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{
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  "signature": "sha256:6b48ddfed32d102e3a04253b5fd6b8c5feb3acf3121f0e960d076687c58877bd"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 14: Performance measurement and monitering"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Figure 14.10, Page Number: 529"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import scipy as sp\n",
      "from scipy import special\n",
      "\n",
      "%matplotlib inline\n",
      "\n",
      "#variable declaration\n",
      "sigma = 1                      #pulse width\n",
      "\n",
      "#calculation\n",
      "fdB_optical = 0.187/sigma\n",
      "fdB_electrical = 0.133/sigma\n",
      "\n",
      "#result\n",
      "print \"F_dB Optical =\",fdB_optical\n",
      "print \"F_dB Electrical =\",fdB_electrical\n",
      "\n",
      "#plot\n",
      "t = arange(-3.0, 3.0, 0.1) \n",
      "p = (1/(sigma*sqrt(2*pi)))*exp((-t**2)/(2*(sigma**2)))    \n",
      "plot(t,p)\n",
      "ylabel('Relative pulse amplitude P(t)')\n",
      "xlabel('Time t')\n",
      "title('Definations of pulse shape parameters')\n",
      "text(-0.7,0.25,'RMS pulse width')\n",
      "text(-1.0,0.20,'Full width half maximum')\n",
      "text(-0.5,0.15,'1/e width')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "F_dB Optical = 0.187\n",
        "F_dB Electrical = 0.133\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.text.Text at 0xa411898>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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qVKpUqcIRRxyRTV+wc37KlSuXsVymTBkOHz6cb5kGDaBuXfjww6BOYZQAcjUO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\n2owwcdddLn6SGQajNFOjBvz7326I66G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       "text": [
        "<matplotlib.figure.Figure at 0xa49efd0>"
       ]
      }
     ],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}