simpeg/MT Script-3D_halfspace.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:653b143c4d16cc4ab9cf69a5c5a6b35ab1752aa01ac028f281eaf261a1490775"
 },
 "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": [
      "# 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)],[(100,10,-1.5),(100.,10),(100,10,1.5)]], x0=['C','C','C'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print M.vectorNz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[-17499.51171875 -11733.0078125   -7888.671875    -5325.78125     -3617.1875\n",
        "  -2478.125       -1718.75        -1212.5          -875.           -650.\n",
        "   -500.           -400.           -300.           -200.           -100.\n",
        "      0.            100.            200.            300.            400.\n",
        "    500.            650.            875.           1212.5          1718.75\n",
        "   2478.125        3617.1875       5325.78125      7888.671875\n",
        "  11733.0078125   17499.51171875]\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set parameters\n",
      "freq = 10\n",
      "conds = [0.01]\n",
      "elev = 300\n",
      "# Setup the models\n",
      "sig = np.zeros(M.nC) + 1e-8\n",
      "sig[M.gridCC[:,2]<=elev] = conds[0]\n",
      "# sig[M.gridCC[:,2]<-600] = 1e-1\n",
      "sigBG = np.zeros(M.nC) + 1e-8\n",
      "sigBG[M.gridCC[:,2]<=elev] = conds[0]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot the models\n",
      "colorbar(M.plotImage(log10(sig)))\n",
      "colorbar(M.plotImage(log10(sigBG)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0x7f12d2e2d128>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2xAiyT51i6YsvApDqKtQJy5bRrE+fPOv/qXNnMn7/nZS1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\nnYQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f12dd0b9a10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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i5X33Mee++0hZv57a117LbRMmkJ2Rwdr33w94nvAyZTgWH8+2WbO4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\nnYQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f12dd0b9c50>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "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": [
      "# Form the A matrices\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": "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",
      "# Solve the systems for each polarization\n",
      "Ainv = simpeg.SolverLU(A)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 34.7 s, sys: 420 ms, total: 35.2 s\n",
        "Wall time: 35.5 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() # conjugate to comply with phase behavior\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 #Negative to comply with phase orientation.\n",
      "\n",
      "eBG_x = np.vstack((simpeg.Utils.mkvc(ex_x,2),ey_x,ez_x))\n",
      "# Note 100% sure why this has to be negative.\n",
      "rhs_x = -ABG*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 ex_x\n",
      "for i in arange(M.vnEy[0]):\n",
      "    for j in arange(M.vnEy[1]):\n",
      "        ey_y[i,j,:] = e0_1d        \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "eBG_y = np.r_[ex_y,simpeg.Utils.mkvc(ey_y),ez_y]\n",
      "# Note 100% sure why this has to be negative.\n",
      "rhs_y = -ABG*eBG_y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "# Solve each polarization\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 346 ms, sys: 1 ms, total: 347 ms\n",
        "Wall time: 480 ms\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "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": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "j_x = Meinv*Msig*e_x\n",
      "j_y = Meinv*Msig*e_y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "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": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "e_x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "array([  6.95763292e-07 -5.19497296e-07j,\n",
        "         6.95763292e-07 -5.19497296e-07j,\n",
        "         6.95763292e-07 -5.19497296e-07j, ...,\n",
        "        -9.19690759e-13 +1.90393072e-10j,\n",
        "        -9.42272240e-13 +1.17287969e-10j,  -2.84432632e-12 -1.10938670e-10j])"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "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_y_CC), vType='CCv', view='vec', streamOpts={'color': 'k'}, normal='X', ax = ax[0])\n",
      "cb0 = plt.colorbar(dat0[0], ax = ax[0])\n",
      "dat1 = M.plotSlice(abs(j_y_CC), vType='CCv', view='vec', streamOpts={'color': 'k'}, normal='X', 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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r1x++9GuufOfXeOLOZXmHYkU3uoXJzJr0MPBNYBGpx0GzNpQ4Z/fZ4VgZPXn3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\nM6sYD+LNzMzMzCrm/wEbOnDDzdi0nwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f12d2758a50>"
       ]
      }
     ],
     "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(-250,251,50),np.arange(-250,251,50))\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": 19
    },
    {
     "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))\n",
      "b_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",
      "b_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": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.constants import mu_0\n",
      "# 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'] = b_x_loc[:,0]/mu_0\n",
      "combMat['hy1'] = b_x_loc[:,1]/mu_0\n",
      "combMat['hz1'] = b_x_loc[:,2]/mu_0\n",
      "combMat['hx2'] = b_y_loc[:,0]/mu_0\n",
      "combMat['hy2'] = b_y_loc[:,1]/mu_0\n",
      "combMat['hz2'] = b_y_loc[:,2]/mu_0\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "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": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zxy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "array([-0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j, -0.06237698-0.06472069j,\n",
        "       -0.06237698-0.06472069j])"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "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",
      "\n",
      "print appResPhs(freq,zxy[ind])\n",
      "print appResPhs(freq,zyx[ind])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(array([ 102.33002434]), array([-133.94357304]))\n",
        "(array([ 102.33002434]), array([ 46.05642696]))\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "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": [
        "[  6.95763292e-07 -5.19497296e-07j   9.74760218e-06 -1.09092785e-05j\n",
        "   1.74907169e-04 +1.24344756e-04j  -5.21083562e-04 +1.34839517e-03j\n",
        "  -4.87010903e-03 +6.63069113e-04j  -8.61854919e-03 -6.03020882e-03j\n",
        "  -7.68552978e-03 -1.53974787e-02j  -3.16876824e-03 -2.35863583e-02j\n",
        "   2.49400722e-03 -2.94018473e-02j   7.73825040e-03 -3.31542272e-02j\n",
        "   1.19706555e-02 -3.54839735e-02j   1.51424717e-02 -3.69189921e-02j\n",
        "   1.86057886e-02 -3.82344505e-02j   2.23709926e-02 -3.94030035e-02j\n",
        "   2.64473102e-02 -4.03949222e-02j   3.08425733e-02 -4.11780213e-02j\n",
        "   3.55629651e-02 -4.17175972e-02j   4.06127458e-02 -4.19763792e-02j\n",
        "   4.59939588e-02 -4.19144958e-02j   5.15406437e-02 -4.16710354e-02j\n",
        "   5.70873289e-02 -4.14275746e-02j   6.54073573e-02 -4.10623825e-02j\n",
        "   7.78874014e-02 -4.05145921e-02j   9.66074704e-02 -3.96929008e-02j\n",
        "   1.24687581e-01 -3.84603474e-02j   1.66807761e-01 -3.66114701e-02j\n",
        "   2.29988062e-01 -3.38380118e-02j   3.24758579e-01 -2.96773825e-02j\n",
        "   4.66914483e-01 -2.34350351e-02j   6.80148567e-01 -1.40669735e-02j\n",
        "   1.00000000e+00 -0.00000000e+00j] [  2.28193925e-05 +1.98808291e-05j  -2.58228538e-04 +3.34422815e-04j\n",
        "  -3.80760340e-03 -1.84599759e-03j   6.28457755e-04 -2.07183543e-02j\n",
        "   4.66852476e-02 -3.79022309e-02j   1.23507371e-01 -7.33469297e-03j\n",
        "   1.85411921e-01 +7.40235581e-02j   2.12886863e-01 +1.72701395e-01j\n",
        "   2.14025522e-01 +2.62118992e-01j   2.02514232e-01 +3.32494576e-01j\n",
        "   1.87732553e-01 +3.83973226e-01j   1.74175989e-01 +4.20174709e-01j\n",
        "   1.57301859e-01 +4.57751431e-01j   1.36813469e-01 +4.96570158e-01j\n",
        "   1.12404317e-01 +5.36469120e-01j   8.37593755e-02 +5.77255592e-01j\n",
        "   5.05566063e-02 +6.18703401e-01j   1.24687508e-02 +6.60550390e-01j\n",
        "  -1.93361233e-02 +6.92017214e-01j  -3.08346503e-02 +7.02495869e-01j\n",
        "  -3.08347046e-02 +7.02495910e-01j  -3.08347965e-02 +7.02495972e-01j\n",
        "  -3.08349577e-02 +7.02496064e-01j  -3.08352521e-02 +7.02496199e-01j\n",
        "  -3.08358123e-02 +7.02496397e-01j  -3.08369191e-02 +7.02496682e-01j\n",
        "  -3.08391789e-02 +7.02497084e-01j  -3.08439181e-02 +7.02497626e-01j\n",
        "  -3.08540630e-02 +7.02498307e-01j  -3.08761114e-02 +7.02499028e-01j\n",
        "  -3.08957218e-02 +7.02499433e-01j] (array([  1.04250623e+01,   1.51842290e+01,   3.25755099e+01,\n",
        "         6.16004358e+01,   8.46106546e+01,   9.15416501e+01,\n",
        "         9.41057689e+01,   9.54534366e+01,   9.62980028e+01,\n",
        "         9.68560992e+01,   9.72291393e+01,   9.74787343e+01,\n",
        "         9.77427801e+01,   9.80109244e+01,   9.82749421e+01,\n",
        "         9.85284962e+01,   9.87669033e+01,   9.89869077e+01,\n",
        "         1.02330024e+02,   1.12522515e+02,   1.27437698e+02,\n",
        "         1.52771347e+02,   1.97433785e+02,   2.79416850e+02,\n",
        "         4.36117589e+02,   7.47052417e+02,   1.38419269e+03,\n",
        "         2.72406252e+03,   5.59822214e+03,   1.18542471e+04,\n",
        "         2.56141002e+04]), array([ -77.81035558, -175.89268984, -170.45525015, -160.60855616,\n",
        "       -148.68117232, -141.62175107, -138.28949602, -136.70192166,\n",
        "       -135.91915906, -135.51770546, -135.30300073, -135.18323205,\n",
        "       -135.08656033, -135.01054362, -134.95270735, -134.91062064,\n",
        "       -134.88195438, -134.86452348, -133.94357304, -131.46909534,\n",
        "       -128.48120651, -124.63366007, -119.99533648, -114.8495013 ,\n",
        "       -109.65593591, -104.892614  , -100.88348042,  -97.73537134,\n",
        "        -95.3881792 ,  -93.70146842,  -92.51822905]))\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the analytic solution for the halfspace.\n",
      "import simpegMT as simpegmt\n",
      "sig1DBG = M.r(sigBG,'CC','CC','M')[0,0,:]\n",
      "anaEd, anaEu, anaHd, anaHu = simpegmt.Utils.MT1Danalytic.getEHfields(mesh1d,sig1DBG,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": 26
    },
    {
     "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": [
        "[ 0.06283185+0.06283185j  0.06283185+0.06283185j  0.06283185+0.06283185j\n",
        "  0.06283185+0.06283185j  0.06283185+0.06283185j  0.06283185+0.06283185j\n",
        "  0.06283185+0.06283185j  0.06283185+0.06283185j  0.06283185+0.06283185j\n",
        "  0.06283185+0.06283185j  0.06283185+0.06283185j  0.06283185+0.06283185j\n",
        "  0.06283185+0.06283185j  0.06283185+0.06283185j  0.06283185+0.06283185j\n",
        "  0.06283185+0.06283185j  0.06283185+0.06283185j  0.06283185+0.06283185j\n",
        "  0.06283185+0.06283185j  0.06283186+0.07072753j  0.06283186+0.0786232j\n",
        "  0.06283186+0.09046671j  0.06283188+0.10823197j  0.06283192+0.13487985j\n",
        "  0.06283203+0.17485166j  0.06283233+0.23480933j  0.06283320+0.32474574j\n",
        "  0.06283584+0.4596502j   0.06284399+0.66200657j  0.06286981+0.96554066j\n",
        "  0.06295315+1.42084151j] (array([   100.        ,    100.        ,    100.        ,    100.        ,\n",
        "          100.        ,    100.        ,    100.        ,    100.        ,\n",
        "          100.        ,    100.        ,    100.        ,    100.        ,\n",
        "          100.        ,    100.        ,    100.        ,    100.        ,\n",
        "          100.        ,    100.        ,    100.        ,    113.3559252 ,\n",
        "          128.29098377,    153.65444595,    198.36160419,    280.41175545,\n",
        "          437.21314017,    748.29899993,   1385.66608888,   2725.87734814,\n",
        "         5600.55464041,  11857.38236111,  25618.47494334]), array([ 44.99999841,  44.99999841,  44.99999841,  44.99999841,\n",
        "        44.99999841,  44.99999841,  44.99999841,  44.99999841,\n",
        "        44.99999841,  44.99999841,  44.99999841,  44.99999841,\n",
        "        44.99999841,  44.99999841,  44.99999841,  44.99999841,\n",
        "        44.99999841,  44.99999841,  44.99999841,  48.38323441,\n",
        "        51.36984352,  55.21885339,  59.86356014,  65.02219199,\n",
        "        70.23436618,  75.01927161,  79.04947649,  82.2157119 ,\n",
        "        84.57718798,  86.27452602,  87.46305803]))\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
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 ]
}