{ "metadata": { "name": "", "signature": "sha256:e45efbc918d6e082147a3abf5687f38acaf875f9fd899adb5638679d888fbdcb" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Chapter 6: Vaccum Tubes" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Example 6.1 Page no.195" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Given\n", "from pylab import *\n", "#Given Circuit Data\n", "V=[0,0.5,1,1.5,2] #V, voltage\n", "I=[0,1.6,4,6.7,9.4] #mA, current\n", "\n", "#Calculation\n", "dVp=0.5 #V, change in plate voltage\n", "dIp=2.7*10**(-3) #A, change in plate current\n", "rp=dVp/dIp # Dynamic Plate Resistance\n", "\n", "#Result\n", "print \"The Dynamic Plate Resistance is rp= \",rp,\"ohm\"\n", "\n", "#plot\n", "\n", "a=plot(V,I)\n", "xlabel(\"V\") \n", "ylabel(\"I (mA)\") \n", "\n", "show(a)\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The Dynamic Plate Resistance is rp= 185.185185185 ohm\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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} ], "prompt_number": 3 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Example 6.2 Page no.202" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "from math import *\n", "from pylab import *\n", "#Calculation\n", "dip=(14.0-10.7)*10**(-3) #A\n", "dvp=20 #V\n", "rp=dvp/dip\n", "diP=(12.4-5.3)*10**(-3) #A\n", "dvG=1 #V\n", "gm=diP/dvG\n", "u=gm*rp\n", "ut=(192-150)/1\n", "\n", "# Result\n", "print \" The Plate AC Resistance is rp= \",round(rp/10**(3),2),\"kohm\"\n", "print \" The Mutual Conductance is gm= \",gm/10**(-3),\"mS\"\n", "print \" The Graphical Amplification Factor is u= \",round(u,2)\n", "print \" The Theoretical Amplification Factor is ut= \",ut\n", "\n", "\n", "#plot\n", "#At Vg=0\n", "V1=[0,50,100,150]\n", "I1=[0,3.5,11.2,20.0]\n", "\n", "#at Vg=-1\n", "V2=[60,100,150,200]\n", "I2=[0,4,12.4,21.5]\n", "\n", "\n", "#at Vg=-2\n", "V3=[100,150,200]\n", "I3=[0,5.4,14.1]\n", "\n", "#at Vg=-3\n", "V4=[160,200,250]\n", "I4=[0,3.4,12.4]\n", "\n", "#at Vg=-4\n", "V5=[220,250,300]\n", "I5=[0,2.5,11.3]\n", "\n", "figure(1)\n", "import numpy \n", "import pylab\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "\n", "\n", "a1=plot(V1,I1)\n", "a2=plot(V2,I2)\n", "a3=plot(V3,I3)\n", "a4=plot(V4,I3)\n", "a5=plot(V5,I4)\n", "xlabel(\"Vp (V)\") \n", "ylabel(\"Ip(mA)\") \n", "ax.annotate('vg=0', xy=(152,21),\n", " arrowprops=dict(facecolor='black', shrink=0.5),\n", " )\n", "ax.annotate('vg=-1', xy=(200,21.5), \n", " arrowprops=dict(facecolor='black', shrink=0.5),\n", " )\n", "ax.annotate('vg=-2', xy=(200,14.1), \n", " arrowprops=dict(facecolor='black', shrink=0.5),\n", " )\n", "ax.annotate('vg=-3', xy=(250,12.4),\n", " arrowprops=dict(facecolor='black', shrink=0.5),\n", " )\n", "ax.annotate('vg=-4',xy=(300,11.3), \n", " arrowprops=dict(facecolor='black', shrink=0.5),\n", " )\n", "show(a1)\n", "show(a2)\n", "show(a3)\n", "show(a4)\n", "show(a5)\n", "\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " The Plate AC Resistance is rp= 6.06 kohm\n", " The Mutual Conductance is gm= 7.1 mS\n", " The Graphical Amplification Factor is u= 43.03\n", " The Theoretical Amplification Factor is ut= 42\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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3bLmHFpi7zLWjwf1yPbNPeYxGjRopRqMx++9Go1Fp1KjR4/7ZE8lFLGGBjh9X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\nQhMbOGQtN+LXxhM+OpymG5pS4XkrHSi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