simpeg/notebooks/CircModel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from SimPEG import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "actind = mesh.gridCC[:,1] < -1.\n",
    "meshact = Mesh.TensorMesh([mesh.hx, mesh.hy[:-2]], x0=mesh.x0)\n",
    "actmap = Maps.ActiveCells(mesh, actind, 1e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "circmap = Maps.CircleMap(meshact)\n",
    "circmap.slope = 1e5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mapping = actmap*circmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "# mapping = Maps.CircleMap(mesh)\n",
    "mtrue = np.r_[np.log(1e0), np.log(1e-3), 0., -3., 2.]\n",
    "m0 = np.r_[np.log(1e-3), np.log(1e-3), -3, -5., 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import simpegDCIP as DC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "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)*19, np.r_[-0.25])\n",
    "xz_M = Utils.ndgrid(xr, np.r_[-0.25])\n",
    "xz_N = Utils.ndgrid(np.ones_like(xr)*-19, np.r_[-0.25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "survey = DC.SurveyDC(txList)\n",
    "problem = DC.ProblemDC_CC(mesh, mapping = mapping)\n",
    "problem.pair(survey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pymatsolver import MumpsSolver\n",
    "problem.Solver = MumpsSolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dini = survey.dpred(m0)\n",
    "dtrue  = survey.dpred(mtrue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Im/Iv5zm7dvLM7ReRjCQc3+PqvpfzJ3t/m/AjA0RkPzHRTzLYz8ZkP5dvy7B9+Equ2pHl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\nGIZhGDUxQWEYhmHU5P8DBvKRdFTa1XkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1075971d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(dini)\n",
    "plot(dtrue)\n",
    "figsize(12, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   2.,   5.,   4.,  10.,  11.,  13.,  20.,  17.,  20.,  23.,\n",
       "         10.,   0.,   0.,   4.,  17.,  29.,  36.,  32.,  18.]),\n",
       " array([-0.21911748, -0.05445749,  0.1102025 ,  0.27486249,  0.43952249,\n",
       "         0.60418248,  0.76884247,  0.93350246,  1.09816245,  1.26282245,\n",
       "         1.42748244,  1.59214243,  1.75680242,  1.92146242,  2.08612241,\n",
       "         2.2507824 ,  2.41544239,  2.58010239,  2.74476238,  2.90942237,\n",
       "         3.07408236]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10999b5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(np.log10(abs(dtrue)), bins = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m1D = Mesh.TensorMesh([5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x10a7e8f80>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sgkang/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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SnXsZ+G3fTfp4mPe1iLi+S1WVu1Mkz7nnVLlz76JO5zJhERF1fihd0r40VrL7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a5c0a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(14*0.5,7*0.5)\n",
    "circmodelest = mapping*mtrue\n",
    "dat = mesh.plotImage(np.log10(circmodelest), clim=(-4, 1), grid=True, gridOpts={'alpha':0.5})\n",
    "plot(xz_A[:,0], xz_A[:,1], 'w.')\n",
    "plot(xz_B[:,0], xz_B[:,1], 'k.')\n",
    "plot(xz_N[:,0], xz_N[:,1], 'r.')\n",
    "# plot(temp.rxList[0].locs[0][:,0], temp.rxList[0].locs[0][:,1], 'bo')\n",
    "plt.colorbar(dat[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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 and solverOpts as the problem***\n",
      "SimPEG.SaveModelEveryIteration will save your models as: '###-InversionModel-2016-02-04-11-31.npy'\n",
      "============================ Inexact Gauss Newton ============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "   0  0.00e+00  4.69e+03  0.00e+00  4.69e+03    3.97e+04      0              \n",
      "   1  0.00e+00  2.80e+03  4.41e-03  2.80e+03    1.09e+03      0              \n",
      "   2  0.00e+00  2.59e+03  3.14e+01  2.59e+03    1.43e+04      1              \n",
      "   3  0.00e+00  1.31e+03  4.23e+01  1.31e+03    1.48e+04      3              \n",
      "   4  0.00e+00  1.22e+03  5.74e+01  1.22e+03    1.49e+04      4              \n",
      "   5  0.00e+00  8.55e+02  3.58e+01  8.55e+02    2.20e+04      0              \n",
      "   6  0.00e+00  7.83e+02  5.46e+01  7.83e+02    2.04e+04      3              \n",
      "   7  0.00e+00  7.38e+02  4.95e+01  7.38e+02    2.38e+04      2              \n",
      "   8  0.00e+00  6.90e+02  3.33e+01  6.90e+02    2.15e+04      1   Skip BFGS  \n",
      "   9  0.00e+00  5.63e+02  5.39e+01  5.63e+02    1.79e+04      2   Skip BFGS  \n",
      "  10  0.00e+00  4.88e+02  5.12e+01  4.88e+02    1.60e+04      2              \n",
      "  11  0.00e+00  4.81e+02  5.04e+01  4.81e+02    1.60e+04      6   Skip BFGS  \n",
      "  12  0.00e+00  4.79e+02  4.87e+01  4.79e+02    1.67e+04      5   Skip BFGS  \n",
      "  13  0.00e+00  3.90e+02  3.14e+01  3.90e+02    1.41e+04      1   Skip BFGS  \n",
      "  14  0.00e+00  3.64e+02  5.04e+01  3.64e+02    1.61e+04      2              \n",
      "  15  0.00e+00  3.49e+02  4.45e+01  3.49e+02    1.58e+04      2              \n",
      "  16  0.00e+00  2.92e+02  5.24e+01  2.92e+02    1.31e+04      3              \n",
      "  17  0.00e+00  2.67e+02  5.12e+01  2.67e+02    1.08e+04      1              \n",
      "  18  0.00e+00  2.18e+02  4.72e+01  2.18e+02    1.15e+04      2              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 4.6952e+02\n",
      "0 : |xc-x_last| = 3.4124e-01 <= tolX*(1+|x0|) = 1.2421e-19\n",
      "0 : |proj(x-g)-x|    = 1.1527e+04 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 1.1527e+04 <= 1e3*eps       = 1.0000e-02\n",
      "0 : maxIter   =      30    <= iter          =     19\n",
      "------------------------- DONE! -------------------------\n"
     ]
    }
   ],
   "source": [
    "survey.dobs = dtrue\n",
    "dmis = DataMisfit.l2_DataMisfit(survey)\n",
    "dmis.Wd = 1./(0.01*abs(dtrue)+1.)\n",
    "reg = Regularization.BaseRegularization(m1D)\n",
    "opt = Optimization.InexactGaussNewton(maxIter=30,tolX=1e-20, maxIterLS=20)\n",
    "opt.remember('xc')\n",
    "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
    "invProb.beta = 0.\n",
    "betaSched = Directives.BetaSchedule(coolingFactor=1, coolingRate=1)\n",
    "targetmis = Directives.TargetMisfit()\n",
    "savemodel = Directives.SaveModelEveryIteration()\n",
    "inv = Inversion.BaseInversion(invProb, directiveList=[betaSched,targetmis, savemodel])\n",
    "reg.mref = m0\n",
    "mopt = inv.run(m0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "XC = opt.recall('xc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact, IntSlider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def viewinv(iteration):\n",
    "#     iteration = 15\n",
    "    figsize(10,6)\n",
    "    ax1 = plt.subplot(211)\n",
    "    circmodelest = mapping*mtrue\n",
    "    if iteration > opt.iter-1:\n",
    "        circmodeltrue = mapping*mopt\n",
    "    else:\n",
    "        circmodeltrue = mapping*XC[iteration]\n",
    "    mesh.plotImage(np.log10(circmodelest), ax=ax1, clim=(-3, 0), grid=True, gridOpts={'alpha':0.5})\n",
    "    ax2 = plt.subplot(212)\n",
    "    mesh.plotImage(np.log10(circmodeltrue), ax=ax2, clim=(-3, 0), grid=True, gridOpts={'alpha':0.5})\n",
    "    plt.show()\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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1kZe5W+k8urWRR7c28jJ3K52XudswRpMAAACVMJoEAAAYFaPJnvWpbHfSrY28zN1K59Gt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\nsp1QWiOq5BkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c77fa10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(viewinv, iteration=IntSlider(min=0, max=opt.iter, step = 1, value=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c57bb50>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gntWItB6FYHCiYF/kX4T3Wt8zJNIuWR7DR407sGNgI+IL7kSfdRfij98BbD8TAJJEYQVK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\nUkgTEREROQf9f6bfBJxNvz1YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c57be10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(invProb.dpred, '.')\n",
    "plot(dtrue)"
   ]
  },
  {
   "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": "2.7.11"
  }
 },
 "nbformat": 4,
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