timedomainresonace/testing.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:665d4d927eafbb088c892eb5950f2a0975c691bdbb983ca96c13a6336ba30584"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from timedomaineuler import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f=85.785                                  #Frequency of oscillation\n",
      "L=1.                                      #Length of tube\n",
      "gp=300                                    #Number of gridpoints\n",
      "\n",
      "nr_p_period=50                  #Number of save per oscillation period\n",
      "CFL=0.5;                        # CFL number\n",
      "periods=100                      #Number of periods to compute\n",
      "\n",
      "T=1/f\n",
      "gc=cvar.gc #Reference!\n",
      "dx=L/(gp-1);                    # One left and right gp, so\n",
      "\n",
      "gc.setfreq(f)\n",
      "tube=TubeLF(L,gp)\n",
      "dt=min(CFL*dx/gc.c0(),T/nr_p_period)\n",
      "intsteps=int(floor(1./(gc.getfreq()*dt)/nr_p_period))\n",
      "x=linspace(0,L,gp)\n",
      "# Create tube instance\n",
      "tube=TubeLF(L,gp)\n",
      "# To create a ni"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "4.873360558484559e-06"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tube.DoIntegration(dt,1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sol=tube.getSol()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(sol.u())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f3d19ffaa90>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EYqq2Hd6qWYiI5KGG7PBWshARibETToBrrsl8h7eaoUREYm5bh3dhIYwZU3OH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\niEhS/x/tUvneg5B9DwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f3d1b8e7d30>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}