simpeg/GCEtools/PetscExample.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from SimPEG import *\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M = mesh.TensorMesh([20,30]) \n",
      "A = M.faceDiv*M.faceDiv.T + sp.identity(M.nC)\n",
      "b = np.random.rand(M.nC)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "phi = Solver(A).solve(b)\n",
      "colorbar(M.plotImage(phi))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0x6c411b8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FbGwsBQUFF9/hZY8GOkURiQQj8rpvX2vzfa6yHaZFPSYmhtLSUhYsWIDb7aa4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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x67e6050>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print np.linalg.norm(A*phi-b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1.5220841846e-13\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import petsc4py\n",
      "import sys\n",
      "petsc4py.init(sys.argv)\n",
      "from petsc4py import PETSc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import PETScIO as IO\n",
      "Apetsc = PETSc.Mat().createAIJ(size=A.shape,csr=(A.indptr, A.indices, A.data))\n",
      "bpetsc = IO.arrayToVec(b)\n",
      "xpetsc = IO.arrayToVec(0*b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ksp = PETSc.KSP().create()\n",
      "pc = PETSc.PC().create()\n",
      "\n",
      "ksp.setOperators(Apetsc)\n",
      "\n",
      "ksp.setType(ksp.Type.CG)\n",
      "pc = ksp.getPC()\n",
      "pc.setType(pc.Type.HYPRE)\n",
      "ksp.view()\n",
      "\n",
      "ksp.solve(bpetsc, xpetsc)\n",
      "print ksp.its"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3\n"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = IO.vecToArray(xpetsc)\n",
      "print np.linalg.norm(A*X-b)\n",
      "M.plotImage(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.000199616893249\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "<matplotlib.collections.QuadMesh at 0x792ae50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x77479d0>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}