{
 "metadata": {
  "name": "jarkus_transect_validate"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from netCDF4 import Dataset\n",
      "import numpy as np\n",
      "from jarkus.transects import Transects"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url0 = 'http://opendap.deltares.nl/thredds/dodsC/opendap/rijkswaterstaat/jarkus/profiles/transect.nc'\n",
      "url1 = 'http://dtvirt5.deltares.nl:8080/thredds/dodsC/opendap/rijkswaterstaat/jarkus/profiles/transect_r20150930.nc'\n",
      "\n",
      "def getdata(url, variables=[], tidx=None):\n",
      "    with Dataset(url) as ds:\n",
      "        data = {}\n",
      "        for var in variables:\n",
      "            if tidx is None:\n",
      "                data[var] = ds.variables[var][:]\n",
      "            else:\n",
      "                data[var] = ds.variables[var][tidx,]\n",
      "    return data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "getdata(url0, variables=['time'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "{'time': array([ -1645.,  -1280.,   -915.,   -549.,   -184.,    181.,    546.,\n",
        "          912.,   1277.,   1642.,   2007.,   2373.,   2738.,   3103.,\n",
        "         3468.,   3834.,   4199.,   4564.,   4929.,   5295.,   5660.,\n",
        "         6025.,   6390.,   6756.,   7121.,   7486.,   7851.,   8217.,\n",
        "         8582.,   8947.,   9312.,   9678.,  10043.,  10408.,  10773.,\n",
        "        11139.,  11504.,  11869.,  12234.,  12600.,  12965.,  13330.,\n",
        "        13695.,  14061.,  14426.,  14791.,  15156.,  15522.,  15887.,\n",
        "        16252.,  16617.])}"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "getdata(url1, variables=['time'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "{'time': array([ -1645.,  -1280.,   -915.,   -549.,   -184.,    181.,    546.,\n",
        "          912.,   1277.,   1642.,   2007.,   2373.,   2738.,   3103.,\n",
        "         3468.,   3834.,   4199.,   4564.,   4929.,   5295.,   5660.,\n",
        "         6025.,   6390.,   6756.,   7121.,   7486.,   7851.,   8217.,\n",
        "         8582.,   8947.,   9312.,   9678.,  10043.,  10408.,  10773.,\n",
        "        11139.,  11504.,  11869.,  12234.,  12600.,  12965.,  13330.,\n",
        "        13695.,  14061.,  14426.,  14791.,  15156.,  15522.,  15887.,\n",
        "        16252.,  16617.])}"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with Dataset(url0) as ds:\n",
      "    cs = ds.variables['cross_shore'][:]\n",
      "    z = ds.variables['altitude'][-1,500,]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "['altitude', 'cross_shore']"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['altitude'].shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "(2268, 1925)"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.interpolate import Rbf, InterpolatedUnivariateSpline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ius = InterpolatedUnivariateSpline(z, cs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(data['cross_shore'], data['altitude'][100,])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(7510.0,\n",
        " masked_array(data = [-- -- -- ..., -- -- --],\n",
        "             mask = [ True  True  True ...,  True  True  True],\n",
        "       fill_value = -9999.0)\n",
        ")"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ius(3)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "array(nan)"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tr = Transects()\n",
      "tr.set_filter(id=7003800, time=-1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = tr.get_data('cross_shore')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = tr.get_data('altitude')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(x, z.flatten())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "[<matplotlib.lines.Line2D at 0xb4ad750>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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77SopKVFJSYmeeuqpmBSazILBoNslJAzWxVWsi6tYF86IOgD8fr/q6+v1wAMP3PBabm6u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\nAABYigAAAEsRAABgKQIAACz1P5OZJSLvVMvgAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.patches as mpatches\n",
      "\n",
      "x0 = np.array([0, 0, 45, 50, 80, 280, 680])\n",
      "z0 = np.array([-5, 0, 15, 15, 3, -1, -5])\n",
      "\n",
      "x1,z1 = np.copy(x0), np.copy(z0)\n",
      "x1[4] = 75\n",
      "x1[5:] += 95\n",
      "z1[4] = 5\n",
      "\n",
      "x2,z2 = np.copy(x0), np.copy(z0)\n",
      "x2[4] = 81.25\n",
      "x2[5:] -= 23.75\n",
      "z2[4] = 2.5\n",
      "\n",
      "x = {0:x0, 1:x1, 2:x2}\n",
      "z = {0:z0, 1:z1, 2:z2}\n",
      "\n",
      "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14,3), sharey=True, sharex=True,\n",
      "    subplot_kw=dict(xlim=(799,0), ylim=(-5,16), xlabel='kustdwarse afstand [m]'))\n",
      "axes[0].set_ylabel('hoogte [m] t.o.v. NAP')\n",
      "fig.subplots_adjust(left=.04, right=.98, wspace=.02)\n",
      "\n",
      "titles = ('a) gevoeligheid waterlijn', 'b) gevoeligheid duinvoet bij hoog strand', 'c) gevoeligheid duinvoet bij laag strand')\n",
      "\n",
      "for i, ax in enumerate(axes):\n",
      "    ax.set_title(titles[i])\n",
      "    ax.add_patch(mpatches.Polygon(np.vstack([x[i], z[i]]).T, fc='yellow', ec='orange'))\n",
      "\n",
      "for i, ax in enumerate(axes):\n",
      "    xlim = ax.get_xlim()\n",
      "    ax.hlines(3, xmin=xlim[0], xmax=xlim[1], linestyle=':')\n",
      "    ax.add_patch(mpatches.Rectangle([0,-5], -np.diff(xlim), 4, alpha=.3, zorder=-10))\n",
      "    ax.add_patch(mpatches.Rectangle([0,-5], -np.diff(xlim), 6, alpha=.3, zorder=-10))\n",
      "\n",
      "axes[0].annotate(\"\", xy=(230, 2), xytext=(230, 0), arrowprops=dict(arrowstyle=\"<->\"))\n",
      "axes[0].annotate(\"\", xy=(230, 2), xytext=(130, 2), arrowprops=dict(arrowstyle=\"<->\"))\n",
      "\n",
      "axes[1].annotate(\"\", xy=(175, 5), xytext=(175, 3), arrowprops=dict(arrowstyle=\"<->\"))\n",
      "axes[1].annotate(\"\", xy=(75, 5), xytext=(175, 5), arrowprops=dict(arrowstyle=\"<->\"))\n",
      "\n",
      "axes[2].annotate(\"\", xy=(85, 4), xytext=(85, 2), arrowprops=dict(arrowstyle=\"<->\"))\n",
      "axes[2].annotate(\"\", xy=(85, 4), xytext=(78.75, 4), arrowprops=dict(arrowstyle=\"<->\"))\n",
      "\n",
      "fig.savefig('profile_sensitivity.png', dpi=600)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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bCg0NVampqeq9995TV199tVdZ3bt3V4sWLfL5OS6ah4t6/vnn1cCBA1VKSkqJ8ZXUIT90\n6FCJZSmlVFZWljKZTJ6Gbmmfw8WLF6vu3bt7nd+iRQvpkFfATz/9pBo3buzzsyZtJmPbTOvXr1dx\ncXFejxXtDJelQ17WtsyECRPUvffeq5RSKjs7W5nNZs/fizZt2nh+bFBKqf/85z8qISFBKeX/fSxr\nh7xofh86dKiaNm1aic+hrB3y840bN049/vjjJe4rml+cTqcKDQ31/CCglPu1Of91LrRgwQLVo0cP\nT/u0qJLyvslkUuvWrfP5HJQq3t7t06ePZ9+uXbtURESEUsr/56M2kiHrF4j33nuPLl26EB0dTXR0\nNDt37vQaclQ4UU5UVFSxc48ePUrz5s29His6dCYtLY2ZM2d6yo6OjubIkSMcPXoUgJEjR7Js2TIA\nli5dyp133ukpt2HDhpjNZk9ZLVu2JCMjw1Pubbfd5imzY8eOhISEcPz48VKfa2Zmpld8LVq0KHZM\ns2bNPP+PiIjAZrN56vz444+9nsuPP/7IsWPH/NbTsmXLUuPq1asXSUlJbNy4kV69etGrVy/Wr1/P\nhg0b6NWrl+e4V199lY4dOxIVFUV0dDTnzp3zGqZaVFpaGvn5+cTGxnriHTNmDCdPnvQcU9ZhTiWJ\niYkhKOiPNBEZGel5rcSF4fzPeOH3upDVavWZNxo2bEi9evU8j7Vo0cIzNNDfd+38vHHbbbdRr149\nTp06RX5+Pq1atfKKq2jeGDdunKfMmJgYAM9+X8qSN5o2ber5//l5Y/PmzV7PZenSpSXmqvLkjaNH\njxIdHU1ERITnsVatWlVqCHLR3Ff0+zx48GA2bdrEsWPH2LBhA0FBQVx33XWemM9/vZ1OJ8ePHy9T\nzizLfeT+PmcVeX4RERHoul7iczCbzcTExJCRkUFmZmax96FVq1aefed/jkvLqU899RRt27alb9++\ntGnThpdfftlvzEXLKygo4Nlnn6Vt27Y0aNCAiy66CMDrb4Cvz+HRo0eLfW4rk/8vZIcPH6ZVq1Ze\nf/+KkjaTsW2mkl7Dot/f8iqtLTNixAg+++wz8vLy+Oyzz+jatasnzqNHjxbLfYXvk7/3sSxKyu9F\nb6WpiM2bN9O7d2+aNGlCVFQUc+fO9bT3XS5XsfxiMpk4deoUJ0+exOl0+v0sFBo1ahQ333wzw4cP\np3nz5jzzzDNe94aXlPfPz0f+2rtFc11kZCQ5OTkUFBT4/HxU5m9jdZMO+QUgLS2NBx54gLfeeosz\nZ86QlZXFpZde6vXB3bNnDwkJCdSvX7/Y+bGxscUas0XvyWzZsiX/+Mc/yMrK8vyz2WwMGzYMgDvu\nuIOkpCQyMjL44osvGDlyJABxcXGcOXPGq4OXnp7uSQAtW7bkq6++8irXbrcTGxtb6vONjY3l8OHD\nnu2i//enZcuWjBo1yqtOq9XK008/XWI9RV8HX/epFurVqxfr1q1j48aNJCYmejro69ev93TIN27c\nyCuvvMLHH3/M2bNnycrKokGDBp736vwEFx8fT3h4OKdPn/bEe+7cOc99TCWdI0R5nP8Zj4uL82xn\nZGSQl5fnuR+sqNjYWM6cOYPD4fA8dvjwYc/n0d937aabbuLkyZP89ttvfPjhh5680ahRI0JDQz1L\n9RXGVTRvvPPOO17l6rrONddcU+rzrGze6NWrV7Hn8tZbb5VYT1nzRmxsrCfvFUpLS/O8hmaz2Wuf\ny+Xy+jGuPKKjo+nbty/Lly9n6dKljBgxwrMvLi6u2OsdEhJCs2bN/L6PZc0/578m5ze2Kuv856Dr\nOqdPn6ZFixbExcWRlpbmdXxaWhotWrQo8XNc2ntWv359Xn31VQ4cOMDKlSt57bXXWLduHeD7tSj6\n+JIlS1i5ciXfffcd586d49ChQ0DZJiuKi4vz+twqpcr1ORZ/iI+PJz09HZfLVeJ+aTP98VyMaDOV\n9BoW/Y6en/tK+gGgrLmnY8eOtGrVirVr17J06VLP6w0l577C3OTvfSxL/SXl96J/YyvSfhs5ciSD\nBg3iyJEjnD17ljFjxnjyx9KlS4vlF/X7PBONGzcmJCSkzJ+FkJAQnn/+eXbt2sVPP/3E6tWree+9\n90qNu+jj/tq7pfH1+ajN7V3pkF8AdF3HZDLRqFEjCgoKWLhwITt37vQ6Zv369fzpT38q8fwePXoQ\nHBzMm2++idPpZMWKFfz3v//17L///vt5++232bJlC0opdF3nyy+/9PzRaNy4MYmJidx99920bt3a\n03iPj4+nR48ejB8/ntzcXLZv386CBQu46667ABgzZgzPPfecJ2mfPHmSlStX+n2+Q4cO5V//+hdH\njx7l7NmzvPzyy8W+pL6+8HfddRerVq3i66+/xuVykZOT4/nDWFI9b7zxBhkZGWRlZTFjxoxS4yrs\nkOfk5BAXF8d1113HV199xZkzZzwTtFmtVkJCQmjUqBF5eXlMnTrVa/KkZs2akZqa6ok/NjaWvn37\n8sQTT2C1WikoKODAgQNs2LDB7+skhD9KKd566y0yMjI4c+YML7zwgtf6u+vXr+fGG28kNDS02Lmt\nWrWiW7duTJ48mfz8fDZt2sTq1as9+/1910JDQxkyZAhPPvkkWVlZ9OnTB4Dg4GCGDh3KP/7xD2w2\nG2lpacyaNcsrb7z44oueiWrOnTvnNcHa+c+v8Ls0dOhQFi5cSHJyMna7nWnTphU71pc///nP7Nu3\njw8++ID8/Hzy8/P573//61mm5vx6Fi1axJ49e7Db7cUm3CnpNZw0aRL5+fn88MMPXq9h+/btycnJ\nYc2aNeTn5zN9+nSvSczKoujzGjlyJIsXL+bTTz/1apSOGDGCWbNmkZqais1m47nnnmP48OEEBQX5\nfR+bNm3KgQMH/MYxffp0HA4Hu3btYtGiRZ5GbWUVPr8RI0awcOFCfvvtN3Jzc3nuuee45ppraNmy\nJbfeeiv79u1j2bJlOJ1Oli9fTnJyMv369aNly5Ylfo59Nfy+/PJLUlJSUEphsVgIDg72XJkry2th\ns9kIDw+nYcOG6LrOc889V+LzKcmf/vQndu3axeeff47T6eSNN94osaMi/Lv66quJjY3l2WefxW63\nk5OTw08//eTZL20mN6PaTD169CAkJIQ33niD/Px8PvvsM6/X8LLLLmPXrl389ttv5OTkMHny5GLx\nludq6ciRI3n99dfZuHGj16R6I0aMYPr06Zw6dYpTp04xdepUz+vt730sa+4rzO8bN27kyy+/9NRf\n3udQyGazER0dTVhYGFu2bPGaKK60/BIcHMzgwYOZPHkyDoeD5ORk3n//fZ+5LikpiR07duByudA0\njdDQUIKDg8v83P21d0vTvXv3Uj8ftZF0yC8AHTt25O9//zvdu3enWbNm7Ny50zMUsdCHH37Igw8+\nWOL5oaGhfPbZZ8yfP5/o6GiWLFlCv379CAsLA6Br1668++67PPLIIzRs2JB27dp5fiUrNHLkSL77\n7juvRh7AsmXLSE1NJS4ujsGDBzN16lTPmo3jxo1jwIAB9O3bF4vFQvfu3dmyZYvnXF9J4v7776dv\n37507tyZrl278uc//9mrUXT+uUXXLmzRogUrVqzgxRdfpEmTJrRs2ZKZM2eWmBTvv/9+br75Zi67\n7DK6devG7bffXuqvc+3atUPTNM+MlRaLhTZt2nDttdd6zrvlllu45ZZbaN++PQkJCURERHgN6ypM\n1DExMXTr1g1w346Ql5fnmVV6yJAhnkZYSesynv9YSft97RMXFpPJxJ133ukZftuuXTsmTJjg2b9k\nyRLGjBnj8/wlS5awadMmYmJimDhxIsOGDfPkDV/ftaIz+xbmjSFDhnh9f2fPno3ZbKZ169b07NmT\nO++8k3vuuQeAQYMG8cwzzzB8+HAaNGhAp06d+M9//uP1nIr+v+h379FHH6V37960b9+e7t27AxAe\nHl7s2PPL0jSNr7/+mg8//JDmzZsTGxvL+PHjPbPxnl/PY489xg033ED79u258cYbS/2eLV26lM2b\nN9OwYUOmTp3K6NGjPfsaNGjAnDlz+Otf/0qLFi2oX7++15BAf9/18x8bMGAAKSkpxMbGeq2vfO+9\n9zJq1Ciuv/56WrduTWRkJLNnzwb858xx48bxySef0LBhQx577LESn6PJZKJXr160bduWm266iaee\nesoz43hl1pYteu6NN97ItGnTuP3224mLi+PQoUN8+OGHgDufrl69mpkzZ9KoUSNeffVVVq9eTcOG\nDYHSP8fn279/P3369EHTNHr06MHDDz/sGQE1fvx4pk+fTnR0NK+99lqx1x/gL3/5C61ataJ58+Zc\neumldO/e3edntuhj4B498vHHH/Pss8/SqFEjUlJSiv2tF2UTFBTEqlWrSElJoWXLlsTHx/PRRx95\n9kubydg2U+FruGjRImJiYvjoo4+4/fbbPfvbt2/P888/z0033cTFF19Mz549S/2e+MshI0aMYMOG\nDdx4442e7z3AhAkT6NatG507d6Zz585069bN8zfQ3/tY0ve9KJPJ5LndMC4ujlGjRjF37lzat29f\n4nMoTdHj5syZw/PPP4/FYmHatGleP276yy9vvvkm586do1mzZowePZoRI0b4zHXHjh1jyJAhNGjQ\ngI4dO3rWHoey5X1/7d3Scl1YWFipn4/ayKRq84B7ERCrVq1iyZIlnsZJWVx99dX87W9/82oc1lRr\n167loYce8hp2JEo2evToYp0uIUqyfft2HnroIX788ccynzNs2DA6duzIpEmTDIwsMPbs2UOnTp3I\ny8vzeR+pqFoHDx7k4osv9rnMXlWpTZ9jEXjSZhJV7fvvv+f+++8v0xX3QHrmmWc4ceIECxcurNJ6\nL0TSyhD079/f7x+WDRs2cOzYMZxOJ4sXL2bnzp3ccsstVRRh+RQO4XQ6nWRkZDBlyhQGDx5c3WHV\neE6nk71793omERKiNJ07d/bbGf/ll184cOAABQUFrF27lpUrVzJo0KAqirD8Pv/8c3Jzc8nKyuKZ\nZ55hwIAB0hmvQXbu3ElCQkKV11vbPsfCWNJmElVt586dtG7d2vB69u7dy/bt21FKsWXLFhYsWMBt\nt91meL0CQqo7AFE77N27l6FDh6LrOm3atOGTTz7xmv2wJlFKMXnyZIYPH05ERAT9+vVj6tSp1R1W\njdesWTOuvPLKWj/sR9Qcx44dY/DgwZw+fZr4+HjefvttLrvssuoOy6d33nmHe+65h+DgYBITE5kz\nZ051hyR+99prr/HKK6/w5ptvVnndte1zLKqftJlEoIwbN47Vq1ezePFiw+uyWq2MGDGCo0eP0rRp\nU5588kkGDBhgeL1ChqwLIYQQQgghhBDVokZfIZcJpYQQdVlJv4dK3hNC1GWS94QQF5KyXPuu8TfH\nFU77X9P+TZo0qdpjkNgktprwT2Kr2D/JexJbTfgnsUlsVfmvtuW9mvxaSmwSm8RW82MrqxrfIRdC\nCCGEEEIIIeoi6ZALIYQQQgghhBDVQDrkFZSYmFjdIfgksVWMxFYxEtuFoya/nhJbxUhsFSOxXRhq\n8mspsVWMxFYxEpuxDJ9l/d577+XLL7+kSZMm7NixA4DJkyczb948GjduDMBLL71U4vqMJpOpXOPv\nhRCitvCV3yTvCSHqKsl7QogLSVlzm+FXyO+55x6++uorr8dMJhNPPPEE27ZtY9u2bSV2xoUQQggh\nhBBCiLrM8A55z549iY6OLva4/BIqhKhVnA73P1VQ3ZEIIYTxlCqS96TNJoS4AFRT3qu2dchnz57N\ne++9R7du3Zg5cyZRUVElHjd58mTP/xMTE+vEfQJCiFom/VP4cQhggvZ/ha5zy11EUlISSUlJZTpW\n8p4QotqlzIVf/gaYoNPzcOmkchcheU8IUavsmgI7pgEKuv0b2j1YrtPLk/OKMvwecoDU1FT69+/v\nuYf8xIkTnvvHJ06cSGZmJvPnzy8enNxTJISoCXZOBdcUaFoAOy+Hm7ZVuki5l1IIUaP98iDUfwci\ngPSboedXfk/xR/KeEKJG+3EAxK2CPCD7HrhyQaWKqzH3kJekSZMmmEwmTCYTf/3rX9myZUt1hCGE\nEGWjJ4O5ADTAeqi6oxFCCOPpe8HM73lvb3VHI4QQxrOnQiRgAbJ3VFm11dIhz8zM9Pz/888/p1On\nTtURhhCaCYDfAAAgAElEQVRClI1tv7thGgnk6ZBvre6IhBDCWHpakQ55hsyfIYSo+/SjRfLewSqr\n1vB7yEeMGMH69es5deoU8fHxTJkyhaSkJH799VdMJhMXXXQRc+eW/35MIYSoMvbD7gRtArRIsKZA\nwy7VHZUQQhhHP+7Oe6FAaAg4jkJki+qOSgghjFHghJyz7tt0TEButntyt5AIw6s2vEO+bNmyYo/d\ne++9RlcrhBCBoQpAP+2+Og6gKbDukw65EKLuyjsLyglhv29bwiB7n3TIhRB1lyMTwsMh2O7eNkeA\nLQWijB/JXS1D1oUQotZwHIOw0D9+vtTs7g65EELUVXqauzFq+n1bywfr/moNSQghDGVPB3PoH9ua\nqcrynnTIhRCiNHoamMP+2NZcYK38LOtCCFFj6WlgNv2xrdnBuqv64hFCCKPp6RBZZK4MiwOyq2ZC\nS+mQCyFEaexp7hnWC2mANbnawhFCCMPpaWDO+WNbA6zbqy0cIYQwnP0wmB1/bGv5VZb3pEMuhBCl\nsaV6J2gLkJ1WXdEIIYTx9P1gzv1jW0OGrAsh6jZ9H0Q6/9jWqLKRQdIhF0KI0ujJYC6SoMNxT3aU\ne7raQhJCCEPpye4Z1gtpgO2EexZiIYSoi+z7/5jAF37vkKdWSdXSIRdCiNLY93s3TE2AFiFXi4QQ\ndZee6p33goGIcPdQdiGEqIv0w955LwJw5kDeOcOrlg65EEKUxpbunaDBPbFbtsy0LoSoo/TMEvJe\niKwwIYSou+zHva+QV+EFGOmQCyGEL0qBfqJ4w9Sig7VqZt4UQogq5dTdV4Xqnfe4liMjg4QQdVO+\nFVx57tsSi9KUdMiFEKJa5Z6GYBOEnve4pmTpMyFE3aSnQ2SRNcgLabmQLTOtCyHqIPthMJeU9/Qq\nWVlHOuRCCOGLngrm838u5feJPuQKuRCiDtLTwFxC89ACWHdUeThCCGE4PR0iS8h7WgFk/2p49dIh\nF0IIX/S04sPV4fcO+RH3kHYhhKhL9FQw5xV/XAOsB6s6GiGEMJ79MJjziz+uIVfIhRCiWulpYM4p\n/ngYEBIMjswqD0kIIQylHwSzo/jjZsCRBa4ScqIQQtRmeipElpD3LID1sOEXYKRDLoQQvuh7S75S\nBKCFyQRHQoi6R99T8sigIMAcCTa5Si6EqGPsyRBZQqc7HDAVQO4pQ6uXDrkQQvii7yu5YQqg5csS\nQEKIukc/6DvvWUyy5KMQou7RD5XS3qtneHtPOuRCCOGLr3vIATQ7WPdUaThCCGE4/Wgpec8hI4OE\nEHWPPcN7DfKiNKfheU865EII4Yt+rJSGKWA1fuZNIYSoMq5cyLVChI/9Wr7kPSFE3aIKwH7ad4fc\nYodsYy/ASIdcCCFKkncOlNM9gVtJLEC2XCkSQtQh9sMQUc9361ADrLuqMiIhhDBWznEIC4MQH/s1\nBdZthoYgHXIhhCiJngbmCDD52F8f9xX0AldVRiWEEMbR08Dsq1XK7x3y1KqKRgghjKenQ6Svqy/8\nnveqccj6yZMn+eWXXzh79qyhQQghRI2jp/oerg7uX1LDw8GeXkUBCSGEwfQ0MDt9748E8nTIt1ZZ\nSEIIYSj7YTAX+N6vAdYM99B2g/jskM+bN49LLrmEsWPHcvHFF7NixQrDghBCiBpHT/O95FkhLUQm\nOBJC1B36QTDbfe83AVokWFOqLCQhhDCUng6Rub73hwKhIeA4algIPjvks2bNYteuXWzatIlNmzbx\n0ksvGRaEEELUOHoKmHNKP0bLlaXPhBB1h54M5hLW4i1KU5L3hBB1hz0FIv1cgLGEGbrko88OeVhY\nGI0bNwagdevW5OaW8suBEELUNXpy6UPWAbQcyN5ZJeEIIYTh9ANlyHt2sO6tknCEEMJw+r4y5L18\nQ3+I9Dlzx5EjR3j00UdRyv1LaUZGhmfbZDLxxhtvGBaUEEJUO/2Q/wRtAY5tr4pohBDCePqRMjRM\nXXBClj4TQtQR9rQy/hC527AQfHbIX3nlFUymP6YX7tq1q+f/RR8XQog6Sc8sQ4IGrAeqIhohhDBW\ngRMcWb7X4i1kAVKMXZNXCCGqjH7Mf97TgJPG/RDps0N+9913l/i4w+Fg1apVRsUjhBDVz2kHZw7U\n83NcfcB+Glx5EFzKkhlCCFHTOY66V44ILmVSN/j9h0hZXUIIUQc4HZDv8N/eM/gCTJnWIXe5XHz5\n5ZfcddddJCQksHz5csMCEkKIaqenQWQpa5AXCsK9VrntYFVEJYQQxtFTwRzq/7hwQDkh97TREQkh\nhLHsh8vW3tMA2wn3SCID+LxCrpRi/fr1LFu2jDVr1nD11VezceNGDh06RGSkv+v6QghRi+lpYC7T\n75WgBbuXPmvQwdiYhBDCSHoamF3+jzMBWoQ774XHGB6WEEIYxn4YzMH+jwsGIsLdeVJrE/AwfLY4\n4+PjefHFF+nduzfJycl88sknREZGSmdcCFH3lWUN8kKaQ5YAEkLUfnpq6WuQF6W5DF0CSAghqoSe\nDpFlvOqthRjW3vPZIb/jjjtISUlh+fLlrFq1Cl3XDQlACCFqHP0AmB1lO1bLA+tvxsYjhBBG05PB\nXFC2Yy06WJONjUcIIYxmT4PIsv4QmWPYD5E+O+Svv/46KSkpjB07lu+++46LL76YkydPsnz5cmw2\nmyHBCCFEjaDv8T/DeiENsO4yMhohhDCevr8ceU+BVZY+E0LUcnoymFXZjtVywbrDkDBKvUkyKCiI\nG264gXfffZeDBw+ybNkyVqxYQatWrQwJRgghaoSyrEFeyAJkHzIyGiGEMJ5+uJw/RO41MhohhDCe\n/WD52nvV0SEvKiwsjP79+7N06VKOHDlS5gruvfdemjZtSqdOnTyPnTlzhj59+tC+fXv69u3L2bNn\nyxe1EEIYSc8oe4KOBPKs4JTbeoQQtZQqAPupcnbIj4Aq45UlIYSoifQj/tcgL6QBVmNW1fHZIe/d\nu3eJ/2644Qb+/Oc/l7mCe+65h6+++srrsRkzZtCnTx/27dvHjTfeyIwZMyr+DIQQIpBceZBrhYgy\nHm8C6keCNcXIqIQQwjg5xyEkpJS1d84TBoQEgyPTyKiEEMI4SoH9ZNk75GbAkQWunICH4jP1vvLK\nK57/m0zuxdl+/vlnXn75ZZo0aVLmCnr27ElqaqrXYytXrmT9+vUAjB49msTEROmUCyFqBvthiKgH\nQeW44q3hnnkz+jLDwhJCCMPoaWAOB8rR0NTC3HkvMs6wsIQQwjC5pyA4GELLOMt6EGCOBOsBiLok\noKH47JB369bN8/+kpCSmT5+Ow+Fg7ty53HrrrZWq9Pjx4zRt2hSApk2bcvz4cZ/HTp482fP/xMRE\nEhMTK1W3EEKUSk8Fc1kvE/1Os/tdCiMpKYmkpKQyFSd5TwhRpfS0ss+wXkjLd69F3jTR5yGS94QQ\nNZb98O8/ROaW/RyLyZ33fHTIy5PzijIp5fsGoK+++ooXXniBsLAwJkyYQO/evctdAUBqair9+/dn\nxw73jfDR0dFkZWV59jds2JAzZ84UD85kopTwhBAi8A4sgBOPQvdyXCE/AJy4A7p/XOZTfOU3yXtC\niCq3+2XImQBXlPFKEcAuIG8cdHm9zKdI3hNC1BiHv4CDo6FXdtnP2RoK9aZDx6fLdHhZc5vPy0BX\nXnklJ0+e5Mknn6R79+7uGLZu9ey/4ooryhRISZo2bcqxY8do1qwZmZmZ5RoCL4QQhtIPgbmMa1IW\n0oADewwJRwghDKfvhQbl6IyDO++lbjckHCGEMJw9HSLLcXUc3CODzvwW8FB8dsjNZjNms5lPP/2U\nTz/9tNj+devWVbjSAQMGsHjxYp555hkWL17MoEGDKlyWEEIElL4HmpTzSo0GWNMMCUcIIQyn74PY\ncp5jAbL3GxGNEEIYTz8A5vJ2yIG0XQEPxWeHvCLj30syYsQI1q9fz6lTp4iPj2fq1Kk8++yzDB06\nlPnz55OQkMBHH30UkLqEEKLS9ANlX/qnUD3AlQt5WRAWbURUQghhHD2t/HmvPqAfgwIXBAUbEZUQ\nQhjHvhdiynmOBmSnBjyUcs5cVH7Lli0r8fFvv/3W6KqFEKL89CPlb5iaAC3CPdFHzFVGRCWEEMZQ\nCvQT5c97IUB4uHvYZ/2LjIhMCCGMo6eVfcmzQpFAvg75VgjVAhaKz3XIhRDiglPgdK8xWd4EDaAV\nQHbpM60LIUSNk3cGTCb32uLlpYX4XWFCCCFqJHtmBS/ARLovwASQdMiFEKKQ46j7ik9FRl9adLDu\nDXhIQghhKD3196V/KkDLDXjDVAghDOfKhVyr+5bD8tKUdMiFEMIwehqYQyt2rqbAui2w8QghhNEq\ncv94IUsOZO8IaDhCCGE4RwZERFSsJ6zZA34BpkId8i5dugQ0CCGEqBH0VDC7KnauhlwhF0LUPnoa\n1M+p2LkaYJUOuRCiltEPQ2QFp1LTXGD9NaDh+O2QF117vNC2bXIVSAhRB+mpYHZU7FwNyD7sniBJ\nCCFqC30/ROZV7FwNsB4IaDhCCGE4ezpEVvACjAXI3hPQcPx2yJ944gk6dOjAxIkT2blzZ0ArF0KI\nGkVPrvgV8nAg2AQ5xwMakhBCGEpPrviQ9fqA/TS4KtihF0KI6qCnVe4CjDU9oOH47ZAnJSWxbt06\nGjVqxIMPPkinTp2YNm1aQIMQQogaQd9f8YYpgBYuExwJIWoXPc3dsa6IIMAcAbaDgYxICCGMZd9b\nuQswygm5pwMWTpnuIY+NjWXcuHG8/fbbXHbZZUydOjVgAQghRI2hH65kh9wpSwAJIWoX/VjFlnos\npAVJ3hNC1C76gYrnPROgRQR0qVu/HfLdu3czefJkLr30Uh555BF69OhBRkZGwAIQQogaQRWA/VQl\nO+Q6WAN7X5EQQhgmP9s93LyCq54BoOXIyCAhRO1ir+wFGFdA857f6eXuu+8+hg0bxtdff01cXFzA\nKhZCiBol5ziEhECIs+JlWIDUwM68KYQQhtHToH4kmLIrXoaWB2d/C1xMQghhJKVAP1m5kUEWHazJ\nAQvJb4d806ZNAatMCCFqLD0NzOFABZf/gd8n+pArRUKIWkJPA7OpcmVowOFdAQlHCCEMl38WTArC\nKlGGpuBI4FYdq9A65JMmTQpYAEIIUSPoaWAuqFwZ9QHbUffwdyGEqOn0VIjMrVwZFiD7UCCiEUII\n4+mHIbJe5crQCOjcGRXqkHfr1i1gAQghRI1QmSUwCoUCYWHue5OEEKKm01OgfiVGBYF72GeeFZx6\nQEISQghD2dMDMzLIesQ9/D0Aytwht9ls2Gw2APr37x+QyoUQosbQk8FcifvHC2mhMmxdCFE7VGYN\n8kIm3PehW1MCEZEQQhhLT6/8yKAwICQYHJkBCclvh3zHjh106dKFjh070rFjR7p27crOnTsDUrkQ\nQtQY+r7KN0wBLHkBXQpDCCEMYztUuYmNCgV4+KYQQhjGfhDMlRwZBKCFBSzv+e2QP/DAA7z22muk\np6eTnp7OzJkzeeCBBwJSuRBC1Bh6emA65JoDrDLBkRCiFrBnBijv2aVDLoSoHfS9AfohMj9gIyL9\ndsjtdju9e/f2bCcmJqLrcp+QEKIOUQr04wFqmAJWWQJICFHDOR2QZ4eIAJRlcUK2LPkohKgF7KkB\n/CEyMBdg/HbIL7roIqZNm0ZqaiqHDh1i+vTptG7dOiCVCyFEjZB3Bkymyi2BUUgDrAcCUJAQQhhI\nTwNzhPse8MrSAOvuABQkhBAG048G7lad7MBcgPHbIV+wYAEnTpxg8ODB3H777Zw8eZIFCxYEpHIh\nhKgRPGuQB0B9QD8FBfmBKU8IIYxgT4PI4MCUpQHW9MCUJYQQRilwQs65wHTILQRsMssQfwc0bNiQ\n2bNnB6QyIYSokfTUwAxfAggGIuq5J0uytA9QoUIIEWB6GtQP0A+H9QBXLuRlQVh0YMoUQohAc2RC\neDgE2StfVn3AdgwKXBBUuR83K7QO+dy5cytVqRBC1Ch6WuXX4i3KEixLnwkhajb9IJgD0CgF97B3\nLULynhCiZrOngzk0MGWFAPXC3WVWUoU65EIIUafo+yEyL3DlaTky47AQomaz7QnMsM1ClgJZ8lEI\nUbPp6RBZELjytJCAtPf8dsgPHjxY7LE+ffpUumIhhKgx9OTADVkH0HLBuiOABQohRIDZD7qHXAaK\npoN1bwALFEKIALOng9kRuPK03ID8EOm3Q3777bcXe2zIkCGVrlgIIWoMPS2wHXILkC0dciFEDaZn\nBPiHSAXWbQEsUAghAkzfB5HOwJVnyQHrzkoX43NStz179rB7927OnTvHZ599hlIKk8lEdnY2OTkB\nvNdSCCGqm34swA1TwFp8dJEQQtQIrjz3TMOBWIO8kAZkJwewQCGECDD7fogNYHkakLm90sX47JDv\n27ePVatWce7cOVatWvVHvZrGu+++W+mKhRCiRsjPdjdOA7TqGeC+LzPnHDgdEBLIFq8QQgSA4whE\nRECQHrgyNcB6BJQCUyAWNxdCiADTDwd27owAXYDx2SEfOHAgAwcO5KeffqJHjx6VrkgIIWokPQ3M\nEWCyBq7MIKB+JNhSIKpT4MoVQohA0NPA7Hfl2/IJB4JNkHMcIpoFtmwhhAgE/XhgR0TWB+yn3Rd2\ngsMqXIzfe8ilMy6EqNP0NKhvwIITmkmWABJC1Ey2VDAH8D7KQlq45D0hRM2Ub4WCfKh4v7m4INwX\ndWyVu0ouy54JIS5seipE5ga+XIsDsmXGYSFEDWRPhcgArUFelOaUJR+FEDWT/fDvIyIDXK4WVOm8\nJx1yIcSFTU8BswETVWr5YP0t8OUKIURl6Xugvgp8uZoO2bsDX64QQlSWng6RRoyIzKl0h9znDURj\nx471e3KDBg2YPn16pQIQQohqpSdDQwPK1YBD0jAVQtRAthRIMKBcC5AqP0QKIWogezqY8wNfrpYH\nZys307rPDvnKlSuZOnWqZ7mz8ymlmDFjRqU65AkJCVgsFoKDgwkNDWXLli0VLksIISrEdiiwM24W\n0gBrqgEFCyFqs5ycHJKTk7n88ssNq8PhcLB//346d+5c8gH6EQPzngxZF0LUQHoqRDoCX64GpFdu\nLXKfHfLHHnuM0aNHl3pyVlZWpSo3mUwkJSXRsKERl6eEEKIM7JmBnXGzUATgzIG8cxDWwIAKhBC1\nSU5ODvPmzWPGjBn07duXBQsWGFbX8ePHGThwIO3atWPSpElce+21f+wscIHjjDF5rz5gywRVACa5\nK1IIUYPYk6GpAbfqWKj0BRiTUsqAyMrmoosu4pdffiEmJqbE/SaTiWoMTwhR1zkd8IkGw1yBn+QD\nYK0FrvoOYroV2+Urv0neE6JuUUrx+OOP8/7779O6dWuGDBlC27ZtDa83Pz+f9evX8+mnnxIbG8u0\nadPo378/ZO+H7y6H2wyY1A3g80jouxvMrYrtkrwnhKg233aBS3+FQK/KqICPQmBwFoTW99pV1tzm\ndxHKgwcPMnv2bFJTU3E6nZ7CV65cWbGgzwvypptuIjg4mAcffJD777+/2DGTJ0/2/D8xMZHExMRK\n1yuEEECRNchtxpSvKfcSQDHdSEpKIikpqUynSd4Tou5wuVx8+OGH2O12TCYTmzZt4ueff66SunNz\nc9E0jR07dvDpkrfp3+gDOLIS2hvxC+TvtFB33jO3krwnhKg59AxjRgaZgPoRYEsh6bezZc55XkX4\nu0LeuXNn/vrXv3LppZcSFOQefmQymejVq1dFQvaSmZlJbGwsJ0+epE+fPsyePZuePXv+EZz8YiqE\nMFLmf2D3MLjxnDHl/xYEQROg05Riu+RKkRAXDqUU33zzDZMmTeLcuXPMmDGDAQMGGFafw+Fg8uTJ\nzJs3j+G3duLZvqnER52Ctg5oXQD1DKsatkRA1KvQ/m/FdkneE0JUC1UAy8NgiAuCDSh/gwUS5kHL\nIV4PB+wKeb169Xj00UcrHmApYmNjAWjcuDG33XYbW7Zs8eqQCyGEofQ0qG/AjJuFtALI/NW48oUQ\nNVJBQQEpKSm0b98ecDfK+vbtS58+ffjmm2/YtGmToR3yY/vWQ+ZX/PqCTnzH/0E7m3uYpoEXxj00\nB1grN8GREEIEVM5xCAuDYAMmdYPfl3zcW+HT/XbIx44dy+TJk7n55psJDw/3PH7FFVdUuFIAu92O\ny+VC0zR0Xefrr79m0qRJlSpTCCHKxXYAzAbdRwnumTf3JRtXvhCiRvrnP//JxIkTycrKon79P+4p\nLOyY9+3bN/CVunLh8Cew/xUusu3j5afyoY0TInMDX1dpNOB45ZYAEkKIgNLTITIMMKhDbnHBiYov\n+ei3Q75r1y7ef/991q1b5xmyDrBu3boKVwruGUBvu+02AJxOJ3feeacxf6CEEMIXPRliDSxfA6yH\nQSkoYflIIUTd43Q6efvttwkJCWHOnDk8/fTTxlZoPQApb8LB+RANdLBCc6C6JjnXAGtKNVUuhBAl\nsKeDucC48jXgwO4Kn+63Q/7xxx9z6NAhwsLCKlxJSS666CJ+/VWGcgohqpH9oHuZHqOEA6YCyD0F\n9RobWJEQoqb48MMPadiwIZGRkcycOZO//e1vXlfJA6LACRmrIeVVOPM/aO2CPvnu5XeqW31APw0F\n+RAUWt3RCCEuAFu3bqVt27ZYLD6SoJ6OI8jB9s1w9dUGBKAB1vQKn+7399NOnTpVer1xIYSokYya\ncbOQCdDqgXWfgZUIIWoKp9PJtGnTeOCBB4iIiKB3797MmTMncBXYj8KO52FlU9jzF0j4EQblQJca\n0hkH94RJkfXAdqi6IxFCXCA++ugj2rRpwwsvvEB2drbXPofDwevzPqXNbU7eecegAOoBrjzIPVOh\n0/12yLOysujQoQN9+/alf//+9O/f39CJSIQQokq48iDnHEQYXI/mhGzpkAtxITh79iw33XQT3bp1\nA2DKlCnYbJVcVlEVwLFvYeMtsKYN5LwCvc5AXytchDEzBleWFiw/RAohqsyMGTPYuHEje/bsoU2b\nNjz00EMsXbqUxx9/nDZt2rDhvymsWQzz5xsUgOcCzP4Kne53yPqUKSUv1yOEELWa4whERECQbmw9\nFjtYZWI3IS4EkZGR3HrrrZ52UlxcHN27d69YYbmn4eACSJkFwVb3TOnXALVhFLiWU+GGqRBCVES7\ndu3o378/GzduZO3atZw6dYpt27bhcrno3w0u6WJwAJYCd95rVP4x8X475ImJiRUJSQghajY9Dcx+\nU2DlaQrStxlfjxCi2jmdTu6++24WLFgAwKuvvkpmZia33npr2QpQCk79DCkz4ciX0CIIrrFDI6pm\nybJA0XLhXMVnHBZCiPJYs2YNTz31FJqmMXfuXG6++WbPD6MbN25kysO9mfY5vDQDhg0zKAhNr/AF\nGJ+t0X79+rF69epSTy7LMUIIUSPZUsHsNL4eDblSJMQFwmKx8NBDD7Fw4UKcTidvvfUWW7Zs8X9i\nvhVSP4D9r4LrOLR1wBUF7okhayMLcGRXdUchhLhA7N27l5kzZ3o64gUFBezYsYPOnTvTs3s3vn0O\nNsbB+g0GBqEpOFqxCct9dsg3btxI//79Sz151y5JtkKIWsqeCpEGrkFeSAOsGe77QE3VtQ6REKKq\nPP7447Ru3Zrg4GAGDRpE69atfR+ctR32z4L05dA0CK7QoSm162p4STTAerC6oxBCXCAef/xxr+1Z\ns2bxzDPPcPLkSaKDT0JkBD2vt9HzegODsADJAb5CvmLFCr8nh4fX1p9uhRAXPH0PNFbG1xMKhIaA\n4yhEtjC+PiFEtWrYsCF33HEH8+fP5x//+EfxA1w5kP4x7P+ne+nFNrnwJxdEVn2shokEcrPB6YAQ\no2fOFEKIP7hcLt58801CQ0P517/+xeQx14G5Cma/1ADrEfetR+Wcb81nh1zuHRdC1Gm2FEiooros\nYe6Z1qVDLsQFYfr06TRr1sz76nj2fkiZDYcWQkOgow3iKMN6N7VQEGCOcOfZqE7VHY0Q4gKyfPly\noqKiCA0N5c033+SxgY2IiqyCWxTDgGAT5ByHiGblOrUu/hkQQgj/9CNVd0VKy5MlgIS4gDRr1ozp\n06dDgRMOfwbfd4dvOoPpbehrg942aEHdboVpJlnyUQhRpVwuF1OnTuVvf/sbYWFhDBgwgNff/QTM\nVXCLIoAWXqH2Xq35U+B0OnnvvfdkW7ZlW7Yrv60f573/nAJz4X4osjvw2xEO3lv2KeVls9n46KOP\nZFu2Zbu2bduPYPv5KT56ugEk3w2tf8Z2Uw4f7c93D2sEbDYocnrd2w7R+ejjTyivU6dOsXLlStmW\nbdmW7XJvp6am0q5dO6655hoAHnnkEfamHIRI9fvxUOTwwG+78ln5+ceUV5k65Ha7nb1795a78EAq\nKCjghx9+kG3Zlm3Zrvj2qZ9h0xAKVrbih8wgCC7cD0UOD/y2GX74707KKzc3l82bN8u2bMt2bdjO\ncbD5++WwoQ+saUeu7Q0259ihjxUSINcJRQ4nN7eOb4e52Py/rZSXzWZj69atsi3bsi3b5d7Oz8+n\nXr16niXPwsLCUPk2zwUYmw2KHB747SA7W7f9j/IyKaVKndVo5cqVPPXUU+Tm5pKamsq2bduYNGmS\n168RRjGZTPgJTwghSpdvhdQlsP8VcP6+nFDrAqhXhTGcBX6Ig34Znod85TfJe0LUMjmn4OB8SHkd\nQm3QzgatcE/oeCE7DmzvBH22ex6SvCeEMJLdbichIYH58+czfvx4hgwZQtoPL7PgEwc0qIIA0oHU\nG+H6b4Gy5zafk7oVmjx5Mps3b6Z3794AdOnShYMHZSkLIUQNd3aHezmhtA+hSRB00aEZ1bOckAbY\nTrjvJw3ym3aFEDWdUnDyR0h5DTLWQrwJejgghtq/ZFmgaIA1tbqjEEJcQCIjI3n88ceZO3cuLpeL\n2bNn8/PEPM8VcsNpVOgecr8tw9DQUKKiorweCwqqNbeeCyEuJK4cSP/EvZyQngJt8+FPzupfTigY\niIteP8oAACAASURBVAgHPRW0ttUcjBCiwvKz4dD7sP9VKDgJ7ezQVYGsAltcBODMgbxzEFYVl6aE\nEMJ93/irr76Ky+ViUP9baNv8CwhxVU3lGmDLBFUAprL3l/0eeckll7BkyRKcTif79+9n7Nix9OjR\nozKhCiFEYFlTYNtj8EVjOPQQ/N8OGOiATjWgM15ICwHr/uqOQghREWe2wZbR8EUzOP4MdE2Ffjp0\nkM64TyZAi5C8J4SoUpqmMWrUKM6dO8eEccPBXIVJOgQICwP74XKd5rdDPnv2bHbt2kV4eDgjRozA\nYrHw+uuvVzRMIUQt9euvv3LgwAFD6/jtt99ISUkp28EFTjj8OXzfA77uBMxxLyd0gw3iqXlrSGg5\nsgSQELWJ0wEHF8N/LoEN10HkEujngJ7VePtLbaMpWfJRCFHlJk2axNSpU2nbVFX9hRkttNztPb9N\n1jVr1vDiiy/yyy+/8Msvv/DCCy+watWqCscohKhdtm7dysCBA+nXrx/p6emG1rVr1y66d+/OPffc\n47tjbs+AHRNgRRPYMxou2gSDcqDLH8sJ1UhaLlh3VHcUQgh/svfB1kdgRWNIfwQu2Q0D7HCpyz0M\nW5SdpoO1elfpEUJceKKjo5k4cSLo6WDOrdrKLXnlHhnkd5b1Ll26sG3bNr+PGUFm3RSi+vzyyy/c\nd999pKenc/PNN3PttdcSGmr8tMF2u51169axbt06OnfuzPLly4lv0RyOfQv7Z8KJDdBKQdtciDY8\nnMDJAPZeBTe41wWS2YaFqEEK8uHICvf8E+d2QGuXew6K+tUdWC13EMgcANeuACTvCSGqkCsPttwN\nDZZBxyqsdw9gfwi6zqn8LOtr165lzZo1ZGRk8Oijj3oKs1qtVdIoF0JUr4MHD5KamkpkZCQOh4Nd\nu3ZVSb1KKfLz8wkPD2f/vr0c3/Qy8fU+hZDflxPqTu1cTsgCWGWFCiFqFP0wpMyBg2+D5oJ2VmiB\neyJGUXkasC+5uqMQQlwoClxwciOkzofDn0GDYLikimPQgOO/lesUnx3yuLg4unbtyooVK+jataun\nQ26xWJg1a1al4hRC1HxDhw7ltttuY8mSJUybNg2bzcZbb71Fhw4dDKvzxx9/5JFHHgGnzoKn2zPg\noq2Y4he41w5vRO2+Z9MMOLLcM8EHV+Ui6EIILwUuOPY17H8FTm6CBAW9cyHK/6minCyA9bB7mThT\nbU7gQogaSynI2gqpiyDtA6jnglY2uFVV3XJnRWmAtXxzLvkdsp6fn19tV8RlCJMQVWvXrl1ERUXR\nvHlzr8fz8/NZunQpbdq04brrrjOm8vxsVs57CpX5BQO62TC1c0DrOjaD8SoNem6CqEtk6KYQVS3n\nBBx4F1L+BWEO94ibBMqwAKyolE/CoV861GsieU8IETjZe90d8NT5oLKhVQ4kuKC6V1l0AR8Hw1AH\npuCwMuU2vx3yTp06FUuUDRo04Morr2TChAnExMRUOm6fwUmCFqLKKKWIi4ujZcuWbN68ueoqzvoN\n9s+CtI+gWRC006EptftquC/rLdB6McQPkoapEFVBKffwxf2vwdH/QLwJ2jng/9u79+io6zOP4+/f\nby6ZmWSSkJDr5Eq4iQkhgNBacbUuaquA2trW7tF6Y3fbXfe03XZbz3YtPdZLq2yrtlZLrRV0wSpq\nxQpaK5eACiRcguAFNYGA4IWLIfe5fPePSQYQueTGDOTzOodzkpnf/H4Pk/Awz3ee+T4D99JFPu2F\nNBj/V8j6gvKeiPRN6w7YNh8a5kDbDigOQ0knZJBYrxufTYHzarHSRvXtM+TdLr74YpxOJ9/85jcx\nxrBgwQJaW1vJycnh2muv1Y7rIqeJF198kY6ODjZv3szWrVsZMWLEwF0s1Abbn4B3fgmt9VDWAZeE\nE2dm+EDxt2kEkMjJ0PkJ1D8C78wGsxeGt8BZBtzxDmwQ8oeiO9dnfSHekYjIqahjDzQuhIYHYP8W\nKLCgqh2ySbwRt938do9e7x23IH/ppZcO21F97NixsV3WKyoqehekiCQUYwyzZs1i0qRJOBwOfv7z\nn/PII4/0/4WatsI790L9n6KrmWOaIZ/ETaj9zR+EvT3b6ENEemBvbbTjZvtCyLNhYmv0RVsivXMy\n2KS2wgFt7CYiPRBqgR3Pwrbfw4evQp4TRrVEXzOeCptu+tt7NPrsuAV5OBxm9erVTJ48GYA1a9YQ\niUSiD3bqg1cip4MXX3yRpqYmJkyYQGFhIXfffTdvv/02I0eO7PvJI8FoUt36S9hfFx0ndNEgHSfk\nB7adnN3qRQaNUCtsWxDdpK1je3Qk4qWaGZ4w/Aa2D/yoXBE5xYU7oxtuNsyB91+EoS4oOQBnA66T\nPEu8r1I7Yf+JvwFz3Ir6oYce4rrrrqO5uRkAv9/PQw89REtLCzfffHPvAxWRhNHR0cF9993HM888\ng9fr5Ve/+hWffPJJ307a0gjv/i76J6VrnFAhp8bK5kDxA00N8Y5C5PTwyRvwzn3R1vShNlQ0Qx6D\np+PmVOFHH9URkc9mIvBhNWz7Y7QtPdWG4gMwAfC0xzu63vMDja+f8OHHLcjPOussXn/99diL87S0\ng1vXfe1rX+t5gD30wAP6jLrIQHvnnS14PD42barngw/ayc0tZOvWJmprd/fwTBEKHeupTH6aXMcb\nUAzO84MaJ9TNB5GOZh5+4PFjHqa8J/LZbIKUOl+jMvkpMhyNWMPDOL4Ujs9oGzkxfgh/soM5D/zl\nmIcp74kMFoah9nuMSvo7I5KWYXvDOMvasS+O05iygeCH9o/68TPk+/fv52c/+xkrVqwA4LzzzuOW\nW245rDAfSIHAtJNyHZHB7L33Wlm48B7KyiaQnFzCY4/9ip/85FkCgfEn9Hh35COKQn9gWOTX2J5W\nnKObsUqA+ExMTFwWhH3JjPAee8M85T2Rw3kjDZSE7qc49CCkRXCd0QwFDO6Om1OFC4zTTVnesf8/\nUd4TOb0lR96mIDSPwsjDuBz7sUs7sEtDp+ebNj5wm9YTPvy4Bfn1119PRUUFTzzxBMYY5s2bx3XX\nXcdTTz3VpzhFJHGcffZXWbDgZ3z8cSP79u2irGw8ZWXHKcaNISO8kmGR/yU7tBhTYOMc3ZZ4oycS\nTaohed+Jb/QhMmiZMNnhJZRF7mJIeDWUGByjOuI/Y1Z6zKS4SW5X3hMZbDyRneSHFlBsfo/XNEJx\nGMewzujoydP5taINYa8POHBChx+3IH/33XcPK75nzZpFZWVlr+MTkcTjcDj4+tdv4Q9/+C7BYDu3\n3vr3ox7rNJ9QEJxLWWQ2btce7DEt2MMMJJ3EgE9hjvRWkve8Fe8wRBJWUmQ3RaE5lIbvxfZ14Drj\nABRzAq9YJFFZaZ2ktOpz5CKDgcvsJS+4kGIexB96PfqGzbA2yGFw7fHhP/EVh+P+9+b1eqmurmbK\nlCkArFy5Ep/vdB8WLDL4fOELV/LAA98hM7OQ4cMnHHF/WngdpeFfkx96ApPjwDm6JZpcT+cVzgFg\np4ZJszfEOwyRxGIMmeHlDIvcTVboJSi0cIxuj3bcyCnPmd5Gyq4t8Q5DRAaIw7SQE1pEkZlDRmgV\nJseJs+wUGlM2ABzp/diy/sADD3DNNdfENnUbMmRIv80nXrJkCd/97ncJh8PceOON/OhHP+qX84pI\nzzkcDr73vXkMGZJ78DbTSn7occoid+G1tmGP7MAernFCfZIKKeaNeEchkhBcZh8FwUcoi8zG5d6P\nY0wLVqkBd7wjk37lhzRLC5EipxPLBMkKv0hR5CGyQ4uJZLhwlXVN1DnVxpQNADstdMLHHrcgHzdu\nHHV1dTQ1NQGQmpra+8gOEQ6H+fd//3deeuklAoEAZ511FtOnT+eMM87ol/OLSM+dddYlACRH3qI0\ndA+FobmYTAvX6OboKudgajUaKH7whrbHOwqRuEoLr6Us/L/khp7B5No4z2iFLNRxc7ryQ3L4nXhH\nISJ9ZSJkhFdSGHmY/NCTGL+Fs+wAVhE4vKfwmLKB4D/xQ+O2y/qaNWsYPnw4JSUlAHzjG9/gL3/5\niwpykTixTCe5ob8w3PwSf+R1rLIw9sggpMQ7stNMElic+KqpyOnCYVoIBOdTZu7CY+/AHtWOPTwC\nnnhHJgPOD+7QR/GOQkR6wxhSIxsoCM+lMDwXyxPEMaIFuySi14jH0p8F+UDtsr5z504KCwtj3xcU\nFLB69eojjps/f1bs6/Ly86ioOK9P1xWRw3kj2ykO3U9J+AFIjUQ3UNI4oX63bFn0D0DkOB3rynty\nOkkJb2ZY+B4CocdgqIXzjBbIQ++GDwKH5j2z2RzzWOU9kcTii7xDQehRiiIP4XLsi44pG3aajinr\nJ4fmPI6d8g5jGWOOeXhlZSUbN2487m09tXDhQpYsWcKcOXMAePTRR1m9ejX33XffweAsi2ef7cHf\nRkROjAmTHX6ha5zQa1ASwTGqU+OETpLg037cVxzgs9Kv8p6cDmzTQW7oKcrMXfgjb2IND2KPDEFy\nvCOTeAk/noTzGx3KeyIJLCmyq2tM2YP4zDYoMjiGdcBQtIjaUxGwHHxmzvu0uO2yHggEaGxsjH3f\n2NhIQUFBn88rIkfnjnxAUWgOw8L3YvvaNU5IRPqVN1JPaei3FIV+D+kc7LjR/hMiIgnJZfaRG3yK\nYh4gNbQJE7Bxlg3CMWVxFLdd1idOnMjWrVtpaGggPz+fxx9/nPnz5/f5vCLyKcaQEV5BWWQ2WaEX\nMQUWztHtkBnvwETktGDC5IT/SlnkbtLDa6G0q+Omf/aAFRGRfuYwrWSHnqPY/J6MYDUmx3VwTJne\npDnp4rbLutPp5De/+Q0XXXQR4XCYG264QRu6ifQjp9lPYfARhkVm43btw3FmC9YwjRMSkf6RFNlF\nUej3DAvfh5XcGX03vAi9mBMRSUCWCTI0/BJFkT+QE3w+OqZseFcXk7sz3uENasf9b7O9vZ2FCxfS\n0NBAOBzGGINlWdxyyy19vviXvvQlvvSlL/X5PCJyUFq4hmHhX5EXeio6Tmh0K2Sjz/6ISN8ZQ2Z4\nKWWRuxkaehmKLByj2iEj3oGJiMgRTISM8CsURv5IfugJTIqFc7jGlCWa4xbkM2bMID09nQkTJuDx\naDaJSCJymFbyu8YJea3G6Dihsgh44x2ZiJwOXGYvBcGHKYv8Ly53E44xzVilqONGRCTRGENqpI6C\n8CMUhudiJ3Vgj2jVmLIEdtyCfOfOnbzwwgsnIxYROVHG4DE7SA/XkGUWUxB6DIbaOEc3R8cJaRMO\nEekLY/CZd0kP15BrniI3tAiTZ+Ec3QZZqONGRCSBuCMfkxapJSOyigLzJ9zWHuzSTuzSrjFlytkJ\n7bgF+dlnn01dXR1jx449GfEcYfHiNXG5rkgi8bv2UJzyBiWpmxmRuY4C71YcjhDhNCfuQBuOsojG\nCZ1CwqHwMe9X3pOTy5CRtJuilDcoSX2dkZnryPe8h3FaRNJtkgrbsEsNqElO+iASiRzzfuU9kRPj\ncTRTlPImxSlbGDl0HUUpb+C1W+hMcePO7cBZEtKYslPMUQvyiooKAMLhMA8//DClpaUkJSUB0XmR\ndXV1JyXAzMxJJ+U6IonC69hDvqeWgHctw1KXkpe0HrfdQijVgyu3GUdWOPp5TR9gdcQ7XOkFy3Ic\n837lPRlIKc5dBDw1FHhXU5q2lFzXJixHiEi6C3deM3ZmJDqFQQW49CPLOnbrlvKeyJFcVgt5nvUE\nPDWUpi0l4F1LsvUxwRQfzuw2nDmd0deEfnBawXiHK7101IJ80aJFJzMOkUEpyW4iz7OOgGcNpWnL\nCHhq8Vj7Cfq9uHJacWYHoy+Mk8Gp4ltEesjn+Jh8Tw0B7xqGpS0jz70ep91OOM2NK7fl4AKfF7Da\n4h2uiMig5bTayUmqI+BdS4l/BYW+1/Db7xP0+XBkd+DK7oi+JkwFh/1JvMOVfnTUgrykpOQkhiFy\n+ouucm4g31NDaepyCnyrSbY+ojPZhyunDWd21ypnKjgtjZ8QkZ7x2PujC3xdxXd+0jqS7E8I+r24\nc1twZIWiOSYZXJZ21xURiRebIDlJr5PvraE4pZqilFdIt7YT9Hqxs4K4c9qixXcaOBxN8Q5XBpim\nhYoMAIfVQW5SHQHPWopTo6ucqfZOgl4fjuxOXDntsVVOr1Y5RaSH3FYzeZ715HtrKE1dRoF3DT5r\nD50pn1rg82uBT0QknizCZLnfJOBdS1HKKopTVjLEfpeQx4OdGcad1xrN1+ngcKrtfDBSQS7SRzZB\nspM2E/DWUJSykuKUVaRb26KrnEODuHO1yikivee02slN2tjVxricguTV+K3dBH3eaBtjTkesu0YL\nfCIi8WMRIcP9DgFPDYXJr1DiX8FQx9uE3S7IgKS85uhrwiHgdKn4ligV5CI9YBFmqPut6Cpn8iqK\n/SvJsN8hlOTBHhrBndsSfWE8RKucItJzNkFyPJsIeGoo9ldTlPwKadZ2gl5ftI0xty2aY9LA4VCO\nERGJH0O6axv5nhoKfa9SmrqcbOcWIi4bhti48pqxMw1kgNOtfYDk6FSQixyVIcP1DgFvDYXJr1Li\nX06W463YKqcrtxl7KFrlFJFesQiTlfRGtPhOWUlRykoy7PcIejzYQ8O4cw9tY1R3jYhIPPmdO6MT\nKnyrKU1dRo6rDssRITLEhTu3GXtoJJqzNaFCekgFuQgAhjTn9mjx7XuVktTlZDs3g9MmkmF3JVqt\ncopI7xxsY1zb1cZY3dXG6IZME21j7O6u0QKfiEhc+RwfHVZ857o34LQ7CKe5o68JsyIHR9CiCRXS\nNyrIZVDyO98n31NDgXdNdA6vuw7LDh+cw6tVThHpNUO6q+HgZwhTV5Dt2ELE5YAhVlcbI1rgExFJ\nAB57H/meWgLetZSmLSM/qZYk+wAhv7drPKQmVMjAUkEupz2f4yPyPbUUeFdTmraMPPeGz57Dq1VO\nEekxg9/5fvSdb99qStKWkePahOUwRNKduHMPxLprtMAnIhJfbvsAeUnrCXhrKE1dSsBbg9faQyjF\nhzOnFWd2UBMq5KRTQS6nFY+9n3xPLfnetV1zeGtJsps0h1dE+kWy48Nod01XG2OeeyMOu4Nwuvvw\nzxD64h2piMjg5rTayPNsIN9TQ0nqcgp9q0mxPohOqMjpwJV9cEKFUxMqJI5UkMspq3sOb8C7lpLU\nZRR41x6yyqk5vCLSNx57HwFvDQHPwTZGt91CMNWDO6drgS8T8GmBT0Qknhx0kuPZFC2+/SsoTH6V\nNKuRoM+HI6sTV077wRG0tvbpkMSiglxOCdE5vBsIeGs+NYfXhyO7/bA5vFrlFJGectsHyPesI9+z\nlmFpS8n31OKz9tLp9+E6tI0xBZyWPvctIhIvNqGuCRVrKUpZSVHKKjLs+sMnVGQSnVDh0IQKSXwq\nyCXhOOgk2/N61xzeFRQlv3qMObwqvkWkZ1xWK7mejQQ8aylJXU6Bb020jTHZizO7Pdpdkwn4wasF\nPhGRuLGIkOl+O7pJZsorlPhXkGlvJZSUhJURISmvJZqvh4DDqXe+5dSkglziqnuVM99TQ3HKqqPP\n4R2iVU4R6TmH1UFO0qauBb7lFCW/Rqq1Q22MIiIJxzDEVd81HvJVSlJXkOV4g4jLCRngym3GHgoM\nAadbH0WU04cKcjlponN4txLw1FCUsooSfzWZ9tuE3UmQ2bXKqTm8ItJL0QW+LQQ8ayn2r6QoeRVD\n7AaCHu/h3TVqYxQRiTNDqnPHwVnfaUvJcb4OToikO7pG0HZNqEiKd6wiA0sFuQyQ7lXOGgq6Vjm7\n5/CaDDs6Cig2h1ernCLSMxbhWBtj9wJfhv1OtI0xM0JSrtoYRUQSRbLjAwLetRR411Catoxc10Yc\njk7Cae6u4rtrQoU33pGKnHwqyKUfGFKdO8n31FDoe43StOXkODeB85A5vFkGhqA5vCLSC4Yhrvco\n8K6lwPcqpakrGOp4g4jLFW1jzGs+uMDn0gKfiEg8eR17yPfUEvCuZVjqUvKS1uO2WwilenDlNuPI\nCsfGQ2pChYgKcumFZMeHBLxrCXjXHJzD6+iIrnJqDq+I9IkhzdlIvreGQm+0jTHbuTnaxjikq40x\ns7uNUbudi4jEU5LdRJ5nHQFP9J3vgKcWj7WfkN+LM6cFZ3ZIEypEjkMFuRyT195Lvre2aw7vUvKT\n1h2cw5vbjGNoWHN4RaTXUhy7Y8V3SayNMUQ43XX4Ap/aGEVE4spltZDn2UC+p4bS1OUU+FaTbH0U\nnVCR0zWhonsEraVuJZETpYJcYg6ucq6lNG1ZbA5v0O/DqTm8ItJHXsceAp6aaHdN2lLy3Rtw2a2E\nUpNw5bZ8qo2xLd7hiogMWg6rg9ykuugmmakrKPS9Rqq9k6DXhyO7a0JF9whaTagQ6RMV5INUdA7v\nBgKeGkpSl3XN4f2QYLIPZ3bbYXN4HZrDKyI9lGR/Qv4hbYz5nlo81icE/V5c3Qt8mUCyFvhEROLJ\nJkh20mYC3hqKUlZSnLKKdGsbQa8Xe6gmVIgMNBXkg0D3Kme+p4Zi/4oj5/DmHrrKqeJbRHom2sa4\nnoCnhtK0pQS8a0m2PlYbo4hIgrEIM9T9VrT4Tl5JsX9l14QKT3RCxaEjaDWhQuSkUEF+momucm7p\nKr6rKUp+5ZA5vCHcua1a5RSRXnNa7eQk1RHwrqXEH21j9Nvvdy3wdeDK6Yi+852qNkYRkfgyZLje\nIeCtoTD5VUr8y8lyvEXY3TWhIvfQCRXK1yLxooL8FNY9h7fAu5bC5Fe65vBuPbjKqTm8ItIHNkFy\nkl4n31tDccpKilJWkW5tP7yNMZNod40W+ERE4siQ5tweLb59r1KSurxrQoVNZIh92IQKpyZUiCQU\nFeSnDEOG610C3pquObzLyXK8SdjlgkytcopI31iEyXK/GSu+i1NWMsR+l1CSB3toBHf3Al+6FvhE\nROItxbmLgGctBV2bZOa6NmF3T6jIa8bO1IQKkVOFCvKEFJ3DG/CupcD3GqWpy8l2vq5VThHpFxYR\nMtzvEPAcbGMc6ng71saYlNcc667RAp+ISHz5HB+T3zWhYljaUvLcG3DabYTTuiZUdI+g9WpChcip\nSAV5L23btozi4vP65VzRVc6aQ1Y567AdQcLpbly5zTh6OId32TI4r39C63eKrXcUW+8kcmwnjyHd\ntY18T7SNMbrAtwXjtIlk2F2zvrsW+NzHXuBL5OdTsfWOYusdxTY49OdrvRPlsfeT76kl37uWYWnL\nyE+qJclqIpTqwZXTiiM7BBmwbC2cd35ivimTyL+Diq13FNvAiktBPmvWLP7whz+QlZUFwB133MHF\nF18cj1B6rbdJ+tBVztK0ZeS7N+DsmsPr7p7DG1vlbO9VbIn8i6nYekex9U4ixzZQ/M6d0QU+32pK\nU5eR46rDckSIdLcxdi/weXp+7kR+PhVb7yi23lFsg8NAF+Ruq5k8z3ryvTWUpi6lwFuDz/qYzhQf\nrpy2gxMq/EdOqFi2HM47f8BC65NE/h1UbL2j2AZWXApyy7L4/ve/z/e///14XP6kic7hrSXgWRsd\nBeRZR5L1CSG/B2dO22FzeF2awysiPeRzfETAU0PAu5phacvIdW/AaXcQTnNH3/nO6iq+fQBqYxQR\niRen1U5u0sauCRXLKUhejd/aRdCXjCO7PTqhoms8pFcjaEUGlbi1rBtj4nXpAeWgkxmBGylNWUa6\n3Ri73TgswmkuIslObEJE2l10bnfB9v6PIbw9SGe1q/9P3A8UW+8ott5J1Ngcwd51v0C0CJ+RP5Oi\n5FdJsT6M3W5cFuE0NxGvA5sQoWYPNAP1/RBwl0R9PkGx9ZZi6x3F1nPOSO/z3qns/KyfUpH+OJnO\nd7AJA2AsiKS6CPtd2FYQE3LQudMHO0/8vIn6cwbF1luKrXcSOTZoPaGjLBOHyvhnP/sZDz/8MGlp\naUycOJHZs2eTnp5+ZHCWdbJDExE5aT4r/SrvicjpTHlPRAaTEym1B6wgnzp1Krt37z7i9ttuu43P\nfe5zsc+P/8///A+7du3ioYceGogwRERERERERBJSXN4hP1RDQwPTpk1j06ZN8QxDRERERERE5KSy\n43HRXbt2xb5++umnqaioiEcYIiIiIiIiInETl3fIr7nmGjZs2IBlWZSWlvLggw+Sk5NzssMQERER\nERERiZu4vEM+d+5c6urq2LhxI88880zCFeNvvfUWVVVVsT9paWnce++97N27l6lTpzJy5EguvPBC\n9u/fH3vMHXfcwYgRIxg9ejQvvvjigMa3f/9+vvrVr3LGGWcwZswYVq9enTCxlZSUMHbsWKqqqpg0\naRJAwsQGEA6HqaqqYtq0aQkTW3t7O5MnT2bcuHGMGTOGm2++OWFia2xs5Pzzz+fMM8+kvLyce++9\nN2Fiu/7668nJyTmswyYR4jqeJUuWMHr0aEaMGMEvfvGLuMXxacp7vae813PKe72jvNe/lPd6L5Hz\nXiLmPFDe661BkfeMHFM4HDa5ublm+/bt5oc//KH5xS9+YYwx5s477zQ/+tGPjDHGbN682VRWVprO\nzk5TX19vysrKTDgcHrCYrrnmGvPQQw8ZY4wJBoNm//79CRNbSUmJ2bNnz2G3JUpsxhgze/Zs881v\nftNMmzYtoWJraWkxxkR/npMnTzbV1dUJEduuXbvM+vXrjTHGHDhwwIwcOdJs2bIlIWJbsWKFWbdu\nnSkvL4/dlghxHUsoFDJlZWWmvr7edHZ2msrKSrNly5aTHsfxKO/1jPJe7yjv9Zzy3sBR3uuZRM57\niZrzjFHe643BkPdUkB/HCy+8YM455xxjjDGjRo0yu3fvNsZEf3FHjRpljDHm9ttvN3feeWfsMRdd\ndJF59dVXBySe/fv3m9LS0iNuT4TYjIkm6I8//jghY2tsbDQXXHCBefnll82ll16aULF1a2lpMRMn\nTjSvv/56wsVmjDEzZswwf/vb3xImtvr6+sMSdKLEdTSvvPKKueiii2Lf33HHHeaOO+446XEcxL2p\nGwAADNBJREFUj/Jezyjv9Y3yXs8o7w0M5b2eSdS8dyrkPGOU93rqdM97cWlZP5UsWLCAq666CoAP\nPvgg1l6fk5PDBx98AMD7779PQUFB7DEFBQXs3LlzQOKpr68nKyuL6667jvHjxzNz5kxaWloSIjaI\nzhL9x3/8RyZOnMicOXOAxHjeAL73ve9x1113YdsHf+0TJbZIJMK4cePIycmJtQwlSmzdGhoaWL9+\nPZMnT0642Lolalzddu7cSWFhYdzjOB7lvZ5R3usd5b3+kahxdVPe6x3lvd5J5JwHynv9JVHj6tbT\nvKeC/Bg6OztZtGgRV1555RH3WZaFZVlHfeyx7uuLUCjEunXr+M53vsO6detITk7mzjvvTIjYAFat\nWsX69etZvHgxv/3tb6murk6I2J577jmys7OpqqrCHGUfw3g+b7Zts2HDBnbs2MGKFStYunRpwsQG\n0NzczFe+8hXuuece/H5/QsV2rOsmWlzxei56Qnmv55T3ekd5r/8lYlzKe72jvNdziZ7zQHlvICRi\nXD29pgryY1i8eDETJkwgKysLiK7A7N69G4iObsvOzgYgEAjQ2NgYe9yOHTsIBAIDElNBQQEFBQWc\nddZZAHz1q19l3bp15Obmxj02gLy8PACysrK4/PLLWbNmTUI8b6+88grPPvsspaWlXHXVVbz88stc\nffXVCRHbodLS0rjkkkuora1NmNiCwSBf+cpXuPrqq7nsssuAxPi38FkSNa5un46jsbHxsJXcRKC8\n13PKe32jvNc3iRpXN+W93lHe67lTJeeB8l5fJWpc3Xqa91SQH8P8+fNj7UsA06dP55FHHgHgkUce\nif2yTp8+nQULFtDZ2Ul9fT1bt26N7TjZ33JzcyksLOTtt98G4KWXXuLMM89k2rRpcY+ttbWVAwcO\nANDS0sKLL75IRUVFQjxvt99+O42NjdTX17NgwQK++MUvMm/evISI7eOPP47tDtnW1sbf/vY3qqqq\nEiI2Yww33HADY8aM4bvf/W7s9kSI7bMkalzdJk6cyNatW2loaKCzs5PHH3+c6dOnn/Q4jkV5r2eU\n93pHea//JGpc3ZT3ekd5r+cSOeeB8l5/StS4uvU47w3sR9pPXc3NzSYzM9M0NTXFbtuzZ4+54IIL\nzIgRI8zUqVPNvn37YvfddtttpqyszIwaNcosWbJkQGPbsGGDmThxohk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      }
     ],
     "prompt_number": 167
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}