diff --git a/notebooks/MT1Dinversion_Scipy2015.ipynb b/notebooks/MT1Dinversion_Scipy2015.ipynb
deleted file mode 100644
index 7909e3b5..00000000
--- a/notebooks/MT1Dinversion_Scipy2015.ipynb
+++ /dev/null
@@ -1,1587 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import SimPEG as simpeg\n",
-    "import simpegMT as simpegmt\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "## Setup the problem\n",
-    "\n",
-    "# Frequency\n",
-    "nFreq = 33\n",
-    "freqs = np.logspace(3,-3,nFreq)\n",
-    "# freqs = np.array([100,10,1,0.1,0.01])\n",
-    "# Make the mesh\n",
-    "ct = 10\n",
-    "air = simpeg.Utils.meshTensor([(ct,15,1.3)])\n",
-    "core = np.concatenate( (  np.kron(simpeg.Utils.meshTensor([(ct,15,-1.2)]),np.ones((5,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n",
-    "bot = simpeg.Utils.meshTensor([(core[0],10,-1.3)])\n",
-    "x0 = -np.array([np.sum(np.concatenate((core,bot)))])\n",
-    "# Change to use no air\n",
-    "m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)\n",
-    "\n",
-    "## Setup model varibles\n",
-    "active = m1d.vectorCCx<0.\n",
-    "layer1 = (m1d.vectorCCx<-200.) & (m1d.vectorCCx>=-600.)\n",
-    "layer2 = (m1d.vectorCCx<-2000.) & (m1d.vectorCCx>=-4000.)\n",
-    "actMap = simpeg.Maps.ActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)\n",
-    "mappingExpAct = simpeg.Maps.ExpMap(m1d) * actMap\n",
-    "sig_half = 2e-3\n",
-    "sig_air = 1e-8\n",
-    "sig_layer1 = 1\n",
-    "sig_layer2 = .1\n",
-    "# Make the true model\n",
-    "sigma_true = np.ones(m1d.nCx)*sig_air\n",
-    "sigma_true[active] = sig_half\n",
-    "sigma_true[layer1] = sig_layer1\n",
-    "sigma_true[layer2] = sig_layer2\n",
-    "m_true = np.log(sigma_true[active])\n",
-    "# Make the background model\n",
-    "sigma_0 = np.ones(m1d.nCx)*sig_air\n",
-    "sigma_0[active] = sig_half\n",
-    "m_0 = np.log(sigma_0[active])\n",
-    "\n",
-    "# Receivers \n",
-    "# 1D impedance at the surface (elevation 0)\n",
-    "rxList = []\n",
-    "for rxType in ['z1dr','z1di']:\n",
-    "    rxList.append(simpegmt.SurveyMT.RxMT(simpeg.mkvc(np.array([0.0]),2).T,rxType))\n",
-    "# Source list\n",
-    "srcList =[]\n",
-    "for freq in freqs:\n",
-    "        srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,freq,sigma_0))\n",
-    "# Make the survey\n",
-    "survey = simpegmt.SurveyMT.SurveyMT(srcList)\n",
-    "survey.mtrue = m_true\n",
-    "# Set the problem\n",
-    "problem = simpegmt.ProblemMT1D.eForm_psField(m1d,mapping=mappingExpAct)\n",
-    "from pymatsolver import MumpsSolver\n",
-    "problem.solver = MumpsSolver\n",
-    "problem.pair(survey)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# problem.mapping.sigmaMap._transform(m_0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.002"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sig_half"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "## Make the observed data \n",
-    "# Project the data\n",
-    "d_true = survey.dpred(m_true)\n",
-    "survey.dtrue = d_true\n",
-    "# Add noise\n",
-    "std = 0.05 # 5% std\n",
-    "noise = std*abs(survey.dtrue)*np.random.randn(*survey.dtrue.shape)\n",
-    "# Assign the dobs\n",
-    "survey.dobs = survey.dtrue + noise\n",
-    "survey.std = survey.dobs*0 + std\n",
-    "# Assign the data weight\n",
-    "survey.Wd = 1/(abs(survey.dobs)*std)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "## Setup the inversion proceedure\n",
-    "C =  simpeg.Utils.Counter()\n",
-    "\n",
-    "# Set the optimization\n",
-    "opt = simpeg.Optimization.InexactGaussNewton(maxIter = 50)\n",
-    "opt.counter = C\n",
-    "opt.LSshorten = 0.5\n",
-    "opt.remember('xc')\n",
-    "# Data misfit\n",
-    "dmis = simpeg.DataMisfit.l2_DataMisfit(survey)\n",
-    "# Regularization\n",
-    "# regMesh = simpeg.Mesh.TensorMesh([m1d.hx[problem.mapping.sigmaMap.maps[-1].indActive]])\n",
-    "# reg = simpeg.Regularization.Tikhonov(regMesh)\n",
-    "reg = simpeg.Regularization.Tikhonov(m1d,mapping=mappingExpAct)\n",
-    "reg.alpha_s = 1e-5\n",
-    "reg.alpha_x = 1.\n",
-    "reg.alpha_xx = .1\n",
-    "# Inversion problem\n",
-    "invProb = simpeg.InvProblem.BaseInvProblem(dmis, reg, opt)\n",
-    "invProb.counter = C\n",
-    "# Beta cooling\n",
-    "beta = simpeg.Directives.BetaSchedule()\n",
-    "betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)\n",
-    "saveModel = simpeg.Directives.SaveModelEveryIteration()\n",
-    "# Create an inversion object\n",
-    "inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest])#,saveModel]) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<SimPEG.Maps.ActiveCells at 0x7f38720f2690>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "problem.mapping.sigmaMap.maps[-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "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",
-      "SimPEG.l2_DataMisfit is creating default weightings for Wd.\n",
-      "============================ Inexact Gauss Newton ============================\n",
-      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
-      "-----------------------------------------------------------------------------\n",
-      "   0  2.01e+05  1.38e+06  6.91e-07  1.38e+06    2.03e+05      0              \n",
-      "   1  2.01e+05  1.66e+05  6.15e-06  1.66e+05    2.43e+04      0              \n",
-      "   2  2.01e+05  8.65e+04  1.09e-05  8.65e+04    1.51e+04      0   Skip BFGS  \n",
-      "   3  2.51e+04  7.32e+04  1.31e-05  7.32e+04    1.32e+04      0   Skip BFGS  \n",
-      "   4  2.51e+04  3.60e+04  3.07e-05  3.60e+04    7.51e+03      0   Skip BFGS  \n",
-      "   5  2.51e+04  2.97e+04  3.89e-05  2.97e+04    6.45e+03      0   Skip BFGS  \n",
-      "   6  3.14e+03  2.60e+04  4.57e-05  2.60e+04    5.81e+03      0   Skip BFGS  \n",
-      "   7  3.14e+03  1.40e+04  9.70e-05  1.40e+04    3.59e+03      0   Skip BFGS  \n",
-      "   8  3.14e+03  1.13e+04  1.25e-04  1.13e+04    3.04e+03      0   Skip BFGS  \n",
-      "   9  3.92e+02  9.74e+03  1.48e-04  9.74e+03    2.72e+03      0   Skip BFGS  \n",
-      "  10  3.92e+02  5.04e+03  3.06e-04  5.04e+03    1.66e+03      0   Skip BFGS  \n",
-      "  11  3.92e+02  3.98e+03  3.93e-04  3.98e+03    1.39e+03      0   Skip BFGS  \n",
-      "  12  4.90e+01  3.40e+03  4.63e-04  3.40e+03    1.23e+03      0   Skip BFGS  \n",
-      "  13  4.90e+01  1.79e+03  8.93e-04  1.79e+03    7.41e+02      0   Skip BFGS  \n",
-      "  14  4.90e+01  1.42e+03  1.13e-03  1.42e+03    6.04e+02      0   Skip BFGS  \n",
-      "  15  6.13e+00  1.23e+03  1.32e-03  1.23e+03    5.26e+02      0   Skip BFGS  \n",
-      "  16  6.13e+00  7.15e+02  2.19e-03  7.15e+02    3.08e+02      0   Skip BFGS  \n",
-      "  17  6.13e+00  5.94e+02  2.65e-03  5.94e+02    2.48e+02      0   Skip BFGS  \n",
-      "  18  7.66e-01  5.31e+02  2.98e-03  5.31e+02    2.16e+02      0   Skip BFGS  \n",
-      "  19  7.66e-01  3.63e+02  4.30e-03  3.63e+02    1.35e+02      0   Skip BFGS  \n",
-      "  20  7.66e-01  3.01e+02  4.92e-03  3.01e+02    1.15e+02      0   Skip BFGS  \n",
-      "  21  9.58e-02  2.61e+02  5.36e-03  2.61e+02    1.03e+02      0   Skip BFGS  \n",
-      "  22  9.58e-02  1.70e+02  7.17e-03  1.70e+02    7.52e+01      0   Skip BFGS  \n",
-      "  23  9.58e-02  1.12e+02  8.27e-03  1.12e+02    5.46e+01      0   Skip BFGS  \n",
-      "  24  1.20e-02  7.86e+01  9.19e-03  7.86e+01    4.19e+01      0   Skip BFGS  \n",
-      "  25  1.20e-02  4.42e+01  1.18e-02  4.42e+01    3.46e+01      0   Skip BFGS  \n",
-      "  26  1.20e-02  2.82e+01  1.32e-02  2.82e+01    1.50e+01      0   Skip BFGS  \n",
-      "  27  1.50e-03  2.39e+01  1.40e-02  2.39e+01    1.10e+01      0   Skip BFGS  \n",
-      "  28  1.50e-03  1.94e+01  1.62e-02  1.94e+01    1.02e+01      0   Skip BFGS  \n",
-      "  29  1.50e-03  1.82e+01  1.76e-02  1.82e+01    2.73e+00      0   Skip BFGS  \n",
-      "  30  1.87e-04  1.80e+01  1.87e-02  1.80e+01    8.26e-01      0   Skip BFGS  \n",
-      "  31  1.87e-04  1.79e+01  1.95e-02  1.79e+01    1.04e+00      0   Skip BFGS  \n",
-      "  32  1.87e-04  1.79e+01  1.97e-02  1.79e+01    7.23e-01      0              \n",
-      "  33  2.34e-05  1.77e+01  2.30e-02  1.77e+01    2.97e+00      0   Skip BFGS  \n",
-      "  34  2.34e-05  1.77e+01  2.85e-02  1.77e+01    1.93e+00      0   Skip BFGS  \n",
-      "  35  2.34e-05  1.76e+01  2.77e-02  1.76e+01    4.66e-01      0              \n",
-      "  36  2.92e-06  1.76e+01  2.87e-02  1.76e+01    4.19e-01      0              \n",
-      "  37  2.92e-06  1.76e+01  3.20e-02  1.76e+01    1.03e+00      0              \n",
-      "  38  2.92e-06  1.76e+01  3.92e-02  1.76e+01    1.95e+00      0              \n",
-      "  39  3.65e-07  1.76e+01  4.02e-02  1.76e+01    4.95e-01      0              \n",
-      "  40  3.65e-07  1.76e+01  3.91e-02  1.76e+01    3.65e-01      0              \n",
-      "  41  3.65e-07  1.76e+01  3.91e-02  1.76e+01    3.16e-01      0              \n",
-      "  42  4.57e-08  1.76e+01  3.93e-02  1.76e+01    3.42e-01      0              \n",
-      "  43  4.57e-08  1.75e+01  4.67e-02  1.75e+01    1.26e+00      0   Skip BFGS  \n",
-      "  44  4.57e-08  1.75e+01  5.23e-02  1.75e+01    1.70e+00      1              \n",
-      "  45  5.71e-09  1.75e+01  5.63e-02  1.75e+01    9.87e-01      0              \n",
-      "  46  5.71e-09  1.75e+01  6.77e-02  1.75e+01    1.55e+00      1   Skip BFGS  \n",
-      "  47  5.71e-09  1.75e+01  6.29e-02  1.75e+01    9.07e-01      0              \n",
-      "  48  7.14e-10  1.75e+01  6.29e-02  1.75e+01    8.89e-01      0   Skip BFGS  \n",
-      "  49  7.14e-10  1.75e+01  6.56e-02  1.75e+01    6.85e-01      0              \n",
-      "  50  7.14e-10  1.75e+01  6.72e-02  1.75e+01    6.46e-01      0              \n",
-      "------------------------- STOP! -------------------------\n",
-      "1 : |fc-fOld| = 8.4462e-04 <= tolF*(1+|f0|) = 1.3818e+05\n",
-      "1 : |xc-x_last| = 1.0965e-01 <= tolX*(1+|x0|) = 5.9957e+00\n",
-      "0 : |proj(x-g)-x|    = 6.4640e-01 <= tolG          = 1.0000e-01\n",
-      "0 : |proj(x-g)-x|    = 6.4640e-01 <= 1e3*eps       = 1.0000e-02\n",
-      "1 : maxIter   =      50    <= iter          =     50\n",
-      "------------------------- DONE! -------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Runn the inversion\n",
-    "mopt = inv.run(m_0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "## Setup the inversion proceedure\n",
-    "C =  simpeg.Utils.Counter()\n",
-    "\n",
-    "# Set the optimization\n",
-    "optc = simpeg.Optimization.InexactGaussNewton(maxIter = 20)\n",
-    "optc.counter = C\n",
-    "optc.LSshorten = 0.5\n",
-    "optc.remember('xc')\n",
-    "# Data misfit\n",
-    "dmisc = simpeg.DataMisfit.l2_DataMisfit(survey)\n",
-    "# Regularization\n",
-    "# regMesh = simpeg.Mesh.TensorMesh([m1d.hx[problem.mapping.sigmaMap.maps[-1].indActive]])\n",
-    "# reg = simpeg.Regularization.Tikhonov(regMesh)\n",
-    "regc = simpeg.Regularization.Tikhonov(m1d,mapping=mappingExpAct)\n",
-    "regc.alpha_s = 1e-5\n",
-    "regc.alpha_x = 1.\n",
-    "# Inversion problem\n",
-    "invProbc = simpeg.InvProblem.BaseInvProblem(dmisc, regc, optc)\n",
-    "invProbc.counter = C\n",
-    "# Beta cooling\n",
-    "betac = simpeg.Directives.BetaSchedule()\n",
-    "betaestc = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)\n",
-    "saveModel = simpeg.Directives.SaveModelEveryIteration()\n",
-    "# Create an inversion object\n",
-    "invc = simpeg.Inversion.BaseInversion(invProbc, directiveList=[betac,betaestc])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "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",
-      "SimPEG.l2_DataMisfit is creating default weightings for Wd.\n",
-      "============================ Inexact Gauss Newton ============================\n",
-      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
-      "-----------------------------------------------------------------------------\n",
-      "   0  1.22e+03  1.75e+01  6.95e-02  1.02e+02    1.27e+02      0              \n",
-      "   1  1.22e+03  2.83e+01  2.63e-02  6.03e+01    6.19e+01      0              \n",
-      "   2  1.22e+03  2.04e+01  2.86e-02  5.52e+01    6.08e+01      0              \n",
-      "   3  1.52e+02  1.84e+01  2.72e-02  2.25e+01    8.98e+00      0              \n",
-      "   4  1.52e+02  1.78e+01  2.46e-02  2.16e+01    3.60e+00      0              \n",
-      "   5  1.52e+02  1.77e+01  2.44e-02  2.14e+01    2.75e+00      0              \n",
-      "   6  1.90e+01  1.77e+01  2.39e-02  1.82e+01    2.01e+00      0              \n",
-      "   7  1.90e+01  1.76e+01  2.46e-02  1.81e+01    9.52e-01      0              \n",
-      "   8  1.90e+01  1.76e+01  2.45e-02  1.81e+01    6.50e-01      0              \n",
-      "   9  2.38e+00  1.76e+01  2.44e-02  1.77e+01    7.01e-01      0              \n",
-      "  10  2.38e+00  1.76e+01  2.44e-02  1.77e+01    1.05e+00      0   Skip BFGS  \n",
-      "  11  2.38e+00  1.76e+01  2.45e-02  1.77e+01    6.53e-01      0              \n",
-      "  12  2.97e-01  1.76e+01  2.45e-02  1.76e+01    7.44e-01      0              \n",
-      "  13  2.97e-01  1.76e+01  2.46e-02  1.76e+01    9.88e-01      0              \n",
-      "  14  2.97e-01  1.76e+01  2.60e-02  1.76e+01    6.11e-01      0              \n",
-      "  15  3.71e-02  1.76e+01  2.60e-02  1.76e+01    7.53e-01      0              \n",
-      "  16  3.71e-02  1.76e+01  2.87e-02  1.76e+01    1.45e+00      1              \n",
-      "  17  3.71e-02  1.76e+01  2.90e-02  1.76e+01    1.29e+00      0              \n",
-      "  18  4.64e-03  1.76e+01  2.91e-02  1.76e+01    1.02e+00      0              \n",
-      "  19  4.64e-03  1.76e+01  2.90e-02  1.76e+01    1.35e+00      0              \n",
-      "  20  4.64e-03  1.76e+01  3.08e-02  1.76e+01    1.38e+00      0   Skip BFGS  \n",
-      "------------------------- STOP! -------------------------\n",
-      "1 : |fc-fOld| = 2.1982e-03 <= tolF*(1+|f0|) = 1.0299e+01\n",
-      "1 : |xc-x_last| = 3.4790e-01 <= tolX*(1+|x0|) = 4.5116e+00\n",
-      "0 : |proj(x-g)-x|    = 1.3805e+00 <= tolG          = 1.0000e-01\n",
-      "0 : |proj(x-g)-x|    = 1.3805e+00 <= 1e3*eps       = 1.0000e-02\n",
-      "1 : maxIter   =      20    <= iter          =     20\n",
-      "------------------------- DONE! -------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "mopt2 = invc.run(mopt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "moptc=mopt2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "## Setup the inversion proceedure\n",
-    "C =  simpeg.Utils.Counter()\n",
-    "\n",
-    "# Set the optimization\n",
-    "optc1 = simpeg.Optimization.InexactGaussNewton(maxIter = 20)\n",
-    "optc1.counter = C\n",
-    "optc1.LSshorten = 0.1\n",
-    "optc1.remember('xc')\n",
-    "# Data misfit\n",
-    "dmisc1 = simpeg.DataMisfit.l2_DataMisfit(survey)\n",
-    "# Regularization\n",
-    "# regMesh = simpeg.Mesh.TensorMesh([m1d.hx[problem.mapping.sigmaMap.maps[-1].indActive]])\n",
-    "# reg = simpeg.Regularization.Tikhonov(regMesh)\n",
-    "regc1 = simpeg.Regularization.Tikhonov(m1d,mapping=mappingExpAct)\n",
-    "regc1.alpha_s = 1e-5\n",
-    "regc1.alpha_x = 1.\n",
-    "regc1.mref = reg.mref\n",
-    "# Inversion problem\n",
-    "invProbc1 = simpeg.InvProblem.BaseInvProblem(dmisc1, regc1, optc1)\n",
-    "invProbc1.counter = C\n",
-    "# Beta cooling\n",
-    "betac1 = simpeg.Directives.BetaSchedule()\n",
-    "betaestc1 = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)\n",
-    "betaestc1.beta0 = 3.60e-03\n",
-    "saveModel = simpeg.Directives.SaveModelEveryIteration()\n",
-    "# Create an inversion object\n",
-    "invc1 = simpeg.Inversion.BaseInversion(invProbc1, directiveList=[betac1,betaestc1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
-      "                    ***Done using same solver as the problem***\n",
-      "SimPEG.l2_DataMisfit is creating default weightings for Wd.\n",
-      "============================ Inexact Gauss Newton ============================\n",
-      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
-      "-----------------------------------------------------------------------------\n",
-      "   0  1.30e-02  1.76e+01  2.85e-02  1.76e+01    1.38e+00      0              \n",
-      "   1  1.30e-02  1.75e+01  2.85e-02  1.75e+01    1.17e+00      1              \n",
-      "   2  1.30e-02  1.75e+01  2.85e-02  1.75e+01    9.35e-01      1   Skip BFGS  \n",
-      "   3  1.62e-03  1.75e+01  2.85e-02  1.75e+01    7.47e-01      1   Skip BFGS  \n",
-      "   4  1.62e-03  1.75e+01  2.85e-02  1.75e+01    1.00e+00      2   Skip BFGS  \n",
-      "   5  1.62e-03  1.75e+01  2.85e-02  1.75e+01    1.00e+00      3   Skip BFGS  \n",
-      "   6  2.03e-04  1.75e+01  2.85e-02  1.75e+01    1.01e+00      3   Skip BFGS  \n",
-      "   7  2.03e-04  1.75e+01  2.85e-02  1.75e+01    1.01e+00      3   Skip BFGS  \n",
-      "   8  2.03e-04  1.75e+01  2.85e-02  1.75e+01    1.02e+00      3   Skip BFGS  \n",
-      "   9  2.53e-05  1.75e+01  2.85e-02  1.75e+01    1.02e+00      3   Skip BFGS  \n",
-      "  10  2.53e-05  1.75e+01  2.85e-02  1.75e+01    1.03e+00      3   Skip BFGS  \n",
-      "  11  2.53e-05  1.75e+01  2.85e-02  1.75e+01    1.04e+00      3   Skip BFGS  \n",
-      "  12  3.16e-06  1.75e+01  2.85e-02  1.75e+01    1.05e+00      3   Skip BFGS  \n",
-      "  13  3.16e-06  1.75e+01  2.85e-02  1.75e+01    1.06e+00      3   Skip BFGS  \n",
-      "  14  3.16e-06  1.75e+01  2.85e-02  1.75e+01    1.07e+00      3   Skip BFGS  \n",
-      "  15  3.96e-07  1.75e+01  2.85e-02  1.75e+01    1.08e+00      3   Skip BFGS  \n",
-      "  16  3.96e-07  1.75e+01  2.85e-02  1.75e+01    1.10e+00      3   Skip BFGS  \n",
-      "  17  3.96e-07  1.75e+01  2.85e-02  1.75e+01    1.11e+00      3   Skip BFGS  \n",
-      "  18  4.94e-08  1.75e+01  2.85e-02  1.75e+01    1.13e+00      3   Skip BFGS  \n",
-      "  19  4.94e-08  1.75e+01  2.85e-02  1.75e+01    1.14e+00      3   Skip BFGS  \n",
-      "  20  4.94e-08  1.75e+01  2.85e-02  1.75e+01    1.16e+00      3   Skip BFGS  \n",
-      "------------------------- STOP! -------------------------\n",
-      "1 : |fc-fOld| = 2.2454e-04 <= tolF*(1+|f0|) = 1.8553e+00\n",
-      "1 : |xc-x_last| = 1.9180e-01 <= tolX*(1+|x0|) = 4.4390e+00\n",
-      "0 : |proj(x-g)-x|    = 1.1563e+00 <= tolG          = 1.0000e-01\n",
-      "0 : |proj(x-g)-x|    = 1.1563e+00 <= 1e3*eps       = 1.0000e-02\n",
-      "1 : maxIter   =      20    <= iter          =     20\n",
-      "------------------------- DONE! -------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "moptc1 = invc1.run(mopt2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Counters:\n",
-      "  InexactGaussNewton.doEndIteration       :       50\n",
-      "  InexactGaussNewton.doStartIteration     :       51\n",
-      "  InexactGaussNewton.scaleSearchDirection :       50\n",
-      "\n",
-      "Times:                                        mean      sum\n",
-      "  BaseInvProblem.evalFunction             : 3.24e+00, 3.33e+02,  103x\n",
-      "  InexactGaussNewton.findSearchDirection  : 2.07e+01, 1.04e+03,   50x\n",
-      "  InexactGaussNewton.minimize             : 1.37e+03, 1.37e+03,    1x\n",
-      "  InexactGaussNewton.modifySearchDirection: 1.93e+00, 9.63e+01,   50x\n",
-      "  InexactGaussNewton.projection           : 4.69e-05, 9.75e-03,  208x\n"
-     ]
-    }
-   ],
-   "source": [
-    "opt.counter.summary()\n",
-    "xc = opt.recall('xc')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# import matplotlib.pyplot as plt\n",
-    "# # plt.figure(1)\n",
-    "# # for i in range(problem.G.shape[0]):\n",
-    "# #     plt.plot(problem.G[i,:])\n",
-    "# meshPts = np.concatenate((mesh.gridN[0:1],np.kron(mesh.gridN[1::],np.ones(2))[:-1]))\n",
-    "# modelPts = np.kron(1./model,np.ones(2,))\n",
-    "# axM.semilogx(modelPts,meshPts,color=col)\n",
-    "# plt.figure(2)\n",
-    "# plt.plot(m1d.vectorCCx[active], np.log10(mappingExpAct*survey.mtrue)[active], 'b-')\n",
-    "# plt.plot(m1d.vectorCCx[active], np.log10(mappingExpAct*mopt)[active], 'r-')\n",
-    "# plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def plotMT1DModelData(problem,models,symList=None):\n",
-    "    # Make the analytic solution\n",
-    "    # \tdef makeAnalyticSolution(mesh,model,elev,freqs):\n",
-    "    # \t\tdata1D = []\n",
-    "    # \t\tfor freq in freqs:\n",
-    "    # \t\t\tanaEd, anaEu, anaHd, anaHu = simpegmt.Utils.MT1Danalytic.getEHfields(mesh,model,freq,elev)\n",
-    "    # \t\t\tanaE = anaEd+anaEu\n",
-    "    # \t\t\tanaH = anaHd+anaHu\n",
-    "    # \t\t\t# Scale the solution\n",
-    "    # \t\t\t# anaE = (anaEtemp/anaEtemp[-1])#.conj()\n",
-    "    # \t\t\t# anaH = (anaHtemp/anaEtemp[-1])#.conj()\n",
-    "    # \t\t\tanaZ = anaE/anaH\n",
-    "    # \t\t\t# Add to the list\n",
-    "    # \t\t\tdata1D.append((freq,0,0,elev,anaZ[0]))\n",
-    "    # \t\tdataRec = np.array(data1D,dtype=[('freq',float),('x',float),('y',float),('z',float),('zxx',complex)])\n",
-    "    # \t\treturn dataRec\n",
-    "    def appResPhs(freq,z):\n",
-    "        fr = simpeg.mkvc(freq,2)*np.ones(z.shape)\n",
-    "        app_res = ((1./(8e-7*np.pi**2))/fr)*np.abs(z)**2\n",
-    "        app_phs = np.arctan2(z.imag,z.real)*(180/np.pi)\n",
-    "        return app_res, app_phs\n",
-    "    \n",
-    "    # Setup the figure\n",
-    "    fontSize = 15\n",
-    "\n",
-    "    fig = plt.figure(figsize=[9,7])\n",
-    "    axM = fig.add_axes([0.075,.1,.25,.875])\n",
-    "    axM.set_xlabel('Resistivity [Ohm*m]',fontsize=fontSize)\n",
-    "    axM.set_xlim(1e-1,1e5)\n",
-    "    axM.set_ylim(-10000,5000)\n",
-    "    axM.set_ylabel('Depth [km]',fontsize=fontSize)\n",
-    "    axR = fig.add_axes([0.42,.575,.5,.4])\n",
-    "    axR.set_xscale('log')\n",
-    "    axR.set_yscale('log')\n",
-    "    axR.invert_xaxis()\n",
-    "    # axR.set_xlabel('Frequency [Hz]')\n",
-    "    axR.set_ylabel('Apparent resistivity [Ohm m]',fontsize=fontSize)\n",
-    "\n",
-    "    axP = fig.add_axes([0.42,.1,.5,.4])\n",
-    "    axP.set_xscale('log')\n",
-    "    axP.invert_xaxis()\n",
-    "    axP.set_ylim(0,90)\n",
-    "    axP.set_xlabel('Frequency [Hz]',fontsize=fontSize)\n",
-    "    axP.set_ylabel('Apparent phase [deg]',fontsize=fontSize)\n",
-    "\n",
-    "    # if not symList:\n",
-    "    # \tsymList = ['x']*len(models)\n",
-    "    sys.path.append('/home/gudni/Dropbox/code/python/MTview')\n",
-    "    import plotDataTypes as pDt\n",
-    "    # Loop through the models.\n",
-    "    modelList = [problem.survey.mtrue]\n",
-    "    modelList.extend(models)\n",
-    "    if False:\n",
-    "        modelList = [problem.mapping.sigmaMap*mod for mod in modelList]\n",
-    "    for nr, model in enumerate(modelList):\n",
-    "        # Calculate the data\n",
-    "        if nr==0:\n",
-    "            data1D = problem.dataPair(problem.survey,problem.survey.dobs).toRecArray('Complex')\n",
-    "        else:\n",
-    "            data1D = problem.dataPair(problem.survey,problem.survey.dpred(model)).toRecArray('Complex')\n",
-    "        # Plot the data and the model \n",
-    "        colRat = nr/((len(modelList)-2)*1.)\n",
-    "        if colRat > 1.:\n",
-    "            col = 'k'\n",
-    "        else:\n",
-    "            col = plt.cm.seismic(1-colRat)\n",
-    "        # The model - make the pts to plot\n",
-    "        meshPts = np.concatenate((problem.mesh.gridN[0:1],np.kron(problem.mesh.gridN[1::],np.ones(2))[:-1]))\n",
-    "        modelPts = np.kron(1./(problem.mapping.sigmaMap*model),np.ones(2,))\n",
-    "        axM.semilogx(modelPts,meshPts,color=col)\n",
-    "\n",
-    "        ## Data\n",
-    "        # Appres\n",
-    "        pDt.plotIsoStaImpedance(axR,np.array([0,0]),data1D,'zyx','res',pColor=col)\n",
-    "        # Appphs\n",
-    "        pDt.plotIsoStaImpedance(axP,np.array([0,0]),data1D,'zyx','phs',pColor=col)\n",
-    "        try:\n",
-    "            allData = np.concatenate((allData,mkvc(data1D['zyx'],2)),1)\n",
-    "        except:\n",
-    "            allData = simpeg.mkvc(data1D['zyx'],2)\n",
-    "    freq = data1D['freq']\n",
-    "    res, phs = appResPhs(freq,allData)\n",
-    "\n",
-    "    stdCol = 'gray'\n",
-    "    axRtw = axR.twinx()\n",
-    "    axRtw.set_ylabel('Std of log10',color=stdCol)\n",
-    "    [(t.set_color(stdCol), t.set_rotation(-45)) for t in axRtw.get_yticklabels()]\n",
-    "    axPtw = axP.twinx()\n",
-    "    axPtw.set_ylabel('Std ',color=stdCol)\n",
-    "    [t.set_color(stdCol) for t in axPtw.get_yticklabels()]\n",
-    "    axRtw.plot(freq, np.std(np.log10(res),1),'--',color=stdCol)\n",
-    "    axPtw.plot(freq, np.std(phs,1),'--',color=stdCol)\n",
-    "\n",
-    "    # Fix labels and ticks\n",
-    "\n",
-    "    yMtick = [l/1000 for l in axM.get_yticks().tolist()]\n",
-    "    axM.set_yticklabels(yMtick)\n",
-    "    [ l.set_rotation(90) for l in axM.get_yticklabels()]\n",
-    "    [ l.set_rotation(90) for l in axR.get_yticklabels()]\n",
-    "    [(t.set_color(stdCol), t.set_rotation(-45)) for t in axRtw.get_yticklabels()]\n",
-    "    [t.set_color(stdCol) for t in axPtw.get_yticklabels()]\n",
-    "    for ax in [axM,axR,axP]:\n",
-    "        ax.xaxis.set_tick_params(labelsize=fontSize)\n",
-    "        ax.yaxis.set_tick_params(labelsize=fontSize)\n",
-    "    return fig\n",
-    "# plotMT1DModelData(problem,[mopt])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
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-       "/yhwnHM1pWx+NLl6oDDA9znAGt30zcmFbyM0Nv9gZi3Vzl+9Ufh530y43fa6mR1Y4yw5V/eSK45f\n",
-       "MbN1uw1cY0k/72FmtlMJYUcTbl9vBjxuZq1VytMAYCdCY3sTy2jaUufyNeoVRJeiMFvCbsApwMWl\n",
-       "NA5Tfg0syQ23089EhH6dO+BXEZ1rGJL+n6TDCYPv/7rMw/9Dz57ULtUSQuPQ6xxXU/2tD2J/NZFw\n",
-       "q2YGcHoZx21JeyVVzcnp69UFJNOO0f6UpXOua93d0q4H1xP6VP7ezK4p8ZhZtI8BWc07Kluk3j9X\n",
-       "NJRzVea3mJ1zzjnnXAd+i9k555xzznXgDUTnnHPOOdeBNxCdc84551wH3kB0zjnnnHMdeAPROeec\n",
-       "c8514A1E55xzzjnXwf8HOnyht85O8qsAAAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f384bb67990>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plotMT1DModelData(problem,[mopt,moptc,moptc1])\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "modelList = [problem.survey.mtrue]\n",
-    "modelList.extend([mopt])\n",
-    "# problem.mapping.sigmaMap*mopt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[['zyxr'], ['zyxi']]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "src = survey.srcList[0]\n",
-    "[[rx.rxType.replace('z1d','zyx')] for rx in src.rxList ]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'zyxr'"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'z1dr'.replace('z1d','zyx')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import SimPEG as simpeg\n",
+    "import simpegMT as simpegmt\n",
+    "import numpy as np, os\n",
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Frequency\n",
+    "nFreq = 31\n",
+    "freqs = np.logspace(3,-3,nFreq)\n",
+    "# Set mesh parameters\n",
+    "ct = 10\n",
+    "air = simpeg.Utils.meshTensor([(ct,25,1.3)])\n",
+    "core = np.concatenate( (  np.kron(simpeg.Utils.meshTensor([(ct,5,-1.2)]),np.ones((3,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n",
+    "bot = simpeg.Utils.meshTensor([(core[0],25,-1.3)])\n",
+    "x0 = -np.array([np.sum(np.concatenate((core,bot)))])\n",
+    "# Make the model\n",
+    "m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)\n",
+    "# Setup model varibles\n",
+    "active = m1d.vectorCCx<0.\n",
+    "layer1 = (m1d.vectorCCx<-200.) & (m1d.vectorCCx>=-600.)\n",
+    "layer2 = (m1d.vectorCCx<-2000.) & (m1d.vectorCCx>=-4000.)\n",
+    "# Set the conductivity values\n",
+    "sig_half = 2e-3\n",
+    "sig_air = 1e-8\n",
+    "sig_layer1 = 1\n",
+    "sig_layer2 = .1\n",
+    "# Make the true model\n",
+    "sigma_true = np.ones(m1d.nCx)*sig_air\n",
+    "sigma_true[active] = sig_half\n",
+    "sigma_true[layer1] = sig_layer1\n",
+    "sigma_true[layer2] = sig_layer2\n",
+    "# Extract the model \n",
+    "m_true = np.log(sigma_true[active])\n",
+    "# Make the background model\n",
+    "sigma_0 = np.ones(m1d.nCx)*sig_air\n",
+    "sigma_0[active] = sig_half\n",
+    "m_0 = np.log(sigma_0[active])\n",
+    "\n",
+    "\n",
+    "# Set the mapping# Set the mapping\n",
+    "actMap = simpeg.Maps.ActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)\n",
+    "mappingExpAct = simpeg.Maps.ExpMap(m1d) * actMap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Setup the layout of the survey, set the sources and the connected receivers\n",
+    "\n",
+    "# Receivers \n",
+    "rxList = []\n",
+    "for rxType in ['z1dr','z1di']:\n",
+    "    rxList.append(simpegmt.SurveyMT.RxMT(simpeg.mkvc(np.array([0.0]),2).T,rxType))\n",
+    "# Source list\n",
+    "srcList =[]\n",
+    "for freq in freqs:\n",
+    "        srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,freq))\n",
+    "# Make the survey\n",
+    "survey = simpegmt.SurveyMT.SurveyMT(srcList)\n",
+    "survey.mtrue = m_true\n",
+    "# Set the problem\n",
+    "problem = simpegmt.ProblemMT1D.eForm_psField(m1d,sigmaPrimary=sigma_0,mapping=mappingExpAct)\n",
+    "from pymatsolver import MumpsSolver\n",
+    "problem.solver = MumpsSolver\n",
+    "problem.pair(survey)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "## Forward model observed data \n",
+    "# Project the data\n",
+    "d_true = survey.dpred(m_true)\n",
+    "survey.dtrue = d_true\n",
+    "# Add noise to the true data\n",
+    "std = 0.05 # 5% std\n",
+    "noise = std*abs(survey.dtrue)*np.random.randn(*survey.dtrue.shape)\n",
+    "# Assign the dobs\n",
+    "survey.dobs = survey.dtrue + noise\n",
+    "survey.std = survey.dobs*0 + std\n",
+    "# Assign the data weight\n",
+    "survey.Wd = 1/(abs(survey.dobs)*std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "modList = []\n",
+    "modFiles = glob('*52.npy')\n",
+    "modFiles.sort()\n",
+    "for f in modFiles:\n",
+    "    modList.append(np.load(f))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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r3vjiiy/YsmULEyZM8Ds1nhCi4ZCkT4gIYa1NB7oBLwATgTVKqaVKqRuUUk2P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\nCl2EEG2FEIOFEMMrH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a1wP0DHIq4eyuhTKJwQIcQ1wEqgAFiL1tcyBHgIeFQIMVxKWetK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\nDwMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6d2961e810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,modList[10],modList[15],modList[24]])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR: No traceback has been produced, nothing to debug.\n"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(modList)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/scipy2015/MT1D_dobs.npy b/notebooks/scipy2015/MT1D_dobs.npy
new file mode 100644
index 00000000..80d57253
Binary files /dev/null and b/notebooks/scipy2015/MT1D_dobs.npy differ
diff --git a/notebooks/scipy2015/MT1D_dtrue.npy b/notebooks/scipy2015/MT1D_dtrue.npy
new file mode 100644
index 00000000..1b21c982
Binary files /dev/null and b/notebooks/scipy2015/MT1D_dtrue.npy differ
diff --git a/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping.ipynb b/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping.ipynb
new file mode 100644
index 00000000..6cbdb115
--- /dev/null
+++ b/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping.ipynb
@@ -0,0 +1,321 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import SimPEG as simpeg\n",
+    "import simpegMT as simpegmt\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Setup the modelling\n",
+    "# Setting up 1D mesh and conductivity models to forward model data.\n",
+    "\n",
+    "# Frequency\n",
+    "nFreq = 31\n",
+    "freqs = np.logspace(3,-3,nFreq)\n",
+    "# Set mesh parameters\n",
+    "ct = 10\n",
+    "air = simpeg.Utils.meshTensor([(ct,25,1.3)])\n",
+    "core = np.concatenate( (  np.kron(simpeg.Utils.meshTensor([(ct,5,-1.2)]),np.ones((3,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n",
+    "bot = simpeg.Utils.meshTensor([(core[0],25,-1.3)])\n",
+    "x0 = -np.array([np.sum(np.concatenate((core,bot)))])\n",
+    "# Make the model\n",
+    "m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)\n",
+    "\n",
+    "# Setup model varibles\n",
+    "active = m1d.vectorCCx<0.\n",
+    "layer1 = (m1d.vectorCCx<-200.) & (m1d.vectorCCx>=-600.)\n",
+    "layer2 = (m1d.vectorCCx<-2000.) & (m1d.vectorCCx>=-4000.)\n",
+    "# Set the conductivity values\n",
+    "sig_half = 2e-3\n",
+    "sig_air = 1e-8\n",
+    "sig_layer1 = 1\n",
+    "sig_layer2 = .1\n",
+    "# Make the true model\n",
+    "sigma_true = np.ones(m1d.nCx)*sig_air\n",
+    "sigma_true[active] = sig_half\n",
+    "sigma_true[layer1] = sig_layer1\n",
+    "sigma_true[layer2] = sig_layer2\n",
+    "# Extract the model \n",
+    "m_true = np.log(sigma_true[active])\n",
+    "# Make the background model\n",
+    "sigma_0 = np.ones(m1d.nCx)*sig_air\n",
+    "sigma_0[active] = sig_half\n",
+    "m_0 = np.log(sigma_0[active])\n",
+    "\n",
+    "# Set the mapping\n",
+    "actMap = simpeg.Maps.ActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)\n",
+    "mappingExpAct = simpeg.Maps.ExpMap(m1d) * actMap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Setup the layout of the survey, set the sources and the connected receivers\n",
+    "\n",
+    "# Receivers \n",
+    "rxList = []\n",
+    "for rxType in ['z1dr','z1di']:\n",
+    "    rxList.append(simpegmt.SurveyMT.RxMT(simpeg.mkvc(np.array([0.0]),2).T,rxType))\n",
+    "# Source list\n",
+    "srcList =[]\n",
+    "for freq in freqs:\n",
+    "        srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,freq))\n",
+    "# Make the survey\n",
+    "survey = simpegmt.SurveyMT.SurveyMT(srcList)\n",
+    "survey.mtrue = m_true\n",
+    "# Set the problem\n",
+    "problem = simpegmt.ProblemMT1D.eForm_psField(m1d,sigmaPrimary=sigma_0,mapping=mappingExpAct)\n",
+    "from pymatsolver import MumpsSolver\n",
+    "problem.solver = MumpsSolver\n",
+    "problem.pair(survey)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Read the data\n",
+    "std = 0.05 # 5% std\n",
+    "# Load the files if they exist\n",
+    "if os.path.isfile('MT1D_dtrue.npy') and os.path.isfile('MT1D_dobs.npy'):\n",
+    "    d_true = np.load('MT1D_dtrue.npy')\n",
+    "    d_obs = np.load('MT1D_dobs.npy')\n",
+    "else:\n",
+    "    # Forward model\n",
+    "    d_true = survey.dpred(m_true)\n",
+    "    np.save('MT1D_dtrue.npy',d_true)\n",
+    "    d_obs = d_true + (std*abs(d_true)*np.random.randn(*d_true.shape))\n",
+    "    np.save('MT1D_dobs.npy',d_obs)\n",
+    "# Assign the datas to the survey object\n",
+    "survey.dtrue = d_true\n",
+    "survey.dobs = d_obs\n",
+    "survey.std = survey.dobs*0 + std\n",
+    "# Assign the data weight\n",
+    "survey.Wd = 1/(abs(survey.dobs)*survey.std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Setup the inversion proceedure\n",
+    "\n",
+    "# Define a counter\n",
+    "C =  simpeg.Utils.Counter()\n",
+    "# Set the optimization\n",
+    "opt = simpeg.Optimization.InexactGaussNewton(maxIter = 30)\n",
+    "opt.counter = C\n",
+    "opt.LSshorten = 0.5\n",
+    "opt.remember('xc')\n",
+    "# Data misfit\n",
+    "dmis = simpeg.DataMisfit.l2_DataMisfit(survey)\n",
+    "# Regularization\n",
+    "# Note: We want you use a mesh the corresponds to the domain we want to solve, the active cells.\n",
+    "if False:\n",
+    "    regMesh = simpeg.Mesh.TensorMesh([m1d.hx[problem.mapping.sigmaMap.maps[-1].indActive]],m1d.x0)\n",
+    "    reg = simpeg.Regularization.Tikhonov(regMesh)\n",
+    "else:\n",
+    "    reg = simpeg.Regularization.Tikhonov(m1d,mapping=mappingExpAct)\n",
+    "reg.alpha_s = 1e-8\n",
+    "reg.alpha_x = 1.\n",
+    "# Inversion problem\n",
+    "invProb = simpeg.InvProblem.BaseInvProblem(dmis, reg, opt)\n",
+    "invProb.counter = C\n",
+    "# Beta cooling\n",
+    "beta = simpeg.Directives.BetaSchedule()\n",
+    "betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)\n",
+    "saveModel = simpeg.Directives.SaveModelEveryIteration()\n",
+    "saveModel.fileName = 'Inversion_NoStopping'\n",
+    "# Create an inversion object\n",
+    "inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest,saveModel]) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": 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",
+      "SimPEG.l2_DataMisfit is creating default weightings for Wd.\n",
+      "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_NoStopping.npy'\n",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  3.97e+05  1.32e+06  1.77e-07  1.32e+06    3.23e+05      0              \n",
+      "   1  3.97e+05  1.89e+05  3.57e-07  1.89e+05    4.74e+04      0              \n",
+      "   2  3.97e+05  3.88e+04  4.77e-06  3.88e+04    1.11e+04      0   Skip BFGS  \n",
+      "   3  4.96e+04  3.69e+04  5.13e-06  3.69e+04    1.05e+04      0   Skip BFGS  \n",
+      "   4  4.96e+04  2.71e+04  8.22e-06  2.71e+04    7.80e+03      0   Skip BFGS  \n",
+      "   5  4.96e+04  2.34e+04  1.04e-05  2.34e+04    6.80e+03      0   Skip BFGS  \n",
+      "   6  6.21e+03  2.11e+04  1.23e-05  2.11e+04    6.19e+03      0   Skip BFGS  \n",
+      "   7  6.21e+03  1.27e+04  2.87e-05  1.27e+04    3.95e+03      0   Skip BFGS  \n",
+      "   8  6.21e+03  1.07e+04  3.77e-05  1.07e+04    3.41e+03      0   Skip BFGS  \n",
+      "   9  7.76e+02  9.53e+03  4.53e-05  9.53e+03    3.09e+03      0   Skip BFGS  \n",
+      "  10  7.76e+02  5.51e+03  1.06e-04  5.51e+03    1.97e+03      0   Skip BFGS  \n",
+      "  11  7.76e+02  4.57e+03  1.39e-04  4.57e+03    1.69e+03      0   Skip BFGS  \n",
+      "  12  9.70e+01  4.02e+03  1.66e-04  4.02e+03    1.53e+03      0   Skip BFGS  \n",
+      "  13  9.70e+01  2.27e+03  3.59e-04  2.27e+03    9.73e+02      0   Skip BFGS  \n",
+      "  14  9.70e+01  1.83e+03  4.62e-04  1.83e+03    8.24e+02      0   Skip BFGS  \n",
+      "  15  1.21e+01  1.57e+03  5.44e-04  1.57e+03    7.36e+02      0   Skip BFGS  \n",
+      "  16  1.21e+01  8.91e+02  1.06e-03  8.91e+02    4.57e+02      0   Skip BFGS  \n",
+      "  17  1.21e+01  6.95e+02  1.35e-03  6.95e+02    3.66e+02      0   Skip BFGS  \n",
+      "  18  1.51e+00  5.92e+02  1.57e-03  5.92e+02    3.13e+02      0   Skip BFGS  \n",
+      "  19  1.51e+00  3.56e+02  2.57e-03  3.56e+02    1.75e+02      0   Skip BFGS  \n",
+      "  20  1.51e+00  3.22e+02  3.06e-03  3.22e+02    1.53e+02      0   Skip BFGS  \n",
+      "  21  1.89e-01  2.75e+02  3.38e-03  2.75e+02    1.25e+02      0              \n",
+      "  22  1.89e-01  1.98e+02  4.78e-03  1.98e+02    8.83e+01      0   Skip BFGS  \n",
+      "  23  1.89e-01  1.51e+02  5.79e-03  1.51e+02    7.53e+01      0              \n",
+      "  24  2.37e-02  1.19e+02  6.62e-03  1.19e+02    6.26e+01      0   Skip BFGS  \n",
+      "  25  2.37e-02  8.19e+01  1.04e-02  8.19e+01    4.51e+01      0   Skip BFGS  \n",
+      "  26  2.37e-02  7.08e+01  1.12e-02  7.08e+01    3.79e+01      1              \n",
+      "  27  2.96e-03  5.49e+01  1.16e-02  5.49e+01    3.51e+01      0              \n",
+      "  28  2.96e-03  4.67e+01  1.23e-02  4.67e+01    3.03e+01      1              \n",
+      "  29  2.96e-03  3.27e+01  1.32e-02  3.27e+01    3.13e+01      0              \n",
+      "  30  3.70e-04  2.51e+01  1.36e-02  2.51e+01    2.30e+01      0              \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 7.6530e+00 <= tolF*(1+|f0|) = 1.3164e+05\n",
+      "1 : |xc-x_last| = 4.1960e+00 <= tolX*(1+|x0|) = 4.2689e+00\n",
+      "0 : |proj(x-g)-x|    = 2.2988e+01 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 2.2988e+01 <= 1e3*eps       = 1.0000e-02\n",
+      "1 : maxIter   =      30    <= iter          =     30\n",
+      "------------------------- DONE! -------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run the inversion, given the background model as a start.\n",
+    "mopt = inv.run(m_0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "modList = []\n",
+    "modFiles = glob('*Inversion_NoStopping.npy')\n",
+    "modFiles.sort()\n",
+    "for f in modFiles:\n",
+    "    modList.append(np.load(f))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,modList)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,modList)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,modList[-3:-1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping_regMesh.ipynb b/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping_regMesh.ipynb
new file mode 100644
index 00000000..dc52b296
--- /dev/null
+++ b/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping_regMesh.ipynb
@@ -0,0 +1,448 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import SimPEG as simpeg\n",
+    "import simpegMT as simpegmt\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Setup the modelling\n",
+    "# Setting up 1D mesh and conductivity models to forward model data.\n",
+    "\n",
+    "# Frequency\n",
+    "nFreq = 31\n",
+    "freqs = np.logspace(3,-3,nFreq)\n",
+    "# Set mesh parameters\n",
+    "ct = 10\n",
+    "air = simpeg.Utils.meshTensor([(ct,25,1.3)])\n",
+    "core = np.concatenate( (  np.kron(simpeg.Utils.meshTensor([(ct,5,-1.2)]),np.ones((3,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n",
+    "bot = simpeg.Utils.meshTensor([(core[0],25,-1.3)])\n",
+    "x0 = -np.array([np.sum(np.concatenate((core,bot)))])\n",
+    "# Make the model\n",
+    "m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)\n",
+    "\n",
+    "# Setup model varibles\n",
+    "active = m1d.vectorCCx<0.\n",
+    "layer1 = (m1d.vectorCCx<-200.) & (m1d.vectorCCx>=-600.)\n",
+    "layer2 = (m1d.vectorCCx<-2000.) & (m1d.vectorCCx>=-4000.)\n",
+    "# Set the conductivity values\n",
+    "sig_half = 2e-3\n",
+    "sig_air = 1e-8\n",
+    "sig_layer1 = 1\n",
+    "sig_layer2 = .1\n",
+    "# Make the true model\n",
+    "sigma_true = np.ones(m1d.nCx)*sig_air\n",
+    "sigma_true[active] = sig_half\n",
+    "sigma_true[layer1] = sig_layer1\n",
+    "sigma_true[layer2] = sig_layer2\n",
+    "# Extract the model \n",
+    "m_true = np.log(sigma_true[active])\n",
+    "# Make the background model\n",
+    "sigma_0 = np.ones(m1d.nCx)*sig_air\n",
+    "sigma_0[active] = sig_half\n",
+    "m_0 = np.log(sigma_0[active])\n",
+    "\n",
+    "# Set the mapping\n",
+    "actMap = simpeg.Maps.ActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)\n",
+    "mappingExpAct = simpeg.Maps.ExpMap(m1d) * actMap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Setup the layout of the survey, set the sources and the connected receivers\n",
+    "\n",
+    "# Receivers \n",
+    "rxList = []\n",
+    "for rxType in ['z1dr','z1di']:\n",
+    "    rxList.append(simpegmt.SurveyMT.RxMT(simpeg.mkvc(np.array([0.0]),2).T,rxType))\n",
+    "# Source list\n",
+    "srcList =[]\n",
+    "for freq in freqs:\n",
+    "        srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,freq))\n",
+    "# Make the survey\n",
+    "survey = simpegmt.SurveyMT.SurveyMT(srcList)\n",
+    "survey.mtrue = m_true\n",
+    "# Set the problem\n",
+    "problem = simpegmt.ProblemMT1D.eForm_psField(m1d,sigmaPrimary=sigma_0,mapping=mappingExpAct)\n",
+    "from pymatsolver import MumpsSolver\n",
+    "problem.solver = MumpsSolver\n",
+    "problem.pair(survey)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Read the data\n",
+    "std = 0.05 # 5% std\n",
+    "# Load the files if they exist\n",
+    "if os.path.isfile('MT1D_dtrue.npy') and os.path.isfile('MT1D_dobs.npy'):\n",
+    "    d_true = np.load('MT1D_dtrue.npy')\n",
+    "    d_obs = np.load('MT1D_dobs.npy')\n",
+    "else:\n",
+    "    # Forward model\n",
+    "    d_true = survey.dpred(m_true)\n",
+    "    np.save('MT1D_dtrue.npy',d_true)\n",
+    "    d_obs = d_true + (std*abs(d_true)*np.random.randn(*d_true.shape))\n",
+    "    np.save('MT1D_dobs.npy',d_obs)\n",
+    "# Assign the datas to the survey object\n",
+    "survey.dtrue = d_true\n",
+    "survey.dobs = d_obs\n",
+    "survey.std = np.abs(survey.dobs*std) + 0.01*np.linalg.norm(survey.dobs)\n",
+    "# Assign the data weight\n",
+    "survey.Wd = 1./survey.std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Setup the inversion proceedure\n",
+    "\n",
+    "# Define a counter\n",
+    "C =  simpeg.Utils.Counter()\n",
+    "# Set the optimization\n",
+    "opt = simpeg.Optimization.InexactGaussNewton(maxIter = 30)\n",
+    "opt.counter = C\n",
+    "opt.LSshorten = 0.5\n",
+    "opt.remember('xc')\n",
+    "# Data misfit\n",
+    "dmis = simpeg.DataMisfit.l2_DataMisfit(survey)\n",
+    "# Regularization\n",
+    "# Note: We want you use a mesh the corresponds to the domain we want to solve, the active cells.\n",
+    "if True:\n",
+    "    regMesh = simpeg.Mesh.TensorMesh([m1d.hx[problem.mapping.sigmaMap.maps[-1].indActive]],m1d.x0)\n",
+    "    reg = simpeg.Regularization.Tikhonov(regMesh)\n",
+    "# else:\n",
+    "#     reg = simpeg.Regularization.Tikhonov(m1d,mapping=mapAct)\n",
+    "reg.alpha_s = 1e-8\n",
+    "reg.alpha_x = 1.\n",
+    "# Inversion problem\n",
+    "invProb = simpeg.InvProblem.BaseInvProblem(dmis, reg, opt)\n",
+    "invProb.counter = C\n",
+    "# Beta cooling\n",
+    "beta = simpeg.Directives.BetaSchedule()\n",
+    "betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)\n",
+    "saveModel = simpeg.Directives.SaveModelEveryIteration()\n",
+    "saveModel.fileName = 'Inversion_NoStoppingregMesh'\n",
+    "# Create an inversion object\n",
+    "inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest,saveModel]) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": 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",
+      "SimPEG.l2_DataMisfit is creating default weightings for Wd.\n",
+      "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_NoStoppingregMesh.npy'\n",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  3.07e+06  5.84e+06  2.95e-02  5.93e+06    1.45e+06      0              \n",
+      "   1  3.07e+06  6.43e+05  2.81e-02  7.29e+05    1.64e+05      0              \n",
+      "   2  3.07e+06  6.97e+04  2.75e-02  1.54e+05    1.99e+04      0   Skip BFGS  \n",
+      "   3  3.84e+05  1.01e+04  2.79e-02  2.08e+04    2.95e+03      0   Skip BFGS  \n",
+      "   4  3.84e+05  5.52e+03  2.97e-02  1.69e+04    3.93e+02      0   Skip BFGS  \n",
+      "   5  3.84e+05  5.44e+03  2.93e-02  1.67e+04    7.12e+01      0   Skip BFGS  \n",
+      "------------------------------------------------------------------\n",
+      "0 :    ft     = 1.6712e+04 <= alp*descent     = 1.6712e+04\n",
+      "1 : maxIterLS =      10    <= iterLS          =     10\n",
+      "------------------------- End Linesearch -------------------------\n",
+      "The linesearch got broken. Boo.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run the inversion, given the background model as a start.\n",
+    "mopt = inv.run(m_0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "modList = []\n",
+    "modFiles = glob('*Inversion_NoStopping.npy')\n",
+    "modFiles.sort()\n",
+    "for f in modFiles:\n",
+    "    modList.append(np.load(f))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/gudni/anaconda/lib/python2.7/site-packages/numpy/ma/core.py:2834: FutureWarning: Numpy has detected that you (may be) writing to an array returned\n",
+      "by numpy.diagonal or by selecting multiple fields in a record\n",
+      "array. This code will likely break in a future numpy release --\n",
+      "see numpy.diagonal or arrays.indexing reference docs for details.\n",
+      "The quick fix is to make an explicit copy (e.g., do\n",
+      "arr.diagonal().copy() or arr[['f0','f1']].copy()).\n",
+      "  if (obj.__array_interface__[\"data\"][0]\n",
+      "/home/gudni/anaconda/lib/python2.7/site-packages/numpy/ma/core.py:2835: FutureWarning: Numpy has detected that you (may be) writing to an array returned\n",
+      "by numpy.diagonal or by selecting multiple fields in a record\n",
+      "array. This code will likely break in a future numpy release --\n",
+      "see numpy.diagonal or arrays.indexing reference docs for details.\n",
+      "The quick fix is to make an explicit copy (e.g., do\n",
+      "arr.diagonal().copy() or arr[['f0','f1']].copy()).\n",
+      "  != self.__array_interface__[\"data\"][0]):\n"
+     ]
+    }
+   ],
+   "source": [
+    "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,modList)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,modList[-3:-1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.mref"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n",
+       "       -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m_0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ -7.62973638e+04,  -5.87387576e+04,  -4.52321375e+04,\n",
+       "        -3.48424297e+04,  -2.68503468e+04,  -2.07025907e+04,\n",
+       "        -1.59735476e+04,  -1.23358221e+04,  -9.53757167e+03,\n",
+       "        -7.38507138e+03,  -5.72930192e+03,  -4.45563311e+03,\n",
+       "        -3.47588787e+03,  -2.72223768e+03,  -2.14250677e+03,\n",
+       "        -1.69655992e+03,  -1.35352387e+03,  -1.08965000e+03,\n",
+       "        -8.86670089e+02,  -7.30531699e+02,  -6.10425246e+02,\n",
+       "        -5.18035666e+02,  -4.46966758e+02,  -3.92298368e+02,\n",
+       "        -3.50245760e+02,  -3.17897600e+02,  -2.93014400e+02,\n",
+       "        -2.68131200e+02,  -2.43248000e+02,  -2.22512000e+02,\n",
+       "        -2.01776000e+02,  -1.81040000e+02,  -1.63760000e+02,\n",
+       "        -1.46480000e+02,  -1.29200000e+02,  -1.14800000e+02,\n",
+       "        -1.00400000e+02,  -8.60000000e+01,  -7.40000000e+01,\n",
+       "        -6.20000000e+01,  -5.00000000e+01,  -4.00000000e+01,\n",
+       "        -3.00000000e+01,  -2.00000000e+01,  -1.00000000e+01,\n",
+       "        -5.82076609e-11])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regMesh.gridN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ -7.62973638e+04,  -5.87387576e+04,  -4.52321375e+04,\n",
+       "        -3.48424297e+04,  -2.68503468e+04,  -2.07025907e+04,\n",
+       "        -1.59735476e+04,  -1.23358221e+04,  -9.53757167e+03,\n",
+       "        -7.38507138e+03,  -5.72930192e+03,  -4.45563311e+03,\n",
+       "        -3.47588787e+03,  -2.72223768e+03,  -2.14250677e+03,\n",
+       "        -1.69655992e+03,  -1.35352387e+03,  -1.08965000e+03,\n",
+       "        -8.86670089e+02,  -7.30531699e+02,  -6.10425246e+02,\n",
+       "        -5.18035666e+02,  -4.46966758e+02,  -3.92298368e+02,\n",
+       "        -3.50245760e+02,  -3.17897600e+02,  -2.93014400e+02,\n",
+       "        -2.68131200e+02,  -2.43248000e+02,  -2.22512000e+02,\n",
+       "        -2.01776000e+02,  -1.81040000e+02,  -1.63760000e+02,\n",
+       "        -1.46480000e+02,  -1.29200000e+02,  -1.14800000e+02,\n",
+       "        -1.00400000e+02,  -8.60000000e+01,  -7.40000000e+01,\n",
+       "        -6.20000000e+01,  -5.00000000e+01,  -4.00000000e+01,\n",
+       "        -3.00000000e+01,  -2.00000000e+01,  -1.00000000e+01])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m1d.gridN[active]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD.ipynb b/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD.ipynb
new file mode 100644
index 00000000..5f82a465
--- /dev/null
+++ b/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD.ipynb
@@ -0,0 +1,291 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import SimPEG as simpeg\n",
+    "import simpegMT as simpegmt\n",
+    "import numpy as np, os\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Setup the modelling\n",
+    "# Setting up 1D mesh and conductivity models to forward model data.\n",
+    "\n",
+    "# Frequency\n",
+    "nFreq = 31\n",
+    "freqs = np.logspace(3,-3,nFreq)\n",
+    "# Set mesh parameters\n",
+    "ct = 10\n",
+    "air = simpeg.Utils.meshTensor([(ct,25,1.3)])\n",
+    "core = np.concatenate( (  np.kron(simpeg.Utils.meshTensor([(ct,5,-1.2)]),np.ones((3,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n",
+    "bot = simpeg.Utils.meshTensor([(core[0],25,-1.3)])\n",
+    "x0 = -np.array([np.sum(np.concatenate((core,bot)))])\n",
+    "# Make the model\n",
+    "m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)\n",
+    "\n",
+    "# Setup model varibles\n",
+    "active = m1d.vectorCCx<0.\n",
+    "layer1 = (m1d.vectorCCx<-200.) & (m1d.vectorCCx>=-600.)\n",
+    "layer2 = (m1d.vectorCCx<-2000.) & (m1d.vectorCCx>=-4000.)\n",
+    "# Set the conductivity values\n",
+    "sig_half = 2e-3\n",
+    "sig_air = 1e-8\n",
+    "sig_layer1 = 1\n",
+    "sig_layer2 = .1\n",
+    "# Make the true model\n",
+    "sigma_true = np.ones(m1d.nCx)*sig_air\n",
+    "sigma_true[active] = sig_half\n",
+    "sigma_true[layer1] = sig_layer1\n",
+    "sigma_true[layer2] = sig_layer2\n",
+    "# Extract the model \n",
+    "m_true = np.log(sigma_true[active])\n",
+    "# Make the background model\n",
+    "sigma_0 = np.ones(m1d.nCx)*sig_air\n",
+    "sigma_0[active] = sig_half\n",
+    "m_0 = np.log(sigma_0[active])\n",
+    "\n",
+    "# Set the mapping\n",
+    "actMap = simpeg.Maps.ActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)\n",
+    "mappingExpAct = simpeg.Maps.ExpMap(m1d) * actMap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Setup the layout of the survey, set the sources and the connected receivers\n",
+    "\n",
+    "# Receivers \n",
+    "rxList = []\n",
+    "for rxType in ['z1dr','z1di']:\n",
+    "    rxList.append(simpegmt.SurveyMT.RxMT(simpeg.mkvc(np.array([0.0]),2).T,rxType))\n",
+    "# Source list\n",
+    "srcList =[]\n",
+    "for freq in freqs:\n",
+    "        srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,freq))\n",
+    "# Make the survey\n",
+    "survey = simpegmt.SurveyMT.SurveyMT(srcList)\n",
+    "survey.mtrue = m_true\n",
+    "# Set the problem\n",
+    "problem = simpegmt.ProblemMT1D.eForm_psField(m1d,sigmaPrimary=sigma_0,mapping=mappingExpAct)\n",
+    "from pymatsolver import MumpsSolver\n",
+    "problem.solver = MumpsSolver\n",
+    "problem.pair(survey)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Forward model observed data \n",
+    "# Project the data\n",
+    "std = 0.05 # 5% std\n",
+    "if os.path.isfile('MT1D_dtrue.npy') and os.path.isfile('MT1D_dobs.npy'):\n",
+    "    d_true = np.load('MT1D_dtrue.npy')\n",
+    "    d_obs = np.load('MT1D_dobs.npy')\n",
+    "else:\n",
+    "    d_true = survey.dpred(m_true)\n",
+    "    np.save('MT1D_dtrue.npy',d_true)\n",
+    "    d_obs = std*abs(d_true)*np.random.randn(*d_true.shape)\n",
+    "    np.save('MT1D_dobs.npy',d_obs)\n",
+    "# Assign the dobs\n",
+    "survey.dtrue = d_true\n",
+    "survey.dobs = d_obs\n",
+    "survey.std = survey.dobs*0 + std\n",
+    "# Assign the data weight\n",
+    "survey.Wd = 1/(abs(survey.dobs)*survey.std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Setup the inversion proceedure\n",
+    "\n",
+    "# Define a counter\n",
+    "C =  simpeg.Utils.Counter()\n",
+    "# Set the optimization\n",
+    "opt = simpeg.Optimization.InexactGaussNewton(maxIter = 50)\n",
+    "opt.counter = C\n",
+    "opt.LSshorten = 0.5\n",
+    "opt.remember('xc')\n",
+    "# Data misfit\n",
+    "dmis = simpeg.DataMisfit.l2_DataMisfit(survey)\n",
+    "# Regularization\n",
+    "# Either have to use \n",
+    "if False:\n",
+    "    regMesh = simpeg.Mesh.TensorMesh([m1d.hx[problem.mapping.sigmaMap.maps[-1].indActive]],m1d.x0)\n",
+    "    reg = simpeg.Regularization.Tikhonov(regMesh)\n",
+    "else:\n",
+    "    reg = simpeg.Regularization.Tikhonov(m1d,mapping=mappingExpAct)\n",
+    "reg.alpha_s = 1e-6\n",
+    "reg.alpha_x = 1.\n",
+    "\n",
+    "# Inversion problem\n",
+    "invProb = simpeg.InvProblem.BaseInvProblem(dmis, reg, opt)\n",
+    "invProb.counter = C\n",
+    "# Beta cooling\n",
+    "beta = simpeg.Directives.BetaSchedule()\n",
+    "betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)\n",
+    "targmis = simpeg.Directives.TargetMisfit()\n",
+    "saveModel = simpeg.Directives.SaveModelEveryIteration()\n",
+    "saveModel.fileName = 'Inversion_TargMisEqnD'\n",
+    "# Create an inversion object\n",
+    "inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest,targmis,saveModel]) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": 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",
+      "SimPEG.l2_DataMisfit is creating default weightings for Wd.\n",
+      "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_TargMisEqnD.npy'\n",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  7.52e+05  1.32e+06  4.79e-07  1.32e+06    3.23e+05      0              \n",
+      "   1  7.52e+05  1.91e+05  1.61e-06  1.91e+05    4.88e+04      0              \n",
+      "   2  7.52e+05  8.47e+04  3.06e-06  8.47e+04    2.41e+04      0   Skip BFGS  \n",
+      "   3  9.41e+04  7.39e+04  3.58e-06  7.39e+04    2.12e+04      0   Skip BFGS  \n",
+      "   4  9.41e+04  4.00e+04  7.90e-06  4.00e+04    1.19e+04      0   Skip BFGS  \n",
+      "   5  9.41e+04  3.37e+04  1.00e-05  3.37e+04    1.02e+04      0   Skip BFGS  \n",
+      "   6  1.18e+04  3.00e+04  1.18e-05  3.00e+04    9.10e+03      0   Skip BFGS  \n",
+      "   7  1.18e+04  1.71e+04  2.61e-05  1.71e+04    5.44e+03      0   Skip BFGS  \n",
+      "   8  1.18e+04  1.41e+04  3.38e-05  1.41e+04    4.61e+03      0   Skip BFGS  \n",
+      "   9  1.47e+03  1.25e+04  4.02e-05  1.25e+04    4.12e+03      0   Skip BFGS  \n",
+      "  10  1.47e+03  6.91e+03  8.84e-05  6.91e+03    2.48e+03      0   Skip BFGS  \n",
+      "  11  1.47e+03  5.65e+03  1.15e-04  5.65e+03    2.09e+03      0   Skip BFGS  \n",
+      "  12  1.84e+02  4.93e+03  1.36e-04  4.93e+03    1.87e+03      0   Skip BFGS  \n",
+      "  13  1.84e+02  2.73e+03  2.85e-04  2.73e+03    1.14e+03      0   Skip BFGS  \n",
+      "  14  1.84e+02  2.22e+03  3.66e-04  2.22e+03    9.55e+02      0   Skip BFGS  \n",
+      "  15  2.30e+01  1.95e+03  4.32e-04  1.95e+03    8.48e+02      0   Skip BFGS  \n",
+      "  16  2.30e+01  1.16e+03  8.20e-04  1.16e+03    5.20e+02      0   Skip BFGS  \n",
+      "  17  2.30e+01  9.61e+02  1.03e-03  9.61e+02    4.28e+02      0   Skip BFGS  \n",
+      "  18  2.87e+00  8.51e+02  1.19e-03  8.51e+02    3.75e+02      0   Skip BFGS  \n",
+      "  19  2.87e+00  5.60e+02  2.03e-03  5.60e+02    2.24e+02      0   Skip BFGS  \n",
+      "  20  2.87e+00  4.64e+02  2.44e-03  4.64e+02    1.82e+02      0   Skip BFGS  \n",
+      "  21  3.59e-01  4.09e+02  2.74e-03  4.09e+02    1.59e+02      0   Skip BFGS  \n",
+      "  22  3.59e-01  2.75e+02  3.99e-03  2.75e+02    1.10e+02      0   Skip BFGS  \n",
+      "  23  3.59e-01  2.28e+02  4.59e-03  2.28e+02    8.98e+01      0              \n",
+      "  24  4.48e-02  1.85e+02  4.88e-03  1.85e+02    8.62e+01      0   Skip BFGS  \n",
+      "  25  4.48e-02  1.26e+02  6.70e-03  1.26e+02    7.39e+01      0   Skip BFGS  \n",
+      "  26  4.48e-02  8.27e+01  7.76e-03  8.27e+01    5.01e+01      0              \n",
+      "  27  5.61e-03  6.54e+01  8.37e-03  6.54e+01    5.22e+01      1              \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.3164e+05\n",
+      "0 : |xc-x_last| = 5.4748e+00 <= tolX*(1+|x0|) = 4.2689e+00\n",
+      "0 : |proj(x-g)-x|    = 5.2197e+01 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 5.2197e+01 <= 1e3*eps       = 1.0000e-02\n",
+      "0 : maxIter   =      50    <= iter          =     28\n",
+      "------------------------- DONE! -------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run the inversion, given the background model as a start.\n",
+    "mopt = inv.run(m_0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+m4YNadChA/3+9KcKH6cqy8jIICYmhmbNmpVeWAgRcP5cB4wxNYArgUk4d11m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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fef773ac550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD_regMesh.ipynb b/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD_regMesh.ipynb
new file mode 100644
index 00000000..3f3ba509
--- /dev/null
+++ b/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD_regMesh.ipynb
@@ -0,0 +1,284 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import SimPEG as simpeg\n",
+    "import simpegMT as simpegmt\n",
+    "import numpy as np, os\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Setup the modelling\n",
+    "# Setting up 1D mesh and conductivity models to forward model data.\n",
+    "\n",
+    "# Frequency\n",
+    "nFreq = 31\n",
+    "freqs = np.logspace(3,-3,nFreq)\n",
+    "# Set mesh parameters\n",
+    "ct = 10\n",
+    "air = simpeg.Utils.meshTensor([(ct,25,1.3)])\n",
+    "core = np.concatenate( (  np.kron(simpeg.Utils.meshTensor([(ct,5,-1.2)]),np.ones((3,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n",
+    "bot = simpeg.Utils.meshTensor([(core[0],25,-1.3)])\n",
+    "x0 = -np.array([np.sum(np.concatenate((core,bot)))])\n",
+    "# Make the model\n",
+    "m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)\n",
+    "\n",
+    "# Setup model varibles\n",
+    "active = m1d.vectorCCx<0.\n",
+    "layer1 = (m1d.vectorCCx<-200.) & (m1d.vectorCCx>=-600.)\n",
+    "layer2 = (m1d.vectorCCx<-2000.) & (m1d.vectorCCx>=-4000.)\n",
+    "# Set the conductivity values\n",
+    "sig_half = 2e-3\n",
+    "sig_air = 1e-8\n",
+    "sig_layer1 = 1\n",
+    "sig_layer2 = .1\n",
+    "# Make the true model\n",
+    "sigma_true = np.ones(m1d.nCx)*sig_air\n",
+    "sigma_true[active] = sig_half\n",
+    "sigma_true[layer1] = sig_layer1\n",
+    "sigma_true[layer2] = sig_layer2\n",
+    "# Extract the model \n",
+    "m_true = np.log(sigma_true[active])\n",
+    "# Make the background model\n",
+    "sigma_0 = np.ones(m1d.nCx)*sig_air\n",
+    "sigma_0[active] = sig_half\n",
+    "m_0 = np.log(sigma_0[active])\n",
+    "\n",
+    "# Set the mapping\n",
+    "actMap = simpeg.Maps.ActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)\n",
+    "mappingExpAct = simpeg.Maps.ExpMap(m1d) * actMap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Setup the layout of the survey, set the sources and the connected receivers\n",
+    "\n",
+    "# Receivers \n",
+    "rxList = []\n",
+    "for rxType in ['z1dr','z1di']:\n",
+    "    rxList.append(simpegmt.SurveyMT.RxMT(simpeg.mkvc(np.array([0.0]),2).T,rxType))\n",
+    "# Source list\n",
+    "srcList =[]\n",
+    "for freq in freqs:\n",
+    "        srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,freq))\n",
+    "# Make the survey\n",
+    "survey = simpegmt.SurveyMT.SurveyMT(srcList)\n",
+    "survey.mtrue = m_true\n",
+    "# Set the problem\n",
+    "problem = simpegmt.ProblemMT1D.eForm_psField(m1d,sigmaPrimary=sigma_0,mapping=mappingExpAct)\n",
+    "from pymatsolver import MumpsSolver\n",
+    "problem.solver = MumpsSolver\n",
+    "problem.pair(survey)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Forward model observed data \n",
+    "# Project the data\n",
+    "std = 0.05 # 5% std\n",
+    "if os.path.isfile('MT1D_dtrue.npy') and os.path.isfile('MT1D_dobs.npy'):\n",
+    "    d_true = np.load('MT1D_dtrue.npy')\n",
+    "    d_obs = np.load('MT1D_dobs.npy')\n",
+    "else:\n",
+    "    d_true = survey.dpred(m_true)\n",
+    "    np.save('MT1D_dtrue.npy',d_true)\n",
+    "    d_obs = std*abs(d_true)*np.random.randn(*d_true.shape)\n",
+    "    np.save('MT1D_dobs.npy',d_obs)\n",
+    "# Assign the dobs\n",
+    "survey.dtrue = d_true\n",
+    "survey.dobs = d_obs\n",
+    "survey.std = survey.dobs*0 + std\n",
+    "# Assign the data weight\n",
+    "survey.Wd = 1/(abs(survey.dobs)*survey.std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Setup the inversion proceedure\n",
+    "\n",
+    "# Define a counter\n",
+    "C =  simpeg.Utils.Counter()\n",
+    "# Set the optimization\n",
+    "opt = simpeg.Optimization.InexactGaussNewton(maxIter = 50)\n",
+    "opt.counter = C\n",
+    "opt.LSshorten = 0.5\n",
+    "opt.remember('xc')\n",
+    "# Data misfit\n",
+    "dmis = simpeg.DataMisfit.l2_DataMisfit(survey)\n",
+    "# Regularization\n",
+    "# Either have to use \n",
+    "if True:\n",
+    "    regMesh = simpeg.Mesh.TensorMesh([m1d.hx[problem.mapping.sigmaMap.maps[-1].indActive]],m1d.x0)\n",
+    "    reg = simpeg.Regularization.Tikhonov(regMesh)\n",
+    "else:\n",
+    "    reg = simpeg.Regularization.Tikhonov(m1d,mapping=mappingExpAct)\n",
+    "reg.alpha_s = 1e-6\n",
+    "reg.alpha_x = 1.\n",
+    "\n",
+    "# Inversion problem\n",
+    "invProb = simpeg.InvProblem.BaseInvProblem(dmis, reg, opt)\n",
+    "invProb.counter = C\n",
+    "# Beta cooling\n",
+    "beta = simpeg.Directives.BetaSchedule()\n",
+    "betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)\n",
+    "targmis = simpeg.Directives.TargetMisfit()\n",
+    "saveModel = simpeg.Directives.SaveModelEveryIteration()\n",
+    "saveModel.fileName = 'Inversion_TargMisEqnDregMesh'\n",
+    "# Create an inversion object\n",
+    "inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest,targmis,saveModel]) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": 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",
+      "SimPEG.l2_DataMisfit is creating default weightings for Wd.\n",
+      "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_TargMisEqnDregMesh.npy'\n",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  5.15e+05  1.32e+06  2.95e+00  2.83e+06    3.23e+05      0              \n",
+      "   1  5.15e+05  1.66e+05  2.77e+00  1.59e+06    3.93e+04      0              \n",
+      "   2  5.15e+05  2.22e+04  2.75e+00  1.44e+06    7.05e+03      0   Skip BFGS  \n",
+      "------------------------------------------------------------------\n",
+      "0 :    ft     = 1.4405e+06 <= alp*descent     = 1.4405e+06\n",
+      "1 : maxIterLS =      10    <= iterLS          =     10\n",
+      "------------------------- End Linesearch -------------------------\n",
+      "The linesearch got broken. Boo.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run the inversion, given the background model as a start.\n",
+    "mopt = inv.run(m_0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/gudni/anaconda/lib/python2.7/site-packages/numpy/ma/core.py:2834: FutureWarning: Numpy has detected that you (may be) writing to an array returned\n",
+      "by numpy.diagonal or by selecting multiple fields in a record\n",
+      "array. This code will likely break in a future numpy release --\n",
+      "see numpy.diagonal or arrays.indexing reference docs for details.\n",
+      "The quick fix is to make an explicit copy (e.g., do\n",
+      "arr.diagonal().copy() or arr[['f0','f1']].copy()).\n",
+      "  if (obj.__array_interface__[\"data\"][0]\n",
+      "/home/gudni/anaconda/lib/python2.7/site-packages/numpy/ma/core.py:2835: FutureWarning: Numpy has detected that you (may be) writing to an array returned\n",
+      "by numpy.diagonal or by selecting multiple fields in a record\n",
+      "array. This code will likely break in a future numpy release --\n",
+      "see numpy.diagonal or arrays.indexing reference docs for details.\n",
+      "The quick fix is to make an explicit copy (e.g., do\n",
+      "arr.diagonal().copy() or arr[['f0','f1']].copy()).\n",
+      "  != self.__array_interface__[\"data\"][0]):\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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9McDj9AJmA42Az3FG6xZ/y2wK9ARGAseA4dbaVQEcM+yfvzvGj2fNjBnEt2pF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\n6UoC9PiJSDpeT+QyvJlmrfC+GJYBHgoQd66IvAK8WcK4I+EUvB7TeXh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xJvk5jjPLdd3m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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0bd6798710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/MT3DforData1Dinv.ipynb b/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/MT3DforData1Dinv.ipynb
new file mode 100644
index 00000000..049b716d
--- /dev/null
+++ b/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/MT3DforData1Dinv.ipynb
@@ -0,0 +1,47 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "## Forward model a data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Define the model\n"
+   ]
+  }
+ ],
+ "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
+}
diff --git a/notebooks/scipy2015/simpegMT1DinvResults_NoStop_1.png b/notebooks/scipy2015/simpegMT1DinvResults_NoStop_1.png
new file mode 100644
index 00000000..b26d277d
Binary files /dev/null and b/notebooks/scipy2015/simpegMT1DinvResults_NoStop_1.png differ
diff --git a/simpegMT/BaseMT.py b/simpegMT/BaseMT.py
index 68f74945..bebd7cc3 100644
--- a/simpegMT/BaseMT.py
+++ b/simpegMT/BaseMT.py
@@ -2,14 +2,14 @@ from simpegEM.FDEM import BaseFDEMProblem
 from SurveyMT import SurveyMT
 from DataMT import DataMT
 from FieldsMT import FieldsMT
-from SimPEG import SolverLU as SimpegSolver, mkvc
+from SimPEG import SolverLU as SimpegSolver, Utils, mkvc
 import numpy as np
 
 class BaseMTProblem(BaseFDEMProblem):
 
     def __init__(self, mesh, **kwargs):
         BaseFDEMProblem.__init__(self, mesh, **kwargs)
-
+        Utils.setKwargs(self, **kwargs)
     # Set the default pairs of the problem
     surveyPair = SurveyMT
     dataPair = DataMT
diff --git a/simpegMT/ProblemMT1D/Problems.py b/simpegMT/ProblemMT1D/Problems.py
index 156e6bb5..8b1a8085 100644
--- a/simpegMT/ProblemMT1D/Problems.py
+++ b/simpegMT/ProblemMT1D/Problems.py
@@ -31,7 +31,10 @@ class eForm_psField(BaseMTProblem):
     @property
     def sigmaPrimary(self):
         return self._sigmaPrimary
-
+    @sigmaPrimary.setter
+    def sigmaPrimary(self, val):
+        # Note: TODO add logic for val, make sure it is the correct size.
+        self._sigmaPrimary = val
 
     def getA(self, freq):
         """
diff --git a/simpegMT/ProblemMT3D/Problems.py b/simpegMT/ProblemMT3D/Problems.py
index 30f73b0d..61ea254b 100644
--- a/simpegMT/ProblemMT3D/Problems.py
+++ b/simpegMT/ProblemMT3D/Problems.py
@@ -71,7 +71,7 @@ class eForm_ps(BaseMTProblem):
 
         return C.T*Mmui*C + 1j*omega(freq)*Msig
 
-    def getADeriv(self, freq, u, v, adjoint=False):
+    def getADeriv_m(self, freq, u, v, adjoint=False):
 
         dsig_dm = self.curModel.sigmaDeriv
         dMe_dsig = self.MeSigmaDeriv( v=u)
@@ -94,7 +94,7 @@ class eForm_ps(BaseMTProblem):
         S_e = Src.S_e(self)
         return -1j * omega(freq) * S_e
 
-    def getRHSderiv(self, freq, u, v, adjoint=False):
+    def getRHSderiv_m(self, freq, u, v, adjoint=False):
         """
         The derivative of the RHS with respect to sigma
         """
@@ -227,6 +227,7 @@ class eForm_Tp(BaseMTProblem):
         Abg = C.T*mui*C + 1j*omega(freq)*MeBack
 
         return Abg*eBG_bp, eBG_bp
+
     def getRHSderiv(self, freq, backSigma, u, v, adjoint=False):
         raise NotImplementedError('getRHSDeriv not implemented yet')
         return None
diff --git a/simpegMT/Utils/dataUtils.py b/simpegMT/Utils/dataUtils.py
index 12d0090b..20723798 100644
--- a/simpegMT/Utils/dataUtils.py
+++ b/simpegMT/Utils/dataUtils.py
@@ -1,5 +1,7 @@
 # Utils used for the data,
-import numpy as np
+import numpy as np, matplotlib.pyplot as plt, sys
+import SimPEG as simpeg
+
 def getAppRes(MTdata):
     # Make impedance
     zList = []
@@ -21,4 +23,103 @@ def appResPhs(freq,z):
     return app_res, app_phs
 
 def rec2ndarr(x,dt=float):
-    return x.view((dt, len(x.dtype.names)))
\ No newline at end of file
+    return x.view((dt, len(x.dtype.names)))
+
+def makeAnalyticSolution(mesh,model,elev,freqs):
+  data1D = []
+  for freq in freqs:
+      anaEd, anaEu, anaHd, anaHu = simpegmt.Utils.MT1Danalytic.getEHfields(mesh,model,freq,elev)
+      anaE = anaEd+anaEu
+      anaH = anaHd+anaHu
+
+      anaZ = anaE/anaH
+      # Add to the list
+      data1D.append((freq,0,0,elev,anaZ[0]))
+  dataRec = np.array(data1D,dtype=[('freq',float),('x',float),('y',float),('z',float),('zyx',complex)])
+  return dataRec
+
+def plotMT1DModelData(problem,models,symList=None):
+
+    # Setup the figure
+    fontSize = 15
+
+    fig = plt.figure(figsize=[9,7])
+    axM = fig.add_axes([0.075,.1,.25,.875])
+    axM.set_xlabel('Resistivity [Ohm*m]',fontsize=fontSize)
+    axM.set_xlim(1e-1,1e5)
+    axM.set_ylim(-10000,5000)
+    axM.set_ylabel('Depth [km]',fontsize=fontSize)
+    axR = fig.add_axes([0.42,.575,.5,.4])
+    axR.set_xscale('log')
+    axR.set_yscale('log')
+    axR.invert_xaxis()
+    # axR.set_xlabel('Frequency [Hz]')
+    axR.set_ylabel('Apparent resistivity [Ohm m]',fontsize=fontSize)
+
+    axP = fig.add_axes([0.42,.1,.5,.4])
+    axP.set_xscale('log')
+    axP.invert_xaxis()
+    axP.set_ylim(0,90)
+    axP.set_xlabel('Frequency [Hz]',fontsize=fontSize)
+    axP.set_ylabel('Apparent phase [deg]',fontsize=fontSize)
+
+    # if not symList:
+    #   symList = ['x']*len(models)
+    sys.path.append('/home/gudni/Dropbox/code/python/MTview')
+    import plotDataTypes as pDt
+    # Loop through the models.
+    modelList = [problem.survey.mtrue]
+    modelList.extend(models)
+    if False:
+        modelList = [problem.mapping.sigmaMap*mod for mod in modelList]
+    for nr, model in enumerate(modelList):
+        # Calculate the data
+        if nr==0:
+            data1D = problem.dataPair(problem.survey,problem.survey.dobs).toRecArray('Complex')
+        else:
+            data1D = problem.dataPair(problem.survey,problem.survey.dpred(model)).toRecArray('Complex')
+        # Plot the data and the model
+        colRat = nr/((len(modelList)-2)*1.)
+        if colRat > 1.:
+            col = 'k'
+        else:
+            col = plt.cm.seismic(1-colRat)
+        # The model - make the pts to plot
+        meshPts = np.concatenate((problem.mesh.gridN[0:1],np.kron(problem.mesh.gridN[1::],np.ones(2))[:-1]))
+        modelPts = np.kron(1./(problem.mapping.sigmaMap*model),np.ones(2,))
+        axM.semilogx(modelPts,meshPts,color=col)
+
+        ## Data
+        # Appres
+        pDt.plotIsoStaImpedance(axR,np.array([0,0]),data1D,'zyx','res',pColor=col)
+        # Appphs
+        pDt.plotIsoStaImpedance(axP,np.array([0,0]),data1D,'zyx','phs',pColor=col)
+        try:
+            allData = np.concatenate((allData,simpeg.mkvc(data1D['zyx'],2)),1)
+        except:
+            allData = simpeg.mkvc(data1D['zyx'],2)
+    freq = simpeg.mkvc(data1D['freq'],2)
+    res, phs = appResPhs(freq,allData)
+
+    stdCol = 'gray'
+    axRtw = axR.twinx()
+    axRtw.set_ylabel('Std of log10',color=stdCol)
+    [(t.set_color(stdCol), t.set_rotation(-45)) for t in axRtw.get_yticklabels()]
+    axPtw = axP.twinx()
+    axPtw.set_ylabel('Std ',color=stdCol)
+    [t.set_color(stdCol) for t in axPtw.get_yticklabels()]
+    axRtw.plot(freq, np.std(np.log10(res),1),'--',color=stdCol)
+    axPtw.plot(freq, np.std(phs,1),'--',color=stdCol)
+
+    # Fix labels and ticks
+
+    yMtick = [l/1000 for l in axM.get_yticks().tolist()]
+    axM.set_yticklabels(yMtick)
+    [ l.set_rotation(90) for l in axM.get_yticklabels()]
+    [ l.set_rotation(90) for l in axR.get_yticklabels()]
+    [(t.set_color(stdCol), t.set_rotation(-45)) for t in axRtw.get_yticklabels()]
+    [t.set_color(stdCol) for t in axPtw.get_yticklabels()]
+    for ax in [axM,axR,axP]:
+        ax.xaxis.set_tick_params(labelsize=fontSize)
+        ax.yaxis.set_tick_params(labelsize=fontSize)
+    return fig
\ No newline at end of file