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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 2 : Circuit Concepts"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 2.1  Page No : 21"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "from matplotlib.pyplot import subplot,plot,xlabel,ylabel,suptitle\n",
      "from numpy import linspace,arange,array,sin\n",
      "from scipy.integrate import quad\n",
      "import math \n",
      "\n",
      "\n",
      "# Given\n",
      "#resistance used is 4 ohm\")\n",
      "#Current flow is i = 2.5*math.sin(w*t)\")\n",
      "#Angular frequency(w) = 500 rad/s\")\n",
      "R = 4;\n",
      "iamp = 2.5\n",
      "w = 500;\n",
      "t = arange(0,0.012566+0.001,.001)\n",
      "i = 2.5*sin(w*t)\n",
      "\n",
      "# Calculation and Results\n",
      "Vamp = iamp*R;\n",
      "print \"v = %d*math.sin%d*t)V)\"%(Vamp,w)\n",
      "\n",
      "pamp = iamp*iamp*R;\n",
      "print \"p = %d sin%d*t))**2W)\"%(pamp,w)\n",
      "p = pamp*sin(w*t)**2;\n",
      "\n",
      "#On integrating p with respect to t\n",
      "W = 25*(t/2-sin(2*w*t)/(4*w))\n",
      "\n",
      "def f(t):\n",
      "    return pamp*sin(w*t)**2\n",
      "    \n",
      "w1 = quad(f,0,2*math.pi/w)[0];\n",
      "\n",
      "\n",
      "subplot(221)\n",
      "plot(t,i)\n",
      "suptitle ('i vs wt')\n",
      "xlabel('wt')\n",
      "ylabel('i ');\n",
      "\n",
      "subplot(222)\n",
      "plot(t,p)\n",
      "suptitle('p vs wt')\n",
      "xlabel('wt')\n",
      "ylabel('p ');\n",
      "\n",
      "\n",
      "\n",
      "subplot(223)\n",
      "plot(t,W)\n",
      "suptitle ('w vs wt')\n",
      "xlabel('wt')\n",
      "ylabel('w ');\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "v = 10*math.sin500*t)V)\n",
        "p = 25 sin500*t))**2W)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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usAXAS2bVyV4qVJA1KNdeKz/Y+fEaKCfJqFFw440wenTmXAS8ZswYuVP9W2yo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\nAD4HGIY00LovgXwNwAeMlSu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RFEVRFEVRFEVRFCV89DKtgKIoihJ+fjetgKIoihJ8ogs6\nDkRWwAO0Bz5BilMuAN72XzVFUTKNIBV6VNxlCtLQCaTXSiWknlYrYBxSBqUJcK0R7RRFySjUmGQu\n84G/AlWQelQzEKPSGmn0pCiK4hrlTSugeMY+pIrtDUgjqMWIi6sesNycWoqiKErY6I1U822P9Nj4\nGYmXgJSk15sJRVFcQd1cmc1UpIf5DKSt8G6KXVyvIrMVDcAriqIoiqIoiqIoiqIoiqIoiqIoiqIo\niqIoiqIoiqIoiqIoiqIoiqIoiqIoihIM/h9hoa5htwQhEAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bd54f50>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 2.2  Page No : 25"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import math\n",
      "from numpy import arange,sin,cos \n",
      "from matplotlib.pyplot import plot,subplot,xlabel,ylabel,suptitle\n",
      "#Example 2.2\")\n",
      "# Given\n",
      "#Inducmath.tance used is 30mH\")\n",
      "#Current flow is i = 10*math.sin(50*t)\")\n",
      "L = 30*10**-3;\n",
      "iamp = 10;\n",
      "t = arange(0,0.06283+0.01,0.01);\n",
      "i = 10*sin(50*t)\n",
      "#v = L*d/dt(i)\n",
      "#d/dt(math.sin 50t) = 50*math.cos t\n",
      "vamp = L*iamp*50;\n",
      "v = vamp*cos(50*t)\n",
      "\n",
      "#math.sinA*math.cosB = (math.sin(A+B)+math.sin(A-B))/2\n",
      "\n",
      "pamp = vamp*iamp/2;\n",
      "p = pamp*sin(100*t)\n",
      "#On integrating 'p' w.r.t  t\n",
      "\n",
      "wL = 0.75*(1-cos(100*t));\n",
      "\n",
      "\n",
      "subplot(221)\n",
      "plot(t,i)\n",
      "suptitle ('i vs wt')\n",
      "xlabel('wt')\n",
      "ylabel('i');\n",
      "\n",
      "subplot(222)\n",
      "plot(t,v)\n",
      "suptitle ('v vs wt')\n",
      "xlabel('wt')\n",
      "ylabel('c');\n",
      "\n",
      "\n",
      "subplot(223)\n",
      "plot(t,p)\n",
      "suptitle ('p vs wt')\n",
      "xlabel('wt')\n",
      "ylabel('p');\n",
      "\n",
      "subplot(224)\n",
      "plot(t,wL)\n",
      "suptitle ('wL vs wt')\n",
      "xlabel('wt')\n",
      "ylabel('wL');\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jgBeBU4GngJvC/h7zuu+hQ6KdNHasrLsqzvHbb3DppbB4sf+ru+mehjPs3y+1WDp0kOqX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\nUZKP/wf7h0iHn2nZYwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c0773d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 2.3  Page No : 30"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import math\n",
      "from numpy import cos,arange,sin\n",
      "from matplotlib.pyplot import plot,xlabel,ylabel,suptitle \n",
      "\n",
      "#Example 2.3\")\n",
      "# Given\n",
      "#capacitance used is 20uF\")\n",
      "#Voltage is v = 50*math.sin(200*t)\")\n",
      "C = 20*10**-6;\n",
      "# Given that v = 50*math.sin(200*t);\n",
      "vamp = 50;\n",
      "t = arange(0,0.015+0.001,0.001);\n",
      "#q = C*v\n",
      "qamp = vamp*C\n",
      "q = qamp*sin(200*t)\n",
      "#i = C*d/dt(v)\n",
      "#d/dt(math.sin 200t) = 200*math.cos t\n",
      "iamp = C*vamp*200;\n",
      "i = iamp*cos(200*t)\n",
      "\n",
      "#math.sinA*math.cosB = (math.sin(A+B)+math.sin(A-B))/2\n",
      "\n",
      "pamp = vamp*iamp/2;\n",
      "p = pamp*sin(400*t)\n",
      "\n",
      "#On integrating 'p' w.r.t  t\n",
      "\n",
      "wC = 12.5*(1-cos(400*t));\n",
      "\n",
      "plot(t,wC)\n",
      "suptitle ('wC vs wt')\n",
      "xlabel('wt')\n",
      "ylabel('wC (mJ)');\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sMddJgs9VS+JY4PX47eLAy8CQHD8PXUviyy+hVi1YvNj+8YlIsK1aZVvlzJ9v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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bdaab10>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 2.4  Page No : 33"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math \n",
      "\n",
      "\n",
      "# Given\n",
      "#Current through diode is 30mA\")\n",
      "#From the table the nearest value is at v = 0.74V\n",
      "V = 0.74;\n",
      "I = 28.7*10**-3;\n",
      "R = V/I;\n",
      "delV = 0.75-0.73\n",
      "\n",
      "# Calculation\n",
      "delI = 42.7*10**-3-19.2*10**-3\n",
      "r = delV/delI\n",
      "p = (V*I)*10**3\n",
      "\n",
      "# Results\n",
      "print \"  Static resistance is %3.2fohm\"%(R)\n",
      "print \"Dynamic resistance is %3.2fohm\"%(r)\n",
      "print \"Power consumption is %3.2fmW\"%(p)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  Static resistance is 25.78ohm\n",
        "Dynamic resistance is 0.85ohm\n",
        "Power consumption is 21.24mW\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 2.5  Page No : 34"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math \n",
      "\n",
      "# Given\n",
      "#a)\")\n",
      "#Current through diode is 10mA\")\n",
      "#From the table the  value is at v = 2.5V\n",
      "V = 2.5;\n",
      "I = 10*10**-3;\n",
      "R = V/I;\n",
      "delV = 3.-2\n",
      "delI = 11.*10**-3-9*10**-3\n",
      "\n",
      "# Calculation and Results\n",
      "r = delV/delI\n",
      "p = (V*I)*10**3\n",
      "print \"  Static resistance is %3.2fohm\"%(R)\n",
      "print \"Dynamic resistance is %3.2fohm\"%(r)\n",
      "print \"Power consumption is %3.2fmW\"%(p)\n",
      "\n",
      "#b)\")\n",
      "#Current through diode is 15mA\")\n",
      "#From the table the  value is at v = 5V\n",
      "V = 5;\n",
      "I = 15*10**-3;\n",
      "R = V/I;\n",
      "delV = 5.5-4.5\n",
      "delI = 16*10**-3-14*10**-3\n",
      "r = delV/delI\n",
      "p = (V*I)*10**3\n",
      "print \"  Static resistance is %3.2fohm\"%(R)\n",
      "print \"Dynamic resistance is %3.2fohm\"%(r)\n",
      "print \"Power consumption is %3.2fmW\"%(p)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  Static resistance is 250.00ohm\n",
        "Dynamic resistance is 500.00ohm\n",
        "Power consumption is 25.00mW\n",
        "  Static resistance is 333.33ohm\n",
        "Dynamic resistance is 500.00ohm\n",
        "Power consumption is 75.00mW\n"
       ]
      }
     ],
     "prompt_number": 11
    }
   ],
   "metadata": {}
  }
 ]
}