simpeg/MT Script-3D_layerTest.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:090d72f749f721ee8a9ec2a32ec92230d1a8d709fd7a2d443382e8716798661a"
 },
 "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": [],
     "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 0x7f49fad5ae60>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XwyFOYRCRDBHJEZHF7tQvQLuTROQdEVklIitF5GyvdsWskFfWwjzo2N2ZnzoH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\nmeQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f49faf630d0>"
       ]
      }
     ],
     "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)\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": 10
    },
    {
     "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": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "(19,)"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "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[2]):\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": 68
    },
    {
     "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": 69
    },
    {
     "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 1s, sys: 692 ms, total: 1min 2s\n",
        "Wall time: 1min 3s\n"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "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 442 ms, sys: 7 ms, total: 449 ms\n",
        "Wall time: 873 ms\n"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "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": 72
    },
    {
     "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": 73
    },
    {
     "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": 74
    },
    {
     "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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3dqXZDAH+JsrIfaExRonILUASP/CkFRgluhwUquqGUjCM3LxW7aO8Y5KwmiwlkFrI1Hpt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\nXY5qP1ZOfGH5ChQYW9gq3L0PzHkBHPxN2OUlE31GBSYzdm4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3Q2nuLGoDQzu2MaWRbdp3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\nJCR0GP4/lchGUTCxr0EAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7f4b5e6f10>"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "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": 76
    },
    {
     "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": 77
    },
    {
     "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": 98
    },
    {
     "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": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zxy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "array([ 103.08034974+93.33312557j,  102.77545813+93.44979787j,\n",
        "        102.47308710+93.56743107j,  102.29608745+93.58878322j,\n",
        "        102.12043418+93.61118429j,  102.03854087+93.59991998j,\n",
        "        101.95718301+93.5893647j ,  101.94174620+93.57351209j,\n",
        "        101.92630594+93.55792438j,  101.95796202+93.54393898j,\n",
        "        101.98916063+93.52979246j,  102.05716977+93.5111035j ,\n",
        "        102.12416754+93.49191324j,  102.22305017+93.45331517j,\n",
        "        102.32007506+93.41406643j,  102.44488440+93.33387177j,\n",
        "        102.56648651+93.253287j  ,  102.70737308+93.10606995j,\n",
        "        102.84300993+92.95955571j,  102.98041066+92.72421867j,\n",
        "        103.10984421+92.49217487j,  103.04861775+93.3581011j ,\n",
        "        102.74071637+93.47494095j,  102.43537003+93.59276115j,\n",
        "        102.25606292+93.6135275j ,  102.07812067+93.63536852j,\n",
        "        101.99485628+93.62326119j,  101.91213383+93.61188394j,\n",
        "        101.89617372+93.59528018j,  101.88020924+93.57895428j,\n",
        "        101.91199608+93.56448122j,  101.94331979+93.54985155j,\n",
        "        102.01195260+93.53098315j,  102.07956452+93.51160968j,\n",
        "        102.17950023+93.47310804j,  102.27756410+93.43394328j,\n",
        "        102.40389697+93.35401182j,  102.52700083+93.27366804j,\n",
        "        102.66995374+93.12663616j,  102.80762045+92.98027486j,\n",
        "        102.94753505+92.74460789j,  103.07941959+92.51219865j,\n",
        "        103.04319208+93.36652086j,  102.73399438+93.48328289j,\n",
        "        102.42737040+93.60103561j,  102.24696355+93.62138706j,\n",
        "        102.06793128+93.64282778j,  101.98396919+93.63020579j,\n",
        "        101.90055233+93.61832617j,  101.88431104+93.60129305j,\n",
        "        101.86806489+93.58454546j,  101.89991440+93.56982811j,\n",
        "        101.93129806+93.55495676j,  102.00025785+93.53605788j,\n",
        "        102.06819242+93.51665143j,  102.16867400+93.47831136j,\n",
        "        102.26727773+93.43930055j,  102.39436496+93.3596509j ,\n",
        "        102.51821467+93.2795761j ,  102.66210841+93.1328029j ,\n",
        "        102.80070309+92.98668309j,  102.94161506+92.75102982j,\n",
        "        103.07447735+92.51861718j,  103.03776119+93.37495406j,\n",
        "        102.72726973+93.49163503j,  102.41937018+93.60931671j,\n",
        "        102.23786516+93.62925141j,  102.05774410+93.6502897j ,\n",
        "        101.97308488+93.63715194j,  101.88897394+93.6247686j ,\n",
        "        101.87245118+93.60730522j,  101.85592290+93.59013488j,\n",
        "        101.88783415+93.57517225j,  101.91927669+93.5600581j ,\n",
        "        101.98856217+93.54112738j,  102.05681798+93.52168654j,\n",
        "        102.15784399+93.483506j  ,  102.25698601+93.44464701j,\n",
        "        102.38482603+93.36527577j,  102.50941984+93.28546642j,\n",
        "        102.65425227+93.13894606j,  102.79377247+92.99306162j,\n",
        "        102.93567768+92.75741245j,  103.06951310+92.52498634j,\n",
        "        103.03848088+93.37897699j,  102.72743094+93.49555632j,\n",
        "        102.41898326+93.61314146j,  102.23696565+93.63281885j,\n",
        "        102.05633708+93.65360768j,  101.97133835+93.64017504j,\n",
        "        101.88688946+93.62750356j,  101.87022948+93.60980448j,\n",
        "        101.85356358+93.59240265j,  101.88551479+93.57731764j,\n",
        "        101.91699577+93.56208243j,  101.98645948+93.54315685j,\n",
        "        102.05489127+93.52371959j,  102.15620340+93.48565456j,\n",
        "        102.25562865+93.44690677j,  102.38384124+93.36771445j,\n",
        "        102.50880394+93.28807725j,  102.65405999+93.14171732j,\n",
        "        102.79399910+92.99598488j,  102.93629109+92.76036616j,\n",
        "        103.07050753+92.52796281j,  103.03919125+93.38300761j,\n",
        "        102.72758420+93.49948344j,  102.41858953+93.61696992j,\n",
        "        102.23606009+93.63638868j,  102.05492462+93.65692649j,\n",
        "        101.96958651+93.64319798j,  101.88479968+93.63023721j,\n",
        "        101.86800209+93.61230159j,  101.85119807+93.5946673j ,\n",
        "        101.88318846+93.57945909j,  101.91470705+93.56410189j,\n",
        "        101.98434806+93.54518051j,  102.05295486+93.52574581j,\n",
        "        102.15455233+93.48779496j,  102.25425997+93.44915698j,\n",
        "        102.38284469+93.37014142j,  102.50817581+93.29067408j,\n",
        "        102.65385536+93.14447083j,  102.79421320+92.99888639j,\n",
        "        102.93689167+92.76329184j,  103.07148872+92.5309046j ,\n",
        "        103.04055530+93.38521604j,  102.72870919+93.50161789j,\n",
        "        102.41948115+93.61903275j,  102.23672036+93.63830807j,\n",
        "        102.05535617+93.65870639j,  101.96986118+93.64482203j,\n",
        "        101.88491820+93.63170885j,  101.86805920+93.61365265j,\n",
        "        101.85119350+93.59589991j,  101.88321003+93.58063318j,\n",
        "        101.91475393+93.56521807j,  101.98449018+93.54630597j,\n",
        "        102.05319102+93.52687963j,  102.15493487+93.4889941j ,\n",
        "        102.25478741+93.45041917j,  102.38355271+93.37149757j,\n",
        "        102.50906270+93.29212082j,  102.65493176+93.14599726j,\n",
        "        102.79547734+93.00048856j,  102.93830727+92.76490659j,\n",
        "        103.07305433+92.5325284j ,  103.04191039+93.38742831j,\n",
        "        102.72982605+93.50375514j,  102.42036541+93.62109716j,\n",
        "        102.23737370+93.64022816j,  102.05578119+93.66048596j,\n",
        "        101.97012931+93.64644501j,  101.88503014+93.63317858j,\n",
        "        101.86810938+93.61500114j,  101.85118157+93.59712919j,\n",
        "        101.88322362+93.58180334j,  101.91479216+93.56632964j,\n",
        "        101.98462300+93.54742616j,  102.05341722+93.52800751j,\n",
        "        102.15530703+93.4901865j ,  102.25530406+93.45167375j,\n",
        "        102.38425007+93.37284486j,  102.50993911+93.29355735j,\n",
        "        102.65599852+93.14751129j,  102.79673272+93.00207596j,\n",
        "        102.93971555+92.76650285j,  103.07461410+92.53412977j,\n",
        "        103.04269346+93.3884377j ,  102.73051959+93.50472704j,\n",
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        "        101.88533767+93.63385721j,  101.86839407+93.61562957j,\n",
        "        101.85144329+93.5977083j ,  101.88349856+93.58235923j,\n",
        "        101.91507995+93.56686254j,  101.98495265+93.5479646j ,\n",
        "        102.05378821+93.52855106j,  102.15574029+93.49075849j,\n",
        "        102.25579895+93.45227323j,  102.38481896+93.37348338j,\n",
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        "        102.25478741+93.45041917j,  102.38355271+93.37149757j,\n",
        "        102.50906270+93.29212082j,  102.65493176+93.14599726j,\n",
        "        102.79547734+93.00048856j,  102.93830727+92.76490659j,\n",
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        "        102.72758420+93.49948344j,  102.41858953+93.61696992j,\n",
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        "        101.86800209+93.61230159j,  101.85119807+93.5946673j ,\n",
        "        101.88318846+93.57945909j,  101.91470705+93.56410189j,\n",
        "        101.98434806+93.54518051j,  102.05295486+93.52574581j,\n",
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        "        103.03848088+93.37897699j,  102.72743094+93.49555632j,\n",
        "        102.41898326+93.61314146j,  102.23696565+93.63281885j,\n",
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        "        101.88688946+93.62750356j,  101.87022948+93.60980448j,\n",
        "        101.85356358+93.59240265j,  101.88551479+93.57731764j,\n",
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        "        103.07050753+92.52796281j,  103.03776119+93.37495406j,\n",
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        "        102.23786516+93.62925141j,  102.05774410+93.6502897j ,\n",
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        "        101.87245118+93.60730522j,  101.85592290+93.59013488j,\n",
        "        101.88783415+93.57517225j,  101.91927669+93.5600581j ,\n",
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        "        102.15784399+93.483506j  ,  102.25698601+93.44464701j,\n",
        "        102.38482603+93.36527577j,  102.50941984+93.28546642j,\n",
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        "        102.93567768+92.75741245j,  103.06951310+92.52498634j,\n",
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        "        101.90055233+93.61832617j,  101.88431104+93.60129305j,\n",
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        "        102.51821467+93.2795761j ,  102.66210841+93.1328029j ,\n",
        "        102.80070309+92.98668309j,  102.94161506+92.75102982j,\n",
        "        103.07447735+92.51861718j,  103.04861775+93.3581011j ,\n",
        "        102.74071637+93.47494095j,  102.43537003+93.59276115j,\n",
        "        102.25606292+93.6135275j ,  102.07812067+93.63536852j,\n",
        "        101.99485628+93.62326119j,  101.91213383+93.61188394j,\n",
        "        101.89617372+93.59528018j,  101.88020924+93.57895428j,\n",
        "        101.91199608+93.56448122j,  101.94331979+93.54985155j,\n",
        "        102.01195260+93.53098315j,  102.07956452+93.51160968j,\n",
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        "        102.40389697+93.35401182j,  102.52700083+93.27366804j,\n",
        "        102.66995374+93.12663616j,  102.80762045+92.98027486j,\n",
        "        102.94753505+92.74460789j,  103.07941959+92.51219865j,\n",
        "        103.08034974+93.33312557j,  102.77545813+93.44979787j,\n",
        "        102.47308710+93.56743107j,  102.29608745+93.58878322j,\n",
        "        102.12043418+93.61118429j,  102.03854087+93.59991998j,\n",
        "        101.95718301+93.5893647j ,  101.94174620+93.57351209j,\n",
        "        101.92630594+93.55792438j,  101.95796202+93.54393898j,\n",
        "        101.98916063+93.52979246j,  102.05716977+93.5111035j ,\n",
        "        102.12416754+93.49191324j,  102.22305017+93.45331517j,\n",
        "        102.32007506+93.41406643j,  102.44488440+93.33387177j,\n",
        "        102.56648651+93.253287j  ,  102.70737308+93.10606995j,\n",
        "        102.84300993+92.95955571j,  102.98041066+92.72421867j,\n",
        "        103.10984421+92.49217487j])"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}