simpeg/simpegPF/notebooks/MagInversion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "from SimPEG import *\n",
    "from simpegPF import BaseMag\n",
    "from scipy.constants import mu_0\n",
    "from simpegPF.MagAnalytics import spheremodel, CongruousMagBC\n",
    "from simpegPF.Magnetics import MagneticsDiffSecondary, MagneticsDiffSecondaryInv\n",
    "# import SeogiUtils as SeUtils\n",
    "# import simpegEM.Utils.Solver.Mumps as Mumps\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mag Inversion\n",
    "\n",
    "## Step1: Generating mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cs = 25.\n",
    "hxind = [(cs,5,-1.3), (cs, 31),(cs,5,1.3)]\n",
    "hyind = [(cs,5,-1.3), (cs, 31),(cs,5,1.3)]\n",
    "hzind = [(cs,5,-1.3), (cs, 30),(cs,5,1.3)]\n",
    "mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step2: Generating Model: Use Combo model\n",
    "\n",
    "### Here we combined $\\mu$ model$^1$, Depth model$^2$ and Active model$^3$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "chibkg = 1e-5\n",
    "chiblk = 0.1\n",
    "chi = np.ones(mesh.nC)*chibkg\n",
    "sph_ind = spheremodel(mesh, 0., 0., -150., 80)\n",
    "chi[sph_ind] = chiblk\n",
    "active = mesh.gridCC[:,2]<0\n",
    "actMap = Maps.ActiveCells(mesh, active, chibkg)\n",
    "dweight = np.ones(mesh.nC)\n",
    "dweight[active] = (1/abs(mesh.gridCC[active, 2]-13.)**1.5)\n",
    "baseMap = BaseMag.BaseMagMap(mesh)\n",
    "depthMap = BaseMag.WeightMap(mesh, dweight)\n",
    "dmap = baseMap*actMap\n",
    "rmap = depthMap*actMap\n",
    "model = (chi)[active]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sph_ind_ini = spheremodel(mesh, 0., 0., -200., 150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "chi_ini = np.ones_like(chi)*chibkg\n",
    "chi_ini[sph_ind_ini] = chiblk*0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-500, 0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x18eab5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize = (5, 5))\n",
    "dat1 = mesh.plotSlice(chi, ax = ax, normal = 'X')\n",
    "plt.colorbar(dat1[0], orientation=\"horizontal\", ax = ax)\n",
    "ax.set_ylim(-500, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33620L,)\n",
      "(67240L,)\n"
     ]
    }
   ],
   "source": [
    "print model.shape\n",
    "print chi.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step3: Generating Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "survey = BaseMag.BaseMagSurvey()\n",
    "const = 20\n",
    "Inc = 90.\n",
    "Dec = 0.\n",
    "Btot = 51000\n",
    "survey.setBackgroundField(Inc, Dec, Btot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xr = np.linspace(-300, 300, 81)\n",
    "yr = np.linspace(-300, 300, 81)\n",
    "X, Y = np.meshgrid(xr, yr)\n",
    "Z = np.ones((xr.size, yr.size))*(0.)\n",
    "rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]\n",
    "survey.rxLoc = rxLoc\n",
    "prob = MagneticsDiffSecondary(mesh, mapping = dmap)\n",
    "prob.pair(survey)\n",
    "prob.Solver = Utils.SolverUtils.SolverWrapD(Mumps, factorize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dsyn = survey.dpred(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "survey.dtrue = Utils.mkvc(dsyn)\n",
    "std = 0.05\n",
    "noise = std*abs(survey.dtrue)*np.random.randn(*survey.dtrue.shape)\n",
    "survey.dobs = survey.dtrue+noise\n",
    "survey.std = survey.dobs*0 + std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nrfNa1TweS/ck4WOsLOsIH5Pnx+zmJaX0CHha07Z+lkUKk01jWbHALXF1Wrrd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\n6yaDWjHtWgoudaSd1bqUICmg7esWGC1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HlHUOucoTxgx5yo6yNrStJTK1MaXnXwfh2GFr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\nOdclxezpyTc1xVLMFG5TRzz3qNt+glOPqHoDmrUObeu0cIE36BdkUz1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A93+NH4B1RGIa+svcXlb9/kdO0cyZULDOMmD4J96rUlqssZX1l+g51zd4jWbW7WLrJ7bou96hbW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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3c47c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize = (8,5) )\n",
    "dat = ax[0].imshow(np.reshape(noise, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)])\n",
    "plt.colorbar(dat, ax = ax[0], orientation=\"horizontal\")\n",
    "dat2 = ax[1].imshow(np.reshape(survey.dobs, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)])\n",
    "plt.colorbar(dat2, ax = ax[1], orientation=\"horizontal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# m0 = (1e-5*np.ones(mesh.nC))[active]\n",
    "m0 = chi_ini[active]/dweight[active] \n",
    "dmisfit = DataMisfit.l2_DataMisfit(survey)\n",
    "valmin = abs(survey.dobs).max()\n",
    "dmisfit.Wd = 1/(np.ones(survey.dobs.size)*valmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MGNlix/CQDLJtEkHhMbQeYzklF0y+MHLJPuDWed4qH30fKWPv4ylI3UbNa56X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\nDMMwDMMwDMMwDMMwDMMwDGNd/geOBdPxiEgCrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb793dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_ini = survey.dpred(m0)\n",
    "fig, ax = plt.subplots(1,2, figsize = (8,5) )\n",
    "dat1 = ax[0].imshow(np.reshape(d_ini, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)])\n",
    "vmin = d_ini.min()\n",
    "vmax = d_ini.max()\n",
    "plt.colorbar(dat1, ax = ax[0], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 3)], format = FormatStrFormatter('$%5.5f$'))\n",
    "dat2 = ax[1].imshow(np.reshape(survey.dobs, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)])\n",
    "vmin = survey.dobs.min()\n",
    "vmax = survey.dobs.max()\n",
    "plt.colorbar(dat2, ax = ax[1], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reg = Regularization.Tikhonov(mesh, mapping = rmap)\n",
    "opt = Optimization.ProjectedGNCG(maxIter = 30)\n",
    "opt.lower = 1e-10\n",
    "opt.maxIterLS = 50\n",
    "invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt)\n",
    "beta = Directives.BetaSchedule(coolingFactor=8, coolingRate=2)\n",
    "betaest = Directives.BetaEstimate_ByEig(beta0_ratio=10**0)\n",
    "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaest])\n",
    "opt.tolG = 1e-20\n",
    "opt.eps = 1e-20\n",
    "reg.alpha_s = 1e-9\n",
    "reg.alpha_x = 1.\n",
    "reg.alpha_y = 1.\n",
    "reg.alpha_z = 1.\n",
    "prob.counter = opt.counter = Utils.Counter()\n",
    "opt.LSshorten = 0.1\n",
    "opt.remember('xc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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",
      "=============================== Projected GNCG ===============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "   0 -8.78e+04  1.08e+06  2.70e-01  1.06e+06    1.38e+05      0              \n",
      "   1 -8.78e+04  3.89e+05  1.52e+09 -1.34e+14    1.57e+08      0              \n",
      "   2 -1.10e+04  2.49e+05  1.52e+09 -1.67e+13    1.96e+07     15   Skip BFGS  \n",
      "   3 -1.10e+04  2.49e+05  1.52e+09 -1.67e+13    1.96e+07     19   Skip BFGS  \n",
      "   4 -1.37e+03  2.49e+05  1.52e+09 -2.09e+12    2.45e+06     19   Skip BFGS  \n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-e70b86582598>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmopt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
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      "\u001b[1;32m/home/seogi/Documents/simpeg/SimPEG/Inversion.pyc\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, m0)\u001b[0m\n\u001b[0;32m     60\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvProb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstartup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdirectiveList\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'initialize'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 62\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvProb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevalFunction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvProb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     63\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdirectiveList\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'finish'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/seogi/Documents/simpeg/SimPEG/Utils/CounterUtils.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     90\u001b[0m         \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'counter'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountTic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     93\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountToc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/seogi/Documents/simpeg/SimPEG/Optimization.pyc\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(self, evalFunction, x0)\u001b[0m\n\u001b[0;32m    179\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprintIter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstoppingCriteria\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 181\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msearchDirection\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfindSearchDirection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    182\u001b[0m             \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mH\u001b[0m \u001b[1;31m#: Doing this saves memory, as it is not needed in the rest of the computations.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    183\u001b[0m             \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscaleSearchDirection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msearchDirection\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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      "\u001b[1;32m/home/seogi/Documents/simpeg/SimPEG/Optimization.pyc\u001b[0m in \u001b[0;36mfindSearchDirection\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    974\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    975\u001b[0m                 \u001b[1;31m#  Form product Hessian*pc.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 976\u001b[1;33m                 \u001b[0mHp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mH\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mpc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    977\u001b[0m                 \u001b[0mHp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mActive\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mHp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    978\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/scipy/sparse/linalg/interface.pyc\u001b[0m in \u001b[0;36mmatvec\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    132\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'dimension mismatch'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 134\u001b[1;33m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_matvec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    135\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    136\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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      "\u001b[1;32m/home/seogi/Documents/simpeg/SimPEG/DataMisfit.pyc\u001b[0m in \u001b[0;36meval2Deriv\u001b[1;34m(self, m, v, u)\u001b[0m\n\u001b[0;32m    135\u001b[0m         \u001b[0mprob\u001b[0m   \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprob\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    136\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mu\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mu\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfields\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 137\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mprob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mJtvec_approx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mWd\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mWd\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mprob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mJvec_approx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
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      "\u001b[1;32m/home/seogi/Documents/simpeg/SimPEG/Problem.pyc\u001b[0m in \u001b[0;36mJtvec_approx\u001b[1;34m(self, m, v, u)\u001b[0m\n\u001b[0;32m    399\u001b[0m             \u001b[1;33m:\u001b[0m\u001b[1;32mreturn\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mJTv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    400\u001b[0m         \"\"\"\n\u001b[1;32m--> 401\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mJtvec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    402\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    403\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfields\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/seogi/Documents/simpeg/SimPEG/Utils/CounterUtils.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     90\u001b[0m         \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'counter'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountTic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     93\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountToc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/seogi/Documents/simpegpf/simpegPF/Magnetics.pyc\u001b[0m in \u001b[0;36mJtvec\u001b[1;34m(self, m, v, u)\u001b[0m\n\u001b[0;32m    328\u001b[0m         \u001b[0mAtemp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMfMu0\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mB0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdMfMuI\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdmudm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    329\u001b[0m         \u001b[0mBtemp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDiv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdMfMuI\u001b[0m\u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdmudm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 330\u001b[1;33m         \u001b[0mJtv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAtemp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mP\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mBtemp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mP\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mCtv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    331\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    332\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmkvc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mJtv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/scipy/sparse/base.pyc\u001b[0m in \u001b[0;36m__mul__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m    294\u001b[0m             \u001b[1;31m# Fast path for the most common case\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mul_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    297\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mul_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mM\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/scipy/sparse/compressed.pyc\u001b[0m in \u001b[0;36m_mul_vector\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m    445\u001b[0m         \u001b[1;31m# csr_matvec or csc_matvec\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    446\u001b[0m         \u001b[0mfn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_sparsetools\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'_matvec'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 447\u001b[1;33m         \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mM\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindptr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    448\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    449\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "mopt = inv.run(m0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counters:\n",
      "  ProjectedGNCG.activeSet                 :       30\n",
      "  ProjectedGNCG.doEndIteration            :       30\n",
      "  ProjectedGNCG.doStartIteration          :       31\n",
      "  ProjectedGNCG.projection                :      394\n",
      "  ProjectedGNCG.scaleSearchDirection      :       30\n",
      "\n",
      "Times:                                        mean      sum\n",
      "  MagneticsDiffSecondary.Jtvec            : 3.10e-01, 5.64e+01,  182x\n",
      "  MagneticsDiffSecondary.Jtvec_approx     : 3.48e-01, 5.26e+01,  151x\n",
      "  MagneticsDiffSecondary.Jvec             : 3.48e-01, 5.26e+01,  151x\n",
      "  MagneticsDiffSecondary.Jvec_approx      : 3.48e-01, 5.26e+01,  151x\n",
      "  ProjectedGNCG.findSearchDirection       : 3.66e+00, 1.10e+02,   30x\n",
      "  ProjectedGNCG.minimize                  : 2.57e+02, 2.57e+02,    1x\n",
      "  ProjectedGNCG.modifySearchDirection     : 4.71e+00, 1.41e+02,   30x\n"
     ]
    }
   ],
   "source": [
    "opt.counter.summary()\n",
    "xc = opt.recall('xc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    <button onclick=\"animRJMMSRKUSXJHUFOV.faster()\">+</button>\n",
       "  <form action=\"#n\" name=\"_anim_loop_selectRJMMSRKUSXJHUFOV\" class=\"anim_control\">\n",
       "    <input type=\"radio\" name=\"state\" value=\"once\" > Once </input>\n",
       "    <input type=\"radio\" name=\"state\" value=\"loop\" checked> Loop </input>\n",
       "    <input type=\"radio\" name=\"state\" value=\"reflect\" > Reflect </input>\n",
       "  </form>\n",
       "</div>\n",
       "\n",
       "\n",
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       "  /* The IDs given should match those used in the template above. */\n",
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       "AiAAAACAxP3/VTyFPmX9fb4AAAAASUVORK5CYII=\\\n",
       "\"\n",
       "\n",
       "\n",
       "    /* set a timeout to make sure all the above elements are created before\n",
       "       the object is initialized. */\n",
       "    setTimeout(function() {\n",
       "        animRJMMSRKUSXJHUFOV = new Animation(frames, img_id, slider_id, 40, loop_select_id);\n",
       "    }, 0);\n",
       "  })()\n",
       "</script>\n"
      ],
      "text/plain": [
       "<matplotlib.animation.FuncAnimation at 0x48a3810>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from JSAnimation import IPython_display\n",
    "from matplotlib import animation\n",
    "from SimPEG import *\n",
    "\n",
    "fig, ax = subplots(1,2, figsize = (16, 5))\n",
    "ax[0].set_xlabel('Easting (m)')\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "ax[1].set_xlabel('Easting (m)')\n",
    "ax[1].set_ylabel('Depth (m)')\n",
    "\n",
    "\n",
    "def animate(i_id):\n",
    "    indx = 18\n",
    "    temp = dmap*(xc[i_id])\n",
    "    minval = (temp).min()\n",
    "    maxval = (temp).max()\n",
    "    \n",
    "    frame1 = mesh.plotSlice(temp, vType='CC', ind=indx, normal='X',ax = ax[1], grid=False, gridOpts={'color':'b','lw':0.3, 'alpha':0.5}, )\n",
    "    frame2 = mesh.plotSlice(chi, vType='CC', ind=indx, normal='X',ax = ax[0], grid=False, gridOpts={'color':'b','lw':0.3, 'alpha':0.5}, );\n",
    "    ax[0].set_title('True model', fontsize = 16)\n",
    "    ax[1].set_title('Estimated model at iteration = ' + str(i_id+1), fontsize = 16)\n",
    "    ax[0].set_ylim(-500, 0)\n",
    "    ax[1].set_ylim(-500, 0)\n",
    "    return frame1[0]\n",
    "\n",
    "animation.FuncAnimation(fig, animate, frames=10, interval=40, blit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/matplotlib/lines.py:503: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  return np.alltrue(x[1:] - x[0:-1] >= 0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x5b96550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "indx = 18\n",
    "iteration = 9\n",
    "fig, axes = subplots(1,2, figsize = (12, 5))\n",
    "vmin = chi.min()\n",
    "vmax = chi.max()\n",
    "ps1 = mesh.plotSlice(chi, vType='CC', ind=indx, normal='X',ax = axes[0], grid=True, gridOpts={'color':'b','lw':0.3, 'alpha':0.5});\n",
    "axes[0].set_title('$\\chi_{true}$', fontsize = 16)\n",
    "axes[0].set_ylim(-500, 0.)\n",
    "cb1 = colorbar(ps1[0], ax =  axes[0], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)], format = FormatStrFormatter('$%5.3f$'))\n",
    "axes[0].set_xlabel('Easting (m)')\n",
    "axes[0].set_ylabel('Depth (m)')\n",
    "\n",
    "vmin = (actMap*xc[iteration]).min()\n",
    "vmax = (actMap*xc[iteration]).max()\n",
    "ps2 = mesh.plotSlice(actMap*xc[iteration], vType='CC', ind=indx, normal='X', ax = axes[1], grid=True, gridOpts={'color':'b','lw':0.3, 'alpha':0.5});\n",
    "axes[1].set_title('$\\chi_{pred}$', fontsize = 16)\n",
    "axes[1].set_ylim(-500, 0.)\n",
    "cb2 = colorbar(ps2[0], ax =  axes[1], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)], format = FormatStrFormatter('$%5.3f$'))\n",
    "cb1.set_label('Susceptibility (dimensionless)')\n",
    "cb2.set_label('Susceptibility (dimensionless)')\n",
    "axes[1].set_xlabel('Easting (m)')\n",
    "axes[1].set_ylabel('Depth (m)')\n",
    "fig.savefig('model.png', dpi = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jnvasX8cwEhor2+gZouxDP9PHyQk/5nQUzT0cLYfw5WeiGtCSk7ZLo1w+r7pp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\nX9b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      "text/plain": [
       "<matplotlib.figure.Figure at 0x5be7210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dpred_xc = survey.dpred(xc[iteration])\n",
    "fig, ax = plt.subplots(1,2, figsize = (12,7) )\n",
    "vmin = survey.dobs.min()\n",
    "vmax = survey.dobs.max()\n",
    "dat2 = ax[0].imshow(np.reshape(survey.dobs, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)], vmin = vmin, vmax = vmax)\n",
    "cb1 = plt.colorbar(dat2, ax = ax[0], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)])\n",
    "dat = ax[1].imshow(np.reshape(dpred_xc, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)], vmin = vmin, vmax = vmax)\n",
    "cb2 = plt.colorbar(dat, ax = ax[1], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)])\n",
    "ax[0].plot(rxLoc[:,0],rxLoc[:,1],'w.', ms=1)\n",
    "ax[1].plot(rxLoc[:,0],rxLoc[:,1],'w.', ms=1)\n",
    "ax[0].set_title('Observed', fontsize = 16)\n",
    "ax[1].set_title('Predicted', fontsize = 16)\n",
    "ax[0].set_xlabel('Easting (m)')\n",
    "ax[0].set_ylabel('Northing (m)')\n",
    "ax[1].set_xlabel('Easting (m)')\n",
    "ax[1].set_ylabel('Northing (m)')\n",
    "cb1.set_label('Total magnetic intensity (nT)')\n",
    "cb2.set_label('Total magnetic intensity (nT)')\n",
    "fig.savefig('obspred.png', dpi = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "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
}