diff --git a/MT Script-3D_twoHalfspace-simpegMT.ipynb b/MT Script-3D_twoHalfspace-simpegMT.ipynb
deleted file mode 100644
index b9067c19..00000000
--- a/MT Script-3D_twoHalfspace-simpegMT.ipynb	
+++ /dev/null
@@ -1,3144 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import SimPEG as simpeg\n",
-    "from scipy.constants import mu_0\n",
-    "def omega(freq):\n",
-    "    \"\"\"Change frequency to angular frequency, omega\"\"\"\n",
-    "    return 2.*np.pi*freq"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pylab inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2529.536000000001"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sum(100*np.cumprod(np.ones(5)*1.6))\n",
-    "\n",
-    "   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# M = Mesh.TensorMesh([[(100.,32)],[(100.,34)],[(100.,18)]], x0='CCC')\n",
-    "# M = simpeg.Mesh.TensorMesh([[(100,5,-1.5),(100.,5),(100,5,1.5)],[(100,5,-1.5),(100.,5),(100,5,1.5)],[(100,5,-1.5),(100.,10),(100,5,1.5)]], x0=['C','C','C'])\n",
-    "M = simpeg.Mesh.TensorMesh([[(1000,6,-1.5),(1000.,6),(1000,6,1.5)],[(1000,6,-1.5),(1000.,2),(1000,6,1.5)],[(1000,10,-1.3),(1000.,2),(1000,10,1.3)]], x0=['C','C','C'])# Setup the model\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ -5.64053465e+04  -4.26194973e+04  -3.20149979e+04  -2.38576907e+04\n",
-      "  -1.75828390e+04  -1.27560300e+04  -9.04310000e+03  -6.18700000e+03\n",
-      "  -3.99000000e+03  -2.30000000e+03  -1.00000000e+03   7.27595761e-12\n",
-      "   1.00000000e+03   2.30000000e+03   3.99000000e+03   6.18700000e+03\n",
-      "   9.04310000e+03   1.27560300e+04   1.75828390e+04   2.38576907e+04\n",
-      "   3.20149979e+04   4.26194973e+04   5.64053465e+04]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print M.vectorNz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar instance at 0x7f32ce3c70e0>"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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-      "text/plain": [
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-     "metadata": {},
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-       "O50AAAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f32cde13850>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Setup the model\n",
-    "# conds = [1,1e-2]\n",
-    "# elev = 300\n",
-    "# sig = simpeg.Utils.ModelBuilder.defineBlock(M.gridCC,[-100000,-100000,-200],[100000,100000,0],conds)\n",
-    "# sig[M.gridCC[:,2]>elev] = 1e-8\n",
-    "# sig[M.gridCC[:,2]<-500] = 1e-1\n",
-    "# sig[M.gridCC[:,2]<-900] = 1e-2\n",
-    "elev=0\n",
-    "conds = [1,1e-2]\n",
-    "sig = np.ones(M.nC)*conds[0]\n",
-    "sig[M.gridCC[:,0]>0] = conds[1]\n",
-    "sig[M.gridCC[:,2]>elev] = 1e-8\n",
-    "# sigBG = np.zeros(M.nC) + conds[0]\n",
-    "# sigBG[M.gridCC[:,2]>0] = 1e-8\n",
-    "sigBG = np.ones(M.nC)*conds[0]\n",
-    "sigBG[M.gridCC[:,2]>elev] = 1e-8\n",
-    "colorbar(M.plotImage(log10(sig)))\n",
-    "colorbar(M.plotImage(log10(sigBG)))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Get the mass matrix \n",
-    "# The model\n",
-    "Msig = M.getEdgeInnerProduct(sig)\n",
-    "MsigBG = M.getEdgeInnerProduct(sigBG)\n",
-    "Mmu = M.getFaceInnerProduct(mu_0, invProp=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "freq = 1.0\n",
-    "C = M.edgeCurl\n",
-    "A = C.T*Mmu*C - 1j*omega(freq)*Msig\n",
-    "ARH = -(C.T*Mmu*C - 1j*omega(freq)*MsigBG)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 13.9 s, sys: 1.17 s, total: 15 s\n",
-      "Wall time: 15 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# Solve the systems for each polarization\n",
-    "Ainv = simpeg.SolverLU(A)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Need to solve x and y polarizations of the source.\n",
-    "from simpegMT.Utils import get1DEfields\n",
-    "# Get a 1d solution for a halfspace background\n",
-    "mesh1d = simpeg.Mesh.TensorMesh([M.hz],np.array([M.x0[2]]))\n",
-    "e0_1d = get1DEfields(mesh1d,M.r(sigBG,'CC','CC','M')[0,0,:],freq,sourceAmp=None).conj()\n",
-    "# Setup the primary field (p) for the x (east) polarization (_px)\n",
-    "ex_px = np.zeros((M.vnEx),dtype=complex)\n",
-    "ey_px = np.zeros(M.nEy,dtype=complex)\n",
-    "ez_px = np.zeros(M.nEz,dtype=complex)\n",
-    "# Assign the source to ex_x\n",
-    "for i in arange(M.vnEx[0]):\n",
-    "    for j in arange(M.vnEx[1]):\n",
-    "        ex_px[i,j,:] = -e0_1d\n",
-    "ep_px = np.r_[simpeg.Utils.mkvc(ex_px),ey_px,ez_px]\n",
-    "rhs_px = ARH.dot(ep_px)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Setup y (north) polarization (_y)\n",
-    "ex_py = np.zeros(M.nEx, dtype='complex128')\n",
-    "ey_py = np.zeros((M.vnEy), dtype='complex128')\n",
-    "ez_py = np.zeros(M.nEz, dtype='complex128')\n",
-    "# Assign the source to ey_y\n",
-    "for i in arange(M.vnEy[0]):\n",
-    "    for j in arange(M.vnEy[1]):\n",
-    "        ey_py[i,j,:] = e0_1d  \n",
-    "        \n",
-    "ep_py = np.r_[ex_py,simpeg.Utils.mkvc(ey_py),ez_py]\n",
-    "rhs_py = ARH.dot(ep_py)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We are using splu in scipy package. This is bit slow, but on the cluster you can use mumps, which might a lot faster. We can think about having better iterative solver. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 146 ms, sys: 73 µs, total: 146 ms\n",
-      "Wall time: 145 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "es_px = Ainv*rhs_px\n",
-    "es_py = Ainv*rhs_py"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Need to sum the ep and es to get the total field.\n",
-    "e_x = es_px #+ ep_px\n",
-    "e_y = es_py #+ ep_py"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I want to visualize electrical field, which is a vector, so I average them on to cell center. Also I want to see current density ($\\vec{j} = \\sigma \\vec{e}$)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "Meinv = M.getEdgeInnerProduct(np.ones_like(sig), invMat=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "j_x = Meinv*Msig*e_x\n",
-    "j_y = Meinv*Msig*e_x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(18598,)"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "e_x.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "e_x_CC = M.aveE2CCV*e_x\n",
-    "e_y_CC = M.aveE2CCV*e_y\n",
-    "j_x_CC = M.aveE2CCV*j_x\n",
-    "j_y_CC = M.aveE2CCV*j_y\n",
-    "# j_x_CC = Utils.sdiag(np.r_[sig, sig, sig])*e_x_CC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then use \"plotSlice\" function, to visualize 2D sections"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
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-       "6KRao9FoNBqNRqNxEJ1UazQajUaj0Wg0DqKTao1Go9FoNBqNxkF0Uq3RaDQajUaj0TiITqo1Go1G\n",
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f32ce162250>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
-    "dat0 = M.plotSlice(np.log10(np.abs(e_x_CC)), vType='CCv', view='vec', streamOpts={'color': 'k'}, normal='Y', ax = ax[0])\n",
-    "cb0 = plt.colorbar(dat0[0], ax = ax[0])\n",
-    "dat1 = M.plotSlice(np.log10(np.abs(j_x_CC)), vType='CCv', view='vec', streamOpts={'color': 'k'}, normal='Y', ax = ax[1])\n",
-    "cb1 = plt.colorbar(dat1[0], ax = ax[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Is it reasonable?: Based on that you put resistive target that makes sense to me; current does not want to flow on resistive target so they just do roundabout:). And see air interface. It is continuous on current but not on electric field, which looks reasonable. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Calculate the data\n",
-    "rx_x, rx_y = np.meshgrid(np.arange(-3000,3001,500),np.arange(-1000,1001,500))\n",
-    "rx_loc = np.hstack((simpeg.Utils.mkvc(rx_x,2),simpeg.Utils.mkvc(rx_y,2),elev+np.zeros((np.prod(rx_x.shape),1))))\n",
-    "# Get the projection matrices\n",
-    "Qex = M.getInterpolationMat(rx_loc,'Ex')\n",
-    "Qey = M.getInterpolationMat(rx_loc,'Ey')\n",
-    "Qez = M.getInterpolationMat(rx_loc,'Ez')\n",
-    "Qfx = M.getInterpolationMat(rx_loc,'Fx')\n",
-    "Qfy = M.getInterpolationMat(rx_loc,'Fy')\n",
-    "Qfz = M.getInterpolationMat(rx_loc,'Fz')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "e_x_loc = np.hstack([simpeg.Utils.mkvc(Qex*e_x,2),simpeg.Utils.mkvc(Qey*e_x,2),simpeg.Utils.mkvc(Qez*e_x,2)])\n",
-    "e_y_loc = np.hstack([simpeg.Utils.mkvc(Qex*e_y,2),simpeg.Utils.mkvc(Qey*e_y,2),simpeg.Utils.mkvc(Qez*e_y,2)])\n",
-    "Ciw = -C/(1j*omega(freq)*mu_0)\n",
-    "h_x_loc = np.hstack([simpeg.Utils.mkvc(Qfx*Ciw*e_x,2),simpeg.Utils.mkvc(Qfy*Ciw*e_x,2),simpeg.Utils.mkvc(Qfz*Ciw*e_x,2)])\n",
-    "h_y_loc = np.hstack([simpeg.Utils.mkvc(Qfx*Ciw*e_y,2),simpeg.Utils.mkvc(Qfy*Ciw*e_y,2),simpeg.Utils.mkvc(Qfz*Ciw*e_y,2)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Make a combined matrix\n",
-    "dt = np.dtype([('ex1',complex),('ey1',complex),('ez1',complex),('hx1',complex),('hy1',complex),('hz1',complex),('ex2',complex),('ey2',complex),('ez2',complex),('hx2',complex),('hy2',complex),('hz2',complex)])\n",
-    "combMat = np.empty((len(e_x_loc)),dtype=dt)\n",
-    "combMat['ex1'] = e_x_loc[:,0]\n",
-    "combMat['ey1'] = e_x_loc[:,1]\n",
-    "combMat['ez1'] = e_x_loc[:,2]\n",
-    "combMat['ex2'] = e_y_loc[:,0]\n",
-    "combMat['ey2'] = e_y_loc[:,1]\n",
-    "combMat['ez2'] = e_y_loc[:,2]\n",
-    "combMat['hx1'] = h_x_loc[:,0]\n",
-    "combMat['hy1'] = h_x_loc[:,1]\n",
-    "combMat['hz1'] = h_x_loc[:,2]\n",
-    "combMat['hx2'] = h_y_loc[:,0]\n",
-    "combMat['hy2'] = h_y_loc[:,1]\n",
-    "combMat['hz2'] = h_y_loc[:,2]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def calculateImpedance(fieldsData):\n",
-    "    ''' \n",
-    "    Function that calculates MT impedance data from a rec array with E and H field data from both polarizations\n",
-    "    '''\n",
-    "    zxx = (fieldsData['ex1']*fieldsData['hy2'] - fieldsData['ex2']*fieldsData['hy1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
-    "    zxy = (-fieldsData['ex1']*fieldsData['hx2'] + fieldsData['ex2']*fieldsData['hx1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
-    "    zyx = (fieldsData['ey1']*fieldsData['hy2'] - fieldsData['ey2']*fieldsData['hy1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
-    "    zyy = (-fieldsData['ey1']*fieldsData['hx2'] + fieldsData['ey2']*fieldsData['hx1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
-    "    return zxx, zxy, zyx, zyy\n",
-    "\n",
-    "zxx, zxy, zyx, zyy = calculateImpedance(combMat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-3000., -1000.,     0.],\n",
-       "       [-3000.,  -500.,     0.],\n",
-       "       [-3000.,     0.,     0.],\n",
-       "       [-3000.,   500.,     0.],\n",
-       "       [-3000.,  1000.,     0.],\n",
-       "       [-2500., -1000.,     0.],\n",
-       "       [-2500.,  -500.,     0.],\n",
-       "       [-2500.,     0.,     0.],\n",
-       "       [-2500.,   500.,     0.],\n",
-       "       [-2500.,  1000.,     0.],\n",
-       "       [-2000., -1000.,     0.],\n",
-       "       [-2000.,  -500.,     0.],\n",
-       "       [-2000.,     0.,     0.],\n",
-       "       [-2000.,   500.,     0.],\n",
-       "       [-2000.,  1000.,     0.],\n",
-       "       [-1500., -1000.,     0.],\n",
-       "       [-1500.,  -500.,     0.],\n",
-       "       [-1500.,     0.,     0.],\n",
-       "       [-1500.,   500.,     0.],\n",
-       "       [-1500.,  1000.,     0.],\n",
-       "       [-1000., -1000.,     0.],\n",
-       "       [-1000.,  -500.,     0.],\n",
-       "       [-1000.,     0.,     0.],\n",
-       "       [-1000.,   500.,     0.],\n",
-       "       [-1000.,  1000.,     0.],\n",
-       "       [ -500., -1000.,     0.],\n",
-       "       [ -500.,  -500.,     0.],\n",
-       "       [ -500.,     0.,     0.],\n",
-       "       [ -500.,   500.,     0.],\n",
-       "       [ -500.,  1000.,     0.],\n",
-       "       [    0., -1000.,     0.],\n",
-       "       [    0.,  -500.,     0.],\n",
-       "       [    0.,     0.,     0.],\n",
-       "       [    0.,   500.,     0.],\n",
-       "       [    0.,  1000.,     0.],\n",
-       "       [  500., -1000.,     0.],\n",
-       "       [  500.,  -500.,     0.],\n",
-       "       [  500.,     0.,     0.],\n",
-       "       [  500.,   500.,     0.],\n",
-       "       [  500.,  1000.,     0.],\n",
-       "       [ 1000., -1000.,     0.],\n",
-       "       [ 1000.,  -500.,     0.],\n",
-       "       [ 1000.,     0.,     0.],\n",
-       "       [ 1000.,   500.,     0.],\n",
-       "       [ 1000.,  1000.,     0.],\n",
-       "       [ 1500., -1000.,     0.],\n",
-       "       [ 1500.,  -500.,     0.],\n",
-       "       [ 1500.,     0.,     0.],\n",
-       "       [ 1500.,   500.,     0.],\n",
-       "       [ 1500.,  1000.,     0.],\n",
-       "       [ 2000., -1000.,     0.],\n",
-       "       [ 2000.,  -500.,     0.],\n",
-       "       [ 2000.,     0.,     0.],\n",
-       "       [ 2000.,   500.,     0.],\n",
-       "       [ 2000.,  1000.,     0.],\n",
-       "       [ 2500., -1000.,     0.],\n",
-       "       [ 2500.,  -500.,     0.],\n",
-       "       [ 2500.,     0.,     0.],\n",
-       "       [ 2500.,   500.,     0.],\n",
-       "       [ 2500.,  1000.,     0.],\n",
-       "       [ 3000., -1000.,     0.],\n",
-       "       [ 3000.,  -500.,     0.],\n",
-       "       [ 3000.,     0.,     0.],\n",
-       "       [ 3000.,   500.,     0.],\n",
-       "       [ 3000.,  1000.,     0.]])"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rx_loc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(array([ 6.56710354]), array([ 58.13551072]))\n",
-      "(array([ 1.37802989]), array([-155.52240108]))\n"
-     ]
-    }
-   ],
-   "source": [
-    "ind = np.where(np.sum(np.power(rx_loc - np.array([-1000,0,elev]),2),axis=1)< 5)\n",
-    "def appResPhs(freq,z):\n",
-    "    app_res = ((1./(8e-7*np.pi**2))/freq)*np.abs(z)**2\n",
-    "    app_phs = np.arctan2(z.imag,z.real)*(180/np.pi)\n",
-    "    return app_res, app_phs\n",
-    "print appResPhs(freq,zyx[ind])\n",
-    "print appResPhs(freq,zxy[ind])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 14405.34243898+71568.97959157j] [ -1.45986245e+08 +2.02536180e+08j]\n",
-      "(array([ 1.08290485]), array([-155.59666352]))\n"
-     ]
-    }
-   ],
-   "source": [
-    "e0_1d = e0_1d.conj()\n",
-    "Qex = mesh1d.getInterpolationMat(np.array([elev]),'Ex')\n",
-    "Qfx = mesh1d.getInterpolationMat(np.array([elev]),'Fx')\n",
-    "h0_1dC = -(mesh1d.nodalGrad*e0_1d)/(1j*omega(freq)*mu_0)\n",
-    "h0_1d = mesh1d.getInterpolationMat(mesh1d.vectorNx,'Ex')*h0_1dC\n",
-    "indSur = np.where(mesh1d.vectorNx==elev)\n",
-    "\n",
-    "print (Qfx*e0_1d),(Qex*h0_1dC)#e0_1d, h0_1d\n",
-    "print appResPhs(freq,(Qfx*e0_1d)/(Qex*h0_1dC).conj())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import simpegMT as simpegmt\n",
-    "sig1D = M.r(sig,'CC','CC','M')[0,0,:]\n",
-    "anaEd, anaEu, anaHd, anaHu = simpegmt.Utils.MT1Danalytic.getEHfields(mesh1d,sig1D,freq,mesh1d.vectorNx)\n",
-    "anaEtemp = anaEd+anaEu\n",
-    "anaHtemp = anaHd+anaHu\n",
-    "# Scale the solution\n",
-    "anaE = (anaEtemp/anaEtemp[-1])#.conj()\n",
-    "anaH = (anaHtemp/anaEtemp[-1])#.conj()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.00019869+0.00019869j  0.00019869+0.00019869j  0.00019869+0.00019869j\n",
-      "  0.00019869+0.00019869j  0.00019869+0.00019869j  0.00019869+0.00019869j\n",
-      "  0.00019869+0.00019869j  0.00019869+0.00019869j  0.00019869+0.00019869j\n",
-      "  0.00019869+0.00019869j  0.00019869+0.00019869j  0.00019869+0.00019869j\n",
-      "  0.00019869+0.00027765j  0.00019869+0.00038029j  0.00019869+0.00051373j\n",
-      "  0.00019869+0.0006872j   0.00019869+0.00091271j  0.00019869+0.00120587j\n",
-      "  0.00019869+0.00158698j  0.00019869+0.00208242j  0.00019869+0.00272649j\n",
-      "  0.00019869+0.00356379j  0.00019870+0.00465228j]\n",
-      "(array([], dtype=float64), array([], dtype=float64))\n",
-      "(array([], dtype=float64), array([], dtype=float64))\n"
-     ]
-    }
-   ],
-   "source": [
-    "anaZ = anaE/anaH\n",
-    "indSur = np.where(mesh1d.vectorNx==elev)\n",
-    "print anaZ\n",
-    "print appResPhs(freq,anaZ[indSur])\n",
-    "print appResPhs(freq,-anaZ[indSur])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ -5.64053465e+04,  -4.26194973e+04,  -3.20149979e+04,\n",
-       "        -2.38576907e+04,  -1.75828390e+04,  -1.27560300e+04,\n",
-       "        -9.04310000e+03,  -6.18700000e+03,  -3.99000000e+03,\n",
-       "        -2.30000000e+03,  -1.00000000e+03,   7.27595761e-12,\n",
-       "         1.00000000e+03,   2.30000000e+03,   3.99000000e+03,\n",
-       "         6.18700000e+03,   9.04310000e+03,   1.27560300e+04,\n",
-       "         1.75828390e+04,   2.38576907e+04,   3.20149979e+04,\n",
-       "         4.26194973e+04,   5.64053465e+04])"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mesh1d.vectorNx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "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": "2.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/3D theroy overview-Copy0.ipynb b/notebooks/3D theroy overview-Copy0.ipynb
similarity index 100%
rename from 3D theroy overview-Copy0.ipynb
rename to notebooks/3D theroy overview-Copy0.ipynb
diff --git a/3D theroy overview.ipynb b/notebooks/3D theroy overview.ipynb
similarity index 100%
rename from 3D theroy overview.ipynb
rename to notebooks/3D theroy overview.ipynb
diff --git a/MT 1D code test.ipynb b/notebooks/MT 1D code test.ipynb
similarity index 100%
rename from MT 1D code test.ipynb
rename to notebooks/MT 1D code test.ipynb
diff --git a/MT 1D code_testLayered.ipynb b/notebooks/MT 1D code_testLayered.ipynb
similarity index 100%
rename from MT 1D code_testLayered.ipynb
rename to notebooks/MT 1D code_testLayered.ipynb
diff --git a/MT Script - 3D.ipynb b/notebooks/MT Script - 3D.ipynb
similarity index 100%
rename from MT Script - 3D.ipynb
rename to notebooks/MT Script - 3D.ipynb
diff --git a/MT Script - 3D_seogi.ipynb b/notebooks/MT Script - 3D_seogi.ipynb
similarity index 100%
rename from MT Script - 3D_seogi.ipynb
rename to notebooks/MT Script - 3D_seogi.ipynb
diff --git a/MT Script-3D_halfspace.ipynb b/notebooks/MT Script-3D_halfspace.ipynb
similarity index 100%
rename from MT Script-3D_halfspace.ipynb
rename to notebooks/MT Script-3D_halfspace.ipynb
diff --git a/MT Script-3D_layerTest-working.ipynb b/notebooks/MT Script-3D_layerTest-working.ipynb
similarity index 100%
rename from MT Script-3D_layerTest-working.ipynb
rename to notebooks/MT Script-3D_layerTest-working.ipynb
diff --git a/MT Script-3D_layerTest.ipynb b/notebooks/MT Script-3D_layerTest.ipynb
similarity index 100%
rename from MT Script-3D_layerTest.ipynb
rename to notebooks/MT Script-3D_layerTest.ipynb
diff --git a/notebooks/MT Script-3D_twoHalfspace-simpegMT.ipynb b/notebooks/MT Script-3D_twoHalfspace-simpegMT.ipynb
new file mode 100644
index 00000000..4230295b
--- /dev/null
+++ b/notebooks/MT Script-3D_twoHalfspace-simpegMT.ipynb	
@@ -0,0 +1,2980 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import SimPEG as simpeg\n",
+    "from scipy.constants import mu_0\n",
+    "def omega(freq):\n",
+    "    \"\"\"Change frequency to angular frequency, omega\"\"\"\n",
+    "    return 2.*np.pi*freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2529.536000000001"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(100*np.cumprod(np.ones(5)*1.6))\n",
+    "\n",
+    "   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# M = Mesh.TensorMesh([[(100.,32)],[(100.,34)],[(100.,18)]], x0='CCC')\n",
+    "# M = simpeg.Mesh.TensorMesh([[(100,5,-1.5),(100.,5),(100,5,1.5)],[(100,5,-1.5),(100.,5),(100,5,1.5)],[(100,5,-1.5),(100.,10),(100,5,1.5)]], x0=['C','C','C'])\n",
+    "M = simpeg.Mesh.TensorMesh([[(1000,6,-1.5),(1000.,6),(1000,6,1.5)],[(1000,6,-1.5),(1000.,2),(1000,6,1.5)],[(1000,10,-1.3),(1000.,2),(1000,10,1.3)]], x0=['C','C','C'])# Setup the model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ -5.64053465e+04  -4.26194973e+04  -3.20149979e+04  -2.38576907e+04\n",
+      "  -1.75828390e+04  -1.27560300e+04  -9.04310000e+03  -6.18700000e+03\n",
+      "  -3.99000000e+03  -2.30000000e+03  -1.00000000e+03   7.27595761e-12\n",
+      "   1.00000000e+03   2.30000000e+03   3.99000000e+03   6.18700000e+03\n",
+      "   9.04310000e+03   1.27560300e+04   1.75828390e+04   2.38576907e+04\n",
+      "   3.20149979e+04   4.26194973e+04   5.64053465e+04]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print M.vectorNz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar instance at 0x7fda385d5c20>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": [
+       "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEKCAYAAAD3tSVSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
+       "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl4FeX1wPHvySrIDhoWQZCiZd8EVGrBBUStKBUBrUAV\n",
+       "64KKVftT0VZCFxVbtVqXSivWBURcEBFRUAmKCigCgoCAEEhCghDWiCwJ5/fHTOAm3DW5u+fzPPdh\n",
+       "7sw7c8+5N5w79513ZkRVMcYYk5xSYh2AMcaYyLEib4wxScyKvDHGJDEr8sYYk8SsyBtjTBKzIm+M\n",
+       "MUnMirwxxiQxK/ImoYnIVSLypYjsFZEtIvKuiPR2l50qIq+JyDYR2SUiy0XkdhGxv3vzk2F/7CZh\n",
+       "icgdwGPAX4ETgebAU8BAEWkNLAI2AR1UtR5wBdAdqB2biI2JPrEzXk0iEpG6QD7wW1V9w8vyl4G6\n",
+       "qnpJ1IMzJo7YnrxJVGcCxwHTfSw/D3g9euEYE5+syJtE1RDYrqqH/SwvjGI8xsQlK/ImURUDjfwc\n",
+       "RC0GmkYxHmPikhV5k6g+Bw4Ag3ws/wC4PHrhGBOfrMibhKSqu4H7gadE5FIRqSki6SJyoYhMAMYB\n",
+       "Z4nIwyKSBSAiPxORl9yDtsb8JFiRNwlLVR8F7gD+CHwPbAZGA9NVdQPOwdmWwDcisgvnQOwXwN6Y\n",
+       "BGxMDNgQSmOMiSIRGQD8E0gF/quqE7y0eQK4ENiHM0x4aVVfz/bkjTEmSkQkFXgSGAC0A64UkbaV\n",
+       "2lwE/ExV2wDXA89U5zWtyBtjTPT0BNaraq6qHgKmApdWajMQeAFAVRcB9cqPK1WFFXljjImeZkCe\n",
+       "x/N8d16gNidV9QWtyBtjTPQEexBUqrjeMdKqumK8EBE7cmyMCZqqVi6gIQm15lR6vQKcC+mVa46z\n",
+       "p46fNie586ok4Yu8IzvWAfgwDzgn5LVUxwEgMj7M8XiqWmxVVZ4Tx+6heCUi2aqaHbGAqqFSbOX/\n",
+       "4atVOMIlTO9bRHKKg89URcKT0l+DbPfHY2d9CbQRkZbAFmAocGWlNm8DtwBTReQMYJeqbq1iqMlS\n",
+       "5I0xJnrSq7ieqpaKyC3A+zhDKJ9T1dUicoO7/FlVfVdELhKR9cAPwDXVidWKvDHGhKg6hVNVZwOz\n",
+       "K817ttLzW6rxEhVYkY+olrEOwI+WsQ4gkJxYB+BHTqwD8CMn1gH4kRPrAMKlRqwDCIEV+YhqFesA\n",
+       "/Ijn2EBVc2Idgy8WW9XEc2yhqmp3TSxYkTfGmBAlUuFMpFiNMSYu2J68McYksUQqnIkUqzHGxIVE\n",
+       "2pP3e1kDETlORBaJyDIRWSUiD7rzG4jIXBFZKyJzRKSexzpjRWSdiKwRkf4e87uLyAp32eMe8zNF\n",
+       "5FV3/kIROdlj2Uj3NdaKyIjwpm6MMVWTHuQjHvgt8qq6HzhHVbsAnYBzROQXwD3AXFU9FfjQfY6I\n",
+       "tMM5g6sdzqU0n5ajp5g9A4xyL5/Zxr2mMsAooNid/xgwwd1WA5w7//R0H+M8v0yMMSZWagT5iAcB\n",
+       "L1CmqvvcyQycM7R24nEpTPffy9zpS4FXVPWQquYC64FeItIEqK2qi912L3qs47mtN4Dz3OkLgDmq\n",
+       "uktVdwFzcb44jDEmptKCfMSDgEVeRFJEZBmwFZinqt8AWR7XUtgKlF/ruCkVL7ZTfhnNyvMLOHp5\n",
+       "zSOX1VTVUmC3iDT0sy1jjImpROquCfhlo6qHgS7uzY/fF5FzKi1XuxKkMeanJF720oMRdKyqultE\n",
+       "ZgHdga0i0lhVi9yumO/dZt4ukZnvzj/Jy/zydVoAW0QkDairqsUiUgD09VinOfCR9+jmeUy3JN7P\n",
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fda38893e90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "O50AAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fda38893e50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Setup the model\n",
+    "# conds = [1,1e-2]\n",
+    "# elev = 300\n",
+    "# sig = simpeg.Utils.ModelBuilder.defineBlock(M.gridCC,[-100000,-100000,-200],[100000,100000,0],conds)\n",
+    "# sig[M.gridCC[:,2]>elev] = 1e-8\n",
+    "# sig[M.gridCC[:,2]<-500] = 1e-1\n",
+    "# sig[M.gridCC[:,2]<-900] = 1e-2\n",
+    "elev=0\n",
+    "conds = [1,1e-2]\n",
+    "sig = np.ones(M.nC)*conds[0]\n",
+    "sig[M.gridCC[:,0]>0] = conds[1]\n",
+    "sig[M.gridCC[:,2]>elev] = 1e-8\n",
+    "# sigBG = np.zeros(M.nC) + conds[0]\n",
+    "# sigBG[M.gridCC[:,2]>0] = 1e-8\n",
+    "sigBG = np.ones(M.nC)*conds[0]\n",
+    "sigBG[M.gridCC[:,2]>elev] = 1e-8\n",
+    "colorbar(M.plotImage(log10(sig)))\n",
+    "colorbar(M.plotImage(log10(sigBG)))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Get the mass matrix \n",
+    "# The model\n",
+    "Msig = M.getEdgeInnerProduct(sig)\n",
+    "MsigBG = M.getEdgeInnerProduct(sigBG)\n",
+    "Mmu = M.getFaceInnerProduct(mu_0, invProp=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "freq = 1.0\n",
+    "C = M.edgeCurl\n",
+    "A = C.T*Mmu*C - 1j*omega(freq)*Msig\n",
+    "ARH = -(C.T*Mmu*C - 1j*omega(freq)*MsigBG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2.46 s, sys: 24 ms, total: 2.49 s\n",
+      "Wall time: 2.48 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# Solve the systems for each polarization\n",
+    "sys.path.append('/media/gudni/ExtraDrive1/Codes/python/pymatsolver/')\n",
+    "from pymatsolver import MumpsSolver\n",
+    "Ainv = MumpsSolver(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Need to solve x and y polarizations of the source.\n",
+    "from simpegMT.Utils import get1DEfields\n",
+    "# Get a 1d solution for a halfspace background\n",
+    "mesh1d = simpeg.Mesh.TensorMesh([M.hz],np.array([M.x0[2]]))\n",
+    "e0_1d = get1DEfields(mesh1d,M.r(sigBG,'CC','CC','M')[0,0,:],freq,sourceAmp=None).conj()\n",
+    "# Setup the primary field (p) for the x (east) polarization (_px)\n",
+    "ex_px = np.zeros((M.vnEx),dtype=complex)\n",
+    "ey_px = np.zeros(M.nEy,dtype=complex)\n",
+    "ez_px = np.zeros(M.nEz,dtype=complex)\n",
+    "# Assign the source to ex_x\n",
+    "for i in arange(M.vnEx[0]):\n",
+    "    for j in arange(M.vnEx[1]):\n",
+    "        ex_px[i,j,:] = -e0_1d\n",
+    "ep_px = np.r_[simpeg.Utils.mkvc(ex_px),ey_px,ez_px]\n",
+    "rhs_px = ARH.dot(ep_px)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Setup y (north) polarization (_y)\n",
+    "ex_py = np.zeros(M.nEx, dtype='complex128')\n",
+    "ey_py = np.zeros((M.vnEy), dtype='complex128')\n",
+    "ez_py = np.zeros(M.nEz, dtype='complex128')\n",
+    "# Assign the source to ey_y\n",
+    "for i in arange(M.vnEy[0]):\n",
+    "    for j in arange(M.vnEy[1]):\n",
+    "        ey_py[i,j,:] = e0_1d  \n",
+    "        \n",
+    "ep_py = np.r_[ex_py,simpeg.Utils.mkvc(ey_py),ez_py]\n",
+    "rhs_py = ARH.dot(ep_py)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are using splu in scipy package. This is bit slow, but on the cluster you can use mumps, which might a lot faster. We can think about having better iterative solver. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 42.8 ms, sys: 0 ns, total: 42.8 ms\n",
+      "Wall time: 43.6 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "es_px = Ainv*rhs_px\n",
+    "es_py = Ainv*rhs_py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Need to sum the ep and es to get the total field.\n",
+    "e_x = es_px #+ ep_px\n",
+    "e_y = es_py #+ ep_py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I want to visualize electrical field, which is a vector, so I average them on to cell center. Also I want to see current density ($\\vec{j} = \\sigma \\vec{e}$)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Meinv = M.getEdgeInnerProduct(np.ones_like(sig), invMat=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "j_x = Meinv*Msig*e_x\n",
+    "j_y = Meinv*Msig*e_x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(18598,)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "e_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e_x_CC = M.aveE2CCV*e_x\n",
+    "e_y_CC = M.aveE2CCV*e_y\n",
+    "j_x_CC = M.aveE2CCV*j_x\n",
+    "j_y_CC = M.aveE2CCV*j_y\n",
+    "# j_x_CC = Utils.sdiag(np.r_[sig, sig, sig])*e_x_CC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then use \"plotSlice\" function, to visualize 2D sections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
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+       "VjEAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fda2780a550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
+    "dat0 = M.plotSlice(np.log10(np.abs(e_x_CC)), vType='CCv', view='vec', streamOpts={'color': 'k'}, normal='Y', ax = ax[0])\n",
+    "cb0 = plt.colorbar(dat0[0], ax = ax[0])\n",
+    "dat1 = M.plotSlice(np.log10(np.abs(e_y_CC)), vType='CCv', view='vec', streamOpts={'color': 'k'}, normal='Y', ax = ax[1])\n",
+    "cb1 = plt.colorbar(dat1[0], ax = ax[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Is it reasonable?: Based on that you put resistive target that makes sense to me; current does not want to flow on resistive target so they just do roundabout:). And see air interface. It is continuous on current but not on electric field, which looks reasonable. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the data\n",
+    "rx_x, rx_y = np.meshgrid(np.arange(-3000,3001,500),np.arange(-1000,1001,500))\n",
+    "rx_loc = np.hstack((simpeg.Utils.mkvc(rx_x,2),simpeg.Utils.mkvc(rx_y,2),elev+np.zeros((np.prod(rx_x.shape),1))))\n",
+    "# Get the projection matrices\n",
+    "Qex = M.getInterpolationMat(rx_loc,'Ex')\n",
+    "Qey = M.getInterpolationMat(rx_loc,'Ey')\n",
+    "Qez = M.getInterpolationMat(rx_loc,'Ez')\n",
+    "Qfx = M.getInterpolationMat(rx_loc,'Fx')\n",
+    "Qfy = M.getInterpolationMat(rx_loc,'Fy')\n",
+    "Qfz = M.getInterpolationMat(rx_loc,'Fz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e_x_loc = np.hstack([simpeg.Utils.mkvc(Qex*e_x,2),simpeg.Utils.mkvc(Qey*e_x,2),simpeg.Utils.mkvc(Qez*e_x,2)])\n",
+    "e_y_loc = np.hstack([simpeg.Utils.mkvc(Qex*e_y,2),simpeg.Utils.mkvc(Qey*e_y,2),simpeg.Utils.mkvc(Qez*e_y,2)])\n",
+    "Ciw = -C/(1j*omega(freq)*mu_0)\n",
+    "h_x_loc = np.hstack([simpeg.Utils.mkvc(Qfx*Ciw*e_x,2),simpeg.Utils.mkvc(Qfy*Ciw*e_x,2),simpeg.Utils.mkvc(Qfz*Ciw*e_x,2)])\n",
+    "h_y_loc = np.hstack([simpeg.Utils.mkvc(Qfx*Ciw*e_y,2),simpeg.Utils.mkvc(Qfy*Ciw*e_y,2),simpeg.Utils.mkvc(Qfz*Ciw*e_y,2)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Make a combined matrix\n",
+    "dt = np.dtype([('ex1',complex),('ey1',complex),('ez1',complex),('hx1',complex),('hy1',complex),('hz1',complex),('ex2',complex),('ey2',complex),('ez2',complex),('hx2',complex),('hy2',complex),('hz2',complex)])\n",
+    "combMat = np.empty((len(e_x_loc)),dtype=dt)\n",
+    "combMat['ex1'] = e_x_loc[:,0]\n",
+    "combMat['ey1'] = e_x_loc[:,1]\n",
+    "combMat['ez1'] = e_x_loc[:,2]\n",
+    "combMat['ex2'] = e_y_loc[:,0]\n",
+    "combMat['ey2'] = e_y_loc[:,1]\n",
+    "combMat['ez2'] = e_y_loc[:,2]\n",
+    "combMat['hx1'] = h_x_loc[:,0]\n",
+    "combMat['hy1'] = h_x_loc[:,1]\n",
+    "combMat['hz1'] = h_x_loc[:,2]\n",
+    "combMat['hx2'] = h_y_loc[:,0]\n",
+    "combMat['hy2'] = h_y_loc[:,1]\n",
+    "combMat['hz2'] = h_y_loc[:,2]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def calculateImpedance(fieldsData):\n",
+    "    ''' \n",
+    "    Function that calculates MT impedance data from a rec array with E and H field data from both polarizations\n",
+    "    '''\n",
+    "    zxx = (fieldsData['ex1']*fieldsData['hy2'] - fieldsData['ex2']*fieldsData['hy1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
+    "    zxy = (-fieldsData['ex1']*fieldsData['hx2'] + fieldsData['ex2']*fieldsData['hx1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
+    "    zyx = (fieldsData['ey1']*fieldsData['hy2'] - fieldsData['ey2']*fieldsData['hy1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
+    "    zyy = (-fieldsData['ey1']*fieldsData['hx2'] + fieldsData['ey2']*fieldsData['hx1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
+    "    return zxx, zxy, zyx, zyy\n",
+    "\n",
+    "zxx, zxy, zyx, zyy = calculateImpedance(combMat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# rx_loc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(array([ 2.58052711]), array([ 41.12190413]))\n",
+      "(array([ 1.49556255]), array([-154.79729349]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "ind = np.where(np.sum(np.power(rx_loc - np.array([-3000,0,elev]),2),axis=1)< 5)\n",
+    "def appResPhs(freq,z):\n",
+    "    app_res = ((1./(8e-7*np.pi**2))/freq)*np.abs(z)**2\n",
+    "    app_phs = np.arctan2(z.imag,z.real)*(180/np.pi)\n",
+    "    return app_res, app_phs\n",
+    "print appResPhs(freq,zyx[ind])\n",
+    "print appResPhs(freq,zxy[ind])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  1.48260668e+48 +2.81495302e+48j] [  4.12341913e+49 +9.25329081e+50j]\n",
+      "(array([ 1.49426011]), array([ 149.6731804]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "e0_1d = e0_1d.conj()\n",
+    "Qex = mesh1d.getInterpolationMat(np.array([elev]),'Ex')\n",
+    "Qfx = mesh1d.getInterpolationMat(np.array([elev]),'Fx')\n",
+    "h0_1dC = -(mesh1d.nodalGrad*e0_1d)/(1j*omega(freq)*mu_0)\n",
+    "h0_1d = mesh1d.getInterpolationMat(mesh1d.vectorNx,'Ex')*h0_1dC\n",
+    "indSur = np.where(mesh1d.vectorNx==elev)\n",
+    "\n",
+    "print (Qfx*e0_1d),(Qex*h0_1dC)#e0_1d, h0_1d\n",
+    "print appResPhs(freq,(Qfx*e0_1d)/(Qex*h0_1dC).conj())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import simpegMT as simpegmt\n",
+    "sig1D = M.r(sig,'CC','CC','M')[0,0,:]\n",
+    "anaEd, anaEu, anaHd, anaHu = simpegmt.Utils.MT1Danalytic.getEHfields(mesh1d,sig1D,freq,mesh1d.vectorNx)\n",
+    "anaEtemp = anaEd+anaEu\n",
+    "anaHtemp = anaHd+anaHu\n",
+    "# Scale the solution\n",
+    "anaE = (anaEtemp/anaEtemp[-1])#.conj()\n",
+    "anaH = (anaHtemp/anaEtemp[-1])#.conj()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.00198692+0.00198692j  0.00198692+0.00198692j  0.00198692+0.00198692j\n",
+      "  0.00198692+0.00198692j  0.00198692+0.00198692j  0.00198692+0.00198692j\n",
+      "  0.00198692+0.00198692j  0.00198692+0.00198692j  0.00198692+0.00198692j\n",
+      "  0.00198692+0.00198692j  0.00198692+0.00198692j  0.00198692+0.00198692j\n",
+      "  0.00198692+0.0098826j   0.00198692+0.02014699j  0.00198693+0.03349069j\n",
+      "  0.00198697+0.05083751j  0.00198708+0.07338836j  0.00198737+0.10270447j\n",
+      "  0.00198810+0.14081541j  0.00198983+0.19035962j  0.00199390+0.25476708j\n",
+      "  0.00200329+0.33849678j  0.00202471+0.44734538j]\n",
+      "(array([], dtype=float64), array([], dtype=float64))\n",
+      "(array([], dtype=float64), array([], dtype=float64))\n"
+     ]
+    }
+   ],
+   "source": [
+    "anaZ = anaE/anaH\n",
+    "indSur = np.where(mesh1d.vectorNx==elev)\n",
+    "print anaZ\n",
+    "print appResPhs(freq,anaZ[indSur])\n",
+    "print appResPhs(freq,-anaZ[indSur])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ -5.64053465e+04,  -4.26194973e+04,  -3.20149979e+04,\n",
+       "        -2.38576907e+04,  -1.75828390e+04,  -1.27560300e+04,\n",
+       "        -9.04310000e+03,  -6.18700000e+03,  -3.99000000e+03,\n",
+       "        -2.30000000e+03,  -1.00000000e+03,   7.27595761e-12,\n",
+       "         1.00000000e+03,   2.30000000e+03,   3.99000000e+03,\n",
+       "         6.18700000e+03,   9.04310000e+03,   1.27560300e+04,\n",
+       "         1.75828390e+04,   2.38576907e+04,   3.20149979e+04,\n",
+       "         4.26194973e+04,   5.64053465e+04])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mesh1d.vectorNx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "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": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/MT1DfwdProblem.ipynb b/notebooks/MT1DfwdProblem.ipynb
similarity index 100%
rename from MT1DfwdProblem.ipynb
rename to notebooks/MT1DfwdProblem.ipynb
diff --git a/MTanalytic1D_layerIssue.ipynb b/notebooks/MTanalytic1D_layerIssue.ipynb
similarity index 100%
rename from MTanalytic1D_layerIssue.ipynb
rename to notebooks/MTanalytic1D_layerIssue.ipynb
diff --git a/MTanayltic1Dtest-Copy1.ipynb b/notebooks/MTanayltic1Dtest-Copy1.ipynb
similarity index 100%
rename from MTanayltic1Dtest-Copy1.ipynb
rename to notebooks/MTanayltic1Dtest-Copy1.ipynb
diff --git a/MTanayltic1Dtest.ipynb b/notebooks/MTanayltic1Dtest.ipynb
similarity index 100%
rename from MTanayltic1Dtest.ipynb
rename to notebooks/MTanayltic1Dtest.ipynb
diff --git a/simpegMT/ProblemMT.py b/simpegMT/ProblemMT.py
index 92f1791f..34ed5fc9 100644
--- a/simpegMT/ProblemMT.py
+++ b/simpegMT/ProblemMT.py
@@ -1,8 +1,8 @@
 from SimPEG import Survey, Problem, Utils, Models, np, sp, SolverLU as SimpegSolver
 from simpegEM.FDEM import BaseFDEMProblem
-from simpegEM.Utils import omega
+from simpegEM.Utils.EMUtils import omega
 from scipy.constants import mu_0
-from SurveyMT import SurveyMT, FieldsMT
+from SurveyMT import SurveyMT, FieldsMT, DataMT
 import multiprocessing, sys, time
 
 
@@ -14,7 +14,7 @@ class BaseMTProblem(BaseFDEMProblem):
 
 
     surveyPair = SurveyMT
-    dataPair = Survey.Data
+    dataPair = DataMT
 
     Solver = SimpegSolver
     solverOpts = {}
@@ -25,14 +25,6 @@ class BaseMTProblem(BaseFDEMProblem):
     # Might need to add more stuff here.
 
 
-    @property
-    def MeSigmaBG(self):
-        #TODO: hardcoded to sigma as the model
-        if getattr(self, '_MeSigmaBG', None) is None:
-            sigmaBG = self.backModel
-            self._MeSigmaBG = self.mesh.getEdgeInnerProduct(sigmaBG)
-        return self._MeSigmaBG
-
     
 class ProblemMT_eForm_ps(BaseMTProblem):
     """ 
@@ -44,6 +36,7 @@ class ProblemMT_eForm_ps(BaseMTProblem):
     _fieldType = 'e'
     _eqLocs    = 'FE'
     fieldsPair = FieldsMT
+
     # Set new properties 
     # Background model
     @property
@@ -62,6 +55,15 @@ class ProblemMT_eForm_ps(BaseMTProblem):
             if hasattr(self, prop):
                 delattr(self, prop)
 
+    @property
+    def MeDeltaSigma(self):
+        #TODO: hardcoded to sigma as the model
+        if getattr(self, '_MeDeltaSigma', None) is None:
+            sigma = self.curModel
+            sigmaBG = self.backModel
+            self._MeDeltaSigma = self.mesh.getEdgeInnerProduct(sigma - sigmaBG)
+        return self._MeDeltaSigma
+
     def __init__(self, mesh, **kwargs):
         BaseMTProblem.__init__(self, mesh, **kwargs)
 
@@ -104,8 +106,136 @@ class ProblemMT_eForm_ps(BaseMTProblem):
         # Get the background electric fields
         from simpegMT.Sources import homo1DModelSource
         eBG_bp = homo1DModelSource(self.mesh,freq,backSigma)
-        deltM = self.curModel - self.backModel
-        Abg = -1j*omega(freq)*deltM*eBG_bp
+        deltM = self.MeDeltaSigma
+        Abg = -1j*omega(freq)*deltM
+
+        return Abg*eBG_bp, eBG_bp
+
+    def getRHSderiv(self, freq, backSigma, u, v, adjoint=False):
+        raise NotImplementedError('getRHSDeriv not implemented yet')
+        return None
+
+    def fields(self, m, m_back):
+        '''
+        Function to calculate all the fields for the model m.
+
+        :param np.ndarray (nC,) m: Conductivity model
+        :param np.ndarray (nC,) m_back: Background conductivity model
+        '''
+        self.curModel = m
+        self.backModel = m_back
+        # RHS, CalcFields = self.getRHS(freq,m_back), self.calcFields
+
+        F = FieldsMT(self.mesh, self.survey)
+        for freq in self.survey.freqs:
+            if self.verbose:
+                startTime = time.time()
+                print 'Starting work for {:.3e}'.format(freq)
+                sys.stdout.flush()
+            A = self.getA(freq)
+            rhs, e_p = self.getRHS(freq,m_back)
+            Ainv = self.Solver(A, **self.solverOpts)
+            e_s = Ainv * rhs 
+            e = e_p + e_s
+            # Store the fields
+            Src = self.survey.getSources(freq)
+            # Store the fieldss
+            F[Src, 'e_px'] = e[:,0]
+            F[Src, 'e_py'] = e[:,1]
+            # Note curl e = -iwb so b = -curl/iw
+            b = -( self.mesh.edgeCurl * e )/( 1j*omega(freq) )
+            F[Src, 'b_px'] = b[:,0]
+            F[Src, 'b_py'] = b[:,1]
+            if self.verbose:
+                print 'Ran for {:f} seconds'.format(time.time()-startTime)
+                sys.stdout.flush()
+        return F
+        
+class ProblemMT_eForm_Tp(BaseMTProblem):
+    """ 
+    A MT problem solving a e formulation and a primary/secondary fields decompostion.
+
+    Solves the equation
+    """
+
+    _fieldType = 'e'
+    _eqLocs    = 'FE'
+    fieldsPair = FieldsMT
+
+    # Set new properties 
+    # Background model
+    @property
+    def backModel(self):
+        """
+            Sets the model, and removes dependent mass matrices.
+        """
+        return getattr(self, '_backModel', None)
+
+    @backModel.setter
+    def backModel(self, value):
+        if value is self.backModel:
+            return # it is the same!
+        self._backModel = Models.Model(value, self.mapping)
+        for prop in self.deleteTheseOnModelUpdate:
+            if hasattr(self, prop):
+                delattr(self, prop)
+
+    @property
+    def MeSigmaBack(self):
+        #TODO: hardcoded to sigma as the model
+        if getattr(self, '_MeSigmaBack', None) is None:
+            sigma = self.curModel
+            sigmaBG = self.backModel
+            self._MeSigmaBack = self.mesh.getEdgeInnerProduct(sigmaBG)
+        return self._MeSigmaBack
+
+    def __init__(self, mesh, **kwargs):
+        BaseMTProblem.__init__(self, mesh, **kwargs)
+
+    def getA(self, freq):
+        """
+            Function to get the A matrix.
+
+            :param float freq: Frequency
+            :rtype: scipy.sparse.csr_matrix
+            :return: A
+        """
+        mui = self.MfMui
+        sig = self.MeSigma
+        C = self.mesh.edgeCurl
+
+        return C.T*mui*C + 1j*omega(freq)*sig
+
+    def getADeriv(self, freq, u, v, adjoint=False):
+        sig = self.curTModel
+        dsig_dm = self.curTModelDeriv
+        dMe_dsig = self.mesh.getEdgeInnerProductDeriv(sig, v=u)
+
+        if adjoint:
+            return 1j * omega(freq) * ( dsig_dm.T * ( dMe_dsig.T * v ) )
+
+        return 1j * omega(freq) * ( dMe_dsig * ( dsig_dm * v ) )
+
+    def getRHS(self, freq, backSigma):
+        """
+            Function to return the right hand side for the system.
+            :param float freq: Frequency
+            :param numpy.ndarray (nC,) backSigma: Background conductivity model
+            :rtype: numpy.ndarray (nE, 2)
+            :return: one RHS for both polarizations
+        """
+        # Get sources for the frequency
+        src = self.survey.getSources(freq)
+        # Make sure that there is 2 polarizations. 
+        # assert len()
+        # Get the background electric fields
+        from simpegMT.Sources import homo1DModelSource
+        eBG_bp = homo1DModelSource(self.mesh,freq,backSigma)
+        MeBack = self.MeSigmaBack
+        # Set up the A system
+        mui = self.MfMui
+        C = self.mesh.edgeCurl
+        Abg = C.T*mui*C + 1j*omega(freq)*MeBack
 
         return Abg*eBG_bp, eBG_bp
     def getRHSderiv(self, freq, backSigma, u, v, adjoint=False):
@@ -124,28 +254,27 @@ class ProblemMT_eForm_ps(BaseMTProblem):
         # RHS, CalcFields = self.getRHS(freq,m_back), self.calcFields
 
         F = FieldsMT(self.mesh, self.survey)
-
-        if verbose:
-            startTime = time.time()
-            print 'Starting work for {:.3e}'.format(freq)
-            sys.stdout.flush()
-        A = self.getA(freq)
-        rhs, e_p = self.getRHS(freq,m_back)
-        Ainv = self.Solver(A, **self.solverOpts)
-        e_s = Ainv * rhs 
-        e = e_p + e_s
-        # Store the fields
-        Src = self.survey.getSources(freq)
-        # Store the fieldss
-        F[Src, 'e_px'] = e[:,0]
-        F[Src, 'e_py'] = e[:,1]
-        # Note curl e = -iwb so b = -curl/iw
-        b = -( self.mesh.edgeCurl * e )/( 1j*omega(freq) )
-        F[Src, 'b_px'] = b[:,0]
-        F[Src, 'b_py'] = b[:,1]
-        if verbose:
-            print 'Ran for {:f} seconds'.format(time.time()-startTime)
-            sys.stdout.flush()
+        for freq in self.survey.freqs:
+            if self.verbose:
+                startTime = time.time()
+                print 'Starting work for {:.3e}'.format(freq)
+                sys.stdout.flush()
+            A = self.getA(freq)
+            rhs, e_p = self.getRHS(freq,m_back)
+            Ainv = self.Solver(A, **self.solverOpts)
+            e_s = Ainv * rhs 
+            e = e_p + e_s
+            # Store the fields
+            Src = self.survey.getSources(freq)
+            # Store the fieldss
+            F[Src, 'e_px'] = e[:,0]
+            F[Src, 'e_py'] = e[:,1]
+            # Note curl e = -iwb so b = -curl/iw
+            b = -( self.mesh.edgeCurl * e )/( 1j*omega(freq) )
+            F[Src, 'b_px'] = b[:,0]
+            F[Src, 'b_py'] = b[:,1]
+            if self.verbose:
+                print 'Ran for {:f} seconds'.format(time.time()-startTime)
+                sys.stdout.flush()
         return F
         
-
diff --git a/simpegMT/SurveyMT.py b/simpegMT/SurveyMT.py
index b2797c62..47d6103c 100644
--- a/simpegMT/SurveyMT.py
+++ b/simpegMT/SurveyMT.py
@@ -244,10 +244,10 @@ class DataMT(Survey.Data):
             tArrRec = np.concatenate((src.freq*np.ones((locs.shape[0],1)),locs,np.nan*np.ones((locs.shape[0],8))),axis=1).view(dtRI)
             # np.array([(src.freq,rx.locs[0,0],rx.locs[0,1],rx.locs[0,2],np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ) for rx in src.rxList],dtype=dtRI)
             # Get the type and the value for the DataMT object as a list
-            typeList = [[rx.rxType,self[src,rx][0]] for rx in src.rxList]
+            typeList = [[rx.rxType,self[src,rx]] for rx in src.rxList]
             # Insert the values to the temp array
             for nr,(key,val) in enumerate(typeList):
-                tArrRec[key] = val
+                tArrRec[key] = mkvc(val,2)
             # Masked array 
             mArrRec = np.ma.MaskedArray(rec2ndarr(tArrRec),mask=np.isnan(rec2ndarr(tArrRec))).view(dtype=tArrRec.dtype)
             # Unique freq and loc of the masked array