Result of SimPEG hackathon

- Incorporate field class on DC
- Add IP forward modelling and inversion
- Modify notebooks
- change folder name form simpegDC to simpegDCIP

notebooks/DC_schumberger_FWD.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:b7556968258042b90534fa310f5e2522502708190e2655f8d1be84dd18c34ec2"
+  "signature": "sha256:788d43ec10deaa6de8f97cf0db8cb6daa952455b30356e2a2a83472d9d0d4c10"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,7 +13,7 @@
      "collapsed": false,
      "input": [
       "from SimPEG import *\n",
-      "import simpegDC as DC\n",
+      "import simpegDCIP as DC\n",
       "from simpegem1d import Utils1D\n",
       "%pylab inline"
      ],
@@ -26,9 +26,18 @@
        "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": 12
+     "prompt_number": 1
     },
     {
      "cell_type": "heading",
@@ -66,7 +75,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 13
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -77,7 +86,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
@@ -91,13 +100,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x110fe1850>"
+        "<matplotlib.figure.Figure at 0x104395510>"
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 4
     },
     {
      "cell_type": "heading",
@@ -117,7 +126,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 16
+     "prompt_number": 5
     },
     {
      "cell_type": "heading",
@@ -183,7 +192,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 17
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -194,7 +203,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 18
+     "prompt_number": 7
     },
     {
      "cell_type": "code",
@@ -213,7 +222,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 19
+     "prompt_number": 8
     },
     {
      "cell_type": "code",
@@ -243,7 +252,7 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 20,
+       "prompt_number": 9,
        "text": [
         "(-600, 600)"
        ]
@@ -253,11 +262,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAADNCAYAAACcnje3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10d9c1f90>"
+        "<matplotlib.figure.Figure at 0x10e08b5d0>"
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 9
     },
     {
      "cell_type": "code",
@@ -284,9 +293,9 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 21,
+       "prompt_number": 10,
        "text": [
-        "<matplotlib.text.Text at 0x10da73a50>"
+        "<matplotlib.text.Text at 0x10ee9dcd0>"
        ]
       },
       {
@@ -294,11 +303,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAEiCAYAAADNgWQ8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x112b59190>"
+        "<matplotlib.figure.Figure at 0x10e069890>"
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
@@ -313,14 +322,14 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 22
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "survey = DC.SurveyDC(txlist)\n",
-      "problem = DC.ProblemDC(mesh)\n",
+      "problem = DC.ProblemDC_CC(mesh)\n",
       "problem.pair(survey)\n",
       "try:\n",
       "    from pymatsolver import MumpsSolver\n",
@@ -331,7 +340,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 23
+     "prompt_number": 13
     },
     {
      "cell_type": "heading",
@@ -355,12 +364,12 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "CPU times: user 4.93 s, sys: 612 ms, total: 5.54 s\n",
-        "Wall time: 5 s\n"
+        "CPU times: user 5.26 s, sys: 554 ms, total: 5.81 s\n",
+        "Wall time: 4.06 s\n"
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -394,9 +403,9 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 26,
+       "prompt_number": 15,
        "text": [
-        "<matplotlib.legend.Legend at 0x10d9ed410>"
+        "<matplotlib.legend.Legend at 0x10f990a10>"
        ]
       },
       {
@@ -404,11 +413,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAErCAYAAAAfcL5EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x112b6ca50>"
+        "<matplotlib.figure.Figure at 0x10ea722d0>"
        ]
       }
      ],
-     "prompt_number": 26
+     "prompt_number": 15
     },
     {
      "cell_type": "code",
@@ -417,7 +426,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 21
+     "prompt_number": 15
     },
     {
      "cell_type": "code",
@@ -426,7 +435,25 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 21
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 15
     },
     {
      "cell_type": "code",

notebooks/DC_schumberger_Inv.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:420f2b8a60ef08ab473ad159b33d15f48d4551a725f31a27a51cc03264657fbc"
+  "signature": "sha256:29c8b78e5b8f768bcf861afb186d1d85bff67ec768eba74cf6c3e85da81d0c01"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,7 +13,7 @@
      "collapsed": false,
      "input": [
       "from SimPEG import *\n",
-      "import simpegDC as DC\n",
+      "import simpegDCIP as DC\n",
       "from simpegem1d import Utils1D\n",
       "%pylab inline"
      ],
@@ -26,18 +26,9 @@
        "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 20 days\n"
-       ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -51,7 +42,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 2
+     "prompt_number": 5
     },
     {
      "cell_type": "heading",
@@ -97,7 +88,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -108,7 +99,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 7
     },
     {
      "cell_type": "code",
@@ -124,11 +115,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10439a3d0>"
+        "<matplotlib.figure.Figure at 0x10c83c990>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 8
     },
     {
      "cell_type": "heading",
@@ -154,7 +145,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 6
+     "prompt_number": 9
     },
     {
      "cell_type": "code",
@@ -187,11 +178,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10db67550>"
+        "<matplotlib.figure.Figure at 0x10e1d9710>"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
@@ -212,7 +203,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 8
+     "prompt_number": 11
     },
     {
      "cell_type": "heading",
@@ -278,7 +269,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 15
+     "prompt_number": 12
     },
     {
      "cell_type": "code",
@@ -297,7 +288,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 16
+     "prompt_number": 13
     },
     {
      "cell_type": "code",
@@ -319,7 +310,7 @@
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 14
     },
     {
      "cell_type": "code",
@@ -349,7 +340,7 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 18,
+       "prompt_number": 15,
        "text": [
         "(-0.2, 0.6)"
        ]
@@ -359,11 +350,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAADVCAYAAAAfHeVEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10e28ea50>"
+        "<matplotlib.figure.Figure at 0x1104a8b50>"
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 15
     },
     {
      "cell_type": "code",
@@ -392,11 +383,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAbEAAAEyCAYAAAB09O7HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x114a31710>"
+        "<matplotlib.figure.Figure at 0x10e2f0790>"
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -419,7 +410,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 22
+     "prompt_number": 17
     },
     {
      "cell_type": "heading",
@@ -433,7 +424,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "problem = DC.ProblemDC(mesh, mapping=mapping)\n",
+      "problem = DC.ProblemDC_CC(mesh, mapping=mapping)\n",
       "problem.pair(survey)\n",
       "try:\n",
       "    from pymatsolver import MumpsSolver\n",
@@ -444,7 +435,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 29
+     "prompt_number": 19
     },
     {
      "cell_type": "heading",
@@ -468,12 +459,12 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "CPU times: user 4.7 s, sys: 582 ms, total: 5.28 s\n",
-        "Wall time: 4.41 s\n"
+        "CPU times: user 6.05 s, sys: 708 ms, total: 6.76 s\n",
+        "Wall time: 6.01 s\n"
        ]
       }
      ],
-     "prompt_number": 31
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -497,8 +488,20 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 32
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 21,
+       "text": [
+        "array([ 0.0186522 ,  0.0211157 ,  0.02374998,  0.02677094,  0.02987307,\n",
+        "        0.03446037,  0.03920482,  0.04541832,  0.05344991,  0.062497  ,\n",
+        "        0.07516488,  0.09013051,  0.11364832,  0.1407387 ,  0.18918521,\n",
+        "        0.27485483])"
+       ]
+      }
+     ],
+     "prompt_number": 21
     },
     {
      "cell_type": "code",
@@ -533,7 +536,7 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 34,
+       "prompt_number": 22,
        "text": [
         "(-500.0, 0.0)"
        ]
@@ -543,11 +546,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x110aad290>"
+        "<matplotlib.figure.Figure at 0x10e2d5d10>"
        ]
       }
      ],
-     "prompt_number": 34
+     "prompt_number": 22
     },
     {
      "cell_type": "heading",
@@ -573,7 +576,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 35
+     "prompt_number": 23
     },
     {
      "cell_type": "code",
@@ -610,7 +613,7 @@
         "\n",
         "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
         "-----------------------------------------------------------------------------\n",
-        "   0  4.33e-01  6.26e+03  4.48e+00  6.27e+03    8.79e+03      0              "
+        "   0  2.61e-01  6.27e+03  4.48e+00  6.27e+03    8.80e+03      0              "
        ]
       },
       {
@@ -618,7 +621,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   1  4.33e-01  6.16e+02  3.99e+00  6.18e+02    2.57e+03      0              "
+        "   1  2.61e-01  5.22e+02  3.96e+00  5.23e+02    2.50e+03      0              "
        ]
       },
       {
@@ -626,7 +629,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   2  8.65e-02  2.66e+02  3.93e+00  2.66e+02    1.05e+03      0              "
+        "   2  5.23e-02  1.67e+02  3.91e+00  1.67e+02    8.21e+02      0              "
        ]
       },
       {
@@ -634,7 +637,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   3  8.65e-02  9.60e+01  3.92e+00  9.64e+01    3.61e+02      0   Skip BFGS  "
+        "   3  5.23e-02  7.93e+01  4.08e+00  7.95e+01    2.62e+02      0   Skip BFGS  "
        ]
       },
       {
@@ -642,7 +645,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   4  1.73e-02  7.46e+01  4.01e+00  7.46e+01    2.16e+02      0   Skip BFGS  "
+        "   4  1.05e-02  6.35e+01  4.25e+00  6.36e+01    1.71e+02      0   Skip BFGS  "
        ]
       },
       {
@@ -650,7 +653,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   5  1.73e-02  4.24e+01  4.41e+00  4.24e+01    1.20e+02      0   Skip BFGS  "
+        "   5  1.05e-02  3.01e+01  5.08e+00  3.02e+01    2.36e+02      0   Skip BFGS  "
        ]
       },
       {
@@ -658,7 +661,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   6  3.46e-03  2.73e+01  4.78e+00  2.73e+01    8.23e+01      0   Skip BFGS  "
+        "   6  2.09e-03  2.33e+01  5.32e+00  2.33e+01    1.57e+02      0   Skip BFGS  "
        ]
       },
       {
@@ -666,7 +669,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   7  3.46e-03  1.10e+01  5.73e+00  1.10e+01    2.42e+01      0   Skip BFGS  "
+        "   7  2.09e-03  1.01e+01  6.69e+00  1.01e+01    1.34e+02      0   Skip BFGS  "
        ]
       },
       {
@@ -675,16 +678,16 @@
        "text": [
         "\n",
         "------------------------- STOP! -------------------------\n",
-        "1 : |fc-fOld| = 1.6315e+01 <= tolF*(1+|f0|) = 6.2674e+02\n",
-        "0 : |xc-x_last| = 7.2794e-01 <= tolX*(1+|x0|) = 2.6641e-14\n",
-        "0 : |proj(x-g)-x|    = 2.4186e+01 <= tolG          = 1.0000e-01\n",
-        "0 : |proj(x-g)-x|    = 2.4186e+01 <= 1e3*eps       = 1.0000e-02\n",
+        "1 : |fc-fOld| = 1.3195e+01 <= tolF*(1+|f0|) = 6.2702e+02\n",
+        "0 : |xc-x_last| = 7.9764e-01 <= tolX*(1+|x0|) = 2.6641e-14\n",
+        "0 : |proj(x-g)-x|    = 1.3417e+02 <= tolG          = 1.0000e-01\n",
+        "0 : |proj(x-g)-x|    = 1.3417e+02 <= 1e3*eps       = 1.0000e-02\n",
         "1 : maxIter   =       7    <= iter          =      7\n",
         "------------------------- DONE! -------------------------\n"
        ]
       }
      ],
-     "prompt_number": 36
+     "prompt_number": 24
     },
     {
      "cell_type": "code",
@@ -695,7 +698,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 37
+     "prompt_number": 25
     },
     {
      "cell_type": "code",
@@ -730,14 +733,22 @@
      "outputs": [
       {
        "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
+       "output_type": "pyout",
+       "prompt_number": 26,
        "text": [
-        "<matplotlib.figure.Figure at 0x10ed29dd0>"
+        "<matplotlib.text.Text at 0x11049df90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA/4AAAGrCAYAAAB5QEygAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x114071f10>"
        ]
       }
      ],
-     "prompt_number": 38
+     "prompt_number": 26
     },
     {
      "cell_type": "code",
@@ -758,7 +769,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 39
+     "prompt_number": 27
     },
     {
      "cell_type": "code",

notebooks/DCexample.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:47e57cb7825ee0b5628ee304d569cc545b931cba68c34571eb6de7caf4d5ca9a"
+  "signature": "sha256:9354750ce3568fae04f3b8199a2bd9f439e665cb37041f567e2cebfbe9d7a3bb"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,7 +13,7 @@
      "collapsed": false,
      "input": [
       "from SimPEG import *\n",
-      "import simpegDC as DC\n",
+      "import simpegDCIP as DC\n",
       "%pylab inline"
      ],
      "language": "python",
@@ -32,7 +32,7 @@
        "text": [
         "Vendor:  Continuum Analytics, Inc.\n",
         "Package: mkl\n",
-        "Message: trial mode expires in 20 days\n"
+        "Message: trial mode expires in 29 days\n"
        ]
       }
      ],
@@ -92,8 +92,8 @@
        "output_type": "pyout",
        "prompt_number": 5,
        "text": [
-        "(<matplotlib.collections.QuadMesh at 0x10da55750>,\n",
-        " <matplotlib.lines.Line2D at 0x10da55cd0>)"
+        "(<matplotlib.collections.QuadMesh at 0x10d902650>,\n",
+        " <matplotlib.lines.Line2D at 0x10d902bd0>)"
        ]
       },
       {
@@ -101,7 +101,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEZCAYAAAC5AHPcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10d99ce50>"
+        "<matplotlib.figure.Figure at 0x10d864050>"
        ]
       }
      ],
@@ -139,7 +139,7 @@
        "output_type": "pyout",
        "prompt_number": 7,
        "text": [
-        "[<matplotlib.lines.Line2D at 0x10d9f2490>]"
+        "[<matplotlib.lines.Line2D at 0x10449ead0>]"
        ]
       },
       {
@@ -147,7 +147,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAVIAAAFRCAYAAAAmQSVBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHAlJREFUeJzt3X2sHOd13/HfuW+kwkr30o1Fy3oxRYVMLCSAjFQvjhyY\njnlV9gKxxCKJYySxk6qBAcEvQJNCdqwiFzWg1nUtp43hIK5tWXETMYoLKkxoWqRSNkjLlozaSFZB\ny6QKXtFkJNFUSMoiI9630z92Lu+Qmp2d4ZmZfeH3Ayy0c57zPM/cxepwZ57ZWXN3AQAu3VC3dwAA\n+h2FFACCKKQAEEQhBYAgCikABFFIASCIQoq+Zma/amZ/ldr+gZmt7d4e4XJEIUXPM7N3mdleMztl\nZq+Y2X83s3+UlevuV7r7TMXzf8TMnjKz183s4Yvabk7a/i7Zv/9hZu+qcn70vpFu7wCQx8yukvTn\nkj4s6TFJKyT9tKRzDe7GMUmflvSPJV2R0fbzkmaS7Y9I+oaktzS1c+g+PpGi122Q5O7+x97yurvv\ndvdns5LNbNHM1iXPrzCzz5nZTPJp8a/MbGXSdkfyKfekmT1tZu9utwPuvs3d/1TSKxltp939sLe+\nIjgsaVHSixX83egjfCJFr/uupAUz+5qkrZL2ufvJgn3/vaS3S3qnpJcl3SZp0cyuVetT7i+7+7fM\nbJOk/2JmP+buJ3LGs7YNZqckrZL0t5J+puD+YUDwiRQ9zd1/IOldklzSf5J03Mz+1MyuzutnZkOS\nfk3Sx939RXdfdPf/5e6zkn5Z0jfd/VvJHE9KekrSVKfdydnPCUnjahX7PzGztkUXg4dCip7n7s+5\n+6+5+/WSflzSWyX9ToduPyxppaT/l9H2Nkk/nxzWnzSzk5LuVOfzmrnF0d3PSvqEWqcjfqLDWBgg\nFFL0FXf/rqRH1CqoeU5Iel3Sj2S0HZH0dXdfnXpc6e7/rtP0BXZxWK3/r84WyMWAoJCip5nZj5rZ\nv0jOa8rMrpf0AUn/M6+fuy9K+qqkh8zsGjMbNrN3mtmYpP8s6WfN7K4kvtLMNi7NkbEPw8ki1Yik\nYTNbYWbDSdsmM7slyblK0kOSvuvuz1f1GqD3UUjR634g6XZJ+8zsNbUK6Lcl/UbS7rrwk2L6+W9K\nelbSX6u14v5vJA25+1FJd0v6LUnH1fqE+htq///Dv1LrE+b9ap1f/XtJn0raJiQ9KumUWgtjb5b0\nvkv7U9GvjBs7A0AMn0gBIIhCCgBBFFIACKKQAkDQwH1F1MxYPQNQC3fP/FLGwBXSlumG5qhynqLj\n7ZH0nkD/Mvl5OVltRWLR7SrHXHot65yj7jHz4p3ayuQUyW/33ozMVUQdY7abJxuH9gAQRCEFgCAK\nad9Z2+0dGCBru70DA2Ztt3egayikfefGbu/AAOG1rNbl+3pSSAEgiEIKAEEUUgAIopACQBCFFACC\nKKQAEEQhBYAgCikABFFIASCIQgoAQRRSAAiikAJAEIUUAIIopAAQRCEFgCAKKQAEUUgBIIhCCgBB\nFFIACKKQAkCQuXu396FSZjZYfxCAnuHulhUfaXpHmjHd0BxVzhMdr2z/Ivl5OVltRWLR7TrGbGKO\nusbMi3dqK5MTya+qb5NjtpsnG4f2ABBEIQWAIAopAARRSAEgiEIKAEEUUgAIopACQBCFFACCKKQA\nEEQhBYAgCikABFFIASCIQgoAQRRSAAiikAJAEIUUAIIopAAQRCEFgCB+swkACuI3m2qZo8p5ouOV\n7V8kPy8nq61ILLpdx5hNzFHXmHnxTm1lciL5VfVtcsx282Tj0B4AgiikABBEIQWAoK4WUjObMbNv\nm9nfmNn+JPYmM9ttZgfNbJeZTaTyP2lmh8zsOTO7q3t7DgDLuv2J1CVtdPd3uPttSewTkna7+wZJ\nf5Fsy8xulvR+STdL2izpi2bW7f0HgK4XUkm6+HKC90l6JHn+iKR7kud3S3rU3efcfUbS85JuEwB0\nWbcLqUt60syeMrNfT2Jr3P3l5PnLktYkz98q6Wiq71FJ1zazmwDQXrevI73T3V80szdL2m1mz6Ub\n3d07XGDPxfcAuq6rhdTdX0z++30z26bWofrLZvYWd3/JzK6RdDxJPybp+lT365JYhj2p52sl3Vjt\njgO4DByWNFMos2uH9mb2Q2Z2ZfJ8laS7JD0rabukDyVpH5L0ePJ8u6RfNLMxM7tR0npJ+7NHf0/q\nQREFcClu1IW1pL1ufiJdI2mbmS3txx+6+y4ze0rSY2Z2r1r/HPyCJLn7ATN7TNIBSfOS7vNBu1EA\ngL7UtULq7ocl3ZIR/ztJm9r0eVDSgzXvGgCU0u1VewDoexRSAAjifqQAUBD3I61ljirniY5Xtn+R\n/LycrLYiseh2HWM2MUddY+bFO7WVyYnkV9W3yTHbzZONQ3sACKKQAkAQhRQAgiikABBEIQWAIAop\nAARRSAEgiEIKAEEUUgAIopACQBDftQeAgviufS1zVDlPdLyy/Yvk5+VktRWJRbfrGLOJOeoaMy/e\nqa1MTiS/qr5Njtlunmwc2gNAEIUUAIIopAAQRCEFgCAKKQAEUUgBIIhCCgBBXJAPAAVxQX4tc1Q5\nT3S8sv2L5OflZLUViUW36xiziTnqGjMv3qmtTE4kv6q+TY7Zbp5sHNoDQBCFFACCKKQAEEQhBYAg\nCikABHH5EwAUxOVPtcxR5TzR8cr2L5Kfl5PVViQW3a5jzCbmqGvMvHintjI5kfyq+jY5Zrt5snFo\nDwBBFFIACKKQAkAQi00AUBCLTbXMUeU80fHK9i+Sn5eT1VYkFt2uY8wm5qhrzLx4p7YyOZH8qvo2\nOWa7ebJxaA8AQRRSAAiikAJAEItNAFAQi021zFHlPNHxyvYvkp+Xk9VWJBbdrmPMJuaoa8y8eKe2\nMjmR/Kr6Njlmu3mycWgPAEEUUgAI4hwpABTEOdJa5qhynuh4ZfsXyc/LyWorEotu1zFmE3PUNWZe\nvFNbmZxIflV9mxyz3TzZOLQHgCAKKQAEcY4UAAriHGktc1Q5T3S8sv2L5OflZLUViUW36xiziTnq\nGjMv3qmtTE4kv6q+TY7Zbp5sHNoDQBCFFACCOEcKAAVxjrSWOaqcJzpe2f5F8vNystqKxPK3f//3\nr9GGDXdp3brVmpj6Gc0vnNX83JhWrBjR6Z3/tU3sTs0vjGl+biGJbdPE1BbNL3gSm9fpnXuTvq7t\n939MWz77u1q5cljnzi1ofuGstt//qLZ89t6c2FnNL4xp+/1fTWLSuXNKxvuqtnz2l7Ry5aqk7/Ic\nC4uuQ4de0cLCou7U0VKvBedIuzlmu3myDWghRb/asGGDNm5c29q4aoWkFZqdW9TY6JDGbxhvE7tK\nklKxG5I8ZfSVpqamNP6lL0mSVqwYkbRCU1PrNf6lFTmxq5K+6ZhSsfFU3wvnuGndah089EodLxd6\nBIUUPeXs2bOSpFOnXtfE3r3av3+/Tp/+h5qcvKlD7JhOn349iZ3KiKXynnlGk3v3tmITK1vjPfNS\ngdixVOyUJiYmLoql81pz7N9/THfd9XWd1jk186kJ3cA5UvSUhx9+WFNTU5qdndWaLVv02pkzmp+f\n1/j4uI5v21ZJbOcDD+jnPv95ubuGzGqNnTlzRgcPHtTCwoLu7PaLi7B250gHtJBONzDTtDhHenFb\nkVj+9p4979bGjRtbGz/1U5KWD8+1d28lseOPP66r77nngr08/vhuXX3PZIWx5TlOnDirg4de4Rxp\nuG+TY2bP066QcvkTesq6deskSQsLi5Kk+fl5Lf1jnx9bTMUWMmLLeXNzc5Kkpc8Q8/PzmptbKBBb\nTMU8I5bOmzv/fOaF08FXBb2OQoqecuTIEUnS8HDrrTkyMqIhswKxoVRsOCO2nDc2NiZJSpo0MjKi\nsbHhArGhVMwyYum8sfPP175tPPiqoOe5+0A9JDmP/n3s2LHD3d1Pnjzp7u779u3zXbt29W1s3759\nPj4+3vXXlUc1j3Z1Z0BX7acbmqPKeaLjle1fJD8vJ6utSCx/+/jx4zp+/IxmZ02rJu/Q+jOvan5+\nTLPjK/XatifaxG7V+jMLmp9fTGLf0KrJzVp/ZjaJjeq1bU8mfWe184Hf1Oytd+gKX9ScDWn9mVe1\n84E/7hBzrT+zoJ0PPJLE5jVnI8l4j2j21lt1hVvSd3mO9Wdm9cRr41rQlZwjDfdtcsx282Tj0B49\nZe3atbr66lW67rpxjY4OafXEhMbHV2psdCgnNqrVEytTseuSvKXYaKrvSm3atEljo0NaMTZyfrxN\nm9Z1iI0mfZdiY6nx1mlsdDTVd3mO1RMrddO61d1+WVEzCil6CotN6EcUUvQUFpvQl7q9OMRiE4/0\ng8UmHr38YLGpljmqnCc6Xtn+RfLzcrLaisTyt1lsKrPdKd6prUxOJL+qvk2O2W6ebBzao6ew2IR+\nRCFFT2GxCf2IQ/uemic6Xtn+RfLzcrLaisTabx858l7dcMMNFyws+dycpOEOsaFUbDgj1mmx6VyB\n2FAqZhmxdF56sWlUBw/NlH4tim13indqK5MTya+qb5NjFsdNSy7ZtDhHenFbkVj+9o4dt2pqaurC\n29mlb5nXNnbRbfSWbnGXvo3e+bwDmpycbGSO87fRO32u49/OOdJLVceY2fP45XWHfPSr5cWmea2a\n3JixsJQV++mCi00bu7TY9IoWtMht9AYY50jRU1hsQj8a0E+k0306T3S8sv2L5OflZLUVibXfXrfu\nBUmtBaNhLS0sLUoaqyy2vNjkMrMkdqri2NJi07xmXviOpNnSr0Wx7U7xTm1lciL5VfVtcsziKKSh\nOaqcJzpe2f5F8vNystqKxPK3jxw5kiw2LS0YjcjnWivuVcWWF5ssFZuoODZ2/vnat709+cLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10d9f24d0>"
+        "<matplotlib.figure.Figure at 0x10449e510>"
        ]
       }
      ],
@@ -163,20 +163,20 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 10
+     "prompt_number": 8
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "survey = DC.SurveyDC([tx])\n",
-      "problem = DC.ProblemDC(mesh)\n",
+      "problem = DC.ProblemDC_CC(mesh)\n",
       "problem.pair(survey)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 11
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
@@ -191,7 +191,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 12
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -202,7 +202,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 13
+     "prompt_number": 12
     },
     {
      "cell_type": "code",
@@ -214,7 +214,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 13
     },
     {
      "cell_type": "code",
@@ -226,14 +226,14 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 15
+     "prompt_number": 14
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "j1 = Msig1*mesh.cellGrad*u1\n",
-      "j2 = Msig2*mesh.cellGrad*u2"
+      "j1 = Msig1*mesh.cellGrad*u1[tx, 'phi_sol']\n",
+      "j2 = Msig2*mesh.cellGrad*u2[tx, 'phi_sol']"
      ],
      "language": "python",
      "metadata": {},
@@ -249,7 +249,18 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
+     "outputs": [
+      {
+       "ename": "TypeError",
+       "evalue": "unsupported operand type(s) for -: 'FieldsDC_CC' and 'FieldsDC_CC'",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-17-fd20083b1fc4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mu2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mjs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mj1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mj2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'FieldsDC_CC' and 'FieldsDC_CC'"
+       ]
+      }
+     ],
      "prompt_number": 17
     },
     {
@@ -276,7 +287,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10de455d0>"
+        "<matplotlib.figure.Figure at 0x10de3b550>"
        ]
       }
      ],
@@ -292,25 +303,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 19,
-       "text": [
-        "(-300, 0)"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x10f693c10>"
-       ]
-      }
-     ],
-     "prompt_number": 19
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -320,8 +313,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 20
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -332,19 +324,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 0.14499957  0.80336393 -0.2125369 ]\n",
-        " [ 0.14499957  0.80336393 -0.2125369 ]\n",
-        " [ 0.14499957  0.80336393 -0.2125369 ]]\n",
-        "[ 0.7358266  0.7358266  0.7358266]\n"
-       ]
-      }
-     ],
-     "prompt_number": 21
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -359,8 +339,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 22
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -372,8 +351,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 23
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -386,8 +364,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 24
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -403,17 +380,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x111e2cdd0>"
-       ]
-      }
-     ],
-     "prompt_number": 25
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -421,8 +388,47 @@
      "input": [],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 49
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}

notebooks/DCinverse.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:8f8d2c689741d8d56146a511dd2c67e7956c3d1fe0dcef0cf550c0b5c5f9c52d"
+  "signature": "sha256:9107d99fbfb21822b71a94de16f078e82c12cf73d2e85893fd26dfda06c8ce14"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,7 +13,7 @@
      "collapsed": false,
      "input": [
       "from SimPEG import *\n",
-      "import simpegDC as DC\n",
+      "import simpegDCIP as DC\n",
       "%pylab inline\n",
       "from pymatsolver import MumpsSolver"
      ],
@@ -33,7 +33,7 @@
        "text": [
         "Vendor:  Continuum Analytics, Inc.\n",
         "Package: mkl\n",
-        "Message: trial mode expires in 20 days\n"
+        "Message: trial mode expires in 29 days\n"
        ]
       }
      ],
@@ -156,9 +156,9 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 8,
+       "prompt_number": 7,
        "text": [
-        "[<matplotlib.lines.Line2D at 0x10ea54b90>]"
+        "[<matplotlib.lines.Line2D at 0x10db0fa50>]"
        ]
       },
       {
@@ -166,11 +166,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAFRCAYAAABANxtTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10e8888d0>"
+        "<matplotlib.figure.Figure at 0x10d9ac710>"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 7
     },
     {
      "cell_type": "code",
@@ -179,7 +179,7 @@
       "expmap = Maps.ExpMap(mesh)\n",
       "m2to3  = Maps.Map2Dto3D(mesh,normal='Y')\n",
       "imap   = Maps.IdentityMap(mesh)\n",
-      "problem = DC.ProblemDC(mesh, mapping= imap )\n",
+      "problem = DC.ProblemDC_CC(mesh, mapping= imap )\n",
       "problem.Solver = MumpsSolver\n",
       "problem.pair(survey)"
      ],
@@ -204,16 +204,16 @@
        "output_type": "pyout",
        "prompt_number": 10,
        "text": [
-        "[<matplotlib.lines.Line2D at 0x1106dd910>,\n",
-        " <matplotlib.lines.Line2D at 0x1106ddb90>]"
+        "[<matplotlib.lines.Line2D at 0x1038621d0>,\n",
+        " <matplotlib.lines.Line2D at 0x103862450>]"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10d9bb3d0>"
+        "<matplotlib.figure.Figure at 0x10e82c610>"
        ]
       }
      ],
@@ -283,7 +283,7 @@
         "\n",
         "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
         "-----------------------------------------------------------------------------\n",
-        "   0  6.59e+00  1.84e+03  1.05e+04  7.11e+04    2.15e+03      0              "
+        "   0  7.28e+00  1.86e+03  1.05e+04  7.84e+04    2.20e+03      0              "
        ]
       },
       {
@@ -291,7 +291,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   1  6.59e+00  1.98e+02  1.07e+04  7.10e+04    1.89e+02      0              "
+        "   1  7.28e+00  6.57e+02  1.06e+04  7.80e+04    1.15e+03      1              "
        ]
       },
       {
@@ -299,7 +299,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   2  1.32e+00  1.70e+02  1.07e+04  1.43e+04    2.63e+02      0              "
+        "   2  1.46e+00  5.58e+02  1.06e+04  1.61e+04    1.03e+03      3   Skip BFGS  "
        ]
       },
       {
@@ -307,7 +307,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   3  1.32e+00  9.16e+01  1.07e+04  1.42e+04    1.81e+02      0              "
+        "   3  1.46e+00  1.36e+02  1.08e+04  1.59e+04    1.69e+02      0   Skip BFGS  "
        ]
       },
       {
@@ -315,7 +315,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "------------------------------------------------------------------"
+        "   4  2.91e-01  8.24e+01  1.07e+04  3.20e+03    2.05e+02      0              "
        ]
       },
       {
@@ -323,10 +323,37 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "0 :    ft     = 1.4181e+04 <= alp*descent     = 1.4181e+04\n",
-        "1 : maxIterLS =      10    <= iterLS          =     10\n",
-        "------------------------- End Linesearch -------------------------\n",
-        "The linesearch got broken. Boo.\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"
        ]
       }
      ],
@@ -346,15 +373,15 @@
        "output_type": "pyout",
        "prompt_number": 14,
        "text": [
-        "<matplotlib.colorbar.Colorbar instance at 0x10daf85a8>"
+        "<matplotlib.colorbar.Colorbar instance at 0x10fcbaa28>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10eeb9190>"
+        "<matplotlib.figure.Figure at 0x103879a90>"
        ]
       }
      ],
@@ -385,21 +412,30 @@
        "output_type": "pyout",
        "prompt_number": 16,
        "text": [
-        "[<matplotlib.lines.Line2D at 0x110194e10>,\n",
-        " <matplotlib.lines.Line2D at 0x1101a10d0>]"
+        "[<matplotlib.lines.Line2D at 0x10f42a790>,\n",
+        " <matplotlib.lines.Line2D at 0x10f42aa10>]"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10ed69dd0>"
+        "<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,

notebooks/IPinverse.ipynb

@@ -0,0 +1,586 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:7a25294a07f33a379f31ea5a89ae7d8acb31779a85ce8f5d01cd1d33d466c89b"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "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 30 days\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "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": [
+      "eta = np.zeros_like(sigma)\n",
+      "eta[blk_ind] = 0.1"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sigmaInf = sigma.copy()\n",
+      "sigma0 = sigma*(1-eta)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "dat = mesh.plotSlice(eta, normal='Y')\n",
+      "plt.colorbar(dat[0])\n",
+      "axis('equal')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 7,
+       "text": [
+        "(-600.0, 600.0, -600.0, 0.0)"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x10d87e190>"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "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": 48
+    },
+    {
+     "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": 49
+    },
+    {
+     "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": 50,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x1120eb310>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x111b05c50>"
+       ]
+      }
+     ],
+     "prompt_number": 50
+    },
+    {
+     "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",
+      "try:\n",
+      "    problem.pair(survey)\n",
+      "except Exception, e:\n",
+      "    survey.unpair()\n",
+      "    problem.pair(survey)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 51
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "phi0 = survey.dpred(sigma0)\n",
+      "phiInf = survey.dpred(sigmaInf)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 52
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "phiIP_true = phi0-phiInf"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 53
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# F = problem.fields(mesh, survey)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 54
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "surveyIP = DC.SurveyIP(srcList)\n",
+      "problemIP = DC.ProblemIP(mesh, sigma=sigma)\n",
+      "problemIP.pair(surveyIP)\n",
+      "problemIP.Solver = MumpsSolver"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 55
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 55
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "datasyn = surveyIP.dpred(eta)\n",
+      "surveyIP.makeSyntheticData(eta,std=0.02,force=True)\n",
+      "plot(np.c_[surveyIP.dobs,datasyn])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 56,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x111ccbf10>,\n",
+        " <matplotlib.lines.Line2D at 0x111ccbfd0>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x111af69d0>"
+       ]
+      }
+     ],
+     "prompt_number": 56
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "problemIP.mapping = imap"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 57
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "dmis = DataMisfit.l2_DataMisfit(surveyIP)\n",
+      "dmis.Wd = 1./abs(phi0)*1e3\n",
+      "reg = Regularization.Tikhonov(mesh,mapping=imap)\n",
+      "# opt = Optimization.InexactGaussNewton(maxIter=4,tolX=1e-15)\n",
+      "opt = Optimization.ProjectedGNCG(maxIter=3,tolX=1e-15)\n",
+      "opt.remember('xc')\n",
+      "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
+      "beta = Directives.BetaEstimate_ByEig(beta0_ratio=1e-1)\n",
+      "betaSched = Directives.BetaSchedule(coolingFactor=5, coolingRate=4)\n",
+      "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaSched])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 64
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "reg.alpha_s = 1e-4\n",
+      "reg.alpha_x = 1.\n",
+      "reg.alpha_z = 1.\n",
+      "reg.alpha_y = 1."
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 65
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "opt.upper = Inf\n",
+      "opt.lower = 0."
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 66
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "m0 = np.ones(problemIP.mapping.nP)*1e-10\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",
+        "=============================== Projected GNCG ==============================="
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+        "-----------------------------------------------------------------------------\n",
+        "   0  5.74e+00  9.43e+01  4.96e-16  9.43e+01    1.69e+03      0              "
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "   1  5.74e+00  7.46e+00  6.14e-03  7.49e+00    3.03e+02      0              "
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "   2  5.74e+00  3.18e+00  1.34e-02  3.25e+00    2.68e+02      0   Skip BFGS  "
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "   3  5.74e+00  2.15e+00  1.83e-02  2.26e+00    2.91e+02      0   Skip BFGS  "
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "------------------------- STOP! -------------------------\n",
+        "1 : |fc-fOld| = 9.9700e-01 <= tolF*(1+|f0|) = 9.5290e+00\n",
+        "0 : |xc-x_last| = 4.8192e-02 <= tolX*(1+|x0|) = 1.0000e-15\n",
+        "0 : |proj(x-g)-x|    = 2.9060e+02 <= tolG          = 1.0000e-01\n",
+        "0 : |proj(x-g)-x|    = 2.9060e+02 <= 1e3*eps       = 1.0000e-02\n",
+        "1 : maxIter   =       3    <= iter          =      3\n",
+        "------------------------- DONE! -------------------------\n"
+       ]
+      }
+     ],
+     "prompt_number": 67
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "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": 74
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "colorbar(mesh.plotSlice(mopt, normal='Y', ind=17, grid=True, gridOpts={'alpha': 0.2, 'color':'black'})[0])\n",
+      "axis('equal')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 75,
+       "text": [
+        "(-600.0, 600.0, -600.0, 0.0)"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x11255c8d0>"
+       ]
+      }
+     ],
+     "prompt_number": 75
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# sigma_est = expmap * m2to3 * mopt"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 69
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "dpred = surveyIP.dpred(mopt)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 70
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot(np.c_[dpred,surveyIP.dobs], '.-')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 71,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x112618d10>,\n",
+        " <matplotlib.lines.Line2D at 0x112618f50>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x111cc5750>"
+       ]
+      }
+     ],
+     "prompt_number": 71
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 24
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}

simpegDCIP/BaseDC.py

@@ -1,27 +1,70 @@
 from SimPEG import *
 
 
+
+class FieldsDC_CC(Problem.Fields):
+    knownFields = {'phi_sol':'CC'}
+    aliasFields = {
+                    'phi' : ['phi_sol','CC','_phi'],
+                    'e' : ['phi_sol','F','_e'],
+                    'j' : ['phi_sol','F','_j']
+                  }
+
+    def __init__(self,mesh,survey,**kwargs):
+        super(FieldsDC_CC, self).__init__(mesh, survey, **kwargs)
+
+    def startup(self):
+        self._cellGrad = self.survey.prob.mesh.cellGrad
+
+    def _phi(self, phi_sol, srcList):
+        phi = phi_sol
+        for i, src in enumerate(srcList):
+            phi_p = src.phi_p(self.survey.prob)
+            if phi_p is not None:
+                phi[:,i] += phi_p     
+        return phi
+
+    def _e(self, phi_sol, srcList):
+        e = -self._cellGrad*phi_sol
+        for i, src in enumerate(srcList):
+            e_p = src.e_p(self.survey.prob)
+            if e_p is not None:
+                e[:,i] += e_p     
+        return e
+    
+    def _j(self, phi_sol, srcList):
+        j = -self.survey.prob.Msig*self._cellGrad*phi_sol
+        for i, src in enumerate(srcList):
+            j_p = src.j_p(self.survey.prob)
+            if j_p is not None:
+                j[:,i] += j_p
+        return j
+
+
+
 class SrcDipole(Survey.BaseSrc):
     """A dipole source, locA and locB are moved to the closest cell-centers"""
 
     current = 1
     loc  = None
-    _rhsDict = None
+    # _rhsDict = None
 
     def __init__(self, rxList, locA, locB, **kwargs):
         self.loc = (locA, locB)
         super(SrcDipole, self).__init__(rxList, **kwargs)
 
-    def getRhs(self, mesh):
-        if getattr(self, '_rhsDict', None) is None:
-            self._rhsDict = {}
-        if mesh not in self._rhsDict:
-            pts = [self.loc[0], self.loc[1]]
-            inds = Utils.closestPoints(mesh, pts)
-            q = np.zeros(mesh.nC)
-            q[inds] = - self.current * ( np.r_[1., -1.] / mesh.vol[inds] )
-            self._rhsDict[mesh] = q
-        return self._rhsDict[mesh]
+    def eval(self, prob):
+        # Recompute rhs 
+        # if getattr(self, '_rhsDict', None) is None:
+        #     self._rhsDict = {}
+        # if mesh not in self._rhsDict:
+        pts = [self.loc[0], self.loc[1]]
+        inds = Utils.closestPoints(prob.mesh, pts)
+        q = np.zeros(prob.mesh.nC)
+        q[inds] = - self.current * ( np.r_[1., -1.] / prob.mesh.vol[inds] )
+        # self._rhsDict[mesh] = q
+        # return self._rhsDict[mesh]
+        return q
 
 
 class RxDipole(Survey.BaseRx):
@@ -53,7 +96,7 @@ class SurveyDC(Survey.BaseSurvey):
     def __init__(self, srcList, **kwargs):
         self.srcList = srcList
         Survey.BaseSurvey.__init__(self, **kwargs)
-        self._rhsDict = {}
+        # self._rhsDict = {}
         self._Ps = {}
 
     def projectFields(self, u):
@@ -64,13 +107,7 @@ class SurveyDC(Survey.BaseSurvey):
                 d_\\text{pred} = Pu(m)
         """
         P = self.getP(self.prob.mesh)
-        return P*mkvc(u)
-
-    def getRhs(self, mesh):
-        if mesh not in self._rhsDict:
-            RHS = np.array([src.getRhs(mesh) for src in self.srcList]).T
-            self._rhsDict[mesh] = RHS
-        return self._rhsDict[mesh]
+        return P*mkvc(u[self.srcList, 'phi_sol'])
 
     def getP(self, mesh):
         if mesh in self._Ps:
@@ -82,9 +119,7 @@ class SurveyDC(Survey.BaseSurvey):
         return self._Ps[mesh]
 
 
-
-
-class ProblemDC(Problem.BaseProblem):
+class ProblemDC_CC(Problem.BaseProblem):
     """
         **ProblemDC**
 
@@ -94,6 +129,8 @@ class ProblemDC(Problem.BaseProblem):
 
     surveyPair = SurveyDC
     Solver     = Solver
+    fieldsPair = FieldsDC_CC
+    Ainv = None
 
     def __init__(self, mesh, **kwargs):
         Problem.BaseProblem.__init__(self, mesh)
@@ -143,13 +180,25 @@ class ProblemDC(Problem.BaseProblem):
             self._A = self._A.tocsc()
         return self._A
 
+    def getRHS(self):
+        # if self.mesh not in self._rhsDict:
+        RHS = np.array([src.eval(self) for src in self.survey.srcList]).T
+        # self._rhsDict[mesh] = RHS
+        # return self._rhsDict[mesh]
+        return RHS
+
     def fields(self, m):
+
+        F = self.fieldsPair(self.mesh, self.survey)        
         self.curModel = m
         A    = self.A
-        Ainv = self.Solver(A, **self.solverOpts)
-        Q    = self.survey.getRhs(self.mesh)
-        Phi  = Ainv * Q
-        return Phi
+        self.Ainv = self.Solver(A, **self.solverOpts)
+        RHS    = self.getRHS()
+        Phi  = self.Ainv * RHS   
+        Srcs = self.survey.srcList
+        F[Srcs, 'phi_sol'] = Phi
+
+        return F
 
     def Jvec(self, m, v, u=None):
         """
@@ -178,10 +227,9 @@ class ProblemDC(Problem.BaseProblem):
         sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
         if u is None:
             # Run forward simulation if $u$ not provided
-            u = self.fields(self.curModel)
+            u = self.fields(self.curModel)[self.survey.srcList, 'phi_sol']
         else:
-            shp = (self.mesh.nC, self.survey.nSrc)
-            u = u.reshape(shp, order='F')
+            u = u[self.survey.srcList, 'phi_sol']
 
         D = self.mesh.faceDiv
         G = self.mesh.cellGrad
@@ -199,9 +247,12 @@ class ProblemDC(Problem.BaseProblem):
         # Take derivative of $C(m,u)$ w.r.t. $u$
         dCdu = self.A
         # Solve for $\deriv{u}{m}$
-        dCdu_inv = self.Solver(dCdu, **self.solverOpts)
+        # dCdu_inv = self.Solver(dCdu, **self.solverOpts)
+        if self.Ainv is None:
+            self.Ainv = self.Solver(dCdu, **self.solverOpts)
+
         P        = self.survey.getP(self.mesh)
-        J_x_v    = - P * mkvc( dCdu_inv * dCdm_x_v )
+        J_x_v    = - P * mkvc( self.Ainv * dCdm_x_v )
         return J_x_v
 
     def Jtvec(self, m, v, u=None):
@@ -209,10 +260,12 @@ class ProblemDC(Problem.BaseProblem):
         self.curModel = m
         sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
         if u is None:
-            u = self.fields(self.curModel)
+            # Run forward simulation if $u$ not provided
+            u = self.fields(self.curModel)[self.survey.srcList, 'phi_sol']
+        else:
+            u = u[self.survey.srcList, 'phi_sol']
 
-        shp = (self.mesh.nC, self.survey.nSrc)
-        u = u.reshape(shp, order='F')
+        shp = u.shape
         P = self.survey.getP(self.mesh)
         PT_x_v = (P.T*v).reshape(shp, order='F')
 
@@ -221,9 +274,13 @@ class ProblemDC(Problem.BaseProblem):
         A = self.A
         mT_dm = self.mapping.deriv(m)
 
+        # We probably always need this due to the linesearch .. (?)
         dCdu = A.T
-        Ainv = self.Solver(dCdu, **self.solverOpts)
-        w = Ainv * PT_x_v
+        self.Ainv = self.Solver(dCdu, **self.solverOpts)
+        # if self.Ainv is None:
+        #     self.Ainv = self.Solver(dCdu, **self.solverOpts)
+
+        w = self.Ainv * PT_x_v
 
         Jtv = 0
         for i, ui in enumerate(u.T):  # loop over each column

simpegDCIP/BaseIP.py

@@ -0,0 +1,182 @@
+from SimPEG import *
+from BaseDC import SurveyDC, FieldsDC_CC
+
+class SurveyIP(SurveyDC):
+    """
+        **SurveyDC**
+
+        Geophysical DC resistivity data.
+
+    """
+
+    def __init__(self, srcList, **kwargs):
+        self.srcList = srcList
+        Survey.BaseSurvey.__init__(self, **kwargs)
+        self._Ps = {}
+    
+    def dpred(self, m, u=None):
+        """
+            Predicted data.
+
+            .. math::
+                d_\\text{pred} = Pu(m)
+        """
+
+        return self.prob.forward(m)
+
+
+class ProblemIP(Problem.BaseProblem):
+    """
+        **ProblemIP**
+
+        Geophysical IP resistivity problem.
+
+    """
+
+    surveyPair = SurveyDC
+    Solver     = Solver
+    sigma = None
+    Ainv = None
+    u = None
+
+    def __init__(self, mesh, **kwargs):
+        Problem.BaseProblem.__init__(self, mesh)
+        self.mesh.setCellGradBC('neumann')
+        Utils.setKwargs(self, **kwargs)
+
+    # deleteTheseOnModelUpdate = ['_A', '_Msig', '_dMdsig']
+
+    @property
+    def Msig(self):
+        if getattr(self, '_Msig', None) is None:
+            # sigma = self.curModel.transform
+            sigma = self.sigma
+            Av = self.mesh.aveF2CC
+            self._Msig = Utils.sdiag(1/(self.mesh.dim * Av.T * (1/sigma)))
+        return self._Msig
+
+    @property
+    def dMdsig(self):
+        if getattr(self, '_dMdsig', None) is None:
+            # sigma = self.curModel.transform
+            sigma = self.sigma
+            Av = self.mesh.aveF2CC
+            dMdprop = self.mesh.dim * Utils.sdiag(self.Msig.diagonal()**2) * Av.T * Utils.sdiag(1./sigma**2)
+            self._dMdsig = lambda Gu: Utils.sdiag(Gu) * dMdprop
+        return self._dMdsig
+
+    @property
+    def A(self):
+        """
+            Makes the matrix A(m) for the DC resistivity problem.
+
+            :param numpy.array m: model
+            :rtype: scipy.csc_matrix
+            :return: A(m)
+
+            .. math::
+                c(m,u) = A(m)u - q = G\\text{sdiag}(M(mT(m)))Du - q = 0
+
+            Where M() is the mass matrix and mT is the model transform.
+        """
+        if getattr(self, '_A', None) is None:
+            D = self.mesh.faceDiv
+            G = self.mesh.cellGrad
+            self._A = D*self.Msig*G
+            # Remove the null space from the matrix.
+            self._A[-1,-1] /= self.mesh.vol[-1]
+            self._A = self._A.tocsc()
+        return self._A
+
+    def getRHS(self):
+        # if self.mesh not in self._rhsDict:
+        RHS = np.array([src.eval(self) for src in self.survey.srcList]).T
+        # self._rhsDict[mesh] = RHS
+        # return self._rhsDict[mesh]
+        return RHS
+
+    def fields(self, m):
+        if self.u is None:
+	        A    = self.A
+	        if self.Ainv == None:
+	        	self.Ainv = self.Solver(A, **self.solverOpts)	        	
+	        Q    = self.getRHS()
+	        self.u  = self.Ainv * Q
+        return self.u
+
+    def forward(self, m, u=None):
+        # Set current model; clear dependent property $\mathbf{A(m)}$
+        self.curModel = m
+        # sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
+        sigma = self.sigma
+        if self.u is None:
+            # Run forward simulation if $u$ not provided
+            u = self.fields(sigma)
+
+        shp = (self.mesh.nC, self.survey.nSrc)
+        u = self.u.reshape(shp, order='F')
+
+        D = self.mesh.faceDiv
+        G = self.mesh.cellGrad
+        # Derivative of model transform, $\deriv{\sigma}{\m}$
+        # dsigdm_x_v = self.curModel.transformDeriv * v
+
+        dsigdm_x_v = Utils.sdiag(sigma) * self.curModel.transformDeriv *  m
+
+        # Take derivative of $C(m,u)$ w.r.t. $m$
+        dCdm_x_v = np.empty_like(u)
+        # loop over fields for each source
+        for i in range(self.survey.nSrc):
+            # Derivative of inner product, $\left(\mathbf{M}_{1/\sigma}^f\right)^{-1}$
+            dAdsig         = D * self.dMdsig( G * u[:,i] )
+            dCdm_x_v[:, i] = dAdsig *  dsigdm_x_v
+
+        # Take derivative of $C(m,u)$ w.r.t. $u$
+
+        if self.Ainv == None:
+        	self.Ainv = self.Solver(A, **self.solverOpts)
+
+        # dCdu = self.A
+        # Solve for $\deriv{u}{m}$
+        # dCdu_inv = self.Solver(dCdu, **self.solverOpts)
+        P        = self.survey.getP(self.mesh)
+        J_x_v    = - P * mkvc( self.Ainv * dCdm_x_v )
+        return -J_x_v
+
+    def Jvec(self, m, v, u=None):
+        return self.forward(v)
+
+    def Jtvec(self, m, v, u=None):
+
+        self.curModel = m
+        # sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
+        sigma = self.sigma
+        if self.u is None:
+            u = self.fields(sigma)
+        else:
+        	u = self.u
+        shp = (self.mesh.nC, self.survey.nSrc)
+        u = u.reshape(shp, order='F')
+        P = self.survey.getP(self.mesh)
+        PT_x_v = (P.T*v).reshape(shp, order='F')
+
+        D = self.mesh.faceDiv
+        G = self.mesh.cellGrad
+        A = self.A
+        mT_dm = Utils.sdiag(sigma)*self.mapping.deriv(m)
+        # mT_dm = self.mapping.deriv(m)
+
+        # dCdu = A.T
+        # Ainv = self.Solver(dCdu, **self.solverOpts)
+        # if self.Ainv == None:
+    	self.Ainv = self.Solver(A.T, **self.solverOpts)
+
+        w = self.Ainv * PT_x_v
+
+        Jtv = 0
+        for i, ui in enumerate(u.T):  # loop over each column
+            Jtv += self.dMdsig( G * ui ).T * ( D.T * w[:,i] )
+
+        Jtv = - mT_dm.T * ( Jtv )
+        return -Jtv
+

simpegDCIP/Examples/Verification.py

@@ -1,5 +1,5 @@
 from SimPEG import *
-import simpegDC as DC
+import simpegDCIP as DC
 import matplotlib.pyplot as plt
 
 
@@ -26,7 +26,7 @@ def run(plotIt=False):
     rx = DC.RxDipole(xyz_rxP, xyz_rxN)
     src = DC.SrcDipole([rx], [-200, 0, -12.5], [+200, 0, -12.5])
     survey = DC.SurveyDC([src])
-    problem = DC.ProblemDC(mesh)
+    problem = DC.ProblemDC_CC(mesh)
     problem.pair(survey)
     try:
         from pymatsolver import MumpsSolver

simpegDCIP/Examples/WennerArray.py

@@ -1,5 +1,5 @@
 from SimPEG import *
-import simpegDC as DC
+import simpegDCIP as DC
 import matplotlib.pyplot as plt
 
 
@@ -55,7 +55,7 @@ def example(aSpacing=2.5, nElecs=10, plotIt=False):
 
     srcList = getSrcList(nElecs, aSpacing, in2D=True)
     survey = DC.SurveyDC(srcList)
-    problem = DC.ProblemDC(mesh)
+    problem = DC.ProblemDC_CC(mesh)
     problem.pair(survey)
 
     return mesh, survey, problem

simpegDCIP/Tests/test_forward_DCproblem.py

@@ -38,8 +38,8 @@ class DCProblemTests(unittest.TestCase):
         u = np.random.rand(self.mesh.nC*self.survey.nSrc)
         v = np.random.rand(self.mesh.nC)
         w = np.random.rand(self.survey.dobs.shape[0])
-        wtJv = w.dot(self.p.Jvec(self.m0, v, u=u))
-        vtJtw = v.dot(self.p.Jtvec(self.m0, w, u=u))
+        wtJv = w.dot(self.p.Jvec(self.m0, v))
+        vtJtw = v.dot(self.p.Jtvec(self.m0, w))
         passed = np.abs(wtJv - vtJtw) < 1e-10
         print 'Adjoint Test', np.abs(wtJv - vtJtw), passed
         self.assertTrue(passed)

simpegDCIP/Tests/test_forward_DCproblem_analytic.py

@@ -8,6 +8,5 @@ class DCAnalyticTests(unittest.TestCase):
         self.assertTrue(DC.Examples.Verification.run() < 0.1)
 
 
-
 if __name__ == '__main__':
     unittest.main()

simpegDCIP/Tests/test_forward_IPproblem.py

@@ -0,0 +1,57 @@
+import unittest
+import simpegDC as DC
+from SimPEG import *
+from pymatsolver import MumpsSolver
+
+class IPforwardTests(unittest.TestCase):
+
+    def test_IPforward(self):
+ 
+		cs = 12.5
+		nc = 500/cs+1
+		hx = [(cs,7, -1.3),(cs,nc),(cs,7, 1.3)]
+		hy = [(cs,7, -1.3),(cs,int(nc/2+1)),(cs,7, 1.3)]
+		hz = [(cs,7, -1.3),(cs,int(nc/2+1))]
+		mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')
+		sighalf = 1e-2
+		sigma = np.ones(mesh.nC)*sighalf
+		p0 = np.r_[-50., 50., -50.]
+		p1 = np.r_[ 50.,-50., -150.]
+		blk_ind = Utils.ModelBuilder.getIndecesBlock(p0, p1, mesh.gridCC)
+		sigma[blk_ind] = 1e-3
+		eta = np.zeros_like(sigma)
+		eta[blk_ind] = 0.1
+		sigmaInf = sigma.copy()
+		sigma0 = sigma*(1-eta)
+
+		nElecs = 11
+		x_temp = np.linspace(-250, 250, nElecs)
+		aSpacing = x_temp[1]-x_temp[0]
+		y_temp = 0.
+		xyz = Utils.ndgrid(x_temp, np.r_[y_temp], np.r_[0.])
+		srcList = DC.Examples.WennerArray.getSrcList(nElecs,aSpacing)
+		survey = DC.SurveyDC(srcList)
+
+		imap   = Maps.IdentityMap(mesh)
+		problem = DC.ProblemDC_CC(mesh, mapping= imap )
+		problem.Solver = MumpsSolver
+		problem.pair(survey)
+		
+		phi0 = survey.dpred(sigma0)
+		phiInf = survey.dpred(sigmaInf)
+
+		phiIP_true = phi0-phiInf
+
+		surveyIP = DC.SurveyIP(srcList)
+		problemIP = DC.ProblemIP(mesh, sigma=sigma)
+		problemIP.pair(surveyIP)
+		problemIP.Solver = MumpsSolver
+		phiIP_approx = surveyIP.dpred(eta)
+
+		err =  np.linalg.norm(phiIP_true-phiIP_approx) / np.linalg.norm(phiIP_true)
+		
+		self.assertTrue(err < 0.02)
+
+
+if __name__ == '__main__':
+    unittest.main()

simpegDCIP/Tests/test_sens_IPproblem.py

@@ -0,0 +1,80 @@
+import unittest
+from SimPEG import *
+import simpegDC as DC
+from pymatsolver import MumpsSolver
+
+
+
+class IPProblemTests(unittest.TestCase):
+
+    def setUp(self):
+
+        cs = 12.5
+        nc = 500/cs+1
+        hx = [(cs,0, -1.3),(cs,nc),(cs,0, 1.3)]
+        hy = [(cs,0, -1.3),(cs,int(nc/2+1)),(cs,0, 1.3)]
+        hz = [(cs,0, -1.3),(cs,int(nc/2+1))]
+        mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')
+        sighalf = 1e-2
+        sigma = np.ones(mesh.nC)*sighalf
+        p0 = np.r_[-50., 50., -50.]
+        p1 = np.r_[ 50.,-50., -150.]
+        blk_ind = Utils.ModelBuilder.getIndecesBlock(p0, p1, mesh.gridCC)
+        sigma[blk_ind] = 1e-3
+        eta = np.zeros_like(sigma)
+        eta[blk_ind] = 0.1
+
+        nElecs = 5
+        x_temp = np.linspace(-250, 250, nElecs)
+        aSpacing = x_temp[1]-x_temp[0]
+        y_temp = 0.
+        xyz = Utils.ndgrid(x_temp, np.r_[y_temp], np.r_[0.])
+        srcList = DC.Examples.WennerArray.getSrcList(nElecs,aSpacing)
+        survey = DC.SurveyIP(srcList)
+        imap   = Maps.IdentityMap(mesh)
+        problem = DC.ProblemIP(mesh, sigma=sigma, mapping= imap)
+        problem.pair(survey)
+        problem.Solver = MumpsSolver
+
+        mSynth = eta
+        survey.makeSyntheticData(mSynth)
+
+        # Now set up the problem to do some minimization
+        dmis = DataMisfit.l2_DataMisfit(survey)
+        reg = Regularization.Tikhonov(mesh)
+        opt = Optimization.InexactGaussNewton(maxIterLS=20, maxIter=10, tolF=1e-6, tolX=1e-6, tolG=1e-6, maxIterCG=6)
+        invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta=1e4)
+        inv = Inversion.BaseInversion(invProb)
+
+        self.inv = inv
+        self.reg = reg
+        self.p = problem
+        self.mesh = mesh
+        self.m0 = mSynth
+        self.survey = survey
+        self.dmis = dmis
+
+    def test_misfit(self):
+        derChk = lambda m: [self.survey.dpred(m), lambda mx: self.p.Jvec(self.m0, mx)]
+        passed = Tests.checkDerivative(derChk, self.m0*0, plotIt=False)
+        self.assertTrue(passed)
+
+    def test_adjoint(self):
+        # Adjoint Test
+        u = np.random.rand(self.mesh.nC*self.survey.nSrc)
+        v = np.random.rand(self.mesh.nC)
+        w = np.random.rand(self.survey.dobs.shape[0])
+        wtJv = w.dot(self.p.Jvec(self.m0, v))
+        vtJtw = v.dot(self.p.Jtvec(self.m0, w))
+        passed = np.abs(wtJv - vtJtw) < 1e-10
+        print 'Adjoint Test', np.abs(wtJv - vtJtw), passed
+        self.assertTrue(passed)
+
+    def test_dataObj(self):
+        derChk = lambda m: [self.dmis.eval(m), self.dmis.evalDeriv(m)]
+        passed = Tests.checkDerivative(derChk, self.m0, plotIt=False)
+        self.assertTrue(passed)
+
+
+if __name__ == '__main__':
+    unittest.main()

simpegDCIP/__init__.py

@@ -1,2 +1,3 @@
 from BaseDC import *
+from BaseIP import *
 import Examples