mbeltagy/nonLinearHW9.1.ipynb

## nonLinearHW9.1.ipynb
{
 "metadata": {
  "language": "Julia",
  "name": "",
  "signature": "sha256:19de60cc0ce37db9b934e27d50488e54e531a3f84828fc420cbeaaa2b7ff1733"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "using Requests\n",
      "using Interact\n",
      "using PyPlot\n",
      "res=get(\"http://tuvalu.santafe.edu/~jgarland/CapDimData.dat\")\n",
      "capDimData=readcsv(IOBuffer(res.data));\n",
      "currentView=capDimData[:,[1,3]] ; # The x and z coordinates\n",
      "plot(currentView[:,1],currentView[:,2],\",\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<script charset=\"utf-8\">(function ($, undefined) {\n",
        "\n",
        "    function createElem(tag, attr, content) {\n",
        "\t// TODO: remove jQuery dependency\n",
        "\tvar el = $(\"<\" + tag + \"/>\").attr(attr);\n",
        "\tif (content) {\n",
        "\t    el.append(content);\n",
        "\t}\n",
        "\treturn el[0];\n",
        "    }\n",
        "\n",
        "    // A widget must expose an id field which identifies it to the backend,\n",
        "    // an elem attribute which is will be added to the DOM, and\n",
        "    // a getState() method which returns the value to be sent to the backend\n",
        "    // a sendUpdate() method which sends its current value to the backend\n",
        "    var Widget = {\n",
        "\tid: undefined,\n",
        "\telem: undefined,\n",
        "\tlabel: undefined,\n",
        "\tgetState: function () {\n",
        "\t    return this.elem.value;\n",
        "\t},\n",
        "\tsendUpdate: undefined\n",
        "    };\n",
        "\n",
        "    var Slider = function (typ, id, init) {\n",
        "\tvar attr = { type:  \"range\",\n",
        "\t\t     value: init.value,\n",
        "\t\t     min:   init.min,\n",
        "\t\t     max:   init.max,\n",
        "\t\t     step:  init.step },\n",
        "\t    elem = createElem(\"input\", attr),\n",
        "\t    self = this;\n",
        "\n",
        "\telem.onchange = function () {\n",
        "\t    self.sendUpdate();\n",
        "\t}\n",
        "\n",
        "\tthis.id = id;\n",
        "\tthis.elem = elem;\n",
        "\tthis.label = init.label;\n",
        "\n",
        "\tInputWidgets.commInitializer(this); // Initialize communication\n",
        "    }\n",
        "    Slider.prototype = Widget;\n",
        "\n",
        "    var Checkbox = function (typ, id, init) {\n",
        "\tvar attr = { type: \"checkbox\",\n",
        "\t\t     checked: init.value },\n",
        "\t    elem = createElem(\"input\", attr),\n",
        "\t    self = this;\n",
        "\n",
        "\tthis.getState = function () {\n",
        "\t    return elem.checked;\n",
        "\t}\n",
        "\telem.onchange = function () {\n",
        "\t    self.sendUpdate();\n",
        "\t}\n",
        "\n",
        "\tthis.id = id;\n",
        "\tthis.elem = elem;\n",
        "\tthis.label = init.label;\n",
        "\n",
        "\tInputWidgets.commInitializer(this);\n",
        "    }\n",
        "    Checkbox.prototype = Widget;\n",
        "\n",
        "    var Button = function (typ, id, init) {\n",
        "\tvar attr = { type:    \"button\",\n",
        "\t\t     value:   init.label },\n",
        "\t    elem = createElem(\"input\", attr),\n",
        "\t    self = this;\n",
        "\tthis.getState = function () {\n",
        "\t    return null;\n",
        "\t}\n",
        "\telem.onclick = function () {\n",
        "\t    self.sendUpdate();\n",
        "\t}\n",
        "\n",
        "\tthis.id = id;\n",
        "\tthis.elem = elem;\n",
        "\tthis.label = init.label;\n",
        "\n",
        "\tInputWidgets.commInitializer(this);\n",
        "    }\n",
        "    Button.prototype = Widget;\n",
        "\n",
        "    var Text = function (typ, id, init) {\n",
        "\tvar attr = { type:  \"text\",\n",
        "\t\t     placeholder: init.label,\n",
        "\t\t     value: init.value },\n",
        "\t    elem = createElem(\"input\", attr),\n",
        "\t    self = this;\n",
        "\tthis.getState = function () {\n",
        "\t    return elem.value;\n",
        "\t}\n",
        "\telem.onkeyup = function () {\n",
        "\t    self.sendUpdate();\n",
        "\t}\n",
        "\n",
        "\tthis.id = id;\n",
        "\tthis.elem = elem;\n",
        "\tthis.label = init.label;\n",
        "\n",
        "\tInputWidgets.commInitializer(this);\n",
        "    }\n",
        "    Text.prototype = Widget;\n",
        "\n",
        "    var Textarea = function (typ, id, init) {\n",
        "\tvar attr = { placeholder: init.label },\n",
        "\t    elem = createElem(\"textarea\", attr, init.value),\n",
        "\t    self = this;\n",
        "\tthis.getState = function () {\n",
        "\t    return elem.value;\n",
        "\t}\n",
        "\telem.onchange = function () {\n",
        "\t    self.sendUpdate();\n",
        "\t}\n",
        "\n",
        "\tthis.id = id;\n",
        "\tthis.elem = elem;\n",
        "\tthis.label = init.label;\n",
        "\n",
        "\tInputWidgets.commInitializer(this);\n",
        "    }\n",
        "    Textarea.prototype = Widget;\n",
        "\n",
        "    // RadioButtons\n",
        "    // Dropdown\n",
        "    // HTML\n",
        "    // Latex\n",
        "\n",
        "    var InputWidgets = {\n",
        "\tSlider: Slider,\n",
        "\tCheckbox: Checkbox,\n",
        "\tButton: Button,\n",
        "\tText: Text,\n",
        "\tTextarea: Textarea,\n",
        "\tdebug: false,\n",
        "\tlog: function () {\n",
        "\t    if (InputWidgets.debug) {\n",
        "\t\tconsole.log.apply(console, arguments);\n",
        "\t    }\n",
        "\t},\n",
        "\t// a central way to initalize communication\n",
        "\t// for widgets.\n",
        "\tcommInitializer: function (widget) {\n",
        "\t    widget.sendUpdate = function () {};\n",
        "\t}\n",
        "    };\n",
        "\n",
        "    window.InputWidgets = InputWidgets;\n",
        "\n",
        "})(jQuery, undefined);\n",
        "</script>"
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "html": [
        "<script charset=\"utf-8\">(function (IPython, $, _, MathJax, Widgets) {\n",
        "    $.event.special.destroyed = {\n",
        "\tremove: function(o) {\n",
        "\t    if (o.handler) {\n",
        "\t\to.handler.apply(this, arguments)\n",
        "\t    }\n",
        "\t}\n",
        "    }\n",
        "\n",
        "    var redrawValue = function (container, type, val) {\n",
        "\tvar selector = $(\"<div/>\");\n",
        "\tvar oa = new IPython.OutputArea(_.extend(selector, {\n",
        "\t    selector: selector,\n",
        "\t    prompt_area: true,\n",
        "\t    events: IPython.events,\n",
        "\t    keyboard_manager: IPython.keyboard_manager\n",
        "\t})); // Hack to work with IPython 2.1.0\n",
        "\n",
        "\tswitch (type) {\n",
        "\tcase \"image/png\":\n",
        "            var _src = 'data:' + type + ';base64,' + val;\n",
        "\t    $(container).find(\"img\").attr('src', _src);\n",
        "\t    break;\n",
        "\tdefault:\n",
        "\t    var toinsert = IPython.OutputArea.append_map[type].apply(\n",
        "\t\toa, [val, {}, selector]\n",
        "\t    );\n",
        "\t    $(container).empty().append(toinsert.contents());\n",
        "\t    selector.remove();\n",
        "\t}\n",
        "\tif (type === \"text/latex\" && MathJax) {\n",
        "\t    MathJax.Hub.Queue([\"Typeset\", MathJax.Hub, toinsert.get(0)]);\n",
        "\t}\n",
        "    }\n",
        "\n",
        "\n",
        "    $(document).ready(function() {\n",
        "\tWidgets.debug = false; // log messages etc in console.\n",
        "\tfunction initComm(evt, data) {\n",
        "\t    var comm_manager = data.kernel.comm_manager;\n",
        "\t    comm_manager.register_target(\"Signal\", function (comm) {\n",
        "\t\tcomm.on_msg(function (msg) {\n",
        "\t\t    //Widgets.log(\"message received\", msg);\n",
        "\t\t    var val = msg.content.data.value;\n",
        "\t\t    $(\".signal-\" + comm.comm_id).each(function() {\n",
        "\t\t\tvar type = $(this).data(\"type\");\n",
        "\t\t\tif (val[type]) {\n",
        "\t\t\t    redrawValue(this, type, val[type], type);\n",
        "\t\t\t}\n",
        "\t\t    });\n",
        "\t\t    delete val;\n",
        "\t\t    delete msg.content.data.value;\n",
        "\t\t});\n",
        "\t    });\n",
        "\n",
        "\t    // coordingate with Comm and redraw Signals\n",
        "\t    // XXX: Test using Reactive here to improve performance\n",
        "\t    $([IPython.events]).on(\n",
        "\t\t'output_appended.OutputArea', function (event, type, value, md, toinsert) {\n",
        "\t\t    if (md && md.reactive) {\n",
        "\t\t\t// console.log(md.comm_id);\n",
        "\t\t\ttoinsert.addClass(\"signal-\" + md.comm_id);\n",
        "\t\t\ttoinsert.data(\"type\", type);\n",
        "\t\t\t// Signal back indicating the mimetype required\n",
        "\t\t\tvar comm_manager = IPython.notebook.kernel.comm_manager;\n",
        "\t\t\tvar comm = comm_manager.comms[md.comm_id];\n",
        "\t\t\tcomm.send({action: \"subscribe_mime\",\n",
        "\t\t\t\t   mime: type});\n",
        "\t\t\ttoinsert.bind(\"destroyed\", function() {\n",
        "\t\t\t    comm.send({action: \"unsubscribe_mime\",\n",
        "\t\t\t\t       mime: type});\n",
        "\t\t\t});\n",
        "\t\t    }\n",
        "\t    });\n",
        "\n",
        "\t    // Set up communication for Widgets\n",
        "\t    Widgets.commInitializer = function (widget) {\n",
        "\t\tvar comm = comm_manager.new_comm(\n",
        "\t\t    \"InputWidget\", {widget_id: widget.id}\n",
        "\t\t);\n",
        "\t\twidget.sendUpdate = function () {\n",
        "\t\t    // `this` is a widget here.\n",
        "\t\t    // TODO: I have a feeling there's some\n",
        "\t\t    //       IPython bookkeeping to be done here.\n",
        "\t\t    // Widgets.log(\"State changed\", this, this.getState());\n",
        "\t\t    comm.send({value: this.getState()});\n",
        "\t\t}\n",
        "\t    };\n",
        "\t}\n",
        "\n",
        "\ttry {\n",
        "\t    // try to initialize right away. otherwise, wait on the status_started event.\n",
        "\t    initComm(undefined, IPython.notebook);\n",
        "\t} catch (e) {\n",
        "\t    $([IPython.events]).on('status_started.Kernel', initComm);\n",
        "\t}\n",
        "    });\n",
        "})(IPython, jQuery, _, MathJax, InputWidgets);\n",
        "</script>"
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO: Loading help data...\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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",
       "text": [
        "Figure(PyObject <matplotlib.figure.Figure object at 0x7fdff3ca4f90>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "1-element Array{Any,1}:\n",
        " PyObject <matplotlib.lines.Line2D object at 0x7fe00fa66190>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "function boxCount2D(data,\u03f5)\n",
      "    xmin, xmax =extrema(data[:,1])\n",
      "    ymin, ymax =extrema(data[:,2])\n",
      "    num_x=iceil((xmax-xmin)/\u03f5)\n",
      "    num_y=iceil((ymax-ymin)/\u03f5)\n",
      "    n=size(data,1)\n",
      "    boxarray=zeros(Int,num_x, num_y)\n",
      "    for i=1:n\n",
      "        x=data[i,1]\n",
      "        y=data[i,2]\n",
      "        l_x=ifloor((x-xmin)/\u03f5)+1\n",
      "        l_y=ifloor((y-ymin)/\u03f5)+1\n",
      "        boxarray[l_x,l_y]+=1  \n",
      "    end\n",
      "    N=sum(boxarray.>0)\n",
      "    return N#,boxarray\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "boxCount2D (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "1 (a)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N=boxCount2D(currentView,0.01);\n",
      "N"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "13998"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "1 (b)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N=boxCount2D(currentView,0.5);\n",
      "N"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "4341"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "2"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "log\u03f5R=[-4:0.05:5]\n",
      "\u03f5R=1./exp(log\u03f5R);\n",
      "N\u03f5=[boxCount2D(currentView,\u03f5) for \u03f5 in \u03f5R];\n",
      "plot(log\u03f5R,log(N\u03f5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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",
       "text": [
        "Figure(PyObject <matplotlib.figure.Figure object at 0x7fe00fa9b110>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "1-element Array{Any,1}:\n",
        " PyObject <matplotlib.lines.Line2D object at 0x7fdff3c546d0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Based on the above plot, the scalling range can be determined in the region of [-2,0.5], we the do the calculation and regression "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "log\u03f5R=[-2:0.05:0.5]\n",
      "\u03f5R=1./exp(log\u03f5R);\n",
      "N\u03f5=[boxCount2D(currentView,\u03f5) for \u03f5 in \u03f5R];\n",
      "a, b = linreg(log\u03f5R,log(N\u03f5)) # Linear regression\n",
      "plot(log\u03f5R, log(N\u03f5), \"o\") # Plot (x,y) points\n",
      "plot(log\u03f5R, [a+b*i for i in log\u03f5R]) # Plot the line determined by the linear regression\n",
      "print(\"The capcity dimension is \", b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The capcity dimension is 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ".7064645097572657"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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L3S4Zbzuhnn841S5JkgqMtHqkqLS/lNkC5/8T7oyAk+bDe69w4gdnZwqdyl/c8ZQkSQVGcnIxCNsHUc9Au0cgJADTH4L5fSG5NCn13svrJeoPGDwlSVKBkBpIZVfdtdC9PpTbAItuh5mDYc+h+zStR8rvDJ6SJCnfm7JqCv0n9+fnqKWQ0BGmTIVfzvjdUdYj5Xfe4ylJkvJUUlISffr0p1Gj7tSv35NGjbrTp09/kpKS+GbTN1zw5gV0eaMLpcNK8/GlH1N3MfDLFtJqkUj/My69Hik6734Q/Sl3PCVJUp45aj3Sjx/zv+Sm7D5jE3Uq12HiFRO5rOFlhISEEBkXaT1SAWXwlCRJeeaIeqQSO+GcWGjzFDsPliBqSwdmDfqUEsVKZJxjPVLB5UftkiQpz2TUI4UehLP/C3efDm2ehPn/hP+sZufUUplCpwo2dzwlSVKeOZgcCg0+gPMGQNUf4OvrYNpw+PVkIL0+SYWGwVOSJOWJBT8tYH2XuRD+MSSeBxPHwcazDjvCeqTCxuApSZJyTFLSoeeiJ/xu8Cc6Y/Bn1bZV3Df1PiYsn0DlitXYM/ZpWNmXtMGiw1mPVNgYPCVJUo446oR6QjyzZ/di0vT/8sLyFxi5cCThZcN5pecrXFDzAtqOv5pEWpH2KMxQ0uqRFqTXI43Pw59IOc3gKUmScsQRE+oAhEJYMxJPbMiZo5tSslRxhpw7hHta30OZ4mUAiIsbbz1SEWHwlCRJOSJtQj32txdCUuHMt6DTIKiwnnKravHdCwupXrZ6pvOsRyo6rFOSJEk5Im0CPf0+zVOnwy0t4bLrYEMLGLmc8PimR4ROFS3ueEqSpBwRFpYC4cugSwzU+wR+ioJXZsO6tkAqYSc4oV7UGTwlSVK2bdi5gYMX/Ahlm8L2U2HCBEj4P36bVHdCXQZPSZL0B/6sHmnXgV08MfcJnox7kpLVSlJ1Th22fvYypLTl0FS7E+o6xOApSZKy9Ef1SLPmXMHtL13Ek4ueZPu+7fwz6p/c1+4+Dt50MD2oPu6Euo5g8JQkSVnKuh4pBOolsarLWvrP7M91Ta5jWMdh1K5UO+3tUjihrqMyeEqSpCwdUY9UcyF07Q+nzoRVnTnty1q8PuT1PFufCh7rlCRJUpYy6pEqrYbLr4ZbI6H0Vhj7Cbw+meJbquT1ElXAuOMpSZKyFFJmL3S9FyJHwt6q8MFo+OoGCBQDUtPqk6TjYPCUJKmI+LMJ9UP2J+/nufjnWN1jDiTPg1n3Q9y9cLDsYVezHknHz+ApSVIR8EcT6rNn9yIubjxVq1Vl/LLxDJw2kB93/Mi1Ta5lxoMrWbv8PKB0+pWsR9JfZ/CUJKkIyHpCPRRoRWLiw1w/+C62tEhk0c+L6Fm/J59e8ykNqjUg6dxDu6TDrUdSthk8JUkqAo6YUD+k2rfQ5RE+q/ExLWnJzBtm0r52+4y3w8PDrUdSjjF4SpJUBGRMqB9SbiN0eBCaj4Ydp3Di3ObM/3w+oSEW3ij3GDwlSSoC0ibQA1B8D7R5Cs6JhZQS8MUTsPA2qtT/P0Oncp3BU5KkAupYp9QBzo5sQEKpgdDxVSj9C8TfBbMGwb7KQJwT6goKg6ckSQXQsUyph4eHEwgE+GTFJ8Q3/wRO/Q6WdoVp/4XtdUibUI9zQl1B4566JEkFUOYp9UP3bv42pR4dHcuSDUvo/HpnLhp3ESdWOJEv/u8LbqjQhIiad1GvXk8iInpwww3vZoRUKbe54ylJUgF01Cl1gIon8l7IW7w66kkaVmvIR70/ovsZ3QkJCaHLmC5BXad0OIOnJEkF0BFT6gCltkO7RyDqGXYdhBcvepEbz7qRsFD/5175g/8SJUkqgDKm1AmBYgeg5fPQfhiE7YM5MZzxy3xufezWvF6mlInBU5KkAigyMoKEhPnQ6EfofB9UWgNf3gTTH4Jda2h1w568XqJ0BIOnJEn5xPHUI13Wtz3jynVjf7Wd8H13GPchJDXE56grPzN4SpKUDxxrPdL3W74nZkoMH3z/AU0bNuXEb+rz49e7SK58H2HhPkdd+ZvBU5KkfCBzPdIhv9Uj3TVgCFUug1GLR3FShZN487I3uarxVT5tSAVKtv613nDDDYSGhh71Kz4+PqfWKUlSoZZWjxR15BvF90D7KbxTYxTjlo3jsfMe47t/fMfVZ15t6FSBk60dzwceeIA77rgj02uBQIAePXpQunRpWrZsma3FSZJUVBxRjxSSAs1eg46DoWwSFVbUZuXL8VQtUzXP1ihlV7aCZ506dahTp06m12bOnMmWLVsYPHgwISEhRzlTkiQd7rd6JOD0z6FLNJzwDSzrBVOHU7PGPw2dKvBy/B7Pl19+mdDQUG666aacvrQkSYVWZGQECb+8Dl3fgDpTYW07eGkBrI8E4oi8OCKvlyhlW44Gzx07djBx4kQ6d+7MKaeckpOXliSp0Fq3Yx27u66F2k/C1lNg3Lvw/SWk7YDGWY+kQiNHg+e4cePYt2+fu52SpCLvWDo5d+zbwaNzHmXE/BFULFWRx899nOWvb2RRsdEk1xtz2DnWI6lwyNHg+fLLL1OtWjUuvfTSnLysJEkFyp91cs6c8wb/W/s/hs4cyp6De4g+J5r+bfpTvmR56JjHi5dyUY4Fz6VLl7J48WL69u1L8eLF//T4vn37UqlSpUyv9e7dm969e+fUkiRJyhNH7+SMIrFkZxo+15jdJX6lT7M+DO04lJrla+bRSqW0T6zHjRuX6bXt27fnyvfKseD58ssvA3DzzTcf0/EjRoygefPmOfXtJUnKN9I6OWMzv3jyPOjaD06OI7AhnK8e+oozTzgzT9YnHS6rjb8lS5bQokWLHP9eOdI8u3//fsaOHUtUVBQREU7dSZKKtkydnFVWwJWXw03nQNg+eG0KNae3MnSqSMqR4Pn++++zbdu2Y97tlCSpMAsLS4Eym+GCu+DOCKi1EN59HUYtgtUd0zs7paInRz5qf+WVVyhXrhxXXXVVTlxOkqR851im1AH2HtxLqS47oXQdIAymPQwL7obkUulHxBEZ6aeDKppyJHh+/vnnOXEZSZLypT+bUo+LG0+VqlV4Y+kbDJ4+mE1VN1Fx+Qns+GgU7OlG2geMqcACOzlVpOX4k4skSSpsjj6l3orExIe55oE72Nx0BV9v+porIq7g0c6PUiGlAtGlY4mPf+53O6R2cqroMnhKkvQnspxSBzhhKXQZwuQTJ9O2ZFvm3zSfqJOiMt4eM+aJ4C1SKgAMnpIk/YlMU+oAFX6CjoOh2Wuw9QxqzjqbWVNmERISctRrSDJ4SpL0p9Km0ANQciec8zi0fhoOlIdPnoPFN1GpwWWGTukYGDwlSfoTZ0fWJ6FcPzj3dSixG+L+BXOjYX8FnFKXjp3BU5JU5BxrNVIgEOC9795jTtMPoPYq+Ko7TH8efj2FtCn1OKfUpeNg8JQkFSnHUo0UHh5O3I9x9J/cn7k/zqVb3W683O1lXls7ifiTbndKXfqLDJ6SpCLlz6qRbht4H6FddzAxYSJNT2jKF9d+QZe6XQDoMKZDHqxYKjwMnpKkIuWo1UhltkD7cbxb4xVO+qkWr178Ktc2uZZiocWCvkapsDJ4SpKKlCOqkcL2QtQz0O4RCAlQ7Zv6/DBhCaWLl86zNUqFlcFTklSkZFQjhQSgyVjodD+U2wCLboeZg6h+6o2GTimXGDwlSQXesU6pA0RGRpCw71noMgZqfAUJl8GUx+CXM7AaScpdBk9JUoF2rFPqAN9s+oZ17ZfAqU/Cj43h5dnwY1usRpKCIzSvFyBJUnZknlI/dO/mb1Pq0dGxrP91PTd9cBPNXmzGul3reLnby/wtpRsR5R+lXr2eRET04IYb3s0UUiXlPHc8JUkF2lGn1AFKRPDxnusZ/+xIyhQvw4huI/j72X+nRLES3Ngq61Mk5R6DpySpQDtiSh0g9CA0Hw0dHmRrqS3EREUzoO0AKpaqmCdrlJTG4ClJKtAyptQJSfuz/ofQJQaq/gBfX8vpP/7Eo8MezeNVSgKDpySpgIuMjCAhYQHUCoGu/aH2bEjsAhPfho17OeeGd/N6iZLSGTwlSQXaHYOu5B0uYvepm2HTmTD2U1jZFVjglLqUzxg8JUn5zrH0cm7ds5Xhs4YzcuFIqjaqSrO13fhlVigpoc8TFvFs+vFOqUv5icFTkpSv/Fkv5/TZr/H2qrd5ePbDpARSGHLuEO5pfQ9lipfJ45VL+jMGT0lSvpK5l/OQUAiJJLFMexo+35h9xXfz9xZ/Z0iHIVQvWz2vlirpOBk8JUn5Spa9nKdOh679oOYSQn86geWPxlO/Wv08WZ+kv84nF0mS8pVMvZzhy+Hqi+CGTpBaHF6ZRY1ZkYZOqYAyeEqS8pWwsBQo9zP0uAVubwLVvoV3xsPoOFh3Tnpvp6SCyI/aJUn5xq4DuyjedRuUqQvJZeCLp2Dh7ZBSMv2I+URGRuTpGiX9dQZPSVKuOpZqpOTUZF5e8jJDZgxhe9XtVFpane0fjYZ955H24Vwq9nJKBZ/BU5KUa/6sGmnevLdZsG0BMVNi+HbLt1zb5FqGdxxOmYNliC4bS3z8f34XVu3llAoyg6ckKdcctRqJViTuvZYm/27BplI/0em0Toy9bCzNazTPOGrMmCeCvVxJuczgKUnKNVlWI1VaA50Hwpnj2L6jHJNunMQFp19ASEhIXixRUhAZPCVJuSZTNVKpbdD+YYh8FvZWhQ9f4pTd73Ph0xfm6RolBY/BU5KUa8LCUqDYPoh8HtoPh2IHYNb9EHcvHCxN8Yj38nqJkoLI4ClJyhWpgVSqdQyFLnWh4iZYfAvMHAK7Tkw/Is5qJKmIMXhKknLczDUz6T+5PwvDF1Lmx2rsefMN2NILq5Gkos3gKUk6Ln/Uy7mFLcRMieGjHz6iZc2WzPjbDCLKRhCdEkt8/FirkaQizuApSTpmR+3lXPcp7x5oxu76mzi54smMu3wcVza6ktCQtCczW40kCQyekqTjcEQvZ/Hd0OYpOCeWX1NCabm1HbMHfkbJsJJ/eB1JRVNoXi9AklRwpPVyRkFoMjQfDXefAe0ehkW3wTOr2D2ljKFT0lG54ylJOmYHk0PhjE+gSwxUXw7f9IapD8P204BDvZ2SlDWDpyTpmCzZsIT1nePghI9hzbkwKh5+bnnYEalpvZ2SdBQGT0kqwv5oQv3QxPna7WsZNG0Qb37zJhUrV4G3YuGHfmQ8kSjDAns5Jf0hg6ckFVFHnVBPiGf27F58NmMUo74dxTMLnqFSqUq8eNGL9DipB+3GX0MibYEo7OWUdDwMnpJURB0xoQ5AKBRrTmJ4ExqPOpNipUIZ0HYA/dr0o1yJcgDExY1P3yUdbi+npONi8JSkIiptQj32sFcC0GgCdB4IldZQenUtEkYuoEb5GpnOCw8Pt5dT0l9inZIkFVFpE+jp92meMhtubg1XXAVJEfDfpVRf0OyI0ClJ2eGOpyQVUWFhKVD1O+gyABp8AD83h1enwZqOQCph4U6oS8pZBk9JKoI27dpEyvnroVxj+PUk+N+bsOwqCBz6IMwJdUk5z+ApSYXIn9Uj7Tm4h6fjnubxuY9TLLwYVeedytZPR0Nye5xQl5TbDJ6SVEj8UT3SrDlXcvfLlxC7KJak3Un8I/IfDGo3iNSbUtOD6hNOqEvKdQZPSSoksq5HCoHTt7Gqyzr6Tu9Lr0a9eKTzI9SpXCft7TI4oS4paJxql6RCIq0eKeq3F078Eq7vAtdeCPtqcdqUNrz9f2//FjolKcgMnpJUSGTUI1VcB5f8Df7eAsqvh3EfwJiZFN9cNa+XKKmI86N2SSokQkrvg/NioNV/YF9FmDQSltwCqWFAalp9kiTlIYOnJOVjfzalDnAg5QAvLHqBNT3nQOosmBsNc/vDgfKHXcl6JEl5z+ApSfnUH02pz57di3nz3mbWllncN/U+Vm1bRe/GvZkzbDVrl10AlE2/ivVIkvIPg6ck5VNZT6mHAq1IPHAVjf99FkmlfuaC0y/g3Svf5cwTziSp46Ed0uHWI0nKdwznowOhAAAgAElEQVSekpRPpU2px2Z+scoKOG8ARLzLzm0VmHLLFDrX6Zzxdnh4uPVIkvItp9olKZ/KmFIHKJMEF9wFd0ZArYXw7uuc/Gn7TKFTkvI7dzwlKZ8KC0uB4rsh6hlo+1jai9OGw4K7IbkkxSPeztsFStJxMnhKUj6UkppC5U6p0K0OlPsFFt4BswbDnmrpR8Q5pS6pwDF4SlI+80XiF0RPjubral9Tdm04u197C365nLS7o5xSl1RwGTwlKZ9Yumkp/Sf354vEL2hzchvm3TiP00udTnQglvj4V51Sl1TgGTwlKUiOVgbf94FrGbF0BK999RqnVzmdd698l0saXEJISNpgkVPqkgoLg6ckBUGWZfAlt5NwYl9ee6U5VctX4dkLnuXWFrdSvFjxvF6uJOUKg6ckBUGmMvjQg9BiFHR4CErsJDDvWrrWrsid0Xfm9TIlKVcZPCUpCNLK4B+HBu+lFcBXXQFf/Q2mD4Nfa/JVRI+8XqIk5TqDpyQFwc6Kv0Kfc6H2HFjZDd6ZAJuaZryfVhYvSYVbjjy5aM6cOVx44YVUqVKFMmXKUK9ePYYPH54Tl5akAm3lLyu54p0r+LHbHCi5E17/AsZ+lil0QmpaWbwkFXLZ3vF86623uP766+nVqxdvvPEG5cqVY+XKlWzYsCEn1idJ+dbRptRjY6MJKRvCsJnD+O+i/3JCuRNom3Q+c168HwLnZHGlBZbBSyoSshU8169fz6233sptt93Gc889l/H6ueeem+2FSVJ+luWUOqkk/DCLD39pRXKrLQQI8FCHh+jbqi+7tu+i9We9SEx8GIjCMnhJRVG2gufo0aPZs2cPMTExObUeSSoQMk2pA4SkwplvQudB/FLuZxpsacKsoZ8TXjat5L10eGni4san75AOtwxeUpGUreA5a9YsqlatSkJCAj169GD58uVUqVKFyy67jNjYWMqXL59T65SkfCVtSj027S+nTYWu/aHGl5BwGUx9mNAT/kX4U5nDZHh4uGXwkoq0bH/Uvnv3bq688koGDhxI69atiY+PZ8iQISxbtozZs2fn1DolKV9JTi4G1ZdBl2g44zP4sRW8PAd+TLuHM7mqU+qS9HvZCp6pqans27ePBx98kOjoaADat29PiRIl6Nu3L9OmTaNTp045slBJyi/W/7qeza2+hFObwfbTYMI7kHA5afd5glPqkpS1bAXPqlWrsnLlSrp165bp9fPPPx+AL7/88qjBs2/fvlSqVCnTa71796Z3797ZWZIk5Zqd+3cSOzeWp+KeIrU28Pk/YdFjkFLid0c6pS6p4Bg3bhzjxo3L9Nr27dtz5XtlK3g2a9aMBQsWHPX9kJCQo743YsQImjdvnp1vL0k55o+qkSpXrczoJaMZMmMIv+7/lb5Rfbm5wc10e+sWElMW45S6pIIsq42/JUuW0KJFixz/XtkKnpdffjkvvvgin3zyCU2b/laGPGnSJACioqKytzpJCoKjViMlLOCz1Z0od+k+Ercncl3T6xjWcRinVDwFwCl1STpO2Qqe5513HhdddBFDhw4lNTWVqKgoFi1axNChQ+nRowfnnJNVUbIk5S9HVCMB1FoEXQaw8dRl1NhQm8X/XMxZNc7KdJ5T6pJ0fLL9yMwJEybQt29fRo0axYUXXsiLL77Ivffey8SJE3NifZKU69KqkdI/oam8Ci7vDbdEQeltMHYSlT6KOCJ0SpKOX7YfmVmqVCkeffRRHn300ZxYjyQFXXJysbSQ2X44RD4Hu8Phg5fhq79BoBgp9V7I6yVKUqGQ7eApSQXZvuR9bG+0Ai6tC6HJMGMIzL8HDpZJP8JqJEnKKQZPSUVSaiCVt5e9zcCpA0lqsg4WXgoz/wu7q//uSKuRJCmnZPseT0kqaKavnk7kS5Fc8+41nFXjLOZePZe632+D3YmkVSKR/mdcejVSdB6uVpIKD3c8JRVKWfVy1junOrvbrGfy2slE1Ypi1g2zaFe7HWA1kiQFg8FTUqFzRC9nuY3Q8QESarxC2LISvHT5S9zU6qZMD7mwGkmScp/BU1Khk9HLWaIxtHkI2jwJySXh86dJXnQWc7d+xM2tj/5kNUlS7jB4Sip0FixcBi3qQsdLoNR2WHA3zB4I+yoBqcTHP57XS5SkIsngKanQCAQCfPzDxyR2nQMVP4Ovr4Vpw2FH7cOOCk3r7ZQkBZ3BU1KhsOjnRfT7oh8z186kzL6qHHh7BmxokcWR9nJKUl6xTklSgbZm+xqu/t/VtHypJVv2bGHS1ZO4Yu8NsOHgUc6wl1OS8oo7npIKhN/XI4WU3kdYp22sqLyUqmWq8lKPl7ih2Q2EhYbRMrYlc2b3IjHxYdKewR5KWi/ngvRezvF5+8NIUhFl8JSU72WqRyo2DCKfT3uuerF9VF5Sk3kjp3NqzVMzjg8PD7eXU5LyIYOnpHwvJuYJElcNh8ZroPPVUHEdLLkZZjzItl2reajSyCM6OO3llKT8x+ApKd+bvno23DwTai2E73vAm5NgS8P0d6sTHz88T9cnSTo2DhdJyre+TfqWnuN6srbjAiAAY2bAuA8PC51gPZIkFRzueErKdzbu2siDMx5k9JLRnFzxZGrFNWP9F/MhkFXAtB5JkgoKdzwlBV1SUhJ9+vSnUaPu1K/fk0aNutOnT3/W/LyGoTOHcvozpzNh+QRiu8Ty3Z3f0aXGeRBYeJSrWY8kSQWFO56SgirThDqxQAiEHCSh1BDe+E99ipWDu6LuYlC7QVQuXRmA2NhoZluPJEkFnsFTUlDFxDyRHjpbAQE44xPoEg3Vl5PyTRcuqVybJwc/mekc65EkqXAweEoKqvj4BCAWaixJC5x1psKac2HUQvi5Od9G9MjyPOuRJKngM3hKCqq9JfbDpddD07GQ1BDe+hB+uAgIAXBCXZIKMYOnpKDYvm87j8x+hDU9psPecPjoRfjyRkg9/L+GnFCXpMLM4Ckp237/HPXf7r+MpmKVijy/8HmGzRrGvuR9NNkZxdfPD4cDnbK4khPqklSYGTwlZUuWU+qkkpCwgE/XdaTURbv4ceeP3HTWTTzU4SHC9oXR+qNeJCaWxgl1SSpaDJ6SsiXzlHq6U+ZB135sOmk5J22sw9J/LaVR9UZp75XHCXVJKqIMnpKyJWNKHaDq93DeAGj4PvzcHF6dQoUyT9Po8UaZznFCXZKKJp9cJClbkpOLQdkk6H4H3NkorSbpf2PhpYWwprNT6pKkDO54SvrL9hzcw7Yzv4fL66Y9R33KoxB/FySXSj/CKXVJ0m8MnpKOW0pqCq9//Tr3T7+frY03wvwrYNZI2Fv1d0c6pS5J+o0ftUs6Lp+v/JyzXjyLGz+8kXantGP+tfOpu3Iz7P2BtOl00v+MS59Sj87D1UqS8hN3PCVlcrROzuuiL+CxRY8xedXktMB503yiTooCnFKXJB0bg6ekDFl2clZYS0K923l1/JOcXvl03u/1Pj3r9yQkJCTjPKfUJUnHwuApKUOmTs6SO6Dt49Dq37C/AkzqR5vGKVz8z4vzepmSpALK4CkpQ3x8AhQbDi2ehXOHQondMK8fzI2GA2VZtKdHXi9RklSAGTwlARAIBNhWYxN0bgyVV8FXfWD6UNhZM+MYOzklSdnhVLsk5v04j7Zj2rKh3WL45Qx44Sv4cHSm0GknpyQpuwyeUhG2YusKLp9wOee8cg57Du6h68Yr4M0HYPOZWRxtJ6ckKXv8qF0qgpJ2JzF05lBeWPwCNcrV4PVLXueaJtewdctWWk/uRWLiw0AUaf/fNBVYkN7JOT5vFy5JKtAMnlIhd3gv54EA/NpwJdsar6FUqZIM7zicu6PupnTx0kBaLZKdnJKk3GLwlAqxjF7OVcOgSSPoNBjKbYT4y6i1bj033nxjRug8xE5OSVJu8R5PqRCLiXmCxMAl8Pc74dI+8FMrGPktfDaeNQlPEB0dm9dLlCQVIe54SoXU0k1LeafUq3D9FljXBkbPg59aH3ZEFPHxw/NqeZKkIsjgKRUyP/36E4OnD+a1r14jrEwZePtd+O4SIOR3R4bayylJCio/apcKiV/3/8qgqYOo92w9Pv7hY5694FlO/7zdUUIn2MspSQo2dzylAubwKfXk5GIUK36QCp0OknjS1+w6uIt7W91LTNsYKpSswKKWa/h2+QKgVRZXspdTkhRcBk+pAMmYUk98BHgcGnwAHQdAlRWU/+YE5g+dT9PTmmYcHxsbzezZ9nJKkvIHg6dUgMTEPJEWOk8CupwLtefAyq7wzgR2btrDiLCxjBnzW/C0l1OSlJ8YPKUCZM7yhXDFWmj0DmxsAm98Dold099NzXJK3V5OSVJ+YfCUCoCte7YybNYwVp4/C3bXhPfHwNfXQeDwqXSn1CVJ+ZvBU8rH9iXv45kFz/DI7EdIDaRSffkZbP5oCRwsm8XRTqlLkvI365SkfCg1kMrYpWOp/1x9Bk0bxHVNriPx7kQurNATDn5zlLOcUpck5W/ueEp56PfVSGFhKZzcvhwbzvyOpUlLuazhZTza+VHqVa0HOKUuSSrYDJ5SHslcjRQL1ZdDl/4knDiBkssr8NHfP+KiJhdlOscpdUlSQWbwlPJIRjVS+VOg4y3QbAxsPw0mvMP+hJr8b897XDTmoiPOc0pdklRQGTylPBK3ZCl0LAWtn4bk0vD5v2HRbZBSgrRqpIfzeomSJOUog6cUZMmpyYxeMpqVF8yAsFkwvy/MGQD7Kx52lNVIkqTCx+ApBUkgEODD7z8kZkoMP2z9gQqbarLjvbmwo3YWR1uNJEkqfKxTkoIgfn08HV7rwCXjL+Hkiiez+NbFXEpv2LHhKGdYjSRJKnzc8ZRy0O/rkVIr7iS5wwbWlP2extUb8+k1n9KtbjdCQkKIjT3JaiRJUpFi8JRySKZ6pNIx0P4RiHwOdlckfF4DJr8+mRNPODHjeKuRJElFjcFTyiExMU+QuHYItJkD7S6A0GSY+QDE3UPSwaXcN+CpI2qQrEaSJBUlBk8pB6QGUpm8cTL84x2o8BMsvhVmDIHdJ6QfEUV8/PA8XaMkSXnN4SIpm6avnk7kS5Gsb/U1bGwGzy+DSc8fFjrBeiRJkgye0lElJSXRp09/GjXqTv36PWnUqDt9+vQnKSkJgISkBC566yI6vd6JYqHFqD2tFbz9HmxpkMXVrEeSJMmP2qUsHPEcdUKAVBIS4pm+6BLaP1CXN799k1Mrncr4/xvPFRFXcOPcaF6dtQBolcUVrUeSJMngKWUh4znqh4fIEnugzWesbfMlE75ZwlMXPsXtZ99OybCSAMTGRluPJEnSHzB4SlmIj08gbaeTtOn0s16BDkOg9DZYcBenbv6avkP7ZjrHeiRJkv5YtoLnjBkz6NSpU5bvzZ8/n8jIyOxcXsozGYNA9T6GLtEQ/i0svQamDYftpxKo1zPL86xHkiTp6HJkx/PRRx+lY8eOmV5r1KhRTlxayhMHw7dC605w2gxY3RHefQM2tEh/10EhSZL+ihwJnmeccYa7myoU1mxfw8CpA1ndZR5sPg3e/BhWXEjacNEhDgpJkvRX5EidUiAQyInLSHlm295t9PuiH/Wfq8/0NdN5qsNT1PniFFhRBTj07zsViEsfFIrOw9VKklQw5ciO55133slVV11FmTJlaN26NYMHD+acc87JiUtLOSYpKSl98CchY/CnRWQ9Tr+6MiOWjOBAygEGtRvEva3vpVyJclw37zoHhSRJykHZCp6VKlWib9++dOjQgapVq7JixQqeeOIJOnTowKRJk+jatWtOrVPKliN7OYHG40io2B/mbOD6xtfx+AWPc2K5EzPOcVBIkqScla3g2axZM5o1a5bx93POOYdLL72UM888k5iYGIOn8o1MvZy1Z0HXflBrIXzfA97sTehFSzjxihP/9DqSJOmvy/Eez4oVK9K9e3defPFF9u/fT8mSJbM8rm/fvlSqVCnTa71796Z37945vSQprZez2o1w3sXQ4ENYfzaMmQFrzwVSiY8fm9dLlCQpT4wbN45x48Zlem379u258r1ytUA+JCTkqO+NGDGC5s2b5+a3lwDYtGsT689aDnXPhB0nw8S3YHkvCByarQv9rbdTkqQiJquNvyVLltCiRYujnPHX5Xjw3LZtGx999BFnnXUWJUqUyOnLS8ds94HdPB33NLHzYtlzyn6Y/DjE/wNSfr8Lby+nJEnBkK3gec0113DaaafRvHlzqlSpwooVK3jqqadISkri9ddfz6k1SsclJTWFMV+N4YHpD7B171buiryLn8fvY1zcOUBWt37YyylJUjBkK3g2adKE8ePHM3LkSHbt2kWVKlVo164db775Zq5sz0p/JBAI8OnKT4meHM3ypOX0btybhzs9zGmVTyPprCTiZ/YiMfFhIIq0CttUYEF6L+f4vF28JElFQLaCZ0xMDDExMTm1FukvW7JhCf0n92fa6mmcW/tc4m+Op2Wtlhnvh4eHExc33l5OSZLyUK4OF0m5KSkpiTvuu5/JKZPYcep6Svxals57L+Wt21+gevXqRxxvL6ckSXnL4KkCacWPK4i89zy2N9gI+yrDRy9w4Ms+TE1dQpsvriIuzl1MSZLymxx5VrsULAdSDvCf+f/hzJeasr3eZpgzAJ5ZAYv/DqklgFYkJj5MdHRsXi9VkiT9jjueKhACgQATEyZy39T7WL19NRXW1mL/e3Gws1YWR0cRHz886GuUJEl/zB1P5QtJSUn06dOfRo26U79+Txo16k6fPv1JSkpi7rq5tHmlDVdOvJL61eqz9LalVJ/f7CihEyyElyQpf3LHU3lu8+bNtGlzVfqz1GOBECCVhE0TeSc0gt2nbKF5jeZMvX4qnU7rBJBe+B5IP/b3LISXJCk/csdTeS4m5on00NkKCIGym+HCu+DOq9ldoRjtki5k4S0LM0InkF74vuAoV7QQXpKk/MjgqTwXH58AREHxPdDuYbj7dGjyJkx5FJ5bxdbpEBqS+Z9qbGw0desOBOJIK4In/c+49EL46OD+EJIk6U/5Ubvy3MGUUGj2KnQanLbbufAOmDkY9lYFyPJ+TQvhJUkqeAyeylOfr/ycdRfMhEofw7IrYeojsK3uYUcc/X5NC+ElSSpYDJ7KFUlJSem7kQm/242MJjw8nK82fkX05Ggmr5pM9VK12PzSv2H9TVlcyfs1JUkqLAyeynFHnVJPiGf64ouJGngy73z/DmdUPYP3er1HmyptaPPOVSQSAUSRdutxKrAg/X7N8Xn400iSpJxi8FSOyzylnq7kTmj7AWtbLWZTwlc81+M5bml+C8WLFQfwfk1JkooAg6dyXNqUevojK4sdgBYvwrlDocRuiOtH7S0LuWPoHZnO8X5NSZIKP4OnclzGFHrD/8F5A6BKInzZB6YPhZ21CNTrmbcLlCRJecLgqRx34IQt0O4cODkOVpwPE/4Hm5qkv+tThSRJKqoMnsoxK7auYMDUAazpHAcbzoDXJ8Oq8353lFPqkiQVVT65SNmWtDuJuz65i4jnI1i4fiHPdnqWOlNqwaqy+FQhSZJ0iDue+lNH6+R86JG7eHPlmzw29zEAhncczt1Rd1O6eGl6xfVySl2SJGVi8NQfyrKTM+QgCcWH8sa/6xFSPoU7zr6DwecOplqZahnnOaUuSZJ+z+CpP3REJ2edydC1P5z4NSnLO3JZxTr8Z/B/8nSNkiSpYPAeT/2htE7OKDhhKVx7PlzfFQ6UhdHz4J0pfDdvQ14vUZIkFRDueOoP7S1+AC6+CZq9Cr+cDm+/C99dQtpjMA/r7JQkSfoTBk9l6df9v/L4nMdZ02Ma7K8CnzwLi2+F1OKHHWUnpyRJOnYGzyLoaFPqsbHRVKpSiVGLR/HQzIfYeWAnjXe15Jv/Dof9v+/jBDs5JUnS8TB4FjFZTqmTSkLCAj5d04lyl+xl1fZVXN/0eoZ1HEapA6Vo/VEvEhPLAlGk3RacCixI7+Qcn4c/jSRJKkgMnkXMEVPqACfFQ5doNtVeRrENtfmy75c0PbFpxttxcePt5JQkSdlm8Cxi0qbUY9P+UjkRzrsPGr0DG5vAG59SqeSzNH2saaZz7OSUJEk5weBZxCQnF4MyW6H9MGj5X9hdHd5/Bb6+HgLFSK73fF4vUZIkFVIGzyJkX/I+tjf6AS47HUJSYcaDML8vHCyTfoRT6pIkKfcYPIuA1EAqb33zFoOmDSKpyU+w8FKY+XzabmcmTqlLkqTc45OLCoGkpCT69OlPo0bdqV+/J40adadPn/4kJSUxbfU0Wr7Ukuveu44WNVow75q51P3+F9idSNp0Oul/xqVPqUfn4U8iSZIKM3c8C7ij1iNtGceE4g3YU+sXWp3Uitl9ZtP2lLaAU+qSJClvGDwLuCPqkcr/DB0fgGZj2LO9Bh0292DaAx8QEhKScY5T6pIkKS/4UXsBl1aPFAUldkKHIXDXGdDgffj83zByJZtnpGQKnZIkSXnFHc8C7mBKCJz9InR4EEptT5tSnzMA9lUC0uuTJEmS8gGDZwEVCAT46IePWNt9BpT/BJZeC9OGw45TDjvKeiRJkpR/GDwLoIXrF9Jvcj9mrZ1FjeKnsOHFkbDxb1kcaT2SJEnKPwye+UxSUlL6xHnC7ybOo9kVtouB0wby9rK3aVy9MZ9c/QktKragzcSrSKQeEEXabbupwIL0eqTxefsDSZIkpTN45iNHrUZaPZn39rRgb+NNVC1TldE9RnNDsxsoFpp2/6b1SJIkqSAweOYjR1Qjhe2DliOh/XB2hB7grK0tmR3zOWVLlM10nvVIkiSpILBOKR/JqEYKSYUz34I7G0KXGFjWG55Zyf7JFY8InZIkSQWFO575SHJyMTh1JnTpD7UWwXc94c1PYUuD396XJEkqoAye+URCUgI/d1gANTvCT5EwZiasbX/YEVYjSZKkgs3gmcv+aEo9PDycjbs2MmT6EEZ/OZoy4eXhnWGwfBBpg0WHsxpJkiQVbAbPXHTUKfWEeGbGXc7/Pd2K5796nhLFSvBklye54rQr6PD29STSGauRJElSYWPwzEVHTKkDhKZCs29Y3TGBp+LncW+bexjYbiCVS1cGrEaSJEmFl8EzF6VNqcem/y0AZ3wCXaKhegIs7U2ddRt44sHMNUhWI0mSpMLKOqVclDaFHgI1FsPfOsM1F8Hu6jBqIbz7FqG/ls/rJUqSJAWNO565KLXCTrjsGmjyFmyOgDc/hhUXcuheT6fUJUlSUWLwzAXb9m7jkdmPsKr7bNhVCT4cBV/1gdTD/+N2Sl2SJBUtBs8ctD95P88vfJ5hs4ZxIOUA/4q8l4n3zmf194357a4Gp9QlSVLRZPDMAYFAgAnLJ3Df1PtYu2MttzS/hQc7PMiJ5U6kf8skp9QlSZIweGbbrLWz6PdFPxb+vJAe9Xow6epJNAxvmPG+U+qSJElpDJ5/0XdbviNmSgwffv8hZ9c8mxl/m8G5p56b18uSJEnKtwyexyEpKYm7BgzhiwOT2FbnR4rvKUX7vd2ZcPvLnFD9hLxeniRJUr5m8DxGq9evpsU/OrOt4SZILQGTn+Bg/B3MSvmacz7rTVyc92xKkiT9EQvk/0RKagqjl4ym0Qtnsq3xT7D4dngm8f/bu/+gqus9j+Ov7wE1I7EVUX6UOup4UdSkMohczc1rme2O1NS9OJY/plx/NKXVQpPRDhWmZls5OmUyyZZChDc3pzLXUIsKZJFb5o/utpQ1/rgiGnjP1Szgs3+I1AmMwznf8z0ceD5mzpgfvt/P+TDveder8/1+z0cqfVhq6CkpRVVVOcrIWNHmXAAAAF0Zn3hehDFGW/9vqzK2Z2j/if2KPBKrs5s/lmoHt3J0ssrLn3Z8jQAAAKGETzxbUXmsUpNen6Sp+VPV99K+Kr+3XDGfXHuR0ClJrqbtMQEAAHAxXfoTzxMnLnzH5gHV14epMdKtxhuP6+vLDiihb4K2/HGLbht2myzLatre0uj8dpe/xvaXAAAAbemywbO6ulqpqX9UVdVS6ZLHpHHLpJQXpR8i1LdsmHbk7VBs/9jm46+7boQOHNgtKaWV2dj+EgAAoC1d9lJ7ZuazqjqULaWUSQ8Mla5bLX2cKa06pJr38/TYo//hcfyKFRkaMuQxSaU6v+2lmv4sbdr+MsPh3wAAACC0dMlPPI0x+uBosbTwLenyQ9Kf50i7sqW/xTUd0fJhoejoaJWWFrL9JQAAgI86VfD89T2bPwfDjOZg+Ml3n+iR7Y/ocOqfpf+9VXrjbal65K9mav1hIba/BAAA8F2nCZ4e92xqhc4/BNSoAwfKVVLyB23YulQrKldo85eblRSTpIG7rtO3u94RDwsBAAA4o9Pc45mZ+WxT6EzRz2HSJUUMVtXv/kGpG1O159gevZ72uirmVmjioPGSdl9kNh4WAgAAsJutwTM3N1cul0u9evWyc1qvlJcfkJT880C3M9I/5px/cGh0saL3/k5/uf8vmjF6hlyWi4eFAAAAHGbbpfYjR47okUceUVxcnE6fPm3XtF47f0+mJVkN0lWvS//0uBRRLZUvlD56XJdfOVuXhF/SfDwPCwEAADjLtuA5b948TZw4UZdffrk2bdpk17ReCw9vkIa8L/0+U4rZK+27SypeKn0/RBe7Z5OHhQAAAJxjy6X2DRs2qKSkRGvWrJExxo4p2+Xzv36u2n/eL909RToXKa0rkzYVNoVOiXs2AQAAgs/v4Hn8+HEtWrRIy5YtU1xcXNsn2Ojw6cOa9V+zlLQ2ST1jwxWza6S0frl0ZGzTEdyzCQAA0FH4fal94cKFGjFihObNm2fHerxS90Odln+yXM+XPa9e3Xtp9a2rdd/V96l2em3TPZs53LMJAADQwfgVPDdt2qR33nlHn3/+uV3r+U0/NfyktXvWKvvDbP39x7/r4esfVsYNGYrsESmJezYBAAA6Mp+Dp9vt1v33368HHnhA/fv3V21trSTpxx9/lCTV1dUpPDxcERERrZ4/bqJnqb4AAAvaSURBVNwk9e3bRyNGDFX37t0lSenp6UpPT5fkuQvRT/UunR14VKev+0Z/61ar2WNm68mJTyo+Mt7X5QMAAEBSQUGBCgoKPMYu5Dq7WcbHp4EOHTqkwYMH/+Yx06ZN01tvveUxVllZqWuuuUbS/0iq15Ahj6m01PNSuMcuRFc0SpP/TRrwqfRViq74skGVW9/l0jkAAECAXMhre/bs0dVXX23bvD5/4hkbG6udO3fKsn7ectIYo2XLlunDDz/U+++/r759+/7GDC5JKaqqylFGxgqPS+SZmc+q6vv50l0rpRF/kv56lfTaf0tf/16HVdrieAAAAHR8PgfPHj16aMKECS3G169fr7CwMI0fP97LmZJVXv50899qztRoy09F0sIXJHeMtPk/pb0zJONq9XgAAACEBtu+QP4Cy7I8PgVtm0v19WE6+9NZvbj7RT3z8TNyDzwj7XxKKntQqu/Z6vEAAAAILbbu1S6d/8SzXVtmWvVyD/lWw1YPU9bOLM28aqaGbp0gfZzZSuiULrYLEQAAADo224NnuwzeLv3rCB1N3qvk+GQdWHBAq6asUupVSZJ2X+QkdiECAAAIRbZfavfalIVScpl6VEfqT2nvaOroqc0/WrEiQyUlf1BVVY6kZJ3Px42SdjftQlQYpEUDAADAV0ELnuFRn2lc9b+o8N/XqV+/fh4/i46OVmlpYdP3eD7NLkQAAACdQNCC58cP7FLy2OSL/pxdiAAAADqXoN3j2S2sW7DeGgAAAEEQ3IeLAAAA0GUQPAEAAOAIgicAAAAcQfAEAACAIwieAAAAcATBEwAAAI4geAIAAMARBE8AAAA4guAJAAAARxA8AQAA4AiCJwAAABxB8AQAAIAjCJ4AAABwBMETAAAAjiB4AgAAwBEETwAAADiC4AkAAABHEDwBAADgCIInAAAAHEHwBAAAgCMIngAAAHAEwRMAAACOIHgCAADAEQRPAAAAOILgCQAAAEcQPAEAAOAIgicAAAAcQfAEAACAIwieAAAAcATBEwAAAI4geAIAAMARBE8AAAA4guAJAAAARxA8AQAA4AiCJwAAABxB8AQAAIAjCJ4AAABwBMETAAAAjiB4AgAAwBEETwAAADiC4AkAAABHEDwBAADgCIInAAAAHEHwBAAAgCMIngAAAHAEwRMAAACOIHgCAADAEQRPAAAAOILgCQAAAEcQPAEAAOAIgicAAAAcQfAEAACAIwieAAAAcATBEwAAAI4geAIAAMARBE8AAAA4guAJAAAARxA8AQAA4AiCJwAAABxB8AQAAIAjCJ4AAABwBMETAAAAjvAreH722WeaOnWqBg4cqEsvvVRRUVFKTU3Vxo0b7VofAAAAOgm/gmddXZ0GDBigZ555Rlu3btVrr72mQYMG6e6771ZOTo5da0SIKygoCPYS4CDq3bVQ766FesNffgXPCRMm6KWXXtL06dM1YcIETZ06Vfn5+UpOTtYrr7xi1xoR4vgXVddCvbsW6t21UG/4KyD3eEZFRSk8PDwQUwMAACBE2ZIOjTFqaGjQ999/r6KiIm3btk2rVq2yY2oAAAB0ErYEz/nz5zdfWg8LC9PKlSs1f/58O6YGAABAJ2FL8FyyZInmzp2r6upqbdmyRQ899JB++OEHZWZmXvScgwcP2vHWCAG1tbWqrKwM9jLgEOrdtVDvroV6dx2BymmWMcbYPemCBQuUm5urI0eOKDo62uNnx44d07XXXqujR4/a/bYAAACwSVxcnCoqKhQbG2vbnAF5Amjs2LF6+eWX9c0337QInrGxsaqoqNCxY8cC8dYAAACwQWxsrK2hUwpQ8Ny5c6fCwsI0ZMiQVn8eiF8EAAAAHZtfwXPu3Lnq3bu3xo4dq/79+6umpkZFRUV68803lZGRoaioKLvWCQAAgBDn1z2eeXl5Wr9+vQ4ePKja2lpddtllGjNmjO69915Nnz7dznUCAAAgxAXk4SIAAADg1wKyc9EFxcXFmjlzpoYNG6aIiAhdccUVmjZtWru+iqG6ulqzZs1SdHS0IiIilJqaqh07dgRw1fCV2+1WRkaGJk+erOjoaLlcLmVnZ3t9fl5enlwuV6uv6urqAK4cvvC33hL9HWrcbrcWLVqk+Ph49ezZU0lJSSosLPTqXPq74/KnrvRw6PG13nb1cED3tVy7dq1OnDihxYsXKzExUSdOnNBzzz2nlJQUbdu2TRMnTvzN88+dO6ebbrpJp0+f1qpVq9SvXz+tXr1at9xyiz744AONHz8+kMtHO9XU1GjdunUaM2aM0tLSlJubK8uy2j1PXl6eEhISPMb69Olj1zJhE3/rTX+Hnttvv10VFRVavny5hg0bpo0bNyo9PV2NjY1KT0/3ag76u+Pxta70cGjyt4/97mETQMePH28x5na7TUxMjJk0aVKb569Zs8ZYlmXKysqax+rr601iYqJJTk62da2wV01NjbEsy2RnZ3t9zvr1641lWWbPnj0BXBkCwZd609+h5d133zWWZZk33njDY3zy5MkmPj7eNDQ0/Ob59HfH5E9d6eHQ40+97erhgF5q79evX4uxiIgIDR8+XIcPH27z/M2bNyshIUHJycnNY2FhYZoxY4bKy8v5LtAOzPhx67A/5yI4fKkZ/R1aNm/erF69eunOO+/0GJ89e7aOHj2q3bt3ezUP/d2x+FNXejj02NHH/vZwQINna+rq6lRZWanExMQ2j923b59Gjx7dYnzUqFGSpP3799u+PgTfbbfdpvDwcEVFRemOO+6gzp0U/R1a9u3bp+HDh8vl8vzPRnvrRX93LP7UlR4OPXb0sb89HNB7PFuzcOFCnT17VkuWLGnz2FOnTrV638CFsZMnT9q+PgRPbGysHn/8caWkpCgyMlJ79+7VsmXLlJKSok8//bS5MdA50N+h5eTJkxo6dGiLcW/rRX93TP7UlR4OPf7U264e9voTz127dl30aaZfv/bu3dvqHFlZWcrPz9fzzz+vpKQkb98aQWBHvdvr5ptv1pNPPqlbb71V48aN04IFC1RSUiLLsvTEE0/Y8h5oXTDqjeChvwG0l1097PUnngkJCcrNzfXq2CuvvLLFWHZ2tnJycrR06VItWLDAq3mioqJ06tSpFuMXxtgZKXD8rbddBg4cqBtuuEFlZWUBew8Ep970d/C0p94DBgyQdL4erX0a4k+96O/g86eu9HDosbuPfelhr4NnTEyM5syZ064FXZCdnd38evTRR70+b9SoUa3+3/YXX3whSRo5cqRP60Hb/Kl3IPjytUzwXjDqTX8Hjy/1Hj16tAoKCtTY2Ohxf5gd9aK/g8efutLDoSdQfdyeHg74w0VPPfWUsrOzlZWVpaysrHadm5aWpi+//FLl5eXNY/X19dqwYYNSUlIUExNj93LRwXz99dcqKSnR9ddfH+ylwGb0d2hJS0uT2+3Wpk2bPMbz8vIUHx/v8WSzt+jv4POnrvRw6LG7j33qYb++jKkNK1euNJZlmSlTppiysjJTWlrq8fqlOXPmmPDwcPPdd981j507d86MHDnSDBgwwOTn55vt27ebtLQ00717d/PRRx8Fcunw0XvvvWeKiorMq6++aizLMnfddZcpKioyRUVF5syZM83HtVbvSZMmmaVLl5q3337bFBcXmxdeeMHExcWZ3r17m/379wfj10Eb/Kk3/R16Jk+ebPr06WPWrVtnduzYYe677z5jWZbJz8/3OI7+Di3e1JUe7jx8rbddPRzQ4HnjjTcal8tlLMtq8XK5XB7Hzpo1y7hcLvPtt996jB8/ftzMnDnTREVFmZ49e5rU1FRTXFwcyGXDD4MGDfKo8S//+Ze1ba3eixcvNomJiSYyMtJ069bNxMfHm3vuucd89dVXwfhV4AV/6m0M/R1q3G63efDBB01sbKzp0aOHGTNmjCksLGxxHP0dWrypKz3cefhab7t62DKGb/MFAABA4Dn+BfIAAADomgieAAAAcATBEwAAAI4geAIAAMARBE8AAAA4guAJAAAARxA8AQAA4AiCJwAAABxB8AQAAIAjCJ4AAABwBMETAAAAjiB4AgAAwBH/D5IkftYFuslUAAAAAElFTkSuQmCC",
       "text": [
        "Figure(PyObject <matplotlib.figure.Figure object at 0x7fdff3ccdf90>)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "3"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "function boxCountGen(data,\u03f5)\n",
      "    n=size(data,1)\n",
      "    n_dim=size(data,2)\n",
      "    dminmax=zeros(n_dim,2)\n",
      "    num=zeros(Int,n_dim)\n",
      "    for d=1:n_dim\n",
      "        dminmax[d,1],dminmax[d,2]=extrema(data[:,d])\n",
      "        num[d]=iceil((dminmax[d,2]-dminmax[d,1])/\u03f5)+1\n",
      "    end\n",
      "    #println(n_dim,\" \", n, \" \", data[3,:],\" \", dminmax[:,1])\n",
      "    #println(num)\n",
      "    boxarray=zeros(Bool,num...)\n",
      "    for i=1:n\n",
      "        l=ifloor((vec(data[i,:])-dminmax[:,1])/\u03f5)+1\n",
      "        boxarray[l...]+=1  \n",
      "    end\n",
      "    N::Int=sum(boxarray)\n",
      "    return N#,boxarray\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "boxCountGen (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "log\u03f5R=[-4:0.05:2.5]\n",
      "\u03f5R=1./exp(log\u03f5R);\n",
      "N\u03f5=[boxCountGen(capDimData,\u03f5) for \u03f5 in \u03f5R];\n",
      "plot(log\u03f5R,log(N\u03f5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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",
       "text": [
        "Figure(PyObject <matplotlib.figure.Figure object at 0x7fdfec474fd0>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "1-element Array{Any,1}:\n",
        " PyObject <matplotlib.lines.Line2D object at 0x7fdfe7fbbc90>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Based on the above plot we select the region -3 to 0 as the scalling region"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "log\u03f5R=[-3:0.05:0]\n",
      "\u03f5R=1./exp(log\u03f5R);\n",
      "N\u03f5=[boxCountGen(capDimData,\u03f5) for \u03f5 in \u03f5R];\n",
      "a, b = linreg(log\u03f5R,log(N\u03f5)) # Linear regression\n",
      "plot(log\u03f5R, log(N\u03f5), \"o\") # Plot (x,y) points\n",
      "plot(log\u03f5R, [a+b*i for i in log\u03f5R]) # Plot the line determined by the linear regression\n",
      "print(\"The capcity dimension is \", b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The capcity dimension is 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ".7026053962040222"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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",
       "text": [
        "Figure(PyObject <matplotlib.figure.Figure object at 0x7fdf92f11a90>)"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We conclude hence that the capacity dimension did not differ from the 2D case, and hence for this fractal the 2-D projection was sufficient."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using the assignment 8 reconstruction function"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "function reconstructspace(data,\u03c4,m)\n",
      "    datalen=length(data)\n",
      "    outlen=datalen-(m-1)*\u03c4\n",
      "    output=Array(Float64,outlen,m)\n",
      "    for i=1:outlen\n",
      "        for j=0:m-1\n",
      "            output[i,j+1]=data[i+j*\u03c4]\n",
      "        end\n",
      "    end\n",
      "    output\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "reconstructspace (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f=figure()\n",
      "xview=capDimData[:,1];\n",
      "@manipulate for \u03c4=1:1:100, m=2:1:15; \n",
      "    withfig(f) do\n",
      "        lorenzrecon=reconstructspace(xview,\u03c4,m)\n",
      "        plot(lorenzrecon[:,1], lorenzrecon[:,2],\",\")\n",
      "        gcf()\n",
      "    end\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Slider{Int64}([Input{Int64}] 50,\"\u03c4\",50,1:1:100)"
       ]
      },
      {
       "html": [],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Slider{Int64}([Input{Int64}] 8,\"m\",8,2:1:15)"
       ]
      },
      {
       "metadata": {
        "comm_id": "f3d2aa81-7279-40bd-a216-096aa1cf657f",
        "reactive": true
       },
       "output_type": "pyout",
       "png": 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",
       "prompt_number": 11,
       "text": [
        "Figure(PyObject <matplotlib.figure.Figure object at 0x7fdfe7ffc0d0>)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Based on the above figure, it appears that m=2 and $\\tau=55$ recover the original attractor"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lorenzrecon=reconstructspace(xview,80,2);\n",
      "log\u03f5R=[-4:0.05:2.5]\n",
      "\u03f5R=1./exp(log\u03f5R);\n",
      "N\u03f5=[boxCountGen(lorenzrecon,\u03f5) for \u03f5 in \u03f5R];\n",
      "plot(log\u03f5R,log(N\u03f5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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",
       "text": [
        "Figure(PyObject <matplotlib.figure.Figure object at 0x7fdfe7f6ad50>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "1-element Array{Any,1}:\n",
        " PyObject <matplotlib.lines.Line2D object at 0x7fdfe7dfe4d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Based on the above plot we work in the range of -1 to 1"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "log\u03f5R=[-2:0.05:0.5]\n",
      "\u03f5R=1./exp(log\u03f5R);\n",
      "N\u03f5=[boxCountGen(lorenzrecon,\u03f5) for \u03f5 in \u03f5R];\n",
      "a, b = linreg(log\u03f5R,log(N\u03f5)) # Linear regression\n",
      "plot(log\u03f5R, log(N\u03f5), \"o\") # Plot (x,y) points\n",
      "plot(log\u03f5R, [a+b*i for i in log\u03f5R]) # Plot the line determined by the linear regression\n",
      "print(\"The capcity dimension is \", b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The capcity dimension is 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ".6854253830826444"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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",
       "text": [
        "Figure(PyObject <matplotlib.figure.Figure object at 0x7fdfe7ed7150>)"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It is not too far off. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "5"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "function boxCountGenSmart(data,\u03f5)\n",
      "    n=size(data,1)\n",
      "    n_dim=size(data,2)\n",
      "    dminmax=zeros(n_dim,2)\n",
      "    num=zeros(Int,n_dim)\n",
      "    for d=1:n_dim\n",
      "        dminmax[d,1],dminmax[d,2]=extrema(data[:,d])\n",
      "        num[d]=iceil((dminmax[d,2]-dminmax[d,1])/\u03f5)+1\n",
      "    end\n",
      "    boxset=Set{tuple([Int for i=1:n_dim]...)}()\n",
      "    for i=1:n\n",
      "        l=ifloor((vec(data[i,:])-dminmax[:,1])/\u03f5)+1\n",
      "        push!(boxset,tuple(l...))\n",
      "    end\n",
      "    N::Int=length(boxset)\n",
      "    return N\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "boxCountGenSmart (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Checking out the three algorithms"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N=boxCount2D(currentView,0.055)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "13944"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N=boxCountGen(currentView,0.055)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "13944"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N=boxCountGenSmart(currentView,0.055)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "13944"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now going for something BIG"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N=boxCountGen(capDimData,0.055)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "13999"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N=boxCountGenSmart(capDimData,0.055)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "13999"
       ]
      }
     ],
     "prompt_number": 19
    }
   ],
   "metadata": {}
  }
 ]
}