Create import functions for UBCDC2D model and mesh.

Start driver function for forward model data from example.

notebooks/2DinvDC.ipynb

@@ -2,11 +2,22 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Efficiency Warning: Interpolation will be slow, use setup.py!\n",
+      "\n",
+      "            python setup.py build_ext --inplace\n",
+      "    \n"
+     ]
+    }
+   ],
    "source": [
     "from SimPEG import *\n",
     "import simpegDCIP as DC\n",
@@ -15,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -31,7 +42,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "WARNING: pylab import has clobbered these variables: ['linalg', 'inv']\n",
+      "WARNING: pylab import has clobbered these variables: ['linalg']\n",
       "`%matplotlib` prevents importing * from pylab and numpy\n"
      ]
     }
@@ -42,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -55,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 4,
    "metadata": {
     "collapsed": true
    },
@@ -66,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -82,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -93,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -110,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -123,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -134,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -158,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -166,18 +177,18 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10c7a9210>]"
+       "[<matplotlib.lines.Line2D at 0x15be0278>]"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEPCAYAAAC5sYRSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGctJREFUeJzt3X2wXXV97/H3pwkgyAAiMwkhaRPmJlPCWAWn3Eh1CEqc\nGGIwdhDog9h6e5lLEYaxjDx47eHWaXE6ttQy9EaMTtqKFIOmgUDlyM0hdfTyGDHkYQKa0DzcHKw8\njArk4fC9f+yVuM/J3nv99j5nZz3k85pZw95r/dZvfReQT9b5nbXWTxGBmZnVy68VXYCZmU08h7uZ\nWQ053M3MasjhbmZWQw53M7MacribmdVQKcNd0kJJWyQ9J+kzRddjZjYeKZkm6UvZ9mcknTPeY5Yu\n3CVNAu4AFgJzgSsknVVsVWZmvUnJNEmLgP8SEbOB/w78w3iPW7pwB84Dno+I7RGxH7gHuKTgmszM\nepWSaUuAFQAR8RhwiqQp4zloGcP9DGBH0/ed2TozsypKybRWbaaP56BlDHe/D8HM6iQ109Tjfi1N\nHs/OfbILmNH0fQaNv8UOkeS/AMwsWUSMDc6udJs5Y46Xm2kt2kzP1vWsjOH+JDBb0kxgN3AZcMXh\nzQZ6PsAIA4f+inyQGSzmkx1arwUu7PlYqZYtW8ycOW/ntdf283u/dx+vvrq3q+3d9vHRj/4P9u59\nX+F1TGwfo/9bleFcJuIYixfv4tOfvrnwf+cTL//P1iOPDKDsD+v3vz+Dz36205/VdgZ62Odwn09s\n99nDV6Vk2mrgGuAeSfOAVyJiuMdSgRIOy0TEARon+R1gE/AvEbF5Io+hpmXRqGGu4syZ83bmz5/J\nokWzWbZscdfbu+1j8eI5paij7n1MxDHe/vYTSnGuRZB+tZx/frF/Vo9JXMZql2mSrpJ0VdbmQeAn\nkp4HlgFXj7feMl65ExEPAQ/1/Tg0rtzL4LXX9gPw+OO7uOqqB7re3m0fDzywFTj8l/FHuo669zER\nx9i/f6Tvdaa2KUpE48q9SOMJy1aZFhHLxny/ZhyHOIyq+D73xvjXQM/7P8ByFrEjYUgGYBswq+dj\npTr55ONYtmwxV131QMsfh/O2d9/HFlqd15GvYyL7GP3fqgznMhHHeOtbd7F8+bWF/zufePl/tj7/\n+eWcf/6OcQzJAAxMyJj7nYltr2b8Y/wT4agMdzM7mkxMuN+V2PZPKEe4l3JYxsysbKoWllWr18ys\nEK1+WVpmDnczswRVC8uq1WtmVghfuZuZ1ZDD3cysho4vuoAuOdzNzBJULSyrVq+ZWSE8LGNmVkNV\nC8uq1WtmVghfuZuZ1VDVwrJq9ZqZFaJqV+6le5+7mVkZHZ+4dEPSqZIGJW2V9LCkU1q0mSFpraSN\nkp6VdG1K3w53M7MEvU7WkeNGYDAi5gCPZN/H2g9cHxFnA/OAP5V0Vl7HDnczswSTE5cuLQFWZJ9X\nAB8Z2yAi9kTED7PPvwA2A9NS6jUzsxzHpKblga66ndI0V+owraZHa5LNw3oO8Fhexw53M7MEk9uk\n5b+PwPfebL+fpEFgaotNtzR/iYhoTETUtp8TgZXAddkVfOd68xqYmRkcM6n1+vdPgvc3fb/tl6O3\nR8SCdn1KGpY0NSL2SDodeLFNu2OA+4B/johVKfV6zN3MLMHkyWlLl1YDV2afrwQOC25JApYDmyLi\n9tSOHe5mZgmOOS5t6dJtwAJJW2n8AHAbgKRpktZkbX4H+APgQknrs2VhXsceljEzS9GHtIyIl4CL\nWqzfDVycff4ePVyIO9zNDIABBoouoS8GJqqjiqVlxco1MytIxdKyYuWamRWkzd0yZeVwNzNLUbG0\nrFi5ZuVQ1/Fp66D7O2EK5XA3M0tRsbSsWLlmZgWpWFpWrFwzs4L4F6pmZjVUsbSsWLlWNf7Fo9VG\nxdKyYuWamRWkYmlZsXLNzAriWyHNzGqoYmlZunIlDQD/DfhptuqmiPi34io6cjw+bVZifbhbRtKp\nwL8AvwFsBz4WEa+0aTsJeBLYGREfzuu7jO9zD+BvIuKcbDkqgt3MSq4/M2TfCAxGxBzgkex7O9cB\nm2hkZK4yhjuAii7AzGyU/oT7EmBF9nkF8JFWjSRNBxYBXyExH8sa7p+S9Iyk5ZJOKboYMzMmJS7d\nmRIRw9nnYWBKm3Z/C9wAdJiKe7RCxtxzZgP/B+B/Zd//Avgi8MnDm65t+jwTmDWRJZpZRW2jMXg9\n4dqk5dBuGPp/7XfLybtDIiIkHTbkImkx8GJErJc0P7VcRSQN3xRC0kzg/oh4x5j1MVBEQWZWOQNA\nRIxrqFdSxDWJbe9IP56kLcD8iNgj6XRgbUT85pg2fwn8IXAAeAtwEnBfRHy8U9+lG5bJTvCgpcCG\nomoxMzukP8Myq4Ers89XAqvGNoiImyNiRkTMAi4H/k9esEMJb4UEviDpXTR+I7wNuKrgeszM+pWW\ntwH3Svok2a2QAJKmAXdFxMUt9kkabilduKf8jWRmdsT1IS0j4iXgohbrdwOHBXtEPAo8mtJ36cLd\nzKyU/MpfM7MaqlhaVqxcM7OCVCwtK1aumVlB/FZIM7MaqlhaVqxcM7OCVCwtK1aumVlBfLeMmVkN\nVSwtK1aumVlBKpaWFSvXzKwgHpYxM6uhtxRdQHcc7mZmKSqWlhUr18ysIBUblind+9zNzEqpD3Oo\nSjpV0qCkrZIebjetqKRTJK2UtFnSJknz8vp2uJuZpejPBNk3AoMRMQd4JPveyt8BD0bEWcBvAZvz\nOna4m5ml6M9MTEuAFdnnFcBHxjaQdDLwvoj4KkBEHIiIV/M69pi7mVmK/twtMyUihrPPw8CUFm1m\nAT+V9DXgncBTwHUR8Vqnjn3lbmaWoscr92xMfUOLZUlzu4gIWk+hNxk4F7gzIs4Ffkn74ZtRO5mZ\nWZ42aTn0FAw93X63iFjQbpukYUlTI2KPpNOBF1s02wnsjIgnsu8rcbibmU2QNmk5/782loNuXd5V\nr6uBK4EvZP9cNbZBFvw7JM2JiK005lzd2GO5ZmY2Sn/S8jbgXkmfBLYDHwOQNA24KyIOTpL9KeDr\nko4Ffgz8UTHlmpnVTR8eYoqIl2hciY9dvxu4uOn7M8Bvd9O3w93MLEXF0rJi5ZqZFcRzqJqZ1VDF\n0rJi5ZqZFaRiaVmxcs3MClKxtKxYuWZmxYiKvfLX4W5mlmCkYmlZsXLNzIrhcDczq6G9xx2b2HJf\nX+tI5XA3M0swMqlag+4OdzOzBCMVm0TV4W5mluCAw93MrH5GKhaXhczEJOlSSRsljUg6d8y2myQ9\nJ2mLpA8WUZ+Z2VgjTEpauiHp1Gympq2SHpZ0Spt210t6NpvB6W5JuW+6KWqavQ3AUmBd80pJc4HL\ngLnAQuBOSZ4K0MwK149wpzGj0mBEzAEeocUMS5LOoPE+93dHxDtovHz48ryOC/k5IyK2AEgau+kS\n4BsRsR/YLul54Dzg/x7ZCs3MRttL6q2QXVkCXJB9XgEM0XoKvcnACZJGgBOAXXkdl+2qeBqN+QIP\n2gmcUVAtZmaHjDA5aenSlIgYzj4PA1PGNoiIXcAXgf8AdgOvRMR38zru25W7pEFgaotNN0fE/V10\n1Wo2cDOzI6rXWyE7ZOEtzV8iIiQdlneS3kbjCn8m8CrwTUm/HxFf73TcvoV7pxm/O9gFzGj6Pp02\nP36sbfo8E5jVw8HMrH620ZiMdKK1C/cnh37Jk0Ovtd2vUxZKGpY0NZsE+3TgxRbNLgK2RcTPsn2+\nBZwPFBPuXWgeeF8N3C3pb2gMx8wGHm+104VHoDAzq55ZjL7Ye3SC+m13n/u75p/Eu+afdOj7l2/9\nz266XQ1cCXwh++eqFm1eAOZJOh54g0bYt8zFZkXdCrlU0g5gHrBG0kMAEbEJuBfYBDwEXB0RHpYx\ns8L1acz9NmCBpK3A+7PvSJomaQ1ARDwOrASeBn6U7fflvI5VxeyUFANFF2FmlTAARMRht+Z1Q1J8\nL96d1Pa9emrcx5sIZRiWMTMrvX39uRWybxzuZmYJ/G4ZM7Maqtq7ZapVrZlZQfzKXzOzGnK4m5nV\nkMfczcxqaB+5b9ktFYe7mVkCD8uYmdWQh2XMzGrIt0KamdWQh2XMzGrI4W5mVkMOdzOzGtpbsVsh\nyzaHqplZKY0wKWnphqRLJW2UNCLp3A7tFkraIuk5SZ9J6dvhbmaWoB/hDmwAlgLr2jWQNAm4A1gI\nzAWukHRWXse54S7p2myCVjOzo9YBJiUt3YiILRGxNafZecDzEbE9IvYD9wCX5PWdcuU+BXhC0r3Z\njwaFzzBiZnak9WmavRRnADuavu/M1nWUW0lE3CLpfwIfBD4B3CHpXmB5RPy4t1rNzKql3ZDL9qEX\neGHohbb7SRoEprbYdHNE3J9w6J7mQk36ayYi3pS0BxgGRoC3ASslfTcibujlwGZmVdIu3GfMP5MZ\n88889H3drd8btT0iFozz0LuAGc2HpHH13lFuuEu6Dvg48DPgK8CfRcR+Sb8GPAc43M2s9vb2fw7V\ndkPeTwKzJc0EdgOXAVfkdZZy5X4q8NGIGPVzR3Y1/+GE/c3MKq8f4+mSlgJfAk4D1khaHxEfkjQN\nuCsiLo6IA5KuAb4DTKIxJL45r++UMfc/77BtU/JZmJlVWD+eUI2IbwPfbrF+N3Bx0/eHgIe66dtP\nqJqZJfDrB8zMasjvczczqyG/z93MrIY8LGNmVkP7+n8r5IRyuJuZJfCYu5lZDXnM3cyshjzmbmZW\nQw53M7Ma8pi7mVkNeczdzKyGfCukmVkNVW1YppAJstvN+C1ppqTXJa3PljuLqM/MbKx+TLPXLgvH\ntJkhaW3W7llJ16b0XdSV+8EZv5e12PZ8RJxzhOsxM+uoT3fLdMrCg/YD10fEDyWdCLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEPCAYAAAC5sYRSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ab76b50>"
+       "<matplotlib.figure.Figure at 0x15968710>"
       ]
      },
      "metadata": {},
@@ -194,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -202,18 +213,18 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10c039b50>]"
+       "[<matplotlib.lines.Line2D at 0x15e72518>]"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEPCAYAAAC5sYRSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEPCAYAAAC5sYRSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bfe44d0>"
+       "<matplotlib.figure.Figure at 0x15968198>"
       ]
      },
      "metadata": {},
@@ -221,7 +232,7 @@
     }
    ],
    "source": [
-    "dat = mesh.plotImage(np.log10(mappingfwd*m0), clim=(-2, 0))\n",
+    "dat = mesh.plotImage(np.log10(mappingfwd*m0_poly), clim=(-2, 0))\n",
     "plt.colorbar(dat[0])\n",
     "plot(xz_A[:,0], xz_A[:,1], 'w.')\n",
     "plot(xz_B[:,0], xz_B[:,1], 'y.')\n",
@@ -230,18 +241,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 14,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
-    "from pymatsolver import MumpsSolver"
+    "from SimPEG import SolverLU"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -249,18 +260,18 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10d8c2d90>]"
+       "[<matplotlib.lines.Line2D at 0x160b5eb8>]"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEACAYAAACtVTGuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEACAYAAACtVTGuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10cd0a810>"
+       "<matplotlib.figure.Figure at 0x160c1278>"
       ]
      },
      "metadata": {},
@@ -273,16 +284,17 @@
     "survey = DC.SurveyDC(txList)\n",
     "problem = DC.ProblemDC_CC(mesh, mapping = mapping)\n",
     "problem.pair(survey)\n",
-    "problem.Solver = MumpsSolver\n",
+    "problem.Solver = SolverLU\n",
     "dtrue  = survey.dpred(mtrue)\n",
     "d0  = survey.dpred(m0)\n",
+    "j = problem.fields\n",
     "plot(dtrue)\n",
     "plot(d0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -290,10 +302,10 @@
     {
      "data": {
       "text/plain": [
-       "0.004688525906217933"
+       "0.0046885259087758868"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -304,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -315,22 +327,22 @@
        "(array([  1.,   2.,   3.,   4.,   5.,   4.,   6.,   7.,   8.,   9.,   8.,\n",
        "         10.,  17.,  17.,  18.,  33.,  41.,  30.,  32.,  17.]),\n",
        " array([-2.32896368, -2.11250236, -1.89604103, -1.67957971, -1.46311839,\n",
-       "        -1.24665706, -1.03019574, -0.81373442, -0.59727309, -0.38081177,\n",
+       "        -1.24665706, -1.03019574, -0.81373441, -0.59727309, -0.38081177,\n",
        "        -0.16435044,  0.05211088,  0.2685722 ,  0.48503353,  0.70149485,\n",
        "         0.91795617,  1.1344175 ,  1.35087882,  1.56734014,  1.78380147,\n",
        "         2.00026279]),\n",
        " <a list of 20 Patch objects>)"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEACAYAAAB8nvebAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEACAYAAAB8nvebAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a2299d0>"
+       "<matplotlib.figure.Figure at 0x15ca0828>"
       ]
      },
      "metadata": {},
@@ -343,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -354,7 +366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -371,23 +383,22 @@
       "   0  1.00e+00  1.40e+08  0.00e+00  1.40e+08    8.32e+06      0              \n",
       "   1  1.00e+00  1.88e+07  6.18e-03  1.88e+07    1.13e+06      0              \n",
       "   2  1.00e+00  2.52e+06  2.45e-02  2.52e+06    1.55e+05      0   Skip BFGS  \n",
-      "   3  1.25e-01  3.50e+05  5.43e-02  3.50e+05    2.18e+04      0   Skip BFGS  \n",
-      "   4  1.25e-01  6.62e+04  9.32e-02  6.62e+04    3.26e+03      0   Skip BFGS  \n",
-      "   5  1.25e-01  1.56e+04  7.26e+00  1.56e+04    4.80e+02      1   Skip BFGS  \n",
-      "   6  1.56e-02  4.47e+03  1.84e+01  4.47e+03    5.41e+02      1              \n",
-      "   7  1.56e-02  2.73e+03  4.08e+01  2.73e+03    2.91e+03      0   Skip BFGS  \n",
-      "   8  1.56e-02  5.74e+02  3.75e+01  5.75e+02    2.97e+02      0              \n",
-      "   9  1.95e-03  3.37e+02  3.70e+01  3.37e+02    2.87e+02      0   Skip BFGS  \n",
-      "  10  1.95e-03  2.47e+02  3.98e+01  2.47e+02    8.70e+01      0              \n",
-      "  11  1.95e-03  1.99e+02  3.89e+01  1.99e+02    1.22e+02      0              \n",
-      "  12  2.44e-04  1.60e+02  4.09e+01  1.60e+02    1.45e+02      0              \n",
-      "  13  2.44e-04  1.38e+02  4.20e+01  1.38e+02    8.79e+01      0   Skip BFGS  \n",
+      "   3  1.25e-01  3.49e+05  5.43e-02  3.49e+05    2.17e+04      0   Skip BFGS  \n",
+      "   4  1.25e-01  6.57e+04  9.31e-02  6.57e+04    3.25e+03      0   Skip BFGS  \n",
+      "   5  1.25e-01  1.53e+04  7.19e+00  1.53e+04    4.83e+02      1   Skip BFGS  \n",
+      "   6  1.56e-02  4.40e+03  1.81e+01  4.40e+03    5.23e+02      1              \n",
+      "   7  1.56e-02  2.56e+03  4.02e+01  2.56e+03    2.82e+03      0   Skip BFGS  \n",
+      "   8  1.56e-02  5.47e+02  3.69e+01  5.47e+02    2.89e+02      0              \n",
+      "   9  1.95e-03  3.14e+02  3.71e+01  3.14e+02    3.22e+02      0   Skip BFGS  \n",
+      "  10  1.95e-03  2.18e+02  3.92e+01  2.18e+02    1.07e+02      0              \n",
+      "  11  1.95e-03  1.77e+02  3.86e+01  1.77e+02    8.57e+01      0              \n",
+      "  12  2.44e-04  1.44e+02  4.09e+01  1.44e+02    9.30e+01      0              \n",
       "------------------------- STOP! -------------------------\n",
-      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.3961e+07\n",
-      "1 : |xc-x_last| = 1.1216e+00 <= tolX*(1+|x0|) = 4.8945e+01\n",
-      "0 : |proj(x-g)-x|    = 8.7946e+01 <= tolG          = 1.0000e-01\n",
-      "0 : |proj(x-g)-x|    = 8.7946e+01 <= 1e3*eps       = 1.0000e-02\n",
-      "0 : maxIter   =      30    <= iter          =     14\n",
+      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.3960e+07\n",
+      "1 : |xc-x_last| = 3.1882e+00 <= tolX*(1+|x0|) = 4.8945e+01\n",
+      "0 : |proj(x-g)-x|    = 9.3043e+01 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 9.3043e+01 <= 1e3*eps       = 1.0000e-02\n",
+      "0 : maxIter   =      30    <= iter          =     13\n",
       "------------------------- DONE! -------------------------\n"
      ]
     }
@@ -411,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 20,
    "metadata": {
     "collapsed": true
    },
@@ -422,9 +433,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 21,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -448,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -456,9 +467,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmIAAAFHCAYAAAAC3yD+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmIAAAFHCAYAAAAC3yD+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11286c350>"
+       "<matplotlib.figure.Figure at 0x1ab1e9b0>"
       ]
      },
      "metadata": {},
@@ -480,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -488,18 +499,18 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10d8cdb50>]"
+       "[<matplotlib.lines.Line2D at 0x1ba516d8>]"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEACAYAAABRQBpkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEACAYAAABRQBpkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d8cd110>"
+       "<matplotlib.figure.Figure at 0x1ef46908>"
       ]
      },
      "metadata": {},

simpegDCIP/Dev/DC2D_fwr_obs.py

@@ -0,0 +1,30 @@
+import os
+
+home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegdc\simpegDCIP\Dev'
+
+inpfile = 'MAG3Cfwr.inp'
+
+os.chdir(home_dir)
+
+#%%
+from SimPEG import np, Utils, Mesh
+import simpegDCIP as DC
+import pylab as plt
+
+from readUBC_DC2DMesh import readUBC_DC2DMesh
+from readUBC_DC2DModel import readUBC_DC2DModel
+
+# Load UBC mesh 2D
+mesh = readUBC_DC2DMesh('mesh2d.txt')
+
+# Load model
+model = readUBC_DC2DModel('model2d.con')
+
+# load obs file
+
+#%% Plot model
+fig, axs = plt.subplots(1,1, figsize=(10,7))
+h1 = mesh.plotImage(model, ax = axs)
+plt.ylim([-1000,0])
+plt.xlim([0,2000])
+

simpegDCIP/Dev/mesh2d.txt

@@ -0,0 +1,43 @@
+29
+-4349.9 -2908.3 1
+    -1947.17 1
+    -1306.45 1
+    -879.30 1
+    -594.53 1
+    -404.69 1
+    -278.13 1
+    -193.75 1
+    -137.50 1
+    -100.00 1
+    -75.00 1
+    -50.00 1
+    -25.00 1
+    0.00 1
+    2000.00 80
+    2025.00 1
+    2050.00 1
+    2075.00 1
+    2100.00 1
+    2137.50 1
+    2193.75 1
+    2278.13 1
+    2404.69 1
+    2594.53 1
+    2879.30 1
+    3306.45 1
+    3947.17 1
+    4908.25 1
+    6349.88 1
+
+11
+0.00 975.00 39
+    1462.50 13
+    1518.75 1
+    1603.13 1
+    1729.69 1
+    1919.53 1
+    2204.30 1
+    2631.45 1
+    3272.17 1
+    4233.25 1
+    5674.88 1

simpegDCIP/Dev/obs2d_East.loc

@@ -0,0 +1,325 @@
+0 0  75 150
+0 0  150 225
+0 0  225 300
+0 0  300 375
+0 0  375 450
+0 0  450 525
+0 0  525 600
+0 0  600 675
+0 0  675 750
+0 0  750 825
+0 0  825 900
+0 0  900 975
+0 0  975 1050
+0 0  1050 1125
+0 0  1125 1200
+0 0  1200 1275
+0 0  1275 1350
+0 0  1350 1425
+0 0  1425 1500
+0 0  1500 1575
+0 0  1575 1650
+0 0  1650 1725
+0 0  1725 1800
+0 0  1800 1875
+0 0  1875 1950
+75 75  150 225
+75 75  225 300
+75 75  300 375
+75 75  375 450
+75 75  450 525
+75 75  525 600
+75 75  600 675
+75 75  675 750
+75 75  750 825
+75 75  825 900
+75 75  900 975
+75 75  975 1050
+75 75  1050 1125
+75 75  1125 1200
+75 75  1200 1275
+75 75  1275 1350
+75 75  1350 1425
+75 75  1425 1500
+75 75  1500 1575
+75 75  1575 1650
+75 75  1650 1725
+75 75  1725 1800
+75 75  1800 1875
+75 75  1875 1950
+150 150  225 300
+150 150  300 375
+150 150  375 450
+150 150  450 525
+150 150  525 600
+150 150  600 675
+150 150  675 750
+150 150  750 825
+150 150  825 900
+150 150  900 975
+150 150  975 1050
+150 150  1050 1125
+150 150  1125 1200
+150 150  1200 1275
+150 150  1275 1350
+150 150  1350 1425
+150 150  1425 1500
+150 150  1500 1575
+150 150  1575 1650
+150 150  1650 1725
+150 150  1725 1800
+150 150  1800 1875
+150 150  1875 1950
+225 225  300 375
+225 225  375 450
+225 225  450 525
+225 225  525 600
+225 225  600 675
+225 225  675 750
+225 225  750 825
+225 225  825 900
+225 225  900 975
+225 225  975 1050
+225 225  1050 1125
+225 225  1125 1200
+225 225  1200 1275
+225 225  1275 1350
+225 225  1350 1425
+225 225  1425 1500
+225 225  1500 1575
+225 225  1575 1650
+225 225  1650 1725
+225 225  1725 1800
+225 225  1800 1875
+225 225  1875 1950
+300 300  375 450
+300 300  450 525
+300 300  525 600
+300 300  600 675
+300 300  675 750
+300 300  750 825
+300 300  825 900
+300 300  900 975
+300 300  975 1050
+300 300  1050 1125
+300 300  1125 1200
+300 300  1200 1275
+300 300  1275 1350
+300 300  1350 1425
+300 300  1425 1500
+300 300  1500 1575
+300 300  1575 1650
+300 300  1650 1725
+300 300  1725 1800
+300 300  1800 1875
+300 300  1875 1950
+375 375  450 525
+375 375  525 600
+375 375  600 675
+375 375  675 750
+375 375  750 825
+375 375  825 900
+375 375  900 975
+375 375  975 1050
+375 375  1050 1125
+375 375  1125 1200
+375 375  1200 1275
+375 375  1275 1350
+375 375  1350 1425
+375 375  1425 1500
+375 375  1500 1575
+375 375  1575 1650
+375 375  1650 1725
+375 375  1725 1800
+375 375  1800 1875
+375 375  1875 1950
+450 450  525 600
+450 450  600 675
+450 450  675 750
+450 450  750 825
+450 450  825 900
+450 450  900 975
+450 450  975 1050
+450 450  1050 1125
+450 450  1125 1200
+450 450  1200 1275
+450 450  1275 1350
+450 450  1350 1425
+450 450  1425 1500
+450 450  1500 1575
+450 450  1575 1650
+450 450  1650 1725
+450 450  1725 1800
+450 450  1800 1875
+450 450  1875 1950
+525 525  600 675
+525 525  675 750
+525 525  750 825
+525 525  825 900
+525 525  900 975
+525 525  975 1050
+525 525  1050 1125
+525 525  1125 1200
+525 525  1200 1275
+525 525  1275 1350
+525 525  1350 1425
+525 525  1425 1500
+525 525  1500 1575
+525 525  1575 1650
+525 525  1650 1725
+525 525  1725 1800
+525 525  1800 1875
+525 525  1875 1950
+600 600  675 750
+600 600  750 825
+600 600  825 900
+600 600  900 975
+600 600  975 1050
+600 600  1050 1125
+600 600  1125 1200
+600 600  1200 1275
+600 600  1275 1350
+600 600  1350 1425
+600 600  1425 1500
+600 600  1500 1575
+600 600  1575 1650
+600 600  1650 1725
+600 600  1725 1800
+600 600  1800 1875
+600 600  1875 1950
+675 675  750 825
+675 675  825 900
+675 675  900 975
+675 675  975 1050
+675 675  1050 1125
+675 675  1125 1200
+675 675  1200 1275
+675 675  1275 1350
+675 675  1350 1425
+675 675  1425 1500
+675 675  1500 1575
+675 675  1575 1650
+675 675  1650 1725
+675 675  1725 1800
+675 675  1800 1875
+675 675  1875 1950
+750 750  825 900
+750 750  900 975
+750 750  975 1050
+750 750  1050 1125
+750 750  1125 1200
+750 750  1200 1275
+750 750  1275 1350
+750 750  1350 1425
+750 750  1425 1500
+750 750  1500 1575
+750 750  1575 1650
+750 750  1650 1725
+750 750  1725 1800
+750 750  1800 1875
+750 750  1875 1950
+825 825  900 975
+825 825  975 1050
+825 825  1050 1125
+825 825  1125 1200
+825 825  1200 1275
+825 825  1275 1350
+825 825  1350 1425
+825 825  1425 1500
+825 825  1500 1575
+825 825  1575 1650
+825 825  1650 1725
+825 825  1725 1800
+825 825  1800 1875
+825 825  1875 1950
+900 900  975 1050
+900 900  1050 1125
+900 900  1125 1200
+900 900  1200 1275
+900 900  1275 1350
+900 900  1350 1425
+900 900  1425 1500
+900 900  1500 1575
+900 900  1575 1650
+900 900  1650 1725
+900 900  1725 1800
+900 900  1800 1875
+900 900  1875 1950
+975 975  1050 1125
+975 975  1125 1200
+975 975  1200 1275
+975 975  1275 1350
+975 975  1350 1425
+975 975  1425 1500
+975 975  1500 1575
+975 975  1575 1650
+975 975  1650 1725
+975 975  1725 1800
+975 975  1800 1875
+975 975  1875 1950
+1050 1050  1125 1200
+1050 1050  1200 1275
+1050 1050  1275 1350
+1050 1050  1350 1425
+1050 1050  1425 1500
+1050 1050  1500 1575
+1050 1050  1575 1650
+1050 1050  1650 1725
+1050 1050  1725 1800
+1050 1050  1800 1875
+1050 1050  1875 1950
+1125 1125  1200 1275
+1125 1125  1275 1350
+1125 1125  1350 1425
+1125 1125  1425 1500
+1125 1125  1500 1575
+1125 1125  1575 1650
+1125 1125  1650 1725
+1125 1125  1725 1800
+1125 1125  1800 1875
+1125 1125  1875 1950
+1200 1200  1275 1350
+1200 1200  1350 1425
+1200 1200  1425 1500
+1200 1200  1500 1575
+1200 1200  1575 1650
+1200 1200  1650 1725
+1200 1200  1725 1800
+1200 1200  1800 1875
+1200 1200  1875 1950
+1275 1275  1350 1425
+1275 1275  1425 1500
+1275 1275  1500 1575
+1275 1275  1575 1650
+1275 1275  1650 1725
+1275 1275  1725 1800
+1275 1275  1800 1875
+1275 1275  1875 1950
+1350 1350  1425 1500
+1350 1350  1500 1575
+1350 1350  1575 1650
+1350 1350  1650 1725
+1350 1350  1725 1800
+1350 1350  1800 1875
+1350 1350  1875 1950
+1425 1425  1500 1575
+1425 1425  1575 1650
+1425 1425  1650 1725
+1425 1425  1725 1800
+1425 1425  1800 1875
+1425 1425  1875 1950
+1500 1500  1575 1650
+1500 1500  1650 1725
+1500 1500  1725 1800
+1500 1500  1800 1875
+1500 1500  1875 1950
+1575 1575  1650 1725
+1575 1575  1725 1800
+1575 1575  1800 1875
+1575 1575  1875 1950
+1650 1650  1725 1800
+1650 1650  1800 1875
+1650 1650  1875 1950
+1725 1725  1800 1875
+1725 1725  1875 1950
+1800 1800  1875 1950

simpegDCIP/Dev/readUBC_DC2DMesh.py

@@ -0,0 +1,70 @@
+def readUBC_DC2DMesh(fileName):
+
+    from SimPEG import np
+    """
+        Read UBC GIF 2DTensor mesh and generate 2D Tensor mesh in simpeg
+
+        Input:
+        :param fileName, path to the UBC GIF mesh file
+
+        Output:
+        :param SimPEG TensorMesh 2D object
+        :return
+        
+        Created on Thu Nov 12 13:14:10 2015
+
+        @author: dominiquef
+
+    """
+    
+    # Open file 
+    fopen = open(fileName,'r')
+    
+    # Read down the file and unpack dx vector
+    def unpackdx(fid,nrows):
+        for ii in range(nrows):
+            
+            line = fid.readline()
+            var = np.array(line.split(),dtype=float)
+            
+            if ii==0:
+                x0= var[0]
+                xvec = np.ones(int(var[2])) * (var[1] - var[0]) / int(var[2])
+                xend = var[1]            
+                
+            else:
+                xvec = np.hstack((xvec,np.ones(int(var[1])) * (var[0] - xend) / int(var[1])))
+                xend = var[0] 
+                
+        return x0, xvec
+        
+    #%% Start with dx block
+    # First line specifies the number of rows for x-cells
+    line = fopen.readline()
+    nl = np.array(line.split(),dtype=float)
+
+    [x0, dx] = unpackdx(fopen,nl) 
+    
+
+    #%% Move down the file until reaching the z-block
+    line = fopen.readline()
+    if not line:
+        line = fopen.readline()
+        
+    #%% End with dz block
+    # First line specifies the number of rows for z-cells
+    line = fopen.readline()
+    nl = np.array(line.split(),dtype=float)
+
+    [z0, dz] = unpackdx(fopen,nl) 
+    
+    # Flip z0 to be the bottom of the mesh for SimPEG
+    z0 = z0 - sum(dz)
+    dz = dz[::-1]
+    #%% Make the mesh using SimPEG
+    
+    from SimPEG import Mesh
+    tensMsh = Mesh.TensorMesh([dx,dz],(x0, z0))
+    return tensMsh
+
+

simpegDCIP/Dev/readUBC_DC2DModel.py

@@ -0,0 +1,39 @@
+def readUBC_DC2DModel(fileName):
+
+    from SimPEG import np, mkvc
+    """
+        Read UBC GIF 2DTensor model and generate 2D Tensor model in simpeg
+
+        Input:
+        :param fileName, path to the UBC GIF 2D model file
+
+        Output:
+        :param SimPEG TensorMesh 2D object
+        :return
+        
+        Created on Thu Nov 12 13:14:10 2015
+
+        @author: dominiquef
+
+    """
+    
+    # Open fileand skip header... assume that we know the mesh already
+    fopen = open(fileName,'r')
+    lines = fopen.readlines()[:]
+    fopen.close()
+
+    dim = np.array(lines[0].split(),dtype=float)
+    
+    model = np.array(lines[1:],dtype=float)
+    
+    # Permute the second dimension to flip the order
+    model = model.reshape(dim[1],dim[0])
+    
+    model = model[::-1,:]
+    model = np.transpose(model, (1, 0))
+    model = mkvc(model)
+
+
+    return model
+
+