{
 "metadata": {
  "name": "",
  "signature": "sha256:29c8b78e5b8f768bcf861afb186d1d85bff67ec768eba74cf6c3e85da81d0c01"
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from SimPEG import *\n",
      "import simpegDCIP as DC\n",
      "from simpegem1d import Utils1D\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "matplotlib.rcParams.update({'font.size': 20, 'text.usetex': True})\n",
      "# matplotlib.rcParams.update({'font.size': 16, 'font.family': 'arial'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "1D DC inversion of Schlumberger array"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is an example for 1D DC Sounding inversion. This 1D inversion usually use analytic foward modeling, which is efficient. However, we choose different approach to show flexibility in geophysical inversion through mapping. Here mapping ($M$)indicates transformation of our model to a different space:\n",
      "\n",
      "$$\n",
      "    \\mathbf{m} = M(\\mathbf{\\sigma})\n",
      "$$\n",
      "\n",
      "Now we consider a transformation, which maps 3D conductivity model to 1D layer model. That is, 3D distribution of conducitivity can be parameterized as 1D model. Once we can compute derivative of this transformation, we can change our model space, based on the transformation. \n",
      "\n",
      "Following example will show you how user can implement this set up with 1D DC inversion example. Note that we have 3D forward modeling mesh."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Step1: Generate mesh"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cs = 25.\n",
      "npad = 11\n",
      "hx = [(cs,npad, -1.3),(cs,41),(cs,npad, 1.3)]\n",
      "hy = [(cs,npad, -1.3),(cs,17),(cs,npad, 1.3)]\n",
      "hz = [(cs,npad, -1.3),(cs,20)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mesh.plotGrid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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U4Ng3z9Hah98PoRCsWCFx220OdB2WLYsxcaKBJMHkyQ4mT9Y57DCDDz+UeOml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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c83c990>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Step2: Generating model and mapping (1D to 3D)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mapping = Maps.ExpMap(mesh)*Maps.Vertical1DMap(mesh)\n",
      "siglay1 = 1./(100.)\n",
      "siglay2 = 1./(500.)\n",
      "sighalf = 1./(100.)\n",
      "sigma = np.ones(mesh.nCz)*siglay1\n",
      "sigma[mesh.vectorCCz<=-100.] = siglay2\n",
      "sigma[mesh.vectorCCz<-150.] = sighalf\n",
      "mtrue = np.log(sigma)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,2, figsize = (18*0.8, 7*0.8))\n",
      "Utils1D.plotLayer(np.log(sigma), mesh.vectorCCz, 'linear', showlayers=True, ax = ax[0])\n",
      "ax[0].invert_xaxis()\n",
      "ax[0].set_ylim(-500, 0)\n",
      "ax[0].set_xlim(-7, -4)\n",
      "ax[0].set_xlabel('$log(\\sigma)$', fontsize = 25)\n",
      "ax[0].set_ylabel('Depth (m)', fontsize = 25)\n",
      "ax[0].text(-7., 10., '(a)', fontsize = 30)\n",
      "dat = mesh.plotSlice((mapping*mtrue), normal='Y', ind = 9, ax = ax[1])\n",
      "cb = plt.colorbar(dat[0], ax =ax[1])\n",
      "ax[1].set_title(\"Vertical section\", fontsize = 25)\n",
      "cb.set_label(\"Conductivity (S/m)\", fontsize = 25)\n",
      "ax[1].set_xlabel('Easting (m)', fontsize = 25)\n",
      "ax[1].set_ylabel(' ', fontsize = 25)\n",
      "ax[1].set_xlim(-1000., 1000.)\n",
      "ax[1].set_ylim(-500., 0.)\n",
      "ax[1].text(-1000., 13., '(b)', fontsize = 30)\n",
      "fig.savefig('mappingDC.png', dpi=200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DVMYc0BoxBxRAEzAnuhjmgPbGHFAAzVHjHND/vo/6/9b4zgGNa4ouSbooaUrS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\n/wdChVAKeWV3DQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e1d9710>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# fig, ax = plt.subplots(1,1, figsize = (7, 5))\n",
      "# dat = mesh.plotSlice((mapping*mtrue), normal='Y', ind = 9, ax = ax)\n",
      "# cb = plt.colorbar(dat[0], ax =ax)\n",
      "# ax.set_title(\"Vertical section\", fontsize = 16)\n",
      "# cb.set_label(\"Conductivity (S/m)\", fontsize = 16)\n",
      "# ax.set_xlabel('Easting (m)', fontsize = 16)\n",
      "# ax.set_ylabel('Depth (m)', fontsize = 16)\n",
      "# ax.set_xlim(-1000., 1000.)\n",
      "# ax.set_ylim(-500., 0.)\n",
      "# ax.text(-1000., 20., '(b)', fontsize = 20)\n",
      "# fig.savefig('cond3d.png', dpi=200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Step3: Design survey: Schulumberger array"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"http://www.landrinstruments.com/_/rsrc/1271695892678/home/ultra-minires/additional-information-1/schlumberger-soundings/schlum%20array.JPG\"> </img>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "$$ \\rho_a = \\frac{V}{I}\\pi\\frac{b(b+a)}{a}$$"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Let $b=na$, then we rewrite above equation as:"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "$$ \\rho_a = \\frac{V}{I}\\pi na(n+1)$$"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Since AB/2 can be a good measure for depth of investigation, we express "
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "$$AB/2 = \\frac{(2n+1)a}{2}$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ntx = 16"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xtemp_txP = np.arange(ntx)*(25.)-500.\n",
      "xtemp_txN = -xtemp_txP\n",
      "ytemp_tx = np.zeros(ntx)\n",
      "xtemp_rxP = -50.\n",
      "xtemp_rxN = 50.\n",
      "ytemp_rx = 0.\n",
      "abhalf = abs(xtemp_txP-xtemp_txN)*0.5\n",
      "a = xtemp_rxN-xtemp_rxP\n",
      "b = ((xtemp_txN-xtemp_txP)-a)*0.5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print a\n",
      "print b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100.0\n",
        "[ 450.  425.  400.  375.  350.  325.  300.  275.  250.  225.  200.  175.\n",
        "  150.  125.  100.   75.]\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1, figsize = (12,3))\n",
      "for i in range(ntx):\n",
      "    ax.plot(np.r_[xtemp_txP[i], xtemp_txP[i]], np.r_[0., 0.4-0.01*(i-1)], 'k-', lw = 1)\n",
      "    ax.plot(np.r_[xtemp_txN[i], xtemp_txN[i]], np.r_[0., 0.4-0.01*(i-1)], 'k-', lw = 1)\n",
      "    ax.plot(xtemp_txP[i], ytemp_tx[i], 'bo')\n",
      "    ax.plot(xtemp_txN[i], ytemp_tx[i], 'ro')\n",
      "    ax.plot(np.r_[xtemp_txP[i], xtemp_txN[i]], np.r_[0.4-0.01*(i-1), 0.4-0.01*(i-1)], 'k-', lw = 1)    \n",
      "\n",
      "ax.plot(np.r_[xtemp_rxP, xtemp_rxP], np.r_[0., 0.2], 'k-', lw = 1)\n",
      "ax.plot(np.r_[xtemp_rxN, xtemp_rxN], np.r_[0., 0.2], 'k-', lw = 1)\n",
      "ax.plot(xtemp_rxP, ytemp_rx, 'ko')\n",
      "ax.plot(xtemp_rxN, ytemp_rx, 'go')\n",
      "ax.plot(np.r_[xtemp_rxP, xtemp_rxN], np.r_[0.2, 0.2], 'k-', lw = 1)    \n",
      "\n",
      "ax.grid(True)    \n",
      "ax.set_ylim(-0.2,0.6)\n",
      "# ax.set_xlim(-600,600)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "(-0.2, 0.6)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AjSpZ9tDM5iSdkrSlbB3yTXe/P0L8oqSfRokBAOyPGnIAqF/0AbmZ9W4ENJ9ru2lmnWHu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\noxWyVSEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1104a8b50>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1, figsize = (6,4))\n",
      "ax.plot(xtemp_txP, ytemp_tx, 'bo')\n",
      "ax.plot(xtemp_txN, ytemp_tx, 'ro')\n",
      "ax.plot(xtemp_rxP, ytemp_rx, 'ko')\n",
      "ax.plot(xtemp_rxN, ytemp_rx, 'go')\n",
      "ax.legend(('A (C+)', 'B (C-)', 'M (P+)', 'N (C-)'), fontsize = 14)\n",
      "mesh.plotSlice(np.log10(mapping*mtrue), grid=True, ax = ax, pcolorOpts={'cmap':'binary'})\n",
      "ax.set_xlim(-600, 600)\n",
      "ax.set_ylim(-200, 200)\n",
      "ax.set_title('Survey geometry (Plan view)')\n",
      "ax.set_xlabel('Easting (m)')\n",
      "ax.set_ylabel('Northing (m)')\n",
      "ax.text(-600, 210, '(a)', fontsize = 16)\n",
      "fig.savefig('DCsurvey.png', dpi = 200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fuqYsPL94Mve5dbos5+n8ev3ZjKMTpp80PWPExCzpfV893nlqogWU4IkWYFttZT2LkReq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\nJLXQiBFCCEkt/z8zAwkz3y21HgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e2f0790>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We generate tx and rx lists:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "txlist = []\n",
      "rx = DC.RxDipole(np.r_[xtemp_rxP, ytemp_rx, -12.5], np.r_[xtemp_rxN, ytemp_rx, -12.5])\n",
      "for i in range(ntx):    \n",
      "    tx = DC.SrcDipole([rx], [xtemp_txP[i], ytemp_tx[i], -12.5],[xtemp_txN[i], ytemp_tx[i], -12.5])\n",
      "    txlist.append(tx)\n",
      "survey = DC.SurveyDC(txlist)    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Step4: Set up problem and pair with survey"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "problem = DC.ProblemDC_CC(mesh, mapping=mapping)\n",
      "problem.pair(survey)\n",
      "try:\n",
      "    from pymatsolver import MumpsSolver\n",
      "    problem.Solver = MumpsSolver\n",
      "except Exception, e:\n",
      "    problem.Solver = SolverLU"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Step5: Run survey.dpred to comnpute syntetic data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "data = survey.dpred(mtrue)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 6.05 s, sys: 708 ms, total: 6.76 s\n",
        "Wall time: 6.01 s\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$ \\rho_a = \\frac{V}{I}\\pi\\frac{b(b+a)}{a}$$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To make synthetic example you can use survey.makeSyntheticData, which generates related setups"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survey.makeSyntheticData(mtrue,std=0.01,force=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "array([ 0.0186522 ,  0.0211157 ,  0.02374998,  0.02677094,  0.02987307,\n",
        "        0.03446037,  0.03920482,  0.04541832,  0.05344991,  0.062497  ,\n",
        "        0.07516488,  0.09013051,  0.11364832,  0.1407387 ,  0.18918521,\n",
        "        0.27485483])"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "appres = data*np.pi*b*(b+a)/a\n",
      "appres_obs = survey.dobs*np.pi*b*(b+a)/a\n",
      "fig, ax = plt.subplots(1,2, figsize = (16, 4))\n",
      "ax[1].semilogx(abhalf, appres, 'k.-')\n",
      "ax[1].set_xscale('log')\n",
      "ax[1].set_ylim(100., 180.)\n",
      "ax[1].set_xlabel('AB/2')\n",
      "ax[1].set_ylabel('Apparent resistivity ($\\Omega m$)')\n",
      "ax[1].grid(True)\n",
      "# ax[1].text(100, 183, '(c)', fontsize = 16)\n",
      "# ax[1].legend(('Observed', 'Predicted'), loc = 1)\n",
      "\n",
      "dat = mesh.plotSlice((mapping*mtrue), normal='Y', ind = 9, ax = ax[0])\n",
      "cb = plt.colorbar(dat[0], ax =ax[0])\n",
      "ax[0].set_title(\"Vertical section\")\n",
      "cb.set_label(\"Conductivity (S/m)\")\n",
      "ax[0].set_xlabel('Easting (m)')\n",
      "ax[0].set_ylabel('Depth (m)')\n",
      "ax[0].set_xlim(-1000., 1000.)\n",
      "ax[0].set_ylim(-500., 0.)\n",
      "# ax[0].text(-1000, 20, '(b)')\n",
      "# fig.savefig('DCfwd.png', dpi=200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "(-500.0, 0.0)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1PYnjUnH3vYk6DgB7FFiLiIg0V6afNfEmv54w5G0lcU6ZmQGcJiR42U/IujrW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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e2d5d10>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Step6: Run inversion"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dmis = DataMisfit.l2_DataMisfit(survey)\n",
      "reg = Regularization.Tikhonov(mesh,mapping=mapping)\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": 23
    },
    {
     "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  2.61e-01  6.27e+03  4.48e+00  6.27e+03    8.80e+03      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   1  2.61e-01  5.22e+02  3.96e+00  5.23e+02    2.50e+03      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   2  5.23e-02  1.67e+02  3.91e+00  1.67e+02    8.21e+02      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   3  5.23e-02  7.93e+01  4.08e+00  7.95e+01    2.62e+02      0   Skip BFGS  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   4  1.05e-02  6.35e+01  4.25e+00  6.36e+01    1.71e+02      0   Skip BFGS  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   5  1.05e-02  3.01e+01  5.08e+00  3.02e+01    2.36e+02      0   Skip BFGS  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   6  2.09e-03  2.33e+01  5.32e+00  2.33e+01    1.57e+02      0   Skip BFGS  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   7  2.09e-03  1.01e+01  6.69e+00  1.01e+01    1.34e+02      0   Skip BFGS  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "------------------------- STOP! -------------------------\n",
        "1 : |fc-fOld| = 1.3195e+01 <= tolF*(1+|f0|) = 6.2702e+02\n",
        "0 : |xc-x_last| = 7.9764e-01 <= tolX*(1+|x0|) = 2.6641e-14\n",
        "0 : |proj(x-g)-x|    = 1.3417e+02 <= tolG          = 1.0000e-01\n",
        "0 : |proj(x-g)-x|    = 1.3417e+02 <= 1e3*eps       = 1.0000e-02\n",
        "1 : maxIter   =       7    <= iter          =      7\n",
        "------------------------- DONE! -------------------------\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "appres = data*np.pi*b*(b+a)/a\n",
      "appres_obs = survey.dobs*np.pi*b*(b+a)/a\n",
      "appres_pred = invProb.dpred*np.pi*b*(b+a)/a\n",
      "fig, ax = plt.subplots(1,2, figsize = (17, 6))\n",
      "ax[0].plot(abhalf, appres_obs, 'k.-')\n",
      "ax[0].plot(abhalf, appres_pred, 'r.-')\n",
      "ax[0].set_xscale('log')\n",
      "ax[0].set_ylim(100., 180.)\n",
      "ax[0].set_xlabel('AB/2', fontsize=25)\n",
      "ax[0].set_ylabel('Apparent resistivity ($\\Omega m$)', fontsize=25)\n",
      "ax[0].grid(True)\n",
      "ax[0].legend(('Observed', 'Predicted'), loc = 1, fontsize=20)\n",
      "ax[0].text(100., 181, '(a)', fontsize = 28)\n",
      "ax[1].plot(1., 1., 'k', lw = 2)\n",
      "ax[1].plot(1., 1., 'r', lw = 2)\n",
      "ax[1].legend(('True', 'Predicted'), loc = 3, fontsize = 20)\n",
      "Utils1D.plotLayer((np.exp(mopt)), mesh.vectorCCz, 'log', ax = ax[1], **{'lw':2, 'color':'r'})\n",
      "Utils1D.plotLayer((np.exp(mtrue)), mesh.vectorCCz, 'log', showlayers=True, ax = ax[1], **{'lw':2})\n",
      "ax[1].set_ylim(-500, 0)\n",
      "ax[1].set_xlabel('Conductivity (S/m)', fontsize = 25)\n",
      "ax[1].set_ylabel('Depth (m)', fontsize = 25)\n",
      "ax[1].text(1e-3, 10., '(b)', fontsize = 28)\n",
      "# fig.savefig('obspredDC.png', dpi=200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<matplotlib.text.Text at 0x11049df90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GoAM4ABxOVy6bMmnOyeq/gRQWWidccgHBJrJNZXovWHePf0qrQr27T234QkWm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\nCxMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114071f10>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# fig, ax = plt.subplots(1,1, figsize = (7.5, 7))\n",
      "# ax.plot(1., 1., 'k', lw = 2)\n",
      "# ax.plot(1., 1., 'r', lw = 2)\n",
      "# ax.legend(('True', 'Predicted'), loc = 3, fontsize = 16)\n",
      "# Utils1D.plotLayer((np.exp(mopt)), mesh.vectorCCz, 'log', ax = ax, **{'lw':2, 'color':'r'})\n",
      "# Utils1D.plotLayer((np.exp(mtrue)), mesh.vectorCCz, 'log', showlayers=True, ax = ax, **{'lw':2})\n",
      "# ax.set_ylim(-500, 0)\n",
      "# ax.set_xlabel('Conductivity (S/m)', fontsize = 20)\n",
      "# ax.set_ylabel('Depth (m)', fontsize = 20)\n",
      "# ax.text(1e-3, 10., '(b)', fontsize = 30)\n",
      "# fig.savefig('obspred_dc1d_mod.png', dpi=200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}