{
 "metadata": {
  "name": "",
  "signature": "sha256:2a1db8a0c9fc38bb4ca7413d2237b02181f6fade17006921d3d457a24f1c5681"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook generates tidal comparisons for CMOS. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# imports\n",
      "%matplotlib inline\n",
      "import matplotlib.pylab as plt\n",
      "import numpy as np\n",
      "import netCDF4 as NC\n",
      "from scipy.optimize import curve_fit\n",
      "from salishsea_tools import tidetools\n",
      "from salishsea_tools import viz_tools\n",
      "import collections\n",
      "import pandas as pd\n",
      "import csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# grid\n",
      "bathy, X, Y = tidetools.get_SS_bathy_data()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pathname for data\n",
      "#This run has bf=1e-3, K1 phase and amplitude corrected and M2 phase corrected\n",
      "\n",
      "name = '/data/nsoontie/MEOPAR/SalishSea/results/tides/tide_M2phase/'\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#constants and fitting\n",
      "# M2\n",
      "M2freq = 28.984106 # degrees per hour\n",
      "M2freq = M2freq*np.pi/180. # radians per hour\n",
      "#K1\n",
      "K1freq = 15.041069*np.pi/180.\n",
      "\n",
      "# initialize fit\n",
      "M2amp = 1.; M2pha = 180.\n",
      "K1amp = 1.; K1pha = 180."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# function for fit\n",
      "def double(x, M2amp, M2pha, K1amp, K1pha):\n",
      "    return (M2amp*np.cos(M2freq*x-M2pha*np.pi/180.)+\n",
      "            K1amp*np.cos(K1freq*x-K1pha*np.pi/180.))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# initial phase calculation\n",
      "# our start is currently Oct 26, 2002\n",
      "# data for phase output from bdytides.F90; found in ocean.output\n",
      "K1ft = 1.050578\n",
      "K1uvt = 296.314842\n",
      "M2ft = 0.987843\n",
      "M2uvt = 245.888564"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Observations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load some observations from a text file.\n",
      "/data/nsoontie/MEOPAR/analysis/compare_tides/obs_tidal_wlev_const_all.csv\n",
      "Note: This file contains a mix of M2/K1 measurements from Foreman et al (1995), US tidal harmonics, Foreman et al (2004) and Foreman et al (2012) (for Northern tides)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filename = '/data/nsoontie/MEOPAR/analysis/compare_tides/obs_tidal_wlev_const_all.csv'\n",
      "\n",
      "harm_obs = pd.read_csv(filename,sep=';',header=0)\n",
      "harm_obs = harm_obs.rename(columns={'Site': 'site', 'Lat': 'lat', 'Lon': 'lon', \n",
      "                                    'M2 amp': 'M2_amp', 'M2 phase (deg UT)': 'M2_pha',\n",
      "                                   'K1 amp': 'K1_amp', 'K1 phase (deg UT)': 'K1_pha'})\n",
      "print harm_obs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                 site       lat       lon  M2_amp  M2_pha  K1_amp  K1_pha\n",
        "0               Sooke  48.36700  123.7330    43.8   282.7    56.9   266.4\n",
        "1        Port Angeles  48.12500  123.4400    51.8   307.4    66.9   261.4\n",
        "2          Pedder Bay  48.33100  123.5490    34.2   308.0    62.7   269.0\n",
        "3           Esquimalt  48.43300  123.4330    36.7   317.1    64.3   268.1\n",
        "4        Clover Point  48.40500  123.3470    40.3   320.3    64.2   269.8\n",
        "5            Victoria  48.41700  123.3670    37.3   316.1    62.7   269.2\n",
        "6       Finnerty Cove  48.47300  123.2950    44.7   357.7    70.8   277.5\n",
        "7       Port Townsend  48.14500  122.7550    65.2   350.0    75.0   270.8\n",
        "8              Sidney  48.65000  123.4000    55.4     5.9    76.7   277.6\n",
        "9        Patricia Bay  48.65000  123.4500    60.3    14.4    76.0   281.3\n",
        "10          Maple Bay  48.81700  123.6170    68.5    17.0    79.3   281.2\n",
        "11    Fulford Harbour  48.76700  123.4500    58.2    12.7    75.3   280.0\n",
        "12          Ladysmith  48.98300  123.8000    70.8    16.3    79.8   281.8\n",
        "13       Patos Island  48.78300  122.9670    68.0    25.0    79.0   285.6\n",
        "14      Tumbo Channel  48.79200  123.1080    72.6    31.0    81.1   286.9\n",
        "15         Whaler Bay  48.88500  123.3250    83.4    32.9    84.7   287.5\n",
        "16          Silva Bay  49.15300  123.7000    92.2    32.0    86.5   286.7\n",
        "17           Ferndale  48.83300  122.7170    72.3    23.8    80.1   283.6\n",
        "18             Blaine  48.99000  122.7600    77.4    25.1    82.3   284.3\n",
        "19         Tsawwassen  48.99000  123.1330    81.1    27.8    83.4   284.8\n",
        "20          Sandheads  49.10000  123.3000    86.9    30.9    83.7   286.5\n",
        "21         Point Grey  49.25000  123.2670    94.5    33.9    90.6   287.0\n",
        "22     Point Atkinson  49.33300  123.2500    91.8    31.2    86.2   286.1\n",
        "23           Squamish  49.70000  123.1500    94.2    31.2    87.4   286.8\n",
        "24    Gibsons Landing  49.40000  123.5000    94.7    30.1    87.2   285.2\n",
        "25       Halfmoon Bay  49.51700  123.9170    96.4    31.5    88.0   285.8\n",
        "26    Irvines Landing  49.63300  124.0500    98.8    31.9    88.0   286.7\n",
        "27         Winchelsea  49.30000  124.0830    95.2    32.6    87.5   286.7\n",
        "28      Northwest Bay  49.30000  124.2000    95.6    32.7    87.2   286.7\n",
        "29       Cherry Point  48.86300  122.7570    73.2    21.8    81.5   281.9\n",
        "30     Friday Harbour  48.54500  123.0100    56.4    11.0    76.3   279.4\n",
        "31            Rosario  48.64700  122.8700    58.1     4.3    75.3   277.3\n",
        "32        North Beach  48.71200  122.9080    68.4    18.1    79.2   281.8\n",
        "33         Bellingham  48.74500  122.4950    65.3    15.2    77.0   281.1\n",
        "34          Anacortes  48.52300  122.6130    62.2     5.2    77.3   277.2\n",
        "35    Reservation Bay  48.41500  122.6520    58.1   344.7    73.8   269.5\n",
        "36      Hanbury Point  48.58000  123.1720    52.8   357.3    75.3   275.1\n",
        "37   Sheringham Point  48.37500  123.9180    48.7   263.3    54.8   261.5\n",
        "38           Neah Bay  48.36670  124.6117    78.9   246.2    49.6   248.5\n",
        "39  Sekiu Clallam Bay  48.26330  124.2967    66.9   258.0    54.2   252.2\n",
        "40       Port Angeles  48.12500  123.4400    51.4   307.1    66.7   261.8\n",
        "41      Port Townsend  48.11170  122.7567    68.5   349.7    76.6   270.7\n",
        "42         Budd Inlet  47.09830  122.8950   145.9    30.3    87.7   289.6\n",
        "43            Seattle  47.60170  122.3383   107.2    10.6    83.4   277.0\n",
        "44             Bangor  47.74830  122.7267   102.4     4.1    83.5   274.8\n",
        "45  Foulweather Bluff  47.92670  122.6167    88.3     3.4    76.5   275.9\n",
        "46            Everett  47.98000  122.2217   100.9    11.6    80.9   276.9\n",
        "47     Sneeoosh Point  48.40000  122.5467   102.6    18.3    78.4   282.0\n",
        "48         Turner Bay  48.44500  122.5550    94.4    16.7    75.4   281.4\n",
        "49    Armitage Island  48.53500  122.7967    57.3     0.5    75.6   276.4\n",
        "50     Friday Harbour  48.54670  123.0100    56.5     9.7    75.8   278.8\n",
        "51         Richardson  48.44670  122.9000    52.2   340.1    71.3   270.9\n",
        "52       Cherry Point  48.86330  122.7567    73.4    22.8    81.7   282.8\n",
        "53             Blaine  48.99167  122.7650    76.3    24.8    78.4   286.3\n",
        "54       Port Renfrew  48.55000  124.4300    70.8   241.1    45.3   254.1\n",
        "55       Little River  49.74000  124.9200    99.4    32.9    90.2   287.0\n",
        "56        Twin Islets  50.03000  124.9300   101.3    35.4    90.4   287.5\n",
        "57     Campbell River  50.04000  125.2400    82.5    18.4    84.6   284.0\n",
        "58    Seymour Narrows  50.13000  125.3400    94.6   320.1    69.2   272.1\n",
        "59           Owen Bay  50.31000  125.2200    85.0   319.9    67.8   272.7\n",
        "                  ...       ...       ...     ...     ...     ...     ...\n",
        "\n",
        "[77 rows x 7 columns]\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Model Output"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is a list of the stations for which we have model results."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stations = ['BrownBay', 'CampbellRiver', 'ChathamPoint', 'CloverPoint', \n",
      "            'Esquimalt', 'FinnertyCove', 'FulfordHarbour', 'GibsonsLanding',\n",
      "            'HalfmoonBay', 'IrvinesLanding', 'KelseyBay', 'Lund',\n",
      "            'MaudeIslandE', 'NympheCove', 'PatosIsland','PedderBay',\n",
      "            'PointAtkinson','PointGrey','PortRenfrew',\n",
      "            'PowellRiver', 'Sandheads','SeymourNarrows','SheringhamPoint',\n",
      "            'Tsawwassen','TumboChannel','TwinIslets','Victoria',\n",
      "            'WhalerBay','YorkeIsland']\n",
      "numsta=len(stations)\n",
      "#again with spaces because the text file likes that\n",
      "stations_obs = ['Brown Bay', 'Campbell River', 'Chatham Point', 'Clover Point', \n",
      "                'Esquimalt', 'Finnerty Cove', 'Fulford Harbour', 'Gibsons Landing', \n",
      "                'Halfmoon Bay','Irvines Landing', 'Kelsey Bay', 'Lund',\n",
      "                'Maude Island E', 'Nymphe Cove', 'Patos Island','Pedder Bay',\n",
      "                'Point Atkinson','Point Grey','Port Renfrew',\n",
      "                'Powell River', 'Sandheads','Seymour Narrows','Sheringham Point',\n",
      "                'Tsawwassen','Tumbo Channel','Twin Islets','Victoria',\n",
      "                'Whaler Bay','Yorke Island']\n",
      "\n",
      "#Removed Port Townsend because it was recorded twice in the file. Which to use?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M2_amp=[]\n",
      "M2_pha=[]\n",
      "K1_amp=[]\n",
      "K1_pha=[]\n",
      "\n",
      "M2_amp_obs= np.zeros(numsta)\n",
      "M2_pha_obs=np.zeros(numsta)\n",
      "K1_amp_obs=np.zeros(numsta)\n",
      "K1_pha_obs=np.zeros(numsta)\n",
      "\n",
      "ts = 240\n",
      "  \n",
      "for stn in range(numsta):\n",
      "    print stations[stn]\n",
      "    fT1 = NC.Dataset(name+stations[stn]+'.nc','r')\n",
      "    time = fT1.variables[\"time_counter\"][:]/3600.  # want hours not seconds\n",
      "    ssh = fT1.variables[\"sossheig\"][:,0,0]\n",
      "\n",
      "    fitted, cov = curve_fit(double,time[ts:],ssh[ts:]) \n",
      "    if fitted[0] < 0:\n",
      "        fitted[0] = -fitted[0]\n",
      "        fitted[1] = fitted[1]+180\n",
      "\n",
      "    M2_amp.append(fitted[0]*M2ft)\n",
      "    pha = fitted[1]+M2uvt\n",
      "    if  pha > 360:\n",
      "        pha=pha-360\n",
      "    M2_pha.append(pha)\n",
      "\n",
      "    K1_amp.append(fitted[2]*K1ft)\n",
      "    pha= fitted[3]+K1uvt\n",
      "    if  pha > 360:\n",
      "        pha=pha-360\n",
      "    K1_pha.append(pha)   \n",
      "\n",
      "    #now the observations\n",
      "    location=stations_obs[stn]\n",
      "    M2_amp_obs[stn]=harm_obs.M2_amp[harm_obs.site==location]/100\n",
      "    M2_pha_obs[stn]=harm_obs.M2_pha[harm_obs.site==location]\n",
      "    K1_amp_obs[stn]=harm_obs.K1_amp[harm_obs.site==location]/100\n",
      "    K1_pha_obs[stn]=harm_obs.K1_pha[harm_obs.site==location]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "BrownBay\n",
        "CampbellRiver\n",
        "ChathamPoint\n",
        "CloverPoint\n",
        "Esquimalt\n",
        "FinnertyCove"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "FulfordHarbour\n",
        "GibsonsLanding\n",
        "HalfmoonBay\n",
        "IrvinesLanding\n",
        "KelseyBay"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Lund\n",
        "MaudeIslandE\n",
        "NympheCove\n",
        "PatosIsland\n",
        "PedderBay"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "PointAtkinson\n",
        "PointGrey\n",
        "PortRenfrew\n",
        "PowellRiver\n",
        "Sandheads"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "SeymourNarrows\n",
        "SheringhamPoint\n",
        "Tsawwassen\n",
        "TumboChannel\n",
        "TwinIslets"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Victoria\n",
        "WhalerBay\n",
        "YorkeIsland\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print M2_amp_obs\n",
      "print K1_amp_obs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.935  0.825  0.903  0.403  0.367  0.447  0.582  0.947  0.964  0.988\n",
        "  1.17   1.022  0.556  0.615  0.68   0.342  0.918  0.945  0.708  1.007\n",
        "  0.869  0.946  0.487  0.811  0.726  1.013  0.373  0.834  1.171]\n",
        "[ 0.679  0.846  0.654  0.642  0.643  0.708  0.753  0.872  0.88   0.88\n",
        "  0.577  0.889  0.811  0.77   0.79   0.627  0.862  0.906  0.453  0.904\n",
        "  0.837  0.692  0.548  0.834  0.811  0.904  0.627  0.847  0.558]\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Plotting - M2\n",
      "font = {\n",
      "        'size'   : 18}\n",
      "plt.rc('font', **font)\n",
      "\n",
      "fig=tidetools.plot_scatter_pha_amp(M2_amp,M2_amp_obs,M2_pha,M2_pha_obs,'M2',figsize=(14,6))\n",
      "\n",
      "ax_amp,ax_pha = fig.axes\n",
      "min_value, max_value = ax_amp.set_xlim(0, 1.2)\n",
      "ax_amp.plot([min_value, max_value], [min_value, max_value], color='red',lw=2)\n",
      "\n",
      "min_value, max_value = ax_pha.set_xlim(0, 360)\n",
      "ax_pha.plot([min_value, max_value], [min_value, max_value], color='red',lw=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "[<matplotlib.lines.Line2D at 0x4f262d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DEhERmbFnwhJWrMCCRTJ88IFinAdbWyAwENi9G7C3N3WxxsNhxYmIDGDzZiA8\nHMjKAiQJGGifgGWPIiHBcsMS753Gw2tNREanJSwt/FaGkSMVn/MpOTsD33wDDB5sulJ14bDiREQW\n4OZNRTujDEsA0EckYKmFhyUiIrJiWsJSAWQYO1Y9LAFAdjZw+bJpyjQVkwamGTNmoFevXvD394dM\nJkONGjWKdZzffvsNbdq0Qbly5eDm5oa33noLaWlppVssEdFzZGUBLVoAP/zwJCz1RQLiEAkZBH4O\nZFgyV6dPn0bfvn1Rp04dVKhQAS4uLggICMCIESNw4cIFrduHhYWhUqVKKFeuHNq3b4+dO3dqPXZB\nQQHmzp2L2rVrw8nJCT4+Phg3bhyylH9JiIhMSUtYgkyG3FzFomfZ2QFt2xq/TFMyaZc8mUwGNzc3\nNGnSBAcPHsQLL7yA8+fPF+kYP/30E8LDw9G4cWO8++67uHv3LubNmwcbGxscPHgQnp6eGvuwqwMR\nGUJyMtCzJ/DggeL7p8PSTNtoNNsSg44dTVtjSVjzvXPHjh2IiYlB69at4e3tDVtbWxw7dgwrVqyA\nra0tDh8+rPpQ79y5c2jRogXs7e0xZswYlC9fHkuXLsWJEyewefNmdHzml/z+++8jNjYWPXr0QGho\nKP755x/ExsaiXbt22LZtGyRJ0qjHmq81EZkRHWFJqUUL4PBhQC5/ssunnwIzZpigVj0Y7N4pTOjC\nhQuqPwcGBooaNWoUaf/c3Fzh5eUl/Pz8xMOHD1XLjxw5ImxsbMSQIUO07mfi0yYiK7V1qxCurkIA\nQvRFvJBDEgIQi9yixfr1pq6u5MrivXPt2rVCkiTx+eefq5b16tVL2NraiqNHj6qWZWZmCl9fX1Gr\nVi21/U+cOCEkSRLh4eFqy2NjY4UkSSIxMVHrzy2L15qIjGzrViFsbRWNVmSkEHK5xiY3bwoRFCSE\nk5MQXl6KXcyZoe6dJu2S5+fnV6L9d+3ahWvXrmHw4MFwdnZWLW/YsCGCg4ORlJQE+dORmIjIgNq2\nBSpXBiJlT54sJdWMxrD0GISFmbo6Kg4fHx8AgP3j4aAePnyIX375BcHBwWjQoIFqOxcXFwwePBhn\nzpzBgQMHVMtXr14NABgzZozacd999104OzsjISHB0KdARKTpOU+WlCpXBlJSFN3Mr1wBXnnF+KWa\nA4se9EHZKLVu3VpjXcuWLXH//n2cOXPG2GURURn1zz/A7EYJ+L5AEZZSXo5G95N8Z8mSPHr0COnp\n6bh8+TIQfiRRAAAgAElEQVS2bt2KoUOHwsfHB++88w4A4NixY8jNzdXZ7gDAwYMHVcsOHDgAGxsb\ntGjRQm1bBwcHNGzYUC1cEREZhZ5hiZ6w6Ktz9epVAEC1atU01imXXblyxag1EVHZlJQEzG+dgG4/\nKUbDW1YlGm12xJSpeSqswdKlS+Hh4QEfHx907twZdnZ22LNnD6pUqQKg6O3O1atX4e7uDjs7O63b\np6enI1/bW9VERIbAsFQsFn2FlCMMOTg4aKxzdHRU24aIyJB2DErAd3mKJ0vTEI0xmTFYu9bUVVFR\nde/eHdu2bcOGDRswadIknDt3DkFBQaoBiYra7mRlZWndVtf2REQGw7BUbLamLqAklO8tPXr0SGNd\nTk6O2jZERAaTkIBFWU/C0meIgUM+cPu2qQujoqpWrZrqSdGbb76Jnj17onnz5hg7dix+/vnnIrc7\nzs7OSE9P1/qzcnJyIEkS2ykiMjyGpRKx6MDk5eUFQNH9oVatWmrrlF0itHWbAIDJkyer/hwcHIzg\n4GCD1EhEVi4hAYhUhKUZsmh8VqB4Z0kmA4KCTFxbCaWkpCAlJcXUZZhU/fr10ahRI+zevRuAervz\nLG3tjpeXF1JTU5GXl6fRLe/KlStwd3eHra32ppjtFBGVCisOS8Zqpyw6MClfot23bx9CQkLU1v31\n11944YUXEBAQoHXfpxsiIqKievQIyFycgEpjIiEJgayx0dh+LAZ2u4EXXgCWLAEaNjR1lSXz7D/S\np0yZYrpiTCg7Oxuyx/+4qF+/PhwcHLBv3z6N7f766y8AQLNmzVTLWrRogeTkZPz9999o+9RMjzk5\nOThy5EihIYjtFBGVmBWHJcB47ZTFXLHr168jNTUV2dnZqmVBQUHw9PTEsmXL8PDhQ9Xyo0ePIiUl\nBb169YKNjY0pyiUiK/bTT8Aw1wRUfF8Rli4PiIbz1zHYtg3IzQVu3QK6dzd1lVQUN27c0Lp8586d\nOHHihGoy2nLlyqFr165ISUnBsWPHVNtlZmZi2bJlCAgIQPPmzVXLIyIiIEkS5s2bp3bcpUuXIjs7\nG3379jXA2RARodCwlJUFvPceEBgIvP46kJZm2lLNnfR4kieTiI+Px8WLFwEAsbGxyMvLwwcffABA\nMUdTv379VNtGRUUhLi4OO3fuRNBT/VzWrVuHiIgINGzYEIMHD8b9+/cxd+5c2NjY4NChQ/D09NT4\nuZxBnYiK6/JlYJJ/ApY9NcDDnAoxuH4d0PFuv9Ww5ntn9+7dcf36dYSEhMDHxwc5OTk4dOgQkpKS\n4Obmhr1796JGjRoAgHPnzqFFixaws7PD2LFj4erqiqVLl+LkyZPYtGkTXnlmopLRo0dj/vz56N69\nO0JDQ3Hq1CnExsaibdu22LFjh9Z6rPlaE5ERPOfJ0muvAbt3Azk5gI0NUKkScOYMUKGCCWsuBYa6\nd+oMTB06dIAkScU+8Jw5c9C4ceNCt+nQoQN27dqlKOTxz1KWExwcrNaQDBw4UBWY2rdvr3acTZs2\nYdq0aTh27BgcHBzQqVMnzJw5U9W4PYsNEREV17GPE1DvK/UBHlxcgGPHAH9/U1dnWNZ871y7di3i\n4uJw9OhR3Lp1C5Ikwd/fH6Ghofj4449RuXJlte1TU1Px6aefYteuXcjNzUXTpk0xefJkje7hAFBQ\nUIB58+ZhyZIlSEtLQ+XKlREREYGpU6fqHPDBmq81ERnYc8LS/fuAm5titZKrKxAXB4ufZN3ogUkm\nk8Hd3b3Io/cUFBTg8uXL2LZtm9aGwxywISIifcjlwNWrik/cXF0BJCRA9FfMs6QMSwDg6Kjohleu\nnGnrNTTeO42H15qIikWPd5ayshTv2j4bmFavVnTPs2SGuncWOujD3Llzi9y/Oj09HR4eHiUqiojI\n1M6dA0JCgPR0IC8PWBeWgDfXKcLSrrbRmHE4BuVtFQ3OokXWH5aIiMjM6TnAg7Mz0K8fsGaNIjzZ\n2wNVqijaPNJOZ2Cyt7fXOdRpYSRJgr29PQdbICKLFhameF+poADoiwS8sTYSgACioxEUE4O/jgMX\nLihemH3xRVNXS0REZVoRR8NbtkwxkmtKClCjhuLPy5YBrVsDTw30SY+ZdNAHU2FXByJ6HltbRZe8\nvkhAHBTvLP3dMRott8WYujST4b3TeHitiUhvJRg6XC4HXn0V+PtvxZ8lCVi4EIiKMmzJhmKoe6fF\nDCtORGRMVauqh6VZdtG4NrLshiUiIjJDJZxnacsWYP9+4OFDxYh52dnA8OEAP69Rx8BERKTF9oHq\nYelgWAy6dTN1VURERI+VwqS0t25phqO8PMXk7PREkV5SSktLw5IlS3D27Fncvn1b6yMvXXNKEBFZ\njIQE1IpRvLN0NiIaQWNj8FELRVcFIiIikyuFsAQAbdqoByYbG8W7uY6OpVirFdA7MP3yyy8IDw9H\nfn4+ypcvjwpaZrYqybxNRERmISFB0fgIxQAPNWNiUNPUNRERESmVUlgCgIAAxWh5AwYAd+8CjRsD\nP/9cyvVaAb0HfahTpw5yc3OxYcMG1K9f39B1GRRfpiUirZ4JS4jhO0tP473TeHitiUirUgxLzxLC\n8ntSmHzQh7S0NIwePdriwxIRkVYMS0REZM4MGJYAyw9LhqR3lzw/Pz884htgRGSNGJbM3pQpU0rU\n7bt///6oUaNGKVZERGREBg5LVDi9u+QtXrwYc+bMweHDh1HOwqe0Z1cHIlJhWNKbKe+dshL8w0CS\nJCQnJyPEgqaxZztFRCoMS3oz1L1T7ydMQ4cOxe3btxEYGIgBAwagRo0asLGx0dguMjKyVAskIioN\nDx8CBw4oRv5p1kwxMe3TYelbt2h8OC8G9bYpXoD19TV1xfSsuXPnolsRx3a/c+cOmjZtaqCKiIgM\njGHJLOgdmK5du4b169fj0qVLmDZtmtZtJEliYCIis/Pff0Dr1kBmJlBQANStC+wZmgD7wYqwNNsh\nGh/dVjxZ2r9fMUrQjRuAnZ2JCyc1lStXhp+fX5H2sfQeEURUhjEsmQ29A9OwYcNw5MgRjB07Fm3b\ntkXFihUNWRcRUakZMkQRgORyxfd1DyfA7h3FPEvne0fjs/Xq3fDu3AG+/hr45BPj10ra7du3Dy+9\n9FKR96tYsSL27duHwMBAA1RFRGQgDEtmRe93mMqXL493330Xc+bMMXRNBse+4URli78/cOGC4s99\nkYA4REIGxTtLh3rEoEULxZOnp7VrB+zebfxazRnvncbDa01UhjEsFZvJhxV3cHAo1qd7RESm1rw5\nYG+vHpb2v6IY4KFJE6ByZc193N2NXycREZVxDEtmSe8nTIMGDcK9e/fw448/Gromg+Mnd0RlS0YG\nMKdxAr74TxGWfqoTjW7HY6Act+bwYaBNGyA3V/G9iwvw559AvXqmq9kcmdO9c+XKlYUOMy5JEpyc\nnODj44MmTZrA1lbvHuhmwZyuNREZCcNSiRnq3ql3YMrIyEDnzp3RsmVLjBkzBv7+/iWaE8OU2BAR\nlTEJCRCRkZCEQOb70Sg3T3Po8NOnFYPmSRLQvz/AB+qazOneWZRhxt3d3TFt2jQMGTLEgBWVLnO6\n1kRkBAxLpcLkgamwxklZnCRJkCvfqjZjbIiIyhDOs1RqzOneuW3bNowfPx4ZGRkYNmwYAgICAACn\nT5/Gt99+Czc3N0yYMAHnzp3DggULcPHiRSQlJSE8PNzElevHnK41ERkYw1KpMfk8TPoMF26pT5yI\nyEoxLFmtffv2IScnB8eOHYOLi4vauvfeew+tWrXCkSNHMHnyZAwdOhQNGjTA3LlzLSYwEVEZwbBk\nEfR+wmRN+MkdURnAsFTqzOne6evri1GjRmHcuHFa18+ePRuxsbG4ePEiAGDKlCmYPXs2Hjx4YMwy\ni82crjURGQjDUqkz+Sh5REQWg2HJ6t28ebPQLuByuRw3btxQfe/p6Yn8/HxjlEZE9HwMSxaFvxki\nsigXLwKDBwPdugFxcYpMpOapsJTaMxp7u8RobkMWLyAgAMuXL8e9e/c01t29exffffcdatWqpVqW\nlpYGDw8PY5ZIRKSdjrBUUABMmQLUrAk0aqTYjMyDzi55dnZ2iI+Px9tvv12kA96+fRuenp7YunUr\ngoODS6PGUseuDkSW6fp1IDAQuHcPkMsBZ2fgs8+ATz99vMFTYWmWXTRinGIglwOdOwNr1ypGwKPi\nM6d7548//ohevXqhSpUqiIqKUoWj1NRUfP/997h58ybWrFmD8PBwyOVy1KxZE61bt0ZiYqKJK9eP\nOV1rIipFz4SlHf1XYN9fMnh5AWfOALGxQFaWYlNnZ2DnTqBFC9OWbEmMPuiDXC5HQUFBsQ6an59f\n7H2JiHT54Qfg4UNFWAIUjcrMmY8D01Nhaa5zND7JigHyFNv9/juwcSPQtavJSqdS1rNnT6xevRof\nfPABZs6cqbbO09MTq1atUg3wUFBQgM2bN/MJExGZ1jNhaW6DFZjYTYacHMDREcjLU3wpZWUBa9Yw\nMJmDQkfJGzt2LCZOnFikA1rCsOJEZJny84FnP4uRy6EWlgrGR2PczBiN/R6/+09WJCIiAj179sSh\nQ4dw4cIFAECNGjXQrFkz2ChnJYaix0Tt2rVNVSYRkUZYki9bgU/LyVQTpmdlafaCsLEBnJyMXypp\n0tklryTd6SRJwpw5c9CkSZNiH8OQ2NWByDKdPw80bAhkZiq+d3YGvm2bgP7J6gM8BAYCqalPwpWz\nM7B9O9Cqlelqtwa8dxoPrzWRFdHyzlJWjgzlyz/pMQEonjIVFAC5uYrxH8qXB44eBXx8TFe6pTH5\nxLXWhA0RkeX63/+ADz8EMjKASf4J6L4hEtIzo+GdPw907Ahcu6bIUbNmAe+/b+LCrYA53jt37dqF\nrVu34ubNm/jwww9Ru3ZtZGZm4vDhw6hfvz4qVqxo6hKLxRyvNREVQyGj4TVvDhw5olgFAC4uwLff\nAjt2KMLS2LGAr68Ja7dADEyliA0RkRV4ztDhQgC3bikaHUdHE9VoZczp3imXy9G7d2+sW7cOgKK2\n5ORkhISEIDs7G9WqVcOHH36ICRMmmLjS4jGna01ExfScocNv3QIiIoC//wY8PICVK4H27U1YrxXg\nPExEREp6zLMkSYoGiGHJOs2cORM//fQTvv76a5w6dUqtgXRyckL37t2xefNmE1ZIRGWaHvMsVa6s\neJr08CFw4QLDkjljYCIiy8JJaQlAXFwc+vfvjzFjxsDNzU1jfe3atXH27FkTVEZEZR4npbU6/O0R\nkeVgWKLH0tLS0KZNG53rK1SogDt37hixIiIiMCxZKf4GicgyMCzRU1xdXZGRkaFz/blz51C5cmUj\nVkREZR7DktXib5GIzB/DEj2jbdu2SEhI0DpJ+p07d7B8+XJ06NDBBJURUZnEsGTV+JskIvPGsERa\nTJgwAWfOnEFISAg2btwIADhy5Ai+/fZbNG7cGJmZmfj0009NXCURlQkMS1avSL/N//77DwMHDkS1\natVgZ2eHHTt2AABu3bqFgQMH4sCBAwYpkois34ULQFAQ4OkJvPaaYg4lhiXSpVmzZvjpp5+QmpqK\nQYMGAQDGjRuH9957Dzk5OdiwYQMCAwNNXCURWT2GpTLBVt8NL1y4gJYtW+LRo0do2bIlrl27plpX\nuXJlHDx4EMuWLUPz5s0NUigRWa+sLODll4EbNxSznKenA7MbJWD2Lc1JaYmUXn/9daSlpSE5OVk1\ntHhAQABee+01ODs7m7o8IrJ2DEtlht6BacKECZDJZDh+/DicnZ3h4eGhtr5Lly6qbhFEREVx5Ihi\nHgrl6ygR+Qn46mYkJDAsUeEcHR3RtWtXdO3a1dSlEFFZwrBUpuj9m922bRvee+89+Pj4aF3v6+uL\nS5culVphRFR2ODsDcrniz32RgDhEQgaBeyMZloiIyMwwLJU5ej9hun//Pry8vHSuz83NRX5+fqkU\nRURlS4MGii55XjsT8F2eIiytrxuN7rEMS6RQo0YNSJKk9/ZCCEiShPPnzxuwKiIqcxiWyiS9A5O3\ntzdOnjypc/3ff/+NmjVrlkpRRFS2yGTApt4JsNmq6IZ3pEs0uv3KsERP+Pr6aiy7cuUKzp07B1dX\nV/j7+wMAzp8/jwcPHuDFF1+Et7e3scskImvGsFRm6R2YevbsiUWLFmHQoEEaT5p+/PFHrFmzBlOm\nTCn1AomoDEhIgO2gSODxO0uN2A2PnpGSkqL2/aFDh/DKK69g7ty5GD58OOzt7QEAjx49wqJFizB1\n6lQkJSWZoFIiskoMS2Wa3r/p6OhoVK9eHa1atUK/fv0AADNnzkSrVq3Qq1cvNGzYEB9++GGRfnhB\nQQHmzp2L2rVrw8nJCT4+Phg3bhyysrL02j8/Px+LFi1C8+bN4ebmhvLly6NevXr44osv8ODBgyLV\nQkSGJwTw2WeAiwvg5AQMGwbIV3LocCq6cePGoVevXnj//fdVYQkAHBwcMGbMGISHh2PcuHF6H+/M\nmTOYNGkSWrVqBQ8PD5QvXx6NGzfG9OnTn9smLVq0CDKZDDKZDBkZGRrrS9rWEZGJMSyRKIK7d++K\n0aNHCzc3NyFJkpAkSVSsWFGMHDlS3Lt3ryiHEkIIMXr0aCFJkujZs6dYtmyZ+OCDD4SdnZ0ICQkR\nBQUFz91/4MCBQpIk0alTJzF//nyxePFi8fbbbwtJkkSrVq107lfE0yaiUrJkiRDOzkIo0pEQA+3i\nRYFi4HAhoqONWkt+vhBffSVEaKgQI0YIkZ5u1B9vkczp3uni4iIWLVqkc/3ChQuFi4uL3sf75JNP\nhKurq+jXr5+qPYmIiBCSJImGDRuK7OxsrftduXJFlC9fXri6ugqZTCZu376tsU1x2jpzutZEZdrW\nrULY2iraqchIIeRyU1dEhTDUvbNYRy0oKBA3btwQ169fF/Ji/sU5ceKEkCRJhIeHqy2PjY0VkiSJ\nxMTEQvfPzs4WNjY2olmzZhrr+vXrJyRJEkePHtW6LxsiItPo0uVJWOqLeCE3UVgSQoioqCfhzc5O\nCD8/ITIzjV6GRTGne6ebm5sYMGCAzvWRkZHCzc1N7+MdPHhQ3L9/X2P5xIkThSRJYv78+Vr3CwsL\nE02bNhX9+/cXkiRpBKbitnXmdK2JyiyGJYtjqHtnsZ4nSpIEDw8PVKlSBbJiPpJcvXo1AGDMmDFq\ny9999104OzsjISGh0P3t7Ozg4OCAKlWqaKzz9PQEALi4uBSrNiIyDE9PwMZGfejwxBrG74aXkwPE\nxysmzAWAvDzg9m1FrwuyDN27d0dcXBymTJmCzMxM1fIHDx5g8uTJiI+PR1hYmN7Ha9q0KVxdXTWW\nv/XWWwCgddCj9evX49dff8W3336rsy0saVtHRCZSSt3w8vKAf/9VTMhOlkvnoA+7d+8u1gHbt2+v\n13YHDhyAjY0NWrRoobbcwcEBDRs2xIEDBwrd38bGBpMmTcKECRMwa9Ys9OjRA7a2tkhJScGiRYvQ\nv39/vPjii8U6ByIyjMmTAbukBCzIVISlr+yj0cUEo+HJ5YC2EaqVc0GR+Zs1axaOHj2KKVOmICYm\nRvVB2dWrVyGXy9GkSRN89dVXJf45ly9fBgCND+fu37+PkSNHYtiwYWjWrJnO/Uva1hGRCZRSWDp7\nFggOBu7eVQSnMWOAmTNLv1wyAl2PnpTvKD37JZPJdC6TyWR6P9qqV6+eqFq1qtZ1vXr1EpIkiby8\nvOceZ8mSJcLR0VGtlkmTJhW6TyGnTUSGFB8vCiRFN7wDr0aL//4zXSlduwrh5KToaWFjI4SHhxAZ\nGaarxxKY270zNzdXLF68WISGhopatWqJWrVqidDQULF48WKRm5tb4uPn5+eL1q1bC3t7e3HmzBm1\ndcOGDRNeXl6qbnwDBgzQ2iWvuG2duV1rojKjFLvhNWggxOMmTwBCuLgIsXlzKdZKGgx179T5hGn5\n8uXPBivExsbi33//Rd++fVGnTh0AwD///IPExEQEBARg1KhRege1rKwsODg4aF3n6Oio2qZ8+fI6\njzFr1iyMHz8e4eHh6NmzJwBg3bp1+OKLL+Dg4IDo6Gi96yEiA0tQjIYnPR4Nr5mJR8NbswYYPx5I\nSQF8fYFvvgEqVjRpSVREdnZ2GDJkCIYMGWKQ448ZMwZ//fUXZsyYgZdeekm1fO/evViyZAkSExO1\nduN7Wmm0dURkJKU8Gl5qqiIqKT16BBw9CnTuXAq1klHpDExRUVFq33/zzTe4desWUlNTUa1aNbV1\nn332GVq3bo379+/r/YOdnZ2RrqNDZ05ODiRJgrOzs879jx8/jvHjxyMiIgKJiYmq5W+99RZ69+6N\nSZMmITw8HAEBAXrXREQGkmB+Q4c7OgJz55q6CjJXn332GRYsWIChQ4fik08+US3Pzc3FkCFD8Mor\nryAiIuK5xylpW0dERmKAocO9vYHz55987+AA1KxZwjrJJPSeuHb+/PkYOnSoRlgCAG9vbwwdOhTz\n58/H6NGj9Tqel5cXUlNTkZeXBzs7O7V1V65cgbu7O2xtdZe3Y8cOCCHQq1cvjXXh4eFISkrC3r17\ndQamyZMnq/4cHByM4OBgveomoiIyw7BE+klJSdGYMNZUEhMT0aZNG/j5+RVpv9zcXKxbtw4dO3bU\nOkiQNpMnT0ZMTAwGDRqERYsWqa1bsGABTp8+ja+//hpnz55VLVfO/Xf+/HncvXsX/v7+AErW1rGd\nIjISA82ztGYN0LGj4s95eUDXrkCPHiU+LD3FaO2Uvn33HBwcxNdff61z/Zw5c4SDg4PefQGVQ7Xu\n2bNHbXl2drZwdnYWXbp0KXT/r776SkiSJNasWaOx7ocffhCSJIklS5Zo3bcIp01EJREf/6QDt46h\nw7/7ToiGDYVo0kSI9euNXB8ViSnvnZIkiVWrVhV5v1u3bglJksT27dv12v7zzz8XkiSJgQMHal0/\nZswYne/4Kr/KlSun2r64bR3bKSIjMfDQ4RkZQuzYIcSRI0LoMcUolZCh7p16P2Hy8/NDfHw8hg8f\nrup3rZSdnY34+PgiffIXERGB6dOnY968eWjbtq1q+dKlS5GdnY2+ffuqll2/fh13796Fr68vnJyc\nAEA14tDKlSs1njKtXLkSANC8eXO96yGiUqbHk6XvvwdGjXoyvHefPsD69cBrrxm3VLIMp06dKvII\nrnfv3tV726lTp2Lq1KmIjIzUeI9XaeDAgWjXrp3G8vnz5yMlJQUrVqxAxadehitKW0dERmagJ0tP\nq1gR6NChVA9JpqBvslqyZImQJEkEBgaKhQsXih07dogdO3aIBQsWiLp16wpJksTixYuLlNZGjRol\nJEkSPXr0EEuXLlXNft6hQwe17ZSjD6WkpKgt79Kli5AkSbRv317MnTtXzJ07V7Rr105IkiQiIiJ0\n/twinDYRFYceT5aEEKJ58yejBym/wsKMWCcViSnvnc97qvO8r+c9YZo/f76QJEn4+vqKuLg4ER8f\nr/aVnJxc6P66RskTQv+27mlsp4gMjJPSWiVD3Tv1fsL07rvv4uHDh5gwYQJGjBihts7JyQmzZ88u\n8khF8+bNg5+fH5YsWYJNmzahcuXKGD16NKZOnaq2nSRJqq+nrV+/Hl999RVWr16N8ePHAwACAgIw\na9YsfPDBB0WqhYieEELxgdszr1zopwjvLNnbay575gE2EQDNkVuLQpIk1K1bt9BtDh48CEmScOnS\nJQwYMEBjfXBwMDp16lToz3i2jVLSt60jIiMxwpMlsi7S4zSmt7t372Lr1q04/3jYjxdffBGvvPIK\nKlSoYJACDUGSJBTxtInKjPXrgQEDgMxMoG5d4LffAB8fPXcu4gAPyclAt25Adrbie2dnYPduoGnT\nkp0DGQbvncbDa01kIAxLVs1Q984iByZrwIaISLvUVEVYUb5TJJMBtWoB//yjx87FHA1vzx7g228B\nW1vFLOiNGxe/fjIs3juNh9eayAAYlqyeoe6denfJIyLr9/ff6m1HQQFw5oziCdDj8Va0K8HQ4e3a\nKb6IiIgMhmGJSkDvwFSjRg2d/bMBQAgBSZJUXfWIyPJom6bG3v457xVxniUiIjJnDEtUQnoHJl9f\nX41l+fn5uHDhAq5du4aaNWtqndSWiCzHq68CQUHArl2Kp0sAsHgxoPOzEoYlIiIyZ0UMS0Io2j8b\nGyPWSGavVN5hWr16NT744AOkpKSgVq1apVGXQbFvOJFuBQWKgR6uXQNatgQaNNCxIcNSmcN7p/Hw\nWhOVgiKEJSGAKVOAGTMAuRzo2hVITHxOd3QyO2Y/6MPw4cPx33//YdOmTaVxOINiQ0RUQgxLZRLv\nncbDa01UQkV8srR6NTB48JNBjxwdFSPGfvutkeqlUmGoe2epdeBs1KhRkWdgJyILxLBEZiQzMxPJ\nyclYtWoVrl+/bupyiMgcFOOdpS1bnoQlAMjJAbZuNXCdZDFKLTAdPXoUMr5AR2TdGJbIjCxcuBDV\nqlXDa6+9hsjISPzzePz7GzduwMHBAUuWLDFxhURkdMUc4KF6dc3J1KtWNVCNZHH0HvRB19OjjIwM\nJCcnY8mSJejRo0epFUZEZoZhiczIjz/+iJEjR6Jbt27o2rUrBg8erFpXpUoVhIaG4ueff8aQIUNM\nWCURGVUJRsP78ENg1SogPf3JoA8LFxq4XrIYegem4ODgQtd36tQJsbGxJa2HiMwRwxKZma+++grB\nwcFYv3490tPTNdY3bdoUy5YtM0FlRGQSJRw6vGJF4Phx4OefFd3xXnsN8PY2YL1kUfQOTMuXL9dY\nJkkSKlWqhICAAIsYHY+IioFhiczQ8ePHMXPmTJ3rPT09cePGDSNWREQmU0rzLJUrB/Tta4D6yOLp\nHZiioqIMWAYRmSWGJTJTNjY2KFBOFqbFtWvX4OLiYsSKiMgkOCktGYHef6M6dOiA7du361y/c+dO\nhISElEpRRGQGGJbIjDVo0AC///671nUFBQVYu3YtmjdvbuSqiMioGJbISPT+W7Vr165CuzfcuHED\nKQraANsAACAASURBVCkppVETEZkawxKZuVGjRmHz5s2YOHEiMjIyAAByuRypqakIDw/HiRMnMHr0\naBNXSUQGw7BERqR3l7znuXfvHhwcHErrcERkKgxLZAEiIiJw/PhxTJ8+HTNmzAAAdO7cWTVh4eTJ\nk9GlSxdTlkhEhsKwREZWaGA6evQojh49qmqA9uzZg/z8fI3tbt++jYULF6Ju3bqGqZKIiu3SJeDc\nOeDFFxXzTBSKYYksyLRp09CjRw+sWrUKp06dghACAQEB6N+/P5o1a2bq8ojIEBiWyAQkoUxDWkye\nPBlTp07V60Curq744YcfEBoaWmrFGYokSSjktImsxnffAaNGKSbjy80F5s8HBg3SsTHDEj0H753G\nw2tNpAXDEj2Hoe6dhQamtLQ0pKWlAQBCQkIQHR2NTp06aRRWrlw5BAYGwtHRsdQLNAQ2RFQW3LgB\n+Pkp5pNQcnQELl4EPDye2ZhhifRgCffOQ4cOISMjA+3atbOYNkkbS7jWREbFsER6MNS9s9AueX5+\nfvDz8wOgmIcpKCgINWrUKPUiiKj0XbyoeLL0dGCyt9cSmBiWyALNnj0bu3btwq+//qpa1rt3byQl\nJQEA/P39sXfvXlSpUsVUJRJRaWFYIhPT+29bVFQUwxKRBfH3V7QtT8vPVyxXYVgiC/XDDz+g+lMv\n5e3YsQNJSUno3bs3pk+fjuvXrxc6sS0RWQiGJTIDOp8wrVy5EpIkoV+/fpDJZIiLi9PrgJGRkaVW\nHBEVn7s7sGqVYtZyGxtALgcSEwE3t8cbMCyRBUtLS1ObUH3Dhg2oWrUq4uPjIZPJkJ6ejl9++QVf\nf/216YokopJhWCIzofMdJplMBkmSkJ2dDXt7e8j0+AsqSRLkcnmpF1na2DecypIHDxQj5VWvDri6\nPl7IsETFYE73TicnJ8yfPx/vvPMOAKBevXpo0qSJ6sO97777DqNGjUJWVpYpyyw2c7rWRCbBsETF\nYPR3mHbs2AEAsLOzU/ueiCyLqyugNuL/U2GpYHw0ktvH4EYc0LIlUKuWycokKhIvLy8cO3YMAHDx\n4kX8888/GDt2rGr9nTt3ODcgkaViWCIzozMwBQcHF/o9EVmgZ8JS2IkY7IxVrJLLFV34unc3bYlE\n+njzzTexYMECyOVy/PXXX7C3t8frr7+uWn/y5EnVoEVEZEEYlsgMFTqsuLViVwcqk57phrexdQx6\n9wYyM59s4uoK3LsHSJLpyiTzZU73zoyMDPTq1Qs7d+6Eg4MD5s2bh6FDhwIAsrKy4OnpiXfeecdi\n32Eyp2tNZDQMS1RCRu+St3v37mIdsH379sUuhogMRMs7S9eWAgUF6ptlZiraqcc9cYnMVqVKlbB9\n+3bcu3cPTk5OsLe3V62TJAm7du2Cj4+PCSskoiJhWCIzVuigD0U+GAd9IDI/OgZ4OHYMaN0aUL4T\nL5MBdeoAJ06YsFYya7x3Gg+vNZUpDEtUSoz+hGn58uWl/sOIyMgKGQ2vQQNg8WLg3XeBvDzgpZeA\nTZtMWCtRMeTn5+P06dO4c+cOCp59ZAr2eiAyewxLZAH4DhORtdJz6PCCAiAnB3B2NnJ9ZHHM7d75\n5Zdf4ssvv8T9+/fVlivrtJReD9qY27UmMgiGJSplhrp38m8lkTUqwjxLMhnDElme7777DtHR0Wjc\nuDGmTZsGABg7diw+/vhjVKxYEc2aNWNPCSJzxrBEFqRIT5iEEFizZg3Wr1+PCxcuAAD8/f0RFhaG\niIgIgxVZ2vjJHVk1TkpLBmJO985mzZrBzs4O+/btw+3bt+Hh4YFt27YhJCQE165dQ6NGjTB9+nTV\nxLaWxpyuNVGpY1giAzH5E6aHDx/ilVdeQe/evbFmzRqcOXMGZ86cQVJSEnr37o2QkBA8fPiw1Ask\noiJgWKIy4tSpU3jrrbcgSRKkx+PgK7vfeXp6YsiQIfi///s/U5ZIRNowLJEF0vtv6IQJE7Bjxw6M\nHj0aV69exZ07d3Dnzh1cuXIFo0ePRkpKCqKjow1ZKxEVhmGJyhAbGxu4uLgAgOq/t2/fVq339fXF\nmTNnTFIbEenAsEQWSu+/pUlJSQgPD8e8efNQtWpV1XJPT0/MmzcPPXv2xJo1awxSJBE9B8MSlTHV\nq1dXdQ13dHSEt7e32vyBBw8eRKVKlUxVHhE9i2GJLJjef1Pv37+PkJAQnes7dOiAe/fulUpRRP/P\n3p2HRVX2bwC/D6DIsEiIuCEimmvmlpI7bhiWiiv5qoimkq+JW/a6pZaVLfZzGZcSNfcyU9NKTVDR\n1Nwyc19QNBUzyYUdBJ7fHxOj4wAOzHLOzNyf65qrOMvwfQ7O83BzznkOFQPDEtmhdu3a4ccff9R+\n3a9fP3z55ZcYMmQIBg8ejOjoaHTt2lXGColIi2GJrFyhz2F6WoMGDXD58uVC18fHx+PFF180SVFE\nZCCGJbJTUVFRaNiwIdLT06FSqTBz5kxcunQJq1atgiRJCA4Oxscffyx3mUTEsEQ2wOBZ8mJjY9Gz\nZ0+sW7cO3bt311m3detWDBw4EFu3bi3yLJRScPYhsgkMS2Rh1tB3PnjwAI6OjnB3d5e7FKNYw7Em\neiaGJbIwc/WdBgemIUOG4MSJEzh9+jTq1KmDunXrAtDMVHTx4kW88MILaNq0qd5+SnwOBgcisnoM\nSyQD9p2Ww2NNVo9hiWQge2ByKOE/8ry8vBLtZ04ciMiqMSyRTJTYd6anp+PatWv4559/Cqytbdu2\nMlRlPCUeayKDMSyRTMzVdxp8D5MSgw+R3WFYIgKgeTbg+PHj8dVXXyEnJ6fAbSRJ0j6biYgshGGJ\nbJDBgYmIZMawRKQ1duxYLF++HF27dkX79u1Rrlw5uUsiIoYlslEGX5JnS3ipA1kdhiVSACX1nd7e\n3ggODsb69evlLsUslHSsiQzCsEQKYK6+s1j/kg8ePIj//Oc/aN68OWrUqIGAgADtq3r16ggICCjW\nN8/Ly8PcuXNRp04duLi4wM/PD2+//TbS09MNfo+cnBwsWLAATZo0gZubGzw9PdG0aVMsXbq0WLUQ\nKRbDEpGezMxMtG/f3qTvOXv2bPTt2xcBAQFwcHBA9erVi9x+586d6NKlC6pUqQKVSoWaNWtixIgR\n2gfqPskU4x2RYjEskY0z+JK86OhoREZGwtnZGbVr10bVqlX1tpEkqVjffNy4cVCr1ejVqxcmTpyI\nc+fOYcGCBfj9998RGxv7zPfLzs5G9+7dERcXh4EDB+K///0vcnJycOnSJfz555/FqoVIkRiWiArU\ntGnTIp8NWBJTp05FuXLl0KRJEzx8+LDIMWj16tWIiIhArVq1MGbMGHh7e+PMmTNYunQpNm3ahNOn\nT6Ny5cra7Y0d74gUi2GJ7IEwkL+/v2jcuLG4e/euobsU6cyZM0KSJNGnTx+d5Wq1WkiSJNavX//M\n95g2bZpwcnIScXFxxfrexWg2kXzWrBFCkoQAhJgyRe5qiBTVdx46dEiUK1dOHD161GTvmZCQoP3/\n+vXri+rVqxe6batWrYSzs7P4559/dJYvW7ZMSJIk5s2bp11WkvFOSceaqFC7dgnh5KQZp8LDhcjN\nlbsisnPm6jsNPsN0584dTJw4Ed7e3iYJal9//TUAzY27Txo+fDgmTZqEtWvXon///oXun5aWhvnz\n5yM0NBTt2rWDEAKpqalW/7BCIgA8s0T0lCFDhuidhalatSpatGiBFi1aICAgAI6Ojnr7FedZgP7+\n/gZv6+rqCmdnZ3h6euosr1SpEgDAzc1Nu8zY8Y5IkXhmieyIwYGpTp06uHfvnsm+8bFjx+Do6Ijm\nzZvrLHd2dkbDhg1x7NixIvf/5ZdfkJqaiiZNmmDMmDFYsWIF0tLS4O3tjeHDh+P9998vcPAkUjyG\nJSI9q1atKnTdwYMHcfDgwQLXmevh6ZMnT8arr76KwYMHY+LEiShXrhzOnDmDCRMmoF69enj99de1\n2xo73hEpDsMS2RmD/3VPnToVixcvxq1bt0zyjRMTE+Ht7Y1SpUrpratSpQqSkpIKfbYGAFy8eBEA\nMG/ePGzZsgVz5szBt99+i5YtW2L27Nl44403TFInkUUxLBEVKC8vr0QvcwkKCkJsbCz27t2LRo0a\noWrVqggJCUGNGjXw66+/wtXVVbutseMdkaIwLJEdMvgMU+/evfHw4UPUrVsXoaGhqF69eoFncKZP\nn27Q+6Wnp8PZ2bnAdWXKlNFu4+HhUeA2KSkpAID79+/j7NmzeP755wEAffr0QYcOHbB69WpMmjQJ\nderUMageItkxLBFZjT179iA0NBS1atXCrFmz4OXlhQMHDkCtVuP111/H1q1b4eSkGWKNHe+IFINh\nieyUwYHp/PnzmDFjBlJTU7F27dpCtzM0MKlUKiQlJRW4LjMzE5IkQaVSFbq/i4sLAODll1/WhqV8\n4eHhiIuLw759+woNTDNnztT+f1BQEIKCggyqm8gsGJZIgeLi4hAXFyd3GTouXbqEBQsW4MqVK/D2\n9kZ4eDg6d+5s0Rqys7MRHh4OHx8fHDx4UBuGevTogZo1a2LkyJFYtWqV9kqHko53HKdIURiWSIEs\nNU4ZHJhGjRqF+/fvY/78+WjdujWee+45o75x5cqVceHCBTx69EjvMoVbt27B29tb+9e5guRPa16x\nYkW9dfnL7t+/X+j+Tw5ERLJiWCKFevqX9Pfee0++YgCcO3cOLVq00F5hAADr16/HypUrMWjQIIvV\ncf78eSQmJmL06NF6Z4769OmDkSNHYv/+/drAVNLxjuMUKQbDEimUpcYpg/+1Hz16FBMmTMDo0aPR\nuHFj+Pv7F/gyVPPmzZGbm4sjR47oLM/MzMTJkyfx0ksvPXN/ALh586beuvxlPj4+BtdDJAuGJSKD\nffDBB8jIyMCcOXNw6tQpbNq0CZUrV8akSZMsWsejR48AALm5uXrr8u9FevKeJGPHOyJZMSwRGR6Y\nPDw8TBpAwsLCIEkS5s2bp7M8OjoaGRkZGDBggHbZX3/9hQsXLiAjI0O7zN/fH61atcKRI0fw+++/\na5fn5uYiOjoapUqVQnBwsMnqJTI5hiWiYtm/fz8GDx6M8ePH44UXXkDPnj0xd+5c3L59WzsRkCW8\n8MILUKlU2LJlCx4+fKizbuXKlQCAZs2aaZcVZ7wjUhSGJSIAgPTvQ56eafz48fjjjz+we/duk33z\nqKgoLFy4ED179kRISAjOnz8PtVqN1q1bY8+ePdrtIiIisHr1auzduxft2rXTLj958iTatGmD0qVL\nIyoqCl5eXtiwYQMOHTqEGTNmYMaMGQV+X0mSYGCzicyDYYmskNx9Z+nSpbFkyRKdWVBv3LiBatWq\nIS4uDm3btjXq/desWYPr168DANRqNR49eoTx48cD0PyRbuDAgdptP/30U0yaNAn+/v4YPnw4nnvu\nORw8eBDr169HjRo1cOLECZ1nMRk63uWT+1gTMSyRNTJb32noE27PnTsnmjVrJrp37y5iY2PF1atX\nxfXr1/VexZGbmys+//xzUbt2beHs7Cx8fX3FhAkTRFpams52ERERwsHBQezbt0/vPU6dOiW6d+8u\nPD09RZkyZUSTJk3EqlWrivy+xWg2kemtWSOEJGmejD5litzVEBlM7r5TkiSxbt06nWV3794VkiSJ\n3bt3G/3+QUFBQpIkIUmScHBwEA4ODtqv27dvr7f9119/LVq3bi3c3d1FqVKlRPXq1cVbb70lkpKS\n9LY1dLzLJ/exJju3a5cQTk6acSo8XIjcXLkrIjKIufpOg88wORjwVwVJkgq8pltp+Jc7kg3PLJEV\nk7vvdHBwwNq1a/Gf//xHu+yff/5B+fLlERsbiw4dOshWm6nJfazJjvHMElkxc/WdBgcmQ2frsYZZ\nfTgQkSwYlsjKyd13Ojg4oEyZMjozygkhkJaWVuBySZKQnJwsR6lGk/tYk51iWCIrJ3tgsiUciMji\nGJbIBsjddxb3OUSSJGHv3r3mKcbM5D7WZIcYlsgGKD4wJSYmYvXq1Raf3rUkOBCRRTEskY1g32k5\nPNZkUQxLZCMUGZhycnKwbds2rFixAj///DPy8vJ4DxPRkxiWyIaw77QcHmuyGIYlsiHm6jv1Hy1u\ngHPnzmH58uVYu3Yt7t69C5VKhdDQUPTu3dvU9RFZL4YlIiJSMoYlIoMYHJhSUlLwzTffYPny5Th6\n9Kg2wb377ruYNGkSXFxczFknkXVhWCIiIiVjWCIy2DM/Gfv370dERAQqVaqEyMhIJCcn47PPPsOB\nAwcAAA0bNmRYInoSwxIRESkZwxJRsRR5hqlWrVqIj4+HSqVC3759MWzYMLRq1QoAEB8fb5ECiawK\nwxIRESkZwxJRsRUZmOLj4+Hq6gq1Wo2BAwfqPOOCiJ7CsERERErGsERUIkV+SiZOnAg3NzcMHToU\nvr6+mDhxIi5evGip2oisB8MSEREpGcMSUYk9c1rxnJwc/PTTT1i+fDl27NiB3NxctGzZEsHBwZg5\ncya+++479OrVy1L1mgSnayWTYlgiOyFn3/nnn3+WaD8/Pz8TV2IZHKfIpBiWyE4o4jlMf/31F1at\nWoUVK1bg8uXLAIDg4GC8+eabeOWVV1CmTBmTF2gOHIjIZBiWyI7I2Xc6FPLLXUE15S+TJMkqng1Y\nEI5TZDIMS2RHFBGYnvTLL79g+fLl+O6775Ceng5XV1e88sor2Lhxo6lrNDkORGQSDEtkZ+TsO2fO\nnKm37IcffsDvv/+O4OBg1K1bF4DmOYGxsbFo1KgRunfvjhkzZli4UtPgOEUmwbBEdkZxgSnf089n\nysvLM1VtZsOBiIzGsER2SEl95/r16zFq1Cjs2bMHjRs31ll34sQJdOzYEYsXL0b//v1lqtA4SjrW\nZKUYlsgOKTYwPens2bOoX7++qd7ObDgQkVEYlshOKanvbNCgAUJDQzFr1qwC10+bNg3btm3DqVOn\nLFyZaSjpWJMVYlgiO2WuvtOknx5rCEtERmFYIlKE+Ph4VKhQodD1Pj4+uHTpkgUrIlIIhiUik+Mn\niMhQDEtEilGxYkVs2rSpwL8k5uXlYfPmzahYsaIMlRHJiGGJyCz4KSIyBMMSkaKMGDEC+/btQ3Bw\nMHbs2IGEhAQkJCRg+/btCA4Oxv79+zFixAi5yySyHIYlIrMx6T1M1oLXhlOxMCwRAVBW35mXl4fx\n48djwYIFeuskScKoUaMwf/58SJIkQ3XGU9KxJivAsEQEwEomfbAWHIjIYAxLRFpK7DsvXryIrVu3\n4urVqwCAGjVqoHv37qhdu7bMlRlHiceaFIphiUiLgcmEOBCRQYwIS0IASUlA6dJA2bJmrJHIgth3\nWg6PNRmEYYlIh7n6TqfCVvz5558lekM/P78SF0OkGEaEpeRkICQEOH5cs/ugQUB09OMxLDcXOHgQ\nSEsDAgMBLy8ztYHIDqSmpuLw4cO4c+cOOnbsyIkeyH4wLBFZTKGByd/fv8DlBSW3/GWSJCE3N9ek\nBRJZnJGX4Y0aBfz2G5Cdrfn6m2+Al14CRo7ULOvQAfjjD8245uQEHDgA1K1rhnYQ2bjFixdj8uTJ\nSElJgSRJiImJQcWKFXHnzh34+flBrVZz4geyTQxLRBZVaGCaPn263rIffvgBv//+O4KDg1H339/w\nzp07h9jYWDRu3Bjdu3c3X6VElmCCe5YOHQKysh5/nZ6uCUUjRwJffgmcOAFkZGjWSRIweDBw9Kj+\n+1y+DNy8CdSvD/j4lLA9RDZq06ZNeOutt9CjRw9069YNw4YN066rUKECQkJCsHXrVgYmsj0MS0QW\nV2hgmjlzps7X69evx9WrV/Hbb7+hcePGOutOnDiBjh07olatWmYpksgiTDTBQ0AAcO0akJen+drZ\nGXj+ec3/X778OCwBmm91/br+e0yZAsybp7kHKicH2LIF6Ny5ROUQ2aTPPvsMQUFB2LJlC5KSkvTW\nN23aFMuWLZOhMiIzYlgikoXBn7LZs2fjrbfe0gtLANCkSROMGjUKs2fPNmlxRBZjwtnwvvhCc1+S\nhwfg7g7Urg1MnKhZ9/LLgKvr421LldJcrvek48eB+fM1werhQ829Tr17Pw5gRAScPn0avXr1KnR9\npUqVcOfOHQtWRGRmDEtEsin0DNPT4uPjUaFChULX+/j44NKlSyYpisiiTDx1eI0amjNJBw9qzi61\nbas5UwQA/fsDv/6quTTP0RGoVQtYuVJ3//h4zbonZWUBDx5wggiifI6Ojsgr4q8It2/fhuuTf50g\nsmYMS0SyMvjTVrFiRWzatKnAqfry8vKwefNmzk5E1sdMz1ny9ARefRXo1OlxWAI09yyp1cDdu8DV\nq8DJk0D58rr71q+vmUnvSe7uwHPPmaQ0Ipvw4osv4ueffy5wXV5eHjZu3IhmzZpZuCoiM2BYIpKd\nwZ+4ESNGYN++fQgODsaOHTuQkJCAhIQEbN++HcHBwdi/fz9vriXrIuNDacuWBSpV0gSopzVoAHz8\nsebslLu7Jnxt317wtkT2avTo0dixYwemTZuGe/fuAQByc3Nx4cIF9OnTB2fOnEFUVJTMVRIZiWGJ\nSBEMfnBtXl4exo8fjwULFui/iSRh1KhRmD9/PiQr+K2ODwQkOcOSoe7dA+7cAfz9ARcXuashUl7f\nOW3aNHz00Uc6j7bIr2/mzJkFzvZqLZR2rEkGDEtExWauvtPgwJTv4sWL2Lp1K65evQoAqFGjBrp3\n747atWubvDhz4UBk56wgLBEpkRL7zhMnTmDdunU4f/48hBCoVasWBg0ahJeenk3FyijxWJMFMSwR\nlYhiApMt4EBkxxiWiEqMfafl8FjbMYYlohIzV99p8Cx5+VJTU3H48GHcuXMHHTt25EQPZD0YloiI\nSMkYlogUqViBafHixZg8eTJSUlIgSRJiYmJQsWJF3LlzB35+flCr1Zz4gZSJYYnIqr333nslukfW\nmu9jIjvDsESkWAZfkrdp0yb07dsXPXr0QLdu3TBs2DDExsaiQ4cOAIDQ0FA8evQIP/30k1kLNgVe\n6mBnGJaITELOvtOhhL84FvWsJiXjOGVnGJaITEL2S/I+++wzBAUFYcuWLUhKStJb37RpUyxbtsyk\nxREZjWGJyCbkTzREZHMYlogUz+DAdPr0aXzyySeFrq9UqRLu3LljkqKITIJhichm+Pv7y10Ckekx\nLBFZBYM/lY6OjkVe2nD79m24urqapCgioxUzLKWkAAMHAtWqAS1bAmfPWqhOIiKyTwxLRFbD4DNM\nL774In7++ecCn5yel5eHjRs3olmzZiYtjqhESnBmqUcP4NAhICsLuHEDaNUKuHgRqFDBAvUS0TOt\nWrWqRJM+hIeHm6EaIiMxLBFZFYMnfdiwYQP69++PKVOmIDw8HHXq1MHPP/+MqlWrYsqUKfj+++/x\n448/omvXruau2Wi8mdaGlSAspaUBnp6acSufuzsQHQ2EhZmxViIrY22TPkiShNzcXDNUY34cp2wY\nwxKR2cg+6UNYWBhOnz6Njz76CLNnzwYAvPLKK9qiZs6caRVhiWxYCe9ZKlWq4OUuLiasjYiMsmfP\nHrlLIDIewxKRVTL4DFO+EydOYN26dTh//jyEEKhVqxYGDRqEl156qdjfPC8vD/Pnz8eXX36J69ev\no3z58ujXrx/ef/99qFSqYr9fWFgYNm7ciPr16+P06dOFbse/3NkgIyd4ePtt4IsvNGebnJ2BgADg\nxAmgTBkz1UtkhWy975w9ezZOnDiB3377DdeuXUO1atWQkJBQ4LZr167Fjz/+iOPHj+P27dvw9vZG\no0aNMHXqVDRv3lxv++KOd7Z+rO0SwxKR2Zmr7yx2YDKlMWPGQK1Wo1evXggJCcG5c+egVqvRpk0b\nxMbGFut69R9//BGhoaFwdnZGjRo1cOrUqUK35UBkY0wwG54QwLp1wJ49QPXqwLhxgJubGWolsmK2\n3nc6ODigXLlyaNKkCY4fP46yZcsWOJ15ZmYmVCoVGjdujFdffRXVq1dHYmIivvjiCyQmJmL16tUY\nMGCAzj7FHe9s/VjbHYYlIoswW98pZHLmzBkhSZLo06ePznK1Wi0kSRLr1683+L1SUlJE1apVxZgx\nY4S/v79o0KBBkdvL2GwytTVrhJAkIQAhpkyRuxoim6a0vvP69esiIiJCVK5cWTg5OYndu3cLIYT4\n+++/RUREhDh69Gix3i8hIUH7//Xr1xfVq1cvcLucnByxf/9+veV37twR3t7eokKFCiIvL0+7vCTj\nndKONRlh1y4hnJw041R4uBC5uXJXRGSzzNV3FnoP03vvvVeiGYmmT59u0HZff/01AGDs2LE6y4cP\nH45JkyZh7dq16N+/v0HvNXXqVAghMGvWLHz//ffFK5isF5+zRCaUlwfcuaOZAIT3rylfQkICAgMD\nkZWVhcDAQNy+fVu7rnz58jh+/DiWLVtWrNlbDX3Wk6OjI9q0aaO33MfHB23btsWWLVtw9+5d+Pj4\nADDteEdWhmeWiGxCkYGpJAwNTMeOHYOjo6Petd7Ozs5o2LAhjh07ZtD7HD16FIsWLcI333wDd3f3\nYtdLVophiUzoyhWgQwfg7781wemTT4CnfrclhZk6dSocHBxw+vRpqFQqbTjJ17VrV/z4448Wr+vm\nzZtwdnaGp6endpmpxjuyMgxLRDaj0MBU0HXbppSYmAhvb2+UKmCKsipVquDXX39FTk4OnJwKn8gv\nJycHw4YNQ5cuXdCnTx9zlktKwrBEJtatm+b5W/mXPU+dCrz8suZFyhQbG4u33noLfn5+SEpK0ltf\nrVo13Lhxw6I1bd++HceOHUN4eDhKly6tXW6K8Y6sDMMSkU0ptHc29NKEkkpPT4ezs3OB68r8OzVZ\neno6PDw8Cn2Pzz77DFeuXMG2bdvMUiMpEMMSmVheHnDhwuOwlL/st98YmJQsOTkZlStXLnR9dnY2\ncp58uJqZXb58GYMGDYKvry8+//xznXWmGO/IijAsEdkc2T7BKpUKWVlZBa7LzMyEJElFTi0eTXyV\nBgAAIABJREFUHx+PWbNmYdq0aWYPd6QQDEtkBg4OgLe37jJHR6BaNXnqIcP4+vri7Nmzha4/cuQI\natasaZFaEhIS0LFjRzg6OmLHjh0oV66cznpjxzuyIgxLRDap0DNMq1atKtGkD+Hh4QZtV7lyZVy4\ncAGPHj3Su0zh1q1b8Pb2LvLyhAkTJsDLywuhoaGIj4/XLs/JyUFWVhauXLkClUqFSpUqFbj/zJkz\ntf8fFBSEoKAgg+ommTAskRlt2KC5LM/REcjNBV59VfOyd3FxcYiLi5O7jAL17t0bS5YswdChQ/XO\nNG3atAnffvttie/FLY5r166hffv2SE9Px+7du1G/fn29bUo63nGcsjIMS0QWZ7FxqrDp8yRJKvbL\nwcHB4On5pk2bJiRJEr/88ovO8oyMDKFSqUTXrl2L3L9Ro0bPrKdbt24F7ltEs0mJOHU4WcDNm0Js\n3SrEoUNCPDEjND1BSX3ngwcPRL169YRKpRKvvPKKkCRJBAcHi8DAQCFJkmjcuLFIT08v8fsXNa14\nvoSEBFGtWjXh5eUlTpw4Ueh2JRnvlHSsyQCcOpxIEczVdxZ6CmfPnj1mDWphYWH46KOPMG/ePLRu\n3Vq7PDo6GhkZGToP/fvrr7/w4MEDVKtWDS7/zvc7Z84cPHz4UOc9hRD473//CxcXF/zf//1foWeX\nyIrwzBJZSJUqmhdZh7Jly+LQoUOYPn061q1bBwCIiYmBp6cnRo0ahQ8//FA7XpjD9evX0b59eyQn\nJyMmJgaNGzcudNvijHdkhXhmicjmSf+mMVlERUVh4cKF6NmzJ0JCQnD+/Hmo1Wq0bt1aJ7BFRERg\n9erV2Lt3L9q1a1fke/r7+8PDwwOnTp0qdBs+Qd1KMCwRKYpS+04hBO7evQshBMqXLw+HEv6yumbN\nGly/fh0AoFar8ejRI4wfPx6AZmwZOHAgACAlJQUNGzbEtWvXMHr06AKf9RQcHKwz1bmh410+pR5r\negrDEpGimKvvlDUw5eXlYd68eVi6dCmuXbuG8uXLIywsDO+//77ODbBDhgzRBqa2bdsW+Z7Vq1eH\nu7s7A5O1Y1giUhxb7zvbt2+Pffv2AYD2Ht789gYFBWmDzbVr1xAQEFDo8ZAkSW+8MnS8e/I9bPlY\n2wSGJSLFUURg+vPPPzFjxgzs2rULf//9N37++Wd06NABd+/exTvvvIP//ve/xXqqulw4ECkcwxKR\nIsnZd+7fv79E+z3rj2xKxXFK4RiWiBTJXH2nwU/JS0hIQGBgILKyshAYGIjbt29r15UvXx7Hjx/H\nsmXLrCIwkYIxLBFRAQqbIa6gwTF/mSRJyM3NtUB1ZFcYlojsjsGBaerUqXBwcMDp06ehUql0rs0G\ngK5du+LHH380eYFkRxiWiKgQK1as0PlaCAG1Wo3Lly9jwIABqFu3LgDg3LlzWL9+PWrVqoXRo0fL\nUSrZMoYlIrtkcGCKjY3FW2+9BT8/PyQlJemtr1atGm7cuGHS4siOmCksXb0KxMcDzz8PVK9ukrck\nIhlERETofD1//nzcvXsXFy5cQJWnpjd899130aJFCyQnJ1uwQrJ5DEtEdsvgT3pycrLewwGflJ2d\njZycHJMURXbGTGFp8WLghReAfv2A+vWB6GiTvC0RKcDChQsRGRmpF5YAwNfXF5GRkVi4cKEMlZFN\nYlgismsGf9p9fX1x9uzZQtcfOXIENWvWNElRZEfMFJZu3wYmTAAyMoCHDzX/jYoC/v7bJG9PRDK7\nceMGXF1dC12vUqnw559/WrAislkMS0R2z+BPfO/evbF8+XKcPn1aO91qvk2bNuHbb79Fv379TF4g\n2TAz3rN0/Trg7Ky7rHRpgFeNEtkGf39/rFmzBpmZmXrrMjIysGbNGvj7+1u+MLItDEtEhGJMK/7w\n4UO0bNkS165dQ9u2bfHzzz+jc+fOePjwIY4ePYpGjRrh4MGDZn2yuqlwulYFMPMED//8A/j5Aenp\nj5e5ugI3bwKenib9VkR2Q0l9Z3R0NCIjI1GvXj2MGjUKderUAQCcP38eixYtwvnz5/HFF19gxIgR\nMldaMko61naLYYnI6ijiOUwPHz7E9OnTsW7dOty7dw8A4OnpiQEDBuDDDz+Eh4eHyQs0Bw5EMrPQ\nbHg//AC8/rpmfBMC2LgRCAkxy7cisgtK6zvnzZuHqVOnIiMjQ2e5i4sLZs2ahfHjx8tUmfGUdqzt\nDsMSkVVSRGDKJ4TA3bt3IYRA+fLl4WBlnQgHIhlZeOrw9HTg1i2gShVApTLrtyKyeUrsOx88eIBd\nu3bh6tWrAICAgAAEBwfD08pPJSvxWNsNhiUiq6WowGTtOBDJhM9ZIrJq7Dsth8daJgxLRFbNXH1n\noc9h2r9/f4nesG3btiUuhmwYwxIRmcHDhw8RGxuLhIQEAJozTJ07d4a7u7vMlZHVYVgiokIUeoap\nsMvsCkpu+cskSUJubq7pqzQx/uXOwhiWiGyC0vrO6OhoTJgwAampqTrL3d3d8fnnn2PYsGEyVWY8\npR1rm8ewRGQTLH6GacWKFTpfCyGgVqtx+fJlDBgwAHXr1gUAnDt3DuvXr0etWrUwevRokxdIVo5h\niYjMYNu2bYiMjERAQAA++OAD1KtXD4BmTFKr1YiMjISPjw+6d+8uc6WkeAxLRPQMBt/DNH/+fMyZ\nMweHDx/We7L6zZs30aJFC0ycOBFRUVFmKdSU+Jc7C2FYIrIpSuo7W7dujXv37uHIkSN6l9+lpKQg\nMDAQXl5eOHDggEwVGkdJx9qmMSwR2RRz9Z0G9woLFy5EZGSkXlgCAF9fX0RGRmLhwoUmLY6sGMMS\nEZnRH3/8gYiIiALvVXJ3d0dERAROnjwpQ2VkNRiWiMhABvcMN27cgKura6HrVSoV/vzzT5MURVaO\nYYmIzCz/vtnCFLWOiGGJiIrD4Evy6tSpA5VKhUOHDqFMmTI66zIyMtCyZUtkZGTgwoULZinUlHip\ngxkxLBHZLCX1na1atcKDBw9w5MgRuLm56axLTU1FYGAgPD09cfDgQZkqNI6SjrXNYVgislkWn/Th\naRMmTEBkZCReeukljBo1CnXq1AEAnD9/HosWLcL58+fxxRdfmLxAsiIMS0RkIRMnTkSvXr3QpEkT\nREVFoX79+gCAM2fOQK1WIz4+Hps3b5a5SlIchiUiKoFiPbh23rx5mDp1KjIyMnSWu7i4YNasWRg/\nfrzJCzQH/uXODBiWiGye0vrOxYsX45133kF6errOcldXV3z66acYOXKkTJUZT2nH2iYwLBHZPHP1\nncUKTADw4MED7Nq1C1evXgWgeUhgcHAwPD09TV6cuXAgMjGGJSK7oMS+8/79+4iJidE+uLZGjRro\n3LkzypYtK3NlxlHisbZqDEtEdkExgckWcCAyIYYlIrvBvtNyeKxNiGGJyG7Ifg9TvocPHyI2Nlb7\n17yAgAB07ty5wKldycYxLBERkZIxLBGRCRQrMEVHR2PChAlITU3VWe7u7o7PP/8cw4YNM2lxpGAM\nS0RkYe3bty/2dOF79uwxUzWkeAxLRGQiBl+St23bNoSGhiIgIABRUVGoV68eAODcuXNQq9W4evUq\ntmzZgu7du5u1YFPgpQ5GYlgiskty950ODg5wcnKCs7MzADyzFkmSkJKSYonSTE7uY231GJaI7JLs\n9zC1bt0a9+7dw5EjR/Quv0tJSUFgYCC8vLxw4MABkxdpahyIjMCwRGS35O47S5cuDQDo2rUrhgwZ\ngtdeew2Ojo6y1WNOch9rq8awRGS3zNV3GtyD/PHHH4iIiCjwXiV3d3dERETg5MmTJi2OFIZhiYhk\ndPPmTcyePRvx8fHo2bMnfH198c4771jFA9PJQhiWiMgMDO5FhBBFXjte3OvKycowLBGRzHx8fDBh\nwgScOXMGhw8fRo8ePbB06VLUq1cPLVq0QHR0tN49tmRHGJaIyEwMviSvVatWePDgAY4cOQI3Nzed\ndampqQgMDISnpycOHjxolkJNiZc6FBPDEhFBmX1nRkYGNm/ejBUrViAuLg4uLi744osvMHDgQLlL\nM4oSj7WiMSwRERQwrfjEiRPRq1cvNGnSBFFRUahfvz4A4MyZM1Cr1YiPj8fmzZtNXiDJjGGJiBTM\nxcUFAwYMgL+/P2bOnIndu3fjypUrcpdFlsSwRERmVqwH1y5evBjvvPMO0tPTdZa7urri008/xciR\nI01eoDnwL3cGYlgioicore9MTEzEqlWrsHLlSly+fBlVqlRBeHg43nzzTVStWlXu8oyitGOtWAxL\nRPQE2WfJy3f//n3ExMRoH1xbo0YNdO7cGWXLljV5cebCgcgADEtE9BQl9J3Z2dnYunUrvvrqK+za\ntQtOTk7o3r07hgwZguDgYJuZNU8Jx1rxGJaI6CmKCUy2gAPRMzAsEVEB5O47R48ejfXr1+P+/ft4\n8cUXMXToUAwcOBBeXl6y1WQuch9rxWNYIqICMDCZEAeiIjAsEVEh5O47HRwcUKZMGfTs2RNNmjQx\naHbW8ePHW6Ay05P7WCsawxIRFUKWwNS+fftiTxe+Z88eo4syNw5EhWBYIqIiyN13OpTgl+K8vDwz\nVGJ+ch9rxWJYIqIiyDJL3r59++Dk5ARnZ2cAeGYBfBaTFWNYIiKFs4Y/yJEZMSwRkUyKPMNUunRp\nAEDXrl0xZMgQvPbaazZxQy3/cvcUhiUiMgD7TsvhsX4KwxIRGcBcfWeRvc3Nmzcxe/ZsxMfHo2fP\nnvD19cU777yDCxcumLwQkgnDEhERKRnDEhHJzOBJH44ePYoVK1bgm2++QXJyMgIDAzF06FD0798f\nbm5u5q7TpPiXu38xLBFRMbDvtBwe638xLBFRMShmlryMjAxs3rwZK1asQFxcHFxcXPDFF19g4MCB\nJi/OXDgQgWGJiIqNfafl8FiDYYmIik2WS/IK4uLiggEDBuD9999Hhw4dkJ6ejitXrpi8MDIjKw1L\ne/YAL7wA+PoCo0cD2dlyV0RERGbBsEREClKsM0yJiYlYtWoVVq5cicuXL6NKlSoIDw/Hm2++iapV\nq5qzTpOy67/cWWlYOn0aePllID1d87WLCzBgABAdLW9dRPbErvtOC7PrY82wREQlJNslednZ2di6\ndSu++uor7Nq1C05OTujevTuGDBmC4OBgq5w1z24HIoWHJSGA27cBlQrw9NRd99FHwPTpQG7u42Ue\nHsDDh5atkcie2W3fKQO7PdYMS0RkBFkuyRs9ejQqVaqEsLAwJCYm4v/+7/+QmJiIb7/9FiEhISYJ\nS3l5eZg7dy7q1KkDFxcX+Pn54e2330Z6/qmEIjx48ADz589HcHAw/Pz8oFKpUKdOHURGRuLmzZtG\n12ZTFB6W7t4FGjYEatQAKlQA3npLU2o+lQpweuqpYWXKWLZGIqKn3bt3D2+//TZq1qwJFxcX+Pj4\noEOHDjhw4IDOdhcvXkRoaCi8vLzg5uaGtm3bYu/evTJVrVAMS0SkUEWeYXJwcECZMmXQs2dPNGnS\nxKAH044fP75YBYwZMwZqtRq9evVCSEgIzp07B7VajTZt2iA2NrbI77lz505069YNnTp1QocOHeDt\n7Y3Tp0/jyy+/ROnSpXHo0CHUrVtXbz+7+8udwsMSAAQHA3v3asZJAHB1BZYuBf7zH83X//yjuX/p\nn3+AR480AUqtBoYOla9mIntjd33nM1y/fh1BQUFIT0/HG2+8gVq1auHBgwc4ffo0unTpgn79+gEA\nrly5gubNm6N06dIYO3YsPDw8EB0djTNnzmDHjh3o2LGj3nvb3bFmWCIiE5DlkjyHEnRWeXl5Bm97\n9uxZNGjQAL1798bGjRu1yxcuXIioqCisW7cO/fv3L3T/69evIy8vD9WrV9dZvnv3bnTu3FnvffPZ\n1UCk8LAkBDB2LLBggf660aN1l9+9CyxcCCQlAT16aEIWEVmOXfWdBmjTpg3+/PNPHD16FBUqVCh0\nu379+mHLli347bff8OKLLwIA0tLSUL9+fZQpU6bAZxva1bFmWCIiEzFX3+lU1Mo9e/aY/Bs+6euv\nvwYAjB07Vmf58OHDMWnSJKxdu7bIwFStWrUCl3fs2BHPPfcczp49a7pirZHCwxIAbNwILF+uv9zF\nBXj+ed1l5csD771nmbqIiIqyf/9+HDx4EGq1GhUqVMCjR4/w6NEjqFQqne3S0tKwbds2BAUFacMS\nALi6umLYsGGYPn06jh07hmbNmlm6CcrAsEREVqDIwBQUFGTWb37s2DE4OjqiefPmOsudnZ3RsGFD\nHDt2rETv+/DhQ6SkpOgMTnbHCsISABw5AqSl6S9v0gSIjLR8PUREhti+fTsAoGrVqujWrRt27tyJ\n3NxcPP/885g+fToGDBgAADh16hSys7PRokULvfcIDAwEABw/ftw+AxPDEhFZCVl7psTERHh7e6NU\nqVJ666pUqYKkpCTk5N/UUgwffvghcnJyMHjwYFOUaX2sJCwBQM2amvuRnvT888C+fUDp0vLURET0\nLBcvXgSguSLiwYMHWL16NVasWIHSpUtj0KBBWLlyJQDNOAdoxrSn5S+7deuWZYpWEoYlIrIiRZ5h\nMrf09HQ4OzsXuK7Mv1Ogpaenw8PDw+D3/O677zBnzhyEhIQgIiLCFGVaF4WGpaQkYMkS4P59zf1H\n7dpplg8bBnzzDXDihGasdHQEtmzR/JeISKlSUlIAAB4eHti7dy+c/p3GMzQ0FAEBAZgyZQoGDx6s\nnfG1oLHuyXHOrjAsEZGVkTUwqVQqJCUlFbguMzMTkiTpXQ9elO3bt2PAgAFo1qwZNmzYYKoyrYeC\nw1KDBo9nuPvyS2DZMqB/f6BUKWDPHuDQISA1FQgMBLy85K6YiKhoLi4uAID+/ftrwxIAeHp6olu3\nblizZg0uXryoHcOysrL03iMzMxMAijXOWT2GJSKyQrIGpsqVK+PChQt49OiR3mV5t27dgre3t85A\nVJSdO3eiV69eaNCgAXbt2gU3N7cit585c6b2/4OCgsx+v5bZKTQsAcDKlcC9e5qwBADp6cDEiZrA\nBGjOJrVpI1t5RFSIuLg4xMXFyV2GIvn6+gIAKlasqLeuUqVKADTPCizqsrv8ZQVdrgfY4DjFsERE\nJmapcUrWwNS8eXPExMTgyJEjaN26tXZ5ZmYmTp48afDgsHPnToSGhqJevXqIjY1F2bJln7nPkwOR\n1VNwWAI0Z47yw1I+e7sChcgaPf1L+nucplIrMDAQX375JW7cuKG3Lv/B6T4+PvDx8YGzszMOHTqk\nt93hw4cBAC+99FKB38OmximGJSIyA0uNU7L2VmFhYZAkCfPmzdNZHh0djYyMDO0sQwDw119/4cKF\nC8jIyNDZdteuXejZsyfq1q2L3bt3w9PT0yK1K4bCwxIAdOsG/HupPgDNlOF9+shXDxGRsUJDQ+Hu\n7o61a9ci7YmpPm/fvo3vv/8etWvXRkBAANzc3NCtWzfExcXh1KlT2u1SU1OxbNky1KpVy/ZnyGNY\nIiIrV+SDay0hKioKCxcuRM+ePRESEoLz589DrVajdevWOs+BioiIwOrVq7F37160+3fGgOPHj6PN\nv9dyffzxxyhXrpze+w8cOFBvmc08ENAKwlK+HTuAMWOA5GSgVy9g3jzOgkdkbWym7zSR6OhoREZG\non79+hg6dCiysrKwZMkS3LlzBz/++CM6deoEALhy5QqaN2+OUqVKYdy4cXB3d0d0dDTOnj2Ln376\nCZ07d9Z7b5s51gxLRGRBZus7hcxyc3PF559/LmrXri2cnZ2Fr6+vmDBhgkhLS9PZLiIiQjg4OIh9\n+/Zpl61cuVJIkiQcHByEJEl6LwcHhwK/pwKabbw1a4SQJCEAIaZMkbsaIrIDNtF3mtjmzZvFyy+/\nLFxdXYW7u7vo0qWLOHTokN5258+fFz169BCenp5CpVKJNm3aiN27dxf6vjZxrHftEsLJSTNOhYcL\nkZsrd0VEZOPM1XfKfoZJDlb/lzsrOrNERLbD6vtOK2L1x5pnlohIBubqO9l7WRuGJSIiUjKGJSKy\nMezBrAnDEhERKRnDEhHZIPZi1oJhiYiIlIxhiYhsFHsya8CwRERESsawREQ2jL2Z0jEsERGRkjEs\nEZGNY4+mZAxLRESkZAxLRGQH2KspFcMSEREpGcMSEdkJ9mxKxLBERERKxrBERHaEvZvSMCwREZGS\nMSwRkZ1hD6ckDEtERKRkDEtEZIfYyykFwxIRESkZwxIR2Sn2dErAsERERErGsEREdoy9ndwYloiI\nSMkYlojIzrHHkxPDEhERKRnDEhERA5NsGJaIiEjJGJaIiAAwMMmDYYmIiBQiORk4fBi4ckXzdXQ0\nEFU3BjldGJaIiABAEkIIuYuwNEmSIFuzGZaIyErJ2nfaGUsd699+Azp1ArKyNK/SpYFWmTHYga4o\nhRysRjhaX/4KATUZlohI+czVdzIwWRLDEhFZMQYmy7HUsa5QAfj778dfd8TjsLQK4RiCr/BaNwds\n22b2UoiIjGauvpN/MrIUhiUiIlKQbdueHZYEHHD/vnw1EhEpAQOTJTAsERGRQuTlAf/3f0D//o+X\nFRaWAM3wRURkz5zkLsDmMSwREZGCvPMOsGQJkJ6u+bqosNS/PzB8uIzFEhEpAO9hMieGJSKyIbyH\nyXLMcayzsoBdu4A+fYDsbM2yosKShweQlASUKmXSMoiIzMZc4xTPMJkLwxIRESlEWhrQogWQkPDs\nsOTsDLi6Ajt2MCwREQG8h8k8GJaIiEhB1Grg0iUgNVXz9dNhabTrV3Au44B33wXi44E7d4DmzeWt\nmYhIKXiGydQYloiISGESEjSX5AG6YSnOLxx1vvkKP+U4oEoVICBA3jqJiJSIgcmUGJaIiEiB2rcH\n1q0DXk57HJb2+Ycj6MpXgAMvNiEiKgonfTAVhiUisnGc9MFyjDnWOTnAH38AkgS8+CLg5KQZmpaF\nxSBioyYs7fQJR4sLX6HscwxLRGQ7zDVOMTCZAsMSEdkBBibLKemxfvgQaNNGcwkeoLnE7pdfAI8j\nMUDXrkBODh71D0eptTyzRES2x1zjFHtLYzEsERGRQkye/Hhyh9RU4OJFYOXAx2EJ4QxLRETFxR7T\nGAxLREQks8xMzTAEAKdOPZ7cAQBaZ8Xgvz88Dkv4imGJiKi42GuWFMMSERHJ6MYN4IUXNM9McnXV\nDEtNmwJlymjW58+G5wSGJSIyjdRUYORIoFEjoF8/4PZtuSuyDN7DVBIMS0Rkh3gPk+UYcqwbNgTO\nngVyczVfq1RAbCwwbhzg/XsMtmRrJnjgPUtEZApCAG3bAsePa85sOzkBlSsD589r+h8l4D1MSsGw\nREREMsvJAU6ffhyW8p06BRx6LwY/5GrCkhjEsEREpnH79uOwBGj6ofv3gV9/lbcuS2APWhwMS0RE\npABOToC7u+4yBwegwZ0YOLzWFVKu5jI8aSXDEhGZhqPj4/sln15u69iLGophiYiIFGTlSsDFBXBz\n07zeqhODFrM4wQMRmUeFCkCXLo8vv3N2BqpWBVq2lLcuS+A9TIZgWCIi4j1MFmTosb5wAThyBKh7\nMwbNZnaFxLBERGb06BHw6afAwYNA3brAjBmAh4fcVT3GB9eaULEOJsMSEREABiZLKuhY79sH7NgB\neHsDw4cDZcv+uyJG9zlLDEtEZK8YmEzI4IPJsEREpMXAZDlPH+vVq4E339TcbF26NFCxomaCB48j\nDEtERPkYmEzIoIPJsEREpIOByXKePtblywNJSY/Xu7gA37wRg+5fMCwREeVjYDKhZx5MhiUiIj0M\nTJbz9LF2dQXS0x+vD3bQPJTWIY9hiYgoH5/DZCkMS0REpDDdugFlymj+vyNi8GMewxIRkaXwDNOT\nGJaIiArFM0yW8/SxTk8HRo4EUrbE4NuUrnACwxIR0dNs9gxTXl4e5s6dizp16sDFxQV+fn54++23\nkf7ktQfPsH37drRs2RJubm4oV64c+vXrh2vXrhWvEIYlIiIyE2PHOpUKWDUwBpszGJaIiPJlZwPX\nr2smxDEn2XvacePGYcKECXjhhRewcOFC9O3bFwsWLEC3bt0MSoibN2/Ga6+9hqysLMyZMwcTJ07E\n/v370apVK9y+fduwIhiWiIjIjIwd6zh1OBGRrv37AR8foF49wMsL+P57M34zIaMzZ84ISZJEnz59\ndJar1WohSZJYv359kftnZ2eLypUrC39/f5GWlqZdfvLkSeHo6ChGjBhR4H46zV6zRghJEgIQYsqU\nkjeGiMjGyTxkWK2SjHU6x3rXLiGcnDTjVHi4ELm55i6ZiEjR0tKE8PDQdIv5L5XKfOOUrH+e+vrr\nrwEAY8eO1Vk+fPhwqFQqrF27tsj99+3bh9u3b2PYsGFQqVTa5Q0bNkRQUBA2bNiA3Nzcwt+AZ5aI\niMjMjBrreGaJiEjP9euaX9+fVKqU+b6frL3usWPH4OjoiObNm+ssd3Z2RsOGDXHs2LFn7g8ALVq0\n0FsXGBiI5ORkXLp0qeCdbTgsxcXFyV2C2dhy2wC2z9rZevuoZEo81tlwWLLlz4ottw1g+6ydrbSv\nUiXg0SPdZdnZ5vt+sva8iYmJ8Pb2RqkCImGVKlWQlJSEnJycIvfP37ag/QHg1q1bBe9so2EJsJ0P\nQ0FsuW0A22ftbL19VDIlHutsNCwBtv1ZseW2AWyftbOV9nl6AosWaR7i7eGh+e+775rv+8na+6an\np8PZ2bnAdWX+feBEUTMI5a8r6D2eub+NhiUiIlKWEo91NhqWiIhMYehQ4MwZYP164LffgMmTzfe9\nnMz31s+mUqmQlJRU4LrMzExIkqRzb1JB+wNAVlZWgfs/uY0ehiUiIrKAEo91DEtEREUKCNC8zM4s\nU0kYKDg4WDg5OYns7Gy9dS1bthQ+Pj5F7v/RRx8JSZLE7t279dZNmTJFSJIkzp07p7euRo0aAgBf\nfPHFF1/FeDVs2LDkHb4dK8lYx3GKL7744qv4rxo1apilH5f1DFPz5s0RExODI0eOoHV9ZhxLAAAg\nAElEQVTr1trlmZmZOHnyJIKCgp65PwAcOnQIHTp00Fl3+PBhlC1bFrVq1dLbLz4+3vjiiYiIDFCS\nsY7jFBGRcsh6nj8sLAySJGHevHk6y6Ojo5GRkYEBAwZol/3111+4cOECMjIytMvatWuHSpUqYdmy\nZUhLS9Mu/+OPPxAXF4e+ffvC0dHR/A0hIiIqRHHGOiIiUh5JCEMeMW4+UVFRWLhwIXr27ImQkBCc\nP38earUarVu3xp49e7TbRUREYPXq1di7dy/atWunXf7dd98hLCwMDRs2xLBhw5CcnIy5c+fC0dER\nv/32GypVqiRHs4iIiLQMHeuIiEh5ZA9MeXl5mDdvHpYuXYpr166hfPnyCAsLw/vvv69zE+yQIUO0\ngalt27Y67/HTTz/hgw8+wKlTp+Ds7IxOnTrhk08+QfXq1S3dHCIiIj2GjnVERKQ8sk+94+DggPHj\nx+PChQvIzMzEjRs3MGfOHL0B5KuvvkJubq5eWAKAkJAQ9OvXD1WrVkVGRgYOHz6MRYsWFTkl+dO2\nb9+Oli1bws3NDeXKlUO/fv1w7do1Y5tnEnl5eZg7dy7q1KkDFxcX+Pn54e233zaofQ8ePMD8+fMR\nHBwMPz8/qFQq1KlTB5GRkbh586YFqi+aMW0rSFhYGBwcHNCgQQMTV1oypmhfTk4OFixYgCZNmsDN\nzQ2enp5o2rQpli5dasbKDWNs+3JycrBkyRI0a9YM5cqVg4eHB1544QXMmjULKSkpZq6+aLNnz0bf\nvn0REBAABweHEv8BRql9i7HtU3rfojTPGutM3RfK7d69e3j77bdRs2ZNuLi4wMfHBx06dMCBAwd0\ntrt48SJCQ0Ph5eUFNzc3tG3bFnv37pWp6seK8/lYu3YtXn/9ddSsWROurq6oVq0aevTogaNHjxa4\nvRJ+1sX9/O/cuRNdunRBlSpVoFKpULNmTYwYMQIJCQl628rdvkuXLmH69Ol4+eWX4ePjAw8PDzRu\n3BgfffTRM2tYsmQJHBwc4ODggHv37umtl7ttgOYzM2DAANStWxeenp5wdXVFrVq1MGrUqAJ/HsX5\njFlj+wDNiZNOnTrBy8sLrq6uqF27NkaPHq23nVHtM8tUEhYWFRUlJEkSvXv3FsuWLRPjx48XpUqV\nEh06dBB5eXnP3H/Tpk1CkiTRpEkTsWTJEjF79mxRoUIFUblyZZGYmGiBFhTNmPbt2LFDODk5iVde\neUV8+umnYsWKFWLcuHFCpVIJT0/PAmcRtCRjf3ZP+uGHH4Sjo6NQqVSiQYMGZqq4eIxtX1ZWlujS\npYtwdnYWb7zxhoiOjhZLliwR48aNE1OnTrVAC4pmbPuGDBkiJEkSnTp1EgsXLhRffvmleP3114Uk\nSeLll1+2QAsKJ0mS8Pb2FsHBwcLLy0tUr1692O+h5L7F2PYpvW+xNqbsC+V27do14e/vL3x8fMTk\nyZPFV199JebOnSuGDh0qNmzYoN0uPj5eeHl5iYoVK4qPP/5YLF68WDRu3FiUKlVKxMbGytgCwz8f\nGRkZ2s/4u+++K1asWCE++OAD4evrKxwcHMTatWv19lHCz7o4n/9Vq1YJSZJE7dq1xSeffCKWL18u\nxo0bJ1xdXYWXl5e4deuWzvZyt+9///ufcHd3FwMHDtSOK2FhYUKSJNGwYUORkZFR4H63bt0SHh4e\nwt3dXTg4OIh//vlHbxu52yaEELt37xYdOnQQU6dOFUuWLBHR0dFi9OjRws3NTXh6eoqrV69qty3u\nZ8za2ieEEDNnzhSSJImQkBChVqvF8uXLxfTp00XPnj1N2j6rD0xnzpwRkiSJPn366CxXq9VCkiSx\nfv36IvfPzs4WlStXFv7+/iItLU27/OTJk8LR0VGMGDHCLHUbytj2Xbt2Te8flxBCxMbGFvi+lmRs\n256UkpIiqlatKsaMGSP8/f0VEZhM0b5p06YJJycnERcXZ64yS8zY9mVkZAhHR0fx0ksv6a0bOHCg\nkCRJ/PHHHyatuTgSEhK0/1+/fv1iBwql9y3Gtk/JfYu1MWVfqAStW7cWfn5+4q+//ipyu759+won\nJyedz3lqaqqoVq2aqF27trnLLJKhn4+cnByxf/9+veV37twR3t7eokKFCjq/iCnlZ12cz3+rVq2E\ns7OzXoBYtmyZkCRJzJs3T7tMCe07fvy4SE5O1ls+bdo0IUmSWLhwYYH7hYaGiqZNm4pBgwYJSZL0\n2quEthVl48aNQpIkMWPGDO2y4nzGrLF9MTExQpIk8cEHHzxzf2PbZ/WBaerUqUKSJHHgwAGd5ZmZ\nmcLV1VV07dq1yP2LOtgdO3YUZcuWFTk5OSatuTiMbV9RvLy8RN26dY0tscRM2baoqCjh6+srkpOT\nRbVq1RQRmIxtX2pqqnB3d9d+uPPy8gocBORibPtycnKESqUSr776qt66iRMnCkmSRHx8vElrLqmS\nBAql9y1PKkn7iiJ332JtzNnPW9q+fft0finNzs7W+YNBvtTUVOHs7Cw6deqkt27WrFlCkiRx9OhR\ns9driJJ+Pnr16iUkSRJ37tzRLlPiz/pZ7QsODhYeHh4iNzdXZ/lPP/0kJEkSy5Yt0y5TYvvynTp1\nSkiSJEaOHKm3bvPmzcLR0VEcO3ZMDB48uMDApOS2CSHEkSNHhCRJ4sMPPxRCFP8zZm3tE0KI9u3b\ni4oVK2r/baakpOj9O81nbPtkv4fJWMeOHYOjo6P2mUz5nJ2d0bBhQxw7duyZ+wNAixYt9NYFBgYi\nOTkZly5dMl3BxWRs+wrz8OFDpKSkoEKFCqYos0RM1bajR49i0aJFmDt3Ltzd3c1RaokY275ffvkF\nqampaNKkCcaMGQMPDw+ULVsWPj4+mDp1KnJzc81Z/jMZ2z5HR0dMnz4dO3fuxKeffor4+Hhcu3YN\nK1euxJIlSzBo0CDUqFHDnE0wK6X3LeaihL7F2pirn5fD9u3bAQBVq1ZFt27doFKp4Obmhtq1a2Pd\nunXa7U6dOoXs7OxCPx8AcPz4ccsUbSY3b96Es7MzPD09tcus8Wc9efJk5OTkYPDgwTh16hRu3bqF\nn3/+GRMmTEC9evXw+uuva7dVcvvy7618um9KTk7GW2+9hTfffBMvvfRSofsrrW1ZWVlISkrCzZs3\nsWvXLkRGRsLPzw9vvPEGgOJ/xqytfWlpadi/fz8CAwMRHR2NKlWqwMPDA+7u7ujfvz/+/vtvnfcz\ntn1WH5gSExPh7e2NUqVK6a2rUqUKkpKSkJOTU+T++dsWtD8A3Lp1y0TVFp+x7SvMhx9+qO0A5WKK\ntuXk5GDYsGHo0qUL+vTpY65SS8TY9l28eBEAMG/ePGzZsgVz5szBt99+i5YtW2L27NnaTkMupvj5\n/e9//8OSJUswY8YM1KpVCwEBAXjjjTcwfvx4rFq1ylylW4TS+xZzUULfYm3M1c/LIb/fGj58OB48\neIDVq1djxYoVKF26NAYNGoSVK1cCsP3Px/bt23Hs2DGEhYWhdOnS2uXW+LMOCgpCbGws9u7di0aN\nGqFq1aoICQlBjRo18Ouvv8LV1VW7rVLbl5ubi1mzZqFUqVL4z3/+o7Puf//7HwDNRBhFUVrboqOj\n4ePjAz8/P7zyyisoVaoUfvnlF20gLO5nzNraFx8fj7y8PPz6668YO3YsIiMjsWXLFrz55pvYuHEj\n2rdvr/PsVmPb52T6JlpWeno6nJ2dC1xXpkwZ7TYeHh6F7g+gwPd4cn+5GNu+gnz33XeYM2cOQkJC\nEBERYYoyS8QUbfvss89w5coVbNu2zSw1GsPY9uXPEnf//n2cPXsWzz//PACgT58+6NChA1avXo1J\nkyahTp06Zqj+2Uzx8/v0008xefJk9OnTB7179wag+fc5a9YsODs7Y8qUKaYv3EKU3reYg1L6Fmtj\njn5eLvn9loeHB/bu3QsnJ82vGaGhoQgICMCUKVMwePBgm/58XL58GYMGDYKvry8+//xznXXW+LPe\ns2cPQkNDUatWLcyaNQteXl44cOAA1Go1Xn/9dWzdulX7c1Zq+8aOHYvDhw9j9uzZ2rEUAA4ePIil\nS5di/fr1z7xCRWlt69mzJ+rVq4fU1FScOHECarUa7dq1Q2xsLAICAor9GbO29uX3NXfv3sWyZcsw\ndOhQAECPHj3g4eGB9957D6tWrcKbb76prd2Y9ln9GSaVSoWsrKwC12VmZkKSpCKfcZG/rqD3yMzM\n1NlGDsa272nbt2/HgAED0KxZM2zYsMFUZZaIsW2Lj4/HrFmzMG3aNPj7+5upypIztn0uLi4AgJdf\nflmngweA8PBwAMC+fftMVG3xGdu+06dPY/LkyQgLC8OGDRvQr18/9OvXD99++y3CwsIwffp0q75k\nTel9i6kpqW+xNqbu5+WU32/1799f+0s0AHh6eqJbt27466+/cPHiRZv9fCQkJKBjx45wdHTEjh07\nUK5cOZ311vazzs7ORnh4OHx8fHDw4EEMGTIEPXr0wGeffYb58+djx44dOlcDKLF97777LhYtWoTI\nyEjt2SRA07YRI0agc+fOCAsLe+b7KK1tVapUQYcOHdC9e3fMnDkTcXFxSExMxLhx47T1AoZ/xqyt\nffl9jaOjIwYNGqSzb/4VDk/+jmRs+6w+MFWuXBlJSUl49OiR3rpbt27B29tbp9MuaP/8bQvaHyj4\ndKalGNu+J+3cuRO9evVCgwYNsGvXLri5uZm63GIxtm0TJkyAl5cXQkNDER8fr33l5OQgKysLV65c\nwe3bt83ZhCIZ276qVasCACpWrKi3Ln/Z/fv3TVRt8Rnbvj179kAIgb59++qt69OnD/Ly8nDw4EGT\n1mxJSu9bTElpfYu1MWU/LzdfX18ABfdblSpVAqB5hldRl91Z6+fj2rVraN++PdLT0xETE4P69evr\nbWNtP+vz588jMTERr776qt5f5/Mvg9+/f792mdLaN3PmTHz44YcYOnQolixZorNu0aJFuHjxIsaN\nG6fzO0T+mYurV6/i6tWr2u2V1ranNWjQAI0aNdL+PIo7BllL+/JDUH5f89xzz+ldZlfQ70jGts/q\nA1Pz5s2Rm5uLI0eO6CzPzMzEyZMni7yBL3///2/vzsOiONI/gH+ruWYYTgUUPJBbBBQRlTVGjvWI\nJlEWgz4RFTyj0TVCdGOOJ6JExY0CogRvAUWzEaImaqKQGGMOH+8cu+AmilckUUQFAlGB9/cHv5ml\nnRkEuc37eR6eh6mu6a63erprqqe6GgC++eYbrWXHjx+HpaUl3N3dm67ADdTY+NQ+/fRThIaGolev\nXsjNzYWlpWVzFLdBGhvblStXcP36dXh5ecHd3V3zd/36dfz0009wc3PDSy+91Jwh1KmpPpu6HgKq\nTrOzs2ui0jZcY+NTn7R0jRlWp7W1sfwN0dbPLU2lLZ5b2pumOs+3Beqbya9evaq1rPZ5y9vbGyYm\nJnqPDwDtKu5Lly4hKCgIpaWlyMnJQZ8+fXTma2/7Wn2e1jXJkK7zdFuKLzY2FkuXLkVUVBQ2b96s\ntfzKlSuorq7GyJEjZd8h9uzZA6Amltr7sS3Fpk9FRQUkqearvY+PT4OOsfYSn4GBAYCayTu6deuG\n4uJi2b1KgO7vSI2Or3GT/LW+H374gSRJorFjx8rSk5OTSQhBmZmZmrTCwkLKy8uj8vJyTdqDBw/I\nwcGBHB0dqaysTJN+7tw5kiSJZsyY0fxB1KGx8RERHTp0iBQKBfn6+lJxcXGLlLs+Ghtbbm4uZWdn\ny/6ysrLIzs6OHB0dKTs7m7755psWi+dhTbHvBg8eTJIk0ZkzZzRplZWVNGDAADI2NqarV682bxB1\naGx86umHdU0rPnLkSBJC0NmzZ5svgAZ41LS77fHcUtvjxEfUds8t7U1DjqW27vbt22RhYUFdu3aV\nfe6vX79OKpWKevbsqUkLDw8nAwMD2TNiSktLqXv37q3+HKbaHnV8qB/Ua21tTadOnapzXW1xX9cV\nX0VFBalUKnJwcKA7d+7IlsXHx5MQghITEzVpbSW+JUuWkBCCIiMj9eb57rvvtL5DZGdnU3BwMAkh\nKC0tjfbt26fJ31Zi0/d8s88//5wkSaLw8HBNWkOOsfYYn/rZWrU/g0REMTExJISgXbt2adIaG1+7\n7zAREf39738nIQSFhYXRpk2bNE/uDQ4OluVTz63/8ENAd+/eTZIkUd++fSklJYVWrFhBdnZ2ZG9v\nT9evX2/JUHRqTHwnT54khUJBCoWCkpKSaPv27Vp/ramx+06XtvIcJqLGx3f27FkyMzOjDh06UGxs\nLCUnJ9NTTz1FQgiKjY1tyVB0amx8o0aNIiEEDRkyhBITEykxMZGefvppEkLQ+PHjWzIULRkZGRQX\nF0dxcXFkZ2dH1tbWmtcPHzft8dzS2Pja+rmlvanvsdQebNy4kYQQ5O3tTQkJCbRixQrq3r07mZiY\nUE5Ojibfzz//TB06dKBOnTpRfHw8paSkkK+vLxkZGdHhw4dbMYL6Hx8lJSXk5OREQgiaN2+ezuOg\n9nOYiNrGvm7I8b9y5UoSQpCTkxMtX76cUlNTaeLEiSRJErm5uVFpaWmbim/dunUkhCBHR0fKyMjQ\n2h+1P4O66HsOE1Hrx0ZU84DdgIAAeuONN2j9+vWUlJREkyZNImNjY7K3t5c9ULyhx1h7i6+kpIQ8\nPT3JwMCAZs+eTampqRQREUFCCBo6dKjsodGNje+J6DBVVVXR6tWrycPDg0xMTKhr16706quvaj0s\nLyoqiiRJoqNHj2qtY//+/RQQEECmpqZkbW1N4eHhOp9i3xoaE19aWhoJIUiSJBJCaP1JktTS4cg0\nxb57WI8ePdpMh6kp4vv+++9p9OjRZGVlRQqFgvz8/Cg9Pb2lQqhTY+O7d+8evfPOO+Tl5aX58t27\nd29699139T58rqUEBQXJjpPax9DDJ9f2eG5pbHxt/dzS3tT3WGovPvzwQwoICCCVSkXm5uY0YsQI\nnb/45+Xl0ZgxY8jKyopMTU3p6aefps8++6wVSixX3+OjoKDgkcfBw+eFtrCvG3L8ExHt2rWLBg8e\nTObm5mRkZEROTk40d+5cKioq0srb2vGpz1f69smjvhyr36+rw9TasRERffDBB/Tcc89Rt27dSKFQ\nkFKpJC8vL1qwYAHduHFDK39DjrH2GF9RURHNnj2bHBwcyNjYmFxcXOitt96ie/fuaeVtTHyCiKgJ\nhhUyxhhjjDHG2BOn3U/6wBhjjDHGGGPNhTtMjDHGGGOMMaYHd5gYY4wxxhhjTA/uMDHGGGOMMcaY\nHtxhYowxxhhjjDE9uMPEGGOMMcYYY3pwh4kxxhhjjDHG9OAOE2OMMcYYY4zpwR0mpuWLL76AJElI\nT09v7aK0CbGxsZAkCVeuXGntojSJtLQ0SJKEL7/8UpPWWvtcV1maQlBQECRJgiRJMDIyatJ1qykU\nCs02goODm2UbjLHG4zZNrrnaNEmSMGXKlCZdZ0tSt0fqv507d9brfUFBQXBycmrm0tU4fvy4rIxL\nlixpke0y7jD9KZSUlCAuLg5+fn6wsLCASqWCl5cX/vGPf+DGjRt63yeEaMFSPnlee+01SJIEd3f3\n1i5KvQghZPv80qVLiI2NxXfffdeKpXp8tra22LFjB7Zv394s609PT8f27dthY2PDxwpjLYjbtJZT\n++KTJEkwNjZG165dMWHCBPznP//Ryv8k1PGbb76JHTt2YNCgQfV+T0vF7ebmhh07diAxMbFFt8sA\nw9YuAGte//3vfzFixAhcuXIFY8eOxYwZM2BkZIRvv/0Wa9aswbZt2/Dxxx8jICCgtYv6RKmsrERG\nRgZUKhV+/vlnfPnllxgyZEhrF0uvwMBAVFRUwNDwf6eES5cuYenSpXB2dkafPn1asXSPR6VSYcKE\nCc22/vHjxwOoaVwZYy2D27SWp1AosHnzZgBARUUFjh8/jvT0dBw4cAAnT55sNxcF62vYsGFttr3u\n2LEjJkyYgEuXLiE6Orq1i/Onwh2mJ1h5eTmef/55FBYWYv/+/Rg5cqRm2fTp0/Hyyy9j6NChGDNm\nDH744QfY2dm1YmnrRkQoLy+HSqVq7aLUy4EDB/Dbb79h69atmDt3LrZu3dpmT8BAzVUqY2NjncuI\nqIVLwxhj2rhNax2Ghoayi0/Tpk2Dp6cnFixYgOTkZKxbt64VS8dYy+AheU+wLVu24KeffsL8+fNl\nDYtav379sHz5cty8eRPvvvuu1nIiwtq1a+Hu7g6lUgkPDw+dJ8Z///vfCA8PR5cuXaBQKGBvb4+Q\nkBAcPHhQlu/evXtYvnw5vLy8oFQqYW1tjdGjR+PcuXOyfLXHm6ekpKBXr15QKpVYtWoVxo8fDxMT\nExQXF2uV4/z585AkCTExMbL0f/3rXxg8eLBm6EZAQACys7O13l9dXY0VK1bAyckJSqUSPj4+9R7D\n/LAtW7bAzs4OEydORHh4OLKyslBaWqqVTz2WPC8vDzExMXBwcICZmRlCQkKQl5cHAMjOzoafnx9M\nTU3h5OSETZs2aa1HPXY8NzcXAQEBUKlUsLe3x/z58/H7778/srzqOs/IyABQM5Y7JCQEADBlyhSt\ne3XquvdI33juTZs2oWfPnlAoFHBzc8OaNWv0dsbu3r2L1157Da6urlAoFLCzs8OECRNQUFDwyFge\nJSoqCpIkobi4GFOnToWtrS0sLCwwZswYFBYWAgA2bNgAT09PKJVKeHp64qOPPmr0dhljjcNtWo3W\naNMeNnz4cADAhQsXtJZ9++23CAwMhJmZGWxsbDBjxgytdig/Px8vv/wyvLy8NHH4+/tjy5YtWusr\nLi5GdHQ0XFxcoFQqYWNjA39/f6xatUorb33r5nHcvn0bM2bMgI2NDczMzBAcHIzTp0/rzX/q1Cn8\n7W9/g62tLRQKBXr27Inly5ejqqpKK292djb69OkDpVIJR0dHLF26FLm5uXzvXVtC7Ik1ZMgQkiSJ\nLly4oDdPeXk5GRkZkbOzsybtyJEjJISgfv36UZcuXSguLo6SkpJowIABJISgJUuWaPIWFRWRnZ0d\nde7cmWJjY2nbtm30z3/+k8aNG0eLFy/W5Lt//z4FBQWRiYkJzZgxg9avX0/x8fHk4uJCpqamdOrU\nKa3t+/r6Urdu3SguLo42bdpEn376KR08eJCEELRu3TqtWN544w0SQtC5c+c0aW+++SYJIWjUqFG0\nZs0aWrt2LQUHB5MQglJSUmTvf+WVV0gIQUFBQbR27Vp66623yMrKivz8/EgIQZcvX65XvRcWFpKh\noSEtWLCAiIi++uorEkLQxo0btfIuXryYhBDUv39/CgkJoXXr1tHbb79NKpWKunbtSlu2bCF7e3uK\ni4ujlJQU6tu3Lwkh6KuvvpKtRwhBvXv3JjMzM4qJiaHU1FQKDw8nIQT99a9/perqak3ebdu2kRCC\njh49qlXn6enpRER08eJFTd3NmjWLMjMzKTMzk3Jzc/WuQy0wMJCcnJxkaYmJiSSEoL59+1JCQgLF\nxcVR165dNfHUXs+dO3eoV69eZG5uTvPnz6dNmzbRkiVLqFOnTmRra1uv/aCrDGqRkZGaOg8LC6PU\n1FSKiYkhQ0ND8vf3p2XLlpGbmxutXLmSkpKSyNnZmYyMjKigoEDn+hwdHSk4OPiRZWKMNQ63aS3f\npgUGBpK5ublW+ocffkhCCJo4caImTR1jx44daeHChbRx40Z68cUXSQhBM2fOlL1//fr15O3tTYsW\nLaINGzZQQkICBQQEkBCCVqxYIcsbEhJCRkZGNG/ePNq8eTOtXbuWZs+eTc8995wsX0PqRpe62rX7\n9+9T//79SQhBkZGRlJqaSjNnziRra2tydXXVam/2799PxsbG5O3tTfHx8bRx40aKiooiAwMDCg8P\nl+V9//33SQhB7u7uFB8fT++++y55e3uTv7+/rF2uraCgQOuzy5oXd5ieYB06dCBLS8tH5vPx8SFJ\nkuj3338nov+d3C0sLOiXX37R5Lt//z4NGDCAjIyM6Nq1a0REtG/fPhJC0O7du+vcRkJCAgkh6PDh\nw7L0kpIS6t69OwUFBWnS1Nvv2LEj3bx5U5a/qqqK7O3tacCAAbL06upq6t69O/Xp00eTdvr0aRJC\n0JtvvqlVntDQULKwsKDS0lIiIsrPzychBA0dOlTWuThz5gwJIUiSpHp3mOLj40kIQfn5+Zo0T09P\nGjhwoFZedYdp9OjRsvTk5GQSQpC5ubmmromIbt68SQqFgl588UVZfiEECSFo3759snR1g/n+++9r\n0urTYdKXVtc61B7urNy+fZtMTU3Jy8uLKioqNOnXrl0jMzMzkiRJtp558+aRqakpff/997L1Xr58\nmSwsLCgqKkprm48qQ23qDtPcuXNl6TExMSSEoO7du2s+F0RE33//PQkh6PXXX9e5Pu4wMdYyuE1r\n+TYtMDCQzMzMqKioiG7evElXrlyh3bt3U9euXUmSJFn8QggyMDCgEydOyNbx7LPPkpGRkWZ/EJHs\n/9oxBwUFkaWlJT148ICIai6gCSFozpw5dZazIXWjT13t2oYNG0gIQbGxsbL0pKQkEkLI2puKigrq\n1KkTBQYGUlVVlSy/+uLhF198QUREDx48IAcHB+rcuTPduXNHk6+srIycnZ25w9SG8JC8J1hJSQks\nLS0fmc/CwgJAzTCo2iIiIuDg4KB5bWRkhOjoaFRWVuLjjz8GAM36Dx48qHPImdqOHTvg6ekJPz8/\nFBUVaf7u3buHoUOH4quvvsK9e/dk75k8eTJsbGxkaZIkISIiAidPnsT58+c16V988QWuXr2KyMhI\nTVpmZiaEEJg8ebJsm0VFRXj++edRWlqK48ePAwD27dsHAIiJiZHNOtO3b18MHz68QffxbN26FYMG\nDYKHh4cmbdq0aThx4oTOWYUAYN68ebLXgwcPBgCEhoaiS5cumnQbGxt4eHjg559/1lpHz549MXr0\naFnaokWLAAB79uypd/mb2uHDh1FRUYE5c+ZAoVBo0rt06YKIiAhZ3RIRMjMzMZamhCIAAAyQSURB\nVGTIEDg4OMj2mampKQYOHIjDhw83Sbnmz58ve62u88jISJiZmWnSfXx8YGFhobPOGWMth9u01mnT\nfv/9d9ja2sLOzg6Ojo4YN24cqqurkZaWhmHDhsny/uUvf0H//v1lacHBwaisrMSlS5c0aaamppr/\n//jjD9y6dQu3bt3CsGHDUFJSoqkLpVIJExMTHD9+HJcvX9ZbxvrUzbffflvvmB+2d+9eGBoa4tVX\nX5Wlz549G+bm5rK0nJwc3LhxA1FRUSguLpaVRT2UVN2OnT59GoWFhYiKipJ9tlUqFWbNmvXY5WVN\njyd9eIJZWFigpKTkkfnUeR5uiDw9PbXyqtPU95IEBgZi8uTJSEtLQ2ZmJvr374+hQ4di/Pjxsvfn\n5eXhjz/+gK2trc4yCCFQVFQk6xzom3knMjISq1evRkZGBpYtWwYAyMjIgKGhISIiImTbJCL07NlT\n7zZ/++03AMDFixcBQGdeT0/Pen9JP3bsGH766SdERETIvmAPGjQIQghs2bIFq1ev1nqfs7Oz7LW1\ntTUA6LwXyMrKClevXtVZzod17twZlpaWTXLvz+N6VN3WdvPmTRQXF+PQoUN6PysGBgZNUq6G1vmt\nW7eaZLuMscfDbVrLt2lAzSx5+/fvB1AzAUSnTp1kFwRre/i8CtTM7AZAdg4tKytDbGwsPvjgA1y7\ndk3rPbdv3wYAGBsbIykpCa+88gqcnJzQq1cvhISEIDQ0VHOfLVC/uqlryvlHuXjxIuzt7WUX09Tl\nc3Z2lnXO1fcfT5069ZFlUX/udNXnkzb7YHvHHaYnmLe3N44dO4YLFy7AxcVFZ57y8nLk5+ejR48e\nsis+DZGWloaFCxfik08+wbFjx7B69WosW7YMSUlJmDNnDoCaXw569+6NhIQEvet5+MqbvvJ4e3vD\n19cXmZmZWLZsGcrLy5GdnY3hw4fLZkUiIggh8Omnn+r9kt2rV6+Ghlsn9Q2rsbGxiI2N1Vq+Y8cO\nrFy5UjZ9N6C/E6AvvSFXB5tDXc9+qKysfOz1quMaNmwYXnvttcdeT33oi6Gt1jljf3bcprV8mwbU\ndJJqd07qUtcFrdrn0AkTJuDAgQN46aWXMGTIEHTs2BEGBgY4cOAAEhMTUV1drcn70ksvYcyYMThw\n4ACOHj2KrKwsrFu3DuPHj8euXbs0626NutFFHeeqVavg6+urM0/tXzpZ+8AdpifY2LFjcezYMWze\nvBkrVqzQmScjIwOVlZUICwvTWqZr+Jg67eGrSF5eXvDy8sKCBQtw9+5dDBw4EIsWLdI0Lu7u7rhx\n4waCg4Ob5EFrkZGRiI6OxpEjR3D9+nWUlZXJhi6ot3no0CF069ZN71UnNXXjm5eXp/ULg75hdA8r\nLS1FVlYWhg8fjpkzZ2ot/+677xAXF4ePPvpIZ303lvqqVm2FhYW4e/euzqt+j1LXfurQoQMA6JzZ\nqaCgACYmJprXtetWPcue2sN1a2trCysrK9y9e7feDTRj7M+B27SWbdOay507d7B//35ERkbivffe\nky3T98tX586dMW3aNEybNg3V1dWYNGkSdu3ahQULFqBfv34NqpvH4ezsjJycHJSWlsqG4N27dw8X\nL17U/IoG/O+XIVNT00e2Y+p9k5+fr7Ws9hBN1vr4HqYn2PTp0+Hq6oqEhAQcOnRIa/mZM2fw+uuv\nw87ODgsXLtRanpmZiV9++UXz+v79+0hMTIShoSGee+45ADU/m9e+EgTUDIPo0aMHKioqNGO4J0+e\njF9//VXv1Tj1MIL6mjBhAgwNDZGRkYGMjAxYWVlhzJgxsjyTJk0CALzxxhtaZXx4m6NHj4YQAgkJ\nCbK8Z86cQW5ubr0axPfffx/l5eWYNWsWwsLCtP4WLVoEU1NTbN26tUGx1tf58+c149bVVq5cCaDm\nXqiGUg890DUUTd0g5OTkyNJ37dqlmZpbbdiwYVAqlUhJSUFFRYUm/dq1a9i5c6esbtXj+U+cOKF3\nKtibN282OJaH8dPRGWt/uE1r2TatuRgYGEAIoRVDYWEhNm/eLCtbRUUFysvLZfkkSYKPjw+A/120\na0jdPI7Q0FBUVVVpDalPTU3VutdtxIgRsLOzQ3x8vGZoYW0VFRUoKysDAPj7+8Pe3h5paWm4c+eO\nJk9ZWRnWr1/fqDKzpsW/MD3BTE1N8dFHH+GZZ57Bs88+i7FjxyIwMBCGhoY4ceIEtm/fDgsLC+zd\nu1fnA/7c3d0xcOBAzJo1C2ZmZti5cydOnTqFt99+WzMuOz09HYmJiQgLC4OLiwuMjIxw9OhRHD58\nWPN8CQB45ZVXkJOTg4ULF+Lzzz9HcHAwLCwscOXKFXz22WdQKpX4/PPP6x2bra0tRo4ciaysLPzx\nxx+YPn261oNX/f39NUPjfH19ER4eDnt7exQWFuL06dP45JNPNI2fh4cH5syZg3Xr1iEkJARhYWG4\nceMGUlJS4Ovri7Nnzz6yTFu2bIFKpcIzzzyjc7lSqcTIkSOxd+9eXL9+vVE/yesaHubt7Y2JEydi\nxowZcHV1xZEjR5CdnY2goCCMHz++wdvw8vKCubk53nvvPZiamsLS0hKdOnVCcHAwPDw8MHToUGzY\nsAFEhD59+uDcuXPYu3cvXF1d8eDBA816rKysEBcXhwULFmDQoEGYNGkSysvLsWHDBri7u2vV7bJl\ny/D1119j3LhxGDduHAYOHAhjY2NcvnwZBw8ehL+/P7Zt29bwSquFh9cx1v5wm9aybZpaU58vzc3N\nMXz4cOzYsQNKpRL+/v64fPkyNm7cCGdnZ5w6dUqT9/z58wgMDERYWBi8vLxgbW2NvLw8rF+/Hs7O\nznj66acbXDePY8qUKdi4cSOWLl2KgoICBAQE4OzZs8jKyoKLi4tsKLqpqSkyMjIQGhoKDw8PTJ06\nFS4uLrhz5w7y8/OxZ88e7N27F0OGDIGBgQFWrVqFiIgIDBgwANOmTYOBgQHS0tLQsWNHXLp0iS/w\ntRUtOicfaxV3796lpUuXkq+vL5mZmZFSqSRPT09auHAh/fbbb1r5jxw5QpIkUXp6OiUnJ5ObmxuZ\nmJiQu7s7JScny/KeO3eOIiMjydXVlVQqFVlYWJCvry8lJCTQ/fv3ZXkrKyspOTmZ+vfvTyqVilQq\nFbm7u9PEiRMpJydH5/brkp2drZke9ZtvvtGb78CBAzRixAjq0KEDmZiYUPfu3WnUqFG0YcMGWb7q\n6mpatmwZOTo6komJCfn4+NDOnTspNjb2kVOw/vjjjySEoBdeeKHOMu/atYskSdI8Z2Lx4sU6113X\nlKFBQUFaU2YLIWjKlCn02Wef0cCBA0mpVFLnzp1p3rx5VFZWJsu7bds2ram89dX5wYMHyc/PjxQK\nBQkhZNNn//rrrxQeHk4WFhZkZmZGo0aNovz8fJ3lI6qZltXDw4NMTEzIzc2N1qxZo7MsRDXPUomL\niyMfHx9SKpVkbm5OvXr1opkzZ2pNWatLXdOKR0VFkSRJWul1fe569Oihd+pwnlacsZbFbVrzt2lq\nQUFBOp/DpIu6HXqYrvN8UVERTZ8+nRwcHEihUFDv3r1p8+bNlJaWJst769Ytio6OJl9fX7KysiKl\nUklubm4UHR1Nv/7662PXjS51TStORFRcXEzTpk2jjh07kkqlouDgYDp9+rTeNu/HH3+kiRMnUpcu\nXcjY2Jg6depETz31FL3zzjtUXFwsy7t7927q3bu3psyLFy+mPXv26J3inqcVb3mCiC+1MtbeSZKE\nqKioZhvu194EBQWhoKAAZ86cAQDZ+PKmcuvWLRAR/Pz84Orq2qCryYwxxtqWtLQ0TJ06FXv37sWg\nQYNgYWGh9StfS1q9ejUWLlyI48ePY8CAAQCAqqoq3L59G1evXkW/fv0QGxuLt99+u9XK+GfC9zAx\nxp44QghcvXoVtra2sLe3b5ZtdOnSBXZ2djqnxGWMMda+qIe+hYaGws7ODllZWS2y3QcPHqCqqkqW\nVlZWhpSUFNjY2MDPz0+TfvLkSdjZ2aFfv348VK+F8T1MjLEnzurVqzU30EpS81wXOnTokKaRUz/D\niTHGWPs0YsQI5Obmal57eXm1yHYvXLiAkSNH4sUXX0SPHj1QWFiI9PR0XL58GampqbLHkHh5ecnK\nqOu5gax58JA8xp4APCSPMcYYa3+Ki4sxd+5cfP3117hx4wYMDQ3Ru3dvREdH44UXXmjt4rH/xx0m\nxhhjjDHGGNOD72FijDHGGGOMMT24w8QYY4wxxhhjenCHiTHGGGOMMcb04A4TY4wxxhhjjOnBHSbG\nGGOMMcYY04M7TIwxxhhjjDGmx/8BDVAioQ7+ocEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x36b4e10>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Plotting - K1\n",
      "font = {\n",
      "        'size'   : 16}\n",
      "plt.rc('font', **font)\n",
      "fig=tidetools.plot_scatter_pha_amp(K1_amp,K1_amp_obs,K1_pha,K1_pha_obs,'K1',figsize=(14,6))\n",
      "\n",
      "ax_amp,ax_pha = fig.axes\n",
      "min_value, max_value = ax_amp.set_xlim(0, 1.2)\n",
      "ax_amp.plot([min_value, max_value], [min_value, max_value], color='red',lw=2)\n",
      "\n",
      "min_value, max_value = ax_pha.set_xlim(0, 360)\n",
      "ax_pha.plot([min_value, max_value], [min_value, max_value], color='red',lw=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "[<matplotlib.lines.Line2D at 0x38c4c50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5czH06tVL5/qzZ88KLy8voVAoxJdffqmxj0KhUP8z/yf/72dp3bq1UCgU4uDBg0/dbsuW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\nkW8xISIiIiIiIt9iQkRERERERL71C5RTsptk2QH8AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4f02490>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Port Townsend\n",
      "stn='PortTownsend'\n",
      "fT1 = NC.Dataset(name+stn+'.nc','r')\n",
      "time = fT1.variables[\"time_counter\"][:]/3600.  # want hours not seconds\n",
      "ssh = fT1.variables[\"sossheig\"][:,0,0]\n",
      "\n",
      "fitted, cov = curve_fit(double,time[ts:],ssh[ts:]) \n",
      "if fitted[0] < 0:\n",
      "    fitted[0] = -fitted[0]\n",
      "    fitted[1] = fitted[1]+180\n",
      "\n",
      "M2_ampPT=fitted[0]*M2ft\n",
      "pha = fitted[1]+M2uvt\n",
      "if  pha > 360:\n",
      "    pha=pha-360\n",
      "M2_phaPT=pha\n",
      "\n",
      "K1_ampPT=fitted[2]*K1ft\n",
      "pha= fitted[3]+K1uvt\n",
      "if  pha > 360:\n",
      "    pha=pha-360\n",
      "K1_phaPT=pha\n",
      "\n",
      "print M2_ampPT,M2_phaPT,K1_ampPT, K1_phaPT"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.537417179642 343.912392102 0.750546673222 270.711264264\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "At Port Townsend, M2 is pretty off from both Foreman et al 1995 and 2004. (Closer to Foreman et al 1995 )"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def rms(diff):\n",
      "    return np.sqrt((diff**2).mean())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diff =M2_amp-M2_amp_obs\n",
      "print 'M2 amp mean error', np.mean(abs(diff))\n",
      "print 'M2 amp rms error', rms(diff)\n",
      "\n",
      "diff =M2_pha-M2_pha_obs\n",
      "diff = diff +360*(diff<-180) - 360*(diff>180)\n",
      "\n",
      "print 'M2 pha mean error', np.mean(abs(diff))\n",
      "print 'M2 pha rms error', rms(diff)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M2 amp mean error 0.0712630845562\n",
        "M2 amp rms error 0.125563550289\n",
        "M2 pha mean error 6.8524786232\n",
        "M2 pha rms error 13.1054555665\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diff =K1_amp-K1_amp_obs\n",
      "print 'K1 amp mean error', np.mean(abs(diff))\n",
      "print 'K1 amp rms error', rms(diff)\n",
      "\n",
      "diff =K1_pha-K1_pha_obs\n",
      "diff = diff +360*(diff<-180) - 360*(diff>180)\n",
      "\n",
      "\n",
      "print 'K1 pha mean error', np.mean(abs(diff))\n",
      "print 'K1 pha rms error', rms(diff)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "K1 amp mean error 0.02552682652\n",
        "K1 amp rms error 0.0447435264431\n",
        "K1 pha mean error 2.70244129003\n",
        "K1 pha rms error 4.52104797531\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diff =M2_amp-M2_amp_obs\n",
      "print diff"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[-0.12226278 -0.04050405 -0.01621579 -0.17383465 -0.15478292 -0.08726851\n",
        " -0.09313465 -0.02236256  0.00449838 -0.00519835  0.04330538  0.00559139\n",
        " -0.06427212 -0.13347857  0.0309048  -0.14615282  0.00803197 -0.01661162\n",
        " -0.02174928 -0.00504801  0.01896887 -0.54028503 -0.05359559  0.00642456\n",
        "  0.0449969   0.00783555 -0.15361937  0.00809151  0.03760346]\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "bad station for M2 Amp living in index 21"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "print stations[21]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SeymourNarrows\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def complex_diff(Ao,go,Am,gm):\n",
      "    #calculates complex differences between observations and model\n",
      "    import numpy as np\n",
      "    import math\n",
      "    D = np.sqrt((Ao*np.cos(np.pi*go/180)-Am*np.cos(np.pi*gm/180))**2 + \n",
      "                (Ao*np.sin(np.pi*go/180)-Am*np.sin(np.pi*gm/180))**2)\n",
      "    \n",
      "    return D"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "D_M2= complex_diff(np.array(M2_amp_obs),np.array(M2_pha_obs), np.array(M2_amp),np.array(M2_pha))\n",
      "\n",
      "print 'M2 Complex diff mean:', np.mean(D_M2)\n",
      "\n",
      "D_K1= complex_diff(np.array(K1_amp_obs),np.array(K1_pha_obs), np.array(K1_amp),np.array(K1_pha))\n",
      "\n",
      "print 'K1 Complex diff mean:', np.mean(D_K1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M2 Complex diff mean: 0.115614209695\n",
        "K1 Complex diff mean: 0.043362009465\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}