diff --git a/simpegPF/notebooks/MagInversion.ipynb b/simpegPF/notebooks/MagInversion.ipynb
index b18124c2..c4b26028 100644
--- a/simpegPF/notebooks/MagInversion.ipynb
+++ b/simpegPF/notebooks/MagInversion.ipynb
@@ -1,2779 +1,761 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:ea5924570857853319c35ba53aa8dd01074562c5d5f8e317e0328471779ef10d"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Populating the interactive namespace from numpy and matplotlib\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Mag Inversion\n",
-      "\n",
-      "## Step1: Generating mesh"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "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$^3$"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: pylab import has clobbered these variables: ['linalg']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "sph_ind_ini = spheremodel(mesh, 0., 0., -200., 150)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "chi_ini = np.ones_like(chi)*chibkg\n",
-      "chi_ini[sph_ind_ini] = chiblk*0.1"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 35
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fig, ax = plt.subplots(1,1, figsize = (5, 5))\n",
-      "dat1 = mesh.plotSlice(rmap*test, ax = ax, normal = 'X')\n",
-      "plt.colorbar(dat1[0], orientation=\"horizontal\", ax = ax)\n",
-      "ax.set_ylim(-500, 0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 29,
-       "text": [
-        "(-500, 0)"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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Q+cYWaTv+XsQTTKIpPqkvSGHxM9JPUb3uk9rI2UEF0d8YwwiYprdhUaRj4DcLty/S+eYR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\nkiRJkiRJkjQevwNC9FbcDf9oPgAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x4593050>"
-       ]
-      }
-     ],
-     "prompt_number": 29
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print model.shape\n",
-      "print chi.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(33620,)\n",
-        "(67240,)\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Step3: Generating Data"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "survey = BaseMag.BaseMagSurvey()\n",
-      "const = 20\n",
-      "Inc = 90.\n",
-      "Dec = 0.\n",
-      "Btot = 51000\n",
-      "survey.setBackgroundField(Inc, Dec, Btot)\n",
-      "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)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "dsyn = survey.dpred(model)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "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": [
-        "<matplotlib.figure.Figure at 0x3c47c90>"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 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)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 36
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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MGNlix/CQDLJtEkHhMbQeYzklF0y+MHLJPuDWed4qH30fKWPv4ylI3UbNa56X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\nDMMwDMMwDMMwDMMwDMMwDGNd/geOBdPxiEgCrgAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0xb793dd0>"
-       ]
-      }
-     ],
-     "prompt_number": 37
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 40
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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",
-        "=============================== Projected GNCG ==============================="
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\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              "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "   1 -8.78e+04  3.89e+05  1.52e+09 -1.34e+14    1.57e+08      0              "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "   2 -1.10e+04  2.49e+05  1.52e+09 -1.67e+13    1.96e+07     15   Skip BFGS  "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "   3 -1.10e+04  2.49e+05  1.52e+09 -1.67e+13    1.96e+07     19   Skip BFGS  "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "   4 -1.37e+03  2.49e+05  1.52e+09 -2.09e+12    2.45e+06     19   Skip BFGS  "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      },
-      {
-       "ename": "KeyboardInterrupt",
-       "evalue": "",
-       "output_type": "pyerr",
-       "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/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: "
-       ]
-      }
-     ],
-     "prompt_number": 41
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "opt.counter.summary()\n",
-      "xc = opt.recall('xc')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "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"
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "\n",
-        "<script language=\"javascript\">\n",
-        "  /* Define the Animation class */\n",
-        "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
-        "    this.img_id = img_id;\n",
-        "    this.slider_id = slider_id;\n",
-        "    this.loop_select_id = loop_select_id;\n",
-        "    this.interval = interval;\n",
-        "    this.current_frame = 0;\n",
-        "    this.direction = 0;\n",
-        "    this.timer = null;\n",
-        "    this.frames = new Array(frames.length);\n",
-        "\n",
-        "    for (var i=0; i<frames.length; i++)\n",
-        "    {\n",
-        "     this.frames[i] = new Image();\n",
-        "     this.frames[i].src = frames[i];\n",
-        "    }\n",
-        "    document.getElementById(this.slider_id).max = this.frames.length - 1;\n",
-        "    this.set_frame(this.current_frame);\n",
-        "  }\n",
-        "\n",
-        "  Animation.prototype.get_loop_state = function(){\n",
-        "    var button_group = document[this.loop_select_id].state;\n",
-        "    for (var i = 0; i < button_group.length; i++) {\n",
-        "        var button = button_group[i];\n",
-        "        if (button.checked) {\n",
-        "            return button.value;\n",
-        "        }\n",
-        "    }\n",
-        "    return undefined;\n",
-        "  }\n",
-        "\n",
-        "  Animation.prototype.set_frame = function(frame){\n",
-        "    this.current_frame = frame;\n",
-        "    document.getElementById(this.img_id).src = this.frames[this.current_frame].src;\n",
-        "    document.getElementById(this.slider_id).value = this.current_frame;\n",
-        "  }\n",
-        "\n",
-        "  Animation.prototype.next_frame = function()\n",
-        "  {\n",
-        "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
-        "  }\n",
-        "\n",
-        "  Animation.prototype.previous_frame = function()\n",
-        "  {\n",
-        "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
-        "  }\n",
-        "\n",
-        "  Animation.prototype.first_frame = function()\n",
-        "  {\n",
-        "    this.set_frame(0);\n",
-        "  }\n",
-        "\n",
-        "  Animation.prototype.last_frame = function()\n",
-        "  {\n",
-        "    this.set_frame(this.frames.length - 1);\n",
-        "  } \n",
-        "\n",
-        "  Animation.prototype.slower = function()\n",
-        "  {\n",
-        "    this.interval /= 0.7;\n",
-        "    if(this.direction > 0){this.play_animation();}\n",
-        "    else if(this.direction < 0){this.reverse_animation();}\n",
-        "  }\n",
-        "\n",
-        "  Animation.prototype.faster = function()\n",
-        "  {\n",
-        "    this.interval *= 0.7;\n",
-        "    if(this.direction > 0){this.play_animation();}\n",
-        "    else if(this.direction < 0){this.reverse_animation();}\n",
-        "  }\n",
-        "\n",
-        "  Animation.prototype.anim_step_forward = function()\n",
-        "  {\n",
-        "    this.current_frame += 1;\n",
-        "    if(this.current_frame < this.frames.length){\n",
-        "      this.set_frame(this.current_frame);\n",
-        "    }else{\n",
-        "      var loop_state = this.get_loop_state();\n",
-        "      if(loop_state == \"loop\"){\n",
-        "        this.first_frame();\n",
-        "      }else if(loop_state == \"reflect\"){\n",
-        "        this.last_frame();\n",
-        "        this.reverse_animation();\n",
-        "      }else{\n",
-        "        this.pause_animation();\n",
-        "        this.last_frame();\n",
-        "      }\n",
-        "    }\n",
-        "  }\n",
-        "\n",
-        "  Animation.prototype.anim_step_reverse = function()\n",
-        "  {\n",
-        "    this.current_frame -= 1;\n",
-        "    if(this.current_frame >= 0){\n",
-        "      this.set_frame(this.current_frame);\n",
-        "    }else{\n",
-        "      var loop_state = this.get_loop_state();\n",
-        "      if(loop_state == \"loop\"){\n",
-        "        this.last_frame();\n",
-        "      }else if(loop_state == \"reflect\"){\n",
-        "        this.first_frame();\n",
-        "        this.play_animation();\n",
-        "      }else{\n",
-        "        this.pause_animation();\n",
-        "        this.first_frame();\n",
-        "      }\n",
-        "    }\n",
-        "  }\n",
-        "\n",
-        "  Animation.prototype.pause_animation = function()\n",
-        "  {\n",
-        "    this.direction = 0;\n",
-        "    if (this.timer){\n",
-        "      clearInterval(this.timer);\n",
-        "      this.timer = null;\n",
-        "    }\n",
-        "  }\n",
-        "\n",
-        "  Animation.prototype.play_animation = function()\n",
-        "  {\n",
-        "    this.pause_animation();\n",
-        "    this.direction = 1;\n",
-        "    var t = this;\n",
-        "    if (!this.timer) this.timer = setInterval(function(){t.anim_step_forward();}, this.interval);\n",
-        "  }\n",
-        "\n",
-        "  Animation.prototype.reverse_animation = function()\n",
-        "  {\n",
-        "    this.pause_animation();\n",
-        "    this.direction = -1;\n",
-        "    var t = this;\n",
-        "    if (!this.timer) this.timer = setInterval(function(){t.anim_step_reverse();}, this.interval);\n",
-        "  }\n",
-        "</script>\n",
-        "\n",
-        "<div class=\"animation\" align=\"center\">\n",
-        "    <img id=\"_anim_imgRJMMSRKUSXJHUFOV\">\n",
-        "    <br>\n",
-        "    <input id=\"_anim_sliderRJMMSRKUSXJHUFOV\" type=\"range\" style=\"width:350px\" name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\" onchange=\"animRJMMSRKUSXJHUFOV.set_frame(parseInt(this.value));\"></input>\n",
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-        "\"></button>\n",
-        "    <button onclick=\"animRJMMSRKUSXJHUFOV.pause_animation()\"><img class=\"anim_icon\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+H\n",
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-        "\"></button>\n",
-        "    <button onclick=\"animRJMMSRKUSXJHUFOV.next_frame()\"><img class=\"anim_icon\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+H\n",
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-        "\"></button>\n",
-        "    <button onclick=\"animRJMMSRKUSXJHUFOV.last_frame()\"><img class=\"anim_icon\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+H\n",
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-        "\"></button>\n",
-        "    <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",
-        "<script language=\"javascript\">\n",
-        "  /* Instantiate the Animation class. */\n",
-        "  /* The IDs given should match those used in the template above. */\n",
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-        "\"\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"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 21,
-       "text": [
-        "<matplotlib.animation.FuncAnimation at 0x48a3810>"
-       ]
-      }
-     ],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib\n",
-      "matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "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"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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-       "text": [
-        "<matplotlib.figure.Figure at 0x5b96550>"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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jnvasX8cwEhor2+gZouxDP9PHyQk/5nQUzT0cLYfw5WeiGtCSk7ZLo1w+r7pp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\nX9b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-       "text": [
-        "<matplotlib.figure.Figure at 0x5be7210>"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 18
     }
    ],
-   "metadata": {}
+   "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",
+    "from pymatsolver import MumpsSolver\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mag Inversion\n",
+    "\n",
+    "## Step1: Generating mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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": 14,
+   "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": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sph_ind_ini = spheremodel(mesh, 0., 0., -200., 150)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-500, 0)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112bcd750>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,1, figsize = (5, 5))\n",
+    "dat1 = mesh.plotSlice(rmap*model, ax = ax, normal = 'X')\n",
+    "plt.colorbar(dat1[0], orientation=\"horizontal\", ax = ax)\n",
+    "ax.set_ylim(-500, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(33620,)\n",
+      "(67240,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print model.shape\n",
+    "print chi.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step3: Generating Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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",
+    "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 = MumpsSolver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'NotImplementedType' object is not callable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-12-63f359d726f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdsyn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msurvey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpred\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/Users/sgkang/Projects/simpeg/SimPEG/Utils/CounterUtils.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     81\u001b[0m         \u001b[0mcounter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'counter'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcounter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/sgkang/Projects/simpeg/SimPEG/Utils/codeutils.pyc\u001b[0m in \u001b[0;36mrequiresVarWrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    221\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextra\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    225\u001b[0m         \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequiresVarWrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/sgkang/Projects/simpeg/SimPEG/Survey.pyc\u001b[0m in \u001b[0;36mdpred\u001b[0;34m(self, m, u)\u001b[0m\n\u001b[1;32m    307\u001b[0m         \"\"\"\n\u001b[1;32m    308\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mu\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 309\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mUtils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkvc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    310\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/sgkang/Projects/simpeg/SimPEG/Utils/CounterUtils.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     81\u001b[0m         \u001b[0mcounter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'counter'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcounter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/sgkang/Projects/simpeg/SimPEG/Survey.pyc\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, u)\u001b[0m\n\u001b[1;32m    320\u001b[0m                 \u001b[0md_\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mmathbf\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m         \"\"\"\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'eval is not yet implemented.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mUtils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'NotImplementedType' object is not callable"
+     ]
+    }
+   ],
+   "source": [
+    "dsyn = survey.dpred(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "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": 15,
+   "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": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZdEMAfl9gt670HLCnwN5o8sF5XA7Y7bq9+4C7bkKrfJ4K7hbIUwCvL4BGV1CY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W5ysqKvZQf42qss7CKWvp6nlzw6AK+IKs4s0n3G1U7ce2tTc+daIdDKcorfPB7S91nyxpxHui47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\n9bxSfn7VvNyX9+r5Ocb4Whx0a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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f499090>"
+      ]
+     },
+     "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": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "reg = Regularization.Tikhonov(mesh, mapping = rmap)\n",
+    "opt = Optimization.ProjectedGNCG(maxIter = 2)\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**2)\n",
+    "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaest])\n",
+    "opt.tolG = 1e-20\n",
+    "opt.eps = 1e-20\n",
+    "reg.alpha_s = 1e-5\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": 23,
+   "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 -4.89e+06  1.16e+06  2.70e-01 -1.59e+05    1.45e+05      0              \n",
+      "   1 -4.89e+06  8.02e+05  2.15e+05 -1.05e+12    2.87e+07      0              \n",
+      "   2 -6.12e+05  8.00e+05  2.15e+05 -1.32e+11    3.59e+06     10   Skip BFGS  \n",
+      "------------------------- STOP! -------------------------\n",
+      "0 : |fc-fOld| = 9.2136e+11 <= tolF*(1+|f0|) = 1.5946e+04\n",
+      "1 : |xc-x_last| = 4.6116e-05 <= tolX*(1+|x0|) = 1.0566e+02\n",
+      "0 : |proj(x-g)-x|    = 3.5921e+06 <= tolG          = 1.0000e-20\n",
+      "0 : |proj(x-g)-x|    = 3.5921e+06 <= 1e3*eps       = 1.0000e-17\n",
+      "1 : maxIter   =       2    <= iter          =      2\n",
+      "------------------------- DONE! -------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "mopt = inv.run(m0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Counters:\n",
+      "  ProjectedGNCG.activeSet                 :        2\n",
+      "  ProjectedGNCG.doEndIteration            :        2\n",
+      "  ProjectedGNCG.doStartIteration          :        3\n",
+      "  ProjectedGNCG.projection                :       24\n",
+      "  ProjectedGNCG.scaleSearchDirection      :        2\n",
+      "\n",
+      "Times:                                        mean      sum\n",
+      "  MagneticsDiffSecondary.Jtvec            : 5.36e-01, 5.89e+00,   11x\n",
+      "  MagneticsDiffSecondary.Jtvec_approx     : 6.45e-01, 5.16e+00,    8x\n",
+      "  MagneticsDiffSecondary.Jvec             : 6.31e-01, 5.05e+00,    8x\n",
+      "  MagneticsDiffSecondary.Jvec_approx      : 6.31e-01, 5.05e+00,    8x\n",
+      "  ProjectedGNCG.findSearchDirection       : 4.77e+00, 9.54e+00,    2x\n",
+      "  ProjectedGNCG.minimize                  : 2.02e+01, 2.02e+01,    1x\n",
+      "  ProjectedGNCG.modifySearchDirection     : 4.53e+00, 9.06e+00,    2x\n"
+     ]
+    }
+   ],
+   "source": [
+    "opt.counter.summary()\n",
+    "xc = opt.recall('xc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<script language=\"javascript\">\n",
+       "  /* Define the Animation class */\n",
+       "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
+       "    this.img_id = img_id;\n",
+       "    this.slider_id = slider_id;\n",
+       "    this.loop_select_id = loop_select_id;\n",
+       "    this.interval = interval;\n",
+       "    this.current_frame = 0;\n",
+       "    this.direction = 0;\n",
+       "    this.timer = null;\n",
+       "    this.frames = new Array(frames.length);\n",
+       "\n",
+       "    for (var i=0; i<frames.length; i++)\n",
+       "    {\n",
+       "     this.frames[i] = new Image();\n",
+       "     this.frames[i].src = frames[i];\n",
+       "    }\n",
+       "    document.getElementById(this.slider_id).max = this.frames.length - 1;\n",
+       "    this.set_frame(this.current_frame);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.get_loop_state = function(){\n",
+       "    var button_group = document[this.loop_select_id].state;\n",
+       "    for (var i = 0; i < button_group.length; i++) {\n",
+       "        var button = button_group[i];\n",
+       "        if (button.checked) {\n",
+       "            return button.value;\n",
+       "        }\n",
+       "    }\n",
+       "    return undefined;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.set_frame = function(frame){\n",
+       "    this.current_frame = frame;\n",
+       "    document.getElementById(this.img_id).src = this.frames[this.current_frame].src;\n",
+       "    document.getElementById(this.slider_id).value = this.current_frame;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.next_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.previous_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.first_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(0);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.last_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(this.frames.length - 1);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.slower = function()\n",
+       "  {\n",
+       "    this.interval /= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.faster = function()\n",
+       "  {\n",
+       "    this.interval *= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_forward = function()\n",
+       "  {\n",
+       "    this.current_frame += 1;\n",
+       "    if(this.current_frame < this.frames.length){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.first_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.last_frame();\n",
+       "        this.reverse_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.last_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_reverse = function()\n",
+       "  {\n",
+       "    this.current_frame -= 1;\n",
+       "    if(this.current_frame >= 0){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.last_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.first_frame();\n",
+       "        this.play_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.first_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.pause_animation = function()\n",
+       "  {\n",
+       "    this.direction = 0;\n",
+       "    if (this.timer){\n",
+       "      clearInterval(this.timer);\n",
+       "      this.timer = null;\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.play_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = 1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function(){t.anim_step_forward();}, this.interval);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.reverse_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = -1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function(){t.anim_step_reverse();}, this.interval);\n",
+       "  }\n",
+       "</script>\n",
+       "\n",
+       "<div class=\"animation\" align=\"center\">\n",
+       "    <img id=\"_anim_imgIZPNPUQZWJQOVJBN\">\n",
+       "    <br>\n",
+       "    <input id=\"_anim_sliderIZPNPUQZWJQOVJBN\" type=\"range\" style=\"width:350px\" name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\" onchange=\"animIZPNPUQZWJQOVJBN.set_frame(parseInt(this.value));\"></input>\n",
+       "    <br>\n",
+       "    <button onclick=\"animIZPNPUQZWJQOVJBN.slower()\">&#8211;</button>\n",
+       "    <button onclick=\"animIZPNPUQZWJQOVJBN.first_frame()\"><img class=\"anim_icon\" src=\"data:image/png;base64,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\"></button>\n",
+       "    <button onclick=\"animIZPNPUQZWJQOVJBN.previous_frame()\"><img class=\"anim_icon\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAgyTCyQ6wAAANRJREFUKM9jYBjO4AiUfgzFGGAp4+yayUvX6jMwMDCsYmBgOCS4OAOrSYmMgcc8/pd5Q3irC+Neh/1AlmeBMVgZmP8yMLD8/c/cqv9r90whzv/MX7Eq/MfAwMDIwCuZdfSV8U8WDgZGRmYGrAoZGRgY/jO8b3sj/J2F6T8j4z80pzEhmIwMjAxsSbqqlkeZGP//Z8SlkJnhPwMjwx/Guoe1NhmRwk+YGH5jV8jOwMPHzcDBysAwh8FrxQwtPU99HrwBXsnAwMDAsJiBgYGBoZ1xmKYqALHhMpn1o7igAAAAAElFTkSuQmCC\"></button>\n",
+       "    <button onclick=\"animIZPNPUQZWJQOVJBN.reverse_animation()\"><img class=\"anim_icon\" src=\"data:image/png;base64,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\"></button>\n",
+       "    <button onclick=\"animIZPNPUQZWJQOVJBN.pause_animation()\"><img class=\"anim_icon\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAkR91DQ2AAAAKtJREFUKM9jYCANTEVib2K4jcRbzQihGWEC00JuNjN8Z2Q0Zo3VYWA4lL005venH9+c3ZK5IfIsMIXMBtc12Bj+MMgxMDAwMPzWe2TBzPCf4SLcZCYY4/9/RgZGBiaYFf8gljFhKiQERhUOeoX/Gf8y/GX4y/APmlj+Mfxj+MfwH64Qnnq0zr9fyfLrPzP3eQYGBobvk5x4GX4xMIij23gdib0cRWYHiVmAAQDK5ircshCbHQAAAABJRU5ErkJggg==\"></button>\n",
+       "    <button onclick=\"animIZPNPUQZWJQOVJBN.play_animation()\"><img class=\"anim_icon\" src=\"data:image/png;base64,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\"></button>\n",
+       "    <button onclick=\"animIZPNPUQZWJQOVJBN.next_frame()\"><img class=\"anim_icon\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAkd/uac8wAAAMhJREFUKM9jYBie4DEUQ8B+fEq3+3UrMzAwMFxjYGBgYJizYubaOUxYFUaXh/6vWfRfEMIL/+//P5gZJoei4/f/7wxnY1PeNUXdE2RgYGZgYoCrY2BBVsjKwMDAwvCS4f3SG/dXxm5gYESSQ1HIwvCPgZmB8f8Pxv+Kxxb/YfiPJIdi9T8GJgaG/38ZFd4Fx0xUYsZt4h8GBgb2D2bLy7KnMTAwMEIxFoVCXIYr1IoDnkF4XAysqNIwUMDAwMDAsADKS2NkGL4AAIARMlfNIfZMAAAAAElFTkSuQmCC\"></button>\n",
+       "    <button onclick=\"animIZPNPUQZWJQOVJBN.last_frame()\"><img class=\"anim_icon\" src=\"data:image/png;base64,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\"></button>\n",
+       "    <button onclick=\"animIZPNPUQZWJQOVJBN.faster()\">+</button>\n",
+       "  <form action=\"#n\" name=\"_anim_loop_selectIZPNPUQZWJQOVJBN\" 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",
+       "<script language=\"javascript\">\n",
+       "  /* Instantiate the Animation class. */\n",
+       "  /* The IDs given should match those used in the template above. */\n",
+       "  (function() {\n",
+       "    var img_id = \"_anim_imgIZPNPUQZWJQOVJBN\";\n",
+       "    var slider_id = \"_anim_sliderIZPNPUQZWJQOVJBN\";\n",
+       "    var loop_select_id = \"_anim_loop_selectIZPNPUQZWJQOVJBN\";\n",
+       "    var frames = new Array(0);\n",
+       "    \n",
+       "  frames[0] = \"data:image/png;base64,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\"\n",
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+       "\n",
+       "\n",
+       "    /* set a timeout to make sure all the above elements are created before\n",
+       "       the object is initialized. */\n",
+       "    setTimeout(function() {\n",
+       "        animIZPNPUQZWJQOVJBN = new Animation(frames, img_id, slider_id, 40, loop_select_id);\n",
+       "    }, 0);\n",
+       "  })()\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<matplotlib.animation.FuncAnimation at 0x120049b50>"
+      ]
+     },
+     "execution_count": 26,
+     "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=2, 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": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+AMBOmUQED7N7S1lR+cPszgd5a8XYeou5v3Wbsr4yy6rUqaNNHW0huQD8uPB8BXBJj/11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\nIjX6/yfMAN86wQ4DAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11022bc50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "indx = 18\n",
+    "iteration = 1\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": []
   }
- ]
-}
\ No newline at end of file
+ ],
+ "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,
+ "nbformat_minor": 0
+}
diff --git a/simpegPF/notebooks/model.png b/simpegPF/notebooks/model.png
index e39034a0..115c43dc 100644
Binary files a/simpegPF/notebooks/model.png and b/simpegPF/notebooks/model.png differ
diff --git a/simpegPF/notebooks/obspred.png b/simpegPF/notebooks/obspred.png
index ca5a5e7c..0ecd657d 100644
Binary files a/simpegPF/notebooks/obspred.png and b/simpegPF/notebooks/obspred.png differ
diff --git a/simpegPF/notebooks/tutorials/Tutorial_1_Mag forward modeling.ipynb b/simpegPF/notebooks/tutorials/Tutorial_1_Mag forward modeling.ipynb
index 2f383f33..5ecc225e 100644
--- a/simpegPF/notebooks/tutorials/Tutorial_1_Mag forward modeling.ipynb	
+++ b/simpegPF/notebooks/tutorials/Tutorial_1_Mag forward modeling.ipynb	
@@ -1,443 +1,471 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:550ca3df316c026a67f55f9de3baa38df81944304bfe8103f9a2d9e8a43c20c7"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from SimPEG import *\n",
-      "from simpegPF.MagAnalytics import spheremodel, MagSphereAnalFun, CongruousMagBC, MagSphereAnalFunA\n",
-      "from simpegPF.Magnetics import MagneticsDiffSecondary, MagneticsDiffSecondaryInv, BaseMag\n",
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Populating the interactive namespace from numpy and matplotlib\n"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib\n",
-      "matplotlib.rcParams.update({'font.size': 16, 'text.usetex': True})"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Forward problem: Magnetics"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
      ]
     },
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This is a tutorial for Mag forward problem using simpegPF package. We first start with analytic solution for susceptible sphere in a whole space. Then we solve steady-state Maxwell's equatoins for Mag problem (<a href=\"http://simpegpf.readthedocs.org/en/latest/api_PF.html\">See Doc</a>) using simpegPF package. "
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Step1: Discretize the earth"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We use TensorMesh class in SimPEG to discretize the 3D earth (<a href=\"http://docs.simpeg.xyz/en/latest/api_MeshCode.html?highlight=tensormesh#module-SimPEG.Mesh.TensorMesh\">See Doc</a>). Let's visualize discretized mesh on section views:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "cs = 12.5\n",
-      "ncx, ncy, ncz, npad = 41, 41, 40, 5\n",
-      "hx = [(cs,npad,-1.4), (cs,ncx), (cs,npad,1.4)]\n",
-      "hy = [(cs,npad,-1.4), (cs,ncy), (cs,npad,1.4)]\n",
-      "hz = [(cs,npad,-1.4), (cs,ncz), (cs,npad,1.4)]\n",
-      "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCC')\n",
-      "fig, ax = plt.subplots(1,2, figsize=(12, 5))\n",
-      "dat0 = mesh.plotSlice(np.zeros(mesh.nC), grid=True, ax=ax[0]); ax[0].set_title('XY plane')\n",
-      "dat1 = mesh.plotSlice(np.zeros(mesh.nC), grid=True, normal='X', ax=ax[1]); ax[1].set_title('YZ plane')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 17,
-       "text": [
-        "<matplotlib.text.Text at 0x3a99ad0>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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5M6zx2PrY/qnnW9kAgKXjcA4AWLCq4OzU+VZ/qO6Pd/VZPWSQUUJG33hqz5De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\n2gfe3Ob6RUQdAGAr/IbK561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-       "text": [
-        "<matplotlib.figure.Figure at 0x3b6b310>"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Step2: Compose suceptibility model: susceptible sphere in whole space"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "- $\\mu = \\mu_0(1+\\chi)$\n",
-      "- $\\mu$: magnetic permeability\n",
-      "- $\\mu_0$: magnetic permeability of vacuum space\n",
-      "- $\\chi$: magnetic susceptibility"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from scipy.constants import mu_0\n",
-      "mu0 = 4*np.pi*1e-7\n",
-      "chibkg = 0.     # Background susceptibility\n",
-      "chiblk = 0.01   # Susceptibility for a sphere\n",
-      "chi = np.ones(mesh.nC)*chibkg"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "sph_ind = spheremodel(mesh, 0, 0, -100, 80) # A sphere is located at (0, 0, 0) and radius of the sphere is 100 m\n",
-      "chi[sph_ind] = chiblk # Assign susceptibility value for the sphere\n",
-      "mu = (1.+chi)*mu0"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fig, ax  =  plt.subplots(1,2, figsize=(12, 7))\n",
-      "indz = int(np.argmin(abs(mesh.vectorCCz-(-100.)))); indx = int(np.argmin(abs(mesh.vectorCCx-0.)))\n",
-      "dat0 = mesh.plotSlice(chi, grid=True, ind=indz, ax=ax[0]); ax[0].set_title(('XY plane at z=%5.2f')%(mesh.vectorCCz[indz]))\n",
-      "dat1 = mesh.plotSlice(chi, grid=True, normal='X', ind=indx, ax=ax[1]); ax[1].set_title(('YZ plane at x=%5.2f')%(mesh.vectorCCx[indx]))\n",
-      "cb0 = plt.colorbar(dat0[0], orientation='horizontal',  ax=ax[0], ticks = linspace(0, 0.01, 5)); \n",
-      "cb1 = plt.colorbar(dat1[0], orientation='horizontal',  ax=ax[1], ticks = linspace(0, 0.01, 5)); \n",
-      "cb0.set_label(\"Suceptibility\")\n",
-      "cb1.set_label(\"Suceptibility\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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q5DGK4WsUP7ZMPZKpavjaQ+N984fu76u9FiLnbABAiNBF/5uSbkl6knAsKZxv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\nrOGqyzX9dfUF/7ak17U4gr+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-       "text": [
-        "<matplotlib.figure.Figure at 0x42450d0>"
-       ]
-      }
-     ],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Step3: Set up an airborne MAG survey"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We have discretized 3D earth and generated suceptibility model, which means that we have discretized earth and physical property distribution. We can compute magnetic fields everywhere in our domain by solving parial differental equation (PDE), but our measurements are confined to finite locations. Therefore, we need to project computed fields $\\mathbf{u}$, which is defined everywhere in our domain to certain locations where we have receiving points. For instance in airborne mag survey these are the points where a plane or helicopter measure earth magnetic fields. This projection can be expressed as:\n",
-      "\n",
-      "$$ \\mathbf{d} = P(\\mathbf{u})$$\n",
-      "\n",
-      "where $P(\\cdot)$ is a projection from computed field to the measured data, and $d$ is the measure data. "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's assume that we have a survey area: 400 m $\\times$ 400 m. We have 21 lines of airborne MAG survey, and we measure magnetic fields for every 20 m on each line. A pilot for this helicopter is really talented so that the flight height is constant for 30 m above the surface.  "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "xr = np.linspace(-200, 200, 21)\n",
-      "yr = np.linspace(-200, 200, 21)\n",
-      "X, Y = np.meshgrid(xr, yr)\n",
-      "Z = np.ones((size(xr), size(yr)))*30."
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fig, ax  =  plt.subplots(1,1, figsize=(5, 5))\n",
-      "indz = int(np.argmin(abs(mesh.vectorCCz-(0.))));\n",
-      "dat0 = mesh.plotSlice(chi, grid=True, ind=indz, ax=ax); ax.set_title(('XY plane at z=%5.2f')%(mesh.vectorCCz[indz]))\n",
-      "ax.plot(X.flatten(), Y.flatten(), 'w.', ms=5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 24,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x56e54d0>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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-       "text": [
-        "<matplotlib.figure.Figure at 0x482ae10>"
-       ]
-      }
-     ],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Step4: Analytic solution"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We have an analytic solution when we have a sphere in a whole-space. simpegPF provides this function so that you can compute magnetic field on your receiving locations. Another input you need to put is direction of the earth magnetic fieds, and the strength, you can easily get this information from <a href=\"http://www.ngdc.noaa.gov/geomag-web/\">NOAA's website</a>. We assume that we have veritcal earth fields and the strength is 1. "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "Bxra, Byra, Bzra = MagSphereAnalFunA(X, Y, Z, 80., 0., 0., -100, chiblk, np.array([0., 0., 1.]), flag)\n",
-      "Bxra = np.reshape(Bxra, (size(xr), size(yr)), order='F')\n",
-      "Byra = np.reshape(Byra, (size(xr), size(yr)), order='F')\n",
-      "Bzra = np.reshape(Bzra, (size(xr), size(yr)), order='F')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fig, ax = plt.subplots(1,3, figsize=(18, 4))\n",
-      "dat0=ax[0].contourf(X, Y, Bxra, 30); ax[0].set_title('Bx'); cb0 = plt.colorbar(dat0, ax=ax[0])\n",
-      "dat1=ax[1].contourf(X, Y, Byra, 30); ax[1].set_title('By'); cb1 = plt.colorbar(dat0, ax=ax[1])\n",
-      "dat2=ax[2].contourf(X, Y, Bzra, 30); ax[2].set_title('Bz'); cb2 = plt.colorbar(dat0, ax=ax[2])\n",
-      "for i in range(3):\n",
-      "    ax[i].plot(X.flatten(), Y.flatten(), 'k.', ms=3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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hOQQQyn5CRZKY6JEk4ggLI0z75BBF1gM4G8A7ATyjab8ZwFcAfL15vQ312sX3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\nD7zAAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x56f2150>"
-       ]
-      }
-     ],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Step5: Solve PDE"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Note that data for this case magnetic fields projected to earth field direction, which is typical data type for airborne mag survey. "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "First, set survey class"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "survey = BaseMag.BaseMagSurvey() # survey class for mag problem\n",
-      "Inc = 90.\n",
-      "Dec = 0.\n",
-      "Btot = 1\n",
-      "survey.setBackgroundField(Inc, Dec, Btot) # set inclination, declination, and strength of magnetic field\n",
-      "rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]\n",
-      "survey.rxLoc = rxLoc # set receiver locations"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Second, set problem class then pair with survey"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "prob = MagneticsDiffSecondary(mesh)\n",
-      "prob.pair(survey)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Third, run forward modeling"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "data = survey.dpred(mu)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 29
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now we viualize computed solution and compare with analytic solutions! Note that you may always need to make sure that your numerical solution is reasonable enough. "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fig, ax = plt.subplots(1,3, figsize=(18, 4))\n",
-      "vmin = Bzra.min()\n",
-      "vmax = Bzra.max()\n",
-      "residual = data.reshape((xr.size, yr.size), order='F')-Bzra\n",
-      "dat0=ax[0].contourf(X, Y, Bzra, 30, vmin=vmin, vmax=vmax)\n",
-      "dat1=ax[1].contourf(X, Y, data.reshape((xr.size, yr.size), order='F'), 30, vmin=vmin, vmax=vmax)\n",
-      "dat2=ax[2].contourf(X, Y, residual, 30)\n",
-      "cb0 = plt.colorbar(dat0, ax=ax[0])\n",
-      "cb1 = plt.colorbar(dat0, ax=ax[1])\n",
-      "cb2 = plt.colorbar(dat2, ax=ax[2])\n",
-      "ax[0].set_title('Bz (analytic)')\n",
-      "ax[1].set_title('Bz (simpegPF)')\n",
-      "ax[2].set_title('Residual')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 30,
-       "text": [
-        "<matplotlib.text.Text at 0x4858f10>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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qZkoFE2VKOl3NyIbumtuHgS2aKgjlLO1/J/R+Ss81pqnSxUxZ2mftQowVYVz6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\nl2CMAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x6054210>"
-       ]
-      }
-     ],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a><br />This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International License</a>."
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: pylab import has clobbered these variables: ['linalg']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "from SimPEG import *\n",
+    "from simpegPF.MagAnalytics import spheremodel, MagSphereAnaFun, CongruousMagBC, MagSphereAnaFunA\n",
+    "from simpegPF.Magnetics import MagneticsDiffSecondary, MagneticsDiffSecondaryInv, BaseMag\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.rcParams.update({'font.size': 16, 'text.usetex': True})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Forward problem: Magnetics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a tutorial for Mag forward problem using simpegPF package. We first start with analytic solution for susceptible sphere in a whole space. Then we solve steady-state Maxwell's equatoins for Mag problem (<a href=\"http://simpegpf.readthedocs.org/en/latest/api_PF.html\">See Doc</a>) using simpegPF package. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step1: Discretize the earth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use TensorMesh class in SimPEG to discretize the 3D earth (<a href=\"http://docs.simpeg.xyz/en/latest/api_MeshCode.html?highlight=tensormesh#module-SimPEG.Mesh.TensorMesh\">See Doc</a>). Let's visualize discretized mesh on section views:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x3a99ad0>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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5M6zx2PrY/qnnW9kAgKXjcA4AWLCq4OzU+VZ/qO6Pd/VZPWSQUUJG33hqz5De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\n2gfe3Ob6RUQdAGAr/IbK561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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x3b6b310>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cs = 12.5\n",
+    "ncx, ncy, ncz, npad = 41, 41, 40, 5\n",
+    "hx = [(cs,npad,-1.4), (cs,ncx), (cs,npad,1.4)]\n",
+    "hy = [(cs,npad,-1.4), (cs,ncy), (cs,npad,1.4)]\n",
+    "hz = [(cs,npad,-1.4), (cs,ncz), (cs,npad,1.4)]\n",
+    "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCC')\n",
+    "fig, ax = plt.subplots(1,2, figsize=(12, 5))\n",
+    "dat0 = mesh.plotSlice(np.zeros(mesh.nC), grid=True, ax=ax[0]); ax[0].set_title('XY plane')\n",
+    "dat1 = mesh.plotSlice(np.zeros(mesh.nC), grid=True, normal='X', ax=ax[1]); ax[1].set_title('YZ plane')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step2: Compose suceptibility model: susceptible sphere in whole space"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- $\\mu = \\mu_0(1+\\chi)$\n",
+    "- $\\mu$: magnetic permeability\n",
+    "- $\\mu_0$: magnetic permeability of vacuum space\n",
+    "- $\\chi$: magnetic susceptibility"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.constants import mu_0\n",
+    "mu0 = 4*np.pi*1e-7\n",
+    "chibkg = 0.     # Background susceptibility\n",
+    "chiblk = 0.01   # Susceptibility for a sphere\n",
+    "chi = np.ones(mesh.nC)*chibkg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sph_ind = spheremodel(mesh, 0, 0, -100, 80) # A sphere is located at (0, 0, 0) and radius of the sphere is 100 m\n",
+    "chi[sph_ind] = chiblk # Assign susceptibility value for the sphere\n",
+    "mu = (1.+chi)*mu0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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q5DGK4WsUP7ZMPZKpavjaQ+N984fu76u9FiLnbABAiNBF/5uSbkl6knAsKZxv\naHskaX/sgQBAZl7T4mh6LjrM2UWioQxRO7a/L98Vt9ub8nw51KBGDjXa2mNzuuSmrDG90EX/h+XH\nV5JuaD5/AOxIOrHaTsvPZzWP7wEAungm6VtJFyUdW7HzMkfQXxp5TD4d5uwi0VCKnrVj+/vyXXG7\nvSnPl0MNauRQo609NqdLbsoarrrjCl30vyjpkqQ3NK8/ALa1eCNYpdqh7GjUHcgQtWP7+/Jdcbvd\nt92lTx418hhFihpFUNZcvpvufdraQ+N984fuH+VFmYX/65I+tmIvjDmQQB3mbABAiK6T/r7MTuR1\nSVuSvlaefwDsy4ypvhPZk/RAZudij/W59Le1zV1J5wYaSqF+O/vY/r58V9xu92136ZNPjTxGMXyN\n4seWqUcyVQ1fe2i8b/7Q/eu+0/IB/C+k5Tn9maSfS/oXMnfDuSHp78vYeUn3Zf4oyEmHORsAZmvU\ngy9nOvb7k8xif0fmrgo/1+IMwF1Jb2n1dPIUTmR2FHXVtuOPk1cTDgcAhnJOywclvmhKei7zJti7\nkm5L+oXMwZpcdZizi0RDKXrWju3vy3fF7famPF8ONaiRQ4229ticLrkpa7jqjitm0X9Oi0t8qjdb\n3ZNZ4N+S9FjmKM2N8uNXww2zs6+1uB60siOzA3Qo0o1mkBfekPmuuN3u2+7SJ48aeYwiRY0iKGsu\n3033Pm3tofG++UP37+SezBz+B5nLfT6YYhABOszZAIAQoYv+6ujLqczO43013z7tnsw9lo8GGd0w\nPtTyPZ6rP0wcikTDKHrWju3vy3fF7Xbfdpc++dTIYxTD1yh+bJl6JFPV8LWHxvvmD93fV7vVqcxZ\n2WuS3kk0iCFEztkAgBChi/6bkv6dpG8Ccj+SOfKfi6sy17MeaHFtqP2GNgBYNz+V+a+2tupyn/1x\nhxMscs4uEg6lb+3Y/r58V9xub8rz5VCDGjnUaGuPzemSm7LG9EIX/e/6U370uMtAEsv1VDYApNK0\n4K/cKz9yFTFnF4mGUPSsHdvfl++K2+1Neb4calAjhxpt7bE5XXJT1nDVHVfoon+DFBnXju3vy3fF\n7Xbfdpc+edTIYxQpahRBWXP5brr3aWsPjffNH7o/AADxWPSvKBLW7VM7tr8v3xW3233bXfrkUyOP\nUQxfo/ixZeqRTFXD1x4a75s/dH9fbQAAmrHoBwDMWJFx7dj+vnxX3G5vyvPlUIMaOdRoa4/N6ZKb\nssb0WPRlfJSUAAAgAElEQVQDAGasSFi3T+3Y/r58V9xub8rz5ax3jalGkevzkW+NtvbYnC65KWu4\n6o6LRf+KIuPasf19+a643e7b7tInjxp5jCJFjSIoay7fTfc+be2h8b75Q/cHACAei/4VRcK6fWrH\n9vflu+J2u2+7S588axQqMhhF9xr5jCSHGr720Hjf/KH7+2oDANCMRT8AYMaKjGvH9vflu+J2e1Oe\nL2d9a0w1ilyfj7xrtLXH5nTJTVljeiz6AQAzViSs26d2bH9fvitutzfl+XLWq0Yeo1jdzmckudZo\na4/N6ZKbsoar7rhY9K8oMq4d29+X74rb7b7tLn3yq1F4M8YYRZ8+uYwklxq+9tB43/yh+wMAEI9F\n/4oiYd0+tWP7+/Jdcbvdt92lzzxqFCoyGIV7O5+R5FjD1x4a75s/dH9fbQAAmrHoBwDMWJFx7dj+\nvnxX3G5vyvPlrE+NPEbR1CeXkeRco609NqdLbsoa02PRDwCYsSJh3T61Y/v78l1xu70pz5czzxqF\nigxG0XW7CMqax3czVI229ticLrkpa7jqjotF/4oi49qx/X35rrjd7tvu0if/GoUjY9xRtOXkMpJc\na/jaQ+N984fuDwBAPBb9K4qEdfvUju3vy3fF7Xbfdpc+864x9iiKH7f6VBliJHOq4WsPjffNH7q/\nrzYAAM1Y9AMAZqzIuHZsf1++K263N+X5cuZXo3BkjDuKrttFUNZcvpvharS1x+Z0yU1ZY3ovTD2A\nzDyfegAA0MOmzenP05456VM7tr8v3xW325vyfDnzrFGoyGAUXbeLoKx5fDdD1Whrj83pkpuyhqvu\nuHP2mTEfbB6KhHX71I7t78t3xe1233aXPutVY+hRsDMYooavPTTeN3/o/r7aAAA0e3HqAQAAAABI\nay5H+i9J+oWkqw2xK5IeStopt29GxgEAs1VkXDu2vy/fFbfbm/J8OfOrUTgyxh1F1+0iKGsu381w\nNdraY3O65KasMb3cr/+8IOm8pIuSvpX091b8mqRPJH1Wbh9J+lLSncC4jWv6AcxZLnP6WAdquKY/\n20vshq9RqMhgFGlqLL6zqUcyZY229ticLrkpa7jqck1/3R/Kj59I2m6IH0p6t7Z9t9y+ExhvUHQd\nq0fRs3Zsf1++K263+7a79KEGNVLX8LWHxvvmD93fV3ty9oEaW9OBmAO1H6ipxwEAHc35mv7zDW2P\nJO0HxgEAw/qDpA8kfa3mI1iHWizoJXMg5q2IOACgo9yP9LfZkXRitZ2Wn88GxJ+kGxoAwJLoQE3R\nZ0wefWvH9vflu+J2e1OeLyf/GoU3Y4xRpKpROLLGH8m0NdraY3O65KasMb1crv/0OZK5vOe3tbZL\nkj7U4rpPlTknkvZkridtix83PA7X9AOYs1zm9KY5e1/SdUk/rbXtSXpQ5v6NJ950oIZr+rO9xG74\nGoWKDEaRpsbiO5t6JFPWaGuPzemSm7KGq+76X9O/rfbF9ePAOqcNbdUC/yQg7lAEPnysomft2P6+\nfFfcbvdtd+lDDWqkruFrD433zR+6v6921ra1fBBGWszFOwFxzs4CQA9jL/oPZN7g1eZUzXd8sJ1o\n9c291faTgLjD57WvdyWdCxgKAIztOzWfsBzchh6oGaJ2bH9fvitutzfl+XLyr1F4M8YYRaoahSNr\n/JFMW6OtPTanS27KGtMbe9F/R8PdheFrre4kdmTe+BUSd3h1gKEBQGrntHxQ4osUDzKDAzVFwEN3\nUfSsHdvfl++K2+1Neb6cedQoVGQwijQ1Ft/Z1COZskZbe2xOl9yUNVx1xzX2or8r1zVPH2r5dm77\nkm5ExBsUHYcYom/t2P6+fFfcbvdtd+lDDWqkruFrD433zR+6/6hmcKAGABAi90X/KzIL9QNJfy1z\n3+d7kr4p41dl/pHLgRZv+Pq41t8Xb1AMNPSmun1qx/b35bvidrtvu0sfalAjdQ1fe2i8b/7Q/X21\nszHigRoAQIjcF/3flB8ftOS0xULiAIBhrNGBmiFqx/b35bvidntTni8n/xqFN2OMUaSqUTiyxh/J\ntDXa2mNzuuSmrDG93Bf9AID5mOBATRGXHlW3T+3Y/r58V9xub8rz5cyjRqEig1GkqbH4zqYeyZQ1\n2tpjc7rkpqzhqjsuFv0rioxrx/b35bvidrtvu0sfalAjdQ1fe2i8b/7Q/QEAiMeif0WRsG6f2rH9\nffmuuN3u2+7ShxrUSF3D1x4a75s/dH9fbQAAmrHoBwDMWJFx7dj+vnxX3G5vyvPl5F+j8GaMMYpU\nNQpH1vgjmbZGW3tsTpfclDWmx6IfADBjRcK6fWrH9vflu+J2e1OeL2eeNQoVGYyi63YRlDWP72ao\nGm3tsTldclPWcNUdF4v+FUXGtWP7+/Jdcbvdt92lDzWokbqGrz003jd/6P4AAMRj0b+iSFi3T+3Y\n/r58V9xu92136UMNaqSu4WsPjffNH7q/rzYAAM1Y9AMAZqzIuHZsf1++K263N+X5cuZXo3BkjDuK\nrttFUNZcvpvharS1x+Z0yU1ZY3qu/5q4qZ5PPQAA6GHT5vTnac+c9Kkd29+X74rb7U15vpx51ihU\nZDCKrttFUNY8vpuharS1x+Z0yU1Zw1V33Dn7zJgPNg9Fwrp9asf29+W74na7b7tLH2pQI3UNX3to\nvG/+0P19tQEAaPbi1AMAAAAAkBZH+gEAM1ZkXDu2vy/fFbfbm/J8OfOrUTgyxh1F1+0iKGsu381w\nNdraY3O65KasMb1Nu/7Th2v6AczZps3pXNOf7SV209XIYxSr2/mMJNcabe2xOV1yU9Zw1eWa/okV\nCev2qR3b35fvitvtvu0ufahBjdQ1fO2h8b75Q/f31QYAoBnX9AMAAABrjiP9AIAZKzKuHdvfl++K\n2+1Neb6c9amRxyia+uQykpxrtLXH5nTJTVljept2/acP1/QDmLNNm9O5pj/bS+zyqTHVKHJ9PvKt\n0dYem9MlN2UNV12u6Z9YkbBun9qx/X35rrjd7tvu0oca1Ehdw9ceGu+bP3R/X20AAJpxTT8AAACw\n5jjSDwCYsSLj2rH9ffmuuN3elOfLWd8aU40i1+cj7xpt7bE5XXJT1pjeHK7/vFJ+/qWkLyV90BB/\nKGmn3L4ZGa/jmn4AczaHOX1IXNOf7SV21KBGbI229ticLrkpa7jqck1/3ZGkq7Xt++XnauF/TdIn\nkj6r5R9IuhMYb1D0G3Fr3T61Y/v78l1xu9233aUPNaiRuoavPTTeN3/o/r7aWRjzQA0AIFDO1/Rv\nSfrearsh6b3a9qEWC3pJuivprYg4AGA4RzKL/A8k/UbSG1r8ESCZAzFfyRx4uSnpZZkDMaFxAEBH\nOR/p/4nMDuC2pOOy7ZGk7fLr8w19HknaD4wDAIbjOlBzTYuj/YeS3q3F75bbdwLjDYqOww3Rt3Zs\nf1++K263N+X5cqhBjRxqtLXH5nTJTVljerlf//kzSX+ubd+QtCvpVzKL9+uSflqL70l6IPOHwd94\n4k8aHo9r+gHM2ZRzejW/7mlxoOaSpFsyZ5XPS7qnxWU7KtvuB8abcE1/tpfYUYMasTXa2mNzuuSm\nrOGqyzX9dfUF/7ak17U4gr+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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x42450d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax  =  plt.subplots(1,2, figsize=(12, 7))\n",
+    "indz = int(np.argmin(abs(mesh.vectorCCz-(-100.)))); indx = int(np.argmin(abs(mesh.vectorCCx-0.)))\n",
+    "dat0 = mesh.plotSlice(chi, grid=True, ind=indz, ax=ax[0]); ax[0].set_title(('XY plane at z=%5.2f')%(mesh.vectorCCz[indz]))\n",
+    "dat1 = mesh.plotSlice(chi, grid=True, normal='X', ind=indx, ax=ax[1]); ax[1].set_title(('YZ plane at x=%5.2f')%(mesh.vectorCCx[indx]))\n",
+    "cb0 = plt.colorbar(dat0[0], orientation='horizontal',  ax=ax[0], ticks = linspace(0, 0.01, 5)); \n",
+    "cb1 = plt.colorbar(dat1[0], orientation='horizontal',  ax=ax[1], ticks = linspace(0, 0.01, 5)); \n",
+    "cb0.set_label(\"Suceptibility\")\n",
+    "cb1.set_label(\"Suceptibility\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step3: Set up an airborne MAG survey"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have discretized 3D earth and generated suceptibility model, which means that we have discretized earth and physical property distribution. We can compute magnetic fields everywhere in our domain by solving parial differental equation (PDE), but our measurements are confined to finite locations. Therefore, we need to project computed fields $\\mathbf{u}$, which is defined everywhere in our domain to certain locations where we have receiving points. For instance in airborne mag survey these are the points where a plane or helicopter measure earth magnetic fields. This projection can be expressed as:\n",
+    "\n",
+    "$$ \\mathbf{d} = P(\\mathbf{u})$$\n",
+    "\n",
+    "where $P(\\cdot)$ is a projection from computed field to the measured data, and $d$ is the measure data. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's assume that we have a survey area: 400 m $\\times$ 400 m. We have 21 lines of airborne MAG survey, and we measure magnetic fields for every 20 m on each line. A pilot for this helicopter is really talented so that the flight height is constant for 30 m above the surface.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "xr = np.linspace(-200, 200, 21)\n",
+    "yr = np.linspace(-200, 200, 21)\n",
+    "X, Y = np.meshgrid(xr, yr)\n",
+    "Z = np.ones((size(xr), size(yr)))*30."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x56e54d0>]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x482ae10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax  =  plt.subplots(1,1, figsize=(5, 5))\n",
+    "indz = int(np.argmin(abs(mesh.vectorCCz-(0.))));\n",
+    "dat0 = mesh.plotSlice(chi, grid=True, ind=indz, ax=ax); ax.set_title(('XY plane at z=%5.2f')%(mesh.vectorCCz[indz]))\n",
+    "ax.plot(X.flatten(), Y.flatten(), 'w.', ms=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step4: Analytic solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have an analytic solution when we have a sphere in a whole-space. simpegPF provides this function so that you can compute magnetic field on your receiving locations. Another input you need to put is direction of the earth magnetic fieds, and the strength, you can easily get this information from <a href=\"http://www.ngdc.noaa.gov/geomag-web/\">NOAA's website</a>. We assume that we have veritcal earth fields and the strength is 1. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Bxra, Byra, Bzra = MagSphereAnalFunA(X, Y, Z, 80., 0., 0., -100, chiblk, np.array([0., 0., 1.]), flag)\n",
+    "Bxra = np.reshape(Bxra, (size(xr), size(yr)), order='F')\n",
+    "Byra = np.reshape(Byra, (size(xr), size(yr)), order='F')\n",
+    "Bzra = np.reshape(Bzra, (size(xr), size(yr)), order='F')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hOQQQyn5CRZKY6JEk4ggLI0z75BBF1gM4G8A7ATyjab8ZwFcAfL15vQ312sX3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\nD7zAAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x56f2150>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,3, figsize=(18, 4))\n",
+    "dat0=ax[0].contourf(X, Y, Bxra, 30); ax[0].set_title('Bx'); cb0 = plt.colorbar(dat0, ax=ax[0])\n",
+    "dat1=ax[1].contourf(X, Y, Byra, 30); ax[1].set_title('By'); cb1 = plt.colorbar(dat0, ax=ax[1])\n",
+    "dat2=ax[2].contourf(X, Y, Bzra, 30); ax[2].set_title('Bz'); cb2 = plt.colorbar(dat0, ax=ax[2])\n",
+    "for i in range(3):\n",
+    "    ax[i].plot(X.flatten(), Y.flatten(), 'k.', ms=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step5: Solve PDE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that data for this case magnetic fields projected to earth field direction, which is typical data type for airborne mag survey. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, set survey class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "survey = BaseMag.BaseMagSurvey() # survey class for mag problem\n",
+    "Inc = 90.\n",
+    "Dec = 0.\n",
+    "Btot = 1\n",
+    "survey.setBackgroundField(Inc, Dec, Btot) # set inclination, declination, and strength of magnetic field\n",
+    "rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]\n",
+    "survey.rxLoc = rxLoc # set receiver locations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Second, set problem class then pair with survey"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "prob = MagneticsDiffSecondary(mesh)\n",
+    "prob.pair(survey)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Third, run forward modeling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data = survey.dpred(mu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we viualize computed solution and compare with analytic solutions! Note that you may always need to make sure that your numerical solution is reasonable enough. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x4858f10>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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qZkoFE2VKOl3NyIbumtuHgS2aKgjlLO1/J/R+Ss81pqnSxUxZ2mftQowVYVz6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\nl2CMAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x6054210>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,3, figsize=(18, 4))\n",
+    "vmin = Bzra.min()\n",
+    "vmax = Bzra.max()\n",
+    "residual = data.reshape((xr.size, yr.size), order='F')-Bzra\n",
+    "dat0=ax[0].contourf(X, Y, Bzra, 30, vmin=vmin, vmax=vmax)\n",
+    "dat1=ax[1].contourf(X, Y, data.reshape((xr.size, yr.size), order='F'), 30, vmin=vmin, vmax=vmax)\n",
+    "dat2=ax[2].contourf(X, Y, residual, 30)\n",
+    "cb0 = plt.colorbar(dat0, ax=ax[0])\n",
+    "cb1 = plt.colorbar(dat0, ax=ax[1])\n",
+    "cb2 = plt.colorbar(dat2, ax=ax[2])\n",
+    "ax[0].set_title('Bz (analytic)')\n",
+    "ax[1].set_title('Bz (simpegPF)')\n",
+    "ax[2].set_title('Residual')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a><br />This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International License</a>."
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "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
+}