1 files changed, 399 insertions, 0 deletions
diff --git a/OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/Chapter8.ipynb b/OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/Chapter8.ipynb
new file mode 100644
index 00000000..2cb88119
--- /dev/null
+++ b/OpAmps_And_Linear_Integrated_Circuits_by_Gayakwad/Chapter8.ipynb
@@ -0,0 +1,399 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Chapter 8. Comparators and Converters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Example 8.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEZCAYAAABiu9n+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERERERERf/p/LsxYpbABTCwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5548e4e5d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "#Example 8.1\n",
+    "#In the circuit of figure 8-4(a), R1=100 ohm,R2=56 kilo Ohm, Vin=V pp sine wave\n",
+    "#and the opamp is type 741 with supply voltages 15 V, -15 V.\n",
+    "#Determine the threshold voltages Vul and Vut and draw the output waveform.\n",
+    "\n",
+    "from __future__ import division #to perform decimal division\n",
+    "%matplotlib inline\n",
+    "import math\n",
+    "import array\n",
+    "import numpy as np\n",
+    "#Variable declaration\n",
+    "R1=100\n",
+    "R2=56*10**3\n",
+    "vin=1                        #Input voltage in volt\n",
+    "pos_Vsat=14                  #Positive saturation voltage in volt\n",
+    "neg_Vsat=-14                 #Negative saturation voltage in volt\n",
+    "Vut=(R1/(R1+R2))*(pos_Vsat)  #Upper threshold voltage\n",
+    "\n",
+    "\n",
+    "#calculation\n",
+    "Vut=(R1/(R1+R2))*(pos_Vsat)  #Upper threshold voltage\n",
+    "Vlt=(R1/(R1+R2))*(neg_Vsat)  #Lower threshold voltage\n",
+    "\n",
+    "t=np.arange(0,2*math.pi,0.1)\n",
+    "vut=0.5*np.sin(t)\n",
+    "subplot(211)\n",
+    "plt.plot(t,vut)\n",
+    "plt.ylabel('Vin')\n",
+    "plt.xlabel('t')\n",
+    "plt.title(r'$Input voltage$')\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "t1=math.asin(0.025/0.5)\n",
+    "t2=math.pi-math.asin(-0.025/0.5)\n",
+    "t3=2*math.pi\n",
+    "x=[0,t1,t2,t3]\n",
+    "y=[-14,14,-14,14]\n",
+    "plt.subplot(212)\n",
+    "plt.step(x,y)                #Plotting square wave\n",
+    "plt.title('Output Waveform')\n",
+    "plt.xlabel('t')\n",
+    "plt.ylabel('Vo')\n",
+    "\n",
+    "#result\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Example 8.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#Since zener diode is forward biased\n",
+      "Output voltage during positive half-cycle of the input is -0.7 V\n",
+      "Output voltage during negative half-cycle of the input is 5.1 V\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEZCAYAAABiu9n+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/T8NRSbI+0GhoAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f55408e2f50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Example 8.2\n",
+    "#In the circuit of figure 8-7(a), Vin=500 mV peak 60-Hz sinewave, R=100 ohm.\n",
+    "#IN3826 zener with Vz=5.1 V and the supply voltages= 15 V, -15V.\n",
+    "#Determine the output voltage swing.Assume that the  voltage drop across\n",
+    "#the forward biased zener=0.7V.\n",
+    "\n",
+    "%matplotlib inline\n",
+    "from __future__ import division #to perform decimal division\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import math\n",
+    "import array\n",
+    "\n",
+    "#Variable declaration\n",
+    "vin=5*10**-3\n",
+    "R=100\n",
+    "Vd1=-0.7   # Output voltage during positive half-cycle of the input\n",
+    "Vd2=5.1    # Output voltage during negative half-cycle of the input\n",
+    "\n",
+    "#calculation\n",
+    "\n",
+    "\n",
+    "t=np.arange(0,2*math.pi,0.1)\n",
+    "vut=0.5*np.sin(t)\n",
+    "subplot(211)\n",
+    "plt.plot(t,vut)\n",
+    "plt.ylabel('Vin')\n",
+    "plt.xlabel('t')\n",
+    "plt.title(r'$Input voltage$')\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "t1=math.pi\n",
+    "t2=2*math.pi\n",
+    "x=[0,t1,t2]\n",
+    "y=[-0.7,-0.7,5.1]\n",
+    "subplot(2,1,2)\n",
+    "plt.step(x,y)\n",
+    "plt.title('Output Waveform')\n",
+    "plt.xlabel('t')\n",
+    "plt.ylabel('Vo')\n",
+    "\n",
+    "#result\n",
+    "print \"#Since zener diode is forward biased\"\n",
+    "print \"Output voltage during positive half-cycle of the input is\",Vd1,\"V\"\n",
+    "print \"Output voltage during negative half-cycle of the input is\",Vd2,\"V\" \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Example 8.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEZCAYAAAB2AoVaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/x/a2OqnzxDEugAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5522d5d710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output freq Fo is  2000 Hz\n",
+      "Output freq Fo/2 is 1000 Hz\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "#Example 8.3\n",
+    "#The V..F converter of figure 8-12 is initially adjusted for a 10 kHz full scale\n",
+    "#output frequency.Determine the output frequencies Fo and Fo/2 if the output\n",
+    "#signal Vin=2 V.\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.pyplot import subplot\n",
+    "from __future__ import division #to perform decimal division\n",
+    "import math\n",
+    "import array\n",
+    "\n",
+    "\n",
+    "\n",
+    "#Variable declaration\n",
+    "Vin=2       # Input voltage\n",
+    "Fo1=2*10**3 # Output freq Fo when Vin=2V\n",
+    "Fo2=1*10**3 # Output freq Fo/2 when Vin=2V\n",
+    "\n",
+    "#calculation\n",
+    "import array\n",
+    "v=array.array('i',(0 for i in range(0,50)))\n",
+    "\n",
+    "\n",
+    "count=1\n",
+    "for i in range(1,50):   #for 5 cycles\n",
+    " if count<4:\n",
+    "    v[i]=5\n",
+    " else:\n",
+    "    v[i]=0\n",
+    " if count<10:\n",
+    "    count=count+1\n",
+    " else:\n",
+    "    count=1\n",
+    "\n",
+    "\n",
+    "plt.subplot(211)\n",
+    "plt.plot(v)\n",
+    "plt.title('Output Waveform')\n",
+    "plt.xlabel('t(microsec)')\n",
+    "plt.ylabel('Pulse freq output,Fo(V)')\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "for i in range(1,50):   #for 5 cycles\n",
+    " if count<10:\n",
+    "    v[i]=5\n",
+    " else:\n",
+    "    v[i]=0\n",
+    " if count<20:\n",
+    "    count=count+1\n",
+    " else:\n",
+    "    count=1\n",
+    "\n",
+    "plt.subplot(2,1,2)\n",
+    "plt.plot(v)\n",
+    "plt.title('Output Waveform')\n",
+    "plt.xlabel('t(microsec)')\n",
+    "plt.ylabel('Pulse freq output,Fo(V)')\n",
+    "plt.show()\n",
+    "\n",
+    "#result\n",
+    "print \"Output freq Fo is \",Fo1,\"Hz\"\n",
+    "print \"Output freq Fo/2 is\",Fo2,\"Hz\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Example 8.4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output voltage is 0.28 Volts\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "#Example 8.4\n",
+    "#The F/V converterof figure 8-14(a) is initially adjusted for Vo=2.8V at Fin max\n",
+    "#of 10 kHz. Determine the output voltage Vo if fin= 1 kHz.\n",
+    "\n",
+    "#Variable decclaration\n",
+    "Vo=2.8       #At Finmax of 10kHz\n",
+    "\n",
+    "#Calculation\n",
+    "Vo1=Vo/10    #Output voltage at Fin=1kHz\n",
+    "\n",
+    "#Result\n",
+    "print \"Output voltage is\",Vo1,\"Volts\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Example 8.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEZCAYAAAB4hzlwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5522c75350>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Example 8.5\n",
+    "#In the circuit of figure 8-25(a), Vin=200 mV peak to peak sine wave at 100 Hz.\n",
+    "#Briefly describe the operation of the circuit and draw the output waveform.\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from __future__ import division #to perform decimal division\n",
+    "import numpy as np\n",
+    "import math\n",
+    "import array\n",
+    "\n",
+    "\n",
+    "#Variable declaration\n",
+    "Vin=100*10**-3           # Input voltage\n",
+    "\n",
+    "\n",
+    "#calculation\n",
+    "\n",
+    "t=np.arange(0,math.pi,0.1)  #time scale\n",
+    "v=Vin*np.sin(t)\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.xlim(0,2*math.pi)\n",
+    "plt.ylim(0,0.1)\n",
+    "plt.plot(t,v)\n",
+    "plt.ylabel('Vin')\n",
+    "plt.xlabel('t')\n",
+    "plt.title(r'$Input voltage$')\n",
+    "\n",
+    "\n",
+    "#result\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}