MT Script-3D_layerTest.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:10cac7f43ce75cbe563e4200a1f752d3ab5dff458ffd49c1559eac07e93f8e3c"
 },
 "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.,10),(100,5,1.5)],[(100,5,-1.5),(100.,10),(100,5,1.5)],[(100,5,1.6),(100.,10),(100,3,2)]], x0=['C','C',-3529.5360])"
     ],
     "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": [
        "[ -3.52953600e+03  -3.36953600e+03  -3.11353600e+03  -2.70393600e+03\n",
        "  -2.04857600e+03  -1.00000000e+03  -9.00000000e+02  -8.00000000e+02\n",
        "  -7.00000000e+02  -6.00000000e+02  -5.00000000e+02  -4.00000000e+02\n",
        "  -3.00000000e+02  -2.00000000e+02  -1.00000000e+02   4.54747351e-13\n",
        "   2.00000000e+02   6.00000000e+02   1.40000000e+03]\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Setup the model\n",
      "conds = [1e-2,1]\n",
      "sig = simpeg.Utils.ModelBuilder.defineBlock(M.gridCC,[-1000,-1000,-400],[1000,1000,-200],conds)\n",
      "sig[M.gridCC[:,2]>0] = 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": 6,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0x7f24725c9368>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XwyFOYRCRDBHJEZHF7tQvQLuTROQdEVklIitF5GyvdsWskFfWwjzo2N2ZnzoH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\nmeQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f24727970d0>"
       ]
      }
     ],
     "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": [
      "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": [
      "# 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=None)\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(M.r(ex_x,'Ex','Ex','V'),2),ey_x,ez_x))\n",
      "rhs_x = ABG.dot(eBG_x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in arange(M.vnEx[0]):\n",
      "    for j in arange(M.vnEx[1]):\n",
      "        ex_x[i,j,:] = e0_1d"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "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",
      "# eBG_y = np.vstack((ex_y,Utils.mkvc(M.r(ey_y,'Ey','Ey','V'),2),ez_y))\n",
      "# rhs_y = ABG.dot(eBG_y)"
     ],
     "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",
      "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",
      "# Solve the systems for each polarization\n",
      "Ainv = simpeg.SolverLU(A)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 1min 16s, sys: 761 ms, total: 1min 17s\n",
        "Wall time: 1min 18s\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "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 497 ms, sys: 1 ms, total: 498 ms\n",
        "Wall time: 534 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_x"
     ],
     "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": "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='X', 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='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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rYzvWaXlzgJzv9uriF8DbsMWr/imtp2P36jFMof4bxt73OuyedWPhJD5Mfn2+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\n6wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2471e8ce10>"
       ]
      }
     ],
     "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),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",
      "h_x_loc = np.hstack([simpeg.Utils.mkvc(Qfx*C*e_x,2),simpeg.Utils.mkvc(Qfy*C*e_x,2),simpeg.Utils.mkvc(Qfz*C*e_x,2)])\n",
      "h_y_loc = np.hstack([simpeg.Utils.mkvc(Qfx*C*e_y,2),simpeg.Utils.mkvc(Qfy*C*e_y,2),simpeg.Utils.mkvc(Qfz*C*e_y,2)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "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": 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": 23,
       "text": [
        "array([ 103.21972098+93.23211088j,  102.90475057+93.32910177j,\n",
        "        102.58986124+93.42601702j,  102.39619991+93.42582359j,\n",
        "        102.20255534+93.42570773j,  102.09543991+93.39475409j,\n",
        "        101.98830179+93.36384863j,  101.93711201+93.33580298j,\n",
        "        101.88590678+93.30777082j,  101.87105299+93.29712749j,\n",
        "        101.85619645+93.28648454j,  101.87105299+93.29712749j,\n",
        "        101.88590678+93.30777082j,  101.93711201+93.33580298j,\n",
        "        101.98830179+93.36384863j,  102.09543991+93.39475409j,\n",
        "        102.20255534+93.42570773j,  102.39619991+93.42582359j,\n",
        "        102.58986124+93.42601702j,  102.90475057+93.32910177j,\n",
        "        103.21972098+93.23211088j,  103.18844918+93.25637175j,\n",
        "        102.87026907+93.35333401j,  102.55217722+93.45021873j,\n",
        "        102.35592664+93.44923714j,  102.15969494+93.44833625j,\n",
        "        102.05084774+93.41639946j,  101.94197761+93.38451306j,\n",
        "        101.88984420+93.35571748j,  101.83769506+93.32693623j,\n",
        "        101.82254720+93.31601774j,  101.80739677+93.30509988j,\n",
        "        101.82254720+93.31601774j,  101.83769506+93.32693623j,\n",
        "        101.88984420+93.35571748j,  101.94197761+93.38451306j,\n",
        "        102.05084774+93.41639946j,  102.15969494+93.44833625j,\n",
        "        102.35592664+93.44923714j,  102.55217722+93.45021873j,\n",
        "        102.87026907+93.35333401j,  103.18844918+93.25637175j,\n",
        "        103.18370715+93.264238j  ,  102.86414600+93.36098376j,\n",
        "        102.54467838+93.45765065j,  102.34719288+93.45610491j,\n",
        "        102.14972799+93.45464162j,  102.03999958+93.42207021j,\n",
        "        101.93024829+93.38955054j,  101.87761440+93.36028118j,\n",
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        "        101.88590678+93.30777082j,  101.93711201+93.33580298j,\n",
        "        101.98830179+93.36384863j,  102.09543991+93.39475409j,\n",
        "        102.20255534+93.42570773j,  102.39619991+93.42582359j,\n",
        "        102.58986124+93.42601702j,  102.90475057+93.32910177j,\n",
        "        103.21972098+93.23211088j])"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zyx\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
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        "       -103.18034361-93.27573251j, -103.17895780-93.27210029j,\n",
        "       -103.18370715-93.264238j  , -103.18844918-93.25637175j,\n",
        "       -103.21972098-93.23211088j])"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zxy + zyx\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
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       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}