Changing figures for simpegPaper

.gitignore

@@ -38,3 +38,6 @@ nosetests.xml
 *.sublime-project
 *.sublime-workspace
 docs/_build/
+
+
+*.ipynb_checkpoints

notebooks/DC_schumberger_FWD.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:4c81eac7c432b351e702d9aa1f39f2d14f9487903d874e4c95342ece5a8bb266"
+  "signature": "sha256:73d0cc81c1a231c2a808e89facb3717f7496e7473309088a86f2512a257912a1"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -67,7 +67,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 113
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -78,7 +78,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 114
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
@@ -89,16 +89,24 @@
      "language": "python",
      "metadata": {},
      "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "C:\\Users\\SEOGI\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\matplotlib\\lines.py:503: RuntimeWarning: invalid value encountered in greater_equal\n",
+        "  return np.alltrue(x[1:] - x[0:-1] >= 0)\n"
+       ]
+      },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x4ecf510>"
+        "<matplotlib.figure.Figure at 0x3d01c18>"
        ]
       }
      ],
-     "prompt_number": 115
+     "prompt_number": 4
     },
     {
      "cell_type": "heading",
@@ -118,7 +126,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 121
+     "prompt_number": 5
     },
     {
      "cell_type": "heading",
@@ -184,7 +192,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 140
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -195,7 +203,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 141
+     "prompt_number": 7
     },
     {
      "cell_type": "code",
@@ -214,7 +222,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 142
+     "prompt_number": 8
     },
     {
      "cell_type": "code",
@@ -244,7 +252,7 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 124,
+       "prompt_number": 9,
        "text": [
         "(-600, 600)"
        ]
@@ -252,49 +260,46 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x65e8150>"
+        "<matplotlib.figure.Figure at 0xa8d19b0>"
        ]
       }
      ],
-     "prompt_number": 124
+     "prompt_number": 9
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "fig, ax = plt.subplots(1,1, figsize = (5,3))\n",
-      "mesh.plotSlice(sigma, grid=True, ax = ax, pcolorOpts={'cmap':'binary'})\n",
+      "fig, ax = plt.subplots(1,1, figsize = (6,4))\n",
       "ax.plot(xtemp_txP, ytemp_tx, 'bo')\n",
       "ax.plot(xtemp_txN, ytemp_tx, 'ro')\n",
       "ax.plot(xtemp_rxP, ytemp_rx, 'ko')\n",
       "ax.plot(xtemp_rxN, ytemp_rx, 'go')\n",
+      "ax.legend(('A (C+)', 'B (C-)', 'M (P+)', 'N (C-)'), fontsize = 14)\n",
+      "mesh.plotSlice(sigma, grid=True, ax = ax, pcolorOpts={'cmap':'binary'})\n",
       "ax.set_xlim(-600, 600)\n",
       "ax.set_ylim(-200, 200)\n",
-      "ax.set_title('Survey geometry (Plan view)')"
+      "ax.set_title('Survey geometry (Plan view)')\n",
+      "ax.set_xlabel('x (m)')\n",
+      "ax.set_ylabel('y (m)')\n",
+      "ax.text(-600, 210, '(a)', fontsize = 16)\n",
+      "fig.savefig('DCsurvey.png', dpi = 200)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 144,
-       "text": [
-        "<matplotlib.text.Text at 0x62fa710>"
-       ]
-      },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAEiCAYAAADNgWQ8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3U1v21ie7/GvnBokq5LiegOxlN7HsqvvrtsoPy3SuAO0\nH1Izq7lAOfYFbtKbip10N9CZRU/FyawSDCq2az0zsZ1ZxYuK7YartnGc2rct1RuIY2eVAJXwLg5p\nURRJUU8UJf0+gGCJ/yPqiJL4N885PAQRERERERERERERERERERERERERaZ1lYDRi2QywDqRbVx0R\nEelGy8A3NT5nENhvQV1ERKRLjQHHdT53HbjVxLq0whgmMe4Dh5g6Z4GXwEAb6yWVBmnPZ3Kd0vf4\nOuZ78tG+HVP5/Rm0yy7b8Y/AXIz1jSILvKH19XqOfkdSg23g2zqfO0X9ySoOY5idxCXXslHMD/Ej\ncKUNdZJgK0Rv1m2W68D3Psv3gQ+Uf0fS9vKPlOo5aD/+qoV1rEcG893/fYtfZ8B+HTWvS1UZ/H8s\nGWAD89/LIeZHNhXyfL9YEmzj31Q4iqn3F/FWR6o4JN7PJIv5HlzyiW0HxKbs5euedSQt4cTpHqXt\nIRH0tbsCbTJs/y14lm9g/mOZAC4DO/YyrxPPepImCwz5LN8FDmKui4RbxnxeqZhf8yXwcw3PKdp/\nB0NL9ZYVYBo1rUXWqwkna//1Nou9ASzXY2dwwKUq60mal5Sa1W5R/oOYseNgdjxOM9sXrmVO+/wt\nn2VTmOaYbXs9jym1+z+xy08DR55l2M/bxxxBPqe8T8BZx/eYpD8WsA4v56jUvd5lSkeo1V7bseiK\nu0ciLlLeX7FhPz60H2coHRUc+qy32vuect1/TunINGib72P+s653e2Gvb6dKGS/nn6tq/7CsYN6H\n0/dzzxVbpPR9m7PLOtuyWp/oCuHfs3XMd9H73XUEfQaLrvV+i9mWLyl9nr+3yzqv4245cJLwUpW6\nS4+bJrgvI23HH1Nqt/YrF+WH3S7uNnfn9gbznrz/jTlNJe4mnQF72deuZU5z3D37OY8xbf2XKCWd\nT13lb1HeR+bsQJ0yzuumXeW99Zij+jbeBl4EvB/n9au99gpm5+K4R/k/I05/xbG93jRmp+XslK7Y\nyw6p/Cem2mv7bX9H2Davd3uNEd4U5iRP9/cki9nhOq/tLPOuZ4Py7ejUf85nmfPcTykl0Gr9WM77\nD/ue+X13q30Gzuu71+skIMcc/n2+h/j3hYmccX4s3h/5NOYL9BXmh+V8MYMSTq1DquM2ivmR/h3z\nQ3V+6O7/wp0dkHtbOH1UX/uU+7v9OE2pY9b5kbv/m92n9AN21ufeXhmf57j7CMDs1C+FvsPK9Xp3\nhNVe2ynvfq/O+3H+S85TuQOa8nmekwScOkd5384/P34JJ2ybQ33by3m9oE5199Gae5Tat551+yWc\nx8Brz/q879/Zlt7EGOX3VO175q6X87lE+Qyc53g/b/c/D+v4b9uXJHsAUaL0apOa03fjbhLLYr5U\n94DvMO3bnwU833nei4B4O2Uw/9GB6bNZAH6F6ZNyDv3vNLB+pynmFPgf+37RXn7dfpzFbOO39mOn\nOWacUpPGOua/Zvcon/uU2sSdbfxzlfqsYpoJHdOYZlGnntVeO2/H3f15TlPJmOe1Xrrun/o8z1mW\nifjaUfltc6hve51UiTvGMPUfxnx3/m+EdS9gfjNjmN/Rc3t5xqes32/Hr5xbte+ZnyifQQHTVDhv\nP563bxnMPxYZ+7V+DngNK2C5eHzS7gq00SpmcMB39mP3TuI7zBds0V6WA35yPTePaaJy//iToh9z\n+O9tVy4CDzDvxbsj9Qrrm3oZsHwZ89/xHGbAwmNXzNm2jyltbz/fYLa588N/HFLWcR+YpfTfuIV5\njz9HfO3pCK/h8PtPNmwHHvV9uw3Yr3PqWha0zevZXk6C7I9Yn1pMYxLNY+C/gduYI4WwetQq7Hvm\nJ+pnsGLfxuz1zrtu/SGvk6H+9yI9JI35Ybv/03Ta7g8xh/yfYv4jek15k8dLkjsc1GkuCOqEXaa8\nOcOvSe06wU1qYec3HNo373+vTlOI90ebofIEvXXKm5GqeUN5k4pXtdf2Np9BZbOM08Tifu9h281p\ngo3yvr3r2XA9P8o2r3V7QWXzoFvQsGgvb5Oa89iv6cr9Wn7bslqdvIK+Z+56fO15HOW75zSjOc+d\no/q27YSmdUmIOWofRz9NcgcLQOlH/oHKpJPF/KCueJa527MzlAYcuNvKnZ1f2LlHzg/0a5+Ys2N0\nnp/BJHNv/5jTQe+3Di/3zmTRvl2n8giu2ms/pryze5nyvghnJ+neQTl9Ie6ObifhuPvIqr22e/s7\n294RZZvXsr0ctwienskZoVVt+LNT7znPY3cz2rZrmfOPXdB78ksKQcK+Z97+GIj+3VunfFBCmvDB\nQc734lLEektCDWK+VLcwXwLviJlbmB/6LcqPUMJiQaaIfpZ3mvKdcBJlMP+ROaN/nDbrdfx/ZGC2\ngdNJ/ITSTuwj5r9IJ/7B/hv2H91rgo847lEaybVP8MmOhyHr8HIPl3XfvDvUaq/t7ISfUzq6hdJA\nkg+Y7XoL8910L3O+q8euZe7/4Ku9tvvI2om5X7faNq9lezmOKU8q7qltPtjxF/j/hqYozUhw7Kqb\nsw2OMSO3rriWPaH8e+Rsy0FKI/4+EP0ox+97Nuqq12vKt1mU794olSPO1gPKgjkarXe2EkmINJXD\nKN3/fe57ym6ExHQWcGfIUP4fcNQfcRb/s/Sd/4C7Vb3by83ZOUt9xkjmoCGpUZ7yBOM0EX1qx557\nyh+7nhcUk2Q7wnzmWSqHuYbxNn05rtPdO4N6t5fXIMmfhDapojb/SQe45Lo/RmmnMk3lUYtzdndQ\nTBNVJp/T7PI9tbWHpyn1vzhDXvcx//HXuxPuBPVuLxGpYp1Se/h1ghOO3wAAJRwRkYRI+omfc5jx\n/M75Lq+pPDmsH3PuxXFATEREEiDJJ36OYtqq/+ZaVsA/ifyESZ5BsTK5XM46OjpqRh1FRHrFEWbW\nibol9QgnjzlicZKNczb4K0+5LGasP1TOYuuOlTk6OsKyLCzLzEjh3I9yi1I+rEyrY3/5y18SU5de\nen/NWp/7/SXlven9df77q6VMUHnMDB4NSeIRjjPyxu0I2LTvO+c8FIDPKR9CHRYTEZE2SmLCKRB+\n5PWK0pHO0xpiIiLSRkltUpM6jYyMtLsKLaX319n0/npbnJe1TRLLaZ9MpVJnbZVRRCkfVkax7owl\npR6KKRamkf1dKpWCBnOGjnBERCQWPXuE0+4KiEiwTz75hF9++aXd1eg5EbZ7QzkjiYMGYqEmNcXU\npNaZMWkd73b3aVJriJrUREQkFko4IiISCyUcEZEWWVpaYm1trabn7O7u+q5nYmLi7Lazs8Pw8HCk\n9RWLRYrFYk11aBUlHBGRFlldXWVlZSVy+aWlpYq+kvHxcd6+fcvz5895/vw5GxsbzM/PR04iAwMD\nLC0t8eqVd2YwiYvlcN+PIkr5sDKKdWcsKfXohVin2N7etlKplNXX12cVCoVI5WdmZsqWLS8vW6lU\nqqLswcGB7/IgJycnVi6Xq1rOu929+8pGd7w6whERaQHn6MayrEhHOfPz8ywsLJQtu337NjMzMxVl\nBwcHyeUq59IsFou+TXLpdJp8Pl9z816zKeGISMfZ2vqRyck/MzJyl8nJP7O19WMi1uV2cHDA3Nwc\ng4ODrK6uhpYtFAoUi8WyfpmDAzMBfjab9X3O/r53jmN4+fIlS0tLvuWHh4drat5rhZ49D0dEOtPW\n1o/84Q/fc3T017NlR0d/AuDq1d+0bV1um5ubjI+PA/Dll1+ytLTE7u4uo6OjvuWd5PLpp6UroxcK\nBQA+++wz3+ek0+mKZWHnymQymbPXaRcd4YhIR3n48HlZggA4Ovorjx75Xv4qtnW5rayscP36dQDm\n5ubOlgVxkoubc2Tz+vXryK9rhZws299vrk/59u3byOtrNh3hiEhHef/ef7f17t25tq7LcXJywu7u\n7lmicWxubnJ6eup7ZOLXbOYs80tGYPp87t+/z4sXL5iYmCiL9fWZY4mZmRmePHlSFnMfRcVNRzgi\n0lHOn/ef6+vChQ9tXZdjdXWV+/fvs7+/f3bb2NgAYH193fc5fgknk8kwPT3N9rb/0VaxWCSdTjM2\nNkahUKBQKLC8vEw+nz977B4kUCgUGBoaqvt9Sf0ChwHWOmyw1jKKdWcsKfXohdizZz9YudwfLbDO\nbrncHevZsx8CnxPHuhyZTMY6ODjwXR42NDmXy1k7Oztly5zhzPPz82XLZ2ZmrN3d3Yp1bGxsWEND\nQ77rn56eth48eBBad+929+4rW7tbbi+/tL4MfASOMZehHnTFspjLS4/afyuPW0ss3XTTLdm3MM+e\n/WBNTv7Z+u1v/2JNTv65oQTRrHW9efPGymazVl9fn3Xx4kVrbW3NsiyTNMbGxqy+vj6rr6/PGh4e\ntk5OTiqev7m5WXEejmN+ft4aHx8/u/klG2cdw8PDvnWLeh5OlVtDknh5glEgBzymsslvDggaSL4P\nOGMK03a52YCy9rbVbNGKNSeWlHr0Qqybzc7OcufOHQYHB6sXrsHCwgKzs7N88cUXoeUizBbddRdg\n2wXCB61XymOOehynwFjTaiQiEoP19fXAQQL1KhaLLCwsVE02cejEUWpTwAkwDnyDSS5Ze5nbMXAF\n+CnW2omINGBqaqqp6xsYGGjq+hrRaQlnH3BmoDvGHA0NA/1tq5GIiESSxCa1MK889/PAp5jkk/GU\nVRISEUmQTko4ecwRjtdboIB/glFzmohIQnRSk9oRps/GMQZs2Pe9EwRl8R9Wfebu3btn9/Line truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x4ec6210>"
+        "<matplotlib.figure.Figure at 0x16750ba8>"
        ]
       }
      ],
-     "prompt_number": 144
+     "prompt_number": 36
     },
     {
      "cell_type": "code",
@@ -309,7 +314,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 129
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -318,12 +323,13 @@
       "survey = DC.SurveyDC(txlist)\n",
       "problem = DC.ProblemDC(mesh)\n",
       "problem.pair(survey)\n",
-      "problem.Solver = SolverWrapD(EMUtils.Solver.Mumps)"
+      "problem.Solver = SolverLU\n",
+      "# problem.Solver = SolverWrapD(EMUtils.Solver.Mumps)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 133
+     "prompt_number": 12
     },
     {
      "cell_type": "heading",
@@ -342,7 +348,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 134
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -366,29 +372,31 @@
       "ax.set_xlabel('AB/2')\n",
       "ax.set_ylabel('Apparent resistivity ($\\Omega m$)')\n",
       "ax.grid(True)\n",
-      "ax.legend(('True', 'simpegDC'), loc = 1, fontsize = 14)"
+      "ax.text(100, 122, '(b)', fontsize = 16)\n",
+      "ax.legend(('True', 'simpegDC'), loc = 1, fontsize = 14)\n",
+      "fig.savefig('comp_dc.png')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 148,
-       "text": [
-        "<matplotlib.legend.Legend at 0x9a06b90>"
-       ]
-      },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x934c3d0>"
+        "<matplotlib.figure.Figure at 0x171e7dd8>"
        ]
       }
      ],
-     "prompt_number": 148
+     "prompt_number": 40
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}

notebooks/DC_schumberger_Inv.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:19c3127b59d003240c9fc3ef942fdeb1b3f06fc6f97ca83dc7d01ca16af87352"
+  "signature": "sha256:18a2474a85f20bef4b2c8819f0b010465a78239cea5edd4974658b8b0ed69b50"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -16,20 +16,26 @@
       "import simpegDC as DC\n",
       "from simpegem1d import Utils1D\n",
       "import simpegEM.Utils as EMUtils\n",
-      "%pylab inline"
+      "%matplotlib inline"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Populating the interactive namespace from numpy and matplotlib\n"
-       ]
-      }
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib\n",
+      "import matplotlib.pyplot as plt\n",
+      "# matplotlib.rcParams.update({'font.size': 16, 'text.usetex': True, 'font.family': 'arial'})\n",
+      "matplotlib.rcParams.update({'font.size': 16, 'font.family': 'arial'})"
      ],
-     "prompt_number": 89
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 10
     },
     {
      "cell_type": "heading",
@@ -75,7 +81,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 90
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -86,7 +92,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 91
+     "prompt_number": 12
     },
     {
      "cell_type": "code",
@@ -100,13 +106,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXeYFGX2tu+q6tw9gSQqSJKcBAMguwYUCYKiIAZQUcGA\niSAqa0L0p366KoqgGFZ0ESMKohIE11UMgC5IEkRAEBEkTexc4fujqJrunp7cM0MPdV/XXDI91W9X\n2289feq85z0PWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWBwjCKX9UdM0raZO\nxMLCwqKuIAhCidoq1uSJWFhYWBzrWKJrYWFhUYNYomthYWFRg1iia2FhYVGDWKJrYWFhUYNYomth\nYWFRg1iia2FhYVGDWKJrYWFhUYNYomthYWFRg1iia2FhYVGDWKJrYWFhUYNYomthYWFRg1iia1Fl\nFEVBlmWs/kgWFmVjq+0TsEhPNE1D0zSi0SiRSARZljEaK0mShN1uR5IkRFFEFEVKabpkYXFMYYmu\nRYWIFVu/348oithsNgRBQBRFwuEwsiyjKErc80RRRJIk88cSY4tjFaufrkW5iBVbVVUBCAQCqKqK\noihommYKqCAI2O12U1gTx4jFEmOLukhp/XQt0bUoFU3TUFUVWZZRVRVBEFBVlXA4TCgUQpIk3G63\nGdlGIhFTgFVVNf9tiKkhrLFzMvY4A0uMLdIZS3QtKkxJYhsKhYhEIjgcDkAXR7vdjizLZnpBEATz\n78Y4iT+applCGvtjzFUjKk4mxoYg22w2S4wtjkpKE10rp2sRh6ZpcdUIsZFtJBLB6XSSlZWFKIoE\ng8FiomiMYSAIghmtJh4TK8JG2iKZGAuCECfGoVAIRVFwOp3meEZu2YiKJUmKe56FxdGCJboWQHKx\n1TSNQCBANBqNE9vSMJ5XFlURY2P8RDGOTW0YJKYoLDG2qG0s0T3GKUtsXS4XHo+nTLFNFeURY+Nc\njUi7tMg4caHP+G+sGMfmmS0xtqhuLNE9RjHE1u/3m/lRVVUJBoPIsozL5cLr9ZYpQjWV9k8UY0VR\ncLlcFU5TGF8eyaouAEuMLaodS3SPMTRNM+tojVtyh8NBKBQyxdbn85VLZI4GIUp1ztgQY9ArMYzF\nOuM1Ehfvjob/BxbphSW6xwiG2MqyDOhipSgKiqIQDAZxu93lFtt0oDQxVhQlLlVh1B3HlrSJooiq\nqnH/VhSFSCQSN54lxhYVxRLdOk6i2AKm0Bqi4nK54ioBUvGaRyuCIGCzxU97ozTNEGJFUYhGo2be\nOFaIY4XVeE6iGBtpDEuMLZJhiW4dJZnYyrJMKBRCVVXcbjcOh4PCwsJKC0GySoV0FBVDDBMXC/1+\nv/llFCvGRt1ySXXGlhhblIYlunWMksQ2GAwC4HK5cDgccVt2qxqZGmP5/dCzZxa//y5xxx0ROnZU\n6NBBpX17FY+nSi9RjJqIpg0xTJaiSBYZV1aMFUXBbrcnbRJkiXHdwxLdOoJxMZcktm63G7vdXi0X\nsd8PL73kYNo0Fzk5+vizZ9sBOwDRKJx4omaKcMeO+k/r1ipHNq5ViJoSomQbP4zXTxYZV1aMQ6GQ\nmTdOfJ1kW6FrqnzPonqwtgGnOcZiUGxXr2g0SigUAsoWW6NkzOVyVfi1Dx+OMGuWyIwZHg4fjh/f\n6y2aOrIMdnvx5xcW6s+pX1/l1lujdOyo0qGDQosWGgnBZRyyLBONRnG73RU+54rg9/txu91VFrlE\nMY79MSLg2N10ZTUJMsQ4NkVhbPqwODqwei/UQQyxNXoheL1eotEowWDQXBwrT2RbGdHdvRvatCl9\n4c0QXVkGSYJY3TLENpaMjKKppijQtq1qpiYWLLAxZ06Qk07SEIT0E92SMAQ1EAhgt9vjhLmkyDjx\nubG/J+aLkzUXsqgZLNGtQ8TWnAKm0IJe8uR2u83+tuUhEAggCEK5BKygAGbNknjwwbKzUh6PLpDR\nqB7lCkJysTXw+eJFOtnxmZnaESGWads2QqdOEpIE55yjJBuyylS36BoUFhbGbUQxBDW2tK28HdsS\nGwUlirHVsa1msBrepDnJetmCXrxvCK7P58Oe7B4+BRQUwEsvSUybJpk527IIBIqOSyhtLRVVhVAo\n+WuoKvz8s8imTQ4KC4si7YYNVTp1Us188datIhMmRGjUKD1jhtJyxuXd8JEoqoqimF+wxjwxjrPE\nuGaxIt2jmJLE1uhla7PZsNvthMNhsrKyKvUapUW6BQXQqFHq6ndLw+vV8PtLv9h9Pq3E1IQgQH5+\n/N+aNlVp2VJlxQobd94Z4bLLorRureJ2U2rO2KC2It2KUt72mbIsm3Mm9rlWY/nUY6UX0ozEXrYG\nsWJrpBFkWcbv91dadI2mMZ6Ymq78/KLINje3bl9oU6eGsdk0rr8+is8X/7eaEF1N0/D7/VUS3dLG\njhXhWPPQsiJjS4yrhiW6aUJpLg3hcBi73Y7b7Y6rG1UUhYKCArKzsyv1mrGim58Pxx1XM5FtOjBx\nYiF33KFSv75AdemKIbq+RMWvBoLBoFkLXJnG8sb5WmJcNpboHuWU1ji8JLE1qKrohkIhfvlF4+OP\nfcycKZGXd+xeKOXlqadCjBoVxeWiymJcG6KbuA069lxSJcayLJuvdSyKsSW6RylGvjYajZqTMVZs\nHQ4HLpcrqdgaqKpKXl4e9erVq/Dr5+XB889rPP54xWt0LeI54QSVp54KM3CgXKENH0eT6JZEZcTY\n7/ebKSvjrs3gWIiMLdE9yoiNbCORCOFwGJ/PRzAYNFstljeXWBnRzcuDc8+1s3mztbOpujnjDIVH\nHw3Ts6eSdPHO6GHs9Xqr/VwCgQBOp7PUL/GKELutOVaMDZJt3ogtaUvc8VeXxNgS3aOEZGmESCRC\nIBAAwOl04nK5KrRwo2kaOTk51K9fv8xjv/lG4O67baxda4ltbXPppVEeeCBMq1YKoVB6im4yYjd8\nOByOYrvvSutLUZoYJ274ONrF2BLdWqaknG0wGCQajQKUy3+spLFzcnKoV69eiZNwxQqBCy6oRJMD\nixpl0KAoY8ZE6dZNoUGD1I9fUyVwyVImZW2FrooYH43+d5bo1hKJLg2JYmts1S0sLKz0QhjA4cOH\nk4ru22+L3HBD9WyYsKgZpkwJ07Gjgt8vcOmlcrnqi0uipkS3IimTVIhxKBTC4XAgiiLr1q1j27Zt\njB49ulrfY1lYO9JqmGQuDSX5j8U2qkkVb70lMmaMJbZ1galTi0r4brgBTjtNoX17lblz7Tz7bIjB\ng2UaN9aqraStuklFx7bYrc579uzh0KFDtfRuyocV6aaQZGJruDQYYutyueIi0qpUHxjk5OSQlZXF\n3Lk2brzREtu6isOhEYnEX7IZGRo2G3TsqLBjh8jevSJLlgRo314hMc1f1Z1v5UVRFMLhcNyGm1SR\nLDI2rrfLL78cURTxeDyMHDmSTp060alTJxzlKCd59dVXeeqpp9izZw/dunXj2WefpVevXpU+Tyu9\nUM0kE1ujA5jhWut0OpNO9ooshJXEyy8HGTeu8ukJi/TF5dKK9aqoX18lFBLMBkEdO6o0aaLyyy8K\njz+ukJGRvqKbiJE/9ng8bNu2jX//+9/s27cPgE2bNjFnzhy6d+9e6hhvvvkmo0ePZsqUKZxxxhlM\nnz6db7/9lnXr1tGiRYtKnZclutWEUVNreI1BvP9YaWJrUJ6FsJL46iuB/v2tBTKLeLKzNcJhcLs1\nNE2Ia1Lkcmkcf7whxkVN5du0UUmVTV5Ntd6E4ot2jz/+OOeeey79+/cv9/NbtmzJoEGDmDlzJqCf\nf7t27Rg8eDDPP/98pc7LyummmFiXBr/fb66gJvqPlUdEK3Ord+gQNGlibde1SE44DE4nHD5cfMFM\nkvRGRqtXi6xeXfR3v19AkvT/XnVVlEGDZDp0UGjVqvSG8rVNYkVDRXdnbtu2jd9//52LL77YfMxm\nszFo0CCWLFmS0nM1x6+WUesoyVwaNE0jHA4jCEIx/7HyYqzGlvU8S2wtykMwKHCk42cxHA5IvH9N\nFOd33rHz+ecSmqaP1aZNUdvMDh0UOnZUzYbyySjPXE4Via+Vn59foeZPW7duBaB169Zxj7ds2ZLt\n27dXy3uxRLccJBNbw39MVVXsdnuVFijKMoc8eBA6dHBQUJCmS9QWRw2CUDTPVFUotYtcKCTgcsGe\nPQJ79kgsXy4BdhRFQFGgZUuV9eslnngixG23Rc3n1WZWsqKL0vn5+QBkZGTEPZ6RkYGqqtWyRdsS\n3RIoqZdtov9Y7OJZqjl4EJo2tSJbi9SRLOWQiM2moSj6fBbFRH82CIV0QV6/Xs87fP+9FCe6+nG1\nF+lWJL1gfEGUdL7VUdNs7QdNwChHMXoixNri5OfnEw6HcbvdZGZmmqmEqn6zJ45x4AA88IBEu3bW\nIplFzeNw6MKabGHt4EGxWLVE06bx87820wvRaLRCDipGKqKgoCDu8YKCAiRJqpYKDCvSPUJJjcMj\nkYhpke31eov5j6Vichmie+AADBtmj1vgsLCoaQyrJf2GTkCSiiLfZDRpopb4t5qkrKg1GW3atAFg\nx44dtGrVynx8x44dtGvXLrUneIRj/uo2+iJEIhEikYj5wUUiEfLz84lGo3i9XjIzM5O666Yi0j14\nUODBBx20a+ewBNfiqKM0wQU44YSjJ9KFiovuSSedxPz5883HotEon332Geeff37KzjOWYzbSTdaE\nBvSm3oYljs/nK7P3aFVEd/9+aNbMCVh5W4v0pUmT2ls4i712K3MdCoLA5MmTuf3226lXrx69e/dm\nxowZHD58mAkTJqT6dIFjUHRjxTa2rjZWbDMyMsrd6Lkyort/P0ybJvHyy0dxAaSFRTlp3DLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x4702410>"
+        "<matplotlib.figure.Figure at 0xa8833c8>"
        ]
       }
      ],
-     "prompt_number": 92
+     "prompt_number": 13
     },
     {
      "cell_type": "heading",
@@ -132,7 +138,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 138
+     "prompt_number": 14
     },
     {
      "cell_type": "code",
@@ -154,43 +160,41 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEKCAYAAAD6q1UVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x5424110>"
+        "<matplotlib.figure.Figure at 0x39f9080>"
        ]
       }
      ],
-     "prompt_number": 139
+     "prompt_number": 15
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "fig, ax = plt.subplots(1,1, figsize = (3, 6))\n",
-      "Utils1D.plotLayer(1./sigma, mesh.vectorCCz, 'log', ax = ax)\n",
-      "ax.set_ylim(-600, 0)"
+      "fig, ax = plt.subplots(1,1, figsize = (7, 7))\n",
+      "Utils1D.plotLayer(np.log(sigma), mesh.vectorCCz, 'linear', showlayers=True, ax = ax)\n",
+      "ax.invert_xaxis()\n",
+      "ax.set_ylim(-500, 0)\n",
+      "ax.set_xlim(-7, -4)\n",
+      "ax.set_xlabel('$log(\\sigma)$', fontsize = 20)\n",
+      "ax.set_ylabel('z (m)', fontsize = 22)\n",
+      "ax.text(-7., 10., '(a)', fontsize = 30)\n",
+      "fig.savefig('logcond1d.png', dpi=200)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 140,
-       "text": [
-        "(-600, 0)"
-       ]
-      },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x4b9a090>"
+        "<matplotlib.figure.Figure at 0xab254a8>"
        ]
       }
      ],
-     "prompt_number": 140
+     "prompt_number": 16
     },
     {
      "cell_type": "code",
@@ -199,32 +203,28 @@
       "fig, ax = plt.subplots(1,1, figsize = (7, 5))\n",
       "dat = mesh.plotSlice((mapping*mtrue), normal='Y', ind = 9, ax = ax)\n",
       "cb = plt.colorbar(dat[0], ax =ax)\n",
-      "ax.set_title(\"Vertical section\")\n",
-      "cb.set_label(\"Conductivity (S/m)\")\n",
+      "ax.set_title(\"Vertical section\", fontsize = 16)\n",
+      "cb.set_label(\"Conductivity (S/m)\", fontsize = 16)\n",
+      "ax.set_xlabel('x (m)', fontsize = 16)\n",
+      "ax.set_ylabel('z (m)', fontsize = 16)\n",
       "ax.set_xlim(-1000., 1000.)\n",
-      "ax.set_ylim(-500., 0.)"
+      "ax.set_ylim(-500., 0.)\n",
+      "ax.text(-1000., 20., '(b)', fontsize = 20)\n",
+      "fig.savefig('cond3d.png', dpi=200)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 213,
-       "text": [
-        "(-500.0, 0.0)"
-       ]
-      },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x6cda950>"
+        "<matplotlib.figure.Figure at 0xaaec3c8>"
        ]
       }
      ],
-     "prompt_number": 213
+     "prompt_number": 17
     },
     {
      "cell_type": "heading",
@@ -290,7 +290,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 142
+     "prompt_number": 33
     },
     {
      "cell_type": "code",
@@ -309,7 +309,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 143
+     "prompt_number": 34
     },
     {
      "cell_type": "code",
@@ -331,7 +331,7 @@
        ]
       }
      ],
-     "prompt_number": 144
+     "prompt_number": 35
     },
     {
      "cell_type": "code",
@@ -361,7 +361,7 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 145,
+       "prompt_number": 36,
        "text": [
         "(-0.2, 0.6)"
        ]
@@ -369,13 +369,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x4da7b90>"
+        "<matplotlib.figure.Figure at 0xad05e48>"
        ]
       }
      ],
-     "prompt_number": 145
+     "prompt_number": 36
     },
     {
      "cell_type": "code",
@@ -397,21 +397,21 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 220,
+       "prompt_number": 37,
        "text": [
-        "<matplotlib.text.Text at 0xee2d3d0>"
+        "<matplotlib.text.Text at 0xec092e8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x77ef110>"
+        "<matplotlib.figure.Figure at 0xe4a4630>"
        ]
       }
      ],
-     "prompt_number": 220
+     "prompt_number": 37
     },
     {
      "cell_type": "markdown",
@@ -434,7 +434,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 147
+     "prompt_number": 41
     },
     {
      "cell_type": "heading",
@@ -450,12 +450,13 @@
      "input": [
       "problem = DC.ProblemDC(mesh, mapping=mapping)\n",
       "problem.pair(survey)\n",
-      "problem.Solver = SolverWrapD(EMUtils.Solver.Mumps)"
+      "problem.Solver = SolverLU\n",
+      "# problem.Solver = SolverWrapD(EMUtils.Solver.Mumps)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 216
+     "prompt_number": 42
     },
     {
      "cell_type": "heading",
@@ -474,7 +475,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 149
+     "prompt_number": 43
     },
     {
      "cell_type": "markdown",
@@ -499,7 +500,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 217
+     "prompt_number": 44
     },
     {
      "cell_type": "code",
@@ -507,37 +508,40 @@
      "input": [
       "appres = data*np.pi*b*(b+a)/a\n",
       "appres_obs = survey.dobs*np.pi*b*(b+a)/a\n",
-      "fig, ax = plt.subplots(1,1, figsize = (6, 4))\n",
-      "ax.semilogx(abhalf, appres, '.-')\n",
-      "ax.semilogx(abhalf, appres_obs, '.-')\n",
-      "ax.set_xscale('log')\n",
-      "ax.set_ylim(100., 180.)\n",
-      "ax.set_xlabel('AB/2')\n",
-      "ax.set_ylabel('Apparent resistivity ($\\Omega m$)')\n",
-      "ax.grid(True)\n",
-      "ax.legend(('Observed', 'Predicted'), loc = 1)"
+      "fig, ax = plt.subplots(1,2, figsize = (12, 4))\n",
+      "ax[1].semilogx(abhalf, appres, 'k.-')\n",
+      "ax[1].set_xscale('log')\n",
+      "ax[1].set_ylim(100., 180.)\n",
+      "ax[1].set_xlabel('AB/2')\n",
+      "ax[1].set_ylabel('Apparent resistivity ($\\Omega m$)')\n",
+      "ax[1].grid(True)\n",
+      "ax[1].text(100, 183, '(c)', fontsize = 16)\n",
+      "# ax[1].legend(('Observed', 'Predicted'), loc = 1)\n",
+      "\n",
+      "dat = mesh.plotSlice((mapping*mtrue), normal='Y', ind = 9, ax = ax[0])\n",
+      "cb = plt.colorbar(dat[0], ax =ax[0])\n",
+      "ax[0].set_title(\"Vertical section\", fontsize = 16)\n",
+      "cb.set_label(\"Conductivity (S/m)\", fontsize = 14)\n",
+      "ax[0].set_xlabel('x (m)', fontsize = 16)\n",
+      "ax[0].set_ylabel('z (m)', fontsize = 16)\n",
+      "ax[0].set_xlim(-1000., 1000.)\n",
+      "ax[0].set_ylim(-500., 0.)\n",
+      "ax[0].text(-1000, 20, '(b)', fontsize = 16)\n",
+      "fig.savefig('DCfwd.png', dpi=200)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 214,
-       "text": [
-        "<matplotlib.legend.Legend at 0xee25050>"
-       ]
-      },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEXCAYAAABsyHmSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEjCAYAAACciN+wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0xe215c90>"
+        "<matplotlib.figure.Figure at 0xebfdc88>"
        ]
       }
      ],
-     "prompt_number": 214
+     "prompt_number": 72
     },
     {
      "cell_type": "heading",