{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA - Principal Component Analysis\n",
    "\n",
    "**Problem**: you have a multidimensional set of data (such as a set of hidden unit activations) and you want to see which points are closest to others.\n",
    "\n",
    "PCA allows you to identify the dimensions of greatest variance, to the dimensions of least variance. PCA1 has greatest variance.\n",
    "\n",
    "## Example\n",
    "\n",
    "Let's look at a dataset that has nothing to do with networks: measurements of flowers, specifically Irises."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.get(\"feature_names\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://rpubs.com/sarthakdasadia11/iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2],\n",
       "       [ 5.4,  3.9,  1.7,  0.4],\n",
       "       [ 4.6,  3.4,  1.4,  0.3],\n",
       "       [ 5. ,  3.4,  1.5,  0.2],\n",
       "       [ 4.4,  2.9,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5.4,  3.7,  1.5,  0.2],\n",
       "       [ 4.8,  3.4,  1.6,  0.2],\n",
       "       [ 4.8,  3. ,  1.4,  0.1],\n",
       "       [ 4.3,  3. ,  1.1,  0.1],\n",
       "       [ 5.8,  4. ,  1.2,  0.2],\n",
       "       [ 5.7,  4.4,  1.5,  0.4],\n",
       "       [ 5.4,  3.9,  1.3,  0.4],\n",
       "       [ 5.1,  3.5,  1.4,  0.3],\n",
       "       [ 5.7,  3.8,  1.7,  0.3],\n",
       "       [ 5.1,  3.8,  1.5,  0.3],\n",
       "       [ 5.4,  3.4,  1.7,  0.2],\n",
       "       [ 5.1,  3.7,  1.5,  0.4],\n",
       "       [ 4.6,  3.6,  1. ,  0.2],\n",
       "       [ 5.1,  3.3,  1.7,  0.5],\n",
       "       [ 4.8,  3.4,  1.9,  0.2],\n",
       "       [ 5. ,  3. ,  1.6,  0.2],\n",
       "       [ 5. ,  3.4,  1.6,  0.4],\n",
       "       [ 5.2,  3.5,  1.5,  0.2],\n",
       "       [ 5.2,  3.4,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.6,  0.2],\n",
       "       [ 4.8,  3.1,  1.6,  0.2],\n",
       "       [ 5.4,  3.4,  1.5,  0.4],\n",
       "       [ 5.2,  4.1,  1.5,  0.1],\n",
       "       [ 5.5,  4.2,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5. ,  3.2,  1.2,  0.2],\n",
       "       [ 5.5,  3.5,  1.3,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 4.4,  3. ,  1.3,  0.2],\n",
       "       [ 5.1,  3.4,  1.5,  0.2],\n",
       "       [ 5. ,  3.5,  1.3,  0.3],\n",
       "       [ 4.5,  2.3,  1.3,  0.3],\n",
       "       [ 4.4,  3.2,  1.3,  0.2],\n",
       "       [ 5. ,  3.5,  1.6,  0.6],\n",
       "       [ 5.1,  3.8,  1.9,  0.4],\n",
       "       [ 4.8,  3. ,  1.4,  0.3],\n",
       "       [ 5.1,  3.8,  1.6,  0.2],\n",
       "       [ 4.6,  3.2,  1.4,  0.2],\n",
       "       [ 5.3,  3.7,  1.5,  0.2],\n",
       "       [ 5. ,  3.3,  1.4,  0.2],\n",
       "       [ 7. ,  3.2,  4.7,  1.4],\n",
       "       [ 6.4,  3.2,  4.5,  1.5],\n",
       "       [ 6.9,  3.1,  4.9,  1.5],\n",
       "       [ 5.5,  2.3,  4. ,  1.3],\n",
       "       [ 6.5,  2.8,  4.6,  1.5],\n",
       "       [ 5.7,  2.8,  4.5,  1.3],\n",
       "       [ 6.3,  3.3,  4.7,  1.6],\n",
       "       [ 4.9,  2.4,  3.3,  1. ],\n",
       "       [ 6.6,  2.9,  4.6,  1.3],\n",
       "       [ 5.2,  2.7,  3.9,  1.4],\n",
       "       [ 5. ,  2. ,  3.5,  1. ],\n",
       "       [ 5.9,  3. ,  4.2,  1.5],\n",
       "       [ 6. ,  2.2,  4. ,  1. ],\n",
       "       [ 6.1,  2.9,  4.7,  1.4],\n",
       "       [ 5.6,  2.9,  3.6,  1.3],\n",
       "       [ 6.7,  3.1,  4.4,  1.4],\n",
       "       [ 5.6,  3. ,  4.5,  1.5],\n",
       "       [ 5.8,  2.7,  4.1,  1. ],\n",
       "       [ 6.2,  2.2,  4.5,  1.5],\n",
       "       [ 5.6,  2.5,  3.9,  1.1],\n",
       "       [ 5.9,  3.2,  4.8,  1.8],\n",
       "       [ 6.1,  2.8,  4. ,  1.3],\n",
       "       [ 6.3,  2.5,  4.9,  1.5],\n",
       "       [ 6.1,  2.8,  4.7,  1.2],\n",
       "       [ 6.4,  2.9,  4.3,  1.3],\n",
       "       [ 6.6,  3. ,  4.4,  1.4],\n",
       "       [ 6.8,  2.8,  4.8,  1.4],\n",
       "       [ 6.7,  3. ,  5. ,  1.7],\n",
       "       [ 6. ,  2.9,  4.5,  1.5],\n",
       "       [ 5.7,  2.6,  3.5,  1. ],\n",
       "       [ 5.5,  2.4,  3.8,  1.1],\n",
       "       [ 5.5,  2.4,  3.7,  1. ],\n",
       "       [ 5.8,  2.7,  3.9,  1.2],\n",
       "       [ 6. ,  2.7,  5.1,  1.6],\n",
       "       [ 5.4,  3. ,  4.5,  1.5],\n",
       "       [ 6. ,  3.4,  4.5,  1.6],\n",
       "       [ 6.7,  3.1,  4.7,  1.5],\n",
       "       [ 6.3,  2.3,  4.4,  1.3],\n",
       "       [ 5.6,  3. ,  4.1,  1.3],\n",
       "       [ 5.5,  2.5,  4. ,  1.3],\n",
       "       [ 5.5,  2.6,  4.4,  1.2],\n",
       "       [ 6.1,  3. ,  4.6,  1.4],\n",
       "       [ 5.8,  2.6,  4. ,  1.2],\n",
       "       [ 5. ,  2.3,  3.3,  1. ],\n",
       "       [ 5.6,  2.7,  4.2,  1.3],\n",
       "       [ 5.7,  3. ,  4.2,  1.2],\n",
       "       [ 5.7,  2.9,  4.2,  1.3],\n",
       "       [ 6.2,  2.9,  4.3,  1.3],\n",
       "       [ 5.1,  2.5,  3. ,  1.1],\n",
       "       [ 5.7,  2.8,  4.1,  1.3],\n",
       "       [ 6.3,  3.3,  6. ,  2.5],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 7.1,  3. ,  5.9,  2.1],\n",
       "       [ 6.3,  2.9,  5.6,  1.8],\n",
       "       [ 6.5,  3. ,  5.8,  2.2],\n",
       "       [ 7.6,  3. ,  6.6,  2.1],\n",
       "       [ 4.9,  2.5,  4.5,  1.7],\n",
       "       [ 7.3,  2.9,  6.3,  1.8],\n",
       "       [ 6.7,  2.5,  5.8,  1.8],\n",
       "       [ 7.2,  3.6,  6.1,  2.5],\n",
       "       [ 6.5,  3.2,  5.1,  2. ],\n",
       "       [ 6.4,  2.7,  5.3,  1.9],\n",
       "       [ 6.8,  3. ,  5.5,  2.1],\n",
       "       [ 5.7,  2.5,  5. ,  2. ],\n",
       "       [ 5.8,  2.8,  5.1,  2.4],\n",
       "       [ 6.4,  3.2,  5.3,  2.3],\n",
       "       [ 6.5,  3. ,  5.5,  1.8],\n",
       "       [ 7.7,  3.8,  6.7,  2.2],\n",
       "       [ 7.7,  2.6,  6.9,  2.3],\n",
       "       [ 6. ,  2.2,  5. ,  1.5],\n",
       "       [ 6.9,  3.2,  5.7,  2.3],\n",
       "       [ 5.6,  2.8,  4.9,  2. ],\n",
       "       [ 7.7,  2.8,  6.7,  2. ],\n",
       "       [ 6.3,  2.7,  4.9,  1.8],\n",
       "       [ 6.7,  3.3,  5.7,  2.1],\n",
       "       [ 7.2,  3.2,  6. ,  1.8],\n",
       "       [ 6.2,  2.8,  4.8,  1.8],\n",
       "       [ 6.1,  3. ,  4.9,  1.8],\n",
       "       [ 6.4,  2.8,  5.6,  2.1],\n",
       "       [ 7.2,  3. ,  5.8,  1.6],\n",
       "       [ 7.4,  2.8,  6.1,  1.9],\n",
       "       [ 7.9,  3.8,  6.4,  2. ],\n",
       "       [ 6.4,  2.8,  5.6,  2.2],\n",
       "       [ 6.3,  2.8,  5.1,  1.5],\n",
       "       [ 6.1,  2.6,  5.6,  1.4],\n",
       "       [ 7.7,  3. ,  6.1,  2.3],\n",
       "       [ 6.3,  3.4,  5.6,  2.4],\n",
       "       [ 6.4,  3.1,  5.5,  1.8],\n",
       "       [ 6. ,  3. ,  4.8,  1.8],\n",
       "       [ 6.9,  3.1,  5.4,  2.1],\n",
       "       [ 6.7,  3.1,  5.6,  2.4],\n",
       "       [ 6.9,  3.1,  5.1,  2.3],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 6.8,  3.2,  5.9,  2.3],\n",
       "       [ 6.7,  3.3,  5.7,  2.5],\n",
       "       [ 6.7,  3. ,  5.2,  2.3],\n",
       "       [ 6.3,  2.5,  5. ,  1.9],\n",
       "       [ 6.5,  3. ,  5.2,  2. ],\n",
       "       [ 6.2,  3.4,  5.4,  2.3],\n",
       "       [ 5.9,  3. ,  5.1,  1.8]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, n_components=2, whiten=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = pca.transform(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "        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"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f3f814f0d30>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=iris.target)\n",
    "plt.scatter([X[10][0]], [X[10][1]], s=[200], c=[\"r\"], alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}