simpeg/MT Script-3D_layerTest-working.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:7b047ea0d01a07946777c0203bdee5b1513cf1b7fad4ebbb2354487852002c17"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import SimPEG as simpeg\n",
      "from scipy.constants import mu_0\n",
      "def omega(freq):\n",
      "    \"\"\"Change frequency to angular frequency, omega\"\"\"\n",
      "    return 2.*np.pi*freq"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Efficiency Warning: Interpolation will be slow, use setup.py!\n",
        "\n",
        "            python setup.py build_ext --inplace\n",
        "    \n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.sum(100*np.cumprod(np.ones(5)*1.6))\n",
      "\n",
      "   "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "2529.536000000001"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# M = Mesh.TensorMesh([[(100.,32)],[(100.,34)],[(100.,18)]], x0='CCC')\n",
      "M = simpeg.Mesh.TensorMesh([[(100,5,-1.5),(100.,5),(100,5,1.5)],[(100,5,-1.5),(100.,5),(100,5,1.5)],[(100,5,-1.5),(100.,10),(100,5,1.5)]], x0=['C','C','C'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print M.vectorNz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[-2478.125 -1718.75  -1212.5    -875.     -650.     -500.     -400.     -300.\n",
        "  -200.     -100.        0.      100.      200.      300.      400.      500.\n",
        "   650.      875.     1212.5    1718.75   2478.125]\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Setup the model\n",
      "conds = [1,1e-2]\n",
      "elev = 300\n",
      "sig = simpeg.Utils.ModelBuilder.defineBlock(M.gridCC,[-10000,-10000,-200],[10000,10000,0],conds)\n",
      "sig[M.gridCC[:,2]>elev] = 1e-8\n",
      "sig[M.gridCC[:,2]<-600] = 1e-1\n",
      "sigBG = np.zeros(M.nC) + conds[0]\n",
      "sigBG[M.gridCC[:,2]>0] = 1e-8\n",
      "colorbar(M.plotImage(log10(sig)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0x7f4226103c68>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9IC3LjSzZMAPWToVeo6DkACy6z9XZV4UEXPQxfO8RyKxT1s/a6jyXbIo+jn77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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4221670e90>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the mass matrix \n",
      "# The model\n",
      "Msig = M.getEdgeInnerProduct(sig)\n",
      "MsigBG = M.getEdgeInnerProduct(sigBG)\n",
      "Mmu = M.getFaceInnerProduct(mu_0, invProp=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "freq = 1e1\n",
      "C = M.edgeCurl\n",
      "A = C.T*Mmu*C - 1j*omega(freq)*Msig\n",
      "ABG = C.T*Mmu*C - 1j*omega(freq)*MsigBG"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "# Solve the systems for each polarization\n",
      "Ainv = simpeg.SolverLU(A)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 15.9 s, sys: 271 ms, total: 16.1 s\n",
        "Wall time: 16.6 s\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Need to solve x and y polarizations of the source.\n",
      "from simpegMT.Utils import get1DEfields\n",
      "# Get a 1d solution for a halfspace background\n",
      "mesh1d = simpeg.Mesh.TensorMesh([M.hz],np.array([M.x0[2]]))\n",
      "e0_1d = get1DEfields(mesh1d,M.r(sigBG,'CC','CC','M')[0,0,:],freq,sourceAmp=1).conj()\n",
      "# Setup x (east) polarization (_x)\n",
      "ex_x = np.zeros(M.vnEx,dtype=complex)\n",
      "ey_x = np.zeros((M.nEy,1),dtype=complex)\n",
      "ez_x = np.zeros((M.nEz,1),dtype=complex)\n",
      "# Assign the source to ex_x\n",
      "for i in arange(M.vnEx[0]):\n",
      "    for j in arange(M.vnEx[1]):\n",
      "        ex_x[i,j,:] = -e0_1d\n",
      "eBG_x = np.vstack((simpeg.Utils.mkvc(ex_x,2),ey_x,ez_x))\n",
      "rhs_x = -ABG.dot(eBG_x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Setup y (north) polarization (_y)\n",
      "ex_y = np.zeros(M.nEx, dtype='complex128')\n",
      "ey_y = np.zeros((M.vnEy), dtype='complex128')\n",
      "ez_y = np.zeros(M.nEz, dtype='complex128')\n",
      "# Assign the source to ey_y\n",
      "for i in arange(M.vnEy[0]):\n",
      "    for j in arange(M.vnEy[1]):\n",
      "        ey_y[i,j,:] = e0_1d  \n",
      "        \n",
      "eBG_y = np.r_[ex_y,simpeg.Utils.mkvc(ey_y),ez_y]\n",
      "rhs_y = -ABG.dot(eBG_y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We are using splu in scipy package. This is bit slow, but on the cluster you can use mumps, which might a lot faster. We can think about having better iterative solver. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "e_x = Ainv*rhs_x\n",
      "e_y = Ainv*rhs_y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 194 ms, sys: 1 ms, total: 195 ms\n",
        "Wall time: 431 ms\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I want to visualize electrical field, which is a vector, so I average them on to cell center. Also I want to see current density ($\\vec{j} = \\sigma \\vec{e}$)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Meinv = M.getEdgeInnerProduct(np.ones_like(sig), invMat=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "j_x = Meinv*Msig*e_x\n",
      "j_y = Meinv*Msig*e_x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "e_x_CC = M.aveE2CCV*e_x\n",
      "e_y_CC = M.aveE2CCV*e_y\n",
      "j_x_CC = M.aveE2CCV*j_x\n",
      "j_y_CC = M.aveE2CCV*j_y\n",
      "# j_x_CC = Utils.sdiag(np.r_[sig, sig, sig])*e_x_CC"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Then use \"plotSlice\" function, to visualize 2D sections"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
      "dat0 = M.plotSlice(abs(e_x_CC), vType='CCv', view='vec', streamOpts={'color': 'k'}, normal='Y', ax = ax[0])\n",
      "cb0 = plt.colorbar(dat0[0], ax = ax[0])\n",
      "dat1 = M.plotSlice(abs(j_x_CC), vType='CCv', view='vec', streamOpts={'color': 'k'}, normal='Y', ax = ax[1])\n",
      "cb1 = plt.colorbar(dat1[0], ax = ax[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CZVwwpVqXlW+yZ3U09DAD57l9Lxbgjb8Of/k6WN4hBV8jwMSDjFnJubjALkk9\nMGscTrdqj9ZRnJJh1Ja0ruh8dPmkdcw1rsGl8i/hXl95KS54N4xJkhOfnZPdrCMCJxzR7VYkkLuz\nMTk2rIGznuuC8RVLut0ao6ex31qPYU/fvclioA/3TrkF48YUyInPzsluGnOB+/6l2y0w5gzm+Qxj\nBvjNbjfAmCvkxGfnZDcNwzA8cvJ+omEYxpwgJz7bgnLDMPKHeT7DMIzeISc+OyVpnGEYxhyl1MZg\nGIZhdIcO+WwROV9EHhSRX4rIWxPqXBXYfyoiJ3vlnxKRbSJyX6T+dSJydzA8IiJ3B+XrReSgZ/t4\nK7tpGD3BF77jUiWuPqzbLTF6npz8FWoY3WUzLiXeyi63w+h5OuCzRaQIfAw4D5ff8w4RuUFVN3p1\nLgCOVtUNInIa8Ang9MB8LfBR4DP+elX1d73lPwTs9sybVPVkWsSCcqNneP17XUrE170c/uYNFpwb\nU8A8n2HMAN/G5fVej8vAYsG50Sad8dmn4oLkzeB6uIELgY1enVcRKDCr6u0islBEVqjqk6r6AxFZ\nn7RyERHgNcA57TYwd7emWg3u/EVdsKcWjmsuk2yt1ljeUEfrOcLHK8nLl0tO6Ca6fDgM9MGB0RgF\nw4IbD/bD/kA/oElcKLCHy4dl/rjfE76JU/QsFqHi5aOOlbPQiDxGpFJfGUZTxHWGBmD/oWT7gkF4\n5kCyfXio2T5egWoNPnUjXHuTE545Zi288Ph63vaQeRnb7y+niwOJNO9zlGIBKilaFwN9cNDbRnR9\n0WMQtY/Mq4sTxQlFjcyD3fs8OZNIHd8ed60sGIQ9B+rzqqC1+nR/HxyMu04j8+G1H3e9Dw3A3gPx\nv6NaDZYtgieejv/t1RTWL4eHn3Dn86b3JR/rSZM7z9frbAfGCK60oEwT5uOmBacymLRMASdcEycu\npLhuukqMDc8et7y/fY2UE1M3bj4kbGMSJVwu9yT6gNEUe3+GPWv5Ptw58tmD259HgH/GiSgtxgkZ\n9aWsK4kCdbXIOEqkiy2UY9roEz0G0XMxGXvceYxuP1rHv86i9qRrK1oWXgfR6zwcD+AEipKu9fk4\nJdak39kwriM4yb6QukDSq5ll6UxX4xLnh2wBTmuhzmrSlblCzgS2qaovXXhE8DrLHuAvVfXWtBXk\n7tZUU/jTq5yLDAWAQiGfQiDyMzzkAjq/zJ9etgie3pO8/MrFsG1XUBYVESKw744RJwrWs3wRPLW7\nURDIFy6z6yiUAAAgAElEQVRaPOyCrdDWMMYFMAc8vxFV9CwXm4PJqOhQnAiRXzZQritsxiECB1KC\n4nIxPShOU9SsKRQFRisuaDw01hyUlzLWD+mKpeEtNI1SwT0kJFHTugz9xHojxzXabt9cLEKf9wuN\nnuvB/vp5jBORmjfgjkM437R8HyxcELnOavW6pYLbh1ghLW867jfir0Oplzf8ZnAPsDX17JF1lYrO\nXs6dpzIa+REuMBdvwJteiLvnRcvD+YXAMwk2wQUae2OWD6eHaFZa9Md9NAbEUWGgAs0eJU1sKK48\nLuD0l4kLiqPrTHOKRdKD+kLM9idjB/dQUQmGdj5pK5IelAvZDy5pyyc9OPnrTxN9EpLfsxAaQ664\nZf2HjqztJI39ay3OHh7DpGvdt8f9nsL1J9n9ts6nY3TmHpB1Ww+JHvBWl7sY+Kw3vxVYq6q7ROQU\n4Msicryq7o1fPIdBeakId2S+at8lcnc2JseHPw8lhd85G979Rlhv/4R2HlP0NGYlr+52A4y22Ix7\n2FmDU/M8vKutMXqYFnz2LQ+7IYXHcX/ThKzF9YSn1VkTlKUiIiVcYv5TwjJVHSN4ElbVu0TkIWAD\ncFfSeuzWZPQM//gWp+hpwbgxZexDT8OYAc7C9eBbMG5MkRZ89tkb3BByxXeaqtwJbAjeC98KXITr\n3fa5AbgMuE5ETgd2q+q2Flp4HrBRVbeGBSKyFNilqlURORIXkKc+NlhQbvQMrz+/2y0w5gzm+Qxj\nBji62w0w5god8NmqWhGRy4Bv4ML8a1R1o4hcGtivVtUbReQCEdkE7AcuCZcXkc/hnjSXiMhjwF+r\n6rWB+SLgc5FN/hrwLhEZx70TdKmq7iYFuzUZhpE/zPMZhmH0Dh3y2ap6E3BTpOzqyPxlCctGe9V9\n2yUxZV8CvjSZ9tmtyTCM/GGvrxiGYfQOOfHZFpQbhpE/zPMZhmH0Djnx2TnZTWMucNIb3Yee73yt\nS0tpGG1jns8wZoAvAwdw38At63JbjJ4mJz47J7vp+Ntz39LtJswqSqn5XGcfP3vso9z/qPLxGwuc\n+kcncM47T2X+sqFuN2taqUzyPzsN1IGceI+6fOuBss+E8FA4PTEOlouMteZPB/VrGogMOYWfWlin\nppEhWK6qE+vSWnM9FGrVWvOywXShKFRGqxSKwrNeeZS3px+e2oHNyV+hhtFdduAyzj0MHIkF53FE\nRXgkUhZXL2ucJrBVy6iXNUCzEJc/hLniFTgCJ1bUAXLis3MVlIMLAP7rsu9MBBV+IFALpot9BcYP\nVFxA0RRsKINLBti/7UBioLFw3TA7H9rdFLyEgc1hxy5m2/1PNwQ5fiC0/DlLefKepyau/2jAtOyE\nJWy77+mmQCscLTpyhF3B9qM2gMFF/RzcOUq4gTjlymJfgepoPWhvUptcPZ89W/YlHudlxwf7mMCa\nU1ew5SfJAlmHv3Alj/7oiYayykR7avzoqnv40VX3AFAeKiERVZ4VJy7lyft2JK5/4eHz2f1ocvtL\nA0XGD6Yn7R4Y6efQ7mSFu8VHj/D0L70PrSPHcNXzlvH4nds9e2OFdWesYvOtWxtsfpV1Z67mV9/f\nkqg8uvaFK9lyuzvGEqj9TBwmEQ47bjE7HtwV2HDHMBhLQRhaNsjBHYeQQrBsQQKRK5lYlxRlon59\nqNefd9ggB3eNIgUoFKL1hOE189n35IGGZX37yOEL2Lt1P+V55UhQPkVy5/l6nVuBXSQHAkPAvhS7\nr0IYNywCniY5SFmcYA+nR4L1EykPxwPAIa+MyDQ0K3ZG7QuoCyDFcRhOYCmJVaSnW15Dc8rm6PJb\nU+zLgGjmuFCMqAL8IhjA/QCj+iyhKmoag9RFnOJYTF1NMo6VpO/DWhrFHKPnIMu+mvoxjnPMWedo\nHu6fhZA4ASFfvIeY8QJc0pAkIa0luN9SVPAnHJbijmGSfXHG8qFQl+COd4eC8pz47JzspocIy09c\nmhgESEEaAo1CsTmQKBSDoCRu2YJQKAmoF7yE2/GmQQJ7PWAqBPMTgZEn41kPmNzPsVCYMDQEWg5F\nAqnIuq2+HlVFCkLJUzaLKk2GdRoPnaTaG5YHCnGyoP7yWfbI+t+99Gpq41WKfUU2vHwdZ7/jBSw+\ncoTyQPNlrNq8T5OzK4Wo3Ga0Dhn7QMwx8GbdNqL2xmNcKzRLeoZValq/DkRIbYsRIX+er8dZjJMw\nTwoEQqXGJHvYzZakQFiIKSdhOikYiqoZ+uVKs4JlXN1W5+PKNaZelj1tfVGylo+zfwYXpJZx5/Ac\nXHDf38b6W2Uq+5C1/FTs4XWQdP0YqeTEZ+dkN+sUCsJpbzqp282YFfTa6yvzlg6w9vSVvPw9Z3DY\nMfl4qTzt9ZV2RKqNgNx5vl7nuG43wGiL+bje0pcB67EA1GibnPjsnOymMRd4+5Y/6nYTDMMwjJZJ\nTOtsGEYM1tlmGEb+KLYxxCAi54vIgyLySxF5a0Kds0XkbhG5X0Ru6fCeGIZhzH065LNnO10NykXk\nUyKyTUTu88oWi8jNIvILEfmmiCz0bG8Pbn4PisjLvPLnich9ge3vZ3o/DMPoMUptDBFEpAh8DDgf\n937FxSJybKTOQuAfgN9Q1ROA356W/ZkhzGcbhtEVOuCze4Fu95Rfi7uh+bwNuFlVjwG+HcwjIscB\nF+FufucDH5f6l22fAN6oqhuADSISXadhGEadzjj4U4FNqrpZVceB64ALI3V+D7heVbcAqGpySqDe\nwHy2YRgzjwXl04+q/gCXW8fnVcCng+lPA68Opi8EPqeq46q6GdgEnCYiK4EFqvqToN5nvGWMOcQD\nNzzEvu0HsisaRhad+St0NY350bYEZT4bgMUi8l0RuVNEXtu5nZh5zGcbk+Mx0lMAGkaL5OT1ldn4\nLLFcVcNkp9uA5cH0KuA2r154AxynMbnq4zTfGI05wOcuuhGAU//4xFwIBxnTSGc8X0KG+AbKwCnA\nS3DJtH8sIrep6i870oLZgflsI4Fv4k71Bkw4yJgSszFanQZm9W6qqopIKze+qWyDWlXRQFWwVlWq\nlRrUnBphaHNjpRZOB3a3TM2JCAU2EaiO1xrqhEOtqkhRqI5VG8SHfCGjQl+RysFKg2CQr7BYHPDt\nEzsyMQqFf5LEg6QgaLVGAZ1QgHQV/OMSLWg8boVSgep4jSTKg0XGDyanXCwPlRjfP55sn19mfF+j\nvVapUasot338p9z2iXtZcvQIhz17MeteuLIpp3lpsOSOUQKFcoHqWHL7EQVNT99VKAi1avI6in1F\nT/CIJnGg8rwyY/4+Rux98/s4uLdSP0e+mBQwMNzHoT1jE9eIb0Ohb7iP0d2jEfXO+rhvfpnRZ8Y8\nkatACCu41or9JSoHxpvUNmsRsayk34LW3DYO7hyt26r130GtWmNkzQJ2PrxnYh1hnXC8/MSlbL1r\nO33zy7zlkQ5m32nB891yN9xyT2qVx3FqIiFraVZfeQzYoaoHgYMi8n3gucBcCsonmAmfXRfuqUXG\ncWVpdfx5EsrDaaGuUhgnMBTaidjx7L6vSBMRSpoveG2Ko0RdrCeOMjCWYu/LsJcz1l/CiQT5PINr\n7y9wf5QswInTrA22N1mix7GVNvhk7UMrxyhZMK55+ei5ip6j6HXgC0jFXUfRcdy1GG4jSShrACcu\nlHStD9Mo1BX9DS3FPXsn/a6W4wSaFLiEjj2IzepotXPMxt3cJiIrVPXJ4G/O8L+v6A0wlB97PJj2\ny2Nly75z+Y+oVWt8/70/Ca4jF6BMiAQVC0gB5q+Yx8GdhybK3FgoFAtIUVh85Ah7tuxrEBeaEBkq\nFlh89EL2/OqZRjEiT1xo0fph9mzZWy/zBYYKwoJV89n35P66MJAnLCQCQ4cNcvDpQ26nYsSD+kf6\nGNs77hc1iAcV+orUxqsUwh+0JzoTTkpBGgN2GsVpykOlVMXLgeE+Dj2T7NyGFg9wcFeycxtcPMDB\nnYcaysLm1KpKoQgHnj7EM4/v45mt+5uC8v7hMqPPpAT9Q0UqKQ8NE/e/FAqlArVqcvzRN7/c/ODh\nNXOgqoxGj5Fnl4LUg/aIcBAiVCtKrVKbWC4qOFUsFSj2FxuUOn2xqvJgifJguS5iNXEtOnuh5MRQ\nEkW2QgGtYiHxt1AoBfV8m18//F0Vwun67ywU6SqUCjx221a+c/mP0k/IZGjhr82zn++GkCv+panK\nnbj3odfj7kIX0ZwD7ivAx4KPQvuB04CPtNHi2cy0+Wz4bjC+G9gbTPtBaij8swp4MlLmj1cAT0XK\n/OlQxdC3+cMwTjE0Wk4wHqQerMWJwxSpO5Qk0aC4TgC/rEx8wBnW6ac5YPSXz1LDHKJRTTLKIE6V\nNIm47Ud97CHceXyGeAGhLLIcc9aDxQDpQXWovOoTFWBKs+NtP07IyW9/9NyH10maOFW0fpy4VZwQ\nlj8UqT8oRn8PSb+PpOk0+2PA/THHoU169HWUyTIbg/IbgNcD7w/GX/bKPysiH8H91bkB+EnQM/OM\niJwG/AR4LXBV3IrPvfxFqCpnvfP0xuAhp0qIvSYedNvHf4qW4NmvPCI3AkJp4kF54thXHc2xrzp6\nYv67V/x4aivsgOdT1YqIXAZ8A3fLuEZVN4rIpYH9alV9UES+DtyLuxv/s6o+MPWtzyqmzWc7BUiA\nM4Oxf+M3Zj+P4QL95TgBoSO62xxjBjk2GEK+N7XVzcZodRro6m6KyOeAs4ClIvIY8NfA+4AviMgb\ngc3AawBU9QER+QLwAK674E1a78p9E/AvuEf5G1X16ynbjJVlN2Y/r7zyLI48Z20ugnFjmumQC1DV\nm4CbImVXR+Y/BHyoM1vsLt3w2Q7z2b3JmbjeawvGjSmSExcg0VcU5ioiou/Wt3S7GbOKXuspzyPW\nUx7PX8mHUc146T8BEVFNf1c8frmTaHubxuRx76Zf3u1mGIbRES43n90COXn2MAzD8LBnHcMwjN4h\nJz672+JBhmEYhmEYhpF7rKfcMIz8YZ7PMAyjd8iJz7aecqNn+NCGf+GLf/BNdj+2N7OuYaSSE8lm\nw+guXwSuJTHjpWG0Sk58dq4+9Fymv+p2M1Ip2oeXE8Qdiy0Dx0OtBsUiQxe/kuEr3kxp7copb6va\n5stqWb8drdVS022qamNit8j6GuwTokAasYsrmxCI8uo1DI1lWqvV52vqjqsq1Zq46Zqi1WowXYNq\nzc1Xq1CtQbUazNcmyrRScdOVwFapum2NjUOl4sorFTc9XkErVSiXYO/+oLwKlUrDtCwcofbkdujr\nY/gf3zOx79tl3dQ+Gnq4jeWO7L2PhnoZ+9BzLvBJXGr6Ei4r5svornhrVrzTij2at3wy60oTimpF\nLCgcJw3QuqhW0rTgxIdqCUMJl6s9yT6Ay+lfA84FRoJ2TfFDzw75bBE5H7gS95b6J1X1/THbuwp4\nBS6f5xtU9e6g/FPArwPbVfVEr/7lwB/ixBAA3hFk5kJE3g78AS5p/5+p6jfT2tyjzxLto9Uqo5//\nqgsIwkBhfNwLCFwwqAcPxdvHK8j8IXTHrnrgES5XqUC1RmHVcqoPPxoTuFTRao3isUdRvffBiWDI\nBT8KNTcun3Qs43feNxEwqdaDJlQpn3Qc43f9rCEY84Oy4ob1VH/xSEO5m3YjWbIQ3bHLK292HtJX\nRkfHEusU162iujm596N00rFU7tmYaC+/6BTGf/jfifa+s05l7Hs/iTeOVzhw7fUcuPb65PWfchzj\ndyWngy4esYbqI1HxRY/BATiYJpQBsmgE3bUneRvPPpLqzx9JbuOpz2H8jvsiK637j74XP4+xH97V\nbAvGfWc8j7Ef/Xe9bEJgyI3Lzz/RXSehoFBYp1AAEUrHrKf60KMT8xQK3iAUli5Cd++FYlBWLCJF\nN6ZYRAuClEpIsQil4sSYYhEJxyML0NExpFRydYIxpRJSKiILh9FKxZUP9EOpRCGwUyoi8+dRPHod\nMtCO0EgKufN8vc4mnApheOOvkh4oRO2DONGapEBiBCcelKQEugjYQXMQFNoXBsvj2fzpARqFe+KC\nsVCNMinQGwaS/Y3LBf5kin0t8GiKfR2Q1nF1eMbyK1K2XwnW/c8pyxdpFhuKkiWAtAR4OsW+mvRe\n+6xjsI70Y5CigzUhUrXDmycyPY+6gFOSeFCWONA83DFKEvsZxv2W/LKCNz8ctKGQMMzDnacCTtAq\nau/D/V7C6Q7RAZ8diLh9DDgPd6LuEJEbVHWjV+cC4GhV3RBoKXwCOD0wXwt8FPhMZNUKfERVG4Th\nROQ4nKjccbiL71sicoyqJipg5fLWNPpf324KDiiVoFxy5YMDoOoCgTBIKJfrQcXQINRq9eXC8jAo\n6e9zP59iEYoFF6wE0y5gKUGhMQiSYoFiQaFQQEWQIDAinPYCKkVdGTQEY/64oYe2YTroYQ2Xj6tD\ndp1Ye3T5tF5iSLUXtNJkf3zwBPeAUyoy+Lu/zoK/+V+UDl8Vu57M7atSCNrfbk/5XCQ3xyKXnq+X\neRTYTXKgMIgLkIskBwpLUpYPr/skRdAk5UJfyCguWCJlHJ2Om/fLor200frt2CeDkv7Ga9z6r8H1\nlJdxqqvn4WKTuHa00r6sNhhzls747FOBTaq6GUBErgMuBPwexFcBnwZQ1dtFZGGoWKyqPwgUnOOI\nu3gvBD6nquPAZhHZFLThtqQG5u7WJMUiI5/9aLebEctMvb7SilvOqjPd9oI0O97imhX0/9oLGL78\nzygdvip9/RkqrXlVcTUCcuf5ep1zu90AI5M4nzqCe+Bp5bWVTtyZjDlLZ3z2apzMbMgW4LQW6qwm\n/W8ogP8lIq8D7gTeoqq7cU+ifgAerisRuzUZPcPKTd/udhOMOYLm5A8Bw+guv9PtBhhzhFZ89i23\nwi0/TF9Ni5uLPv1lLfcJ4F3B9LuBDwNvbKcNFpQbhpE7qub5DMMweoZWfPaZZ7sh5F0faKryOO7j\nipC1uN7rtDppHwoAoKrbw2kR+STw1XbXZS9nGYaRO6qlyQ+GYRhGd+iQz74T2CAi60WkD/cR5g2R\nOjcArwMQkdOB3aq6La1tIuKngftNIMzgcAPwuyLSJyJHABuAhAwWDrvVGIaROyrFdvojEj+YNwzD\nMKaRTvhsVa2IyGXAN3AfO1yjqhtF5NLAfrWq3igiFwQfZe4HLgmXF5HPAWcBS0TkMeCvVfVa4P0i\nchLu1ZRHgHB9D4jIF4AHcCmI3qQZuZQtT/kswvKU14k7Fnve9kH6X34m/Wef1tEPNXOTcaQFeuVY\nTDVP+Z7K5FN1jZTGLE/5DGJ5yucCd+Jin+dAj/gWY7qYWp7yvPhs6yk3eoa9//da9l31GUrHHsnI\nh97e8eDcmDyq6nLwj1dcPv+xcTcer6Bj4044KJhnbKxeFpRrrYYeODRhY2wcHRsLxuPI4AC1p55G\nikXmf+Ad3d5dwzAmxT3AVuDbwEuw4Hy24Ofyr0am/bK4AWAsxV7GdTBXgRfj8p4brWJBeQZardYD\nhdEgqBgdc4HGRIAxho5XJuqpKhwcnRAbqisYujED/eievRNiQ6FSolSdWJEsGkaf2ol6SouhsqLW\nlMKShdSe2tmo2AgTqo0yMt+JvkCTAiSADPQ7caS6oXnHJVB2nKgTMc8fQvfuT1yHLF2EPrWTJAqr\nllF7PPk1reKa5dS2RDIQVVygN37XA+w47/XQV6Zw2GL6nn9CU870wrLF1LYnb19GFqC7nwl2LSbP\neankznEK0tfXKLAUobBohNrO3d5KG49RYcVh1J7YnmgvHr6S6q+2Ntq8OsV1q52Ak2qjkmcwLh6+\niurmLYHGSV18yql5QnHVYVQffSIQsKrVx4GYlSwcprZ9R7NA1oQyZzAvAuUy0ld2uf7LZQimC8sP\ng/0HJualrw/6XF3pKyMrl8He/RNl9PXV19PXhwwNUly1HJk3lHouJku1aIHB3CUuqKgklIeD4AIN\nX1DIFyAqAqMkKyOWA3ucwmKYW7vizRMznTXfF7Qxqe4QdeGZOPsCnIBSkn0YeIZkFmTY5+GCMZ+n\nccdnH+712q/icsqvoFlYpkD2K2KhwFISUZGmKPODtiSRJdCUZU86RuGxDo9R0jXQR/06Cm1pSp5x\nqp2D1BU144alwDbcNR3m6PenlwO7vPLosJC6uFCcvYw7zuF8Z8iLz85dUK6VCrvPf91EgM3YmAus\ngmBbx8YpLBiiunW7682r1Vyg0B8EE/19FI85gtrW7W6+XAoCinqwUVi32gWkpZKzB+JDEowLy5a4\noDwUFwqEiwrFgboK4uBAXTQoFBIK5mVwAI4KAkZfqZFgulxyIjshUSEhicn2GhUPIiaXtz9fKDQH\n8769XHJBWxLlkgvoEij2Fd358Tj4lW851dP+PqRcou+c0ymfeAzlk45taquUSi5wTKJQhEBUq5b0\nvXNWL7xIetbcYrHxwSa6zrhj5NtLRRcgR22hcmep5B7owrZERaRKBacUGxGfkuCa0WLRLVMsuGut\nWKRWKNWFrqRQv359ka0JsawCWixSKPWeG+mV13SMkBtxgUQYYEeD6qW4pAZhIB0NFFbjFLCTAo1F\nuEAmDDSiwkJDuIDYFw8q4m6hobLhQNDWJKVFPDsJZUnzSWU+BdKzrZVIV8wskh4UZwXNcfZtNKpL\nrsEF5KtoL/xoRQEj7Ri0s48yBXs75zVOfCp6zfgKnHGiVtFrOKra2Xv+Ly8+u/fuplNg++cPd4Hk\n6ZdDqS8Y+t243D9RVi2WoTzgygpFF8AE6wifR6eF8nStuAso6b/7VhR4mzpS3grzB+Did6Ev/yNG\ny/2MTmb5dkmK7bM+x0jr0IFsRen0jvrpoRvb7AKVnDj4ucNzcBdnieaAOgyMfZu91jY7uB3Xs/wC\n4ExcT7FhTJ68+OxcBeWA6y088bxut8Joh/f+ANad4B6WDGMKVHPo+nqbNd1ugNEWF+L+QbBg3Jga\nefHZ+dhLY25w9PO63QJjjpCXv0INo7ss6XYDjDlCXny2BeWGYeSOvDh4wzCMuUBefLYF5YZh5I68\nOHjDMIy5QF58tgXlRu/w6bfB814BJ5zV7ZYYM4EqVMZg/BCMj8LIso6tOi8fDRmGYcwF8uKzLSg3\neoevfBj+6yo4/Hi45EMWnM8EtaoLjKPD+KgbqmMwdigInIPBn6+Mw6G9MHaweRg/CIPD8NTmePvY\nQdhwOjx2vwvIr/xlx3YrLx8NGUZ3qVJPw2cY7ZMXn52PvZwpwp69sUNQGa338FVG60FMZdQFOmMH\noToeBDnjbppxN903APt3u3pxw+AC2LcrEIMJRGEIBIZQGFgAB3bX2xTaw+nygAuIGgRnmsVpwjze\nDQJFYb2+eTC6L1Lfm16wBPbuiLcBLF4Ju7YmH8vFq+HpLY1ltSpUK7DpTvjLc6HcB8PLYMMLmnOK\nL1wOu5PFiZi30B3DJEpld27Ay4EZ2YdiUKdh37zpeYtg387mYxfOL1oJOx9vLPPPxWHrYdvD9WWj\n52H5kfDEpojNuw5WHA1bfx4IB9WC68ebXrYetv7CHdNaxY0r4/XpkRWw87HG1KENQ787B+VBd82W\nvSGcHxwGKbjxyHLoG2wcBua79UTLw6E4PS6qU3+Fisj5wJW4PHyfVNX3R+xnA18BwhN5var+bUc2\nbhiznutwAkIvA56FBedGu9jrK3MVVdjxKIzuh9EDjeOxA3BovwucD+5tLBs7UK83OAxPPeIC69ED\njT17hQIccwZs+Vk913m5PwhggvGqZ8POLS7wK5ZdgFMsQ7nsykZWuKC6UGwcwvzpA/NdoCOeKEw4\njbhgprIyIjYj9bqFYj24E0+UICo+01TmTXviO40BcTBdLLnAz8ev19cXPIgkUOp3vbA+t/0naNUd\nx2IZTjgH1p8AR5zUHJQXSi64TEK89ifWCdZZjSmLThNT7h8jIsdWxB2jajX5uE8s79n981Ao4Z4Y\nvHMbd00UivXrpVAMxIQKgShVyV1zhVJwzoJxsezqFEvZIko9SCccvIgUgY8B5+GUa+4QkRtUdWOk\n6vdU9VVT3qBh9ByHgJ3A9Ti1y5cAa4PpuedXjOnDgvK5yPdwgeK/nwmleVAagnIwLs2DcjDuXxQE\nKyNQWunKBodgOKw3D9YNQGkQioPB8oNuKEzhkA5kV+ko032Np60/S1gnLl6X18LACJzxLjjxD13g\nDo1KxCHTpfCUppAdJUscqBU6sY7JcGiGt9fbnApsUtXNACJyHS4xczQot+hjynwX58CTWAf8agr2\nTqxjrtunso5xXHD+H9O0/pmyz4Y2zHa7X+cs4JyMuoZPvoJycEHzax/tdiuMdrj4Vlh6Yj0YN4w2\n6dBHQ6uBx7z5LcBpkToKvEhEforrTf9zVX2gExvPF+dgN/de5Brcz6IIHAucCyzqaouM3sQ+9DSM\n2caK53e7BcYcoUMfDWX9VwJwF7BWVQ+IyCuALwPHdGLjhjH7WQEsxIJxY6rYh56GYRhzlFbeT7zr\nlr3cfcvetCqP416QDVmL6xacQFX3etM3icjHRWSxqu6cXIsNoxf59W43wJgj2DvlhjHTjO2Dvvnd\nboWRA1px8M89eyHPPXvhxPy1VzwRrXInsEFE1gNbgYuAi/0KIrIc2K6qKiKnAmIBuZEfLCWi0Rks\nKJ+L3NPtBmQwr9sN6CKj2+AHq+CwC+Ho98K8Z3W7Rflk/yTq1sahth+qe4NhH9S86YnxHqg+Ux8q\n3vyzvwaDR0/b7iTRCQevqhURuQz4Bu6l2WtUdaOIXBrYrwZ+G/gTEakAB4DfnfKGc8WCbjcgg3K3\nGzDL+b/AKPBqLCXidJGSxaxlNFjPGO58jXnTo5HpAvCMV2fMW3YMWIz71GYc+J+4V5imjgXlxtxg\nIkd5DZeSRF0GGvHyWk/YvXzmaPI8RNIJxuU5j75uGzPvF40+DlKGp74CO26ERb8Gqy6BwaOgvLjd\nnU+3pb4RnNV+3L4KMfsczTnulft5yAEI88zXaD7WBdBxGs6Pfx4pgo4Gueqrwbq8aSlBdT9opT7U\nxpSDx2UAACAASURBVOvThX4Yfxpqo27QURgbrc8X58HYVqgdcEP1QH26dgBkyO1/cT4UF0BhQX16\nYrwYikNQXgbFESgOQ2nYjYvD0H942kmYNjr10ZCq3gTcFCm72pv+B+AfOrIxo4cJf7PR6fC3Hk0X\nFZfmKc5htfJZg183LSieqr0EDEXKDgDbgE8CS4FX4jJztPuwtSfDPt372MJ9IbVe3Dg6HV4Ptch0\nDffPg+LSl1WD8WgwXfXKwkDZH8KyPmAXjcH3GK5foQ9Ygwu6+4B+bxxOD+N6EBcF89Gh7I0719No\nH3oa7VE75HoCa/uDnsN9QW+hN43A+FNQOwh6yI1rh6BwEKqHoH8lHPh5EECNBWNvet5xsPeuIPiq\nunE4UIN5J8Deu6k7+iBnNQIUYOBwFwRP5CEveNMC0kc9F5802sKhvAgqu6k7sEge84H1cCiaNslz\ndvOOgwNe5rhaxe0f6oLDnTfDzm9BYdAdjyjznwP77k0+D/OOhf0PJtsH1sFoShae4ojryU1qP7ig\nV4Pj5OcYD8d9S2Hs6cbc4v7xDI/RxLmJHOvBo+HQI9TPT6HxPA4eCYceDcoKLve6P92/CsZ3uHZK\nORiXoBBOL4LCgEv9WegPgvR+Vyb97tgXBqAw5IbiUH26MOTW06Pk5aMhI4mwZ/BgMIx642jv4Bju\nVrkbF/CMe+NqMF4EPEk9cArH4fTCYHlJGELNAZ+oDsJg0MY4G7geyrQ3o1YCTa9geaylMZnQZO0n\nACPA12NsY7g3vP4pZXmAo4CHUuxhcJjEAiDtO5DDgKdS7Ktxn4ok0fTZSMzyoSheNLgXYBmwncZz\n79cdxv1dGd6XC5HpPiY6ZCgF44I3HQbWRVyO5bI3RANmP+AuM/05kqdGXnx2PvZyMmgFxndDdRdU\ndtXHlV1Q2enGWoGxLe5v+coeqO4OpncDCgteDKObXW9hYX7QazgfCvPcuG+tC7KLC6BwWD34GQzG\nxUAcqNAXBFPlxmkJxF4mAjFvoAhIYPeDvFnOoUfhh0e7wLA47F5hWfF7U8v7bkyeyby+0sPk5a/Q\nuY/iAr4DuIs3HA5Exv24YOmgN4SB7hCud3Av9aDPD1jm43r8VuKCl1IwlKkHPsVgHAZPoS8u0Bhc\n5YELvOm/wwXBBeAFwPlYFpZO04nXV2Y/efHZ+Yt4auOw5YogwI4Zqnuhf72rW1oEpcXBeBEUF0F5\nufsrfviM4O/4ESgtrE8XBtoPgvP8TnlpISw4Gdb8qQXjxrSTFwc/d/gRrpc3GnyHvYpH4oLqebgg\nOxwvoa4g+QJcEB4Onfinp3f/LZoZ1uLOjQXjxtTolM8WkfOBK3FPzZ9U1ffH1LkKeAXO4bxBVe8O\nyj+FSym0XVVP9Op/EPdu1hjur55LVHVPkARgIxD+bf9jVX1TWvvyF/lIyQXOQyd4Affi+lAcCV4B\nMGaU0jCcenu3W2HkhLy8nzh3KAHLaQy6w6Gvi+0y0nldtxtgzBE64bNFpAh8DDgP957SHSJyg6pu\n9OpcABytqhtE5DTgE8Dpgfla4KPAZyKr/ibwVlWticj7gLcDbwtsm1T15FbbmK+g/B5wvSp/2eWG\nJDDTPeXWyTP7mel/JnPz+kq+XF/vsjoY/2ZXW5GMOVGj2+Tl9ZWO+OxTcUHyZgARuQ64ENebHfIq\n4NMAqnq7iCwUkRWq+qSq/iDo/W5AVW/2Zm8H/ke7DbQuYcMwckeV4qQHwzAMo6dZTePXyluoP/lP\npk4afwDc6M0fISJ3i8gtIvLirIWtu8gwDMMwDMOYtXSoY6TVHKLRDwNbWk5E3gmMqepng6KtwFpV\n3SUipwBfFpHjfaXnKBaUG93n0Neg74VQaDcfuTFjaA3YARzWG1l9ErCeb8PoBD8ANtApgRhjOqnh\n8o8P04svSbTisx+45Sk23pKW8pLHcV8fh8TluIzWWUN6nkwAROQNuNRDLwnLVDVMAo+q3iUiD+F+\nMHclrceCcqP77PotoAjz/hfMf6sF5zOFKnAAartBg6G6G2p7QLaDPhkM2+pjngJGYOAhN+5R7ENP\nw+gE/4SLOU4Dfg8LzmeacVzWoTDN535c4L3TG54Oht24D9fej8ub31u04rOPOXsFx5xdvwb/84qN\n0Sp3AhuC98K3AhcBF0fq3ABcBlwnIqcDu1V1W9p2g4wufwGcpaqHvPKlwC5VrYrIkbiA/OG0dVlQ\n3kvUDgIHnLgOo054iFFvPlTs8oSEfCEL9ZXBFMY9Fc8JZcki9Q9Hompj4PL2TlxzzcgQaNrXgvNo\n/pqwCozB/o/A/iuhdKwbyqc1Z8IproOBC1PWn4GOwoF/zKhUoFnIw6cPJyqSxCDoAX+jjWaZB7qP\neDU3cOnb9rjzVQvPjafuJgtAd1I/z96gVZDhIIg+FFwj/vigs1cfckE4JSgsBFnoxIRqC4GFIEtA\nVkDhGDeW5W7MMpcnv8exDz2NqRP4rQmxIV9UyJ+uUM+nXosMGjOOU3gMFH4nRN3iFD/LwTai5SG+\n+FAcQ7gMcJOxj+H274fAbTjxn5NxH+Z22k8ocF1GnSL1YxRHC7479RiE968kldUhYJ9ni57LeYE9\n7pzXcHnv9+KOadxQwqmaHsCdyxqNaT4HcQH3QpyY1HpcWtDF1BU4e5NO+GxVrYjIZcA3cBfLNaq6\nUUQuDexXq+qNInKBiGzCnexLwuVF5HPAWcASEXkM+GtVDTOy9AE3i/sHOUx9eBZwhYiM407Wpaq6\nO62N+bozHYwqNM4GdgGb3XBwN/AI7ul2d2DbFczvwj1kbaEuajFAo8jFYpzDiYpXRMdxamGhutgQ\nLuiOUepEcH99pSmmLQ7amsQS3FO7T+i4AgnhyhaoFODQIpr/ZisCfwX8bco2zsT9rZrEC3A9O0lk\nOfYh0m9uI7jeCh//VY9FuPMblkfV+arU1f8g+TwVI0N4jgeBY3HXhz/418wCnOPujzx/ZH3JH/bI\n9Db2+kpeUZyPjOY7H8cFO7640AFvehGug8tX/KxS/z2tx73WFYoLRccLcAFsgWZBofB3C43+oBAp\nK+J+rHFqntFl4ghVIKfDHm5/F/D5YIjjOOCBlG1k2Y8HTkyxFyFVpKlM+uvBodJlElG1ax/BXQ9x\n5zKc7sfdQ31/7vv1MnWF16ShH+f/w1z7lZT2zh065bNV9SbgpkjZ1ZH5yxKWjfaqh+UbEsqvB66f\nTPvyFZR3lUdxmXLuxwXem4OhhnPo63HOZhnwLNxNIHy6DacHO9ym2XL6/wnnXF4OvBsXUGbx19Pa\notlDPhzuTGNBea9Sw3UahAHzgWAYo96DGKfquRin33EA5/d8gaF5uOQKo9R7GxdRD3zCIQzAwyGU\nLM8zNwTjF+PeBFjWxbYYc5m8+OzZEpXNMWq4p/0f4/7Suw3n8F8InIJLi7keOALn/MOn6d5/LaA9\nPgCcS2vBuJE/xoA7gDM6tsa8OPi5wwdxqYQPUu8pDIPmIWAVrrd7CJiPExryRYb8abvtdY434V5Z\nsWDcmF7y4rPNO3WMHcB/ArcC3wUOw4lAnQO8Ayc13LvZKqaXP+12A4xZyf3Al3EiascCX6JTPZP2\noWev8UfUe7h7L3PE3OWF3W6AkRPy4rMtKJ8Se4GvAf+B68l7GfDbuF4d6zkwjMnzDPBF4F+BJ3E9\ncTfjHmo7h33o2Wv0XrYIwzA6R158dj72coLNHVjHGK43/CbgR8DzgFfg3oUeCuocaHNbvf8BndHr\ndPq7hVZQ4G7cP03fxSkh/wHwIuofTW3u6Bbz8ldor/NXem+3m5CKXUd1ZvJYFFM/xM8XvXIs3j3F\nFwXy8lubM0F5kCfyStxd/JOq+v7ObuHnuI9of4X7+O4VwDuxHhzDaJcdwHeAf8O9kvCbwP/GZeiZ\nXvLi4Gcz0++zDcOYK+TFZ8+JoFxEisDHgPNwykt3iMgNqtqUOX5y7Ae+jgvGd+CChjfgPioyDGPy\nVHD/NH0Zp+PwG8C7gOcyk99c5OX9xNnK9PlswzCM3mVOBOW4/7s3qepmABG5DpfipA0Hr8DPcB+V\nfRN4PvAnNP6VbhjG5HgUF4jfAKzEPeD+HS4bhpFDOuizjdlKdaxCoVwkEFQxjLbJS0dKZlAuIt8B\nPqyqX/PK/klV/3haWzY5VgOPefNbSFeHieExXBD+AO6+8Fu4wNw+2DSM9tiOE3H6Gk545ZXAPwJH\nd7NRwNz+aCg/PtuY7Vx/4XXs2rSTcz7wUja8+tkWnBttM5d9tk8re3kE8FYReb6qXhGUvWAa29QO\naRJdHp/wpp+Pew3lZpzi6pO4f1IvxuUSt7RbhjE5wvz83w+Gx3H/MP0+TmV1Knn478C97tIZ5vj7\niXPGZ3/v8u9PTK87ex3rz143bQ1qF60ptUoNVQV186qK1hrnUdwYmqbBm59YcXRD6YfMLZ8S9Grc\nSltevauQElRXtdC0+QPb97N7006++vtfYmjZEKf+xRmseP4q5q+c3xygZzQf3LEUkcSPGzOamLmP\nqprahIxD2LR80/a8ggZbMFO/TlxZdIxCrVoLxu4aK9QqE9NaU7Tqrsdateauy3C+UqNWVUT4/9k7\n7zg7qvvsf882tVXvEkICIboBQSgGgwGDDdgGY2PAvRA3bOOWYhy/Mdh588YpTuIUh8RO4jgucWLH\ncRJsA8aiIxBFCIEB9b6SVtrVaust5/3jmaOZHd1795a5bWeez+d8ZubeaXfumd95zq+SGkhpO5Ul\nk8qSTWXIprXePrmdwf0DRz7PpLJkRjLevhk6F3TSs6mHzEiGTMr7fCRzZP/ZJ81mz9N7GD48wmk3\nncLkOZOJAuNcZh9BMaS8B1V2+box5r+B91T3lsrCTmBJYHsJ0ryEcCnKnnIv8EdAFxqrrkflfd2f\n/lLVbjRBgvEDi8TDyyh7yjOocMvZwNuBE/FFzIYKr9WJ3l+Hv6vobONcwI8bmX35nQ8DsAsYuPNh\nNqAi5+HWgup85voug3IK9Xrr2dB3WVTCba+3Hm4ZZC/d7W3bwNLxqvko6ihcVN1tT8HPrRUkbcH1\nVu+8+TANJeHNh7nAvgLfL/J+Qz4cg/6UQsfvKvH6h71leiDFoS293PfxuwG9zeE3sI2x6xdPQGX4\n8mEmcLDA9/PR/5wLlrGf0Vjfz0esIojgfxy+v/AEYBLqx8HvcvUp11rw5zKuTUIspyXQTGB9CnqG\nrYHPgusdoc9aQ+tt3vnyfd+yfv+Rzzr/5ukj/7M/vS4P41xmH0FR9gBrbRq41RjzfmSPnlnNmyoD\na4AVxphlSG7chFTeOdCCXpu3A6cxftzq44Kn0f/3OpIS17VEL/I22OE1t94CrETK2bcAC+p1gyVh\nvAv48SWz9dYfwCcA4daJiIgjDR2h7zsQYQoTiFFkIsd6LlJjAsspNG9JuFqMfF9H0SStwFnAVTRe\nR3QYa0LQqIhLIuXxLrMdinkvj6ikrLX/bIxZR4OVYLTWpo0xn0B+KK3At/JH8behHMgJmhNrgFWo\nwMzbkMtRQs6Lh0XDzzCiMSMor/4hr/UGlm7doInQMUiheQyqVnsMMJ1mpCXjPGhonMlsRSM0Ipqv\n59cWxyANeiOT8QTNgXEus49gTFJurb0rtP0UDchqrbU/QxV9mghZIIWIUZAkuZZCBMotw80ZV3MZ\nXbOIrA7hG1ltqHUig+go57bAcgayhIc/dwgb48IOdNMDx+fCVAobZHMZKzeg39aP8lv/KzLsLvHO\nF0Y7en75MAXfyJoLY/2GsZ7BLKA79FlwH/eMLUf/D+H/KFfrQLqSfIZ3ZxAfwO9jLejZduDrEA16\njtO95eLA+izvPscPBYkqaKjYXNvGmHOBx4AbrbU/juTieZDI7ASNgrfW+wYSjBskgZ4JciCLCFYP\nvmYx7W07Eu2IzyTkZegItyPfbns68k5zxtYgSepA1UFbvO9ztXav2cB+YaNrOyJm+bzS2r37D3s5\nuu0ORhPaMClzx5Nnn7G8BMfyojQcTXJ7kVegu88lyHXiOHKn18t1jiBaxvi+FQpWTGvL8X3YUzST\n4zu3bMF/BsFn77ZzeQ2GPVZzGdzDBvkJ+H2sGTQOfehdc9r6NHBZZGePwhRabK5tb7+voqIH42dm\nkyBBggQ1QuK+Mi5RSCubCwPAc8h/dh0i2SchzepUr81DhMERaUeqJ6DgUUeeOwLLDnwyni/LSz8a\n56eivM6NjEKa6KjxLOI1Z6PCM42asrKSTCPVgrOwNBqyyALyHPAKfqjUTPz3rANlTIoGEQn4YnNt\nfxL5WzVaBpSGx5e+X+87GAMTany9Jhuxf70DFsyAGZ31vpMaotYitlDka42RycLhIRgYhv5hWDIb\nJnhD4R15I0aKPHdCyuOMIeC/gOcR6Tsb+dDOI7cUPgD8D3ADIublXvNZlM/5GeSSsBgp4RqdlNcS\nbwCuBObU+0ZqhBSaoPWjSeKAt55B/S74nVs/jDKVXF+je9yIvEdLZSgWpTpcjVyAzkb3vAgRcYNS\nlf43sAWNPtGwoIj8E8fMtW2MWYyI+uWIlBeZvjVBgubHTX8Mr+yCz71FLVbkvAFhLQynYGDEJ85H\nliNaptJwsF/rh4e0DK7P7oQXd/qfB1sqDVecDmu3wZQJcPfvwkkRFUBPfMpji23A3wAnA7+NfGoL\n4Ungu8i0Xk6n6QfuBh4BjkfawCuB2VQnV3oWudtMpzncGMKYVe8bKANpfNLsiPUwcssIkung+myU\nmtOiid5k5J7jlvMQaZ3vbQe/c+vVxgDwI6Th/iRwbInH/gDYivKYr8ixzyNIwfwm5BIdXX+NyD+x\nGIL9F8DnrbXWKDFz4r4yDpFOQyoD6ayISTq47i2zXv7prIVsNs+2dz5rj6SlPrIOkG3x14Pfg/Jz\nZ7L+d8F9AFqN7iWUHv3IdluLfgOhzx3aWov4PqQl7u6DwRH46o/hT/4TLj4Vzjoezl4OHaFXcCxH\nw1zXDG+3tOgZBD8Prre26L/JkSIc0D0Np45+Nm45sV3k1X1mQ//ZxA4YGMj/P7rj3eeuD7h9Olr1\nfSZ7dEtn9IyGUjDi9beRNIwMw4i3nkrrPL0Deu5DqaOXJy2EPb0wuUPEefIEb9kBUybC0jnad4r3\n+cwpcMws6Jyo7emT4eYL/e+PtIn6fdWqD5X4lMcSXWgMfQ9K8zYWViNS8ttIaVYKrHf8f3jX+jJj\nTwCKRQZpGHd7y26v7UcaySnA7Yj4JciPLH4MwDCyZgwFtgeQRSPchlBMwQ5vnww+sXZtQeDzmUjT\nnIt8d1BdHrcT5RnP5a+dBe4DXsvRGupDwLdQH7qT0iYBGeAfUF6G/0NuMfQISszxe1TDKlKMKXT3\nqpfZs+rlQrsUk2v7HOAHXqGUOcDVxpiUtfanJd1wgophrUhJTz/0DGjZO6D1kTTsPSRtoNMIBpcL\npsPzO0TYhgJtOA1DI3DmUnhhp4htWyu0t2rp2uJZsP+QSGOLUTPesqVFZCxjvUgR73U3xt82RsRn\nMOVvw+jv507TNYLHB/dZPBt2HTj6O7d97FzYvn/0MwtKnpzfB3ZYNBN2h5KE9/ZrmUqLED/wPKzf\nqjYpJFImT5DWthBM+KZCm52ToH/o6Htz63OmaqKQ7/sFM6CrZ/RnwWe1cBbsOZj7+QPMnwH7evL/\nj7M61e/cf++Odd9Pmag+1trit442LV2/am2B9jZ93tEGHVbL9lYtJ7ZrcjCpw1tvD6x36PgEjYuE\nlB/BIHAXyrVcDCHfCPwb8DnKI+Q/QuP3rUhDXglGgF8D65HmcQfKlvEqROqOQ5bz2UjT3Ij+zllE\nZgfxg2XDmWfcuvH2dVlGLKOzjrSFvs+Eli3edVKhNhK63iB6VhPxAyUneNvTvfNM8trMwPokNMGa\nRG2IdTkYQm4hjwLX5vh+BPgmmlRcHPpuAE1ez0SeGaXiZ+i/uInc2u9t+JPd+rkpLbz0RBZeeuKR\n7bV3/m94lzFzbVtrj7zcxph/Av47IeSVw1oR6r2HYN8hmdu7emF/n0hX92Fv/bC2l82F+9fr2JlT\nYMYUmDHZa1NgxQIRws6JMH+6CLDTDHZOhKkT5RvriM0ER346td1WK6Njk43Yp9wKm7tg7nT46vvg\nptdAazMaaEtBjH3Kq4nEpzx2+Bkir5cUsW8KaQnfT+mEHKQBfB74Hcp3M8igeLIngLXAUuAU5Je7\npILz5sIhpGVfRnEuNVmOzn3dF9juQBMHp1UeQiRwoteO867XzuhsM259GpJEuUp7uGwjExmdhSRc\nk8xlpwkG3baHmjtHOWjEiY/Ds8h15CSk5Q5baIaBb6Pf8ClG/5Ys8EPkbpKLzI+F7cADwO+Tm5Bn\ngX9C3LZ6sRRRCPh8ubaNMR/xvr+r4AkSFI3f+zd4cqNPwvcdkvZv7jSYN02a6uEUzJ4qn9cVC2DO\nNK3P7pSGcuYUEehIUetAzybDh14PC2bGhIwnqCriQsqNDTtljVMYY6yIYi5sB16N3EmKIdl/ju/v\nWir+B/g8yo52TOi7fy7yHC+jQFSQdfwcpLktFcVO6R9Cv3USsiKci+79IAo23Bdq05DrzDQUsBde\nTsfXOE/yloUy0cQZUc6bh4B/RxlO3gcsz7HPu5HCdyGqxxf+T/4SxUDcTek+3hb5h18LfCTPPv+M\ncs/fS2HrwmSstWWZH4wx9p32WyUf9z1zS9nXTFA6jDGW93jj0657gSxMmAsT58HEudDaAIy4mcsp\nFsr0Gkc0K+ebVO8bKBLfMYnMLgKJphyAPwI+THGEfC8i5b8s4zpbkbvKTziakBeDw8B/Ig35jch9\noBb9bSbSlg6gycgj3uftSHs+12srvOUsEhVSo2Ej0n6fCHwBTYTCsMgdK4X6eJiQvwL8KfAg5Y1g\n9yAvj1vyfN8H/Aki5tXt13GJ5B83WHRlve8gQYIEdURUMruYgm/GmK8DVyPS835r7TPe5/8IvBHY\na619VWD/WcifeSlKFXajtbbH++52lKkgA9xmrb2n0P0lpJwHkTn9sSL3/wpwM7mzRRRCGv0vn0Uu\nJqViI/IBXoxM/7lIVVQY9K63CfWvbci9BNSPFyFNa6PmCE/gIwP8L/Idvxk4q8C+96F84fdytPtN\nBvgoChA+roz7GAA+jTIb5RM7f4ifObC6iEskf4IECRKMB0Qhs4sp+GaMuQY4wVq7whhzPvANlBMb\n5Fv5V8C/hE79eeBea+0fG2N+19v+vDHmVGR6PhWRt/uMMSdaa/NWTYz5yDQM3Ab8P3KXaA9jLdJS\n/3sZ1/pTpD2+rYxjfw38I/Be4PQyji8GIyhQ9GmUiu80pPW+Emn1XZaM64DX0HiBiwmOxh6kdZ6K\nyHQhF6c1wK9QjEKuLEB/h/7zj5Z5L3+Iau1cnuf7tcD3UYrR6iMu/okJYozhbrn4tCXJwRM0P2pY\n8O1aZFbGWrvaGDPDGLPAWrvHWvuQF9gfxrUoTRnesasQMb8O+L61NgVsMcZs8O7h8Xw3GHNS/mfI\nnP/mIvbNAB8HPoTcOUrBk4jUPEzpftNrUR70DwMnlHjsWEghwv808ALKM302CrILB4pehyYEcyO+\nhwTRIwXcjwj2a1H2lEKTqA0oePM2crtVvQT8PcqIUo7f/zrgO9795EIG5Tm/g1r1r4SUJxj3ePYT\nsOunsOIzcOJnoaMZazzUGDYL2RTYlJbh9WwK7Ahkwy2lpc1Cph+yw9rOeEu33ToJhvZAdggyXguu\nn/BxOPZd9X4KhWEtHN4EU3PFJFUPEcnsMQu+5dlnMdJy5cN8a60rRd2FCoiA3AqCBNydKy9iTMpf\nQVaJYt1W/hZpHN9b4nUOI7eVP6d0P/I1KMDyE5RWmGUs7EAThUdQn1kJvJXC1oJceazjAov+xy6v\nzUKZbhoNFhXy+TEK1PwoY5PcLpQz/P3k7p99aJL2acqbFGZQ//0SvpwK45vIilTo3fpXNHkuJ6D5\naCQ+5THG8EHofgIWvaHed1I5rBXhSw+CHR5NAof2Q2YAXvoTePlPYPYlMP10mHY6tE2CdIBw2pRH\nSEfApsFmtMwG1lsmQvoQkNW+NqMl3nr7bBjZ631m/aVbn7gYhnYEqvWElpOWwMBWf9ta/3gsTFwC\ng1sD18yOvtbEhTC4zbuvHK19Fgzv8X6f+22Bls0AGTDt0OK18PrE+ZDuh5YO7/OO0W3igsD3E0Yv\n26dB+0yYMEfPstVrLYHllHJcA6sMm4WD62Dvg9D1oJatk+DaF6CtFkXqhGJkdveq5zmwan2hXYrN\nbBLWYhWdEcUrFFdo/4LnihkpX+0tXUDbTcitaOcYx+0B/i/yh82n7cuHP0H+54sC1y8Gq1HWk9u8\nY6PAJpT1ZRvwemRdmRHRuZsdWUanbdyDT8K7UJ+Z77WoijxFiV1oAteL78I2FrqQy8hbAvsH+6hF\nZHo5moSU0n8d/hNlfcl3/H6kIf8L8ruuPIQmxQuBaEzxiU95k+A734jgJFkUZP+C13ajCeYrVJ5y\no6/E/TMoZsdV9nXrae9crl6CK1rmtqeg9zVctyGDrFdzkOLApY1tRXIMaXYtsO+XsO8RVLisk9Fp\nYl2qWBidXtYwOpVsNvB5uA143wcL1waP7+boBABB7tODL1tNjn1SSAYEr0lgvQXV4nDr+Zbud+dq\nBqzRY82VnaY/x2dHoVA6XO8/4XCe7x8u5gIhFON6WwosekdeRJneNqL+sgK9N58EZsP3vx3xdQuj\nGJk949KzmHGpHze14c4fhncppuBbeJ9jGJskdjkXF2PMQpQRpKxzxXRkug8JwLcWsa9FWu4bKV3T\n/TDKCf0PJR73BMrQchuV52q2yP3g54gAvR654LRT+yoH1YLFH6SChYBc5c2R0NKi1I29+HnU+5HL\nzjQUyNiGAqnPQ0S8k8byo8+i37ITuZXsB65BefaLIRrbENG9DqUDzYXveef9IuX99n3Ive4vye/2\n8tso5mZZnu/3AF9Dk+LofGMT95XxjjSKkXkFTQano4nnm9Eksxp1BCwi2QcQAT0QaBO9e0l5Alv9\n3QAAIABJREFU68HqvpOQ9c16303Dr6EwwVu6OgowumaDq7mQCz8GnvH2uQjFAlUzQUCC5oVFE9dn\nvZYCfgONf+8kKgtlJYhIZo9Z8A34KTLv/sAYcwHQE3BNyYefouwXX/WWPwl8/j1jzNeQ28oKxtDs\nxpCUH0L+3f+X4sjLLxExuanE6+xEPut/RGmFfB5FWVYqJeRZ5MrwC6SpfAPKatHIZMSi/+cg0iS4\n1h/anoivNXLN4A9kx6KB0A1qrrltV9b+VCRspnmtkZ8N6D+9A00i3G+2SDv0RYoXnC8jl5F3kj8b\nyxNIy/0N9NzKwV8h0r80x3f7gd9D2X3+Ms/xKVTc6B0Up/kvHgkpH6/Yiywrq5FG+NWon1XDIjgC\nbEYT3K1IuziACLZri1Fl5ZnADUj+VFKPIVXCvsd517sUadoTJAijC3gKKRA7kCvrB9AY2khKqNoV\nfLPW3m2MucYLyuxHDwQAY8z3UaDWbGPMduD3rbX/hIjeD40xt+ClRPTO94Ix5ofIRJcGbrVjFAeK\nISnvQQT75CL3/S6qvFnKoxpG5Ok9qGriWNiHgnX/FZHSL1IZId+A0uCNIM34mTRWYZ7D+IWG9jK6\n8FAbvkDoRINJJ0q/2Om1SRxdgTMOJKsFPYdub9uiQf8LFC9An0P97IPkfwd2IBlzB7l90jPAXajQ\nUD5XnlXe/YWDljJIs/+P6D0p5A70faRBf3ue78tH4lM+HpDBd/XYg8jFM0jm/Q5y6YgaFmm8H0Xu\ngBPQpPMMVBhrBo0ja1d6LcH4Rj9K7dyFYoOKwW6Ubes5lO3v48hNtrGIeDVgrf0ZKuEe/Oyu0PYn\n8hwb1qq7zw8gk2+u7/4QpR8rCjEk5cdSXNCkRf7gF1F6UN9fIU3s9WPstwcR8G34LhinUr4P+QGk\n3dzkXfscavOSPYEI1omINAavOYwCmbcgbdIWRKIn4xcdOt07bi6lWRXihAEkRLfga8g7UDBnsf/x\n48iqdiv53UX2IUJzKyIaufBN1Mfyad/2Ie33H3K0lv2z+EoDyO8S9oJ3r98kDgNFglKQRn10CE3G\nLbIiLUc1HKL2sXXYD/wAke5TkIxtFAKeIH44iKxCzyDFWzGBy1lk/b8X1cC5kWahgXFRpDTHv1EX\n/Bd+EFop+Dmafbq8zoUwBZGtDHpZOlCWk1J9vUeQn/yDyKf4Hd65alVHeQ1yiXABNDPwzayHkRl5\nKSLfb0Rm3XzPphTzbCNjLJ/VDEpHuYTCgaMHkdb5EWQG/yyaBN2Dn41krP4yjLTTB5Cr3II8xxxC\nOfuvI8+k37uXVah/5xKSWe8cbyP3ZPZdwJcD189VgGoIaepvQ30leiSBns2MNmSBXIcv45aid6Na\nE7hnkGw/G3gd/tB5oErXS9C8qPYYZpGl5hUky79Ice5ZWVTzJkv1LEnVQ1xkdjx+Zcl4ARVu+mtK\nCwjagsjKn1Ocxncq8BmUBcVpXEpNO/cKmvVORi9arXPRZpGG+yV8YbQXDZKXo8Ez6WaCRT6oa1Aw\nzRykqShEyh9DE7ffRr7jIPP8ZJR/fCxsRjnCTwB+k/yBXv0o8PP15I+f2IKypPwx+f3X/x0R7pxW\nPmQJmoTIzcPkTpP4LRQPc2mec1SOxKe82XEDIuWgPn0r1SPkLjXtrUSbmjZBglIxhKzhB5GMnUnx\nhPxfUTzSxyg/Tqh+iIvMTtjSKKTwg9tew+hMNmNhBPgK8s0qNtfodqQR/DyaBBxD8b7RWaQdfwBp\nH6MNhCuMEUTCn0cZDpyG3CKf74+gGXwCYTfyd12DJnm/gbR6xWgqXp/jsw5yV8bsQ65QXahvrUdC\n/BZk3syHQUTITyF/rvDDyDXgo8hNKRdeQX7g+bToINeui1De8+0c7WqwFmniv1ngfitHXAT8+MVa\nRMYHULKDarmsrMbPhBVVatoECcrBbuDfkOvhDRSvMMyiTFrdaGLZfIQc4iOzE1IOyAR6Lwpe66O8\ncuL/jIR2PrN/GAeA30UBd1cirefzRR7bj7Sfg8BvUXqF0XJgkR/xI8itpx25o1yB3HC+gITFB0j8\nwi0inM8jgrwR+fffgiZe1dLo/QiRFfDN+jdSmJAPIxJ9HKoUnOvesmjyeDZwVZ7z9CIz6sfJPyF7\nGD0TR7jDk95BlFHqM1Q7BVdcBPz4xFNIIfFZ9J4V6t+V4HHkxhhFatrxjm7kGnE+knXhfOTVwAh+\nXYm+0HICY8d0NQsGkKJiI3JPPbuEY11gfQ/SkNfif6kO4iKzE1IOSEv9PfxCS62Upul9Cvkbfovi\nCNcwCiK9GvlYg7Q+xbwwm9EEYCXyKa52R+1HPsyPetsXImEX1EylEJFaQHwDn9Io6816r7UjH/CL\nkfa5Fs/lakTKg362rymw/wjKoT8f5ezP13e/h4T6l/J8vxkRl3byT0p7kevL7yP3lVz4OxRcemGB\ne44GcQkaGn94GWkLnea6WtrrR1AGq0/RXFa/fuQ+WOvKkMPoHb8XTZjOAU5D41oLo9PXptFEfwS/\nSk+4tSFyPZynzUCa42loLHJpbacjxUctFFXVRgZZah5Az/IDlFarYRDJ9zYKuy5WCwfQ/xGNrI2L\nzE5IOQA3o8wgjyBiPpfiOlIG+B9ENt5P8YLgnxAxeXeJ9/k8irZ+G/kzY0SBoFZ8PRIINwPHk5+4\nxdG0ewD1m3XInWc+sh58BKUqrGXWkFcQWVmEsvpA4VSCaaTZWkjhLBLPon53B0ebS/cgIv0oGmw/\nT/7f/EvkcpOv367zrvU3Be45OsQlaGh8YSdSfDiLU7XwMMqY9mlyByI3EvrQpHiL13pRFpplRCt/\nssiFrddrg97SVSY9iMZDp9h6zGttiEC3h9os77jWPK0DufdNGKONx8xMaZQE4D7EKT5I7tibQjiA\nZOmJaByoVCnUixQzuWpO5EIPKvr2DsQfKkdcZHY8fuUR5MtSMRFpNF9BnXlpgX0d1gBfx6+m+vYi\njgG9bL8A/p6js6MUitregDSWt6Bgo2pEeGeRpnUd0kJcgFwaXOq7fL9vvFQGLQRXBXRzoKURCV+B\nrBZB60GtnskAKjb1EiLXr0KWlCwi3Ln6SQYF/WSQpSZYUzp4330ok8ptaGANfvc40pwb79gOFEya\n73df6+2X63uLgqrfjd7F6j+7uJhCmx9uIngAxfrcjN65auEB5CrwWxRHyPNZfaqFA4h8b0DuDP1I\nWbIcWZgWUZ5mMouI1H7kitKHxrYerx1Cv3WG1xYiUjwLjQ9ZlC4S7/pXIFeLalROHY9wFTXXoDF4\nGZLnxdRTCT/jrUieXum1Sicu/d75zqe4RBT9aEJwKfmL05WOuMjsmJHyfBhCfq63I0E8Fin4W1Q9\n1RGZTooLnhhBVT4/Rmnmta3Ih/w9VCf635HxexEpuhIJg/GohSgGFg1Ee9EguAGR8A5kFj4epUWb\nQ/2ekUVp2n6KfGp/B988+V58jVUYWeCH+AGg+QSdRZPOC5AwDuMkFFz8a287zdjWknzXesS7r9eO\ncXx0iIuAHx9Iobz3r0Nlv6sBC9yN+uJnyF00q9awKCZlMyLgThHgSPhrKN9l8AlUZXy/1w4icj3H\nawvQGOBI+HQKE+xB7z7OQ3EncY8rcuhFCq5jOfqZZFChtk1IyWFQEoDPUH4WtWeQBfTdyIWoUgyj\n4PxTyB9PFMQQftKAYvYvHnGR2QkpB5TG7RSkZSwGVyGT/V4kOIv1Ofwe0jBcVsK97UbuLjdRerrE\nsRAm49chc1cjk/EBNLE5C2liKgkIHEYaoG40+HWh/3QvIuDzkTA9HVXraxQ/xc3IbWom8jMMmxTz\nDdIWZRc6AHyIwq//L9Fg8bd5vp+JJrEf9LanUZ5WLIM0+x+mlvEIcfFPHB9oR+6B1fKTtigYbh2a\n3BaTYq4ayCKXsI34ioAJiISvQMVholIE9KN3+ATvnLOpTKs9CVnVGnnsqAdeAH6MnssUpPTrQc97\nCyLfZ6EMakso//n1IEvFThQHsayCe3ZII/fEecgTYKx7G0Ya9WXIxTbavhAXmZ2QcvYhDcmfl3DM\n8ch/9nbUEYvRXm9AgUPfoPjO2o0CNa4j2pSHGVTgqJnIuEMGEfPHkHZhOSLNnWhgGEGaNbd0Wu/D\nORpooM8iAr4UaXrm0Ziani7Uh3aiieE5FE9ks8hPdg/SkBey7Dhf8T+icPDxN1FqrteiLBjl4H7k\n9nNumceXh7j4J44fVIuQu3RxW1AtgFIC6SqF04RvwHdHmYyI8lnIfaFaE4RSFEPFohnGj1ohg5Q7\n/YFtlymmFcnvd1J5f8uiSs8/RXL4FqJxGcoC/4ju9X2MPc4MIY36HETIo1ewxEVmx+NXFsS/oFzQ\npUbYfx9pG5cztl9hBvmQf5Tiq2g5l5o3oEwrUcCioL2fo9/bqGR8EJlU9yICvt9b9iMynQ3s+5LX\nJiArRDsinB3e+nRvfQkSgMHWLOmhelEFz3VoMH0PpQneFOqvh5FmvVAU/ghK1XkjhS0za1EQ8Ge9\n85VDmlLo/fsdat0H42IKTVAIzkpzAPgctfEPH0Qy+GWvuViMVyESXt1UoAmihkX+9/vwA/93oMnW\ndEYH/Lcj6+7VRCPvdiMrfzuKgVgcwTlBv+l7aNz5NGPHKAwh97KFyG2mOhbPuMjsmJPyzUjb+s8l\nHveid+yXKM6X/Meoo+cq+JILWeC7SFjn8uctFRYFsd6NH9x3CvUn44eRYNmDhJprw8incy7yU56P\nNEiT0cD516jrtiA/09cwPrtyGlkzHkUa/M9TugZ/ALk/TUWTyEJkfifwB2jyc0OB/TLoP/golaXZ\n+hmaLBXrNpYgQVRIIUVJGgUyV3OCPogmsGuRS9hyFAdyNbWvwJygfOxDFsF9odaOlG0nIGXXOWjc\ncn3qH5Di6G1UbhG0yKrzGBo3X41iwKIiwing24gbfIKxlT+DiJAvRi44cU2JHB3GI5MpAf+ATEhT\nxtoxhP9FqX6KIeS7ka/X1ymeBP8CzT6vK/G+cmELIj89yGR2JvV5cVIosGgbEmzbEWF8FXrxF3jr\nc5GGodCzmoEmFa7c/HhFC3oOn6U8f/Z9aMK5AmU/yfe/d6MiKb9GhPuGAvuCTKWzKJwDfSwMoYnn\nVyo4R/mIi9YlQS4cRulD21H60moMg7mIuPMdrnW+6ATR4AWkCZ+LgmAv9tbHGoOuQWNVsekEc2EI\n1UN5DI2lr0b8YArRjee9KIZoNorhGIvfDKB00MsQH6quki8uMjvGpPxppBm8s8Tj1nrttiL2tWgW\n+XaKNy09i16+T1HZ37MLkfFdSCD8BtUvNBTEIWRN6EJkby8SYMeizB1XIO1COQLl9ojusdHRQnkR\n7CPIV/thlKrxPPILzN0ocBbUXydQOH6hyzvvbQXOWQx+gUjKiRWco3xksvEQ8AnC2IWsPCupju/r\nDlRL4iB6l85Cip9ap05MED3KzQ5VrlvJEBpD16EYsBMREV9O9P12O3ovLkJjxliyvQ9xmxNQEorq\nW93jIrNjRsqf9pZZFJRwGerwxcKi/JuvRX6BxVxvFxL+T4+xL2gW/iM0S21D2pZSsQV4EA0K56IJ\nQTsiatWC9a63JdAG0Qx6BTLTLuLomXcwP3YYB5Hfd7Pmua1GHvmxEIwZOBaVvJ+BhHs+TEMa74cC\n5+gjd3/NojiHFUi73l3mffajIKJb81yn+kinoxHwxpirkLqoFfimtfaroe+vA76MHl4W+G1r7f2R\nXDxBiViLTPM3EF3V2ElIhq1FAXcH0PjwdkbXLYgKcagJ0ewol1b1oGBf1/Yi976VwFuoXrzBs+i9\neCfFudfsQBr1S1DMW23cYKOS2Y2OmJFyh2fRGFqqL+t6JBSLSYjfj9xc3k9xGuq9KF3ddZReHdOi\noKEHEKG6GL3I1SK0WXS/WxExcxObZV67CGnFK5nNfxcNcK9GA2ipLka54Eo7DyKiGly6ktDNiMOI\njG9FQbE3UHzgZQZNgE5F2pLDyHyZC65yZ6X5xO9D71D9ckFn0pWLPmNMK1IvXYHMbk8aY35qrX0x\nsNt91tr/8vZ/FXrJo85tmqAgLJqo3o8mqssjOm8/KjT0AHLnugy5B8aDPCQoFxn8RAZ7keLuZTQW\nLffajUixUk2lVPC9uI3ixoynUOG5m1ANi9ohCpndDIjHrxyFFDKd30xpM7wM6sBvojiy+T+IeCwp\nYt8upIG8gtJK0mYQGXNazotResCoB4U0mh1v9do2RJKXIgFyPhqUopgxW0Sc097yIa/NQX7V873r\npAIti4JD0/ja9/C60wC3I+3WxNByBc1Fyp3P6jrEB09C/W0FxU+G9qF4h+OQNSOD/ttcA8FOJLxv\nLeH8+a65FmW7qB8y0WhdzgM2WGu3ABhjfoBm1UdIubW2P7B/JxqNE9QMLsPPLuT2FkVgZTdy4bof\nKXY+QnWKuiVoXowgZUc3owm4K0g3Eykl5iG3lCvQ2FareK/dKK6ig+LeiyyKO3ocZWSpxD++PEQk\nsxseMSTluxAJKTWF2y9RfvKTith3HXr5PlzEvo6QX03pmvsfoeCMK4k2tWEf0ppuQ9qg55EAWYoi\ny68nWtPsDuAn3rUGkGByaQ+z3vZBb3sGfk7y9kBr81prjmUrPhlv5hd7AGXRWYd8DZcjc+O7KC7o\nOIi1yJJzhXcOg57NKTn23YqIzQ0Un9IzF7LItepSorF8lI+IBPxiRido30GOdEnGmLegyioLUYBH\ngppgP5Kts1DazUozrHSjDFZPIdP9V0h8xeOILLIo9iKXk4NeO+C1g0hWz0R9b6G3vgKR8DnUzy1z\nECkMH0UBqJcxNg08iDJ4ZYDfQy6PtUdCysctllL6LG8dsAb4JGMT35eRhfqDjD0I7EK+tdcAZ1O6\nH/K1VK7dTaPUSsGsKMNIw78Ekf03Ud2UYa7gwCQUyd6BnssW77OrgTOIV7qlLCIVbnK0HQ0AJyBr\nyg2U99/vRn15A3KtGstV6tfAD5E59eQyrhfEvUhT/pYKz1M50qmxBbx95EHsow8V3KWYa1lrfwL8\nxBhzMfAdipvZJ6gIq5Em8FrkblWJwqIbBc2vwSfjTilRj9iRRkYvmrRcQHNlxnJF6cIF5gaQ3O1F\nyQt6vc8nI3I6HRHumUipNgsR72k01niVRVruHyNr+p0UJte9SPHzDCLwJwGfoZ6UsRiZPR4QQ1Je\nKnbhk+yxtMObkDvAe4FjCuyXRi4ZT6FB44wy7y0Kd4tn0AC2BM3kLye6Us7FYiLSJgRxOiKgFzL+\nu2kaaVj2I9K8A5HwSeh/ORZ5SiygPE1/Fk0WH/WucT5yQyk00bLAk8jV631Ubq58DgV1fpJGsFZk\nM0X0qQsuV3P4s/8X3mMno/3TlqA/LyestQ8ZY9qMMbOtteVGySYoiH5U+GQbymBVSr/djKxCDn3o\nnTmE6iEEyXiC3NiOUqbejdwpX0d1q6Rm8Cs4D4eWzg2yH2mIc7V2NMYPIXkbLjDXicamkxEBn47I\nbCEZ1mjj1WZUPA4UU1HIS+BlVM15EN9NdAEqTlRfFCWzxwHi8SvLxn4UcHgthUk2qBN/H0UwLyuw\n3yZE8meh6or5gupqhXOpdYnz4nAeIpNb0cDaSFqHUmGR0HeaFpe5ZL+37EPCfjbqZ+cCbyVaAvAU\nssacTuHX3qJCF/egAelDlF7tNox1SEvzXmpbxrwAojGFrgFWGGOWoZH9JpSw9wiMMcuBTdZaa4w5\nGyAh5NXCiygv/5nAFynduvc4CtxsRWQvi9zl/g+VvwPNCEtuwhtcz+DH86SRUsHgp2W9H2mV5yD5\nlg21GUgGum0bWs5Fr5aLEUqH1peiufEEZGF1S9dmeudxVtg53rprE5ErXZT5vhsBLvnDo+i9uA4l\nTRjrNy7Af7agSctvVukeS0SNMmZ5+3wdmegHgPdba58pdKwXT+QsoDOAHmvtSm9seBGZnAEes9be\nWuj+ElKeExZ4AmkJ30hx2VYmoQCIfL6yfcgEugGR/NOof0XNRkc38C1EWK9AFoV6a1ndIDSUow2j\nd7gP3/zp1tsRIZ2N3tnZyDIxGw0c1fxdLcjvfCxk0PPuR3EKp1PZQJVC/osve9ePqgx0BIhAwFtr\n08aYTyBB0Qp8y1r7ojHmI973dyG/rPcaY1KoI9xc8YUThJBCio4nkVXn9DLPcyUi5c4lZS4y8zdr\nWlaHLHqnXTuMH79zmPxa5EF8a2yQ9AbXO73zu5gegz+utaBxcRkyIs3Dj10x3vetgX1dM6HvXQvH\nDbUF9k8gDOBPLi2K33kHxVnV0yjmx+JPTI+hHkGdORGBzC4mY5Yx5hrgBGvtCmPM+cA3gAsKHWut\nvTlw/J8inyeHDdbalcXeY0LKj0IP8B9IIH0ERUQXi1yEvAdF6m9GZqPPUV3/7EaH04CkkBAILoNZ\nU7IowKQNaZd/ggITFyLhPg9fo+JaGyLGwc+C+0xAQsudPxNan+Rdy2V1SYfWJ+MXBZmABJ1rbnsy\n8tPuRJpuZwJthoG9FcU3HENlZDyLlANPof/kUzRcZpt0NAO5tfZnaLYd/OyuwPofA38cycUS5MBW\n4N+R7P19yrMuWeRa9SOkAOhF/fbTNO57m0UT/kOBpVt32zDaNWMKkkVTAuudaPLhNMeTGa1FLvX3\nv4wMSDNR7Ei9KkjHDdsREV+DgvXfRWnJH7agXOUzgC8ha+k/IQVigyAamT1mxiz0o78NYK1dbYyZ\nYYxZgAhcwWONMQYFYF1W7g0mpPwIhvDJ83IUHFTJzGwPmnW+gKppvgd1+GZFitza4WAbCSzDvn3D\naADYiwRFG37WFLeciwYTpxlxBB188tzl3YvF16Y4zcpk7zqG0doWt97hnbclz7LdW3f31B5ab/XO\nMZ41M5WkdsugNI2/RM/qCjRAjOfnlaA+GEIp2p5AmsBzKK+fbUSkfhh4N5pQ34407vMiudPS4dK3\nHkDWwh5GZ/no9doSJAunIj/nqcgtcpm37tpkakeMl6KsY6fW8JpxhEWuQi8gzrIFBSHfSWk8YwC5\nKj6IuOT56D26ECnAllVwj2uRNbihAn6LyZiVa5/FSDiMdezFQJe1dmPgs+OMMc+gl/aL1tqHC91g\nQsoZBh5BhPxENLsvt6iJRS/HA+i/uwil4mqoTonI7SCjo8wH0UAwiF7UgdA6HK0dDrdpiLwGffqC\npk5Haoud7BwA/hx101OQeTmKPMMJokUPIkdPoODcq5F7XQOT8aQwYhNjLQrmPBm4g/K04wcQqd8E\nXMVof9s/o/oyO4Oft9rFlrhlN5KTK7x9XYaPY5GMnYEvaxsNEyjffShBYRxGrskvIOVsG5r8vBZZ\n9UtRIg4i5ckvEU+5g6OzsZSaNtrhACL661BgaUTvUjQyu6iMWZQ/eL0DCSeHXcASa+1BL6boJ8aY\n06y1fflOEGNSPoyCIB5Cwu9jVEbGX0Q+jV1oxvouai80ne/goRzNzaydL2EwwKUTCf4JSDvkzJjB\nVg8N8VQ0Yz+XyvJjJ4geWaRlfAwRm5UoICjqgDgXePoY0mRG9E4lpLwJ0YOyW21H6Txz5dQfCymU\nmvNeJKd/j6Ndq6Im5EPIBdVlVdqBLKkno/fIBUGe5C3n5Lgnh6TjxgMuJe4uxCnWoj5zAiLiVyG+\nYihNJg4hIn4fmjx9ntJcdAshgwJ770ZZd+4o8d7GQDFd/8lVsGZVoT2KyZgV3ucYb5/2QscaY9pQ\nEZez3WfWWucqgLX2aWPMRkQ4n853gzEk5SlExh9ExYBK9RsPIotmg79CL4cz11fTbOdMm/vQS7vf\nW29BJMlprJ05cxoyKU5HM+JONOjUO2CyGLQjrWuC4pFBMqBaRU36kTx5HImPV6OkI1HHSVikFboP\nSeMribTPJtymiWCRvP4JItIfpPRiWaC0nP+GzPK3Uz33lF6kzdztXbPHu6ZLb3oRsoTHObYogWCR\n0mw34oK7vGUXUpgtQhb86xBfKZfkDiGeci/iKL9LtAqUjcC/Is4R9bk9FCOzV16q5vB3d4b3GDNj\nFsrp+QngB8aYC1AmlS5jTPcYx14BvGit3eU+MMbMAQ5aazPGmOMRId9U6CfEjJQPIsKyG6Vnmxf4\nvBRkkLB9GJGfy9CzNkgDHxUsItzbkC/hJmTabMfXrsxBZqbZiHgX85daElZSS/Qh144zKV/jb1Hf\ncgUtXDGLcOtHmX3eVuZ1cvWLfnyzqdPWXItIhsF3h4oCFmUoWoXe1UvxJ7oRvltJzZcmQdDK91FE\nbl0MS7HYj1xV9iL3RKdhP5T3iNKQQQGnv/baATQenIJ80+dy9IQyyncmQWMhKFyyqJ/14Ff7DFYA\n7UFk2+UDX4ziIxZytLXEpYMMY6x+lAb+CCnnPopPmKPo/wMoAcMLwJuRxdREdO4QIpDZxWTMstbe\nbYy5xhizAQ1+Hyh0bOD0N+EnhHe4BPiyl30rC3zEWttDARhri3WxaW4YY6zMKZUgBTyLyPgs9LyX\nEZ1bRwYNPtuQkN+GtCmuCulc/DyrCZoH21EkewuaCF6Ib7oO+u27lkGTL+fv79KYtSBLxzHePtNy\ntE6i0Sj3IZesF5FSwJlNT6A6Gj6LJp2/QoTrUgoHi92BtbasF88YY3mkDLl3kSn7mglKh2T2n1Vw\nhiHk9nQ/UpxcQnR6qGFEwNeijCMzEQk/GcnqZrBEJigfWUanv+3Br0Ph2iHvuxOQnJ8ZaLMC67XI\nTNVHtHUvsshi+j8oVfHVjM1LPpfI7CJQF025MebtiCGfDJxrrX068N3tyD6ZAW6z1t7jfX4Oqgwx\nEbjbWvsp7/MJwL8gP55u4CZrbbAsWwRIoQ64Drl+3MBo16JK4II3dgHPoyCepcjf640ULoWboH5w\nhTXCVeRcIQ1XRW4ICWxnndiFUm46uOwIwTYTaTNc+jKXuqwck32xSHn3tgVNCHchbd95aFCpZnzE\nZjTZ3YGClirNj14EEkNRSWgumZ1FVuqfoT78OaLJfJVFffVJJKtPQo/jLSRy2iEDfAecl3P9AAAg\nAElEQVRpe0+jOTKwWCS7XbxVuI0grXYwMUI/6tZTERfIIkv1XJS9zVX/nEpjOCRESci3IleyWcAt\nRMeFxkBMZHa9ess65BB/V/BDY8ypyARwKrLj3GeMWWGlzv8GcIu19gljzN3GmKustT9HvaLbS/R+\nE/BVIivQkUZl6B9CJOka5ONVKQ7huwPsxjd1XkmiBW8GfBuRV5dNJlxYYzbqO65s8zQUrOhyqS9G\ngTrzqd+gNYQ0+M4qsxsNKMciTf4yqi8etiDNeB/SZF5LzTSMMRHwEaJJZPZm5KpiUDBoFIVPuhHJ\nX4Pe83PRWJAQ8aORQV3lJaRQuAYV36tEzln8mhLBWhbBCp8uni4VWrqWq9CbW2/xlmHlyGTvN0xH\nsjpYe2IK8bOGHESuKpvQ/3o2NR2/YiKz60LKrbW/BlCe9VG4Dvi+tTYFbPF8es43xmwFplprn/D2\n+xeknvg5Gsm/5H3+I1RxqUKkkebuIURUbkQuA5XgECLhLyDfxhOBC9CsuhFTWyXIj5vxK8sVgyxK\nETUVdfETqnRf+a59CMUmuHYIkfFFiLRcgrQdtQo824p8xnuQZvxV1HyAi4mAjwqNL7ODhOGNyLe1\nEsLgyOWjaMK6EsUhHUNDp/qsCM76N4SsfM76F6w9ESa/Heg9dgTZOf66/b6LMsS1I2uFK+aW9doC\nZJXLMrrQm1uf533fmqO1IYKcwk/BG07J24af/CBY5C1c+K2aVshmxzBSnjyCgpTfTl2ClGMisxvB\nrhLEIpTWwcElbU8xOm3NTvya3UcSvXuO+L3GmFnW2gOlX94FcD6AtJ2VuqlYfHPnDkTAX4MCOxrt\n0ScoHqUKpBYUK7KI6vzvWWRSDfozduFn6JmIH4+wCJmWF1TpXgphOxLuB9BE4Ezqpm2KiYCvAeos\ns11q2/uJhjAMAquRQmYWqgVyKs0lry2j61A4NwznUhd002hDSqKg1thV85yINMQDjCa6rjlt8mx8\nJUULsgLjfTYLucAtRKS8Bb+gW65lsNibW29l/E6EGhkZ5ClwN+Isn0WulXVCTGR21SSNMeZecufF\n+YK19r+rdd3y4LQiD6BMJm+lssqGQygA6EkkTM5FCqUGKzWeoIYotz9l0CDaF2iHGV3Zrw/1LefH\nOB/143MRGa9nv7NIM/4wuu9zERmvM8mJiYAvBc0ls9NoLnAfCjT7DJUVFjuAiPiTyE/8/dTMV7Zo\npBkdTOjWg77OTj60Iy3yHERunSvGZESOp+DXowhqjSt9L7NozJuHDCMuK1mC5kEWxUzcg8TBe6ms\nsmdEiInMrtrIaK29sozD8iVt38lo/xH3uTvmWGCXl7x9en6Ny68C68uQ6d6RcedasKyM23bYjfwO\n1yOt+Ju8ayRCqbnxQ9TlziYagusCQZ32KldzBHwQDZyuZHYn0lYch0/CG7G6nwVeQUSnH1mIzqA8\nkdOF3t15RPYuxUTAl4LGlNm/CKwvRxq7tSiIcw4qWFWJa+FWJP9fQRrdz1EfbaBFZPpAoPUhS5cj\n4INIBkxn9Lsf9nfupH7yoAX4AnqG4Xd1M7JoXEllSq8EPizqI6+gwNrpFZwrSMbbkN/4KZQnc3cC\n/4lIfURBpjGR2Y1gkwv+4z8FvmeM+Royca4AnrDWWmPMIWPM+Sjh83uArweOeR9Sm9yAylXlwWXe\nMouI898ibcGbEMkpp/M5TeCDSCAtQaVlo4x2TlBfbERpz+5HWt7zEDl3ucODLYVvJnbZVwYDzQ2+\nTnMVbrNo7oAi92497G078385/r17EWHagtxdzsEXWQ9UdJdJnvKKUEOZ/YbA+kvAX6C+9Hb8MvTl\nYDM++TgBxQ3VwqI0iHL970HWrp34Oas78FPlzUKTjhX4BHwKzZHNJJ/Fohu5tryCuoobd4uF8zsP\nBn2mGB3cGWxzaDxrRxToRc/QNVA/Kfd9iJKM70NhIxtRmsSgzL6nzPuLF+qVEvF6JKDnAP9rjHnG\nWnu1tfYFY8wPUTRkGrjV+onUb0XptSah9Fo/9z7/FvAdY8wr6K0fI4p/AOWMnoA6zfGUT8Y3IE3g\nYSrTBMYdLrreBQo5gRsWwO6zcFBQMDCoxTuHJXfgUCsiyuHzuutPQoNkULiPeMcCPOU1EGmehJ91\nZQIi063e5zO8ZbA5M/F4s56MIDL+ECIPr6N80/V+FAi6GVUMvY4kEKu+qK/MtkhmdyHCcAblvz+b\nEDnoRn30N6henMc+5Dq/J9D6kWZ7AfKzvoja5quOChlkrQjK7XRouw1fEeHiXlww6Rb8+F4nF8My\nfT4yrgTlvgvybEFW7S7vOi7IM9hOp/lJeQr9xt2oP61Dz3E5ShZxBXoly3kfMkhm34OeaSVk/KB3\nnvUoeP9GIg8GjYkiJYbFgyzKPHEs5XW+LNLYPIiEzyVIE9hM2syokGa0FjgctR/WIrsc3k6jHGwG\nCdKZaOKUK9q+FZlr+8kdEGQC3+cKJDKIMI5wdBS/W+/w9g8K9x8i7UQ7cqG4Fg0YCTRgrEGDxamI\nMC0r81zdSAO+AZHx88gv2CssHvTdMuTeu5qvEEUzY3TxoG1Iu1qunN2ISMMBRGR+o4Jz5UIWvQsb\nA20mIkwL8Un4TJpD2z0WssilzMnOdm89uO3Is5PBL6MYAOt9dibKvDQHvedhWe0CP1tD52oWbEKT\nj5WM7RKVRVls9iACvstbduP3oeXIVWsxlfWhg8hItdo716uR60s5z7YPGbqe8s5zKbIC50KFxYNi\nIrNjqNY1lJe71pHx+9FjuwQVjxgPAhZ8V4x8Ps793j7d+CQ8w2gtsPNldFrjDqQ5DmqSXcqqsFaj\nkSc109FvfxNJ4BJoErUeCeIe5Gv/Mcr3ZzzgnetplCb0GqquNYyJf+L4Qbk+yJuQOb0HkfFziE7W\n7EPuGI6ET0aW19PRxL2OmSqqjhZkaSgFh9A4egHwesa/i+cWZE34BQq4X4nGSGdJ6GZ0DMFyNM4s\nQhrr1yElUBQ0zfGXR737Ohv4KLnjuovBACL2v0Lv1O9Q9f8zJjI7hqS8VGRRqfFVSOi+HvkgNhMx\nc6ZDF63f5633hdbnoZctl5/zEvzofWdu7KC5nkO5uBlNKBp54lBtWKQJfBYF2i1GpvcVlP9cepDF\n6UWkZbmNmhXPiomAjy82IzJ0APmln0U0728X6v9rEbFajrS9byGaqqHjGacCX2b8FcgLZshymXF6\nUB90lZx3e80gK+sKNN6ejMbXWVQn9/dBpOx4HI3dFwLvruBag0hmP4ysop9GKTFrgJjI7ISU54VF\nWpBVSJg3OhkfwJ95O6HgWi8i0cuQAJmGZrXzAutTaS6fxloinzluvCOLAtFe9JpBWsAPU5kWsBf5\nnq9HbgSfpObPOCYCPn7Yhsh4F9KMn0vlZHwPIuHPITl7JvA2JE/Hi6W0FnCxNo2ODKNzuweXToEV\nTD85gOTXYiQjZyCr4fGoP6YR1boaBb5Xu8843/N1KD7nfBRnXUnGm2Eksx9EWvxPIbeaGiImMjsh\n5UfBIt83lz7xchRQUQkZ34/8ZC+o7NYACYC9jK7QuBdpbU709pmBTGCn4guIRkuZl6AxkUHZhBwR\nn4SE8I3I1FnJe9CHBPtzyHz6CWR9qQNiIuDjg52IjO9AZv8PUNnwdhgljXkOEbEzUKKYpTQOEXdF\ngg7hk8VjSOJdykEG+Coi3yNIQRXM7T4FPwXlYkanqM2XIWsAuBf5g78PubBUAxb5oDsi3o+sN1cj\nS04lk9IRVMlzFVJKfpy69a+YyOyElB+BRX6BTyGN82VU5jOeQeT+SaS1WYk0j6WcL+MduxXNuB1h\nmuu1eYiIz0XColG1+AkaFy5AbavXutCAdAoqoBKFNqQXCfbNaJBogJShMRHw4x97UMDaM0iB8h7K\nV0C4JACPohRxp6PMP5UQ8ay3LPV453LYg1wQnNUzjQiYI+Jt6F1yFs8ZJKS8HLQCH8J3z4xi4jUZ\nKR6insi52K5NSKb2IsXfq1Cq0GMjuJ6rlPsycnX5KJpc1BExkdkJKceijv0rpHW4HPl5ldupDyMf\nrjVIUJ6LIpuLedQppPHZhgjSDu8cxyKStAQJ3YR8JygXufrYVNTHTgauorICFEF0IzL+ApqUvgf1\n5wZATAT8+MVelE3lFSSzb6d8P9kUIvWPIO3mhcCbkRa0UvwEjQdnIevQMvyxJYXI1L7AcgSlUXQu\nhzOQq5hbzkLvkiPh1fBDjiuqockuJQ97PmSRP/pmfCIOco85DqUgnE80vGAA+Ys/ghQob6Sy4lwR\nIiYyO+akfCvKptKHUvmcTvlkPFjN81QUHLioxHOsRybTpcjX9q3UzbyfoMmRRRq2oIvTPmRtaae6\nfawLualsQpPS22g4v/yY5Lwdf9iPXAJeRBmwbqD8WJheZMl8EE1KryL6jFpTUarYx71ruVoKbagT\nuiJBc717mOPdxwwSl8O4wSLrRxeyALllO7KSHI+Uc29E/SZK5dwh9B6sRkrEjyNLfAMhJjI7pqR8\nO9KMH0CzzDMo3+9qO+rMe9AgcQXlB7Oc5bUECYpBFk0ogybuPqQJ70ZEeB4a8I9HMQ1zqU4hHou0\n7g97178ApZCsNHjYFen6NSIrERGVTDSnSVArHEBk/HlUqO12ypezB5Ay5lkUePcpossg4dzBtnvt\nJfzCYxn0PpyDcvAvIN4ZneIIi3y+g6kQB5ECowv1h/leW4SsK/OJxmqTC642xNPetT5D/oqspcC5\nlZ1P+WkXQ4iJzI4hKe9HJsULKT9NlkVa9gfQS/UaFAiXaDYSRIlhfN/RYDDXAfysOq5yqDNxL0Xm\n7bnUxrQ9jIKL1uD7ot9ANO/CJka7lUVIYGJiCm1+uIqQ30V9+zP4ZHywxHPtx0/BeS5K5+asRKWe\nKx/+zjvXYmT2fy3wX0g7fhbKwe/ejZGIrpmgcZDCT43oZPYwsqQ7xUkro12S5iP5Np/8Vsuo+ido\n4vgyssrvRuPFp/CJfyXX2odk9kbU9ydVeL4AYiKzY0jKp6Dgi3JMPy4Y9EHkO34xlWnZE1QGi/6H\nZipCkUVC6rDX+kPLYO54l74y2BYiE/tM6ptVJ1jJcxmyEB1PNKb/rUiwH6Jyt7I8iEjAG2OuAv4C\nCYFvWmu/Gvr+XaiyhkF/7Mestc9Fc/W4wAC3UL65vgu/UuwFwGepXmq+32T0sDqCyM8VKHtFAr0G\nnTRPbJRFLkhhme1SImaRZrgXEXAXeDsNyeg5SOs902v1Sj18CCWyCMa7vYNoxpBulKHlJaTwvI4k\n3qE8xJCUQ+nCwJHxZ5Bv7sXI7yoh4/XFXuAbKCDldZTuw18uXEGIYSSs3XIEBcoM5llapC2ZgJ9K\nK7iczWiBPpHGGrhGkJZxDdL4nENllTzD2IHcCqJwK6s+jDGtwF8jxrUTeNIY81Nr7YuB3TYBl1hr\nez0C//dEkxs1ZijnPXBkfDsiINdSfUIUHlI7UOaKBD6+hrTEr0fB5bWScRkkp4MyexhfPrsWltvt\niHwHZfVUb30xkn8XIJk9hcZJmQmaMGxEMnsjytDyLqIbKw8iMv4iclX5LFV7xxJNeQJhMyIKg4go\nvI3GeunGK7LoLcwElhnvc+st96EuvBGVDp6DCPocJCDdvm7/FkQsw+d0rQ1pP1JeGwmtT0PpyIbQ\nQDIBCaAJXnOZcSYhf+7Z+Cm2Jnv75stp26gYRhkuXkDPeTmVV/IMYxeK9t/lnfssqi6aohHw5wEb\nrLVbAIwxP0AqoiOk3Fr7WGD/1TRMKoPxjP1IZm9EroVvoTpxFAlGI4svS9P4MjYos90++4B/w6+D\nMBffrz8ot0HyNxM6t1s3aGwewZfXQbmdRZrtYW9/J6sn4svuqah/TEKWyMmBNslrzaT1zaDxcD2S\n28uQ3L6e6AjzIUTGt6O0zJ+m6sH8CSmPO7YhwX4IkfFXkZDxQkjhayGCbTiwPuJtO8E5ElqfhGbe\naSSQ2xDxc8u5SENr0H/hBDP4Od33I+I7x9vHBPafgQR0a57WgcyLHUg74pbBdSfUx3P8wCAyQ76I\nJqUuJecbiTZTSxdyU9mJT55q9FyjieRfjEYlhx1IXZQPtwB3R3LlBDlwAPWnl4BXk5jQx0IGveu5\n5LZroDEwKKeDbS5+1coso+V1G3pF9iL56+Sxy92eRkqQJ9H/tMg7zgT2nerdY/C8wfV2fDc+J6fD\n8tuR8HYay/IYJdJoEroeBcXPRNb83yTaypuHkfvuM8hS+j6qF4QaQpJ9JS7YhDqzm533eNstwBtQ\nSeVm0mxGhTQisM5vzlWOG8jT2tAzm5inBTUSQeEZ3G4LNCeUC2EvcJe3r0sltrTC3x1HuIwRWxBB\nfhnlvz0VEeWo/W/3IS3LFqQZfxs1n+QUE8m/fRXsWFVoD1voyyCMMZcBH0Q/OEGk6EH9aT2aEwWD\nQeOELJLFfYHmSsS7MvHB8vEjKLhwmKPl9SR8y55LzzghtAwqLRxZLob03uHtNxGNsWeQKLxKhUWT\n0C1Idj+NMm2dhgofzoz4ev0os9YaZMm8jZrHckWUfWWsOCBvn6+jkqgDwPuttc8UOtYYcweaAe3z\nTvEFa+3PvO9uR7I/A9xmrb2n0P0lpJxdqHJVcHydh5Ra41XLMoIItsvg0YtI+B78AJZhfP+5TvSS\nOxeNRYw28U1GArrWWohORB4v9JbjVQsSNTIo6n4rEurb0bNcioIq30x1+n430rK8gjSZ11bpOkWg\nGFPowkvVHB6/M7zHTlTRy2EJ0paPgjHmDOAfgKustQdLu9EE+dGH+lMX0sh+hobLhx8ZMvgy28lt\nt+1kdj8iuq78u4tPmYI02sGy8c6drh5k+FQUFL6SeCq8yoFFVuAtyHq5xftsGXIfuY3qFGYbQPxo\nNRobPo44QB0QgftKMXFAxphrgBOstSuMMeejwLULxjjWAl+z1n4tdL1TgZtQp18M3GeMOdFamyUP\nElLO8chNxZHyucBHaG5h4fJXH0BE6ACy/exAwtz5R08PtHnoWTgSPpnG115MBt5d75tocGTQ/9/l\ntUPIvDkdkfCzkJm/mibI/aiY0GHkUn0b9ctA4CEa/8Q1wApjzDI0u78JpTM4AmPMscCPgXdbazdE\nctXYox/1p6cQsbuRmpnQqwaLCFB3oPUha2Av+s2diBBN95bzUEYXV92zk+YYt26s9w00OCz6z13x\noMMoy1UrIuHHoxSKs6meIsqR8ZdRnvGPEU3+8gpQozggpC36NoC1drUxZoYxZgHS/BU6NtefcR3w\nfWttCthijNng3cPj+W4wxqT8ADJ5bkBBEJtQp38nzSHYQOS7Gz8d0w78ggQT0Us021suQQRsOtKW\nJFrl8QWLBvH9+ATc+dh34hekOB1lPahFpdhgZc/zkXtRg7gVROCfaK1NG2M+AfwCCY1vWWtfNMZ8\nxPv+LuD3kZnpG8YYgJS19rzKrx5HDKKA4NUoxueTVEc7WE04wrUHWbp345NwkLx2bRmadMxApLtZ\nxqUExWMQyehgBc89iJot8NpSZFmcSfXH7cPITeUp5ApzE9EV1qoQtYsDyrXPYuQiUOjYTxpj3ouU\nNZ+z1vZ4xzweOmZxoRuMISkfAu7BT+FzGxJ2f43IStS+WFFhBGlN9iBBvsfb7kQR48eggWqW18ar\n602ckUWa7oP4ky9nCTmIXIhO8JbHoECcedS+L+xCbgXb0WBSLXeYChCRf6LnN/iz0Gd3BdZ/E/ka\nJigbFvWnR1AKvVtpXDkdRBq9n7sDbQ+yQC5E4/UK9I7MQpa/RFkyvhCs4NmdY5lFdSdaEQE/hepW\n8MyHQ0iB8iyKo6ujm0o+RCOzi40DKvVF/AbwZW/9K8CfIR/oku8hhqS8HQn0TzLa//BTNJa7RhpN\nqjbja/GHkDBfgIJj5lN3N4AEEWIE31e0l9E+/z1IE74ECXJnBXETsXoWpQDJmc3AY4h4XAS8lYZN\nRReT9FrjAy4o8MNEm0kiariA6U1eO4DeCyezX+OtN1OxswSF4Xz9nYwO+vy79aVII+6s1ivwrSH1\nnojtQ9lvngHOpi4BnMWiGJm9ZxV0rSq0RzFxQOF9jvH2ac93rLV2r/vQGPNN4L8LnGtnoRuMISlv\nRcV/wqg3Ic8iTYoj4TvQAHQ8qmq4hIYlOHXFRhr72bi0Yy77gcto41qwimcavbMG33d0aWB9Go2X\ninEIeA4JdouIx01EL1pSRPrbE1LeZCiUabJesEjbuSnQJiF3yJXIBbUWbmLNhh1InjUo+SOL5Fow\nY00vo+W2a4OIZA/i+/ovRBrvGV5rMCshGRRXtBq/GOKnib6vptA4ENGkoxiZPedSNYfnjgrOHzMO\nCPgpKvv+A2PMBUCPtbbLGNOd71hjzEJr7W7v+OtREIA71/eMMV9DbisrUInfvIghKW8kZFAGjOeR\n+8FhRMLPA95Ow/jfNiwywHeQ0LsIDdzVEIAZ/Ly8wbzrwTYYWAaF+RB+8SCXXmwiMk/OQwOTC9Ka\nRPOYr7uQfFuH+uw1yAc26vsfQG4L65B1KyJiHpOctwmihkWWoHUoHqkfke8TUcxEg5n8GxI/QpOZ\nlSh9XzWeWRY/t3qwcqeT0cH87CC/7qDc7sDPVLMI/e+dqGbD1EBrtAqehdCHZPaT6Jmfj/zGo6aB\naeSTvgolYijoQl08ahQHZK292xhzjReU2Q98oNCx3qm/aow5C99k7M73gjHmh6iKUxq41Vpb0H3F\njPH9uIExxio/ar2RRcUWnkd+7dNQ8N2pNIefZDVg8QVosIpnOrTt9nM55TNoIgp+XvM5yES4GL+C\nnKsm14HesQxHVwtN4xcvChfIcMdO884VzuUbzu8bTD02ieYR2mMhhaLxn0CD6jleq0aw3RByhXkC\nvRuXIE2Uwx1Ya8uaARhjLNeXIff+05R9zQSlQzL7D+p9Gx66kMxeh2TFq5DcXsj4eb9LgUXPwcns\nsKx2zZHjoBy+FxFEV9htJnLlWIy4jg3sD35Fz/A1gp8NM1pmp/Fzqc/y9g3K7OByCn5Odie3x0tQ\nreMbq1Eq2tMRGV9YhWtlkE/6r1AWu9cxuoDxFyuT2TeUIbP/o/lkdqIprxl2ow67Hr34p6F88g0S\n2VwR0viuGbk0x+6zVkaTXidIU953SxDZC1aEC7aZyJoQrNTpkPW2+5AwHmR0Zbg27/NpHF11Llwh\nriPUIjTBNR1SSCO4Hgn104BzkXm2GgPXCBpAHkOWvg9R91RcCWKKHiSzn0Py63RU6Mq5mDUzskhG\nusJwQQ1ycHsCcnEIy+wR9AyOQxMWJ1/DcnsGGhuCFT2dytN624e9Y50V0cltV5BoArnldS6ZPQFf\nZsdxsgSjFX/rEQE/AWX6q4b1Petd635kTbgBWU0jRkTB+Y2OhJRXFWn0UjyBBNOZqCzt3HreVJHI\nIJJ9CBHdQ0h4hivEucpwk9EEw3K0NmJOYDtMeF0rV4A+h7rx8cCVNMezbXSk8Es2v4yE+mnIPF+t\nrAApYC0yeS5FFsMq/peJT3mCnLCo769GBVrOQ2TmWBqf5Llc54cCbRi/wFB/oA0imbzU+25SoE3E\n942egixVjuw64ttO+fThrxHRnwO8EcnuZp/k1BtZlO3qea9NRpPID1I9OWrR+HAP6g9vQvEUVfov\nYyKzE1JeFfQg361nUNT9JUjr10hCfRA/OvwgIt4H8Ul4P3qxXVU450O3gNFmPmf6i/q37UY+12Np\nYy9H/pwLIr5+3HAQucLtw++3p6Ey2NVMz5X2rvcQ0rq9m5r8lzER8AmKxRDSiq9GsuwCFNfTSAHk\nKfSeOpl9EMnxffhKE2cNdDJ7DlKWHIsvt138StSWrn3eNcfKAnURGjtOJCHjlWAAyextyK1qIiLi\nH0BjZ7VgkdX0l972FShVaZX/y5jI7ISURwbn3/8ECt48A70c9Urh5bQm+712CGknnFDP4keHz/SW\nS2iM6nAZ4C7vHi5FRY/yddVLqngfWSQJUoy/LAp9qL+6lkKk+ASUo7bamREyyMrxACINNzLa/7DK\nSAI9EwCSiatRX1yOtOLLqB9ZHEbyeh++3N6H5PYQfoaPmV47BslHR8TrmZ3pB8j98EKUhWlynv1W\nVvEenK97Ct+NZbxgGFlvgik3lyK5/X6qS8QdNgH3ocng65AVpUbKxpjI7PHUY+uELPASKm4xAwn2\n66ldGiSLBLer4NiNT8TB15TMRbNoR8TrnR8VfAE6jB+wk8J/+w6juiz3oCCg2Sg3ezbU2tGA5YKD\nXHPbzsc8k6e1IsuAu/ZIYN35Sv4WzRv8k8IvPHUQpcM6jMjHcah4yVxq0x+yyC1mFZp0XY8Glhoj\nJv6JCfJhJ+qDKaSMqHV10EHvHvbiy+t9SI7NRnJ7DhpPzkYEfCqNYW3NIJntZGQw2DKNKkI+gixe\nc1H2khZGy2zwg/fzye0so4M6w60dpSoMyusR71odyOpWB9kSCVy17l1Ibm9B4/ti5O7zZvzA2Fpg\nGyLjPcg6fQY174sxkdkJKS8bWZTl5kH0YlyCKnNVs6NaRKp2h5pB5kmn7V6JBPsU6k+8AR5FM+zh\nQHNBQwaRw334BDjYLTPoWe9E2l2LHzQUDB7KMjoANLjejl+mOldzAUMd+EFD7V5rhEGwFAyigT5Y\nQbAb9YcFSLP2Vm+9lr/NvS+Po//wGurqSxoTU2iCMLYhMr4H5Wc+h+q7qBxG5GoXeid3ISvmAkSs\n5qLA6TlIE94IMuclZPUdztGyaLzpxpeTbUixAX7QfRf6nVnkWtGKL7MdmXRB+GG5HQzQL1Zmu/Vm\nU56MoEmZ6xtObk9BE5qFKF5qCbW3hGxHKRQ3ovSVK6nb842JzE5IecnIIP+th5Bf3hXIX7wa5CKD\nX1BoLwqqmIBe0oUoCMlViGsE8p0Px6IBZ0KguYChfF3wTu+7eSjAcEme/eKIDJqcdeNbRty6czta\niJ7ZeegZ1susnUGBRw+h//syqhoMVCxiIuATgO9auAqZ/C8B3kl1hj+L3kfnYjLlRXgAACAASURB\nVLAbTZQXIoJ1GiJYs2gM8p0Pc1CWpQk5Wr5sVH+LiPgUJLNPp7F/Yy3hLNr7c7Q+9Lzn4/eRhdSv\nTolFmvlV6P4uR25ddaaLMZHZCSkvGo5c/ApppN+ITP9RkossIt/Oz3erd61l+NkvmtG3uRxf4fNQ\n8MhxEd9LM8Ai7VpPjtaKtBbBIK4FaACcTeOYuNMom8rDyC3gahoqy0JM/BPjDZdJ5X6kxX0tyoAV\ntabvIKOreragvn4y8HpEwBuk3xcNVwa+FKxEk/86alPrCpc8wQXhuvXDaFzvwHdLcq5Jc5BrUiM8\nL4vS365C93wJel8ahCbGRGY3yNNuZIQD0t5CtDk4XR7oDaiY0CRERM8ErqM5SXgUuLrI/VxBgWYZ\n9FJI4OUq2XwImXk3IAE+I9DmI/eoGSgrRD0DugohBTyNfErnovelAf06Y+KfGE9YRI5/iYjS5Uip\nEdVk1eWBXu8texAJP967VjOS8Cjw6hL2dTnKmwEuPbCT0cE0wX1IAbEb9QsXgOuCcY8LrDdqhW4X\nF7cKye/XIiVPI0wUAoiJzE5IeV44N5UHkJ9flAFpIyil0AuIgC1CPoUXk5RpLhW/Qin1XPBJLQVJ\niqOLJY0gYe1yuIfzui9A2pNgmsmpiMBOQ+4nb6WxUrEVg4OotPIOZOK+icjKK1cDMTGFxg+bERnv\nQ65SUQWkZZFJfz2S264A3PVowtwsBLMRsBH4HsrSciG1JasZji5uN4L6S7AOR3B9irdfUGZPQ26Z\nbn269zuaqR8MojSgzyAqeAniIY1gac2BmMjshJQfBVedahUiSNcSjQvFCPIJfwEJpcUondA1xFcb\nXipyRecPIIF6N8rScj6+sDShfV0Ll2gOruOdcyRPm4aCX1y2l3ChpHne+aagCVaunO7NJLgLIYsm\nlU8iMn4mKiARdRrQzUj7fh2RiayYCPj4YAsi4734ZLzSCboj4s8juT0VEfFbqF+q22ZEWAYPIBn5\nkNdWIjI4Db3f4f0tfpaXoKx2264qaFhWu4QC05HMHvSOmcDoQklzvGtMQfJ7SqhNpmGJalnYhdKA\nrkfxcNcghWOU49J+pNB8LZG9KzGR2QkpPwKXqm0denmjqjS2DxUScnlwl3vnHs9EPIuEodNGOI1E\nmsKEtwOZgsNk2S078Es2uwh9Z9NyKbFWeduT8YsauX1b0ERrhKPLQbttVyink9EVR4MV7SZ6rZ3x\nQ7BLwWGkXXkKPedzqU6hFRecdwgJ9wifdUz8E8c/tqEJ2wZExs+icjJ+GPXtNSgeZgHwIUr3sW4m\nOOI7yGgt8hB+1hWXctCR3f/f3rkH21XVef6zyOPmSW6eJCQhCRAeASEQeSgDCSoQ0AFUFO1qW9CZ\npsehtWuqenz0VI1d849293TZTldbVrUzrc4g7ZRdDtZgt+iIra0QkETCI4GoN0AgIeSdm9zcx1nz\nx3ct9j7nnnPvubn7nL3P2b9P1ap93mfvc9b+7t/6rd/6/WIbCdt6To4RZBQfIdHimAo38kRoPUh3\n03rt0CziEerr9VQ0WKogLeqlfvXR6DjpoZyaPYRsmy3IiXUV8EdkXxjuDaTZL6BwplYWnutOzCh/\n0xj/CTpxb2Tyxvgwig9/EmXEuAK4j84NTYkxdcdJyjXXhmecQCfgbiTaaSGMRuyC8FlRMGsN31js\noVZ40wJc67H4EfK2TEMr1jejcCAjWwZQyNWzKF3XGmSIZx2ikl75fwxNqb6FwsU3GjmzGy3gPIAK\njL2HyV3OYoaWJ1A/X0cSgtWJRlwFGdVxvUptKF1sU9AixAGkrWnNnokM3bQ+z2O0oyKmsm3k6IjG\ndeRF4H+Fx2eh9UPr6MzfuciMoLUVzyJjeRo6Vy4ge89/rTH+Hxi/susEKYkjpcRGea0xvpnJp2o7\niDws29A0WMwgUmSDYhB5IQ4j0T5I9eLDGFs3CxliJ0lCMWahqan0/eiNaNcxx6p2N6OwFSM7TqIF\nQM8hQ3kVunjeQfZxoG02xkuyaKj7qDXGx6r22wwnkF5vQYbKVShksaiL8kCdN2r2cUZr9tHweA9K\nixorEsd2Nolep8Pq2mUOzEUOqhvROd5NoSF5M4zCY59FReIWoEWbG9G1Mmv2o0X9z9EyYzxSEs0u\noVFea4xnkaptD0r9NoSm2u6lODGHFSTgB0jypKZT7J0iKd0cK68tQ6PpuKhlNsUdWFwZmpENR5F3\n5RkUGrCGZEFbKwyVmCnjJ8iQuBEZ/i3ubyWJT+weao3xyabdO4yMiT1I++5AWbWK4K2NKVHfQOfj\n61Rr9nE0y9hLUtVxETpX42LEORQ3Q9NSFDphZMMAydqHnSTVu99B62bnX0cOlF3ofNxMy4zxSEk0\nu4RG+SnkGZmsMR6NiZ+hC8XbkXGYV9aMfjRqfQN5TmIxmcPIG7IQeU1mIaM7ptqbjXkqyswxJOh9\naPr+JFokdzlwF60T2phD+lF0UbmBthYbKYnAdz6xFPtjaHAY84zXxiU3y+vIGN+J9PouZMhC+11x\ngySaHfU6avdUEs2ejgzu6Dw5k+YGJNbJu5NTyGHyW6Tb+5F2LkOG+Jmp12bdB/ahKuZ9wLVofVxP\ni76rhpJ05xIa5TOBj0zi/bFc+L+gXnIdOiHa9VNWkHDvDW1f2A4i7/ZUNGV1ORL1+XReej2jNXiU\nujBWie1DXrdV6KJ/FQq7aqVh7FFM6U9Qn92IPONtHhiWJD6xO3DA+yf5GS8jB8orKEPTp2hfiEqs\n5hi1Our2ERTeCNLqC8N2QRv3zSg+x1Gf6SOp7r0czezcjMI3W21/7EXG+G7kgLydxBhvEyXR7BIa\n5afLMFq9/FPkbd5IaxZM1NKPRsW7kYjvQt7tpaFdGba9FGPq1SgGw8iDsrem9SBDYAGwAeVYbodB\nXEGLn3cgg2QjuebELUl8YrmJA8DHSWYz76L1YR2DyPjfjWainkf9/Cyk1ReRpIoralig0X4qyGkS\ntfq1sB0mKRz3LmSEtys06WUUWbATnT93kpuTrySabUb5uNRWKLwDLShslQF8FIl5bEfRFOYqZMTc\nQctjt4wO4iTV094HkLi/gGZJ4uDtgrBtdyrOISTqPw/ffR2KT885ZKokU6HlJFYo/Gf0R2+itQvu\nB0gcJ7vRoHMpiWbfiOK8DQOkiWm9jpq9A82QRM1+a9jOo70OtwoazP4Lsj+uBW4h9xn3kmi2GeUN\nOYXSYz2GRqatqlA4gqaldqJR8itIzFchL/hZmDelzIwgYYzZFg4jT9zLSMyHkdd7YWhr0eDxveS7\n0OsEOn+2oPPmTrKriJsBGQm8c24z8CV0kv6t9/6LNc9fBPwPtDLxT7z3/zWbbzZGExfx/xT9HTcg\nD2PWA0CPPJg7kHG1A/XxVSimdwW5GzBGjsSFurWa/QrS7OPIYRI1eyWaNdmMZuHzIkYD/BydP9fR\nlkX3zWJGeVk5iaY7t6CFoB9BhnGWDKAwlB1hG+MJr6T1Mb1GcRhCAl2bgnIIXfRjpoXZJIu8elF/\nvAD1m1hsoygcQOfOr5B38qOoTxeMDOITnXNTgL9Gc8p7gCeccw95759PvewA8IdoVGK0hBFUnO2n\n6Fy5CTifbM+LYeQF34EcKFNQ/95AppVmjYITa3bUavYIqpR5JLTpSKujbi9CxvfC8FhBDF0gSQv6\nC+TQ2Uw2hRMzxmLKy8Yx5BXfi6YaP0a2aQ1PoJCC7cjLeQ4S9ZupXi1tTJxvIC/VNeQX2hMr4sVK\neOmiSrWFlkD9bAgZ1XNT27nI6L4IiXcsPV1khlDc7FNo6v5q4BMUul9nE594NbDLe98H4Jx7EFlo\nbxrl3vv9wH7n3Lsz+UYjxSCqLPsi8pLfTrblwodQhqBnqHae/C4yXgpmtHQU/4R0chP5FtUbplqz\nG+n2TJLsVLMYrdmLUN+LhnjRZ0oqaIb+KXT+bAB+B2VwKSgWU14WDqLYqWdRKrj3kF2S/REk6ltR\n+sT1yBv+Qdq+crmreSm0nyHD/O3Unwb0SIwqJGWg65WFjuWkG7VTSMjTpajTFfF6kSekXpGl+Nhc\nJPSdPCvyGurb21GO+7eiwUQHyIrP5FOWoxF2JKb2MFrKSTQbswV5HzeGbRZ45PHchozxVcjrfgsW\nF54le9Cp8wwa6LwDDXrqMYI0u1anm9Xs6GI9wmjNriDNXklSJC9q9JnISI33b6bYNTua4Qjq21vR\nwOFK4DbyDZtpkmw0e9yQw/CaL6O82SeAe7z3W8d6r3Puz5HxOIiMvnu990ecc6tJMhwA/MJ7/4mx\n9q8Drp6t4jVkjP8aGRP3o5FvFryOOv7TyEBbT/GrxLWCmEt4gMSQjUZtFNHa7XQUQx3FdqTm9gLk\nja2Ez6/NV/yz0EAx1fE18fVnoYiCdEno9O1Z4XXTqC4vPR2J9DQ0oIqlqNOtqMU6suQ4mr5/EnmR\nrgDuI19vV25kdJkwmuMoms18Chly9yCPdRYcQ3q9DenJesrbr0dIjNZTSJcHaKzZ09B/U0+vh9F1\n9RDVOnyMRLufDQ2kwY7EeRJPsSk01uypyFj2jNbsOWE7M+xn1Oqo39Po/hmPQeQU/CUaCF0CfAA5\nUrr92KtpJuTQOXcbcL73fq1z7hrgK8C147z3B8CnvfcV59wXgM8Cnwkfuct7f0Wz+1hCo/wk8B0U\nPvA2NLjJIuRhEA2ItiCBuhzF02Z10cibETRoPE71VN+JmvtTUDGMKOig37cnbBeH56LRG7c9SEBn\nhtekRXhKzX2HPMwutK+i338qWq1+Q/iMmanXnZG6bTSPR//nTjTY34+8K+8AzqN9nv6DaEDWTh4N\nrSF7qHbRrkTeciNzvo/WKVwG/AHZGMsxy8Qv0UzbRagYSpYhMHlSQdqcDseo1esYmvEqiSE+QrVm\nLw2fEQ3dtHbPCu+P6R3r6fUUqnXYAd9FBmL8jBvQ7z6H0XrdybOJeXEMhcvuRGEq60iM8XaF1hxG\ns0uFmlkYN+QQeVC/DuC9f9w51+ucW4qKedR9r/f+kdT7H2cShRVyMcobufrDc59FAd0jwCe99z8I\nj28A/g6pxMPe+0+Fx3tQUPGVyAV6t/d+d+Nv70HCfjfZeDYPIUN8G/rPNtFeY2WyVEhWih8N7Xid\ndhKJ70J0bLNSbT4adUeBjt6IHtrjPY7G/maym8YuMyPogrkztJgndxMqWNHOQlkvomwAR1Gcejtn\nIzaFFvnT2hc8CawNU5SvIlH5cIMP62grL1/NBmnrDWST0vMkmr7fgjTrGnQN7ZSQQo+M6ZiVqT9s\n6+n2dHTOpkMzZiLjd0nqsWiAt8t7fGZoN5NL8bCuw6MZ+qjZB1DY1VtQJq52ztK/ijT718gxubSN\n3z0uzYQc1nvNcmTkNBOu+DHgW6n7a5xzW9FJ+p+89z+r8543yctTXtfV75xbhy5s69CP8EPn3Frv\nvUdTCB/33m9xzj3snNvsvf9H4OPAgTDVcDfwReBDjb/6DGSUTwaPFn08jjwsVwC/T3ax6FkyjPrC\nobA9SLJCPBrgM5FAzgttNvJ8zEm1WRRsxJvifoq/sKbIDCEhjbH5FXQRvxB5VpbSXptyCMWp/wJJ\n1NspVGqugPd+2Dl3P1q1NgX4mvf+eefcfeH5rwYPyxPoBKs45z4FrPPeH89tx0+PHDUbksqXk+F1\nZIg/g1KH3oXSFxaNCvJ0HkJ6HW+ndXsqiV4vCffPplqz51DcyfA7STzoxsSpoP4cNbsf9ZULgXei\nRBLt/O8raDH0VjSBeA3ZRSFMhEcZZ3az2ZDD07rgOef+BBj03j8QHnoVWOm9P+ScuxL4rnPuEu/9\nsUafkcsZO4ar/w7gW977IaDPObcLuMY5txuY673fEl73DXRW/yOaavjP4fHvoJifFjGI4g4fD/ej\nhyVvg3CEpBjBfiTgUdCPoymk+Wj6vxeJeBT0uRRXuJsl79+/0ziOBvkvoYH/XjTTsBLF0p5DPova\njiP79UlkYLQylCCb/Fre+++j2Ir0Y19N3d5LF0zfdK5mR2PhMWTEbAD+Pfkv2vTIwN5Pot1Rtw8j\nYyZq9mJ0TsZsTJ2Q3WM8yrD+JktOkThOXg5tNuoX55LkOm/3hNwQCit7DNkR1yNpaIVN0YxmXxda\nZNTsZjMhh7WvWRFeM22s9zrn7kGrZt8ZH/Pex9XGeO+fcs79GnkEnmp0BEWwxtKu/rPRvxuJ0wZD\nVP9we0gq+bw51RA8V0eccwu89wez28XDyFh4CjmEbkXTqe0+AUaQaL+OxDxuD6KLzDJkdC8L+zmf\nJBOIUT6GSPrJvrB9nUS8z0HVBpeT79T9PuQV3wFcSrYL+BpRkkoUraEDNHsAhRQ+jq6pl6OY2nZf\n8jzybNdq9n5kWC9Gej0fGVfzQ+t0o9s4PaKDLWp11O0zkEafgwaWd5JdYorT4RiJA2UFcqCsprU2\nUSaa3UzI4UNo+v1B59y1wGHv/T7n3IFG7w1ZWf4Y2Oi9H4gf5JxbBBzy3o84585FBvlvxtrBlimU\nc+4R6gcTfc57/73wmlpXf4Hw6LrxGApVuRz4t7RvsZlHg4E9qXYCnbSLkbf7AjQqXISJeFmpoIv+\nQZLZkWHkHTyC+usSlHXm6nC7l/xDnCso5vAX6KJzNaqxk0XMcDOUpBLFBOh8zQb1/8eR9+48FE+7\nkvb1936qNXsPSQrUJciAuQJpeAekoTNagEf9JOr1QbTO4SW0qH4O0uslyEmxBK3lKoJz7TVkE+1E\n8epZ13MZi8lrdjMhh977h51zt4VZv37g3rHeGz76vyEj7BHnHCSpDzcCf+qcG0IXvfu894fH2seW\nGeXe+5vGer6eq5/G0wZ7qA7+i4/H95wDvOqcmwrMa+xx+XHq9mrk7a4lpmh6DHlbrkGzra2OjRpC\nh7KbRMwdOtTlaMHZ2W3YD6NYjCCvRHohbuwrB9HAbRYyvuN093K0hm4hxZgMS9OPPJhPov28DF14\nxtvP36IsAllhnvJaOleza9f4XAn8OxTm0UoqKPTrJZJDPoF0ejlKtXsH+YfKGO2lgnQuLsQ9isJP\n9pI4T6YwWrMvQ4O1oi04HgKeQ55xh5yBtzD+oLKYmj1eyGG4f3+z7w2Pr23w+u+gEL2mySv7Sl1X\nP5o2eMA595eol64FtnjvvXPuaMgZuQX4CPDl1Hs+iqzou4AfNf7mG8fYqyMoNdZr6M/fFL6+VQtR\nKuG7fhNanPVdjuJ6343iB/P2aBqtYRgJdz+jMyY41B+OhudjIYsYU7oYXfDjVHfR4zM9Op4nUJqu\nC1Hc4XKa799rqDbIfjLJfTJP+UQopmYPoDU+O9G50uo1Ph4ZVVGz+9C5uRZluthIkp3K6D7iAvjj\njNZtj67nR5ATZTrVawAWoPCpaIh3Qs2Sg8h5sg2FWF2HDPJmPfam2adDXm60uq5+7/1zzrlvo2HZ\nMPCJsIoflA/t71Bvfjis4gf4GvBN59yLaLXMOKv401SQuD6BPNSXobzwZ03m2MbgEErxFgV9Looj\nvBYtaDMveH3+ConZuyheGeB0caRTqdv9JPmAa7dzkXdtFqOzJcxHIr6OZCFuEaYtT4cBlOniSbTW\n5a0obWURpu3LIfAZUhDNBnkcn0AzmmuQsbCa1hjDJ1CYVTTEK0izL0Jri85swXd2A/+Awm5vQoZc\nkZxLI4zW60GSfO7pPO7x9jz0/89AA7Fa3T4THWc0xIvuKGnECAp9fAL9f5ejZEmNqq22k3Jotkv0\ns7txznn4fLiXnkLvAa5CU+hZTxt5dAHZEVo/8qjEEaQJenN8AYnnVGSUX4vi2HoYXbGzMsZjcduo\n8ly8PUTj6nVDyEg+FPbJk+T4jduF4btiTuB621l0p0ctxrNvR4uUFqHzaw3ZHu/n8d6fbtoqr6nV\nibLmtL/TmDjVmh2n0J9EIVsbUJhKKzT0CNLr55H38xLkqDmXfDJcdCL/E+nANGTEXofCemLlzYnq\ndoXGmj2MtKWfxpp9JgovGgiv76Fat88Krx1Ls2fTuQ6SsYjr555BA92zUZ+/hGwHF6bZzVC0gNM2\ncBJl4LoAeB8KdczyP6ugDv48EnaAi1Eo5kq60xCDpDTzSRIvRL02FV30hhq0RSjUYSTV4sBxmCQd\nFMiLXFspbh5JCEjtc2cgZ1+F6mpz6Sp0U0kuJL0kletqK5CmiyN11DnfAkbQzM8zqM/HBUq30b6F\nmxOlHF6X7uHr6Lx7OxObQm8Gj7KhREP8cPiOa5Eh3q2L6CtIk8fT7DNI1rLUa7PRAsV6mj2Efs//\nG+7PRL9nrMYctXlqeE8j3Z4VPqtWs+N2FtLk6KWurTwaq0bPSH1/mfHIabId6fY0tHDzXtq3cHOi\nlEOzS2iUzwT+iGy94jFm9lfIOJmCDPEPoRF4pwnACJqyizHP6dsjSGRP1rQh9NuuRALeU6fFqb9e\nEkO3tk1JbWP7C2Twn4G8YxsprrFXFoZQyNcLyLsyDxnim2j9ArsssIWencVHyH4m8wAySl5FM5oX\noXCLVXSeR7SCNLK/pp1A16cDjNbsU+g3XYUcJfU0ezrJmpZGmh0dGWnN/t9okDMVzZLdhAbrRn7E\nhcm/QZECQ0izP0xn2indSQmNcshO3N9Aov40MhgvB36H9qVNnCgjyIt8rE47GrYOHddMJMZx2i62\nBWh6a2ZNm07rZgGWhO+9EctkkBexjPOvQ3sZ/S/rUFqsIsQcToRyeF26h6w0ux8NIp9GIWiXokH+\n2RTTKInp8+ppdtTtOcjQigZ0us1CTpCFjNbsGbROsxciY/xmircOqEwcoXpNxCzkEb+D7KMEWk05\nHCklNconQz+a7nkaeYzfgkqRLyP/Dj6MTsLDodXePoYWEvYg4za2VanbUciLFGZzT947UEJixcGX\nSAzxaWg6fwNKmtGODAKnkBH1IjrPitQvjc5gCGVoeRrN7lyADPFzyd8jHusM1NPtI6GtJFkgHttS\ndBxzkVFetHjnMbNrGi3jBIqd34U0+wQaHJ2H/pN2zGKOhO/ehrKnZuWwKYcjxYzyphhGRkGc8pmN\nvLZraL8QRhE/UKcdQ9OMZyLvSG/Yx16SVeH2lxv1GEDT+HtQKFZMKX0x8qhspH0zQBU0GNiKpsBX\nozShWVIOr0t5iYvXtiED16HsWu+n/XmgPTKO6mn2QXR+jSCN7kVe+4tJdLtbY9qNyTGM4sJjjvxX\n0Ez4pcj59j40eGuXI2M/Ot9+hfrtFWRbdbQcmm0W2pjsRZ3sabT44Qo0Xd8uUR9AJ93e0A6gbAAx\nw0ds54btfIrlLTGKR5wOj+W+D6BV7YeRgK9Asz+3ImFt5+zPYbTQbgvyyq9H3p1WlJMuh9elfBxF\nRsG2cH89WufQrkxXw+i82ou0ei+a7TlCtWZfkrptRrcxHgOoX+0nCUnZR1J4aDXKcLOY9s4mDiDN\n/iXS78tRCYLFLfiucmi2GeWjOIHixLeG2+tpT57OUySj3WiEH0Nxu0uR92QDGhxYPnNjPDzymhwg\nMcBfDw0kmrHs93q00CePAd0g8oZvRX3+ShQa0+oY33J4XcpBDE/ZhvRzHXAnrY+ZHUGG96uhvYbO\nt/lIs5ehxaNL0Oxq3uGNRvE5ifpQNMCjdp9A1/4lKJwp1uzIo/pnBTlytqGF/ucD14dtK68h5dBs\nM8qBJAZqK/Iinok8dFnnVk7Tj6boX0JxjvuRIXI2EvJNaCBgnm+jETHd2GGS8s2Hwu3DyNu8Gg3i\nlqAp8cXI85yngRCzFW1FuadXoAHnhbSv6EY5vC7di0eG8DYUWrgQeek+SOs8z4PIcbI7tD3IAD8f\n9eG3osFtpxaOMVrPCJrNqafZh5DBG4stLUF9agkKZcp7Pc1BkvCUmShy4FbaVwyuHJpdcqN8PzIM\nniaJgbqE1ixgGyBZAb0bnZgr0CLLW5AxbmJuRAaRp/toqh2p2Z4i6TexfPM54XYvxZtRieEpv0RG\n1XpU9DGPIlrl8Lp0H8eRXm9D58h6tBC8twXfNUziNInhAmchzX4b8lh2Qrl0oz0Mo/4ZdbtWr48g\nZ9x5SLujZl8YtguQgVukGZWTyHHyLJrJvAylUFyaw76UQ7NLaJQPoZHeVnSSXAb8Hq3JoXoATe+8\ngLwqK4G1aIo+r3CBMlMB/hkNvFoR8zYWI1SXb44lnPtJhDzdKsijvQBd+OeRrB+Ii3lnk7/3ZDyi\nqD+NpmLXAbejcyHPi085vC7dQwxx6kMzibehAWjW/b8fed5fQIb4IqQX70Sxuxb/3X6eRDq4hvZq\nRsz9HrU66vYAsh1qNXsQafI5SO/PRLp9NkmihbkU/7ofqzI/jc6BNcA1aDCRp8lYDs0uoVEO8n5s\nRJ0syxNkJHx2NMRPISP8GtSx84j/MhIGgUeBnyGj8EYkoGPh0f86WKcNI7EeSLVTqdsOTfnFQh2x\nXHNsMRf8grAfc1Kth2J5TCZCzFb0NJoZOhdVR1xLcSSnHF6X7uEl5FF8H9kXftuH+utONHu6JnzX\nrVhdhCLwU2T09iLNvpjxr9tpzR5K3T4VtgM01m2PQkkG0CAsanXU7fmoXyyjWrNnUnwnSSNitqKn\nkVd8MXJYvof2haeMRzk0uyhXyDYyDQl7VnjkBY+dOcaFvw+dtJ16kraSERJxHEEn21DYDqfu+7Ct\n1LSRsJ1GIqL12kwSr7MnOamH0UKVPvT/9CChHa5pi5ExABLn2tYb9mUGSbXShan7M0jEvIfu7guD\nyKuyE8WLz0aifjvFnOIvh9ele7g54887iBb0b0fn7Dy0jmc1pbwsjkuFamdEI80mvKZWq2MbT7N7\nkDc6/Z6T4XMOAN8J3zEdaTY1+zAdebErjNbrWIF0Rk2bV+exaIgX3as9GSrIEN+BCgYeQpr9+yS/\nbZEoh2ab+pw2byBDfDtJDtx70XRnN5MOwxhgtKe41vMQje9080gk5yIhjaWaY4v3Z6MT8YxUm5K6\n7cLrXIMWvRzp128PxzElfM+FyGt+Vs0+TA2vmY6dJo04jmaEdqJBznL0hOmWfQAADlZJREFUe26i\nNTG+WVIOgTfSHEeOk+3IKL+EYoRStZoK0uF0+EW9dhJp3mGqvcrReTIdneMHaazZveE7arU63p8e\nto00expJppr4vt+GfYgafh6K64/hRI00u5v/09Ml7Tx5AV2DLwLegeLEi/yblUOzzdqYEMfQQrWt\n4faltCd9W6s5hS5Yx1LbITRyTsdA95OEYSxAx1zrYZiDBiY94X4P1d6KHhJRzoPvIeG/Ea1st1Og\neSpoqn83Mm5eRxfIS1AKuiJ6xBtRjqlQYxCFpmxFXsELgBvIPnSx3QxRHc8ctbufas2O8dDTSEIv\n6ul2XBg+M7w2anXay5yXZj+Jju2taMBflHCKTsCjQdZLaG1P2nmykWJ6xBtRDs02i2RchtGIchvq\n2BtQjtBWpkvMCk+yKCW9AvwI2veXkZiDjOlYsnkOmtJbgQRwNkk83QyKf9xj8btIlIoQ3z+MLprT\nyNegjesgVqH/PO5LNML7QtuN+sKlyLBZQ+dKSDm8LuUkxsduRU6UC2l9usQsGaRaq6N2n0Be6ug0\nmU21Zs9B3s5ZdVonD0DegzSpCLNvFfQ/ePJdb3AM+Dm6lq0gKfQW4+H7kF73oVmOS+lM50n56NQr\nahvYi0R9O4otXk8+JZrHYwSJ9sGaNhWtoAadsOl2fthej4RlOsXy9H8FXVw2kf1I/tyMPy/Gqqen\ngGunhSvUn3UYRhfMd6J0nHnxBhL4LSThRDGecz6Ks30LujjawjejqNRW87yC/FJujkUFeX7Ten2I\npIDcEEnmjrhdgc69eWE7k2Jp9kMo5vtdKBwoS5Zl/HkgnTtFfb0+GV5ziNEzDwPot78EeHcL9qtZ\nTgG/QNfuEZLQnmG0f2uQbl+P1kwUqa+cLuVwpJhRXkU/irXaQlLN89+gUI28GUKZAfahsIEhFBt2\nFIl0zHO6AHk856N4yaLlqm6Gw+hYn0UZO65HF6MzSIxFX3O7UUu/tnbRUbxfu8BzqOa2I0l5VRtr\nOYji0Y8iMaydFp5BcmFNe65mk2+GlQE08HwVeVPiQi7C9nx0gT0rj51rA+WYCu1+hlB4yi+RQduu\nap7NMIIM7teRbp9As1KHkDG1INUuQprdS2dW/zyCPLPfQOGLNyJjOj3AT7exNDu2tF7XardHOtxI\ns6ej60i9NU2n0MDhderrdVz8eTbVs8Rx8WdeM8XDaJ9jBVlHotkV5Dy8BTmeOq3/NEM5NNuM8qpq\nnr9BHsFWV/Mcj6Mk5ZujoB9FI94loS1FaeZ6yf9v9CRpptKppaKXuJ/66akG0YBiH4mgxswshPc+\nHxoki4TSi4fmo9+m0cKhGSSLhNLvTd+uXeAZV+jH29NT29pYy6IvBD2JPOGxHQiP/xoZ3MvQ9P5u\n9LtPB+5GMbfdTDm8Lt1JuprnM6gPr0f9Nq/wlJMkg9zoOHkD6dtZSLPXoLjo+eQ/41o7w5fW7RE0\ngKiXBnYQ7fshqh0Z/eFzh9C164FwPy4ATS/cjPrraazbs8M+NVowOid8V1qz48L+qN8rqNbq9O0i\nLwQdQjqd1m2PHIYLUH9fho7vZXQsm4DrKO4xZUE5NLvI1kSLqVfNM4/0bcNIzF9GqeReRp0vVhZd\nh7wOC2lfXGBc+HmcJNSiXuGbE8gLvBt1pbiwM+1xiKkGpyMhTS8cSi/8TBvFX0EXhykoJ+07KUY8\nYRHx6P85nGoDqB+9gfrSItR/FqFB52LgA1T3pxfDez5KawppFY1svC7Ouc3Al9CP+bfe+y/Wec2X\nUdLrE8A93vutmXx56UhX8xxChvh9tF8bKujcinr9Cjrvzgv7shrVplhM+wYJ6WqSJ0m0u7bwzQmk\nyb8J74s6ndbtXpKUgjORxtemF6x1ZHwPhUtOQ4OQzchoNOozQLVmnyLR7GPI+F4U2tqwvYPq/jQV\nDQLfj66T3U7+mt3ovc65BcDfozCFPuCD3vvD4bnPAh9Do91Peu9/MNb+ldAoHwC+iabbLkdGSKPq\njr9F3o1W8AIqirAXnYCx2uc7SDKbtIr0cf0/NEBJr+SHZOHQEpKc3/F+upBCXLGf5YBhHurbNyFj\nciK08j/Lm3hsjyFPdxT0mIostiVoCnMR8tQ105f+NfJC5bEIKI//bPJeF+fcFOCvUZzPHuAJ59xD\n3vvnU6+5DTjfe7/WOXcNGnFeO+kvLx3fRTNm41XzbGVfOgA8jP7qmcjoXAFcRXsqNMdj24YM4LRm\nx4Wfc9Div0Gqi93UhmDMQAZ0VsxHv8GtaFAyEcqg2S8CTyG9PoTss6jX85ENchXS7F6a60vrUZhh\nHg6r8mn2OO/9DPCI9/7PnHOfDvc/45xbh6bw1qET84fOuQu895VG+1hCo3wG+k3PYfyO30frOt58\n5AHPIxNIH8lxLUFiml7Bn/fCzz+YxHv76F6B70PHFoU7tizWDczO4DNOlz7a/59l4nW5Gtjlve8D\ncM49iNxZz6deczvwdQDv/ePOuV7n3Fne+31Z7EB52IAMvvG0so/W9aVZKPzkvUgn200fOrZ5KOQs\nGuGxmmSemj2ZRY99dL9mz0Wz33HdwCwm/39FZ0we9FEyzV6KDrjRe29HOSYJ730UGeZ3AN/y3g8B\nfc65XWEfHmu0gyU0yqEYArCYxh76dnJp3jtgTJjz896BLiCT+MTlaM458gqKWxjvNStQ4LHRNFln\n9DgdZlKMMIEiXL+MibE0NOP0yVWzl6OVv43em3a07CPJkHA21QZ4/KyGlNQoNwyj3GTidfFNvq7W\nJdbs+wzDMAwgZ81u9JpRn+e99865sb5nzH0omVH++dN4z0+y3omC0K3HBXZsnUi7j+vzWXzIHqpd\nuCuRJ2Ss16wIjxlN8fnTeE+3niPQvcfWrccF3XtspdLsV9AijEZavs85t9R7v9c5twylX2r0WWPq\nf2mMcu99N+cKMgyjSTLUgieBtc651SgNwt3Ah2te8xBwP/Cgc+5a4LDFkzeHabZhGFAMzXbOHRjj\nvQ+hrCFfDNvvph5/wDn3lyhsZS0qhNOQ0hjlhmEYWeK9H3bO3Q/8E1o1/jXv/fPOufvC81/13j/s\nnLstLPDpB+7NcZcNwzBKy2Q0u9F7w0d/Afi2c+7jhJSI4T3POee+DTyH4m8+4b0fM3zFjfO8YRiG\nYRiGYRgtJq+SlbnjnPtz59zzzrlfOef+wTk3L/XcZ51zLzrndjjnbk49vsE5tz0891epx3ucc38f\nHn/MObeq3ceT2pcPOOeedc6NOOeurHmuY49rPJxzm8NxvRjyhBYe59x/d87tc85tTz22wDn3iHPu\nBefcD5xzvannJvT/5YVzbqVz7sehHz7jnPtkeLzjj83Ij27V7LA/ptsdoNum2Z13bB2H976UDVWm\nOSPc/gLwhXB7HarOMA1VQdhFMqOwBbg63H4Y2BxufwL4m3D7buDBHI/rIuAC4MfAlanHO/q4xjnm\nKeF4Vofj2wZcnPd+NbHf16NSsttTj/0Z8B/D7U9Ppl/meFxLgfXh9hxUH/ribjg2a7n2q67U7LAP\nptsdoNum2Z13bJ3WSusp994/4pOqSo+T1AR+M9m7V5L4XcA1Titq53rvY5D+N4A7w+03k80D30F1\n4XPBe7/De/9Cnac6+rjG4c2CAF5J+mNS/0Ljvf8pKu+WJv2bf53kvzid/y8XvPd7vffbwu3jqLjC\ncrrg2Iz86FbNBtPtTtFt0+zOO7ZOo7RGeQ0fQyM6ULL3dIqcdOL49ON7SJLAv5ls3ns/DBxxzi1o\n5Q6fBt16XNA42X8nMlYRgon+f7njtFL9CmREddWxGblSBs2G7j62btHtrtI10+x86ersK865R6hf\nRutz3vvvhdf8CTDovX+grTs3CZo5rpLRlauVvR+3CEGhcc7NQZ66T3nvjzmXZLXq9GMzWkO3ajaY\nbteh687/Ttc10+z86Wqj3Ht/01jPO+fuAW6jenqvUeL4PSTTpenH43vOAV51zk0F5nnvD05q58dg\nvONqQOGPaxI0UxCgU5hIEYJG/1/uxWmcc9OQuH/Tex9ztnbFsRmto1s1G0y369Atut0VumaaXQxK\nG77inNsM/DFwh/d+IPXUQ8CHnHPTnXNrCMnevfd7gaPOuWucho8fAf5P6j0fDbfvAn7UloMYn3TC\n/W46rlreLAjgnJuOFjc9lPM+nS7p37y2CEGz/993az+0nYT9+BrwnPf+S6mnOv7YjPwoiWaD6Xan\n0fG6ZppdIPJeaZpXA14EdgNbQ/ub1HOfQwsXdgC3pB7fAGwPz3059XgP8O3wmY8Bq3M8rveiOL2T\nwF7g+91wXE0c961oxfgu4LN570+T+/wtVBlsMPxn9wILgB8CLwA/AHpP9//L8bj+FVBBq/Pj+bW5\nG47NWq79qis1O+yP6XYH6LZpducdW6c1Kx5kGIZhGIZhGDlT2vAVwzAMwzAMwygKZpQbhmEYhmEY\nRs6YUW4YhmEYhmEYOWNGuWEYhmEYhmHkjBnlhmEYhmEYhpEzZpQbhmEYhmEYRs6YUW4YhmEYhmEY\nOWNGuWEYhmEYhmHkjBnlRsfjnLvKOfcr51yPc262c+4Z59y6vPfLMAzDqI/ptmGMxip6Gl2Bc+6/\nADOAmcDL3vsv5rxLhmEYxhiYbhtGNWaUG12Bc24a8CRwEnibt45tGIZRaEy3DaMaC18xuoVFwGxg\nDvK6GIZhGMXGdNswUpin3OgKnHMPAQ8A5wLLvPd/mPMuGYZhGGNgum0Y1UzNewcMY7I4534POOW9\nf9A5dwbwc+fcJu/9oznvmmEYhlEH023DGI15yg3DMAzDMAwjZyym3DAMwzAMwzByxoxywzAMwzAM\nw8gZM8oNwzAMwzAMI2fMKDcMwzAMwzCMnDGj3DAMwzAMwzByxoxywzAMwzAMw8gZM8oNwzAMwzAM\nI2fMKDcMwzAMwzCMnPn/eV0qK2mTn2kAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4224fcd610>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Is it reasonable?: Based on that you put resistive target that makes sense to me; current does not want to flow on resistive target so they just do roundabout:). And see air interface. It is continuous on current but not on electric field, which looks reasonable. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Calculate the data\n",
      "rx_x, rx_y = np.meshgrid(np.arange(-500,501,50),np.arange(-500,501,50))\n",
      "rx_loc = np.hstack((simpeg.Utils.mkvc(rx_x,2),simpeg.Utils.mkvc(rx_y,2),elev+np.zeros((np.prod(rx_x.shape),1))))\n",
      "# Get the projection matrices\n",
      "Qex = M.getInterpolationMat(rx_loc,'Ex')\n",
      "Qey = M.getInterpolationMat(rx_loc,'Ey')\n",
      "Qez = M.getInterpolationMat(rx_loc,'Ez')\n",
      "Qfx = M.getInterpolationMat(rx_loc,'Fx')\n",
      "Qfy = M.getInterpolationMat(rx_loc,'Fy')\n",
      "Qfz = M.getInterpolationMat(rx_loc,'Fz')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "e_x_loc = np.hstack([simpeg.Utils.mkvc(Qex*e_x,2),simpeg.Utils.mkvc(Qey*e_x,2),simpeg.Utils.mkvc(Qez*e_x,2)])\n",
      "e_y_loc = np.hstack([simpeg.Utils.mkvc(Qex*e_y,2),simpeg.Utils.mkvc(Qey*e_y,2),simpeg.Utils.mkvc(Qez*e_y,2)])\n",
      "Ciw = -C/(1j*omega(freq)*mu_0)\n",
      "h_x_loc = np.hstack([simpeg.Utils.mkvc(Qfx*Ciw*e_x,2),simpeg.Utils.mkvc(Qfy*Ciw*e_x,2),simpeg.Utils.mkvc(Qfz*Ciw*e_x,2)])\n",
      "h_y_loc = np.hstack([simpeg.Utils.mkvc(Qfx*Ciw*e_y,2),simpeg.Utils.mkvc(Qfy*Ciw*e_y,2),simpeg.Utils.mkvc(Qfz*Ciw*e_y,2)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make a combined matrix\n",
      "dt = np.dtype([('ex1',complex),('ey1',complex),('ez1',complex),('hx1',complex),('hy1',complex),('hz1',complex),('ex2',complex),('ey2',complex),('ez2',complex),('hx2',complex),('hy2',complex),('hz2',complex)])\n",
      "combMat = np.empty((len(e_x_loc)),dtype=dt)\n",
      "combMat['ex1'] = e_x_loc[:,0]\n",
      "combMat['ey1'] = e_x_loc[:,1]\n",
      "combMat['ez1'] = e_x_loc[:,2]\n",
      "combMat['ex2'] = e_y_loc[:,0]\n",
      "combMat['ey2'] = e_y_loc[:,1]\n",
      "combMat['ez2'] = e_y_loc[:,2]\n",
      "combMat['hx1'] = h_x_loc[:,0]\n",
      "combMat['hy1'] = h_x_loc[:,1]\n",
      "combMat['hz1'] = h_x_loc[:,2]\n",
      "combMat['hx2'] = h_y_loc[:,0]\n",
      "combMat['hy2'] = h_y_loc[:,1]\n",
      "combMat['hz2'] = h_y_loc[:,2]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calculateImpedance(fieldsData):\n",
      "    ''' \n",
      "    Function that calculates MT impedance data from a rec array with E and H field data from both polarizations\n",
      "    '''\n",
      "    zxx = (fieldsData['ex1']*fieldsData['hy2'] - fieldsData['ex2']*fieldsData['hy1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
      "    zxy = (-fieldsData['ex1']*fieldsData['hx2'] + fieldsData['ex2']*fieldsData['hx1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
      "    zyx = (fieldsData['ey1']*fieldsData['hy2'] - fieldsData['ey2']*fieldsData['hy1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
      "    zyy = (-fieldsData['ey1']*fieldsData['hx2'] + fieldsData['ey2']*fieldsData['hx1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
      "    return zxx, zxy, zyx, zyy\n",
      "\n",
      "zxx, zxy, zyx, zyy = calculateImpedance(combMat)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "array([-0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
        "       -0.00673393-0.02865668j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673390-0.0286567j , -0.00673390-0.0286567j ,\n",
        "       -0.00673390-0.0286567j , -0.00673390-0.0286567j ,\n",
        "       -0.00673390-0.0286567j , -0.00673390-0.0286567j ,\n",
        "       -0.00673390-0.0286567j , -0.00673390-0.0286567j ,\n",
        "       -0.00673390-0.0286567j , -0.00673390-0.0286567j ,\n",
        "       -0.00673390-0.0286567j , -0.00673390-0.0286567j ,\n",
        "       -0.00673390-0.0286567j , -0.00673390-0.0286567j ,\n",
        "       -0.00673390-0.0286567j , -0.00673390-0.0286567j ,\n",
        "       -0.00673390-0.0286567j , -0.00673390-0.0286567j ,\n",
        "       -0.00673390-0.0286567j , -0.00673390-0.0286567j ,\n",
        "       -0.00673390-0.0286567j , -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673391-0.02865669j, -0.00673391-0.02865669j,\n",
        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
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        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
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        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
        "       -0.00673393-0.02865668j, -0.00673393-0.02865668j,\n",
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        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673395-0.02865663j, -0.00673395-0.02865663j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
        "       -0.00673398-0.02865658j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673399-0.02865637j, -0.00673399-0.02865637j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
        "       -0.00673401-0.02865616j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
        "       -0.00673385-0.02865476j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
        "       -0.00673364-0.02865302j])"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ind = np.where(np.sum(np.power(rx_loc - np.array([0,0,elev]),2),axis=1)< 5)\n",
      "def appResPhs(freq,z):\n",
      "    app_res = ((1./(8e-7*np.pi**2))/freq)*np.abs(z)**2\n",
      "    app_phs = np.arctan2(z.imag,z.real)*(180/np.pi)\n",
      "    return app_res, app_phs\n",
      "print appResPhs(freq,zxy[ind])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(array([ 10.97500457]), array([-103.22375266]))\n"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "e0_1d\n",
      "h0_1dC = -(mesh1d.nodalGrad*e0_1d)/(1j*omega(freq)*mu_0)\n",
      "h0_1d = mesh1d.getInterpolationMat(mesh1d.vectorNx,'CC')*h0_1dC\n",
      "\n",
      "print e0_1d, h0_1d, appResPhs(freq,e0_1d/h0_1d)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ -6.20279168e-09 -4.39881198e-09j  -3.59098481e-07 -1.31996127e-07j\n",
        "   2.74444236e-06 -9.30035530e-06j   1.09368959e-04 +1.54406904e-05j\n",
        "   1.03302758e-04 +5.78395328e-04j  -1.18516132e-03 +1.18309882e-03j\n",
        "  -3.21180911e-03 +4.16527153e-04j  -5.56733355e-03 -2.88598734e-03j\n",
        "  -5.64417373e-03 -1.05842922e-02j   2.63600826e-03 -2.27390580e-02j\n",
        "   2.88702308e-02 -3.28125152e-02j   6.80583153e-02 -3.14884619e-02j\n",
        "   1.07246400e-01 -3.01644081e-02j   1.46434485e-01 -2.88403534e-02j\n",
        "   1.85622570e-01 -2.75162976e-02j   2.24810656e-01 -2.61922403e-02j\n",
        "   2.83592784e-01 -2.42061510e-02j   3.71765978e-01 -2.12270077e-02j\n",
        "   5.04025769e-01 -1.67582648e-02j   7.02415460e-01 -1.00550655e-02j\n",
        "   1.00000000e+00 -0.00000000e+00j] [  2.12811704e-06 -5.88572986e-06j   1.38473321e-04 +4.42315497e-05j\n",
        "  -4.65316847e-04 +2.43179550e-03j  -1.93844533e-02 +1.39561396e-03j\n",
        "  -4.33100215e-02 -6.54109382e-02j   3.78293705e-02 -1.97523000e-01j\n",
        "   2.57677892e-01 -2.77504299e-01j   6.96635025e-01 -1.54031289e-01j\n",
        "   1.25721039e+00 +5.19482689e-01j   1.40761866e+00 +2.18565020e+00j\n",
        "   5.54062471e-01 +4.14291599e+00j  -1.67693342e-01 +4.96322889e+00j\n",
        "  -1.67693429e-01 +4.96322893e+00j  -1.67693556e-01 +4.96322895e+00j\n",
        "  -1.67693722e-01 +4.96322898e+00j  -1.67693927e-01 +4.96322901e+00j\n",
        "  -1.67694309e-01 +4.96322905e+00j  -1.67695046e-01 +4.96322910e+00j\n",
        "  -1.67696524e-01 +4.96322916e+00j  -1.67699578e-01 +4.96322923e+00j\n",
        "  -1.67702245e-01 +4.96322927e+00j] (array([  1.86964165e-02,   8.77304283e-02,   1.94267016e-01,\n",
        "         4.09088305e-01,   7.10419412e-01,   8.78131542e-01,\n",
        "         9.26369448e-01,   9.78429149e-01,   9.84798832e-01,\n",
        "         9.81985367e-01,   1.38473542e+00,   2.88794941e+00,\n",
        "         6.37406946e+00,   1.14393261e+01,   1.80837193e+01,\n",
        "         2.63072489e+01,   4.16034243e+01,   7.12096690e+01,\n",
        "         1.30608494e+02,   2.53433007e+02,   5.13553916e+02]), array([ -74.53559993, -177.53243593, -174.39163289, -167.84610729,\n",
        "       -156.61696865, -145.79203492, -140.26761361, -140.13080193,\n",
        "       -140.51973521, -140.6048989 , -131.03954507, -116.7636348 ,\n",
        "       -107.64443593, -103.07696138, -100.36712929,  -98.5805876 ,\n",
        "        -96.8138101 ,  -95.20305726,  -93.83947719,  -92.75532863,\n",
        "        -91.93522727]))\n"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import simpegMT as simpegmt\n",
      "sig1D = M.r(sig,'CC','CC','M')[0,0,:]\n",
      "anaEd, anaEu, anaHd, anaHu = simpegmt.Utils.MT1Danalytic.getEHfields(mesh1d,sig1D,freq,mesh1d.vectorNx)\n",
      "anaEtemp = anaEd+anaEu\n",
      "anaHtemp = anaHd+anaHu\n",
      "# Scale the solution\n",
      "anaE = (anaEtemp/anaEtemp[-1])#.conj()\n",
      "anaH = (anaHtemp/anaEtemp[-1])#.conj()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anaZ = anaE/anaH\n",
      "print anaZ, appResPhs(freq,anaZ)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.01986918+0.01986918j  0.01986918+0.01986918j  0.01986918+0.01986918j\n",
        "  0.01986918+0.01986918j  0.01986918+0.01986918j  0.02022782+0.03020735j\n",
        "  0.02093098+0.03672601j  0.02206142+0.04291033j  0.02364531+0.04871254j\n",
        "  0.00963058+0.00415596j  0.00619719+0.00527786j  0.00624787+0.01305949j\n",
        "  0.00649834+0.02074006j  0.00705940+0.02830392j  0.00705940+0.0361996j\n",
        "  0.00705940+0.04409528j  0.00705940+0.05593881j  0.00705941+0.07370409j\n",
        "  0.00705944+0.10035202j  0.00705951+0.14032392j  0.00705974+0.20028176j] (array([  10.        ,   10.        ,   10.        ,   10.        ,\n",
        "         10.        ,   16.73887525,   22.63143218,   29.48449815,\n",
        "         37.13437077,    1.39342121,    0.83920449,    2.65444118,\n",
        "          5.98274409,   10.77736658,   17.22771793,   25.25720583,\n",
        "         40.26231869,   69.43197022,  128.1759076 ,  250.01810043,\n",
        "        508.66554665]), array([ 44.99999984,  44.99999984,  44.99999984,  44.99999984,\n",
        "        44.99999984,  56.19244978,  60.32020862,  62.79101927,\n",
        "        64.10783458,  23.34204936,  40.41952465,  64.43279136,\n",
        "        72.60301804,  75.99535101,  78.96506301,  80.90445816,\n",
        "        82.80736989,  84.52887198,  85.97605635,  87.11995179,  87.98121528]))\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}