{
 "metadata": {
  "name": "",
  "signature": "sha256:3d437ebdf0e958eb2361cad44156f40592bb9d64cab9430f55235831db4ce11a"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "import pyaudio"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This makes a sound!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PyAudio = pyaudio.PyAudio\n",
      "\n",
      "BITRATE = 24000 #number of frames per second/frameset.      \n",
      "FREQUENCY = 261.6 #Hz, waves per second, 261.63=C4-note.\n",
      "LENGTH = 1. #seconds to play sound\n",
      "\n",
      "NUMBEROFFRAMES = int(BITRATE * LENGTH)\n",
      "RESTFRAMES = NUMBEROFFRAMES % BITRATE\n",
      "WAVEDATA = ''    \n",
      "\n",
      "for x in xrange(NUMBEROFFRAMES):\n",
      " WAVEDATA = WAVEDATA+chr(int(math.sin(x/((BITRATE/FREQUENCY)/math.pi))*127+128))    \n",
      "\n",
      "#fill remainder of frameset with silence\n",
      "for x in xrange(RESTFRAMES): \n",
      " WAVEDATA = WAVEDATA+chr(128)\n",
      "\n",
      "p = PyAudio()\n",
      "stream = p.open(format = p.get_format_from_width(1), \n",
      "                channels = 1, \n",
      "                rate = BITRATE, \n",
      "                output = True)\n",
      "stream.write(WAVEDATA)\n",
      "stream.stop_stream()\n",
      "stream.close()\n",
      "p.terminate()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Write a file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "from matplotlib import pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from struct import pack\n",
      "from math import sin, pi"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# create a sound file in AU format playing a sine wave\n",
      "# of a given frequency, duration and volume\n",
      "def au_file(name='test.au', freq=440, dur=1000, vol=0.5, lead=0, tail=0):\n",
      "    \"\"\"\n",
      "    creates an AU format sine wave audio file\n",
      "    of frequency freq (Hz)\n",
      "    of duration dur (milliseconds)\n",
      "    and volume vol (max is 1.0)\n",
      "    \"\"\"\n",
      "    fout = open(name, 'wb')\n",
      "    \n",
      "    total = lead + dur + tail\n",
      "    \n",
      "    values = []\n",
      "    \n",
      "    # header needs size, encoding=2, sampling_rate=8000, channel=1\n",
      "    fout.write(pack('>4s5L', '.snd'.encode(\"utf8\"), 24, 8*total, 2, 8000, 1))\n",
      "    factor = 2 * pi * freq/8000\n",
      "    \n",
      "    # write lead\n",
      "    for seg in range(8 * lead):\n",
      "        val = pack('b', 0)\n",
      "        fout.write(val)\n",
      "        values.append(0)\n",
      "        \n",
      "    # write data\n",
      "    for seg in range(8 * dur):\n",
      "        # sine wave calculations\n",
      "        sin_seg = sin(seg * factor)\n",
      "        v = vol * 127 * sin_seg\n",
      "        val = pack('b', int(v))\n",
      "        fout.write(val)\n",
      "        values.append(v)\n",
      "        \n",
      "    # write tail\n",
      "    for seg in range(8 * tail):\n",
      "        val = pack('b', 0)\n",
      "        fout.write(val)\n",
      "        values.append(0)\n",
      "        \n",
      "    fout.close()\n",
      "    print(\"File %s written\" % name)\n",
      "    \n",
      "    return values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sound = au_file(name='sound440_2.au', freq=150, dur=500, vol=1.0, lead=500, tail=500)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "File sound440_2.au written\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(20,4))\n",
      "plt.plot(sound)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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VR4tqman1cdvV7G/Jq+RT2/Vt265PaH9zvl8M1Tbnmp+TPgAAAClaVi49UF21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\nhT5ufaUptB2rT+3xuA5t+9tNfe3110/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       "text": [
        "<matplotlib.figure.Figure at 0x107d23650>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}