diff --git a/MT 1D code test.ipynb b/MT 1D code test.ipynb
index cae26cb7..b412bad1 100644
--- a/MT 1D code test.ipynb	
+++ b/MT 1D code test.ipynb	
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:f84d7f6fac432ae58377d9da7313463a2286c599c51cd1ac6b9a6ad5cb6ebd91"
+  "signature": "sha256:c4b44464df09aacf6b083748f1a6b724084741da8ed0844c16bc39b2fbc25d29"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -25,14 +25,26 @@
      "input": [
       "# import the simpegMT module\n",
       "from simpegMT.Utils import MT1Danalytic, MT1Dsolutions\n",
-      "\n",
+      "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": [],
+     "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": 2
     },
     {
@@ -40,16 +52,44 @@
      "collapsed": false,
      "input": [
       "# Set up the mesh.\n",
-      "freq = 10.0\n",
-      "hz = [(100.,50)]\n",
+      "freq = 10\n",
+      "z = 100.\n",
+      "hz = [(z,10,-1.5),(z,10),(z,10,1.5)]\n",
       "M = simpeg.Mesh.TensorMesh([hz],'C')\n",
       "sig = np.zeros(M.nC) + 1e-8\n",
-      "sig[M.vectorCCx<=0] = 1.0\n"
+      "sig[M.vectorCCx<=300] = 0.01\n"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "M.vectorNx"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 27,
+       "text": [
+        "array([-17499.51171875, -11733.0078125 ,  -7888.671875  ,  -5325.78125   ,\n",
+        "        -3617.1875    ,  -2478.125     ,  -1718.75      ,  -1212.5       ,\n",
+        "         -875.        ,   -650.        ,   -500.        ,   -400.        ,\n",
+        "         -300.        ,   -200.        ,   -100.        ,      0.        ,\n",
+        "          100.        ,    200.        ,    300.        ,    400.        ,\n",
+        "          500.        ,    650.        ,    875.        ,   1212.5       ,\n",
+        "         1718.75      ,   2478.125     ,   3617.1875    ,   5325.78125   ,\n",
+        "         7888.671875  ,  11733.0078125 ,  17499.51171875])"
+       ]
+      }
+     ],
+     "prompt_number": 27
     },
     {
      "cell_type": "code",
@@ -57,22 +97,26 @@
      "input": [
       "# Get the fields\n",
       "anaEd, anaEu, anaHd, anaHu = MT1Danalytic.getEHfields(M,sig,freq,M.vectorNx)\n",
-      "anaEtemp = anaEd+anaEu\n",
+      "anaEtemp = (anaEd+anaEu)\n",
+      "anaHtemp = (anaHd+anaHu)\n",
       "# Scale the solution\n",
-      "anaE = anaEtemp/anaEtemp[-1]\n",
+      "anaZ = (anaEtemp/anaHtemp)[np.argmin(M.vectorNx**2)]\n",
+      "anaEcor = anaEtemp/anaEtemp[-1] #.real/np.abs(anaEtemp[-1].real)+1j*anaEtemp.imag/np.abs(anaEtemp[-1].imag)\n",
+      "anaHcor = anaHtemp/anaEtemp[-1] # .real/np.abs(anaEtemp[-1].real)+1j*anaHtemp.imag/np.abs(anaEtemp[-1].imag)\n",
       "\n",
-      "solE = MT1Dsolutions.get1DEfields(M,sig,freq)\n"
+      "solE = MT1Dsolutions.get1DEfields(M,sig,freq,sourceAmp=1).conj()\n",
+      "solH = -M.nodalGrad*solE/(1j*omega(freq)*mu_0)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 28
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "plot(solE.real,M.vectorNx,'r*--',anaE.real,M.vectorNx,'b+:')"
+      "anaEtemp[-1]"
      ],
      "language": "python",
      "metadata": {},
@@ -80,28 +124,130 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 5,
+       "prompt_number": 29,
        "text": [
-        "[<matplotlib.lines.Line2D at 0x7f5b370f1a50>,\n",
-        " <matplotlib.lines.Line2D at 0x7f5b370f1cd0>]"
+        "(922807.04800415318-689021.35510797054j)"
+       ]
+      }
+     ],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "np.hstack((simpeg.mkvc(anaEcor,2),simpeg.mkvc(solE,2)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 30,
+       "text": [
+        "array([[  6.95763292e-07 +5.19497296e-07j,\n",
+        "          6.95763292e-07 -5.19497296e-07j],\n",
+        "       [ -1.40834457e-05 -2.93173032e-05j,\n",
+        "          9.74760218e-06 -1.09092785e-05j],\n",
+        "       [  3.35815988e-04 +1.40732501e-04j,\n",
+        "          1.74907169e-04 +1.24344756e-04j],\n",
+        "       [ -7.70118008e-04 +1.65141317e-03j,\n",
+        "         -5.21083562e-04 +1.34839517e-03j],\n",
+        "       [ -5.32123445e-03 +3.24450003e-04j,\n",
+        "         -4.87010903e-03 +6.63069113e-04j],\n",
+        "       [ -8.64982593e-03 -6.64134560e-03j,\n",
+        "         -8.61854919e-03 -6.03020882e-03j],\n",
+        "       [ -7.46721859e-03 -1.59079740e-02j,\n",
+        "         -7.68552978e-03 -1.53974787e-02j],\n",
+        "       [ -2.91038457e-03 -2.39785146e-02j,\n",
+        "         -3.16876824e-03 -2.35863583e-02j],\n",
+        "       [  2.72167021e-03 -2.97359468e-02j,\n",
+        "          2.49400722e-03 -2.94018473e-02j],\n",
+        "       [  7.92975642e-03 -3.34680056e-02j,\n",
+        "          7.73825040e-03 -3.31542272e-02j],\n",
+        "       [  1.21356998e-02 -3.57924928e-02j,\n",
+        "          1.19706555e-02 -3.54839735e-02j],\n",
+        "       [  1.52903430e-02 -3.72269264e-02j,\n",
+        "          1.51424717e-02 -3.69189921e-02j],\n",
+        "       [  1.87388388e-02 -3.85404390e-02j,\n",
+        "          1.86057886e-02 -3.82344505e-02j],\n",
+        "       [  2.24915402e-02 -3.97057953e-02j,\n",
+        "          2.23709926e-02 -3.94030035e-02j],\n",
+        "       [  2.65576291e-02 -4.06933593e-02j,\n",
+        "          2.64473102e-02 -4.03949222e-02j],\n",
+        "       [  3.09448819e-02 -4.14710213e-02j,\n",
+        "          3.08425733e-02 -4.11780213e-02j],\n",
+        "       [  3.56594159e-02 -4.20041369e-02j,\n",
+        "          3.55629651e-02 -4.17175972e-02j],\n",
+        "       [  4.07054159e-02 -4.22554788e-02j,\n",
+        "          4.06127458e-02 -4.19763792e-02j],\n",
+        "       [  4.60848403e-02 -4.21852042e-02j,\n",
+        "          4.59939588e-02 -4.19144958e-02j],\n",
+        "       [  5.16310109e-02 -4.19401825e-02j,\n",
+        "          5.15406437e-02 -4.16710354e-02j],\n",
+        "       [  5.71771819e-02 -4.16951603e-02j,\n",
+        "          5.70873289e-02 -4.14275746e-02j],\n",
+        "       [  6.54964388e-02 -4.13276262e-02j,\n",
+        "          6.54073573e-02 -4.10623825e-02j],\n",
+        "       [  7.79753255e-02 -4.07763228e-02j,\n",
+        "          7.78874014e-02 -4.05145921e-02j],\n",
+        "       [  9.66936582e-02 -3.99493614e-02j,\n",
+        "          9.66074704e-02 -3.96929008e-02j],\n",
+        "       [  1.24771163e-01 -3.87089021e-02j,\n",
+        "          1.24687581e-01 -3.84603474e-02j],\n",
+        "       [  1.66887432e-01 -3.68481633e-02j,\n",
+        "          1.66807761e-01 -3.66114701e-02j],\n",
+        "       [  2.30061859e-01 -3.40569049e-02j,\n",
+        "          2.29988062e-01 -3.38380118e-02j],\n",
+        "       [  3.24823538e-01 -2.98695515e-02j,\n",
+        "          3.24758579e-01 -2.96773825e-02j],\n",
+        "       [  4.66966101e-01 -2.35870424e-02j,\n",
+        "          4.66914483e-01 -2.34350351e-02j],\n",
+        "       [  6.80179899e-01 -1.41584953e-02j,\n",
+        "          6.80148567e-01 -1.40669735e-02j],\n",
+        "       [  1.00000000e+00 +0.00000000e+00j,\n",
+        "          1.00000000e+00 -0.00000000e+00j]])"
+       ]
+      }
+     ],
+     "prompt_number": 30
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot(solE.real,M.vectorNx,'r*--',anaEcor.real,M.vectorNx,'b+:')\n",
+      "#axis([-.2,.2,-10000,10000])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 31,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x7f4f0d873110>,\n",
+        " <matplotlib.lines.Line2D at 0x7f4f0d873390>]"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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B3MiGWo2UgxCR0FAOIgSyIxGaDxvG9vzDlYMQkdDQCCLJYjmIXd/8msN31VAO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\nIiIJKUCIiEhC/w8ZM2f6EBPGOgAAAABJRU5ErkJggg==\n",
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5vMX/0SenIR0GDCClU+ewQxORak5dVUkqPd0YPmQ1Z/ofqM8Wevp4hg9ZTXp6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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f5b379ae790>"
+        "<matplotlib.figure.Figure at 0x7f4f0dbf3a90>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 31
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "plot(solE.imag,M.vectorNx,'r*--',-anaE.imag,M.vectorNx,'b+:')"
+      "plot((abs(solE.real)-abs(anaEcor.real))/abs(anaEcor.real),M.vectorNx,'b+:')"
      ],
      "language": "python",
      "metadata": {},
@@ -109,28 +255,27 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 6,
+       "prompt_number": 32,
        "text": [
-        "[<matplotlib.lines.Line2D at 0x7f5b36fded50>,\n",
-        " <matplotlib.lines.Line2D at 0x7f5b36fdefd0>]"
+        "[<matplotlib.lines.Line2D at 0x7f4f0d7d6e10>]"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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E7Gzeu/IlPq7fN2/Wzuuvw4wZ8NoFY+H113l3WyKfbjuNx856C2rWJPDbW1gQ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\nAUNDvu2HAq8ANYD3nXMz/fU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n3DpW2kZ6KHCvXtClS1iL64orwmTENWtCZ3smE5q9eveGE04I97vnvl5EKocC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\nrkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f5b379c6810>"
+        "<matplotlib.figure.Figure at 0x7f4f0d80a210>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 32
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "semilogx(abs(solE),M.vectorNx,'r*--',abs(anaE),M.vectorNx,'b+:')"
+      "plot(solE.imag,M.vectorNx,'r*--',anaEcor.imag,M.vectorNx,'b+:')"
      ],
      "language": "python",
      "metadata": {},
@@ -138,28 +283,28 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 7,
+       "prompt_number": 33,
        "text": [
-        "[<matplotlib.lines.Line2D at 0x7f5b36fcda50>,\n",
-        " <matplotlib.lines.Line2D at 0x7f5b36f1fd50>]"
+        "[<matplotlib.lines.Line2D at 0x7f4f0d71a410>,\n",
+        " <matplotlib.lines.Line2D at 0x7f4f0d71a690>]"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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2Tat+wwk1voKtW3l4YSc6Dz2R7t3D97v8chgwAC6tPR/69ePhKrdxzrFrOavF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\noRgUo6EAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f5b370013d0>"
+        "<matplotlib.figure.Figure at 0x7f4f0d7f8550>"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 33
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "anaE[1].conj()"
+      "plot((abs(solE.imag)-abs(anaEcor.imag))/abs(anaEcor.imag),M.vectorNx,'b+:')"
      ],
      "language": "python",
      "metadata": {},
@@ -167,13 +312,201 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 8,
+       "prompt_number": 34,
        "text": [
-        "(-1.2220068301411678e-08-1.5509245597740526e-09j)"
+        "[<matplotlib.lines.Line2D at 0x7f4f0d657a10>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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z+1NeAGhmZr3CicMamrumzHqeu6rMzPopd1WZmVmvcOIwM7OKOHGYmVlFnDjM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\nM6uIE4eZmVXEicPMzCry/w6nUYe43AHvAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4f0d81f850>"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 34
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "semilogx(abs(solE),M.vectorNx,'r*--',abs(anaEcor),M.vectorNx,'b+:')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 35,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x7f4f0d638650>,\n",
+        " <matplotlib.lines.Line2D at 0x7f4f0d58b9d0>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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hmdkvzewjM9tuZkeXO5ZjZoVmttTMBkSV9zCzReGxu6PKM83s8bD8LTPrGHXs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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4f0d6175d0>"
+       ]
+      }
+     ],
+     "prompt_number": 35
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot((abs(solE)-abs(anaEcor))/abs(anaEcor),M.vectorNx,'b+:')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 36,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x7f4f0d35efd0>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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UU9CjR5w4Jk4M27/kEtK8edCzp85mkeqyxcTh7sfn+4Xuvh5YH90/b2ZvAHWE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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4f0d462bd0>"
+       ]
+      }
+     ],
+     "prompt_number": 36
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def appResPhs(freq,z):\n",
+      "    app_res = ((1./(8e-7*np.pi**2))/freq)*np.abs(z)**2\n",
+      "    app_phs = np.arctan2(z.imag,z.real)*(180/np.pi)\n",
+      "    return app_res, app_phs\n",
+      "app_rAna, app_pAna = appResPhs(freq,anaZ)\n",
+      "app_rSol, app_pSol = appResPhs(freq,solE[np.argmin(M.hx**2)]/solH[np.argmin(M.hx**2)])\n",
+      "print app_rAna, app_pAna\n",
+      "print app_rSol, app_pSol"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "100.0 44.999998407\n",
+        "91.3634893888 -137.014649098\n"
+       ]
+      }
+     ],
+     "prompt_number": 37
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "M.nodalGrad.dot(solE).shape"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 38,
+       "text": [
+        "(30,)"
+       ]
+      }
+     ],
+     "prompt_number": 38
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "M.nN\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 39,
+       "text": [
+        "31"
+       ]
+      }
+     ],
+     "prompt_number": 39
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot(-solH.imag,M.vectorCCx,'r*--',anaHcor.imag,M.vectorNx,'b+:')\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 42,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x7f4f0d209810>,\n",
+        " <matplotlib.lines.Line2D at 0x7f4f0d209a90>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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SbHmUL18VlBP8LABw913AVjNrjohILZFIwNq1MH36ngdU3f/BB1zw6KMVP6Y3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\nrkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4f0d733610>"
+       ]
+      }
+     ],
+     "prompt_number": 42
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "M.vectorCCx"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 41,
+       "text": [
+        "array([-14616.25976562,  -9810.83984375,  -6607.2265625 ,  -4471.484375  ,\n",
+        "        -3047.65625   ,  -2098.4375    ,  -1465.625     ,  -1043.75      ,\n",
+        "         -762.5       ,   -575.        ,   -450.        ,   -350.        ,\n",
+        "         -250.        ,   -150.        ,    -50.        ,     50.        ,\n",
+        "          150.        ,    250.        ,    350.        ,    450.        ,\n",
+        "          575.        ,    762.5       ,   1043.75      ,   1465.625     ,\n",
+        "         2098.4375    ,   3047.65625   ,   4471.484375  ,   6607.2265625 ,\n",
+        "         9810.83984375,  14616.25976562])"
+       ]
+      }
+     ],
+     "prompt_number": 41
     },
     {
      "cell_type": "code",
@@ -182,7 +515,15 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 8
+     "prompt_number": 41
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}
diff --git a/MT Script-3D_layerTest-working.ipynb b/MT Script-3D_layerTest-working.ipynb
new file mode 100644
index 00000000..d422e161
--- /dev/null
+++ b/MT Script-3D_layerTest-working.ipynb	
@@ -0,0 +1,818 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:7b047ea0d01a07946777c0203bdee5b1513cf1b7fad4ebbb2354487852002c17"
+ },
+ "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.,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'])"
+     ],
+     "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": [
+        "[-2478.125 -1718.75  -1212.5    -875.     -650.     -500.     -400.     -300.\n",
+        "  -200.     -100.        0.      100.      200.      300.      400.      500.\n",
+        "   650.      875.     1212.5    1718.75   2478.125]\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Setup the model\n",
+      "conds = [1,1e-2]\n",
+      "elev = 300\n",
+      "sig = simpeg.Utils.ModelBuilder.defineBlock(M.gridCC,[-10000,-10000,-200],[10000,10000,0],conds)\n",
+      "sig[M.gridCC[:,2]>elev] = 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": 35,
+       "text": [
+        "<matplotlib.colorbar.Colorbar instance at 0x7f4226103c68>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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9IC3LjSzZMAPWToVeo6DkACy6z9XZV4UEXPQxfO8RyKxT1s/a6jyXbIo+jn77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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4221670e90>"
+       ]
+      }
+     ],
+     "prompt_number": 35
+    },
+    {
+     "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": [
+      "%%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 15.9 s, sys: 271 ms, total: 16.1 s\n",
+        "Wall time: 16.6 s\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "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=1).conj()\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(ex_x,2),ey_x,ez_x))\n",
+      "rhs_x = -ABG.dot(eBG_x)"
+     ],
+     "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 ey_y\n",
+      "for i in arange(M.vnEy[0]):\n",
+      "    for j in arange(M.vnEy[1]):\n",
+      "        ey_y[i,j,:] = e0_1d  \n",
+      "        \n",
+      "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",
+      "e_x = Ainv*rhs_x\n",
+      "e_y = Ainv*rhs_y"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "CPU times: user 194 ms, sys: 1 ms, total: 195 ms\n",
+        "Wall time: 431 ms\n"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "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": 14
+    },
+    {
+     "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": 15
+    },
+    {
+     "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": 16
+    },
+    {
+     "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='Y', 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='Y', 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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CZVwwpVqXlW+yZ3U09DAD57l9Lxbgjb8Of/k6WN4hBV8jwMSDjFnJubjALkk9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\nI2fMKDcMwzAMwzCMnPn/eV0qK2mTn2kAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4224fcd610>"
+       ]
+      }
+     ],
+     "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),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')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 36
+    },
+    {
+     "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",
+      "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)])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 37
+    },
+    {
+     "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": 38
+    },
+    {
+     "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": 39
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 40,
+       "text": [
+        "array([-0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
+        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
+        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
+        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
+        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
+        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
+        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
+        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
+        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
+        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
+        "       -0.00673364-0.02865302j, -0.00673374-0.02865389j,\n",
+        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
+        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
+        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
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+        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
+        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
+        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
+        "       -0.00673364-0.02865302j, -0.00673364-0.02865302j,\n",
+        "       -0.00673364-0.02865302j])"
+       ]
+      }
+     ],
+     "prompt_number": 40
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ind = np.where(np.sum(np.power(rx_loc - np.array([0,0,elev]),2),axis=1)< 5)\n",
+      "def appResPhs(freq,z):\n",
+      "    app_res = ((1./(8e-7*np.pi**2))/freq)*np.abs(z)**2\n",
+      "    app_phs = np.arctan2(z.imag,z.real)*(180/np.pi)\n",
+      "    return app_res, app_phs\n",
+      "print appResPhs(freq,zxy[ind])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "(array([ 10.97500457]), array([-103.22375266]))\n"
+       ]
+      }
+     ],
+     "prompt_number": 43
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "e0_1d\n",
+      "h0_1dC = -(mesh1d.nodalGrad*e0_1d)/(1j*omega(freq)*mu_0)\n",
+      "h0_1d = mesh1d.getInterpolationMat(mesh1d.vectorNx,'CC')*h0_1dC\n",
+      "\n",
+      "print e0_1d, h0_1d, appResPhs(freq,e0_1d/h0_1d)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[ -6.20279168e-09 -4.39881198e-09j  -3.59098481e-07 -1.31996127e-07j\n",
+        "   2.74444236e-06 -9.30035530e-06j   1.09368959e-04 +1.54406904e-05j\n",
+        "   1.03302758e-04 +5.78395328e-04j  -1.18516132e-03 +1.18309882e-03j\n",
+        "  -3.21180911e-03 +4.16527153e-04j  -5.56733355e-03 -2.88598734e-03j\n",
+        "  -5.64417373e-03 -1.05842922e-02j   2.63600826e-03 -2.27390580e-02j\n",
+        "   2.88702308e-02 -3.28125152e-02j   6.80583153e-02 -3.14884619e-02j\n",
+        "   1.07246400e-01 -3.01644081e-02j   1.46434485e-01 -2.88403534e-02j\n",
+        "   1.85622570e-01 -2.75162976e-02j   2.24810656e-01 -2.61922403e-02j\n",
+        "   2.83592784e-01 -2.42061510e-02j   3.71765978e-01 -2.12270077e-02j\n",
+        "   5.04025769e-01 -1.67582648e-02j   7.02415460e-01 -1.00550655e-02j\n",
+        "   1.00000000e+00 -0.00000000e+00j] [  2.12811704e-06 -5.88572986e-06j   1.38473321e-04 +4.42315497e-05j\n",
+        "  -4.65316847e-04 +2.43179550e-03j  -1.93844533e-02 +1.39561396e-03j\n",
+        "  -4.33100215e-02 -6.54109382e-02j   3.78293705e-02 -1.97523000e-01j\n",
+        "   2.57677892e-01 -2.77504299e-01j   6.96635025e-01 -1.54031289e-01j\n",
+        "   1.25721039e+00 +5.19482689e-01j   1.40761866e+00 +2.18565020e+00j\n",
+        "   5.54062471e-01 +4.14291599e+00j  -1.67693342e-01 +4.96322889e+00j\n",
+        "  -1.67693429e-01 +4.96322893e+00j  -1.67693556e-01 +4.96322895e+00j\n",
+        "  -1.67693722e-01 +4.96322898e+00j  -1.67693927e-01 +4.96322901e+00j\n",
+        "  -1.67694309e-01 +4.96322905e+00j  -1.67695046e-01 +4.96322910e+00j\n",
+        "  -1.67696524e-01 +4.96322916e+00j  -1.67699578e-01 +4.96322923e+00j\n",
+        "  -1.67702245e-01 +4.96322927e+00j] (array([  1.86964165e-02,   8.77304283e-02,   1.94267016e-01,\n",
+        "         4.09088305e-01,   7.10419412e-01,   8.78131542e-01,\n",
+        "         9.26369448e-01,   9.78429149e-01,   9.84798832e-01,\n",
+        "         9.81985367e-01,   1.38473542e+00,   2.88794941e+00,\n",
+        "         6.37406946e+00,   1.14393261e+01,   1.80837193e+01,\n",
+        "         2.63072489e+01,   4.16034243e+01,   7.12096690e+01,\n",
+        "         1.30608494e+02,   2.53433007e+02,   5.13553916e+02]), array([ -74.53559993, -177.53243593, -174.39163289, -167.84610729,\n",
+        "       -156.61696865, -145.79203492, -140.26761361, -140.13080193,\n",
+        "       -140.51973521, -140.6048989 , -131.03954507, -116.7636348 ,\n",
+        "       -107.64443593, -103.07696138, -100.36712929,  -98.5805876 ,\n",
+        "        -96.8138101 ,  -95.20305726,  -93.83947719,  -92.75532863,\n",
+        "        -91.93522727]))\n"
+       ]
+      }
+     ],
+     "prompt_number": 42
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "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()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 32
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "anaZ = anaE/anaH\n",
+      "print anaZ, appResPhs(freq,anaZ)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[ 0.01986918+0.01986918j  0.01986918+0.01986918j  0.01986918+0.01986918j\n",
+        "  0.01986918+0.01986918j  0.01986918+0.01986918j  0.02022782+0.03020735j\n",
+        "  0.02093098+0.03672601j  0.02206142+0.04291033j  0.02364531+0.04871254j\n",
+        "  0.00963058+0.00415596j  0.00619719+0.00527786j  0.00624787+0.01305949j\n",
+        "  0.00649834+0.02074006j  0.00705940+0.02830392j  0.00705940+0.0361996j\n",
+        "  0.00705940+0.04409528j  0.00705940+0.05593881j  0.00705941+0.07370409j\n",
+        "  0.00705944+0.10035202j  0.00705951+0.14032392j  0.00705974+0.20028176j] (array([  10.        ,   10.        ,   10.        ,   10.        ,\n",
+        "         10.        ,   16.73887525,   22.63143218,   29.48449815,\n",
+        "         37.13437077,    1.39342121,    0.83920449,    2.65444118,\n",
+        "          5.98274409,   10.77736658,   17.22771793,   25.25720583,\n",
+        "         40.26231869,   69.43197022,  128.1759076 ,  250.01810043,\n",
+        "        508.66554665]), array([ 44.99999984,  44.99999984,  44.99999984,  44.99999984,\n",
+        "        44.99999984,  56.19244978,  60.32020862,  62.79101927,\n",
+        "        64.10783458,  23.34204936,  40.41952465,  64.43279136,\n",
+        "        72.60301804,  75.99535101,  78.96506301,  80.90445816,\n",
+        "        82.80736989,  84.52887198,  85.97605635,  87.11995179,  87.98121528]))\n"
+       ]
+      }
+     ],
+     "prompt_number": 33
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/MT Script-3D_layerTest.ipynb b/MT Script-3D_layerTest.ipynb
index fd96492a..4916c56f 100644
--- a/MT Script-3D_layerTest.ipynb	
+++ b/MT Script-3D_layerTest.ipynb	
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:090d72f749f721ee8a9ec2a32ec92230d1a8d709fd7a2d443382e8716798661a"
+  "signature": "sha256:10cac7f43ce75cbe563e4200a1f752d3ab5dff458ffd49c1559eac07e93f8e3c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -20,7 +20,18 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
+     "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
     },
     {
@@ -120,7 +131,7 @@
        "output_type": "pyout",
        "prompt_number": 6,
        "text": [
-        "<matplotlib.colorbar.Colorbar instance at 0x7f49fad5ae60>"
+        "<matplotlib.colorbar.Colorbar instance at 0x7f24725c9368>"
        ]
       },
       {
@@ -128,7 +139,7 @@
        "output_type": "display_data",
        "png": 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XwyFOYRCRDBHJEZHF7tQvQLuTROQdEVklIitF5GyvdsWskFfWwjzo2N2ZnzoH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\nmeQAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f49faf630d0>"
+        "<matplotlib.figure.Figure at 0x7f24727970d0>"
        ]
       }
      ],
@@ -171,7 +182,7 @@
       "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)\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",
@@ -186,7 +197,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 10
+     "prompt_number": 9
     },
     {
      "cell_type": "code",
@@ -199,7 +210,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 15
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
@@ -207,17 +218,8 @@
      "input": [],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 17,
-       "text": [
-        "(19,)"
-       ]
-      }
-     ],
-     "prompt_number": 17
+     "outputs": [],
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
@@ -229,7 +231,7 @@
       "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[2]):\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)"
@@ -237,7 +239,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 68
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -249,7 +251,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 69
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -273,12 +275,12 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "CPU times: user 1min 1s, sys: 692 ms, total: 1min 2s\n",
-        "Wall time: 1min 3s\n"
+        "CPU times: user 1min 16s, sys: 761 ms, total: 1min 17s\n",
+        "Wall time: 1min 18s\n"
        ]
       }
      ],
-     "prompt_number": 70
+     "prompt_number": 13
     },
     {
      "cell_type": "code",
@@ -295,12 +297,12 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "CPU times: user 442 ms, sys: 7 ms, total: 449 ms\n",
-        "Wall time: 873 ms\n"
+        "CPU times: user 497 ms, sys: 1 ms, total: 498 ms\n",
+        "Wall time: 534 ms\n"
        ]
       }
      ],
-     "prompt_number": 71
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -318,7 +320,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 72
+     "prompt_number": 15
     },
     {
      "cell_type": "code",
@@ -330,7 +332,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 73
+     "prompt_number": 16
     },
     {
      "cell_type": "code",
@@ -345,7 +347,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 74
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -370,13 +372,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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cJoXFZAqfeGeypxSReE0ukybZk+wJTXyhQmXbWHK5Q04TFsqqR7yUKpjuLEaR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\nXY5qP1ZOfGH5ChQYW9gq3L0PzHkBHPxN2OUlE31GBSYzdm4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3Q2nuLGoDQzu2MaWRbdp3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\naj+NG9FE8k7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rYzvWaXlzgJzv9uriF8DbsMWr/imtp2P36jFMof4bxt73OuyedWPhJD5Mfn2+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\n6wAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7f4b5e6f10>"
+        "<matplotlib.figure.Figure at 0x7f2471e8ce10>"
        ]
       }
      ],
-     "prompt_number": 75
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -403,7 +405,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 76
+     "prompt_number": 19
     },
     {
      "cell_type": "code",
@@ -417,7 +419,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 77
+     "prompt_number": 20
     },
     {
      "cell_type": "code",
@@ -442,7 +444,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 98
+     "prompt_number": 21
     },
     {
      "cell_type": "code",
@@ -463,7 +465,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 99
+     "prompt_number": 22
     },
     {
      "cell_type": "code",
@@ -477,233 +479,932 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 102,
+       "prompt_number": 23,
        "text": [
-        "array([ 103.08034974+93.33312557j,  102.77545813+93.44979787j,\n",
-        "        102.47308710+93.56743107j,  102.29608745+93.58878322j,\n",
-        "        102.12043418+93.61118429j,  102.03854087+93.59991998j,\n",
-        "        101.95718301+93.5893647j ,  101.94174620+93.57351209j,\n",
-        "        101.92630594+93.55792438j,  101.95796202+93.54393898j,\n",
-        "        101.98916063+93.52979246j,  102.05716977+93.5111035j ,\n",
-        "        102.12416754+93.49191324j,  102.22305017+93.45331517j,\n",
-        "        102.32007506+93.41406643j,  102.44488440+93.33387177j,\n",
-        "        102.56648651+93.253287j  ,  102.70737308+93.10606995j,\n",
-        "        102.84300993+92.95955571j,  102.98041066+92.72421867j,\n",
-        "        103.10984421+92.49217487j,  103.04861775+93.3581011j ,\n",
-        "        102.74071637+93.47494095j,  102.43537003+93.59276115j,\n",
-        "        102.25606292+93.6135275j ,  102.07812067+93.63536852j,\n",
-        "        101.99485628+93.62326119j,  101.91213383+93.61188394j,\n",
-        "        101.89617372+93.59528018j,  101.88020924+93.57895428j,\n",
-        "        101.91199608+93.56448122j,  101.94331979+93.54985155j,\n",
-        "        102.01195260+93.53098315j,  102.07956452+93.51160968j,\n",
-        "        102.17950023+93.47310804j,  102.27756410+93.43394328j,\n",
-        "        102.40389697+93.35401182j,  102.52700083+93.27366804j,\n",
-        "        102.66995374+93.12663616j,  102.80762045+92.98027486j,\n",
-        "        102.94753505+92.74460789j,  103.07941959+92.51219865j,\n",
-        "        103.04319208+93.36652086j,  102.73399438+93.48328289j,\n",
-        "        102.42737040+93.60103561j,  102.24696355+93.62138706j,\n",
-        "        102.06793128+93.64282778j,  101.98396919+93.63020579j,\n",
-        "        101.90055233+93.61832617j,  101.88431104+93.60129305j,\n",
-        "        101.86806489+93.58454546j,  101.89991440+93.56982811j,\n",
-        "        101.93129806+93.55495676j,  102.00025785+93.53605788j,\n",
-        "        102.06819242+93.51665143j,  102.16867400+93.47831136j,\n",
-        "        102.26727773+93.43930055j,  102.39436496+93.3596509j ,\n",
-        "        102.51821467+93.2795761j ,  102.66210841+93.1328029j ,\n",
-        "        102.80070309+92.98668309j,  102.94161506+92.75102982j,\n",
-        "        103.07447735+92.51861718j,  103.03776119+93.37495406j,\n",
-        "        102.72726973+93.49163503j,  102.41937018+93.60931671j,\n",
-        "        102.23786516+93.62925141j,  102.05774410+93.6502897j ,\n",
-        "        101.97308488+93.63715194j,  101.88897394+93.6247686j ,\n",
-        "        101.87245118+93.60730522j,  101.85592290+93.59013488j,\n",
-        "        101.88783415+93.57517225j,  101.91927669+93.5600581j ,\n",
-        "        101.98856217+93.54112738j,  102.05681798+93.52168654j,\n",
-        "        102.15784399+93.483506j  ,  102.25698601+93.44464701j,\n",
-        "        102.38482603+93.36527577j,  102.50941984+93.28546642j,\n",
-        "        102.65425227+93.13894606j,  102.79377247+92.99306162j,\n",
-        "        102.93567768+92.75741245j,  103.06951310+92.52498634j,\n",
-        "        103.03848088+93.37897699j,  102.72743094+93.49555632j,\n",
-        "        102.41898326+93.61314146j,  102.23696565+93.63281885j,\n",
-        "        102.05633708+93.65360768j,  101.97133835+93.64017504j,\n",
-        "        101.88688946+93.62750356j,  101.87022948+93.60980448j,\n",
-        "        101.85356358+93.59240265j,  101.88551479+93.57731764j,\n",
-        "        101.91699577+93.56208243j,  101.98645948+93.54315685j,\n",
-        "        102.05489127+93.52371959j,  102.15620340+93.48565456j,\n",
-        "        102.25562865+93.44690677j,  102.38384124+93.36771445j,\n",
-        "        102.50880394+93.28807725j,  102.65405999+93.14171732j,\n",
-        "        102.79399910+92.99598488j,  102.93629109+92.76036616j,\n",
-        "        103.07050753+92.52796281j,  103.03919125+93.38300761j,\n",
-        "        102.72758420+93.49948344j,  102.41858953+93.61696992j,\n",
-        "        102.23606009+93.63638868j,  102.05492462+93.65692649j,\n",
-        "        101.96958651+93.64319798j,  101.88479968+93.63023721j,\n",
-        "        101.86800209+93.61230159j,  101.85119807+93.5946673j ,\n",
-        "        101.88318846+93.57945909j,  101.91470705+93.56410189j,\n",
-        "        101.98434806+93.54518051j,  102.05295486+93.52574581j,\n",
-        "        102.15455233+93.48779496j,  102.25425997+93.44915698j,\n",
-        "        102.38284469+93.37014142j,  102.50817581+93.29067408j,\n",
-        "        102.65385536+93.14447083j,  102.79421320+92.99888639j,\n",
-        "        102.93689167+92.76329184j,  103.07148872+92.5309046j ,\n",
-        "        103.04055530+93.38521604j,  102.72870919+93.50161789j,\n",
-        "        102.41948115+93.61903275j,  102.23672036+93.63830807j,\n",
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-        "        102.05319102+93.52687963j,  102.15493487+93.4889941j ,\n",
-        "        102.25478741+93.45041917j,  102.38355271+93.37149757j,\n",
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-        "        101.91507995+93.56686254j,  101.98495265+93.5479646j ,\n",
-        "        102.05378821+93.52855106j,  102.15574029+93.49075849j,\n",
-        "        102.25579895+93.45227323j,  102.38481896+93.37348338j,\n",
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-        "        102.73120553+93.50570001j,  102.42157156+93.62296816j,\n",
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-        "        101.88376573+93.58291147j,  101.91535941+93.56739127j,\n",
-        "        101.98527347+93.54849841j,  102.05414985+93.52908951j,\n",
-        "        102.15616397+93.4913249j ,  102.25628405+93.45286657j,\n",
-        "        102.38537844+93.37411505j,  102.51121450+93.29490207j,\n",
-        "        102.65742220+93.14891813j,  102.79830356+93.00354163j,\n",
-        "        102.94139561+92.76797361j,  103.07640294+92.53560259j,\n",
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-        "        101.91536348+93.56739327j,  101.98527777+93.5485006j ,\n",
-        "        102.05415439+93.5290919j ,  102.15616860+93.49132749j,\n",
-        "        102.25628876+93.45286938j,  102.38538292+93.37411813j,\n",
-        "        102.51121874+93.29490545j,  102.65742580+93.14892199j,\n",
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-        "        102.73120553+93.50570001j,  102.42157156+93.62296816j,\n",
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-        "        101.91507995+93.56686254j,  101.98495265+93.5479646j ,\n",
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      ],
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+     "prompt_number": 23
+    },
+    {
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+     "collapsed": false,
+     "input": [
+      "zyx\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 24,
+       "text": [
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+       ]
+      }
+     ],
+     "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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+        "         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",
diff --git a/MTanayltic1Dtest.ipynb b/MTanayltic1Dtest.ipynb
new file mode 100644
index 00000000..97bbdca8
--- /dev/null
+++ b/MTanayltic1Dtest.ipynb
@@ -0,0 +1,128 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:260d0d5c93203805d78b8fa9e20a9079ce0e65e4b6a3453fedfcda305fffaa80"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import SimPEG as simpeg\n",
+      "import simpegMT as simpegmt\n"
+     ],
+     "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": [
+      "#Define the mesh\n",
+      "m1d = simpeg.Mesh.TensorMesh([[(100,5,1.5),(100.,10),(100,5,1.5)]], x0=['C'])\n",
+      "sigma = np.zeros(m1d.nC) + 2e-3\n",
+      "sigma[m1d.gridCC[:]>200] = 1e-8\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Calculate the analytic fields\n",
+      "freqs = np.logspace(4,-1,26)\n",
+      "Z = []\n",
+      "for freq in freqs:\n",
+      "    Ed, Eu, Hd, Hu = simpegmt.Utils.getEHfields(m1d,sigma,freq,np.array([200]))\n",
+      "    Z.append((Ed + Eu)/(Hd + Hu))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "Zarr = np.concatenate(Z)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def appResPhs(freq,z):\n",
+      "    app_res = ((1./(8e-7*np.pi**2))/freq)*np.abs(z)**2\n",
+      "    app_phs = np.arctan2(-z.imag,z.real)*(180/np.pi)\n",
+      "    return app_res, app_phs\n",
+      "app_r, app_p = appResPhs(freqs,Zarr)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "app_r"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 21,
+       "text": [
+        "array([ 499.99998067,  499.99999231,  499.99999694,  499.99999878,\n",
+        "        499.99999951,  499.99999981,  499.99999992,  499.99999997,\n",
+        "        499.99999999,  500.        ,  500.        ,  500.        ,\n",
+        "        500.        ,  500.        ,  500.        ,  500.        ,\n",
+        "        500.        ,  500.        ,  500.        ,  500.        ,\n",
+        "        500.        ,  500.        ,  500.        ,  500.        ,\n",
+        "        500.        ,  500.        ])"
+       ]
+      }
+     ],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/simpegMT/Examples/simple3DfowardProblem.py b/simpegMT/Examples/simple3DfowardProblem.py
index 0ce43746..d4f73281 100644
--- a/simpegMT/Examples/simple3DfowardProblem.py
+++ b/simpegMT/Examples/simple3DfowardProblem.py
@@ -26,12 +26,12 @@ for loc in rx_loc:
         rxList.append(simpegmt.SurveyMT.RxMT(simpeg.mkvc(loc,2).T,rxType))
 # Source list
 srcList =[]
-for freq in np.logspace(3,-1,5):
+for freq in np.logspace(3,-3,7):
     srcList.append(simpegmt.SurveyMT.srcMT(freq,rxList))
 # Survey MT 
 survey = simpegmt.SurveyMT.SurveyMT(srcList)
 
-## Setup the problem objec
+## Setup the problem object
 problem = simpegmt.ProblemMT.MTProblem(M)
 problem.pair(survey)
 
diff --git a/simpegMT/ProblemMT.py b/simpegMT/ProblemMT.py
index 76500d2a..e68ad41b 100644
--- a/simpegMT/ProblemMT.py
+++ b/simpegMT/ProblemMT.py
@@ -1,4 +1,4 @@
-from SimPEG import Survey, Problem, Utils, Models, np, sp, Solver as SimpegSolver
+from SimPEG import Survey, Problem, Utils, Models, np, sp, SolverLU as SimpegSolver
 from scipy.constants import mu_0
 from SurveyMT import SurveyMT, FieldsMT
 
@@ -120,7 +120,8 @@ class MTProblem(Problem.BaseProblem):
             # Store the fields
             F[Src, 'e_px'] = e[:,0]
             F[Src, 'e_py'] = e[:,1]
-            b = self.mesh.edgeCurl * e     
+            # 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]
         return F
@@ -138,7 +139,7 @@ class MTProblem(Problem.BaseProblem):
         sig = self.MeSigma
         C = self.mesh.edgeCurl
 
-        return C.T*mui*C + 1j*omega(freq)*sig
+        return C.T*mui*C - 1j*omega(freq)*sig
 
     def getAbg(self, freq):
         """
@@ -152,7 +153,7 @@ class MTProblem(Problem.BaseProblem):
         sigBG = self.MeSigmaBG
         C = self.mesh.edgeCurl
 
-        return C.T*mui*C + 1j*omega(freq)*sigBG
+        return C.T*mui*C - 1j*omega(freq)*sigBG
 
     def getADeriv(self, freq, u, v, adjoint=False):
         sig = self.curTModel
@@ -181,7 +182,7 @@ class MTProblem(Problem.BaseProblem):
         eBG_bp = homo1DModelSource(self.mesh,freq,backSigma)
         Abg = self.getAbg(freq)
 
-        return Abg*eBG_bp
+        return -Abg*eBG_bp
 
     ##################################################################
     # Inversion stuff
diff --git a/simpegMT/Sources/backgroundModelSources.py b/simpegMT/Sources/backgroundModelSources.py
index 21b35d83..c878baf5 100644
--- a/simpegMT/Sources/backgroundModelSources.py
+++ b/simpegMT/Sources/backgroundModelSources.py
@@ -16,7 +16,8 @@ def homo1DModelSource(mesh,freq,m_back):
     from simpegMT.Utils import get1DEfields
     # Get a 1d solution for a halfspace background
     mesh1d = simpeg.Mesh.TensorMesh([mesh.hz],np.array([mesh.x0[2]]))
-    e0_1d = get1DEfields(mesh1d,mesh.r(m_back,'CC','CC','M')[0,0,:],freq)
+    # 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)
@@ -24,18 +25,19 @@ def homo1DModelSource(mesh,freq,m_back):
     # 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(mesh.r(ex_px,'Ex','Ex','V'),2),ey_px,ez_px))
+            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
+            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
diff --git a/simpegMT/SurveyMT.py b/simpegMT/SurveyMT.py
index 2d5be7eb..21a7193c 100644
--- a/simpegMT/SurveyMT.py
+++ b/simpegMT/SurveyMT.py
@@ -269,7 +269,7 @@ class DataMT(Survey.Data):
                 from numpy.lib import recfunctions as recFunc
                 dt = dtCP 
                 for uniFL in uniFLmarr:
-                    mTemp = simpeg.mkvc(rec2ndarr(mArrRec[np.ma.where(mArrRec[['freq','x','y','z']].data == np.array(uniFL))][impList]).sum(axis=0),2).T
+                    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:
diff --git a/simpegMT/Utils/MT1Danalytic.py b/simpegMT/Utils/MT1Danalytic.py
index 02406fc4..6d08e091 100644
--- a/simpegMT/Utils/MT1Danalytic.py
+++ b/simpegMT/Utils/MT1Danalytic.py
@@ -47,12 +47,19 @@ def getEHfields(m1d,sigma,freq,zd):
         UDp[:,lnr+1] = elamh.dot(Pjinv.dot(Pj1)).dot(UDp[:,lnr])
 
     # Calculate the fields
-    Ed = np.zeros((zd.size,),dtype=complex)
-    Eu = np.zeros((zd.size,),dtype=complex)
-    Hd = np.zeros((zd.size,),dtype=complex)
-    Hu = np.zeros((zd.size,),dtype=complex)
+    Ed = np.empty((zd.size,),dtype=complex)
+    Eu = np.empty((zd.size,),dtype=complex)
+    Hd = np.empty((zd.size,),dtype=complex)
+    Hu = np.empty((zd.size,),dtype=complex)
 
     # Loop over the layers and calculate the fields
+    # In the halfspace below the mesh
+    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]))
+    Hd[dind] = (k[0]/(w*mu[0]))*UDp[1,0]*np.exp(-1j*k[0]*(dup-zd[dind]))
+    Hu[dind] = -(k[0]/(w*mu[0]))*UDp[0,0]*np.exp(1j*k[0]*(dup-zd[dind]))
     for ki,mui,epsi,dlow,dup,Up,Dp in zip(k[1::],mu[1::],eps[1::],m1d.vectorNx[:-1],m1d.vectorNx[1::],UDp[0,1::],UDp[1,1::]):
         dind = np.logical_and(dup >= zd, zd > dlow)
         Ed[dind] = Dp*np.exp(-1j*ki*(dup-zd[dind]))
diff --git a/simpegMT/Utils/MT1Dsolutions.py b/simpegMT/Utils/MT1Dsolutions.py
index fd3347d5..4e07b989 100644
--- a/simpegMT/Utils/MT1Dsolutions.py
+++ b/simpegMT/Utils/MT1Dsolutions.py
@@ -20,10 +20,12 @@ def get1DEfields(m1d,sigma,freq,sourceAmp=1.0):
     Aio = A[1:-1,[0,-1]]
 
     # Set the boundary conditions
-    Ed_low, Eu_low, Hd_low, Hu_low = getEHfields(m1d,sigma,freq,np.array([m1d.vectorNx[0]]))
-    Etot_low = Ed_low + Eu_low
+    Ed, Eu, Hd, Hu = getEHfields(m1d,sigma,freq,m1d.vectorNx)
+    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
-    bc = np.r_[Etot_low.conj(),sourceAmp]
+    bc = np.r_[Etot[0],Etot[-1]]
     # The right hand side
     rhs = -Aio*bc
     # Solve the system