Figures for simpegPaper

notebooks/DC_schumberger_Inv.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:18a2474a85f20bef4b2c8819f0b010465a78239cea5edd4974658b8b0ed69b50"
+  "signature": "sha256:6f2469c3448302728c618fff9269b3bdf8a5a014311fdeb21e9d78722d80229d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -16,11 +16,19 @@
       "import simpegDC as DC\n",
       "from simpegem1d import Utils1D\n",
       "import simpegEM.Utils as EMUtils\n",
-      "%matplotlib inline"
+      "%pylab inline"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n"
+       ]
+      }
+     ],
      "prompt_number": 1
     },
     {
@@ -29,13 +37,13 @@
      "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'})"
+      "matplotlib.rcParams.update({'font.size': 16, 'text.usetex': True, 'font.family': 'arial'})\n",
+      "# matplotlib.rcParams.update({'font.size': 16, 'font.family': 'arial'})"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 10
+     "prompt_number": 2
     },
     {
      "cell_type": "heading",
@@ -81,7 +89,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 11
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
@@ -92,7 +100,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 12
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -103,16 +111,24 @@
      "language": "python",
      "metadata": {},
      "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "/usr/local/lib/python2.7/dist-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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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0xa8833c8>"
+        "<matplotlib.figure.Figure at 0x2825f10>"
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 5
     },
     {
      "cell_type": "heading",
@@ -138,35 +154,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "dat = mesh.plotImage(mapping*mtrue)\n",
-      "print (mapping*mtrue).min()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0.002\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAETCAYAAAAlCTHcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x39f9080>"
-       ]
-      }
-     ],
-     "prompt_number": 15
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -188,13 +176,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0xab254a8>"
+        "<matplotlib.figure.Figure at 0x42f2950>"
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 7
     },
     {
      "cell_type": "code",
@@ -218,13 +206,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0xaaec3c8>"
+        "<matplotlib.figure.Figure at 0x43f5a90>"
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 8
     },
     {
      "cell_type": "heading",
@@ -290,7 +278,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 33
+     "prompt_number": 9
     },
     {
      "cell_type": "code",
@@ -309,7 +297,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 34
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
@@ -331,7 +319,7 @@
        ]
       }
      ],
-     "prompt_number": 35
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -361,7 +349,7 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 36,
+       "prompt_number": 12,
        "text": [
         "(-0.2, 0.6)"
        ]
@@ -369,49 +357,46 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0xad05e48>"
+        "<matplotlib.figure.Figure at 0x43f1b10>"
        ]
       }
      ],
-     "prompt_number": 36
+     "prompt_number": 12
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "fig, ax = plt.subplots(1,1, figsize = (5,3))\n",
-      "mesh.plotSlice(np.log10(mapping*mtrue), 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.set_xlim(-1000, 1000)\n",
+      "ax.legend(('A (C+)', 'B (C-)', 'M (P+)', 'N (C-)'), fontsize = 14)\n",
+      "mesh.plotSlice(np.log10(mapping*mtrue), 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": 37,
-       "text": [
-        "<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 0xe4a4630>"
+        "<matplotlib.figure.Figure at 0x43efed0>"
        ]
       }
      ],
-     "prompt_number": 37
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -434,7 +419,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 41
+     "prompt_number": 14
     },
     {
      "cell_type": "heading",
@@ -450,13 +435,13 @@
      "input": [
       "problem = DC.ProblemDC(mesh, mapping=mapping)\n",
       "problem.pair(survey)\n",
-      "problem.Solver = SolverLU\n",
-      "# problem.Solver = SolverWrapD(EMUtils.Solver.Mumps)"
+      "# problem.Solver = SolverLU\n",
+      "problem.Solver = SolverWrapD(EMUtils.Solver.Mumps)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 42
+     "prompt_number": 15
     },
     {
      "cell_type": "heading",
@@ -474,8 +459,17 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 43
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "/usr/local/lib/python2.7/dist-packages/mumps/__init__.py:204: RuntimeWarning: undestroyed DMumpsContext\n",
+        "  RuntimeWarning)\n"
+       ]
+      }
+     ],
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -500,7 +494,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 44
+     "prompt_number": 17
     },
     {
      "cell_type": "code",
@@ -508,7 +502,7 @@
      "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(1,2, figsize = (12, 4))\n",
+      "fig, ax = plt.subplots(1,2, figsize = (14, 4))\n",
       "ax[1].semilogx(abhalf, appres, 'k.-')\n",
       "ax[1].set_xscale('log')\n",
       "ax[1].set_ylim(100., 180.)\n",
@@ -535,13 +529,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEjCAYAAACciN+wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA2EAAAEpCAYAAAAamCoXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0xebfdc88>"
+        "<matplotlib.figure.Figure at 0x4bce110>"
        ]
       }
      ],
-     "prompt_number": 72
+     "prompt_number": 20
     },
     {
      "cell_type": "heading",
@@ -567,7 +561,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 162
+     "prompt_number": 21
     },
     {
      "cell_type": "code",
@@ -604,7 +598,7 @@
         "\n",
         "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
         "-----------------------------------------------------------------------------\n",
-        "   0  9.67e-02  6.33e+03  2.24e+04  8.50e+03    9.06e+03      0              "
+        "   0  4.75e-01  6.33e+03  2.24e+04  1.70e+04    1.16e+04      0              "
        ]
       },
       {
@@ -612,7 +606,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   1  9.67e-02  4.67e+02  7.43e+03  1.19e+03    2.80e+03      0              "
+        "   1  4.75e-01  1.14e+03  5.22e+03  3.61e+03    4.96e+03      0              "
        ]
       },
       {
@@ -620,7 +614,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   2  1.93e-02  1.95e+02  4.57e+03  2.84e+02    4.36e+02      0              "
+        "   2  9.49e-02  2.92e+02  4.20e+03  6.90e+02    1.07e+03      0   Skip BFGS  "
        ]
       },
       {
@@ -628,7 +622,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   3  1.93e-02  1.02e+02  7.15e+03  2.40e+02    1.26e+02      0              "
+        "   3  9.49e-02  2.21e+02  4.47e+03  6.45e+02    1.21e+02      0   Skip BFGS  "
        ]
       },
       {
@@ -636,7 +630,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   4  3.87e-03  1.11e+02  6.52e+03  1.37e+02    2.18e+02      0              "
+        "   4  1.90e-02  2.01e+02  4.51e+03  2.87e+02    3.76e+02      0              "
        ]
       },
       {
@@ -644,7 +638,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   5  3.87e-03  3.10e+01  1.73e+04  9.80e+01    1.06e+02      0              "
+        "   5  1.90e-02  1.20e+02  7.00e+03  2.53e+02    1.17e+02      0              "
        ]
       },
       {
@@ -652,7 +646,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   6  7.73e-04  3.94e+01  1.45e+04  5.07e+01    1.01e+02      0              "
+        "   6  3.80e-03  1.28e+02  6.48e+03  1.52e+02    2.18e+02      0              "
        ]
       },
       {
@@ -660,7 +654,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   7  7.73e-04  1.04e+01  3.03e+04  3.38e+01    3.64e+01      0              "
+        "   7  3.80e-03  4.19e+01  1.76e+04  1.09e+02    1.41e+02      0              "
        ]
       },
       {
@@ -669,27 +663,27 @@
        "text": [
         "\n",
         "------------------------- STOP! -------------------------\n",
-        "1 : |fc-fOld| = 1.6880e+01 <= tolF*(1+|f0|) = 8.5000e+02\n",
-        "0 : |xc-x_last| = 1.3315e+00 <= tolX*(1+|x0|) = 2.6641e-14\n",
-        "0 : |proj(x-g)-x|    = 3.6437e+01 <= tolG          = 1.0000e-01\n",
-        "0 : |proj(x-g)-x|    = 3.6437e+01 <= 1e3*eps       = 1.0000e-02\n",
+        "1 : |fc-fOld| = 4.3690e+01 <= tolF*(1+|f0|) = 1.6953e+03\n",
+        "0 : |xc-x_last| = 1.4581e+00 <= tolX*(1+|x0|) = 2.6641e-14\n",
+        "0 : |proj(x-g)-x|    = 1.4105e+02 <= tolG          = 1.0000e-01\n",
+        "0 : |proj(x-g)-x|    = 1.4105e+02 <= 1e3*eps       = 1.0000e-02\n",
         "1 : maxIter   =       7    <= iter          =      7\n",
         "------------------------- DONE! -------------------------\n"
        ]
       }
      ],
-     "prompt_number": 163
+     "prompt_number": 22
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})"
+      "# matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 207
+     "prompt_number": 23
     },
     {
      "cell_type": "code",
@@ -706,8 +700,9 @@
       "ax.set_xlabel('AB/2')\n",
       "ax.set_ylabel('Apparent resistivity ($\\Omega m$)')\n",
       "ax.grid(True)\n",
-      "ax.legend(('Observed', 'Predicted'), loc = 1)\n",
-      "fig.savefig('obspred_dc1d_dat.png')"
+      "ax.legend(('Observed', 'Predicted'), loc = 1, fontsize=14)\n",
+      "ax.text(100., 185, '(a)', fontsize = 20)\n",
+      "fig.savefig('obspred_dc1d_dat.png', dpi=200)"
      ],
      "language": "python",
      "metadata": {},
@@ -715,26 +710,29 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x6cbdf90>"
+        "<matplotlib.figure.Figure at 0x656c250>"
        ]
       }
      ],
-     "prompt_number": 218
+     "prompt_number": 40
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "fig, ax = plt.subplots(1,1, figsize = (3, 6))\n",
+      "fig, ax = plt.subplots(1,1, figsize = (7, 7))\n",
       "ax.plot(1., 1., 'k', lw = 2)\n",
       "ax.plot(1., 1., 'r', lw = 2)\n",
-      "ax.legend(('True', 'Recovered'), loc = 4, fontsize = 14)\n",
-      "Utils1D.plotLayer(1./(np.exp(mopt)), mesh.vectorCCz, 'log', ax = ax, **{'lw':2, 'color':'r'})\n",
-      "Utils1D.plotLayer(1./(np.exp(mtrue)), mesh.vectorCCz, 'log', ax = ax, **{'lw':2})\n",
+      "ax.legend(('True', 'Predicted'), loc = 3, fontsize = 16)\n",
+      "Utils1D.plotLayer((np.exp(mopt)), mesh.vectorCCz, 'log', ax = ax, **{'lw':2, 'color':'r'})\n",
+      "Utils1D.plotLayer((np.exp(mtrue)), mesh.vectorCCz, 'log', showlayers=True, ax = ax, **{'lw':2})\n",
       "ax.set_ylim(-600, 0)\n",
-      "fig.savefig('obspred_dc1d_mod.png')"
+      "ax.set_xlabel('Conductivity (S/m)', fontsize = 20)\n",
+      "ax.set_ylabel('Depth (m)', fontsize = 20)\n",
+      "ax.text(1e-3, 10., '(b)', fontsize = 30)\n",
+      "fig.savefig('obspred_dc1d_mod.png', dpi=200)"
      ],
      "language": "python",
      "metadata": {},
@@ -742,13 +740,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0xdbb1050>"
+        "<matplotlib.figure.Figure at 0xc678450>"
        ]
       }
      ],
-     "prompt_number": 219
+     "prompt_number": 41
     }
    ],
    "metadata": {}