path: root/Principles_of_Electronics_____by_V.K._Mehta_and_Rohit_Mehta/chapter18_6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CHAPTER 18 : SOLID-STATE SWITCHING CIRCUITS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.1 : Page number 472"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input voltage required to saturate the transistor switch=5.4V.\n"
     ]
    }
   ],
   "source": [
    "#Variable declaration\n",
    "VCC=10;             #Supply voltage, V\n",
    "RC=1.0;               #Collector resistor, kΩ\n",
    "RB=47.0;              #Base resistor, kΩ\n",
    "beta=100.0;           #Base current amplification factor\n",
    "VBE=0.7;            #Base-emitter voltage, V\n",
    "\n",
    "#Calculation\n",
    "IC_sat=VCC/RC;              #Collector saturation current, mA\n",
    "IB=IC_sat/beta;             #Base current, mA\n",
    "V=IB*RB+VBE;                #Input voltage, V\n",
    "\n",
    "#Result\n",
    "print(\"Input voltage required to saturate the transistor switch=%.1fV.\"%V);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.2 : Page number 475"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(i) The collector emitter voltage at cut-off=9.99V.\n",
      "(ii) The collector emitter voltage at saturation=0.7V.\n"
     ]
    }
   ],
   "source": [
    "#Variable declaration\n",
    "VCC=10;             #Supply voltage, V\n",
    "RC=1.0;               #Collector resistor, kΩ\n",
    "ICBO=10.0;            #Collector leakage current, μA\n",
    "V_knee=0.7;         #Knee voltage, V\n",
    "\n",
    "\n",
    "#Calculation\n",
    "#(i)\n",
    "IC=ICBO;                    #Collector current, μA\n",
    "VCE=VCC-(ICBO/1000)*RC;            #Collector-emitter voltage, V\n",
    "\n",
    "print(\"(i) The collector emitter voltage at cut-off=%.2fV.\"%VCE);\n",
    "\n",
    "#(ii)\n",
    "#Since, saturation current=IC_sat=(VCC-V_knee)/RC;         \n",
    "VCE=V_knee;                    #Collector-emitter voltage, V\n",
    "\n",
    "print(\"(ii) The collector emitter voltage at saturation=%.1fV.\"%VCE);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.3 : Page number 475-476"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(i) Minimum β=19.4.\n",
      "(ii) The transistor will not be saturated.\n"
     ]
    }
   ],
   "source": [
    "#Variable declaration\n",
    "VCC=10;             #Supply voltage, V\n",
    "RC=1;               #Collector resistor, kΩ\n",
    "VBB=2;              #Supply voltage to base, V\n",
    "RB=2.7;             #Base resistor, kΩ\n",
    "V_knee=0.7;         #Knee voltage, V\n",
    "VBE=0.7;            #Base-emitter voltage, V\n",
    "\n",
    "#Calculation\n",
    "#(i)\n",
    "IB=round((VBB-VBE)/RB,2);           #Base current, mA\n",
    "Ic_sat=(VCC-V_knee)/RC;             #Collector saturation current, mA\n",
    "beta_min=Ic_sat/IB;                 #Minimum value of base current amplification factor\n",
    "print(\"(i) Minimum β=%.1f.\"%beta_min);\n",
    "\n",
    "#(ii)\n",
    "VBB=1;                       #Supply voltage to base(changed), V\n",
    "beta=50;                     #Base current amplification factor\n",
    "IB=(VBB-VBE)/RB;             #Base current, mA\n",
    "IC=beta*IB;                  #Collector current,mA\n",
    "\n",
    "if(IC<Ic_sat):\n",
    "    print(\"(ii) The transistor will not be saturated.\");\n",
    "else:\n",
    "    print(\"(ii) The transistor will be saturated.\");\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.4 : Page number 480"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time period of the square wave=0.14 m sec.\n",
      "Time frequency of the square wave=7 kHz.\n"
     ]
    }
   ],
   "source": [
    "#Variable declaration\n",
    "R2=10;                  #Resistor R2, kΩ\n",
    "R3=10;                  #Resistor R3, kΩ\n",
    "C1=0.01;                #Capacitor of 1st transistor, μF\n",
    "C2=0.01;                #Capacitor of 2nd transistor, μF\n",
    "\n",
    "#Calculation\n",
    "R=R2*1000;                   #Resistance, Ω\n",
    "C=C1*10**-6;                 #Capacitance, F\n",
    "T=round((1.4*R*C)*1000,2);   #Time period,m sec\n",
    "f=1/(T*10**-3);              #Frequency, Hz\n",
    "f=f/1000;                    #Frequency, kHz\n",
    "\n",
    "#Result\n",
    "print(\"Time period of the square wave=%.2f m sec.\"%T);\n",
    "print(\"Time frequency of the square wave=%d kHz.\"%f);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.6 : Page number 485"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The output voltage=0.55V.\n"
     ]
    }
   ],
   "source": [
    "#Variable declaration\n",
    "R=10;               #Resistance in differentiating circuit, kΩ\n",
    "C=2.2;              #Capacitance in differentiating circuit, μF\n",
    "d_ei=10;            #Change in input voltage, V\n",
    "dt=0.4;             #Time in which change occurs, s\n",
    "\n",
    "#Calculation\n",
    "eo=R*1000*C*10**-6*d_ei/dt\n",
    "\n",
    "\n",
    "#Result\n",
    "print(\"The output voltage=%.2fV.\"%eo);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.7 : Page number 489"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The peak output voltage=11.3V.\n"
     ]
    }
   ],
   "source": [
    "#Variable declaration\n",
    "Vin_peak=12;                #Peak value of input voltage, V\n",
    "V_D=0.7;                    #Forward bias voltage of diode, V\n",
    "\n",
    "#Calculation\n",
    "Vout_peak=Vin_peak-V_D;         #Peak value of output voltage, V\n",
    "\n",
    "#Result\n",
    "print(\"The peak output voltage=%.1fV.\"%Vout_peak);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.8 : Page number 489"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The peak output voltage=8V.\n"
     ]
    }
   ],
   "source": [
    "#Variable declaration\n",
    "Vin_peak=10;                #Peak value of input voltage, V\n",
    "R=1;                        #Input resistor, kΩ\n",
    "RL=4;                       #Load resistor, kΩ\n",
    "\n",
    "#Calculation\n",
    "Vout_peak=(Vin_peak*RL)/(R+RL);         #Peak output voltage, V\n",
    "\n",
    "\n",
    "#Result\n",
    "print(\"The peak output voltage=%dV.\"%Vout_peak);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.9 : Page number 490"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The diode will be forward biased for the negative half-cycle of input signal.\n",
      "The output voltage=-0.7V.\n",
      "The voltage across R=-9.3V.\n"
     ]
    }
   ],
   "source": [
    "#Variable declaration\n",
    "Vin=-10;                #Input voltage, V\n",
    "V_D=0.7;                #Forward bias voltage of the diode, V\n",
    "R=1;                    #Resistance, kΩ\n",
    "\n",
    "\n",
    "print(\"The diode will be forward biased for the negative half-cycle of input signal.\");\n",
    "Vout=-V_D;              #Output voltage, V\n",
    "V_R=Vin-(-V_D);         #Voltage across resistor R, V\n",
    "\n",
    "#Result\n",
    "print(\"The output voltage=%.1fV.\"%Vout);\n",
    "print(\"The voltage across R=%.1fV.\"%V_R);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.10 : Page number 490-491"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "During the positive half cycle, the diode is foward biased and can be replaced by battery of 0.7V.\n",
      "Therefore, Vout=0.7V.\n",
      "During the negative half cycle, the diode is reverse biased and hence behaves as an open circuit.\n",
      "Therefore, Vout_peak=-8.33V.\n"
     ]
    }
   ],
   "source": [
    "#Variable declaration\n",
    "V_F=0.7;                        #Forward bias voltage of diode, V\n",
    "R=200.0;                          #Input resistor of the circuit,  Ω\n",
    "RL=1.0;                           #Load resistor, kΩ\n",
    "Vin_peak=10.0;                    #Peak input voltage, V\n",
    "\n",
    "\n",
    "#Calculations\n",
    "\n",
    "#Positive half-cycle:\n",
    "print(\"During the positive half cycle, the diode is foward biased and can be replaced by battery of %.1fV.\"%V_F);\n",
    "print(\"Therefore, Vout=%.1fV.\"%V_F);\n",
    "\n",
    "#Negative half-cycle:\n",
    "print(\"During the negative half cycle, the diode is reverse biased and hence behaves as an open circuit.\");\n",
    "Vout_peak=RL*(-Vin_peak)/(R/1000+RL);\n",
    "print(\"Therefore, Vout_peak=%.2fV.\"%Vout_peak);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.12 : Page number 491"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEZCAYAAABmTgnDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa23cd37e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from math import sin\n",
    "from math import pi\n",
    "\n",
    "V_biasing=10.0;                        #Biasing voltage, V\n",
    "vin=[30*sin(t/10.0) for t in range(0,(int)(2*pi*10))]      #input voltage  waveform, V\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.plot(vin);\n",
    "plt.xlabel('t-->');\n",
    "plt.ylabel('Vin(V)');\n",
    "plt.title('Input waveform');\n",
    "\n",
    "vout=[];                             #Output voltage waveform, V\n",
    "for v in vin[:]:\n",
    "    if(v-V_biasing)>0 :              #Diode is forward biased.\n",
    "        vout.append(v-V_biasing);\n",
    "    else:                            #Diode is reverse biased.\n",
    "        vout.append(0);\n",
    "\n",
    "plt.subplot(212)        \n",
    "plt.plot(vout);\n",
    "plt.xlabel('t-->');\n",
    "plt.ylabel('Vout(V)');\n",
    "plt.title('Output waveform');\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.13 : Page number 492"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEZCAYAAACAZ8KHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5CKPkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa23dcfd668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "Vin=[];      #Input voltage waveform, V\n",
    "t1=50;         #Assumed time interval, s\n",
    "t2=100;         #Assumed time interval, s\n",
    "V_biasing=10;  #Biasing voltage, V\n",
    "for t in range(0,151):                  #time interval from 0s to 151s\n",
    "    if(t<=t1):                      \n",
    "        Vin.append(15);               #Value of input voltage for time 0 to t1 seconds \n",
    "    elif(t<=t2 and t>t1):\n",
    "        Vin.append(-30);             #Value of input voltage for time t1 to t2 seconds\n",
    "    else :\n",
    "        Vin.append(15);             #Value of input voltage after t2 seconds\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.plot(Vin);\n",
    "plt.xlim([0,160])\n",
    "plt.ylim([-35,20])\n",
    "plt.xlabel('t-->');\n",
    "plt.ylabel('Vin(V)');\n",
    "plt.title('Input waveform');\n",
    "\n",
    "\n",
    "vout=[];                 #Output voltage waveform, V\n",
    "for v in Vin[:]:                  #Loop iterating input voltage \n",
    "    if(v<=0):\n",
    "        vout.append(0);               #Diode reverse biased\n",
    "    else:\n",
    "        vout.append(v-V_biasing);      #Diode forward biased\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(vout);\n",
    "plt.xlim(0,160)\n",
    "plt.ylim(-35,20)\n",
    "plt.xlabel('t-->');\n",
    "plt.ylabel('Vout(V)');\n",
    "plt.title('Output waveform');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.14 : Page number 492-493"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEZCAYAAABmTgnDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ng4WZmdXkYGFmZjU5WJiZWU3/Hz1pk1JFdhIyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa23c928d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "Vin=[];      #Input voltage waveform, V\n",
    "t1=50;         #Assumed time interval, s\n",
    "t2=100;         #Assumed time interval, s\n",
    "V_biasing=5;  #Biasing voltage, V\n",
    "for t in range(0,151):                  #time interval from 0s to 151s\n",
    "    if(t<=t1):                      \n",
    "        Vin.append(10);               #Value of input voltage for time 0 to t1 seconds \n",
    "    elif(t<=t2 and t>t1):\n",
    "        Vin.append(-10);             #Value of input voltage for time t1 to t2 seconds\n",
    "    else :\n",
    "        Vin.append(0);\n",
    "\n",
    "plt.subplot(211)        \n",
    "plt.plot(Vin);\n",
    "plt.xlim(0,101)\n",
    "plt.ylim(-20,20)\n",
    "plt.xlabel('t-->');\n",
    "plt.ylabel('Vin(V)');\n",
    "plt.title('Input waveform');\n",
    "\n",
    "\n",
    "vout=[];                 #Output voltage waveform, V\n",
    "for v in Vin[:]:                  #Loop iterating input voltage \n",
    "    if(v<=0):\n",
    "        vout.append(v);               #Diode reverse biased\n",
    "    else:\n",
    "        vout.append(v-V_biasing);      #Diode forward biased\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(vout);\n",
    "plt.xlim(0,101)\n",
    "plt.ylim(-20,20)\n",
    "plt.xlabel('t-->');\n",
    "plt.ylabel('Vout(V)');\n",
    "plt.title('Output waveform');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.15 : Page number 493"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEZCAYAAACNebLAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa23cd370b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "Vin=[];      #Input voltage waveform, V\n",
    "t1=50;         #Assumed time interval, s\n",
    "t2=100;         #Assumed time interval, s\n",
    "V_D1=0.6;          #Forward Biasing voltage of the 1st diode, V\n",
    "V_D2=0.6;          #Forward Biasing voltage of the 2nd diode, V\n",
    "for t in range(0,151):                  #time interval from 0s to 151s\n",
    "    if(t<=t1):                      \n",
    "        Vin.append(10);               #Value of input voltage for time 0 to t1 seconds \n",
    "    elif(t<=t2 and t>t1):\n",
    "        Vin.append(-10);             #Value of input voltage for time t1 to t2 seconds\n",
    "    else :\n",
    "        Vin.append(0);\n",
    "\n",
    "plt.subplot(211);\n",
    "plt.plot(Vin);\n",
    "plt.xlim(0,110)\n",
    "plt.ylim(-20,20)\n",
    "plt.xlabel('t-->');\n",
    "plt.ylabel('Vin(V)');\n",
    "plt.title('Input waveform');\n",
    "\n",
    "vout=[];                 #Output voltage waveform, V\n",
    "for v in Vin[:]:                  #Loop iterating input voltage \n",
    "    if(v<=0):\n",
    "        vout.append(-V_D1);               #Diode D1 forward biased, \n",
    "    else:\n",
    "        vout.append(V_D2);      #Diode D2 forward biased\n",
    "\n",
    "plt.subplot(212)        \n",
    "plt.plot(vout);\n",
    "plt.xlim(0,110)\n",
    "plt.ylim(-1,1)\n",
    "plt.xlabel('t-->');\n",
    "plt.ylabel('Vout(V)');\n",
    "plt.title('Output waveform');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.16 : Page number 493-494"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEZCAYAAABmTgnDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa23cac80f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from math import sin\n",
    "from math import pi\n",
    "\n",
    "VZ=20;                   #Assumed zener voltage, V\n",
    "VF=0.7;          #Assumed forward biasing voltage of the zener diode, V\n",
    "Vin=[];      #Input voltage waveform, V\n",
    "for t in range(0,(int)(2*pi*10)):                  #time interval from 0s to 151s\n",
    "    Vin.append(30*sin(t/10.0));\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.plot(Vin);\n",
    "plt.xlabel('t-->');\n",
    "plt.ylabel('Vin(V)');\n",
    "plt.title('Input waveform');\n",
    "\n",
    "\n",
    "vout=[];                 #Output voltage waveform, V\n",
    "for v in Vin[:]:                  #Loop iterating input voltage \n",
    "    if(v<=-VF):\n",
    "        vout.append(-VF);               #Zener diode forward biased, \n",
    "    elif(v>=VZ):\n",
    "        vout.append(VZ);        #Input voltage exceeds zener voltage\n",
    "    else:\n",
    "        vout.append(v);           #Zener diode reverse biased\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(vout);\n",
    "plt.xlim([0,80])\n",
    "plt.ylim([-1,40])\n",
    "plt.xlabel('t-->');\n",
    "plt.ylabel('Vout(V)');\n",
    "plt.title('Output waveform');\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 18.17 : Page number 494"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEZCAYAAABmTgnDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsgghhFCu/w+ip4Q9s46W/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa23c83bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from math import sin\n",
    "from math import pi\n",
    "\n",
    "VZ1=20;                   #Assumed zener voltage, V\n",
    "VF1=0.7;          #Assumed forward biasing voltage of the zener diode, V\n",
    "VZ2=20;                   #Assumed zener voltage, V\n",
    "VF2=0.7;          #Assumed forward biasing voltage of the zener diode, V\n",
    "Vin=[];      #Input voltage waveform, V\n",
    "for t in range(0,(int)(2*pi*10)):                  #time interval from 0s to 151s\n",
    "    Vin.append(30*sin(t/10.0));\n",
    "    \n",
    "plt.subplot(211)\n",
    "plt.plot(Vin);\n",
    "plt.xlabel('t-->');\n",
    "plt.ylabel('Vin(V)');\n",
    "plt.title('Input waveform');\n",
    "\n",
    "\n",
    "vout=[];                 #Output voltage waveform, V\n",
    "for v in Vin[:]:                  #Loop iterating input voltage \n",
    "    if(v<=-(VZ1+VF2)):\n",
    "        vout.append(-(VZ1+VF2));               #Zener diode forward biased, \n",
    "    elif(v>=VZ2+VF1):\n",
    "        vout.append(VZ2+VF1);        #Input voltage exceeds zener voltage\n",
    "    else:\n",
    "        vout.append(v);           #Zener diode reverse biased\n",
    "plt.subplot(212)\n",
    "plt.plot(vout); \n",
    "plt.xlim([0,80])\n",
    "plt.ylim([-40,40])\n",
    "plt.xlabel('t-->');\n",
    "plt.ylabel('Vout(V)');\n",
    "plt.title('Output waveform');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}