{
 "metadata": {
  "name": "",
  "signature": "sha256:9107d99fbfb21822b71a94de16f078e82c12cf73d2e85893fd26dfda06c8ce14"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from SimPEG import *\n",
      "import simpegDCIP as DC\n",
      "%pylab inline\n",
      "from pymatsolver import MumpsSolver"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "Vendor:  Continuum Analytics, Inc.\n",
        "Package: mkl\n",
        "Message: trial mode expires in 29 days\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cs = 12.5\n",
      "nc = 500/cs+1\n",
      "hx = [(cs,7, -1.3),(cs,nc),(cs,7, 1.3)]\n",
      "hy = [(cs,7, -1.3),(cs,int(nc/2+1)),(cs,7, 1.3)]\n",
      "hz = [(cs,7, -1.3),(cs,int(nc/2+1))]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')\n",
      "print mesh"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  ---- 3-D TensorMesh ----  \n",
        "   x0: -541.97\n",
        "   y0: -416.97\n",
        "   z0: -548.22\n",
        "  nCx: 55\n",
        "  nCy: 35\n",
        "  nCz: 28\n",
        "   hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
        "   hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
        "   hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sighalf = 1e-2\n",
      "sigma = np.ones(mesh.nC)*sighalf\n",
      "p0 = np.r_[-50., 50., -50.]\n",
      "p1 = np.r_[ 50.,-50., -150.]\n",
      "blk_ind = Utils.ModelBuilder.getIndecesBlock(p0, p1, mesh.gridCC)\n",
      "sigma[blk_ind] = 1e-3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nElecs = 21\n",
      "x_temp = np.linspace(-250, 250, nElecs)\n",
      "aSpacing = x_temp[1]-x_temp[0]\n",
      "y_temp = 0.\n",
      "xyz = Utils.ndgrid(x_temp, np.r_[y_temp], np.r_[0.])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "srcList = DC.Examples.WennerArray.getSrcList(nElecs,aSpacing)\n",
      "survey = DC.SurveyDC(srcList)\n",
      "print len(survey.srcList)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "63\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1, figsize = (6.5,5))\n",
      "indz = 22\n",
      "dat = mesh.plotSlice(sigma, grid=True, ax = ax, ind=indz)\n",
      "ax.set_xlim(-300, 300)\n",
      "ax.set_ylim(-300, 300)\n",
      "cb = plt.colorbar(dat[0])\n",
      "ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n",
      "\n",
      "txLocs = np.array([[tx.loc[0][0], tx.loc[1][0]] for tx in survey.srcList]).flatten()\n",
      "ax.plot(txLocs, txLocs*0, 'w.', ms = 3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10db0fa50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ri9L3M1Mbz8vlfARYExH/lr7fBRwFHNyvrqRPAecAnwNeHhH/VdSspp3DOQi4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\nWSX+P3faf3379MqEAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d9ac710>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expmap = Maps.ExpMap(mesh)\n",
      "m2to3  = Maps.Map2Dto3D(mesh,normal='Y')\n",
      "imap   = Maps.IdentityMap(mesh)\n",
      "problem = DC.ProblemDC_CC(mesh, mapping= imap )\n",
      "problem.Solver = MumpsSolver\n",
      "problem.pair(survey)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datasyn = survey.dpred(sighalf*np.ones(mesh.nC))\n",
      "survey.makeSyntheticData(sigma,std=0.01,force=True)\n",
      "plot(np.log(np.c_[survey.dobs,datasyn]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1038621d0>,\n",
        " <matplotlib.lines.Line2D at 0x103862450>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+92fW7+bNUFXVu+Px8+fDRRcFM36OO673+hHJBd0Nff1UlqfMjGjrYOq37OhS+1/9Cj7+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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e82c610>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "problem.mapping = expmap * m2to3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dmis = DataMisfit.l2_DataMisfit(survey)\n",
      "reg = Regularization.Tikhonov(mesh,mapping=m2to3)\n",
      "opt = Optimization.InexactGaussNewton(maxIter=7,tolX=1e-15)\n",
      "opt.remember('xc')\n",
      "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
      "beta = Directives.BetaEstimate_ByEig(beta0_ratio=1e1)\n",
      "betaSched = Directives.BetaSchedule(coolingFactor=5, coolingRate=2)\n",
      "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaSched])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m0 = np.log(np.ones(problem.mapping.nP)*sighalf)\n",
      "mopt = inv.run(m0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SimPEG.InvProblem will set Regularization.mref to m0.\n",
        "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
        "                    ***Done using same solver as the problem***\n",
        "SimPEG.l2_DataMisfit is creating default weightings for Wd."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "============================ Inexact Gauss Newton ============================"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
        "-----------------------------------------------------------------------------\n",
        "   0  7.28e+00  1.86e+03  1.05e+04  7.84e+04    2.20e+03      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   1  7.28e+00  6.57e+02  1.06e+04  7.80e+04    1.15e+03      1              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   2  1.46e+00  5.58e+02  1.06e+04  1.61e+04    1.03e+03      3   Skip BFGS  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   3  1.46e+00  1.36e+02  1.08e+04  1.59e+04    1.69e+02      0   Skip BFGS  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   4  2.91e-01  8.24e+01  1.07e+04  3.20e+03    2.05e+02      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   5  2.91e-01  4.61e+01  1.07e+04  3.16e+03    1.35e+02      0   Skip BFGS  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   6  5.83e-02  4.40e+01  1.07e+04  6.65e+02    1.16e+02      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   7  5.83e-02  3.99e+01  1.07e+04  6.61e+02    1.35e+02      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "------------------------- STOP! -------------------------\n",
        "1 : |fc-fOld| = 4.7528e+00 <= tolF*(1+|f0|) = 7.8397e+03\n",
        "0 : |xc-x_last| = 4.6314e-01 <= tolX*(1+|x0|) = 1.8172e-13\n",
        "0 : |proj(x-g)-x|    = 1.3494e+02 <= tolG          = 1.0000e-01\n",
        "0 : |proj(x-g)-x|    = 1.3494e+02 <= 1e3*eps       = 1.0000e-02\n",
        "1 : maxIter   =       7    <= iter          =      7\n",
        "------------------------- DONE! -------------------------\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colorbar(mesh.plotSlice(np.log10(expmap * m2to3 * mopt), normal='Y', ind=indz, grid=True)[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0x10fcbaa28>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AY8DLwM/N4ue+64HjJb0GHBvWC16G4ziO0146yZvIzEbn2b4OOCWxvhBYmKPe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\njuM4+GDgOI7j4IOB4ziOA/w/91GdssuxpB0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x103879a90>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dpred = survey.dpred(mopt)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(np.c_[dpred,survey.dobs])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10f42a790>,\n",
        " <matplotlib.lines.Line2D at 0x10f42aa10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10fa4d110>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}