pletchm/GuitarDecay.ipynb

## GuitarDecay.ipynb
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "from pylab import *\n",
      "from scipy.optimize import curve_fit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "osc=np.loadtxt(\"GuitarDecay.csv\", delimiter=',')\n",
      "osc.size"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "85"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t=arange(0,85,1)\n",
      "t.size"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "85"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is the my decaying oscillations on a wave graph"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(t,osc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7fe246fbbbd0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fe2470ee6d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def func(t,a,b,c):\n",
      "    return a*np.exp(-b*t)+c\n",
      "popt,pocv=curve_fit(func,t,osc)\n",
      "print popt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 1.01268155  0.58554939  0.00466181]\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here are the decay constants"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a=1.248286\n",
      "b=1.54487411\n",
      "c=0.01144054"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Plotting this function gives a accurately approximated graph."
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "decay=a*np.exp(-b*t)+c\n",
      "xlim(0,7)\n",
      "plot(t,decay)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7fe24703fd90>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEACAYAAACuzv3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njWJEsQwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe24703fd10>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}