simpeg/notebooks/2DinvDC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from SimPEG import *\n",
    "import simpegDCIP as DC\n",
    "from numpy.polynomial import polynomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['linalg', 'inv']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cs = 0.5\n",
    "mesh = Mesh.TensorMesh([np.ones(100)*cs, np.ones(50)*cs], \"CN\")\n",
    "x = mesh.vectorCCx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "actx = (x>-15.)&(x<15.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "order = 3\n",
    "Vobs = polynomial.polyvander(x, order)\n",
    "dobs = 0.05*x-4\n",
    "H = np.dot(Vobs.T, Vobs)\n",
    "g = np.dot(Vobs.T, dobs)\n",
    "mest = np.linalg.solve(H, g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dpred = Vobs.dot(mest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "V = polynomial.polyvander(x, order)\n",
    "m1D = Mesh.TensorMesh([V.shape[1]+2])\n",
    "weight = (1./(V**2).sum(axis=0))**0.5\n",
    "weight = weight / weight.max()\n",
    "weightmap = Maps.Weighting(m1D, weights=np.r_[1., 1., weight])\n",
    "m0_poly = np.r_[np.log(1e-3), np.log(1e-3), np.r_[-3., np.zeros(V.shape[1]-1)] / weight]\n",
    "mtrue_poly = np.r_[np.log(1e-3), np.log(1e0), mest / weight]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "polymap = Maps.PolyMap(mesh, V.shape[1]-1, logSigma=True, normal='Y')\n",
    "mappingfwd = polymap*weightmap\n",
    "mapping = Maps.ExpMap(mesh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sigma = mappingfwd*mtrue_poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xr = np.linspace(-15, 15, 20)\n",
    "xz_A = Utils.ndgrid(xr, np.r_[-0.25])\n",
    "xz_B = Utils.ndgrid(np.ones_like(xr)*22, np.r_[-0.25])\n",
    "xz_M = Utils.ndgrid(xr, np.r_[-0.25])\n",
    "xz_N = Utils.ndgrid(np.ones_like(xr)*-22, np.r_[-0.25])\n",
    "\n",
    "ntx = xz_A.shape[0]\n",
    "txList = []\n",
    "for i in range(ntx):\n",
    "    offset = abs(xz_A[i,0]-xz_M[:,0])\n",
    "    actrx = offset > 5.\n",
    "    rx = DC.RxDipole(xz_M[actrx,:], xz_N[actrx,:])\n",
    "    src = DC.SrcDipole([rx], xz_A[i,:], xz_B[i,:])\n",
    "    txList.append(src)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c7a9210>]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wXNtmVhV9ep97bhZGxB5gT/b5F5I2A9OAjuFeyLBMjlnZkMyQpPcWXYyZGTTCPWXpp2yq\nvXOAx/La9u3KvccZv3cDMyLi5Wz8aZWksyPi5/2q08wsxV6O62m/HrOwVT8nAiuB6yLiF3nt+xbu\nvcz4HRH7gH3Z56cl/RiYDTw9tu3aps8zgVm9lWlmNbMN2N6Hfttdlb829ASvDT3Zdr9esnAsSccA\n9wH/HBGrUvYpw62QhwabJJ0GvBwRI5LOpBHsP2m104VHqDgzq5ZZjL7Ye3SC+m0X7sfNn8dx8+cd\n+v7Srf+710O0HHhXY0B+ObApIm5P7ayoWyGXStoBzKMx4/fBiV8vAJ6RtB74JnBVRLxSRI1mZs0O\nMClp6Ua7LJQ0TdKarNnvAH8AXNh0m/jC3L4jorszLAFJMVB0EWZWCQNARIzr1jxJMT2eS2q7U7PH\nfbyJUIZhGTOz0vNbIc3MasjhbmZWQ3v3+cVhZma1M3KgWnFZrWrNzAoycsDDMmZmteNwNzOroQP7\nHe5mZrXz5ki14rJa1ZqZFcXDMmZmNfRGteKyWtWamRXlQNEFdMfhbmaWwuFuZlZDFQv3Mk6zZ2ZW\nPvsTly5IulTSRkkj2exzndpOyl73mzR7k8PdzCzFSOLSnQ3AUmBdQtvrgE1A0nvaHe5mZikOJC5d\niIgtEbE1r52k6cAi4Cu0mbFpLI+5m5mleKPQo/8tcANwUuoODnczsxQ9/kJV0iAwtcWmmyMid/xc\n0mLgxYhYL2l+6nEd7mZmKdqF+4YheHao7W4RsWCcRz4fWCJpEfAW4CRJ/xgRH++0k+dQNbNaG2Bi\n5lDlvsSs/F11fTxJa4E/i4inctpdkLX7cF6f/oWqmVmK/twKuVTSDmAesEbSQ9n6aZLWtNkt6W8Z\nD8uYmaXo/jbHXBHxbeDbLdbvBi5usf5R4NGUvh3uZmYpKvaEqsPdzCxFsbdCds3hbmaWwlfuZmY1\n5HA3M6shh7uZWQ11eZtj0RzuZmYp+nArZD853M3MUvhuGTOzGvKYu5lZDXnM3cyshjzmbmZWQx6W\nMTOrIYe7mVkNVWzMvZD3uUv6a0mbJT0j6VuSTm7adpOk5yRtkfTBIuozMzvM3sSlC5IulbRR0oik\nczu0O0XSyiw3N0mal9d3UZN1PAycHRHvBLYCNwFImgtcBswFFgJ3SvKEImZWvAOJS3c2AEuBdTnt\n/g54MCLOAn4L2JzXcSHBGRGDEfFm9vUxYHr2+RLgGxGxPyK2A88D5xVQopnZaH2YiSkitkTE1k5t\nspGN90XEV7N9DkTEq3l9l+Gq+I+BB7PP04CdTdt2Amcc8YrMzMYaSVwm3izgp5K+JulpSXdJOiFv\np779QlXSIDC1xaabI+L+rM0twL6IuLtDVy3nC1zb9HkmjbM3M9sGbO9Hx+2GXP5zCH421Ha3lCzM\nMRk4F7gmIp6QdDtwI/C5vJ36IiIWdNou6RPAIuADTat3ATOavk/P1h3mwnHWZ2b1NIvRF3tJE46m\naBfup8xvLAdtvXXU5rwsTLAT2BkRT2TfV9II946KultmIXADcElENL+OZzVwuaRjJc0CZgOPF1Gj\nmdkofRhzH0OtVkbEHmCHpDnZqouAjXmdFXWf+98DxwKDkgB+EBFXR8QmSfcCm2j8PXl1RLQcljEz\nO6K6vM0xhaSlwJeA04A1ktZHxIckTQPuioiLs6afAr4u6Vjgx8Af5fZdxeyUFANFF2FmlTAARETL\nq+JUkoL3JGblDzTu400EP6FqZpaiYk+oOtzNzFL4rZBmZjXkF4eZmdWQw93MrIY85m5mVkN9uBWy\nnxzuZmYpPCxjZlZDHpYxM6sh3wppZlZDHpYxM6shh7uZWQ15zN3MrIYqduVehmn2zMyOSpIulbRR\n0oikczu0u17Ss5I2SLpb0nF5fTvczcyKswFYCqxr10DSGTTe5/7uiHgHMAm4PK9jD8uYmRUkIrYA\nZJMWdTIZOEHSCHACbaYfHbuDmZnlKuY3qhGxS9IXgf8AXge+ExHfzdvP4W5mlqTdb1TX0WFUBUmD\nwNQWm26OiPvzjirpbcASYCbwKvBNSb8fEV/vtJ/D3cwsSbsr9/dky0F/OWprRCwY54EvArZFxM8A\nJH0LOB9wuJuZjd/r/T5Au4H3F4B5ko4H3qAR9o/ndea7ZczMkuxPXNJJWippBzAPWCPpoWz9NElr\nACLicWAl8DTwo2zXL+f2HZE4o3eJSIqBoosws0oYACIi93aUTiQFbEtsPWvcx5sIHpYxM0tSrfcP\nONzNzJJU6/0DDnczsyS+cjczq6G+3y0zoRzuZmZJPCxjZlZDHpYxM6shX7mbmdWQr9zNzGrIV+5m\nZjXkK3czsxryrZBmZjXkK3czsxqq1ph7Ia/8lfTXkjZLekbStySdnK2fKel1Seuz5c4i6jMzO1xf\nXvnbMgtbtFsoaYuk5yR9JqXvot7n/jBwdkS8E9gK3NS07fmIOCdbri6mvF9Jfcln1dTxvOp4TuDz\nKo8DiUtXOmUhAJImAXcAC4G5wBWSzsrruJBwj4jBiHgz+/oYML2IOlJsL7qAPtledAF9sL3oAvpk\ne9EF9Mn2ogvo2sRfuSdm4Xk0Lnq3R8R+4B7gkry+yzAT0x8DDzZ9n5UNyQxJem9RRZmZjdaXK/dm\nY7PwoDOAHU3fd2brOurbL1RTZvyWdAuwLyLuzrbtBmZExMuSzgVWSTo7In7erzrNzNL0ditkj1nY\nrKfp8gqbZk/SJ4A/AT4QEW+0abMW+HREPD1mffXmBjSzwkzMNHv9OV5eFkqaBwxExMLs+03AmxHx\nhU79FnIrpKSFwA3ABc0nI+k04OWIGJF0JjAb+MnY/cswP6GZHT36lTntsnCMJ4HZkmbSGN24DLgi\nt+8irtwlPQccC7yUrfpBRFwt6XeBW2n8VuJN4HMRseaIF2hmdgR0yMJpwF0RcXHW7kPA7cAkYHlE\n/FVu30UNy5iZWf+U4W6ZUur0cIGkm7KHCbZI+mCRdXZD0qWSNkoayX5h3bytkud0UC8PeZSRpK9K\nGpa0oWndqZIGJW2V9LCkU4qssVuSZkham/2/96yka7P1lT6vsnO4t9fy4QJJc2mMec2l8VDBnZKq\n8u9xA7AUWNe8suLn1PNDHiX1NRrn0exGYDAi5gCPZN+rZD9wfUScDcwD/jT771P18yq1yvwBPtI6\nPFxwCfCNiNgfEduB52k8ZFB6EbElIra22FTZc8r09JBHGUXEvwMvj1m9BFiRfV4BfOSIFjVOEbEn\nIn6Yff4FsJnGfdqVPq+yc7inaX64YBqNhwgOSnqgoOSqfk49PeRRIVMiYjj7PAxMKbKY8cju+DiH\nxgVTbc6rjI7qt0JOwMMFB5Xmt9Ip55SoNOeUoEq1jktERFWf85B0InAfcF1E/Fz61d2FVT6vsjqq\nwz0iFnTanj1csAj4QNPqXcCMpu/Ts3WlkHdObZT6nBKMrX8Go38SqbphSVMjYo+k04EXiy6oW5KO\noRHs/xQRq7LVlT+vMvOwTBtNDxdcMubhgtXA5ZKOlTSLxoNWjxdR4zg1P5RR9XM69JCHpGNp/HJ4\ndcE1TaTVwJXZ5yuBVR3alo4al+jLgU0RcXvTpkqfV9n5Pvc22j1ckG27mcY4/AEaP2J+p5gquyNp\nKfAl4DTgVWB9RHwo21bJczqol4c8ykjSN4ALaPw3GgY+B/wrcC/w6zRepvixiHilqBq7lb0AcB3w\nI341hHYTjQuIyp5X2TnczcxqyMMyZmY15HA3M6shh7uZWQ053M3MasjhbmZWQw53M7MacribmdWQ\nw93MrIYc7lZ5kn47m1TlOElvzSaEmFt0XWZF8hOqVguS/gJ4C3A8sCNvZnizunO4Wy1kbx18Engd\neE/4f2w7ynlYxuriNOCtwIk0rt7Njmq+crdakLQauBs4Ezg9Ij5VcElmhTqqJ+uwepD0cWBvRNyT\nTez9fUnzI2Ko4NLMCuMrdzOzGvKYu5lZDTnczcxqyOFuZlZDDnczsxpyuJuZ1ZDD3cyshhzuZmY1\n5HA3M6uh/w9x5JF4EBHHlQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ab76b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat = mesh.plotImage(np.log10(sigma), clim=(-2, 0))\n",
    "plt.colorbar(dat[0])\n",
    "plot(xz_A[:,0], xz_A[:,1], 'w.')\n",
    "plot(xz_B[:,0], xz_B[:,1], 'y.')\n",
    "plot(xz_N[:,0], xz_N[:,1], 'r.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c039b50>]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bfe44d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat = mesh.plotImage(np.log10(mappingfwd*m0), clim=(-2, 0))\n",
    "plt.colorbar(dat[0])\n",
    "plot(xz_A[:,0], xz_A[:,1], 'w.')\n",
    "plot(xz_B[:,0], xz_B[:,1], 'y.')\n",
    "plot(xz_N[:,0], xz_N[:,1], 'r.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pymatsolver import MumpsSolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10d8c2d90>]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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phKJH9wyMKILpl/CI6IECY5NOJJ2cdqZCEQqGktrbvSbpsJ19SV6JJzv72vER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\nuA2o7hsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cd0a810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtrue = np.log(sigma)\n",
    "m0 = np.log(np.ones(mesh.nC)*1e-3)\n",
    "survey = DC.SurveyDC(txList)\n",
    "problem = DC.ProblemDC_CC(mesh, mapping = mapping)\n",
    "problem.pair(survey)\n",
    "problem.Solver = MumpsSolver\n",
    "dtrue  = survey.dpred(mtrue)\n",
    "d0  = survey.dpred(m0)\n",
    "plot(dtrue)\n",
    "plot(d0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004688525906217933"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(dtrue).min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   2.,   3.,   4.,   5.,   4.,   6.,   7.,   8.,   9.,   8.,\n",
       "         10.,  17.,  17.,  18.,  33.,  41.,  30.,  32.,  17.]),\n",
       " array([-2.32896368, -2.11250236, -1.89604103, -1.67957971, -1.46311839,\n",
       "        -1.24665706, -1.03019574, -0.81373442, -0.59727309, -0.38081177,\n",
       "        -0.16435044,  0.05211088,  0.2685722 ,  0.48503353,  0.70149485,\n",
       "         0.91795617,  1.1344175 ,  1.35087882,  1.56734014,  1.78380147,\n",
       "         2.00026279]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G2yZV9bMkfwV8mZWPCl5tcEvS5tjwmbckaX46vT0+yfuT3JlkV5IvJ9na5fE2W5K/T3LP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a2299d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(np.log10(abs(dtrue)), bins = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "noise = 0.05*abs(dtrue)*np.random.randn(dtrue.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "                    ***Done using same solver as the problem***\n",
      "============================ Inexact Gauss Newton ============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "   0  1.00e+00  1.40e+08  0.00e+00  1.40e+08    8.32e+06      0              \n",
      "   1  1.00e+00  1.88e+07  6.18e-03  1.88e+07    1.13e+06      0              \n",
      "   2  1.00e+00  2.52e+06  2.45e-02  2.52e+06    1.55e+05      0   Skip BFGS  \n",
      "   3  1.25e-01  3.50e+05  5.43e-02  3.50e+05    2.18e+04      0   Skip BFGS  \n",
      "   4  1.25e-01  6.62e+04  9.32e-02  6.62e+04    3.26e+03      0   Skip BFGS  \n",
      "   5  1.25e-01  1.56e+04  7.26e+00  1.56e+04    4.80e+02      1   Skip BFGS  \n",
      "   6  1.56e-02  4.47e+03  1.84e+01  4.47e+03    5.41e+02      1              \n",
      "   7  1.56e-02  2.73e+03  4.08e+01  2.73e+03    2.91e+03      0   Skip BFGS  \n",
      "   8  1.56e-02  5.74e+02  3.75e+01  5.75e+02    2.97e+02      0              \n",
      "   9  1.95e-03  3.37e+02  3.70e+01  3.37e+02    2.87e+02      0   Skip BFGS  \n",
      "  10  1.95e-03  2.47e+02  3.98e+01  2.47e+02    8.70e+01      0              \n",
      "  11  1.95e-03  1.99e+02  3.89e+01  1.99e+02    1.22e+02      0              \n",
      "  12  2.44e-04  1.60e+02  4.09e+01  1.60e+02    1.45e+02      0              \n",
      "  13  2.44e-04  1.38e+02  4.20e+01  1.38e+02    8.79e+01      0   Skip BFGS  \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.3961e+07\n",
      "1 : |xc-x_last| = 1.1216e+00 <= tolX*(1+|x0|) = 4.8945e+01\n",
      "0 : |proj(x-g)-x|    = 8.7946e+01 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 8.7946e+01 <= 1e3*eps       = 1.0000e-02\n",
      "0 : maxIter   =      30    <= iter          =     14\n",
      "------------------------- DONE! -------------------------\n"
     ]
    }
   ],
   "source": [
    "survey.dobs = dtrue +noise\n",
    "dmis = DataMisfit.l2_DataMisfit(survey)\n",
    "dmis.Wd = 1./(0.05*abs(dtrue)+ 0.1)\n",
    "reg = Regularization.Tikhonov(mesh)\n",
    "opt = Optimization.InexactGaussNewton(maxIter=30,maxIterLS=20)\n",
    "opt.remember('xc')\n",
    "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
    "betaSched = Directives.BetaSchedule(coolingFactor=8, coolingRate=3)\n",
    "targetmis = Directives.TargetMisfit()\n",
    "savemodel = Directives.SaveModelEveryIteration()\n",
    "inv = Inversion.BaseInversion(invProb, directiveList=[betaSched,targetmis])\n",
    "reg.alpha_s = 1e-5\n",
    "reg.mref = m0\n",
    "mopt = inv.run(m0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "XC = opt.recall('xc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact, IntSlider\n",
    "def viewInv(iteration):\n",
    "    fig  = plt.figure(num=0,figsize = (10,5))\n",
    "    ax = plt.subplot(111)\n",
    "    dat = mesh.plotImage(np.log10(mapping*XC[iteration]), grid=True, ax=ax, clim=(-3, 0), gridOpts={'alpha':0.2}, pcolorOpts={'cmap':cm.RdPu})\n",
    "#     ax.set_xlim(mesh.vectorNx.min(), mesh.vectorNx.max())\n",
    "#     ax.set_ylim(mesh.vectorNy.min(), mesh.vectorNy.max())\n",
    "    ax.plot(mesh.vectorCCx, dpred, 'r--')\n",
    "    ax.plot(xz_A[:,0], xz_A[:,1], 'k.')\n",
    "    ax.plot(xz_B[:,0], xz_B[:,1], 'r.')\n",
    "    ax.plot(xz_N[:,0], xz_N[:,1], 'b.')    \n",
    "#     plt.colorbar(dat[0], ax=ax)\n",
    "#     ax.set_ylim (-15, 0.)\n",
    "#     ax.set_xlim (-15, 15.)\n",
    "    plt.show()\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GvZiz5V0v5mwM6wMAAESIRgwAACAQGjEAAIBAaMQAAAACSXZYn1WTAAAgBaya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\nAAAgEBoxAACAQGjEAAAAAvn/WBe8gjsi3HIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11286c350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(viewInv, iteration = IntSlider(min=0, max=opt.iter-1,step=1, value=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10d8cdb50>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jDMKlpY62cAPc+/we/MnFBKmje2hunsGuPQZLVto/wT2fUGKQh6Hh7GI+4mCX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\n8P+lWV/BXY4nkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d8cd110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(survey.dobs)\n",
    "plt.plot(invProb.dpred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}