MLS Signal.ipynb

{
 "metadata": {
  "name": "MLS Signal"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from MlsDb import MlsDb\n",
      "\n",
      "mls_db = MlsDb()\n",
      "\n",
      "mls = mls_db.getMls(5)\n",
      "\n",
      "# convert into -1's and 1s\n",
      "mls = -2 * mls + 1\n",
      "\n",
      "# hold values to produce plots\n",
      "mls_plot = reduce(lambda x, y: x + y, zip(mls, mls))\n",
      "bins_plot = reduce(lambda x, y: x + y, zip(arange(len(mls)), arange(1, len(mls) + 1)))\n",
      "\n",
      "plot(bins_plot, mls_plot)\n",
      "ylim(-1.1, 1.1)\n",
      "yticks([-1, 0, 1])\n",
      "xlabel(\"Bins\")\n",
      "ylabel(\"Amplitude\")\n",
      "axhline(y=-1, ls=\"--\", color=\"gray\")\n",
      "axhline(y=0, ls=\"--\", color=\"gray\")\n",
      "axhline(y=1, ls=\"--\", color=\"gray\")\n",
      "subplots_adjust(left=0.10, right=0.97, top=0.97, bottom=0.10)\n",
      "xlim(0, 31)\n",
      "tight_layout()\n",
      "savefig(\"5_tap_mls_signal.eps\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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AcwgnAIA5Zm8lBwD4X10R5PqDX2+EwbwEALiIaT0AgDmEEwDAHMIJAGCOuWtO+fn5WrFi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\nAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from SignalGenerator import SignalGenerator\n",
      "from BaseDelegate import BaseDelegate"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scikits/__init__.py:1: UserWarning: Module argparse was already imported from /Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/argparse.pyc, but /Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages is being added to sys.path\n",
        "  __import__('pkg_resources').declare_namespace(__name__)\n",
        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scikits/__init__.py:1: UserWarning: Module numpy was already imported from /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/__init__.pyc, but /Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages is being added to sys.path\n",
        "  __import__('pkg_resources').declare_namespace(__name__)\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bd = BaseDelegate()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sig = SignalGenerator(bd.measurement_settings)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fs = 44100.0\n",
      "t = arange(0, len(sig.signal) / fs, 1 / fs)\n",
      "plot(t[::500], sig.signal[::500])\n",
      "xlabel(\"Time (s)\")\n",
      "ylabel(\"$s[n]$\")\n",
      "xlim(0, 20)\n",
      "grid(True)\n",
      "savefig(\"excitation_signal_measurement.eps\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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2b95smr9e8bCMROrVq0e9TnsW7eaR2vezZcsW03gs+LVOhObI1mP2TE7kZjl+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\nIpHYRioRiUQikdjm/wMkd5MHNXKongAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "7.430385487528345"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}