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  "name": ""
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 6 : Design for Single Reactions"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 6.1 page no : 125"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Variables\n",
      "V1 = 50.       # volume liters\n",
      "V2 = 30.       # volume liters\n",
      "V3 = 40.;      # volume liters\n",
      "\n",
      "# Calculations\n",
      "VD = V1+V2;\n",
      "VE = V3;\n",
      "m = VE/VD\n",
      "fr_D = 1./(1+m);\n",
      "\n",
      "# Results\n",
      "print \" Fraction of feed going to branch D is %.3f \"%(fr_D)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Fraction of feed going to branch D is 0.667 \n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 6.2 page no : 129"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Variables\n",
      "kCot = 90.        # reactant\n",
      "kCot = 180.       \n",
      "X = 97.4;         # conversion\n",
      "\n",
      "print \" Part a\"\n",
      "print \" The conversion in percentage is %.2f \"%(X)\n",
      "#For 90% Conversion & N = 2.from graph\n",
      "kCot = 27.5;\n",
      "\n",
      "# Calculations\n",
      "ratio = 90*2/27.5;\n",
      "\n",
      "# Results\n",
      "print \" Part b\"\n",
      "print \" Treatment rate can be increased by  %.1f \"%(ratio)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Part a\n",
        " The conversion in percentage is 97.40 \n",
        " Part b\n",
        " Treatment rate can be increased by  6.5 \n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 6.3 pageno : 144"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "\n",
      "import math \n",
      "from numpy import *\n",
      "from matplotlib.pyplot import *\n",
      "\n",
      "# Variables\n",
      "CAo = array([2,5,6,6,11,14,16,24]);         #mmol/m3\n",
      "CA = array([0.5,3,1,2,6,10,8,4])            #mmol/m3\n",
      "t = array([30,1,50,8,4,20,20,4])            #min\n",
      "vo = 0.1                                    #m3/min\n",
      "\n",
      "# Calculations\n",
      "inv_rA = zeros(8)\n",
      "for i in range(8):\n",
      "    inv_rA[i] = t[i]/(CAo[i]-CA[i]);\n",
      "\n",
      "for i in range(8):\n",
      "    for j in range(i,8):\n",
      "        if CA[i]>CA[j]:\n",
      "            temp = CA[i];\n",
      "            CA[i] = CA[j];\n",
      "            CA[j] = temp;\n",
      "            temp1 = inv_rA[i];\n",
      "            inv_rA[i] = inv_rA[j];\n",
      "            inv_rA[j] = temp1;\n",
      "\n",
      "# Results\n",
      "plot(CA,inv_rA)\n",
      "plot(CA,inv_rA,\"go\")\n",
      "suptitle(\"Arrangement with smallest volume\")\n",
      "xlabel(\"CA, m mol/m**3\")\n",
      "ylabel(\"-1/ra, m**3.min/m mol\")\n",
      "print ('From the graph,we can see that we should use plug flow with recycle')\n",
      "CAin = 6.6;#mmol/m3\n",
      "R = (10-6.6)/(6.6-1);\n",
      "#V = t*vo = area*vo\n",
      "V = (10-1)*1.2*vo;\n",
      "vr = vo*R;\n",
      "print \" Part a\"\n",
      "print \"  The vol of reactor is %.2f m**3\"%(V)\n",
      "print \"  The recycle flow rate is %.4f \"%(vr),\n",
      "print \"m3/min\"\n",
      "\n",
      "#Part b,from fig\n",
      "t = (10-1)*10;\n",
      "t1 = (10-2.6)*0.8;\n",
      "t2 = (2.6-1)*10;\n",
      "#For 1  math.tank\n",
      "V = t*vo;\n",
      "#For  2 math.tank\n",
      "V1 = t1*vo;\n",
      "V2 = t2*vo;\n",
      "Vt = V1+V2;\n",
      "print \" Part b\"\n",
      "print \"  For 1 tank volume is %.2f m**3\"%(V)\n",
      "print \"  For 2 tank the volume is %.2f m**3\"%(Vt)\n",
      "\n",
      "print \" Part c\"\n",
      "print (' We should use mixed flow followed by plug flow')\n",
      "#For MFR\n",
      "tm = (10-4)*0.2;\n",
      "Vm = tm*vo;\n",
      "#For PFR\n",
      "tp = 5.8;#by graphical integration\n",
      "Vp = tp*vo;\n",
      "Vtotal = Vp+Vm;\n",
      "print \" For MFR volume is %.2f m**3\"%(Vm)\n",
      "print \" For PFR volume is %.2f m**3\"%(Vp)\n",
      "print \" Total volume is %.2f m**3\"%(Vtotal),\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        "From the graph,we can see that we should use plug flow with recycle\n",
        " Part a\n",
        "  The vol of reactor is 1.08 m**3\n",
        "  The recycle flow rate is 0.0607  m3/min\n",
        " Part b\n",
        "  For 1 tank volume is 9.00 m**3\n",
        "  For 2 tank the volume is 2.19 m**3\n",
        " Part c\n",
        " We should use mixed flow followed by plug flow\n",
        " For MFR volume is 0.12 m**3\n",
        " For PFR volume is 0.58 m**3\n",
        " Total volume is 0.70 m**3\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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h4uICPz8/7baYmBi4urpCLpdDLpcjJSXFGKc2iC1/IiIjJf9p06bVSO4ymQx//etfkZOT\ng5ycHERGRhrj1Aax5U9EBLQwxkFDQkKgVCprbK/PaHRMTIz2sUKhgEKhaLrAoLuer0zWpIcmIjKJ\n9PR0pKenP9IxjFbqqVQqMWrUKJw4cQIAsGTJEqxfvx5t2rRBUFAQPvnkE7Rt21Y3GCOXegLqpN+u\nHXDunHqWTyIiS9eY3Flr8h81alSdJ0pKSqrzwA8n/8uXL6Njx44AgMWLF+PixYtYu3btI7+Bxnjq\nKeDTT4GBA41+KiIio2tM7qy122fBggV1nqihOnXqpH08Y8aMOi8uxqYZ9GXyJyJbVWvyr97Xfu/e\nPZw9exYymQyenp6ws7Nr8IkuXryILl26AAC2bNmiUwlkahz0JSJbZ3DANz09HVOmTEH37t0BAAUF\nBYiLi8OQIUNq3eeFF17Anj17cPXqVXTt2hVLlixBeno6jh07BplMBjc3N3z55ZdN9y4ayMMDSEyU\n7PRERJIzOOAbGBiIhIQEeHp6AgDOnj2L6OhoZGdnN30wJurzP34cePFF4ORJo5+KiMjojDK3T3l5\nuTbxA4CHhwfKy8sbHp0Z6dkTyMtTT/RGRGSLDHb79O3bFzNmzMCkSZMghEB8fDyCgoJMEZvRVF/P\n181N6miIiEzPYLdPWVkZVq9ejQMHDgBQ38A1Z84ctGrVqumDMVG3DwCEhQFvvglIdKMxEVGTadI6\nfymYMvnPmQN4eQFz55rkdERERmOUPv/t27dDLpfD2dkZTk5OcHJyQuvWrRsdpLngBG9EZMsM9vnP\nnz8fW7Zsga+vL5o1s54ZoD08gB07pI6CiEgaBrO5q6srfHx8rCrxA2z5E5FtM9jnn5mZiffeew+h\noaFo2bKleqf/Tc/c5MGYsM+f6/kSkbUwSp//4sWL4ejoiLKyMqhUKqhUKty+fbvRQZoLrudLRLbM\nYJ//xYsXsXv3blPEYnKarh9/f6kjISIyLYMt/2eeeQa7du0yRSwmxwneiMhWGezzd3R0RGlpKVq2\nbKmdzVMmk+HWrVtNH4wJ+/wBYN06YM8eIC7OZKckImpyTTqfv4ZKpWp0QObOwwP497+ljoKIyPSs\nq36zgaqv50tEZEtsOvn/b1VJXLsmbRxERKZm08lfJuOgLxHZJoN9/gBQUlKCgoICVFSbAD8wMNBo\nQZkS1/MlIltkMPkvXrwYGzZswJNPPqkzxUNaWppRAzMVtvyJyBYZTP6bN29GXl6edmoHa8P1fInI\nFhns8/fx8UFJSYkpYpGEpydb/kRkewze5HXkyBE8++yz8PX11a7eJZPJkJSU1PTBmPgmLwC4cwfo\n0AFQqdTz/RARWRqj3OQ1efJkvP322zrz+ctkssZFaIa4ni8R2SKDyd/R0RFzrXytQ82gL5M/EdkK\ng8k/JCQEixYtwujRo3UWbbeWUk9AnfzPnuVi7kRkOwwm/+zsbMhkMmRmZupst5ZST4CDvkRke2pN\n/hkZGQgODkZ6eroJw5EG1/MlIltTa6nnxo0bERgYiOjoaGzYsAHFxcWmjMukNN0+RES2wmCp56lT\np7Bz506kpqbixo0bCA0NxYgRIzBw4EA0b+LaSClKPQGu50tElq0xudNg8q+utLQUaWlp2LlzJw4e\nPIisrKwGB1lnMBIlfwDw9lbf6cslHYnI0hilzh8AKioqcOnSJZSXl8PX1xe+vr7o3r17o4I0V5pB\nXyZ/IrIFBpP/qlWrsGTJEnTq1EnbzSOTyZCbm2v04EyJ/f5EZEsMJv8VK1bgzJkzaN++vSnikYyH\nB7B3r9RREBGZhsGJ3bp164bWrVubIhZJsdafiGyJwZa/m5sbQkNDERUVpZ3WWSaT4a9//avRgzOl\n6uv5WtHURUREehlM/t26dUO3bt1w//593L9/H0IIq5rYTUOznu/Vq1WPiYisVYNKPY1NylJPAHjq\nKeDTT7mkIxFZliYt9Zw3bx5iY2MxatQovScyxnz+UtNU/DD5E5G1qzX5T548GQCwYMECkwUjNQ76\nEpGtYLdPNd98o77L94cfJAuBiKjBGpM7DZZ6bt++HXK5HM7OznBycoKTk5PVln6y5U9EtsJgy9/d\n3R1btmzRWcbRaMFI3PK/cwdo3179J9fzJSJLYZSWv6urK3x8fIye+M1B9fV8iYismcE6/2XLlmHE\niBEIDQ216pu8NDRdP1zPl4ismcHm/OLFi+Ho6IiysjKoVCqoVCrcvn3bFLFJghO8EZEtMNjyv3jx\nInbv3m2KWMwCB32JyBYYbPk/88wz2LVrlyliMQts+RORLTCY/L/44guMGDEC9vb29S71nD59Olxc\nXODn56fddv36dYSHh8PDwwPDhw/HjRs3Hj36Jpa8OxkfxUVgX6ECEdMikLw7WeqQiIiMwig3ee3b\ntw+Ojo6YPHkyTpw4AQBYuHAhOnTogIULF2LZsmUoKSnB0qVLdYORsNQzeXcy5q2ehzx5nnabe447\nYl+NRVR4lCQxERHVh9HX8G0IpVKJUaNGaZO/l5cX9uzZAxcXFxQXF0OhUOD06dO6wUiY/COmRSC1\nR2rN7QURSFmbIkFERET1Y7Q1fB8ml8uRk5PToH0uXboEFxcXAICLiwsuXbqk9/diYmK0jxUKBRQK\nRWNCbLB74p7e7WUVZSY5PxFRfaWnpyM9Pf2RjtGo5N/QxP8wmUxW65oA1ZO/KbWStdK73b65vYkj\nISKq28MN4yVLljT4GCa7bVfT3QOoy0c7depkqlPXy9yJc+Ge466z7cksd7we/bpEERERGU+tyf/4\n8eMICwtDdHQ08vPzERoaijZt2iAkJATnz59v8IlGjx6NuLg4AEBcXBzGjBnT+KiNICo8CrGvxiKi\nIAJD8oeg3XcReL4vB3uJyEqJWgwYMEAkJSWJr7/+WnTu3Fl8/fXXoqKiQiQlJYnw8PDadhNCCBEd\nHS26dOki7OzshKurq1i3bp24du2aGDZsmOjVq5cIDw8XJSUlNfarIxyTi4sTYuRIqaMgIjKsMbmz\n1mqf6oO6PXv21GntN2bAtz6kntWzujt3AFdX4NQpoHNnqaMhIqpdk87qWVFRoX388CRuDx48aGBo\nlsfBARg7FoiPlzoSIqKmV2vynzNnjnYCtzlz5mi3nz9/HmFhYcaPzAxMnQps2ACYyZcRIqImU+dN\nXmVlZbC3t9f+afRgzKjbBwAqK4FevdTLO/btK3U0RET6NfliLrNnz8bdu3d1Wv62pFkzYMoUdeuf\niMia1Jr89+zZg6CgIAwePBh9+/bFnj17TBmX2Zg8GUhIAO7pvwGYiMgi1dnyb9asGSorK2u9G9cW\n9OgB+PsDO3ZIHQkRUdOpNfkPHjwYhw8fxr59+3D06FEMGTLElHGZFc3ALxGRteCAbz2w5p+IzBkH\nfI2ENf9EZG044FtPrPknImvCAd96GjQIKC0FsrOljoSI6NFxwLeeWPNPRNaEA74NoFQCQUFAURHQ\nSv/aL0REJtfkyzja29vjt99+w6pVq6BUKlFeXq49UVJSUuMjtVDVa/7HjZM6GiKixjO4gLu/vz9m\nzJgBX19fNGum7iWSyWRG6QYy95Y/AGzcCHz7LbB9u9SREBGpNSZ3Gkz+/fv3x+HDhx8psHoHYwHJ\nnzX/RGRujJL8v/rqK+Tl5SEiIgKtqnV0BwYGNi7KuoKxgOQPANOnAz4+wIIFUkdCRGSk5P/222/j\nq6++Qs+ePbXdPgCQlpbWuCjrCsZCkv/evcCrrwK5uQCrYIlIakZJ/u7u7jh16hRatmz5SMHVKxgL\nSf6c55+IzEmTT+8AAH5+figpKWl0UNaINf9EZOkMtvyHDBmC3Nxc9OvXT9vnb6xST0tp+QOs+Sci\n89Hkdf4AsGTJEr0nsnWs+SciS2aw5W9KltTyB1jzT0TmwSgDvqZkacmfNf9EZA6MMuBLteM8/0Rk\nqZj8HxHn+SciS9Tg5D9lyhT85S9/wcmTJ40Rj8XhPP9EZIkanPxfffVVDBs2DBs3bjRGPBaHNf9E\nZIk44NsEWPNPRFIySp3/5cuX8X//93/45ZdfUFZWpj3Rzz//3LgorRBr/onI0hjs9nnxxRfh5eWF\n/Px8xMTEoEePHggKCjJFbBZFM/BLRGQJDHb7BAYGIjs7G/7+/sjNzQUABAUF4ejRo00fjIV2+wCs\n+Sci6Rilzl8zm2fnzp2xY8cOZGdnc6I3PVjzT0SWxGDLf8eOHRg0aBAKCwvx+uuv49atW4iJicHo\n0aObPhgLbvkDnOefiKTR5AO+FRUVOHv2LEaOHIm2bdsiPT39UeKzetVr/jnPPxGZszq7fZo3b46E\nhARTxWLxWPNPRJbCYLfPG2+8gQcPHuD555+Hg4MDhBCQyWQ2vYZvXVjzT0SmZpRZPRUKhd75+215\nDV9Dhg5V9/2z5p+ITKFJk39GRgaCg4NNunCLtSR/zvNPRKbUpMl/9uzZOHToEDw9PREZGYnIyEh0\nNnIBu7Ukf9b8E5EpGaXb59SpU9i5cydSU1Nx48YNDB06FJGRkRg4cCCaN2/+SAHXCMZKkj8ATJ8O\n+PgACxZIHQkRWTujr+RVWlqKtLQ07Ny5EwcPHkRWVlaDg6wzGCtK/qz5JyJTMdkyjrdv34aTk1ND\ndzMcjBUl/8pKoFcv4JtvWPNPRMZlsmUcfXx8GrObTWHNPxGZs1rv8P3kk09q3en27dtGCcbaTJ6s\nrvn/+GPW/BOReam15f/OO++gpKQEKpVK5+f27duorKw0ZYwWq/o8/0RE5qTWPv/g4GCsWrVK79z9\nXbt2RWFhYdMHY0V9/hqs+SciY2vSAd/Tp0+jffv26NixY43XiouLG13z36NHD7Ru3RrNmzeHnZ0d\nDh8+XBWMFSZ/1vwTkbEk707Gyq9XInVDqnGrfS5evIguXbo0OMDq3NzckJWVhXbt2tUMxgqTP8Ca\nfyJqesm7kzFv9TzkyfOAGBi32icqKqpBB6+NNSb4umiWeLSxt01ERrTy65XqxN9IBhdwr64pkrZM\nJkNYWBiaN2+OV155BTNnztR5PSYmRvtYoVBAoVA88jmlxnn+iagppaen40zOGUDZ+GM0KPk/nKgb\n48CBA+jSpQuuXLmC8PBweHl5ISQkRPt69eRvLarX/DP5E9GjUigU6Onvid/df1dvSG/4MRrU7TNn\nzpyGn+EhmjGDjh07YuzYsToDvtZs8mQgIQG4d0/qSIjIkp05A8ybBxxKnguHH90bfZxG3eHbWKWl\npdobxO7cuYPU1FT4+fmZMgTJsOafiBqrvBzYsgUIDweGDAEcHYFfcqKw+R+xiCiIaNQxGzW3T2Pl\n5+dj7NixAIDy8nK8+OKLWLRoUVUwVlrto8GafyJqiEuXgP/8B/jyS6Br16pFoh6eMcBkE7sZi7Un\nf9b8E5EhQgAZGcDq1cDOncCECcCcOUBAQO37mGxiN2ocBwdg7FggPl7qSIjI3Ny5A/z73+okP20a\n0L8/kJ9fta2pseVvYpznn4iqO3MG+OILYNMmICREnR+GDVNXCdYXW/4WoHrNPxHZJs0AblgYMHiw\negA3JwfYulU9qNuQxN9YDarzp0fHmn8i23XpErBmjXoAt1u32gdwTYHdPhJQKtXz/BcVcZ5/Imsn\nBHDggLprp74DuA3Fbh8LwZp/IuunUlUN1k6fbvwB3IZi8peIZrI3IrIumjtwu3cHfvxRvZLf6dPA\n/PlA27ZSR1eF3T4SYc0/kfUoL1ffvLl6NXDiBDBjBvDKK+p+fVPgTV4WhvP8E1k2cxnAZZ+/heE8\n/0SWRwhg/35g4kTAywv4/Xd1q//AAfU2SyniYPKXEGv+iSxHbQO4a9aYxwBuQ7HOX0Ks+Scyfw/f\ngfvxxw2/A9ccsc9fYqz5JzI/Ug/gNlRjcidb/hKrXvM/bpzU0RDZNnMZwDUFC//iYh1Y808kHWsZ\nwG0odvuYAdb8E5meSqWeXv2LL4C7d9VTLkydal43YtUXSz0tFOf5JzKd06er7sDdudN878A1NiZ/\nM8GafyLjqT6FsmYNXFNPoWxuOOBrJqrX/LPsk6hpPDyAO2cOMH689fbjN4QNXu/MU/WafyKqXfLu\nZERMi4BiqgIR0yKQvDtZ53XNAO4LLwCenroDuC++yMSvwQFfM8Kaf6K6Je9OxrzV85Anz9Nuc89x\nR+yrsRiBWgOVAAAObUlEQVQSHGU1A7gNxYndrMDQoVW1xUSkK2JaBFJ7pNbY3m1XBFRnUhq9Bq6l\n401eVkAz8MvkT1TTPXFP/wt2ZcjJMd87cM2RDV0bLcO4cer+yuJiqSMhMh/37gE//QQoz+rvD+3d\n056Jv4GY/M1MekYy/tQrAoMm6R/MIrIVv/2mnltn1CigY0dg8WJgoNdcPHHQXef33LPd8Xr06xJF\nabnY7WNGNINZF6LUg1l5APJWqx9HhUdJGBmR8d29C6SnAykp6puvbt0CIiPVFTobNgDt2wNAFJJ3\nA6sSV6Gsogz2ze3x+muv8/9HI3DA14zUNpgVURCBlLUpEkREZDxCAOfOqRP9zp3qUsyAAGDECPVP\nnz62NWj7KDjga+FqG8wqqygzcSRExqFSAWlpVa37+/fVrfsZM4DERNsoyzQXTP5mpJVM/2BWswp7\nE0dC1DSEAH79tSrZHzoE9OunTvjbtgG+voBMJnWUtondPmZE3w0srXe5o/JMLObMiMKbb6oHvojM\n2a1bwH//q072KSnq5K7pyhk6FHBykjpC68ObvKxA8u5k3cGs6Nfh7xWFpUvVX4tnzAAvAmRWhABy\nc6uSfVYWEBysTvaRkeo58tm6Ny4mfytXWAheBMgslJSo6+41Cf9Pf6pK9gqFeppyMh0mfxvBiwCZ\nWmWlegpkTbLPzVUvZh4ZqU76PXtKHaFtY/K3MbwIkDFdvQqkpqqT/a5dgLNzVd99SAjw2GNSR0ga\nTP42ihcBagoVFcCRI1WVOadPq7twIiPVP25uUkdItWHyt3G8CFBDXbqkbtWnpKhb+V26VHXlDBzI\nqcUtBZM/AeBFgGpXXg5kZla17vPy1NMfjxgBREQAXbtKHSE1BpM/6eBFgAD14kC7dqmT/U8/AT16\nVFXmBAcDdnZSR0iPismf9OJFwPIl707Gyq9X4p64h1ayVpg7cW6tk5ndvw9kZFRV5vzxh3qR8shI\ndeu+SxcTB09Gx+RPdeJFwDLVtXSh5gJQUFDVlZOWBnh4VPXd9+sHtOBELlaNyZ/qhRcBy1LbbK9B\nOREY7J6ClBTg8mV1qz4yEhg+HOjUSYJASTKNyZ2cMNUGde2qXiTj2DH1LIteXsBbbwFXrlT9TvLu\nZERMi4BiKheVkcrt28DZs8ClEv2zvZ7NL0Pbtuq57i9dAjZtAiZNYuKn+uGXQRumuQi8/bb6m4CX\nl/qbQJ9+yXhvk243AxeVaTq3bwMXLwIXLqh/NI8f3lZRATz+OHDNvhUgr3mc4CB7LF5s+vjJOrDb\nh7Q03UH/To5A+TQuKtNQDU3qmp8uXXT/1Dxu3Vo9IZrePv9sd8S+FsuLMQHgYi70iDTfBLKv30Om\nntfvltvmojKPmtTlcv1Jvb40CZ5LF1JTYvKnGlrb67+tc+9/7dG+vf5W6sN/NsWdoQ0pb2yM+ib1\nykr97zMwUHdbQ5N6Q0SFRzHZU5Ni8jdT6enpUCgUkpx77sS5yFudV6Ob4bO1r2NAYM1kefo08PPP\nVduKi9ULdhjq1qjrIqHT1aEE0KP+4w7Vk3pdyd0cknpDSfnvwtzws3g0Jk/+KSkpmD9/PioqKjBj\nxgy89dZbpg7BIkj5D9tQN0PHjurFtWtTWQlcu1a/i0Tr1vovDv/ZuRJ5/f938VFCnfzleVi2dhUc\n7KKsLqnXFxNeFX4Wj8akyb+iogKvvfYafvrpJzzxxBPo168fRo8ejd69e5syDKqHR+lmaNZMfYFo\n7EXi1Cmg6LL+8sajuWV47z3rS+pEpmbS5H/48GH07NkTPXr0AABER0dj27ZtTP42qq6LxPlprVCz\n3ggYHGyPlLUmCY/Iqpm01PO7777Drl27sGbNGgDApk2bcOjQIaxatUodDJtrRESNYtalnoaSO2v8\niYhMw6TTOzzxxBMoLCzUPi8sLISrq6spQyAiIpg4+QcFBeHcuXNQKpW4f/8+Nm/ejNGjR5syBCIi\ngom7fVq0aIHPP/8cERERqKiowMsvv8zBXiIiCZh8Vs8RI0bgzJkzOH/+PBYtWqTdnpKSAi8vL/Tq\n1QvLli0zdVhmpbCwEKGhofDx8YGvry9WrlwpdUiSqqiogFwux6hRo6QORVI3btzA+PHj0bt3b3h7\neyMzU98kHLbho48+go+PD/z8/DBx4kTcu6e/NNgaTZ8+HS4uLvDz89Nuu379OsLDw+Hh4YHhw4fj\nxo0bBo9jFlM6a+r/U1JS8OuvvyIhIQGnTp2SOizJ2NnZ4bPPPsMvv/yCzMxMrF692qY/j9jYWHh7\ne9t8Ndi8efPwzDPP4NSpU8jNzbXZb81KpRJr1qxBdnY2Tpw4gYqKCiQmJkodlslMmzYNKSm6Eywu\nXboU4eHhOHv2LIYNG4alS5caPI5ZJP/q9f92dnba+n9b1blzZwQEBAAAHB0d0bt3b1y4cEHiqKTx\nxx9/4Mcff8SMGTNsuhrs5s2b2LdvH6ZPnw5A3YXapk0biaOSRuvWrWFnZ4fS0lKUl5ejtLQUTzzx\nhNRhmUxISAicnZ11tiUlJWHKlCkAgClTpmDr1q0Gj2MWyb+oqAhdu3bVPnd1dUVRUZGEEZkPpVKJ\nnJwcPPXUU1KHIok33ngDy5cvR7NmZvFPVTL5+fno2LEjpk2bhsDAQMycOROlpaVShyWJdu3aYcGC\nBejWrRsef/xxtG3bFmFhYVKHJalLly7BxcUFAODi4oJLly4Z3Mcs/kfZ+tf52qhUKowfPx6xsbFw\ndHSUOhyT27FjBzp16gS5XG7TrX4AKC8vR3Z2NubMmYPs7Gw4ODjU66u9NcrLy8OKFSugVCpx4cIF\nqFQqxMfHSx2W2ZDJZPXKqWaR/Fn/X9ODBw8wbtw4TJo0CWPGjJE6HElkZGQgKSkJbm5ueOGFF/Dz\nzz9j8uTJUoclCVdXV7i6uqJfv34AgPHjxyM7O1viqKRx9OhRPP3002jfvj1atGiBP//5z8jIyJA6\nLEm5uLiguLgYAHDx4kV0qsdanmaR/Fn/r0sIgZdffhne3t6YP3++1OFI5sMPP0RhYSHy8/ORmJiI\noUOHYuPGjVKHJYnOnTuja9euOHv2LADgp59+go+Pj8RRScPLywuZmZm4e/cuhBD46aef4O3tLXVY\nkho9ejTi4uIAAHFxcfVrMAoz8eOPPwoPDw/h7u4uPvzwQ6nDkdS+ffuETCYTffr0EQEBASIgIEDs\n3LlT6rAklZ6eLkaNGiV1GJI6duyYCAoKEv7+/mLs2LHixo0bUockmWXLlglvb2/h6+srJk+eLO7f\nvy91SCYTHR0tunTpIuzs7ISrq6tYt26duHbtmhg2bJjo1auXCA8PFyUlJQaPY1Zr+BIRkWmYRbcP\nERGZFpM/EZENYvInIrJBTP5ERDaIyZ/MSnFxMaKjo9GzZ08EBQUhKioK586d076+YsUKPPbYY7h1\n65aEUdZu6tSp+P7777XPExMT8eGHHzboGEuWLAGgu7iRZpvGyy+/jICAAPj7+2Ps2LG4efPmI0RN\ntojJn8yGEAJjx47F0KFDcf78eRw9ehQfffSRzq3qCQkJCA8Pxw8//CBhpLV7+M7KlJQUjBgxol77\nHjt2DPPmzcP169exbds2vPvuuzW2vfPOOwDUF8Fjx44hNzcXTz75pHYpVKL6Mul8/kR1SUtLQ8uW\nLTFr1iztNn9/f+3jvLw8PHjwAH/729/w/vvvY+rUqXUeT6lUIjIyEsHBwcjIyEBQUBCmTJmCJUuW\n4MqVK4iPj9feMauxYcMGbN26FaWlpTh37hwWLFiAsrIyfP3112jVqhV+/PFHODs749ixY5g9ezbu\n3r0Ld3d3rFu3Dm3bttU5lhACx44dg1wuR0xMDPLz85Gfn4+CggJ8+umnyMjIQGpqKp544gls374d\nAQEB+Mtf/oKnn34a5eXlWL16NQDo3ebk5KQ9x927d9GrV69Gf+5km9jyJ7Nx8uRJ9O3bt9bXExMT\n8dxzz2HAgAE4f/48Ll++bPCYeXl5ePPNN3H69GmcOXMGmzdvxoEDB/Dxxx/X2h3zyy+/YMuWLThy\n5AjeeecdtG7dGtnZ2QgODtbeYTx58mQsX74cx48fh5+fX41uGQDIycnRzs4KqCdnS0tLQ1JSEiZN\nmoTw8HDk5ubiscceQ3JyMo4fP45//etfeOmllzB8+HAsXrxY7zaNadOmoUuXLsjNzcWMGTMMfhZE\n1TH5k9kwNBlVYmIiJkyYAAAYM2YMvv32W4PHdHNzg4+PD2QyGXx8fLSzP/r6+kKpVOqNITQ0FA4O\nDujQoQPatm2rXUTGz88PSqUSt27dws2bNxESEgJAPYXu3r17a7yP6l0+MpkMI0aMQPPmzeHr64vK\nykpEREToHLdPnz5YsWIF2rVrh2effRZ///vf9W7TWL9+PS5cuAB/f3988MEHBj8LouqY/Mls+Pj4\nICsrS+9rJ06cwLlz5xAWFgY3NzckJiYiISHB4DFbtWqlfdysWTO0bNlS+7i8vLxe+2ie17ZPbTfJ\n7969G8OHD9c+r35uOzs7nXNUP+77779f41j6tmn2jY6OxpEjR/S+TlQbJn8yG0OHDsW9e/ewZs0a\n7bbc3Fzs378fCQkJWLJkibbfvKioCBcuXEBBQQGKioqabD73umY70bzWunVrODs7Y//+/QCAr776\nCgqFQud3b968ifLy8hqLbjSV8+fPa2NKSkqCXC43ynnIenHAl8zKli1bMH/+fCxbtgz29vZwc3PD\nZ599hs2bN2Pnzp06vzt27Fhs3rwZoaGhaNFC/z/lh7uSqj/X18308FzoDz/WPI+Li8Ps2bNRWloK\nd3d3rF+/Xuc4u3fvRnh4eL3P3ZA1LYQQmDp1qrbcNSgoSDsQTFRfnNiNLN7q1avRvXt3jBw5UupQ\ntGbOnImZM2eif//+UodCpBeTPxGRDWKfPxGRDWLyJyKyQUz+REQ2iMmfiMgGMfkTEdkgJn8iIhv0\n/4qV3Dpe155RAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1e37810>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}