwBS1QoIAcOXJEZs6cKY888ogtxp03b145fPiwIC/19evXJWnSpLboFztBAiQQfARmzZolI0eOlK5du+pEOZhGeOLECalVq5bA5Qvfs2fPlpQpUwbTEDkWEiCBECZAC4AQvvgcOgmQwP0RQPq/1157TU9+5513xC7ZVF944QUNUoUXck7+7+/a8iwSIIHYEYDic9y4cUE3+T9+/LjUr19fJ/+VKlWSGTNmcPIfu1uCpUiABBxCgBYADrlQ7CYJkIC9CNy4cUMQDwAr7mPGjJHHHnvMXh1kb0iABEiABOJEAFZd1uS/TJkysmDBAsmcOXOc6mBhEiABErA7AVoA2P0KsX8kQAK2JIB0UB988IH27a233pIrV67Ysp/sFAmQAAmQQMwEdu3aFWH2X7FiRVm4cCEn/zFjYwkSIAEHEqACwIEXjV0mARKwB4GnnnpK8KJ48OBB6du3rz06xV6QAAmQAAnEicCKFSukevXqcuDAAalataog9V+mTJniVAcLkwAJkIBTCNAFwClXiv0kARKwJYENGzZoHuy///5b8BL54IMP2rKf7BQJkAAJkMC9BCZOnChQ5l67dk2aNWumcQ0Y8O9eTtxDAiQQPARoARA815IjIQESCACB8uXLS8+ePQUKgMcff1wuXLgQgF6wSRIgARKIXwJXr16VVatWya1bt+K3YR+1hr/ZCOLarl07nfw///zzMnXqVAb88xFfVkMCJGBfAlQA2PfasGckQAIOIfD++++rK8CePXukc+fOtskK4BB87CYJkIADCdSuXVuqVKkiHTp0cFzvz549K82bN5ePP/5YkNVlwIABMmzYMEmUKJHjxsIOkwAJkEBcCVABEFdiLE8CJEACbgSSJ08ukyZNkowZM8q0adPk008/dSvh/599+vQR9KN3797+b4wtkAAJhDSB27dvy5YtW5QB3KCcJEuWLJFy5crJrFmzNMjf3Llz5ZVXXnHSENhXEiABEvCKAGMAeIWPJ5MACZDAvwR+//13adq0qSRMmFAVAdiOL0mTJo1cvnxZUqVKpd/x1S7bIQESCE0C48aNk7Fjx0qPHj2kQYMGtocAV4X+/fvrqv+dO3ekRo0amsI1b968tu87O0gCJEACviRACwBf0mRdJEACIU2gSZMmAncAvFy2bdtW00jFFxDEIciQIYPGI4ivNtkOCZBA6BKA6T985p0w+V+/fr0Ga4UCIDw8XH3/Fy9eLJz8h+79y5GTQCgToAVAKF99jp0ESMAvBF588UX53//+J6lTp5Z58+ZpWim/NMRKSYAEgprAlStXZO/evVK2bNmgHqe/Bgd+mPR/+eWXAreFwoULy4gRI6RWrVr+apL1kgAJkIDtCdACwPaXiB0kARJwGoHBgwdrMECY5MMqAJGyKSRAAiQQVwIvvPCCBhiFUvHMmTNxPT2kyyMuS4kSJTQmCyL+w89/48aNnPyH9F3BwZMACYAAFQC8D0iABEjAxwQSJEggP/zwg7oBnD9/XurWrStTpkzxcSusjgRIIJgJDBkyRH7++Wd1KYJFUZEiRWTgwIFy8+bNYB6212P766+/pE6dOvr39/Dhw/LAAw/IypUrNdJ/ypQpva6fFZAACZCA0wlQAeD0K8j+kwAJ2JIA0kkhQNZzzz2nOaYRE+Crr76yZV/ZKRIgAXsRWLFihbz22mvaqc8//1waN24s586dk1dffVWKFi2qCkaYtFP+JYCsBO3atVOXK0T6z5w5swwdOlSgEKhcufK/BblFAiRAAiFOgAqAEL8BOHwSIAH/EUB+6e+++04++eQTDTyFQH1du3YV+KVSSIAESMATgRMnTujqNaLWv/7669KrVy+ZPXu2zJgxQ8qUKSMHDx7UvyMwb8fflxs3bniqxu/7jh8/Ltu3b/d7OzE1gAB/ULAiTsLEiRMlRYoU8vbbb2vsBLhQICsLhQRIgARI4F8CDAL4LwtukQAJkIDfCIwfP17jAly/fl1X8H755Rc1TfVbg6yYBEjAcQQwmW/UqJEsXbpUzdj/+OMPgSLREkSwR/q9fv36ya5du3R3tmzZ5OWXX1ZroyxZslhF/fqNwITlypVTZeaPP/4oXbp08Wt77pXDpx8KkQEDBkRkW0mePLkyePPNNyVnzpzup/A3CZAACZDAPwSoFuWtQAIkQALxQKB9+/ayZs0aXaXCi3v16tU1H7UvzHixCpcvXz71ET5w4EA8jIZNkAAJ+JoAJvedO3fWyX/u3Ll1ou86+Ud7iC/SsWNH2bZtm/z6668aIBAWA++8847gnMcff1zP93Xf3OvbuXNnhCUTVuDjS44dO6Z/NxEPoUWLFjr5T5MmjaY/3b9/vwwaNIiT//i6GGyHBEjAsQRoAeDYS8eOkwAJOJEAVvjeeustXbnCCz/MeL/++mv18b3f8Xz88cc6AcD5yECAiOHxJViJGzlypJw+fVp69+4dX82yHRIIOgJ4fr744gtJmzatLFu2TM39YzPI+fPna3DAWbNmacBAnFOwYEF54oknVCGAmAG+ljt37ujfsSNHjghiFOTKlcvXTUTUd/HiRZk2bZoqPObMmRNpjD169JBnnnlGmUWcwA0SIAESIIFoCVABEC0eHiQBEiAB/xCAaW/37t1l9+7d2kDTpk01V3WxYsXi3CBW43B+0qRJ1Vc4b968ca7jfk6YO3euvPHGG5paq2HDhjJv3rz7qYbnkEDIE/jmm2/UjD9JkiT6DNevXz/OTBDxHjEBYJJ/9OjRiPMRN6Bly5a6Yl6xYkW1Iog4aNONsLAwgUJj+vTpgkk/XKcg+BuHlf9nn31WXSXo32/TC8hukQAJ2JoAFQC2vjzsHAmQQDATQJAvmKz2799fLly4IMgc0Lp1a51UI3WVXQUv5Ojz8uXLI7qYLl06OXv2LANuRRDhBgnEjgCyhWC1HtY0o0eP1u3Ynem5FOpZtGiRIM7IpEmT9G+LVTJr1qzSoEED/SA9aaFChaxDAf3G3z/EPUC/FyxYIBs2bNDAqegUJvm1a9eWDh06aLA/RPenkAAJkAAJ3D8BKgDunx3PJAESIAGfEDh58qS89957unJn5fjGyznSgDVp0kSwKhhouXTpkowZM0awUrl161btTqZMmaRPnz66D6uPSMNVqlSpQHc14O3DZBlm3BQSiIkAgoN26tRJzdqRLQTPky8FSkZMqqdOnaqr6XhOXQWT6apVq2qaPFgK4AP3AX+urOPvHWIYbN68WeOirF69WmDFBMWFJSlTplQlRbNmzaR58+aSI0cO6xC/SYAESIAEvCRABYCXAHk6CZAACfiKAMxeBw4cKMOGDYtYtcMkG7mtMUmoWbNmvJrvQhkBVwUEG8NK4tWrV3WoiLCNfOTdunWT1KlTa9+wiglrBvjkhrIgxSOio8N82R++16HMNtjGjmcKAf0QCBRR/d9//32/DxETbcQMwAcWPAgg6C6Ipl+gQAFVBOAbk29kGsAnQ4YMkipVKn3ukW4PQQnxgVy7dk0/+Dtx5swZ/Zw6dUrdEQ4dOqTpC/fs2aP73dtMliyZPPjgg5r5AMrPGjVqCPpBIQESIAES8D0BKgB8z5Q1kgAJkIBXBLDaPnz4cPnpp590lcyqDBNv+AbjU69ePcmfP791yCffWIFDhgKY4sK/Hx+sZkPwkg8zXOTVRs5t1+jksAxA9HH0Cea7oSxY0YWpMiZL4Ifc5BQScCcwZcoUvU+wQv/uu++qS417mfj4jawhK1euFETyx4o8rHjcrQR83Y/06dNr8FNYC1WqVEnToeI5gX8/hQRIgARIwP8EqADwP2O2QAIkQAL3TQAv5PDlxQr7wYMHI9WDYH94cUYmgZIlS+o3VutgNYBVuqgECgYECUMEb3w2bdoka9eu1UkAjrlK+fLlpU2bNjrBx2qgJ4H/LvKPQ4Fw/PhxCWUfXZgsz5w5UzFhogNLgGrVqnnCxn0hSgBZM7p27aor/8gIgiwedpLLly/Lvn37BGn1oCDAMw1LAXzwrOM4/k7g46oIhEUAPjDfh6UA/g7gg79JSFOKj2VRYKfxsi8kQAIkEGoEqAAItSvO8ZIACTiWAHzvFy5cqKvsixcv1qB7UQ0GJrUZM2bUF3KsMsLMGB+Y6eIFPirJnj27uho0atRIHnrooVhbGcBPd8aMGfLZZ5+FbDpA+DYjHRqsJRo3bqw8oIiB/zWyJFBIAM+H5ecfyJV/b68ElIKTJ0+WKlWqqAWBt/XxfBIgARIggfgjQAVA/LFmSyRAAiTgMwKWuT6Caa1YsUJdBpAqCytvMNu3/PU9NYigglixt1bkkHoQwb8qVKgQ6wm/e71Y6UYqQqzwwc/Xn0HE3Nu2y+8PPvhA/biRcm3ixIny3HPPaWBHKGPgzgF/b0poEggPD5eePXvK119/rc8GYn289NJLjoWR37gfwSIJ9zbiXiCDCYUESIAESMAZBBI7o5vsJQmQAAmQgCsBTLCLFy+uH5jowjQX8uKLL6pP8Y0bNzTYFhQBUBagPExzsSKNlH2+locffliDhsF0GCuDiBMQSoJJEIIgQl5//XWdEP3www/KesCAAfLYY4/Jjh07NNibFTQtlPiE8lhhdfP000/LuHHj1M8dqf7at2/vaCTffvutDB48WF2DOPl39KVk50mABEKQAC0AQvCic8gkQALBRQArccjtjUn/nDlzNB5AIEaISQGCBCImAQKKhdLEwDLtRqYGBFF0FaRORErHO3fu6MQP1gDwlaYEPwEE1INFyLp16yRNmjTqDoIgnhQSIAESIAESCBQBKgACRZ7tkgAJkECQEUCMAVgl7N27V77//nt59tlng2yEnocDCwyk/ENQNET+R/wEd5k9e7ZGfYd7RuXKldVKInfu3O7F+DuICCxZskRTeCI2ROHChWXatGkBU84FEVYOhQRIgARIwEsCCb08n6eTAAmQAAmQgBJARPAPP/xQtxHoDDnAQ0EwVkz+W7Ro4XHyDwZwkUCshoIFC8rq1as13sLvv/8eCnhCcowwj0fgR0z+ERAS1xyZOigkQAIkQAIkEGgCVAAE+gqwfRIgARIIIgIIdIcJz5kzZ+SVV14JopF5Hsr06dNl1KhRkjx5cvnqq688F/pnLyaAq1atkiZNmsjp06c1aCLSwMFyghIcBHDft2rVSnr06CHIvtG7d29NBYmUkBQSIAESIAESsAMBugDY4SqwDyRAAiQQRASQP7x06dKaieDHH3+ULl26BNHo/h0KTP/Lli2rlg6Y/MPPPzaCiPCffvqp9O3bVyf/iBvwyy+/SN68eWNzOsvYlABSdD755JNy9OhRwYR/+PDhIRcM06aXht0iARIgARJwIUALABcY3CQBEiABEvCeAFIBDhkyRCvq3r27BgT0vlZ71XDz5k0N6Ac3h4ceekheffXVWHcQWQDefPNNwYQRcQCWLVumChNMGCnOI4B7AZYcMPnH5B8KnY0bNwbt5B9ZRVauXBmRecR5V4w9JgESIIHQJkAFQGhff46eBEiABPxCAKv+zzzzjCAFWrNmzXRi5JeGAlRp165dNdo/JvAjR46U+0nth4ni+vXrdaKIGAL/+c9/1H0CkeMpziCwfPlyKV++vHzyySd6D7z//vuyaNGioLbmQKrRatWq6biReYRCAiRAAiTgLAJUADjrerG3JEACJOCRAHzRs2XLJo888oj6HnssFM87YQVQo0YNOXTokAbBO3/+fDz3wD/NvfHGG4Jc7qlTp5YZM2ZI9uzZ77uhzJkzy4QJE+TXX38VbCOLANwnhg0bJlhppdiTABQ2mAjXqlVLtm/frtkvkP6xX79+QZ/+ctOmTXpRkH4UWS0oJEACJEACziJABYCzrhd7SwIkQAIeCXz77bcacRyR5Tdv3uyxTHzvRGA8KCYQ/G7Lli2C/OdOzwzQq1cv+fzzzyVJkiQybtw4KVeunE+wdujQQbZu3SqtW7fWSVW3bt00XSBMrSn2IgCFDe7p//3vf4LMF4jlsGHDBl0Vt1dP/dObgQMHanrD7777TrJkyeKfRlgrCZAACZCA3wgwCKDf0LJiEiABEog/ApMmTZKnn35aKlWqJHPmzJGkSZPGX+MxtAS/6AYNGsjOnTulWLFiusrttIB3iOiOeAbff/+9ssUk8NFHH41h5Pd3GNfy9ddfV8sJuBZ07txZgwZmzZr1/irkWT4hsGbNGg30iJgNkCpVquj9AIsNCgmQAAmQAAk4hQAVAE65UuwnCZAACTiYgJUPHSulWDUcP3681K1b1xEjQmq3Nm3ayOLFiyVlypTa96ZNm/q171evXpX/+7//U2sD+Fmn7/Y0jQAAQABJREFUTZtWevbsqYoBuB5Q4o8AFFhvv/22un0ggwMUMf379xfEgUiYkIaU8Xcl2BIJkAAJkIAvCFAB4AuKrIMESIAESCBGAhcuXJDHHntM4KYA02lMopAnPVGiRDGeG6gCCxYs0BX4I0eOSM6cOeW3335TK4v46s/evXs1wwBiDUCgPEHEebgIwMWC4j8CSPOIdI1wr0Ewy2TJkum1gDIAChkKCZAACZAACTiRABUATrxq7DMJkAAJOJQAAtu9++67urqNISCa+E8//SRFixa11YguX74s7733ngwYMECw6otghrBagBIgEIIAc5h4WubnyD6AVILItJAiRYpAdClo2wwLC5PPPvssYuIPN4y2bduqMgApLikkQAIkQAIk4GQCVAA4+eqx7yRAAiTgUAKIdv/ss88KVtYRr+Dll19WxUC6dOkCOiJM9seMGSOI9H/s2DG1VEBkd6y628FSYdasWfLOO+9o0DmAQuaAl156SSPSY5ty/wQQo2LQoEEyYsQIuX79uqb1a9WqlUb2L1u27P1XzDNJgARIgARIwEYEqACw0cVgV0iABEgglAjAJQBR9THhgmUAJrAIfgfz9vTp08crCrSP4HsfffSRbNy4UduuWrWqIJVhxYoV47UvMTUGJcWUKVN0lfqvv/7S4ohNgCCQCFSICPWU2BEASwTNRGR7fOM3VvyRjQGKnzJlysSuohAphSweuOeQ0QP3HIUESIAESMB5BKgAcN41Y49JgARIIEoCmMjevn3bVlkAouzsPwfWr1+vE/9FixbpnjRp0qhpOya0vkqzF1UfEJxw1KhRMnz4cNm1a5cWy5UrlyoCnnrqKZ0MRnWuHfYvWbJEAwXOnDlTJ6/oE9wV/vOf/2iqNroHeL5KJ06ckF9++UWQyg4r/xBMaJ988km1RqES5V5ud+7cUVedffv2SbNmzTTF572luIcESIAESMDuBKgAsPsVYv9IgARIIJYEEK0ePvUwq588ebI8/PDDsTzTHsXmz5+vftbz5s2L6BAUAPC/btKkia7EY3XWWwEfTJinT5+uKQmR4g8C/2741Xfp0sVRChT0fdu2bTJ48GCd1F68eBG71IqiU6dOGngRSgFfsNOKHfoPsikgiOPIkSN1tR+KMghSUr744ovy3HPPSYYMGRw6Ov93G/zABwER4RJhWcr4v2W2QAIkQAIk4EsCVAD4kibrIgESIIEAEsCkFitzEKQow6q2EwWpAn/44QcZO3asQKlhCSLgI/d6pUqVpHz58jphx+QtqkkbJiwHDx6U/fv36yrvqlWr1Hx5z549VpXq44+UfuAFJYMd/PwjOncfG1euXJFx48bptV+5cmVEDbBqaNeunXTo0EEZhooyAOkUEW9i6tSpMm3aNDl//rwySZIkieC6Q9mDZ8bp1z3iQvt5Axk84H4CVxM8gxQSIAESIAHnEaACwHnXjD0mARIgAY8EMNnBajkmvTBrx0TZyXLz5k1dqUXgu9mzZ8uBAwc8Dgfp8GC+DXN3TOzAAVH88e1JUqdOLY0aNZLmzZvrJBB53YNRNm/erBYByF4AJYglUAbAOgSfhg0bxnu8Basf/vpG8Eb482PCj8k/VqwtwTPRuXNntYpg0ESLCr9JgARIgARCiQAVAKF0tTlWEiABEnAwAfger127Vj9btmxRRcehQ4fEMnl3H1rixInVvBum/YUKFVKFCCwISpcuHXIrvrB+gCIAn8OHD0egwso33EagCKhevbog8CFiMDhJEMcB8SMWLlyoH8unH2OApQOuecuWLfVTrFgxJw2NfSUBEiABEiABnxOgAsDnSFkhCZAACZBAfBJAyjas9mOlF/78sAbAKj+jlHu+CvDdtuIfwE3AioGA0gkTJlQFCZQBlrIEAfHswhIm/OvWrVMlkPW9e/fuSAOFAqN27dpq4fHoo49Kjhw5Ih3nDxIgARIgARIIZQJUAITy1efYSYAESIAEQpIA0t5BnnnmGVmwYIEgm8Cff/6pk2u4XrgKlAIFCxZUxQCUAfny5Yv4IAaDr5UD586dk7CwMNm7d69gco8P4jbgG+4t7oL2a9asKfXq1dMPzPxh/UEhARIgARIgARK4lwAVAPcy4R4SIAESIAESCFoCsJjAhB4CtwrEULAEx+BmYSkD4GoBk3pXKwGrrPWdMWNGyZQpk+AbARnxnT59ekmWLJlOxDEZtz6IvA9LDXwsq40LFy4IzPiRmg/f7goIqx18I84DMkNgko9PxYoVpVSpUpzwu0LiNgmQAAmQAAlEQ4AKgGjg8BAJkAAJkAAJBBsBrP6/+uqrOqwBAwbIK6+8Eu0QMfmHEgDKgF27dukqPFbiEX8BH2Rb8KWkS5dOsmfPrlkeihQpIoULFxZ844N4DozY70vasasLCpvRo0cLYijUqVMndiexFAmQAAmQgC0JUAFgy8vCTpEACZCAdwRgQr19+3Z9WeeEyTuWwXS2tfqP+wMC/3h3K4C4jDc8PFxTNSJdI0z38Tl79qym28NKPlb8XT+wBMAqvvWB+T589rNlyybIxoBvWA5Q7EUACiMojuAOgr8rRYsWtVcH2RsSIAESIIFYE6CTXKxRsSAJkAAJOIMAouKXLVtWTp8+LS+99JJ88803zug4e+l3At9++63611sNQRGAfTFZAVjl3b8RZR/p9JhSz51McP2GEgcChc+dO3eCa3AcDQmQAAmEGAFaAITYBedwSYAEgp8AzLLz58+vL+vI9f77778H/6A5whgJuK/+Wyd4awVg1cPv4CVw+fJl+f7776V48eKCvykUEiABEiAB5xJI9L4R53afPScBEiABEnAnAB9qRGpHMLaPPvpIA7S5l+Hv0CMwZMgQmThx4j0Dx+QuS5YsUrVq1XuOcQcJgEDSpEn1/kA8BgoJkAAJkICzCdACwNnXj70nARIgARIggRgJRLX6b51IKwCLBL9JgARIgARIILgJJAzu4XF0JEACJEACJEAC7r7/7kSsWADu+/mbBEiABEiABEgguAjQAiC4ridHQwIkQAIkQAKRCMS0+m8VphWARYLfJEACJEACJBC8BGgBELzXliMjARIgARIgAY3yb6X9iw4HrQCio8NjJEACJEACJBAcBGgBEBzXkaMgARIgARIggXsIxHb13zqRVgAWCX6DwN69e+XDDz+UBx54QF588UVCIQESIAESCAICiYNgDBwCCZAACZBAFASQtxv+3wcOHJDu3btL3rx5oyjJ3cFIYObMmZoFIlOmTBHDu3Hjhhw8eFB/I1tEsmTJIo5hA+e0adMm0j7+CE0CI0eOlP379+unWbNmml0kNElw1CRAAiQQPARoARA815IjIQESIAESIIEYCXz55ZfSq1cvLffFF19Iz549YzyHBUiABEiABEiABIKDABUAwXEdOQoSIAESIAESiBWBPXv2SOPGjbXsnDlzhLndY4WNhUiABEiABEggKAhQARAUl5GDIAESIAESIAESIAESIAESIAESIIHoCTALQPR8eJQESIAESIAESIAESIAESIAESIAEgoIAFQBBcRk5CBIgARIgARIgARIgARIgARIgARKIngAVANHz4VESIAESIAESIAESIAESIAESIAESCAoCVAAExWXkIEiABEiABEiABEiABEiABEiABEggegJUAETPh0dJgARIwLYEhgwZIgkSJJCNGzf6pY8ff/yx9OjRI6Lu/Pnzy6lTpyJ+x7Tx2WefyQcffBBTMenUqZNMmzYtxnIsQAJOJcBnNfZXrmLFirJ48eLYn8CSJEACJEACcSJABUCccLEwCZAACdiHwE8//SQvvPCCjBo1yi+dQqq4du3a+aVuVho4Anv37pVixYrpZ9++fYHrSAi1zGc19he7T58+UqRIkdifwJIkQAIkQAJxIkAFQJxwsTAJkAAJ2IPA5s2bJXHixNK3b1/59ddf5c6dOz7vWKVKlaR27do+r5cVBpbA1KlTZdeuXfqZMmVKYDsTAq3zWY3bRe7QoYPkzJkzbiexNAmQAAmQQKwJUAEQa1QsSAIkQAL2IYAVxccff1xflEuWLClz586N6NyOHTukc+fOEb+tjYsXL0qTJk3k9u3bumvFihVSr149yZ49u2TOnFk6duwop0+ftorLpEmT5Ntvv4347b4R0/lWeUw4a9WqJdmyZZM6depE6qtVxvX71q1b8sYbb+gKda5cuaRVq1Zy7Ngx1yLc9oJAixYtpECBAvrBNsW/BPis/su3f//+MmPGjH93/LP1448/RvytgdvRzp07I8ps27ZNmjVrpn/rMmbMqNv79++POM4NEiABEiCBuBGgAiBuvFiaBEiABAJOABP4cePGCVbKIFAEuLoBwLx70aJFsnv37kh9nTx5suAFGpYDR48elfbt20u/fv3k+PHjsn79ekmZMqW89tprEeegzIEDByJ+u27E5nyUx8v+4MGDZfTo0drmO++8I0899ZQsXLjQtbpI2xjXpUuXZOXKlYIXfUxS69atK9evX49Ujj/uj0DhwoUFpv/4YJviPwJ8ViOzhbJy2LBhkXeaXwMHDhT4/kMQ0+Ty5cu6ffXqVZ3wQ6GJvzlQDJQuXVq6dOmix/kPCZAACZBA3AlQARB3ZjyDBEiABAJKYNasWVK+fHnJkiWL9qNNmza6qn7hwgX9jcCACKw3Zsz/t3cf4HIV9QLAJyQEkN4FQuhI0dBrRHoPXRAEnkpROioIGIr0JkXg0ZtK+6RXAQUEqQoivbcgvVeRuu/8573dt3fvzd5NcpO75Tffd9mz58w5O/Mb9ubO/8zMubBLOS+44ILc+Y6df/3rX3MAIDrWkWafffYUnfM777wzv+/tP42eH3+wX3LJJSkWEIzAwxprrJGOP/74dPDBB/f4EXfddVcOOpxyyilp2mmnTYMHD85/7Md51UGOHk+2k0CTCfiudm2QuJP/97//vctIo0cffTR9+umnaamlluqauXj3j3/8Iy299NJ5LZL4vRa/82LaUwQsx8e0p24FsIMAAQJtKCAA0IaNqkoECLS3QAyX3WqrrSqVnHLKKdPqq6+eO9rlnVtvvXWKDn85vfrqqymmBqy22mp51xZbbJGOPfbYvF0qlfLdtbhbH3feG0mNnr/uuuvmjnz1NSNgEdMHylMRqo/deOONeWRD/LFfnVZZZZV8TvU+2wSaXcB3tWsLTTLJJGnDDTdMF198ceVAdWCysvP/NmLqUKxxUk6vv/56uuaaa/IIgc8++6y82ysBAgQIjIHAoDHIKysBAgQI9LNAPIbvuuuuS6NGjUonnnhipTQxRz5+tttuu7wvhtpGYODee+/Nd9bij+iY4z9w4MB8/Kuvvko33HBDOuOMM9J9992X5+cvu+yylev1ttHo+XPMMUe3S8Vd/bi7H+UdOnRol+MxLP2mm25KZ599dpf98XnLLLNMl33eEGhmAd/VnlsngpP77LNP2mmnnVIEH+N3U73H/sVoo/g9FdOaYgrT8ssvnyabbLKeL24vAQIECPQqIADQK5EMBAgQaB6BuFsWd9D23HPPboVab7318pz5WOAtUowSiPwxtDZezznnnMo5559/fopnk8fc2xhiO9FEE6WPP/64y525SuYeNho9/+233+7h7JTv4E0zzTTdjsW+mIqwyy67dDtmB4FWEvBd7bm1vv3tb+fgX6zvEfP64/dVTEHqKUWnP4KaEQCIxRRjGlGkmFYkESBAgMDYCZgCMHZuziJAgEC/CMQfwTvuuGPutEfHvfpn0003zYvtlQsWw/QvvfTSFHNsY77ssGHDyofSWWedlRcAjLv+0fmPVL3ydiXjaDYaPT/u7MVdvuoUj0WL0QlTTTVV9e68vfDCC/e4DkF0pqLuEoFWEfBd7bmlYnpPLFwaa5TE9zpGBIwuxe+ZCHbGFKBy5z+eVPLOO++M7hT7CRAgQKAXAQGAXoAcJkCAQLMIxMJXcUc9HqXXU4qF/6oXyovH+8WK2T/+8Y+7/ZE96aSTdlmIKx4ROHLkyNTovNpGz4/VvI866qhKceNz4jFfEcToKcWohZtvvrnLowLjSQQxImCBBRbo6RT7xlAgplnEkyLiJ7alvhfwXa3/XY3veTwZJKYzffe73x1tA9T+nokngcSTSmIqUywcKBEgQIDAmAuYAjDmZs4gQIBAvwjEgmJxV798x762EMstt1xeWC9W8h8+fHg+HHfX4hFal112WZfs0SmPEQPlBbbefffdPB0gHsEXC/fFH+b1UqPnx/DdGOYbj/gaMmRIXr07HusXc4B7SlNMMUW6/vrrc8AipgPEXb8YwXDcccelMVmjoKdr2/e/AldccUV66qmn8pvY3mOPPdD0sYDvav31RCKYF6OAllxyyRTf+dGlAw44IK2zzjr5saGTTz55njpw6KGHphdffDEvfHr77benCBJIBAgQINC4wIBiaGbXsZmNnysnAQIECLSwwOeff547gtNPP32K0QKR4g7bJ5980m3l/p6qOSbnx5DdCATMP//8KVYCbyTFkwsiMDHPPPM0fE4j1+30PE8//XRac801M0M8dWG++ebrdJKmr/+YfNd6qsyYnN9s39VYADT+n42F/8qLhkZ94rGnM8wwQ0/VtY8AAQIE6ggIANTBcYgAAQIECBAgQIAAAQIECLSLgDUA2qUl1YMAAQIECBAgQIAAAQIECNQREACog+MQAQIECBAgQIAAAQIECBBoFwEBgHZpSfUgQIAAAQIECBAgQIAAAQJ1BAQA6uA4RIAAAQIECBAgQIAAAQIE2kXAYwDbpSXVgwABAgQIEOh3gTfeeCOvWh+PrYunXnzta1/r9zLVFiAeABWP0nvhhRfyE0DmnnvuNPHEE9dm854AAQIE2lDACIA2bFRVIkCgMwVGjBiRzjnnnKaofHQsFl544UpZdtttt3T22WdX3tvoP4HnnnsuxXPY4ye2pb4RiEdWbrrppmmppZZKxxxzTBo5cmSac8450wknnNA3H9DAVZ599tk0bNiwujkfeeSRtMwyy6T4fXHyySen7bffPs0777wpHgk5IdPVV1+dVl555Qn5kT6LAAECBAoBIwD8b0CAAAECBDpI4IorrkhPPvlkrnFs77HHHh1U+/FX1R133DFNPfXU6ZlnnqncTX/11VfTt7/97TwSYO211x5/H97glT///PO0wQYbpL322iv95Cc/qZx11113pY022ij94x//SEOGDKnsH58b3/rWt9Luu+8+Pj/CtQkQIECgBwEjAHpAsYsAAQIECLSrwPrrr5/vTMfd6diWxl0ghtTHHe2DDz640vmPq84yyyy5k3vxxReP+4f0wRUefvjh9NVXX3Xp/Mdll19++Xw3/vrrr++DT2nsEnPNNVfacMMNG8ssFwECBAj0mYAAQJ9RuhABAgSaXyA6KkcddVRaZJFF0swzz5z/6L/pppu6Ffy0005LSyyxRJptttnS1ltv3W148Jtvvpl+9KMf5buF0047be5AxF3ERtJ3v/vdFEOVa9NOO+2U70DW7ve+bwXmm2++9Pzzz+ef2JbGXWDAgAFpuummSy+//HK3i2277bbp8MMPr+xfffXV03vvvZdif3wHoyMc36X333+/kic2YiRB3JWfddZZ87XXXHPNysiNOP7nP/85/frXv06XXXZZWnrppfNr7K9Nv//97/Mw/7j7P8MMM+TP/uijj2qzpVNOOSXFd7Oc/vOf/6SYuhP/j8SogO9973spvvfV6cwzz0yrrrpqmn322dOWW26Zbr311urDqd7x+++/P+2zzz7d8scUiplmmikNHz481QZO1lhjjRSjKjbbbLPsstBCC6XzzjuvyzW8IUCAAIH6AgIA9X0cJUCAQFsJ/PznP0/R4b/qqqvyH9LRMfnxj3+c/vSnP1XqGQGC+KM6/viOOeJxly7+4K7+4z46AzFvODqS//rXv3IHI4YWRyejtxSdiQsvvLBLttdeey13YHqbv9zlJG8INJFAdOIjWHbzzTfnu+zlosVigDESoJwiUBbfn9VWWy1/d6IjPHjw4ByMiwBdpPgerbvuumnjjTfOQYWnn346d4i///3vly+TO+M33HBDnsd/yCGHpAgQ1KaLLrooHXbYYenAAw/MIxOGDh2aFl988bTJJpukRx99tEv2CGBEMK+cYo2AKNd9992XgxErrbRSLuOXX36Zs8R0gf/+7/9Op556ag7obbHFFnlESSwuGKm34xEEqS7DsccemyLwGAGL119/PW/HiIrf/e53+XrxnzvvvDNtvvnmOXAQv3d+85vfpP322y//TqtkskGAAAEC9QWKf2wkAgQIEGgDgaLDUCoW2httTYo/zEvTTz996YMPPuiS57rrrisVi4Llfe+8806puEtYKv4A75Ln0EMPLW211VZ539tvv10q5u92OR5virtxpWKIcd5fBAby+3KmXXfdtXTWWWflt0WHorTggguWD+XXYqG00g477NBlnzcEWkmg6BiXik5sqbhjXyruYJeKBQFLv/3tb0sff/xxl2pMNtlkpYMOOqjLvmJYfmmxxRYrXXPNNXl/fEeKDniXPJ9++mlpqqmmqlzvggsuKE066aSl+D6WUzFqoPLdvPTSS0tFkK40atSo8uH8WixWWIrv45RTTpmPF4sAlq699tpSlKGciqkApRVWWKH8tvJaBDhKRVAhvy/WESgdcMABlWOxsffee+drxXZvx4tASaWOH374Yf69UyweGqdW0gMPPFCaY445Sl988UXeVzxRoWJUzlSsI1AqRiqU33olQIAAgV4EjACoHx9xlAABAm0jcMcdd6RVVlklFX/4d6nTWmutlR566KH0ySefpLg7ueSSS+YhuNWZ9t1338qduLhTGPnLKe7k3XLLLSkef1b8IV/ePdrXmFoQQ6aLP+4reWJEQAwhlgi0qsBEE02UYoTNSy+9lGIufQxlP+644/ICgNXfl6hf3MWuTvF9iBE05Wk08R0pggGVLDH0vgjU5e9oEVCo7P/Od76TpwdUdvzfRtGhT3FH/qSTTkpx1786TTPNNOnEE09Mb731VjrjjDNSERTMo4DiyQDlaQjxRIA4vzbF74+777477476xVNH/vjHP6byqIAjjzwyj1yIDL0dr772P//5zzzVoOjsV+/OU5UGDhyYRxqVD6y44orlzfwa0x96mtLQJZM3BAgQIFAREACoUNggQIBAewvEkNmYq1ubouMSc25j/nI8vq/2j/By/shXTjF0N1bwnrNYSG655ZZLxd39vAJ6+XhvrzFUujwNINYDiCG/MedXItDqAtGZj2H2v/jFL9KDDz6Yh/Fvt912XapV2ymPgzHXP+a3l1N0tMvrbETHOzraU0wxRflwfq2eWlA+EEPwf/rTn6Zf/vKXee2B4kZQ+VCX1/K0gyOOOCI99dRTqRhNkH71q1/lPDH1pxj1k4MX888/f+U19sXaAJFivYD9998/D8efccYZUzFCKN1zzz35WCPHKxmLjdH9boo8UccIqpRTrUEECEZXx/I5XgkQIEDg/wX+/6+5/99niwABAgTaUCAWHCuGC/dYs3iGeQQB4u5g3NGvl+JOf3TWV1pppTyH9/HHH8+d+dEFDnq6VtztjzUG4g/3mKccc5uj4yQRaEWBeKxizFfvKUUgIO5wf/bZZ5XDPX0P4zsYi/RFinn3se5GfC+iMx6r98eCerWjd6ITX5viexQjEIppBnne/7nnnlvJEkGEYupA5X15I9Yp2HnnndPf/va3vCt+D5QDAxEcKP/EwoSnn356+bQ8ciBGNzz22GMp7swX0x5SrEtQTrG+SL3j5XyN/G4q5/V7oizhlQABAmMnIAAwdm7OIkCAQMsJfPOb38yLaMVjwKpTLNYVi38V84vTwgsvnGKqQDHntjpL7qyXh97GIl0xhDlWKI+OQzmVny1ffl/vNUYizD333PmzIgBg+H89rb49Fh3KBRZYIBXrMOTOZd9evTOvVsxNz3fMe5oCEwv6xfekurMeK/jXplg8cNFFF827Y0RNjLCJJwaUz4uh/z09ZaD2OvHdKj/dIYb4x1398ur9Ub6YltBTinJGxz9S/B6IBfdqU5TrD3/4Q94di++V83z961/PC4HuueeeDR+vvnas5h9TgqqnN8TxqG/8xO8KiQABAgT6RkAAoG8cXYUAAQJNLxDziuORY/HosHKKP7h/9rOfVR7HFR2QyBOripdTsTBgHkocw5EjxVDhmD9cThFQiJXGY2RAsVBZeXevrzENYOTIkWmSSSZJ0QGQJozAFVdckR8n98QTT6TYlsZdIDrdxaJ5+Y54rKVRTvF9iPUzap93Xyx62WVee3SqY1pN3EGPVPsdi9ED8T2Nu99j8h2bZ555UrHgX4qOeaQIKMQTN+J3QPWw+QgQHH300TmoF/m22Wab/FSO22+/Pd7mFCN99thjj8p3NQITMc2gPP8/MkUQMKYyROrteM70f/+JYf7rr79+vl65XBGEjMcQ7rLLLtmjOr9tAgQIEBh7AQGAsbdzJgECBJpOIP5gjkW9an+iExIp7t7HHf5FFlkkrbfeevkucDw+LIbqltMll1yS88RdwHjudsxXXmedddIPf/jDnCU6B7FYWCzyFY8pi0UDYwRBsYp/2mmnnfJw5fK16r3GHOIYfeDufz2lvj8WHa2YrhE/sS31jcD555+fF6OLAFos6Bed/pg/Hx3aWIyvnKITH4+2i+9OLMAZgbmYWx+jAsp3+/fZZ58UC/HFNJu4VqyzEd/B+D5Gm8V0gUZTLEwYUwj+8pe/5AUDI+gT0wBiFEgEHKIMEfiL65Z/D8RUhHhU6LbbbpsDG8svv3we4h8jCoongOSPLlb5TzH3P/LGIwPj90DM5Y+yR+rteM5U9Z/jjz8+/16JkSlRlhjFEIGV4kkDVblsEiBAgMC4Cgwo/mHqeXWYcb2y8wkQIECgaQVinn8MrY0OysQTT9xjOaOTHwuKxfDb6qH+5cyxYGDcjYw/1GOBwPjnJEYBxHzeRlLcKY3gwiOPPNLwOY1cVx4C/SkQ36unn346j2yJO/CxtkZ1iu9Seah7fL8GDRpUuWtenS++TzHnPo5HUCFS3BWPzn90vMclla9dPCIwBwXiO1y7vkD5+rEAX6yyH3Xp6XdFjB6IOg8ZMqSyhkH53Hjt7Xh13tiOz4rfLcUjDN35r8XxngABAn0gIADQB4guQYAAAQJjLhCLmt166609Lko25ldzBoHWEKgOALRGiZWSAAECBNpJYFA7VUZdCBAgQKD5BWIu8d57710Zltz8JVZCAgQIECBAgEB7CBgB0B7tqBYECBBoGYEY+n///ffnucTx5AGJQCcJxKiXmNsvESBAgACB/hAQAOgPdZ9JgAABAgQIECBAgAABAgQmsICnAExgcB9HgAABAgQIECBAgAABAgT6Q0AAoD/UfSYBAgQIEOgngeeeey4//jEetxbbEgECBAgQINA5AgIAndPWakqAAIEJLvDll1+m1157LT/aa4J/uA/sUSCeA//EE0/kn9iWCBAgQIAAgc4READonLZWUwIECExwgYEDB6avf/3raYopppjgn+0DexZYf/310xxzzJF/YltqbYGPPvooXXfddendd99t7YooPQECBAhMEAGLAE4QZh9CgAABAgQIEOh7gVVXXTXdcsstaeGFF06PPPJI33+AKxIgQIBAWwkYAdBWzakyBAgQaB2BP/7xj2m22WZLG2ywQfr8889bp+BKSqCJBJ5//vlcmhdffDF99dVXTVQyRSFAgACBZhQQAGjGVlEmAgQIdIDAKaeckl555ZV09dVXpwcffLADaqyKBPpe4KKLLkrbbLNNuvzyy9NEE/mzru+FXZEAAQLtJWAKQHu1p9oQIECgZQQuvvji9MMf/jAtvvjieQjz4MGDW6bsCkqAAAECBAgQaEUBAYBWbDVlJkCAQJsIlEqlNGDAgDapjWoQIECAAAECBJpbwFix5m4fpSNAgEBbC+j8t3XzqhwBAgQIECDQZAICAE3WIIpDgAABAikvCrjbbrulzTbbLL388stI+lAgFo1baKGF8k95Abk+vLxLjSeBZ555Ju23337p7rvvHk+f4LIECBAg0AkCAgCd0MrqSIAAgRYTiCcEnHTSSemSSy5Jxx57bIuVvrmLG4vFPf744/kntqXWENh8883TYYcdltZcc8302WeftUahlZIAAQIEmk5AAKDpmkSBCBAgQCDuUE8xxRQZYqmllgLShwLrrbdeGjp0aP6Jbak1BKaccspc0MknnzwNHDiwNQqtlAQIECDQdAIWAWy6JlEgAgQIEAiBN954I33wwQdp3nnnBUKg4wXeeeeddOWVV6aVV145zTXXXB3vAYAAAQIExk5AAGDs3JxFgAABAgQIECBAgAABAgRaSsAUgJZqLoUlQIAAgRC4+uqr04wzzpjWXnvtvGAgFQIECBAgQIAAgd4FBAB6N5KDAAECBJpM4IwzzkhvvfVWuuGGG9KDDz7YZKVTHAJjL3Dvvfem2267bewv4EwCBAgQIFBHQACgDo5DBAgQINCcAttuu22aaqqp0korrZSGDRvWnIVUKgJjKBAd/6WXXjr/f33hhReO4dmyEyBAgACB3gUG9Z5FDgIECBAg0FwCG220UYofiUA7Cbz88suV6lRvV3baIECAAAEC4yggADCOgE4nQIAAgeYTeOaZZ9KZZ56Z1llnnbTiiis2XwH7sUTPP/98GjFiRC7Btddea0X5fmyL2o/ebLPN0qhRo9K///3vtMsuu9Qe9p4AAQIECIyzgKcAjDOhCxAgQIBAswkst9xy6Z577kmTTTZZevPNN1M8O136X4Fjjz027bnnnvnNMccck/bYYw80BAgQIECAQIcIWAOgQxpaNQkQINBJAtNNN12u7pRTTpkmnnjisa76J598MtbnNuuJ6623Xho6dGj+iW2JAAECBAgQ6BwBIwA6p63VlAABAh0j8MEHH+RHBQ4fPnychrivscYa6dBDD80Ls3UMnoqOV4H33nsv7bjjjqlUKqVTTz01TTvttOP181ycAAECBAhUCwgAVGvYJkCAAIGOEHj11VfT2WefndcHWGGFFXqs88MPP5yfMDDTTDPl6QRzzTVXj/nsJDAmAieccEL66U9/mk85/vjjK9tjcg15CRAgQIDA2ApYBHBs5ZxHgAABAi0rEI8RvP7669Mkk0ySXnnllVSeMlBdobg7G+mNN95Ia6+9drrrrrt6zFd9jm0CvQnEY/4GDx6csy2zzDK9ZXecAAECBAj0qYA1APqU08UIECBAoBUEJp100lzMWB9g4MCB3Yr80UcfpfPPP7+y/8knn0wbbrhh+vTTTyv7bBAYG4FYoPLFF1/MP7EtESBAgACBCSkgADAhtX0WAQIECDSFwLnnnptOO+20dOedd6app566W5mi8//hhx922X/77benH/zgB3nudpcD3hAYQ4GZZ545xY9EgAABAgQmtIAAwIQW93kECBAg0O8C0en/yU9+kuf491SY8vD/2mN/+MMf0j777FO723sCXQROPPHEtPjii6cLLrigy35vCBAgQIBAfwtYBLC/W8DnEyBAgEBTCcRc/3h6QL10yimn5JXc6+Vp1mPPP/98GjFiRC7etddeO05PSWjWOvZnub744os02WSTpXidbbbZ0ksvvdSfxfHZBAgQIECgi4ARAF04vCFAgACBThcY3d3/apddd901Ree5FdPll1+eHnvssfwT21LfCgwaNCjF4yMjrbPOOn17cVcjQIAAAQLjKGAEwDgCOp0AAQIE2kfgrbfeSkOGDGlosb/JJ5883XbbbWmJJZZoKYCnnnoqrbbaarnMN910U5p//vlbqvytUNhSqZRee+21NMsss7RCcZWRAAECBDpIwAiADmpsVSVAgACB+gKxOGCjK/1//PHHeSj9Cy+8UP+iTXY0OvzlVeh1/seucR588MG01157pXvvvbfHCwwYMEDnv0cZOwkQIECgvwWMAOjvFvD5BAgQINAUAnHXdr755kvPPvtst/LEXO6JJuo5Zr7YYoulWByw/GjBbifb0XQCzzzzTJpnnnlSdNTHJs0999wp1lKYaaaZ0uuvvz42l3AOAQIECBDoF4FB/fKpPpQAAQIECDSZwC233JI++uijtNBCC6Xpp5++y88hhxySBg8e3GQlVpyxEfjb3/6W1l133RTTHxZddNGxuUSabrrpcgBgmmmmGavznUSAAAECBPpLQACgv+R9LgECBAg0lcCqq66a5203VaEUpk8F/vjHP6ZNN900/fvf/0433njjWAcArr/++nTDDTek1VdfvU/L52IECBAgQGB8C/Q8nnF8f6rrEyBAgAABAgQmoECs77DBBhvkzn987J/+9KcePz0e37fRRhulmWeeOV155ZU95plxxhnT1ltvnb7+9a/3eNxOAgQIECDQrALWAGjWllEuAgQIECBAoE8EjjjiiDRy5Mgu14opHe+8806KpzlUp4cffjgNGzYs74o7/KMLFFSfY5sAAQIECLSKgBEArdJSykmAAAECBAiMkcBXX32Vdt11126d/7jIZ599lm699dZu11tggQXSKquskr72ta+lH/3oR92O20GAAAECBFpZwBoArdx6yk6AAAECBAj0KBCPc4xh+pdcckmPx2Nn3N2PBQGr08QTT5xuvvnm6l22CRAgQIBA2wgIALRNU6oIAQIECPSXwBtvvJGefvrpPJx8/vnnz3eP+6ssvX3uW2+9lcsaHd147OHUU0/d2yktd/z9999PG264YY93+KsrEwsBSgQIECBAoJMETAHopNZWVwIECBDoU4F33303ryq/1FJLpWOOOSYPNZ9zzjnTCSec0KefU+9izz77bGXOer18999/f4pyLrHEEunoo49OW2yxRV7obvfdd8/D4eudOzbH4g58rLbf1+m9996re8lXX301fec73+m18x8XefLJJ9OoUaPqXs9BAgQIECDQTgICAO3UmupCgAABAhNUYMcdd8x30J955pl0xRVXpHjM3IMPPphOPPHEFI+Ka5b0wAMP5E7xL37xi9zhjbLGiIWnnnoqxaJ3O++8c58X9fe//32Pc+/H5YM+/PDDFHP0R5eiQ7/88sunhx56aHRZuu23yF83EjsIECBAoI0FBADauHFVjQABAgTGn0CpVEpXX311Ovjgg1MMpy+nWWaZJcVd9Ysvvri8q19fo5zRwT/00EPTZptt1qUsQ4cOTRdeeGGKzvprr73W5VgrvrnuuuvSN77xjRSP6Ws0mQbQqJR8BAgQINAOAgIA7dCK6kCAAAECE1xgwIABabrppksvv/xyt8/edttt0+GHH17ZH4+Ti6HrsT+eLz/XXHPlFeZjrnp1ipEE8Qz6WWedNV97zTXXzMPUy3n+/Oc/p1//+tfpsssuS0svvXR+LR+rfo0O/fbbb58+//zzfMc/7ohvs8021Vkq2/Es+8svvzx98cUXlX0ximGttdZKEcxYcMEFU4wciFXzyynqdsstt6QDDzwwLbzwwrm8MRqinOekk07K0wxiAb4Yjh9rJESKaQE/+9nP8toDQ4YMyQGJ8rE4/vOf/7zbon1R17j2Y489lsIjHt0X17zooovilC4pzr/hhhvy57344ovpyiuvTAcccEBe6C/q0lOKBf++/PLLng7ZR4AAAQIE2k+guDMgESBAgAABAmMhsN9++5WKO86lm266qVR0Ikd7heKRcqU11lijVNxtLxWd4FLRiS39+Mc/Li222GKl4lF1+byi81wqFhAsFZ33vK9YrK900EEHlRZffPHKdS+44IJS8Yi60sorr1wqOrqlYkh8qQgalL71rW9V8sRnxHVeeumlvC/KVgQLKsd723jkkUdKRZCiVAQFclmLDnou63rrrVc59b/+679Kc889d+n0008vFYGD0uOPP14qFt0r7bDDDjlPlOvYY48tFUGI0uuvv16xKTrwpZ/+9KelIhhS+uSTT/L5RYChVAQq8nlFJ79UBAbyObGjGJVQKoIhpWIKQyl8ivUOSsXd/Xz8448/zueMyX+K9QFK1157bakYtVHaYIMNSrPPPnup+MuudNddd43JZeQlQIAAAQItK5BatuQKToAAAQIE+lkgOv3R0Y1O6kwzzVTadNNNS7/97W9LtZ3TySabLHfmq4sbHf8IAFxzzTV593333VcaMWJEdZbcAZ9qqqkq14sAwKSTTlp6++23K/mqAwCXXnppad555y0VC9tVjkcnfZNNNqm8721j8803Lx1//PFdskVZI6hw99135/0RAPjBD37QJc/tt9+eHco7zzjjjFIxFaL8tlSMXigV8/Mr78sbP/rRj0rnnXde+W3+7GKqQn4fQYVixEPl2AcffJCDE5UdfbARAY4IUkgECBAgQKATBEwBaL9BHWpEgAABAhNIYKKJJsrD1ou77XnRv1hl/7jjjkvxKMDaheiKjnWXUsUUguIudCruPuf9sTp/EQyo5HnzzTdTzGkv7pSnIqBQ2R/D32PqQW0q7mznlf1j+H3M7S+n+Jzq4f3l/aN7jaH9tWsFxDVqH6u34oordrlETEn46KOPuuyrfhNz7WsN4ngxoiEVgYVK1lg/IepeBBhSTJGIYf3jM8V6AUXwZnx+hGsTIECAAIGmERjUNCVREAIECBAg0KIC0UEuhurnn5gvv9tuu6Xtttsu/f3vf6/UqLpTXt4Zc/3LAYDYFx3h4s55irn+0047bVp22WXTFFNMUc6eX3uayx7z3Yuh9emXv/xlXnsg5spHmSLFXPtXXnklb/f2n5ijX4wuyHP/a/PG58YaBeVUW66BAwfGqMLy4W6vzz33XF5wMAIU1akYXZBWWmmlyq4odzH1Ic/zv/POO1MEWSQCBAgQIECgbwT8q9o3jq5CgAABAh0mEI+ciycA9JQiCPDPf/6zsihe5ImOdW1699130wwzzJB3F1MA8p3373//+yk6y/F4vjPPPDNNOeWUXU4bPHhwl/fxJjrN8djB6DjHEwnOPffcSp5YxC9GI/T0+eVMMXIhFgKcZJJJ8ufVLk4Y+aKs1XfKywGG8jV6e51mmmnSIYcckh89GI8fLP9EUOGss86qnB4L8o0cOTIvWrj//vvXDSpUTrJBgAABAgQINCQgANAQk0wECBAgQKCrQLGwX360XjybvjbF6vuTTz55qu6sx1392hQr0C+66KJ5d3SCY/h7PDGgfF4M/e/pKQO11ykWs8sr68f+GEHwq1/9Kg+jj/dzzjlnvsN+9tlnx9tu6Y477sid8Vj1P9I3v/nNVMzn75YvpgYMGzas2/5Gd8TTAuKOfm0655xz8siA8v4jjzwyFQsMpijv9NNPn0488cTyIa8ECBAgQIDAOAoIAIwjoNMJECBAoDMFotO9wgorpGI1/zxPv6wQw+j33XffPGe+vC9eTzjhhPT8889Xdv3hD39Ijz76aCoWDsz7isX9UrHyf+V4PFIvHpkXd9rjmo2meeaZJ+26665pzz33rJwSd9IPO+ywbo/Oi5EBW221VS5bBDQixTSCKH/Mwy+neKxgPH5v/fXXL+/q9TXqE4GQcvrhD3+YrrrqqnTbbbeVd+XAQ9RxoYUWyvvuvffeFAGBcqf/5JNPzmsqPPHEE/l4jG7wyL4Knw0CBAgQIDDGAtYAGGMyJxAgQIAAgf8VOP/883MAYK655krLLLNM7qzH0P/lllsuVc91j0787373u7Txxhun4hF7uXMdHfwYFVC+27/PPvuktddeO91zzz1p6qmnTrGwYHTcYwRAdLxjtECjKRbOi0X5/vKXv6TikYFp+PDh+bO23HLLvKhelDWmBERAIgIDseBeOa2zzjqpeIpAPj865sWj8/JohlhkcEzm48dihXvttVcqVtlPRx11VL6rH4scRiAgFt6L9QJiGsCpp56aR0HEaIcIRsQIhqh/pMhXPGUhbb311nl9hAgqxEiC4mkJebpE8TSCcrG9EiBAgAABAg0IDIhHHTSQTxYCBAgQIEBgNALRSX/66afzHPq4A189Vz5OiekA5ZX8Y8G+QYMGpVgAsDbFP8kxJz6OR1AhUqzgH/PvozPcFyk65DHyIEYwxFD70XXq4057dNDjc8vrFIzp58daAmGzwAILdPmc2Fc80i+FVTkA0ui1Y9HAxx57LE9tqF2IsNFryEeAAAECBDpVQACgU1tevQkQIEBggglUBwAm2If6IAIECBAgQIBAjYA1AGpAvCVAgAABAgQIECBAgAABAu0oIADQjq2qTgQIECDQVALXXXddU5VHYQgQIECAAIHOFDAFoDPbXa0JECBAgAABAgQIECBAoMMEjADosAZXXQIECBAgQIAAAQIECBDoTAEBgM5sd7UmQIAAAQIECBAgQIAAgQ4TEADosAZXXQIECBAgQIAAAQIECBDoTAEBgM5sd7UmQIAAAQIECBAgQIAAgQ4TEADosAZXXQIECBAgQIAAAQIECBDoTAEBgM5sd7UmQIAAAQIECBAgQIAAgQ4TEADosAZXXQIECBAgQIAAAQIECBDoTAEBgM5sd7UmQIAAgQ4VeOGFF9KwYcPyT2xLBAgQIECAQOcICAB0TlurKQECBAgQSJdddll6+OGH809sSwQIECBAgEDnCAgAdE5bqykBAgQIEEgjRoxIs802W/6JbYkAAQIECBDoHIEBpSJ1TnXVlAABAgQIECBAgAABAgQIdKaAEQCd2e5qTYAAAQIECBAgQIAAAQIdJiAA0GENrroECBAgQIAAAQIECBAg0JkCAgCd2e5qTYAAAQIECBAgQIAAAQIdJiAA0GENrroECBAgQIAAAQIECBAg0JkCAgCd2e5qTYAAAQIECBAgQIAAAQIdJiAA0GENrroECBAg0NkCL7zwQho2bFj+GTVqVGdjqD0BAgQIEOgwAQGADmtw1SVAgACBzha47LLL0sMPP5x/Lr300s7GUHsCBAgQINBhAgIAHdbgqkuAAAECnS2w7rrrpllnnTX/jBgxorMx1J4AAQIECHSYwIBSkTqszqpLgAABAgQ6WqD8T/+AAQM62kHlCRAgQIBApwkM6rQKqy8BAgQIEOh0AR3/Tv8/QP0JECBAoFMFTAHo1JZXbwIECBAgQIAAAQIECBDoKAEBgI5qbpUlQIAAAQIECBAgQIAAgU4VEADo1JZXbwIECBAgQIAAAQIECBDoKAEBgI5qbpUlQIAAAQIECBAgQIAAgU4VEADo1JZXbwIECBDoSIFRo0alRRZZJP/EtkSAAAECBAh0joAAQOe0tZoSIECAAIF06aWXpoceeij/xLZEgAABAgQIdI6AAEDntLWaEiBAgACBtO6666ZZZ501/8S2RIAAAQIECHSOwIBSkTqnumpKgAABAgQIlP/pHzBgAAwCBAgQIECggwQGdVBdVZUAAQIECBAoBHT8/W9AgAABAgQ6U8AUgM5sd7UmQIAAAQIECBAgQIAAgQ4TEADosAZXXQIECBAgQIAAAQIECBDoTAEBgM5sd7UmQIAAAQIECBAgQIAAgQ4TsAZAhzW46hIgQIBA5wjcc8896c477xyjCg8fPjwtu+yyY3SOzAQIECBAgEBrCHgKQGu0k1ISIECAAIExFnjmmWfSAgsskL788suGzh04cGB64okn0rzzzttQfpkIECBAgACB1hIwBaC12ktpCRAgQIBAwwLRkd9qq60azh95df4b5pKRAAECBAi0nIARAC3XZApMgAABAgQaF2h0FIC7/42bykmAAAECBFpVwAiAVm055SZAgAABAg0INDoKwN3/BjBlIUCAAAECLS5gBECLN6DiEyBAgACB3gR6GwXg7n9vgo4TIECAAIH2EDACoD3aUS0IECBAgMBoBXobBeDu/2jpHCBAgAABAm0lYARAWzWnyhAgQIAAgZ4FRjcKwN3/nr3sJUCAAAEC7ShgBEA7tqo6ESBAgACBGoHRjQJw978GylsCBAgQINDGAkYAtHHjqhoBAgQIEKgWqB0F4O5/tY5tAgQIECDQ/gJGALR/G6shAQIECBDIArWjANz99z8GAQIECBDoLAEjADqrvdWWAAECBDpcoDwKIBieeOKJFEEBiQABAgQIEOgMgUGdUU21JECAAAECBEKgehSAzr//JwgQIECAQGcJGAHQWe2ttgQIECAwDgKjXvzXOJzdPKe+8MILuTBzzjln8xRqHEoyx9DZx+FspxIgQIAAgc4READonLZWUwIECBAYR4HDjzpuHK/g9PEhMHLvn4+Py7omAQIECBBoOwFTANquSVWIAAECBMa3wNDZh4zvj3D9BgRe/NdLDeSShQABAgQIECgLCACUJbwSIECAAIEGBbb6/mYN5pRtfAoYkTE+dV2bAAECBNpRwGMA27FV1YkAAQIECBAgQIAAAQIECNQICADUgHhLgAABAgQIECBAgAABAgTaUUAAoB1bVZ0IECBAgAABAgQIECBAgECNgABADYi3BAgQIECAAAECBAgQIECgHQUEANqxVdWJAAECBAgQIECAAAECBAjUCAgA1IB4S4AAAQIECBAgQIAAAQIE2lFAAKAdW1WdCBAgQIAAAQIECBAgQIBAjYAAQA2ItwQIECBAgAABAgQIECBAoB0FBADasVXViQABAgQIECBAgAABAgQI1AgIANSAeEuAAAECBAgQIECAAAECBNpRQACgHVtVnQgQIECAAAECBAgQIECAQI2AAEANiLcECBAgQIAAAQIECBAgQKAdBQQA2rFV1YkAAQIECBAgQIAAAQIECNQICADUgHhLgAABAgQIECBAgAABAgTaUUAAoB1bVZ0IECBAgAABAgQIECBAgECNgABADYi3BAgQIECAAAECBAgQIECgHQUEANqxVdWJAAECBAgQIECAAAECBAjUCAgA1IB4S4AAAQIECBAgQIAAAQIE2lFAAKAdW1WdCBAgQIAAAQIECBAgQIBAjYAAQA2ItwQIECBAgAABAgQIECBAoB0FBADasVXViQABAgQIECBAgAABAgQI1AgIANSAeEuAAAECBAgQIECAAAECBNpRQACgHVtVnQgQIECAAAECBAgQIECAQI2AAEANiLcECBAgQIAAAQIECBAgQKAdBQQA2rFV1YkAAQIECBAgQIAAAQIECNQICADUgHhLgAABAgQIECBAgAABAgTaUUAAoB1bVZ0IECBAgAABAgQIECBAgECNgABADYi3BAgQIECAAAECBAgQIECgHQUEANqxVdWJAAECBAgQIECAAAECBAjUCAgA1IB4S4AAAQIECBAgQIAAAQIE2lFAAKAdW1WdCBAgQIAAAQIECBAgQIBAjYAAQA2ItwQIECBAgAABAgQIECBAoB0FBADasVXViQABAgQIECBAgAABAgQI1AgIANSAeEuAAAECBAgQIECAAAECBNpRQACgHVtVnQgQIECAAAECBAgQIECAQI2AAEANiLcECBAgQIAAAQIECBAgQKAdBQQA2rFV1YkAAQIECBAgQIAAAQIECNQICADUgHhLgAABAgQIECBAgAABAgTaUUAAoB1bVZ0IECBAgAABAgQIECBAgECNgABADYi3BAgQIECgGQS++uqrdOONN6ZHH320oeK8/fbbOf+rr77aUH6ZCBAgQIAAgc4TEADovDZXYwIECBBoAYHPPvssrbXWWunEE09sqLT3339/zn/zzTc3lF8mAgQIECBAoPMEBAA6r83VmAABAgQIECBAgAABAgQ6UEAAoAMbXZUJECBAgAABAgQIECBAoPMEBAA6r83VmAABAgQIECBAgAABAgQ6UGBQB9ZZlQkQIECAQEcJPPvss+m0005LTz/9dPr000/TnHPOmTbeeOO0+uqrj9bhxRdfTNdcc03629/+lt5///202GKL5TUGll122W7n3HXXXemxxx5L2223XXruuefyYoR33HFH+v3vf58GDhzYLb8dBAgQIECAQP8IGAHQP+4+lQABAgQITBCBiy++OC244ILp9NNPT2fCEmkAABA5SURBVAMGDEjTTz99uuqqq9Iaa6yRNt9881QqlbqV47rrrkuLLLJIOuKII1IsRjjTTDOlK664Ig0fPjwddNBBKZ5QUJ0iULDvvvumu+++O5+30047pb/+9a/VWWwTIECAAAECTSBgBEATNIIiECBAgACB8SHwySef5Lvy0ZmPu/QTTzxx5WMuuuiitOWWW+ZRANtuu21l/2233ZbWW2+9tM0226STTz45TTLJJJVjZ599dtpxxx3TzDPPnHbYYYfK/tj48ssv87Hvfe976cgjj0wzzDBDl+PeECBAgAABAv0vYARA/7eBEhAgQIAAgfEi8Mgjj6QPP/wwd8yrO//xYVtssUVabrnl8p396g/fe++901JLLZXOPPPMLp3/yBOBguj4H3744XlkQPV5b7/9dpp77rnTWWedpfNfDWObAAECBAg0kYAAQBM1hqIQIECAAIG+FJhnnnnyHPxbbrmlx8vGsP64y19O99xzT57zv//+++fpAuX91a8xAuBf//pX+stf/lK9O2/vsssu3fbZQYAAAQIECDSPgCkAzdMWSkKAAAECBPpUYLrppksjRoxIF1xwQXr99dfT7rvvnlZaaaU0xRRT5M+Juf3VKRbyi3UCYs2At956q/pQZXuaaaZJE000URo1alRlX3mjpwUCy8e8EiBAgAABAv0vIADQ/22gBAQIECBAoJvAoEGDcmf8gw8+6Haspx0fffRR3l071P/SSy9Nhx56aH4KQMztj+suv/zyaaONNkpbb711XhSwfL0nn3wyLwo477zzlneN9jWeElCdppxyyvS1r32tepdtAgQIECBAoMkEBACarEEUhwABAgQIhEB01Gedddb08ssvNwTyyiuv5HxzzDFHl/xxnQMPPDD96le/Sg888EC6/fbb8wr9e+21Vzr66KPThRdemEcFxEmTTjppvrsfjwuM8+ql6PBXp8kmm6z6rW0CBAgQIECgCQXq/+vehAVWJAIECBAg0CkCcSf+hRdeaKi65Tvyo7t7H0P7F1tssfyz2267pci/2mqr5QUCH3/88fwZsYhf+RF/Q4cObehzZSJAgAABAgRaR8AigK3TVkpKgAABAh0msPTSS+e59vFovnrp888/z3fyo9Ne/fi9U089Nc0111zp/fff73Z65N15553TE088kZ577rl8fIkllsivV111Vbf85R3XXHNNWnTRRdPNN99c3uWVAAECBAgQaBEBAYAWaSjFJECAAIHOE4hh+rHo3gEHHJD+85//jBbgN7/5TXrppZfSYYcd1iXPsGHD8giC3/72t132l9/EwoAx1L+8GOA3v/nNvGhgTA149dVXy9kqr7EeQTwFIM5bYYUVKvttECBAgAABAq0hIADQGu2klAQIECDQgQJxNz8643fccUceun/11VfnR/CVSqX07rvvprvvvjttueWWKQIFq6++et6uZho+fHjaZJNN8vF999033XrrrenTTz9Nb775ZjrhhBPSMccck37wgx9UngoQ5x555JF5IcAll1wyXXzxxem1115Lb7zxRjrnnHPSuuuum9+fd955afDgwdUfZZsAAQIECBBoAQFrALRAIykiAQIECHSuwPbbb5/iTv4222yTNthggwwRne/PPvssb8cj/aIzv8suu+SnBtRKnXvuuWnkyJG5Y3/44YfnRf5inv+0006b9thjj3TIIYd0OWXhhRdO999/fw4MRHDhiy++yNeNoEMcu/HGG9Oqq67a5RxvCBAgQIAAgdYQGFD8g15qjaIqJQECBAgQ6F+Bw486Lhdg5N4/n+AFiX+uY+G+eFTfs88+m2acccb0jW98I80333x59f7eChTrADz22GPpnXfeSUOGDEnzzz9/6m3l/ph28NBDD6X33nsv548nDMRigs2S+rM9msVAOQgQIECAwJgIGAEwJlryEiBAgACBfhKIjnd0wGsf89docaaeeuq03HLLNZo954vHAsZChBIBAgQIECDQHgLWAGiPdlQLAgQIECBAgAABAgQIECBQV0AAoC6PgwQIECBAgAABAgQIECBAoD0EBADaox3VggABAgQIECBAgAABAgQI1BUQAKjL4yABAgQIECBAgAABAgQIEGgPAQGA9mhHtSBAgAABAgQIECBAgAABAnUFBADq8jhIgAABAgQIECBAgAABAgTaQ0AAoD3aUS0IECBAgAABAgQIECBAgEBdAQGAujwOEiBAgAABAgQIECBAgACB9hAQAGiPdlQLAgQIECBAgAABAgQIECBQV0AAoC6PgwQIECBAgAABAgQIECBAoD0EBADaox3VggABAgQIECBAgAABAgQI1BUQAKjL4yABAgQIECBAgAABAgQIEGgPAQGA9mhHtSBAgAABAgQIECBAgAABAnUFBADq8jhIgAABAgQIECBAgAABAgTaQ0AAoD3aUS0IECBAgAABAgQIECBAgEBdAQGAujwOEiBAgAABAgQIECBAgACB9hAQAGiPdlQLAgQIECBAgAABAgQIECBQV0AAoC6PgwQIECBAgAABAgQIECBAoD0EBADaox3VggABAgQIECBAgAABAgQI1BUQAKjL4yABAgQIECBAgAABAgQIEGgPAQGA9mhHtSBAgAABAgQIECBAgAABAnUFBADq8jhIgAABAgQIECBAgAABAgTaQ0AAoD3aUS0IECBAgAABAgQIECBAgEBdAQGAujwOEiBAgAABAgQIECBAgACB9hAQAGiPdlQLAgQIECBAgAABAgQIECBQV0AAoC6PgwQIECBAgAABAgQIECBAoD0EBADaox3VggABAgQIECBAgAABAgQI1BUQAKjL4yABAgQIECBAgAABAgQIEGgPAQGA9mhHtSBAgAABAgQIECBAgAABAnUFBADq8jhIgAABAgQIECBAgAABAgTaQ0AAoD3aUS0IECBAgAABAgQIECBAgEBdAQGAujwOEiBAgAABAgQIECBAgACB9hAQAGiPdlQLAgQIECBAgAABAgQIECBQV0AAoC6PgwQIECBAgAABAgQIECBAoD0EBADaox3VggABAgQIECBAgAABAgQI1BUQAKjL4yABAgQIECBAgAABAgQIEGgPAQGA9mhHtSBAgAABAgQIECBAgAABAnUFBtU96iABAgQIECDQTWDUi//qts8OAgQIECBAgECzCwgANHsLKR8BAgQINJ3ABRdd0nRlUiACBAgQIECAQG8CAgC9CTlOgAABAgT+T2Do7ENYECBAgAABAgRaVmBAqUgtW3oFJ0CAAAECBAgQIECAAAECBBoSsAhgQ0wyESBAgAABAgQIECBAgACB1hYQAGjt9lN6AgQIECBAgAABAgQIECDQkIAAQENMMhEgQIAAAQIECBAgQIAAgdYWEABo7fZTegIECBAgQIAAAQIECBAg0JCAAEBDTDIRIECAAAECBAgQIECAAIHWFhAAaO32U3oCBAgQIECAAAECBAgQINCQgABAQ0wyESBAgAABAgQIECBAgACB1hYQAGjt9lN6AgQIECBAgAABAgQIECDQkIAAQENMMhEgQIAAAQIECBAgQIAAgdYWEABo7fZTegIECBAgQIAAAQIECBAg0JCAAEBDTDIRIECAAAECBAgQIECAAIHWFhAAaO32U3oCBAgQIECAAAECBAgQINCQgABAQ0wyESBAgAABAgQIECBAgACB1hYQAGjt9lN6AgQIECBAgAABAgQIECDQkIAAQENMMhEgQIAAAQIECBAgQIAAgdYWEABo7fZTegIECBAgQIAAAQIECBAg0JCAAEBDTDIRIECAAAECBAgQIECAAIHWFhAAaO32U3oCBAgQIECAAAECBAgQINCQgABAQ0wyESBAgAABAgQIECBAgACB1hYQAGjt9lN6AgQIECBAgAABAgQIECDQkIAAQENMMhEgQIAAAQIECBAgQIAAgdYWEABo7fZTegIECBAgQIAAAQIECBAg0JCAAEBDTDIRIECAAAECBAgQIECAAIHWFhAAaO32U3oCBAgQIECAAAECBAgQINCQgABAQ0wyESBAgAABAgQIECBAgACB1hYQAGjt9lN6AgQIECBAgAABAgQIECDQkIAAQENMMhEgQIAAAQIECBAgQIAAgdYWEABo7fZTegIECBAgQIAAAQIECBAg0JCAAEBDTDIRIECAAAECBAgQIECAAIHWFhAAaO32U3oCBAgQIECAAAECBAgQINCQgABAQ0wyESBAgAABAgQIECBAgACB1hYQAGjt9lN6AgQIECBAgAABAgQIECDQkIAAQENMMhEgQIAAAQIECBAgQIAAgdYWEABo7fZTegIECBAgQIAAAQIECBAg0JCAAEBDTDIRIECAAAECBAgQIECAAIHWFhAAaO32U3oCBAgQIECAAAECBAgQINCQgABAQ0wyESBAgAABAgQIECBAgACB1hYQAGjt9lN6AgQIECBAgAABAgQIECDQkIAAQENMMhEgQIAAAQIECBAgQIAAgdYWEABo7fZTegIECBAgQIAAAQIECBAg0JCAAEBDTDIRIECAAAECBAgQIECAAIHWFhAAaO32U3oCBAgQIECAAAECBAgQINCQgABAQ0wyESBAgAABAgQIECBAgACB1hYQAGjt9lN6AgQIECBAgAABAgQIECDQkIAAQENMMhEgQIAAAQIECBAgQIAAgdYWEABo7fZTegIECBAgQIAAAQIECBAg0JCAAEBDTDIRIECAAAECBAgQIECAAIHWFhAAaO32U3oCBAgQIECAAAECBAgQINCQgABAQ0wyESBAgAABAgQIECBAgACB1hYQAGjt9lN6AgQIECBAgAABAgQIECDQkIAAQENMMhEgQIAAAQIECBAgQIAAgdYWEABo7fZTegIECBAgQIAAAQIECBAg0JCAAEBDTDIRIECAAAECBAgQIECAAIHWFhAAaO32U3oCBAgQIECAAAECBAgQINCQgABAQ0wyESBAgAABAgQIECBAgACB1hYQAGjt9lN6AgQIECBAgAABAgQIECDQkIAAQENMMhEgQIAAAQIECBAgQIAAgdYWEABo7fZTegIECBAgQIAAAQIECBAg0JCAAEBDTDIRIECAAAECBAgQIECAAIHWFhAAaO32U3oCBAgQIECAAAECBAgQINCQgABAQ0wyESBAgAABAgQIECBAgACB1hYQAGjt9lN6AgQIECBAgAABAgQIECDQkIAAQENMMhEgQIAAAQIECBAgQIAAgdYWEABo7fZTegIECBAgQIAAAQIECBAg0JCAAEBDTDIRIECAAAECBAgQIECAAIHWFhAAaO32U3oCBAgQIECAAAECBAgQINCQgABAQ0wyESBAgAABAgQIECBAgACB1hYQAGjt9lN6AgQIECBAgAABAgQIECDQkIAAQENMMhEgQIAAAQIECBAgQIAAgdYWEABo7fZTegIECBAgQIAAAQIECBAg0JCAAEBDTDIRIECAAAECBAgQIECAAIHWFhAAaO32U3oCBAgQIECAAAECBAgQINCQgABAQ0wyESBAgAABAgQIECBAgACB1hYQAGjt9lN6AgQIECBAgAABAgQIECDQkMD/AAHuV/DlTzwfAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Pandafy a Spark DataFrame\n",
    "Suppose you've run a query on your huge dataset and aggregated it down to something a little more manageable.\n",
    "\n",
    "Sometimes it makes sense to then take that table and work with it locally using a tool like pandas. Spark DataFrames make that easy with the .toPandas() method. Calling this method on a Spark DataFrame returns the corresponding pandas DataFrame. It's as simple as that!\n",
    "\n",
    "This time the query counts the number of flights to each airport from SEA and PDX.\n",
    "\n",
    "Remember, there's already a SparkSession called spark in your workspace!\n",
    "\n",
    "```python\n",
    "query = \"SELECT origin, dest, COUNT(*) as N FROM flights GROUP BY origin, dest\"\n",
    "\n",
    "# Run the query\n",
    "flight_counts = spark.sql(query)\n",
    "\n",
    "# Convert the results to a pandas DataFrame\n",
    "pd_counts = flight_counts.toPandas()\n",
    "\n",
    "# Print the head of pd_counts\n",
    "print(pd_counts.head())\n",
    "\n",
    "```\n",
    "### Put some Spark in your data\n",
    "In the last exercise, you saw how to move data from Spark to pandas. However, maybe you want to go the other direction, and put a pandas DataFrame into a Spark cluster! The SparkSession class has a method for this as well.\n",
    "\n",
    "The .createDataFrame() method takes a pandas DataFrame and returns a Spark DataFrame.\n",
    "\n",
    "The output of this method is stored locally, not in the SparkSession catalog. This means that you can use all the Spark DataFrame methods on it, but you can't access the data in other contexts.\n",
    "\n",
    "For example, a SQL query (using the .sql() method) that references your DataFrame will throw an error. To access the data in this way, you have to save it as a temporary table.\n",
    "\n",
    "You can do this using the .createTempView() Spark DataFrame method, which takes as its only argument the name of the temporary table you'd like to register. This method registers the DataFrame as a table in the catalog, but as this table is temporary, it can only be accessed from the specific SparkSession used to create the Spark DataFrame.\n",
    "\n",
    "There is also the method .createOrReplaceTempView(). This safely creates a new temporary table if nothing was there before, or updates an existing table if one was already defined. You'll use this method to avoid running into problems with duplicate tables.\n",
    "\n",
    "Check out the diagram to see all the different ways your Spark data structures interact with each other.\n",
    "\n",
    "![spark_figure.png](attachment:spark_figure.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Table(name='flights', database=None, description=None, tableType='TEMPORARY', isTemporary=True), Table(name='temp', database=None, description=None, tableType='TEMPORARY', isTemporary=True)]\n",
      "[Table(name='flights', database=None, description=None, tableType='TEMPORARY', isTemporary=True), Table(name='temp', database=None, description=None, tableType='TEMPORARY', isTemporary=True)]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# Create pd_temp\n",
    "pd_temp = pd.DataFrame(np.random.random(10))\n",
    "\n",
    "# Create spark_temp from pd_temp\n",
    "spark_temp = spark.createDataFrame(pd_temp)\n",
    "\n",
    "# Examine the tables in the catalog\n",
    "print(spark.catalog.listTables())\n",
    "\n",
    "# Add spark_temp to the catalog\n",
    "spark_temp.createOrReplaceTempView(\"temp\")\n",
    "\n",
    "# Examine the tables in the catalog again\n",
    "print(spark.catalog.listTables())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping the middle man\n",
    "Now you know how to put data into Spark via pandas, but you're probably wondering why deal with pandas at all? Wouldn't it be easier to just read a text file straight into Spark? Of course it would!\n",
    "\n",
    "Luckily, your SparkSession has a .read attribute which has several methods for reading different data sources into Spark DataFrames. Using these you can create a DataFrame from a .csv file just like with regular pandas DataFrames!\n",
    "\n",
    "The variable file_path is a string with the path to the file airports.csv. This file contains information about different airports all over the world.\n",
    "\n",
    "A SparkSession named spark is available in your workspace.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+--------------------+----------------+-----------------+----+---+---+\n",
      "|faa|                name|             lat|              lon| alt| tz|dst|\n",
      "+---+--------------------+----------------+-----------------+----+---+---+\n",
      "|04G|   Lansdowne Airport|      41.1304722|      -80.6195833|1044| -5|  A|\n",
      "|06A|Moton Field Munic...|      32.4605722|      -85.6800278| 264| -5|  A|\n",
      "|06C| Schaumburg Regional|      41.9893408|      -88.1012428| 801| -6|  A|\n",
      "|06N|     Randall Airport|       41.431912|      -74.3915611| 523| -5|  A|\n",
      "|09J|Jekyll Island Air...|      31.0744722|      -81.4277778|  11| -4|  A|\n",
      "|0A9|Elizabethton Muni...|      36.3712222|      -82.1734167|1593| -4|  A|\n",
      "|0G6|Williams County A...|      41.4673056|      -84.5067778| 730| -5|  A|\n",
      "|0G7|Finger Lakes Regi...|      42.8835647|      -76.7812318| 492| -5|  A|\n",
      "|0P2|Shoestring Aviati...|      39.7948244|      -76.6471914|1000| -5|  U|\n",
      "|0S9|Jefferson County ...|      48.0538086|     -122.8106436| 108| -8|  A|\n",
      "|0W3|Harford County Ai...|      39.5668378|      -76.2024028| 409| -5|  A|\n",
      "|10C|  Galt Field Airport|      42.4028889|      -88.3751111| 875| -6|  U|\n",
      "|17G|Port Bucyrus-Craw...|      40.7815556|      -82.9748056|1003| -5|  A|\n",
      "|19A|Jackson County Ai...|      34.1758638|      -83.5615972| 951| -4|  U|\n",
      "|1A3|Martin Campbell F...|      35.0158056|      -84.3468333|1789| -4|  A|\n",
      "|1B9| Mansfield Municipal|      42.0001331|      -71.1967714| 122| -5|  A|\n",
      "|1C9|Frazier Lake Airpark|54.0133333333333|-124.768333333333| 152| -8|  A|\n",
      "|1CS|Clow Internationa...|      41.6959744|      -88.1292306| 670| -6|  U|\n",
      "|1G3|  Kent State Airport|      41.1513889|      -81.4151111|1134| -4|  A|\n",
      "|1OH|     Fortman Airport|      40.5553253|      -84.3866186| 885| -5|  U|\n",
      "+---+--------------------+----------------+-----------------+----+---+---+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "file_path = 'data/airports.csv'\n",
    "\n",
    "#Read in the airports path\n",
    "airports = spark.read.csv(file_path, header=True)\n",
    "\n",
    "airports.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyspark.sql.dataframe.DataFrame"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(airports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Database(name='default', description='Default Hive database', locationUri='file:/Users/aysbt/AnacondaProjects/DataCamp/spark-warehouse')]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_spark.catalog.listDatabases()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Table(name='flights', database=None, description=None, tableType='TEMPORARY', isTemporary=True),\n",
       " Table(name='temp', database=None, description=None, tableType='TEMPORARY', isTemporary=True)]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_spark.catalog.listTables()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating data\n",
    "### Creating columns\n",
    "In this chapter, you'll learn how to use the methods defined by Spark's DataFrame class to perform common data operations.\n",
    "\n",
    "Let's look at performing column-wise operations. In Spark you can do this using the .withColumn() method, which takes two arguments. First, a string with the name of your new column, and second the new column itself.\n",
    "\n",
    "The new column must be an object of class Column. Creating one of these is as easy as extracting a column from your DataFrame using df.colName.\n",
    "\n",
    "Updating a Spark DataFrame is somewhat different than working in pandas because the Spark DataFrame is immutable. This means that it can't be changed, and so columns can't be updated in place.\n",
    "\n",
    "Thus, all these methods return a new DataFrame. To overwrite the original DataFrame you must reassign the returned DataFrame using the method like so:\n",
    "```python\n",
    "df = df.withColumn(\"newCol\", df.oldCol + 1)\n",
    "```\n",
    "The above code creates a DataFrame with the same columns as df plus a new column, newCol, where every entry is equal to the corresponding entry from oldCol, plus one.\n",
    "\n",
    "To overwrite an existing column, just pass the name of the column as the first argument!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|tailnum|flight|origin|dest|air_time|distance|hour|minute|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "|2014|   12|  8|     658|       -7|     935|       -5|     VX| N846VA|  1780|   SEA| LAX|     132|     954|   6|    58|\n",
      "|2014|    1| 22|    1040|        5|    1505|        5|     AS| N559AS|   851|   SEA| HNL|     360|    2677|  10|    40|\n",
      "|2014|    3|  9|    1443|       -2|    1652|        2|     VX| N847VA|   755|   SEA| SFO|     111|     679|  14|    43|\n",
      "|2014|    4|  9|    1705|       45|    1839|       34|     WN| N360SW|   344|   PDX| SJC|      83|     569|  17|     5|\n",
      "|2014|    3|  9|     754|       -1|    1015|        1|     AS| N612AS|   522|   SEA| BUR|     127|     937|   7|    54|\n",
      "|2014|    1| 15|    1037|        7|    1352|        2|     WN| N646SW|    48|   PDX| DEN|     121|     991|  10|    37|\n",
      "|2014|    7|  2|     847|       42|    1041|       51|     WN| N422WN|  1520|   PDX| OAK|      90|     543|   8|    47|\n",
      "|2014|    5| 12|    1655|       -5|    1842|      -18|     VX| N361VA|   755|   SEA| SFO|      98|     679|  16|    55|\n",
      "|2014|    4| 19|    1236|       -4|    1508|       -7|     AS| N309AS|   490|   SEA| SAN|     135|    1050|  12|    36|\n",
      "|2014|   11| 19|    1812|       -3|    2352|       -4|     AS| N564AS|    26|   SEA| ORD|     198|    1721|  18|    12|\n",
      "|2014|   11|  8|    1653|       -2|    1924|       -1|     AS| N323AS|   448|   SEA| LAX|     130|     954|  16|    53|\n",
      "|2014|    8|  3|    1120|        0|    1415|        2|     AS| N305AS|   656|   SEA| PHX|     154|    1107|  11|    20|\n",
      "|2014|   10| 30|     811|       21|    1038|       29|     AS| N433AS|   608|   SEA| LAS|     127|     867|   8|    11|\n",
      "|2014|   11| 12|    2346|       -4|     217|      -28|     AS| N765AS|   121|   SEA| ANC|     183|    1448|  23|    46|\n",
      "|2014|   10| 31|    1314|       89|    1544|      111|     AS| N713AS|   306|   SEA| SFO|     129|     679|  13|    14|\n",
      "|2014|    1| 29|    2009|        3|    2159|        9|     UA| N27205|  1458|   PDX| SFO|      90|     550|  20|     9|\n",
      "|2014|   12| 17|    2015|       50|    2150|       41|     AS| N626AS|   368|   SEA| SMF|      76|     605|  20|    15|\n",
      "|2014|    8| 11|    1017|       -3|    1613|       -7|     WN| N8634A|   827|   SEA| MDW|     216|    1733|  10|    17|\n",
      "|2014|    1| 13|    2156|       -9|     607|      -15|     AS| N597AS|    24|   SEA| BOS|     290|    2496|  21|    56|\n",
      "|2014|    6|  5|    1733|      -12|    1945|      -10|     OO| N215AG|  3488|   PDX| BUR|     111|     817|  17|    33|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "flights = spark.read.csv('data/flights_small.csv', header=True)\n",
    "flights.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Table(name='flights', database=None, description=None, tableType='TEMPORARY', isTemporary=True),\n",
       " Table(name='temp', database=None, description=None, tableType='TEMPORARY', isTemporary=True)]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.name = flights.createOrReplaceTempView('flights')\n",
    "my_spark.catalog.listTables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|tailnum|flight|origin|dest|air_time|distance|hour|minute|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "|2014|   12|  8|     658|       -7|     935|       -5|     VX| N846VA|  1780|   SEA| LAX|     132|     954|   6|    58|\n",
      "|2014|    1| 22|    1040|        5|    1505|        5|     AS| N559AS|   851|   SEA| HNL|     360|    2677|  10|    40|\n",
      "|2014|    3|  9|    1443|       -2|    1652|        2|     VX| N847VA|   755|   SEA| SFO|     111|     679|  14|    43|\n",
      "|2014|    4|  9|    1705|       45|    1839|       34|     WN| N360SW|   344|   PDX| SJC|      83|     569|  17|     5|\n",
      "|2014|    3|  9|     754|       -1|    1015|        1|     AS| N612AS|   522|   SEA| BUR|     127|     937|   7|    54|\n",
      "|2014|    1| 15|    1037|        7|    1352|        2|     WN| N646SW|    48|   PDX| DEN|     121|     991|  10|    37|\n",
      "|2014|    7|  2|     847|       42|    1041|       51|     WN| N422WN|  1520|   PDX| OAK|      90|     543|   8|    47|\n",
      "|2014|    5| 12|    1655|       -5|    1842|      -18|     VX| N361VA|   755|   SEA| SFO|      98|     679|  16|    55|\n",
      "|2014|    4| 19|    1236|       -4|    1508|       -7|     AS| N309AS|   490|   SEA| SAN|     135|    1050|  12|    36|\n",
      "|2014|   11| 19|    1812|       -3|    2352|       -4|     AS| N564AS|    26|   SEA| ORD|     198|    1721|  18|    12|\n",
      "|2014|   11|  8|    1653|       -2|    1924|       -1|     AS| N323AS|   448|   SEA| LAX|     130|     954|  16|    53|\n",
      "|2014|    8|  3|    1120|        0|    1415|        2|     AS| N305AS|   656|   SEA| PHX|     154|    1107|  11|    20|\n",
      "|2014|   10| 30|     811|       21|    1038|       29|     AS| N433AS|   608|   SEA| LAS|     127|     867|   8|    11|\n",
      "|2014|   11| 12|    2346|       -4|     217|      -28|     AS| N765AS|   121|   SEA| ANC|     183|    1448|  23|    46|\n",
      "|2014|   10| 31|    1314|       89|    1544|      111|     AS| N713AS|   306|   SEA| SFO|     129|     679|  13|    14|\n",
      "|2014|    1| 29|    2009|        3|    2159|        9|     UA| N27205|  1458|   PDX| SFO|      90|     550|  20|     9|\n",
      "|2014|   12| 17|    2015|       50|    2150|       41|     AS| N626AS|   368|   SEA| SMF|      76|     605|  20|    15|\n",
      "|2014|    8| 11|    1017|       -3|    1613|       -7|     WN| N8634A|   827|   SEA| MDW|     216|    1733|  10|    17|\n",
      "|2014|    1| 13|    2156|       -9|     607|      -15|     AS| N597AS|    24|   SEA| BOS|     290|    2496|  21|    56|\n",
      "|2014|    6|  5|    1733|      -12|    1945|      -10|     OO| N215AG|  3488|   PDX| BUR|     111|     817|  17|    33|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "only showing top 20 rows\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Create the DataFrame flights\n",
    "flights_df = my_spark.table('flights')\n",
    "print(flights_df.show())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|tailnum|flight|origin|dest|air_time|distance|hour|minute|      duration_hrs|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "|2014|   12|  8|     658|       -7|     935|       -5|     VX| N846VA|  1780|   SEA| LAX|     132|     954|   6|    58|               2.2|\n",
      "|2014|    1| 22|    1040|        5|    1505|        5|     AS| N559AS|   851|   SEA| HNL|     360|    2677|  10|    40|               6.0|\n",
      "|2014|    3|  9|    1443|       -2|    1652|        2|     VX| N847VA|   755|   SEA| SFO|     111|     679|  14|    43|              1.85|\n",
      "|2014|    4|  9|    1705|       45|    1839|       34|     WN| N360SW|   344|   PDX| SJC|      83|     569|  17|     5|1.3833333333333333|\n",
      "|2014|    3|  9|     754|       -1|    1015|        1|     AS| N612AS|   522|   SEA| BUR|     127|     937|   7|    54|2.1166666666666667|\n",
      "|2014|    1| 15|    1037|        7|    1352|        2|     WN| N646SW|    48|   PDX| DEN|     121|     991|  10|    37|2.0166666666666666|\n",
      "|2014|    7|  2|     847|       42|    1041|       51|     WN| N422WN|  1520|   PDX| OAK|      90|     543|   8|    47|               1.5|\n",
      "|2014|    5| 12|    1655|       -5|    1842|      -18|     VX| N361VA|   755|   SEA| SFO|      98|     679|  16|    55|1.6333333333333333|\n",
      "|2014|    4| 19|    1236|       -4|    1508|       -7|     AS| N309AS|   490|   SEA| SAN|     135|    1050|  12|    36|              2.25|\n",
      "|2014|   11| 19|    1812|       -3|    2352|       -4|     AS| N564AS|    26|   SEA| ORD|     198|    1721|  18|    12|               3.3|\n",
      "|2014|   11|  8|    1653|       -2|    1924|       -1|     AS| N323AS|   448|   SEA| LAX|     130|     954|  16|    53|2.1666666666666665|\n",
      "|2014|    8|  3|    1120|        0|    1415|        2|     AS| N305AS|   656|   SEA| PHX|     154|    1107|  11|    20| 2.566666666666667|\n",
      "|2014|   10| 30|     811|       21|    1038|       29|     AS| N433AS|   608|   SEA| LAS|     127|     867|   8|    11|2.1166666666666667|\n",
      "|2014|   11| 12|    2346|       -4|     217|      -28|     AS| N765AS|   121|   SEA| ANC|     183|    1448|  23|    46|              3.05|\n",
      "|2014|   10| 31|    1314|       89|    1544|      111|     AS| N713AS|   306|   SEA| SFO|     129|     679|  13|    14|              2.15|\n",
      "|2014|    1| 29|    2009|        3|    2159|        9|     UA| N27205|  1458|   PDX| SFO|      90|     550|  20|     9|               1.5|\n",
      "|2014|   12| 17|    2015|       50|    2150|       41|     AS| N626AS|   368|   SEA| SMF|      76|     605|  20|    15|1.2666666666666666|\n",
      "|2014|    8| 11|    1017|       -3|    1613|       -7|     WN| N8634A|   827|   SEA| MDW|     216|    1733|  10|    17|               3.6|\n",
      "|2014|    1| 13|    2156|       -9|     607|      -15|     AS| N597AS|    24|   SEA| BOS|     290|    2496|  21|    56| 4.833333333333333|\n",
      "|2014|    6|  5|    1733|      -12|    1945|      -10|     OO| N215AG|  3488|   PDX| BUR|     111|     817|  17|    33|              1.85|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#include a new column called duration_hrs\n",
    "flights = flights.withColumn('duration_hrs', flights.air_time / 60)\n",
    "flights.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering Data\n",
    "Let's take a look at the .filter() method. As you might suspect, this is the Spark counterpart of SQL's WHERE clause. The .filter() method takes either a Spark Column of boolean (True/False) values or the WHERE clause of a SQL expression as a string.\n",
    "\n",
    "For example, the following two expressions will produce the same output:\n",
    "```python\n",
    "flights.filter(flights.air_time > 120).show()\n",
    "flights.filter(\"air_time > 120\").show()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|tailnum|flight|origin|dest|air_time|distance|hour|minute|      duration_hrs|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "|2014|    1| 22|    1040|        5|    1505|        5|     AS| N559AS|   851|   SEA| HNL|     360|    2677|  10|    40|               6.0|\n",
      "|2014|    4| 19|    1236|       -4|    1508|       -7|     AS| N309AS|   490|   SEA| SAN|     135|    1050|  12|    36|              2.25|\n",
      "|2014|   11| 19|    1812|       -3|    2352|       -4|     AS| N564AS|    26|   SEA| ORD|     198|    1721|  18|    12|               3.3|\n",
      "|2014|    8|  3|    1120|        0|    1415|        2|     AS| N305AS|   656|   SEA| PHX|     154|    1107|  11|    20| 2.566666666666667|\n",
      "|2014|   11| 12|    2346|       -4|     217|      -28|     AS| N765AS|   121|   SEA| ANC|     183|    1448|  23|    46|              3.05|\n",
      "|2014|    8| 11|    1017|       -3|    1613|       -7|     WN| N8634A|   827|   SEA| MDW|     216|    1733|  10|    17|               3.6|\n",
      "|2014|    1| 13|    2156|       -9|     607|      -15|     AS| N597AS|    24|   SEA| BOS|     290|    2496|  21|    56| 4.833333333333333|\n",
      "|2014|    9| 26|     610|       -5|    1523|       65|     US| N127UW|   616|   SEA| PHL|     293|    2378|   6|    10| 4.883333333333334|\n",
      "|2014|   12|  4|     954|       -6|    1348|      -17|     HA| N395HA|    29|   SEA| OGG|     333|    2640|   9|    54|              5.55|\n",
      "|2014|    6|  4|    1115|        0|    1346|       -3|     AS| N461AS|   488|   SEA| SAN|     133|    1050|  11|    15| 2.216666666666667|\n",
      "|2014|    6| 26|    2054|       -1|    2318|       -6|     B6| N590JB|   907|   SEA| ANC|     179|    1448|  20|    54|2.9833333333333334|\n",
      "|2014|    6|  7|    1823|       -7|    2112|      -28|     AS| N512AS|   815|   SEA| LIH|     335|    2701|  18|    23| 5.583333333333333|\n",
      "|2014|    4| 30|     801|        1|    1757|       90|     AS| N407AS|    18|   SEA| MCO|     342|    2554|   8|     1|               5.7|\n",
      "|2014|   11| 29|     905|      155|    1655|      170|     DL| N824DN|  1598|   SEA| ATL|     229|    2182|   9|     5| 3.816666666666667|\n",
      "|2014|    6|  2|    2222|        7|      55|       15|     AS| N402AS|    99|   SEA| ANC|     190|    1448|  22|    22|3.1666666666666665|\n",
      "|2014|   11| 15|    1034|       -6|    1414|      -26|     AS| N589AS|   794|   SEA| ABQ|     139|    1180|  10|    34| 2.316666666666667|\n",
      "|2014|   10| 20|    1328|       -1|    1949|        4|     UA| N68805|  1212|   SEA| IAH|     228|    1874|  13|    28|               3.8|\n",
      "|2014|   12| 16|    1500|        0|    1906|       19|     US| N662AW|   500|   SEA| PHX|     151|    1107|  15|     0|2.5166666666666666|\n",
      "|2014|   11| 19|    1319|       -6|    1821|      -14|     DL| N309US|  2164|   PDX| MSP|     169|    1426|  13|    19| 2.816666666666667|\n",
      "|2014|    5| 21|     515|        0|     757|        0|     US| N172US|   593|   SEA| PHX|     143|    1107|   5|    15|2.3833333333333333|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "only showing top 20 rows\n",
      "\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|tailnum|flight|origin|dest|air_time|distance|hour|minute|      duration_hrs|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "|2014|    1| 22|    1040|        5|    1505|        5|     AS| N559AS|   851|   SEA| HNL|     360|    2677|  10|    40|               6.0|\n",
      "|2014|    4| 19|    1236|       -4|    1508|       -7|     AS| N309AS|   490|   SEA| SAN|     135|    1050|  12|    36|              2.25|\n",
      "|2014|   11| 19|    1812|       -3|    2352|       -4|     AS| N564AS|    26|   SEA| ORD|     198|    1721|  18|    12|               3.3|\n",
      "|2014|    8|  3|    1120|        0|    1415|        2|     AS| N305AS|   656|   SEA| PHX|     154|    1107|  11|    20| 2.566666666666667|\n",
      "|2014|   11| 12|    2346|       -4|     217|      -28|     AS| N765AS|   121|   SEA| ANC|     183|    1448|  23|    46|              3.05|\n",
      "|2014|    8| 11|    1017|       -3|    1613|       -7|     WN| N8634A|   827|   SEA| MDW|     216|    1733|  10|    17|               3.6|\n",
      "|2014|    1| 13|    2156|       -9|     607|      -15|     AS| N597AS|    24|   SEA| BOS|     290|    2496|  21|    56| 4.833333333333333|\n",
      "|2014|    9| 26|     610|       -5|    1523|       65|     US| N127UW|   616|   SEA| PHL|     293|    2378|   6|    10| 4.883333333333334|\n",
      "|2014|   12|  4|     954|       -6|    1348|      -17|     HA| N395HA|    29|   SEA| OGG|     333|    2640|   9|    54|              5.55|\n",
      "|2014|    6|  4|    1115|        0|    1346|       -3|     AS| N461AS|   488|   SEA| SAN|     133|    1050|  11|    15| 2.216666666666667|\n",
      "|2014|    6| 26|    2054|       -1|    2318|       -6|     B6| N590JB|   907|   SEA| ANC|     179|    1448|  20|    54|2.9833333333333334|\n",
      "|2014|    6|  7|    1823|       -7|    2112|      -28|     AS| N512AS|   815|   SEA| LIH|     335|    2701|  18|    23| 5.583333333333333|\n",
      "|2014|    4| 30|     801|        1|    1757|       90|     AS| N407AS|    18|   SEA| MCO|     342|    2554|   8|     1|               5.7|\n",
      "|2014|   11| 29|     905|      155|    1655|      170|     DL| N824DN|  1598|   SEA| ATL|     229|    2182|   9|     5| 3.816666666666667|\n",
      "|2014|    6|  2|    2222|        7|      55|       15|     AS| N402AS|    99|   SEA| ANC|     190|    1448|  22|    22|3.1666666666666665|\n",
      "|2014|   11| 15|    1034|       -6|    1414|      -26|     AS| N589AS|   794|   SEA| ABQ|     139|    1180|  10|    34| 2.316666666666667|\n",
      "|2014|   10| 20|    1328|       -1|    1949|        4|     UA| N68805|  1212|   SEA| IAH|     228|    1874|  13|    28|               3.8|\n",
      "|2014|   12| 16|    1500|        0|    1906|       19|     US| N662AW|   500|   SEA| PHX|     151|    1107|  15|     0|2.5166666666666666|\n",
      "|2014|   11| 19|    1319|       -6|    1821|      -14|     DL| N309US|  2164|   PDX| MSP|     169|    1426|  13|    19| 2.816666666666667|\n",
      "|2014|    5| 21|     515|        0|     757|        0|     US| N172US|   593|   SEA| PHX|     143|    1107|   5|    15|2.3833333333333333|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Filter flights with a SQL string\n",
    "long_flights1 = flights.filter('distance > 1000')\n",
    "# Filter flights with a boolean column\n",
    "long_flights2 = flights.filter(flights.distance > 1000 )\n",
    "\n",
    "long_flights1.show()\n",
    "long_flights2.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting\n",
    "The Spark variant of SQL's SELECT is the .select() method. This method takes multiple arguments - one for each column you want to select. These arguments can either be the column name as a string (one for each column) or a column object (using the df.colName syntax). When you pass a column object, you can perform operations like addition or subtraction on the column to change the data contained in it, much like inside .withColumn().\n",
    "\n",
    "The difference between .select() and .withColumn() methods is that .select() returns only the columns you specify, while .withColumn() returns all the columns of the DataFrame in addition to the one you defined. It's often a good idea to drop columns you don't need at the beginning of an operation so that you're not dragging around extra data as you're wrangling. In this case, you would use .select() and not .withColumn()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+----+-------+\n",
      "|origin|dest|carrier|\n",
      "+------+----+-------+\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     AS|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     AS|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "+------+----+-------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Select the first set of columns as a string\n",
    "selected_1 = flights.select('tailnum', 'origin', 'dest')\n",
    "\n",
    "# Select the second set of columns usinf df.col_name\n",
    "temp = flights.select(flights.origin, flights.dest, flights.carrier)\n",
    "\n",
    "# Define first filter to only keep flights from SEA to PDX.\n",
    "FilterA = flights.origin == 'SEA'\n",
    "FilterB =flights.dest == 'PDX'\n",
    "\n",
    "# Filter the data, first by filterA then by filterB\n",
    "selected_2 = temp.filter(FilterA).filter(FilterB)\n",
    "\n",
    "selected_2.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to SQL, you can also use the .select() method to perform column-wise operations. When you're selecting a column using the df.colName notation, you can perform any column operation and the .select() method will return the transformed column. For example,\n",
    "\n",
    "```python\n",
    "flights.select(flights.air_time/60)\n",
    "```\n",
    "returns a column of flight durations in hours instead of minutes. You can also use the .alias() method to rename a column you're selecting. So if you wanted to .select() the column duration_hrs (which isn't in your DataFrame) you could do\n",
    "\n",
    "```python\n",
    "flights.select((flights.air_time/60).alias(\"duration_hrs\"))\n",
    "```\n",
    "The equivalent Spark DataFrame method .selectExpr() takes SQL expressions as a string:\n",
    "\n",
    "flights.selectExpr(\"air_time/60 as duration_hrs\")\n",
    "with the SQL as keyword being equivalent to the .alias() method. To select multiple columns, you can pass multiple strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+----+-------+------------------+\n",
      "|origin|dest|tailnum|         avg_speed|\n",
      "+------+----+-------+------------------+\n",
      "|   SEA| LAX| N846VA| 433.6363636363636|\n",
      "|   SEA| HNL| N559AS| 446.1666666666667|\n",
      "|   SEA| SFO| N847VA|367.02702702702703|\n",
      "|   PDX| SJC| N360SW| 411.3253012048193|\n",
      "|   SEA| BUR| N612AS| 442.6771653543307|\n",
      "|   PDX| DEN| N646SW|491.40495867768595|\n",
      "|   PDX| OAK| N422WN|             362.0|\n",
      "|   SEA| SFO| N361VA| 415.7142857142857|\n",
      "|   SEA| SAN| N309AS| 466.6666666666667|\n",
      "|   SEA| ORD| N564AS| 521.5151515151515|\n",
      "|   SEA| LAX| N323AS| 440.3076923076923|\n",
      "|   SEA| PHX| N305AS|431.29870129870125|\n",
      "|   SEA| LAS| N433AS| 409.6062992125984|\n",
      "|   SEA| ANC| N765AS|474.75409836065575|\n",
      "|   SEA| SFO| N713AS| 315.8139534883721|\n",
      "|   PDX| SFO| N27205| 366.6666666666667|\n",
      "|   SEA| SMF| N626AS|477.63157894736844|\n",
      "|   SEA| MDW| N8634A|481.38888888888886|\n",
      "|   SEA| BOS| N597AS| 516.4137931034483|\n",
      "|   PDX| BUR| N215AG| 441.6216216216216|\n",
      "+------+----+-------+------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Create a table of the average speed of each flight both ways.\n",
    "#Calculate average speed by dividing the distance by the air_time (converted to hours).Use the .alias() method name\n",
    "# Define avg_speed\n",
    "avg_speed = (flights.distance/(flights.air_time/60)).alias(\"avg_speed\")\n",
    "speed_1 = flights.select('origin','dest','tailnum', avg_speed)\n",
    "\n",
    "#Using the Spark DataFrame method .selectExpr() \n",
    "speed_2 =flights.selectExpr('origin','dest','tailnum','distance/(air_time/60) as avg_speed')\n",
    "speed_2.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregating\n",
    "All of the common aggregation methods, like `.min()`, `.max()`, and `.count()` are GroupedData methods. These are created by calling the `.groupBy()` DataFrame method. For now, all you have to do to use these functions is call that method on your DataFrame. For example, to find the minimum value of a column, col, in a DataFrame, df, you could do\n",
    "```python\n",
    "df.groupBy().min(\"col\").show()\n",
    "```\n",
    "This creates a GroupedData object (so you can use the .min() method), then finds the minimum value in col, and returns it as a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[summary: string, year: string, month: string, day: string, dep_time: string, dep_delay: string, arr_time: string, arr_delay: string, carrier: string, tailnum: string, flight: string, origin: string, dest: string, air_time: string, distance: string, hour: string, minute: string, duration_hrs: string]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------------+-----------------+\n",
      "|summary|          air_time|         distance|\n",
      "+-------+------------------+-----------------+\n",
      "|  count|              9925|            10000|\n",
      "|   mean|152.88423173803525|        1208.1516|\n",
      "| stddev|  72.8656286392139|656.8599023464376|\n",
      "|    min|              20.0|             93.0|\n",
      "|    max|             409.0|           2724.0|\n",
      "+-------+------------------+-----------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#arr_time: string and distance: string, so to find min() and max() we need to convert this float \n",
    "flights = flights.withColumn('distance', flights.distance.cast('float'))\n",
    "flights = flights.withColumn('air_time', flights.air_time.cast('float'))\n",
    "\n",
    "flights.describe('air_time', 'distance').show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+\n",
      "|min(distance)|\n",
      "+-------------+\n",
      "|        106.0|\n",
      "+-------------+\n",
      "\n",
      "+-------------+\n",
      "|max(air_time)|\n",
      "+-------------+\n",
      "|        409.0|\n",
      "+-------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Find the length of the shortest (in terms of distance) flight that left PDX \n",
    "flights.filter(flights.origin =='PDX').groupBy().min('distance').show()\n",
    "\n",
    "#Find the length of the longest (in terms of time) flight that left SEA\n",
    "flights.filter(flights.origin == 'SEA').groupBy().max('air_time').show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+\n",
      "|     avg(air_time)|\n",
      "+------------------+\n",
      "|188.20689655172413|\n",
      "+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#get the average air time of Delta Airlines flights  that left SEA. \n",
    "flights.filter(flights.carrier == 'DL').filter(flights.origin == 'SEA').groupBy().avg('air_time').show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+\n",
      "| sum(duration_hrs)|\n",
      "+------------------+\n",
      "|25289.600000000126|\n",
      "+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#get the total number of hours all planes in this dataset spent in the air by creating a column called duration_hrs\n",
    "flights.withColumn('duration_hrs', flights.air_time/60).groupBy().sum('duration_hrs').show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping and Aggregating I\n",
    "Part of what makes aggregating so powerful is the addition of groups. PySpark has a whole class devoted to grouped data frames: `pyspark.sql.GroupedData`, which you saw in the previous exercises.\n",
    "\n",
    "You've learned how to create a grouped DataFrame by calling the .groupBy() method on a DataFrame with no arguments.Now you'll see that when you pass the name of one or more columns in your DataFrame to the .groupBy() method, the aggregation methods behave like when you use a GROUP BY statement in a SQL query!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-----+\n",
      "|tailnum|count|\n",
      "+-------+-----+\n",
      "| N442AS|   38|\n",
      "| N102UW|    2|\n",
      "| N36472|    4|\n",
      "| N38451|    4|\n",
      "| N73283|    4|\n",
      "| N513UA|    2|\n",
      "| N954WN|    5|\n",
      "| N388DA|    3|\n",
      "| N567AA|    1|\n",
      "| N516UA|    2|\n",
      "| N927DN|    1|\n",
      "| N8322X|    1|\n",
      "| N466SW|    1|\n",
      "|  N6700|    1|\n",
      "| N607AS|   45|\n",
      "| N622SW|    4|\n",
      "| N584AS|   31|\n",
      "| N914WN|    4|\n",
      "| N654AW|    2|\n",
      "| N336NW|    1|\n",
      "+-------+-----+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Group bu tailnum colum\n",
    "by_plane = flights.groupBy('tailnum')\n",
    "\n",
    "#Use the .count() method with no arguments to count the number of flights each plane made\n",
    "by_plane.count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+------------------+\n",
      "|origin|     avg(air_time)|\n",
      "+------+------------------+\n",
      "|   SEA| 160.4361496051259|\n",
      "|   PDX|137.11543248288737|\n",
      "+------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#group by origin column\n",
    "by_origin = flights.groupBy('origin')\n",
    "\n",
    "#Find the .avg() of the air_time column to find average duration of flights from PDX and SEA\n",
    "by_origin.avg('air_time').show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping and Aggregating II\n",
    "In addition to the GroupedData methods you've already seen, there is also the `.agg()` method. This method lets you pass an aggregate column expression that uses any of the aggregate functions from the pyspark.sql.functions submodule.\n",
    "\n",
    "This submodule contains many useful functions for computing things like standard deviations. All the aggregation functions in this submodule take the name of a column in a GroupedData table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import pyspark.sql.functions as F\n",
    "import pyspark.sql.functions as F\n",
    "\n",
    "# Group by month and dest\n",
    "by_month_dest = flights.groupBy('month', 'dest')\n",
    "\n",
    "#convert to dep_delay to numeric column\n",
    "flights = flights.withColumn('dep_delay', flights.dep_delay.cast('float'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+----+--------------------+\n",
      "|month|dest|      avg(dep_delay)|\n",
      "+-----+----+--------------------+\n",
      "|   11| TUS| -2.3333333333333335|\n",
      "|   11| ANC|   7.529411764705882|\n",
      "|    1| BUR|               -1.45|\n",
      "|    1| PDX| -5.6923076923076925|\n",
      "|    6| SBA|                -2.5|\n",
      "|    5| LAX|-0.15789473684210525|\n",
      "|   10| DTW|                 2.6|\n",
      "|    6| SIT|                -1.0|\n",
      "|   10| DFW|  18.176470588235293|\n",
      "|    3| FAI|                -2.2|\n",
      "|   10| SEA|                -0.8|\n",
      "|    2| TUS| -0.6666666666666666|\n",
      "|   12| OGG|  25.181818181818183|\n",
      "|    9| DFW|   4.066666666666666|\n",
      "|    5| EWR|               14.25|\n",
      "|    3| RDM|                -6.2|\n",
      "|    8| DCA|                 2.6|\n",
      "|    7| ATL|   4.675675675675675|\n",
      "|    4| JFK| 0.07142857142857142|\n",
      "|   10| SNA| -1.1333333333333333|\n",
      "+-----+----+--------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Average departure delay by month and destination\n",
    "by_month_dest.avg('dep_delay').show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Joining II\n",
    "In PySpark, joins are performed using the DataFrame method `.join()`. This method takes `three arguments`. The `first` is the `second DataFrame` that you want to join with the first one. The `second argument`, on, is the `name of the key column(s)` as a string. The names of the key column(s) must be the same in each table. The `third argument`, `how,` specifies the kind of join to perform. In this course we'll always use the value `how=\"leftouter`\".\n",
    "\n",
    "The flights dataset and a new dataset called airports are already in your workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+--------------------+----------------+-----------------+----+---+---+\n",
      "|faa|                name|             lat|              lon| alt| tz|dst|\n",
      "+---+--------------------+----------------+-----------------+----+---+---+\n",
      "|04G|   Lansdowne Airport|      41.1304722|      -80.6195833|1044| -5|  A|\n",
      "|06A|Moton Field Munic...|      32.4605722|      -85.6800278| 264| -5|  A|\n",
      "|06C| Schaumburg Regional|      41.9893408|      -88.1012428| 801| -6|  A|\n",
      "|06N|     Randall Airport|       41.431912|      -74.3915611| 523| -5|  A|\n",
      "|09J|Jekyll Island Air...|      31.0744722|      -81.4277778|  11| -4|  A|\n",
      "|0A9|Elizabethton Muni...|      36.3712222|      -82.1734167|1593| -4|  A|\n",
      "|0G6|Williams County A...|      41.4673056|      -84.5067778| 730| -5|  A|\n",
      "|0G7|Finger Lakes Regi...|      42.8835647|      -76.7812318| 492| -5|  A|\n",
      "|0P2|Shoestring Aviati...|      39.7948244|      -76.6471914|1000| -5|  U|\n",
      "|0S9|Jefferson County ...|      48.0538086|     -122.8106436| 108| -8|  A|\n",
      "|0W3|Harford County Ai...|      39.5668378|      -76.2024028| 409| -5|  A|\n",
      "|10C|  Galt Field Airport|      42.4028889|      -88.3751111| 875| -6|  U|\n",
      "|17G|Port Bucyrus-Craw...|      40.7815556|      -82.9748056|1003| -5|  A|\n",
      "|19A|Jackson County Ai...|      34.1758638|      -83.5615972| 951| -4|  U|\n",
      "|1A3|Martin Campbell F...|      35.0158056|      -84.3468333|1789| -4|  A|\n",
      "|1B9| Mansfield Municipal|      42.0001331|      -71.1967714| 122| -5|  A|\n",
      "|1C9|Frazier Lake Airpark|54.0133333333333|-124.768333333333| 152| -8|  A|\n",
      "|1CS|Clow Internationa...|      41.6959744|      -88.1292306| 670| -6|  U|\n",
      "|1G3|  Kent State Airport|      41.1513889|      -81.4151111|1134| -4|  A|\n",
      "|1OH|     Fortman Airport|      40.5553253|      -84.3866186| 885| -5|  U|\n",
      "+---+--------------------+----------------+-----------------+----+---+---+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "airports.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename the faa column\n",
    "airports = airports.withColumnRenamed('faa','dest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+--------+--------+----+------+------------------+--------------------+---------+-----------+----+---+---+\n",
      "|dest|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|tailnum|flight|origin|air_time|distance|hour|minute|      duration_hrs|                name|      lat|        lon| alt| tz|dst|\n",
      "+----+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+--------+--------+----+------+------------------+--------------------+---------+-----------+----+---+---+\n",
      "| LAX|2014|   12|  8|     658|     -7.0|     935|       -5|     VX| N846VA|  1780|   SEA|   132.0|   954.0|   6|    58|               2.2|    Los Angeles Intl|33.942536|-118.408075| 126| -8|  A|\n",
      "| HNL|2014|    1| 22|    1040|      5.0|    1505|        5|     AS| N559AS|   851|   SEA|   360.0|  2677.0|  10|    40|               6.0|       Honolulu Intl|21.318681|-157.922428|  13|-10|  N|\n",
      "| SFO|2014|    3|  9|    1443|     -2.0|    1652|        2|     VX| N847VA|   755|   SEA|   111.0|   679.0|  14|    43|              1.85|  San Francisco Intl|37.618972|-122.374889|  13| -8|  A|\n",
      "| SJC|2014|    4|  9|    1705|     45.0|    1839|       34|     WN| N360SW|   344|   PDX|    83.0|   569.0|  17|     5|1.3833333333333333|Norman Y Mineta S...|  37.3626|-121.929022|  62| -8|  A|\n",
      "| BUR|2014|    3|  9|     754|     -1.0|    1015|        1|     AS| N612AS|   522|   SEA|   127.0|   937.0|   7|    54|2.1166666666666667|            Bob Hope|34.200667|-118.358667| 778| -8|  A|\n",
      "| DEN|2014|    1| 15|    1037|      7.0|    1352|        2|     WN| N646SW|    48|   PDX|   121.0|   991.0|  10|    37|2.0166666666666666|         Denver Intl|39.861656|-104.673178|5431| -7|  A|\n",
      "| OAK|2014|    7|  2|     847|     42.0|    1041|       51|     WN| N422WN|  1520|   PDX|    90.0|   543.0|   8|    47|               1.5|Metropolitan Oakl...|37.721278|-122.220722|   9| -8|  A|\n",
      "| SFO|2014|    5| 12|    1655|     -5.0|    1842|      -18|     VX| N361VA|   755|   SEA|    98.0|   679.0|  16|    55|1.6333333333333333|  San Francisco Intl|37.618972|-122.374889|  13| -8|  A|\n",
      "| SAN|2014|    4| 19|    1236|     -4.0|    1508|       -7|     AS| N309AS|   490|   SEA|   135.0|  1050.0|  12|    36|              2.25|      San Diego Intl|32.733556|-117.189667|  17| -8|  A|\n",
      "| ORD|2014|   11| 19|    1812|     -3.0|    2352|       -4|     AS| N564AS|    26|   SEA|   198.0|  1721.0|  18|    12|               3.3|  Chicago Ohare Intl|41.978603| -87.904842| 668| -6|  A|\n",
      "| LAX|2014|   11|  8|    1653|     -2.0|    1924|       -1|     AS| N323AS|   448|   SEA|   130.0|   954.0|  16|    53|2.1666666666666665|    Los Angeles Intl|33.942536|-118.408075| 126| -8|  A|\n",
      "| PHX|2014|    8|  3|    1120|      0.0|    1415|        2|     AS| N305AS|   656|   SEA|   154.0|  1107.0|  11|    20| 2.566666666666667|Phoenix Sky Harbo...|33.434278|-112.011583|1135| -7|  N|\n",
      "| LAS|2014|   10| 30|     811|     21.0|    1038|       29|     AS| N433AS|   608|   SEA|   127.0|   867.0|   8|    11|2.1166666666666667|      Mc Carran Intl|36.080056| -115.15225|2141| -8|  A|\n",
      "| ANC|2014|   11| 12|    2346|     -4.0|     217|      -28|     AS| N765AS|   121|   SEA|   183.0|  1448.0|  23|    46|              3.05|Ted Stevens Ancho...|61.174361|-149.996361| 152| -9|  A|\n",
      "| SFO|2014|   10| 31|    1314|     89.0|    1544|      111|     AS| N713AS|   306|   SEA|   129.0|   679.0|  13|    14|              2.15|  San Francisco Intl|37.618972|-122.374889|  13| -8|  A|\n",
      "| SFO|2014|    1| 29|    2009|      3.0|    2159|        9|     UA| N27205|  1458|   PDX|    90.0|   550.0|  20|     9|               1.5|  San Francisco Intl|37.618972|-122.374889|  13| -8|  A|\n",
      "| SMF|2014|   12| 17|    2015|     50.0|    2150|       41|     AS| N626AS|   368|   SEA|    76.0|   605.0|  20|    15|1.2666666666666666|     Sacramento Intl|38.695417|-121.590778|  27| -8|  A|\n",
      "| MDW|2014|    8| 11|    1017|     -3.0|    1613|       -7|     WN| N8634A|   827|   SEA|   216.0|  1733.0|  10|    17|               3.6| Chicago Midway Intl|41.785972| -87.752417| 620| -6|  A|\n",
      "| BOS|2014|    1| 13|    2156|     -9.0|     607|      -15|     AS| N597AS|    24|   SEA|   290.0|  2496.0|  21|    56| 4.833333333333333|General Edward La...|42.364347| -71.005181|  19| -5|  A|\n",
      "| BUR|2014|    6|  5|    1733|    -12.0|    1945|      -10|     OO| N215AG|  3488|   PDX|   111.0|   817.0|  17|    33|              1.85|            Bob Hope|34.200667|-118.358667| 778| -8|  A|\n",
      "+----+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+--------+--------+----+------+------------------+--------------------+---------+-----------+----+---+---+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Join the DataFrames\n",
    "flights_with_airports= flights.join(airports, on='dest', how='leftouter')\n",
    "flights_with_airports.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting started with machine learning pipelines\n",
    "\n",
    "### Machine Learning Pipelines\n",
    "\n",
    "At the core of the `pyspark.ml` module are the Transformer and Estimator classes. Almost every other class in the module behaves similarly to these two basic classes. Transformer classes have a `.transform()` method that takes a DataFrame and returns a new DataFrame; usually the original one with a new column appended. For example, you might use the class Bucketizer to create discrete bins from a continuous feature or the class PCA to reduce the dimensionality of your dataset using principal component analysis. Estimator classes all implement a `.fit() ` method. These methods also take a DataFrame, but instead of returning another DataFrame they return a model object. This can be something like a StringIndexerModel for including categorical data saved as strings in your models, or a RandomForestModel that uses the random forest algorithm for classification or regression.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----+--------------------+----------------+--------+-------+-----+-----+---------+\n",
      "|tailnum|year|                type|    manufacturer|   model|engines|seats|speed|   engine|\n",
      "+-------+----+--------------------+----------------+--------+-------+-----+-----+---------+\n",
      "| N102UW|1998|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N103US|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N104UW|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N105UW|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N107US|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N108UW|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N109UW|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N110UW|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N111US|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N11206|2000|Fixed wing multi ...|          BOEING| 737-824|      2|  149|   NA|Turbo-fan|\n",
      "| N112US|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N113UW|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N114UW|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N117UW|2000|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N118US|2000|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N119US|2000|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N1200K|1998|Fixed wing multi ...|          BOEING| 767-332|      2|  330|   NA|Turbo-fan|\n",
      "| N1201P|1998|Fixed wing multi ...|          BOEING| 767-332|      2|  330|   NA|Turbo-fan|\n",
      "| N12114|1995|Fixed wing multi ...|          BOEING| 757-224|      2|  178|   NA|Turbo-jet|\n",
      "| N121DE|1987|Fixed wing multi ...|          BOEING| 767-332|      2|  330|   NA|Turbo-fan|\n",
      "+-------+----+--------------------+----------------+--------+-------+-----+-----+---------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "planes = my_spark.read.csv('data/planes.csv', header=True)\n",
    "planes.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename year column on panes to avoid duplicate column name\n",
    "planes = planes.withColumnRenamed('year', 'plane_year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----+-----+---+--------+---------+--------+---------+-------+------+------+----+--------+--------+----+------+------------------+----------+--------------------+--------------+-----------+-------+-----+-----+---------+\n",
      "|tailnum|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|flight|origin|dest|air_time|distance|hour|minute|      duration_hrs|plane_year|                type|  manufacturer|      model|engines|seats|speed|   engine|\n",
      "+-------+----+-----+---+--------+---------+--------+---------+-------+------+------+----+--------+--------+----+------+------------------+----------+--------------------+--------------+-----------+-------+-----+-----+---------+\n",
      "| N846VA|2014|   12|  8|     658|     -7.0|     935|       -5|     VX|  1780|   SEA| LAX|   132.0|   954.0|   6|    58|               2.2|      2011|Fixed wing multi ...|        AIRBUS|   A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N559AS|2014|    1| 22|    1040|      5.0|    1505|        5|     AS|   851|   SEA| HNL|   360.0|  2677.0|  10|    40|               6.0|      2006|Fixed wing multi ...|        BOEING|    737-890|      2|  149|   NA|Turbo-fan|\n",
      "| N847VA|2014|    3|  9|    1443|     -2.0|    1652|        2|     VX|   755|   SEA| SFO|   111.0|   679.0|  14|    43|              1.85|      2011|Fixed wing multi ...|        AIRBUS|   A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N360SW|2014|    4|  9|    1705|     45.0|    1839|       34|     WN|   344|   PDX| SJC|    83.0|   569.0|  17|     5|1.3833333333333333|      1992|Fixed wing multi ...|        BOEING|    737-3H4|      2|  149|   NA|Turbo-fan|\n",
      "| N612AS|2014|    3|  9|     754|     -1.0|    1015|        1|     AS|   522|   SEA| BUR|   127.0|   937.0|   7|    54|2.1166666666666667|      1999|Fixed wing multi ...|        BOEING|    737-790|      2|  151|   NA|Turbo-jet|\n",
      "| N646SW|2014|    1| 15|    1037|      7.0|    1352|        2|     WN|    48|   PDX| DEN|   121.0|   991.0|  10|    37|2.0166666666666666|      1997|Fixed wing multi ...|        BOEING|    737-3H4|      2|  149|   NA|Turbo-fan|\n",
      "| N422WN|2014|    7|  2|     847|     42.0|    1041|       51|     WN|  1520|   PDX| OAK|    90.0|   543.0|   8|    47|               1.5|      2002|Fixed wing multi ...|        BOEING|    737-7H4|      2|  140|   NA|Turbo-fan|\n",
      "| N361VA|2014|    5| 12|    1655|     -5.0|    1842|      -18|     VX|   755|   SEA| SFO|    98.0|   679.0|  16|    55|1.6333333333333333|      2013|Fixed wing multi ...|        AIRBUS|   A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N309AS|2014|    4| 19|    1236|     -4.0|    1508|       -7|     AS|   490|   SEA| SAN|   135.0|  1050.0|  12|    36|              2.25|      2001|Fixed wing multi ...|        BOEING|    737-990|      2|  149|   NA|Turbo-jet|\n",
      "| N564AS|2014|   11| 19|    1812|     -3.0|    2352|       -4|     AS|    26|   SEA| ORD|   198.0|  1721.0|  18|    12|               3.3|      2006|Fixed wing multi ...|        BOEING|    737-890|      2|  149|   NA|Turbo-fan|\n",
      "| N323AS|2014|   11|  8|    1653|     -2.0|    1924|       -1|     AS|   448|   SEA| LAX|   130.0|   954.0|  16|    53|2.1666666666666665|      2004|Fixed wing multi ...|        BOEING|    737-990|      2|  149|   NA|Turbo-jet|\n",
      "| N305AS|2014|    8|  3|    1120|      0.0|    1415|        2|     AS|   656|   SEA| PHX|   154.0|  1107.0|  11|    20| 2.566666666666667|      2001|Fixed wing multi ...|        BOEING|    737-990|      2|  149|   NA|Turbo-jet|\n",
      "| N433AS|2014|   10| 30|     811|     21.0|    1038|       29|     AS|   608|   SEA| LAS|   127.0|   867.0|   8|    11|2.1166666666666667|      2013|Fixed wing multi ...|        BOEING|  737-990ER|      2|  222|   NA|Turbo-fan|\n",
      "| N765AS|2014|   11| 12|    2346|     -4.0|     217|      -28|     AS|   121|   SEA| ANC|   183.0|  1448.0|  23|    46|              3.05|      1992|Fixed wing multi ...|        BOEING|    737-4Q8|      2|  149|   NA|Turbo-fan|\n",
      "| N713AS|2014|   10| 31|    1314|     89.0|    1544|      111|     AS|   306|   SEA| SFO|   129.0|   679.0|  13|    14|              2.15|      1999|Fixed wing multi ...|        BOEING|    737-490|      2|  149|   NA|Turbo-jet|\n",
      "| N27205|2014|    1| 29|    2009|      3.0|    2159|        9|     UA|  1458|   PDX| SFO|    90.0|   550.0|  20|     9|               1.5|      2000|Fixed wing multi ...|        BOEING|    737-824|      2|  149|   NA|Turbo-fan|\n",
      "| N626AS|2014|   12| 17|    2015|     50.0|    2150|       41|     AS|   368|   SEA| SMF|    76.0|   605.0|  20|    15|1.2666666666666666|      2001|Fixed wing multi ...|        BOEING|    737-790|      2|  151|   NA|Turbo-jet|\n",
      "| N8634A|2014|    8| 11|    1017|     -3.0|    1613|       -7|     WN|   827|   SEA| MDW|   216.0|  1733.0|  10|    17|               3.6|      2014|Fixed wing multi ...|        BOEING|    737-8H4|      2|  140|   NA|Turbo-fan|\n",
      "| N597AS|2014|    1| 13|    2156|     -9.0|     607|      -15|     AS|    24|   SEA| BOS|   290.0|  2496.0|  21|    56| 4.833333333333333|      2008|Fixed wing multi ...|        BOEING|    737-890|      2|  149|   NA|Turbo-fan|\n",
      "| N215AG|2014|    6|  5|    1733|    -12.0|    1945|      -10|     OO|  3488|   PDX| BUR|   111.0|   817.0|  17|    33|              1.85|      2001|Fixed wing multi ...|BOMBARDIER INC|CL-600-2C10|      2|   80|   NA|Turbo-fan|\n",
      "+-------+----+-----+---+--------+---------+--------+---------+-------+------+------+----+--------+--------+----+------+------------------+----------+--------------------+--------------+-----------+-------+-----+-----+---------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#join the flights and plane table use key as tailnum column\n",
    "model_data = flights.join(planes, on='tailnum', how='leftouter')\n",
    "model_data.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data types\n",
    "Before you get started modeling, it's important to know that Spark only handles `numeric data`. That means all of the columns in your DataFrame must be either integers or decimals (called 'doubles' in Spark).\n",
    "\n",
    "When we imported our data, we let Spark guess what kind of information each column held. Unfortunately, Spark doesn't always guess right and you can see that some of the columns in our DataFrame are strings containing numbers as opposed to actual numeric values.\n",
    "\n",
    "To remedy this, you can use the .cast() method in combination with the .withColumn() method. It's important to note that .cast() works on columns, while .withColumn() works on DataFrames.\n",
    "\n",
    "The only argument you need to pass to .cast() is the kind of value you want to create, in string form. For example, to create integers, you'll pass the argument \"integer\" and for decimal numbers you'll use \"double\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[summary: string, tailnum: string, year: string, month: string, day: string, dep_time: string, dep_delay: string, arr_time: string, arr_delay: string, carrier: string, flight: string, origin: string, dest: string, air_time: string, distance: string, hour: string, minute: string, duration_hrs: string, plane_year: string, type: string, manufacturer: string, model: string, engines: string, seats: string, speed: string, engine: string]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_data = model_data.withColumn('arr_delay', model_data.arr_delay.cast('integer'))\n",
    "model_data = model_data.withColumn('air_time' , model_data.air_time.cast('integer'))\n",
    "model_data = model_data.withColumn('month', model_data.month.cast('integer'))\n",
    "model_data = model_data.withColumn('plane_year', model_data.plane_year.cast('integer'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------------+------------------+------------------+-----------------+\n",
      "|summary|         arr_delay|          air_time|             month|       plane_year|\n",
      "+-------+------------------+------------------+------------------+-----------------+\n",
      "|  count|              9925|              9925|             10000|             9354|\n",
      "|   mean|2.2530982367758186|152.88423173803525|            6.6438|2001.594398118452|\n",
      "| stddev|31.074918600451877|  72.8656286392139|3.3191600205962097|58.92921992728455|\n",
      "|    min|               -58|                20|                 1|                0|\n",
      "|    max|               900|               409|                12|             2014|\n",
      "+-------+------------------+------------------+------------------+-----------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_data.describe('arr_delay', 'air_time','month', 'plane_year').show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a new column\n",
    "Create the column `plane_age` using the `.withColumn()` method and subtracting the year of manufacture (column `plane_year`) from the `year` (column year) of the flight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_data =model_data.withColumn('plane_age', model_data.year - model_data.plane_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making a Boolean\n",
    "\n",
    "Use the `.withColumn()` method to create the column` is_late`. This column is equal to `model_data.arr_delay > 0`.\n",
    "Convert this column to an `integer column` so that you can use it in your model and name it `label` (this is the default name for the response variable in Spark's machine learning routines).\n",
    "`Filter out missing values `"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[tailnum: string, year: string, month: int, day: string, dep_time: string, dep_delay: float, arr_time: string, arr_delay: int, carrier: string, flight: string, origin: string, dest: string, air_time: int, distance: float, hour: string, minute: string, duration_hrs: double, plane_year: int, type: string, manufacturer: string, model: string, engines: string, seats: string, speed: string, engine: string, plane_age: double, is_late: boolean, label: int]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data = model_data.withColumn('is_late', model_data.arr_delay >0)\n",
    "\n",
    "model_data = model_data.withColumn('label', model_data.is_late.cast('integer'))\n",
    "\n",
    "model_data.filter(\"arr_delay is not NULL and dep_delay is not NULL and air_time is not NULL and plane_year is not NULL\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strings and factors\n",
    "As you know, Spark requires numeric data for modeling. So far this hasn't been an issue; even boolean columns can easily be converted to integers without any trouble. But you'll also be using the airline and the plane's destination as features in your model. These are coded as strings and there isn't any obvious way to convert them to a numeric data type.\n",
    "\n",
    "Fortunately, PySpark has functions for handling this built into the `pyspark.ml.features` submodule. You can create what are called `'one-hot vectors'` to represent the carrier and the destination of each flight. A one-hot vector is a way of representing a categorical feature where every observation has a vector in which all elements are zero except for at most one element, which has a value of one (1).\n",
    "\n",
    "Each element in the vector corresponds to a level of the feature, so it's possible to tell what the right level is by seeing which element of the vector is equal to one (1).\n",
    "\n",
    "The first step to encoding your categorical feature is to create a `StringIndexer`. Members of this class are Estimators that take a DataFrame with a column of strings and map each unique string to a number. Then, the Estimator returns a Transformer that takes a DataFrame, attaches the mapping to it as metadata, and returns a new DataFrame with a numeric column corresponding to the string column.\n",
    "\n",
    "The second step is to encode this numeric column as a one-hot vector using a `OneHotEncoder`. This works exactly the same way as the StringIndexer by creating an Estimator and then a Transformer. The end result is a column that encodes your categorical feature as a vector that's suitable for machine learning routines!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Carrier\n",
    "In this exercise you'll create a` StringIndexer` and a `OneHotEncoder` to code the carrier column. To do this, you'll call the class constructors with the arguments inputCol and outputCol. The `inputCol` is the name of the column you want to index or encode, and the `outputCol` is the name of the new column that the Transformer should create."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import StringIndexer, OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a StringIndexer\n",
    "carr_indexer = StringIndexer(inputCol='carrier', outputCol='carrier_index')\n",
    "#Create a OneHotEncoder\n",
    "carr_encoder = OneHotEncoder(inputCol='carrier_index', outputCol='carr_fact')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Destination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# encode the dest column just like you did above\n",
    "dest_indexer = StringIndexer(inputCol='dest', outputCol='dest_index')\n",
    "dest_encoder = OneHotEncoder(inputCol='dest_index', outputCol='dest_fact')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assemble a  Vector\n",
    "The last step in the Pipeline is to combine all of the columns containing our features into a single column. pyspark.ml.feature submodule contains a class called VectorAssembler. This Transformer takes all of the columns you specify and combines them into a new vector column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import  VectorAssembler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec_assembler =VectorAssembler(inputCols=['month', 'air_time','carr_fact','dest_fact','plane_age'],\n",
    "                              outputCol='features')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the pipeline\n",
    "You're finally ready to create a` Pipeline!` Pipeline is a class in the `pyspark.ml module` that combines all the Estimators and Transformers that you've already created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_pipe = Pipeline(stages=[dest_indexer, dest_encoder, carr_indexer, carr_encoder, vec_assembler])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test vs Train\n",
    "After you've cleaned your data and gotten it ready for modeling, one of the most important steps is to split the data into a `test set` and a` train set`. After that, `don't touch your test data` until you think you have a good model! As you're building models and forming hypotheses, you can test them on your training data to get an idea of their performance.\n",
    "\n",
    "Once you've got your favorite model, you can see how well it predicts the new data in your test set. This never-before-seen data will give you a much more realistic idea of your model's performance in the real world when you're trying to predict or classify new data.\n",
    "\n",
    "In Spark it's important to make sure you split the data after all the transformations. This is because operations like StringIndexer don't always produce the same index even when given the same list of strings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "piped_data =flights_pipe.fit(model_data).transform(model_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----+-----+---+--------+---------+--------+---------+-------+------+------+----+--------+--------+----+------+------------------+----------+--------------------+--------------+-----------+-------+-----+-----+---------+---------+-------+-----+----------+---------------+-------------+--------------+--------------------+\n",
      "|tailnum|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|flight|origin|dest|air_time|distance|hour|minute|      duration_hrs|plane_year|                type|  manufacturer|      model|engines|seats|speed|   engine|plane_age|is_late|label|dest_index|      dest_fact|carrier_index|     carr_fact|            features|\n",
      "+-------+----+-----+---+--------+---------+--------+---------+-------+------+------+----+--------+--------+----+------+------------------+----------+--------------------+--------------+-----------+-------+-----+-----+---------+---------+-------+-----+----------+---------------+-------------+--------------+--------------------+\n",
      "| N846VA|2014|   12|  8|     658|     -7.0|     935|       -5|     VX|  1780|   SEA| LAX|     132|   954.0|   6|    58|               2.2|      2011|Fixed wing multi ...|        AIRBUS|   A320-214|      2|  182|   NA|Turbo-fan|      3.0|  false|    0|       1.0| (68,[1],[1.0])|          8.0|(10,[8],[1.0])|(81,[0,1,10,13,80...|\n",
      "| N559AS|2014|    1| 22|    1040|      5.0|    1505|        5|     AS|   851|   SEA| HNL|     360|  2677.0|  10|    40|               6.0|      2006|Fixed wing multi ...|        BOEING|    737-890|      2|  149|   NA|Turbo-fan|      8.0|   true|    1|      21.0|(68,[21],[1.0])|          0.0|(10,[0],[1.0])|(81,[0,1,2,33,80]...|\n",
      "| N847VA|2014|    3|  9|    1443|     -2.0|    1652|        2|     VX|   755|   SEA| SFO|     111|   679.0|  14|    43|              1.85|      2011|Fixed wing multi ...|        AIRBUS|   A320-214|      2|  182|   NA|Turbo-fan|      3.0|   true|    1|       0.0| (68,[0],[1.0])|          8.0|(10,[8],[1.0])|(81,[0,1,10,12,80...|\n",
      "| N360SW|2014|    4|  9|    1705|     45.0|    1839|       34|     WN|   344|   PDX| SJC|      83|   569.0|  17|     5|1.3833333333333333|      1992|Fixed wing multi ...|        BOEING|    737-3H4|      2|  149|   NA|Turbo-fan|     22.0|   true|    1|       9.0| (68,[9],[1.0])|          1.0|(10,[1],[1.0])|(81,[0,1,3,21,80]...|\n",
      "| N612AS|2014|    3|  9|     754|     -1.0|    1015|        1|     AS|   522|   SEA| BUR|     127|   937.0|   7|    54|2.1166666666666667|      1999|Fixed wing multi ...|        BOEING|    737-790|      2|  151|   NA|Turbo-jet|     15.0|   true|    1|      23.0|(68,[23],[1.0])|          0.0|(10,[0],[1.0])|(81,[0,1,2,35,80]...|\n",
      "| N646SW|2014|    1| 15|    1037|      7.0|    1352|        2|     WN|    48|   PDX| DEN|     121|   991.0|  10|    37|2.0166666666666666|      1997|Fixed wing multi ...|        BOEING|    737-3H4|      2|  149|   NA|Turbo-fan|     17.0|   true|    1|       2.0| (68,[2],[1.0])|          1.0|(10,[1],[1.0])|(81,[0,1,3,14,80]...|\n",
      "| N422WN|2014|    7|  2|     847|     42.0|    1041|       51|     WN|  1520|   PDX| OAK|      90|   543.0|   8|    47|               1.5|      2002|Fixed wing multi ...|        BOEING|    737-7H4|      2|  140|   NA|Turbo-fan|     12.0|   true|    1|      10.0|(68,[10],[1.0])|          1.0|(10,[1],[1.0])|(81,[0,1,3,22,80]...|\n",
      "| N361VA|2014|    5| 12|    1655|     -5.0|    1842|      -18|     VX|   755|   SEA| SFO|      98|   679.0|  16|    55|1.6333333333333333|      2013|Fixed wing multi ...|        AIRBUS|   A320-214|      2|  182|   NA|Turbo-fan|      1.0|  false|    0|       0.0| (68,[0],[1.0])|          8.0|(10,[8],[1.0])|(81,[0,1,10,12,80...|\n",
      "| N309AS|2014|    4| 19|    1236|     -4.0|    1508|       -7|     AS|   490|   SEA| SAN|     135|  1050.0|  12|    36|              2.25|      2001|Fixed wing multi ...|        BOEING|    737-990|      2|  149|   NA|Turbo-jet|     13.0|  false|    0|      12.0|(68,[12],[1.0])|          0.0|(10,[0],[1.0])|(81,[0,1,2,24,80]...|\n",
      "| N564AS|2014|   11| 19|    1812|     -3.0|    2352|       -4|     AS|    26|   SEA| ORD|     198|  1721.0|  18|    12|               3.3|      2006|Fixed wing multi ...|        BOEING|    737-890|      2|  149|   NA|Turbo-fan|      8.0|  false|    0|       6.0| (68,[6],[1.0])|          0.0|(10,[0],[1.0])|(81,[0,1,2,18,80]...|\n",
      "| N323AS|2014|   11|  8|    1653|     -2.0|    1924|       -1|     AS|   448|   SEA| LAX|     130|   954.0|  16|    53|2.1666666666666665|      2004|Fixed wing multi ...|        BOEING|    737-990|      2|  149|   NA|Turbo-jet|     10.0|  false|    0|       1.0| (68,[1],[1.0])|          0.0|(10,[0],[1.0])|(81,[0,1,2,13,80]...|\n",
      "| N305AS|2014|    8|  3|    1120|      0.0|    1415|        2|     AS|   656|   SEA| PHX|     154|  1107.0|  11|    20| 2.566666666666667|      2001|Fixed wing multi ...|        BOEING|    737-990|      2|  149|   NA|Turbo-jet|     13.0|   true|    1|       3.0| (68,[3],[1.0])|          0.0|(10,[0],[1.0])|(81,[0,1,2,15,80]...|\n",
      "| N433AS|2014|   10| 30|     811|     21.0|    1038|       29|     AS|   608|   SEA| LAS|     127|   867.0|   8|    11|2.1166666666666667|      2013|Fixed wing multi ...|        BOEING|  737-990ER|      2|  222|   NA|Turbo-fan|      1.0|   true|    1|       4.0| (68,[4],[1.0])|          0.0|(10,[0],[1.0])|(81,[0,1,2,16,80]...|\n",
      "| N765AS|2014|   11| 12|    2346|     -4.0|     217|      -28|     AS|   121|   SEA| ANC|     183|  1448.0|  23|    46|              3.05|      1992|Fixed wing multi ...|        BOEING|    737-4Q8|      2|  149|   NA|Turbo-fan|     22.0|  false|    0|       5.0| (68,[5],[1.0])|          0.0|(10,[0],[1.0])|(81,[0,1,2,17,80]...|\n",
      "| N713AS|2014|   10| 31|    1314|     89.0|    1544|      111|     AS|   306|   SEA| SFO|     129|   679.0|  13|    14|              2.15|      1999|Fixed wing multi ...|        BOEING|    737-490|      2|  149|   NA|Turbo-jet|     15.0|   true|    1|       0.0| (68,[0],[1.0])|          0.0|(10,[0],[1.0])|(81,[0,1,2,12,80]...|\n",
      "| N27205|2014|    1| 29|    2009|      3.0|    2159|        9|     UA|  1458|   PDX| SFO|      90|   550.0|  20|     9|               1.5|      2000|Fixed wing multi ...|        BOEING|    737-824|      2|  149|   NA|Turbo-fan|     14.0|   true|    1|       0.0| (68,[0],[1.0])|          4.0|(10,[4],[1.0])|(81,[0,1,6,12,80]...|\n",
      "| N626AS|2014|   12| 17|    2015|     50.0|    2150|       41|     AS|   368|   SEA| SMF|      76|   605.0|  20|    15|1.2666666666666666|      2001|Fixed wing multi ...|        BOEING|    737-790|      2|  151|   NA|Turbo-jet|     13.0|   true|    1|      11.0|(68,[11],[1.0])|          0.0|(10,[0],[1.0])|(81,[0,1,2,23,80]...|\n",
      "| N8634A|2014|    8| 11|    1017|     -3.0|    1613|       -7|     WN|   827|   SEA| MDW|     216|  1733.0|  10|    17|               3.6|      2014|Fixed wing multi ...|        BOEING|    737-8H4|      2|  140|   NA|Turbo-fan|      0.0|  false|    0|      30.0|(68,[30],[1.0])|          1.0|(10,[1],[1.0])|(81,[0,1,3,42],[8...|\n",
      "| N597AS|2014|    1| 13|    2156|     -9.0|     607|      -15|     AS|    24|   SEA| BOS|     290|  2496.0|  21|    56| 4.833333333333333|      2008|Fixed wing multi ...|        BOEING|    737-890|      2|  149|   NA|Turbo-fan|      6.0|  false|    0|      24.0|(68,[24],[1.0])|          0.0|(10,[0],[1.0])|(81,[0,1,2,36,80]...|\n",
      "| N215AG|2014|    6|  5|    1733|    -12.0|    1945|      -10|     OO|  3488|   PDX| BUR|     111|   817.0|  17|    33|              1.85|      2001|Fixed wing multi ...|BOMBARDIER INC|CL-600-2C10|      2|   80|   NA|Turbo-fan|     13.0|  false|    0|      23.0|(68,[23],[1.0])|          2.0|(10,[2],[1.0])|(81,[0,1,4,35,80]...|\n",
      "+-------+----+-----+---+--------+---------+--------+---------+-------+------+------+----+--------+--------+----+------+------------------+----------+--------------------+--------------+-----------+-------+-----+-----+---------+---------+-------+-----+----------+---------------+-------------+--------------+--------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "piped_data.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "training, test = piped_data.randomSplit([.6, .4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model tuning and selection\n",
    "\n",
    "### What is logistic regression?\n",
    "The model you'll be fitting in this chapter is called a logistic regression. This model is very similar to a linear regression, but instead of predicting a numeric variable, it predicts the probability (between 0 and 1) of an event.\n",
    "\n",
    "To use this as a classification algorithm, all you have to do is assign a cutoff point to these probabilities. If the predicted probability is above the cutoff point, you classify that observation as a 'yes' (in this case, the flight being late), if it's below, you classify it as a 'no'!\n",
    "\n",
    "You'll tune this model by testing different values for several hyperparameters. A hyperparameter is just a value in the model that's not estimated from the data, but rather is supplied by the user to maximize performance\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross validation\n",
    "In the next few exercises you'll be tuning your logistic regression model using a procedure called `k-fold cross validation`. This is a method of estimating the model's performance on unseen data (like your test DataFrame).\n",
    "\n",
    "It works by splitting the training data into a few different partitions. The exact number is up to you, but in this course you'll be using PySpark's default value of three. Once the data is split up, one of the partitions is set aside, and the model is fit to the others. Then the error is measured against the held out partition. This is repeated for each of the partitions, so that every block of data is held out and used as a test set exactly once. Then the error on each of the partitions is averaged. This is called the cross validation error of the model, and is a good estimate of the actual error on the held out data.\n",
    "\n",
    "You'll be using cross validation to choose the hyperparameters by creating a grid of the possible pairs of values for the two hyperparameters, elasticNetParam and regParam, and using the cross validation error to compare all the different models so you can choose the best one!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the evaluator\n",
    "The first thing you need when doing cross validation for model selection is a way to compare different models. Luckily, the pyspark.ml.evaluation submodule has classes for evaluating different kinds of models. Your model is a binary classification model, so you'll be using the `BinaryClassificationEvaluator` from the `pyspark.ml.evaluation` module. This evaluator calculates the area under the ROC. This is a metric that combines the two kinds of errors a binary classifier can make (false positives and false negatives) into a simple number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark.ml.evaluation as evals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "evalator = evals.BinaryClassificationEvaluator(metricName='areaUnderROC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make a grid\n",
    "Next, you need to create a grid of values to search over when looking for the optimal hyperparameters. The submodule `pyspark.ml.tuning` includes a class called `ParamGridBuilder` that does just that (maybe you're starting to notice a pattern here; PySpark has a submodule for just about everything!).\n",
    "\n",
    "You'll need to use the `.addGrid()` and `.build()` methods to create a grid that you can use for cross validation. The .addGrid() method takes a model parameter (an attribute of the model Estimator, lr, that you created a few exercises ago) and a list of values that you want to try. The .build() method takes no arguments, it just returns the grid that you'll use later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark.ml.tuning as tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the parameter grid\n",
    "grid = tune.ParamGridBuilder()\n",
    "# Add the hyperparameter\n",
    "grid = grid.addGrid(lr.regParam, np.arange(0., .1, .01))\n",
    "grid = grid.addGrid(lr.elasticNetParam, [0., 1.])\n",
    "# Build the grid\n",
    "grid = grid.build()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make the validator\n",
    "The submodule pyspark.ml.tuning also has a class called CrossValidator for performing cross validation. This Estimator takes the modeler you want to fit, the grid of hyperparameters you created, and the evaluator you want to use to compare your models.\n",
    "\n",
    "The submodule `pyspark.ml.tune` has already been imported as `tune`. You'll create the CrossValidator by passing it the logistic regression Estimator `lr`, the parameter `grid`, and the evaluator you created in the previous exercises."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = tune.CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evalator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fit the model(s)\n",
    "You're finally ready to fit the models and select the best one!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "#models = cv.fit(training)\n",
    "#when I fit the data I saw a problem on it , Figure it out the problem then continue the last two step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "# Fit cross validation models\n",
    "models = cv.fit(training)\n",
    "\n",
    "# Extract the best model\n",
    "best_lr = models.bestModel\n",
    "\n",
    "# Use the model to predict the test set\n",
    "test_results = best_lr.transform(test)\n",
    "\n",
    "# Evaluate the predictions\n",
    "print(evaluator.evaluate(test_results))\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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