diff --git a/notebooks/Test FDEM Mag Dipole Wholespace.ipynb b/notebooks/Test FDEM Mag Dipole Wholespace.ipynb
new file mode 100644
index 00000000..7c7ac2c2
--- /dev/null
+++ b/notebooks/Test FDEM Mag Dipole Wholespace.ipynb	
@@ -0,0 +1,558 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:0c740be2795f45fed7e9988b98793c1d5c028cb904190367f5ac4b4fa72b1bc6"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "FDEM Inversion for Conductivity"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Forward simulation and inversion for a block (fracture) on a whole space"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from SimPEG import *\n",
+      "import simpegEM as EM\n",
+      "from pymatsolver import MumpsSolver\n",
+      "from scipy.constants import mu_0"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Set up Mesh"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "cs = 5\n",
+      "ncore = 35\n",
+      "ncore_x = 15\n",
+      "pad = 8\n",
+      "padfactor = 1.4\n",
+      "hx = [(cs,pad,-padfactor),(cs,ncore_x),(cs,pad,padfactor)]\n",
+      "h = [(cs,pad,-padfactor),(cs,ncore),(cs,pad,padfactor)]\n",
+      "mesh = Mesh.TensorMesh([hx, h, h],'CCC')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sigwh = 1e-2 # conductivity of the wholespace background\n",
+      "sigma = 1e-2*np.ones(prod(mesh.nC))  # whole space\n",
+      "sigmaB = 1e-2*np.ones(prod(mesh.nC)) # with block \n",
+      "sigmaB[(np.abs(mesh.gridCC[:,0]) < 10.) & (np.abs(mesh.gridCC[:,1]) < 25.) & (np.abs(mesh.gridCC[:,2]) < 25.)] = 1\n",
+      "mesh.plotSlice(sigmaB,grid=True) # todo: fix axes\n",
+      "mesh.plotImage(sigmaB)           # "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/lines.py:503: RuntimeWarning: invalid value encountered in greater_equal\n",
+        "  return np.alltrue(x[1:] - x[0:-1] >= 0)\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 3,
+       "text": [
+        "<matplotlib.collections.QuadMesh at 0x10a2c6790>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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OHDCTJk0yu3fvDsV76/2e7auvvjIZGRmmrq7OdHR0mLy8PFNTU+P1sqL2zjvv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\nLsoAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x109c147d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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b15qdO0excGEM+/ertkrWRKOf33ZcXFYzcmQknTq1Zds2X5Ua1d0ZHj6CjRvP\nEh2dVus26vgJaXV2pqbWrNwTErJJTMzhq6+Os3TpCaZNewWpVLVWdXZKpRIMDGQEBOzi2LF0Tp3K\nZNSoH3nxRRO8ve21pvNhvr4OmJi0YM2aBJX6NNHZp097tmwZyTffnKR37zUMHLiB8vIqdu4chSr/\nRNXZ+OWXx4iMTCY6OpDy8nkcPfq+8hlmQ79pShT3f02klqgZAIsavH9S0kQ6dmyDTCZFLtejtLQC\nqVRCy5Zy7t6tecrVrVsIWVlF9d5+1KjuhIePwNg4mJKSCuV2d/dO7NkzhiVLjiuP2QEoFAv/17m4\nyRsf1revNSdPBtG9eygpKXla0VlzVkW18nqJRIJUKqGqqpp//zuRSZP2aUVnfYYOtWP//ndp3345\nubl3taJz3bq3GDvWGZnsi1r7ZWfPYOnSE6xaFa8VnQ87dux9iorK66yOtaFzy5aRGBsb1Gqztjbi\n2rVpeHpu5MiRwCZvvE8iAUvL1uTm3mXoUDv27vWnd+81JCbmoFAsRCKR8LhR3uwP13h5haOvr0dY\nmA8HDlwmMvI8Cxe6c+9eFcHBJwDIzi5+7O319KT/+/8H37q9ve2JjPRl3rxovv32lFY2Pm6fx53C\n2BSdjo6hta53c7MmLOwthgzZzIULN7Wmsz6urlaUlFSQl9f4F2LV3XnsWDoBAT146SUzLl3KB8DU\ntCXt2hmSlnbrsffzvDvv69atHf37d1TbGV/q7pRIqLUIgQer48Y+29TUY6lQPLidv78TV64UkpiY\n06CmZj/kMzPvIJVKcHa24KOP9nL16i2cnCxYtCiGq1dr/8OfPv1VLly4SWpqAQqFgt6927NsmSe7\ndqUozwbw9XUgPHwES5YcJyLiHBYWrQCoqlI0+gte3Y1BQS4UFpaRnHyTsrJKHB3NWbbMk4SE6yqd\naaHuzgsX8mrdxty85rG8eDGP3Ny7WtP56aevkJ5+i+TkmygUNav4uXNf41//+g9VVY1/oqvuzi1b\nkpg79zXCwnyYMuUgFRVVLFvmSWpqvkovuqu7877x43tz/XoRUVEXG92myc4dO1IIDx/B1Kl9iYq6\nhIlJC5YsGURW1h3i4zPrS3jujZ07m/CXv3QkLi4DIyMDgoJc8PPrzhtvNPx1g2Y/5AFcXCy5d6+K\nS5fyMTY2oHv3Fzh2LL3OfjKZlK+/HoytbVuqqxWkpd3iX//6rdZqfdKk3ujpSViwwL3WDx2kpd3C\nzm6VVjQZoYebAAAgAElEQVRWVlYzd+5r2NmZIJNJyci4w44dF9Ryiqc6O+ujrqOD6uzU05OwZMkg\nOnQwpqKimtTUfKZMOUhYWKJWdZaVVeLpuYlvvx1KTEwgJSUVxMSk4em5iYqK6jr32VSdAC1ayBg7\n1pnvvvsNdR4QVmdnZOR5WrfW55NP3PjiiwGUlFQQF5fJ0KGbuXu3/sN4z7tRKpXw8cd9CAnxRqFQ\n8NtvWQwcuIHY2Iw69/c4zf6Y/PPWmGOJTUF0qpfoVK/m0NkcGoGnHpPXibNrBEEQhPpp5Upey5IE\nQRC0mljJC4Ig/Elp5Quv2nwMrDkdpwPRqS6iU72aQ2dzaIQHnY8jVvKCIAg6TAx5QRAEHSaGvCAI\ngg4TQ14QBEGHiSEvCIKgw8SQFwRB0GFiyAuCIOgwMeQFQRB0mBjygiAIOkwMeUEQBB0mhrwgCIIO\nE0NeEARBh4khLwiCoMPEkBcEQdBhOj3kF/3vf9quOTSC6FSn5vRvszl0Co+n00NeEAThz04r3zRE\nXZrLCkR0qldz6GwOjdB8OoXH06mVfE7ODFxdrQA4evR9Ro92VF73wQc9OXIkgBs3ZnL79mf85z//\nx5gxjo+7KywsWpGdPYOqqgVYWbXWulZ3905UVS2o8/HBBz21phFAKpUwe3Z/UlImU1o6l5ycGYSE\neKutUR2d69a9Ve9jWVk5HzOzllrTCTBqVHdOn/6IoqI55OTM4Mcf36FzZxO1NaqrMyjIhT/+mEBx\n8RzS0qayYIH7c2scMsSOkyc/5MaNmZSUfE5q6if8/e8DkMlqjzt7e1MOHnyX4uI53Lgxk9DQ12nZ\nUv3rXlVbLSxasXnzcM6dm0h5+Tx++eW9Z/r8OrOSt7MzwdBQTmJiNnK5lN6923PixDXl9QMG2LJz\nZwozZx6ioKCU4cO7snHjcCorq9m+PbnWfUkkEB4+gvj4TN5882WtbnVxWU12dpHy8p0797Sqcf36\nt+jb14ZZsw5x5kwORkYG2Nq2VUujujqnTDnArFmHlLeRSCTs2jWK4uJy8vNLtaazX78OhIePYO7c\nI2zdmoSZmSHLlw9h3z5/unUL0ZrOceNcWbnSi/Hj93L8eDpOThasWfMGcrmU+fOjNd54+3YZK1ac\nIinpBkVF5bi6WrFmzRsYGekzbdrPALRqJefw4QDOnMnh1VfXYmZmSFiYD23b+uDvv0PlRnW2GhjI\nyM8vZfnyOPz8HNDTe7a1uc4M+f79OxIfn4VCAX36WJOfX0Jm5h3l9QEBu2rtv2LFKdzdO+Hn173O\n4Jw/352yskpWrDilkSGvzta8vBJu3izRykYPD1tGj3bE2fkHUlLylPsmJd3Qqs6ionKKisqV+9jb\nm9K3rw3vvLNdqzr79GlPYWEZy5bFApCefpvly+PYvXs0rVvrU1xcjqrU0RkY2IP168+wefMfys5l\ny2L58suBfPXVccrKKjXaGB+fRXx8lvJyZuYdPDxscXfvpNzm7++EmZkh/v47lI/b5Mn72bvXnzlz\nDpOeflulRnW2Xrt2m6lTDwI1z+CtrY2eqaHZD/nCwtkoFAoMDGRIpRIKCmYhl+thYKBHQcEsFAow\nM/u63tuamLTkypXCWts8PGwZN84FF5fVODqaa3UrwIkTH2BoKOfy5QJWr05g06Y/tKZx5MhuXLlS\nyJAhdkRFjUFfX4+4uAxmzjxU6x96U3c+avz43uTkFLNrV4pKjeru/PXXKyxdOghfXwd++ikZY2MD\nxo515sSJayoPeHV2Ghjoce9eVa19ysoqMTSU11nJPo/Gl182w8vLjh07Hvx99u/fgZMnM2o9bocO\nXaG6WkG/fh1UHvLqbFVVsx/yzs7fI5FIOHUqiAkT9nHmTA5bt44kIiKJ3bsf/0C9+64TfftaM2XK\nAeU2c/NWbNo0nICAnWp7mq6p1uvXi5g0aR+//36d6moF3t72rFnzJl26mLJwYYxWNNrZmdCxYxve\ne8+JoKA9lJdX8dVXAzlyJABHx+8pL6967P09z86H6evrERjYg9WrE6iuVjS6TxOd58/fxNd3O+Hh\nIwgPH4FMJiU+PpPXX4/Qqs4DBy4zeXIftm8/T1xcJl27tmPatFcAaN/+2VahqjRmZEyjXTtD9PX1\nWLfuDPPmHVFeZ2VlRE5Oca39KyurKSgoxcqq8Y2aaFVVsx/yGRl3cHIyRy7XIyrqIq1b69OzpyU+\nPlvJy6v/MIaPz8usWfMmH364h7Nnc5Xbw8NHsHHjWaKj02rtL5FItK41NbWA1NQC5eXExBz09KTM\nnPkqixcf1YpGqVSCgYGMgIBdysM1o0b9SHb2DLy97VVaKauz82G+vg6YmLRgzZqERrdpqrNPn/Zs\n2TKSZctiiYq6iKlpSxYv9mDnzlEMGLABhQrfk9TZ+eWXx3jhhVZERwcilUooLCxj1ap4/v73ASp9\n43zWxv79wzA0lOPqasWyZZ6sXOmlPOyhUOXBes6tqmrWQz4paSIdO7ZBJpMil+tx+/ZnysFy5coU\nALp1CyEr68ELk6NGdWfdurcYNy6KiIhzte5v4MAXcXfvxN/+1g94MNzT0qby738nMmnSPq1prU98\nfCatWunzwguGWtGYnV2MQqGodTw+L6+EvLwSOnZs06hGTXQ+bMKEXvz883+5dk31Y7Lq7pw+/VVO\nnLjGkiXHldvefXcH165Nw8PDts7ipKk6KyqqmTRpH5Mn78PSsjW5uXcZOtQOgP/+t4DGaEzj/b/D\nlJQ8qqqqCQ8fwZw5hykpqSA7u5gOHYxrfQ6ZTIqpactaJzJoQ6uqnjrkMzIyCAgI4MaNG0gkEj76\n6COmTJlCQUEBo0aNIj09HVtbWyIjI2nbtuasiaVLlxIWFoaenh6rVq1iyJAhACQkJPD+++9TVlaG\nt7c3K1euVCneyyscfX09wsJ8OHDgMpGR51m40J1796oIDj4B1Aya+8aNc2XVKi8CAnbx44/Jde7P\n0TG01mU3N2vCwt5iyJDNXLhwU6ta6+PqakVJScVjV17Pu/HYsXQCAnrw0ktmXLqUD4CpaUvatTMk\nLe1Woxo10Xlft27t6N+/I8OHb2t0myY7JRKoqqqute3+yliVZ5uaejwVige38/d34sqVQhITc55L\n46Pun5Gip1fzOMXGZrBypVetF6wHD+6MVCohNjajUY2aan3Usz4Jeeq5OHK5nBUrVnD+/HlOnTpF\nSEgIFy5cIDg4mMGDB3Pp0iUGDRpEcHAwAMnJyWzbto3k5GQOHjzIpEmTlE+NJk6cyNq1a0lNTSU1\nNZWDB1V7OpKZeYe0tFs4O1uwc2cKV6/ewsnJgr17L3H16i2uXr2l/CL49NNXCA31ZurUgxw/no6F\nRSssLFphYtJCeX8XLuTV+rg/iC5ezCM3965WtX766SsMH96Vl18246WXzPjkEzfmzn2NkJD/UFXV\nuKei6m7csiWJq1cLCQvzwdXVCicnczZvHk5qaj4HDqRqzWN53/jxvbl+vYioqIuNbtNk544dKQwb\nZs/UqX3p3NmEXr2sWL/+bbKy7hAfn6k1nZ07mxAQ0AN7e1NcXa0ICfHGz6+7Ss+En6Vx+vRXGTas\nC126mGJnZ8KoUd1ZtsyTXbtSlGdRRUScIy+vhIiIETg5mePhYUtIiDdbtyap/CxO3a0APXpY0KOH\nBaamLTEy0sfZueZyQzx1JW9paYmlpSUArVu3plu3bmRlZbFnzx6OHq059hsYGIiHhwfBwcHs3r2b\nMWPGIJfLsbW1pUuXLsTHx9OpUyeKiopwc3MDICAggF27duHl5fVsj+AjXFwsuXevikuX8jE2NqB7\n9xc4diy9zn5TprghlUr44Yc3+OGHN5TbY2LSGDRo42PvX53H7tTZqqcnYcmSQXToYExFRTWpqflM\nmXKQsLBErWksK6vE03MT3347lJiYQEpKKoiJScPTcxMVFdV17rOpOgFatJAxdqwz3333m0rHtjXZ\nGRl5ntat9fnkEze++GIAJSUVxMVlMnToZu7eVe1pvTo7pVIJH3/ch5AQbxQKBb/9lsXAgRtUXiE3\ntFEmk/L114OxtW1LdbWCtLRb/Otfv/Htt6eU+5SUVODpuZHvvhtGXFwQpaWVbN+ezPTpP6vUqIlW\ngNOnxyv/W6FQkJg4HoVCgUz2xVNbJIpnmGJpaWm4u7uTlJREx44dKSwsVH5SU1NTCgsL+eSTT3jl\nlVd49913ARg3bhzDhg3D1taWzz77jEOHan7o5Pjx43z99ddERUXVDpJIQIt/lFqhWAiARLK4iUue\nTHSql+hUr+bQ2RwaoaZTIpE8dkHa4B+dKi4uZuTIkaxcuRIjo9qnGEkkErWdgSIIgiCoT4POrqmo\nqGDkyJGMHTuWt99+GwALCwtycnKwtLQkOzsbc/OaHxyytrYmI+PB07LMzExsbGywtrYmMzOz1nZr\na+t6P9/ChQ++I3l4eODh4fHMfzBNu/9dXtuJTvUSnerVHDq1sTEmJoaYmBgAFi1a9MR9n3q4RqFQ\nEBgYiJmZGStWrFBunzVrFmZmZsyePZvg4GBu3bpFcHAwycnJ+Pv789tvv5GVlYWnpyeXL19GIpHQ\nt29fVq1ahZubG6+//jpTpkypc0z+SU87BEEQhLqeNDefOuRPnDjBX//6V5ydnZWHZJYuXYqbmxt+\nfn5cu3atzimUS5YsISwsDJlMxsqVKxk6dCjw4BTK0tJSvL29WbVqVb2x4pi86kSneolO9WoOnc2h\nEZ5+TP6ph2v+8pe/UF1d/5kQv/76a73bP//8cz7//PM623v16sW5c0//oR5BEARBPXTq98kLgiAI\ntYkhLwiCoMPEkBcEQdBhYsgLgiDoMDHkBUEQdJgY8oIgCDpMDHlBEAQdJoa8IAiCDhNDXhAEQYeJ\nIS8IgqDDxJAXBEHQYWLIC4Ig6DAx5AWhCSz63/+0nehUr6ZoFENeEARBh4khLwiCoMMa9PZ/giCo\nV3M4tACiU93E4RoV5eTMwNXVCoCjR99n9GhH5XUODi8QGenLxYsfU1k5nzVr3qz3PqRSCbNn9ycl\nZTKlpXPJyZlBSIi31rWuW/cWVVUL6nxUVs7HzKylVjQCjBrVndOnP6KoaA45OTP48cd36NzZRC19\n6uwMCnLhjz8mUFw8h7S0qSxY4K7Wxqd1fvBBT44cCeDGjZncvv0Z//nP/zFmjGOd+7C3N+XgwXcp\nLp7DjRszCQ19nZYt1btWU7XTwqIVmzcP59y5iZSXz+OXX95Ta5+6On18XmbfPn+uX59OcfEczp2b\nyCefuGldZ8+elkRHB5KdPYPS0rmkpU1l1aphGBsbNOjz68xK3s7OBENDOYmJ2cjlUnr3bs+JE9eU\n17dsKSMt7Ta7d19k+vRXH/tWWevXv0XfvjbMmnWIM2dyMDIywNa2rda1TplygFmzDikvSyQSdu0a\nRXFxOfn5pVrR2K9fB8LDRzB37hG2bk3CzMyQ5cuHsG+fP926hajcqK7OceNcWbnSi/Hj93L8eDpO\nThasWfMGcrmU+fOjn0vngAG27NyZwsyZhygoKGX48K5s3Dicyspqtm9PBqBVKzmHDwdw5kwOr766\nFjMzQ8LCfGjb1gd//x1a02lgICM/v5Tly+Pw83NAT0/9a0l1dLq7dyI2NoPFi4+Sm1uMu7stoaHe\ntGgh45tvTmpNZ1lZJWFhiSQm5lBYWErXru0ICfGmQwdjhg/f9tQGnRny/ft3JD4+C4UC+vSxJj+/\nhMzMO8rrExKySUjIBmpWbfXx8LBl9GhHnJ1/ICUlT7k9KemG1rUWFZVTVFSuvGxvb0rfvja88852\nrWns06c9hYVlLFsWC0B6+m2WL49j9+7RtG6tT3Fxeb23e96dgYE9WL/+DJs3/6HsXLYsli+/HMhX\nXx2nrKxS450BAbtq7b9ixSnc3Tvh59dd+cXu7++EmZkh/v47lI/d5Mn72bvXnzlzDpOeflsrOq9d\nu83UqQeBmkFqbW2kcpcmOmfM+KXWPhs3nsXV1Qo/v+5qG/Lq6ExJyas1j7KyiggN/Z2FCxv2bLPZ\nD/nCwtkoFAoMDGRIpRIKCmYhl+thYKBHQcEsFAowM/u6Qfc1cmQ3rlwpZMgQO6KixqCvr0dcXAYz\nZx6q9RejDa2PGj++Nzk5xezalaI1jb/+eoWlSwfh6+vATz8lY2xswNixzpw4cU3lAa/OTgMDPe7d\nq6q1raysEkNDeZ2V1/PsNDFpyZUrhcrL/ft34OTJjFqP3aFDV6iuVtCvXweVhrw6OzVJ050mJi3U\nsvjQZKeNjTG+vt04cCC1QS3Nfsg7O3+PRCLh1KkgJkzYx5kzOWzdOpKIiCR27362gWdnZ0LHjm14\n7z0ngoL2UF5exVdfDeTIkQAcHb+nvLzq6XfynFofpq+vR2BgD1avTqC6uv7DUE3ReP78TXx9txMe\nPoLw8BHIZFLi4zN5/fUIlRrV3XngwGUmT+7D9u3niYvLpGvXdkyb9goA7durtgptbOe77zrRt681\nU6YcUG6zsjIiJ6e41n6VldUUFJRiZaU9nZqkyU53906MHu3YoEMgTdEZG/shPXta0qKFjJ9/vkxQ\n0J4GtTT7F14zMu7Qpo0BcrkeUVEXKSwspWdPS7ZuTSIj4w4ZGQ1fgUulEgwMZAQE7OLYsXROncpk\n1KgfefFFE7y97bWq9WG+vg6YmLRgzZoErWrs06c9W7aM5JtvTtK79xoGDtxAeXkVO3eOQiLRns4v\nvzxGZGQy0dGBlJfP4+jR99m0qebQjarfNBvT6ePzMmvWvMmHH+7h7Nlc5fbHvY6kDurs1CRNdfbt\na83OnaNYuDCG/fsbtkJ+3p1+fttxcVnNyJGRdOrUlm3bfBvU0qxX8klJE+nYsQ0ymRS5XI/btz9T\nDuorV6YA0K1bCFlZRQ26v+zsYhQKRa3jX3l5JeTlldCxYxutan3YhAm9+Pnn/3LtmmrHZNXdOH36\nq5w4cY0lS44rt7377g6uXZuGh4ct0dFpWtFZUVHNpEn7mDx5H5aWrcnNvcvQoXYA/Pe/BY1qbGzn\nqFHdWbfuLcaNiyIi4lyt+8vOLqZDB+Na22QyKaamLcnOfvZ/N5rq1BRNdbq7d2LPnjEsWXJc+fqR\nNnbe3//SpXyys4s4eTKIrl3bPbWnWQ95L69w9PX1CAvz4cCBy0RGnmfhQnfu3asiOPgEUPOF0VDH\njqUTENCDl14y49KlfABMTVvSrp0haWm3tKr1vm7d2tG/f0e1PMVUd6NEAlVV1bW23V8ZS1RYymvq\nsVQoHtzO39+JK1cKSUzMeW6d48a5smqVFwEBu/jxx+Q69xcbm8HKlV61XrQePLgzUqmE2NgMrel8\nlLqegGii09vbnshIX+bNi+bbb09pbeej7p+xJJM9/WBMsz5ck5l5h7S0Wzg7W7BzZwpXr97CycmC\nvXsvcfXqLa5evaUcKjKZlB49LOjRwwIjIwPMzFrSo4cF3bo9+E64ZUsSV68WEhbmg6urFU5O5mze\nPJzU1PwGv8jxvFrvGz++N9evFxEVdVGlPk007tiRwrBh9kyd2pfOnU3o1cuK9evfJivrDvHxmVrT\n2bmzCQEBPbC3N8XV1YqQEG/8/LozadK+Rjc+a+enn75CaKg3U6ce5PjxdCwsWmFh0QoTkxbK+4uI\nOEdeXgkRESNwcjLHw8OWkBBvtm5NUulZnLo7AeVjbmraEiMjfZyday6rQt2dvr4O7Nw5im++OUlE\nxDnlPu3aGWpVZ1CQCyNGdKNr13bY2rbljTde4v/9vzdJSLjeoDP/mvVKHsDFxZJ796q4dCkfY2MD\nund/gWPH0uvsZ21txOnT44GaY5uurlYMH96NtLRb2NmtAmrOqPD03MS33w4lJiaQkpIKYmLS8PTc\nREVFdZ37bMpWgBYtZIwd68x33/2mttWSOhsjI8/TurU+n3zixhdfDKCkpIK4uEyGDt3M3bsVWtMp\nlUr4+OM+hIR4o1Ao+O23LAYO3KDS6vhZO6dMcUMqlfDDD2/www9vKLfHxKQxaNBGAEpKKvD03Mh3\n3w0jLi6I0tJKtm9PZvr0n7WqE1A+5lDzuCcmjkehUCCTfaE1nZMm9UZPT8KCBe61fvjt0a+zpu6s\nrKxm7tzXsLMzQSaTkpFxhx07LjT4NE+JQpOv5jRCzdP4RU2d8VgKxUIAJJLFTVzyZKJTvUSnejWH\nzubQCDWdEonksS/MN+vDNYIgCMKTaeVKXsuSBEEQtJpYyQuCIPxJaeULr9p8DKw5HacD0akuolO9\nmkNnc2iEB52PI1bygiAIOkwMeUEQBB0mhrwgCIIOE0NeEARBh4khLwiCoMPEkBcEQdBhYsgLgiDo\nMDHkBUEQdJgY8oIgCDrsqUP+ww8/xMLCAicnJ+W2goICBg8ezEsvvcSQIUO4devBG2osXboUe3t7\nunbtyi+/PHg39ISEBJycnLC3t2fq1Klq/mMIgiAI9XnqkP/ggw84ePBgrW3BwcEMHjyYS5cuMWjQ\nIIKDgwFITk5m27ZtJCcnc/DgQSZNmqT8pTkTJ05k7dq1pKamkpqaWuc+BUEQBPV76pB/7bXXMDEx\nqbVtz549BAYGAhAYGMiuXbsA2L17N2PGjEEul2Nra0uXLl2Ij48nOzuboqIi3NzcAAgICFDeRhAE\nQdCcRh2Tz83NxcKi5q28LCwsyM2teWfx69evY2Njo9zPxsaGrKysOtutra3JyspSpVsQBEFoAJV/\nC6VEIlHpTZnrF/3Qf9sCL6r5/gVBEJqzq0AaAIsWPfn9Nxq1krewsCAnp+Zd7LOzszE3NwdqVugZ\nGQ/eFzMzMxMbGxusra3JzMystd3a2voJn2HAQx9iwAuCINT2Ivdn5KJFi564Z6OGvI+PDxs2bABg\nw4YNvP3228rtW7dupby8nKtXr5KamoqbmxuWlpYYGxsTHx+PQqFg06ZNytsIgiAImvPUIT9mzBj6\n9evHxYsX6dChA+vWreOzzz7j0KFDvPTSSxw5coTPPvsMAAcHB/z8/HBwcGDYsGGEhoYqD+WEhoYy\nbtw47O3t6dKlC15eXpr9kz1BTs4MXF2tADh69H1Gj3asdf3HH7tx/vwkiovnkJU1nXXr3uKFFwy1\nplFPT8Lf/taPCxcmU1LyORcvfszEib2fa4ODwwtERvpy8eLHVFbOZ82aN+u9D3t7Uw4efJfi4jnc\nuDGT0NDXadlSve9Vo2qnhUUrNm8ezrlzEykvn8cvv7yn1j51dfr4vMy+ff5cvz6d4uI5nDs3kU8+\ncdO6zp49LYmODiQ7ewalpXNJS5vKqlXDMDY20JrGh1lYtCI7ewZVVQuwsmqttkZ1tbq7d6KqakGd\njw8+6Nmgz//Ur7YtW7bUu/3XX3+td/vnn3/O559/Xmd7r169OHfuXIOiNMnOzgRDQzmJidnI5VJ6\n927PiRPXlNePHu3I8uVDmDBhL7/+eoUOHdrwww+vs3HjcIYNC9eKxsWLB/B//+fK//1fFGfP5tCv\nXwfWrHmT8vIq1q5NfC4NLVvKSEu7ze7dF5k+/dV631+yVSs5hw8HcOZMDq++uhYzM0PCwnxo29YH\nf/8dWtNpYCAjP7+U5cvj8PNzQE9P/T8jqI5Od/dOxMZmsHjxUXJzi3F3tyU01JsWLWR8881Jreks\nK6skLCyRxMQcCgtL6dq1HSEh3nToYMzw4du0ovE+iQTCw0cQH5/Jm2++rHKbJltdXFaTnV2kvHzn\nzr0GNWjl2/9pUv/+HYmPz0KhgD59rMnPLyEz847y+r59rfnjj1zWrTsDQEbGHdasOc3ixR5a0xgY\n2IN//OMke/ZcBCA9/TZubtbMnfua2ob80xoSErJJSMgGICjIpd778Pd3wszMEH//HRQXlwMwefJ+\n9u71Z86cw6Sn39aKzmvXbjN1as3Pbbi7d8La2kjlLk10zpjxS63LGzeexdXVCj+/7mob8uroTEnJ\nIyUlT3k5K6uI0NDfWbjQXWsa75s/352yskpWrDilkSGvzta8vBJu3ix55oY/zZAvLJyNQqHAwECG\nVCqhoGAWcrkeBgZ6FBTMQqEAM7OvOXDgMkFBLvz1r504diwdC4tWvPOOA3v3XtKaRgMDPe7dq6p1\n27KySjp1aouNjXGtf0SaamiI/v07cPJkhnLAAxw6dIXqagX9+nVQacirs1OTNN1pYtKi1uOrjZ02\nNsb4+nbjwIFUrWr08LBl3DgXXFxW4+horlKbplsBTpz4AENDOZcvF7B6dQKbNv3RoNv9aYa8s/P3\nSCQSTp0KYsKEfZw5k8PWrSOJiEhi9+4U5X6//PJfPv30Z37++T2kUgkymZS9ey8xbtwerWk8cOAy\nU6a4cfjwFc6fv4mbmzUffuiCQqGgfXsjlYZ8QxsawsrKiJyc4lrbKiurKSgoxcpKtdWyOjs1SZOd\n7u6dGD3aUS2HQDTRGRv7IT17WtKihYyff75MUJBqX0PqbDQ3b8WmTcMJCNhJfn6pSl2abr1+vYhJ\nk/bx++/Xqa5W4O1tz5o1b9KliykLF8Y89fZ/miGfkXEHJydz5HI9oqIu0rq1Pj17WuLjs5W8vAdP\ngd588yVWrBjKtGk/c/x4OjY2xnzzzWDCwt5i7NidWtE4depBfvjhdc6cmYBCoSArq4h///s0n332\nF6qrn3zOrLoaGuJJxxdVpc5OTdJUZ9++1uzcOYqFC2PYv1+1FbKmOv38ttOqlT4ODi/w1VcD2bbN\nl7ffbvw3JHU2hoePYOPGs0RHp9Xarq6f+VFna2pqAampBcrLiYk56OlJmTnzVRYvPvrU2/8phnxS\n0kQ6dmyDTCZFLtfj9u3PkEolGBjIuHJlCgDduoWQlVXE55+/xubNf/DDD78DcP78TYqLyzl27AMW\nLIh+0qd5bo23bpUxevRP6OntwNy8FdnZxcqza65cKXwuDQ2RnV1Mhw7GtbbJZFJMTVvWegGpqTs1\nRVOd7u6d2LNnDEuWHGfZslit7by//6VL+WRnF3HyZBBdu7ardby+qRoHDnwRd/dO/O1v/YAHwz0t\nba7/yroAABKjSURBVCr//ncikybte+ZGTbXWJz4+k1at9Bt01t+fYsh7eYWjr69HWJgPBw5cJjLy\nPAsXunPvXhXBwSeAmoEENa+2V1XVXoHeXx2r/yd7G9d4X1WVQrltzBhHjh5No6Cg8U89G9PwJLGx\nGaxc6UXr1vrK48aDB3dGKpUQG5vxlFs/v85HqesJiCY6vb3tiYz0Zd68aL799pTWdj7q/hlLMlnj\nzlxSd6OjY2ity25u1oSFvcWQIZu5cOFmoxo11VofV1crSkoqGvSs4E8x5DMz7yCVSnB2tuCjj/Zy\n9eotnJwsWLQohqtXb9Xad8eOFBYs+Cv/+U8Wx49fw8bGmG+/HcrZszkqrZLV2dirlxUvvmjC6dPZ\nmJu3YsaMV3F2tuAvf1n33BpkMindu78AgJGRAWZmLenRw4Ly8iouXKhZqUVEnGP+/L8SETGCuXOP\nYGZmSEiIN1u3JnHtWuNfdFV3J0CPHjW/i8nUtCVGRvo4O1sgkcDZs7la0+nr60B4+AiWLDlORMQ5\nLCxaATXf7FU59KPuzqAgFwoLy0hOvklZWSWOjuYsW+ZJQsJ1kpJuaEXjw3/3UHOMHuDixTxyc+82\nqlFTrZ9++grp6bdITr6JQgFDh9oxd+5r/Otf/6mzIK3Pn2LIA7i4WHLvXhWXLuVjbGxA9+4vcOxY\nep39vv46FoVCwZw5f+H779tw61YZR45cZc6cw1rTaGAgY8GCv2JnZ0p5eRVHj6bRr18YycmqrUCe\npcHa2ojTp8cDNcfeXV2tGD68G2lpt7CzWwVASUkFnp4b+e67YcTFBVFaWsn27clMn/7z/2/v3KOi\nKvc+/t3DDBwNMvIy4GCRw0Wuw5BA5fI1825icbQCT+gbYOU55aUyskzNd6ViqUeTMl3peet0edVV\nQgZY3oJMQRHrlJfsMFMwDpogCiK3md/7B8dBkFEGh7234++z1l46M3vP/vjzme/seZ5n7y0rTwC2\ndS6vV1LyDIgISuX/yMbzr38dDDc3AQsWDMOCBa3TEdv/W6T2bG624rXXhkKr9YZSqUBZ2QV8/vmx\nG57m6ez/8/Y4cwzJma5ubgKWLBmBAQNuR1OTFSdPVmLmzDxs3Ni56dICdefoWBdo6RJZJLWGXYgW\nAgAE4Q2JTa4NezoX9nQuN4PnzeAItHgKgmD3S4pv/8cwDOPCcMgzDMO4MLLsrpGZEsMwjKzh7hqG\nYZhbFFnOrpHzQMfNNBgDsKezYE/ncjN43gyOQKunPfhInmEYxoXhkGcYhnFhOOQZhmFcGA55hmEY\nF4ZDnmEYxoXhkGcYhnFhOOQZhmFcGA55hmEYF4ZDnmEYxoXhkGcYhnFhOOQZhmFcGA55hmEYF4ZD\nnmEYxoXhkGcYhnFhOOQZhmFcGA55hmEYF+aWC/mKihcRHe0LAPj22/9GYmJ4m9djYzXYty8FdXWv\nwmR6AW+++RAEQQrTa7uGhvbF5s2TceLEc2hufh3r18dLI4lrez71VBR2756KM2dewvnzr+DgwelI\nSgq391aSOI4ercX336fgzJmXUFf3Kk6efB6LFw+HUin+x+N67fMyISF9UFs7D42N88XUs3Etz2nT\ndLBYFly1DB/uLytPAOjRQ4mlS0egtHQm6utfQ1nZHMyf/1+y8tyzZ1qH9aypmdep95blnaG6C63W\nGz17qlBSYoZKpcDgwf3x3Xe/217387sd33yTjC1bjiI1NRtBQb2xceNECIKAV1/dJSvXHj2UMBrP\nIyvrBF544X7J7ot7Pc/hw/3xxRfH8dJL36Cq6hISEgbhww8T0NxsxZYtR2XheP58PVatOoCffjqD\nmppGREf7Yv36CfDycsecOTtEceyM52V69FBi8+bHsGuXAWPHBojm54inxWJF//4r2xwgnTtXLytP\nhULAV19NgaenO55+ejtOnDiL3r17ok+fnrLyTEj4P6hUrQccCoWAgwenIy/v3516/1sq5IcMuQuF\nhSYQATExGlRW1qG8/ILt9RkzBqO6uh5padkAgOPHz+L11/dg+fJRWLz4W9TXN8vGtbjYjOJiMwAg\nNVUvmld7ruc5deq2NuuvWnUAw4bdjccfDxMt5K/nWFhoQmGhyfa4vPwCHnzQH8OG3S2KX2c9L5OZ\nOR75+b+hsNCEcePED/nOep49Wye625Vcv23qEB3tC612DSorLwEAysqu/ndI7Vld3fbLceTIgdBo\nbse6dYc69f63RMifO5cOIoKHhxIKhYCqqpehUrnBw8MNVVUvgwjo3Xs5hgwZgK+/bvvtuGPHv7F2\n7Xjo9T7Yv79cNq5ScyOe3t49UFp6TraOwcG9MXasFp9/frzbHR31TE6OxL339kdMzAbRu70c8XRz\nU+DXX59Hjx4qnDhxFm+/vR85OSdl5TlpUgiKikyYM+d+JCdHoqnJgl27DHjllZ2i/Oroavt89tl7\ncfiwGYcPmzu1n1si5CMj34MgCDhwIBXPPvsVjhypwGefTcInn/yErKzWD7KPjycKCtr+7KyoqAUA\n+Pp6ycpVarrq+Ze/RCAuToOZM3Nl51hWNgd9+vSEu7sbNm06gvnzd3e7oyOegwb1wdtvj8aDD/4D\njY0WUdy64nn8+Fk89VQWfvihAh4eSjz+eBi+/DIJaWnZ2LTpiGw8tVpv+PvfAYuFMHnyZnh6umPV\nqjHYti0Rw4b9QzaeV+Lj44n4+GD87W85nd7PLRHyZWUXEBHRDyqVG7788gQ8Pd0RFeWDiRM/k/wn\nZXtuFteueE6cGIz16+ORkpKNH344LTvHIUM2omdPFaKjfZGRMRKrV4/FrFl5svB0d3fDli2PYf78\n3Th27Gy3O3XVE7i6+6uoyIQ77/wT0tOHiBLynfVUKFoGDBITt+L8+QYAQEpKNg4enA6dTi0bzytJ\nSdHj0qUmfPLJvzq9H5cP+Z9+moG77uoFpVIBlcoN58+/AoVCgIeHEqWlMwEAISGZMJlqYDbXXnXE\nrlbfBgAwm2tk5SolXfF84okwbNr0CNLSvnSogYrp+Pvv5wG0HIlaLFZ8/PGfMW/eLtTVNUnuqVQq\nEBraF5mZ45GZOR4AIAgCFAoBjY3z8frre5CRsU9yT3tts7DQhClTIrrNryueZnPtf9ZpsG1/9Ogf\nAIC7775DNp6XEQRg+vRofPzxvxxqky4f8mPHfgx3dzds3DgRubm/YvPmn7Fw4TA0NFiwbNl3AACz\nuaVLZt++MiQnR7bbPgAXLzaipKRCVq5S4qhnWlo01qwZi6lTt2HrVnEGW2+0lm5uiv/82b3zZzvr\nKQhAePi7bbZ99NFBeOONB6HTrcOZMxdl4WmP6Ghf25eoXDzz83/Dyy8PgZeXO2pqGgG0jMcAgNFY\nLRvP1m0CcNddvfD++8UO7cvl58mXl1+A0ViNyEg1vvjiOAyGakREqLF9+y8wGKphMFTDam2Zfvje\newfRq5cHNmyIR2hoX8THB2Hx4uF4550iUWbWOOKqVCqg06mh06nh5eWB3r17QKdTIySkj6w8Z8++\nD+++Ox6zZuWhoOA3qNW3Qa2+Dd7ef5KN4wsv3I9x4wIQEHAntFpvPPFEGDIyRmLbtuO2D7/UnhYL\n4dixs22WU6dajvKOHTtrmx0itScALFw4DGPHBkCr9UZoaF8sWDAMKSl6rFx5oFsdHfV8992DqKtr\nwocfJiA0tC9iYvpjw4Z47N1rxI8/dm93oiOel3nmmXtRVGRy2E30I/m8vDzMnj0bFosFaWlpSE9P\n7/Z96vU+aGiw4JdfKnH77R4IC+uL/PzfrlrPZKrB6NH/xMqVo3Ho0HRUV9fj/feLRRuAc8RVo/HC\n4cPPAACICNHRvkhICIHRWA2tdo1sPGfOjIVCIWDduglYt26C7fm9e40YMeJDWTgqlQosXz4K/v53\nwGolGI3VWLu2CH//e/eHkiOeHSHm6RGd9fTy8kBm5nj4+Hji0qUmHDt2Fo89tgXbtokzcaCznqdP\nX8RDD/0vVq4cg4MHp6Oq6hK++uok0tO/kZUnAPTv74Xx4wPx9NPbHd6PQCKeRWOxWBAcHIydO3dC\no9EgJiYGn376KUJCQlqFBAHAIrGUOokBwD0AAKKFAABBeENCn8u0erVHOk/7Th0hnqdjXu3pPs8b\n82qPczyd69QRXfPsfq8r6ZyjuE4dQbQQgiDYPSFS1O6aoqIiBAQEwN/fHyqVComJicjKyhJToYsY\npRawg1FqgQ4wSi1gB6PUAnYwSi3QAUapBexglFqgA4xSC1wXUUPeZDJhwIABtsd+fn4wmUzX2IJh\nGIa5IUhEtm7dSmlpabbHH330ET333HNt1hFZqVMsXLhQaoUOkaOXHJ2I2MsR5OhEJE8vuThdKzdF\nTdT9+/fTmDFjbI+XLFlCy5Yta7OOVqslALzwwgsvvHRy0el0dnNX1IHX5uZmBAcHY9euXejfvz9i\nY2OvGnhlGIZhnIeoUyiVSiXWrl2LMWPGwGKxIDU1lQOeYRimGxH1SJ5hGIYRF9mc8ZqXl4dBgwYh\nMDAQGRkZou7b398fkZGR0Ov1iI2NBQBUVVVh1KhRCAoKwujRo1Fd3Xqa89KlSxEYGIhBgwbh66+/\ndppHSkoK1Go1IiJar/HRFY/i4mJEREQgMDAQs2bNcrrTokWL4OfnB71eD71ej9zc1itKiuEEAGVl\nZRg+fDjCwsIQHh6ONWtaTgCTul72vKSsWX19PeLi4hAVFYXQ0FDMm9dyRyGpa2XPSw7ty2KxQK/X\nIz6+5Y5rUtfqhuiWEVYHaW5uJq1WSwaDgRobG0mn09HRo0dF27+/vz9VVla2eW7u3LmUkZFBRETL\nli2j9PR0IiL6+eefSafTUWNjIxkMBtJqtWSxWJzikZ+fT4cPH6bw8PAueVitViIiiomJocLCQiIi\nGjduHOXm5jrVadGiRbRixYqr1hXLiYjIbDZTSUkJERHV1NRQUFAQHT16VPJ62fOSumYXL14kIqKm\npiaKi4ujgoICyWtlz0vqWhERrVixgqZMmULx8fFEJP3n8EaQxZG8HE6Sona9VtnZ2Zg2bRoAYNq0\nadi2reUOR1lZWUhKSoJKpYK/vz8CAgJQVFTkFIehQ4fC29u7yx6FhYUwm82oqamx/SKZOnWqbRtn\nOQFX10tMJwDw8fFBVFQUAMDT0xMhISEwmUyS18ueFyBtzXr2bLmlXWNjIywWC7y9vSWvlT0vQNpa\nlZeXIycnB2lpaTYPOdSqq8gi5KU+SUoQBIwcORKDBw/Ghg0bAACnT5+GWt1yTWm1Wo3Tp1suCnTq\n1Cn4+fmJ5uqoR/vnNRpNt/i988470Ol0SE1Ntf10lcrJaDSipKQEcXFxsqrXZa/77rsPgLQ1s1qt\niIqKglqttnUnyaFWHXkB0tZqzpw5eOutt6BQtMajHGrVVWQR8sKVd/uVgH379qGkpAS5ubnIzMxE\nQUFBm9cFQbimo1j+1/MQixkzZsBgMODIkSPw9fXFiy++KJlLbW0tJk2ahNWrV8PLq+29AKSsV21t\nLSZPnozVq1fD09NT8popFAocOXIE5eXlyM/Px549e9q8LlWt2nvt3btX0lpt374d/fr1g16vt3st\nGLl8DjuLLEJeo9GgrKzM9risrKzNt2B34+vrCwDo27cvEhISUFRUBLVajYqKlmvIm81m9OvXr0PX\n8vJyaDSabnNzxMPPzw8ajQbl5eVtnne2X79+/WwNPS0tzdZdJbZTU1MTJk2ahOTkZDz66KMA5FGv\ny15PPvmkzUsuNevVqxcefvhhFBcXy6JW7b0OHTokaa2+//57ZGdn45577kFSUhJ2796N5ORkWdXK\nYSQZCWhHU1MTDRw4kAwGAzU0NIg68Hrx4kW6cOECERHV1tbSAw88QDt27KC5c+fazsZdunTpVQMt\nDQ0NVFpaSgMHDrQNtDgDg8Fw1cCrox6xsbF04MABslqtThnwae906tQp299XrlxJSUlJojtZrVZK\nTk6m2bNnt3le6nrZ85KyZn/88QedO3eOiIjq6upo6NChtHPnTslrZc/LbDbb1pGqfRER7d27lyZM\nmEBE0rerG0EWIU9ElJOTQ0FBQaTVamnJkiWi7be0tJR0Oh3pdDoKCwuz7buyspJGjBhBgYGBNGrU\nKFtjJCJ68803SavVUnBwMOXl5TnNJTExkXx9fUmlUpGfnx9t3LixSx6HDh2i8PBw0mq19PzzzzvV\n6YMPPqDk5GSKiIigyMhIeuSRR6iiokJUJyKigoICEgSBdDodRUVFUVRUFOXm5kper468cnJyJK3Z\njz/+SHq9nnQ6HUVERNDy5cuJqGtt3Jm1suclh/ZF1BLyl2fXSF2rG4FPhmIYhnFhZNEnzzAMw3QP\nHPIMwzAuDIc8wzCMC8MhzzAM48JwyDMMw7gwHPIMwzAuDIc8wzCMC8MhzzAM48L8P30xDCtzqEiX\nAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x109c14750>"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Comparing against analytic"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Start by comparing the solution for the whole-space model to what we expect for a dipole in a whole-space"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "x = np.linspace(-50,50,10)\n",
+      "XYZ = Utils.ndgrid(np.r_[0],np.r_[50],x)\n",
+      "\n",
+      "Rx0 = EM.FDEM.RxFDEM(XYZ, 'bzr')\n",
+      "Rx1 = EM.FDEM.RxFDEM(XYZ, 'bzi')\n",
+      "\n",
+      "txList = []\n",
+      "freq = 500.\n",
+      "Tx = EM.FDEM.TxFDEM(np.r_[0.,0.,0.], 'VMD',freq,[Rx0,Rx1])\n",
+      "txList.append(Tx)\n",
+      "\n",
+      "survey = EM.FDEM.SurveyFDEM(txList)\n",
+      "mapping = Maps.ExpMap(mesh)\n",
+      "prb = EM.FDEM.ProblemFDEM_b(mesh, mapping=mapping)\n",
+      "prb.pair(survey)\n",
+      "prb.Solver = MumpsSolver"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# SimPEG soln\n",
+      "fTest = prb.fields(np.log(sigma))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "Bx, By, Bz = EM.Analytics.FDEM.AnalyticMagDipoleWholeSpace(Rx0.locs,Tx.loc,sigwh,freq, m = 1.0, orientation='Z')\n",
+      "Bx0, By0, Bz0 = EM.Analytics.FDEM.AnalyticMagDipoleWholeSpace(Rx0.locs,Tx.loc,sigwh,0.0, m = 1.0, orientation='Z')\n",
+      "BxA = Bx - Bx0\n",
+      "ByA = By - By0\n",
+      "BzA = Bz - Bz0\n",
+      "# XYZ2 = Utils.ndgrid(np.r_[0],np.r_[100],x)\n",
+      "# Bx, By, Bz = EM.Analytics.FDEM.AnalyticMagDipoleWholeSpace(np.r_[0,100,0],Tx.loc,sigwh,100., m = 1.0, orientation='Z')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 61
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot(x, np.c_[BzA.real/data[Tx,Rx0]])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 66,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x114c64f90>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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db3rCmC0FBYk9I155RSQLZ9X+4XndunXWXcBSB8XVq1fJ39+f9Ho9NTc3d9pJ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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x111fd8650>"
+       ]
+      }
+     ],
+     "prompt_number": 66
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot(x, np.c_[np.arctan2(Bz.imag,Bz.real)*180./np.pi, np.arctan2(data[Tx,Rx1],data[Tx,Rx0])*180./np.pi])\n",
+      "legend(('ana','data'))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 53,
+       "text": [
+        "<matplotlib.legend.Legend at 0x110a13a90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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g13VGR0cLALzxxhtvvPlxi46O9qv/DsjPF4huo856vR4HDhzApk2b0NTUhLKy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\nAmnyJCjCAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x110a13e50>"
+       ]
+      }
+     ],
+     "prompt_number": 53
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "data = survey.projectFields(fTest)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot(x, np.c_[data[Tx,Rx0], Bz.real])\n",
+      "legend(('data','Ana'))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 19,
+       "text": [
+        "<matplotlib.legend.Legend at 0x10a9688d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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XvTk718KRy0eQfCUZY9uPlR2FjATLnPSGa+eamxM3BzO7zOSsnKqMZU564/2y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\n5BSsLRPnVh4AAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10a968950>"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fTest"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 48,
+       "text": [
+        "<simpegEM.FDEM.SurveyFDEM.FieldsFDEM at 0x122bed68>"
+       ]
+      }
+     ],
+     "prompt_number": 48
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "x = np.linspace(-50,50,10)\n",
+      "XYZ = Utils.ndgrid(np.r_[0],np.r_[50],x)\n",
+      "Rx0 = EM.FDEM.RxFDEM(XYZ, 'bzr')\n",
+      "Rx1 = EM.FDEM.RxFDEM(XYZ, 'bzi')\n",
+      "txList = []\n",
+      "for z in np.linspace(-50,50,10):\n",
+      "    for freq in [300.,600.,900.]:\n",
+      "        Tx = EM.FDEM.TxFDEM(np.r_[0.,-50.,z], 'VMD', freq, [Rx0,Rx1])\n",
+      "        txList.append(Tx)\n",
+      "survey = EM.FDEM.SurveyFDEM(txList)\n",
+      "mapping = Maps.ExpMap(mesh)\n",
+      "prb = EM.FDEM.ProblemFDEM_b(mesh, mapping=mapping)\n",
+      "prb.pair(survey)\n",
+      "prb.Solver = MumpsSolver"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fB = prb.fields(np.log(sigmaB))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 41
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "dB = survey.projectFields(fB)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "f300 = dB[survey.getTransmitters(300.)[-1],Rx0]\n",
+      "f600 = dB[survey.getTransmitters(600.)[-1],Rx0]\n",
+      "f900 = dB[survey.getTransmitters(900.)[-1],Rx0]\n",
+      "\n",
+      "semilogy(x, np.abs(np.c_[f300,f600,f900]))\n",
+      "legend(('3','6','9'))\n",
+      "# plot(x, np.c_[dpred[Tx0,Rx0], dB[Tx0,Rx0]])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 71,
+       "text": [
+        "<matplotlib.legend.Legend at 0x10da95950>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEDCAYAAAA849PJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10da95710>"
+       ]
+      }
+     ],
+     "prompt_number": 71
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "survey.dobs = survey.dpred(None, u=fB)\n",
+      "survey.std = 0.01"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 80
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "reg = Regularization.Tikhonov(mesh)\n",
+      "dmis = DataMisfit.l2_DataMisfit(survey)\n",
+      "opt = Optimization.InexactGaussNewton(maxIter=3)\n",
+      "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
+      "beta = Directives.BetaSchedule()\n",
+      "betaest = Directives.BetaEstimate_ByEig()\n",
+      "inv = Inversion.BaseInversion(invProb, directiveList=[beta, betaest])\n",
+      "m0 = np.ones_like(sigmaB)*1e-2"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 81
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "inv.run(np.log(m0))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "SimPEG.InvProblem will set Regularization.mref to m0.\n",
+        "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
+        "                    ***Done using same solver as the problem***\n",
+        "SimPEG.l2_DataMisfit is creating default weightings for Wd."
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "============================ Inexact Gauss Newton ============================"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+        "-----------------------------------------------------------------------------\n",
+        "   0  4.46e+03  6.92e+04  0.00e+00  6.92e+04    2.31e+04      0              "
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "   1  4.46e+03  6.51e+03  2.09e+00  1.58e+04    3.71e+02      0              "
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "   2  4.46e+03  6.15e+03  2.16e+00  1.58e+04    2.17e+01      0              "
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "   3  5.57e+02  6.13e+03  2.16e+00  7.33e+03    3.68e+03      0   Skip BFGS  "
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "------------------------- STOP! -------------------------\n",
+        "0 : |fc-fOld| = 8.4323e+03 <= tolF*(1+|f0|) = 6.9198e+03\n",
+        "1 : |xc-x_last| = 4.2277e-02 <= tolX*(1+|x0|) = 8.0690e+01\n",
+        "0 : |proj(x-g)-x|    = 3.6813e+03 <= tolG          = 1.0000e-01\n",
+        "0 : |proj(x-g)-x|    = 3.6813e+03 <= 1e3*eps       = 1.0000e-02\n",
+        "1 : maxIter   =       3    <= iter          =      3\n",
+        "------------------------- DONE! -------------------------\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 82,
+       "text": [
+        "array([-4.62771738, -4.62789076, -4.62806113, ..., -4.62159749,\n",
+        "       -4.62166636, -4.62176178])"
+       ]
+      }
+     ],
+     "prompt_number": 82
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "mopt = _"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 83
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "colorbar(mesh.plotSlice(mapping*mopt)[0])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 90,
+       "text": [
+        "<matplotlib.colorbar.Colorbar instance at 0x1141e51b8>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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22i+fLBYLli1bhosXL0quOhQKBaDwaZGk/Cw7YqKV++4m7Tdupvck5smKgne7at8EQRpP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\nEZEsrDiIiEgWVhxERCQLKw4iIpLl/wP3+YICw+bS+gAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x11473f510>"
+       ]
+      }
+     ],
+     "prompt_number": 90
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 85
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/simpegEM/Analytics/FDEM.py b/simpegEM/Analytics/FDEM.py
index 3c6f7ca7..8166075a 100644
--- a/simpegEM/Analytics/FDEM.py
+++ b/simpegEM/Analytics/FDEM.py
@@ -1,7 +1,9 @@
+from __future__ import division
 import numpy as np
 from scipy.constants import mu_0, pi
 from scipy.special import erf
 import matplotlib.pyplot as plt
+from SimPEG import Utils
 
 def hzAnalyticDipoleF(r, freq, sigma, secondary=True):
     """
@@ -12,7 +14,7 @@ def hzAnalyticDipoleF(r, freq, sigma, secondary=True):
         import matplotlib.pyplot as plt
         import simpegEM as EM
         freq = np.logspace(-1, 6, 61)
-        test = EM.Utils.Ana.FEM.hzAnalyticDipoleF(100, freq, 0.001, secondary=False)
+        test = EM.Analytics.FDEM.hzAnalyticDipoleF(100, freq, 0.001, secondary=False)
         plt.loglog(freq, abs(test.real))
         plt.loglog(freq, abs(test.imag))
         plt.title('Response at $r$=100m')
@@ -36,7 +38,7 @@ def hzAnalyticDipoleF(r, freq, sigma, secondary=True):
 
     return hz
 
-def AnalyticMagDipoleWholeSpace(x,y,z,sig,f,xs=0.,ys=0.,zs=0.,m=1.,orientation='X'):
+def AnalyticMagDipoleWholeSpace(XYZ, txLoc, sig, f, m=1., orientation='X'):
     """
     Analytical solution for a dipole in a whole-space.
 
@@ -47,14 +49,30 @@ def AnalyticMagDipoleWholeSpace(x,y,z,sig,f,xs=0.,ys=0.,zs=0.,m=1.,orientation='
         - add E-fields
         - handle multiple frequencies
         - add divide by zero safety
+
+
+    .. plot::
+
+        import simpegEM as EM
+        import matplotlib.pyplot as plt
+        freqs = np.logspace(-2,5,100)
+        Bx, By, Bz = EM.Analytics.FDEM.AnalyticMagDipoleWholeSpace([0,100,0], [0,0,0], 1e-2, freqs, m=1, orientation='Z')
+        plt.loglog(freqs, np.abs(Bz.real)/mu_0, 'b')
+        plt.loglog(freqs, np.abs(Bz.imag)/mu_0, 'r')
+        plt.legend(('real','imag'))
+        plt.show()
+
+
     """
 
-    dx = x-xs
-    dy = y-ys
-    dz = z-zs
+    XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
+
+    dx = XYZ[:,0]-txLoc[0]
+    dy = XYZ[:,1]-txLoc[1]
+    dz = XYZ[:,2]-txLoc[2]
 
     r  = np.sqrt( dx**2. + dy**2. + dz**2.)
-    k  = np.sqrt(-1j*2.*np.pi*f*mu_0*sig)
+    k  = np.sqrt( -1j*2.*np.pi*f*mu_0*sig )
     kr = k*r
 
     front = m / (4.*pi * r**3.) * np.exp(-1j*kr)