{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "IPython's Rich Display System"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In Python, objects can declare their textual representation using the `__repr__` method.  IPython expands on this idea and allows objects to declare other, richer representations including:\n",
      "\n",
      "* HTML\n",
      "* JSON\n",
      "* PNG\n",
      "* JPEG\n",
      "* SVG\n",
      "* LaTeX\n",
      "\n",
      "A single object can declare some or all of these representations; all are handled by IPython's *display system*. This Notebook shows how you can use this display system to incorporate a broad range of content into your Notebooks."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Basic display imports"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The `display` function is a general purpose tool for displaying different representations of objects. Think of it as `print` for these rich representations."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import display"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A few points:\n",
      "\n",
      "* Calling `display` on an object will send **all** possible representations to the Notebook.\n",
      "* These representations are stored in the Notebook document.\n",
      "* In general the Notebook will use the richest available representation.\n",
      "\n",
      "If you want to display a particular representation, there are specific functions for that:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import display_pretty, display_html, display_jpeg, display_png, display_json, display_latex, display_svg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Images"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To work with images (JPEG, PNG) use the `Image` class."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = Image(filename='logo/logo.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Returning an `Image` object from an expression will automatically display it:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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L0amUWggcqrXO8gg2FKHG2CdW4Uem9XvBlUflu7RUaiByU3lPa92ZKN8cSav8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\nHMRRca/E5hVINNIVwI2B56z6/3ExLRI31pXNAAAAAElFTkSuQmCC\n",
       "prompt_number": 5,
       "text": [
        "<IPython.core.display.Image at 0x7fa15951cc10>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or you can pass it to `display`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "display(i)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<IPython.core.display.Image at 0x7fa15951cc10>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "An image can also be displayed from raw data or a url"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(url='http://python.org/images/python-logo.gif')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://python.org/images/python-logo.gif\"/>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<IPython.core.display.Image at 0x7fa15951c250>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "SVG images are also supported out of the box (since modern browsers do a good job of rendering them):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import SVG\n",
      "SVG(filename='python-logo.svg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "svg": [
        "<svg height=\"115.02pt\" id=\"svg2\" inkscape:version=\"0.43\" sodipodi:docbase=\"/home/sdeibel\" sodipodi:docname=\"logo-python-generic.svg\" sodipodi:version=\"0.32\" version=\"1.0\" width=\"388.84pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:cc=\"http://web.resource.org/cc/\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:inkscape=\"http://www.inkscape.org/namespaces/inkscape\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\" xmlns:sodipodi=\"http://inkscape.sourceforge.net/DTD/sodipodi-0.dtd\" xmlns:svg=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
        "  <metadata id=\"metadata2193\">\n",
        "    <rdf:RDF>\n",
        "      <cc:Work rdf:about=\"\">\n",
        "        <dc:format>image/svg+xml</dc:format>\n",
        "        <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
        "      </cc:Work>\n",
        "    </rdf:RDF>\n",
        "  </metadata>\n",
        "  <sodipodi:namedview bordercolor=\"#666666\" borderopacity=\"1.0\" id=\"base\" inkscape:current-layer=\"svg2\" inkscape:cx=\"243.02499\" inkscape:cy=\"71.887497\" inkscape:pageopacity=\"0.0\" inkscape:pageshadow=\"2\" inkscape:window-height=\"543\" inkscape:window-width=\"791\" inkscape:window-x=\"0\" inkscape:window-y=\"0\" inkscape:zoom=\"1.4340089\" pagecolor=\"#ffffff\"/>\n",
        "  <defs id=\"defs4\">\n",
        "    <linearGradient id=\"linearGradient2795\">\n",
        "      <stop id=\"stop2797\" offset=\"0\" style=\"stop-color:#b8b8b8;stop-opacity:0.49803922\"/>\n",
        "      <stop id=\"stop2799\" offset=\"1\" style=\"stop-color:#7f7f7f;stop-opacity:0\"/>\n",
        "    </linearGradient>\n",
        "    <linearGradient id=\"linearGradient2787\">\n",
        "      <stop id=\"stop2789\" offset=\"0\" style=\"stop-color:#7f7f7f;stop-opacity:0.5\"/>\n",
        "      <stop id=\"stop2791\" offset=\"1\" style=\"stop-color:#7f7f7f;stop-opacity:0\"/>\n",
        "    </linearGradient>\n",
        "    <linearGradient id=\"linearGradient3676\">\n",
        "      <stop id=\"stop3678\" offset=\"0\" style=\"stop-color:#b2b2b2;stop-opacity:0.5\"/>\n",
        "      <stop id=\"stop3680\" offset=\"1\" style=\"stop-color:#b3b3b3;stop-opacity:0\"/>\n",
        "    </linearGradient>\n",
        "    <linearGradient id=\"linearGradient3236\">\n",
        "      <stop id=\"stop3244\" offset=\"0\" style=\"stop-color:#f4f4f4;stop-opacity:1\"/>\n",
        "      <stop id=\"stop3240\" offset=\"1\" style=\"stop-color:#ffffff;stop-opacity:1\"/>\n",
        "    </linearGradient>\n",
        "    <linearGradient id=\"linearGradient4671\">\n",
        "      <stop id=\"stop4673\" offset=\"0\" style=\"stop-color:#ffd43b;stop-opacity:1\"/>\n",
        "      <stop id=\"stop4675\" offset=\"1\" style=\"stop-color:#ffe873;stop-opacity:1\"/>\n",
        "    </linearGradient>\n",
        "    <linearGradient id=\"linearGradient4689\">\n",
        "      <stop id=\"stop4691\" offset=\"0\" style=\"stop-color:#5a9fd4;stop-opacity:1\"/>\n",
        "      <stop id=\"stop4693\" offset=\"1\" style=\"stop-color:#306998;stop-opacity:1\"/>\n",
        "    </linearGradient>\n",
        "    <linearGradient gradientTransform=\"translate(100.2702,99.61116)\" gradientUnits=\"userSpaceOnUse\" id=\"linearGradient2987\" x1=\"224.23996\" x2=\"-65.308502\" xlink:href=\"#linearGradient4671\" y1=\"144.75717\" y2=\"144.75717\"/>\n",
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        "</svg>"
       ],
       "text": [
        "<IPython.core.display.SVG at 0x7fa15951c290>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Links to local files"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If we want to create a link to one of them, we can call use the `FileLink` object."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import FileLink, FileLinks\n",
      "FileLink('Part 1 - Running Code.ipynb')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<a href='files/Part 1 - Running Code.ipynb' target='_blank'>Part 1 - Running Code.ipynb</a><br>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "/home/takluyver/Code/ipython/examples/notebooks/Part 1 - Running Code.ipynb"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Alternatively, if we want to link to all of the files in a directory, we can use the `FileLinks` object, passing `'.'` to indicate that we want links generated for the current working directory. Note that if there were other directories under the current directory, `FileLinks` would work in a recursive manner creating links to files in all sub-directories as well."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "FileLinks('.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "./<br>\n",
        "&nbsp;&nbsp;<a href='files/./Progress Bars.ipynb' target='_blank'>Progress Bars.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./animation.m4v' target='_blank'>animation.m4v</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Cell Magics.ipynb' target='_blank'>Cell Magics.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./python-logo.svg' target='_blank'>python-logo.svg</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Frontend-Kernel Model.ipynb' target='_blank'>Frontend-Kernel Model.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Part 4 - Markdown Cells.ipynb' target='_blank'>Part 4 - Markdown Cells.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Part 5 - Rich Display System.ipynb' target='_blank'>Part 5 - Rich Display System.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Script Magics.ipynb' target='_blank'>Script Magics.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Part 3 - Plotting with Matplotlib.ipynb' target='_blank'>Part 3 - Plotting with Matplotlib.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Part 1 - Running Code.ipynb' target='_blank'>Part 1 - Running Code.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./User Interface.ipynb' target='_blank'>User Interface.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./R Magics.ipynb' target='_blank'>R Magics.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Animations Using clear_output.ipynb' target='_blank'>Animations Using clear_output.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./README.md' target='_blank'>README.md</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Importing Notebooks.ipynb' target='_blank'>Importing Notebooks.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./SymPy Examples.ipynb' target='_blank'>SymPy Examples.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Cython Magics.ipynb' target='_blank'>Cython Magics.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Octave Magic.ipynb' target='_blank'>Octave Magic.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Typesetting Math Using MathJax.ipynb' target='_blank'>Typesetting Math Using MathJax.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Data Publication API.ipynb' target='_blank'>Data Publication API.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Custom Display Logic.ipynb' target='_blank'>Custom Display Logic.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Part 2 - Basic Output.ipynb' target='_blank'>Part 2 - Basic Output.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./Trapezoid Rule.ipynb' target='_blank'>Trapezoid Rule.ipynb</a><br>\n",
        "./.ipynb_checkpoints/<br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/Data Publication API-checkpoint.ipynb' target='_blank'>Data Publication API-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/User Interface-checkpoint.ipynb' target='_blank'>User Interface-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/Cython Magics-checkpoint.ipynb' target='_blank'>Cython Magics-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/Animations Using clear_output-checkpoint.ipynb' target='_blank'>Animations Using clear_output-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/Frontend-Kernel Model-checkpoint.ipynb' target='_blank'>Frontend-Kernel Model-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/Part 5 - Rich Display System-checkpoint.ipynb' target='_blank'>Part 5 - Rich Display System-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/Part 1 - Running Code-checkpoint.ipynb' target='_blank'>Part 1 - Running Code-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/User Experience-checkpoint.ipynb' target='_blank'>User Experience-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/R Magics-checkpoint.ipynb' target='_blank'>R Magics-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/Importing Notebooks-checkpoint.ipynb' target='_blank'>Importing Notebooks-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/Part 3 - Plotting with Matplotlib-checkpoint.ipynb' target='_blank'>Part 3 - Plotting with Matplotlib-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/Progress Bars-checkpoint.ipynb' target='_blank'>Progress Bars-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/Part 2 - Basic Output-checkpoint.ipynb' target='_blank'>Part 2 - Basic Output-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/Custom Display Logic-checkpoint.ipynb' target='_blank'>Custom Display Logic-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/Script Magics-checkpoint.ipynb' target='_blank'>Script Magics-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/SymPy Examples-checkpoint.ipynb' target='_blank'>SymPy Examples-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/Trapezoid Rule-checkpoint.ipynb' target='_blank'>Trapezoid Rule-checkpoint.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./.ipynb_checkpoints/Cell Magics-checkpoint.ipynb' target='_blank'>Cell Magics-checkpoint.ipynb</a><br>\n",
        "./images/<br>\n",
        "&nbsp;&nbsp;<a href='files/./images/menubar_toolbar.png' target='_blank'>menubar_toolbar.png</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./images/edit_mode.png' target='_blank'>edit_mode.png</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./images/command_mode.png' target='_blank'>command_mode.png</a><br>\n",
        "./logo/<br>\n",
        "&nbsp;&nbsp;<a href='files/./logo/logo.png' target='_blank'>logo.png</a><br>\n",
        "./nbimp/<br>\n",
        "&nbsp;&nbsp;<a href='files/./nbimp/__init__.py' target='_blank'>__init__.py</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./nbimp/mynotebook.ipynb' target='_blank'>mynotebook.ipynb</a><br>\n",
        "./nbimp/nbs/<br>\n",
        "&nbsp;&nbsp;<a href='files/./nbimp/nbs/other.ipynb' target='_blank'>other.ipynb</a><br>\n",
        "&nbsp;&nbsp;<a href='files/./nbimp/nbs/__init__.py' target='_blank'>__init__.py</a><br>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "./\n",
        "  Progress Bars.ipynb\n",
        "  animation.m4v\n",
        "  Cell Magics.ipynb\n",
        "  python-logo.svg\n",
        "  Frontend-Kernel Model.ipynb\n",
        "  Part 4 - Markdown Cells.ipynb\n",
        "  Part 5 - Rich Display System.ipynb\n",
        "  Script Magics.ipynb\n",
        "  Part 3 - Plotting with Matplotlib.ipynb\n",
        "  Part 1 - Running Code.ipynb\n",
        "  User Interface.ipynb\n",
        "  R Magics.ipynb\n",
        "  Animations Using clear_output.ipynb\n",
        "  README.md\n",
        "  Importing Notebooks.ipynb\n",
        "  SymPy Examples.ipynb\n",
        "  Cython Magics.ipynb\n",
        "  Octave Magic.ipynb\n",
        "  Typesetting Math Using MathJax.ipynb\n",
        "  Data Publication API.ipynb\n",
        "  Custom Display Logic.ipynb\n",
        "  Part 2 - Basic Output.ipynb\n",
        "  Trapezoid Rule.ipynb\n",
        "./.ipynb_checkpoints/\n",
        "  Data Publication API-checkpoint.ipynb\n",
        "  User Interface-checkpoint.ipynb\n",
        "  Cython Magics-checkpoint.ipynb\n",
        "  Animations Using clear_output-checkpoint.ipynb\n",
        "  Frontend-Kernel Model-checkpoint.ipynb\n",
        "  Part 5 - Rich Display System-checkpoint.ipynb\n",
        "  Part 1 - Running Code-checkpoint.ipynb\n",
        "  User Experience-checkpoint.ipynb\n",
        "  R Magics-checkpoint.ipynb\n",
        "  Importing Notebooks-checkpoint.ipynb\n",
        "  Part 3 - Plotting with Matplotlib-checkpoint.ipynb\n",
        "  Progress Bars-checkpoint.ipynb\n",
        "  Part 2 - Basic Output-checkpoint.ipynb\n",
        "  Custom Display Logic-checkpoint.ipynb\n",
        "  Script Magics-checkpoint.ipynb\n",
        "  SymPy Examples-checkpoint.ipynb\n",
        "  Trapezoid Rule-checkpoint.ipynb\n",
        "  Cell Magics-checkpoint.ipynb\n",
        "./images/\n",
        "  menubar_toolbar.png\n",
        "  edit_mode.png\n",
        "  command_mode.png\n",
        "./logo/\n",
        "  logo.png\n",
        "./nbimp/\n",
        "  __init__.py\n",
        "  mynotebook.ipynb\n",
        "./nbimp/nbs/\n",
        "  other.ipynb\n",
        "  __init__.py"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Embedded vs Non-embedded Images"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By default, image data is embedded in the Notebook document so that the images can be viewed offline. However it is also possible to tell the `Image` class to only store a *link* to the image. Let's see how this works using a webcam at Berkeley."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "img_url = 'http://www.lawrencehallofscience.org/static/scienceview/scienceview.berkeley.edu/html/view/view_assets/images/newview.jpg'\n",
      "\n",
      "# by default Image data are embedded\n",
      "Embed      = Image(img_url)\n",
      "\n",
      "# if kwarg `url` is given, the embedding is assumed to be false\n",
      "SoftLinked = Image(url=img_url)\n",
      "\n",
      "# In each case, embed can be specified explicitly with the `embed` kwarg\n",
      "# ForceEmbed = Image(url=img_url, embed=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is the embedded version. Note that this image was pulled from the webcam when this code cell was originally run and stored in the Notebook. Unless we rerun this cell, this is not todays image."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Embed"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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oiU5/KuePkyw/1rplhjl7jStraK0hEEO7aCSNzFjyc9TTax7a9Orq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\nNHLAZGTV1HYN0PPUms2D/9k=\n",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<IPython.core.display.Image at 0x7fa158870c90>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is today's image from same webcam at Berkeley, (refreshed every minutes, if you reload the notebook), visible only with an active internet connection, that should be different from the previous one. Notebooks saved with this kind of image will be lighter and always reflect the current version of the source, but the image won't display offline."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SoftLinked"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://www.lawrencehallofscience.org/static/scienceview/scienceview.berkeley.edu/html/view/view_assets/images/newview.jpg\"/>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<IPython.core.display.Image at 0x7fa158870b90>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Of course, if you re-run this Notebook, the two images will be the same again."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Audio"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPython makes it easy to work with sounds interactively. The `Audio` display class allows you to create an audio control that is embedded in the Notebook. The interface is analogous to the interface of the `Image` display class. All audio formats supported by the browser can be used. Note that no single format is presently supported in all browsers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Audio\n",
      "Audio(url=\"http://www.nch.com.au/acm/8k16bitpcm.wav\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "                <audio controls=\"controls\" >\n",
        "                    <source src=\"http://www.nch.com.au/acm/8k16bitpcm.wav\" type=\"audio/x-wav\" />\n",
        "                    Your browser does not support the audio element.\n",
        "                </audio>\n",
        "              "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<IPython.lib.display.Audio at 0x7fa158870950>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A Numpy array can be auralized automatically. The Audio class normalizes and encodes the data and embed the result in the Notebook.\n",
      "\n",
      "For instance, when two sine waves with almost the same frequency are superimposed a phenomena known as [beats](https://en.wikipedia.org/wiki/Beat_%28acoustics%29) occur. This can be auralised as follows"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "max_time = 3\n",
      "f1 = 220.0\n",
      "f2 = 224.0\n",
      "rate = 8000.0\n",
      "L = 3\n",
      "times = np.linspace(0,L,rate*L)\n",
      "signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)\n",
      "\n",
      "Audio(data=signal, rate=rate)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "                <audio controls=\"controls\" >\n",
        "                    <source 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rLudBZKiidaExYN5ht2MwZbYo76z9sX22SDv0ARhGigvhELgU7Niim4Jd+17D31legV0H2r+XApNvDqjJloRifvV5enQaL3pq/Sc/pBliGuDOILWhDCLF5U+oj6yncTM2C7uHATvGfsunUI8VE9jYW8UeCN9Z37We4J1nWtzXmpO/ju+J0cSQfxU5izRbb2xq4KcVpCMh2iCE4GYg+OJxJPuoPywcsO/10ftYQNkGaMue0JQVJdj22+7ePJ9WH/gfJ92xWycX41PEj28KCgT//zr5pjRrL3Bq2ScDJAYh86BWIDBgveIyZLsn/qvdsLR1mzsoALBGCMuIEIcVItj92/9eFh+33+EfVt3k214YG9Q9T2aKfsTwv2Y5yzSI74ZrJqcH5AIh5yBBoBRgmyIKJI4nzmvrMED1qHr3AEJGHwtjUGhUyxjtW/beFZ+/H/AfbR3B24EYQ9Rpj5WKr8Uh/5b6ObS0b62rCKdjpBch9OBHYBJgkSI4ZHVnrquFsFZ1ebqFgE9F64sxEDiUntiF29UeOp9r3+Ufap3IG4/YWxRIj/vKnAVTf8w6cbTtL+XrfydWpEUiHKCn4CpgoCI9pHCnn+utMDT1D/qUABfFr4rxz/fUXphHm5rdxd9+H7/fDx33G0pYYRRaD9iKw4WEAAW6sjUy8C7riWfgJIviXmDioFygyCJaJIBn4quicBz1Kvpjv9xFasqlz6aUClgy2wfdt172X0DfGt2PW3AYFZRdz+vK5YW0gAL6+rVE8IgsJyg/5OqiuaE3oKihCOKNpOTn9uulsA61C7pz/51FHkpNz0WT4xeIWt0dD16UXyfejZ1QWwFYOJQTj/TKwgXjgEO7CzXi8PEsWCi1ZWGjLiGmoQ5homLYJR4oHOv3MAq1MnoFv5tEykoqDtUTaNcIGlpcjl4YnrVeJ9z6Gr2XiZQ7D7OK2EXQwIb7YrYMMWks22kAJi+ju6Iu4Y2iFKN5pWwoVKwW8FF1HzoZf1bEr4m7jlXS3Jay2YDcNN1DnimdqVxNGmVXSNPUD6eK6AX8AIx7gHa/8a9tcGmfppSkYWLQYmXinyPyJc6o3mxFMKK1EvovvxBETolCjghSftXJWRCbQxzV3UTdEtvJWfiW9lNej1DK8MXkgNO75Hb98gOuFqpS50/lHqOKYxbjQaSBJoXpeeyCMP71DboIfwiEJ8jADa2RkFVMWEqauhvQHIgcZJsvWTcWUhMaTy7KsoXKARv8DbdE8uTujSsZaCBl8yRcI9+kO2UmZxEp5y0NsSZ1T3okvv/DvAh0TMYREdS8l2+ZmpsyW7NbXxp/GGHV29KHTsHKrMXsQST8e7eU81JvUyvyaMWm3aVFJMAlDGYhZ/CqZe2n8Vk1mToEfvaDS8ggTFLQRFPa1oCY5Ro+Godagxm5V7iVFFIlzklKX4XKgW48rfgss8twKCycqf6nneZEpfcl86bx6KOrNi4Qsdc16nooPq2DGAeEy9QPqFLoFb4Xmpkz2YUZkNieVvvUe5F1jcWKCkXkgXb843iLtI8wyu2Xqspo8mdZpsQnMKfW6anr127H8mC2A7pQfqUC4MciiwtO/xHlFKkWvBfUWK2YSVevFewTkdD3TXZJrMW6AX79G/kxNRzxuq5ia+gp2miDKCZoAmkQKoLsyW+NcvW2ZTp9Pl3Cp0a6SnlNyVES04LVipbg10EXbVZsFMoS15AqjNvJR0WKwYW9lrmcNfNydi97rNarFSnAaVypaGocq63ti7BhM1X2zvqu/lfCa8YMid6NB9AykkxUR1WaFgEWPZUWE9YRzU9QDHYI2UVWQYp90voMNpIzfPBirhTsYSsQaqYqoWt77KqunfECdAE3QPrmPlQCLsWaSTwMPA7FUUbTMtQBVO6Uu1PuEpEQ845oC4UIowUcgY0+D/qAd3f0DbGV72GtvWxxq8GsLKys7ffvv3HxNLe3u3rivlLB8UUkSFMLZs3MUDMRjxLX00pTZ1K0kXuPio2zCslIJITdAY0+Tbs39+P1JzKUsLvu6K3jLW4tSS4urxUw73LstXj4Pjsk/lRBs8Srh6RKSUzITtKQXJFe0dXRwtFqkBaOk0yxCgLHnUSXwYo+ivuyOJV2CLPdceIwYe9jrupu9a9AcIGyLbP0tgS4yXutPlkBdsQwRvCJZEu6zWaO3Q/XEFIQTo/RjuLNTkuiyXHGzgRMwYO+x3wt+Us3MPTvcxOx57Dx8HUwcPDgsfyzOTTI9xr5XPv7fmFBOwOzxjkIeQpkzDBNUY5CjsCOzE5qDWFMPIpJCJaGdoP7gXk+wryq+gQ4HvYI9I6zeHJMcg0yOjJO80S0kXYoN/r5+LwPvq2AwMN2hX5HSIlHyvEL+4yiTSJNPMy1i9LK3olkB7HFlsOkAWr/O7zoOv/40XdpNdH00zQyM7Ezj3QJtNk19TcSeOR6nDyqPr5AiML5hIHGlEgkyWoKXIs3i3kLYcs1CniJdQg0xoOFL0MGQVf/cn1k+705x3iOt1w2djWhdV+1cDWPtni3I7hG+db7R30LPtOAk4J9Q8RFnMb9B9zI9clECcXJ/AlpyNPIAYc7hYzEQELiQQB/pj3gfHr6/7m4eKv34HdY9xc3Gndft+K4nDmEutI8On1yPu1AYUHCg0aEo8WSBoqHSIfJCApIDYfVB2VGhEX5hI2DicJ4QOP/ln5aPTg7+XrkugA5kDkXONZ4zPk4uVV6HbrK+9V89H3ffwxAcwFKQomDqcRkhTTFlsYIBkfGV0Y4Ba5FPsRvQ4aCzAHIAMJ/wv7RPfR88zwSu5c7A/raupu6hrrZOxA7pzwY/N/9tX5Sv3CACMEUwc5CsEM2Q5zEIURChIAEmoRUhDBDscMdgrhBx8FRgJu/6v8E/q697D1A/S+8unxhvGX8Rby/vJF9N31uPfF+fP7MP5oAIwCiwRWBuEHIgkQCqgK6ArQCmUKrQmwCHoHFQaOBPUCVQG9/zj+0vyW+4v6uPkh+cf4rPjL+CP5rPlf+jX7JPwk/Sr+Lf8kAAkB1AGBAgsDcAOvA8kDwAOXA1MD+AKNAhcCnQEkAbIATAD2/7H/gP9j/1v/Zf9//6X/1P8GADgAZQCJAJ8ApQCXAHYAQAD3/5z/Mv++/kT+y/1X/e/8mvxc/Dr8Ovxd/Kb8FP2n/Vz+Lv8YABMBGAIdAxkEAwXRBXsG+AZCB1MHKAe/BhkGNwUfBNgCaQHf/0T+pPwO+475NPgL9x72efUi9SD1dvUj9if3e/gX+vD7+v0kAGACmgTCBsQIkAoVDEUNFA54DmwO7Q37DJsL1gm3B0wFqALe/wP9Lfp19+/0svLQ8FzvZO7x7Qrusu7n76Hx1/N49nP5svwaAJQDBAdOClcNBhBEEv0TIBWjFX0VrRQ3EyMRfg5bC9EH+gPz/9r70ff282nwSe2u6rDoYefO5v/m9ees6RnsLO/O8uf2Vvv7/7EEVAm/Dc8RYRVZGJ0aGBy7HIAcZRtvGaoWKxMKD2QKXgUdAMv6j/WU8AHs/eeo5B7id+DB3wXgReF545Pmfuod70306fnF/7MFhwsRESYWnBpPHh8h9CK8I3AjDSKcHy0c2Re+EgMN0wZeANX5avNR7bvn0+LD3q3bqtnO2CLZp9pT3RXh0eVm66rxbPh6/5wGnA1DFFsatB8hJH4nrymfKkUqoCi6JaYhgxx2FqsPWAizAPn4ZPEx6pnj0N0F2WHVAdP80VzSINQ/16PbK+Gv5/7u4PYa/2sHkw9SF2oeoyTKKbYtRDBfMfswGC/CKxEnJSEsGloS6gkdATj4fu81557f9thy0z/PgsxQy7jLuM1D0UHWjtz6403sSPWm/h8IaRE7Gk8iZilGL8AzrTb1N4s3cDWwMWcsvCXfHQ0ViAubAZH3ue1f5M7bStQOzk7JMcbRxDzFcsdky/bQ/9dK4JnppfMg/rgIHRP8HAkm+i2RNJc55TxdPvE9ojt/N6YxQiqKIcEXMA0rAgb3Fuyy4SzYz8/eyJHDE8CDvu6+VcGnxcXLg9Ol3Obm+PGH/TYJrxSVH5MpWjKlOTg/5kKQRCZEqUEpPcc2sy4qJXMa4A7MApT2luov37rUicvmww2+L7psuNS4ZrsSwLTGHc8M2TbkRfDd/JsJHRYCIuwshTaAPp5ErEiKSiVKf0epQsc7DDO6KCAdlxB+Az32OenY3HvRfMcqv8e4ibSRsvGyqrWousfB0sqE1Yzhje4j/OUJaBdDJBIwdjodQ8NJMk5FUOlPH036R6BASDc4LMYfUBI/BAD2AOit2nDOqMOsusOzJa/3rE2tJ7BwtQO9psYR0uve0uxb+xUKjxhXJgEzKj55R6VOc1O9VW1VhFIYTU5FYzuhL2IiDBQNBdz16+ax2JzLE8BytgWvB6qip+un4KpvsG24nsK1zlXcF+uH+i0Kkhk8KLk1oEGRSz9TbFjsWqxaqlf9Uc1JWD/vMvAkxhXoBdH1++Xj1gDJvrx9spCqNaWYos+i3KWoqwe0vL51y8/ZXemn+SwKcBryKTg41ERiT45XF13QX6JfjFylVhdOJEMgNm0nfhfNBt31L+VG1Z7Gq7nRrmimsaDcnQCeHqEgp9ivBrtUyFvXqOe9+BQKKht3K306xkfpUo9bcmFkZEpkJmELWypSxEYwOdgpMRm7BwD2h+TY03fE3LZwq5CigJxymYCZqpzbouOrf7dVxfvU+eXL9+YJwBvMLIY8ckojVj5feWWjaKFocmUtXwBWMkocPCws2xqwCDn2A+Sc0o3CU7RdqAqfpJhelVOVhZjeniyoKrR8wrPSU+TT9qMJMhzwLVI+10wPWZhiKGmKbKJsbmkFY5dZbE3hPmcufByrCYb2ouOQ0d/AEbKZpdqbIZWkkX6Rs5Qsm7ekDLHMv4bQuOLX9UwJghzjLuE/9U6qW51lfmwXcEtwFm2QZupcblB8QYYwEB6oCuf2Y+O10G+/GLAnowKZ+pFFjgOON5HJl4ehJ65IvXbOLOHY9OIIsBymLzJByVDzXUhod29Gc5dzZnDKafZfNVPoQ4YylR+oC1n3RuML0D2+aK4HoYSWMI9Gi+aKFI64lJ+ef6v0uobMr9/Z82cIvBw4MEVCVFLpX5lqEXIVdoV2W3OxbLhivVUkRmU0CSGmDNz3SuOQz0m9Aq07n2CUxoypiCmITYv8kQOcF6nRuLnKRN7b8t0HqBybMBpDlVOLYY9sSnSBeBB583VBby1lBFgsSB82aSKjDW74beNFz5K856vEnZmSvopvhs+F5IiYj7aZ8abkthLJ79zh8UUHdhzPMLJDi1TXYiduInaIejh7K3h4cVJnCFr/SbM3tCOaDgz5r+Mozxm8FqujnDCRGImahNqD3YaOjbqXEaUttZLHsNvs8KAGJhzVMAxEN1XOY2FvlXcqfPt8AXpUcyVpxFuYSx455iSMD7b5DeQ5z927j6rWmyWQ14csg0uCOYXiixGWeaOxszzGitr/7/EFuhutMCpEmFVwZD1wpHhkfVZ+c3vRdKNqOF33TF46/iV1EGr6h+R3z927U6pgm3iP+oYmgiSB+oOUir6UKqJxshPFgNkb7zoFMxtbMA1EsFW8ZLpwT3k2fkl/gHzvdctrYV4YTnE7+iZTESX7GuXfzxi8X6o+myqPgoaJgWWAIYOmicKTKKFusRjElNhD7nsEkxrdL7VDgFWzZNlwlHmfftJ/JX2tdptsPV/7TlM81yclEub7xuVx0Iy8tKpxmzqPb4ZUgRGAroIZiR+Tc6CssE7Dxtd47bgD3Bk3LyRDCFVXZJtwdHmffvJ/ZH0IdxFtyl+dTwU9lCjoEqv8iOYq0Tm9T6v3m6iPwYaJgSaApILviNaSDKArsLXCGte87PECEBlqLlxCSVSnY/9v8Hg2fqh/O30BdyxtCWD8T4Q9LymcE3P9XucK0h2+MKzPnHKQeIcmgqWAAYMoieiS95/ur1HCkdYR7CoCMBh4LV5BRlOmYgdvCHhlffR+qXyXdu1s9l8YUM49pik9FDr+R+gO0zW/Va35nZiRkYgqg46BxoPDiVWTMqD0ryHCLdZ662MBPxdjLCxAAFJVYbRtvnYtfNd9sHvJdVJskl/vT+I99ynKFAD/QOk01IDAvK5xnxeTDIqWhN+C84TCih+UvqA/sCjC7tX36p4APhYsK8g+eVC2XwlsE3WOelJ8UHqZdFpr3V6BT789IipCFcL/SOp71fzBYrA3oe6U54tnhpiEh4YkjEOVnaHQsGfC19WL6uD/LxXXKTU9s07KXQZqCHOLeGZ6iXgGcwhq1V3NTmU9JSqjFX4AXOvf1qbDRrJHoxuXII6biLeGgIjojcSWzqKosd7C6dU26ib/FhRlKHQ7sUyWW69noXAmdhR4XnYScVpoe1zSTdI8/inrFTQBe+xe2HzFZrSfpZuZtZAxizqJ3YoMkJ+YUKTGso3DJdb76XX+8xLYJok5dkoaWQdl321gc191z3O+blJm0FqRTAU8rikZFuABoe332XvHvbY9qGyco5MnjiCMnI2QktOaJKYqtHfEi9ba6c39yRE0JXY3A0hbVg9ixWo8cEly4HALbPFj1FgKS/86MiksFoICzu6n26HJSrkdq4qf6JZ4kWWPvJBylWCdSKjUtZnFHNfV6TH9mhB6Iz41XUVaU8teV2e9bNVukW37aDlhiFY9ScA5iigiFhgD/+9r3evLCbw9rvOif5ojlQeTOpSvmESgvKrEt/XG2tft6aP8aA+tIeMyhkIcUD5bl2PmaAZr5mmRZSpe7VMrR0c4tyf7FZ8DMvFA31bO976ZsaKmZ54lmQOXEphGnH2jfa34uYvIxNgj6iP8NQ7PH2kwgT+kTG1XiV+8ZN5m4WXPYchaBVHURJU2tya2FRcEZPIl4d/QEsIttZWqmqJ4nVabQpwzoAini7BwvFnK2tl46rP7Aw3kHdItUjz1SFlTMVtAYGFihmG3XRRX0007Qqo0iiVRFX4ElfMW44PTVMX2uMeuFqcbovufxqBzpOOq47Mqv2DMHdvt6lX71AvtGyEr/DgTRQlPk1Z4W5Rd2VxOWRJTVkphP4gyMiTNFNMEwfQS5T/WvMjwvDWz1asHp/Ckm6UEqQyvgrckwp3Oi9yB6wr7qwruGVsogzUCQX9Ks1FoVnpY3VeXVMROlEZHPC8wrSIpFBUF6PUV5xDZRcwXwdq31LA6rC6qvarhrX6zaLtexRHRJt427NL6iAnoF4El6zHHPMFFlkwTURhTllKVTy5KjULvOKEt/CBjE0IFBvcd6fLb7M9mxbG8DravsbOvKLAHsze4j7/UyLrT7d8L7bD6bgjeFZciNy5kONJAQEeAS3JNCU1MSlJFRT5bNd4qIB9+ElkFG/gn6+PerNPaybbBfrtgt3m11rVxuDS998OEzJbW3eEB7qP6XwfUE6EfayrfM7c7t0GyRY1HO0fBRDZAvjmPMeonGx13EVsFJPkx7d7hg9duzuXGIMFJvXu7w7scvnDCm8hs0KPZ+OMY7636XAbKEaAciiY9L3U2/zuwP25BMEH4Ptw6/jSMLcUk7BpQEEUFIfo57+LkbNsf0znM7sZkw7XB68ECxObHeM2I1N/cOuZP8M/6ZgXFD5oZmiKAKhExHTZ9ORs77Tr2OEk1BjBWKXEhlRgJDxcFD/s88evnZN/n16zR48ytySHISMgfypTNidLX2EngpOim8Qj7gATFDZAWnB6uJZArFjAgM5g0dzTAMoIv2yrwJPIdGRaiDdIE7vs48/bqZuPB3DrX+dId0LnO1c5u0HPTzNdU3d3jNOsd81r7qQPNC4UTlhrMIPYl8CmeLOwt1C1aLIspgSVdIEkadxMdDHQEu/wr9f/tb+er4d7cLNmv1njVjNXp1oHZPN384Zrn5+2y9MT75QLgCX0QjBbdG0kgsSP8JRwnCifKJWkj/B+gG3oWshB5Cv4Ddv0S9wTxeuue5pPid99e3VjcaNyL3bbf1eLM5nzrvPBk9kb8MwL/B3sNgBLmFo4aXR1BHy4gHiAXHyEdURq+FoYSzQ24CG8DHv7t+AP0hu+X61To0uUk5FPjY+NP5A/mkejA64DvsvM0+OH8lQEsBoIKdg7sEckU+xZyGCcZFhlEGLkWhBS6EXIOyArbBsgCsv64+vf2jPOR8BvuOuz76mPqduov64bsbe7T8KPzxPYe+pX9CwFpBJQHcwrzDAEPjxCWEQ4S+BFYETUQmw6ZDEEKqAfjBAkCMv9y/N75i/eI9eTzqPLc8YPxnfEm8hbzZPQC9uL38/kj/GD+lwC4ArQEegb/BzkJIAqxCukKygpZCpsJmQheB/UFbQTSAjIBnP8Z/rb8fvt4+qn5F/nD+Kz40fgs+bn5cPpI+zn8Ov0//kL/OAAbAeQBjgIVA3cDswPKA74DkwNMA/AChAINApMBGwGpAEQA7/+s/33/Yv9b/2b/gf+p/9j/CgA8AGkAiwCgAKQAlQByADoA8P+T/yn/tP46/sH9Tv3n/JT8WPw5/Dv8Yvyt/B/9tf1s/kH/LAApAS4CMgMuBBUF4QWHBgAHRgdSByIHtAYIBiIFBgS6AkkBvv8h/oH87fpw+Rn49PYN9m71HvUk9YH1NvZA95v4PPoa/Cf+UwCQAskE7wbtCLMKMgxaDSAOfA5mDt0N4gx5C6wJhgcWBW0Cov/G/PL5Pfe89IbyrfBD71Xu7e0T7sfuB/DM8Qv0tfa2+fn8ZADfA0wHkgqUDTsQbhIbFDEVphVyFZUUERPwEEAOFAuCB6UDnP+D+3z3p/Mi8AvtfeqN6E3ny+YM5xPo2elU7HTvIvND97j7XgAVBbUJGg4gEqcVkRjEGi4cvxxxHEMbOxlnFtkSqw78CfAErf9a+iL1LvCm66/naeTy4V7gvd8W4Gvhs+Pf5trqhu/C9GT6QwAxBgEMgxGMFvMalB5QIQ8jwSNdI+MhXB/ZG3MXSRKDDEwG0/9K+eXy1exL53Tid95324zZydg32dXamd1x4UHm5us28gD5EQA0By8OyxTVGhwgdCS5J9AppSovKm4obiVCIQoc6xUUD7cHDgBV+MbwnukV41/drNgh1d7S9tF00lbUkdcO3K3hROih7473zP8bCD0Q8Bf4HhwlKir6LWowZTHhMN8uayueJpogjRmrETEJXwB798jui+YG33TYC9P2zlnMScvTy/TNoNG71iLdo+QH7Q32cf/oCCoS7xrxIvAptC8ONNk2/TdvNzA1TjHmKx8lKx1IFLcKxAC89uzsoOMj27jTms37yALGyMRaxbbHy8t+0aTYCOFp6oH0Av+ZCfYTxh2+JpQuDDXvORY9Zj7SPVs7EzcXMZQpwyDmFkkMPQEZ9jLr3eBt1yzPXMg0w9+/eL4Ov5/BGcZbzDnUdd3L5+vygP4vCp4VdCBbKgUzLjqaPx5Dm0QFRFxBszwrNvUtTySDGeINyAGR9ZvpRt7p09bKV8OnvfW5X7j1uLW7jMBXx+PP79kw5U7x7f2pCiIX9iLHLUA3FT8JRepIl0oCSixHKkIeOz8yzicdHIMPYwIk9Sno2tuX0LnGjr5XuEi0grIVs/+1LLt2wqfLedaZ4qvvSf0JC4IYSyX+MEA7v0M4SnZOVVDFT8hMckfrP2w2OyuvHikRDwPR9NvmnNl6zdbCBLpKs96u5axxrYCw/LW+vYrHFtMK4APulfxOC70Zcif/MwQ/KEgjT71Tz1VIVSlSiEyORHk6ky45IdESygOZ9LPljdeVyjK/vbWCrrqpjqcQqD6rArExuY7Dys+H3Vvs1Pt5C9MaaSnINohCTEzHU7xYAluHWktXZlEDSWE+0jG2I3kUkQR69LDkrtXpx867vLEEquKkgaL1oj2mQqzWtLm/mMwR27PqB/uMC8QbMCtXOctFKVAfWG1d6V99XylcB1ZETSJC9TQkJiAWZAV19NLj/9N4xa24Ba7TpVigwp0lnoKhwKewsA+8hcms2A7pLvqFC48cxiyrO8pIu1MoXM5hf2QlZMBgaFpOUbZF+TeAKMIXQgaH9BnjgtJCw9G1maryoSGcVZmlmRGdgaPDrJK4k8Za1m/nTPlnCzUdKi7CPYJLAFffX9tlwmh8aAplhV4dVRpJ2TrGKl4ZJwew9IbiNtFKwTyze6dknj+YPZV4le+YiJ8TqUa1xcMf1NflY/gzC7cdXS+bP/RN9llCY5BprWx+bARpWGKtWEtMkz31LPEaEwjv9BbiHdCPv++wrqQtm7eUgJGikR6V2pukpTCyH8H+0UnkdffqChQeXTA3QR1Qm1xOZutsPXAocKts4GX6W0ZPJEAJL3kcBAlC9crhNc8TvuuuMqJNmIqRHo4mjqOReph5olOvpL74z8jigvaNCk4eLDGTQvtR7V4Baepvb3N1c/pvF2kCXwZSiEL/MPMd+Amp9aHhf87WvDGtCqDHlbuOG4sHi4CObJWWn7GsWLwRzlXhjvUdCmYeyjGxQ5BT62BZa4lyQXZkdu9y/GvAYYlUvUTWMl4f7Qoh9pvh+s3Yu8KrNp6dk0yMeohJiLmLspL+nE6qPLpMzPPfmfSdCVweNjKPRNlUlGJVbcd0sXjyeId1i24xZMtWv0aJNLYg4Aup9rXhps0Zu5+quJzQkT6KPYbthVGJT5Czmi2oU7iryqXep/MNCTIecjIvRdZV52PzbqN2vHoce8B3wXBUZstYjUgXNvoh0QxA9+7hgc2ZusepkJtikJSIZYT1g0mHRo65mE+mobYwyWzdufJvCOkdfzKQRYlW5GQycBt4YHzgfJZ5nHImaIVaI0p+NycjvA3k90bijM1WujqpvppSj0+H9IJkgqOFmYwRl7mkKLXex0vc0PHGB4MdXjK0RfBWi2UTcS55nX0+fgp7GnSjafdbf0u7ODwkoQ6T+LvixM1RuvmoQpqhjm6G64E7gWKES4u+lWuj6bO3xkTb8PASBwAdDzKbRQxX22WUcdx5cn4zfxh8OXXLaiBdoEzMOTUlfA9L+UvjKc6IugGpHZpPjvOFS4F6gIeDW4rBlGii57K9xVnaGvBWBmMclTFHRd9W1mW2cSR63n6/f8B8+HWca/1dgk2uOhImTRAK+vXjuM76ulOpTZpcjt6FE4EjgBODzYkclLKhJLLyxIvZT++UBa4b8TC4RGlWfGV6cQd64X7hfwF9VnYVbIxeJk5hO88mERHQ+rbkcc+lu+2p0ZrIji2GRYE1gAWDoInRk0qhobFYxN7Yku7MBOEaJDDwQ6xVzWTgcIV5e36af9p8UnYzbM1eiE7iO2wnxhGY+43lUdCJvM2qqZuQj+KG4IGygGCD1YnfkzChYLHxw1LY5e0DBP8ZMS/xQqhUzWPpb594rH3pfkx863X3a75eqE4wPOcnaxJj/HnmV9GivfOr0py1kPmH44KXgSKEbYpJlGehYrG9w+nXSu04AwsZGS69QWFTe2KXblZ3dXzOfVZ7InVga19ehE5JPD4o/RIt/XbngdLwvlutS540knOJTITmgkuFaIsNle6hqLG+w6XXwexuAgUY3ixWQNhR2mDrbKt12XpMfPp59nNvaq9dHE4tPG8oexP1/YPozNNwwAWvE6AMlE6LHIachNuGxYwtlseiMrL2w4fXTuyoAfEWgyu+Pg5Q7V7naqFz13hiejd4aHIiaa5cb03aO3ko5BO6/p7pNtUfwu6wJqI7loiNT4i5htCIhI6nl/GjArNkxJDX8evmANAVCir4PAhOtVyOaDlxcnYTeBB2eXB7Z1xbfUxQO1woNRR4/8Xqvdb8wxKzgqS9mB2Q5Io6iSmLo5B7mWulF7QKxcLXq+srAKMUdSgGO8ZLNVrjZXZurXNhdYVzKm56ZblZRUuOOhYobhQuAPbrX9gDxnC1JaeRmw2T2Y0djOWNIZOomzancbXpxR3Yf+t5/28TxybqOExJcFfnYlpriXBNcppwfWshY8ZXyEmTOaYnjRTcAC7tGdozyAW4DKq0nlSWK5FgjwCR/JUtnlGpELcAx6LYbuvR/jQSAiWoNp5GaVSfX+lnCm3bbk9tdGhwYIRVBkhgOAsnkBR/AWvu6duHys26M60hou6Z1pQAk3mUM5kIobqr9LhPyFLZeOs1/vQQKCNCNL1DI1ENXCdkM2kNa6hpEGVqXfRSAEb0NkYmeBQVAqzvy93+zMW9lrDXpdmd2Jj7lk2Yw5w4pHCuHLvXyS3an+um/bMPPSG7Ma5Aok01WBVgB2XnZqhlVWESWhhQt0NRNVUlQhScAu7wvt+Uz+rAM7TQqRCiLZ1Mm3mcqaC5p3Kxhr2WyzPb5Osm/XEOQx8XL3M96kkbVLlbimBsYlJhRV1oVvFMLEF1Mzkk7xMUAy/yv+FG0jnEBrgKrpCm0aHwn/mg4qSLq760MsCNzWXcR+y2/DENPB1YLBE6/UXCTxZXwVugXalc41hvUoFJYD5jMfIifBN8A23zy+MR1a7HC7yAslWrwKbjpMqla6mpr1G4HsO6z8LdyexY/PYLKxuBKYo24UEwSzFSrlaHWLFXM1QsTstFVTscL38h6xLQA6b04OXy10bLPcAtt1qw9ashqueqQa4RtCm8R8Yc0krfau0N/MAKExmXJuMymT1oRg1NV1EmU25SOU+gSdJBDTifLOIfOhISBNj1++fl2vvOmsQOvJq1bbGlr02wX7PAuEPArcmz1P3gK+7V+5EJ9habIx8vKjlvQbFHwEuATeZM+EnQRJg9ijTvKRoeaRE+BAL3Gero3czSG8kewRG7Ibdrtfi1wrixvZ3ETc1819niC++z+20I1xSSIEMrlzRKPCBC70WbRxtHdES+PyA5zzANJykceBBWBCH4OOz34LTWvs1YxrrADr1tu+G7Zb7iwjLJI9F22t7kDPCn+1MHuRJ+HVEn5i/9NmA86D99QRNBsz5wOm003Sz8IxAaaA9XBDX5Vu4P5K/afdK3y5HGLsOnwQbCRMRNyADOL9We3QvnLPGx+0cGnRBjGk4jGiuMMXU2sTkpO9Q6uDjoNIQvuSi9IM8XOA5BBDr6b/At57neVdc30Y7Me8kTyF/IWcrvzQPTa9nz4F/pbPLS+0gFhg5DFz4fOCb+K2YwTzOlNGI0iTIsL2gqZSRSHWkV6QwUBDH7g/JN6s7iQNzT1q7S8c+tzunOoNDE0zfY1t1z5NjryvML/FoEdwwjFCQbRSFXJjcqxyz4LcItKyxBKR4l5B++Gd4SewvPAxf8jfRt7evmOuGE3OvYidZt1Z3VFNfF2ZjdbOIa6HTuR/Vc/HwDcQoFEQUXRBycIO0jICYmJ/smoiUqI6gfOhsEFjEQ8AlyA+z8jfaJ8AzrP+ZH4kDfPt1O3HbcsN3w3yLjKufn6zLx4PbF/LECdwjsDeQSPBfSGo8dXx82IBIg9h7uHAwaahYmEmMNSAj+Aq79gfie8yzvS+sW6KblCuRM427jbeQ95s/oC+zV7w/0lvhG/fkBiwbaCsUOLxL/FCIXiRgtGQwZKhiQFk4UeREoDncKhQZxAlz+Zfqq9kjzV/Dt7Rns5+pf6n/qRuup7JzuC/Hj8wr3Z/rf/VQBrwTUB6wKIw0nD6sQpRESEvARRBEXEHMOagwMCm4HpgTMAfb+Ofyq+V33YfXF85Py0PGB8aTxNvIu84P0KPYM+CD6UvyP/sUA5ALcBJ0GHAhPCTAKuQrqCsQKTAqHCYAIQQfWBUsErwIQAXr/+v2b/Gb7ZPqb+Q75v/it+Nb4NvnH+YH6XPtO/E/9Vf5X/0wALQHzAZsCHwN9A7YDygO7A44DRQPnAnoCAwKJAREBoAA8AOn/p/96/2H/W/9o/4T/rP/c/w4AQABsAI4AoQCkAJMAbgA1AOn/i/8f/6r+MP63/UX94PyO/FT8OPw9/Gb8tfwq/cP9ff5U/0EAPgFEAkgDQgQoBfAFkwYIB0kHUAcbB6gG9wUMBewDnQIpAZz//v1f/Mz6Uvn/99/2/fVk9Rz1KPWM9Un2Wve7+GL6RPxV/oMAvwL4BBsHFQnWCk4Mbw0sDn4OXw7MDcgMVwuBCVUH3wQzAmX/ify3+QX3ivRb8orwKu9H7uvtHO7c7ifw9/FA9PL2+vlB/a4AKQSUB9UK0Q1uEJcSOBRCFagVZhV7FOkSvBABDssKMgdQA0X/LPso91jz3O/P7E3qa+g758nmG+cx6Afqkey973bzn/ca/MMAeQUVCnQOcRLrFccY6hpCHMEcYRwgGwcZIhaFEkwOlAmBBDv/6fm19MjvS+ti5y3kx+FH4LvfKeCS4e7jLec36/HvNvXg+sEArwZ6DPQR8RZJG9gefyEoI8MjSCO3IRsfhBsMF9QRAgzDBUj/wPhf8lns3OYW4i3eQ9tx2cfYTtkF2+HdzuGy5mfsw/KV+aoAywfADlIVThuCIMQk8SfuKagqFio6KCAl3SCQG2AVew4WB2r/svcp8AvpkuLw3FTY5NS90vPRj9KO1OXXe9ww4troRvA7+HwAywjmEI0YhB+TJYgqPC6OMGkxxTCjLhErKSYNIOwY+xB3CKL/v/YT7uPlcN7116bSsM4zzEXL8cs0zv/RONe43U7lwu3T9joAsAnrEqIbkSN4Kh8wWTQCNwI4TzftNOkwYiuAJHYcgRPmCe//6PUf7OLiedoo0yjNq8jWxcPEe8X8xzbMCdJM2cfhO+te9eT/eQrOFI8ecSctL4Q1RDpFPWw+sD0RO6M2hTDkKPofChZgC08ALfVP6grgsdaMzt7H28Kuv3G+Mr/swY3G9Mzx1Ejesujf83r/JguMFlEhISuuM7M6+D9SQ6NE4EMMQTk8izU1LXMjkhjkDMMAjvSi6F7dGdMmysvCRL2+uVa4G7kJvAvB/cer0NXaK+ZY8v3+twsmGOgjny74N6c/cEUjSaFK3EnWRqZBcjpuMeAmFxtvDkgBC/Qa597atc/5xfW967cMtHeyPLNYtrS7KcN/zHDXqOPJ8G7+LQybGVAm6DEIPF5EqUq1TmFQnU9tTOZGMz+MNTwqlx0AEN8Bo/O45Y3Yh8wHwl+51LKbrtismq3esIy2fL5wyB7ULOE279D9hwzqGooo+zTaP9NInU8CVN5VH1XJUfRLy0OLOYMtDiCVEYUCV/N85GvWkclUvgy1A65yqX6nOqigq5qx+rmCxOLQut6g7SL9xgwTHJQq1DdtQwNNSlQHWRNbXVrnVspQNUhnPbMweiIrEzoDJfNm43rU1cbiugCxfKmUpG+iH6OjpuCsqbW6wL/NVdwK7Gf86gwWHWwsczq+RutQq1i+XfxfUl/CW2VVbUwbQcgz2CTAFPsDDfN34rvSVMS0tz2tQqUEoK2dUJ7soWWojLEbvbnK/9l26qD79gzzHRIu1jzKSYhUvFwlYpZk+mNVYMBZblCkRL42JSdSFsgEDvOt4S7REMLLtMapWaHHmz2Z0Jl+nSukp62oudTHvNfm6M766AypHoYv+z6PTNhXfGA3ZttoUmidZNddNVT/R5I5XinfF54FJ/MK4dPPCsApsp6mxJ3glyOVopVdmTig/qlnthHFjtVd5/T5www6H8cw4kAMT9ha52PyaclsVGyVaKdhvlcnS0E8gCtlGXwGWPOM4KzOQ77Rr8ejhJpSlGKRzJGOlY6clqZZs3bCeNPc5RP5iAylH9UxiUJAUYZd+2ZTbV1w/m86bCplBVsZTsg+iS3hGmAHnvMz4LfNu7zCrUKhnZcfkf2NT44UkjKZcaODsATAfdFm5C34NwzsH7Ay8UMpU+FftWlXcJJzTXOIb19oCF7SUCRBdi9QHEgI+PP+3/bMc7v+qxGfEZVLjveKL4vyjiaWkqDorb+9n8/94kP30wsPIFgzGUXHVOdhE2z7cmh2PXZ8ckFrwmBPU1FDRDGxHTIJZvTt32fMa7qHqjad4JLXi1OIb4gsjG6T/p2Kq6q74s2k4Vj2XAsPIM4zAUYYVphjFW4+ddp4zXgUdc5tMGONVU5F8DIBHx0K5fT+3wnMo7lcqbGbDZHFiRKGEYbDiQ2Rtpttqce5R8xd4G711QrtHxM0qUYeV/JkuW8ed+h6+HpNdwNwUWWJVxdHeDQ/IAYLdfUw4N3LG7l9qIKamY8XiDeEGIS6hwSPvZmSpxm40soq34b0PgqsHyc0EUfWV/Rl/XCaeJB8v3wled5xIWdBWalI2zVnIewLEvaB4OHL07jqp6uZhI7OhsOChIIUhleNFpj+paK2g8kO3qPzmglLHws0OkdDWKBm4nGxedB9H36aelxznmiyWgNKFDd3IswMvPbx4BTMyLijpyuZz43phbeBWIHShAiMw5axpGW1X8gK3cby6gjMHsEzJkdkWPVmZ3Jjeqh+Fn+qe310xmnaWyNLIzhvI6UNcfd94XXM+7iopwGZeo1rhROBlYD0gxeLxpWuo2S0Zscg3PLxMQgyHkoz1UY6WPNmjXKuehd/pX9UfD11mGq3XAZMBjlKJHUOL/gl4gLNa7n3py6ZhI1ThdmAO4B+g4aKH5X2oqCzmsZT2yjxbwd+HacySEbGV5tmU3KTehx/yn+XfJ51E2tJXatMujkJJTkP9Pjm4rrNFbqPqLGZ7Y2ghQmBS4Bug1eK0ZSMohuz/8Wk2mrwpwaxHNsxgUUKV+5lu3EUerh+hX90fJx1NGuMXQ9NPTqoJfAPvvm+45vO+LpvqYeatY5ShqGBxYDFg4mK3ZRvoteylMUV2rrv2wXOG+YwgUQGVu5kxXAveex91n7oezl1/GqCXTNNjzomJpgQjPqs5KLPE7yVqrGb2Y9oh6GCqIGEhB6LQpWiotSyW8Wo2RvvDQXWGswvS0O+VJxjc2/nd7d8v332enN0amonXRRNrTqCJi8RW/ut5c/QY70ArCudWJHiiAmE9IKqhRSMApYkoxSzV8Ve2Y3uPgTMGY0u4UEyU/phx209dhx7P3ydeUxzfWl9XLJMmDq6JrMRKfzA5h/S576srfSeMZO8iteFqIQ2h22NG5f2o5izh8U42RLucQOxGCwtREBlUQpgwmszdBx5WHred8NxNmiDWw1MTDrMJiQS9fzi55DTnMCZrwqhYZX2jAmIwoYniSaPj5gYpWC07sU52aztpwKIF6wreD5aT89dZ2nLcbl2DHi7ddlvlWY4WiNLyzm4Jn4SvP0R6R/Vf8LDsWuj5peMj56KQIl8iz+RXJqLpmy1isZh2V3t4gFTFg8qfzwSTUpbuWYHb/RzXHU1c5BtnGSeWPVJEzl8JsISfv5M6srWjsQntBKmvZp9kpKNIYwzjreTgpxNqLy2Xsex2SbtJQEUFVcoWjqSSoBYumPqa9FwS3JNcOpqSmK0VoJIIzgYJuwSN/+P647Yx8bDtv+o453FleOQYY9KkYyW/55eqlC4acgq2gjtcADNE4cmDjjbR3JVbWB3aFJt224Hbedno198VMxG/TaLJf0S5//Z7GnaJsmUuSysVaFimY+UAJO+lLyZ0aG9rCm6q8nM2gTtx/+AEqIknjXxRCVS1lyxZHppD2tlaYpkplz3UdJEnzXTJPMSigAo7lncqMuWvJivEKVQnZGY+JaOmEWd+KRor0O8JcuY2xztKP8wEakiCzPXQZxO+FicYE5l62ZpZdZgVlkmT5ZCCjTyI84SIgF571reSs7Hvz6zEKmLoeecR5u1nCShcKhesqC+1syO3FDtmP7fD6AgWTCRPtpK11Q8XNBgcmIYYc1ctlULTBlAPjLmIosSqwHK8GrgCtEiwxq3Ua0Qpo2h6Z8woValN6ydtT7BvM6v3aLtF/6ODokejC0iO+RGeFCUVwRcp11zXHNYyFGoSFw9PDCwISsSJAIa8ofi5NOkxiq7z7HZqn2m26T9pdepS7AjuRrE2dD73hHupv1ADWccpiqON71C3UuqUu9WkFiAV8tTj00ARWA6BS5QIKwRjAJm863k1dZJymi/hbbkr7WrGKoWq6WuqLTuvDTHKtNw4J/uR/33Cz0aqifYM2k+DEeATZZRL1NCUthODkkUQSg3myvGHg8R4QKs9Nvm2tkOztDDb7sqtS+xm694sLyzTLn6wIrKrtUP4kvv+/y0Cg0YnSQFMO05CUIeSPxLik2+TJ9JSUToPLYz/SgTHVMQIwPq9Q3p79zv0V/IicCouue2YLUethe5Mr5GxRjOZNjX4xbww/x6CdoVgSEYLE012TyGQihGpkf3RiNEQz9+OAwwLyY2G3gPUQMf90DrEeDn1RDNzsVZwNe8YrsDvLK+V8PNyd7RStvH5QHxofxKCKYTWh4VKI0wgTe+PB1Ah0H0QGo+ADrbMywsMSMzGX4OaQNJ+HTtPeP02d7ROcs3xvvCnMEjwonEt8iMztfVXt7e5wryk/wnB3QRKhsAJLErBTLLNuI5Mzu4Onc4hDQBLxkoByAIF2YNawNm+aTvb+YQ3sXWxdA9zE3JCch5yJbKTs6A0wLan+Eb6jLznfwRBkYP9hfdH8AmaiyzMHszrzRKNFAy1C70KdcjsRy5FC8MVgN1+s7xpek54sDbbdZm0sfPo87/ztbQFtSk2FreCuV97Hj0vfwKBSANwBSwG7whtiZ6Ku4sAS6uLfkr9Ci4JGkfMhlFEtoKKgN0+/Hz2+xp5szgLNys2GXWZNWw1ULXDNr23d7inOgC79z19fwTBAILixF9F6oc7SAnJEEmLifqJnkl6SJSH9IajRWvD2cJ5wJi/An2DvCe6uLl/eEL3x/dR9yG3NbdK+Bw44jnU+yo8V33RP0uA+8IWw5HE5AXFRu/HXofPSADINMeuBzFGRUWxRH5DNkHjAI+/RX4O/PT7v/q2+d85fLjRuN744zkbeYO6VfsLPBt9Pn4q/1cAuoGMgsTD3ESMxVHF54YMhkAGQ4YZhYXFDYR3A0lCi4GGQIG/hP6XvYF8x7wwO3569bqW+qK6l7rzuzL7kTxI/RQ97H6Kf6dAfUEFAjlClMNTA/FELQRFBLnETAR+A9MDjoM1gk0B2oEjwG6/gD8dvkv9zv1qPN+8sXxgPGs8UfyR/Ok9E72N/hO+oL8v/70ABADAwXABjkIZQk+CsAK6gq9Cj4KdAloCCQHtgUpBI0C7QBZ/9v9f/xP+1H6jfkF+bv4rvjc+EH51fmS+m/7Y/xl/Wv+bP9fAD4BAwKnAigDgwO5A8oDuQOJAz4D3wJwAvkBfwEHAZgANQDj/6P/d/9f/1z/av+H/7D/4f8TAEQAbwCQAKIAowCRAGoALwDi/4L/Fv+g/ib+rf08/dj8iPxR/Df8P/xs/L78Nf3R/Y7+Z/9VAFQBWQJdA1YEOgX/BZ8GEAdMB04HFAebBuYF9gTRA38CCQF6/9z9Pfyr+jT55PfJ9u71W/UZ9S31mfVc9nX32/iI+m/8gv6yAO8CJwVHBz0J+QpqDIINNw6ADlcOuw2tDDMLVgkjB6gE+AEo/0z8fPnO9lj0MPJp8BLvOu7q7Sfu8u5J8CTydvQv9z36if34AHIE2wcYCw0OoRDAElQUUBWpFVkVXxTBEocQwA2CCuIG+wLt/tX61PYK85bvlOwf6kvoKufI5irnUeg36s/sCPDL8/z3fPwnAd0FdQrNDsASLxb8GA8bVRzCHE4c+xrQGNsVMRLrDSoJEgTJ/nj5SfRk7/LqFufx457hMuC63z7gu+Er5Hvnlutd8Kz1XPtAAS0H8gxjElQXnRsZH60hQCPDIzEjiSHXHiwboxZdEYELOwW8/jX42/He62/muuHl3RHbV9nG2GfZN9sq3i7iJOfp7FDzK/pCAWIIUQ/YFcUb5yATJSgoCiqoKvspBCjQJHUgFBvTFOENdAbF/g73jO966BHig9z/16nUn9Ly0azSyNQ72OvctuJy6evw6fguAXsJjxEqGQ8gCCbkKnsurjBqMaYwZS61KrIlfh9KGEsQvQfk/gP2X+085dvdd9dE0mzOD8xEyxLMds5h0rfXUN775X7umfcEAXgKqxNUHC8k/SqIMKE0JzcEOC03pzSCMNwq3yO/G7oSFAkZ/xT1VOsm4tLZmtK5zF7IrsXAxJ/FRsijzJfS9tmI4g3sPPbFAFkLpBVWHyIowi/6NZU6cD1vPoo9xDowNvAvMSgvHywVdwpi/0H0bek53/bV7s1ix4XCgL9uvlm/PMIFx5DNrNUd35rp1PRyAB0MeRctIuUrUzQ1O1NAgkOnRLhDuEC8O+k0cSyVIp8X5Au//4vzqud43E3SeclDwuW8i7lRuEW5YLyNwabId9G82yjnYvMMAMUMKRnYJHUvrTg2QNRFWUmnSrFJfUYgQcI5mzDvJREaWg0tAPLyDebk2dfOPMVgvYO307NwsmiztLY/vN7DWs1p2Ljk6fGU/1ANsxpUJ9AyzDz5RBZL8E5oUHBPDUxXRnc+qjQ6KX0c1g6vAHXyl+SA15fLO8G+uGOyXa7QrMetQLEgtz2/Wcko1U/iavAK/74NFhygKfM1rUB6SRNQQ1TnVfFUZFFcSwRDmzhxLOIeVxBBARXyR+NM1ZHIer1ftImtL6l0p2ioB6w2sse6ecX80e/f5+5w/hEOUh28K904T0S2TchUTVkfWy1aflYrUGNHaTyRLz0h2xHjAdDxHuJJ08TF+rlHsPmoSqRhok+jDqeErYC2vsHozprdY+3H/UgOZx6lLYw7rUepUTJZCl4LYCJfVVu/VJFLEUCXMosjXxOSAqbxHeF50TTDvrZ5rLektZ+dnYCeWqIPqWyyKr7wy1Tb3+sS/WUOVR9cL/09xkpRVUxddmKnZMpj5V8TWYlPj0OANcgl4RRNA5bxQ+Dcz+HAyLP4qMagc5sqmQCa753bpJCuw7oYyR/ZX+pR/GgOGyDfMDBAmE2rWBRhjmbvaCFoKmQlXUlT30ZIOPQnYBYUBKDxkN9zzs6+G7HGpSidhpcNldOV0ZntoO+qi7dhxv/W5OiF+1IOuyAuMiRCIVC1W4ZkT2rgbCRsH2jvYMtW/knsOgkq1xfjBMHxBN8+zfq8t67louGZ85NJkfuRBJZHnY2nhrTQw/XUcOey+iUONCFJM9hDX1JtXqFntW12cM9vw2tvZAxa6ExqPQcsRxm6Bfrxnt49zGa7nqxXoPOWu5DijX6OjJLumW2kt7FowQTTBebY+eENhyExNEtFU1TQYGJqvXCvcx9zEG+gZwldmk+9P+otqxqXBknyXd5vyxO60KoenmCU4o3Zil2La4/mlpOhI68rvzDRp+T5+IgNtiHkNH5G+VXeYsdsZ3OHdhB2BHKAar5fEVLjQa8vAxx3B6zyQd7WygK5UKk7nCmSaYsxiJuIpYwwlAOfyqwcvXrPV+MY+BsNwCFkNW5HU1eWZM9urnX9eKF4m3QLbSliSlTZQ1QxSx1ZCCLzSd5wyjK4HaivmlCQU4nuhTyGPIrQkb6csao/u+bNF+I295wMpyGxNR5IYFj3ZXhwk3cOe8561XY/b0dkQ1adRdcygh47Carzct48yqK3N6d6mdeOoIcPhEGEM4jIj8ea2qiVuXbM6uBU9g0MbCHMNY5IH1n/ZsJxE3m5fJd8rXgZcRZm+FcsRzQ0pB8bCkH0vt45ylO3nqadmL2NU4aYgquCi4YcjiGZSKchuCvL0t929W4LESG2Nb1IkVmwZ6xyLnr8ffl9I3qYcpJnZ1mESGs1sSD3Cub0KN9nykO3U6YZmASNa4WJgX2BR4XLjM+X/KXmtgnK0d6d9MIKlyBwNa1ItlkJaDVz43rXfvN+NXu6c7toj1qiSXg2piHNC5j1sd/EynO3U6bsl6uM6YTjgLiAaITZi9CW+aTltRHJ6d3L8wsK/x/8NF9IkFkKaF1zMXtJf4R/4Xt8dI9pbVuFSlo3gSKbDFT2V+BPy9+3oKYWmLKMzoSmgFuA74NHiyiWQKQgtUbIHN0B80oJTB9bNNRHHlm1ZyZzGXtRf6t/J3zfdAtqAFwrSw84QCNgDRn3F+EGzIi4NqeWmBmNGYXTgGmA3YMVi9iV06OZtKjHa9xC8oIIfx6PMw1HY1gKZ5BynHrvfml/BnzgdC9qR1yTS5U44SMZDuX38OHnzGy5Fqhsmd+NyYVpgeCAMYREi+CVs6NRtDrH2tuQ8bQHmh2ZMg1GYFcKZptxuXklfr1+fnuAdPtpQFy6S+s4YyTEDrX44eLxzYi6PKmVmgKP3oZngsCB7YTUi0GW4aNKtP3GaNvs8OIGnxx8MdZEFla4ZEpwcnjzfKl9j3q/c21p6luhSw45xCRgD4j55uMhz9u7qaoQnIGQVojNgwmDD4bHjPyWXqSFtPPGGNtZ8A4GkBs5MGhDiFQUY51uyXZZeyx8OnmccoZoRltFS/84AiXqD1z6/uR10GK9WKzbnVuSMIqZhbqEl4cajhCYKqUCtRzH7NrX7zoFcBrTLsdBuFIiYZdsv3RbeUd6f3cYcUVnU1qnSrs4HCViEDD7J+bs0Ry/Sa70n4yUaozKh9GGhInOj32ZRabCtXrH5Npp72gEPxlNLfU/p1DjXjtqVnL4dv53YHU0b6tlEFnFSUM4EiXGEAD8XueC0wbBeLBYohOXAY9dikyJ1YvikUObr6fFtg3IAtsQ75kDARinK/Q9Wk5bXIpnkW81dFF13nLxbLljflehSJU34CQUEcz8o+g21R3D4rIEpe6Z85FRjSuMh45UlGGdaakLuNbIR9vN7tECtxbmKcg700uLWYdkc2wScUJy+29Qam9hnlU5R7E2iCRLEZH98ekF117FhrX2pxidPZWikGmPmpEil9afcKuUudXJs9uj7g8CZRULKHI5FEl4VjZh/miTbdVuumxUZ9BecFOORZc1CCRpEU7+SOvr2MfHX7grq4+g3JhNlASTCpVLmqCixK1huwvLSNyR7lcBCxQZJvY2IUYjU5pdNmW8aQxrHGn/Y9xb9VChQ0Y0XyNuEQH/pOzo2lXKa7uerk+kzZxQmPuW1JjNnb2lZLBvvXbMBd2a7qoArBITJFg0/UKST7dZHWGPZepmJWVSYJZYME5yQcAyjSJYEaj/BO733ATNpr5MslWoC6GnnEib9pykoSupTrO+vxjO7N2+7goASxH7IZkxqz/HS49VuVwQYXJi2WBRXABVIUsDPwMxkiEmEUEAZu8X39LPDcIztp2slKVNoeifbaHOpeisgbZNwu/P+9797nj/6Q/UH70uMDzHRyhRDVhDXKpdOVz+Vx1Ry0dUPBIvbSDYEMwAx/BE4bvSnMVMuiKxYqpAptikNaZIqvGw+rkaxfrRNeBa7/T+iQ6hHcgrjziVQ4ZMHlMsV5NYS1deU+9MMERoOe0sHx9tEEcBJvJ847zVT8mWvuG1cq95qxOqSasOr0S1t70kyDnUl+HU74L+LQ1lG7woyzQ1P6xH703RUTRTEVJzTnlIUkBBNpQqqR3kD7EBgPO85dHYI80Kw9S6vrT2sJavprAdtNy5tcFpy6vWIuNs8CH+1gsiGZ0l6DCsOqBChkg0TJBNkkxCSb9DNDzfMgooCRw9DwgC0/QB6PjbE9Gmx/i/Q7qwtlq1R7Zvube+8sXnzk7Z1eQi8dT9hwraFm8i6yz/NWU96EJdRq1H0EbPQ8Q+2TdGL08lQhp4DkwCHfZK6i3fHdVlzEjF+r+kvFu7KbwCv9DDasqb0iDcsOb28Zr9QQmSFDQf1ygwMQI4GD1PQI5B0UAePo05RTN4K2UiVBiVDXsCXveT7G3iOtlB0b7K4MXLwpXBRMLRxCTJGs+C1iHfsejo8nb9BghKEvAbsCRGLHoyHjcPOjs7mTozOB00ei54J08fQBaTDJQCkvjZ7rTlad041lbQ7sshyQLIlsjWyq/O/tOa2k3i2Or582f92QYGEKcYeyBFJ9Ms/TCkM7Y0LzQUMnkufSlIIw4cBxR0C5gCufka8f/opOFD2wrWINKgz5zOGM8O0WvUFNng3qLlI+0n9W/9uQXIDVsVOhwxIhMnvCoTLQguly3FK6UoUSTtHqQYqxE3CoUC0fpU80vs6eVf4Nbbb9hD1l7VxdVy11XaVd5R4x/pkO9y9o39qQSSCxAS8xcPHT0hXyRhJjQn1iZNJaYi+h5oGhUVLQ/eCFsC2PuF9ZTvMuqG5bXh194D3ULcl9z+3Wjgv+Po58DsHvLZ98L9qwNnCckOqRPjF1cb7R2UH0Eg8x+vHoEcfhm+FWMRjwxpBxoCzfyq99jye+616qDnUuXb40LjieOs5J7mTumj7IPwzPRc+Q/+vwJJB4kLYQ+yEmcVaxeyGDUZ9BjyFzsW3xPzEJAN0gnYBcIBsP3B+RP2wvLm75Tt2+vF6ljqlep36/Ps/O5+8WX0l/f6+nP+5gE6BVQIHQuBDXEP3hDBERYS3REbEdkPIw4KDKAJ+gYtBFIBf/7I+0P5AvcW9YvzavK78X/xtfFY8mHzxPR09mL4fPqx/O/+IgE7AysF4gZVCHsJTQrHCuoKtgowCmAJTwgHB5YFBwRqAssAOP+9/WT8N/s++n/5/Pi3+LD44/hL+eP5o/qD+3j8e/2B/oH/cwBQARICswIxA4kDvAPKA7YDgwM3A9YCZwLvAXUB/QCPAC0A3f+e/3T/Xv9c/2z/iv+0/+X/FwBIAHMAkgCjAKMAjwBmACkA2v96/wz/lv4b/qP9M/3Q/IL8Tfw2/EH8cfzG/EH94P2f/nr/agBpAW8CcwNqBEsFDgaqBhcHTgdMBwwHjgbUBd8EtwNhAugAV/+5/Rv8i/oW+cv3tPbf9VP1GPUz9ab1cfaQ9/34rvqa/LD+4gAfA1UFcgdlCRoLhQyVDUEOgQ5ODqkNkgwPCyoJ8AZwBL0B6/4P/EH5l/Yn9AbySPD77i7u6e0y7grvbPBR8qz0bveC+tH9QgG8BCMIWgtIDtMQ5xJvFF4VqRVLFUMUlxJREIANOAqRBqYClv5/+oH2vfJR71rs8eks6BvnyeY753PoaOoO7VPwIPRZ+N78jAFABtUKJQ8PE3EWLxkyG2ccwRw7HNUamRiUFdsRig3BCKIDWP4I+d3zAO+Z6szmt+N34R7gvN9U4ObhaeTM5/bryfAi9tj7vwGqB2oN0hK3F+8bWh/YIVUjwiMYI1ohkh7UGjkW5RD+CrEEMf6s91fxZOsD5l/hnt3h2j/ZyNiC2Wvbdt6P4pjnbO3f88D62wH4COEPXRY7HEkhYCVcKCQqpirdKcsnfiQMIJYaRRRHDdIFIP5r9vDu6ueR4RjcrNdx1IPS9NHL0gXVk9hc3T3jCuqR8Zj54AEqCjYSxBmYIHsmPiu4Ls0waDGEMCQuVyo4Je4epheZDwIHJv5H9avsl+RJ3fzW5NErzu/LRcs1zLvOxtI42OreqeY872D4zwFAC2oUBR3LJIEr7jDnNEo3AjgHN140FzBTKjwjBhvxEUEIQv5A9Inqa+Es2Q/STMwUyInFwcTGxZLIE80n06LaS+Ph7Bv3qAE5DHoWHCDRKFUwbDbkOpc9bz5hPXQ6uzVZL30nYh5NFI0JdP5W84zoad4+1VPN6cYywla/br6Dv5DCgMcuzmnW89+E6sn1bAEUDWUYByOmLPY0tDuqQK5DqESMQ2FAPDtENKwrtSGrFuQKuv6J8rPmlNuC0c7IvsGJvFy5ULhyubq8EsJSyUXSptwm6G30HAHSDSsaxiVIMF85wkA0RotJqUqCSR9GlkAQOcUv/SQIGUQME//a8QHl7Nj7zYLEz7wet5+zbrKYsxW3zryXxDfOZdnL5QnzuQByDskbVii1M409kUV/SydPbFA/T6lLw0W4PcUzNShhG6wNgP9J8XbjddaqynPAIbj2sSSuy6z5raaxuLcCwEbKNdZ0457xRAD1DkAdtSrpNn1BHUqFUH9U7FW+VPxQwEo5Qqc3XCu0HRkP/v/U8BPiLtSTx6O8trMTrfCobqecqHKs1rKXu3TGGdMm4S7wvv9cD5Ae4izjOSxFZE5CVY9ZJlv5WRBWh0+NRmg7bC79H4oQiwB88NjgGtK2xBW5k697qAakWaKEo36nLK5at8XCFNDi3rzuKP+mD7cf3S6hPJhIY1K0WVFeFGDtXuNaE1SxSgQ/ZDE7Iv0RKAFA8MXfOdAXwsy1uqsxpGyfk522ns6ivalQsz6/Ks2s3ErthP7UD7UgozAhP75LFVbWXcNismSUY29fYlihTnZCPzRqJG8T0wEf8Nrejc62v8qyL6g3oCSbHpk2mmaekKV9r+G7XsqF2tnr0/3oD4whNjJjQZxOelmmYeBm/WjrZ7FjblxYUrxF+zaHJt4UigIY8BjeFc2VvRCw8qSSnDKX/pQJlkuap6Hjq7O4tMdy2GzqF/3hDzoikzNkQzBRjVwgZaVq8Wzua6RnM2DSVdFIlDmQKEkWSwMs8H3d0su0u6GtCKJEmZqTN5ExkoCWBp6IqLe1LcV01gXpUfzCD8EiuzQkRXpTTl9CaBBuinCab0ZrrmMNWbNLBzyCKqwXFQRX8ArdxMoVun6rcZ9Oll2QzY2zjgqTsZpvpfCyzsKO1Kbng/uKDyEjrzWiRndVumEKax5xxXPqcpFu3GYFXF5OUj5bLAUZ5gSa8L3c7Mm4uKepMJ20k3+NwYqRi+qPq5eaomKwmcDD0lLmsPo8D1sjbTbeRydX0GN2bcxzoXbddYRxuWm2Xs9QcEAXLlMavAXy8JfcSMmctx6oRZt4kQGLFojOiCSN+JQNoA+ukr4W0Qvl2PnaDm8j9jbYSIpYj2WEbxh2GXlveBx0QmwdYQNTYEK1L5MblQZf8ZXc2cjDtuOmspmZj+eI0IVthruKmZLLnfururyIz9Pj/vhjDl4jTDeQSZ1Z9mYycQF4LnueelZ2dW45Y/hUH0QyMcMccAff8bfcncgttvald5gajjCH74NwhLKIk5DXmyeqFbsdzqziI/jaDSsjbjcGSmNaBWiBcoV523xofC94T3AGZapWqkWLMuAdSghw8vzclMjXtVillZf8jN+FdILZggmH5o4ympeopLnWzJnhSvdBDdUiXjc7Stpaumhvc6R6In7MfaZ5znGCZhhYAEe/M+keIQkR82LdvcjDtQelDJc/jPOEYoGpgcSFlY3fmEynari3y5vgdPaZDGAiHTcwSgRbF2n8c1x7/37Ifrl68HKrZz9ZHUjKNNwf9Qm/8+fdFsnutQSl25bii26EuoDhgOOEoozgl0imarfAyrTfpPXlC8shrDblSeFaHGkodK57c39cf2d7tXN/aB5aAEmsNbYgwQp69IrenslYtk2lA5fni1CEeoCCgGiEDYw3l46lpLb0yefe2/QlCxkhDDZcSXJayWjzc5h7fn+Gf7B7GXT9aLNaqEliNnYhhgs/9UrfVMoAt+KlgZdLjJmEpICNgFKE2Ivklh+lG7ZUyTXeG/RcCkwgQDWWSLhZIGhecx17H39Gf5J7HnQlafxaEkrqNhkiQQwL9iTgNsvjt8GmVZgQjUeFOIEBgaSEBIzplvyk0LXjyKDdZ/OMCWUfSTSWR7RYIWdrcjx6V36efg17wXP0aPlaPUpDN54i7wzf9hfhQcwAueinfpkyjlqGNILegVyFkYxGlyalxbWiyCvdv/K2CGceKjNcRmlXzmUacfd4J32MfSF6BHNraKhaKUpsNwQjkA229yDidc1Wulap+pqxj9GHmIMlg3uGf437l52l+rWSyNXcJvLdB1Qd4zHrRNlVKWRtb053kHsRfNB45nGJZwpa00ljN0kjIQ6Q+D3jzs7huwirx5yLkaqJYoXShP+Hzo4KmWOmcLa0yKLcnfECBy0ceDBGQwVUNWJnbUV1k3kwehl3Z3BOZh1ZPUknN2sjoQ5r+W3kS9Cgvf2s456+k+SLkYfnhuiJfJBxmnanKLcKyZHcJvEoBvUa6i5uQfFR818Ia9xyMnfqd/90iG67ZOJXZEi4NmkjDg9E+q3l6dGQvzGvSqFHlnyOI4pfiTSMipIwnNmoIriTyaXcxPBQBa4ZPS1nP55PZl1VaBZwb3Q/dYFyTGzQYllWSEcUNkMjZg8a+/vmpdOuwaKx+6MkmW+RFo07jOKO9pRGnomqXrlRyt/cdvB9BFoYcisyPRBNkVpPZfZsTXEzcqNvsmmPYIJU60U8NfYiqQ/r+1ToftX5w02086ZSnLqUZpB2j/CRvpeyoIas3LpFyz/dPvCvA/sWjCnTOklKeFf6YYBpz23JbmZsvGb4XV9STEQuNIMi1A+1/LfpcNdsxi+3LqrNn1yYEpQPk1uV4Jpzo9CunLxtzMbdH/DpApQVjidMOE1HHVRZXrVl92kCa85obmMOW/BPa0LrMugh6A92/SLredkFyUS6qK2So0+cFZgDlyCZWp6GpmSxnb7KzXTeGPAtAicUeyWiNR9Eg1BwWpphymXiZtxkyV/RVzZNSkBzMSYh4Q8t/pHsltvBy4q9X7Gep5GgbJxOmz2dKqLrqUK038Bdz0vfLPB8AbYSVCPWMsJAsExDVjJdSmFtYpRgz1tGVDRK6T3HLzsgwA/Y/gPuxd2czvzAT7Xtqx2lE6Hsn6+hTKadrWi3X8Mj0UngWvDYAEQRHiHsLzs9pkjUUYJYfFynXfpbhVdtUOtGSjvmLSgfhA91/3bvA+CU0ZjEc7l5sPCpB6bapHGmvqqcsdS6HcYe03DhpPBCANIP2h7nLIw5aUQqTY5TZFeSWBBX7VJKTF1DbTjSK+0dLA8CAOfwTOKk1FnIx71AtQSvQqsUqoGre6/jtYO+F8lL1cDiC/G8/2IOjBzLKbo1/j9ISFpOB1I1U9xRCk7fR40/VjWLKYoctw6AAFTyn+TL1zvMSMI9ule0wbCUr9mwgbRvunPCS8yq1zfkjvFH//cMNRqbJskxaTsyQ+tIaEySTWFM4kgxQ347BTIUJ/8aJg7tALzz+OYD2zvQ8MZqv+G5frZXtXW2zLk+v6HGuM862tXlLvLk/pML2hdaI7strjbuPUZDjkawR6RGd0NCPjI3fi5sJE0Zdw1GARz1VelL3lTUvMvExJ+/dLxYu1K8Vr9MxArLWtP53Jvn7PKU/jcKfBUNIJcp0jGAOHA9fUCSQapAzj0XOa0ywiqXIXQXqgyMAXP2s+ue4YPYp9BFyozFn8KRwWnCHMWUyavPL9fk34bpx/NY/uUIHxO1HF4l2SztMm43Ojo/O3c67De0M/Et1CaVHncVwAu+Ab/3D+765MTcrdXpz6LL+cj+x7bIGcsSz3/UNdv74pbrwPQx/qAHxBBXGRYhyCc6LUUxyzO7NBE01TEcLgQptyJpG1UTuQraAf74Z/Ba6BLhyNqp1dzRfM+YzjPPSNHC1IXZaN885srt1vUg/mgGbw72FcMcpCJtJ/sqNS0MLn0tjytUKOgjbx4WGBARlQngAS76ufK762rl9N+C2zXYI9Za1dzVpNeg2rfexeOj6R/wCPcl/kAFIQyUEmgYcR2KIZUkfiY4J8EmHyViIqEe/RmcFKkOVAjPAU/7AvUb78fpLOVu4abe6Nw+3KvcKN6m4BHkSegu7ZbyVvhB/igE3gk3DwoUNBiXGxoerB9EIOIfiR5JHDQZZxUAESMM+AaoAV78P/d28iXubOpn5yvlxeM/45njzuTR5o/p8ezb8Cv1wPl0/iIDpwfgC60P8hKYFY0XxRg3GeUY1BcPFqYTrxBDDX8JgQVqAVr9b/nI9YDyr+9p7b3rtepX6qLqkesZ7S3vuPGm9N33RPu+/i8CfgWSCFQLrw2UD/YQzhEXEtIRBRG4D/oN2QtpCb8G8QMVAUP+kPsQ+db28fRu81fysvF/8b/xa/J78+b0m/aO+Kv64fwe/1ABZgNSBQMHcAiPCVoKzQroCq0KIQpMCTUI6gZ1BeUDRwKpABj/n/1J/CH7LPpy+fT4tPiy+On4Vvny+bX6lvuN/JH9l/6W/4YAYQEgAr8COgOPA78DygOzA34DMAPNAl0C5QFqAfQAhgAmANf/mv9x/13/Xf9u/43/uP/p/xsATAB2AJQApACiAIwAYgAjANP/cf8D/4v+Ef6Z/Sr9yfx9/Er8NvxE/Hf8z/xN/e79sP6N/38AfwGFAogDfQRdBR0GtQYdB1AHSQcEB4EGwgXJBJwDQwLIADX/lv35+2r6+fix96D20PVK9Rb1OfWz9YX2q/ce+dX6xfze/hEBTwOEBZ4HjAk8C58MqA1LDoEORQ6WDXUM6gr+CL0GOASBAa7+0/sG+WD29vPd8Sfw5e4j7urtP+4i74/wf/Lj9Kz3xvoa/o0BBgVpCJsLgg4EEQ0TiRRrFagVOxUlFGwSGhA+De0JPwZQAj7+KPou9nDyDe8h7MXpDugM58vmTueV6JnqTu2f8Hf0t/hB/fEBpAY0C30PXROyFmIZVBt3HL4cJRyuGmAYSxWFESgNVggzA+b9l/hx853uQuqC5n/jUeEN4L/fbOAT4qnkHehX7DfxmfZV/D4CJwjiDUATGBhBHJgfAiJpI74j/SIpIUseehrOFWwQewooBKX9IvfT8OvqmOUG4Vrds9oq2czYn9mh28Pe8eIN6PDtbvRX+3MCjglwEOEWrxyrIasljig7KqIqvSmRJyokoR8XGrUTrAwvBXv9yfVV7lznE+Gv21vXO9Rp0vjR7dJE1e3Yz93G46XqOPJH+pEC2QrdEl4aICHsJpUr8i7oMGQxYDDgLfYpvSRcHgEX5g5HBmj9jPT56/LjuNyD1ofR7M3Ry0rLXMwDzy3Tu9iF31nn+u8n+ZkCBwwoFbMdZiUCLFExKjVqN/433zYSNKovyCmXIkwaJxFuB2z9bfPA6bHgiNiG0ePLzcdnxcXE8cXiyIbNutNQ2xDktu3694oCGA1PF98gfinmMNw2Lzu8PWs+NT0gOkI1vy7GJpMdbROiCIX9a/Ks55vdiNS7zHPG4sEvv3K+sb/owv7H0M4p18zgbuu/9mYCCg5QGeAjZS2WNTA8/kDXQ6VEXEMGQLg6mzPjKtMgthXjCbT9iPG+5bHautAnyDzBMbwwuVO4o7kYvZvCAcoV05HdJul59SwC3g4rG7ImGTEOOklBkUa5SaZKUEm9RQhAWjjtLggk/xctC/j9w/D34/bXIc3Mw0G8vrZus3CyzLN6t2C9VMUYz2Pa3uYq9N8BlA/eHFUplzRLPiRG40taT2pQCk9CSyxF9jzdMi8nRBqADE/+HPBX4mzVv8muv4e3jbHvrcysL64RslS4y8A1y0TXm+TT8n4BLBBpHscr3DdJQrxK8lC3VO1Vh1SOUB9KakGxNkQqhBzaDbn+k+/h4BPTmMbQuxGzoqy1qG2n1KjirHqza7xxxzjUX+J38QwBphDLHwYu5joGRg9PuFXLWShbwFmeVd5OtEVkOkUtvB45DzT/KO+T3+7QrMM1uOOuAqjHo1aivaPzp9iuObjQw0PRLOAX8IgAAxEFIREwsz1/SRhTMlqTXhhgs15sWmNTzUnzPS4w6SCaEL//2u5u3v3O/cDftACrr6Mnn46d8J5Ho3GqObRVwGfOBd617vb/QhEUIugxQkCyTNVWXF4KY7hkWWP1XqtXtE1aQfwyCSP8EVgAqO5z3UDNjr7QsWqnrp/bmhaZcZrjnkmmbrADvajL7dtU7Vb/ZhH7IoozkUKcT0NaM2IsZwVpsGczY7FbYlGVRKs1GCVcE/8Aku6h3LrLX7wLrySkAZzklvSURJbKmmai3azfuQrJ59n166n+bxG4I/U0n0Q8UmBdtWX2avxss2skZ3Ff1VShRzk4FSe4FLIBl+7422nKcrqQrDChrJhGkyuRbZICl8qeiKnsto7G9deb6vH9XRFNJCs2bEaQVCpg3mhmbpdwXm/DauhiClh6SqI6+ygPFm8Cte5420/Jx7hiqpGesJUFkL6N7o6Nk3mbdaYttDjEGtZJ6S/9MxG5JCo39UeXVp9irGt5cdVzr3INbhJm/FoeTeM8yipdFzQD7O4g22vIX7eCqEecD5Mija+Ky4tvkHaYpaOmsQzCWdT/52f88BD+JPM3O0lRWL1kHm4rdLR2o3X/cO1oqF2IT/o+fiyiGAAEOu/u2r3HO7bwplWazJCgigKICImqjcWVHaFZrwvAs9LB5pj7lxAcJYY4Pkq7WYNmMnB8di95NniWc3RrDGC3UeRAFC7ZGdAEnu/k2kXHWbWtpbuY6I6BiLmFpYZBi2mT3p5IrTm+LdGQ5cb6KRAUJeQ4/krWWvBn5nFpeEZ7Z3rQdaVtJWKoU55Ciy8CG6QFFvD+2gHHu7S6pHqXZI3GhtSDp4Q3iWOR7Jx4q5i8x89w5PL5pw/oJA05e0uiWwRpOXPxefd8M3yqd39v8GNXVSVE3zAaHHgGoPA82/LGX7QWpJOWQYxxhVeCDYOOh7aPSJvqqSq7hM5h4x75FA+YJAM5tksfXL9pLHQTe0B+mX0ief5wa2XEVnhFDzIfHUsHPPGd2xXHRrTAowWWgIuChEOB24FHhmWO9pmgqPO5Z81m4kz4cA4mJMY4r0tNXCBqvHTPeyB/l343eiFylGbqV5RGGTMQHhsI5/Ef3GvHbbS5o9GVIIv6g5eAEYFkhXCN9pidp/O4ccyB4X73vg2VI1g4Z0stXChq63QjfJd/LX/neudyamfKWHhH+jPpHucIoPLA3PHH1bQApPWVIYvZg1WAr4DnhNqMS5jipiy4pcu04Lb2/wzkIro34ErAW9hpuXQRfKV/Wn8ye05z6mdgWSFIsTSpH6wJZfN/3abIe7WTpHGWhIsfhHyAt4DPhKOM9pdwpqG3BMsB4PX1NQwXIu82G0oHWzBpJ3SYe0h/HX8Xe1VzFGitWY1IPDVPIGgKM/Ra3ojJXrZxpUWXRozLhA2BKYEdhcuM95dKplO3kMpp3z71YwsvIfc1GkkEWjFoNXO5eoN+d36Vevxy52etWb1ImTXYIBoLCfVP35XKfLeZpm6YaI3dhQeCBILShVSNUJhvpkO3Ssrv3pL0igouINU030e4WN9m5HF1eVV9aH2teUNyY2diWa1IxzVCIb8L5fVc4MzL1LgIqOqZ545Th2mDR4Pthj6OAZnhpnK3NMqU3vPzrAkWH4oza0YmVzllOHDNd7978XtgeClxhmbJWF5IxDWOIVYMxfZ+4SnNY7q9qbmbwZAriTKF8oRtiIiPCZqgp+K3T8pZ3mTzygjpHRoywURPVUJjMG7EdcR5EnqtdrBvUWXjV85HkDW3Id4Mp/e14qzOJ7y2q9ed9ZJli1+HA4dSijGRaputqJK4nMpA3uXy6AepHIYw5EI2U/1g0Gtbc2V3z3eXdNdtxWOwVv5GKjW/IVQNifj941HQHb7vrUKggJX8jfCJeYmZjDmTIp0HqoK5HMtK3njyBwdZG9Au1UDdUG1eGmmVcKN0J3UecqBr4mEwVe1FkTSjIbcNaflV5RbSQ8BmsPeiYJjwkOGMUYxDj56VMJ+tq7W6z8t53h/yKAb7GfwsmD5JTpNbEmZ0bYJxHnJFbw1pqV9iU5pEwzNiIQYORvq55vnTlsIYs/SlkZs9lDGQio9Lkl+Yk6GhrSi8t8zM3tvxTgWQGAsrMDx6S3NYuWL8aQVut24NbB9mG11JUQZDwjL8ID8OHfsp6PfVFMUCtjWpEZ/hl9yTIJOxlXqbS6Tfr9y90s1F367xewQcFwEpnzl1SBFVE18vZi1q82p6aNhiOlrlTjJBjTFwIGAO7Pug6QzYuMchubes26LWm9+XEZdymeyeVadostC/Ic/l35jxrwOhFeEm6DY9RXBRJVsRYgBm1maNZDpfCFc4TB8/JDC9H2oOs/wf6zjagMpxvHew7aYboDacWZuKnbSir6o6tQPCpNCr4Jvx7gIhFKwkEDTWQZRN8VamXYBhY2JLYElbh1NCScw8hy7jHloObv2h7HXcac3vv2+0QaurpN6g9Z/2oc6mV65TuHTEW9KZ4bjxOQKdEmYiGDFCPoBJfFLyWLFcn122WwdXuU8GRjw6tyziHTAOHf4l7sPeb9CXw5241a+CqdOl4aSzpjirSrKxuyPHRNSu4u/xkAEZEREgBC6HOjlFyk35U5hXjFjRVndSoUuGQm83tCq5HOsNv/6p7x7hj9Nlx/y8pLScrg+rGaq9q+2vhrZTvw3KYNbq40Ly9gCWD7Ed2CqnNsNA4EjATjlSMFOjUZ1NQkfFPmk0gChpG4sNUf8q8YPjxtZWy4nBqbnzs5CwmK8Qseq0B7s0wzDNrNhN5bDybAAXDkgblyemMiI8wUNMSZhMkE0tTH1IoELEOikxHCbzGQ4N0/+m8u/lEdplzz7G4L6DuU+2WbWntiy6yr9Tx4vQKNvX5jvz9P+eDNgYRCSJLlo3cz6gQ7tGr0d1RhtDvT2HNrMtiCNWGHUMQQAc9GHoa92P0xfLRMRIv0i8Wbt+vK2/y8StyxvU092H6OPzjv8sC2YW4yBUKnAy+zjEPadAkkGAQHs9njgSMgoqxyCTFr8LngCJ9dTq0eDP1xDQ0Mk8xXbCkcGQwmvFBso+0N7XquBc6qf0O//ECfMTeB0KJmktXTO6N2E6QDtSOqI3RzNmLS4m2h2sFOwK5wDs9kbtQeQh3CTVfs9Zy9PI/MfZyF/LeM8D1dHbrONV7Ij1/P5nCIIRBRqwIUkony2KMe4zvDTxM5QxvC2JKCUixBqhEv0JHAFD+LXvtueC4E7aS9Wb0VrPls5Sz4XRHNX42fHf1uZx7oX20v4XBxUPjhZKHRUjxSc4K1QtDi5hLVcrASh9I+8dhhd0EPIIOwGM+R/yLevs5IvfMNv81wXWWNX21djX7doZ3zvkKOqv8J73vv7VBbAMFxPcGNId1iHJJJkmOiepJu8kGyJGHpEZIhQlDssHRAHF+n/0o+5d6dTkKeF33s/cPdzA3FPe5+Bj5Kzone0O89P4wP6kBFQKow9qFIUY1RtFHsIfRiDOH2IeDxzqGA4VnBC3C4gGNwHu+9b2FfLP7SXqMOcF5bLjPuOr4/HkBOfR6UDtM/GL9SP62f6EAwQINQz4DzETyRWvF9YYOBnWGLUX4RVsE2oQ9gwsCSsFEwEF/R75fvVA8nnvP+2h66fqV+qw6qzrQO1f7/Px6PQl+I77CP93AsMF0QiKC9wNtw8OEdkRFhLGEe4Qlw/QDagLMgmEBrQD2AAI/lj73fiq9s30U/NF8qnxgPHJ8X7ylvMH9cP2ufjZ+hH9Tv9+AZEDeAUlB4sIpAlnCtMK5wqlChIKNwkcCMwGVQXDAyQChgD3/oH9L/wK+xr6Zfns+LL4tPjw+GH5AfrG+qr7o/yn/az+q/+ZAHIBLwLLAkIDlAPBA8kDrwN4AygDxAJTAtoBYAHqAH4AHgDR/5b/b/9d/17/cP+R/7z/7f8fAFAAeQCWAKQAoQCJAF0AHQDL/2j/+f6B/gf+kP0h/cL8d/xH/Db8R/x9/Nj8Wf39/cL+of+UAJUBmwKdA5EEbgUrBr8GJAdSB0UH+wZzBq8FsQSBAyUCpwAT/3P91/tK+tz4mPeM9sL1Q/UW9UD1wvWb9sf3QPn9+vD8DP9BAX4DsgXJB7IJXAu5DLkNVA6ADjsOgg1YDMUK0QiKBgAERgFx/pb7zPgq9sbztPEI8NDuGe7s7UzuO++08K3yG/Xs9wv7Y/7XAU8FsAjdC7wONREyE6IUdhWlFSoVBxRAEuIP+wyiCe0F+gHn/dL52/Uk8sru6eua6fLnAOfO5mLnuejN6o/t7fDO9Bb5pP1VAgcHkwvUD6kT8haTGXQbhRy6HA4chBolGAEVLRHFDOsHwgJ0/Sf4B/M77uvpO+ZI4y3h/d/D34bgQeLr5HDoueyl8RH30vy8AqMIWA6tE3gYkBzVHyoieiO5I+Ei9SADHh4aYhXyD/cJngMZ/Zn2UfBz6i7lruAX3YfaFtnS2L/Z2dsT31bjg+h17v707fsLAyQK/hBkFyIdCiL0Jb4oUCqcKpspVCfUIzQflxklEw8MjATW/Cb1u+3O5pfgSNsM1wfUUtL/0RLThtVK2UTeUORA6+Dy9vpDA4gLgxP2GqYhXCfqKyovATFdMTkwmi2TKUAkxx1bFjIOiwWq/NHzR+tP4yncDNYs0bDNtstRy4XMTc+W00DZI+AK6Lrw7/ljA84M5RVhHv4lgCyyMWo1hzf3N7M2wzM7Lzsp8CGRGVwQmgaV/Jry+Oj53+fX/9B8y4nHSMXNxB7GNcn7zU/UANzW5Izu2fhtA/YNIhiiISkqdDFJN3c73T1kPgU9yTnHNCIuDCbDHIwStweX/ILxzebP3NTTJcwAxpbBDL95vuO/QsN/yHPP69em4VrstvdfA/8OORq2JCIuMzapPE5B/UOeRClDpz8yOvAyGSrvH8AU4Qiv/IjwyuTR2fTPgse9wN27CblauNi5er0nw7PK6NN/3ibqhvY9A+oPKRycJ+gxujrOQelG4kmgShlJWEV3P6E3ES4RI/MWFgrd/K3v7eID10rMGMO3u2G2QrN2sgS04rf3vRPG+89j2/PnTPUFA7UQ8R1SKnY1Bj+0RkRMiE9lUNFO1kqRRDA88jEmJiUZVAsf/fHuOuFl1NjI7L7ytiixvq3RrGqugLL0uJfBKMxW2MPlCfS5AmERkB/WLMw4EkNYS1tR6lToVUtUHFB7SZhAtzUrKVMbmgx0/VTusN/70aDFALtxsjWsgKhwpxGpVq0jtEO9csha1ZnjwPJaAu8RBSEnL+Y73Ua1TylWA1olW4NZJ1UxTtZEXDkcLHkd5g3c/dXtUN7Ez6TCWLc4ro2njaNZovyjbKiIrxy53sR00nfhcvHpAV4SUSJEMcI+Y0rJU6pa0F4XYHNe8FmvUuZI3jz2LpYfNg9V/nXtGt3Czee/9bNKqjOj6J6OnTCfxKMpqya1b8Gnz2DfIfBoAbASciMqM2BBok2QV9xeS2O5ZBljdV7wVsJMOUC2MaYhhxDe/jLtDtz2y2q92rCqpiqfl5oVmbKaZJ8Ip2SxKb71zFfd0O7YAOQSaCTbNL1DmFAIW7ticmcIaW9nsGLwWmhQakNYNKcj2BF2/w3tLdthyi67Ca5ao3abm5bxlIaWT5sqo9utD7tiyl7bgO05APwSNCVVNtdFQ1MvXkRmQmsAbXFrnmarXtRTbEbaNpclJxMZAAPtddoDyTS5hKtdoBmY+ZIlka6SiZeTn46qJbjyx3nZM+yQ//gS1iWXN7BHolUBYXRptm6fcB1vO2odYgFXPUk5OXIncRTJABTt6Nndx363TKm2nRaVs4+1jS+PFpRGnICnbrWmxanX7Orb/toSTyaiOERJs1d+Y0lszXHfc25yg21DZe5Z2UtyOzYptBWCAT7thNntxgu2Yqdkm3CSy4ykigyM+ZBHmbak7rKBw/HVreke/qMSnyZ2OZRKdVmkZcFuhHTAdmJ1dHAaaJVcPU6BPeEq7xZEAoLtSNk1xt20yKVrmSeQRYr0h0eJNY6ZljGiprCIwVTUeOhZ/VQSxyYSOqBL6FpxZ9pw2XY+efd3CnOfavZeZ1BkP3AsHhgLA9ztNNm0xfOzfaTKlz2OIoioheSGzYs+lPafm668v9TST+eP/O4RyCZ5OmhMClzlaJNyynhYeyl6RHXPbAxhVFIZQeEtQBnXA03uRtloxU6zg6ODlrSMY4bBg+SEw4k5kgaezqwfvnPRNObC+3MRoiapOuxM3Fz+aexzVnoMffd7HneobtViAFSdQjEvUxqlBNHuftlTxeyy2aKWlY2LCoVBgkmDGYiNkGScQqu1vDTQKuXz+uUQWCalOixNXl2+auJ0fHtYfl99mHgncE9ka1XtQ14wVBt0BWjv2tlxxc2yfqIElceKGIQqgRSC0YY7jxKb+ql/uxrPMuQl+kYQ6yVsOilNkV0ja3d1O3w7f19+rnlLcXllkVYIRWUxQhxCBhDwWNrDxfGydKLMlGSKjIN7gEiB7IVFjhKa9qh/uiXOT+NZ+ZYPXCUBOuVMdF0va6l1kny1f/d+YHoTck9mcVfrRUYyGh0MB8fw99pGxlWzt6LtlGKKaYM2gOSAbYWtjWWZOqi4uVnNguKR+NgOrSRmOWBMCl3hanp1gnzFfyd/rXp8ctJmCViVRv4y2x3RB4vxttv6xvqzSaNolcOKrINbgOmAUoVzjQ2ZxqcrubbMzuHP9w4O4COaOJxLUlw6aul0DHxrf+1+lXqGcv9mWFgFR4szgx6PCFvykdzdx920JqQ6loOLVoTqgFiBnYWZjQuZnKfauD/MNOEW9zoN9iKhN5tKT1s9afhzLnuofkl+F3oxctVmXVg4R+wzDx9ECTTzid3syP21TqVil6SMZoXhgTCCT4YejmCZvafGuPXLteBm9l0M8iF9Nl5JAlrqZ6ly7Hl8fT19Mnl8cVVmFlgtRx80fx/uCRT0md4lyle3v6bfmCKO24ZBg3CDZocDjwyaKqjvuNnLVODC9XoL1iAuNedHblhDZvxwRnjoe8l76XdncH5lg1flRiM00B+LCvr0wd+Iy+m4d6ivmv2Ps4gIhRiF4ohHkA6b4qhXue3LE+Ar9ZIKox+5MzlGk1ZKZPNuPXbvee55O3bzbk9kpFZdRvczAiAbC+P1/+AQzbK6c6rQnDKS7IozhyaHworrkWic6Kn/uTLM8d+k9KgJXB4eMlZEdlQCYpJs1HORd613KXQgbcpieVWVRZkzEyCaC872T+K8zq+8saw+n7+UhI3DiZiJBY3tkxmeOavnuqjM8d8t9L0IAx1hMEFCGFJuX9ppDnHRdAl1tXHvau9gAlSNRAozAiAIDLj3sOOK0Ny+Lq/4oaGXeJCzjG6MqY9Llh+g16wPvFDNFODJ89MHmhuDLvw/fU+PXM5m7G2xcQNy4W5jaL5ePlJFQ0kyzR9iDKD4H+V30jjB57H7pNaaxpMCkKOPrZIFmXqiwK53vSvOW+B48+0GJBqILIo9p0xqWXJjcmo0bp5urmt8ZTlcL1C9QVQxdB+oDIT5m+aA1L/D2bRCqFmea5eskzeTDpYZnCil9LAevznPxuA98wwGoxhyKu86mUkCVshfo2Zeat1qIGg8YmJZ1032Py0w9h7ZDGL6Ieii1m7GAbjLqymiY5uulyWXyZmEnyiocbMFwXrQV+EY8zIFGhdEKCw4WEZZUtVbgmIwZsNmOWSnXjpWNUvwPdIuUx7yDDj7runb2ELJXLuSr0Cmq58GnGqb251Eo3irNrYqw+7RDeIK82AEihUBJkc15UJ0TptXFF6wYVNi/F++WsNSTEisO0UtiR3zDAX8QOso2zjM5b6Ts5uqPqSuoASgQqJVpxWvQrmNxZTT6uIW85kD9hOrI0EyRj9XSiBTXVnhXJFdbFuEVgBPHkUrOYYrmhzbDMb81eyG3U3PmcLLtzavGamjpe2k+aa2q/2yk7wsyGzV7eM6894CYBJGIR4vfjsGRmZOYFTGV4JYjlb9UfRKrEFvNpUphBupDHv9a+7y333SdcY1vAy0N67hqiKq/qtjsC23JsAFy3bXFuV58zACyhDUHuIrkDeEQXRJIk9mUihTZVEsTaFG+T15M3MnSBpdDCH+APBp4sTVdMrNwBq5k7NjsJ+vS7FXtaK7+MMYzrDZZebT85IBNw9YHJAogTPXPExEqEnETIlN9EsVSAtCBzpLMCIl5hj1C7j+kfHp5CDZks6OxVq+Krkktl+13baQulnACMhh0Rjc2udJ9AMBqA3VGSslVS8DOPU+90PlRqpHQka8QjU92jXnLKIiXhdyCz3/HPNu543cy9J1ysfD9L4gvF27rrwIwE3FUszf1K/edOna9IYAIQxOF7ghDysMM3M5FD7OQI9BUkAlPSI4dDFPKfYfsRXTCrH/oPT26QbgHNd7z13J78RQwpTBu8K8xXzK09CP2HHhMuuH9RwAogrFFDketCb3LcozBDiFOj07KjpVN9gy2CyHJR4d4RMYChEAGvZ/7IrjgNue1BfPE8uxyP7H/siny+HPiNVv3F7kFe1Q9sb/LQk+ErIaSCLIKAEuzDEPNLs0zTNQMVotCyiQIR0a7RFBCV4AifcF7xTn89/X2e/UXdE7z5fOcs/E0XjVbdp84HPnGu8194T/xQe6DyYX0B2FIxsocitxLQ0uQy0cK6wnECNuHfUW1w9OCJYA6viF8aDqcOQj3+Daxtfq1VjVEtYP2Dzbft+y5K7qQPE1+Fb/awY9DZkTThkyHh8i+ySyJjknjya9JNMh6h0kGacToQ1AB7gAPfr+8yzu9Oh85OXgSd643D3c2NyB3ijht+QP6QzuhvNR+T//IAXKCg8QyRTTGBIcbh7XH0UguR85HtMbnhi1FDgQSwsXBsUAf/ts9rTxe+3e6fnm4OSf4z/jvuMW5TnnFeqP7Yzx6/WH+j3/5gNhCIoMQxBvE/kVzxflGDcZxRiVF7MVMRMkEKgM2AjUBLsAsPzO+DX1//FE7xftheua6ljqvurI62ntku8v8iv1bPjX+1L/vwIGBg4JwAsJDtkPJBHkERUSuRHWEHYPpQ12C/sISQZ3A5sAzf0h+6v4f/ap9DjzNPKh8YLx1PGR8rLzKvXr9uX4CPtB/X3/qwG8A54FRQemCLcJdArYCuQKnAoDCiEJAQiuBjQFoQMCAmQA1/5j/RT89PoJ+lj55fiw+Lf4+Pht+RD62Pq++7j8vP3C/r//rACDAT0C1gJLA5oDwwPIA6wDcgMgA7sCSQLQAVYB4QB1ABcAy/+S/23/XP9f/3L/lP/A//L/JABUAHwAmAClAJ8AhgBZABcAxP9g/+/+d/79/Yb9Gf27/HL8Rfw2/Er8g/zh/GX9Df7T/rT/qQCrAbECsgOkBH8FOQbKBioHUwdBB/IGZQacBZoEZQMGAoYA8P5R/bX7Kvq/+ID3efa09Tz1FvVH9dD1sfbk92P5JPsc/Tv/cQGuA+AF9AfYCX0L0gzKDVwOfw4wDm0NOwyfCqMIVgbHAwoBNP5a+5L49PWW843x6e+87hDu7u1b7lXv2fDd8lT1LPhR+6z+IQKZBfYIHQz1DmQRVxO6FIEVoRUZFecTExKpD7gMVgmbBaQBj/17+Yn12PGH7rLrcOnW5/Tm0+Z3597oAevR7TvxJvV1+Qf+ugJqB/ELKxD1EzEXwxmTG5IctBz2G1oa6he1FNQQYgx/B1ICAv24953y2u2W6fTlEuMK4e7fyt+h4HHiLuXE6BztFPKJ90/9OwMgCc4OGRTWGN4cECBQIoojsiPCIsAguR3BGfQUdw9zCRQDjvwQ9s/v/OnG5Fjg1txe2gXZ2tjg2RPcZN+84/vo++6P9YT8pAO5CowR5heUHWgiOybsKGMqkyp3KRUnfCPGHhUZkxJzC+kDMfyF9CLtQuYc4OLav9bV0z7SCNI508nVqdm73tzk3euJ86b79QM2DCgUjRsqIsknPCxgLxcxUzEPMFItLSnAIzIdsxV9Dc8E7PsX85fqruKb25jV1NB3zZ7LW8uxzJrPAtTI2cLgveh68bj6LgSUDaEWDR+VJv0sETKnNaE37TeFNnIzyS6rKEch0xiRD8YFv/vI8THoQ99H13zQGMtIxyzF18RPxovJdM7m1LLcneVj77n5TwTUDvUYYiLRKgAysze8O/o9WT7SPG85STSDLVEl8RuqEcsGqfuY8PDlBNwj05PLkMVNwe2+g74YwKDDA8ka0K/YguJH7a34WQT0DyIbiiXdLs42Hj2bQR9Ek0TyQkY/qDlCMkwpCR/IE98HqvuI79fj89gxz+DGQsCMu+W4ZbgRuuC9tsNpy73Ubt8p65P3TQT1ECcdhCizMmM7T0I+RwhKlUrfSO9E4j7lNjQtGCLnFf4IwvuX7ubhEdZ2y2jCMLsIthqzgLJAtE+4kb7WxuHQZdwK6W/2KwTVEQMfTStTNr0/QEehTLNPW1CTTmZK8kNmOwUxGyUFGCgK7vvG7R7gYdPzxy6+YLbHsJKt2qyprvOymLlnwh3Nadnt5kD18wOWErYg5C25OddD70u/URlV31ULVKZP00jDP7o0DiggGloLMPwV7YHe5NCrxDW61LHNq0+oeadSqc+t0LQfvnbJf9bV5Ar0qAM3Ez4iRjDiPK9HV1CVVjZaHVtAWatUgE31Q1I47yo1HJMMhPyE7A7dnc6gwX+2ka0dp1ejYKI/pOqoPrADuu/FqNPE4s/ySgO5E5sjczLOP0JLdVQeWwdfEGAvXnBZ9lH6R8c7ui1BHtEN7PwR7Mfbi8zUvg+zmKm7oq6elJ12n0ek5asWto3C6dC94I/x2gIcFM0kajR6Qo5OR1hXX4djtGTTYvBdMFbNSxY/bDBCIBIPZP2+66var8pJvOiv7qWrnliaGZn5muufy6deslK/Rc7D3k3wWgJhFNMlKTbkRJBRyFs9Y7NnBWkoZydiKVpqTztCAjM1IlQQ6/2I67rZDMkAugytlqLwmliW85TNltmb86PdrkO8vsvY3AzvywGIFK4msTcMR0ZU+F7NZodrAG0qaxJm313NUjNFeTUXJJURgf5w6/XYoMf6t3yqkJ+Ml7GSJZH2kheYYaCXq2K5WMn+2sztLwGSFF4nADnwSK9W02EEagBvoHDVbqxpTGH0VftHzTfnJdISJP9z61rYbcY4tjqo35yDlGaPs413j6aUGZ2QqLS2Fsc52ZHshwCBFOMnFzqPSspYWGTfbBty43MncvNsb2TbWJBK/TmhJwoU0v+S6+rXcsW7tEemhprWkXuMn4pTjIqRHZrLpTq0+sSL113r1f9VFD4o9jrpS5VahWZdb9d0xnYcdeNvQ2d+W+5MBDxDKTsViADL66TXsMSDs6SkhZiHj/CJ7YeOiceOcpdLo/ixCMP21TDqGv8PFHAonDv+TA9cWWh8cTB3R3mxd3hyxWnaXRJP4T3KKmIWRgEc7IfXJsSRslKj3paYjcmHnoUph1+MGZUTofKvQsF91A/pWP6yE3koCjzOTTld02k7cyV5ZHvkebJ09GvtX/tQkD80LH0XCwKF7JHX08PksVGikZUKjAaGtIMnhVWKFZMmnyiuqr8i0/vnkv0/E1soQjxYThJe82qYdLV6Gn20e412zG21YaVSEEF/LYoY0wID7cLXt8N8saGhoJTeiqqEMoKLg6qIaZGFnZ+sQ77n0fXmyPy2EhcoQzyeTplet2uTdd57aX4dfQd4S28uYw1UXkKpLocZnQOV7RnY0MNYsUKhCZQVirSDF4FUgmGHF5AznFirD73P0AHm/fsbEq4nDzygTtBeIWwrdqB8Tn8gfh55cHBXZDNVd0OvL3MaaAQ67pTYHsR4sTOhzZOuiSaDZoCFgXuGII8zm1SqELzczx/lNPtuESInqDtfTrZeL2xgdvt8y3+7ftN5OHEvZRNWW0SPMEobMAXv7jHZn8TasXSh65Oqif+CH4AfgfmFho6EmpepSLsPz1LkbPqxEHUmDjvcTU5e5Gszdu183X/tfiJ6pHGzZa1WBkVIMQsc9gWz7+7ZUsV+sgOiZJQIikCDQYAigdyFSo4qmiGpubprzp3jqvnmD6clQzoZTZhdP2uldXl8hn+1fg16sXHjZf9WeEXXMbUctQaE8MvaNMZgs+CiNZXHiuiDzYCOgSSGbI4lmvSoZbrxzQDj7vgQD7wkSTkXTJVcQmq1dJ57xX4VfpJ5X3G+ZQdXr0U8MkUdbQdf8cTbRceBtAmkXZbmi/aEwoFjgtKG7Y51mhCpTLqizX3iOvgwDrUjIjjYSkdb72hnc116nH0MfbF4rnBCZcZWqkV0MrodHAhE8tncgsjdtXul2pdkjWqGIIOgg+WHzo8cm3epcLqBzRfikfdHDZQi0DZfSbFZR2e6cbd4C3ybe2x3nm9wZDlWZ0V/MhIewAgv8wbe6clztzWnrJk/j0CI5IREhV2JDZEZnCqq0bqOzc7h8/ZZDFshVTWtR9NXTWWxb692E3rDecJ1MG5IY2FV50RaMkseVwkg9Erfd8tBuTWpz5t1kXmKDodPhzmLq5JsnSercbvKzaThY/ZnCw0gtDPERbJVAmNObUd0t3eGd7RzY2zKYT5UKEQGMmUe3wkT9aLgKs1Du3erQJ4ElBGNnIm/iXeNp5QVn3CsT7w2zprh4/VyCqse7zGoQ09TamCUaoBx+XTldEVxOWr2X89SKkOBMV8eVwoI9g3iAc94vfqt/qDplgaQi4yRjBaQ/5YToQWubb3UzrLhc/V+CTgdCDBcQa1Qh12GZ11u2nHicXZus2fOXRVR7UHLMDYevgr89ofj99Dcv7qwBqQgmlWT2I/DjxWTspllo+Ovyb6jz+zhFvWLCLcbAi7hPtBNXFomZOJqXm6Abklr02RSWxFPcUDjL+odEQvt9w/lC9NtwrWzU6eonfyWgpNTk3CWvpwKpgyyZcCj0EnizfSdByka4Ss7PLpK7VZ4YBFniGrDasFnnGGFWMRMtj7KLnodUAvZ+KLmOtUnxea246p7ofaahJc+lyWaIaAAqX60PsLW0cvimfS0BpEYpiltOW5HPVN/XO9iW2arZt9jDl5nVS9Kvjx+LecceQu++T7ogNcHyEq6sq6YpUCf25uBmzKe2KNFrDa3VcQ603Hje/TSBfIWVCd7NvFDUE9AWH5e22E+YqhfLVr7UVNHiDoBLC4ciwuc+uDp3NkLy969vLL5qdejg6AXoJOi4afXrzW6qcbQ1DzkdPT5BE0V7yRoM0dAKku+U8NZC11/XR5b/FVETjJEFzhSKlAbhgtv+4frStwuzp/B/baarrWoeaX+pESnOay0s3i9OMmX1i3lh/QsBKUTeSI2MHI8z0b+TsJU8FdyWEZWflFDSs5AazVzKE0aZws2/C/tx95t0YfFcrt4s9etuKoxqkOs3bDYt/zAAcyO2EPmsvRrA/0R9h/qLHY4QkIESoBPj1IbUyJRtkz8RSo9hjJkJiUZLwvx/NfuUeHF1JTJFcCOuDizO7Crr4qxyLVBvMDEAs+12n7n9/S3AlUQZx2HKVk0ij3URAFK60x+TbhLqUdyQUc5aS8mJNcX3Aqd/X3w4+My2MHN4sTWvdS4/rVptRa397rrwMDIOdIK3d7oV/UTArIO0BoRJh0wqTh0P0tEC0ehRwtGWUKpPCo1Fyy6IWUWbwo4/h7yfeax2wvS1clNw6S++7tlu+K8ZsDSxfvMpdWM32Lq0fWAARUNNRiLIscrpTPnOWE+8kCIQSFAzDykN9QwkygjH84U5wnD/rfzGuk932zW6c7tyKXELsKawerCEcb0ymzRQtk64grsZ/b+AH8LlxX4Hlwngi41NEo4pjo4O/45BTdmMkgs3SRgHBQTRAk8/0n1uOvV4uHaGtSyztDKkcgDyCfJ8stM0BDWD90R5dbtGPePAPMJ+RJdG94iRSlgLgwyLTS3NKczCjH1LIwn+iB1GTgRhQih/8/2Ve5z5mbfYtmV1CHRH8+bzpbPBtLW1eTaCOEQ6MPv5fc0AHIIXxC8F1Me8iNvKKorjC0KLiIt3ypUJ6Ei6xxiFjoPqwfy/0n47PAV6vbjvt6S2pLX0dVb1TDWR9iM2+TfK+U169HxzPjv/wAHyg0aFL8Zjx5nIislySY3J3QmiSSJIYwdtRgrExsNtgYtALT5ffO37Y3oJ+Sk4B3eo9w/3PHcsN5s4Q3ldOl97v/zz/m9/5wFPgt6ECYVIRlNHJUe6h9DIKIfDh6XG1EYWhTSD94KpgVTABD7BPZV8SftmenF5r3kj+NB49LjPOVv51nq4O3m8Uv26/qi/0gEvQjeDIwQrBMnFu0X9Bg1GbMYcxeDFfUS3g9ZDIQIfQRkAFv8fvjs9MDxEO/v7Gvrjupa6s/q5euS7cXvbPJu9bT4Ifyc/wcDSgZLCfULNA75DzkR7RETEqsRvRBTD3oNRAvDCA4GOgNeAJP96vp6+FT2h/Qe8yPymvGE8eDxpvLO8031E/cR+Tf7cP2t/9kB5gPEBWUHwAjKCX8K3AriCpIK8wkMCecHkAYTBX8D3wFCALf+Rv36+976+PlM+d74rvi6+AD5efkf+uv60vvO/NL91/7U/78AlAFLAuECUwOeA8UDxwOoA2wDGQOyAj8CxgFMAdcAbQAQAMb/jv9r/1z/YP91/5f/xP/2/ygAVwB/AJoApQCeAIMAVAARALz/V//l/m3+8/18/RD9tPxt/EL8NvxN/In86/xy/Rz+5f7I/74AwQHGAscDuASQBUcG1AYvB1QHPQfpBlcGiAWCBEoD5wFkAM7+Lv2T+wv6ovhn92X2p/U29Rf1T/Xg9cf2AfiG+Uz7SP1p/6AB3QMNBh4I/gmcC+oM2g1jDn0OJA5YDRwMeAp1CCIGjgPOAPf9HftY+L/1Z/Nl8cvvqO4I7vLtau5x7//wDfON9Wz4l/v1/msC4gU8CV0MLQ+TEXoT0RSKFZ0VBRXGE+URbw90DAkJSAVOATj9Jfk49Y7xRu5860fpvOfq5tnmjucF6TfrFO6K8X711Plr/h8DzAdODIAQQBRvF/EZsRudHK0c3BsuGqwXaRR6EP0LEwfiAZH8SPcz8nrtQumv5d/i6eDi39Lfv+Cj4nLlGumA7YXyAfjN/boDnAlED4QUNBkrHUogdSKYI6gjoiKJIG0dYhmGFPwO7giKAgL8h/VO74bpYOQE4JfcNtr22OTYBNpP3LffI+R06YPvIPYb/TwETgsZEmYYBB7EIn8mFyl0KogqUSnUJiIjVR6RGAES1QpFA4z74/OJ7Lflo99/2nXWptMs0hTSYtMQ1graNN9q5XvsM/RX/KYE4wzMFCMcrCI0KI0ski8rMUcx4y8HLcYoPyOaHAoVyAwSBC77XvLn6Q7iENsl1X7QQM2Jy2jL4Mzqz3DUUtpk4XDpO/KA+/gEWg5cF7cfKSd3LWwy4TW4N983UzYdM1QuGSicIBUYxA7xBOn69/Br54/eqtb6z7fKCscTxeXEg8bjye/OgNVm3WbmO/Ca+jEFsQ/GGSEjeCuIMho4/jsVPks+nDwSOccz4SyUJB4bxxDfBbv6sO8U5TvbdNICyyTFCMHQvpK+UMABxIvJw9B22WDjNu6k+VMF6BAIHF0mlC9lN5A95EE9RIVEt0LgPhs5kjF9KCIezxLcBqX6ie7l4hbYcc5Cxsq/P7vFuHO4TrpJvknEIcyV1V/gLOyh+F0F/xEjHmopfDMJPMxCj0cqSodKoEiCREo+JjZULB0h2RTlB6f6gu3g4CLVpcq7wa66tLX3so6ygLTAuC6/nMfJ0WndIuqS91AF9RITIEYsLDdxQMlH+UzYT01QUU7ySU9DmjoUMA4k4xb6CL76newE31/SEcdzvdK1a7Bqreis7a5qsz+6OcMVzn/aGOh49i4FyhPaIe8uojqYRIJMH1JDVdJVxlMsTyZI6T67M/Am6xgYCuv61+tT3dHPucNsuTyxaasjqIanmKlNroG1/r59yqXXEuZV9fYEfxR1I2Mx3D1+SPRQ/FZlWhBb+VgrVMtMEENEN8Ep7ho/Cy37M+vO23jNn8Cqte6ssqYno2yiiKRtqfew7boEx9/UE+Qs9KsEExXjJKAz1kAdTB1VjVs6XwVg5V3qWDlRCkesOn0s6hxrDIL7r+p22lbLxb0tsuyoSaJ6np+dwJ/PpKesC7euwy7SHOL98k0EiBUmJqc1kEN1T/hYzV++Y6pkiGJmXWtV00ruPSEv2x6cDen7SupJ2WvJLLv7rjilMZ4fmiKZRJt3oJOoXLN/wJfPMeDL8d0D3RU8J3U3CEaDUoNcumPuZ/1o22aZYV5ZZ04JQaoxwCDODmH8BepJ2LnH1bgTrNahb5oalvqUGpdpnMKk5K96vR3NU96Y8F0DExYmKAs5PEhEVbxfUWfGa/ls3WqBZQ1dw1H3QxU0lSIBEOj83ul210DGw7Z4qceeBZdvkiuRQ5OpmDWhpqyjusLKhtxm788CKxbkKGc6LEq4V6BijmpEb5twiG4YaXZg41S2Rl82WSQyEX791OnO1gHF9rQspw+c9ZMgj7eNxI87lfGdpan9t4rIzNo37jMCJhZ1KYk71kvcWS1lcG1kcuBz2XFdbJRjw1dDSYU4CSZfEiD+5+lS1vvDb7Mwpa6ZQpExjKGKoIwhkvma5qaKtXfGKNkN7YsBBRbbKXM8Ok2wW2Fn9G8jdcZ2z3RLb2VmYVqaS4Q6oieFE8z+FeoC1i7DLrKFo6aX7o6iieyH2olfj1GYaqRPs4zEm9fq69oAyhUXKiI9WE4yXTxpGHKAd0l5ZXfgceVouVy6TVo8ISmkFIL/Xerb1ZvCM7EroviV+ox3h5qFdYf4jPqVNqJNscvCKdbR6iAAdRUpKpk9ME9jXr1q3HN5eWh7mXkZdBJryV6eTwQ+hSq4FT4Avure1UHCf7AjoaaUZ4uwha6DcoXtiveTSqCHrzjB1NTC6WL/CRUSKtg9wU9CX+FrPnUNeyF9anv0delsj2BEUYA/zCvAFgABNusJ1h7CEbBuoK6TN4pQhCmC04NCiUySq54ArtS/ndPB6J3+hhTTKd89DFDOX6tsPXY6fHJ+1nxvd2huB2KrUstA8iy5F8YBw+ta1jLC6K8KoBOTaYlXgwyBm4L4h/mQWp26rKK+iNLQ59b97xNvKa89ElAJYBht2Hb/fFt/2n2IeI5vMWPQU+NB9i2iGI0CZOzR1nzCBLD3n9SS/4jFgliAyoEQhwGQWZy3q6S9ltHw5g/9RBPmKEs91U/0XyptEXdcfdp/d34/eVhwCWSxVMdC1i54GVUDGO1s1/vCY7A2oO+S+Iicgg6AYYGMhmWPqZv4qty8ydAk5kj8iBI6KLI8VE+NX+Fs53ZSfe9/rH6RecZwj2RMVXRDjy86GhoE2+0p2K3DBbHDoGWTU4nbgi6AYYFshiePTZuAqku8I9Bt5YX7vRFsJ+g7kk7YXj5sWnbffJt/d35+edZwwmShVehDITDmGtsEre4G2Y/E6LGfoTWUEYqAg7eAyoGxhkaPRJtQqvO7pc/N5Mb65RCAJu06j03WXUJrbHUGfNx+2n0GeYhwoGStVSNEiTB5G5YFi+8C2qLFCbPIol2VL4uNhKqBnIJch8OPkJtoqta7U89G5A/6ARB2JcQ5T0yHXO9pHnTHerV91HwpeNtvKmRxVSNExzDzG0oGdPAa2+LGaLQ8pNuWrIz/hQWD1oNriJ+QMpzJqvS7K8/a42D5FA9QJG4400rvWkZocnIjeSZ8ZnvodtBuXWPqVOdD2DBRHPQGZvFM3E3IAbb4pa6YiI7Vh8iEeIXeidmRKZ11q068Mc+K47v4Hw4RI+42HEkPWUpmaHAcdzF6kXlCdWdtPGIZVG5DuzCTHJIHXfKX3eHJ1Lf8p9Oav5ANivCGf4e2i3GTdZ5rrOa8Zc9Y4yP4JQ28IUc1L0fpVv1jBG6zdNd3V3c6c6BrxWD+UrdCcDC2HCQIWvP33pvL3LlDqkidT5OljHuJ64nvjWeVF6Csrbu9yM9E45n3JwxRIHozDEWBVGFhSGvscRp1unTQcHxp+V6YUcNB9S+6HKcIWPRr4HrNGLzLrAqgNpaaj2mMuoyJkLiXDaI3r86+WtBQ4x73KAvUHooxuELZUXleN2jJbv1xu3EGbv5m2VzoT5FASy+dHBkJWPXw4XrPhL6SrxajcJnqkrWP6Y+Ck2OaVqQLsR/AHdF+47T2KQpIHXovNED0Tklb1GRNa4JuXG7faiZkZ1rvTSE/cC5eHHoJVfaE45nRH8GUsmmm+5ySll6TdpPYlmid8aYps67BENLO4132LQmtG00thD3WS9RXImF7Z61qompcZ/Zgo1etS3M9ZC39G8cJUPcl5dTT5MPOtQCq06COml+XXZeHmsKg3amOtXrDNNNA5Br2NQgHGgUrqzqBSB1UJV1WY4BmjmaAY3BdkFQkSYk7KCx5GwAKRfjP5ijW0MY8udat9aTanrabnJuOnnGkF606uIPFidTW5Oz1QwdYGKUorDf6RChQ4FjiXgBiJGJPX5hZL1FVRmI5uirRGiMKM/mC6JPY4MncvOmxW6l0o16gMKDponKonrAsu8jHDtaQ5dT1WQajFjEmizREQflLWFQkWjFdaF3LWnBVg01CQwA3HSkFGi8KGPo66hDbEc2owDO2A65WqFSlFKWUp8Gsb7RgvkfKxNdu5tP1eQXpFKsjSzFiPZNHkU8gVRVYXlj5VftQjkntP2Q0TycVGSQK8/r0657dX9CdxLK66LJ8rZOqQ6qNrFuxh7jWwf/Mqdlx5+v1pQQuExUh7y1aOf1CkErZT7NSCVPbUD1MVEVYPJAxUyUAGAAKwfuw7TrgyNO4yGC/Brjhshewu6/OsT2247yKxe7PvduY6Bv23QNzEXQefCouNTk+WEVWSg9Nb013SzlH1kCEOIUuKCPHFsMJgvxp7+DiRtf0zDnEV72BuNu1drVUt2K7gMF7yRTT/t3j6WX2IwO7D8ob9CbjMEw57j+aRCxHlEfRRfNBGjx3NEYr0SBqFWwJNP0g8Y3l1tpN0TnJ18JXvtq7cLsavcjAW8amzW3Wa+BR68r2eQIIDhoZWyN9LDs0WTqrPhJBfUHtP288IjcxMNQnTh7qE/oI1f3Q8j/odt6+1VrOgchexA/Co8Ebw2nGb8sG0vfZBOPj7Ej34AFbDGcWth8BKAovnDSOOMQ6LzvPObI28jG2KzIkoRtHEm8IZf549PPqIeJE2pnTUM6QynXIC8hTyUDMutCa1rHdxeWX7uH3WgG4CrMTBhxyI78pvS5IMkg0sDR+M8EwjiwKJ2IgyxiCEMgH4/4W9qbt1OXb3u/YPtTo0AXPoc68z0rSNtZd25bhruht8JX45gAgCQIRURjVHl0kwSjgK6QtBC7/LJ8q+yYwImcczxWcDgcHTf+o91Xwiul941reRtpg17vVYdVR1oLY39tM4KXlvetk8mT5hgCUB1YOmhQvGuserSJZJd4mMidWJlMkPSEtHUUYrxKWDCsGov8t+f3yQu0n6NPjZODz3ZDcRNwM3eHeseFk5drp7+559E36OwAXBrML4xCCFW0Zhxy7HvsfPyCJH+IdWBsDGP8TbA9xCjUF4/+i+pz19vDV7Fbpkeab5IDjRePo42Plp+ef6jHuQfKs9k/7BQCqBBkJMg3UEOcTVBYLGAEZMhmfGFAXUhW3EpcPCgwvCCYEDQAG/C/4pfSC8d3uyexT64PqXerg6gPsvO3676nysvX8+Gz85v9OA40GiAkpDF8OGRBOEfYRDxKcEaQQMA9ODRELiwjTBf0CIgBY/bT6Sfgq9mX0BfMT8pTxiPHs8bvy6/Nw9Tz3Pvlm+6D93P8GAhAE6QWFB9kI3QmKCuAK3gqHCuIJ9QjMB3IG8gRcA7wBIQCX/ij94fvJ+uf5QfnY+K34vvgI+YX5L/r9+uf74/zo/e3+6P/SAKQBWQLsAloDowPGA8YDpANmAxEDqQI1ArwBQgHOAGQACQDA/4r/af9b/2H/d/+b/8j/+v8sAFsAgQCbAKUAnQCAAE8ACgC0/07/3P5j/un9c/0I/a38afxA/Df8UfyQ/PX8f/0s/vf+3P/TANYB3ALcA8sEoQVUBt0GNQdUBzkH3wZIBnUFagQuA8gBQwCr/gv9cvvr+Yb4UPdT9pv1MPUY9Vj18PXe9h/4qvl1+3T9mP/QAQ0EOwZICCMKuwsCDeoNaQ56DhcOQg39C1AKRgjtBVUDkgC6/eH6H/iK9TnzP/Gu75buAO727Xvuje8n8T/zx/Wt+N37Pv+2AisGgQmdDGQPwBGcE+YUkhWWFfEUpBO2ETQPLwy8CPUE9wDg/ND45vRD8QXuR+sg6aPn4ebg5qXnLelu61nu2vHX9TT6z/6DAy8IqwzVEIoUqxceGs0bpxykHMAbABpuFxsUIBCYC6cGcQEf/Nn2yvEa7e/oa+Ws4srg19/c397g1uK45XHp5u328nv4S/44BBcKuA/uFJAZdh2CIJcipCOdI38iUSAfHQIZFRR/DmgI/wF3+//0ze4S6frjsd9Z3BDa6djw2CnajdwM4I3k7+kL8LL2sv3UBOMLpRLlGHIeHiPCJkApgip7KigpkCbGIuMdDBhtETcKoQLn+kPz8ust5SzfHtos1nnTHNIi0o7TWNZt2q/f+eUa7d30B/1YBZANbxW3HCwjnCjaLMMvPDE4MbQvuSxcKLsiARxgFBIMVQNw+qXxOelw4YbatdQq0A3Nd8t3yxLNPNDh1N7aB+Im6v3ySvzCBR4PFRhgILsn7i3FMhg2zDfPNx82xjLdLYUn8B9VF/YNHAQS+ibwpubc3Q7We89Yys/G/sT2xLrGP8ptzxzWHN4x5xTxe/sTBo0QlRrdIxwsDjN+OD08LD45PmI8sjhDMz0s1CNJGuMP8gTN+cjuOeR12sfRdcq6xMbAuL6kvozAZsQVym/RPto/5CXvnPpMBtsR7hwuJ0ow+jf/PSpCV0RyRHlCeD6LON4wqyc4HdUR2AWh+Yvt9eE817PNpsVWv/W6qbiFuI66tr7fxNvMb9ZS4THtr/ltBggTHR9OKkM0rDxGQ9xHSEp0Sl5IEUSuPWQ1cSshIMoTzAaM+W7s29801NbJEsEuumO117KhssS0NLnPv2XItNJv3jvrtfh2BhMUIiE8LQM4IUFNSE5N+k86UAtOekmpQso5IS//IsEVzAeO+XTr7N1g0TLGvLxItROwRq37rDWv5bPruhDEEM+X20XpsPdoBv0U/CL3L4k7VkURTXtSaFW/VXxTrU52Rw0+uDLQJbUX1win+ZvqKNzAzsvCqLiosAqr/KeYp+Opz642tuG/hsvO2FHnofZFBsUVqSR8MtI+SUmOUV9Xjlr/WqxYplMSTChCMzaQKKYZ6gnV+ePpkdpWzKK/2LRQrEum/KJ9otak9am1sdu7G8gX1mPlifUMBmwWKibLNNtB9UzAVfdbZ1/0X5ddYFh3UBZGjTk8K5EbBQsY+k3pJ9kjyrm8ULFEqNuhSp6vnRCgXKVsrQS408R2033jbPS/BfIWfifhNqNEWVClWT5g8GOaZDdi11yiVNVJxDzSLXMdJQxu+tfo6tcqyBK6Ea6HpL2d7JkxmZabCKFgqV+0sMHt0KDhSvNgBVcXpCi+OChHclM5XTJkJGjuaIlmBmGOWGBN0z9OMEkfSA3W+oLo29Zpxq+3HqsbofSZ45UIlWyX/pyVpe+wtb5/ztHfJvLvBJwXnClhOmlJPlZ7YM9nAGzsbIpq6mQ3XLRQt0KtMhIhbQ5P+03o+dXjxJG1eagEnoSWM5I3kZaTQpkOormt6LsvzBDeAfFuBMMXZyrKO2RLvFhnYxNrgm+QcDRuf2ibX8xTbUXtNMoikQ/Y+zboRdWXw7izJKZDm26T4I7AjRiQ1pXPnr+qSrkAymHc3u/fA8oXBSv4PBlN6Vr8ZfptpXLXc4ZxwWu1YqZW8kcJN28kshBu/D3ovdSGwieyHqTbmLWQ7YupivSMvpLbmwWo37b2x8bav+5CA7UXdivsPYdOxlw4aIRwaXW/dnt0rm6CZT9ZQ0oBOf8lzxEQ/WDoYtSwwd2wa6LMlluOWonyhy2K/Y81mY6lqbQSxkLZpe2bAoQXuyulPq5PUF4aaq5yyndFeRJ3QnEAaJNbXEzQOnYn5RK9/Z/oMtQUwdqvCqEYlWGMK4eehceHl43gll2jrLJYxNfXk+zqATcX1iskP41QiF+ga3d0x3lme0h5enMraqBdPE51PNQo8hNy/vjoLdSywB6v+5+/k8qKYYWvg8OFjIvflHSh6rDJwofWi+sxAdIWxitqPyVRbWDKbN11XnsifRp7VnUBbGRf4E/tPRYq9RQu/2rpUdSJwKquQJ/DkpWJ/YMngiOE4Yk1k9efZq9pwVXVj+pyAFUWjSt3P3ZR/2CYbeB2jnx2fod80XaAbdtgRVE1Pzgr6hXw//LpntSYwHyu154kksSIAYMIgemClYjikYaeIa45wELUoemw/8EVLStMP4FRPmEKboB3V31hf4597HembgRiaVJMQDos0BazAJDqEdXewJSuwZ7gkVaIbIJRgBaCrIfpkIWdHq08v1LTw+jq/hoVpyrqPkZRK2Efbrx3t33jfy1+pHhyb95iSlMvQRotpRd5AUHrqtVawfGu/J75kUyIQIIEgKqBJYdLkNScX6xzvoTS9+ck/l8U/SlUPsdQyGDYbZR3r336f2R++Hjhb2Zj51PdQdQtaBg+AgXsZtYLwpGviJ9tkqWIfIIhgKiBA4cJkHWc5Kvgvd3RP+dg/ZQTLymKPQZQFGA3bQl3P32ofzJ+6Hj0b5xjP1RUQmguFRkBA9jsRNfuwnOwZKA7k2GJIIOogA6CRYclkGmcsKuFvV3RnOaf/LkSQSiOPARPEl88bB12aHztfph9dHiqb35jT1STQtQurBm/A7ntQtgCxJaxjaFjlH6KKoSZgd2C7IefkLGcxKtjvQXREebk+9IRNCdjO8JNwl3patB0KnvIfZV8m3cCbwxjF1SYQhcvKxp3BKbuXdlExfayAqPhlfuLm4XxghOE94h2kU2dIKx8vdjQn+Uw++AQCiYKOkJMKFw/aSNzh3k7fCp7Xnb7bUVillNiQi4vkBonBZ3vldq0xpO0wKS1l9aNb4eyhLGFZoqrkj6exazPvdfQSOWE+uUPxiSFOIhKRVpBZxlxgXdIell5vXSYbCphzVLxQRkv2RrOBZzw5ttNyGq2x6bcmQ6Qp4nYhraHOYw9lISftK1fvgLRDeXk+eMOaSPXNpVIHFjyZLRuGXXwdyN3uXLXarpfuVFDQdcuBRtoBqHxTt0Pyni4E6lUnJ+SPoxiiR6KbY4slh6h7K4rv1vR8ORP+dsN9iECNWxGrlVSYvdrUnI1dYl0VHC6aPZdXVBZQGcuFBv1Bqnyy972y7u6oasan4iVNI9NjOmMApF2mAujbbAzwOPR8eTJ+NEMbyAJMxBEAFNmX+NoL28Zco1xkG1DZt9bt04xP8gtAxt0B7TzW+AAzjC9bq4rosaYhZKYjxWQ9ZMam0ulN7J5wZnSEuVT+McL1x7vMIRBFFAxXH1lsmugbjJubmpyY3ZZyUzNPfos0RriB7/0++Eq0NS/eLGEpVScLpY/k56TRZcXnt2nSbT7wn/TVOXu970KLx22Lso+7ky2WMdh3mfMantq8WZLYLxWk0otPPwrfxo+CMf1qeNx0qPCu7QhqTCgK5o/l4KX75ppob6qo7a6xJXUt+Wc97YJextiLOY7kEn4VMZdt2OgZmtmHGPOXLRTFkhQOs8qChqGCMz2YuXS1JvFM7j/rFakep6Vm76b754Qpe6tQrm0xtrVPeZd97QIvRn0Kdo4/kX7UHxZQl8hYgVi8V7/WF5QVEU5OHIpcxm6CMr3JedL17nI3bsascOoFqM9oE6gRKMHqWmxJrzpyE/X5eY097kH+BdwJ6w1PULDTO5UgVpRXUxdc1rgVL5MT0LmNeUnuRjZCMH47ujZ2fjLtb9utXGt+6czpS6l6adNrS61TL9Yy/PYsecg98YGLBbaJF0yTz5USCBQeVU2WEVYp1V0UNVICD9bMyom3BfhCK/5uup43FXPt8P2uV2yJK1zqluq26zesTm5s8IAzsXaoOgk994FXhQzIvIuOjqzQxdLLlDTUvNSkFC/S6dEgjuYMEAk2xbRCJL6iewl383S38evvoK3jrL4r9CvFrK2tom9WMbe0Mbcs+lA9wIFkBKAH24rADbkPthFpkouTVxNM0vFRjdAvzefLSkithWpCGj7V+7e4VzWKcyTw9u8M7i9tYi1lbfRuxnCOcrx0/Pe6ep09zMEwxDCHNUnpjHrOWZA5kRKR4RHk0WJQYg7wTNyKuYfbxRoCC/8IvCf5P7ZkdCfyGTCDr69u3+7Vb0twebGU84310zhQuzC93ID+g7+GSokMS3ONMc68T4uQW9BtT8PPJ02iy8TJ3gdBRMNCOj86fFm57HdE9XOzRfIG8T0wbDBUMPExu3Lo9Kv2s/jve0p+MICNw02F3IgpCiQLwA1zjjeOiM7njlcNnsxIiuEI+AaeRGZB4/9qPMv6m/hqtka0/DNU8pbyBbIgsmRzCrRJtdU3nvmWu+r+CMCfAtsFK4cBCQ3KhcvgjJgNKY0UzN1MCUshybIHyAYyw8LByX+XfX47DblUd5/2OnTsdDuzqrO5M+R0pjW2Nsm4k7pGPFG+ZcBzAmlEeUYVh/HJBApEyy6Lfwt2SxeKp8mviHiGzoV/Q1iBqj+CPe+7wHpBuP43f3ZMden1WjVdNa+2DTctuAg5kbs9vL8+R4BKAjiDhgVnBpFH/EihSXxJisnNiYcJO8gzBzUFzESDwygBRf/pfh98s7sw+eA4ybgy91/3ErcKd0U3/jhvOVB6mHv8/TL+rkAkQYmDEwR3RW3Gb8c3x4KIDogbx+0HRkbtBeiEwUPAwrEBHL/NPo09ZnwhOwT6V/me+Ry40rjAOSM5eDn5eqE7pzyDve0+2kACwV0CYQNHBEhFIAWJxgMGS0ZihgsFyAVehJPD7sL2wfOA7f/svvg9130RPGr7qPsO+t56mLq8uoi7OftL/Dm8vb1RPm2/C8AlgPPBsQJXQyJDjgQYRH9EQsSjRGJEAsPIg3eClMIlwXAAub/Hv1++hn4APZD9O3yBPKP8Yzx+fHR8gj0lPVl92v5lfvQ/QoAMwI5BA4GpAfyCO4JlQrjCtoKfQrRCd8IsQdTBtEEOgOaAQAAd/4L/cj7tPrX+Tb50vis+ML4EPmS+T/6D/v7+/n8/v0C//3/5AC1AWcC9wJiA6cDxwPEA6ADYAMJA6ACKwKyATgBxQBcAAIAu/+H/2f/W/9i/3n/nv/M//7/MABeAIQAnQClAJsAfQBKAAQArP9E/9L+Wf7f/Wn9//ym/GT8Pvw4/FX8l/z//Iz9O/4J//D/6ADsAfIC8APeBLEFYgbnBjkHVAc0B9UGOAZhBVEEEQOpASIAif7p/FD7zPlr+Dj3QfaP9Sv1GvVh9QD29vY9+M75nvug/cf/AAI8BGgGcghICtoLGQ35DW8Odg4KDisN3QsoChcIuAUcA1YAff2l+ub3VvUL8xnxku+E7vrt/O2M7qrvT/Fx8wH27/gk/Ij/AAN0BsYJ2wybD+0RvRP7FJkVjxXcFIEThhH4DukLbgiiBKAAifx6+Jb0+vDF7RPr+eiM59nm6ea/51bppeue7iryMfaU+jP/6AORCAgNKRHTFOYXShrnG7AcmhyjG9EZLhfME8QPMgs6BgABrvtr9mLxvOyd6Cjle+Kt4M7f6N/+4AvjAObJ6UzuZ/P0+Mn+twSSCiwQVxXrGcAduCC4Iq4jkCNbIhYg0BygGKQTAQ7iB3QB6/p49E7unuiX42DfHtzs2d7Y/9hR2s3cYuD35GvqlPBF90r+bQV3DDATYxnfHnYjAydoKY4qayr9KEsmaCJvHYYX2BCYCf0BQvqj8lvrpeS23r7Z5tVP0w/SM9K906PW0tos4Inmuu2I9bj9CQY8DhEWSR2rIwMpJi3wL0oxJjGDL2ks8Cc2ImYbtRNaC5gCs/nt8Izo0+D/2UfU2c/bzGfListGzZHQVNVs26vi3OrA8xP9jAbjD84YBiFMKGMuGzNNNt03uzfnNWwyYy3vJkEfkxYoDUYDPPlW7+LlK9111f/O/MmXxuzECsX0xp7K7c+71tXe/efu8Vz89QZoEWQbmCS9LJIz3zh4PD8+JT4lPE84vDKWKxMjchn+DgYE4Pjh7WDjsNkc0evJVMSHwKO+ub7MwM7Eosoe0gnbIOUV8JX7RQfNEtId/Cf9MIs4az5sQm5EXUQ3Qgs+9zcoMNgmTRzaENUEnPiN7AfhZNb4zA7F5b6vupG4nLjSuia/eMWZzUzXR+I37r76fAcQFBYgMCsGNUs9vEMlSGJKXkoXSJ1DDz2fNIwqIh+6ErIFcfhb69jeSdMKyWzAs7kWtbuyt7INtay5dMAxyaLTd99V7Nr5mwcxFS8iMS7XOM5BzkieTRdQJFDBTf5I/0H3OCsu7iGcFJ0GXvhM6tXcYtBXxQm8w7S/ryetEa2Br2W0mrvpxA3Qsdxz6un4ogcvFhwk/TBtPBBGnE3SUolVqVUuUypOwkYtPbMxrSR+FpQHY/hf6f7asc3gwee3GLCvqtmnr6czqlWv77bHwJPM+dmS6O33kgcLF9wlkzPEPw9KI1K9V7Ja6FpbWB1TVEs7QR81XSddGJUIfviU6FXZNsunvgu0tqvppdWilKIopYGqd7LNvDbJUte15uj2bAfEF28n8jXcQshNX1ZcXI9f3l9DXdFXsU8fRWw4+ik3Gp0Jr/js59nX9Miwu3ewoKdzoSCexZ1koO2lNq4BufrFwNTf5Nz1MQdbGNMoGDiyRThRTVqqYBxkhWThYUNc01PTSJY7gSwJHK0K9Phm54zW68b8uC2t2qNOnb6ZRpnsm5+hMqpltePCRNIS48r04gbRGAkqAzpFSFxU612kZFRo2mgyZm5guVdVTJk+8C7RHcELTPkB527VHcWMti+qZqB/mbGVG5XEl5mdbab/sfS/489Q4bTzgQYlGRArtDuSSjNXNWFIaDRs2Wwxak5kXFugT3NBQzGMH9cMt/m95n7UicNitH+nRp0Ilv6RSJHvk9+Z7KLQrjC9n82c353yDgZZGegrKj2ZTLtZKWSSa7pvf3Dbbd9nul6xUiBEeDM4Ie8NMvqZ5r3TMcJ/siClfprskqeO0Y1xkHaWsp/dq5u6esv43YbxiwVtGZIsZD5YTvFbxmZ/buFyyHMscR9r0GGFVZ1GizXTIgUPvPqU5irTFcHjsBKjDpgtkK+Lt4pOjWCTwpwpqTe4eMln3HHw+gRjGQ8tYj/QT9ddCWkPcal1snYidApummQZWOdIezdZJBgQVPut5sTSNcCPr1ah+JXOjRiJ/oeHiqGQH5q3pgi2nMfs2mHvXAQ8GV4tJUD/UGlf8Wo+cw54Onm5dp1wFGdpWvtKQznJJSUR9/vj5ovSkL+Eru2fPZTPi+aGqIUgiDuOzZeJpBC06MWI2VfuswP4GIEtrEDmUadgfWwLdQ56XnvveNVyPmlyXNdM4johJysSpfw0537SJr/Crdme35IzihiFt4MahjGMzZWkolKyXsQ92FXtAQOaGHgt+UCFUpJhrW12dql7HH3DerF0E2s0XndOVjxdKCgTW/2f55zS975HrRie3pH6iLGDLIJ5hIWKI5QIodCwAcMP117sRwIiGEUtC0HbUipigG59d9x8cn4yfC12kmyqX9pPmz19KRkUGP4i6OTSAb8UraqdOpEliLGCCoE9gzmJ0JK4n42v1MH/1XPriAGTF+os5UDqUm1i9W4heKh9YH87fUl3uG3TYP1QsT58Kv0U2v696FPTQ78prZCd85CzhxqCUYBogk6I1pG2noqu18AQ1ZfqxQDuFmYsh0CzUl5iDm9geAt+5H/cfQN4hW6tYd9RlD9bK9EVnv9s6erTvb+DrcidCZGnh+uBAYD6gcWHNpEDnsmtDsBD1MvpAAA=\" type=\"audio/wav\" />\n",
        "                    Your browser does not support the audio element.\n",
        "                </audio>\n",
        "              "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<IPython.lib.display.Audio at 0x7fa1587bab90>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Video"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "More exotic objects can also be displayed, as long as their representation supports the IPython display protocol.  For example, videos hosted externally on YouTube are easy to load (and writing a similar wrapper for other hosted content is trivial):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import YouTubeVideo\n",
      "# a talk about IPython at Sage Days at U. Washington, Seattle.\n",
      "# Video credit: William Stein.\n",
      "YouTubeVideo('1j_HxD4iLn8')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"http://www.youtube.com/embed/1j_HxD4iLn8\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x7fa15855d710>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using the nascent video capabilities of modern browsers, you may also be able to display local\n",
      "videos.  At the moment this doesn't work very well in all browsers, so it may or may not work for you;\n",
      "we will continue testing this and looking for ways to make it more robust.  \n",
      "\n",
      "The following cell loads a local file called  `animation.m4v`, encodes the raw video as base64 for http\n",
      "transport, and uses the HTML5 video tag to load it. On Chrome 15 it works correctly, displaying a control\n",
      "bar at the bottom with a play/pause button and a location slider."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "from base64 import b64encode\n",
      "video = open(\"animation.m4v\", \"rb\").read()\n",
      "video_encoded = b64encode(video).decode('ascii')\n",
      "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n",
      "HTML(data=video_tag)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<video controls alt=\"test\" 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       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<IPython.core.display.HTML at 0x7fa15855df50>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "HTML"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Python objects can declare HTML representations that will be displayed in the Notebook. If you have some HTML you want to display, simply use the `HTML` class."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = \"\"\"<table>\n",
      "<tr>\n",
      "<th>Header 1</th>\n",
      "<th>Header 2</th>\n",
      "</tr>\n",
      "<tr>\n",
      "<td>row 1, cell 1</td>\n",
      "<td>row 1, cell 2</td>\n",
      "</tr>\n",
      "<tr>\n",
      "<td>row 2, cell 1</td>\n",
      "<td>row 2, cell 2</td>\n",
      "</tr>\n",
      "</table>\"\"\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h = HTML(s); h"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table>\n",
        "<tr>\n",
        "<th>Header 1</th>\n",
        "<th>Header 2</th>\n",
        "</tr>\n",
        "<tr>\n",
        "<td>row 1, cell 1</td>\n",
        "<td>row 1, cell 2</td>\n",
        "</tr>\n",
        "<tr>\n",
        "<td>row 2, cell 1</td>\n",
        "<td>row 2, cell 2</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<IPython.core.display.HTML at 0x7fa15855d8d0>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas makes use of this capability to allow `DataFrames` to be represented as HTML tables."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is a small amount of stock data for APPL:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file data.csv\n",
      "Date,Open,High,Low,Close,Volume,Adj Close\n",
      "2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50\n",
      "2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26\n",
      "2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48\n",
      "2012-03-01,548.17,621.45,516.22,599.55,26486000,596.99\n",
      "2012-02-01,458.41,547.61,453.98,542.44,22001000,540.12\n",
      "2012-01-03,409.40,458.24,409.00,456.48,12949100,454.53"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Writing data.csv\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Read this as into a `DataFrame`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pandas.read_csv('data.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And view the HTML representation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Date</th>\n",
        "      <th>Open</th>\n",
        "      <th>High</th>\n",
        "      <th>Low</th>\n",
        "      <th>Close</th>\n",
        "      <th>Volume</th>\n",
        "      <th>Adj Close</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 2012-06-01</td>\n",
        "      <td> 569.16</td>\n",
        "      <td> 590.00</td>\n",
        "      <td> 548.50</td>\n",
        "      <td> 584.00</td>\n",
        "      <td> 14077000</td>\n",
        "      <td> 581.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2012-05-01</td>\n",
        "      <td> 584.90</td>\n",
        "      <td> 596.76</td>\n",
        "      <td> 522.18</td>\n",
        "      <td> 577.73</td>\n",
        "      <td> 18827900</td>\n",
        "      <td> 575.26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2012-04-02</td>\n",
        "      <td> 601.83</td>\n",
        "      <td> 644.00</td>\n",
        "      <td> 555.00</td>\n",
        "      <td> 583.98</td>\n",
        "      <td> 28759100</td>\n",
        "      <td> 581.48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 2012-03-01</td>\n",
        "      <td> 548.17</td>\n",
        "      <td> 621.45</td>\n",
        "      <td> 516.22</td>\n",
        "      <td> 599.55</td>\n",
        "      <td> 26486000</td>\n",
        "      <td> 596.99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 2012-02-01</td>\n",
        "      <td> 458.41</td>\n",
        "      <td> 547.61</td>\n",
        "      <td> 453.98</td>\n",
        "      <td> 542.44</td>\n",
        "      <td> 22001000</td>\n",
        "      <td> 540.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 2012-01-03</td>\n",
        "      <td> 409.40</td>\n",
        "      <td> 458.24</td>\n",
        "      <td> 409.00</td>\n",
        "      <td> 456.48</td>\n",
        "      <td> 12949100</td>\n",
        "      <td> 454.53</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>6 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "         Date    Open    High     Low   Close    Volume  Adj Close\n",
        "0  2012-06-01  569.16  590.00  548.50  584.00  14077000     581.50\n",
        "1  2012-05-01  584.90  596.76  522.18  577.73  18827900     575.26\n",
        "2  2012-04-02  601.83  644.00  555.00  583.98  28759100     581.48\n",
        "3  2012-03-01  548.17  621.45  516.22  599.55  26486000     596.99\n",
        "4  2012-02-01  458.41  547.61  453.98  542.44  22001000     540.12\n",
        "5  2012-01-03  409.40  458.24  409.00  456.48  12949100     454.53\n",
        "\n",
        "[6 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "External sites"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can even embed an entire page from another site in an iframe; for example this is today's Wikipedia\n",
      "page for mobile users:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import IFrame\n",
      "IFrame('http://en.mobile.wikipedia.org/?useformat=mobile', width=700, height=350)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"700\"\n",
        "            height=350\"\n",
        "            src=\"http://en.mobile.wikipedia.org/?useformat=mobile\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "<IPython.lib.display.IFrame at 0x7fa13db22d50>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "LaTeX"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And we also support the display of mathematical expressions typeset in LaTeX, which is rendered\n",
      "in the browser thanks to the [MathJax library](http://mathjax.org)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Math\n",
      "Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<IPython.core.display.Math at 0x7fa13db94c10>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With the `Latex` class, you have to include the delimiters yourself.  This allows you to use other LaTeX modes such as `eqnarray`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Latex\n",
      "Latex(r\"\"\"\\begin{eqnarray}\n",
      "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
      "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
      "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
      "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
      "\\end{eqnarray}\"\"\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "\\begin{eqnarray}\n",
        "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
        "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
        "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
        "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
        "\\end{eqnarray}"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<IPython.core.display.Latex at 0x7fa13db94dd0>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or you can enter latex directly with the `%%latex` cell magic:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%latex\n",
      "\\begin{align}\n",
      "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
      "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
      "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
      "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
      "\\end{align}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "\\begin{align}\n",
        "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
        "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
        "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
        "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
        "\\end{align}"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Latex at 0x7fa13db94f50>"
       ]
      }
     ],
     "prompt_number": 28
    }
   ],
   "metadata": {}
  }
 ]
}