Untitled0.ipynb

{
 "metadata": {
  "name": "Untitled0"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from oceanoptics import USB4000"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dev = USB4000()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-05-16 13:20:34,064 - DEBUG - In init!\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = dev.request_spectra()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-05-16 13:20:51,522 - DEBUG - Requesting spectra\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-05-16 13:20:51,522 - DEBUG - Sending '\\t'\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-05-16 13:20:51,810 - DEBUG - obtained spectra\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dev.set_integration_time(400000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-05-16 13:16:35,284 - DEBUG - setting 400000 for the integration time\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-05-16 13:16:35,285 - DEBUG - Sending '\\x02\\x80\\x1a\\x06\\x00'\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "5"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(12,8))\n",
      "plot(data)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10da9fb50>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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iQgUFBZKkgoICbdy4UZJUXl6u/Px8JSYmKiUlRampqaqurlZjY6NaWlqUmZkp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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d93a210>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dev.reset()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-05-15 22:54:40,639 - DEBUG - resetting device\n"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dev.get_status()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-05-15 22:55:26,242 - DEBUG - getting status parameters\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-05-15 22:55:26,242 - DEBUG - Sending '\\xfe'\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-05-15 22:55:26,244 - DEBUG - status is bytearray(b\"\\x00\\x0f\\x10\\'\\x00\\x00\\x00\\x00\\x00\\x0f\\x00\\x00\\x00\\x00\\x80U\")\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "{'acq_status': 0,\n",
        " 'integration_time': 10000,\n",
        " 'lamp_enable': False,\n",
        " 'num_pixels': 3840,\n",
        " 'packet_count': 0,\n",
        " 'packets_in_spectra': 15,\n",
        " 'power_down': False,\n",
        " 'trigger_mode': 0,\n",
        " 'usb_speed': 128}"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}