IainNZ/gist:7340901

## gistfile1.txt
{
 "metadata": {
  "name": "What is multiple dispatch?"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Multiple Dispatch helps make Julia fast. <br>\nIt also makes Julia extensible, programmable, and downright fun to play with. <br>\nIt may just be a breakthrough for parallel computation..."
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "1. Roman Numeral Example\n2. Function Example\n3. Parallel Computing Example"
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "1. Roman Numeral Example (just for fun)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "type Roman \n    n::Int\nend\n\n# Cool roman numeral printing magic (0:9 for demo only)\nimport Base: promote_rule, convert, show\nBase.show(io::IO, r::Roman)=print(io,r.n%10==0?'0':'\u2170'+r.n%10-1)\n\n[Roman(1) Roman(2) Roman(5) Roman(9)]    ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": "1x4 Array{Roman,2}:\n \u2170  \u2171  \u2174  \u2178"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "*(i::Roman,j::Roman)=Roman(i.n*j.n)                     # Multiply like a Roman\n\n*(i::Number,j::Roman)=eye(i)*j.n                        # Multiply like a matrix constructor\n\n*(i::Roman,j::Number)=print(['\u263a' for k=1:(i.n)*j]')    # Multiply like a kid",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": "* (generic function with 129 methods)"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "[Roman(2)*Roman(3)  Roman(3)^2]                        # WOW! Squaring just worked",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": "1x2 Array{Roman,2}:\n \u2175  \u2178"
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "7*Roman(4)                                             # Multiply like a matrix constructor",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": "7x7 Array{Float64,2}:\n 4.0  0.0  0.0  0.0  0.0  0.0  0.0\n 0.0  4.0  0.0  0.0  0.0  0.0  0.0\n 0.0  0.0  4.0  0.0  0.0  0.0  0.0\n 0.0  0.0  0.0  4.0  0.0  0.0  0.0\n 0.0  0.0  0.0  0.0  4.0  0.0  0.0\n 0.0  0.0  0.0  0.0  0.0  4.0  0.0\n 0.0  0.0  0.0  0.0  0.0  0.0  4.0"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "Roman(2)*4                                            # Multiply like a kid",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\u263a\t\u263a\t\u263a\t\u263a\t\u263a\t\u263a\t\u263a\t\u263a\n"
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "2. Function Example (glimpse of why multiple dispatch is useful)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "\n*(\u03b1::Number,  g::Function)= x->\u03b1*g(x)                # Scalar times function\n*(f::Function,\u03bb::Number)  = x->f(\u03bb*x)                # Scale the argument\n*(f::Function,g::Function)= x->f(g(x))               # Function Composition",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": "* (generic function with 132 methods)"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "using PyPlot",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "x=pi*(0:.01:4)\n\nplot(x,(12*sin)(x),\"g\")\nplot(x,(sin*12)(x),\"r\")\nplot(x,(5*sin*exp)(x),\"b\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "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",
       "svg": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (http://matplotlib.org/) -->\n<svg height=\"381pt\" version=\"1.1\" viewBox=\"0 0 494 381\" width=\"494pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"\nM0 381.421\nL494.697 381.421\nL494.697 0\nL0 0\nz\n\" style=\"fill:#ffffff;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"\nM34.4575 358.607\nL480.857 358.607\nL480.857 13.0069\nL34.4575 13.0069\nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"line2d_1\">\n    <path clip-path=\"url(#p5a34f26e57)\" d=\"\nM34.4575 137.338\nL38.4644 133.617\nL41.4695 131.219\nL43.473 129.913\nL45.4764 128.924\nL47.4799 128.334\nL48.4816 128.217\nL49.4833 128.235\nL50.485 128.402\nL51.4867 128.731\nL52.4884 129.238\nL53.4902 129.937\nL54.4919 130.842\nL55.4936 131.97\nL56.4953 133.336\nL57.497 134.955\nL58.4988 136.842\nL59.5005 139.011\nL60.5022 141.474\nL62.5056 147.329\nL64.5091 154.469\nL66.5125 162.908\nL68.516 172.584\nL70.5194 183.335\nL74.5263 206.743\nL77.5314 223.755\nL79.5349 233.308\nL80.5366 237.162\nL81.5383 240.213\nL82.54 242.317\nL83.5417 243.333\nL84.5435 243.13\nL85.5452 241.593\nL86.5469 238.632\nL87.5486 234.191\nL88.5503 228.257\nL89.5521 220.872\nL90.5538 212.143\nL92.5572 191.454\nL95.5624 157.534\nL96.5641 147.397\nL97.5658 138.827\nL98.5675 132.446\nL99.5693 128.834\nL100.571 128.485\nL101.573 131.739\nL102.574 138.725\nL103.576 149.293\nL104.578 162.966\nL106.581 195.948\nL107.583 212.58\nL108.585 227.112\nL109.586 237.801\nL110.588 243.07\nL111.59 241.765\nL112.592 233.414\nL113.593 218.468\nL114.595 198.438\nL116.598 154.266\nL117.6 137.369\nL118.602 128.761\nL119.604 130.916\nL120.605 144.412\nL121.607 167.352\nL123.611 221.635\nL124.612 239.367\nL125.614 242.646\nL126.616 229.137\nL127.617 201.563\nL128.619 167.925\nL129.621 139.726\nL130.623 128.211\nL131.624 139.572\nL132.626 171.016\nL133.628 210.02\nL134.629 238.371\nL135.631 240.532\nL136.633 212.987\nL137.635 169.061\nL138.636 134.304\nL139.638 132.17\nL140.64 166.95\nL141.641 216.315\nL142.643 243.161\nL143.645 223.513\nL144.647 170.656\nL145.648 130.67\nL146.65 143.657\nL147.652 200.38\nL148.653 242.213\nL149.655 219.66\nL150.657 155.462\nL151.659 129.412\nL152.66 179.71\nL153.662 239.366\nL154.664 218.619\nL155.666 145.37\nL156.667 139.468\nL157.669 215.385\nL158.671 236.639\nL159.672 158.773\nL160.674 136.33\nL161.676 220.123\nL162.678 226.255\nL163.679 137.296\nL164.681 168.59\nL165.683 243.394\nL166.684 163.061\nL167.686 147.938\nL168.688 242.194\nL169.69 164.834\nL170.691 155.639\nL171.693 242.611\nL172.695 138.717\nL173.696 200.189\nL174.698 206.883\nL175.7 139.934\nL176.702 242.418\nL177.703 129.275\nL178.705 236.814\nL179.707 141.106\nL180.709 226.44\nL181.71 145.52\nL182.712 229.688\nL183.714 135.536\nL184.715 242.156\nL185.717 129.382\nL186.719 228.853\nL187.721 174.218\nL188.722 155.23\nL189.724 242.962\nL190.726 149.594\nL191.727 159.816\nL192.729 242.453\nL194.733 128.25\nL195.734 174.907\nL196.736 236.433\nL197.738 233.131\nL198.739 187.289\nL199.741 146.875\nL200.743 129.813\nL201.745 129.072\nL202.746 132.673\nL203.748 133.35\nL204.75 130.079\nL205.751 128.625\nL206.753 142.094\nL207.755 182.717\nL208.757 233.663\nL209.758 230.681\nL210.76 152.544\nL211.762 145.773\nL212.764 240.388\nL213.765 164.285\nL214.767 173.996\nL215.769 214.125\nL216.77 156.665\nL217.772 199.872\nL218.774 205.392\nL219.776 130.961\nL220.777 220.113\nL221.779 232.517\nL222.781 170.958\nL223.782 134.351\nL224.784 128.207\nL225.786 128.427\nL226.788 129.686\nL227.789 152.431\nL228.791 215.018\nL229.793 234.951\nL230.794 136.807\nL231.796 207.236\nL232.798 172.893\nL233.8 216.764\nL234.801 128.853\nL235.803 204.942\nL236.805 243.398\nL237.807 232.238\nL238.808 229.721\nL239.81 242.528\nL240.812 218.258\nL241.813 132.398\nL242.815 212.885\nL243.817 159.647\nL244.819 238.492\nL245.82 154.242\nL246.822 128.207\nL247.824 129.527\nL248.825 131.967\nL249.827 196.734\nL250.829 223.162\nL251.831 147.463\nL252.832 177.173\nL253.834 239.105\nL254.836 243.399\nL255.837 232.169\nL256.839 148.462\nL257.841 200.599\nL258.843 140.59\nL259.844 212.021\nL260.846 222.401\nL261.848 169.231\nL262.849 151.644\nL263.851 202.162\nL264.853 242.29\nL265.855 240.467\nL266.856 219.066\nL267.858 136.23\nL268.86 183.923\nL269.862 213.493\nL270.863 155.621\nL271.865 191.607\nL272.867 129.732\nL273.868 132.507\nL274.87 167.785\nL275.872 183.576\nL276.874 228.293\nL277.875 169.901\nL278.877 202.174\nL279.879 146.996\nL280.88 208.566\nL281.882 150.834\nL282.884 181.717\nL283.886 130.893\nL284.887 193.587\nL285.889 163.683\nL286.891 208.559\nL287.892 184.873\nL288.894 226.569\nL289.896 241.701\nL290.898 178.239\nL291.899 238.021\nL292.901 145.858\nL293.903 205.361\nL294.905 131.571\nL295.906 129.332\nL296.908 241.02\nL297.91 242.419\nL298.911 172.818\nL299.913 242.623\nL300.915 243.217\nL301.917 143.028\nL302.918 238.924\nL303.92 242.328\nL304.922 180.955\nL305.923 181.276\nL306.925 133.879\nL307.927 163.807\nL309.93 131.309\nL310.932 221.09\nL311.934 188.877\nL312.935 162.268\nL313.937 219.891\nL314.939 141.208\nL315.941 242.361\nL316.942 143.804\nL317.944 144.792\nL318.946 177.354\nL319.947 152.443\nL320.949 148.634\nL321.951 209.573\nL322.953 242.943\nL323.954 205.879\nL324.956 183.761\nL325.958 174.009\nL326.96 217.522\nL327.961 229.898\nL328.963 145.764\nL329.965 188.08\nL330.966 130.868\nL331.968 239.674\nL332.97 155.494\nL333.972 131.384\nL334.973 131.989\nL335.975 142.901\nL336.977 156.716\nL337.978 149.753\nL338.98 128.663\nL339.982 230.13\nL340.984 129.772\nL341.985 129.598\nL342.987 241.446\nL343.989 228.896\nL344.99 140.652\nL345.992 133.3\nL346.994 128.286\nL348.997 239.965\nL349.999 154.186\nL351.001 128.35\nL352.003 129.845\nL353.004 234.051\nL354.006 169.612\nL355.008 241.832\nL356.009 243.003\nL358.013 139.612\nL359.015 241.597\nL360.016 210.126\nL361.018 224.774\nL362.02 160.331\nL363.021 159.993\nL364.023 243.098\nL365.025 131.051\nL366.027 236.339\nL367.028 190.966\nL368.03 192.504\nL369.032 181.37\nL370.033 135.22\nL371.035 134.577\nL372.037 129.997\nL373.039 175.189\nL374.04 138.39\nL375.042 173.406\nL376.044 224.355\nL377.045 236.985\nL378.047 133.424\nL379.049 134.7\nL380.051 154.991\nL381.052 231.005\nL382.054 224.862\nL383.056 160.766\nL384.058 242.979\nL385.059 132.942\nL386.061 198.416\nL387.063 154.285\nL388.064 237.565\nL389.066 195.35\nL390.068 232.658\nL391.07 144.284\nL392.071 241.951\nL393.073 197.423\nL394.075 128.348\nL395.076 193.483\nL396.078 191.942\nL397.08 129.6\nL398.082 241.142\nL399.083 129.747\nL400.085 128.286\nL401.087 131.604\nL402.088 236.004\nL403.09 135.745\nL404.092 133.239\nL405.094 132.482\nL406.095 230.359\nL407.097 192.98\nL408.099 227.392\nL409.101 189.792\nL411.104 142.682\nL412.106 154.814\nL413.107 205.243\nL414.109 128.585\nL415.111 128.672\nL416.113 131.491\nL417.114 142.242\nL418.116 206.734\nL419.118 148.737\nL420.119 174.274\nL421.121 235.644\nL422.123 224.806\nL423.125 171.616\nL424.126 153.132\nL425.128 172.577\nL426.13 225.277\nL427.131 237.121\nL428.133 187.892\nL429.135 182.346\nL430.137 242.188\nL431.138 211.479\nL432.14 154.558\nL433.142 220.793\nL434.143 157.979\nL435.145 222.822\nL435.145 222.822\" style=\"fill:none;stroke:#0000ff;stroke-linecap:square;\"/>\n   </g>\n   <g id=\"line2d_2\">\n    <path clip-path=\"url(#p5a34f26e57)\" d=\"\nM34.4575 185.807\nL42.4713 151.428\nL47.4799 130.905\nL51.4867 115.437\nL54.4919 104.551\nL57.497 94.3871\nL60.5022 85.0343\nL63.5074 76.5758\nL65.5108 71.4713\nL67.5142 66.8179\nL69.5177 62.6341\nL71.5211 58.9365\nL73.5246 55.7395\nL75.528 53.0559\nL77.5314 50.8961\nL79.5349 49.2688\nL80.5366 48.6569\nL81.5383 48.1804\nL82.54 47.8397\nL83.5417 47.6351\nL84.5435 47.5669\nL85.5452 47.6351\nL86.5469 47.8397\nL87.5486 48.1804\nL88.5503 48.6569\nL89.5521 49.2688\nL91.5555 50.8961\nL93.5589 53.0559\nL95.5624 55.7395\nL97.5658 58.9365\nL99.5693 62.6341\nL101.573 66.8179\nL103.576 71.4713\nL105.58 76.5758\nL108.585 85.0343\nL111.59 94.3871\nL114.595 104.551\nL117.6 115.437\nL121.607 130.905\nL125.614 147.239\nL131.624 172.797\nL143.645 224.375\nL148.653 244.667\nL152.66 259.88\nL155.666 270.535\nL158.671 280.439\nL161.676 289.502\nL164.681 297.645\nL166.684 302.527\nL168.688 306.948\nL170.691 310.89\nL172.695 314.339\nL174.698 317.281\nL176.702 319.704\nL178.705 321.598\nL180.709 322.957\nL181.71 323.433\nL182.712 323.774\nL183.714 323.979\nL184.715 324.047\nL185.717 323.979\nL186.719 323.774\nL187.721 323.433\nL188.722 322.957\nL189.724 322.345\nL191.727 320.718\nL193.731 318.558\nL195.734 315.874\nL197.738 312.677\nL199.741 308.98\nL201.745 304.796\nL203.748 300.142\nL205.751 295.038\nL208.757 286.579\nL211.762 277.227\nL214.767 267.062\nL217.772 256.177\nL221.779 240.709\nL225.786 224.375\nL231.796 198.816\nL243.817 147.239\nL248.825 126.947\nL252.832 111.734\nL255.837 101.079\nL258.843 91.1751\nL261.848 82.1115\nL264.853 73.9684\nL266.856 69.087\nL268.86 64.6662\nL270.863 60.7236\nL272.867 57.2746\nL274.87 54.3328\nL276.874 51.9099\nL278.877 50.0155\nL280.88 48.6569\nL281.882 48.1804\nL282.884 47.8397\nL283.886 47.6351\nL284.887 47.5669\nL285.889 47.6351\nL286.891 47.8397\nL287.892 48.1804\nL288.894 48.6569\nL289.896 49.2688\nL291.899 50.8961\nL293.903 53.0559\nL295.906 55.7395\nL297.91 58.9365\nL299.913 62.6341\nL301.917 66.8179\nL303.92 71.4713\nL305.923 76.5758\nL308.929 85.0343\nL311.934 94.3871\nL314.939 104.551\nL317.944 115.437\nL321.951 130.905\nL325.958 147.239\nL331.968 172.797\nL343.989 224.375\nL348.997 244.667\nL353.004 259.88\nL356.009 270.535\nL359.015 280.439\nL362.02 289.502\nL365.025 297.645\nL367.028 302.527\nL369.032 306.948\nL371.035 310.89\nL373.039 314.339\nL375.042 317.281\nL377.045 319.704\nL379.049 321.598\nL381.052 322.957\nL382.054 323.433\nL383.056 323.774\nL384.058 323.979\nL385.059 324.047\nL386.061 323.979\nL387.063 323.774\nL388.064 323.433\nL389.066 322.957\nL390.068 322.345\nL392.071 320.718\nL394.075 318.558\nL396.078 315.874\nL398.082 312.677\nL400.085 308.98\nL402.088 304.796\nL404.092 300.142\nL406.095 295.038\nL409.101 286.579\nL412.106 277.227\nL415.111 267.062\nL418.116 256.177\nL422.123 240.709\nL426.13 224.375\nL432.14 198.816\nL435.145 185.807\nL435.145 185.807\" style=\"fill:none;stroke:#008000;stroke-linecap:square;\"/>\n   </g>\n   <g id=\"line2d_3\">\n    <path clip-path=\"url(#p5a34f26e57)\" d=\"\nM34.4575 185.807\nL35.4592 181.566\nL36.4609 177.921\nL37.4627 175.383\nL38.4644 174.31\nL39.4661 174.851\nL40.4678 176.931\nL41.4695 180.257\nL44.4747 192.578\nL45.4764 195.534\nL46.4781 197.123\nL47.4799 197.123\nL48.4816 195.534\nL49.4833 192.578\nL51.4867 184.363\nL52.4884 180.257\nL53.4902 176.931\nL54.4919 174.851\nL55.4936 174.31\nL56.4953 175.383\nL57.497 177.921\nL58.4988 181.566\nL60.5022 190.048\nL61.5039 193.693\nL62.5056 196.23\nL63.5074 197.304\nL64.5091 196.763\nL65.5108 194.683\nL66.5125 191.357\nL69.5177 179.036\nL70.5194 176.08\nL71.5211 174.491\nL72.5228 174.491\nL73.5246 176.08\nL74.5263 179.036\nL76.5297 187.251\nL77.5314 191.357\nL78.5331 194.683\nL79.5349 196.763\nL80.5366 197.304\nL81.5383 196.23\nL82.54 193.693\nL83.5417 190.048\nL85.5452 181.566\nL86.5469 177.921\nL87.5486 175.383\nL88.5503 174.31\nL89.5521 174.851\nL90.5538 176.931\nL91.5555 180.257\nL94.5607 192.578\nL95.5624 195.534\nL96.5641 197.123\nL97.5658 197.123\nL98.5675 195.534\nL99.5693 192.578\nL101.573 184.363\nL102.574 180.257\nL103.576 176.931\nL104.578 174.851\nL105.58 174.31\nL106.581 175.383\nL107.583 177.921\nL108.585 181.566\nL110.588 190.048\nL111.59 193.693\nL112.592 196.23\nL113.593 197.304\nL114.595 196.763\nL115.597 194.683\nL116.598 191.357\nL119.604 179.036\nL120.605 176.08\nL121.607 174.491\nL122.609 174.491\nL123.611 176.08\nL124.612 179.036\nL126.616 187.251\nL127.617 191.357\nL128.619 194.683\nL129.621 196.763\nL130.623 197.304\nL131.624 196.23\nL132.626 193.693\nL133.628 190.048\nL135.631 181.566\nL136.633 177.921\nL137.635 175.383\nL138.636 174.31\nL139.638 174.851\nL140.64 176.931\nL141.641 180.257\nL144.647 192.578\nL145.648 195.534\nL146.65 197.123\nL147.652 197.123\nL148.653 195.534\nL149.655 192.578\nL151.659 184.363\nL152.66 180.257\nL153.662 176.931\nL154.664 174.851\nL155.666 174.31\nL156.667 175.383\nL157.669 177.921\nL158.671 181.566\nL160.674 190.048\nL161.676 193.693\nL162.678 196.23\nL163.679 197.304\nL164.681 196.763\nL165.683 194.683\nL166.684 191.357\nL169.69 179.036\nL170.691 176.08\nL171.693 174.491\nL172.695 174.491\nL173.696 176.08\nL174.698 179.036\nL176.702 187.251\nL177.703 191.357\nL178.705 194.683\nL179.707 196.763\nL180.709 197.304\nL181.71 196.23\nL182.712 193.693\nL183.714 190.048\nL185.717 181.566\nL186.719 177.921\nL187.721 175.383\nL188.722 174.31\nL189.724 174.851\nL190.726 176.931\nL191.727 180.257\nL194.733 192.578\nL195.734 195.534\nL196.736 197.123\nL197.738 197.123\nL198.739 195.534\nL199.741 192.578\nL201.745 184.363\nL202.746 180.257\nL203.748 176.931\nL204.75 174.851\nL205.751 174.31\nL206.753 175.383\nL207.755 177.921\nL208.757 181.566\nL210.76 190.048\nL211.762 193.693\nL212.764 196.23\nL213.765 197.304\nL214.767 196.763\nL215.769 194.683\nL216.77 191.357\nL219.776 179.036\nL220.777 176.08\nL221.779 174.491\nL222.781 174.491\nL223.782 176.08\nL224.784 179.036\nL226.788 187.251\nL227.789 191.357\nL228.791 194.683\nL229.793 196.763\nL230.794 197.304\nL231.796 196.23\nL232.798 193.693\nL233.8 190.048\nL235.803 181.566\nL236.805 177.921\nL237.807 175.383\nL238.808 174.31\nL239.81 174.851\nL240.812 176.931\nL241.813 180.257\nL244.819 192.578\nL245.82 195.534\nL246.822 197.123\nL247.824 197.123\nL248.825 195.534\nL249.827 192.578\nL251.831 184.363\nL252.832 180.257\nL253.834 176.931\nL254.836 174.851\nL255.837 174.31\nL256.839 175.383\nL257.841 177.921\nL258.843 181.566\nL260.846 190.048\nL261.848 193.693\nL262.849 196.23\nL263.851 197.304\nL264.853 196.763\nL265.855 194.683\nL266.856 191.357\nL269.862 179.036\nL270.863 176.08\nL271.865 174.491\nL272.867 174.491\nL273.868 176.08\nL274.87 179.036\nL276.874 187.251\nL277.875 191.357\nL278.877 194.683\nL279.879 196.763\nL280.88 197.304\nL281.882 196.23\nL282.884 193.693\nL283.886 190.048\nL285.889 181.566\nL286.891 177.921\nL287.892 175.383\nL288.894 174.31\nL289.896 174.851\nL290.898 176.931\nL291.899 180.257\nL294.905 192.578\nL295.906 195.534\nL296.908 197.123\nL297.91 197.123\nL298.911 195.534\nL299.913 192.578\nL301.917 184.363\nL302.918 180.257\nL303.92 176.931\nL304.922 174.851\nL305.923 174.31\nL306.925 175.383\nL307.927 177.921\nL308.929 181.566\nL310.932 190.048\nL311.934 193.693\nL312.935 196.23\nL313.937 197.304\nL314.939 196.763\nL315.941 194.683\nL316.942 191.357\nL319.947 179.036\nL320.949 176.08\nL321.951 174.491\nL322.953 174.491\nL323.954 176.08\nL324.956 179.036\nL326.96 187.251\nL327.961 191.357\nL328.963 194.683\nL329.965 196.763\nL330.966 197.304\nL331.968 196.23\nL332.97 193.693\nL333.972 190.048\nL335.975 181.566\nL336.977 177.921\nL337.978 175.383\nL338.98 174.31\nL339.982 174.851\nL340.984 176.931\nL341.985 180.257\nL344.99 192.578\nL345.992 195.534\nL346.994 197.123\nL347.996 197.123\nL348.997 195.534\nL349.999 192.578\nL352.003 184.363\nL353.004 180.257\nL354.006 176.931\nL355.008 174.851\nL356.009 174.31\nL357.011 175.383\nL358.013 177.921\nL359.015 181.566\nL361.018 190.048\nL362.02 193.693\nL363.021 196.23\nL364.023 197.304\nL365.025 196.763\nL366.027 194.683\nL367.028 191.357\nL370.033 179.036\nL371.035 176.08\nL372.037 174.491\nL373.039 174.491\nL374.04 176.08\nL375.042 179.036\nL377.045 187.251\nL378.047 191.357\nL379.049 194.683\nL380.051 196.763\nL381.052 197.304\nL382.054 196.23\nL383.056 193.693\nL384.058 190.048\nL386.061 181.566\nL387.063 177.921\nL388.064 175.383\nL389.066 174.31\nL390.068 174.851\nL391.07 176.931\nL392.071 180.257\nL395.076 192.578\nL396.078 195.534\nL397.08 197.123\nL398.082 197.123\nL399.083 195.534\nL400.085 192.578\nL402.088 184.363\nL403.09 180.257\nL404.092 176.931\nL405.094 174.851\nL406.095 174.31\nL407.097 175.383\nL408.099 177.921\nL409.101 181.566\nL411.104 190.048\nL412.106 193.693\nL413.107 196.23\nL414.109 197.304\nL415.111 196.763\nL416.113 194.683\nL417.114 191.357\nL420.119 179.036\nL421.121 176.08\nL422.123 174.491\nL423.125 174.491\nL424.126 176.08\nL425.128 179.036\nL427.131 187.251\nL428.133 191.357\nL429.135 194.683\nL430.137 196.763\nL431.138 197.304\nL432.14 196.23\nL433.142 193.693\nL434.143 190.048\nL435.145 185.807\nL435.145 185.807\" style=\"fill:none;stroke:#ff0000;stroke-linecap:square;\"/>\n   </g>\n   <g id=\"matplotlib.axis_1\">\n    <g id=\"xtick_1\">\n     <g id=\"line2d_4\">\n      <defs>\n       <path d=\"\nM0 0\nL0 -4\" id=\"m93b0483c22\" style=\"stroke:#000000;stroke-width:0.5;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"34.4575\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_5\">\n      <defs>\n       <path d=\"\nM0 0\nL0 4\" id=\"m741efc42ff\" style=\"stroke:#000000;stroke-width:0.5;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"34.4575\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_1\">\n      <!-- 0 -->\n      <defs>\n       <path d=\"\nM31.7812 66.4062\nQ24.1719 66.4062 20.3281 58.9062\nQ16.5 51.4219 16.5 36.375\nQ16.5 21.3906 20.3281 13.8906\nQ24.1719 6.39062 31.7812 6.39062\nQ39.4531 6.39062 43.2812 13.8906\nQ47.125 21.3906 47.125 36.375\nQ47.125 51.4219 43.2812 58.9062\nQ39.4531 66.4062 31.7812 66.4062\nM31.7812 74.2188\nQ44.0469 74.2188 50.5156 64.5156\nQ56.9844 54.8281 56.9844 36.375\nQ56.9844 17.9688 50.5156 8.26562\nQ44.0469 -1.42188 31.7812 -1.42188\nQ19.5312 -1.42188 13.0625 8.26562\nQ6.59375 17.9688 6.59375 36.375\nQ6.59375 54.8281 13.0625 64.5156\nQ19.5312 74.2188 31.7812 74.2188\" id=\"BitstreamVeraSans-Roman-30\"/>\n      </defs>\n      <g transform=\"translate(31.4340625 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_2\">\n     <g id=\"line2d_6\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"98.2289285714\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_7\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"98.2289285714\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_2\">\n      <!-- 2 -->\n      <defs>\n       <path d=\"\nM19.1875 8.29688\nL53.6094 8.29688\nL53.6094 0\nL7.32812 0\nL7.32812 8.29688\nQ12.9375 14.1094 22.625 23.8906\nQ32.3281 33.6875 34.8125 36.5312\nQ39.5469 41.8438 41.4219 45.5312\nQ43.3125 49.2188 43.3125 52.7812\nQ43.3125 58.5938 39.2344 62.25\nQ35.1562 65.9219 28.6094 65.9219\nQ23.9688 65.9219 18.8125 64.3125\nQ13.6719 62.7031 7.8125 59.4219\nL7.8125 69.3906\nQ13.7656 71.7812 18.9375 73\nQ24.125 74.2188 28.4219 74.2188\nQ39.75 74.2188 46.4844 68.5469\nQ53.2188 62.8906 53.2188 53.4219\nQ53.2188 48.9219 51.5312 44.8906\nQ49.8594 40.875 45.4062 35.4062\nQ44.1875 33.9844 37.6406 27.2188\nQ31.1094 20.4531 19.1875 8.29688\" id=\"BitstreamVeraSans-Roman-32\"/>\n      </defs>\n      <g transform=\"translate(95.4520535714 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-32\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_3\">\n     <g id=\"line2d_8\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"162.000357143\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_9\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"162.000357143\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_3\">\n      <!-- 4 -->\n      <defs>\n       <path d=\"\nM37.7969 64.3125\nL12.8906 25.3906\nL37.7969 25.3906\nz\n\nM35.2031 72.9062\nL47.6094 72.9062\nL47.6094 25.3906\nL58.0156 25.3906\nL58.0156 17.1875\nL47.6094 17.1875\nL47.6094 0\nL37.7969 0\nL37.7969 17.1875\nL4.89062 17.1875\nL4.89062 26.7031\nz\n\" id=\"BitstreamVeraSans-Roman-34\"/>\n      </defs>\n      <g transform=\"translate(158.812857143 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-34\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_4\">\n     <g id=\"line2d_10\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"225.771785714\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_11\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"225.771785714\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_4\">\n      <!-- 6 -->\n      <defs>\n       <path d=\"\nM33.0156 40.375\nQ26.375 40.375 22.4844 35.8281\nQ18.6094 31.2969 18.6094 23.3906\nQ18.6094 15.5312 22.4844 10.9531\nQ26.375 6.39062 33.0156 6.39062\nQ39.6562 6.39062 43.5312 10.9531\nQ47.4062 15.5312 47.4062 23.3906\nQ47.4062 31.2969 43.5312 35.8281\nQ39.6562 40.375 33.0156 40.375\nM52.5938 71.2969\nL52.5938 62.3125\nQ48.875 64.0625 45.0938 64.9844\nQ41.3125 65.9219 37.5938 65.9219\nQ27.8281 65.9219 22.6719 59.3281\nQ17.5312 52.7344 16.7969 39.4062\nQ19.6719 43.6562 24.0156 45.9219\nQ28.375 48.1875 33.5938 48.1875\nQ44.5781 48.1875 50.9531 41.5156\nQ57.3281 34.8594 57.3281 23.3906\nQ57.3281 12.1562 50.6875 5.35938\nQ44.0469 -1.42188 33.0156 -1.42188\nQ20.3594 -1.42188 13.6719 8.26562\nQ6.98438 17.9688 6.98438 36.375\nQ6.98438 53.6562 15.1875 63.9375\nQ23.3906 74.2188 37.2031 74.2188\nQ40.9219 74.2188 44.7031 73.4844\nQ48.4844 72.75 52.5938 71.2969\" id=\"BitstreamVeraSans-Roman-36\"/>\n      </defs>\n      <g transform=\"translate(222.751160714 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-36\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_5\">\n     <g id=\"line2d_12\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"289.543214286\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_13\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"289.543214286\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_5\">\n      <!-- 8 -->\n      <defs>\n       <path d=\"\nM31.7812 34.625\nQ24.75 34.625 20.7188 30.8594\nQ16.7031 27.0938 16.7031 20.5156\nQ16.7031 13.9219 20.7188 10.1562\nQ24.75 6.39062 31.7812 6.39062\nQ38.8125 6.39062 42.8594 10.1719\nQ46.9219 13.9688 46.9219 20.5156\nQ46.9219 27.0938 42.8906 30.8594\nQ38.875 34.625 31.7812 34.625\nM21.9219 38.8125\nQ15.5781 40.375 12.0312 44.7188\nQ8.5 49.0781 8.5 55.3281\nQ8.5 64.0625 14.7188 69.1406\nQ20.9531 74.2188 31.7812 74.2188\nQ42.6719 74.2188 48.875 69.1406\nQ55.0781 64.0625 55.0781 55.3281\nQ55.0781 49.0781 51.5312 44.7188\nQ48 40.375 41.7031 38.8125\nQ48.8281 37.1562 52.7969 32.3125\nQ56.7812 27.4844 56.7812 20.5156\nQ56.7812 9.90625 50.3125 4.23438\nQ43.8438 -1.42188 31.7812 -1.42188\nQ19.7344 -1.42188 13.25 4.23438\nQ6.78125 9.90625 6.78125 20.5156\nQ6.78125 27.4844 10.7812 32.3125\nQ14.7969 37.1562 21.9219 38.8125\nM18.3125 54.3906\nQ18.3125 48.7344 21.8438 45.5625\nQ25.3906 42.3906 31.7812 42.3906\nQ38.1406 42.3906 41.7188 45.5625\nQ45.3125 48.7344 45.3125 54.3906\nQ45.3125 60.0625 41.7188 63.2344\nQ38.1406 66.4062 31.7812 66.4062\nQ25.3906 66.4062 21.8438 63.2344\nQ18.3125 60.0625 18.3125 54.3906\" id=\"BitstreamVeraSans-Roman-38\"/>\n      </defs>\n      <g transform=\"translate(286.543214286 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-38\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_6\">\n     <g id=\"line2d_14\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"353.314642857\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_15\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"353.314642857\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_6\">\n      <!-- 10 -->\n      <defs>\n       <path d=\"\nM12.4062 8.29688\nL28.5156 8.29688\nL28.5156 63.9219\nL10.9844 60.4062\nL10.9844 69.3906\nL28.4219 72.9062\nL38.2812 72.9062\nL38.2812 8.29688\nL54.3906 8.29688\nL54.3906 0\nL12.4062 0\nz\n\" id=\"BitstreamVeraSans-Roman-31\"/>\n      </defs>\n      <g transform=\"translate(346.737142857 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n       <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_7\">\n     <g id=\"line2d_16\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"417.086071429\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_17\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"417.086071429\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_7\">\n      <!-- 12 -->\n      <g transform=\"translate(410.711071429 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n       <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-32\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_8\">\n     <g id=\"line2d_18\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"480.8575\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_19\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"480.8575\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_8\">\n      <!-- 14 -->\n      <g transform=\"translate(474.218125 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n       <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-34\"/>\n      </g>\n     </g>\n    </g>\n   </g>\n   <g id=\"matplotlib.axis_2\">\n    <g id=\"ytick_1\">\n     <g id=\"line2d_20\">\n      <defs>\n       <path d=\"\nM0 0\nL4 0\" id=\"m728421d6d4\" style=\"stroke:#000000;stroke-width:0.5;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"34.4575\" xlink:href=\"#m728421d6d4\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_21\">\n      <defs>\n       <path d=\"\nM0 0\nL-4 0\" id=\"mcb0005524f\" style=\"stroke:#000000;stroke-width:0.5;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"480.8575\" xlink:href=\"#mcb0005524f\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"text_9\">\n      <!-- \u221215 -->\n      <defs>\n       <path d=\"\nM10.5938 35.5\nL73.1875 35.5\nL73.1875 27.2031\nL10.5938 27.2031\nz\n\" id=\"BitstreamVeraSans-Roman-2212\"/>\n       <path d=\"\nM10.7969 72.9062\nL49.5156 72.9062\nL49.5156 64.5938\nL19.8281 64.5938\nL19.8281 46.7344\nQ21.9688 47.4688 24.1094 47.8281\nQ26.2656 48.1875 28.4219 48.1875\nQ40.625 48.1875 47.75 41.5\nQ54.8906 34.8125 54.8906 23.3906\nQ54.8906 11.625 47.5625 5.09375\nQ40.2344 -1.42188 26.9062 -1.42188\nQ22.3125 -1.42188 17.5469 -0.640625\nQ12.7969 0.140625 7.71875 1.70312\nL7.71875 11.625\nQ12.1094 9.23438 16.7969 8.0625\nQ21.4844 6.89062 26.7031 6.89062\nQ35.1562 6.89062 40.0781 11.3281\nQ45.0156 15.7656 45.0156 23.3906\nQ45.0156 31 40.0781 35.4375\nQ35.1562 39.8906 26.7031 39.8906\nQ22.75 39.8906 18.8125 39.0156\nQ14.8906 38.1406 10.7969 36.2812\nz\n\" id=\"BitstreamVeraSans-Roman-35\"/>\n      </defs>\n      <g transform=\"translate(7.45125 361.918125)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-2212\"/>\n       <use x=\"83.7890625\" xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n       <use x=\"147.412109375\" xlink:href=\"#BitstreamVeraSans-Roman-35\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_2\">\n     <g id=\"line2d_22\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"34.4575\" xlink:href=\"#m728421d6d4\" y=\"301.006875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_23\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"480.8575\" xlink:href=\"#mcb0005524f\" y=\"301.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_10\">\n      <!-- \u221210 -->\n      <g transform=\"translate(7.2 304.318125)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-2212\"/>\n       <use x=\"83.7890625\" xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n       <use x=\"147.412109375\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_3\">\n     <g id=\"line2d_24\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"34.4575\" xlink:href=\"#m728421d6d4\" y=\"243.406875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_25\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"480.8575\" xlink:href=\"#mcb0005524f\" y=\"243.406875\"/>\n      </g>\n     </g>\n     <g id=\"text_11\">\n      <!-- \u22125 -->\n      <g transform=\"translate(15.08625 246.718125)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-2212\"/>\n       <use x=\"83.7890625\" xlink:href=\"#BitstreamVeraSans-Roman-35\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_4\">\n     <g id=\"line2d_26\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"34.4575\" xlink:href=\"#m728421d6d4\" y=\"185.806875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_27\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"480.8575\" xlink:href=\"#mcb0005524f\" y=\"185.806875\"/>\n      </g>\n     </g>\n     <g id=\"text_12\">\n      <!-- 0 -->\n      <g transform=\"translate(24.410625 189.118125)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_5\">\n     <g id=\"line2d_28\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"34.4575\" xlink:href=\"#m728421d6d4\" y=\"128.206875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_29\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"480.8575\" xlink:href=\"#mcb0005524f\" y=\"128.206875\"/>\n      </g>\n     </g>\n     <g id=\"text_13\">\n      <!-- 5 -->\n      <g transform=\"translate(24.796875 131.518125)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-35\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_6\">\n     <g id=\"line2d_30\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"34.4575\" xlink:href=\"#m728421d6d4\" y=\"70.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_31\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"480.8575\" xlink:href=\"#mcb0005524f\" y=\"70.606875\"/>\n      </g>\n     </g>\n     <g id=\"text_14\">\n      <!-- 10 -->\n      <g transform=\"translate(17.3025 73.918125)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n       <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_7\">\n     <g id=\"line2d_32\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"34.4575\" xlink:href=\"#m728421d6d4\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_33\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"480.8575\" xlink:href=\"#mcb0005524f\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_15\">\n      <!-- 15 -->\n      <g transform=\"translate(17.55375 16.318125)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n       <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-35\"/>\n      </g>\n     </g>\n    </g>\n   </g>\n   <g id=\"patch_3\">\n    <path d=\"\nM34.4575 13.0069\nL480.857 13.0069\" style=\"fill:none;stroke:#000000;\"/>\n   </g>\n   <g id=\"patch_4\">\n    <path d=\"\nM480.857 358.607\nL480.857 13.0069\" style=\"fill:none;stroke:#000000;\"/>\n   </g>\n   <g id=\"patch_5\">\n    <path d=\"\nM34.4575 358.607\nL480.857 358.607\" style=\"fill:none;stroke:#000000;\"/>\n   </g>\n   <g id=\"patch_6\">\n    <path d=\"\nM34.4575 358.607\nL34.4575 13.0069\" style=\"fill:none;stroke:#000000;\"/>\n   </g>\n  </g>\n </g>\n <defs>\n  <clipPath id=\"p5a34f26e57\">\n   <rect height=\"345.6\" width=\"446.4\" x=\"34.4575\" y=\"13.006875\"/>\n  </clipPath>\n </defs>\n</svg>\n",
       "text": "Figure(PyObject <matplotlib.figure.Figure object at 0x7826d10>)"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": "1-element Array{Any,1}:\n PyObject <matplotlib.lines.Line2D object at 0x75f5750>"
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<img src=\"https://lh4.googleusercontent.com/--z5eKJbB7sg/UffjL1iAd4I/AAAAAAAABOc/S_wDVyDOBfQ/gauss.jpg\">"
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": " \"Sin^2 phi is odious to me, even though Laplace made use of it; should\n it be feared that sin^2 phi might become ambiguous, which would perhaps\n never occur, or at most very rarely when speaking of sin(phi^2), well\n then, let us write (sin phi)^2, but not sin^2 phi, which by analogy\n should signify sin(sin phi).\""
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "x=(0:.01:2)*pi;\nplot(x,(sin^2)(x),\"b\")     # Squaring just works, y=sin(sin(x)), Gauss would be pleased!\nplot(x,sin(x).^2,\"r\")         ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAq8AAAIQCAYAAACv/Yc9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3Xd4VNXWBvB3Qigh9EiXLgJC6F3pvUhvAcKEhBJ6lAtYKNIUxBI6CIFMAoReRYoICCi9SFWRJkhPBKSHZO4fyyhIy8ycM3tmzvt7Hh6/LzI760aYWWfvtdcyWa1WK4iIiIiI3ICX6gCIiIiIiJKLySsRERERuQ0mr0RERETkNpi8EhEREZHbYPJKRERERG6DySsRERERuQ0mr0RERETkNpi8EhEREZHbYPJKRERERG5D9+T19u3bGDx4MOrXr4+sWbPCy8sLI0eOTPbrr169iqCgIGTNmhW+vr6oWrUqNm/erGPEREREROSqdE9er1+/jlmzZiE+Ph4tW7YEAJhMpmS99sGDB6hTpw62bNmCSZMmYfXq1ciePTsaNmyIbdu26Rk2EREREbkgb72/Qf78+fHnn38CAGJjYzF79uxkvzYiIgLHjh3Dzp07UalSJQBAzZo1UapUKQwePBi7du3SJWYiIiIick1OrXm1Wq02/f4VK1agaNGi/ySuAJAiRQp07twZe/bswaVLl7QOkYiIiIhcmEtf2Dp69ChKliz51Nf9/f0BAMeOHXN2SERERESkkEsnr3FxcciSJctTX0/6WmxsrLNDIiIiIiKFdK95VeHSpUssKSAiIiJyYTlz5kTOnDltfp1LJ69+fn6Ii4t76utJX/Pz83vq3126dAnly5fHxYsXdY+PiIiIiOyTK1cu7Nu3z+YE1qWTV39/fxw+fPiprx85cgQAUKJEiaf+3aVLl3Dx4kXMmzcPxYoV0z1GowoLC0N4eLjqMF7szh1g7Vpg0SLg7Fkgc2agfHmgbFkgZ04gSxYgTRrg5k0gLg44dQrYuxc4cgRISABq1gTat5fXJLO9m1bc4ufr5vgz1pfH/nwTEoDt24GFC+X9Ik0aoHRpeZ/Il0/eVzJkkPefuDjgwgVg3z7gwAHg9m2geHF5X6lXD0iVyqFQPPZn7EL4M9bPiRMn0LlzZ1y6dMmzkteWLVuid+/e2LNnDypWrAgAePToEebNm4fKlSsjR44cz31tsWLFULZsWWeFajiZMmVy3Z9vYiJgsQDvvQfExgItWwJz5wI1aiQvCf3rL2DePGDKFCA0FKhdG5g0ST50nMSlf74egj9jfXnkz/eHH4B+/YCDB4HKleV9ok0bIHXql782IQH45htg8mRg+HAgMhKYOBFo2tTucDzyZ+xi+DN2TU65sLVu3TosXboUa9asASBdApYuXYqlS5fi3r17AICQkBCkTJkS58+f/+d1wcHBKF68ONq2bYuYmBhs2rQJ7dq1w8mTJzF+/HhnhE7u5vBhoGpVIDgYqFNHdlOXLJFd1OTunqZPD/TqBRw9Cnz9NXD+PFCqFPDuu8Ddu7qGT0QuKDYW6NIFeOstIEUKYMcOYOdOoFOn5CWugLzu7beBjRvldKdQIfn/mzQBzp3TN34iD+OUndfevXvj3N9/OU0mE5YsWYIlS5bAZDLhzJkzyJs3LxITE5GYmPhEL9hUqVLhu+++w+DBg9GvXz/cvXsXZcqUwbp161CtWjVnhE7uwmoFZs+WXZHXXgO2bpWdVkeYTPLBUrcu8OWXwKhRwKZNkgwXKaJJ2ETk4nbulGP+O3eAWbPkwdjLwX2fEiWADRuAlSuBAQOAMmWAqCiHdmGJjMQpO69nzpz5JzlNSEh44v/OmzcvAGDu3LlP/P9JsmXLhsjISFy/fh13797FDz/8gNq1azsjbHIXd+/KrkiPHkBQkNSXOZq4Pi51ailB2LMHePhQatsWLdJufSJyPVarPLRWrw68+ipw6BDQrZvjiWsSk0lKmg4dkh3dt98GBg+W8gIieiGX7vNKrisgIEB1CCIuTi4+LF8OzJ8PzJghFyj0UKKEJMbNmgEdOgCffabP94EL/Xw9GH/G+nLrn29iItC/v5QK9e8PfP89kCePPt8rSxZg1Sp5P/niC6BdO+D+/WS91K1/xm6CP2PXZLLaOrPVxR04cADlypXD/v37WWTt6f74A2jYELh0SS5C/H2pT3dWKzBsGDB2rOyUjBvn9G4ERKSThw/lBGfhQmD6dKBnT+d97zVrJHmtUkVKCjJkcN73JnIyR/I1l+42QPRc58/Lcd6jR9K2xplt0UwmYMwY4JVXgHfekVZb06czgSVydw8fAq1aAd9+CyxeLJ0EnCnpQtfbb0uXk+++AzJmdG4MRG6AZQPkfq5dk1IBQFrXqOrnGxYGzJkDzJwJvP++mhiISBsJCVI7/+23sgPq7MQ1SbVqcuH01ClJYv/uyENE/+LOK7mXW7ekVODGDWlX858Lfk7XtavEFBYmQxCGDFEbDxHZzmoF+vSRTiJLlwL166uNp3RpKYWqWxdo2xZYsQJImVJtTEQuhDuv5D7i44EWLWRHYuNGaYnlCgYMkKbj770njceJyL189JGcoMyeLR0AXEGVKpK0btwoXQ4863oKkUO480ruY8AA2W3dtAkoWVJ1NE/66CO5ONazp/SArVJFdURElBxLlkgP508+kZMUV1K/vjwQd+oE+PsD//uf6oiIXAJ3Xsk9zJwpl6KmTpWLWq7GZJJxshUrys7NhQuqIyKilzl0SDoLBAS4bslPx45yqjNkCLB+vepoiFwCk1dyfTt2AH37Ar17A927q47m+VKlknq5VKmkvIEXLYhc1/Xr8ve0aFEpF3DlbiFjxkitf4cOwMmTqqMhUo7JK7m269dlNGPVqkB4uOpoXi57dunPeOwYj/iIXFVionQWuHNH6krTplUd0YulSAEsWCDvL23bJnuIAZGnYvJKrstqBUJCgAcP5I3bXW7bli0rk3KmTZPJOUTkWiZNAtatAywW9R1LkitjRhlLfeKElBEQGRiTV3Jd06YBq1cDc+cCuXOrjsY2oaFyJBkczPpXIldy8KDUj4aFAY0bq47GNqVLA59+CkycCKxdqzoaImWYvJJrOnoUGDhQal3fflt1NLYzmaSOzscHCAyUY0oiUuvOHbmc9cYbMtbZHfXvDzRpIhfNLl1SHQ2REkxeyfU8egSYzdLHdcIE1dHYz88PmDdPpuVMnao6GiIaOhQ4dw6IiQFSp1YdjX1MJjmN8vICevVi/1cyJCav5Ho++0xa2MydC6RJozoax9SsKZN73nsPOHNGdTRExvXjj3LcPmaMdBhwZ1mz/ltTv3ix6miInI7JK7mWEyeAESPkpn6FCqqj0ca4cfJhwyk5RGrcuyf15xUrSq2rJ2jdGmjTRkqrrl1THQ2RUzF5JdeRkCAfMAUKyMQqT5EundS/bt4MfPWV6miIjGfkSDn5mDNH2k55iilT5IG4Xz/VkRA5FZNXch0zZwK7dgEREXLRyZPUrSttvwYPBq5cUR0NkXEcPiylSCNGyEUtT5I9u5RCLFokrb+IDILJK7mGq1eBDz+Uo/U331QdjT7Gj5detYMHq46EyBisVqk5L1zYc4eGdOwI1K4tu68cXkAGweSVXMOQIXKL9pNPVEeiHz8/qX+NigK2b1cdDZHnmzdPxktPmSJjmz2RyST/+86dc+/uLEQ2YPJK6v3wAxAZKYnrK6+ojkZfSZdG+vSRlmBEpI8bN2S3tX17oE4d1dHoq1gx4N13gY8/ZlcTMgQmr6RWQoIkcuXLS8mAp/PykhY3R4+y9yuRnkaMkKEEn3+uOhLnGDZMTnc8pZsC0QsweSW15s4FfvpJjr086Rbwi5QrB/ToITeg4+JUR0PkeX75RR4Shw1zv9HS9kqXThL11aulswmRB2PySurcvi0fLgEBQKVKqqNxrpEjgfh4YPRo1ZEQeZ7BgyVpHTBAdSTO1a4dULmyjNZOSFAdDZFumLySOp9+Cvz5p2df0nqe7NmB99+X0oHfflMdDZHn2LpVdh/HjXP/CX22Mplk9/XQISA6WnU0RLph8kpqXLggvRfDwoB8+VRHo0ZYmCSxQ4aojoTIMyQmyq5jxYpyUcuIqlYF2raV1oN37qiOhkgXTF5JjWHDAF9f2X00qrRp5Xbw8uXSzoeIHDN/PnDgAPDFF7ILaVTjxgHXr8vPgcgDMXkl5zt+XHqdDh8OZMyoOhq1OnUCypSRJN5qVR0Nkft6+FDeU1q08NxBJ8lVsKB0cZkwAYiNVR0NkeaYvJLzDR8O5MkjN+6NzssLGDNGdl43bFAdDZH7mj1bGvWPGaM6EteQ9EA8frzqSIg0x+SVnGv/fmDZMunBmDq16mhcQ6NGslP04YfcfSWyx927krR26gQUL646GteQNavU1U+ZAly8qDoaIk0xeSXnGjoUKFIECAxUHYnrMJmk9vXAAal/JSLbTJ0KXLsGfPSR6khcy8CB0nFh7FjVkRBpylt1AGQgO3YA69cDixcD3s7/o5eQIBMjb9wAHjyQErmEBBl5niqV3J/KnFnukTn9rkf16kD9+pLct2hhnIENRI66eVMuKHXrBhQqpDoa15Ipk3QzGTZMRuUWKKA6IiJNMHkl5xkxAihVCmjdWrdvcfMmcPiw3Ak7fhw4fRo4f15+JXeYVcqUQI4cUpabNy/w+uvAG2/IaWTRojrm3WPHAhUqAIsWAR076vRNiDzM5MnSEmroUNWRuKa+fYEvv5TTnVmzVEdDpAkmr+QcO3bIyMLly+WSkkbOnJFlt20D9uwBfv5Zvp4ihWzCvPYaUKWKDJ7Jlg3IkkU2I9Kkkd1WLy8ZdBUfLwO//vxTktxLl4DffwfOnpX1r16VddOmBcqWlSE2tWsDb70FpE+v0f+Y8uWBJk2kdq99e+6+Er3MX39JO6ju3Y0zBtZWvr7AoEHAe+9Jgm/UvtrkUZi8knOMHg34+wPNmzu0THy8JKqrVwNr1kjy6uUFlC4tyeR770ly+frr2t4Hi40Fjh4F9u6VXwsWyIwFb2/pCd6smfwqXNjBbzRsmGTGy5ZJxk1Ezzd1quy6ctDHi4WGSmnFuHHA9OmqoyFyGJNX0t+uXcDGjVLraseua2Ii8MMP0n98yRLZGc2TB3j7bSkTrV5dalX15OcH1KghvwBpCnDyJPDdd8A338iGxv/+J1URnToBAQHAq6/a8Y0qVQIaNJBkv00bTXepiTzK7dsyCjU42M6/bAbi6ytvUMOGAR98IG+gRG6Mn4ykv9GjpWjUxlrXa9ekx/brr0uCum6dtIbdv1/aOU6dKhu5eieuz2IySVy9eskO8PXrUhHx+uvSxjZvXqkAWLNGLoXZZPhw2eZduVKX2Ik8wowZcvvyvfdUR+IeeveWGqdPP1UdCZHDmLySvvbt+3drMpm7iMeP/7uZMmyYHMt//72UCHzyiZQFuNrkR19foGVL2Vy+ckXuRVy9KqUEBQvKfYm//krmYlWrAnXqAKNGse8r0bPcvStPtkFBrOFMrvTpgXfflTcn9n0lN8fklfQ1bpzcmkpG/ea+fVIKULy4VBmMHg1cuCCTZKtXd58T9AwZgJCQf+tja9QABg+W3dhhw+RS2EsNHQr89JO0FiOiJ82dK8cd3HW1Td++cls1PFx1JEQOcZN0gNzSL7/IWfqgQS+8OX/4sLQ2rVAB+O03IDJSWlwNHgy88orzwtVD+fKSfJ8+DXTtKiV6BQrIpuqtWy94YY0aQMWKkvwT0b8ePZLbku3asa+rrTJmlFqnpJILIjfF5JX089lnQPbsQJcuz/zXly7JDmXp0lLiGR0t/zSbpY2VJ8mTRzr6nDkjSezHH0tnglmznlMTazLJrtK2bcDOnU6Pl8hlLV4sPezYYcA+AwbIhBZ2HSA3xuSV9HHxomw5vvOOHFM95uFDYPx4udy0apX0GD9xAujc2fNbm2bPLvWvJ09Kp4QePaSGd/v2Z/zm5s1llO748U6Pk8glWa3y96FhQ3nqJdvlyCG1wuHhwL17qqMhsguTV9JHeLgkrT17PvHlH36QZO3DD2Wa48mTQJ8+MtXKSPLkkZ3m3bsBHx+p6e3e/T9TwLy8pHZi1SrJ7omMbv16qTPirqtj/vc/qRm2WFRHQmQXJq+kvRs3pKaqd2+psYL0Ee/TRyZS+fpKu6svv1TT5sqVVKwI/PijnOAtWSLjZ5cte+w3dOokk4PY3oZIdl0rVfq34TLZ57XXpI/0hAl29PIjUo/JK2kvIgK4fx/o3x+A7LaWKiUXhCdOlGStVCnFMboQLy8ZgHPiBPDmm/KZEhj4932K1KmlRm3BAunBRWRUBw5Iz7xBg1yvV547GjRIbpKuXq06EiKbMXklbT16JEWsAQF4lDUnhg+XI/Fs2aTzU//+nl/Xaq+cOaU5Q1SUDDfw9wd27IDUV6RMKbvZREY1cSKQP7+0JiHHlS8vR2Fsm0VuiMkraWvVKuDcOVwNGIA6dYCxY4GRI+VCUuHCqoNzfSaT7LoeOSIttWrWBD6enhnWLmZg2jTgwQPVIRI53+XLQEyM9Cnl0692BgyQjiYHDqiOhMgmTF5JW+HhuOFfDcUDy+LUKWDrVum3z88b2+TJA2zeDLz/vvz8uh/pLyO7Fi5UHRqR802fLv3zQkJUR+JZWrSQ6SkTJ6qOhMgmTF5JM9a9+4AdO9DtWBhKlwYOHQKqVVMdlfvy9pYpYxs2ACuOF8GWtI1xb1w4R8aSsdy/L8lr165Apkyqo/Es3t5Av36yq335supoiJKNyStp4sEDYGeHiTiD/CgwoDnWrXP/6Viuol49GZ27MHsYfH4+hO1jt6kOich5YmKAa9f+uQBKGgsJkV1tDi0gN8LklRz2559Ax5oXUf70IsR17IcJX6SAt7fqqDxLgQLAF4fr4nyGN3B9WDjvWJAxWK1yoahpUxbN6yVzZhlaMH267HITuQEmr+SQM2eAqlWBKoemw8snNcpNY02aXnzTmZD70zA0xypMeuc0+vdni0bycFu3ylCCsDDVkXi2/v1ldzsmRnUkRMnC5JXsdviwJK5eD+4hzGcGvLt1/WcoAenDq0tnePllwcrakzFtGhAQwAYE5MHCw4ESJYDatVVH4tlefx1o0kR+3qypJzfA5JXssnOnDLnJmRPY2W8BvG/ESuE/6cvHB+jZEyX3RmBV9C2sXg00ayYTzIg8ym+/ScPjsDAOJXCGsDDZkdi6VXUkRC/F5JVs9t13QN26siGyZbMVGeawJs2pevcG7t1Dk6tzsW6dTCyrXx+4eVN1YEQamjwZ8PMDOnZUHYkx1KkDFC/OoQXkFpi8kk02bZI8tVo1aeGUcf9m4OhR1qQ5U+7cQLt2wKRJqFU9AZs3y2jZBg2YwJKHuHkTmDNH5ib7+KiOxhhMJnkfX7NGdr2JXBiTV0q2TZuAt9+W8rOVK4G0aSHNrf39gVq1VIdnLGFhMpd87VpUqCD/bX79lTuw5CHmzpWb7716qY7EWDp1ArJkAaZMUR0J0QsxeaVk2bLl38R12TIgTRoAv/8OrF0L9OnDmjRnq1BBfv3dm7FsWUlgT56UHdi//lIcH5G9rFZgxgygdWsgVy7V0RiLj4/0fbVYgLt3VUdD9FxMXuml9uyRS0HVqz+WuALArFmAry9r0lTp1UtqN06fBvBvAnviBNC8OVs2kpvauhX45RfuuqrSsydw4wawaJHqSIiei8krvdDRo0CjRkDJksDy5Y8lrvHxwOzZQOfOQPr0SmM0rPbtpTXZrFn/fKlsWeDrr4Fdu+Rfx8crjI/IHjNmAMWKydMyOV/BgnJ8M2OG6kiInovJKz3X2bNSQ5k3r1QH+Po+9i9XrZJZ2KGhqsKjtGkBsxmIiHii2Wu1avKgsW6dnACybSO5jcuX5Q9vaChLkVTq1UuO3A4cUB0J0TMxeaVniouTHVcfH2D9eiBTpv/8hhkzZEJByZJK4qO/9ewpk3FWrHjiyw0bAtHR8mvoUEWxEdlqzhwgZUqgSxfVkRhbkybAq69y95VcFpNXesr9+1Izee2aJK7Zs//nN/z6qzR7ZU2aesWKATVr/nNx63Ht2wMTJgAff8zPIHIDCQnAzJkyNu6pp2VyKm9voHt3YP58ti8hl8TklZ6QmCgn0fv2Sbu/Z84dmDlTmoe3aeP0+OgZQkOBbduA48ef+lcDB8rgsz59pPSDyGWtXy8dTFiK5BpCQqQcad481ZEQPYXJKz1h5Ehg8WJ54K5S5Rm/4d496cHYtetjt7dIqZYtgWzZnrm9ajIBX34pbc46dJALeEQuafp0oFw5aQFH6uXOLUdw06ezcJ5cDpNX+seiRcCoUcDYsUCrVs/5TUuWAH/+CfTo4dTY6AVSpZJdkqgo4M6dp/51ihSyeVKwoCSx164piJHoRc6dA775hruuriY0FDh2DPjhB9WRED2BySsBkDKBoCAZsPL++y/4jdOnA/XqPaeegJTp0QO4dQtYuPCZ/zpdOmD1auk73ro18PChk+MjepGvvpKWewEBqiOhx9WpA7z22jNr6olUYvJKuHZNdlpLlpTWrc/tUHPokDQQ5e6I68mfX9pDvOBmVr580pRg1y7g3XedFxrRCz18KO3eunT5Tz8+Us7LSzqaLF3KIxtyKUxeDe7RI6mFvH//P9OznmXGDCBnTjl7JtcTGipb6Pv2Pfe3VK0KTJoETJ0qbbSIlFu5ErhyhQ/FriooSHY05s5VHQnRP5i8GtzQoTKNcdEiaev3XH/9Jbe4uneXPozkeho3BvLkeWlfrJ49paNEz57ATz85KTai55kxQyZrFC+uOhJ6lldeAdq2lS4ziYmqoyECwOTV0FauBMaPl1+1ar3kN8fESMFkt25OiY3skCKF1L7GxEj963OYTFLCVrSolIvcuOHEGIked/IksGWLPEmR6woNBU6flv7eRC6AyatBnT0r3a5atpReoC81e7aMbcqTR+/QyBFdu0oNyHMubiXx8ZEykdhY2UxnJxxSIiICyJxZbhGS66paVZ52IyJUR0IEgMmrIcXHS51rxozyXvTSEeJHjgB790o7JnJtuXPLQ0YyPmQKFJBnkqVLOYGLFIiPBywWaXHCntGuzWSS9/8VK+SJl0gxJq8G9OGHwP79UueaOXMyXhARIU3wmzbVPTbSQLduwJ498tDxEm3aAL17A++8I80kiJzmm2+Ay5dZiuQuunSRmtf581VHQsTk1Wg2bpR59598AlSqlIwXPHgg19K7dJFm+OT6mjaVh41kHvF9/jlQrJjsxj9jxgGRPiIiZKJWqVKqI6HkyJYNaNZMjmtYZ0SKMXk1kOvX5ZZ5/fo29PlcuRKIi2PJgDtJmVL+Q0dHy8PHS6RJI3e8fv8dGDTICfERXbwoO6/cdXUv3brJic4L2vEROQOTV4OwWuUi+sOH0q7PK7n/5SMigDfflGJ9ch/BwfLQsXJlsn570aKyAzt9OvD11zrHRmSxyEkOJ2q5l/r1paciL26RYkxeDWLuXKm1nzULyJUrmS86exb49lvujrijokWBt96y6UMmNBRo0kTy3itXdIyNjM1qlT+XbdvKrVFyHylSSEeTBQtYY0RKMXk1gLNngQED5D2nVSsbXjh3rswbb9tWr9BITyEh8vBx9myyfrvJ9G+u27Mny9pIJ99/D5w6xVIkd9W1qwytWbpUdSRkYExePVxionxGZMkChIfb8MKEBEleO3TgvHF31batPHzYMNYxe3YZpLNqlWyuEGkuIgIoXFimapH7KVAAqFOHpQOkFJNXDzdjBrB5s7zPZMhgwwu//RY4f54lA+7M11dqCufOlYeRZGrZUl7Wrx9w6ZKO8ZHx3LghO3YhIcloME0uq1s3YPt24JdfVEdCBsXk1YOdPg0MHixHwHXr2vjiiAigRAmgQgVdYiMnCQmRh5Bvv7XpZZMny30alg+QphYskOEEZrPqSMgRLVpIk/A5c1RHQgbF5NVDWa0y9vOVV6Svq02uXZNz427duDvi7ipUAPz9bT7i8/OT8oE1a1g+QBqKiJA+xDlyqI6EHJEmDRAYKF0j4uNVR0MGxOTVQ0VHS7nAzJlS9mjzi00moHNnXWIjJ0oa67hqlTyU2KB5c6BdO5m+FRenU3xkHAcPAgcO8KKWpwgJkbYka9eqjoQMiMmrB4qNBQYOlLrFBg1sfLHVKkdBLVrI9hu5v86dJYmNjrb5peHhMufgvfd0iIuMZc4cIGdOoFEj1ZGQFkqWlJMdXtwiBZi8eqDBg4FHj4AvvrDjxfv3A8eOSTsU8gx+frKNGhlpcwFrzpzAuHHSH3jHDn3CIwN48EDqTwIDAW9v1dGQVrp2BdatY2Nocjomrx5m2zbZ4Bg3zs6yMotFphjUq6d5bKRQUJCMdTx0yOaX9uwJVKok/3z4UPvQyADWrpXaE17U8iwdOsjggvnzVUdCBsPk1YM8eCAJRpUqclnLrgUWLJBj5hQpNI+PFKpfX55mIiNtfqmXl9RO//KLjJAlspnFIkfMb7yhOhLSUubMUmI2dy7bkpBTMXn1IBMmAL/9JomGlz3/Zbk74rm8veWhZP58u7ZPS5UC3n0XGDVKhiMRJdvVq8A33/B9xVOZzcDRo3Ihj8hJmLx6iN9+A8aMkYta/v52LmKxAOXLc3fEU5nNcpvvm2/sevmIETKBq08fbrKQDRYskAuDHTqojoT04MCpDpG9mLx6AKsV6NVLLtcMH27nIkm7I0FBWoZGrqRECXk4sfNDxtcXmDIF2LABWLRI29DIg1ksQLNm7F7iqby95SLeggUsiienYfLqARYvBjZtAqZNA9KmtXMR7o4Yg9ks5SFXr9r18qZNgdatpffrX39pHBt5np9+kkuCLBnwbEmnOuz5Sk7C5NXN3bsnrbGaN3ewfaLFArz9NndHPF1AgDykODA268svgZs3gU8+0TAu8kwWC5A1K9CwoepISE/Fi8uFPJYOkJMweXVzX3wBXLpkxwjYxx0+LLsjLBnwfH5+coRrsdi9RJ48wKBB8mfvzBkNYyPPEh8vFwQ7dwZSplQdDekt6VSHPV/JCZi8urGLF2X3q39/oHBhBxbi7oixBAXJw4odPV+TDB4sefCQIdqFRR5mwwYpT2HJgDEk9Xx14FSHKLmCzwMqAAAgAElEQVSYvLqxDz4AfHyAoUMdWCQ+Hpg3D+jUibsjRtGggbQNcGD31ddXBmEsWSKDMYieEhkpPdZKlVIdCTmDBqc6RMnF5NVN7dsn7xGjRwOZMjmwEHdHjCdlSnlYmTfPodvBnToBFSsCYWFAQoKG8ZH7i4sD1qzh+4rRBAX9e0mPSEdMXt2Q1SoJg78/0K2bg4tZLLIzUrq0JrGRmzCbgevXZS65nby8gPBw6U0eFaVhbOT+Fi4EEhPlCYeMI+lUhxe3SGdMXt3QkiXADz/IrW9vbwcWiosDVq/m7ogRlSwJlC3r8BFflSrSwOCDD9g6ix4TGSntT7JlUx0JOZODk/yIkovJq5tJao3VrBlQp46Diy1cKOe9HTtqEhu5maAgOdq9ds2hZcaNA27ckH8S4cQJYO9ePhQbVdKpjp2T/IiSg8mrm/niC+ky8NlnGixmscjuSPbsGixGbiep52tMjEPL5M0rrbM+/5ytswjyvpI5s0y0IOPx9wfKlWPpAOmKyasbuXZNdrf69nWwNRYguyN79rC3q5G98ookGBp8yAweDGTJ4mDnC3J/CQlAdLSc5qROrToaUiUoSHq+OniqQ/Q8TF7dyMcfyyWZDz/UYDHujhAgHzIHD8qgCgekSweMGCGbuA4uRe5s0yY5GmLJgLFpMMmP6EWYvLqJ338Hpk0D/vc/DSa4Ju2OBARwd8ToGjWSARUa9GYMDgYKFdLo4Yrck8UCFCsGlC+vOhJSyc9Pxo2zdIB0wuTVTYwcCWTMKC2yHPbdd7I7wpIBerzna3y8w0uNGgV8/bV0wyCDuXkTWLFC3ldMJtXRkGoaTPIjeh4mr27g55/lAfbDD4H06TVYMDKSuyP0r6AgGVSxfr3DS7VvL22DP/hA+hGTgSxeLO2ROndWHQm5goYNpVUaJ26RDpi8uoFhw4BXXwVCQzVYLGl3xGzm7giJpCEVGhzxeXkBY8fKyNgNGxwPjdyIxQLUqwfkyqU6EnIFKVP+2/PVwVMdov9ySvJ6+/ZthIWFIXfu3PDx8UGZMmWwaNGil74uMjISXl5ez/x19epVJ0Su3v79wNKlwEcfaVSeumQJd0foaWaz9HyNjXV4qcaNgTfflN3XxEQNYiPX99tvUivCi1r0OLNZOg44MMmP6Fmckry2atUKUVFR+Oijj7B+/XpUqFABAQEBiElmf8nIyEjs2rXriV9ZsmTROWrX8MEHQNGiQGCgRgsm7Y7kzq3RguQROnaUc/6FCx1eymQCPvlEmhgsXapBbOT6oqKADBmAFi1UR0KupGRJoEwZlg6Q5hwZLpos33zzDTZt2oSYmBi0b98eAFCjRg2cO3cOgwYNQvv27eHl9eIcukSJEihbtqzeobqcrVuBjRslAXBoDGyS334Dduxg+xJ6WrZssmUaGQn06ePwctWqSSODYcOAVq00+vNLrikxUZKT9u0BHx/V0ZCrMZtliklsrAatcoiE7juvK1asQPr06dG2bdsnvt61a1dcvHgRu3fvfukaVgPe/LBagffflztVrVpptCh3R+hFzGZg3z7g+HFNlhs7Fvj1V3bL8Xjffy+9/FgyQM+SdKrj4CQ/osfpnrwePXoUxYoVe2p31d/fHwBw7Nixl67RtGlTeHt7w8/PD61bt07Wa9zd118Du3bJYAJN7lUlJkryyt0Rep4mTWRMlkZHfGXKyB+3kSOB+/c1WZJckcUCvPYaULWq6kjIFWXNKqc6LB0gDemevMbGxj6zPjXpa7EvuCCSM2dODB06FBEREdi6dStGjx6NvXv3onLlyjhy5IhuMatmtcoHfvXqQN26Gi36/ffAuXPcHaHnS51adkmio4FHjzRZcuRIaSk8d64my5GruX1b6prYvYReJChITnUMsPFEzuHSrbIaNGiAUaNGoXHjxnjrrbfQu3dvbN++HSaTCcOHD1cdnm7WrZMuAyNGaPh5wN0RSg6zGbh0ScZ8aqBIEaBDB7nA9fChJkuSK1m2DLhzR8MbpeSRmjSRelfuvpJGdL9G4efn98zd1bi4uH/+vS3y5cuHN998E7t27Xrh7wsLC0OmTJme+FpAQAACAgJs+n7OZrXKlKKqVYFatTRaNGl3ZMgQ7o7Qi5UrB7zxhnzINGyoyZIffgiUKCFLdu+uyZLkKiwWeaPKl091JOTKUqWSceTz5kktHG9wGk5MTMxTHaZu3Lhh93q6/wkqWbIkYmJikJiY+ETda9Kxf4kSJexa1/SSJCw8PNwtOxRs2gTs3i3DjjTLM5cvl92RLl00WpA8lskkR3zDhgE3bgD/eQC0xxtvAG3ayO5rUJD0LicPcO4csGULd9MoeYKCgClT5ENOowdjch/P2jw8cOAAypUrZ9d6upcNtGzZErdv38bS/zR8jIyMRO7cuVGpUiWb1jt9+jS2b9+OKlWqaBmmS0iqda1YEahfX8OFIyO5O0LJ17mzTMRZvFizJYcOBc6ckWE75CGiogBfXw3boZBHK1sWKF6c7UdIE7rvvDZs2BD16tVDr169cOvWLRQqVAgxMTHYuHEj5s+f/88OakhICKKionD69GnkyZMHAFCvXj3Url0bxYsXR7p06XDkyBF8+umn8Pb2xujRo/UO3em2bpUhNWvWaLjrmrQ7wjcMSq6cOeXpyWIBevTQZMmSJaVD29ixkhvz1NDNWa2SvLZpA6RLpzoacgcmk9TUa3iqQ8bllAtby5cvR2BgIIYPH45GjRph7969WLhw4RNbyImJiUhMTHyip6u/vz/mz5+PwMBANGzYEBMmTEDdunWxb98+vPHGG84I3alGjZL2Qk2aaLhodLTsjrRureGi5PHMZuDHH6VRq0aGDZM5GRoM8SLVfvxR/mOyewnZQodTHTImk9XDJgAk1VDs37/frWpet2+X1lgrVmg4Q8BqBV5/XQbNc+eVbHHvnuzA9u0LjBmj2bJvvy05z9GjQIoUmi1Lzta9O/Dtt8Dp08BLJiQSPaFxY9l5/fFH1ZGQYo7ka3zXcRGjR8vRarNmGi7K3RGyl4+PTBiIipIBFxoZNgz4+WdpfkFu6t492Tnr0oWJK9nObAZ27tT0VIeMh+88LmDnTtnEGDpU488Ci0UuadWooeGiZBhmM3D+vNRMa6RiRaBBA3lY0zAnJmdauRK4dYvdS8g+zZsDGTPKgzGRnZi8uoDRo6WdkKZlqffuAYsWSfNw7o6QPapUAQoX1rwV0vDhMmhnxQpNlyVniYyUUqTXXlMdCbmjNGlkconGpzpkLMxqFDt0SCZqffCBxjlm0u5IUJCGi5KhJN0OXrYM+OsvzZatWhWoXVv6vnpWxb0B/PGH9Ons2lV1JOTOdDjVIWNh8qrYhAlyst++vcYLR0YC1aoBhQppvDAZSmCg7OIvW6bpskOGyAhkfna5mehoIHVqoG1b1ZGQO6tcWZdTHTIOJq8KnT0rJ/sDB2rc9/LCBSmi5UUtclTevDLgQuNuFfXqAaVKAZ9+qumypCerVf4ctGoFZMigOhpyZzqd6pBxMHlV6MsvpW49OFjjhaOjpa6IuyOkhaAg4PvvZUSWRkwmYPBgYMMG4KefNFuW9LR7N/DLLyxFIm0kneqw9QjZgcmrIrGxwOzZ0kbT11fDhZN2R1q35u4IaaNVK5miFB2t6bJt20rJzIQJmi5LeomMBPLkkZ14IkflzSvF7ywdIDsweVVk2jS5aNm3r8YL79ol/fO4O0Ja8fWVMaAWi6Y3rFKmBN59VyZunTun2bKkh3v35D9Uly6cLkHaMZs1P9UhY2DyqsC9e8CkSVIukDWrxotbLNwdIe0FBck0pR07NF02JERKZ778UtNlSWurVwM3b7K3K2lLp1Md8nxMXhWIjATi4mTXSVNJuyNmM3u7kraqVQPy59f8iM/XF+jTB5g1S0ppyEVFRkqPs9dfVx0JeRJfX6kf0vhUhzwfMxwnS0gAPvtMTmE172K1ahV3R0gfXl7y52rxYuDuXU2X7ttXSmimT9d0WdLKH38AGzeyFIn0YTbrcqpDno3Jq5MtXy5/TwcN0mHxpMk3hQvrsDgZXpcu0tZG49FY2bJJz/tJk+TwgFzMvHlAqlRAu3aqIyFPpNOpDnk2Jq9OZLUC48fLBcvy5TVe/I8/pLcrd0dIL4UKyQeNDh8yAwdK2QA/v1xMUveSli2lOJlIa15esvuqw6kOeS4mr060datMFRoyRIfF583j5BvSn9ks40HPn9d02UKFpJTms8+ktIZcxN69wM8/86GY9KXTqQ55LiavTvT550DJkjJdSFOPT77h7gjpqW1bGYAxb57mSw8aBJw6JaXb5CIiI4HcuYE6dVRHQp6sYEHdTnXIMzF5dZJffwXWrgXeeUemC2lqzx7ZHeE4WNJbhgzykKTD7eDy5YG33gImTtR0WbLX/ftATAx7u5JzBAXJqc6FC6ojITfA5NVJJk+Wnq4dOuiweGQk8OqrUkxLpDezWcaE7t6t+dIDBgDbtgEHD2q+NNlq9Wrgxg0+FJNztGkjpzrs+UrJwOTVCW7cAObOBXr1kr+bmuLuCDlb7dpylKzDEV+LFjI1kruvLiAyEqhcGShSRHUkZAQ6nuqQ52Hy6gQREcDDh5K8ai6ptyt3R8hZUqQAAgNlIMb9+5ou7e0N9Osnz2NXrmi6NNni4kVgwwZe1CLnSjrV2bNHdSTk4pi86uzRIykZ6NAByJFDh29gsXDyDTmf2SxHCqtXa750SIgksTNmaL40Jdf8+UDKlED79qojISOpXVtK4CIjVUdCLo7Jq85WrwbOnZNaPs1xd4RUKVoUqFRJl9KBzJnlj/S0acCDB5ovTy/zeG/XTJlUR0NGouOpDnkWJq86mzhRblCXK6fD4px8QyqZzfLwdPmy5kv37w9cvQosWqT50vQy+/YBx4+zFInUSDrVWbNGdSTkwpi86ujAAbk5HRamw+KcfEOqdeggOyXz52u+dJEiQKNGQHg47244XWQkkCuXDg2piZKhSBG5KMier/QCTF51NHEikC8f0Ly5Dovv3g2cOMGSAVInc2ZpDzBnji4ZZliYtMzavl3zpel57t0DFiyQo1t2LyFVgoKAdeukNI7oGZi86uTyZSnb6dtXLp9oLiJCegrVravD4kTJFBwsR8w69HytVw8oVoxts5xqxQo5sg0OVh0JGVmHDjLunLuv9BxMXnUyY4Zc1g0J0WHx27clM+7aFfDif0JSqG5deYiKiNB8aZNJLjquXAmcOaP58vQsERFA9ersXkJqZcwoQwsiIlg3RM/EzEcHDx4A06fLyUfmzDp8g8WLgTt3JHklUilFCvlzuHChPFRpLDBQPsemTNF8afqvU6eAzZt1euImslG3bvJn8vvvVUdCLojJqw6WLJGb0v366fQNIiLkTDVfPp2+AZENunaVh6nFizVfOm1aoHt3Kau9e1fz5elxc+fKlKM2bVRHQgRUqwYULqzLqQ65PyavOpg2TU5TdZmqeOIE8OOP8lRK5Ary5ZOHKZ0+ZEJDZYhcTIwuyxMg01QiI4GOHeWJgUg1k0lOAZYulTpsoscwedXYwYPAzp1A7946fYOICMDPD2jWTKdvQGSHkBB5qDpxQvOlCxQAGjcGpk5l+ZtuNmwA/viDJQPkWsxmID5eOmAQPYbJq8amTZPpdm+/rcPiDx/K7cvAQLmJSeQqmjeXhyqddl/79JEHQx2aGhAg/91KldJpmgqRnXLkAJo0AWbPVh0JuRgmrxr680/p196zp07tsdasAa5f5+4IuZ7UqeWhKipKHrI01qABULCg7L6Sxq5ckfeWkBA5qiVyJd26yZPrwYOqIyEXwuRVQxaLlI7pVo4aESHz5EuU0OkbEDkgJAS4dk2XsY5eXkCvXnIn7No1zZc3tqgo6RrRqZPqSIie1qgRkDMnL27RE5i8aiQxUUoGWreWkw7NnT8PrF/PXVdyXSVKyMOVTh8ySW2N+RmmIatVfqCtWgFZsqiOhuhp3t5S+zpvnkyAIwKTV8189x1w8qTU5ukiMlJuAXfooNM3INJAt25y+ef8ec2X9vOTP/4zZgAJCZovb0w//gj88gu7l5BrCw6WliPLl6uOhFwEk1eNTJ0K+PsDb76pw+KJidLosl07IH16Hb4BkUbatwd8fORhSwe9ewPnzgHffKPL8sYze7a0c6hZU3UkRM9XuDBQowYvbtE/mLxq4PffpcyvTx+d7jts3gycPcvdEXJ96dPLQ9acOfLQpbEKFeQXL25p4NYtKSIODuaYaXJ93boBW7fK1C0yPL5jaWDmTCBdOh3vO8yeDRQtClSpotM3INJQt27ysPXdd7os37u3VCb89psuyxvHwoXA/fsyx5rI1bVuLbOiuftKYPLqsAcPgFmzpJ48XTodvsGVK1Ln07Mn29iQe6hSRS5vzZihy/Lt28vdounTdVneGKxW+e/TtKk0piZydT4+QJcucqqjQzs+ci9MXh20bJm07unVS6dvMGeOtLHp0kWnb0CkMZNJZrquWgVcvKj58j4+ctI9dy5w967myxvDnj3SNzM0VHUkRMnXsydw9SqwYoXqSEgxJq8Omj4dqFULKFZMh8UTEoCvvpIr1mxjQ+4kMBBIk0a3I75evWTc+aJFuizv+WbMAPLnl+kPRO6ieHGgenXdTnXIfTB5dcCxY8COHTpuXmzYILWD3B0hd5MhA9Cxo9TUPHqk+fIFCwL16smzHdkoLk7qXXv25EUtcj+hoXJx68QJ1ZGQQnzncsCsWUDWrECLFjp9gxkzgNKlgYoVdfoGRDoKDQUuXADWrtVl+Z49gV27gMOHdVnec0VFyalOcLDqSIhs16qVfPDOnKk6ElKIyaud7t2Tz4CuXYFUqXT4Br//Lh/6vXrxoha5p7Jl5cFLpyO+t98GsmeXh0hKpqSLWq1bA9myqY6GyHapU8uDl8XConcDY/Jqp6VLgT//BLp31+kbzJoF+PrK0SuRuwoNlfKX06c1XzplSvkMi47mZ1iybd0qE7VYikTurEcPmbjFonfDYvJqp6++AurUAV57TYfF4+PloktgoE79t4icpH176c2oU3Fq9+7yGbZ4sS7Le57p0+V2afXqqiMhsl/BgnLZkP3yDIvJqx2SLmr16KHTN1i1Crh8mbsj5P7SppUmyBER0hRZYwUKAPXr8+JWsly+LC2GQkNZikTur1cvYO9eYP9+1ZGQAkxe7aD7Ra3p04E33wT8/XX6BkROFBoKXL8uTZF10KMHsHMncOSILst7jogIqbVgz2jyBE2aAHnysG2WQTF5tZHuF7V++QXYvJm7ruQ5ihYFatbU7UOmWTO5uMXd1xdI6hkdEABkyqQ6GiLHpUghdUMLFkjtEBkKk1cb6X5Ra+ZMwM8PaNNGp29ApECvXsD27cDRo5ovzYtbybBunXQw0W0UIJEC3bpJOVJ0tOpIyMmYvNpI14ta9+4BkZGyrZsmjQ7fgEiRFi1ke1Sn3ddu3Xhx64VmzADKlQPKl1cdCZF2cuaU95bp06UNHBkGk1cb6H5RKyZGZl727KnTNyBSJFUqyTCjooBbtzRfvmBBXtx6rlOngG++4a4reabevYHjx4Hvv1cdCTkRk1cb6HpRy2oFwsOBpk112tYlUqxXLzldmDtXl+V5ces5Jk+WUiT2jCZPVKsWUKKEfH6SYTB5TSbdL2pt2SKfumFhOixO5AJy5wbatQMmTZILRBrjxa1nuHULmDNHLoD6+KiOhkh7JpN8bq5eLacMZAhMXpNp+XK5qNWtm07fIDxcWmPVqqXTNyByAQMGyLStr7/WfOmUKeXhMjpaHjYJkrjev8+SAfJsHTvK6cLkyaojISdh8ppMERFAjRpA4cI6LH7ypHyYh4WxeTh5tooVgapVdTviCw6Wi1srVuiyvHtJSJBd7nbtgFy5VEdDpB8fH7krMmeOLjX15HqYvCbDqVNyqh8SotM3YE0aGUlYGLB1K3DokOZLFy4sk08jIjRf2v2sWQOcOcNSJDKG3r3lyGXOHNWRkBMweU2GyEggQwagdWsdFr9xQ/6y9erF9lhkDC1bymSciRN1WT4kROZ8nDmjy/LuIzxcJvWxPRYZQa5cQPv2utXUk2th8voSCQmSvAYEyJh2zc2ZAzx8yJo0Mg5vb6BfP5mMc+WK5su3bg2kT69bUwP3cPCgtA7irisZSViYPLWuWaM6EtIZk9eX+PZb4MIFnUoGHj2Sp8QOHaTZMpFRdOsmSawOQwt8feVhMzLSwBswEycCefPq1NePyEWVLy+nDWyb5fGYvL5ERIS0kNPl5G31auDcObmBTWQkmTMDQUHAtGky3lFjwcHA+fPApk2aL+36Ll+WgSf9+skDApGRhIXJqcPBg6ojIR0xeX2B69eBVatk11WXJgDh4UC1ajK2kcho+vcHrl4FFi7UfOmKFYHixQ16d2PGDOkbptsNUyIX1qKFnDroVFNProHJ6wvMmyf/7NxZh8X37we2b2dNGhlXkSJA48byEKfxXHKTSXK3lSuB2FhNl3Zt9+/LnPegINndJjKapJr6mBg5hSCPxOT1OaxW2bVp3hx45RUdvkF4OJA/v3wDIqMKC5OWWTrMJe/cWf4eJz2EGkJMjOxm9++vOhIidUJC5PRh+nTVkZBOmLw+x759Mq01OFiHxX//XY5K+/cHUqTQ4RsQuYm6daWofMIEzZfOmlVGxkZEaL6x65oSE4HPPgOaNgVef111NETqZM4s4/amTgXu3FEdDemAyetzzJkjo9jr19dh8S++kF4+3bvrsDiRGzGZgCFDgG++AQ4f1nz54GB5CN2/X/OlXc/XXwPHjwPvvac6EiL1Bg6UPuqcWOKRmLw+w9270oIyKEiHjdHYWGDWLKnJSZdO48WJ3FD79kC+fMCnn2q+dIMG8hDq8Re3rFZg3DjgrbekVRCR0eXPLz3zPv8ciI9XHQ1pjMnrMyxfLuORdSkZmDJFPmj69dNhcSI3lDKl7JIsXAicPavp0ilSyEPoggUyOdJj7dgB7Nwpu9hEJAYP/rdMjzwKk9dniIgAatUCChbUeOE7d4DJk6VBuy63wIjcVEiI1Kl9/rnmS3ftCty8KQ+lHmvcOKkdbtxYdSRErsPfH2jSBBg/XmrCyWMwef2PU6eArVt12nWNiJAanHff1WFxIjeWNq2cRkREyG15DRUqBNSs6cGlb4cPS83w4MGAF9/SiZ4wZAhw7Biwdq3qSEhDfKf7j7lzgYwZZT66ph48kJq+jh2lFoeIntS3r5zzf/ml5ksHBwNbtsjDqccZO1beUzp0UB0Jket56y35NWaMQdqOGAOT18ckJMg89IAAwMdH48XnzgUuXQI+/FDjhYk8RJYssvs6ZYrmkwVatwYyZJC/3x7l+HFgyRLggw+kdpiInmQyAcOHA3v2ABs3qo6GNMLk9TEbNwJ//KHDVMWHD4FPPpGdkSJFNF6cyIO8+67sjoSHa7ps2rTyUBoZKQ+pHmPsWODVVwGzWXUkRK6rbl2gcmVg5EjuvnoIJq+PmTNH6rvLldN44ago4Px57roSvcwrrwC9ewOTJgF//qnp0iEhwIULwLffarqsOr/8Ireo338fSJVKdTREritp93XnTmDzZtXRkAaYvP7t2jVg1Sr5gDOZNFw4Pl52R9q2Bd54Q8OFiTzUwIHy92biRE2XLV9eHk495uLW2LFAzpw63S4l8jANG8qbwKhRqiMhDTB5/dv8+fLPTp00XjgqSnpXDh2q8cJEHip7diA0VEoHNNx9NZkkz1u1Crh+XbNl1fj1V3nTGjwYSJ1adTREri9p93XbNrm9SW6Nyevf5s4FmjfXuP3qgwdSY9OunWz5EFHyDBkiu6+ffabpsp07yz8XLNB0WecbMQLIlQvo0UN1JETuo2lToEIFKeFj7atbY/IK4NAhaZWo+Z2HmTPlBhiPKYhskz07MGCA7L5euaLZsq+8Ip9fFotmSzrfTz9Jrevw4UCaNKqjIXIfJpOU2+zcyb6vbo7JK+SDLHt2mYOumTt35C+J2cwOA0T2GDRI2j998ommy5rNwIEDwJEjmi7rPMOGAa+9JnNvicg2devK1JKhQzl1y40ZPnmNj5fSsU6dNG6TmHRbesQIDRclMpDMmSWBnT5d5pNrpHFjIGtWN9193bkTWLNGypHY15XIdkm7rz/9JD2SyS0ZPnldt046DWhaMvDnnzJNq2dPIF8+DRcmMpgBA2Tk3ciRmi2ZMqUMups3D3j0SLNl9We1yjACf39O0yJyRNWqQJMmcooRH686GrKD4ZNXiwUoXRooWVLDRceMkb8Q7OtK5Jh06eR4LzJS03N+s1lKaTds0GxJ/a1dC2zdCnz8MeBl+LduIsd8/DHw22/AV1+pjoTsYOh3wNhYOYHTtHTs1Clg8mTgvfeAHDk0XJjIoEJDgUKFgP/9T7Mlkx5Y3WZcbHy8lFDUri07RkTkmJIlga5dgY8+Am7eVB0N2cjQyWtMjJzEdeyo4aLvvQdkyyZjLonIcalSSRnOxo3A+vWaLGkyye7r6tVAXJwmS+pr1iyZqPX55xpPUSEysNGjgbt3ZReW3Iqhk1eL5d/LG5r44Qdg6VIpBk+bVqNFiQjNmwPVqsnuq0aFqp06AQkJwKJFmiynn5s35eJnly6yZUxE2siVS040wsNlmBC5DcMmr8ePA/v2aXhRKzFRxlqWKQMEBmq0KBEBkN3GL74Ajh3TbL5r9uxAo0ZuUDrw8cf/tt4jIm0NGgT4+cmpKbkNwyavFov8eW3aVKMFIyOB3bvlCY6XKYi0V768PG1+8IEUrGsgKAjYswc4cUKT5bT388/Al1/KxLHcuVVHQ+R5fH2ll/SiRRwb60YMmWU9egRERwMBAVJO57C4OPlw6dwZqF5dgwWJ6JnGj5ez/g8+0GS5pk2lnaxL9ny1WoF+/YA8eeT9hYj0ERgIvPkm0KcPW2e5CUMmr5s2AZcuaVgyMHQo8PAhMGGCRgsS0TNlzy6t6GbNki1TB6W4fVoAACAASURBVKVOLRc2o6MlJ3YpS5fKm9WkSRwDS6QnLy9g6lS5FDlxoupoKBkMmbxaLMAbbwDlymmw2P79wIwZwKhRbI1F5AyhoUCpUrJLokHGaTYDFy9Knugybt8G3nlHLqqxNRaR/kqVAvr2ldZZFy6ojoZewnDJ640bwIoVUuvmcMeZR49kipa/v3yQEpH+vL2BadPkxuW0aQ4vV768PMy6VOnAsGFSjhQerjoSIuMYNUoGo/TvL2U75LIMl7wuXiwlLZ07a7DY558DBw/KEaa3twYLElGyVKkC9O4NvP8+cOaMQ0sl9XxdscJFepXv3ClHl6NHA/nzq46GyDgyZgSmTJE3g6VLVUdDL2C45NViAerXB3LmdHChn3+W3osDBwIVK2oSGxHZYNw4aRnSvbvDuySdO0vZ+uLFGsVmr/v3geBgoEIFICxMcTBEBtSmDdC6tZymXr+uOhp6DkMlrydPAj/+qMFFrYQE+YDJmxcYOVKT2IjIRunTy6nHd9853Ps1Vy55qFXe83XUKOD0aWDOHCBFCsXBEBnUlCnyOT9ggOpI6DkMlbxaLHIq0Ly5gwuFhwO7dskHjI+PJrERkR3q15cHyYEDHZ6QYzbLw+3Jk9qEZrM9e2QM7vDhQPHiioIgIuTIIaU7CxZICQG5HMMkr4mJ0g6nfXsH880DB6TO7p13gLfe0iw+IrLT559Ls9ZOnRwaHdu8uTzcKrm4deuWNJ4uVw4YPFhBAET0hE6dgFatgJAQ4Px51dHQfxgmed26Ffj9dwdLBm7fBjp0kO4Cn3yiVWhE5IhMmWSHZPduOXa3k4+PPNxGR8vDrtNYrUCvXsC1a0BMDJAypRO/ORE9k8kkZUm+vlIU73KNoI3NMMmrxQIULiyXlO3Wr580hIyJ0Wg0FxFpompV6c84Zgzw/fd2LxMUJA+5W7dqFVgyREdL8j1jBlCwoBO/MRG9UJYswPz5wI4dwMcfq46GHmOI5PWvv6TrhdnsQG/XyEj5NXUq8PrrGkZHRJp4/30Zz9yxo4zQs0PlyvKQ67SLW0ePSssvs1niJiLXUr26TNEcOVIuh5JLMETyumwZcO+ejC+2y65dMoygWzegSxdNYyMijaRIIacigLS6efDA5iVMJtl9XbZMHnp1FRcnhbaFCslDMRG5pmHDgDp1gHbtpBsIKWeI5NViAWrVks5WNrt4UYq2K1SQDxiHx3IRkW5y5pTbwQcOyI6mHf1fAwPlYVfXHuWPHkmB7c2bwKpVUldHRK7J2xtYuFDKCJo3l/svpJTHJ69nz0r9ml0Xte7cAVq2lB2dZctY50rkDipWBL76SlrZTZxo88vz5JFNFt26DlitwLvvAlu2SIbMKVpEri9zZnnQPHtWnnB5gUspj09eo6JkVHHr1ja+8OFDedGxY8DKlUD27LrER0Q66NIFGDRIWtrNn2/zy81muffl4OTZZ/v4Y2DyZGmEXrOmDt+AiHTxxhtSmrR6NRAa6vBkP7KfRyevVqskr23a2Hgql5AgH35btkjiWq6cbjESkU7Gjwe6dpVMdM0am17asqU89EZFaRzTtGly+WPUKPnwIyL30rSpTPSbPVsuiZISHp28/vADcOqUjSUDCQnyobJkiTxh1a2rW3xEpCOTScoHmjWTixY23BT29ZWXWCwa9nyNigL69gXCwiSBJSL3FBQEfPmlPCCPHas6GkPy6OQ1MhLIl086XSTLw4cyVWPOHPnVqpWe4RGR3ry9pYdqzZpA48ZSu55MZrOUDezYoUEcEyfKgsHBMhGMFz+J3FtYGDB6tDyIDhrEEgIn0z15vX37NsLCwpA7d274+PigTJkyWLRoUbJee/XqVQQFBSFr1qzw9fVF1apVsXnz5mS99t49YPFi+bzwSs7/ytu35ThgxQrZdXVoFBcRuYw0aeSiRatWsp06a1ayXlatGlCggIMXt6xW+XALC5MPuFmzkvmGREQub+hQYNIk4LPPpETJgfHUZBvd30VbtWqFqKgofPTRR1i/fj0qVKiAgIAAxCT1Y3yOBw8eoE6dOtiyZQsmTZqE1atXI3v27GjYsCG2bdv20u+7dav0aUxWW9ZffwXefFP6ua5fzx1XIk+TKpVc3OrdG+jRQ5LJhw9f+BKTSZ5hFy+WxiM2u3VLxkmPHQt8+qn84o4rkWfp109Od+bPB+rXBy5fVh2RMVh1tHbtWqvJZLIuXLjwia/Xr1/fmjt3bmtCQsJzXzt16lSryWSy7tq165+vPXr0yFq8eHFrpUqVnvu6/fv3WwFYK1feb33rrWQEGRNjtaZLZ7UWKWK1HjmSjBcQkdtKTLRaJ02yWlOmtForVrRaz5594W8/fdpqBazW6Ggbv8+hQ1Zr4cJWa4YMVuuSJfbHS0TuYetWqzVHDqs1e3ardfNm1dG4haR8bf/+/Ta/Vted1xUrViB9+vRo27btE1/v2rUrLl68iN27d7/wtUWLFkWlSpX++VqKFCnQuXNn7NmzB5deMv5x166XnPxfuCC7IgEBcqFj3z6gRIlk/e8iIjdlMslOyQ8/AFevAqVKybHfc477ChSQmvlklw7cvQuMGAFUqiS3vvbvl3YnROTZatQADh2SPKJuXbmcGRenOiqPpWvyevToURQrVgxe/6nx8vf3BwAcO3bsha8tWbLkU19PzmsBIHVq4D85s7h1S/osFikitQVRUcC8edIXh4iMoUIFmcLVvr2UEJQtC2zc+MxLF2azNCo4f/4F6z16BCxaBBQrBowbBwwcCOzcCbz2mn7/G4jItWTPDmzYAEyYILnF668DM2YA9++rjszj6Jq8xsbGIkuWLE99PelrsbGxz31tXFyc3a8FZBxsxox//z9WK3DkiDwJ5c4tOyOhocAvv8ikDNahERlP5szAzJnA3r3y8NqgAVC8uPRifWzHpE0bufM1b94z1rh0SZLVQoXkJMffXwabjB0rLyIiY0mRQibo/for0KQJ0KuXjO374APg9GnV0XkMb9UB6CUo69fAhO+Agwdl2MDly0C2bDJxp2dPSWKJiMqVkzKCbdtk6lX//vKgW7o0UKMGMhQqhPHlsuGnaRlhzRcH07Wr8uC7ZQvw88+SpHbsCPTpIzu4REQ5cki90dChwNSp8uuTT2QcdK1a8qCbLRsOXMiGIm1Lwrcgp3jaQtfk1c/P75k7pHF/72r4+fm98LVxz6gXSc5rAWBq9AjM9/aWHZVXXgEqVUJA794ISFb7ASIyFJNJatZq1AAuXpQSgi1bpMXWhQvoFx8vv68TJFnNl09+74gRQL16wEvej4jIoAoXBsLDgTFjgE2b5H1l69Z/2piUBbDlaARqRQerjlRXMTExT3WZunHjht3r6Zq8lixZEjExMUhMTHyi7vXIkSMAgBIvuCDl7++Pw4cPP/X15LwWAMJ//BFlq1SxJ2wiMrJcuWSCTlCQ/P9WKxL+vIWqb9xAlaZ+CJ/ly1IjIrJNunRAixby62+Tx93B5OHXsHtUJoWBOUdAQAACAgKe+NqBAwdQrlw5u9bTtea1ZcuWuH37NpYuXfrE1yMjI5E7d+4nOgk867U///wz9uzZ88/XHj16hHnz5qFy5crIkSPHi7956tQOxU5EBAAwmZAiS0bUCc4Hy7J0uP+AiSsROW7OIl+UbJYfmQt4fvKqNV2T14YNG6JevXro1asXZs+ejS1btqBHjx7YuHEjPv30U5j+3r0ICQlBypQpcf6x67zBwcEoXrw42rZti5iYGGzatAnt2rXDyZMnMX78eD3DJiJ6itkM3LgBrFmjOhIicneHD0tnLQ7ztI/uE7aWL1+OwMBADB8+HI0aNcLevXuxcOHCJ7aPExMTkZiYCOtjbWpSpUqF7777DrVq1UK/fv3QrFkzXLlyBevWrUO1atX0DpuI6AlFikj7VofGxRIRQTppZc0KNGyoOhL3ZLJan9HY0I0l1VDs378fZXnzl4g0NH26zDj44w9p6UhEZKtHj4BXX5XueuHhqqNRx5F8TfedVyIiT9G+vbRxnD9fdSRE5K6+/Ra4cgVg8yP7MXklIkqmLFlkmjRLB4jIXhaLTJEtU0Z1JO6LySsRkQ3M5n8vWxAR2eLGDWDlStl1Zcc9+zF5JSKyQYMGMqwvMlJ1JETkbhYtAuLjgc6dVUfi3pi8EhHZIGVK+eBZsEA+hIiIkstikQfgnDlVR+LemLwSEdnIbAauXQPWrVMdCRG5i19/BXbu/Hd4H9mPySsRkY1KlgRKl+bFLSJKPosFyJRJLn2SY5i8EhHZwWyWaVuxsaojISJXl5Aggwk6dADSpFEdjftj8kpEZIeOHQGrFVi4UHUkROTqtmwBLlzgOFitMHklIrJDtmxAo0YsHSCil7NY/h0xTY5j8kpEZCezGdi7Fzh+XHUkROSqbt0Cli2T9wv2dtUGk1ciIjs1bQpkzszdVyJ6vqVLgfv3gcBA1ZF4DiavRER2Sp0aCAgA5s2TCxlERP8VGQnUrQu8+qrqSDwHk1ciIgcEBQEXLwKbNqmOhIhczalTwPbtvKilNSavREQOKF8eKFaMpQNE9LSoKCB9eqBlS9WReBYmr0REDjCZZFdlxQrg5k3V0RCRq0hMlOS1XTsgbVrV0XgWJq9ERA7q3Bl4+BBYskR1JETkKrZvB86eZcmAHpi8EhE5KHduuZDB0gEiShIZCRQsCLz1lupIPA+TVyIiDZjNwI4dwG+/qY6EiFS7fVtOYtjbVR9MXomINNCihVzMiIpSHQkRqbZ8OXDnDtCli+pIPBOTVyIiDaRNKxczoqLkogYRGZfFAtSsCeTPrzoSz8TklYhII0FBwLlzwLZtqiMhIlXOnQM2b5b3A9IHk1ciIo28+SZQqBAvbhEZWXQ04OsLtG6tOhLPxeSViEgjJpPUuC1dKvVuRGQsVqs8vLZuDaRLpzoaz8XklYhIQ126yE3j5ctVR0JEzvbjj9JxhCUD+mLySkSkofz5gRo1pMcjERmLxQLkyyfvAaQfJq9ERBozm4EtW4Dff1cdCRE5y717wKJFcvrixexKV/zxEhFprE0bwMdHLm4QkTGsXAncusXers7A5JWISGPp0wOtWskRotWqOhoicobISOk48tprqiPxfExeiYh0YDYDJ08Cu3apjoSI9PbHH8CmTbyo5SxMXomIdFCrFpAnD3u+EhlBdDSQKhXQtq3qSIyBySsRkQ5SpAACA4GFC+UiBxF5pqTerq1aARkzqo7GGJi8EhHp5P/t3XucznX+//HndZmhcVjMNMaMnFJy/qLV6ETOsw6bYaVZZJDzypCG24pCB0RLiyITi2mi2cKmVsIU7cpOB+d2a0UHNmMGGTk21++P948S1QzXNe/r87ke99vtutVeM9fluZ9GPb2vz/v1Tk6Wjh0zGzkAuNOWLdLHH3PLQHGivAJAgNx4o3TnnVJamu0kAAIlLc3Mdm3TxnaS0EF5BYAAGjBAWr9e+uwz20kA+Ft+vpnt2q8fs12LE5caAALod78zo7M4cQtwn5dflk6c4JaB4kZ5BYAAKlNGuvdeadEi6bvvbKcB4E9paVLbtua2ARQfyisABFj//tIXX5jbBwC4w7//Lb37rvn9jeJFeQWAAIuPl+rVY+MW4CYvvCBVrCh17Wo7SeihvAJAgHk8ZnVm5UopN9d2GgBX6+xZM9u1Vy/pmmtspwk9lFcAKAZ9+kgFBVJ6uu0kAK7WG29IX39tpomg+FFeAaAYVKok/fa35tYBn892GgBXIy1NatpUatzYdpLQRHkFgGLSv7+0fbv0wQe2kwC4Uv/7n7RmDRu1bKK8AkAx6dBBiotj4xbgZEuWSGFh0u9/bztJ6KK8AkAxCQuT+vaVXnxROnnSdhoAReXzmSkD3bqZSQOwg/IKAMWof3/p2DHplVdsJwFQVP/4h5nvykYtuyivAFCMbrhBatnSrN4AcJYXXpBq1JBatbKdJLRRXgGgmPXvL23YIO3dazsJgMI6flxavlzq10/y0p6s4vIDQDH73e+kcuWkxYttJwFQWC+/LH37rZScbDsJKK8AUMxKl5aSkqRFi6TvvrOdBkBhpKVJ7dpJ1arZTgLKKwBYMGCA9OWX0rp1tpMA+CV79pjNWmzUCg6UVwCwoFkzqX59Nm4BTrBokRQZKd19t+0kkCivAGCFx2NWcVaulA4ftp0GwE85e1b6y1+k3r2lUqVsp4FEeQUAa3r3Nn9dutRuDgA/bc0a6dAhjoMNJpRXALAkOlpKTJSef96c3AMg+CxYIN1yi/R//2c7Cc6jvAKARYMGmc0gmzfbTgLgx/btk/7+d/P7FMGD8goAFrVqZU7dWrDAdhIAP5aWJpUtK917r+0k+CHKKwBY5PVKAweaAei5ubbTADjv7FlTXnv3lsqUsZ0GP0R5BQDLkpOlggI2bgHBZM0a6eBBafBg20nwY5RXALCsUiWzcWvBAjZuAcGCjVrBi/IKAEGAjVtA8GCjVnCjvAJAEGjVSqpVi41bQDBgo1Zwo7wCQBDwes0qz8svS3l5ttMAoevcOTZqBTvKKwAEifMbt5YssZ0ECF2vvcZGrWBHeQWAIFGpktS1Kxu3AJvYqBX8KK8AEEQGD2bjFmALG7WcgfIKAEGEjVuAPWzUcgbKKwAEkR+euHX4sO00QOjgRC3noLwCQJDp39/c87poke0kQOhYudJs1Bo61HYS/BLKKwAEmeho6Z57pGeflb77znYaIDTMnSvdeafUsKHtJPgllFcACELDh0uffSatXWs7CeB+u3ZJb79tft8h+FFeASAIxcdLTZpI8+bZTgK437x5UkyMlJhoOwkKg/IKAEHI4zGrQK+/blZgAQTG8ePmYJBBg6SSJW2nQWFQXgEgSCUlSeXLS889ZzsJ4F5Ll0onTzLb1UkorwAQpEqXlvr1M+N7Tp2ynQZwH5/P3DLw299K111nOw0Ki/IKAEFs6FApN1dascJ2EsB93nnHbNZio5azUF4BIIjdeKPUvr0Z4wPAv+bNk266SWrd2nYSFAXlFQCC3LBh0tatUna27SSAexw8KL3yivn95fHYToOioLwCQJDr3FmqVo2xWYA/LVhgpgvcd5/tJCgqyisABLkSJaTBg6WMDCkvz3YawPnOnjXltXdvqUIF22lQVJRXAHCA++83R8W+8ILtJIDzrVolHThgNkTCeSivAOAAlSpJ994rzZkjnTtnOw3gbLNnS3fcITVubDsJrgTlFQAcYuRIaf9+afVq20kA53r/fWnzZiklxXYSXCnKKwA4xM03m9Wi2bNtJwGca/ZsqXp16e67bSfBlaK8AoCDpKSYweoffmg7CeA8Bw9KL70kjRghhYXZToMrRXkFAAe5+24zNovVV6DonnvOjMcaMMB2ElwNyisAOEhYmFk1ysiQ/vc/22kA5zh1Snr2WSk5mfFYTkd5BQCHGTBACg83q0gACicjQ8rJkR54wHYSXC3KKwA4TMWKUt++ZhXp9GnbaYDg5/OZW206dpRq17adBleL8goADvTAA9KhQ2bzCYCf9/bb0rZtjMdyC8orADjQTTeZVaTZs82qEoCfNnu2VK+e1Lat7STwB8orADjUyJFmZNamTbaTAMFr715zHOzIkZLHYzsN/IHyCgAO1a6dVLeuNGuW7SRA8Przn8194r17204Cf6G8AoBDeTzmHr5Vq8zqEoCLHTsmpaVJgwZJpUvbTgN/obwCgIP16SNFRUlPP207CRB8FiwwEzlGjLCdBP5EeQUAB4uIMJMHXnjBzLAEYJw+bW6p6dNHiouznQb+RHkFAIcbNszcQjB3ru0kQPB48UXpwAFpzBjbSeBvlFcAcLjISGngQGnOHOnECdtpAPsKCqSnnpLuvluqU8d2Gvgb5RUAXGDUKOnoUWnRIttJAPvWrJH27JFSU20nQSBQXgHABapXl3r2lGbOlM6ds50GsGv6dOn226XbbrOdBIFAeQUAl3joIWnfPikz03YSwJ5//EPavJlVVzejvAKASzRuLLVvb1adODIWoeqpp8x9rp07206CQKG8AoCLpKaaI2PXr7edBCh+H39sDu146CHJS8NxLf7RAoCLtG4tNW1qVl+BUDNzplS5stSrl+0kCCTKKwC4iMdjVl/XrTMrsECoOHhQWrLEHJlcqpTtNAgkyisAuEz37lLNmtLUqbaTAMVn1ixTWgcPtp0EgUZ5BQCXCQuTxo2TXn7ZzLoE3O7wYXPC3IgRUvnyttMg0AJeXvPz85WSkqIqVaooIiJCTZo00fLlywv12sWLF8vr9V72cejQoQAnBwDn6ttXqlJFeuIJ20mAwJs1y/x11Ci7OVA8wgL9C3Tr1k3Z2dmaNm2aateurfT0dCUlJamgoEBJSUmFeo/Fixerzo/Od4uMjAxEXABwhVKlzOrrAw9IEydKN95oOxEQGEeOSM88Iw0bJl17re00KA4BLa+vv/663nrrLWVkZKhnz56SpJYtW2r//v166KGH1LNnT3kLMcuiQYMGatq0aSCjAoDrDBggPf64WX3l2Fi41ezZ5lS5Bx+0nQTFJaC3Dbz66qsqV66cevTocdHz/fr104EDB/Tee+8V6n18TNsGgCK75hozeWDpUmnvXttpAP87dsyU18GDpZgY22lQXAJaXnfu3Km6detesrrasGFDSdKuXbsK9T6dO3dWWFiYoqKi1L1790K/DgBC3aBBUlSU9OSTtpMA/jdnjnTypDmUAKEjoOU1Nzf3svemnn8uNzf3Z18fGxurhx9+WGlpacrKytKUKVP0r3/9S82bN9eOHTsCkhkA3KR0afMf9r/8Rdq/33YawH+OH5eefloaOFCKi7OdBsWp0OU1KyvrJ3f+//ixfft2v4Tr0KGDJk+erI4dO+qOO+7QsGHDtGnTJnk8Hk2cONEvvwYAuN2QIWZ80LRptpMA/vPss1J+vjR2rO0kKG6F3rBVp04dLVy4sFDfW61aNUlSVFTUZVdX8/LyLny9qKpXr67bb79dW7Zs+dnvS0lJUYUKFS56LikpqdATDgDALcqWlUaPlh59VPrjH6XrrrOdCLg6J05IM2ZI/frx8+wEGRkZysjIuOi5o0ePXvH7Fbq8Vq5cWf379y/Smzdq1EgZGRkqKCi46L7X8x/5N2jQoEjv90Mej+dnvz5r1iwmFADA/zd8uPTUU9L06WasEOBk8+ebEVnjxtlOgsK43OLhBx98oJtvvvmK3i+g97wmJiYqPz9fmZmZFz2/ePFiValSRfHx8UV+z71792rTpk269dZb/RUTAFzvV78yA9yff146cMB2GuDKnTxp/hDWt69Uo4btNLAhoHNeExIS1K5dOw0dOlTffPONatWqpYyMDL355ptKT0+/aPV0wIABWrJkifbu3auqVatKktq1a6fWrVurfv36Klu2rHbs2KHp06crLCxMU6ZMCWR0AHCdESOkP/3JzH6dO9d2GuDKzJkj5eaaW2AQmgJ+wtYrr7yi8ePHa+LEicrLy1PdunX10ksv6Z577rno+woKClRQUHDRTNeGDRsqPT1dX3zxhU6ePKlKlSqpbdu2mjBhgm644YZARwcAV6lQwWxuefhhM9D9+uttJwKK5uhRM/Zt4EB+fkOZx+eyEwDO30Px/vvvc88rAPzIt99KN9wgtWljDi8AnOThh814rP/+V4qNtZ0GV+Nq+lpA73kFAASX0qWlCROk9HSJcdlwkq+/lmbNkh54gOIa6iivABBiBgyQatY0q1iAUzz+uBQWxlxXUF4BIOSULClNniytXi3985+20wC/bN8+6bnnpNRUqWJF22lgG+UVAEJQUpLUsKHZse2unQ9wo0cflSIjpZEjbSdBMKC8AkAI8nrNx7BZWdK6dbbTAD9t926zufDhh6UyZWynQTCgvAJAiOrcWbr1VlZfEdwefliqVk0aNMh2EgQLyisAhCiPx8zMfP996a9/tZ0GuNTWrdKrr0qTJpl7tQGJ8goAIa1lS6lDB7P6euaM7TTA93w+M1mgXj2pVy/baRBMKK8AEOKeesoMfZ83z3YS4HsrV5p7smfMkEqUsJ0GwYTyCgAhrmFDcz/hpEnS4cO20wDS6dPSmDHSb35jHsAPUV4BAJo8WSookB55xHYSQJo9W9q/X5o503YSBCPKKwBA0dHSxIlmEPzOnbbTIJR9/bX02GPSsGFS3bq20yAYUV4BAJKkESOk66+XRo9mdBbsmTDBHAPLpwD4KZRXAIAkM4poxgxzaMGaNbbTIBR99JG0cKE5USsqynYaBCvKKwDggt/+VmrdWnrwQUZnoXj5fNKoUdJNN0lDh9pOg2BGeQUAXODxSH/6k/Tpp4zOQvFatcqMxnr6aSk83HYaBDPKKwDgIo0aSQMHMjoLxef8aKyEBEZj4ZdRXgEAl5g8+fsTjoBAmz6d0VgoPMorAOASlSpJU6dKL7wgvfOO7TRws08+kR5/3Ky81qtnOw2cgPIKALisQYOk5s2lIUPYvIXA8PnM5qy4ODMiCygMyisA4LK8Xmn+fOk//5Geesp2GrjRiy9K69dLc+dKpUvbTgOnoLwCAH5So0ZmbNaUKWYCAeAveXlmNFbPnmzSQtFQXgEAP2viRKlyZXNcJydvwV/GjTNTBv70J9tJ4DSUVwDAzypTxsx8XbdOysiwnQZusHmz9PzzZlNgbKztNHAayisA4Bd17Cj16GE+5j1yxHYaONmZM2YTYHy8NHiw7TRwIsorAKBQZs2STp1i9iuuzsyZ0scfSwsWmE2BQFHxYwMAKJS4OOnJJ83HvRs22E4DJ9qzx5zcNnq02QwIXAnKKwCg0IYMke66S+rfXzp+3HYaOMm5c1JyslSjhimwwJWivAIACs3rldLSpMOHpdRU22ngJDNmSNnZ0uLFUkSE7TRwMsorAKBIrr/enEX/3HPSW2/ZTgMn2LVLeuQRcwRs8+a208DpKK8AgCIbMkRq3Vrq14/pA/h5Z85IffpItWpxuwD8g/IKACgyr9d8/Hv8uDR8uO003F8B3wAAE6VJREFUCGaPPCLt3CktWyZdc43tNHADyisA4IpUrWpuHcjIkNLTbadBMHr7bWnaNHO8cNOmttPALSivAIArdu+9Uq9e5ujY/fttp0EwOXpUuu8+6c47zb2ugL9QXgEAV2XuXKliRen3v5fOnrWdBsHA55MGDpSOHZOWLJFKlLCdCG5CeQUAXJXy5aWXXpK2bpUmTrSdBsFg/nwpM9OMVate3XYauA3lFQBw1Zo3lx5/XJo6VVq71nYa2LRtm5SSYm4l6d7ddhq4EeUVAOAXY8ZICQlS797SV1/ZTgMbjh+XevaU6tSRZs60nQZuRXkFAPiF12vubyxVSurRw8z3ROjw+czc3wMHpBUrGIuFwKG8AgD8Jjra3OuYnS2NHm07DYrTjBnSX/8q/eUvUu3attPAzSivAAC/at5ceuYZM4Vg6VLbaVAcNmyQxo2Txo6VEhNtp4HbUV4BAH43eLCUnCwNGmRWYeFe+/aZ+1xbtZIee8x2GoQCyisAwO88HunZZ6VGjaS77zb3QcJ9jh+XunSRfvUrMy4tLMx2IoQCyisAICCuuUZaudJs5OraVTp50nYi+NN335nT1fbvl1avlq691nYihArKKwAgYGJjpVWrpJ07zU70ggLbieAvf/yjtGaNWXGtX992GoQSyisAIKCaNjUbt1asMIUHzjdvnjR9upnl2rGj7TQINZRXAEDAde8uPf20NG2amUIA51q1ShoxwpyilZJiOw1CEbdWAwCKRUqK9PnnpvjExTFSyYm2bJGSkqRu3ThBC/aw8goAKDYzZpjTt+69V1q/3nYaFMX27dJvfiPdfLO5DcRLg4Al/OgBAIqN12uKT+vWZoTWP/9pOxEK45NPpPbtpZo1pdde4+hX2EV5BQAUq5IlzTGiTZqYzT4ffWQ7EX7O559LbdtKFStKa9dK5cvbToRQR3kFABS70qXNCl6tWlKbNtK2bbYT4XI+/1y66y6zYr5unRQdbTsRQHkFAFhSvrwpRDVrmtsIKLDB5XxxlaSsLOm662ymAb5HeQUAWFOx4sUFNjvbdiJI0t693xfXjRul6tWtxgEuQnkFAFh1vsDWrm0KbFaW7UShbedO6Y47pLAw88+C4opgQ3kFAFh3vsDGx0sJCdLf/mY7UWh67z2pRQupUiVp0yapWjXbiYBLUV4BAEGhbFmziatTJ3OAwfz5thOFltWrzcp3vXpmxTUmxnYi4PIorwCAoFGqlLRihTR0qDRkiDR2rFRQYDuV+/35z1LXrmbVe906qUIF24mAn8bxsACAoFKihPTMM2aM1ujR0n//Ky1ebFZm4V9nz0oPPmjK64MPStOnc3IWgh8/ogCAoOPxSCkp0iuvmMH4t91mdsDDf3JyzKlZzz4rzZtnju6luMIJ+DEFAAStrl2lLVukkyelX/9a+vvfbSdyh+xscz1375Y2bDC3aQBOQXkFAAS1+vWlrVul5s2l3/xGGjfOfNyNovP5pFmzzEp2TIwpsXfeaTsVUDSUVwBA0KtY0UwimDbNfLzdsqX02We2UzlLTo5ZyR41ShoxQtq8Wapa1XYqoOgorwAAR/B6pdRU6Z13pAMHpEaNpIULzWoift7q1VKDBtK775q/nzlTKlnSdirgylBeAQCOcttt0vbt0j33SAMHSp07S198YTtVcMrNlZKTpbvvlm65xZye1aWL7VTA1aG8AgAc51e/ktLSzElcH34o1a1r7uU8d852suDg80lLl0p16kgrV5prtXq1VLmy7WTA1aO8AgAcq3Nnac8eqV8/MxO2WTPp7bdtp7Lro4/MSVn33Se1bSt9/LHUv78ZPwa4AeUVAOBo5cubIftbtpgTuu66S+reXfr0U9vJitfBg+Y2iqZNpf/9z4wVy8hgtRXuQ3kFALjCLbdI//iHtGyZGa1Vp44pc/v3204WWDk50kMPmRPJXnnFnE62fbvUoYPtZEBgUF4BAK7h9Uq9ekn/+Y/01FPSqlXSjTeaEvvvf9tO519ffimNGSNdf700f775+//+V/rDH6TwcNvpgMChvAIAXCciwswz3btXmjLFzIitW9fMOV2/XioosJ3wyvh8ZlW5b1+pZk0zKmzECPP/c/JkqUIF2wmBwKO8AgBcq2xZaexYad8+U/Q+/dRsYqpTxxx2cOCA7YSFk5cnPfecdPPNUny82ZQ2fboZEfbEE9K119pOCBQfyisAwPVKlTI77nfsMIcc/PrX0vjx5oSpdu3MKKlDh2ynvNjRo2bDVdeuZtPV8OFSlSrSmjXm9oBRo6Ry5WynBIpfmO0AAAAUF49HuvNO8zhyRPrrX6X0dGnQIPOR/K23SgkJZtTULbcU772j330nbdsmbdggrV0rZWWZubXNmpn7d3v2ZHIAIFFeAQAhqmJF6f77zSMnx6xorlplbieYOFEqU8YUx/OP+vWlG27wz7Gq331n7lPdvVvKzjb3sW7dalZbIyKkFi2k2bPNaVhVq179rwe4CeUVABDyoqPNMarJyWa188MPzcrne+9Jy5eblU9JCgszG6WqVTOPypWlyEjziIgwxTY83LzHmTPSyZNmhTcvT/r6a3OP6hdfmI/9T5827xkTY1Z5H3xQatnS3NPqj4IMuBXlFQCAHwgL+3619bycHHOS1+7d0iefmAK6e7e0caMpp8eO/fT7lS1rym10tCm8bdtKgwdL9eqZCQhxcZx+BRQF5RUAgF8QHW0eLVpc/uvnzpmV1DNnzCM83KyelirFzFXA3yivAABcpbAw8yhTxnYSwP0YlQUAAADHoLwCAADAMSivAAAAcAzKKwAAAByD8goAAADHoLwCAADAMSivAAAAcAzKKwAAAByD8goAAADHoLwCAADAMSivAAAAcAzKKwAAAByD8goAAADHoLwCAADAMSivAAAAcAzKKwAAAByD8goAAADHoLwCAADAMSivAAAAcAzKKwAAAByD8goAAADHoLwCAADAMSivAAAAcAzKKwAAAByD8goAAADHoLwCAADAMSivAAAAcAzKKwAAABwjoOU1Pz9fqampat++vaKjo+X1ejVp0qQivcehQ4eUnJys6OholSlTRrfddps2bNgQoMQAAAAIZgEtr4cPH9bzzz+vs2fPKjExUZLk8XgK/frTp0+rTZs22rhxo5555hmtXr1aMTExSkhI0DvvvBOo2CiEjIwM2xFcjesbeFzjwOL6Bh7XOPC4xsEpoOW1Ro0aOnLkiDZu3Kgnn3yyyK9PS0vTrl27tGLFCiUlJalNmzbKzMxU7dq1lZqaGoDEKCx+QwcW1zfwuMaBxfUNPK5x4HGNg1Ox3fPq8/mK/JpXX31VderUUXx8/IXnSpQood69e2vr1q06ePCgPyMCAAAgyAX1hq2dO3eqUaNGlzzfsGFDSdKuXbuKOxIAAAAsCurympeXp8jIyEueP/9cbm5ucUcCAACARWGF/casrCy1bt26UN/70UcfXXbFtDjt2bPH6q/vdkePHtUHH3xgO4ZrcX0Dj2scWFzfwOMaBx7XOHCupqcVurzWqVNHCxcuLNT3Vq1a9YoD/VBUVJTy8vIuef78c1FRUZd8LTY2VnFxcerdu7dfMuCn3XzzzbYjuBrXN/C4xoHF9Q08rnHgcY0DJy4uTrGxsUV+XaHLa+XKldW/f/8i/wJXo2HDhtq+ffslz+/YsUOS1KBBg0u+Fhsbq+zsbDZzAQAABLHY2NjAllcbEhMTNWzYMG3dulW33HKLJOncuXNatmyZmjdvrsqVK1/2dVd6MQAAABDcAl5e33jjDZ04cULHjx+XZCYEZGZmSpI6deqkiIgISdKAAQO0ZMkS7d2798JtB/3799fcuXPVo0cPTZ06VdHR0Zo3b54++eQTvfXWW4GODgAAgCDj8V3JANYiqFmzpvbv329+MY/nwrxXj8ejzz77TNWqVZMk9evXT0uWLLnoOckcD5uamqrXXntN3377rZo0aaIpU6YUevMYAAAA3CPg5RUAAADwl6Ce81oU+fn5SklJUZUqVRQREaEmTZpo+fLltmO5Rn5+vlJTU9W+fXtFR0fL6/Vq0qRJtmO5xvr169W3b1/Vrl1bZcqU0XXXXaeuXbsyosWPPvroI3Xq1EnVq1dX6dKlFRUVpdtuu03p6em2o7nWwoUL5fV6Va5cOdtRXCErK0ter/eyj61bt9qO5xqbN29Wx44dFRkZqdKlS6t27dp67LHHbMdyheTk5J/8GS7Kz3FQb9gqim7duik7O1vTpk1T7dq1lZ6erqSkJBUUFCgpKcl2PMc7fPiwnn/+eTVu3FiJiYlauHChPB6P7ViuMX/+fOXk5GjUqFGqX7++cnJyNHPmTDVv3lxr165Vq1atbEd0vGPHjqlatWrq1auXqlSpovz8fKWnp6tPnz7at2+fxo8fbzuiq3z11VcaM2aM4uLi9M0339iO4ypPPvnkJf9OqF+/vqU07vLiiy/qvvvuU8+ePbV06VKVLVtWn376KROM/GTixIkaNmzYRc/5fD516dJFERERatasWaHexxW3Dbz++uvq3LmzMjIy1LNnzwvPd+jQQbt27dLnn38ur9c1i8zW5ebmKjo6Wo8++qgmTpxoO44rHDp0SJUqVbrouRMnTuiGG25QgwYNtG7dOkvJ3O/WW2/VgQMHLtybD//o0qWLwsLCVKFCBWVmZl7YtIsrd/6woMzMTHXr1s12HNf56quvdNNNNyk5OVlz5syxHSdkvP3222rVqpUmTJhQ6E90XdHoXn31VZUrV049evS46Pl+/frpwIEDeu+99ywlcycX/Hkn6Py4uEpSmTJlVLduXX355ZcWEoWOqKgohYW55kOooLBs2TJt2rRJc+fO5d8XAcA1DYyFCxfq22+/1dixY21HCSlpaWnyer0aMGBAoV/jivK6c+dO1a1b95LV1YYNG0oy47kApzl27Jg++OADPg70M5/Pp3PnziknJ0fz5s3T2rVrNWbMGNuxXOPrr79WSkqKpk6dqri4ONtxXGn48OEKDw9X+fLllZCQoHfffdd2JFd45513FBUVpd27d6tx48YKDw9XTEyMhg4dyicHAXLs2DFlZmaqTZs2F02a+iWuKK+5ubmKjIy85Pnzz+Xm5hZ3JOCqDR8+XCdPnuReTD8bOnSoSpYsqZiYGI0cOVIzZszQ0KFDbcdyjeHDh6tevXoaMmSI7SiuU6FCBaWkpGjBggXKysrS7Nmz9cUXX+iuu+7Sm2++aTue43311Vc6ceKE7rnnHiUlJWn9+vV66KGHtGTJEnXs2NF2PFfKyMjQqVOnirTqKrlowxbgJhMmTNCLL76oOXPmqEmTJrbjuMr48eM1aNAgHTp0SKtXr9bo0aN16tQpPir0g8zMTL322mvatm2b7Siu1LhxYzVu3PjC/7799tuVmJiohg0bauzYsWrfvr3FdM5XUFCgU6dO6dFHH1VqaqokqUWLFipZsqRSUlK0YcMGZsz7WVpamq699lolJiYW6XWuWHmNioq67OpqXl7eha8DTjFp0iQ9/vjjeuKJJy7ZlYmrV7VqVTVt2lQJCQmaN2+eBg8erAkTJignJ8d2NEfLz8/XH/7wBz3wwAOKiYnR0aNHdfToUZ05c0aS+XjwxIkTllO6T/ny5dWpUydt27ZNp0+fth3H0c53hQ4dOlz0fEJCgiTpww8/LPZMbrZ9+3a9//776t27t8LDw4v0WleU10aNGmnPnj0qKCi46PkdO3ZIkho0aGAjFlBkkyZNuvAYN26c7TghoVmzZjp37pw+++wz21Ec7fDhwzp06JBmzJihyMjIC4+XXnpJJ06cUMWKFdWnTx/bMV2N8YVX54er2pfD9fWvtLQ0SdL9999f5Ne6orwmJiYqPz9fmZmZFz2/ePFiValSRfHx8ZaSAYU3ZcoUTZo0SRMmTNCECRNsxwkZGzduVIkSJVSrVi3bURwtNjZWGzduVFZW1oXHxo0b1aFDB11zzTXKyspi0HsAHDlyRH/729/UpEkTlSxZ0nYcR+vevbskM37zh9asWSNJdAk/On36tJYtW6b4+HjVq1evyK93xT2vCQkJateunYYOHapvvvlGtWrVUkZGht58802lp6fzpyU/eeONN3TixIkLuy537dp14Q8MnTp1UkREhM14jjZz5kw98sgjSkhIUMeOHbVly5aLvt68eXNLydxj0KBBKl++vJo1a6aYmBgdPnxYL7/8slasWKHU1FRuL7pKpUqVUsuWLS95ftGiRSpRooRatGhhIZW79OrVSzVr1lTTpk0VGRmpTz75RDNnzlROTo6WLFliO57jtW3bVp07d9bkyZNVUFCg+Ph4ZWdna/LkyerSpYtuv/122xFdY+XKlTpy5MgVrbpKknwukZ+f7xs5cqQvNjbWV6pUKV/jxo19y5cvtx3LVWrUqOHzeDw+j8fj83q9F/39/v37bcdztLvuuuuia/rDh9frtR3PFRYtWuRr0aKFLzo62hceHu6rWLGir1WrVr709HTb0VwtOTnZV65cOdsxXGHq1Km+Jk2a+CpUqOALCwvzVapUyde9e3dfdna27WiucfLkSd+4ceN81apV84WHh/tq1KjhGz9+vO/MmTO2o7lK+/btfeXKlfPl5+df0etdccIWAAAAQoMr7nkFAABAaKC8AgAAwDEorwAAAHAMyisAAAAcg/IKAAAAx6C8AgAAwDEorwAAAHAMyisAAAAcg/IKAAAAx6C8AgAAwDEorwAAAHAMyisAAAAc4/8BWXA0Q8L0DK0AAAAASUVORK5CYII=",
       "svg": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (http://matplotlib.org/) -->\n<svg height=\"381pt\" version=\"1.1\" viewBox=\"0 0 494 381\" width=\"494pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"\nM0 381.421\nL494.684 381.421\nL494.684 0\nL0 0\nz\n\" style=\"fill:#ffffff;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"\nM38.2713 358.607\nL484.671 358.607\nL484.671 13.0069\nL38.2713 13.0069\nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"line2d_1\">\n    <path clip-path=\"url(#p1a591c6a9e)\" d=\"\nM38.2713 185.807\nL48.2884 158.885\nL54.2988 143.275\nL60.3091 128.386\nL64.316 118.97\nL68.3228 110.025\nL72.3297 101.594\nL76.3366 93.7131\nL80.3435 86.4041\nL84.3503 79.6813\nL88.3572 73.5496\nL92.3641 68.0057\nL96.371 63.039\nL100.378 58.6333\nL104.385 54.7681\nL108.392 51.4195\nL112.398 48.5623\nL116.405 46.1711\nL120.412 44.2215\nL124.419 42.6912\nL128.426 41.5611\nL132.433 40.8165\nL136.44 40.4468\nL140.447 40.4468\nL144.453 40.8165\nL148.46 41.5611\nL152.467 42.6912\nL156.474 44.2215\nL160.481 46.1711\nL164.488 48.5623\nL168.495 51.4195\nL172.502 54.7681\nL176.509 58.6333\nL180.515 63.039\nL184.522 68.0057\nL188.529 73.5496\nL192.536 79.6813\nL196.543 86.4041\nL200.55 93.7131\nL204.557 101.594\nL208.564 110.025\nL212.57 118.97\nL216.577 128.386\nL222.588 143.275\nL228.598 158.885\nL236.612 180.38\nL248.632 212.729\nL254.643 228.339\nL260.653 243.228\nL264.66 252.644\nL268.667 261.589\nL272.674 270.019\nL276.68 277.901\nL280.687 285.21\nL284.694 291.932\nL288.701 298.064\nL292.708 303.608\nL296.715 308.575\nL300.722 312.98\nL304.729 316.846\nL308.735 320.194\nL312.742 323.051\nL316.749 325.443\nL320.756 327.392\nL324.763 328.923\nL328.77 330.053\nL332.777 330.797\nL336.784 331.167\nL340.79 331.167\nL344.797 330.797\nL348.804 330.053\nL352.811 328.923\nL356.818 327.392\nL360.825 325.443\nL364.832 323.051\nL368.839 320.194\nL372.845 316.846\nL376.852 312.98\nL380.859 308.575\nL384.866 303.608\nL388.873 298.064\nL392.88 291.932\nL396.887 285.21\nL400.894 277.901\nL404.9 270.019\nL408.907 261.589\nL412.914 252.644\nL416.921 243.228\nL422.931 228.339\nL428.942 212.729\nL436.956 191.234\nL438.959 185.807\nL438.959 185.807\" style=\"fill:none;stroke:#0000ff;stroke-linecap:square;\"/>\n   </g>\n   <g id=\"line2d_2\">\n    <path clip-path=\"url(#p1a591c6a9e)\" d=\"\nM38.2713 185.807\nL40.2747 185.636\nL42.2781 185.126\nL44.2816 184.276\nL46.285 183.092\nL48.2884 181.578\nL50.2919 179.74\nL52.2953 177.584\nL54.2988 175.12\nL56.3022 172.357\nL58.3056 169.306\nL60.3091 165.979\nL62.3125 162.39\nL66.3194 154.48\nL70.3263 145.702\nL74.3331 136.194\nL78.34 126.106\nL84.3503 110.236\nL98.3744 72.7078\nL102.381 62.6195\nL106.388 53.1114\nL110.395 44.3334\nL114.402 36.424\nL116.405 32.8345\nL118.409 29.5078\nL120.412 26.4569\nL122.416 23.694\nL124.419 21.2298\nL126.423 19.0742\nL128.426 17.2356\nL130.429 15.7213\nL132.433 14.5373\nL134.436 13.6882\nL136.44 13.1774\nL138.443 13.0069\nL140.447 13.1774\nL142.45 13.6882\nL144.453 14.5373\nL146.457 15.7213\nL148.46 17.2356\nL150.464 19.0742\nL152.467 21.2298\nL154.471 23.694\nL156.474 26.4569\nL158.478 29.5078\nL160.481 32.8345\nL162.484 36.424\nL166.491 44.3334\nL170.498 53.1114\nL174.505 62.6195\nL178.512 72.7078\nL184.522 88.5781\nL198.546 126.106\nL202.553 136.194\nL206.56 145.702\nL210.567 154.48\nL214.574 162.39\nL216.577 165.979\nL218.581 169.306\nL220.584 172.357\nL222.588 175.12\nL224.591 177.584\nL226.594 179.74\nL228.598 181.578\nL230.601 183.092\nL232.605 184.276\nL234.608 185.126\nL236.612 185.636\nL238.615 185.807\nL240.619 185.636\nL242.622 185.126\nL244.625 184.276\nL246.629 183.092\nL248.632 181.578\nL250.636 179.74\nL252.639 177.584\nL254.643 175.12\nL256.646 172.357\nL258.649 169.306\nL260.653 165.979\nL262.656 162.39\nL266.663 154.48\nL270.67 145.702\nL274.677 136.194\nL278.684 126.106\nL284.694 110.236\nL298.718 72.7078\nL302.725 62.6195\nL306.732 53.1114\nL310.739 44.3334\nL314.746 36.424\nL316.749 32.8345\nL318.753 29.5078\nL320.756 26.4569\nL322.76 23.694\nL324.763 21.2298\nL326.766 19.0742\nL328.77 17.2356\nL330.773 15.7213\nL332.777 14.5373\nL334.78 13.6882\nL336.784 13.1774\nL338.787 13.0069\nL340.79 13.1774\nL342.794 13.6882\nL344.797 14.5373\nL346.801 15.7213\nL348.804 17.2356\nL350.808 19.0742\nL352.811 21.2298\nL354.815 23.694\nL356.818 26.4569\nL358.821 29.5078\nL360.825 32.8345\nL362.828 36.424\nL366.835 44.3334\nL370.842 53.1114\nL374.849 62.6195\nL378.856 72.7078\nL384.866 88.5781\nL398.89 126.106\nL402.897 136.194\nL406.904 145.702\nL410.911 154.48\nL414.918 162.39\nL416.921 165.979\nL418.925 169.306\nL420.928 172.357\nL422.931 175.12\nL424.935 177.584\nL426.938 179.74\nL428.942 181.578\nL430.945 183.092\nL432.949 184.276\nL434.952 185.126\nL436.956 185.636\nL438.959 185.807\nL438.959 185.807\" style=\"fill:none;stroke:#ff0000;stroke-linecap:square;\"/>\n   </g>\n   <g id=\"matplotlib.axis_1\">\n    <g id=\"xtick_1\">\n     <g id=\"line2d_3\">\n      <defs>\n       <path d=\"\nM0 0\nL0 -4\" id=\"m93b0483c22\" style=\"stroke:#000000;stroke-width:0.5;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"38.27125\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_4\">\n      <defs>\n       <path d=\"\nM0 0\nL0 4\" id=\"m741efc42ff\" style=\"stroke:#000000;stroke-width:0.5;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"38.27125\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_1\">\n      <!-- 0 -->\n      <defs>\n       <path d=\"\nM31.7812 66.4062\nQ24.1719 66.4062 20.3281 58.9062\nQ16.5 51.4219 16.5 36.375\nQ16.5 21.3906 20.3281 13.8906\nQ24.1719 6.39062 31.7812 6.39062\nQ39.4531 6.39062 43.2812 13.8906\nQ47.125 21.3906 47.125 36.375\nQ47.125 51.4219 43.2812 58.9062\nQ39.4531 66.4062 31.7812 66.4062\nM31.7812 74.2188\nQ44.0469 74.2188 50.5156 64.5156\nQ56.9844 54.8281 56.9844 36.375\nQ56.9844 17.9688 50.5156 8.26562\nQ44.0469 -1.42188 31.7812 -1.42188\nQ19.5312 -1.42188 13.0625 8.26562\nQ6.59375 17.9688 6.59375 36.375\nQ6.59375 54.8281 13.0625 64.5156\nQ19.5312 74.2188 31.7812 74.2188\" id=\"BitstreamVeraSans-Roman-30\"/>\n      </defs>\n      <g transform=\"translate(35.2478125 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_2\">\n     <g id=\"line2d_5\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"102.042678571\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_6\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"102.042678571\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_2\">\n      <!-- 1 -->\n      <defs>\n       <path d=\"\nM12.4062 8.29688\nL28.5156 8.29688\nL28.5156 63.9219\nL10.9844 60.4062\nL10.9844 69.3906\nL28.4219 72.9062\nL38.2812 72.9062\nL38.2812 8.29688\nL54.3906 8.29688\nL54.3906 0\nL12.4062 0\nz\n\" id=\"BitstreamVeraSans-Roman-31\"/>\n      </defs>\n      <g transform=\"translate(99.4383035714 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_3\">\n     <g id=\"line2d_7\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"165.814107143\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_8\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"165.814107143\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_3\">\n      <!-- 2 -->\n      <defs>\n       <path d=\"\nM19.1875 8.29688\nL53.6094 8.29688\nL53.6094 0\nL7.32812 0\nL7.32812 8.29688\nQ12.9375 14.1094 22.625 23.8906\nQ32.3281 33.6875 34.8125 36.5312\nQ39.5469 41.8438 41.4219 45.5312\nQ43.3125 49.2188 43.3125 52.7812\nQ43.3125 58.5938 39.2344 62.25\nQ35.1562 65.9219 28.6094 65.9219\nQ23.9688 65.9219 18.8125 64.3125\nQ13.6719 62.7031 7.8125 59.4219\nL7.8125 69.3906\nQ13.7656 71.7812 18.9375 73\nQ24.125 74.2188 28.4219 74.2188\nQ39.75 74.2188 46.4844 68.5469\nQ53.2188 62.8906 53.2188 53.4219\nQ53.2188 48.9219 51.5312 44.8906\nQ49.8594 40.875 45.4062 35.4062\nQ44.1875 33.9844 37.6406 27.2188\nQ31.1094 20.4531 19.1875 8.29688\" id=\"BitstreamVeraSans-Roman-32\"/>\n      </defs>\n      <g transform=\"translate(163.037232143 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-32\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_4\">\n     <g id=\"line2d_9\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"229.585535714\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_10\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"229.585535714\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_4\">\n      <!-- 3 -->\n      <defs>\n       <path d=\"\nM40.5781 39.3125\nQ47.6562 37.7969 51.625 33\nQ55.6094 28.2188 55.6094 21.1875\nQ55.6094 10.4062 48.1875 4.48438\nQ40.7656 -1.42188 27.0938 -1.42188\nQ22.5156 -1.42188 17.6562 -0.515625\nQ12.7969 0.390625 7.625 2.20312\nL7.625 11.7188\nQ11.7188 9.32812 16.5938 8.10938\nQ21.4844 6.89062 26.8125 6.89062\nQ36.0781 6.89062 40.9375 10.5469\nQ45.7969 14.2031 45.7969 21.1875\nQ45.7969 27.6406 41.2812 31.2656\nQ36.7656 34.9062 28.7188 34.9062\nL20.2188 34.9062\nL20.2188 43.0156\nL29.1094 43.0156\nQ36.375 43.0156 40.2344 45.9219\nQ44.0938 48.8281 44.0938 54.2969\nQ44.0938 59.9062 40.1094 62.9062\nQ36.1406 65.9219 28.7188 65.9219\nQ24.6562 65.9219 20.0156 65.0312\nQ15.375 64.1562 9.8125 62.3125\nL9.8125 71.0938\nQ15.4375 72.6562 20.3438 73.4375\nQ25.25 74.2188 29.5938 74.2188\nQ40.8281 74.2188 47.3594 69.1094\nQ53.9062 64.0156 53.9062 55.3281\nQ53.9062 49.2656 50.4375 45.0938\nQ46.9688 40.9219 40.5781 39.3125\" id=\"BitstreamVeraSans-Roman-33\"/>\n      </defs>\n      <g transform=\"translate(226.706473214 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-33\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_5\">\n     <g id=\"line2d_11\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"293.356964286\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_12\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"293.356964286\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_5\">\n      <!-- 4 -->\n      <defs>\n       <path d=\"\nM37.7969 64.3125\nL12.8906 25.3906\nL37.7969 25.3906\nz\n\nM35.2031 72.9062\nL47.6094 72.9062\nL47.6094 25.3906\nL58.0156 25.3906\nL58.0156 17.1875\nL47.6094 17.1875\nL47.6094 0\nL37.7969 0\nL37.7969 17.1875\nL4.89062 17.1875\nL4.89062 26.7031\nz\n\" id=\"BitstreamVeraSans-Roman-34\"/>\n      </defs>\n      <g transform=\"translate(290.169464286 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-34\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_6\">\n     <g id=\"line2d_13\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"357.128392857\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_14\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"357.128392857\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_6\">\n      <!-- 5 -->\n      <defs>\n       <path d=\"\nM10.7969 72.9062\nL49.5156 72.9062\nL49.5156 64.5938\nL19.8281 64.5938\nL19.8281 46.7344\nQ21.9688 47.4688 24.1094 47.8281\nQ26.2656 48.1875 28.4219 48.1875\nQ40.625 48.1875 47.75 41.5\nQ54.8906 34.8125 54.8906 23.3906\nQ54.8906 11.625 47.5625 5.09375\nQ40.2344 -1.42188 26.9062 -1.42188\nQ22.3125 -1.42188 17.5469 -0.640625\nQ12.7969 0.140625 7.71875 1.70312\nL7.71875 11.625\nQ12.1094 9.23438 16.7969 8.0625\nQ21.4844 6.89062 26.7031 6.89062\nQ35.1562 6.89062 40.0781 11.3281\nQ45.0156 15.7656 45.0156 23.3906\nQ45.0156 31 40.0781 35.4375\nQ35.1562 39.8906 26.7031 39.8906\nQ22.75 39.8906 18.8125 39.0156\nQ14.8906 38.1406 10.7969 36.2812\nz\n\" id=\"BitstreamVeraSans-Roman-35\"/>\n      </defs>\n      <g transform=\"translate(354.298080357 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-35\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_7\">\n     <g id=\"line2d_15\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"420.899821429\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_16\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"420.899821429\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_7\">\n      <!-- 6 -->\n      <defs>\n       <path d=\"\nM33.0156 40.375\nQ26.375 40.375 22.4844 35.8281\nQ18.6094 31.2969 18.6094 23.3906\nQ18.6094 15.5312 22.4844 10.9531\nQ26.375 6.39062 33.0156 6.39062\nQ39.6562 6.39062 43.5312 10.9531\nQ47.4062 15.5312 47.4062 23.3906\nQ47.4062 31.2969 43.5312 35.8281\nQ39.6562 40.375 33.0156 40.375\nM52.5938 71.2969\nL52.5938 62.3125\nQ48.875 64.0625 45.0938 64.9844\nQ41.3125 65.9219 37.5938 65.9219\nQ27.8281 65.9219 22.6719 59.3281\nQ17.5312 52.7344 16.7969 39.4062\nQ19.6719 43.6562 24.0156 45.9219\nQ28.375 48.1875 33.5938 48.1875\nQ44.5781 48.1875 50.9531 41.5156\nQ57.3281 34.8594 57.3281 23.3906\nQ57.3281 12.1562 50.6875 5.35938\nQ44.0469 -1.42188 33.0156 -1.42188\nQ20.3594 -1.42188 13.6719 8.26562\nQ6.98438 17.9688 6.98438 36.375\nQ6.98438 53.6562 15.1875 63.9375\nQ23.3906 74.2188 37.2031 74.2188\nQ40.9219 74.2188 44.7031 73.4844\nQ48.4844 72.75 52.5938 71.2969\" id=\"BitstreamVeraSans-Roman-36\"/>\n      </defs>\n      <g transform=\"translate(417.879196429 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-36\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_8\">\n     <g id=\"line2d_17\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"484.67125\" xlink:href=\"#m93b0483c22\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_18\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"484.67125\" xlink:href=\"#m741efc42ff\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_8\">\n      <!-- 7 -->\n      <defs>\n       <path d=\"\nM8.20312 72.9062\nL55.0781 72.9062\nL55.0781 68.7031\nL28.6094 0\nL18.3125 0\nL43.2188 64.5938\nL8.20312 64.5938\nz\n\" id=\"BitstreamVeraSans-Roman-37\"/>\n      </defs>\n      <g transform=\"translate(481.85875 371.725)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-37\"/>\n      </g>\n     </g>\n    </g>\n   </g>\n   <g id=\"matplotlib.axis_2\">\n    <g id=\"ytick_1\">\n     <g id=\"line2d_19\">\n      <defs>\n       <path d=\"\nM0 0\nL4 0\" id=\"m728421d6d4\" style=\"stroke:#000000;stroke-width:0.5;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"38.27125\" xlink:href=\"#m728421d6d4\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_20\">\n      <defs>\n       <path d=\"\nM0 0\nL-4 0\" id=\"mcb0005524f\" style=\"stroke:#000000;stroke-width:0.5;\"/>\n      </defs>\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"484.67125\" xlink:href=\"#mcb0005524f\" y=\"358.606875\"/>\n      </g>\n     </g>\n     <g id=\"text_9\">\n      <!-- \u22121.0 -->\n      <defs>\n       <path d=\"\nM10.5938 35.5\nL73.1875 35.5\nL73.1875 27.2031\nL10.5938 27.2031\nz\n\" id=\"BitstreamVeraSans-Roman-2212\"/>\n       <path d=\"\nM10.6875 12.4062\nL21 12.4062\nL21 0\nL10.6875 0\nz\n\" id=\"BitstreamVeraSans-Roman-2e\"/>\n      </defs>\n      <g transform=\"translate(7.2 361.918125)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-2212\"/>\n       <use x=\"83.7890625\" xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n       <use x=\"147.412109375\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n       <use x=\"179.19921875\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_2\">\n     <g id=\"line2d_21\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"38.27125\" xlink:href=\"#m728421d6d4\" y=\"272.206875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_22\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"484.67125\" xlink:href=\"#mcb0005524f\" y=\"272.206875\"/>\n      </g>\n     </g>\n     <g id=\"text_10\">\n      <!-- \u22120.5 -->\n      <g transform=\"translate(7.45125 275.518125)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-2212\"/>\n       <use x=\"83.7890625\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n       <use x=\"147.412109375\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n       <use x=\"179.19921875\" xlink:href=\"#BitstreamVeraSans-Roman-35\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_3\">\n     <g id=\"line2d_23\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"38.27125\" xlink:href=\"#m728421d6d4\" y=\"185.806875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_24\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"484.67125\" xlink:href=\"#mcb0005524f\" y=\"185.806875\"/>\n      </g>\n     </g>\n     <g id=\"text_11\">\n      <!-- 0.0 -->\n      <g transform=\"translate(16.775625 189.118125)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n       <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n       <use x=\"95.41015625\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_4\">\n     <g id=\"line2d_25\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"38.27125\" xlink:href=\"#m728421d6d4\" y=\"99.406875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_26\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"484.67125\" xlink:href=\"#mcb0005524f\" y=\"99.406875\"/>\n      </g>\n     </g>\n     <g id=\"text_12\">\n      <!-- 0.5 -->\n      <g transform=\"translate(17.026875 102.718125)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n       <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n       <use x=\"95.41015625\" xlink:href=\"#BitstreamVeraSans-Roman-35\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_5\">\n     <g id=\"line2d_27\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"38.27125\" xlink:href=\"#m728421d6d4\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"line2d_28\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"484.67125\" xlink:href=\"#mcb0005524f\" y=\"13.006875\"/>\n      </g>\n     </g>\n     <g id=\"text_13\">\n      <!-- 1.0 -->\n      <g transform=\"translate(17.3025 16.318125)scale(0.12 -0.12)\">\n       <use xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n       <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n       <use x=\"95.41015625\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n      </g>\n     </g>\n    </g>\n   </g>\n   <g id=\"patch_3\">\n    <path d=\"\nM38.2713 13.0069\nL484.671 13.0069\" style=\"fill:none;stroke:#000000;\"/>\n   </g>\n   <g id=\"patch_4\">\n    <path d=\"\nM484.671 358.607\nL484.671 13.0069\" style=\"fill:none;stroke:#000000;\"/>\n   </g>\n   <g id=\"patch_5\">\n    <path d=\"\nM38.2713 358.607\nL484.671 358.607\" style=\"fill:none;stroke:#000000;\"/>\n   </g>\n   <g id=\"patch_6\">\n    <path d=\"\nM38.2713 358.607\nL38.2713 13.0069\" style=\"fill:none;stroke:#000000;\"/>\n   </g>\n  </g>\n </g>\n <defs>\n  <clipPath id=\"p1a591c6a9e\">\n   <rect height=\"345.6\" width=\"446.4\" x=\"38.27125\" y=\"13.006875\"/>\n  </clipPath>\n </defs>\n</svg>\n",
       "text": "Figure(PyObject <matplotlib.figure.Figure object at 0xa087b10>)"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": "1-element Array{Any,1}:\n PyObject <matplotlib.lines.Line2D object at 0x927dfd0>"
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "3. Parallel Computing Examples (How did we live without it!!??)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "cumprod(1:10)'      %Prefix product gives factorials",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": "1x10 Array{Int64,2}:\n 1  2  6  24  120  720  5040  40320  362880  3628800"
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "function prefix_serial(y,*)\n    for i in 2:length(y)\n        y[i]=y[i-1]*y[i]\n    end\n    y\nend",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": "prefix_serial (generic function with 1 method)"
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "prefix_serial([1:10],*)'",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": "1x10 Array{Int64,2}:\n 1  2  6  24  120  720  5040  40320  362880  3628800"
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "prefix_serial([1:10],+)'",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": "1x10 Array{Int64,2}:\n 1  3  6  10  15  21  28  36  45  55"
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "prefix_serial([1:10],min)'",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": "1x10 Array{Int64,2}:\n 1  1  1  1  1  1  1  1  1  1"
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "A=[rand(1:3,2,2) for i=1:4];A[1]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": "2x2 Array{Int64,2}:\n 2  2\n 3  3"
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "prefix_serial(A,*)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": "4-element Array{Array{Int64,2},1}:\n 2x2 Array{Int64,2}:\n 2  2\n 3  3        \n 2x2 Array{Int64,2}:\n  8  4\n 12  6      \n 2x2 Array{Int64,2}:\n 20  24\n 30  36    \n 2x2 Array{Int64,2}:\n  68   88\n 102  132"
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "prefix_serial(A,kron)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 90,
       "text": "4-element Array{Array{Int64,2},1}:\n 2x2 Array{Int64,2}:\n 2  2\n 3  3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      \n 4x4 Array{Int64,2}:\n 16   8  16   8\n 24  12  24  12\n 24  12  24  12\n 36  18  36  18                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n 8x8 Array{Int64,2}:\n  320   384  160  192   320   384  160  192\n  480   576  240  288   480   576  240  288\n  480   576  240  288   480   576  240  288\n  720   864  360  432   720   864  360  432\n  480   576  240  288   480   576  240  288\n  720   864  360  432   720   864  360  432\n  720   864  360  432   720   864  360  432\n 1080  1296  540  648  1080  1296  540  648                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n 16x16 Array{Int64,2}:\n  21760   28160   26112   33792  10880  14080  13056  16896   21760   28160   26112   33792  10880  14080  13056  16896\n  32640   42240   39168   50688  16320  21120  19584  25344   32640   42240   39168   50688  16320  21120  19584  25344\n  32640   42240   39168   50688  16320  21120  19584  25344   32640   42240   39168   50688  16320  21120  19584  25344\n  48960   63360   58752   76032  24480  31680  29376  38016   48960   63360   58752   76032  24480  31680  29376  38016\n  32640   42240   39168   50688  16320  21120  19584  25344   32640   42240   39168   50688  16320  21120  19584  25344\n  48960   63360   58752   76032  24480  31680  29376  38016   48960   63360   58752   76032  24480  31680  29376  38016\n  48960   63360   58752   76032  24480  31680  29376  38016   48960   63360   58752   76032  24480  31680  29376  38016\n  73440   95040   88128  114048  36720  47520  44064  57024   73440   95040   88128  114048  36720  47520  44064  57024\n  32640   42240   39168   50688  16320  21120  19584  25344   32640   42240   39168   50688  16320  21120  19584  25344\n  48960   63360   58752   76032  24480  31680  29376  38016   48960   63360   58752   76032  24480  31680  29376  38016\n  48960   63360   58752   76032  24480  31680  29376  38016   48960   63360   58752   76032  24480  31680  29376  38016\n  73440   95040   88128  114048  36720  47520  44064  57024   73440   95040   88128  114048  36720  47520  44064  57024\n  48960   63360   58752   76032  24480  31680  29376  38016   48960   63360   58752   76032  24480  31680  29376  38016\n  73440   95040   88128  114048  36720  47520  44064  57024   73440   95040   88128  114048  36720  47520  44064  57024\n  73440   95040   88128  114048  36720  47520  44064  57024   73440   95040   88128  114048  36720  47520  44064  57024\n 110160  142560  132192  171072  55080  71280  66096  85536  110160  142560  132192  171072  55080  71280  66096  85536"
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# staged prefix (example with 8 elements)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "function prefix8(y,*)\n    if length(y)!=8; error(\"length 8 only\"); end\n    for i in [2,4,6,8]; y[i]=y[i-1]*y[i]; end\n    for i in [  4,  8]; y[i]=y[i-2]*y[i]; end\n    for i in [      8]; y[i]=y[i-4]*y[i]; end\n    for i in [    6  ]; y[i]=y[i-2]*y[i]; end\n    for i in [ 3,5,7 ]; y[i]=y[i-1]*y[i]; end\n    y\nend",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": "prefix8 (generic function with 1 method)"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "prefix8([1:8],*)'",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": "1x8 Array{Int64,2}:\n 1  2  6  24  120  720  5040  40320"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "addprocs(7)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": "7-element Array{Any,1}:\n 2\n 3\n 4\n 5\n 6\n 7\n 8"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "@spawnat : One of many parallel programming primitives in Julia"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "r=@spawnat 3 x=randn(4,4)    # Execute on processor 3, return a pointer to the answer",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": "RemoteRef(3,1,6)"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fetch(r)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": "4x4 Array{Float64,2}:\n 1.06088   -0.734633   0.580743   0.659417 \n 0.755944   0.735749  -0.139172   0.0996647\n 1.11658   -0.616243  -0.180218  -0.507076 \n 0.273514  -0.933803  -2.05133   -1.38384  "
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "*(r1::RemoteRef,r2::RemoteRef)=@spawnat r2.where fetch(r1)*fetch(r2)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": "* (generic function with 127 methods)"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "The above makes the prefix code parallel "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "A=[@spawnat i randn(5000,5000) for i=1:8]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": "8-element Array{Any,1}:\n RemoteRef(1,1,8) \n RemoteRef(2,1,9) \n RemoteRef(3,1,10)\n RemoteRef(4,1,11)\n RemoteRef(5,1,12)\n RemoteRef(6,1,13)\n RemoteRef(7,1,14)\n RemoteRef(8,1,15)"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "@time @sync prefix8(A,*)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "elapsed time: 168"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": ".724089338 seconds (274694192 bytes allocated)\n"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": "8-element Array{Any,1}:\n RemoteRef(1,1,8) \n RemoteRef(2,1,16)\n RemoteRef(3,1,24)\n RemoteRef(4,1,20)\n RemoteRef(5,1,25)\n RemoteRef(6,1,23)\n RemoteRef(7,1,26)\n RemoteRef(8,1,22)"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "A=randn(5000,5000);B=randn(5000,5000);\ntic();A*B;t=toc();t*11",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "elapsed time: "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "40.91440721 seconds\n"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": "450.05847931"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}