Minor correction for notebooks

notebooks/2DinvDC.ipynb

@@ -6,18 +6,7 @@
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from SimPEG import *\n",
     "import simpegDCIP as DC\n",
@@ -177,18 +166,26 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x15be0278>]"
+       "[<matplotlib.lines.Line2D at 0x1078d47d0>]"
       ]
      },
      "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sgkang/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  if self._edgecolors == str('face'):\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15968710>"
+       "<matplotlib.figure.Figure at 0x1075ce290>"
       ]
      },
      "metadata": {},
@@ -213,7 +210,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x15e72518>]"
+       "[<matplotlib.lines.Line2D at 0x107a11410>]"
       ]
      },
      "execution_count": 12,
@@ -222,9 +219,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15968198>"
+       "<matplotlib.figure.Figure at 0x1079291d0>"
       ]
      },
      "metadata": {},
@@ -241,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -252,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
@@ -260,18 +257,18 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x160b5eb8>]"
+       "[<matplotlib.lines.Line2D at 0x10797cc90>]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x160c1278>"
+       "<matplotlib.figure.Figure at 0x10796f5d0>"
       ]
      },
      "metadata": {},
@@ -294,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -302,10 +299,10 @@
     {
      "data": {
       "text/plain": [
-       "0.0046885259087758868"
+       "0.0046885259085681129"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -316,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -334,15 +331,15 @@
        " <a list of 20 Patch objects>)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15ca0828>"
+       "<matplotlib.figure.Figure at 0x1075b7c90>"
       ]
      },
      "metadata": {},
@@ -355,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -366,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -376,29 +373,30 @@
      "output_type": "stream",
      "text": [
       "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
-      "                    ***Done using same solver as the problem***\n",
+      "                    ***Done using same Solver and solverOpts as the problem***\n",
       "============================ Inexact Gauss Newton ============================\n",
       "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
       "-----------------------------------------------------------------------------\n",
       "   0  1.00e+00  1.40e+08  0.00e+00  1.40e+08    8.32e+06      0              \n",
       "   1  1.00e+00  1.88e+07  6.18e-03  1.88e+07    1.13e+06      0              \n",
       "   2  1.00e+00  2.52e+06  2.45e-02  2.52e+06    1.55e+05      0   Skip BFGS  \n",
-      "   3  1.25e-01  3.49e+05  5.43e-02  3.49e+05    2.17e+04      0   Skip BFGS  \n",
-      "   4  1.25e-01  6.57e+04  9.31e-02  6.57e+04    3.25e+03      0   Skip BFGS  \n",
-      "   5  1.25e-01  1.53e+04  7.19e+00  1.53e+04    4.83e+02      1   Skip BFGS  \n",
-      "   6  1.56e-02  4.40e+03  1.81e+01  4.40e+03    5.23e+02      1              \n",
-      "   7  1.56e-02  2.56e+03  4.02e+01  2.56e+03    2.82e+03      0   Skip BFGS  \n",
-      "   8  1.56e-02  5.47e+02  3.69e+01  5.47e+02    2.89e+02      0              \n",
-      "   9  1.95e-03  3.14e+02  3.71e+01  3.14e+02    3.22e+02      0   Skip BFGS  \n",
-      "  10  1.95e-03  2.18e+02  3.92e+01  2.18e+02    1.07e+02      0              \n",
-      "  11  1.95e-03  1.77e+02  3.86e+01  1.77e+02    8.57e+01      0              \n",
-      "  12  2.44e-04  1.44e+02  4.09e+01  1.44e+02    9.30e+01      0              \n",
+      "   3  1.25e-01  3.50e+05  5.43e-02  3.50e+05    2.18e+04      0   Skip BFGS  \n",
+      "   4  1.25e-01  6.61e+04  9.31e-02  6.61e+04    3.26e+03      0   Skip BFGS  \n",
+      "   5  1.25e-01  1.57e+04  7.18e+00  1.57e+04    4.85e+02      1   Skip BFGS  \n",
+      "   6  1.56e-02  4.44e+03  1.84e+01  4.44e+03    5.43e+02      1              \n",
+      "   7  1.56e-02  2.74e+03  4.09e+01  2.74e+03    2.92e+03      0   Skip BFGS  \n",
+      "   8  1.56e-02  5.47e+02  3.77e+01  5.48e+02    2.94e+02      0              \n",
+      "   9  1.95e-03  3.27e+02  3.71e+01  3.27e+02    2.70e+02      0   Skip BFGS  \n",
+      "  10  1.95e-03  2.42e+02  3.97e+01  2.42e+02    8.87e+01      0              \n",
+      "  11  1.95e-03  1.96e+02  3.88e+01  1.96e+02    1.22e+02      0              \n",
+      "  12  2.44e-04  1.56e+02  4.13e+01  1.56e+02    9.85e+01      0              \n",
+      "  13  2.44e-04  1.37e+02  4.19e+01  1.37e+02    1.23e+02      0              \n",
       "------------------------- STOP! -------------------------\n",
-      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.3960e+07\n",
-      "1 : |xc-x_last| = 3.1882e+00 <= tolX*(1+|x0|) = 4.8945e+01\n",
-      "0 : |proj(x-g)-x|    = 9.3043e+01 <= tolG          = 1.0000e-01\n",
-      "0 : |proj(x-g)-x|    = 9.3043e+01 <= 1e3*eps       = 1.0000e-02\n",
-      "0 : maxIter   =      30    <= iter          =     13\n",
+      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.3959e+07\n",
+      "1 : |xc-x_last| = 7.6786e-01 <= tolX*(1+|x0|) = 4.8945e+01\n",
+      "0 : |proj(x-g)-x|    = 1.2300e+02 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 1.2300e+02 <= 1e3*eps       = 1.0000e-02\n",
+      "0 : maxIter   =      30    <= iter          =     14\n",
       "------------------------- DONE! -------------------------\n"
      ]
     }
@@ -422,7 +420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {
     "collapsed": true
    },
@@ -433,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -459,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -467,9 +465,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ab1e9b0>"
+       "<matplotlib.figure.Figure at 0x10d44d890>"
       ]
      },
      "metadata": {},
@@ -491,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -499,18 +497,18 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1ba516d8>]"
+       "[<matplotlib.lines.Line2D at 0x10d44d590>]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1ef46908>"
+       "<matplotlib.figure.Figure at 0x10cb0ca10>"
       ]
      },
      "metadata": {},
@@ -548,7 +546,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "version": "2.7.11"
   }
  },
  "nbformat": 4,

notebooks/CircModel.ipynb

@@ -11,15 +11,6 @@
     "from SimPEG import *"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -202,7 +193,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x107614750>"
+       "<matplotlib.figure.Figure at 0x1075971d0>"
       ]
      },
      "metadata": {},
@@ -243,7 +234,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a2ad990>"
+       "<matplotlib.figure.Figure at 0x10999b5d0>"
       ]
      },
      "metadata": {},
@@ -275,18 +266,26 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar instance at 0x10a7b12d8>"
+       "<matplotlib.colorbar.Colorbar instance at 0x10a7e8f80>"
       ]
      },
      "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sgkang/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  if self._edgecolors == str('face'):\n"
+     ]
+    },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAADzCAYAAADJsGLOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a698690>"
+       "<matplotlib.figure.Figure at 0x10a5c0a90>"
       ]
      },
      "metadata": {},
@@ -316,8 +315,8 @@
      "output_type": "stream",
      "text": [
       "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
-      "                    ***Done using same solver as the problem***\n",
-      "SimPEG.SaveModelEveryIteration will save your models as: '###-InversionModel-2015-11-10-16-33.npy'\n",
+      "                    ***Done using same Solver and solverOpts as the problem***\n",
+      "SimPEG.SaveModelEveryIteration will save your models as: '###-InversionModel-2016-02-04-11-31.npy'\n",
       "============================ Inexact Gauss Newton ============================\n",
       "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
       "-----------------------------------------------------------------------------\n",
@@ -327,20 +326,25 @@
       "   3  0.00e+00  1.31e+03  4.23e+01  1.31e+03    1.48e+04      3              \n",
       "   4  0.00e+00  1.22e+03  5.74e+01  1.22e+03    1.49e+04      4              \n",
       "   5  0.00e+00  8.55e+02  3.58e+01  8.55e+02    2.20e+04      0              \n",
-      "   6  0.00e+00  6.43e+02  5.20e+01  6.43e+02    1.71e+04      3              \n",
-      "   7  0.00e+00  4.57e+02  4.43e+01  4.57e+02    5.20e+03      0              \n",
-      "   8  0.00e+00  3.83e+02  6.55e+01  3.83e+02    1.54e+04      2              \n",
-      "   9  0.00e+00  3.67e+02  5.78e+01  3.67e+02    5.76e+03      1              \n",
-      "  10  0.00e+00  3.31e+02  6.61e+01  3.31e+02    1.20e+04      1              \n",
-      "  11  0.00e+00  3.30e+02  6.15e+01  3.30e+02    1.76e+04      2              \n",
-      "  12  0.00e+00  2.01e+02  4.84e+01  2.01e+02    8.75e+03      0              \n",
-      "  13  0.00e+00  1.54e+02  6.25e+01  1.54e+02    8.56e+03      2              \n",
+      "   6  0.00e+00  7.83e+02  5.46e+01  7.83e+02    2.04e+04      3              \n",
+      "   7  0.00e+00  7.38e+02  4.95e+01  7.38e+02    2.38e+04      2              \n",
+      "   8  0.00e+00  6.90e+02  3.33e+01  6.90e+02    2.15e+04      1   Skip BFGS  \n",
+      "   9  0.00e+00  5.63e+02  5.39e+01  5.63e+02    1.79e+04      2   Skip BFGS  \n",
+      "  10  0.00e+00  4.88e+02  5.12e+01  4.88e+02    1.60e+04      2              \n",
+      "  11  0.00e+00  4.81e+02  5.04e+01  4.81e+02    1.60e+04      6   Skip BFGS  \n",
+      "  12  0.00e+00  4.79e+02  4.87e+01  4.79e+02    1.67e+04      5   Skip BFGS  \n",
+      "  13  0.00e+00  3.90e+02  3.14e+01  3.90e+02    1.41e+04      1   Skip BFGS  \n",
+      "  14  0.00e+00  3.64e+02  5.04e+01  3.64e+02    1.61e+04      2              \n",
+      "  15  0.00e+00  3.49e+02  4.45e+01  3.49e+02    1.58e+04      2              \n",
+      "  16  0.00e+00  2.92e+02  5.24e+01  2.92e+02    1.31e+04      3              \n",
+      "  17  0.00e+00  2.67e+02  5.12e+01  2.67e+02    1.08e+04      1              \n",
+      "  18  0.00e+00  2.18e+02  4.72e+01  2.18e+02    1.15e+04      2              \n",
       "------------------------- STOP! -------------------------\n",
       "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 4.6952e+02\n",
-      "0 : |xc-x_last| = 5.6930e-01 <= tolX*(1+|x0|) = 1.2421e-19\n",
-      "0 : |proj(x-g)-x|    = 8.5645e+03 <= tolG          = 1.0000e-01\n",
-      "0 : |proj(x-g)-x|    = 8.5645e+03 <= 1e3*eps       = 1.0000e-02\n",
-      "0 : maxIter   =      30    <= iter          =     14\n",
+      "0 : |xc-x_last| = 3.4124e-01 <= tolX*(1+|x0|) = 1.2421e-19\n",
+      "0 : |proj(x-g)-x|    = 1.1527e+04 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 1.1527e+04 <= 1e3*eps       = 1.0000e-02\n",
+      "0 : maxIter   =      30    <= iter          =     19\n",
       "------------------------- DONE! -------------------------\n"
      ]
     }
@@ -417,9 +421,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmIAAAF/CAYAAADq9qU+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c979dd0>"
+       "<matplotlib.figure.Figure at 0x10c77fa10>"
       ]
      },
      "metadata": {},
@@ -441,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -449,18 +453,18 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10c8ed390>]"
+       "[<matplotlib.lines.Line2D at 0x10c57bb50>]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c8ed2d0>"
+       "<matplotlib.figure.Figure at 0x10c57be10>"
       ]
      },
      "metadata": {},
@@ -498,7 +502,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "version": "2.7.11"
   }
  },
  "nbformat": 4,

notebooks/CircModel_app.ipynb

@@ -395,6 +395,18 @@
    "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": "2.7.11"
   }
  },
  "nbformat": 4,

notebooks/DCR Pseudo-section Simulation.ipynb

@@ -13,18 +13,7 @@
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from SimPEG import *\n",
     "from SimPEG.Examples import DC_PseudoSection_Simulation\n",
@@ -45,15 +34,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Transmitter 8 of 9 -> Time:0.957999944687 sec Transmitter 8 of 9\n",
+      "Transmitter 8 of 9 -> Time:0.747934818268 sec Transmitter 8 of 9\n",
       "Forward completed\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sgkang/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  if self._edgecolors == str('face'):\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x38afc88>"
+       "<matplotlib.figure.Figure at 0x1077915d0>"
       ]
      },
      "metadata": {},

notebooks/DC_Forward_Live.ipynb

@@ -11,10 +11,6 @@
      "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",
       "Populating the interactive namespace from numpy and matplotlib\n"
      ]
     },
@@ -83,19 +79,27 @@
     {
      "data": {
       "text/plain": [
-       "(<matplotlib.collections.QuadMesh at 0x157f41d0>,\n",
-       " <matplotlib.lines.Line2D at 0x15631320>)"
+       "(<matplotlib.collections.QuadMesh at 0x10809aa90>,\n",
+       " <matplotlib.lines.Line2D at 0x10778f390>)"
       ]
      },
      "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sgkang/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  if self._edgecolors == str('face'):\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x15482e10>"
+       "<matplotlib.figure.Figure at 0x1077370d0>"
       ]
      },
      "metadata": {},
@@ -105,7 +109,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1577eba8>"
+       "<matplotlib.figure.Figure at 0x1080825d0>"
       ]
      },
      "metadata": {},
@@ -142,7 +146,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x15be3940>]"
+       "[<matplotlib.lines.Line2D at 0x1081aabd0>]"
       ]
      },
      "execution_count": 7,
@@ -151,9 +155,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x155f4a20>"
+       "<matplotlib.figure.Figure at 0x1081aad50>"
       ]
      },
      "metadata": {},
@@ -222,19 +226,7 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "No module named pymatsolver",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-11-d6799536a06f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpymatsolver\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMumpsSolver\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mImportError\u001b[0m: No module named pymatsolver"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pymatsolver import MumpsSolver"
    ]
@@ -264,7 +256,7 @@
      "evalue": "expected an indented block (<ipython-input-13-ff092c0e869a>, line 3)",
      "output_type": "error",
      "traceback": [
-      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-13-ff092c0e869a>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m    \u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-13-ff092c0e869a>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    \u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
      ]
     }
    ],
@@ -276,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },

notebooks/DCinverse.ipynb

@@ -1,451 +1,407 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:9107d99fbfb21822b71a94de16f078e82c12cf73d2e85893fd26dfda06c8ce14"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from SimPEG import *\n",
-      "import simpegDCIP as DC\n",
-      "%pylab inline\n",
-      "from pymatsolver import MumpsSolver"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Populating the interactive namespace from numpy and matplotlib\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "Vendor:  Continuum Analytics, Inc.\n",
-        "Package: mkl\n",
-        "Message: trial mode expires in 29 days\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "cs = 12.5\n",
-      "nc = 500/cs+1\n",
-      "hx = [(cs,7, -1.3),(cs,nc),(cs,7, 1.3)]\n",
-      "hy = [(cs,7, -1.3),(cs,int(nc/2+1)),(cs,7, 1.3)]\n",
-      "hz = [(cs,7, -1.3),(cs,int(nc/2+1))]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')\n",
-      "print mesh"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "  ---- 3-D TensorMesh ----  \n",
-        "   x0: -541.97\n",
-        "   y0: -416.97\n",
-        "   z0: -548.22\n",
-        "  nCx: 55\n",
-        "  nCy: 35\n",
-        "  nCz: 28\n",
-        "   hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
-        "   hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
-        "   hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "sighalf = 1e-2\n",
-      "sigma = np.ones(mesh.nC)*sighalf\n",
-      "p0 = np.r_[-50., 50., -50.]\n",
-      "p1 = np.r_[ 50.,-50., -150.]\n",
-      "blk_ind = Utils.ModelBuilder.getIndecesBlock(p0, p1, mesh.gridCC)\n",
-      "sigma[blk_ind] = 1e-3"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "nElecs = 21\n",
-      "x_temp = np.linspace(-250, 250, nElecs)\n",
-      "aSpacing = x_temp[1]-x_temp[0]\n",
-      "y_temp = 0.\n",
-      "xyz = Utils.ndgrid(x_temp, np.r_[y_temp], np.r_[0.])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "srcList = DC.Examples.WennerArray.getSrcList(nElecs,aSpacing)\n",
-      "survey = DC.SurveyDC(srcList)\n",
-      "print len(survey.srcList)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "63\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fig, ax = plt.subplots(1,1, figsize = (6.5,5))\n",
-      "indz = 22\n",
-      "dat = mesh.plotSlice(sigma, grid=True, ax = ax, ind=indz)\n",
-      "ax.set_xlim(-300, 300)\n",
-      "ax.set_ylim(-300, 300)\n",
-      "cb = plt.colorbar(dat[0])\n",
-      "ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n",
-      "\n",
-      "txLocs = np.array([[tx.loc[0][0], tx.loc[1][0]] for tx in survey.srcList]).flatten()\n",
-      "ax.plot(txLocs, txLocs*0, 'w.', ms = 3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x10db0fa50>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x10d9ac710>"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expmap = Maps.ExpMap(mesh)\n",
-      "m2to3  = Maps.Map2Dto3D(mesh,normal='Y')\n",
-      "imap   = Maps.IdentityMap(mesh)\n",
-      "problem = DC.ProblemDC_CC(mesh, mapping= imap )\n",
-      "problem.Solver = MumpsSolver\n",
-      "problem.pair(survey)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "datasyn = survey.dpred(sighalf*np.ones(mesh.nC))\n",
-      "survey.makeSyntheticData(sigma,std=0.01,force=True)\n",
-      "plot(np.log(np.c_[survey.dobs,datasyn]))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x1038621d0>,\n",
-        " <matplotlib.lines.Line2D at 0x103862450>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x10e82c610>"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "problem.mapping = expmap * m2to3"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "dmis = DataMisfit.l2_DataMisfit(survey)\n",
-      "reg = Regularization.Tikhonov(mesh,mapping=m2to3)\n",
-      "opt = Optimization.InexactGaussNewton(maxIter=7,tolX=1e-15)\n",
-      "opt.remember('xc')\n",
-      "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
-      "beta = Directives.BetaEstimate_ByEig(beta0_ratio=1e1)\n",
-      "betaSched = Directives.BetaSchedule(coolingFactor=5, coolingRate=2)\n",
-      "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaSched])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "m0 = np.log(np.ones(problem.mapping.nP)*sighalf)\n",
-      "mopt = inv.run(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  7.28e+00  1.86e+03  1.05e+04  7.84e+04    2.20e+03      0              "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "   1  7.28e+00  6.57e+02  1.06e+04  7.80e+04    1.15e+03      1              "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "   2  1.46e+00  5.58e+02  1.06e+04  1.61e+04    1.03e+03      3   Skip BFGS  "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "   3  1.46e+00  1.36e+02  1.08e+04  1.59e+04    1.69e+02      0   Skip BFGS  "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "   4  2.91e-01  8.24e+01  1.07e+04  3.20e+03    2.05e+02      0              "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "   5  2.91e-01  4.61e+01  1.07e+04  3.16e+03    1.35e+02      0   Skip BFGS  "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "   6  5.83e-02  4.40e+01  1.07e+04  6.65e+02    1.16e+02      0              "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "   7  5.83e-02  3.99e+01  1.07e+04  6.61e+02    1.35e+02      0              "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "------------------------- STOP! -------------------------\n",
-        "1 : |fc-fOld| = 4.7528e+00 <= tolF*(1+|f0|) = 7.8397e+03\n",
-        "0 : |xc-x_last| = 4.6314e-01 <= tolX*(1+|x0|) = 1.8172e-13\n",
-        "0 : |proj(x-g)-x|    = 1.3494e+02 <= tolG          = 1.0000e-01\n",
-        "0 : |proj(x-g)-x|    = 1.3494e+02 <= 1e3*eps       = 1.0000e-02\n",
-        "1 : maxIter   =       7    <= iter          =      7\n",
-        "------------------------- DONE! -------------------------\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "colorbar(mesh.plotSlice(np.log10(expmap * m2to3 * mopt), normal='Y', ind=indz, grid=True)[0])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 14,
-       "text": [
-        "<matplotlib.colorbar.Colorbar instance at 0x10fcbaa28>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x103879a90>"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "dpred = survey.dpred(mopt)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plot(np.c_[dpred,survey.dobs])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 16,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x10f42a790>,\n",
-        " <matplotlib.lines.Line2D at 0x10f42aa10>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x10fa4d110>"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: pylab import has clobbered these variables: ['linalg']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n"
+     ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "from SimPEG import *\n",
+    "import simpegDCIP as DC\n",
+    "%pylab inline\n",
+    "from pymatsolver import MumpsSolver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "cs = 12.5\n",
+    "nc = 500/cs+1\n",
+    "hx = [(cs,7, -1.3),(cs,nc),(cs,7, 1.3)]\n",
+    "hy = [(cs,7, -1.3),(cs,int(nc/2+1)),(cs,7, 1.3)]\n",
+    "hz = [(cs,7, -1.3),(cs,int(nc/2+1))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  ---- 3-D TensorMesh ----  \n",
+      "   x0: -541.97\n",
+      "   y0: -416.97\n",
+      "   z0: -548.22\n",
+      "  nCx: 55\n",
+      "  nCy: 35\n",
+      "  nCz: 28\n",
+      "   hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
+      "   hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
+      "   hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n"
+     ]
+    }
+   ],
+   "source": [
+    "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')\n",
+    "print mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sighalf = 1e-2\n",
+    "sigma = np.ones(mesh.nC)*sighalf\n",
+    "p0 = np.r_[-50., 50., -50.]\n",
+    "p1 = np.r_[ 50.,-50., -150.]\n",
+    "blk_ind = Utils.ModelBuilder.getIndecesBlock(p0, p1, mesh.gridCC)\n",
+    "sigma[blk_ind] = 1e-3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "nElecs = 21\n",
+    "x_temp = np.linspace(-250, 250, nElecs)\n",
+    "aSpacing = x_temp[1]-x_temp[0]\n",
+    "y_temp = 0.\n",
+    "xyz = Utils.ndgrid(x_temp, np.r_[y_temp], np.r_[0.])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "63\n"
+     ]
+    }
+   ],
+   "source": [
+    "srcList = DC.Examples.WennerArray.getSrcList(nElecs,aSpacing)\n",
+    "survey = DC.SurveyDC(srcList)\n",
+    "print len(survey.srcList)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10db0fa50>]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d9ac710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,1, figsize = (6.5,5))\n",
+    "indz = 22\n",
+    "dat = mesh.plotSlice(sigma, grid=True, ax = ax, ind=indz)\n",
+    "ax.set_xlim(-300, 300)\n",
+    "ax.set_ylim(-300, 300)\n",
+    "cb = plt.colorbar(dat[0])\n",
+    "ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n",
+    "\n",
+    "txLocs = np.array([[tx.loc[0][0], tx.loc[1][0]] for tx in survey.srcList]).flatten()\n",
+    "ax.plot(txLocs, txLocs*0, 'w.', ms = 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "expmap = Maps.ExpMap(mesh)\n",
+    "m2to3  = Maps.Map2Dto3D(mesh,normal='Y')\n",
+    "imap   = Maps.IdentityMap(mesh)\n",
+    "problem = DC.ProblemDC_CC(mesh, mapping= imap )\n",
+    "problem.Solver = MumpsSolver\n",
+    "problem.pair(survey)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1038621d0>,\n",
+       " <matplotlib.lines.Line2D at 0x103862450>]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e82c610>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "datasyn = survey.dpred(sighalf*np.ones(mesh.nC))\n",
+    "survey.makeSyntheticData(sigma,std=0.01,force=True)\n",
+    "plot(np.log(np.c_[survey.dobs,datasyn]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "problem.mapping = expmap * m2to3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dmis = DataMisfit.l2_DataMisfit(survey)\n",
+    "reg = Regularization.Tikhonov(mesh,mapping=m2to3)\n",
+    "opt = Optimization.InexactGaussNewton(maxIter=7,tolX=1e-15)\n",
+    "opt.remember('xc')\n",
+    "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
+    "beta = Directives.BetaEstimate_ByEig(beta0_ratio=1e1)\n",
+    "betaSched = Directives.BetaSchedule(coolingFactor=5, coolingRate=2)\n",
+    "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaSched])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SimPEG.InvProblem will set Regularization.mref to m0.\n",
+      "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
+      "                    ***Done using same solver as the problem***\n",
+      "SimPEG.l2_DataMisfit is creating default weightings for Wd.\n",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  7.28e+00  1.86e+03  1.05e+04  7.84e+04    2.20e+03      0              \n",
+      "   1  7.28e+00  6.57e+02  1.06e+04  7.80e+04    1.15e+03      1              \n",
+      "   2  1.46e+00  5.58e+02  1.06e+04  1.61e+04    1.03e+03      3   Skip BFGS  \n",
+      "   3  1.46e+00  1.36e+02  1.08e+04  1.59e+04    1.69e+02      0   Skip BFGS  \n",
+      "   4  2.91e-01  8.24e+01  1.07e+04  3.20e+03    2.05e+02      0              \n",
+      "   5  2.91e-01  4.61e+01  1.07e+04  3.16e+03    1.35e+02      0   Skip BFGS  \n",
+      "   6  5.83e-02  4.40e+01  1.07e+04  6.65e+02    1.16e+02      0              \n",
+      "   7  5.83e-02  3.99e+01  1.07e+04  6.61e+02    1.35e+02      0              \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 4.7528e+00 <= tolF*(1+|f0|) = 7.8397e+03\n",
+      "0 : |xc-x_last| = 4.6314e-01 <= tolX*(1+|x0|) = 1.8172e-13\n",
+      "0 : |proj(x-g)-x|    = 1.3494e+02 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 1.3494e+02 <= 1e3*eps       = 1.0000e-02\n",
+      "1 : maxIter   =       7    <= iter          =      7\n",
+      "------------------------- DONE! -------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "m0 = np.log(np.ones(problem.mapping.nP)*sighalf)\n",
+    "mopt = inv.run(m0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar instance at 0x10fcbaa28>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x103879a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colorbar(mesh.plotSlice(np.log10(expmap * m2to3 * mopt), normal='Y', ind=indz, grid=True)[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dpred = survey.dpred(mopt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10f42a790>,\n",
+       " <matplotlib.lines.Line2D at 0x10f42aa10>]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10fa4d110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.c_[dpred,survey.dobs])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
- ]
-}
+ ],
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