{
 "cells": [
  {
   "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",
    "from numpy.polynomial import polynomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cs = 0.5\n",
    "mesh = Mesh.TensorMesh([np.ones(100)*cs, np.ones(50)*cs], \"CN\")\n",
    "x = mesh.vectorCCx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "actx = (x>-15.)&(x<15.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "order = 3\n",
    "Vobs = polynomial.polyvander(x, order)\n",
    "dobs = 0.05*x-4\n",
    "H = np.dot(Vobs.T, Vobs)\n",
    "g = np.dot(Vobs.T, dobs)\n",
    "mest = np.linalg.solve(H, g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dpred = Vobs.dot(mest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "V = polynomial.polyvander(x, order)\n",
    "m1D = Mesh.TensorMesh([V.shape[1]+2])\n",
    "weight = (1./(V**2).sum(axis=0))**0.5\n",
    "weight = weight / weight.max()\n",
    "weightmap = Maps.Weighting(m1D, weights=np.r_[1., 1., weight])\n",
    "m0_poly = np.r_[np.log(1e-3), np.log(1e-3), np.r_[-3., np.zeros(V.shape[1]-1)] / weight]\n",
    "mtrue_poly = np.r_[np.log(1e-3), np.log(1e0), mest / weight]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "polymap = Maps.PolyMap(mesh, V.shape[1]-1, logSigma=True, normal='Y')\n",
    "mappingfwd = polymap*weightmap\n",
    "mapping = Maps.ExpMap(mesh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sigma = mappingfwd*mtrue_poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xr = np.linspace(-15, 15, 20)\n",
    "xz_A = Utils.ndgrid(xr, np.r_[-0.25])\n",
    "xz_B = Utils.ndgrid(np.ones_like(xr)*22, np.r_[-0.25])\n",
    "xz_M = Utils.ndgrid(xr, np.r_[-0.25])\n",
    "xz_N = Utils.ndgrid(np.ones_like(xr)*-22, np.r_[-0.25])\n",
    "\n",
    "ntx = xz_A.shape[0]\n",
    "txList = []\n",
    "for i in range(ntx):\n",
    "    offset = abs(xz_A[i,0]-xz_M[:,0])\n",
    "    actrx = offset > 5.\n",
    "    rx = DC.RxDipole(xz_M[actrx,:], xz_N[actrx,:])\n",
    "    src = DC.SrcDipole([rx], xz_A[i,:], xz_B[i,:])\n",
    "    txList.append(src)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15be0278>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GADzXtpmVRY+e5942CyNiP7A/e/1TSTuA2UDLcM9lWKaNedmQzKCkt+ZdjJkZ1MI9Zeml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\nswpyuJuZVZDD3cysgv4T1DGdandC938AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15968710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat = mesh.plotImage(np.log10(sigma), 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",
    "plot(xz_N[:,0], xz_N[:,1], 'r.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15e72518>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EOoZ7IcMyOeZkQzLDkt5ZdDFmZtAI95Sln7Kp9s4Hnshr27cz9x5n/N4LDETEy9n40xpJ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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15968198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "plot(xz_N[:,0], xz_N[:,1], 'r.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from SimPEG import SolverLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x160b5eb8>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JRY/uGRhRBNMv4RHRAwXGJp1IOjntTIUiFAwltbd7TdJhO/uSvBJPdva14yOK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\nV9T6sRBynTgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x160c1278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtrue = np.log(sigma)\n",
    "m0 = np.log(np.ones(mesh.nC)*1e-3)\n",
    "survey = DC.SurveyDC(txList)\n",
    "problem = DC.ProblemDC_CC(mesh, mapping = mapping)\n",
    "problem.pair(survey)\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": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0046885259087758868"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(dtrue).min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(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, -0.81373441, -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": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LSVoEG26bVNXPkvwFcAPLlwpeZXBL0ubY8Jm3JGl2er09PsmHktyRZG+Sm5Kc3OfxNluS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15ca0828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(np.log10(abs(dtrue)), bins = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "noise = 0.05*abs(dtrue)*np.random.randn(dtrue.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "                    ***Done using same solver as the problem***\n",
      "============================ Inexact Gauss Newton ============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "   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  3.49e+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|) = 1.3960e+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"
     ]
    }
   ],
   "source": [
    "survey.dobs = dtrue +noise\n",
    "dmis = DataMisfit.l2_DataMisfit(survey)\n",
    "dmis.Wd = 1./(0.05*abs(dtrue)+ 0.1)\n",
    "reg = Regularization.Tikhonov(mesh)\n",
    "opt = Optimization.InexactGaussNewton(maxIter=30,maxIterLS=20)\n",
    "opt.remember('xc')\n",
    "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
    "betaSched = Directives.BetaSchedule(coolingFactor=8, coolingRate=3)\n",
    "targetmis = Directives.TargetMisfit()\n",
    "savemodel = Directives.SaveModelEveryIteration()\n",
    "inv = Inversion.BaseInversion(invProb, directiveList=[betaSched,targetmis])\n",
    "reg.alpha_s = 1e-5\n",
    "reg.mref = m0\n",
    "mopt = inv.run(m0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "XC = opt.recall('xc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact, IntSlider\n",
    "def viewInv(iteration):\n",
    "    fig  = plt.figure(num=0,figsize = (10,5))\n",
    "    ax = plt.subplot(111)\n",
    "    dat = mesh.plotImage(np.log10(mapping*XC[iteration]), grid=True, ax=ax, clim=(-3, 0), gridOpts={'alpha':0.2}, pcolorOpts={'cmap':cm.RdPu})\n",
    "#     ax.set_xlim(mesh.vectorNx.min(), mesh.vectorNx.max())\n",
    "#     ax.set_ylim(mesh.vectorNy.min(), mesh.vectorNy.max())\n",
    "    ax.plot(mesh.vectorCCx, dpred, 'r--')\n",
    "    ax.plot(xz_A[:,0], xz_A[:,1], 'k.')\n",
    "    ax.plot(xz_B[:,0], xz_B[:,1], 'r.')\n",
    "    ax.plot(xz_N[:,0], xz_N[:,1], 'b.')    \n",
    "#     plt.colorbar(dat[0], ax=ax)\n",
    "#     ax.set_ylim (-15, 0.)\n",
    "#     ax.set_xlim (-15, 15.)\n",
    "    plt.show()\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C2KIHLgPTU8DUNDA1BVyfwMilLdj17S9ausb+tRiIXoD7+J/DTU8D00n/3MQcml/+LORt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\ncBADAACo5H9ra/3eqvM+uwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ab1e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(viewInv, iteration = IntSlider(min=0, max=opt.iter-1,step=1, value=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ba516d8>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9qm55BolwlIoh7xn4J8QgIKLSRWu6L82q5nqa4nHpaWemaTWKS9XFSEbC0pvng+kcAWKA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\nFpHzBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef46908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(survey.dobs)\n",
    "plt.plot(invProb.dpred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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
}
