timedomainresonace/timedomain.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:f48effc45d7e0ce53ecece3d2b83f5c45c2f29c363f0233fd7f2579385deda58"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from timedomaineuler import *\n",
      "#import timedomaineuler"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Globalconf accessable with cvar.gc\n",
      "f=85.785\n",
      "T=1/f\n",
      "loglevel=20\n",
      "L=1.0\n",
      "gp=400\n",
      "gc=cvar.gc #Reference!\n",
      "dx=L/(gp-1);                    # One left and right gp, so\n",
      "CFL=0.01;\n",
      "gc.setfreq(f)\n",
      "tube=TubeLF(L,gp)\n",
      "dt=min(CFL*dx/gc.c0(),T/50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "7.303933869608436e-08"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gc.c0()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "343.1391506142924"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nr_p_period=1\n",
      "intsteps=int(floor(1./(gc.getfreq()*dt)/nr_p_period))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "intsteps=100"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(\"DoIntegration relative time step of one period: %0.2e\" %(dt*gc.getfreq()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DoIntegration relative time step of one period: 6.27e-06\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tube.DoIntegration(dt,intsteps)\n",
      "sol=tube.getSol()\n",
      "u=sol.u()\n",
      "p=sol.p()\n",
      "rho=sol.rho()\n",
      "figure(figsize=(9,6))\n",
      "subplot(221)\n",
      "plot(p)\n",
      "subplot(222)\n",
      "plot(u)\n",
      "subplot(223)\n",
      "plot(rho)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f338420d828>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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m4sSJzJw5k87Ozv2efJomKC+MiHlpv7yrImJWCjweJJtD8xiwFrgwbUGzjGxS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\n0+DWW+FVr6pyZc2sX5KIiIO6/WxmVk3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       "text": [
        "<matplotlib.figure.Figure at 0x7f3384430a90>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}