{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['linalg']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "from SimPEG import *\n",
    "import simpegDCIP as DC\n",
    "%pylab inline\n",
    "from pymatsolver import MumpsSolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cs = 12.5\n",
    "nc = 500/cs+1\n",
    "hx = [(cs,7, -1.3),(cs,nc),(cs,7, 1.3)]\n",
    "hy = [(cs,7, -1.3),(cs,int(nc/2+1)),(cs,7, 1.3)]\n",
    "hz = [(cs,7, -1.3),(cs,int(nc/2+1))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  ---- 3-D TensorMesh ----  \n",
      "   x0: -541.97\n",
      "   y0: -416.97\n",
      "   z0: -548.22\n",
      "  nCx: 55\n",
      "  nCy: 35\n",
      "  nCz: 28\n",
      "   hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
      "   hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
      "   hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n"
     ]
    }
   ],
   "source": [
    "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')\n",
    "print mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sighalf = 1e-2\n",
    "sigma = np.ones(mesh.nC)*sighalf\n",
    "p0 = np.r_[-50., 50., -50.]\n",
    "p1 = np.r_[ 50.,-50., -150.]\n",
    "blk_ind = Utils.ModelBuilder.getIndicesBlock(p0, p1, mesh.gridCC)\n",
    "sigma[blk_ind] = 1e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "eta = np.zeros_like(sigma)\n",
    "eta[blk_ind] = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sigmaInf = sigma.copy()\n",
    "sigma0 = sigma*(1-eta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-600.0, 600.0, -600.0, 0.0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O6OnXBNH/A8wG7i4n2/yD7cttb5N0K7AN2A9c3pC5Lwc+ARxPcfX5sEQfETHTqsyzt71f\n0grgToqly2+wvV3SZeXx1bbXS1omaZxiqOaShrd4O/ApSbOBbzQdO0RfevbTKT37iOHUr579l3xu\n1/VfpXsrna+Ketz6FRExoJ5uPaVy4CTZR0RUkGfjREQMgbo8G6ceUUZEDKg84jgiYggk2UdEDIGM\n2UdEDIGneU6/Q+hKkn1ERAUZxomIGAIZxomIGAKZehkRMQQyjBMRMQSS7CMihkCSfUTEEHiqJlMv\nB2EN2oiI2qq4LCGSlkraIekhSS0XHpG0qjy+RdKihvKdkr4q6T5JGzvFmZ59REQFVYZxJI0A1wEX\nUCw1uEnSuhYrVZ1le4GkxcD1wJLysIFR2/8y2bmS7CMiKqg4z/48YNz2TgBJa4GLgO0NdS4E1gDY\n3iBpjqRTbO8pj3e1GMqkwziS/p+kX2oq+7Nu3jwi4mh3gFldby3MBR5p2N9VlnVbx8A9kjZLelun\nOLvp2Z8JXCHpZbYn1vB7eRftIiKOep2GcXaOPczDYw93at7tGqrteu+vsv2opBdRrOm9w/YXW1Xs\nJtk/AbwGWCXpr4Ff7zK4iIijXqdkP3/0xcwfffHB/S9c/aXmKruB+Y1NKHrunerMK8uw/Wj55+OS\nPksxLNQy2Xc1G8f2ftuXA58u3+hF3bSLiDjaPcXsrrcWNgMLJJ0haTbwRmBdU511wMUAkpYAT9je\nI+m5kk4oy58HvA7Y2i7Obnr2H514YfsTkrYC/6OLdhERR70qz8axvV/SCuBOYAS4wfZ2SZeVx1fb\nXi9pmaRxYC9wSdn8VOAzkqDI5Z+yfVe7c00ape3VTfv3Am85gs8VEXHUqXoHre3bgdubyprz7ooW\n7f4ReGm358nUy4iICvK4hIiIIZDn2UdEDIE8zz4iYghkGCciYgg83XpK5cBJso+IqCBj9hERQyBj\n9hERQyBj9hERQyDJPiJiCGTMPiJiCGTMPiJiCGTqZUTEEKjLME5Xz7OfLpLeJekZSSc1lF1ZrqK+\nQ9LrGsrPlbS1PHZtfyKOiDhUxWUJkbS0zHcPSbqiTZ1V5fEtkhY1HRuRdF+5uFRbfUv2kuYDrwUe\nbihbSPHw/oXAUuAjKh/WTLGi+lttL6B42P/SGQ45IuIwBxjpemsmaQS4jiLfLQSWSzq7qc4y4Kwy\n911KkQsbvRPYxiRLHPazZ/8h4Pebyi4CbrG9r1xtfRxYLOk04ATbG8t6NwFvmLFIIyLaqJLsKZYR\nHLe90/Y+YC1FHmx0IbAGwPYGYI6kUwAkzQOWAX9O+3VqgT4le0kXAbtsf7Xp0Okcuv7ixCrqzeW7\nOXwF9oiIGVcx2c8FHmnYn8h53db5U+D3gGcmi3PaLtBKupti2axm7wGupFgv8WD16YojImI6PcVz\nqjTvOPTSoDlHStIvA9+yfZ+k0cneYNqSve3XtiqXdA5wJrClHI6fB9wraTGtV1HfVZbPayrf3e7c\nK1euPPh6dHSU0dHRI/kIEXGUGRsbY2xsbErfs9MdtD8c28QPxzZ3at6c8+Zz6ChGqzoT+e+/ABeW\nY/rHAS+QdJPti1udSHa3XyzTQ9I3gXNt/0t5gfZminGsucA9FBcmLGkD8A5gI/C3wCrbd7R4P/fy\nmaSrp+BTRES/2Vf13EYSto94ZEGSf9wPdF3/GzrnkPNJmgU8CPwC8ChFfltue3tDnWXACtvLJC0B\nPmx7SVMc5wO/a/v17c49CPPsD2Zm29sk3UpxZXk/cHlD5r4c+ARwPLC+VaKPiJhpVebZ294vaQVw\nJzAC3GB7u6TLyuOrba+XtEzSOLAXuKTd23U6V9979lMtPfuI4dSvnv08P9R1/V1aUOl8VQxCzz4i\norby1MuIiCGQZB8RMQSeejoPQouIOOod2F+PNFqPKCMiBtSB/RnGiYg46iXZR0QMgf37kuwjIo56\nzxyoRxqtR5QREYMqwzgREUPgyXqk0XpEGRExqPb3O4DuJNlHRFSRZB8RMQRqkuz7uQZtRET97eth\na0HSUkk7JD0k6Yo2dVaVx7dIWlSWHSdpg6T7JT0gaWWnMNOzj4io4sCRN5U0AlwHXECx+tQmSeta\nLF5ylu0F5Yp+1wNLbD8p6edt/7BcBOVLkm4vFyU/THr2ERFV7O9hO9x5wLjtnbb3AWuBi5rqXAis\nASgT+RxJp5T7PyzrzAaOpcPC40n2ERFVPNnDdri5wCMN+7vKssnqzIPiNwNJ9wN7gLtsb2oXZpJ9\nREQV1Xr23S6r17y6lQFsH7D9Uorkv1jST7Z7g4zZR0RU0Wk2ztYxeGCsU+vdwPyG/fkUPfdOdeaV\nZQfZ/p6kzwNLga+1OlGSfUREFZ2S/dmjxTZh7WFrXm8GFkg6A3gUeCOwvKnOOmAFsFbSEuAJ23sk\nnQzst/2EpOOB1wLXtAslyT4iooo2Uyq7YXu/pBXAncAIcIPt7ZIuK4+vtr1e0jJJ48Be4JKy+WnA\nmnJGzzHAX9he3+5csrsdMqoHSe7lM0mHfdNGRA3ZV/XcRhK2m8fDe2lvPtVDDv3VauerIj37iIgq\nanIHbZJ9REQVradUDpwk+4iIKtKzj4gYAkn2ERFDIMk+ImIIVJh6OZOS7CMiqqjw1MuZNPTJ/kjm\n5kZEHJTZOBERQyBj9hERQyBj9hERQyBj9hERQyDDOBERQyDJPiJiCNRkzL5vyxJKeruk7ZIekPT+\nhvIrJT0kaYek1zWUnytpa3ns2v5EHRHR5KkethYkLS3z3UOSrmhTZ1V5fIukRWXZfEmfl/S1Mo++\no1OYfenZS/p5ihXTf8r2PkkvKssXUqzUspBikd17JC0oH1B/PfBW2xslrZe01PYd/Yg/IuKgCsM4\n5cIj1wEXUCw1uEnSOtvbG+osA86yvUDSYopcuITid4rftn2/pOcD90q6u7Fto3717H8TeJ/tfQC2\nHy/LLwJusb3P9k5gnGIR3dOAE2xvLOvdBLxhhmOOiDjcvh62w50HjNveWebDtRR5sNGFwBoA2xuA\nOZJOsf2Y7fvL8h8A24HT24XZr2S/APg5SV+WNCbpZWX56Ry62O4uih5+c/nusjwior8O9LAdbi7w\nSMP+RM6brM68xgrlGraLgA3twpy2YRxJdwOntjj0nvK8J9peIunlwK3Ai6fq3CtXrjz4enR0lNHR\n0al664iosbGxMcbGxqb2TTsN43x7DL7T8XzdrmnYvJThwXblEM5twDvLHn7rN+jHGrSSbgeusf13\n5f44xRjUbwDYvqYsvwO4CngY+Lzts8vy5cD5tv97i/fuaQ3aiBheU7IG7S/2kG9uP/R8kpYAK20v\nLfevBJ6x3Thp5aPAmO215f4Oivy3R9KxwN8At9v+cKdT92sY53PAawAkvQSYbfvbwDrgTZJmSzqT\nYrhno+3HgH+VtFiSgF8v3yMior+qjdlvBhZIOkPSbIoJKuua6qwDLoaDXw5PlIlewA3AtskSPfRv\nnv2NwI2StgJPU34Q29sk3Qpso/jl6PKGbvrlwCeA44H1mYkTEQOhzZTKbtjeL2kFcCcwAtxge7uk\ny8rjq22vl7SsHAHZC1xSNn8l8GvAVyXdV5Zd2S439mUYZzplGCciujUlwziv6CHf/EO181WRO2gj\nIqqoyR20SfYREVXkqZcREUMgD0KLiBgCSfYREUMgY/YREUOgwtTLmZRkHxFRRYZxIiKGQIZxIiKG\nQKZeRkQMgQzjREQMgST7iIghkDH7iIghUJOefb+eZx8REYCkpZJ2SHpI0hVt6qwqj2+RtKih/EZJ\ne8rHxXeUZB8R0SeSRoDrgKXAQmC5pLOb6iwDzrK9ALgUuL7h8MfLtpNKso+I6J/zgHHbO23vA9YC\nFzXVuRBYA2B7AzBH0qnl/heB73ZzoiT7iIhKKq1LOBd4pGF/V1nWa51J5QJtREQlna7QfqHc2up2\nmavm1a16Xo4vyT4iopJOcy9fUW4T3ttcYTcwv2F/PkXPvVOdeWVZTzKMExFRyb/1sB1mM7BA0hmS\nZgNvBNY11VkHXAwgaQnwhO09vUaZnn1ERCVHfleV7f2SVgB3AiPADba3S7qsPL7a9npJyySNA3uB\nSybaS7oFOB/4EUmPAH9o++OtziW756GfgSbJR9tniojpIQnbzePhvbQ3fLOHFmdWOl8V6dlHRFRS\nj+clJNlHRFRSj+clJNlHRFSSnn1ExBBoOctm4CTZR0RUkmGciIghkGGciIghkJ59RMQQSM8+ImII\npGcfETEE0rOPiBgCmXoZETEE0rOPiBgC9Riz78vz7CW9VNKXJd0naZOklzccu7JcRX2HpNc1lJ8r\naWt57Np+xB0RcbhKyxIiaWmZ7x6SdEWbOqvK41skLeql7YR+LV7yAeAq24uAPyz3kbSQ4uH9CylW\nTP+IpInHgV4PvLVcYX2BpK5WVB90Y2Nj/Q6hZ4l5+tUtXqhnzFNjfw/boSSNANdR5LuFwHJJZzfV\nWQacVea+SylyYVdtG/Ur2T8DvLB8PYdnl9i6CLjF9j7bO4FxYLGk04ATbG8s690EvGEG4502dfwP\nkpinX93ihXrGPDUq9ezPA8Zt77S9D1hLkQcbXQisAbC9AZgj6dQu2x7UrzH73wLulPQnFF84E4s0\nng58uaHexCrq+zh0XcbdHMHq6hERU6/SmP1c4JGG/V3A4i7qzKXIl5O1PWjakr2ku4FTWxx6D3AB\n8Fu2PyvpV4AbgddOVywREdOn0tTLbpfVq766le0Z3ygWzJ14LeB75et3A+9uOHYHxTfVqcD2hvLl\nwEfbvLezZcuWrdutYi6rdD5gCXBHw/6VwBVNdT4KvKlhfwdwSjdtG7d+DeM8Kul8238HvAb4elm+\nDrhZ0ocofk1ZAGy0bUn/KmkxsBH4dWBVqzfu1/qOETF8piDfbKaYcHIG8CjFBJXlTXXWASuAtZKW\nUHSW90j6ThdtD+pXsn8bcK2kWRS/A10KYHubpFuBbRQDYZc3rB5+OfAJ4Hhgve07ZjzqiIgpZHu/\npBXAncAIcIPt7ZIuK4+vtr1e0jJJ48Be4JJObdudS8/m0oiIOFr1a+rllJD0dknbJT0g6f0N5QN9\nY5akd0l6RtJJDWUDF7OkPy5/vlskfUbSCxuODVy8rfRy08lMkjRf0uclfa389/uOsvwkSXdL+rqk\nuyTNaWjT8mc+w3GPlDdD/nVN4p0j6bby3/E2SYsHPeZp048LtFN0kffngbuBY8v9F5V/LgTuB44F\nzqCYqz/xG8xG4Lzy9XpgaR/ink9x4fmbwEmDHDPFDKljytfXANcMcrwt4h8pYzujjPV+4Ox+/9st\nYzsVeGn5+vnAg8DZFDcY/n5ZfsUkP/Nj+hD37wCfAtaV+4Me7xrgLeXrWRT39wx0zNO11bln/5vA\n+1zcTIDtx8vyQb8x60PA7zeVDWTMtu+2/Uy5uwGYN8jxttDTTSczyfZjtu8vX/8A2E4xKeHgDTTl\nnxM/v1Y/8/NmMmZJ84BlwJ/z7FTAQY73hcCrbd8IxRi37e8NcszTqc7JfgHwcyqesTMm6WVl+ekc\negNW4w0Ifb0xS9JFwC7bX206NLAxN3gLRU8d6hEvtL8ZZaCUsykWUXyhnmJ7T3loD8UUO2j/M59J\nfwr8HsUd8BMGOd4zgcclfVzSVyR9TNLzGOyYp81AP/VykhuzZgEn2l6i4kFqtwIvnsn4Wpkk5iuB\nxnHAvk8T7RDvH9ieGJd9D/C07ZtnNLjqBn72gaTnA58G3mn7+88+CqqYkC2p02eYsc8n6ZeBb9m+\nT9Joy2AGKN7SLOBngBW2N0n6MMW9PM8GNHgxT5uBTva2295VK+k3gc+U9TaVFzxPpuhNzm+oOo/i\nG3o3zw5DTJTvZoq1i1nSORQ9jS3lf+h5wL3lvQN9i7nTzxhA0pspfnX/hYbivv6Me9Ac53wO7bn1\nlaRjKRL9J21/rizeI+lU24+Vw2LfKstb/cxn8mf7s8CFKh7KdRzwAkmfHOB4ofi73mV7U7l/G0WH\n67EBjnn69PuiwZFuwGXA1eXrlwD/5EMvssymSK7f4NmLhxso7sgV/b942OoC7UDFTPE0va8BJzeV\nD2S8LeKfVcZ2RhnrIF2gFcU1jT9tKv8A5V2QFL3Q5ouHh/3M+xD7+cBf1yFe4AvAS8rXK8t4Bzrm\naftZ9DvAryDtAAABY0lEQVSACn+JxwKfBLYC9wKjDcf+gOLiyg7gPzaUn1vWHwdW9Tn+f5xI9oMa\nM/AQ8DBwX7l9ZJDjbfMZfpFipss4cGW/42mI61UUY9/3N/x8lwInAfdQ3FV+FzBnsp95H2I/n2dn\n4wx0vMBPA5uALRQjAS8c9Jina8tNVRERQ6DOs3EiIqJLSfYREUMgyT4iYggk2UdEDIEk+4iIIZBk\nHxExBJLsIyKGQJJ9RMQQSLKPo5akl5cLrzxH0vPKRUIW9juuiH7IHbRxVJP0RxQP7joeeMT2+ydp\nEnFUSrKPo1r5ZMnNFAvbv8L5Bx9DKsM4cbQ7GXgexdJ/x/c5loi+Sc8+jmqS1gE3Uyxsc5rtt/c5\npIi+GOjFSyKqkHQx8JTttZKOAf5e0qjtsT6HFjHj0rOPiBgCGbOPiBgCSfYREUMgyT4iYggk2UdE\nDIEk+4iIIZBkHxExBJLsIyKGQJJ9RMQQ+P+rRvrwHFPwUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10986eb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat = mesh.plotSlice(eta, normal='Y')\n",
    "plt.colorbar(dat[0])\n",
    "axis('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nElecs = 21\n",
    "x_temp = np.linspace(-250, 250, nElecs)\n",
    "aSpacing = x_temp[1]-x_temp[0]\n",
    "y_temp = 0.\n",
    "xyz = Utils.ndgrid(x_temp, np.r_[y_temp], np.r_[0.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63\n"
     ]
    }
   ],
   "source": [
    "srcList = DC.Examples.WennerArray.getSrcList(nElecs,aSpacing)\n",
    "survey = DC.SurveyDC(srcList)\n",
    "print len(survey.srcList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "DATA = Survey.Data(survey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tx0 = srcList[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c5f9fd3e-9e73-4561-b2c2-52a2747439e8'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tx0.uid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import SimPEG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "SimPEG.Survey.Data??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1089f8550>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pEfGmona1cobTyb13CrNG5/fLKyp3vP5tO97MeBPbNGi8bHmvvDL5pZXopddtzl59dD/e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\ngLV4JFNPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1087a6750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize = (6.5,5))\n",
    "indz = 22\n",
    "dat = mesh.plotSlice(sigma, grid=True, ax = ax, ind=indz)\n",
    "ax.set_xlim(-300, 300)\n",
    "ax.set_ylim(-300, 300)\n",
    "cb = plt.colorbar(dat[0])\n",
    "ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n",
    "\n",
    "txLocs = np.array([[tx.loc[0][0], tx.loc[1][0]] for tx in survey.srcList]).flatten()\n",
    "ax.plot(txLocs, txLocs*0, 'w.', ms = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "expmap = Maps.ExpMap(mesh)\n",
    "m2to3  = Maps.Map2Dto3D(mesh,normal='Y')\n",
    "imap   = Maps.IdentityMap(mesh)\n",
    "problem = DC.ProblemDC_CC(mesh, mapping= imap )\n",
    "problem.Solver = MumpsSolver\n",
    "try:\n",
    "    problem.pair(survey)\n",
    "except Exception, e:\n",
    "    survey.unpair()\n",
    "    problem.pair(survey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.49 s, sys: 705 ms, total: 7.2 s\n",
      "Wall time: 4.94 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "phi0 = survey.dpred(sigma0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  ---- 3-D TensorMesh ----  \n",
      "   x0: -541.97\n",
      "   y0: -416.97\n",
      "   z0: -548.22\n",
      "  nCx: 55\n",
      "  nCy: 35\n",
      "  nCz: 28\n",
      "   hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
      "   hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
      "   hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n"
     ]
    }
   ],
   "source": [
    "print mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phiInf = survey.dpred(sigmaInf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phiIP_true = phi0-phiInf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# F = problem.fields(mesh, survey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "surveyIP = DC.SurveyIP(srcList)\n",
    "problemIP = DC.ProblemIP(mesh, sigma=sigma)\n",
    "problemIP.pair(surveyIP)\n",
    "problemIP.Solver = MumpsSolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10984ab90>,\n",
       " <matplotlib.lines.Line2D at 0x10984ae10>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ii6d4gXA1OYnMrV2BiMfhPe+B6emVHsmFhxYIjSEkzDE620qfZ7+jexO+2aOk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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1089dcf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasyn = surveyIP.dpred(eta)\n",
    "surveyIP.makeSyntheticData(eta,std=0.02,force=True)\n",
    "plot(np.c_[surveyIP.dobs,datasyn])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "problemIP.mapping = imap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dmis = DataMisfit.l2_DataMisfit(surveyIP)\n",
    "dmis.Wd = 1./abs(phi0)*1e3\n",
    "reg = Regularization.Tikhonov(mesh,mapping=imap)\n",
    "# opt = Optimization.InexactGaussNewton(maxIter=4,tolX=1e-15)\n",
    "opt = Optimization.ProjectedGNCG(maxIter=3,tolX=1e-15)\n",
    "opt.remember('xc')\n",
    "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
    "beta = Directives.BetaEstimate_ByEig(beta0_ratio=1e-1)\n",
    "betaSched = Directives.BetaSchedule(coolingFactor=5, coolingRate=4)\n",
    "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaSched])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reg.alpha_s = 1e-4\n",
    "reg.alpha_x = 1.\n",
    "reg.alpha_z = 1.\n",
    "reg.alpha_y = 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "opt.upper = Inf\n",
    "opt.lower = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SimPEG.InvProblem will set Regularization.mref to m0.\n",
      "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "                    ***Done using same solver as the problem***\n",
      "=============================== Projected GNCG ===============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "   0  5.84e+00  9.43e+01  0.00e+00  9.43e+01    1.69e+03      0              \n",
      "   1  5.84e+00  7.43e+00  6.13e-03  7.46e+00    3.04e+02      0              \n",
      "   2  5.84e+00  3.10e+00  1.33e-02  3.18e+00    2.65e+02      0   Skip BFGS  \n",
      "   3  5.84e+00  2.13e+00  1.82e-02  2.24e+00    2.49e+02      0   Skip BFGS  \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 9.4407e-01 <= tolF*(1+|f0|) = 9.5287e+00\n",
      "0 : |xc-x_last| = 4.4997e-02 <= tolX*(1+|x0|) = 1.0000e-15\n",
      "0 : |proj(x-g)-x|    = 2.4936e+02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 2.4936e+02 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =       3    <= iter          =      3\n",
      "------------------------- DONE! -------------------------\n"
     ]
    }
   ],
   "source": [
    "m0 = np.ones(problemIP.mapping.nP)*1e-10\n",
    "mopt = inv.run(m0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  ---- 3-D TensorMesh ----  \n",
      "   x0: -541.97\n",
      "   y0: -416.97\n",
      "   z0: -548.22\n",
      "  nCx: 55\n",
      "  nCy: 35\n",
      "  nCz: 28\n",
      "   hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
      "   hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
      "   hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n"
     ]
    }
   ],
   "source": [
    "print mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-600.0, 600.0, -600.0, 0.0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QZID/UIbDk/Bhf/cTppaO5x9ZzsPl7cwv7U9peVIRlGbBOmBBKNVWQ+lAWHHO+awt72BB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\n4yavLtDJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10faf04d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colorbar(mesh.plotSlice(mopt, normal='Y', ind=17, grid=True, gridOpts={'alpha': 0.2, 'color':'black'})[0])\n",
    "axis('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# sigma_est = expmap * m2to3 * mopt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dpred = surveyIP.dpred(mopt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11345b510>,\n",
       " <matplotlib.lines.Line2D at 0x11345b750>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QwpFj7sSc+N8r468vBvZJKV+PH88npRwv+tOVwPh4vNAo/lucDQuiXF1Mfj8Y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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f219190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(np.c_[dpred,surveyIP.dobs], '.-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
