QuickTour.ipynb

{
 "metadata": {
  "name": "QuickTour"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "Fluid everyday scientific computing\n",
      "<!--====-->"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.special as spec\n",
      "x = linspace(0, 20, 200)\n",
      "f, ax = plt.subplots()\n",
      "for n in range(0,10,3):\n",
      "    plot(x, spec.jn(n, x), label=r'$J_%i(x)$' % n)\n",
      "grid()\n",
      "legend()\n",
      "title('Bessel Functions')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<matplotlib.text.Text at 0x42d6650>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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fV6uPUQ6jcHXBVbx18i24nnLF76/9jida85AkDkcXuLi4wMXFReF5AwMDzJ8/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\nuwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4213bd0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%qtconsole"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "<!--====-->\n",
      "## Document your workflows\n",
      "\n",
      "You can *italicize*, **boldface**\n",
      "\n",
      "* build\n",
      "* lists\n",
      "\n",
      "and embed code meant for illustration instead of execution in Python:\n",
      "\n",
      "    def f(x):\n",
      "        \"\"\"a docstring\"\"\"\n",
      "        return x**2\n",
      "\n",
      "or other languages:\n",
      "\n",
      "    if (i=0; i<n; i++) {\n",
      "      printf(\"hello %d\\n\", i);\n",
      "      x += 4;\n",
      "    }"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "source": [
      "Courtesy of MathJax, you can include mathematical expressions both inline: \n",
      "$e^{i\\pi} + 1 = 0$  and displayed:\n",
      "\n",
      "$$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "<!--====-->\n",
      "## Rich displays: include anyting a browser can show\n",
      "\n",
      "### Images"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(filename='fig/logo.png')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "png": 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L0amUWggcqrXO8gg2FKHG2CdW4Uem9XvBlUflu7RUaiByU3lPa92ZKN8cSav8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\nHMRRca/E5hVINNIVwI2B56z6/3ExLRI31pXNAAAAAElFTkSuQmCC\n",
       "prompt_number": 4,
       "text": [
        "<IPython.core.display.Image at 0x363f050>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML(\"\"\"<table> <tr>\n",
      "<th>Header 1</th>\n",
      "<th>Header 2</th></tr>\n",
      "<tr><td>row 1, cell 1</td>\n",
      "<td>row 1, cell 2</td></tr>\n",
      "<tr><td>row 2, cell 1</td>\n",
      "<td>row 2, cell 2</td></tr></table>\"\"\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table> <tr>\n",
        "<th>Header 1</th>\n",
        "<th>Header 2</th></tr>\n",
        "<tr><td>row 1, cell 1</td>\n",
        "<td>row 1, cell 2</td></tr>\n",
        "<tr><td>row 2, cell 1</td>\n",
        "<td>row 2, cell 2</td></tr></table>"
       ],
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<IPython.core.display.HTML at 0x486ff10>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "Loading routine datasets"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas\n",
      "from IPython.display import display\n",
      "pandas.core.format.set_printoptions(notebook_repr_html=True)\n",
      "df = pandas.read_csv('data.csv', parse_dates=True)\n",
      "display(df.head())\n",
      "plt.plot(df.index, df['Adj Close']);"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\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",
        "      <td><strong>0</strong></td>\n",
        "      <td> 2013-02-12</td>\n",
        "      <td> 200.01</td>\n",
        "      <td> 200.74</td>\n",
        "      <td> 199.02</td>\n",
        "      <td> 200.04</td>\n",
        "      <td> 2461800</td>\n",
        "      <td> 200.04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 2013-02-11</td>\n",
        "      <td> 200.98</td>\n",
        "      <td> 201.95</td>\n",
        "      <td> 199.75</td>\n",
        "      <td> 200.16</td>\n",
        "      <td> 2944700</td>\n",
        "      <td> 200.16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 2013-02-08</td>\n",
        "      <td> 199.97</td>\n",
        "      <td> 202.09</td>\n",
        "      <td> 199.68</td>\n",
        "      <td> 201.68</td>\n",
        "      <td> 2893300</td>\n",
        "      <td> 201.68</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 2013-02-07</td>\n",
        "      <td> 200.62</td>\n",
        "      <td> 200.91</td>\n",
        "      <td> 198.68</td>\n",
        "      <td> 199.74</td>\n",
        "      <td> 3076700</td>\n",
        "      <td> 199.74</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 2013-02-06</td>\n",
        "      <td> 200.39</td>\n",
        "      <td> 201.29</td>\n",
        "      <td> 199.56</td>\n",
        "      <td> 201.02</td>\n",
        "      <td> 3624200</td>\n",
        "      <td> 201.02</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "display_data",
       "text": [
        "         Date    Open    High     Low   Close   Volume  Adj Close\n",
        "0  2013-02-12  200.01  200.74  199.02  200.04  2461800     200.04\n",
        "1  2013-02-11  200.98  201.95  199.75  200.16  2944700     200.16\n",
        "2  2013-02-08  199.97  202.09  199.68  201.68  2893300     201.68\n",
        "3  2013-02-07  200.62  200.91  198.68  199.74  3076700     199.74\n",
        "4  2013-02-06  200.39  201.29  199.56  201.02  3624200     201.02"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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csN+y372b2ldGRlIfgP795X3WxL6jg8pyVFQACxY4ekcM4x5Y7P2MiAhg3Dj6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\nyDvqxunfnx4YIilKKbbOjEN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       "text": [
        "<matplotlib.figure.Figure at 0x48737d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "<!--====-->\n",
      "### Video"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "video = open(\"fig/animation.m4v\", \"rb\").read()\n",
      "video_encoded = video.encode(\"base64\")\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": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "html": [
        "<video controls alt=\"test\" src=\"data:video/x-m4v;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAAqiW1kYXQAAAKMBgX//4jcRem9\n",
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        "IGNocm9tYV9tZT0xIHRyZWxsaXM9MSA4eDhkY3Q9MSBjcW09MCBkZWFkem9uZT0yMSwxMSBmYXN0\n",
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        "\">"
       ],
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<IPython.core.display.HTML at 0x4c30f90>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "source": [
      "And more exotic objects can also be displayed, as long as their representation supports \n",
      "the IPython display protocol.\n",
      "\n",
      "For example, videos hosted externally on YouTube are easy to load (and writing a similar wrapper for other\n",
      "hosted content is trivial):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import YouTubeVideo\n",
      "YouTubeVideo('F4rFuIb1Ie4')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "html": [
        "\n",
        "            <iframe\n",
        "                width=\"400\"\n",
        "                height=\"300\"\n",
        "                src=\"http://www.youtube.com/embed/F4rFuIb1Ie4\"\n",
        "                frameborder=\"0\"\n",
        "                allowfullscreen\n",
        "            ></iframe>\n",
        "        "
       ],
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x4c31c10>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "### Embed entire websites\n",
      "<!--====-->"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=650 height=350>')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "html": [
        "<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=650 height=350>"
       ],
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<IPython.core.display.HTML at 0x4c31c90>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "<!--====-->\n",
      "## Load remote code\n",
      "\n",
      "Use ``%load`` with any local or remote url, such as [the always useful Matplotlib Gallery!](http://matplotlib.org/gallery.html)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "%load http://matplotlib.org/mpl_examples/api/hinton_demo.py"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Initial idea from David Warde-Farley on the SciPy Cookbook\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from matplotlib.patches import Rectangle\n",
      "from matplotlib.ticker import NullLocator\n",
      "#from matplotlib.collections import RegularPolyCollection\n",
      "#from matplotlib.colors import BoundaryNorm, ListedColormap\n",
      "\n",
      "def hinton(W, maxWeight=None, ax=None):\n",
      "    \"\"\"\n",
      "    Draws a Hinton diagram for visualizing a weight matrix.\n",
      "    \"\"\"\n",
      "    if not ax:\n",
      "        fig = plt.figure()\n",
      "        ax = fig.add_subplot(1, 1, 1)\n",
      "\n",
      "    if not maxWeight:\n",
      "        maxWeight = 2**np.ceil(np.log(np.abs(W).max())/np.log(2))\n",
      "\n",
      "    ax.patch.set_facecolor('gray')\n",
      "    ax.set_aspect('equal', 'box')\n",
      "    ax.xaxis.set_major_locator(NullLocator())\n",
      "    ax.yaxis.set_major_locator(NullLocator())\n",
      "\n",
      "    for (x,y),w in np.ndenumerate(W):\n",
      "        if w > 0: color = 'white'\n",
      "        else:     color = 'black'\n",
      "        size = np.sqrt(np.abs(w))\n",
      "        rect = Rectangle([x - size / 2, y - size / 2], size, size,\n",
      "            facecolor=color, edgecolor=color)\n",
      "        ax.add_patch(rect)\n",
      "    ax.autoscale_view()\n",
      "\n",
      "    # Reverse the yaxis limits\n",
      "    ax.set_ylim(*ax.get_ylim()[::-1])\n",
      "\n",
      "## Potential way using polygon collections that just has an issue with\n",
      "## easily getting the squares scaled by the data.\n",
      "\n",
      "#    height,width = W.shape\n",
      "#    x = np.arange(width)\n",
      "#    y = np.arange(height)\n",
      "#    X,Y = np.meshgrid(x, y)\n",
      "#    xy = np.array([X.flatten(),Y.flatten()]).T\n",
      "#    scaled_data = W.flatten() / maxWeight\n",
      "#    cmap = ListedColormap(['black', 'white'])\n",
      "#    norm = BoundaryNorm([-1., 0., 1.], cmap.N)\n",
      "\n",
      "#    rect_col = RegularPolyCollection(4, rotation=np.pi/4,\n",
      "#        sizes=np.abs(scaled_data) * 72 / ax.figure.get_dpi(), offsets=xy,\n",
      "#        transOffset=ax.transData, norm=norm, cmap=cmap, edgecolor='none')\n",
      "#    ax.add_collection(rect_col)\n",
      "#    rect_col.set_array(scaled_data)\n",
      "#    ax.autoscale_view()\n",
      "\n",
      "if __name__ == '__main__':\n",
      "    hinton(np.random.rand(20, 20) - 0.5)\n",
      "    plt.title('Hinton Example')\n",
      "    plt.show()\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ofHFNp/RtRzSnx0xtD903EqHqE5GpgVJaYp4HL9aXUtgOtGs6pW877EKUB0iEaqwrnxt6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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x486fd50>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "<!--====-->\n",
      "## LaTeX rendering and SymPy support\n",
      "\n",
      "Objects can have a LaTeX representation, rendered thanks to MathJax:"
     ]
    },
    {
     "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": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "latex": [
        "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$"
       ],
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<IPython.core.display.Math at 0x4c1f110>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Latex\n",
      "Latex(r\"\"\"\\begin{eqnarray}\n",
      "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \n",
      "\\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\\, \n",
      "\\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = & \\vec{\\mathbf{0}} \\\\\n",
      "\\nabla \\cdot \\vec{\\mathbf{B}} & = & 0 \n",
      "\\end{eqnarray}\"\"\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "latex": [
        "\\begin{eqnarray}\n",
        "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \n",
        "\\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\\, \n",
        "\\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = & \\vec{\\mathbf{0}} \\\\\n",
        "\\nabla \\cdot \\vec{\\mathbf{B}} & = & 0 \n",
        "\\end{eqnarray}"
       ],
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.Latex at 0x1af71d0>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "<!--====-->\n",
      "## This plays very nicely with SymPy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext sympyprinting\n",
      "from __future__ import division\n",
      "from sympy import *\n",
      "x, y = symbols(\"x y\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "eq = ((x+y)**2 * (x+1)); eq"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAHQAAAAbCAYAAACtOKuoAAAABHNCSVQICAgIfAhkiAAAA+lJREFU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\n",
       "prompt_number": 15,
       "text": [
        "\n",
        "               2\n",
        "(x + 1)\u22c5(x + y) "
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expand(eq)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAQ0AAAAbCAYAAABm6to6AAAABHNCSVQICAgIfAhkiAAABP5JREFU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\n7l6NeSaTyWQymUwmk8lkMpke518QvOZokU+z1gAAAABJRU5ErkJggg==\n",
       "prompt_number": 16,
       "text": [
        "\n",
        " 3      2      2      2            2\n",
        "x  + 2\u22c5x \u22c5y + x  + x\u22c5y  + 2\u22c5x\u22c5y + y "
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(1/cos(x)).series(x, 0, 6)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAMEAAAAfCAYAAABedqnDAAAABHNCSVQICAgIfAhkiAAABkNJREFU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\nNfEf/bCaiZS/zxYAAAAASUVORK5CYII=\n",
       "prompt_number": 17,
       "text": [
        "\n",
        "     2      4        \n",
        "    x    5\u22c5x     \u239b 6\u239e\n",
        "1 + \u2500\u2500 + \u2500\u2500\u2500\u2500 + O\u239dx \u23a0\n",
        "    2     24         "
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diff(cos(x**2)**2 / (1+x), x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAL4AAAAoCAYAAABAS0DDAAAABHNCSVQICAgIfAhkiAAABjZJREFU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\nAABJRU5ErkJggg==\n",
       "prompt_number": 18,
       "text": [
        "\n",
        "                           2    \n",
        "         \u239b 2\u239e    \u239b 2\u239e       \u239b 2\u239e\n",
        "  4\u22c5x\u22c5sin\u239dx \u23a0\u22c5cos\u239dx \u23a0   cos \u239dx \u23a0\n",
        "- \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 - \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
        "         x + 1                 2\n",
        "                        (x + 1) "
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "<!--====-->\n",
      "Use other tools seamlessly: talk to R (or Octave)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "%load_ext rmagic"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "source": [
      "A simple dataset plotted in Python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "X = np.array([0,1,2,3,4])\n",
      "Y = np.array([3,5,4,6,7])\n",
      "plt.scatter(X, Y);"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "Figure(480x320)"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "## Use R to do a linear fit\n",
      "<!--====-->"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i X,Y -o XYcoef\n",
      "XYlm = lm(Y~X)\n",
      "XYcoef = coef(XYlm)\n",
      "print(summary(XYlm))\n",
      "par(mfrow=c(2,2))\n",
      "plot(XYlm)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "display_data",
       "text": [
        "\n",
        "Call:\n",
        "lm(formula = Y ~ X)\n",
        "\n",
        "Residuals:\n",
        "   1    2    3    4    5 \n",
        "-0.2  0.9 -1.0  0.1  0.2 \n",
        "\n",
        "Coefficients:\n",
        "            Estimate Std. Error t value Pr(>|t|)  \n",
        "(Intercept)   3.2000     0.6164   5.191   0.0139 *\n",
        "X             0.9000     0.2517   3.576   0.0374 *\n",
        "---\n",
        "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1 \n",
        "\n",
        "Residual standard error: 0.7958 on 3 degrees of freedom\n",
        "Multiple R-squared:  0.81,\tAdjusted R-squared: 0.7467 \n",
        "F-statistic: 12.79 on 1 and 3 DF,  p-value: 0.03739 \n",
        "\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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g0NQ+Qt5gMktHjUp+9owu8fDc+PIl+d692r17S8vKaBFPaam2trZA2lhj1NXV\n9+zZc/78+R07dqiqqlJaFwKAmpoa7j+6UlJSTS8PRhBESUkJADx58uTDhw8AYGtrK04DmVuLqKNP\nnz59+vTp1q2bioqKhoaGioqKiYkJIQjJycnN3HP+/Pk//fTTD3Z6+5YYOZKoqWltWAIXHU3Mny/q\nIJp08iTh7S3qIL7z+++///3339SV/+jRIz09PRMTky1btly4cIG6inhqVnsWL0+fPh05cmRUVNST\nJ0+GDx/+9u1bnruVlZURBBETE/PkyRPhBkgtwbbn786gBX7Vu7KyktwQ5NVzgoBly8DXFxrcgy96\nAwdCYiJ8/SrqOBqRmwv+/lBn1u/2oPUrqqAWGTZs2G+//fbnn39evHjxyJEj9S7uVVRUEASRlpZG\njrvt37+/qampiCJtA3jkOPKqN4vFMjc3z87O5qPQ+Pj48ePHq6io6OvrKyoqjh8/XpCrg586BUOH\n0nf2uEmT4OpVUQfRiFWrYOfO9raIIhUrqqCmmZqa7t+/f8+ePX379iWfIQiitrb206dP58+fr6ys\n1NXVbeUMVu0EjwRNXvVOSEg4dOgQf1e9lyxZsmbNmuzs7Pfv3+fk5Kxbt05gI21zcyEgALy8BFMa\nFWbPhnPnRB0EL9eugawsDBsm6jiEjYoVVVCL1NbWnj17tqCgQEFBYe7cuVRfFhYnPEZxtP6qN4XT\nM65cCbt2NXN2HtHo2BEIAjIzgVZj6UtKwMcH/v5b1HGIgJ+f36lTpwSyokpz3L9/v+5kmFlZWe35\nktfVq1e7d+/eu3dvZ2dnUcfSJn2XN7du3ert7X348GE5OTlylMzBgwc3bdrU0kKpulHl6lVQUAAT\nEwEURalZs+DcOeBrhCJVvL3B0xPk5UUdhwicP3/e1dVVQkICAE6fPj137lxKq4uOjq47bqGgoKAd\n9qv8888/1dXVw4cPnzRpkqhjadu+S9DkfEbkDAatQcmNKiUlsGNH2zgHtLeHESNolKCfPIHsbBg/\nXtRxiMbq1asvXrwYGBgoJycXEhJCdYL28PCo+5AcTUhpjfSRmJiYkJDg4ODQHmbJEI7vEjS5/KKN\njU1+fr6CgkJAQEDDVQqbQ4DTM1ZXVz948KCsrGz09etSXl5t4xxQSgr69IHoaPh+/VDRqKyEX3+F\noCBRxyEy5ubmc+fOtbW1PX/+vKhjEU8FBYQmCsUAACAASURBVAV///23k5NTz549e9L26n3bxOMi\noaura2Ji4qZNm9hs9sKFC4UfE1dlZaWtrW1kZGTN48dPL15MbBPTXJDmzgWa3LS2Ywc4O0OnTqKO\nQ5QmTJhw8ODByZMnC3I0UbtXWVl5+vTp4uJiVVVVZ2fnhktPodbj8TetqqqaOHFienr66tWrRfvr\nLCQkZOzYsV6rVztERmpeurR79+6ampoHDx64038V7eHD4cULqKoScRjx8RARAQsWiDgMkZoxYwYA\nDBw48MqVK63vvkMAcP369ZSUFElJyVmzZikqKoo6HHHGY3CFhISEt7f3kCFDwsLCPn36JPyYuPLz\n87t06QJMJuzf31lfP3/v3tzc3OfPn1eJPPE1x4gRcOcO2NmJLACCgFWrYPduYDBEFoNIkRe9yduv\nyGcoXDWiHXj69CmbzR48ePCYMWPIJdJx8Vyq8TiD9vHxUVNTW7lyZUJCgr+/v/Bj4rKxsQkICCj8\n/Lm6b99t27aNHz9eQ0NDtL0uLfDTT3DqlCgDOHoUzMxArKcSbRr3onddog6q7UlOTg4NDQWAAQMG\nkKt5sNvlymciweMfwF69evXq1au0tFTkqbBHjx7r1q1zcnIie13mz58v2nhaRkcHPn2CggIQyQQ9\nmZlw/jzcuyeCqmmDvOjN34VulJeX9/TpU3t7+y5dunTt2hUAeK8sjKjEI0E/fvx43rx56urqdnZ2\nvXr1mjJlivDD4rK2tra2thZhAK0yYwb89Re4uYmgajc32LUL2vcvUFzyig/l5eV37twZNWqUvLz8\nqFGjoM4KHkj4eHRx0HxyGUVFRT8/P1FH0TyOjnDxogjqvXgR9PRAfFfSbCZc8qr5CIIIDQ398OED\ni8WysLCQlpaWkpJqh7fY0A2PBI2TywiMvDxoaEBCglArLSqCvXth2zahVkpjrZ/8S7y9fv06MTGR\nwWD07NmzY8eOLBYLB2bQB48EjZPLCNKcOXDmjFBrXLsW1q8H7C78pvWTf3ERBFFUVETUWQCo7UpP\nT3/x4gUAqKqqkjOCampq4lhmuuHxefj5+Q0cONDQ0FBDQ+P48ePCj0msjBwJDx5Ak4tKCFJoKHz9\nCra2QqquLfD09Hz79u2aNWsyMjL4m/xr6dKlABATE9OzZ09TU1NDQ8Nnz54JOkwhKS4ufvLkCQBI\nSkp2794dADp37oyj5Wjruw+muLj47Nmz3bp1W7BgwevXr5OSklavXn3gwAG+SycIori4WFFRkdFe\nh+ICkwmWlvDgAYwcSXldZWWwYQNcu0Z5RW3KsmXLoqOjWzMyLDIyEgC8vLyCg4P79euXnZ3t5OT0\n8OFDgYVIvYqKihcvXgwZMqSmpkZPT+/Dhw8JCQm6urry8vIAUFNT8/jx40uXLvn6+oo6UvSd786g\np02b9vDhw3379s2cOXPBggWPHj3ib704cTrjEACh3fa9dSssWgQqKsKoq+0YPHiwmZnZ2rVrvb29\nvVuxmoy0tDQ5KaOGhkbT6+zRB0EQMTExxcXFlZWVqqqqLBZLRUUlPDx85syZT58+Xb58+Y4dOwCg\nLd3/1c58dwadk5Nz586diooKbW3t9PR0vofXiMEZhyD16gXv30NJCcjJUVjLy5cQG4vXBhuaOHFi\nKxfvSElJcXBwyMjICAwMdHZ23rlzJ/2vzaSlpXE4HDU1NSaT2aFDBzabLfet+fn6+t69e5ecNX/E\niBFubm7k/V/r1q0TaciIh+8StLKyMgBwOJxevXq1fvBjWzzjoAo53s7Fharya2pg1So4eZKq8tsy\ne3t77nZUVBQfJeTn56elpSUlJZFfEDabfezYMYHFJ1AfP3788uWLjo5OYWFhjx49WCxWvanYCYJg\nsVjcNU26du2ak5NTb9lARB+UXBxoi2cc1HJyAicnChO0ry/Y2YGODlXlt2XBwcFnzpypra0lCCI9\nPZ07L0fzSUhI6Ovrc8dQr1y5UtAxtlZ5eXlWVpa+vj53AZdBjUx1y2AwFBQUIiIihg0blpKSkpCQ\noKenJ9xgUQt8l6AjIyPJ6VwzMjK487omJia2tNA2dMYhJMrKICMDqanQtavgC09JgcuX4f59wZcs\nFo4ePbp58+ZTp07NmDHj9u3bAilTR0cnPT1dIEW1Rk1NTVpamp6eXnZ2NvkjtTlLFx07dmzFihVe\nXl6ysrInT54kh9a1pfu/2pPvEnRGRoZACqX/GYcIzJkDZ89CKy5SNWrFCvD1BQkJwZcsFphMpqmp\n6blz52xsbA4dOiSQMu+L+p/D7OxsVVXVwsLC3NxcfX39Fp0Fq6mpnT17lrrYkAB9l6BVKZvWhyZn\nHKJkZwc7d4KXl4An/zx7Fvr0AYEs+SimpKSkgoKCKisrDx06VFpa2pqiKisrJSUlAaCJW8bHjRtX\ndzhEQkLCYMHdc19YWMjhcJhMZnx8vIWFRadOnTq176UYxJ6QBqg3ccbRsEGTUxqKGzYbjI0hIgJM\nTQVWZl4eHDkCoaECK1AcnT9/Pjs729zc/MiRIxs3buSjhPj4+DVr1kRERMjIyJSUlJiZme3du7ex\nqaVv3rxZ9+GCBQtav+pFRUVFWVmZgoLCs2fPTExMlJWVRwphWD2iAWoTdHPOOKho0DQ1Zw6cOCHI\nBL16NWzbBt+uyCOeZGRkyFEKmzdv5q+EJUuWbNmyZejQoRwOp7y8PCoqatmyZXfu3BFomDzU1NSU\nlJQoKipGRkZqaWkpKiqOGzeO6koRrVBy6318fPz48eNVVFT09fUVFRXHjx+Pa8EBABgbw7//QlmZ\nYEq7fRvYbLC0FExp4oicob979+6qqqqampqqqqrDhg3joxwmk2lubk4OTZOSkiKnXhJ0sN/5/Pkz\nAMTExCQnJwOAubm5rq4upTUieqKknYnqjKMNGD8eQkJg2rTWlvPlC2zeDN//+ED1kCPqZs+evWjR\nIhMTk4iIiKNHj/JRjpGR0dixY4cNG6arq5uenv7s2bPmDJbgw9evX2VkZNLS0t6+fTtq1CgjIyMq\nakFtCCVn0MI/42gzZs8GgVxA37gRVqwABQUBFCXuWj/d6K5duzw9PdlsdmxsLJPJ9PT03L59uwAj\nrK6urqmpKS4uvnr1alVVla6uLjlTPkKU5E2hnXG0PRoaUF0NOTnQmovvERGQlgZ79gguLHFGTjdq\naWl5//59/qYbZTAYFhYWFhYWAODh4WFmZiaQwMh7ZwDg0qVLI0eOVFZWnjlzpkBKRmKDkjNoqs84\n2rZZs+B//+P/7VVVsHYt4KxjzXbmzJlWTjdaV2ZmZutDIm8quXbtWkpKioSExLRp08hbuhCqh5Iz\naIrOOMSEgwOMGgW//MLn23fvhhkzoJ3fOt8S6urqewT3a2P8+PGtLOHp06cAYGpqWneSEIR4orxr\nWCBnHGJFWhoMDCAmBgYMaPF7ExMhNBTwcmtL+Pr61r02yMdcHHU5Ozvz98b4+PiUlJTx48ebCnCc\nJRJ3lCfo1p9xiKG5c+HPP1ucoAkCVqyAQ4cA1yVqieDg4EePHqmIbppsNTW12tra3r179+7dW1Qx\noDaK8q8632cc4szcHCIioKXzo/v7w7BhYGBATUxiS09PT7Q9vPn5+bjWH+IPjn4TBQYDRo+G27eh\n+T8vsrLg5El48IDKsMRTbW2tsbGxtbU1OdbTx8dH1BEh1FyYoEXExQXWrWtBgl61CvbuBUlJKmMS\nT9Naf1sQQiKCCVpEunaFwkIoKgIlpR/vfOUKqKrC0KHUhyWG6l4F4W9FFYREBRO06EybBn/9BYsX\n/2C34mLYvh3u3RNKTGKo9SuqICQqeO1CdKZPh+DgH+/m5QUbN1K74KxYO3r06Lp16zQ0NFatWmVn\nZyfqcBBqAUzQoiMvD+rq0PSKYuHhUFgIOMlkK5ArqnA4HBsbG3JyOITaCkzQIjVnDgQGNvpqeTms\nXg1//CHEgMSQAFdUQUjIMEGL1OjRcO8eNLZAgY8PLFgAHTsKNyZxc/78+UGDBm3atCk3N5e/FVUQ\nEhUhraiCeJOQAHNzePQIrK3rv/Tvv/D8OWBCabUbN25MnToVADZv3nz27Fn+5uxvPjc3t+rqau7D\n8PDwXr16UVojEmOUJOgWreHW3rm4wK5d9RN0TQ38/DOcOCHgFWbbmaCgoKCgoOfPn//1118AQBDE\nmzdvZs2aRWmlHh4e5CSipE+fPklJSVFaIxJjuKKKqPXpAykp8OULyMr+/5NHjsDo0dC1q+jCEgc2\nNjb9+vXbt2/fypUryWc6Ut9f1PX7T01OTk5s19hE1KMkQZMrqjAYDMAVVZpjyhS4fBlmz/7v4fv3\ncOECDnxuPVVVVVVVVXIqu8+fPxMEoYBr0KA2hZKLhOSKKr/99tvp06c3b948btw4XFGlKU5O303h\nv2wZ7N8P+E9aq7148cLY2LiwsPDhw4cGBgYDBgy4cuWKqINCqAUoyQK7du0KCwsLDw+PjY1VVlb2\n9PQcPnw4FRWJCTU1kJKCjAzQ1oa//oIePaB/f1HHJA7c3d3Pnj2rrKy8ZcuWGzdu6Ovr29jY4DT5\nqA2hfEUV1BxxAwYk2ts/7tlz++vXHf75R9ThiAkmk2lgYFBWVlZYWDho0CAAYLPZog4KoRYQ0jho\nHR0d4VTUFj1+/HjD8+cTGIzN5eUb2OyE9HRRRyQmvnz5Ultbe+fOncGDBwNAVVUVJmjUtgipo/P+\n/fuNvbR7925yDU1SfHx8t27dhBKUKIWFhR06dKhDhw5OTk4XL17csmMHx9eXU1Jiu2PHpUuXvLy8\nRB2gOJg6derAgQPz8/Nv3LiRlJS0YsUKnIsDtS1CStD6+vqNvTRo0KC645A+fvyora0tlKBE6fXr\n18ePH8/Lyzt48KCMjMzXr19h/35gMktv3JCRkRF1dGLC09Nz7NixampqWlpacXFxs2fPxrmhUdsi\n+qECNjY2dR9mZWW1h3GjS5cujYqKWrBggY+Pj7a29k8//eTt7V1RUbFjx46rV6+KOjoxwWAwyK5n\nADA0NDQ0NBRtPAi1FCUJevfu3Q2f9PDwoKKuNiooKGjy5MlPnjxxcnK6cuXK+fPnz507JykpGRIS\noq6uLuroEEK0QEmCVlJSWrJkya5duyQkJKgoXwxISko6OzsrKyuTdx7r6OisW7dO1EGhHyAIori4\nWFFRkYG34COhoCRBu7q6njt3zs3NDW8gbIy9vT0OyG0rli5devjw4ZiYmOnTpzOZTCaT6e/vb2Ji\nIuq4kPijapjdvXv3MDsj8RAZGQkAXl5ewcHBCQkJd+/e5fvnTnV19d27d69du1ZYWAgAYWFhM2bM\ncHV1DQ0NFWTESFxQPg4au56ReJCWliZnLNDQ0Kg7MLT5Kisrx4wZ8/Dhw+Tk5DFjxiQmJpKDeTw9\nPa9fvy7oeJE4oDxBZ2ZmUl0FQpRKSUlxcHBITU0NDAwEgJ07d2poaPBRTkhIyLhx47Zt2/bLL7+c\nOnVq9+7dS5cuffPmjaOjo62traCjRuKA8l6IuoveI9QW5efnp6WlJSUlKSsrAwCbzT527Bh/5XTp\n0oXc1tLSys/PrzuYZ8yYMYIMGokFyhO0s7Mz1VUgRCkJCQl9fX3uzVbc2aVbysbGxt3dfdSoUfLy\n8tu2bRs/fny9wTwI1YPX8RBqMR0dnfSWT5nSo0ePdevWOTk5VVVVTZw4cf78+QwGAwfzoCZggkao\nxZqYW+bixYt1b4VNTU3t1KkT96G1tbV1w/UnEWoEHRN0VFSUbN31n5qnpqaGHA6FRMXQ0FBOTq6l\n74qLi+PekN1WNDG3TGpqat0xHoqKirW1tcHBwS0qv6ysLCYmhv/4kIAYGBiQFx6aT7DtmVF3gUua\n4O8KTGhoaE5ODt8TLZWVlb148cLMzIy/twPA3bt3R40axffb37x5Iy0t3Zr4o6KizM3N+Q6g9fEb\nGBjwEQCDwZgxYwYfmb2t4KM9X7hwgclkqqmpUREPqZUf9w+1sj03B9WHkJGRIS8vP2HChBa9S7Dt\nmY4Jmj++vr4aGhpTpkzh7+05OTkrV648d+4c3wFYW1s/ePCA77cfPHiwU6dOjo6O/L09Nzd3+fLl\n58+f5zuAVsZ/6NAhdXX1qVOn8l0CbQl/bhkvLy87OztTU1Pqqmjlx/1DrWzPzUH1IVy6dCkzM/Pn\nn3+mroofEtKE/Qi1XUpKSp6enmw2W6oOUQeF2gU69kEjRCs4twwSFWxwCP3YvXv3RB0Cao+wiwOh\nFsC5ZZAwic9FQoSEYMaMGa25EotQi+AZNEItgHPLIGHCM2iEEKIpPINGCCGawgSNEEI0hQkaIYRo\nChM0QgjRFCZohBCiKUzQCCFEU20yQZeVlU2ZMmXEiBHGxsbPnj2r+1JNTY2ysrKxsbGxsfHSpUsp\nDaOysrJfv35paWnCD6CJWoQTwJEjR6ytrfv37x8RESH82sWVv79/w5nzCILw8PCwtra2tLSMj4/n\nr+QmCmn9R9ZE4QIJnur466LuI+AT0QadOHFizZo1BEE8fvzY2tq67kvJycn29vbCCWPLli0cDic1\nNVX4ATRRixACiIuLMzIyqqysfPXq1dChQ4Vcu7gaOXKkpKTkrl276j3/6NGj0aNH19bWPnr0yM7O\njr/Cmyik9R9ZE4ULJPimyxFgk6P0I+BPmzyDNjExWbJkCQAQBKGoqFj3paSkpIyMDEdHx/Hjx1O6\nJkViYmJkZOTQoUPrPS+cAJqoRQgB3LhxY+rUqWw2u2/fvteuXRNy7eLq1q1bO3fubPh8eHi4qakp\ng8EYMmRIvd+LzddEIa3/yJooXCDBUx0/F6UfAX/aZILu06ePrq7uggULRo8eXW+JZWVl5RUrVgQH\nB2/btm369OkENfdJ1tbWLl++/I8//mAwGPVeEk4ATdQihAByc3MTEhLGjBljaWkZHR3dzMBQ01gs\nloSERMPnCwoKdHV1AUBKSkpaWrqyspKPwpsopPUfWROFCyR4quPnovQj4DMkodUkQJ8/f5aWlj5+\n/Linp+eIESPevXvHTZRDhw4lz2r79+/PZDKLi4uVlJQEHsDRo0dHjRqlp6fX8CXhBNBELUIIQE5O\nLisrKyQkJDc3d9CgQZmZmRwOR2i1i5PAwMDr16936dKF56ItJGVlZXIF8fLy8pKSEklJST7Kb6KQ\n1n9kTRTemuCbWYUQmpygjoIPbfIM+vfffz969CgAcDicev+a7du3j/yRkp2dzWAw6nWACEpkZOTf\nf/9ta2v76tWruXPn1r1OKJwAmqhFCAGYm5vLy8uz2Ww5OTkmk1n3nEU4hy82nJ2dz58/30R2BgBL\nS0vyZ3VUVNTw4cP5K7+JQlr/kTVReGuCb2Y5QmhygjoKfgizw1tQ8vLyxo4da2pqOmDAgBs3bhAE\nkZycrK2tTRBEcXHxtGnTzMzMLCwsnj59SnUklpaW5EVCIQfQsBZhBlBbW7t27dohQ4b0798/KChI\nyLWLMV9fX+4VKu6ftLa2dvXq1ba2tiNHjkxMTOSv5IaFCPAja6JwgQRPdfx1UfcR8Adns0MIIZpq\nk10cCCHUHmCCRgghmsIEjRBCNIUJGiGEaAoTNEII0RQmaIQQoilM0AghRFOYoBFCiKYwQSOEEE1h\ngkYIIZrCBI0QQjSFCRohhGgKEzRCCNEUJmiEEKIpTNAIIURTmKBbLDMzs0OHDlbfuLu7BwUFbd26\ntaCgICgoCAC4G00zNDRsUb0XLlzw9vbmM2gk7hYtWmRlZdWjRw8dHR0rK6vJkydfv35dsA2G27DJ\nBv/D/Ru22Pj4+AkTJlhYWJibmy9cuPDjx4+CCkZcvx1tck1CkevatevDhw/rPZmYmBgUFDRt2jSy\n6UybNk0UoaF2ilwEzs/PLzMzk8ye169fF2wV3IbNX9vOy8ubNGnS+fPnjYyMCII4deqUvb39o0eP\nmEx+ThPrBXPhwgU+CqE/PIMWgAsXLmzatGnfvn3Pnz8PDg7mblRVVbm6upqamlpYWDx+/BgA8vPz\nx44dO3r06NmzZ1dUVNQtZMyYMVFRUQBw+/btWbNmff36deLEiaNHjx49enTdxhcYGOjj4wMA5eXl\nxsbGANCwloSEhEmTJo0fP37y5MmFhYXC/FMgWnnx4oWzs7OFhYWvry/waiqlpaUzZswYO3ashYXF\n/fv3AeDKlSuurq59+/Z99epVvZ25DZts8CUlJdOnT7e1tbWwsPj3338ba7Fcf/75p7Ozs5GREQAw\nGIx58+axWKznz583bNINi7p+/fqcOXPmzZtnbm5OHku9YMgqxO+7gGfQ/EhNTbWysiK3yQUrAWDl\nypUfP36cOnVq3759yY3jx49LSko+efIkLy/P1NT03bt3Pj4+EydOXLJkSXh4+K1bt+qWOWfOnL/+\n+svY2PjPP/9ctGhRdna2s7PztGnTnj596uPj4+jo2Fgwp06dqlfL7du3+/btu2XLlmvXruXn5ysr\nK1P2l0C09vHjx5s3b+bn59vY2Li7uzdsKn5+foaGht7e3hkZGRYWFuTyx3FxcdHR0Q135rZwMmke\nOHDA0NBw/fr1YWFh4eHhHA6n6RabmppqYWFR95mePXumpKQ0DJtn44+Li3vx4kVhYaGlpaW7u3u9\nYEji913ABM2Pel0cGRkZPHd7/fr1mzdv5s2bBwDq6uqlpaVJSUnz588HABMTEw6HU3dne3v77du3\nr1+/PjEx0dzcPDc3NzQ09PHjx5WVlbW1tQ0Lr66ubqwWFxeXPXv2jBs3TkdHh1yRHrVP1tbWDAZD\nXV2dfNiwqSQmJjo5OQGAtrY2AHz9+hUArKys2Gx2w53rFR4XF7d48WIAMDc3Nzc3z8nJabrF6unp\nvXnzhtyuqamRkJBISEj46aefuE9ym7SsrGzDoshjUVFRaeJ4xe+7gF0cgsRdgZfcMDAwMDU1PXXq\n1P79++3s7Dp06NCrV68HDx4AQERERL0ujg4dOgwZMsTDw8PZ2ZnBYBw+fLhfv34HDx6cMWNG3YV9\nmUxmXl4eAISGhpLPNKwlKCho9OjRt27d0tPTCwgIEM6xIxpis9l1HzZsKj179nzy5AkApKWlEQQh\nIyPDfVfDnaFOCyd3CA8PB4AHDx6sW7eusRbLNXv27MDAwBcvXgDAmTNn7O3tJSQkjIyMGjZpnkXV\nO5Z6wTR2gG39u4Bn0ALTsWPHN2/enD17dty4ceTG/Pnz58+fP2LEiNLS0tWrVwPAmjVrZs+efeHC\nhc6dO3fv3r1eCXPmzLG1tc3KygIAW1vbzZs3379/v1u3bunp6WT3NABYWVn5+/tPmjTJ0NCQ/Do1\nrGXQoEHz5s1TVFRkMpnHjh0T6l8B0VjDprJ48eJ58+aNGjWqvLz81KlTTe/MbeHkj7+VK1fOmzdv\nxIgRZWVl/v7+RUVFPFssl7q6+pUrV9asWVNYWFhSUmJgYAAAhYWFDZt0Y42/rnrBNBZzW/8uMHj+\nW4cQQlR7//69hoaGhISEqAOhL0zQCCFEU9gHjRBCNIUJGiGEaAoTNEII0RQmaIQQoilM0AghRFOY\noBFCiKYwQSOEEE1hgkYIIZrCBI0QQjSFCRohhGgKEzRCCNEUJmiEEKIpTNAIIURTmKARQoimMEEj\nhBBNYYJGCCGawgSNEEI0hQkaIYRoChM0QgjRFCZohBCiKUzQCCFEU5igEUKIpjBBI4QQTWGCRggh\nmsIEjRBCNIUJGiGEaAoTNEII0RQmaIQQoilM0AghRFOYoBFCiKYwQSOEEE1hgkYIIZrCBI0QQjSF\nCRohhGgKEzRCCNEUJmiEEKIpTNAIIURTmKARQoimMEEjhBBNYYJGCCGawgSNEEI0hQkaIYRoChM0\nQgjRFCZohBCiKUzQCCFEU5igEUKIpjBBI4QQTWGCRgghmsIEjRBCNIUJmodXr16NHj1aQUFBRUVl\n4sSJb9++bf57Y2JiDA0Nm7Pnly9fGAxGQUEBv2HywGKxqquro6KijI2NBVgsEqEuXbowvpGTk7Oz\ns8vOzm7+23k2huLiYkVFxRaF0fyG3UxUtH/xgwm6vpqaGjs7uyFDhrx8+TIuLs7AwMDBwYEgCFHH\n1QJdu3bdvHmzqKNAAnPz5s2ioqLCwsLo6OjPnz97eXk1/73YGNo0TND1ZWVlZWZmrl27Vk9Pr3Pn\nzj4+Pl27dv306RMABAUFde/eXUVFZcmSJRUVFQBw/Pjxrl27SktLm5iYvHnzpl5RYWFhAwcO7NCh\ng62tbVZWVjMDuHTpkoGBgYKCwpQpU/Lz88knm1P16NGja2pq9PX14+LiNmzY0FhpiYmJZmZmu3fv\n1tTU7Nq16/379wXwV0NUkpOTU1RUVFJS6t69u7Ozc0pKCvl8wwZWXV29ZMkSJSUlVVXVLVu2AEBq\naiq3Mfj6+mppaWlpaQUEBJDPPHv2zMTEpOF2Ew27YRVctra2x44dI7d37do1Y8aMJnZuTMODalgs\nz93i4uKsrKy2bt3ar1+/xg7h1KlTurq6urq6p0+f1tXVbaxGGiHQ96qqqgwNDceMGXPr1q2vX79y\nn3/z5o2KisrTp0/fvXtnZGR04sSJjIwMSUnJR48e5efnu7i4LFy4kCCIly9f9unThyCIgoICFRWV\na9euFRYWurm5WVlZ1auopKQEAPLz8+s+mZKSoqCgcOfOnY8fP7q4uEyfPr35VRMEISEhUVVV9fz5\ncyMjo8ZKS0hI6NChw/bt20tLS9esWTNs2DAK/5qo1TQ1NcPCwsjtrKwse3v7bdu2EY00sKCgIAMD\ng9TU1OjoaA6Hk5yczG0Mjx8/VlJSevToUWZmpo2NjYKCAkEQERERQ4cOJQvnbjfdsBtWwQ31yJEj\nEydOJLdNTU0vX77cxM482z/Pg2pYLM/dXr9+raCg4OLiEhcXx/MQXr16paqq+s8//2RlZZmZmeno\n6DRWI31gguahvLz88OHDtra2KioqY8aMiYyMJAhiy5Yt7u7u5A4xMTGPHj0qKytLT08nCOLLly8e\nHh5k+uO241OnTk2ZMoXcv6ysTEZGI6kVMgAAIABJREFUprq6um4tPBvovn375s6dS27n5eWx2ezq\n6upmVk00SNA8S0tISJCXl6+qqiII4vXr1wYGBgL94yEB09TU7NChg4KCgry8PACYmJiQDYlnAwsK\nCtLT0/vnn39qa2vz8/MrKiq4jWH58uXr1q0j93/y5EkTCbrpht2wCm6o2dnZsrKyZWVlHz58UFRU\nLCsra2Jnnu2f50E1LJbnbq9fv5aUlCwvL2/sEDw9PVevXk2+6+rVq2SC/uH3VLRYIj19p6PKykqC\nIJYsWUJ2Jpw7d87c3DwsLCwzM7N79+7kPv379weA6urqEydO3Lp1S0FBgcPhyMnJ1S3n/fv3d+7c\n4f6MkpSUzMvLu3Dhwvr16wFg796906ZNa1h7Tk4O9y1qamqSkpL5+fktrbrp0gCgU6dOLBYLAMj/\nI5o7efLk4MGDAaCgoGDmzJmBgYFz587l2cAmT578+fPnhQsX5ubmurm5rVq1iltITk7OyJEjyW09\nPb2GtRDfLrSwWKwmWlcTVXTu3NnQ0PDhw4cZGRmTJk2SkpJqYmeeeB5Uw2J57gYAWlpaHA6nsUPI\nysoi/4wAoK2t3XSNTccpNPj9rC84ODggICA0NBQAOByOi4vL6dOnX7582bFjx8zMTHKfiIiI5ORk\nFot148aNu3fvKisrBwYGXr9+vW45nTt3HjVq1MWLFwGgpqbm5cuXnTp1cnd3d3d3J3f48uVLw9o7\ndeoUGxtLbpNnHKqqqi2tuunSiouLGQxGK/9KSJg6d+5MZhBdXd0pU6a8fPly7ty5PBtYcnKyjY2N\nq6trRkbG1KlTFRUVud3KGhoa7969I7dTU1O5hVdXV5Mb3DYWHBzcROtKSUmpV8WyZcu4rzo4ONy4\ncePdu3c///zzD3fmeaQND6phsTx3+/jxI/eEg+chdO7cOSMjg9zh/fv3TddIE3iRsL5Ro0ZFR0dv\n3Ljx7du3b968OXLkyIsXL6ytradMmXLmzJl//vknJSVlxYoVBQUFHz9+lJWVlZaWzsvL8/X1LSsr\nq1uOnZ1dWFjYzZs3CwoK1q1bt2LFCp5p8dOnT8XfVFRUTJgw4dKlS6GhoUVFRR4eHg4ODiwWq0VV\n1837PEuj7k+HhKBTp05kcuHZwK5duzZz5szc3NyampqKigppaWnuGx0dHY8ePRoWFpadnb1hwway\nNSooKMTGxsbExHz8+PHQoUPknk037CaqAAB7e/vLly/HxsaOGDHihztDg/bf2LemXrE//HLxPARH\nR8eTJ09GRUV9+PBhz5495J7N/J6KjKj7WOgoKSnJzs6uY8eOcnJyw4YNu3XrFvn86dOnu3btKi8v\nP2/evIqKiuLi4lGjRikrK5uamoaEhHTs2PHPP//kdtURBHH79u0+ffrIyMhYW1u/e/euXi1kH1xd\nx48fJwjiwoULPXr0kJOTs7e3z83NbX7VBEFMmzZNTk7u4cOHZLcjz9ISEhK4/c51txE91b1ISBDE\njRs31NXVP336RPBqYCUlJfb29h06dFBWVl68eHFlZSW3D5ogCF9f3y5dumhqagYEBGhqahIEUVtb\n6+7uLisr27dv3+DgYLIPuumG3bCKegH37t170aJF5HYTOzfW/hv71tQtlududS+oNPYF8fPz69y5\nc48ePfz8/Jr5PRUtBtGmRvgihBB/EhMTc3NzLS0tAeDOnTvbt29/8OCBqIP6Aaq6OKqrq6uqqrgP\nyaG7CCEkKkVFRTNnzszLyysrK/P19R03bpyoI/oxShL0pUuXNDU1e/TocfToUfKZoUOHUlERQgg1\n07Bhw37++eeBAwd27969c+fObm5uoo7oxyjp4ujXr9/9+/dlZGTGjBmzfft2MzOzAQMGxMTECLwi\nhBASY5Rc0+/UqZOqqioA+Pn5ubi4hIeHN/+9xcXFQUFBVESF6InJZDo7O0tJSYk6EEpge25vBNue\nKUnQurq6P/300y+//GJoaDhhwoQpU6YUFhY2871Xr16NjY21srKiIjBEQ1euXNHS0hozZoyoA/kx\ngiDIeeCaPxKrYXtmsViSkpJfv36lJERhUcjI6H/69Adj4+QxYwim8Ebr9rhxQzYnJ8HevkxFRWiV\ntohg2zMlCfrgwYMPHjyoqakBgPXr11+5cuX27dvNf7uxsfHUqVOpCIw+wsLCDh061KFDBycnJ3Jo\nZ7uVnJws6hB+YOnSpYcPH46JiZk+fTqTyWQymf7+/twbQH6oXnsmRwTLyMhQE6wQrVzZ8dixAd27\nw6BBwqt06lR4+FB/+3YYNQo8PIRXb7MJtj1TkqAlJSW5/4AwGAwHBwcHB4fGdi4qKqr7sLS0tOFo\ndvHz+vXr48eP5+XlHTx4cMSIEefOnfvf//7HYrEWLVpka2sr6ujQdyIjIwHAy8srODi4X79+2dnZ\nTk5ODx8+5K80CQkJMenPYTJh8eL/tkNCwNwcWjjHNJ+srMDKCsjbZ0pL4eNH+HbftvgR0n1lOjo6\n6enpPF9atmxZ3QF5r169GjJkyLx584QTmKgsXbo0KipqwYIFPj4+t27dunnz5oULFyoqKpycnDp2\n7Dhw4EBRB4jqk5aWJuex1NDQIH8d8qekpOTjx4/cyR/EhKws2NvD9OmwYAEI525V8jSupATc3EBd\nHby8gNcEI22dkBJ0E5MOnz17tu7DBQsW1NbWUh9RU+7duxcdHd2rV68JEyYAQExMjI+Pj7y8vKWl\n5axZs1pb+ufPUFwcFBQ0efDgJ97eTl5eM6Sl9w0ezNm4kTNnzqpVq0JCQjBB00pKSoqDg0NGRkZg\nYKCzs/POnTs1NDT4Lk1GRkZCQkKA4dGCtTWYmcH+/XDlCjg6Cq/eTp0gJAQiI2HjRvDzgw4dhFe1\nUAgpQevr6wunotbbunVrdnb2hAkTHj16dPfu3QMHDkRFRR04cEBFRWXy5Mn1E3RVFRQXw6dP8OkT\nFBX9/3bdDe7kGAwGEAQoKICCgmRBgfPp08oKCrOGDCnPycnS0lLt1Qs8PT9Mm9bStYgQ1fLz89PS\n0pKSkpSVlQGAzWZz54/ng/h0cdTDZv9/p3BEBMjLQ58+Qqp6yBA4c+a/7d9/h4EDwdYWaDWlBr9w\n6pzv1NbW3r59OywsDF6/HmthsePXX8v37ZtfUVGwbZvHgweLlZRg7Fio0yEDbDYoKoKCAigq/vef\nggJoaJBZ+L+HsrINK7IHsP+2nZGRMXPmzLlz51q/f393z5699+4J41BRs0lISOjr63NPMlauXNma\n0sSzi6MeNTVYuxakpGDLFmH3PMydC4cPg48PbNwINjZCrZoClCTo3bt3N3zSg5aXXOupqamRkZGB\nqCj45RdwcFDgcCoVFZOqq0+9fr322LFO3bqBoqLAu9i0tbVDQkKuX7+ePGXK8dhYlpKSYMtHAtfE\nNZVx48bVvaaSmpo6ZsyYutdUxLOLo55u3eDiRYiKguvX4eefhVq1piZs2wZfvgA5GVNJCZSVgbq6\nUGMQHCYA3Lp1q7i4WICFKikpeXp6stlsqToEWD512Gy2jpJSwcyZn06cuNGjx3V1dfl58/Y/e5ZR\nUeFx8OBGX1+KLoAoKyvPmTNnrKcnq6ICkpKoqAIJUBPXVG7evHm3Dmtr6/Ly8ro7iG0XR0PGxv9l\n5+xscHeHDx+EV7WsLJCT7mdmwuTJMGcOvHghvNoFhwUAFy5cINcNW7BgQb2XORxOaGhoS9uTq6vr\nuXPn3Nzc2uLsw4fZ7EtDhpxeuXLAgAFnzpwBAH9/f+FV/+uvsHMnnDghvBpRy7Xmmkq76OKoR0MD\nJk+GuXNBXx/27gVhjqPt1QvCw+HpUzh+HPr1AzZbeFULwv8n0IEDB0ZFRQFAYWFhfn6+goJCQECA\no6Mjf//a32ujHannz7OkpacdO8ZjNSrhGDoUvL0hI0OMh3a2c+2hiyMrK+vChQssFmvmzJnKysox\nMTE+R4/K6+paamnNIm87/PQJFBSEF5CpKZia/rft5gZKSrBwYZv4ivE4w3V1dXVxcbl165a+vv7C\nhQv5HpDf9qSnwx9/QGioiMPw8IB9+2DfPhGHgb4R7DUVse/iSEpK+umnn1asWFFeXj5hwoTLly9/\nNw6KwwEA2L4dYmLAwwO+LZMoPORYQFdXmDkTfvpJ2LW3EI+b6KuqqiZOnJienr569WqRD0kWnpoa\ncHWFw4dFP5RyzBiIjob8fBGHIS4KCwvfvHmTk5Pz+++/J/HVvy/YayolJSXclfHE0rFjx3bt2uXo\n6Ojs7Lxy5cqzZ8/Onz+fyWR6eHgs5t556OMDhw/DrVsgzP5DEosFjo5w9+5/2fndO9i3DwoKhB1G\n8/A4g5aQkPD29h4yZEhYWNinT5+EH5No7NoFI0YIdVaBJri7w8GD8Ntvoo5DHLT+F6Fgr6mIfRfH\n169flb6NRFJSUnrz5s2rV69OnTq1du3a79Zj1dODbwsDwr//wsWLsHAhCH/BVh0d0NKCefOAw4GA\nAJCXF3YATeJxBu3j46OmprZy5cqEhAShXh8TochIePAA1q4VdRzfTJ4MoaHQYNE2xAeB/CK8d++e\noK54i30Xx9SpU3/99decnJz09PTt27fb29vv378/IyPDw8Nj48aNvN/TqxcMGgSLF8OMGSDkef7I\nE+qQENizB8gZrMLCIDtbqDE07rs25+3tzd0m+90yMjKMjY2FHZSQffkC7u5w8SIIcdbEH2Ay4aef\n4OhRes7X1bbQ7Reh2I/iIEcWuru7S0pKbt68uU+fPj8+z2MyYfx4GD8e8vP/G+Nx/z4MGADKykII\n+D86Ov8fzPLlUFMDS5eKoIv8e98l6AEDBogqDlH6v/bOPRCq9I3jX+ResVa16Z5uWrQhSW5Rcltd\nRG1JipKWdJVV24Xaii7blo12a5Uuot1EikpXYaN+aVXYVNRaRVcJ4zK/P8ZqMIm5nHNmvJ+/Zs7M\nPO/XOOeZ93nO+z7PihXw90fv3nTraMrs2TAxgY8PJHq2RQFbtmxJTk52d3ePiYlhQkQo8SkOALa2\ntra2tvx8slu3hge1tZgzB127wscHY8YIUdunGTsWY8eitBR5eQDAZiMnBzo6lGr4jyYOelqLKiec\nhXeSTGwsKivh5ES3jhbIymLmTBw8CC8vuqWIK8yMCCU+xSEcrK1hbY3Hj/H4MQDU1uLpU1AZdnTr\n1vBrUVODX35BRgYcHODjQ+mknudNwtjY2KioqPr6ejabXVhYmJOTQ6UgSikuxs6dOHeObh0fYf58\nWFrCw4Oi+o0SBzMjQolPcQiT/v0bnPL79wgIwOvXcHbGrFmUhpVycvjpJ1RXIz4ez55BTQ0vX6JT\nJ2puJ/K48iMiIoKCgiIjI2fMmNGuTihiBpuN+fOxaxfPYkaMQEkJ9vaIjcU339AtRSxhZkTYEVIc\nwqdrV0RH4907xMXh338xYADKyqCsTN2mRHl5NLbFefAAAQHo1g3z5kHErdp43BaTlpY2NjaWl5e3\ntLRkfjsi/tm1C6NGYdQounW0yuLF2LsXIui83nGIjY11dHR0cHCwt7d3d3enWw5JcQhA585wdcWA\nAQBw5w6srDBjBhITqZZhaIiLFxEaioICcNYF/fMP74u0rg6XLsHXl++heDhoBQWFmJgYFosVFhZW\nUVHBt2lGk52NU6fAlaNkKCoqMDKi4RSUICIiIgICAjQ0NJYvX25vb0+3HMnfqEIRlpZIS8P69Wj8\nMu/dA4tFnYC+fbFoUcPSr5gYGBlh2TLcvdvkPc+eITOzSYHidsLDQUdHR+vp6a1fv/7Zs2cfXbco\n1lRWYuFCHDggHrnd5cs/rOcntB+mRYRKSkrdxbb6JeMYNgze3g2Pr1yBhQXc3HDjBtUyli5FWhq+\n/hoFBU+ePPn7778bjmtoYMECQQzz8FBKSkqDBg0CEBQUJIjpNmJsbMzi+t0rLCw0bixrIiICArBw\nYUOgxHx69MCwYbh6FWZmdEsRS5gWEZIUh6jw9oa3N/76q6GfbGUlEhJgZYXPP6didBkZjBsHoCAs\nrLfw1uzycNDc65CUlJSuXr0qrMF4kpaWxv1U5D0JT59GaSnmzBHhEELH3x+LFxMHzR/R0dHFxcWm\npqZ79+5lQkRIVnGIlsYFy7KyKC7GzJmQlkZoKLS1aZXFJzwcdGpqKoDa2tqUlJQb1AcLIqWkBEFB\nzF1X9zEGDICKCm7ehL4+3VLED4ojwlevXnE/ra6ulm1ag5is4qCITp2wZAmWLEF5OThfeHo6zp6F\nnR0MDanbNqyqivBwvj/Nw0E3xl+TJk06cOAA36YZB5sNT09s3w5x7MoaGIigIERH061D/KA4Ipw1\naxZ3y6v79+/rN/1ZJSkOqunSpeGBoSHevcORIwgIQHw80+oi8YSHg96yZQvnwfPnz5mQsxMa4eHQ\n0YGpKd06+GL4cHC6YQ0ZQrcUMYPiiPDMmTPcT1um7EiKgzZkZDBhAiZM+HAkLAy//w4LCzg6gpHb\nmnjM83v/h4GBQVRjM3Nx56+/cPw4goPp1iEAAQEICaFbhPjBKd/cuXPnSZMmMWFbLFnFwSC+/RYJ\nCdDXR2OTycuXmVPKDjxn0K6urtTrEC0sFr79VmzW1X0M0g2LL5gWEZIUB7NQVoa9PRoXyJeUYNcu\nlJbCwwNcvdjpoonD0tbWBlBdXf3q1St5efnq6urBgwenp6fTpE14rFkDV1cMGkS3DoEh3bDaT+Oa\np759+65cuZJeMSApDoYzYwZmzEBtLTiVaVksODvD0BDjxsHQkPoZXpPxOAHg7Nmzvby8jIyM0tPT\nIyIiKBYkfFJSUFQkIcmBiRPxww8oLf1QlZHwKZgWEZJVHGJAp04Nq6fl5HDoEK5dw4kTePsWNjZ4\n9w4PHmDECEhJUSCERw66pKTExMSkU6dOpqamxUxKx/DDixcICMCePXTrEB6cbliENqCtra2trT14\n8GB1dfVevXqpq6uPobiyMC9IikPMUFGBgwN27ICNDQC8f4/ISNjaYupUFBR84rMFBXB2hosLMjMb\njqSkwNwcrq5o2+093sWSwsPD79+/HxYWpkhZsSgR4eWFH36AujrdOoQH6YbVZnJycnJycoyMjOLi\n4goLC0+ePKmpqUm3KFKLQ8zp3h0//oikJOzf39DlIyUFjo7YsQP/+1/zN4eHY80a/Pzzh7TknTsN\nC7Ha1v6Uh4OOior6+++//f39i4qKDh48yPcfQj8HDqBv3yaraiSAxm5YhLbBtIiQrOKQED77DPLy\nAGBlhf370bcvoqLw7l1tbe3bt28b3vPPP9DUhLo6Gu9OW1tj2zbs3InvvmvLIE0c9MaNGwH8/PPP\nXbp00dfXV1RU3CO+0XR+Pg4cwObNdOsQAbNnIzYWVVV06xAPmBYRdpQUx59/YvNmdJBYoVs3TJuG\nHTvQubOMjEznxhLzGhp49AgvXkBZueHI3buQlUXXrm1MYTe5SThkyBD8t5ZDWNTV1bFYLKovjJoa\neHpi376GnzgJg3TDag9RUVFbt25NTEwcPnw4EyLCjrKKIzQUVlbw9kZNDWbNgpMTcztjCBUpKSnp\nxn3kCxdi3TpISWHZMly4gDdv0LMn5s5FTQ2WLWuLtSYzaBcXFwCWlpY6OjomJib5+fm6urp8SPzz\nzz9NTU3t7OwyMjIGDx7ct2/fmJgYPuzwT1AQnJ0xfDilg1LJ/Pn47TfU1tKtg9EwMyLsECmOigq8\negVvbyQmIiYG1dWYNAmOjoiNFaQ4svgxaBCOHMHhwzAwwPjxcHKCqSmOH8cff8DcvC0GeCzr8/Dw\ncHd3P3v2rKam5oIFCy5fvtxeVT4+Pnv37n3+/LmpqWleXp6qquq4ceM43p8KLl/GnTuIi6NoOFpQ\nUoKDA+mG1TpCjwjZbPbr169VVVWlBFhi1SFSHMnJDWseAKiqYsECLFiAvDwcO4atW6Gv39C0nvAp\neNwkrKmpcXR0LCwsXLlyJX+VP2VkZAwMDMaPH9+7d+9+/fqpqalRd0a+eoWVK/HLL9SsUqQTX1/S\nDat1hBURLlq0CMDt27eHDRtmbGysra2dkZHBt6oOsYrj5ElMmdL84NChWL8eN25g9mxERcHICOvX\no7CQDn1iA48ZtIyMzJo1awwNDa9du/aGs52mnWhra0+ePPnt27eDBg1auXKlmpoadTHd4sUIDobE\nh5Dg6obl4EC3FEYjeETIKbG0evXq2NhYXV3d4uLimTNn8mGHg+RvVGGx8OjRRzfuSkvDxAQmJqiq\nQkIC/PxQXY1vvoGT04c7aYT/4DGD3rJlS7du3ZYuXXr//v39+/fzYTQiImL+/Pnr169PTk7u27dv\nZWUlRTdnjhxB164fYiuJh3TDagOCR4QcFBUVORNwDQ2Nuro6vu1Iforj6lVOb5FPoKAAZ2fExWH/\nfrx6BTs7uLnhwgUSFHLDYwatpaWlpaVVUVGxgN9uWjIyMo3dOZcsWcK/unbx+DHCw3H+PEXDMQHS\nDasNCB4RPnz4cMqUKUVFRYcPH3Z1dQ0JCdHQ0OBbj+Sv4jh5Eh4e7Xi/hgb8/ODnh7t3ERWFgACM\nHw8PDwweLDKJYgMPB3316tW5c+d2797d3t5eS0vLyclJ8GH69etX+JFk07Zt27jnI9nZ2QMHDmz3\nAHV18PTE3r2Q7LlJS0g3rE+xZcuW5ORkd3f3mJgY/iLC0tLSx48f5+fnq6mpAZCVld23bx/feiQ8\nxcFmIzu7jdvkmvPll9iyBXV1uHQJwcF4/BhOTpg1S6J2ArcTHg569erVaWlpq1at8vX1tbGxEYqD\nvthYbrUFenp63IHnlStX+Dl9N2+GjY2Yth0TCNIN61MIJSLU1NRs3Ca+dOnSVt5869YtNleQXlZW\npqKi0syaJKc4MjMFPRVlZDB+PMaPx+vXiI+HuzukpTF7NiZPRtPmYR0BHg66rq6Oc09PRUVFWbC0\nPYvFkpOTA9BKDQRLS0vup8ePH293ojAjA6mpaNrJogNBumG1CsUR4R9//FHLtT790aNHg5reLpPw\nFAfP9Rv8oaoKNze4uTWszwsNxciRWLCgQ81FeDjoYcOGLVq0qKioaM2aNfzl2u7du+fv75+enq6k\npFReXm5iYrJjx44hIurVVF6OxYtx8iR1XSCZBumG1SoUR4Sc3TGNvHjxotmEQ8JTHFevouk3IAQ4\n6/PWrkVaGvbtw507mDwZc+bgiy+EPNCnSElJCQoK6tOnz8SJE2fPnn3t2rVz586pqanNnz+/s2j2\nSfJwakuWLBk5cqS2traGhsYvv/zCh1Fvb29/f//i4uInT56UlJQEBAT4+PgILPUjLFmC775Dr16i\nsi8WkG5YH0e4ESHngSBV8SQ5xXH/PoYMgYh+fjjr8yIicOkSBg7EwoWwtcWhQ6CwS86dO3c4E009\nPb3jx4/v3r3bxsZGXV3d0dGxurpaFCPymEHPmjXr1q1bsgKke6SlpU1NTTm7rRQUFDi1xPjX2ArH\nj0NKSmghlfhCumF9HKZFhJKc4oiLo+Ji5KzPc3ZGcTFiY+HggD594OYGKytRb0+ztraeN28ei8Xy\n8PCoqqo6deqUoqLi2LFjCwoKUlNTrayshD4iD785atQoExMTCwsLjo/e2P6ARV9f39bWdsyYMf37\n9y8sLMzIyOBvB9cnKCrCzp1ISRG+ZXGEdMP6CH5+fn/++aesrKyGhsb333/PhwVvb+/g4ODRo0fL\ny8tXVVVlZWX5+PicO3eOPz2SnOI4fx6ULatFi/V5gYGwtISnp+ia2929e3fAgAEKCgpSUlJsNptz\ngw2AgoKCIEvjW4GHg3Z0dHR0dBTEaGho6LVr11JTU7Ozs9XU1AIDA8eOHSuIQR7U1cHDA2FhZPdR\nA6Qb1kdwdXVlVEQosSmOJ0/w+eegpaAr9/q8oKCG9Xmurg1tq4RHz549586dW1NTs2zZstzcXB8f\nnxUrVhQVFSUkJPj6+gp3LA48zrPJkycLaFRKSsrMzMxMpItzd+yAuXmHup/7aTjdsDZsoFsHs2Ba\nRCixKY74eEyaRKeAxvV5b97g1Cm4u0NJCa6usLUVVrNXU1NTU1NTzmMzM7Pff/89KCioV69esbGx\nAt7e+BhUN6kVDpmZOH8eSUl062AYU6di1y6sWIEuXeiWwiCYFhFKbIojMRFHj9ItAgCgotKwPq+o\n6MP6PHd3jBwpxEGkpKSmTZs2bdo0IdpsiRg66PfvsWQJjh/vuOvqPkZjN6wVK+iWwiCYFhFKZorj\n5UsAUFWlW0dT+vbFqlVYtQo3byIyEn5+sLeHuzt69KBbWVsRQx+3YgWWLm1o10hohqsrYmNRWUm3\nDsJHkcxyo6dPM7qqor4+du3CuXMYOBBeXrCzw6FDeP+eblmfpomDZmab+ibEx6O8HCIOK8QYTjes\nQ4fo1kH4KJLZUSU+HlyRSn19/d27d/Py8mhUxINm9fO+/pr59fOaOGhmtqn/wL//YuNGMKBrEaMh\n3bCYjQSmOCoq8PIl/ltj/ubNGwsLi927d2/evNne3r5xdw+D6NkTfn5IScGqVbhwAUZGCAhAQQHd\nsnjAI8XBtDb1DbDZ8PTEzp1oWnqG0JzGblgdHmZGhBKY4jh/HtbWjc927969bNmy8PDwyMjICRMm\nHGXInUOecNbnpaVh/HgEB2PiROzbh/JyumV9gIeDZlSbehaLFRMTc/DgwbebNkFfv87I6NKlSyJa\ncig5MLUbVkFBwW+//Xbq1ClBCue3HWZGhBKY4oiLA1eFk0ePHuno6HAe6+vrP3z4kCZZbYazPi8y\nEjExUFDAzJmYPh0JCUwIQ3k46KioqL///tvf37+oqIjeNvWVlZX29vZFRUUqhYUF27bddXJ69uxZ\nZmZmTYdqDMwHjd2wmMSlS5e8vLw6dep0+/btadOmUeOjwbyIUNJSHLW1KCjgLq5vamp66NAhAPX1\n9YcOHWpcOCwGcNbnJSRg2zbcuwcrK/j54fZtGhXxcNDdu3ffvn17dHT01q1b1WktlR0fH29nZ7di\n8eLJ58+rHD0aunOnhoYG31V72uNTAAAgAElEQVR9OxZ+fvjpJ7pFNCEkJCQ2Nnb2zJnr1q0bOHBg\nWloaNeMyKiKE5KU4WjT0mTNnDpvN5uzpGDFixIQJE+iSxj99+mDVKly5Ajc3/PYbzM2xdSueP6de\nCEUdVfjjzZs3PXv2hKws9uz5vH//N3yV1uug9OqFfv2QlgZjY7qlNFBTXa16+DCysnDwoIaGxuvX\nr6kZNyoqauvWrYmJicOHD6c3IuQgaRtV4uIwezb3ASkpqaCgILrkCBl9fejro7oa587Bzw9v3mDG\nDDg7U7ajnccMmlM/d+jQob6+vtu2baNGB08mTJiwb9++0rKy2i+/XLt2reA7DjoWgYHYvJluEf+R\nlbW/sDDx2jX2b79x0sHCL8/yEZgTEXKQqBQHm42sLBgY0K1DxMjL4+uvcewYDh9GVRVcXODlhdRU\nCm7z8HDQQqyf2xYePXr0kIvy8vLGjkEDBgwICgry9PScMGHC8OHD58yZA0BVVTU8PFzUqiSBxm5Y\n9FJRAT8/bN/e68qVewYGZubmq1atCgsL++yzz6gZ/+rVq5qamuPHj9+4cePvv/8u6uH279+/j4vc\n3Nw+ffqw2WwWi1VSUsJms1++fHnv3j3uI2L84PZt1ujRJc+eMUWPqB+wWOz581m//14yZw47Oprl\n6FgSE9PsPcKt3C+SjirtYtu2bdwtgrKzs7W0tBqfmpiYmJiYiFqDxLJ6NTZsoLMb1oULCA7GihX4\n+utOgL+/v7+/P8USRNFRpRVUVVW573/Ky8s33tOWlpYGoKio2NilUPq/cgXi+uDUKdjb0y+D+geD\nBmH3brx/L33vXrOXhHz3m81mz5s3r6CggP0f1dXVERERvr6+YWFhVVVVbGrx9PScN28exYNKMpMn\ns3NzaRi3rIzt5sb28mK/etX6G3/44YekpCTRCRkzZkx9fT3nzpWVlZXoBuIJz/O5rq6OYhmiwsSE\nXVNDtwhmYW1tvWnTJmFZ4zGDjo6O9vDw4NzHOHjwICexQBBXON2w9u+ndNDYWISFITgYDFhiRXFE\n+Ekkp9xoXh4GDhRWJU8CT3jkoFeuXDl16tTy8nIACQkJlEsiCJXRo/HkCShb1/X0KZyccPcuzp5l\ngncGEB4eLmCPTeEiORtV4uJA7tuLGB6/fqampnPmzLGxsYmmMXdJECLUdMOqr8fu3UhIwPbtGDFC\ntGO1B6ZFhJKziuPcOZA9vSKGd7nRr7/+es+ePVOnTs3Pz6dYEEH4WFvj1i2UlopwiNxc2NsDQFIS\no7wzmBcRSshGladPoaICJSW6dUg4PBz0jBkzAIwcOTIuLk5bW5tySQQR4OuL3btFYpnFwvr1WLkS\nYWHw82NgRtLU1NTT09PGxubJkyd0awEkJsWRkEDyGxTQ5HLauHHjmjVrOCVmOEf47i1PYBacbljl\n5ULuhpWZiVWr4O6OdetE3fFeEL7++uvevXtPnTq1urqabi2SkuI4fRpRUXSLkHyaOGiOOyazZglE\n6N2wKioQGIjnz3H0KL74Qjg2RQN3RLhy5Uq65UjEKo5Xr1BXBzU1unVIPk0ctIuLCwBRt0Ek0IOr\nK0xM8O23QigjwLX9RBjKRAUzI0JJqMVx5gyjG1xJEE0cNGfuXF1d/erVK3l5+erq6sGDB6enp9Ok\njSBUGrtheXnxb+TFCyxfDlVVxMczv3OC0CNCNpv9+vVrVVVVKQHyOZKQ4oiLw48/0i2iQ9DEQXMm\nGrNnz/by8jIyMkpPT4+IiKBJGEEEzJ8PS0t4ePB5K+/QIRw4wJDtJ21BWBHhokWLfv7559u3b0+f\nPl1aWlpaWnr//v1GRkb8WRP7FEdlJcrK0KsX3To6BOLT8oogOHx3w3ryBE5OePgQSUni4p0hvJZX\nN27cALB69erY2Nj79++fP38+ICCAb1Viv4rjwgWIY4lnEZCQkODj47N27doXL16IaAgqWl49fvxY\ncCME4dDeblj19di1C/PmYd06rF8PsYrNhdvySlFRUVdXF4CGhkZdXR3fdsQ+xXHyJKZMoVsE/Rw4\ncODkyZOLFi0yNDScMmXK+/fvRTEKDwe9du1aAVtePW7KnDlziI9mCioqGDOmrd2wcnNhZwcAZ89C\nV1ekukSH4BHhw4cPp0yZ8ujRo8OHDwMICQkRpKaHeG9Uqa1FXh646k12WI4ePRoeHj58+HAHB4eJ\nEyempqaKYhQeuciFCxfeunVLVlaWb6OOjo75+fmjR4/m3Ev566+/3N3dL1++zLdBgjBZtgwzZnzi\nLjyLhR9+wK1b+PlnDBxIlTKRwIkIzc3NL168yF9EWFpa+vjx4/z8fDU1NQCysrL79u3jW494r+K4\nfl2MclwipbG+KID6+noR/U95OOhRo0aZmJhYWFhwfPTGjRvbazQrKyskJCQzM/PHH38cMGCAjY1N\nUlKSEMQShEKPHhg2DFeuwNyc9xtu3EBAAPO3n7SRwMDA+Ph4QVpeycjIaGpqNqZHli5dKoge8U5x\nnDyJb76hWwQjcHNzc3V19fPze/jw4blz50S0xJ6Hg3Z0dHR0dBTEqJyc3Jo1a3Jzc729vcePH1/L\ngO7lhCb4+8PXl4eDfvcOy5ejuhrHjqFHDzqUCR8fHx8BI8KW9OvXr7CwkL/PivEqDk6DK1FX3RIT\nXF1de/bsmZSUpKamlpCQIKIfXR4Omrv1X1ZWFt+mhw0bdubMmYiIiD59+vBthCASBgyAqipu3oS+\n/oeD589j40asXClhexAEjwhbcvHixY+9tGbNGu4ZSWZmZrM7k2Kc4vjf/6CrKwFBlbCwsrKysrIS\n6RA8HHRsbGxUVFR9fT2bzS4sLGzchcUH0tLS3t7e3t7eAigkiIQSD48nLi7fDx5sYmLiP2+e3LJl\n6NFDLLaftBfBI8JGWCyWnJwcgFZWg0ydOpXNtUgmLy+vWZM6MU5xxMWR9RsUw8NBR0REBAUFRUZG\nzpgxIzk5WSjDtBIS2tnZNTZtA3D//v1Ro0YJZVDCx2CxWM5r18b36XN6+/aroaFFhoaDoqMhob0f\nBY8I79275+/vn56erqSkVF5ebmJismPHjo/tGtfT0+N+qq6u3qxJnRinOK5cwfff0y2iY8HDQUtL\nSxsbGx87dszS0jIsLEwow7QSEp45c4b76fz584XcdZHQgrt3744aNeqzKVNgb285ZYrNgAFJEuqd\nIYyI0NvbOzg4ePTo0fLy8lVVVVlZWT4+PufOneNPj7imOPLz0bcvhJrKJ3wSHuugFRQUYmJiWCxW\nWFhYRUWFINZZLBbngSAbBAhCR0VF5cWLFzA1xc2bNSEh1cwr4ixEIiIiAgICNDQ0li9fbs/pKtBO\npKWlTU1N5eXlASgoKHBWVfOtR1xTHPHxpAA09fBw0NHR0Xp6euvXr3/27Nm6dev4MHrv3j0HB4fP\nP/9cU1NTVVXVwcGBdGZhFAMHDqypqdmwYUPsxYsuLi4eHh50KxIhnIhQXl7e0tLywYMHfFjQ19e3\ntbXdsGHDwYMHg4KC7OzsdAXYtiOuG1WSk2FrS7eIDgePiYCSktKgQYMABAUF8WdUuCEhQRQcOXLk\nzJkzf//9d1BQkI6ODt1yRIjgEWFoaOi1a9dSU1Ozs7PV1NQCAwPHjh3Ltx6xTHH8+y+UlUmDK+oR\nSblRTkjI2UYoeEhIEAVSUlL8xftiR3R0dHFxsamp6d69e/mLCKWkpMzMzMzMzACsWLHCRLB8vVim\nOOLjMWkS3SI6IiIpN8oJCceMGdO/f//CwsKMjAxBQkICQRAEjwi5efr0qYAWxHIVx+nTiIykW0RH\nRCTlRkNDQwMDA2VlZbOzs6WlpQMDAzdv3iywVAKhfQir3Cg3DgLv4hG/cqOvX4PFwuef061DDCko\ngLMzXFyQmdlwJCUF5uZwdW1jR0fey+wELC7DHRISCHQhigYUrq6uAloQvxTH2bMNRQ0JbYDNZn9Y\nKBwejjVr0KsXFi/G0aMAcOcOhgxBZSWarpf/GDxm0FFRUQKWGyUQmAPTGlCI3yqOU6fIArtPUFqK\nP/5Afj6Aurq68vLyhuP//ANNTairo/HutLU1tm3Dzp347ru2GOYxg+7evfv27duFIptAoB3BI0Lh\nImarOKqqUFKCfv3o1sFU3r6FgwNUVWFhgTFjAHTq1EmlsV6ChgYePYKGBpSVG47cvYsBA6Cg0MaS\nJjwc9O7du7kjQUFqcRAItBMVFbV161ZByo0KFzFLcaSkQMT1gMSMixdx+jSysuDujnnz0LUrrl79\n6JsXLmyo2btsGS5cwJs36NkTc+eipgbLlrVlNN7Fkq5cufI5uSdAkAiYFhGK2SqOuDj4+dEtglaq\nq5GairIyTJ8OAPn5mDYNISFt6rw8aBCOHGl+sD0dD3iMMXDgQE7nCAJBAmBaRChOKY66OuTmQlub\nbh30sXo1rl2DiQmcnBqOLFxI5fg8HHR9fb2BgcG4ceM4u0u2bNlCpSACQbgwLSIUpxRHWhoEXpgo\nTpSW4tQpXLkCFgvR0ZCSwqZN9Cri4aBdXFyo10EgiAimRYTilOKIi/swc5RUqquRmYmvvkLnzrh1\nC2w2Nm1C3750y2qAh4PmXoovSEcVAoEJMC0iFKcUR3o6QkPpFiEyHjyAvz9KS2FhgREjAGDiRLo1\nNUe0HVUIBNqhOCJs2YDCwMCA+w1ik+LIzoauLqR5bJUQV+rqkJWFkycxcSLGjcMXX2D3bvTqRbes\n1qCoowqBQBcUR4SfbEAhNimOkyclan9KVRXs7TF8OCZNalhH0bkzmnYjYyAUdVQhEOiCaRGh2KQ4\nLl/G6tV0ixCAsjLExeH332Fmhu++g4ICUlKo1lBXh6tX8ccf2L2bPwM8HLQQO6q0hW3bttXV1TU+\nzc7O7ke2LRGEB9MiQvFIcTx8CA0NsWxw9eZNQ+Pjn3+Gujp++w1ffEGbmGfPkJkJrpRXexFJR5V2\noaenp8+Furo6p7cQgSAUBO+oIlzEoxaH2DXwLi3Frl2ws8OKFQ1H1q7FokWUeufsbKxateju3c9r\naxuOaGhgwQJBTPKYQScmJjo7OwMICgo6cuSI4BUaW8fS0pL76fHjx0nTWIIQoTgi/CTikeJISsIf\nf9Atog2UlKBrVygpIS8P/fvjjz9AfXTy9i26dgWbjdhYTJlyT0VFT19fWLabOOiYmJiYmJjMzMzj\nx48DYLPZeXl5s2bNEtZgBAL1CN5RRbiIQYqjpAQKCoy+gVZTg19/xenTUFFBSAiUlEB9W/pnz3Dk\nCJKTMWoUNm6ElBQ2bgSAS5eEOEgTB21paamrq7tz586lS5dyjvTo0UOIgxEI1ENxRPhJxGAVx+nT\nDG1wVVGB16/RqxfevIGiIo4fp+FXpLKyoRZdaiq++AJxcWi9RKKqKsLD+R6tiYNWV1dXV1fnFC54\n+/Ytm83+UDePQBA3mBkRikGKIz4ev/5Kt4imZGYiLAy5uVi1ClOmQF0d7u6UCmCzcfEifvsNT58i\nNhbdulGzx7KJg75586aXl9e5c+fu3LnzzTffKCgo7Ny5c7IkrYUkdCSYGREyPcXx9i0qK8GQplyc\n9C6A+/fh44OmW34opaAA168jOBgDBlA5bBMH7evre+TIETU1teDg4MTERE1NTUtLS+KgCWIKMyNC\npqc4kpLob3BVU4OkJERFQUYGR49CSgpublRrKCvD4cM4fhze3nBzw6BBWLuWag3NltlJS0sPHTq0\nsrLy5cuXenp6KioqsuK4EJJAAADcvHnTwMDg5cuXly9fHjp06FdffRUXFyeIQTab/erVKzabLYgR\npjeNZcICu+vXkZWFHTtw7FgbO48In3XroKyM5GQafhu4aOKg3717V19ff+7cuVGjRgGoqakhDpog\nvjSLCG/fvh0cHMyHnUWLFgG4ffv2sGHDjI2NtbW1MzIy+FbF6BQHi4V//gEts/u0NEyfjqQkALCw\nwIYN6N2bUgHFxQgK+pBZDgvD/PkNCRb6aJLicHZ2HjlyZGlpaWJiYn5+/pIlS+zt7elSRiAISLOI\nEAB/E44bN24AWL16dWxsrK6ubnFx8cyZMy9fvsyfKmamOMrKyrZu3fp5ZqZVly56dXVU38acNg19\n+mDjRgweTOm4jZw+jYMHMWcOAgPpEfARPjjo2tpab2/vsWPHqqur9+rV6/79+1OnTp1Ce7BDIPCL\ncCNCRUVFXV1dABoaGtzFCdqLqFdx/PXXX/7+/u/fvy8vL7ezswsODpb6VJagpqZmzJgxdnZ2fn37\nHlZRiVu7VkpKqnfv3gvb0D1EW1s7JycnJiYmPz9/zZo13C+VlZVdvHjxo9UE//0XJ09i0SIAOHGi\njX+dMCkqwoEDsLODoSEcHMBVVIs5fHDQf//9d8tl/BcuXIiMjGRuREYgfBxhRYQPHz6cMmVKUVHR\n4cOHXV1dQ0JCNDQ0+FYl0hTHu3fvZs6ceezYMW1t7draWjs7u+PHj8+YMaP1T929e7d3795aWlqK\nJ0/OP3TIwsJCT0/vyJEjmZmZM2fOtGpD01ieXrisrCwmJobHS8XFWLsW//6L/1bXUE1NDdzcUFmJ\nOXOgp0ePhrbRCYCcnNyePXvMzc23bdvWlzGtBAgEAQkMDLS1te3WrVufPn1ycnJmz57NX23o0tLS\nx48f5+fnczqzyMrK7tu3j29VH1IcL17g4kUAkJKCtTW6dm3TkVZJTEwcN26ctrY2gE6dOkVHR1dX\nV1dUVHh4eLx586aiomL9+vWWlpbNjvTq1YvFYgF4duiQs5lZRUVFz549Y2Nj79y5s2DBAi0tLTk5\nuV9//bWxMU1paambm1tdXV2PHj2qq6sBnDhxIicnZ/r06QEBAXV1dZz379y5MzMzMzY21t7efsaM\nGVVVVQAWLFgwbdiw019+GcNiyRw58mDDBhcXF19f3/Lyck9Pzzdv3rx//37v3r1DhgxZuHDh/fv3\nO3XqtHHjRjMzM76/8A+8fYuMDFhbQ1YWP/2Ebt2EYFPEdAIQGhp648aN//3vf7GxsUVFRUpKSrq6\nuiNHjhw5cuSQIUOkBajYzWKx5OTkhKeWQGgHUlJSev/Nj7S1tbX5bX4qIyOjqampqanJebpUsHnf\nhxTHixd4+LDh6KtXDe74k0da5eHDhwO4FupyXOr27du1tbXXrFlTVFRkZmb2+PHj8PBw7iOPHj16\n+/ZtXFxcSEiIlpbWzJkzFy5cmJWVNW/ePDMzsyNHjsTHx5eWljY66C1btjg6Onp7e6empp49e7Zx\nuOTkZB0dneDgYM77ly5d+uLFC2dn5wcPHrgaGbn8+WfaqFFbDh2aFh+Px49zoqJu3rz58uVLc3Nz\nX1/fn376SVtb+/vvv7927VpqampaWpqcnNz169efP39ubGxcUFAgyHeOJ0+wbRtycuDr23BEHLwz\nOA66c+fOlpaWjUWL3r9/f+fOnd9//33evHkGBgbXrl1rr9F79+75+/unp6crKSmVl5ebmJjs2LFj\nyJAhQtZOINBEv379CgsL+fvshxTHkCFYtarJa2050ir9+/dPS0trfPrnn3/euHEjNzd35syZADjx\n8fv375sdqays9PT03Lx5c+fOnfX19RcuXBgTEzN16tTMzEwLCws7O7t+/fqNHj260Wx+fr6npycA\nIyMj7tqT7u7u27dvb3z/69evOcc7b9iQcuPGVUNDVlFRYym0cePGSUlJNTbzzcnJ4aS8TU1NTU1N\nFy9enJeXN3fuXADdu3evqKhQVlZu+/fQnPx8TJ+OXbv4t0ATPGbH8vLy69evLy0tTUhI4O9Wtbe3\nt7+/f3Fx8ZMnT0pKSgICAnx8fARVSiAwhouctAMvjI2NDbg4c+ZMszuTIi03amdnd+HChVu3bgGo\nqanZsGFD165dhw0bdv36dQCPHz9ms9lKSkotj8jIyKxbt+769evR0dElJSVycnKurq7ffPPNrFmz\nzp49O3DgwAMHDjSOoqWldenSJQDp6emcFAeHmJgYa2vrD+9/+JBdXw/g5wEDdBcv3hMVNWPGjMZV\n5M2+lqFDh6ampgK4dOlSQEDA0KFDjY2NIyMjd+3aZW9v327v/O4dwsNhbo7sbACwsoKxcfu/Tvrh\nUW6UE39t3ry5Z8+e/BmVlpY2NTXl3DtWUFAwMTHh9OskECSDxnRHS7gnsAAOHjzYrHyuSFdxqKio\nHDt2bPny5e/evausrJw0aZKbm9v79+/nzp07YcKEqqqqyMhIAAsXLmx2BICUlFSPHj3Wrl3r4+Nz\n4sSJyZMnZ2VlzZ079/z589LS0txpd39//9mzZ584caJnz56DuRbG6enpzZ07V1VVVZrF2tejR/fM\nzLy8vCNHjtjY2AQFBV28eHHQoEGFhYU8G48tXbp07ty5VlZWlZWV+/fvHzhwoKenp5WVVUVFxcqV\nK9v3LbBYmDIFjo44cwaCzLsZgBTPbVFVVVWC3GhesWJFTk7OmDFj+vfvX1hYmJGRoaur28Zuypwe\nbvv37+d7dIJ4sXnzZj09vYnMa6gsFDgOmhOqN1JfXy/IrR2m8/IlPDwQEACurAgVPHqEixfh4UHp\noC0Q7vnMe2Ir4DKg0NBQTqY/OztbTU0tMDBw7NixghgkEGhk27ZtLQ+uaOzc0U6YuVFFCNTUICUF\nNjZQU8PJk5QO/egR1qxBVRWWL6d0XNEjksyDlJSUmZlZG1fG3Lp1i3sWX1ZWxoSKNgRCI5999pm3\nt3doaKhQUhNiUG6UDy5cwPff01a24uVLBARAR4ee0UUJRanhVu5679+/v7axhRdQWlqqpaVFjSoC\noS14eHgcO3bs22+/FcqtFEbX4uCPzEwcPYq4OFBZzTU9HZs2wccHNjYQXosppkGRg27lrndYWBj3\n05Y3VQgE2rlw4YKwTElOioPNRn4+hg7FqFEYNYrSob/5Bl27YudO2mp3UIVo71Rwtieh1bveBIIY\nwXfquRGmlxttIwUFcHBoKD5HGXl54KRDjx1DRITEe2eIyEHfu3fPwcHh888/19TUVFVVdXBwyM/P\nF8VABALFPH36VEALkpDiuHwZc+ciJAR+fhSNeO8eXFywfj240qEdAZGkOLy9vYODg0ePHi0vL19V\nVZWVleXj43Pu3Lk2fjwrK6tz+3tB1tXVccpCEuhCW1u7S5cu7f1UTk6OHrML1nDj0P6aZ83O506d\nOr18+fLu3btC1UU5Bgagai2s2tu3DteunR89+l919Xbtq6QAKSkpQ0ND7kWTwj2fReKgBdmoMmfO\nnOrq6levXrV30JSUlJKSEr6LPVVWVt68edNEgObt58+fnzBhAt8fz8vLU1RUFER/VlaWqakp3wIE\n119aWsqHAAsLC2Px2eXl6urarve3PJ8LCwszMzOF1R0xPT1dR0eHjwlNS+rq6i5fvtxK7TrThw8H\nlZX9ZmjYFmsCns8AlFmsYc+e3ezTpxTY8+bNBDYbpaV8W2uktLT06dOnI0eOFNwUgPT0dFVVVe7/\nppDPZ7YIWL58+cSJE9evXx8ZGblhwwZbW9tVq1aJYiBufvrppxMnTvD98X///ZezD5VvLCwsBPn4\n7t27Y2Nj+f54SUnJ9OnTBREgoP49e/bExMQIYqGDcP369cDAQGFZc3d3f/TokVBMVVdXW1tb836t\nvJz9zTfslSvZ1dVttCbQ+Vxdzd69mz16NDspiXNAwJOTm/T09ICAAGFZmzdvXkFBgbCstUQkM2iy\nUYVAkCg6d8bRo9QNJycHHx+QAj5M2KhCIBAIBJ5IbkEAAoFAEHOIgyYQCASGwruaHYFAIBBoh8yg\nCQQCgaEQB00gEAgMhThoAoFAYCjEQRMIBAJDIQ6aQCAQGApx0AQCgcBQiIMmEAgEhkIcNIFAIDAU\nsXTQlZWVTk5OVlZWBgYGGRkZ3C/V1dWpqakZGBgYGBgsWrRIpDJYLJauru7jx4+pF9DKKNQI2Lt3\n77hx40aMGJGenk796GIEm81esWLFuHHjzM3N792718aX2muKw/79+3k2IG+vNRaL5eXlZW1traur\n+8cffwhiqpVLlQ9rjfJaXnd8WGvvudq6sI9dEYIiukJ5ouPXX3/19/dns9lXr14dN24c90sPHjyY\nPHkyNTKCg4Pl5eWbFXukRkAro1AgICcnR19fn8Vi3blzZ/To0RSPLl5cuXLF2tq6vr7+ypUr9vb2\nbXypvabYbPb48ePl5ORCQ0MFF5aUlDR37lw2m/38+fPu3bvX1NTwbaqVS5UPaxx4Xnd8WGvvudqK\nqVauCAERyxm0kZGRt7c3ADabraqqyv1Sfn5+UVHRtGnTHBwcbt++LToNubm5N27cGD16dLPj1Aho\nZRQKBCQmJjo7O8vKyuro6MTHx1M8uniRmppqbGzM6bvRbArZykvtNQXg7NmzISEhQhH2xRdfLFu2\nDICysrKSkhL7U9UgWjHVyqXKhzV8/Lrjw1p7z9VWTLVyRQiIWDroL7/8sn///vPnz7e2tl66dCn3\nS2pqakuWLImNjd20aROngL0oBNTX1/v5+f3444+crjHUC2hlFAoEPHv27P79+xMnTjQ3N79161Yb\nhXVMysrKOA28FRQUFBUVG9sot/5Se00B6NSpk4yMjFCEjRgxQltb++7duxMnTgwICJCVleXbVCuX\nKh/WWrnu+LDW3nO1FVOtXBECIpJ60KLm7du3ioqKv/zyS2BgoJWVVUFBQeM/bPTo0Zxf1xEjRkhL\nS79+/fqzzz4TuoCIiIgJEyYMHDiw5UvUCGhlFAoEdOnS5Z9//klISHj27Jment7Tp0/l5eUpG128\nUFNTKywsBFBVVVVeXi4nJ9eWl9prSrjC2Gz2pk2bTp06tXPnzrY0gWvFVCuXKh/WWrnu+LDW3nO1\nFVOtXBECIpYz6B9++CEiIgKAvLx8s3nEzp07OYFecXGxlJRUG6Oq9nLjxo2kpCQbG5s7d+7MmTOH\n+34FNQJaGYUCAaampl27dpWVle3SpYu0tDT31IOaP1+MMDc354TDWVlZzfoKtfJSe00JV1hcXFxG\nRkZqamobW3S2YqqVSz5MytIAAAVTSURBVJUPa61cd3xYa++52oqpVq4IQRFiPpsynj9/bmtra2xs\n/NVXXyUmJrLZ7AcPHvTt25fNZr9+/drFxcXExMTMzCwtLU3USszNzTk3KygW0HIUKgXU19evWrXK\n0NBwxIgRnD6EdH3/zKe+vn7lypU2Njbjx4/Pzc1lc31XLV/i2xSH3bt3t/0mYSvWvLy8+vTpM+I/\nqqqq+DbV8lIV/M9kc113glhr77na+r+y2RUhLEg9aAKBQGAoYpniIBAIhI4AcdAEAoHAUIiDJhAI\nBIZCHDSBQCAwFOKgCQQCgaEQB00gEAgMhThoAoFAYCjEQRMIBAJDIQ6aQCAQGApx0AQCgcBQiIMm\nEAgEhkIcNIFAIDAU4qAJBAKBoRAHTSAQCAyFOGgCgUBgKMRBt5unT58qKytb/Ievr29MTMzGjRvL\nyspiYmIAND5oHW1t7XaNe+LEiTVr1vApmkBoJ1lZWTY2NnSr6OiIZU9C2hkwYMDly5ebHczNzY2J\niXFxceE4aBcXFzqkEQgEyYHMoIXAiRMn1q9fv3PnzszMzNjY2MYHNTU1Hh4exsbGZmZmV69eBVBa\nWmpra2ttbT179uzq6mpuIxMnTszKygKQnJw8a9as9+/fOzo6WltbW1tbnzhxovFthw8f3rJlC4Cq\nqioDAwMALUe5f//+pEmTHBwcpk6d+vLlSyq/CoIE0/JMa3bStnxDXFych4eHjo7OzZs3m53Pb968\ncXJycnBw8PLyMjQ05GmfQGbQ/PDo0SMLCwvOY07fSQBLly598eKFs7Ozjo4O58Evv/wiJyd3/fr1\n58+fGxsbFxQUbNmyxdHR0dvbOzU19ezZs9w23dzcjh8/bmBgcOjQIS8vr+LiYldXVxcXl7S0tC1b\ntkybNu1jYiIjI5uNkpycrKOjExwcHB8fX1paqqamJrJvgtCBaHmmNTtpW74BQE5Ozq1btwoLC5ud\nzzt27DAwMPjuu+/Onj2bkpLC0z7dfzH9EAfND81SHEVFRTzf9tdff+Xl5c2dOxdA9+7dKyoq8vPz\nPT09ARgZGTVrzD558uTNmzd///33ubm5pqamz549S0lJuXr1KovFqq+vb2m8trb2Y6O4u7tv377d\nzs6uX79+nMbyBILgtDzTmp20J06caPYGABYWFrKysp07d252Pv/111/Lly8HYGxs/DH7ysrKdP2x\nDIGkOIRJYwdezoOhQ4caGxtHRkbu2rXL3t5eWVlZS0vr0qVLANLT05ulOJSVlQ0NDVesWOHq6iol\nJfXzzz/r6uru2bNnxowZ3I19paWlnz9/DoAz6eA5SkxMjLW19dmzZwcOHHjgwAFq/naCxNPyTGt2\n0rZ8AwBZWVkALc/nYcOGXbt2DcD169c/Zp++v5UpEActNHr06JGXl3fkyJHGB56enoWFhVZWVhMn\nTtTS0gLg7++fkJBgaWkZHh4+ePDgZhbc3NwOHTrk5uYGwMbGJiEhwcnJKTk5ubCwkJPpA2BhYZGd\nnT1p0qQbN24oKSkBaDmKnp7eokWLTE1NExMTnZycKP0WCBJERkaGyX9cvXq15ZmGpictzzdwaHk+\nr1y58vr167a2thcuXFBUVGz94x0WKe7ZGYFAIFBDcnKytLT0hAkT0tLStm7deurUKboVMRHioAkE\nAg38888/bm5uysrKLBYrJCREV1eXbkVMhDhoAoFAYCgkB00gEAgMhThoAoFAYCjEQRMIBAJDIQ6a\nQCAQGApx0AQCgcBQiIMmEAgEhkIcNIFAIDAU4qAJBAKBoRAHTSAQCAyFOGgCgUBgKMRBEwgEAkMh\nDppAIBAYCnHQBAKBwFD+D74wycNWxPM1AAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "source": [
      "And now we have the *python* variable `XYcoef`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "XYcoef"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "[ 3.2  0.9]"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {
      "slideshow": {
       "slide_start": true,
       "slide_type": "slide"
      }
     },
     "source": [
      "<!--====-->\n",
      "Seamless Cython"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext cythonmagic"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def f(x):\n",
      "    return x**2-x\n",
      "\n",
      "def integrate_f(a, b, N):\n",
      "    s = 0; dx = (b-a)/N\n",
      "    for i in range(N):\n",
      "        s += f(a+i*dx)\n",
      "    return s * dx"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "cdef double fcy(double x) except? -2:\n",
      "    return x**2-x\n",
      "\n",
      "def integrate_fcy(double a, double b, int N):\n",
      "    cdef int i\n",
      "    cdef double s, dx\n",
      "    s = 0; dx = (b-a)/N\n",
      "    for i in range(N):\n",
      "        s += fcy(a+i*dx)\n",
      "    return s * dx"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit integrate_f(0, 1, 100)\n",
      "%timeit integrate_fcy(0, 1, 100)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_start": false
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10000 loops, best of 3: 34.3 \u00b5s per loop\n",
        "1000000 loops, best of 3: 599 ns per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}