From 6b3f9b94789cbc2092a31ecbad33eafd0ebc5cda Mon Sep 17 00:00:00 2001
From: GudniRos <gkrosen@gmail.com>
Date: Fri, 3 Apr 2015 10:44:56 -0700
Subject: [PATCH] Fix import bug of sys

---
 3D theroy overview-Copy0.ipynb                |   95 +
 3D theroy overview.ipynb                      |   95 +
 MT 1D code test.ipynb                         | 2406 +++++++++++++----
 MT 1D code_testLayered.ipynb                  | 2068 ++++++++++++++
 MT Script-3D_layerTest-working.ipynb          | 2228 +++++++++------
 MT1DfwdProblem.ipynb                          |  898 +++---
 MTanayltic1Dtest-Copy1.ipynb                  |  925 +++++++
 simpegMT/ProblemMT.py                         |    2 +-
 .../Tests/test_ApparentResistivityLayerpy     |   48 +
 simpegMT/Utils/MT1Dsolutions.py               |    2 +-
 10 files changed, 6988 insertions(+), 1779 deletions(-)
 create mode 100644 3D theroy overview-Copy0.ipynb
 create mode 100644 3D theroy overview.ipynb
 create mode 100644 MT 1D code_testLayered.ipynb
 create mode 100644 MTanayltic1Dtest-Copy1.ipynb
 create mode 100644 simpegMT/Tests/test_ApparentResistivityLayerpy

diff --git a/3D theroy overview-Copy0.ipynb b/3D theroy overview-Copy0.ipynb
new file mode 100644
index 00000000..d94031f6
--- /dev/null
+++ b/3D theroy overview-Copy0.ipynb	
@@ -0,0 +1,95 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:74c8f271a46170528d2e89be943c0f766fa137ce8e32b8bc8ad86623db691b9d"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The Maxwell's equations are\n",
+      "\n",
+      "\\begin{align}\n",
+      "\\frac{\\partial \\vec{B}}{\\partial t} &= - \\nabla \\times \\vec{E}            \\hspace{2cm} \\text{Faraday equations}\\\\\n",
+      "\\frac{\\partial \\vec{D}}{\\partial t} &= - \\nabla \\times \\vec{H} - \\vec{J} - \\vec{s} \\hspace{2cm} \\text{Ampere's law.}\n",
+      "\\end{align}\n",
+      "\n",
+      "where $\\vec{E}$ is the electric field, $\\vec{H}$ is the magnetic field, $\\vec{J}$ is the electric flux, $\\vec{D}$ is the displacement flux, $\\vec{B}$ is the magnetic flux and $\\vec{s}$ is the source."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The constitutive realtations between the fields and flux are as follows.\n",
+      "\n",
+      "\\begin{align}\n",
+      "\\vec{J} &= \\sigma \\vec{E}\\\\\n",
+      "\\vec{B} &= \\mu \\vec{H}\\\\\n",
+      "\\vec{D} &= \\epsilon \\vec{E}\n",
+      "\\end{align}"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Using the contitutive relations, the Maxwell's equations can written in terms of the electric field and the magnetic flux \n",
+      "\\begin{align}\n",
+      "\\mu \\frac{\\partial \\vec{B}}{\\partial t} &= - \\nabla \\times \\vec{E}  \\\\\n",
+      "\\epsilon \\frac{\\partial \\vec{E}}{\\partial t} &= -  \\nabla \\times \\frac{1}{\\mu} \\vec{B} - \\sigma \\vec{E} - \\vec{s} \n",
+      "\\end{align}\n",
+      "\n",
+      "Futher by droping the $\\epsilon$ term and using a $e^{-i\\omega t}$ Fourier time relation, Maxwell's equations can be written in the frequency domain as\n",
+      "\n",
+      "\\begin{align}\n",
+      "-i \\omega \\mu  \\hat{\\vec{B}} &= - \\nabla \\times \\hat{\\vec{E}}  \\\\\n",
+      "- \\vec{s} &= -  \\nabla \\times \\frac{1}{\\mu} \\hat{\\vec{B}} - \\sigma \\hat{\\vec{E}}\n",
+      "\\end{align}\n",
+      "\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
+      "\n",
+      "The weak form is \n",
+      "\\begin{align}\n",
+      "-i \\omega \\mu (\\hat{\\vec{B}},\\hat{\\vec{F}}) + (\\nabla \\times \\hat{\\vec{E}},\\hat{\\vec{F}}) &= 0 \\\\\n",
+      "(\\nabla \\times \\mu^{-1} \\hat{\\vec{B}},\\hat{\\vec{W}}) + (\\sigma \\hat{\\vec{E}},\\hat{\\vec{W}}) &= (\\hat{\\vec{s}},\\hat{\\vec{W}})\n",
+      "\\end{align}\n",
+      "\n",
+      "\n",
+      "$ (\\mu^{-1} B, \\nabla \\times W) + (\\sigma E, W) = (\\mu^{-1}B W )|_0^{end} $\n",
+      "\n",
+      "$ (i \\omega B,F) + (\\nabla \\times E , f) = 0 $"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/3D theroy overview.ipynb b/3D theroy overview.ipynb
new file mode 100644
index 00000000..d94031f6
--- /dev/null
+++ b/3D theroy overview.ipynb	
@@ -0,0 +1,95 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:74c8f271a46170528d2e89be943c0f766fa137ce8e32b8bc8ad86623db691b9d"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The Maxwell's equations are\n",
+      "\n",
+      "\\begin{align}\n",
+      "\\frac{\\partial \\vec{B}}{\\partial t} &= - \\nabla \\times \\vec{E}            \\hspace{2cm} \\text{Faraday equations}\\\\\n",
+      "\\frac{\\partial \\vec{D}}{\\partial t} &= - \\nabla \\times \\vec{H} - \\vec{J} - \\vec{s} \\hspace{2cm} \\text{Ampere's law.}\n",
+      "\\end{align}\n",
+      "\n",
+      "where $\\vec{E}$ is the electric field, $\\vec{H}$ is the magnetic field, $\\vec{J}$ is the electric flux, $\\vec{D}$ is the displacement flux, $\\vec{B}$ is the magnetic flux and $\\vec{s}$ is the source."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The constitutive realtations between the fields and flux are as follows.\n",
+      "\n",
+      "\\begin{align}\n",
+      "\\vec{J} &= \\sigma \\vec{E}\\\\\n",
+      "\\vec{B} &= \\mu \\vec{H}\\\\\n",
+      "\\vec{D} &= \\epsilon \\vec{E}\n",
+      "\\end{align}"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Using the contitutive relations, the Maxwell's equations can written in terms of the electric field and the magnetic flux \n",
+      "\\begin{align}\n",
+      "\\mu \\frac{\\partial \\vec{B}}{\\partial t} &= - \\nabla \\times \\vec{E}  \\\\\n",
+      "\\epsilon \\frac{\\partial \\vec{E}}{\\partial t} &= -  \\nabla \\times \\frac{1}{\\mu} \\vec{B} - \\sigma \\vec{E} - \\vec{s} \n",
+      "\\end{align}\n",
+      "\n",
+      "Futher by droping the $\\epsilon$ term and using a $e^{-i\\omega t}$ Fourier time relation, Maxwell's equations can be written in the frequency domain as\n",
+      "\n",
+      "\\begin{align}\n",
+      "-i \\omega \\mu  \\hat{\\vec{B}} &= - \\nabla \\times \\hat{\\vec{E}}  \\\\\n",
+      "- \\vec{s} &= -  \\nabla \\times \\frac{1}{\\mu} \\hat{\\vec{B}} - \\sigma \\hat{\\vec{E}}\n",
+      "\\end{align}\n",
+      "\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
+      "\n",
+      "The weak form is \n",
+      "\\begin{align}\n",
+      "-i \\omega \\mu (\\hat{\\vec{B}},\\hat{\\vec{F}}) + (\\nabla \\times \\hat{\\vec{E}},\\hat{\\vec{F}}) &= 0 \\\\\n",
+      "(\\nabla \\times \\mu^{-1} \\hat{\\vec{B}},\\hat{\\vec{W}}) + (\\sigma \\hat{\\vec{E}},\\hat{\\vec{W}}) &= (\\hat{\\vec{s}},\\hat{\\vec{W}})\n",
+      "\\end{align}\n",
+      "\n",
+      "\n",
+      "$ (\\mu^{-1} B, \\nabla \\times W) + (\\sigma E, W) = (\\mu^{-1}B W )|_0^{end} $\n",
+      "\n",
+      "$ (i \\omega B,F) + (\\nabla \\times E , f) = 0 $"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/MT 1D code test.ipynb b/MT 1D code test.ipynb
index b412bad1..7417948c 100644
--- a/MT 1D code test.ipynb	
+++ b/MT 1D code test.ipynb	
@@ -1,532 +1,1886 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:c4b44464df09aacf6b083748f1a6b724084741da8ed0844c16bc39b2fbc25d29"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Test 1D solution of MT problem and compare to a analytic solution\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# import the simpegMT module\n",
-      "from simpegMT.Utils import MT1Danalytic, MT1Dsolutions\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": [
-      {
-       "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
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Set up the mesh.\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<=300] = 0.01\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "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",
-     "collapsed": false,
-     "input": [
-      "# Get the fields\n",
-      "anaEd, anaEu, anaHd, anaHu = MT1Danalytic.getEHfields(M,sig,freq,M.vectorNx)\n",
-      "anaEtemp = (anaEd+anaEu)\n",
-      "anaHtemp = (anaHd+anaHu)\n",
-      "# Scale the solution\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,sourceAmp=1).conj()\n",
-      "solH = -M.nodalGrad*solE/(1j*omega(freq)*mu_0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "anaEtemp[-1]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 29,
-       "text": [
-        "(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",
-       "png": 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5vMX/0SenIR0GDCClU+ewQxORak5dVUkqPd0YPmQ1Z/ofqM8Wevp4hg9ZTXp6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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f4f0dbf3a90>"
-       ]
-      }
-     ],
-     "prompt_number": 31
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plot((abs(solE.real)-abs(anaEcor.real))/abs(anaEcor.real),M.vectorNx,'b+:')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 32,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x7f4f0d7d6e10>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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n3DpW2kZ6KHCvXtClS1iL64orwmTENWtCZ3smE5q9eveGE04I97vnvl5EKocC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\nrkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f4f0d80a210>"
-       ]
-      }
-     ],
-     "prompt_number": 32
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plot(solE.imag,M.vectorNx,'r*--',anaEcor.imag,M.vectorNx,'b+:')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 33,
-       "text": [
-        "[<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 0x7f4f0d7f8550>"
-       ]
-      }
-     ],
-     "prompt_number": 33
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plot((abs(solE.imag)-abs(anaEcor.imag))/abs(anaEcor.imag),M.vectorNx,'b+:')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 34,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x7f4f0d657a10>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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z+1NeAGhmZr3CicMamrumzHqeu6rMzPopd1WZmVmvcOIwM7OKOHGYmVlFnDjM\nzKwiThxmZlYRJw4zM6tIlxOHpK9JekLSo5J+JGn/knOXSFojabWkcSXlJ0lalZ27rqR8L0m3ZuXL\nJB1ecm6apCezxzldra+ZmfWM7rQ4lgBvi4h3Ak8ClwBIOg44CzgOmABcL71xx+gbgOkRMRIYKWlC\nVj4d2JKVXwtclb3WYOBLwMnZY6Yk37pnFwreEvYNfi8Svw85vxfd1+XEERFLI2JHFj4EHJodTwLm\nR8T2iFgHrAXGSDoY2DcilmfXzQMmZ8cTgbnZ8e3AqdnxeGBJRGyNiK3AUlIysg74P0bO70Xi9yHn\n96L7emqM4xPAoux4OLCh5NwG4JB2yjdm5WRf1wNERCuwTdKQDl7LzMxqpMNt1SUtBYa1c+rSiLgr\nu+YLwGsRcUsV6mdmZvUmIrr8AM4FHgT2Lim7GLi4JF4MjCEloCdKyqcCN5Rc8+7seADwbHY8BfhW\nyXO+DZy1k7qEH3744YcflT268ru/yzdyyga2/wUYGxGvlpxaCNwi6RpSt9JIYHlEhKQXJI0BlgNn\nA7NKnjMNWAacAdyXlS8BvpoNiAs4Dfh8e/XpykZdZmZWue7cAXA2sCewNJs09YuImBERj0u6DXgc\naAVmlGxbOwOYAwwEFkXE4qz8JuBmSWuALaSWBhHxnKQrgF9m112WDZKbmVmNNMy26mZm1jv65Mpx\nSWdKekzS65JO7OC6CdkixDWS2u3i6uskDZa0NFsguWRn61wkrZP0a0krJS1v75q+qjM/Z0mzsvOP\nShrd23XsLbt6LyQ1SdqWfQ5WSvpiLepZbZK+K6lF0qoOrukvn4kO34sufSa6MzheqwdwLHAMcD9w\n4k6u2Z20huQIYA/gV8CoWte9Cu/F1cDnsuPPA1fu5LrfAoNrXd8q/Pt3+XMGPkTqGoU0UWNZretd\nw/eiCVhY67r2wnvxXmA0sGon5/vFZ6KT70XFn4k+2eKIiNUR8eQuLjsZWBsR6yJiO/AD0uLERlO6\neHIu+aLK9jTiBILO/JzfeI8i4iFgkKSDereavaKzn/lG/ByUiYifAc93cEl/+Ux05r2ACj8TfTJx\ndNIbiwozjbp48KCIaMmOW4CdffgD+LGkFZL+vneq1is683Nu75pDaTydeS8C+Muse2ZRtkVQf9Rf\nPhOdUfFnojuzqqqqM4sPd6FhRv07eC++UBpEREja2b/7lIjYJOlA0ky41dlfIn1dZ3/Obf+iapjP\nR4nO/JuUlyP5AAABkklEQVQeAUZExB8kfRBYQOr27Y/6w2eiMyr+TNRt4oiI07r5EhuBESXxCMq3\nL+kzOnovskGvYRGxOdsP7JmdvMam7Ouzku4gdWs0QuLozM+57TWHZmWNZpfvRUS8WHJ8j6TrJQ2O\niOd6qY71or98JnapK5+JRuiq2lnf3ArSDrxHSNqTtGPvwt6rVq8pLp4k+7qg7QWS9pG0b3b8JmAc\nsNPZJn1MZ37OC4FzACS9G9ha0r3XSHb5Xkg6qLhbtaSTSVPy+1vSgP7zmdilrnwm6rbF0RFJp5NW\nnQ8F7pa0MiI+KGk4cGNE/HVEtEq6ALiXNNvkpoh4oobVrpYrgdskTQfWAR8FKH0vSN1cP8o+GwOA\n70fEktpUt2ft7Ocs6bzs/LcjYpGkD0laC7wM/G0Nq1w1nXkvSDszfEpSK/AHssW2jUbSfGAsMFTS\nemAmaaZZv/pMwK7fC7rwmfACQDMzq0gjdFWZmVkvcuIwM7OKOHGYmVlFnDjMzKwiThxmZlYRJw4z\nM6uIE4eZmVXEicPMzCry/w6nUYe43AHvAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f4f0d81f850>"
-       ]
-      }
-     ],
-     "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",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 41
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "# Test 1D solution of MT problem and compare to a analytic solution\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# import the simpegMT module\n",
+    "from simpegMT.Utils import MT1Danalytic, MT1Dsolutions\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set up the mesh.\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<=300] = 0.01\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "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])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M.vectorNx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Get the fields\n",
+    "anaEd, anaEu, anaHd, anaHu = MT1Danalytic.getEHfields(M,sig,freq,M.vectorNx)\n",
+    "anaEtemp = (anaEd+anaEu)\n",
+    "anaHtemp = (anaHd+anaHu)\n",
+    "# Scale the solution\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,sourceAmp=1).conj()\n",
+    "solH = -M.nodalGrad*solE/(1j*omega(freq)*mu_0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(922807.04800415318-689021.35510797054j)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anaEtemp[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "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]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.hstack((simpeg.mkvc(anaEcor,2),simpeg.mkvc(solE,2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7feb9a686510>,\n",
+       " <matplotlib.lines.Line2D at 0x7feb9a686790>]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "ETK9PKZGxKuSvp5s/3VEzJB0oqRlwD+A81MMuVqqcn1kptBvD0xJvpXviIjD04q5Oqp4fQ1SFf82\n",
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+       "paE+qjIzs5Q4cZiZWbU4cZiZWbU4cZiZWbU4cZiZWbU4cZiZWbU4cZiZWbU4cZiZWbX8f1SwREq/\n",
+       "7HBwAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7feb9a72ad50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(solE.real,M.vectorNx,'r*--',anaEcor.real,M.vectorNx,'b+:')\n",
+    "#axis([-.2,.2,-10000,10000])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7feb9981fad0>]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "zN6lrBeM5v7GpFebqhoDBinfn/M/naRtJW1ftp8PHA10/dsdxWbvz/ZZtmfb3p9q3Mst3ZI0RmE0\n",
+       "v79dGm/rSNqGavDnHZNWw4kZzf2JavzSvbY/M4l1a4XN3l+P+S7VbN37Sdqaanbv65qOuQ44GUDS\n",
+       "4cBjtea6bjea+2sYXV+p7Z77AnYCvkE1nfsSYMdSvhdwfdl+EXBn+foBcGan693K+2s6/gjguk7X\n",
+       "u8W/v1cB3y+/v7uBD3S63i2+v9dR9U3dSZUQ7wDmdbrurbq/El8JPAT8hqqN/X92uu6buKc3Aj+i\n",
+       "ms37zFL2DuAdtWM+V/bfBRzS6Tq38v6omiUfBB6neqHhp8B2I10vAwAjImJMerWpKiIiOiSJIyIi\n",
+       "xiSJIyIixiSJIyIixiSJIyIixiSJIyIixiSJIyIixiSJIyIixuT/A4kEhS0tjZoYAAAAAElFTkSu\n",
+       "QmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7feb9a5f5ed0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot((abs(solE.real)-abs(anaEcor.real))/abs(anaEcor.real),M.vectorNx,'b+:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7feb997604d0>,\n",
+       " <matplotlib.lines.Line2D at 0x7feb99760750>]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7feb9a57e510>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(solE.imag,M.vectorNx,'r*--',anaEcor.imag,M.vectorNx,'b+:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/gudni/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: RuntimeWarning: invalid value encountered in divide\n",
+      "  if __name__ == '__main__':\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7feb9970fad0>]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "j+7lOR4u/z4m6a+oujUGIXFM5fc80aLUkQ7Xqwn7fC1s/2vt+DOSrpZ0sO3Hu1THXjFX3hP7NJ33\n",
+       "xCB0Ve2tb+5LVDvwHivpAKode9d2r1pd01o8Sfn31vEXSPpRSfPL8fOA5cBeZ5v0man8ntcCFwBI\n",
+       "ejXwRK17b5Ds87WQtLC1W7WkE6mm5M+1pAFz5z2xT9N5T/Rsi2Myks6iWnV+KPBpSffZPk3SEcCf\n",
+       "2v5527slvRW4nWq2ybW2H2iw2p3yAeBmSauArcA5APXXgqqb6xPlvbEf8FHbdzRT3dm1t9+zpLeU\n",
+       "8x+2vU7SayVtAb4DvLHBKnfMVF4Lqp0Zfk3SbuC7lMW2g0bSGuBk4FBJDwGXUc00m1PvCdj3a8E0\n",
+       "3hNZABg91tKJAAAAOklEQVQREW0ZhK6qiIjooiSOiIhoSxJHRES0JYkjIiLaksQRERFtSeKIiIi2\n",
+       "JHFERERbkjgiIqIt/x+4vI9l3pPo8wAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7febc43946d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot((abs(solE.imag)-abs(anaEcor.imag))/abs(anaEcor.imag),M.vectorNx,'b+:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7feb9967c590>,\n",
+       " <matplotlib.lines.Line2D at 0x7feb99638890>]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "nj5DKSeQOmBoxv5QSutJH8dfPKUcOzj+Yiv1+CFPn6GUE8hTJKsa7iipB8mjfvcWOabWcPzFU8qx\n",
+       "g+MvtlKPH/L1GYrduZNlB9BtwGLgXZL7dqel5UcA/yR5mmBKseN0/MWPtTPF7viL/yr1+Av9GTyZ\n",
+       "opmZtUkp38IyM7MicgIxM7M2cQIxM7M2cQIxM7M2cQIxM7M2cQIxM7M2cQIxM7M2cQIxM7M2cQIx\n",
+       "M7M2+f/DvAf0HfArswAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7feb996d74d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "semilogx(abs(solE),M.vectorNx,'r*--',abs(anaEcor),M.vectorNx,'b+:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7feb993ce610>]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "PtcBbwBQ9fTm+trtuKaNJv+W0U2kbnrgps3Bnn2oBl3vpZpdPr20HwB8pdbv5VSDmncDX6BaymRS\n",
+       "5A88D7irvH4InNN03mP9/tf6Hwtc13TeY/z+vwz4fvn+/wB4T9N5jzH/11CNLd1FVfDuBOY2nftY\n",
+       "/v0AVwIPAE9S3aP/kwZzfj3w71SrfZ9T2s4Czqr1+fty/m7gqKa/z2PJn+q24v3ABuAR4D+APbf2\n",
+       "eZkAGBERYzJZb1VFRERDUjgiImJMUjgiImJMUjgiImJMUjgiImJMUjgiImJMUjgiImJMUjgiImJM\n",
+       "/j93KY9Nb6nlqgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7feb9954b1d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot((abs(solE)-abs(anaEcor))/abs(anaEcor),M.vectorNx,'b+:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100.0 44.999998407\n",
+      "91.3634893888 -137.014649098\n"
+     ]
+    }
+   ],
+   "source": [
+    "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(30,)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M.nodalGrad.dot(solE).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M.nN\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7feb99304550>,\n",
+       " <matplotlib.lines.Line2D at 0x7feb993047d0>]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "uLyomS2vmGo1bCB1Vh0yWS4UOr4fozCd+6NAt1TeG3io6LjvUfgAnEZh9t2NyiSuTYHXKcwWXC7v\n",
+       "1W4UOlZfAO4DNs8zJmB74Pm0/BO4sBzep6LjDwQm5R0ThTupnkvv04vA+eXwNwV8mkI/0PMUEtlU\n",
+       "YETe/34UvqwtAN6h0O5/agaxHErhxpzZtX+3wBnAGUXHXJf2v0Dhzs+s/802GBOFJsB5FJr136DQ\n",
+       "N7xZfdfzAEAzM2uU1tpUZWZmOXHiMDOzRnHiMDOzRnHiMDOzRnHiMDOzRnHiMDOzRnHiMDOzRnHi\n",
+       "MDOzRvn/0De/+koUcWoAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7feb9936d610>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(-solH.imag,M.vectorCCx,'r*--',anaHcor.imag,M.vectorNx,'b+:')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "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])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M.vectorCCx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/MT 1D code_testLayered.ipynb b/MT 1D code_testLayered.ipynb
new file mode 100644
index 00000000..0170b5a9
--- /dev/null
+++ b/MT 1D code_testLayered.ipynb	
@@ -0,0 +1,2068 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test 1D solution of MT problem and compare to a analytic solution\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# import the simpegMT module\n",
+    "from simpegMT.Utils import MT1Danalytic, MT1Dsolutions\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set up the mesh.\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",
+    "conds = [1,1e-2]\n",
+    "elev = 300\n",
+    "sig = np.zeros(M.nC) + conds[1]\n",
+    "sig[np.logical_and(M.gridCC>-200,M.gridCC<0)] = conds[0]\n",
+    "sig[M.gridCC>elev] = 1e-8\n",
+    "sig[M.gridCC<-500] = 1e-1\n",
+    "sig[M.gridCC<-900] = conds[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f28e1428f50>]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "/DWwE/gUcGLPsXOa/TuBN3Y43g8DtwA3U34Qjl+K8Y7zApwBfLYZz9u7jmeRsT+LckfFTcCOmfgp\n",
+       "dy59gvIu5mvpuWtpsc9rR+P6KLAXOND8PJ4zyjHVmqv9Ft+AI0mVsVUiSZWxcEtSZSzcklQZC7ck\n",
+       "VcbCLUmVsXBLUmUs3JJUGQu3JFXGwq0VJyJe0vxnxCMiYk1E7IiI53cdl7RQvnNSK1JEvBM4EjgK\n",
+       "2J2Zl3QckrRgFm6tSBGxmvLPph4AXp7+IKgitkq0Uj0ZWAMcTZl1S9Vwxq0VKSI+BnwEeDbw1Mw8\n",
+       "v+OQpAVbMR+kIM2IiLOBb2bm5RHxOODfI2IqM6c7Dk1aEGfcklQZe9ySVBkLtyRVxsItSZWxcEtS\n",
+       "ZSzcklQZC7ckVcbCLUmVsXBLUmX+H3zfa/Qjde8mAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f28e25e0050>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "M.plotImage(log10(sig))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "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])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M.vectorNx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Get the fields\n",
+    "anaEd, anaEu, anaHd, anaHu = MT1Danalytic.getEHfields(M,sig,freq,M.vectorNx)\n",
+    "anaEtemp = (anaEd+anaEu)\n",
+    "anaHtemp = (anaHd+anaHu)\n",
+    "# Scale the solution\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,sourceAmp=1).conj()\n",
+    "solH = -M.nodalGrad*solE/(1j*omega(freq)*mu_0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12836609.712174654+24128916.329733729j)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anaEtemp[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  1.71846041e-08 -3.23018213e-08j,\n",
+       "          1.71846041e-08 +3.23018213e-08j],\n",
+       "       [ -1.13091973e-06 +7.74148973e-07j,\n",
+       "         -5.08843373e-07 -3.09514912e-07j],\n",
+       "       [  3.71261251e-06 -1.48868560e-05j,\n",
+       "          3.65512026e-06 -7.95948811e-06j],\n",
+       "       [  7.37072093e-05 +2.15078894e-05j,\n",
+       "          5.80305408e-05 +1.06357942e-05j],\n",
+       "       [  4.75612845e-05 +2.19547586e-04j,\n",
+       "          6.36366910e-05 +1.90231770e-04j],\n",
+       "       [ -2.21258264e-04 +4.02750922e-04j,\n",
+       "         -1.76226223e-04 +3.91452069e-04j],\n",
+       "       [ -6.14770210e-04 +4.12789798e-04j,\n",
+       "         -5.58922388e-04 +4.25303107e-04j],\n",
+       "       [ -9.80056634e-04 +2.74642565e-04j,\n",
+       "         -9.21632381e-04 +3.06492631e-04j],\n",
+       "       [ -1.25588450e-03 +7.69129746e-05j,\n",
+       "         -1.19789524e-03 +1.23674848e-04j],\n",
+       "       [ -1.43995677e-03 -3.53489427e-04j,\n",
+       "         -1.41049571e-03 -2.73526014e-04j],\n",
+       "       [ -1.49344998e-03 -9.54835366e-04j,\n",
+       "         -1.49148850e-03 -8.51549869e-04j],\n",
+       "       [ -1.48131966e-03 -1.44956637e-03j,\n",
+       "         -1.49169515e-03 -1.33110967e-03j],\n",
+       "       [ -1.45773633e-03 -1.95598586e-03j,\n",
+       "         -1.48139177e-03 -1.82244743e-03j],\n",
+       "       [ -1.41870158e-03 -2.47390502e-03j,\n",
+       "         -1.45669893e-03 -2.32548179e-03j],\n",
+       "       [ -2.90180962e-04 -3.48330897e-03j,\n",
+       "         -5.04762046e-04 -3.40934866e-03j],\n",
+       "       [  3.59895776e-03 -4.54053788e-03j,\n",
+       "          3.13908864e-03 -4.89175966e-03j],\n",
+       "       [  9.19763090e-03 -4.77509117e-03j,\n",
+       "          8.73344053e-03 -5.12251551e-03j],\n",
+       "       [  1.48339589e-02 -4.93699809e-03j,\n",
+       "          1.43682382e-02 -5.28431488e-03j],\n",
+       "       [  2.05091907e-02 -4.98175514e-03j,\n",
+       "          2.00447591e-02 -5.33266719e-03j],\n",
+       "       [  2.62040772e-02 -4.95302628e-03j,\n",
+       "          2.57423326e-02 -5.30188588e-03j],\n",
+       "       [  3.18989638e-02 -4.92429721e-03j,\n",
+       "          3.14399061e-02 -5.27110437e-03j],\n",
+       "       [  4.04412937e-02 -4.88120312e-03j,\n",
+       "          3.99862664e-02 -5.22493164e-03j],\n",
+       "       [  5.32547886e-02 -4.81656058e-03j,\n",
+       "          5.28058071e-02 -5.15567122e-03j],\n",
+       "       [  7.24750311e-02 -4.71959260e-03j,\n",
+       "          7.20351186e-02 -5.05177662e-03j],\n",
+       "       [  1.01305395e-01 -4.57412782e-03j,\n",
+       "          1.00879087e-01 -4.89592257e-03j],\n",
+       "       [  1.44550940e-01 -4.35589030e-03j,\n",
+       "          1.44145040e-01 -4.66210323e-03j],\n",
+       "       [  2.09419254e-01 -4.02840425e-03j,\n",
+       "          2.09043975e-01 -4.31125116e-03j],\n",
+       "       [  3.06721710e-01 -3.53675174e-03j,\n",
+       "          3.06392386e-01 -3.78457151e-03j],\n",
+       "       [  4.52675331e-01 -2.79787645e-03j,\n",
+       "          4.52415018e-01 -2.99322787e-03j],\n",
+       "       [  6.71605530e-01 -1.68492364e-03j,\n",
+       "          6.71448995e-01 -1.80181306e-03j],\n",
+       "       [  1.00000000e+00 +0.00000000e+00j,\n",
+       "          1.00000000e+00 -0.00000000e+00j]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.hstack((simpeg.mkvc(anaEcor,2),simpeg.mkvc(solE,2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f28e1642950>,\n",
+       " <matplotlib.lines.Line2D at 0x7f28e1642410>]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "7/iBcj/wFQBJw4HXs5rs8t1HPp+kPYA/AF+OiGUpxFgfH/l8EbF3ROwVEXuRqQV/vQkkDajZv80/\n",
+       "AUdIaiWpA5lO1kWNHGdd1eT5FgPHACTt/4OAfzdqlLlT68+VvK1xfIQrgbsljQdWAF8CkNQH+HVE\n",
+       "fI5MM8eXgQWS5ifXTY73Jx3mlYiokHQO8AiZUR7TI+JFSV9Ljt8UEQ9JOl7SMuBt4KwUQ66Vmjwf\n",
+       "mSX0uwI3Jt/Kt0XE0LRiro0aPl+TVMN/m4slzQQWAJVk/j9sEomjhn93VwC/lfQ8mS/c34uI11IL\n",
+       "uhYk3QkcCfSQtAqYQqZpsc6fK54AaGZmtdJUm6rMzCwlThxmZlYrThxmZlYrThxmZlYrThxmZlYr\n",
+       "ThxmZlYrThxmZlYrThxmZlYr/x/lWzp+eZujggAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f28e149ae50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(solE.real,M.vectorNx,'r*--',anaEcor.real,M.vectorNx,'b+:')\n",
+    "#axis([-.2,.2,-10000,10000])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f28e24abf10>]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "MSImkY1rua1eRaMfNpl/+tR4CbA4Ir5dx9x6cxfZjMy7ShpJNoNz95PS9cBHAdITeGsKt+TKtsn8\n",
+       "Je0M/BQ4PiKWlpBjbzaZe0TsFhGT0u/7T4BPDJGiAf373bkOODA9Bbk12cMVi+ucZ2/6k/+DZOdI\n",
+       "Ur/eG4GHe33Hsnv8B/iUwFjgFrLp3OcBY1L7TsCNheP2JRs8eC/Zf6ih8lRVv/IvHH8QQ+upqk3m\n",
+       "DxxI1jdzD7Awfc0oOe9DyZ7uWgqckdo+Dny8cMz30v57gf3K/llXkj/ZQxSrCz/vBWXnXMnPvnDs\n",
+       "D4EPlJ3zAH53TiX7cLoIOKXsnCv83dkeuCH93i8CPtzX+3kAoJmZVaRRb1WZmVlJXDjMzKwiLhxm\n",
+       "ZlYRFw4zM6uIC4eZmVXEhcPMzCriwmFmZhVx4TAzs4r8f6jZeSiIHLknAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f28e166af10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot((abs(solE.real)-abs(anaEcor.real))/abs(anaEcor.real),M.vectorNx,'b+:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f28e13c1750>,\n",
+       " <matplotlib.lines.Line2D at 0x7f28e13c19d0>]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": [
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f28e1672450>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(solE.imag,M.vectorNx,'r*--',anaEcor.imag,M.vectorNx,'b+:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/gudni/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: RuntimeWarning: invalid value encountered in divide\n",
+      "  if __name__ == '__main__':\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f28e12fd450>]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "D2Xc7TCqi4tG1+8R/km6SmBv4MtUy7nfCMwq7QcCX6gd93aqD+GlVIOSOw5ons+imuR4O9V/2H5f\n",
+       "VdVVnrXjj6Wdq6q2mSfwfKoxmNuAW8tjQZ/yO5HqKq4VwDml7Q3AG2rH/FPZfztwVL+/h93kSXXx\n",
+       "w9ra92/JoOXYOPbjwEsH8XtZ4j+vfQ69eRDzpDpru678Xi4FXrW118sEwIiIGJOp2lUVEREtSeGI\n",
+       "iIgxSeGIiIgxSeGIiIgxSeGIiIgxSeGIiIgxSeGIiIgxSeGIiIgx+f/5YB+A7bUhOAAAAABJRU5E\n",
+       "rkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f28e13589d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot((abs(solE.imag)-abs(anaEcor.imag))/abs(anaEcor.imag),M.vectorNx,'b+:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f28e1261d50>,\n",
+       " <matplotlib.lines.Line2D at 0x7f28e1238110>]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "5P8DEIvS62qo2M0AAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f28e1293c90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "semilogx(abs(solE),M.vectorNx,'r*--',abs(anaEcor),M.vectorNx,'b+:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f28e1042c50>]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "U8BmScemqjOAh9sT3l7tNX5J+0uano4PABYCpT9dklTz+/9wRMyNiGPI5uV8s11JowrV/P4Pzp/2\n",
+       "kbQf2eTTtW2LcGzVxC+y+VHrI+KqNsZWjb3GP858l2w176MlTSFb/XtFRZsVwDsBlD29uaPQHVe2\n",
+       "auLPVTeRuuyBmzoHe2aRDbo+Qja7fGaqnw18rdDu58kGNR8A/pFsKZOOiB94NXB/ej0ELC077lp/\n",
+       "/4X2pwIryo67xt//a4Hvpd//g8AHy467xvh/mWxs6X6yhLcWWFR27LX8+wFuAbYCL5H10f92iTG/\n",
+       "CfgB2WrfS1PdxcDFhTZ/nc4/AJxc9u+5lvjJuhU3AzuB54D/AA4c7f08AdDMzGrSqV1VZmZWEicO\n",
+       "MzOriROHmZnVxInDzMxq4sRhZmY1ceIwM7OaOHGYmVlNnDjMzKwm/x8YpMuvBuSa0gAAAABJRU5E\n",
+       "rkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f28e11d9d50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot((abs(solE)-abs(anaEcor))/abs(anaEcor),M.vectorNx,'b+:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.849336540898 41.0291116761\n",
+      "10.1269411518 -150.251552846\n"
+     ]
+    }
+   ],
+   "source": [
+    "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(30,)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M.nodalGrad.dot(solE).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M.nN\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f28e0f08c90>,\n",
+       " <matplotlib.lines.Line2D at 0x7f28e0f08f10>]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "ZYxpY+MSufFZsyLiv8ocz0bFVCFPk5v5e0BqVTguxVbobuAkAOWeXlxZ0MyWVUx5GzeQulwdMuVc\n",
+       "yHV8TyE3nfskoFsq7wvcW1Dvx+Q+AGeQm323fTOJqzOwjNxswc3lZ7ULuY7V54E7gK5ZxgRsCzyX\n",
+       "lheBs5rDz6mg/r7A3VnHRO5JqmfSz+kF4EfN4XcK2JtcP9Bz5BLZs8CYrP/9yP2xtghYTa7d/2tl\n",
+       "iOUQcg/mzMv/3gLfAL5RUOdXaf/z5J78LPe/2afGRK4JcAG5Zv03yPUNb76h83kAoJmZNUpLbaoy\n",
+       "M7OMOHGYmVmjOHGYmVmjOHGYmVmjOHGYmVmjOHGYmVmjOHGYmVmjOHGYmVmj/P+o5FkWr3OFFQAA\n",
+       "AABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f28e0fdce10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(-solH.imag,M.vectorCCx,'r*--',anaHcor.imag,M.vectorNx,'b+:')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "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])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M.vectorCCx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/MT Script-3D_layerTest-working.ipynb b/MT Script-3D_layerTest-working.ipynb
index d422e161..1724abf9 100644
--- a/MT Script-3D_layerTest-working.ipynb	
+++ b/MT Script-3D_layerTest-working.ipynb	
@@ -1,818 +1,1424 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:7b047ea0d01a07946777c0203bdee5b1513cf1b7fad4ebbb2354487852002c17"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import SimPEG as simpeg\n",
+    "from scipy.constants import mu_0\n",
+    "def omega(freq):\n",
+    "    \"\"\"Change frequency to angular frequency, omega\"\"\"\n",
+    "    return 2.*np.pi*freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "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. "
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
      ]
-    },
-    {
-     "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\nMGscTrdqj9ZRnJJh1Ja0ruh8dPmkdcw1rsGl8i/hXl95KS54N4xJkhOfnZPdrCMCJxzR7VYkkLuz\nMTk2rIGznuuC8RVLut0ao6ex31qPYU/fvclioA/3TrkF48YUyInPzsluGnOB+/6l2y0w5gzm+Qxj\nBvjNbjfAmCvkxGfnZDcNwzA8cvJ+omEYxpwgJz7bgnLDMPKHeT7DMIzeISc+OyVpnGEYxhyl1MZg\nGIZhdIcO+WwROV9EHhSRX4rIWxPqXBXYfyoiJ3vlnxKRbSJyX6T+dSJydzA8IiJ3B+XrReSgZ/t4\nK7tpGD3BF77jUiWuPqzbLTF6npz8FWoY3WUzLiXeyi63w+h5OuCzRaQIfAw4D5ff8w4RuUFVN3p1\nLgCOVtUNInIa8Ang9MB8LfBR4DP+elX1d73lPwTs9sybVPVkWsSCcqNneP17XUrE170c/uYNFpwb\nU8A8n2HMAN/G5fVej8vAYsG50Sad8dmn4oLkzeB6uIELgY1enVcRKDCr6u0islBEVqjqk6r6AxFZ\nn7RyERHgNcA57TYwd7emWg3u/EVdsKcWjmsuk2yt1ljeUEfrOcLHK8nLl0tO6Ca6fDgM9MGB0RgF\nw4IbD/bD/kA/oElcKLCHy4dl/rjfE76JU/QsFqHi5aOOlbPQiDxGpFJfGUZTxHWGBmD/oWT7gkF4\n5kCyfXio2T5egWoNPnUjXHuTE545Zi288Ph63vaQeRnb7y+niwOJNO9zlGIBKilaFwN9cNDbRnR9\n0WMQtY/Mq4sTxQlFjcyD3fs8OZNIHd8ed60sGIQ9B+rzqqC1+nR/HxyMu04j8+G1H3e9Dw3A3gPx\nv6NaDZYtgieejv/t1RTWL4eHn3Dn86b3JR/rSZM7z9frbAfGCK60oEwT5uOmBacymLRMASdcEycu\npLhuukqMDc8et7y/fY2UE1M3bj4kbGMSJVwu9yT6gNEUe3+GPWv5Ptw58tmD259HgH/GiSgtxgkZ\n9aWsK4kCdbXIOEqkiy2UY9roEz0G0XMxGXvceYxuP1rHv86i9qRrK1oWXgfR6zwcD+AEipKu9fk4\nJdak39kwriM4yb6QukDSq5ll6UxX4xLnh2wBTmuhzmrSlblCzgS2qaovXXhE8DrLHuAvVfXWtBXk\n7tZUU/jTq5yLDAWAQiGfQiDyMzzkAjq/zJ9etgie3pO8/MrFsG1XUBYVESKw744RJwrWs3wRPLW7\nURDIFy6z6yiUAAAgAElEQVRaPOyCrdDWMMYFMAc8vxFV9CwXm4PJqOhQnAiRXzZQritsxiECB1KC\n4nIxPShOU9SsKRQFRisuaDw01hyUlzLWD+mKpeEtNI1SwT0kJFHTugz9xHojxzXabt9cLEKf9wuN\nnuvB/vp5jBORmjfgjkM437R8HyxcELnOavW6pYLbh1ghLW867jfir0Oplzf8ZnAPsDX17JF1lYrO\nXs6dpzIa+REuMBdvwJteiLvnRcvD+YXAMwk2wQUae2OWD6eHaFZa9Md9NAbEUWGgAs0eJU1sKK48\nLuD0l4kLiqPrTHOKRdKD+kLM9idjB/dQUQmGdj5pK5IelAvZDy5pyyc9OPnrTxN9EpLfsxAaQ664\nZf2HjqztJI39ay3OHh7DpGvdt8f9nsL1J9n9ts6nY3TmHpB1Ww+JHvBWl7sY+Kw3vxVYq6q7ROQU\n4Msicryq7o1fPIdBeakId2S+at8lcnc2JseHPw8lhd85G979Rlhv/4R2HlP0NGYlr+52A4y22Ix7\n2FmDU/M8vKutMXqYFnz2LQ+7IYXHcX/ThKzF9YSn1VkTlKUiIiVcYv5TwjJVHSN4ElbVu0TkIWAD\ncFfSeuzWZPQM//gWp+hpwbgxZexDT8OYAc7C9eBbMG5MkRZ89tkb3BByxXeaqtwJbAjeC98KXITr\n3fa5AbgMuE5ETgd2q+q2Flp4HrBRVbeGBSKyFNilqlURORIXkKc+NlhQbvQMrz+/2y0w5gzm+Qxj\nBji62w0w5god8NmqWhGRy4Bv4ML8a1R1o4hcGtivVtUbReQCEdkE7AcuCZcXkc/hnjSXiMhjwF+r\n6rWB+SLgc5FN/hrwLhEZx70TdKmq7iYFuzUZhpE/zPMZhmH0Dh3y2ap6E3BTpOzqyPxlCctGe9V9\n2yUxZV8CvjSZ9tmtyTCM/GGvrxiGYfQOOfHZFpQbhpE/zPMZhmH0Djnx2TnZTWMucNIb3Yee73yt\nS0tpGG1jns8wZoAvAwdw38At63JbjJ4mJz47J7vp+Ntz39LtJswqSqn5XGcfP3vso9z/qPLxGwuc\n+kcncM47T2X+sqFuN2taqUzyPzsN1IGceI+6fOuBss+E8FA4PTEOlouMteZPB/VrGogMOYWfWlin\nppEhWK6qE+vSWnM9FGrVWvOywXShKFRGqxSKwrNeeZS3px+e2oHNyV+hhtFdduAyzj0MHIkF53FE\nRXgkUhZXL2ucJrBVy6iXNUCzEJc/hLniFTgCJ1bUAXLis3MVlIMLAP7rsu9MBBV+IFALpot9BcYP\nVFxA0RRsKINLBti/7UBioLFw3TA7H9rdFLyEgc1hxy5m2/1PNwQ5fiC0/DlLefKepyau/2jAtOyE\nJWy77+mmQCscLTpyhF3B9qM2gMFF/RzcOUq4gTjlymJfgepoPWhvUptcPZ89W/YlHudlxwf7mMCa\nU1ew5SfJAlmHv3Alj/7oiYayykR7avzoqnv40VX3AFAeKiERVZ4VJy7lyft2JK5/4eHz2f1ocvtL\nA0XGD6Yn7R4Y6efQ7mSFu8VHj/D0L70PrSPHcNXzlvH4nds9e2OFdWesYvOtWxtsfpV1Z67mV9/f\nkqg8uvaFK9lyuzvGEqj9TBwmEQ47bjE7HtwV2HDHMBhLQRhaNsjBHYeQQrBsQQKRK5lYlxRlon59\nqNefd9ggB3eNIgUoFKL1hOE189n35IGGZX37yOEL2Lt1P+V55UhQPkVy5/l6nVuBXSQHAkPAvhS7\nr0IYNywCniY5SFmcYA+nR4L1EykPxwPAIa+MyDQ0K3ZG7QuoCyDFcRhOYCmJVaSnW15Dc8rm6PJb\nU+zLgGjmuFCMqAL8IhjA/QCj+iyhKmoag9RFnOJYTF1NMo6VpO/DWhrFHKPnIMu+mvoxjnPMWedo\nHu6fhZA4ASFfvIeY8QJc0pAkIa0luN9SVPAnHJbijmGSfXHG8qFQl+COd4eC8pz47JzspocIy09c\nmhgESEEaAo1CsTmQKBSDoCRu2YJQKAmoF7yE2/GmQQJ7PWAqBPMTgZEn41kPmNzPsVCYMDQEWg5F\nAqnIuq2+HlVFCkLJUzaLKk2GdRoPnaTaG5YHCnGyoP7yWfbI+t+99Gpq41WKfUU2vHwdZ7/jBSw+\ncoTyQPNlrNq8T5OzK4Wo3Ga0Dhn7QMwx8GbdNqL2xmNcKzRLeoZValq/DkRIbYsRIX+er8dZjJMw\nTwoEQqXGJHvYzZakQFiIKSdhOikYiqoZ+uVKs4JlXN1W5+PKNaZelj1tfVGylo+zfwYXpJZx5/Ac\nXHDf38b6W2Uq+5C1/FTs4XWQdP0YqeTEZ+dkN+sUCsJpbzqp282YFfTa6yvzlg6w9vSVvPw9Z3DY\nMfl4qTzt9ZV2RKqNgNx5vl7nuG43wGiL+bje0pcB67EA1GibnPjsnOymMRd4+5Y/6nYTDMMwjJZJ\nTOtsGEYM1tlmGEb+KLYxxCAi54vIgyLySxF5a0Kds0XkbhG5X0Ru6fCeGIZhzH065LNnO10NykXk\nUyKyTUTu88oWi8jNIvILEfmmiCz0bG8Pbn4PisjLvPLnich9ge3vZ3o/DMPoMUptDBFEpAh8DDgf\n937FxSJybKTOQuAfgN9Q1ROA356W/ZkhzGcbhtEVOuCze4Fu95Rfi7uh+bwNuFlVjwG+HcwjIscB\nF+FufucDH5f6l22fAN6oqhuADSISXadhGEadzjj4U4FNqrpZVceB64ALI3V+D7heVbcAqGpySqDe\nwHy2YRgzjwXl04+q/gCXW8fnVcCng+lPA68Opi8EPqeq46q6GdgEnCYiK4EFqvqToN5nvGWMOcQD\nNzzEvu0HsisaRhad+St0NY350bYEZT4bgMUi8l0RuVNEXtu5nZh5zGcbk+Mx0lMAGkaL5OT1ldn4\nLLFcVcNkp9uA5cH0KuA2r154AxynMbnq4zTfGI05wOcuuhGAU//4xFwIBxnTSGc8X0KG+AbKwCnA\nS3DJtH8sIrep6i870oLZgflsI4Fv4k71Bkw4yJgSszFanQZm9W6qqopIKze+qWyDWlXRQFWwVlWq\nlRrUnBphaHNjpRZOB3a3TM2JCAU2EaiO1xrqhEOtqkhRqI5VG8SHfCGjQl+RysFKg2CQr7BYHPDt\nEzsyMQqFf5LEg6QgaLVGAZ1QgHQV/OMSLWg8boVSgep4jSTKg0XGDyanXCwPlRjfP55sn19mfF+j\nvVapUasot338p9z2iXtZcvQIhz17MeteuLIpp3lpsOSOUQKFcoHqWHL7EQVNT99VKAi1avI6in1F\nT/CIJnGg8rwyY/4+Rux98/s4uLdSP0e+mBQwMNzHoT1jE9eIb0Ohb7iP0d2jEfXO+rhvfpnRZ8Y8\nkatACCu41or9JSoHxpvUNmsRsayk34LW3DYO7hyt26r130GtWmNkzQJ2PrxnYh1hnXC8/MSlbL1r\nO33zy7zlkQ5m32nB891yN9xyT2qVx3FqIiFraVZfeQzYoaoHgYMi8n3gucBcCsonmAmfXRfuqUXG\ncWVpdfx5EsrDaaGuUhgnMBTaidjx7L6vSBMRSpoveG2Ko0RdrCeOMjCWYu/LsJcz1l/CiQT5PINr\n7y9wf5QswInTrA22N1mix7GVNvhk7UMrxyhZMK55+ei5ip6j6HXgC0jFXUfRcdy1GG4jSShrACcu\nlHStD9Mo1BX9DS3FPXsn/a6W4wSaFLiEjj2IzepotXPMxt3cJiIrVPXJ4G/O8L+v6A0wlB97PJj2\ny2Nly75z+Y+oVWt8/70/Ca4jF6BMiAQVC0gB5q+Yx8GdhybK3FgoFAtIUVh85Ah7tuxrEBeaEBkq\nFlh89EL2/OqZRjEiT1xo0fph9mzZWy/zBYYKwoJV89n35P66MJAnLCQCQ4cNcvDpQ26nYsSD+kf6\nGNs77hc1iAcV+orUxqsUwh+0JzoTTkpBGgN2GsVpykOlVMXLgeE+Dj2T7NyGFg9wcFeycxtcPMDB\nnYcaysLm1KpKoQgHnj7EM4/v45mt+5uC8v7hMqPPpAT9Q0UqKQ8NE/e/FAqlArVqcvzRN7/c/ODh\nNXOgqoxGj5Fnl4LUg/aIcBAiVCtKrVKbWC4qOFUsFSj2FxuUOn2xqvJgifJguS5iNXEtOnuh5MRQ\nEkW2QgGtYiHxt1AoBfV8m18//F0Vwun67ywU6SqUCjx221a+c/mP0k/IZGjhr82zn++GkCv+panK\nnbj3odfj7kIX0ZwD7ivAx4KPQvuB04CPtNHi2cy0+Wz4bjC+G9gbTPtBaij8swp4MlLmj1cAT0XK\n/OlQxdC3+cMwTjE0Wk4wHqQerMWJwxSpO5Qk0aC4TgC/rEx8wBnW6ac5YPSXz1LDHKJRTTLKIE6V\nNIm47Ud97CHceXyGeAGhLLIcc9aDxQDpQXWovOoTFWBKs+NtP07IyW9/9NyH10maOFW0fpy4VZwQ\nlj8UqT8oRn8PSb+PpOk0+2PA/THHoU169HWUyTIbg/IbgNcD7w/GX/bKPysiH8H91bkB+EnQM/OM\niJwG/AR4LXBV3IrPvfxFqCpnvfP0xuAhp0qIvSYedNvHf4qW4NmvPCI3AkJp4kF54thXHc2xrzp6\nYv67V/x4aivsgOdT1YqIXAZ8A3fLuEZVN4rIpYH9alV9UES+DtyLuxv/s6o+MPWtzyqmzWc7BUiA\nM4Oxf+M3Zj+P4QL95TgBoSO62xxjBjk2GEK+N7XVzcZodRro6m6KyOeAs4ClIvIY8NfA+4AviMgb\ngc3AawBU9QER+QLwAK674E1a78p9E/AvuEf5G1X16ynbjJVlN2Y/r7zyLI48Z20ugnFjmumQC1DV\nm4CbImVXR+Y/BHyoM1vsLt3w2Q7z2b3JmbjeawvGjSmSExcg0VcU5ioiou/Wt3S7GbOKXuspzyPW\nUx7PX8mHUc146T8BEVFNf1c8frmTaHubxuRx76Zf3u1mGIbRES43n90COXn2MAzD8LBnHcMwjN4h\nJz672+JBhmEYhmEYhpF7rKfcMIz8YZ7PMAyjd8iJz7aecqNn+NCGf+GLf/BNdj+2N7OuYaSSE8lm\nw+guXwSuJTHjpWG0Sk58dq4+9Fymv+p2M1Ip2oeXE8Qdiy0Dx0OtBsUiQxe/kuEr3kxp7copb6va\n5stqWb8drdVS022qamNit8j6GuwTokAasYsrmxCI8uo1DI1lWqvV52vqjqsq1Zq46Zqi1WowXYNq\nzc1Xq1CtQbUazNcmyrRScdOVwFapum2NjUOl4sorFTc9XkErVSiXYO/+oLwKlUrDtCwcofbkdujr\nY/gf3zOx79tl3dQ+Gnq4jeWO7L2PhnoZ+9BzLvBJXGr6Ei4r5svornhrVrzTij2at3wy60oTimpF\nLCgcJw3QuqhW0rTgxIdqCUMJl6s9yT6Ay+lfA84FRoJ2TfFDzw75bBE5H7gS95b6J1X1/THbuwp4\nBS6f5xtU9e6g/FPArwPbVfVEr/7lwB/ixBAA3hFk5kJE3g78AS5p/5+p6jfT2tyjzxLto9Uqo5//\nqgsIwkBhfNwLCFwwqAcPxdvHK8j8IXTHrnrgES5XqUC1RmHVcqoPPxoTuFTRao3isUdRvffBiWDI\nBT8KNTcun3Qs43feNxEwqdaDJlQpn3Qc43f9rCEY84Oy4ob1VH/xSEO5m3YjWbIQ3bHLK292HtJX\nRkfHEusU162iujm596N00rFU7tmYaC+/6BTGf/jfifa+s05l7Hs/iTeOVzhw7fUcuPb65PWfchzj\ndyWngy4esYbqI1HxRY/BATiYJpQBsmgE3bUneRvPPpLqzx9JbuOpz2H8jvsiK637j74XP4+xH97V\nbAvGfWc8j7Ef/Xe9bEJgyI3Lzz/RXSehoFBYp1AAEUrHrKf60KMT8xQK3iAUli5Cd++FYlBWLCJF\nN6ZYRAuClEpIsQil4sSYYhEJxyML0NExpFRydYIxpRJSKiILh9FKxZUP9EOpRCGwUyoi8+dRPHod\nMtCO0EgKufN8vc4mnApheOOvkh4oRO2DONGapEBiBCcelKQEugjYQXMQFNoXBsvj2fzpARqFe+KC\nsVCNMinQGwaS/Y3LBf5kin0t8GiKfR2Q1nF1eMbyK1K2XwnW/c8pyxdpFhuKkiWAtAR4OsW+mvRe\n+6xjsI70Y5CigzUhUrXDmycyPY+6gFOSeFCWONA83DFKEvsZxv2W/LKCNz8ctKGQMMzDnacCTtAq\nau/D/V7C6Q7RAZ8diLh9DDgPd6LuEJEbVHWjV+cC4GhV3RBoKXwCOD0wXwt8FPhMZNUKfERVG4Th\nROQ4nKjccbiL71sicoyqJipg5fLWNPpf324KDiiVoFxy5YMDoOoCgTBIKJfrQcXQINRq9eXC8jAo\n6e9zP59iEYoFF6wE0y5gKUGhMQiSYoFiQaFQQEWQIDAinPYCKkVdGTQEY/64oYe2YTroYQ2Xj6tD\ndp1Ye3T5tF5iSLUXtNJkf3zwBPeAUyoy+Lu/zoK/+V+UDl8Vu57M7atSCNrfbk/5XCQ3xyKXnq+X\neRTYTXKgMIgLkIskBwpLUpYPr/skRdAk5UJfyCguWCJlHJ2Om/fLor200frt2CeDkv7Ga9z6r8H1\nlJdxqqvn4WKTuHa00r6sNhhzls747FOBTaq6GUBErgMuBPwexFcBnwZQ1dtFZGGoWKyqPwgUnOOI\nu3gvBD6nquPAZhHZFLThtqQG5u7WJMUiI5/9aLebEctMvb7SilvOqjPd9oI0O97imhX0/9oLGL78\nzygdvip9/RkqrXlVcTUCcuf5ep1zu90AI5M4nzqCe+Bp5bWVTtyZjDlLZ3z2apzMbMgW4LQW6qwm\n/W8ogP8lIq8D7gTeoqq7cU+ifgAerisRuzUZPcPKTd/udhOMOYLm5A8Bw+guv9PtBhhzhFZ89i23\nwi0/TF9Ni5uLPv1lLfcJ4F3B9LuBDwNvbKcNFpQbhpE7qub5DMMweoZWfPaZZ7sh5F0faKryOO7j\nipC1uN7rtDppHwoAoKrbw2kR+STw1XbXZS9nGYaRO6qlyQ+GYRhGd+iQz74T2CAi60WkD/cR5g2R\nOjcArwMQkdOB3aq6La1tIuKngftNIMzgcAPwuyLSJyJHABuAhAwWDrvVGIaROyrFdvojEj+YNwzD\nMKaRTvhsVa2IyGXAN3AfO1yjqhtF5NLAfrWq3igiFwQfZe4HLgmXF5HPAWcBS0TkMeCvVfVa4P0i\nchLu1ZRHgHB9D4jIF4AHcCmI3qQZuZQtT/kswvKU14k7Fnve9kH6X34m/Wef1tEPNXOTcaQFeuVY\nTDVP+Z7K5FN1jZTGLE/5DGJ5yucCd+Jin+dAj/gWY7qYWp7yvPhs6yk3eoa9//da9l31GUrHHsnI\nh97e8eDcmDyq6nLwj1dcPv+xcTcer6Bj4044KJhnbKxeFpRrrYYeODRhY2wcHRsLxuPI4AC1p55G\nikXmf+Ad3d5dwzAmxT3AVuDbwEuw4Hy24Ofyr0am/bK4AWAsxV7GdTBXgRfj8p4brWJBeQZardYD\nhdEgqBgdc4HGRIAxho5XJuqpKhwcnRAbqisYujED/eievRNiQ6FSolSdWJEsGkaf2ol6SouhsqLW\nlMKShdSe2tmo2AgTqo0yMt+JvkCTAiSADPQ7caS6oXnHJVB2nKgTMc8fQvfuT1yHLF2EPrWTJAqr\nllF7PPk1reKa5dS2RDIQVVygN37XA+w47/XQV6Zw2GL6nn9CU870wrLF1LYnb19GFqC7nwl2LSbP\neankznEK0tfXKLAUobBohNrO3d5KG49RYcVh1J7YnmgvHr6S6q+2Ntq8OsV1q52Ak2qjkmcwLh6+\niurmLYHGSV18yql5QnHVYVQffSIQsKrVx4GYlSwcprZ9R7NA1oQyZzAvAuUy0ld2uf7LZQimC8sP\ng/0HJualrw/6XF3pKyMrl8He/RNl9PXV19PXhwwNUly1HJk3lHouJku1aIHB3CUuqKgklIeD4AIN\nX1DIFyAqAqMkKyOWA3ucwmKYW7vizRMznTXfF7Qxqe4QdeGZOPsCnIBSkn0YeIZkFmTY5+GCMZ+n\nccdnH+712q/icsqvoFlYpkD2K2KhwFISUZGmKPODtiSRJdCUZU86RuGxDo9R0jXQR/06Cm1pSp5x\nqp2D1BU144alwDbcNR3m6PenlwO7vPLosJC6uFCcvYw7zuF8Z8iLz85dUK6VCrvPf91EgM3YmAus\ngmBbx8YpLBiiunW7682r1Vyg0B8EE/19FI85gtrW7W6+XAoCinqwUVi32gWkpZKzB+JDEowLy5a4\noDwUFwqEiwrFgboK4uBAXTQoFBIK5mVwAI4KAkZfqZFgulxyIjshUSEhicn2GhUPIiaXtz9fKDQH\n8769XHJBWxLlkgvoEij2Fd358Tj4lW851dP+PqRcou+c0ymfeAzlk45taquUSi5wTKJQhEBUq5b0\nvXNWL7xIetbcYrHxwSa6zrhj5NtLRRcgR22hcmep5B7owrZERaRKBacUGxGfkuCa0WLRLVMsuGut\nWKRWKNWFrqRQv359ka0JsawCWixSKPWeG+mV13SMkBtxgUQYYEeD6qW4pAZhIB0NFFbjFLCTAo1F\nuEAmDDSiwkJDuIDYFw8q4m6hobLhQNDWJKVFPDsJZUnzSWU+BdKzrZVIV8wskh4UZwXNcfZtNKpL\nrsEF5KtoL/xoRQEj7Ri0s48yBXs75zVOfCp6zfgKnHGiVtFrOKra2Xv+Ly8+u/fuplNg++cPd4Hk\n6ZdDqS8Y+t243D9RVi2WoTzgygpFF8AE6wifR6eF8nStuAso6b/7VhR4mzpS3grzB+Did6Ev/yNG\ny/2MTmb5dkmK7bM+x0jr0IFsRen0jvrpoRvb7AKVnDj4ucNzcBdnieaAOgyMfZu91jY7uB3Xs/wC\n4ExcT7FhTJ68+OxcBeWA6y088bxut8Joh/f+ANad4B6WDGMKVHPo+nqbNd1ugNEWF+L+QbBg3Jga\nefHZ+dhLY25w9PO63QJjjpCXv0INo7ss6XYDjDlCXny2BeWGYeSOvDh4wzCMuUBefLYF5YZh5I68\nOHjDMIy5QF58tgXlRu/w6bfB814BJ5zV7ZYYM4EqVMZg/BCMj8LIso6tOi8fDRmGYcwF8uKzLSg3\neoevfBj+6yo4/Hi45EMWnM8EtaoLjKPD+KgbqmMwdigInIPBn6+Mw6G9MHaweRg/CIPD8NTmePvY\nQdhwOjx2vwvIr/xlx3YrLx8NGUZ3qVJPw2cY7ZMXn52PvZwpwp69sUNQGa338FVG60FMZdQFOmMH\noToeBDnjbppxN903APt3u3pxw+AC2LcrEIMJRGEIBIZQGFgAB3bX2xTaw+nygAuIGgRnmsVpwjze\nDQJFYb2+eTC6L1Lfm16wBPbuiLcBLF4Ju7YmH8vFq+HpLY1ltSpUK7DpTvjLc6HcB8PLYMMLmnOK\nL1wOu5PFiZi30B3DJEpld27Ay4EZ2YdiUKdh37zpeYtg387mYxfOL1oJOx9vLPPPxWHrYdvD9WWj\n52H5kfDEpojNuw5WHA1bfx4IB9WC68ebXrYetv7CHdNaxY0r4/XpkRWw87HG1KENQ787B+VBd82W\nvSGcHxwGKbjxyHLoG2wcBua79UTLw6E4PS6qU3+Fisj5wJW4PHyfVNX3R+xnA18BwhN5var+bUc2\nbhiznutwAkIvA56FBedGu9jrK3MVVdjxKIzuh9EDjeOxA3BovwucD+5tLBs7UK83OAxPPeIC69ED\njT17hQIccwZs+Vk913m5PwhggvGqZ8POLS7wK5ZdgFMsQ7nsykZWuKC6UGwcwvzpA/NdoCOeKEw4\njbhgprIyIjYj9bqFYj24E0+UICo+01TmTXviO40BcTBdLLnAz8ev19cXPIgkUOp3vbA+t/0naNUd\nx2IZTjgH1p8AR5zUHJQXSi64TEK89ifWCdZZjSmLThNT7h8jIsdWxB2jajX5uE8s79n981Ao4Z4Y\nvHMbd00UivXrpVAMxIQKgShVyV1zhVJwzoJxsezqFEvZIko9SCccvIgUgY8B5+GUa+4QkRtUdWOk\n6vdU9VVT3qBh9ByHgJ3A9Ti1y5cAa4PpuedXjOnDgvK5yPdwgeK/nwmleVAagnIwLs2DcjDuXxQE\nKyNQWunKBodgOKw3D9YNQGkQioPB8oNuKEzhkA5kV+ko032Np60/S1gnLl6X18LACJzxLjjxD13g\nDo1KxCHTpfCUppAdJUscqBU6sY7JcGiGt9fbnApsUtXNACJyHS4xczQot+hjynwX58CTWAf8agr2\nTqxjrtunso5xXHD+H9O0/pmyz4Y2zHa7X+cs4JyMuoZPvoJycEHzax/tdiuMdrj4Vlh6Yj0YN4w2\n6dBHQ6uBx7z5LcBpkToKvEhEforrTf9zVX2gExvPF+dgN/de5Brcz6IIHAucCyzqaouM3sQ+9DSM\n2caK53e7BcYcoUMfDWX9VwJwF7BWVQ+IyCuALwPHdGLjhjH7WQEsxIJxY6rYh56GYRhzlFbeT7zr\nlr3cfcvetCqP416QDVmL6xacQFX3etM3icjHRWSxqu6cXIsNoxf59W43wJgj2DvlhjHTjO2Dvvnd\nboWRA1px8M89eyHPPXvhxPy1VzwRrXInsEFE1gNbgYuAi/0KIrIc2K6qKiKnAmIBuZEfLCWi0Rks\nKJ+L3NPtBmQwr9sN6CKj2+AHq+CwC+Ho98K8Z3W7Rflk/yTq1sahth+qe4NhH9S86YnxHqg+Ux8q\n3vyzvwaDR0/b7iTRCQevqhURuQz4Bu6l2WtUdaOIXBrYrwZ+G/gTEakAB4DfnfKGc8WCbjcgg3K3\nGzDL+b/AKPBqLCXidJGSxaxlNFjPGO58jXnTo5HpAvCMV2fMW3YMWIz71GYc+J+4V5imjgXlxtxg\nIkd5DZeSRF0GGvHyWk/YvXzmaPI8RNIJxuU5j75uGzPvF40+DlKGp74CO26ERb8Gqy6BwaOgvLjd\nnU+3pb4RnNV+3L4KMfsczTnulft5yAEI88zXaD7WBdBxGs6Pfx4pgo4Gueqrwbq8aSlBdT9opT7U\nxpSDx2UAACAASURBVOvThX4Yfxpqo27QURgbrc8X58HYVqgdcEP1QH26dgBkyO1/cT4UF0BhQX16\nYrwYikNQXgbFESgOQ2nYjYvD0H942kmYNjr10ZCq3gTcFCm72pv+B+AfOrIxo4cJf7PR6fC3Hk0X\nFZfmKc5htfJZg183LSieqr0EDEXKDgDbgE8CS4FX4jJztPuwtSfDPt372MJ9IbVe3Dg6HV4Ptch0\nDffPg+LSl1WD8WgwXfXKwkDZH8KyPmAXjcH3GK5foQ9Ygwu6+4B+bxxOD+N6EBcF89Gh7I0719No\nH3oa7VE75HoCa/uDnsN9QW+hN43A+FNQOwh6yI1rh6BwEKqHoH8lHPh5EECNBWNvet5xsPeuIPiq\nunE4UIN5J8Deu6k7+iBnNQIUYOBwFwRP5CEveNMC0kc9F5802sKhvAgqu6k7sEge84H1cCiaNslz\ndvOOgwNe5rhaxe0f6oLDnTfDzm9BYdAdjyjznwP77k0+D/OOhf0PJtsH1sFoShae4ojryU1qP7ig\nV4Pj5OcYD8d9S2Hs6cbc4v7xDI/RxLmJHOvBo+HQI9TPT6HxPA4eCYceDcoKLve6P92/CsZ3uHZK\nORiXoBBOL4LCgEv9WegPgvR+Vyb97tgXBqAw5IbiUH26MOTW06Pk5aMhI4mwZ/BgMIx642jv4Bju\nVrkbF/CMe+NqMF4EPEk9cArH4fTCYHlJGELNAZ+oDsJg0MY4G7geyrQ3o1YCTa9geaylMZnQZO0n\nACPA12NsY7g3vP4pZXmAo4CHUuxhcJjEAiDtO5DDgKdS7Ktxn4ok0fTZSMzyoSheNLgXYBmwncZz\n79cdxv1dGd6XC5HpPiY6ZCgF44I3HQbWRVyO5bI3RANmP+AuM/05kqdGXnx2PvZyMmgFxndDdRdU\ndtXHlV1Q2enGWoGxLe5v+coeqO4OpncDCgteDKObXW9hYX7QazgfCvPcuG+tC7KLC6BwWD34GQzG\nxUAcqNAXBFPlxmkJxF4mAjFvoAhIYPeDvFnOoUfhh0e7wLA47F5hWfF7U8v7bkyeyby+0sPk5a/Q\nuY/iAr4DuIs3HA5Exv24YOmgN4SB7hCud3Av9aDPD1jm43r8VuKCl1IwlKkHPsVgHAZPoS8u0Bhc\n5YELvOm/wwXBBeAFwPlYFpZO04nXV2Y/efHZ+Yt4auOw5YogwI4Zqnuhf72rW1oEpcXBeBEUF0F5\nufsrfviM4O/4ESgtrE8XBtoPgvP8TnlpISw4Gdb8qQXjxrSTFwc/d/gRrpc3GnyHvYpH4oLqebgg\nOxwvoa4g+QJcEB4Onfinp3f/LZoZ1uLOjQXjxtTolM8WkfOBK3FPzZ9U1ffH1LkKeAXO4bxBVe8O\nyj+FSym0XVVP9Op/EPdu1hjur55LVHVPkARgIxD+bf9jVX1TWvvyF/lIyQXOQyd4Affi+lAcCV4B\nMGaU0jCcenu3W2HkhLy8nzh3KAHLaQy6w6Gvi+0y0nldtxtgzBE64bNFpAh8DDgP957SHSJyg6pu\n9OpcABytqhtE5DTgE8Dpgfla4KPAZyKr/ibwVlWticj7gLcDbwtsm1T15FbbmK+g/B5wvSp/2eWG\nJDDTPeXWyTP7mel/JnPz+kq+XF/vsjoY/2ZXW5GMOVGj2+Tl9ZWO+OxTcUHyZgARuQ64ENebHfIq\n4NMAqnq7iCwUkRWq+qSq/iDo/W5AVW/2Zm8H/ke7DbQuYcMwckeV4qQHwzAMo6dZTePXyluoP/lP\npk4afwDc6M0fISJ3i8gtIvLirIWtu8gwDMMwDMOYtXSoY6TVHKLRDwNbWk5E3gmMqepng6KtwFpV\n3SUipwBfFpHjfaXnKBaUG93n0Neg74VQaDcfuTFjaA3YARzWG1l9ErCeb8PoBD8ANtApgRhjOqnh\n8o8P04svSbTisx+45Sk23pKW8pLHcV8fh8TluIzWWUN6nkwAROQNuNRDLwnLVDVMAo+q3iUiD+F+\nMHclrceCcqP77PotoAjz/hfMf6sF5zOFKnAAartBg6G6G2p7QLaDPhkM2+pjngJGYOAhN+5R7ENP\nw+gE/4SLOU4Dfg8LzmeacVzWoTDN535c4L3TG54Oht24D9fej8ub31u04rOPOXsFx5xdvwb/84qN\n0Sp3AhuC98K3AhcBF0fq3ABcBlwnIqcDu1V1W9p2g4wufwGcpaqHvPKlwC5VrYrIkbiA/OG0dVlQ\n3kvUDgIHnLgOo054iFFvPlTs8oSEfCEL9ZXBFMY9Fc8JZcki9Q9Hompj4PL2TlxzzcgQaNrXgvNo\n/pqwCozB/o/A/iuhdKwbyqc1Z8IproOBC1PWn4GOwoF/zKhUoFnIw6cPJyqSxCDoAX+jjWaZB7qP\neDU3cOnb9rjzVQvPjafuJgtAd1I/z96gVZDhIIg+FFwj/vigs1cfckE4JSgsBFnoxIRqC4GFIEtA\nVkDhGDeW5W7MMpcnv8exDz2NqRP4rQmxIV9UyJ+uUM+nXosMGjOOU3gMFH4nRN3iFD/LwTai5SG+\n+FAcQ7gMcJOxj+H274fAbTjxn5NxH+Z22k8ocF1GnSL1YxRHC7479RiE968kldUhYJ9ni57LeYE9\n7pzXcHnv9+KOadxQwqmaHsCdyxqNaT4HcQH3QpyY1HpcWtDF1BU4e5NO+GxVrYjIZcA3cBfLNaq6\nUUQuDexXq+qNInKBiGzCnexLwuVF5HPAWcASEXkM+GtVDTOy9AE3i/sHOUx9eBZwhYiM407Wpaq6\nO62N+bozHYwqNM4GdgGb3XBwN/AI7ul2d2DbFczvwj1kbaEuajFAo8jFYpzDiYpXRMdxamGhutgQ\nLuiOUepEcH99pSmmLQ7amsQS3FO7T+i4AgnhyhaoFODQIpr/ZisCfwX8bco2zsT9rZrEC3A9O0lk\nOfYh0m9uI7jeCh//VY9FuPMblkfV+arU1f8g+TwVI0N4jgeBY3HXhz/418wCnOPujzx/ZH3JH/bI\n9Db2+kpeUZyPjOY7H8cFO7640AFvehGug8tX/KxS/z2tx73WFYoLRccLcAFsgWZBofB3C43+oBAp\nK+J+rHFqntFl4ghVIKfDHm5/F/D5YIjjOOCBlG1k2Y8HTkyxFyFVpKlM+uvBodJlElG1ax/BXQ9x\n5zKc7sfdQ31/7vv1MnWF16ShH+f/w1z7lZT2zh065bNV9SbgpkjZ1ZH5yxKWjfaqh+UbEsqvB66f\nTPvyFZR3lUdxmXLuxwXem4OhhnPo63HOZhnwLNxNIHy6DacHO9ym2XL6/wnnXF4OvBsXUGbx19Pa\notlDPhzuTGNBea9Sw3UahAHzgWAYo96DGKfquRin33EA5/d8gaF5uOQKo9R7GxdRD3zCIQzAwyGU\nLM8zNwTjF+PeBFjWxbYYc5m8+OzZEpXNMWq4p/0f4/7Suw3n8F8InIJLi7keOALn/MOn6d5/LaA9\nPgCcS2vBuJE/xoA7gDM6tsa8OPi5wwdxqYQPUu8pDIPmIWAVrrd7CJiPExryRYb8abvtdY434V5Z\nsWDcmF7y4rPNO3WMHcB/ArcC3wUOw4lAnQO8Ayc13LvZKqaXP+12A4xZyf3Al3EiascCX6JTPZP2\noWev8UfUe7h7L3PE3OWF3W6AkRPy4rMtKJ8Se4GvAf+B68l7GfDbuF4d6zkwjMnzDPBF4F+BJ3E9\ncTfjHmo7h33o2Wv0XrYIwzA6R158dj72coLNHVjHGK43/CbgR8DzgFfg3oUeCuocaHNbvf8BndHr\ndPq7hVZQ4G7cP03fxSkh/wHwIuofTW3u6Bbz8ldor/NXem+3m5CKXUd1ZvJYFFM/xM8XvXIs3j3F\nFwXy8lubM0F5kCfyStxd/JOq+v7ObuHnuI9of4X7+O4VwDuxHhzDaJcdwHeAf8O9kvCbwP/GZeiZ\nXvLi4Gcz0++zDcOYK+TFZ8+JoFxEisDHgPNwykt3iMgNqtqUOX5y7Ae+jgvGd+CChjfgPioyDGPy\nVHD/NH0Zp+PwG8C7gOcyk99c5OX9xNnK9PlswzCM3mVOBOW4/7s3qepmABG5DpfipA0Hr8DPcB+V\nfRN4PvAnNP6VbhjG5HgUF4jfAKzEPeD+HS4bhpFDOuizjdlKdaxCoVwkEFQxjLbJS0dKZlAuIt8B\nPqyqX/PK/klV/3haWzY5VgOPefNbSFeHieExXBD+AO6+8Fu4wNw+2DSM9tiOE3H6Gk545ZXAPwJH\nd7NRwNz+aCg/PtuY7Vx/4XXs2rSTcz7wUja8+tkWnBttM5d9tk8re3kE8FYReb6qXhGUvWAa29QO\naRJdHp/wpp+Pew3lZpzi6pO4f1IvxuUSt7RbhjE5wvz83w+Gx3H/MP0+TmV1Knn478C97tIZ5vj7\niXPGZ3/v8u9PTK87ex3rz143bQ1qF60ptUoNVQV186qK1hrnUdwYmqbBm59YcXRD6YfMLZ8S9Grc\nSltevauQElRXtdC0+QPb97N7006++vtfYmjZEKf+xRmseP4q5q+c3xygZzQf3LEUkcSPGzOamLmP\nqprahIxD2LR80/a8ggZbMFO/TlxZdIxCrVoLxu4aK9QqE9NaU7Tqrsdateauy3C+UqNWVUT4/9k7\n7zg7qvvsf882tVXvEkICIboBQSgGgwGDDdgGY2PAvRA3bOOWYhy/Mdh588YpTuIUh8RO4jgucWLH\ncRJsA8aiIxBFCIEB9b6SVtrVaust5/3jmaOZHd1795a5bWeez+d8ZubeaXfumd95zq+SGkhpO5Ul\nk8qSTWXIprXePrmdwf0DRz7PpLJkRjLevhk6F3TSs6mHzEiGTMr7fCRzZP/ZJ81mz9N7GD48wmk3\nncLkOZOJAuNcZh9BMaS8B1V2+box5r+B91T3lsrCTmBJYHsJ0ryEcCnKnnIv8EdAFxqrrkflfd2f\n/lLVbjRBgvEDi8TDyyh7yjOocMvZwNuBE/FFzIYKr9WJ3l+Hv6vobONcwI8bmX35nQ8DsAsYuPNh\nNqAi5+HWgup85voug3IK9Xrr2dB3WVTCba+3Hm4ZZC/d7W3bwNLxqvko6ihcVN1tT8HPrRUkbcH1\nVu+8+TANJeHNh7nAvgLfL/J+Qz4cg/6UQsfvKvH6h71leiDFoS293PfxuwG9zeE3sI2x6xdPQGX4\n8mEmcLDA9/PR/5wLlrGf0Vjfz0esIojgfxy+v/AEYBLqx8HvcvUp11rw5zKuTUIspyXQTGB9CnqG\nrYHPgusdoc9aQ+tt3vnyfd+yfv+Rzzr/5ukj/7M/vS4P41xmH0FR9gBrbRq41RjzfmSPnlnNmyoD\na4AVxphlSG7chFTeOdCCXpu3A6cxftzq44Kn0f/3OpIS17VEL/I22OE1t94CrETK2bcAC+p1gyVh\nvAv48SWz9dYfwCcA4daJiIgjDR2h7zsQYQoTiFFkIsd6LlJjAsspNG9JuFqMfF9H0SStwFnAVTRe\nR3QYa0LQqIhLIuXxLrMdinkvj6ikrLX/bIxZR4OVYLTWpo0xn0B+KK3At/JH8behHMgJmhNrgFWo\nwMzbkMtRQs6Lh0XDzzCiMSMor/4hr/UGlm7doInQMUiheQyqVnsMMJ1mpCXjPGhonMlsRSM0Ipqv\n59cWxyANeiOT8QTNgXEus49gTFJurb0rtP0UDchqrbU/QxV9mghZIIWIUZAkuZZCBMotw80ZV3MZ\nXbOIrA7hG1ltqHUig+go57bAcgayhIc/dwgb48IOdNMDx+fCVAobZHMZKzeg39aP8lv/KzLsLvHO\nF0Y7en75MAXfyJoLY/2GsZ7BLKA79FlwH/eMLUf/D+H/KFfrQLqSfIZ3ZxAfwO9jLejZduDrEA16\njtO95eLA+izvPscPBYkqaKjYXNvGmHOBx4AbrbU/juTieZDI7ASNgrfW+wYSjBskgZ4JciCLCFYP\nvmYx7W07Eu2IzyTkZegItyPfbns68k5zxtYgSepA1UFbvO9ztXav2cB+YaNrOyJm+bzS2r37D3s5\nuu0ORhPaMClzx5Nnn7G8BMfyojQcTXJ7kVegu88lyHXiOHKn18t1jiBaxvi+FQpWTGvL8X3YUzST\n4zu3bMF/BsFn77ZzeQ2GPVZzGdzDBvkJ+H2sGTQOfehdc9r6NHBZZGePwhRabK5tb7+voqIH42dm\nkyBBggQ1QuK+Mi5RSCubCwPAc8h/dh0i2SchzepUr81DhMERaUeqJ6DgUUeeOwLLDnwyni/LSz8a\n56eivM6NjEKa6KjxLOI1Z6PCM42asrKSTCPVgrOwNBqyyALyHPAKfqjUTPz3rANlTIoGEQn4YnNt\nfxL5WzVaBpSGx5e+X+87GAMTany9Jhuxf70DFsyAGZ31vpMaotYitlDka42RycLhIRgYhv5hWDIb\nJnhD4R15I0aKPHdCyuOMIeC/gOcR6Tsb+dDOI7cUPgD8D3ADIublXvNZlM/5GeSSsBgp4RqdlNcS\nbwCuBObU+0ZqhBSaoPWjSeKAt55B/S74nVs/jDKVXF+je9yIvEdLZSgWpTpcjVyAzkb3vAgRcYNS\nlf43sAWNPtGwoIj8E8fMtW2MWYyI+uWIlBeZvjVBgubHTX8Mr+yCz71FLVbkvAFhLQynYGDEJ85H\nliNaptJwsF/rh4e0DK7P7oQXd/qfB1sqDVecDmu3wZQJcPfvwkkRFUBPfMpji23A3wAnA7+NfGoL\n4Ungu8i0Xk6n6QfuBh4BjkfawCuB2VQnV3oWudtMpzncGMKYVe8bKANpfNLsiPUwcssIkung+myU\nmtOiid5k5J7jlvMQaZ3vbQe/c+vVxgDwI6Th/iRwbInH/gDYivKYr8ixzyNIwfwm5BIdXX+NyD+x\nGIL9F8DnrbXWKDFz4r4yDpFOQyoD6ayISTq47i2zXv7prIVsNs+2dz5rj6SlPrIOkG3x14Pfg/Jz\nZ7L+d8F9AFqN7iWUHv3IdluLfgOhzx3aWov4PqQl7u6DwRH46o/hT/4TLj4Vzjoezl4OHaFXcCxH\nw1zXDG+3tOgZBD8Prre26L/JkSIc0D0Np45+Nm45sV3k1X1mQ//ZxA4YGMj/P7rj3eeuD7h9Olr1\nfSZ7dEtn9IyGUjDi9beRNIwMw4i3nkrrPL0Deu5DqaOXJy2EPb0wuUPEefIEb9kBUybC0jnad4r3\n+cwpcMws6Jyo7emT4eYL/e+PtIn6fdWqD5X4lMcSXWgMfQ9K8zYWViNS8ttIaVYKrHf8f3jX+jJj\nTwCKRQZpGHd7y26v7UcaySnA7Yj4JciPLH4MwDCyZgwFtgeQRSPchlBMwQ5vnww+sXZtQeDzmUjT\nnIt8d1BdHrcT5RnP5a+dBe4DXsvRGupDwLdQH7qT0iYBGeAfUF6G/0NuMfQISszxe1TDKlKMKXT3\nqpfZs+rlQrsUk2v7HOAHXqGUOcDVxpiUtfanJd1wgophrUhJTz/0DGjZO6D1kTTsPSRtoNMIBpcL\npsPzO0TYhgJtOA1DI3DmUnhhp4htWyu0t2rp2uJZsP+QSGOLUTPesqVFZCxjvUgR73U3xt82RsRn\nMOVvw+jv507TNYLHB/dZPBt2HTj6O7d97FzYvn/0MwtKnpzfB3ZYNBN2h5KE9/ZrmUqLED/wPKzf\nqjYpJFImT5DWthBM+KZCm52ToH/o6Htz63OmaqKQ7/sFM6CrZ/RnwWe1cBbsOZj7+QPMnwH7evL/\nj7M61e/cf++Odd9Pmag+1trit442LV2/am2B9jZ93tEGHVbL9lYtJ7ZrcjCpw1tvD6x36PgEjYuE\nlB/BIHAXyrVcDCHfCPwb8DnKI+Q/QuP3rUhDXglGgF8D65HmcQfKlvEqROqOQ5bz2UjT3Ij+zllE\nZgfxg2XDmWfcuvH2dVlGLKOzjrSFvs+Eli3edVKhNhK63iB6VhPxAyUneNvTvfNM8trMwPokNMGa\nRG2IdTkYQm4hjwLX5vh+BPgmmlRcHPpuAE1ez0SeGaXiZ+i/uInc2u9t+JPd+rkpLbz0RBZeeuKR\n7bV3/m94lzFzbVtrj7zcxph/Av47IeSVw1oR6r2HYN8hmdu7emF/n0hX92Fv/bC2l82F+9fr2JlT\nYMYUmDHZa1NgxQIRws6JMH+6CLDTDHZOhKkT5RvriM0ER346td1WK6Njk43Yp9wKm7tg7nT46vvg\nptdAazMaaEtBjH3Kq4nEpzx2+Bkir5cUsW8KaQnfT+mEHKQBfB74Hcp3M8igeLIngLXAUuAU5Je7\npILz5sIhpGVfRnEuNVmOzn3dF9juQBMHp1UeQiRwoteO867XzuhsM259GpJEuUp7uGwjExmdhSRc\nk8xlpwkG3baHmjtHOWjEiY/Ds8h15CSk5Q5baIaBb6Pf8ClG/5Ys8EPkbpKLzI+F7cADwO+Tm5Bn\ngX9C3LZ6sRRRCPh8ubaNMR/xvr+r4AkSFI3f+zd4cqNPwvcdkvZv7jSYN02a6uEUzJ4qn9cVC2DO\nNK3P7pSGcuYUEehIUetAzybDh14PC2bGhIwnqCriQsqNDTtljVMYY6yIYi5sB16N3EmKIdl/ju/v\nWir+B/g8yo52TOi7fy7yHC+jQFSQdfwcpLktFcVO6R9Cv3USsiKci+79IAo23Bdq05DrzDQUsBde\nTsfXOE/yloUy0cQZUc6bh4B/RxlO3gcsz7HPu5HCdyGqxxf+T/4SxUDcTek+3hb5h18LfCTPPv+M\ncs/fS2HrwmSstWWZH4wx9p32WyUf9z1zS9nXTFA6jDGW93jj0657gSxMmAsT58HEudDaAIy4mcsp\nFsr0Gkc0K+ebVO8bKBLfMYnMLgKJphyAPwI+THGEfC8i5b8s4zpbkbvKTziakBeDw8B/Ig35jch9\noBb9bSbSlg6gycgj3uftSHs+12srvOUsEhVSo2Ej0n6fCHwBTYTCsMgdK4X6eJiQvwL8KfAg5Y1g\n9yAvj1vyfN8H/Aki5tXt13GJ5B83WHRlve8gQYIEdURUMruYgm/GmK8DVyPS835r7TPe5/8IvBHY\na619VWD/WcifeSlKFXajtbbH++52lKkgA9xmrb2n0P0lpJwHkTn9sSL3/wpwM7mzRRRCGv0vn0Uu\nJqViI/IBXoxM/7lIVVQY9K63CfWvbci9BNSPFyFNa6PmCE/gIwP8L/Idvxk4q8C+96F84fdytPtN\nBvgoChA+roz7GAA+jTIb5RM7f4ifObC6iEskf4IECRKMB0Qhs4sp+GaMuQY4wVq7whhzPvANlBMb\n5Fv5V8C/hE79eeBea+0fG2N+19v+vDHmVGR6PhWRt/uMMSdaa/NWTYz5yDQM3Ab8P3KXaA9jLdJS\n/3sZ1/pTpD2+rYxjfw38I/Be4PQyji8GIyhQ9GmUiu80pPW+Emn1XZaM64DX0HiBiwmOxh6kdZ6K\nyHQhF6c1wK9QjEKuLEB/h/7zj5Z5L3+Iau1cnuf7tcD3UYrR6iMu/okJYozhbrn4tCXJwRM0P2pY\n8O1aZFbGWrvaGDPDGLPAWrvHWvuQF9gfxrUoTRnesasQMb8O+L61NgVsMcZs8O7h8Xw3GHNS/mfI\nnP/mIvbNAB8HPoTcOUrBk4jUPEzpftNrUR70DwMnlHjsWEghwv808ALKM302CrILB4pehyYEcyO+\nhwTRIwXcjwj2a1H2lEKTqA0oePM2crtVvQT8PcqIUo7f/zrgO9795EIG5Tm/g1r1r4SUJxj3ePYT\nsOunsOIzcOJnoaMZazzUGDYL2RTYlJbh9WwK7Ahkwy2lpc1Cph+yw9rOeEu33ToJhvZAdggyXguu\nn/BxOPZd9X4KhWEtHN4EU3PFJFUPEcnsMQu+5dlnMdJy5cN8a60rRd2FCoiA3AqCBNydKy9iTMpf\nQVaJYt1W/hZpHN9b4nUOI7eVP6d0P/I1KMDyE5RWmGUs7EAThUdQn1kJvJXC1oJceazjAov+xy6v\nzUKZbhoNFhXy+TEK1PwoY5PcLpQz/P3k7p99aJL2acqbFGZQ//0SvpwK45vIilTo3fpXNHkuJ6D5\naCQ+5THG8EHofgIWvaHed1I5rBXhSw+CHR5NAof2Q2YAXvoTePlPYPYlMP10mHY6tE2CdIBw2pRH\nSEfApsFmtMwG1lsmQvoQkNW+NqMl3nr7bBjZ631m/aVbn7gYhnYEqvWElpOWwMBWf9ta/3gsTFwC\ng1sD18yOvtbEhTC4zbuvHK19Fgzv8X6f+22Bls0AGTDt0OK18PrE+ZDuh5YO7/OO0W3igsD3E0Yv\n26dB+0yYMEfPstVrLYHllHJcA6sMm4WD62Dvg9D1oJatk+DaF6CtFkXqhGJkdveq5zmwan2hXYrN\nbBLWYhWdEcUrFFdo/4LnihkpX+0tXUDbTcitaOcYx+0B/i/yh82n7cuHP0H+54sC1y8Gq1HWk9u8\nY6PAJpT1ZRvwemRdmRHRuZsdWUanbdyDT8K7UJ+Z77WoijxFiV1oAteL78I2FrqQy8hbAvsH+6hF\nZHo5moSU0n8d/hNlfcl3/H6kIf8L8ruuPIQmxQuBaEzxiU95k+A734jgJFkUZP+C13ajCeYrVJ5y\no6/E/TMoZsdV9nXrae9crl6CK1rmtqeg9zVctyGDrFdzkOLApY1tRXIMaXYtsO+XsO8RVLisk9Fp\nYl2qWBidXtYwOpVsNvB5uA143wcL1waP7+boBABB7tODL1tNjn1SSAYEr0lgvQXV4nDr+Zbud+dq\nBqzRY82VnaY/x2dHoVA6XO8/4XCe7x8u5gIhFON6WwosekdeRJneNqL+sgK9N58EZsP3vx3xdQuj\nGJk949KzmHGpHze14c4fhncppuBbeJ9jGJskdjkXF2PMQpQRpKxzxXRkug8JwLcWsa9FWu4bKV3T\n/TDKCf0PJR73BMrQchuV52q2yP3g54gAvR654LRT+yoH1YLFH6SChYBc5c2R0NKi1I29+HnU+5HL\nzjQUyNiGAqnPQ0S8k8byo8+i37ITuZXsB65BefaLIRrbENG9DqUDzYXveef9IuX99n3Ive4vye/2\n8tso5mZZnu/3AF9Dk+LofGMT95XxjjSKkXkFTQano4nnm9Eksxp1BCwi2QcQAT0QaBO9e0l5Alv9\n3QAAIABJREFU68HqvpOQ9c16303Dr6EwwVu6OgowumaDq7mQCz8GnvH2uQjFAlUzQUCC5oVFE9dn\nvZYCfgONf+8kKgtlJYhIZo9Z8A34KTLv/sAYcwHQE3BNyYefouwXX/WWPwl8/j1jzNeQ28oKxtDs\nxpCUH0L+3f+X4sjLLxExuanE6+xEPut/RGmFfB5FWVYqJeRZ5MrwC6SpfAPKatHIZMSi/+cg0iS4\n1h/anoivNXLN4A9kx6KB0A1qrrltV9b+VCRspnmtkZ8N6D+9A00i3G+2SDv0RYoXnC8jl5F3kj8b\nyxNIy/0N9NzKwV8h0r80x3f7gd9D2X3+Ms/xKVTc6B0Up/kvHgkpH6/Yiywrq5FG+NWon1XDIjgC\nbEYT3K1IuziACLZri1Fl5ZnADUj+VFKPIVXCvsd517sUadoTJAijC3gKKRA7kCvrB9AY2khKqNoV\nfLPW3m2MucYLyuxHDwQAY8z3UaDWbGPMduD3rbX/hIjeD40xt+ClRPTO94Ix5ofIRJcGbrVjFAeK\nISnvQQT75CL3/S6qvFnKoxpG5Ok9qGriWNiHgnX/FZHSL1IZId+A0uCNIM34mTRWYZ7D+IWG9jK6\n8FAbvkDoRINJJ0q/2Om1SRxdgTMOJKsFPYdub9uiQf8LFC9An0P97IPkfwd2IBlzB7l90jPAXajQ\nUD5XnlXe/YWDljJIs/+P6D0p5A70faRBf3ue78tH4lM+HpDBd/XYg8jFM0jm/Q5y6YgaFmm8H0Xu\ngBPQpPMMVBhrBo0ja1d6LcH4Rj9K7dyFYoOKwW6Ubes5lO3v48hNtrGIeDVgrf0ZKuEe/Oyu0PYn\n8hwb1qq7zw8gk2+u7/4QpR8rCjEk5cdSXNCkRf7gF1F6UN9fIU3s9WPstwcR8G34LhinUr4P+QGk\n3dzkXfscavOSPYEI1omINAavOYwCmbcgbdIWRKIn4xcdOt07bi6lWRXihAEkRLfga8g7UDBnsf/x\n48iqdiv53UX2IUJzKyIaufBN1Mfyad/2Ie33H3K0lv2z+EoDyO8S9oJ3r98kDgNFglKQRn10CE3G\nLbIiLUc1HKL2sXXYD/wAke5TkIxtFAKeIH44iKxCzyDFWzGBy1lk/b8X1cC5kWahgXFRpDTHv1EX\n/Bd+EFop+Dmafbq8zoUwBZGtDHpZOlCWk1J9vUeQn/yDyKf4Hd65alVHeQ1yiXABNDPwzayHkRl5\nKSLfb0Rm3XzPphTzbCNjLJ/VDEpHuYTCgaMHkdb5EWQG/yyaBN2Dn41krP4yjLTTB5Cr3II8xxxC\nOfuvI8+k37uXVah/5xKSWe8cbyP3ZPZdwJcD189VgGoIaepvQ30leiSBns2MNmSBXIcv45aid6Na\nE7hnkGw/G3gd/tB5oErXS9C8qPYYZpGl5hUky79Ice5ZWVTzJkv1LEnVQ1xkdjx+Zcl4ARVu+mtK\nCwjagsjKn1Ocxncq8BmUBcVpXEpNO/cKmvVORi9arXPRZpGG+yV8YbQXDZKXo8Ez6WaCRT6oa1Aw\nzRykqShEyh9DE7ffRr7jIPP8ZJR/fCxsRjnCTwB+k/yBXv0o8PP15I+f2IKypPwx+f3X/x0R7pxW\nPmQJmoTIzcPkTpP4LRQPc2mec1SOxKe82XEDIuWgPn0r1SPkLjXtrUSbmjZBglIxhKzhB5GMnUnx\nhPxfUTzSxyg/Tqh+iIvMTtjSKKTwg9tew+hMNmNhBPgK8s0qNtfodqQR/DyaBBxD8b7RWaQdfwBp\nH6MNhCuMEUTCn0cZDpyG3CKf74+gGXwCYTfyd12DJnm/gbR6xWgqXp/jsw5yV8bsQ65QXahvrUdC\n/BZk3syHQUTITyF/rvDDyDXgo8hNKRdeQX7g+bToINeui1De8+0c7WqwFmniv1ngfitHXAT8+MVa\nRMYHULKDarmsrMbPhBVVatoECcrBbuDfkOvhDRSvMMyiTFrdaGLZfIQc4iOzE1IOyAR6Lwpe66O8\ncuL/jIR2PrN/GAeA30UBd1cirefzRR7bj7Sfg8BvUXqF0XJgkR/xI8itpx25o1yB3HC+gITFB0j8\nwi0inM8jgrwR+fffgiZe1dLo/QiRFfDN+jdSmJAPIxJ9HKoUnOvesmjyeDZwVZ7z9CIz6sfJPyF7\nGD0TR7jDk95BlFHqM1Q7BVdcBPz4xFNIIfFZ9J4V6t+V4HHkxhhFatrxjm7kGnE+knXhfOTVwAh+\nXYm+0HICY8d0NQsGkKJiI3JPPbuEY11gfQ/SkNfif6kO4iKzE1IOSEv9PfxCS62Upul9Cvkbfovi\nCNcwCiK9GvlYg7Q+xbwwm9EEYCXyKa52R+1HPsyPetsXImEX1EylEJFaQHwDn9Io6816r7UjH/CL\nkfa5Fs/lakTKg362rymw/wjKoT8f5ezP13e/h4T6l/J8vxkRl3byT0p7kevL7yP3lVz4OxRcemGB\ne44GcQkaGn94GWkLnea6WtrrR1AGq0/RXFa/fuQ+WOvKkMPoHb8XTZjOAU5D41oLo9PXptFEfwS/\nSk+4tSFyPZynzUCa42loLHJpbacjxUctFFXVRgZZah5Az/IDlFarYRDJ9zYKuy5WCwfQ/xGNrI2L\nzE5IOQA3o8wgjyBiPpfiOlIG+B9ENt5P8YLgnxAxeXeJ9/k8irZ+G/kzY0SBoFZ8PRIINwPHk5+4\nxdG0ewD1m3XInWc+sh58BKUqrGXWkFcQWVmEsvpA4VSCaaTZWkjhLBLPon53B0ebS/cgIv0oGmw/\nT/7f/EvkcpOv367zrvU3Be45OsQlaGh8YSdSfDiLU7XwMMqY9mlyByI3EvrQpHiL13pRFpplRCt/\nssiFrddrg97SVSY9iMZDp9h6zGttiEC3h9os77jWPK0DufdNGKONx8xMaZQE4D7EKT5I7tibQjiA\nZOmJaByoVCnUixQzuWpO5EIPKvr2DsQfKkdcZHY8fuUR5MtSMRFpNF9BnXlpgX0d1gBfx6+m+vYi\njgG9bL8A/p6js6MUitregDSWt6Bgo2pEeGeRpnUd0kJcgFwaXOq7fL9vvFQGLQRXBXRzoKURCV+B\nrBZB60GtnskAKjb1EiLXr0KWlCwi3Ln6SQYF/WSQpSZYUzp4330ok8ptaGANfvc40pwb79gOFEya\n73df6+2X63uLgqrfjd7F6j+7uJhCmx9uIngAxfrcjN65auEB5CrwWxRHyPNZfaqFA4h8b0DuDP1I\nWbIcWZgWUZ5mMouI1H7kitKHxrYerx1Cv3WG1xYiUjwLjQ9ZlC4S7/pXIFeLalROHY9wFTXXoDF4\nGZLnxdRTCT/jrUieXum1Sicu/d75zqe4RBT9aEJwKfmL05WOuMjsmJHyfBhCfq63I0E8Fin4W1Q9\n1RGZTooLnhhBVT4/Rmnmta3Ih/w9VCf635HxexEpuhIJg/GohSgGFg1Ee9EguAGR8A5kFj4epUWb\nQ/2ekUVp2n6KfGp/B988+V58jVUYWeCH+AGg+QSdRZPOC5AwDuMkFFz8a287zdjWknzXesS7r9eO\ncXx0iIuAHx9Iobz3r0Nlv6sBC9yN+uJnyF00q9awKCZlMyLgThHgSPhrKN9l8AlUZXy/1w4icj3H\nawvQGOBI+HQKE+xB7z7OQ3EncY8rcuhFCq5jOfqZZFChtk1IyWFQEoDPUH4WtWeQBfTdyIWoUgyj\n4PxTyB9PFMQQftKAYvYvHnGR2QkpB5TG7RSkZSwGVyGT/V4kOIv1Ofwe0jBcVsK97UbuLjdRerrE\nsRAm49chc1cjk/EBNLE5C2liKgkIHEYaoG40+HWh/3QvIuDzkTA9HVXraxQ/xc3IbWom8jMMmxTz\nDdIWZRc6AHyIwq//L9Fg8bd5vp+JJrEf9LanUZ5WLIM0+x+mlvEIcfFPHB9oR+6B1fKTtigYbh2a\n3BaTYq4ayCKXsI34ioAJiISvQMVholIE9KN3+ATvnLOpTKs9CVnVGnnsqAdeAH6MnssUpPTrQc97\nCyLfZ6EMakso//n1IEvFThQHsayCe3ZII/fEecgTYKx7G0Ya9WXIxTbavhAXmZ2QcvYhDcmfl3DM\n8ch/9nbUEYvRXm9AgUPfoPjO2o0CNa4j2pSHGVTgqJnIuEMGEfPHkHZhOSLNnWhgGEGaNbd0Wu/D\nORpooM8iAr4UaXrm0Ziani7Uh3aiieE5FE9ks8hPdg/SkBey7Dhf8T+icPDxN1FqrteiLBjl4H7k\n9nNumceXh7j4J44fVIuQu3RxW1AtgFIC6SqF04RvwHdHmYyI8lnIfaFaE4RSFEPFohnGj1ohg5Q7\n/YFtlymmFcnvd1J5f8uiSs8/RXL4FqJxGcoC/4ju9X2MPc4MIY36HETIo1ewxEVmx+NXFsS/oFzQ\npUbYfx9pG5cztl9hBvmQf5Tiq2g5l5o3oEwrUcCioL2fo9/bqGR8EJlU9yICvt9b9iMynQ3s+5LX\nJiArRDsinB3e+nRvfQkSgMHWLOmhelEFz3VoMH0PpQneFOqvh5FmvVAU/ghK1XkjhS0za1EQ8Ge9\n85VDmlLo/fsdat0H42IKTVAIzkpzAPgctfEPH0Qy+GWvuViMVyESXt1UoAmihkX+9/vwA/93oMnW\ndEYH/Lcj6+7VRCPvdiMrfzuKgVgcwTlBv+l7aNz5NGPHKAwh97KFyG2mOhbPuMjsmJPyzUjb+s8l\nHveid+yXKM6X/Meoo+cq+JILWeC7SFjn8uctFRYFsd6NH9x3CvUn44eRYNmDhJprw8incy7yU56P\nNEiT0cD516jrtiA/09cwPrtyGlkzHkUa/M9TugZ/ALk/TUWTyEJkfifwB2jyc0OB/TLoP/golaXZ\n+hmaLBXrNpYgQVRIIUVJGgUyV3OCPogmsGuRS9hyFAdyNbWvwJygfOxDFsF9odaOlG0nIGXXOWjc\ncn3qH5Di6G1UbhG0yKrzGBo3X41iwKIiwing24gbfIKxlT+DiJAvRi44cU2JHB3GI5MpAf+ATEhT\nxtoxhP9FqX6KIeS7ka/X1ymeBP8CzT6vK/G+cmELIj89yGR2JvV5cVIosGgbEmzbEWF8FXrxF3jr\nc5GGodCzmoEmFa7c/HhFC3oOn6U8f/Z9aMK5AmU/yfe/d6MiKb9GhPuGAvuCTKWzKJwDfSwMoYnn\nVyo4R/mIi9YlQS4cRulD21H60moMg7mIuPMdrnW+6ATR4AWkCZ+LgmAv9tbHGoOuQWNVsekEc2EI\n1UN5DI2lr0b8YArRjee9KIZoNorhGIvfDKB00MsQH6quki8uMjvGpPxppBm8s8Tj1nrttiL2tWgW\n+XaKNy09i16+T1HZ37MLkfFdSCD8BtUvNBTEIWRN6EJkby8SYMeizB1XIO1COQLl9ojusdHRQnkR\n7CPIV/thlKrxPPILzN0ocBbUXydQOH6hyzvvbQXOWQx+gUjKiRWco3xksvEQ8AnC2IWsPCupju/r\nDlRL4iB6l85Cip9ap05MED3KzQ5VrlvJEBpD16EYsBMREV9O9P12O3ovLkJjxliyvQ9xmxNQEorq\nW93jIrNjRsqf9pZZFJRwGerwxcKi/JuvRX6BxVxvFxL+T4+xL2gW/iM0S21D2pZSsQV4EA0K56IJ\nQTsiatWC9a63JdAG0Qx6BTLTLuLomXcwP3YYB5Hfd7Pmua1GHvmxEIwZOBaVvJ+BhHs+TEMa74cC\n5+gjd3/NojiHFUi73l3mffajIKJb81yn+kinoxHwxpirkLqoFfimtfaroe+vA76MHl4W+G1r7f2R\nXDxBiViLTPM3EF3V2ElIhq1FAXcH0PjwdkbXLYgKcagJ0ewol1b1oGBf1/Yi976VwFuoXrzBs+i9\neCfFudfsQBr1S1DMW23cYKOS2Y2OmJFyh2fRGFqqL+t6JBSLSYjfj9xc3k9xGuq9KF3ddZReHdOi\noKEHEKG6GL3I1SK0WXS/WxExcxObZV67CGnFK5nNfxcNcK9GA2ipLka54Eo7DyKiGly6ktDNiMOI\njG9FQbE3UHzgZQZNgE5F2pLDyHyZC65yZ6X5xO9D71D9ckFn0pWLPmNMK1IvXYHMbk8aY35qrX0x\nsNt91tr/8vZ/FXrJo85tmqAgLJqo3o8mqssjOm8/KjT0AHLnugy5B8aDPCQoFxn8RAZ7keLuZTQW\nLffajUixUk2lVPC9uI3ixoynUOG5m1ANi9ohCpndDIjHrxyFFDKd30xpM7wM6sBvojiy+T+IeCwp\nYt8upIG8gtJK0mYQGXNazotResCoB4U0mh1v9do2RJKXIgFyPhqUopgxW0Sc097yIa/NQX7V873r\npAIti4JD0/ja9/C60wC3I+3WxNByBc1Fyp3P6jrEB09C/W0FxU+G9qF4h+OQNSOD/ttcA8FOJLxv\nLeH8+a65FmW7qB8y0WhdzgM2WGu3ABhjfoBm1UdIubW2P7B/JxqNE9QMLsPPLuT2FkVgZTdy4bof\nKXY+QnWKuiVoXowgZUc3owm4K0g3Eykl5iG3lCvQ2FareK/dKK6ig+LeiyyKO3ocZWSpxD++PEQk\nsxseMSTluxAJKTWF2y9RfvKTith3HXr5PlzEvo6QX03pmvsfoeCMK4k2tWEf0ppuQ9qg55EAWYoi\ny68nWtPsDuAn3rUGkGByaQ+z3vZBb3sGfk7y9kBr81prjmUrPhlv5hd7AGXRWYd8DZcjc+O7KC7o\nOIi1yJJzhXcOg57NKTn23YqIzQ0Un9IzF7LItepSorF8lI+IBPxiRido30GOdEnGmLegyioLUYBH\ngppgP5Kts1DazUozrHSjDFZPIdP9V0h8xeOILLIo9iKXk4NeO+C1g0hWz0R9b6G3vgKR8DnUzy1z\nECkMH0UBqJcxNg08iDJ4ZYDfQy6PtUdCysctllL6LG8dsAb4JGMT35eRhfqDjD0I7EK+tdcAZ1O6\nH/K1VK7dTaPUSsGsKMNIw78Ekf03Ud2UYa7gwCQUyd6BnssW77OrgTOIV7qlLCIVbnK0HQ0AJyBr\nyg2U99/vRn15A3KtGstV6tfAD5E59eQyrhfEvUhT/pYKz1M50qmxBbx95EHsow8V3KWYa1lrfwL8\nxBhzMfAdipvZJ6gIq5Em8FrkblWJwqIbBc2vwSfjTilRj9iRRkYvmrRcQHNlxnJF6cIF5gaQ3O1F\nyQt6vc8nI3I6HRHumUipNgsR72k01niVRVruHyNr+p0UJte9SPHzDCLwJwGfoZ6UsRiZPR4QQ1Je\nKnbhk+yxtMObkDvAe4FjCuyXRi4ZT6FB44wy7y0Kd4tn0AC2BM3kLye6Us7FYiLSJgRxOiKgFzL+\nu2kaaVj2I9K8A5HwSeh/ORZ5SiygPE1/Fk0WH/WucT5yQyk00bLAk8jV631Ubq58DgV1fpJGsFZk\nM0X0qQsuV3P4s/8X3mMno/3TlqA/LyestQ8ZY9qMMbOtteVGySYoiH5U+GQbymBVSr/djKxCDn3o\nnTmE6iEEyXiC3NiOUqbejdwpX0d1q6Rm8Cs4D4eWzg2yH2mIc7V2NMYPIXkbLjDXicamkxEBn47I\nbCEZ1mjj1WZUPA4UU1HIS+BlVM15EN9NdAEqTlRfFCWzxwHi8SvLxn4UcHgthUk2qBN/H0UwLyuw\n3yZE8meh6or5gupqhXOpdYnz4nAeIpNb0cDaSFqHUmGR0HeaFpe5ZL+37EPCfjbqZ+cCbyVaAvAU\nssacTuHX3qJCF/egAelDlF7tNox1SEvzXmpbxrwAojGFrgFWGGOWoZH9JpSw9wiMMcuBTdZaa4w5\nGyAh5NXCiygv/5nAFynduvc4CtxsRWQvi9zl/g+VvwPNCEtuwhtcz+DH86SRUsHgp2W9H2mV5yD5\nlg21GUgGum0bWs5Fr5aLEUqH1peiufEEZGF1S9dmeudxVtg53rprE5ErXZT5vhsBLvnDo+i9uA4l\nTRjrNy7Af7agSctvVukeS0SNMmZ5+3wdmegHgPdba58pdKwXT+QsoDOAHmvtSm9seBGZnAEes9be\nWuj+ElKeExZ4AmkJ30hx2VYmoQCIfL6yfcgEugGR/NOof0XNRkc38C1EWK9AFoV6a1ndIDSUow2j\nd7gP3/zp1tsRIZ2N3tnZyDIxGw0c1fxdLcjvfCxk0PPuR3EKp1PZQJVC/osve9ePqgx0BIhAwFtr\n08aYTyBB0Qp8y1r7ojHmI973dyG/rPcaY1KoI9xc8YUThJBCio4nkVXn9DLPcyUi5c4lZS4y8zdr\nWlaHLHqnXTuMH79zmPxa5EF8a2yQ9AbXO73zu5gegz+utaBxcRkyIs3Dj10x3vetgX1dM6HvXQvH\nDbUF9k8gDOBPLi2K33kHxVnV0yjmx+JPTI+hHkGdORGBzC4mY5Yx5hrgBGvtCmPM+cA3gAsKHWut\nvTlw/J8inyeHDdbalcXeY0LKj0IP8B9IIH0ERUQXi1yEvAdF6m9GZqPPUV3/7EaH04CkkBAILoNZ\nU7IowKQNaZd/ggITFyLhPg9fo+JaGyLGwc+C+0xAQsudPxNan+Rdy2V1SYfWJ+MXBZmABJ1rbnsy\n8tPuRJpuZwJthoG9FcU3HENlZDyLlANPof/kUzRcZpt0NAO5tfZnaLYd/OyuwPofA38cycUS5MBW\n4N+R7P19yrMuWeRa9SOkAOhF/fbTNO57m0UT/kOBpVt32zDaNWMKkkVTAuudaPLhNMeTGa1FLvX3\nv4wMSDNR7Ei9KkjHDdsREV+DgvXfRWnJH7agXOUzgC8ha+k/IQVigyAamT1mxiz0o78NYK1dbYyZ\nYYxZgAhcwWONMQYFYF1W7g0mpPwIhvDJ83IUHFTJzGwPmnW+gKppvgd1+GZFitza4WAbCSzDvn3D\naADYiwRFG37WFLeciwYTpxlxBB188tzl3YvF16Y4zcpk7zqG0doWt97hnbclz7LdW3f31B5ab/XO\nMZ41M5WkdsugNI2/RM/qCjRAjOfnlaA+GEIp2p5AmsBzKK+fbUSkfhh4N5pQ34407vMiudPS4dK3\nHkDWwh5GZ/no9doSJAunIj/nqcgtcpm37tpkakeMl6KsY6fW8JpxhEWuQi8gzrIFBSHfSWk8YwC5\nKj6IuOT56D26ECnAllVwj2uRNbihAn6LyZiVa5/FSDiMdezFQJe1dmPgs+OMMc+gl/aL1tqHC91g\nQsoZBh5BhPxENLsvt6iJRS/HA+i/uwil4mqoTonI7SCjo8wH0UAwiF7UgdA6HK0dDrdpiLwGffqC\npk5Haoud7BwA/hx101OQeTmKPMMJokUPIkdPoODcq5F7XQOT8aQwYhNjLQrmPBm4g/K04wcQqd8E\nXMVof9s/o/oyO4Oft9rFlrhlN5KTK7x9XYaPY5GMnYEvaxsNEyjffShBYRxGrskvIOVsG5r8vBZZ\n9UtRIg4i5ckvEU+5g6OzsZSaNtrhACL661BgaUTvUjQyu6iMWZQ/eL0DCSeHXcASa+1BL6boJ8aY\n06y1fflOEGNSPoyCIB5Cwu9jVEbGX0Q+jV1oxvouai80ne/goRzNzaydL2EwwKUTCf4JSDvkzJjB\nVg8N8VQ0Yz+XyvJjJ4geWaRlfAwRm5UoICjqgDgXePoY0mRG9E4lpLwJ0YOyW21H6Txz5dQfCymU\nmvNeJKd/j6Ndq6Im5EPIBdVlVdqBLKkno/fIBUGe5C3n5Lgnh6TjxgMuJe4uxCnWoj5zAiLiVyG+\nYihNJg4hIn4fmjx9ntJcdAshgwJ770ZZd+4o8d7GQDFd/8lVsGZVoT2KyZgV3ucYb5/2QscaY9pQ\nEZez3WfWWucqgLX2aWPMRkQ4n853gzEk5SlExh9ExYBK9RsPIotmg79CL4cz11fTbOdMm/vQS7vf\nW29BJMlprJ05cxoyKU5HM+JONOjUO2CyGLQjrWuC4pFBMqBaRU36kTx5HImPV6OkI1HHSVikFboP\nSeMribTPJtymiWCRvP4JItIfpPRiWaC0nP+GzPK3Uz33lF6kzdztXbPHu6ZLb3oRsoTHObYogWCR\n0mw34oK7vGUXUpgtQhb86xBfKZfkDiGeci/iKL9LtAqUjcC/Is4R9bk9FCOzV16q5vB3d4b3GDNj\nFsrp+QngB8aYC1AmlS5jTPcYx14BvGit3eU+MMbMAQ5aazPGmOMRId9U6CfEjJQPIsKyG6Vnmxf4\nvBRkkLB9GJGfy9CzNkgDHxUsItzbkC/hJmTabMfXrsxBZqbZiHgX85daElZSS/Qh144zKV/jb1Hf\ncgUtXDGLcOtHmX3eVuZ1cvWLfnyzqdPWXItIhsF3h4oCFmUoWoXe1UvxJ7oRvltJzZcmQdDK91FE\nbl0MS7HYj1xV9iL3RKdhP5T3iNKQQQGnv/baATQenIJ80+dy9IQyyncmQWMhKFyyqJ/14Ff7DFYA\n7UFk2+UDX4ziIxZytLXEpYMMY6x+lAb+CCnnPopPmKPo/wMoAcMLwJuRxdREdO4QIpDZxWTMstbe\nbYy5xhizAQ1+Hyh0bOD0N+EnhHe4BPiyl30rC3zEWttDARhri3WxaW4YY6zMKZUgBTyLyPgs9LyX\nEZ1bRwYNPtuQkN+GtCmuCulc/DyrCZoH21EkewuaCF6Ib7oO+u27lkGTL+fv79KYtSBLxzHePtNy\ntE6i0Sj3IZesF5FSwJlNT6A6Gj6LJp2/QoTrUgoHi92BtbasF88YY3mkDLl3kSn7mglKh2T2n1Vw\nhiHk9nQ/UpxcQnR6qGFEwNeijCMzEQk/GcnqZrBEJigfWUanv+3Br0Ph2iHvuxOQnJ8ZaLMC67XI\nTNVHtHUvsshi+j8oVfHVjM1LPpfI7CJQF025MebtiCGfDJxrrX068N3tyD6ZAW6z1t7jfX4Oqgwx\nEbjbWvsp7/MJwL8gP55u4CZrbbAsWwRIoQ64Drl+3MBo16JK4II3dgHPoyCepcjf640ULoWboH5w\nhTXCVeRcIQ1XRW4ICWxnndiFUm46uOwIwTYTaTNc+jKXuqwck32xSHn3tgVNCHchbd95aFCpZnzE\nZjTZ3YGClirNj14EEkNRSWgumZ1FVuqfoT78OaLJfJVFffVJJKtPQo/jLSRy2iEDfAecl3P9AAAg\nAElEQVRpe0+jOTKwWCS7XbxVuI0grXYwMUI/6tZTERfIIkv1XJS9zVX/nEpjOCRESci3IleyWcAt\nRMeFxkBMZHa9ess65BB/V/BDY8ypyARwKrLj3GeMWWGlzv8GcIu19gljzN3GmKustT9HvaLbS/R+\nE/BVIivQkUZl6B9CJOka5ONVKQ7huwPsxjd1XkmiBW8GfBuRV5dNJlxYYzbqO65s8zQUrOhyqS9G\ngTrzqd+gNYQ0+M4qsxsNKMciTf4yqi8etiDNeB/SZF5LzTSMMRHwEaJJZPZm5KpiUDBoFIVPuhHJ\nX4Pe83PRWJAQ8aORQV3lJaRQuAYV36tEzln8mhLBWhbBCp8uni4VWrqWq9CbW2/xlmHlyGTvN0xH\nsjpYe2IK8bOGHESuKpvQ/3o2NR2/YiKz60LKrbW/BlCe9VG4Dvi+tTYFbPF8es43xmwFplprn/D2\n+xeknvg5Gsm/5H3+I1RxqUKkkebuIURUbkQuA5XgECLhLyDfxhOBC9CsuhFTWyXIj5vxK8sVgyxK\nETUVdfETqnRf+a59CMUmuHYIkfFFiLRcgrQdtQo824p8xnuQZvxV1HyAi4mAjwqNL7ODhOGNyLe1\nEsLgyOWjaMK6EsUhHUNDp/qsCM76N4SsfM76F6w9ESa/Heg9dgTZOf66/b6LMsS1I2uFK+aW9doC\nZJXLMrrQm1uf533fmqO1IYKcwk/BG07J24af/CBY5C1c+K2aVshmxzBSnjyCgpTfTl2ClGMisxvB\nrhLEIpTWwcElbU8xOm3NTvya3UcSvXuO+L3GmFnW2gOlX94FcD6AtJ2VuqlYfHPnDkTAX4MCOxrt\n0ScoHqUKpBYUK7KI6vzvWWRSDfozduFn6JmIH4+wCJmWF1TpXgphOxLuB9BE4Ezqpm2KiYCvAeos\ns11q2/uJhjAMAquRQmYWqgVyKs0lry2j61A4NwznUhd002hDSqKg1thV85yINMQDjCa6rjlt8mx8\nJUULsgLjfTYLucAtRKS8Bb+gW65lsNibW29l/E6EGhkZ5ClwN+Isn0WulXVCTGR21SSNMeZecufF\n+YK19r+rdd3y4LQiD6BMJm+lssqGQygA6EkkTM5FCqUGKzWeoIYotz9l0CDaF2iHGV3Zrw/1LefH\nOB/143MRGa9nv7NIM/4wuu9zERmvM8mJiYAvBc0ls9NoLnAfCjT7DJUVFjuAiPiTyE/8/dTMV7Zo\npBkdTOjWg77OTj60Iy3yHERunSvGZESOp+DXowhqjSt9L7NozJuHDCMuK1mC5kEWxUzcg8TBe6ms\nsmdEiInMrtrIaK29sozD8iVt38lo/xH3uTvmWGCXl7x9en6Ny68C68uQ6d6RcedasKyM23bYjfwO\n1yOt+Ju8ayRCqbnxQ9TlziYagusCQZ32KldzBHwQDZyuZHYn0lYch0/CG7G6nwVeQUSnH1mIzqA8\nkdOF3t15RPYuxUTAl4LGlNm/CKwvRxq7tSiIcw4qWFWJa+FWJP9fQRrdz1EfbaBFZPpAoPUhS5cj\n4INIBkxn9Lsf9nfupH7yoAX4AnqG4Xd1M7JoXEllSq8EPizqI6+gwNrpFZwrSMbbkN/4KZQnc3cC\n/4lIfURBpjGR2Y1gkwv+4z8FvmeM+Royca4AnrDWWmPMIWPM+Sjh83uArweOeR9Sm9yAylXlwWXe\nMouI898ibcGbEMkpp/M5TeCDSCAtQaVlo4x2TlBfbERpz+5HWt7zEDl3ucODLYVvJnbZVwYDzQ2+\nTnMVbrNo7oAi92497G078385/r17EWHagtxdzsEXWQ9UdJdJnvKKUEOZ/YbA+kvAX6C+9Hb8MvTl\nYDM++TgBxQ3VwqI0iHL970HWrp34Oas78FPlzUKTjhX4BHwKzZHNJJ/Fohu5tryCuoobd4uF8zsP\nBn2mGB3cGWxzaDxrRxToRc/QNVA/Kfd9iJKM70NhIxtRmsSgzL6nzPuLF+qVEvF6JKDnAP9rjHnG\nWnu1tfYFY8wPUTRkGrjV+onUb0XptSah9Fo/9z7/FvAdY8wr6K0fI4p/AOWMnoA6zfGUT8Y3IE3g\nYSrTBMYdLrreBQo5gRsWwO6zcFBQMDCoxTuHJXfgUCsiyuHzuutPQoNkULiPeMcCPOU1EGmehJ91\nZQIi063e5zO8ZbA5M/F4s56MIDL+ECIPr6N80/V+FAi6GVUMvY4kEKu+qK/MtkhmdyHCcAblvz+b\nEDnoRn30N6henMc+5Dq/J9D6kWZ7AfKzvoja5quOChlkrQjK7XRouw1fEeHiXlww6Rb8+F4nF8My\nfT4yrgTlvgvybEFW7S7vOi7IM9hOp/lJeQr9xt2oP61Dz3E5ShZxBXoly3kfMkhm34OeaSVk/KB3\nnvUoeP9GIg8GjYkiJYbFgyzKPHEs5XW+LNLYPIiEzyVIE9hM2syokGa0FjgctR/WIrsc3k6jHGwG\nCdKZaOKUK9q+FZlr+8kdEGQC3+cKJDKIMI5wdBS/W+/w9g8K9x8i7UQ7cqG4Fg0YCTRgrEGDxamI\nMC0r81zdSAO+AZHx88gv2CssHvTdMuTeu5qvEEUzY3TxoG1Iu1qunN2ISMMBRGR+o4Jz5UIWvQsb\nA20mIkwL8Un4TJpD2z0WssilzMnOdm89uO3Is5PBL6MYAOt9dibKvDQHvedhWe0CP1tD52oWbEKT\nj5WM7RKVRVls9iACvstbduP3oeXIVWsxlfWhg8hItdo716uR60s5z7YPGbqe8s5zKbIC50KFxYNi\nIrNjqNY1lJe71pHx+9FjuwQVjxgPAhZ8V4x8Ps793j7d+CQ8w2gtsPNldFrjDqQ5DmqSXcqqsFaj\nkSc109FvfxNJ4BJoErUeCeIe5Gv/Mcr3ZzzgnetplCb0GqquNYyJf+L4Qbk+yJuQOb0HkfFziE7W\n7EPuGI6ET0aW19PRxL2OmSqqjhZkaSgFh9A4egHwesa/i+cWZE34BQq4X4nGSGdJ6GZ0DMFyNM4s\nQhrr1yElUBQ0zfGXR737Ohv4KLnjuovBACL2v0Lv1O9Q9f8zJjI7hqS8VGRRqfFVSOi+HvkgNhMx\nc6ZDF63f5633hdbnoZctl5/zEvzofWdu7KC5nkO5uBlNKBp54lBtWKQJfBYF2i1GpvcVlP9cepDF\n6UWkZbmNmhXPiomAjy82IzJ0APmln0U0728X6v9rEbFajrS9byGaqqHjGacCX2b8FcgLZshymXF6\nUB90lZx3e80gK+sKNN6ejMbXWVQn9/dBpOx4HI3dFwLvruBag0hmP4ysop9GKTFrgJjI7ISU54VF\nWpBVSJg3OhkfwJ95O6HgWi8i0cuQAJmGZrXzAutTaS6fxloinzluvCOLAtFe9JpBWsAPU5kWsBf5\nnq9HbgSfpObPOCYCPn7Yhsh4F9KMn0vlZHwPIuHPITl7JvA2JE/Hi6W0FnCxNo2ODKNzuweXToEV\nTD85gOTXYiQjZyCr4fGoP6YR1boaBb5Xu8843/N1KD7nfBRnXUnGm2Eksx9EWvxPIbeaGiImMjsh\n5UfBIt83lz7xchRQUQkZ34/8ZC+o7NYACYC9jK7QuBdpbU709pmBTGCn4guIRkuZl6AxkUHZhBwR\nn4SE8I3I1FnJe9CHBPtzyHz6CWR9qQNiIuDjg52IjO9AZv8PUNnwdhgljXkOEbEzUKKYpTQOEXdF\ngg7hk8VjSOJdykEG+Coi3yNIQRXM7T4FPwXlYkanqM2XIWsAuBf5g78PubBUAxb5oDsi3o+sN1cj\nS04lk9IRVMlzFVJKfpy69a+YyOyElB+BRX6BTyGN82VU5jOeQeT+SaS1WYk0j6WcL+MduxXNuB1h\nmuu1eYiIz0XColG1+AkaFy5AbavXutCAdAoqoBKFNqQXCfbNaJBogJShMRHw4x97UMDaM0iB8h7K\nV0C4JACPohRxp6PMP5UQ8ay3LPV453LYg1wQnNUzjQiYI+Jt6F1yFs8ZJKS8HLQCH8J3z4xi4jUZ\nKR6insi52K5NSKb2IsXfq1Cq0GMjuJ6rlPsycnX5KJpc1BExkdkJKceijv0rpHW4HPl5ldupDyMf\nrjVIUJ6LIpuLedQppPHZhgjSDu8cxyKStAQJ3YR8JygXufrYVNTHTgauorICFEF0IzL+ApqUvgf1\n5wZATAT8+MVelE3lFSSzb6d8P9kUIvWPIO3mhcCbkRa0UvwEjQdnIevQMvyxJYXI1L7AcgSlUXQu\nhzOQq5hbzkLvkiPh1fBDjiuqockuJQ97PmSRP/pmfCIOco85DqUgnE80vGAA+Ys/ghQob6Sy4lwR\nIiYyO+akfCvKptKHUvmcTvlkPFjN81QUHLioxHOsRybTpcjX9q3UzbyfoMmRRRq2oIvTPmRtaae6\nfawLualsQpPS22g4v/yY5Lwdf9iPXAJeRBmwbqD8WJheZMl8EE1KryL6jFpTUarYx71ruVoKbagT\nuiJBc717mOPdxwwSl8O4wSLrRxeyALllO7KSHI+Uc29E/SZK5dwh9B6sRkrEjyNLfAMhJjI7pqR8\nO9KMH0CzzDMo3+9qO+rMe9AgcQXlB7Oc5bUECYpBFk0ogybuPqQJ70ZEeB4a8I9HMQ1zqU4hHou0\n7g97178ApZCsNHjYFen6NSIrERGVTDSnSVArHEBk/HlUqO12ypezB5Ay5lkUePcpossg4dzBtnvt\nJfzCYxn0PpyDcvAvIN4ZneIIi3y+g6kQB5ECowv1h/leW4SsK/OJxmqTC642xNPetT5D/oqspcC5\nlZ1P+WkXQ4iJzI4hKe9HJsULKT9NlkVa9gfQS/UaFAiXaDYSRIlhfN/RYDDXAfysOq5yqDNxL0Xm\n7bnUxrQ9jIKL1uD7ot9ANO/CJka7lUVIYGJiCm1+uIqQ30V9+zP4ZHywxHPtx0/BeS5K5+asRKWe\nKx/+zjvXYmT2fy3wX0g7fhbKwe/ejZGIrpmgcZDCT43oZPYwsqQ7xUkro12S5iP5Np/8Vsuo+ido\n4vgyssrvRuPFp/CJfyXX2odk9kbU9ydVeL4AYiKzY0jKp6Dgi3JMPy4Y9EHkO34xlWnZE1QGi/6H\nZipCkUVC6rDX+kPLYO54l74y2BYiE/tM6ptVJ1jJcxmyEB1PNKb/rUiwH6Jyt7I8iEjAG2OuAv4C\nCYFvWmu/Gvr+XaiyhkF/7Mestc9Fc/W4wAC3UL65vgu/UuwFwGepXmq+32T0sDqCyM8VKHtFAr0G\nnTRPbJRFLkhhme1SImaRZrgXEXAXeDsNyeg5SOs902v1Sj18CCWyCMa7vYNoxpBulKHlJaTwvI4k\n3qE8xJCUQ+nCwJHxZ5Bv7sXI7yoh4/XFXuAbKCDldZTuw18uXEGIYSSs3XIEBcoM5llapC2ZgJ9K\nK7iczWiBPpHGGrhGkJZxDdL4nENllTzD2IHcCqJwK6s+jDGtwF8jxrUTeNIY81Nr7YuB3TYBl1hr\nez0C//dEkxs1ZijnPXBkfDsiINdSfUIUHlI7UOaKBD6+hrTEr0fB5bWScRkkp4MyexhfPrsWltvt\niHwHZfVUb30xkn8XIJk9hcZJmQmaMGxEMnsjytDyLqIbKw8iMv4iclX5LFV7xxJNeQJhMyIKg4go\nvI3GeunGK7LoLcwElhnvc+st96EuvBGVDp6DCPocJCDdvm7/FkQsw+d0rQ1pP1JeGwmtT0PpyIbQ\nQDIBCaAJXnOZcSYhf+7Z+Cm2Jnv75stp26gYRhkuXkDPeTmVV/IMYxeK9t/lnfssqi6aohHw5wEb\nrLVbAIwxP0AqoiOk3Fr7WGD/1TRMKoPxjP1IZm9EroVvoTpxFAlGI4svS9P4MjYos90++4B/w6+D\nMBffrz8ot0HyNxM6t1s3aGwewZfXQbmdRZrtYW9/J6sn4svuqah/TEKWyMmBNslrzaT1zaDxcD2S\n28uQ3L6e6AjzIUTGt6O0zJ+m6sH8CSmPO7YhwX4IkfFXkZDxQkjhayGCbTiwPuJtO8E5ElqfhGbe\naSSQ2xDxc8u5SENr0H/hBDP4Od33I+I7x9vHBPafgQR0a57WgcyLHUg74pbBdSfUx3P8wCAyQ76I\nJqUuJecbiTZTSxdyU9mJT55q9FyjieRfjEYlhx1IXZQPtwB3R3LlBDlwAPWnl4BXk5jQx0IGveu5\n5LZroDEwKKeDbS5+1coso+V1G3pF9iL56+Sxy92eRkqQJ9H/tMg7zgT2nerdY/C8wfV2fDc+J6fD\n8tuR8HYay/IYJdJoEroeBcXPRNb83yTaypuHkfvuM8hS+j6qF4QaQpJ9JS7YhDqzm533eNstwBtQ\nSeVm0mxGhTQisM5vzlWOG8jT2tAzm5inBTUSQeEZ3G4LNCeUC2EvcJe3r0sltrTC3x1HuIwRWxBB\nfhnlvz0VEeWo/W/3IS3LFqQZfxs1n+QUE8m/fRXsWFVoD1voyyCMMZcBH0Q/OEGk6EH9aT2aEwWD\nQeOELJLFfYHmSsS7MvHB8vEjKLhwmKPl9SR8y55LzzghtAwqLRxZLob03uHtNxGNsWeQKLxKhUWT\n0C1Idj+NMm2dhgofzoz4ev0os9YaZMm8jZrHckWUfWWsOCBvn6+jkqgDwPuttc8UOtYYcweaAe3z\nTvEFa+3PvO9uR7I/A9xmrb2n0P0lpJxdqHJVcHydh5Ra41XLMoIItsvg0YtI+B78AJZhfP+5TvSS\nOxeNRYw28U1GArrWWohORB4v9JbjVQsSNTIo6n4rEurb0bNcioIq30x1+n430rK8gjSZ11bpOkWg\nGFPowkvVHB6/M7zHTlTRy2EJ0paPgjHmDOAfgKustQdLu9EE+dGH+lMX0sh+hobLhx8ZMvgy28lt\nt+1kdj8iuq78u4tPmYI02sGy8c6drh5k+FQUFL6SeCq8yoFFVuAtyHq5xftsGXIfuY3qFGYbQPxo\nNRobPo44QB0QgftKMXFAxphrgBOstSuMMeejwLULxjjWAl+z1n4tdL1TgZtQp18M3GeMOdFamyUP\nElLO8chNxZHyucBHaG5h4fJXH0BE6ACy/exAwtz5R08PtHnoWTgSPpnG115MBt5d75tocGTQ/9/l\ntUPIvDkdkfCzkJm/mibI/aiY0GHkUn0b9ctA4CEa/8Q1wApjzDI0u78JpTM4AmPMscCPgXdbazdE\nctXYox/1p6cQsbuRmpnQqwaLCFB3oPUha2Av+s2diBBN95bzUEYXV92zk+YYt26s9w00OCz6z13x\noMMoy1UrIuHHoxSKs6meIsqR8ZdRnvGPEU3+8gpQozggpC36NoC1drUxZoYxZgHS/BU6NtefcR3w\nfWttCthijNng3cPj+W4wxqT8ADJ5bkBBEJtQp38nzSHYQOS7Gz8d0w78ggQT0Us021suQQRsOtKW\nJFrl8QWLBvH9+ATc+dh34hekOB1lPahFpdhgZc/zkXtRg7gVROCfaK1NG2M+AfwCCY1vWWtfNMZ8\nxPv+LuD3kZnpG8YYgJS19rzKrx5HDKKA4NUoxueTVEc7WE04wrUHWbp345NwkLx2bRmadMxApLtZ\nxqUExWMQyehgBc89iJot8NpSZFmcSfXH7cPITeUp5ApzE9EV1qoQtYsDyrXPYuQiUOjYTxpj3ouU\nNZ+z1vZ4xzweOmZxoRuMISkfAu7BT+FzGxJ2f43IStS+WFFhBGlN9iBBvsfb7kQR48eggWqW18ar\n602ckUWa7oP4ky9nCTmIXIhO8JbHoECcedS+L+xCbgXb0WBSLXeYChCRf6LnN/iz0Gd3BdZ/E/ka\nJigbFvWnR1AKvVtpXDkdRBq9n7sDbQ+yQC5E4/UK9I7MQpa/RFkyvhCs4NmdY5lFdSdaEQE/hepW\n8MyHQ0iB8iyKo6ujm0o+RCOzi40DKvVF/AbwZW/9K8CfIR/oku8hhqS8HQn0TzLa//BTNJa7RhpN\nqjbja/GHkDBfgIJj5lN3N4AEEWIE31e0l9E+/z1IE74ECXJnBXETsXoWpQDJmc3AY4h4XAS8lYZN\nRReT9FrjAy4o8MNEm0kiariA6U1eO4DeCyezX+OtN1OxswSF4Xz9nYwO+vy79aVII+6s1ivwrSH1\nnojtQ9lvngHOpi4BnMWiGJm9ZxV0rSq0RzFxQOF9jvH2ac93rLV2r/vQGPNN4L8LnGtnoRuMISlv\nRcV/wqg3Ic8iTYoj4TvQAHQ8qmq4hIYlOHXFRhr72bi0Yy77gcto41qwimcavbMG33d0aWB9Go2X\ninEIeA4JdouIx01EL1pSRPrbE1LeZCiUabJesEjbuSnQJiF3yJXIBbUWbmLNhh1InjUo+SOL5Fow\nY00vo+W2a4OIZA/i+/ovRBrvGV5rMCshGRRXtBq/GOKnib6vptA4ENGkoxiZPedSNYfnjgrOHzMO\nCPgpKvv+A2PMBUCPtbbLGNOd71hjzEJr7W7v+OtREIA71/eMMV9DbisrUInfvIghKW8kZFAGjOeR\n+8FhRMLPA95Ow/jfNiwywHeQ0LsIDdzVEIAZ/Ly8wbzrwTYYWAaF+RB+8SCXXmwiMk/OQwOTC9Ka\nRPOYr7uQfFuH+uw1yAc26vsfQG4L65B1KyJiHpOctwmihkWWoHUoHqkfke8TUcxEg5n8GxI/QpOZ\nlSh9XzWeWRY/t3qwcqeT0cH87CC/7qDc7sDPVLMI/e+dqGbD1EBrtAqehdCHZPaT6Jmfj/zGo6aB\naeSTvgolYijoQl08ahQHZK292xhzjReU2Q98oNCx3qm/aow5C99k7M73gjHmh6iKUxq41Vpb0H3F\njPH9uIExxio/ar2RRcUWnkd+7dNQ8N2pNIefZDVg8QVosIpnOrTt9nM55TNoIgp+XvM5yES4GL+C\nnKsm14HesQxHVwtN4xcvChfIcMdO884VzuUbzu8bTD02ieYR2mMhhaLxn0CD6jleq0aw3RByhXkC\nvRuXIE2Uwx1Ya8uaARhjLNeXIff+05R9zQSlQzL7D+p9Gx66kMxeh2TFq5DcXsj4eb9LgUXPwcns\nsKx2zZHjoBy+FxFEV9htJnLlWIy4jg3sD35Fz/A1gp8NM1pmp/Fzqc/y9g3K7OByCn5Odie3x0tQ\nreMbq1Eq2tMRGV9YhWtlkE/6r1AWu9cxuoDxFyuT2TeUIbP/o/lkdqIprxl2ow67Hr34p6F88g0S\n2VwR0viuGbk0x+6zVkaTXidIU953SxDZC1aEC7aZyJoQrNTpkPW2+5AwHmR0Zbg27/NpHF11Llwh\nriPUIjTBNR1SSCO4Hgn104BzkXm2GgPXCBpAHkOWvg9R91RcCWKKHiSzn0Py63RU6Mq5mDUzskhG\nusJwQQ1ycHsCcnEIy+wR9AyOQxMWJ1/DcnsGGhuCFT2dytN624e9Y50V0cltV5BoArnldS6ZPQFf\nZsdxsgSjFX/rEQE/AWX6q4b1Petd635kTbgBWU0jRkTB+Y2OhJRXFWn0UjyBBNOZqCzt3HreVJHI\nIJJ9CBHdQ0h4hivEucpwk9EEw3K0NmJOYDtMeF0rV4A+h7rx8cCVNMezbXSk8Es2v4yE+mnIPF+t\nrAApYC0yeS5FFsMq/peJT3mCnLCo769GBVrOQ2TmWBqf5Llc54cCbRi/wFB/oA0imbzU+25SoE3E\n942egixVjuw64ttO+fThrxHRnwO8EcnuZp/k1BtZlO3qea9NRpPID1I9OWrR+HAP6g9vQvEUVfov\nYyKzE1JeFfQg361nUNT9JUjr10hCfRA/OvwgIt4H8Ul4P3qxXVU450O3gNFmPmf6i/q37UY+12Np\nYy9H/pwLIr5+3HAQucLtw++3p6Ey2NVMz5X2rvcQ0rq9m5r8lzER8AmKxRDSiq9GsuwCFNfTSAHk\nKfSeOpl9EMnxffhKE2cNdDJ7DlKWHIsvt138StSWrn3eNcfKAnURGjtOJCHjlWAAyextyK1qIiLi\nH0BjZ7VgkdX0l972FShVaZX/y5jI7ISURwbn3/8ECt48A70c9Urh5bQm+712CGknnFDP4keHz/SW\nS2iM6nAZ4C7vHi5FRY/yddVLqngfWSQJUoy/LAp9qL+6lkKk+ASUo7bamREyyMrxACINNzLa/7DK\nSAI9EwCSiatRX1yOtOLLqB9ZHEbyeh++3N6H5PYQfoaPmV47BslHR8TrmZ3pB8j98EKUhWlynv1W\nVvEenK97Ct+NZbxgGFlvgik3lyK5/X6qS8QdNgH3ocng65AVpUbKxpjI7PHUY+uELPASKm4xAwn2\n66ldGiSLBLer4NiNT8TB15TMRbNoR8TrnR8VfAE6jB+wk8J/+w6juiz3oCCg2Sg3ezbU2tGA5YKD\nXHPbzsc8k6e1IsuAu/ZIYN35Sv4WzRv8k8IvPHUQpcM6jMjHcah4yVxq0x+yyC1mFZp0XY8Glhoj\nJv6JCfJhJ+qDKaSMqHV10EHvHvbiy+t9SI7NRnJ7DhpPzkYEfCqNYW3NIJntZGQw2DKNKkI+gixe\nc1H2khZGy2zwg/fzye0so4M6w60dpSoMyusR71odyOpWB9kSCVy17l1Ibm9B4/ti5O7zZvzA2Fpg\nGyLjPcg6fQY174sxkdkJKS8bWZTl5kH0YlyCKnNVs6NaRKp2h5pB5kmn7V6JBPsU6k+8AR5FM+zh\nQHNBQwaRw334BDjYLTPoWe9E2l2LHzQUDB7KMjoANLjejl+mOldzAUMd+EFD7V5rhEGwFAyigT5Y\nQbAb9YcFSLP2Vm+9lr/NvS+Po//wGurqSxoTU2iCMLYhMr4H5Wc+h+q7qBxG5GoXeid3ISvmAkSs\n5qLA6TlIE94IMuclZPUdztGyaLzpxpeTbUixAX7QfRf6nVnkWtGKL7MdmXRB+GG5HQzQL1Zmu/Vm\nU56MoEmZ6xtObk9BE5qFKF5qCbW3hGxHKRQ3ovSVK6nb842JzE5IecnIIP+th5Bf3hXIX7wa5CKD\nX1BoLwqqmIBe0oUoCMlViGsE8p0Px6IBZ0KguYChfF3wTu+7eSjAcEme/eKIDJqcdeNbRty6czta\niJ7ZeegZ1susnUGBRw+h//syqhoMVCxiIuATgO9auAqZ/C8B3kl1hj+L3kfnYjLlRXgAACAASURB\nVLAbTZQXIoJ1GiJYs2gM8p0Pc1CWpQk5Wr5sVH+LiPgUJLNPp7F/Yy3hLNr7c7Q+9Lzn4/eRhdSv\nTolFmvlV6P4uR25ddaaLMZHZCSkvGo5c/ApppN+ITP9RkossIt/Oz3erd61l+NkvmtG3uRxf4fNQ\n8MhxEd9LM8Ai7VpPjtaKtBbBIK4FaACcTeOYuNMom8rDyC3gahoqy0JM/BPjDZdJ5X6kxX0tyoAV\ntabvIKOreragvn4y8HpEwBuk3xcNVwa+FKxEk/86alPrCpc8wQXhuvXDaFzvwHdLcq5Jc5BrUiM8\nL4vS365C93wJel8ahCbGRGY3yNNuZIQD0t5CtDk4XR7oDaiY0CRERM8ErqM5SXgUuLrI/VxBgWYZ\n9FJI4OUq2XwImXk3IAE+I9DmI/eoGSgrRD0DugohBTyNfErnovelAf06Y+KfGE9YRI5/iYjS5Uip\nEdVk1eWBXu8texAJP967VjOS8Cjw6hL2dTnKmwEuPbCT0cE0wX1IAbEb9QsXgOuCcY8LrDdqhW4X\nF7cKye/XIiVPI0wUAoiJzE5IeV44N5UHkJ9flAFpIyil0AuIgC1CPoUXk5RpLhW/Qin1XPBJLQVJ\niqOLJY0gYe1yuIfzui9A2pNgmsmpiMBOQ+4nb6WxUrEVg4OotPIOZOK+icjKK1cDMTGFxg+bERnv\nQ65SUQWkZZFJfz2S264A3PVowtwsBLMRsBH4HsrSciG1JasZji5uN4L6S7AOR3B9irdfUGZPQ26Z\nbn269zuaqR8MojSgzyAqeAniIY1gac2BmMjshJQfBVedahUiSNcSjQvFCPIJfwEJpcUondA1xFcb\nXipyRecPIIF6N8rScj6+sDShfV0Ll2gOruOdcyRPm4aCX1y2l3ChpHne+aagCVaunO7NJLgLIYsm\nlU8iMn4mKiARdRrQzUj7fh2RiayYCPj4YAsi4734ZLzSCboj4s8juT0VEfFbqF+q22ZEWAYPIBn5\nkNdWIjI4Db3f4f0tfpaXoKx2264qaFhWu4QC05HMHvSOmcDoQklzvGtMQfJ7SqhNpmGJalnYhdKA\nrkfxcNcghWOU49J+pNB8LZG9KzGR2QkpPwKXqm0denmjqjS2DxUScnlwl3vnHs9EPIuEodNGOI1E\nmsKEtwOZgsNk2S078Es2uwh9Z9NyKbFWeduT8YsauX1b0ERrhKPLQbttVyink9EVR4MV7SZ6rZ3x\nQ7BLwWGkXXkKPedzqU6hFRecdwgJ9wifdUz8E8c/tqEJ2wZExs+icjJ+GPXtNSgeZgHwIUr3sW4m\nOOI7yGgt8hB+1hWXctCR3f/f3rkH21XVef6zyOPmSW6eJCQhCRAeASEQeSgDCSoQ0AFUFO1qW9CZ\npsehtWuqenz0VI1d849293TZTldbVrUzrc4g7ZRdDtZgt+iIra0QkETCI4GoN0AgIeSdm9zcx1nz\nx3ct9j7nnnPvubn7nL3P2b9P1ap93mfvc9b+7t/6rd/6/WIbCdt6To4RZBQfIdHimAo38kRoPUh3\n03rt0CziEerr9VQ0WKogLeqlfvXR6DjpoZyaPYRsmy3IiXUV8EdkXxjuDaTZL6BwplYWnutOzCh/\n0xj/CTpxb2Tyxvgwig9/EmXEuAK4j84NTYkxdcdJyjXXhmecQCfgbiTaaSGMRuyC8FlRMGsN31js\noVZ40wJc67H4EfK2TEMr1jejcCAjWwZQyNWzKF3XGmSIZx2ikl75fwxNqb6FwsU3GjmzGy3gPIAK\njL2HyV3OYoaWJ1A/X0cSgtWJRlwFGdVxvUptKF1sU9AixAGkrWnNnokM3bQ+z2O0oyKmsm3k6IjG\ndeRF4H+Fx2eh9UPr6MzfuciMoLUVzyJjeRo6Vy4ge89/rTH+Hxi/susEKYkjpcRGea0xvpnJp2o7\niDws29A0WMwgUmSDYhB5IQ4j0T5I9eLDGFs3CxliJ0lCMWahqan0/eiNaNcxx6p2N6OwFSM7TqIF\nQM8hQ3kVunjeQfZxoG02xkuyaKj7qDXGx6r22wwnkF5vQYbKVShksaiL8kCdN2r2cUZr9tHweA9K\nixorEsd2Nolep8Pq2mUOzEUOqhvROd5NoSF5M4zCY59FReIWoEWbG9G1Mmv2o0X9z9EyYzxSEs0u\noVFea4xnkaptD0r9NoSm2u6lODGHFSTgB0jypKZT7J0iKd0cK68tQ6PpuKhlNsUdWFwZmpENR5F3\n5RkUGrCGZEFbKwyVmCnjJ8iQuBEZ/i3ubyWJT+weao3xyabdO4yMiT1I++5AWbWK4K2NKVHfQOfj\n61Rr9nE0y9hLUtVxETpX42LEORQ3Q9NSFDphZMMAydqHnSTVu99B62bnX0cOlF3ofNxMy4zxSEk0\nu4RG+SnkGZmsMR6NiZ+hC8XbkXGYV9aMfjRqfQN5TmIxmcPIG7IQeU1mIaM7ptqbjXkqyswxJOh9\naPr+JFokdzlwF60T2phD+lF0UbmBthYbKYnAdz6xFPtjaHAY84zXxiU3y+vIGN+J9PouZMhC+11x\ngySaHfU6avdUEs2ejgzu6Dw5k+YGJNbJu5NTyGHyW6Tb+5F2LkOG+Jmp12bdB/ahKuZ9wLVofVxP\ni76rhpJ05xIa5TOBj0zi/bFc+L+gXnIdOiHa9VNWkHDvDW1f2A4i7/ZUNGV1ORL1+XReej2jNXiU\nujBWie1DXrdV6KJ/FQq7aqVh7FFM6U9Qn92IPONtHhiWJD6xO3DA+yf5GS8jB8orKEPTp2hfiEqs\n5hi1Our2ERTeCNLqC8N2QRv3zSg+x1Gf6SOp7r0czezcjMI3W21/7EXG+G7kgLydxBhvEyXR7BIa\n5afLMFq9/FPkbd5IaxZM1NKPRsW7kYjvQt7tpaFdGba9FGPq1SgGw8iDsrem9SBDYAGwAeVYbodB\nXEGLn3cgg2QjuebELUl8YrmJA8DHSWYz76L1YR2DyPjfjWainkf9/Cyk1ReRpIoralig0X4qyGkS\ntfq1sB0mKRz3LmSEtys06WUUWbATnT93kpuTrySabUb5uNRWKLwDLShslQF8FIl5bEfRFOYqZMTc\nQctjt4wO4iTV094HkLi/gGZJ4uDtgrBtdyrOISTqPw/ffR2KT885ZKokU6HlJFYo/Gf0R2+itQvu\nB0gcJ7vRoHMpiWbfiOK8DQOkiWm9jpq9A82QRM1+a9jOo70OtwoazP4Lsj+uBW4h9xn3kmi2GeUN\nOYXSYz2GRqatqlA4gqaldqJR8itIzFchL/hZmDelzIwgYYzZFg4jT9zLSMyHkdd7YWhr0eDxveS7\n0OsEOn+2oPPmTrKriJsBGQm8c24z8CV0kv6t9/6LNc9fBPwPtDLxT7z3/zWbbzZGExfx/xT9HTcg\nD2PWA0CPPJg7kHG1A/XxVSimdwW5GzBGjsSFurWa/QrS7OPIYRI1eyWaNdmMZuHzIkYD/BydP9fR\nlkX3zWJGeVk5iaY7t6CFoB9BhnGWDKAwlB1hG+MJr6T1Mb1GcRhCAl2bgnIIXfRjpoXZJIu8elF/\nvAD1m1hsoygcQOfOr5B38qOoTxeMDOITnXNTgL9Gc8p7gCeccw95759PvewA8IdoVGK0hBFUnO2n\n6Fy5CTifbM+LYeQF34EcKFNQ/95AppVmjYITa3bUavYIqpR5JLTpSKujbi9CxvfC8FhBDF0gSQv6\nC+TQ2Uw2hRMzxmLKy8Yx5BXfi6YaP0a2aQ1PoJCC7cjLeQ4S9ZupXi1tTJxvIC/VNeQX2hMr4sVK\neOmiSrWFlkD9bAgZ1XNT27nI6L4IiXcsPV1khlDc7FNo6v5q4BMUul9nE594NbDLe98H4Jx7EFlo\nbxrl3vv9wH7n3Lsz+UYjxSCqLPsi8pLfTrblwodQhqBnqHae/C4yXgpmtHQU/4R0chP5FtUbplqz\nG+n2TJLsVLMYrdmLUN+LhnjRZ0oqaIb+KXT+bAB+B2VwKSgWU14WDqLYqWdRKrj3kF2S/REk6ltR\n+sT1yBv+Qdq+crmreSm0nyHD/O3Unwb0SIwqJGWg65WFjuWkG7VTSMjTpajTFfF6kSekXpGl+Nhc\nJPSdPCvyGurb21GO+7eiwUQHyIrP5FOWoxF2JKb2MFrKSTQbswV5HzeGbRZ45PHchozxVcjrfgsW\nF54le9Cp8wwa6LwDDXrqMYI0u1anm9Xs6GI9wmjNriDNXklSJC9q9JnISI33b6bYNTua4Qjq21vR\nwOFK4DbyDZtpkmw0e9yQw/CaL6O82SeAe7z3W8d6r3Puz5HxOIiMvnu990ecc6tJMhwA/MJ7/4mx\n9q8Drp6t4jVkjP8aGRP3o5FvFryOOv7TyEBbT/GrxLWCmEt4gMSQjUZtFNHa7XQUQx3FdqTm9gLk\nja2Ez6/NV/yz0EAx1fE18fVnoYiCdEno9O1Z4XXTqC4vPR2J9DQ0oIqlqNOtqMU6suQ4mr5/EnmR\nrgDuI19vV25kdJkwmuMoms18Chly9yCPdRYcQ3q9DenJesrbr0dIjNZTSJcHaKzZ09B/U0+vh9F1\n9RDVOnyMRLufDQ2kwY7EeRJPsSk01uypyFj2jNbsOWE7M+xn1Oqo39Po/hmPQeQU/CUaCF0CfAA5\nUrr92KtpJuTQOXcbcL73fq1z7hrgK8C147z3B8CnvfcV59wXgM8Cnwkfuct7f0Wz+1hCo/wk8B0U\nPvA2NLjJIuRhEA2ItiCBuhzF02Z10cibETRoPE71VN+JmvtTUDGMKOig37cnbBeH56LRG7c9SEBn\nhtekRXhKzX2HPMwutK+i338qWq1+Q/iMmanXnZG6bTSPR//nTjTY34+8K+8AzqN9nv6DaEDWTh4N\nrSF7qHbRrkTeciNzvo/WKVwG/AHZGMsxy8Qv0UzbRagYSpYhMHlSQdqcDseo1esYmvEqiSE+QrVm\nLw2fEQ3dtHbPCu+P6R3r6fUUqnXYAd9FBmL8jBvQ7z6H0XrdybOJeXEMhcvuRGEq60iM8XaF1hxG\ns0uFmlkYN+QQeVC/DuC9f9w51+ucW4qKedR9r/f+kdT7H2cShRVyMcobufrDc59FAd0jwCe99z8I\nj28A/g6pxMPe+0+Fx3tQUPGVyAV6t/d+d+Nv70HCfjfZeDYPIUN8G/rPNtFeY2WyVEhWih8N7Xid\ndhKJ70J0bLNSbT4adUeBjt6IHtrjPY7G/maym8YuMyPogrkztJgndxMqWNHOQlkvomwAR1Gcejtn\nIzaFFvnT2hc8CawNU5SvIlH5cIMP62grL1/NBmnrDWST0vMkmr7fgjTrGnQN7ZSQQo+M6ZiVqT9s\n6+n2dHTOpkMzZiLjd0nqsWiAt8t7fGZoN5NL8bCuw6MZ+qjZB1DY1VtQJq52ztK/ijT718gxubSN\n3z0uzYQc1nvNcmTkNBOu+DHgW6n7a5xzW9FJ+p+89z+r8543yctTXtfV75xbhy5s69CP8EPn3Frv\nvUdTCB/33m9xzj3snNvsvf9H4OPAgTDVcDfwReBDjb/6DGSUTwaPFn08jjwsVwC/T3ax6FkyjPrC\nobA9SLJCPBrgM5FAzgttNvJ8zEm1WRRsxJvifoq/sKbIDCEhjbH5FXQRvxB5VpbSXptyCMWp/wJJ\n1NspVGqugPd+2Dl3P1q1NgX4mvf+eefcfeH5rwYPyxPoBKs45z4FrPPeH89tx0+PHDUbksqXk+F1\nZIg/g1KH3oXSFxaNCvJ0HkJ6HW+ndXsqiV4vCffPplqz51DcyfA7STzoxsSpoP4cNbsf9ZULgXei\nRBLt/O8raDH0VjSBeA3ZRSFMhEcZZ3az2ZDD07rgOef+BBj03j8QHnoVWOm9P+ScuxL4rnPuEu/9\nsUafkcsZO4ar/w7gW977IaDPObcLuMY5txuY673fEl73DXRW/yOaavjP4fHvoJifFjGI4g4fD/ej\nhyVvg3CEpBjBfiTgUdCPoymk+Wj6vxeJeBT0uRRXuJsl79+/0ziOBvkvoYH/XjTTsBLF0p5DPova\njiP79UlkYLQylCCb/Fre+++j2Ir0Y19N3d5LF0zfdK5mR2PhMWTEbAD+Pfkv2vTIwN5Pot1Rtw8j\nYyZq9mJ0TsZsTJ2Q3WM8yrD+JktOkThOXg5tNuoX55LkOm/3hNwQCit7DNkR1yNpaIVN0YxmXxda\nZNTsZjMhh7WvWRFeM22s9zrn7kGrZt8ZH/Pex9XGeO+fcs79GnkEnmp0BEWwxtKu/rPRvxuJ0wZD\nVP9we0gq+bw51RA8V0eccwu89wez28XDyFh4CjmEbkXTqe0+AUaQaL+OxDxuD6KLzDJkdC8L+zmf\nJBOIUT6GSPrJvrB9nUS8z0HVBpeT79T9PuQV3wFcSrYL+BpRkkoUraEDNHsAhRQ+jq6pl6OY2nZf\n8jzybNdq9n5kWC9Gej0fGVfzQ+t0o9s4PaKDLWp11O0zkEafgwaWd5JdYorT4RiJA2UFcqCsprU2\nUSaa3UzI4UNo+v1B59y1wGHv/T7n3IFG7w1ZWf4Y2Oi9H4gf5JxbBBzy3o84585FBvlvxtrBlimU\nc+4R6gcTfc57/73wmlpXf4Hw6LrxGApVuRz4t7RvsZlHg4E9qXYCnbSLkbf7AjQqXISJeFmpoIv+\nQZLZkWHkHTyC+usSlHXm6nC7l/xDnCso5vAX6KJzNaqxk0XMcDOUpBLFBOh8zQb1/8eR9+48FE+7\nkvb1936qNXsPSQrUJciAuQJpeAekoTNagEf9JOr1QbTO4SW0qH4O0uslyEmxBK3lKoJz7TVkE+1E\n8epZ13MZi8lrdjMhh977h51zt4VZv37g3rHeGz76vyEj7BHnHCSpDzcCf+qcG0IXvfu894fH2seW\nGeXe+5vGer6eq5/G0wZ7qA7+i4/H95wDvOqcmwrMa+xx+XHq9mrk7a4lpmh6DHlbrkGzra2OjRpC\nh7KbRMwdOtTlaMHZ2W3YD6NYjCCvRHohbuwrB9HAbRYyvuN093K0hm4hxZgMS9OPPJhPov28DF14\nxtvP36IsAllhnvJaOleza9f4XAn8OxTm0UoqKPTrJZJDPoF0ejlKtXsH+YfKGO2lgnQuLsQ9isJP\n9pI4T6YwWrMvQ4O1oi04HgKeQ55xh5yBtzD+oLKYmj1eyGG4f3+z7w2Pr23w+u+gEL2mySv7Sl1X\nP5o2eMA595eol64FtnjvvXPuaMgZuQX4CPDl1Hs+iqzou4AfNf7mG8fYqyMoNdZr6M/fFL6+VQtR\nKuG7fhNanPVdjuJ6343iB/P2aBqtYRgJdz+jMyY41B+OhudjIYsYU7oYXfDjVHfR4zM9Op4nUJqu\nC1Hc4XKa799rqDbIfjLJfTJP+UQopmYPoDU+O9G50uo1Ph4ZVVGz+9C5uRZluthIkp3K6D7iAvjj\njNZtj67nR5ATZTrVawAWoPCpaIh3Qs2Sg8h5sg2FWF2HDPJmPfam2adDXm60uq5+7/1zzrlvo2HZ\nMPCJsIoflA/t71Bvfjis4gf4GvBN59yLaLXMOKv401SQuD6BPNSXobzwZ03m2MbgEErxFgV9Looj\nvBYtaDMveH3+ConZuyheGeB0caRTqdv9JPmAa7dzkXdtFqOzJcxHIr6OZCFuEaYtT4cBlOniSbTW\n5a0obWURpu3LIfAZUhDNBnkcn0AzmmuQsbCa1hjDJ1CYVTTEK0izL0Jri85swXd2A/+Awm5vQoZc\nkZxLI4zW60GSfO7pPO7x9jz0/89AA7Fa3T4THWc0xIvuKGnECAp9fAL9f5ejZEmNqq22k3Jotkv0\ns7txznn4fLiXnkLvAa5CU+hZTxt5dAHZEVo/8qjEEaQJenN8AYnnVGSUX4vi2HoYXbGzMsZjcduo\n8ly8PUTj6nVDyEg+FPbJk+T4jduF4btiTuB621l0p0ctxrNvR4uUFqHzaw3ZHu/n8d6fbtoqr6nV\nibLmtL/TmDjVmh2n0J9EIVsbUJhKKzT0CNLr55H38xLkqDmXfDJcdCL/E+nANGTEXofCemLlzYnq\ndoXGmj2MtKWfxpp9JgovGgiv76Fat88Krx1Ls2fTuQ6SsYjr555BA92zUZ+/hGwHF6bZzVC0gNM2\ncBJl4LoAeB8KdczyP6ugDv48EnaAi1Eo5kq60xCDpDTzSRIvRL02FV30hhq0RSjUYSTV4sBxmCQd\nFMiLXFspbh5JCEjtc2cgZ1+F6mpz6Sp0U0kuJL0kletqK5CmiyN11DnfAkbQzM8zqM/HBUq30b6F\nmxOlHF6X7uHr6Lx7OxObQm8Gj7KhREP8cPiOa5Eh3q2L6CtIk8fT7DNI1rLUa7PRAsV6mj2Efs//\nG+7PRL9nrMYctXlqeE8j3Z4VPqtWs+N2FtLk6KWurTwaq0bPSH1/mfHIabId6fY0tHDzXtq3cHOi\nlEOzS2iUzwT+iGy94jFm9lfIOJmCDPEPoRF4pwnACJqyizHP6dsjSGRP1rQh9NuuRALeU6fFqb9e\nEkO3tk1JbWP7C2Twn4G8YxsprrFXFoZQyNcLyLsyDxnim2j9ArsssIWencVHyH4m8wAySl5FM5oX\noXCLVXSeR7SCNLK/pp1A16cDjNbsU+g3XYUcJfU0ezrJmpZGmh0dGWnN/t9okDMVzZLdhAbrRn7E\nhcm/QZECQ0izP0xn2indSQmNcshO3N9Aov40MhgvB36H9qVNnCgjyIt8rE47GrYOHddMJMZx2i62\nBWh6a2ZNm07rZgGWhO+9EctkkBexjPOvQ3sZ/S/rUFqsIsQcToRyeF26h6w0ux8NIp9GIWiXokH+\n2RTTKInp8+ppdtTtOcjQigZ0us1CTpCFjNbsGbROsxciY/xmircOqEwcoXpNxCzkEb+D7KMEWk05\nHCklNconQz+a7nkaeYzfgkqRLyP/Dj6MTsLDodXePoYWEvYg4za2VanbUciLFGZzT947UEJixcGX\nSAzxaWg6fwNKmtGODAKnkBH1IjrPitQvjc5gCGVoeRrN7lyADPFzyd8jHusM1NPtI6GtJFkgHttS\ndBxzkVFetHjnMbNrGi3jBIqd34U0+wQaHJ2H/pN2zGKOhO/ehrKnZuWwKYcjxYzyphhGRkGc8pmN\nvLZraL8QRhE/UKcdQ9OMZyLvSG/Yx16SVeH2lxv1GEDT+HtQKFZMKX0x8qhspH0zQBU0GNiKpsBX\nozShWVIOr0t5iYvXtiED16HsWu+n/XmgPTKO6mn2QXR+jSCN7kVe+4tJdLtbY9qNyTGM4sJjjvxX\n0Ez4pcj59j40eGuXI2M/Ot9+hfrtFWRbdbQcmm0W2pjsRZ3sabT44Qo0Xd8uUR9AJ93e0A6gbAAx\nw0ds54btfIrlLTGKR5wOj+W+D6BV7YeRgK9Asz+3ImFt5+zPYbTQbgvyyq9H3p1WlJMuh9elfBxF\nRsG2cH89WufQrkxXw+i82ou0ei+a7TlCtWZfkrptRrcxHgOoX+0nCUnZR1J4aDXKcLOY9s4mDiDN\n/iXS78tRCYLFLfiucmi2GeWjOIHixLeG2+tpT57OUySj3WiEH0Nxu0uR92QDGhxYPnNjPDzymhwg\nMcBfDw0kmrHs93q00CePAd0g8oZvRX3+ShQa0+oY33J4XcpBDE/ZhvRzHXAnrY+ZHUGG96uhvYbO\nt/lIs5ehxaNL0Oxq3uGNRvE5ifpQNMCjdp9A1/4lKJwp1uzIo/pnBTlytqGF/ucD14dtK68h5dBs\nM8qBJAZqK/Iinok8dFnnVk7Tj6boX0JxjvuRIXI2EvJNaCBgnm+jETHd2GGS8s2Hwu3DyNu8Gg3i\nlqAp8cXI85yngRCzFW1FuadXoAHnhbSv6EY5vC7di0eG8DYUWrgQeek+SOs8z4PIcbI7tD3IAD8f\n9eG3osFtpxaOMVrPCJrNqafZh5DBG4stLUF9agkKZcp7Pc1BkvCUmShy4FbaVwyuHJpdcqN8PzIM\nniaJgbqE1ixgGyBZAb0bnZgr0CLLW5AxbmJuRAaRp/toqh2p2Z4i6TexfPM54XYvxZtRieEpv0RG\n1XpU9DGPIlrl8Lp0H8eRXm9D58h6tBC8twXfNUziNInhAmchzX4b8lh2Qrl0oz0Mo/4ZdbtWr48g\nZ9x5SLujZl8YtguQgVukGZWTyHHyLJrJvAylUFyaw76UQ7NLaJQPoZHeVnSSXAb8Hq3JoXoATe+8\ngLwqK4G1aIo+r3CBMlMB/hkNvFoR8zYWI1SXb44lnPtJhDzdKsijvQBd+OeRrB+Ii3lnk7/3ZDyi\nqD+NpmLXAbejcyHPi085vC7dQwxx6kMzibehAWjW/b8fed5fQIb4IqQX70Sxuxb/3X6eRDq4hvZq\nRsz9HrU66vYAsh1qNXsQafI5SO/PRLp9NkmihbkU/7ofqzI/jc6BNcA1aDCRp8lYDs0uoVEO8n5s\nRJ0syxNkJHx2NMRPISP8GtSx84j/MhIGgUeBnyGj8EYkoGPh0f86WKcNI7EeSLVTqdsOTfnFQh2x\nXHNsMRf8grAfc1Kth2J5TCZCzFb0NJoZOhdVR1xLcSSnHF6X7uEl5FF8H9kXftuH+utONHu6JnzX\nrVhdhCLwU2T09iLNvpjxr9tpzR5K3T4VtgM01m2PQkkG0CAsanXU7fmoXyyjWrNnUnwnSSNitqKn\nkVd8MXJYvof2haeMRzk0uyhXyDYyDQl7VnjkBY+dOcaFvw+dtJ16kraSERJxHEEn21DYDqfu+7Ct\n1LSRsJ1GIqL12kwSr7MnOamH0UKVPvT/9CChHa5pi5ExABLn2tYb9mUGSbXShan7M0jEvIfu7guD\nyKuyE8WLz0aifjvFnOIvh9ele7g54887iBb0b0fn7Dy0jmc1pbwsjkuFamdEI80mvKZWq2MbT7N7\nkDc6/Z6T4XMOAN8J3zEdaTY1+zAdebErjNbrWIF0Rk2bV+exaIgX3as9GSrIEN+BCgYeQpr9+yS/\nbZEoh2ab+pw2byBDfDtJDtx70XRnN5MOwxhgtKe41vMQje9080gk5yIhjaWaY4v3Z6MT8YxUm5K6\n7cLrXIMWvRzp128PxzElfM+FyGt+Vs0+TA2vmY6dJo04jmaEdqJBznL0hOmWfQAADlZJREFUe26i\nNTG+WVIOgTfSHEeOk+3IKL+EYoRStZoK0uF0+EW9dhJp3mGqvcrReTIdneMHaazZveE7arU63p8e\nto00expJppr4vt+GfYgafh6K64/hRI00u5v/09Ml7Tx5AV2DLwLegeLEi/yblUOzzdqYEMfQQrWt\n4faltCd9W6s5hS5Yx1LbITRyTsdA95OEYSxAx1zrYZiDBiY94X4P1d6KHhJRzoPvIeG/Ea1st1Og\neSpoqn83Mm5eRxfIS1AKuiJ6xBtRjqlQYxCFpmxFXsELgBvIPnSx3QxRHc8ctbufas2O8dDTSEIv\n6ul2XBg+M7w2anXay5yXZj+Jju2taMBflHCKTsCjQdZLaG1P2nmykWJ6xBtRDs02i2RchtGIchvq\n2BtQjtBWpkvMCk+yKCW9AvwI2veXkZiDjOlYsnkOmtJbgQRwNkk83QyKf9xj8btIlIoQ3z+MLprT\nyNegjesgVqH/PO5LNML7QtuN+sKlyLBZQ+dKSDm8LuUkxsduRU6UC2l9usQsGaRaq6N2n0Be6ug0\nmU21Zs9B3s5ZdVonD0DegzSpCLNvFfQ/ePJdb3AM+Dm6lq0gKfQW4+H7kF73oVmOS+lM50n56NQr\nahvYi0R9O4otXk8+JZrHYwSJ9sGaNhWtoAadsOl2fthej4RlOsXy9H8FXVw2kf1I/tyMPy/Gqqen\ngGunhSvUn3UYRhfMd6J0nHnxBhL4LSThRDGecz6Ks30LujjawjejqNRW87yC/FJujkUFeX7Ten2I\npIDcEEnmjrhdgc69eWE7k2Jp9kMo5vtdKBwoS5Zl/HkgnTtFfb0+GV5ziNEzDwPot78EeHcL9qtZ\nTgG/QNfuEZLQnmG0f2uQbl+P1kwUqa+cLuVwpJhRXkU/irXaQlLN89+gUI28GUKZAfahsIEhFBt2\nFIl0zHO6AHk856N4yaLlqm6Gw+hYn0UZO65HF6MzSIxFX3O7UUu/tnbRUbxfu8BzqOa2I0l5VRtr\nOYji0Y8iMaydFp5BcmFNe65mk2+GlQE08HwVeVPiQi7C9nx0gT0rj51rA+WYCu1+hlB4yi+RQduu\nap7NMIIM7teRbp9As1KHkDG1INUuQprdS2dW/zyCPLPfQOGLNyJjOj3AT7exNDu2tF7XardHOtxI\ns6ej60i9NU2n0MDhderrdVz8eTbVs8Rx8WdeM8XDaJ9jBVlHotkV5Dy8BTmeOq3/NEM5NNuM8qpq\nnr9BHsFWV/Mcj6Mk5ZujoB9FI94loS1FaeZ6yf9v9CRpptKppaKXuJ/66akG0YBiH4mgxswshPc+\nHxoki4TSi4fmo9+m0cKhGSSLhNLvTd+uXeAZV+jH29NT29pYy6IvBD2JPOGxHQiP/xoZ3MvQ9P5u\n9LtPB+5GMbfdTDm8Lt1JuprnM6gPr0f9Nq/wlJMkg9zoOHkD6dtZSLPXoLjo+eQ/41o7w5fW7RE0\ngKiXBnYQ7fshqh0Z/eFzh9C164FwPy4ATS/cjPrraazbs8M+NVowOid8V1qz48L+qN8rqNbq9O0i\nLwQdQjqd1m2PHIYLUH9fho7vZXQsm4DrKO4xZUE5NLvI1kSLqVfNM4/0bcNIzF9GqeReRp0vVhZd\nh7wOC2lfXGBc+HmcJNSiXuGbE8gLvBt1pbiwM+1xiKkGpyMhTS8cSi/8TBvFX0EXhykoJ+07KUY8\nYRHx6P85nGoDqB+9gfrSItR/FqFB52LgA1T3pxfDez5KawppFY1svC7Ouc3Al9CP+bfe+y/Wec2X\nUdLrE8A93vutmXx56UhX8xxChvh9tF8bKujcinr9Cjrvzgv7shrVplhM+wYJ6WqSJ0m0u7bwzQmk\nyb8J74s6ndbtXpKUgjORxtemF6x1ZHwPhUtOQ4OQzchoNOozQLVmnyLR7GPI+F4U2tqwvYPq/jQV\nDQLfj66T3U7+mt3ovc65BcDfozCFPuCD3vvD4bnPAh9Do91Peu9/MNb+ldAoHwC+iabbLkdGSKPq\njr9F3o1W8AIqirAXnYCx2uc7SDKbtIr0cf0/NEBJr+SHZOHQEpKc3/F+upBCXLGf5YBhHurbNyFj\nciK08j/Lm3hsjyFPdxT0mIostiVoCnMR8tQ105f+NfJC5bEIKI//bPJeF+fcFOCvUZzPHuAJ59xD\n3vvnU6+5DTjfe7/WOXcNGnFeO+kvLx3fRTNm41XzbGVfOgA8jP7qmcjoXAFcRXsqNMdj24YM4LRm\nx4Wfc9Div0Gqi93UhmDMQAZ0VsxHv8GtaFAyEcqg2S8CTyG9PoTss6jX85ENchXS7F6a60vrUZhh\nHg6r8mn2OO/9DPCI9/7PnHOfDvc/45xbh6bw1qET84fOuQu895VG+1hCo3wG+k3PYfyO30frOt58\n5AHPIxNIH8lxLUFiml7Bn/fCzz+YxHv76F6B70PHFoU7tizWDczO4DNOlz7a/59l4nW5Gtjlve8D\ncM49iNxZz6deczvwdQDv/ePOuV7n3Fne+31Z7EB52IAMvvG0so/W9aVZKPzkvUgn200fOrZ5KOQs\nGuGxmmSemj2ZRY99dL9mz0Wz33HdwCwm/39FZ0we9FEyzV6KDrjRe29HOSYJ730UGeZ3AN/y3g8B\nfc65XWEfHmu0gyU0yqEYArCYxh76dnJp3jtgTJjz896BLiCT+MTlaM458gqKWxjvNStQ4LHRNFln\n9DgdZlKMMIEiXL+MibE0NOP0yVWzl6OVv43em3a07CPJkHA21QZ4/KyGlNQoNwyj3GTidfFNvq7W\nJdbs+wzDMAwgZ81u9JpRn+e99865sb5nzH0omVH++dN4z0+y3omC0K3HBXZsnUi7j+vzWXzIHqpd\nuCuRJ2Ss16wIjxlN8fnTeE+3niPQvcfWrccF3XtspdLsV9AijEZavs85t9R7v9c5twylX2r0WWPq\nf2mMcu99N+cKMgyjSTLUgieBtc651SgNwt3Ah2te8xBwP/Cgc+5a4LDFkzeHabZhGFAMzXbOHRjj\nvQ+hrCFfDNvvph5/wDn3lyhsZS0qhNOQ0hjlhmEYWeK9H3bO3Q/8E1o1/jXv/fPOufvC81/13j/s\nnLstLPDpB+7NcZcNwzBKy2Q0u9F7w0d/Afi2c+7jhJSI4T3POee+DTyH4m8+4b0fM3zFjfO8YRiG\nYRiGYRgtJq+SlbnjnPtz59zzzrlfOef+wTk3L/XcZ51zLzrndjjnbk49vsE5tz0891epx3ucc38f\nHn/MObeq3ceT2pcPOOeedc6NOOeurHmuY49rPJxzm8NxvRjyhBYe59x/d87tc85tTz22wDn3iHPu\nBefcD5xzvannJvT/5YVzbqVz7sehHz7jnPtkeLzjj83Ij27V7LA/ptsdoNum2Z13bB2H976UDVWm\nOSPc/gLwhXB7HarOMA1VQdhFMqOwBbg63H4Y2BxufwL4m3D7buDBHI/rIuAC4MfAlanHO/q4xjnm\nKeF4Vofj2wZcnPd+NbHf16NSsttTj/0Z8B/D7U9Ppl/meFxLgfXh9hxUH/ribjg2a7n2q67U7LAP\nptsdoNum2Z13bJ3WSusp994/4pOqSo+T1AR+M9m7V5L4XcA1Titq53rvY5D+N4A7w+03k80D30F1\n4XPBe7/De/9Cnac6+rjG4c2CAF5J+mNS/0Ljvf8pKu+WJv2bf53kvzid/y8XvPd7vffbwu3jqLjC\ncrrg2Iz86FbNBtPtTtFt0+zOO7ZOo7RGeQ0fQyM6ULL3dIqcdOL49ON7SJLAv5ls3ns/DBxxzi1o\n5Q6fBt16XNA42X8nMlYRgon+f7njtFL9CmREddWxGblSBs2G7j62btHtrtI10+x86ersK865R6hf\nRutz3vvvhdf8CTDovX+grTs3CZo5rpLRlauVvR+3CEGhcc7NQZ66T3nvjzmXZLXq9GMzWkO3ajaY\nbteh687/Ttc10+z86Wqj3Ht/01jPO+fuAW6jenqvUeL4PSTTpenH43vOAV51zk0F5nnvD05q58dg\nvONqQOGPaxI0UxCgU5hIEYJG/1/uxWmcc9OQuH/Tex9ztnbFsRmto1s1G0y369Atut0VumaaXQxK\nG77inNsM/DFwh/d+IPXUQ8CHnHPTnXNrCMnevfd7gaPOuWucho8fAf5P6j0fDbfvAn7UloMYn3TC\n/W46rlreLAjgnJuOFjc9lPM+nS7p37y2CEGz/993az+0nYT9+BrwnPf+S6mnOv7YjPwoiWaD6Xan\n0fG6ZppdIPJeaZpXA14EdgNbQ/ub1HOfQwsXdgC3pB7fAGwPz3059XgP8O3wmY8Bq3M8rveiOL2T\nwF7g+91wXE0c961oxfgu4LN570+T+/wtVBlsMPxn9wILgB8CLwA/AHpP9//L8bj+FVBBq/Pj+bW5\nG47NWq79qis1O+yP6XYH6LZpducdW6c1Kx5kGIZhGIZhGDlT2vAVwzAMwzAMwygKZpQbhmEYhmEY\nRs6YUW4YhmEYhmEYOWNGuWEYhmEYhmHkjBnlhmEYhmEYhpEzZpQbhmEYhmEYRs6YUW4YhmEYhmEY\nOWNGuWEYhmEYhmHkjBnlRsfjnLvKOfcr51yPc262c+4Z59y6vPfLMAzDqI/ptmGMxip6Gl2Bc+6/\nADOAmcDL3vsv5rxLhmEYxhiYbhtGNWaUG12Bc24a8CRwEnibt45tGIZRaEy3DaMaC18xuoVFwGxg\nDvK6GIZhGMXGdNswUpin3OgKnHMPAQ8A5wLLvPd/mPMuGYZhGGNgum0Y1UzNewcMY7I4534POOW9\nf9A5dwbwc+fcJu/9oznvmmEYhlEH023DGI15yg3DMAzDMAwjZyym3DAMwzAMwzByxoxywzAMwzAM\nw8gZM8oNwzAMwzAMI2fMKDcMwzAMwzCMnDGj3DAMwzAMwzByxoxywzAMwzAMw8gZM8oNwzAMwzAM\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",
-        "       -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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-        "       -0.00673374-0.02865389j, -0.00673374-0.02865389j,\n",
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-        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
-        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
-        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
-        "       -0.00673385-0.02865476j, -0.00673396-0.02865562j,\n",
-        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
-        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
-        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
-        "       -0.00673396-0.02865562j, -0.00673396-0.02865562j,\n",
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-        "       -0.00673401-0.02865616j, -0.00673401-0.02865616j,\n",
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-        "       -0.00673398-0.02865658j, -0.00673398-0.02865658j,\n",
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-        "       -0.00673385-0.02865476j, -0.00673385-0.02865476j,\n",
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-        "       -0.00673385-0.02865476j, -0.00673374-0.02865389j,\n",
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-        "       -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",
-        "       -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",
-        "       -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",
-        "       -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])"
-       ]
-      }
-     ],
-     "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": {}
+   "source": [
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2529.536000000001"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(100*np.cumprod(np.ones(5)*1.6))\n",
+    "\n",
+    "   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# M = Mesh.TensorMesh([[(100.,32)],[(100.,34)],[(100.,18)]], x0='CCC')\n",
+    "M = simpeg.Mesh.TensorMesh([[(100,5,-1.5),(100.,5),(100,5,1.5)],[(100,5,-1.5),(100.,5),(100,5,1.5)],[(100,5,-1.5),(100.,10),(100,5,1.5)]], x0=['C','C','C'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "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"
+     ]
+    }
+   ],
+   "source": [
+    "print M.vectorNz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar instance at 0x7fcef35a7d40>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": [
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fcee87a3d10>"
+      ]
+     },
+     "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",
+    "# sigBG = np.zeros(M.nC) + conds[0]\n",
+    "# sigBG[M.gridCC[:,2]>0] = 1e-8\n",
+    "sigBG = sig\n",
+    "colorbar(M.plotImage(log10(sig)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "freq = 0.0001\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": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7.68 s, sys: 27.9 ms, total: 7.7 s\n",
+      "Wall time: 7.7 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# Solve the systems for each polarization\n",
+    "Ainv = simpeg.SolverLU(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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": 11,
+   "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": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 100 ms, sys: 0 ns, total: 100 ms\n",
+      "Wall time: 99.4 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "es_px = Ainv*rhs_px\n",
+    "es_py = Ainv*rhs_py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Meinv = M.getEdgeInnerProduct(np.ones_like(sig), invMat=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "j_x = Meinv*Msig*e_x\n",
+    "j_y = Meinv*Msig*e_x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(15200,)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "e_x.shape"
+   ]
+  },
+  {
+   "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,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
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+       "bcCU4vEJwFURsTHShOKrgZmS9gF2jojlxXFXAG8qHj83MTlpmaqjR7v+Q4mIlRFx3yBFWbdrGM9N\n",
+       "Hh8RG4GtE8D3tIj4CfDHAbsb/80vZ9vfop2/XyUi4pGI+Hnx+CnSRPyT6YO2WXX6NWaD43Yucdsx\n",
+       "O7+2Vam2CfYA7yJ904I0mfjahrLGScYb968r9kPDxOQRsQl4XNLuo1nhNvRru2DoieFztFdEPFo8\n",
+       "fhTYq3jczt+vckp3XB9KSoj6qm1WqTrEbOjvtvVL3O6ruOaYXZ6+nkVE0o0MPsP92RHx/eKYjwEb\n",
+       "IuLKrlauA620q2b68k7diAhlPBewpJ1IPWgfiIgnpW0zLeXeNhsd/RqzwXF7EH33+c89rjlml6uv\n",
+       "E+yIOLZZuaRTgfls/xPaOtJygVtNIX0jW8e2nyQb9289Z1/gIUk7ALtGxB86qnwTw7VrCD3frg4M\n",
+       "bNtUtv8WnZNHJe0dEY8UP7f9ttg/kr/fuq7UtAlJ40iB+msRcU2xuy/aZqOnX2M2OG4Pol/idl/E\n",
+       "Ncfs8tV2iIikecCHgRMi4tmGoiXAWyWNl7Q/MB1YHhGPAE9Imqn0te7twPcaznln8fhE4IddacTw\n",
+       "Gidn76d2DfTc5PGSxpNu7FlScZ3a1fhv/k7gmob9rf79rhl40W4q6nEZcG9EfL6hKPu2WXVqErPB\n",
+       "cTs32cc1x+xRUvVdllVtwCrgAeDOYvtCQ9nZpEH7K4HXN+w/HLi7KLuwYf8E4BvFNW8FplXYrjeT\n",
+       "xrU9AzwCXNsP7Wqh3ceR7nxeDZxVdX1arPNVpBWhNhR/s9OA3YEfAPcBNwC7tfv3q7Bdrwa2kO4y\n",
+       "3/r5mtcPbfNW6fuqL2N2UR/H7QzitmN2fm2rcvNCM2ZmZmZmJartEBEzMzMzs9HgBNvMzMzMrERO\n",
+       "sM3MzMzMSuQE28zMzMysRE6wzczMzMxK5ATbzMzMzKxETrDNzMzMzErkBNvMzMzMrEROsC17kl4h\n",
+       "6S5JEyTtKOmXkmZUXS8zMxuc47b1O6/kaH1B0jnARGAS8GBEnFtxlczMrAnHbetnTrCtL0gaB9wB\n",
+       "PAO8MvzGNjPraY7b1s88RMT6xZ7AjsBOpN4QMzPrbY7b1rfcg219QdIS4ErgAGCfiHh/xVUyM7Mm\n",
+       "HLetn+1QdQXMOiXpHcCfI2KxpDHATyXNiYhlFVfNzMwG4bht/c492GZmZmZmJfIYbDMzMzOzEjnB\n",
+       "NjMzMzMrkRNsMzMzM7MSOcE2MzMzMyuRE2wzMzMzsxI5wTYzMzMzK5ETbDMzMzOzEjnBNjMzMzMr\n",
+       "0f8HMpKKgpyCorYAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fcee86ce690>"
+      ]
+     },
+     "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='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])"
+   ]
+  },
+  {
+   "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),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": 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",
+    "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": 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": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(array([ 91.22293905]), array([ 42.49194681]))\n",
+      "(array([ 91.22293906]), array([-137.50805341]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "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,zyx[ind])\n",
+    "print appResPhs(freq,zxy[ind])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.00551946-0.00563967j] [ 2776.87342066+2515.31936068j]\n",
+      "(array([ 91.22293906]), array([ 41.84924238]))\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": 25,
+   "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": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.00019869+0.00019869j  0.00019869+0.00019869j  0.00019869+0.00019869j\n",
+      "  0.00019869+0.00019869j  0.00019868+0.0001971j   0.00019867+0.00019605j\n",
+      "  0.00019867+0.00019605j  0.00019867+0.00019605j  0.00019867+0.00019605j\n",
+      "  0.00019842+0.00018849j  0.00019791+0.00018125j  0.00019791+0.00018125j\n",
+      "  0.00019790+0.00018126j  0.00019789+0.00018127j  0.00019789+0.00018135j\n",
+      "  0.00019789+0.00018143j  0.00019789+0.00018154j  0.00019789+0.00018172j\n",
+      "  0.00019789+0.00018199j  0.00019789+0.00018239j  0.00019789+0.00018299j]\n",
+      "(array([ 91.21391269]), array([ 42.48936455]))\n",
+      "(array([ 91.21391269]), array([-137.51063545]))\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": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-2478.125, -1718.75 , -1212.5  ,  -875.   ,  -650.   ,  -500.   ,\n",
+       "        -400.   ,  -300.   ,  -200.   ,  -100.   ,     0.   ,   100.   ,\n",
+       "         200.   ,   300.   ,   400.   ,   500.   ,   650.   ,   875.   ,\n",
+       "        1212.5  ,  1718.75 ,  2478.125])"
+      ]
+     },
+     "execution_count": 27,
+     "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": []
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/MT1DfwdProblem.ipynb b/MT1DfwdProblem.ipynb
index f875cb31..0ddd7733 100644
--- a/MT1DfwdProblem.ipynb
+++ b/MT1DfwdProblem.ipynb
@@ -1,447 +1,465 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:74ba4ac0804b189d65dce7f7af6f88a605b83052cf38c5acb492600297a13398"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "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": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Simple notebook on performing a 1D MT problem.\n"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#from IPython.display import Latex"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Maxwell's equations in 1D are as follows\n",
-      "\n",
-      "\n",
-      "$i \\omega b = - \\partial_z e $ \n",
-      "\n",
-      "$  s =  \\partial_z(\\mu^{-1} b) - \\sigma(z) e$\n",
-      "\n",
-      "$b(0) = 1 \\hspace{1cm} ;\\hspace{1cm} b(-\\infty) = 0$\n",
-      "\n",
-      "\n",
-      "\n",
-      "where $e = \\widehat{\\overrightarrow{E}}_x $ and $b = \\widehat{\\overrightarrow{B}}_y$\n",
-      "\n",
-      "In weak form the equations become\n",
-      "\n",
-      "$i \\omega(b,f) = - (\\partial_ze,f)$ \n",
-      "\n",
-      "$(s,w) = -(\\mu^{-1} b, \\partial_z w) - (\\sigma(z) e, w)$\n",
-      "\n",
-      "\n",
-      "where f and w are abritrary functions living in same discritizational space as b and e, respectivily.\n",
-      "\n",
-      "We consider e on nodes and b on cell centers. This way the derivative of any nodal function becomes\n",
-      "\n",
-      "$ (\\partial_z u)_k \\approx $\n",
-      "\n",
-      "$h_{k}^{-1} ( u_{k+\\frac{1}{2}} - u_{k-\\frac{1}{2}})  + O(h^2) $\n",
-      "\n",
-      "Matrix form\n",
-      "\n",
-      "$ e_z \\approx \\textbf{L}^{-1} \\textbf{G} e = \\begin{bmatrix} h_1^{-1} & & & \\\\\\\\ & h_2^{-1} & & \\\\\\\\ & &  \\ddots & \\\\\\\\ & & & h_n^{-1} \\end{bmatrix}^{(n,n)}\n",
-      "\\begin{bmatrix} -1 & 1 & & & & \\\\\\\\ & -1 & 1 & & & \\\\\\\\ & & \\ddots & \\ddots & \\\\\\\\ & & & -1 & 1 \\end{bmatrix}^{(n,n+1)} \n",
-      "\\begin{bmatrix} e_1 \\\\\\\\ \\\\\\\\ \\vdots \\\\\\\\ \\\\\\\\ e_{n+1} \\end{bmatrix}^{(n+1,1)} $\n",
-      "\n",
-      "where $ \\textbf{L} = diag(h) $ is the cell size and $ \\textbf{G}$ is the gradient operator with -1,1 representing the topology of the mesh, taking the difference between adjoint cells.\n",
-      "\n",
-      "We need to compute 2 inner products, on cell centers and from nodes to cell centers.\n",
-      "\n",
-      "Cell centers inner product is\n",
-      "\n",
-      "$ (b,f) \\approx \\sum\\limits_k h_k \\textbf{b}_k \\textbf{f}_k + O(h^2) $\n",
-      "\n",
-      "and in matrix from\n",
-      "\n",
-      "$ (b,) \\approx \\textbf{b}^T \\textbf{M}^f \\textbf{f}$  and $ (\\mu^{-1} b,f) \\approx \\textbf{b}^T \\textbf{M}_{\\mu}^f \\textbf{f}$ \n",
-      "\n",
-      "where $ \\textbf{M}_{\\mu}^f = diag(\\textbf{h} \\odot \\mu^{-1}) $ and $ \\textbf{M}^f = diag(\\textbf{h}) $ are the matrices.\n",
-      "Nodes to cell centers inner product is\n",
-      "\n",
-      "$ (\\sigma e, w) \\approx \\sum\\limits_k \\frac{h_k \\sigma_k}{4} ( e_{k+\\frac{1}{2}} w_{k+\\frac{1}{2}} + e_{k+\\frac{1}{2}} w_{k+\\frac{1}{2}} )$\n",
-      "\n",
-      "and in matrix from\n",
-      "\n",
-      "$ (\\sigma e, w ) \\approx (\\textbf{h} \\odot \\sigma )^T ( \\textbf{A}_v (\\textbf{e} \\odot \\textbf{w} = \\textbf{w}^T diag(\\textbf{A}_v^T (\\textbf{h} \\odot \\sigma)) \\textbf{e} $\n",
-      "\n",
-      "Here $\\odot$ is a point wise Hadamard product and $\\textbf{A}_v$ is the averaging operator/matrix from nodes to cell centers\n",
-      "\n",
-      "$ \\textbf{A}_v = \\begin{bmatrix} \\frac{1}{2} & \\frac{1}{2} &  & \\\\\\\\ & \\ddots & \\ddots & \\\\\\\\ &  &  \\frac{1}{2} & \\frac{1}{2} \\end{bmatrix} ^{(n+1 , n)} $\n",
-      "\n",
-      "The sigma mass matrix is defined as \n",
-      "\n",
-      "$ \\textbf{M}_{\\sigma}^{e} = diag(\\textbf{A}_v^T (\\textbf{h} \\odot \\sigma)) $\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the MT problem there is no source in the domain, so $ s = 0 $. How ever the boundary conditions provide the right hand side where \n",
-      "\n",
-      "$ (\\partial_z \\mu^{-1} b, w ) = - (\\mu^{-1} b, \\partial_z w ) + (\\mu^{-1} b w )|_0^{end} $\n",
-      "\n",
-      "where \n",
-      "\n",
-      "$ (\\mu^{-1} b w )|_0^{end} = \\textbf{bc}^T (\\textbf{BC w}) $\n",
-      "\n",
-      "here $\\textbf{BC}$ is an matrix operator that extracts the boundary elements from $ \\textbf{w}$ and $\\textbf{bc} $ are the known boundary condintions. For the 1D case with homogenous boundary conditions we have \n",
-      "\n",
-      "$ \\textbf{B} = \\begin{bmatrix} -1 & 0 \\\\\\\\ \\vdots & \\vdots \\\\\\\\ 0 & 1 \\end{bmatrix} $ \n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The weak form is \n",
-      "\n",
-      "$ (\\mu^{-1} b, \\partial_z w) + (\\sigma e, w) = (\\mu^{-1} b w )|_0^{end} $\n",
-      "\n",
-      "$ (i \\omega b,f) + (\\partial_z e , f) = 0 $\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Using the above matrix represntation we get the Maxwells equations in following form\n",
-      "\n",
-      "$ i \\omega \\textbf{f}^T \\textbf{M}^f \\textbf{b} + \\textbf{f}^T \\textbf{M}^f \\textbf{L}^{-1} \\textbf{G} \\textbf{e} = 0 $ \n",
-      "\n",
-      "$ \\textbf{w}^T \\textbf{G}^T \\textbf{L}^{-1}  \\textbf{M}^f_{\\mu} \\textbf{b} + \\textbf{w}^T \\textbf{M}_{\\sigma}^e \\textbf{e} = \\textbf{w}^T \\textbf{bc}^T \\textbf{BC} $ \n",
-      "\n",
-      "Here we use that \n",
-      "\n",
-      "$ (\\textbf{b},\\textbf{f}) \\approx \\textbf{b}^T \\textbf{M}^f \\textbf{f} = \\textbf{f}^T  \\textbf{M}^f \\textbf{b} $ \n",
-      "\n",
-      "since $\\textbf{M}^f$ is a symmetric diaoganal matrix of size n by n and $\\textbf{b}$ and $\\textbf{f}$ are vectors of length n."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We eliminate testing vectors and get system of equations to solve\n",
-      "\n",
-      "$ i \\omega \\textbf{b} + \\textbf{L}^{-1} \\textbf{G} \\textbf{e} = 0 $ \n",
-      "\n",
-      "$ \\textbf{G}^T \\textbf{L}^{-1}  \\textbf{M}^f_{\\mu} \\textbf{b} +  \\textbf{M}_{\\sigma}^e \\textbf{e} = \\textbf{bc}^T \\textbf{BC} $ \n",
-      "\n",
-      "and as $ \\textbf{A} \\textbf{x} = \\textbf{bc} $ system that we will solve, where \n",
-      "\n",
-      "$ \\textbf{A} = \\begin{bmatrix} \\textbf{G}^T \\textbf{L}^{-1}  \\textbf{M}^f_{\\mu}  & \\textbf{M}_{\\sigma}^e  \\\\\\\\ i \\omega  &  \\textbf{L}^{-1} \\textbf{G}  \\end{bmatrix} $\n",
-      "\n",
-      "$ \\textbf{x} = \\begin{bmatrix} \\textbf{b} \\\\\\\\ \\textbf{e} \\end{bmatrix} $\n",
-      "\n",
-      "$ \\textbf{bc} = \\begin{bmatrix} \\textbf{bc}^T \\textbf{BC} \\\\\\\\ \\textbf{0} \\end{bmatrix} $ "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import sys\n",
-      "sys.path.append('C:/GudniWork/Codes/python/simpeg')\n",
-      "import SimPEG as simpeg, numpy as np, scipy, scipy.sparse as sp"
-     ],
-     "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": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We have\n",
-      "\n",
-      "$ i \\omega b = - \\partial_z e \\hspace{1cm} ; \\hspace{1cm} \\partial_z(\\mu^{-1} b) - \\sigma(z) e \\hspace{1cm} ;\\hspace{1cm} b(0) = 1 \\hspace{1cm} ;\\hspace{1cm} b(-\\infty) = 0 $\n",
-      "\n",
-      " \n",
-      "To deal with boundary: we assume that below depth L both $ \\sigma $ and $ \\mu $ are constants ($ z < - L $). At the boundary we have that \n",
-      "\n",
-      "$ e = c \\exp(ikz) \\hspace{0.2cm} where \\hspace{0.2cm} k = \\sqrt{i\\omega\\mu\\sigma} $. \n",
-      "\n",
-      "Therefore for $ z < - L $ we have that\n",
-      "\n",
-      "$\\omega b - k e = 0 $.\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We discretize the e field on the nodes and b field at the cell centers. The system we want to solve is \n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "$\\begin{bmatrix} i  \\omega & \\frac{\\partial}{\\partial z} \\\\\\\\ \\frac{1}{\\mu} \\frac{\\partial}{\\partial z} & -\\sigma \\end{bmatrix} \n",
-      "\\begin{bmatrix} b \\\\\\\\ e \\end{bmatrix}  = \\begin{bmatrix} s1 \\\\\\\\ s2 \\end{bmatrix}$\n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      " # Set up  the problem\n",
-      "mu = 4*np.pi*1e-7\n",
-      "eps0 = 8.85e-12\n",
-      "# Frequency\n",
-      "fr = np.array([1e1]) #np.logspace(0,5,200) #np.array([2000]) #np.logspace(-4,5,82)\n",
-      "omega = 2*np.pi*fr\n",
-      "# Mesh\n",
-      "sig0 = 1e-2\n",
-      "#L = 3*np.sqrt(2/(mu*omega[0]*sig0))\n",
-      "#nn=np.ceil(np.log(0.3*L + 1)/np.log(1.3))\n",
-      "#h = 5*(1.3**(np.arange(nn+1)))\n",
-      "\n",
-      "h = np.ones(18)\n",
-      "x0 = np.array([0])\n",
-      "#sig = sig0*np.ones((len(h),1)) \n",
-      "#sig[0:50] = 0.1\n",
-      "#sig[50:100] = 1\n",
-      "# Make the mesh\n",
-      "mesh = simpeg.Mesh.TensorMesh([h],x0)\n",
-      "sig = np.zeros(mesh.nC) + 1e-8\n",
-      "sig[mesh.vectorCCx<=0] = 1e-2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fr,omega"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "(array([ 10.]), array([ 62.83185307]))"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Make the operators\n",
-      "G = mesh.nodalGrad\n",
-      "Av = mesh.aveN2CC\n",
-      "Li = scipy.sparse.spdiags(1/mesh.hx,0,mesh.nNx,mesh.nNx)\n",
-      "Mmu = scipy.sparse.spdiags(mesh.hx/mu,0,mesh.nCx,mesh.nCx)\n",
-      "Msig = scipy.sparse.spdiags(Av.T.dot(mesh.hx*sig.ravel()),0,mesh.nNx,mesh.nNx)\n",
-      "# The boundaries\n",
-      "bc_b = np.zeros((mesh.nCx,1))\n",
-      "bc_b[0] = -1 # Set the top b field to 1\n",
-      "bc_e = np.zeros((mesh.nNx,1))\n",
-      "# Make the sparse matrix\n",
-      "bc = sp.vstack((bc_b,bc_e))\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = np.empty((mesh.nCx,len(omega)),dtype=np.complex64)\n",
-      "e = np.empty((mesh.nNx,len(omega)),dtype=np.complex64)\n",
-      "# Loop all the frequencies\n",
-      "for nrOm, om in enumerate(omega):\n",
-      "    # Left hand side\n",
-      "    A = sp.vstack((sp.hstack(( -G.conj().T.dot(Mmu), - Msig)), sp.hstack((1j*om*scipy.sparse.identity(mesh.nCx) , G))))\n",
-      "    #A = A.tocsr\n",
-      "    # Solve the system\n",
-      "    bef = scipy.sparse.linalg.spsolve(A,bc)\n",
-      "    # Sort the output\n",
-      "    b[:,nrOm] = bef[0:mesh.nCx]\n",
-      "    e[:,nrOm] = bef[mesh.nCx::]\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/home/Gudni/anaconda/lib/python2.7/site-packages/scipy/sparse/linalg/dsolve/linsolve.py:90: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n",
-        "  SparseEfficiencyWarning)\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt\n",
-      "# Plot the solution\n",
-      "z=e[0,:]/(b[0,:]/mu)\n",
-      "app_res = ((1./(8e-7*np.pi**2))/fr)*np.abs(z)**2\n",
-      "app_phs = np.arctan(z.imag/z.real)*(180/np.pi)\n",
-      "ax_res = plt.subplot(2,1,1)\n",
-      "ax_res.loglog(fr,app_res)\n",
-      "ax_phs = plt.subplot(2,1,2)\n",
-      "ax_phs.semilogx(fr,app_phs)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x7fa8671e1fd0>]"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Calculate the impedance\n",
-      "z = e[0,:]/(b[0,:]/mu)\n",
-      "z"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 9,
-       "text": [
-        "array([-5714285.5+0.00050081j], dtype=complex64)"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "app_res"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "array([  4.13555827e+17])"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
     }
    ],
-   "metadata": {}
+   "source": [
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Simple notebook on performing a 1D MT problem.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#from IPython.display import Latex"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Maxwell's equations in 1D are as follows\n",
+    "\n",
+    "\n",
+    "$i \\omega b = - \\partial_z e $ \n",
+    "\n",
+    "$  s =  \\partial_z(\\mu^{-1} b) - \\sigma(z) e$\n",
+    "\n",
+    "$b(0) = 1 \\hspace{1cm} ;\\hspace{1cm} b(-\\infty) = 0$\n",
+    "\n",
+    "\n",
+    "\n",
+    "where $e = \\widehat{\\overrightarrow{E}}_x $ and $b = \\widehat{\\overrightarrow{B}}_y$\n",
+    "\n",
+    "In weak form the equations become\n",
+    "\n",
+    "$i \\omega(b,f) = - (\\partial_ze,f)$ \n",
+    "\n",
+    "$(s,w) = -(\\mu^{-1} b, \\partial_z w) - (\\sigma(z) e, w)$\n",
+    "\n",
+    "\n",
+    "where f and w are abritrary functions living in same discritizational space as b and e, respectivily.\n",
+    "\n",
+    "We consider e on nodes and b on cell centers. This way the derivative of any nodal function becomes\n",
+    "\n",
+    "$ (\\partial_z u)_k \\approx $\n",
+    "\n",
+    "$h_{k}^{-1} ( u_{k+\\frac{1}{2}} - u_{k-\\frac{1}{2}})  + O(h^2) $\n",
+    "\n",
+    "Matrix form\n",
+    "\n",
+    "$ e_z \\approx \\textbf{L}^{-1} \\textbf{G} e = \\begin{bmatrix} h_1^{-1} & & & \\\\\\\\ & h_2^{-1} & & \\\\\\\\ & &  \\ddots & \\\\\\\\ & & & h_n^{-1} \\end{bmatrix}^{(n,n)}\n",
+    "\\begin{bmatrix} -1 & 1 & & & & \\\\\\\\ & -1 & 1 & & & \\\\\\\\ & & \\ddots & \\ddots & \\\\\\\\ & & & -1 & 1 \\end{bmatrix}^{(n,n+1)} \n",
+    "\\begin{bmatrix} e_1 \\\\\\\\ \\\\\\\\ \\vdots \\\\\\\\ \\\\\\\\ e_{n+1} \\end{bmatrix}^{(n+1,1)} $\n",
+    "\n",
+    "where $ \\textbf{L} = diag(h) $ is the cell size and $ \\textbf{G}$ is the gradient operator with -1,1 representing the topology of the mesh, taking the difference between adjoint cells.\n",
+    "\n",
+    "We need to compute 2 inner products, on cell centers and from nodes to cell centers.\n",
+    "\n",
+    "Cell centers inner product is\n",
+    "\n",
+    "$ (b,f) \\approx \\sum\\limits_k h_k \\textbf{b}_k \\textbf{f}_k + O(h^2) $\n",
+    "\n",
+    "and in matrix from\n",
+    "\n",
+    "$ (b,) \\approx \\textbf{b}^T \\textbf{M}^f \\textbf{f}$  and $ (\\mu^{-1} b,f) \\approx \\textbf{b}^T \\textbf{M}_{\\mu}^f \\textbf{f}$ \n",
+    "\n",
+    "where $ \\textbf{M}_{\\mu}^f = diag(\\textbf{h} \\odot \\mu^{-1}) $ and $ \\textbf{M}^f = diag(\\textbf{h}) $ are the matrices.\n",
+    "Nodes to cell centers inner product is\n",
+    "\n",
+    "$ (\\sigma e, w) \\approx \\sum\\limits_k \\frac{h_k \\sigma_k}{4} ( e_{k+\\frac{1}{2}} w_{k+\\frac{1}{2}} + e_{k+\\frac{1}{2}} w_{k+\\frac{1}{2}} )$\n",
+    "\n",
+    "and in matrix from\n",
+    "\n",
+    "$ (\\sigma e, w ) \\approx (\\textbf{h} \\odot \\sigma )^T ( \\textbf{A}_v (\\textbf{e} \\odot \\textbf{w} = \\textbf{w}^T diag(\\textbf{A}_v^T (\\textbf{h} \\odot \\sigma)) \\textbf{e} $\n",
+    "\n",
+    "Here $\\odot$ is a point wise Hadamard product and $\\textbf{A}_v$ is the averaging operator/matrix from nodes to cell centers\n",
+    "\n",
+    "$ \\textbf{A}_v = \\begin{bmatrix} \\frac{1}{2} & \\frac{1}{2} &  & \\\\\\\\ & \\ddots & \\ddots & \\\\\\\\ &  &  \\frac{1}{2} & \\frac{1}{2} \\end{bmatrix} ^{(n+1 , n)} $\n",
+    "\n",
+    "The sigma mass matrix is defined as \n",
+    "\n",
+    "$ \\textbf{M}_{\\sigma}^{e} = diag(\\textbf{A}_v^T (\\textbf{h} \\odot \\sigma)) $\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the MT problem there is no source in the domain, so $ s = 0 $. How ever the boundary conditions provide the right hand side where \n",
+    "\n",
+    "$ (\\partial_z \\mu^{-1} b, w ) = - (\\mu^{-1} b, \\partial_z w ) + (\\mu^{-1} b w )|_0^{end} $\n",
+    "\n",
+    "where \n",
+    "\n",
+    "$ (\\mu^{-1} b w )|_0^{end} = \\textbf{bc}^T (\\textbf{BC w}) $\n",
+    "\n",
+    "here $\\textbf{BC}$ is an matrix operator that extracts the boundary elements from $ \\textbf{w}$ and $\\textbf{bc} $ are the known boundary condintions. For the 1D case with homogenous boundary conditions we have \n",
+    "\n",
+    "$ \\textbf{B} = \\begin{bmatrix} -1 & 0 \\\\\\\\ \\vdots & \\vdots \\\\\\\\ 0 & 1 \\end{bmatrix} $ \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The weak form is \n",
+    "\n",
+    "$ (\\mu^{-1} b, \\partial_z w) + (\\sigma e, w) = (\\mu^{-1} b w )|_0^{end} $\n",
+    "\n",
+    "$ (i \\omega b,f) + (\\partial_z e , f) = 0 $\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the above matrix represntation we get the Maxwells equations in following form\n",
+    "\n",
+    "$ i \\omega \\textbf{f}^T \\textbf{M}^f \\textbf{b} + \\textbf{f}^T \\textbf{M}^f \\textbf{L}^{-1} \\textbf{G} \\textbf{e} = 0 $ \n",
+    "\n",
+    "$ \\textbf{w}^T \\textbf{G}^T \\textbf{L}^{-1}  \\textbf{M}^f_{\\mu} \\textbf{b} + \\textbf{w}^T \\textbf{M}_{\\sigma}^e \\textbf{e} = \\textbf{w}^T \\textbf{bc}^T \\textbf{BC} $ \n",
+    "\n",
+    "Here we use that \n",
+    "\n",
+    "$ (\\textbf{b},\\textbf{f}) \\approx \\textbf{b}^T \\textbf{M}^f \\textbf{f} = \\textbf{f}^T  \\textbf{M}^f \\textbf{b} $ \n",
+    "\n",
+    "since $\\textbf{M}^f$ is a symmetric diaoganal matrix of size n by n and $\\textbf{b}$ and $\\textbf{f}$ are vectors of length n."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We eliminate testing vectors and get system of equations to solve\n",
+    "\n",
+    "$ i \\omega \\textbf{b} + \\textbf{L}^{-1} \\textbf{G} \\textbf{e} = 0 $ \n",
+    "\n",
+    "$ \\textbf{G}^T \\textbf{L}^{-1}  \\textbf{M}^f_{\\mu} \\textbf{b} +  \\textbf{M}_{\\sigma}^e \\textbf{e} = \\textbf{bc}^T \\textbf{BC} $ \n",
+    "\n",
+    "and as $ \\textbf{A} \\textbf{x} = \\textbf{bc} $ system that we will solve, where \n",
+    "\n",
+    "$ \\textbf{A} = \\begin{bmatrix} \\textbf{G}^T \\textbf{L}^{-1}  \\textbf{M}^f_{\\mu}  & \\textbf{M}_{\\sigma}^e  \\\\\\\\ i \\omega  &  \\textbf{L}^{-1} \\textbf{G}  \\end{bmatrix} $\n",
+    "\n",
+    "$ \\textbf{x} = \\begin{bmatrix} \\textbf{b} \\\\\\\\ \\textbf{e} \\end{bmatrix} $\n",
+    "\n",
+    "$ \\textbf{bc} = \\begin{bmatrix} \\textbf{bc}^T \\textbf{BC} \\\\\\\\ \\textbf{0} \\end{bmatrix} $ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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 sys\n",
+    "sys.path.append('C:/GudniWork/Codes/python/simpeg')\n",
+    "import SimPEG as simpeg, numpy as np, scipy, scipy.sparse as sp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have\n",
+    "\n",
+    "$ i \\omega b = - \\partial_z e \\hspace{1cm} ; \\hspace{1cm} \\partial_z(\\mu^{-1} b) - \\sigma(z) e \\hspace{1cm} ;\\hspace{1cm} b(0) = 1 \\hspace{1cm} ;\\hspace{1cm} b(-\\infty) = 0 $\n",
+    "\n",
+    " \n",
+    "To deal with boundary: we assume that below depth L both $ \\sigma $ and $ \\mu $ are constants ($ z < - L $). At the boundary we have that \n",
+    "\n",
+    "$ e = c \\exp(ikz) \\hspace{0.2cm} where \\hspace{0.2cm} k = \\sqrt{i\\omega\\mu\\sigma} $. \n",
+    "\n",
+    "Therefore for $ z < - L $ we have that\n",
+    "\n",
+    "$\\omega b - k e = 0 $.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We discretize the e field on the nodes and b field at the cell centers. The system we want to solve is \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$\\begin{bmatrix} i  \\omega & \\frac{\\partial}{\\partial z} \\\\\\\\ \\frac{1}{\\mu} \\frac{\\partial}{\\partial z} & -\\sigma \\end{bmatrix} \n",
+    "\\begin{bmatrix} b \\\\\\\\ e \\end{bmatrix}  = \\begin{bmatrix} s1 \\\\\\\\ s2 \\end{bmatrix}$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    " # Set up  the problem\n",
+    "mu = 4*np.pi*1e-7\n",
+    "eps0 = 8.85e-12\n",
+    "# Frequency\n",
+    "fr = np.array([1e1]) #np.logspace(0,5,200) #np.array([2000]) #np.logspace(-4,5,82)\n",
+    "omega = 2*np.pi*fr\n",
+    "# Mesh\n",
+    "sig0 = 1e-2\n",
+    "#L = 3*np.sqrt(2/(mu*omega[0]*sig0))\n",
+    "#nn=np.ceil(np.log(0.3*L + 1)/np.log(1.3))\n",
+    "#h = 5*(1.3**(np.arange(nn+1)))\n",
+    "\n",
+    "h = np.ones(18)\n",
+    "x0 = np.array([0])\n",
+    "#sig = sig0*np.ones((len(h),1)) \n",
+    "#sig[0:50] = 0.1\n",
+    "#sig[50:100] = 1\n",
+    "# Make the mesh\n",
+    "mesh = simpeg.Mesh.TensorMesh([h],x0)\n",
+    "sig = np.zeros(mesh.nC) + 1e-8\n",
+    "sig[mesh.vectorCCx<=0] = 1e-2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 10.]), array([ 62.83185307]))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fr,omega"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Make the operators\n",
+    "G = mesh.nodalGrad\n",
+    "Av = mesh.aveN2CC\n",
+    "Li = scipy.sparse.spdiags(1/mesh.hx,0,mesh.nNx,mesh.nNx)\n",
+    "Mmu = scipy.sparse.spdiags(mesh.hx/mu,0,mesh.nCx,mesh.nCx)\n",
+    "Msig = scipy.sparse.spdiags(Av.T.dot(mesh.hx*sig.ravel()),0,mesh.nNx,mesh.nNx)\n",
+    "# The boundaries\n",
+    "bc_b = np.zeros((mesh.nCx,1))\n",
+    "bc_b[0] = -1 # Set the top b field to 1\n",
+    "bc_e = np.zeros((mesh.nNx,1))\n",
+    "# Make the sparse matrix\n",
+    "bc = sp.vstack((bc_b,bc_e))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/Gudni/anaconda/lib/python2.7/site-packages/scipy/sparse/linalg/dsolve/linsolve.py:90: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n",
+      "  SparseEfficiencyWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = np.empty((mesh.nCx,len(omega)),dtype=np.complex64)\n",
+    "e = np.empty((mesh.nNx,len(omega)),dtype=np.complex64)\n",
+    "# Loop all the frequencies\n",
+    "for nrOm, om in enumerate(omega):\n",
+    "    # Left hand side\n",
+    "    A = sp.vstack((sp.hstack(( -G.conj().T.dot(Mmu), - Msig)), sp.hstack((1j*om*scipy.sparse.identity(mesh.nCx) , G))))\n",
+    "    #A = A.tocsr\n",
+    "    # Solve the system\n",
+    "    bef = scipy.sparse.linalg.spsolve(A,bc)\n",
+    "    # Sort the output\n",
+    "    b[:,nrOm] = bef[0:mesh.nCx]\n",
+    "    e[:,nrOm] = bef[mesh.nCx::]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fa8671e1fd0>]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "# Plot the solution\n",
+    "z=e[0,:]/(b[0,:]/mu)\n",
+    "app_res = ((1./(8e-7*np.pi**2))/fr)*np.abs(z)**2\n",
+    "app_phs = np.arctan(z.imag/z.real)*(180/np.pi)\n",
+    "ax_res = plt.subplot(2,1,1)\n",
+    "ax_res.loglog(fr,app_res)\n",
+    "ax_phs = plt.subplot(2,1,2)\n",
+    "ax_phs.semilogx(fr,app_phs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-5714285.5+0.00050081j], dtype=complex64)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate the impedance\n",
+    "z = e[0,:]/(b[0,:]/mu)\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  4.13555827e+17])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "app_res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/MTanayltic1Dtest-Copy1.ipynb b/MTanayltic1Dtest-Copy1.ipynb
new file mode 100644
index 00000000..2e519da7
--- /dev/null
+++ b/MTanayltic1Dtest-Copy1.ipynb
@@ -0,0 +1,925 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import SimPEG as simpeg\n",
+    "import simpegMT as simpegmt\n",
+    "from scipy.constants import mu_0\n",
+    "\n",
+    "def omega(freq):\n",
+    "    \"\"\"Change frequency to angular frequency, omega\"\"\"\n",
+    "    return 2.*np.pi*freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#Define the mesh\n",
+    "z = 100.\n",
+    "hz = [(z,5,-1.5),(z,10),(z,5,1.5)]\n",
+    "M = simpeg.Mesh.TensorMesh([hz],'C')\n",
+    "# sig = np.zeros(M.nC) + 1e-8\n",
+    "conds = [1,1e-2]\n",
+    "elev = 300\n",
+    "sig = np.zeros(M.nC) + conds[1]\n",
+    "sig[np.logical_and(M.gridCC>-200,M.gridCC<0)] = conds[0]\n",
+    "sig[M.gridCC>elev] = 1e-8\n",
+    "sig[M.gridCC<-500] = 1e-1\n",
+    "sig[M.gridCC<-900] = conds[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-2478.125, -1718.75 , -1212.5  ,  -875.   ,  -650.   ,  -500.   ,\n",
+       "        -400.   ,  -300.   ,  -200.   ,  -100.   ,     0.   ,   100.   ,\n",
+       "         200.   ,   300.   ,   400.   ,   500.   ,   650.   ,   875.   ,\n",
+       "        1212.5  ,  1718.75 ,  2478.125])"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M.vectorNx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Calculate the analytic fields\n",
+    "freqs = np.logspace(4,-4,33)\n",
+    "Zana = []\n",
+    "for freq in freqs:\n",
+    "    Ed, Eu, Hd, Hu = simpegmt.Utils.getEHfields(M,sig,freq,np.array([elev]))\n",
+    "    Zana.append((Ed + Eu)/(Hd + Hu))\n",
+    "ZanaArr = np.concatenate(Zana)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the synthetic solution\n",
+    "Zsyn = []    \n",
+    "Qex = M.getInterpolationMat(np.array([elev]),'Ex')\n",
+    "Qfx = M.getInterpolationMat(np.array([elev]),'Fx')\n",
+    "for freq in freqs:\n",
+    "    e = simpegmt.Utils.get1DEfields(M,sig,freq,sourceAmp=None)\n",
+    "    h = -(M.nodalGrad*e)/(1j*omega(freq)*mu_0)\n",
+    "    Zsyn.append((Qfx*e).conj()/(Qex*h).conj())\n",
+    "ZsynArr = np.concatenate(Zsyn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([12]),)"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.where(freqs==10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.000197894493906+0.000181285727378j)\n",
+      "[ 1.00551946+0.00563967j] [ 2776.87342066-2515.31936068j]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print ZsynArr[-1]\n",
+    "print (Qfx*e).conj(), (Qex*h).conj()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "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",
+    "appAna_r, appAna_p = appResPhs(freqs,ZanaArr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  99.9982606   100.03796243   99.63440411   99.33759941  106.29388449\n",
+      "  120.23667968  122.60558712  101.23963102   70.5016044    45.28589223\n",
+      "   28.43953028   17.6198611    10.84432222    6.83742416    4.62137881\n",
+      "    3.61487549    3.53761463    4.30735297    5.99370277    8.77556001\n",
+      "   12.87536967   18.46051811   25.52731592   33.81862185   42.83565417\n",
+      "   51.95659295   60.60094607   68.35309357   75.00153541   80.50894752\n",
+      "   84.9530758    88.47009307   91.21391269]\n",
+      "[ 44.9980325   45.00483104  45.04645859  44.4151918   43.72907936\n",
+      "  46.72033941  54.60169587  63.94626768  71.19189243  75.42083811\n",
+      "  77.37907592  77.73640994  76.058035    71.73541117  64.19535734\n",
+      "  53.36978096  41.16658711  30.81763511  24.08982259  20.77904552\n",
+      "  19.99288987  20.90312313  22.8822938   25.46014498  28.27778379\n",
+      "  31.06840674  33.65137133  35.92447275  37.84909319  39.43106457\n",
+      "  40.70229309  41.70642543  42.48936455]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print appAna_r\n",
+    "print appAna_p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "appSyn_r, appSyn_p = appResPhs(freqs,ZsynArr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 148.47846913  175.57907495  170.44187937  154.82139762  151.64437874\n",
+      "  157.04097616  142.11024881  103.42128503   66.07607566   41.13098488\n",
+      "   26.10536117   16.64437152   10.50362449    6.72485409    4.58294174\n",
+      "    3.60236081    3.53787296    4.31623649    6.00810219    8.793518\n",
+      "   12.8970497    18.48663881   25.55782824   33.85235462   42.87059902\n",
+      "   51.99052864   60.6320452    68.38024378   75.02434148   80.52754625\n",
+      "   84.96791113   88.48173456   91.22293906]\n",
+      "[ 24.90447028  35.50740917  43.45028334  46.88513715  48.17221695\n",
+      "  52.65990261  61.40691455  69.96355574  75.14671736  77.09349558\n",
+      "  77.37322373  76.95784679  75.19022669  71.03384758  63.68398281\n",
+      "  53.00055411  40.91266512  30.67116253  24.02349762  20.75523371\n",
+      "  19.98819197  20.90730695  22.89080611  25.4705669   28.28863141\n",
+      "  31.07873966  33.66064252  35.93243359  37.85570553  39.43641995\n",
+      "  40.70654829  41.709758    42.49194635]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print appSyn_r\n",
+    "print appSyn_p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
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+       "BxOpSAfLeT3wSDCRinSwnOuA64KJVKSD5dwKXB1MpJ8o9vcHIEk+02hyPky4uAYpmpzJ8Dt/yJz7\n",
+       "RPsdGuTVQck3I1005Ywv5YyPZM+3j3LGV9xzBlkEUmXtIeWML+WMj2TPt49yxlfccwZZBFJl7SHl\n",
+       "jC/ljI9kz7ePcsZX/HMmaDb+OSAf2EF4PGtAZPvZwELCs913JiKLcipnquVM9nzKmdo5k/JmMRER\n",
+       "SYykXDZCREQSQ0VARCSDqQiIiGQwFQERkQymIiAiksFUBEREMpiKgIhIBlMREBHJYP8f64VSM4TA\n",
+       "JcIAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc5b9632550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "loglog(freqs,np.abs(ZanaArr),freqs,np.abs(ZsynArr))\n",
+    "gca().invert_xaxis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "VyXNv5+fA/cn1nWlBT/XDOK8jBb6Pc0gxmNpwb/55sZJ8K0p5fyZ08TfiOSvzRDc7Tuk7oW739vi\n",
+       "Ee2owRjdfT4wP5yQGtRYnJcCt4cTUoMai/Nz4KJwQmpQY3HWAOeHE9IOdvr3A5Ann2sqcd5GcFIN\n",
+       "Syox5sPffJNx1kk1f4Yxqyd/riY3rhBiBMWZbYUQZyHECIURZyHECDmIM4zEXwg1fgohRlCc2VYI\n",
+       "cRZCjFAYcRZCjJCDOMNI/IVQ46cQYgTFmW2FEGchxAiFEWchxAi5iDPHV6hnAP8gePD6CuC8xPpR\n",
+       "wDsEV6qvy/WV8kKPUXFGM85CiLFQ4iyEGFsyzry6gUtERHIvr0o2iIhI7inxi4hEjBK/iEjEKPGL\n",
+       "iESMEr+ISMQo8YuIRIwSv4hIxCjxi4hEzP8DY8K8YajT1GUAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc5b8713f90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "loglog(freqs,appAna_r,'bo--',freqs,appSyn_r,'gx:')\n",
+    "gca().invert_xaxis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
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+       "ZrHcmHNJKyu+gpf/qy2DflU9VcGzD3Rhc/8ra+62/4IZdUxGNmDrIRz11+oRMH94AtaNANq1zuDf\n",
+       "ymWpBou7mW0ys5din28HXgc+B5wHzIntNgc4P10hncsPnQpYcRksuDUoxkuugy9/HQqOHi5xEICi\n",
+       "GsidfTowqNVsCu6tbuEPbH0Xf/7GZRr24xel2M/dBy3K2dKvxgyOP/cZHN1ejbqhKqk3cAKwjGCx\n",
+       "3LLYW2VAUUqTOZd3akxVUDUccc5CqBy/0Iz3FVV74Ke8V/wJ8+/rwlk3wPYe0GEzPL60E22vGcJh\n",
+       "FfcBNwNgZbdz/DeP3dvnXjWD49I9PoOjS7y4S+oI/AW42swqpOq+/mB2MMXt35M0rcbLUjMrTS6q\n",
+       "czlu36kKqovxnQAWsR3AME0bV0plIbz0DZgwqPqBo8odq2ze77+393w+AiZvSCoBSlJ5zoSKu6TW\n",
+       "BIX9HjN7KLa5TFJ3M9skqQewOd6xZjYtJUmdy3UJF+NYC/+4P9T7wFFdT8e63BNr9JZWvZYUaeo5\n",
+       "GxwKqaCJPgf40My+V2P7z2LbbpF0A1BoZjfsc6wPhXSukfyBI5eK2plIcf8C8DTwb6Bq5xuB54D7\n",
+       "gcOADcA4s9rL6Xlxd67x4k5GVrTq7mTHw7vck5Hi3qSTe3F3zrlGy8hDTM4553KPF3fnnMtDXtyd\n",
+       "cy4PeXF3zrk85MXdOefykBd355zLQ17cnXMuD3lxd865POTF3Tnn8pAXd+ecy0Ne3J1zLg95cXfO\n",
+       "uTzkxd055/KQF3fnnMtDXtydcy4PeXF3zrk85MXdOefykBd355zLQ17cnXMuD3lxd865POTF3Tnn\n",
+       "8pAXd+ecy0MNFndJd0sqk/RyjW3dJC2QtFrS45IK0xvTOedcYyTScv8dcM4+224AFphZMfBk7HXO\n",
+       "klQSdoaG5EJG8Jyp5jlTK1dypkKDxd3M/gls3WfzecCc2OdzgPNTnCvTSsIOkICSsAMkqCTsAAkq\n",
+       "CTtAgkrCDpCgkrADJKgk7ACZkmyfe5GZlcU+LwOKEjko3m/N+n6TJvNbNhW/mT1n045J5PhcyNnQ\n",
+       "ORt7zTD+zZO5rufMru/NZDX5hqqZGWAJ7l6S4LZE3kvlMYmco77zJnPNZI5J5Bz1nTeZayZzTCLH\n",
+       "13feZK6ZzDENHd/QORt7zcbun+g5GjpvY6/b2P0TPUdD523sdRu7f6LnqO+8yVwzmWOaTEFtbmAn\n",
+       "qTfwDzP7fOz1SqDEzDZJ6gEsMrP+cY5LtOg755yrwczUlONbJXncw8BlwC2xjw/F26mp4ZxzziWn\n",
+       "wZa7pHuBM4EDCfrXfwz8HbgfOAzYAIwzs/K0JnXOOZewhLplnHPO5RZ/QtU55/KQF3fnnMtDGS3u\n",
+       "kjpIel7SmExetzEk9Zc0S9L9kq4IO09dJH1J0p2S7pM0Iuw8dZHUR9JvJT0Qdpb6xL4358S+ppeE\n",
+       "nac+OfQ1zZXv0Zz4mYfG1dCM9rlLigIVwOtm9mjGLpwESS2A+8xsXNhZ6hOb1+dWM7sy7Cz1kfSA\n",
+       "mY0NO0ddJI0HtpjZo5LuM7OLw87UkGz/mlbJoe/RrP+Zb0wNbVLLPd6kYrHt50haKekNST+IbRsB\n",
+       "vAa835RrpjtnbPsXgUeB+7I5Z8x/AndkLmXSOTOukTk/B2yMfb4nC/OFJgU5M/I92pScmfyZTzZn\n",
+       "o2uomSX9BxgCnAC8XGNbS2AN0BtoDbwEHAX8N/BLYD7BuHg15drpyrnPcX/PVMYkvp4ieM7grExm\n",
+       "TPbrCTyQzTmBrwFjYvvcm4X5xsd+fg7J9Nc02ZyZ/h5t6tcztn/af+ab8PVsVA1N9iEmIJhULPb0\n",
+       "ak2nAmvMbAOApPuAL5nZf8ZeXwa8b7G/USY0Jqekg4GvAAXAokxlhMblBIYDZwGdJR1uZr/JxpyS\n",
+       "yoCbgOMl/cDMbsnGnMBM4I5YX+bD2ZbPzG4G7olt60YGv6ZNyDmZDH6PNiHnmWTwZz7ZnAT/A0q4\n",
+       "hjapuNeh5n9vAd4GBla9MLM5+x0Rjrg5zewp4KlwIsVVV85JwO3hRIqrrpxbgG+HEymuunLuAC4P\n",
+       "J1It9f78AGTJ1zSRnDMJfmmGKZGc2fAz32DOKonW0HSMlsmVp6I8Z2p5ztTI9nxVPGdqpTxnOor7\n",
+       "O0DPGq97EvwWyjaeM7U8Z2pke74qnjO1Up4zHcX9BeAISb0ltQEuIkN9mI3kOVPLc6ZGtuer4jlT\n",
+       "K/U5m3jX917gXWAXQX/RN2LbRwGrCO7+3piJO+We03PmWs5sz+c5czunTxzmnHN5yOeWcc65POTF\n",
+       "3Tnn8pAXd+ecy0Ne3J1zLg95cXfOuTzkxd055/KQF3fnnMtDXtydcy4PeXF3zrk89P+s0mbHL+3g\n",
+       "UgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc5b8c06050>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "semilogx(freqs,appAna_p,'bo--',freqs,appSyn_p,'gx:')\n",
+    "gca().invert_xaxis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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/simpegMT/ProblemMT.py b/simpegMT/ProblemMT.py
index b3ca41a1..5540105d 100644
--- a/simpegMT/ProblemMT.py
+++ b/simpegMT/ProblemMT.py
@@ -1,7 +1,7 @@
 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
+import multiprocessing, sys
 
 def omega(freq):
     """Change frequency to angular frequency, omega"""
diff --git a/simpegMT/Tests/test_ApparentResistivityLayerpy b/simpegMT/Tests/test_ApparentResistivityLayerpy
new file mode 100644
index 00000000..8fbd3202
--- /dev/null
+++ b/simpegMT/Tests/test_ApparentResistivityLayerpy
@@ -0,0 +1,48 @@
+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/Utils/MT1Dsolutions.py b/simpegMT/Utils/MT1Dsolutions.py
index 4e07b989..16a45711 100644
--- a/simpegMT/Utils/MT1Dsolutions.py
+++ b/simpegMT/Utils/MT1Dsolutions.py
@@ -21,7 +21,7 @@ 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
+    Etot = (Ed + Eu).conj()
     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