{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from SimPEG import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "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 0x107614750>"
      ]
     },
     "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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YkiAMAMCQBGEAAIYkCAMAMCRBGACAIQnCAAAMSRAGAGBIgjAAAEMShAEAGJIgDADAkARh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\nIQnCAAAM6f8DvOoF0hV0WScAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a2ad990>"
      ]
     },
     "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 0x10a7b12d8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SnXsZ+G3fTfp4mPe1iLi+S1WVu1Mkz7nnVLlz76JO5zJhERF1fihd0r40VrL7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a698690>"
      ]
     },
     "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 as the problem***\n",
      "SimPEG.SaveModelEveryIteration will save your models as: '###-InversionModel-2015-11-10-16-33.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  6.43e+02  5.20e+01  6.43e+02    1.71e+04      3              \n",
      "   7  0.00e+00  4.57e+02  4.43e+01  4.57e+02    5.20e+03      0              \n",
      "   8  0.00e+00  3.83e+02  6.55e+01  3.83e+02    1.54e+04      2              \n",
      "   9  0.00e+00  3.67e+02  5.78e+01  3.67e+02    5.76e+03      1              \n",
      "  10  0.00e+00  3.31e+02  6.61e+01  3.31e+02    1.20e+04      1              \n",
      "  11  0.00e+00  3.30e+02  6.15e+01  3.30e+02    1.76e+04      2              \n",
      "  12  0.00e+00  2.01e+02  4.84e+01  2.01e+02    8.75e+03      0              \n",
      "  13  0.00e+00  1.54e+02  6.25e+01  1.54e+02    8.56e+03      2              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 4.6952e+02\n",
      "0 : |xc-x_last| = 5.6930e-01 <= tolX*(1+|x0|) = 1.2421e-19\n",
      "0 : |proj(x-g)-x|    = 8.5645e+03 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 8.5645e+03 <= 1e3*eps       = 1.0000e-02\n",
      "0 : maxIter   =      30    <= iter          =     14\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": {
      "image/png": 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JtzbyMncrnUe3NvLo1kZe5m6l8zJ3G8ZoEgAAoBJGkwAAAKNiNNmzPpXtTrq1kZe5W+k8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c979dd0>"
      ]
     },
     "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": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c8ed390>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SkWbD6cqEkwbuZqVFTWcix/GC9SxfC6VqTlqqBgWV9WhWnKQjODQvWBeRJmQz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\niIjIGUghTUREROQMpJAmIiIicgb6/xeqKQDKaAuMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c8ed2d0>"
      ]
     },
     "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
