willycs40/Weather_Effect.ipynb

## Weather_Effect.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:503d38c40d1fd02e418e2cd040a58785aee3079a8751191621640a79fee5055c"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sqlalchemy import create_engine # database connection\n",
      "import pyodbc\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import pyproj\n",
      "import datetime as dt\n",
      "from scipy import spatial"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# set up the connection to the database\n",
      "sqllite_engine = create_engine('sqlite:///Weather.db')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "q = '''\n",
      "    SELECT SignificantWeather\n",
      "    FROM Weather as w\n",
      "    GROUP BY SignificantWeather\n",
      "'''\n",
      "weather_types_df = pd.read_sql_query(q, sqllite_engine)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_types = list(weather_types_df['SignificantWeather'].values)\n",
      "weather_types = filter(None, weather_types)\n",
      "weather_types.append(u'None')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 174
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_types"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "[u'(Black) Low-level cloud',\n",
        " u'(White) Medium-level cloud',\n",
        " u'Clear sky (Night)',\n",
        " u'Drizzle',\n",
        " u'Fog',\n",
        " u'Hail shower (Day)',\n",
        " u'Hail shower (Night)',\n",
        " u'Heavy Rain',\n",
        " u'Heavy rain shower (Day)',\n",
        " u'Heavy rain shower (Night)',\n",
        " u'Heavy snow',\n",
        " u'Heavy snow shower (Day)',\n",
        " u'Heavy snow shower (Night)',\n",
        " u'Light rain',\n",
        " u'Light rain shower (Day)',\n",
        " u'Light rain shower (Night)',\n",
        " u'Light snow',\n",
        " u'Light snow shower (Day)',\n",
        " u'Light snow shower (Night)',\n",
        " u'Mist',\n",
        " u'Partly cloudy (Night)',\n",
        " u'Sleet',\n",
        " u'Sunny (Day)',\n",
        " u'Sunny intervals',\n",
        " u'Thunder storm',\n",
        " u'Thundery shower (Day)',\n",
        " u'Thundery shower (Night)',\n",
        " u'None']"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "speed_hist_bins = np.arange(0,3.1,0.01)\n",
      "distance_hist_bins = np.arange(0, 100, 1)\n",
      "\n",
      "speed_histograms = {}\n",
      "for weather_type in weather_types:\n",
      "    speed_histograms[weather_type], discard = np.histogram([], speed_hist_bins)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "q = '''\n",
      "    SELECT s.Speed, s.Distance, ifnull(w.SignificantWeather,'None') as SignificantWeather\n",
      "    FROM gps_to_station s \n",
      "    INNER JOIN Weather as w\n",
      "    ON w.SiteCode = s.SiteCode\n",
      "    and date(w.ObservationDate) = s.Date\n",
      "    and w.ObservationTime = s.Hour\n",
      "    WHERE s._ROWID_ >= (abs(random()) % (SELECT max(_ROWID_) FROM gps_to_station))\n",
      "    LIMIT 20000\n",
      "'''"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 162
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# loop through the queried rows building up histograms for each weather type\n",
      "\n",
      "tart = dt.datetime.now()\n",
      "chunksize = 1000\n",
      "j = 0\n",
      "index_start = 1\n",
      "\n",
      "# Loop through the source data, pulling 'chunksize' rows at a timelawfo\n",
      "for df in pd.read_sql_query(sql=q, con=sqllite_engine, chunksize=chunksize):\n",
      "    \n",
      "    df.index += index_start\n",
      "    \n",
      "    for weather_type in weather_types:\n",
      "        \n",
      "        data = df[df['SignificantWeather']==weather_type]['Speed'].values\n",
      "        hist, discard = np.histogram(data, speed_hist_bins)\n",
      "        speed_histograms[weather_type] = speed_histograms[weather_type] + hist\n",
      "        \n",
      "    j+=1\n",
      "    print '{} seconds: completed {} rows'.format((dt.datetime.now() - start).seconds, j*chunksize)\n",
      "\n",
      "    index_start = df.index[-1] + 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "8 seconds: completed 1000 rows\n",
        "16 seconds: completed 2000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "25 seconds: completed 3000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "33 seconds: completed 4000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "41 seconds: completed 5000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "49 seconds: completed 6000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "58 seconds: completed 7000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "67 seconds: completed 8000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "75 seconds: completed 9000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "83 seconds: completed 10000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "90 seconds: completed 11000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "98 seconds: completed 12000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "106 seconds: completed 13000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "114 seconds: completed 14000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "122 seconds: completed 15000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "130 seconds: completed 16000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "138 seconds: completed 17000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "146 seconds: completed 18000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "154 seconds: completed 19000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "162 seconds: completed 20000 rows"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 163
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#plt.figure(figsize=(10, 10))\n",
      "#plt.hist(df['Distance'].values, bins=np.arange(0,20,0.01))\n",
      "#plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 175
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "condition_count = len(weather_types)\n",
      "\n",
      "plt.figure(figsize=(10, 80))\n",
      "bin_centers = (speed_hist_bins[:-1] + speed_hist_bins[1:]) / 2    # get the x positions\n",
      "\n",
      "i = 1\n",
      "\n",
      "for idx, weather_type in enumerate(weather_types):\n",
      "    \n",
      "    data = speed_histograms[weather_type]\n",
      "\n",
      "    if sum(data)>0:\n",
      "        plt.subplot(condition_count+1,1,i)\n",
      "        plt.title(weather_type + ' ' + str(sum(data)))\n",
      "        plt.bar(bin_centers, data, align='center', width = 0.005)\n",
      "        i += 1\n",
      "        \n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6GrA78KNiqxdP/EfTnqROz40EKfj4OKmDcm0tFqTO3t+QVPxj/hipr80sCmO3\n5We4t0Xemyk+96+Ae3IAWt0G2EPS84DL8/bGiHhOzXUOBI6UVKw5mcL251sC/JekPwLeAPwoBxXQ\n/HmbmU0KmlsFPfuS3oWiW3nibA6Nzu1beUfENYXN8yTNJzVp/6ek44E/Jc0ZuEHS7+b9zye9ax8A\n/k+z60t6GrAc+H8R8ZVu82k2zBxkmXXnBlJT3I8K+75H+tf+rcDn8r6rgPl53w79sTpQ26x2Dyno\nOCwi7qp3gqT9SDVnP6t3/PELR/xK0reA9wG/VSfJbcA7a/4gV+9xF3BoYXt3Uu1TPb8kBYbVtDuT\ngsWORZpbsTbArc3zdyPimAbnr5V0K6kG662kvmnFcxs975wm97wdOEDSzrnmrZE7SIFd0YHAt/L6\nL4EnF47NZPvP/07aL+9uFN+z3yV92LEBICKuzD/vl5CCzlnA2hzk7wbsJulOYL+IiNz8uRy4NCLO\n7WMezYaKmwvNunM5aSypou8BLyDVXP1X3ncj8AzS5/TdBlmjwP7VTuy5tuTTwAW5tgBJ+0kqBhVH\nAStras4aORs4KiJqa1ggdbr/qKQD8n2elms5IDXpvVbSS5UGzP1rGv9OWQ/sqjQ+zy6kpqgntZG3\nbnwTmCvpbZJ2ycuLcpNo1cXAB0k1N18t7G/2vM1cB9xFqhHaXdKuuWN4rW/lvL1F0hRJJwOHkPpz\nAawBTsnHXgi8sXDuJbRf3tWPFnYljXEmpY8lpuZjs6vXyXn9EClgq763PwF+X9JB+TpHk74WvDE/\nw4GkJvDnkfoCrgYOzwHWU0jj+10dEU0755tNdA6yzLrzReA4Fb7oi4hbSE1ed0XEg3lfkP4A7wkU\n++jUdnCmznbVSuAmYLOkapPaWaQxjK6V9ACpo3dx/qw/IAUMLUXEXQ36DwF8gjRC9HJJDwLXkPof\nEWni1D8mBSx3kmarL35B+PgzRpqi4nTSkAKbSMML1E1bs6/Zdu2x6r22AseQ+qjdQQp+ziXV7FUt\nIQXDK2PH0ZobPm+zPOTA93WkjxBuy892UuGcat7uJfUZ+1NSjeSfkYZRqObhw6QaxS3ACNs75BMR\nN9G8vGsdRRoH65uk5sxfkcasgvQ+XpivsYlUXr9X6Jz+GdKwC98jjZN1AfCeiFgfEY9ExN3VJR9/\nJK8DvJ704cc7lb6I3ar0xagnIbZJR9u7PDRIIJ0J/CGpHf7TEfGJ/IXMV0j/mtlIGnDx/px+IelL\nlN8AZ0QiB9AbAAAgAElEQVTE8vKybzZ+JP0tcHd0MeJ7mdTjiO9mZtYfTYMsSb9N+hffi0ifg19B\n6rvxXlKn1I9JOgvYOyIW5E+ML87p9wO+TRrpdywGXzQzMzMbGK2aCw8hfRH169yZ87ukPgLHA4tz\nmsXAiXn9BNJ8a49GxEZSc8YRmJmZmU0yrYKsnwIvlzQ9f8lyHGnwuxmxfdbuUbZ/3rzDJ8Z5vZ1P\nk83MzMwmlKZDOETEOknnkz7F/SXpy5ff1KQJSa06pO6gRXozMzOzgRIRnQ6w3Prrwoj4XES8MCKO\nIn3xsh4YlTQTQNIstg8ieAfpK5aq/fO+etf10uWyaNGicc/DMC8uP5edy284F5efy268lm61DLKq\n03zkcWPeQOrYvow0wCL5v5fm9WWkMV6mSjoIOJg0A7uZmZnZpNLOiO9fk/RU0teFp0fEA5LOA5ZK\nOo08hAM8PpLyUtKks4/l9G4aNDMzs0mnZZAVEa+os+8+4DUN0n8U+GjvWbNG5s2bN95ZGGouv+65\n7Hrj8uuNy697Lrvx0XIw0lJuKrmCy8zMzIaCJKKMju9mZmZm1jkHWWZmZmYlcJBlZmZmVgIHWWZm\nZmYlaGecrIWSbpJ0o6SLJT0pT7OzQtJ6ScslTatJf4ukdZKOKTf7ZmZmZoOpaZAlaQ7wbuAFEfEc\nYGfgFGABsCIi5gIr8zaSDgNOBg4DjgUulOTaMjMzM5t0WgVAD5IGId1d0hRgd+BO4HhgcU6zGDgx\nr58ALImIRyNiI7ABOKJVJiQhdfxlpJmZmdnAahpk5UFHPw7cRgqu7o+IFcCMiBjNyUaBGXl9X2BT\n4RKbgP36mmMzMzOzIdCqufC3gA8Cc0gB1B6S3lZMk0cVbTayqEcdNTMzs0mn1bQ6LwS+HxH3Akj6\nOvBiYLOkmRGxWdIs4O6c/g5gduH8/fO+JxgZGekl32ZmZmalqFQqVCqVnq/TdFodSc8DvgS8CPg1\n8AXgeuBA4N6IOF/SAmBaRCzIHd8vJvXD2g/4NvDM2jl0aqfVqfbH8lQ7ZmZmNmi6nVanaU1WRPxE\n0heBHwLbgB8D/wbsCSyVdBqwETgpp18raSmwFngMON2TFJqZmdlkNBATRLsmy8zMzAaVJ4g2MzMz\nGyAOsszMzMxK4CDLzMzMrAQOsszMzMxK4CDLzMzMrAQOsszMzMxK0DLIkvQsSasLywOSzpA0XdIK\nSeslLZc0rXDOQkm3SFon6ZhyH8HMzMxs8HQ0TpaknUjT5BwBfAC4JyI+JuksYO+aUd9fxPZR3+dG\nxLbCdTxOlpmZmQ2FsRon6zXAhoi4HTgeWJz3LwZOzOsnAEsi4tGI2AhsIAVlZmZmZpNGp0HWKcCS\nvD4jIkbz+igwI6/vC2wqnLOJVKPVFkmP12yZmZmZDaumcxcWSZoKvA44q/ZYRISkZm19Tzg2MjLS\n7q3NzMzMxkylUqFSqfR8nbb7ZEk6AfijiDg2b68D5kXEZkmzgFURcYikBQARcV5OdwWwKCKuK1yr\nYZ8s988yMzOzQTIWfbLewvamQoBlwPy8Ph+4tLD/FElTJR0EHAxc32nGzMzMzIZZWzVZkp4M3Aoc\nFBFb877pwFLgAGAjcFJE3J+PnQ28C3gMODMirqy5nmuyzMzMbCh0W5PV0RAO/eIgy8zMzIbFWA3h\nYGZmZmZtcJBlZmZmVgIHWWZmZmYlcJBlZmZmVgIHWWZmZmYlaCvIkjRN0tck3SxpraQjJU2XtELS\neknLJU0rpF8o6RZJ6yQdU172zczMzAZTuzVZnwAuj4hDgecC64AFwIqImAuszNtIOgw4GTgMOBa4\nUJJrzMzMzGxSaRn8SNoLeHlEfA4gIh6LiAeA44HFOdli4MS8fgKwJCIejYiNwAbgiH5n3MzMzGyQ\ntVPDdBDwC0mfl/RjSZ/OI8DPiIjRnGYUmJHX9wU2Fc7fBOzXtxybmZmZDYEpbaZ5AfD+iPiBpAvI\nTYNVERGSmg3R/oRjIyMjneTTzMzMbExUKhUqlUrP12k5rY6kmcA1EXFQ3n4ZsBB4BvDKiNgsaRaw\nKiIOkbQAICLOy+mvABZFxHWFa3paHTMzMxsKpU2rExGbgdslzc27XgPcBFwGzM/75gOX5vVlwCmS\npko6CDgYuL7TjJmZmZkNs3aaCwE+AHxJ0lTgv4F3AjsDSyWdBmwETgKIiLWSlgJrgceA08PVUmZm\nZjbJtGwuLOWmbi40MzOzIVFac+F4kvR40GVmZmY2TAY6yDIzMzMbVg6ySuAaODMzM3OQVSIHW2Zm\nZpOXg6wx4oDLzMxscmkryJK0UdINklZLuj7vmy5phaT1kpZLmlZIv1DSLZLWSTqmrMybmZmZDap2\na7ICmBcRz4+I6mTPC4AVETEXWJm3kXQYcDJwGHAscKGkSV1j5imEzMzMJp+2xsmS9HPghRFxb2Hf\nOuCoiBjNU+9U8rQ6C4FtEXF+TncFMBIR1xbObWucrGEdM6tZs+CwPYuZmdlkV/Y4WQF8W9IPJb07\n75sREaN5fRSYkdf3BTYVzt0E7NdpxszMzMyGWbvT6rw0Iu6S9DRgRa7FelxEhKRmVTSuvjEzM7NJ\npa0gKyLuyv/9haRvAEcAo5JmRsRmSbOAu3PyO4DZhdP3z/t24H5KZmZmNogqlQqVSqXn67TskyVp\nd2DniNgq6cnAcuAc4DXAvRFxvqQFwLSIWJA7vl9MCsT2A74NPLPYCct9sszMzGxYdNsnq52arBnA\nN3LgMAX4UkQsl/RDYKmk04CNwEkAEbFW0lJgLfAYcHo4sjAzM7NJpq2vC/t+U9dkmZmZ2ZAo++tC\nMzMzM+uAgywzMzOzEjjIMjMzMyuBgywzMzOzEjjIMjMzMyuBgywzMzOzErQVZEnaWdJqSZfl7emS\nVkhaL2m5pGmFtAsl3SJpnaRjysq4mZmZ2SBrtybrTNLgotVBnhYAKyJiLrAyb5NHez8ZOAw4FrhQ\nkmvLzMzMbNJpGQBJ2h84DvgMUB2I63hgcV5fDJyY108AlkTEoxGxEdhAml7HzMzMbFJpp5bpH4EP\nAdsK+2ZExGheHyVNvQOwL7CpkG4Taf5CMzMzs0ml6dyFkl4L3B0RqyXNq5cmIkJSs7li6h4bGRlp\nN49mZmZmY6ZSqVCpVHq+TtO5CyV9FDiVNNHzrsBTgK8DLwLmRcRmSbOAVRFxiKQFABFxXj7/CmBR\nRFxXc13PXWhmZmZDoZS5CyPi7IiYHREHAacA34mIU4FlwPycbD5waV5fBpwiaaqkg4CDges7zZSZ\nmZnZsGvaXFhHtRrmPGCppNOAjcBJABGxVtJS0peIjwGnh6tuzMzMbBJq2lxY2k3dXGhmZmZDopTm\nQjMzMzPrjoMsMzMzsxI4yDIzMzMrgYMsMzMzsxIMdZAlqWknczMzM7Px0jTIkrSrpOskrZG0VtK5\nef90SSskrZe0XNK0wjkLJd0iaZ2kY8p+ADMzM7NB1HIIB0m7R8TDkqYAVwN/Rpog+p6I+Jiks4C9\nI2KBpMOAi0kjwu8HfBuYGxHbaq7Z8RAO9YZzGNQhHjyEg5mZ2cRR2hAOEfFwXp0K7AxsIQVZi/P+\nxcCJef0EYElEPBoRG4ENwBGdZmoicxOnmZnZ5NAyyJK0k6Q1wChpjsKbgBkRMZqTjAIz8vq+wKbC\n6ZtINVpmZmZmk0rLaXVyU9/hkvYCrpT0yprjIalZG1jdYyMjI53kc8LJVY/jnQ0zMzOrUalUqFQq\nPV+no2l1JH0Y+BXwh8C8iNgsaRaphusQSQsAIuK8nP4KYFFEXFdznUnbJ6to0PJtZmZmT1RKnyxJ\n+1S/HJS0G3A0sBpYBszPyeYDl+b1ZcApkqZKOgg4GLi+00yZmZmZDbtWzYWzgMWSdiIFZBdFxEpJ\nq4Glkk4DNgInAUTEWklLgbXAY8DpMUbVNYNaq2VmZmaTU0fNhX27aQnNhYMUZLm50MzMbOIobQgH\nMzMzM+ucgywzMzOzEjjIGhAepNTMzGxiaTlOlpXLgZWZmdnE5JosMzMzsxK0M63ObEmrJN0k6aeS\nzsj7p0taIWm9pOXV8bTysYWSbpG0TtIxZT7ARDPZR8I3MzObKFoO4SBpJjAzItZI2gP4EWlC6HcC\n90TExySdBewdEQskHQZcDLyING/ht4G5eXqe6jVLHcJhvIdz6GQIh3ppPbSDmZnZ4ChtCIeI2BwR\na/L6Q8DNpODpeGBxTraYFHgBnAAsiYhHI2IjsAE4otOMDSv3sTIzMzPosE+WpDnA84HrgBkRMZoP\njQIz8vq+wKbCaZtIQZmZmZnZpNF2kJWbCi8BzoyIrcVjue2vWRtX39q/PNSBmZmZDYO2hnCQtAsp\nwLooIqqTQY9KmhkRmyXNAu7O++8AZhdO3z/v24E7eLdnvPuXmZmZTTaVSoVKpdLzddrp+C5Sn6t7\nI+L/FvZ/LO87X9ICYFpNx/cj2N7x/ZnFnu69dHyv6rST/FjpRy1bo7IxMzOzsddtx/d2gqyXAd8D\nbmB7s99C4HpgKXAAsBE4KSLuz+ecDbwLeIzUvHhlzTUdZDXhIMvMzGxwlBZklWG8g6wyA5eygqx6\nx8zMzKx8pQ3hMBG4/5eZmZmNtUlRk5Uj0Lr3K+HZer6Ga7LMzMwGh2uyJhEPY2FmZjb4HGQNIAdR\nZmZmw6+tcbKG1WQKVPwVopmZ2WCZ0EFWJ4Y1SJlMgaSZmdkwadlcKOlzkkYl3VjYN13SCknrJS2X\nNK1wbKGkWyStk3RMWRk3MzMzG2Tt9Mn6PHBszb4FwIqImAuszNvk0d5PBg7L51woaeD7fTUa4qGT\nvlGTsR9VvWf2cBlmZmZJW0M4SJoDXBYRz8nb64CjImJU0kygEhGHSFoIbIuI83O6K4CRiLi25npj\nMoRDbfp69ytut0rXTBkBVifPXTw+VuqVT+1wGWZmZsNurIdwmBERo3l9FJiR1/cFNhXSbSLNX2hm\nZmY2qfTc8T0iQlKzqou6xwa1WamTKXnK7izfbe3YeE4rVHv9Yf2gwMzMJq9KpUKlUun5Ot0GWaOS\nZkbEZkmzgLvz/juA2YV0++d9T1AMss4555yOM1AMQNoJ2Dr9Y98qwBmW/lfjGXANaiBtZmbWzLx5\n85g3b97j293EKdB9n6yPAfdGxPmSFgDTImJB7vh+MXAEqZnw28Azo+Ym/eiT1UirtI2m3Wl1zdq8\ndnLPfuqlr1pVv2qaOnlm12SZmdmwKq1PlqQlwPeBZ0m6XdI7gfOAoyWtB16Vt4mItcBSYC3wLeD0\n2gDLBptrn8zMzPpjaCeIbqTTtMX7t0rXKO1Eqslq9XXgyMhIV9Wm7p9lZmbDakJPEN1J7UoZwU6r\n+49X7c949Avrtl26HteamZnZRDYUNVllabcmq9O0ZVq0aFHTQKfXmq5WNVllPb9rt8zMbFB1W5M1\nqYOsiWhYg6x6hiHwchOomdnE122Q5QmiJ5luml7HM+Ad5NHtm03HVE3rIMzMbPIaij5Z1r5WQVQ/\n+1SVrd3xz/oVALZzrWoaSS3LsnYst2bXH+/+aeN9fzOzCSki+r6QJodeB9wCnFXneBSRRoVvuO6l\n/KX4s2j2cxmkZdGiRXXzWLVo0aKopzZd7f561+8kL+2UdaufQb1napTvfmjnuvXuP1b5myhWrVo1\n3lkYai6/7rnsepN/t3UcD/W9JkvSzsA/kwKtw4C3SDq03/ex/irWstTWuAxq37hiTVJtfqs1Tc2e\nq7iv3jN2UuvXSdp2y7NRumKeGz1f7TMVa9IaPW+963ZTUziotWL9rPXsVj+m6ZjMXH7dc9mNjzKa\nC48ANkTExoh4FPgycEKnFxnUX9Q2nOr9cR20d6zM5sR6QWAvQVSzQLwY3LYK2Ood77Rptcwm4zLv\nZWaTQDfVX80W4E3ApwvbbwM+WZOmbvNKtUrOixcvnS2Nmk4HcemkabXVUu9ZWz1/8Xg7eSk2F9Tu\na/R7rFFzQy/H66WpbRKv94zd3K+dvIyHRl0AxtOgllWtQSy7YZJ/xh3HRH0fwkHSG4FjI+Ldeftt\nwJER8YFCmv7e1MzMzKxEMSBDONwBzC5szwY2FRN0k1EzMzOzYVJGn6wfAgdLmiNpKnAysKyE+5iZ\nmZkNrL7XZEXEY5LeD1wJ7Ax8NiJu7vd9zMzMzAbZuEyrY2ZmZjbRlTriu6RjJa2TdIuksxqk+ad8\n/CeSnl9mfoZNq/KTNE/SA5JW5+UvxyOfg0jS5ySNSrqxSRq/e3W0Kju/d81Jmi1plaSbJP1U0hkN\n0vn9q6Od8vM7WJ+kXSVdJ2mNpLWSzm2Qzu9eHe2UX8fvXjefJLazkJoKNwBzgF2ANcChNWmOAy7P\n60cC15aVn2Fb2iy/ecCy8c7rIC7Ay4HnAzc2OO53r/uy83vXvPxmAofn9T2An/l3X9/Lz+9g4/Lb\nPf93CnAt8LKa4373eiu/jt69Mmuy2hmU9HhgMUBEXAdMkzSjxDwNk3YHdfWXmnVExFXAliZJ/O41\n0EbZgd+7hiJic0SsyesPATcD+9Yk8/vXQJvlB34H64qIh/PqVNI/1u+rSeJ3r4k2yg86ePfKDLL2\nA24vbG/K+1ql2b/EPA2TdsovgJfkKt/LJR02Zrkbfn73uuf3rk2S5pBqBa+rOeT3rw1Nys/vYAOS\ndpK0BhgFVkXE2pokfveaaKP8Onr3yhgnq5iRdtRGhO6Jn7RTDj8GZkfEw5J+D7gUmFtutiYUv3vd\n8XvXBkl7AF8Dzsw1Mk9IUrPt96+gRfn5HWwgIrYBh0vaC7hS0ryIqNQk87vXQBvl19G7V2ZNVstB\nSeuk2T/vs/YGdd1ardqMiG8Bu0iaPnZZHGp+97rk9641SbsAlwD/HhGX1kni96+JVuXnd7C1iHgA\n+CbwwppDfvfa0Kj8On33ygyy2hmUdBnwdgBJvwPcHxGjJeZpmLQsP0kzlGerlXQEaUiOeu3H9kR+\n97rk9665XDafBdZGxAUNkvn9a6Cd8vM7WJ+kfSRNy+u7AUcDq2uS+d1roJ3y6/TdK625MBoMSirp\nvfn4v0bE5ZKOk7QB+CXwzrLyM2zaKT/SZNx/JOkx4GHglHHL8ICRtAQ4CthH0u3AItJXmn73WmhV\ndpTw3kn6FHBHRPxNl+dXgIsi4rO95qUPXgq8DbhBUvUX9NnAAeD3rw0tyw//7mtkFrBY0k6kSpSL\nImKl/+62rWX50eG758FIzaxvJG0Eng48BvwGWAt8Efi3KPGXjaRVpF+In+vwvKeS/mX/LFIg+T/A\nOcUmKkkfBt4D7En6V+0fVzvDSnqIHfuz7AZcGBF1x8Yys8ml1MFIzWzSCeC1EfEUUs3DecBZpOaf\nuvK/GsfLQ8C7gKdHxF7ACLA0d7pG0vHA+0hjh00HrgEuqp4cEXtExJ4RsSdpfKdfAUvH9AnMbGA5\nyDKzUuQOopeR+hPOr37qLOkLkj6VP39+CHhl3veRfPwySVsLy28kzZf0oZr9j0qqW3Ml6V1KIzbf\nJ+kKSQc0yOP/RsTPImJbDva2AfcAj+QkzwauzuPVbQO+BDT6ZPtNwGhEXN1diZnZROMgy8xKFRE/\nIH0Z+/LC7rcAH4mIPYCrSTVgkdO/rlA7dBJwF/DtiPi7wv5DgbuBr9TeT9IJwELg9cA+wFXAkmZ5\nlHQDqRbqC8DrI6IaZK0EXizp4PzF23zgWw0uM5/UNGpmBjjIMrOxcSepua3q0oi4BlJtUt63w9g9\nkuaSgp6TIuKOwv7dgP8ALoiIK+vc633AudUaKuBc0rg3s+ukJefhuaQ+VyPAJdXmwoi4njQ69s9I\nnVzfCPxJ7fmSDgRekdOamQEOssxsbOzP9ukpgh1HnH6CPBDgfwB/ERHfrzn8WeDmiPi7BqcfCHxC\n0hZJW4B78/7aGRN2EBGPRMQnga3Aq3I+3g+8Ouf/ScBfA9/JgV7RqcBVEXFrs3uY2eTiIMvMSiXp\nRaS559rqq5T7Rl0MrIyIz9QcWwA8EzitySVuA94TEXsXlidHxLVtZnkKqdYK4FhgSUTcGRHbImIx\nsDepubLo7bgWy8xqOMgys36rDtT3FEmvJfWHuigibioer3dO9rfA7sAHd0iQprD4APCGQhNjPf8C\nnF3oaL+XpDfXzah0pKSXSZoqaTdJZwG7AtWA7AbgJElPV5rT7FRSELahcI2XkILIrzbJk5lNQmXO\nXWhmk9NleaC+bcBNwMdJgU/V453cG+w7BZgBbMkDKwO8lzT68tOAmwv7L4qI03e4UMSluU/Vl3Nf\nqQeA5dQPgp4E/BPwDOB/geuBYwtz5f1NPn4DaQysW4A3RsSDhWu8HbgkIn7ZqEDMbHLqeTBSSTuT\npoDZFBGvU5rD5yukfhEbSZ1W7+81o2ZmZmbDpB/NhWeSRnWuRmsLgBURMZf0+fOCPtzDzMzMbKj0\nFGRJ2h84DvgM2/tUHM/2DqCLgRN7uYeZmZnZMOq1JusfgQ+R+l5UzSjM6D1K6lthZmZmNql03fE9\nfzV0d0SsljSvXpqICElP6PRVb5+ZmZnZoIqIel9GN9VLTdZLgOMl/Zz0ifarJF0EjEqaCSBpFmnq\ni3qZ9dLlsmjRonHPwzAvLj+XnctvOBeXn8tuvJZudR1kRcTZETE7Ig4ifXL9nYg4FVhGmsOL/N9L\nu86dmZmZ2ZDq52Ck1VDvPOBoSetJU1Oc18d7mJmZmQ2FvgxGGhHfBb6b1+8DXtOP61p98+bNG+8s\nDDWXX/dcdr1x+fXG5dc9l9346Hkw0q5uKsV43NfMzMysU5KIMe74bmZmZmYNOMgyMzMzK4GDLDMz\nM7MSOMgyMzMzK4GDLDMzM7MSOMgyMzMzK0HXQZakXSVdJ2mNpLWSzs37RyRtkrQ6L8f2L7tmZmZm\nw6GncbIk7R4RD0uaAlwN/BnwamBrRPxDk/N2GCdLSkNPeOwsMzMzGzTjMk5WRDycV6cCOwNbqvnp\n5bpmZmZmw66nIEvSTpLWAKPAqoi4KR/6gKSfSPqspGk959LMzMxsyPQ0d2FEbAMOl7QXcKWkecCn\ngL/OST4CfBw4rfbckZGRXm5tZmZmVopKpUKlUun5On2bu1DSh4FfRcTfF/bNAS6LiOfUpHWfLDMz\nMxsKY94nS9I+1aZASbsBRwOrJc0sJHs9cGO39zAzMzMbVr00F84CFkvaiRSsXRQRKyV9UdLhQAA/\nB97bh3yamZmZDZW+NRd2dFM3F5qZmdmQGJchHMzMzMysPgdZZmZmZiVwkGVmZmZWAgdZZmZmZiVw\nkGVmZmZWAgdZZmZmZiVwkGVmZmZWgl5GfN9V0nWS1khaK+ncvH+6pBWS1kta7gmizczMbDLqaTBS\nSbtHxMOSpgBXA38GHA/cExEfk3QWsHdELKg5z4ORmpmZ2VAYl8FII+LhvDoV2BnYQgqyFuf9i4ET\ne7mHmZmZ2TDqKciStJOkNcAosCoibgJmRMRoTjIKzOgxj2ZmZmZDp5cJoomIbcDhkvYCrpT0yprj\nIaluG+DIyEgvtzYzMzMrRaVSoVKp9Hydvk0QLenDwK+APwTmRcRmSbNINVyH1KR1nywzMzMbCmPe\nJ0vSPtUvByXtBhwNrAaWAfNzsvnApd3ew8zMzGxY9dJcOAtYLGknUrB2UUSslLQaWCrpNGAjcFLv\n2TQzMzMbLn1rLuzopm4uNDMzsyExLkM4mJmZmVl9DrLMzMzMSuAgy8zMzKwEDrLMzMzMSuAgy8zM\nzKwEDrLMzMzMSuAgy8zMzKwEvYz4PlvSKkk3SfqppDPy/hFJmyStzsux/cuumZmZ2XDoejBSSTOB\nmRGxRtIewI+AE0kjvG+NiH9ocq4HIzUzM7Oh0O1gpF1PqxMRm4HNef0hSTcD+1Xz0+11zczMzCaC\nvvTJkjQHeD5wbd71AUk/kfTZ6iTSZmZmZpNJz0FWbir8GnBmRDwEfAo4CDgcuAv4eK/3+P/s3Xvc\n7WVd5//Xm1OeUjQm5OiuCSZNTUoBT7HNadqRg06ah1TUaYyxPOTUeMqR2/k1mU2lY5bhBA5qwjhY\nCIWiGUsxFUPZgALqTihQ2B44eMAS5PP74/u9YbFY677X6bvvtfb9ej4e67HX+n6v7/e61nVf+96f\nfV3X97okSZKWzdTDhQBJ9gbeA7yzqs4EqKqv9J3/M+DsYdeurKzMkrUkSVIner0evV5v5vvMMvE9\nwKnA16vqpX3HD6iqa9v3LwUeUVW/NHCtE98lSdJSmHbi+yxB1mOAjwCXAKs3eRXwDJqhwgKuBE6o\nqp0D1xpkSZKkpbDLg6xZGGRJkqRlMW2Q5YrvkiRJHTDIkiRJ6oBBliRJUgcMsiRJkjpgkCVJktQB\ng6wOJLn9iUlJkrQ5GWRJkiR1YOogK8khSc5L8tkkn0ny4vb4/ZJ8MMnnk3zADaIlSdJmNMuK7/cH\n7l9V29tNoj8FPAl4HvC1qvq9JC8H7ltVrxi4dqzFSJd1kdJlLbckSbqrXb4YaVVdV1Xb2/ffAi4H\nDgKOo9nTkPbPJ02bhyRJ0rKay5ysJFuAI4ALgP379ircCew/jzwkSZKWycxBVjtU+B7gJVX1zf5z\n7ZigY2aSJGnT2WuWi5PsTRNgvaOqzmwP70xy/6q6LskBwFeGXbuysjLqnoDzmSRJ0sbo9Xr0er2Z\n7zPLxPfQzLn6elW9tO/477XHXp/kFcC+k0x8H/V+mSxruSVJ0l1NO/F9liDrMcBHgEu4Y0jwlcAn\ngXcDhwJXAU+tqhsHrjXIkiRJS2GXB1mzMMiSJEnLYpcv4SBJkqTRDLIkSZI6YJC1C4x6klKSJO2+\nnJPVgcFyt2O5G1kkSZI0JedkSZIkLRCDLEmSpA4YZEmSJHVg6iArySlJdia5tO/YSpJrklzUvrbN\np5iSJEnLZZaerLcBg0FUAX9YVUe0r/fPcH9JkqSlNXWQVVXnAzcMOTXx7HtJkqTdTRdzsl6U5OIk\nJyfZt4P7S5IkLbx5B1lvAX4IeBhwLfAHc76/JEnSUthrnjerqq+svk/yZ8DZo9K6CrokSVpEvV6P\nXseHGbYAACAASURBVK83831mWvE9yRbg7Kp6SPv5gKq6tn3/UuARVfVLQ65zxXdJkrQUpl3xfeqe\nrCSnAccA+yW5GjgR2JrkYTRPGV4JnDDt/SVJkpaZexd2YLXc/ZbtO0iSpIZ7F0qSJC0QgyxJkqQO\nGGTtIkmGDiNKkqTdk0GWJElSBwyyJEmSOmCQJUmS1AGDLEmSpA4YZEmSJHVg6iArySlJdia5tO/Y\n/ZJ8MMnnk3wgyb7zKaYkSdJymaUn623AtoFjrwA+WFWHAx9qP0uSJG06UwdZVXU+cMPA4eOAU9v3\npwJPmvb+kiRJy2zec7L2r6qd7fudwP5zvr8kSdJS2KurG1dVJRm5K/LKyspE91vWzaIlSdJy6fV6\n9Hq9me+TWYKWJFuAs6vqIe3nK4CtVXVdkgOA86rqR4dcV/359gdQ47xfdGttn7MM5ZckSXdIQlVN\nvDfevIcLzwKe075/DnDmnO8vSZK0FKbuyUpyGnAMsB/N/KvXAO8F3g0cClwFPLWqbhxy7W7bkzXu\nJtCL/j0mtSw/H0mSJjVtT9ZMw4XTMsha/O8xqWX5+UiSNKlFGS6UJEkSSx5kJRm752jR7U7fRZIk\nLXmQJUmStKgMsiRJkjpgkCVJktSBTRdkOfdJkiTtCpsuyJIkSdoVOtm7MMlVwDeA7wG3VNWRXeQj\nSZK0qLrqySqaPQyP2IgAa3BIcNLNqDU+h18lSRqukxXfk1wJPLyqvj7i/FxWfB+1yvjg8Xal1jWv\nmZdpV3xf1hXTB7/vspVfkqT1LNqK7wX8TZILkzy/ozwkSZIWVidzsoBHV9W1Sf4V8MEkV1TV+f0J\nph3CG9bjs7KysnRDgqu9aw61SZK0WHq9Hr1eb+b7dL5BdJITgW9V1R/0HZt6uHDV4LG1hhEXcbhw\ntQzLPty27OWXJGk9CzNcmOQeSb6/fX9P4N8Bl847H0mSpEXWxZys/YHzk2wHLgD+qqo+MO9Mxukx\n8sm3bq1Xt8s2hDsN25gkaZTOhwuHZjqH4cJBw+437LzDhfOzVj2vnl+m7zONZX0qVJI0voUZLpQk\nSZJBliRJUid2myBrGefGTFLe9b7frvz+a+W1srJyp/Oj3o9jM8zpkiTtvnabOVmrRqVdxDlZw4wq\n13rl3pVzg6b5jqNW7V8vn0Wf6+ScLEna/TknS5IkaYEsRZDVxbDRYG/M6lDWOMNbw453NVw3TV7T\nlGVXfqdJrPXzWK9dzLvdzGvj8UUa+t1dWYeTc3hemr+lGC6cxLjDhf3HBo8PK8tA+de9flqj8lqv\nXKPqcFzr3XOtMo1j2uHCta5fbzhx3sONw/LvL9+095n0vNZnHU5uGYbnpY2yUMOFSbYluSLJF5K8\nvIs8pGnNYz+qzcq6m431Nxvrb3rW3cboYludPYE3A9uABwHPSPLAeeczqfWeiBuWvv/84FDietdP\na728Bsu41hDWqPPDhkaH3XdU+aYxqt7WK8s49530uv5fNusNRw4r2yQ/my7M4+c2WO+jzg++3xW/\nqGf9+zTq+kUYQhxWf+OWa72/J7vy79BGWa2/tdqrhhv1d9dh4o5V1VxfwCOB9/d9fgXwioE01Q+o\n1WPD3k/ymuS6wfynyWuW67v+fuOWdb16H/azmvU7rFWWYecHyzjs/KBRx0888cR1296k33VY3muV\na9S5Wc5PUhfj1v3g+/6668pa333c69f7ThtlWP2NW65x2vs032+9+y6S1fpbxJ/tohv1d9d6G09b\nTxPHRF0MFx4EXN33+Zr2mCRJ0qYx94nvSZ4MbKuq57efnwUcVVUv6ksz30wlSZI6VFNMfN+rg3J8\nCTik7/MhNL1Zt5umoJIkScuki+HCC4HDkmxJsg/wNOCsDvKRJElaWHPvyaqqW5O8EDgX2BM4uaou\nn3c+kiRJi2xDFiOVJEna3XW6rc44i5ImeVN7/uIkR3RZnmWzXv0l2ZrkpiQXta9Xb0Q5F1GSU5Ls\nTHLpGmlse0OsV3e2u7UlOSTJeUk+m+QzSV48Ip3tb4hx6s82OFySuyW5IMn2JJcled2IdLa9Icap\nv4nb3jTrPozzohkq3AFsAfYGtgMPHEhzLHBO+/4o4BNdlWfZXmPW31bgrI0u6yK+gMcCRwCXjjhv\n25u+7mx3a9ff/YGHte/vBXzO331zrz/b4Oj6u0f7517AJ4DHDJy37c1WfxO1vS57so4EdlTVVVV1\nC3A68MSBNMcBpwJU1QXAvkn277BMy2Sc+gPwSc0hqup84IY1ktj2Rhij7sB2N1JVXVdV29v33wIu\nBw4cSGb7G2HM+gPb4FBVdXP7dh+a/6xfP5DEtreGMeoPJmh7XQZZ4yxKOizNwR2WaZmMU38FPKrt\n8j0nyYN2WemWn21vera7MSXZQtMreMHAKdvfGNaoP9vgCEn2SLId2AmcV1WXDSSx7a1hjPqbqO11\nsU5Wf0HGMRgROhO/MU49fBo4pKpuTvJzwJnA4d0Wa7di25uO7W4MSe4FnAG8pO2RuUuSgc+2vz7r\n1J9tcISqug14WJL7AOcm2VpVvYFktr0Rxqi/idpelz1Z6y5KOiTNwe0xjbeo6zdXuzar6n3A3knu\nt+uKuNRse1Oy3a0vyd7Ae4B3VtWZQ5LY/tawXv3ZBtdXVTcBfw08fOCUbW8Mo+pv0rbXZZA1zqKk\nZwHHAyQ5GrixqnZ2WKZlsm79Jdk/7dbzSY6kWZJj2Pix7sq2NyXb3draujkZuKyq3jgime1vhHHq\nzzY4XJL9kuzbvr878DPARQPJbHsjjFN/k7a9zoYLa8SipElOaM+fVFXnJDk2yQ7g28DzuirPshmn\n/oCnAC9IcitwM/D0DSvwgklyGnAMsF+Sq4ETaZ7StO2tY726w3a3nkcDzwIuSbL6C/pVwKFg+xvD\nuvWHbXCUA4BTk+xB04nyjqr6kP/ujm3d+mPCtudipJJ2uSRXAT8IfK89VMDhVXXdHPN4KvDrwI8D\nn6yqx/WdeyxwzsAl9wSeXFV/OXCfDwGPA/Zq52tI0lg6XYxUkkYo4AlV9f3t697zDLBaXwf+EPjd\nu2RedX5f3t8PPAH4FvD+/nRJnknT4+//RiVNzCBL0sJI8n1J3pjkS+3rDe2cxNXzL0vy5STXJPlP\nSW5L8sPD7lVVH6qqM4Brx8j6ucD/q6rv9OV1H+A1wMtwTSZJUzDIkrRRhgUuv0WzEO+Pt68jgVdD\ns80U8FLg8cBhNCsvz9zDlOSewJNpF2js8zvAn9CslyNJEzPIkrQRApyZ5Ib29Rft8WcC/72qvlZV\nXwNeCzy7PfdU4JSqurztcTqR+fQw/QLw1ar6yO2FSx4OPBL4ozncX9Im1eVipJI0SgFPrKq/HTh+\nAPCPfZ//iTu2VDkA+GTfucF196b1HODtqx/aJ4v+BPj1qrqtfVobHDKUNCF7siQtki/TbIq+6lDu\nWCjxWu66QO84Rg4pJjmEZrmKt/cdvjfwk8D/TXItdwR21yR59Jh5SpI9WZIWymnAq5P8ffv5NcA7\n2/fvBk5J8g6aHq7/ttaN2h6pfWjW+NojyfcBt7Ubrq96NvB3VXXl6oGqujHJAX1pDqUJtH4C+NrU\n30zSpmNPlqRF8ts0ux1c0r4ubI9RVe8H3gScB3we+Hh7zb+MuNfxNIsF/gnwWOA7wEkDaZ7NXSe8\nU1VfWX3RBFYF7BwI0CRpTVMvRprkFODnga9U1UPaY0cCb6b5n+OtwK9W1d+PvoskTSfJA4FLgX1c\nJFTSIpqlJ+ttwLaBY78H/LeqOoKmm//3Zri/JN1Jkv/QrqV1X+D1wFkGWJIW1dRBVlWdD9wwcPha\n4D7t+31xZ29J8/UrNOtW7QBuAV6wscWRpNFm2rswyRbg7L7hwgcAH6WZv7AH8Miqunr2YkqSJC2X\neT9deDLw4qr6yyS/CJwC/MxgoiTuAyZJkpZGVU28Vt68ny48sm8H+zNotsQYqqp8Tfk68cQTN7wM\ny/yy/qw76285X9afdbdRr2nNO8jakeSY9v1P0zxmLUmStOlMPVyY5DSalZL3S3I1zdOEvwL8cbvo\n33faz5IkSZvO1EFWVT1jxKmjpr2nxrN169aNLsJSs/6mZ93NxvqbjfU3PetuY8z0dOHUmSa1EflK\nkiRNKgm1ABPfJUmSxIIEWUlIJg4QJUmSFtZCBFmSJEm7G4MsSZKkDkwdZCU5JcnOJJcOHH9RksuT\nfCbJ62cvoiRJ0vKZpSfrbcC2/gNJHgccBzy0qh4M/P4M95ckSVpaUwdZVXU+cMPA4RcAr6uqW9o0\nX52hbJIkSUtr3nOyDgN+KsknkvSSPHzO95ckSVoKU6/4vsb97ltVRyd5BPBu4IeHJVxZWZlz1pIk\nSbPr9Xr0er2Z7zPTiu9JtgBnV9VD2s/vA363qj7cft4BHFVVXx+47k4rvq+ukeUq8JIkadEsyorv\nZwI/3RbocGCfwQBLkiRpM5h6uDDJacAxwA8kuRp4DXAKcEq7rMN3gePnUkpJkqQlsxAbRDtcKEmS\nFtWiDBdKkiQJgyxJkqROGGRJkiR1wCBLkiSpAwZZkiRJHZg6yEpySpKd7XINg+d+I8ltSe43W/Ek\nSZKW0yw9WW8Dtg0eTHII8DPAP85wb0mSpKU2dZBVVecDNww59YfAy6YukSRJ0m5grnOykjwRuKaq\nLpnnfSVJkpbN1NvqDEpyD+BVNEOFtx+e1/0lSZKWydyCLOBfA1uAi9ttcg4GPpXkyKr6ymDilZWV\nOWYtSZI0H71ej16vN/N9Ztq7MMkW4OyqesiQc1cCP1lV1w85596FkiRpKezyvQuTnAZ8DDg8ydVJ\nnjeQxIhJkiRtWjP1ZE2dqT1ZkiRpSezynixJkiSNZpAlSZLUAYMsSZKkDhhkSZIkdcAgS5IkqQMG\nWZIkSR2YKchKckqSnUku7Tv2P5NcnuTiJH+R5D6zF1OSJGm5zNqT9TZg28CxDwA/VlU/DnweeOWM\neUiSJC2dmYKsqjofuGHg2Aer6rb24wU0exhKkiRtKl3PyfqPwDkd5yFJkrRw9urqxkl+C/huVb1r\n2PmVlZWuspYkSZpar9ej1+vNfJ+Z9y5MsgU4u6oe0nfsucDzgcdX1T8Puca9CyVJ0lKYdu/Cufdk\nJdkG/FfgmGEBliRJ0mYwU09WktOAY4D9gJ3AiTRPE+4DXN8m+3hV/erAdfZkSZKkpTBtT9bMw4XT\nMMiSJEnLYtogyxXfJUmSOmCQJUmS1AGDLEmSpA4YZEmSJHXAIEuSJKkDUwdZSU5JsjPJpX3H7pfk\ng0k+n+QDSfadTzElSZKWyyw9WW8Dtg0cewXwwao6HPhQ+1mSJGnTmTrIqqrzgRsGDh8HnNq+PxV4\n0rT3lyRJWmbznpO1f1XtbN/vBPaf8/0lSZKWwtz3LlxVVZVk5BLuKysrXWUtSZI0tV6vR6/Xm/k+\ns+5duAU4u6oe0n6+AthaVdclOQA4r6p+dMh1bqsjSZKWwqJsq3MW8Jz2/XOAM+d8f0mSpKUwdU9W\nktOAY4D9aOZfvQZ4L/Bu4FDgKuCpVXXjkGvtyZIkSUth2p6smYYLp2WQJUmSlsWiDBdKkiQJgyxJ\nkqROGGRJkiR1wCBLkiSpAwZZkiRJHegkyEryyiSfTXJpkncl+b4u8pEkSVpUcw+y2lXgnw/8RLsS\n/J7A0+edjyRJ0iLrYu/CbwC3APdI8j3gHsCXOshHkiRpYc29J6uqrgf+APgn4MvAjVX1N/POR5Ik\naZF1MVz4r4FfB7YABwL3SvLMeecjSZK0yLoYLnw48LGq+jpAkr8AHgX8eX+ilZWVDrKWJEmaTa/X\no9frzXyfue9dmOTHaQKqRwD/DPwf4JNV9cd9ady7UJIkLYWF2buwqi4G3g5cCFzSHn7rvPORJEla\nZHPvyRorU3uyJEnSkliYnixJkiQZZEmSJHXCIEuSJKkDCx1kJbl9vtY80+5qLlchSdLms9AT3yeZ\nEL/Ik+fbCXMbXQxJkjQFJ75LkiQtkE6CrCT7JjkjyeVJLktydBf5SJIkLaouttUB+F/AOVX1lCR7\nAffsKB9JkqSF1MW2OvcBLqqqH14jjXOyJEnSUlikOVk/BHw1yduSfDrJ/05yjw7ykSRJWlhdDBfu\nBfwE8MKq+vskbwReAbymP9Gkyxosck+VJEnaffR6PXq93sz36WK48P7Ax6vqh9rPjwFeUVVP6Esz\n8XDhekHWIgdhDhdKkrS8Fma4sKquA65Ocnh76N8Cn513PpIkSYusq6cLXwT8eZJ9gH8AntdRPpIk\nSQtpaVZ8d7hQkiRthIUZLpQkSZJBliRJUicWLshKcvvQ3zTnx02zq/WXaRHLJ0mS5mvhgixJkqTd\ngUGWJElSBzoLspLsmeSiJGd3lYckSdKi6rIn6yXAZYBrF0iSpE2nkyArycHAscCfAc7wliRJm05X\nPVlvAP4rcFtH95ckSVpoc99WJ8kTgK9U1UVJto5Kt7KyMo+8gMVb5X2a5RlWrznxxBPnUjeSJGk6\nvV6PXq83833mvq1Okt8Bng3cCtwNuDfwnqo6vi/NyG111nu/apy0GxV8rRVkjSpf/zWLFjRKkrSZ\nTbutTqd7FyY5BvjNqvr3A8cNsjDIkiRpGSzy3oVGDJIkadPptCdrZKb2ZN3+ftg19mRJkrQ4Frkn\nS5IkadMxyJIkSeqAQZYkSVIHNmWQlWSqtax2tf5yTlvmZfmukiTtbjZlkCVJktS1rvYuPCTJeUk+\nm+QzSV7cRT6SJEmLau7b6rRuAV5aVduT3Av4VJIPVtXlHeUnSZK0UDrpyaqq66pqe/v+W8DlwIFd\n5CVJkrSIOp+TlWQLcARwQdd5SZIkLYpOg6x2qPAM4CVtj5YkSdKm0NWcLJLsDbwHeGdVnTl4fmVl\nZdb7j512ZWWF1772tWOdH2dLm3lt2zPqPoPfba3vupFbCG309kWbzbj17c9FkmbT6/Xo9Xoz36eT\nvQvT/JY/Ffh6Vb10yPmZ9y7st97ehaOuHXZ+HkHWuGVd69ha109Tlo5+zp3dW3dlkCVJG2PR9i58\nNPAs4HFJLmpf2zrKS5IkaeF0MlxYVR/FhU4lSdImZiAkSZLUAYMsSZKkDhhkSZIkdWChgqxRT9it\nt9zDqPNJ1l3+YNj5weMrKyu3HxuWfr3zg/fuwmre/XXRn9ew8/3Hp8lrWD7zzmvY9aPuP497Tnv9\n4PtJrhsn3eDPdfDa/jbYn25aa5Vvknrvok6mNc/7j3uvebRRScurkyUc1s10xBIO/SZZ1mAe1w27\nfpolIGbNc5y0/UbV3VrlGrV8xrjWynPw/Kx5TZLvPO456f3WWjpk3OsmKV+/9f4OjVuWtfIddn37\nKPPM95kl7TTmef9Jfn4upSEtv0VbwkGSJGlT6yTISrItyRVJvpDk5V3ksZnNYxXazcz6m551Nxvr\nbzbW3/Ssu40x9yAryZ7Am4FtwIOAZyR54Lzz2cz8yzIb62961t1srL/ZWH/Ts+42Rhc9WUcCO6rq\nqqq6BTgdeGIH+UiSJC2sLoKsg4Cr+z5f0x6TJEnaNOb+dGGSJwPbqur57ednAUdV1Yv60vi4jSRJ\nWhrTPF3Yxd6FXwIO6ft8CE1v1u2mKagkSdIy6WK48ELgsCRbkuwDPA04q4N8JEmSFtbce7Kq6tYk\nLwTOBfYETq6qy+edjyRJ0iLbkBXfJUmSdnedrvg+zqKkSd7Unr84yRFdlmfZrFd/SbYmuSnJRe3r\n1RtRzkWU5JQkO5NcukYa294Q69Wd7W5tSQ5Jcl6Szyb5TJIXj0hn+xtinPqzDQ6X5G5JLkiyPcll\nSV43Ip1tb4hx6m/itldVnbxohgp3AFuAvYHtwAMH0hwLnNO+Pwr4RFflWbbXmPW3FThro8u6iC/g\nscARwKUjztv2pq87293a9Xd/4GHt+3sBn/N339zrzzY4uv7u0f65F/AJ4DED5217s9XfRG2vy56s\ncRYlPQ44FaCqLgD2TbJ/h2VaJuMu6uqTmkNU1fnADWskse2NMEbdge1upKq6rqq2t++/BVwOHDiQ\nzPY3wpj1B7bBoarq5vbtPjT/Wb9+IIltbw1j1B9M0Pa6DLLGWZR0WJqDOyzTMhmn/gp4VNvle06S\nB+2y0i0/2970bHdjSrKFplfwgoFTtr8xrFF/tsERkuyRZDuwEzivqi4bSGLbW8MY9TdR2+tinaz+\ngoxjMCJ0Jn5jnHr4NHBIVd2c5OeAM4HDuy3WbsW2Nx3b3RiS3As4A3hJ2yNzlyQDn21/fdapP9vg\nCFV1G/CwJPcBzk2ytap6A8lseyOMUX8Ttb0ue7LWXZR0SJqD22Mab1HXb652bVbV+4C9k9xv1xVx\nqdn2pmS7W1+SvYH3AO+sqjOHJLH9rWG9+rMNrq+qbgL+Gnj4wCnb3hhG1d+kba/LIGucRUnPAo4H\nSHI0cGNV7eywTMtk3fpLsn+StO+PpFmSY9j4se7KtjelzdLukjwzyblTXBfgZOCyqnrjiGS2vxHG\nqb/N0gYnlWS/JPu27+8O/Axw0UAy294I49TfpG2vs+HCGrEoaZIT2vMnVdU5SY5NsgP4NvC8rsqz\nbMapP+ApwAuS3ArcDDx9wwq8YJKcBhwD7JfkauBEmqc0bXvrWK/uaNrdG5L8E/AV2naX5LnAL1fV\nYzei3MMkuQr4QeB7ND/nDwK/VlXfWO/aqvpz4M+nyPbRwLOAnUl+jeY/s58F/hK41va3rtX6uyTJ\n6j9wrwIOBX/3reMA4NQke9C0u3dU1Yf8d3ds69YfE7Y9FyOVNLEkV9IEVH/bd+y5LF6QdXs52yeo\nzgU+UFUv6zjf44C30AQM/wT8NvCzVfWTXeYrabF0uhippE3lTv9jS3Jgkvck+UqSLyZ5Ud+5I5N8\nPMkNSb6c5I/aeTgkeUuS/zlwr/cmeWmS30xyxsC5NyUZNSx3R+GaIZEPAD/Wd+0rkuxI8o00i18+\nqe/cc5Oc3/f5tiQnJPl8W+43r5HdjwEfbZdguY2mR8wn4KRNxiBL0rQGn1C6/XPb3X42zXyGA4HH\nA7+e5N+1SW4FXgL8APDI9vyvtufeRTMHcfVe96WZG3Ea8E5gW/vkD0n2atOeul45kxwMbOPOywHs\noFls8N7Aa4F3Zu01g36eZiLsQ4GnJvnZEek+BDwyyWFt8Pgc4H1r3FfSbsggS9I0ApzZ9ujckOQG\n4I+5ozfrEcB+VfXbVXVrVV0J/Bnt/IWq+nRVfbKqbquqfwTeSjMPDOCjQCVZHXZ8CvCxdpHK64Dz\ngV9sz20DvlpVg5N7B8v5DZphu3+gGbqjLccZ7T2pqncDX6BZBXuU362qb1TV1cB5wMOGJaqqT9IE\nfp+jmbfxZOC/rHFfSbshgyxJ0yjgiVV139UXTU/Uam/WA4ADB4KwV9JMQifJ4Un+Ksm1SW4C/gdN\nrxbVTBQ9HXhGe69f4s4T0E+lmRhN++c7xijnvWm2w/hp+h7JTnJ8mv3HVsv44NVyjHBd3/ubabZ9\nuYv2oZXH0zwe/33Afwf+tn1iSdImYZAlaV76hw+vBq7sD8Kq6t5V9YT2/FuAy4Afqar7AL/FnX8f\nnQY8JckDaLaYek/fufcCD03yYJrhu7GeAKyqjwB/BLweoL33W4FfA+7XBoqf4a7DoNPYBpxWVV9u\ne+tOBe4LPHAO95a0JAyyJHXhk8A3k7wsyd2T7JnkwUlWe5HuBXwTuDnJjwIv6L+43bvuazRDjO/v\nX3Khqr5DE3S9C7igqgYXOV7LG4EjkxwF3JOmp+trwB5JnkfTkzWutYKxS2jmbP1gmm06nk2zZM6O\nCe4vackZZEmal2pfVNX3gCfQzFn6IvBVml6je7dpf5NmGPAb7fHT4S5be7yLZnjvXUPyOpUmIFpr\nqPCuBaz6Wnvty9s9yf4A+DjNMOCDaeaD3eX79H1mjfP9fptmPtYlNJttvwR48jjrc0nafUy0TlaS\nU2i6579SVQ8ZkeZNwM/RzFd47hoTUiVpKkkOAa4A9h+xL6AkbbhJe7LeRjPXYKgkx9LMsTgM+BWa\neReSNDft8hC/QTPnyQBL0sKaaFudqjo/yZY1khxHu15NVV2QZN8k+7svkqR5SHJPYCdwJWv8h0+S\nFsG89y48iOapolXX0DzCbJAlaWZV9W1GLJsgSYumi4nvg0/cuDmiJEnadObdk/Ul4JC+zwe3x+4k\niYGXJElaGlU18Rp68+7JOgs4HiDJ0cCNo+ZjVZWvKV8nnnjihpdhGV+r+utvlva4TO14krIO1kn/\n50nb3rL+fe+q3P7dtf6su+V8TWuinqwkp9HsL7ZfkquBE4G9219GJ1XVOUmOTbID+DbwvKlLJkmS\ntMQmfbrwGWOkeeH0xZEkSdo9uOL7Etq6detGF2GpWX/Ts+5mY/3NxvqbnnW3MSZa8X1umSa1Eflq\nc0uaOYv9bW/YsUnutyzteJKyDtbJrHU07bUbaVnLLakb7e/QDZ/4LkmSJAyyJEmSOmGQJUmS1AGD\nLEmSpA4YZEmSJHXAIEuSJKkDBlmSJEkdMMiSJEnqgEGWJElSBwyyJEmSOmCQJUmS1AGDLEmSpA4Y\nZEmSJHXAIEuSJKkDBlmSJEkdmDjISrItyRVJvpDk5UPO75fk/Um2J/lMkufOpaSSJElLJFU1fuJk\nT+BzwL8FvgT8PfCMqrq8L80K8H1V9cok+7Xp96+qW/vS1CT5SvOQBID+tjfs2CT3W5Z2PElZB+tk\n1jqa9tqNtKzlltSN9ndoJr1u0p6sI4EdVXVVVd0CnA48cSDNtcC92/f3Br7eH2BJkiRtBntNmP4g\n4Oq+z9cARw2k+d/A3yb5MvD9wFOnL54kSdJymrQna5y+81cB26vqQOBhwB8n+f6JSyZJkrTEJu3J\n+hJwSN/nQ2h6s/o9CvgfAFX1D0muBP4NcGF/opWVldvfb926la1bt05YFEmSpPnr9Xr0er2Z7zPp\nxPe9aCayPx74MvBJ7jrx/Q+Bm6rqtUn2Bz4FPLSqru9L48R37XJOfHfi+7iWtdySujHtxPeJLhxd\nJgAAIABJREFUerKq6tYkLwTOBfYETq6qy5Oc0J4/Cfgd4G1JLqYZjnxZf4AlSZK0GUzUkzW3TO3J\n0gawJ8uerHEta7kldWNXLeEgSZKkMRhkSZIkdcAgS5IkqQMGWZIkSR0wyJIkSeqAQZYkSVIHDLIk\nSZI6YJAlSZLUAYMsSZKkDhhkSZIkdcAgS5IkqQMGWZIkSR0wyJIkSeqAQZYkSVIHDLIkSZI6YJAl\nSZLUAYMsSZKkDkwUZCXZluSKJF9I8vIRabYmuSjJZ5L05lJKSZKkJZOqGi9hsifwOeDfAl8C/h54\nRlVd3pdmX+DvgJ+tqmuS7FdVXxtyrxo3X2lekgDQ3/aGHZvkfsvSjicp62CdzFpH0167kZa13JK6\n0f4OzaTXTdKTdSSwo6quqqpbgNOBJw6k+SXgPVV1DcCwAEuSJGkzmCTIOgi4uu/zNe2xfocB90ty\nXpILkzx71gJKkiQto70mSDtOv/newE8AjwfuAXw8ySeq6guDCVdWVm5/v3XrVrZu3TpBUSRJkrrR\n6/Xo9Xoz32eSOVlHAytVta39/Ergtqp6fV+alwN3r6qV9vOfAe+vqjMG7uWcLO1yzslyTta4lrXc\nkrqxK+ZkXQgclmRLkn2ApwFnDaR5L/CYJHsmuQdwFHDZpIWSJEladmMPF1bVrUleCJwL7AmcXFWX\nJzmhPX9SVV2R5P3AJcBtwP+uKoMsSZK06Yw9XDjXTB0u1AZwuNDhwnEta7kldWNXDBdKkiRpTAZZ\nkiRJHTDIkiRJ6oBBliRJUgcMsiRJkjpgkCVJktQBgyxJkqQOGGRJkiR1wCBLkiSpAwZZkiRJHTDI\nkiRJ6oBBliRJUgcMsiRJkjpgkCVJktQBgyxJkqQOGGRJkiR1wCBLkiSpAxMFWUm2JbkiyReSvHyN\ndI9IcmuSX5i9iJIkSctn7CAryZ7Am4FtwIOAZyR54Ih0rwfeD2RO5ZQkSVoqk/RkHQnsqKqrquoW\n4HTgiUPSvQg4A/jqHMonSZK0lCYJsg4Cru77fE177HZJDqIJvN7SHqqZSidJkrSk9pog7TgB0xuB\nV1RVJQlrDBeurKzc/n7r1q1s3bp1gqJIkiR1o9fr0ev1Zr5PqsbrbEpyNLBSVdvaz68Ebquq1/el\n+SJ3BFb7ATcDz6+qswbuVePmK81LE/dDf9sbdmyS+y1LO56krIN1MmsdTXvtRlrWckvqRvs7dOJ5\n5pP0ZF0IHJZkC/Bl4GnAM/oTVNUP9xXobcDZgwGWJEnSZjB2kFVVtyZ5IXAusCdwclVdnuSE9vxJ\nHZVRkiRp6Yw9XDjXTB0u1AZwuNDhwnEta7kldWPa4UJXfJckSeqAQZYkSVIHDLIkSZI6YJAlSZLU\nAYMsSZKkDhhkSZIkdcAgS5IkqQMGWZIkSR0wyJIkSeqAQZYkSVIHDLIkSZI6YJAlSZLUAYMsSZKk\nDhhkSZIkdcAgS5IkqQMGWZIkSR0wyJIkSerAxEFWkm1JrkjyhSQvH3L+mUkuTnJJkr9L8tD5FFWS\nJGl5TBRkJdkTeDOwDXgQ8IwkDxxI9kXgp6rqocD/B7x1HgWVJElaJpP2ZB0J7Kiqq6rqFuB04In9\nCarq41V1U/vxAuDg2YspSZK0XCYNsg4Cru77fE17bJRfBs6ZtFCSJEnLbq8J09e4CZM8DviPwKOH\nnV9ZWbn9/datW9m6deuERZEkSZq/Xq9Hr9eb+T6pGjtuIsnRwEpVbWs/vxK4rapeP5DuocBfANuq\naseQ+9Qk+UrzkASA/rY37Ngk91uWdjxJWQfrZNY6mvbajbSs5ZbUjfZ3aCa9btLhwguBw5JsSbIP\n8DTgrIGCHEoTYD1rWIAlSZK0GUw0XFhVtyZ5IXAusCdwclVdnuSE9vxJwGuA+wJvaf83eEtVHTnf\nYkuSJC22iYYL55apw4XaAA4XOlw4rmUtt6Ru7KrhQkmSJI3BIEuSJKkDBlmSJEkdMMiSJEnqgEGW\nJElSBwyyJEmSOmCQJUmS1AGDLEmSpA4YZEmSJHXAIEuSJKkDBlmSJEkdMMiSJEnqgEGWJElSBwyy\nJEmSOmCQJUmS1AGDLEmSpA4YZEmSJHVg4iArybYkVyT5QpKXj0jzpvb8xUmOmL2Y6tfr9Ta6CEvN\n+puedTcb62821t/0rLuNMVGQlWRP4M3ANuBBwDOSPHAgzbHAj1TVYcCvAG+ZU1nV8i/LbKy/6Vl3\ns7H+ZmP9Tc+62xiT9mQdCeyoqquq6hbgdOCJA2mOA04FqKoLgH2T7D9zSSVJkpbIpEHWQcDVfZ+v\naY+tl+bgyYsmSZK0vFJV4ydOngxsq6rnt5+fBRxVVS/qS3M28LtV9Xft578BXlZVn+5LM36mkiRJ\nG6yqMuk1e02Y/kvAIX2fD6HpqVorzcHtsdtNU1BJkqRlMulw4YXAYUm2JNkHeBpw1kCas4DjAZIc\nDdxYVTtnLqkkSdISmagnq6puTfJC4FxgT+Dkqro8yQnt+ZOq6pwkxybZAXwbeN7cSy1JkrTgJpqT\nJUmSpPF0uuK7C5fOZr36S7I1yU1JLmpfr96Ici6iJKck2Znk0jXS2PaGWK/ubHdrS3JIkvOSfDbJ\nZ5K8eEQ6298Q49SfbXC4JHdLckGS7UkuS/K6Eelse0OMU38Tt72q6uRFM5y4A9gC7A1sBx44kOZY\n4Jz2/VHAJ7oqz7K9xqy/rcBZG13WRXwBjwWOAC4dcd62N33d2e7Wrr/7Aw9r398L+Jy/++Zef7bB\n0fV3j/bPvYBPAI8ZOG/bm63+Jmp7XfZkuXDpbMapPwCf1Byiqs4HblgjiW1vhDHqDmx3I1XVdVW1\nvX3/LeBy4MCBZLa/EcasP7ANDlVVN7dv96H5z/r1A0lse2sYo/5ggrbXZZDlwqWzGaf+CnhU2+V7\nTpIH7bLSLT/b3vRsd2NKsoWmV/CCgVO2vzGsUX+2wRGS7JFkO7ATOK+qLhtIYttbwxj1N1Hbm3Sd\nrEmMO6N+MCJ0Jn5jnHr4NHBIVd2c5OeAM4HDuy3WbsW2Nx3b3RiS3As4A3hJ2yNzlyQDn21/fdap\nP9vgCFV1G/CwJPcBzk2ytap6A8lseyOMUX8Ttb0ue7LmsnDpJrZu/VXVN1e7NqvqfcDeSe6364q4\n1Gx7U7LdrS/J3sB7gHdW1ZlDktj+1rBe/dkG11dVNwF/DTx84JRtbwyj6m/SttdlkOXCpbNZt/6S\n7J8k7fsjaZbkGDZ+rLuy7U1p3HaX5JlJzp1z3luS3Jak0yejZ9HWzcnAg4HzRiSba/tL8vtJ/vO0\n1y+Svvq7rKreOCKNv/uGSLJfkn3b93cHfga4aCCZv/tGGKf+Jm17nQ0XlguXzmSc+gOeArwgya3A\nzcDTN6zACybJacAxwH5JrgZOpHlKc+HaXpKrgF+uqg/1HXtue+yxG1CeNeuOMdtdVf058Oe7pNCL\n5dHAs4BvAf+n/X38WeAZwHeBW4BrgX9JciXwDWZvf78PfDLJye2DMneS5LHAOQOH7wk8uar+Msmf\nAs/sO7c38N2quveM5ZrGav1dkmT1H7hXAYeCv/vWcQBwavufkD2Ad1TVh/x3d2zr1h8Ttj0XI5U2\nWPsP7S9X1d/2HXsuGxRkjSPJXlV16wbkuwX4IrBXO3diQ42qhyR/TTPUdVr7+UTgX1fV8Un2BP4N\n8FrgUcBPVtV1cyjLB4CTquo9Y6Q9Bjgb2L+qvjPk/NuA71XVf5q1XNJmtrBd7tImd6f//SQ5MMl7\nknwlyReTvKjv3JFJPp7khiRfTvJH7ZwWkrwlyf8cuNd7k7w0yW8mOWPg3JuSjBqiuSrJy5JcAnwz\nyZ5JXpFkR5JvpFk88kl96Z+b5Py+z7clOSHJ59uyvnnUl2+/04VpFv27LskfDCR5VpJ/TPLVJK/q\nu+77krwxyZfa1xvSDLeT5MNJfqF9/+i2PMe2nx/f12tCkv+YZjHC65O8P8mhA9/jV5N8gWYNp8Gy\n7wM8Dvhw/+H2RVV9r31i6WnAV4HfaK+7b5K/an/G1yc5O8lB7blfTHLhQD7/JUn/fKUe8POj6nTA\nc4H/NyLAuifwZNrH/CVNzyBLWgyDT/vc/rntuj6bZm7AgcDjgV9P8u/aJLcCLwF+AHhke/5X23Pv\novnHfPVe96WZZ3Aa8E5gW5qnaEiyV5t2rX9cnw78HLBvVX2PZsHcx7TDSq8F3pm119z5eZqJpA8F\nnprkZ0ek+1/AG6rqPsAPA+8eOP9omid6Hg+8Jsm/aY//Fs0acz/evo4EVldk7tEsJAjNcOgXgZ/q\n+9wDSPJE4JXAfwD2A86nqa9+TwQeAQx7fPsw4Laq+vKI7wbc/hTTe2kWf4XmZ34yzbDYocB3gNVA\n9Czgh5L8aN8tns2df1ZXtN95TWMEUU8GvtKulyZpBgZZ0sYLcGbbu3NDkhuAP+aO3qxHAPtV1W9X\n1a1VdSXwZ7RzAarq01X1yaq6rar+EXgrTdAA8FGg2jk50Mwn+Fi74ON1NAHEL7bntgFfrarBibKr\nCnhTVX2pqv6lzfuM1aGuqno38AWaVaRH+d2q+kZVXU0zKfxhI9J9l+bBj/2q6uZ20cR+r62qf6mq\nS4CLuSO4+CXgv1fV16rqazSB37Pbcx/pq5fHAq/r+3wMd/Q8/WfgdVX1uTYQeh3NI939T2S9rqpu\nXK2HAfsC31yjDvpdC9wPoKqur6q/rKp/bpcs+J3V8rX5vJtmrhJJfgx4APBXfff6Zpv3en6B5uf8\nkRHnnwO8fczyS1qDQZa08Qp4YlXdd/VF0xO12pv1AODAgSDslcAPAiQ5vB1mujbJTcD/oOnVoppJ\nl6fTTLqGJgjpn4x+Ku0/3O2f71inrP2LGJLk+DT7d62W68GreY/QP/foZpptU4b5ZZqeqsuTfDLJ\n4DDYqPscCPxj37l/4o7Vwj8OHJ7kB2mCu7cDhyT5AZpAdjXoeADwv/q+09fb4/2LAd+pHgbcAHz/\nGuf7HbR6/yT3SHJSOyx7E03Qd58kq+3gVJqfHzSB4/8dmOT+/cCNY+Q5Mohqh0WPGXVe0mQMsqTF\n1D98eDVwZX8QVlX3rqontOffAlwG/Eg7vPZb3Pnv9mnAU5I8gGb4rH9i9HuBhyZ5MM1Q3npPA94+\nV6y931uBXwPu1waHn+GuQ58Tq6odVfVLVfWvgNcDZ6R5pHo9X6bZ73PVoe2x1e0yPgX8Os2+jLcA\nH6OZE7Wj7zHsfwJ+ZaC+71lVn+gv4hpl2EGzEsEBa6Vvh4H/PU1vIm05DgeObH+Ox3DnuVyfAL6b\n5KdogubBgPiBNHucjtT2xq0VRD0b+GhVXbXWfSSNxyBLWnyfpJlo/rIkd28nnD84yeoiefeiGSq6\nuZ2z84L+i6vZB+5rNEOM76+qb/Sd+w5N0PUu4IKqGlwweC33pAkevgbskeR5ND1Z4xoZjCV5VpJ/\n1X68qc1nnKcJTwNenWa9m/2A13DnYOTDNEHh6tBgD3ghd56k/qfAq9Jul5HkPkl+kTFV1XeBv+GO\n+V9w5zl2eyV5YFvWHwT+sD11L5p5WDelWdzwxCG3fwfNPK3vVtXHBs4dA7xvneI9G/i7dsh5mOOB\n/7POPSSNySBLWkzVvmgnmD+BZojrizRPpL0VWF3D6DdphpG+0R4/nbv2nLwL+On2z0Gn0gRH6w0V\n3rmAzRNyf0AzDHdde4+PDvsOfZ9Z43y/nwU+k+SbwBuAp/fNf1qrF+m3aRbyvaR9XdgeW/VhmmBm\ndWjwIzTB4u3zk6pZYfz1wOntsN2lbXlGfY9hTuKOuWCr1zyt/T430vQgfpU7L9/wRuDuNEHrx2gC\npsG83gH8GM1DC7dre80eSLPFx1oGJ8v33+ORNEOr/2+de0ga08TrZKVZ4+VC4Jqq+vdDzr+J5umj\nm4HnrjGJVtICaIeQrqBZM2nYHnuaQpKPAr9WVRfP8Z53p9m49oiq+oe+479PM+T5p/PKS9Lsplnx\n/SU08z/uMrGzXXPmR6rqsCRH0cwVOXq2IkrqSjsv6DeA0wyw5quqHtPBbV8AfLI/wGrz+s0O8pI0\no4mCrCQHA8fSPL30X4YkOY62K7qqLkiyb5L9y32RpIXTrpe0E7iSZvkGLbA02y8V8KR1kkpaEJP2\nZL0B+K/cMRdk0EHc+dHma2h2+DbIkhZMVX2b0UsoaMFU1ZaNLoOkyYw98T3JE2hWAb6ItR/RHjzn\n5oiSJGnTmaQn61HAce28q7sB907y9qo6vi/Nl4D+VZEPbo/dSRIDL0mStDSqauI1AMfuyaqqV1XV\nIVX1QzTbefztQIAFzf5axwMkORq4cdR8rKq6/TXs2FrHN/vrxBNP3PAyLPPL+rPurL/lfFl/1t1G\nvaY1zdOFt8c+AElOaAOhk6rqnCTHJtkBfBt43gz3lyRJWlpTBVlV9WHaFZKr6qSBcy+cQ7kkSZKW\nmiu+L6GtW7dudBGWmvU3PetuNtbfbKy/6Vl3G2PiFd/nkmlS/fmubjI/WJZRxyVJknaVJFSXE98l\nSZI0PoMsSZKkDhhkSZIkdcAgS5IkqQMGWZIkSR2YKMhKcrckFyTZnuSyJK8bkmZrkpuSXNS+Xj2/\n4kqSJC2HiRYjrap/TvK4qro5yV7AR5M8pqo+OpD0w1V13PyKKUmStFwmHi6sqpvbt/sAewLXD0k2\n8VoSkiRJu5OJg6wkeyTZDuwEzquqywaSFPCoJBcnOSfJg+ZRUEmSpGUyTU/WbVX1MOBg4KeSbB1I\n8mngkKr6ceCPgDNnLqUkSdKSmWqDaICquinJXwMPB3p9x7/Z9/59Sf4kyf2q6k7DiisrK9NmLUmS\n1Jler0ev15v5PhPtXZhkP+DWqroxyd2Bc4HXVtWH+tLsD3ylqirJkcC7q2rLwH3cu1CSJC2Fafcu\nnLQn6wDg1CR70Aw1vqOqPpTkBICqOgl4CvCCJLcCNwNPn7RQkiRJy26inqy5ZWpPliRJWhLT9mS5\n4rskSVIHDLIkSZI6YJAlSZLUAYMsSZKkDhhkSZIkdcAgS5IkqQMGWZIkSR0wyJIkSerAREFWkrsl\nuSDJ9iSXJXndiHRvSvKFJBcnOWI+RZUkSVoeE22rU1X/nORxVXVzkr2AjyZ5TFV9dDVNkmOBH6mq\nw5IcBbwFOHq+xZYkSVpsEw8XVtXN7dt9gD2B6weSHAec2qa9ANi33TRakiRp05g4yEqyR5LtwE7g\nvKq6bCDJQcDVfZ+vAQ6evoiSJEnLZ6LhQoCqug14WJL7AOcm2VpVvYFkg5so3mWH55WVlaH33x02\nhV79DquqauzvtbKyMrJuJElS93q9Hr1eb+b7ZJZgJsl/A75TVb/fd+xPgV5Vnd5+vgI4pqp29qWp\n/nz7A5BR75fJLEFWu9N3Z2WTJEmTaf9tHuxAWtekTxful2Tf9v3dgZ8BLhpIdhZwfJvmaODG/gBL\nkiRpM5h0uPAA4NQke9AEaO+oqg8lOQGgqk6qqnOSHJtkB/Bt4HnzLbIkSdLim2m4cOpMHS5c89pl\n+76SJO3OdslwoSRJksZjkCVJktQBgyxJkqQOGGRJkiR1wCBLkiSpAwZZkiRJHTDIkiRJ6sCkK74f\nkuS8JJ9N8pkkLx6SZmuSm5Jc1L5ePb/iSpIkLYdJV3y/BXhpVW1Pci/gU0k+WFWXD6T7cFUdN58i\nSpIkLZ+JerKq6rqq2t6+/xZwOXDgkKQTr4oqSZK0O5l6TlaSLcARwAUDpwp4VJKLk5yT5EHTF0+S\nJGk5TTpcCEA7VHgG8JK2R6vfp4FDqurmJD8HnAkcPniPlZWVabKWJEnqVK/Xo9frzXyfiTeITrI3\n8FfA+6rqjWOkvxL4yaq6vu+YG0Svce2yfV9JknZnu2SD6DSRwsnAZaMCrCT7t+lIciRNIHf9sLSS\nJEm7q0mHCx8NPAu4JMlF7bFXAYcCVNVJwFOAFyS5FbgZePqcyipJkrQ0Jh4unEumDheuee2yfV9J\nknZnu2S4UJIkSeMxyJIkSeqAQZYkSVIHDLIkSZI6YJAlSZLUAYMsSZKkDhhkSZIkdWDSFd8PSXJe\nks8m+UySF49I96YkX2g3iT5iPkWVJElaHpOu+H4L8NKq2t5uEv2pJB+sqstXEyQ5FviRqjosyVHA\nW4Cj51dkSZKkxTdRT1ZVXVdV29v33wIuBw4cSHYccGqb5gJg3yT7z6GskiRJS2PqOVlJtgBHABcM\nnDoIuLrv8zXAwdPmI0mStIwmHS4EoB0qPAN4SdujdZckA5/vshnfysrKNFkPlqO5+Tp7A66srPDa\n1752rLRdGPyus+a/SHs6TlKWwbQrKyvrtoNF+q6SpM2h1+vR6/Vmvs/EG0Qn2Rv4K+B9VfXGIef/\nFOhV1ent5yuAY6pqZ1+auWwQPW7a/g2bd0WQNbhBdL9xyjrOJtKjrt/VZgmylu27SpI2p12yQXSa\nf/FOBi4bFmC1zgKOb9MfDdzYH2BJkiRtBpMOFz4aeBZwSZKL2mOvAg4FqKqTquqcJMcm2QF8G3je\n3EorSZK0JCYeLpxLpg4Xjsx/2YbQHC6UJO3udslwoSRJksZjkCVJktQBgyxJkqQOGGRJkiR1wCBL\nkiSpAwZZkiRJHTDIkiRJ6sCkK76fkmRnkktHnN+a5KYkF7WvV8+nmJIkSctl0hXf3wb8EfD2NdJ8\nuKqOm75IkiRJy2+inqyqOh+4YZ1kE6+IKkn6/9m793DJqvrO/+8Pt6CitATTEGgHkwlG1AhmRH+K\n2sY4QeKgzhhHjZdJTPSXjJcxN9FfEg46idFnNMZkYkxEbdGgRCOCokAIlZD8IkZDC3JRmYCiQuOl\nQRCjIN/5Y+8DRVF1Tt12n1N93q/nqaer9l57r1XrLM75stbaa0na3cx7TlYBj07ymSRnJTlizveX\nJElaCJMOF67mX4AtVXVLkicDpwOHD0u4tLQ056wlSZJm1+v16PV6M99n4g2ikxwGnFlVDx0j7VXA\nT1bVNweOu0H0iPwXbdNkN4iWJO3u1sUG0Uk2p/2rmORomiDum6tcJkmStNuZaLgwyanA44EDk1wD\nnAjsDVBVbwOeAfxKktuAW4Bnzbe4kiRJi2Hi4cK5ZOpw4cj8F20IzeFCSdLubl0MF0qSJKlhkCVJ\nktQBgyxJkqQOGGRJkiR1wCBLkiSpAwZZkiRJHTDIkiRJ6sBEQVaSdyTZkeSSFdK8JckX2k2ij5q9\niJIkSYtn0p6sdwLHjjqZ5Djg31fVjwEvAt46Q9kkSZIW1kRBVlVdAOxcIcnxwLY27YXApiSbpy+e\nJEnSYpr3nKxDgGv6Pn8ZOHTOeUiSJK17E20QPabBvX2Gbjq3tLQ09g2XlpY46aSTmptNsIfdSnsI\nDt5/3PIM7oM4aV6D6YbtzTfpsWmvn9Wo77yr9oY88cQTJ2pHg9dP05am2aMR7trGRv3cdpV5/Hxm\nrQ9Nx7qUdo1er0ev15v5PhNvEJ3kMODMqnrokHN/BvSq6n3t5yuAx1fVjoF0E20Q3W+c6wbPD14/\nzUbFw+45TZA1TllWuv+k9bLSsVlNEuQNOz7tBtGzBilrEWT1f1eDLE3LupTWxnrZIPoM4PltgR4F\n3DAYYEmSJG0EEw0XJjkVeDxwYJJrgBOBvQGq6m1VdVaS45JcCXwb+IV5F1iSJGkRTDxcOJdMHS5c\nsSwr3d/hQocLZ+Vw4eKyLqW1sV6GCyVJkoRBliRJUicMsiRJkjpgkCVJktQBgyxJkqQOGGRJkiR1\nwCBLkiSpAxMHWUmOTXJFki8keeWQ81uT3Jjkovb12/MpqiRJ0uKYdMX3PYE/AX4a+Arwz0nOqKrL\nB5L+XVUdP6cySpIkLZxJe7KOBq6sqqur6lbgfcBTh6SbfPlzSZKk3cikQdYhwDV9n7/cHutXwKOT\nfCbJWUmOmKWAkiRJi2ii4UKaAGo1/wJsqapbkjwZOB04fOKSSZIkLbBJg6yvAFv6Pm+h6c26Q1Xd\n1Pf+Y0n+NMkBVfXN/nRLS0sTZi1JktS9Xq9Hr9eb+T6ZZDf3JHsBnwOeCHwV+CTw7P6J70k2A9dX\nVSU5Gjitqg4buE/159u/s/yw9/3GuW7w/OD1g2nb3bXHrYMVyzKJUWVZ6f6T1stKx2Y1TlmHpZ+k\n3lf7uU7zfaapi0muGVXm1X7Gu8o82sKs9aHpWJfS2mh/h0/8h36inqyqui3JS4CzgT2Bk6vq8iQv\nbs+/DXgG8CtJbgNuAZ41aaEkSZIW3UQ9WXPL1J6sFcuy0v3tybIna1b2ZC0u61JaG9P2ZLniuyRJ\nUgcMsiRJkjpgkCVJktQBgyxJkqQOGGRJkiR1wCBLkiSpAwZZkiRJHZg4yEpybJIrknwhyStHpHlL\ne/4zSY6avZiSJEmLZaIgK8mewJ8AxwJHAM9O8qCBNMcB/76qfgx4EfDWOZVV0hqbx15eG5n1Nxvr\nb3rW3dqYtCfraODKqrq6qm4F3gc8dSDN8cA2gKq6ENjU7mcoacH5i3o21t9srL/pWXdrY9Ig6xDg\nmr7PX26PrZbm0MmLJkmStLgmDbLG3TBrcH8fN9qSJEkbykQbRCd5FLBUVce2n18F3F5Vr+9L82dA\nr6re136+Anh8Ve3oS2PQJUmSFsY0G0TvNWH6TwE/luQw4KvAfwWePZDmDOAlwPvaoOyG/gBr2oJK\nkiQtkomCrKq6LclLgLOBPYGTq+ryJC9uz7+tqs5KclySK4FvA78w91JLkiStcxMNF0qSJGk8na74\n7sKls1mt/pJsTXJjkova12+vRTnXoyTvSLIjySUrpLHtDbFa3dnuVpZkS5Lzk1ya5LNJXjYine1v\niHHqzzY4XJJ9k1yYZHuSy5K8bkQ6294Q49TfxG2vqjp50QwnXgkcBuwNbAceNJDmOOCs9v0jgU90\nVZ5Fe41Zf1uBM9a6rOvxBTwWOAq4ZMR52970dWe7W7n+DgKObN/vB3zO331zrz/b4Oj6u2f7717A\nJ4BjBs7b9marv4naXpc9WS5cOptx6g/uvlyGgKq6ANi5QhLb3ghj1B3Y7kaqquuqanuMM8xMAAAg\nAElEQVT7/mbgcuCHB5LZ/kYYs/7ANjhUVd3Svt2H5n/WvzmQxLa3gjHqDyZoe10GWS5cOptx6q+A\nR7ddvmclOWKXlW7x2famZ7sbU/sk9lHAhQOnbH9jWKH+bIMjJNkjyXZgB3B+VV02kMS2t4Ix6m+i\ntjfpEg6TcOHS2YxTD/8CbKmqW5I8GTgdOLzbYu1WbHvTsd2NIcl+wAeAl7c9MndLMvDZ9tdnlfqz\nDY5QVbcDRybZHzg7ydaq6g0ks+2NMEb9TdT2uuzJ+gqwpe/zFpqIeaU0h7bHNEb9VdVNy12bVfUx\nYO8kB+y6Ii40296UbHerS7I38EHgPVV1+pAktr8VrFZ/tsHVVdWNwEeB/zBwyrY3hlH1N2nb6zLI\numPh0iT70CxcesZAmjOA58Mdq8nfbeHSDWzV+kuyOUna90fTLMkxbPxYd2fbm5LtbmVt3ZwMXFZV\nbx6RzPY3wjj1ZxscLsmBSTa17+8BPAm4aCCZbW+Ecepv0rbX2XBhuXDpTMapP+AZwK8kuQ24BXjW\nmhV4nUlyKvB44MAk1wAn0jyladtbxWp1x5B2l+SxwF9U1Y+Pcf+twClVtWW1tPOSZguwH6mqX94F\n2T0GeC5wcZLlX9CvBu4Ptr8xrFp/+LtvlIOBbUn2oOlEOaWqzvPv7thWrT8mbHsuRippbEmuBl5Y\nVefNcI+trBBkJXkXcE1V/c60ecxbkocDb6aZhP1t4Per6i19518OvBz4IeBLwFOr6gtJDgL+HPhJ\nml/gh1XVl3Z1+SWtjU4XI5W02ynW2STZJF0+wEOSA4GPAW8FDgB+FDin7/wvAb8IHFdV+wE/C3y9\nPX07cBbwX7oso6T1ySBL0szaVZCv6fv88HY15G8lOS3J+5O8duCaX0uzsvxXk/y39tiLgOcAv5Xk\npiQfHpHf7Ul+NckXaBarJMkfJflSmtWYP5XkmL70S0lOad8f1l7//CRfTPK1JK9e4ev9GvDxqjq1\nqm6tqm9X1RXtvfagGU79H8vHquqqqtrZvr++qv6MZo6lpA3GIEvSXLUPanwIeAdwX+BU4GnctQfs\nIOA+NItMvhD430n2r6o/B94LvL6q7l1VwxbgXfZU4BHA8jo1nwQe1ub5l8BftWWB4b1vj6F59PqJ\nwO8mGTWf7JHAziT/2AaFZyRZHuo8lGbdoYe2Ad6/tgGdC2VKMsiSNHePAvasqj+uqu9X1YdoAqB+\ntwKvac9/DLgZeGDf+XGClNdV1Q1V9V2AqnpvVe2sqtur6k3AD/Tdc9j9Tqqq71bVxcBnaAK0YbYA\nLwBeRjP5+iqawBHuXMTxScBDgCcAz6YJHCVtcAZZkubth7n7ujvXDHz+Rrvo37JbaPapm8Rd7pnk\nN9Js6npDkp3A/sCBK1x/3UD+9xqR7hbgr6vq021AdxLNis/3Br7TpnlDVX2rqr4IvI1mfzhJG5xB\nlqR5u5a7bwF1/wmuH3di/R3p2iUkfhP4uaraVFX3BW5kPvvbXbzCuc8B31upbJI2LoMsSZPaJ8m+\nfa89B87/E/D9JC9JsleS5blT49oB/MiEZbo3cBvw9ST7JPldmjlfkxgVkL0TeHqSh6VZifx3gAv6\nVn5+P81E/f2SHAr8MvCRO26a7Avs237ct/0saQMwyJI0qbNohtCWXyfSt7RDVX0P+M8085J2Aj9P\nE3T09/is1NNzMnBEkp1J/npEmsHrP96+Pg9cTTOM96WB9DXwebV7NgerzqdZDPOj3BkAPqcvyUto\n5pR9Ffj/gfdW1Tv7zt8CfKu9/xU062xJ2gBmXoy0/b/YTwFfrqr/lGQJ+CXga22SV1XVx2fKRNJC\nS3Ih8KdVtW2tyyJJu8o8FvF7OXAZTXc9NP+39qb26R5JG1CSx9H0Kn2dpifrITQ9TZK0Ycw0XNjO\nPzgOeDt3zmcI85lsKmlxPRDYTjNc+ArgGW5CK2mjmXVO1h/SPNHT/yh2AS9N8pkkJy/vaC1p46iq\nv6iqg9oFRY9s18KSpA1l6uHCJE8Brq+qi9oNX5e9FXhN+/61wBsZWJgviY83S5KkhVFVE4/SzdKT\n9Wjg+CTLqx//VJJ3t3t1VTUz6t8OHD2isL6mfJ144olrXoZFfll/1p31t5gv68+6W6vXtKYOsqrq\n1VW1paoeADwL+Nuqen6Sg/uSPR24ZOrSSZIkLah5PF0IzUT35VDvDUke1n6+CnjxnPKQJElaGHMJ\nsqqqB/Ta98+bxz012tatW9e6CAvN+puedTcb62821t/0rLu1MfNipFNlmtRa5CtJkjSpJNQunvgu\nSZKkEQyyJEmSOmCQJUmS1IF1EWQlIXEnHkmStPuYOchKsmeSi5Kc2X4+IMm5ST6f5By31ZEkSRvR\nPHqyXg5cxp3rZJ0AnFtVhwPntZ8lSZI2lJmCrCSHAsfRbJ+zPN53PLCtfb8NeNoseUiSJC2iWXuy\n/hD4TeD2vmObq2pH+34HsHnGPCRJkhbO1EFWkqcA11fVRdzZi3UX7YqjrjoqSZI2nFm21Xk0cHyS\n44B9gfskOQXYkeSgqrqu3Sz6+mEXLy0tzZC1JElSN3q9Hr1eb+b7zGVbnSSPB36jqv5TkjcA36iq\n1yc5AdhUVScMpL/LtjrLyze41Y4kSVpv1sO2OssR0h8AT0ryeeCn2s+SJEkbyrrYINqeLEmStF6t\nh54sSZIktQyyJEmSOmCQJUmS1AGDLEmSpA4YZEmSJHXAIEuSJKkDs2yrs2+SC5NsT3JZkte1x5eS\nfDnJRe3r2PkVV5IkaTHMtE5WkntW1S1J9gL+AfgN4InATVX1phWuc50sSZK0ENZknayquqV9uw+w\nJ7BzuTyz3FeSJGnRzRRkJdkjyXZgB3B+VV3annppks8kOTnJpplLKUmStGD2muXiqrodODLJ/sDZ\nSbYCbwVe0yZ5LfBG4IWD1y4tLc2StSRJUid6vR69Xm/m+8xt78IkvwN8p6r+V9+xw4Azq+qhA2md\nkyVJkhbCLp+TleTA5aHAJPcAngRclOSgvmRPBy6ZNg9JkqRFNctw4cHAtiR70ARrp1TVeUneneRI\noICrgBfPoZwLxZ45SZI0t+HCiTLdzYcLd7fvI0nSRrYmSzhIkiRpOIMsSZKkDhhkSZIkdcAgS5Ik\nqQMGWZIkSR2YZZ2sfZNcmGR7ksuSvK49fkCSc5N8Psk5bqsjSZI2oqmDrKr6N+AJVXUk8BPAE5Ic\nA5wAnFtVhwPntZ8lSZI2lJmGC6vqlvbtPsCewE7geGBbe3wb8LRZ8pAkSVpEMwVZSfZIsh3YAZxf\nVZcCm6tqR5tkB7B5xjJKkiQtnFm21aGqbgeOTLI/cHaSJwycryQuey5JkjacmYKsZVV1Y5KPAj8J\n7EhyUFVdl+Rg4Pph1ywtLc0ja0mSpLnq9Xr0er2Z7zP13oVJDgRuq6obktwDOBs4CfgZ4BtV9fok\nJwCbquqEgWvdu1CSJC2EafcunKUn62BgW5I9aOZ2nVJV5yW5CDgtyQuBq4FnzpCHJEnSQpq6J2um\nTO3JkiRJC2LanixXfJckSeqAQZYkSVIHDLIkSZI6YJAlSZLUAYMsSZKkDkwdZCXZkuT8JJcm+WyS\nl7XHl5J8OclF7evY+RVXkiRpMcyyGOlBwEFVtT3JfsCnaTaDfiZwU1W9aYVrXcJBkiQthF2+GGlV\nXQdc176/OcnlwCHL5Zn2vpIkSbuDuczJSnIYcBTwifbQS5N8JsnJSTbNIw9JkqRFMnOQ1Q4VfgB4\neVXdDLwVeABwJHAt8MZZ85AkSVo0s+xdSJK9gQ8C76mq0wGq6vq+828Hzhx27dLS0ixZS5IkdaLX\n69Hr9Wa+zywT3wNsA75RVa/oO35wVV3bvn8F8Iiqes7AtU58lyRJC2Haie+zBFnHAH8PXAws3+TV\nwLNphgoLuAp4cVXtGLjWIEuSJC2EXR5kzWKlIGt3CFAGv8PS0hInnXTSXY5JkqTFYJC1jgx+h+XP\n/cckSdJimDbIclsdSZKkDhhkSZIkdcAgS5IkqQPrOshKcpf5TJIkSYtiXQdZkiRJi2rqICvJliTn\nJ7k0yWeTvKw9fkCSc5N8Psk57l0oSZI2oll6sm4FXlFVDwYeBfz3JA8CTgDOrarDgfPaz5IkSRvK\n1EFWVV1XVdvb9zcDlwOHAMfTbLdD++/TZi2kJEnSopnLnKwkhwFHARcCm/u20dkBbJ5HHpIkSYtk\nr1lvkGQ/4IPAy6vqpoHVzSvJ0CXOl5aWZs1akiRp7nq9Hr1eb+b7zLStTpK9gY8AH6uqN7fHrgC2\nVtV1SQ4Gzq+qHx+4bqxtdRZ1ix231ZEkafexy7fVSRM5nAxcthxgtc4AXtC+fwFw+rR5SJIkLaqp\ne7KSHAP8PXAxsHyTVwGfBE4D7g9cDTyzqm4YuHZD9GQNs2jfRZKkjW7anqyZhgunZZAlSZIWxS4f\nLpQkSdJoBlmSJEkdMMiSJEnqgEGWJElSBwyyJEmSOjDLOlnvSLIjySV9x5aSfDnJRe3r2PkUU5Ik\nabHM0pP1TmAwiCrgTVV1VPv6+Az3lyRJWlhTB1lVdQGwc8ipideRmFaSFdekkiRJWitdzMl6aZLP\nJDk5yaYO7i9JkrTuzTvIeivwAOBI4FrgjXO+vyRJ0kLYa543q6rrl98neTtw5qi0S0tL88x6oSzq\ndkGSJG0EvV6PXq83831m2rswyWHAmVX10PbzwVV1bfv+FcAjquo5Q66by96F6zVYWW3vwvVabkmS\ndHfT7l04dU9WklOBxwMHJrkGOBHYmuRImqcMrwJePO39h+QH7H6Bye76vSRJ2uhm6smaOtMperKG\nBSPrNUCZpCdrvX4HSZLUmLYnyxXfJUmSOmCQJUmS1AGDLEmSpA4YZEmSJHXAIGvO3OZHkiTBDEFW\nknck2ZHkkr5jByQ5N8nnk5zjtjqSJGmjmqUn653AsQPHTgDOrarDgfPaz5IkSRvO1EFWVV0A7Bw4\nfDywrX2/DXjatPefVJKFGKobVcZFKb8kSRrPvOdkba6qHe37HcDmOd9fkiRpIXQ28b1d0t1lzCVJ\n0oY09d6FI+xIclBVXZfkYOD6UQmXlpamymCjbkOzUb+3JEm7Wq/Xo9frzXyfmfYuTHIYcGZVPbT9\n/AbgG1X1+iQnAJuq6m6T32fZu3DZet0DcJJ5VaO+10r3XevvJ0nSRjPt3oVTB1lJTgUeDxxIM//q\nd4EPA6cB9weuBp5ZVTcMudYgC4MsSZIWwS4PsmbRdZC1lgHJrgiyDLgkSdp1pg2yXPFdkiSpAwZZ\nkiRJHdiQQZYLf0qSpK5tyCBLkiSpawZZ68zS0tIdPW39r1HG7ZXrqvdupftOuxaaJEm7gw35dGGX\nT+fN+nThJNdM8qRhV995pfu2T2PMNT9Jkna1aZ8unPeK78uFuRr4FvB94NaqOrqLfCRJktarToIs\nmj0Lt1bVNzu6/25hI0y+d00vSdJG1eWcrN0/gpAkSRqhqyCrgL9J8qkkv9xRHpIkSetWV8OFj6mq\na5PcDzg3yRVVdUF/gl395NlaToZfzyb93ktLSyv+7JaWljjppJPmUTRJktZEr9ej1+vNfJ/Ony5M\nciJwc1W9se/YLn+6cJLzM37fma5fzbyfLpz0ew8+MTh4/ajvv9GCV0nS7mPd7F2Y5J5J7t2+vxfw\nH4FL5p2PJEnSetbFnKzNwAVJtgMXAh+pqnNmueGw4alpe4yGXbfa0OUiLKrZvyjoLAuPjvNdh91/\nnouduu3RnawLSVpcC7EY6WomGS4cvG7w3KyLaq7VcOGo84PlWm24cLXvOlhXk/yMxrVR58cNY11I\n0tpbN8OFkiRJ2k2CrGmH+yYZilnUPfoGy72ey7oWphmOm6bdDNb7SveYtl2uNGTcn/+0Q5DjXmcb\nk6TGbjFc2G/YcNmk+wkOKe+q56fJaxrTPnU5mGbZRh8unCavSa4Z1XZW2/Nx+dwkS4+sdN2wNjpp\n/U7yJKvDm5J2Jw4XSpIkrSMGWQPGGRJZyye+Bod9pjHNcNSoJwonzXOloctZhtDm8XOb9PzS0tLI\n4cAuddH+Vir/uE+TzusJ19Xy6fK6aa73CVBJo3QyXJjkWODNwJ7A26vq9QPn1+1wYf99Bsu30v13\n1XDhaiapw9W+y/LnLso4Tl6TLBY7zlDWsDTTDM2NMslQ8yzDheMMEU46XLjSEN9qQ9Kj0o36fpPo\nemhzHtfvyuFtSWtj3QwXJtkT+BPgWOAI4NlJHjTvfKRpzWOrBGkatr3ZWH/Ts+7WRhfDhUcDV1bV\n1VV1K/A+4Kkd5DNU//9Nz9KNP48hsrUwz2Grrr7rvIaVVntqtH84rz+P1X7ZDBsCHKeMq32vYWUZ\nVtZheU7yBO2sP7dRbX/W+43KY/A1yc91mrwnST/pU5mrpRnW9ga//zj37mIYdZKnsNdqiHTWQGFU\nuRdxyHfSMo9bd4tYF+taVc31BTwD+Iu+z88F/nggTfUDavnYsPfr9dVf/sHvsh5eq5VnsL7X8rsM\ny29UG1mprOOU+8QTTxz7umnqoqu6m+a7rlZXq/3cR/1cVvoZTZt2mu86yrjpVrtutXobVcZR+tve\npGWdJJ9x7jHs3LjXTJv/rIbV3yTG+RkuiknLPG7dLWJd7AptnUwcE3XRk1Ud3FOSJGmhzH3ie5JH\nAUtVdWz7+VXA7dU3+T2JgZgkSVoYNcXE9y6CrL2AzwFPBL4KfBJ4dlVdPteMJEmS1rG95n3Dqrot\nyUuAs2mWcDjZAEuSJG00a7KtjiRJ0u6u0xXfkxyb5IokX0jyyhFp3tKe/0ySo7osz6JZrf6SbE1y\nY5KL2tdvr0U516Mk70iyI8klK6Sx7Q2xWt3Z7laWZEuS85NcmuSzSV42Ip3tb4hx6s82OFySfZNc\nmGR7ksuSvG5EOtveEOPU38Rtb5pHEsd50QwVXgkcBuwNbAceNJDmOOCs9v0jgU90VZ5Fe41Zf1uB\nM9a6rOvxBTwWOAq4ZMR52970dWe7W7n+DgKObN/vRzNH1d99860/2+Do+rtn++9ewCeAYwbO2/Zm\nq7+J2l6XPVnjLEp6PLANoKouBDYl2dxhmRbJuIu6umrcEFV1AbBzhSS2vRHGqDuw3Y1UVddV1fb2\n/c3A5cAPDySz/Y0wZv2BbXCoqrqlfbsPzf+sf3MgiW1vBWPUH0zQ9roMsg4Brun7/OX22GppDu2w\nTItknPor4NFtl+9ZSY7YZaVbfLa96dnuxpTkMJpewQsHTtn+xrBC/dkGR0iyR5LtwA7g/Kq6bCCJ\nbW8FY9TfRG1v7k8XDhRkHIMRoTPxG+PUw78AW6rqliRPBk4HDu+2WLsV2950bHdjSLIf8AHg5W2P\nzN2SDHy2/fVZpf5sgyNU1e3AkUn2B85OsrWqegPJbHsjjFF/E7W9LnuyvgJs6fu8hSZiXinNoe0x\njVF/VXXTctdmVX0M2DvJAbuuiAvNtjcl293qkuwNfBB4T1WdPiSJ7W8Fq9WfbXB1VXUj8FHgPwyc\nsu2NYVT9Tdr2ugyyPgX8WJLDkuwD/FfgjIE0ZwDPhztWir+hqnZ0WKZFsmr9JdmcdifPJEfTLMkx\nbPxYd2fbm5LtbmVt3ZwMXFZVbx6RzPY3wjj1ZxscLsmBSTa17+8BPAm4aCCZbW+Ecepv0rbX2XBh\njViUNMmL2/Nvq6qzkhyX5Erg28AvdFWeRTNO/dFsxv0rSW4DbgGetWYFXmeSnAo8HjgwyTXAiTRP\naS5M20vyWJrN1n98jLRbgVOqastqace414p1x53t7gCatjnsgYxp897KnL5Hl5L8AM2wwU8N+QP1\nGOC5wMVJln9Bvxq4P0zX/pJ8AHh7VX18nt9jnVq1/vB33ygHA9uS7EHTiXJKVZ3n392xrVp/TNj2\nXIxUWmNJrgZeWFXnzXCPrawQnCR5F3BNVf3OtHnsCgsUZL2UZlmBX20/vwt4NvDdNskXgTOBP6iq\nb80hv0cAb62qwaGf5fNHAO8GfoTmj8OlwCur6h/60jwceDPNRPJvA79fVW9pz50PPBjYl2bo6E1V\n9Rezllva6DpdjFTSWIp1NvE0zR6kG94K9fBi4JS+zwW8vqruAxxI0zvwKOAfk9xz1nJU1T8D90ny\nkyOSfAX4OeAHgfvSLPnygeWTSQ4EPga8FTgA+FHgnL7rXwYc0pb/BcAfJ3ngrOWWNjqDLGmdalcW\nvqbv88PbFYa/leS0JO9P8tqBa34tzWrtX03y39pjLwKeA/xWkpuSfHhEfrcn+dUkX6BZAJIkf5Tk\nS2lWOP5UkmP60i8lOaV9f1h7/fOTfDHJ15K8eoXvdlyaFb2/leTLSX59te/RHt8/ybuTXJ/k6iT/\nX9/8iC+2vTUk+fm2PA9qP78wyYfa93skOSHJlUm+3tbjfQe+xy8m+SLwN0PKfn+aHqPBZQUCUFXf\nq6pP0axH9IO0wzFJfjTJ37Z5fi3Je9I8wUSS32yHBPvzeUuS/jlJPeBnh9VnVd1YVVdVMzSxJ3A7\ncG1fkl8DPl5Vp1bVrVX17aq6ou/6S9r1+JbdDMzcAydtdAZZ0gJI8/DDh4B30PRUnAo8jbv2gB0E\n3Idm4cYXAv87yf5V9efAe2l6Wu5dVSvNoXoq8Ahgee2XTwIPa/P8S+Cv2rLA8N63x9A8zvxE4HeT\njJpPdjLworbn5MHA3672PdpzfwzcG3gAzbyx53PnnJIezWrMtOf+T/vv8ude+/6lNAHQ42jmYOwE\n/vdA+R4H/DjwM0PK/lDgX9tHvUdqlx04l2YF/WW/1+b5IJonvJba46cAx/YFXXvRPOyyre/ay2l+\nFiMluQH4DvBbNHNHlj0S2JnkH9vg9YwkWwau/UiS79DU0y9WVX+QJmkKBlnSYngUsGdV/XFVfb+q\nPkQTAPW7FXhNe/5jNL0R/UM+46xS/LqquqGqvgtQVe+tqp1VdXtVvQn4gb57DrvfSVX13aq6GPgM\no4OC7wEPTnKfthem/wmeod8jyZ40gcer2p6YLwJvBJ7XXvd33BlUHQO8ru/z49rz0Az1/XZVfbXt\nvTkJeEY72XXZUlV9Z7keBmwCbhrxvQZdSzM8R1X9n6o6r+1J+jrwh8vlq6rrgAtohvwAjgW+NlAv\nN7d5j1RVm4D9aYYL/6rv1BaaYcCX0Uwgv4omUO+/9ik029g8H3hX22MnaQYGWdJi+GHuvpbNNQOf\nvzHQu3ILzR/NSdzlnkl+I81GqTck2UnzB/zAFa6/biD/e41I919o9lC7OkkvzaPky0Z9jwNpnnL8\nYt+5L3HnTgh/Dzw2yUE0Q2Z/BTwmyb8D9q92qxaa/UA/lGRn+50uA24D+rcWGazbfjtpetPGcQjw\nDbjj0e/3tcOjN9L0Xv1gX9ptNE/V0f57Cnd1b+CG1TJs1/A5ATg8yU+0h28B/rqqPt0GjifRrFp9\n74Frv19VH6AZCn36mN9R0ggGWdJiuJa7b6s0SU/DuBPr70iXZgmJ3wR+rqo2VdV9gRuZw55xVfWp\nqnoacD+aFZNPG+Oyr9P0ch3Wd+z+tIv0VtWVNMHES4G/q6qbaIK+F9H0Ei37EnBsVd2373XPgeGx\nlerrYuABAz1fd7smzYrlP92X9+8D3wceUlX70/TA9d/jw8BPJHkIzdyr9w7c/0E0G8WPY8/23sv7\nsF085nXL9qZ5AlHSDAyypPVhnyT79r32HDj/T8D3k7wkyV5JludOjWsHzWTtSdybpofn60n2SfK7\nNHOlJnG3gCzJ3u3E9P2r6vs0Q2/fX+1GbdrTgN9Lsl/bQ/UK4D19yf4OeAl3Dg32Bj4D/Bnw+8vD\nYUnul+T4cb9QVX0ZuJJmntMdX6t9keQH0jwFeDpNL9Y72zT70QQu30pyCE0A23/f79Cscv6XwIVt\nPv0eR/OE4N0k+ekkRybZM8l9gDcBn2sDT9oyPD3Jw9Kspv47wAVVdVOSByZ5cpJ7tD+b59Kscn3O\nsLwkjc8gS1ofzqLpdVh+nUjf0g5V9T3gP9NMBN8J/DzwEZq5TctW6n05GTiiHSL76xFpBq//ePv6\nPHA1zYTqLw2kr4HPq91z2XOBq9phsxfRfJ/VroGml+rbwL/S9BC9lzuDGGiCqf1ohg6HfQb4I5pV\nr89J8i2aAPboMfNf9jbunAu2fM1vtff7Os3Q3z8Dj26DJ2iG6B5O0xt4Jk1ANZjXNuAhDAwVplkn\n66b2qcVhNtHMsbqB5snQ+9FM7m8KV3U+zYKeH+XOgPs5y7enaW87aHr+fgn42arq/1lLmsLUi5G2\nT6a8G/ghml8Uf15Vb0myRPMf6dfapK/aIKsUS7tUkguBP62qbasm1ly1T1hexPAV32e57xbgCmBz\n9W2KnI214ru025glyDoIOKiqtrdzDz5N80j5M2n+j+tN8yumpCSPo+lV+jpNz8+fAj8yzz/yWjvt\nHK83AftV1S+tdXkkzW7qVZ3bR46va9/fnORy7pyYO/PEWEl380CaOUn3olkD6hkGWLuHJPeiGa67\nimb5Bkm7gbnsXZjkMJq5Dw8Gfp1mccAbgU8Bv15Vqz52LEmStDuZOchqhwp7wP+sqtOT/BB3zsd6\nLXBwVb1w4Jp1tU+bJEnSSqpq4lG6mZ4ubB8F/iDwnqo6vS3E9dUC3s5dn9rpL6yvKV8nnnjimpdh\nkV/Wn3Vn/S3my/qz7tbqNa2pg6x2U9aTgcuq6s19xw/uS/Z04JKpSydJkrSgpp74TrMR7HOBi5Ms\n76/1auDZSY6kWdbhKpp9wiRJkjaUWZ4u/AeG94QNXZFY87N169a1LsJCs/6mZ93NxvqbjfU3Petu\nbczl6cKJM02qP99m5JGZxj0lSZK6kITa1RPfJUmSNJxBliRJUgcMsiRJkjpgkCVJktQBgyxJkqQO\nrLsgK8kdTxtKkiQtqnUXZEmSJO0ODLIkSZI6YJAlSZLUAYMsSZKkDhhkSZIkdcAgS5IkqQMGWZIk\nSR0wyJIkSeqAQZYkSVIHDLIkSZI6MHWQlWRLkvOTXJrks0le1h4/IMm5ST6f5GXhYZoAACAASURB\nVJwkm+ZXXEmSpMWQqpruwuQg4KCq2p5kP+DTwNOAXwC+XlVvSPJK4L5VdcLAtdWf7/JehVV1l/eS\nJElrLQlVNfHGylP3ZFXVdVW1vX1/M3A5cAhwPLCtTbaNJvCSJEnaUOYyJyvJYcBRwIXA5qra0Z7a\nAWyeRx6SJEmLZK9Zb9AOFX4QeHlV3bQ83AdQVZVk6Ljf0tLSrFlLkiTNXa/Xo9frzXyfqedkASTZ\nG/gI8LGqenN77Apga1Vdl+Rg4Pyq+vGB65yTJUmSFsIun5OVJho6GbhsOcBqnQG8oH3/AuD0afOQ\nJElaVLM8XXgM8PfAxcDyTV4FfBI4Dbg/cDXwzKq6YeBae7IkSdJCmLYna6bhwmkZZEmSpEWxy4cL\nJUmSNJpBliRJUgcMsiRJkjpgkCVJktQBgyxJkqQOGGTtAq5uL0nSxuMSDrtA++jnWhdDkiRNwSUc\nJEmS1hGDLEmSpA4YZEmSJHXAIEuSJKkDBlmSJEkdMMiSJEnqgEGWJElSBwyyJEmSOmCQJUmS1AGD\nLEmSpA5MHWQleUeSHUku6Tu2lOTLSS5qX8fOp5iSJEmLZZaerHcCg0FUAW+qqqPa18dnuL8kSdLC\nmjrIqqoLgJ1DTk28gaIkSdLupos5WS9N8pkkJyfZ1MH9JUmS1r295ny/twKvad+/Fngj8MJhCZeW\nluactSRJ0ux6vR69Xm/m+6Sqpr84OQw4s6oeOuG56s83aUYYq+ou73cXSXar7yNJ0kbS/h2feDrU\nXIcLkxzc9/HpwCWj0kqSJO3Oph4uTHIq8HjgwCTXACcCW5McSfOU4VXAi+dSSkmSpAUz03Dh1Jk6\nXChJkhbEtMOF8574rj7LAaMkSdp43FZHkiSpAwZZkiRJHTDIkiRJ6oBBliRJUgfWdZCVxMnjkiRp\nIa3rIEuSJGlRGWRJkiR1wCBLkiSpAwZZkiRJHTDIkiRJ6oBBliRJUgcMsiRJkjpgkCVJktQBgyxJ\nkqQOGGRJkiR1YOogK8k7kuxIcknfsQOSnJvk80nOSbJpPsVcLMO2A3KLIEmSNpZZerLeCRw7cOwE\n4NyqOhw4r/0sSZK04UwdZFXVBcDOgcPHA9va99uAp017f0mSpEU27zlZm6tqR/t+B7B5zveXJEla\nCJ1NfK+qAqqr+0uSJK1ne835fjuSHFRV1yU5GLh+VMKlpaU5Zy1JkjS7Xq9Hr9eb+T5pOpymvDg5\nDDizqh7afn4D8I2qen2SE4BNVXW3ye9Jqj/f5afuqmrk+0Wy0lOEi/ZdJEna6JJQVRMvETB1kJXk\nVODxwIE0869+F/gwcBpwf+Bq4JlVdcOQaw2yJEnSQtjlQdYsDLIkSdKimDbIcsV3SZKkDix0kLWI\nq6gvYpklSdLkFjrIkiRJWq8MsiRJkjpgkCVJktQBgyxJkqQOLEyQtSgTxhehjJIkqXsLE2RJkiQt\nEoMsSZKkDhhkSZIkdcAgS5IkqQMGWbuJRXkwQJKkjcIgS5IkqQMGWZIkSR0wyJIkSeqAQZYkSVIH\ndpsga1dN/F6rCebraWL7eiqLJEnr1V5d3DTJ1cC3gO8Dt1bV0V3kI0mStF51EmQBBWytqm92dH9J\nkqR1rcvhQseTJEnShtVVkFXA3yT5VJJf7igPSZKkdaur4cLHVNW1Se4HnJvkiqq6oKO8JEmS1p1O\ngqyqurb992tJPgQcDdwlyFpaWuoi66kNPi1XVXccq6pO8xx1/67y7/p7SZK0yHq9Hr1eb+b7pIM/\n4PcE9qyqm5LcCzgHOKmqzulLU/359v/RH+f9atdNWe67fB4nz3HuM8o4ZZ7k+03y/edVVwZpkqSN\nIAlVNfFc8y56sjYDH2r/EO8FvLc/wJIkSdoI5h5kVdVVwJHzvq8kSdIi2W1WfJckSVpPDLJmMK/t\nZZbvs3yvwfv2v1+PDwwM1oHb7kiS1MHE97Ey3U0mvq90fjX9149zfDDNqPLv6onv0zwcIEnSIpl2\n4rs9WZIkSR0wyJIkSeqAQZYkSVIHdusga3kC9mqTxYedX8+TzVczycTzSSep99fFtJPbnRgvSdoI\nduuJ7/1/yFf6nuP8wV++fhEmvs9zJflR165UprXaLkiSpC448V2SJGkdMciSJEnqgEGWJElSBxYi\nyBqceD44cXq184PmMfG6q4nb40yyX2l1+FFp12Ky+aQPHKxUxlHndtX36jr/UXXVxf27qrNJ2qUk\nbQQLMfG932oTw1e6blh+/Z9XslLaeU58n8Swe6xWh+M8ULCSSSa+j7rvqPMrlWXUuV01ib7r/NtJ\nlWPnO8v9u6qzee7AIEnriRPfJUmS1hGDLEmSpA4YZEmSJHVg4YKs1eYxTXJ+XiujLy0tTTzRdx6T\ngofdY5LV6VebZD7JhPlxVtef1wMHw/Ka5GGHWSaxj7MTwLzyGnXNrniQYdh3GPUznvSBha53UJj0\nQZguTJvnIj0wsEhlldZKJxPfkxwLvBnYE3h7Vb1+4PzUE9+nNckk993FahPfB48vW+n8pPU37n37\nz682WX7c/IZdv9Kk7HHzH1YXk+xEMCyvcSa+r1Z/K5l04vsk+Y7TxgbTdzkhfvD+azEJf1SevV6P\nrVu3TnzderQWZV2t/jSadTebdTPxPcmewJ8AxwJHAM9O8qB55yNJi6bX6611ERaa9Tc9625tdDFc\neDRwZVVdXVW3Au8DntpBPpIkSetWF0HWIcA1fZ+/3B6TJEnaMOY+JyvJfwGOrapfbj8/F3hkVb20\nL836n3AgSZLUmmZO1l4dlOMrwJa+z1toerPuME1BJUmSFkkXw4WfAn4syWFJ9gH+K3BGB/lIkiSt\nW3Pvyaqq25K8BDibZgmHk6vq8nnnI0mStJ6tyQbRkiRJu7tOV3xPcmySK5J8IckrR6R5S3v+M0mO\n6rI8i2a1+kuyNcmNSS5qX7+9FuVcj5K8I8mOJJeskMa2N8RqdWe7W1mSLUnOT3Jpks8medmIdLa/\nIcapP9vgcEn2TXJhku1JLkvyuhHpbHtDjFN/E7e9qurkRTNUeCVwGLA3sB140ECa44Cz2vePBD7R\nVXkW7TVm/W0Fzljrsq7HF/BY4CjgkhHnbXvT153tbuX6Owg4sn2/H/A5f/fNvf5sg6Pr757tv3sB\nnwCOGThv25ut/iZqe132ZI2zKOnxwDaAqroQ2JRkc4dlWiTjLurqk5pDVNUFwM4Vktj2Rhij7sB2\nN1JVXVdV29v3NwOXAz88kMz2N8KY9Qe2waGq6pb27T40/7P+zYEktr0VjFF/MEHb6zLIGmdR0mFp\nDu2wTItknPor4NFtl+9ZSY7YZaVbfLa96dnuxpTkMJpewQsHTtn+xrBC/dkGR0iyR5LtwA7g/Kq6\nbCCJbW8FY9TfRG2vi3Wy+gsyjsGI0Jn4jXHq4V+ALVV1S5InA6cDh3dbrN2KbW86trsxJNkP+ADw\n8rZH5m5JBj7b/vqsUn+2wRGq6nbgyCT7A2cn2VpVvYFktr0Rxqi/idpelz1Zqy5KOiTNoe0xjbeo\n603LXZtV9TFg7yQH7LoiLjTb3pRsd6tLsjfwQeA9VXX6kCS2vxWsVn+2wdVV1Y3AR4H/MHDKtjeG\nUfU3advrMsgaZ1HSM4DnAyR5FHBDVe3osEyLZNX6S7I5Sdr3R9MsyTFs/Fh3Z9ubku1uZW3dnAxc\nVlVvHpHM9jfCOPVnGxwuyYFJNrXv7wE8CbhoIJltb4Rx6m/SttfZcGGNWJQ0yYvb82+rqrOSHJfk\nSuDbwC90VZ5FM079Ac8AfiXJbcAtwLPWrMDrTJJTgccDBya5BjiR5ilN294qVqs71rjdtXMgtlXV\nI3ZBXj9A82TvY6vq62Ne9hjgucDFSZZ/Qb8auD/Y/sawav3h775RDga2JdmDphPllKo6z7+7Y1u1\n/piw7bkYqSQAkhwDvAE4Avg+zVNd/6OqPrWmBRuQ5IPA+6vqtPbz1cAPAbfRlPsy4N3An9ccfsEl\n+U1gc1X9xojzPwD8AfBM4B7AqTTziG5rz78HeCJwL+DrNP/D9HvtuSPasv4IzS/1S4FXVtU/zFpu\nSWuv08VIJS2GJPcBPgL8EXBfmieQTgK+u5blGpTkYJp1avrn6RTwlKq6D01vxx8Ar6QZcpqHU4EX\ntPOEhjkBeDjwYJoJsA8H+hcofB3wgLZ8TwZemuRn2nNfAX4O+EGaen8fzWRvSbsBgyxJ0AQHVVXv\nr8a/VdW5VXUJQJKlJKcsJ27nCt7edquTpJfkNUn+Icm3kpyd5AcH0j4/yReTfC3Jq9tzByX5dv/E\n0SQPT3J9kj2HlPNJwKer6nvDvkQ7KfVMmjmML0jy4PaeP5tmdeYbk3wpyYl9+X20HZqn79jFSZ7a\n3vPLNOuG/T8j6u4pwB9X1Q3tkOJbgF/sK9OlVfVvfelvA77Wnruxqq5qe9z2BG4Hrh2Rj6QFY5Al\nCZpVtb+f5F1ptnO678D5cYbdng38N5qhu32AweG1x9AEc08EfjfJA6vqOqBHM9S27HnAqVX1/SF5\nPLQt64qq6p9pnsY9pj10M/Dcqtof+FmaORXLi/u+i2YOEABJHkaz+OVH+255OfCwFbLsfyR+D+DQ\nJPfuu+efJvk2zXDg/6yqf7nLxckNwHeA36KZ8yFpN2CQJYmquokmICngL4Drk3w4yQ+1SVZb4biA\nd1bVlW2vzWnAkQNpTqqq71bVxcBnuDNoeTdtkNP2Xj0LOIXh9qcJmMbxVeCA9vv9XVVd2r6/hGZY\n7vFtujOBw5P8aPv5ecD7ludUtW4CNo3I5+PAy9snkw4CXkZTH/dcTlBVv0qzRcxPA/+zfSqJvvOb\n2u/2PuCvlp9ekrTYDLIkAVBVV1TVL1TVFuAhNL05o5YgGOa6vvffoQkqRp2/pe/8h4Ej0qzu/STg\nxhUm2+8E7j3i3KBDaLfESPLINJsOX9/2Gr2YZh4UfUHh89rgZliQd29GbzX0ezSPeW8H/gH4EHDb\n4GPx7TBsD/grml4/Bs7fQjO/63CaHjtJC84gS9LdVNXnaPY3e0h76Nv09czQbOI7r7z+jSbweG77\nevcKyS9mjJW9kzyCJshafkrvL2kmyx/a9hr9GXf9/bcN+HmanqZb2j3d+j2IpvdtaPmr6qVVdWhV\n/XuawG6lJzL3pqnPYfZsy3XLiPOSFohBliSSPDDJryU5pP28haa35Z/aJNuBxyXZkma7iVcNu82k\n2fa9fzfNej3HM3qoEOBvgIenWaD3bvdKcp8kT6F5IvCU5SFCml6znVX1vXao7jn0zTOrqn9qP/8v\nBoK8tk4OAD4x9EskP9y+0i7u+Ns0a4uR5H5JnpXkXkn2bJ8q/Dma3juS/HSSI9tz9wHeBHyuqq5c\noQ4kLQiDLEnQzDl6JHBhkptpgquLgV8HqKpzgfe3x/6ZZh7T4GT4Gng/+HlQf5DzjzRP1n26qq4Z\nknY53Q7gb4GnDZw6M8m3gC/RBIBv5K6LLP4q8Jo2ze+032XQu2mG6d4zcPw5wLuq6tYRxfpR4B9p\n5oq9k2adq7/p+47/L80k/G8ArwWe107Mh2ae16nADTQT+u9HE2hK2g3MtBhpuwjgt2gWALy1qo5u\nH8V+P/DvgKuBZ1bVDbMXVdLuLMnfAH9ZVe9YJd2DaFZ8P3qldFPk/zzgl6vqcX3HplnxXZKA2YOs\nq4Cf7N+3J8kbgK9X1RuSvBK4b1WdMHtRJe2u2jlUZ9Psbj9qvlKX+d+TpofsT6pqsCdLkqYyj+HC\nwXkYx9NMIqX9d7BbX5LukGQbcC7NFj5rEWD9DHA9zSKgf7mr85e0+5q1J+tfgRtphgvfVlV/kWRn\nVd23PR/gm8ufJUmSNoq9Zrz+MVV1bZL7AecmuaL/ZFVVkrtFccOOSZIkrVdVNfEiwTMNF1bVte2/\nX6NZgO9oYEe76vHyZq7Xj7h26Gu1876KE088cc3LsMgv68+6s/4W82X9WXdr9ZrW1EFWknsu782V\n5F7AfwQuAc4AXtAmewHNAoCSJEkbyizDhZuBD7VbbO0FvLeqzknyKeC0JC+kXcJh5lJKkiQtmKmD\nrKq6irtvAEs1yzn89CyF0sq2bt261kVYaNbf9Ky72Vh/s7H+pmfdrY2Zni6cOtOkRuW7vPn8WpRL\nkiRpUBJqV098lyRJ0nAGWZIkSR0wyJIkSeqAQZYkSVIHDLIkSZI6YJAlSZLUAYMsSZKkDqyLICvJ\nHetjSZIk7Q7WRZAlSZK0uzHIkiRJ6oBBliRJUgcMsiRJkjpgkCVJktQBgyxJkqQOGGRJkiR1wCCr\nQ67/JUnSxjVzkJVkzyQXJTmz/XxAknOTfD7JOUk2zV5MSZKkxTKPnqyXA5cB1X4+ATi3qg4Hzms/\nS5IkbSgzBVlJDgWOA94OLI+LHQ9sa99vA542Sx6SJEmLaNaerD8EfhO4ve/Y5qra0b7fAWyeMQ9J\nkqSFs9e0FyZ5CnB9VV2UZOuwNFVVSWrYuaWlpXHyWL7PtMWUJEmaSK/Xo9frzXyfTBvAJPl94HnA\nbcC+wH2AvwYeAWytquuSHAycX1U/PnBt9efbH0yNer+IFr38kiSp+XteVRMvFzD1cGFVvbqqtlTV\nA4BnAX9bVc8DzgBe0CZ7AXD6tHlIkiQtqnmuk7XcXfMHwJOSfB74qfazJEnShjL1cOFMmTpcKEmS\nFsQuHy7UZFz9XZKkjcUgS5IkqQMGWZIkSR0wyJIkSeqAQZYkSVIHDLIkSZI6YJAlSZLUAYMsSZKk\nDhhk7QLjbIYtSZJ2L6743qFhi48u6neRJGmjcsV3SZKkdcQgS5IkqQMGWZIkSR0wyJIkSeqAQZYk\nSVIHDLIkSZI6MHWQlWTfJBcm2Z7ksiSva48fkOTcJJ9Pck6STfMrriRJ0mKYOsiqqn8DnlBVRwI/\nATwhyTHACcC5VXU4cF77WZIkaUOZabiwqm5p3+4D7AnsBI4HtrXHtwFPmyWPRZRk6EKkkiRp45gp\nyEqyR5LtwA7g/Kq6FNhcVTvaJDuAzTOWUZIkaeHsNcvFVXU7cGSS/YGzkzxh4HwlcR8ZSZK04cwU\nZC2rqhuTfBT4SWBHkoOq6rokBwPXD7vGTZMlSdJ61Ov16PV6M99n6g2ikxwI3FZVNyS5B3A2cBLw\nM8A3qur1SU4ANlXVCQPX7tYbRK80H2vRvoskSRvdtBtEz9KTdTCwLckeNHO7Tqmq85JcBJyW5IXA\n1cAzZ8hDkiRpIU3dkzVTpvZkSZKkBTFtT5YrvkuSJHVgoYOs/vWoXJtKkiStJwsdZEmSJK1XBlmS\nJEkdMMiSJEnqgEGWJElSBwyydjEn6EuStDEYZEmSJHXAIEuSJKkDBlmSJEkdMMiSJEnqgEGWJElS\nBwyyJEmSOmCQJUmS1AGDLEmSpA4YZEmSJHXAIEuSJKkDUwdZSbYkOT/JpUk+m+Rl7fEDkpyb5PNJ\nzkmyaX7FlSRJWgyz9GTdCryiqh4MPAr470keBJwAnFtVhwPntZ8lSZI2lKmDrKq6rqq2t+9vBi4H\nDgGOB7a1ybYBT5u1kJIkSYtmLnOykhwGHAVcCGyuqh3tqR3A5nnkIUmStEhmDrKS7Ad8EHh5Vd3U\nf66qCqhZ85AkSVo0e81ycZK9aQKsU6rq9PbwjiQHVdV1SQ4Grh927dLS0ixZS5IkdaLX69Hr9Wa+\nT5rOpikuTEIz5+obVfWKvuNvaI+9PskJwKaqOmHg2urPt7kVVNXI9yPKMHbaXWm5LCtZD+WUJEmr\nS0JVrf7HffC6GYKsY4C/By7mziHBVwGfBE4D7g9cDTyzqm4YuNYgax2UU5IkrW6XB1mzMMhaH+WU\nJEmrmzbIcsV3SZKkDhhkSZIkdcAgS5IkqQMGWZIkSR0wyJIkSeqAQZYkSVIHFibISjLW0gi7+l6z\nlEGSJO2+FibIkiRJWiQGWf+3vbuPluyq6/z//uTpBwgSYrATksZETIBWRjJKYHiQ64wwIeME1ogQ\nGCQgIwg/BJwnEmTsG1wqutY4iIjy04ANaDCDGhsICZHJ1fhAA5JAoBNI1DhpMI2YEBMaNCHf3x/n\n3FCp1POt07fq9vu1Vq0+VWefc761a997v733PrvmzB4qSZIEJlmSJEmdMMmSJEnqwJZMshZhYvsk\nliVOSZI0vS2ZZEmSJG02kyxJkqQOmGRJkiR1wCRLkiSpA1s6yXJiuSRJ2iwzJ1lJ3p5kf5Jrel47\nJsnlST6X5ENJjp5PmJIkSctlIz1Z7wDO6HvtXODyqjoV+HD7XJIk6ZAzc5JVVVcCt/a9fBawq93e\nBTxr1vMPM8sQ4Orq6rzD6My0sTokKknSYkpVzX5wchLwvqp6TPv81qp6SLsd4Jb1533HVe9115OE\nqhq7vW6asuuvD7revM2S8PTHNk1cXb4XSZJ0z9/mqf/AH9FFMABVVUmG/uVftN6lYQmbJmOdSZK2\nirW1NdbW1jZ8nnn3ZF0HrFTVzUmOB66oqkcNOG7herLmlWQdqj1ZixKHJEnzNmtP1ryXcNgNnNNu\nnwNcPOfzS5IkLYWNLOFwIfDnwCOT3JTkxcAbgacl+Rzwr9vnkiRJh5wNDRfOfFGHC0fG5nChJEmL\nY1GGCyVJkkSHdxcuk1nvdJxn701vD9i4847ridtsixSLJEmbxeFC7p3gTDNcOCrZmcYkMQ+77qB6\n2QwugSFJ2qocLpyT/h6lg7Ga+iTXcGV3SZKWi0mWJElSB0yyJEmSOrC0E997h842OnF9Hroaylvk\nIcJZY3POliTpULC0E9+HmWbi+6RmmYA+D9NM+B8XaxeG1fMsd0dKkrSonPh+EG3mhPhF+GLtSd//\n6uqqE/aXkJ+ZJM2HPVkTnrMv/omu2bXN6ska9V5nXXFfi8PPR5LuzZ4sSZKkBXLIJVmz9DgtwhDd\nrOY19LN+nnF1MWr/vIehDubnMkns07y/9bKLPDS3qHFJ0rI45IYLZ/3DMWq4cZGHC+c19LPR99fF\n0Oa0X6a90WvB6JinqetFWal/kP5vQJCkQ53Dha1hycBGewwWscehi3h6e4fm+Z43stzD+rEHq/dn\n3PkHxTROf73OK5ZJzeNzXcSfgWkse/ySls+W68nq2rhlFTbbRnuyenuHDsYSFZPEMyqGLtrvoDqb\npo0OimlQvfbqermLYZ/rqGv2l1n2CfHLHr+kzWNPliRJ0gIxyZrSoq5d1WvQsMiwIa6tNITS/16m\n+VwmrYdJzt/1kObB/Mw2Y3L+wfh5GvaettLPw7wt2u+5Q51tdTl0MlyY5AzgTcDhwG9W1S/07V/a\n4cJFN8mK8KOGuJZ5uHDUe+m1trbGysrK2OtMs7baJDdczGu4cJZhr1mHCwfperitqxsaJhna7XpI\ncVDbWxYH80aTYZa5/uZt2rZq3W3MwgwXJjkceAtwBrADeF6SR8/7OtKs1tbWNjsEHaJsextj/c3O\nutscXQwXng7cUFU3VtWdwHuAZ27khHZTT27cXWyT3OV2sLqhe792Z9CjP6ZZYh00NHr++eeTZODX\n/vQPB85y9+C4608T66jz99fVtMPAo46f1KDPbdydjP31OuhzH9ceRp1/nN74pv15GNY2hx077e+u\nSc8/TVva6PXncV6Y/fd47zV7f3ZHld3Ien6TxjLJ6/O4fhfTHnTwzH24MMmzgX9bVT/WPn8B8Piq\n+omeMlMNF6p744ayNuOzmHV9tEHvZdHb0yRtf9LPaNJh4GHHTxLrumHHjBqanPVzGXbX5rB9w8qO\ni3VYvOOGksfdVQrNH8xxC/ZOcv5x73uaepn0uGH1M825Zz1u/ZrjYpg0xnnEMsnr87j+sLLTtotx\nbU+jtZ/D1H9Eukiyfgg4Y1ySNdeLSpIkdWiWJOuIDuL4PLC95/l2YF9vgVkClSRJWiZdzMn6OHBK\nkpOSHAU8F9jdwXUkSZIW1tx7sqrqriSvBC6jWcLhgqq6dt7XkSRJWmSb8rU6kiRJW12nK74nOSPJ\ndUmuT/LaIWXe3O7/ZJLTuoxn2YyrvyQrSW5LclX7eP1mxLmIkrw9yf4k14woY9sbYFzd2e5GS7I9\nyRVJPpPk00leNaSc7W+ASerPNjhYkvsl2ZPk6iR7k/z8kHK2vQEmqb+p215VdfKgGSq8ATgJOBK4\nGnh0X5kzgUva7ccDH+kqnmV7TFh/K8DuzY51ER/AU4DTgGuG7LftzV53trvR9Xcc8Nh2+4HAZ/3d\nN/f6sw0Or78HtP8eAXwEeHLfftvexupvqrbXZU/WJIuSngXsAqiqPcDRSbZ1GNMymXRRV+/UHKCq\nrgRuHVHEtjfEBHUHtruhqurmqrq63b4DuBZ4WF8x298QE9Yf2AYHqqoD7eZRNP9Zv6WviG1vhAnq\nD6Zoe10mWScAN/U839e+Nq7MiR3GtEwmqb8Cnth2+V6SZMdBi2752fZmZ7ubUJKTaHoF9/Ttsv1N\nYET92QaHSHJYkquB/cAVVbW3r4htb4QJ6m+qttfFOlm9gUyiPyN0Jn5jknr4BLC9qg4keQZwMXBq\nt2FtKba92djuJpDkgcB7gVe3PTL3KdL33PbXY0z92QaHqKq7gccmeTBwWZKVqlrrK2bbG2KC+puq\n7XXZkzV2UdIBZU5sX9Nki7revt61WVUfBI5McszBC3Gp2fZmZLsbL8mRwO8B766qiwcUsf2NMK7+\nbIPjVdVtwAeA7+3bZdubwLD6m7btdZlkTbIo6W7ghQBJngB8uar2dxjTMhlbf0m2pf2yqiSn0yzJ\nMWj8WPdl25uR7W60tm4uAPZW1ZuGFLP9DTFJ/dkGB0tybJKj2+37A08DruorZtsbYpL6m7btdTZc\nWEMWJU3ysnb/26rqkiRnJrkB+Arw4q7iWTaT1B/wbODlSe4CDgBnb1rACybJhcBTgWOT3ATspLlL\n07Y3xri6Y07tLsl5wLdX+z2niyzJGvCuqrpgguJPAl4AfCrJ+i/o1wEPB9vfBMbWH/7uG+Z4YFeS\nw2g6Ud5VVR/27+7ExtYfU7Y9FyOVtqAkTwZ+EdgBfJ3mDq3XVNXHNzWw54lwjgAAIABJREFUGSVZ\nBR5RVT+ySde/guYX7tunPO7JwP+imbPxJeCNVfUb7b4X0fTYHOg55N9V1Z+0+08Efg14IvDPNPOT\nXlNVX28nhP81zR/JdW+sqp+d+s1J6kyXE98lbYIk3wy8H3gZcBHw/9CsffVPmxnXZkpyeFV9/WBf\nE/gD4HVV9RtJvhe4IsmeqvpUW+zPqur7hpzizTSJ2fHAQ4DLgVcAv9JT5pvL/ylLC6vTFd8lbYpT\ngaqq363G16rq8qq6BppeoSTvWi/czvu7u+0iJ8lakjck+dMk/5jksiTf0lf2hUn+NsnfJ3ldu++4\nJF/pnQSa5F8m+WKbcNxLbxxjznsGcB7w3CS3rw8hJXlwkguSfCHJviQ/0/MeXpTkz5L8UpIvAT+T\n5NYk39lz/YcmOdDOw3hIkve3sd6S5H1J+pdMWT/uO5L8cZIvt3G+Z8jnsA34FuBdNB/Ix2l6FB/d\ne7phHyLwncDvVtU/t3NmLm1f6+XvcGmB+QMqbT2fBb6e5LfSfDXTQ/r2T9Lz8TzgRcC30izK91/7\n9j+JJpn7N8BPJ3lkVd0MrAHP6Sn3I8CFQ3qRBsUx6LyXAj8HvKeqHlRV618D8ls0w2iPoFlL6enA\nf+o51+nAX7Xv4Q3A77fva91zgLWq+hJNsnMBzbyfhwNfBd4yID6AnwEuraqjadYcevOgQlX1BeBT\nwI8mOTzJE4FvA/605/2f1iZqn03y+r5k9DLg+Unu3yZ8zwA+2HeZv01yU5qvQvqWIfFK2iQmWdIW\nU1W3A0+m+SP+G8AXk/xhkm9ti4xbrbiAd1TVDVX1NZohx8f2lTm/qv6pHfb6JPDd7evvpJm0vD5c\ndjZtT84Ag+IYdt70lk+zQvUzgJ+sqq9W1d8Db+Lek1C/UFW/WlV3t+/jd/r2P799jaq6par+oO31\nu4MmqXvqkLj/GTgpyQltL9OfDykH8FLgfOBrwB/TDB2u3y7/J8B3VtVDgR+iSQD/W8+xq8B3Af9I\ns3jkx6rqD9t9f09za/nDge8BHgT89og4JG0CkyxpC6qq66rqxVW1neYP9cNokpBJ3dyz/VWa75Ab\ntv9Az/4/BHa0E7OfBtw25WT7Yeft9200dzz+XTsMeCvw68BDe8rc1HfMGvCAJKe38X03zZwpkjwg\nyduS3JjkNpqE6MFJBiWC/50m4ftomi8wHnh3Vtv79H7g+VV1JM1Q32uTnAlQVX9TVX/bbn+aprft\n2e2xoenJ+t/AA4BjgWOS/EJb/itV9Yk2gfwi8Erg6Um+aUh9SdoEJlnSFldVn6X5rrLval/6Cs0f\n7nXHzfFaX6NJDF7QPt45qvg0p+57fhPNRP5vqaqHtI8HV9Vjhh3TDlleRNNj9DzgfVW1fnfef6EZ\npjy9qh5M04t1r96znvPsr6qXVtUJNDcXvDXJtw+I+YnAvqq6vD3uczSLGz5jxPtcv96xND1Ub6mq\nO9t1eH6L5st9R/F3urRA/IGUtpgkj0zyn9cnbifZTpNU/EVb5Grg+5JsT/PVEecNOs20l+3ZfifN\n2jtnMXyocNpr3EwzRBeAqvo74EPALyV5UJrvG3tEkmF36q1bHzK8Z6iw9UCaHrvb2on7O4cGnfxw\nu7wCwJdpkrm7BxT9NPDIJN+fxiOAH6QZBiXJM9phT5I8Cng9zVd0QHNX4d/RrMdzeJoFEs/pOfb0\n9nM+rJ2L9Waa71m7fcz7l3QQmWRJW8/twOOBPUnuoEmuPkXTW0Pbs/K77WsfA97HfXuKqm+7/3m/\ne16rqj+jSTr+sqr6h+z6jxl33nX/u/33H5KsDz++kGZS/l7glrbMeq9c/7nXY/socAfNsgi9k8jf\nBNyfJrn583bfsHi+F/hIkttphkdfVVU3DrjWtcDLgV8FbqMZrnxvVf1mW+RfA59sP6MP0HyNzM+1\nxxbwH4B/38Z0PU3P3U+2x357G+M/AtfQJIi9k/olLYCJFiNt/xf1mzRzCormf6nX0/yi/jbgRuA5\nVfXltvx5wI/SLIL4qqr6UBfBS1pMSf4I+J1pF++UpK1k0p6sXwYuqapHA/8CuA44F7i8qk4FPtw+\nJ8kOmu/Z2wGcQTNfwR4z6RCR5HHAv6T5T5gkHbLGJj/tnI2nrP+PtKruqubbqc+imUxL+++z2u1n\n0qyLc2fbhX4DzXo1kra4JLtoViZ/Tc+kckk6JE3ytTonA3+f5B00tzz/JfAaYFt945u799OsbgzN\nreIf6Tl+H82CfZK2uKo6Z7NjkKRFMUmSdQRN1/8rq+pjSd5EOzS4rqoqyajJXffaN6asJEnSQqmq\nae+6nmhO1j6atV4+1j5/L03SdXOS4wCSHA98sd3/eWB7z/Entq/1B+tjxsfOnTs3PYZlflh/1p31\nt5wP68+626zHrMYmWdV8H9lNSU5tX/oB4DM0t32vDw2cwzfWd9kNnJ3kqCQnA6cAH505QkmSpCU0\nyXAhwE8Av53kKJovXH0xcDhwUZKX0C7hAFBVe5NcRLN2zV3AK2ojaaAkSdISmijJqqpPAo8bsOsH\nhpT/OdpF9TR/Kysrmx3CUrP+ZmfdbYz1tzHW3+ysu80x0WKkc79oYueWJElaCkmojia+S5IkaUom\nWZIkSR0wyZIkSeqASZYkSVIHTLIkSZI6YJIlSZLUAZMsSZKkDphkSZIkdcAkS5IkqQMmWZIkSR0w\nyZIkSerARElWkhuTfCrJVUk+2r52TJLLk3wuyYeSHN1T/rwk1ye5LsnTuwpekiRpUU3ak1XASlWd\nVlWnt6+dC1xeVacCH26fk2QH8FxgB3AG8NYkI6+ThGTq712UJElaWNMMF/ZnQWcBu9rtXcCz2u1n\nAhdW1Z1VdSNwA3A6kiRJh5BperL+KMnHk/xY+9q2qtrfbu8HtrXbDwP29Ry7Dzhhw5FKkiQtkSMm\nLPekqvq7JA8FLk9yXe/OqqokNeL4++xbXV2dPEpJkqSDZG1tjbW1tQ2fJ1WjcqMBByQ7gTuAH6OZ\np3VzkuOBK6rqUUnOBaiqN7blLwV2VtWennNU73XX52NNG4skSVLXklBVU08eHztcmOQBSR7Ubn8T\n8HTgGmA3cE5b7Bzg4nZ7N3B2kqOSnAycAnx02sAkSZKW2STDhduAP2h7m44AfruqPpTk48BFSV4C\n3Ag8B6Cq9ia5CNgL3AW8ouyikiRJh5iphwvnclGHCyVJ0pLobLhQkiRJ0zPJkiRJ6oBJliRJUgdM\nsiRJkjpgkiVJktQBkyxJkqQOmGRJkiR1wCRLkiSpAyZZkiRJHTDJkiRJ6oBJliRJUgdMsiRJkjpg\nkiVJktSBiZKsJIcnuSrJ+9rnxyS5PMnnknwoydE9Zc9Lcn2S65I8vavAJUmSFtmkPVmvBvYC1T4/\nF7i8qk4FPtw+J8kO4LnADuAM4K1J7C2TJEmHnLEJUJITgTOB3wTSvnwWsKvd3gU8q91+JnBhVd1Z\nVTcCNwCnzzNgSZKkZTBJL9P/Av4bcHfPa9uqan+7vR/Y1m4/DNjXU24fcMJGg5QkSVo2R4zameQH\ngS9W1VVJVgaVqapKUoP2rRcZ9OLq6uqkMUqSJB00a2trrK2tbfg8qRqeHyX5OeBHgLuA+wHfDPw+\n8DhgpapuTnI8cEVVPSrJuQBV9cb2+EuBnVW1p++81XvdpBmFHBWLJEnSZkhCVWV8yXsbOVxYVa+r\nqu1VdTJwNvB/qupHgN3AOW2xc4CL2+3dwNlJjkpyMnAK8NFpg5IkSVp2I4cLB1jvanojcFGSlwA3\nAs8BqKq9SS6iuRPxLuAVNWX3lL1akiRpKxg5XNjZRUcMF5pkSZKkRdLJcKEkSZJmY5IlSZLUAZMs\nSZKkDphkSZIkdcAkS5IkqQMmWZIkSR0wyZIkSeqASZYkSVIHTLIkSZI6YJLVoST3rGAvSZIOLSZZ\nkiRJHTDJkiRJ6sDIJCvJ/ZLsSXJ1kr1Jfr59/Zgklyf5XJIPJTm655jzklyf5LokT99IcA63SZKk\nZZWqGl0geUBVHUhyBPCnwH8FzgK+VFW/mOS1wEOq6twkO4DfAR4HnAD8EXBqVd3dd87qve56IlVV\nQ7eX0bLHL0mSmr/nVTV1r8/Y4cKqOtBuHgUcDtxKk2Ttal/fBTyr3X4mcGFV3VlVNwI3AKdPG5Qk\nSdKyG5tkJTksydXAfuCKqvoMsK2q9rdF9gPb2u2HAft6Dt9H06MlSZJ0SDliXIF2qO+xSR4MXJbk\n+/v2V5JR42ED962urk4T51JbXV3l/PPPBxw6lCRp0a2trbG2trbh84ydk3Wvwsn/AL4K/Cdgpapu\nTnI8TQ/Xo5KcC1BVb2zLXwrsrKo9fec5pOZk9VrW9yJJ0qGqkzlZSY5dv3Mwyf2BpwFXAbuBc9pi\n5wAXt9u7gbOTHJXkZOAU4KPTBiVJkrTsxg0XHg/sSnIYTUL2rqr6cJKrgIuSvAS4EXgOQFXtTXIR\nsBe4C3hFHYJdNy47IUmSphounNtFt/hw4agka9neiyRJh7rOlnCQJEnS9EyyJEmSOmCSJUmS1AGT\nLEmSpA6YZEmSJHXAJEuSJKkDJlmSJEkdMMmSJEnqgEmWJElSB0yyJEmSOmCSJUmS1AGTLEmSpA6Y\nZEmSJHVgbJKVZHuSK5J8Jsmnk7yqff2YJJcn+VySDyU5uueY85Jcn+S6JE/v8g1IkiQtolTV6ALJ\nccBxVXV1kgcCfwk8C3gx8KWq+sUkrwUeUlXnJtkB/A7wOOAE4I+AU6vq7p5zVu91kwBQVUO3l8l6\n3IMs23uRJOlQl4SqGv7HfYixPVlVdXNVXd1u3wFcS5M8nQXsaovtokm8AJ4JXFhVd1bVjcANwOnT\nBiZJkrTMppqTleQk4DRgD7Ctqva3u/YD29rthwH7eg7bR5OUSZIkHTKOmLRgO1T4e8Crq+r23iGx\nqqoko8bB7rNvdXV1ijAlSZIOjrW1NdbW1jZ8nrFzsgCSHAm8H/hgVb2pfe06YKWqbk5yPHBFVT0q\nybkAVfXGttylwM6q2tNzPudkSZKkpdDZnKw0GcMFwN71BKu1Gzin3T4HuLjn9bOTHJXkZOAU4KPT\nBiZJkrTMJrm78MnAnwCf4hvDfufRJE4XAQ8HbgSeU1Vfbo95HfCjwF00w4uX9Z3TnixJkrQUZu3J\nmmi4cN7mlWQtahI2Kslat2gxS5KkwTobLpQkSdL0libJSjJRD5EkSdIiWJoka6sxYZQkaWszyZIk\nSeqASZYkSVIHJl7xXd3oHzb0rkNJkrYGe7IkSZI6YJIlSZLUgS2TZLnEgyRJWiRbJsmSJElaJIdk\nkmWvlyRJ6tohmWRJkiR1bWySleTtSfYnuabntWOSXJ7kc0k+lOTonn3nJbk+yXVJnt5V4IcKe90k\nSVpOk/RkvQM4o++1c4HLq+pU4MPtc5LsAJ4L7GiPeWuSufaWbaWkY9D7WF1dPfiBSJKkucski18m\nOQl4X1U9pn1+HfDUqtqf5DhgraoeleQ84O6q+oW23KXAalV9pO981Xvd9WSjqsZurxtVdoL3M3HZ\nac0jARxWN5Ik6eBLQlVN/Qd+1l6mbVW1v93eD2xrtx8G7Osptw84YcZrzGwr9XZJkqTltOGv1amq\nSjKqm2XgPofFpmOPliRJB8fa2hpra2sbPs9GhgtXqurmJMcDV7TDhecCVNUb23KXAjurak/f+Tod\nLuxPSFZXV++V1C3jcOGgfZIkqXsHe7hwN3BOu30OcHHP62cnOSrJycApwEdnvMbcnH/++ZsdgiRJ\nOsSMHS5MciHwVODYJDcBPw28EbgoyUuAG4HnAFTV3iQXAXuBu4BXlF0vc+fQoSRJi2+i4cK5X/Qg\nDxe23XwDr9fBe9vwOcYNFw6K38RLkqRuHOzhwi1l0Sbhe3ekJEnLz56sAfs3+N7mch6Y7H33X9ee\nLEmS5sueLB109rhJkjScSVafcYnDMiQWyxCjJElbnUmWNsyETpKk+9rwiu+LbJo//v1l5zlPa1aT\nxL8oCc6s3yMpSdJWZU+W5qr3Ts3V1dV7DV06jClJOpRs6bsL+8sPul7v82FGrVs16ppdmLZeBplX\nT9M079keLknSsjrk7i6cNqnZSC/KuHW0DuY6W/O+1rT1Muv17cGSJB1qlrYnq9fOnTvv+X7CaXqV\nJu3JmrbswTJtD9+6ab5Mu19XK9q75pckaVHN2pO1JZKsXl0lWctiI8OsMH7C/zyTrNXV1Xslx/3X\nMMmSJC2CLT1c2NVw3KE4EXuauuxq0vr6udYTrFkt0tchbTSWRXovkqT5WIqerGkczEnoi2iaodNx\nPVkb/SymMegaO3fuHJh89JYbFOs4s9wYMOkQbL9Jf77mvWSIvYGSND8L1ZOV5Iwk1yW5Pslru7jG\niGsfzMstnI32DsF9e60ORp0Ousb5558/dQ/aoPXO5hl/77n6l6iY5zUmPefB7o1dW1sbW8ZeueEm\nqT8NZ/3NzrrbHHNPspIcDrwFOAPYATwvyaPnfR2NN+6P7zIlpOvJxCRJ1PprvX/sBx0/bh2vcUnU\nJAltbyy9MfQ/ujaoLiaJr/+9fP/3f//A99dbdtpEf1C9b1X+odsY62921t3m6KIn63Tghqq6saru\nBN4DPLOD60gjjftj37t/WE/awYplPYb+HsRBSdCgsv3lJ7n+JInRJMnpoPgnTSTHxTqPhHTUcVs5\noZO0AKpqrg/g2cBv9Dx/AfArfWWqF3Cfx86dOwe+7sOHj/s+Jvl5GfXztmixjjt+1Hvpfb1/u6om\nOn5Q2UHlB72XfqOuMS6W/uuPMiyGcecaFveo/aPeb5fW38M09TKJcZ/htOYZ3yTnmuUz1HTa+ps6\nJ5r7xPckPwScUVU/1j5/AfD4qvqJnjLzvagkSVKHaoaJ7118QfTnge09z7cD+3oLzBKoJEnSMuli\nTtbHgVOSnJTkKOC5wO4OriNJkrSw5t6TVVV3JXklcBlwOHBBVV077+tIkiQtsk1ZjFSSJGmr6/Rr\ndSZZlDTJm9v9n0xyWpfxLJtx9ZdkJcltSa5qH6/fjDgXUZK3J9mf5JoRZWx7A4yrO9vdaEm2J7ki\nyWeSfDrJq4aUs/0NMEn92QYHS3K/JHuSXJ1kb5KfH1LOtjfAJPU3ddub5ZbESR40Q4U3ACcBRwJX\nA4/uK3MmcEm7/XjgI13Fs2yPCetvBdi92bEu4gN4CnAacM2Q/ba92evOdje6/o4DHttuPxD4rL/7\n5l5/tsHh9feA9t8jgI8AT+7bb9vbWP1N1fa67MmaZFHSs4BdAFW1Bzg6ybYOY1omky7q6p2aA1TV\nlcCtI4rY9oaYoO7AdjdUVd1cVVe323cA1wIP6ytm+xtiwvoD2+BAVXWg3TyK5j/rt/QVse2NMEH9\nwRRtr8sk6wTgpp7n+9rXxpU5scOYlskk9VfAE9su30uS7Dho0S0/297sbHcTSnISTa/gnr5dtr8J\njKg/2+AQSQ5LcjWwH7iiqvb2FbHtjTBB/U3V9rpYJ6s3kEn0Z4TOxG9MUg+fALZX1YEkzwAuBk7t\nNqwtxbY3G9vdBJI8EHgv8Oq2R+Y+Rfqe2/56jKk/2+AQVXU38NgkDwYuS7JSVWt9xWx7Q0xQf1O1\nvS57ssYuSjqgzInta5psUdfb17s2q+qDwJFJjjl4IS41296MbHfjJTkS+D3g3VV18YAitr8RxtWf\nbXC8qroN+ADwvX27bHsTGFZ/07a9LpOsSRYl3Q28ECDJE4AvV9X+DmNaJmPrL8m2tN98m+R0miU5\nBo0f675se6O9hiFDCPNud0lelOTKWY9fNG3dXADsrao3DSlm+xtikvrzd99gSY5NcnS7fX/gacBV\nfcVse0NMUn/Ttr3OhgtryKKkSV7W7n9bVV2S5MwkNwBfAV7cVTzLZpL6o/ky7pcnuQs4AJy9aQEv\nmCQXAk8Fjk1yE7CT5i5N2x6Q5A6+MUTwTcDXgK+3z68Bvgv4pkF1x5K2uyQvAl5SVU/p+FJPAl4A\nfCrJ+i/o1wEPp5l0/HDgUcAn+ttfkpcC/x14KPA54DVV9Wcdx7toRtXfUrfBg+B4YFeSw2g6Ud5V\nVR/27+7ExtYfU7Y9FyOVDnFJ/oYm+fg/Pa+tAo+oqh85CNd/ETMmP0kOa+dQdHqd9vjDq+rr40uO\nPMcLgS8CPw58oqre0LPvscCVwPdV1VVJfhx4A7Ct/EUtLaVOFyOVtLQKOCrJriT/mGZRyO9Z35nk\n7iTf3vP8t5L8TLu9kmRfkv+cZlHTL7QJznrZb0myO82CfnuAR/ReOMmjklye5B/SLMb7w33X+bX2\nrp47aNasoe/4FyX5qzbuv07y/CSPAn4d+FdJbk9yS1v2wUnemeSLSW5M8lM9QwEvSvJnSX4pyZeA\n1STvSPLW9vq3J7kyyXFJfjnJrUmubZOlwZVa9c6quhS4nftOPt5BM0S23nvzLuBY4FuHnU/SYjPJ\nkjRIaIa2LgQeTDOP4y0jyhf3vkNpG/DNNOsbvQT41TR36wD8Kk03+3HAj9IMVxRAkm8CLgfeTTNk\ndjbw1iSP7jn384CfqaoHAvcaSmuP/2XgjKr6ZuBfAVdX1XXAy4C/qKoHVdX6RNVfAR4EnEwzvPxC\n7j18cjrwVzSJzs+29fLDwE/RJED/TLNg4ceAY2juhvulEfU0ypXAyUlOT3J4WzdXOV9GWl4mWZKG\nubKqLm2Hqt4NfPeY8r09M3cCb6iqr7d34NwBPLJNHv4D8NNV9dWq+gzNwojrx/4g8DdVtauq7m4X\npfx9msRm3cVV9RcAVfVPA+K4G3hMkvtX1f6edW7u1XPUxvJc4Lyq+kpV/S3wP4HeIdIvVNWvtrF8\njSYZ/P2quqq99h8AX6mqd7f1dBHNuk5Tq6qbgNfTJI5fA/4HTWIoaUmZZEkaprcH5QBwv3ZC6CT+\noW+u1AGar0h5KM0NN72LIf7fnu1vAx7fDr3dmuRW4Pk0PWPQJDm9x95LVX2FJnH6ceALSd6f5JFD\nih9LM6H/b/ti6V30d9C1vtiz/bW+51+leZ9TS3IW8F9ovkLmSJpk7/1Jjp/lfJI2n0mWpEHGTbQ+\nADyg5/nxExwD8PfAXbR3irV6t/8v8MdV9ZCex4Oq6v+dJGiAqvpQVT2dZjjyOuA31nf1Ff0STY/b\nSX2x9K5H1+WE8/5z/1vgA1V1A0BVXQb8Hc2Qp6QlZJIlaZBx3811NfAfkxye5Azg+yY5aXt33u/T\nTCK/f5qvpDiHbyQcHwBOTfKCJEe2j8e1E9fHxpXkW5M8s52bdSfNLerrdwTuB05Ms9DleiwXAT+b\n5IFJvg34SZqh0aGXmOR9jojviCT3o1mW5cgkvb2DnwT+XZKT03gazUrSn97INSVtHpMsSYP0T2Sn\n7/mrgX9P80XSz6eZmzSsbL9X0gyp3Qy8vX00B1XdDjydZsL752l6cn6e5stah8XV6zCaROnzwD8A\nTwFe3u77MPAZ4OYk60N8P0GTiP01zcTz3wbeMeJa/a+Nq6d+v8k31tb5qXb7BT37/hD4E+A24E3A\nS6vqcyPOJ2mBTbVOVpLtwDtp7rQp4P+rqjcPKPdm4Bk0v0Be1HNLsiRJ0iFh2hXf7wR+sqquTvPl\nnX+Z5PKquna9QJIzge+oqlOSPB74NeAJ8wtZkiRp8U01XFhVN7e3VNN+K/q1NOvg9DqL5pZsqmoP\ncHSSbUiSJB1CZp6TleQkmvVg9vTtOoF73/a8jyFfNCtJkrRVzZRktUOF7wVe3fZo3adI33O/d0uS\nJB1Spp2TRXv78+8B766qiwcU+Tywvef5ie1rvecw6ZIkSUujqqZewmWqnqz2i1MvoPkS0zcNKbab\n5vu/SPIE4MuDvnurqrbso+v3uHPnzk1/j8v8sP6sO+tvOR/Wn3W3WY9ZTduT9SSaNV0+lWR9WYbX\n0a7YXFVvq6pLkpyZ5Aaa9WdePPhUkiRJW9dUSVZV/SkT9H5V1StnjkiSJGkLcMX3JbSysrLZISw1\n62921t3GWH8bY/3NzrrbHFOt+D63iya1Gdc9WJqpa2xoHFeSJC2GJFTXE98lSZI0GZMsSZKkDphk\nSZIkdcAkS5IkqQMmWZIkSR0wyZIkSeqASZYkSVIHTLIkSZI6YJIlSZLUAZMsSZKkDphkSZIkdcAk\nS5IkqQMmWZIkSR0wyZIkSeqASZYkSVIHTLIkSZI6YJIlSZLUAZMsSZKkDphkSZIkdcAkS5IkqQMm\nWZIkSR0wyZIkSerAVElWkrcn2Z/kmiH7V5LcluSq9vH6+YQpSZK0XI6Ysvw7gF8B3jmizB9X1Vmz\nhyRJkrT8purJqqorgVvHFMvs4UiSJG0N856TVcATk3wyySVJdsz5/JIkSUth2uHCcT4BbK+qA0me\nAVwMnDqo4Orq6j3bKysrrKyszDkUSZKk6a2trbG2trbh86SqpjsgOQl4X1U9ZoKyfwN8T1Xd0vd6\nTXvdZZI0I6Zb+T1KknSoSEJVTT0daq7DhUm2pc0wkpxOk8TdMuYwSZKkLWeq4cIkFwJPBY5NchOw\nEzgSoKreBjwbeHmSu4ADwNnzDVeSJGk5TD1cOJeLOlwoSZKWxEIMF0qSJKlhkiVJktQBkyxJkqQO\nmGRJkiR1wCRLkiSpAyZZkiRJHTDJkiRJ6oBJliRJUgdMsiRJkjpgkiVJktQBkyxJkqQOmGRJkiR1\nwCRLkiSpAyZZkiRJHTDJkiRJ6oBJliRJUgdMsiRJkjpgkiVJktQBkyxJkqQOmGRJkiR1wCRLkiSp\nAyZZkiRJHTDJkiRJ6sBUSVaStyfZn+SaEWXenOT6JJ9MctrGQ5QkSVo+0/ZkvQM4Y9jOJGcC31FV\npwAvBX5tA7FJkiQtramSrKq6Erh1RJGzgF1t2T3A0Um2zR6eJEnScpr3nKwTgJt6nu8DTpzzNSRJ\nkhbeER2cM33Pa1Ch1dXVe7ZXVlZYWVm574nSnKqq7rU9NoABZQed654Apzz/pIadc1x8i2x1dfVe\nn926QfW7c+fOgWW3omH1osWxLD9jkjbf2toaa2trGz5Ppv2Fk+Th/y+QAAAHVklEQVQk4H1V9ZgB\n+34dWKuq97TPrwOeWlX7+8rVtMnSMiVZg84/TXyLLMnAGIfV76K/n3kZVi9aHMvyMyZp8bS/4/s7\nkcaa93DhbuCFbUBPAL7cn2BJkiQdCqYaLkxyIfBU4NgkNwE7gSMBquptVXVJkjOT3AB8BXjxvAOW\nJElaBlMPF87log4XjoxvkTlcOJjDhYtvWX7GJC2eRRkulCRJEiZZkiRJnTDJkiRJ6oBJliRJUgdM\nsiRJkjpgkiVJktQBkyxJkqQOmGRJkiR1wCRLkiSpAyZZkiRJHTDJkiRJ6oBJliRJUgdMsiRJkjpg\nkiVJktQBkyxJkqQOmGRJkiR1wCRLkiSpAyZZkiRJHTDJkiRJ6oBJliRJUgdMsiRJkjpgkiVJktQB\nkyxJkqQOTJ1kJTkjyXVJrk/y2gH7V5LcluSq9vH6+YQqSZK0PI6YpnCSw4G3AD8AfB74WJLdVXVt\nX9E/rqqz5hSjJEnS0pm2J+t04IaqurGq7gTeAzxzQLlsODJJkqQlNm2SdQJwU8/zfe1rvQp4YpJP\nJrkkyY6NBChJkrSMphoupEmgxvkEsL2qDiR5BnAxcOrUkUmSJC2xaZOszwPbe55vp+nNukdV3d6z\n/cEkb01yTFXd0ltudXX1nu2VlRVWVlamDEWSJGn+1tbWWFtb2/B5UjVJ51RbODkC+Czwb4AvAB8F\nntc78T3JNuCLVVVJTgcuqqqT+s5Tk1w3aaZ2VdW9tqc5btS51k17/kmv33v+aeJbZEkGxjisfhf9\n/czLsHrR4liWnzFJi6f9HT/1fPOperKq6q4krwQuAw4HLqiqa5O8rN3/NuDZwMuT3AUcAM6eNihJ\nkqRlN1VP1twuak/WyPgWmT1Zg9mTtfiW5WdM0uKZtSfLFd8lSZI6YJIlSZLUAZMsSZKkDphkSZIk\ndcAkS5IkqQMmWZIkSR0wyZIkSeqASZYkSVIHTLIkSZI6YJIlSZLUAZMsSZKkDphkSZIkdcAkS5Ik\nqQMmWZIkSR0wyZIkSeqASZYkSVIHTLIkSZI6YJIlSZLUAZMsSZKkDphkSZIkdcAkS5IkqQMmWZIk\nSR0wyZIkSerA1ElWkjOSXJfk+iSvHVLmze3+TyY5beNhSpIkLZepkqwkhwNvAc4AdgDPS/LovjJn\nAt9RVacALwV+bU6xqrW2trbZISw162921t3GWH8bY/3NzrrbHNP2ZJ0O3FBVN1bVncB7gGf2lTkL\n2AVQVXuAo5Ns23Ckuoc/LBtj/c3OutsY629jrL/ZWXebY9ok6wTgpp7n+9rXxpU5cfrQJEmSlte0\nSVZNWC4zHidJkrQlpGry/CfJE4DVqjqjfX4ecHdV/UJPmV8H1qrqPe3z64CnVtX+njImXZIkaWlU\nVX8H0lhHTFn+48ApSU4CvgA8F3heX5ndwCuB97RJ2Zd7E6xZA5UkSVomUyVZVXVXklcClwGHAxdU\n1bVJXtbuf1tVXZLkzCQ3AF8BXjz3qCVJkhbcVMOFkiRJmkynK767cOnGjKu/JCtJbktyVft4/WbE\nuYiSvD3J/iTXjChj2xtgXN3Z7kZLsj3JFUk+k+TTSV41pJztb4BJ6s82OFiS+yXZk+TqJHuT/PyQ\ncra9ASapv6nbXlV18qAZTrwBOAk4ErgaeHRfmTOBS9rtxwMf6SqeZXtMWH8rwO7NjnURH8BTgNOA\na4bst+3NXne2u9H1dxzw2Hb7gcBn/d039/qzDQ6vvwe0/x4BfAR4ct9+297G6m+qttdlT5YLl27M\nJPUH910uQ0BVXQncOqKIbW+ICeoObHdDVdXNVXV1u30HcC3wsL5itr8hJqw/sA0OVFUH2s2jaP6z\nfktfEdveCBPUH0zR9rpMsly4dGMmqb8Cnth2+V6SZMdBi2752fZmZ7ubUHsn9mnAnr5dtr8JjKg/\n2+AQSQ5LcjWwH7iiqvb2FbHtjTBB/U3V9qZdwmEaLly6MZPUwyeA7VV1IMkzgIuBU7sNa0ux7c3G\ndjeBJA8E3gu8uu2RuU+Rvue2vx5j6s82OERV3Q08NsmDgcuSrFTVWl8x294QE9TfVG2vy56szwPb\ne55vp8mYR5U5sX1NE9RfVd2+3rVZVR8EjkxyzMELcanZ9mZkuxsvyZHA7wHvrqqLBxSx/Y0wrv5s\ng+NV1W3AB4Dv7dtl25vAsPqbtu11mWTds3BpkqNoFi7d3VdmN/BCuGc1+fssXHoIG1t/SbYlSbt9\nOs2SHIPGj3Vftr0Z2e5Ga+vmAmBvVb1pSDHb3xCT1J9tcLAkxyY5ut2+P/A04Kq+Yra9ISapv2nb\nXmfDheXCpRsySf0BzwZenuQu4ABw9qYFvGCSXAg8FTg2yU3ATpq7NG17Y4yrO2x34zwJeAHwqSTr\nv6BfBzwcbH8TGFt/2AaHOR7YleQwmk6Ud1XVh/27O7Gx9ceUbc/FSCVJkjrQ6WKkkiRJhyqTLEmS\npA6YZEmSJHXAJEuSJKkDJlmSJEkdMMmSJEnqgEmWJElSB0yyJEmSOvD/A0nvHKezjsK/AAAAAElF\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2178a7f0>"
       ]
      }
     ],
     "prompt_number": 164
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# aggregate the sunny, cloudy, rainy weather type histograms together\n",
      "\n",
      "sunnyset = [t for t in weather_types if 'sun' in t.lower()]\n",
      "cloudset= [t for t in weather_types if 'cloud' in t.lower()]\n",
      "rainset = [t for t in weather_types if 'rain' in t.lower() or 'drizzle' in t.lower() or 'storm' in t.lower()]\n",
      "\n",
      "sunny_data, blah = np.histogram([], speed_hist_bins)\n",
      "cloud_data, blah = np.histogram([], speed_hist_bins)\n",
      "rain_data, blah = np.histogram([], speed_hist_bins)\n",
      "\n",
      "for w in sunnyset:\n",
      "    if sum(speed_histograms[w])>0:\n",
      "        sunny_data += speed_histograms[w]\n",
      "for w in cloudset:\n",
      "    if sum(speed_histograms[w])>0:\n",
      "        cloud_data += speed_histograms[w]\n",
      "for w in rainset:\n",
      "    if sum(speed_histograms[w])>0:\n",
      "        rain_data += speed_histograms[w]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 170
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# normalise each set\n",
      "sunny_data = sunny_data.astype(float) / sum(sunny_data)\n",
      "cloud_data = cloud_data.astype(float) / sum(cloud_data)\n",
      "rain_data = rain_data.astype(float) / sum(rain_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 171
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(20, 10))\n",
      "plt.bar(bin_centers, rain_data, align='center', color='b', edgecolor='b', width=0.01, alpha=0.3)\n",
      "plt.bar(bin_centers, sunny_data, align='center', color='y', edgecolor='y', width=0.01, alpha=0.3)\n",
      "#plt.bar(bin_centers, cloud_data, align='center', color='g', edgecolor='g', width=0.01, alpha=0.3)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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A7mhu1gUAAAAAcDQIigAAAABIckqXngF3t7SUjMYtnvR0AgAAOEUERfvUb5qs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\nCBpDXblpAbdWpTQ2UAJrKEU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       "text": [
        "<matplotlib.figure.Figure at 0x1b868550>"
       ]
      }
     ],
     "prompt_number": 177
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "newhistograms = {}\n",
      "for i in histograms:\n",
      "    newhistograms[i] = histograms[i].tolist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "import json\n",
      "with open('results.json', 'wb') as fp:\n",
      "    json.dump(newhistograms,fp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 119
    }
   ],
   "metadata": {}
  }
 ]
}