diff --git a/.gitignore b/.gitignore
index aef7f0e8..1d24ca05 100644
--- a/.gitignore
+++ b/.gitignore
@@ -42,3 +42,249 @@ nosetests.xml
 *.sublime-workspace
 docs/_build/
 *.ipynb_checkpoints
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diff --git a/.travis.yml b/.travis.yml
index b02a7708..310a44e3 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,8 +1,10 @@
 language: python
 python:
-  - "2.7"
-virtualenv:
-  system_site_packages: true
+  - 2.7
+# virtualenv:
+#   system_site_packages: true
+
+sudo: false
 
 # Setup anaconda
 before_install:
@@ -12,12 +14,12 @@ before_install:
   - export PATH=/home/travis/anaconda/bin:/home/travis/miniconda/bin:$PATH
   - conda update --yes conda
   # The next couple lines fix a crash with multiprocessing on Travis and are not specific to using Miniconda
-  - sudo rm -rf /dev/shm
-  - sudo ln -s /run/shm /dev/shm
+  # - sudo rm -rf /dev/shm
+  # - sudo ln -s /run/shm /dev/shm
 
 # Install packages
 install:
-  - conda install --yes pip python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib cython
+  - conda install --yes pip python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib cython gdal
   - pip install nose-cov python-coveralls
   - cd ../
   - git clone https://github.com/simpeg/simpeg.git
@@ -26,6 +28,12 @@ install:
   - cd ../
   - echo export PYTHONPATH=$PYTHONPATH:/home/travis/build/simpeg/simpeg >> .bashrc
   - source .bashrc
+  - git clone https://github.com/simpeg/simpegem.git
+  - cd simpegem/
+  - python setup.py build_ext --inplace
+  - cd ../
+  - echo export PYTHONPATH=$PYTHONPATH:/home/travis/build/simpeg/simpegem >> .bashrc
+  - source .bashrc
   - cd simpegmt
 
 # Run test
@@ -40,3 +48,4 @@ notifications:
   email:
     - rowanc1@gmail.com
     - gkrosen@gmail.com
+    - lindseyheagy@gmail.com
diff --git a/MT Script-3D_layerTest.ipynb b/MT Script-3D_layerTest.ipynb
deleted file mode 100644
index 4916c56f..00000000
--- a/MT Script-3D_layerTest.ipynb	
+++ /dev/null
@@ -1,1421 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:10cac7f43ce75cbe563e4200a1f752d3ab5dff458ffd49c1559eac07e93f8e3c"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Efficiency Warning: Interpolation will be slow, use setup.py!\n",
-        "\n",
-        "            python setup.py build_ext --inplace\n",
-        "    \n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Populating the interactive namespace from numpy and matplotlib\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "np.sum(100*np.cumprod(np.ones(5)*1.6))\n",
-      "\n",
-      "   "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "2529.536000000001"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# M = Mesh.TensorMesh([[(100.,32)],[(100.,34)],[(100.,18)]], x0='CCC')\n",
-      "M = simpeg.Mesh.TensorMesh([[(100,5,-1.5),(100.,10),(100,5,1.5)],[(100,5,-1.5),(100.,10),(100,5,1.5)],[(100,5,1.6),(100.,10),(100,3,2)]], x0=['C','C',-3529.5360])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print M.vectorNz"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[ -3.52953600e+03  -3.36953600e+03  -3.11353600e+03  -2.70393600e+03\n",
-        "  -2.04857600e+03  -1.00000000e+03  -9.00000000e+02  -8.00000000e+02\n",
-        "  -7.00000000e+02  -6.00000000e+02  -5.00000000e+02  -4.00000000e+02\n",
-        "  -3.00000000e+02  -2.00000000e+02  -1.00000000e+02   4.54747351e-13\n",
-        "   2.00000000e+02   6.00000000e+02   1.40000000e+03]\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Setup the model\n",
-      "conds = [1e-2,1]\n",
-      "sig = simpeg.Utils.ModelBuilder.defineBlock(M.gridCC,[-1000,-1000,-400],[1000,1000,-200],conds)\n",
-      "sig[M.gridCC[:,2]>0] = 1e-8\n",
-      "sig[M.gridCC[:,2]<-600] = 1e-1\n",
-      "sigBG = np.zeros(M.nC) + conds[0]\n",
-      "sigBG[M.gridCC[:,2]>0] = 1e-8\n",
-      "colorbar(M.plotImage(log10(sig)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "<matplotlib.colorbar.Colorbar instance at 0x7f24725c9368>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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XwyFOYRCRDBHJEZHF7tQvQLuTROQdEVklIitF5GyvdsWskFfWwjzo2N2ZnzoH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\nmeQAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f24727970d0>"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 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)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "freq = 1e1\n",
-      "C = M.edgeCurl\n",
-      "A = C.T*Mmu*C - 1j*omega(freq)*Msig\n",
-      "ABG = C.T*Mmu*C - 1j*omega(freq)*MsigBG"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 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)\n",
-      "# Setup x (east) polarization (_x)\n",
-      "ex_x = np.zeros(M.vnEx,dtype=complex)\n",
-      "ey_x = np.zeros((M.nEy,1),dtype=complex)\n",
-      "ez_x = np.zeros((M.nEz,1),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_x[i,j,:] = e0_1d\n",
-      "eBG_x = np.vstack((simpeg.Utils.mkvc(M.r(ex_x,'Ex','Ex','V'),2),ey_x,ez_x))\n",
-      "rhs_x = ABG.dot(eBG_x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for i in arange(M.vnEx[0]):\n",
-      "    for j in arange(M.vnEx[1]):\n",
-      "        ex_x[i,j,:] = e0_1d"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Setup y (north) polarization (_y)\n",
-      "ex_y = np.zeros(M.nEx, dtype='complex128')\n",
-      "ey_y = np.zeros((M.vnEy), dtype='complex128')\n",
-      "ez_y = np.zeros(M.nEz, dtype='complex128')\n",
-      "# Assign the source to ex_x\n",
-      "for i in arange(M.vnEy[0]):\n",
-      "    for j in arange(M.vnEy[1]):\n",
-      "        ey_y[i,j,:] = e0_1d        \n",
-      "# eBG_y = np.vstack((ex_y,Utils.mkvc(M.r(ey_y,'Ey','Ey','V'),2),ez_y))\n",
-      "# rhs_y = ABG.dot(eBG_y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "eBG_y = np.r_[ex_y,simpeg.Utils.mkvc(ey_y),ez_y]\n",
-      "rhs_y = ABG.dot(eBG_y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "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",
-     "collapsed": false,
-     "input": [
-      "%%time\n",
-      "# Solve the systems for each polarization\n",
-      "Ainv = simpeg.SolverLU(A)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "CPU times: user 1min 16s, sys: 761 ms, total: 1min 17s\n",
-        "Wall time: 1min 18s\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%time\n",
-      "e_x = Ainv*rhs_x\n",
-      "e_y = Ainv*rhs_y"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "CPU times: user 497 ms, sys: 1 ms, total: 498 ms\n",
-        "Wall time: 534 ms\n"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "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",
-     "collapsed": false,
-     "input": [
-      "Meinv = M.getEdgeInnerProduct(np.ones_like(sig), invMat=True)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "j_x = Meinv*Msig*e_x\n",
-      "j_y = Meinv*Msig*e_x"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Then use \"plotSlice\" function, to visualize 2D sections"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
-      "dat0 = M.plotSlice(abs(e_x_CC), vType='CCv', view='vec', streamOpts={'color': 'k'}, normal='X', ax = ax[0])\n",
-      "cb0 = plt.colorbar(dat0[0], ax = ax[0])\n",
-      "dat1 = M.plotSlice(abs(j_x_CC), vType='CCv', view='vec', streamOpts={'color': 'k'}, normal='X', ax = ax[1])\n",
-      "cb1 = plt.colorbar(dat1[0], ax = ax[1])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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rYzvWaXlzgJzv9uriF8DbsMWr/imtp2P36jFMof4bxt73OuyedWPhJD5Mfn2+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\n6wAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f2471e8ce10>"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "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",
-     "collapsed": false,
-     "input": [
-      "# Calculate the data\n",
-      "rx_x, rx_y = np.meshgrid(np.arange(-500,501,50),np.arange(-500,501,50))\n",
-      "rx_loc = np.hstack((simpeg.Utils.mkvc(rx_x,2),simpeg.Utils.mkvc(rx_y,2),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')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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",
-      "h_x_loc = np.hstack([simpeg.Utils.mkvc(Qfx*C*e_x,2),simpeg.Utils.mkvc(Qfy*C*e_x,2),simpeg.Utils.mkvc(Qfz*C*e_x,2)])\n",
-      "h_y_loc = np.hstack([simpeg.Utils.mkvc(Qfx*C*e_y,2),simpeg.Utils.mkvc(Qfy*C*e_y,2),simpeg.Utils.mkvc(Qfz*C*e_y,2)])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 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"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "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)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "zxy"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 23,
-       "text": [
-        "array([ 103.21972098+93.23211088j,  102.90475057+93.32910177j,\n",
-        "        102.58986124+93.42601702j,  102.39619991+93.42582359j,\n",
-        "        102.20255534+93.42570773j,  102.09543991+93.39475409j,\n",
-        "        101.98830179+93.36384863j,  101.93711201+93.33580298j,\n",
-        "        101.88590678+93.30777082j,  101.87105299+93.29712749j,\n",
-        "        101.85619645+93.28648454j,  101.87105299+93.29712749j,\n",
-        "        101.88590678+93.30777082j,  101.93711201+93.33580298j,\n",
-        "        101.98830179+93.36384863j,  102.09543991+93.39475409j,\n",
-        "        102.20255534+93.42570773j,  102.39619991+93.42582359j,\n",
-        "        102.58986124+93.42601702j,  102.90475057+93.32910177j,\n",
-        "        103.21972098+93.23211088j,  103.18844918+93.25637175j,\n",
-        "        102.87026907+93.35333401j,  102.55217722+93.45021873j,\n",
-        "        102.35592664+93.44923714j,  102.15969494+93.44833625j,\n",
-        "        102.05084774+93.41639946j,  101.94197761+93.38451306j,\n",
-        "        101.88984420+93.35571748j,  101.83769506+93.32693623j,\n",
-        "        101.82254720+93.31601774j,  101.80739677+93.30509988j,\n",
-        "        101.82254720+93.31601774j,  101.83769506+93.32693623j,\n",
-        "        101.88984420+93.35571748j,  101.94197761+93.38451306j,\n",
-        "        102.05084774+93.41639946j,  102.15969494+93.44833625j,\n",
-        "        102.35592664+93.44923714j,  102.55217722+93.45021873j,\n",
-        "        102.87026907+93.35333401j,  103.18844918+93.25637175j,\n",
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-        "        102.20255534+93.42570773j,  102.39619991+93.42582359j,\n",
-        "        102.58986124+93.42601702j,  102.90475057+93.32910177j,\n",
-        "        103.21972098+93.23211088j])"
-       ]
-      }
-     ],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "zyx\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 24,
-       "text": [
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-        "       -103.21972098-93.23211088j])"
-       ]
-      }
-     ],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "zxy + zyx\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 26,
-       "text": [
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-        "         2.22022304e-01 +7.09463552e-02j,\n",
-        "         1.12154497e-01 +3.50026122e-02j,\n",
-        "        -2.58637556e-12 +1.70530257e-13j,\n",
-        "        -5.42187202e-02 -3.13356663e-02j,\n",
-        "        -1.08514503e-01 -6.21795484e-02j,\n",
-        "        -1.24699904e-01 -7.41034910e-02j,\n",
-        "        -1.40373562e-01 -8.56290921e-02j,\n",
-        "        -1.24699904e-01 -7.41034910e-02j,\n",
-        "        -1.08514503e-01 -6.21795484e-02j,\n",
-        "        -5.42187202e-02 -3.13356663e-02j,\n",
-        "        -5.82645043e-13 +1.35003120e-12j,\n",
-        "         1.12154497e-01 +3.50026122e-02j,\n",
-        "         2.22022304e-01 +7.09463553e-02j,\n",
-        "         4.20316757e-01 +7.56543199e-02j,\n",
-        "         6.08718653e-01 +8.37235816e-02j,\n",
-        "         9.19251597e-01 -7.16808567e-03j,\n",
-        "         1.19529669e+00 -8.25155575e-02j,\n",
-        "         1.08628561e+00 -1.15392123e-01j,\n",
-        "         8.08757489e-01 -4.08955418e-02j,\n",
-        "         4.97590348e-01 +4.94935648e-02j,\n",
-        "         3.08567222e-01 +4.09977830e-02j,\n",
-        "         1.10097982e-01 +3.61134042e-02j,\n",
-        "        -5.11590770e-13 +7.67386155e-13j,\n",
-        "        -1.12154497e-01 -3.50026122e-02j,\n",
-        "        -1.66453648e-01 -6.63951572e-02j,\n",
-        "        -2.20706782e-01 -9.71779170e-02j,\n",
-        "        -2.36931719e-01 -1.09139227e-01j,\n",
-        "        -2.52565263e-01 -1.20608513e-01j,\n",
-        "        -2.36931719e-01 -1.09139227e-01j,\n",
-        "        -2.20706782e-01 -9.71779170e-02j,\n",
-        "        -1.66453648e-01 -6.63951572e-02j,\n",
-        "        -1.12154497e-01 -3.50026122e-02j,\n",
-        "        -4.54747351e-13 +1.59161573e-12j,\n",
-        "         1.10097982e-01 +3.61134042e-02j,\n",
-        "         3.08567222e-01 +4.09977830e-02j,\n",
-        "         4.97590348e-01 +4.94935648e-02j,\n",
-        "         8.08757489e-01 -4.08955418e-02j,\n",
-        "         1.08628561e+00 -1.15392123e-01j,\n",
-        "         9.77788266e-01 -1.49975229e-01j,\n",
-        "         6.99118138e-01 -7.62683974e-02j,\n",
-        "         3.87656331e-01 +1.36804967e-02j,\n",
-        "         1.98328238e-01 +4.90943742e-03j,\n",
-        "        -3.26849658e-13 -4.12114787e-13j,\n",
-        "        -1.10097982e-01 -3.61134042e-02j,\n",
-        "        -2.22022304e-01 -7.09463552e-02j,\n",
-        "        -2.76269723e-01 -1.02268985e-01j,\n",
-        "        -3.30347986e-01 -1.32863762e-01j,\n",
-        "        -3.46570199e-01 -1.44816109e-01j,\n",
-        "        -3.62121355e-01 -1.56182657e-01j,\n",
-        "        -3.46570199e-01 -1.44816109e-01j,\n",
-        "        -3.30347986e-01 -1.32863762e-01j,\n",
-        "        -2.76269723e-01 -1.02268985e-01j,\n",
-        "        -2.22022304e-01 -7.09463552e-02j,\n",
-        "        -1.10097982e-01 -3.61134042e-02j,\n",
-        "         8.81072992e-13 -3.48165941e-12j,\n",
-        "         1.98328238e-01 +4.90943741e-03j,\n",
-        "         3.87656331e-01 +1.36804967e-02j,\n",
-        "         6.99118138e-01 -7.62683974e-02j,\n",
-        "         9.77788266e-01 -1.49975229e-01j,\n",
-        "         7.82757885e-01 -1.53723302e-01j,\n",
-        "         5.02091486e-01 -8.06075906e-02j,\n",
-        "         1.89983971e-01 +8.97376924e-03j,\n",
-        "        -2.13162821e-13 -9.09494702e-13j,\n",
-        "        -1.98328238e-01 -4.90943742e-03j,\n",
-        "        -3.08567222e-01 -4.09977830e-02j,\n",
-        "        -4.20316757e-01 -7.56543198e-02j,\n",
-        "        -4.74567880e-01 -1.06900271e-01j,\n",
-        "        -5.28490429e-01 -1.37306224e-01j,\n",
-        "        -5.44729133e-01 -1.49248763e-01j,\n",
-        "        -5.60197871e-01 -1.60512571e-01j,\n",
-        "        -5.44729133e-01 -1.49248763e-01j,\n",
-        "        -5.28490429e-01 -1.37306224e-01j,\n",
-        "        -4.74567880e-01 -1.06900271e-01j,\n",
-        "        -4.20316757e-01 -7.56543198e-02j,\n",
-        "        -3.08567222e-01 -4.09977830e-02j,\n",
-        "        -1.98328238e-01 -4.90943742e-03j,\n",
-        "         7.53175300e-13 -4.93116659e-12j,\n",
-        "         1.89983971e-01 +8.97376924e-03j,\n",
-        "         5.02091486e-01 -8.06075906e-02j,\n",
-        "         7.82757885e-01 -1.53723302e-01j,\n",
-        "         5.93845915e-01 -1.61779013e-01j,\n",
-        "         3.11968778e-01 -8.92349727e-02j,\n",
-        "         2.27373675e-13 -3.41060513e-13j,\n",
-        "        -1.89983971e-01 -8.97376924e-03j,\n",
-        "        -3.87656331e-01 -1.36804967e-02j,\n",
-        "        -4.97590348e-01 -4.94935648e-02j,\n",
-        "        -6.08718653e-01 -8.37235816e-02j,\n",
-        "        -6.62728712e-01 -1.14702155e-01j,\n",
-        "        -7.16250356e-01 -1.44728566e-01j,\n",
-        "        -7.32429175e-01 -1.56591360e-01j,\n",
-        "        -7.47738834e-01 -1.67682421e-01j,\n",
-        "        -7.32429175e-01 -1.56591360e-01j,\n",
-        "        -7.16250356e-01 -1.44728566e-01j,\n",
-        "        -6.62728712e-01 -1.14702155e-01j,\n",
-        "        -6.08718653e-01 -8.37235816e-02j,\n",
-        "        -4.97590348e-01 -4.94935648e-02j,\n",
-        "        -3.87656331e-01 -1.36804967e-02j,\n",
-        "        -1.89983971e-01 -8.97376924e-03j,\n",
-        "        -4.40536496e-13 +1.37845291e-12j,\n",
-        "         3.11968778e-01 -8.92349727e-02j,\n",
-        "         5.93845915e-01 -1.61779013e-01j,\n",
-        "         2.83698609e-01 -7.27300238e-02j,\n",
-        "         9.94759830e-14 +2.55795385e-13j,\n",
-        "        -3.11968778e-01 +8.92349727e-02j,\n",
-        "        -5.02091486e-01 +8.06075906e-02j,\n",
-        "        -6.99118138e-01 +7.62683974e-02j,\n",
-        "        -8.08757489e-01 +4.08955418e-02j,\n",
-        "        -9.19251597e-01 +7.16808566e-03j,\n",
-        "        -9.73007788e-01 -2.34670227e-02j,\n",
-        "        -1.02611931e+00 -5.30845919e-02j,\n",
-        "        -1.04222919e+00 -6.48383612e-02j,\n",
-        "        -1.05737970e+00 -7.57566535e-02j,\n",
-        "        -1.04222919e+00 -6.48383612e-02j,\n",
-        "        -1.02611931e+00 -5.30845919e-02j,\n",
-        "        -9.73007788e-01 -2.34670227e-02j,\n",
-        "        -9.19251597e-01 +7.16808566e-03j,\n",
-        "        -8.08757489e-01 +4.08955418e-02j,\n",
-        "        -6.99118138e-01 +7.62683974e-02j,\n",
-        "        -5.02091486e-01 +8.06075906e-02j,\n",
-        "        -3.11968778e-01 +8.92349727e-02j,\n",
-        "        -2.62900812e-12 +3.09796633e-12j,\n",
-        "         2.83698609e-01 -7.27300238e-02j,\n",
-        "         2.13162821e-13 +1.42108547e-14j,\n",
-        "        -2.83698609e-01 +7.27300238e-02j,\n",
-        "        -5.93845915e-01 +1.61779013e-01j,\n",
-        "        -7.82757885e-01 +1.53723302e-01j,\n",
-        "        -9.77788266e-01 +1.49975229e-01j,\n",
-        "        -1.08628561e+00 +1.15392123e-01j,\n",
-        "        -1.19529669e+00 +8.25155575e-02j,\n",
-        "        -1.24835785e+00 +5.25007978e-02j,\n",
-        "        -1.30061689e+00 +2.35682982e-02j,\n",
-        "        -1.31652401e+00 +1.20259957e-02j,\n",
-        "        -1.33138063e+00 +1.38247490e-03j,\n",
-        "        -1.31652401e+00 +1.20259957e-02j,\n",
-        "        -1.30061689e+00 +2.35682982e-02j,\n",
-        "        -1.24835785e+00 +5.25007978e-02j,\n",
-        "        -1.19529669e+00 +8.25155575e-02j,\n",
-        "        -1.08628561e+00 +1.15392123e-01j,\n",
-        "        -9.77788266e-01 +1.49975229e-01j,\n",
-        "        -7.82757885e-01 +1.53723302e-01j,\n",
-        "        -5.93845915e-01 +1.61779013e-01j,\n",
-        "        -2.83698609e-01 +7.27300238e-02j,  -3.48165941e-12 +5.25801624e-13j])"
-       ]
-      }
-     ],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
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/notebooks/Derivative test MT1D.ipynb b/notebooks/Derivative test MT1D.ipynb
new file mode 100644
index 00000000..751e9879
--- /dev/null
+++ b/notebooks/Derivative test MT1D.ipynb	
@@ -0,0 +1,890 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Testing derivaties for 1D MT problem.\n",
+    "\n",
+    "Especially the rx.projectFieldsDeriv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import SimPEG as simpeg\n",
+    "import simpegEM as simpegem, simpegMT as simpegmt\n",
+    "from SimPEG.Utils import meshTensor\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "simpegMT.FieldsMT.FieldsMT_1D"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simpegmt.FieldsMT.FieldsMT_1D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Setup the problem\n",
+    "sigmaHalf = 1e-2\n",
+    "# Frequency\n",
+    "nFreq = 33\n",
+    "# freqs = np.logspace(3,-3,nFreq)\n",
+    "freqs = np.array([100])\n",
+    "# Make the mesh\n",
+    "ct = 5\n",
+    "air = meshTensor([(ct,25,1.3)])\n",
+    "# coreT0 = meshTensor([(ct,15,1.2)])\n",
+    "# coreT1 = np.kron(meshTensor([(coreT0[-1],15,1.3)]),np.ones((7,)))\n",
+    "core = np.concatenate( (  np.kron(meshTensor([(ct,15,-1.2)]),np.ones((10,))) , meshTensor([(ct,20)]) ) )\n",
+    "bot = 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))], x0=x0)\n",
+    "# Make the model\n",
+    "sigma = np.zeros(m1d.nC) + sigmaHalf\n",
+    "sigma[ m1d.gridCC > 0 ] = 1e-8\n",
+    "\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",
+    "tD = False\n",
+    "if tD:\n",
+    "    for freq in freqs:\n",
+    "        srcList.append(simpegmt.SurveyMT.srcMT_polxy_1DhomotD(rxList,freq))\n",
+    "else:\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",
+    "\n",
+    "# Set the problem\n",
+    "problem = simpegmt.ProblemMT1D.eForm_psField(m1d)\n",
+    "problem.sigmaPrimary = sigma\n",
+    "problem.pair(survey)\n",
+    "\n",
+    "# Get the fields\n",
+    "fields = problem.fields(sigma)\n",
+    "\n",
+    "# Project the data\n",
+    "data = survey.projectFields(fields)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR: No traceback has been produced, nothing to debug.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%debug\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need calculate this derivative. \n",
+    "\\begin{align}\n",
+    "\\underbrace{\\frac{\\partial P(f(u(m)),m^{fix})}{\\partial f}}_{Rx}\n",
+    "\\end{align}\n",
+    "\n",
+    "Use the rule\n",
+    "\\begin{align}\n",
+    "\\frac{d}{dx}\\left( \\frac{a(x)}{b(x)} \\right)  = \\frac{\\frac{d }{dx} a(x)  b(x) - a(x)\\frac{d }{dx} b(x)  }{ b(x)^2 }\n",
+    "\\end{align}\n",
+    "\n",
+    "In the case of the 1D MT problem the data is calculated as \n",
+    "\\begin{align}\n",
+    "MT1Ddata = P(f(m)) &= \\frac{P_{ex} f_e(src,m)}{P_{bx} f_b(src,m) \\frac{1}{\\mu_0}} = \\frac{P_e u}{P_b f_b(u)} \\\\\n",
+    "\\frac{\\partial P(f(m))}{\\partial u} v &=  \\frac{P_e}{P_b \\frac{1}{mu_0} f_b(u)}v - \\frac{P_e u}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)^2} P_b \\frac{1}{mu_0} \\frac{d f_b}{du} v\n",
+    "\\end{align}\n",
+    "where u is the fields that we solve for. \n",
+    "\\begin{align}\n",
+    "\\frac{d f_b}{du} = - \\frac{1}{i \\omega} \\nabla \n",
+    "\\end{align}\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Unused code &= \\frac{ P_{ex} P_{bx} \\frac{1}{\\mu_0} \\left( f_b(src,m) - f_e(src,m) \\right) } { \\left(P_{bx}f_b(src,m) \\frac{1}{\\mu_0} \\right)^2 }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As matrices the formulas above can be written as\n",
+    "\\begin{align}\n",
+    "\\left[ \\frac{\\partial P(f(m))}{\\partial u} v \\right] = \\left[ diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right] [P_e v] , diag[P_e u] diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right]^T diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right] \\left[ P_b  \\frac{1}{mu_0} \\frac{d f_b}{du}(v)  \\right] \\right]\n",
+    "\\end{align}\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The adjoint problem is done simliarly\n",
+    "\\begin{align}\n",
+    "\\left[ \\frac{\\partial P(f(m))}{\\partial u} v \\right]^T = [P_e]^T diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right]^T v - \\left[ P_b   \\frac{d f_b}{du} \\frac{1}{mu_0} \\right]^T diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right] diag \\left[ \\frac{1}{\\left(P_b \\frac{1}{mu_0} f_b(u)\\right)} \\right]^T diag \\left[ P_e u  \\right]^T v\n",
+    "\\end{align}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# def projectFields(self, src, mesh, u):\n",
+    "#         '''\n",
+    "#         Project the fields and return the\n",
+    "#         '''\n",
+    "\n",
+    "#         if self.projType is 'Z1D':\n",
+    "#             Pex = mesh.getInterpolationMat(self.locs,'Fx')\n",
+    "#             Pbx = mesh.getInterpolationMat(self.locs,'Ex')\n",
+    "#             ex = Pex*mkvc(u[src,'e_1d'],2)\n",
+    "#             bx = Pbx*mkvc(u[src,'b_1d'],2)/mu_0\n",
+    "#             f_part_complex = ex/bx\n",
+    "#         real_or_imag = self.projComp\n",
+    "#         f_part = getattr(f_part_complex, real_or_imag)\n",
+    "#         return f_part"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Initate things for the derivs Test\n",
+    "src = survey.srcList[0]\n",
+    "rx = src.rxList[0]\n",
+    "v = np.random.randn(m1d.nN)\n",
+    "v0 = np.random.randn(m1d.nF+m1d.nE)\n",
+    "u0 = np.random.randn(m1d.nN)+np.random.randn(m1d.nN)*1j\n",
+    "f0 = problem.fieldsPair(m1d,survey)\n",
+    "f0[src,'e_1dSolution'] = u0\n",
+    "# f0[src,'b_1d'] = -1/(1j*simpegem.Utils.EMUtils.omega(src.freq))*m1d.nodalGrad*u0"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    3.089e-04     2.730e-05      nan\n",
+      " 1   1.00e-02    2.841e-05     2.582e-07      2.024\n",
+      " 2   1.00e-03    2.818e-06     2.568e-09      2.002\n",
+      " 3   1.00e-04    2.816e-07     2.567e-11      2.000\n",
+      " 4   1.00e-05    2.816e-08     2.567e-13      2.000\n",
+      "========================= PASS! =========================\n",
+      "The test be workin!\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Run a test\n",
+    "def fun(u):\n",
+    "    f = problem.fieldsPair(m1d,survey)\n",
+    "    f[src,'e_1dSolution'] = u\n",
+    "    return rx.projectFields(src,m1d,f), lambda t: rx.projectFieldsDeriv(src,m1d,f0,t)\n",
+    "simpeg.Tests.checkDerivative(fun,u0,num=5,plotIt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.00285083])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rx.projectFieldsDeriv(src,m1d,f0,u0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.00130618]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rx.projectFields(src,m1d,f0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Test the Jvec derivative."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    1.537e-05     1.005e-07      nan\n",
+      " 1   1.00e-02    1.541e-06     1.008e-09      1.999\n",
+      " 2   1.00e-03    1.541e-07     1.008e-11      2.000\n",
+      " 3   1.00e-04    1.541e-08     1.008e-13      2.000\n",
+      "========================= PASS! =========================\n",
+      "The test be workin!\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print '%s formulation - %s' % (fdemType, comp)\n",
+    "CONDUCTIVITY = 0.01\n",
+    "m0 = np.log(np.ones(problem.mesh.nC)*CONDUCTIVITY)\n",
+    "# mu = np.log(np.ones(problem.mesh.nC)*MU)\n",
+    "\n",
+    "if True:\n",
+    "    m0  = m0 + np.random.randn(problem.mesh.nC)*CONDUCTIVITY*1e-1 \n",
+    "#     mu = mu + np.random.randn(prb.mesh.nC)*MU*1e-1\n",
+    "\n",
+    "# prb.mu = mu\n",
+    "# survey = prb.survey\n",
+    "def fun(x):\n",
+    "    \n",
+    "    return survey.dpred(x), lambda x: problem.Jvec(m0, x)\n",
+    "simpeg.Tests.checkDerivative(fun, m0, num=4, plotIt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR: No traceback has been produced, nothing to debug.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%debug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "### Adjoint test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have \n",
+    "\\begin{align}\n",
+    "Jvec =&"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adjoint e formulation - projectFieldsDeriv\n",
+      "0.000177264079929 0.000177264079929 3.52365706058e-19 1e-07 True\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Run a test\n",
+    "TOL = 1e-4\n",
+    "FLR = 1e-20\n",
+    "\n",
+    "def projectFieldsAdjointTest(fdemType, comp):\n",
+    "    print 'Adjoint %s formulation - %s' % (fdemType, comp)\n",
+    "\n",
+    "    m  = np.log(np.ones(problem.mesh.nC)*0.01)\n",
+    "    if True:\n",
+    "        m  = m + np.random.randn(problem.mesh.nC)*0.01*1e-1 \n",
+    "\n",
+    "    u = problem.fields(m)\n",
+    "    v = np.random.randn(1)#+np.random.randn(1)*1j\n",
+    "    # print prb.PropMap.PropModel.nP\n",
+    "    w = np.random.randn(m1d.nN)+np.random.randn(m1d.nN)*1j\n",
+    "\n",
+    "    vJw = v.dot(rx.projectFieldsDeriv(src,m1d,f0,w))\n",
+    "    wJtv = w.dot(rx.projectFieldsDeriv(src,m1d,f0,v,adjoint=True)).real\n",
+    "    tol = np.max([TOL*(10**int(np.log10(np.abs(vJw)))),FLR]) \n",
+    "    print vJw, wJtv, vJw - wJtv, tol, np.abs(vJw - wJtv) < tol\n",
+    "    return np.abs(vJw - wJtv) < tol\n",
+    "projectFieldsAdjointTest('e','projectFieldsDeriv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adjoint test e formulation - getADeriv_m\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "'Source does not have a uid: e_1dSolution'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-21-4c7e7058b8e9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[0mvJw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwJtv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvJw\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mwJtv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvJw\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mwJtv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mtol\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvJw\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mwJtv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mtol\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[0mgetADeriv_mAdjointTest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-21-4c7e7058b8e9>\u001b[0m in \u001b[0;36mgetADeriv_mAdjointTest\u001b[1;34m()\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[0mw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm1d\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#+np.random.randn(m1d.nN)*1j\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m     \u001b[0mvJw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetADeriv_m\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mf0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m     \u001b[0mwJtv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetADeriv_m\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mf0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0madjoint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m     \u001b[0mtol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mTOL\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog10\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvJw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mFLR\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpegmt/simpegMT/ProblemMT1D/Problems.py\u001b[0m in \u001b[0;36mgetADeriv_m\u001b[1;34m(self, freq, u, v, adjoint)\u001b[0m\n\u001b[0;32m     67\u001b[0m         \u001b[0mMeMui\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmesh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetEdgeInnerProduct\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mmu_0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     68\u001b[0m         \u001b[1;31m#\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 69\u001b[1;33m         \u001b[0mu_src\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'e_1dSolution'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     70\u001b[0m         \u001b[0mdMf_dsig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmesh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetFaceInnerProductDeriv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurModel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msigma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu_src\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurModel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msigmaDeriv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     71\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0madjoint\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Fields.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    151\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 152\u001b[1;33m         \u001b[0mind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_indexAndNameFromKey\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'get'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    153\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Fields.pyc\u001b[0m in \u001b[0;36m_indexAndNameFromKey\u001b[1;34m(self, key, accessType)\u001b[0m\n\u001b[0;32m    131\u001b[0m         \u001b[0msrcTestList\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    132\u001b[0m         \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_nameIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccessType\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 133\u001b[1;33m         \u001b[0mind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_srcIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrcTestList\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    134\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    135\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Fields.pyc\u001b[0m in \u001b[0;36m_srcIndex\u001b[1;34m(self, srcTestList)\u001b[0m\n\u001b[0;32m    100\u001b[0m             \u001b[0mind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msrcTestList\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m             \u001b[0mind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msurvey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetSourceIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrcTestList\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    103\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mind\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Survey.pyc\u001b[0m in \u001b[0;36mgetSourceIndex\u001b[1;34m(self, sources)\u001b[0m\n\u001b[0;32m    339\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0msrc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msources\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'uid'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Source does not have a uid: %s'\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m         \u001b[0minds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0msrc\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sourceOrder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msources\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minds\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Source does not have a uid: e_1dSolution'"
+     ]
+    }
+   ],
+   "source": [
+    "# Run a test\n",
+    "TOL = 1e-4\n",
+    "FLR = 1e-20\n",
+    "\n",
+    "def getADeriv_mAdjointTest():\n",
+    "    print 'Adjoint test e formulation - getADeriv_m' \n",
+    "\n",
+    "    m  = np.log(np.ones(problem.mesh.nC)*0.01)\n",
+    "    if True:\n",
+    "        m  = m + np.random.randn(problem.mesh.nC)*0.01*1e-1 \n",
+    "\n",
+    "    u = problem.fields(m)\n",
+    "    v = np.random.randn(m1d.nN)#+np.random.randn(1)*1j\n",
+    "    # print prb.PropMap.PropModel.nP\n",
+    "    w = np.random.randn(m1d.nC)#+np.random.randn(m1d.nN)*1j\n",
+    "\n",
+    "    vJw = v.dot(problem.getADeriv_m(freq,f0,w))\n",
+    "    wJtv = w.dot(problem.getADeriv_m(freq,f0,v,adjoint=True))\n",
+    "    tol = np.max([TOL*(10**int(np.log10(np.abs(vJw)))),FLR]) \n",
+    "    print vJw, wJtv, vJw - wJtv, tol, np.abs(vJw - wJtv) < tol\n",
+    "    return np.abs(vJw - wJtv) < tol\n",
+    "getADeriv_mAdjointTest()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> \u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpegmt/simpegMT/ProblemMT1D/Problems.py\u001b[0m(69)\u001b[0;36mgetADeriv_m\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32m     68 \u001b[1;33m        \u001b[1;31m#\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[1;32m---> 69 \u001b[1;33m        \u001b[0mu_src\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'e_1dSolution'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[1;32m     70 \u001b[1;33m        \u001b[0mdMf_dsig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmesh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetFaceInnerProductDeriv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurModel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msigma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu_src\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurModel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msigmaDeriv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "ipdb> u\n",
+      "> \u001b[1;32m<ipython-input-16-0d25a7e16094>\u001b[0m(17)\u001b[0;36mgetADeriv_mAdjointTest\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32m     16 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[1;32m---> 17 \u001b[1;33m    \u001b[0mvJw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetADeriv_m\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mu0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[1;32m     18 \u001b[1;33m    \u001b[0mwJtv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetADeriv_m\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mu0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0madjoint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "ipdb> d\n",
+      "> \u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpegmt/simpegMT/ProblemMT1D/Problems.py\u001b[0m(69)\u001b[0;36mgetADeriv_m\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32m     68 \u001b[1;33m        \u001b[1;31m#\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[1;32m---> 69 \u001b[1;33m        \u001b[0mu_src\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'e_1dSolution'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[1;32m     70 \u001b[1;33m        \u001b[0mdMf_dsig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmesh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetFaceInnerProductDeriv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurModel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msigma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu_src\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurModel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msigmaDeriv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "ipdb> p u\n",
+      "array([  7.09826705e-01 -3.42993400e-01j,\n",
+      "         2.06495724e-02 -1.13236591e+00j,\n",
+      "         7.19940065e-01 +6.18299116e-01j,\n",
+      "        -3.76573540e-01 +9.09687373e-01j,\n",
+      "        -5.96166731e-01 +2.23309643e-01j,\n",
+      "         3.30533731e-01 +7.98068064e-01j,\n",
+      "        -1.41356795e+00 -1.74137927e-01j,\n",
+      "         1.51554175e+00 -2.16363375e-01j,\n",
+      "         1.59469627e+00 -1.87971566e+00j,\n",
+      "        -5.54637698e-01 -1.01936458e+00j,\n",
+      "         1.01869865e+00 +8.33443055e-01j,\n",
+      "        -4.92616671e-01 -1.97036224e+00j,\n",
+      "         1.00389102e+00 -1.52567261e-01j,\n",
+      "         7.90254779e-01 -5.68065046e-01j,\n",
+      "         9.17329964e-01 -1.18879097e+00j,\n",
+      "        -1.38317362e+00 -2.17789273e+00j,\n",
+      "         2.58324368e-01 -1.51003598e+00j,\n",
+      "        -2.04803634e+00 -1.44299943e-01j,\n",
+      "         3.33011285e+00 +1.52905384e+00j,\n",
+      "         2.91429972e-01 -1.91849967e+00j,\n",
+      "        -1.10273784e+00 -8.65794290e-01j,\n",
+      "        -2.80103156e-01 -1.42653192e+00j,\n",
+      "        -9.81404885e-01 +9.06845144e-02j,\n",
+      "         6.37579045e-01 +3.84148006e-01j,\n",
+      "        -6.83597988e-01 +6.91187965e-01j,\n",
+      "        -8.98575981e-01 +3.80347476e-02j,\n",
+      "         1.02647319e+00 -1.13876879e-01j,\n",
+      "         8.28006255e-01 -9.95903406e-01j,\n",
+      "         3.53931083e-01 -2.80188697e-01j,\n",
+      "        -1.09339690e+00 +1.43646692e+00j,\n",
+      "         2.59276626e+00 +4.09662561e-01j,\n",
+      "        -5.97873868e-01 -5.23852065e-01j,\n",
+      "         8.87054287e-01 -1.69872744e+00j,\n",
+      "         2.29767481e-01 -5.88143928e-01j,\n",
+      "        -6.97610315e-01 +5.68810360e-01j,\n",
+      "        -4.47049016e-01 +3.29354463e-01j,\n",
+      "        -3.31498311e-01 -8.27120276e-01j,\n",
+      "         1.57689998e-01 +6.68378872e-01j,\n",
+      "         9.47018631e-02 +2.60180446e-01j,\n",
+      "        -4.59177257e-01 -7.66896049e-01j,\n",
+      "        -1.16791204e+00 +4.20585804e-01j,\n",
+      "         2.72405147e-01 +3.48079787e-01j,\n",
+      "        -2.45880514e-02 +1.90226778e-01j,\n",
+      "        -7.03428363e-01 +1.20531568e+00j,\n",
+      "         1.60362217e+00 +6.88472259e-01j,\n",
+      "        -5.49144335e-01 +6.96208292e-01j,\n",
+      "        -9.83200346e-01 -1.15726575e+00j,\n",
+      "         1.22826524e+00 -8.58070855e-01j,\n",
+      "        -5.99020439e-01 -2.19182124e+00j,\n",
+      "        -5.69992871e-01 +1.68586900e+00j,\n",
+      "         9.26752644e-01 +1.05767829e+00j,\n",
+      "         8.64247444e-01 +1.51282356e-01j,\n",
+      "        -6.24547862e-01 -1.54517937e+00j,\n",
+      "        -1.72043526e+00 +1.91659834e+00j,\n",
+      "        -1.84501951e+00 -2.40165623e-01j,\n",
+      "         1.15210121e+00 -1.19627222e+00j,\n",
+      "        -3.93036395e-01 -1.94918396e+00j,\n",
+      "        -1.18266273e+00 -5.24652722e-01j,\n",
+      "         2.67461628e-01 -8.07279138e-01j,\n",
+      "        -1.56363641e+00 +1.59257615e-03j,\n",
+      "        -3.17211883e-01 -3.23379690e+00j,\n",
+      "        -1.08658939e-01 -8.66079039e-01j,\n",
+      "        -9.30350013e-01 -1.22668219e+00j,\n",
+      "        -1.07263137e+00 -5.56847000e-02j,\n",
+      "         1.27878013e+00 -1.34173537e+00j,\n",
+      "         7.91441492e-01 +6.42111414e-01j,\n",
+      "        -2.60121575e+00 -1.08140886e+00j,\n",
+      "        -1.15027808e+00 -4.47110142e-01j,\n",
+      "        -5.69747800e-01 +2.34065916e-01j,\n",
+      "         1.52155089e+00 -5.80340775e-01j,\n",
+      "         2.50052750e-01 -6.58793179e-01j,\n",
+      "         2.96944135e-01 +1.27455206e+00j,\n",
+      "         9.18939766e-01 -4.58122761e-01j,\n",
+      "         4.90015531e-01 -8.86288179e-01j,\n",
+      "        -1.65155083e+00 -6.57733967e-01j,\n",
+      "         9.72148169e-01 -1.19446820e+00j,\n",
+      "         2.08365350e+00 -1.53585595e-01j,\n",
+      "        -4.11101072e-01 +1.01418536e+00j,\n",
+      "         9.72580562e-01 -2.20470840e-01j,\n",
+      "         1.72922147e-01 -6.86360934e-01j,\n",
+      "         3.80255034e-01 +1.96862619e-01j,\n",
+      "        -1.53628572e+00 +5.31946011e-01j,\n",
+      "        -4.65939167e-01 +9.11725611e-01j,\n",
+      "        -1.06390365e+00 -3.07658816e-02j,\n",
+      "        -2.40734296e+00 -7.60530882e-01j,\n",
+      "         1.28242272e+00 +3.32085973e+00j,\n",
+      "         3.17004751e+00 +1.62654811e+00j,\n",
+      "        -8.49758476e-01 +9.86658359e-01j,\n",
+      "         4.99578557e-01 +4.01392488e-01j,\n",
+      "        -9.48695998e-01 +4.04322381e-01j,\n",
+      "         1.75371116e-01 +7.85458813e-01j,\n",
+      "         4.90232886e-01 -1.39280042e-01j,\n",
+      "        -2.31430252e-01 -8.44065839e-01j,\n",
+      "        -1.02319133e+00 +1.90560784e+00j,\n",
+      "         1.08940667e+00 +1.63046186e+00j,\n",
+      "        -4.34233324e-01 +2.90439312e-01j,\n",
+      "         4.47622371e-01 +1.26507976e+00j,\n",
+      "        -2.07317470e+00 -2.21558532e+00j,\n",
+      "        -3.19553487e-01 -9.19210427e-01j,\n",
+      "         6.09870390e-01 +3.05692898e-01j,\n",
+      "         4.92081860e-01 -2.18326059e-01j,\n",
+      "         1.48544627e+00 -2.02054490e-01j,\n",
+      "        -1.59612367e-01 +7.90853777e-02j,\n",
+      "         2.85854411e-01 +2.16567814e-01j,\n",
+      "         5.28063071e-01 +2.08838949e+00j,\n",
+      "         6.17580558e-01 +9.90748539e-01j,\n",
+      "        -2.64539211e-01 +1.16598665e+00j,\n",
+      "        -1.07357542e+00 -7.58002064e-01j,\n",
+      "         6.13399713e-02 +2.52338250e+00j,\n",
+      "        -4.84180135e-01 -1.45046020e+00j,\n",
+      "        -6.57519374e-01 -1.52719059e+00j,\n",
+      "        -1.54949084e-01 -7.64796563e-01j,\n",
+      "        -2.01172915e-02 +1.07377565e+00j,\n",
+      "         2.19840636e-01 +6.89622678e-01j,\n",
+      "        -2.64578500e+00 +1.46933966e+00j,\n",
+      "         1.22168696e+00 +2.33458454e-01j,\n",
+      "         2.71303177e+00 +2.77858685e-01j,\n",
+      "         4.39426665e-01 +1.79924695e-01j,\n",
+      "        -1.73858667e+00 -3.03473636e-01j,\n",
+      "         2.68736300e-01 +8.32359634e-01j,\n",
+      "         2.57517784e+00 +1.88097576e-01j,\n",
+      "         2.76775469e-01 -6.49891901e-01j,\n",
+      "        -9.87790301e-01 +4.50328269e-01j,\n",
+      "         9.19033669e-01 +1.01453748e+00j,\n",
+      "        -1.35706747e+00 +5.90240590e-01j,\n",
+      "         6.60118738e-01 +5.10361208e-01j,\n",
+      "        -2.17652642e-01 -4.26632365e-01j,\n",
+      "        -1.39163540e+00 +9.12739752e-01j,\n",
+      "        -1.76241829e+00 -1.04835908e+00j,\n",
+      "        -7.28279529e-01 +2.75961750e-01j,\n",
+      "        -1.32043513e+00 -3.28790183e-01j,\n",
+      "         5.88227093e-01 +9.41865970e-01j,\n",
+      "         1.97695607e+00 +1.04535450e+00j,\n",
+      "        -3.02599128e+00 +1.02031744e+00j,\n",
+      "        -1.34308763e+00 -4.48222977e-01j,\n",
+      "        -1.86687861e+00 +2.26767568e+00j,\n",
+      "         5.61472023e-01 -5.28652462e-01j,\n",
+      "        -5.26747946e-01 +2.75545054e-01j,\n",
+      "         6.16116514e-01 +3.41765793e-01j,\n",
+      "         1.38709750e+00 +5.87537003e-01j,\n",
+      "        -2.27923203e-01 +7.19317677e-01j,\n",
+      "        -8.66404713e-01 -1.80932081e-01j,\n",
+      "        -4.58146668e-01 +1.75955171e+00j,\n",
+      "        -5.12526647e-01 -1.91939928e-01j,\n",
+      "        -6.26532699e-01 -5.71301355e-01j,\n",
+      "        -3.35566040e-01 -5.06173544e-01j,\n",
+      "        -3.86124497e-01 -8.00560567e-01j,\n",
+      "         2.47252265e+00 +3.83928843e-01j,\n",
+      "         3.35561169e-01 +1.19309445e+00j,\n",
+      "        -6.19075444e-01 -1.19021628e+00j,\n",
+      "        -1.17335297e+00 +5.80500783e-01j,\n",
+      "        -2.35111248e-01 -1.59785495e+00j,\n",
+      "        -1.24819583e-01 -8.05281256e-01j,\n",
+      "        -9.18606131e-01 -1.25312332e-01j,\n",
+      "         3.14400972e-01 +1.18650079e+00j,\n",
+      "         6.50630049e-01 -6.99853497e-01j,\n",
+      "         1.28269722e+00 -1.29832850e+00j,\n",
+      "         2.07880202e-01 +2.56385692e-02j,\n",
+      "        -1.45089461e+00 -9.35093103e-01j,\n",
+      "         6.84146951e-01 +7.74422758e-01j,\n",
+      "         9.45439196e-01 -3.50695577e-01j,\n",
+      "         1.00057580e+00 -1.27412936e+00j,\n",
+      "        -2.01665427e-01 +1.73591867e+00j,\n",
+      "         1.76623768e+00 -1.42964638e+00j,\n",
+      "         1.12282181e+00 +9.02767375e-01j,\n",
+      "        -2.22663798e+00 +7.34672692e-02j,\n",
+      "         8.26230180e-02 -7.46601441e-01j,\n",
+      "        -4.92810641e-01 -5.49544995e-01j,\n",
+      "         1.06703148e-01 +1.70461156e+00j,\n",
+      "        -6.84949746e-01 -1.46950185e-01j,\n",
+      "         1.47509281e+00 +8.77421590e-01j,\n",
+      "        -9.83722735e-03 -6.30597166e-01j,\n",
+      "         2.36006569e-03 +2.12883413e-01j,\n",
+      "        -3.67899785e-01 -9.95508241e-02j,\n",
+      "         1.26752410e+00 +2.49370098e-01j,\n",
+      "        -1.19439605e+00 +2.94935402e-01j,\n",
+      "        -2.28617888e+00 +5.70564449e-02j,\n",
+      "         3.95617530e-02 -2.80995512e-01j,\n",
+      "        -7.51027805e-01 -8.14567233e-01j,\n",
+      "        -1.24160227e+00 -4.13553574e-01j,  -4.76626111e-01 +4.16480574e-01j])\n",
+      "ipdb> u_src\n",
+      "*** NameError: name 'u_src' is not defined\n",
+      "ipdb> c\n"
+     ]
+    }
+   ],
+   "source": [
+    "%debug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adjoint test e formulation - getRHSDeriv_m\n",
+      "(-10077.7224119-28916.4723594j) (-10077.7224119-28916.4723594j) (1.81898940355e-12-3.63797880709e-12j) 1.0 True\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Run a test\n",
+    "TOL = 1e-4\n",
+    "FLR = 1e-20\n",
+    "\n",
+    "def getRHSDeriv_mAdjointTest():\n",
+    "    print 'Adjoint test e formulation - getRHSDeriv_m'\n",
+    "\n",
+    "    m  = np.log(np.ones(problem.mesh.nC)*0.01)\n",
+    "    if True:\n",
+    "        m  = m + np.random.randn(problem.mesh.nC)*0.01*1e-1 \n",
+    "\n",
+    "    u = problem.fields(m)\n",
+    "    v = np.random.randn(m1d.nN)#+np.random.randn(1)*1j\n",
+    "    # print prb.PropMap.PropModel.nP\n",
+    "    w = np.random.randn(m1d.nC)#+np.random.randn(m1d.nN)*1j\n",
+    "\n",
+    "    vJw = v.dot(problem.getRHSDeriv_m(freq,w))\n",
+    "    wJtv = w.dot(problem.getRHSDeriv_m(freq,v,adjoint=True))\n",
+    "    tol = np.max([TOL*(10**int(np.log10(np.abs(vJw)))),FLR]) \n",
+    "    print vJw, wJtv, vJw - wJtv, tol, np.abs(vJw - wJtv) < tol\n",
+    "    return np.abs(vJw - wJtv) < tol\n",
+    "getRHSDeriv_mAdjointTest( )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "simpeg.mkvc(np.random.randn(survey.nD)+np.random.randn(survey.nD)*1j,2)\n",
+    "\n",
+    "print survey.nD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "TOL = 1e-4\n",
+    "FLR = 1e-20\n",
+    "\n",
+    "def JvecAdjointTest():\n",
+    "    print 'Adjoint e formulation - Jvec' \n",
+    "\n",
+    "    m  = np.log(np.ones(problem.mesh.nC)*0.01)\n",
+    "    if True:\n",
+    "        m  = m + np.random.randn(problem.mesh.nC)*0.01*1e-1 \n",
+    "\n",
+    "    u = problem.fields(m)\n",
+    "\n",
+    "    v = np.random.rand(survey.nD)\n",
+    "    # print prb.PropMap.PropModel.nP\n",
+    "    w = np.random.rand(problem.mesh.nC)\n",
+    "\n",
+    "    vJw = v.dot(problem.Jvec(m, w, u))\n",
+    "    wJtv = w.dot(problem.Jtvec(m, v, u))\n",
+    "    tol = np.max([TOL*(10**int(np.log10(np.abs(vJw)))),FLR]) \n",
+    "    print vJw, wJtv, vJw - wJtv, tol, np.abs(vJw - wJtv) < tol\n",
+    "    return np.abs(vJw - wJtv) < tol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adjoint e formulation - Jvec\n",
+      "1.09508355274e-05 1.09508355274e-05 -1.01643953671e-20 1e-08 True\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "JvecAdjointTest()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
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 98%
rename from MT 1D code_testLayered.ipynb
rename to notebooks/MT 1D code_testLayered.ipynb
index 0170b5a9..7cf83993 100644
--- a/MT 1D code_testLayered.ipynb	
+++ b/notebooks/MT 1D code_testLayered.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -70,10 +70,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f28e1428f50>]"
+       "[<matplotlib.lines.Line2D at 0x7f2e0ac0b590>]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -186,7 +186,7 @@
        "ZSzcklQZC7ckVcbCLUmVsXBLUmX+H3zfa/Qjde8mAAAAAElFTkSuQmCC\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f28e25e0050>"
+       "<matplotlib.figure.Figure at 0x7f2e0b5d9810>"
       ]
      },
      "metadata": {},
@@ -199,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -217,7 +217,7 @@
        "         7888.671875  ,  11733.0078125 ,  17499.51171875])"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -228,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
@@ -260,7 +260,7 @@
        "(12836609.712174654+24128916.329733729j)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -271,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
@@ -343,7 +343,7 @@
        "          1.00000000e+00 -0.00000000e+00j]])"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -354,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -362,11 +362,11 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f28e1642950>,\n",
-       " <matplotlib.lines.Line2D at 0x7f28e1642410>]"
+       "[<matplotlib.lines.Line2D at 0x7f2e0a452750>,\n",
+       " <matplotlib.lines.Line2D at 0x7f2e0a4529d0>]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -562,7 +562,7 @@
        "ThxmZlYrThxmZlYrThxmZlYr/x/lWzp+eZujggAAAABJRU5ErkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f28e149ae50>"
+       "<matplotlib.figure.Figure at 0x7f2e0a4dcd10>"
       ]
      },
      "metadata": {},
@@ -576,7 +576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
@@ -584,10 +584,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f28e24abf10>]"
+       "[<matplotlib.lines.Line2D at 0x7f2e0a397350>]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -788,7 +788,7 @@
        "ZlYRFw4zM6uIC4eZmVXEhcPMzCriwmFmZhVx4TAzs4r8f6jZeSiIHLknAAAAAElFTkSuQmCC\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f28e166af10>"
+       "<matplotlib.figure.Figure at 0x7f2e0a4dc2d0>"
       ]
      },
      "metadata": {},
@@ -801,7 +801,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
@@ -809,11 +809,11 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f28e13c1750>,\n",
-       " <matplotlib.lines.Line2D at 0x7f28e13c19d0>]"
+       "[<matplotlib.lines.Line2D at 0x7f2e0a2dc1d0>,\n",
+       " <matplotlib.lines.Line2D at 0x7f2e0a2dc450>]"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1048,7 +1048,7 @@
        "c865pPGk4pxzLmk8qTjnnEsaTyrOOeeS5v8Bn8/HW3Alj/EAAAAASUVORK5CYII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f28e1672450>"
+       "<matplotlib.figure.Figure at 0x7f2e0a4dcb90>"
       ]
      },
      "metadata": {},
@@ -1061,7 +1061,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
@@ -1077,10 +1077,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f28e12fd450>]"
+       "[<matplotlib.lines.Line2D at 0x7f2e0a224050>]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1252,7 +1252,7 @@
        "rkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f28e13589d0>"
+       "<matplotlib.figure.Figure at 0x7f2e0a4dc310>"
       ]
      },
      "metadata": {},
@@ -1265,7 +1265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
@@ -1273,11 +1273,11 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f28e1261d50>,\n",
-       " <matplotlib.lines.Line2D at 0x7f28e1238110>]"
+       "[<matplotlib.lines.Line2D at 0x7f2e0a18c850>,\n",
+       " <matplotlib.lines.Line2D at 0x7f2e0a15cc50>]"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1485,7 +1485,7 @@
        "5P8DEIvS62qo2M0AAAAASUVORK5CYII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f28e1293c90>"
+       "<matplotlib.figure.Figure at 0x7f2e0a27e310>"
       ]
      },
      "metadata": {},
@@ -1498,7 +1498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
@@ -1506,10 +1506,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f28e1042c50>]"
+       "[<matplotlib.lines.Line2D at 0x7f2e0a77b990>]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1689,7 +1689,7 @@
        "rkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f28e11d9d50>"
+       "<matplotlib.figure.Figure at 0x7f2e0a1b9e90>"
       ]
      },
      "metadata": {},
@@ -1702,7 +1702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
@@ -1729,7 +1729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false
    },
@@ -1740,7 +1740,7 @@
        "(30,)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1751,7 +1751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -1762,7 +1762,7 @@
        "31"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1773,7 +1773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -1781,11 +1781,11 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f28e0f08c90>,\n",
-       " <matplotlib.lines.Line2D at 0x7f28e0f08f10>]"
+       "[<matplotlib.lines.Line2D at 0x7f2e0a501b10>,\n",
+       " <matplotlib.lines.Line2D at 0x7f2e0a501d90>]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 26,
      "metadata": {},
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     },
@@ -1985,7 +1985,7 @@
        "AABJRU5ErkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f28e0fdce10>"
+       "<matplotlib.figure.Figure at 0x7f2e0a5fe5d0>"
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      "metadata": {},
@@ -1998,7 +1998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
@@ -2016,7 +2016,7 @@
        "         9810.83984375,  14616.25976562])"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2049,18 +2049,6 @@
    "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,
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/notebooks/MT Script-3D_layerTest.ipynb b/notebooks/MT Script-3D_layerTest.ipynb
new file mode 100644
index 00000000..734097c2
--- /dev/null
+++ b/notebooks/MT Script-3D_layerTest.ipynb	
@@ -0,0 +1,1404 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Efficiency Warning: Interpolation will be slow, use setup.py!\n",
+      "\n",
+      "            python setup.py build_ext --inplace\n",
+      "    \n"
+     ]
+    }
+   ],
+   "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.,10),(100,5,1.5)],[(100,5,-1.5),(100.,10),(100,5,1.5)],[(100,5,1.6),(100.,10),(100,3,2)]], x0=['C','C',-3529.5360])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ -3.52953600e+03  -3.36953600e+03  -3.11353600e+03  -2.70393600e+03\n",
+      "  -2.04857600e+03  -1.00000000e+03  -9.00000000e+02  -8.00000000e+02\n",
+      "  -7.00000000e+02  -6.00000000e+02  -5.00000000e+02  -4.00000000e+02\n",
+      "  -3.00000000e+02  -2.00000000e+02  -1.00000000e+02   4.54747351e-13\n",
+      "   2.00000000e+02   6.00000000e+02   1.40000000e+03]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print M.vectorNz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'M' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-fe06aef8ad6f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Setup the model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mconds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1e-2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msimpeg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mModelBuilder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdefineBlock\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mM\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgridCC\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m400\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mconds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0msig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mM\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgridCC\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m>\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1e-8\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0msig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mM\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgridCC\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m<\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m600\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1e-1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'M' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# Setup the model\n",
+    "conds = [1e-2,1]\n",
+    "sig = simpeg.Utils.ModelBuilder.defineBlock(M.gridCC,[-200,-200,-400],[200,200,-200],conds)\n",
+    "sig[M.gridCC[:,2]>0] = 1e-8\n",
+    "sig[M.gridCC[:,2]<-600] = 1e-1\n",
+    "sigBG = np.zeros(M.nC) + conds[0]\n",
+    "sigBG[M.gridCC[:,2]>0] = 1e-8\n",
+    "colorbar(M.plotImage(log10(sig)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Receivers \n",
+    "# 3D impedance at the surface (elevation 0)\n",
+    "rxList = []\n",
+    "for rxType in ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi',]:\n",
+    "    rxList.append(simpegmt.SurveyMT.RxMT(simpeg.mkvc(np.array([0.0]),2).T,rxType))\n",
+    "# Source list\n",
+    "srcList =[]\n",
+    "tD = False\n",
+    "if tD:\n",
+    "    for freq in freqs:\n",
+    "        srcList.append(simpegmt.SurveyMT.srcMT_polxy_1DhomotD(rxList,freq))\n",
+    "else:\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": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Make the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Setup y (north) polarization (_y)\n",
+    "ex_y = np.zeros(M.nEx, dtype='complex128')\n",
+    "ey_y = np.zeros((M.vnEy), dtype='complex128')\n",
+    "ez_y = np.zeros(M.nEz, dtype='complex128')\n",
+    "# Assign the source to ex_x\n",
+    "for i in arange(M.vnEy[0]):\n",
+    "    for j in arange(M.vnEy[1]):\n",
+    "        ey_y[i,j,:] = e0_1d        \n",
+    "# eBG_y = np.vstack((ex_y,Utils.mkvc(M.r(ey_y,'Ey','Ey','V'),2),ez_y))\n",
+    "# rhs_y = ABG.dot(eBG_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "eBG_y = np.r_[ex_y,simpeg.Utils.mkvc(ey_y),ez_y]\n",
+    "rhs_y = ABG.dot(eBG_y)"
+   ]
+  },
+  {
+   "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 1min 16s, sys: 761 ms, total: 1min 17s\n",
+      "Wall time: 1min 18s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# Solve the systems for each polarization\n",
+    "Ainv = simpeg.SolverLU(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 497 ms, sys: 1 ms, total: 498 ms\n",
+      "Wall time: 534 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "e_x = Ainv*rhs_x\n",
+    "e_y = Ainv*rhs_y"
+   ]
+  },
+  {
+   "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": [],
+   "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": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rYzvWaXlzgJzv9uriF8DbsMWr/imtp2P36jFMof4bxt73OuyedWPhJD5Mfn2+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\n6wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f2471e8ce10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
+    "dat0 = M.plotSlice(abs(e_x_CC), vType='CCv', view='vec', streamOpts={'color': 'k'}, normal='X', ax = ax[0])\n",
+    "cb0 = plt.colorbar(dat0[0], ax = ax[0])\n",
+    "dat1 = M.plotSlice(abs(j_x_CC), vType='CCv', view='vec', streamOpts={'color': 'k'}, normal='X', 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": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the data\n",
+    "rx_x, rx_y = np.meshgrid(np.arange(-500,501,50),np.arange(-500,501,50))\n",
+    "rx_loc = np.hstack((simpeg.Utils.mkvc(rx_x,2),simpeg.Utils.mkvc(rx_y,2),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": 20,
+   "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",
+    "h_x_loc = np.hstack([simpeg.Utils.mkvc(Qfx*C*e_x,2),simpeg.Utils.mkvc(Qfy*C*e_x,2),simpeg.Utils.mkvc(Qfz*C*e_x,2)])\n",
+    "h_y_loc = np.hstack([simpeg.Utils.mkvc(Qfx*C*e_y,2),simpeg.Utils.mkvc(Qfy*C*e_y,2),simpeg.Utils.mkvc(Qfz*C*e_y,2)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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": 22,
+   "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": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 103.21972098+93.23211088j,  102.90475057+93.32910177j,\n",
+       "        102.58986124+93.42601702j,  102.39619991+93.42582359j,\n",
+       "        102.20255534+93.42570773j,  102.09543991+93.39475409j,\n",
+       "        101.98830179+93.36384863j,  101.93711201+93.33580298j,\n",
+       "        101.88590678+93.30777082j,  101.87105299+93.29712749j,\n",
+       "        101.85619645+93.28648454j,  101.87105299+93.29712749j,\n",
+       "        101.88590678+93.30777082j,  101.93711201+93.33580298j,\n",
+       "        101.98830179+93.36384863j,  102.09543991+93.39475409j,\n",
+       "        102.20255534+93.42570773j,  102.39619991+93.42582359j,\n",
+       "        102.58986124+93.42601702j,  102.90475057+93.32910177j,\n",
+       "        103.21972098+93.23211088j,  103.18844918+93.25637175j,\n",
+       "        102.87026907+93.35333401j,  102.55217722+93.45021873j,\n",
+       "        102.35592664+93.44923714j,  102.15969494+93.44833625j,\n",
+       "        102.05084774+93.41639946j,  101.94197761+93.38451306j,\n",
+       "        101.88984420+93.35571748j,  101.83769506+93.32693623j,\n",
+       "        101.82254720+93.31601774j,  101.80739677+93.30509988j,\n",
+       "        101.82254720+93.31601774j,  101.83769506+93.32693623j,\n",
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+       "        103.21972098+93.23211088j])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zxy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "       -103.18370715-93.264238j  , -103.18844918-93.25637175j,\n",
+       "       -103.21972098-93.23211088j])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zyx\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ -2.84217094e-14 -1.13686838e-13j,\n",
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+       "        -1.98328238e-01 -4.90943742e-03j,\n",
+       "         7.53175300e-13 -4.93116659e-12j,\n",
+       "         1.89983971e-01 +8.97376924e-03j,\n",
+       "         5.02091486e-01 -8.06075906e-02j,\n",
+       "         7.82757885e-01 -1.53723302e-01j,\n",
+       "         5.93845915e-01 -1.61779013e-01j,\n",
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+       "         2.27373675e-13 -3.41060513e-13j,\n",
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+       "        -7.16250356e-01 -1.44728566e-01j,\n",
+       "        -7.32429175e-01 -1.56591360e-01j,\n",
+       "        -7.47738834e-01 -1.67682421e-01j,\n",
+       "        -7.32429175e-01 -1.56591360e-01j,\n",
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+       "        -3.87656331e-01 -1.36804967e-02j,\n",
+       "        -1.89983971e-01 -8.97376924e-03j,\n",
+       "        -4.40536496e-13 +1.37845291e-12j,\n",
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+       "         5.93845915e-01 -1.61779013e-01j,\n",
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+       "         9.94759830e-14 +2.55795385e-13j,\n",
+       "        -3.11968778e-01 +8.92349727e-02j,\n",
+       "        -5.02091486e-01 +8.06075906e-02j,\n",
+       "        -6.99118138e-01 +7.62683974e-02j,\n",
+       "        -8.08757489e-01 +4.08955418e-02j,\n",
+       "        -9.19251597e-01 +7.16808566e-03j,\n",
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+       "        -1.02611931e+00 -5.30845919e-02j,\n",
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+       "        -9.19251597e-01 +7.16808566e-03j,\n",
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+       "        -3.11968778e-01 +8.92349727e-02j,\n",
+       "        -2.62900812e-12 +3.09796633e-12j,\n",
+       "         2.83698609e-01 -7.27300238e-02j,\n",
+       "         2.13162821e-13 +1.42108547e-14j,\n",
+       "        -2.83698609e-01 +7.27300238e-02j,\n",
+       "        -5.93845915e-01 +1.61779013e-01j,\n",
+       "        -7.82757885e-01 +1.53723302e-01j,\n",
+       "        -9.77788266e-01 +1.49975229e-01j,\n",
+       "        -1.08628561e+00 +1.15392123e-01j,\n",
+       "        -1.19529669e+00 +8.25155575e-02j,\n",
+       "        -1.24835785e+00 +5.25007978e-02j,\n",
+       "        -1.30061689e+00 +2.35682982e-02j,\n",
+       "        -1.31652401e+00 +1.20259957e-02j,\n",
+       "        -1.33138063e+00 +1.38247490e-03j,\n",
+       "        -1.31652401e+00 +1.20259957e-02j,\n",
+       "        -1.30061689e+00 +2.35682982e-02j,\n",
+       "        -1.24835785e+00 +5.25007978e-02j,\n",
+       "        -1.19529669e+00 +8.25155575e-02j,\n",
+       "        -1.08628561e+00 +1.15392123e-01j,\n",
+       "        -9.77788266e-01 +1.49975229e-01j,\n",
+       "        -7.82757885e-01 +1.53723302e-01j,\n",
+       "        -5.93845915e-01 +1.61779013e-01j,\n",
+       "        -2.83698609e-01 +7.27300238e-02j,  -3.48165941e-12 +5.25801624e-13j])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zxy + zyx\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/MT Script-3D_twoHalfspace-simpegMT.ipynb b/notebooks/MT Script-3D_twoHalfspace-simpegMT.ipynb
new file mode 100644
index 00000000..36847d04
--- /dev/null
+++ b/notebooks/MT Script-3D_twoHalfspace-simpegMT.ipynb	
@@ -0,0 +1,2977 @@
+{
+ "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": [
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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",
+    "m\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/notebooks/MT1D inversion test.ipynb b/notebooks/MT1D inversion test.ipynb
new file mode 100644
index 00000000..fae17ea4
--- /dev/null
+++ b/notebooks/MT1D inversion test.ipynb	
@@ -0,0 +1,790 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import SimPEG as simpeg\n",
+    "import simpegMT as simpegmt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "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",
+    "tD = False\n",
+    "if tD:\n",
+    "    for freq in freqs:\n",
+    "        srcList.append(simpegmt.SurveyMT.srcMT_polxy_1DhomotD(rxList,freq))\n",
+    "else:\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": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m1d.nC"
+   ]
+  },
+  {
+   "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 = 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",
+    "# 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",
+    "\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 0x7fcfc8df5650>"
+      ]
+     },
+     "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  4.83e+05  1.46e+06  6.90e-07  1.46e+06    2.09e+05      0              \n",
+      "   1  4.83e+05  1.80e+05  5.76e-06  1.80e+05    2.64e+04      0              \n",
+      "   2  4.83e+05  1.29e+05  7.58e-06  1.29e+05    2.05e+04      0   Skip BFGS  \n",
+      "   3  6.04e+04  1.10e+05  8.89e-06  1.10e+05    1.82e+04      0   Skip BFGS  \n",
+      "   4  6.04e+04  5.48e+04  1.97e-05  5.48e+04    1.05e+04      0   Skip BFGS  \n",
+      "   5  6.04e+04  4.57e+04  2.46e-05  4.57e+04    9.11e+03      0   Skip BFGS  \n",
+      "   6  7.55e+03  4.04e+04  2.88e-05  4.04e+04    8.25e+03      0   Skip BFGS  \n",
+      "   7  7.55e+03  2.22e+04  6.11e-05  2.22e+04    5.14e+03      0   Skip BFGS  \n",
+      "   8  7.55e+03  1.81e+04  7.86e-05  1.81e+04    4.38e+03      0   Skip BFGS  \n",
+      "   9  9.44e+02  1.58e+04  9.29e-05  1.58e+04    3.94e+03      0   Skip BFGS  \n",
+      "  10  9.44e+02  8.37e+03  1.97e-04  8.37e+03    2.43e+03      0   Skip BFGS  \n",
+      "  11  9.44e+02  6.71e+03  2.54e-04  6.71e+03    2.05e+03      0   Skip BFGS  \n",
+      "  12  1.18e+02  5.78e+03  3.01e-04  5.78e+03    1.83e+03      0   Skip BFGS  \n",
+      "  13  1.18e+02  3.04e+03  6.06e-04  3.04e+03    1.11e+03      0   Skip BFGS  \n",
+      "  14  1.18e+02  2.42e+03  7.75e-04  2.42e+03    9.25e+02      0   Skip BFGS  \n",
+      "  15  1.47e+01  2.09e+03  9.10e-04  2.09e+03    8.15e+02      0   Skip BFGS  \n",
+      "  16  1.47e+01  1.21e+03  1.66e-03  1.21e+03    4.81e+02      0   Skip BFGS  \n",
+      "  17  1.47e+01  9.97e+02  2.07e-03  9.97e+02    3.82e+02      0   Skip BFGS  \n",
+      "  18  1.84e+00  8.79e+02  2.36e-03  8.79e+02    3.30e+02      0   Skip BFGS  \n",
+      "  19  1.84e+00  6.16e+02  3.62e-03  6.16e+02    1.99e+02      0   Skip BFGS  \n",
+      "  20  1.84e+00  5.31e+02  4.24e-03  5.31e+02    1.64e+02      0   Skip BFGS  \n",
+      "  21  2.30e-01  4.79e+02  4.68e-03  4.79e+02    1.45e+02      0   Skip BFGS  \n",
+      "  22  2.30e-01  3.38e+02  6.36e-03  3.38e+02    1.01e+02      0   Skip BFGS  \n",
+      "  23  2.30e-01  2.80e+02  7.02e-03  2.80e+02    8.93e+01      0   Skip BFGS  \n",
+      "  24  2.88e-02  2.32e+02  7.55e-03  2.32e+02    8.14e+01      0   Skip BFGS  \n",
+      "  25  2.88e-02  1.53e+02  1.08e-02  1.53e+02    6.94e+01      0   Skip BFGS  \n",
+      "  26  2.88e-02  8.67e+01  1.27e-02  8.67e+01    3.84e+01      0              \n",
+      "  27  3.60e-03  6.54e+01  1.40e-02  6.54e+01    3.13e+01      0   Skip BFGS  \n",
+      "  28  3.60e-03  3.48e+01  1.69e-02  3.48e+01    2.06e+01      0   Skip BFGS  \n",
+      "  29  3.60e-03  2.63e+01  1.86e-02  2.63e+01    1.07e+01      0   Skip BFGS  \n",
+      "  30  4.50e-04  2.19e+01  2.17e-02  2.19e+01    7.17e+00      0   Skip BFGS  \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 4.3709e+00 <= tolF*(1+|f0|) = 1.4560e+05\n",
+      "1 : |xc-x_last| = 2.1874e+00 <= tolX*(1+|x0|) = 5.9957e+00\n",
+      "0 : |proj(x-g)-x|    = 7.1673e+00 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 7.1673e+00 <= 1e3*eps       = 1.0000e-02\n",
+      "1 : maxIter   =      30    <= iter          =     30\n",
+      "------------------------- DONE! -------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Runn the inversion\n",
+    "mopt = inv.run(m_0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'left': <function SimPEG.Optimization.<lambda>>,\n",
+       "  'right': <function SimPEG.Optimization.<lambda>>,\n",
+       "  'stopType': 'critical',\n",
+       "  'str': '%d : maxIter   =     %3d    <= iter          =    %3d'}]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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  9.87e+02  2.19e+01  2.35e-02  4.51e+01    3.23e+01      0              \n",
+      "   1  9.87e+02  2.38e+01  1.74e-02  4.09e+01    7.61e+00      0              \n",
+      "   2  9.87e+02  2.30e+01  1.79e-02  4.06e+01    2.68e+00      0              \n",
+      "   3  1.23e+02  2.28e+01  1.79e-02  2.50e+01    1.54e+01      0              \n",
+      "   4  1.23e+02  2.07e+01  2.24e-02  2.35e+01    5.36e+00      0              \n",
+      "   5  1.23e+02  2.03e+01  2.22e-02  2.30e+01    6.78e+00      0   Skip BFGS  \n",
+      "   6  1.54e+01  2.00e+01  2.33e-02  2.03e+01    7.34e+00      0   Skip BFGS  \n",
+      "   7  1.54e+01  1.95e+01  3.01e-02  2.00e+01    7.09e+00      0              \n",
+      "   8  1.54e+01  1.92e+01  3.30e-02  1.97e+01    5.00e+00      0              \n",
+      "   9  1.93e+00  1.91e+01  3.72e-02  1.91e+01    5.68e+00      0              \n",
+      "  10  1.93e+00  1.88e+01  6.62e-02  1.89e+01    8.43e+00      1              \n",
+      "  11  1.93e+00  1.82e+01  1.12e-01  1.84e+01    5.37e+00      0              \n",
+      "  12  2.41e-01  1.81e+01  1.21e-01  1.81e+01    5.14e+00      0              \n",
+      "  13  2.41e-01  1.80e+01  1.18e-01  1.80e+01    3.93e+00      0              \n",
+      "  14  2.41e-01  1.80e+01  1.43e-01  1.80e+01    6.08e+00      0              \n",
+      "  15  3.01e-02  1.78e+01  1.19e-01  1.78e+01    4.97e+00      1              \n",
+      "  16  3.01e-02  1.78e+01  1.13e-01  1.78e+01    2.41e+00      0   Skip BFGS  \n",
+      "  17  3.01e-02  1.78e+01  1.37e-01  1.78e+01    4.47e+00      0              \n",
+      "  18  3.77e-03  1.76e+01  1.28e-01  1.76e+01    6.24e+00      0   Skip BFGS  \n",
+      "  19  3.77e-03  1.76e+01  1.07e-01  1.76e+01    7.34e+00      0              \n",
+      "  20  3.77e-03  1.75e+01  1.25e-01  1.75e+01    6.14e+00      1              \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 4.7935e-02 <= tolF*(1+|f0|) = 4.6080e+00\n",
+      "1 : |xc-x_last| = 3.4384e+00 <= tolX*(1+|x0|) = 4.0560e+00\n",
+      "0 : |proj(x-g)-x|    = 6.1413e+00 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 6.1413e+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": 24,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "moptc=mopt2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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": 27,
+   "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  1.79e-02  1.75e+01  1.23e-01  1.75e+01    6.14e+00      0              \n",
+      "   1  1.79e-02  1.75e+01  1.23e-01  1.75e+01    6.14e+00      3              \n",
+      "   2  1.79e-02  1.75e+01  1.23e-01  1.75e+01    6.15e+00      3   Skip BFGS  \n",
+      "   3  2.23e-03  1.75e+01  1.23e-01  1.75e+01    6.16e+00      3   Skip BFGS  \n",
+      "   4  2.23e-03  1.75e+01  1.23e-01  1.75e+01    6.17e+00      3   Skip BFGS  \n",
+      "   5  2.23e-03  1.75e+01  1.23e-01  1.75e+01    6.17e+00      3   Skip BFGS  \n",
+      "   6  2.79e-04  1.75e+01  1.23e-01  1.75e+01    6.17e+00      3   Skip BFGS  \n",
+      "   7  2.79e-04  1.75e+01  1.23e-01  1.75e+01    6.17e+00      3   Skip BFGS  \n",
+      "   8  2.79e-04  1.75e+01  1.23e-01  1.75e+01    6.17e+00      3   Skip BFGS  \n",
+      "   9  3.49e-05  1.75e+01  1.23e-01  1.75e+01    6.17e+00      3   Skip BFGS  \n",
+      "  10  3.49e-05  1.75e+01  1.23e-01  1.75e+01    6.17e+00      3   Skip BFGS  \n",
+      "  11  3.49e-05  1.75e+01  1.23e-01  1.75e+01    6.17e+00      3   Skip BFGS  \n",
+      "  12  4.36e-06  1.75e+01  1.23e-01  1.75e+01    6.16e+00      3   Skip BFGS  \n",
+      "  13  4.36e-06  1.75e+01  1.23e-01  1.75e+01    6.16e+00      3   Skip BFGS  \n",
+      "  14  4.36e-06  1.75e+01  1.23e-01  1.75e+01    6.29e+00      2   Skip BFGS  \n",
+      "  15  5.46e-07  1.75e+01  1.23e-01  1.75e+01    6.32e+00      2   Skip BFGS  \n",
+      "  16  5.46e-07  1.75e+01  1.23e-01  1.75e+01    6.31e+00      2   Skip BFGS  \n",
+      "  17  5.46e-07  1.75e+01  1.23e-01  1.75e+01    6.29e+00      2   Skip BFGS  \n",
+      "  18  6.82e-08  1.75e+01  1.23e-01  1.75e+01    6.26e+00      2   Skip BFGS  \n",
+      "  19  6.82e-08  1.75e+01  1.23e-01  1.75e+01    6.22e+00      2   Skip BFGS  \n",
+      "  20  6.82e-08  1.75e+01  1.23e-01  1.75e+01    6.18e+00      2   Skip BFGS  \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 3.9817e-03 <= tolF*(1+|f0|) = 1.8547e+00\n",
+      "0 : |xc-x_last| = 4.1858e+01 <= tolX*(1+|x0|) = 5.7798e+00\n",
+      "0 : |proj(x-g)-x|    = 6.1825e+00 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 6.1825e+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": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Counters:\n",
+      "  InexactGaussNewton.doEndIteration       :       30\n",
+      "  InexactGaussNewton.doStartIteration     :       31\n",
+      "  InexactGaussNewton.scaleSearchDirection :       30\n",
+      "\n",
+      "Times:                                        mean      sum\n",
+      "  BaseInvProblem.evalFunction             : 3.35e+00, 2.04e+02,   61x\n",
+      "  InexactGaussNewton.findSearchDirection  : 2.11e+01, 6.34e+02,   30x\n",
+      "  InexactGaussNewton.minimize             : 8.39e+02, 8.39e+02,    1x\n",
+      "  InexactGaussNewton.modifySearchDirection: 1.90e+00, 5.71e+01,   30x\n",
+      "  InexactGaussNewton.projection           : 4.65e-05, 5.86e-03,  126x\n"
+     ]
+    }
+   ],
+   "source": [
+    "opt.counter.summary()\n",
+    "xc = opt.recall('xc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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": 13,
+   "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": 14,
+   "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": [
+    "plotMT1DModelData(problem,[mopt,mopt2])\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "modelList = [problem.survey.mtrue]\n",
+    "modelList.extend([mopt])\n",
+    "# problem.mapping.sigmaMap*mopt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['zyxr'], ['zyxi']]"
+      ]
+     },
+     "execution_count": 16,
+     "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": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'zyxr'"
+      ]
+     },
+     "execution_count": 17,
+     "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
+}
diff --git a/MT1DfwdProblem.ipynb b/notebooks/MT1DfwdProblem.ipynb
similarity index 100%
rename from MT1DfwdProblem.ipynb
rename to notebooks/MT1DfwdProblem.ipynb
diff --git a/notebooks/MT3D derivatives test.ipynb b/notebooks/MT3D derivatives test.ipynb
new file mode 100644
index 00000000..91f11d1b
--- /dev/null
+++ b/notebooks/MT3D derivatives test.ipynb	
@@ -0,0 +1,938 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import SimPEG as simpeg\n",
+    "import simpegEM as simpegem, simpegMT as simpegmt\n",
+    "from SimPEG.Utils import meshTensor\n",
+    "import numpy as np\n",
+    "import simpegMT.Tests.test_Problem3D_againstAnalytic as t3Dmt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sigmaHalf = 0.01\n",
+    "survey, problem = t3Dmt.setupSimpegMTfwd_eForm_ps(t3Dmt.halfSpace(sigmaHalf),comp='zxyi',singleFreq=1)\n",
+    "if False:\n",
+    "    fields = problem.fields(problem.curModel.sigma)\n",
+    "    data = survey.dpred(problem.curModel.sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    3.111e+11     3.481e+10      nan\n",
+      " 1   1.00e-02    3.112e+10     3.421e+08      2.008\n",
+      " 2   1.00e-03    3.116e+09     3.420e+06      2.000\n",
+      "========================= PASS! =========================\n",
+      "Once upon a time, a happy little test passed.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def testDeriv_dA_dm(prb,cond):\n",
+    "    TOL = 1e-4\n",
+    "    FLR = 1e-20\n",
+    "    x0 = np.log(np.ones(prb.mesh.nC)*cond)\n",
+    "    prb.mapping = simpeg.Maps.ExpMap(prb.mesh)\n",
+    "    if True:\n",
+    "        x0  = x0 + np.random.randn(prb.mesh.nC)*cond*1e-1 \n",
+    "    survey = prb.survey\n",
+    "    src = survey.srcList[0]\n",
+    "    freq = src.freq\n",
+    "    v1 = np.random.randn(prb.mesh.nE,1)\n",
+    "    v2 = np.random.randn(prb.mesh.nE,1)\n",
+    "    v = np.hstack(( simpeg.mkvc(v1,2), simpeg.mkvc(v2,2)))\n",
+    "    u_0 = prb.fieldsPair(prb.mesh,prb.survey)\n",
+    "    u_0[src,'e_pxSolution'] = v1\n",
+    "    u_0[src,'e_pySolution'] = v2\n",
+    "    def fun(x):\n",
+    "        prb.curModel = x\n",
+    "        A = prb.getA(freq) #\n",
+    "#         return simpeg.mkvc(A*v1)+simpeg.mkvc(A*v2), lambda t: simpeg.mkvc(prb.getADeriv_m(freq, u_0[src], t))\n",
+    "        return A*v, lambda t: (prb.getADeriv_m(freq, u_0[src], t))\n",
+    "    return simpeg.Tests.checkDerivative(fun, x0, num=3, plotIt=False, eps=FLR)\n",
+    "testDeriv_dA_dm(problem,0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    3.187e+11     3.398e+10      nan\n",
+      " 1   1.00e-02    3.176e+10     3.369e+08      2.004\n",
+      " 2   1.00e-03    3.178e+09     3.372e+06      2.000\n",
+      "========================= PASS! =========================\n",
+      "That was easy!\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def testDeriv_dRHS_dm(prb,cond):\n",
+    "    TOL = 1e-4\n",
+    "    FLR = 1e-20\n",
+    "    x0 = np.log(np.ones(prb.mesh.nC)*cond)\n",
+    "    prb.mapping = simpeg.Maps.ExpMap(prb.mesh)\n",
+    "    if True:\n",
+    "        x0  = x0 + np.random.randn(prb.mesh.nC)*cond*1e-1 \n",
+    "    survey = prb.survey\n",
+    "    src = survey.srcList[0]\n",
+    "    \n",
+    "    u0 = prb.fields(x0)\n",
+    "    freq = src.freq\n",
+    "    A = prb.getA(freq) #\n",
+    "    A_I = prb.Solver(A, **prb.solverOpts)\n",
+    "\n",
+    "    ftype = prb._fieldType + 'Solution'\n",
+    "    u0_src = u0[src, ftype]\n",
+    "    v = np.random.randn(prb.mesh.nE,1)\n",
+    "    \n",
+    "    def fun(x):\n",
+    "        prb.curModel = x\n",
+    "        return simpeg.mkvc(np.sum(prb.getRHS(freq))), lambda x: simpeg.mkvc(prb.getRHSDeriv_m(freq, x))\n",
+    "#         return simpeg.mkvc(prb.fields(x)[src,prb._fieldType + 'Solution']), lambda x: simpeg.mkvc(prb.getADeriv_m(freq, u0_src, x))\n",
+    "    return simpeg.Tests.checkDerivative(fun, x0, num=3, plotIt=False, eps=FLR)\n",
+    "testDeriv_dA_dm(problem,0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    2.873e+10     3.211e+09      nan\n",
+      " 1   1.00e-02    2.879e+09     3.204e+07      2.001\n",
+      " 2   1.00e-03    2.882e+08     3.207e+05      2.000\n",
+      "========================= PASS! =========================\n",
+      "You are awesome.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def testDeriv_S_e(prb,cond):\n",
+    "    # Initate things for the derivs Test\n",
+    "    TOL = 1e-4\n",
+    "    FLR = 1e-20\n",
+    "    \n",
+    "    src = prb.survey.srcList[0]\n",
+    "    rx = src.rxList[0]\n",
+    "\n",
+    "    x0 = np.log(np.ones(prb.mesh.nC)*cond)\n",
+    "#     prb.mapping = simpeg.Maps.ExpMap(prb.mesh)\n",
+    "    if True:\n",
+    "        x0  = x0 + np.random.randn(prb.mesh.nC)*cond*1e-1 \n",
+    "    def fun(x):\n",
+    "        prb.curModel = x\n",
+    "        return src.S_e(prb), lambda t: src.S_eDeriv_m(prb,t)\n",
+    "    simpeg.Tests.checkDerivative(fun,x0,num=3,plotIt=False)\n",
+    "survey, problem = t3Dmt.setupSimpegMTfwd_eForm_ps(t3Dmt.random(1),comp='All',singleFreq=1)\n",
+    "testDeriv_S_e(problem,0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    1.436e+01     1.792e-11      nan\n",
+      " 1   1.00e-02    1.436e+00     1.792e-11      0.000\n",
+      " 2   1.00e-03    1.436e-01     1.730e-11      0.015\n",
+      "========================= PASS! =========================\n",
+      "That was easy!\n",
+      "\n",
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    1.433e+01     1.792e-11      nan\n",
+      " 1   1.00e-02    1.433e+00     1.776e-11      0.004\n",
+      " 2   1.00e-03    1.433e-01     1.763e-11      0.003\n",
+      "========================= PASS! =========================\n",
+      "The test be workin!\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def testDeriv_epx(prb):\n",
+    "    # Initate things for the derivs Test\n",
+    "    src = prb.survey.srcList[0]\n",
+    "    rx = src.rxList[0]\n",
+    "    rx.locs = np.array([[0.,0.,0,],[1.,1.,1.]])\n",
+    "    u0x = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j\n",
+    "    u0y = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j\n",
+    "#     u0 = np.vstack((simpeg.mkvc(u0x,2),simpeg.mkvc(u0y,2)))\n",
+    "    u0 = np.r_[u0x, u0y]\n",
+    "    f0 = prb.fieldsPair(survey.mesh,survey)\n",
+    "    # u0 = np.hstack((simpeg.mkvc(u0_px,2),simpeg.mkvc(u0_py,2)))\n",
+    "    f0[src,'e_pxSolution'] =  u0[:len(u0)/2]#u0x\n",
+    "    f0[src,'e_pySolution'] = u0[len(u0)/2::]#u0y\n",
+    "    # f0[src,'b_1d'] = -1/(1j*simpegem.Utils.EMUtils.omega(src.freq))*m1d.nodalGrad*u0\n",
+    "    # Run a test\n",
+    "    def fun(u):\n",
+    "        f = prb.fieldsPair(survey.mesh,survey)\n",
+    "        f[src,'e_pxSolution'] = u[:len(u)/2]\n",
+    "        f[src,'e_pySolution'] = u[len(u)/2::]\n",
+    "    #     f[src,'b_1d'] = -(m1d.nodalGrad*u)/(1j*simpegem.Utils.EMUtils.omega(src.freq))\n",
+    "        return f._e_px(f[src,'e_pxSolution'],[src]).ravel(), lambda t: f._e_pxDeriv_u([src],t)[:len(u0)/2].ravel()\n",
+    "    simpeg.Tests.checkDerivative(fun,u0,num=3,plotIt=False)\n",
+    "    \n",
+    "def testDeriv_epy(prb):\n",
+    "    # Initate things for the derivs Test\n",
+    "    src = prb.survey.srcList[0]\n",
+    "    rx = src.rxList[0]\n",
+    "    rx.locs = np.array([[0.,0.,0,],[1.,1.,1.]])\n",
+    "    u0x = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j\n",
+    "    u0y = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j\n",
+    "#     u0 = np.vstack((simpeg.mkvc(u0x,2),simpeg.mkvc(u0y,2)))\n",
+    "    u0 = np.r_[u0x, u0y]\n",
+    "    f0 = prb.fieldsPair(survey.mesh,survey)\n",
+    "    # u0 = np.hstack((simpeg.mkvc(u0_px,2),simpeg.mkvc(u0_py,2)))\n",
+    "    f0[src,'e_pxSolution'] =  u0[:len(u0)/2]#u0x\n",
+    "    f0[src,'e_pySolution'] = u0[len(u0)/2::]#u0y\n",
+    "    # f0[src,'b_1d'] = -1/(1j*simpegem.Utils.EMUtils.omega(src.freq))*m1d.nodalGrad*u0\n",
+    "    # Run a test\n",
+    "    def fun(u):\n",
+    "        f = prb.fieldsPair(survey.mesh,survey)\n",
+    "        f[src,'e_pxSolution'] = u[:len(u)/2]\n",
+    "        f[src,'e_pySolution'] = u[len(u)/2::]\n",
+    "        return f._e_py(f[src,'e_pySolution'],[src]).ravel(), lambda t: f._e_pyDeriv_u([src],t).ravel()\n",
+    "    simpeg.Tests.checkDerivative(fun,u0,num=3,plotIt=False)\n",
+    "    \n",
+    "survey, problem = t3Dmt.setupSimpegMTfwd_eForm_ps(t3Dmt.random(.1),comp='zxyr',singleFreq=.01)\n",
+    "testDeriv_epx(problem)\n",
+    "testDeriv_epy(problem)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    3.995e-01     1.107e-13      nan\n",
+      " 1   1.00e-02    3.995e-02     1.105e-13      0.001\n",
+      " 2   1.00e-03    3.995e-03     1.110e-13      -0.002\n",
+      "========================= PASS! =========================\n",
+      "The test be workin!\n",
+      "\n",
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    4.075e-01     1.086e-13      nan\n",
+      " 1   1.00e-02    4.075e-02     1.049e-13      0.015\n",
+      " 2   1.00e-03    4.075e-03     1.025e-13      0.010\n",
+      "========================= PASS! =========================\n",
+      "Happy little convergence test!\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def testDeriv_bpx(prb):\n",
+    "    # Initate things for the derivs Test\n",
+    "    src = prb.survey.srcList[0]\n",
+    "    rx = src.rxList[0]\n",
+    "    rx.locs = np.array([[0.,0.,0,],[1.,1.,1.]])\n",
+    "    u0x = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j\n",
+    "    u0y = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j\n",
+    "#     u0 = np.vstack((simpeg.mkvc(u0x,2),simpeg.mkvc(u0y,2)))\n",
+    "    u0 = np.r_[u0x, u0y]\n",
+    "    f0 = prb.fieldsPair(survey.mesh,survey)\n",
+    "    # u0 = np.hstack((simpeg.mkvc(u0_px,2),simpeg.mkvc(u0_py,2)))\n",
+    "    f0[src,'e_pxSolution'] =  u0[:len(u0)/2]#u0x\n",
+    "    f0[src,'e_pySolution'] = u0[len(u0)/2::]#u0y\n",
+    "    # f0[src,'b_1d'] = -1/(1j*simpegem.Utils.EMUtils.omega(src.freq))*m1d.nodalGrad*u0\n",
+    "    # Run a test\n",
+    "    def fun(u):\n",
+    "        f = prb.fieldsPair(survey.mesh,survey)\n",
+    "        f[src,'e_pxSolution'] = u[:len(u)/2]\n",
+    "        f[src,'e_pySolution'] = u[len(u)/2::]\n",
+    "    #     f[src,'b_1d'] = -(m1d.nodalGrad*u)/(1j*simpegem.Utils.EMUtils.omega(src.freq))\n",
+    "        return f._b_px(f[src,'e_pxSolution'],[src]).ravel(), lambda t: f._b_pxDeriv_u(src,t).ravel()\n",
+    "    simpeg.Tests.checkDerivative(fun,u0,num=3,plotIt=False)\n",
+    "    \n",
+    "def testDeriv_bpy(prb):\n",
+    "    # Initate things for the derivs Test\n",
+    "    src = prb.survey.srcList[0]\n",
+    "    rx = src.rxList[0]\n",
+    "    rx.locs = np.array([[0.,0.,0,],[1.,1.,1.]])\n",
+    "    u0x = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j\n",
+    "    u0y = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j\n",
+    "#     u0 = np.vstack((simpeg.mkvc(u0x,2),simpeg.mkvc(u0y,2)))\n",
+    "    u0 = np.r_[u0x, u0y]\n",
+    "    f0 = prb.fieldsPair(survey.mesh,survey)\n",
+    "    # u0 = np.hstack((simpeg.mkvc(u0_px,2),simpeg.mkvc(u0_py,2)))\n",
+    "    f0[src,'e_pxSolution'] =  u0[:len(u0)/2]#u0x\n",
+    "    f0[src,'e_pySolution'] = u0[len(u0)/2::]#u0y\n",
+    "    # f0[src,'b_1d'] = -1/(1j*simpegem.Utils.EMUtils.omega(src.freq))*m1d.nodalGrad*u0\n",
+    "    # Run a test\n",
+    "    def fun(u):\n",
+    "        f = prb.fieldsPair(survey.mesh,survey)\n",
+    "        f[src,'e_pxSolution'] = u[:len(u)/2]\n",
+    "        f[src,'e_pySolution'] = u[len(u)/2::]\n",
+    "        return f._b_py(f[src,'e_pySolution'],[src]).ravel(), lambda t: f._b_pyDeriv_u(src,t).ravel()\n",
+    "    simpeg.Tests.checkDerivative(fun,u0,num=3,plotIt=False)\n",
+    "    \n",
+    "survey, problem = t3Dmt.setupSimpegMTfwd_eForm_ps(t3Dmt.random(.1),comp='zyxr',singleFreq=.01)\n",
+    "testDeriv_bpx(problem)\n",
+    "testDeriv_bpy(problem)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# %debug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    5.819e+11     7.675e+10      nan\n",
+      " 1   1.00e-02    5.440e+10     8.852e+08      1.938\n",
+      " 2   1.00e-03    5.403e+09     4.494e+07      1.294\n",
+      " 3   1.00e-04    5.399e+08     4.428e+06      1.006\n",
+      " 4   1.00e-05    5.399e+07     4.428e+05      1.000\n",
+      " 5   1.00e-06    5.399e+06     4.428e+04      1.000\n",
+      "*********************************************************\n",
+      "<<<<<<<<<<<<<<<<<<<<<<<<< FAIL! >>>>>>>>>>>>>>>>>>>>>>>>>\n",
+      "*********************************************************\n",
+      "You break it, you fix it.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.constants import mu_0\n",
+    "def testDeriv_Hd(prb):\n",
+    "    src = prb.survey.srcList[0]\n",
+    "    rx = src.rxList[0]\n",
+    "    rx.locs = np.array([[0.,0.,0,],[1.,1.,1.]])\n",
+    "    u0x = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j\n",
+    "    u0y = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j\n",
+    "#     u0 = np.vstack((simpeg.mkvc(u0x,2),simpeg.mkvc(u0y,2)))\n",
+    "    u0 = np.r_[u0x, u0y]\n",
+    "    f0 = prb.fieldsPair(survey.mesh,survey)\n",
+    "    # u0 = np.hstack((simpeg.mkvc(u0_px,2),simpeg.mkvc(u0_py,2)))\n",
+    "    f0[src,'e_pxSolution'] =  u0[:len(u0)/2]#u0x\n",
+    "    f0[src,'e_pySolution'] = u0[len(u0)/2::]#u0y\n",
+    "    # Run a testdef testDeriv_ZijN(rx):\n",
+    "    def getHdcomp(rx,f,v=None):\n",
+    "        if rx.locs.ndim == 3:\n",
+    "            eFLocs = rx.locs[:,:,0]\n",
+    "            bFLocs = rx.locs[:,:,1]\n",
+    "        else:\n",
+    "            eFLocs = rx.locs\n",
+    "            bFLocs = rx.locs\n",
+    "        # Get the projection\n",
+    "        Pbx = prb.mesh.getInterpolationMat(bFLocs,'Fx')\n",
+    "        Pby = prb.mesh.getInterpolationMat(bFLocs,'Fy')\n",
+    "        # Get the fields at location\n",
+    "        # px: x-polaration and py: y-polaration.\n",
+    "        hx_px = Pbx*f[src,'b_px']/mu_0\n",
+    "        hy_px = Pby*f[src,'b_px']/mu_0\n",
+    "        hx_py = Pbx*f[src,'b_py']/mu_0\n",
+    "        hy_py = Pby*f[src,'b_py']/mu_0\n",
+    "        # Derivatives as lambda functions\n",
+    "        spPe = simpeg.Utils.spzeros(rx.nD,prb.mesh.nE)\n",
+    "        spPb = simpeg.Utils.spzeros(rx.nD,prb.mesh.nF)\n",
+    "\n",
+    "        # NOTE: Think b_p?Deriv_u should return a 2*nF size matrix\n",
+    "        hx_px_u = lambda vec: Pbx*f._b_pxDeriv_u(src,vec)/mu_0\n",
+    "        hy_px_u = lambda vec: Pby*f._b_pxDeriv_u(src,vec)/mu_0\n",
+    "        hx_py_u = lambda vec: Pbx*f._b_pyDeriv_u(src,vec)/mu_0\n",
+    "        hy_py_u = lambda vec: Pby*f._b_pyDeriv_u(src,vec)/mu_0\n",
+    "        # Update the input vector\n",
+    "        sdiag = lambda t: simpeg.Utils.sdiag(simpeg.mkvc(t,2))\n",
+    "        # Define the components of the derivative\n",
+    "        if v is not None:\n",
+    "            return (sdiag(hy_py)*hx_px_u(v)) + (sdiag(hx_px)*hy_py_u(v)) - (sdiag(hx_py)*hy_px_u(v)) - (sdiag(hy_px)*hx_py_u(v))\n",
+    "        else:\n",
+    "            return (sdiag(hx_px)*hy_py) - (sdiag(hx_py)*hy_px)\n",
+    "    def fun(u):\n",
+    "        f = prb.fieldsPair(survey.mesh,survey)\n",
+    "        f[src,'e_pxSolution'] = u[:len(u)/2]\n",
+    "        f[src,'e_pySolution'] = u[len(u)/2::]\n",
+    "        return getHdcomp(rx,f), lambda t: getHdcomp(rx,f0,t)\n",
+    "    simpeg.Tests.checkDerivative(fun,u0,num=6,plotIt=False)\n",
+    "survey, problem = t3Dmt.setupSimpegMTfwd_eForm_ps(t3Dmt.random(.01),comp='zxyr',singleFreq=.0001)\n",
+    "testDeriv_Hd(problem)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    1.733e+05     7.391e+02      nan\n",
+      " 1   1.00e-02    1.734e+04     6.892e+01      1.030\n",
+      " 2   1.00e-03    1.734e+03     6.896e+00      1.000\n",
+      " 3   1.00e-04    1.734e+02     6.896e-01      1.000\n",
+      " 4   1.00e-05    1.734e+01     6.897e-02      1.000\n",
+      " 5   1.00e-06    1.734e+00     6.897e-03      1.000\n",
+      "*********************************************************\n",
+      "<<<<<<<<<<<<<<<<<<<<<<<<< FAIL! >>>>>>>>>>>>>>>>>>>>>>>>>\n",
+      "*********************************************************\n",
+      "You break it, you fix it.\n",
+      "\n",
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    3.075e+05     3.072e+03      nan\n",
+      " 1   1.00e-02    3.057e+04     1.202e+02      1.407\n",
+      " 2   1.00e-03    3.055e+03     1.169e+01      1.012\n",
+      " 3   1.00e-04    3.055e+02     1.168e+00      1.000\n",
+      " 4   1.00e-05    3.055e+01     1.168e-01      1.000\n",
+      " 5   1.00e-06    3.055e+00     1.168e-02      1.000\n",
+      "*********************************************************\n",
+      "<<<<<<<<<<<<<<<<<<<<<<<<< FAIL! >>>>>>>>>>>>>>>>>>>>>>>>>\n",
+      "*********************************************************\n",
+      "Did you put your clever trousers on today?\n",
+      "\n",
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    4.726e+05     1.575e+04      nan\n",
+      " 1   1.00e-02    4.791e+04     1.217e+03      1.112\n",
+      " 2   1.00e-03    4.797e+03     1.216e+02      1.001\n",
+      " 3   1.00e-04    4.798e+02     1.216e+01      1.000\n",
+      " 4   1.00e-05    4.798e+01     1.216e+00      1.000\n",
+      " 5   1.00e-06    4.798e+00     1.216e-01      1.000\n",
+      "*********************************************************\n",
+      "<<<<<<<<<<<<<<<<<<<<<<<<< FAIL! >>>>>>>>>>>>>>>>>>>>>>>>>\n",
+      "*********************************************************\n",
+      "Coffee break?\n",
+      "\n",
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    4.323e+05     2.689e+04      nan\n",
+      " 1   1.00e-02    4.326e+04     4.536e+02      1.773\n",
+      " 2   1.00e-03    4.326e+03     3.671e+01      1.092\n",
+      " 3   1.00e-04    4.327e+02     3.660e+00      1.001\n",
+      " 4   1.00e-05    4.327e+01     3.660e-01      1.000\n",
+      " 5   1.00e-06    4.327e+00     3.660e-02      1.000\n",
+      "*********************************************************\n",
+      "<<<<<<<<<<<<<<<<<<<<<<<<< FAIL! >>>>>>>>>>>>>>>>>>>>>>>>>\n",
+      "*********************************************************\n",
+      "No gold star for you.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def testDeriv_ZijN(prb):\n",
+    "    src = prb.survey.srcList[0]\n",
+    "    rx = src.rxList[0]\n",
+    "    rx.locs = np.array([[0.,0.,0,],[1.,1.,1.]])\n",
+    "    u0x = np.random.randn(survey.mesh.nE)+np.random.randn(prb.mesh.nE)*1j\n",
+    "    u0y = np.random.randn(survey.mesh.nE)+np.random.randn(prb.mesh.nE)*1j\n",
+    "#     u0 = np.vstack((simpeg.mkvc(u0x,2),simpeg.mkvc(u0y,2)))\n",
+    "    u0 = np.r_[u0x, u0y]\n",
+    "    f0 = prb.fieldsPair(prb.mesh,prb.survey)\n",
+    "    # u0 = np.hstack((simpeg.mkvc(u0_px,2),simpeg.mkvc(u0_py,2)))\n",
+    "    f0[src,'e_pxSolution'] =  u0[:len(u0)/2]#u0x\n",
+    "    f0[src,'e_pySolution'] = u0[len(u0)/2::]#u0y\n",
+    "    def getZijNcomp(rx,f,v=None):\n",
+    "        if rx.locs.ndim == 3:\n",
+    "            eFLocs = rx.locs[:,:,0]\n",
+    "            bFLocs = rx.locs[:,:,1]\n",
+    "        else:\n",
+    "            eFLocs = rx.locs\n",
+    "            bFLocs = rx.locs\n",
+    "        # Get the projection\n",
+    "        Pex = prb.mesh.getInterpolationMat(eFLocs,'Ex')\n",
+    "        Pey = prb.mesh.getInterpolationMat(eFLocs,'Ey')\n",
+    "        Pbx = prb.mesh.getInterpolationMat(bFLocs,'Fx')\n",
+    "        Pby = prb.mesh.getInterpolationMat(bFLocs,'Fy')\n",
+    "        # Get the fields at location\n",
+    "        # px: x-polaration and py: y-polaration.\n",
+    "        ex_px = Pex*f[src,'e_px']\n",
+    "        ey_px = Pey*f[src,'e_px']\n",
+    "        ex_py = Pex*f[src,'e_py']\n",
+    "        ey_py = Pey*f[src,'e_py']\n",
+    "        hx_px = Pbx*f[src,'b_px']/mu_0\n",
+    "        hy_px = Pby*f[src,'b_px']/mu_0\n",
+    "        hx_py = Pbx*f[src,'b_py']/mu_0\n",
+    "        hy_py = Pby*f[src,'b_py']/mu_0\n",
+    "        # Derivatives as lambda functions\n",
+    "        # The size of the deratives should be nD,nU\n",
+    "        ex_px_u = lambda vec: Pex*f._e_pxDeriv_u(src,vec)\n",
+    "        ey_px_u = lambda vec: Pey*f._e_pxDeriv_u(src,vec)\n",
+    "        ex_py_u = lambda vec: Pex*f._e_pyDeriv_u(src,vec)\n",
+    "        ey_py_u = lambda vec: Pey*f._e_pyDeriv_u(src,vec)\n",
+    "        # NOTE: Think b_p?Deriv_u should return a 2*nF size matrix\n",
+    "        hx_px_u = lambda vec: Pbx*f._b_pxDeriv_u(src,vec)/mu_0\n",
+    "        hy_px_u = lambda vec: Pby*f._b_pxDeriv_u(src,vec)/mu_0\n",
+    "        hx_py_u = lambda vec: Pbx*f._b_pyDeriv_u(src,vec)/mu_0\n",
+    "        hy_py_u = lambda vec: Pby*f._b_pyDeriv_u(src,vec)/mu_0\n",
+    "\n",
+    "        # Update the input vector\n",
+    "        # Define the components of the derivative# Calculate components\n",
+    "        # Update the input vector\n",
+    "        sdiag = lambda t: simpeg.Utils.sdiag(simpeg.mkvc(t,2))\n",
+    "        # Define the components of the derivative\n",
+    "        if 'zxx' in rx.rxType:\n",
+    "            if v is not None:\n",
+    "                return  sdiag(hy_py)*ex_px_u(v) + sdiag(ex_px)*hy_py_u(v) - sdiag(ex_py)*hy_px_u(v) - sdiag(hy_px)*ex_py_u(v)\n",
+    "            return ( sdiag(ex_px)*hy_py - sdiag(ex_py)*hy_px)\n",
+    "        elif 'zxy' in rx.rxType:\n",
+    "            if v is not None:\n",
+    "                return -sdiag(hx_py)*ex_px_u(v) - sdiag(ex_px)*hx_py_u(v) + sdiag(ex_py)*hx_px_u(v) + sdiag(hx_px)*ex_py_u(v)\n",
+    "            return (-sdiag(ex_px)*hx_py + sdiag(ex_py)*hx_px)\n",
+    "        elif 'zyx' in rx.rxType:\n",
+    "            if v is not None:\n",
+    "                return  ey_px_u(v)*hy_py + ey_px*hy_py_u(v) - ey_py*hy_px_u(v) - ey_py_u(v)*hy_px\n",
+    "            return ( ey_px*hy_py - ey_py*hy_px)\n",
+    "        elif 'zyy' in rx.rxType:\n",
+    "            if v is not None:\n",
+    "                return -ey_px_u(v)*hx_py - ey_px*hx_py_u(v) + ey_py*hx_px_u(v) + ey_py_u(v)*hx_px\n",
+    "            return (-ey_px*hx_py + ey_py*hx_px)\n",
+    "        \n",
+    "    def fun(u):\n",
+    "        f = prb.fieldsPair(survey.mesh,prb.survey)\n",
+    "        f[src,'e_pxSolution'] = u[:len(u)/2]\n",
+    "        f[src,'e_pySolution'] = u[len(u)/2::]\n",
+    "        return getZijNcomp(rx,f), lambda t: getZijNcomp(rx,f0,t)\n",
+    "    simpeg.Tests.checkDerivative(fun,u0,num=6,plotIt=False)\n",
+    "            \n",
+    "surveyxx, problemxx = t3Dmt.setupSimpegMTfwd_eForm_ps(t3Dmt.random(.01),comp='zxxr',singleFreq=.001)\n",
+    "testDeriv_ZijN(problemxx)\n",
+    "surveyxy, problemxy = t3Dmt.setupSimpegMTfwd_eForm_ps(t3Dmt.random(.01),comp='zxyr',singleFreq=.0001)\n",
+    "testDeriv_ZijN(problemxy)\n",
+    "surveyyx, problemyx = t3Dmt.setupSimpegMTfwd_eForm_ps(t3Dmt.random(.01),comp='zyxr',singleFreq=.0001)\n",
+    "testDeriv_ZijN(problemyx)\n",
+    "surveyyy, problemyy = t3Dmt.setupSimpegMTfwd_eForm_ps(t3Dmt.random(.01),comp='zyyr',singleFreq=.0001)\n",
+    "testDeriv_ZijN(problemyy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# %debug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# dA_dm = problem.getADeriv_m(src.freq,u_src,w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# dA_dm.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# a, b, sig, d, e = t3Dmt.halfSpace(.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================== checkDerivative ====================\n",
+      "iter    h         |ft-f0|   |ft-f0-h*J0*dx|  Order\n",
+      "---------------------------------------------------------\n",
+      " 0   1.00e-01    5.050e-06     1.906e-09      nan\n",
+      " 1   1.00e-02    5.049e-07     1.949e-11      1.990\n",
+      " 2   1.00e-03    5.048e-08     1.969e-13      1.996\n",
+      " 3   1.00e-04    5.048e-09     1.377e-15      2.155\n",
+      " 4   1.00e-05    5.048e-10     1.073e-15      0.108\n",
+      " 5   1.00e-06    5.049e-11     4.179e-15      -0.590\n",
+      " 6   1.00e-07    5.059e-12     1.079e-14      -0.412\n",
+      " 7   1.00e-08    5.092e-13     4.411e-15      0.388\n",
+      "========================= PASS! =========================\n",
+      "Yay passed!\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def testDeriv_ProjFields(prb):\n",
+    "    # Initate things for the derivs Test\n",
+    "    src = prb.survey.srcList[0]\n",
+    "    rx = src.rxList[0]\n",
+    "#     rx.locs = np.array([[0.,0.,0,],[1.,1.,1.]])\n",
+    "#     u0x = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j\n",
+    "#     u0y = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j\n",
+    "#     u0 = np.vstack((simpeg.mkvc(u0x,2),simpeg.mkvc(u0y,2)))\n",
+    "#     u0 = np.r_[u0x, u0y]\n",
+    "#     f0 = prb.fieldsPair(survey.mesh,survey)\n",
+    "    # u0 = np.hstack((simpeg.mkvc(u0_px,2),simpeg.mkvc(u0_py,2)))\n",
+    "#     f0[src,'e_pxSolution'] =  u0[:len(u0)/2]#u0x\n",
+    "#     f0[src,'e_pySolution'] = u0[len(u0)/2::]#u0y\n",
+    "\n",
+    "#     prb.mapping = simpeg.Maps.ExpMap(prb.mesh)\n",
+    "#     if True:\n",
+    "#         x0  = x0 + np.random.randn(prb.mesh.nC)*cond*1e-1 \n",
+    "    survey = prb.survey\n",
+    "    src = survey.srcList[0]\n",
+    "    \n",
+    "    f0 = prb.fields(prb.curModel)\n",
+    "    u0 = np.r_[f0[src,'e_pxSolution'],f0[src,'e_pySolution']]\n",
+    "#     def fun(x):\n",
+    "#         prb.curModel = x\n",
+    "    # f0[src,'b_1d'] = -1/(1j*simpegem.Utils.EMUtils.omega(src.freq))*m1d.nodalGrad*u0\n",
+    "    # Run a test\n",
+    "    def fun(u):\n",
+    "        f = prb.fieldsPair(survey.mesh,survey)\n",
+    "        f[src,'e_pxSolution'] = u[:len(u)/2]\n",
+    "        f[src,'e_pySolution'] = u[len(u)/2::]\n",
+    "    #     f[src,'b_1d'] = -(m1d.nodalGrad*u)/(1j*simpegem.Utils.EMUtils.omega(src.freq))\n",
+    "        return rx.projectFields(src,survey.mesh,f), lambda t: rx.projectFieldsDeriv(src,survey.mesh,f0,t)\n",
+    "    simpeg.Tests.checkDerivative(fun,u0,num=8,plotIt=False)\n",
+    "survey, problem = t3Dmt.setupSimpegMTfwd_eForm_ps(t3Dmt.halfSpace(1),comp='zxyr',singleFreq=.1)\n",
+    "testDeriv_ProjFields(problem)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# %debug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "src = survey.srcList[0]\n",
+    "rx = src.rxList[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "rx.locs = np.array([[0, 0, 0],[1,1,1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 19.30432593,   8.59301637,   7.75725432, ...,   2.18228759,\n",
+       "        16.72231401,   7.67029076])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "problem.curModel.sigma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "u0x = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j\n",
+    "u0y = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j    \n",
+    "src = survey.srcList[0]\n",
+    "rx = src.rxList[0]\n",
+    "f0 = problem.fieldsPair(survey.mesh,survey)\n",
+    "u0 = np.vstack((simpeg.mkvc(u0x,2),simpeg.mkvc(u0y,2)))\n",
+    "f0[src,'e_pxSolution'] = u0x\n",
+    "f0[src,'e_pySolution'] = u0y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20536, 1)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f0._e_px(f0[src,'e_pxSolution'],[src]).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 1)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f0._e_pxDeriv_u([src],u0)[len(u0)/2::].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "simpeg.Utils.spzeros"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.00031425],\n",
+       "       [ 0.00030875]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rx.projectFields(src,survey.mesh,f0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR: No traceback has been produced, nothing to debug.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%debug"
+   ]
+  },
+  {
+   "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/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/notebooks/Test ProblemMT1D.eFrom_TotalField.ipynb b/notebooks/Test ProblemMT1D.eFrom_TotalField.ipynb
new file mode 100644
index 00000000..d4061ba1
--- /dev/null
+++ b/notebooks/Test ProblemMT1D.eFrom_TotalField.ipynb	
@@ -0,0 +1,263 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Notebook to test 1D code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import SimPEG as simpeg\n",
+    "import simpegMT as simpegmt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "ct = 10\n",
+    "m1d = simpeg.Mesh.TensorMesh([[(ct,20,-1.5),(ct,100),(ct,20,1.5)]], x0=['C'])\n",
+    "sigma = np.zeros(m1d.nC) + 2e-3\n",
+    "sigma[m1d.gridCC[:]>200] = 1e-8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Make the rx and src\n",
+    "freqs = np.logspace(3,-3,31)\n",
+    "rxList = []\n",
+    "for rxType in ['zxyr','zxyi']:\n",
+    "    rxList.append(simpegmt.SurveyMT.RxMT(simpeg.mkvc(np.array([405]),2).T,rxType))\n",
+    "# Source list\n",
+    "srcList =[]\n",
+    "for freq in freqs: \n",
+    "    srcList.append(simpegmt.SurveyMT.srcMT(freq,rxList))  \n",
+    "survey = simpegmt.SurveyMT.SurveyMT(srcList)\n",
+    "problem = simpegmt.ProblemMT1D.eForm_TotalField(m1d)\n",
+    "problem.pair(survey)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "fields = problem.fields(sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  1.52442595e-125 -7.20764170e-126j,\n",
+       "          7.74672164e-101 +1.76864736e-100j,\n",
+       "         -2.07557099e-080 -3.74608600e-081j, ...,\n",
+       "          3.10393307e-001 -2.86962945e-001j,\n",
+       "          4.10341179e-001 -2.87349774e-001j,\n",
+       "          5.05105758e-001 -2.74745005e-001j],\n",
+       "       [  8.67440346e-030 +6.35706181e-030j,\n",
+       "         -1.53155881e-027 +3.75444030e-027j,\n",
+       "         -1.08396611e-024 -4.98500246e-026j, ...,\n",
+       "         -4.05147339e-001 +2.76576534e-001j,\n",
+       "         -4.96380502e-001 +2.67133906e-001j,\n",
+       "         -5.80363237e-001 +2.48768467e-001j],\n",
+       "       [ -6.16527128e-026 +8.41573172e-026j,\n",
+       "         -2.29821697e-023 -9.36787887e-024j,\n",
+       "          1.90707675e-022 -4.18621866e-021j, ...,\n",
+       "         -4.75055964e-001 +2.59780137e-001j,\n",
+       "         -5.57847068e-001 +2.46025119e-001j,\n",
+       "         -6.32948088e-001 +2.25820916e-001j],\n",
+       "       ..., \n",
+       "       [ -4.42815267e-001 +4.54129730e-002j,\n",
+       "         -4.45450945e-001 +2.94352343e-002j,\n",
+       "         -4.46746292e-001 +1.94083017e-002j, ...,\n",
+       "         -7.96037136e-001 +1.08182287e-001j,\n",
+       "         -8.29064328e-001 +1.00291076e-001j,\n",
+       "         -8.58609530e-001 +9.06236881e-002j],\n",
+       "       [ -6.64333571e-001 +4.65810415e-002j,\n",
+       "         -6.66721482e-001 +2.99034482e-002j,\n",
+       "         -6.67823222e-001 +1.93821193e-002j, ...,\n",
+       "         -8.77622277e-001 +6.49094362e-002j,\n",
+       "         -8.97438594e-001 +6.01746869e-002j,\n",
+       "         -9.15165716e-001 +5.43742395e-002j],\n",
+       "       [  1.00000000e+000 +0.00000000e+000j,\n",
+       "          1.00000000e+000 +0.00000000e+000j,\n",
+       "          1.00000000e+000 +0.00000000e+000j, ...,\n",
+       "          1.00000000e+000 +0.00000000e+000j,\n",
+       "          1.00000000e+000 +0.00000000e+000j,\n",
+       "          1.00000000e+000 +0.00000000e+000j]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fields[:,'e_1d']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "m1d.nN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "self = problem\n",
+    "Mmui = self.MfMui\n",
+    "Msig = self.mesh.getFaceInnerProduct(self.curModel)\n",
+    "C = self.mesh.nodalGrad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "self"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Mmui"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "self.Me[1,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "self.mesh.vol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "m1d.h"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "m1d.gridCC"
+   ]
+  },
+  {
+   "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": [
+    "## 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 = 20\n",
+    "air = simpeg.Utils.meshTensor([(ct,16,1.4)])\n",
+    "core = np.concatenate( (  np.kron(simpeg.Utils.meshTensor([(ct,10,-1.3)]),np.ones((5,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n",
+    "bot = simpeg.Utils.meshTensor([(core[0],10,-1.4)])\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<-500.) & (m1d.vectorCCx>=-800.)\n",
+    "layer2 = (m1d.vectorCCx<-3500.) & (m1d.vectorCCx>=-5000.)\n",
+    "# Set the conductivity values\n",
+    "sig_half = 2e-3\n",
+    "sig_air = 1e-8\n",
+    "sig_layer1 = .2\n",
+    "sig_layer2 = .2\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": 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\nY9MpBFYAdCPO5d6IkNiiBVcvWEBUTAyvDB/OkePpUmyg4uPjmTx5MgkJCeEORQgRAaROnxARxFr7\nC3C/UkrjTMA9BXgceFwpNQeYZq2t7jC9ZcC9SqmvrLU+Z/LwDOS4F/gy0I3+9JMLgMLCDDIyMkhP\nT69mWP4VFBQAEJOQwCUzZvDZffcxvnNnUk86idjExHJtpZCzfzJTQ8O0adMmOnToQEyMnOIjjTEm\nDkBrXRCO/ctvhBARyFprlVLLgBOBXsCpwDnAZKXUKmCKtXZFZdso43fAJ8AWpdTHwFqgpFZKMnAS\ncD6QDwwPNMbevV0ArF3r4pdf0hk8GBo1CnTtyq1cuZKDBw+SkpKCiopi+N/+xn9nz6b71197tZVC\nzkKUt2zZMnbv3s3pp58e7lCEhzEmAadO6p1AtjFmltb67VDHIUmfEBFGKZWO08N3KVAEzARutNZ+\nq5TqDTwJvAqcHMj2rLWrPev9BhiNk9iVXMY9gJMEPgI8a60NuHDewoWnAdCsWRz5+S7uuAOuuw4G\nDAh0C95z9HriJSsri9GjR7NgwQKaNHGqyTRt2xZ+/jnwjQvRQI0YMYLp06fTr18/GgXrm5ioMWNM\nM+AqnC/Xs4D1wIvGmB+11iGtoyRJnxARwnNJ9xqgE7AYuBmYba09NorBWvuTUuo+YEl1tm2tPQD8\n3fMIir17c4AW9O7dlt/+Fn78EZ5/HhYvhtWrXfiqA1yxvMt0P5dlrbXceOONXHTRRXzwwQdy4gpA\ndnY2BQUFtGzZMtyhiDBLTU2lZ8+efP7554wcOTLc4TRonsu5k4C+wMNa6yWe5dtxBu+FlCR9QkSO\nm4DpwEvW2soGXqwFrgv2zpVSjYBUXyN8fSs/uPjkk+HRR+Htt2H6dOjZ0+W1RlaW9zI/sfDss89y\n9dVXc9lll/HOO+8EFlIDlZeXx4wZM+jXr58kfQKA9PR0nnnmGQYOHEhKSkq4w2nIzgTGAg9prZcY\nY6KB8ThTZX4DYIxRWusq66MaY04CLgbaeRZtB97TWq8JNBgZvStE5Ghvrf1zFQkf1tr91trptbD/\nCznOW+Ti4uDKK6Fjx+MPJjo6mpdffpnY2FgmT56M20/N6INbthz/zuqwoqIi3njjDdLS0hgyZEi4\nwxERomnTpgwcOJClS5eGO5QGyxgTg/Nlfo7WerHn9VnAYJyEz12NhO9enFt9AL7yPKKAmcaYPwUa\nk/T0CRG2ZnhCAAAgAElEQVQ5CpVSp1trl1d8Qyk1APjKWlvbxdaqUb13HdACa9t4vROsCiGxsbHM\nmjWLsWPHsn3HDtTQoeUKDBfl55O3Zg2f/t//ce6DDza44sNut5s5c+bQpEkTRo0a1eA+v6jcmWee\nGe4QGjoL5AElI3UnAv08r6drrYuNMQrAGHMBsF9rvczPtq4HemmtC8suNMY8BqwmwFt3pKdPiMhR\n2Rk7FmdQR/U3qtRCpdRnVT1wZuOoxtyGPYCWfP/9JnbsCGyu3Pz86scfHx/PO++8w97sbBasX88m\na9kMbAa2x8fTYtQoNsyfz8e3397g5h798MMPycvLY9y4cZLwCS9xcXHExcVV3VDUCq11Mc7Au7uN\nMRk4V1N+AR7RWh8yxkSV6eXbBUw1xoz2s7liSi/rlnWC572ASE+fEGGklOqIMw1ayRm7v1KqYj9Z\nAs5o3s013M1QnG651VW0q9ZoiWHDYrHWkp3dhgED7mDatNsYNeq0StfZvBnmzIGLLoLqlBBr3Lgx\n3bt354svvmDXrl0V4hjGtQsXMmP0aN6/8UbGPPssUQ1k9omOHTsyYsQIqccmRAhlZDi1SQOhtf7O\nGDMcpzzWZq11PoAxJtqTFGKMidFarzDG/BZ42BiD1vrDCpv6A/CJMWYDsM2zrANOQf9bA41d1dVv\nxkqpQOaFF6JWHe9E20opF3B/AE1zgRusta/XYB8rgTXW2olVtLsMeNNaW+UVgIrH3+LFPzJp0mNc\nfXU6Dzwwmdtue8DnnLxJSdCli4uDB+Hmm8FHxRa/0tPTWbRokdfyYcOGkZGRQf7hw7xx0UU0PeEE\nxr38MlGSCAkhQiDQ84AxZiJO4veV53W5+/mMMW1xzgc3AR211tsqrB8NDMLp8bPADuAbrXXAV4Hk\nr6IQ4fVfYLbn+UqcWk6rKrQpALZaa/NquI8vcerzBZXL5SI9PZ309HSGDj2ZFSue4Oqr/0V6+p9p\n23YPWVnZXus0btyKP/0JFi2CBx+EnTtdJCZCxSuTFUu7BCK+aVMmzZ/Pm5deyvjOnUlJS0NFlc9f\nZfYO0ZC53W6iouSurjBaDPSHY717RZ6SLsk4yV43nMF0V1RM+ODY5WKvWZOMMU201j5nW6pIkj4h\nwshauwfYA6CU6gzstNYGe3qeR4APVNXd4x8AnQPdqMvlKvc6NTWZ+fM1Dz88h/vvv5/Cwq4+1tqD\nUpCeDn36wOjR0KSJy6tVoKVdKopt1IiJ77zDjHbt6LzEu5ShzN4hGiprLS+//DIjR46kffv24Q6n\nQdJa78L5OwvOyN1WOPdSJ+Jcqr0ZyNJa76/mplfjzN5UpZAnfUqp4Ti9Dj1xZgWwlM4K8KG19rNQ\nxyREuCilEoFcTzK2B4hRSvk9Lq21R6u7D08JmErLwHja5VLz+wYBiIqK4o9/vIxZs17g++8rb9u8\nOZwY0J+pyu3evbvc65j4eFJ793aqRNcju3btQilFmzbeo6WFqIpSikGDBjF37lxuuukmYmNjwx1S\ng+Xp3bseuAeYB0wDvtRaF5SM5vWxzp2VbLJpoPsOWdKnlGoOzMWpUbMJWEPpF+9mwCXAnUqpJcB4\na211M10h6qIcYAiw3PO8MhaoEyMUkpMbA4VVtqsOX1O2HT16lFWrVvHGG29wxRVXHFte30ay7tu3\nj9dff50LL7xQkj5RY71792bt2rV89tlnnH/++eEOp8HyJHdzgS1a65KePzyjed1+Vvsb8Cjef1gV\n1ajEEsqevieB1sBga633rOkcq0U2w9N2cghjEyJcfo0zhL/kuQAyM6GgwCn2XMLflG2rVq1i5MiR\nJCQkMG7cuNAEGELZ2dm8+uqrnHvuufTs2TPc4Yg6bvTo0Tz77LP06NHD5xcpERpa6504s3IcS/Yq\nSfgAVgBztdbfVHzDGBPwDE2hTPrGAFP8JXwA1tpvlFL3Ai+HLiwhwqfszBq1NMtGrSk7kCNQW7dm\nYa2tsieusBDuuccZ4du9e+XbPOWUU/jggw8YPXo0CQkJjBo1KuB4Il1ubi6vvfYaAwcO5NRTTw13\nOKIeSExMZMyYMbz77rvccsstUu4nAlSR7JX4FeCvIOrAQPcVspItSqn9wHXW2kon0VRKjceZe7RZ\nFe2kZIsIu+Mt2VJhW6/iTLPzsbU24GKb4VDV8Tdlym/ZvHlPuWV5eYVs2LCLoUPHM23a70lObszN\nN7t8lnZJTYVrrnHx0ktw1llwxRXle/18Wbp0KePGjePNN99k7vTpHNy8+dh7xYWF7PruO9r068dr\nX3oNfotIbreb6dOn06FDB0aOHBnucEQ9s3PnTk444YRwh1EvBPM8UNtCmfRNwykSe6219nM/bc4E\nXgEWWWsrvdQlSZ+IBEFO+r4GTgP2A+8AbwCfReIvek2Pv/z8Qu6440UWLPie2bPvpU+fTpW2z86G\nl16CTZtg714XBT7GNZct77Jw4UImTpzIu+++y+mnn16u3f6NG5l29tmMee45eowdW+3Yw2Hr1q10\n6NCh3t2jKER9EsqkzxjzPs793SX7s0A28DXwnNa60tJeoUz6koE3gZHAbpzRugc9b6fgjOZtA/wP\nmGitPVTF9iLxXCgamGAf7J6yLRM9jz44I3pnA7Ostd41SMLkeI+/115byO23v0jPnkeIjva+Bzkt\nrRXTpz9z7PVXX8ENN7jo2dPl1dZaF2+9Vbr8o48+Yty4cfTu3ZumTcsPaktt0oRBy5czad482g0a\nVOP4hRCiRIiTvieBljhXhRTOuSIbcANJWuurK1s/ZBfzPUnc+Uqp0ylfsgUgC1iCU7LF32TDQtR7\n1tpfcCbO/rtSqgfOAX05cLNSaoe1tkNYAwySyZPPoW/fTgwaNJa8PF+9feUvDQ8eDF26BLbtUaNG\n0bVrV7777juv94YNG8bFL73EGxdfzK+WLKF5V1+1BIUQImKdobUeUOb1e8aYb7TWA4wxP1W1cshL\nc1trv7TW3m+tvdxaO9LzmGit1ZLwCVHKWrsOp37TNJzecV+TbYeNy+UKeP5JX045JY3TTgswk6N6\nc/W2bNnS73vdx4xhmMvFjNGjOeLrhkIhGqDCwuCWWBK1prExpmPJC8/zxp6XVRb2r9PDdsrOCFDd\nUYRC1ER1JtquKaVUW2ACTi/fEJzbIObg3OMXMSrOyFETMTHROFclau7IkeqvM+Cmmzi0dStvXHQR\n13z6KbGJiccVw/Gy1rJ06VI6duwosyWIkDty5AhTp07lhhtuoHHjxlWvIMLpTmCJMaak1Fdn4GZj\nTGMCqHwSsnv6AqWUegGIkoEcoi4I8kCOm3Eu5Z6FU6j5XWAWsMBaG1Ffw4N1/KWnX8qiRd4fbejQ\nGBYtmlNu2YQJLpRyebVdt87FLbe4mDwZSnK39PR0Fi1a5NV22LBhx5J2ay1zr72W/OxsLn/7baKi\nw1P3uri4mHnz5pGZmcmVV17pdR+iEKHwySefsG/fPi6//HIZOFRNoR69a4xJAHp4Xq6ravBGWZHY\n05dOHZl1QIggewR4H7gM+MhaG/CBXN/8+ONWDh7MISWlybFlqam+5+QdPBjcbrjrLrjxRujXz/92\n8/JKf6RKKS564QUu7tiRGR060Lxbt3Inu5S0NJ7wUxA6WHJzc5k1axYJCQlMmTKFuKrq0ghRS9LT\n05k6dSqrVq2iT58+4Q5H+OGZwu0mnGooABnGmGe11gF1DERc0metlTurRUPVylpbg4uVdVdaWisq\nDtpwuy1ZWckMGHAHc+b86VhZl5KyLP6sXAnPPQe9e8POnQdp2bJjufePHj3Ed9+tYMeOHbRr59we\nGR0XR/MuXejyxRewa1e59puoXSVTq/Xo0YMRI0YQFRXyW6yFOCYmJoZx48bx2muvkZaWRlJSUrhD\nEr49g5O7PY0zevdqz7LrA1k54pI+pVQc0MZauzXcsQgRSg0t4QPKlWWpaMaMDIYPv4/HH7+OyZPP\nqXJbffrAo4/CjBnQqNE4+vVzebVZs2YEw4cPJyMj49gctlFhmpFgz549nH766QwYMKDqxkKEQNu2\nbRk0aBDz588vN5e1CB5PTx1a6yoHXfgxUGtdtiv2U2PMykBXDulXS6XUrUqpX5RSeUqpH5RS1/ho\n1p/a/5ItRERQSmUppU4t87yyx56qthdKxzt6typXXZXOp58+gDFv8LvfPUdBQdVXLxo1guuvh3Z+\nxjn37HkWkyZNYsSIEWSFeeTuSSedJAmfiDhDhw5l9OjR4Q6j3jHGJBhjRgLvAa8ZYy6t4aaKjDHH\nrogaY7oARYGuHLKvuEqpK4AncQoKfg+cDkxTSl0MXFXh/iW5i1Q0FE9Ten3z6XAGUl3BGL1blT59\nOvH1149xzTVP0K7dGXTt2pb4+NhybSoWcgaobADifffdR0FBASNGjOCzzz7z2y7/8OHjil2Iukgp\nRXJycrjDqFeMMc2Aq4DzcQbnrQdeNMb8qLVeV83N3Q18Zowp6RxLw5mXNyChvK5xF/CYtfbukgVK\nqeHA60CGUmqMtXZvCOMRIuystS5fz0WplJQmzJ37Z7p0+ZhlywAq9vgF3gFaWOic1B544AEKCgo4\n77zz6NGokc+2mStX8s2zz3LaTTcd92hGa62MiBSiAfJczp0E9AUe1lov8SzfDjSv7va01p8aY7rj\njN61OKN38wNdP5RJXw+cxO8Ya+2nSqnBwIfAl0qpUSGMR4iIopT6DLjZWrvWx3vdgWetteeGPrLw\ni4qKomPHVmzefHyVa375BT78EM4/X/HPf/6T22+/neeeeYamSUlEVUjKkpOS+OaZZ9j6+eeMefZZ\n4po08bPVyh05coTZs2czevRoWrVqdVzxCxEu8sWlxs4ExgIPaa2XGGOigfHATuCbQDfiuRxcMudu\n2bl3uxpj0FrP8btyGaG8p+8wznxx5VhrN+P8ULKApcDAEMYkRCRJB/wNmUsGhoUulLotNdWZk7fi\no39/WLYM7rsPtm9XPP744zRr0YKs7GwyDx0q92jbuTPXffkl0bGxvDB4MFlr1lQ7jpycHF588UXa\nt29PampqLXxSIWrf+vXrmTlzJkVFAd86JgBjTAxOeZU5WuvFntdnAYNxEr7qVKYf63mMKfPvmDLL\nAxLKnr4VwDicyePLsdbuV0qNAN4C/o2TxQohAKVUPHAOzlRsooIjR7zLGVZW3sXthk8/BZfL6fGz\nNtZnu59/3kxsYiIXT5vGipdeYvrQofx40kkoH6VVfNX0s9Yyb948evXqxfDhw6vzkYSIKF26dOH7\n77/nzTff5PLLLycmTCPe6yAL5FE6PdpEoJ/n9XStdXGgG9JaTwlGQCGbkUMpdTnwB2CMtXa/nzYx\nwH+BkdZaX7Owl20rM3KIsDveSuxKKQ3oAJs/Yq29t6b7CqZwHH/+Zu+IidnAK688w5VXVq8jdP9+\neOEF+Otf0zhyZIvX+y1bdiQra/Ox17u//57Jp5/O2XneSeamYcOYXmEk88qVK/niiy+44YYb5CQp\n6rzi4mLefvttiouLufzyy4kO0ww2kaiy84Axpj/wKs7VzJ3AEmCm1vpgmTbNcb7Yu4EsrfXntRZr\nXU2cJOkTkSAISd8gYJDn5ZPAY0DFDKQAWGOtXVLT/QRbOI6/KVN+y+bN3oM2kpIa8dNPiVx44QAe\nffRXxMX57rnzxVpo3jyNgwerTvoArjnrLKeQcwUVk77i4mKefvppJkyYQNu2bQOOR4hIVlxczOzZ\nzsW6yy67rMEmfhXnYDfGVHoeMMa0wblFZ3PFQRfGmJuArkBv4BPgZuA2rfX8Wghdkj4hjkeQ596d\nAsyrC6PYI+34O3Agh2uueZz9+w/z5pv30q5di4DXTU1NY+9e76SvRYsO7N1bvkb8lPR0OvmY09dX\nT19BQYFMqybqneLiYj744AOGDh1KSkpKuMOJCIGeB4wxE4EtWutlntdTcEq5fA+8oLVeZ4wZBbiA\nMVrrvWXWnaC1fssY01lr/UtNY5VrDkJEjhlUmHdaKXU+cBKw2Fr7XVii8sPlcpGenk56enq4Q6FZ\nsya8++7/8Y9/zGbgwDs55ZQ88vO9LwX7qumXmJhCy5ZpZZZYsrPXcvjwfo4ePUpiYmKNYpKET9RH\n0dHRXHTRReEOo65ajDMBBcaYzsAAYD+wD6dg8zit9UfGmJVlEz6PP+OMe3gbOLWmAUjSJ0TkmAUc\nBH4NoJS6DXgCyAeilVKXWmvfD2N85YSiOHN1REVF8ec/X86gQd254IJJFBb6msbb+/LwoEHjUMpV\nbpm1xXzySX+GDLmIJUveIzm5ZomfEEKU0FrvAj7wvBwCNAFu0VrvNca0xqleskNrvdPH6vuMMQuA\nTsaYiucBq7UOKBOXpE+IyDEYZ7ATyimIdTfwL8+/T+N804uYpC9SjRjRj/79u/LVV4G1T02FrCyX\n1/KLL76YH374hVNOuYi3336PgQMTSUlLOzZHZH52Nnt++on2gwfTLC0tWOELUScVFRWxePFiTj31\nVJo1axbucCKaMSYKaA/86En4uuCU5PqkktUuwOklfA14lPIzlwV8r40kfUJEjhbALs/zU4B2OAWZ\nrVJqNjA5bJHVMQkJsXjP3OFbZeVdiouLGTPmWi655GJuv/1djsSmcSQ1zXkzFQqPpJHpbknjxBPY\nt28fzZs3lwK2okEqKiqiqKiIqVOncuKJJzJo0CA6deokx4MPWmu3MeY94GNjTBOcGq3v44zs9bdO\nAbDMGHO61jrLsx5a65zq7FsGcghxHII8kGML8Bdr7atKqbtxZufo5HnvQmCGtTYi7pyO9OPPX3mX\noUNjWbTo7Wptq7i4mMmTr2XVqkxgEL16/c2rTXT0/fTv35TrrruO5s2rPbOSEPVGQUEBq1atYvny\n5bjdbkaOHEn37t3DHVatqul5wBjTDRiJc09fhtY6M4B1TgFewekkAKcUzLVa6x8D2af09AkROd4C\n/qmU6gtMwbmkW6IfziTd4jisXbuNnJxcmjTxPd+uL9HR0bz22stcc801zJ79JLt3L0ap8qUqLr6o\nB4MHj5CETzR4cXFxnHbaafTv358tW7b4rVGZmZlJUVERsbGxxMbGEhcXx913P0JWlnfb1NTyPfI3\n3+wKqF112wZq//79fPbZZ+zfv5+mTZvWaBsAWuv1VP/v+vPAHVrrhQDGmHTPsjMCWVmSPiEix5+A\nbJybeZ8BHirz3gCcgR4iAGlprag4aKO42M2uXYkMGnQXs2ffS69eJwa8vejoaF555RVmz05i377y\ndVP79+9PXGw0pw8eHIzQhagXlFKkpaX5Tbo6d4auXdtRUFBAYWEhhYWFpKZCbu61HDlSfm6GrCwX\nxcXFx+oCfrlkLd1OesNrm18uueLY8yNHjpCZmYnbDe3b9yEhYTcQxcaNvzm2zRJHjx5lwYIFLF26\ngiNHnFl7oqJAKdi92ztBTEhIYOnSH9m7FwoKCLXEkoQPQGudYYxpHOjKkvQJESGstYXAX/28Nz7E\n4dRpFcuylDVt2icMG/ZnHn/8OiZPPifgbUZHR9O0aUv27Sut3ZecnMzw4cN5a+Y8bjjjLU69Rm67\nFKKsrCy8RscDbNzo4t57byAnBw4fdh5/+YuL5s29J+PavRsefvgFrC0kLq4Nnbu1p0mT9eTltaWo\nqMmxdoWFScybB1FRBaxY8RSNG7fGWjhypBP79p1OYWHp3TH5+bB3L8TFgbXRtGvXgUOHVhAVdS5R\nUQW43fHk56eiVE+vgV6JiYls21b2c5kg/KQCtskYcx/OLB8Kp85fwHX7JOkTQjQov/rVCE47rQsT\nJjzM4sU/8e9/30CjRvEBrVvxpvQzzjiDZcuWkXUghyff6s89A4o5qVfDnKVANByBXjItLobcXPBV\n6nLNGrj3Xmja1Hk0aeK09SUmBnr1up68vH3k5u4mPm4ZLVp8SULCbjZsuJXiYmcHlmj27oXCwjhS\nUu6huFjxww8uTjzRu6zd1q1w331OT11BQTwFBf358cf36N17qFfbX34BrZ0EMTbW+XfHDmjfPqAf\nV7D9GifLnON5vcSzLCCS9AkRRkqpLOA8a+0Kz/PKWGttq1DEVd/16dOJr79+jBtvfJp27c6gW7e2\nXomfr0LOsRVmePv4448BaN26PV1z3+Cxf/6RvgMS+eEHF4cOee/3eO4jEiJS+Ou927PHxc8/w+rV\nTlK3bh3s3AldfZTM7NULpk4tv2z5ct/7a9kSLr44GmgFtOIPv5tFbMI3nndLZysrzNvIlCklx6zz\nBe2xx+aycWOG1zYTEw/yzDOln8Fa6N9nrc/9F+Vv5vLLobDQSRILC+GDD3w2rXVa6/3A72q6viR9\nQoTX05TefPZ0ZQ2pRi0mUbWkpERmzryL7t0/YPnyKLxLvHgXcu7ePY1du0qnbHO73QB07tyBiXef\nyfy7htHivOUsXw49e7q81vdVD1CI+mLdOieR69ULzj0XbrkFhp3pO5HasNZ7+fLlczl6NMNreWLi\nQcBFTmYmXz35JPl5hzmU4z0dYkJMMl8/8wz9pkwhtpEzWOvo0YPs3fuDV9uWLTuWe60UbNq8lO27\n073aFub9Qu/e5Zf9/LPvWCOdJH1ChJG11uXruQgNpRTt2rVgw4bAavqlVSjCbK1l48aNrFmzhpTT\nTqNRI0W/+Lfp5H1bkhD1Xrdu8Mgj5ZdVlkhVtH/vFnKOeidojRsl8cEtt/DjzJn0mjgRFVXkc/9K\nFfLDe+/xkdYMvOkmBtx4I7lHD/psm3fUuyu+sOgoh/Z6J5OJCd5zeftLJiOdJH1CRDCl1ElAD2C5\ntdbX1DxhE0lz74bK9OnTvZZZa/njH//I8OHDefbuu1n8wAPEdRsX+uCECIGNG2HTJmf0bUXRFW5n\nLcrLoyB/P4dytnm1jY9JYPYzz5CbkMDegwfZuXMnhQW+6wwfyc3mkuefp9haip991m9seYVHuevL\nLykuKqLoH/+g+MEH/ZZozzl6kJTkZKIA5Xaj3G6O5h312TY37wDnpKcTFx9PTEwMsbGxHNi/w28c\nkUySPiEihFLqecBtrf2N5/VEYAYQBeQopUZba78IZ4xlRdrcu8G2a9cBrLXlBm+sXLmS7t27k5CQ\ncGyZUop//OMfJCQkcN1DDzElOpqj+/bRONXXNuHQIUhODsUnECJ49u+H11+HVaugIG+XzzYb1q6l\nICeH9R9+yNo5c/h5/nxii3PxNT6joCiPP95zD41yc2mWkEDbtm0pKnL73G58TBz7Dx0gLi6O6Oho\n2rZtR2amdwytWrdl9+7S78bZ27eTemInCqx3z2AMij8oRYvevWnRuzfNe/TgqvvvZ/9R78SvsbJ0\n+fprUvv2pfVpp5Haty8Z//sfh3J99zjWBmPMf8q8tFSYhk1rfVsg25GkT4jIcT7O/LolHgBmAvcA\nT+KUcxkehrgapO3b93LVVY/x7LM3k5SUyPfff8+iRYvo1KlTuaQPnMTPGENcXBz/ffppWh5aQ2cf\nSZ9ScMcdMHYsXHCBMwpQiEiWnw/vvw/z58OIEfDEE3BC6hw2rfMuG6qUm7+1bcuhrl3ZnpTEtykp\nHM7O9rndVsnJbDh4EOt2c2jrVrJWr2bOmPFgvQvfua0iscwQ4Lw838XxKi5Pat8eGxUNxT6Ss6g4\n9IED5b7UHf3T//ncblFMHE9u2cKGjz5i/fz5bLz3Xo7m5vtsW4u+9fx7BtALp26rAiYAPwW6EZmG\nTYjjEORp2HJxRvIuUUp1B9YCfa21q5RS5wGzrLURMZN5fTr+pkz5LZs3ew/aaN++JU2b9uV///ue\n//xnMqtXL+faa68lNdVHNlfGo48+yr1330PTpqnExpef+aN58xQyMr5nxgznEtmVV8Lrrwd/xgAh\nqqtiGRZrnV7pffvgmmtcTJoErTy1A9qkpJDpY3h6jFIkNG7MkCFDGDVqFOeffz4DTh1AfpF3gpQQ\nG09uQV65ZXExCRQWe7eNiYrnx9UbKCwsprCwiOHDh3LgQJ5Xu2bNEvjww8+Ijo469hjYvw8Fxd7f\nruJjCjiQvZe4uJhjRZ/j4xIpKPTul4yLbUR+QWkPoLuoiPj4xhS5S5PMYJ0HqmKM+Qo4S2td6Hkd\nC3yutQ6oOrz09AkROfYDbTzPhwOZ1tpVntcKkAJwtaCyQs4Ar776EYsWLaB58x7cddd9bNninaGV\nLe9y11138eSjj7ItMxMOl2/Xu3cabdvCXXc5ZS1eecUpU9G5s8trmzLSV4RSxTIsSkGzZhAX5+IP\nfwhsG00aNWJHZma5Xjmrony2LXRH8cwz89mxYz87duxjx459FBbHAd6TSxS5Cxk79kFiY6OJjY2h\nsLAl0NG7XdFWbrttKkVFxRQXu52Huy3Q06ttftFamjWbREFBEUopYmOjKSiMBbynaCx2uxky5C7i\n4mKIi4slLi6GYnccUFIYen/VPxwPY0wcgNa6pnN5pABJOPP1AjT1LAuIJH1CRI4PAaOUaoVzSffN\nMu/1BjaHI6iG7PDhw2RlrWb06NH8+c9zWbv2aw4caOejZfmewk49ejhJXyV69YKHHoJvv620mRBh\nVbGwsru4mCNHjvhsGx8bWy7hKyoqpnGTZhQc8E6koqJgxYpfaNeuBWeddRLt2rXgh29ns2e/d1G/\ndq038vPPpQM40tMvZdEi7yEa/ft3ISPj0XLL2rfpzQ4fh2K71rFs3/02AMXFxRQUFDFy5ES++ML7\nvsK+fYt44onrKSgoOvZYuuhVDh3t5Wnxvs+fR1nGmATgbOBOINsYM0tr/XaVK3r7B/CdMWYhTmfA\nMMAV6MqS9AkROe4C/gX8BlgM3F/mvUuAj8IRVEPWuHFjxo8fT6dOnVi0aBBdugzjwIGq19u4bp3P\n5RVrk0VFwd7da2nm46K9rzpmQoTTT6tWceXIkeQU+R7AcOhoHgcO5PDRR9/y/vtf8/HHKygo8N0r\nd8YZsTz//K3llp10ykns8ZHMde3p3VMXqK49e7Ijs/JtRkdH06hRNDEx0YB30te0aSOGDCkfQ5Om\n8f7vfswAACAASURBVBzyPdjXizGmGc50aefj3Iu3HnjRGPOj1tr3Hws/tNbTjDEfAYNxBnT8UWvt\ne2SND5L0CREhrLUH8TOdjrX2rBCHI4CoqCg6eYruxcbG0LlzG7Ztq7qmX1Ge9/1G/pb7a1tQ2Iyc\nHGd6KiFqk7XO/LZt2/p+/+jRo7j+8heeeeopLunZk7X7siks8r73La9Q0bHjdQwbdjJjxw7kkUd+\nxVVX3eizV86XtLRW+CqK7iyvfrvqtq0Of8lkRZ7LuZOAvsDDWuslnuXbgebV3a8x5lOt9XBgro9l\nVZKkT4gIo5TqBZwGdACmWWt3KaW64tzjd7jytavc9vPW2huDEaeovqNHjrD0sceIT0o69nD76TWB\nGH7/exgzBkaPhgoDhoUICmth+nRYvXoua9dmVHwXpbbSs8eLtMrO5pnJk5k0dSoft+tMZqZ3Tb3k\n5Hh27nyFxMTA5rKuqKr7a6vbrrpta5pMLvKu51zWmcBY4CGt9RJjTDQwHtgJfFPpmmUYYxoBiUCq\nMaZsspgE+LrnxCdJ+oSIEEqpJsA04FKcOcFicC7p7gIeArbiXAI+HqOPc33hw7Zte3G73URFOTet\nN0lIIKHC6MY8IKuoiA8++4zBbdtScPgw+dnZFBzYwKaF6V7bjG6axwMPwKxZ8Pvfw/jx8O67Lvbu\n9d6/jPQVNeF2w0svOSPJ4+IOsnu39wwT0VHR3NC8GVfffz+n33EHSinat+9NZqZ3+tCvX6xXwldb\nPW21oabJZNmyL2UZY2KAm4A5WuvFntdn4lya/QZwG2OU1jqQUgg3Ab8HTqC0fAs4w8WeCjRuSfqE\niBz/Ak7HGbn7BU6eUGI+cDcBJH1KKd8VTh31o85KLVm1ahWdO3emcWPvEYTg+wSWm1vApk25jBx5\nPy+//Afat2/JWT170snHQI5vTzuN6f/P3nnHR1VlD/x70iGhJIAgigSlo4iKWECJBXXFAirFtmJd\nK2tdXcu+PNd17XV1XSv+UBAVpdjXEkBURLCtgoAaEBBCCYRA+pzfH3cCSWYmmYRpmdzv5/M+mXn3\nzH3nMsybM+ee8s03ZI8axaWXXgrAf3Ny6OHHVfDrgOF07QrXXQe//AKvvAKffw69e+f6yNpMX0tj\n8Xjg6adhzRq47TZ48Tn/YQYZniqu+te/2H/cOEpLy/n736fx7bf5gG/ChT8aY0jFIYq5j1dn6o4D\nBnmfT3Icp6pa0HXdvYA2juP4DeZ1HOcR4BHXdSc6jvNYUxWyRp/FEjucAVyrqp+ISN3P5ir8RUP7\nZy1wsKrWsk7E/Bxdtftqxic//PADH374IRdd5DesEgj8BVZVVcW9907n4IOv47HHLg34+qyMDPLy\n8jjhhBPYsmULN910U0DZojW72jztuy/ceit8+mkQC7FYGsDjgaeegvXrzf+rVq0Cx5ampKez/7hx\nfPbZEi6++HH69dubwYN78sUXEVa6GeI4TpXruo8Bk13XnYC5N88DpjqOs3MrwHXdfTBevKtc1x3t\nOM67dedyXfdQYHW1wee67gWYXaF8INdxnKDqxlijz2KJHVoBfjbvAFOLqSrAWF1mA72p45JSVRWR\n95uuXvzy22+/8c4773D++efTrgk90hITE7n11rGccMJBnHfeQ/y+ej2pbfcloc6uT9bqTfTq1YtP\nP/2UESNGsHnzZtp1786vw4fXkqsoKWH70qW8d+21nPDggyR4i8cGcEASJ3WyLRGgqgqefBIKC+Gv\nf90VKxqw2HpiEhMnPs3rr3/G449fxplnHsmECVeQmto8tmzDQV5eHnl5eUHJOo6z2HXd44B2QL7j\nOGUArusmOI7jcV13b0zFhmTgYuAfrutWOI7zYZ2pnsbbkcl13aMxpVuuBg7yjp0VjD62I4fFshuE\nuCPHHGCtqp7t9fSVA4NVdbGI/B/QSVVjIiYvnj5/hYWFPP/885x22mn06tVrt+fbsaOMXr1yWLvW\nt3PH8OHJ5OWZ0lwbN27kpJNO4tBDD+Vf//rXzq4A1ZQUFvLamDEkpaVx5tSppLZpw5gxubUK6Faz\nfHkujz6ay9Chvk3vLS0Xf1021qwx/0c++iiXVG/4XVVVFSnJKXjUX2RIGhdc8E8eeuhisrLaRETv\n5kaw3wOu644DVjmO87n3eRJwPPAWMNxxnPmu6+YAIzCJH9trvPZbx3EO9D5+AtjgOE5u3bGGsJ4+\niyV2uB34UEQ+Al7znjtZRK7H/Io7OmqaxSllZWVMmTKFo446KiQGH0Dr1qn06tWVtWvrL+fQsWNH\nPv74Y3r37s306dPp27fvzkQQgOzsbJ57913eufpqXhg2jLNnBy4Au+ee8PHH8NprJuHj6KNh4kTb\n3q2l46/LRrduUFW1y+DzeDxceumlXm9xJqbe7y6SE8uZNCnIlhyWhpiHKbSP67qpXq/fe67rXg/c\n6bru2Y7j5Lmu+6XjOHWrACa6rpvsbb92PFCzCkPQtpw1+iyWGMHbc/dYjNv+ce9pF/gCOE5Vv9yd\n+UWkDaZ6ex/M3R2gENPjd46q+tZgiHNSUlI4/vjj6dOnT1Su37Zt253bvRv8WGiJycmc8tRTfPHw\nwzx3xBGkDx3Nds31kcvOhtxc09pt+nRz/PQTdOjgK2uTPizVvy1Ulauvvpofv/uOtmSylSN9ZLMy\nfoiwdvGL4zhrgbWu6x6JKbPymneb9zHXdQdiSrLgx+ADmArMcV13I7ADY0Dium4vYEuwOlijz2KJ\nAUQkFePNW6iqR4lIa4xhtkVV/fc8Cn7uBIzxeD0mbnAHxtjDe43WwA4ReQhw4mbfNghEJKIG388/\nr6OkpIxWrXaVtai7rVsXEeGI668nq2dP3hk7lsz99iO9U+2t45T0bMC0duvfH5Ytg/PPhw4dQr4E\nS5ygqtxwww189sknnLVpE7+2aePTKxogyRaIDAdrgae9nrsp3iSN04BHA73AcZx/uK77MaY/+weO\n41TvxQtwTbAXtkafxRIblAPPYdr0LFPVHRjjLBQ4wHWY/ozTVLVWBq+IdMOUEnAwJQacEF3XUoey\nsgoGDbqWF16YyJFH9mvUa/ucdhqdBgyg7+LFPmO/1nneuzfss89uKGqJe26//XbemT6ds7dv5+yp\nU5l9+2MU+NlL2J0WaBb/OI6T77rueGCK67pDgfHAHY7jfN/A6z73c25ZY65tjT6LJQbwZtZ+j8m6\nrb++e+O5BLhBVf8T4Nq/AQ+ISBHG4LNG324SuCDtIZx66vmceeY9nHPO0fz97+cFnKO42He3PbXN\n7gfSr10Lq1ZZo7AlUFQE/pLRl/w4h4V5/+Oy9HQunTePqswufPfdr8C+EdexpeI4zv9c1z0V2AOY\n7DhORIrgWKPPYokdrgVeFJF1wLuqGqg/V2NpD6wIQu5ndsX6xSXr1q2jdevWtG3bNqzXaagg7fDh\n+zNx4tMMGvRnNm780a/M4sWL+eijjzjuuKBaagZNcjLcdRd07w6nngoHHABXXWWTPuKN5cvhyy9n\nkJAwiRr5QezYsZWykiLuH3wIl739NpUprcnJuZU+ffamfftkn3laShmWaOA4zkpgZSSvaY0+iyV2\nmIGJr5sJqIgUUruDhqpqU+7AXwA3i8iCQMka3hZwNwM+2weByM3N3fk4JyeHnJycJqgWOYqKipgy\nZQojR44Mu9HXEB07tmXKlBt5883POfPMV/DXd719+yTOOeccHnroIc4999x65/MXhtmpk/+kjf79\n4ZFHTKHnSZNM+Y7lyyEz01fWJn00TwoK4IEHoFXKNn4v8LUp2qekcM28eVSqMPKkXIYN689jjz0S\nsJ2YJX6wRp/FEjs80cB4UxMsrgE+BFZ6izMvZVe2VzugHyaWsAxv8c9gqGn0xTrl5eVMmTKFww47\nLGqZuv4YPfoIjjhiOJ995vvWDhyYzBNP3MnJJ5/MmjVr6u3esXn5clS11pd2Qx66Y4+FY46Bb781\nDeMz49rH23LYsQPuuQdOPx1mTtvkVyYlLQ0Skxh/1j3suWcmjz56qTX4WgjW6LNYYgRVP7U4QjPv\njyIyAFP1/Q8Yw65uyZb7gadUNejU/+aCx+Nh+vTpdO3alSOP9C1JEW2Sk5MA/zX9BgwYwPz58zn5\n5JNZvXo1bffZx6d7h6eykqqffuLda67hD48/3qgvbxEYNMhs9VqaP5WV8NBDMGAA/KHeMu7Cn/70\nBCUl5bz66l9q1Ye0xDcRf6dF5DgReUBE3hKR+SLyqYjMFpH7vTXKLBZLiFHVQlX9p6oeraqdVTXF\ne3RW1eGqek9jDb7c3NygWxFFkw8++ICKigpGjhzZrLwZ69dvQVXZe++9mTdvHv/73/+YOWcOv3g8\n5MPOY1VSEh1GjGDtV1/x7sSJgdtpNYEVK+Ddd433yBLbqMLzz5vt+gkTjEEfiOJS4YcfVjF9+i2k\npPjG8Vnil4i1YRORLEzM0jBMhYEl7NpiygT6Aj0wBQdHq2q9zYPjqQ2UpfkSyjZs0UZEWmFava0K\nQrZZfP5UlUWLFjFgwABatWoVbXX8kpNzJnPm+Hr60tNXcsQRp/Hvf19Bz55dKSsro1u3bn6LOA8f\nPpz3Zs7kpRNOYK/DDuOkRx9tlIEbqL1bcXEuJ5yQy7ffwmGHwfz5uZSW+r7eJnxEn1mzYN48uPNO\nqP6v3qlNGzb6yQJPlEzWb8inQ4foxrbGC83peyCS27uPAZ2Bw1R1oT8BERkMvOyVDVzLwGKxhIOR\nwDQgbrq3igiDBw+Othr1Eqi8yz77HMGgQYdw+OE3cd11p3PTTaPp16+fX6MPIK1dO857/30mn3AC\n7117LSc9EnxgfqCkj+xsuPZa2LIFPvkEvv8eevf2lbMJH9Hlyy/hnXdMVna1wbft99/ZXFyMAMkk\nIkAVCVSSSGpyuTX4WiiR9PRtASao6owG5EYBL6qqn+pCteSahafBEt80p194DSEiZwGvqmqDYR8i\noo7jNIus3ebOypUFXH31f/j553WsW/cahYW+Rl/nznuybt1aAEq3bGHyCSfwQXExrTt18jH82mdn\n88ikSU3S5ayzcklIyPU57/Hk8vrrvuct4eHKK3eV2Ckp2VV3cZ99jMe1dMsWLj7gAKatWU+VHk9d\n/85enX9m9TrbXi1UNKfvgUh6+jzU7eTsH/HKWiyWECAinxBc5u8eQcoBzSt7tznTvfsezJp1u7e8\nywt+ZUpLy3c+TmvfnvM/+IDXunVj/yVLfGTrdu9oDIEchytWwCuvwNFHQ9eu5lxNw6Qmdit499mw\ngZ3b8a1bQ3XTjA0bcqnYsYO7c3J4a+NGDjokh6++8v2at102Wi6RNPpmYqr+b1DVT/0JiMhQ4AHg\nzQjqZbHEBCLiAQ5XVZ9mSN7QhwWq2pSt16OBnwD/VYB3EZtBb41g69atVFZW0iHOms6KCGeccSRt\n27amqKjEZ7ykZAcej2dnFmZa+/Z0HjgQPvssIvp16wZlZeA4sMcexvhbtw6SknJ9ZO1WcPhQ9fD8\n6NE8tWIFz0+ezOP/mkqgzHBLyySSRt+1wKvAXG/HgZq1wtpjEjm6AB9g+oRaLJZdJANN7dDxA7BE\nVcfVJ1S9vdvEa0SdkpISXnrpJQ499NC4M/qqadUqnaKius5YDx7PDsaPH8+kSZNo3bo1AInJkcvK\nTEuDCy6A886D774zdf+WL4d+jWsvbAmSkhLj4avLxiVLeKJgMX/8058486yzeOzxqZFXzlIvruum\nADiOU96QbDiImNGnqluBE0XkCEytsL7sqhW2AZO1+66qRqT/nMUSC4hId6A7u0IfDhaRtDpiacAE\nTIWOpvA55jMXUnJzc2Mmpq+iooKpU6fSq1cvhgwZEm11wkbfvoNZv97Xc5OV9Tsej8ninTlzJl2r\n91hDTKCEj06dzN/ERDjoIHPMnet/Do8N3tkt5s83MXz+dmh//v079jugP/fcey8Aa9ZsxNRft0Qb\n13XTgKOAG4Ai13WnOY4zPdJ6RLw4s6p+TiNaPVkscc6FwN9qPH8ygFwJcGkTr3E/8LY0nP30No3o\nuB4rMX3VxZfbt2/PiBEjoq1OVEhJSeGTT5I49NBeDBlyGLNmzQzLdRoTi5cYIBBh2TLTBu6ww4xx\nmJZm4/+CweOB114zxnSCxzcyc+3at1i/7Te+enUuSUlJLFjwE6tXb2LIkLa0apVSS9b2040srutm\nAudiOh9NA5YDz7mu+z/HcX6KpC62I4fFEl2eBF73Pv4Oc2P4vo5MObBKVf1USGsYVV0BrAhCroSm\nexOjgqry9ttvU1FRwZgxY5pV8eWmEKi8S3b2QG677Q6uv/45qqo2cMwxx1FRXk5lYupOGfUoqh7S\nv/qaSZFT2YeePWH//eHjj+Gpp8xj2/u3fkpL4YknTOmcf/wDjj74J379JGfneFlVCUu2fM0+XXrR\nuXNnNm/exrhx9zN16jOMHn1E9BS3VG/nngMcCNznOM487/nV+Gu6HWZizugTkWeBBFW9qCHZ5tbw\nvbmQlXUOhYW+BT1hdsR1iXdUtQDvt7iI7AusVdWoxHo0R0SE7OxsevfuTWIg11IcMWnSv+sdnz37\nDt57bxGXX/5PVq70v3NUXlJGZWkpSWl1owhCS6Ct4D33hOOPN0dxMXz1Fbz+uu39G4hNm+C++0yy\nzB13QEoK7NiRT9HG9YBJt/8daAOIZxMej4cLLniEM844whp8scFQ4FTgbsdx5rmumwiMBtYCX0Va\nmZgz+oAcgiwOGyvbS7FKYOOtfpKTM5gwYRYv+K8OUWP+LAoLC5ukW2ZmJps319t0pVkQSs+SquZ7\n50wF9sLE8tWVaSgDt8VxwAEHRFuFmOKkkw5h2bKptGnTgfLybT7jkpDA7MsuY9SLL4bVMxrMtmxG\nBuTkmPpy/li3Dr75Bvr3N8YOtKyt4BUr4IEH4KST4PTTd5XMKS4tZX0d2UIgpbSUBx+cwYYNW5k+\n/ZZIq2upg+u6ScCfgDccx5nrfT4UOAxj8EU8wjXmjD5V7RltHaJFU420QCQnZzBmzKxa59LTadCY\nC5ZaRtvYsfBq8ImfWVlZjfrCiRcjsT5EZC/gaQInXSgx1C0jlhI5LLVJSUmmVasUyv34jJNbt2bD\nDz8w/777GHbzzZFXrhEkJsIbb8BDD5nEhUGDYPVqSEvL9ZFt7lvBdY3ZrVuN0duvH4walVtLNlBo\nbnklPPDAmyxc+JDtqRsbKFCKCdEBGAcM8j6f5DhOleu64jhOxDpNxJzRF48Ea8xlZmagustIu/BC\n2L49sHwoDbhIsTvewRbAM8DBmJJFS9h1o4hJrKe9eVJeXslZ099g0rChdOrXjz6nnRZtlQLSqZPp\nJbt9u2kB9803sHIl9OkT3Oubk1ewZsFlgPbtzeHx5PrIeir8197bsiOR2dMmss8+ncKkpaUxeI26\nx4DJrutOwGzpzgOmOo6z1XXdRMdxqiKpU8SNPhFpAwwH+rCrZEshpm7fHFUNnasrStQ28t4j2OKY\nhYUg0ri6Sk3sphQegvDcZWZmBvyVamEocJmqTou2IrHK2rVrSUxMpHPnztFWpdlSVlbCkSfdww3X\n3M3Miy/hgo8/onOUt8gbKgWTng6HH26Ojz7yP8fq1ab/bJ8+0L07JCX5GlLVxKJXsKrK6NwQpVu3\nUlriW6AbIL1VAiNHHhpizSx1ycvLIy8vLyhZx3EWu657HKZ2Tr7jOGU1xqoAXNc9DKjCxPodCPzL\ncZz3Qq03RNDoE5EEwAWux1T+34Ex9sAYf62BHSLyEODEUmPde7OyKC0s5B6Mn7YxpAGBIiuWnr6J\nqhSTvJNYvpm+M5tvQVlnzJhGbe9a/LIB87mw+KGgoIApU6Zw6qmnWqMvCDp29L2fVFVVUVa2g27d\nVvP4ywug3UhWjTibwpxDWbOuyEc+O3uPBpNHQkEovG4ZGcbw++gjY+zttx8UFEAw/1XC5RFsaN6t\nW2HhQvjyy+CLWT93+eWUiICfr8iDDunfZF0twVM3rMV13XrlHcdZB6xzXXec67r5juMs8L7u/4Bl\nwDHAJOBQjH20p+u6CY7jhDzmL5KePgezbZULTFPVVTUHRaQbZr/bweyDOxHUrV5yCwspxXipSgLE\nlWVlnQPA5s1TgprzwguNQbhrezaLRrQ9jT3Gjo22BvHA34CbRWSut5i5xcumTZt46aWXOPHEE+kT\n7N5eC2fFCv/lv7Zv385FF11EYeECLrj0Zv75t21smDaXSgb4zrE0QIXlGKR9e7jsMvO4uNgYUR9/\n7F+2sBCWLoW99zbGYmM8go0xEAPN+9NPueTmQn4+HHigSWZZtKiexXmZ/+ab3Prqq2R13IPyctOg\np7LSw/btpbRp04q1a9c1PIklmszDxPThuu4gjM1zluM4d7muOxJYBfwX+G84DD6IrNF3CXCDqv7H\n36Cq/obpzVuEMfhixugrJXDgbPVWbmZmRlAGX3WcXnOMx7OEndHAPkC+iCxkV5tCMB07VFVjxrqO\nVCLHli1bmDx5Mjk5OTZTNwSkp6fzyiuv8MADD3D33RN5cdqLjDrpEir93OIqS5tUGjKsNLQVDMaQ\nO+gg0wfYHzt2wOTJxjOYkmKMrx49fOWqqoxDrWbkSrAGYnk5VFTsyjquSUkJnHIKDBy4a/zhh/3r\nWs1vq1Zx+vjx/PmCC5j7y1bmzKkdNrRtGxx8sE3eiGUcx1mLievDcZxvXNc9E3jYdd1Dga7AHOBj\nx3HClrUYSaOvPUEUiAV+ZlesX8xTWFhcK/miIbZvt7ugloB0wvz/FyAFqP7KUu+5mHIFRyKRo7Ky\nksmTJ3P44Ydz8MEHh/16LQUR4aabbmLQoEGcd955lMsW0Pk+clt3xF4uUSi2gvfayxQ5VoXNm03P\nYH8sWwZnn21+pFcfK1dCdrav7K+/wvXXm3t8cbGZ+5df/LdL22svGDy49rn6jNmNGzdy9JAhHLvX\nXrjPPccxx5zV6DVbYofqrVvHcd5yXXc4cDPwACbBo6k91oMikkbfF5itqwWBkjVEJAOz+Lhs03bh\nheamYbH4Q1Vzoq1DrJGUlMSYMWPo0qVLtFWJS0aMGMEXX3zBvvvuC/g6F0or0vjk7fnknHwkIsK1\nEyawJT/fR659djaPxFRWmaEhr6AIdOhgPIP+6NcPXnrJeAaLi41B9803/mU7d4ZrrzVzpacbD15j\nol4CGbPbtm1j+FFHkb11K0988gkiYpPhmjnVW7eu647BePiexNhjYW8pFEmj7xrgQ2CliLyPydat\n3r5qB/TD9KUrA46LoF4R4cILzV+7pWsJBjFFDPcENqhqcOnfcYo1+MJLjx49SEpIpdJT5jMmeBh9\n2p10Tiph/CFtmf7tAhJ3+IYaJS1dySORULaRhMIrmJQEbduaAwIbiK1bBy4y3VRKS0s5/fTT6Vhc\nzF+uu45O/fqhqqxY8TtmY8DSzPkMY+i9CSQ6jhP2e33EjD5V/VFEBgCXY4rPHodvyZb7gadUdYv/\nWaJDGmY7ZHcKBNttXUswiMhITDzrIEwh5kOBxSLyDKak0UvR1M8SnyQlQqWfsPHUZGH9ttn855Hp\nPPDwW6wraUclvhminUt/iICW4SOYOMFIz1tRUcG4ceNo7fFwXFUVw2+/HQDXncrWrTbJPx5wHGeN\n67rTvaVbIvLjPqJ1+lS1EPin92g23AI4qnHfzN0SXUTkj8DzwMvAE0BNv/By4GLAGn2WkNMhqz1r\n1tdt7AVZme1ITU1h4s1nc+UNY+nQtg9F/kvENWsa4xFsjCHXmHknTJhAvnfrXFVZunQpFeXl7FdZ\nySnTppHcujWPPTabKVPmMnLkYNav9/WNZGcHyFyxxCxxX5y5OZOZmbnbHj+LpR5uAx5Q1VtEJIna\nRt8PwI3RUSsyVFVVMXv2bIYNG0bHjh2jrU6Lomffvn6NvrKqKrZu3Uq7du1ISkqkVYr6Nfo8LSjE\nLFydPPLz85kzZ47P+e0dO9Lr5JN56aVPuP/+N5k3759kZ9s6lZamYY2+RlBt6FmPnyVMdAc+CDBW\nCrSNoC4NEsqSLR6PhzfffJOKigoyM5tN8n7ckO0nHdXj8bB+/XqGDBnCm2++Sf/+gQv/bihK5Lrr\nnuWaa05h331tDGYoyerZk7feWsiNN77Axx/fZQ0+y25hjb4mUO3xq6Yh75/N2rUEyWpM711/JWUP\nIbiSRxEjVCVbysvLmT59Oh6Ph3HjxpGYmBiSeS3BM6mezNsXX3yR4cOH8+STT5KUlgZ+yoZnppaQ\nkpLEkCE3MGxYf/7851OZNOnfrFxZ4CMbqS4fzY2iIt+OKADFJZVceOGjvPXWHfTvH+JMEUuLwxp9\nTaCmcSdyGqqz6vX+2SQOS5A8Czgisg6Y6T2XICLHA38B/h41zcJEcXExU6dOZY899uCUU06xBl8M\ncsEFFzBw4EDOOOMMiku30rbt+lrFilUhYXsxlwzfg7/97TkmT/6Eq676DytXzmfHjmw/M/oagi2Z\n4uJibr/9dr4JUAvmu+9+4f33n+aww2wnGsvuI8213o+IRKw9b3XvXX/cw4neR+/77cubBpw6Rhnw\nWnxvCbfU3rvemlkheXO9/akfx2S4ezDZu5Xev0+p6lWhuE4oCNXnb8mSJaxbt46cnBwbNhHjbNq0\niZ49e7Jli28CwZCBAxm3aRNXfP89rTIzUVUOPPAkvv8+1Ud2+PBk8vKmR0LlmOedd97hyiuvJCcn\nh3feeIMN27b5yLRr1Z4tO/x//1hig1B+D4Qb6+kLgpvrSdpwqPb2+f8CrP4ic5qpcR00tvfubqOq\nHuAqEXkYU9KoI6Zi7seq6r+RajOnX79+9Aumy7wl6nTo0IGBAwcyd65vP95WmZn0Peoo3ps4kdGT\nJyMiZGVl4K8KRUHBVsrLK0hJabktwwoKCrj22mtZsGABzzzzDCNGjKDNK6+TivF0K0I5SSRSRVXL\nLtNpCTHW6LNYYgARaYWJlhqrqjOIsfg9iwXqT2I7/t57eerAA1k6YwZ9R40KKLd27Wa6dbuI9pDI\nLAAAIABJREFUCy44lksvPZFevboyYcIV5OfHX/xfzTIsYEqxrF+/nvz8fCZOnMgzzzzDtuXL+fCW\nW0gqb0Uxx9Z6fRWQntq8ayBaYgtr9FksMYCqlohIAWY712JpVlRUVJCSns6oSZN4bcwY9hk2LKDs\noEE9ePrpe3j22Q8YNuxmBgzYh99/X8bSpf6y3Zp3/N+H773ntxROh3bt+EOrVrxw8MFUlZczYPx4\nktPTwW+DUosldFijLwRkZmYgchqZmRls3jylzlgmr70mNBSuZGv/WYD/ABNF5ANVjb1O97uBqvLx\nxx/Tp08f9t5772irYwkxixYt4vPPP+eIYcPY/5xzePvKK72Fgv1773r33ov77ruQu+46j5kzF3DZ\nZe8C8VfioLLUX6Q3VG7dSvm2bYyePJmuhx5KcXEJxQ+9HmHtLNHAdd0UAMdxonKPt0ZfCKg29ERO\n8zNmDLmxY+vPc7BB7BZMD+r9gV9F5CNgPVArGFRV/xINxfwRbJ2+yspKZsyYwbZt2zjyyCMjo5wl\nLPir5weQkJDAqFGjuPHGG5l45508O3gwN7kuAxqI9U1JSWbMmGE88UQP5szxjV2rqIhos4KguHbC\nBLbU2LKtpn12No94S9+UFRXx3pQpFPpJzABIbduWEx96CIAZM77gmmuehgT/metJaWkh0dsSXVzX\nTQOOAm4AilzXneY4TsQzmqzRZ7HEDmcBZZgG3EfVGROMARhTRl9DlJSUMG3aNNLT0zn//PNJSrK3\nnOZMffX8Vq5cyfjx48nLy+OfjzzCuxdcQPfhw8no3PRiwgsW/MTZZ9/PhAnHcfzxB5KYmBj1+L8t\n+fn08NM545vffmPauHG8O3cunxQUsD0piUAFiESE1as3cs01T/Pjj78xefJ15OZu9Wv49uzbN8Qr\nsEQa13UzgXOBE4FpmLaaz7mu+z/HcSKapGfvwBZLjKCq2dHWIZSsW7eO6dOn07NnT0444QTrzY5z\nunfvzty5c7n99tsZeckl7Nm6Nc8PGECnAQNqvffZ2dn1Go81OeywPgwb1p/bbnuJiy9+nD/+8Rh+\n/PE3Fi5M8CMdmfi/T5cuJa/OuSqgaNUqPisspGfPnjz0+OOMGj2ajFbp4CnzmWPTtlIOOuharrrq\nZKZOvZG0tJR6t8MtzRfvdu45wIHAfY7jzPOeXw1kRVofa/RZLJawUFRUxLBhwzjwwAOjrYolQiQn\nJ3PvvfcyfPhwTjnlFFSVJXVKvKxYutTndfUZPFddNZKrrhrJ99/nM2nSR3z99S9AzzCtoGGKtm9n\ng5/zaSLM/eSTOv/fk/EXq1jlqWDevHvo23dXfGtzzlK21MtQ4FTgbsdx5rmumwiMBtYCX0VaGWv0\nhZD6Ejoafm1mkzwhNgEkvhDzn2AY0AtT27sWqvpkxJVqIr179462CpYocfLJJ9MhI4ONfmLa/CU3\nBGPwHHBANg8+eDELF77NvHm+Se7l5eFNfPdUVvLZgw9SVuw/xbZd69Y+P3A6ZO3DmvX7+cjuucfP\ntQw+S3zium4S8CfgDcdx5nqfDwUOwxh8Htd1xXGciBXytUZfCKkvoaPh1zbNcLNbZvGDiHTG9N2t\nr1pxszH6LC2bxAR/W7C7T0KC/3veggXLOPbY2zjzzCMZPfpwunbtELL4v4IffuA/48fz2dat+E/N\n8GX79lLaddiTNb4VW+jVz8bptRAUKAWqM3XHAYO8zyc5jrMzU8l13QFAouM434VTIWv0WSyxw4OY\nAs3dgN+AwzEZvOcCfwROiZ5qgdm+fTurVq2ynTUsQVFaHp5KFUce2ZeJE09l+vTPuOOOl+nfvxu/\n//4jv/zSzo90bUOwbhHlavbeay96VlUx+Y032NKqFZdecQV5Dz9Kabmvt7KorILi4hLefvsrXn99\nPh988A0JCZuB1qFZoKXZ4ThOleu6jwGTXdedgNnSnQdMdRxnq+u6iV4ZAVKAKa7rXu84zrvh0ska\nfRZL7DAc+DOwrvqEqq4E7haRRIyX74Qo6eaXZcuWMXv2bAYNGmSNPktQFJWUcHjv3jw9bRoDDzqo\n0a+vL/5v1KjDGTXqcMrLK/joo++48MI8TCWk2lRW1i4F8+q01ygp3eH3ev2zsrjjkUc455JLSElJ\n4amnnvVr9FVUJrDXXhcydGg/zjrrSJ566krOPPNCvxm5luZNXl4eeXl5Qck6jrPYdd3jMP8R8x3H\nKQOoNvi8YomO43ztuu6VwL9d1y10HOeLcOguoWiaHg1C1fA9HJhevLNqnWuoTl/TryUB+/5GlHAt\nMMYJZaNtEdkGjFTVuSKyBThPVd/yjh0HzFTVjFBca3cREZ01axa//PILo0aNonv37tFWyRJj9OzS\nhUo/3SikQweO3ntvpn//PaNOPJFHJ0+mQ4cOYdEhJ+dMv0ZXQsIyunUbxgEHdGfgwGzu+edleNTX\nkEskmV9XrWHLlu1s3lxMYWExf/zjKLZt852zbdtk8vNXkJm56yMa7fIylsgQ7PeA67rjgFWO43xe\n41w6MAr4xHGcta7rOsBix3Fmh0PXgJ4+EbmfOoVhg+RRVV3TdJUslhbLr0B1dPePwHnAW97npwAx\nlbHj8Xi4/PLLSU1NjbYqlhjklJNOqreI8cQZM7j+kkvoseeepLVujaj6xChndezIjytC34Z62LD+\nPPfcnSxatJwZM2bjCdAAp4pEDj/8JjIzM8jKyiAzM4PWrbuzbZtv7cGDDkquZfCBzci1+DAP6A/g\num5bx3GKHMfZ7i3cvMJ13TswxZtHh0uBgJ4+EfFgtpl8iwwFmAsTi3Soqi4OjXr1XCyGPX1ZWedQ\nWLgrwyszM4PTT5/C9u2Qng4vvBDKa2VRWFgYcDxi2b3W0xeKue4BOqvqhSLyB2AWJqavEtgHuFlV\n7w/FtXaXWP78WZoPnspKXnVdzrvrLvz13ujcrh3rtmxp8vxdunRn/XrfbNvMzCTOOut0pk+fTu8e\nPViw6GsUj49ccmIq5ZW1PYCBvIfDhyeTlxfxBguWGKCx3wOu6x4LdHMc50XXdRMcx/G4rjsVeB6o\ncBwnL1y6NhTTN1pVFwQzkYgksStDpUVTt1yLyGk7Db0GuhI14Vr1G3Q2u7f5oKq31Hj8rogcifnF\n1wr4QFXDFtxrsUSDhKQkxv/97/z50Ucp8FPepcrja4gFSrrwV/S5aOtGwDdWb0uhkLZhAzfttRep\nBQUsJJEqP0afxRImfgUedV23wnGcKa7rDgaOA3LD3aGjPqPv/8BvDcpAVHlfs2m3NLJYLACo6kJg\nYbT1CESwvXctloaQAOVdNm7bRpesLIYddRTHnngiRx11FP99913WFvjGya1YupQdO3aQn5/Pzz//\nzC+//AIe/7X72qIclZzMQfffz77HH8+trdpTVdHGRy4xwdePYTtnWHYXx3F+dV13PPCy67pDMBUa\nnEi0ZLOJHBGgZmJHpHdBI5boYbd3QznnicChwJ7A78CXqvpBKK+xuzSnz58l9unSvj3rt271Od+h\nVSvcESOY8/HH/JaYyCoR1gbY7hUgJTWV7t26sU/XruzVsSOvz57N9grfrdg92rRhfVHRzuf9ex7A\n5o2+HsGsjq35ccX3TV+YpUXQ1O8B13X3AToCyY7jBLWrurvYki0WS4wgIl2BGcBgjCuhAOgMdBKR\nRcAomyRlaUkkpaRw1cyZXOnx8PvixSx/911Oz81lq59t3wzghspKUjdsIB1oXV7OLI+H7X7mretZ\ntIadJRo4jrMKWBXJawZt9InIXpj+cV3x3x7qLyHUy2JpiTwNdAGGqepn1SdFZCjwind8ZJR0s1jC\nRlbHjvWel4QEug4eTNfBg0l78EG2+vEKtmrThts3bSIxOXnnudvatwc/shZLSyUoo09ExmPi9cDE\n+dUMdBBMaRdr9Fksu8exwMU1DT4AVZ0vIjcDz0ZHLYslvISiLIskJNQy+AAy0tJI82P0JaX5+C0s\nlhZBsJ6+fwCvA5eralFDwpbaZGZm1OrHWzOhNjMzwyfbN7TXziQrKysyZVssu0sBUBJgrITGJVZZ\nLHFJYwy5+moFWiwtkWCNvo7Ac9bgaxr+jLrqvIeaxmB4rr3Zlm1pPtwNuCLylaqurj4pIt0A1ztu\nsbRoGmPIPVKnhIvF0tIJ1uibAeQAH4VPFYulxTMC6AD8LCKL2ZXIcTDGy3ectx2bAKqqIa76aLHE\nPtaQs1iaTrBG39XAZBF5FvgY8MmZV9V3QqmYxdIC6QQsB6oDnNoBpcBnNcZhVxytxWKxWCxBE6zR\n1wsYCGQDF/kZVyAxRDpZLC0SVc0JxTwikoFJrDoT0xoRYDUwHbhPVX1bH1gsFosl7gnW6HsOKMKU\ni/gZ227NYollXgaWYlq4/eY9tw9wsXcsvIGkFovFYolJgjX6+gBnqOp7obioiLQBegOZ3lOFwDLr\ngbC0dERkIPBXYAimI8da4EvgXlX9Nshp+qnq6XXO/QT8RUSWhUxZi8VisTQr/Dc89OVLdm0TNRkR\nGSEi8zBG3kLgA++xECgUkbkicvzuXsdiaY6IyChgETAIeA24A7MlezCwUERGBzlVsYic5Gf+PwDF\nIVLXYrFYLI3Edd0U13VTonX9YD191wEvikgpJoPXXyKHb+PCGojIWGAq8B4mLnAJxvgD4/HrC4wD\n3heRs1W15TVytbR07gVmAmNqNrYVkb8CrwL3AG8GMc8fgae8iVfVpV/2BvKBC0KpsMVisVgaxnXd\nNOAo4AagyHXdaY7jTI+0HhJM03QR8W10WBtV1XoTOUTkB+Dthtq1ich9wCmq2r8BuWbd8L1mnT7V\nWWG9lrcZdFivsXNBLYymNtoOMNcOYLSqvu9n7CTgTVVt1Yj5OmOMPQFWq+q6UOjpnbtZf/4sFosl\nVDT0PeC6biZwLnAi8AamSsNzwGmO4/wUGS0NwXr6/GXsNpZ9gbeDkHsHmBiC68U06enGTkpOzmiw\nQHNju3ZkZWVRWFi483lmZmY90pYYYhEwAPAx+rznFzVmMlVdD6wPgV4Wi8ViaQLerdxzgAOB+xzH\nmec9vxrIirQ+QRl9qjqpvnERSa5v3MsKTDbhnAbkTsdYwXHNCy9UPzLGXH2OssZ27SgsLAy/Z88S\nDq4DpolICmYbtwDYAzgDk3k7XkRaVws3FFJRF28C1XBMYlbNJKqlwBxVbfHxfnl5eeTk5ERbjZBj\n19W8sOuKK4YCpwJ3O44zz3XdRIwttBb4KtLKBJXIISJ31TPWChOH1BC3A1eJyIcicpmIHC0iA73H\nUd5z/8UUgr49KO0tlvjiS6AHpt3aEmCT9+8/MJ7yLzGJGMVA0JnuIpIgIn8H1gGzMC3dLvAeLjAb\nWCcid0oL79mXl5cXbRXCgl1X88KuKz5wXTcJ+BPwhuM4c73PhwGHYQw+j+u6wSbUhoRgL/ZnEbmt\n7kmv5+A9zNZTvajqTOAYoAp4HMgDvvEec7znqoAcr6zF0tK4qBHHxY2Y18F4EXOBbFXNUNVu3iMD\n6O4dq5YJmpo38UCPg3ke6FwwY02Ra8w8dl12XcGMNUWuMfPYdcX+uvygmK5K1bWNxwGneJ9Pchyn\nynEcD4DrukNc1x0YLkWqCdboOw24VUSurz4hIlmYlmxdMRkpDaKqn6rqiUBbYH/v647yPm6rqiep\n6vxG6G+xxA2qOqm+A3i5zvNguQS4QVXvV9VVfq77m6o+gMkqu6QxOsfrzduuq/7nDelk1xWcXGPm\nseuK/XXVxXGcKuAx4CbXdfMwDS5+Ae53HGdrtZfPdd0umI5nr7iue3JYlPESVPYugIicCMwArvf+\n/cA7NCKUWYHBEm/Zgw3F9DUmwzci2bp1sdm74Zo/ATgWOBuT2dvowF8R2Q6cpqofNSB3HDBbVVvX\nJ+eVjZ8Pn8VisewmDWTvdsH0Us93HKfMey7RaxTWlBuKyeo9x3GcxeHQM2ijD0BMRsGrmFijtcCJ\nqro5pAqJdPPq5eORqCNnjb4AWKMvcoTL6BORIzCG3higM+Yz96qqXtWEuT7ChE6cEShZw9uv9w0g\nUVWPa7LiFovFYvGL67rjgFWO43zufS4AjuNotRHouu5DwGvVMqEmYPauiPhzMVZi0k1PAx4EDq+O\n+1bVd0Kk06+YumL11v2zWOINbwu2s4HxmDi7MiAV413/l6pWNnHqa4APgZUi8j4mW7e6wHo7oB+m\nflQZYA0+i8ViCQ/zMNUTgJ3GXirm3tvDdd1emO+AaeFSIKCnL4iCzDVpsDhz0AqJ/NGr14sNyFlP\nXwCspy9y7K6nT0T2w3zIz8YYX1sx9SzfAL7AdNTIUdW5u6lnJnA58Af8l2x5F3hKVX267VgsFosl\ndLiuewFmF2cH5n68BsjA3I+nOo7zSriuXV+dvn3DddH6UNX/C1Y2Nzd35+OcnJyWWP/HEmHy8vJC\nHfS7HCjBeNBvBD5U1QoAEWkfqouoaiHwT+9hsVgslujxGaaqwuvAlUAF4AE8juNsD+eFAxp9qpof\nzguHgppGn8USCer+uHBdd3enXInZyh2OidvbhKnHF3G8NTc7NRRPG8Q8czDbxgmYTLULvUZns8Ub\nazwJ2BNzc35bVW+OqlIhQkT+jSke21VVI1ozLJyIyP7A/2E8KEuAc+OlAHkcv2dx+Tnzd0/Mzc0d\nCTwDnOI4ziSASNTsC3gBEWnrzRwMmoZeIyLpIvJHEblZREaLiM+WsIjsKyLPN+a6FktzRVV7YCq2\nvwdMAL4Qkd9E5HEgJ8LqjMTE1O4up6jqIFUdCPwM1Ntvu5lQAdzk7Ql+EHCYiJwRZZ1CxcvAwdFW\nIgw8Bdyqqr0xIQzx8P+wmnh9z+L1c+ZzT3QcZwlwBfBn13W7ua6bUF2zL5zUZ9RtAQYHO5GIJHlf\nMyjA+J7A/zBW/B3AdOAHETm0jugemC8/i6VFoKqfq+pEYC/gBEw5pPMwcX0Al/n5nISL3c5EVtVt\nsLPcTAawYXfnjDaquk5VF3sfVwDfAXtHV6vQ4K2fWhBtPUKJiHTGFCJ/z3vqOeDMKKoUUuLxPYP4\n/ZwFuic6jvM9cLTjOL9FwuCDhnvvDhWRjkHO1VAixz8xlan7qOpyb6bio8AcEblAVV8L8joWS1yi\nqlWYLNsPReQKTNLF2Zg+jeeIyDJV7dvYeUXkE0xl+IbYI0i5YK75DuZH43JgYijmjBVEpAMwChgR\nbV0sAdkbkwRVzW9AtyjpYmkC8fY5q+eeGNGQg1Bl79ZkcLWlXme+VRi37bQa5xIwxuCN3rGHRORw\n4LOG4hTiLXv3wgthe43wzcTyzfSd2QGAeziRUlK8I+9hPOCBSQNuCYuWgXHGjLHZu+G7RjpwOjBe\nVU9rwuurgJ+AH+sRS8dsF+2BiaWZq6rH+JmrP6Zl4uEYz/6zgKuqPveLGp/vdqp6eWP1DgUi0hO4\nCTgC0y5yt9YlIqmYD+EsVX04zOoHJNTr8sp6oh0fFqp1ichgTJmjw73PWwHrVbVtRBayS8+Qv091\nXhe19yyca4vW5ywC71fU74nhyN5dE+B8Fqbh+068/0A3i8hK4DER2RtT/LnF8cIL5q8rgqPK2LFZ\nOF6j1qkhV7ccS2PLuYSNsWOjrUHcoqrbMdm9U5o4xQ/AElUdF0jAW3j9Oe/Tn/Dj8fOWffkQE6Zx\nGtATU68zAROyUVdvj4j8HxC28gNB0B/jMf0cc79r8rq8McgvA4uiafB5Cdm6YoxQrWs1tbcF96G2\n5y9ShGQ9InIxcLX3JVeqalgK9zaScKztCmAh0fuchfX9iol7oqpG5MD8A/2lnvEzMaUrvgaqgphP\n45Fc77rGjPE/XnfdcGq4VQqOQArHOd73I2Kfo6YcwH+AVQ3ICHAWxsv3OvCxH5m/YrKLM2qcuwnY\nDrTxPm8PdK4x/jfghSiuXWo8bvK6vOeeBZ6P9vsZ6nXVeP898bQu4FPgD97H9wF/b87r8Td3NN+z\ncK0tmp+zcKwp1u6JkXQLvw9cGii7V1WnYyzsHoQgmNxisezkfuBqqW6f4wc1d6O3qd/D/wfgfa1d\n9mIa0ApTcgZM0efZIvKtiHwL9AZu2B3ldwfvuhqivnUdDSAiQ4GLgENE5GvvcbXvVJEhBOuqfr8Q\nkWeBVYB6M8efDqmyjSCU68J4jf4hIsuAvhjDL6KEeD07iYX3LBxri/bnLEzvV0zdExtK5AglDwKf\nAG0wXQd8UNU8b8/RIRHUy2KJa1R1BbAiCLkSIL8e27APZluj5mtWiUh1Vfm3VPVXmt/nt7519cXU\nCptP/dUOYpEG3y/vuUuioNvuEOy6vqd5lDUJaj11xpvLe9aotTWTz1lj1xRT98SIGX2quhZYW/e8\nN07mv8CfVHW5qi7BFNK0WCyxRSa7evbWpJBdbd2aI3ZdzYt4W1e8racm8bi2Zr2mWLCoBVOEtk2U\n9bBYLBaLxWKJW2LB6LNYLM2DQkwrobpkeseaK3ZdzYt4W1e8racm8bi2Zr2moLd3RWQv4BRM14C0\nuuOqGk8tbiwWiy9LgX41T3h7Zbb2jjVX7LqaF/G2rnhbT03icW3Nek1BefpEZDSmSfC/gIuBMTWO\nsd6/TUJVK4FjgWVNncNisUSEd4ETRSSjxrlxwA5gTnRUCgl2Xc2LeFtXvK2nJvG4tma9pmA9fXdj\nSq5MUNXNoVZCVfNCPafFYgkeb8eCkd6newFtROQs7/O3vZm9T2HaB70hIvcC+2Fqhz9Up3xBzGDX\nZdcVTeJtPTWJx7XF45p8CLJgYTFwfLSKCQbQSeORXFucuVlBMyjOHMwBZGMKM3uAKu9R/XifGnL9\ngI8wv2rXAC41CprG2mHXZddl12PX1pLXVPcI1tP3OX5q01gslvhAVfMJItxDTUml48KuUIiw67Lr\niibxtp6axOPa4nFNdQlo9IlI6xpPrwOmiMh24AP81KhR1R2hV89isVgsFovFEgrq8/T525t+PoCs\nAom7r47FYrFYLBaLJRzUZ/RdFDEtLBaLxWKxWCxhJaDRp6qTIqiHxWKxWCwWiyWMBJXIISK/AKNV\n9Vs/YwcAM1V131Ar11LJysqisLAQf33vMzMz6zzPQOQ0n3ObN08Jp4oWi8VisViaGcFm72YDqQHG\nWgPdQqKNBYDCwsLqtPCdjB0Lr77qK+vPuKtrBFosFovFYrHUl73bDtNfrtrftKeI7FNHLA1TiXpN\neNSzWCwWi8VisYSC+jx91wF/q/H8zXpkbwyNOhaLxWKxWCyWcFCf0TcF+Mr7eBbGsKvbH7cc+ElV\nV4ZBN4vFYrFYLBZLiKgve3cZXiNPRI4FFqnqtkgpZrFYQoeIjALuBHoDa4HHVfVhP3K3AlcAHYCF\nwER/CVwWi8ViaX4ElcihqnkAItIHOBTYE/gd+EpVl4ZNO4vFstuIyFDgDeBZ4HrgcOBeEfGo6qM1\n5P4K3I7x6i8FbgA+FJH9VXV95DW3WCwWSygJtmRLW8wXxpmYxI5iIANQEXkDuFhVi8KmpcVi2R3+\nBsxT1cu8zz8UkfbA30TkSVWtEJE04BbgblV9EkBEvgDygauBO6Kgt8VisTQ7XNd9HhgJFDiOc0Cd\nsRuA+4GOjuNsjrRuDTYW9vIkMAI4H8hQ1bYYo++P3vP/Do96FoslBBwI/LfOuf8CmRivH8CRQBtg\nZ2Egbz/t2cAfIqCjxWKxxAsvACfVPem6bjeMzRS1PIhgjb7Tgb+o6hTvFwGqukNVXwZu8o5bLJbY\nJA2TdFWT6uf9vH/7AlXA8jpyS71jFovFYgkCx3HmAYV+hh4C/hJhdWoRrNG3HRP87Y+1mO1ei8US\nm6zAxOLWZIj3b5b3byZQrHWrgpsbV2sRCbaQu8VisVjq4Lru6cBqx3G+i6YewRp9TwA3ikjrmidF\nJB3j6bPbuxZL7PIUMFpELhGRTBE5EVOHE8ATRb0sFosl7nFdtzVwK+DUOO2n0Wr4CfbXe1ugF7BK\nRP4LFACdMXvTJcBCEbmvWlhVo+q+tFgstXgeE9f3b+BpjOf+FuBxYJ1XphDIEBGp4+3LBHaoamXN\nCUWkrkfQYrFYWiyqWp8Rtx+mne23rusC7A0scl13iOM4BRFQbyfBevrGABWYbdwjgNMwAeDbgErg\nLK/MWO9fi8USI6iqR1WvAToCB2B+sC3wDn/h/bsUSAR61nl5X2BJgHlxHAdVrfdxMM8DnQtmrCly\nDb3ersuuy67LrivYoyEcx/necZzOjuP0cBynB7AaODjSBh8EX6cvO8x6WCyWMKOqW4GtACJyJTBf\nTRF2gM+AIswPt394ZVoDp2K2h/2Sk5PT4ONgngc6F8xYU+QaM49dl11XMGNNkWvMPHZdsb+ualzX\nnQoMBzq4rvsb8DfHcV6oIRK9nZLdsW6jeRjV449c85/B5/yYMcHPAaeGUKMgaYyCcYT3vYr656G+\nAzgMU3D5eOAM4DVgC7B/HblbMFu/VwLHAW9jQjk6+Zkz9P+YMYDjONFWISzYdTUv7LqaF83he6D6\nCHZ7FxE5UEReFZFfRKRcRA72nr9bRGwdL4sldqnAePDexNSPSgOGqur/agqp6j0YL99fMfX5MoAR\nqrohsupGj1D/4o8V7LqaF3ZdlnARlNHnNeq+wsQCvUjtbeEy4JrQq2axWEKBqi5W1SGq2kZV26nq\nqar6QwDZu1W1m6q2VtXh2sL67sbrl5JdV/PCrssSLoL19P0TmKSqw/HG+9TgG+CgkGplsVgsFovF\nYgkpwRp9fYFpAcaK2FXg1WKxWCwWi8USgwRr9G3A1JnxR39gVWjUsVgsFovFYrGEg2CNvqnAnSIy\njBqpxiLSB7gZeDkMulksFovFYrFYQkSwHTn+hvHozWVXBf+ZQBfgfeDu0KvWcsnMzCQrK4vNmzfv\nPJeeDmPH1pZLT4cXXsCHzMwMRE7b+Xjz5inhVNdiaVZ4PB5UFRFBxBTRr/5rsVgs8UywxZlLgVNE\n5DhMra+OwGbgQ1X9bxj1a5Fs3rzZ50vIn3FX1wjc9fpdRl618WexxCtr167ls88+o7RpWOxSAAAg\nAElEQVS0tNaRnZ3NWWed5SP/008/8frrr9esOQhAv379GBvoQ2WxWCxxQLCePgBU9SPgo929qIi0\nAXpj+nqC6fu5TFW37e7cFosl/qiqqmLt2rV069bNZyw9PZ2+ffuSlpbmc/ijX79+3HHHHTufVxt+\nNQ3AmmzcuJGEhASysmy+msViad40aPSJSAIwAlPVv7P39Hrgc4ynL+h2IiIyArNVfAS+8YQeEfkM\nuFNVPwx2TovFEr8UFhayePFivv76a7p06cK5557r4wVv164d7dq1a/I1Gtrizc/PJy8vj9TUVPbb\nbz/2228/evToQUpKSpOvabFYLNGgXqPP23XjFUwT9kpgI8ZYy/K+drmIjFfVrxu6kIiMxSSEvAdc\nhGniXugdzsSUhRkHvC8iZ6vqq01akcViafasWLGCBQsWsGbNGg488EAmTJhAx44do6LL4MGDOeSQ\nQ1i/fj0///wzCxYsYMaMGVx00UV06tQpKjpZLBZLUwho9IlIZ4yB9jvwB2CON7YPEUkDjgHuBd4T\nkQNUtaCBaznAg6r6lwDjC4HJInIfkAtYo89iCREici6m/25PYCsmTOMWVf29jtytwBVAB8xncmI0\nunIUFBQwYMAAxo4dS3JyMgDXTpjAlvx8H9n22dk8MmlSrXOhlhURunTpQpcuXRg6dCibNm2y270W\ni6XZUZ+n7xqgBDhaVbfWHPAaf++KyOfAt17ZO3ynqMW+mAbuDfEOMDEIOYvFEgQicgYwGfgXcD3Q\nFbgLeFtEDqkO0RCRvwK3Y4zDpcANwIcisr+qro+kzkceeaTPuS35+fSYM8fn/K9+Xh8u2cYYkxaL\nxRJr1Gf0nQD8u67BVxNV3SIi/wbOoGGjbwUwGvC9u9bmdGB5AzIWiyV4xgOLVHXnjykRKcKUXeoN\n/OT13t8C3K2qT3plvgDygatp+PPdJDweDwkJwZYL9U9ZUREr586lsqyMqrIyKsvKKF7v30bd+NNP\nvD5+POXbtlG2bRtlRUWsXrqUHv5klyzhvzffTEaXLrTZc08yunRhw48/0nvhQh9ZfwaixWKxxBr1\nGX09gUVBzLEIU6C5IW4HXheR/TFbt0uBLd6xdkA/YAyQA/jWWbBYLLtDUZ3n1T/mqrMXjgTaUCOs\nQlV3iMhsTHhHyI2+bdu2cd/dd7NhyRJKt2ypNVbtOasqL6fghx9Y+9VXfLhggd8bVvl33/HxbbeR\nmJpKUmoqiampfPHrr3zlR1a3b6fP6aeT2qYNqW3bktKmDfMuvxy+/NJHNi0zk1aZmWzJz2fNF19Q\nvG4dBd9/T+/QLN9isVgiTn1GXzt2fTHUxzagbUNCqjpTRI7BfHk8DiTXEakAPgFyVHV+ENe1WCzB\n8TTwloicz66i6ncBH6nqUq9MX6AKXy/7UkyCVUhZs2YN06ZNY9vatew5c6bP+FfLlvHMkCEU/O9/\nZO67L10HD6YU2ORnrs4ZGVw4b16tc39u3541ZWW+sgkJHHD22bXOLVi5En83nKQtWxh2yy21zuXl\n5ICfreDE1FTmzZvHsGHDbKFni8USs9Rn9AV759JgZVX1U+BEEUnF9PKtWafvZ1X1vUtbLC0EEbmf\nGm0OG8Gjqrom0KCqfigilwDPAS96T39GbY96JlDspwRTIdBaRJJUtbIJuvnw7bff8sEHH3Dqqacy\n8t57qfIj49m2jScefpgugwaRkp4OwJ/efBNKS31kSysqmDVr1s5OGx6Ph9Lycr/X9ngLMtc0zIpL\nS/G3GdzZz7UCUbJpEz/88AMJCQkMHTo06NdZLBZLJGmoTt/7ItLQjb5RBZ4BvMbdj419ncUS59yA\naXMY7I8fAbphyioFNPpEZCTwDPAQ8C7G05cLvCkix6uqZzd0bhTz589n0aJFjBk5ktUzZrB5wwa2\n+JFrW1XFy3PnsnLyZFauXMnKlSspKKq7Q20orazk2WefRURISEggISGBMo//JW0oKiI9PZ1OnTqx\nxx57sMcee1DkxyMIkOSnuPOnS5eS50e2bPFizl+9mi9LSmjXrh37779/gH8Bi8ViiR71GWx3NmKe\npngn/CIi3QBR1VWhmtNiaUaMVtUFwQiKSBLg36VVm3uA11X1rzVe+w1m6/Z04E2MRy9DRKSOty8T\n2OHPy5ebm7vzcU5ODjk5OTuf+8tyVVVS2rThmLQ0pjkOfc88E0lLg5ISH4XLKiooLCzkgAMO4JRT\nTqF79+5cccUVzJ/vuxF7+BFHMGvWrFrncnJymONnG3b48OG8/fbbFBQUsGHDBgoKClixYgXLli3z\nkS3cto2zzz6b/v377zxKVFnrIwl7dujA5kWLSKus5J2yMtq0aUP37t39SFosFkv0CGj0qWpuBPWo\nya8YD0ZilK5vsUSL/wM2NEK+yvsaf6FuNdmXXdu6AKjqMhEp8Y6BMQATMQlc/8/emcdFVa4P/PsO\nOwICsriRoKgIbpVLWSpqVlpaVi7l1Wy1bLs3K7PyDtOiprZcy63VrK7dlp/t7olZalnmFoIi4r6g\nIIiAbO/vjzMsw5xBloEZ4P1+PufjzHue855nhGGeedbyeX1RaI3UrShv9FXk+1WrKNSpoM1zcaHj\nAw+QOHIkb61aRaaNEKp/s2bMmTPHYs3VtdpBBV2aNWtGREQEERFaze68efN0jb6oqCiGDx9OQkIC\ny5YtIyEhgRNp+j+eTjEx3PXDD/w2fz7xy5axKCOD/338Mbk5ORZygUFBJCQnW6ypNjAKhaK+sM9f\nUftyL1XPJ1QoGg1SyknVlJdAVa5JBa4ovyCE6AJ4mc+BluOXBYwBXjHLeAMjgMXV0Qts58lRVET8\noUPceOONTH/+eQb178/x09Z93fVCq+Hh4br30luvjqwtfH19mTBhgsVa//79+eWXX6xkd+7cyUsv\nv0z//v0Zs2AB1996K6k6r0uP6vQJVCgUzo/JZPoAuAk4bTQau5nX5gI3o0VnDgD3GI3GqhTL2hWn\nM/qklMuqKltZeEmhqAvi4+OJj493tBrVZQHwlhDiONqUnVC0GdgH0ZqhI6XME0LMBmYIITKAJLRG\nzqBV21eLwvx8vLy8yK0Qug3x8+PHH38sfd6xSxddoy8yKspqbWk1vF7Vka2Ogejioh+AaNOmDefP\nn+e5555j165d5NvwYBZevMimWbPITU8nLyOD3PR0Tu7YodsnUKFQNFg+RPu7Wd6eWQNMMxqNxSaT\naTYwHa03ar3iNEafEKI1cEZKWZUcJaDy8JJCURdU/HJhMpnq5D5CCCO2c2WL0bxyO6WUl2p2jpRy\nobkgawowGa0V0yZgupQyt5zcbCGEAe2PUckYtqFSyiqHnLPT03nm5pvJKixk8uTJfPvttxw+XJae\nW7GdiT08crWlOgaiLYKCgpg7dy4AOTk5tA0JIePCBQsZg8FAYWEheZmZNAsOJqhzZ7wCA/FPSYGd\n1pPuzh8/TmFenq7XU6FQOC9Go3GTyWQKr7C2ttzT34Db61OnEpzC6BNCNAeOojVm/tmx2igUTsFj\ngCfgbX6eDfiYH+eg5d95CCF2AjdeakyalPIdtH59lSKlnAnMrK6yRUVFvP3SS7z0yiuEhYRw87Bh\nHD9+3MLg08MeBld9UhUj1dvbG3ed/MOePXsyePBglp8+TagQXBMdzfXXX88fU6awVWfPgkOH+E/7\n9lz95JNcOXkyHr6+9nkRCoXC0dwLLHfEjevN6LtED7KSr7IPCyFuBpBSPlMviikUzslw4BPgeeA7\nc/jVExiJ1lj5XrPcZ2itWMbXt4KxsbFIKUFKUhMTcTl3jnkvvMC148ezePFiFixYYHVNQ/da1cZI\n3b59O5mnT/P6ggX89ddfxMfHs3nzZgLatOEPnaKXUC8vxq9cya+zZzO/fXt6PfwwX+3bR/bJk1ay\nquhDoWgYmEym54F8o9H4X0fcvz49fVPRQlIZaIUa5Q3AkuGbsWg9yiSgjD5FU+Zt4FUp5RclC1LK\nPOBzIYQvMF9KeYUQ4iXMhRf1TUlLFA+DgUeio5mxbRvuwcEsXryY7Oxs+vbta3VNfYZtHUlgUJDu\nuruHByNHjmTkyJEAJCcn8+H77+vKunp60rJHD25fvpz05GR+nTOHhC+/ZGCRdTtrVfShUNQfNc3t\nNplMk9C+0A+xs0pVpj6Nvv+geSeWoX2YlfYyEEL4A+nAuKrkKCkUTYBuwAkb504C0ebHSWgzcx2G\nr5cXc3fswODiwurVq+natStPPvnkpS9sxFRsy2KLyMhIOnfpwkmdYpZC4KeffiI2NpbAyEhGvPMO\n/9u9G7ZaB4OLC6176KtWMApF3VCT3G6TyXQj8DQw0Gg0Vn3cj52pN6NPSvkvIcS7aBUt9wohnpVS\nflpRrL70USicnP3AP4UQ68uPJzSHeP+JZuyBNl2j0ny+usbF1RWDuap1yBCHfYFtVJRM9Jg6dSpn\nz55l/PjxTJw4ka0HD2LdMAbyfv2VRd270/aqq2h79dWEXX01GQcP0v5n6xRp5RVUKOoWk8m0HBgI\nBJlMpiOAEa1Azh1YazYStxiNxin1rVu9FnJIKROAIUKIO4DXhBCPAE8A1p1RFYqmzeNo7VSOCCHW\nojVtDgGGohV33GSWuxz4yiEa6mCvBspNHV9fX/r3789zzz3H4cOH+fjjjxkyZAhpaWnozcUM8fNj\n5Pvvc3TrVlLWruXnl17iyOHDpZ23FY2TtLQ0AgIC1PvOyTAajXfqLH9Q74ro4JDfFCnll0KIH9As\n33i0eaAKhcKMlDJeCNERzavXG6258km0/k9vSimPm+WmOU5LhT2wleeYm5vL0qVLufvuu5kzZw6z\nZs0iyM+PcxWmfIDWCqdN79606d2bvo89BsAf/frBli11qbrCgeTk5LBkyRI8PDzo3r07l19+OSEh\nIY5WS+HkOOzrgbk/2L+FEB+izQbdCVyo/CqFoukgpTyGlgPilJRMlm3oFbmOprKK4N9++42PPvqI\niRMnEhAQQDNfX12jL6+4mAsXLtCsWbPSNRd397pQV+EknDp1im7dutG/f3/++usvPv74Y7p168b1\n11/vaNUUTowz+IQPoYWtxkopVZhXoSiHECIauBIIAz6QUp40ewBPSSmzHKnbPQCBgaTGxDhSjUZN\n3759EULwxRdf8MADDxAZFcUxnfYuru7utGvXjrvvvpspU6bQoUMHfklMJF5nT8OePXWut6LuKT8/\nesiQIQwaNIgcnS8ECkV5DJcWqXMMaAmPPpcSVCiaCkIIHyHEF8Ae4D3gJaC1+fQraGPUHErqoEHk\n33cfQToj0xT2o0+fPkyYMMFqmkl5unbtyh9//IGrqytXXXUVN998M+cuXuQQWB0Z6emc3ae+Xzc2\nDAYDPj76H6P79+9XBqECcA6jT6FQWPM6cDVaPydftN6WJfwIDKvqRkKIeCFEsY2jbzm554QQR4QQ\nOUKIjUKIHpXtO/aZZ+jZuzdz3n67eq9MUW28vLwALf9v4MCBVkd4eDjh4eG8+uqrHDp0iFGjRpFX\nUKC7V/uOHflo0CBOK49fk0BKSWJiIkuWLKG4uNjR6igcjDOEdxUKhTW3Af+UUm4QQlR8nx6mLKWu\nKjyMZS8/AbwI9ESbr4sQYjrwAvAUkIjWTH2dEKKrrRFvCQkJPPTQQ5V6oC7FpEkPk5pq3aMuPDyE\npUsX1YtsQ6IqE0G8vb257777WLZsGT/rtGzxbdWKoZMns+y66xj/44+0uuKKKt9f9f5reAghGDFi\nBO+++y4pKSlERkY6WiWFA3G40SelLBRCDEa1bQHAMyAAkxB4Yj2cXo+KIm5uAdw+PJmob1po+3ED\nQow0P87nWVbbW2UAjKNH18m+TRgv4IyNc76A9VgGG0gp95Z/LoRwR6sIXi6lLDb3/nsWmCmlXGiW\n2QqkAo8CM/T2veWWW/D29tY7VWVSU0+zcaOeR8raYKsr2YZKdna2zXAe2P77ceDAAVoMHMhNixbx\n6bBhjPvmG9pedVWV7nkuNZWIjdb981XvP+ene/fu7N69Wxl9TRyHG32gtadwtA7OwrT0dEDr5FgT\nhBAUuQdilNJqHyFGlq7bnTFj6mbfpssfwN3AKp1ztwOba7H3jYA/ZQO/+6EZkp+XCEgpc4QQ36GF\nkXWNvg4dOti8QW09bWfOZPHZZz+Tk3OR3Nx8cnIumvcLsJI9evQsH3ywFh8fL3x9vfDx8eT8+Vyq\n+uetIXoFs7KyWLJkCbGxsfTq1ata3lYpJV27dmX06NGMmTmT5SNH8o6bG+cvWDdPCAwKqvJ0EUX9\ncODAAVxcXKo90rBr165s2LCB/Px83FVld5PFKYw+hUJhxQto4dX1QMn83eFCiCeBO4ABtdh7HHBE\nSlky3CEKzXO4v4JcIjC2Jjew5Wk7e/YA8+d/x8GDp0hJOcm2bfuBcCu5Eycy+Prr3/D2dsfLywNv\nbw+kjS8subkX+eWXBM6fzyU7O4/s7DySko6hFwFPSDjCc88to127ENq1C6ZduxAOHDjJL7/oOU6d\n1yvo5+fHvffey1dffUVycjIjR460aNcCtvv/hYeHM3fuXObPn8/YadO4pkcPTv70E9k6slJKTu/Z\nw5HNmzny668c/e03Iuz/chTVYMuWLfTs2bPa1zVr1oyIiAiOHTtWWvWraHooo0+hcEKklJvMaQ+z\n0UYXApiArcAQKeXvNdlXCOENjATKu7ACgGxpbVVlAN5CCFcppdUgiNjY2wFrj9iJE+mkpWWiDQ6x\n5NixsyQlHaN9+1D694/m2LHN/PmntZ7durXjs88sWxRu2/Y9hw9bG5IdO7bmgw+eqKDbfl2jMzDQ\nBy8vd7Zt28+XX/5KauppDhzYC3Syks3Pt5594UxewRYtWnDffffx008/sWTJEm655RYL7+ul8v9e\neuklnn76aRYtWsR3P/2kK3MxK4v/jRpFWL9+tO3Xj5CEBPjjD3u+DEU1yM3N5ejRo4ypYWRlzJgx\ntcrBVTR8lNGnUDgpUspfgf5mQy0AOCelrG0D8xFo1tjySwleihKj6vz5QyxY8AObNyeyefNesrJy\nMduLVtd07x7OggUPlT6fP98L0K8yrQtatgxgxoxxFmsDB97Gzz9bG3i//76P0NAJ9OgRQffu4XTv\nHs6ePYf5808XnZ0d4xV0cXFh6NChREZGEh8fT3h4OC4uevrp4+fnx7Rp03h95kxOZ1m3ffTw9eWx\n/WUOYPfltf61UdSCxMRE2rdvX+PwrDL4FMroUyicHCllDmCvJlvjgP1Syu3l1jIAHyGEqODtCwBy\n9Lx85dm79yh//ZXCkCHdmTFjLJ06tWbw4NE2CiksCQ8PQc9g0tbrR9bWB+E110Tz6advsGtXKjt3\nHmTlyj9JSDiCXji6oo+0vj2CERERhIeH1/hD3eZ1Fdb9w8Mtijayjh0j9+xZOlczv0xRMxISEujR\no9JOSgpFpSijT6FwEswjCatSaSMAKaW8t5r7N0crzJhd4VQi4AJEYpnXFwXsxSZJAISEGPjHP7oR\nGxtbHXUAqmUA1ZWsLYSAtm2DaNs2iOHDewEQG7tV15jdvHkvI0e+TL9+UfTrF+WQPMG68OKczc5m\ny5YtXH311QBWbVmK8vN5u3NnRt1/v93vrbAkNzeXI0eOMFp1SlDUAmX0KRTOQzcsjb7LgGA0S+E0\nEGp+fgZtuEJ1GQW4Yx3a3QxkAWPQpn2U5P6NABbb3q4zAOHhblYGX3U8bY7GHrr26hXJhAmxbN6c\nyNNPL2XbNv08wfqmsLCQ/Pz8S7bWCQwKslqTUuLq6ckdd9zBsGHDmD17NkEV5Fzc3Rnw73/z0wsv\ncPeGDSp8WIe4u7szceJEVXmrqBXK6FMonAQpZa+Sx0JrrvgGMEpKubnc+jXAR2hj2arLOGCHlDKp\nwn3zhBCzgRlCiAw0F96T5tNvUQOq6mmbMiWOtDTr9eBgWLgwrsay1cEeXkEPDzdGj76W0aOvBaB/\n/wO6nr4DB06ydu1fXHttNF5eHkDdhoIPHDjADz/8wMSJE60MtvJU1pYlMzMTo9FITEwMHTp0wM3N\nzcK4k1KS99dfDPjpJ9oPGVIrfRW2cXFxoXXr1pcWrAJpaWmcOnWKrl272mU/RcNBGX0KhXMyG5hR\n3uADrbhDCPFv4FXg26puJoQIAgajtYKxQko5WwhhAKYDLdAmdQyVUuqYWRoDB7oB+h6xqhpoaWkg\nRJyVXFqa3lrVZevKQKyqV9DFxYBe/2wXF0Fc3HJ27TpE376duO66HuzefYjt2/X+FNc+FNy5c2fO\nnj3LqlWrGD9+fI08cc2bN+fNN99k0qRJDBgwgPPnz1vJXBkVxYYZM4gYPFh5+xoAxcXFrF27lpiY\nGPXzamIoo0+hcE4isF28kWM+X2WklGfQQruVycwEZlZ1z/j4r2yeq46BpkdWFmzaBAUFUFioHWfO\naEZbRTIztS4inp7g5aUdx4+Du3vV7l8dA7G2nrfw8FDi4+eQlZXDxo17WLduJ3v3HkWvOMRe9O3b\nl+3bt7N//346dap5yLlnz55cfvnluqPdmoWEcPHsWZJXrqTj8OG1UVdRD4SGhuLl5cWhQ4eq3eRZ\n0bBRRp9C4ZxsB4xCiN+llMdLFoUQbYA4QKe7Xf0yenQcUD3v2enTsHAhnD0L6emQmAhduljLZWbC\nX3+Bq2vZUWijhvj8eVi3DvLyIDdX+zclBaKirGVPnID/+z9o0aLsOHUKXFys9a+NgXgpj6Cfnzcj\nRvRhxIg+7Ny5Src4JDX1NDt2pNCjR0SpN6YmoWAXFxduuOEGVq1aRYcOHarV0qUitrxCQghiTSY2\nzJhB5LBhynvUAOjevTu7du1SRl8TQxl9CoVzMhlYDaQKIf6grJDjSrRCjhscqBtQ5slLS4tDSs27\nl5oKBw/C4cPQznogBkJoxlhgoGZwHbJRjhIWBo8/brm2cqW+bNu28Oyzlmu2Chw9PDSjcOdOzeg8\nexaSkiA62lq2xID09Cxbq6oH0x55goWFRdxxx2zy8wu5+ebejBzZh5SUU2zapGf9Vh4K7tixI7//\n/jt79+6tkzyuoqIiuowaxaZXXiHx66/pMmqU3e/RVCkoKKCwsBAvLy+77tu1a1cWLVrE8OHDcXVV\npkBTQf2kFQonREq5RwgRCdwD9AFaorVW+Rj4UEqZ60j9ypOaCvfeC+7uEBEB4eHg768vGxwMgweX\nPa+F06lGBAbCXXdZrtkyEI8fhwceAB8faN0a2rTRDMUWLepeT4DIyFZs2LCExMSjfPfd77z88uds\n2bIX6Fij/UaPHo2bm5t9lTSze/dusi9cYNCLL7J++nSibrkFYTDUyb2aGn///TdJSUmMHVujiYg2\n8fPzo3Xr1iQlJRETE2PXvRXOizL6FAonxWzYLTQfTktQELz5JjRvXrb2le10PwuCg/XDqHq5e9WR\ntQcdOsBHH2newGPHNCPw4kV92cxMLXQcGgoGQ/XyBCsLBQsh6NIljC5dwnjmmdvp1y+RLVusWzkW\nFOj1BLTEHq0+9EKBUkpOnDjB0KFD+fHHH3Fr1oy/P/+cruPGWW+gqDYJCQl1VmV78803X7Kdj6L6\nmEymD4CbgNNGo7GbeS0Q+B/aUPBUYIzRaDxX37opo0+hUNQKHx9Lgw+qbqBVp5K2OrL2MhANBu2a\n4GDo2RM+/FBfLisLXn4ZsrM1T+fu3dCqlfX99XSqTijY3d0VvbF1W7cmMXy4iTFjruXWW/vi7+9T\nJ61gbM3zlVLy1FNPMXjwYN55/nniX3iB6DvuwKDChrUiLy+PQ4cOcfvtt9fJ/gEBAXWyr4IP0dpd\nLSu39iyw1mg0zjGZTNPMz5/Vu7guUe9IhcJJEEKkA9dVGJFWmbwLkAbESil31aly1aQ2bVHq+/72\nMBDDwmDBAs3oO3gQdApcAa3oJCsL/PzK1uzRXqZfvygmThzE55//whNPvMuAATHs37+PpKRmOtL2\nnwoihGDevHmYTCYmzpjBgwEB7PrkE3pOmmT3ezUlkpKSiIiIwMPDw9GqKKqB0WjcZDKZwissjwQG\nmh9/BMSjjD6FoknjD3QSQuRVUd7VfI1D3sdSxgF1F16tL+xpIPr4QLduWshbj/R0eOwxLeexc2fo\n1AmOHgVPT+s99e5TWSh43LgBjBs3gKysHL777ncef/wbQM/o0yguLsZgx7w7IQRxcXH4+Pjwxmuv\nkTFjBt3uugsXNUGixiQkJKh8u8ZDqNFoPGV+fAqtMK/eUUafQuFc/NfRClSVL76Ic7QK9U5tPZjt\n2mkh4qNHtXY1iYlapXNV2+dVJSzr5+fN+PGxvPtuO91WMJmZOWRkZLB8+XIefPBBu1duPvXUU/j6\n+vLYlCl81bEjIRGWLSXDw8NthokVZUgpad68ea16KyqcE6PRKE0mU1XmrNsdZfQpFM7D4EuL6LLP\nrloo6hSDAS67TDuuvx42btSXO3wYvv8eunbVZKtbIGKLpKSj9O07nfHjO7Jy5TpGjLjR7vl/kydP\nZu6sWew9dIi9hw9X+3qF5jkdXk+NrgsKCsjIyCAkxPlmYzsj8fHxxMfHV/eyUyaTqaXRaDxpMpla\nURd5FlVAGX0KhZMgpYx3tA4K+2CPPEF/fzh5Etau1XIFo6O1noItWljvW9VJJwC9e3di7twn+eST\nNfz662befnsThw4lkZTkoyNd88+lvDz9LIXkxMQa76moG06fPs1XX33FY489phprV4HY2FhiY2NL\nn5tMpqpc9i1wN9oIzbuBr+tCt0uhjD6FogkghHAFngLuA8LQCkC+kFI+WUHuOeBhyubvPi6l3FnP\n6jZ47FHI4ucH99+vPT57Fv7+u+qtcKDy/L8+fTrRp08nVq1aTevWhzGZVgN6Rl/NKbRh9NlaVziO\n1q1bYzAYOHr0KGFhYY5Wp8FjMpmWoxVtBJlMpiPAv9HmqX9uMpnuw9yyxRG6KaNPoWgaLAUGoY1w\nSwQuAywGoAkhpgMvoBmHicBUYJ0QoquU8hSKOqEqXsEWLWDAAK1BtB4HD2rj5Ymv/Z8AACAASURB\nVC6/XGsZIwR4e4cSHGydK16+LdugQbH8/ffb9OsXxfffZ1nJVqX/n6LhI4QoHcumjL7aYzQa77Rx\n6rp6VUQHZfQpFI0cIcSNaN8qu0spdWNrQghPtPYBM6WUC81rW9G+kT4KzKgfbZse9vAKBgdrrWDe\nfFMbH9ezJxw4AM2bW+9d3sD08PBg1KhRrF37h+6+v/2WxMSJbzB58g3069eFe+6ZYvfefwrnoFu3\nbrz33nvceOONtZrPrHBulNGnUDR+7gXW2zL4zPQDfIHPSxaklDlCiO+AYSijz6nx8YFJk7Tj5EnY\nsQPOnbNumq1H+/btycvL1z3Xt28nevQI595738Ld3ZW8vL0kJ/vpSFobgj6ennhmZpY+LwBOAG7K\noKiUixcvsmLFCsaOHVuv+XUBAQEEBQWRnJxM586d6+2+ivpFGX0KReOnD/CtEOJtYALa+34V8KiU\n8oRZJgooAvZXuDYRsO/QT0WNqUoouGVLuPFGeP99/T1OnYLkZGjfXqsIBjhx4jhBQdaG35kz7kyd\nOoonn7yV+PjdjB17P6Bn9FlzbVQUEacsswK2AT+fO8fZQ4do0a5dlfZpaiQlJVFcXOyQgooBAwao\nRtCNHGX0KRROihCiB/A80AtoC1wlpdwuhJgJbJJSrqziVq2AScAONAPOD5gDrACuMssEANlSyoq9\nozIAbyGEq5SysDavR1F77DXpZMECyMmB3r2hVy/o1u0GDAbrvUsacAshGDSoO9HRYbq9/4qLrVuO\n+YeHc7DCWmBxMT47dzKuWzc+/vlnWvbsaYdX07jYu3cv0dHRDrl3hw4dHHJfRf2hjD6FwgkRQgxD\nK/HfjDayx1ju9EXgMaCqRl+Jy+AWKWWGef8TwEYhRKxqFdO0CA2FN96A48dh2zb48kutFUwXc1mP\np+cJPDxOc+FCBwqs7Ttdtm5N4sUXP2Py5BsIDdXmub5powHzqVOn6BoVxYuxsTyxbBmdR460w6tq\nHBQWFpKSksKIESMcrYqikaKMPoXCOZkFLJVSPmBut1Le6NsBPFSNvdKBAyUGn5lfgXwgBm0GZAbg\nI4QQFbx9AUCOnpcvLi6u9HHFvlUKx3OpUHDr1nDLLdrx999l54UoxNc3iVatVpGfD+vXrycyMpK2\nbdvavFePHuEcO3aWqKgpjBzZhyeeGMn8+fNsFn18sGwZj0yeTJvJkxm0bx9XT52q+sMBhw8fJiQk\nBO/yJdYKhR1RRp9C4ZxEobVO0SMLCKzGXnsBT511AZQYeImACxCJZV5flPl6K8obfQrnozqh4PKT\n2HJzwzh6NAwo4vTpl2jeXLBr1xquvba3zdy/rCx3lix5hFmzJvLee2sYNWom585tJytLr/3HaUaM\nGMEPP/zA7rQ0WnzyCfOWLMG3VStEhVnA/uHhNj2GjZGUlBQiIyMdrYaiEaOMPoXCOUkDOgDrdM5F\nA9WZbfU9YBJCtJBSnjWvDQDc0LyGoIWRs9Bau7wCIITwBkYAi6utvaIR4EJAAPTpM5gtWwbz9tuS\nwMAb6NEjzkqyJPcvMNCXZ565nSefvJUePW4gIcH27q+99hpXXHEFI557jtwnn6RHcrKVTMWcwMbO\n4MGDKSxUqbOKukMZfQqFc7IceFEI8TewpWRRCNEZmAZ8UI293gEeB74zF4H4oY0CWiul3AwgpcwT\nQswGZgghMoAkoGRax1u1fTEK58ZWKLhVKxgyRDuysgS33Va1/VxdXQgObo7WqMWSixe1tWbNmvHp\np58yfPhwBkRGwu+/1+IVNA4MBgPu7u6OVoMdO3aQlZXFgAEDHK2Kws44xOgTQvgCndDyhUDLJ9on\npTzvCH0UCifk32gevZ+Bk+a1b4CWwGpgZlU3klKeF0IMBuYDn6Hl8n0N/KuC3GwhhAGYTtkYtqFS\nyrTavRSFs1OVULCfHwQElD338dkHQHZ2J06e1KaClEwDqYxt2/bz4INv8/TTt9GrVy+eeOIJ3p43\njxjAUPmlinrC39+fP/74Qxl9jZB6NfqEEEPRPsyuxvr9XSyE2Ay8KKXUC2kpFE0GKWUecLMQYgja\n6J4gtIKM9VLKNTXY7wBwUxXkZlINg1LRdHFxycPPby/Z2Z0wGGDePPD01MbF9e8P+/alAtZz44KC\nvGjdOpBrrpnGgAExPPXU7bxseon5eNEcLwtZ18RD9fNiFBa0bduWtLQ0cnNz8fLyuvQFigZDvRl9\nQogxaCGrVWgTAvaiefhA8/hFofUQWy2EuFNK+bnuRgpFE0JKuR5Y72g9FIqKZGd3oFWrHxGiiJAQ\neOstSEyEn3+GqVMhP9+doCA3q+s8PDyIi7uLp5++jffeW8OYMXPIK3Aljwucw43yH0ue6Wfq8RUp\nSnB1dSUsLIzU1FS6dOly6QsUDYb69PQZgdeklM/YOL8N+FgIMQdtKLwy+hRNFiFENNBcSrnF/Nwb\nbRRaF+AnKeV8R+qnaJqUz/0rLISLF8HL6yW8vbXpHtHR2nHvvXDs2A34+8dZ7VFS9NGsmSdPPDGS\nKVOG4+X5GUXFoNUSlVFM0/AyZWZmYjAY8PX1dbQqpbRv356UlBRl9DUy6tPoaw/8UAW5H9GSzhWK\npsxCtF56JUUcc4B7gF+AV4UQnlLKOY5STtE0qZj7t2HDBoqKirjuuuss1t3dbc/9TU+H3FwoiRq6\nubni4+tNZmaulaxXE+lXt2XLFry9vZ0qh659+/Z8++23jlZDYWfqM282GRhVBblbsJ7/qagiAQEB\nfPGFQAjrA77TXa/tERhYnZZxiioSA2wFEEK4o83M/ZeU8ga0Qot7HKibQgFAZGQkyTqtVirjwgWY\nMgXefRcOmVP2cnLydGXPn88lKyuntmo6PcnJyXTs2NHRalgQGhrKfffd52g1FHamPj19LwBfCiG6\nooVuE4Fz5nPN0cJWo4FY4I561KtRkZ6ebvPcmDHwuTloLsRIpLTPtzjVSb9OaAZkmh9fBfgAX5mf\n/wWEO0AnhcKCNm3aVHtkWFgYvPYarF8Ps2ZBUBAUF+vLSimJiprCrFkTmDBhEAZD46vvTU9PJy8v\nj5YtWzpaFQuEELi4uDhaDYWdqTejT0r5jRBiEFpe0ltojWHLUwBsAGKllL/Wl14KhZOSilbl/jNw\nK/BXucbKQYBqb6RwOAaDgTZtrCt0ofIxcIGBMHo03HYb/PknvPqqvjHn6mJgxYrpPPbYOyxevIq3\n3nqQt99+3eZ4t6VLF9Xq9TiCEi+f+vKsqA/qtWWLlPIX4AYhhAfatIHyffoOSCkv1qc+CoUT8xqw\nSAgxGrgcy3DuQGCXQ7RSKKpIVXr/ubhAnz7QunV7cnL8S9fz88+QnZ2Cv1cAfft2ZuvWuXz00U+M\nGPEykMTJkyE6u1kbgg2B5ORkevTo4Wg1FE0EhzRnNht3lQzoUSiaNlLK94UQ+4E+wDRz65YSMoA3\nHKOZQmF/+vS5FSHiSp9LKdm0aRiiMJvtm05wRf9W3HPPddx229XExAx1nKJ1QGhoKO3bt3e0Goom\ngtONYRNChAFCSlmd2aIKRaNDSvkzWni34rrRAeooFPWGEIKePd9k/brLmfvGeaLjWzFqFMTENKO4\nWD8gpDWDbngMGTLE0SpUyoULF8jJySE4ONjRqjQoTCbTdOAfQDGwG7jHaDQ6PJrpjFmxB2l6c7YV\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..8f2a35fb
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..12f5fed4
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..85b6d285
--- /dev/null
+++ b/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping.ipynb
@@ -0,0 +1,441 @@
+{
+ "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 = 20\n",
+    "air = simpeg.Utils.meshTensor([(ct,16,1.4)])\n",
+    "core = np.concatenate( (  np.kron(simpeg.Utils.meshTensor([(ct,10,-1.3)]),np.ones((5,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n",
+    "bot = simpeg.Utils.meshTensor([(core[0],10,-1.4)])\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<-500.) & (m1d.vectorCCx>=-800.)\n",
+    "layer2 = (m1d.vectorCCx<-3500.) & (m1d.vectorCCx>=-5000.)\n",
+    "# Set the conductivity values\n",
+    "sig_half = 2e-3\n",
+    "sig_air = 1e-8\n",
+    "sig_layer1 = .2\n",
+    "sig_layer2 = .2\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 = np.abs(survey.dobs*std) + 0.01*np.linalg.norm(survey.dobs) #survey.dobs*0 + std\n",
+    "# Assign the data weight\n",
+    "survey.Wd = 1/survey.std #(abs(survey.dobs)*survey.std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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=mappingExpAct)\n",
+    "reg.smoothModel = True\n",
+    "reg.alpha_s = 1e-7\n",
+    "reg.alpha_x = 1.\n",
+    "# reg.alpha_xx = 0.001\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_smoothTrue'\n",
+    "# Create an inversion object\n",
+    "inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest,saveModel]) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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_smoothTrue.npy'\n",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  2.55e+05  2.18e+05  0.00e+00  2.18e+05    4.01e+04      0              \n",
+      "   1  2.55e+05  2.50e+04  2.21e-03  2.55e+04    5.64e+03      0              \n",
+      "   2  2.55e+05  3.37e+03  4.74e-03  4.58e+03    9.91e+02      0   Skip BFGS  \n",
+      "   3  3.19e+04  1.78e+03  4.90e-03  1.93e+03    2.83e+02      0   Skip BFGS  \n",
+      "   4  3.19e+04  9.92e+02  1.67e-02  1.52e+03    2.15e+02      0   Skip BFGS  \n",
+      "   5  3.19e+04  7.50e+02  1.83e-02  1.33e+03    1.06e+02      0              \n",
+      "   6  3.99e+03  6.23e+02  2.13e-02  7.08e+02    1.40e+02      0   Skip BFGS  \n",
+      "   7  3.99e+03  3.26e+02  5.92e-02  5.62e+02    2.61e+02      0              \n",
+      "   8  3.99e+03  3.58e+02  3.99e-02  5.17e+02    1.18e+02      0              \n",
+      "   9  4.98e+02  3.33e+02  4.07e-02  3.53e+02    1.14e+02      1              \n",
+      "  10  4.98e+02  2.51e+02  1.43e-01  3.22e+02    3.78e+02      0              \n",
+      "  11  4.98e+02  1.75e+02  1.17e-01  2.34e+02    2.27e+02      1              \n",
+      "  12  6.23e+01  7.99e+01  1.45e-01  8.89e+01    5.71e+01      0   Skip BFGS  \n",
+      "  13  6.23e+01  7.36e+01  1.92e-01  8.56e+01    1.16e+02      0   Skip BFGS  \n",
+      "  14  6.23e+01  6.72e+01  1.99e-01  7.96e+01    7.08e+01      0              \n",
+      "  15  7.79e+00  6.04e+01  2.08e-01  6.21e+01    2.97e+01      0              \n",
+      "  16  7.79e+00  5.60e+01  2.35e-01  5.78e+01    4.30e+01      0              \n",
+      "  17  7.79e+00  4.85e+01  3.72e-01  5.14e+01    3.44e+01      0              \n",
+      "  18  9.73e-01  4.34e+01  3.81e-01  4.38e+01    7.57e+00      0              \n",
+      "  19  9.73e-01  4.30e+01  3.60e-01  4.33e+01    1.87e+01      0   Skip BFGS  \n",
+      "  20  9.73e-01  4.20e+01  3.74e-01  4.24e+01    1.23e+01      0              \n",
+      "  21  1.22e-01  4.03e+01  4.12e-01  4.04e+01    1.79e+01      2              \n",
+      "  22  1.22e-01  4.00e+01  4.83e-01  4.01e+01    2.70e+01      0   Skip BFGS  \n",
+      "  23  1.22e-01  3.92e+01  4.79e-01  3.92e+01    2.06e+01      0              \n",
+      "  24  1.52e-02  3.86e+01  5.04e-01  3.86e+01    1.97e+01      2   Skip BFGS  \n",
+      "  25  1.52e-02  3.82e+01  5.02e-01  3.82e+01    3.32e+01      0              \n",
+      "  26  1.52e-02  3.80e+01  4.74e-01  3.80e+01    2.44e+01      0              \n",
+      "  27  1.90e-03  3.76e+01  4.57e-01  3.76e+01    3.62e+01      1   Skip BFGS  \n",
+      "  28  1.90e-03  3.74e+01  4.77e-01  3.74e+01    3.13e+01      1              \n",
+      "  29  1.90e-03  3.74e+01  4.55e-01  3.74e+01    2.68e+01      0              \n",
+      "  30  2.38e-04  3.71e+01  4.30e-01  3.71e+01    2.24e+01      1              \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 3.3322e-01 <= tolF*(1+|f0|) = 2.1833e+04\n",
+      "1 : |xc-x_last| = 1.0086e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
+      "0 : |proj(x-g)-x|    = 2.2396e+01 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 2.2396e+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": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "modList = []\n",
+    "modFiles = glob('*Inversion_NoStoppingregMesh_smoothTrue.npy')\n",
+    "modFiles.sort()\n",
+    "for f in modFiles:\n",
+    "    modList.append(np.load(f))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# simpegmt.Utils.dataUtils.plotMT1DModelData(problem,modList)\n",
+    "# plt.show()\n"
+   ]
+  },
+  {
+   "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"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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8/fXXfPbZZ/Tp04dBgwZFMWp1uPLl8W4k6ONdFQviqVk/kvy4/jq3bk1B0TpR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\ny6OqfM1uAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f7d27dca950>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "fig = simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n",
+    "fig.suptitle('No stopping-useMref')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "reg.alpha_xx = 0.001\n",
+    "saveModel.fileName = 'Inversion_NoStoppingregMesh_smoothTrueWxx'"
+   ]
+  },
+  {
+   "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.SaveModelEveryIteration will save your models as: '###-Inversion_NoStoppingregMesh_smoothTrueWxx.npy'\n",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  2.54e+05  2.18e+05  0.00e+00  2.18e+05    4.01e+04      0              \n",
+      "   1  2.54e+05  2.50e+04  2.21e-03  2.55e+04    5.64e+03      0              \n",
+      "   2  2.54e+05  3.37e+03  4.74e-03  4.57e+03    9.91e+02      0   Skip BFGS  \n",
+      "   3  3.17e+04  1.77e+03  4.92e-03  1.93e+03    2.83e+02      0   Skip BFGS  \n",
+      "   4  3.17e+04  9.89e+02  1.68e-02  1.52e+03    2.15e+02      0   Skip BFGS  \n",
+      "   5  3.17e+04  7.47e+02  1.84e-02  1.33e+03    1.06e+02      0              \n",
+      "   6  3.96e+03  6.19e+02  2.14e-02  7.04e+02    1.39e+02      0   Skip BFGS  \n",
+      "   7  3.96e+03  3.25e+02  5.92e-02  5.60e+02    2.61e+02      0              \n",
+      "   8  3.96e+03  3.55e+02  4.00e-02  5.14e+02    1.17e+02      0              \n",
+      "   9  4.95e+02  3.29e+02  4.10e-02  3.50e+02    1.13e+02      1              \n",
+      "  10  4.95e+02  2.48e+02  1.43e-01  3.19e+02    3.78e+02      0              \n",
+      "  11  4.95e+02  1.75e+02  1.18e-01  2.33e+02    2.31e+02      1              \n",
+      "  12  6.19e+01  7.93e+01  1.45e-01  8.83e+01    5.76e+01      0   Skip BFGS  \n",
+      "  13  6.19e+01  6.97e+01  1.87e-01  8.13e+01    9.12e+01      0   Skip BFGS  \n",
+      "  14  6.19e+01  6.36e+01  1.88e-01  7.53e+01    2.36e+01      0              \n",
+      "  15  7.74e+00  6.22e+01  1.94e-01  6.37e+01    2.16e+01      0              \n",
+      "  16  7.74e+00  5.72e+01  2.23e-01  5.89e+01    3.88e+01      1              \n",
+      "  17  7.74e+00  5.15e+01  3.19e-01  5.40e+01    4.78e+01      1   Skip BFGS  \n",
+      "  18  9.68e-01  4.36e+01  3.51e-01  4.39e+01    1.50e+01      0              \n",
+      "  19  9.68e-01  4.22e+01  4.45e-01  4.27e+01    2.01e+01      0   Skip BFGS  \n",
+      "  20  9.68e-01  4.13e+01  4.20e-01  4.17e+01    9.91e+00      0              \n",
+      "  21  1.21e-01  4.02e+01  4.47e-01  4.02e+01    2.47e+01      1              \n",
+      "  22  1.21e-01  3.96e+01  4.82e-01  3.96e+01    2.93e+01      0   Skip BFGS  \n",
+      "  23  1.21e-01  3.92e+01  4.94e-01  3.93e+01    2.75e+01      0              \n",
+      "  24  1.51e-02  3.90e+01  5.51e-01  3.90e+01    2.27e+01      0   Skip BFGS  \n",
+      "  25  1.51e-02  3.82e+01  5.19e-01  3.82e+01    1.94e+01      0              \n",
+      "  26  1.51e-02  3.79e+01  5.52e-01  3.79e+01    1.71e+01      1   Skip BFGS  \n",
+      "  27  1.89e-03  3.76e+01  5.39e-01  3.76e+01    2.16e+01      1              \n",
+      "  28  1.89e-03  3.76e+01  6.14e-01  3.76e+01    3.82e+01      1   Skip BFGS  \n",
+      "  29  1.89e-03  3.69e+01  5.53e-01  3.69e+01    3.71e+01      0              \n",
+      "  30  2.36e-04  3.57e+01  5.65e-01  3.57e+01    1.91e+01      0              \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 1.1797e+00 <= tolF*(1+|f0|) = 2.1833e+04\n",
+      "1 : |xc-x_last| = 1.0384e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
+      "0 : |proj(x-g)-x|    = 1.9125e+01 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 1.9125e+01 <= 1e3*eps       = 1.0000e-02\n",
+      "1 : maxIter   =      30    <= iter          =     30\n",
+      "------------------------- DONE! -------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "moptWxx = inv.run(m_0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bJDZ0onDSxFCp6JoK/NIY88OGGhhjzgB+6bc9IGzfvr3GGsnVtSQxBG+8T0K8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\n1Cm/mjh27BilpaVs3ryZLVu2cN1118XVHUc86Nq1K9dddx379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EiaZcALIc+sGvhq0LHv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\ngP8P/qhXoxc5BSwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f7d1ef376d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "fig = simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[mopt,moptWxx])\n",
+    "fig.suptitle('No stopping-useMref - Wxx')\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..48e67d50
--- /dev/null
+++ b/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping_regMesh.ipynb
@@ -0,0 +1,623 @@
+{
+ "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 = .2\n",
+    "sig_layer2 = .2\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 = 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": 5,
+   "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.smoothModel = False\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_smoothFalse'\n",
+    "# Create an inversion object\n",
+    "inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest,saveModel]) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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",
+      "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_NoStoppingregMesh_smoothFalse.npy'\n",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  6.86e+04  2.17e+05  0.00e+00  2.17e+05    5.84e+04      0              \n",
+      "   1  6.86e+04  2.43e+04  5.27e-04  2.43e+04    7.87e+03      0              \n",
+      "   2  6.86e+04  3.29e+03  1.23e-03  3.37e+03    1.10e+03      0   Skip BFGS  \n",
+      "   3  8.58e+03  1.40e+03  5.11e-03  1.44e+03    2.44e+02      0   Skip BFGS  \n",
+      "   4  8.58e+03  6.54e+02  4.10e-02  1.01e+03    3.22e+02      0              \n",
+      "   5  8.58e+03  5.71e+02  2.17e-02  7.57e+02    1.52e+02      0              \n",
+      "   6  1.07e+03  5.21e+02  2.55e-02  5.48e+02    1.38e+02      0              \n",
+      "   7  1.07e+03  4.93e+02  3.09e-02  5.26e+02    2.22e+02      0              \n",
+      "   8  1.07e+03  4.81e+02  2.85e-02  5.12e+02    1.93e+02      1              \n",
+      "   9  1.34e+02  4.74e+02  3.50e-02  4.79e+02    1.65e+02      0              \n",
+      "  10  1.34e+02  4.63e+02  3.61e-02  4.68e+02    1.68e+02      1              \n",
+      "  11  1.34e+02  4.50e+02  5.43e-02  4.57e+02    1.66e+02      2              \n",
+      "  12  1.68e+01  4.49e+02  5.21e-02  4.50e+02    1.92e+02      0              \n",
+      "  13  1.68e+01  4.34e+02  1.01e-01  4.35e+02    1.94e+02      3              \n",
+      "  14  1.68e+01  4.16e+02  2.16e-01  4.20e+02    1.91e+02      2              \n",
+      "  15  2.09e+00  3.39e+02  1.47e-01  3.39e+02    2.11e+02      0              \n",
+      "  16  2.09e+00  2.63e+02  7.82e-02  2.64e+02    1.33e+02      0              \n",
+      "  17  2.09e+00  1.84e+02  6.64e-02  1.84e+02    1.81e+02      1   Skip BFGS  \n",
+      "  18  2.62e-01  1.78e+02  1.33e-01  1.79e+02    4.49e+02      0   Skip BFGS  \n",
+      "  19  2.62e-01  9.38e+01  1.22e-01  9.38e+01    5.08e+01      0              \n",
+      "  20  2.62e-01  8.47e+01  1.01e-01  8.47e+01    5.36e+01      0              \n",
+      "  21  3.27e-02  7.92e+01  1.12e-01  7.92e+01    3.55e+01      0              \n",
+      "  22  3.27e-02  6.76e+01  1.22e-01  6.76e+01    5.51e+01      0   Skip BFGS  \n",
+      "  23  3.27e-02  6.16e+01  1.13e-01  6.16e+01    3.68e+01      0              \n",
+      "  24  4.09e-03  6.11e+01  1.14e-01  6.11e+01    3.88e+01      0   Skip BFGS  \n",
+      "  25  4.09e-03  5.82e+01  1.15e-01  5.82e+01    3.34e+01      0              \n",
+      "  26  4.09e-03  5.55e+01  1.22e-01  5.55e+01    3.30e+01      0   Skip BFGS  \n",
+      "  27  5.11e-04  5.48e+01  1.22e-01  5.48e+01    2.57e+01      0              \n",
+      "  28  5.11e-04  5.34e+01  1.16e-01  5.34e+01    2.37e+01      1              \n",
+      "  29  5.11e-04  5.32e+01  1.16e-01  5.32e+01    2.48e+01      0   Skip BFGS  \n",
+      "  30  6.39e-05  5.32e+01  1.16e-01  5.32e+01    2.23e+01      0              \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 4.2159e-02 <= tolF*(1+|f0|) = 2.1667e+04\n",
+      "1 : |xc-x_last| = 2.6596e-01 <= tolX*(1+|x0|) = 4.2689e+00\n",
+      "0 : |proj(x-g)-x|    = 2.2265e+01 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 2.2265e+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": 7,
+   "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": [
+    {
+     "data": {
+      "image/png": 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neOGFF5g/f35xK4wmfXXD7t27WbVqFUOGDPE7lKA2b97M66+/zpAhQxg0aJDf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APxICTJmV3BneePCoZhoKpvicg64BTgPlfqlhK5wAuhGZm7I0eOcO211zJ58mTatWtnH8vJYWly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\n/wHRI9o7K38rSAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f112c6abc10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "fig = simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ExMmlvIJApVQEEON93Fp7ONjXKg2llK1atQYANWvWYM2aVWFuUUFXt2pFi6VLCx2XIFAI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ndHWxtVBn0KAYZLCfPmQWoqK5rTuHD5VlQhiBF+KqedR07tSkofuJ/Wk35iaFXd2KjE8g8/KI/s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\nbbep+3Y7ZO6s5Lln/dXDz+WUU0zsO5iEyxzPPs8UTKaf8Xh8B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supra1LypBMnTqN++7z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PPVBSQ+O3zWGgW8P/DwIXAV83PB6NkmzcVgpQ8v22oBRcXQW8LISwSCmfR+nXWSulbJ04UwFY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\n4ziO4zhODeJGoOM4juM4Tg3yf3NrVpscXsVKAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f112ab04110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "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": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.71407085590263"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.norm(mopt - reg.mref)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.mref"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regMesh.gridN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m1d.gridN[active]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "self = reg\n",
+    "r1 = self.W * ( self.mapping * (m_0) )\n",
+    "r2 = self.Ws * ( self.mapping * (self.mref) )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "( self.mapping * (m_0) )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<45x45 sparse matrix of type '<type 'numpy.float64'>'\n",
+       "\twith 45 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "self.Ws"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<SimPEG.Maps.IdentityMap at 0x7f11340a8250>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "self.mapping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1.7347234759768071e-18"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.5*(r1.dot(r1)-r2.dot(r2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.029467074824486239"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r1.dot(r1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.029467074824486243"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2.dot(r2)"
+   ]
+  },
+  {
+   "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..e57ffcfa
--- /dev/null
+++ b/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD.ipynb
@@ -0,0 +1,309 @@
+{
+ "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 = 20\n",
+    "air = simpeg.Utils.meshTensor([(ct,16,1.4)])\n",
+    "core = np.concatenate( (  np.kron(simpeg.Utils.meshTensor([(ct,10,-1.3)]),np.ones((5,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n",
+    "bot = simpeg.Utils.meshTensor([(core[0],10,-1.4)])\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<-500.) & (m1d.vectorCCx>=-800.)\n",
+    "layer2 = (m1d.vectorCCx<-3500.) & (m1d.vectorCCx>=-5000.)\n",
+    "# Set the conductivity values\n",
+    "sig_half = 2e-3\n",
+    "sig_air = 1e-8\n",
+    "sig_layer1 = .2\n",
+    "sig_layer2 = .2\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 = d_true + 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 = np.abs(survey.dobs*std) + 0.01*np.linalg.norm(survey.dobs) #survey.dobs*0 + std\n",
+    "# Assign the data weight\n",
+    "survey.Wd = 1/survey.std #(abs(survey.dobs)*survey.std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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 = 2)\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.smoothModel = True\n",
+    "reg.alpha_s = 1e-7\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",
+    "targmis = simpeg.Directives.TargetMisfit()\n",
+    "targmis.target = survey.nD\n",
+    "saveModel = simpeg.Directives.SaveModelEveryIteration()\n",
+    "saveModel.fileName = 'Inversion_TargMisEqnD_smoothTrue'\n",
+    "# Create an inversion object\n",
+    "inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest,targmis]) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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  2.13e+05  2.18e+05  0.00e+00  2.18e+05    4.01e+04      0              \n",
+      "   1  2.13e+05  2.50e+04  2.21e-03  2.54e+04    5.65e+03      0              \n",
+      "   2  2.13e+05  3.36e+03  4.80e-03  4.38e+03    9.88e+02      0   Skip BFGS  \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 2.1054e+04 <= tolF*(1+|f0|) = 2.1833e+04\n",
+      "0 : |xc-x_last| = 9.1698e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
+      "0 : |proj(x-g)-x|    = 9.8763e+02 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 9.8763e+02 <= 1e3*eps       = 1.0000e-02\n",
+      "1 : maxIter   =       2    <= iter          =      2\n",
+      "------------------------- DONE! -------------------------\n",
+      " "
+     ]
+    }
+   ],
+   "source": [
+    "##### \n",
+    "# Run the inversion, given the background model as a start.\n",
+    "import cProfile\n",
+    "%prun mopt = inv.run(m_0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'mopt' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-7-915cfd00243c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'matplotlib inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msimpegmt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotMT1DModelData\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mm_0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmopt\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msuptitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Target - smooth true'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'mopt' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "fig = simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n",
+    "fig.suptitle('Target - smooth true')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "reg.alpha_xx = 0.001\n",
+    "saveModel.fileName = 'Inversion_TargMisEqnD_smoothTrue_Wxx'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%timeit\n",
+    "moptWxx = inv.run(m_0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "moptWxx = Out[11]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "fig = simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,moptWxx])\n",
+    "fig.suptitle('Target - smooth true-Wxx as 0.001')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "1/np.exp(moptWxx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  }
+ },
+ "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..d8af4a60
--- /dev/null
+++ b/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD_regMesh.ipynb
@@ -0,0 +1,425 @@
+{
+ "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": true
+   },
+   "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 = 20\n",
+    "air = simpeg.Utils.meshTensor([(ct,16,1.4)])\n",
+    "core = np.concatenate( (  np.kron(simpeg.Utils.meshTensor([(ct,10,-1.3)]),np.ones((5,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n",
+    "bot = simpeg.Utils.meshTensor([(core[0],10,-1.4)])\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Make a model\n",
+    "# Some model varibles\n",
+    "active = m1d.vectorCCx<0.\n",
+    "layer1 = (m1d.vectorCCx<-500.) & (m1d.vectorCCx>=-800.)\n",
+    "layer2 = (m1d.vectorCCx<-3500.) & (m1d.vectorCCx>=-5000.)\n",
+    "# Set the conductivity values\n",
+    "sig_half = 2e-3\n",
+    "sig_air = 1e-8\n",
+    "sig_layer1 = .2\n",
+    "sig_layer2 = .2\n",
+    "# Make the true conductivity\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 a background model\n",
+    "sigma_0 = np.ones(m1d.nCx)*sig_air\n",
+    "sigma_0[active] = sig_half\n",
+    "# Define the background model\n",
+    "m_0 = np.log(sigma_0[active])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Setup the layout of the survey, set the sources and the connected receivers\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 mapping\n",
+    "actMap = simpeg.Maps.ActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)\n",
+    "mappingExpAct = simpeg.Maps.ExpMap(m1d) * actMap\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": [
+    "## 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 = d_true + 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 = 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",
+    "regMesh = simpeg.Mesh.TensorMesh([m1d.hx[problem.mapping.sigmaMap.maps[-1].indActive]],m1d.x0)\n",
+    "reg = simpeg.Regularization.Tikhonov(regMesh)\n",
+    "reg.smoothModel = False\n",
+    "reg.alpha_s = 1e-7\n",
+    "reg.alpha_x = 1.\n",
+    "# reg.alpha_xx = .001\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",
+    "targmis.target = .75 * survey.nD\n",
+    "saveModel = simpeg.Directives.SaveModelEveryIteration()\n",
+    "saveModel.fileName = 'Inversion_TargMisEqnDregMesh_smoothFalse'\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": [
+      "Fri, 10 Jul 2015 17:10:39 +0000\n",
+      "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_smoothFalse.npy'\n",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  2.02e+05  2.18e+05  0.00e+00  2.18e+05    4.01e+04      0              \n",
+      "   1  2.02e+05  2.50e+04  2.21e-03  2.54e+04    5.65e+03      0              \n",
+      "   2  2.02e+05  3.36e+03  4.81e-03  4.33e+03    9.87e+02      0   Skip BFGS  \n",
+      "   3  2.52e+04  1.70e+03  5.46e-03  1.83e+03    2.73e+02      0   Skip BFGS  \n",
+      "   4  2.52e+04  8.89e+02  2.10e-02  1.42e+03    2.47e+02      0              \n",
+      "   5  2.52e+04  8.24e+02  1.77e-02  1.27e+03    1.27e+02      0              \n",
+      "   6  3.15e+03  7.63e+02  1.97e-02  8.25e+02    1.51e+02      0              \n",
+      "   7  3.15e+03  4.83e+02  7.41e-02  7.17e+02    2.52e+02      0              \n",
+      "   8  3.15e+03  4.27e+02  6.43e-02  6.30e+02    1.61e+02      0              \n",
+      "   9  3.94e+02  4.02e+02  7.10e-02  4.30e+02    1.46e+02      0              \n",
+      "  10  3.94e+02  3.31e+02  2.15e-01  4.16e+02    3.40e+02      0              \n",
+      "  11  3.94e+02  3.47e+02  1.68e-01  4.13e+02    2.87e+02      1              \n",
+      "  12  4.92e+01  1.59e+02  2.45e-01  1.71e+02    1.31e+02      0              \n",
+      "  13  4.92e+01  1.32e+02  2.60e-01  1.44e+02    9.22e+01      1              \n",
+      "  14  4.92e+01  1.01e+02  3.62e-01  1.19e+02    1.23e+02      1              \n",
+      "  15  6.15e+00  6.31e+01  5.46e-01  6.65e+01    1.34e+02      0   Skip BFGS  \n",
+      "  16  6.15e+00  4.95e+01  6.83e-01  5.37e+01    5.52e+01      0   Skip BFGS  \n",
+      "  17  6.15e+00  4.75e+01  6.46e-01  5.15e+01    6.07e+01      2   Skip BFGS  \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.1833e+04\n",
+      "1 : |xc-x_last| = 2.8985e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
+      "0 : |proj(x-g)-x|    = 6.0707e+01 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 6.0707e+01 <= 1e3*eps       = 1.0000e-02\n",
+      "0 : maxIter   =      30    <= iter          =     18\n",
+      "------------------------- DONE! -------------------------\n",
+      "CPU times: user 1min 23s, sys: 64 ms, total: 1min 23s\n",
+      "Wall time: 1min 23s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "simpegmt.Utils.dataUtils.printTime()\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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D6NGj6d+/v655qxos5D19FRo35jDgb8Bv8Xr+tKdPRZWGfsMzxuTyU9IHPw10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Pn0Wb6cUaWl9FyypCIRVMFRvjOHN8574QX2LljApOuuY9KkSSctw6OU8p7NZjNUWqAXSIHs6dsM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\nVgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0dcf291f50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n",
+    "plt.suptitle('Target misfit-smooth False')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "reg.alpha_xx = 0.001\n",
+    "saveModel.fileName = 'Inversion_TargMisEqnDregMesh_smoothFalseWxx'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fri, 10 Jul 2015 17:12:05 +0000\n",
+      "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
+      "                    ***Done using same solver as the problem***\n",
+      "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_TargMisEqnDregMesh_smoothFalseWxx.npy'\n",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  2.49e+05  2.18e+05  0.00e+00  2.18e+05    4.01e+04      0   Skip BFGS  \n",
+      "   1  2.49e+05  2.50e+04  2.21e-03  2.55e+04    5.64e+03      0   Skip BFGS  \n",
+      "   2  2.49e+05  3.37e+03  4.75e-03  4.55e+03    9.91e+02      0   Skip BFGS  \n",
+      "   3  3.11e+04  1.77e+03  4.96e-03  1.92e+03    2.82e+02      0   Skip BFGS  \n",
+      "   4  3.11e+04  9.80e+02  1.71e-02  1.51e+03    2.18e+02      0   Skip BFGS  \n",
+      "   5  3.11e+04  7.36e+02  1.87e-02  1.32e+03    1.07e+02      0              \n",
+      "   6  3.89e+03  6.09e+02  2.17e-02  6.93e+02    1.38e+02      0   Skip BFGS  \n",
+      "   7  3.89e+03  3.22e+02  5.93e-02  5.53e+02    2.61e+02      0              \n",
+      "   8  3.89e+03  3.46e+02  4.06e-02  5.04e+02    1.15e+02      0              \n",
+      "   9  4.86e+02  3.19e+02  4.20e-02  3.39e+02    1.11e+02      1              \n",
+      "  10  4.86e+02  2.39e+02  1.44e-01  3.10e+02    3.76e+02      0              \n",
+      "  11  4.86e+02  1.77e+02  1.22e-01  2.37e+02    2.55e+02      1              \n",
+      "  12  6.08e+01  7.81e+01  1.43e-01  8.67e+01    5.12e+01      0   Skip BFGS  \n",
+      "  13  6.08e+01  6.85e+01  1.73e-01  7.90e+01    4.92e+01      0   Skip BFGS  \n",
+      "  14  6.08e+01  6.56e+01  1.76e-01  7.63e+01    2.50e+01      0              \n",
+      "  15  7.59e+00  6.32e+01  1.88e-01  6.46e+01    2.24e+01      0              \n",
+      "  16  7.59e+00  6.29e+01  2.30e-01  6.46e+01    6.69e+01      0              \n",
+      "  17  7.59e+00  4.86e+01  3.65e-01  5.14e+01    3.42e+01      0              \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.1833e+04\n",
+      "1 : |xc-x_last| = 1.3234e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
+      "0 : |proj(x-g)-x|    = 3.4236e+01 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 3.4236e+01 <= 1e3*eps       = 1.0000e-02\n",
+      "0 : maxIter   =      30    <= iter          =     18\n",
+      "------------------------- DONE! -------------------------\n",
+      "CPU times: user 1min 20s, sys: 19.9 ms, total: 1min 20s\n",
+      "Wall time: 1min 20s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "simpegmt.Utils.dataUtils.printTime()\n",
+    "moptWxx = inv.run(m_0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OA37hnNvr978cGOecu5gQICKXAq855+KD6KvXn6IoCqF7DlhrU/AUv33AG3grSTl4BoJH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\nrmIoIieIyJRkxKWq/1TVL5IRV3VEpCNuXsY3vV2Tga+nKj0/yuSls0VVF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OOaRNL4bxH5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\nFUNjjDHGGAPA/wfpfW9E7nyV6wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0dc6721f50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[moptWxx,mopt])\n",
+    "\n",
+    "plt.suptitle('Target misfit-smooth False-Wxx included')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OA37hnNvr978cGOecu5gQICKXAq855+KD6KvXn6IoCqF7DlhrU/AUv33AG3grSTl4BoJH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\nrmIoIieIyJRkxKWq/1TVL5IRV3VEpCNuXsY3vV2Tga+nKj0/yuSls0VVF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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0dc5fdfd50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "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. b/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/MT3DforData1Dinv.
new file mode 100644
index 00000000..e69de29b
diff --git a/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/MT3DforData1Dinv.ipynb b/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/MT3DforData1Dinv.ipynb
new file mode 100644
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--- /dev/null
+++ b/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/MT3DforData1Dinv.ipynb
@@ -0,0 +1,979 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "## Forward model a data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import SimPEG as simpeg\n",
+    "import simpegMT as simpegmt\n",
+    "from simpegMT.Utils.dataUtils import rec2ndarr\n",
+    "import cPickle as pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Make the mesh.\n",
+    "mTensor = simpeg.Utils.meshTensor\n",
+    "cSize = [50,20]\n",
+    "# Cells constant size mesh\n",
+    "hx = mTensor([(cSize[0],50)])\n",
+    "hy = mTensor([(cSize[0],50)])\n",
+    "hz = mTensor([(cSize[1],48)])\n",
+    "x0 = np.array([-1250,-1250,- 30*20])\n",
+    "mesh3dCons = simpeg.Mesh.TensorMesh([hx,hy,hz],x0)\n",
+    "# With padding\n",
+    "hPad = mTensor([(cSize[0],5,1.5)])\n",
+    "aPad = mTensor([(cSize[1],9,1.5)])\n",
+    "bPad = mTensor([(cSize[1],9,-1.5)])\n",
+    "hxPad = np.hstack((hPad[::-1],mTensor([(cSize[0],40)]),hPad))\n",
+    "hyPad = np.hstack((hPad[::-1],mTensor([(cSize[0],40)]),hPad))\n",
+    "hzPad = np.hstack((bPad,mTensor([(cSize[1],30)]),aPad))\n",
+    "x0Pad = np.array([-(np.sum(hPad)+1000),-(np.sum(hPad)+1000),-(np.sum(bPad)+(20*30))])\n",
+    "mesh3d = simpeg.Mesh.TensorMesh([hxPad,hyPad,hzPad],x0Pad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Read the model\n",
+    "modelname = \"simpegTDmodel.con\"\n",
+    "\n",
+    "# Load the model to the uniform cell mesh\n",
+    "modelUniCell = simpeg.Utils.meshutils.readUBCTensorModel(modelname,mesh3dCons)\n",
+    "\n",
+    "# Load the model to the mesh with padding cells\n",
+    "modelT = simpeg.Utils.meshutils.readUBCTensorModel(modelname,mesh3d)\n",
+    "# Adjust the model to reflect changes in the mesh (fewer aircells)\n",
+    "modMat = mesh3d.r(modelT,'CC','CC','M')\n",
+    "modNewMat = np.ones((50,50,48))*modMat[0,0,0]\n",
+    "modNewMat[:,:,9::] = modMat[:,:,:-9]\n",
+    "modelTD = mesh3d.r(modNewMat,'CC','CC','V')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Load the model to the uniform cell mesh\n",
+    "modelUniCell = simpeg.Utils.meshutils.readUBCTensorModel(modelname,mesh3dCons)\n",
+    "# Save as a vtk file\n",
+    "simpeg.Utils.meshutils.writeVTRFile('modelTDuniMesh.vtr',mesh3dCons,{'S/m':modelUniCell})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Load the model to the mesh with padding cells\n",
+    "# modelTD = simpeg.Utils.meshutils.readUBCTensorModel(modelname,mesh3d)\n",
+    "# Save as a vtk file\n",
+    "simpeg.Utils.meshutils.writeVTRFile('modelTDpaddedMesh.vtr',mesh3d,{'S/m':modelTD})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Define the data locations\n",
+    "xG,yG = np.meshgrid(np.linspace(-700,700,8),np.linspace(-700,700,8))\n",
+    "zG = np.zeros_like(xG)\n",
+    "locs = np.hstack((simpeg.mkvc(xG.ravel(),2),simpeg.mkvc(yG.ravel(),2),simpeg.mkvc(zG.ravel(),2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Make the receiver list\n",
+    "rxList = []\n",
+    "for rxType in ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi']:\n",
+    "    rxList.append(simpegmt.SurveyMT.RxMT(locs,rxType))    \n",
+    "# Source list\n",
+    "srcList =[]\n",
+    "freqs = np.logspace(4,0,17)\n",
+    "for freq in freqs:\n",
+    "    srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,freq))\n",
+    "# Survey MT\n",
+    "survey = simpegmt.SurveyMT.SurveyMT(srcList)\n",
+    "\n",
+    "\n",
+    "# Forward model the data\n",
+    "if False:\n",
+    "    fields = problem.fields(modelTD)\n",
+    "    mtData = survey.projectFields(fields)"
+   ]
+  },
+  {
+   "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": [
+    "# Load the data\n",
+    "mtSeogiData = simpegmt.DataMT.DataMT(survey,np.load('seogiModel_MTdata.npy'))\n",
+    "mtSeogirecData = mtSeogiData.toRecArray('Complex')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sys.path.append('/home/gudni/Dropbox/code/python/MTview/')\n",
+    "import interactivePlotFunctions as iPf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "% matplotlib qt\n",
+    "# Plot the data\n",
+    "mtSeogiMap = iPf.MTinteractiveMap([mtSeogirecData])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1088,)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mtSeogirecData.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Add noise to the data\n",
+    "std = 0.05 # 5% std\n",
+    "if os.path.isfile('seogiModel_MTdata.npy') and os.path.isfile('seogiModel_dobs.npy'):\n",
+    "    d_true = np.load('seogiModel_MTdata.npy')\n",
+    "    d_obs = np.load('seogiModel_dobs.npy')\n",
+    "else:\n",
+    "    d_true = simpeg.mkvc(mtSeogiData)\n",
+    "    d_obs = d_true + std*abs(d_true)*np.random.randn(*d_true.shape)\n",
+    "    np.save('seogiModel_dobs.npy',d_obs)\n",
+    "# Assign the dobs\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) #survey.dobs*0 + std\n",
+    "# Assign the data weight\n",
+    "Wd = 1/survey.std #(abs(survey.dobs)*survey.std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Make an MTdata object with dobs\n",
+    "mtSeogiDobs = simpegmt.DataMT.DataMT(survey,survey.dobs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Run the setup\n",
+    "mt1DdataZyxList = simpegmt.Utils.dataUtils.convert3Dto1Dobject(mtSeogiDobs,'zyx')\n",
+    "mt1DdataZxyList = simpegmt.Utils.dataUtils.convert3Dto1Dobject(mtSeogiDobs,'zxy')\n",
+    "mt1DdataZdetList = simpegmt.Utils.dataUtils.convert3Dto1Dobject(mtSeogiDobs,'zdet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "m1d = simpeg.Mesh.TensorMesh([mesh3d.hz],[mesh3d.x0[-1]])\n",
+    "# Setup the problem object\n",
+    "sigma1d = mesh3d.r(modelTD,'CC','CC','M')[0,0,:] # Use the edge column as a background model\n",
+    "# Set the mapping\n",
+    "actMap = simpeg.Maps.ActiveCells(m1d, sigma1d > 1e-7, np.log(1e-8), nC=m1d.nCx)\n",
+    "mappingExpAct = simpeg.Maps.ExpMap(m1d) * actMap\n",
+    "problem = simpegmt.ProblemMT1D.eForm_psField(m1d,sigmaPrimary = sigma1d,mapping=mappingExpAct)\n",
+    "problem.verbose = False\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  5.00000000e-03,   5.00000000e-03,   5.00000000e-03,\n",
+       "         5.00000000e-03,   5.00000000e-03,   5.00000000e-03,\n",
+       "         5.00000000e-03,   5.00000000e-03,   5.00000000e-03,\n",
+       "         5.00000000e-03,   5.00000000e-03,   5.00000000e-03,\n",
+       "         5.00000000e-03,   5.00000000e-03,   5.00000000e-03,\n",
+       "         5.00000000e-03,   5.00000000e-03,   5.00000000e-03,\n",
+       "         5.00000000e-03,   5.00000000e-03,   5.00000000e-03,\n",
+       "         5.00000000e-03,   5.00000000e-03,   5.00000000e-03,\n",
+       "         5.00000000e-03,   5.00000000e-03,   5.00000000e-03,\n",
+       "         5.00000000e-03,   5.00000000e-03,   5.00000000e-03,\n",
+       "         5.00000000e-03,   5.00000000e-03,   5.00000000e-03,\n",
+       "         5.00000000e-03,   1.00000000e-03,   1.00000000e-03,\n",
+       "         1.00000000e-03,   1.00000000e-03,   1.00000000e-03,\n",
+       "         1.00000000e-08,   1.00000000e-08,   1.00000000e-08,\n",
+       "         1.00000000e-08,   1.00000000e-08,   1.00000000e-08,\n",
+       "         1.00000000e-08,   1.00000000e-08,   1.00000000e-08])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sigma1d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Define a function to run the forward problem\n",
+    "def runInversionModel(data,problem,m1d,nameflag):\n",
+    "    problem.unpair()\n",
+    "    data.survey.unpair()\n",
+    "    problem.pair(data.survey)\n",
+    "    # Define a counter\n",
+    "    C =  simpeg.Utils.Counter()\n",
+    "    # Set the optimization\n",
+    "    opt = simpeg.Optimization.InexactGaussNewton(maxIter = 10)\n",
+    "    opt.counter = C\n",
+    "    opt.LSshorten = 0.5\n",
+    "    opt.remember('xc')\n",
+    "    # Data misfit\n",
+    "    dmis = simpeg.DataMisfit.l2_DataMisfit(data.survey)\n",
+    "    dmis.Wd = 1./data.survey.std\n",
+    "    # Regularization\n",
+    "    regMesh = simpeg.Mesh.TensorMesh([m1d.hx[problem.mapping.sigmaMap.maps[-1].indActive]],m1d.x0)\n",
+    "    reg = simpeg.Regularization.Tikhonov(regMesh)\n",
+    "    reg.smoothModel = False\n",
+    "    reg.alpha_s = 1e-6\n",
+    "    reg.alpha_x = 1.\n",
+    "    # reg.alpha_xx = .001\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",
+    "#     targmis.target = .6 * data.survey.nD\n",
+    "#     print 'Target misfit is {:.0f}'.format(targmis.target)\n",
+    "    locs = np.ones((regMesh.nC,1))*data.survey.srcList[0].rxList[0].locs[:,:-1]\n",
+    "    saveModel = simpeg.Directives.SaveModelEveryIteration()\n",
+    "    saveModel.fileName = 'Inversion_modAt{:.0f}_{:.0f}'.format(locs[0,0],locs[0,1])\n",
+    "    print saveModel.fileName\n",
+    "    # Create an inversion object\n",
+    "    inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest,targmis])\n",
+    "    # Run\n",
+    "    m_0 = np.log(1e-2+0*problem.sigmaPrimary[problem.mapping.sigmaMap.maps[-1].indActive])\n",
+    "    problem.survey.mtrue = m_0\n",
+    "    mopt = inv.run(m_0)\n",
+    "    # Save the model\n",
+    "    \n",
+    "    xyzv = np.hstack((locs,simpeg.mkvc(regMesh.gridCC,2),simpeg.mkvc(mopt,2)))\n",
+    "    np.save('xyzmod_{:s}_{:.0f}_{:.0f}.npy'.format(nameflag,locs[0,0],locs[0,1]),xyzv)\n",
+    "    return mopt\n",
+    "    \n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Inversion_modAt-700_-700\n",
+      "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",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  1.37e+03  1.07e+03  0.00e+00  1.07e+03    3.77e+02      0              \n",
+      "   1  1.37e+03  4.40e+01  1.93e-02  7.05e+01    7.43e+01      0              \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.0682e+02\n",
+      "1 : |xc-x_last| = 1.4393e+00 <= tolX*(1+|x0|) = 2.9759e+00\n",
+      "0 : |proj(x-g)-x|    = 7.4277e+01 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 7.4277e+01 <= 1e3*eps       = 1.0000e-02\n",
+      "0 : maxIter   =      10    <= iter          =      2\n",
+      "------------------------- DONE! -------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "dat = mt1DdataZyxList[0]\n",
+    "mopt = runInversionModel(dat,problem,m1d,'zyx')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<simpegMT.SurveyMT.SurveyMT at 0x7f3614079310>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dat.survey"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Inversion_modAt-700_-700\n",
+      "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",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  1.69e+03  1.07e+03  0.00e+00  1.07e+03    3.77e+02      0              \n",
+      "   1  1.69e+03  3.88e+01  1.72e-02  6.80e+01    6.98e+01      0              \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.0682e+02\n",
+      "1 : |xc-x_last| = 1.3066e+00 <= tolX*(1+|x0|) = 2.9759e+00\n",
+      "0 : |proj(x-g)-x|    = 6.9756e+01 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 6.9756e+01 <= 1e3*eps       = 1.0000e-02\n",
+      "0 : maxIter   =      10    <= iter          =      2\n",
+      "------------------------- DONE! -------------------------\n",
+      "Inversion_modAt-700_-500\n",
+      "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",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  1.12e+03  1.10e+03  0.00e+00  1.10e+03    3.79e+02      0              \n",
+      "   1  1.12e+03  5.89e+01  2.11e-02  8.26e+01    9.39e+01      0              \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.1005e+02\n",
+      "1 : |xc-x_last| = 1.8888e+00 <= tolX*(1+|x0|) = 2.9759e+00\n",
+      "0 : |proj(x-g)-x|    = 9.3881e+01 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 9.3881e+01 <= 1e3*eps       = 1.0000e-02\n",
+      "0 : maxIter   =      10    <= iter          =      2\n",
+      "------------------------- DONE! -------------------------\n",
+      "Inversion_modAt-700_-300\n",
+      "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",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  7.28e+02  1.14e+03  0.00e+00  1.14e+03    3.81e+02      0              \n",
+      "   1  7.28e+02  6.54e+01  2.62e-02  8.44e+01    8.78e+01      0              \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.1411e+02\n",
+      "1 : |xc-x_last| = 1.8407e+00 <= tolX*(1+|x0|) = 2.9759e+00\n",
+      "0 : |proj(x-g)-x|    = 8.7820e+01 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 8.7820e+01 <= 1e3*eps       = 1.0000e-02\n",
+      "0 : maxIter   =      10    <= iter          =      2\n",
+      "------------------------- DONE! -------------------------\n",
+      "Inversion_modAt-700_-100\n",
+      "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",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-31-496b6a805e71>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mdat\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmt1DdataZyxList\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m         \u001b[0mrunInversionModel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdat\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mproblem\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mm1d\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'zyx'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mdat\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmt1DdataZxyList\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-22-5072715ac24c>\u001b[0m in \u001b[0;36mrunInversionModel\u001b[1;34m(data, problem, m1d, nameflag)\u001b[0m\n\u001b[0;32m     39\u001b[0m     \u001b[0mm_0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1e-2\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mproblem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msigmaPrimary\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mproblem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmapping\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msigmaMap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmaps\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindActive\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m     \u001b[0mproblem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msurvey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmtrue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mm_0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m     \u001b[0mmopt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     42\u001b[0m     \u001b[1;31m# Save the model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Utils/CounterUtils.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     90\u001b[0m         \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'counter'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountTic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     93\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountToc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Inversion.pyc\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, m0)\u001b[0m\n\u001b[0;32m     60\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvProb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstartup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdirectiveList\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'initialize'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 62\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvProb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevalFunction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvProb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     63\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdirectiveList\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'finish'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Utils/CounterUtils.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     90\u001b[0m         \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'counter'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountTic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     93\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountToc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Optimization.pyc\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(self, evalFunction, x0)\u001b[0m\n\u001b[0;32m    206\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdoStartIteration\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mH\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mevalFunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_g\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_H\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprintIter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstoppingCriteria\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Utils/CounterUtils.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     90\u001b[0m         \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'counter'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountTic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     93\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountToc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/InvProblem.pyc\u001b[0m in \u001b[0;36mevalFunction\u001b[1;34m(self, m, return_g, return_H)\u001b[0m\n\u001b[0;32m    124\u001b[0m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    125\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mreturn_g\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 126\u001b[1;33m             \u001b[0mphi_dDeriv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdmisfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevalDeriv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    127\u001b[0m             \u001b[0mphi_mDeriv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevalDeriv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    128\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/PropMaps.pyc\u001b[0m in \u001b[0;36mfget\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    107\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    108\u001b[0m             \u001b[0mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'%sModel'\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mprop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 109\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mderiv\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mm\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    110\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mproperty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfget\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfget\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    111\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Maps.pyc\u001b[0m in \u001b[0;36mderiv\u001b[1;34m(self, m)\u001b[0m\n\u001b[0;32m    181\u001b[0m         \u001b[0mmi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mmap_i\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mreversed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmaps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 183\u001b[1;33m             \u001b[0mderiv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmap_i\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mderiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mderiv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    184\u001b[0m             \u001b[0mmi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmap_i\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mmi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    185\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mderiv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Maps.pyc\u001b[0m in \u001b[0;36mderiv\u001b[1;34m(self, m)\u001b[0m\n\u001b[0;32m    249\u001b[0m                 \u001b[0;31m\\\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mfrac\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mpartial\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mexp\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mpartial\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m}\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0msdiag\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0mexp\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \"\"\"\n\u001b[1;32m--> 251\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmkvc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    252\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    253\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mReciprocalMap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mIdentityMap\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpeg/SimPEG/Utils/matutils.pyc\u001b[0m in \u001b[0;36msdiag\u001b[1;34m(h)\u001b[0m\n\u001b[0;32m     38\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0msdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m     \u001b[1;34m\"\"\"Sparse diagonal matrix\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 40\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0msp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mspdiags\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmkvc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"csr\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     41\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0msdInv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/home/gudni/anaconda/lib/python2.7/site-packages/scipy/sparse/construct.pyc\u001b[0m in \u001b[0;36mspdiags\u001b[1;34m(data, diags, m, n, format)\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m     \"\"\"\n\u001b[1;32m---> 60\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mdia_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdiags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/home/gudni/anaconda/lib/python2.7/site-packages/scipy/sparse/base.pyc\u001b[0m in \u001b[0;36masformat\u001b[1;34m(self, format)\u001b[0m\n\u001b[0;32m    211\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    212\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 213\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'to'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m     \u001b[1;31m###################################################################\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/home/gudni/anaconda/lib/python2.7/site-packages/scipy/sparse/dia.pyc\u001b[0m in \u001b[0;36mtocsr\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    225\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtocsr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    226\u001b[0m         \u001b[1;31m#this could be faster\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 227\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtocoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtocsr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    228\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    229\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtocsc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/home/gudni/anaconda/lib/python2.7/site-packages/scipy/sparse/dia.pyc\u001b[0m in \u001b[0;36mtocoo\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    250\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mcoo\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcoo_matrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 252\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mcoo_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    253\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    254\u001b[0m     \u001b[1;31m# needed by _data_matrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/home/gudni/anaconda/lib/python2.7/site-packages/scipy/sparse/coo.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, arg1, shape, dtype, copy)\u001b[0m\n\u001b[0;32m    204\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    205\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 206\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    208\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgetnnz\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/home/gudni/anaconda/lib/python2.7/site-packages/scipy/sparse/coo.pyc\u001b[0m in \u001b[0;36m_check\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    257\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnnz\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 259\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    260\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'row index exceeds matrix dimensions'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "if True:\n",
+    "    for dat in mt1DdataZyxList:\n",
+    "        runInversionModel(dat,problem,m1d,'zyx')\n",
+    "\n",
+    "    for dat in mt1DdataZxyList:\n",
+    "        runInversionModel(dat,problem,m1d,'zxy')\n",
+    "    for dat in mt1DdataZdetList:\n",
+    "        runInversionModel(dat,problem,m1d,'zdet')   \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# import multiprocessing, os, numpy as np\n",
+    "# from glob import glob\n",
+    "# import itertools as it\n",
+    "# import subprocess\n",
+    "\n",
+    "\n",
+    "# def runInv_star(args):\n",
+    "#     return runInversionModel(*args)\n",
+    "\n",
+    "\n",
+    "# if __name__ == '__main__':\n",
+    "#     pool = multiprocessing.Pool(processes=6)\n",
+    "#     pool.map(runInversionZxy,it.izip(mt1DdataZxyList,it.repeat( (problem,m1d,'zxy') )))\n",
+    "#     pool.join()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "seogizxy1Dmods = np.vstack(([np.load(f) for f in glob('xyzmod_zxy*.npy')]))\n",
+    "seogizyx1Dmods = np.vstack(([np.load(f) for f in glob('xyzmod_zyx*.npy')]))\n",
+    "seogizdet1Dmods = np.vstack(([np.load(f) for f in glob('xyzmod_zdet*.npy')]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "airInd = modelTD ==1e-8\n",
+    "# Interpolate the 1D models\n",
+    "from scipy.interpolate import griddata\n",
+    "mod3d_zxy1dMod = griddata(seogizxy1Dmods[:,0:3],seogizxy1Dmods[:,3],mesh3d.gridCC,'linear')\n",
+    "mod3d_zxy1dMod[airInd] = np.log(1e-8)\n",
+    "mod3d_zyx1dMod = griddata(seogizyx1Dmods[:,0:3],seogizyx1Dmods[:,3],mesh3d.gridCC,'linear')\n",
+    "mod3d_zyx1dMod[airInd] = np.log(1e-8)\n",
+    "mod3d_zdet1dMod = griddata(seogizdet1Dmods[:,0:3],seogizdet1Dmods[:,3],mesh3d.gridCC,'linear')\n",
+    "mod3d_zdet1dMod[airInd] = np.log(1e-8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "simpeg.Utils.meshutils.writeVTRFile('model3D_zxy1Dinv_paddedMesh.vtr',mesh3d,{'S/m':np.exp(mod3d_zxy1dMod)})\n",
+    "simpeg.Utils.meshutils.writeVTRFile('model3D_zyx1Dinv_paddedMesh.vtr',mesh3d,{'S/m':np.exp(mod3d_zyx1dMod)})\n",
+    "simpeg.Utils.meshutils.writeVTRFile('model3D_zdet1Dinv_paddedMesh.vtr',mesh3d,{'S/m':np.exp(mod3d_zdet1dMod)})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "m_0 = np.log(problem.sigmaPrimary[problem.mapping.sigmaMap.maps[-1].indActive])\n",
+    "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n",
+    "plt.suptitle('Target misfit-smooth False')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(794.79531859664246, 56.319860569098076),\n",
+       " (674.66872912184408, 57.635385492661392),\n",
+       " (559.52741397235366, 58.423832448812171),\n",
+       " (459.43398962262597, 58.410853192444343),\n",
+       " (380.28287396790699, 57.529223784875711),\n",
+       " (322.8521167724395, 56.001248998922648),\n",
+       " (283.43537890603488, 54.287024515800717),\n",
+       " (255.71182522194459, 52.81526241219477),\n",
+       " (233.73967183794295, 51.557597236493933),\n",
+       " (216.97743323521141, 50.281238562685871),\n",
+       " (204.41678397612844, 49.05493425669367),\n",
+       " (196.11922783991341, 47.732975834266547),\n",
+       " (192.81620390650565, 46.626437679858043),\n",
+       " (191.83932042041988, 45.994944912824344),\n",
+       " (191.13999703648773, 45.673623213889364),\n",
+       " (190.40201746939533, 45.487392219519187),\n",
+       " (189.72482164986508, 45.362566052988335)]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simpegmt.Utils.dataUtils.getAppRes(problem.dataPair(problem.survey,problem.survey.dpred(mopt)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model3D_zdet1Dinv_paddedMesh.vtr  xyzmod_zxy_300_500.npy\r\n",
+      "model3D_zxy1Dinv_paddedMesh.vtr   xyzmod_zxy_-300_-700.npy\r\n",
+      "model3D_zyx1Dinv_paddedMesh.vtr   xyzmod_zxy_-300_700.npy\r\n",
+      "modelTDpaddedMesh.vtr             xyzmod_zxy_300_-700.npy\r\n",
+      "modelTDuniMesh.vtr                xyzmod_zxy_300_700.npy\r\n",
+      "MT3DforData1Dinv.                 xyzmod_zxy_-500_-100.npy\r\n",
+      "MT3DforData1Dinv.ipynb            xyzmod_zxy_-500_100.npy\r\n",
+      "MT3DforData1Dinv.py               xyzmod_zxy_500_-100.npy\r\n",
+      "seogiModel_dobs.npy               xyzmod_zxy_500_100.npy\r\n",
+      "seogiModel_MTdata.npy             xyzmod_zxy_-500_-300.npy\r\n",
+      "simpegTDmodel.con                 xyzmod_zxy_-500_300.npy\r\n",
+      "xyzmod_zdet_-100_-100.npy         xyzmod_zxy_500_-300.npy\r\n",
+      "xyzmod_zdet_-100_100.npy          xyzmod_zxy_500_300.npy\r\n",
+      "xyzmod_zdet_100_-100.npy          xyzmod_zxy_-500_-500.npy\r\n",
+      "xyzmod_zdet_100_100.npy           xyzmod_zxy_-500_500.npy\r\n",
+      "xyzmod_zdet_-100_-300.npy         xyzmod_zxy_500_-500.npy\r\n",
+      "xyzmod_zdet_-100_300.npy          xyzmod_zxy_500_500.npy\r\n",
+      "xyzmod_zdet_100_-300.npy          xyzmod_zxy_-500_-700.npy\r\n",
+      "xyzmod_zdet_100_300.npy           xyzmod_zxy_-500_700.npy\r\n",
+      "xyzmod_zdet_-100_-500.npy         xyzmod_zxy_500_-700.npy\r\n",
+      "xyzmod_zdet_-100_500.npy          xyzmod_zxy_500_700.npy\r\n",
+      "xyzmod_zdet_100_-500.npy          xyzmod_zxy_-700_-100.npy\r\n",
+      "xyzmod_zdet_100_500.npy           xyzmod_zxy_-700_100.npy\r\n",
+      "xyzmod_zdet_-100_-700.npy         xyzmod_zxy_700_-100.npy\r\n",
+      "xyzmod_zdet_-100_700.npy          xyzmod_zxy_700_100.npy\r\n",
+      "xyzmod_zdet_100_-700.npy          xyzmod_zxy_-700_-300.npy\r\n",
+      "xyzmod_zdet_100_700.npy           xyzmod_zxy_-700_300.npy\r\n",
+      "xyzmod_zdet_-300_-100.npy         xyzmod_zxy_700_-300.npy\r\n",
+      "xyzmod_zdet_-300_100.npy          xyzmod_zxy_700_300.npy\r\n",
+      "xyzmod_zdet_300_-100.npy          xyzmod_zxy_-700_-500.npy\r\n",
+      "xyzmod_zdet_300_100.npy           xyzmod_zxy_-700_500.npy\r\n",
+      "xyzmod_zdet_-300_-300.npy         xyzmod_zxy_700_-500.npy\r\n",
+      "xyzmod_zdet_-300_300.npy          xyzmod_zxy_700_500.npy\r\n",
+      "xyzmod_zdet_300_-300.npy          xyzmod_zxy_-700_-700.npy\r\n",
+      "xyzmod_zdet_300_300.npy           xyzmod_zxy_-700_700.npy\r\n",
+      "xyzmod_zdet_-300_-500.npy         xyzmod_zxy_700_-700.npy\r\n",
+      "xyzmod_zdet_-300_500.npy          xyzmod_zxy_700_700.npy\r\n",
+      "xyzmod_zdet_300_-500.npy          xyzmod_zyx_-100_-100.npy\r\n",
+      "xyzmod_zdet_300_500.npy           xyzmod_zyx_-100_100.npy\r\n",
+      "xyzmod_zdet_-300_-700.npy         xyzmod_zyx_100_-100.npy\r\n",
+      "xyzmod_zdet_-300_700.npy          xyzmod_zyx_100_100.npy\r\n",
+      "xyzmod_zdet_300_-700.npy          xyzmod_zyx_-100_-300.npy\r\n",
+      "xyzmod_zdet_300_700.npy           xyzmod_zyx_-100_300.npy\r\n",
+      "xyzmod_zdet_-500_-100.npy         xyzmod_zyx_100_-300.npy\r\n",
+      "xyzmod_zdet_-500_100.npy          xyzmod_zyx_100_300.npy\r\n",
+      "xyzmod_zdet_500_-100.npy          xyzmod_zyx_-100_-500.npy\r\n",
+      "xyzmod_zdet_500_100.npy           xyzmod_zyx_-100_500.npy\r\n",
+      "xyzmod_zdet_-500_-300.npy         xyzmod_zyx_100_-500.npy\r\n",
+      "xyzmod_zdet_-500_300.npy          xyzmod_zyx_100_500.npy\r\n",
+      "xyzmod_zdet_500_-300.npy          xyzmod_zyx_-100_-700.npy\r\n",
+      "xyzmod_zdet_500_300.npy           xyzmod_zyx_-100_700.npy\r\n",
+      "xyzmod_zdet_-500_-500.npy         xyzmod_zyx_100_-700.npy\r\n",
+      "xyzmod_zdet_-500_500.npy          xyzmod_zyx_100_700.npy\r\n",
+      "xyzmod_zdet_500_-500.npy          xyzmod_zyx_-300_-100.npy\r\n",
+      "xyzmod_zdet_500_500.npy           xyzmod_zyx_-300_100.npy\r\n",
+      "xyzmod_zdet_-500_-700.npy         xyzmod_zyx_300_-100.npy\r\n",
+      "xyzmod_zdet_-500_700.npy          xyzmod_zyx_300_100.npy\r\n",
+      "xyzmod_zdet_500_-700.npy          xyzmod_zyx_-300_-300.npy\r\n",
+      "xyzmod_zdet_500_700.npy           xyzmod_zyx_-300_300.npy\r\n",
+      "xyzmod_zdet_-700_-100.npy         xyzmod_zyx_300_-300.npy\r\n",
+      "xyzmod_zdet_-700_100.npy          xyzmod_zyx_300_300.npy\r\n",
+      "xyzmod_zdet_700_-100.npy          xyzmod_zyx_-300_-500.npy\r\n",
+      "xyzmod_zdet_700_100.npy           xyzmod_zyx_-300_500.npy\r\n",
+      "xyzmod_zdet_-700_-300.npy         xyzmod_zyx_300_-500.npy\r\n",
+      "xyzmod_zdet_-700_300.npy          xyzmod_zyx_300_500.npy\r\n",
+      "xyzmod_zdet_700_-300.npy          xyzmod_zyx_-300_-700.npy\r\n",
+      "xyzmod_zdet_700_300.npy           xyzmod_zyx_-300_700.npy\r\n",
+      "xyzmod_zdet_-700_-500.npy         xyzmod_zyx_300_-700.npy\r\n",
+      "xyzmod_zdet_-700_500.npy          xyzmod_zyx_300_700.npy\r\n",
+      "xyzmod_zdet_700_-500.npy          xyzmod_zyx_-500_-100.npy\r\n",
+      "xyzmod_zdet_700_500.npy           xyzmod_zyx_-500_100.npy\r\n",
+      "xyzmod_zdet_-700_-700.npy         xyzmod_zyx_500_-100.npy\r\n",
+      "xyzmod_zdet_-700_700.npy          xyzmod_zyx_500_100.npy\r\n",
+      "xyzmod_zdet_700_-700.npy          xyzmod_zyx_-500_-300.npy\r\n",
+      "xyzmod_zdet_700_700.npy           xyzmod_zyx_-500_300.npy\r\n",
+      "xyzmod_zxy_-100_-100.npy          xyzmod_zyx_500_-300.npy\r\n",
+      "xyzmod_zxy_-100_100.npy           xyzmod_zyx_500_300.npy\r\n",
+      "xyzmod_zxy_100_-100.npy           xyzmod_zyx_-500_-500.npy\r\n",
+      "xyzmod_zxy_100_100.npy            xyzmod_zyx_-500_500.npy\r\n",
+      "xyzmod_zxy_-100_-300.npy          xyzmod_zyx_500_-500.npy\r\n",
+      "xyzmod_zxy_-100_300.npy           xyzmod_zyx_500_500.npy\r\n",
+      "xyzmod_zxy_100_-300.npy           xyzmod_zyx_-500_-700.npy\r\n",
+      "xyzmod_zxy_100_300.npy            xyzmod_zyx_-500_700.npy\r\n",
+      "xyzmod_zxy_-100_-500.npy          xyzmod_zyx_500_-700.npy\r\n",
+      "xyzmod_zxy_-100_500.npy           xyzmod_zyx_500_700.npy\r\n",
+      "xyzmod_zxy_100_-500.npy           xyzmod_zyx_-700_-100.npy\r\n",
+      "xyzmod_zxy_100_500.npy            xyzmod_zyx_-700_100.npy\r\n",
+      "xyzmod_zxy_-100_-700.npy          xyzmod_zyx_700_-100.npy\r\n",
+      "xyzmod_zxy_-100_700.npy           xyzmod_zyx_700_100.npy\r\n",
+      "xyzmod_zxy_100_-700.npy           xyzmod_zyx_-700_-300.npy\r\n",
+      "xyzmod_zxy_100_700.npy            xyzmod_zyx_-700_300.npy\r\n",
+      "xyzmod_zxy_-300_-100.npy          xyzmod_zyx_700_-300.npy\r\n",
+      "xyzmod_zxy_-300_100.npy           xyzmod_zyx_700_300.npy\r\n",
+      "xyzmod_zxy_300_-100.npy           xyzmod_zyx_-700_-500.npy\r\n",
+      "xyzmod_zxy_300_100.npy            xyzmod_zyx_-700_500.npy\r\n",
+      "xyzmod_zxy_-300_-300.npy          xyzmod_zyx_700_-500.npy\r\n",
+      "xyzmod_zxy_-300_300.npy           xyzmod_zyx_700_500.npy\r\n",
+      "xyzmod_zxy_300_-300.npy           xyzmod_zyx_-700_-700.npy\r\n",
+      "xyzmod_zxy_300_300.npy            xyzmod_zyx_-700_700.npy\r\n",
+      "xyzmod_zxy_-300_-500.npy          xyzmod_zyx_700_-700.npy\r\n",
+      "xyzmod_zxy_-300_500.npy           xyzmod_zyx_700_700.npy\r\n",
+      "xyzmod_zxy_300_-500.npy\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import multiprocessing, os, numpy as np\n",
+    "from glob import glob\n",
+    "import subprocess\n",
+    "\n",
+    "def runMT3Dfwd(path):\n",
+    "    \"\"\" Worker function \"\"\"\n",
+    "    orgDir = os.getcwd()\n",
+    "    print 'Starting in {:s}\\n'.format(orgDir)\n",
+    "    \n",
+    "    os.chdir(path)\n",
+    "    print '########### \\nRunning in {:s}\\n###########'.format(os.getcwd())\n",
+    "    cmd = '/tera_raid/gudni/Codes/ZTEM_MT3Dinv_06082014/MT3Dfwd'\n",
+    "    # os.system(cmd)\n",
+    "    subprocess.call(cmd)\n",
+    "    os.chdir(orgDir)\n",
+    "\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "    pool = multiprocessing.Pool(processes=12)\n",
+    "    foldList = glob('MT3Dfwd/model100w*/Freq*')\n",
+    "    \n",
+    "    pool.map(runMT3Dfwd,foldList)\n",
+    "    pool.cself.mesh.getEdgeInnerProductlose()\n",
+    "    pool.join()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "locs=rec2ndarr(uniLocs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%debug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "simpeg.mkvc(locs[0,:],2).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "simpegmt.DataMT.DataMT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "sys.path.append('/home/gudni/Dropbox/code/python/MTview/')\n",
+    "import interactivePlotFunctions as iPf\n",
+    "dataRec = mt1DdataList[0].toRecArray('Complex')\n",
+    "iPf.MTinteractiveMap([dataRec])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(979.21054686559228, 60.117584762903384),\n",
+       " (742.46348350358323, 60.178574585305192),\n",
+       " (575.52692078307155, 59.504482832217768),\n",
+       " (448.01468147227973, 57.739339607718549),\n",
+       " (380.47444523645635, 58.103945329921899),\n",
+       " (329.83191047669334, 55.337196869237211),\n",
+       " (289.04291928642527, 54.655448967096305),\n",
+       " (266.75073534275322, 53.22914208405799),\n",
+       " (233.25010687113831, 51.415310785084358),\n",
+       " (235.86124553266205, 49.932520459157615),\n",
+       " (204.17536410665218, 49.937485696638511),\n",
+       " (216.58363015060209, 48.71243562166476),\n",
+       " (213.79479472770714, 45.446839687730105),\n",
+       " (194.30255638458473, 46.234641994868568),\n",
+       " (198.73561291019692, 47.04819962721875),\n",
+       " (206.89598365667399, 44.89666240461478),\n",
+       " (213.17403683446534, 43.140057028057178)]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simpegmt.Utils.dataUtils.getAppRes(mt1DdataZdetList[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "    "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/MT3DforData1Dinv.py b/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/MT3DforData1Dinv.py
new file mode 100644
index 00000000..1534732d
--- /dev/null
+++ b/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/MT3DforData1Dinv.py
@@ -0,0 +1,76 @@
+## Script to run 3D forward model
+## Forward model a data
+import numpy as np, sys, os, time, gzip, cPickle as pickle
+sys.path.append('/tera_raid/gudni/gitCodes/simpegmt')
+sys.path.append('/tera_raid/gudni/gitCodes/simpegem')
+sys.path.append('/tera_raid/gudni/gitCodes/simpeg')
+sys.path.append('/tera_raid/gudni')
+from pymatsolver import MumpsSolver
+import simpegMT as simpegmt, SimPEG as simpeg
+import numpy as np, scipy
+## Setup the forward modeling
+
+# Make the mesh.
+mTensor = simpeg.Utils.meshTensor
+cSize = [50,20]
+# Cells constant size mesh
+hx = mTensor([(cSize[0],50)])
+hy = mTensor([(cSize[0],50)])
+hz = mTensor([(cSize[1],48)])
+x0 = np.array([-1250,-1250,- 30*20])
+mesh3dCons = simpeg.Mesh.TensorMesh([hx,hy,hz],x0)
+# With padding
+hPad = mTensor([(cSize[0],5,1.5)])
+aPad = mTensor([(cSize[1],9,1.5)])
+bPad = mTensor([(cSize[1],9,-1.5)])
+hxPad = np.hstack((hPad[::-1],mTensor([(cSize[0],40)]),hPad))
+hyPad = np.hstack((hPad[::-1],mTensor([(cSize[0],40)]),hPad))
+hzPad = np.hstack((bPad,mTensor([(cSize[1],30)]),aPad))
+x0Pad = np.array([-(np.sum(hPad)+1000),-(np.sum(hPad)+1000),-(np.sum(bPad)+(20*30))])
+mesh3d = simpeg.Mesh.TensorMesh([hxPad,hyPad,hzPad],x0Pad)
+
+# Read the model
+modelname = "simpegTDmodel.con"
+
+# Load the model to the uniform cell mesh
+modelUniCell = simpeg.Utils.meshutils.readUBCTensorModel(modelname,mesh3dCons)
+
+# Load the model to the mesh with padding cells
+modelT = simpeg.Utils.meshutils.readUBCTensorModel(modelname,mesh3d)
+# Adjust the model to reflect changes in the mesh (fewer aircells)
+modMat = mesh3d.r(modelT,'CC','CC','M')
+modNewMat = np.ones((50,50,48))*modMat[0,0,0]
+modNewMat[:,:,9::] = modMat[:,:,:-9]
+modelTD = mesh3d.r(modNewMat,'CC','CC','V')
+
+# Define the data locations
+xG,yG = np.meshgrid(np.linspace(-700,700,8),np.linspace(-700,700,8))
+zG = np.zeros_like(xG)
+locs = np.hstack((simpeg.mkvc(xG.ravel(),2),simpeg.mkvc(yG.ravel(),2),simpeg.mkvc(zG.ravel(),2)))
+
+# Make the receiver list
+rxList = []
+for rxType in ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi']:
+    rxList.append(simpegmt.SurveyMT.RxMT(locs,rxType))
+# Source list
+srcList =[]
+freqs = np.logspace(4,0,17)
+for freq in freqs:
+    srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,freq))
+# Survey MT
+survey = simpegmt.SurveyMT.SurveyMT(srcList)
+
+# Setup the problem object
+sigma1d = mesh3d.r(modelTD,'CC','CC','M')[0,0,:] # Use the edge column as a background model
+problem = simpegmt.ProblemMT3D.eForm_ps(mesh3d,sigmaPrimary = sigma1d)
+problem.verbose = True
+from pymatsolver import MumpsSolver
+problem.Solver = MumpsSolver
+problem.pair(survey)
+
+# Forward model the data
+fields = problem.fields(modelTD)
+mtData = survey.projectFields(fields)
+# Save the data
+np.save('seogiModel_MTdata.npy',simpeg.mkvc(mtData,1))
+
diff --git a/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/seogiModel_MTdata.npy b/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/seogiModel_MTdata.npy
new file mode 100644
index 00000000..e77d59d8
Binary files /dev/null and b/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/seogiModel_MTdata.npy differ
diff --git a/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/simpegTDmodel.con b/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/simpegTDmodel.con
new file mode 100644
index 00000000..a0f10fa7
--- /dev/null
+++ b/notebooks/scipy2015/SeogiModelMT3Dfor21Dinv/simpegTDmodel.con
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+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
+   5.0000000e-03
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
new file mode 100644
index 00000000..7eb2573f
--- /dev/null
+++ b/simpegMT/BaseMT.py
@@ -0,0 +1,144 @@
+from simpegEM.FDEM import BaseFDEMProblem
+from SurveyMT import SurveyMT
+from DataMT import DataMT
+from FieldsMT import FieldsMT
+from SimPEG import SolverLU as SimpegSolver, PropMaps, Utils, mkvc, sp, 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
+    fieldsPair = FieldsMT
+
+    # # Pickleing support methods
+    # def __getstate__(self):
+    #     '''
+    #     Method that makes the dictionary of the object pickleble, removes non-pickleble elements of the object.
+
+    #     Used when doing:
+    #         pickle.dump(pickleFile,object)
+    #     '''
+    #     odict = self.__dict__.copy()
+    #     # Remove fields that are not needed
+    #     del odict['hook']
+    #     del odict['setKwargs']
+    #     # Return the dict
+    #     return odict
+
+    # def __setstate__(self,odict):
+    #     '''
+    #     Function that sets a pickle dictionary in to an object.
+
+    #     Used when doing:
+    #         object = pickle.load(pickleFile)
+    #     '''
+    #     # Update the dict
+    #     self.__dict__.update(odict)
+    #     # Re-hook the methods to the object
+    #     Utils.codeutils.hook(self,Utils.codeutils.hook)
+    #     Utils.codeutils.hook(self,Utils.codeutils.setKwargs)
+
+    # Set the solver
+    Solver = SimpegSolver
+    solverOpts = {}
+
+    verbose = False
+    # Notes:
+    # Use the forward and devs from BaseFDEMProblem
+    # Might need to add more stuff here.
+
+    def Jvec(self, m, v, u=None):
+        """
+        Function to calculate the data sensitivities dD/dm times a vector.
+
+            :param numpy.ndarray (nC, 1) - conductive model
+            :param numpy.ndarray (nC, 1) - random vector
+            :param MTfields object (optional) - MT fields object, if not given it is calculated
+            :rtype: MTdata object
+            :return: Data sensitivities wrt m
+        """
+
+        # Calculate the fields
+        if u is None:
+           u = self.fields(m)
+        # Set current model
+        self.curModel = m
+        # Initiate the Jv object
+        Jv = self.dataPair(self.survey)
+
+        # Loop all the frequenies
+        for freq in self.survey.freqs:
+            dA_du = self.getA(freq) #
+
+            dA_duI = self.Solver(dA_du, **self.solverOpts)
+
+            for src in self.survey.getSrcByFreq(freq):
+                # We need fDeriv_m = df/du*du/dm + df/dm
+                # Construct du/dm, it requires a solve
+                # NOTE: need to account for the 2 polarizations in the derivatives.
+                u_src = u[src,:]
+                # dA_dm and dRHS_dm should be of size nE,2, so that we can multiply by dA_duI. The 2 columns are each of the polarizations.
+                dA_dm = self.getADeriv_m(freq, u_src, v) # Size: nE,2 (u_px,u_py) in the columns.
+                dRHS_dm = self.getRHSDeriv_m(freq, v) # Size: nE,2 (u_px,u_py) in the columns.
+                if dRHS_dm is None:
+                    du_dm = dA_duI * ( -dA_dm )
+                else:
+                    du_dm = dA_duI * ( -dA_dm + dRHS_dm )
+                # Calculate the projection derivatives
+                for rx in src.rxList:
+                    # Get the projection derivative
+                    # v should be of size 2*nE (for 2 polarizations)
+                    PDeriv_u = lambda t: rx.projectFieldsDeriv(src, self.mesh, u, t) # wrt u, we don't have have PDeriv wrt m
+                    Jv[src, rx] = PDeriv_u(mkvc(du_dm))
+        # Return the vectorized sensitivities
+        return mkvc(Jv)
+
+    def Jtvec(self, m, v, u=None):
+        if u is None:
+            u = self.fields(m)
+
+        self.curModel = m
+
+        # Ensure v is a data object.
+        if not isinstance(v, self.dataPair):
+            v = self.dataPair(self.survey, v)
+
+        Jtv = np.zeros(m.size)
+
+        for freq in self.survey.freqs:
+            AT = self.getA(freq).T
+
+            ATinv = self.Solver(AT, **self.solverOpts)
+
+            for src in self.survey.getSrcByFreq(freq):
+                ftype = self._fieldType + 'Solution'
+                u_src = u[src, :]
+
+                for rx in src.rxList:
+                    # Get the adjoint projectFieldsDeriv
+                    # PTv needs to be nE,
+                    PTv = rx.projectFieldsDeriv(src, self.mesh, u, mkvc(v[src, rx],2), adjoint=True) # wrt u, need possibility wrt m
+                    # Get the
+                    dA_duIT = ATinv * PTv
+                    dA_dmT = self.getADeriv_m(freq, u_src, mkvc(dA_duIT), adjoint=True)
+                    dRHS_dmT = self.getRHSDeriv_m(freq, mkvc(dA_duIT), adjoint=True)
+                    # Make du_dmT
+                    if dRHS_dmT is None:
+                        du_dmT = -dA_dmT
+                    else:
+                        du_dmT = -dA_dmT + dRHS_dmT
+                    # Select the correct component
+                    # du_dmT needs to be of size nC,
+                    real_or_imag = rx.projComp
+                    if real_or_imag == 'real':
+                        Jtv +=  du_dmT.real
+                    elif real_or_imag == 'imag':
+                        Jtv +=  -du_dmT.real
+                    else:
+                        raise Exception('Must be real or imag')
+
+        return Jtv
\ No newline at end of file
diff --git a/simpegMT/DataMT.py b/simpegMT/DataMT.py
new file mode 100644
index 00000000..6ad911f6
--- /dev/null
+++ b/simpegMT/DataMT.py
@@ -0,0 +1,128 @@
+from SimPEG import Survey, Utils, Problem, np, sp, mkvc
+from simpegMT.Utils import rec2ndarr
+import simpegMT
+from scipy.constants import mu_0
+import sys
+from numpy.lib import recfunctions as recFunc
+
+
+############
+### Data ###
+############
+class DataMT(Survey.Data):
+    '''
+    Data class for MTdata
+
+    :param SimPEG survey object survey:
+    :param v vector with data
+
+    '''
+    def __init__(self, survey, v=None):
+        # Pass the variables to the "parent" method
+        Survey.Data.__init__(self, survey, v)
+
+    # # Import data
+    # @classmethod
+    # def fromEDIFiles():
+    #     pass
+
+    def toRecArray(self,returnType='RealImag'):
+        '''
+        Function that returns a numpy.recarray for a SimpegMT impedance data object.
+
+        :param str returnType: Switches between returning a rec array where the impedance is split to real and imaginary ('RealImag') or is a complex ('Complex')
+
+        '''
+
+        # Define the record fields
+        dtRI = [('freq',float),('x',float),('y',float),('z',float),('zxxr',float),('zxxi',float),('zxyr',float),('zxyi',float),
+        ('zyxr',float),('zyxi',float),('zyyr',float),('zyyi',float),('tzxr',float),('tzxi',float),('tzyr',float),('tzyi',float)]
+        dtCP = [('freq',float),('x',float),('y',float),('z',float),('zxx',complex),('zxy',complex),('zyx',complex),('zyy',complex),('tzx',complex),('tzy',complex)]
+        impList = ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi']
+        for src in self.survey.srcList:
+            # Temp array for all the receivers of the source.
+            # Note: needs to be written more generally, using diffterent rxTypes and not all the data at the locaitons
+            # Assume the same locs for all RX
+            locs = src.rxList[0].locs
+            if locs.shape[1] == 1:
+                locs = np.hstack((np.array([[0.0,0.0]]),locs))
+            elif locs.shape[1] == 2:
+                locs = np.hstack((np.array([[0.0]]),locs))
+            tArrRec = np.concatenate((src.freq*np.ones((locs.shape[0],1)),locs,np.nan*np.ones((locs.shape[0],12))),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.replace('z1d','zyx'),self[src,rx]] for rx in src.rxList]
+            # Insert the values to the temp array
+            for nr,(key,val) in enumerate(typeList):
+                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
+            uniFLmarr = np.unique(mArrRec[['freq','x','y','z']]).copy()
+
+            try:
+                outTemp = recFunc.stack_arrays((outTemp,mArrRec))
+                #outTemp = np.concatenate((outTemp,dataBlock),axis=0)
+            except NameError as e:
+                outTemp = mArrRec
+
+            if 'RealImag' in returnType:
+                outArr = outTemp
+            elif 'Complex' in returnType:
+                # Add the real and imaginary to a complex number
+                outArr = np.empty(outTemp.shape,dtype=dtCP)
+                for comp in ['freq','x','y','z']:
+                    outArr[comp] = outTemp[comp].copy()
+                for comp in ['zxx','zxy','zyx','zyy','tzx','tzy']:
+                    outArr[comp] = outTemp[comp+'r'].copy() + 1j*outTemp[comp+'i'].copy()
+            else:
+                raise NotImplementedError('{:s} is not implemented, as to be RealImag or Complex.')
+
+        # Return
+        return outArr
+
+    @classmethod
+    def fromRecArray(cls, recArray, srcType='primary'):
+        """
+        Class method that reads in a numpy record array to MTdata object.
+
+        Only imports the impedance data.
+
+        """
+        if srcType=='primary':
+            src = simpegMT.SurveyMT.srcMT_polxy_1Dprimary
+        elif srcType=='total':
+            src = sdsimpegMT.SurveyMT.srcMT_polxy_1DhomotD
+        else:
+            raise NotImplementedError('{:s} is not a valid source type for MTdata')
+
+        # Find all the frequencies in recArray
+        uniFreq = np.unique(recArray['freq'])
+        srcList = []
+        dataList = []
+        for freq in uniFreq:
+            # Initiate rxList
+            rxList = []
+            # Find that data for freq
+            dFreq = recArray[recArray['freq'] == freq].copy()
+            # Find the impedance rxTypes in the recArray.
+            rxTypes = [ comp for comp in recArray.dtype.names if (len(comp)==4 or len(comp)==3) and 'z' in comp]
+            for rxType in rxTypes:
+                # Find index of not nan values in rxType
+                notNaNind = ~np.isnan(dFreq[rxType])
+                if np.any(notNaNind): # Make sure that there is any data to add.
+                    locs = rec2ndarr(dFreq[['x','y','z']][notNaNind].copy())
+                    if dFreq[rxType].dtype.name in 'complex128':
+                        rxList.append(simpegMT.SurveyMT.RxMT(locs,rxType+'r'))
+                        dataList.append(dFreq[rxType][notNaNind].real.copy())
+                        rxList.append(simpegMT.SurveyMT.RxMT(locs,rxType+'i'))
+                        dataList.append(dFreq[rxType][notNaNind].imag.copy())
+                    else:
+                        rxList.append(simpegMT.SurveyMT.RxMT(locs,rxType))
+                        dataList.append(dFreq[rxType][notNaNind].copy())
+            srcList.append(src(rxList,freq))
+
+        # Make a survey
+        survey = simpegMT.SurveyMT.SurveyMT(srcList)
+        dataVec = np.hstack(dataList)
+        return cls(survey,dataVec)
\ No newline at end of file
diff --git a/simpegMT/FieldsMT.py b/simpegMT/FieldsMT.py
new file mode 100644
index 00000000..c5d605d5
--- /dev/null
+++ b/simpegMT/FieldsMT.py
@@ -0,0 +1,348 @@
+from SimPEG import Survey, Utils, Problem, np, sp, mkvc
+from scipy.constants import mu_0
+import sys
+from numpy.lib import recfunctions as recFunc
+from simpegEM.Utils.EMUtils import omega
+
+##############
+### Fields ###
+##############
+class FieldsMT(Problem.Fields):
+    """Field Storage for a MT survey."""
+    knownFields = {}
+    dtype = complex
+
+
+class FieldsMT_1D(FieldsMT):
+    """
+    Fields storage for the 1D MT solution.
+    """
+    knownFields = {'e_1dSolution':'F'}
+    aliasFields = {
+                    'e_1d' : ['e_1dSolution','F','_e'],
+                    'e_1dPrimary' : ['e_1dSolution','F','_ePrimary'],
+                    'e_1dSecondary' : ['e_1dSolution','F','_eSecondary'],
+                    'b_1d' : ['e_1dSolution','E','_b'],
+                    'b_1dPrimary' : ['e_1dSolution','E','_bPrimary'],
+                    'b_1dSecondary' : ['e_1dSolution','E','_bSecondary']
+                  }
+
+    def __init__(self,mesh,survey,**kwargs):
+        FieldsMT.__init__(self,mesh,survey,**kwargs)
+
+    def _ePrimary(self, eSolution, srcList):
+        ePrimary = np.zeros_like(eSolution)
+        for i, src in enumerate(srcList):
+            ep = src.ePrimary(self.survey.prob)
+            if ep is not None:
+                ePrimary[:,i] = ep[:,-1]
+        return ePrimary
+
+    def _eSecondary(self, eSolution, srcList):
+        return eSolution
+
+    def _e(self, eSolution, srcList):
+        return self._ePrimary(eSolution,srcList) + self._eSecondary(eSolution,srcList)
+
+    def _eDeriv_u(self, src, v, adjoint = False):
+        return v
+
+    def _eDeriv_m(self, src, v, adjoint = False):
+        # assuming primary does not depend on the model
+        return None
+
+    def _bPrimary(self, eSolution, srcList):
+        bPrimary = np.zeros([self.survey.mesh.nE,eSolution.shape[1]], dtype = complex)
+        for i, src in enumerate(srcList):
+            bp = src.bPrimary(self.survey.prob)
+            if bp is not None:
+                bPrimary[:,i] += bp[:,-1]
+        return bPrimary
+
+    def _bSecondary(self, eSolution, srcList):
+        C = self.mesh.nodalGrad
+        b = (C * eSolution)
+        for i, src in enumerate(srcList):
+            b[:,i] *= - 1./(1j*omega(src.freq))
+            # There is no magnetic source in the MT problem
+            # S_m, _ = src.eval(self.survey.prob)
+            # if S_m is not None:
+            #     b[:,i] += 1./(1j*omega(src.freq)) * S_m
+        return b
+
+    def _b(self, eSolution, srcList):
+        return self._bPrimary(eSolution, srcList) + self._bSecondary(eSolution, srcList)
+
+    def _bSecondaryDeriv_u(self, src, v, adjoint = False):
+        C = self.mesh.nodalGrad
+        if adjoint:
+            return - 1./(1j*omega(src.freq)) * (C.T * v)
+        return - 1./(1j*omega(src.freq)) * (C * v)
+
+    def _bSecondaryDeriv_m(self, src, v, adjoint = False):
+        # Doesn't depend on m
+        # _, S_eDeriv = src.evalDeriv(self.survey.prob, adjoint)
+        # S_eDeriv = S_eDeriv(v)
+        # if S_eDeriv is not None:
+        #     return 1./(1j * omega(src.freq)) * S_eDeriv
+        return None
+
+    def _bDeriv_u(self, src, v, adjoint=False):
+        # Primary does not depend on u
+        return self._bSecondaryDeriv_u(src, v, adjoint)
+
+    def _bDeriv_m(self, src, v, adjoint=False):
+        # Assuming the primary does not depend on the model
+        return self._bSecondaryDeriv_m(src, v, adjoint)
+
+    def _fDeriv_u(self, src, v, adjoint=False):
+        """
+        Derivative of the fields object wrt u.
+
+        :param MTsrc src: MT source
+        :param numpy.ndarray v: random vector of f_sol.size
+        This function stacks the fields derivatives appropriately
+
+        return a vector of size (nreEle+nrbEle)
+        """
+
+        de_du = v #Utils.spdiag(np.ones((self.nF,)))
+        db_du = self._bDeriv_u(src, v, adjoint)
+        # Return the stack
+        # This doesn't work...
+        return np.vstack((de_du,db_du))
+
+    def _fDeriv_m(self, src, v, adjoint=False):
+        """
+        Derivative of the fields object wrt m.
+
+        This function stacks the fields derivatives appropriately
+        """
+        return None
+
+class FieldsMT_3D(FieldsMT):
+    """
+    Fields storage for the 3D MT solution.
+    """
+    # Define the known the alias fields
+    # Assume that the solution of e on the E.
+    ## NOTE: Need to make this more general, to allow for other solutions formats.
+    knownFields = {'e_pxSolution':'E','e_pySolution':'E'}
+    aliasFields = {
+                    'e_px' : ['e_pxSolution','E','_e_px'],
+                    'e_pxPrimary' : ['e_pxSolution','E','_e_pxPrimary'],
+                    'e_pxSecondary' : ['e_pxSolution','E','_e_pxSecondary'],
+                    'e_py' : ['e_pySolution','E','_e_py'],
+                    'e_pyPrimary' : ['e_pySolution','E','_e_pyPrimary'],
+                    'e_pySecondary' : ['e_pySolution','E','_e_pySecondary'],
+                    'b_px' : ['e_pxSolution','F','_b_px'],
+                    'b_pxPrimary' : ['e_pxSolution','F','_b_pxPrimary'],
+                    'b_pxSecondary' : ['e_pxSolution','F','_b_pxSecondary'],
+                    'b_py' : ['e_pySolution','F','_b_py'],
+                    'b_pyPrimary' : ['e_pySolution','F','_b_pyPrimary'],
+                    'b_pySecondary' : ['e_pySolution','F','_b_pySecondary']
+                  }
+
+    def __init__(self,mesh,survey,**kwargs):
+        FieldsMT.__init__(self,mesh,survey,**kwargs)
+
+    def _e_pxPrimary(self, e_pxSolution, srcList):
+        e_pxPrimary = np.zeros_like(e_pxSolution)
+        for i, src in enumerate(srcList):
+            ep = src.ePrimary(self.survey.prob)
+            if ep is not None:
+                e_pxPrimary[:,i] = ep[:,0]
+        return e_pxPrimary
+
+    def _e_pyPrimary(self, e_pySolution, srcList):
+        e_pyPrimary = np.zeros_like(e_pySolution)
+        for i, src in enumerate(srcList):
+            ep = src.ePrimary(self.survey.prob)
+            if ep is not None:
+                e_pyPrimary[:,i] = ep[:,1]
+        return e_pyPrimary
+
+    def _e_pxSecondary(self, e_pxSolution, srcList):
+        return e_pxSolution
+
+    def _e_pySecondary(self, e_pySolution, srcList):
+        return e_pySolution
+
+    def _e_px(self, e_pxSolution, srcList):
+        return self._e_pxPrimary(e_pxSolution,srcList) + self._e_pxSecondary(e_pxSolution,srcList)
+
+    def _e_py(self, e_pySolution, srcList):
+        return self._e_pyPrimary(e_pySolution,srcList) + self._e_pySecondary(e_pySolution,srcList)
+
+    #NOTE: For e_p?Deriv_u,
+    # v has to be u(2*nE) long for the not adjoint and nE long for adjoint.
+    # Returns nE long for not adjoint and 2*nE long for adjoint
+    def _e_pxDeriv_u(self, src, v, adjoint = False):
+        '''
+        Takes the derivative of e_px wrt u
+        '''
+        if adjoint:
+            # adjoint: returns a 2*nE long vector with zero's for py
+            return np.vstack((v,np.zeros_like(v)))
+        # Not adjoint: return only the px part of the vector
+        return v[:len(v)/2]
+
+    def _e_pyDeriv_u(self, src, v, adjoint = False):
+        '''
+        Takes the derivative of e_py wrt u
+        '''
+        if adjoint:
+            # adjoint: returns a 2*nE long vector with zero's for px
+            return np.vstack((np.zeros_like(v),v))
+        # Not adjoint: return only the px part of the vector
+        return v[len(v)/2::]
+
+    def _e_pxDeriv_m(self, src, v, adjoint = False):
+        # assuming primary does not depend on the model
+        return None
+    def _e_pyDeriv_m(self, src, v, adjoint = False):
+        # assuming primary does not depend on the model
+        return None
+
+    def _b_pxPrimary(self, e_pxSolution, srcList):
+        b_pxPrimary = np.zeros([self.survey.mesh.nF,e_pxSolution.shape[1]], dtype = complex)
+        for i, src in enumerate(srcList):
+            bp = src.bPrimary(self.survey.prob)
+            if bp is not None:
+                b_pxPrimary[:,i] += bp[:,0]
+        return b_pxPrimary
+
+    def _b_pyPrimary(self, e_pySolution, srcList):
+        b_pyPrimary = np.zeros([self.survey.mesh.nF,e_pySolution.shape[1]], dtype = complex)
+        for i, src in enumerate(srcList):
+            bp = src.bPrimary(self.survey.prob)
+            if bp is not None:
+                b_pyPrimary[:,i] += bp[:,1]
+        return b_pyPrimary
+
+    def _b_pxSecondary(self, e_pxSolution, srcList):
+        C = self.mesh.edgeCurl
+        b = (C * e_pxSolution)
+        for i, src in enumerate(srcList):
+            b[:,i] *= - 1./(1j*omega(src.freq))
+            # There is no magnetic source in the MT problem
+            # S_m, _ = src.eval(self.survey.prob)
+            # if S_m is not None:
+            #     b[:,i] += 1./(1j*omega(src.freq)) * S_m
+        return b
+
+    def _b_pySecondary(self, e_pySolution, srcList):
+        C = self.mesh.edgeCurl
+        b = (C * e_pySolution)
+        for i, src in enumerate(srcList):
+            b[:,i] *= - 1./(1j*omega(src.freq))
+            # There is no magnetic source in the MT problem
+            # S_m, _ = src.eval(self.survey.prob)
+            # if S_m is not None:
+            #     b[:,i] += 1./(1j*omega(src.freq)) * S_m
+        return b
+
+    def _b_px(self, eSolution, srcList):
+        return self._b_pxPrimary(eSolution, srcList) + self._b_pxSecondary(eSolution, srcList)
+
+    def _b_py(self, eSolution, srcList):
+        return self._b_pyPrimary(eSolution, srcList) + self._b_pySecondary(eSolution, srcList)
+
+    # NOTE: v needs to be length 2*nE to account for both polarizations
+    def _b_pxSecondaryDeriv_u(self, src, v, adjoint = False):
+        # C = sp.kron(self.mesh.edgeCurl,[[1,0],[0,0]])
+        C = sp.hstack((self.mesh.edgeCurl,Utils.spzeros(self.mesh.nF,self.mesh.nE))) # This works for adjoint = None
+        if adjoint:
+            return - 1./(1j*omega(src.freq)) * (C.T * v)
+        return - 1./(1j*omega(src.freq)) * (C * v)
+
+    def _b_pySecondaryDeriv_u(self, src, v, adjoint = False):
+        # C = sp.kron(self.mesh.edgeCurl,[[0,0],[0,1]])
+        C = sp.hstack((Utils.spzeros(self.mesh.nF,self.mesh.nE),self.mesh.edgeCurl)) # This works for adjoint = None
+        if adjoint:
+            return - 1./(1j*omega(src.freq)) * (C.T * v)
+        return - 1./(1j*omega(src.freq)) * (C * v)
+
+    def _b_pxSecondaryDeriv_m(self, src, v, adjoint = False):
+        # Doesn't depend on m
+        # _, S_eDeriv = src.evalDeriv(self.survey.prob, adjoint)
+        # S_eDeriv = S_eDeriv(v)
+        # if S_eDeriv is not None:
+        #     return 1./(1j * omega(src.freq)) * S_eDeriv
+        return None
+
+    def _b_pySecondaryDeriv_m(self, src, v, adjoint = False):
+        # Doesn't depend on m
+        # _, S_eDeriv = src.evalDeriv(self.survey.prob, adjoint)
+        # S_eDeriv = S_eDeriv(v)
+        # if S_eDeriv is not None:
+        #     return 1./(1j * omega(src.freq)) * S_eDeriv
+        return None
+
+    def _b_pxDeriv_u(self, src, v, adjoint=False):
+        # Primary does not depend on u
+        return self._b_pxSecondaryDeriv_u(src, v, adjoint)
+
+    def _b_pyDeriv_u(self, src, v, adjoint=False):
+        # Primary does not depend on u
+        return self._b_pySecondaryDeriv_u(src, v, adjoint)
+
+    def _b_pxDeriv_m(self, src, v, adjoint=False):
+        # Assuming the primary does not depend on the model
+        return self._b_pxSecondaryDeriv_m(src, v, adjoint)
+
+    def _b_pyDeriv_m(self, src, v, adjoint=False):
+        # Assuming the primary does not depend on the model
+        return self._b_pySecondaryDeriv_m(src, v, adjoint)
+
+    def _f_pxDeriv_u(self, src, v, adjoint=False):
+        """
+        Derivative of the fields object wrt u.
+
+        :param MTsrc src: MT source
+        :param numpy.ndarray v: random vector of f_sol.size
+        This function stacks the fields derivatives appropriately
+
+        return a vector of size (nreEle+nrbEle)
+        """
+
+        de_du = v #Utils.spdiag(np.ones((self.nF,)))
+        db_du = self._b_pxDeriv_u(src, v, adjoint)
+        # Return the stack
+        # This doesn't work...
+        return np.vstack((de_du,db_du))
+
+    def _f_pyDeriv_u(self, src, v, adjoint=False):
+        """
+        Derivative of the fields object wrt u.
+
+        :param MTsrc src: MT source
+        :param numpy.ndarray v: random vector of f_sol.size
+        This function stacks the fields derivatives appropriately
+
+        return a vector of size (nreEle+nrbEle)
+        """
+
+        de_du = v #Utils.spdiag(np.ones((self.nF,)))
+        db_du = self._b_pyDeriv_u(src, v, adjoint)
+        # Return the stack
+        # This doesn't work...
+        return np.vstack((de_du,db_du))
+
+    def _f_pxDeriv_m(self, src, v, adjoint=False):
+        """
+        Derivative of the fields object wrt m.
+
+        This function stacks the fields derivatives appropriately
+        """
+        # The fields have no dependance to the model.
+        return None
+
+    def _f_pyDeriv_m(self, src, v, adjoint=False):
+        """
+        Derivative of the fields object wrt m.
+
+        This function stacks the fields derivatives appropriately
+        """
+        # The fields have no dependance to the model.
+        return None
\ No newline at end of file
diff --git a/simpegMT/ProblemMT.py b/simpegMT/ProblemMT.py
deleted file mode 100644
index 9967a5d4..00000000
--- a/simpegMT/ProblemMT.py
+++ /dev/null
@@ -1,297 +0,0 @@
-from SimPEG import Survey, Problem, Utils, Models, np, sp, SolverLU as SimpegSolver
-from scipy.constants import mu_0
-from SurveyMT import SurveyMT, FieldsMT
-import multiprocessing, sys, time
-
-def omega(freq):
-    """Change frequency to angular frequency, omega"""
-    return 2.*np.pi*freq
-
-
-class MTProblem(Problem.BaseProblem):
-
-    def __init__(self, mesh, **kwargs):
-        Problem.BaseProblem.__init__(self, mesh, **kwargs)
-
-    solType = 'e'
-    storeTheseFields = ['e', 'b']
-
-    surveyPair = SurveyMT
-    dataPair = Survey.Data
-
-    Solver = SimpegSolver
-    solverOpts = {}
-
-    ####################################################
-    # Mass Matrices
-    ####################################################
-
-    @property
-    def MfMui(self):
-        #TODO: assuming constant mu
-        if getattr(self, '_MfMui', None) is None:
-            self._MfMui = self.mesh.getFaceInnerProduct(1/mu_0)
-        return self._MfMui
-
-    @property
-    def Me(self):
-        if getattr(self, '_Me', None) is None:
-            self._Me = self.mesh.getEdgeInnerProduct()
-        return self._Me
-
-    @property
-    def MeSigma(self):
-        #TODO: hardcoded to sigma as the model
-        if getattr(self, '_MeSigma', None) is None:
-            sigma = self.curTModel
-            self._MeSigma = self.mesh.getEdgeInnerProduct(sigma)
-        return self._MeSigma
-
-
-    @property
-    def MeSigmaI(self):
-        #TODO: hardcoded to sigma as the model
-        if getattr(self, '_MeSigmaI', None) is None:
-            sigma = self.curTModel
-            self._MeSigmaI = self.mesh.getEdgeInnerProduct(sigma, invMat=True)
-        return self._MeSigmaI
-
-    curModel = Utils.dependentProperty('_curModel', None, ['_MeSigma', '_MeSigmaI', '_curTModel', '_curTModelDeriv'], 'Sets the current model, and removes dependent mass matrices.')
-
-    @property
-    def curTModel(self):
-        if getattr(self, '_curTModel', None) is None:
-            self._curTModel = self.mapping*self.curModel
-        return self._curTModel
-
-    @property
-    def curTModelDeriv(self):
-        if getattr(self, '_curTModelDeriv', None) is None:
-            self._curTModelDeriv = self.mapping*self.curModel
-        return self._curTModelDeriv
-
-
-    # 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 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
-
-    def fields(self, m, m_back,nrProc=None):
-        '''
-        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)
-
-        def solveAtFreq(self,F,freq):
-            startTime = time.time()
-            print 'Starting work for {:.3e}'.format(freq)
-            sys.stdout.flush()
-            A = self.getA(freq)
-            rhs, ep = self.getRHS(freq,m_back)
-            Ainv = self.Solver(A, **self.solverOpts)
-            e = Ainv * rhs 
-
-            # 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]
-            print 'Ran for {:f} seconds'.format(time.time()-startTime)
-            sys.stdout.flush()
-            return F
-        #NOTE: add print status statements.
-        if nrProc is None:
-            for freq in self.survey.freqs:
-                F = solveAtFreq(self,F,freq)
-        else:
-            pool = multiprocessing.Pool(processes=nrProc)
-            pool.map(solveAtFreq,self.survey.freqs)
-            pool.close()
-            pool.join()
-
-        return F
-
-
-    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 getAbg(self, freq):
-        """
-            Function to get the A matrix for the background model.
-
-            :param float freq: Frequency
-            :rtype: scipy.sparse.csr_matrix
-            :return: A
-        """
-        mui = self.MfMui
-        sigBG = self.MeSigmaBG
-        C = self.mesh.edgeCurl
-
-        return C.T*mui*C - 1j*omega(freq)*sigBG
-
-    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)
-        Abg = self.getAbg(freq)
-
-        return -Abg*eBG_bp, eBG_bp
-
-    ##################################################################
-    # Inversion stuff
-    ##################################################################
-    # Not really used now....
-
-
-    def calcFields(self, sol, freq, fieldType, adjoint=False):
-        e = sol
-        if fieldType == 'e':
-            return e
-        elif fieldType == 'b':
-            if not adjoint:
-                b = -(1./(1j*omega(freq))) * ( self.mesh.edgeCurl * e )
-            else:
-                b = -(1./(1j*omega(freq))) * ( self.mesh.edgeCurl.T * e )
-            return b
-        raise NotImplementedError('fieldType "%s" is not implemented.' % fieldType)
-
-    def calcFieldsDeriv(self, sol, freq, fieldType, v, adjoint=False):
-        e = sol
-        if fieldType == 'e':
-            return None
-        elif fieldType == 'b':
-            return None
-        raise NotImplementedError('fieldType "%s" is not implemented.' % fieldType)
-
-
-    def Jvec(self, m, v, u=None):
-        if u is None:
-           u = self.fields(m)
-
-        self.curModel = m
-
-        Jv = self.dataPair(self.survey)
-
-        for freq in self.survey.freqs:
-            A = self.getA(freq)
-            solver = self.Solver(A, **self.solverOpts)
-
-            for tx in self.survey.getTransmitters(freq):
-                u_tx = u[tx, self.solType]
-                w = self.getADeriv(freq, u_tx, v)
-                Ainvw = solver.solve(w)
-                for rx in tx.rxList:
-                    fAinvw = self.calcFields(Ainvw, freq, rx.projField)
-                    P = lambda v: rx.projectFieldsDeriv(tx, self.mesh, u, v)
-
-                    df_dm = self.calcFieldsDeriv(u_tx, freq, rx.projField, v)
-                    if df_dm is None:
-                        Jv[tx, rx] = - P(fAinvw)
-                    else:
-                        Jv[tx, rx] = - P(fAinvw) + P(df_dm)
-
-        return Utils.mkvc(Jv)
-
-    def Jtvec(self, m, v, u=None):
-        if u is None:
-            u = self.fields(m)
-
-        self.curModel = m
-
-        # Ensure v is a data object.
-        if not isinstance(v, self.dataPair):
-            v = self.dataPair(self.survey, v)
-
-        Jtv = np.zeros(self.mapping.nP)
-
-        for freq in self.survey.freqs:
-            AT = self.getA(freq).T
-            solver = self.Solver(AT, **self.solverOpts)
-
-            for tx in self.survey.getTransmitters(freq):
-                u_tx = u[tx, self.solType]
-
-                for rx in tx.rxList:
-                    PTv = rx.projectFieldsDeriv(tx, self.mesh, u, v[tx, rx], adjoint=True)
-                    fPTv = self.calcFields(PTv, freq, rx.projField, adjoint=True)
-
-                    w = solver.solve( fPTv )
-                    Jtv_rx = - self.getADeriv(freq, u_tx, w, adjoint=True)
-
-                    df_dm = self.calcFieldsDeriv(u_tx, freq, rx.projField, PTv, adjoint=True)
-
-                    if df_dm is not None:
-                        Jtv_rx += df_dm
-
-                    real_or_imag = rx.projComp
-                    if real_or_imag == 'real':
-                        Jtv +=   Jtv_rx.real
-                    elif real_or_imag == 'imag':
-                        Jtv += - Jtv_rx.real
-                    else:
-                        raise Exception('Must be real or imag')
-
-        return Jtv
diff --git a/simpegMT/ProblemMT1D/Problems.py b/simpegMT/ProblemMT1D/Problems.py
new file mode 100644
index 00000000..f237a086
--- /dev/null
+++ b/simpegMT/ProblemMT1D/Problems.py
@@ -0,0 +1,243 @@
+from simpegEM.Utils.EMUtils import omega
+from SimPEG import mkvc
+from scipy.constants import mu_0
+from simpegMT.BaseMT import BaseMTProblem
+from simpegMT.SurveyMT import SurveyMT
+from simpegMT.FieldsMT import FieldsMT_1D
+from simpegMT.DataMT  import DataMT
+from simpegMT.Utils.MT1Danalytic import getEHfields
+import numpy as np
+import multiprocessing, sys, time
+
+
+class eForm_psField(BaseMTProblem):
+    """
+    A MT problem soving a e formulation and primary/secondary fields decomposion.
+
+    Solves the equation
+
+    """
+    # From FDEMproblem: Used to project the fields. Currently not used for MTproblem.
+    _fieldType = 'e_1d'
+    _eqLocs    = 'EF'
+    _sigmaPrimary = None
+
+
+    def __init__(self, mesh, **kwargs):
+        BaseMTProblem.__init__(self, mesh, **kwargs)
+        self.fieldsPair = FieldsMT_1D
+        # self._sigmaPrimary = sigmaPrimary
+
+    @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):
+        """
+            Function to get the A matrix.
+
+            :param float freq: Frequency
+            :rtype: scipy.sparse.csr_matrix
+            :return: A
+        """
+
+        Mmui = self.mesh.getEdgeInnerProduct(1.0/mu_0)
+        Msig = self.mesh.getFaceInnerProduct(self.curModel.sigma)
+        # Note: need to use the code above since in the 1D problem I want
+        # e to live on Faces(nodes) and h on edges(cells). Might need to rethink this
+        # Possible that _fieldType and _eqLocs can fix this
+        # Mmui = self.MfMui
+        # Msig = self.MeSigma
+        C = self.mesh.nodalGrad
+        # Make A
+        A = C.T*Mmui*C + 1j*omega(freq)*Msig
+        # Either return full or only the inner part of A
+        return A
+
+    def getADeriv_m(self, freq, u, v, adjoint=False):
+        """
+        The derivative of A wrt sigma
+        """
+
+        dsig_dm = self.curModel.sigmaDeriv
+        MeMui = self.mesh.getEdgeInnerProduct(1.0/mu_0)
+        #
+        u_src = u['e_1dSolution']
+        dMf_dsig = self.mesh.getFaceInnerProductDeriv(self.curModel.sigma)(u_src) * self.curModel.sigmaDeriv
+        if adjoint:
+            return 1j * omega(freq) * (  dMf_dsig.T * v )
+        # Note: output has to be nN/nF, not nC/nE.
+        # v should be nC
+        return 1j * omega(freq) * ( dMf_dsig * v )
+
+    def getRHS(self, freq):
+        """
+            Function to return the right hand side for the system.
+            :param float freq: Frequency
+            :rtype: numpy.ndarray (nF, 1), numpy.ndarray (nF, 1)
+            :return: RHS for 1 polarizations, primary fields
+        """
+
+        # Get sources for the frequncy(polarizations)
+        Src = self.survey.getSrcByFreq(freq)[0]
+        S_e = Src.S_e(self)
+        return -1j * omega(freq) * S_e
+
+    def getRHSDeriv_m(self, freq, v, adjoint=False):
+        """
+        The derivative of the RHS wrt sigma
+        """
+
+        Src = self.survey.getSrcByFreq(freq)[0]
+        S_eDeriv = Src.S_eDeriv_m(self, v, adjoint)
+        return -1j * omega(freq) * S_eDeriv
+
+    def fields(self, m):
+        '''
+        Function to calculate all the fields for the model m.
+
+        :param np.ndarray (nC,) m: Conductivity model
+        '''
+        # Set the current model
+        self.curModel = m
+
+        F = FieldsMT_1D(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  = self.getRHS(freq)
+            Ainv = self.Solver(A, **self.solverOpts)
+            e_s = Ainv * rhs
+
+            # Store the fields
+            Src = self.survey.getSrcByFreq(freq)[0]
+            # NOTE: only store the e_solution(secondary), all other components calculated in the fields object
+            F[Src, 'e_1dSolution'] = e_s[:,-1] # Only storing the yx polarization as 1d
+
+            # Note curl e = -iwb so b = -curl e /iw
+            # b = -( self.mesh.nodalGrad * e )/( 1j*omega(freq) )
+            # F[Src, 'b_1d'] = b[:,1]
+            if self.verbose:
+                print 'Ran for {:f} seconds'.format(time.time()-startTime)
+                sys.stdout.flush()
+        return F
+
+class eForm_TotalField(BaseMTProblem):
+    """
+    A MT problem solving a e formulation and a Total bondary domain decompostion.
+
+    Solves the equation:
+
+    Math:
+
+
+    """
+
+    # From FDEMproblem: Used to project the fields. Currently not used for MTproblem.
+    _fieldType = 'e'
+    _eqLocs    = 'EF'
+
+
+    def __init__(self, mesh, **kwargs):
+        BaseMTProblem.__init__(self, mesh, **kwargs)
+
+    def getA(self, freq, full=False):
+        """
+            Function to get the A matrix.
+
+            :param float freq: Frequency
+            :param logic full: Return full A or the inner part
+            :rtype: scipy.sparse.csr_matrix
+            :return: A
+        """
+
+        Mmui = self.mesh.getEdgeInnerProduct(1.0/mu_0)
+        Msig = self.mesh.getFaceInnerProduct(self.curModel.sigma)
+        # Note: need to use the code above since in the 1D problem I want
+        # e to live on Faces(nodes) and h on edges(cells). Might need to rethink this
+        # Possible that _fieldType and _eqLocs can fix this
+        # Mmui = self.MfMui
+        # Msig = self.MeSigma
+        C = self.mesh.nodalGrad
+        # Make A
+        A = C.T*Mmui*C + 1j*omega(freq)*Msig
+        # Either return full or only the inner part of A
+        if full:
+            return A
+        else:
+            return A[1:-1,1:-1]
+
+    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):
+        """
+            Function to return the right hand side for the system.
+            :param float freq: Frequency
+            :rtype: numpy.ndarray (nE, 2), numpy.ndarray (nE, 2)
+            :return: RHS for both polarizations, primary fields
+        """
+        # Get sources for the frequency
+        # NOTE: Need to use the source information, doesn't really apply in 1D
+        src = self.survey.getSrcByFreq(freq)
+        # Get the full A
+        A = self.getA(freq,full=True)
+        # Define the outer part of the solution matrix
+        Aio = A[1:-1,[0,-1]]
+        Ed, Eu, Hd, Hu = getEHfields(self.mesh,self.curModel.sigma,freq,self.mesh.vectorNx)
+        Etot = (Ed + Eu)
+        sourceAmp = 1.0
+        Etot = ((Etot/Etot[-1])*sourceAmp) # Scale the fields to be equal to sourceAmp at the top
+        ## Note: The analytic solution is derived with e^iwt
+        eBC = np.r_[Etot[0],Etot[-1]]
+        # The right hand side
+
+        return -Aio*eBC, eBC
+
+    def getRHSderiv(self, freq, backSigma, u, v, adjoint=False):
+        raise NotImplementedError('getRHSDeriv not implemented yet')
+        return None
+
+    def fields(self, m):
+        '''
+        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
+        # RHS, CalcFields = self.getRHS(freq,m_back), self.calcFields
+
+        F = FieldsMT_1D(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_o = self.getRHS(freq)
+            Ainv = self.Solver(A, **self.solverOpts)
+            e_i = Ainv * rhs
+            e = mkvc(np.r_[e_o[0], e_i, e_o[1]],2)
+            # Store the fields
+            Src = self.survey.getSrcByFreq(freq)
+            # NOTE: only store e fields
+            F[Src, 'e_1dSolution'] = e[:,0]
+            if self.verbose:
+                print 'Ran for {:f} seconds'.format(time.time()-startTime)
+                sys.stdout.flush()
+        return F
diff --git a/simpegMT/ProblemMT1D/__init__.py b/simpegMT/ProblemMT1D/__init__.py
new file mode 100644
index 00000000..d1908621
--- /dev/null
+++ b/simpegMT/ProblemMT1D/__init__.py
@@ -0,0 +1 @@
+from Problems import eForm_TotalField, eForm_psField
\ No newline at end of file
diff --git a/simpegMT/ProblemMT2D/Problems.py b/simpegMT/ProblemMT2D/Problems.py
new file mode 100644
index 00000000..e69de29b
diff --git a/simpegMT/ProblemMT2D/__init__.py b/simpegMT/ProblemMT2D/__init__.py
new file mode 100644
index 00000000..fc80254b
--- /dev/null
+++ b/simpegMT/ProblemMT2D/__init__.py
@@ -0,0 +1 @@
+pass
\ No newline at end of file
diff --git a/simpegMT/ProblemMT3D/Problems.py b/simpegMT/ProblemMT3D/Problems.py
new file mode 100644
index 00000000..2ae646b1
--- /dev/null
+++ b/simpegMT/ProblemMT3D/Problems.py
@@ -0,0 +1,265 @@
+from SimPEG import Survey, Problem, Utils, Models, np, sp, mkvc, SolverLU as SimpegSolver
+from simpegEM.Utils.EMUtils import omega
+from scipy.constants import mu_0
+from simpegMT.BaseMT import BaseMTProblem
+from simpegMT.SurveyMT import SurveyMT
+from simpegMT.FieldsMT import FieldsMT_3D
+from simpegMT.DataMT import DataMT
+import multiprocessing, sys, time
+
+
+
+class eForm_ps(BaseMTProblem):
+    """
+    A MT problem solving a e formulation and a primary/secondary fields decompostion.
+
+    Solves the equation:
+
+
+
+    """
+
+    # From FDEMproblem: Used to project the fields. Currently not used for MTproblem.
+    _fieldType = 'e'
+    _eqLocs    = 'FE'
+    fieldsPair = FieldsMT_3D
+    _sigmaPrimary = None
+
+    def __init__(self, mesh, **kwargs):
+        BaseMTProblem.__init__(self, mesh, **kwargs)
+
+    @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):
+        """
+            Function to get the A matrix.
+
+            :param float freq: Frequency
+            :rtype: scipy.sparse.csr_matrix
+            :return: A
+        """
+        Mmui = self.MfMui
+        Msig = self.MeSigma
+        C = self.mesh.edgeCurl
+
+        return C.T*Mmui*C + 1j*omega(freq)*Msig
+
+    def getADeriv_m(self, freq, u, v, adjoint=False):
+
+        # Nee to account for both the polarizations
+        # dMe_dsig = (self.MeSigmaDeriv( u['e_pxSolution'] ) + self.MeSigmaDeriv( u['e_pySolution'] ))
+        # dMe_dsig = (self.MeSigmaDeriv( u['e_pxSolution'] +  u['e_pySolution'] ))
+
+        # # dMe_dsig = self.MeSigmaDeriv( u )
+        # if adjoint:
+        #     return 1j * omega(freq) * ( dMe_dsig.T * v ) # As in simpegEM
+
+        # return 1j * omega(freq) * ( dMe_dsig * v ) # As in simpegEM
+
+        # This considers both polarizations and returns a nE,2 matrix for each polarization
+        if adjoint:
+            dMe_dsigV = sp.hstack(( self.MeSigmaDeriv( u['e_pxSolution'] ).T, self.MeSigmaDeriv(u['e_pySolution'] ).T ))*v
+        else:
+            # Need a nE,2 matrix to be returned
+            dMe_dsigV = np.hstack(( mkvc(self.MeSigmaDeriv( u['e_pxSolution'] )*v,2), mkvc( self.MeSigmaDeriv(u['e_pySolution'] )*v,2) ))
+        return 1j * omega(freq) * dMe_dsigV
+        # Stacking them
+
+        # if adjoint:
+        #     dMe_dsig = sp.vstack((self.MeSigmaDeriv( u['e_pxSolution'] ), self.MeSigmaDeriv( u['e_pxSolution'] ) )).T
+        #         # self.MeSigmaDeriv(u['e_pySolution'] ).T*v,2) ))
+        # else:
+        #     dMe_dsig = sp.vstack((self.MeSigmaDeriv( u['e_pxSolution'] ), self.MeSigmaDeriv( u['e_pxSolution'] ) ))
+        # return 1j * omega(freq) * dMe_dsig*v
+
+
+    def getRHS(self, freq):
+        """
+            Function to return the right hand side for the system.
+            :param float freq: Frequency
+            :rtype: numpy.ndarray (nE, 2), numpy.ndarray (nE, 2)
+            :return: RHS for both polarizations, primary fields
+        """
+
+        # Get sources for the frequncy(polarizations)
+        Src = self.survey.getSrcByFreq(freq)[0]
+        S_e = Src.S_e(self)
+        return -1j * omega(freq) * S_e
+
+    def getRHSDeriv_m(self, freq, v, adjoint=False):
+        """
+        The derivative of the RHS with respect to sigma
+        """
+
+        Src = self.survey.getSrcByFreq(freq)[0]
+        S_eDeriv = Src.S_eDeriv_m(self, v, adjoint)
+        return -1j * omega(freq) * S_eDeriv
+
+    def fields(self, m):
+        '''
+        Function to calculate all the fields for the model m.
+
+        :param np.ndarray (nC,) m: Conductivity model
+        '''
+        # Set the current model
+        self.curModel = m
+
+        F = FieldsMT_3D(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  = self.getRHS(freq)
+            Ainv = self.Solver(A, **self.solverOpts)
+            e_s = Ainv * rhs
+
+            # Store the fields
+            Src = self.survey.getSrcByFreq(freq)[0]
+            # Store the fieldss
+            F[Src, 'e_pxSolution'] = e_s[:,0]
+            F[Src, 'e_pySolution'] = e_s[:,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 eForm_Tp(BaseMTProblem):
+    """
+    A MT problem solving a e formulation and a total/primary fields decompostion.
+
+    Solves the equation
+    """
+
+    _fieldType = 'e'
+    _eqLocs    = 'FE'
+    fieldsPair = FieldsMT_3D
+
+    # 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):
+        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_3D(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_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/ProblemMT3D/__init__.py b/simpegMT/ProblemMT3D/__init__.py
new file mode 100644
index 00000000..4a98814c
--- /dev/null
+++ b/simpegMT/ProblemMT3D/__init__.py
@@ -0,0 +1 @@
+from Problems import eForm_ps, eForm_Tp
\ No newline at end of file
diff --git a/simpegMT/Sources/backgroundModelSources.py b/simpegMT/Sources/backgroundModelSources.py
index c878baf5..fc18783e 100644
--- a/simpegMT/Sources/backgroundModelSources.py
+++ b/simpegMT/Sources/backgroundModelSources.py
@@ -1,45 +1,178 @@
 import SimPEG as simpeg, numpy as np
 
-def homo1DModelSource(mesh,freq,m_back):
+def homo1DModelSource(mesh,freq,sigma_1d):
     '''
         Function that calculates and return background fields
 
         :param Simpeg mesh object mesh: Holds information on the discretization
         :param float freq: The frequency to solve at
-        :param np.array m_back: Background model of conductivity to base the calculations on.
+        :param np.array sigma_1d: Background model of conductivity to base the calculations on, 1d model.
         :rtype: numpy.ndarray (mesh.nE,2)
         :return: eBG_bp, E fields for the background model at both polarizations.
 
     '''
-
     # import
     from simpegMT.Utils import get1DEfields
     # Get a 1d solution for a halfspace background
-    mesh1d = simpeg.Mesh.TensorMesh([mesh.hz],np.array([mesh.x0[2]]))
-    # Note: Need to conjugate the source field to comply with orientations
-    e0_1d = get1DEfields(mesh1d,mesh.r(m_back,'CC','CC','M')[0,0,:],freq).conj()
-    # Setup x (east) polarization (_x)
-    ex_px = np.zeros(mesh.vnEx,dtype=complex)
-    ey_px = np.zeros((mesh.nEy,1),dtype=complex)
-    ez_px = np.zeros((mesh.nEz,1),dtype=complex)
-    # Assign the source to ex_x
-    for i in np.arange(mesh.vnEx[0]):
-        for j in np.arange(mesh.vnEx[1]):
-            ex_px[i,j,:] = -e0_1d
-    # ex_px[1:-1,1:-1,1:-1] = 0
-    eBG_px = np.vstack((simpeg.Utils.mkvc(ex_px,2),ey_px,ez_px))
-    # Setup y (north) polarization (_py)
-    ex_py = np.zeros((mesh.nEx,1), dtype='complex128')
-    ey_py = np.zeros(mesh.vnEy, dtype='complex128')
-    ez_py = np.zeros((mesh.nEz,1), dtype='complex128')
-    # Assign the source to ey_py
-    
-    for i in np.arange(mesh.vnEy[0]):
-        for j in np.arange(mesh.vnEy[1]):
-            ey_py[i,j,:] = e0_1d
-    # ey_py[1:-1,1:-1,1:-1] = 0
-    eBG_py = np.vstack((ex_py,simpeg.Utils.mkvc(ey_py,2),ez_py))
+    if mesh.dim == 1:
+        mesh1d = mesh
+    elif mesh.dim == 2:
+        mesh1d = simpeg.Mesh.TensorMesh([mesh.hy],np.array([mesh.x0[1]]))
+    elif mesh.dim == 3:
+        mesh1d = simpeg.Mesh.TensorMesh([mesh.hz],np.array([mesh.x0[2]]))
+
+    # # Note: Everything is using e^iwt
+    e0_1d = get1DEfields(mesh1d,sigma_1d,freq)
+    if mesh.dim == 1:
+        eBG_px = simpeg.mkvc(e0_1d,2)
+        eBG_py = -simpeg.mkvc(e0_1d,2) # added a minus to make the results in the correct quadrents.
+    elif mesh.dim == 2:
+        ex_px = np.zeros(mesh.vnEx,dtype=complex)
+        ey_px = np.zeros((mesh.nEy,1),dtype=complex)
+        for i in np.arange(mesh.vnEx[0]):
+            ex_px[i,:] = -e0_1d
+        eBG_px = np.vstack((simpeg.Utils.mkvc(ex_px,2),ey_px))
+        # Setup y (north) polarization (_py)
+        ex_py = np.zeros((mesh.nEx,1), dtype='complex128')
+        ey_py = np.zeros(mesh.vnEy, dtype='complex128')
+        # Assign the source to ey_py
+        for i in np.arange(mesh.vnEy[0]):
+            ey_py[i,:] = e0_1d
+        # ey_py[1:-1,1:-1,1:-1] = 0
+        eBG_py = np.vstack((ex_py,simpeg.Utils.mkvc(ey_py,2),ez_py))
+    elif mesh.dim == 3:
+        # Setup x (east) polarization (_x)
+        ex_px = np.zeros(mesh.vnEx,dtype=complex)
+        ey_px = np.zeros((mesh.nEy,1),dtype=complex)
+        ez_px = np.zeros((mesh.nEz,1),dtype=complex)
+        # Assign the source to ex_x
+        for i in np.arange(mesh.vnEx[0]):
+            for j in np.arange(mesh.vnEx[1]):
+                ex_px[i,j,:] = -e0_1d
+        eBG_px = np.vstack((simpeg.Utils.mkvc(ex_px,2),ey_px,ez_px))
+        # Setup y (north) polarization (_py)
+        ex_py = np.zeros((mesh.nEx,1), dtype='complex128')
+        ey_py = np.zeros(mesh.vnEy, dtype='complex128')
+        ez_py = np.zeros((mesh.nEz,1), dtype='complex128')
+        # Assign the source to ey_py
+        for i in np.arange(mesh.vnEy[0]):
+            for j in np.arange(mesh.vnEy[1]):
+                ey_py[i,j,:] = e0_1d
+        # ey_py[1:-1,1:-1,1:-1] = 0
+        eBG_py = np.vstack((ex_py,simpeg.Utils.mkvc(ey_py,2),ez_py))
 
     # Return the electric fields
     eBG_bp = np.hstack((eBG_px,eBG_py))
-    return eBG_bp
\ No newline at end of file
+    return eBG_bp
+
+def analytic1DModelSource(mesh,freq,sigma_1d):
+    '''
+        Function that calculates and return background fields
+
+        :param Simpeg mesh object mesh: Holds information on the discretization
+        :param float freq: The frequency to solve at
+        :param np.array sigma_1d: Background model of conductivity to base the calculations on, 1d model.
+        :rtype: numpy.ndarray (mesh.nE,2)
+        :return: eBG_bp, E fields for the background model at both polarizations.
+
+    '''
+    # import
+    from simpegMT.Utils import getEHfields
+    # Get a 1d solution for a halfspace background
+    if mesh.dim == 1:
+        mesh1d = mesh
+    elif mesh.dim == 2:
+        mesh1d = simpeg.Mesh.TensorMesh([mesh.hy],np.array([mesh.x0[1]]))
+    elif mesh.dim == 3:
+        mesh1d = simpeg.Mesh.TensorMesh([mesh.hz],np.array([mesh.x0[2]]))
+
+    # # Note: Everything is using e^iwt
+    Eu, Ed, _, _ = getEHfields(mesh1d,sigma_1d,freq,mesh.vectorNz)
+    # Make the fields into a dictionary of location and the fields
+    e0_1d = Eu+Ed
+    E1dFieldDict = dict(zip(mesh.vectorNz,e0_1d))
+    if mesh.dim == 1:
+        eBG_px = simpeg.mkvc(e0_1d,2)
+        eBG_py = -simpeg.mkvc(e0_1d,2) # added a minus to make the results in the correct quadrents.
+    elif mesh.dim == 2:
+        ex_px = np.zeros(mesh.vnEx,dtype=complex)
+        ey_px = np.zeros((mesh.nEy,1),dtype=complex)
+        for i in np.arange(mesh.vnEx[0]):
+            ex_px[i,:] = -e0_1d
+        eBG_px = np.vstack((simpeg.Utils.mkvc(ex_px,2),ey_px))
+        # Setup y (north) polarization (_py)
+        ex_py = np.zeros((mesh.nEx,1), dtype='complex128')
+        ey_py = np.zeros(mesh.vnEy, dtype='complex128')
+        # Assign the source to ey_py
+        for i in np.arange(mesh.vnEy[0]):
+            ey_py[i,:] = e0_1d
+        # ey_py[1:-1,1:-1,1:-1] = 0
+        eBG_py = np.vstack((ex_py,simpeg.Utils.mkvc(ey_py,2),ez_py))
+    elif mesh.dim == 3:
+        # Setup x (east) polarization (_x)
+        ex_px = -np.array([E1dFieldDict[i] for i in mesh.gridEx[:,2]]).reshape(-1,1)
+        ey_px = np.zeros((mesh.nEy,1),dtype=complex)
+        ez_px = np.zeros((mesh.nEz,1),dtype=complex)
+        # Construct the full fields
+        eBG_px = np.vstack((ex_px,ey_px,ez_px))
+        # Setup y (north) polarization (_py)
+        ex_py = np.zeros((mesh.nEx,1), dtype='complex128')
+        ey_py = np.array([E1dFieldDict[i] for i in mesh.gridEy[:,2]]).reshape(-1,1)
+        ez_py = np.zeros((mesh.nEz,1), dtype='complex128')
+        # Construct the full fields
+        eBG_py = np.vstack((ex_py,simpeg.Utils.mkvc(ey_py,2),ez_py))
+
+    # Return the electric fields
+    eBG_bp = np.hstack((eBG_px,eBG_py))
+    return eBG_bp
+
+# def homo3DModelSource(mesh,model,freq):
+#     '''
+#         Function that estimates 1D analytic background fields from a 3D model.
+
+#         :param Simpeg mesh object mesh: Holds information on the discretization
+#         :param float freq: The frequency to solve at
+#         :param np.array sigma_1d: Background model of conductivity to base the calculations on, 1d model.
+#         :rtype: numpy.ndarray (mesh.nE,2)
+#         :return: eBG_bp, E fields for the background model at both polarizations.
+
+#     '''
+
+#     if mesh.dim < 3:
+#         raise IOError('Input mesh has to have 3 dimensions.')
+
+
+#     # Get the locations
+#     a = mesh.gridCC[:,0:2].copy()
+#     unixy = np.unique(a.view(a.dtype.descr * a.shape[1])).view(float).reshape(-1,2)
+#     uniz = np.unique(mesh.gridCC[:,2])
+#     # # Note: Everything is using e^iwt
+#     # Need to loop thourgh the xy locations, assess the model and calculate the fields at the phusdo cell centers.
+#     # Then interpolate the cc fields to the edges.
+
+#     e0_1d = get1DEfields(mesh1d,sigma_1d,freq)
+
+#     elif mesh.dim == 3:
+#         # Setup x (east) polarization (_x)
+#         ex_px = np.zeros(mesh.vnEx,dtype=complex)
+#         ey_px = np.zeros((mesh.nEy,1),dtype=complex)
+#         ez_px = np.zeros((mesh.nEz,1),dtype=complex)
+#         # Assign the source to ex_x
+#         for i in np.arange(mesh.vnEx[0]):
+#             for j in np.arange(mesh.vnEx[1]):
+#                 ex_px[i,j,:] = -e0_1d
+#         eBG_px = np.vstack((simpeg.Utils.mkvc(ex_px,2),ey_px,ez_px))
+#         # Setup y (north) polarization (_py)
+#         ex_py = np.zeros((mesh.nEx,1), dtype='complex128')
+#         ey_py = np.zeros(mesh.vnEy, dtype='complex128')
+#         ez_py = np.zeros((mesh.nEz,1), dtype='complex128')
+#         # Assign the source to ey_py
+#         for i in np.arange(mesh.vnEy[0]):
+#             for j in np.arange(mesh.vnEy[1]):
+#                 ey_py[i,j,:] = e0_1d
+#         # ey_py[1:-1,1:-1,1:-1] = 0
+#         eBG_py = np.vstack((ex_py,simpeg.Utils.mkvc(ey_py,2),ez_py))
+
+#     # Return the electric fields
+#     eBG_bp = np.hstack((eBG_px,eBG_py))
+#     return eBG_bp
\ No newline at end of file
diff --git a/simpegMT/SurveyMT.py b/simpegMT/SurveyMT.py
index 95ee6db3..33c28d71 100644
--- a/simpegMT/SurveyMT.py
+++ b/simpegMT/SurveyMT.py
@@ -1,42 +1,48 @@
-from SimPEG import Survey, Utils, Problem, np, sp, mkvc
+from SimPEG import Survey, Utils, Problem, Maps, np, sp, mkvc
+from simpegEM.FDEM.SurveyFDEM import SrcFDEM
+from simpegEM.Utils.EMUtils import omega
 from scipy.constants import mu_0
 import sys
 from numpy.lib import recfunctions as recFunc
+from DataMT import DataMT
+from simpegMT.Sources import homo1DModelSource
+#################
+### Receivers ###
+#################
 
 class RxMT(Survey.BaseRx):
 
     knownRxTypes = {
-                    'zxxr':[['e', 'Ex'],['b','Fx'], 'real'],
-                    'zxyr':[['e', 'Ex'],['b','Fy'], 'real'],
-                    'zyxr':[['e', 'Ey'],['b','Fx'], 'real'],
-                    'zyyr':[['e', 'Ey'],['b','Fy'], 'real'],
-                    'zxxi':[['e', 'Ex'],['b','Fx'], 'imag'],
-                    'zxyi':[['e', 'Ex'],['b','Fy'], 'imag'],
-                    'zyxi':[['e', 'Ey'],['b','Fx'], 'imag'],
-                    'zyyi':[['e', 'Ey'],['b','Fy'], 'imag'],
-
-                    #TODO: Add tipper fractions as well. Bz/B(x|y)
-                    # 'exi':['e', 'Ex', 'imag'],
-                    # 'eyi':['e', 'Ey', 'imag'],
-                    # 'ezi':['e', 'Ez', 'imag'],
-
-                    # 'bxr':['b', 'Fx', 'real'],
-                    # 'byr':['b', 'Fy', 'real'],
-                    # 'bzr':['b', 'Fz', 'real'],
-                    # 'bxi':['b', 'Fx', 'imag'],
-                    # 'byi':['b', 'Fy', 'imag'],
-                    # 'bzi':['b', 'Fz', 'imag'],
+                    # 3D impedance
+                    'zxxr':['Z3D', 'real'],
+                    'zxyr':['Z3D', 'real'],
+                    'zyxr':['Z3D', 'real'],
+                    'zyyr':['Z3D', 'real'],
+                    'zxxi':['Z3D', 'imag'],
+                    'zxyi':['Z3D', 'imag'],
+                    'zyxi':['Z3D', 'imag'],
+                    'zyyi':['Z3D', 'imag'],
+                    # 2D impedance
+                    # TODO:
+                    # 1D impedance
+                    'z1dr':['Z1D', 'real'],
+                    'z1di':['Z1D', 'imag'],
+                    # Tipper
+                    'tzxr':['T3D','real'],
+                    'tzxi':['T3D','imag'],
+                    'tzyr':['T3D','real'],
+                    'tzyi':['T3D','imag']
                    }
     # TODO: Have locs as single or double coordinates for both or numerator and denominator separately, respectively.
     def __init__(self, locs, rxType):
         Survey.BaseRx.__init__(self, locs, rxType)
 
     @property
-    def projField(self,fracPos):
+    def projField(self):
         """
         Field Type projection (e.g. e b ...)
         :param str fracPos: Position of the field in the data ratio
-        
+
         """
         if 'numerator' in fracPos:
             return self.knownRxTypes[self.rxType][0][0]
@@ -46,11 +52,11 @@ class RxMT(Survey.BaseRx):
             raise Exception('{s} is an unknown option. Use numerator or denominator.')
 
     @property
-    def projGLoc(self,fracPos):
+    def projGLoc(self):
         """
         Grid Location projection (e.g. Ex Fy ...)
         :param str fracPos: Position of the field in the data ratio
-        
+
         """
         if 'numerator' in fracPos:
             return self.knownRxTypes[self.rxType][0][1]
@@ -58,105 +64,454 @@ class RxMT(Survey.BaseRx):
             return self.knownRxTypes[self.rxType][0][1]
         else:
             raise Exception('{s} is an unknown option. Use numerator or denominator.')
+    @property
+    def projType(self):
+        """
+        Receiver type for projection.
+
+        """
+        return self.knownRxTypes[self.rxType][0]
 
     @property
     def projComp(self):
         """Component projection (real/imag)"""
-        return self.knownRxTypes[self.rxType][2]
+        return self.knownRxTypes[self.rxType][1]
 
-    def projectFields(self, src, mesh, u):
+    def projectFields(self, src, mesh, f):
         '''
-        Project the fields and return the 
+        Project the fields and return the correct data.
         '''
 
-        # Get the projection
-        # Pex = self.getP(mesh,'Ex')
-        # Pey = self.getP(mesh,'Ey')
-        # Pbx = self.getP(mesh,'Fx')
-        # Pby = self.getP(mesh,'Fy')
-        Pex = mesh.getInterpolationMat(self.locs,'Ex')
-        Pey = mesh.getInterpolationMat(self.locs,'Ey')
-        Pbx = mesh.getInterpolationMat(self.locs,'Fx')
-        Pby = mesh.getInterpolationMat(self.locs,'Fy')
-        # Get the fields at location
-        ex_px = Pex*u[src,'e_px']
-        ey_px = Pey*u[src,'e_px']
-        ex_py = Pex*u[src,'e_py']
-        ey_py = Pey*u[src,'e_py']
-        hx_px = Pbx*u[src,'b_px']/mu_0
-        hy_px = Pby*u[src,'b_px']/mu_0
-        hx_py = Pbx*u[src,'b_py']/mu_0
-        hy_py = Pby*u[src,'b_py']/mu_0
-        if 'zxx' in self.rxType:
-            f_part_complex = (ex_px*hy_py - ex_py*hy_px)/(hx_px*hy_py - hx_py*hy_px)
-        elif 'zxy' in self.rxType:
-            f_part_complex  = (-ex_px*hx_py + ex_py*hx_px)/(hx_px*hy_py - hx_py*hy_px)
-        elif 'zyx' in self.rxType:
-            f_part_complex  = (ey_px*hy_py - ey_py*hy_px)/(hx_px*hy_py - hx_py*hy_px)
-        elif 'zyy' in self.rxType:
-            f_part_complex  = (-ey_px*hx_py + ey_py*hx_px)/(hx_px*hy_py - hx_py*hy_px)
+        if self.projType is 'Z1D':
+            Pex = mesh.getInterpolationMat(self.locs[:,-1],'Fx')
+            Pbx = mesh.getInterpolationMat(self.locs[:,-1],'Ex')
+            ex = Pex*mkvc(f[src,'e_1d'],2)
+            bx = Pbx*mkvc(f[src,'b_1d'],2)/mu_0
+            # Note: Has a minus sign in front, to comply with quadrant calculations.
+            # Can be derived from zyx case for the 3D case.
+            f_part_complex = -ex/bx
+        # elif self.projType is 'Z2D':
+        elif self.projType is 'Z3D':
+            if self.locs.ndim == 3:
+                eFLocs = self.locs[:,:,0]
+                bFLocs = self.locs[:,:,1]
+            else:
+                eFLocs = self.locs
+                bFLocs = self.locs
+            # Get the projection
+            Pex = mesh.getInterpolationMat(eFLocs,'Ex')
+            Pey = mesh.getInterpolationMat(eFLocs,'Ey')
+            Pbx = mesh.getInterpolationMat(bFLocs,'Fx')
+            Pby = mesh.getInterpolationMat(bFLocs,'Fy')
+            # Get the fields at location
+            # px: x-polaration and py: y-polaration.
+            ex_px = Pex*f[src,'e_px']
+            ey_px = Pey*f[src,'e_px']
+            ex_py = Pex*f[src,'e_py']
+            ey_py = Pey*f[src,'e_py']
+            hx_px = Pbx*f[src,'b_px']/mu_0
+            hy_px = Pby*f[src,'b_px']/mu_0
+            hx_py = Pbx*f[src,'b_py']/mu_0
+            hy_py = Pby*f[src,'b_py']/mu_0
+            # Make the complex data
+            if 'zxx' in self.rxType:
+                f_part_complex = ( ex_px*hy_py - ex_py*hy_px)/(hx_px*hy_py - hx_py*hy_px)
+            elif 'zxy' in self.rxType:
+                f_part_complex = (-ex_px*hx_py + ex_py*hx_px)/(hx_px*hy_py - hx_py*hy_px)
+            elif 'zyx' in self.rxType:
+                f_part_complex = ( ey_px*hy_py - ey_py*hy_px)/(hx_px*hy_py - hx_py*hy_px)
+            elif 'zyy' in self.rxType:
+                f_part_complex = (-ey_px*hx_py + ey_py*hx_px)/(hx_px*hy_py - hx_py*hy_px)
+        elif self.projType is 'T3D':
+            if self.locs.ndim == 3:
+                horLoc = self.locs[:,:,0]
+                vertLoc = self.locs[:,:,1]
+            else:
+                horLoc = self.locs
+                vertLoc = self.locs
+            Pbx = mesh.getInterpolationMat(horLoc,'Fx')
+            Pby = mesh.getInterpolationMat(horLoc,'Fy')
+            Pbz = mesh.getInterpolationMat(vertLoc,'Fz')
+            bx_px = Pbx*f[src,'b_px']
+            by_px = Pby*f[src,'b_px']
+            bz_px = Pbz*f[src,'b_px']
+            bx_py = Pbx*f[src,'b_py']
+            by_py = Pby*f[src,'b_py']
+            bz_py = Pbz*f[src,'b_py']
+            if 'tzx' in self.rxType:
+                f_part_complex = (- by_px*bz_py + by_py*bz_px)/(bx_px*by_py - bx_py*by_px)
+            if 'tzy' in self.rxType:
+                f_part_complex = (  bx_px*bz_py - bx_py*bz_px)/(bx_px*by_py - bx_py*by_px)
 
-        # P_num = self.getP(mesh,self.projGLoc('numerator'))
-        # u_num_complex = u[src, self.projField('numerator')]
-        # # Get the denominator information
-        # P_den = self.getP(mesh,self.projGLoc('denominator'))
-        # u_den_complex = u[src, self.projField('denominator')]
-        # # Calculate the fraction
-        # f_part_complex = (P_num*u_num_complex)/(P_den*u_den_complex)
-        # get the real or imag component
+        else:
+            NotImplementedError('Projection of {:s} receiver type is not implemented.'.format(self.rxType))
+        # Get the real or imag component
         real_or_imag = self.projComp
         f_part = getattr(f_part_complex, real_or_imag)
+        # print f_part
         return f_part
 
-    def projectFieldsDeriv(self, src, mesh, u, v, adjoint=False):
-        P = self.getP(mesh)
+    def projectFieldsDeriv(self, src, mesh, f, v, adjoint=False):
+        """
+        The derivative of the projection wrt u
+
+        :param MTsrc src: MT source
+        :param TensorMesh mesh: Mesh defining the topology of the problem
+        :param MTfields f: MT fields object of the source
+        :param numpy.ndarray v: Random vector of size
+        """
+
+        real_or_imag = self.projComp
 
         if not adjoint:
-            Pv_complex = P * v
-            real_or_imag = self.projComp
-            Pv = getattr(Pv_complex, real_or_imag)
-        elif adjoint:
-            Pv_real = P.T * v
+            if self.projType is 'Z1D':
+                Pex = mesh.getInterpolationMat(self.locs[:,-1],'Fx')
+                Pbx = mesh.getInterpolationMat(self.locs[:,-1],'Ex')
+                # ex = Pex*mkvc(f[src,'e_1d'],2)
+                # bx = Pbx*mkvc(f[src,'b_1d'],2)/mu_0
+                dP_de = -mkvc(Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0))*(Pex*v),2)
+                dP_db = mkvc( Utils.sdiag(Pex*mkvc(f[src,'e_1d'],2))*(Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0)).T*Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0)))*(Pbx*f._bDeriv_u(src,v)/mu_0),2)
+                PDeriv_complex = np.sum(np.hstack((dP_de,dP_db)),1)
+            elif self.projType is 'Z2D':
+                raise NotImplementedError('Has not been implement for 2D impedance tensor')
+            elif self.projType is 'Z3D':
+                if self.locs.ndim == 3:
+                    eFLocs = self.locs[:,:,0]
+                    bFLocs = self.locs[:,:,1]
+                else:
+                    eFLocs = self.locs
+                    bFLocs = self.locs
+                # Get the projection
+                Pex = mesh.getInterpolationMat(eFLocs,'Ex')
+                Pey = mesh.getInterpolationMat(eFLocs,'Ey')
+                Pbx = mesh.getInterpolationMat(bFLocs,'Fx')
+                Pby = mesh.getInterpolationMat(bFLocs,'Fy')
+                # Get the fields at location
+                # px: x-polaration and py: y-polaration.
+                ex_px = Pex*f[src,'e_px']
+                ey_px = Pey*f[src,'e_px']
+                ex_py = Pex*f[src,'e_py']
+                ey_py = Pey*f[src,'e_py']
+                hx_px = Pbx*f[src,'b_px']/mu_0
+                hy_px = Pby*f[src,'b_px']/mu_0
+                hx_py = Pbx*f[src,'b_py']/mu_0
+                hy_py = Pby*f[src,'b_py']/mu_0
+                # Derivatives as lambda functions
+                # The size of the diratives should be nD,nU
+                ex_px_u = lambda vec: Pex*f._e_pxDeriv_u(src,vec)
+                ey_px_u = lambda vec: Pey*f._e_pxDeriv_u(src,vec)
+                ex_py_u = lambda vec: Pex*f._e_pyDeriv_u(src,vec)
+                ey_py_u = lambda vec: Pey*f._e_pyDeriv_u(src,vec)
+                # NOTE: Think b_p?Deriv_u should return a 2*nF size matrix
+                hx_px_u = lambda vec: Pbx*f._b_pxDeriv_u(src,vec)/mu_0
+                hy_px_u = lambda vec: Pby*f._b_pxDeriv_u(src,vec)/mu_0
+                hx_py_u = lambda vec: Pbx*f._b_pyDeriv_u(src,vec)/mu_0
+                hy_py_u = lambda vec: Pby*f._b_pyDeriv_u(src,vec)/mu_0
+                # Update the input vector
+                sDiag = lambda t: Utils.sdiag(mkvc(t,2))
+                # Define the components of the derivative
+                Hd = sDiag(1./(sDiag(hx_px)*hy_py - sDiag(hx_py)*hy_px))
+                Hd_uV = sDiag(hy_py)*hx_px_u(v) + sDiag(hx_px)*hy_py_u(v) - sDiag(hx_py)*hy_px_u(v) - sDiag(hy_px)*hx_py_u(v)
+                # Calculate components
+                if 'zxx' in self.rxType:
+                    Zij = sDiag(Hd*( sDiag(ex_px)*hy_py - sDiag(ex_py)*hy_px ))
+                    ZijN_uV =  sDiag(hy_py)*ex_px_u(v) + sDiag(ex_px)*hy_py_u(v) - sDiag(ex_py)*hy_px_u(v) - sDiag(hy_px)*ex_py_u(v)
+                elif 'zxy' in self.rxType:
+                    Zij = sDiag(Hd*(-sDiag(ex_px)*hx_py + sDiag(ex_py)*hx_px ))
+                    ZijN_uV = -sDiag(hx_py)*ex_px_u(v) - sDiag(ex_px)*hx_py_u(v) + sDiag(ex_py)*hx_px_u(v) + sDiag(hx_px)*ex_py_u(v)
+                elif 'zyx' in self.rxType:
+                    Zij = sDiag(Hd*( sDiag(ey_px)*hy_py - sDiag(ey_py)*hy_px ))
+                    ZijN_uV =  sDiag(hy_py)*ey_px_u(v) + sDiag(ey_px)*hy_py_u(v) - sDiag(ey_py)*hy_px_u(v) - sDiag(hy_px)*ey_py_u(v)
+                elif 'zyy' in self.rxType:
+                    Zij = sDiag(Hd*(-sDiag(ey_px)*hx_py + sDiag(ey_py)*hx_px ))
+                    ZijN_uV = -sDiag(hx_py)*ey_px_u(v) - sDiag(ey_px)*hx_py_u(v) + sDiag(ey_py)*hx_px_u(v) + sDiag(hx_px)*ey_py_u(v)
 
-            real_or_imag = self.projComp
+                # Calculate the complex derivative
+                PDeriv_complex = Hd * (ZijN_uV - Zij * Hd_uV )
+            # Extract the real number for the real/imag components.
+            Pv = np.array(getattr(PDeriv_complex, real_or_imag))
+        elif adjoint:
+            # Note: The v vector is real and the return should be complex
+            if self.projType is 'Z1D':
+                Pex = mesh.getInterpolationMat(self.locs[:,-1],'Fx')
+                Pbx = mesh.getInterpolationMat(self.locs[:,-1],'Ex')
+                # ex = Pex*mkvc(f[src,'e_1d'],2)
+                # bx = Pbx*mkvc(f[src,'b_1d'],2)/mu_0
+                dP_deTv = -mkvc(Pex.T*Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0)).T*v,2)
+                db_duv = Pbx.T/mu_0*Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0))*(Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0))).T*Utils.sdiag(Pex*mkvc(f[src,'e_1d'],2)).T*v
+                dP_dbTv = mkvc(f._bDeriv_u(src,db_duv,adjoint=True),2)
+                PDeriv_real = np.sum(np.hstack((dP_deTv,dP_dbTv)),1)
+            elif self.projType is 'Z2D':
+                raise NotImplementedError('Has not be implement for 2D impedance tensor')
+            elif self.projType is 'Z3D':
+                if self.locs.ndim == 3:
+                    eFLocs = self.locs[:,:,0]
+                    bFLocs = self.locs[:,:,1]
+                else:
+                    eFLocs = self.locs
+                    bFLocs = self.locs
+                # Get the projection
+                Pex = mesh.getInterpolationMat(eFLocs,'Ex')
+                Pey = mesh.getInterpolationMat(eFLocs,'Ey')
+                Pbx = mesh.getInterpolationMat(bFLocs,'Fx')
+                Pby = mesh.getInterpolationMat(bFLocs,'Fy')
+                # Get the fields at location
+                # px: x-polaration and py: y-polaration.
+                aex_px = mkvc(mkvc(f[src,'e_px'],2).T*Pex.T)
+                aey_px = mkvc(mkvc(f[src,'e_px'],2).T*Pey.T)
+                aex_py = mkvc(mkvc(f[src,'e_py'],2).T*Pex.T)
+                aey_py = mkvc(mkvc(f[src,'e_py'],2).T*Pey.T)
+                ahx_px = mkvc(mkvc(f[src,'b_px'],2).T/mu_0*Pbx.T)
+                ahy_px = mkvc(mkvc(f[src,'b_px'],2).T/mu_0*Pby.T)
+                ahx_py = mkvc(mkvc(f[src,'b_py'],2).T/mu_0*Pbx.T)
+                ahy_py = mkvc(mkvc(f[src,'b_py'],2).T/mu_0*Pby.T)
+                # Derivatives as lambda functions
+                aex_px_u = lambda vec: f._e_pxDeriv_u(src,Pex.T*vec,adjoint=True)
+                aey_px_u = lambda vec: f._e_pxDeriv_u(src,Pey.T*vec,adjoint=True)
+                aex_py_u = lambda vec: f._e_pyDeriv_u(src,Pex.T*vec,adjoint=True)
+                aey_py_u = lambda vec: f._e_pyDeriv_u(src,Pey.T*vec,adjoint=True)
+                ahx_px_u = lambda vec: f._b_pxDeriv_u(src,Pbx.T*vec,adjoint=True)/mu_0
+                ahy_px_u = lambda vec: f._b_pxDeriv_u(src,Pby.T*vec,adjoint=True)/mu_0
+                ahx_py_u = lambda vec: f._b_pyDeriv_u(src,Pbx.T*vec,adjoint=True)/mu_0
+                ahy_py_u = lambda vec: f._b_pyDeriv_u(src,Pby.T*vec,adjoint=True)/mu_0
+
+                # Update the input vector
+                # Define shortcuts
+                sDiag = lambda t: Utils.sdiag(mkvc(t,2))
+                sVec = lambda t: Utils.sp.csr_matrix(mkvc(t,2))
+                # Define the components of the derivative
+                aHd = sDiag(1./(sDiag(ahx_px)*ahy_py - sDiag(ahx_py)*ahy_px))
+                aHd_uV = lambda x: ahx_px_u(sDiag(ahy_py)*x) + ahx_px_u(sDiag(ahy_py)*x) - ahy_px_u(sDiag(ahx_py)*x) - ahx_py_u(sDiag(ahy_px)*x)
+                # Need to fix this to reflect the adjoint
+                if 'zxx' in self.rxType:
+                    Zij = sDiag(aHd*( sDiag(ahy_py)*aex_px - sDiag(ahy_px)*aex_py))
+                    ZijN_uV = lambda x: aex_px_u(sDiag(ahy_py)*x) + ahy_py_u(sDiag(aex_px)*x) - ahy_px_u(sDiag(aex_py)*x) - aex_py_u(sDiag(ahy_px)*x)
+                elif 'zxy' in self.rxType:
+                    Zij = sDiag(aHd*(-sDiag(ahx_py)*aex_px + sDiag(ahx_px)*aex_py))
+                    ZijN_uV = lambda x:-aex_px_u(sDiag(ahx_py)*x) - ahx_py_u(sDiag(aex_px)*x) + ahx_px_u(sDiag(aex_py)*x) + aex_py_u(sDiag(ahx_px)*x)
+                elif 'zyx' in self.rxType:
+                    Zij = sDiag(aHd*( sDiag(ahy_py)*aey_px - sDiag(ahy_px)*aey_py))
+                    ZijN_uV = lambda x: aey_px_u(sDiag(ahy_py)*x) + ahy_py_u(sDiag(aey_px)*x) - ahy_px_u(sDiag(aey_py)*x) - aey_py_u(sDiag(ahy_px)*x)
+                elif 'zyy' in self.rxType:
+                    Zij = sDiag(aHd*(-sDiag(ahx_py)*aey_px + sDiag(ahx_px)*aey_py))
+                    ZijN_uV = lambda x:-aey_px_u(sDiag(ahx_py)*x) - ahx_py_u(sDiag(aey_px)*x) + ahx_px_u(sDiag(aey_py)*x) + aey_py_u(sDiag(ahx_px)*x)
+
+                # Calculate the complex derivative
+                PDeriv_real = ZijN_uV(aHd*v) - aHd_uV(Zij.T*aHd*v)#
+                # NOTE: Need to reshape the output to go from 2*nU array to a (nU,2) matrix for each polarization
+                # PDeriv_real = np.hstack((mkvc(PDeriv_real[:len(PDeriv_real)/2],2),mkvc(PDeriv_real[len(PDeriv_real)/2::],2)))
+                PDeriv_real = PDeriv_real.reshape((2,mesh.nE)).T
+            # Extract the data
             if real_or_imag == 'imag':
-                Pv = 1j*Pv_real
+                Pv = 1j*PDeriv_real
             elif real_or_imag == 'real':
-                Pv = Pv_real.astype(complex)
-            else:
-                raise NotImplementedError('must be real or imag')
+                Pv = PDeriv_real.astype(complex)
+
 
         return Pv
 
 
-# Call this Source or polarization or something...?
-# Note: Might need to add tests to make sure that both polarization have the same rxList. 
-class srcMT(Survey.BaseSrc):
+###############
+### Sources ###
+###############
+
+class srcMT(SrcFDEM): # Survey.BaseSrc):
     '''
-    Sources for the MT problem. 
+    Sources for the MT problem.
     Use the SimPEG BaseSrc, since the source fields share properties with the transmitters.
 
     :param float freq: The frequency of the source
     :param list rxList: A list of receivers associated with the source
-    :param str srcPol: The polarization of the source
     '''
 
     freq = None #: Frequency (float)
-
     rxPair = RxMT
 
-    def __init__(self, freq, rxList, srcPol = 'pol_xy'): # remove rxType? hardcode to one thing. always polarizations
+
+    def __init__(self, rxList, freq):
+
         self.freq = float(freq)
-        Survey.BaseSrc.__init__(self, None, srcPol, rxList)
+        Survey.BaseSrc.__init__(self, rxList)
+
+# 1D sources
+class srcMT_polxy_1DhomotD(srcMT):
+    """
+    MT source for both polarizations (x and y) for the total Domain. It calculates fields calculated based on conditions on the boundary of the domain.
+    """
+    def __init__(self, rxList, freq):
+        srcMT.__init__(self, rxList, freq)
 
 
-
-class FieldsMT(Problem.Fields):
-    """Fancy Field Storage for a MT survey."""
-    knownFields = {'b_px': 'F','b_py': 'F', 'e_px': 'E','e_py': 'E'}
-    dtype = complex
+    # TODO: need to add the  primary fields calc and source terms into the problem.
 
 
+# Need to implement such that it works for all dims.
+class srcMT_polxy_1Dprimary(srcMT):
+    """
+    MT source for both polarizations (x and y) given a 1D primary models. It assigns fields calculated from the 1D model
+    as fields in the full space of the problem.
+    """
+    def __init__(self, rxList, freq):
+        # assert mkvc(self.mesh.hz.shape,1) == mkvc(sigma1d.shape,1),'The number of values in the 1D background model does not match the number of vertical cells (hz).'
+        self.sigma1d = None
+        srcMT.__init__(self, rxList, freq)
+        # Hidden property of the ePrimary
+        self._ePrimary = None
+
+    def ePrimary(self,problem):
+        # Get primary fields for both polarizations
+        if self.sigma1d is None:
+            # Set the sigma1d as the 1st column in the background model
+            if len(problem._sigmaPrimary) == problem.mesh.nC:
+                if problem.mesh.dim == 1:
+                    self.sigma1d = problem.mesh.r(problem._sigmaPrimary,'CC','CC','M')[:]
+                elif problem.mesh.dim == 3:
+                    self.sigma1d = problem.mesh.r(problem._sigmaPrimary,'CC','CC','M')[0,0,:]
+            # Or as the 1D model that matches the vertical cell number
+            elif len(problem._sigmaPrimary) == problem.mesh.nCz:
+                self.sigma1d = problem._sigmaPrimary
+
+        if self._ePrimary is None:
+            self._ePrimary = homo1DModelSource(problem.mesh,self.freq,self.sigma1d)
+        return self._ePrimary
+
+    def bPrimary(self,problem):
+        # Project ePrimary to bPrimary
+        # Satisfies the primary(background) field conditions
+        if problem.mesh.dim == 1:
+            C = problem.mesh.nodalGrad
+        elif problem.mesh.dim == 3:
+            C = problem.mesh.edgeCurl
+        bBG_bp = (- C * self.ePrimary(problem) )*(1/( 1j*omega(self.freq) ))
+        return bBG_bp
+
+    def S_e(self,problem):
+        """
+        Get the electrical field source
+        """
+        e_p = self.ePrimary(problem)
+        Map_sigma_p = Maps.Vertical1DMap(problem.mesh)
+        sigma_p = Map_sigma_p._transform(self.sigma1d)
+        # Make mass matrix
+        # Note: M(sig) - M(sig_p) = M(sig - sig_p)
+        # Need to deal with the edge/face discrepencies between 1d/2d/3d
+        if problem.mesh.dim == 1:
+            Mesigma = problem.mesh.getFaceInnerProduct(problem.curModel.sigma)
+            Mesigma_p = problem.mesh.getFaceInnerProduct(sigma_p)
+        if problem.mesh.dim == 2:
+            pass
+        if problem.mesh.dim == 3:
+            Mesigma = problem.MeSigma
+            Mesigma_p = problem.mesh.getEdgeInnerProduct(sigma_p)
+        return (Mesigma - Mesigma_p) * e_p
+
+    def S_eDeriv_m(self, problem, v, adjoint = False):
+        '''
+        Get the derivative of S_e wrt to sigma (m)
+        '''
+        # Need to deal with
+        if problem.mesh.dim == 1:
+            # Need to use the faceInnerProduct
+            MsigmaDeriv = problem.mesh.getFaceInnerProductDeriv(problem.curModel.sigma)(self.ePrimary(problem)[:,1]) * problem.curModel.sigmaDeriv
+            # MsigmaDeriv = ( MsigmaDeriv * MsigmaDeriv.T)**2
+        if problem.mesh.dim == 2:
+            pass
+        if problem.mesh.dim == 3:
+            # Need to take the derivative of both u_px and u_py
+            ePri = self.ePrimary(problem)
+            # MsigmaDeriv = problem.MeSigmaDeriv(ePri[:,0]) + problem.MeSigmaDeriv(ePri[:,1])
+            # MsigmaDeriv = problem.MeSigmaDeriv(np.sum(ePri,axis=1))
+            if adjoint:
+                return sp.hstack(( problem.MeSigmaDeriv(ePri[:,0]).T, problem.MeSigmaDeriv(ePri[:,1]).T ))*v
+            else:
+                return np.hstack(( mkvc(problem.MeSigmaDeriv(ePri[:,0]) * v,2), mkvc(problem.MeSigmaDeriv(ePri[:,1])*v,2) ))
+        if adjoint:
+            #
+            return MsigmaDeriv.T * v
+        else:
+            # v should be nC size
+            return MsigmaDeriv * v
+
+class srcMT_polxy_3Dprimary(srcMT):
+    """
+    MT source for both polarizations (x and y) given a 3D primary model. It assigns fields calculated from the 1D model
+    as fields in the full space of the problem.
+    """
+    def __init__(self, rxList, freq):
+        # assert mkvc(self.mesh.hz.shape,1) == mkvc(sigma1d.shape,1),'The number of values in the 1D background model does not match the number of vertical cells (hz).'
+        self.sigmaPrimary = None
+        srcMT.__init__(self, rxList, freq)
+        # Hidden property of the ePrimary
+        self._ePrimary = None
+
+    def ePrimary(self,problem):
+        # Get primary fields for both polarizations
+        self.sigmaPrimary = problem._sigmaPrimary
+
+        if self._ePrimary is None:
+            self._ePrimary = homo3DModelSource(problem.mesh,self.sigmaPrimary,self.freq)
+        return self._ePrimary
+
+    def bPrimary(self,problem):
+        # Project ePrimary to bPrimary
+        # Satisfies the primary(background) field conditions
+        if problem.mesh.dim == 1:
+            C = problem.mesh.nodalGrad
+        elif problem.mesh.dim == 3:
+            C = problem.mesh.edgeCurl
+        bBG_bp = (- C * self.ePrimary(problem) )*(1/( 1j*omega(self.freq) ))
+        return bBG_bp
+
+    def S_e(self,problem):
+        """
+        Get the electrical field source
+        """
+        e_p = self.ePrimary(problem)
+        Map_sigma_p = Maps.Vertical1DMap(problem.mesh)
+        sigma_p = Map_sigma_p._transform(self.sigma1d)
+        # Make mass matrix
+        # Note: M(sig) - M(sig_p) = M(sig - sig_p)
+        # Need to deal with the edge/face discrepencies between 1d/2d/3d
+        if problem.mesh.dim == 1:
+            Mesigma = problem.mesh.getFaceInnerProduct(problem.curModel.sigma)
+            Mesigma_p = problem.mesh.getFaceInnerProduct(sigma_p)
+        if problem.mesh.dim == 2:
+            pass
+        if problem.mesh.dim == 3:
+            Mesigma = problem.MeSigma
+            Mesigma_p = problem.mesh.getEdgeInnerProduct(sigma_p)
+        return (Mesigma - Mesigma_p) * e_p
+
+    def S_eDeriv_m(self, problem, v, adjoint = False):
+        '''
+        Get the derivative of S_e wrt to sigma (m)
+        '''
+        # Need to deal with
+        if problem.mesh.dim == 1:
+            # Need to use the faceInnerProduct
+            MsigmaDeriv = problem.mesh.getFaceInnerProductDeriv(problem.curModel.sigma)(self.ePrimary(problem)[:,1]) * problem.curModel.sigmaDeriv
+            # MsigmaDeriv = ( MsigmaDeriv * MsigmaDeriv.T)**2
+        if problem.mesh.dim == 2:
+            pass
+        if problem.mesh.dim == 3:
+            # Need to take the derivative of both u_px and u_py
+            ePri = self.ePrimary(problem)
+            # MsigmaDeriv = problem.MeSigmaDeriv(ePri[:,0]) + problem.MeSigmaDeriv(ePri[:,1])
+            # MsigmaDeriv = problem.MeSigmaDeriv(np.sum(ePri,axis=1))
+            if adjoint:
+                return sp.hstack(( problem.MeSigmaDeriv(ePri[:,0]).T, problem.MeSigmaDeriv(ePri[:,1]).T ))*v
+            else:
+                return np.hstack(( mkvc(problem.MeSigmaDeriv(ePri[:,0]) * v,2), mkvc(problem.MeSigmaDeriv(ePri[:,1])*v,2) ))
+        if adjoint:
+            #
+            return MsigmaDeriv.T * v
+        else:
+            # v should be nC size
+            return MsigmaDeriv * v
+
+##############
+### Survey ###
+##############
 class SurveyMT(Survey.BaseSurvey):
     """
         Survey class for MT. Contains all the sources associated with the survey.
@@ -192,15 +547,14 @@ class SurveyMT(Survey.BaseSurvey):
         return len(self._freqDict)
 
     # TODO: Rename to getSources
-    def getSources(self, freq):
-        """Returns the transmitters associated with a specific frequency."""
+    def getSrcByFreq(self, freq):
+        """Returns the sources associated with a specific frequency."""
         assert freq in self._freqDict, "The requested frequency is not in this survey."
         return self._freqDict[freq]
 
     def projectFields(self, u):
         data = DataMT(self)
         for src in self.srcList:
-            print 'Project at freq: {:.3e}'.format(src.freq)
             sys.stdout.flush()
             for rx in src.rxList:
                 data[src, rx] = rx.projectFields(src, self.mesh, u)
@@ -209,73 +563,3 @@ class SurveyMT(Survey.BaseSurvey):
     def projectFieldsDeriv(self, u):
         raise Exception('Use Transmitters to project fields deriv.')
 
-class DataMT(Survey.Data):
-    '''
-    Data class for MTdata 
-
-    :param SimPEG survey object survey: 
-    :param v vector with data
-
-    '''
-    def __init__(self, survey, v=None):
-        # Pass the variables to the "parent" method
-        Survey.Data.__init__(self, survey, v)
-
-    def toRecArray(self,returnType='RealImag'):
-        '''
-        Function that returns a numpy.recarray for a SimpegMT impedance data object.
-
-        :param str returnType: Switches between returning a rec array where the impedance is split to real and imaginary ('RealImag') or is a complex ('Complex')
-
-        '''
-
-        def rec2ndarr(x,dt=float):
-            return x.view((dt, len(x.dtype.names)))
-        # Define the record fields
-        dtRI = [('freq',float),('x',float),('y',float),('z',float),('zxxr',float),('zxxi',float),('zxyr',float),('zxyi',float),('zyxr',float),('zyxi',float),('zyyr',float),('zyyi',float)]
-        dtCP = [('freq',float),('x',float),('y',float),('z',float),('zxx',complex),('zxy',complex),('zyx',complex),('zyy',complex)]
-        impList = ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi']
-        for src in self.survey.srcList:
-            # Temp array for all the receivers of the source.
-            # Note: needs to be written more generally, using diffterent rxTypes and not all the data at the locaitons
-            # Assume the same locs for all RX
-            locs = src.rxList[0].locs
-            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]
-            # Insert the values to the temp array
-            for nr,(key,val) in enumerate(typeList):
-                tArrRec[key] = val
-            # 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
-            uniFLmarr = np.unique(mArrRec[['freq','x','y','z']])
-
-            try:
-                outTemp = recFunc.stack_arrays((outTemp,mArrRec))
-                #outTemp = np.concatenate((outTemp,dataBlock),axis=0)
-            except NameError as e:
-                outTemp = mArrRec
-
-            if 'RealImag' in returnType:
-                outArr = outTemp
-            if 'Complex' in returnType:
-                # Add the real and imaginary to a complex number
-
-                outArr = np.empty(outTemp.shape,dtype=dtCP)
-                for comp in ['freq','x','y','z']:
-                    outArr[comp] = outTemp[comp].copy()
-                for comp in ['zxx','zxy','zyx','zyy']:
-                    outArr[comp] = outTemp[comp+'r'].copy() + 1j*outTemp[comp+'i'].copy()
-                # for uniFL in uniFLmarr:
-                #     mTemp = mkvc(rec2ndarr(mArrRec[np.ma.where(mArrRec[['freq','x','y','z']].data == np.array(uniFL))][impList]).sum(axis=0),2).T
-                #     compBlock = np.sum(mTemp.data.reshape((4,2))*np.array([[1,1j],[1,1j],[1,1j],[1,1j]]),axis=1).copy().view(dt[4::])
-                #     dataBlock = mkvc(recFunc.merge_arrays((np.array(uniFL),compBlock),flatten=True),2).T
-                #     try:
-                #         outArr = recFunc.stack_arrays((outArr,dataBlock),usemask=False)
-                #     except NameError as e:
-                #         outArr = dataBlock
-
-        # Return 
-        return outArr
\ No newline at end of file
diff --git a/simpegMT/Tests/test_ApparentResistivityLayerpy b/simpegMT/Tests/test_ApparentResistivityLayerpy
deleted file mode 100644
index 8fbd3202..00000000
--- a/simpegMT/Tests/test_ApparentResistivityLayerpy
+++ /dev/null
@@ -1,48 +0,0 @@
-import unittest
-from SimPEG import *
-import simpegMT as MT
-
-TOL = 1e-6
-
-def appResPhs(freq,z):
-    app_res = ((1./(8e-7*np.pi**2))/freq)*np.abs(z)**2
-    app_phs = np.arctan2(-z.imag,z.real)*(180/np.pi)
-    return app_res, app_phs
-
-def appResNorm(sigmaHalf):
-    nFreq = 26
-
-    m1d = Mesh.TensorMesh([[(100,5,1.5),(100.,10),(100,5,1.5)]], x0=['C'])
-    sigma = np.zeros(m1d.nC) + sigmaHalf
-    sigma[m1d.gridCC[:]>200] = 1e-8
-
-    # Calculate the analytic fields
-    freqs = np.logspace(4,-4,nFreq)
-    Z = []
-    for freq in freqs:
-        Ed, Eu, Hd, Hu = MT.Utils.getEHfields(m1d,sigma,freq,np.array([200]))
-        Z.append((Ed + Eu)/(Hd + Hu))
-
-    Zarr = np.concatenate(Z)
-
-    app_r, app_p = appResPhs(freqs,Zarr)
-
-    return np.linalg.norm(np.abs(app_r - np.ones(nFreq)/sigmaHalf)) / np.log10(sigmaHalf)
-
-
-class TestAnalytics(unittest.TestCase):
-
-    def setUp(self):
-        pass
-    def test_appRes2en1(self):self.assertLess(appResNorm(2e-1), TOL)
-    def test_appRes2en2(self):self.assertLess(appResNorm(2e-2), TOL)
-    def test_appRes2en3(self):self.assertLess(appResNorm(2e-3), TOL)
-    def test_appRes2en4(self):self.assertLess(appResNorm(2e-4), TOL)
-    def test_appRes2en5(self):self.assertLess(appResNorm(2e-5), TOL)
-    def test_appRes2en6(self):self.assertLess(appResNorm(2e-6), TOL)
-
-
-
-
-if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
diff --git a/simpegMT/Tests/test_Problem1D_againstAnalyticHalfspace.py b/simpegMT/Tests/test_Problem1D_againstAnalyticHalfspace.py
new file mode 100644
index 00000000..b815eb83
--- /dev/null
+++ b/simpegMT/Tests/test_Problem1D_againstAnalyticHalfspace.py
@@ -0,0 +1,162 @@
+import unittest
+import SimPEG as simpeg
+import simpegMT as simpegmt
+from SimPEG.Utils import meshTensor
+import numpy as np
+# Define the tolerances
+TOLr = 5e-2
+TOLp = 5e-2
+
+
+def setupSurvey(sigmaHalf,tD=True):
+
+    # Frequency
+    nFreq = 33
+    freqs = np.logspace(3,-3,nFreq)
+    # Make the mesh
+    ct = 5
+    air = meshTensor([(ct,25,1.3)])
+    # coreT0 = meshTensor([(ct,15,1.2)])
+    # coreT1 = np.kron(meshTensor([(coreT0[-1],15,1.3)]),np.ones((7,)))
+    core = np.concatenate( (  np.kron(meshTensor([(ct,15,-1.2)]),np.ones((10,))) , meshTensor([(ct,20)]) ) )
+    bot = meshTensor([(core[0],10,-1.3)])
+    x0 = -np.array([np.sum(np.concatenate((core,bot)))])
+    m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)
+    # Make the model
+    sigma = np.zeros(m1d.nC) + sigmaHalf
+    sigma[m1d.gridCC > 0 ] = 1e-8
+
+    rxList = []
+    for rxType in ['z1dr','z1di']:
+        rxList.append(simpegmt.SurveyMT.RxMT(simpeg.mkvc(np.array([0.0]),2).T,rxType))
+    # Source list
+    srcList =[]
+    if tD:
+        for freq in freqs:
+            srcList.append(simpegmt.SurveyMT.srcMT_polxy_1DhomotD(rxList,freq))
+    else:
+        for freq in freqs:
+            srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,freq))
+
+    survey = simpegmt.SurveyMT.SurveyMT(srcList)
+    return survey, sigma, m1d
+
+def getAppResPhs(MTdata):
+    # Make impedance
+    def appResPhs(freq,z):
+        app_res = ((1./(8e-7*np.pi**2))/freq)*np.abs(z)**2
+        app_phs = np.arctan2(z.imag,z.real)*(180/np.pi)
+        return app_res, app_phs
+    zList = []
+    for src in MTdata.survey.srcList:
+        zc = [src.freq]
+        for rx in src.rxList:
+            if 'i' in rx.rxType:
+                m=1j
+            else:
+                m = 1
+            zc.append(m*MTdata[src,rx])
+        zList.append(zc)
+    return [appResPhs(zList[i][0],np.sum(zList[i][1:3])) for i in np.arange(len(zList))]
+
+def appRes_TotalFieldNorm(sigmaHalf):
+
+    # Make the survey
+    survey, sigma, mesh = setupSurvey(sigmaHalf)
+    problem = simpegmt.ProblemMT1D.eForm_TotalField(mesh)
+    problem.pair(survey)
+
+    # Get the fields
+    fields = problem.fields(sigma)
+
+    # Project the data
+    data = survey.projectFields(fields)
+
+    # Calculate the app res and phs
+    app_r = np.array(getAppResPhs(data))[:,0]
+
+    return np.linalg.norm(np.abs(app_r - np.ones(survey.nFreq)/sigmaHalf)*sigmaHalf)
+
+def appPhs_TotalFieldNorm(sigmaHalf):
+
+    # Make the survey
+    survey, sigma, mesh = setupSurvey(sigmaHalf)
+    problem = simpegmt.ProblemMT1D.eForm_TotalField(mesh)
+    problem.pair(survey)
+
+    # Get the fields
+    fields = problem.fields(sigma)
+
+    # Project the data
+    data = survey.projectFields(fields)
+
+    # Calculate the app  phs
+    app_p = np.array(getAppResPhs(data))[:,1]
+
+    return np.linalg.norm(np.abs(app_p - np.ones(survey.nFreq)*45)/ 45)
+
+def appRes_psFieldNorm(sigmaHalf):
+
+    # Make the survey
+    survey, sigma, mesh = setupSurvey(sigmaHalf,False)
+    problem = simpegmt.ProblemMT1D.eForm_psField(mesh, sigmaPrimary = sigma)
+    problem.pair(survey)
+
+    # Get the fields
+    fields = problem.fields(sigma)
+
+    # Project the data
+    data = survey.projectFields(fields)
+
+    # Calculate the app res and phs
+    app_r = np.array(getAppResPhs(data))[:,0]
+
+    return np.linalg.norm(np.abs(app_r - np.ones(survey.nFreq)/sigmaHalf)*sigmaHalf)
+
+def appPhs_psFieldNorm(sigmaHalf):
+
+    # Make the survey
+    survey, sigma, mesh = setupSurvey(sigmaHalf,False)
+    problem = simpegmt.ProblemMT1D.eForm_psField(mesh, sigmaPrimary = sigma)
+    problem.pair(survey)
+
+    # Get the fields
+    fields = problem.fields(sigma)
+
+    # Project the data
+    data = survey.projectFields(fields)
+
+    # Calculate the app  phs
+    app_p = np.array(getAppResPhs(data))[:,1]
+
+    return np.linalg.norm(np.abs(app_p - np.ones(survey.nFreq)*45)/ 45)
+
+class TestAnalytics(unittest.TestCase):
+
+    def setUp(self):
+        pass
+    # Total Fields
+    # def test_appRes2en1(self):self.assertLess(appRes_TotalFieldNorm(2e-1), TOLr)
+    # def test_appPhs2en1(self):self.assertLess(appPhs_TotalFieldNorm(2e-1), TOLp)
+
+    def test_appRes2en2(self):self.assertLess(appRes_TotalFieldNorm(2e-2), TOLr)
+    def test_appPhs2en2(self):self.assertLess(appPhs_TotalFieldNorm(2e-2), TOLp)
+
+    # def test_appRes2en3(self):self.assertLess(appRes_TotalFieldNorm(2e-3), TOLr)
+    # def test_appPhs2en3(self):self.assertLess(appPhs_TotalFieldNorm(2e-3), TOLp)
+
+    # def test_appRes2en4(self):self.assertLess(appRes_TotalFieldNorm(2e-4), TOLr)
+    # def test_appPhs2en4(self):self.assertLess(appPhs_TotalFieldNorm(2e-4), TOLp)
+
+    # def test_appRes2en5(self):self.assertLess(appRes_TotalFieldNorm(2e-5), TOLr)
+    # def test_appPhs2en5(self):self.assertLess(appPhs_TotalFieldNorm(2e-5), TOLp)
+
+    # def test_appRes2en6(self):self.assertLess(appRes_TotalFieldNorm(2e-6), TOLr)
+    # def test_appPhs2en6(self):self.assertLess(appPhs_TotalFieldNorm(2e-6), TOLp)
+
+    # Primary/secondary
+    def test_appRes2en2_ps(self):self.assertLess(appRes_psFieldNorm(2e-2), TOLr)
+    def test_appPhs2en2_ps(self):self.assertLess(appPhs_psFieldNorm(2e-2), TOLp)
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/simpegMT/Tests/test_Problem1D_totalDvsPSvsAnalytic.py b/simpegMT/Tests/test_Problem1D_totalDvsPSvsAnalytic.py
new file mode 100644
index 00000000..5ab70fd4
--- /dev/null
+++ b/simpegMT/Tests/test_Problem1D_totalDvsPSvsAnalytic.py
@@ -0,0 +1,135 @@
+import unittest
+import SimPEG as simpeg
+import simpegMT as simpegmt
+from SimPEG.Utils import meshTensor
+import numpy as np
+# Define the tolerances
+TOLr = 5e-2
+TOLp = 5e-2
+
+
+def setupSurvey(sigmaHalf,tD=True):
+
+    # Frequency
+    nFreq = 33
+    freqs = np.logspace(3,-3,nFreq)
+    # Make the mesh
+    ct = 5
+    air = meshTensor([(ct,25,1.3)])
+    # coreT0 = meshTensor([(ct,15,1.2)])
+    # coreT1 = np.kron(meshTensor([(coreT0[-1],15,1.3)]),np.ones((7,)))
+    core = np.concatenate( (  np.kron(meshTensor([(ct,15,-1.2)]),np.ones((10,))) , meshTensor([(ct,20)]) ) )
+    bot = meshTensor([(core[0],15,-1.3)])
+    x0 = -np.array([np.sum(np.concatenate((core,bot)))])
+    m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)
+    # Make the model
+    sigma = np.zeros(m1d.nC) + sigmaHalf
+    sigma[m1d.gridCC > 0 ] = 1e-8
+    sigmaBack = sigma.copy()
+    # Add structure
+    shallow = (m1d.gridCC < -200) * (m1d.gridCC > -600)
+    deep = (m1d.gridCC < -3000) * (m1d.gridCC > -5000)
+    sigma[shallow] = 1
+    sigma[deep] = 0.1
+
+    rxList = []
+    for rxType in ['z1dr','z1di']:
+        rxList.append(simpegmt.SurveyMT.RxMT(simpeg.mkvc(np.array([0.0]),2).T,rxType))
+    # Source list
+    srcList =[]
+    if tD:
+        for freq in freqs:
+            srcList.append(simpegmt.SurveyMT.srcMT_polxy_1DhomotD(rxList,freq))
+    else:
+        for freq in freqs:
+            srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,freq))
+
+    survey = simpegmt.SurveyMT.SurveyMT(srcList)
+    return survey, sigma, m1d
+
+def getAppResPhs(MTdata):
+    # Make impedance
+    def appResPhs(freq,z):
+        app_res = ((1./(8e-7*np.pi**2))/freq)*np.abs(z)**2
+        app_phs = np.arctan2(z.imag,z.real)*(180/np.pi)
+        return app_res, app_phs
+    zList = []
+    for src in MTdata.survey.srcList:
+        zc = [src.freq]
+        for rx in src.rxList:
+            if 'i' in rx.rxType:
+                m=1j
+            else:
+                m = 1
+            zc.append(m*MTdata[src,rx])
+        zList.append(zc)
+    return [appResPhs(zList[i][0],np.sum(zList[i][1:3])) for i in np.arange(len(zList))]
+
+def calculateAnalyticSolution(srcList,mesh,model):
+    surveyAna = simpegmt.SurveyMT.SurveyMT(srcList)
+    data1D = simpegmt.DataMT.DataMT(surveyAna)
+    for src in surveyAna.srcList:
+        elev = src.rxList[0].locs[0]
+        anaEd, anaEu, anaHd, anaHu = simpegmt.Utils.MT1Danalytic.getEHfields(mesh,model,src.freq,elev)
+        anaE = anaEd+anaEu
+        anaH = anaHd+anaHu
+        # Scale the solution
+        # anaE = (anaEtemp/anaEtemp[-1])#.conj()
+        # anaH = (anaHtemp/anaEtemp[-1])#.conj()
+        anaZ = anaE/anaH
+        for rx in src.rxList:
+            data1D[src,rx] = getattr(anaZ, rx.projComp)
+    return data1D
+
+def dataMis_AnalyticTotalDomain(sigmaHalf):
+
+    # Make the survey
+
+    # Total domain solution
+    surveyTD, sigma, mesh = setupSurvey(sigmaHalf)
+    problemTD = simpegmt.ProblemMT1D.eForm_TotalField(mesh)
+    problemTD.pair(surveyTD)
+    # Analytic data
+    dataAnaObj = calculateAnalyticSolution(surveyTD.srcList,mesh,sigma)
+    # dataTDObj = simpegmt.DataMT.DataMT(surveyTD, surveyTD.dpred(sigma))
+    dataTD = surveyTD.dpred(sigma)
+    dataAna = simpeg.mkvc(dataAnaObj)
+    return np.all((dataTD - dataAna)/dataAna < 2.)
+    # surveyTD.dtrue = -simpeg.mkvc(dataAna,2)
+    # surveyTD.dobs = -simpeg.mkvc(dataAna,2)
+    # surveyTD.Wd = np.ones(surveyTD.dtrue.shape) #/(np.abs(surveyTD.dtrue)*0.01)
+    # # Setup the data misfit
+    # dmis = simpeg.DataMisfit.l2_DataMisfit(surveyTD)
+    # dmis.Wd = surveyTD.Wd
+    # return dmis.eval(sigma)
+
+
+def dataMis_AnalyticPrimarySecondary(sigmaHalf):
+
+    # Make the survey
+    # Primary secondary
+    surveyPS, sigmaPS, mesh = setupSurvey(sigmaHalf,tD=False)
+    problemPS = simpegmt.ProblemMT1D.eForm_psField(mesh)
+    problemPS.sigmaPrimary = sigmaPS
+    problemPS.pair(surveyPS)
+    # Analytic data
+    dataAnaObj = calculateAnalyticSolution(surveyPS.srcList,mesh,sigmaPS)
+
+    dataPS = surveyPS.dpred(sigmaPS)
+    dataAna = simpeg.mkvc(dataAnaObj)
+    return np.all((dataPS - dataAna)/dataAna < 2.)
+
+
+
+class TestNumericVsAnalytics(unittest.TestCase):
+
+    def setUp(self):
+        pass
+    # Total Fields
+    def test_appRes2en2(self):self.assertTrue(dataMis_AnalyticTotalDomain(2e-2))
+
+    # Primary/secondary
+    def test_appRes2en2_ps(self):self.assertTrue(dataMis_AnalyticPrimarySecondary(2e-2))
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/simpegMT/Tests/test_Problem3D_againstAnalytic.py b/simpegMT/Tests/test_Problem3D_againstAnalytic.py
new file mode 100644
index 00000000..fc8aa939
--- /dev/null
+++ b/simpegMT/Tests/test_Problem3D_againstAnalytic.py
@@ -0,0 +1,291 @@
+# Test functions
+from glob import glob
+import numpy as np, sys, os, time, scipy, subprocess
+import simpegMT as simpegmt, SimPEG as simpeg
+import unittest
+import SimPEG as simpeg
+import simpegMT as simpegmt
+from SimPEG.Utils import meshTensor
+from scipy.constants import mu_0
+
+TOLr = 5e-2
+TOL = 1e-4
+FLR = 1e-20 # "zero", so if residual below this --> pass regardless of order
+CONDUCTIVITY = 1e1
+MU = mu_0
+freq = [1e-1, 2e-1]
+addrandoms = True
+
+
+def getInputs():
+    """
+    Function that returns Mesh, freqs, rx_loc, elev.
+    """
+    # Make a mesh
+    # M = simpeg.Mesh.TensorMesh([[(100,5,-1.5),(100.,10),(100,5,1.5)],[(100,5,-1.5),(100.,10),(100,5,1.5)],[(100,5,1.6),(100.,10),(100,3,2)]], x0=['C','C',-3529.5360])
+    # M = simpeg.Mesh.TensorMesh([[(1000,6,-1.5),(1000.,6),(1000,6,1.5)],[(1000,6,-1.5),(1000.,2),(1000,6,1.5)],[(1000,6,-1.3),(1000.,6),(1000,6,1.3)]], x0=['C','C','C'])# Setup the model
+    M = simpeg.Mesh.TensorMesh([[(1000,6,-1.5),(1000.,4),(1000,6,1.5)],[(1000,6,-1.5),(1000.,4),(1000,6,1.5)],[(500,8,-1.3),(500.,8),(500,8,1.3)]], x0=['C','C','C'])# Setup the model
+    # Set the frequencies
+    freqs = np.logspace(1,-3,5)
+    elev = 0
+
+    ## Setup the the survey object
+    # Receiver locations
+    rx_x, rx_y = np.meshgrid(np.arange(-3000,3001,500),np.arange(-1000,1001,500))
+    rx_loc = np.array([[0, 0, 0]]) #,[-100,-100,0],[100,100,0]]) #np.hstack((simpeg.Utils.mkvc(rx_x,2),simpeg.Utils.mkvc(rx_y,2),elev+np.zeros((np.prod(rx_x.shape),1))))
+
+    return M, freqs, rx_loc, elev
+
+
+def random(conds):
+    ''' Returns a halfspace model based on the inputs'''
+    M, freqs, rx_loc, elev = getInputs()
+
+    # Backround
+    sigBG = np.ones(M.nC)*conds
+    # Add randomness to the model (10% of the value).
+    sig = np.exp( np.log(sigBG) + np.random.randn(M.nC)*(conds)*1e-1 )
+
+    return (M, freqs, sig, sigBG, rx_loc)
+
+def halfSpace(conds):
+    ''' Returns a halfspace model based on the inputs'''
+    M, freqs, rx_loc, elev = getInputs()
+
+    # Model
+    ccM = M.gridCC
+    # conds = [1e-2]
+    groundInd = ccM[:,2] < elev
+    sig = np.zeros(M.nC) + 1e-8
+    sig[groundInd] = conds
+    # Set the background, not the same as the model
+    sigBG = np.zeros(M.nC) + 1e-8
+    sigBG[groundInd] = conds
+
+    return (M, freqs, sig, sigBG, rx_loc)
+
+def twoLayer(conds):
+    ''' Returns a 2 layer model based on the conductivity values given'''
+    M, freqs, rx_loc, elev = getInputs()
+
+    # Model
+    ccM = M.gridCC
+    groundInd = ccM[:,2] < elev
+    botInd = ccM[:,2] < -3000
+    sig = np.zeros(M.nC) + 1e-8
+    sig[groundInd] = conds[1]
+    sig[botInd] = conds[0]
+    # Set the background, not the same as the model
+    sigBG = np.zeros(M.nC) + 1e-8
+    sigBG[groundInd] = conds[1]
+
+
+    return (M, freqs, sig, sigBG, rx_loc)
+
+def setupSimpegMTfwd_eForm_ps(inputSetup,comp='All',singleFreq=False,expMap=True):
+    M,freqs,sig,sigBG,rx_loc = inputSetup
+    # Make a receiver list
+    rxList = []
+    if comp == 'All':
+        for rxType in ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi',]:
+                rxList.append(simpegmt.SurveyMT.RxMT(rx_loc,rxType))
+    else:
+        rxList.append(simpegmt.SurveyMT.RxMT(rx_loc,comp))
+    # Source list
+    srcList =[]
+
+    if singleFreq:
+        srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,singleFreq))
+    else:
+        for freq in freqs:
+            srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,freq))
+    # Survey MT
+    survey = simpegmt.SurveyMT.SurveyMT(srcList)
+
+    ## Setup the problem object
+    sigma1d = M.r(sigBG,'CC','CC','M')[0,0,:]
+    if expMap:
+        problem = simpegmt.ProblemMT3D.eForm_ps(M,sigmaPrimary= np.log(sigma1d) )
+        problem.mapping = simpeg.Maps.ExpMap(problem.mesh)
+        problem.curModel = np.log(sig)
+    else:
+        problem = simpegmt.ProblemMT3D.eForm_ps(M,sigmaPrimary= sigma1d)
+        problem.curModel = sig
+    problem.pair(survey)
+    problem.verbose = False
+    try:
+        from pymatsolver import MumpsSolver
+        problem.Solver = MumpsSolver
+    except:
+        pass
+
+    return (survey, problem)
+
+def setupSimpegMTfwd_eForm_ps_multiRx(inputSetup,comp='All',singleFreq=False,expMap=True):
+    M,freqs,sig,sigBG,rx_loc = inputSetup
+    # Add to the receiver list
+    rx_loc = np.vstack((rx_loc,np.array([[-100,100,0]])))# ,[-100,-100,0],[100,-100,0],[100,100,0]])))
+    # Make a receiver list
+    rxList = []
+    if comp == 'All':
+        for rxType in ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi',]:
+                rxList.append(simpegmt.SurveyMT.RxMT(rx_loc,rxType))
+    else:
+        rxList.append(simpegmt.SurveyMT.RxMT(rx_loc,comp))
+    # Source list
+    srcList =[]
+
+    if singleFreq:
+        srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,singleFreq))
+    else:
+        for freq in freqs:
+            srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,freq))
+    # Survey MT
+    survey = simpegmt.SurveyMT.SurveyMT(srcList)
+
+    ## Setup the problem object
+    sigma1d = M.r(sigBG,'CC','CC','M')[0,0,:]
+    if expMap:
+        problem = simpegmt.ProblemMT3D.eForm_ps(M,sigmaPrimary= np.log(sigma1d) )
+        problem.mapping = simpeg.Maps.ExpMap(problem.mesh)
+        problem.curModel = np.log(sig)
+    else:
+        problem = simpegmt.ProblemMT3D.eForm_ps(M,sigmaPrimary= sigma1d)
+        problem.curModel = sig
+    problem.pair(survey)
+    problem.verbose = False
+    try:
+        from pymatsolver import MumpsSolver
+        problem.Solver = MumpsSolver
+    except:
+        pass
+
+    return (survey, problem)
+def getAppResPhs(MTdata):
+    # Make impedance
+    def appResPhs(freq,z):
+        app_res = ((1./(8e-7*np.pi**2))/freq)*np.abs(z)**2
+        app_phs = np.arctan2(z.imag,z.real)*(180/np.pi)
+        return app_res, app_phs
+    recData = MTdata.toRecArray('Complex')
+    return appResPhs(recData['freq'],recData['zxy']), appResPhs(recData['freq'],recData['zyx'])
+
+def JvecAdjointTest(inputSetup,comp='All',freq=False):
+    (M, freqs, sig, sigBG, rx_loc) = inputSetup
+    survey, problem = setupSimpegMTfwd_eForm_ps(inputSetup,comp='All',singleFreq=freq)
+    print 'Adjoint test of eForm primary/secondary for {:s} comp at {:s}\n'.format(comp,str(survey.freqs))
+
+    m  = sig
+    u = problem.fields(m)
+
+    v = np.random.rand(survey.nD,)
+    # print problem.PropMap.PropModel.nP
+    w = np.random.rand(problem.mesh.nC,)
+
+    vJw = v.ravel().dot(problem.Jvec(m, w, u))
+    wJtv = w.ravel().dot(problem.Jtvec(m, v, u))
+    tol = np.max([TOL*(10**int(np.log10(np.abs(vJw)))),FLR])
+    print ' vJw   wJtv  vJw - wJtv     tol    abs(vJw - wJtv) < tol'
+    print vJw, wJtv, vJw - wJtv, tol, np.abs(vJw - wJtv) < tol
+    return np.abs(vJw - wJtv) < tol
+
+# Test the Jvec derivative
+def DerivJvecTest(inputSetup,comp='All',freq=False,expMap=True):
+    (M, freqs, sig, sigBG, rx_loc) = inputSetup
+    survey, problem = setupSimpegMTfwd_eForm_ps(inputSetup,comp=comp,singleFreq=freq,expMap=expMap)
+    print 'Derivative test of Jvec for eForm primary/secondary for {:s} comp at {:s}\n'.format(comp,survey.freqs)
+    # problem.mapping = simpeg.Maps.ExpMap(problem.mesh)
+    # problem.sigmaPrimary = np.log(sigBG)
+    x0 = np.log(sigBG)
+    # cond = sig[0]
+    # x0 = np.log(np.ones(problem.mesh.nC)*cond)
+    # problem.sigmaPrimary = x0
+    # if True:
+    #     x0  = x0 + np.random.randn(problem.mesh.nC)*cond*1e-1
+    survey = problem.survey
+    def fun(x):
+        return survey.dpred(x), lambda x: problem.Jvec(x0, x)
+    return simpeg.Tests.checkDerivative(fun, x0, num=3, plotIt=False, eps=FLR)
+
+
+def DerivProjfieldsTest(inputSetup,comp='All',freq=False):
+
+    survey, problem = setupSimpegMTfwd_eForm_ps(inputSetup,comp,freq)
+    print 'Derivative test of data projection for eFormulation primary/secondary\n\n'
+    # problem.mapping = simpeg.Maps.ExpMap(problem.mesh)
+    # Initate things for the derivs Test
+    src = survey.srcList[0]
+    rx = src.rxList[0]
+
+    u0x = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j
+    u0y = np.random.randn(survey.mesh.nE)+np.random.randn(survey.mesh.nE)*1j
+    u0 = np.vstack((simpeg.mkvc(u0x,2),simpeg.mkvc(u0y,2)))
+    f0 = problem.fieldsPair(survey.mesh,survey)
+    # u0 = np.hstack((simpeg.mkvc(u0_px,2),simpeg.mkvc(u0_py,2)))
+    f0[src,'e_pxSolution'] =  u0[:len(u0)/2]#u0x
+    f0[src,'e_pySolution'] = u0[len(u0)/2::]#u0y
+
+    def fun(u):
+        f = problem.fieldsPair(survey.mesh,survey)
+        f[src,'e_pxSolution'] = u[:len(u)/2]
+        f[src,'e_pySolution'] = u[len(u)/2::]
+        return rx.projectFields(src,survey.mesh,f), lambda t: rx.projectFieldsDeriv(src,survey.mesh,f0,simpeg.mkvc(t,2))
+
+    return simpeg.Tests.checkDerivative(fun, u0, num=3, plotIt=False, eps=FLR)
+
+
+def appResPhsHalfspace_eFrom_ps_Norm(sigmaHalf,appR=True,expMap=False):
+    if appR:
+        label = 'resistivity'
+    else:
+        label = 'phase'
+    # Make the survey and the problem
+    survey, problem = setupSimpegMTfwd_eForm_ps(halfSpace(sigmaHalf),expMap=expMap)
+    print 'Apperent {:s} test of eFormulation primary/secondary at {:g}\n\n'.format(label,sigmaHalf)
+
+    data = problem.dataPair(survey,survey.dpred(problem.curModel))
+    # Calculate the app  phs
+    app_rpxy, app_rpyx = np.array(getAppResPhs(data))
+    if appR:
+        return np.all(np.abs(app_rpxy[0,:] - np.ones(survey.nFreq)/sigmaHalf) * sigmaHalf < .4)
+    else:
+        return np.all(np.abs(app_rpxy[1,:] + np.ones(survey.nFreq)*135) / 135 < .4)
+
+class TestAnalytics(unittest.TestCase):
+
+    def setUp(self):
+        pass
+    # # Test apparent resistivity and phase
+    def test_appRes1en2(self):self.assertTrue(appResPhsHalfspace_eFrom_ps_Norm(1e-2))
+    def test_appPhs1en2(self):self.assertTrue(appResPhsHalfspace_eFrom_ps_Norm(1e-2,False))
+
+    def test_appRes1en3(self):self.assertTrue(appResPhsHalfspace_eFrom_ps_Norm(1e-3))
+    def test_appPhs1en3(self):self.assertTrue(appResPhsHalfspace_eFrom_ps_Norm(1e-3,False))
+
+    # Do a derivative test
+    def test_derivProj1(self):self.assertTrue(DerivProjfieldsTest(halfSpace(1e-2)))
+
+    # Do a derivative test of Jvec
+    # def test_derivJvec_zxxr(self):self.assertTrue(DerivJvecTest(random(1e-2),'zxxr',.1))
+    # def test_derivJvec_zxxi(self):self.assertTrue(DerivJvecTest(random(1e-2),'zxxi',.1))
+    # def test_derivJvec_zxyr(self):self.assertTrue(DerivJvecTest(random(1e-2),'zxyr',.1))
+    # def test_derivJvec_zxyi(self):self.assertTrue(DerivJvecTest(random(1e-2),'zxyi',.1))
+    # def test_derivJvec_zyxr(self):self.assertTrue(DerivJvecTest(random(1e-2),'zyxr',.1))
+    # def test_derivJvec_zyxi(self):self.assertTrue(DerivJvecTest(random(1e-2),'zyxi',.1))
+    # def test_derivJvec_zyyr(self):self.assertTrue(DerivJvecTest(random(1e-2),'zyyr',.1))
+    # def test_derivJvec_zyyi(self):self.assertTrue(DerivJvecTest(random(1e-2),'zyyi',.1))
+    def test_derivJvec_All(self):self.assertTrue(DerivJvecTest(random(1e-2),'All',.1))
+
+    # Test the adjoint of Jvec and Jtvec
+    # def test_JvecAdjoint_zxxr(self):self.assertTrue(JvecAdjointTest(random(1e-2),'zxxr',.1))
+    # def test_JvecAdjoint_zxxi(self):self.assertTrue(JvecAdjointTest(random(1e-2),'zxxi',.1))
+    # def test_JvecAdjoint_zxyr(self):self.assertTrue(JvecAdjointTest(random(1e-2),'zxyr',.1))
+    # def test_JvecAdjoint_zxyi(self):self.assertTrue(JvecAdjointTest(random(1e-2),'zxyi',.1))
+    # def test_JvecAdjoint_zyxr(self):self.assertTrue(JvecAdjointTest(random(1e-2),'zyxr',.1))
+    # def test_JvecAdjoint_zyxi(self):self.assertTrue(JvecAdjointTest(random(1e-2),'zyxi',.1))
+    # def test_JvecAdjoint_zyyr(self):self.assertTrue(JvecAdjointTest(random(1e-2),'zyyr',.1))
+    # def test_JvecAdjoint_zyyi(self):self.assertTrue(JvecAdjointTest(random(1e-2),'zyyi',.1))
+    def test_JvecAdjoint_All(self):self.assertTrue(JvecAdjointTest(random(1e-2),'All',.1))
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/simpegMT/Utils/MT1Danalytic.py b/simpegMT/Utils/MT1Danalytic.py
index 6d08e091..cf0847d9 100644
--- a/simpegMT/Utils/MT1Danalytic.py
+++ b/simpegMT/Utils/MT1Danalytic.py
@@ -2,19 +2,19 @@
 
 import numpy as np, SimPEG as simpeg
 
-def getEHfields(m1d,sigma,freq,zd):
+def getEHfields(m1d,sigma,freq,zd,scaleUD=True):
     '''Analytic solution for MT 1D layered earth. Returns E and H fields.
 
     :param SimPEG.mesh, object m1d: Mesh object with the 1D spatial information.
     :param numpy.array, vector sigma: Physical property of conductivity corresponding with the mesh.
     :param float, freq: Frequency to calculate data at.
     :param numpy array, vector zd: location to calculate EH fields at
+    :param bollean, scaleUD: scales the output to be 1 at the top, increases numeracal stability.
 
     Assumes a halfspace with the same conductive as the last cell below.
 
     '''
     # Note add an error check for the mesh and sigma are the same size.
-    # Need make the solution e^-iwt dependent
 
     # Constants: Assume constant
     mu = 4*np.pi*1e-7*np.ones((m1d.nC+1))
@@ -28,7 +28,7 @@ def getEHfields(m1d,sigma,freq,zd):
 
     # Initiate the propagation matrix, in the order down up.
     UDp = np.zeros((2,m1d.nC+1),dtype=complex)
-    UDp[1,0] = 1 # Set the wave amplitude as 1 into the half-space at the bottom of the mesh
+    UDp[1,0] = 1. # Set the wave amplitude as 1 into the half-space at the bottom of the mesh
     # Loop over all the layers, starting at the bottom layer
     for lnr, h in enumerate(m1d.hx): # lnr-number of layer, h-thickness of the layer
         # Calculate
@@ -46,6 +46,9 @@ def getEHfields(m1d,sigma,freq,zd):
         # The down and up component in current layer.
         UDp[:,lnr+1] = elamh.dot(Pjinv.dot(Pj1)).dot(UDp[:,lnr])
 
+        if scaleUD:
+             UDp[:,lnr+1::-1] = UDp[:,lnr+1::-1]/UDp[1,lnr+1]
+
     # Calculate the fields
     Ed = np.empty((zd.size,),dtype=complex)
     Eu = np.empty((zd.size,),dtype=complex)
@@ -54,7 +57,7 @@ def getEHfields(m1d,sigma,freq,zd):
 
     # Loop over the layers and calculate the fields
     # In the halfspace below the mesh
-    dup = m1d.vectorNx[0] 
+    dup = m1d.vectorNx[0]
     dind = dup >= zd
     Ed[dind] = UDp[1,0]*np.exp(-1j*k[0]*(dup-zd[dind]))
     Eu[dind] = UDp[0,0]*np.exp(1j*k[0]*(dup-zd[dind]))
diff --git a/simpegMT/Utils/MT1Dsolutions.py b/simpegMT/Utils/MT1Dsolutions.py
index 16a45711..aae83162 100644
--- a/simpegMT/Utils/MT1Dsolutions.py
+++ b/simpegMT/Utils/MT1Dsolutions.py
@@ -13,7 +13,7 @@ def get1DEfields(m1d,sigma,freq,sourceAmp=1.0):
     # Conductivity
     Msig = m1d.getFaceInnerProduct(sigma)
     # Set up the solution matrix
-    A = G.T*Mmu*G - 1j*2.*np.pi*freq*Msig
+    A = G.T*Mmu*G + 1j*2.*np.pi*freq*Msig
     # Define the inner part of the solution matrix
     Aii = A[1:-1,1:-1]
     # Define the outer part of the solution matrix
@@ -21,13 +21,13 @@ def get1DEfields(m1d,sigma,freq,sourceAmp=1.0):
 
     # Set the boundary conditions
     Ed, Eu, Hd, Hu = getEHfields(m1d,sigma,freq,m1d.vectorNx)
-    Etot = (Ed + Eu).conj()
+    Etot = (Ed + Eu)
     if sourceAmp is not None:
         Etot = ((Etot/Etot[-1])*sourceAmp) # Scale the fields to be equal to sourceAmp at the top
-    ## Note: need to use conjugate of the analytic solution. It is derived with e^iwt
+    ## Note: The analytic solution is derived with e^iwt
     bc = np.r_[Etot[0],Etot[-1]]
     # The right hand side
-    rhs = -Aio*bc
+    rhs = Aio*bc
     # Solve the system
     Aii_inv = simpeg.Solver(Aii)
     eii = Aii_inv*rhs
diff --git a/simpegMT/Utils/__init__.py b/simpegMT/Utils/__init__.py
index 73005269..b683f8b4 100644
--- a/simpegMT/Utils/__init__.py
+++ b/simpegMT/Utils/__init__.py
@@ -1,2 +1,4 @@
 from MT1Dsolutions import *  # Add the names of the functions
 from MT1Danalytic import *
+from dataUtils import *
+from ediFilesUtils import *
diff --git a/simpegMT/Utils/dataUtils.py b/simpegMT/Utils/dataUtils.py
new file mode 100644
index 00000000..a2cd89cd
--- /dev/null
+++ b/simpegMT/Utils/dataUtils.py
@@ -0,0 +1,241 @@
+# Utils used for the data,
+import numpy as np, matplotlib.pyplot as plt, sys
+import SimPEG as simpeg
+import simpegMT as simpegmt
+import numpy.lib.recfunctions as recFunc
+from scipy.constants import mu_0
+from scipy import interpolate as sciint
+
+def getAppRes(MTdata):
+    # Make impedance
+    zList = []
+    for src in MTdata.survey.srcList:
+        zc = [src.freq]
+        for rx in src.rxList:
+            if 'i' in rx.rxType:
+                m=1j
+            else:
+                m = 1
+            zc.append(m*MTdata[src,rx])
+        zList.append(zc)
+    return [appResPhs(zList[i][0],np.sum(zList[i][1:3])) for i in np.arange(len(zList))]
+
+def rotateData(MTdata,rotAngle):
+    '''
+    Function that rotates clockwist by rotAngle (- negative for a counter-clockwise rotation)
+    '''
+    recData = MTdata.toRecArray('Complex')
+    impData = rec2ndarr(recData[['zxx','zxy','zyx','zyy']],complex)
+    # Make the rotation matrix
+    # c,s,zxx,zxy,zyx,zyy = sympy.symbols('c,s,zxx,zxy,zyx,zyy')
+    # rotM = sympy.Matrix([[c,-s],[s, c]])
+    # zM = sympy.Matrix([[zxx,zxy],[zyx,zyy]])
+    # rotM*zM*rotM.T
+    # [c*(c*zxx - s*zyx) - s*(c*zxy - s*zyy), c*(c*zxy - s*zyy) + s*(c*zxx - s*zyx)],
+    # [c*(c*zyx + s*zxx) - s*(c*zyy + s*zxy), c*(c*zyy + s*zxy) + s*(c*zyx + s*zxx)]])
+    s = np.sin(-np.deg2rad(rotAngle))
+    c = np.cos(-np.deg2rad(rotAngle))
+    rotMat = np.array([[c,-s],[s,c]])
+    rotData = (rotMat.dot(impData.reshape(-1,2,2).dot(rotMat.T))).transpose(1,0,2).reshape(-1,4)
+    outRec = recData.copy()
+    for nr,comp in enumerate(['zxx','zxy','zyx','zyy']):
+        outRec[comp] = rotData[:,nr]
+    return simpegmt.DataMT.DataMT.fromRecArray(outRec)
+
+
+def appResPhs(freq,z):
+    app_res = ((1./(8e-7*np.pi**2))/freq)*np.abs(z)**2
+    app_phs = np.arctan2(z.imag,z.real)*(180/np.pi)
+    return app_res, app_phs
+
+def skindepth(rho,freq):
+    ''' Function to calculate the skindepth of EM waves'''
+    return np.sqrt( (rho*((1/(freq * mu_0 * np.pi )))))
+
+def rec2ndarr(x,dt=float):
+    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)
+    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)-1.999)*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
+
+def printTime():
+    import time
+    print time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime())
+
+def convert3Dto1Dobject(MTdata,rxType3D='zyx'):
+    # Find the unique locations
+    # Need to find the locations
+    recDataTemp = MTdata.toRecArray()
+    # Check if survey.std has been assigned.
+    ## NEED TO: write this...
+    # Calculte and add the DET of the tensor to the recArray
+    if 'det' in rxType3D:
+        Zon = (recDataTemp['zxxr']+1j*recDataTemp['zxxi'])*(recDataTemp['zyyr']+1j*recDataTemp['zyyi'])
+        Zoff = (recDataTemp['zxyr']+1j*recDataTemp['zxyi'])*(recDataTemp['zyxr']+1j*recDataTemp['zyxi'])
+        det = np.sqrt(Zon.data - Zoff.data)
+        recData = recFunc.append_fields(recDataTemp,['zdetr','zdeti'],[det.real,det.imag] )
+    else:
+        recData = recDataTemp
+
+    uniLocs = rec2ndarr(np.unique(recData[['x','y','z']])).data
+    mtData1DList = []
+    if 'zxy' in rxType3D:
+        corr = -1 # Shift the data to comply with the quadtrature of the 1d problem
+    else:
+        corr = 1
+    for loc in uniLocs:
+        # Make the receiver list
+        rx1DList = []
+        for rxType in ['z1dr','z1di']:
+            rx1DList.append(simpegmt.SurveyMT.RxMT(simpeg.mkvc(loc,2).T,rxType))
+        # Source list
+        locrecData = recData[np.sqrt(np.sum( (rec2ndarr(recData[['x','y','z']]).data - loc )**2,axis=1)) < 1e-5]
+        dat1DList = []
+        src1DList = []
+        for freq in locrecData['freq']:
+            src1DList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rx1DList,freq))
+            for comp  in ['r','i']:
+                dat1DList.append( corr * locrecData[rxType3D+comp][locrecData['freq']== freq].data )
+
+        # Make the survey
+        sur1D = simpegmt.SurveyMT.SurveyMT(src1DList)
+
+        # Make the data
+        dataVec = np.hstack(dat1DList)
+        dat1D = simpegmt.DataMT.DataMT(sur1D,dataVec)
+        sur1D.dobs = dataVec
+        # Need to take MTdata.survey.std and split it as well.
+        std=0.05
+        sur1D.std =  np.abs(sur1D.dobs*std) #+ 0.01*np.linalg.norm(sur1D.dobs)
+        mtData1DList.append(dat1D)
+
+    # Return the the list of data.
+    return mtData1DList
+
+def resampleMTdataAtFreq(MTdata,freqs):
+    '''
+    Function to resample MTdata at set of frequencies
+
+    '''
+    # Make a rec array
+    MTrec = MTdata.toRecArray().data
+
+    # Find unique locations
+    uniLoc = np.unique(MTrec[['x','y','z']])
+    uniFreq = MTdata.survey.freqs
+    # Get the comps
+    dNames = MTrec.dtype
+
+    # Loop over all the locations and interpolate
+    for loc in uniLoc:
+        # Find the index of the station
+        ind = np.sqrt(np.sum((rec2ndarr(MTrec[['x','y','z']]) - rec2ndarr(loc))**2,axis=1)) < 1. # Find dist of 1 m accuracy
+        # Make a temporary recArray and interpolate all the components
+        tArrRec = np.concatenate((simpeg.mkvc(freqs,2),np.ones((len(freqs),1))*rec2ndarr(loc),np.nan*np.ones((len(freqs),12))),axis=1).view(dNames)
+        for comp in ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi','tzxr','tzxi','tzyr','tzyi']:
+            int1d = sciint.interp1d(MTrec[ind]['freq'],MTrec[ind][comp],bounds_error=False)
+            tArrRec[comp] = simpeg.mkvc(int1d(freqs),2)
+
+        # Join together
+        try:
+            outRecArr = recFunc.stack_arrays((outRecArr,tArrRec))
+        except NameError as e:
+            outRecArr = tArrRec
+
+    # Make the MTdata and return
+    return simpegmt.DataMT.DataMT.fromRecArray(outRecArr)
\ No newline at end of file
diff --git a/simpegMT/Utils/ediFilesUtils.py b/simpegMT/Utils/ediFilesUtils.py
new file mode 100644
index 00000000..937e0035
--- /dev/null
+++ b/simpegMT/Utils/ediFilesUtils.py
@@ -0,0 +1,176 @@
+# Functions to import and export MT EDI files.
+from SimPEG import mkvc
+from scipy.constants import mu_0
+from numpy.lib import recfunctions as recFunc
+import simpegMT
+from simpegMT.Utils.dataUtils import rec2ndarr
+
+# Import modules
+import numpy as np
+import os, sys, re
+try:
+    import osr
+except ImportError as e:
+    print 'Could not import osr, missing the gdal package'
+    pass
+
+class EDIimporter:
+    """
+    A class to import EDIfiles.
+
+    """
+    _impUnitEDI2SI = 4*np.pi*1e-4 # Convert Z[mV/km/nT] (as in EDI)to Z[V/A] SI unit
+    _impUnitSI2EDI = 1./_impUnitEDI2SI # ConvertZ[V/A] SI unit to Z[mV/km/nT] (as in EDI)
+
+    # Properties
+    filesList = None
+    comps = None
+
+    # Hidden properties
+    _outEPSG = None
+    _2out = None
+
+
+    def __init__(self, EDIfilesList, compList=None, outEPSG=None):
+
+        # Set the fileList
+        self.filesList = EDIfilesList
+        # Set the components to import
+        if compList is None:
+            self.comps = ['ZXXR','ZXYR','ZYXR','ZYYR','ZXXI','ZXYI','ZYXI','ZYYI','ZXX.VAR','ZXY.VAR','ZYX.VAR','ZYY.VAR']
+        else:
+            self.comps = compList
+        if outEPSG is not None:
+            self._outEPSG = outEPSG
+
+    def __call__(self,comps=None):
+
+        if comps is None:
+            return self._data
+
+        return self._data[comps]
+
+    def importFiles(self):
+        """
+        Function to import EDI files into a object.
+
+
+        """
+
+        # Constants that are needed for convertion of units
+
+        # Temp lists
+        tmpStaList = []
+
+        tmpCompList = ['freq','x','y','z']
+        tmpCompList.extend(self.comps)
+        # Make the outarray
+        dtRI = [(compS.lower().replace('.',''),float) for compS in tmpCompList]
+        # Loop through all the files
+        for nrEDI, EDIfile in enumerate(self.filesList):
+            # Read the file into a list of the lines
+            with open(EDIfile,'r') as fid:
+                EDIlines = fid.readlines()
+            # Find the location
+            latD, longD, elevM = _findLatLong(EDIlines)
+            # Transfrom coordinates
+            transCoord = self._transfromPoints(longD,latD)
+            # Extract the name of the file (station)
+            EDIname = EDIfile.split(os.sep)[-1].split('.')[0]
+            # Arrange the data
+            staList = [EDIname, EDIfile, transCoord[0], transCoord[1], elevM[0]]
+            # Add to the station list
+            tmpStaList.extend(staList)
+
+            # Read the frequency data
+            freq = _findEDIcomp('>FREQ',EDIlines)
+            # Make the temporary rec array.
+            tArrRec = ( np.nan*np.ones( (len(freq),len(dtRI)) ) ).view(dtRI)     #np.concatenate((freq*np.ones((locs.shape[0],1)),locs,np.nan*np.ones((locs.shape[0],8))),axis=1).view(dtRI)
+            # Add data to the array
+            tArrRec['freq'] = mkvc(freq,2)
+            tArrRec['x'] = mkvc(np.ones((len(freq),1))*transCoord[0],2)
+            tArrRec['y'] = mkvc(np.ones((len(freq),1))*transCoord[1],2)
+            tArrRec['z'] = mkvc(np.ones((len(freq),1))*elevM[0],2)
+            for comp in self.comps:
+                # Deal with converting units of the impedance tensor
+                if 'Z' in comp:
+                    unitConvert = self._impUnitEDI2SI
+                else:
+                    unitConvert = 1
+                # Rotate the data since EDI x is *north, y *east but Simpeg uses x *east, y *north (* means internal reference frame)
+                key = [comp.lower().replace('.','').replace(s,t) for s,t in [['xx','yy'],['xy','yx'],['yx','xy'],['yy','xx']] if s in comp.lower()][0]
+                tArrRec[key] = mkvc(unitConvert*_findEDIcomp('>'+comp,EDIlines),2)
+            # Make a masked array
+            mArrRec = np.ma.MaskedArray(rec2ndarr(tArrRec),mask=np.isnan(rec2ndarr(tArrRec))).view(dtype=tArrRec.dtype)
+            try:
+                outTemp = recFunc.stack_arrays((outTemp,mArrRec))
+            except NameError as e:
+                outTemp = mArrRec
+
+        # Assign the data
+        self._data = outTemp
+
+    # % Assign the data to the obj
+    # nOutData=length(obj.data);
+    # obj.data(nOutData+1:nOutData+length(TEMP.data),:) = TEMP.data;
+    def _transfromPoints(self,longD,latD):
+        # Coordinates convertor
+        if self._2out is None:
+            src = osr.SpatialReference()
+            src.ImportFromEPSG(4326)
+            out = osr.SpatialReference()
+            if self._outEPSG is None:
+                # Find the UTM EPSG number
+                Nnr =  700 if latD < 0.0 else 600
+                utmZ = int(1+(longD+180.0)/6.0)
+                self._outEPSG = 32000 + Nnr + utmZ
+            out.ImportFromEPSG(self._outEPSG)
+            self._2out = osr.CoordinateTransformation(src,out)
+        # Return the transfrom
+        return self._2out.TransformPoint(longD,latD)
+
+# Hidden functions
+def _findLatLong(fileLines):
+    latDMS = np.array(fileLines[_findLine('LAT=',fileLines)[0]].split('=')[1].split()[0].split(':'),float)
+    longDMS = np.array(fileLines[_findLine('LONG=',fileLines)[0]].split('=')[1].split()[0].split(':'),float)
+    elevM = np.array([fileLines[_findLine('ELEV=',fileLines)[0]].split('=')[1].split()[0]],float)
+    # Convert to D.ddddd values
+    latS = np.sign(latDMS[0])
+    longS = np.sign(longDMS[0])
+    latD = latDMS[0] + latS*latDMS[1]/60 + latS*latDMS[2]/3600
+    longD = longDMS[0] + longS*longDMS[1]/60 + longS*longDMS[2]/3600
+    return latD, longD, elevM
+
+def _findLine(comp,fileLines):
+    """ Find a line number in the file"""
+    # Line counter
+    c = 0
+    # List of indices for found lines
+    found = []
+    # Loop through all the lines
+    for line in fileLines:
+        if comp in line:
+            # Append if found
+            found.append(c)
+        # Increse the counter
+        c += 1
+    # Return the found indices
+    return found
+
+def _findEDIcomp(comp,fileLines,dt=float):
+    """
+    Extract the data vector.
+
+    Returns a list of the data.
+    """
+    # Find the data
+    headLine, indHead = [(st,nr) for nr,st in enumerate(fileLines) if re.search(comp,st)][0]
+    # Extract the data
+    nrVec = int(headLine.split()[-1])
+    c = 0
+    dataList = []
+    while c < nrVec:
+        indHead += 1
+        dataList.extend(fileLines[indHead].split())
+        c = len(dataList)
+    return np.array(dataList,dt)
\ No newline at end of file
diff --git a/simpegMT/Utils/plotDataTypes.py b/simpegMT/Utils/plotDataTypes.py
new file mode 100644
index 00000000..c0180f00
--- /dev/null
+++ b/simpegMT/Utils/plotDataTypes.py
@@ -0,0 +1,416 @@
+from matplotlib import pyplot as plt, colors, numpy as np
+
+
+def rec2nd(structArray):
+	""" Converts a structured/record array to ndarray to do operations on."""
+	return structArray.view((np.float,len(structArray.dtype.names)))
+
+def plotIsoFreqNSimpedance(ax,freq,array,flag,par='abs',colorbar=True,colorNorm='SymLog',cLevel=True,contour=True):
+
+	indUniFreq = np.where(freq==array['freq'])
+
+
+	x, y = array['x'][indUniFreq],array['y'][indUniFreq]
+	if par == 'abs':
+		zPlot = np.abs(array[flag][indUniFreq])
+		cmap = plt.get_cmap('OrRd_r')#seismic')
+		level = np.logspace(0,-5,31)
+		clevel = np.logspace(0,-4,5)
+		plotNorm = colors.LogNorm()
+	elif par == 'real':
+		zPlot = np.real(array[flag][indUniFreq])
+		cmap = plt.get_cmap('RdYlBu')
+		if cLevel:
+			level = np.concatenate((-np.logspace(0,-10,31),np.logspace(-10,0,31)))
+			clevel = np.concatenate((-np.logspace(0,-8,5),np.logspace(-8,0,5)))
+		else:
+			level = np.linspace(zPlot.min(),zPlot.max(),100)
+			clevel = np.linspace(zPlot.min(),zPlot.max(),10)
+		if colorNorm=='SymLog':
+			plotNorm = colors.SymLogNorm(1e-10,linscale=2)
+		else:
+			plotNorm = colors.Normalize()
+	elif par == 'imag':
+		zPlot = np.imag(array[flag][indUniFreq])
+		cmap = plt.get_cmap('RdYlBu')
+		level = np.concatenate((-np.logspace(0,-10,31),np.logspace(-10,0,31)))
+		clevel = np.concatenate((-np.logspace(0,-8,5),np.logspace(-8,0,5)))
+		plotNorm = colors.SymLogNorm(1e-10,linscale=2)		
+		if cLevel:
+			level = np.concatenate((-np.logspace(0,-10,31),np.logspace(-10,0,31)))
+			clevel = np.concatenate((-np.logspace(0,-8,5),np.logspace(-8,0,5)))
+		else:
+			level = np.linspace(zPlot.min(),zPlot.max(),100)
+			clevel = np.linspace(zPlot.min(),zPlot.max(),10)
+		if colorNorm=='SymLog':
+			plotNorm = colors.SymLogNorm(1e-10,linscale=2)
+		elif colorNorm=='Lin':
+			plotNorm = colors.Normalize()
+	if contour:
+		cs = ax.tricontourf(x,y,zPlot,levels=level,cmap=cmap,norm=plotNorm)#,extend='both')
+	else:
+		uniX,uniY = np.unique(x),np.unique(y)
+		X,Y = np.meshgrid(np.append(uniX-25,uniX[-1]+25),np.append(uniY-25,uniY[-1]+25))
+		cs = ax.pcolor(X,Y,np.reshape(zPlot,(len(uniY),len(uniX))),cmap=cmap,norm=plotNorm)
+	if colorbar:
+		plt.colorbar(cs,cax=ax.cax,ticks=clevel,format='%1.2e')
+		ax.set_title(flag+' '+par,fontsize=8)
+	return cs
+
+def plotIsoFreqNSDiff(ax,freq,arrayList,flag,par='abs',colorbar=True,cLevel=True,mask=None,contourLine=True,useLog=False):
+
+	indUniFreq0 = np.where(freq==arrayList[0]['freq'])
+	indUniFreq1 = np.where(freq==arrayList[1]['freq'])
+	seicmap = plt.get_cmap('RdYlBu')#seismic')
+	x, y = arrayList[0]['x'][indUniFreq0],arrayList[0]['y'][indUniFreq0]
+	if par == 'abs':
+		if useLog:
+			zPlot = (np.log10(np.abs(arrayList[0][flag][indUniFreq0])) - np.log10(np.abs(arrayList[1][flag][indUniFreq1])))/np.log10(np.abs(arrayList[1][flag][indUniFreq1]))
+		else:
+			zPlot = (np.abs(arrayList[0][flag][indUniFreq0]) - np.abs(arrayList[1][flag][indUniFreq1]))/np.abs(arrayList[1][flag][indUniFreq1])
+		if mask:
+			maskInd = np.logical_or(np.abs(arrayList[0][flag][indUniFreq0])< 1e-3,np.abs(arrayList[1][flag][indUniFreq1]) < 1e-3)
+			zPlot = np.ma.array(zPlot)
+			zPlot[maskInd] = mask
+		if cLevel:
+			level = np.arange(-200,201,10) 
+			clevel = np.arange(-200,201,25)
+		else:
+			level = np.linspace(zPlot.min(),zPlot.max(),100)
+			clevel = np.linspace(zPlot.min(),zPlot.max(),10)
+	elif par == 'real':
+		if useLog:
+			zPlot = (np.log10(np.real(arrayList[0][flag][indUniFreq0])) -np.log10(np.real(arrayList[1][flag][indUniFreq1])))/np.log10(np.abs((np.real(arrayList[1][flag][indUniFreq1]))))
+		else:
+			zPlot = (np.real(arrayList[0][flag][indUniFreq0]) -np.real(arrayList[1][flag][indUniFreq1]))/np.abs((np.real(arrayList[1][flag][indUniFreq1])))
+		if mask:
+			maskInd = np.logical_or(np.abs(np.real(arrayList[0][flag][indUniFreq0])) < 1e-3,np.abs(np.real(arrayList[1][flag][indUniFreq1])) < 1e-3)
+			zPlot = np.ma.array(zPlot)
+			zPlot[maskInd] = mask
+		if cLevel:
+			level = np.arange(-200,201,10) 
+			clevel = np.arange(-200,201,25)
+		else:
+			level = np.linspace(zPlot.min(),zPlot.max(),100)
+			clevel = np.linspace(zPlot.min(),zPlot.max(),10)
+	elif par == 'imag':
+		if useLog:
+			zPlot = (np.log10(np.imag(arrayList[0][flag][indUniFreq0])) -np.log10(np.imag(arrayList[1][flag][indUniFreq1])))/np.log10(np.abs((np.imag(arrayList[1][flag][indUniFreq1]))))
+		else:
+			zPlot = (np.imag(arrayList[0][flag][indUniFreq0]) -np.imag(arrayList[1][flag][indUniFreq1]))/np.abs((np.imag(arrayList[1][flag][indUniFreq1])))
+		if mask:
+			maskInd = np.logical_or(np.abs(np.imag(arrayList[0][flag][indUniFreq0])) < 1e-3,np.abs(np.imag(arrayList[1][flag][indUniFreq1])) < 1e-3)
+			zPlot = np.ma.array(zPlot)
+			zPlot[maskInd] = mask
+		if cLevel:
+			level = np.arange(-200,201,10) 
+			clevel = np.arange(-200,201,25)
+		else:
+			level = np.linspace(zPlot.min(),zPlot.max(),100)
+			clevel = np.linspace(zPlot.min(),zPlot.max(),10)
+	cs = ax.tricontourf(x,y,zPlot*100,levels=level*100,cmap=seicmap,extend='both') #,norm=colors.SymLogNorm(1e-2,linscale=2))
+	if contourLine:
+		csl = ax.tricontour(x,y,zPlot*100,levels=clevel*100,colors='k')
+		plt.clabel(csl, fontsize=7, inline=1,fmt='%1.1e',inline_spacing=10)
+	if colorbar:
+		cb = plt.colorbar(cs,cax=ax.cax,ticks=clevel*100,format='%1.1e')
+		for t in cb.ax.get_yticklabels():
+			t.set_rotation(60)
+			t.set_fontsize(8)
+
+	ax.set_title(flag+' '+par,fontsize=8)
+
+def plotIsoFreqNStipper(ax,freq,array,flag,par='abs',colorbar=True,colorNorm='SymLog',cLevel=True,contour=True):
+
+	indUniFreq = np.where(freq==array['freq'])
+
+	x, y = array['x'][indUniFreq],array['y'][indUniFreq]
+	if par == 'abs':
+		cmap = plt.get_cmap('OrRd_r')#seismic')
+		zPlot = np.abs(array[flag][indUniFreq])
+		if cLevel:
+			level = np.logspace(-4,0,33)
+			clevel = np.logspace(-4,0,5)
+		else:
+			level = np.linspace(zPlot.min(),zPlot.max(),100)
+			clevel = np.linspace(zPlot.min(),zPlot.max(),10)
+		if colorNorm=='SymLog':
+			plotNorm = colors.LogNorm()
+		else:
+			plotNorm = colors.Normalize()
+	elif par == 'real':
+		cmap = plt.get_cmap('RdYlBu')
+		zPlot = np.real(array[flag][indUniFreq])
+		if cLevel:
+			level = np.concatenate((-np.logspace(0,-4,33),np.logspace(-4,0,33)))
+			clevel = np.concatenate((-np.logspace(0,-4,5),np.logspace(-4,0,5)))
+		else:
+			level = np.linspace(zPlot.min(),zPlot.max(),100)
+			clevel = np.linspace(zPlot.min(),zPlot.max(),10)
+		if colorNorm=='SymLog':
+			plotNorm = colors.SymLogNorm(1e-4,linscale=2)
+		else:
+			plotNorm = colors.Normalize()
+	elif par == 'imag':
+		cmap = plt.get_cmap('RdYlBu')
+		zPlot = np.imag(array[flag][indUniFreq])
+		if cLevel:
+			level = np.concatenate((-np.logspace(0,-4,33),np.logspace(-4,0,33)))
+			clevel = np.concatenate((-np.logspace(0,-4,5),np.logspace(-4,0,5)))
+		else:
+			level = np.linspace(zPlot.min(),zPlot.max(),100)
+			clevel = np.linspace(zPlot.min(),zPlot.max(),10)
+		if colorNorm=='SymLog':
+			plotNorm = colors.SymLogNorm(1e-4,linscale=2)
+		else:
+			plotNorm = colors.Normalize()
+	if contour:
+		cs = ax.tricontourf(x,y,zPlot,levels=level,cmap=cmap,norm=plotNorm)#,extend='both')
+	else:
+		uniX,uniY = np.unique(x),np.unique(y)
+		X,Y = np.meshgrid(np.append(uniX-25,uniX[-1]+25),np.append(uniY-25,uniY[-1]+25))
+		cs = ax.pcolor(X,Y,np.reshape(zPlot,(len(uniY),len(uniX))),levels=level,cmap=cmap,norm=plotNorm,edgecolors='k', linewidths=0.5)
+	if colorbar:
+		plt.colorbar(cs,cax=ax.cax,ticks=clevel,format='%1.2e')
+	ax.set_title(flag+' '+par,fontsize=8)
+
+def plotIsoStaImpedance(ax,loc,array,flag,par='abs',pSym='s',pColor=None):	
+	
+	appResFact = 1/(8*np.pi**2*10**(-7))
+	treshold = 1.0 # 1 meter
+	indUniSta = np.sqrt(np.sum((rec2nd(array[['x','y']])-loc)**2,axis=1)) < treshold
+	freq = array['freq'][indUniSta]
+	
+	if par == 'abs':
+		zPlot = np.abs(array[flag][indUniSta])
+	elif par == 'real':
+		zPlot = np.real(array[flag][indUniSta])
+	elif par == 'imag':
+		zPlot = np.imag(array[flag][indUniSta])
+	elif par == 'res':
+		zPlot = (appResFact/freq)*np.abs(array[flag][indUniSta])**2
+	elif par == 'phs':
+		zPlot = np.arctan2(array[flag][indUniSta].imag,array[flag][indUniSta].real)*(180/np.pi)
+
+	if not pColor:
+		if 'xx' in flag:
+			lab = 'XX'
+			pColor = 'g'
+		elif 'xy' in flag:
+			lab = 'XY'
+			pColor = 'r'	
+		elif 'yx' in flag:
+			lab = 'YX'
+			pColor = 'b'
+		elif 'yy' in flag:
+			lab = 'YY'
+			pColor = 'y'
+
+	ax.plot(freq,zPlot,color=pColor,marker=pSym,label=flag)
+	
+
+def plotPsudoSectNSimpedance(ax,sectDict,array,flag,par='abs',colorbar=True,colorNorm='None',cLevel=None,contour=True):
+
+	indSect = np.where(sectDict.values()[0]==array[sectDict.keys()[0]])
+
+	# Define the plot axes
+	if 'x' in sectDict.keys()[0]:
+		x = array['y'][indSect]
+	else:
+		x = array['x'][indSect]
+	y = array['freq'][indSect]
+
+	if par == 'abs':
+		zPlot = np.abs(array[flag][indSect])
+		cmap = plt.get_cmap('OrRd_r')#seismic')
+		if cLevel:
+			level = np.logspace(0,-5,31,endpoint=True)
+			clevel = np.logspace(0,-4,5,endpoint=True)
+		else:
+			level = np.linspace(zPlot.min(),zPlot.max(),100,endpoint=True)
+			clevel = np.linspace(zPlot.min(),zPlot.max(),10,endpoint=True)
+		
+	elif par == 'ares':	
+		zPlot = np.abs(array[flag][indSect])**2/(8*np.pi**2*10**(-7)*array['freq'][indSect])
+		cmap = plt.get_cmap('RdYlBu')#seismic)
+		if cLevel:
+			zMax = np.log10(cLevel[1])
+			zMin = np.log10(cLevel[0])
+		else:
+			zMax = (np.ceil(np.log10(np.abs(zPlot).max())))
+			zMin = (np.floor(np.log10(np.abs(zPlot).min())))
+		level = np.logspace(zMin,zMax,(zMax-zMin)*8+1,endpoint=True)
+		clevel = np.logspace(zMin,zMax,(zMax-zMin)*2+1,endpoint=True)
+		plotNorm = colors.LogNorm()
+
+	elif par == 'aphs':
+		zPlot = np.arctan2(array[flag][indSect].imag,array[flag][indSect].real)*(180/np.pi)
+		cmap = plt.get_cmap('RdYlBu')#seismic)
+		if cLevel:
+			zMax = cLevel[1]
+			zMin = cLevel[0]
+		else:
+			zMax = (np.ceil(zPlot).max())
+			zMin = (np.floor(zPlot).min())
+		level = np.arange(zMin,zMax+.1,1)
+		clevel = np.arange(zMin,zMax+.1,10)
+		plotNorm = colors.Normalize()
+
+	elif par == 'real':
+		zPlot = np.real(array[flag][indSect])
+		cmap = plt.get_cmap('Spectral') #('RdYlBu')
+		if cLevel:
+			zMax = np.log10(cLevel[1])
+			zMin = np.log10(cLevel[0])
+		else:
+			zMax = (np.ceil(np.log10(np.abs(zPlot).max())))
+			zMin = (np.floor(np.log10(np.abs(zPlot).min())))
+		level = np.concatenate((-np.logspace(zMax,zMin-.125,(zMax-zMin)*8+1,endpoint=True),np.logspace(zMin-.125,zMax,(zMax-zMin)*8+1,endpoint=True)))
+		clevel = np.concatenate((-np.logspace(zMax,zMin,(zMax-zMin)*1+1,endpoint=True),np.logspace(zMin,zMax,(zMax-zMin)*1+1,endpoint=True)))
+		plotNorm = colors.SymLogNorm(np.abs(level).min(),linscale=0.1)
+	elif par == 'imag':
+		zPlot = np.imag(array[flag][indSect])
+		cmap = plt.get_cmap('Spectral') #('RdYlBu')
+		
+		if cLevel:
+			zMax = np.log10(cLevel[1])
+			zMin = np.log10(cLevel[0])
+		else:
+			zMax = (np.ceil(np.log10(np.abs(zPlot).max())))
+			zMin = (np.floor(np.log10(np.abs(zPlot).min())))
+		level = np.concatenate((-np.logspace(zMax,zMin-.125,(zMax-zMin)*8+1,endpoint=True),np.logspace(zMin-.125,zMax,(zMax-zMin)*8+1,endpoint=True)))
+		clevel = np.concatenate((-np.logspace(zMax,zMin,(zMax-zMin)*1+1,endpoint=True),np.logspace(zMin,zMax,(zMax-zMin)*1+1,endpoint=True)))
+		plotNorm = colors.SymLogNorm(np.abs(level).min(),linscale=0.1)
+	
+	if colorNorm=='SymLog':
+		plotNorm = colors.SymLogNorm(np.abs(level).min(),linscale=0.1)
+	elif colorNorm=='Lin':
+		plotNorm = colors.Normalize()
+	elif colorNorm=='Log':
+		plotNorm = colors.LogNorm()
+	if contour:
+		cs = ax.tricontourf(x,y,zPlot,levels=level,cmap=cmap,norm=plotNorm)#,extend='both')
+	else:
+		uniX,uniY = np.unique(x),np.unique(y)
+		X,Y = np.meshgrid(np.append(uniX-25,uniX[-1]+25),np.append(uniY-25,uniY[-1]+25))
+		cs = ax.pcolor(X,Y,np.reshape(zPlot,(len(uniY),len(uniX))),cmap=cmap,norm=plotNorm)
+	if colorbar:
+		csB = plt.colorbar(cs,cax=ax.cax,ticks=clevel,format='%1.2e')
+		# csB.on_mappable_changed(cs)
+		ax.set_title(flag+' '+par,fontsize=8)
+		return cs, csB
+	return cs,None
+
+
+def plotPsudoSectNSDiff(ax,sectDict,arrayList,flag,par='abs',colorbar=True,colorNorm='SymLog',cLevel=None,contour=True,mask=None,useLog=False):
+
+	def sortInArr(arr):
+		return np.sort(arr,order=['freq','x','y','z'])
+	# Find the index for the slice
+	indSect0 = np.where(sectDict.values()[0]==arrayList[0][sectDict.keys()[0]])
+	indSect1 = np.where(sectDict.values()[0]==arrayList[1][sectDict.keys()[0]])
+	# Extract and sort the mats
+	arr0 = sortInArr(arrayList[0][indSect0])
+	arr1 = sortInArr(arrayList[1][indSect1])
+
+	# Define the plot axes
+	if 'x' in sectDict.keys()[0]:
+		x0 = arr0['y']
+		x1 = arr1['y']
+	else:
+		x0 = arr0['x']
+		x1 = arr1['x']
+	y0 = arr0['freq']
+	y1 = arr1['freq']
+	
+
+	if par == 'abs':
+		if useLog:
+			zPlot = (np.log10(np.abs(arr0[flag])) - np.log10(np.abs(arr1[flag])))/np.log10(np.abs(arr1[flag]))
+		else:
+			zPlot = (np.abs(arr0[flag]) - np.abs(arr1[flag]))/np.abs(arr1[flag])
+		if mask:
+			maskInd = np.logical_or(np.abs(arr0[flag])< 1e-3,np.abs(arr1[flag]) < 1e-3)
+			zPlot = np.ma.array(zPlot)
+			zPlot[maskInd] = mask
+		cmap = plt.get_cmap('RdYlBu')#seismic)
+	elif par == 'ares':
+		arF = 1/(8*np.pi**2*10**(-7))		
+		if useLog:
+			zPlot = (np.log10((arF/arr0['freq'])*np.abs(arr0[flag])**2) - np.log10((arF/arr1['freq'])*np.abs(arr1[flag])**2))/np.log10((arF/arr1['freq'])*np.abs(arr1[flag])**2)
+		else:
+			zPlot = ((arF/arr0['freq'])*np.abs(arr0[flag])**2 - (arF/arr1['freq'])*np.abs(arr1[flag])**2)/((arF/arr1['freq'])*np.abs(arr1[flag])**2)
+		if mask:
+			maskInd = np.logical_or(np.abs(arr0[flag])< 1e-3,np.abs(arr1[flag]) < 1e-3)
+			zPlot = np.ma.array(zPlot)
+			zPlot[maskInd] = mask
+		cmap = plt.get_cmap('Spectral')#seismic)
+	
+	elif par == 'aphs':
+		if useLog:
+			zPlot = (np.log10(np.arctan2(arr0[flag].imag,arr0[flag].real)*(180/np.pi)) - np.log10(np.arctan2(arr1[flag].imag,arr1[flag].real)*(180/np.pi)) )/np.log10(np.arctan2(arr1[flag].imag,arr1[flag].real)*(180/np.pi))
+		else:
+			zPlot = ( np.arctan2(arr0[flag].imag,arr0[flag].real)*(180/np.pi) - np.arctan2(arr1[flag].imag,arr1[flag].real)*(180/np.pi) )/(np.arctan2(arr1[flag].imag,arr1[flag].real)*(180/np.pi))
+		if mask:
+			maskInd = np.logical_or(np.abs(arr0[flag])< 1e-3,np.abs(arr1[flag]) < 1e-3)
+			zPlot = np.ma.array(zPlot)
+			zPlot[maskInd] = mask
+		cmap = plt.get_cmap('Spectral')#seismic)
+	elif par == 'real':
+		if useLog:
+			zPlot = (np.log10(arr0[flag].real) - np.log10(arr1[flag].real))/np.log10(arr1[flag].real)
+		else:
+			zPlot = (arr0[flag].real - arr1[flag].real)/arr1[flag].real
+		if mask:
+			maskInd = np.logical_or(arr0[flag].real< 1e-3,arr1[flag].real < 1e-3)
+			zPlot = np.ma.array(zPlot)
+			zPlot[maskInd] = mask
+		cmap = plt.get_cmap('Spectral') #('Spectral')
+		
+	elif par == 'imag':
+		if useLog:
+			zPlot = (np.log10(arr0[flag].imag) - np.log10(arr1[flag].imag))/np.log10(arr1[flag].imag)
+		else:
+			zPlot = (arr0[flag].imag - arr1[flag].imag)/arr1[flag].imag
+		if mask:
+			maskInd = np.logical_or(arr0[flag].imag< 1e-3,arr1[flag].imag < 1e-3)
+			zPlot = np.ma.array(zPlot)
+			zPlot[maskInd] = mask
+		cmap = plt.get_cmap('Spectral') #('RdYlBu')
+		
+	if cLevel:
+		zMax = np.log10(cLevel[1])
+		zMin = np.log10(cLevel[0])
+	else:
+		zMax = (np.ceil(np.log10(np.abs(zPlot).max())))
+		zMin = (np.floor(np.log10(np.abs(zPlot).min())))
+
+	
+	if colorNorm=='SymLog':
+		level = np.concatenate((-np.logspace(zMax,zMin-.125,(zMax-zMin)*8+1,endpoint=True),np.logspace(zMin-.125,zMax,(zMax-zMin)*8+1,endpoint=True)))
+		clevel = np.concatenate((-np.logspace(zMax,zMin,(zMax-zMin)*1+1,endpoint=True),np.logspace(zMin,zMax,(zMax-zMin)*1+1,endpoint=True)))
+		plotNorm = colors.SymLogNorm(np.abs(level).min(),linscale=0.1)
+	elif colorNorm=='Lin':
+		if cLevel:
+			level = np.arange(cLevel[0],cLevel[1]+.1,(cLevel[1] - cLevel[0])/50.) 
+			clevel = np.arange(cLevel[0],cLevel[1]+.1,(cLevel[1] - cLevel[0])/10.)
+		else:
+			level = np.arange(zPlot.min(),zPlot.max(),(zPlot.max() - zPlot.min())/50.)
+			clevel = np.arange(zPlot.min(),zPlot.max(),(zPlot.max() - zPlot.min())/10.)
+		plotNorm = colors.Normalize()
+	elif colorNorm=='Log':
+		level = np.logspace(zMin-.125,zMax,(zMax-zMin)*8+1,endpoint=True)
+		clevel = np.logspace(zMin,zMax,(zMax-zMin)*2+1,endpoint=True)
+		plotNorm = colors.LogNorm()
+	if contour:
+		cs = ax.tricontourf(x0,y0,zPlot*100,levels=level*100,cmap=cmap,norm=plotNorm,extend='both')#,extend='both')
+	else:
+		uniX,uniY = np.unique(x0),np.unique(y0)
+		X,Y = np.meshgrid(np.append(uniX-25,uniX[-1]+25),np.append(uniY-25,uniY[-1]+25))
+		cs = ax.pcolor(X,Y,np.reshape(zPlot,(len(uniY),len(uniX))),cmap=cmap,norm=plotNorm)
+	if colorbar:
+		csB = plt.colorbar(cs,cax=ax.cax,ticks=clevel*100,format='%1.2e')
+		# csB.on_mappable_changed(cs)
+		ax.set_title(flag+' '+par + ' diff',fontsize=8)
+		return cs, csB
+	return cs,None
diff --git a/simpegMT/Utils/srcUtils.py b/simpegMT/Utils/srcUtils.py
new file mode 100644
index 00000000..f0163564
--- /dev/null
+++ b/simpegMT/Utils/srcUtils.py
@@ -0,0 +1,46 @@
+import SimPEG as simpeg, numpy as np
+
+def homo1DModelSource(mesh,freq,m_back):
+    '''
+        Function that calculates and return background fields for a 3D mesh and model.
+        The calculuations use 1D field solution for a vertical slice throught model (south-western most column),
+        which is assigned at the fields everywhere for the respective polarizations.2
+
+        :param Simpeg mesh object mesh: Holds information on the discretization
+        :param float freq: The frequency to solve at
+        :param np.array m_back: Background model of conductivity to base the calculations on.
+        :rtype: numpy.ndarray (mesh.nE,2)
+        :return: eBG_bp, E fields for the background model at both polarizations.
+
+    '''
+
+    # import
+    from simpegMT.Utils import get1DEfields
+    # Get a 1d solution for a halfspace background
+    mesh1d = simpeg.Mesh.TensorMesh([mesh.hz],np.array([mesh.x0[2]]))
+    # Note: Everything is using e^iwt
+    e0_1d = get1DEfields(mesh1d,mesh.r(m_back,'CC','CC','M')[0,0,:],freq)
+    # Setup x (east) polarization (_x)
+    ex_px = np.zeros(mesh.vnEx,dtype=complex)
+    ey_px = np.zeros((mesh.nEy,1),dtype=complex)
+    ez_px = np.zeros((mesh.nEz,1),dtype=complex)
+    # Assign the source to ex_x
+    for i in np.arange(mesh.vnEx[0]):
+        for j in np.arange(mesh.vnEx[1]):
+            ex_px[i,j,:] = -e0_1d
+    eBG_px = np.vstack((simpeg.Utils.mkvc(ex_px,2),ey_px,ez_px))
+    # Setup y (north) polarization (_py)
+    ex_py = np.zeros((mesh.nEx,1), dtype='complex128')
+    ey_py = np.zeros(mesh.vnEy, dtype='complex128')
+    ez_py = np.zeros((mesh.nEz,1), dtype='complex128')
+    # Assign the source to ey_py
+    
+    for i in np.arange(mesh.vnEy[0]):
+        for j in np.arange(mesh.vnEy[1]):
+            ey_py[i,j,:] = e0_1d
+    # ey_py[1:-1,1:-1,1:-1] = 0
+    eBG_py = np.vstack((ex_py,simpeg.Utils.mkvc(ey_py,2),ez_py))
+
+    # Return the electric fields
+    eBG_bp = np.hstack((eBG_px,eBG_py))
+    return eBG_bp
\ No newline at end of file
diff --git a/simpegMT/__init__.py b/simpegMT/__init__.py
index fe5cf927..a578bbe8 100644
--- a/simpegMT/__init__.py
+++ b/simpegMT/__init__.py
@@ -2,5 +2,7 @@
 import Utils
 # import Tests
 import Sources
-import ProblemMT
-import SurveyMT
+# from BaseMT import SurveyMT, RxMT, srcMT, DataMT, FieldsMT
+import BaseMT
+import SurveyMT, DataMT, FieldsMT
+import ProblemMT1D, ProblemMT2D, ProblemMT3D