{
 "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step1 Generate mesh "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cs = 25.\n",
    "hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
    "hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
    "hz = [(cs,7, -1.3),(cs,20)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "blk1 = Utils.ModelBuilder.getIndicesBlock(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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.collections.QuadMesh at 0x1077c3550>,\n",
       " <matplotlib.lines.Line2D at 0x1077c39d0>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vUtumfF3O2PBjbXJt8qVtZ9m3i3H6nqNp7npewpIkFbGASJKKWEAkSUUsIJKkIhYQSVIR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\nQCRJRSwgkqQiFhBJUpH/D+59VatNuYwRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1077d5550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mesh.plotSlice(sigma, normal='X', grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xtemp = np.linspace(-150, 150, 21)\n",
    "ytemp = np.linspace(-150, 150, 21)\n",
    "xyz_rxM = Utils.ndgrid(xtemp-10., ytemp, np.r_[0.])\n",
    "xyz_rxN = Utils.ndgrid(xtemp+10., ytemp, np.r_[0.])\n",
    "# xyz_rxM = Utils.ndgrid(xtemp, ytemp, np.r_[0.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x108464ed0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10847d090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xyz_rxP[:,0], xyz_rxP[:,1], 'k.')\n",
    "plt.plot(xyz_rxM[:,0], xyz_rxM[:,1], 'r.')\n",
    "plt.plot(xyz_rxN[:,0], xyz_rxN[:,1], 'g.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x108590810>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mWyX9jqRhSV919891eUmo0Ouvvy6p5t9sevrpt/4+09MZv9n0ltjxVCy12ZSZ9zS/2bSM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\nEPT/Ac5ECZ93vwuPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108590610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize = (5,5))\n",
    "mesh.plotSlice(sigma, grid=True, ax = ax)\n",
    "ax.plot(xyz_rxP[:,0],xyz_rxP[:,1], 'w.')\n",
    "ax.plot(xyz_rxN[:,0],xyz_rxN[:,1], 'r.', ms = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<simpegDCIP.BaseDC.RxDipole at 0x10859ae90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rx = DC.RxDipole(xyz_rxM, xyz_rxN)\n",
    "tx = DC.SrcDipole([rx], [-200, 0, -12.5],[+200, 0, -12.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "txList = []\n",
    "txList.append(DC.SrcDipole([rx], [-200, 0, -12.5],[+200, 0, -12.5]))\n",
    "txList.append(DC.SrcDipole([rx], [-200, 0, -12.5],[+100, 0, -12.5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<simpegDCIP.BaseDC.SrcDipole at 0x108657a90>,\n",
       " <simpegDCIP.BaseDC.SrcDipole at 0x108657ad0>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "txList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# survey = DC.SurveyDC([tx])\n",
    "survey = DC.SurveyDC(txList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# survey.unpair()\n",
    "problem = DC.ProblemDC_CC(mesh)\n",
    "problem.pair(survey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    from pymatsolver import MumpsSolver\n",
    "    problem.Solver = MumpsSolver\n",
    "except Exception, e:\n",
    "    problem.Solver = SolverLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.25 s, sys: 177 ms, total: 1.42 s\n",
      "Wall time: 970 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dataP = survey.dpred(sigmahomo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = survey.dpred(sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Data = (data/dataP).reshape((21,21,2), order='F')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x109fa69d0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EvBYRJyT9m6TP9m9Yi0t337BHsHDs7Wb/0Oniw7yYsHvYA6hcNshXSx/4btcB5f4sOACg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b0df390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolor(Data[:,:,0].T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "u1 = problem.fields(sigma)\n",
    "u2 = problem.fields(sigmahomo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Msig1 = Utils.sdiag(1./(mesh.aveF2CC.T*(1./sigma)))\n",
    "# Msig2 = Utils.sdiag(1./(mesh.aveF2CC.T*(1./sigmahomo)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "j1 = Msig1*mesh.cellGrad*u1[tx, 'phi_sol']\n",
    "j2 = Msig2*mesh.cellGrad*u2[tx, 'phi_sol']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# us = u1-u2\n",
    "# js = j1-j2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-300, 0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WhWvyqlOnOBcudKNLl1L4+gYwbtxpkpN1rEebg6OjCdu3V2X+fFc+/vg+Q4aE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108233510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mesh.plotSlice(mesh.aveF2CCV*j1, vType='CCv', normal='Y', view='vec', streamOpts={\"density\":3, \"color\":'w'})\n",
    "xlim(-300, 300)\n",
    "ylim(-300, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'js' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-575f23801c4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmesh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotSlice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmesh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maveF2CCV\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mjs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvType\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'CCv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Y'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mview\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'vec'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstreamOpts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"density\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"color\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mxlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'js' is not defined"
     ]
    }
   ],
   "source": [
    "mesh.plotSlice(mesh.aveF2CCV*js, vType='CCv', normal='Y', view='vec', streamOpts={\"density\":3, \"color\":'w'})\n",
    "xlim(-300, 300)\n",
    "ylim(-300, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.random.randn(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print (a.reshape([1,-1])).repeat(3, axis = 0)\n",
    "print (a.reshape([1,-1])).repeat(3, axis = 0).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def DChalf(txlocP, txlocN, rxloc, sigma, I=1.):\n",
    "    rp = (txlocP.reshape([1,-1])).repeat(rxloc.shape[0], axis = 0)\n",
    "    rn = (txlocN.reshape([1,-1])).repeat(rxloc.shape[0], axis = 0)\n",
    "    rP = np.sqrt(((rxloc-rp)**2).sum(axis=1))\n",
    "    rN = np.sqrt(((rxloc-rn)**2).sum(axis=1))\n",
    "    return I/(sigma*2.*np.pi)*(1/rP-1/rN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_analP = DChalf(np.r_[-200, 0, 0.],np.r_[+200, 0, 0.], xyz_rxP, sighalf)\n",
    "data_analN = DChalf(np.r_[-200, 0, 0.],np.r_[+200, 0, 0.], xyz_rxN, sighalf)\n",
    "data_anal = data_analP-data_analN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Data_anal = data_anal.reshape((21, 21), order = 'F')\n",
    "Data = data.reshape((21, 21), order = 'F')\n",
    "X = xyz_rxM[:,0].reshape((21, 21), order = 'F')\n",
    "Y = xyz_rxM[:,1].reshape((21, 21), order = 'F')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
    "vmin = np.r_[data, data_anal].min()\n",
    "vmax = np.r_[data, data_anal].max()\n",
    "dat0 = ax[0].contourf(X, Y, Data, 60, vmin = vmin, vmax = vmax)\n",
    "dat1 = ax[1].contourf(X, Y, Data_anal, 60, vmin = vmin, vmax = vmax)\n",
    "cb0 = plt.colorbar(dat1, orientation = 'horizontal', ax = ax[0])\n",
    "cb1 = plt.colorbar(dat1, orientation = 'horizontal', ax = ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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
}
