{
 "metadata": {
  "name": "PSF_Photometry_Process"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##This notebook provides a basic introduction for how to do PSF photometry (stellar modeling and subtraction on crowded fields)##"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#first I'll load some basic tools, please make sure you've started this notebook with \"ipython notebook --pylab\"\n",
      "import matplotlib.pyplot as plt\n",
      "import pyfits\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is an example of what a crowded field might look like:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "image=pyfits.getdata('../data/nic2_f110w_dn.fits') #this is a drizzled nicmos image of the galactic bulge in units of DN, exptime ~ 94,072 s\n",
      "plt.imshow(image,vmin=1000,vmax=12000,cmap=plt.cm.gray)\n",
      "plt.colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0x108d8e830>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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G+kwMxLmKdJaBkVOxZMkS4XJbu3YtN9xwAwADAwPiPm1ubqahoYGCggIxM6mqqkKSJF5+\n+WWuv/76k7b11ltvMX/+/NN+/5eOBDMyMhg3bhz19fWMHz+eLVu2UFZWRllZGWvXrmXFihVjdnrJ\nkiXcfvvtfO9736Orq4uGhoaT/EjnCgUFBWLKotFo6OzsxGazkZOTI/w5wWCQmpoaJk+ePGY6vGnT\nJiKRCEeOHCErK4uMjAzx3pEjR1Cr1ZSUlKDT6ejt7SUSieB2uwHw+/2EQiEuueQSPv30U/x+P5FI\nhLS0NHw+35hpr81mw+VyYTKZkCSJf/u3f2PBggV8+OGH+Hw+ACZNmsS6devo6+sjEongcDgIBoMk\nJSVhMBjIyMigoqICp9PJ1q1b2b9/P7NnzyYSidDf309mZiafffYZLpcLv99PIBDg2muvZXBwkPnz\n53PkyBEADh48KEayVqtVuEPMZjO5ubk0NTUxbdo0MfX3+/04nU7KysqwWCxcfvnlKBQKGhoa2LFj\nB5WVlej1esaNG0dbWxvt7e10d3djtVq5/PLLqa2tpbOzE41Gg9/vB8BisdDZ2UlWVha1tbU0Njby\nzDPPYDabcTqdeL1e4X90Op28/fbbPPjgg0yZMoUpU6ag0+nEA6ilpYXu7m5g9CEoj2xkH6her0er\n1VJeXv6F11BLSwvBYJDU1FRsNhsPPPCAmCo/8sgjAOzYsYODBw/S29sr/KJKpZLDhw+LUc75yNn6\nBG+77Ta2b9/OwMAA48aN49///d954oknuPnmm1mzZg15eXn85S9/AUbP3b/+678Kd9R///d/k5iY\nCMDq1au5++67CQQCLFq0iKuvvhqAe++9l+985zsUFxeTnJws/PdfxmmTpZ9//nnuuOMOQqEQhYWF\n/PnPfyYajZ5ypydOnMjNN9/MxIkTUavVrF69+pydDjc0NNDX10cgEMDhcJCfn8+cOXOYOXMmH3zw\nAQAlJSX09vaiUCiYMmUKMOrXy83N5fDhw+Tm5uL1eunp6SEcDvPXv/6VSy65hN27d3P99deTmprK\nb37zG3p6ekhISBAjmXA4zM6dO1GpVNhsNm644QbKy8tpb29nw4YNGAwGWlpaSEpK4qqrrmJ4eJi6\nujrS0tJYtGgRmzZtorCwkJaWFjo6OggGg0QiEXQ6Hd3d3cydO5f58+fj8/l44403WLZsGW+99RYq\nlYqRkRG2bNnC4sWLsdlshMNhhoaGxHQ1IyODrKwsLr/8ctrb22lsbBTTCr/fjyRJmEwmZs6ciU6n\nw+Fw0NvbS0pKijCAAAaDgUmTJrF//34qKyvFdSBPkV0ulzD2wWAQl8tFfn4+N910kzj3/f39pKen\n09rayrx58zh06BDp6enCzeD1eolGoxiNRmbOnMlnn33GwMCACHqUlZWJ7wBYuHDhmGsgPT2ddevW\niQeexWLhiiuuYPfu3QwPD2M0GklJSTnp2nE6nbz55puYzWaKiopO6XgvLS0V/7744ouo1eoxQa60\ntDQaGxu/1jX8TXK2RvC111475fItW7actOzGG2/kxhtvPOX606dP5/Dhwyct1+l0wh6dKac1gpMn\nT+bTTz89afmpdhrgRz/6ET/60Y++0k58E2zevBmfz4dGo0Gr1XL77bcDo/6qJUuW8Ne//hWz2UxH\nRwcGg4F33nmHK664gmg0ysDAADfccIO4wf73//7fjIyMMDg4iM/nY8qUKeTl5aFSqUhNTeXgwYP4\nfD4xtI9Go6hUKiwWCwsXLqSyshKj0UhhYSE7d+5keHiYhIQEHn30UQoLC4lEIvzud78DRoMi4XCY\npqYmRkZGUCgUY6a4VqsVt9vNwYMH6erqIikpiWeeeUaMurq6ukhJSSEYDOL1eqmpqRF+tIsvvhiX\ny0Vubi75+fl88MEHNDU1kZSUJL7Lbrcza9Ys5s+fz0cffURjYyNqtXrMaBhG/aMlJSU0NzefNOKR\nI7QyWq0Wj8fDrbfeKpapVCruvfdeqqurhUuiqKiIHTt2MDw8jN1uZ9y4cbS0tDBv3jwuvvhi9u/f\nz8KFCzly5Ag5OTnCTfNFyNNul8tFKBQiEAjw4YcfYrFYSE1NJTU1lcOHD3PRRReJz7zzzjvU1taS\nk5OD3W4nGo2Sm5tLdXU106ZNO+X33HXXXUSjUdra2hgZGSEpKQmdTofZbP7S/TuX+XtGh79pLqiy\nuZdeeolgMIjD4UCn02EymbBareTl5Y1ZT6PRUFhYSGZmJl1dXfT29uLxePjggw9wOp0UFBQIA+hw\nODCZTGRkZJCbm0tSUhJ+vx+lUslrr71GV1cX0WiUcDiMSqXCYDCgVCoxm83MnDmT+fPni6mYPJqb\nO3cuS5cuFfujVqvR6XTs3buXe++9l0AgQDgcBkYNqlarZWRkhGg0itfrpa6ujvb2drKzs4nFYkyZ\nMoW9e/eSmppKaWkpNpuNyspKnnnmGerq6ggEAmg0GhoaGjCZTHz66afs3r0bhUKBwWDA5/MJR7Pd\nbmfevHm89dZbuFwu1Go1BQUFp/QJ+v1+FAoFKpVqzPJYLIZaraaqqgqdTkd1dTUFBQUYjcYx60mS\ndJJhkaPBAHv27MFgMLBjxw42b96M1WqlqqoKo9GIx+Ohvr6e1NTUL43ChkIhQqEQZrOZWCxGYmIi\nCQkJzJ8/n/Hjx5OYmMj27dsJBAL09PQwNDSEUqmkr6+P73znOwD89re/xel0YrVasdvtY0afAO+9\n9x4ajYbZs2ezYMECdDodsViM3//+91+4X+c6cSN4nhIOh/H7/aSnp1NWVsbmzZuJRCLMnj17zHod\nHR0cPXqUpKQkFi1axK9+9StCoRAtLS3odLoxI5vU1FQxsrvuuusAOHr0KE899RQul4uOjg60Wi2p\nqanC13jTTTcxODiI0+nkvffeIykpCYfDQV9fHy6Xi2uvvfakfe/r62NgYEAEBsLhsJhiykZSzlnM\nyMjAarUiSRKTJk1i4sSJhMNhli5dSk1NDR999BF//vOf6evrEwZJpVLR29uLVqtlYGAAv9+P2Wwm\nOTkZi8VCQkICEydOJCMjA5PJJIJm9fX1lJaWUlhYSDgcFj4xGPUXTpgwYcxxDAwMoFQq2bJlC6mp\nqbjdbvLz84lEIidFHL/IlRKNRtm5cycGgwGNRoNGo0Gn0+HxeIjFYsRiMZqbm9Hr9bS3t2MwGFi2\nbJlIWZHp7++nrq4OlUpFJBIhMzMTm83GPffcg81mw2azMTw8zMcff4zD4SAajZKWliZ8rs888wxG\no5FAIIDL5eLtt9/GarVisVi47rrrqK+vZ/v27YyMjFBaWjomv1OpVDJ9+vTTXrPnKnEjeJ5SWFjI\n4OAgHR0dHDhwAIvFQllZ2UlR3j179qBQKOjt7eXAgQPEYjEGBwdJSEjAYDBgs9kIBAIYDAba29vR\n6/VcfPHF4vOlpaV89tlnbNq0SYymhoeHUSqVJCYm8tFHHwGjvrDW1lYR5FCpVFx00UUnOePXrl1L\nXV0darWakZERtFotZrNZRLEVCgWpqak4HA7S09NZuXIlL7zwAsuWLRMBjPHjxwOjqQYDAwM4nU7h\npFer1SiVSjQaDT6fj2AwSCwWw2q18tRTT7Ft2zY+/fRTFixYgCRJrFu3jqVLlyJJEkePHqWtrY3G\nxkZmzpx50hT0876yQCBALBYjGAwyefJkOjo66OnpYdasWRw9evSUI8rPo1QqaWxsJBwOU15ezh13\n3MELL7yAzWaju7ubhIQEAoGAcDHMnTtXBIF++9vfkpWVJQIksh/TYrGg0+n4j//4D95++22mTp2K\nzWYTD6ZYLEY0GqW/v59gMMjAwIBIrNZqtWRmZhIMBikqKmLevHkolUoCgQBerxcYjW7KWRYyVqv1\ntMd6rvI/SUDhgjKCDQ0NtLW1UVJSgtvtxmazYbfbx4zs5CfcPffcgyRJKBQKfv7zn2O1WnE6nUyb\nNk1EXmG0qmZkZGTMFCgWi+FwOEhOTiYYDOL3+zGZTMIpHolECIVCIpfPYDAQiURQKBRitChPISOR\nCLt37xa+vmg0SjQaFdtMTEzk5ptvZv369SgUCnJzc9FoNOL7TsVVV13Fnj17CAQCIhdRp9Mxe/Zs\ntm3bhtfrFfu0fPlyzGYz2dnZbN++XeQR/uUvf6GoqIjMzEwR+ZTTZk5ENvA9PT1iNKvT6TAYDEiS\nRF1dnQgqXXfddUQikTEBls9z5MgRGhoaCAQC5OXl4Xa72b17N/feey8vvPACM2bMoLKyEo/Hw+uv\nv45SqWTnzp0sXLiQDRs2UFpayp49ewgGg8BoWpfL5aKvrw+FQsHjjz+O3W7H7Xazbt061Go1breb\ncePGMTw8LH4fOapvtVrxeDxEo1GSkpK48sorxb5efPHFIhVKjl4CHD58mIMHDzI4OPjlF+w5THwk\neJ5SV1eHVqtleHiYe+65B7fbTVZWlni/paWFpqYmioqKRAJmd3e3+Ixer+fAgQOUl5fT0tJCfn4+\noVCIcDg8Ju9QqVSSlZVFMBgU1R69vb3Mnz+ftLQ04WPcuXMnCoWC4eFhKisrqampobm5mV/96lfi\npnM4HNhsNjo7O9FqtQQCAVQqFUajUfji2tvbSUhIwOfz0dXVxUsvvYTVahV+w1Nx0UUXYTKZePHF\nF+nu7iYUCrFnzx5GRkYARD6jHNFuaWnh008/JSUlhYGBAS6++GK2bNnCzJkzSUxMJC0tjbq6Oux2\n+xgjZjQasVqt9Pb20tHRQWJiIhqNBqvVyoYNG8RDQp6ufj7hGkbdGDU1NXi9XgYHB4lEIhQWFpKV\nlcXevXvZsWMHhw4dIjk5mezsbBQKBRaLhQceeACv1yv8fXq9noaGBgwGA1qtFkmSuPzyywF4//33\n8Xq9hMNhGhsb8fv9lJWVYTKZmDNnDj6fD0mSWLp0Ka+++iojIyOMjIwQDAZJSUkR5ZWfx+v1Ulxc\nPGaZHDmORCJnctmek8SN4HmKPNqy2+3s2bOH8ePHEw6HUSqVwrCEw2EaGhqYM2cOXq9XRD19Pp/w\nN8lTaqvVisvlwuVy8eabbzJv3jwKCwupqalhZGSEFStWUFNTw65du5g9ezbz588XAYFrrrmG2tpa\n2tvbUSqVHDlyBIvFQigUYmhoiL6+PkKhEOPHj6egoIBXXnkFn88nSuBGRkZoamoiMzNTKGW8/PLL\nfPLJJ8K41tXVceutt5KXl3dK/5o8khsYGBDTm1gsRiQSEVN/lUpFKBRiZGQEg8FAV1cXqampdHR0\ncNlllxEIBJg3bx7Nzc2YTCa6urrGuBfkvK60tDTS09PJzc1lYGCA3t5eMYJcunSpGEnLo+8TaWpq\nwu/34/P5SE9Px+PxYLVaaW1tBSAhIYFwOCyM2ImYzWYcDgdKpZLe3l7MZjM5OTnCDVBWVkZTU5N4\niKSnp4vR6xVXXMH06dN5//332bBhAwqFgtdffx2NRkNCQgIejwefz0d5eTlOp5PW1tYx+x8KhUhN\nTRWuCBmNRjNGSup8JG4Ez1NSUlIwmUzcdNNN+Hw+PB7PGEd+eno66enpOJ1O1q9fT2ZmJh9++CEO\nh0NEHGHUd9fR0SG2YbPZ8Pv95OfnA6M5TLLTe+rUqRw4cACDwYDL5SI5ORmFQsGTTz4pqm6USiUm\nk4lnn32W//zP/0Sj0aBWq3G5XHg8HlH9odFoRDmdQqFAp9ONSd/Iy8tjx44deDweYVBfe+01UlJS\nKCkpobS0VPgI5eOQq1Pcbjc+nw+9Xi+qTB555BG2bNnCsWPHRKJvbm4uTqeT8ePH09vbyx133IHT\n6WTKlClEo9ExSeUnIkekPR6P0HjTaDR8+9vfHrOeQqHgs88+IxqNMn36dAYGBjAajeh0OhITE0Up\n37hx49i3b584Vz6fT0TmP09qairt7e14vV6Sk5PHBJ7WrVsnor5lZWVkZ2fz7rvvCr+iTDQaxe12\nk5KSwmOPPcZzzz2HJEkkJSVRW1uLWq0mGAzy85//nIkTJxIKhXA4HGg0Grq7uxk3bhzTp0+nu7t7\nzOzjfCVuBM9TKisr6e3tZefOnfT29rJs2bKT1pk2bRqffPIJqampVFdXC9+RHERQqVTcfffdvPPO\nO/T29jJp0iS8Xi8LFiwgGAyelOaxe/dukYNms9l49tlnCQaDHDt2TKSPyGVlcrrGihUriEQivPHG\nG2zatIlQItCUAAAgAElEQVSamhqCwSAjIyPodDp0Oh3RaJSCgoIxN9SkSZNwuVxEo1GcTidGo5Fo\nNIparWbjxo14vV4qKyvRarVEo1H++7//m8rKSjZs2IDH40GSJGKxGIWFhTidTl577TUCgQAmkwmz\n2YzJZMLn84mI8cSJE+np6REpKF/kgzyRhIQELrnkEo4fPz7GgMts2rQJh8NBUlISO3bsYOrUqQwN\nDTF58mSMRqM4BzBa0VRaWsrAwABZWVl4vV4SEhLGKP7I5OTkUFZWdtLyhIQE9u7di9/vp66ujtbW\nVsrLy5kwYQJz586lpqaGxsZG+vv7UalUwp9sNptZtGgRr7zyCl6vF7VaTWJiIkqlkra2NhFo6ejo\nEJHqjz76iKysLKZNm3ZGAaBzmf9JRlAhfQNH801VkXz44YfMmjWLAwcOYLVaT1nS19XVxfr169Hr\n9XR1ddHT04NSqSQUChGLxcRIx2AwkJmZSTQa5f7772fTpk0ib0wmGo2iUCh47rnnUCgUtLe34/F4\nGBwcFCIHMHo+5BFNXl6eiNK63W66u7tFhFHensFgIDU1lfT0dPLy8khOTkalUrF161YcDgd+vx+9\nXi+qO6xWKyqVCq1WKyLUTU1NFBQUoFKpCIfDHDt2jEgkgkajYfHixTQ0NIgp7vjx47n++utxuVzo\ndLoxeZUulwuz2XxGBvB0rFu3jqSkJCGdtH//fiKRCHPmzEGSJCFX9fnrp66ujvT0dKxWq3hveHhY\nyIx9GYcPH+all14S8luPPfYYDQ0N5OTkkJqayurVq/n4448JBoNCRkuOBs+ZM4d169YxPDxMNBol\nPT2dZcuWceWVV9Lb24vX6+WNN94Q6jFpaWmo1WpmzpyJUqmkoKCAwsLCr33evipf95ZXKBQcOHDg\njNadPn36OW8wL6iRYE5ODjt27KCvr4+LLrpoTBRWprq6GrvdzsjICPn5+SgUCpxOpyitkiOvkiTh\ndDoxGAw4nU5KSkqEMs2GDRsYP3489fX1ouRu06ZNuN1uPB6PqE9NSEggNzeXY8eOEQ6HCYfDHD16\nVKRjRCIRJEnCaDSKqasc0c3JyeGnP/0pDz74IEajUajF9PX1iQCKXHc7fvx4rr32Wt58803hj9Nq\ntfh8PgwGAyUlJTQ0NJCUlITNZmPOnDlMmzZNaPbdf//9oqTu83R3d58yIPBV+dvf/kY4HBaKMjA6\n0svMzMTtdn9pOsm4ceNwuVzC/wickQEEqKiooKysjNraWjIzM0UVh1xPnZGRIX6HUCiEx+MhJyeH\nWbNm0dPTIxLhY7EYfr+fDz/8kGPHjvHP//zPbN68Ga/XS2ZmJn19fUSjUR5//HEROHrllVfO5lSd\nE8RTZM5TTqxxDoVCbNu2jezsbEpKSti2bRv9/f1MnDgRh8PBFVdcwcDAAG+//TZarZbjx4+j0Wiw\n2WxUVFQI9Rd5dDVz5kwxCpk7dy5WqxWTycT69etxuVyoVCohr2W1WklOTkan0zF16lTy8vJobGzE\n7XbT2dkppnxy5cr06dOJRqMcPHiQ6upqYrEYbW1t3HnnnSJPcOrUqQwMDNDe3k4gEMButxOLxSgt\nLWXatGnk5+dTWFhIV1cXsViM4eFhoWoSiURQKpW43W4xwh0aGkKtVtPV1cXw8LDQ1vs8svqMHGU9\nGzo7O5Ek6aTk4f7+fo4ePTqm5vdUswij0XiSG6K/v5+UlJQv9FGeiMPhICUlBUmSePLJJ9HpdMKH\nGA6HCYVCKBQKkezs9Xppbm7GarWSmJgoRsgulwufz0c4HObll18W+aEAycnJJCUlCV+u1+s9ZxWW\nzoRzfXT3VbigjOBFF11Ec3MzN998MzDa4GXLli1s376daDTKZZddhtfrFdNkg8HAAw88wAcffEBf\nXx8ejwedTsddd90lhANOFeWTRy2ZmZksXLiQjz/+mMHBQbKysvB4PCiVSvLy8njooYfEtO0Pf/gD\nx48fp6CgQIjZ3n777bS1tQmVHlndOBAI0NHRIYxZRkYGhw8fxmazoVareeCBB9Dr9SLSPWHCBFHZ\noFarheGWn+ZtbW0izScQCPDkk09is9loa2sjFovxpz/9iby8PJRKJTabjcsuuwyFQoHH48Hv92M0\nGscI7X4V5MBMTk4ORUVFY97TaDRkZ2dz4MCBr1Rd0dLSgiRJWK1W4T/8IiRJEnqHLS0tYuTvcrnG\nyEHJSeoul4v77ruPDz74gKeffpqamhrhezWZTHg8Hvr6+tixYwexWEyoaZtMJlJSUoQ6eDQaxeFw\nfOXzda4QN4LnKXa7fYw2XFJSEsFgkOHhYTQaDXv27BG5YTAq915YWEhGRgYJCQl4vV5GRkZ4+umn\nKSws/EKFixPJz88fU9Im3xhdXV08++yzXHvttdhsNsaPH09/fz+HDh3CaDQyNDTEe++9Nyb59tFH\nH6W2tpaOjg4RRIHRkjo5R7CkpISysjIyMzNpbW3lkksuERHwBx54gJ///OekpKSIkeCxY8coKioS\nIhmyU18ub7NYLCgUCtxut1B1aW9vR6PRkJyczMUXX8y4cePO2ieoVqtJSUkZo9YSCoU4dOgQPp9P\n+Dy/Cvn5+fh8PgKBAD6fj/7+fpGbB2PTcIaGhpgwYYJIOZIkSQRYZF+sXPcdi8XQaDS89957BAIB\nnn/+eWw2GxaLRfx+coURjBpxg8GA0Wiku7ubtLQ0MU1va2uL5wmeI1xQLTc//vjjk6ZNchnXsmXL\nMJlMtLe383/+z/9h/fr1hMNhPvjgAxGE0Ov1eDweXC7XV0pzyMzMpKioiISEBJFILWsF7ty5k2g0\nyrx58/jJT37CjBkzKCgoICcnh2g0KqT0YTRfTqVSoVarhWinbFTdbjeZmZmidG3t2rUcPHhwTAoQ\njEqWV1ZW8pOf/EQo3ezatUv4R7OyspAkidzcXAwGA36/H7fbLUZCcj+NCRMmcPXVV4tt/D2JRCJM\nnTqVKVOmMG7cuFP6Ik+H7CNsa2sjLS2NhoYGBgcHRVsDWd9RTnK+++670ev1ZGVlkZ6ejlarRa/X\ni+TxE4NibW1tBAIBjh8/jl6vZ9q0aULRW/YR6vV6Zs+ezXXXXcf1119PdnY2kUiENWvWsGfPHiZN\nmsR3v/vdv99J+wfz9xRV/aa5oIxgd3c3b7311phlc+fOJSEhAaPRSFlZGf39/VRXV1NdXc3evXux\n2+0i0ddut4sorjzNkoVMv8hRLEkSM2fOZMmSJUKiatmyZSxatIiioiJRASKTm5vLJZdcIgIfDQ0N\nIq2jsLCQpKQkjEajiP7Kys9qtZpYLMbmzZv5+c9/Tltbm2g4cyI+n49XXnmFP/7xjyK5V5IkCgoK\nmDlzJg899BBGo1GMwtLS0pg4cSIJCQlYLBahgvNFMv1/D+RAUEJCAjU1NWe1jbS0NHp6esjJycFm\ns+F0Otm7dy+ffPIJe/bsQaPR4HQ6MZlM5Ofn43a7ufjii5k/fz4/+MEPRM6jTqcTsmeyeMakSZOI\nRCKUl5dz2223kZyczLp160QyfkpKCklJSaSkpLBw4UKysrIwmUx0dHTgdDqpr68fIw9/PvI/yQhe\nUNPhoaEh9u3bR3FxMXv27OG2224TSioHDx7kyiuvpK6uToiFtrS00N7eTjQaFVPfN998U4witm3b\nJrqJyYEBubOYjFzCZbFYkCQJg8FARUUFXq9XiIR+nqqqKsrKynj22WfHCDO0t7cTiUREzbOsKRgK\nhYRS9C9+8QueffZZJkyYwNDQ0Jjtbt26FQC3283WrVtRKpWiF0ZpaSn33HMPL774IhaLRegNyjd1\nRkYG27dvJyMjg5ycHMxms8hH9Pv92Gy2v7s+nlKpFNUbXxW5nUAgEGBgYICOjg6USiWdnZ309/fT\n0tKC2+0mMTGRjo4Obr311jGR6euvv549e/aIpk/JycniYVdUVERXVxe7du2iqqpK1GnL5+Caa65h\n4cKF7N27l6amJnw+H1arFaPRKH4rp9M5pmPa+cb5YuDOhAvKCMppJ1VVVTz88MO0trYKBRQYldC6\n8847CYVCHDt2jPfff59YLDZGj+7EBOvOzk7+8Ic/CC3BiRMnCmWXz3/vpk2bmD9/Pnq9nueee46B\ngQEGBwex2+1jfFR79uwR9cWRSIT29naxnZycHJKSksjIyBAVG3KbRznq/PLLLxONRmltbRUSWvX1\n9cCoFt/kyZN58sknhRCDxWLBZDIJX1VZWRk9PT0UFRWRlpYmfJlKpZL777//pGRjOcJ8qlHn10Xu\nx9Hf339agdQvwmAwYDAYuPLKK8ckgMuqxHJbhc9v/5ZbbmHhwoX8/ve/Z/HixQwMDPDmm2+iUCjY\nvn27EFNISEhg3LhxQskHRnMUjx07Jjr1vf322yQlJTF//nwMBgOfffYZg4ODohfG+Ug8ReY8xefz\nodVqaW1tFerXDocDr9eL0+kUissGg4HS0lJ27doldOY+z8jICHv37hV5gwcOHEClUp3y6V5bW8ui\nRYvE3/fddx+//OUv6e3tZXBwkMcff5zh4WHcbjcTJkwgKSmJ++67j5/+9Kds27aN9vZ2MjMzueuu\nuyguLmbHjh10d3eLcjKlUkkkEmHKlCls3LgRSZKYNWsWGzduZNeuXSxYsICZM2fS3NwspvJyhUpa\nWhplZWXk5eXx61//muTkZEwmE9/5znf44x//SFpaGmaz+ZQVDvv27aOiouKsosKn4sSHgcPhEOrY\nmZmZ6PV6LBbLWW87Go2Sl5eHXq8Xv7FGoxFR9FPd1FqtlvT0dKqqqqiqqsLj8YhWBjBal2w2m/n2\nt79NZmYmGzZs4NJLL0WSJNFjBkbVZOx2u/h7ypQpuFyuc6od7VclPhI8T1GpVLhcLoaGhvD7/aIH\nrFqtFgX4e/bsweFwMG7cOBobG4lGo3z00UcsWLBgzLZ0Oh2RSISZM2fS2NgoJKlaWlpQKpVjjMbn\njajRaBQCBbFYTAgByGIO//RP/yRy9kZGRujo6OCOO+4A4LrrruPAgQMkJCSwcuVKnnvuOWw2G3/7\n298IBoOoVCqCwSC7d+8WlRxHjhzBZDKNaeOZkZHB9OnTKSkpYc6cORw+fJj+/n4GBgaIRCL853/+\nJzabjZGRkZNEZ2VmzJiBUqmku7ub+vp6iouLv1bum2wAOzs7OXbsGP39/ZjNZkpKSs56mzIDAwOs\nX7+eyy+/nC1btgiBiz//+c+0tbXxzDPPcPnll1NQUCD8n0ajEaVSyccffyx6QIfDYRISEpg2bRpW\nq5WRkRE2bdpEamqqmBbL8vkyBQUFJ+2PLMF1vhI3gucpqampxGIx2tvbkSRJRB3lHhrNzc08//zz\naDQaEaxQq9U0NzePqUf98MMP6ejowOFwcPvtt6PX69HpdKjVakpLS/nVr37FBx98wDXXXEN5ebmY\nJp2InIohSZJo2C23uHzyyScxmUw4nU4yMjJYvny5iEar1WqeeOIJ4X9LS0vj/fffJxqN8u6774rm\nTzDa8ewXv/iFqDKBURGJtLQ0li5dSkFBgRA9zc7OJisri+PHj5Oenk40GkWj0XD77bd/YZmjrHIN\niJ4bZ4NcfyyPBBMTEzEYDNxxxx2iX/DXRQ4wHTp0SGQEbN26ld7eXtFz+s0330SSJCoqKgiFQuh0\nOnbs2IHb7RYjRaPRSH5+Pj/72c8YGhriV7/6lZiuy1qJp+pQNzIywsGDB4lEIrS1tREMBk+pv3i+\nEDeC5ym//vWvef/99zlw4ABHjx7FbDbz1FNP8cMf/pCRkRHUajWDg4NC1kmn02E0GhkYGECr1fLZ\nZ5+hUChEWovBYKC6uprGxkZisRgWi4VPP/0UnU5Ha2urUJWR2b17t0jKlZWPXS6XGAFGo1FRNicL\nt0ajUV544QWWL18udPBODEAsWLCAjo4Oqqur0ev1wmhEIhFeeuklkdYRiUTQarV4vV4sFguvvvoq\nzz77rNhOUlKSqHbo6enBarUK4dOBgYFTGnKVSiVkrb6OMkpmZiaDg4P09vYK49DY2EhxcfFp+4Oc\nrm1lNBpl+/bttLa2olKpWLhwIe+88w7Hjx/n1VdfFSPk1NRUURYpq+bInQaNRiN+v1/0SIZRw+31\nerHZbDQ2NrJ7924KCwu/cMre0NDA7t27RSOrUChEU1PTWZ+zb5r/SUbwgkqRcTqdLF68WEQ9HQ4H\njz32GAkJCUQiEaHXJzdBl31tw8PD/Nu//Rt/+9vf+POf/ywUVeSGR5MnT6anp4ejR4+yfft29u7d\nSzQaHRPU8Hq9bNmyhb6+Po4dOyZ65MqjP1mcVc7JmzhxItOnTycYDDJt2jTRB/fzpKenc8MNN4jp\n14033ojH4xEJx7KRlUe3CoWCgYGBk4I3MOrQV6lUIm1H9vUNDw+LG/ZE8VhANKn/usgiEIODg7S2\nto7px3Eq6uvrRcDni5DLFeXEcrlJVldXlxhxynXc8+bNY+HChSxevJjk5GSGhobQaDRkZWWRm5tL\nWlqaKJ3r7e3lZz/7Gb/73e+ora1lZGSEtLQ0li9fLpRxhoeHcblc9Pb20tLSQnNzM4mJicyePZur\nr76aSy655EsN/LnO10mR+c1vfkNFRQXl5eX85je/AUYzNyorKxk/fjxXXXUVTqdTrL9y5UqKi4sp\nLS1l8+bNYvmBAweoqKiguLiY5cuXn/WxXFAjwRdeeAG32y1GRHJh/ooVK/jNb34j+k7IN3okEiEa\njdLZ2YlarcZgMAgFFbmjXGFhIRUVFWzYsIHBwUFCoRAqlYru7m7Rz0Kn01FbWysa/1itVqE6Lae4\nyDerQqFArVZz5MgRtFotkUhE6OYVFxeL8jWZrVu3smHDBiG08MYbb4jSOjnFxGg0imORm6d/PhBQ\nX18v1i8sLMRmsxEMBtmzZw+ZmZki4HPNNdeMuRBPRW9v71klODudTq666iqOHj1KNBolMTHxlCIX\nMNozRT4Gt9stqmXcbjdGo5GmpiYmTJggHiJHjhyhr6+Pxx9/HI1Gw49+9COSk5Pp6Ojg/fffZ+LE\niaJq5corr2Tnzp1kZGRQXFwsejO/8MILHDt2DI/HIx4UarWa7OxsZsyYMWb/urq62Lt3L4ODg0ye\nPJm6ujrRKOvqq68WfuHzlbMdCR45coQXXniBTz/9FI1Gw9VXX83ixYuFrNsPf/hDnnrqKVatWsWq\nVas4evQob7zxBkePHqWrq4sFCxbQ0NCAQqHgoYceYs2aNcyaNYtFixaxcePGMW0MzpTTGsG8vDws\nFosIIuzbt4+hoSFuueUW2traRPN1WcFj5cqV/OlPf0KlUvHcc89x1VVXffUz9f+I3bt3i/658hRM\nrVbT398/Jt9O9s+pVCrxY4fDYSF6cNlll9Hc3Mwtt9witp2amipk6uVR18DAAMPDw6LaQtadGxkZ\noby8nPr6etRqtRgFymV1Go2GH//4x6xevZrLLrsMtVrNRRddxHvvvcfGjRvRaDQisiwb9ZSUFAYH\nB4X/zmQyiRGgrEatUCgwm82UlZXR3NzMypUr0Wg03HLLLUIEYd++faSlpZGZmSm6553ImRjATz75\nhOnTp5/UyvR0zJgxg5GREdxuN3l5eRw6dAiz2YxOpzulv1F+GMh5mDB6c+7cuVOMmvV6Pdu3bxe+\n10gkQlFRkVC/drvdeL1eqqqqxoitXnrppeL/Go2G9PR0fvzjH9PV1cXzzz9PV1eX0BWU9RVP5Nix\nYzidTkZGRjh8+LD4XXQ6Hc8//zz5+fm0tLR8pfNzLnG2KTLHjx9n9uzZwuV0+eWXs27dOt599122\nb98OjPZpnjdvHqtWrWL9+vXcdtttaDQa8vLyKCoqoqqqitzcXDwej6jzv/POO3nnnXfOygiedjqs\nUCjYtm0bNTU17Nu3D4BVq1ZRWVlJfX098+fPZ9WqVQBjrPbGjRt5+OGHz6l8IjnFQe4t6/F4aG9v\n5wc/+AG1tbX4/X4hoS9XYGg0GqLRKDabjXA4TF9fH2vXrh1T0wujMvKyZp9GoyESiYgKguzsbCFO\najQaKS8vp6ioiOXLl/Poo4+SkpIi1EW0Wi1FRUWiJ63dbuemm27CYrFw+eWX8/DDD+PxeOjo6MDl\ncgklkwcffJC5c+eSlpZGeXm56LWhVquF+otSqUShUNDZ2UliYiI1NTV0dHTQ1NQkpmfyQ2HJkiXi\n2E5s7n46jh8/jsfjEf2PzwQ5kCPXJ0+aNEn4aGXfLYymzbz77rtCmVrmxFHJ8PAwR48epb29ndra\nWrxeLyqVir6+PtRqtRB+kEcW+fn59Pf309vby9DQ0GlHOHLf5eTkZBERHh4e5tChQ3R3d7N161Ze\neeUV9u/fz8DAAENDQzgcDhobGwkEAtTX1xMMBqmvrz/JtXA+8UXT388++4yXXnpJvD5PeXk5O3fu\nFBkaGzZsoLOzc4wAR3p6On19fcBoldeJaWfZ2dl0dXWdtNxut591ytEZTYc/f2F8Fau9b98+5syZ\nc1Y79/dGnr74/X56e3tRKpViNChHdzUajQg+yFMxs9lMUlISkUgEj8fDhAkTxhT8w2g1h9y6Uk6/\ngdFRhCRJou5Yr9ejVqu56aabxGd///vf88QTT3D8+HEsFgter1eoyzQ1NTF79myhqAKj6tfd3d3C\n2LhcLvbt20dzczNXXnkl1157Le+//z7hcJiBgQE0Go2YEisUCq644gq2bdsGjEYt5QdVWVkZf/3r\nX8nMzOTYsWNCJ/B0wYcTCYfDWCwWjh07Rn19PTNmzODKK68UNcw+n0+0+zSZTNTX11NSUkJHRwd5\neXkiOOV0OoXElqzKk5qaSl1dHT6fj1AoJBRg7HY7KSkpqNVq0tPTCQaDdHR04Pf72bp1qwhEud1u\nEUx58cUXicViDAwM4PP52LRpE01NTej1eqLRKN///vfx+/2nnNZXVlayfft2enp6RGDlyJEjfPvb\n3+ajjz5icHAQjUZDIBAgOTmZzMxMPB6PaHtQVFQkHsbnK1/0sJg0aRKTJk0Sf7/88stj3i8tLWXF\nihVcddVVmEwmpkyZcpK7Q24f8Y/itEZQoVCwYMECVCoVDz74IPfff/+XWu0TDZ5stc8V5HaXsggC\njBrGlJQU0WBIni7LGnWXXXYZPp+PnJwc4XtKT08XP5Jcryvr74XDYUwmkyhJGxgYABDdzWTtvxOR\nk3Y1Go0ImJhMJrxeL3feeSeHDx8ec2G1tLSIaKfH48FsNvPhhx+SmJhISkoKycnJ3HnnndTW1uJy\nuUhJSeGKK65g69atop2k7BOVSwYPHTqEyWTC4XDQ09NDQ0ODGDE/+uijZ3yOLRYL1dXVRCIRgsGg\n6JFit9sJBALs2LEDk8mEwWAQnd+2b9/OxIkTaW5uFi0MPB4PnZ2dY3LsvF6viKoajUaCwSA+n48d\nO3ag0+lISUkRfYGLi4vF7zE0NCQCHdFoVJTiyQ2kYrEYkiTR09NDJBJh1qxZ7Nmzh+zsbH73u9+J\nSH1WVhbV1dWimZMsxuH3+0lKSuLRRx8lLS1N6DPK10FSUhLJycm4XC7RKP5b3/oWAPfee+9XuobP\nFb5OdPiee+7hnnvuAeDHP/4x2dnZpKenC19yT0+PqOCx2+10dHSIz8pdIO12+5j6687OzrNO0Tqt\nEfzkk0/IzMzE4XBQWVk5RpIITm+1vykp/VORlJTEwMCA2GfZ8Pj9fjFCMBgMWCwWkpOT+dd//VfW\nr1/PXXfddZJc+7p162hoaKCjowOz2YzVasXhcPCTn/yE/Px81Go1K1eupK6ujtTUVLxeLyaTieTk\n5FP6ymSfZDgcRqvVkpycTHp6Or/4xS9IT0+ns7OTlJQU6urq2LhxIxkZGSLqKd90qampojJFoVBw\n880388c//pEZM2awbNkyIQwhV0nA6Mht165dwKihVigUmEwmhoaGxPn5MmTDvmvXLpRKpRAlaG5u\nxmAw4Ha7aWxsJDU1VUxPXS4X5eXl5OXlnZR6895773HttdeSlZXFJZdcItSwZXdBZmYm5eXlmEwm\nqqqqhN91eHgYs9kspKv6+/uZOXMmpaWlIs1JPlcKhYLnn3+eDz/8kNraWvbv3y+CUomJiWRlZbFw\n4ULhs2ttbcXv9/PJJ58I/7jH4xGq4rLajCyEK+cYyo2x5s+fL1TDS0tLueaaa876Gj5X+DpGUM6r\nbG9v5+2332bv3r20tLSwdu1aVqxYwdq1a4WG5pIlS7j99tv53ve+R1dXFw0NDcyaNUvU5FdVVTFr\n1ixefvnlr/SwPpHTGkE5jJ+amsq3vvUt9u3b95Ws9rmknut0OpEkiXA4jFqtpqKigqysLJqamhga\nGhL9iFtaWkSJVXp6OgcPHmTy5MljtpWXl0dTUxMajYaSkhKOHDmCwWAgJSVFKKyUlpbyne98B4PB\nwMaNG+nt7aW8vJyCggLRlDscDrN+/Xq6urpITExkcHCQ8ePHM3v2bLRaLZ988gmJiYm8+uqrQlpr\nxowZwpe1f/9+YdDS09PHSD7NmDGDTz75hO7ubpYvX47f7xeioXIOolwpk5ycjNvtxmKxMDQ0xKJF\ni9i9e/eY9pmn4vjx4zgcDjweD729vUyfPp0DBw5gs9mEQZB9cpIk0d7ezt133/2FGoEajYZf/vKX\n5OfnMzAwQCAQYNu2bahUKtFkqrm5WcjdywEnpVKJVqsVhtJisQgFmdLSUqqrq2lubhbHKkkSixcv\n5pprruH73/8+PT09zJkzhyVLltDY2IjX6xW9QWKxmJDWCofDKBQKMjIykCSJGTNmEI1G+eyzz3jg\ngQd48803efrpp8U9sW7dOtFoPSkpie7ubv7yl7+MCaqdj3wdI7h06VLhMli9ejVWq5UnnniCm2++\nmTVr1ohgK4yqwN98881MnDgRtVrN6tWrxcBq9erV3H333QQCARYtWnRWQRE4jRH0+/1Eo1HRk3Xz\n5s387Gc/Y8mSJV/Jap8rJCYmiqm7Uqmkvr6euXPn8s///M//H3tvHh11fe//PzL7ZGYyS/Z9DyFh\nCRioMwQAACAASURBVEtYCohYtdS6XeqGtYrVctXaVq0rt3rusdelrbcurRZr3QsCFUQWQUAQZQ1L\nAiSQhOxkmyyTyTJLJjOZ3x85n9clgrbae78/EZ/ncDwOSZjMZ+b1eb1fr+fCTTfdhFqt5tixY2Rk\nZHDllVfy7LPP4nK5zur71traKu7M27Ztw2KxCA9PwZgxY4QwrdPpCAQCXH755YRCId566y0OHTok\nnZMyC9Rqtfj9fm644QYOHz7Mli1bKCkpITo6mo6ODrH3v+KKK1i+fDmxsbGo1Wp+/OMfU1hYeIad\nfGxsLE6nk7Fjx6LT6Thw4IC44Hg8HkKhEA6HQxyvXS4XJpOJ2tpa2VwrRxCADz74QLrNpqYmxo4d\nS0pKCseOHaOqqor169djt9tpb2/HarWKdK+1tZXm5mYCgQBdXV2fWwRbWlpoamrC6XRiNBrlhtXf\n3y98PMU4QnmtNRoN3/nOdygrK5NRx49+9CO5AY8ZM0aOpwp1SMlHVrbr4XCYQ4cOUV9fj8ViYdWq\nVRgMBhoaGtDpdFx33XW8//77BINBIiMjaWtr48477yQ7Oxu9Xo/ZbJacltPJ7D/84Q9ZuXIlUVFR\nNDU1MXv2bC644IIv/+b9muFfWXh+8sknZzzmcDjYtm3bWb9+yZIlovU/HVOmTBEjjH8FX1gEnU6n\nzC6CwSA/+tGPuPTSS5k6deqXrtpfB5hMJkwmE16vVz5EmzdvZtu2beh0Onp6eqiuriYtLY3f/va3\neL1e3G73GSE/5eXl7N69W/IjjEYjwWCQQCDAihUrMJvNZGVl0d7eTkZGBp2dnWg0GoLBIC+++CIw\n0iUr81KDwcDcuXNZuXKlzBR/8Ytf4HA4xMqrs7OTvLw8XC4XeXl5dHd3c+ONN2KxWJgyZYooNpSF\nhtvtxmKxYLPZWLJkiXwwr7jiCp599lmOHDmCSqXCarUSDAZRq9VShCMiIjh16hRRUVFMmTKF9vZ2\n4uLi8Hg8TJ06lR07djA8PCxLg8rKSrZv347T6ZSCl5WVJT+7vb1d5j4dHR1CQLfb7VRVVdHS0kJU\nVBTHjh2joaGB6OhooqOjJelvcHAQq9UqQVXKa6SEVqnVapqbmwmFQiQnJ3PBBRdQVFQ06popWu/Y\n2FgGBwfx+/0cPnyYrKws+vv7ZVYcHR3N4OAgg4ODkvccDodZtWqVeBwqHXNLS4voqgcGBmhoaBBv\nSgWKXruyspL8/PxvRAGEb5Zi5AuLYGZmJmVlZWc8/lWq9tcBCoVF2RIrQUKnq0VcLhddXV1UVlYy\nNDTEuHHjJHtYQWxsLNdccw2vvPIK4XCYCy+8kIaGBgYHB3E6nTKct9vt7Ny5k/7+fj766CMZzg8N\nDUnRVI6KH3/8sfD6ysvLUalUREZGYjabUalUdHR0UFNTQ1ZWFhqNhujoaAYGBrjiiitG2bTn5uby\n1FNPSdqcXq8f5WCTlJTEDTfcQFNTE4FAgMjISIqLi1m7dq28LgaDAZPJxIUXXsgPfvAD2Q4PDw9j\nt9t57bXXCAQCZGVliVt3W1sbg4ODhMNhUlNTZTOtcBSVm+ns2bOF/qPQJCIiIoRfeeWVV0ph6erq\n4plnnkGlUuH3+0lNTRWSuuKsrXzv0NCQBBmd7sGooK2tDZ1Oh9vtJioqilWrVomZhjLfs9vtzJ07\nlzFjxlBVVUVvby+BQIBt27bh9XrJysoiKyuL+Ph4Dh48iNvt5t133yUpKUkyaE5fYAFClcnNzR3F\nCDjXcd4UwW8aPB6PRFNOnTqVKVOm8MQTT+D1eoXgbLfb2bFjByaTSbS4f/nLX8jOzsZgMHDttdeK\n/boi9FepVCQmJjJnzhzGjBnD4sWL6ezsJCMjg4cffphPP/0UGDkSKxpU5Ujq8/kwGAwiS/P5fOj1\negk5nzJlCtOnT2f16tX4/X6Ki4ulkHq9XsxmMxqNhqqqKgKBAC6XiwULFrBx40bpmpQwJAVKYFN/\nfz8ul4uNGzcKNUhxUy4sLKS4uHgUPcZgMLBjxw5CoRCtra10dnZKZ60sdBRrfuVmotBXTofD4aCq\nqoq+vj60Wq2ktV100UWjHGuio6NRqVSy9IGRIq90oe3t7aLrLi8vx2KxMHHiRHbt2oXD4aC3t5cj\nR46wf/9+AoGAbGq9Xi+9vb2i8LFYLCKxGzt2LDabjYkTJ5KQkEBNTQ3bt28XZ5nbb7+dcDhMZWUl\ndXV1MhYJBoOiGT8dLS0tNDQ0MHny5C9FNfq649sieI7C7/fj9XqxWCxUVFRw6NAh8QNUtKpKp5GS\nkiKBR62trdTV1ZGdnS1edAaDAavVysKFC8nMzOTuu+/m5MmTREZGkpyczNGjR7HZbDz99NMEAgHZ\nIE6fPp19+/bJMe90h5dQKCTPQTEvuOeeewDYuHEjfr+fcDjM3r17qaur45FHHpHfzev10tbWhs1m\nIyUlhfvuu48VK1awceNGHnroISZOnMill15KTU2N6GAjIiIkc0OtVmMwGHA4HKSnpzNmzJizbrHn\nzZvH+vXrCQaDQi1RirMyR6yrqyMqKopZs2adkekCiLOyElylBMKfHq0JSJen1+tJSUnB7/czc+ZM\nrr/+egwGA6tWrWLbtm14PB6hTBw6dIiIiAhOnDghoegajUaIyd3d3ZjNZiHb+nw++ZqBgQHWrVvH\n4OAg6enpJCYm0tjYKHGaVquVcDiMWq3mtttuY8mSJQwODkpi3tDQEB0dHfzxj39k7NixnDp1ioqK\nCjQaDW1tbaxYsYKIiAisVitHjx79Wo2Kviy+LYLnKBQahNFoZMmSJfz7v/+7GCWMGzeO+vp6cnJy\nePTRR2VG9vzzz3Pq1CkGBgZobGxkw4YN+P1+3G43VVVVPPfcc9JtBQIBlixZQmVlJUeOHKGmpkay\nfv1+P9HR0SKXa21tJRgMijIFRpY1is2T8mF76KGH0Gg0+Hw+Jk6cyKJFi/jlL395hpFpUVERkyZN\nIhwOywf+hhtuYPz48Tz//PNs2LCBjz76SIqsx+MhLy+Pmpoa6YYUgb/BYBgVf+nz+TAajbLZHB4e\nJiUlRezm1Wo1DQ0NJCQk4Pf7aWhooKioiMsuu4ympibWr18vr//AwAD19fVimNDZ2Uk4HMZsNjM4\nOChyqvr6et544w2Z11VVVRETE0NBQYEsf2666Sb0ej0rVqzA6/UCI5SfvXv3Cn1Ip9OJJLGnp4fI\nyEj6+/uJiYlBp9NRXFyM1Wpl5cqVREREcPDgQaxWK5WVlVKkOjo6JCv48OHD9Pb2cvToUdkcK+FT\nClWpsrKS66+/nqNHj0p3XV9fL9SngwcPYrFY/iH96OuMb4vgOQqlQHR1dfHHP/4RnU4nWcLV1dUk\nJSXx2GOPyZtTmZEpEq7Ozk6WLVvG8PCwBPEotBtlXvanP/1Jjrxer5fKykqsViuTJ0+mpKSEN998\nE4PBIN2X8rwU3TCMdKzDw8OcOnWK5uZmhoeHMRgM+Hw+nn/+efLy8s6wroqIiGDVqlU4nU5+/vOf\ny+PJyclypPT5fCIjU6lU9Pb2iv382LFjKSoqIjExUWz8m5ub2bJlC93d3SQmJrJgwQKqq6tF+vfR\nRx+JNG/s2LHcfPPNLF++HJfLJYUhOTkZk8nE9u3bZUN78cUXU1RUxH/+53+KTPHUqVMcP36cjo4O\nMjIyeOWVVyQKNScnhylTpojN/+loa2sjOjqanp4eVCoV/f39Mi5Qrl04HBYen3Kt1Go1ubm5TJw4\nkS1btpCcnExjYyNGo1HoG8ryQ6fTkZSURGRkJE1NTezYsYOhoSG6u7sJh8NYLBYMBoPILjUaDSaT\niQsuuICpU6fS39/Pb3/7WwYGBmhpaRF362/9BL8eOO+KoHLMPHbsmFhq6fV6rFYrv/nNb0bdnc1m\nM7W1teL3Z7PZ6OzsFO8/ZcajfLAUb73U1FSMRiP79u2Tru7YsWMEAgF8Ph+dnZ1i0KAM9NPT08XY\nFUaOjIrtu/KG6+7uxmq10tvbS3l5uSwbAN544w2OHDlyRtaHzWYjPT0ds9nM8ePH5Rir1WppaWnB\nbDYTHx/Pz372M9mcKhZWyhHU7/dTUVFBT0+PxIbu2rWLhIQEUlJSZC4HiFJGWcZotVouvvhi3nrr\nLZqbm3E4HGzatIm1a9dKpvOBAwfw+/289NJLowjXQ0NDGI1GKisraW5upqioiKKiolHjA4XgrVar\nReYYEREhlCK/349GoyEyMpKhoSHsdjvBYFAWK3l5efh8Ptra2iRzRfFkVPKLbTYbXq+Xu+66i7fe\neks2x4ODg6SkpJCbm4tWq6WpqUkWRC+99BIPPPAAMGI39qMf/Yjly5fT2toq1/dcttL6OnkC/Ks4\nr4pgTEwMBoOB3NxcSkpKhGJhMBgoLCykvb1deG0wYoQZCoUwmUwEAgHpTAKBAKFQSOZ5SqdVXFzM\n3XffDYx0c3fddRdut5v29nZg5DiodIyna3bD4TA33XQTHo+Hp59+muHhYTHyPJ38rCwfFi1axKOP\nPsqTTz7JtddeS1tbG9XV1ULtWLp0KYWFheTk5LBp0yYWL17MsmXLqKmpGZVjrNfrpQvcsWMHc+bM\nOeODOX/+fN5//30effRRnnnmGZKSkqiqquL+++8XSduJEyfweDycOnVKbgS1tbViL6UQsjUajYS7\n6/V6WltbhYcaExNDXV2d3HCUZZPCXzQajZSUlNDW1sZFF11EW1ubFC6HwyEmqCaTCa1Wy+TJk/n4\n44/lBuL3+2Vxk5mZSUpKCiaTiZ07dzJ37lzMZjMTJkxgaGiI1tZWPvroIzIyMqioqKC1tZXMzEx+\n+tOfYrfb6ejokBtJOBzmZz/7GWazmV/96ldioPFZV5msrCwCgYCYOMyaNYvk5GR++9vf/q+/z/9f\n4NtO8ByF1Wpl1qxZXHXVVdxyyy1inBAKhYQCcbqYOykpicLCQkpKSkSVoagswuEwWq1WnFkMBgMW\ni0X4h0rcZX9/vxiVhsNh9Hq9+OQNDw9L5/XGG29Id6EcMRVKT25uLlFRUXR1dbFlyxa2bduGw+Fg\n/PjxDA8PU1tbi9VqpbGxkY6ODoxGI1VVVRQUFJCVlYXP58Pr9QohW3GojoqKoqCgQHSxe/bsweFw\njIqePN1S6/777+eTTz7BbreP0vSOHTuW/v5+1q5dS11dnYQaKb+b2WwmMzOT8vJybDYbhYWFGI1G\n2traJNOkp6cHo9Eo0jclSF4ZH/j9ftLT0+nu7mbdunVceumlFBUVUVFRIW7cRqMRjUbDrFmzhMJ1\nuqxT6YLj4+Pp7+8nPT1dFhopKSnSASt67O3bt6PRaLDZbJJ8p7iIDw0NidXaf/zHf5CQkCAadK1W\ni8vlGuV8vXr1aun+Y2NjmTNnDrm5uf9Xb/X/c3xbBM9R5ObmYrVauf/++7FYLDJMVzTBg4ODo47D\nioZWrVZTVFTEoUOHGBwcBJC5msVikWVGd3e3dGNer5eenh4hSRsMBlJTU4mMjCQ+Pp6f/OQnvPvu\nu+zYsYMpU6bwySefSAFU5nVqtZr09HQWLFhAdnY2b7/9Nr29vbJU+eijj4TaEhMTw+TJkzl+/Dgw\nIqHLy8sjOzub119/nfr6evEbVKvVLFq0iJKSEvHqq6qqoqenh/r6+lFFULH2UnDBBReclfCrBEcl\nJibKcVDJYNZoNFxyySW4XC66u7u56667CAaDbNiwAY/Hg9/vF7fr22+/nbVr11JUVMSqVatENdLR\n0UFERARarZY5c+YIlSYhIYHW1laqqqrw+/0YDAaOHTsm9mE2m01mhSqVCrPZzDXXXEN5eTnBYHBU\nBEJXVxcxMTGMGzeOsWPHsmzZMmpra0XrqhggDA4OYjQa8Xg8tLe309PTg16vp6CgAKvVKrGl//Vf\n/yXd/5gxY+QYrphWKBZi5yK+LYLnKEwmE11dXRQVFWE2mzly5Aitra14PB6am5tZtmwZixcvls7B\n5XKJ00pzczMGg0G6CcV+3uFwyDHzwIED3HHHHUKJUKlUowb0wWCQF154gTfffJNXXnkFn89Hamoq\nJ06cEKv9qVOn0tzcTHNzM2q1GqPRSEpKCmPGjMFqtcrXKu7RoVCItLQ05syZw8MPP0xfXx96vR67\n3U52djZPPfWUGGAonZLdbqesrAy73U5KSgrV1dW43W4hjj/wwAOir83Ly+OKK674pzhu11xzjRhF\nPPXUUzQ2NsoWtrCwkJ07dzJlyhROnjxJbm4ul19+ObNnz+bgwYOsWbNm1CLjww8/BEb8AZX5YCAQ\n4N5775UCp8DtdqPRaLjnnntkrvmHP/yBU6dO4XA4xHhCma+++OKLpKSknHFkPd3aSq1Wi+RTsV5T\nCroyezzdp3FoaIhFixaxfft2uVn29vaKVr2/v1/s+UOhEIcOHZL54LmIb4vgOYqmpiYGBwd5/PHH\ngZFZlhKrGAgE2LNnD93d3WRnZ9Pc3ExFRQVDQ0Po9XrhnBUUFFBTU4NGo6Grq0ve3JGRkeh0OuEi\nKlI6hYNWXFxMcXExL730EjU1NYTDYZ599lmeffZZNm3aJDrkyspKKXLKcf3DDz/knXfeoa+vj76+\nPiEOZ2ZmMm/ePKGvzJgxQ46Gn376Kfv27cPn84ktVjAY5JJLLqGrqwu73U5mZiZ1dXVi/ulyucSg\noK6uTvJI9uzZQ2pqKmPGjBnlgNLd3X2GBnjDhg1otVp+9rOfcc899+B2u0lPT+f+++/nqaeeYt++\nfXIM1Gg0xMTEMH/+fBoaGqiqquL111/HYDDg8XgwGo1kZWXJIiIUChETEyMFXEFPTw9DQ0P09PSQ\nkpKCVqulsLAQp9NJRkYGR44coaCgALfbLRZcgUCA4uLiUc9dKWoejweTyYROpyMuLo5QKCTONMom\nX7HXV2INYmNjeeaZZ5g/fz4FBQXi16hcf6fTKbpir9eL1WqVWfG5iG+L4DmKhIQEZs6cKbwto9FI\nIBDA4/GIzKyyspKqqiopbIpkzmw209bWRl1dHX6/H7PZLG66ii5VKYJZWVm43W7UajUul4tLL72U\n++67DxgJ/7n77rulc1Gkcr29vVxyySWUlJSgVqv5zne+Q3t7O52dnaM21NHR0bjdbpKTkzGbzfT3\n9xMZGUlSUhJXXnklq1evpq+vT2ywlJ+vLCy6urowGAykp6fT29uL1+vF6XSSmJgoczlFzaJSqWhu\nbhZVjFqtZtmyZfJ6DQ4O8uCDD8rru3HjRk6ePElXVxfvvvsuw8PDBAIBTpw4QXp6OkuWLGH+/PnU\n1taSnZ096trExMRw+PBhALkWKSkp3H///Tz55JOyRf/Zz35GXFwc77//vpgxKFJIu90unMYZM2ZQ\nVlZGQ0ODOHbHxsYSERGB1+uluLj4DLoNjBgGK/NIRSZntVpZs2aNuJFrNBrhNur1ehwOB1arlYGB\nAZYvX47H40Gv1xMREYFOp5OTw4wZMygpKZEb6Wc16ecSvklF8LxKm5s8eTLjxo0jFAqxYcMG6urq\n6OnpGWWp39PTQ0xMjLgxZ2ZmEh8fL1bqer0eg8FAOBxm1qxZPPDAA8ycOZPo6Gjxo8vOzuaNN94Q\nS32FAAxQV1dHR0cHra2tdHV1MXPmTCHz7t69m6GhIWw2GwsWLOCRRx4Ryoderyc+Pl7mkDqdjrvu\numsUaVrh5anVagYHB3nxxRdJSEhAo9Gg0+lEd5yXl4fT6cTlcpGQkCALmPvuu4+77rpLvk55XZRi\n+tOf/pT6+noxXVWr1fzhD3/g5ZdfZsWKFaJ1drvdYpWvSBWbm5vRarUcOHCAv//97zQ1NclowePx\ncPz4ccLhMDExMYwfP54LL7yQMWPGoFariYqKIjs7m9bWVvn5TqdTXLOVo+ef/vQn3n//fVauXMne\nvXvJzc3lmmuuYcaMGfh8PtxuN/39/RQUFNDe3s7q1avlRhYMBlmxYoVQgpTFFkB6ejpJSUmo1Wph\nCijpf1qtloKCAm6//Xb8fj8DAwPExsbKdVLcxqOioqiuriY/P1/Cl5T58rkI5b3xj/6cCzivOsH+\n/n5gZAFyepenvKmVIXZ9fT16vZ7c3Fw5sigO1N3d3QSDQbKysvj1r38NwKxZs8Su3WazMTQ0xLp1\n63A4HCINU4b/VqtVYj2Vba9erxf6hEKMXr16NVFRUeTl5UnnBSMLiOLiYrZv3y7Hu9OLrPLG02q1\n3HTTTfKBzMzMRKfTMWbMGOrr6+nt7cXpdMpSx2g0yvFw//798m8ajUZxcXnuuedwOp14PB60Wi1T\npkxhcHCQoaEhLr/8cgAOHz6Mz+cTcwiFoJ6UlERjYyMzZsygs7OTZ599Fp/PJ0VNcWgJh8PSNbvd\nblauXCkuMTExMTIjjIiIEEcXpetSAtzVajUXXXSRHNWnT5/O0qVLiYyM5Ac/+AGZmZlidQ8j+t5V\nq1YxduxYSktLaWtrY9q0aZSWljJhwgTa29txOBxMmzaNurq6UXJBvV7P8PAw69at45JLLpHxyocf\nfkh+fj4lJSU4HA4uuOACJkyYIClrEyZMYN++ff93b/b/Y3yTOsHzqghWV1dTWVnJ8PCwWDYp7iU5\nOTlyRI6IiCA5OZm6ujry8vKIjY1l5syZhEIhnnvuOQYGBkZtTGHEVn7BggXy5njkkUfo6ekhHA6z\nZcsWdu/eTTgcFtcURTPb3t7OokWL2Lp1Kx0dHaSmpsoS5fLLLxfL+9dee40//elPPPTQQxw9ehSf\nz8fTTz+N3W7nzjvvFHMGZVHi8/no7u5maGiI5ORkHA4HkydPprOzE6fTSWdnp0QMJCUlkZaWJrNM\nRZp25MgRoabk5+fL66aoHbZu3coTTzwhmcsWi0XmbUrxVCR/tbW1WCwW9u3bR1RUFL29veh0Onw+\nHzqdTmgrJpOJLVu2YLfbxSzC7/fT29sr3EytVss999zD2rVr0Wq19PT0SEdsMplGhUQpuP3229m1\na5dQe05fiiQnJ4/Kuuju7mbJkiU4HA62bNlCXFycdNQmk4m2tjby8/PZt28f0dHRzJgxgwkTJtDU\n1ERaWhp+v5+qqiqKiopoamri6quvFm/DrKws3nnnHcrLy88aaH+u4NsieI6itLSUvr4+ma+ZTCYx\nDliwYAHLli0T3p9CxG1ububf/u3fyMzMZM+ePdjtdsLhsMzNFDidTp566ilycnIknOmmm27ixRdf\nFFrK8PAwGRkZVFZWipQrHA7zzjvvkJaWJoHwGo2G733vexJlcOTIESwWC7/85S8ZGhqiqqoKvV4v\nx8EHH3xQXLBvueUWBgYG2LhxI4FAgPHjx8sxtb6+ntbWVnp6eoRPqAQTnThxgvb2dr7zne/Q0dFB\neno61dXVOBwOkpKSWLhwIX19fTQ2NtLQ0CB5HS+99BIej4eBgQFaW1uFJqTVaqWgK+Rin8/HhAkT\ncLlcYqyg8Ajz8vI4ceIETqeT3bt3Y7fbiYmJkQ19VFQU6enpEmr+zjvvSPEuLCxEp9PR1dVFWlra\nWa+9Ynn2eRgeHmb79u1yGrDZbELbUW5cNpsNu91OQUEBer2epKQkKisrhTuqhNArhg8bNmwgLi5O\nnMYBjh07htFoZObMmWc1lzhX8G0RPEcRCoXEmcRms4m+1WAw8O6776LT6WhtbRUFgrLsOHz4MJmZ\nmeTk5DBt2jTee+896uvreeKJJyTI5/vf/z5er5f169fLEU2ZK2ZkZDBmzBiJelQiORUqjtlslqAm\nZQZ3eqeixBn09PRI0Vb4c8PDw/T09HD99ddTVlaG3+8XJ+hf//rXGI1Gent7+eCDD0TSpdBxHA4H\n3d3dGI1G2tvbCYVCFBYWMmXKFACKi4vZs2cPnZ2dYhJhtVp54okneOGFF4iIiGD//v1y5I2Li5MC\nqHRoimlFREQEPp+P0tJSWWIo886+vj527NhBTEwMN954IxqNBr/fz8qVK9HpdEydOlW8+AKBAK+8\n8godHR2oVCpiYmLIy8uT1/HzAs1DoZBw9Jqbm7FarbKYcDqdHDhwQCgskZGRGI1GJk+eTENDA4FA\nQMwiZs2aJd8XCoVoa2uT04TD4aCvrw+3283JkyeJiYkhISFBkga1Wq2Y3X42ruFcw7dF8BxFZmYm\nNTU1GAwGBgYGpBAC3HHHHWzZsoW+vj4mTZpEY2OjzM72799PWVmZuE8rs6mKigo58r377ruEQiH6\n+vrIzs6W1DFl0H7bbbdRU1NDRUWFZIsoIv6cnBzsdjulpaW0t7fj9XrZunWrREWeOHGChIQEUSKs\nWLGCBx54gO7ubumkTp06RV9fH8uXL8dqtbJkyRJRQFitVrq7u1GpVNx8882sXLmSU6dO4ff7iYqK\nIj8/X+zBTp8vRkdHc+jQIQwGAy+88AIul4u6ujqmT5/Oww8/zM6dO+nr6yMtLY1LL72Unp4egsEg\nmzdv5rbbbuOpp54S4rhyw1G2qlqtFrvdLrb5Q0NDREZGMnPmTLHTV6lU7N+/f5QZqU6nY+7cuaxZ\ns4bIyEjp/AKBwCjfQkV/XF1dTWxsrCT6+Xw+4uLi2Lp1K5MnTxYKVH5+Pm1tbeIurcxUX3/9dVQq\nFV1dXaIxV6BWq0flWijh9nq9nptvvhm/38/evXvZvXs3c+bMobOzE4vFgsvl4tChQ1K8z0V8WwTP\nUShditfrlTe6xWIhFArx4osvMnfuXK6++mosFgvBYJDnn39egr4NBgMul4vi4mI+/PBDMVPQ6XQS\n4RgMBrFYLCQnJ3PPPffw5z//Ga1WS1JSEr/+9a/RarV4PB6R1ul0OgYHB3n44YcJBoM88sgjuN1u\nVCoVBw8epKysDLfbTWpqKtdffz0bNmwgGAxyxx13SDFOTU0lIiKCiooK/H4/48aN44477pACqMDh\ncDBhwgQKCwuZPHkyLpeL6dOns2jRIgCRgn0WS5Ys4eGHH6axsZFgMEhhYSFTp05FpVIxdepUWlpa\nuOyyyyTsfXBwkPfff5/169czfvx44uLiWLduHTDSOSlbVbPZLJ0tIF1zaWkpxcXFWCwWIiIi/AcK\nAQAAIABJREFUznq8zc/P59Zbb5V5ZXt7u3gAKrDZbBw8eFDC5gcHB1Gr1dLlXnLJJQQCAem4r7ji\nCl5//XX6+vro6ekhNjYWv98v0ZCDg4NnDZM/XdsNiCktjOiVe3p6ZNygOGIrQe+lpaX/6C37tcW3\nRfAcRVxcHE6nE5vNRn5+PnfeeSfr169n8+bNmM1mLrjgApnTaLVaFi5cSExMjMx6tm7dyqZNm4Ru\noQS0m0wm4uPjJXpTSRpLTExEq9VKWpqSa6z8fOW/9913H3a7XSR1Ho+HOXPmsGPHDgkDv+iii9i7\nd68Ekyu8vcceewyVSsWdd95JMBjEbDaL8mF4eFjMVq+99lrhqzmdTkKh0KgFwukf3tOhzLpsNptw\nDRVi9+7duxkeHhbnmv7+fkpLSwkGg5SXl5OZmTlK3aKYkur1eikIP/7xj/nrX//K4OAg1dXVUlQU\nV+nOzk5JNlSg0WhITU2VDJCzBaTDSNpeb28va9euJTk5mTlz5kjRVcYKMEK2VhyhFX9AZWaqQPET\n/CzOxjVUYLPZ5L1y5MgREhIS6O7uFibCuawdPlfoL/8MzqsiWF5eLkTkG264ARj5oOzfv5+4uLgz\n3tCfjZv0+Xz4fD6ZqaWmphIbG8uBAwdIS0vj/vvv5/e//z0nT57kueeeY+zYsfJhj42NJRwOo9Fo\nOHnypGiEGxoaiIyMJBgMSphRbGwsra2twmHz+Xzcdttt6HQ6LBYLeXl5xMXF0dbWxpIlSwgGg0RF\nRWGxWMjPz2fDhg1kZ2ezefNmUlNTmTJlijieDA8Pk5+fTzgcxuFwjPr9Tk9wU6C47ERERGC326mr\nq+MPf/gDFouFo0ePkpyczLp16/B4PDidTglDysvLk5nZ8PAw2dnZzJ07F51Ox/bt29HpdHR0dPC7\n3/1OnHuUUPs9e/bIzM/n8/Hiiy+SnJzMzTffPGqZEBcXJ1b5Cmn5s1CoThMnTjxDbqfAbrdTUlJC\nVVWVuLxER0eLFlmB2Wxmx44dFBQUnGFqezbs2LFDFjfHjx+XnJPU1FRxET9X8VU7waqqKvnswQhv\n9vHHH6enp4e//vWvsjF/8sknRZ301FNP8dprr6FWq3nhhRe49NJLATh06BCLFi3C7/dz2WWX8fzz\nz3+l53ReFcH4+Hj6+vpGSb0aGxvx+/20tLRw9OhRJk2a9LmOv4q2VTEt6OzsFLt2n8/Hs88+S09P\nD06nk8LCQjIyMqTApaSk8Pjjj3PLLbegUqkk6yQiIgKPx0NkZCQej4fCwkKRkJlMJtxuN263W0LR\nv//977Nw4UIiIiL4+c9/Lh1pcnIyCQkJlJeXExMTw5o1azCbzSKPW7ZsGRaLRez1HQ4HJ0+eJDEx\nUVxutm7dyrRp0zhx4gRRUVE0NjaSmprKjBkz0Gq1HD16lMHBQQ4dOsTkyZNFNlhRUcFFF10kUQDH\njh1jw4YNJCYmUl1djUqlYubMmfzwhz9keHiYpqYmdu7cKRrcgYEBIaZ///vf59NPPyUYDOL1ejEY\nDERHR6PX68+6TY2MjPzCLatKpTpr8NLpUMjSynVUuJ7btm0jPz9fpHMTJkyQMPh/pgjOmzePQCDA\n4OAgra2tMhdVeKGnF9hzDV+1CI4ZM0bGAMPDwyQnJ7NgwQJee+017rvvPuGIKjh+/DgrV67k+PHj\ntLS0cPHFF0sTceedd/Lqq68ybdo0LrvsMjZv3vyVsofPqyJ4eniSYpmv2B3FxMRQU1NDW1sbEyZM\nGOUuEggE6O3tFT2tzWbDarVKt6Z0dArxOBQK4XQ6eeutt0hMTBSj0NWrV4vaQ4m5VCR4Spra+PHj\nSUxMFFcUZZOsdIG1tbX8/ve/F73s0NAQaWlpPPzww/zlL38hJiaGgYEBZsyYgd/vp7KyUiy0du3a\nRTAYpKGhgeTkZPr6+pg3bx779++nurqaqKgoYmNjKSoq4uWXXxY7r5KSEgkqCoVCBINB9u7dK8e9\n2NjYUZy38ePHs3btWvbt2zfqg64UvaysLHbt2iXa6NjYWOLi4pg8eTJJSUkSaqTYlfX19WGxWEZZ\nU/2zONvXK9pgBS6Xi3A4TFxcHENDQ5Ih3dHRQWVlJXFxcSQnJ8t298ssNHQ6HTqdjpSUFPr7+yUK\n1GQyySz5XMT/xkxw27Zt5OTkkJqaKqeez+L9999n4cKFaLVaMjIyyMnJYf/+/aSnp9Pf3y8mFzff\nfDNr1679SkXwvJLNLV68WKz033rrLd599122bNlCZGQkJpOJG2+8EZPJJLMmBVVVVfz5z38W+6WY\nmBgWLlyIRqPBaDSSn58vXnJKIYyKimL+/PnMmzdPFiKrV6/G7XYLUdrlcklBjI2NJRQKccUVV3Db\nbbcBI9ZOCQkJREdHExkZSUpKCvPnzxdZV09PD2lpacTHx7Np0yays7Pp6+vj1ltvxev1cuDAAY4f\nP86f/vQn4d6lpaVRXFwsiou8vDyJH+3u7mbnzp0sXbqUCy+8kJaWFg4cOCBZwQaDQTrH4eFhJk6c\nyPjx44ER1YyCUCiE1Wpl7NixREVFERERQWNjI6+++irvvPMOn376KWq1mnHjxjFx4kTxZZw0aZJ0\nhREREVx44YXceuutkq+8efNm+vr6hOT9VTA4OMi2bds4ePAgLS0tHD58mGPHjhEMBrHZbERFRdHd\n3S3cRLPZLJpjZTH08ccfi/ron4WylFKyr4FRhfhcg1K0/tGfL8KKFStYuHAhMLJZ/+Mf/8jEiRO5\n7bbbcLvdABKIpSAlJYWWlpYzHk9OTpYc7y+Lf6oTDIVCTJ06lZSUFNavX4/L5eL666+nsbFRwteV\nYfznnd+/DoiKiuK6666jpaWF7du3Y7VaiY2NFWL0hg0bxOnjdJSWloq/n1qtpru7m6VLl5KUlEQw\nGMRoNIraQymgyuNpaWlcccUVLF++XOygFGuoqKgo8RBUhPn33nuvdE8ajQaXyyXzwujoaNasWUNP\nTw8ejwe73U58fDydnZ3MmzePt99+mwULFhAREUF3d7fk6nq9Xr773e/yb//2b6jVavR6PRs3bgTg\n5ZdfxuPxUFNTg8/nIzIykoSEBD744AOMRqNofEOhEHfffTeRkZFs2rSJ3t5eNBqNGD+c7uoyMDDA\nwoULMRgMlJWV8be//Y0jR45Inkl+fj6tra20traSlpbGokWLyMjI4M033yQcDktxnzx5MjAyi5s/\nfz41NTWUlpaSkpLypc0HwuEwGzdupK2tDYfDQXV1NQCpqamo1WqOHz9OVlYWlZWVo3JYiouLmTBh\nAlqtlv379zNt2jQ2bNhASUkJ3/3ud7/Uczjdqutcx+cVuOrqanltvwiBQID169eLs/add97JY489\nBsCjjz7Kr371K1599dX/vSf8BfiniuDzzz9PQUGB3P2efvppLrnkEh588EF++9vf8vTTT/P000+f\n9fyuzIS+LkhKSiIpKYnx48ezYcMGTCYThYWFMus7nXsGyPFPEccrMZU6nY6mpiZxSNZqtcTFxdHV\n1UVUVBTt7e1kZ2djt9tJT08nJyeH3/3ud/T19YnjsqIXVqz7AdlAxsfHo9FosFgsIuFqa2vj6aef\npq+vD5PJRGpqKj6fj/T0dJYuXYrBYODQoUMsXboUQAwDoqOj+fnPfy5Lj8rKSux2u7gvu1wuUlJS\nKC8vl233iy++yD333CP63KioKFpbW7n22mux2WwyLggGgzidzlGvcU1NjVBRlAQ2peP1+Xwy73Q4\nHDz44IO8+OKLXHDBBahUKrq7u0lNTZVjowKHwyFehKePKv5ZVFRUiGu1QuBWbiL79u1jaGgIp9PJ\nzJkzOXLkCIsXL6ajowOHwyHpfSaTiYqKCsLh8BkWYucbPq8I5ubmjtp6Kzfbz2LTpk1MmTJFxigK\nowJGJI6Km3lycjKnTp2Sv1OEAErE6umPK9LEL4t/WJ2am5v54IMPJHQaRuyGbrnlFgBuueUW1q5d\nC5z9/F5SUvKVntj/NdRqNdHR0RiNRubNm8f8+fP57ne/S1FR0SjVgbI0MJvNpKSk4HA4sNlsMutR\n3KlDoRAnTpxgcHCQxsZG7Hb7qO4oJyeHefPmERUVRUpKCmazWTpOxbtO6T7S0tIknvL2228nNTUV\nk8lETk4OF154IWlpaWRmZjI4OEhsbCx79uxBp9PR3NzM9u3baW9vl4WP0WgkISFhFKUhJyeHDz74\nAIBTp05hMpno7e2Vm5XX6+WWW27B4XCQmZlJYmIiUVFR4ih9ehFS9LSnQzF7PX78uITIKzcfm83G\nhAkTKC4uxufz8fDDD9PT08OYMWO46qqruOeee0hPTz/Dakshl+fn53+lm2pBQYEoV1wuFx0dHXR3\nd/P2229L7orT6USn0zFjxgxg5IOpzC2rqqowGAwSnZmSknJWXuX5gn/VReadd96RozAg4VYA7733\nnoxZrrzySlasWEEgEKC+vp6TJ08ybdo0EhISiIqKEsXS22+/zdVXX/2Vfpd/2Anee++9/P73vx+l\nlXU6nbIdi4+Pl05AMfZUoJzfv45Q7Jc+T2Z16NAh9u3bR29vr8RqejweAoEAQ0NDYnKgbBbj4+Nx\nu91CBlZChk5HQ0MD8+fPp7S0VOgyLpdLgtkVArLRaJTiquiHFVx66aU0NzfjdrvJysqitLRUYjXj\n4+NpaWnB4XDQ3t7O8PDwqBmUgj/+8Y+kpqZSXl5OIBCgtrZWLKIUP0GTySQ29ffddx+rVq3i1KlT\nxMTEjMphAfjb3/7G9OnTSUxMpKWlhdmzZwMjm8C3336buLg4fvnLX/Kf//mfPPnkk1LEDh48yNq1\na/F4PDzxxBOiilFeu6qqKsaMGQMwamOvdKZfBspWVsl46e3tZdq0aUyZMoWIiAhaWlpoa2tj0qRJ\nZ/3+cDiMy+XCarVisVior6+np6cHu93O+PHjcbvdn8tX/CbiX1mMeDwetm3bxiuvvCKPPfTQQ5SV\nlREREUFmZiYvv/wyMHLzuu666ygoKECj0fDSSy/Jieall15i0aJF+Hw+Lrvssq+0FIF/UAQVAfik\nSZPEKfezOD3I5vP+/uuGUCjEkSNHgBEeWWdnp+hxlTjJ9vZ2Tp48KSE8yiJC2f6Gw2HZmCqGCS6X\nS+guFRUVXHXVVVIwjh49Kt1cU1MTOp2O/Px8Vq5cKWHgc+bM4cSJEzz22GP893//t3gYwv/Eeu7a\ntUuyRw4fPszcuXNRqVSUlZVRVFTEM888Q29vL3/4wx/o6upCo9EwZswYPvjgA7xeL/v27SM/P5+k\npCRqa2ulawsEAmRkZKDX67nllls4ceIEPp+PAwcOMDAwwMDAAKtWrWLHjh3Mnz8fr9eL2+1m27Zt\nEtSUnJw8SrqmVqvJyMjA6XTy6KOPMm3atFFdnN1uF7VHVFQURUVFHDlyRI5ALpeLHTt2cNFFF43a\nyH7ZAqjA7/eLeYayVVSuT2pqKoFAgK1bt5KYmMi4ceNGfa9yM9qzZw99fX2EQiFCoRBNTU0MDAxg\nsViE53g+4F8pgkrMxel46623PvfrlyxZwpIlS854fMqUKRw7duwrPw8FX1gE9+zZw7p16/jggw/w\n+/309fXx4x//WAT9CQkJtLW1yXn+bOf3r3pO/7+EWq2WbsDhcOD1eunq6qK5uZmysjL6+vqEgNzb\n24vH4xGn6b6+PnmjKxkgGRkZsryIjY0lMjKS+vp6HnjgAeLj4yUg3Gg0smXLFsxmM5MmTWL16tVE\nRkbS39/PwMAAO3bsICcnh3vvvZef/vSn8sF76aWXxEE5FAphs9k4duwYc+fO5eKLLyY2Npbo6Ggp\nDlarlV/84hfs3LmTrq4u1Go1fX19OJ1OLBYLra2tNDc3s3jxYp577jlCoZAoTR5//HEGBweZNGkS\nTz/9NP39/fz5z3/GZrPR1NREZ2en6K8vv/xyLBYLNptNitdnuW8qlYqOjg6x+Tod1dXVchNVnFiG\nhoYoKysjMjJSxiplZWV0dHQwY8aMzyU8/yMMDg4KKdvn853Rqff29pKamnrW3+F05OTk0NjYSG9v\nLzabjby8PAwGg4RYnS84b2RzTz75JE8++SQAO3fu5JlnnuHtt9/mwQcf5M033+Shhx7izTfflLP4\nlVdeyY033sh9991HS0uLnN+/jhg7diypqakMDQ0RHR3NyZMn6enpwev1kpSURHd3N16vl8jISL73\nve8RHx9PSUkJu3fvxuVyiSvJ4sWLmTRpEhqNhg8//JDLL7+c559/XuZFCg3GbrdTX18vaXctLS0U\nFxcTGxtLU1MT+/fvp7u7mwMHDmA2m3n55Zf5wQ9+QGRkJLNnzyYcDrNr1y6ioqL45JNPMBgMdHV1\nyWBZr9ePcibp6OjAarWi0+mYPXs2mzdvlgKgGJ5WVlYSHx9Pa2srg4OD4pQ8NDREbW0taWlplJSU\nyE1h7Nix1NbWivzv/fffx2q1kpubS1dXl9Bpvv/979PS0kJlZSVtbW2Ew2Hh/wWDQYLBIJ9++iml\npaXk5ubS29uL3+9n+/btZGdno1arycnJIT09fVSY/FddsLW3t3Pq1Cna2tpoamoiOjqao0ePMmHC\nBGBkBrpixQoMBgNWq/ULZ0terxeXy4VWqxXdckdHxxkzzG86zpsi+Fkod7qHH36Y6667jldffVUo\nMvDF5/evGxTLcxjZyDY2NpKXl0d3dzehUIjY2FixfEpLSyM9PZ09e/YwPDyMXq+XwB3l+AuMKigG\ng4G+vj6Sk5Pp7OykvLwcjUaDx+Ph73//Ozqdjh//+MfAyAerr6+PkpIScZDu6+vjvffeG2VyUFZW\nJuFJfX19JCQksGbNGvx+Pw0NDezevZuUlBTy8/Pp7e3FbDYLjeOGG26gtbWV7du309TUJMeLG2+8\nkVtvvRUYOS7+6le/IiYmhtbWVgmXV7oyt9tNcXExvb29VFdXM2/ePLq6ujh+/DhxcXHs3LkTi8VC\neXk5vb29bNq0ifHjx+PxePB4PBw9epRjx45hMBiYNm0aP/nJT8R+/7//+7+x2Wx0dHSQkpLC9OnT\nRx2tvyp6enpEk62EzQeDQSZMmEAwGKSzs5Ndu3ZJ+NTNN9/M0aNHyc/PPyvROiMjg97eXjo7O0lK\nShJVy2dpVd90nJdFcO7cucydOxcYoSso9IrP4vPO719nHD9+XOZFubm5+Hw+WdEPDw9TVlZGf38/\nNptN5oYKeVaZFyqStDfffFN4eunp6aMS6ZRcW4vFIl0IjEi/HA6H5GXExsbS399POBwWN2aTycSJ\nEycYGBgQ49KDBw9SUVEhFJ74+Hi8Xi8ajYaoqKgzeGxJSUnAaGJzOBwmLS2Nuro6cVupr6+nqKiI\n73znO+zevVvkcrfffjs5OTl0dXWxcuVKTpw4QWxsrHjkpaamcvXVV4smNi0tDYfDwaZNm/j0009p\nb2+XGMr29nbGjx+PwWCgpaVFjttms5nW1lbeeecdfv7zn/9L1zUYDGK324WTmZqaSmFhIX6/X2z4\nExMTxfpfMVtIT0+Xa3G6vre2thYYoR55PB7Wr1+PyWQiNjZ2FHH3fMA3yUDh60Pg+/8JSqFRrKZU\nKtUoIq7igXf06FEOHz5MMBhk8eLFFBUVodVqeeutt3A4HLI0UCzgY2NjJclOSYiLjIxk4cKFzJ8/\nn/j4ePbs2UNpaSn79++nvr5erKaUI7TRaGTPnj188sknPPvssxKLCSPzSMUKanh4WPJ1CwoK8Pv9\nXHLJJWf9fSMjI0fN1ZSEvKSkJAkVNxgMJCQkcPHFF5OQkIBOp8NkMrFnzx6sVivp6elER0eLT2Bs\nbCw2m43rr79+VJZueno6FouF6667jltvvVWOw36/H4vFwuTJk5k8eTI1NTW0tLRIlq8iJ/xXofye\nWq2WEydOSDC9klGinFImTpyIyWQiKSlJTFOVOeXp8Hq9OBwOKarhcJju7u4vrR75JuB/QzHydcF5\npR3+LMLhMLW1tVitVsaMGYNKpWLGjBl0d3eP+jqPx0N1dTX9/f0YjUZKS0vp6OggKSkJt9vNQw89\nJBpgo9GI2+3GarWKd6GSmGaz2UQOFA6HOXDgACdPnmR4eJj4+HiKi4sJBoOiTvH5fFgsFioqKsTy\nX9lOA/LY1VdfzezZs8nKyuKTTz7BZDLx4YcfUlhYeAaxWK/XM2XKFAYGBli7dq34Era3txMdHT3K\nFqyqqkrmdYmJiQSDQZYuXYrZbKauro7u7m46OzuJjIzEbrdz6NAhIUl/Fmq1munTp9PZ2UlycjIP\nPPCA/N2ECRM4dOiQSA4VStLy5cu54IIL/uUuq7+/X7bpAJ2dnfJv5OXlUVdXR0JCAunp6cTExMii\nTxlzOJ1O9u7dS0NDA1arldmzZ4sDuRJPcL7hXClw/wzO6yIYERFxRmCSkhZ2OhTXCyVvVuHPKUfW\nYDCIyWQS+3bFsfo3v/kN//Ef/4HJZCIlJQWj0Uh6ejpVVVVYLBaampoYGhrCarVyzTXXjDqq/f73\nvycmJobS0lK0Wq2w44PBoDz3QCBAQkICJpOJ3NxcwuEwWVlZ7NmzRziEzc3NjB07VuZrfr+fjz/+\nGKfTSU1NjZDAzWYzt9xyi3Q4KpWKe++9l+bmZvR6vWx4w+EwRqMRn89HMBiUjN2DBw/idrslqOmz\nUByXFdne6bDZbNhsNgYGBiTJTskM2bRpEwaDgZSUFObNm/eVrrPCBVX4izExMSxfvhybzcbWrVuZ\nMGECV155JS6Xi1OnTomFmrIgqqysFEWP4godGxtLT0+P3NzON3xbBL/hqK2tpaKiguHhYVpaWqTz\ncjgcuFwuoXIoNv3KRlX5cAeDQTQaDXfffTcJCQm43W6eeOIJmpqaePPNN4GRYmS1WuUIvG3bNo4f\nPy5hS8nJyRw4cIDi4mI++eQTcW1WaDmKzC87O5vrrrsOGCmMhw4dEj+/hoYGPB7PqAXDD3/4Q7Zt\n28auXbvIzs7moYceOuP3j4iIYNmyZZjNZhITEyVi0ufziSFqbm4uDQ0NDA0Nyfb7bAUwFArx3nvv\ncezYMVQqFWazWYxlFQNVZQNtNpvFXUUJuVKcv7+sTC0cDtPa2kogEDijG46IiOAXv/gF27dv5+jR\no3JNe3p6RvERFUt+ZYQwODiI2+1maGjoS+uGv2n4tgh+w6HohZXhr9IBKsVNMRpV8ovD4bB0My0t\nLURHR3PixAlxLy4qKkKlUpGRkcEvfvELNm7cyJgxY+jt7eXQoUP09PQwceJEGhsbJTJz69at2O12\njh8/Lt2fw+EQf0KVSkV6ejo2m43Dhw9jMBjYuXOn8Bpfe+01UlNTz8qidzqd5Ofnf6GLiaKlXbx4\nMYmJifzud7/DaDRK0U9PT5cQ9P7+fu69996zCufXrFnDkSNHCIfDcpwPh8P86le/wmg0cvvtt4tz\ni16vl8WOx+ORIKbx48fzve99T36mch2+CEps6hdd4/b2dqZPn87Jkyd59913GTt27Blfl5GRQUZG\nBjBC6P3sTeV8xbdF8BuKgYEBysrKcDqd4p2n0+kYGhpCo9HIEkHZHKakpAhJ1mQyEQ6Hufbaa3nt\ntdcIhULY7XaSk5NJTEyUf6O3t5drrrlG/l8ha7e3t0sC24EDB8jMzJSMEUXwD/DKK6/w1FNPYTQa\nmTp1KjqdjoiICLZu3UpPTw9NTU1MnTqVkpKSs9I2lCVIXV2d/L1SVJRIza6uLonbXLdunRgGNDY2\nyoxzzZo14podExPDr3/9ay688EIJcFfQ0tLCwMAAKSkp4qys1WqFfvPMM8+gUqlISEgQwvbQ0JA4\nYWs0Gq6++upRI4p169axYMGCf+la19XVYbVaOXbsmFjq33jjjV/4PTfffLO4D53v+LYIfkNRX19P\nZ2cnKpVKCMFKvq6yMVSOag6Hg/7+fqKiosRlOhwO89prr43qIH/yk5/IoiUUCp2hLy0oKGB4eFi+\n/7333sNqtXL48GGx8le2vxaLhbvuuovp06cza9YsOWpv27aN8ePHs2zZMsnPVZw8XnnlFXFWPnHi\nBP39/XR2dhITE0NXVxcffvghJSUl4l6tVqu55JJLePTRR+nv72fLli10dXXh8XhEK63cFLxeL3Fx\nceh0OqKioqirq6Ojo0NiAlauXCn8u/r6eux2u+iSCwsLMRqNbN++naioKPR6PXfccQdGo5G///3v\nQueZM2fOKNv/lpYWLr30UlpbW0lISKC2tnaUa4mi5/0s9uzZIxthJRWvubmZcDjMwMAAcXFxVFRU\nkJWV9bkdssfjweVyMTAwwLhx485pe/x/Fd8kisy3RfA0FBQUUFtbK75/pweEK+7SERERxMTEiPGn\n4kitpIip1Woh0KrVav7rv/6Lxx57jBUrVsgWUSE/K1DMD/bu3YtWq6WkpASPxyNegw6HA5PJRH5+\nPgaDgalTp4re9t1335VCqdPpcLvdOBwOcZC2WCysWbOGYDDIwMAAGRkZUlybm5upqakBRsKKgsEg\n0dHRXHzxxQAYjUamTZsmxaKzsxOXy4Xdbsfn85GUlERqaiqPPfYYv/vd75g0aRJVVVX89a9/pba2\nluzsbPLy8jh06JAQyZXvra6uRqvVMnv2bOrq6oiNjeXvf/+7dIlz5swB/seNWkFycjLl5eUUFBRQ\nXl5OVlaWdLJ79+5l+vTpZ1zXhoYGBgcHKSsrQ6VScffdd7N8+XLUarVwAd1uNzt27GD//v0YjUZ+\n9KMfnfFzNm/eTHNzsxCvAbnWXye7uP8X+LYTPEdxtiAhBUqojpIz4fV6RTZnNBqJjIwkEAig1+vJ\nysoiMTFROke73U5bW5vERCqmqV6vF61Wy29+8xsMBgNDQ0PYbDa2bdvGtGnTzjACULq5oaEhMWlQ\nrN6VGE+VSkV7ezs2m43Vq1cTERFBeXm5WOfHx8fzyCOP0NfXx+uvv47BYKCxsZH8/HycTie9vb14\nvV5RpgQCAeLj47nqqquwWCxnxErGxsaydOlSent7xWFZCSgPBAL09fXx/vvvy3Knu7tucIovAAAg\nAElEQVQbm80mN47S0lIKCwvp6uriqquuYufOnRQWFlJTU0M4HCYnJ4fk5GRRcrS0tDB27Fhxivms\nYw2M3Kw2b95MXFwcn3zyCdOnT+fjjz8WEvtnkZKSwtGjR4mJiSEiIoIXXniB66+/XsYP69ato6Oj\nQ5gBp3vbKejs7KS3t1c4lBUVFRQWFsqi6HzDN6kInldX7/MKoBJ6U1NTI55/RqNRBvMKHSQhIYGE\nhASxl1eOgxqNhri4OMaOHcu0adNIS0tDr9eLY8nQ0JAU2bS0NDIyMiSu83Rs376dYDAo3nkxMTEY\njUY0Gg25ubmoVCoqKytFLme1Wtm7d690jWq1mry8PNLT04VjWFtbK8VNo9GIY45y7B8eHiYiIoLs\n7GwKCwsZGBhg8+bNuN1uDh48yNKlS9HpdKhUKpxOJ5mZmUydOpXExET6+/vp7u6mpKSE8vJy2tvb\n6ezspKmpCafTSXV1NS6Xi+bmZpYsWYLT6eTxxx8nJiaG7373u6hUKlpaWpg3bx7Tp0+nuLiY+Ph4\nOSp/0XXs6+vjyJEjZGRkiEyvv7//DOu2I0eO8MYbb+D1emlpaaGrq0u6WKPRiE6nkyjOtrY2VCrV\nKDWPgj179jA0NMTw8DA9PT24XC6JMD0f8S1Z+huEsrIy0tLSaG9vp6CgQHSwbrebYDAoxzOFCK2I\n7E+dOkVkZKTw/KZMmUJ+fj7BYJC//OUvWCwW+dBYrVahxFx11VWj0tEUd5W//vWvaLVatFotNptN\nfAkVCd/3vvc9enp6yM/PZ/fu3cyaNYtHH30UnU5HXFycuFbHxMSIHb7H48FiscgRNioqihkzZrB/\n/34p7krH+vzzz5OWlkZDQwN1dXVs2LCBcDgsR+3h4WHGjRs3iuT86quvUlpaSnd3N//+7//Oq6++\nit/vJxAIAP9j/wUjXpNFRUU4nU6uueYa/va3v/Hggw/y8ccfiyORXq+noKCAqKioL+yuIiIixKK/\noKCAgwcPYjKZqKurY8uWLaSmpuJ2u+ns7ARGjvWtra1C9/msS4zD4ZDoBIPBQGVlJfv376egoACV\nSsXmzZsBpBNWMmP+2Q7Q5/N9YVE/F3GuFLh/Bud9EfR4PPx/7J15dNT1uf9fs2cmmSXbZE/IShZC\ngCiK7KuIgAqKqBUtFVute2/16tXW21bFuoG2LrW4i6KogAoiIPtOIGFJQgLZ93XWzGTW3x85388l\nAkrV9meU9zk9p05mwsxkvs88n+d5L0VFRRiNRkpLS8nKyqKsrIzY2FhaWlqElX5MTAwOh4Pc3FzB\nF5NMNrVarejqKisrycnJwW63s3v3bgDh9jJ37lxxMUhSPLlczgcffEBPT49YvEhF5ze/+Y0gMzsc\nDsLDw9m8eTOXXHIJv/3tb/F4PJjNZhYtWiRUFSUlJXzwwQdiDmaxWMTMa8iQIcycOZOSkhLBe5O2\n3w6HQ9wubcK7u7vF3Eyr1Z5meT9kyBCKi4uJjo6msLAQk8nEzp072bRpEzKZDK1Wi9PpxOVysXXr\nVnp7e1mwYAEAv/jFLzh27BgxMTEcPHiQGTNmAPQLNbLZbELR8vWNbEFBARaLhc8++0zMGO12OyqV\nSpCudTodBoMBr9dLTEwMMTExBINBIVGUitiXX34pOnav18uxY8dwOBxUV1cjk8mEtE9iBjgcDqGj\n/zZYrVZhQmw0Gtm4caOYuQ5knC+CPyHExsZy5MgR2traGDRoEJs3bxYcvsjISBGMJBkolJSUEBUV\nRXh4OB6Ph6FDhxIWFiaOsdKRu7W1lbi4OCorK4mIiBC5CNJcURqwv/POO+Liky5K6YIvKSlh/vz5\nBAIBwsLCsNvtjB8/nttuu01sOmUyWT8qTEFBAStXriQQCGCz2USx0+l04jgqdbOpqakcPXpU5CAb\nDAYefvhh4Vv4+uuvi3gElUp1Go8uGAyKDXpnZyfp6elUVVVx/fXXc+DAAVpbWxk/fjw9PT1s2bKF\nnJwccfH4/X7y8vJETrIE6f2Tjp0qleqMlBSZTEZubi6HDh3CYrEIT0VpU69UKpHL5Vx//fV89tln\nwpnG6/USHR3Nxo0b0Wg0nDhxQnD/HnroIWQyGZs2bcJms9HW1saQIUPIysoS+mOZTEYwGOTYsWOo\nVKpvlPRJyyjpS6y4uLhfLsZAxk+pCP6sZoJfh9SJSA7Oktdca2urmMdlZWVhNpsZN24cHR0d6PV6\ncYFGR0cTFhaGSqXC4/EIuylpmyrFOA4dOpRFixYRDAbRaDR0dHTg8XhYsWIFcrmca6+9Vsy5pCCg\n/Px85s+fL2z3oe9Y9/DDDyOXy3G73YSHh+P3+6murqa1tZXOzk5ee+01QaSWOtSUlBRx8TY0NPCr\nX/2KF154gfvuu49bb71V2IZFREQIErJCoeCyyy4Tvoh+v5+XXnqJhoYGampqBGdw0qRJ5OfnC9up\nwsJCvvjiC9Glut1usrKyhDoG/m9B1dDQwJAhQ87oxadWq0lPTyc5OVnYm339wjObzeL9nTp1KpmZ\nmbS2thIWFoZcLsfhcOD3+5k+fbpIroM+AvSll16KwWAQcsAbbrhBFODJkyeTlpaGx+Ohra2N6Oho\nbDabWPYkJCRgNBq/9TisVCpJSEjAarVSW1tLcXExRqNRjAsGMr5vxsiPCT/bTtDpdJKXlyfCtt1u\nN729vcInTqfTERUVhclkQqlUcuTIEcxmM83NzUyZMgWbzSYMTU+VdUkO0FI3NXjwYC6//HJB45Du\nKy0fZDIZ5eXlgnIDffbxt9xyy2mzpCVLlgi3k4aGBux2Ox6Ph82bN3PkyBEqKysZNGgQPT09YuGh\n0+mor68XW+T09PR+ErSLL76YlpYWtm3b1u85QF+8gsPhEAuUYDDIE088QWJiIldddRWTJk0C+lyb\nd+7cyfHjx0lKSmLhwoX4fD6++OILKioqaGhooKenB5/Px4YNG4iJiaGkpIS0tLRzMkewWCyUlJSI\nf09CV1dXP0qMFJZ04MABACIjI3n11VeZNm0aOTk5YvYoaX0LCgo4fPjwGXNmSkpKcDqdnDx5kj/9\n6U8MGTKEpqYmrrjiCiHDO9eliCTLk8vlQvHzr4bI/9jwU+oEf1ZFcN++fWi1WqxWKzqdTgR/Dxky\nRCw62tvbxTbU7/ejVCrx+XwMHjyYgwcP4vf72b17twgckobqEmbOnCm6puzsbKZMmSIUEPB/F87s\n2bN5+eWXaW9vFy4xsbGxpKamcu211xIIBNBqtSLgacmSJbS3t9Pb20tTU5Pg9EmDfp/PR1JSEiaT\nCZvNxsmTJwVlRKK0KJXK0wwjoI8k7vV6RcTAoUOHaG9vZ8uWLchkMhELKpm/+ny+fgFQGo0Gq9VK\nZWUlCQkJIins+PHjglwMffMxiWg8ZcoUQYg+F6SlpQkidmVlJU1NTaeFUOXk5FBWVkZOTg6dnZ1i\nFFBbW0tpaSnJycn9yOpSNowUaiVBusCl2AOPx0NLSwuZmZkcPnz4jO/hNyE8PJy8vDyqqqpISUnp\ntxgbqDhfBAcopA2t0+kkJiaGQCAgjBAk7zuj0Uh4eDhdXV0MHjwYp9MptolarVYUFYfDITJKvg6Z\nTMbcuXOFXO7UpDQJp3rmSUVS8uQDRBH2+Xy8++67wodPcqyR4jAlvfLo0aPJyckRneVLL72ETqej\nsbERuVyO0+lEq9VSXl7e73nU19fjcDiEO84f//hHrFYrGo2GMWPGsH37dlwuFyEhITQ3NxMdHS0o\nNlInDIic5VO1ynl5eezevRuXyyWyh6Uo0m/SLZ+KyspK7HY7ra2teDweIiIi0Gq1IjP460hJSSEt\nLY38/Hwef/xxrFYrO3bsAPoiB74u6/N6vbS1tfH8888LF+wdO3YI70OlUin00jU1Nf9yB3fw4EGK\ni4sJBoPMmTNHqFkk84iBivNFcIAiLCwMi8VCIBDg5MmTIvtCUkMolUpycnKor69n8ODBWCwWwsPD\nsVgsmEwmDAYDCQkJYlFxtrhOuVzeTy8MfVQYuVwuCqpEvwkNDUWj0WA2m1m4cGG/x1gsFpYsWSKK\nc1dXF3FxcSQnJ6NWq5k2bRqlpaWMGjWqn3tLdnY2jzzyCG+++aZIRZPoNkajkaeeeorY2FjBmZNC\ntORyOQ0NDYSGhqJQKGhoaBCh6T09PYSGhqLVasUW9lT4/X5sNhtqtZpAIEBRURG7d+8W71t0dDQd\nHR0cPHiQlpaWMyoyzgRJEldeXi6WNDNnzjzjUXTbtm1Conjw4EFCQ0Npa2sTc9W6urp+xae1tZXu\n7m5hgLFjxw4xgujo6MDhcIg8aMkGTDKzOFfU1NSILzCJWA99nxHJW3Ig4vsUQYvFwi233CJOQK+/\n/jqZmZlce+211NbWisgOaWzxxBNP8Nprr6FQKHj++eeZNm0a0BeLe/PNN+N2u5kxYwZLly79Ts/n\nZ1UEJaqINJeRFCL79+9HpVKJgG6JNxgbGytUAtAXOq5QKMTW8lzdRLq7u9mwYQMqlQqNRkNnZ6dY\nrOh0OiIjI5k2bZrIJpGWKh9//LHouqQt7KRJk4Q9vkKhID8//4xa2aamJqKjo7nnnnuQyWQ89thj\nYvGhVCrF9jo8PJxPP/1UqD+gr+NNSEggPT2duro6oZwxmUyEhoYSFhZGaWmp0CQDzJkzh/Xr1/Pc\nc88hl8ux2Wy43W7mzZtHQUEBgUCA1atXi6P2vwq/349er0ej0bB9+3ahxklMTMTj8dDZ2Ul+fj7R\n0dEsWbKElpYWOjo6RJSqx+PB5XLxyCOPMGjQIBQKBV6vl5aWFjHaiIyM5OTJk2i1WpKTkzlx4oQw\nfAgLCyMpKYmJEyd+axfX3NxMa2urMG61WCzk5eVhsVgoLS0VX1gD2Yfw+xTBu+++mxkzZrBy5Urh\nSfnYY48xdepU7r//fp588kkWL17M4sWLKS0tZcWKFZSWltLY2MiUKVOorKxEJpNx2223sWzZMkaO\nHMmMGTP44osvvlP28M+qCCYmJpKYmMjBgwcxm80oFAqOHTsmQr8laRggZkdGo1EQmqXuQzJOkDbC\n3xQm5fV6+fLLL8XWtb6+HrfbTXJyMldccUW/i0kqtl6vl08++YQ5c+aI45QUfC51JMFgELvdLp7L\n1zsjvV5PMBjkH//4BzNnzmTy5MmcOHGCwsJCKisrxZb66NGj+P1+7HY7er0ej8dDXFwcw4YNEwl1\nElUoPj6e//mf/2HJkiWnLTRMJhNFRUV4PB56e3vx+/3Mnz9fJODJ5XIuv/xyLBaL6CK/ScZ4Khoa\nGqisrKSnp0c8d51Ox6JFi8R9duzYQXFxMU6nUxg2GAwG0tPTRdRBeXm54BAqlUqioqI4evQora2t\nhISEiDjTnJwccnNzhcuMJKUbPHiwMJz9JsTFxSGTybDb7eJLraamhsrKSgwGAydPnsRsNp/VhXsg\n4LsWQavVyvbt24WvplKpxGg0smbNGrZu3QrATTfdxIQJE1i8eDGrV6/muuuuE/GrGRkZ7N27l5SU\nFOx2u0izXLBgAatWrTpfBL8NEt8sPT0dv9+PTCYjLy/vWx+n0+loa2vDarWK4iJJrr4NkppB6kIl\nrWlvby81NTWkpaX1u7/P52PFihX09PQQFhbG1KlTxQYzNDSUkSNH0tzcTFxcHCdOnKCtrY2NGzeK\nBU9XVxdtbW04HA66uroYNWqU6CRvvPFGOjs7ycvL49ChQxw9epSuri5hB+Z2u4mMjKSzs5Py8nIi\nIiLIzMzE7XZTXV2N2+2mtbVV6IVvv/12FAoFDoeDRx55BJ1OJ8jeoaGhlJSUMHPmTPHa1Go1ra2t\nOJ1Ofv/73zNq1KhzssSSIlCl7k3iP27fvl0YLYSEhAhlh81mEwl5s2fPJikpiU8++YS4uDjKy8vp\n7e3lnnvu4fPPPxfehdISzGKxMHHiRCIjIzl06JA4/ppMJjo6Ohg7dixdXV1nnAWfCofDQU9PjyCt\nSxptlUqF3W6nqqpKOJYPRHxX+kt1dTXR0dH88pe/pKSkhMLCQpYsWSII5dCX2tja2gr0nWguvvhi\n8fjExEQaGxtP42gmJCScJpk8V/ysiqCE5OTkM97+Tcccs9mMXC5HLpeTmJgoOsVv6mZ8Ph+DBg0i\nJSWFo0ePotFohJNLTEzMaQUQ+vTDklvzunXrxJYzIiKCu+66i0OHDjF8+HB2796N1+ulurqa7u5u\n5HI5lZWVhIWFiaINfQUhLy9PEKulI3xqaiq///3veeCBB4SqIiwsDKvVitls5sSJE0RERHDbbbfh\n9/u5//77aW5u5r333sNisVBTU8Njjz3Gb3/7W5599lmCwSB1dXXodDrhaNPd3c3+/fvJyMhAoVCI\nlDyXy0VCQsI5b4flcjmzZs3ivffeo6OjQ6hrFAoFO3fuJDIykr179wr3H4PBgNvtJikpifb2drE1\ndzgcmEwmwsLCWLVqFXV1dWIs4nQ6xZx3y5YtopgbjUZxUc6ZM4d3331XqF6+CdJMuKGhQQRudXd3\nExcXR3d3t8iEHqg4WydYX19PfX39WR/n8/k4ePAgf/vb37jwwgu55557WLx4cb/7nBqC9Z/Az7II\nng3fdsyJiooSG0oJZ/tjNTc3o1QqiYyMJBgMkp6eTmlpKZGRkRQUFJzRqQT6ok0///xzmpqasNvt\nYk44bNgw6urqRCiR0+mkqqqKlpYWNBoNPp9POE1LrthxcXEMHz5c+OWditDQUE6cOCHmUosWLeKp\np54iPDwcuVzOnDlzWLduHb/73e/w+XxCJuj3+/F6vRQWFlJYWEhRUREpKSkiMOpUD0a1Ws2nn34q\nliXSCCElJYWIiAihQKmurkahUJCUlHTW91Oj0XDHHXfwpz/9SaTS7dq1C4vFImI8Q0NDGTNmDM3N\nzbhcLoYOHYrNZuOVV14RG3+/38/Jkyepq6sjKioKv98vDDQSEhKEg83BgwdJTU0lLS0Nh8PByJEj\nhVv3tm3bGDduXL/n19jYSEJCAj6fj7KyMvLz80lISBBqHol0LnE7jUYj11xzDbfffvs3fuZ+rDhb\nEZRGThJ27dp1xp9LW/qrr76aJ554QshUY2NjaW5uFp/XhISEfkW1oaFBqK9OVd80NDR8o5P4N+Eb\nr3q3281FF13EsGHDyM3N5cEHHwT6yJ9Tp04lKyuLadOm9dtyPfHEE2RmZpKdnc2XX375nZ7Ujxnn\n+u0tdYpqtRqNRkN6ejopKSnMnj37rAUQ+jo3qUiEhoai0+kYP348l1xyCfHx8XR0dGA2m4Um1el0\n0t7ejt/vJysri7lz5zJ69GgGDx5MVFQUCQkJJCYmnrbNtdvtLF26lOLiYtra2li8eDF33nknYWFh\nOBwOVq5cSVtbG0eOHKG0tJTm5mb8fj9WqxW/308gEGDcuHFMmzaN0aNHk5ycTFxcHNnZ2WKBcvnl\nl3PrrbeyaNEiFAoFdXV1Iip0/PjxrFmzhjfffJM1a9awb98+SkpK6OrqwuVyYbVa2b17N9u2bROL\nmXfeeYfMzEzhsNPc3ExtbS0+n08od+bOnUtoaChms5mxY8cyZswYRo4cKYqwQqFAr9ejVquxWq0o\nlUpSUlJISUkhPj6emTNnUlFRgVKpRKvVMm7cOCZNmsS+fftISEigoqJCxCIAYskjBTGVlZURHx/P\nunXraGlpoaqqCoPBIMwqbDabKPT/am7Kjwnf1UUmNjaWpKQkEcWwceNG8vLymDVrlpgTvvnmm1x5\n5ZVAH5/2/fffx+PxUF1dTWVlJSNHjiQ2NhaDwcDevXsJBoO8/fbb4jH/KmTBb5lwSh9An8/HmDFj\nePrpp1mzZg1RUVFik9Pd3S02Oddffz379+8Xm5yKiorTOqz/ZKt7Kv6T3Cav14vFYhEedv8KNm3a\nBPQVxEsuuQSTySQuWq/Xy759+4T5q2TwIJfLycnJYfr06eJonJiYiNPppKenh+rqauLj44Vl1Icf\nfthvFhgaGorP58PlcglDWbfbLRLmdDodfr+f0NBQjEYj48ePZ8KECeLb9+WXX2bRokU88sgjgnv5\n5z//WbymhoYGioqKOHr0KFOmTGH//v14vV7q6upQq9Xo9XpBIr7++utxuVzs27eP3t5epkyZwp49\neygsLGT16tWo1WrKysqECkUyn5V8Cf/3f/+XDz/8kLq6OnJycmhubhYmqElJSXR3d7Nnzx6GDh0q\nLPXdbjdvvPGGYA9Ii5KCggKOHz/ONddcw/PPP4/b7cZsNtPZ2UlBQQF6vZ7JkyezfPlykafc3NzM\nbbfdxrvvvovD4aCiokK4zphMJuRyOVlZWQQCAW699dbv/Vn7V/F9rwOZTMa99957Tvd97rnnTvv3\nSkpKuOWWW/B4PKSnp/P666/j9/uZN28edXV1p1FkHn/8cV577TWUSiVLly4VeTMSRcblcjFjxgye\nf/757/Z6vq0ISujp6WH8+PG88cYbzJ07l61btxITE0NLSwsTJkygvLycJ554ArlcLhLMpk+fzqOP\nPtpvsAk/jyIIfZuwfzWP4siRI3g8HjweD6NGjRK3S4TqVatWiY4iNzeX/fv3C+L1hAkThFTOaDQK\n6of0JeR2u4XhqmT2IFFNnE6ncHCRTB6io6M5cOAAKpWK/Px8Ojo6hGmqlAcsOaJUVVVx7NgxysrK\niImJISIiglmzZvV7bZ9++ilZWVl88MEHBAIBkTliMBiw2WzCfuyyyy4TSxvoG1NMnDiRsrIyxo0b\nx7Zt27BYLLS0tOBwOLDZbGIOmZubK9yrOzs7hSzy6quv7qcweeedd5g5c2Y/moqUeyyZ4gL89a9/\nFTNaaZlmtVqFCaz0ZaFSqdDpdIwcOZLi4mISEhKIjo7m008/FTzUhIQETCYTCoVCJOH9/e9//5c+\nHz8EfogieM8995zTfZcsWfKjJ1Z/K2U9EAgwbNgwYmJimDhxInl5ed+4yTl1HiBtcn6u+C6BPPn5\n+RQWFp62tVapVBw4cICkpCRGjhzJVVddRXd3NxEREcTHx3P33XdTWFhITk6OoLmcOHHiNApOamoq\nvb29Yr6pVquFRXxycjIvvPAC6enp2O124Zri9/s5duyYGHvYbDb27t0rcnyhT7e7du1aWlpazhjw\ntHXrVi699FJWrVpFSEgIx48fF9vWmTNnCseViIgIamtrcTqdWK1W5HI53d3dHDt2DKvVyvr16zGZ\nTBQWFrJo0SL0er0IaAoEAtTX11NUVCQWEpK5xdef09SpU0/j6Wm1WqFkUalUHD16VOiRJf1va2sr\nRqORsWPHYjQaaWhooKmpCafTSTAYpKOjA+grNNIMuKOjg6ioKLq7u7HZbIwdOxa/3y/cvQciflam\nqnK5nOLiYqxWK5deeimbN2/u9/Nv2+T8/+r6Bjq+br0vFbxTN8rd3d2YTKZ+NBStVkt2djY7duw4\no0NyVVUVMpmMxsZGQkND6ezsFKoQmUxGSEgI8fHxdHV1YbfbUSgUOJ1O0SFKOuCLLrqonzmpxWKh\nt7eXQCDAgQMH6O7uZuvWreTn51NbW8uCBQt47rnnMBgMopMzGAxs2LABQCyPpk+fTnNzMw6Hg/b2\ndmGwWl5ejtFoFCa3UibIjBkzxOJl+fLlwkw2EAgI15fs7OzTIjobGxs5evSoyA+WurxTvzSkcYPk\nMN7R0YFOp6OpqYkJEyawe/duwsPDaW9vR6PREBYWxp49exg0aBBz584Vx+usrCw6OjowGAz09PSw\nc+dOhgwZIpqHgYiB4hBzLjhn8aLRaOTyyy8XoTktLS0A37rJ+a4bm/P4P/T29tLd3S3ybyXccMMN\nDBky5LT7b9iwgeLiYl588UVeeuklrFYrNTU1/P3vfxfRAQaDge7ubvx+v5D0BQIBHnzwQfR6PXFx\ncdx1110kJSWh0WjIzMwkPj4ejUYjNt7SN31paSnLli3D6XSi0WhobGzE6XRiNpupra1l2rRprFmz\nBoPBwPbt24VJq6QssVgsmM1mUlNTycvLY+LEiej1epKSkkhNTaWwsJBLLrmEjIwMEWvQ1NREVFQU\n0dHRjBkzhvDwcJKSkjAajYSGhgrn7MTERGQyGZ9//rmw1nK73ezZs4eIiAj27t3LsmXL2LZtm7C4\ncrlcvP/++6xcuRLoM0CQir80X7zrrruw2+20tLSgVquFW490jO7t7SUrK4sFCxbQ1NREZGQkXV1d\nJCYmMmHCBBYtWsTcuXP/XR+Zfzt+Np1gR0cHSqUSk8mEy+Viw4YN/PGPf2T27Nm8+eabPPDAA6dt\ncq6//nruu+8+GhsbxSbnPL47gsEgCoXijJxCySz16/eXHKVbW1upqanh2LFjwhQWEHM4iUso5SkX\nFRWh0Wiora3lggsuoKCggKFDh9LR0UFZWZnw9HO5XIwYMYJt27Zx8uRJampqRCxpe3s7MpkMk8lE\ne3s7BQUFrF27loiICA4fPoxSqSQ2Nraf4at0sUj8ze7ubsLCwrjppptOe80PP/ywMJLV6/VkZ2cT\nCAQYNGgQI0aMoLKykpCQEOrr6wkJCUGv15OVlUVnZycqlUrY6C9cuJAtW7ag0Wi48sorhSrmn//8\nJ+np6Zw8eRK/3y9chyIjI2lraxN6ZLVajd1ux2w2iyN0TEwMUVFRyOVyvvrqKy6//HLefvttQfuQ\n5HuSt6HUSAxEDJQCdy74xiLY3NzMTTfdJAwSb7zxRiZPnszw4cOZN28ey5YtE5sc6EsBmzdvHrm5\nuSiVSl588cXzx+EfAF8/ykmQjBckkrekbpg8eTLh4eGsXr2aQCCA0+kUJqBhYWE0NzeLra9SqSQu\nLo7Ozk4haQsEAmRlZWG320W8aEtLixh9yOVynn/+edFxRUZGkpCQgMViESYDFouF1NRUysrKmDx5\nMh999JEIhJLS6NxuN16vF7PZTFRUFBdccAGVlZXU1NScZm9ltVrFkbilpYWsrKx+1B+9Xs9ll13G\nZZddxpNPPonZbMblcpGWlsasWbOEk7SEkJAQVCoVEydOpKSkBL1ez7p167jggrMYepEAACAASURB\nVAtISUmhrKxMRJcmJiZSW1vLP/7xD26++WY8Ho8gWd9www1ERUWJHOfk5GSWL1/OkSNHqK6uRqPR\n0NLSgt/vZ9iwYaJo7969m5MnT/6bPjH/fvxsimB+fj4HDx487faIiAg2btx4xsc89NBDPPTQQz/M\nszuPs36JSHm5gMjHkAqC0Whk6NChHDhwgNraWhH3KS0/Kioq8Pv9qFQqlEolEydOZMWKFYJqc/nl\nlxMXF4der2fmzJl4PB5WrlwpKDoOhwOtVisMV+Pi4jCZTIwdO5aTJ08SHh7OyZMnOX78OHl5eTQ2\nNpKZmcnu3bsxm83k5+cLWzCVSiXyP6CPhSCpa07Fjh07qKurE0sOr9d7xlEAwMiRI9m0aRNhYWG0\nt7fz5JNPnnEsExcXx4YNG7jkkkt46623mD9/vligLFq0iPfff5958+bx2muv0dvby5133im+BLxe\nL3Fxcf1C2F0uFzKZjKysLKqrq2lubsZqtTJr1iz27t1LVlYWY8eOpbOzkz179lBWVvYdPhE/Dvxs\niuB5/PggWT2dWiQ8Hg9NTU3U1taKAKDIyEiio6Npbm4mPDycSZMmiaG8dGSWLPeXL1+OWq0mLCyM\nkJAQbDZbP3H/4MGDSUpKwuPxCCWItEgICQmho6OD22+/ncOHD3Pbbbexfft2tmzZQkhISD8+mUKh\noKWlhYKCAlGoRo0ahU6nY+PGjRw9epT6+noaGhrQ6XS8+uqrImdZ6vzy8vLo7OzkqquuOut7NGbM\nGKqrq2loaKCxsRGfz0dDQwM2m43JkyeLCNKJEyeSm5vLJ598QmJiIqWlpWJ8o1AoMBqNbNiwAavV\nKmaX0Cf9kpL0Tv07SOOGCy+8kGHDhvHMM8/Q1dXFV199xf333y+ex/Hjx5HJZKcV+oGEn1IRHLiu\njj9TfD26MRgMCkKuXC6noqKCffv2sXHjRmGVlZuby6hRo5g6dSpJSUmCO6hWq4XFlNvtZvr06eTk\n5BAbG8uuXbvEBjArK4shQ4ZgNpvRarXidp1OJ7rBw4cP09XVxd69e9m1axdqtZqQkBAxdwS48847\nkcvlnDhxgs8//xy1Wk1JSQnQp0JavXo1H3/8MWq1mt7eXkpLSzl48CCNjY3IZDJmzZqF1Wr9VmqJ\nSqXi5ptvJi8vT+SDBINBiouLefXVV/noo4+ora2lt7eXlStXkpiYyP79+/vNV6VM5IMHD2K324Ve\nWToKS3GkEluioaGh39hCpVIxfPhwUUw//vhjNm7cyNq1a/F6vYwbN+6cjWV/jDifMXIePxpIObjJ\nycls2rQJhUJBfHw8drud2tpaVCoVXq9XbGTj4+NPy7uQy+VotVoOHTqEy+XC5XJRWlqK1+ultrYW\nr9fL8ePHufbaa3n55ZfF0VoyK+3o6OCtt94iOTmZ/fv34/P5GDJkCMeOHesnEfzggw/o6OgQTs9m\nsxm9Xs/mzZux2+3U19cTGRmJxWLh7rvvZtOmTZSUlIjlyTvvvENUVBQ1NTXf+r7I5XLmzp3LV199\nJbz9VCoVsbGxtLW1MWvWLF555RViY2NZu3YtXV1dfP7558LrTy6XYzAYaG5uFh2v1WolEAgIq3+n\n08mBAwcoLy9n+PDhp9mLVVdXExYWJhYptbW1PPbYY+LnhYWFPPXUUz/Ap+A/j59SJ3i+CP4EIG1J\nW1pahHtya2sr2dnZ7NmzB7/fz8qVK3G73TQ3NxMMBoU7Sk9Pj/D/6+7u5le/+pVwytm5cyc1NTXC\n/uvDDz9EqVT2MwWQyWSiGJ44cQKVSkV4eDh79+5Fp9Pxj3/8g/z8fEaNGkVxcbHwZlSr1XR2dop0\nOoVCQWxsLHa7nSuvvJK//e1vBAIBPB4PbrebtWvXYjQahTJk7dq1Iqv4myDZ4/t8PkFYvvrqq1m7\ndi2pqamsXbtWGF2oVCo+//xzTCYTVquVQYMGUVlZSXV1tfh9arWaYDAotOGNjY0sWbKEp556iqys\nLEwmE8FgkAMHDhASEsL06dMpLi6mq6vrtLnkQF4a/pSK4Pnj8ABGb29vv/+eMGECubm5Qn+5e/du\ndDodNpuN6667jkWLFvGHP/yBtLQ0ZDKZCBSXcktCQ0NxOp2MGTOGQYMGiShQg8HA6NGjGTt2rKCS\nREVFifQ8jUYj5Gm9vb04HA5BMs7OzmbUqFE888wzhIeHiy40EAhwwQUXsHv3bpRKpVBgyOVyamtr\nhUje4/EQDAbp6enpF9pUXFzM3r17aWpqYteuXbz//vuCo3cqpLRAg8FAV1cXw4cPp7Ozk9tvv53s\n7GxBhLbZbKhUKnp6eoSKZfTo0SxcuJARI0aIwC2pML/wwguYTCZ0Oh133HEHPp+PZ555hmXLlvHw\nww+TkZGByWSioaEBn89HbGzsaZELAxk/G57gefy44fV6USqV/VylA4EA06ZNY8WKFeLCLygo6Dev\nio+Px+Fw4PV6BVUlGAwyb948Tpw4IYjEUshRW1sbI0aM4OTJkyJvo729nYiICGw2m+DTBYNBIiIi\nRIyAVqtl586dVFdXi7Q6j8cjCuFXX32Fz+fj97//PdnZ2cyZM4elS5dSUFDAqFGjWL58OdXV1SLc\nStp+azQaMUM0m80YDAaMRiNvvPEGUVFRuN1uhg0bRnd3N6WlpaSkpNDe3i464/T0dPbu3Yvb7SYk\nJAS73Y5WqxX8x1ORnJws8psNBoPwiHz44YfFQspgMIikPUk7Lfk6qtVqoqKiGDp0KCtWrGD9+vUM\nGzYMq9XKe++995/5oPwbMFAK3LngfBEcwLDb7f00sTabjQsvvJBVq1aRm5vL0aNH0Wq1p13YISEh\nuFwuYZul0Wjw+/2sXr0am82Gw+Fg8ODB7NixQ4QqSfGUkZGReL1eEfsJfdnFpaWluFwuFi9eTFdX\nF93d3bz33nt0dnZis9nIzMxk06ZNuFwuFAqFMDKNjIykvLycYcOG4ff7iYuLo6KigsOHDws3G8m9\nRiJrS8YUPp+PwsJCRo0aRSAQ4NVXXxUu0Xa7nUsuuUTkoNjtdv785z8jl8spKioSz8HhcBAZGSm8\nF1NSUk57n6XjtEKhQKVSUVVVRVZWFpWVlYIfqFAosNlsonsuLi5m3LhxQlr4/vvvM3ToUJRKJe+9\n9x5VVVU/24yRHxvOF8EBjK8fryQt7lVXXYXVauX48eMEAgH27t1LV1cXvb29VFRUUF5eTkhICD09\nPULFIM3LgsEgzc3N3HPPPSKDOCMjgwMHDrBw4UKmT59OUVERH3/8MV1dXeh0Ovbv349cLic7O5uk\npCSSkpKAvhzgkpIS9u3bJ461gND/RkRE4HA4yM3NFRLLrq4uiouLgb6uNiEhQQTW2+12AoEAsbGx\nuN1uRo0aJez1oS9lr6enB71ej9vtFiad0Nc1Z2RkUFxcjFKpxGw209raKkjhCxcuxGAwsHLlSiZM\nmCAcuN98801qampITEzE7/fT2NiI3+/n0KFDGI1GXC4XPp9PGCJ89NFHGI1Gfve73/XTVs+fPx9A\nzF9Pnjw54A0Ufio4XwQHMKRwJAmdnZ2kpaXx+uuvi5B0i8WC2+3m+PHjImjcZDKJaEqn0ymG/YMG\nDeLEiRMYDAYeeOABQkJCiIiIoLS0lOjoaHHsHTlyJB9++CFms1nkebhcrtMyfcvKyli9erUgQEuK\nFWnzKhWQ559/npiYGBHbKS1uurq6RJeVkZFBaWkpcrkcvV7P/ffff5riQqlU0t7eLuy2jh8/Lopg\nREQEjY2NKBQKLrnkEg4dOkR+fj4nT55EpVLx5ptvipnf008/jUqloqmpCb1eTyAQoLOzk0GDBtHR\n0UEwGGTo0KHIZDLa29tpbW3FbDaL7JqMjIwz0l8k9yVJLjeQIzcHCv3lXHC+CA5gfN0eyu/3U1RU\nJKRy0hzQ6/XS09ODUqkUHVFbWxsJCQkYDAY6OjrQ6/V0dHQQExNDYmIi27ZtQ6VSsXDhQt544w18\nPp/gKHo8Hux2Ox0dHYSFhREeHi4WJqdi8+bNwrRA0isXFBRw6NAhEWXq9Xrp7u7G7Xbz97//nYce\negiZTIbT6RSFJDo6mv3794uZnEqlYsWKFcLLUEJ9fT02m01ElcbHx7Nnzx48Hg9btmzB4/EwefJk\nbDYbs2fPZs+ePahUKmw2G4cPHxbRmh0dHWLGCQjPxcrKSoLBIFdddZUIiHI4HLz44osMHjxYhCjZ\n7fYz/r327t1LRUUF3d3dREVFDWibuZ9SJ3h+OzyAcSrFwuPxsGPHDlEIHnroIeFnKOV+SGoTyfJd\n0vkajUYsFgtPPfUUTz31FDfddBNPP/00aWlpvPXWWyiVyn7E4OXLl4vEtZSUFB599FFMJhODBw8G\noKKiggcffFAEQIWGhvKHP/yBqKgoiouL8fl8zJ8/n1GjRjFx4kR0Oh1ms5n/+q//Qi6Xk56eTnx8\nPFqtVgRVXXzxxUIrLc3zmpqaxOvfunWrmDfa7XbCw8PJzMwkISGBNWvW0NHRQWxsLH6/n7Fjxwoq\nkWSkGggEsFgsOBwO7rjjDmbOnInBYKChoUE4WEsb3lN9IqUvmmnTpjFjxgwGDx6MzWbjySefFPep\nqKjggw8+4Pjx44SEhFBTU0NTU9MZt9kDBee3w+fxo0JlZSVbt27F7XYDfR3i3/72Nzo7O4XCQavV\nMnz4cHbt2iXs9J955hlWr17NunXrSE9P7yfjCg8PJy0tTehf9Xo9W7duZc2aNSJXw2q1otPpWLFi\nBffdd594bFxcHFFRUdTX14v54MaNG0Xq2hVXXEEwGOTXv/61IEBLyW9xcXHU1dWJsPPs7GymTZtG\nQ0MDJpOJ+vp6EhMTRUD64cOHcTqd6HQ6rrjiCsrLy7HZbLS3t7N27VoaGhpQKBSMGzcOg8HA5MmT\n8fl8ZGZmivleXl4eR44cQa1Wc++995KZmQkgjCYsFgtyuZx58+axYsWKfhe3zWbD5XLR1taGSqWi\ntrYWq9VKVFQUzz77LF6vV3S9TU1NuFwu4XkYHh7+b/9s/LswUArcueB8EfwJwOfzYTKZaG1tFUai\nEqVEclxWq9WUl5ejUqlQq9WiCErEX+nCPxUjRowQy5bOzk7a2trEsdpsNuPz+UTA+KnQ6/VotVqm\nTZvGoUOH0Gq1XHHFFfzpT38C4OOPP+aqq65i+fLlyGQy1Go1ERERQp4WFxdHbGyscKUBRFcYEhLC\ntGnTgD6D2KamJvLy8kSxNZvN7Nq1i8TERGpqarDZbGRlZREbG8uFF15IUVERsbGxHD58mPb2dsLD\nw8Xx1Wg0UllZKd6LmJgYFAoFOp0OmUzGqlWrMBqNHDp0iKKiImw2G8ePHychIYEPPvhAmNM6HA4y\nMjLYtm0b8fHxtLe3U1hYSHl5OWlpabS2thIeHn7OkaM/Rpwvgufxo0JbWxsKhULYR914441s2rSJ\nHTt2EAgEhMed0WikpKQEt9stXKAnT57M5s2b2bNnD5MmTRKO1pI8LCIiggMHDuDxeIQJa0JCAldf\nfTUbN24UKWunIhgMUl9fLyzpZTIZjz76qCAjBwIBVq5cKYjKgUBA2HpdeumlzJkzh/b2dnbs2CEI\nx0ajkby8POGWLUV8jh49ut9yCBC0GCkWQgqhkpZDH374IfX19SILWeI3xsXFiW2xXC4XuTqbN2/G\n5XJhMpm45557KC4uZuXKldTW1qLX67HZbAwbNoyamhr27duH3+/nyy+/RC6Xi9D6jRs34vV6qamp\nYcSIEXR3d3PNNdecc2DRjw3ni+B5/KgwZswYbDYbHo+HzZs38/7772Oz2QRhV6vVUl9fj06nw+v1\n4nK5CAaDyOVyNm/ejN/vB/pChcLCwlCr1cyfP5/4+Hhhg3/8+HEx57Hb7UyaNImWlhb279/PunXr\nGDZsGElJSajVav7yl7/g8/nQaDRCtiaTyUTur5T1KxG9w8PDGTZsGCdOnBA+fVKk4qFDh1Cr1acF\nnqenp9PS0nJaAfw6MjMzhaTP5/Oxfv16EdIuk8mIj4/H6XQyb9480tPTUalUvPrqq1x66aW4XC6u\nvvpqwSs0Go0olUqx0NFqtWLbvnz5chEn6vf7ufDCC9m+fTtut1vwMKWCX1tbS3x8PPv37/+3fSb+\n3ThfBM/jRwUpjPzAgQPY7XZB7g0JCWHcuHHs2rULn89HaWkp0McnVKlUgkKjUCiIjIzE7/cLu/ij\nR49y7Ngxmpub2bNnD16vV3RrCoWC22+/neTkZBHVuXTpUrEkMJlMeL1eAoEAV155JYmJiURERPDY\nY4/R0dEhukHpyC5lGJ84cYLExETq6+sJBAI0NjbS1NQkckBOhdTZfhskvp/P5+O1116jsbFRBCpJ\nVv2hoaEoFApMJhMej4fo6Gg++ugjVCoVTz31lFDWnDhxgr/85S+i2LlcLuRyOR6PR2QYB4NB/H6/\nMKCQVD1hYWFCoSKFk0mpjAMRPyWKzPnt8E8AUgZJbm4uBoMBs9ks8oSDwSBhYWEiMF2pVDJq1Cju\nvPNO0dkUFBQwbtw4xowZww033IBOp+Ozzz5j165d7N27l9jYWCIjI0lOTiYQCNDe3o5arWbcuHH8\n9re/ZcSIEXR1ddHe3o7T6RQzSb1eT0JCAjk5Oej1eqZPn45arUatVqNQKMRzgr5oRq/Xy/Lly1mx\nYgXvvPOOsK+XDFe/jrNZUUmvG/poQ729vVRVVYn3QirmgwYNIhAIkJ6ezrBhw4A+g4TLLrtMBN1L\nRO2enh56enqEL6G02ZXiPaVZrNvtxu12097ejkqlErxGjUYjVC/V1dVER0f3224PNHzf7bDf72f4\n8OEilvXRRx8lMTGR4cOHM3z4cNatWyfu+8QTT5CZmUl2djZffvmluL2oqIj8/HwyMzO5++67v/Nr\nOd8JDnB4vV6am5txuVxUV1cL+ZZWq6WtrY333nuPYDCIXq/HbreTnZ0tFCIxMTEYDAYCgQBTp04F\n+j5YEom5vb2drq4uUlNTWbBgAfv376enp0d4CEqzNylpLjY2lt7eXlwuF11dXQQCAcFl1Ol0wnRB\nsqY6NbNEyuyQKDxarRaj0YjRaGTHjh1kZWWd83ty6qLG4/Gg1WoZNmwYKSkprFixAqfTiV6vZ/To\n0axcuZJDhw6h0+m48sorkclkRERE8Itf/IJly5ah1WopLi7GaDSSmprKnDlzsFqtbN68GZVKRV1d\nndj8ajQaenp6RBcs5cNI/ojQp3tOSUkRjj4DFd/3OLx06VJyc3PFUkomk3Hffff1YxlAX4jXihUr\nKC0tpbGxkSlTplBZWYlMJuO2225j2bJljBw5khkzZvDFF18wffr0f/m5nO8EBzhUKpX4X1pamvhQ\ndXZ2imNbb28vkZGRxMfHc8EFF6DX6wkPD8ftdmOxWISG9eDBg2zatEl0PlK3EwwGSU1NJT8/H5VK\nRTAYxGw2s3HjRlauXIlSqUSlUmEwGHjllVdITEwUuSQrVqygqKiI1atXU1RUJLbBwWCQkJAQEQUw\naNAgRo4ciVarJSoqSmQJd3V10dPTQ3l5+Xd6f6RZZkhICHFxcaSmphIWFobFYuHdd98VnVtpaalw\nqIG+jvCWW24BEC7Qt912GzExMWRlZbFo0SJ++ctf8sgjj/CLX/yC0NBQjEYj8fHxREREEBYWRkZG\nBuHh4cKJOyoqiosuuog777yTBQsWsH79+u/zp///iu/TCTY0NLB27VpuueUWcZ+z3X/16tVcd911\nqFQqBg0aREZGBnv37qW5uRm73S6cwBcsWMCqVau+02s53wkOcHR2dhISEkJxcTGBQACj0YjVaiUh\nIYFgMEhoaCjt7e10dHSg0WiIj4+nrKyMBx98UAzrpQjK48ePY7FYsNlsREZGkpSUhNfrJSwsjLff\nflvYySsUCo4fP86MGTOYMmUK9957LwqFgvz8fORyOfPnz2f37t1s3bqVqqoq6urqkMlkeL1e1Gq1\niOy0Wq1AH9F4+PDhPPvssyxevJgnn3xSqFPi4+OJiorCZrN9p/cnPj6+H6m8qqoKpVKJRqMRBVmy\nyaqqqqKgoEDcV6VSYTabCQaDREZG9vu9UkaLQqGgu7tbyAqDwSAXX3wxHo+Hw4cPi9nj+++/Lx5r\ns9mEG89AxffpBO+9916eeuqpfn9TmUzGCy+8wFtvvcUFF1zAM888g8lkoqmpiYsvvljcLzExkcbG\nRlQqVT8T24SEhO+swDlfBAcwJOrIli1bUCqVtLW1CUt9i8XC7NmzKSoqYv/+/fj9fqqqqli6dKkw\nIvB4PMTHx/PLX/6Suro6ysvL0ev1ZGRksHDhQvHvVFVV4ff7BQHb5/OhVCp54403xPY3LCxMhInH\nx8czadIkysvLqa6uFguC6OhoFi9eLBYIr732GrW1tXz11VccPXqUGTNm8Ic//EE4zERFRYmMZJVK\nJVQq/wrMZjOdnZ1UV1fjcDhITEwkGAyi1Wppamqio6ODO+64A4/Hg8lk4osvvhDGEO3t7aJY1dXV\n9fu9KpUKj8cjCmdjYyNpaWnk5eWxZMkS9Hq9UOnk5OT0e6zk4j2QN6xne+7SF+7Z8Nlnn2E2mxk+\nfDhbtmwRt99222384Q9/AOCRRx7hd7/7HcuWLftBn/PZcL4IDmAkJiZisVjIzMykrKwMvV6P1Wol\nJSUFlUpFe3s76enplJSUUFVVBfQZJkjRmRqNBqPRyLZt26ivr6epqQmdTtevAEIfaXjr1q2EhoZi\ns9kE1UWiy4SFhQnr+a+++opJkyZhs9mIiIigoaEBs9nM+PHjMZlMtLW1ie4sNDSUpqYmWlpaqKqq\nYvv27ahUKhwOB6mpqQSDQbq7u0WEQGNjI9OnTz9rBOnZEBkZidVqJSwsTChPdDodiYmJpKam9tsy\nt7a2UlZWJkLcMzIyRF7yhg0bxOzU7Xbz1VdfceLECWw2G7fccguxsbEsW7aMtrY2QUnSarVnTLor\nKyv7SVppSfGjEr4+xti1axdr1qxh7dq1uN1ubDYbCxYs4K233hL3ueWWW8TCJCEhgfr6evGzhoYG\nEhMTSUhIoKGhod/tZ3qfzwXni+AAh8lkQi7vG+3u37+fGTNmiKOb3+8XR06p+9FoNIIj6PV66ezs\nZOvWrZhMJgwGw2nqD4CdO3eyZcsWoRjRarUkJibS3t5Oamoq1dXVyOVySkpKhBmBx+OhrKwMrVbL\nddddR3Z29mm/1+PxiIWI2+0WZqxSlyVpm6X8Y7/fj8Vi6XeRnSvS0tLweDzU1taKwhoeHn4a1aOn\npweDwYDNZkOtVosFSEZGBiNHjhQa423btlFeXk59fT0mkwmn08mzzz5LbW0tnZ2dyOVyXC4XOp2O\nPXv2cNVVV4kj/ssvv4zVaj0tW3kg4btSZB5//HEef/xxoE/v/fTTT/PWW2/R3NwsrOE++eQT8vPz\nAZg9ezbXX3899913H42NjVRWVjJy5EjheL53715GjhzJ22+/zV133fWdntM3FsH6+noWLFhAW1sb\nMpmMW2+9lbvuuouuri6uvfZaamtrRfi69K32xBNP8Nprr6FQKHj++eeFxOk8fjhIx09AJMXZ7XZu\nvPHGfvcLBAJs2rRJaIUjIyPJycnhwIEDDBo0iOLiYnp7e7n77ruFUsTpdPb7HS6XC5vNRlJSktje\nSiFL2dnZNDU1odVq6e3txe12k5KSwvbt25HJZGL29nXfQ+lIHhcXh0KhEN1Sa2srWq0Wt9uNw+Gg\nrq6OuLg4QkND0Wq1KJVKtm7dypw5c75TPkcwGGTEiBGiY5Z4iBKk2Z7D4SA5OZnW1lZGjRpFXV0d\nGRkZuN1uqqqqqKiooL29ndDQUOHQI5fLRTct5Q9LZOq4uDjWrVvHVVddJbpaKSVwoOKHOMoHg0Hx\nd7z//vspKSlBJpORmprKK6+8AkBubi7z5s0jNzcXpVLJiy++KB7z4osvcvPNN+NyuZgxY8Z32gzD\ntxRBlUrFc889x7Bhw3A4HBQWFjJ16lRef/11pk6dyv3338+TTz7J4sWLWbx48RnX2VIU5HmcDr/f\n388a/1whFUDJBUWn0+F0Otm+fXs/k1GVSkVbWxtWq1Xw7SZOnCiMAaQgc6kAQh/37t1330UulxMV\nFcWFF17I1VdfzV//+le0Wi3Z2dlUV1dz6aWXsnXrVuEwIznVVFRUoFarcTgcJCQk4HQ6aWxsFEdO\nae547Ngx0eHp9Xra2towGAy43W7i4uLQaDSEhISg0+mYNGkSJpOJHTt2UFZWxsaNG8Wx9Ntwquei\nRqMhMjISuVxOSEgIbrebiIgIAoEANpuNjo4OfD4f+fn5FBUVER0dTSAQIDU1FZPJRFlZGU6nU0gB\nTSYTJpOJrKwsNmzYQGxsLE1NTWLcoFQqCQ8Px+Px0NDQwMmTJzEajRgMBsLDw4VSZyDihyiCEyZM\nYMKECQC8/fbbZ73fQw89xEMPPXTa7YWFhRw5cuR7P49vLIKxsbHClDIsLIycnBwaGxtZs2YNW7du\nBeCmm25iwoQJLF68+Izr7H379vXb7pzH/+FcC+DZiqWkmpAiLjdv3kwgEBBfOuvXr8dqtYqiZDQa\n+ec//0kwGMRms6FUKsXf91R0dnbS3NxMbGwsXV1dwmRBSlL7r//6L0GLee+990TQksfjEYlzUtB6\naGgoW7ZsIScnR0jnJDrPqcfBtLQ0CgsLRQjUF198gdFoZNiwYWIZMm7cOBISEkhPTz/n91gqgNIW\n/VSC9alW+iaTCbvdzpw5c3jvvffIy8ujsbGRESNG0NTUJJygjx8/zsKFC8nIyOCDDz5g8ODBjB8/\nnoyMDF566SUhqfP7/URERBAeHo5Op6O6upqPPvpIONxoNBoWLlzII488cs6v5ceEgbzU+TrOeSZY\nU1PDoUOHuOiii4RDLiAkQMBZ19nnce44lbfX09ODTqf7xmIp0Tzq6uoIDw+nvr6eAwcOUFVVhcfj\nEd2V9LP4+HjRlYWHh9PY2Eh1dTWpqalA3zeyZP3U0tJCQ0MDgUBA2GMt2RHCigAAIABJREFUWLBA\nFNnJkyeTm5srsjukfGBJHiblGev1enGEueSSSxg1ahTr16/n4MGDdHR0iGVOYWGheF2zZs2iqanp\nNGpKcnKy+P/SkufU0PSz4eu/50xISkqipaWF/Px8jh07RmpqquhyLRYLpaWlwqD10KFDYl6ZkZEB\nwK9//WuKi4tFNorX62X+/Pl89dVXANTW1tLS0iKO9itWrPjW5/Rjxc+uCDocDubOncvSpUtPE6xL\nrf/ZMJCzVf+TkHSscrkcu92O1Wo9Lcz71BkKIKgqTqcTh8NBe3u7GPz39vaSmZnJ0aNHiYqKwul0\nMmLECNrb25k5cyZ2u501a9aQmppKcXExDoeD3bt3EwgE0Gq1tLS0IJPJcDgcJCUlERoayrx5804b\nbXz66adMmjQJl8vF9u3biY+Pp6qqCrlcjlKpRKlUcuONN9LU1ERPTw8RERFEREQwffp0CgsL2b17\nN4cOHRJWWBJkMhmVlZWYTCaio6PF7VLiHCBsr77+vnwfxMbGotfr8fl8jBw5EpvNRk1NDV1dXbjd\nbvx+P8eOHWP48OFMmDChX1GWy+VkZWVRXV2N0+kkOjpazLAkK31pYRIIBIT/40DEz6oIer1e5s6d\ny4033siVV14JIHISYmNjaW5uxmw2A2deZ3/XtfXPDVL+BvQtpM5EA2lraxNB4YFAgN7eXoqKiujp\n6aGmpkbQS0JDQ3G5XHg8HhEGJM2rRo8ezc6dO0lOTsZsNqPX67n44ovp7e2lrq6O+Ph4tm3bRnR0\nNCaTSWxj09PT2bx5M1OmTBEk32AwyLXXXsvJkycJBAI0NDSILI7u7m4CgQAmk4mXX36Z3/zmN+J1\neL1eVCoV0dHRpKWlCQ++U+H1eqmrq6OtrY3Ro0cLHz7Jg+/gwYNkZWWh1WoJBAIoFIofrBiGhoYK\nJUJ9fb14XZLmOBgMUlNTQ2NjI4mJif2+GAYPHizS7pqbm/F6vTgcDmEYERcXR2dnp3CXGaj42RTB\nYDDIr371K3Jzc7nnnnvE7bNnz+bNN9/kgQce4M033xTF8Wzr7PM4N0jHLjjzh8xkMgnSr9SpNTQ0\n0NXVhdPpJCIigmuvvZb169ejUqkICQkRW0qr1UpeXh7FxcWMHTsWuVzOoEGDhIGoRqOhsLCQ2NjY\nfp1PQkICl156KY2NjdhsNpYvX84111xDIBBgz549XHjhhSLS88iRI/T29pKTk8OQIUPYsWOHsJY6\nFacW+CFDhpCRkYFKpWL16tVYrVa6urrQ6/V4PB5mzZolvkhjY2NFoZPJZJSVlXHhhRcKOo1UDH9I\nlJeXk5KSgtVqJSYmRmSIdHV1sXXrVkaNGtXv/jNnzsTr9bJ+/XpB+pYyVnw+nyBhh4SEnJYRM5Dw\nU3KR+cYiuHPnTt555x2GDh3K8OHDgT4KzH//938zb948li1bJigy8M3r7PP4dmg0GmJiYvqFDJ0K\naWHh9/txu93I5XJUKhV6vR6lUkl3dzf79u2jpaUFl8tFRUWFSIibPXu2sKTv7e1Fo9EQFhbGiRMn\nSEtLQy6XiyVJWlqasNfKzMzE4/Fgs9moq6vj9ttvF53P+PHjRXh7aWkpdXV1+P1+rr32WqBvfvfE\nE0+QlJQkOIHQR0WpqanB4XCg1WrF0k1Ku2ttbRVu2dJjfD4fbrebo0ePEhkZSX19PaGhoezYsYPR\no0f3m2v+kMjIyKCmpkYQrh0Oh6AotbW18eKLLzJjxgxiY2MFx/LKK6+kqKgI6FtqSRklqampdHV1\noVAoiIiIOO8s/SPBNxbBMWPGnLXib9y48Yy3n22dfR7njtDQ0DMe7aKiojh48CAOh4Pe3l7kcjkX\nX3wxoaGhVFRUUFpaKlygpaKh0+kYNGiQCEGCvmJrs9kwGAzCdfpUFBQUCA1tR0cHdXV1NDU19aPS\nSL/HbrezatUqKioqaGxsJDw8nI8++oiuri4cDgdut5vGxsZ+r0Wyp3c4HIwcOZKqqirUajUFBQXC\nrkpKp5MiPeVyORUVFezcuROTySQco4cOHUpra+u/pQAC5Ofn093dTWtrKykpKSKsXqfT/b/2zjy4\nyfPO41/dly1Zkm3Z8oGN8YUwtgBDQoCEUMIVQghJFtJA0s1uO2l2uk07u01np9OkswkknXaWTZo0\nPZJNsxtIk1CONJzBxi6XiQ+wsTE2km3JsmXrvu9n/2DepzY2h40xBt7PjGawkPy872u9Pz3P8/v+\nvj8kEglYLBacPHkSFRUVtDyOw+GAEAKr1Qqfz0cDBlPtEA6HEQgERmgy7yTumSDIcvu4su0kcHlP\nMB6PIxqNwu/3QywWU1MEj8eDpqYmKjsRCAQIh8NUxDyU6upq5ObmQi6XX7d0q7OzE4WFhZDL5XC5\nXCMCc1dXF1paWuieWV9fH/bt2wcejwculwuRSESV/YsWLQJwef+TmeU1NzdDpVLB4XBg2rRpSEpK\nQnZ2NoRCIebMmQORSASDwQC3201nj0wACofDMJlM0Ol0E3HJR4XL5SInJwexWAxqtRodHR0wm800\nKcXs0TIZYgZmxhgOh6np7f3334/MzEw0Njair69v1NYEdwpsEGSZFJxOJ7xeL12mhsNh2k9YKBQi\nEomgv78fLpeLbt4zDcSZDnNMNcdQmGZLN0JmZiaOHTuGRx55BDqdDtXV1TCZTFi1ahXUajVqa2sB\nXK4sAf6e/WSMB+RyOR5//HFUVFTAYDAgNzcXfX196OzsBJ/PR2pqKubMmUMbtzNCZY1Gg/b2dqrv\nMxqNEAqFEIvFUKvVmDVrFtXfGQwGTJ8+faIu+6golUq6fFepVBCJRBCJRLRk7qOPPkJpaSm8Xi+a\nm5vhdrvp/mQikUBpaSk2btwIlUqFQCAAp9OJBQsW4L333rulx32rYIMgyy0nHo+jo6MD8XgcFosF\n8Xgcvb29tFqBqYQwm80ghKC3txeRSITWBjPbGMFgEBaLBX/4wx8wc+ZMRCIRlJSUjCqSvpJoNAqb\nzQa9Xo/29nakpKRg+vTpcDgc8Hg86OjoQCAQgN1uRyKRQDwep44vEokES5cuhVgsRmdnJ/bt2wc+\nnw+z2UyzoolEAtFoFGfPnoVOp6N6v5KSEiiVShw5cgQqlQptbW24//77odFo0NnZiZKSEhgMBuqP\nOFQ2M9G0t7fD4XDA6/WitbUVfD4fr732GmKxGHbu3ImcnBzU1dXhu9/9LrZv345oNAqXywWFQoF4\nPE6bxUskEnrN5XI5CCGoqam5Zcd9q2GDIMsth5HMdHZ2wu/3Y/bs2ejp6UEgEKCJEZFIhLVr16Kv\nrw9WqxV8Ph9erxdCoZAupRkTAr1ejxkzZqCjo+OGpRkCgYAKmDMyMmiQys7ORiKRQHNzM23oxFja\nM5laxuJ/7dq1OHz4MDgcDmw2GyKRCADQ5AyHw0E4HIZUKqX7oDKZDO3t7YjFYqivr0dubi4tB8zJ\nycE333wDHo8Hl8t1SwMgAPT29tJMeSQSAZfLRSgUgt1ux4IFC2C321FRUYE//elP8Pv99Ji+853v\n0Bpqpo/LiRMnEAwGMTg4CK/XO+GZ7MnknskOs9w+mJaXhBAMDAwgLy8PFy9epA3VGZFufX098vLy\nsGrVKuzbt482WUpOToZUKkUgEEBhYSENZvPmzRvX8QytyuByuWhra0NDQwNtXZmdnY1oNAqZTIb0\n9HT4fD5qvz9//nwcO3aM9viIxWK0ptbv9yMQCOCDDz6AVqulhgsKhYKKqwkhsNvtUKvVEIvFWLx4\nMdxu9y2tSWcC8qJFi2C32/HZZ58BAF566SU4nc4RiZjTp0/TPVClUgmVSoVFixbB7/cjFovRplVM\nGaJIJKKuMncid9NMkHU2mGIwS0RGojJ//nwsW7YMBw4cgM1mg9/vR0FBASwWCxwOBwoLC5GRkYH8\n/Hz84Ac/oPIkAHRT/sqbzePx3HS1Qn19PRwOBw1EFRUVWL16NVJTU6FSqZCTk0MlIAqFAkqlEhKJ\nBFwuF4QQmsBhrL76+/thtVqRnp6O9vZ2CIVCvPLKK3jqqafA5/OpHyKTmFEoFBgYGEBLS8tNncdo\nRCIRKnERCoWQSqV4+umn8dxzz9EWoVfyi1/8AhUVFVSryOFwUFhYSIMg04iJOQemdO5O5WYbLU0l\n7ty/wl1If38/PB4PsrOzhy3zJBIJzbwKhULY7XaadLiyJlapVEIul9PevkzPjqHs2LEDqamp2LBh\nw7iOkxBC+xH7fD7weDyYzWZs3LgRBoMBHR0dIzRwGo2GLp2ZWRaPx6NLQsZ2ymq1IhgMUuPVwsJC\nXLp0CW1tbZgxYwZsNhstOWOqNxi5z3hgStjC4TC6u7uh1+vR2NhIZS6EECgUCkgkEppMGk37SghB\nLBbD0qVL0dbWht/97nfDerwMpaSkBA6HgwbFO5E7JcDdCGwQnEIw0pHR9rmUSiWdTQwMDCAcDkMm\nkw3zFgSAtLQ0tLS0ULv4Rx99lGZOT548iZMnTwLATenq3nnnHSQSCbr/yCxrf/rTn0Imk8Htdo+w\nwX/22WdhsVhw9uxZHD9+nGZag8EgcnJywOVyqS4wNzeXvp+pqQ6FQtizZw9NwCQlJUGn06G0tBT9\n/f00WztWGPOJtrY2hEIh6PV66mnodDrpcTDNofx+P+3hMpRz584hFouhsrIS58+fh81mo16PhBBM\nnz6d/ru+vv6OrxhhgyDLLYGpwb4SZhOakZcwEhS/34/PPvsMDzzwADweD5qbmzEwMACpVAqZTIbS\n0lKkpqbS/bx4PA6FQoHMzEy6dLPb7cMMCq7F4OAgdu/ejczMTPT390OpVMJisUClUqG7u5s2bhKJ\nROjo6MDWrVuxbt06zJw5E8Dl3iNarRbZ2dnYv38/7Rnicrnwyiuv4PTp05g2bRq0Wi0CgQBqampg\ns9loY51wOIx4PE73DadNm0aXyh6PBzNmzLihQEgIofpHHo+HvLw8+P1+NDY24ty5c2hpacHKlStp\nO82hSRupVAqLxYK3334bzz33HJKTk9Hf3494PI78/HwcPnwYTqeTdutjXLy7u7shEokQDofB4XCg\nVqvZIDhFYPcEpzA9PT2oq6tDW1sb1Go1VCoV9Ho9pFIpMjIykJmZieTkZOzbtw+dnZ3UhIDxEFy6\ndCkyMjKQSCRgMBgQi8XwzDPP4Fvf+hb4fD4OHjyI6upqhMNhOByOax5Le3s7AKCwsBDBYBBPPPEE\nIpEI0tLSEIvFaJaXqVbhcDiIRCIjXIeAy8vBeDyOtLQ0OgM8dOgQvF4vYrEYgMu9gw0GA7q7u6nM\nhNlXFAgEeOCBB6BQKGiSgTFivRGYZTZzvTgcDq3vZcrx+vr6MHPmTCpoZpbAXC4XFosFM2bMwAcf\nfIDt27fj/PnzKC8vh1wuh9lsht/vh9frpefGJHoSiQT4fD5UKhXEYvEdnWFl9wRZJgWLxYK+vj6I\nxWLk5OQgPT2dZkg5HA5mz56Njz/+mGZd8/Pz0dfXh4aGBojFYhw8eJD6/HV2duLll1+mSRKFQgG/\n3w+1Wg2RSASn00mrIEajqKgIHR0dWLhwIXg8HqLRKJRKJQYGBpCWlkbL2+LxOPh8PuLxOPr7+3H0\n6FE8/fTTw5IzhBDqbygWi2EymeD3+2lnOSZZwixLE4kExGIxxGIxAoEAOBwOvv76ayiVSggEAqSm\npg6rfDGZTCOsuUYjKSkJDocD7e3tCIfDWLNmDf7yl7+gr68PWq0WFotlmFUWg9/vh9VqpTNui8VC\n64EzMzNhtVqRnJyMQCAAmUyG733ve2hubsaBAwcQj8cxc+ZMRKPRWy7wvpXcyQH8StggOEUxGAwI\nBoNYsmQJTp06hQsXLmDhwoVQq9WIRCIghCAYDNLmScuXL4dIJILRaERrayu6u7sxMDBAew/PmjVr\nWJUIj8fDgw8+iH379qG+vh5z58695jd3a2vrsPI0Ho+HH/3oR+jo6MAf/vAHSCQSRKNR6mwdjUah\n1WrhcDiwY8cOPPvssyCE4OzZs7h06RICgQCUSiXC4TAyMjLo7PHSpUvUVCEpKYnuO6anp0MqlVLL\ne4fDAYfDgQceeABcLpdmnd1uN2w2G7Kzs0dNYDAWcMDlLxkmIcLhcKj5xKxZs6jLzlCTW+Byl7mu\nri689NJL9LmdO3fi//7v/2C329HY2Ai1Wg2r1UrP4Z133qGd/RgzCo1Gg/Xr19/UZ+R2cqfM8m4E\nNghOMdxuN8xmMy5evIikpCQIhUJwuVzk5ubSjKtWq0UwGERbWxsIIcP2EvPz85GUlET1hBkZGZBI\nJKPu+6WlpSErK4sGLS6XS12lCSEwGAwghGD27NkoKioa5gQDXLbEMhqNKC8vR3d3N3g8Hnp7eyEW\ni2lrBmZmWFVVBbfbjRkzZiA1NRV2u502JPL5fNBqtRAIBIjFYlTaA1z2qFy1atWo14oRVDMmqMz+\n4OzZs2nVRlNTE7X6YpIq0WgUbrcbGRkZaGtrw8qVK7Fr1y5YLBZkZmZCo9FQT8RgMIjz588jFAqh\ns7MTAwMDePHFF4cdx8aNG/H73/8e7e3tSCQScDqdUCgUcDqdCIVC1G4/JycHfD4fEolkmLX/nQgb\nBFluCaFQiBpxCoVCxONxfP3117T/7VCYpSPTG4PH4yEYDKKnpwcul4sG0JSUFJSWliIWi+HMmTNY\nuHDhsBlSVlYWZs2ahT//+c+0soFxn9HpdNRv0GQyjciIhsNhOjNjeg8zNcSlpaV49NFHEQgEYDQa\nMTAwgKeffpq+VyQS4cKFCzQjHIlEwOfzaTBMSkqCzWaj449GYWHhCME00wGOmbHx+XzYbDakpqai\nsbERHo8H9fX1WL16NbhcLnQ6HTweD9RqNTQaDYqLi5Gfn08NbM+ePYuWlha43W4QQkaV4hw/fhyl\npaWor68fZhzBzCCzsrJoIkepVNJM/50MGwRZbglisZi2sWQcYxiTgaENwhlyc3PB4XBgMpmwZ88e\nKp5+6KGH0NraColEAp1OB5/PR2/0AwcOYOnSpRCJRGhra0N1dTWOHDkCDocDp9MJiUQCp9OJkpIS\naoh77Ngx2O12fPHFF3jiiSdoh7nu7m74fD7odDoQQlBSUoLdu3ejpaWFZj6vDKYMKSkpNMsNXJYA\nLVmyBGKxGDabDVarFXK5fNTECsNoFSN8Pp/abjGuzlKpFOfPn6fN4u12OxoaGmgf4VgsBrFYDL1e\nT+2xPB4PDX6MQw5Trvj5559Tp2zGycZgMEAikSAWi1FnaWZGfDday403CIZCITz44IM0ibZu3Tps\n3bp1XG186+vr8fzzzyMUCmH16tXYvn37uI6JDYJTEEYQLJPJkJOTA7fbjZMnTyI1NXVYE3OhUIiC\nggK4XC7YbDYkJyfT9pCFhYW0328sFsNXX32FOXPm4PXXX8fp06chlUrR399Pu6NFo1GEw2E6A2XE\nv83Nzbhw4QLMZjOSkpKwY8cOCAQCBINBaiWfnZ1NLb8ef/xxcLncEW7SV7rWNDc302WpUqlERUUF\nXe4zpXJGoxGXLl0aIR1i7PmHdtYb+n8nTpxAJBKB3W5HcnIyPB4PFixYAC6Xi9OnT1PZzcGDB5Gf\nnw+NRoMVK1bQJlHhcBhffvklDXrJycmw2WzgcDgIBoPo7+/Hhx9+iIyMDKxatQpLliyhS28mkZNI\nJCAUCvEv//IvV/07Mw48dyLjDYJisRhVVVWQSqWIxWJYtGgR/va3v2Hv3r033Ma3o6MDHA4HL774\nIv74xz9i/vz5WL16NQ4cODCu3sNsEJxiRCIRzJo1Cx0dHdSPjhHWjqYrM5lMkEqlNDOckpIChUIx\nbObI5/NhMpmwdetWWtDv8XgQDodp/xGr1YpoNErfYzAY8NZbb9GqBofDAYlEAoPBAKVSCT6fj8HB\nQaSlpY1wwV68eDFMJhPtOhePx3HixAmcPXsWQqEQXq+XNoRPSUmBTCYbkYVVKpWwWq1wu904deoU\nSktL6Tnx+XzY7XYoFIphQbC/vx9OpxNqtRoXLlyAVCqFVqule4LMsYXDYTQ3N1NxelFREYC/l7N9\n8sknGBgYQGFhIZYsWYJvvvkGzc3NNDOdnJxM+8BcunQJBQUFyMjIgE6nw3vvvQeJRAKr1Yq8vDwE\nAoGrVrMcP378hj4TU5GbWQ4zulWm9YJSqRxTG19GT+r1eulqZcuWLdi9e/e4giCrE5xCMLMHxiCB\nmXEFg0HaN/hKwuEwbDYbXC4XsrOz0d7ejoGBgRGv83g8cLlccLlcw/R8TEOjeDxOZQ9JSUkQi8Xw\ner10Ke3z+dDZ2QkAdKZoNBpH7QFssViQnJxM7ea/+eYb1NbWwmazwWw2w2az0QRFOByGRCKB2Wwe\n9jv8fj/kcjnS09NRVFQ0bCbJLO0DgQC8Xi86OjrQ1taGlpYWeDwe9Pf3UxPUoW08h/5uxiGnvLx8\n2A1dV1eHvr4+CAQCRCIRZGdnIz8/f5j8Z/r06Zg+fTpSU1PR398P4PLSXCqVgs/nw+FwQK/XIysr\nC2fOnBnVQfrPf/4z7Hb7yA/BHQIz273e42rvraiogEajwdKlS6HT6a7Zxndo10Wmje+Vz2dlZY27\nvS8bBKcAQ8W1wOUlQ0pKCjIyMuD1emnnOCbpMBRG7weAVn90dHRgz549IISgtrYW7777LrxeL9XV\nMfo7n89HC/z5fD6dbUokErzyyiuIx+NwOp0YHBykshxCCG2fKRKJ8Ne//pWW4jHodDq6tGWqKSKR\nCE1+CAQC8Pl8iEQiZGdn0+DOzESdTicyMjKg0WggkUjg8XiG2U4xQUsul0MkEtEvC8Z0lamMuVoF\nDo/Hw6pVq7Bo0SJaxcHYdtXV1UGlUiEWi4HH4+HDDz9Ea2srfD4fkpOTqbnqs88+C7FYTJ1ymL8j\nl8uFRqPBzJkzIZVKIRAI0NraSsXmwOXmTcxM/07lauJor9eL/v5++hgNLpeLpqYmmM1m1NTUoKqq\natj/X6+N70TDLoenAIxDdFFREb3ZCwsL8fXXX0Or1SKRSCA3N3dUK3yXy4VTp06huLiYdqOTSCTw\ner14//33afBkBM2EENokncfj0UqJyspKpKSk4OjRo+ByuXj55ZdBCKH7hWq1mso7nE4neDwewuEw\notEo/vrXv6K1tRXBYBBz5syBSqVCeno6DAYDQqEQqqurQQihJgTMklIul0MqlUKv1yMpKYkaw4bD\nYQgEAsTjcajVakSj0WEi7qHLdsblxePx0I57arWa7hm2t7cjJyeHvqa2thZpaWl0BsvhcOiMde7c\nuZg7dy7+93//FzweD5cuXYJAIACXyx223ZCbm4t4PE5F6wyDg4NISUmBVqul4m4ul4vy8nJwOBza\nh4Vp0nQnNyG72nJYJpMN2x5hZnSjoVAosGbNGuokfqNtfLOzs5GVlTVs9XAz7X3ZIDgF4PP5dAYz\ndMYzY8YMpKWl0T2UQCAwbFmYSCRw4sQJaLVaOqtjnJ21Wi2t5li6dCnMZjM+//xzcLlcWh3CBJdE\nIoG6ujqqo/N6vTRQAqCJjm3btmHHjh3gcrk4f/48EokEOBwOAoEA2traIBQKwefzUVRUBIvFgrlz\n56KpqQmLFi3C8ePHqXbw4sWLUCqVmD9/Pq1EmT59OlJSUtDT0wORSIT09HQaDK+c0V1pRpqeno54\nPA6tVgu1Wk0dYHp6ehCNRun1k8vlWLx48XUdZ/R6Paqrq7Fs2TKamMnNzUUikUAkEoHb7cb+/ftH\nJHsCgQDtkxKPxzF79myIRCJEo1Hs378fLpcLmZmZMBqNVNx9pzLeY7fZbODz+VQdcPjwYfz85z8f\ncxtfDodDe9fMnz8fH3/8MX7wgx+M65jYIHibiUQiMJvNcLvdMBqN0Gq1iEQiEIvFCIfDw0S1zM2c\nSCRowoBZKnO5XHg8HgSDQbqsVavVWLZsGYDLcpotW7agpqYGdXV1ePjhh3H48GEkEgmIRCLEYjFa\n7M+06YxEInRGJRaL8fbbb9M9wqE2UHw+HzKZDMnJyejs7EQgEKBJi5kzZ+LChQt4+OGHcebMGXR1\nddGZl81mg8/nQ2pqKtxuN60/5nK5dE9vNK50qGGyxMwXCTNrlEqlI2ysbsRyy+l0YuXKlRgcHMTz\nzz+P7u7uEeJmpv9JVVUVNBoNHA4H7HY7reBxOBx0psd4Os6fPx8ZGRnw+/0oKirC7373u+sey1Rl\nvEGwr68Pzz33HN0z3Lx5M5YtWwa9Xj/mNr7vvvsunn/+eQSDQaxevXpcSREA4JDb8HV0u5YBU+2b\n1+PxUI9Aj8cDu90Or9dL3UoefPDBUW21mMqN06dPo7e3F93d3XA6nTSIlZSUgM/nY9GiRVQmw1BV\nVYWmpiaaIRYIBODxePD5fFQSwizzhm5wM8cUCoWoZT+zl5acnIzKykqqpTOZTIhGoyguLkZWVhaK\niopoVrmuro7afCmVSrp/KBKJqFt0PB6/bhe8oXi9Xlo/PTRb7HQ64Xa7kZeXN6a/CyEELS0tKC0t\nhdlspnuTV/Lll1/SMfh8PvLy8qDT6RAMBlFVVYWysjIsXLgQsVgMx48fR2ZmJs1EDwwM4Oc//zl+\n+9vfjunYJoKbvQ84HA51Broera2tU+6+u5JrJkb+8R//ERqNBmVlZfQ5h8OB5cuXo6ioCI888ghc\nLhf9v61bt6KwsBAlJSU4dOjQrTvquwCfzwez2UwrGkKhEFJTU6k9O1M5MRSj0QhCCF2GMfuFjMsK\nIQRZWVkYHByEVqsdthnPUFRURGUpjDML497MJEhEIhFeeuklSKVSurkvlUrh9/tp4kSr1UKpVEIq\nlUIoFGJgYABPPfUUvvvd70KpVFK7qu7ubpjNZrqcnDdvHpKSkpCZmYnKykrk5+fT6gmFQoGkpKQx\nBUAA1K3lSs0gcx0BUONUZk/0WnA4HJSVldHAdjUb/JSUFEgkEhQVFUGn00Gn04HH46GlpQU9PT04\nc+YMqqqqYDQacerUKXg8HvT29sLv9+PTTz+94Y5/U5G7yUXmmjPBw/peAAAUYUlEQVTB2tpaJCUl\nYcuWLWhubgYA/Pu//ztSU1OpqNHpdFJR4zPPPIMzZ85QUePFixdHVfXfjTPBoYX5N0IwGERXVxd4\nPB4KCwtRU1OD5ORkhEIhTJ8+HQKBYNhy8MyZM+BwOJg7dy78fj+6urpgsVjQ3t6OSCSCQCCA9PR0\narpw5swZlJaWIi8vj2rwAoEAXSru3bsXDQ0N9AZ3u91IJBLUSVmv16O+vh7xeBwymQx2u53O0t56\n6y0Eg0H853/+J23xmZqaSrOzjPEoj8dDamoq0tPTUVhYSEvhmOA5mS4qjB/gWGEaQo0GU2/NXDeD\nwYCSkhLs2LGDmjMwYnfGGJbH49G6boFAgP/6r/+62VMbMxMxEywuLr6h17a3t0/5YHjd5XBXVxfW\nrl1Lg2BJSQmOHTtGszkPPfQQLly4gK1bt4LL5eInP/kJAGDlypV49dVXcd99940c9C4Mgm63m0pJ\nBAIBcnJyqPkBQ19fH+1ZGwgEqIaMCToSiYRWJTCJi0AgQLNj999/P4DLQmZGtuH3+6HT6dDY2Age\nj4d58+ahvb0der0earUaPB4Pcrl8VIusgYEB7NixA319fTQYcjgcaDQa2Gw2bNq0iTZQ2rVrF/r6\n+mhwYyz/8/LyYLFYEAqF4PV6IZfLqbCaka9IpVJoNBqUlpZSPV1+fv5t+xwwtcQMXV1diMfjIzSP\np06dwvTp0yGXy+k+5tXo6OiA1Wqlou533nkHcrkc//AP/0C76jU0NMBgMGD+/PnYvn078vPz8cMf\n/vCWnOO1mIggyCzrr8fFixenfBAcs05wrKLGewWFQkGtrL755hvs3bsXHo+Hlo/F43EkJyejqqoK\nPp+P9sYwGo3wer00AAKXN9Jrampw6NAhVFdXw+VyDauo0Gg0iEQiKC0thVwuR0NDAwghSEtLQ1VV\nFdRqNfLy8pCWlgaVSgWj0TjqMaenpyMajWLdunX42c9+hp/97GdYt24dXnjhBWRmZkKr1UIoFEIm\nk2Hz5s1IJBIwmUzo6elBOBzGwMAADAYDfvzjHyMlJQVisRgajQYLFy6ESCSCWCxGWloavTaDg4OQ\nyWTjnpVNBL29vfB4PPRnh8MBl8sFp9M5TNzL+P4pFIrrBkAAyMvLg1KpRCKRQE1NDRQKBfr7+1Ff\nXw8+nw8ej4fKykqo1Wrs3LkTaWlp7HJ4inBTYunriRrvZB3UeHC73RCJRPB4PHC73aiqqqJyDh6P\nh6SkJKxcuRLV1dXYu3cvuru7IZFIUFZWNuxaCYVCrFmzBgKBANnZ2dBqtcM0UMxM6uLFi5g3bx5y\ncnLw4IMPwmg0gs/nw2Kx0GXp0aNH0dfXN+rxWq1WqFQq5OXlUU++8vJy8Pl83HfffcNu/hMnTiA1\nNRWpqakIh8N0Rsjn8/HVV1/R7LJAIIBYLKazQEZUvXbtWtoT5XaZiTJL0qHd4lQqFa1bHjprZ2bQ\nN+r20traSiVNbrcbPT09VI7EBAObzQa73Q6LxXJHC6WBezwIMstgANcVNY5XvHirqKioGPbzRP+h\nAoEAYrEYZDIZuFwurFYrPv/88xGvKysrg9/vRzQahd/vp1sNV7JixQpaPTLUkIDD4SAajUIkEuHs\n2bOQSCQ4deoUNUVwuVz44osvIJFI0NHRgZMnTw6zz/d4PDCZTPjb3/5GkxRXwtT4MiYHPT09sFqt\ndP9PIpHQSg+DwYC+vj6EQiFq6srskz3zzDOQy+VobW2lSZSrBeVbDYfDocmgK2E8/5jrfOHChTH9\n7vLycohEIhw7dgzBYBBqtRppaWk4f/48/vSnP6G2tha7du1CS0sL1Q5eS0g81bmbguCYdYJjFTVO\nJZqamob9PNaZKiMfuRpMEx4mC0kIweDgIFpbW4dJCpiyq6+++gperxcul4uWfQ0VAnO5XKhUqlEN\nOOfMmYPz588jLy8Pp06dgkwmg8FggFAopPXB27ZtozW6H330EVQqFTweD2QyGXg8HgoKCtDd3Y0z\nZ85Ap9PRrDJTNfLJJ5/Q9pZMZYlAIACHwwGfz8dDDz2E/Px8WCwW2Gw22iPEZrNBIBBApVLBZDIh\nHA7DYDAgIyNjVMOFyYIQclXbfalUitbWVtp8KRKJULeasaBQKOBwOKhfYjweh8PhQF1dHbhcLh5/\n/HEcO3YMTqcTXq93Ik7rtnCnBLgb4ZpBcNOmTTh27BhsNhtycnLwi1/8Aq+88sqYRY1The3bt+Nf\n//Vfhz3HfPNfWYUwGkOXS6PdILNmzYLVakUkEoHX66Uau9bWVmRkZFCRr06ng9PpxBNPPIHPPvsM\nubm5OHjwIORyOZYsWUL76TKVH6PB5/NRXl5OxcF2ux1CoRBPPvkkVCoV2tvbabka8/sYp2iVSoWT\nJ08iKSkJZWVlUCgUOHLkCNra2ugSvLOzE8FgkC5vhUIhBgcHkZ2dja6uLhQUFGDJkiUALgeQoqIi\nNDY2DntPMBhEY2MjNBoNkpOTqaD7apKTWw0jNr8SRmY0VAo2e/bscY3B9I62Wq3DGk75fD6IxWLs\n2bOHajCHlv/dadxNQfCeEksDoL05fD4f0tLSYLfbEY1GodFoqNiYae14tZsG+HsQ/NGPfoRf//rX\nAECbgO/fvx9msxnhcJjWODLegMuXL6e/4+zZsygsLMTu3bvpfiKzvSAQCPDwww/f0Ezkiy++gN1u\nh1Qqxbe//W16fT/55BP09PTQ/TqmoiIlJQX/8R//gddee22Y311zczOqq6vR19dHr0NlZSU2bNiA\nnp4eNDY2wul04tKlS7jvvvuwbNkyyGQy2h6zpqaGXjemgkSpVCIrKwsLFy6k2eOphslkoj1Jhkpi\nwuEw6urqsHjx4hv6PVarFQqFArt27UJPTw/MZjOtsWYCIlOrHAwGEY/H8eGHH97KUxuVicgO32h7\ngO7u7ikfMO+5sjmPx4OnnnoKr732GrWGCofDaG9vR0pKCnQ6HUKh0FWDTzweh8FgoFUVW7duBXA5\nKMrlcgSDQaxcuRKffvopgMs3hkAgoNqwgYEBfPvb30ZPTw/Ky8tx8uRJLFu2DAcPHoRCoUA4HIZC\noUBlZeUNBcBQKAStVguXy4WsrKxhXzDBYBAKhYKWpDFOLpFIBL/+9a+xefPmEedGCEFycjK8Xi/N\nMvN4POTn5yMQCKCzsxOpqakoKCjAuXPnaP/j1tZWyGQyqhUUCATUYp6p9byWS/RkwjjhCAQCOJ1O\nZGZm0us2NBEiEonGlM3WaDTw+/0QiUTw+Xzg8XjUHo3pGJiZmQmfz0dVAXcqUz2wjYV7LghWV1dj\n8+bNaG5uhkAgoB9axggzEAhAIpGAy+VCq9UO05OZTCbE43GYzWb09/dTU1CHw4HHH38c58+fh06n\nw9GjR7F+/Xq899571ILK4XCgt7cXqamp+O1vf4tNmzbhwIEDUCgUOHjwIFQqFdWQtbW1Ydq0aaP6\nBw4lFAqhubkZfr8f4XAYtbW1yMzMhFAohNlshsVigcvlQlpaGkQiEfUTZCysurq6hmVqBwcH4fF4\n6M05tP4VuLyXefHiRZSUlCA3Nxd79+5FKBTCwMAAZs+eDa/Xi0uXLgEA7TvCZMuVSuWowvnJZnBw\nEH6/H8FgECkpKSguLqZ9l4fqKRmXacYBpqura0T5ndvtpl8yzN9KJpNhw4YNSE9PR21tLSKRCObP\nn4+GhgaUlZVhyZIlEIlEMJlM6O/vx29+85vJvgQTwt0UBO+55fCJEyeoVIHH49E9KsbZQqVSITk5\nGeXl5aO+32Qy4dKlSxAKhbj//vvpuRgMBkyfPh1tbW0oLS1Fb28vMjMz8fnnnyMQCMBgMFAzAiYg\n8Pl8TJs2DStXrqQF+efOnYNerx9VZH698zpz5gySkpJgtVrh9XqRkpKCJUuWUJF1PB7Hu+++C6vV\nCkIIHnnkERw6dAivv/464vE43n77baxfv/6qtbZvvvkmlixZggULFlBR94cffkjdaBgXZabWWCKR\nID09HXl5eSguLp4yQTAUCsHlclHXGSbh1dzcPGxfkJnNXbhwAf39/eDxeHjooYeovm/Xrl0oKSlB\nIpHArFmzho3j8/lQXV0Np9OJNWvW4MSJE3j00UdHHM/tuBcmYjk8VBN8Lcxm85QPmPdkEAwGg9Rd\n5V7n+9//Pt544w2kpKTg/fffx/e+973rvmdovXhLSwuOHz9OnakZIwM+nw+lUon09HRqnHplVjgl\nJYWOP1kwM12mfprBarUiHo8jGAwOqxypra2lM0fgshsP41ZdW1tLa5GH7vUCfze5+J//+R90dXVB\noVCgoKAAZWVlyM/Pp68ba530RDARQfBG5W+9vb1sEBx10CmWNWZhuZeYiCB4NdXClVgslikfBO+5\nPUEWFpabZ6oHtrHABkEWFpYxc7UmSncibBBkYWEZM+xMkIWF5Z6GDYIsLCz3NGwQZGFhuadhgyAL\nC8s9DRsEWVhY7mnupuzw7a9jYmFhueMYr6nqaB0sX331VWRnZ0Ov10Ov12P//v30/67WwbK+vh5l\nZWUoLCwcYY83npOZdACwD/bBPm7TYyLuX7lcfkOPK8erqakhDQ0NZNasWfS5V199lfzqV78aMc75\n8+dJeXk5iUQixGg0koKCApJIJAghhFRWVpLTp08TQghZtWoV2b9//7jPh50JsrCwjBkyzpng4sWL\nR21vMNpr9+zZg02bNkEgECAvLw8zZszA6dOn0dfXB6/XS53rt2zZgt27d4/7XNggyMLCMmbGGwSv\nxttvv43y8nK88MIL1KDjah0sr3w+KyvrpjpbskGQhYVlzFwt6MViMYTDYfq4EV588UUYjUY0NTUh\nMzMTP/7xj2/x0Q+HDYIsLCxj5mpBkMvlQiAQ0MeNkJ6eTlsP/NM//RPq6uoAjN7BMjs7m7asGPr8\nzXS2ZIMgCwvLmGGaiF3vcSMMbcH6l7/8hWaOH3vsMezcuRORSARGo5F2sMzIyIBcLsfp06dBCMHH\nH39Mu16OB1YnyMLCMmbGst83lCs7WL722muorq5GU1MTOBwO8vPz8f777wO4dgfLd999F88//zyC\nwSBWr16NlStXjvtcWFNVFpZ7jJu95TkcDm0xcD2YHjtTGXYmyMLCMmamemAbC7dkT/DAgQMoKSlB\nYWEh3nzzzVsxBAsLy21koiUyt5MJXw7H43EUFxfjyJEjyMrKQmVlJXbs2IHS0tK/D8ouh1lYbhsT\nsRzm8Xg39Fqml/VUZsJngnV1dZgxYwby8vIgEAiwceNG7NmzZ6KHYWFhuY3cTTPBCQ+Cvb29yMnJ\noT8zKm8WFpa7h4mUyNxuJjwIsktdFhYWhtHqhKcaE54dvlLlbTKZRnSrv1OmySwsLCO52+7fCZ8J\nzps3Dx0dHejq6kIkEsGnn36Kxx57bKKHYWFhYZkQJnwmyOfz8c4772DFihWIx+N44YUXhmWGWVhY\nWKYSt0QnuGrVKrS3t6OzsxM//elP6fOToR8czbnW4XBg+fLlKCoqwiOPPEKteoCrO9eOF5PJhKVL\nl0Kn02HWrFn47//+70k9hlAohAULFqCiogIzZ86k138yr0E8Hoder8fatWsnfey8vDzMnj0ber2e\n+s1N5vgulwtPPvkkSktLMXPmTJw+fXpSx2cZB+O2Yx0jsViMFBQUEKPRSCKRCCkvLyetra0TPs5o\nzrX/9m//Rt58801CCCHbtm0jP/nJTwghozvXxuPxmxq/r6+PNDY2EkII8Xq9pKioiLS2tk7qMfj9\nfkIIIdFolCxYsIDU1tZO6vi/+tWvyDPPPEPWrl1LCJnc65+Xl0fsdvuw5yZz/C1btpA//vGPhJDL\n19/lck3q+CxjZ9KC4IkTJ8iKFSvoz1u3biVbt269JWMZjcZhQbC4uJj09/cTQi4HqeLiYkIIIW+8\n8QbZtm0bfd2KFSvIyZMnJ/RY1q1bRw4fPnxbjsHv95N58+aRlpaWSRvfZDKRZcuWkaNHj5JHH32U\nEDK51z8vL4/YbLZhz03W+C6Xi+Tn5494/nZ+/liuz6RZad1O/aDVaoVGowEAaDQaWK1WAFd3rp0o\nurq60NjYiAULFkzqMSQSCVRUVECj0dCl+WSN//LLL+OXv/wluNy/f7Qm89w5HA6+9a1vYd68efj9\n738/qeMbjUakpaXhO9/5DubMmYN//ud/ht/vv22fP5YbY9KC4FTRDzLmjdf6/4nA5/Nhw4YN2L59\nO5KTkyf1GLhcLpqammA2m1FTU4OqqqpJGf/LL79Eeno69Hr9VWUUt/rcjx8/jsbGRuzfvx+/+c1v\nUFtbO2njx2IxNDQ04Pvf/z4aGhogk8mwbdu2SRufZXxMWhC8Ef3grUKj0aC/vx/AZQPH9PT0UY/p\nZh1qGaLRKDZs2IDNmzdTs8fJPgYAUCgUWLNmDerr6ydl/BMnTmDv3r3Iz8/Hpk2bcPToUWzevHlS\nzz0zMxMAkJaWhvXr16Ourm7Sxs/OzkZ2djYqKysBAE8++SQaGhqQkZEx6X97lhtn0oLg7dQPPvbY\nY/joo48AAB999BENTFdzrr0ZCCF44YUXMHPmTPzwhz+c9GOw2Ww0+xgMBnH48GHo9fpJGf+NN96A\nyWSC0WjEzp078fDDD+Pjjz+etHMPBALwer0AAL/fj0OHDqGsrGzSxs/IyEBOTg4uXrwIADhy5Ah0\nOh3Wrl07aZ8/lnEwmRuQX331FSkqKiIFBQXkjTfeuCVjbNy4kWRmZhKBQECys7PJBx98QOx2O1m2\nbBkpLCwky5cvJ06nk77+9ddfJwUFBaS4uJgcOHDgpsevra0lHA6HlJeXk4qKClJRUUH2798/acdw\n7tw5otfrSXl5OSkrKyNvvfUWIYRM6jUghJDq6mqaHZ6ssQ0GAykvLyfl5eVEp9PRz9hknntTUxOZ\nN28emT17Nlm/fj1xuVyTfu1ZxsZtcZZmYWFhmSqwjZZYWFjuadggyMLCck/DBkEWFpZ7GjYIsrCw\n3NOwQZCFheWehg2CLCws9zT/D1tN9Y0vR+oJAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The above image is about a 20 arcsec FOV on one side. That's a lot of stars in a small area, with lots of spoilers and neighbors. PSF photometry is your best bet\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##General Steps##\n",
      "\n",
      "I'll **bold** highlight things which do well as functions\n",
      "\n",
      "When line identifiers (A, B, ..) repeat, it means those operations are another iteration of the same process"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A. **Find stars in the image and run aperture photometry** (maybe with iraf.daofind and iraf.apphot). __This whole part can be scripted__ for repitition and analysis. It's also a common function you can use for regular aperture photometry\n",
      "\n",
      "  - Be careful of the aperture you use to retrieve the photometry, consider crowding and the structure of the instruments PSF\n",
      "\n",
      "    - Try and choose an aperture at least as big as the FWHM \n",
      "\n",
      "  - Find as many stars as you can, you can always throw the junk out later\n",
      "  - save your photometry parameters so that more stars can be selected with the same defaults later in the process if necessary\n",
      "  - decide if doing photometry on the subtracted image with centroiding on gives you a better or worse location estimate for the first go around\n",
      "  - *only pixels in the good data range defined in your datapars are used for the fit make sure these are set at the beginning of the process*\n",
      "    - be careful when setting the datamax and think about the full-well depth of your detector. Sometimes stars saturate a little below this \n",
      "  - *the detector noise characteristics you enter here are used during the PSF fits*\n",
      "  - if you were just doing aperture photometry, you would stop here and apply your aperture correction to the measured values and be done."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "B. *Examine the PSF structure of the brightest and most isolated stars* in the image to determine what kind of fitting radius you might use, what your model psf radius should be and what kind of analytical fit will be best. The goal is only to use the brightest, most isolated stars to create your model so that you can cleanly fit the other stars which have issues without introducing more problems. \n",
      "\n",
      "  - You want to make sure you have selected stars across your entire field of view to account for any spatial differences. \n",
      "  - You need enough stars so that any noise created by subtracting their neighbors does not adversely affect your model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#put example of psf here"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "C. **Setup all your DAOPARS**\n",
      "  \n",
      "  - it's a good idea to save the pars that you used for future reference.  Either __create a function__ which you can run to automatically set them again, or save them in IRAF using the \":s\" colon command or in PYRAF by selecting \"File: save as\" in the EPAR screen so that they can be loaded again.\n",
      "  - some of the data parameters have important effects on the fitting and photometry, so make sure they are appropriate and used consistantly"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "D. *Select the model PSF stars from your image*\n",
      "\n",
      "  - they shouldn't have any close neighbors (at least as far away as your fitting radius, preferably more)   - use iraf.daophot.pstselect to preselect a group of stars for you\n",
      "    - you can run pstselect interactively if you want to choose more (or specific) stars that the automatic routine doesn't select\n",
      "  - display the image with the selected stars marked and examine them for neighbors or stars which weren't detected by your finding routine\n",
      "      - do photometry on any undetected star and add this information back into your photometry file\n",
      "      - __write a script that takes the list of stellar coordinates you want to add, performs photometry on them, then concatinates this new photometry with the original output photometry file__. \n",
      "\n",
      "          - Make sure to renumber your stellar ids so that there aren't any overlaps. \n",
      "          - useful iraf tasks include daophot.prenumber and daophot.pconcat, but you can also do this in python\n",
      "          - **having a function/script which can manipulate your photometry files by adding and subtracting specific or general stars will be useful later on**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "E. *Create your first PSF image* by running daophot.psf\n",
      " \n",
      "  - input your full photometry file and your list of psf stars (the *.mag.? file and the *.pst.? file using default naming conventions)\n",
      "  - keep this the simplest analytical model available which matches the general shape of your instrumental PSF, i.e use a constant psf. You will make a more complicated and variable model later\n",
      "  - this will produce a psf model file (*psf.?.fits) and a group file (*psg.?) file which contains the list of stars which were in close proximity to your selected model stars\n",
      "   \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#show an example image of what the psf looks like"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "F. **Remove the neighboring spoiler stars**(the group list) from your image using the psf you just created\n",
      "\n",
      "  - Make sure that your PSF stars are not in the group list (usu the first star in each group). __Use your function from D to remove the psf stars in the pst file from the psg file__.\n",
      "  - the fitting radius should not include any neighboring stars\n",
      "  - run daophot.nstar to fit the group stars \n",
      "  - subtract all the stars in the group file from the image (daophot.substar)\n",
      "  - examine how well the subtracted fit was performed and check for more faint companions in the subtracted image which were hiding \n",
      "  - __instead of using group, grpselect, nstar and substar you can just use allstar__\n",
      "  - let allstar re-fit the center of the star when it fits the psf, it will also recompute the sky value for each star\n",
      "    - allstar will reject stars with poor fits and either write them to a rej file or keep them in the allstar output file, users choice\n",
      "    - *only pixels in the good data range defined in your datapars are used for the fit make sure these are set at the beginning of the process*\n",
      "    - use NSTAR if you want to maintain control over how the stars are placed and fitted into groups (maybe important for really dense fields or where the psf is changing significantly over the FOV)\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Worth Considering**\n",
      "\n",
      "You've performed aperture photometery on these stars under varying background conditions. A pass of aperture photometry on the original science image most likely uses the modal value of the pixels in your sky annulus to predict a sky value to subtract from the stellar flux inside your aperture. When you perform PSF photometry, allstar can also recalculate the sky around each star AFTER neighbors have been subtracted using the median pixel values. How accurate either of these assumtions are should be considered for the completeness study you perform afterwards with artificial stars in your science frame.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A. If you found more hidden stars, run your handy __scripted functions__ on them to collect their photometry __from the subtracted image__ and add them to your master photometry file which also renumbers everything again.  If you centered the stars when finding them in the subtracted image, don't recenter them in the aperture photometry later on if you redo the photometry from the original image."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "F. **Remove the neighboring spoiler starts from the original image**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "E. *Recompute your PSF model with daophot.psf*\n",
      "\n",
      "  - use the spoiler  *subtracted* image from the previous step using your master photometry file\n",
      "  - watch out for enhanced noise pixels from the previous subtractions, consider setting your minimum data value to a large negative number for this step\n",
      "  - consider comparing your aperture photometry and psf retrieved photometry with you current model to look for wierd correlation which might suggest you should try a different analytical function for your fit."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "__Repeat steps A, F, and E until you can subtract all the neighboring stars cleanly from the image.__\n",
      " \n",
      "  - every time you subtract neighboring stars use the original science image\n",
      "  - only make a new PSF model from the subtracted image with all the spoiler stars removed\n",
      "  - *increase the variability of your model (varorder=1,2) to extrapolate on the empirical PSF in your image* "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "G. **Run daophot.allstar on your original science image**, to get your final PSF photometry values for each star\n",
      "\n",
      "  - use your complete photometry file\n",
      "  - cleanest PSF model\n",
      "  - decide if you want to set centering off because you have already fit the best centers \n",
      "  - consider your PSF aperture correction for absolute photometry. Verify this on isolated stars if possible, or on your selected PSF stars which have had their neighbors cleared\n",
      "  - compare your aperture and psf photmetry in plots of the field, strange correlations may mean you should use a different analytical model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#show a plot comparing aperture and psf photometry results"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Test for Completeness - If you dont script this you will be sorry##"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using your final PSF model and original science image, write a set of functions:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* which can be used to iteratively add artificial stars into your image and then retrieve them using exactly the same methods as above\n",
      "\n",
      "* generate a random list of star locations and magnitudes, save this for reference later!\n",
      "* use daophot.addstar  to add this list of stars into your science image, it will also add Poisson noise to the stars using the value of the gain\n",
      "     * don't make the crowding problems in your field even worse; if necessary do many runs with fewer stars to populate different magnitude bins\n",
      "* __run A.__ to find stars in the newly created, artifical star populated image, and run aperture photometry\n",
      "* using your PSF model, run daophot.allstar to collect the psf photometry\n",
      "* which matches your list of artificial star locations to the psf photometry information you just retrieved and save a file which contains the input and output information for each artificial star\n",
      "* *make fun comparison plots* of your aperture and psf photometry, your retrieval success, and anything else that sounds exciting after 5 beers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#show an example of a completness plot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}