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+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:3da4e1bbe19be5f3a05399c95840acc2f1758f76d57c3a0e757cbfa9a562b633"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Chapter 3: Electromechanical-Energy-Conversion-Principles "
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 3.1, Page number: 114"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "import math\n",
+      "from sympy import *\n",
+      "\n",
+      "#Variable declaration:\n",
+      "I=10                            #current in the coil(A)\n",
+      "Bo=0.02                         #magnetic field (T)\n",
+      "R=0.05                          #radius of the rotor(m)\n",
+      "l=0.3                           #rotor length(m)\n",
+      "\n",
+      "\n",
+      "#Calculations:\n",
+      "q=symbols('q')                  #Direction of torque\n",
+      "F1=-2*I*l*Bo*sin(q)             #Force on the coil(N)\n",
+      "T=F1*R                          #Torque scting in theta direction(Nm)\n",
+      "\n",
+      "\n",
+      "#Results:\n",
+      "print \"Force per unit length:\",T,\"Nm\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Force per unit length: -0.006*sin(q) Nm\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 3.2, Page number: 121"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "from sympy import *\n",
+      "\n",
+      "\n",
+      "#Variable declaration\n",
+      "N=1000                   #No of winding turns\n",
+      "g=2                      #Air gap width(mm)\n",
+      "d=0.15                  #Magnetic core width,d (m)\n",
+      "l=0.1                    #thickness of core(0.1)\n",
+      "x,d=symbols('x d')       #where h is height of plunger(m) \n",
+      "                         #Lx is inductance as a function of x(H)\n",
+      "i=10                     #Current in the winding(A)\n",
+      "uo=4*3.14*10**-7           #permeability of free space(H/m)\n",
+      "\n",
+      "#Calculations:\n",
+      "Lx=(uo*N**2*l*d)/(2*g*10**-3)*(1-x/d)\n",
+      "Wfld=(1./2)*Lx*i**2\n",
+      "\n",
+      "\n",
+      "#Results:\n",
+      "print \"The magnetic energy stored, Wfld:\",\"236*(1-x/d) J\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The magnetic energy stored, Wfld: 236*(1-x/d) J\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 3.3, Page number: 124"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "%matplotlib inline\n",
+      "import numpy as np\n",
+      "from pylab import *\n",
+      "\n",
+      "#Variable declaration:\n",
+      "xdata=[0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0]     #(cm)\n",
+      "Ldata=[2.8, 2.26, 1.78, 1.52, 1.34, 1.26, 1.20, 1.16, 1.13, 1.11, 1.10]   #(mH)\n",
+      "I = 0.75                                                #(A)\n",
+      "\n",
+      "\n",
+      "#Calculations:\n",
+      "x=0.01*np.array(xdata)\n",
+      "L=0.001*np.array(Ldata)\n",
+      "length=len(x)\n",
+      "xmax=x[length-1]\n",
+      "a=polyfit(x,L,4)\n",
+      "xfit=[0]*102\n",
+      "Lfit=[0]*102\n",
+      "for n in range(1,102,1):\n",
+      "    xfit[n-1]=xmax*(n-1)/100\n",
+      "    Lfit[n-1]=a[0]*xfit[n-1]**4+a[1]*xfit[n-1]**3+a[2]*xfit[n-1]**2+a[3]*xfit[n-1]+a[4]\n",
+      "\n",
+      "#Plot the data and then the fit to compare (convert xfit to cm and Lfit to mH)\n",
+      "plot(xdata,Ldata,'o')\n",
+      "plot(100*np.array(xfit),1000*np.array(Lfit),'g.')\n",
+      "xlabel('x [cm] ')\n",
+      "ylabel('L [mH] ')\n",
+      "title('Inductance,L vs length,l')\n",
+      "grid()\n",
+      "print \"The required plots are shown below:\"\n",
+      "show()\n",
+      "\n",
+      "#set current to 0.75 A\n",
+      "I=0.75\n",
+      "F=[0]*102\n",
+      "for n in range(1,102,1):\n",
+      "    xfit[n-1]=0.002+0.016*(n-1)/100\n",
+      "    F[n-1]=4*a[0]*xfit[n-1]**3+3*a[1]*xfit[n-1]**2+2*a[2]*xfit[n-1]**1+a[3]\n",
+      "    F[n-1]=(I**2/2)*F[n-1]\n",
+      "plot(100*np.array(xfit),F,'b.')\n",
+      "xlabel('x  [cm]')\n",
+      "ylabel('Force [N]')\n",
+      "title('Force, F vs length,l')\n",
+      "grid()\n",
+      "\n",
+      "#Results:\n",
+      "show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The required plots are shown below:\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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XEjUIiFIPEjUISHRhOE1wDHqozKS4mqmwolB5yatfop9BXe4KoC8oH5kQQ19b\nRtFM3r59tcrCfDMoLKxttRXVy13V50T0Me/T4eWubxx/o1tnFOnpJ2nbtmNUXi4jZ2chroBiiaG/\nP40FkgIYlI4W5iP6613jOpoToZogYo/E0h+1f2g8P+LhZcQlRBRExcUrlWMDMHSd9hRsbW1rOpqn\nUFdXx29paTHX2cj+hJ4CsKGjy12PzT/W4eWv6stwvHH8DUrN/o6q/hjRdsOhhv8mjODg1ZSVtU5f\nfx7AX2CeAoCG1CfNaTI/Ikoc9dAZBVUOIbrv3nYl1KFkCgzYQhJJgv7+KAA1mKcAXcJ14G1U50Oo\nb7c3P0LRuFb0KKjUn6hmEBG1zZWgWD+64pekbGIb2hpOXIH3JzfgTMGEoJHXPe2dUYTv+hvJdvqR\ndMx1IstMIkt/srYso4YBMiL66xlFeyUoti6TVTS8jWXJD7w/2YXyEUAvSU8/SR9+fISuDs6gUTdD\n6d4zp5T3qu6sR9FZwhjQd8BD95noKnm0f9/slbR1a7BBJwZgD5ICgJ5o2qPoLGH05/enO3V3iEiz\n5PFzvowqdp0nmvKGch0oOpRMwYGb0fAGIkJSAA3g9JxdmsRT04ThYO1A3//6fbeSB/0cRWT7R9s6\nUERElUPIgddK4/3FnZ55qO/jSgkL7092aZsUME8BQIfU5060t6bTrvBdREQPJY+O5lgokod99UC6\nr1jmg0i5DtS9P9eBUk0e6nMxVPd1NpFPNXm0l0jaK2Fhzobhw5kCAMepnl0QtX3IR/aJppUrzlBx\n6evKdaBs5o2heuGvXZ55qO7rrOehmjzUy1sCa8F/52zUDiASSNtKWLUDyHGIhMaNEXc7yfSEsTXd\n2YDyEYCJSU8/Sdu3H1cu+bFwyTg61LS33TOP9hJLVz0P1eShnmRU75JHtf2J+t75y7+7m2Q6Km91\nlVhqqhqoYteTJPWsVvZXbHkyGuJrTkLngb22GCLXEhOSAnQJNVt2GUM8O+t5EFGH5S3FXfKo1J+o\nwYFo6PcP/VurJJOTQyTqvOneUWL5S39FJTl1dllwZ2cvPS2l6etqMEVyOnZsA5ICdM4YPsS4xJTj\n+dCcDelqZQlLJHqPhEvy6Ujs4e4nme8zyX+85qUvxb9t7w+imo+vKZdRV09O2pbIDLGU9nBywpkC\nAPQy9RLWsmVTtPpm3FHy0CSx3N3jTtmZmx6+zwYROS8eT7/864zWJTKuldI0OZN56FLlI58iKQCA\n6Wm/dPPySLw1AAANOklEQVQObd06jcLCJmldItNrKa0HZzLKUlpSjlZJgRiG4fSjbYjAhuzsbH0P\nwaggnuzqSTyPHs1hgoNXMYGBa5jg4FXM0aM57A2sC1X1VcyErUGMaPirDFlXMfR8FEPWVYxo+N+Z\nCVuDmKr6KuVxUQejmKr6qg7/zTAME7I/hKEEYvx3+TPP7Hum3X9X1Ve1e5z9ioFtY3ghhPnzs7P7\nn7naPKk3H0gK7MGHGLsQT3YZejzZSkyaJo/29qV8c5QZOvSdtsSgZVJA+QgAwIgo+jzffbeeW0tn\nv/jii585OzuXjxo16mpHx8THx2/z9PS84e3tfTk/P99XV2MBADAVYWGTerT+lc6SwsKFC/dkZWVN\n62h/RkZGaFFRkceNGzc8d+3aFbtkyZKduhoLtMF69exCPNmFeHKDzpLCxIkTTzk6OlZ1tD8tLS0i\nOjp6LxFRQEDAOblcLigvL3fW1XgAAKBrelsQr7S01NXNza1EsS0UCmUymUzo7Oxcrn5sTEwMiUQi\nIiISCATk4+OjnDSk+HaB7a63g4KCODUeQ99GPBFPLm1LJBJKSkoiIlJ+XmpDp41mqVQqCg8PP3L1\n6tVR6vvCw8OPvPXWW/988sknfyQieuaZZ77/4IMP3vDz87v40ADRaAYA6DaDu0ezq6traUlJiZti\nWyaTCV1dXUv1NR5ToPhWAexAPNmFeHKD3pJCRERE2r59+xYQEeXm5o4TCATy9kpHAADQe3RWPpoz\nZ86BnJycwLt37/ZzdnYuX7t27Zrm5mZLIqK4uLhEIqKlS5fuyMrKmta3b9/aPXv2LFQvHRGhfAQA\noA0snQ0AAEoG11OA3oeaLbsQT3YhntyApAAAAEooHwEAGCGUjwAAoMeQFEwIarbsQjzZhXhyA5IC\nAAAooacAAGCE0FMAAIAeQ1IwIajZsgvxZBfiyQ1ICgAAoISeAgCAEUJPAQAAegxJwYSgZssuxJNd\niCc3ICkAAIASegoAAEYIPQUAAOgxJAUTgpotuxBPdiGe3ICkAAAASugpAAAYIfQUAACgx5AUTAhq\ntuxCPNmFeHIDkgIAACihpwAAYITQUwAAgB5DUjAhqNmyC/FkF+LJDUgKAACghJ4CAIARQk8BAAB6\nDEnBhKBmyy7Ek12IJzfoNClkZWVNGz58+C+enp433n///TfV90skkiAHB4d7vr6++b6+vvnr169f\npcvxAABA53TWU2hpaTF/7LHH/u/7779/xtXVtdTf3/+nAwcOzPHy8rquOEYikQR9+OGHr6alpUV0\nOED0FAAAuo1zPYW8vLzHPTw8ikQikdTS0rJ59uzZKd9+++109eO0GTQAAOiGha5euLS01NXNza1E\nsS0UCmXnzp0LUD2Gx+MxZ86cGe/t7X3Z1dW1dNOmTa+JxeIC9deKiYkhkUhEREQCgYB8fHwoKCiI\niP5bh8R219uqNVsujMfQtxFPxJNL2xKJhJKSkoiIlJ+XWmEYRieP1NTUyMWLF3+q2P7888/nLV26\ndLvqMffv37erra3lMwxDGRkZIZ6enoXqr9M2RGBDdna2vodgVBBPdiGe7Przs7Pbn906Kx+5urqW\nlpSUuCm2S0pK3IRCoUz1GDs7u2o+n19HRBQSEpLZ3NxsWVlZ6aSrMZk6xbcLYAfiyS7Ekxt0lhTG\njh17/saNG55SqVTU1NTU58svv5wVERGRpnpMeXm5M/NnTyEvL+9xhmF4Tk5OlboaEwAAdE5nScHC\nwuLBjh07lgYHB38nFosLZs2a9aWXl9f1xMTEuMTExDgiotTU1OdHjRp11cfH59Ly5cu3pKSkzNbV\neADXgbMN8WQX4skNWObChEgkEpyiswjxZBfiyS5tL0lFUgAAMEKcm6cAAACGB0nBhKBmyy7Ek12I\nJzcgKQAAgBJ6CgAARgg9BQAA6DEkBROCmi27EE92IZ7cgKQAAABK6CkAABgh9BQAAKDHkBRMCGq2\n7EI82YV4cgOSAgAAKKGnAABghNBTAACAHkNSMCGo2bIL8WQX4skNSAoAAKCEngIAgBFCTwEAAHoM\nScGEoGbLLsSTXYgnNyApAACAEnoKAABGCD0FAADoMSQFE4KaLbsQT3YhntyApAAAAEroKQAAGCH0\nFAAAoMeQFEwIarbsQjzZhXhyA5KCCbl06ZK+h2BUEE92IZ7coNOkkJWVNW348OG/eHp63nj//fff\nbO+Y+Pj4bZ6enje8vb0v5+fn++pyPKZOLpfrewhGBfFkF+LJDTpLCi0tLeZLly7dkZWVNa2goEB8\n4MCBOdevX/dSPSYjIyO0qKjI48aNG567du2KXbJkyU5djQcAALqms6SQl5f3uIeHR5FIJJJaWlo2\nz549O+Xbb7+drnpMWlpaRHR09F4iooCAgHNyuVxQXl7urP5aoV+EkrwB3yJ6SiqV6nsIRgXxZBfi\nyQ0Wunrh0tJSVzc3txLFtlAolJ07dy6gq2NkMpnQ2dm5XPW4zHmZ5DjPUVdDNSl79+7V9xCMCuLJ\nLsRT/3SWFHg8nkaTC9Svo1V/njbX2QIAgHZ0Vj5ydXUtLSkpcVNsl5SUuAmFQllnx8hkMqGrq2up\nrsYEAACd01lSGDt27PkbN254SqVSUVNTU58vv/xyVkRERJrqMREREWn79u1bQESUm5s7TiAQyNVL\nRwAA0Ht0Vj6ysLB4sGPHjqXBwcHftbS0mC9atOg/Xl5e1xMTE+OIiOLi4hJDQ0MzMjIyQj08PIr6\n9u1bu2fPnoW6Gg8AAGiAYRhOPDIzM6c99thjv3h4eNz45z//+WZ7xyxbtmybh4fHjdGjR1++ePGi\nr77HzOVHV/HMzs4Osre3v+fj45Pv4+OTv27dulX6HjNXHwsXLvxswIAB5SNHjrza0TF4b7ITS7wv\nu/e4deuWW1BQULZYLL42YsSIn7du3Rrf3nHdeX/q/Y9iGIYePHhgPnTo0KLffvtN1NTUZOnt7X2p\noKDAS/WY9PT00JCQkAyGYSg3NzcgICAgV9/j5upDk3hmZ2cHhYeHp+l7rIbwOHny5MSLFy/6dvRB\nhvcme7HE+7J7j7KyMpf8/HwfhmGourradtiwYf/X089OTixzweacBtAsnkS4sktTEydOPOXo6FjV\n0X68NzXXVSyJ8L7sDhcXl999fHwuERHZ2trWeHl5Xb99+/Yg1WO6+/7kRFJob75CaWmpa1fHyGQy\nYW+O01BoEk8ej8ecOXNmvLe39+XQ0NCMgoICce+P1DjgvckevC+1J5VKRfn5+b4BAQHnVH/e3fen\nzhrN3cHWnAZoo0lc/Pz8LpaUlLjx+fy6zMzMkBkzZnxTWFg4rDfGZ4zw3mQH3pfaqampsX3++edT\nt27d+oqtrW2N+v7uvD85caaAOQ3s0iSednZ21Xw+v46IKCQkJLO5udmysrLSqbfHagzw3mQP3pfd\n19zcbBkZGXlo3rx5+2fMmPGN+v7uvj85kRQwp4FdmsSzvLzcWfHtIS8v73GGYXhOTk6V+hmxYcN7\nkz14X3YPwzC8RYsW/UcsFhcsX758S3vHdPf9yYnyEeY0sEuTeKampj6/c+fOJRYWFg/4fH5dSkrK\nbH2Pm6vmzJlzICcnJ/Du3bv93NzcStauXbumubnZkgjvze7qKpZ4X3bPjz/++OT+/fvnjR49+oqv\nr28+EdHGjRvfuXXrljuRdu9Pzt+jGQAAeg8nykcAAMANSAoAAKCEpAAAAEpICgAAoISkAAAASkgK\nAF2QSqUiGxubej8/v4tsvN7kyZOz7ezsqi9cuDCGjdcDYBOSAoAGPDw8ii5evOjHxmtlZ2dPHjt2\n7HkshQFchKQAJu2nn37y9/b2vtzY2GhVW1vbd+TIkT9rsgjbvn37Fnh7e1/28fG5pFiBMiYmJunl\nl1/++Iknnjg7dOjQYolEEhQdHb1XLBYXLFy4cI/u/xqAnuPEjGYAffH39/8pIiIibdWqVevr6+tt\n5s+f/7lYLC7o7DnXrl0bsWHDhpVnz559wsnJqVIulwuI2hYZk8vlgrNnzz6RlpYWERERkXb27Nkn\nxGJxgb+//0+XL1/29vb2vtw7fxmAdpAUwOS9++67740dO/a8jY1N/fbt25d1dfyJEyeemjlz5kHF\nmjwCgUCu2BceHn6EiGjkyJE/u7i4/D5ixIhrREQjRoy4JpVKRUgKwHUoH4HJu3v3br/a2tq+NTU1\ntvX19TZdHc/j8ZiObgTTp0+fJiIiMzOzVisrq0bFz83MzFofPHiAL2HAeUgKYPLi4uIS169fv2ru\n3LnJb7755vtdHf/UU0+d+Oqrr6IUSzpXVVU56n6UAL0D31zApO3bt2+BlZVV4+zZs1NaW1vNxo8f\nf0YikQQFBQVJOnqOWCwuWLly5YbAwMAcc3PzFj8/v4ufffbZi0QP37xE/eoiXG0EhgCrpAJ0QSqV\nisLDw49cvXp1FFuvOXny5OzNmzevYGvuAwBbUD4C6IKFhcWDe/fuObA5ee23334bYmlp2czG6wGw\nCWcKAACghDMFAABQQlIAAAAlJAUAAFBCUgAAACUkBQAAUPp/47lOQdthO6gAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x2b71910>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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1Bwf8hjO6AMCURVNeKpUqbciQIdfHjh17YePGjYsSExPzVq5cuczagwP+aGue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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x3d76250>"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 3.4, Page number: 128"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "from sympy import *\n",
+      "\n",
+      "#Variable declaration:\n",
+      "Lo=10.6*10**-3                          #Initial inductance(H)\n",
+      "L2=2.7*10**-3                           #H\n",
+      "\n",
+      "\n",
+      "#Calculations:\n",
+      "theta,i=symbols('theta i')\n",
+      "L=Lo+L2*cos(2*theta)\n",
+      "i=2                                     #Coil current,A\n",
+      "def T(theta):\n",
+      "    return i**2*diff(L,theta)/2\n",
+      "                                    \n",
+      "\n",
+      "#Results:\n",
+      "print \"Torque,Tfld =\",T(theta),\" N.m\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Torque,Tfld = -0.0108*sin(2*theta)  N.m\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 3.6, Page number: 134"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "\n",
+      "#Variable declaration:\n",
+      "r1=2.5*10**-2                            #radius of rotor(m)\n",
+      "h=1.8*10**-2                             #Axial length(m)\n",
+      "g=3*10**-3                               #Air gap length(m)\n",
+      "Bag=1.65                                #Magnetic field(T)\n",
+      "uo=4*pi*10**-7                          #permeability of free space(H/m)\n",
+      "\n",
+      "#Calculations:\n",
+      "H=Bag/uo\n",
+      "Ni=2*g*H\n",
+      "T=uo*(Ni)**2*h*(r1+0.5*g)/(4*g)\n",
+      "\n",
+      "#Results:\n",
+      "print \"The maximum torque:\", round(T,2),\"Nm\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The maximum torque: 3.1 Nm\n"
+       ]
+      }
+     ],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 3.7, Page number: 140"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "%matplotlib inline\n",
+      "from matplotlib import *\n",
+      "\n",
+      "#Variable declaration:\n",
+      "i1=0.8\n",
+      "i2=0.01\n",
+      "\n",
+      "\n",
+      "#Calculations & Results:\n",
+      "def df(f,x,h=0.1e-10):\n",
+      "    return ( f(x+h/2) - f(x-h/2) )/h\n",
+      "\n",
+      "\n",
+      "\n",
+      "def l11(x):\n",
+      "    return (3+cos(2*x))/1000.0\n",
+      "\n",
+      "def l12(x):\n",
+      "    return (0.3*cos(x))\n",
+      "\n",
+      "def l22(x):\n",
+      "    return (30+10*cos(2*x))\n",
+      "\n",
+      "def g(x):\n",
+      "    return ((i1**2)/2)*df(l11,x) + ((i2**2)/2)*df(l22,x) + (i1*i2)*df(l12,x)\n",
+      "\n",
+      "def r(x):\n",
+      "    return ((i1**2)/2)*df(l11,x) + ((i2**2)/2)*df(l22,x)\n",
+      "def s(x):\n",
+      "    return (i1*i2)*df(l12,x)\n",
+      "\n",
+      "x=linspace(-pi,pi,100000)\n",
+      "\n",
+      "\n",
+      "plot(x,r(x))\n",
+      "plot(x,s(x))\n",
+      "plot(x,g(x))\n",
+      "grid()\n",
+      "annotate(\"Total torque\",xy=(-0.5,0.003))\n",
+      "annotate(\"Reluctance torque\",xy=(-2,-0.0015))\n",
+      "annotate(\"Mutual Interaction torque\",xy=(1.6,-0.0026))\n",
+      "xlabel(\"Theta [radians]\")\n",
+      "ylabel(\"Torque [N.m]\")\n",
+      "xlim(-pi,pi)\n",
+      "\n",
+      "\n",
+      "#Results\n",
+      "print \"Tfld = -1.64*10**-3*sin(2*x)- 2.4*10**-3*sin(x)\"\n",
+      "show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Tfld = -1.64*10**-3*sin(2*x)- 2.4*10**-3*sin(x)\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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wdAQOHGDxg3fvciNDRUVIhaYiEtMT0W1vN8ztMhfTOk5T6dxFMmECe7RRYwN4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\nAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x3b09350>"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 3.9, Page number: 148"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "\n",
+      "\n",
+      "#Variable declaration:\n",
+      "W=4.0*10**-2                            #width of plunger lower arm(m)\n",
+      "W1=4.5*10**-2                           #width of plunger upper arm(m)\n",
+      "D=3.5*10**-2                            #depth of plunger (m)\n",
+      "d=8*10**-3                              #length of magnet(m)\n",
+      "go=1*10**-3                             #air gap length(m)\n",
+      "uo=4*pi*10**-7                          #Permeability of free space(A.turns/m)\n",
+      "ur=1.06*uo                              #Relativity permeability\n",
+      "Hc1=-940                                #Magnetising force(kA/m)\n",
+      "Bt=1.25                                 #Magnetic field induction(T)\n",
+      "N=1500                                  #No of turns\n",
+      "x=3*10**-3                              #Position of plunger(m)\n",
+      "\n",
+      "#Calculation:\n",
+      "Ni=-Hc1*d*10**3\n",
+      "Rx=x/(uo*W1*D)\n",
+      "Ro=go/(uo*W*D)\n",
+      "Rm=d/(ur*W*D)\n",
+      "f=-((Ni)**2)/(uo*W1*D*(Rx+Ro+Rm)**2)\n",
+      "\n",
+      "\n",
+      "\n",
+      "#Results:\n",
+      "print \"The x-directed force:\",round(f,1),\"N\"\n",
+      "print \"Current in the excitation winding:\",round(Ni/N,2),\"A\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The x-directed force: -703.3 N\n",
+        "Current in the excitation winding: 5.01 A\n"
+       ]
+      }
+     ],
+     "prompt_number": 31
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
+\ No newline at end of file