DC example: three mesh

notebooks/DC_schumberger_FWD.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:73d0cc81c1a231c2a808e89facb3717f7496e7473309088a86f2512a257912a1"
+  "signature": "sha256:517d797f06003268b2b3d50196af6a2b138058cd669653453dcea0e78f478069"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -15,7 +15,7 @@
       "from SimPEG import *\n",
       "import simpegDC as DC\n",
       "from simpegem1d import Utils1D\n",
-      "import simpegEM.Utils as EMUtils\n",
+      "from pymatsolver import MumpsSolver\n",
       "%pylab inline"
      ],
      "language": "python",
@@ -29,7 +29,7 @@
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 7
     },
     {
      "cell_type": "heading",
@@ -67,7 +67,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 2
+     "prompt_number": 8
     },
     {
      "cell_type": "code",
@@ -78,7 +78,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 9
     },
     {
      "cell_type": "code",
@@ -89,24 +89,16 @@
      "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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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3d01c18>"
+        "<matplotlib.figure.Figure at 0x4cec590>"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 10
     },
     {
      "cell_type": "heading",
@@ -126,7 +118,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 5
+     "prompt_number": 11
     },
     {
      "cell_type": "heading",
@@ -192,7 +184,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 6
+     "prompt_number": 12
     },
     {
      "cell_type": "code",
@@ -203,7 +195,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 7
+     "prompt_number": 13
     },
     {
      "cell_type": "code",
@@ -222,7 +214,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 8
+     "prompt_number": 14
     },
     {
      "cell_type": "code",
@@ -252,7 +244,7 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 9,
+       "prompt_number": 15,
        "text": [
         "(-600, 600)"
        ]
@@ -260,13 +252,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAADNCAYAAACcnje3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0xa8d19b0>"
+        "<matplotlib.figure.Figure at 0x4e1cb50>"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 15
     },
     {
      "cell_type": "code",
@@ -293,13 +285,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAEiCAYAAADNgWQ8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x16750ba8>"
+        "<matplotlib.figure.Figure at 0x4e1c250>"
        ]
       }
      ],
-     "prompt_number": 36
+     "prompt_number": 16
     },
     {
      "cell_type": "code",
@@ -308,13 +300,13 @@
       "txlist = []\n",
       "rx = DC.DipoleRx(np.r_[xtemp_rxP, ytemp_rx, -12.5], np.r_[xtemp_rxN, ytemp_rx, -12.5])\n",
       "for i in range(ntx):    \n",
-      "    tx = DC.DipoleTx([xtemp_txP[i], ytemp_tx[i], -12.5],[xtemp_txN[i], ytemp_tx[i], -12.5], [rx])\n",
+      "    tx = DC.DipoleSrc([xtemp_txP[i], ytemp_tx[i], -12.5],[xtemp_txN[i], ytemp_tx[i], -12.5], [rx])\n",
       "    txlist.append(tx)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 11
+     "prompt_number": 18
     },
     {
      "cell_type": "code",
@@ -324,12 +316,12 @@
       "problem = DC.ProblemDC(mesh)\n",
       "problem.pair(survey)\n",
       "problem.Solver = SolverLU\n",
-      "# problem.Solver = SolverWrapD(EMUtils.Solver.Mumps)"
+      "# problem.Solver = pymatsolver.MumpsSolver"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 12
+     "prompt_number": 19
     },
     {
      "cell_type": "heading",
@@ -348,7 +340,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 13
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -382,13 +374,31 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAErCAYAAAAfcL5EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x171e7dd8>"
+        "<matplotlib.figure.Figure at 0x4cca2d0>"
        ]
       }
      ],
-     "prompt_number": 40
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 21
     },
     {
      "cell_type": "code",

notebooks/DC_schumberger_Inv.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:217ff4129b31cd5fb26fce3dc7aef27cf513d55e30cd6c91c3716bcc65a22097"
+  "signature": "sha256:6a79886b331e74256f87f4817f8611dc05c2f024c9ebc5064c8b977af7cc4517"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -15,7 +15,7 @@
       "from SimPEG import *\n",
       "import simpegDC as DC\n",
       "from simpegem1d import Utils1D\n",
-      "import simpegEM.Utils as EMUtils\n",
+      "from pymatsolver import MumpsSolver\n",
       "%pylab inline"
      ],
      "language": "python",
@@ -29,7 +29,7 @@
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 25
     },
     {
      "cell_type": "code",
@@ -37,13 +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': 20, 'text.usetex': True})\n",
       "# matplotlib.rcParams.update({'font.size': 16, 'font.family': 'arial'})"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 2
+     "prompt_number": 26
     },
     {
      "cell_type": "heading",
@@ -89,7 +89,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 27
     },
     {
      "cell_type": "code",
@@ -100,7 +100,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 28
     },
     {
      "cell_type": "code",
@@ -111,24 +111,16 @@
      "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 0x2825f10>"
+        "<matplotlib.figure.Figure at 0x64d5a90>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 29
     },
     {
      "cell_type": "heading",
@@ -154,21 +146,30 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 6
+     "prompt_number": 30
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "fig, ax = plt.subplots(1,1, figsize = (7.5, 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('Depth (m)', fontsize = 22)\n",
-      "ax.text(-7., 10., '(a)', fontsize = 30)\n",
-      "fig.savefig('logcond1d.png', dpi=200)"
+      "fig, ax = plt.subplots(1,2, figsize = (18*0.8, 7*0.8))\n",
+      "Utils1D.plotLayer(np.log(sigma), mesh.vectorCCz, 'linear', showlayers=True, ax = ax[0])\n",
+      "ax[0].invert_xaxis()\n",
+      "ax[0].set_ylim(-500, 0)\n",
+      "ax[0].set_xlim(-7, -4)\n",
+      "ax[0].set_xlabel('$log(\\sigma)$', fontsize = 25)\n",
+      "ax[0].set_ylabel('Depth (m)', fontsize = 25)\n",
+      "ax[0].text(-7., 10., '(a)', fontsize = 30)\n",
+      "dat = mesh.plotSlice((mapping*mtrue), normal='Y', ind = 9, ax = ax[1])\n",
+      "cb = plt.colorbar(dat[0], ax =ax[1])\n",
+      "ax[1].set_title(\"Vertical section\", fontsize = 25)\n",
+      "cb.set_label(\"Conductivity (S/m)\", fontsize = 25)\n",
+      "ax[1].set_xlabel('Easting (m)', fontsize = 25)\n",
+      "ax[1].set_ylabel(' ', fontsize = 25)\n",
+      "ax[1].set_xlim(-1000., 1000.)\n",
+      "ax[1].set_ylim(-500., 0.)\n",
+      "ax[1].text(-1000., 13., '(b)', fontsize = 30)\n",
+      "fig.savefig('mappingDC.png', dpi=200)"
      ],
      "language": "python",
      "metadata": {},
@@ -176,43 +177,34 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAgIAAAHeCAYAAADpUWMFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA6AAAAGZCAYAAACXL1z9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x6591a50>"
+        "<matplotlib.figure.Figure at 0xad96110>"
        ]
       }
      ],
-     "prompt_number": 46
+     "prompt_number": 31
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "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\", fontsize = 16)\n",
-      "cb.set_label(\"Conductivity (S/m)\", fontsize = 16)\n",
-      "ax.set_xlabel('Easting (m)', fontsize = 16)\n",
-      "ax.set_ylabel('Depth (m)', fontsize = 16)\n",
-      "ax.set_xlim(-1000., 1000.)\n",
-      "ax.set_ylim(-500., 0.)\n",
-      "ax.text(-1000., 20., '(b)', fontsize = 20)\n",
-      "fig.savefig('cond3d.png', dpi=200)"
+      "# 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\", fontsize = 16)\n",
+      "# cb.set_label(\"Conductivity (S/m)\", fontsize = 16)\n",
+      "# ax.set_xlabel('Easting (m)', fontsize = 16)\n",
+      "# ax.set_ylabel('Depth (m)', fontsize = 16)\n",
+      "# ax.set_xlim(-1000., 1000.)\n",
+      "# 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": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAFmCAYAAAAsxZDlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x5637310>"
-       ]
-      }
-     ],
-     "prompt_number": 44
+     "outputs": [],
+     "prompt_number": 32
     },
     {
      "cell_type": "heading",
@@ -278,7 +270,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 9
+     "prompt_number": 33
     },
     {
      "cell_type": "code",
@@ -297,7 +289,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 10
+     "prompt_number": 34
     },
     {
      "cell_type": "code",
@@ -319,7 +311,7 @@
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 35
     },
     {
      "cell_type": "code",
@@ -349,7 +341,7 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 12,
+       "prompt_number": 36,
        "text": [
         "(-0.2, 0.6)"
        ]
@@ -357,13 +349,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x43f1b10>"
+        "<matplotlib.figure.Figure at 0x649b650>"
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 36
     },
     {
      "cell_type": "code",
@@ -390,13 +382,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x6585890>"
+        "<matplotlib.figure.Figure at 0x568a0d0>"
        ]
       }
      ],
-     "prompt_number": 42
+     "prompt_number": 37
     },
     {
      "cell_type": "markdown",
@@ -419,7 +411,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 38
     },
     {
      "cell_type": "heading",
@@ -436,12 +428,12 @@
       "problem = DC.ProblemDC(mesh, mapping=mapping)\n",
       "problem.pair(survey)\n",
       "# problem.Solver = SolverLU\n",
-      "problem.Solver = SolverWrapD(EMUtils.Solver.Mumps)"
+      "problem.Solver = pymatsolver.MumpsSolver"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 15
+     "prompt_number": 39
     },
     {
      "cell_type": "heading",
@@ -459,17 +451,8 @@
      ],
      "language": "python",
      "metadata": {},
-     "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
+     "outputs": [],
+     "prompt_number": 40
     },
     {
      "cell_type": "markdown",
@@ -494,7 +477,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 17
+     "prompt_number": 41
     },
     {
      "cell_type": "code",
@@ -502,25 +485,25 @@
      "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 = (14, 4))\n",
+      "fig, ax = plt.subplots(1,2, figsize = (16, 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].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('Easting (m)', fontsize = 16)\n",
-      "ax[0].set_ylabel('Depth (m)', fontsize = 16)\n",
+      "ax[0].set_title(\"Vertical section\")\n",
+      "cb.set_label(\"Conductivity (S/m)\")\n",
+      "ax[0].set_xlabel('Easting (m)')\n",
+      "ax[0].set_ylabel('Depth (m)')\n",
       "ax[0].set_xlim(-1000., 1000.)\n",
       "ax[0].set_ylim(-500., 0.)\n",
-      "ax[0].text(-1000, 20, '(b)', fontsize = 16)\n",
+      "# ax[0].text(-1000, 20, '(b)')\n",
       "fig.savefig('DCfwd.png', dpi=200)"
      ],
      "language": "python",
@@ -529,13 +512,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAA2EAAAEpCAYAAAAamCoXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3X1wG9d5L/4vaMdx3VYEIDupE2cqLug2aeMJCYKKW6eN\nLABUk/5TmwQoN8nUSYkXJ2mSaSUuqDYJ55dEJAS1nTpNTACaierrOBYJKB63ebGwYOVM3DgWCeq2\nnVzXEhfKJJZvXwQuGNuxFYv7+4N311wCIAACBAjw+5nB2Is92D0Lj3f54DznOSZVVVUQERERERFR\nQ3Q0uwNEREREREQ7CYMwIiIiIiKiBmIQRkRERERE1EAMwoiIiIiIiBqIQRgREREREVEDMQgrIRQK\nIZ1OY2BgAB0dHeju7i7ZVlEUeL1e5PP5BvaQiIh2MlEUkU6nN2zD5xMR0fbEIKwIURRhMpngdDpx\n5swZDA0NwWQylWxvNpsxNjYGp9PZwF4SEdFOJYoiOjo6yj53+HwiItqeGIStI0kS4vE4JiYm9Pcq\nWUqtt7cXgiAgEolsZfeIiGiHK/ac2gifT0RE2w+DsHXC4TCGh4c39dnh4eGKH4pERESbsZnnFJ9P\nRETbC4OwNRRFQTqdRl9fX8E+VVURj8fhcDhgtVoRCoUK2jidTiiKgmQy2YjuEhHRDrPRc0oURTgc\nDjgcDgSDQcM+Pp+IiLYXBmFrzM3NAQAEQSjYJ8sy8vk85ubmMD8/j2PHjhU85Mxms+E4tPoHg81m\nQ0dHBzo6OmC1Wg2/4GrzGrTX2NhYTecrNkk9k8nAYrHg/PnzNR1bEwgEYLVa0dHRUbdjbkYjrpWI\ntpdSzymPx4PTp09jbm4OMzMziMVihvRDPp+IiLYXBmFryLIMALBarQX7TCYTDh06BADo6uqC3+9H\nLBbb8Di0+uBfXFyEy+UCAJw4cQKnTp3S94fDYczPzwNYDSBqTZcJh8MFVcDMZjNuvvnmmo67VjQa\nxczMTN2Ot1mNuFYi2l6KPadkWUYymYQoigCApaUlWCwW2Gy2kp8nIqLmYhC2RrHgS7P+V0dtmyMO\nldH+OHjssccK9l25cgVutxs9PT01n0eSpIJKloIg4MKFC3U5vqaSYi1brVHXSkTbR7HnVCaTAfDG\nc8lut+PKlSu49957G9o3IiKqHIOwNex2OwAgl8sZ3lcUpeQf3VqKx1rF0hl3OqfTCbPZjGQyWTB6\nE41G9SCtFoFAAMD2CJC22k66ViJ6Q7Hn1Ppn1kb4fCIi2h4YhK2hPZzWp2vkcrmC9xYXF2EymbBn\nzx79Pa1Nf3//1na0RY2NjUFVVUxPTxveX1hYwP79+/VtbXL5wMCAYd7d2vlj2WwWx44dg81mQyQS\n0RfXBlbnRgwMDCCdTiMYDOrzt9bPoSo1iV1RFP0Y3d3dcDgcm57MLkmSfi1erxcOhwMLCwtlr3Wj\nPq5doLWaax0YGIDD4TAUlTl27BgsFov+GbfbjY6ODjgcDmSz2U1dMxFtnWLPKe2Zo6V2a9bet/h8\nIiLaZlQyCAQCqsfjMbwnCIJqtVrVWCymqqqqLi0tqRaLRY1EIoZ2MzMzqtVqbVhfW42iKKrJZFJt\nNpv+XjQaVUOhkL7t9/tVh8Ohb3s8HtXtduvbiURCNZlMqiiKqizLqs1mU7u7u/VjmUwmNZ/PG84r\nSZJqMpnUdDqtvzc0NKR/TpZl1WQy6f89XS6X4ZyiKKomk8lwzFQqpZpMJnVhYWHDa7bZbHp/tPNo\nnyl3rRv1sdprHRgY0Lf7+vrUQCCgb2vfqcViUdPptKooitrX16daLJYNr42ImqPYc0r7f1aSJHVp\naUkdGhpSk8mkvp/PJyKi7YUjYeuEw2FIkmRImdNGXubn5zEwMACXy4UjR47ohTo0ExMTCIfDje5y\ny+js7ITL5YIsy/poUCwW01PrZFlGPB7XtwHA6/VCkiRcunQJwBvpd9lsFl1dXZiZmSlbJMNisRi2\nS01i135htlgshrlWDocDAPQ+VEOWZaRSKQCrBV3C4TDMZnPZay3Xx2qvde15hoeHEYvFsLy8DOCN\n7/TIkSPYv38/Ojs7EQgEoCjKpq6ZiLZWsedUOp2Gy+WCx+OBIAh473vfa5gTxucTEdH2cn2zO7Dd\ndHZ2IhwOw+fz6WlzFy5cAABMTU2V/FwikUB3dzdGRkYa0s9WFQgEIEkSotEoRkdHAUBP6dQml09N\nTenffS6Xg81mgyzLhtRPt9sNAOjt7a26D6UmsWump6eRz+eRSCQgSZJe0llRlKrPpf1RpPX14MGD\n2LNnDxKJBIDS16rN8SjVx81eq9YPYDVVcu0fadpcE6AwmCOi7aPYc6qzs7Mg1VvD5xMR0fbDIKwI\nn88Hq9WKdDoNp9NZtr22ftja0utU3ODgIMxmM6anp2E2mw3zoLTA49ixY4Y5YsVoo1MbyWQyhsBi\n/XlKSSQSCIVC+mt+fl4PpKo1MzMDSZKQSqUwPT0NURQNxVxKXWup5Q9K2ey1rsUJ+0Sto9LnFJ9P\nRETbE9MRSxgcHKwoAANWf4GcnJzc4h61D7/fD0VREIlEDL/MlppcnkgkCopEFKtKub50s9/vL3r+\njSaxy7IMr9eLUCiEkZER7NmzZ1MjUMBqYDQ0NITBwUFMTU0hl8vB7/cjlUrpQWSpay030b7Sa9XW\nZ1tcXNTf0yboa/uIIpEIkskk4vE44vF4XdprP2bU43ytotw1a9e7dhHltfur/U4qeU7x+UREtD3t\niCCsXR/4rUqbnzQ0NGR4v7e3Fy6XC6Io6nPGMpkMJicn0dXVZWirFinNrgVmqVQKkiTpC5Vqo0Ha\nZ3p7e2G32zExMYF0Oq1XQzSZTPocCy0FUZZlHDt2DIAxkNGUC9DS6bShQpkgCNi7d2/Za92oj9Vc\nqyAIGBoaQjQa1fsQjUYRCASwa9cuQ1+XlpYK+l/sPWovoiiir68Pg4OD8Pl8WFxc3LAaaLn26XQa\nkUgEsVisYDmKzZyvFZS75kgkgsOHD8Pn88Hn88HlchkCsXb8ToiIqIymlgVpgNHRUUOlOFEU1UQi\n0cQekaquVvJa+99lrUAgoFosFtVisagDAwN6BcBoNKrabDa1o6ND7evr06tVruXxeFSTyaQ6HA41\nn8+rMzMz+me6u7v1amGKoqgej0c/z9pKl6IoqhaLRbXZbKrX61Xz+bzqdrtVq9WqSpKkJhKJosdc\nL5PJqAMDA6ooiqrb7Vb7+vrUYDBY0bWW62M116pdk9aHtdUo137G4XCokiSpsVis4D1qX+urYEqS\nZKjSudn2oigaqnBu9nytpNQ19/X1Fby3trphO38nRERUnElV23u1V6vVapgXk06nEQ6HcebMmSb2\nioio+TKZDFwul+Eemclk4HA4sLKyUlP7UCgERVEMBY2qPV+rKXbNADAwMACz2Yx4PI7Ozk4kk0mY\nTCbce++9bf+dEBFRcW2djqhVhlvLYrFAkqQm9IaIaHvJ5XIF8wu1VFdtCYNa2tf7860qGo0ik8mg\nq6tLT0PUKpPu1O+EiGina+sgjA83IqLSFEUpqKCp3TOLVdastn29P9+qurq6EAgE4HA4IIoizp07\np+/bqd8JEdFO19ZBGB9uRESlFasyqt0b1/+AtZn29f58qxJFEW63G2fOnEEqlUIsFoPX6wWwc78T\nIqKdrq3XCePDjYioNKvVWrAIuba9vnrmZtrX+/OtKJPJwGQyoaenBwDgdDqRzWb1iq878TshIqI2\nD8Kq/gPDZAILchNRI9hsNly8eLHm4/ySyYRXK2z7K7/yK/jZz36mb9vt9oIfq3K5HNxud9HPV9u+\n3p9vRUtLS9i9e7fhvc7OTn2dvmq/k7e//e24fPny1nSWiIgqUo9neFsHYdU+3JYAjG99t1rSPwO4\nu9md2Kb43ZTG76a08SLrzm3GqwC+WGHbv3rppYL3/H4/kskkBgcHAQCSJOlr+QGra+UtLCzo+8u1\n15QqvFvp51tRsWt2Op0Ih8M4fPiw/p6iKBAEQd+u5ju5fPlyye+Wtpfx8XGMj483uxsN14rXvV36\n3Oh+bPX5tuL49TpmrcfR1m2tRVsHYUB7P/CJiADgTTV8dnJyUl/QXpZldHd365X7gNVlPRKJhH4P\nLdd+YWEBkiQhmUxiaWkJNpsNLpcLvb29FX2+FZW75mg0ilAohN27d+s/DE5OTuqfb8fvhIB9+/Y1\nuwtN0YrXvV363Oh+bPX5tuL49Trmdvhv3vbrhAFAJBKBIAiQZRkWiwUjIyNF25lMJo6ElcARjdL4\n3ZTG76a0cZQeLaqGyWTCX1fY9i/qdE5qHpPJxP+GRERNVo97cduPhAEwpIHQ5uxpdge2sT3N7sA2\ntqfZHdghahkJIyIiosbbEUEY1a6r2R3YxvjdlMbvpjF4IyciImotfHYTEbW4X2p2B4iIiKgqDMKI\niFoc0xGJiIhaC4MwIqIWxxs5ERFRa+Gzm4ioxXEkjIiIqLUwCCMianEMwoiIiFoLgzAiohbHGzkR\nEVFr4bObiKjFcSSMiIiotTAIIyJqcSxRT0RE1FoYhBERtTjeyImIiFoLn91ERC2O6YhERESthUEY\nEVGL442ciIiotfDZTUTU4jgSVptkMom5uTnIsgwAEAQB/f39uPfee5vcMyIialcMwoiIWhxv5JsT\niUQwMTEBRVGK7rdYLAiHwxgZGWlwz4iIqN11NLsDRERUmzdV+KJV+XweDocD586dQzweRy6Xw8rK\niuF18eJFRKNRPPTQQxgeHm52l4mIqM2YVFVVm92J7cJkMmG82Z0goh1hHEA9br8mkwn/t8K2v1an\nc7Y6r9eLsbEx9Pb2VtQ+kUhgbm4Ok5OTm2oXCAQQCoXQ1dVV8rORSASCICCXywEAfD5f0XLine truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAExCAYAAACK4HpVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3X2QXFd95//31zY2JkZqSYB/sFUgjc1uQfEgj2TiPGAU\nS7JJFjaxpZH4VUhZqUgzAsLuEmyNtWTx7C9kNZLJJmAeNGNvqsIG//BIMqYSQmyNhNiFJCCNpBAI\nu9gaDdisH/an0UghWH6Qvr8/zmnNnZ5+uPd293TPzOdV1TW6t8899/Rt8O3vPed8j7k7IiIiIiIi\nItIcl7S6ASIiIiIiIiJzmQJvERERERERkSZS4C0iIiIiIiLSRAq8RURERERERJpIgbeIiIiIiIhI\nEynwlpYys4KZXYiv1SnKdyTKL21+C6edf1s89+4ZPm93K847U1p1XUVEREREZoICb2kpd58AjsbN\nrhSHrI9/T7j7WFMaVZ2X/G3V+eeaVl9XEREREZGmUeAt7WAg/k0TeG+Mf/c2qS0AFHvVy7w1DOwE\n9jTz/HOVrquIiIiIzEeXtboBIsAQIfgumNlqdz9QrpCZdQDXEXpFB8qVabBpva/ufgw4NgPnnst0\nXUVERERkXlGPt7Scu58h9Hga1Xu9i8PMR1s0zFxERERERCQzBd7SLopDjKsF3mWHmccEbQNmdiIO\nZT5tZkPlkrWZ2fpY5o64PRC3h+L2nsRQaCsdGl0tyZmZdcbjT8cyJ8ys38wWlvswsS17Stp9xMzW\nVbkGqcXrstPMRkrq31LjmFTXMsvnbvB1LbZpWZmyxe93R9zelvgsJ8xsd6XvQ0RERESkWRR4S7sY\nin8LFQLmAmWGmZtZJ3AS2AIsBU4DCwi94/vN7M4K5zMzG4jHOZPDnx9lamC/h/LzjqcMlzazbuAI\nsC6e/3RszzbgZGmQaGY742deV9LuTmCPmfVXaHcq8XqNAHcCy0vqH6gU4JLxWmb43Hmv6/oy9Rfb\ndKLKQwozs/1AP3ABOAEsA7rjdRERERERmTEKvKUtpBhuviH+LR1mvgdYSAjGF7n7Ene/FOiN7++s\n0FvbQwgwdwIrCAEZ7n6fuxfP5e6+0d03ljn+ohjk7iYEjdvc/VJ3XwJcS8jYXojnKZbvIATEDnQX\ny8d298Ri26qdM4WdhEBzhKnXpXhtu8v0GGe6llk+dx3XtfhAZk+xTcBiJoP4PeV6vgnfZyfQ6e5v\ndPc3Er5ngI5qvf4iIiIiIo2mwFvaSbXs5sV9F3tNY2/rMmC/u3/A3c8W33P3e0gEjGXq6yAEvdvd\n/XgM/PMq1j/o7p9MtOFkot3JntnO+HfY3e9PVuTu9xF6nd3MrqujTSvj396S67KPcA2LPcBA7muZ\n9XNntT3+3R8D9bOx/jMxiB8ufsYyxy4Eutz9eKJdx4BdcbOjjnaJiIiIiGSiwFvaRgwKofxw89VM\nz2ZeDO7KBdYA98W/5QLY06VBbx2Kw9WntSMGobuAweLcYnffCxTc/ZYa9dazpnXx2JvLtGlD7AU+\nmNid51pm+tw5FJPpVWpTcX+5EQ0TJZ+vaDT+LeRsk4iIiIhIZlpOTNrNXkLA1QUcgIvzfGH6MPNi\nr+VWM/tAlTrNzJaWHDtaqXAWcTg0AJUyrbv7XWX2XexRjnV0AGuAtYSe53qCbggPKAaAbfH6DRB6\n2Cst2ZXpWgITxR1ZPndGxetwpML7xbna5XqvKx0jIiIiIjLjFHhLu3mQEHhvALbGfWWzmTM5VHo9\n1QPVcu81JPBmMujLVF9MZLad6UOxi221ehrl7vfFgH57bOPOeN4zhGu8M/ZKF2W9lrk+d1olDzTO\nlivj7hNmViy/oKTcRLljRERERERaQUPNpa0khpsvTAw3X8f0YeYXDyEM2760wuuS+HesSU0uBniL\n0x5gZmuYzNR9ghAUryckArsUqNQrnYm73+Puiwm96IOEHuIFhMRjJ8rMIc9yLU/HY1J/7oxtzxQ4\nVwrORURERETagQJvaUeDxOzmMUiF6cPMISQhA1hSqaK4LnXT5vO6e805w2a2xsy2JOY6F+cm74xz\nrbe7+0PJRGANbuMBd9/q7tcTMo4Xk5Il505nupaJ3vIsnzurM7GeclnLi9nhQb3bIiIiItLmFHgn\nmFlnDBTWmdmdFZahkuYrru+8gcmkX+V6u0cIAXpPmfeKc8PHmVySqllGw+kqrim9h7hEV9wurke+\no0L5AnXM8Y4B8gkzmzbPOQbMxSzgKxJv5bmWWT93Vodjm9ZXeL/YVs3nlrLy/jc9y3Fmtr7S+/P9\nnlLj2qyJ1+ROM+svt8TffL9+IiIytyjwjmLvWX9cb3hfXEKpp1JvmzSPux8g9GIWmMycXTq/GyYD\n122lw6Zjz2yxR7fZgffFHuzS3t24TNdCQhb1sbj7JCGgvKa0IjPbxuR861zzvOMw7WVAZ4WgeG38\nmwxY81zLrJ87q2L920v/fxjnyBfXQq+U9Vzmsbz/Tc9yXByRM0iZB2Xz/Z5S49p0AgvjdJh7YiLG\n8WTwPd+vn4iIzD0KvCf1ArtL9g2gH/WtMqVntVzwFjN0FwPyETMbMrPuGLyeJASfI3UsG2axJ6Zq\nL0tce3uUkHDsZDym28z2EP435Uxdm3x//Hsg9uZ0xvL7gX7CQwejTMCZQXG96j2J61JsUz8lAWue\na5njcxelva4HCMPiC4Q56btj/QNMPjQYrrBsmEje/6bXPM7MlpnZbsL/L8YbfP5ZLeW16U7k8wAu\n5vdYm9g1L6+fiIjMYe6ulzuEHwhLS/YVgAutbtt8fBGGY18AzgN31Ci7O5ZNvs4TsncvKCm7Lr7/\nYI06h5J1JfZvifs+X1J+ISEYPF+mHZvL1P9omTY/BixPnOMC8Egs313uvBk+Q/J1Crit3muZ83Nn\nuq6JY8rVP+07rPX9VjuPXnPrlfe/6VmPAx4HbmrU+efSq8q1OQIsK7P/UV0/vfTSSy+95urL3Otd\nLnj2i0NpxwkZnc+WvHcB6PDmZcWWBog9w2sIgeAocLTe7ywO014GTHjKXvM4THsF4QfiUeBI6f+m\nKpX1RM9t4tzDXkfStXhdOoufgzB6oGoPcZ5rmfFz57muyTZNEK5L1TbJ/JX3v+l5jjOzxwk9uAfr\nqWcuKndt4v5+Qu6GtR4TNcah6Uvd/X5dPxERmYsUeHNxLtnj7j5t6H28ya+pFayIiEh7yPvf9DzH\nVQi8dU+hcuAd3ztCeCjYQ5jOstDj8HNdPxERmYsua3UD2kTTlpsSEZEZl/e/6Y26F+ieUoO7rzSz\nRwnzto8CyZwPun4iIjLnKLmaiIiIzKiYwXwbIaFaByGpozKWi4jInKUe75zMTGP0RUQS3D3XEnjl\nZP1vbCPPLc0Vs573F+dpx4B7D2HFh2vrrFv3ZhERaahG/cZQ4B2MApjZggoJoUbLHdTXzBbNA18H\nfqXVjZjldA3rp2vYGH0trLNMuVz/Ta/juGbVM+fExGkkk6O5+xngZjM7YmY3EYae575+yl0z9/X1\n9dHX19fqZrStuXJ92v1ztLp9M3n+Zp6rGXU3sk6zxj3X11BzwN0nmFyP+KKY4GVC2VNFRGbeZSlf\npfL+N71R9wLdU6paAZyo8N4AIZO5rp9UtWrVqlY3oa3NlevT7p+j1e2byfM381zNqLvV300lCrwn\nDQPXl+zrJAx9ExGRGXZlylcFef+b3qh7ge4p5Y0C11R4r7gcIej6SRXt+qO6XcyV69Pun6PV7VPg\nPbN1NoIC70m9QFfJvu64X5pgaasbMAcsbXUD5oClrW6AVPSylK8Kav433cwKZnYiJvpKfVwZ5cah\n6Z4STLk2iXW7V08pFHqzlyR6s3X9RERkTtE63glmdh2wEThMzLJaaa1QM/O+GWybiEg766PxydU+\nl7LsByucu9Z/0+N841Fgm7vfn+G4hcD2+N76WMcwsL+4FnWaetpVlbnVaY5Ne222EHq+T8VdE+5+\nX0ldma+fmbl+14iISKOYWcN+3yjwzkmBt4jIpD4aH3jfV7sYAFsafO75JGYUX09Y1mslU9fQPg0c\nIQzv3jsb5lYr8BYRkUZqZOCtrOYiItKWdINqHjNbR+iZ7oy7RoEDwDgwQQjAF8f31wK7zGwE2OHu\nD818i0VERGY3/a4REZG2VGX+tuSUWDO7E9gL9Lr7gRTHrQF6gL0xAO+aDT3gIiIi7UKBt4iItCXd\noJqi2Gu9MstB7j5MmKuNmW0jZB9f3PjmiYiIzE36XSMiIm3pFa1uwNzUEdfJzs3dd5nZYKMaJCIi\nMh9oOTEREWlLl6V8SXpZg+7SZb/y1iMiIjLfKfAWEZG2VOc63tIYJ82s38yWtrohIiIis5kCbxER\naUvq8Z45ZrbOzMbN7IKZPWJmO8xsqbuPuvtdQFer2ygiIjKb6TeLiIi0JfVmz6iNwGpgCWH5sJuB\nXjM7QUiqtqSFbRMREZn1FHiLiEhb0g1qRh1292Px38Xs5QVgA2HpsR2tapiIiMhcoN81IiLSltTj\n3VoxgZqyl4uIiDSA5niLiEhbujLlSxriPjPb0upGiIiIzFUKvEVEpC0pudqMcmCFmT1mZpuVxVxE\nRKSx9JtFRETakoaaz6g9wAQwBuwCCmY2AeyPryF3P9u65omIiMxuCrxFRKQt1Rt4m1knsAIYBzqA\no+5+oBHHZSizAThFyAr+YCKBWbvZ4+73FTdiYrU1hAzn24GdZMxsbmbrgdNlrstO4FFgJM4jr3R8\nru9PRESkHSnwFhGRtlTPDcrMOoB+d785sW/IzEbd/WQ9x6UsswbYVlLmiJl1VTt/u4gB8d74yix+\n/kFgfZm3O4E7Y7nS9064+xvzfn8iIiLtSnO8RUSkLb3ssnSvCnqB3SX7Bgg9t9WkOS5NmYLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0xd821fd0>"
+        "<matplotlib.figure.Figure at 0x55daf90>"
        ]
       }
      ],
-     "prompt_number": 43
+     "prompt_number": 42
     },
     {
      "cell_type": "heading",
@@ -561,7 +544,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 21
+     "prompt_number": 43
     },
     {
      "cell_type": "code",
@@ -598,7 +581,7 @@
         "\n",
         "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
         "-----------------------------------------------------------------------------\n",
-        "   0  4.75e-01  6.33e+03  2.24e+04  1.70e+04    1.16e+04      0              "
+        "   0  2.06e-01  6.16e+03  4.48e+00  6.16e+03    8.76e+03      0              "
        ]
       },
       {
@@ -606,7 +589,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   1  4.75e-01  1.14e+03  5.22e+03  3.61e+03    4.96e+03      0              "
+        "   1  2.06e-01  5.04e+02  3.94e+00  5.04e+02    2.75e+03      0              "
        ]
       },
       {
@@ -614,7 +597,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   2  9.49e-02  2.92e+02  4.20e+03  6.90e+02    1.07e+03      0   Skip BFGS  "
+        "   2  4.13e-02  1.61e+02  3.92e+00  1.61e+02    6.53e+02      0              "
        ]
       },
       {
@@ -622,7 +605,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   3  9.49e-02  2.21e+02  4.47e+03  6.45e+02    1.21e+02      0   Skip BFGS  "
+        "   3  4.13e-02  8.31e+01  4.15e+00  8.33e+01    2.36e+02      0   Skip BFGS  "
        ]
       },
       {
@@ -630,7 +613,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   4  1.90e-02  2.01e+02  4.51e+03  2.87e+02    3.76e+02      0              "
+        "   4  8.26e-03  6.26e+01  4.36e+00  6.27e+01    1.67e+02      0   Skip BFGS  "
        ]
       },
       {
@@ -638,7 +621,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   5  1.90e-02  1.20e+02  7.00e+03  2.53e+02    1.17e+02      0              "
+        "   5  8.26e-03  2.53e+01  5.20e+00  2.54e+01    8.12e+01      0   Skip BFGS  "
        ]
       },
       {
@@ -646,7 +629,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   6  3.80e-03  1.28e+02  6.48e+03  1.52e+02    2.18e+02      0              "
+        "   6  1.65e-03  1.34e+01  5.84e+00  1.34e+01    4.74e+01      0   Skip BFGS  "
        ]
       },
       {
@@ -654,7 +637,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "   7  3.80e-03  4.19e+01  1.76e+04  1.09e+02    1.41e+02      0              "
+        "   7  1.65e-03  5.54e+00  6.93e+00  5.55e+00    1.06e+01      0   Skip BFGS  "
        ]
       },
       {
@@ -663,16 +646,16 @@
        "text": [
         "\n",
         "------------------------- STOP! -------------------------\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 : |fc-fOld| = 7.8145e+00 <= tolF*(1+|f0|) = 6.1639e+02\n",
+        "0 : |xc-x_last| = 5.7468e-01 <= tolX*(1+|x0|) = 2.6641e-14\n",
+        "0 : |proj(x-g)-x|    = 1.0608e+01 <= tolG          = 1.0000e-01\n",
+        "0 : |proj(x-g)-x|    = 1.0608e+01 <= 1e3*eps       = 1.0000e-02\n",
         "1 : maxIter   =       7    <= iter          =      7\n",
         "------------------------- DONE! -------------------------\n"
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 44
     },
     {
      "cell_type": "code",
@@ -683,7 +666,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 23
+     "prompt_number": 45
     },
     {
      "cell_type": "code",
@@ -692,17 +675,26 @@
       "appres = data*np.pi*b*(b+a)/a\n",
       "appres_obs = survey.dobs*np.pi*b*(b+a)/a\n",
       "appres_pred = invProb.dpred*np.pi*b*(b+a)/a\n",
-      "fig, ax = plt.subplots(1,1, figsize = (6, 4))\n",
-      "ax.plot(abhalf, appres_obs, 'k.-')\n",
-      "ax.plot(abhalf, appres_pred, 'r.-')\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, fontsize=14)\n",
-      "ax.text(100., 185, '(a)', fontsize = 20)\n",
-      "fig.savefig('obspred_dc1d_dat.png', dpi=200)"
+      "fig, ax = plt.subplots(1,2, figsize = (17, 6))\n",
+      "ax[0].plot(abhalf, appres_obs, 'k.-')\n",
+      "ax[0].plot(abhalf, appres_pred, 'r.-')\n",
+      "ax[0].set_xscale('log')\n",
+      "ax[0].set_ylim(100., 180.)\n",
+      "ax[0].set_xlabel('AB/2', fontsize=25)\n",
+      "ax[0].set_ylabel('Apparent resistivity ($\\Omega m$)', fontsize=25)\n",
+      "ax[0].grid(True)\n",
+      "ax[0].legend(('Observed', 'Predicted'), loc = 1, fontsize=20)\n",
+      "ax[0].text(100., 181, '(a)', fontsize = 28)\n",
+      "ax[1].plot(1., 1., 'k', lw = 2)\n",
+      "ax[1].plot(1., 1., 'r', lw = 2)\n",
+      "ax[1].legend(('True', 'Predicted'), loc = 3, fontsize = 20)\n",
+      "Utils1D.plotLayer((np.exp(mopt)), mesh.vectorCCz, 'log', ax = ax[1], **{'lw':2, 'color':'r'})\n",
+      "Utils1D.plotLayer((np.exp(mtrue)), mesh.vectorCCz, 'log', showlayers=True, ax = ax[1], **{'lw':2})\n",
+      "ax[1].set_ylim(-500, 0)\n",
+      "ax[1].set_xlabel('Conductivity (S/m)', fontsize = 25)\n",
+      "ax[1].set_ylabel('Depth (m)', fontsize = 25)\n",
+      "ax[1].text(1e-3, 10., '(b)', fontsize = 28)\n",
+      "fig.savefig('obspredDC.png', dpi=200)"
      ],
      "language": "python",
      "metadata": {},
@@ -710,42 +702,33 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x656c250>"
+        "<matplotlib.figure.Figure at 0x56c4850>"
        ]
       }
      ],
-     "prompt_number": 40
+     "prompt_number": 46
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "fig, ax = plt.subplots(1,1, figsize = (7.5, 7))\n",
-      "ax.plot(1., 1., 'k', lw = 2)\n",
-      "ax.plot(1., 1., 'r', 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",
-      "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)"
+      "# fig, ax = plt.subplots(1,1, figsize = (7.5, 7))\n",
+      "# ax.plot(1., 1., 'k', lw = 2)\n",
+      "# ax.plot(1., 1., 'r', 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(-500, 0)\n",
+      "# 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": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x51b9310>"
-       ]
-      }
-     ],
+     "outputs": [],
      "prompt_number": 47
     },
     {

notebooks/DCexample.ipynb

@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": ""
+  "name": "",
+  "signature": "sha256:c452407beb91d84a9a33b8a2b4ff3e4e90790c0049f22c5a9936f7462c1e2c5b"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,6 +14,7 @@
      "input": [
       "from SimPEG import *\n",
       "import simpegDC as DC\n",
+      "from pymatsolver import MumpsSolver\n",
       "%pylab inline"
      ],
      "language": "python",
@@ -26,7 +28,7 @@
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 27
     },
     {
      "cell_type": "code",
@@ -40,7 +42,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 30
+     "prompt_number": 28
     },
     {
      "cell_type": "code",
@@ -51,19 +53,52 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 31
+     "prompt_number": 29
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "sighalf = 1e-2\n",
-      "sigma = np.ones(mesh.nC)*sighalf"
+      "\n",
+      "blk1 = Utils.ModelBuilder.getIndecesBlock(np.r_[-50, 75, -50], np.r_[75, -50, -150], mesh.gridCC)\n",
+      "sighalf = 1e-3\n",
+      "sigma = np.ones(mesh.nC)*sighalf\n",
+      "sigma[blk1] = 1e-1\n",
+      "sigmahomo = np.ones(mesh.nC)*sighalf"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 32
+     "prompt_number": 30
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "mesh.plotSlice(sigma, normal='X', grid=True)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 31,
+       "text": [
+        "(<matplotlib.collections.QuadMesh at 0x4c23d10>,\n",
+        " <matplotlib.lines.Line2D at 0x70dc450>)"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x490c1d0>"
+       ]
+      }
+     ],
+     "prompt_number": 31
     },
     {
      "cell_type": "code",
@@ -78,7 +113,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 45
+     "prompt_number": 32
     },
     {
      "cell_type": "code",
@@ -95,33 +130,33 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 46,
+       "prompt_number": 33,
        "text": [
-        "[<matplotlib.lines.Line2D at 0xca18f60>]"
+        "[<matplotlib.lines.Line2D at 0x6153210>]"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAVIAAAFRCAYAAAAmQSVBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0xca186d8>"
+        "<matplotlib.figure.Figure at 0x61532d0>"
        ]
       }
      ],
-     "prompt_number": 46
+     "prompt_number": 33
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "rx = DC.DipoleRx(xyz_rxP, xyz_rxN)\n",
-      "tx = DC.DipoleTx([-200, 0, -12.5],[+200, 0, -12.5], [rx])"
+      "tx = DC.DipoleSrc([-200, 0, -12.5],[+200, 0, -12.5], [rx])"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 47
+     "prompt_number": 34
     },
     {
      "cell_type": "code",
@@ -134,18 +169,138 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 48
+     "prompt_number": 35
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "data = survey.dpred(sigma)"
+      "# problem.Solver = SolverWrapD(EMUtils.Solver.Mumps)\n",
+      "problem.Solver = MumpsSolver"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 49
+     "prompt_number": 36
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "data = survey.dpred(sigmahomo)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 37
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "u1 = problem.fields(sigma)\n",
+      "u2 = problem.fields(sigmahomo)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 38
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "Msig1 = Utils.sdiag(1./(mesh.aveF2CC.T*(1./sigma)))\n",
+      "Msig2 = Utils.sdiag(1./(mesh.aveF2CC.T*(1./sigmahomo)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 39
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "j1 = Msig1*mesh.cellGrad*u1\n",
+      "j2 = Msig2*mesh.cellGrad*u2"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 40
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "us = u1-u2\n",
+      "js = j1-j2"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 41
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "mesh.plotSlice(mesh.aveF2CCV*j1, vType='CCv', normal='Y', view='vec', streamOpts={\"density\":3, \"color\":'w'})\n",
+      "xlim(-300, 300)\n",
+      "ylim(-300, 0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 42,
+       "text": [
+        "(-300, 0)"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEZCAYAAAC99aPhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x4c34490>"
+       ]
+      }
+     ],
+     "prompt_number": 42
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "mesh.plotSlice(mesh.aveF2CCV*js, vType='CCv', normal='Y', view='vec', streamOpts={\"density\":3, \"color\":'w'})\n",
+      "xlim(-300, 300)\n",
+      "ylim(-300, 0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 43,
+       "text": [
+        "(-300, 0)"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEZCAYAAAC99aPhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x61604d0>"
+       ]
+      }
+     ],
+     "prompt_number": 43
     },
     {
      "cell_type": "code",
@@ -156,7 +311,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 50
+     "prompt_number": 44
     },
     {
      "cell_type": "code",
@@ -172,14 +327,14 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[[ 0.46013066  1.09015012  1.85863975]\n",
-        " [ 0.46013066  1.09015012  1.85863975]\n",
-        " [ 0.46013066  1.09015012  1.85863975]]\n",
-        "[ 3.40892053  3.40892053  3.40892053]\n"
+        "[[-0.12692184  2.0157894   0.463918  ]\n",
+        " [-0.12692184  2.0157894   0.463918  ]\n",
+        " [-0.12692184  2.0157894   0.463918  ]]\n",
+        "[ 2.35278557  2.35278557  2.35278557]\n"
        ]
       }
      ],
-     "prompt_number": 51
+     "prompt_number": 45
     },
     {
      "cell_type": "code",
@@ -195,7 +350,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 52
+     "prompt_number": 46
     },
     {
      "cell_type": "code",
@@ -208,7 +363,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 53
+     "prompt_number": 47
     },
     {
      "cell_type": "code",
@@ -222,7 +377,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 54
+     "prompt_number": 48
     },
     {
      "cell_type": "code",
@@ -242,13 +397,22 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0xc854160>"
+        "<matplotlib.figure.Figure at 0x62ce710>"
        ]
       }
      ],
-     "prompt_number": 55
+     "prompt_number": 49
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 49
     }
    ],
    "metadata": {}

notebooks/ThreeMeshDC.ipynb

@@ -0,0 +1,231 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:7ad7ed7a45e44f2928e55e9d6d51495cf85421232c9e6c8ce77f432705518eaf"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from SimPEG import Mesh, Utils, np, SolverLU\n",
+      "import matplotlib.pyplot as plt\n",
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "matplotlib.rcParams.update({'font.size': 16, 'text.usetex': True})"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sz = [15,15]\n",
+      "tM = Mesh.TensorMesh(sz)\n",
+      "qM = Mesh.TreeMesh(sz)\n",
+      "qM.refine(lambda X: 1 if np.sqrt(((X-0.5)**2).sum()) < 0.45 else 0)\n",
+      "rM = Mesh.LogicallyRectMesh(Utils.meshutils.exampleLrmGrid(sz,'rotate'))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 50
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def DCfun(mesh, pts):\n",
+      "    D = mesh.faceDiv\n",
+      "    G = D.T\n",
+      "    sigma = 1e-2*np.ones(mesh.nC)\n",
+      "    Msigi = mesh.getFaceInnerProduct(1./sigma)\n",
+      "    MsigI = Utils.sdInv(Msigi)\n",
+      "    A = D*MsigI*G\n",
+      "    A[-1,-1] /= mesh.vol[-1] # Remove null space\n",
+      "    rhs = np.zeros(mesh.nC)\n",
+      "    txind = Utils.meshutils.closestPoints(mesh, pts)\n",
+      "    rhs[txind] = np.r_[1,-1]\n",
+      "    return A, rhs"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 51
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "tM.vectorCCy"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 52,
+       "text": [
+        "array([ 0.03333333,  0.1       ,  0.16666667,  0.23333333,  0.3       ,\n",
+        "        0.36666667,  0.43333333,  0.5       ,  0.56666667,  0.63333333,\n",
+        "        0.7       ,  0.76666667,  0.83333333,  0.9       ,  0.96666667])"
+       ]
+      }
+     ],
+     "prompt_number": 52
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pts = np.vstack((np.r_[0.25, 0.5], np.r_[0.75, 0.5]))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 96
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "AtM, rhstM = DCfun(tM, pts)\n",
+      "AinvtM = SolverLU(AtM)\n",
+      "phitM = AinvtM*rhstM"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 97
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "AqM, rhsqM = DCfun(qM, pts)\n",
+      "AinvqM = SolverLU(AqM)\n",
+      "phiqM = AinvqM*rhsqM"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 98
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ArM, rhsrM = DCfun(rM, pts)\n",
+      "AinvrM = SolverLU(ArM)\n",
+      "phirM = AinvrM*rhsrM"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 99
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "coreind = (qM.gridCC[:,0]-0.5)**2+(qM.gridCC[:,1]-0.5)**2 >0.43**2\n",
+      "phiqM[coreind] = 0."
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 100
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "Xi = tM.gridCC[:,0].reshape(sz[0], sz[1], order='F')\n",
+      "Yi = tM.gridCC[:,1].reshape(sz[0], sz[1], order='F')\n",
+      "PHItM = griddata(tM.gridCC[:,0], tM.gridCC[:,1], phitM, Xi, Yi, interp='linear')\n",
+      "PHIqM = griddata(qM.gridCC[:,0], qM.gridCC[:,1], phiqM, Xi, Yi, interp='linear')\n",
+      "PHIrM = griddata(rM.gridCC[:,0], rM.gridCC[:,1], phirM, Xi, Yi, interp='linear')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 101
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fig, axes = plt.subplots(1,3,figsize=(14,4))\n",
+      "opts = {}\n",
+      "vmin, vmax = PHItM.min(), PHItM.max()\n",
+      "axes[0].contourf(Xi, Yi, PHItM, 100)\n",
+      "tM.plotGrid(ax=axes[0], **opts)\n",
+      "axes[0].set_title('TensorMesh')\n",
+      "axes[1].contourf(Xi, Yi, PHIqM, 100)\n",
+      "qM.plotGrid(ax=axes[1], **opts)\n",
+      "axes[1].set_title('TreeMesh')\n",
+      "axes[2].contourf(Xi, Yi, PHIrM, 100)\n",
+      "rM.plotGrid(ax=axes[2], **opts)\n",
+      "axes[2].set_title('LogicallyRectMesh')\n",
+      "for i in range(3):\n",
+      "    axes[i].set_xlim(0.025, 0.975)\n",
+      "    axes[i].set_ylim(0.025, 0.975)\n",
+      "    if i==0:        \n",
+      "        axes[i].set_ylabel(\"Northing (m)\")\n",
+      "    else:\n",
+      "        axes[i].set_ylabel(\" \")\n",
+      "    axes[i].set_xlabel(\"Easting (m)\")\n",
+      "fig.savefig(\"./ThreeMesh.png\", dpi=100)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x9120f50>"
+       ]
+      }
+     ],
+     "prompt_number": 103
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}