15-Preview-of-Data-Science-Tools.ipynb

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    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"fig/cover-small.jpg\">\n",
    "*This notebook contains an excerpt from the [Whirlwind Tour of Python](http://www.oreilly.com/programming/free/a-whirlwind-tour-of-python.csp) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/WhirlwindTourOfPython).*\n",
    "\n",
    "*The text and code are released under the [CC0](https://github.com/jakevdp/WhirlwindTourOfPython/blob/master/LICENSE) license; see also the companion project, the [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook).*\n"
   ]
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   "source": [
    "<!--NAVIGATION-->\n",
    "< [String Manipulation and Regular Expressions](14-Strings-and-Regular-Expressions.ipynb) | [Contents](Index.ipynb) | [Resources for Further Learning](16-Further-Resources.ipynb) >"
   ]
  },
  {
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   "source": [
    "# A Preview of Data Science Tools"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "If you would like to spring from here and go farther in using Python for scientific computing or data science, there are a few packages that will make your life much easier.\n",
    "This section will introduce and preview several of the more important ones, and give you an idea of the types of applications they are designed for.\n",
    "If you're using the *Anaconda* or *Miniconda* environment suggested at the beginning of this report, you can install the relevant packages with the following command:\n",
    "```\n",
    "$ conda install numpy scipy pandas matplotlib scikit-learn\n",
    "```\n",
    "Let's take a brief look at each of these in turn."
   ]
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   "source": [
    "## NumPy: Numerical Python\n",
    "\n",
    "NumPy provides an efficient way to store and manipulate multi-dimensional dense arrays in Python.\n",
    "The important features of NumPy are:\n",
    "\n",
    "- It provides an ``ndarray`` structure, which allows efficient storage and manipulation of vectors, matrices, and higher-dimensional datasets.\n",
    "- It provides a readable and efficient syntax for operating on this data, from simple element-wise arithmetic to more complicated linear algebraic operations.\n",
    "\n",
    "In the simplest case, NumPy arrays look a lot like Python lists.\n",
    "For example, here is an array containing the range of numbers 1 to 9 (compare this with Python's built-in ``range()``):"
   ]
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     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 1,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.arange(1, 10)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy's arrays offer both efficient storage of data, as well as efficient element-wise operations on the data.\n",
    "For example, to square each element of the array, we can apply the \"``**``\" operator to the array directly:"
   ]
  },
  {
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    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  9, 16, 25, 36, 49, 64, 81])"
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   "source": [
    "x ** 2"
   ]
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   "source": [
    "Compare this with the much more verbose Python-style list comprehension for the same result:"
   ]
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  {
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   "execution_count": 3,
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    {
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       "[1, 4, 9, 16, 25, 36, 49, 64, 81]"
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     "execution_count": 3,
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   "source": [
    "[val ** 2 for val in range(1, 10)]"
   ]
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   "source": [
    "Unlike Python lists (which are limited to one dimension), NumPy arrays can be multi-dimensional.\n",
    "For example, here we will reshape our ``x`` array into a 3x3 array:"
   ]
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       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
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   "source": [
    "M = x.reshape((3, 3))\n",
    "M"
   ]
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   "source": [
    "A two-dimensional array is one representation of a matrix, and NumPy knows how to efficiently do typical matrix operations. For example, you can compute the transpose using ``.T``:"
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      "text/plain": [
       "array([[1, 4, 7],\n",
       "       [2, 5, 8],\n",
       "       [3, 6, 9]])"
      ]
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    "M.T"
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    "or a matrix-vector product using ``np.dot``:"
   ]
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      "text/plain": [
       "array([ 38,  92, 146])"
      ]
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     "execution_count": 6,
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    "np.dot(M, [5, 6, 7])"
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    "and even more sophisticated operations like eigenvalue decomposition:"
   ]
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     "data": {
      "text/plain": [
       "array([  1.61168440e+01,  -1.11684397e+00,  -1.30367773e-15])"
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    "np.linalg.eigvals(M)"
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    "Such linear algebraic manipulation underpins much of modern data analysis, particularly when it comes to the fields of machine learning and data mining.\n",
    "\n",
    "For more information on NumPy, see [Resources for Further Learning](16-Further-Resources.ipynb)."
   ]
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   "source": [
    "## Pandas: Labeled Column-oriented Data\n",
    "\n",
    "Pandas is a much newer package than NumPy, and is in fact built on top of it.\n",
    "What Pandas provides is a labeled interface to multi-dimensional data, in the form of a DataFrame object that will feel very familiar to users of R and related languages.\n",
    "DataFrames in Pandas look something like this:"
   ]
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   "execution_count": 8,
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    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "  label  value\n",
       "0     A      1\n",
       "1     B      2\n",
       "2     C      3\n",
       "3     A      4\n",
       "4     B      5\n",
       "5     C      6"
      ]
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     "execution_count": 8,
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   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame({'label': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "                   'value': [1, 2, 3, 4, 5, 6]})\n",
    "df"
   ]
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   "source": [
    "The Pandas interface allows you to do things like select columns by name:"
   ]
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       "0    A\n",
       "1    B\n",
       "2    C\n",
       "3    A\n",
       "4    B\n",
       "5    C\n",
       "Name: label, dtype: object"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['label']"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply string operations across string entries:"
   ]
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   "cell_type": "code",
   "execution_count": 10,
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    "collapsed": false
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    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    a\n",
       "4    b\n",
       "5    c\n",
       "Name: label, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['label'].str.lower()"
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  {
   "cell_type": "markdown",
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    "Apply aggregates across numerical entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['value'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, perhaps most importantly, do efficient database-style joins and groupings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>9</td>\n",
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       "       value\n",
       "label       \n",
       "A          5\n",
       "B          7\n",
       "C          9"
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     "metadata": {},
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   "source": [
    "df.groupby('label').sum()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here in one line we have computed the sum of all objects sharing the same label, something that is much more verbose (and much less efficient) using tools provided in Numpy and core Python.\n",
    "\n",
    "For more information on using Pandas, see [Resources for Further Learning](16-Further-Resources.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib MatLab-style scientific visualization\n",
    "\n",
    "Matplotlib is currently the most popular scientific visualization packages in Python.\n",
    "Even proponents admit that its interface is sometimes overly verbose, but it is a powerful library for creating a large range of plots.\n",
    "\n",
    "To use Matplotlib, we can start by enabling the notebook mode (for use in the Jupyter notebook) and then importing the package as ``plt``\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# run this if using Jupyter notebook\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')  # make graphs in the style of R's ggplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create some data (as NumPy arrays, of course) and plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10)  # range of values from 0 to 10\n",
    "y = np.sin(x)           # sine of these values\n",
    "plt.plot(x, y);         # plot as a line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you run this code live, you will see an interactive plot that lets you pan, zoom, and scroll to explore the data.\n",
    "\n",
    "This is the simplest example of a Matplotlib plot; for ideas on the wide range of plot types available, see [Matplotlib's online gallery](http://matplotlib.org/gallery.html) as well as other references listed in [Resources for Further Learning](16-Further-Resources.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SciPy: Scientific Python\n",
    "\n",
    "SciPy is a collection of scientific functionality that is built on NumPy.\n",
    "The package began as a set of Python wrappers to well-known Fortran libraries for numerical computing, and has grown from there.\n",
    "The package is arranged as a set of submodules, each implementing some class of numerical algorithms.\n",
    "Here is an incomplete sample of some of the more important ones for data science:\n",
    "\n",
    "- ``scipy.fftpack``: Fast Fourier transforms\n",
    "- ``scipy.integrate``: Numerical integration\n",
    "- ``scipy.interpolate``: Numerical interpolation\n",
    "- ``scipy.linalg``: Linear algebra routines\n",
    "- ``scipy.optimize``: Numerical optimization of functions\n",
    "- ``scipy.sparse``: Sparse matrix storage and linear algebra\n",
    "- ``scipy.stats``: Statistical analysis routines\n",
    "\n",
    "For example, let's take a look at interpolating a smooth curve between some data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import interpolate\n",
    "\n",
    "# choose eight points between 0 and 10\n",
    "x = np.linspace(0, 10, 8)\n",
    "y = np.sin(x)\n",
    "\n",
    "# create a cubic interpolation function\n",
    "func = interpolate.interp1d(x, y, kind='cubic')\n",
    "\n",
    "# interpolate on a grid of 1,000 points\n",
    "x_interp = np.linspace(0, 10, 1000)\n",
    "y_interp = func(x_interp)\n",
    "\n",
    "# plot the results\n",
    "plt.figure()  # new figure\n",
    "plt.plot(x, y, 'o')\n",
    "plt.plot(x_interp, y_interp);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we see is a smooth interpolation between the points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Data Science Packages\n",
    "\n",
    "Built on top of these tools are a host of other data science packages, including general tools like [Scikit-Learn](http://scikit-learn.org) for machine learning, [Scikit-Image](http://scikit-image.org) for image analysis, and [Statsmodels](http://statsmodels.sourceforge.net/) for statistical modeling, as well as more domain-specific packages like [AstroPy](http://astropy.org) for astronomy and astrophysics, [NiPy](http://nipy.org/) for neuro-imaging, and many, many more.\n",
    "\n",
    "No matter what type of scientific, numerical, or statistical problem you are facing, it's likely there is a Python package out there that can help you solve it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [String Manipulation and Regular Expressions](14-Strings-and-Regular-Expressions.ipynb) | [Contents](Index.ipynb) | [Resources for Further Learning](16-Further-Resources.ipynb) >"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}