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 "metadata": {
  "name": "",
  "signature": "sha256:ead5fcebe635df661bdf968c60ddf9584be9f675c33a1453a9a783d493c7c114"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Geiger counter event recorder through the sound card"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This code snippet shows how to use the provided API to perform event recordings from a Geiger counter which output is fed through the sound card of a computer"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "from counter import Recorder"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Here is an example on how to use the API to perfom a simple recording"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First decide which will the input device (can be a simple microphone, or the Geiger counter audio jack)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Recorder.list_devices()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "[u'Built-in Microph', u'Built-in Output', u'Aggregate Device']"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Instantiate the Recorder object, 1 channel, sampling rate at 44100Hz, recording buffer at 1024 events, input device is Built-in Microph"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "recorder = Recorder(channels=1,rate=44100,frames_per_buffer=1024,input_device_index=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Record for 5 seconds and display the waveform"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "recorder.record(5)\n",
      "recorder.visualize()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[32m[+] Started recording on Built-in Microph, channels=1, rate=44100, frames per buffer=1024\u001b[39m\n",
        "\u001b[31m[-] Stopped recording\u001b[39m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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7znN/5LDtyB/95s2beeONNxK/iQSGDRuGtTa8/fHHH2c1NFwq7q677uK3v/2t38XgzTff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\nKw5RLwpUAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104c34090>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Record for 5 seconds, and trigger on a raising slope above $y=0.1$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig,ax = plt.subplots()\n",
      "t,y = recorder.record_above_threshold(5,threshold=0.1,slope=\"raising\",save_waveform=True)\n",
      "recorder.visualize(fig=fig,ax=ax)\n",
      "recorder.ax.scatter(t,y,color=\"red\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[32m[+] Started recording on Built-in Microph, channels=1, rate=44100, frames per buffer=1024\u001b[39m\n",
        "\u001b[31m[-] Stopped recording\u001b[39m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\u001b[32m[+] Found 15 events on raising threshold=1.00e-01\u001b[39m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.collections.PathCollection at 0x10881f6d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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REZHEqFRERCQxKhUREUmMSkVERBKjUhERkcSoVEREJDEqFZGMCCFUFVk3OI0s\nIr2lUhHJgBDCBcCIIg9NDCEcahdHlUOYSkUkBSGEKSGEr+WXjwRGxhi3tp8XY1wDTA0hDCt1RpHe\nUKmIpONsPrhK8GeBJzuZ+1Pgkwc9kUgCVCoiJRZCOBf4O3K7tsYDR8QY9+Qf+0gI4fwQwiUhhE8C\nxBj/H3BCeolFuk+lIlJiMcZngY0xxoUxxk3kbjzV6gLgfXL3yvldwfqKEkYU6TWVikiJ5bdONhWs\nqixY/ldyN2B7nf3LRmeBSVlQqYiU3mnAayGE00IIQ4F9BY/9dYzx88BHgPML1reUMqBIbx2y96gX\nybCNwHRgTYxxdwhhd8Fj40MIl5O7zfUzACGEADSUPqZIz6lUREosxvgb4DcFq9aHEEbHGLfHGG8v\n8iEnA78qTTqRvtHuL5H0LQQ+1snj5wD/XqIsIn2iUhFJWYzxPWBVCOHo9o+FEE4Eno8x6piKlIUQ\nY0w7g4iIHCK0pSIiIolRqYiISGJUKiIikhiVioiIJEalIiIiiVGpiIhIYlQqIiKSGJWKiIgk5v8D\n5oC5cUsWlnMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10834ae10>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The timings of the above threshold events (in seconds) are saved in the variable $t$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array([ 1.34606379,  1.34642752,  1.34690491,  1.34735957,  1.34783696,\n",
        "        1.34829162,  3.29455091,  3.2948919 ,  3.29498284,  3.2953011 ,\n",
        "        3.29557389,  3.29605129,  3.29648321,  3.29684694,  3.2968924 ])"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can also run the whole recording pipeline automatically: you just have to specify your preferences in a configuration file (or use the default one if you wish)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cat config"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[counter]\r\n",
        "\r\n",
        "num_channels = 1\r\n",
        "rate = 44100\r\n",
        "frames_per_buffer = 1024\r\n",
        "recording_time = 5\r\n",
        "\r\n",
        "[output]\r\n",
        "\r\n",
        "events_file = events.txt\r\n",
        "save_full_waveform = True"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "run geiger.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[33m[*] Reading configuration from config:\u001b[39m\n",
        "\u001b[32m[+] Number of channels: 1\u001b[39m\n",
        "\u001b[32m[+] Acquisition rate: 44100Hz\u001b[39m\n",
        "\u001b[32m[+] Frame buffer size: 1024\u001b[39m\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[33m\n",
        "Enter the recording time in seconds (5.0s) --> \u001b[39m5.0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[32m[+] Recording time set to 5.0s\u001b[39m\n",
        "\u001b[33m\n",
        "[*] Select a recording device (0-2) among:\n",
        "\u001b[39m\n",
        "\u001b[33m0: Built-in Microph\u001b[39m\n",
        "\u001b[33m1: Built-in Output\u001b[39m\n",
        "\u001b[33m2: Aggregate Device\u001b[39m\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-->0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[32m[+] Selected device 0: Built-in Microph\n",
        "\u001b[39m\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[33m[*] Select a threshold: \u001b[39m0.01\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[32m[+] Threshold set to 1.00e-02\n",
        "\u001b[39m\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[33m[*] Select a triggering slope (raising, descending): \u001b[39mraising\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[32m[+] Slope set to raising\n",
        "\u001b[39m\n",
        "\u001b[32m[+] According to the configuration, you want to save the whole waveform\u001b[39m\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[33m[*] Select a file to write the whole waveform to: \u001b[39mwaveform_environment.npy\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[32m[+] The full waveform will be saved to: waveform_environment.npy\n",
        "\u001b[39m\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[33m\n",
        "[*] Press <enter> to start recording:\u001b[39m\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[32m[+] Started recording on Built-in Microph, channels=1, rate=44100, frames per buffer=1024\u001b[39m\n",
        "\u001b[31m[-] Stopped recording\u001b[39m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\u001b[32m[+] Found 4 events on raising threshold=1.00e-02\u001b[39m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\u001b[33m\n",
        "[*] Specify a file on which to save the events (events.txt) \u001b[39m\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[33m-->\u001b[39mcounts_environment.txt\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[32m[+] Saving events to counts_environment.txt\u001b[39m\n",
        "\u001b[32m[+] Saving full waveform (t,y) to waveform_environment.npy\u001b[39m\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[33m[*] Do you want to plot the waveform? (y/n)\u001b[39my\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[33m[+] Plotting waveform to waveform_environment.png\u001b[39m\n",
        "\u001b[31m[-] Event recording complete, bye\u001b[39m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ltuuuu9aKJRP9+vVjwIABgccyTa7/8z//E3+d+C0331GIuTZJPv3004wdOzav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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1080b01d0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And here is the log of the events above threshold"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cat counts_environment.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1.452067562345131568e+00\r\n",
        "1.453567937438905000e+00\r\n",
        "1.468276159949078119e+00\r\n",
        "3.234831435131510169e+00\r\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}