data-visualization/0002-matplotlib-animation-with-regression/matplotlib-linear-regression-animation.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Matplotlib Linear Regression Animation in Jupyter Notebook\n",
    "\n",
    "This is a notebook for the medium article [Matplotlib Linear Regression Animation in Jupyter Notebook](https://bindichen.medium.com/matplotlib-linear-regression-animation-in-jupyter-notebook-2435b711bea2)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from vega_datasets import data\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Miles_per_Gallon</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight_in_lbs</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Year</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>buick skylark 320</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>plymouth satellite</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amc rebel sst</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ford torino</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                        Name  Miles_per_Gallon  Cylinders  Displacement  \\\n",
       "0  chevrolet chevelle malibu              18.0          8         307.0   \n",
       "1          buick skylark 320              15.0          8         350.0   \n",
       "2         plymouth satellite              18.0          8         318.0   \n",
       "3              amc rebel sst              16.0          8         304.0   \n",
       "4                ford torino              17.0          8         302.0   \n",
       "\n",
       "   Horsepower  Weight_in_lbs  Acceleration       Year Origin  \n",
       "0       130.0           3504          12.0 1970-01-01    USA  \n",
       "1       165.0           3693          11.5 1970-01-01    USA  \n",
       "2       150.0           3436          11.0 1970-01-01    USA  \n",
       "3       150.0           3433          12.0 1970-01-01    USA  \n",
       "4       140.0           3449          10.5 1970-01-01    USA  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.cars()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows with NaN\n",
    "df.dropna(subset=['Horsepower', 'Miles_per_Gallon'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform data\n",
    "x = df['Horsepower'].to_numpy().reshape(-1, 1)\n",
    "y = df['Miles_per_Gallon'].to_numpy().reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y, c='g', label='Horsepower vs. Miles_per_Gallon')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Animations with Interactive Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enable interactive plot\n",
    "%matplotlib notebook\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.animation import FuncAnimation\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
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       "    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",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\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",
       "        fig.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 backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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 * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\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 overridden (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",
       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\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"
    },
    {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_data = []\n",
    "y_data = []\n",
    "\n",
    "fig, ax = plt.subplots()     # A tuple unpacking to unpack the only fig\n",
    "ax.set_xlim(30, 250)\n",
    "ax.set_ylim(5, 50)\n",
    "# Plotting \n",
    "scatter, = ax.plot([], [], 'go', label='Horsepower vs. Miles_per_Gallon')\n",
    "line, = ax.plot([], [], 'r', label='Linear Regression')\n",
    "ax.legend()\n",
    "\n",
    "reg = LinearRegression()\n",
    "\n",
    "def animate(frame_num):\n",
    "    # Adding data\n",
    "    x_data.append(x[frame_num])\n",
    "    y_data.append(y[frame_num])\n",
    "    # Convert data to numpy array\n",
    "    x_train = np.array(x_data).reshape(-1, 1)\n",
    "    y_train = np.array(y_data).reshape(-1, 1)\n",
    "    # Fit values to a linear regression\n",
    "    reg.fit(x_train, y_train)\n",
    "\n",
    "    # update data for scatter plot\n",
    "    scatter.set_data((x_data, y_data))\n",
    "    # Predict value and update data for line plot\n",
    "    line.set_data((list(range(250)), reg.predict(np.array([entry for entry in range(250)]).reshape(-1, 1))))\n",
    "\n",
    "anim = FuncAnimation(fig, animate, frames=len(x), interval=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Animations with embedded HTML5 video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.animation import FuncAnimation\n",
    "from IPython import display\n",
    "\n",
    "# Turn off matplotlib plot in Notebook\n",
    "plt.ioff()\n",
    "# Pass the ffmpeg path\n",
    "plt.rcParams['animation.ffmpeg_path'] = '/Users/chen5/Downloads/ffmpeg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video width=\"432\" height=\"288\" controls autoplay loop>\n",
       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAIGZ0eXBNNFYgAAACAE00ViBpc29taXNvMmF2YzEAAAAIZnJlZQABhVxtZGF0AAACrgYF//+q\n",
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_data = []\n",
    "y_data = []\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_xlim(30, 250)\n",
    "ax.set_ylim(5, 50)\n",
    "scatter, = ax.plot([], [], 'go', label='Horsepower vs. Miles_per_Gallon')\n",
    "line, = ax.plot([], [], 'r', label='Linear Regression')\n",
    "ax.legend()\n",
    "\n",
    "reg = LinearRegression()\n",
    "\n",
    "def animate(frame_num):\n",
    "    # Adding data\n",
    "    x_data.append(x[frame_num])\n",
    "    y_data.append(y[frame_num])\n",
    "    # Convert data to numpy array\n",
    "    x_train = np.array(x_data).reshape(-1, 1)\n",
    "    y_train = np.array(y_data).reshape(-1, 1)\n",
    "    reg.fit(x_train, y_train)\n",
    "    \n",
    "    # update data for scatter plot\n",
    "    scatter.set_data((x_data, y_data))\n",
    "    # Predict value and update data for line plot\n",
    "    line.set_data((list(range(250)), reg.predict(np.array([entry for entry in range(250)]).reshape(-1, 1))))\n",
    "\n",
    "anim = FuncAnimation(fig, animate, frames=len(x), interval=20)\n",
    "\n",
    "video = anim.to_html5_video()\n",
    "html = display.HTML(video)\n",
    "display.display(html)\n",
    "plt.close()\n",
    "\n",
    "# Note Github only render static HTML and the embeded HTML5 video won't be displayed, \n",
    "# The embedded video should be working if you host the Notebook or open it locally. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thanks for reading\n",
    "\n",
    "This is a notebook for the medium article [Matplotlib Linear Regression Animation in Jupyter Notebook](https://bindichen.medium.com/matplotlib-linear-regression-animation-in-jupyter-notebook-2435b711bea2)\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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