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authorTrupti Kini2016-03-06 23:30:10 +0600
committerTrupti Kini2016-03-06 23:30:10 +0600
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Added(A)/Deleted(D) following books
A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/Chapter10.ipynb A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/Chapter11.ipynb A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/Chapter12.ipynb A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/Chapter13.ipynb A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/Chapter14.ipynb A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/Chapter2.ipynb A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/Chapter3.ipynb A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/Chapter4.ipynb A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/Chapter5.ipynb A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/Chapter6.ipynb A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/Chapter7.ipynb A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/Chapter8.ipynb A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/Chapter9.ipynb A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/screenshots/1.png A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/screenshots/2.png A Fiber_Optics_and_Optoelectronics_by_R._P._Khare/screenshots/3.png A sample_notebooks/LalitKumar/Ch3.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Chapter13 - Fiber-optic sensors"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 13.1: Page 327"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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PiLiElE64Kpf/yqbhDyfdbPSHXHV4OQtfURaH/TrwxVx99llSELiPVCtyJ2kdlZZF0g6k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\nruR8rlnvqTvYvht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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f99806ea310>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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fqDewpD7ge8A0YFNgb0mb1OnvWOBioKcPO5jV41RqNjKlnhOVtAHwAWAHYFLu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\nkRLLTnneXwa2yJ/9FNinOI9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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f9980722d50>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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kdYBfRMTuA0w3CeiNiMm5+2hgYUQcXxjnd8DXI+LG3H0NcFREzKia11TghYj4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\nZtYEDqhmZmZN4IBqZmbWBP8ftXlamgMlDP4AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f99806db610>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#plot\n",
+ "lod=[0, 20, 40, 60, 80, 100, 160] #in micro meter\n",
+ "slong=[1.0, 0.95, 0.92, 0.89, 0.86, 0.83, 0.80]\n",
+ "lad=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100] #in micro meter\n",
+ "slat=[0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\n",
+ "add=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
+ "sang=[0, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, .12]\n",
+ "from numpy import arange\n",
+ "t=arange(0,201,20)\n",
+ "s1=arange(1.0,0.7,-0.03)\n",
+ "%matplotlib inline\n",
+ "from matplotlib.pyplot import plot, subplot, title, xlabel, ylabel, show\n",
+ "#subplot(131)\n",
+ "plot(t,s1)\n",
+ "title(\"Variation of Slong as a function of delta x (with deltay=fi and delta theta=fi) \")\n",
+ "xlabel(\"Longitudinal displacement delta x (micro meter)\")\n",
+ "ylabel(\"Slong (normalised)\")\n",
+ "show()\n",
+ "t1=arange(0,101,10)\n",
+ "s2=arange(1,-0.1,-0.1)\n",
+ "\n",
+ "#subplot(132)\n",
+ "plot(t1,s2)##\n",
+ "title(\"Variation of Slat as a function of delta y (with deltax=fi and delta theta=fi) \")\n",
+ "xlabel(\"Lateral displacement delta y (micro meter)\")\n",
+ "ylabel(\"Slat (normalised)\")\n",
+ "show()\n",
+ "t2=arange(0,11,1)\n",
+ "s3=arange(1.0,0.7,-0.03)\n",
+ "#subplot(133)\n",
+ "plot(t2,s3)##\n",
+ "title(\"Variation of Sang as a function of delta theta (with deltax=fi and deltay=fi) \")\n",
+ "xlabel(\"Angular displacement delta theta (deg)\")\n",
+ "ylabel(\"Sang (normalised)\")\n",
+ "show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 13.2: Page 332"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "phase change = 106.60 rad/m°C\n"
+ ]
+ }
+ ],
+ "source": [
+ "from math import sqrt, pi\n",
+ "#phase change\n",
+ "#given data :\n",
+ "n=1.45## index of core\n",
+ "a=10**-5## in C**-1\n",
+ "b=5.1*10**-7## in C**-1\n",
+ "lamda=.633*10**-6## in m\n",
+ "# formula:- (1/L)*(del_fi/del_T)=((2*PI)/lamda)[(n/L)*(del_L/del_T)+(del_n/del_T)]\n",
+ "#let we assume a=del_n/del_T, b=(1/L)*(del_L/del_T), c=(1/L)*(del_fi/del_T)\n",
+ "c=((2*pi)/lamda)*((n*b)+a)#\n",
+ "print \"phase change = %0.2f rad/m°C\"%c"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example 13.3: Page 335"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "phase shift, del_fi = 8.99e-05 rad\n"
+ ]
+ }
+ ],
+ "source": [
+ "#phase shift\n",
+ "#given data :\n",
+ "L=500## in m\n",
+ "D=0.1##in m\n",
+ "ohm=7.3*10**-5## in rad s**-1\n",
+ "lamda=0.85*10**-6## in m\n",
+ "c=3*10**8## in m/s\n",
+ "del_fi=(2*pi*L*D*ohm)/(c*lamda)#\n",
+ "print \"phase shift, del_fi = %0.2e rad\"%del_fi"
+ ]
+ }
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