{
 "metadata": {
  "name": "Chapter_13"
 }, 
 "nbformat": 2, 
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown", 
     "source": [
      "<h1>Chapter 13: Basic Op-amp Circuits<h1>"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "<h3>Example 13.1, Page Number: 433<h3>"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "%matplotlib inline"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "", 
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].", 
        "For more information, type 'help(pylab)'."
       ]
      }
     ], 
     "prompt_number": 177
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "", 
      "import pylab", 
      "import numpy", 
      "", 
      "R2=1.0*10**3;", 
      "R1=8.2*10**3;", 
      "V=15.0;", 
      "V_REF=R2*V/(R1+R2);", 
      "print('V_REF = %f V'%V_REF)", 
      "############PLOT##################################", 
      "", 
      "t = arange(0.001, 2.0, 0.005)", 
      "k = arange(0.0001, 0.0529, 0.0005)", 
      "k1= arange(0.0529, 0.447, 0.0005)", 
      "k2= arange(0.447, 1.053, 0.0005)", 
      "k3= arange(1.053,1.447, 0.0005)", 
      "k4= arange(1.447,2.0, 0.0005)", 
      "m=arange(-12,12,0.0005)", 
      "", 
      "x5=(0.0529*m)/m", 
      "x10=(0.447*m)/m", 
      "x15=(1.053*m)/m", 
      "x25=(1.447*m)/m", 
      "subplot(211)", 
      "plot(t, 5*sin(2*pi*t))", 
      "plot(t,(1.63*t)/t,'--')", 
      "ylim( (-5,5) )", 
      "ylabel('Vin')", 
      "title('Input Waveform')", 
      "", 
      "subplot(212)", 
      "plot(k,-12*k/k,'b')", 
      "plot(k1,12*k1/k1,'b')", 
      "plot(k2,-12*k2/k2,'b')", 
      "plot(k3,12*k3/k3,'b')", 
      "plot(k4,-12*k4/k4,'b')", 
      "plot(x5,m,'b')", 
      "plot(x10,m,'b')", 
      "plot(x15,m,'b')", 
      "plot(x25,m,'b')", 
      "", 
      "ylim( (-13,13) )", 
      "ylabel('Vout')", 
      "xlabel('each time the input exceeds +1.63V,o/p switches to +12 and vice-versa')", 
      "title('Output Waveform')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "V_REF = 1.630435 V"
       ]
      }, 
      {
       "output_type": "pyout", 
       "prompt_number": 178, 
       "text": [
        "<matplotlib.text.Text at 0x2f58bd2c>"
       ]
      }, 
      {
       "output_type": "display_data", 
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Vq1eQyWT48ssvMXHiROH10nz4nJNqWBi/qiurFi7kJ5/AQLEjIYro1w9o0oR3niurnj3j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\nvyU4AGgiOABoIjgAaCI4AGgiOABocgcBjJkyNeWsWwAAAABJRU5ErkJggg==\n"
      }
     ], 
     "prompt_number": 178
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "<h3>Example 13.2, Page Number: 436<h3>"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "", 
      "R1=100.0*10**3;", 
      "R2=R1;", 
      "V_out_max=5.0;", 
      "V_UTP=R2*V_out_max/(R1+R2);", 
      "V_LTP=-V_out_max*R2/(R1+R2);", 
      "print('upper trigger point = %.1f Volts'%V_UTP)", 
      "print('lower trigger point = %.1f Volts'%V_LTP)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "upper trigger point = 2.5 Volts", 
        "lower trigger point = -2.5 Volts"
       ]
      }
     ], 
     "prompt_number": 179
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "<h3>Example 13.3, Page Number: 437<h3>"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "import pylab", 
      "import numpy", 
      "", 
      "R1=100.0*10**3;", 
      "R2=47.0*10**3;", 
      "V_R1=4.7+0.7;    #one zener is always forward biased with forward voltage 0.7V ", 
      "#V_R1 can be positive or negative", 
      "I_R1=V_R1/R1;    ", 
      "I_R2=I_R1;", 
      "V_R2=R2*I_R2;", 
      "V_out=V_R1+V_R2;    #positive or negative", 
      "", 
      "print ('I_R1 = \u00b1%d uA' %(I_R1*10**6))", 
      "print ('V_R2 = \u00b1%.2f V' %(V_R2))", 
      "print('max output voltage = \u00b1 %.2f V'%V_out)", 
      "print('\\nV_UTP = + %.2f V' %(V_R2))", 
      "print('V_LTP = - %.2f V' %(V_R2))", 
      "", 
      "################PLOT######################################", 
      "t = arange(0.0, 1.0, 0.0005)", 
      "t1=arange(0.0001,0.085,0.0005)", 
      "t2=arange(0.0001,0.585,0.0005)", 
      "t3=arange(0.585,1.0,0.0005)", 
      "t4=arange(0.085,0.585,0.0005)", 
      "", 
      "m=arange(-7.94,7.94,0.0005)", 
      "x1=(0.085*m)/m", 
      "x5=(0.585*m)/m", 
      "", 
      "subplot(211)", 
      "plot(t, 5*sin(2*pi*t))", 
      "plot(t1,2.54*t1/t1,'--')", 
      "plot(t2,-2.54*t2/t2,'--')", 
      "text(0.09,1.95,'(0.085,2.54)')", 
      "text(0.586,-2,'(0.585,-2.54)')", 
      "#annotate('(0.085,2.54)',(0.085,2.54),(0.3,2.5),arrowprops = dict(facecolor='black',shrink=0.09,xycoords='data',textcoords='axes fraction'))", 
      "ylim( (-6,6) )", 
      "ylabel('Vin')", 
      "title('Input Waveform')", 
      "", 
      "subplot(212)", 
      "plot(t1,7.94*t1/t1,'b')", 
      "plot(t3,7.94*t3/t3,'b')", 
      "plot(t4,-7.94*t4/t4,'b')", 
      "plot(x1,m,'b')", 
      "plot(x5,m,'b')", 
      "ylim( (-9,9) )", 
      "title('Source and output AC voltage')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "I_R1 = \u00b154 uA", 
        "V_R2 = \u00b12.54 V", 
        "max output voltage = \u00b1 7.94 V", 
        "", 
        "V_UTP = + 2.54 V", 
        "V_LTP = - 2.54 V"
       ]
      }, 
      {
       "output_type": "pyout", 
       "prompt_number": 180, 
       "text": [
        "<matplotlib.text.Text at 0x2f9fbf2c>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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hodSrVy9SKBRUUFBAPXv2pP379xMRUUpKChERFRUV0bRp02ju3LlERHTu3Dka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\nB0HRkFnTYYNDAAAAAElFTkSuQmCC\n"
      }
     ], 
     "prompt_number": 180
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "<h3>Example 13.4, Page Number: 440<h3>"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "", 
      "V_IN1=3.0;", 
      "V_IN2=1.0;", 
      "V_IN3=8.0;", 
      "#all resistors are of equal value so weight of each input is 1", 
      "V_OUT=-(V_IN1+V_IN2+V_IN3);", 
      "print('output voltage = %d Volts'%V_OUT)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "output voltage = -12 Volts"
       ]
      }
     ], 
     "prompt_number": 181
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "<h3>Example 13.5, Page Number: 440<h3>"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "", 
      "R_f=10.0*10**3;", 
      "R1=1.0*10**3;", 
      "R2=R1;", 
      "R=R1;", 
      "V_IN1=0.2;", 
      "V_IN2=0.5;", 
      "V_OUT=-(R_f/R)*(V_IN1+V_IN2);", 
      "print('output voltage of the summing amplifier = %d Volts'%V_OUT)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "output voltage of the summing amplifier = -7 Volts"
       ]
      }
     ], 
     "prompt_number": 182
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "<h3>Example 13.6, Page Number: 441<h3>"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "", 
      "R_f=25.0*10**3;", 
      "R1=100.0*10**3;", 
      "R2=R1;", 
      "R3=R1;", 
      "R4=R1;", 
      "R=R1;", 
      "V_IN1=1.0;", 
      "V_IN2=2.0;", 
      "V_IN3=3.0;", 
      "V_IN4=4.0;", 
      "V_OUT=-(R_f/R)*(V_IN1+V_IN2+V_IN3+V_IN4);", 
      "print('output voltage = %.1f Volts'%V_OUT)", 
      "V_IN_avg=(V_IN1+V_IN2+V_IN3+V_IN4)/4;", 
      "if abs(V_OUT)==V_IN_avg:", 
      " print('The amplifier produces an output whose magnitude\\n is the mathematical average of the input voltages')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "output voltage = -2.5 Volts", 
        "The amplifier produces an output whose magnitude", 
        " is the mathematical average of the input voltages"
       ]
      }
     ], 
     "prompt_number": 183
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "<h3>Example 13.7, Page Number: 442<h3>"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "", 
      "V_IN1=3.0;", 
      "V_IN2=2.0;", 
      "V_IN3=8.0;", 
      "R_f=10.0*10**3;", 
      "R1=47.0*10**3;", 
      "R2=100.0*10**3;", 
      "R3=10.0*10**3;", 
      "weight_of_input1=R_f/R1;", 
      "weight_of_input2=R_f/R2;", 
      "weight_of_input3=R_f/R3;", 
      "V_OUT=-(weight_of_input1*V_IN1+weight_of_input2*V_IN2+weight_of_input3*V_IN3);", 
      "print('weight_of_input1 = %f'%weight_of_input1)", 
      "print('weight_of_input2 = %.1f'%weight_of_input2)", 
      "print('weight_of_input3 = %d'%weight_of_input3)", 
      "print('output voltage = %f Volts'%V_OUT)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "weight_of_input1 = 0.212766", 
        "weight_of_input2 = 0.1", 
        "weight_of_input3 = 1", 
        "output voltage = -8.838298 Volts"
       ]
      }
     ], 
     "prompt_number": 184
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "<h3>Example 13.8, Page Number: 444<h3>"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "", 
      "import pylab", 
      "import numpy", 
      "", 
      "", 
      "R_i=10.0*10**3;", 
      "C=0.01*10**-6;", 
      "V_in=2.5-(-2.5);", 
      "PW=100.0*10**-6;    #pulse width", 
      "T=2*PW;", 
      "A=2.5;", 
      "op_change_cap_charge=-V_in/(R_i*C);", 
      "op_change_cap_discharge=V_in/(R_i*C);", 
      "print('rate of change of output voltage with respect to time when capacitor is charging = %.1f V/sec'%op_change_cap_charge)", 
      "print('rate of change of output voltage with respect to time when capacitor is discharging =%.1f V/sec'%op_change_cap_discharge)", 
      "del_V_OUT=op_change_cap_discharge*PW;", 
      "print('\\n\\nwhen input is positive, the slope is negative,\\nwhen input is negative, the slope is negative. \\nSo, the output is a triangular wave varying from zero to %.1f V'%(-del_V_OUT))", 
      "", 
      "##############PLOT#############################", 
      "t = arange(0.0, 10.0, 0.0005)", 
      "t1= arange(10.0, 20.0, 0.0005)", 
      "t2= arange(20.0, 30.0, 0.0005)", 
      "t3= arange(30.0, 40.0, 0.0005)", 
      "", 
      "k = arange(0.0001, 10.0, 0.0005)", 
      "k1= arange(10.0, 20.0, 0.0005)", 
      "k2= arange(20.0, 30.0, 0.0005)", 
      "k3= arange(30.0,40.0, 0.0005)", 
      "", 
      "m=arange(-2.5,2.5,0.0005)", 
      "x1=(0.001*m)/m", 
      "x5=(10*m)/m", 
      "x10=(20*m)/m", 
      "x15=(30*m)/m", 
      "x25=(39.99*m)/m", 
      "", 
      "", 
      "subplot(211)", 
      "plot(k,2.5*k/k,'b')", 
      "plot(k1,-2.5*k1/k1,'b')", 
      "plot(k2,2.5*k2/k2,'b')", 
      "plot(k3,-2.5*k3/k3,'b')", 
      "plot(x1,m,'b')", 
      "plot(x5,m,'b')", 
      "plot(x10,m,'b')", 
      "plot(x15,m,'b')", 
      "plot(x25,m,'b')", 
      "", 
      "ylim( (-3,3) )", 
      "ylabel('Vin')", 
      "xlabel('us')", 
      "title('Input to Opamp Integrator')", 
      "", 
      "subplot(212)", 
      "plot(t,(-0.5*t),'b')", 
      "plot(t1,(0.5*t-5),'b')", 
      "plot(t2,(-0.5*t),'b')", 
      "plot(t3,(0.5*t-5),'b')", 
      "", 
      "ylim( (-5,0) )", 
      "ylabel('Vout')", 
      "xlabel('us')", 
      "title('Output of an Integrator')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "rate of change of output voltage with respect to time when capacitor is charging = -50000.0 V/sec", 
        "rate of change of output voltage with respect to time when capacitor is discharging =50000.0 V/sec", 
        "", 
        "", 
        "when input is positive, the slope is negative,", 
        "when input is negative, the slope is negative. ", 
        "So, the output is a triangular wave varying from zero to -5.0 V"
       ]
      }, 
      {
       "output_type": "pyout", 
       "prompt_number": 185, 
       "text": [
        "<matplotlib.text.Text at 0x303b512c>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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UT3wNbwjDGGOPVxMbwlSYAJYtW4a3334b06ZNK/M9lUqFTz75pMonAwAnJyekpqZqH6em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\ngg==\n"
      }
     ], 
     "prompt_number": 185
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "<h3>Example 13.9, Page Number: 447<h3>"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "", 
      "import pylab", 
      "import numpy", 
      "", 
      "R_f=2.2*10**3;", 
      "C=0.001*10**-6;", 
      "Vc=5.0-(-5.0);", 
      "A=5.0;", 
      "time_const=R_f*C;", 
      "T=10.0*10**-6;", 
      "t=T/2;", 
      "slope=Vc/t;", 
      "V_out=slope*time_const;    #V_out is negative when input is positive and V_out is positive when input is negative", 
      "print('output voltage = %.1f V'%V_out)", 
      "print('max output voltage = %.1f V'%V_out)", 
      "print('min output voltage = %.1f V'%(-V_out))", 
      "", 
      "##################PLOt############################", 
      "", 
      "t = arange(0.0, 5.0, 0.0005)", 
      "t1= arange(5.0, 10.0, 0.0005)", 
      "t2= arange(10.0, 15.0, 0.0005)", 
      "t3= arange(15.0, 20.0, 0.0005)", 
      "", 
      "", 
      "k = arange(0.0001, 5.0, 0.0005)", 
      "k1= arange(5.0, 10.0, 0.0005)", 
      "k2= arange(10.0, 15.0, 0.0005)", 
      "k3= arange(15.0, 20.0, 0.0005)", 
      "", 
      "m=arange(-4.4,4.4, 0.0005)", 
      "x1=(0.001*m)/m", 
      "x5=(5*m)/m", 
      "x10=(10*m)/m", 
      "x15=(15*m)/m", 
      "", 
      "", 
      "subplot(211)", 
      "plot(t,(2*t-5),'b')", 
      "plot(t1,(-2*t+5),'b')", 
      "plot(t2,(2*t-5),'b')", 
      "plot(t3,(-2*t+5),'b')", 
      "ylim( (-6,6) )", 
      "ylabel('Vin')", 
      "xlabel('us')", 
      "title('Triangular wave input')", 
      "", 
      "subplot(212)", 
      "plot(k,-4.4*k/k,'b')", 
      "plot(k1,4.4*k1/k1,'b')", 
      "plot(k2,-4.4*k2/k2,'b')", 
      "plot(k3,4.4*k3/k3,'b')", 
      "plot(x1,m,'b')", 
      "plot(x5,m,'b')", 
      "plot(x10,m,'b')", 
      "plot(x15,m,'b')", 
      "ylim( (-5,5) )", 
      "ylabel('Vout')", 
      "xlabel('us')", 
      "title('Output of Opamp Differentiator')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "output voltage = 4.4 V", 
        "max output voltage = 4.4 V", 
        "min output voltage = -4.4 V"
       ]
      }, 
      {
       "output_type": "pyout", 
       "prompt_number": 186, 
       "text": [
        "<matplotlib.text.Text at 0x30f01b4c>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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MapEACgr4FKw5OfwWu1YtoSMimsrEhPe3L0oCAQGqP+bhw3yyuiNH+NgEQkoS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\nOiytJhKJEBMTg/j4eMTFxQkdjtYZM2b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      }
     ], 
     "prompt_number": 186
    }
   ]
  }
 ]
}