/hmwrk4_Jacob_Burrows.pynb Secret

## hmwrk4_Jacob_Burrows.pynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:13d2a45302ccc79238716ed2cd8c273fc1c52d1b6004de2b3c38e701add1f6e8"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "[Hmwrk 4]\n",
      "Mat 201A Winter Jacob Burrows"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "from __future__ import print_function\n",
      "from __future__ import division"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.io import wavfile"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#input files\n",
      "Samplerate, gloc = wavfile.read((\"glockenspiel.wav\"))\n",
      "Samplerate, piano= wavfile.read((\"piano.wav\"))\n",
      "Samplerate, tom= wavfile.read((\"tom.wav\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#perform dft from fourier analysis\n",
      "X=(abs(fft.rfft(gloc)))\n",
      "Y=(abs(fft.rfft(piano)))\n",
      "Z=(abs(fft.rfft(tom)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#gives index of the highest area\n",
      "argmax((X))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "1158"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(X)\n",
      "xlim(0,8000)\n",
      "title(\"Gloc\")\n",
      "xlabel(\"bin\")\n",
      "ylabel(\"magnitude\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "<matplotlib.text.Text at 0xd7c5570>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Rw3r5uRnfAY4HHgXukXRjRCyqa7ML8F3ghIhYIWnCcJ4DYM0a2LABxo8f7iPNzKxZzYTF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\nzKwhh4WZmTX0/wGsQ+1eJ8De8QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd9365d0>"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#divide by total bin numbers\n",
      "#len is length\n",
      "#plus 1 for exact nyquist\n",
      "ans1=(argmax(X)/len(X))*(Samplerate/2+1)\n",
      "print(ans1, \"Hz\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1323.33426617 Hz\n"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#gives index of the highest area\n",
      "argmax(Y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "58"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot (Y)\n",
      "xlim(0,1000)\n",
      "title(\"Piano\")\n",
      "xlabel(\"bin\")\n",
      "ylabel(\"magnitude\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "<matplotlib.text.Text at 0xcda37b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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l9torXI/qrrtCi2Ly5I7LfeedIQS///3wAbv44lDm7D1Nn15/P/TixeF97Lpr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\nIiIVKShERKQiBYWIiFSkoBARkYr+P4hhwks5EX3zAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xcabd710>"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#divide by total bin numbers\n",
      "#len is length\n",
      "#plus 1 for exact nyquist\n",
      "ans2=(argmax(Y)/len(Y))*(Samplerate/2+1)\n",
      "print(ans2, \"Hz\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "78.0566371681 Hz\n"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#gives index of the highest area\n",
      "argmax(Z)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "81"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(Z)\n",
      "xlim(0,1000)\n",
      "title(\"Tom\")\n",
      "xlabel(\"bin\")\n",
      "ylabel(\"magnitude\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "<matplotlib.text.Text at 0xced33f0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wE/BCZl1LPnvSivQUsEvh+b3AuErv25FrFLsA0919prs3ALcD+9a4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\nkYoUFCIiUpGCQkREKvp/fahMTbbqk5QAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xceb3eb0>"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#divide by total bin numbers\n",
      "#len is length\n",
      "#plus 1 for exact nyquist\n",
      "ans3=(argmax(Z)/len(Z))*(Samplerate/2+1)\n",
      "print(ans3, \"Hz\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "92.5648320896 Hz\n"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print (\"X\", ans1, \"Hz\")\n",
      "print(\"Y\", ans2, \"Hz\")\n",
      "print(\"Z\", ans3,\"Hz\")\n",
      "#these are the Hz values of the gloc(X), piano(Y), and tom(Z)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "X 1323.33426617 Hz\n",
        "Y 78.0566371681 Hz\n",
        "Z 92.5648320896 Hz\n"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "I was able to see that tom sample was similar to a white noise, and the gloc and piano are similar in waveshape. I was expecting that the tom was going to have the highest point.\n",
      "I found out that the Glockenspeil is kind of like a metal rectangular tube. When the Glockenspeil is struck it has tranverse vibrations (which are periodic disturbances for which the particle oscillations of the medium are perpendicular to the direction of propagation). The range of the instrument is a G5 (748Hz) to a C8 (4186HZ). All but the lowest transverse mode die out quickly, but  we hear a clear ring at the frequency of the fundamental transverse mode. On the other hand when the tom drum is struck it reproduces multiple frequencys at various points and it because of these various points which create a big difference in the answer between the to. When taking the (traditional) piano into consideration the hammers could never have as much velocity in the strike nor residence compared to the glockespiel. \n",
      "\n",
      "I gathered this information from the CCRMA at Stanford University. "
     ]
    }
   ],
   "metadata": {}
  }
 ]
}