{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Objective:** \n",
    "\n",
    "In this tutorial we will create a simple two-sphere model and simulate DC Resistivity data for various transmitter-receiver configurations.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "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",
    "import scipy.interpolate as interpolation\n",
    "import time\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dominiquef.MIRAGEOSCIENCE\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n",
      "  \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n",
      "WARNING: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "pylab import has clobbered these variables: ['linalg']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-200, 200)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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RDA2upGdwZQsqOM4oQw2GkG6ETkVInA18CZhlZn+JZS0CLpN0PpF7vyuwzMxM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\n4jiO42TCDYnjOI6TCTckjuM4Tib+P03R8VBVbPvSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15e3dd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First we need to create a mesh and a model.\n",
    "\n",
    "# This is our mesh\n",
    "dx    = 5.\n",
    "\n",
    "hxind = [(dx,15,-1.3), (dx, 75), (dx,15,1.3)]\n",
    "hyind = [(dx,15,-1.3), (dx, 10), (dx,15,1.3)]\n",
    "hzind = [(dx,15,-1.3),(dx, 15)]\n",
    "\n",
    "mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN')\n",
    "\n",
    "# Define our model\n",
    "bckgr = 1e-2\n",
    "cond  = 1e-1\n",
    "resis = 1e-3\n",
    "zloc  = -50.\n",
    "xloc  = 50.\n",
    "yloc  = 0.\n",
    "radi  = 25.\n",
    "\n",
    "# Set background conductivity\n",
    "model = np.ones(mesh.nC) * bckgr\n",
    "\n",
    "# First anomaly (conductor)\n",
    "ind = Utils.ModelBuilder.getIndicesSphere([-xloc,yloc,zloc],radi,mesh.gridCC)\n",
    "model[ind] = cond\n",
    "\n",
    "# Second anomaly (resistor)\n",
    "ind = Utils.ModelBuilder.getIndicesSphere([xloc,yloc,zloc],radi,mesh.gridCC)\n",
    "model[ind] = resis\n",
    "\n",
    "# Get index of the center\n",
    "indy = int(mesh.nCy/2)\n",
    "indz = int(np.argmin( np.abs(mesh.vectorCCz - zloc) ))\n",
    "\n",
    "# Plot the model for reference\n",
    "# Define core mesh extent\n",
    "xlim = 200\n",
    "zlim = 200\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot(1,2,1, aspect='equal')\n",
    "mesh.plotSlice(np.log10(model), ax =ax, normal = 'Y', ind = indy,grid=True)\n",
    "ax.set_title('E-W section at '+str(mesh.vectorCCy[indy])+' m')\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "plt.xlim([-xlim,xlim])\n",
    "plt.ylim([-zlim,0])\n",
    "\n",
    "ax = plt.subplot(1,2,2, aspect='equal')\n",
    "mesh.plotSlice(np.log10(model), ax =ax, normal = 'Z', ind = indz,grid=True)\n",
    "ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "plt.xlim([-xlim,xlim])\n",
    "plt.ylim([-xlim,xlim])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have model in 3D, we can define a survey.\n",
    "There area various ground DC configurations used in the field.\n",
    "We can explore two survey types here: pole-dipole (pdp) and dipole-dipole (dpdp).\n",
    "In both cases we need to specify three important parameter.\n",
    "\n",
    "a: Seperation (m) between the transmitter and receivers\n",
    "\n",
    "b: Dipole seperation (m) of the receiver (and transmitter for dpdp)\n",
    "\n",
    "n: Number of receiver dipoles along line\n",
    "\n",
    "We also need to give a starting and end point for the survey.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-200, 200)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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afm2K5RfpP1lXY6Hk1yW9SYedDQyfaW4YV6e3X+dBijvA9Uk6kOKOcSsyb3MX\nSQdJOjgtvxw4neL1WAGcn1Y7nzzviUaabbfrv5uy3ifpvX89sDYi/qamqzOvTafO2nX7B1hHcWnu\nQ+nnmpq+yymK048B76hpP4HiF7ceuLqDsZxNUfN7HtgM3Jna/wT4WYrvx8A7y4qljNelLq6bgJ8C\nj6Q36+S9xZX5/XMGxZnl9cD8Lr93D6c4Y/1wen/MT+2TgLspbh16FzAxw7aXUZS0Xkjvkw+OtN2c\nv5sGsXyorPcJcDLwu/Q7Gc4pczr12viCBTOzzMZt6cDMbLxwojUzy8yJ1swsMydaM7PMnGjNzDJz\nojUzy8yJ1swsMydaM7PMnGhtnyPpjenuUC+V9PJ0o+dZZcdlvctXhtk+SdIi4GUU30G3MSKuKjkk\n62FOtLZPSndqepDinhBvCv9HsIxcOrB91SHAyym+tuT3So7FepyPaG2fJGkFsBR4LTAlIi4uOSTr\nYZW+8bdZDpI+APy/iPimpJcA/1vSaRFxT8mhWY/yEa2ZWWau0ZqZZeZEa2aWmROtmVlmTrRmZpk5\n0ZqZZeZEa2aWmROtmVlmTrRmZpn9f2OrPHcOE6ghAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15e3d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Survey parameters\n",
    "a = 30.\n",
    "b = 30.\n",
    "n = 20 # Integer number of rx dipoles\n",
    "\n",
    "# Specify the survey type: \"pdp\" | \"dpdp\"\n",
    "stype = 'dpdp'\n",
    "\n",
    "# Then specify the end points of the survey. Let's keep it simple for now and survey above the anomalies, top of the mesh\n",
    "ends = [(-175,0),(175,0)]\n",
    "ends = np.c_[np.asarray(ends),np.ones(2).T*mesh.vectorNz[-1]]\n",
    "\n",
    "# Snap the endpoints to the grid. Easier to create 2D section.\n",
    "indx = Utils.closestPoints(mesh, ends )\n",
    "locs = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*mesh.vectorNz[-1]]\n",
    "\n",
    "# We will handle the geometry of the survey for you and create all the combination of tx-rx along line\n",
    "[Tx, Rx] = DC.gen_DCIPsurvey(locs, mesh, stype, a, b, n)\n",
    "\n",
    "# Here is an example for the first tx-rx array\n",
    "fig, ax = plt.subplots(1,1, figsize = (6.5,5))\n",
    "mesh.plotSlice(np.log10(model), ax =ax, normal = 'Z', ind = indz,grid=True)\n",
    "ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "\n",
    "plt.scatter(Tx[0][0,:],Tx[0][1,:],s=20,c='g')\n",
    "plt.scatter(Rx[0][:,0::3],Rx[0][:,1::3],s=20,c='y')\n",
    "plt.xlim([-xlim,xlim])\n",
    "plt.ylim([-xlim,xlim])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next section will specify all the parameters used to forward model the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Set boundary conditions\n",
    "mesh.setCellGradBC('neumann')\n",
    "\n",
    "# Define the differential operators needed for the DC problem\n",
    "Div = mesh.faceDiv\n",
    "Grad = mesh.cellGrad\n",
    "Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))\n",
    "\n",
    "A = Div*Msig*Grad\n",
    "\n",
    "# Change one corner to deal with nullspace\n",
    "A[0,0] = 1\n",
    "A = sp.csc_matrix(A)\n",
    "\n",
    "# We will solve the system iteratively, so a pre-conditioner is helpful\n",
    "# This is simply a Jacobi preconditioner (inverse of the main diagonal)\n",
    "dA = A.diagonal()\n",
    "P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transmitter 8 of 9 -> Time:0.990999937057 sec Transmitter 8 of 9\n",
      "Forward completed\n"
     ]
    }
   ],
   "source": [
    "# Now we can solve the system for all the transmitters\n",
    "# We want to store the data\n",
    "data = []\n",
    "\n",
    "# There is probably a more elegant way to do this, but we can just for-loop through the transmitters\n",
    "for ii in range(len(Tx)):\n",
    "    \n",
    "    start_time = time.time() # Let's time the calculations\n",
    "    \n",
    "    #print(\"Transmitter %i / %i\\r\" % (ii+1,len(Tx)))\n",
    "    \n",
    "    # Select dipole locations for receiver\n",
    "    rxloc_M = np.asarray(Rx[ii][:,0:3])\n",
    "    rxloc_N = np.asarray(Rx[ii][:,3:])\n",
    "    \n",
    "    # Number of receivers\n",
    "    nrx = rxloc_M.shape[0]\n",
    "  \n",
    "    # For usual cases \"dpdp\" or \"gradient\"\n",
    "    if not re.match(stype,'pdp'): \n",
    "        inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )\n",
    "        RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] )   \n",
    "        \n",
    "    else: \n",
    "        \n",
    "        # Create an \"inifinity\" pole\n",
    "        tx =  np.squeeze(Tx[ii][:,0:1])\n",
    "        tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2\n",
    "        inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)\n",
    "        RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] )  \n",
    "\n",
    "\n",
    "    # Iterative Solve\n",
    "    Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)\n",
    "\n",
    "    # We now have the potential everywhere\n",
    "    phi = mkvc(Ainvb[0])\n",
    "    \n",
    "    # Solve for phi on pole locations\n",
    "    P1 = mesh.getInterpolationMat(rxloc_M, 'CC')\n",
    "    P2 = mesh.getInterpolationMat(rxloc_N, 'CC')\n",
    "        \n",
    "    # Compute the potential difference\n",
    "    dtemp = (P1*phi - P2*phi)*np.pi\n",
    "    \n",
    "    data.append( dtemp )     \n",
    "    print '\\rTransmitter {0} of {1} -> Time:{2} sec'.format(ii,len(Tx),time.time()- start_time),\n",
    "    \n",
    "print 'Transmitter {0} of {1}'.format(ii,len(Tx))\n",
    "print 'Forward completed'\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have our problem, we can use the inversion tools in SimPEG to run our inversion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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JhCOYJFk08UyqVo7numvHcd21J+Ta9faXcOMTYAS/wkLu3q0kBgBSmiz77FLWz3oUoY0C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\n6g0bKS0tTfqZKYrSOrIZBOkIszRtmSUyJqeCIOXXJIjS4dm5cyfrN27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E3HWj92AHaxgO\nDBeR0/ej/rzhUPsD0v7kLodSX0D7k+scav1R4uyzODDGvGqMsZzL94B+zvmZwFPGmGZjTDVQBRwv\nIn2AEmPM+065x8iwN7WiKIqiKAefA+VzcCnwknN+OLDek7ce6BuQXuOkK4qiKIqSQ6QNnywirwJB\nu0HdZIx50SnzfWCCMeYc5/r/gHeNMU861w8CLwPVwM+MMac46ScCNxpjvhBQr8ZOVhRFUQ46Gj7Z\nJu3GS+5AngoRuQQ4A/icJ7kG6O+57odtMaghPvXgptekqFdfjqIoiqK0E/uzWuF04NvAmcaYRk/W\nC8D5IlIkIoOB4cD7zo5Ru0TkeMdB8WLguf1ou6IoiqIoWWCfd2UUkRVAEeDuCf2OMWaWk3cTth9C\nFLjeGPMPJ30i8HugM/CSMea/9qv1iqIoiqIccHJyy2ZFURRFUdqPnIqQKCKnO4GTVojId9q7PfuC\niFSLyCIRmS8i7ztpPUXkVRFZLiKzRaS0vduZChF5WEQ2ichiT1rK9qcKeJUrpOjPLSKy3nlH80Vk\nuicv1/vTX0T+KSJLROQjEfkvJz3v3lGavuTl+xGRiIi8JyILRGSpiPzUSc+7dwNp+5OX78dFRMJO\nu12n+rx8P1nHGJMTBxDGjokwCCgEFgCj27td+9CP1UBPX9rt2CszAL6DvWqj3duaov0nAkcDizO1\nHzvQ1QLnfQ1y3l+ovfvQiv7cDNwQUDYf+lMBjHfOuwKfAKPz8R2l6Us+v58uzmcB8C4wJR/fTYb+\n5O37cdp5A/Ak8IJznbfvJ5tHLlkOjgOqjDHVxphm4I/YAZXyEf9qiy8Cjzrnj5LDwZ+MMW8B233J\nqdofFPDquIPRztaSoj+Q/I4gP/pTa4xZ4JzXA8uw44Xk3TtK0xfI3/fT4JwWYX/h2U4evhuXFP2B\nPH0/ItIPe4Xdg8T7kLfvJ5vkkjjoC6zzXLvBk/INA8wRkXkicoWTVm6M2eScbwLK26dp+0yq9qcK\neJUPXCf2viAPecyIedUfERmEbRV5jzx/R56+vOsk5eX7EZGQiCzAfgf/NMYsIY/fTYr+QJ6+H+DX\n2KvsLE9a3r6fbJJL4uBQ8YycbIw5GpgOfEPsYE8tGNtelbd9bUX786Fv9wCDgfHARuCONGVzsj8i\n0hV4Bns1UJ03L9/ekdOXv2D3pZ48fj/GGMsYMx47jstnReQkX35evZuA/lSSp+9HRGYAm40x8wm2\nfOTd+8kmuSQO/MGT+pOo2vICY8xG53ML8Cy2GWqTiFQAiL3HxOb2a+E+kar9QQGvAgNb5RLGmM3G\nAdu86JoK86I/IlKILQweN8a4sULy8h15+vKE25d8fz8AxpidwN+BieTpu/Hi6c8xefx+TgC+KCKr\ngaeAaSLyOIfA+8kGuSQO5mHv1DhIRIqwd3Z8oZ3b1CZEpIuIlDjnxcCpwGLsfnzdKfZ18i/4U6r2\nBwa8aof2tQnnH4DL2djvCPKgPyIiwEPAUmPMbzxZefeOUvUlX9+PiJS5JnYR6QycAswnD98NpO6P\nO5A65M37McbcZIzpb4wZDJwPvG6MuZg8fT9Zp709Ir0Htin+E2zHj++1d3v2of2Dsb1bFwAfuX0A\negJzsLe2ng2Utndb0/ThKWAD0ITtAzIzXfuBm5z39TFwWnu3vxX9uRR7R9BFwELsfwTledSfKdjz\npQuwB5752Fuj5907StGX6fn6foAjgQ+d/iwCvu2k5927ydCfvHw/vr5NJb5aIS/fT7YPDYKkKIqi\nKEoCuTStoCiKoihKDqDiQFEURVGUBFQcKIqiKIqSgIoDRVEURVESUHGgKIqiKEoCKg4URVEURUlA\nxYGiKIqiKAn8f1A9ajP5f0M7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18d372e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's just convert the 3D format into 2D (distance along line) and plot\n",
    "[Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(Tx,Rx)\n",
    "\n",
    "fig, ax = plt.subplots(1,1, figsize = (8,7))\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "\n",
    "# Plot the location of the spheres for reference\n",
    "circle1=plt.Circle((-xloc-Tx[0][0,0],zloc),radi,color='w',fill=False, lw=3)\n",
    "circle2=plt.Circle((xloc-Tx[0][0,0],zloc),radi,color='k',fill=False, lw=3)\n",
    "ax.add_artist(circle1)\n",
    "ax.add_artist(circle2)\n",
    "\n",
    "# Add the speudo section\n",
    "DC.plot_pseudoSection(Tx2d,Rx2d,data,mesh.vectorNz[-1],stype)\n",
    "\n",
    "plt.xlim([0,2*xlim])\n",
    "plt.ylim([-zlim,0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Back in the days (not so long ago really), geophysicists used to interpret DCR data directly from pseudo-section. Hopefully this example will convince you that interpretating speudo-section is really tricky, arguably impossible.\n",
    "Fortunately for us, we now have inversion techniques to make sense of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125.0\n"
     ]
    }
   ],
   "source": [
    "print -xloc-Tx[0][0,0]"
   ]
  }
 ],
 "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": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
