{
 "metadata": {
  "name": "",
  "signature": "sha256:1e57dcb73985a42b8c9ee1409e50d4315b6a10f8a73cdae8a91714580c2fe346"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 2: Introduction to Engineering Calculations"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 2.2-1, page no. 10"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "#Initialization of variables\n",
      "import math\n",
      "import numpy\n",
      "from numpy import linalg\n",
      "\n",
      "AcclInitial=1.0           #cm/s^2\n",
      "\n",
      "#Calculations and printing :\n",
      "print(\" All the values in the textbook are Approximated hence the values in this code differ from those of Textbook\")\n",
      "AcclFinal=AcclInitial*math.pow(3600*24*365,2)/math.pow(10,5)\n",
      "#the calculations involved are the conversion factors\n",
      "print '%s %.3E' %(\" \\n final acceleration (Km/Yr^2) = \",AcclFinal)\n",
      "raw_input('press enter key to exit')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " All the values in the textbook are Approximated hence the values in this code differ from those of Textbook\n",
        " \n",
        " final acceleration (Km/Yr^2) =  9.945E+09\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "press enter key to exit\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "''"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 2.3-1, page no. 11"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "#Initialization of variables\n",
      "import math\n",
      "import numpy\n",
      "from numpy import linalg\n",
      "\n",
      "Initial=23.0  #lb.ft/min^2\n",
      "\n",
      "#Calculations and printing :\n",
      "\n",
      "Final=Initial*0.453593*100/(3.281*60*60)\n",
      "#the calculations involved are conversion factors\n",
      "print '%s %.4f' %(\"final (kg.cm/s^2) = \",Final)\n",
      "raw_input('press enter key to exit')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "final (kg.cm/s^2) =  0.0883\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "press enter key to exit\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "''"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 2.4-1, page no. 13"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "#Initialization of variables\n",
      "import math\n",
      "import numpy\n",
      "from numpy import linalg\n",
      "\n",
      "density=62.4            #lbm/ft^3\n",
      "volume=2.0              #ft^3\n",
      "\n",
      "#Calculations and printing :\n",
      "mass=volume*density\n",
      "print '%s %.3f' %(\"mass of the water = volume x density(lbm) = \",mass)\n",
      "print(\" \\n At sealevel, g=32.174 ft/s^2\")\n",
      "g=32.174\n",
      "weight=mass*g/32.174\n",
      "print '%s %.3f' %(\" \\n weight at sealevel (lbf) = \",weight)\n",
      "print(\" \\n At denver, g=32.139 ft/s^2\")\n",
      "g=32.139\n",
      "weight=mass*g/32.174\n",
      "print '%s %.3f' %(\"\\n weight at denver (lbf) = \",weight)\n",
      "#the division with 32.174 is to convert lbm.math.pow(ft/s,2) to lbf\n",
      "raw_input('press enter key to exit')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "mass of the water = volume x density(lbm) =  124.800\n",
        " \n",
        " At sealevel, g=32.174 ft/s^2\n",
        " \n",
        " weight at sealevel (lbf) =  124.800\n",
        " \n",
        " At denver, g=32.139 ft/s^2\n",
        "\n",
        " weight at denver (lbf) =  124.664\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "press enter key to exit\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "''"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 2.5-2, page no. 19"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      " \n",
      "#Initialization of variables\n",
      "import math\n",
      "import numpy\n",
      "from numpy import linalg\n",
      "#the badbatches per week are taken as elements of a vector y\n",
      "\n",
      "y=[17, 27, 18, 18, 23, 19, 18, 21, 20, 19, 21, 18]\n",
      "\n",
      "#Calculations and printing :\n",
      "#Here We used standard library functions mean and st_deviation\n",
      "ybar=numpy.mean(y)\n",
      "sy=numpy.std(y)\n",
      "defaultvalue=ybar+3*sy+1\n",
      "print '%s %d' %(\"the maximum allowed value of y i.e. bad batches in a week is \", defaultvalue+1)\n",
      "print(\" \\n in case of 2 standard deviations\")\n",
      "defaultvalue=ybar+2*sy+1\n",
      "print '%s %d' %(\"the limiting value of y i.e. bad batches in a week is \",defaultvalue)\n",
      "raw_input('press enter key to exit')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "the maximum allowed value of y i.e. bad batches in a week is  29\n",
        " \n",
        " in case of 2 standard deviations\n",
        "the limiting value of y i.e. bad batches in a week is  26\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "press enter key to exit\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "''"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 2.7-1, page no. 24"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "#Initialization of variables\n",
      "import math\n",
      "import numpy\n",
      "from numpy import linalg\n",
      "from matplotlib import pyplot\n",
      "%matplotlib inline\n",
      "x=numpy.array([10., 30., 50., 70., 90.])\n",
      "y=numpy.array([20., 52.1, 84.6, 118.3, 151.0])\n",
      "\n",
      "#Calculations and printing :\n",
      "#this program uses least squares fit to solve for slope and intercept.\n",
      "#hence the value differs from textbook a bit.\n",
      "sx=sum(x)\n",
      "sx2=sum(x*x)\n",
      "sy=sum(y)\n",
      "D=numpy.transpose(y)\n",
      "sxy=sum(x*D)\n",
      "n=len(x)\n",
      "A=([[sx,n],[sx2,sx]])\n",
      "B=([[sy],[sxy]])\n",
      "p=numpy.dot(linalg.inv(A),B)\n",
      "m=p[0,0]\n",
      "b=p[1,0]\n",
      "yf=m*x+b\n",
      "pyplot.plot(x,yf)\n",
      "pyplot.xlim(min(x)-1,max(x)+1)\n",
      "pyplot.ylim(min(yf)-1,max(yf)+1)\n",
      "pyplot.xlabel('R')\n",
      "pyplot.ylabel('V')\n",
      "pyplot.show()\n",
      "print(\"in case 2, R=36\")\n",
      "R=36\n",
      "V=m*R+b\n",
      "print '%s %.3f' %(\"then V (L/min) =\",V),\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEKCAYAAADw2zkCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2da0750>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "in case 2, R=36\n",
        "then V (L/min) = 62.226\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 2.7-2, page no. 26"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "#Initialization of variables\n",
      "import math\n",
      "import numpy\n",
      "from numpy import linalg\n",
      "from matplotlib import pyplot\n",
      "%matplotlib inline\n",
      "\n",
      "T=[10., 20., 40., 80.]\n",
      "M=[14.76, 20.14, 27.73, 38.47]\n",
      "sqrtT=numpy.sqrt(T)\n",
      "\n",
      "\n",
      "#Calculations and printing :\n",
      "sx=sum(sqrtT)\n",
      "sx2=sum(T)\n",
      "sy=sum(M)\n",
      "D=numpy.transpose(M)\n",
      "sxy=sum(sqrtT*D)\n",
      "n=len(T)\n",
      "A=([[sx,n],[sx2,sx]]) \n",
      "B=([[sy],[sxy]])\n",
      "p=numpy.dot(linalg.inv(A),B)\n",
      "a=p[0,0]\n",
      "b=p[1,0]\n",
      "yf=a*sqrtT+b\n",
      "x=sqrtT\n",
      "pyplot.plot(x,yf)\n",
      "pyplot.xlim(min(x)-1,max(x)+1)\n",
      "pyplot.ylim(min(yf)-1,max(yf)+1)\n",
      "pyplot.xlabel('sqrtT')\n",
      "pyplot.ylabel('M')\n",
      "pyplot.show()\n",
      "print '%s %.3f' %(\"slope (g/s*K^.5) =\",a)\n",
      "print '%s %.3f' %(\"\\n intercept (g/s) =\",b)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEKCAYAAADzQPVvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHy9JREFUeJzt3XlY1mW+x/H345aWWyBiqalTYS6Qj5NLrg8dzTljmhbh\nTp2ypr2wvabEsbRjmUvZYtpqapqmuVGaAi6gZpnWTOqYmkshSOYCiMDv/HE3nCxARR7uZ/m8rotr\nFI0+XU6fbr6/+3ffLsdxHEREJKBVsh1ARES8T2UvIhIEVPYiIkFAZS8iEgRU9iIiQUBlLyISBKrY\nDlAcj8dDcnKy7RgiIn6le/fuJCUlFftrPrmyT05OxnEcn/oYOXKk9QzKqqzKqqylfZS2SPbJshcR\nkfKlshcRCQIq+zPk8XhsRzhjyuodyuodyloxXI7j+NzZOC6XCx+MJSLi00rrTq3sRUSCgMpeRKQE\nJ0/Ct9/aTlE+VPYiIsXIyoK//AXGjrWdpHyo7EVEfudf/4L27aFtW3j3XdtpyodPvkErImLLsmVw\n883wwgvmfwOFyl5EBHAceOklGD8eFiyATp1sJypfKnsRCXonTsCdd8JXX0FaGlxyie1E5U8zexEJ\nagcPwjXXwJEjsHZtYBY9qOxFJIh9/bV5ENujB8ydCxdcYDuR92iMIyJB6eOP4Y474JVXYMAA22m8\nT2UvIkHFcWDMGHj9dbPz5qqrbCeqGCp7EQkaOTlw663w/fewfj1cfLHtRBVHM3sRCQr790O3blC5\nMiQlBVfRg8peRILAhg3QoQPccAO8/z7UqGE7UcXTGEdEAtqsWXD//TBtGlx/ve009qjsRSQgFRbC\nM8/ABx/A559DVJTtRHap7EUk4Bw7BsOGQWamGeGEhdlOZJ9m9iISUPbsgc6dITTUrOhV9IbKXkQC\nxpo10LGj2V755ptQrZrtRL5DYxwRCQhvvQWPP2522/TqZTuN71HZi4hfKyiARx+FRYsgJQWuuMJ2\nIt+kshcRv/XLLzBwoLkrNi0NQkJsJ/JdmtmLiF/ascPM5y+7zJxxo6IvncpeRPzO559Dly7w4IPw\n8stQtartRL5PYxwR8Suvvgr/+Ad8+CF4PLbT+A+VvYj4hZMn4YEHIDnZ3Ch16aW2E/kXlb2I+LxD\nh+Cmm+D88yE1FWrXtp3I/2hmLyI+7Z//NCdWXnUVLFyooi8rr5V9bm4u7dq1w+12ExERQXx8PAAJ\nCQk0atQIt9uN2+0mMTHRWxFExM8tXWrm8k8/DePGmbPopWxcjuM43vriOTk51KhRg/z8fLp06cLY\nsWNJSUmhVq1ajBgxouRQLhdejCUiPs5xYPx4eOklmDcPrr7adiL/UFp3enVmX+PXGwLy8vIoKCgg\nPDwcQEUuIiU6cQL+9jf4+mvzotQll9hOFBi8OrMvLCykTZs2hIeHEx0dTcuWLQGYMmUKLVq0YOjQ\noWRlZXkzgoj4kfR0uOYac0TxmjUq+vLk1THOf/zyyy/06tWL559/ntatWxMaGgqY+f3OnTuZMWPG\nqaFcLkaOHFn0c4/Hg0cbakUC2ubN5iapW26BkSOhkraPnFZSUhJJSUlFPx81alSJk5MKKXuA0aNH\nU7VqVR5//PGizx04cIDo6Gi2bdt2aijN7EWCyvz5ZnQzZQrExtpO479K606v/bfz0KFDHD16FDAP\napcvX05kZCQZGRlFv2fevHm0atXKWxFExMc5Djz7rHlZKjFRRe9NXntAe+DAAeLi4nAch9zcXAYP\nHkzv3r0ZNmwYW7ZsIS8vjyZNmjB9+nRvRRARH5adDbfdBt9/b64OvOgi24kCW4WNcc6GxjgigW3/\nfjOfv+IKc6PUrxv35BxZGeOIiBRnwwbzRmxMjLlVSkVfMXQ2johUmJkzzXx+2jSzspeKo7IXEa8r\nLDRHHsycCStXQmSk7UTBR2UvIl519CgMGwZZWWaEExZmO1Fw0sxeRLxm927o3NkU/IoVKnqbVPYi\n4hWrV5sDzG67DaZOhWrVbCcKbhrjiEi5mz4dnnjC7Lbp1ct2GgGVvYiUo/x8ePRRWLzYrOybN7ed\nSP5DZS8i5eLwYRg4EAoKYP16uPBC24nktzSzF5FztmMHdOwIERGwbJmK3hep7EXknKxYAV26QHw8\nTJ4MVTQv8En6YxGRMnEcePVVGD0aPvzQ3BUrvktlLyJn7eRJuP9+SEmBdevgT3+ynUhOR2UvImfl\n0CFziFnNmpCaCrVr204kZ0IzexE5Y99+C+3bm48FC1T0/kQrexE5I0uWwP/8D7z4IsTF2U4jZ0tl\nLyKlchwYPx4mTICFC80RCOJ/VPYiUqITJ8xF4F9/bebzl1xiO5GUlWb2IlKs9HSIjoZjx2DNGhW9\nv1PZi8gffPWVeQh77bUwZw5ccIHtRHKuNMYRkVPMmwd33glTpkBsrO00Ul5U9iICmAexzz4Lb74J\niYnw5z/bTiTlSWUvImRnm22Ve/aYEysvush2IilvmtmLBLn9+6FbN6haFZKSVPSBSmUvEsTWr4cO\nHczxB++/D9Wr204k3qIxjkiQ+uADePBBc4Vg376204i3qexFgkxhIfz97zB7NqxcCZGRthNJRVDZ\niwSRo0dh2DD4+WczwgkLs51IKopm9iJBYvdu6NzZFPzy5Sr6YKOyFwkCKSnmALPhw2HqVKhWzXYi\nqWga44gEuOnT4YknYMYMc/yBBCeVvUiAys+HRx4x59CvXg3Nm9tOJDap7EUC0OHDMHAgFBSYB7EX\nXmg7kdimmb1IgNm+HTp2NCv5ZctU9GJ4rexzc3Np164dbrebiIgI4uPjAcjKyqJnz55ERUXRq1cv\nDh8+7K0IIkFnxQro2hVGjIBJk6CKvneXX7kcx3G89cVzcnKoUaMG+fn5dOnShbFjxzJ//nwuvfRS\nHnzwQSZOnMiuXbuYNGnSqaFcLrwYSyTgOI45kvjZZ83LUh6P7URiQ2nd6dUxTo0aNQDIy8ujoKCA\n+vXrs3TpUoYNGwbA0KFDWbJkiTcjiAS8kyfhrrvg9ddh3ToVvRTPq2VfWFhImzZtCA8PJzo6mlat\nWpGRkUFoaCgA9erV4+DBg96MIBLQMjOhZ09zcuW6dfCnP9lOJL7Kq2VfqVIlNm/ezL59+0hJSWHV\nqlXe/NuJBJVvvzUnVnbsCAsWQO3athOJL6uQxzd16tShd+/erF+/nrCwMDIzM6lXrx4ZGRnUr1+/\n2L8mISGh6McejwePvjcVKbJ4sbls5KWXzFk3EpySkpJISko6o9/rtQe0hw4dolq1atSqVYucnBx6\n9erFY489RmJiYtED2gkTJrBr1y4mT558aig9oBUpluPAiy/CxInw0UfmCASR/yitO71W9lu3biUu\nLg7HccjNzWXw4ME888wzZGVlMWDAANLT02nQoAFz5syhbt26ZxxYJFjl5sLf/gZbt8LChdC4se1E\n4muslP25UNmLnOqnn+CGG+Dii+Hdd+GCC2wnEl9kbeuliJy7r74yD2KvvRbmzFHRS9no/ToRHzZv\nHtx5J7z6Ktx0k+004s9U9iI+yHFg9GiYNg0+/RTatrWdSPydyl7Ex2Rnm22Ve/aYEysvush2IgkE\nmtmL+JB9+8xBZtWqQVKSil7Kj8pexEesX28exMbGwnvvQfXqthNJINEYR8QHzJgB8fHw1lvQp4/t\nNBKIVPYiFhUWwlNPwYcfwqpV0Lq17UQSqFT2IpYcPQpDhsAvv5gRTliY7UQSyDSzF7Fg1y7o1AnC\nw2H5chW9eJ/KXqSCpaSYA8xuvx2mTjU7b0S8TWMckQo0bRo8+aR5IHvttbbTSDBR2YtUgPx8ePhh\nWLYMVq+G5s1tJ5Jgo7IX8bLDh2HAAHMEQloaXHih7UQSjDSzF/Gi7dvNi1JXXAFLl6roxR6VvYiX\nLF9ujj546CGYNAmq6PtosUj/9xMpZ44Dr7wCzz1nzp/v3t12IhGVvUi5ysuD++6DtWshNRWaNbOd\nSMRQ2YuUk8xMiImBWrVg3TqoXdt2IpH/p5m9SDn45hto3x46doQFC1T04nu0shc5R4sXm8tGXnoJ\nhg2znUakeCp7kTJyHHjhBbPTZtEis6oX8VUqe5EyyM2FO+4w45u0NGjc2HYikdKVWPZ9+vTB5XLh\nOM4ffs3lcvHJJ594NZiIr/rpJ+jfHxo1MkcfXHCB7UQip1di2aelpdGoUSMGDRpEhw4dAIqK3+Vy\nVUw6ER/z5ZfQrx/cdhs8/TRU0hYH8RMup7ilO5Cfn8/y5cuZNWsWW7dupXfv3gwaNIhWrVp5P1QJ\n31GI2PTRR3DXXfDaa2aLpYivKa07Syz73zpx4gSzZs3i4YcfJiEhgXvvvbfcQ54SSmUvPqSwEEaP\nhunTzbbKtm1tJxIpXmndWeoD2tzcXJYsWcLs2bPZvXs3DzzwAP379/dKSBFflJ0Nt9wCe/fChg3Q\noIHtRCJlU+LKftiwYXz77bf89a9/ZcCAAURGRlZcKK3sxQfs2wfXXw+tWpkbpapXt51IpHRlGuNU\nqlSJC0rYZuByuThy5Ej5JSzm66vsxaa0NLjxRnjgAXjkEdCeBPEHZRrjFBYWei2QiC97/30YMQLe\nfhuuu852GpHyoZeqRH5VUABPPWWOJV61Clq3tp1IpPyo7EWAo0dhyBA4csQ8iK1Xz3YikfKlV0Ik\n6O3aBZ06mZ02n32mopfApLKXoJacbIr+jjvgjTegWjXbiUS8w2tlv3fvXrp160ZkZCTNmzdn3Lhx\nACQkJNCoUSPcbjdut5vExERvRRAp1ZtvQmwsvPeeuV1KO24kkJ3RG7RlkZ6eTkZGBq1bt+bYsWO0\nbduWuXPnsmDBAmrVqsWIESNKDqWtl+JF+fnmEvDERHM0cUSE7UQi5aPMb9Cei/DwcMLDwwGoWbMm\nUVFR7N+/H0BFLtb8/DMMGGBW8evXQ926thOJVIwKmdnv3r2bjRs30rVrVwCmTJlCixYtGDp0KFlZ\nWRURQYTt280FIy1bwpIlKnoJLl4b4/zHsWPHiI6O5qmnnqJfv35kZmYSGhoKmPn9zp07mTFjxqmh\nXC5GjhxZ9HOPx4PH4/FmTAlwn31mrgx87jkYPtx2GpHykZSURFJSUtHPR40adW6nXpbVyZMnue66\n6/jLX/5CfHz8H379wIEDREdHs23btlNDaWYv5cRx4OWXYcwY87JUt262E4l4j5WZveM43HbbbbRs\n2fKUoj948CD169cHYN68eRVyPr4Ep7w8uPdeSE01H82a2U4kYo/XVvZr1qyhW7duREVFFd1sNWbM\nGGbOnMmWLVvIy8ujSZMmTJ8+nYYNG54aSit7OUeZmeYgs7p1YcYMqFXLdiIR7zvny0sqmspezsU3\n30DfvmbXzXPP6epACR5WxjgiNixaZO6HfeklGDrUdhoR36Gyl4DgODBuHEyebAq/QwfbiUR8i8pe\n/F5uLtx+O/zzn+ZFqUaNbCcS8T2aZopf+/FH8HjMzpvVq1X0IiVR2Yvf+vJLM67p3Rtmz4bzz7ed\nSMR3aYwjfmnuXLj7bnj9dbPFUkRKp7IXv1JYCP/4h7kf9rPPwO22nUjEP6jsxW8cPw633AL795sH\nsQ0a2E4k4j80sxe/sHcvdO1q5vKrVqnoRc6Wyl58XmqqeRA7eDC88w6cd57tRCL+R2Mc8Wnvv29u\nlXrrLbjuOttpRPyXyl58UkEBPPkkfPSRGdvocFSRc6OyF59z5AgMGQJHj5oHsfXq2U4k4v80sxef\n8v330KkTNGxotlaq6EXKh8pefEZysin6u+6C116DatVsJxIJHBrjiE+YOhWefho++AB69LCdRiTw\nqOzFqvx8GDHCjGzWrIHLL7edSCQwqezFmp9/hthYqFwZ0tLMFYIi4h2a2YsV27aZF6Vat4bFi1X0\nIt6mspcK99ln0K0bPPYYTJgAVfT9pYjX6V8zqTCOY64NfP5587JU1662E4kED5W9VIi8PLjnHvOS\nVGoqNG1qO5FIcFHZi9dlZEBMjJnLr10LtWrZTiQSfDSzF6/autU8iO3cGT7+WEUvYotW9uI1n3wC\nw4fDxInmeGIRsUdlL+XOceB//xdeecVsq2zf3nYiEVHZS7nKzTWr+e++Mw9jGza0nUhEQDN7KUc/\n/ggejzkCISVFRS/iS1T2Ui42bTIPYq+7DmbNMnfFiojv0BhHztncuXD33fDGG3DDDbbTiEhxVPZS\nZoWFMGqUuQR8+XJo08Z2IhEpicpeyuT4cbj5ZjOn37ABwsNtJxKR0mhmL2fthx+gSxeoWRNWrlTR\ni/gDlb2cldRU6NgRhg6Ft9+G886znUhEzoTXyn7v3r1069aNyMhImjdvzrhx4wDIysqiZ8+eREVF\n0atXLw4fPuytCFLO3nsPrr8epk2Dhx4Cl8t2IhE5Uy7HcRxvfOH09HQyMjJo3bo1x44do23btsyd\nO5dp06Zx6aWX8uCDDzJx4kR27drFpEmTTg3lcuGlWFIGBQXwxBMwf745AqFlS9uJRKQ4pXWn18r+\n92JiYrj11lu577772LBhA6GhoWRmZtKxY0f+/e9/n3FgqVhHjphzbbKzzRbL0FDbiUSkJKV1Z4XM\n7Hfv3s3GjRvp0qULGRkZhP7aGPXq1ePgwYMVEUHK4Pvv4eqroXFj+PRTFb2IP/N62R87doyYmBgm\nTZpE7dq1vf23k3KSlASdOpkLR157DapWtZ1IRM6FV/fZnzx5khtvvJEhQ4bQr18/AMLCwsjMzKRe\nvXpkZGRQv379Yv/ahISEoh97PB48Ho83o8pvTJ0KTz8NM2fCf/2X7TQiUpKkpCSSkpLO6Pd6bWbv\nOA4333wzoaGhTJgwoejz9913X9ED2gkTJrBr1y4mT558aijN7K3Iz4f4eFixwjyIvfxy24lE5GxY\neUC7Zs0aunXrRlRUFK5f9+iNHTuW9u3bM2DAANLT02nQoAFz5syhbt26ZxxYvOPnnyE2FqpUgdmz\noU4d24lE5Gz5xG6cs6Gyr1jffQd9+0KfPjBuHFSubDuRiJSF9d044rs+/RS6dYPHH4fx41X0IoFK\nB6EFKceBSZPM9YHz55uzbkQkcKnsg1BentlSuX69OeumaVPbiUTE21T2QSYjA268EUJCYN06c3Kl\niAQ+zeyDyNat0L69mdHPn6+iFwkmWtkHiYUL4fbbYeJEc9aNiAQXlX2Acxx4/nmYMgWWLIF27Wwn\nEhEbVPYBLDcXhg+HbdvMw9iGDW0nEhFbNLMPUD/+CN27m7PoU1JU9CLBTmUfgDZtMg9i+/Y1h5nV\nqGE7kYjYpjFOgJkzx+yhnzoV+ve3nUZEfIXKPkAUFkJCgrkndvlyaNPGdiIR8SUq+wBw/DjExUF6\nOmzYACVcESAiQUwzez/3ww/mXJs6deDzz1X0IlI8lb0fW7cOOnaEYcNg+nQ47zzbiUTEV2mM46fe\nfRceeQTeeQf++lfbaUTE16ns/UxBgTl7/uOPzaXgLVvaTiQi/kBl70eOHIFBg8ybsevXQ2io7UQi\n4i80s/cTO3fC1VdDkyaQmKiiF5Gzo7L3A6tWQefO5mWpV1+FqlVtJxIRf6Mxjo974w145hmYNQuu\nucZ2GhHxVyp7H3XyJMTHm73za9fCZZfZTiQi/kxl74OysiA2FqpVg7Q088KUiMi50Mzex3z3HXTo\nYM62WbRIRS8i5UNl70MSE839sE8+CS++CJUr204kIoFCYxwf4DgwaRKMG2delurc2XYiEQk0KnvL\nTpyAu++GL76A1FSzj15EpLyp7C06eBBuvBHq1TM7bmrWtJ1IRAKVZvaWbNliHsR6PDBvnopeRLxL\nK3sLFi6E2283c/pBg2ynEZFgoLKvQI4DY8eaIw+WLIF27WwnEpFgobKvIDk5MHw4bN9uTqxs2NB2\nIhEJJprZV4ADB6B7d3MpeEqKil5EKp7K3su++MI8iO3XD2bOhBo1bCcSkWCkMY4Xffgh3HsvTJ0K\n/fvbTiMiwcyrK/tbb72V8PBwIiMjiz6XkJBAo0aNcLvduN1uEhMTvRnBisJCePppeOwxWLFCRS8i\n9rkcx3G89cVXr15NzZo1iYuLY+vWrQCMGjWKWrVqMWLEiJJDuVx4MZZXHT8OcXGQng7z50P9+rYT\niUiwKK07vbqy79q1KxdeeOEfPu+vRX46e/aYc23q1DHn0KvoRcRXWHlAO2XKFFq0aMHQoUPJysqy\nEaHcrVtn7oi9+WaYPh3OO892IhGR/1fhD2jvuecennnmGcDM7++//35mzJjxh9+XkJBQ9GOPx4PH\n46mghGfvnXfg0Ufh3Xfhv//bdhoRCRZJSUkkJSWd0e/16sweYPfu3fTp06doZv9bBw4cIDo6mm3b\ntp0ayk9m9gUF5iHswoXwySfQooXtRCISzKzN7Itz8ODBoh/PmzePVq1aVXSEcnPihPlYv15FLyK+\nzasr+0GDBpGcnExmZibh4eGMGjWKVatWsWXLFvLy8mjSpAnTp0+n4e9eKfWXlb2IiC8prTu9PsYp\nC5W9iMjZ86kxjoiIVDyVvYhIEFDZn6Ez3d7kC5TVO5TVO5S1Yqjsz5A//SErq3coq3coa8VQ2YuI\nBAGVvYhIEPDJrZcej4fk5GTbMURE/Er37t1LHDX5ZNmLiEj50hhHRCQIqOxFRIKAyr4Uubm5tGvX\nDrfbTUREBPHx8bYjnVZBQQFut5s+ffrYjlKqpk2bEhUVhdvtpn379rbjlOrw4cPcdNNNXHnllbRo\n0YLU1FTbkYq1bdu2ous+3W43derUYfLkybZjlWjkyJFERERwxRVXEBMTQ3Z2tu1IJXr++eeJiIig\ndevWTJo0yXacsnGkVNnZ2Y7jOM7JkyedDh06OCtXrrScqHTjx493Bg8e7PTp08d2lFI1bdrUOXTo\nkO0YZyQmJsaZOXOm4ziOU1BQ4Pzyyy+WE51eQUGB06BBA+eHH36wHaVYO3bscJo1a+acOHHCcRzH\niY2NdaZNm2Y5VfG++OILp1WrVk5OTo6Tn5/v9OjRw9myZYvtWGdNK/vTqFGjBgB5eXkUFBQQHh5u\nOVHJ9u3bx9KlSxk+fLhfHCTnDxkPHTrE5s2bGTRoEACVKlWidu3allOd3ooVK7j00ktp3Lix7SjF\nCgkJoWrVqhw/fpz8/Hyys7Np0qSJ7VjF2rZtGx07dqR69epUrlyZ7t27s2jRItuxzprK/jQKCwtp\n06YN4eHhREdH07JlS9uRShQfH88LL7xApUq+/8fqcrno2bMnUVFRvPLKK7bjlGjHjh2EhYURGxtL\n69atiYuL49ixY7Zjndbs2bMZPHiw7RglCgkJ4aGHHuKSSy7h4osvpm7duvTo0cN2rGJFRkaSnJxM\nVlYW2dnZLFmyhL1799qOddZ8vxUsq1SpEps3b2bfvn2kpKT47OvSixcvpn79+rjdbr9YMaelpfHl\nl1/y+eef8/bbb7NixQrbkYpVWFjIxo0beeSRR/jmm28ICQlh9OjRtmOVKi8vj0WLFnHTTTfZjlKi\nnTt3MnHiRHbv3s2BAwc4duwYH3zwge1YxYqMjGTEiBF4PB6io6OJioqyHalMVPZnqE6dOvTu3Zu0\ntDTbUYq1bt06PvnkE5o1a8agQYNYuXIlcXFxtmOVqH79+gCEhYURExPDxo0bLScqXuPGjWnYsCHt\n2rUDICYmhs2bN1tOVbply5bx5z//mbCwMNtRSrRhwwY6depEaGgoVapU4YYbbmDNmjW2Y5Xorrvu\nYsuWLaxfv56LLrqIFn54NZ3KvhSHDh3i6NGjAOTk5LB8+XIiIyMtpyremDFj2Lt3L7t27WL27Nlc\nc801vPfee7ZjFSs7O7to58Xx48dJTEz02espGzduTL169di+fTtgZuG+/i/6rFmzip4x+KrLLruM\ntLQ0cnJycByHFStWcNlll9mOVaLMzEwAfvrpJ+bMmcOAAQMsJzp7VWwH8GUHDhwgLi4Ox3HIzc1l\n8ODB9O7d23asM+JyuWxHKFF6ejr9+vXD5XKRnZ3NwIED6du3r+1YJZo+fTpDhgwpeojoq+MGMP/x\nXLFiBW+++abtKKVq164dMTExREVFUalSJdxuN/fcc4/tWCXq378/R44coWrVqkyZMsWnN2qURMcl\niIgEAY1xRESCgMpeRCQIqOxFRIKAyl5EJAio7EVEgoDKXkQkCKjsRcro66+/ZtmyZQC88847RUcL\nV6tWrej45ieffNJyShFD++xFyqCgoID333+fTZs28fLLL5/ya82aNWPTpk2EhIRYSifyR3qDVoLa\nkSNHGDhwIPv376ewsJC///3vVK9enYcffpiQkBA6dOjArl27WLRoEQkJCezcuZO9e/dy8cUXs3bt\nWnJyclizZg1PPPEEsbGxtv9xREqkspeglpiYSNOmTVm6dCkAR48eJSIigtTUVJo2bcrgwYNPOXri\nu+++IzU1lSpVqvDuu++yadMmn74NSuQ/NLOXoOZ2u/n00095/PHHSUlJ4V//+hcRERE0bdoUgEGD\nBhUdGe1yuejbty9Vqpg1kuM4fnGctAio7CXIXX755WzatInIyEhGjhxJYmLiKb/++zI///zzi37s\ny4fNifyexjgS1H766SdCQkIYMmQIderUYeLEiezYsYM9e/bQpEkTPvzww6JS/33x16hRw6cvyRb5\nLa3sJaht2bKFq666irZt2zJ69GjGjBnD66+/To8ePejQoQNhYWGnjHF+u5qPjo5m06ZNXHnllcyZ\nM6fo81rxiy/S1kuRUiQnJ/Piiy/65QXTIr+llb3IaWilLoFAK3sRkSCglb2ISBBQ2YuIBAGVvYhI\nEFDZi4gEAZW9iEgQUNmLiASB/wN8FSViNpJblAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2d9a8d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "slope (g/s*K^.5) = 4.100\n",
        "\n",
        " intercept (g/s) = 1.798\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 2.7-3, page no. 29"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "#Initilization of variables\n",
      "#Units are not mentioned in this example\n",
      "import math\n",
      "t1=15.\n",
      "t2=30.\n",
      "F1=0.298\n",
      "F2=0.0527\n",
      "#Calculations and printing:\n",
      "print(\"Semi-log plot\")\n",
      "b1=(math.log(F2/F1))/(t2-t1)\n",
      "a1=math.exp(math.log(F1) -b1*t1)\n",
      "print '%s %.3f %s %.4f %s' %(\" \\n F=\",a1,\"exp(\",b1,\"t)\")\n",
      "print(\"\\n Log plot\")\n",
      "b2=(math.log(F2/F1))/(math.log(t2/t1))\n",
      "a2=math.exp(math.log(F1) -b2*math.log(t1))\n",
      "print '%s %.1f %s %.1f %s' %(\" \\n F=\",a2,\"exp(\",b2,\"t)\")\n",
      "raw_input(\"Press the Enter key to quit\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Semi-log plot\n",
        " \n",
        " F= 1.685 exp( -0.1155 t)\n",
        "\n",
        " Log plot\n",
        " \n",
        " F= 259.3 exp( -2.5 t)\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Press the Enter key to quit\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "''"
       ]
      }
     ],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}