simpeg/notebooks/DCexample.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from SimPEG import *\n",
      "import simpegDC as DC\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sighalf = 1e-2\n",
      "sigma = np.ones(mesh.nC)*sighalf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xtemp = np.linspace(-150, 150, 21)\n",
      "ytemp = np.linspace(-150, 150, 21)\n",
      "xyz_rxP = 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.])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "[<matplotlib.lines.Line2D at 0xca18f60>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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65pprNHXqVE2fPl2SdOjQIdXU1GjChAmaPXu2jhw5EsmP9TrNShbSUkNDg/XM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\nAoAhGikAGPr/cpf+VwtHXtcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xca186d8>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rx = DC.DipoleRx(xyz_rxP, xyz_rxN)\n",
      "tx = DC.DipoleTx([-200, 0, -12.5],[+200, 0, -12.5], [rx])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survey = DC.SurveyDC([tx])\n",
      "problem = DC.ProblemDC(mesh)\n",
      "problem.pair(survey)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = survey.dpred(sigma)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.random.randn(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print (a.reshape([1,-1])).repeat(3, axis = 0)\n",
      "print (a.reshape([1,-1])).repeat(3, axis = 0).sum(axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.46013066  1.09015012  1.85863975]\n",
        " [ 0.46013066  1.09015012  1.85863975]\n",
        " [ 0.46013066  1.09015012  1.85863975]]\n",
        "[ 3.40892053  3.40892053  3.40892053]\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QGAp87qp9Nuge0L2vNKIbohU6/MsmBSIy0SPhLys0rmZ7EWh32AeiCOWlBa3R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\npAkhhBBCCNHg/weFbBpJH/3kjAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc854160>"
       ]
      }
     ],
     "prompt_number": 55
    }
   ],
   "metadata": {}
  }
 ]
}