simpeg/MT 1D code test.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Test 1D solution of MT problem and compare to a analytic solution\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# import the simpegMT module\n",
      "from simpegMT.Utils import MT1Danalytic, MT1Dsolutions\n",
      "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": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set up the mesh.\n",
      "freq = 10\n",
      "z = 100.\n",
      "hz = [(z,10,-1.5),(z,10),(z,10,1.5)]\n",
      "M = simpeg.Mesh.TensorMesh([hz],'C')\n",
      "sig = np.zeros(M.nC) + 1e-8\n",
      "sig[M.vectorCCx<=300] = 0.01\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M.vectorNx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "array([-17499.51171875, -11733.0078125 ,  -7888.671875  ,  -5325.78125   ,\n",
        "        -3617.1875    ,  -2478.125     ,  -1718.75      ,  -1212.5       ,\n",
        "         -875.        ,   -650.        ,   -500.        ,   -400.        ,\n",
        "         -300.        ,   -200.        ,   -100.        ,      0.        ,\n",
        "          100.        ,    200.        ,    300.        ,    400.        ,\n",
        "          500.        ,    650.        ,    875.        ,   1212.5       ,\n",
        "         1718.75      ,   2478.125     ,   3617.1875    ,   5325.78125   ,\n",
        "         7888.671875  ,  11733.0078125 ,  17499.51171875])"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the fields\n",
      "anaEd, anaEu, anaHd, anaHu = MT1Danalytic.getEHfields(M,sig,freq,M.vectorNx)\n",
      "anaEtemp = (anaEd+anaEu)\n",
      "anaHtemp = (anaHd+anaHu)\n",
      "# Scale the solution\n",
      "anaZ = (anaEtemp/anaHtemp)[np.argmin(M.vectorNx**2)]\n",
      "anaEcor = anaEtemp/anaEtemp[-1] #.real/np.abs(anaEtemp[-1].real)+1j*anaEtemp.imag/np.abs(anaEtemp[-1].imag)\n",
      "anaHcor = anaHtemp/anaEtemp[-1] # .real/np.abs(anaEtemp[-1].real)+1j*anaHtemp.imag/np.abs(anaEtemp[-1].imag)\n",
      "\n",
      "solE = MT1Dsolutions.get1DEfields(M,sig,freq,sourceAmp=1).conj()\n",
      "solH = -M.nodalGrad*solE/(1j*omega(freq)*mu_0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anaEtemp[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "(922807.04800415318-689021.35510797054j)"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.hstack((simpeg.mkvc(anaEcor,2),simpeg.mkvc(solE,2)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "array([[  6.95763292e-07 +5.19497296e-07j,\n",
        "          6.95763292e-07 -5.19497296e-07j],\n",
        "       [ -1.40834457e-05 -2.93173032e-05j,\n",
        "          9.74760218e-06 -1.09092785e-05j],\n",
        "       [  3.35815988e-04 +1.40732501e-04j,\n",
        "          1.74907169e-04 +1.24344756e-04j],\n",
        "       [ -7.70118008e-04 +1.65141317e-03j,\n",
        "         -5.21083562e-04 +1.34839517e-03j],\n",
        "       [ -5.32123445e-03 +3.24450003e-04j,\n",
        "         -4.87010903e-03 +6.63069113e-04j],\n",
        "       [ -8.64982593e-03 -6.64134560e-03j,\n",
        "         -8.61854919e-03 -6.03020882e-03j],\n",
        "       [ -7.46721859e-03 -1.59079740e-02j,\n",
        "         -7.68552978e-03 -1.53974787e-02j],\n",
        "       [ -2.91038457e-03 -2.39785146e-02j,\n",
        "         -3.16876824e-03 -2.35863583e-02j],\n",
        "       [  2.72167021e-03 -2.97359468e-02j,\n",
        "          2.49400722e-03 -2.94018473e-02j],\n",
        "       [  7.92975642e-03 -3.34680056e-02j,\n",
        "          7.73825040e-03 -3.31542272e-02j],\n",
        "       [  1.21356998e-02 -3.57924928e-02j,\n",
        "          1.19706555e-02 -3.54839735e-02j],\n",
        "       [  1.52903430e-02 -3.72269264e-02j,\n",
        "          1.51424717e-02 -3.69189921e-02j],\n",
        "       [  1.87388388e-02 -3.85404390e-02j,\n",
        "          1.86057886e-02 -3.82344505e-02j],\n",
        "       [  2.24915402e-02 -3.97057953e-02j,\n",
        "          2.23709926e-02 -3.94030035e-02j],\n",
        "       [  2.65576291e-02 -4.06933593e-02j,\n",
        "          2.64473102e-02 -4.03949222e-02j],\n",
        "       [  3.09448819e-02 -4.14710213e-02j,\n",
        "          3.08425733e-02 -4.11780213e-02j],\n",
        "       [  3.56594159e-02 -4.20041369e-02j,\n",
        "          3.55629651e-02 -4.17175972e-02j],\n",
        "       [  4.07054159e-02 -4.22554788e-02j,\n",
        "          4.06127458e-02 -4.19763792e-02j],\n",
        "       [  4.60848403e-02 -4.21852042e-02j,\n",
        "          4.59939588e-02 -4.19144958e-02j],\n",
        "       [  5.16310109e-02 -4.19401825e-02j,\n",
        "          5.15406437e-02 -4.16710354e-02j],\n",
        "       [  5.71771819e-02 -4.16951603e-02j,\n",
        "          5.70873289e-02 -4.14275746e-02j],\n",
        "       [  6.54964388e-02 -4.13276262e-02j,\n",
        "          6.54073573e-02 -4.10623825e-02j],\n",
        "       [  7.79753255e-02 -4.07763228e-02j,\n",
        "          7.78874014e-02 -4.05145921e-02j],\n",
        "       [  9.66936582e-02 -3.99493614e-02j,\n",
        "          9.66074704e-02 -3.96929008e-02j],\n",
        "       [  1.24771163e-01 -3.87089021e-02j,\n",
        "          1.24687581e-01 -3.84603474e-02j],\n",
        "       [  1.66887432e-01 -3.68481633e-02j,\n",
        "          1.66807761e-01 -3.66114701e-02j],\n",
        "       [  2.30061859e-01 -3.40569049e-02j,\n",
        "          2.29988062e-01 -3.38380118e-02j],\n",
        "       [  3.24823538e-01 -2.98695515e-02j,\n",
        "          3.24758579e-01 -2.96773825e-02j],\n",
        "       [  4.66966101e-01 -2.35870424e-02j,\n",
        "          4.66914483e-01 -2.34350351e-02j],\n",
        "       [  6.80179899e-01 -1.41584953e-02j,\n",
        "          6.80148567e-01 -1.40669735e-02j],\n",
        "       [  1.00000000e+00 +0.00000000e+00j,\n",
        "          1.00000000e+00 -0.00000000e+00j]])"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(solE.real,M.vectorNx,'r*--',anaEcor.real,M.vectorNx,'b+:')\n",
      "#axis([-.2,.2,-10000,10000])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f4f0d873110>,\n",
        " <matplotlib.lines.Line2D at 0x7f4f0d873390>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vt6zYTIvTWoUak8gB6qoSSQJf5ezmk3v/CwsWALFnWtSvf3C7koZUBUocIonKz4e5c2HY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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4f0dbf3a90>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot((abs(solE.real)-abs(anaEcor.real))/abs(anaEcor.real),M.vectorNx,'b+:')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f4f0d7d6e10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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n3DpW2kZ6KHCvXtClS1iL64orwmTENWtCZ3smE5q9eveGE04I97vnvl5EKocC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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4f0d80a210>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(solE.imag,M.vectorNx,'r*--',anaEcor.imag,M.vectorNx,'b+:')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f4f0d71a410>,\n",
        " <matplotlib.lines.Line2D at 0x7f4f0d71a690>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2Tat+wwk1voKtW3l4YSc6Dz2R7t3D97v8chgwAC6tPR/69ePhKrdxzrFrOavF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\noRgUo6EAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4f0d7f8550>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot((abs(solE.imag)-abs(anaEcor.imag))/abs(anaEcor.imag),M.vectorNx,'b+:')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f4f0d657a10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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z+1NeAGhmZr3CicMamrumzHqeu6rMzPopd1WZmVmvcOIwM7OKOHGYmVlFnDjM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\nM6uIE4eZmVXEicPMzCry/w6nUYe43AHvAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4f0d81f850>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "semilogx(abs(solE),M.vectorNx,'r*--',abs(anaEcor),M.vectorNx,'b+:')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f4f0d638650>,\n",
        " <matplotlib.lines.Line2D at 0x7f4f0d58b9d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hmdkvzewjM9tuZkeXO5ZjZoVmttTMBkSV9zCzReGxu6PKM83s8bD8LTPrGHXs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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4f0d6175d0>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot((abs(solE)-abs(anaEcor))/abs(anaEcor),M.vectorNx,'b+:')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f4f0d35efd0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UU9CjR5w4Jk4M27/kEtK8edCzp85mkeqyxcTh7sfn+4Xuvh5YH90/b2ZvAHWE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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4f0d462bd0>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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",
      "app_rAna, app_pAna = appResPhs(freq,anaZ)\n",
      "app_rSol, app_pSol = appResPhs(freq,solE[np.argmin(M.hx**2)]/solH[np.argmin(M.hx**2)])\n",
      "print app_rAna, app_pAna\n",
      "print app_rSol, app_pSol"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100.0 44.999998407\n",
        "91.3634893888 -137.014649098\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M.nodalGrad.dot(solE).shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "(30,)"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M.nN\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "31"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(-solH.imag,M.vectorCCx,'r*--',anaHcor.imag,M.vectorNx,'b+:')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f4f0d209810>,\n",
        " <matplotlib.lines.Line2D at 0x7f4f0d209a90>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SbHmUL18VlBP8LABw913AVjNrjohILZFIwNq1MH36ngdU3f/BB1zw6KMVP6Y3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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4f0d733610>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M.vectorCCx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "array([-14616.25976562,  -9810.83984375,  -6607.2265625 ,  -4471.484375  ,\n",
        "        -3047.65625   ,  -2098.4375    ,  -1465.625     ,  -1043.75      ,\n",
        "         -762.5       ,   -575.        ,   -450.        ,   -350.        ,\n",
        "         -250.        ,   -150.        ,    -50.        ,     50.        ,\n",
        "          150.        ,    250.        ,    350.        ,    450.        ,\n",
        "          575.        ,    762.5       ,   1043.75      ,   1465.625     ,\n",
        "         2098.4375    ,   3047.65625   ,   4471.484375  ,   6607.2265625 ,\n",
        "         9810.83984375,  14616.25976562])"
       ]
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     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
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     "input": [],
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 ]
}