simpeg/simpegEM/FDEM/GroundedSource.ipynb

{
 "metadata": {
  "name": "GroundedSource"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from SimPEG import TensorMesh"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pad = 1\n",
      "padfactor = 1.5\n",
      "cs = 100\n",
      "xpad = cs*(np.ones(pad)*padfactor)**np.arange(pad)\n",
      "ypad = cs*(np.ones(pad)*padfactor)**np.arange(pad)\n",
      "zpad = cs*(np.ones(pad)*padfactor)**np.arange(pad)\n",
      "\n",
      "core = 10\n",
      "xcore = cs*np.ones(core)\n",
      "ycore = cs*np.ones(core)\n",
      "zcore = cs*np.ones(core)\n",
      "\n",
      "hx = np.r_[xpad[::-1],xcore, cs, xcore,xpad]\n",
      "hy = np.r_[ypad[::-1],ycore, cs, ycore, ypad]\n",
      "hz = np.r_[zpad[::-1],zcore,zcore, zpad]\n",
      "x0 = np.array([-np.sum(hx)/2, -np.sum(hy)/2, -np.sum(hz)/2], )\n",
      "mesh = TensorMesh([hx, hy, hz],x0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print mesh"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  ---- 3-D TensorMesh ----  \n",
        "   x0: -1150.00\n",
        "   y0: -1150.00\n",
        "   z0: -1100.00\n",
        "  nCx: 23\n",
        "  nCy: 23\n",
        "  nCz: 22\n",
        "   hx: 23*100.00\n",
        "   hy: 23*100.00\n",
        "   hz: 22*100.00\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pts = np.array([[950, 50,  0], \n",
      "                [950, 950, 0],\n",
      "                [-950, 950,0],\n",
      "                [-950, 50, 0]])\n",
      "pts = np.array([[950, 50, -500], \n",
      "                [950, 50, +500]])\n",
      "pts = np.array([[950, 50,  -200],\n",
      "\t\t\t\t[950, 50,  0], \n",
      "                [950, 950, 0],\n",
      "                [-950, 950,0],\n",
      "                [-950, 50, 0],\n",
      "                [-950, 50,  -200]])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print mesh.vectorCCx\n",
      "print mesh.vectorCCy\n",
      "print mesh.vectorCCz\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[-1100. -1000.  -900.  -800.  -700.  -600.  -500.  -400.  -300.  -200.\n",
        "  -100.     0.   100.   200.   300.   400.   500.   600.   700.   800.\n",
        "   900.  1000.  1100.]\n",
        "[-1100. -1000.  -900.  -800.  -700.  -600.  -500.  -400.  -300.  -200.\n",
        "  -100.     0.   100.   200.   300.   400.   500.   600.   700.   800.\n",
        "   900.  1000.  1100.]\n",
        "[-1050.  -950.  -850.  -750.  -650.  -550.  -450.  -350.  -250.  -150.\n",
        "   -50.    50.   150.   250.   350.   450.   550.   650.   750.   850.\n",
        "   950.  1050.]\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ismember(a, b):\n",
      "    tf = np.array([i in b for i in a])\n",
      "    return tf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#def edgeModel = path2edgeModel\n",
      "edm_x = np.zeros(np.prod(mesh.nEx))\n",
      "edm_y = np.zeros(np.prod(mesh.nEy))\n",
      "edm_z = np.zeros(np.prod(mesh.nEz))\n",
      "\n",
      "for ii in range (pts.shape[0]-1):\n",
      "    pt1 = pts[ii,:]\n",
      "    pt2 = pts[ii+1,:]\n",
      "    delta = pt2 - pt1\n",
      "    deltaDim = np.argwhere(delta)\n",
      "    #assert(np.size(deltaDim)==1), \"Path must be orthoginal to mesh\"\n",
      "    if deltaDim == 0:\n",
      "        xLoc = mesh.vectorCCx[(min(pt1[0],pt2[0]) < mesh.vectorCCx ) & (mesh.vectorCCx < max(pt1[0],pt2[0]))]\n",
      "        yLoc = pts[ii,1]\n",
      "        zLoc = pts[ii,2]\n",
      "        delDir = np.sign(pt2[0]-pt1[0])\n",
      "        xyz = np.c_[xLoc, np.ones(np.size(xLoc))*yLoc, np.ones(np.size(xLoc))*zLoc]\n",
      "        edgeInd=ismember(map(tuple,mesh.gridEx),map(tuple,xyz))\n",
      "        edm_x[edgeInd] = delDir\n",
      "        print '>> x-direction', ii\n",
      "        print mesh.gridEx[edgeInd]\n",
      "    if deltaDim == 1:    \n",
      "        xLoc = pts[ii,0]\n",
      "        yLoc = mesh.vectorCCy[(min(pt1[1],pt2[1]) < mesh.vectorCCy ) & (mesh.vectorCCy < max(pt1[1],pt2[1]))]\n",
      "        zLoc = pts[ii,2]\n",
      "        delDir = np.sign(pt2[1]-pt1[1])\n",
      "        xyz = np.c_[np.ones(np.size(yLoc))*xLoc, yLoc, np.ones(np.size(yLoc))*zLoc]\n",
      "        edgeInd=ismember(map(tuple,mesh.gridEy),map(tuple,xyz))\n",
      "        edm_y[edgeInd] = delDir\n",
      "        print '>> y-direction', ii\n",
      "        print mesh.gridEy[edgeInd]\n",
      "    if deltaDim == 2:    \n",
      "        xLoc = pts[ii,0]\n",
      "        yLoc = pts[ii,1]\n",
      "        zLoc = mesh.vectorCCz[(min(pt1[2],pt2[2]) < mesh.vectorCCz ) & (mesh.vectorCCz < max(pt1[2],pt2[2]))]\n",
      "        delDir = np.sign(pt2[2]-pt1[2])\n",
      "        xyz = np.c_[np.ones(np.size(zLoc))*xLoc, np.ones(np.size(zLoc))*yLoc, zLoc]\n",
      "        edgeInd=ismember(map(tuple,mesh.gridEz),map(tuple,xyz))\n",
      "        edm_z[edgeInd] = delDir\n",
      "        print '>> z-direction', ii\n",
      "        print mesh.gridEz[edgeInd]\n",
      "            \n",
      "    edgeModel = np.r_[edm_x, edm_y, edm_z]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ">> z-direction 0\n",
        "[[ 950.   50. -150.]\n",
        " [ 950.   50.  -50.]]\n",
        ">> y-direction 1\n",
        "[[ 950.  100.    0.]\n",
        " [ 950.  200.    0.]\n",
        " [ 950.  300.    0.]\n",
        " [ 950.  400.    0.]\n",
        " [ 950.  500.    0.]\n",
        " [ 950.  600.    0.]\n",
        " [ 950.  700.    0.]\n",
        " [ 950.  800.    0.]\n",
        " [ 950.  900.    0.]]\n",
        ">> x-direction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2\n",
        "[[-900.  950.    0.]\n",
        " [-800.  950.    0.]\n",
        " [-700.  950.    0.]\n",
        " [-600.  950.    0.]\n",
        " [-500.  950.    0.]\n",
        " [-400.  950.    0.]\n",
        " [-300.  950.    0.]\n",
        " [-200.  950.    0.]\n",
        " [-100.  950.    0.]\n",
        " [   0.  950.    0.]\n",
        " [ 100.  950.    0.]\n",
        " [ 200.  950.    0.]\n",
        " [ 300.  950.    0.]\n",
        " [ 400.  950.    0.]\n",
        " [ 500.  950.    0.]\n",
        " [ 600.  950.    0.]\n",
        " [ 700.  950.    0.]\n",
        " [ 800.  950.    0.]\n",
        " [ 900.  950.    0.]]\n",
        ">> y-direction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3\n",
        "[[-950.  100.    0.]\n",
        " [-950.  200.    0.]\n",
        " [-950.  300.    0.]\n",
        " [-950.  400.    0.]\n",
        " [-950.  500.    0.]\n",
        " [-950.  600.    0.]\n",
        " [-950.  700.    0.]\n",
        " [-950.  800.    0.]\n",
        " [-950.  900.    0.]]\n",
        ">> z-direction 4\n",
        "[[-950.   50. -150.]\n",
        " [-950.   50.  -50.]]\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print xyz\n",
      "print mesh.vectorNx\n",
      "print mesh.vectorNy\n",
      "print mesh.vectorNz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[-950.   50. -150.]\n",
        " [-950.   50.  -50.]]\n",
        "[-1150. -1050.  -950.  -850.  -750.  -650.  -550.  -450.  -350.  -250.\n",
        "  -150.   -50.    50.   150.   250.   350.   450.   550.   650.   750.\n",
        "   850.   950.  1050.  1150.]\n",
        "[-1150. -1050.  -950.  -850.  -750.  -650.  -550.  -450.  -350.  -250.\n",
        "  -150.   -50.    50.   150.   250.   350.   450.   550.   650.   750.\n",
        "   850.   950.  1050.  1150.]\n",
        "[-1100. -1000.  -900.  -800.  -700.  -600.  -500.  -400.  -300.  -200.\n",
        "  -100.     0.   100.   200.   300.   400.   500.   600.   700.   800.\n",
        "   900.  1000.  1100.]\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mesh.plotImage(edm_x, imageType='Ex')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<matplotlib.collections.QuadMesh at 0xaad7588>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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KzMSlb19ObN9OUWYmRZmZKNeuATBo/nxGrlvHrrAwsg8c4F4XF+51ceGeDh1M60uOjaUo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\n/kIIYYWk/IUQwgr9H1qERATDahd9AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaac2748>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mesh.plotImage(edm_y, imageType='Ey')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<matplotlib.collections.QuadMesh at 0xafdf9e8>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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5HzlyhPz8fKKjo3nooYcIDW2+XEJoaCg5OTlAc/mHhYVZtgkJCcFkMhEQEICv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\nQggV+v+SqxuYi1KoIAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaac32e8>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mesh.plotImage(edm_z, imageType='Ez')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<matplotlib.collections.QuadMesh at 0xad95e48>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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LyyMtLQ1PT09SU1MxmUwAmEwmpk2bhk6nIz4+HkVRLO8KSkpKSElJwc/Pj+Tk\nZMs2Qgghek635/wPHTpEYWEhsbGxPPjgg4SHt5wuITw8nLy8PKCl/CMiIizbhIWFYTKZCAoKwt/f\n37Jcr9ezbt06Fi1qf8x7ZmYmua2nGQhK0BGcENTd2EII4VKO5JRSmlNm9frdKv+amhpSUlJ48cUX\nGTBgQJcOydRorj5J1rW2z8zMpNnKqzgJIYQaBScEtdsx3vlU5+flsvlQz8bGRmbPns39999PUlIS\nAAaDAbO55fS5ZrMZg8EAgNFopKioyLJtcXExBoOBkJAQKioqLMuLioqIi4uzNZIQQggr2VT+iqKQ\nlpbGuHHjePTRRy3LjUYj2dnZ1NXVkZ2dbSny2NhYtm7dSllZGTk5OWi1Wry9vYGW6aH169dTWVnJ\nhg0bMBqNdhiWEEKIzthU/p999hlvvfUWn3zyCVFRUURFRbFlyxbS09MpKysjLCyM48ePs3DhQgAC\nAgJIT08nMTGRhx9+mJdeesnyWCtWrOD555/HYDAwefJkYmI6v7SeEEKI7rNpzn/SpEk0Nzd3+LON\nGzd2uHzx4sUsXrz4quV6vZ69e/faEkMIIYSN+sw3fLvjNyzlTV6nnBOkMp988tjPPgDGE8VMkq/a\n5nVe6+BKTj2TAVquVzuZeCKJxI/B1FHHXgr4lE/sluF6OR4kjSCCr9qmkUb+wNMOyQBwMz9CTyTB\njOIMlXzHIT5nD/XUOySDBg16IolkHKMZwznOsofP2mUUoq9z+fL3xRd33DlJOW64MYzhlFLabh0F\nhRd4tt2Fyuuoc2iG/+Z+fPFjL19iphB3POhPf7tlsCbHO6zDDTfLfQ0aHiKdgxx0WIbBDCGZ/2IH\n29jBdgYzmDu5Cy1u7GCbQzLoiWQGM9nBNnL4hBDGMovZAPIPgHAZLl/+OoI4xlEUFIYxnDoudniJ\nxp68UPf1MuiJJIRQVvMypzjVazl+uGc9hjF4M5B88hyW4WZ+RBVVlstHVnKaQAKJYoLdyv96GeK4\nlW/4ijxavnB4ilOMZCTxJEj5C5fhsuW/lOXAfy4BuJTlaNHSj34sZTkKCs+2nphLg4ZH+X800UQh\nhXzLPruUsLUZIhlHFecYSzj3MZeLXGAvX1LItzTa4fsNXXktrhRDLOWcoJwTDstwgBImczt6IjlA\nCb74oWccRRQ6LIMnnlziUrttG2hgMEPwwsuu7wqF6C0uW/6reRnQsICFfMBGyinnPlLYxzcUY7as\nV0kl/+Z99pY8AAAONklEQVSfVHASPwYzjpt5mF/xD/5+3atH2SuDH34MwJtwwtnOx3jhxe0kMJox\n/Jt/ditDV3JcaQADCCOcD/mg28/flQzHOcZr/JVf8KClpD9nD1v5yGEZCiggngSOcJhSShlDCJGM\nA2AgPlL+wiW4bPlXU00AAWjRUkIxHngQyFDW8Wa7KZ5jHLVcrL2CCoooZAELuZ2Ebpe/tRm0uNGP\nfmzg35yhEmjZ05xJMv3ox2UuOyTHlaKZwGUus49vuvXcXc0witGkMJfP2MVBDhJAALcxGQ2wpZv/\nAFib4Wv24o03dzEdX3w5wxn28BnxJNBMx0e5CdHXuGT5P0IGPgxC23p7gifRoMENNx7j1wC8zErO\nc77D7YsoJJ4pDstwnmoGMMBS/ABHOIwHHoxE162jjmx5LTRomICBfXxtl2mnrmSI41bKKLUc5XSM\nozTQwCxm8ymf0EBDj2e4xKXWD5y3cQM3UE89cdwKwFnOdPv1EMIZuGT5v8HruOHGLJI5wAEK2U8C\niTTRxC5yAaih5prbhxHOWc46LMMRjhBCKL74Wp43iGAucYmjWH+ipu7maBNCKD74kE9+t57blgwd\n7Vkrrbcrj8bqyQxXavsQfDzRlFBME002ZxDCmbjkZRzPU00V5wggEDNFnGv9/xKKOdd6U2g5idwU\nEgltLd7RjGEGMxmJjs/o/KRI9sxQQB511DGDmYxmDJGMI5Ef8y37uz3l05UcbWIwcJxjVHCyW89t\nS4Y8TIQTwW1MIoAAbuZHTCGR/ezr1nH+XckwjOFEMg5ffJlADL/iUXzwYStb7PJ6COEMXHLPH2Ao\nw7jMZc5QiSee+ONPKUeuWs8DT+5mBgMYQBXnKMbMq6zlOMcclqGeetaSxV3czb2kcJpT7OEzvmV/\ntzN0JQeANwMZSxjv855dnrurGQ7zPRv4F2MJ4zYmU8lpvuYrvqL73wK3NoMbbiQwhZvw5SIXOMpR\ntvLRNacJheiLNEpXzsPcC653BXohhBBXu153uuS0jxBCiM71mWmf39J7F3d/uvULUL2ZwVlyOEMG\nZ8nhDBmEsJXs+QshhApJ+QshhApJ+QshhApJ+QshhApJ+QshhApJ+QshhApJ+QshhApJ+QshhApJ\n+QshhAr1mW/42uo3LOVNXqecE6Qyn3zy2l2HdTBDuIt7GMYwaqnla75iNzsdmmMI/kwhkUCG4osv\ne/nS7idWsyZHFNHcQhQBBHCZyxzkAEV8yyEOOSxDCCFM4cf44ocWLaUc4Tu+w8TnDstwpSEM4SEe\nxg03nuK3ds0gRG9y6fL3xRd33DlJOW64MYzhlFJq+fmN3MgveYiznOFd3mEMoUwhES1adpLjsBzu\nuFNFFcWYmcgkuz1vV3OMYjRmitjKRzTTzC3cwn/zc1bxIuc455AM9TSwh884RQXNNKMjiLuZzkUu\n2O3i6dfL0MYdd+5jLt/zHSGE2uW5hXAWLl3+OoI4xlEUFIYxnDoucp5qy89jiEWDhrVkAfA939NA\nPbdyG5+xy24X7rhejhMc5wTHAYgmxi7PaUuOH14v+GNOEswoJjLJbtfyvV6GKy+rCXCGM4xiNBMw\n2K38r5ehzd1Mp5TDHOMYoYy1y3ML4SxcsvyXshzAcgHwpSxHi5Z+9GMpy1FQeJY/oCPoqguWlFOO\nF14MYQgnu3kxE2tz9LTu5NC03nojQ9teeQihlqttOSrDLYxnGMNZy2pu5kfdfm4hnI1Llv9qXgY0\nLGAhH7CRcsq5jxT28Q3FmC3rDWQgR35wfdxyTrT+zKfb5W9tjp5ma44YDAzBn3/xD4dnWMJv6E9/\ntGj5mK18zh6HZRjMEH7KnbzGX+SyjcJlueTRPtVUcwOeaNFSQjH11BHIUPazj+rWW4uevUiM9Tl6\nli05wolgGnexkQ1UXnFheUdl+Auv8Cpr2cbHTGIy8SQ4JIMbbqQwlx1s4zSnu/2cQjgrl9vzf4QM\nfBiEtvX2BE+iQYMbbjzGrwF4mZWcb70NZVi77dvudzQH3FM5epItOcZxMzNJ5n3es8s8uy0Zqqmi\nmirKOYEGmMTt7GJnhxd4t2cGLVqGMIR7mME9zLBsr0HD73iaHWzvkaPBhHA0lyv/N3gdN9yYRTIH\nOEAh+0kgkSaaLPPGNdQAUEYpk4lHg8Zy8e6hDKWOum7v9XUlB8CRnFKCE4K69Zz2yDGBGO7iHv7F\nPyiisEcybP1iC/Pi7r9mhh9yww0PPLr12YO1r4MGDX9mVbttw9GTyI9Zzctc4ILVz9lTf6bORsbZ\nNznFtM/OnTuJiIggNDSUl19+uVuPdZ5qqjhHAIGYKeJc6/+XUMy51ltb0ReQTzPN/JKHGM0YpnIH\n8Uzhcz7r9lxvV3Jo0eJe7EEggXjigRdeBBLIEIZ0K0NXc9zKRO5hBpvZRBmlDGi9eeFl1wwFW768\nZoaJ3EYoY/HFj2EM51Ymciu3sZ993fozsfZ1aKaZ0z+41bS+IznNaS5y0ernLM0pszlvXyLj7Juc\nYs9/8eLFrF27lqCgIH76058yd+5cBg8ebPPjDWUYl7nMGSrxxBN//CnlyFXrXeQir7KGu7iH+5hD\nLbV8yg52s6sbo+l6joEM5NWHnmq3XQR6qqhiJX9yWI44bkWDhukkMZ0ky/IjHOZvZNstQ3/P/tfM\noEXLHUxjEIO4yEUO8z2b2UQh33br+X+YobPXoSNKD38+JITDKb2sqqpKGT9+vOX+r371K2XTpk2W\n+04Q0SF+97vf9XYEh1HLWGWcrqWvjfN63alpXanXbN++nb/+9a+88847AKxZs4bjx4/z+9//HgCN\npvvHmAshhBp1Vu9OMe3TmV7+t0kIIVxSr3/gazAYKC4uttwvLCwkLi6uFxMJIYTr6/Xy9/HxAVqO\n+Dly5Ajbtm3DaDT2ciohhHBtTjHts3LlSh566CEaGxvJyMjo1pE+Qgghrq/X9/wB4uPjMZvNHDp0\niIyMDMtyex7/3xuOHj3KlClTiIyMJCEhgbfffhuAmpoakpKS0Ol0zJw5k9raWss2q1atIjQ0FL1e\nz+7duy3LzWYz0dHRjB49mmXLljl8LNZoamoiKiqK6dOnA645zgsXLvCLX/yCsWPHotfrMZlMLjlO\ngFdffZWJEycyYcIEHn30UcA1/kxTU1MJCAjg5ptvtiyz57gaGxtJS0sjKCiIhIQETp7s3jnCeowD\njjiy2fjx45Xc3FzlyJEjSlhYmHL69OnejtQl5eXlyldffaUoiqKcPn1aGTVqlHL+/HnlueeeUx55\n5BGlvr5eWbRokfLCCy8oiqIoFRUVSlhYmFJaWqrk5OQoUVFRlse68847lfXr1yuVlZXKbbfdpuTn\n5/fKmDrzpz/9SfnZz36mTJ8+XVEUxSXHuWTJEmX58uVKXV2d0tjYqFRVVbnkOM+cOaMEBwcrtbW1\nSlNTk3LnnXcqW7ZscYmx7ty5U9m7d68ybtw4yzJ7juvvf/+7Mnv2bOXChQvKH//4R2XRokWOHaCV\nnGLPvyPV1S3n1rn99tsJCgrijjvuwGQy9XKqrgkMDGT8+PEADB48mMjISPLz88nLyyMtLQ1PT09S\nU1Mt4zKZTEybNg2dTkd8fDyKolj2QEpKSkhJScHPz4/k5GSney2OHTvG5s2bmT9/vuUILVcc5/bt\n23niiSe44YYb6NevHz4+Pi45Ti8vLxRFobq6mrq6Oi5evMigQYNcYqyTJ0/mpptuarfMnuMymUzM\nmzePG2+8kQULFvT6eK/Facs/Pz+f8PBwy329Xs8XX3zRi4m659ChQxQWFhIbG9tubOHh4eTl5QEt\nvzQRERGWbcLCwjCZTBw6dAh/f3/Lcmd8LR577DFeeOEFtNr//Eq52jiPHTtGfX096enpGI1Gnnvu\nOerq6lxunNBS/llZWQQHBxMYGMhtt92G0Wh0ybGCfX9X8/Ly0Ov1APj6+lJRUUFDQ4OjhmI1py1/\nV1JTU0NKSgovvvgiAwYM6NJ3Fzr6kltXtneETZs24e/vT1RUVLtsrjbO+vp6Dhw4wOzZs8nJyaGw\nsJB3333X5cYJcPr0adLT0ykqKuLIkSN8/vnnbNq0ySXHCvb5XW1briiKzX8PHMlpy99Vjv9vbGxk\n9uzZ3H///SQltZwvx2AwYDa3XDzEbDZjMBgAMBqNFBUVWbYtLi7GYDAQEhJCRUWFZXlRUZFTvRZ7\n9uzh/fffZ9SoUcydO5dPPvmE+++/3+XGGRISQlhYGNOnT8fLy4u5c+eyZcsWlxsntOy9xsXFERIS\ngp+fH/feey+7du1yybGCff5Oth2ifuU2Z8+eJSAgAE9PT0cNxWpOW/6ucPy/oiikpaUxbtw4y9ES\n0PLLkZ2dTV1dHdnZ2Za/DLGxsWzdupWysjJycnLQarV4e3sDLW9F169fT2VlJRs2bHCq1+KZZ57h\n6NGjHD58mPXr15OYmMibb77pcuMECA0NxWQy0dzczIcffsjUqVNdcpyTJ0+moKCAs2fP0tDQwEcf\nfcQdd9zhkmMF+/6dNBqNvPXWW1y4cIFXXnnFKf+xA5z7aJ+cnBwlPDxcGTNmjPLSSy/1dpwu27Vr\nl6LRaJRbbrlFGT9+vDJ+/Hjlo48+Us6fP6/MmDFDGTlypJKUlKTU1NRYtlm5cqUyZswYJSIiQtm5\nc6dleWFhoRIVFaUEBwcr//u//9sbw7FKTk6O5WgfVxxnSUmJYjQalVtuuUVZsmSJUltb65LjVBRF\nee2115Tbb79diYmJUZYvX640NTW5xFjnzJmjDB06VPHw8FBGjBihZGdn23Vcly5dUh588EFl5MiR\nSnx8vFJeXu7Q8Vmr10/sJoQQwvGcdtpHCCFEz5HyF0IIFZLyF0IIFZLyF0IIFZLyF0IIFZLyF0II\nFfr/O/wxcmvA4FsAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x796c630>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subplot(131)\n",
      "plot(edm_x)\n",
      "subplot(132)\n",
      "plot(edm_y)\n",
      "subplot(133)\n",
      "plot(edm_z)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "[<matplotlib.lines.Line2D at 0xb903588>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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kZBQWFgIASkpK8PrrrzsNBiIi8g4ezzHodDrU1tbCZrOhtrYW2dnZTtupVCoYjUZcuXIF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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad85438>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = np.array([2,5,2,6,3,6,2,2,5])\n",
      "B = np.array([3,4,4,3,6])\n",
      "\n",
      "def ismember(a, b):\n",
      "    tf = np.array([i in b for i in a])\n",
      "    return tf\n",
      "\n",
      "temp=ismember(map(tuple,mesh.gridEx),map(tuple,xyz))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print [i in ['x', 'y', 'z'] for i in ['y','w']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[True, False]\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def path2edgeModel(mesh, pts):\n",
      "    edm_x = np.zeros(np.prod(mesh.nEx))\n",
      "    edm_y = np.zeros(np.prod(mesh.nEy))\n",
      "    edm_z = np.zeros(np.prod(mesh.nEz))\n",
      "    \n",
      "    for ii in range (pts.shape[0]-1):\n",
      "        pt1 = pts[ii,:]\n",
      "        pt2 = pts[ii+1,:]\n",
      "        delta = pt2 - pt1\n",
      "        deltaDim = np.argwhere(delta)\n",
      "        #assert(np.size(deltaDim)==1), \"Path must be orthoginal to mesh\"\n",
      "        if deltaDim == 0:\n",
      "            xLoc = mesh.vectorCCx[(min(pt1[0],pt2[0]) < mesh.vectorCCx ) & (mesh.vectorCCx < max(pt1[0],pt2[0]))]\n",
      "            yLoc = pts[ii,1]\n",
      "            zLoc = pts[ii,2]\n",
      "            delDir = np.sign(pt2[0]-pt1[0])\n",
      "            xyz = np.c_[xLoc, np.ones(np.size(xLoc))*yLoc, np.ones(np.size(xLoc))*zLoc]\n",
      "            edgeInd=ismember(map(tuple,mesh.gridEx),map(tuple,xyz))\n",
      "            edm_x[edgeInd] = delDir\n",
      "            print '>> x-direction', ii\n",
      "            print mesh.gridEx[edgeInd]\n",
      "        if deltaDim == 1:    \n",
      "            xLoc = pts[ii,0]\n",
      "            yLoc = mesh.vectorCCy[(min(pt1[1],pt2[1]) < mesh.vectorCCy ) & (mesh.vectorCCy < max(pt1[1],pt2[1]))]\n",
      "            zLoc = pts[ii,2]\n",
      "            delDir = np.sign(pt2[1]-pt1[1])\n",
      "            xyz = np.c_[np.ones(np.size(yLoc))*xLoc, yLoc, np.ones(np.size(yLoc))*zLoc]\n",
      "            edgeInd=ismember(map(tuple,mesh.gridEy),map(tuple,xyz))\n",
      "            edm_y[edgeInd] = delDir\n",
      "            print '>> y-direction', ii\n",
      "            print mesh.gridEy[edgeInd]\n",
      "        if deltaDim == 2:    \n",
      "            xLoc = pts[ii,0]\n",
      "            yLoc = pts[ii,1]\n",
      "            zLoc = mesh.vectorCCz[(min(pt1[2],pt2[2]) < mesh.vectorCCz ) & (mesh.vectorCCz < max(pt1[2],pt2[2]))]\n",
      "            delDir = np.sign(pt2[2]-pt1[2])\n",
      "            xyz = np.c_[np.ones(np.size(zLoc))*xLoc, np.ones(np.size(zLoc))*yLoc, zLoc]\n",
      "            edgeInd=ismember(map(tuple,mesh.gridEz),map(tuple,xyz))\n",
      "            edm_z[edgeInd] = delDir\n",
      "            print '>> z-direction', ii\n",
      "            print mesh.gridEz[edgeInd]\n",
      "    \n",
      "            \n",
      "        edgeModel = np.r_[edm_x, edm_y, edm_z]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Js = path2edgeModel(mesh, pts)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ">> z-direction 0\n",
        "[[ 950.   50. -150.]\n",
        " [ 950.   50.  -50.]]\n",
        ">> y-direction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1\n",
        "[[ 950.  100.    0.]\n",
        " [ 950.  200.    0.]\n",
        " [ 950.  300.    0.]\n",
        " [ 950.  400.    0.]\n",
        " [ 950.  500.    0.]\n",
        " [ 950.  600.    0.]\n",
        " [ 950.  700.    0.]\n",
        " [ 950.  800.    0.]\n",
        " [ 950.  900.    0.]]\n",
        ">> x-direction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2\n",
        "[[-900.  950.    0.]\n",
        " [-800.  950.    0.]\n",
        " [-700.  950.    0.]\n",
        " [-600.  950.    0.]\n",
        " [-500.  950.    0.]\n",
        " [-400.  950.    0.]\n",
        " [-300.  950.    0.]\n",
        " [-200.  950.    0.]\n",
        " [-100.  950.    0.]\n",
        " [   0.  950.    0.]\n",
        " [ 100.  950.    0.]\n",
        " [ 200.  950.    0.]\n",
        " [ 300.  950.    0.]\n",
        " [ 400.  950.    0.]\n",
        " [ 500.  950.    0.]\n",
        " [ 600.  950.    0.]\n",
        " [ 700.  950.    0.]\n",
        " [ 800.  950.    0.]\n",
        " [ 900.  950.    0.]]\n",
        ">> y-direction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3\n",
        "[[-950.  100.    0.]\n",
        " [-950.  200.    0.]\n",
        " [-950.  300.    0.]\n",
        " [-950.  400.    0.]\n",
        " [-950.  500.    0.]\n",
        " [-950.  600.    0.]\n",
        " [-950.  700.    0.]\n",
        " [-950.  800.    0.]\n",
        " [-950.  900.    0.]]\n",
        ">> z-direction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4\n",
        "[[-950.   50. -150.]\n",
        " [-950.   50.  -50.]]\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print pts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 950   50 -200]\n",
        " [ 950   50    0]\n",
        " [ 950  950    0]\n",
        " [-950  950    0]\n",
        " [-950   50    0]\n",
        " [-950   50 -200]]\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    }
   ],
   "metadata": {}
  }
 ]
}