01_SVD_visualizing_data.ipynb

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    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import scipy.stats\n",
    "import sklearn.datasets\n",
    "import sklearn.preprocessing\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "plt.xkcd()\n",
    "\n",
    "None"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "# Singular Value Decomposition for Data Visualization\n",
    "\n",
    "## Displaying high-dimensional data using reduced-rank matrices\n",
    "\n",
    "A data visualization goes a long way in helping you understand the underlying dataset. If the data is highly dimensional, you can use Singular Value Decomposition (SVD) to find a reduced-rank approximation of the data that can be visualized easily."
   ]
  },
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   "cell_type": "markdown",
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    "## Example 1: the Iris dataset\n",
    "\n",
    "We start off with the [Iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set). The data is multivariate, with 150 measurements of 4 features (length and width cm of both sepal and petal) on 3 distinct Iris species. Of the 150 measurements, there are 50 measurements each for _Iris setosa_, _Iris versicolor_, and _Iris virginica_.\n",
    "\n",
    "[Scikit Learn's `datasets`](http://scikit-learn.org/stable/datasets/) includes the Iris dataset, so let's load that up and start exploring."
   ]
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      "Iris dataset has 150 rows and 4 columns\n",
      "\n",
      "Here are the first 5 rows of the data:\n",
      "\n",
      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
      "0                5.1               3.5                1.4               0.2\n",
      "1                4.9               3.0                1.4               0.2\n",
      "2                4.7               3.2                1.3               0.2\n",
      "3                4.6               3.1                1.5               0.2\n",
      "4                5.0               3.6                1.4               0.2\n",
      "\n",
      "Some simple statistics on the Iris dataset:\n",
      "\n",
      "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
      "count         150.000000        150.000000         150.000000   \n",
      "mean            5.843333          3.054000           3.758667   \n",
      "std             0.828066          0.433594           1.764420   \n",
      "min             4.300000          2.000000           1.000000   \n",
      "25%             5.100000          2.800000           1.600000   \n",
      "50%             5.800000          3.000000           4.350000   \n",
      "75%             6.400000          3.300000           5.100000   \n",
      "max             7.900000          4.400000           6.900000   \n",
      "\n",
      "       petal width (cm)  \n",
      "count        150.000000  \n",
      "mean           1.198667  \n",
      "std            0.763161  \n",
      "min            0.100000  \n",
      "25%            0.300000  \n",
      "50%            1.300000  \n",
      "75%            1.800000  \n",
      "max            2.500000  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "iris = sklearn.datasets.load_iris()\n",
    "\n",
    "df_iris = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "\n",
    "print('Iris dataset has {} rows and {} columns\\n'.format(*df_iris.shape))\n",
    "\n",
    "print('Here are the first 5 rows of the data:\\n\\n{}\\n'.format(df_iris.head(5)))\n",
    "\n",
    "print('Some simple statistics on the Iris dataset:\\n\\n{}\\n'.format(df_iris.describe()))"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "As we are exploring the dataset, it would be nice to view the data in order to get an idea of how the 3 species might be distributed with respect to one another in terms of their features. Perhaps we are interested in finding clusters, or maybe we would like to find a way to make class predictions?\n",
    "\n",
    "However, since the data has 4 dimensions, we would be hard-pressed to come up with a good way to graph the data in 4D that we could easily understand.\n",
    "\n",
    "_But what if we could reduce or compress the data so that we could work in 3 dimensions or less?_\n",
    "\n",
    "[Singular value decomposition](http://mathworld.wolfram.com/SingularValueDecomposition.html) lets us do just that."
   ]
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   "source": [
    "## Singular value decomposition\n",
    "\n",
    "Singular value decomposition factorizes an $\\mathbb{R}^{m \\times n}$ matrix $X$ into\n",
    "\n",
    "* matrix $U \\in \\mathbb{R}^{m \\times m}$ are the left-singular vectors of $X$, where columns are the set of orthonormal eigenvectors of $X \\, X^{\\intercal}$\n",
    "* diagonal matrix $\\Sigma$ with entries $\\sigma \\in \\mathbb{R}$ that are the non-negative singular values of $X$\n",
    "* matrix $V \\in \\mathbb{R}^{n \\times n}$ are the right-singular vectors $X$, where the columns are the set of orthonormal eigenvectors of $X^{\\intercal} \\, X$\n",
    "\n",
    "such that \n",
    "\n",
    "\\begin{align}\n",
    "  X &= U \\, \\Sigma \\, V^{\\intercal}\n",
    "\\end{align}\n",
    "\n",
    "We can use [`numpy.linalg.svd`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.svd.html) to factorize the Iris data matrix into three components $U$, $\\Sigma$, and $V^{\\intercal}$."
   ]
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   "source": [
    "U_iris, S_iris, Vt_iris = np.linalg.svd(df_iris)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "#### $U$: left-singular vectors of $X$\n",
    "\n",
    "The rows of the $U$ correspond to the rows of original data matrix $X$, while the columns are the set of ordered, orthornormal eigenvectors of $X \\, X^{\\intercal}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "text": [
      "matrix U has 150 rows, 150 columns\n",
      "\n",
      "        0         1         2         3         4         5         6    \\\n",
      "0 -0.061617  0.129969 -0.000056  0.001058 -0.073179 -0.081571 -0.069303   \n",
      "1 -0.058072  0.111371  0.068439  0.052115 -0.127666 -0.142615 -0.129771   \n",
      "2 -0.056763  0.118295  0.002311  0.009078 -0.010367  0.012712  0.021947   \n",
      "3 -0.056654  0.105608  0.004218 -0.042215 -0.040903 -0.009878 -0.022402   \n",
      "4 -0.061230  0.131431 -0.033908 -0.033254  0.979924 -0.021636 -0.019800   \n",
      "\n",
      "        7         8         9      ...          140       141       142  \\\n",
      "0 -0.071247 -0.062021 -0.066137    ...    -0.102203 -0.108392 -0.081006   \n",
      "1 -0.102197 -0.078402 -0.068525    ...     0.027873  0.029108  0.037220   \n",
      "2 -0.040583 -0.045632 -0.084427    ...     0.059291 -0.040847  0.089081   \n",
      "3 -0.043891 -0.037508 -0.067710    ...     0.176345  0.268409  0.019061   \n",
      "4 -0.016901 -0.013404 -0.013042    ...     0.001694  0.004298 -0.000120   \n",
      "\n",
      "        143       144       145       146       147       148       149  \n",
      "0 -0.097894 -0.104334 -0.103992 -0.088545 -0.093464 -0.096022 -0.080992  \n",
      "1  0.034247  0.003214  0.033050  0.082646  0.036676 -0.032446  0.012727  \n",
      "2  0.100846  0.113471 -0.011387 -0.062039  0.030825  0.195235  0.135568  \n",
      "3  0.067487  0.176562  0.233969  0.115344  0.085373  0.095596 -0.041733  \n",
      "4 -0.000255 -0.001583  0.003997  0.007958  0.001071 -0.007744 -0.004753  \n",
      "\n",
      "[5 rows x 150 columns]\n"
     ]
    }
   ],
   "source": [
    "print('matrix U has {} rows, {} columns\\n'.format(*U_iris.shape))\n",
    "\n",
    "print('{}'.format(pd.DataFrame(U_iris).head(5)))"
   ]
  },
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   "source": [
    "#### $V$: right-singular vectors of $X$\n",
    "\n",
    "[`numpy.linalg.svd`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.svd.html) actually returns $V^{\\intercal}$ instead of $V$, so it is the _columns_ of $V^{\\intercal}$ that correspond to the columns of original data matrix $X$. Hence, the _rows_ are the set of ordered, orthornormal eigenvectors of $X^{\\intercal} \\, X$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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     "text": [
      "matrix Vt has 4 rows, 4 columns\n",
      "\n",
      "          0         1         2         3\n",
      "0 -0.751168 -0.379788 -0.513151 -0.167879\n",
      "1  0.285831  0.544890 -0.708899 -0.344758\n",
      "2  0.499424 -0.675025 -0.054720 -0.540299\n",
      "3  0.323455 -0.321243 -0.480775  0.749023\n"
     ]
    }
   ],
   "source": [
    "print('matrix Vt has {} rows, {} columns\\n'.format(*Vt_iris.shape))\n",
    "\n",
    "print('{}'.format(pd.DataFrame(Vt_iris).head()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### $\\Sigma$: singular values of $X$\n",
    "\n",
    "The elements $\\sigma_{i}$ of diagonal matrix $\\Sigma$ are the non-zero singular values of matrix $X$, which are really just the square roots of the non-zero eigenvalues of $X^{\\intercal} \\, X$ (and also for $X \\, X^{\\intercal}$). These singular values can be used to determine the amount of variance $X^{\\prime}$ explains of the original data matrix $X$ when reducing the dimensions to find a lower rank approximation.\n",
    "\n",
    "\\begin{align}\n",
    "   X^{\\prime}_{k} &=  U_{k} \\, \\Sigma_{k} \\, V^{\\intercal}_{k} \\\\\n",
    "                           &\\approx X_{r} & \\text{ where } rank(X^{\\prime}) = k \\lt rank(X) = r\n",
    "\\end{align}\n",
    "\n",
    "The amount of variance that the reduced rank approximation $X^{\\prime}_{k}$ explains of $X_{r}$ is\n",
    "\n",
    "\\begin{align}\n",
    "  \\text{cum. variance explained} &= \\frac{\\sum_{j=1}^{k} \\sigma_{j}^{2}}{\\sum_{i=1}^{r} \\sigma_{i}^{2}}\n",
    "\\end{align}\n",
    "\n",
    "**NOTE**: [`numpy.linalg.svd`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.svd.html) actually returns a $\\Sigma$ that is not a diagonal _matrix_, but a _list_ of the entries on the diagonal."
   ]
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   "source": [
    "num_sv_iris = np.arange(1, S_iris.size+1)\n",
    "\n",
    "cum_var_explained_iris = [np.sum(np.square(S_iris[0:n])) / np.sum(np.square(S_iris)) for n in num_sv_iris]"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Let's have a look at the cumulative variance explained visually as a function of the number of singular values used when reducing rank to find a lower-ranked matrix $X^{\\prime}$ to approximate $X$. This will inform us as to how many dimensions we should use."
   ]
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       "        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, 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 * 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 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",
       "    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",
       "        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": {
      "text/html": [
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\" width=\"700\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7.0,5.5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "plt.plot(num_sv_iris,\n",
    "         cum_var_explained_iris,\n",
    "         color='#2171b5',\n",
    "         label='variance explained',\n",
    "         alpha=0.65,\n",
    "         zorder=1000)\n",
    "\n",
    "plt.scatter(num_sv_iris,\n",
    "            sklearn.preprocessing.normalize(S_iris.reshape((1,-1))),\n",
    "            color='#fc4e2a',\n",
    "            label='singular values (normalized)',\n",
    "            alpha=0.65,\n",
    "            zorder=1000)\n",
    "\n",
    "plt.legend(loc='center right', scatterpoints=1, fontsize=8)\n",
    "\n",
    "ax.set_xticks(num_sv_iris)\n",
    "ax.set_xlim(0.8, 4.1)\n",
    "ax.set_ylim(0.0, 1.1)\n",
    "ax.set_xlabel(r'Number of singular values used')\n",
    "ax.set_ylabel('Variance in data explained')\n",
    "ax.set_title('Iris dataset: cumulative variance explained & singular values',\n",
    "             fontsize=14,\n",
    "             y=1.03)\n",
    "\n",
    "ax.set_facecolor('0.98')\n",
    "\n",
    "plt.grid(alpha=0.8, zorder=1)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dimension reduction\n",
    "\n",
    "Judging from the curve representing cumulative variance explained in the figure above, we can see that\n",
    "\n",
    "* with 1 singular value, about 96.5% of the variance of $X$ can be explained\n",
    "* with 2 singular values, that number goes up to approximately 99.8%\n",
    "\n",
    "Since graphing the Iris dataset in 1D wouldn't be all that interesting (just dots on a line segment), let's try using the first 2 singular values to represent the data on the $x$- and $y$-axes, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_setosa = np.where(iris.target==0)[0]\n",
    "idx_versicolor = np.where(iris.target==1)[0]\n",
    "idx_virginica = np.where(iris.target==2)[0]\n",
    "\n",
    "setosa_x = U_iris[idx_setosa, 0]\n",
    "setosa_y = U_iris[idx_setosa, 1]\n",
    "\n",
    "versicolor_x = U_iris[idx_versicolor, 0]\n",
    "versicolor_y = U_iris[idx_versicolor, 1]\n",
    "\n",
    "virginica_x = U_iris[idx_virginica, 0]\n",
    "virginica_y = U_iris[idx_virginica, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use different marker shapes and colors to represent the three Iris species on our 2D graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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",
       "            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, 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 * 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 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",
       "    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",
       "        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>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"700\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7.0,5.5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "plt.scatter(setosa_x,\n",
    "            setosa_y,\n",
    "            marker='o',\n",
    "            color='#66c2a5',\n",
    "            label='Iris-setosa',\n",
    "            zorder=1000)\n",
    "\n",
    "plt.scatter(versicolor_x,\n",
    "            versicolor_y,\n",
    "            marker='D',\n",
    "            color='#fc8d62',\n",
    "            label='Iris-versicolor',\n",
    "            zorder=1000)\n",
    "\n",
    "plt.scatter(virginica_x,\n",
    "            virginica_y,\n",
    "            marker='^',\n",
    "            color='#8da0cb',\n",
    "            label='Iris-virginica',\n",
    "            zorder=1000)\n",
    "\n",
    "plt.legend(loc='upper left', scatterpoints=1, fontsize=8)\n",
    "\n",
    "ax.set_xlabel(r'singular value $\\sigma_{1}$')\n",
    "ax.set_ylabel(r'singular value $\\sigma_{2}$')\n",
    "ax.set_title('2D plot of Iris dataset',\n",
    "             fontsize=14,\n",
    "             y=1.03)\n",
    "ax.set_facecolor('0.98')\n",
    "\n",
    "plt.grid(alpha=0.6, zorder=1)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There!\n",
    "\n",
    "Now that we are viewing the originally 4D data with 2 dimensions using the first 2 singular value columns of the $U$ left singular vectors matrix, we can see that there should be a very clear separation for the _Iris setosa_ class and the others. On the other hand, the demarcation between _Iris versicolor_ and _Iris virginica_ might not be as clear cut.\n",
    "\n",
    "Nevertheless, since this 2D reduced-rank matrix representation $X^{\\prime}$ explains nearly 99.8% of the variance in the original dataset, we can be pretty certain that clustering and classification should be possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Countries and Primary Languages\n",
    "\n",
    "### Or, What to do when your data is categorical?\n",
    "\n",
    "In the previous example, we were exploring the Iris dataset which is a matrix $X \\in \\mathbb{R}^{150 \\times 4}$. Singular value decomposition helped us to find a reduced-rank matrix $X^{\\prime} \\in \\mathbb{R}^{150 \\times 2}$ that accurately approximated the original data matrix and let us visualize the 4-dimensional data using 2 dimensions.\n",
    "\n",
    "Let's now consider another dataset where the values are not in $\\mathbb{R}$, but are _categorical_.\n",
    "\n",
    "For this example, we explore a fictional survey of 1000 respondents from each of five countries (Canada, USA, England, Italy and Switzerland), asking them what their primary language is (among English, French, Spanish, German and Italian). So in our data we have categories for both _country_ and _language_.\n",
    "\n",
    "We read in the data from file using [`pandas.dataframe.read_csv`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data has 5000 measurements for 2 variables\n",
      "\n",
      "Here are the first 10 rows...\n",
      "\n",
      "  country language\n",
      "0       2        5\n",
      "1       4        2\n",
      "2       2        3\n",
      "3       1        1\n",
      "4       5        5\n",
      "5       4        5\n",
      "6       5        4\n",
      "7       4        5\n",
      "8       5        4\n",
      "9       5        4\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "fin = os.path.join(os.getcwd(), \n",
    "                   'data', \n",
    "                   'country_language.csv')\n",
    "\n",
    "df_countries = pd.read_csv(fin, dtype='category')\n",
    "\n",
    "print(\"data has {} measurements for {} variables\\n\".format(*df_countries.shape))\n",
    "\n",
    "print(\"Here are the first 10 rows...\\n\\n{}\\n...\".format(df_countries.head(10)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correspondence Analysis\n",
    "\n",
    "#### Contingency table $F$\n",
    "\n",
    "Our next step in exploring the data is to break out the data in terms of the 2 categories.\n",
    "\n",
    "Here we convert the raw observations into a [contingency table](http://onlinestatbook.com/2/chi_square/contingency.html) $F$, with the countries as rows and the languages as columns. [`pandas.crosstab`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.crosstab.html) will do just that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             English  French  Spanish  German  Italian\n",
      "Canada           688     280       10      11       11\n",
      "USA              730      31      190       8       41\n",
      "England          798      74       38      31       59\n",
      "Italy             17      13       11      15      944\n",
      "Switzerland       15     222       20     648       95\n"
     ]
    }
   ],
   "source": [
    "countries = ['Canada', 'USA', 'England', 'Italy', 'Switzerland']\n",
    "languages = ['English', 'French', 'Spanish', 'German', 'Italian']\n",
    "\n",
    "F = pd.crosstab(df_countries.country, df_countries.language, margins=False)\n",
    "F.index = countries\n",
    "F.columns = languages\n",
    "\n",
    "print(\"{}\".format(F))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now say that we are interested in seeing the relation between the countries and the languages.\n",
    "\n",
    "However, we cannot blindly apply singular value decomposition to contingency table $F$ above.\n",
    "\n",
    "Since we are working with 2 distinct categories, we can apply [correspondence analysis](http://www.mathematica-journal.com/2010/09/an-introduction-to-correspondence-analysis/) to transform the contingency table into a form where we can use singular value decomposition. At that point, we should be able to find a reduced-rank matrix that approximates the original data, and that in turn would let us graphically represent the the relations beween countries and languages.\n",
    "\n",
    "The idea is to use the $\\chi^{2}$ distance between rows and columns as our basis for singular value decomposition, as the [$\\chi^{2}$ distribution](https://en.wikipedia.org/wiki/Chi-squared_distribution) lets us calculate the independence of qualitative variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correspondence matrix $P$\n",
    "\n",
    "We start by first calculating the correspondence matrix $P$ where\n",
    "\n",
    "\\begin{align}\n",
    "  P &= \\left[ \\frac{f_{ij}}{\\sum_{i=1}^{I} \\sum_{j=1}^{J} f_{ij}} \\right] \\text{ for } f_{ij} \\in F\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correspondence matrix P:\n",
      "\n",
      "             English  French  Spanish  German  Italian\n",
      "Canada        0.1376  0.0560   0.0020  0.0022   0.0022\n",
      "USA           0.1460  0.0062   0.0380  0.0016   0.0082\n",
      "England       0.1596  0.0148   0.0076  0.0062   0.0118\n",
      "Italy         0.0034  0.0026   0.0022  0.0030   0.1888\n",
      "Switzerland   0.0030  0.0444   0.0040  0.1296   0.0190\n"
     ]
    }
   ],
   "source": [
    "P = F / F.sum().sum()\n",
    "\n",
    "print('correspondence matrix P:\\n\\n{}'.format(P))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Row centroid $p_{i+}$\n",
    "\n",
    "Using correspondence matrix $P$, we next obtain the _row centroid_ $p_{i+}$. The row centroid can be interpreted as the marginal frequency distribution over the sum of the countries (rows), and reflects the fact that there were equally 1000 respondents per country in our fictional study.\n",
    "\n",
    "The row centroid $p_{i+}$ is derived from correspondence matrix $P$ with\n",
    "\n",
    "\\begin{align}\n",
    "  p_{i+} &= \\sum_{j=1}^{J} p_{ij}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "row centroid (marginal frequency distribution over countries):\n",
      "\n",
      "Canada         0.2\n",
      "USA            0.2\n",
      "England        0.2\n",
      "Italy          0.2\n",
      "Switzerland    0.2\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "row_centroid = P.sum(axis=1)\n",
    "\n",
    "print('row centroid (marginal frequency distribution over countries):\\n\\n{}'.format(row_centroid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Column centroid $p_{+j}$\n",
    "\n",
    "Similarly, we obtain the _column centroid_ $p_{+j}$ from $P$. The column centroid can be interpreted as the marginal frequency distribution over the sum of the languages (columns).\n",
    "\n",
    "The column centroid $p_{+j}$ is derived from correspondence matrix $P$ with\n",
    "\n",
    "\\begin{align}\n",
    "  p_{+j} &= \\sum_{i=1}^{I} p_{ij}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column centroid (marginal frequency distribution over languages):\n",
      "\n",
      "English    0.4496\n",
      "French     0.1240\n",
      "Spanish    0.0538\n",
      "German     0.1426\n",
      "Italian    0.2300\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "col_centroid = P.sum(axis=0)\n",
    "\n",
    "print('column centroid (marginal frequency distribution over languages):\\n\\n{}'.format(col_centroid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### $\\chi^{2}$ distances between countries and languages\n",
    "\n",
    "So rather than using the contingency table $F$ as the basis for singular value decomposition, we can look at the $\\chi^{2}$ distances between rows and columns for visualizing the relation between countries and languages.\n",
    "\n",
    "The $\\chi^{2}$ statistic is given by\n",
    "\n",
    "\\begin{align}\n",
    "  \\chi^{2} &= \\sum_{i=1}^{I} \\sum_{j=1}^{J} \\frac{(O_{ij} - E_{ij})^{2}}{E_{ij}} \\\\\n",
    "           &= N \\, \\sum_{i=1}^{I} \\sum_{j=1}^{J} \\frac{(p_{ij} - \\mu_{ij})^{2}}{\\mu_{ij}} \\\\\n",
    "           &= N \\, \\Lambda^{2} \\\\\n",
    "           \\\\\n",
    "  \\Rightarrow \\Lambda^{2} &= \\frac{\\chi^{2}}{N} \\\\\n",
    "   \\Lambda &= \\left[  \\frac{p_{ij} - \\mu_{ij}}{\\sqrt{\\mu_{ij}}} \\right] = \\left[  \\frac{p_{ij} - p_{i+}\\,p_{+j}}{\\sqrt{p_{i+}\\,p_{+j}}} \\right]\n",
    "\\end{align}\n",
    "\n",
    "Inertia matrix $\\Lambda$ measures the distribution of the individual profiles (rows/columns) around the average profile (centroids). Thus inertia represents the observed deviation from independence.\n",
    "\n",
    "Through its relation with the statistic $\\chi^{2}$, inertia $\\Lambda$ (a matrix of _standardized residuals_) provides the basis for using singular value decomposition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inertia Lambda:\n",
      "\n",
      "              English   French   Spanish    German   Italian\n",
      "Canada       0.159004  0.19812 -0.084450 -0.155852 -0.204219\n",
      "USA          0.187016 -0.11811  0.262604 -0.159404 -0.176243\n",
      "England      0.232370 -0.06350 -0.030464 -0.132166 -0.159458\n",
      "Italy       -0.288528 -0.14097 -0.082522 -0.151114  0.665808\n",
      "Switzerland -0.289862  0.12446 -0.065169  0.598536 -0.125888\n"
     ]
    }
   ],
   "source": [
    "Mu_ij = row_centroid.values.reshape((P.index.size,1)) * col_centroid.values.reshape((1,P.columns.size))\n",
    "\n",
    "Lambda = (P - Mu_ij) / np.sqrt(Mu_ij)\n",
    "\n",
    "print('inertia Lambda:\\n\\n{}'.format(Lambda))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Factorizing inertia matrix $\\Lambda$ with singular value decomposition\n",
    "\n",
    "Now that we have transformed contingency table $F$ into inertia matrix $\\Lambda$ where element $\\lambda_{ij} \\in \\mathbb{R}$, we can use singular value decomposition to factorize $\\Lambda$ instead of the original raw data matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "U_lambda, S_lambda, Vt_lambda = np.linalg.svd(Lambda)\n",
    "\n",
    "num_sv_lambda = np.arange(1, S_lambda.size+1)\n",
    "\n",
    "cum_var_explained_lambda = [np.sum(np.square(S_lambda[0:n])) / np.sum(np.square(S_lambda)) for n in num_sv_lambda]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, we look at the cumulative variance explained visually as a function of the number of singular values used when reducing rank to find a lower-ranked inertia matrix $\\Lambda^{\\prime}$ to approximate $\\Lambda$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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",
       "            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, 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 * 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 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",
       "    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",
       "        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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\" width=\"700\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7.0, 5.5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "plt.plot(num_sv_lambda,\n",
    "         cum_var_explained_lambda,\n",
    "         color='#2171b5',\n",
    "         label='variance explained',\n",
    "         alpha=0.65,\n",
    "         zorder=1000)\n",
    "\n",
    "plt.scatter(num_sv_lambda,\n",
    "            sklearn.preprocessing.normalize(S_lambda.reshape((1,-1))),\n",
    "            color='#fc4e2a',\n",
    "            label='singular values (normalized)',\n",
    "            alpha=0.65,\n",
    "            zorder=1000)\n",
    "\n",
    "plt.legend(loc='lower left', scatterpoints=1, fontsize=8)\n",
    "\n",
    "ax.set_xticks(num_sv_lambda)\n",
    "ax.set_xlim(0.9, 5.1)\n",
    "ax.set_ylim(-0.1, 1.1)\n",
    "ax.set_xlabel(r'Number of singular values used')\n",
    "ax.set_ylabel('Variance in data explained')\n",
    "ax.set_title('Countries/languages dataset: cumulative var. explained & singular values',\n",
    "             fontsize=14,\n",
    "             y=1.03)\n",
    "\n",
    "ax.set_facecolor('0.98')\n",
    "\n",
    "plt.grid(alpha=0.8, zorder=1)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dimension reduction\n",
    "\n",
    "Judging from the curve representing cumulative variance explained with respect to the number of singular values used, we see that\n",
    "\n",
    "* with 1 singular value, about 50.6% of the variance of inertia matrix $\\Lambda$ can be explained\n",
    "* with 2 singular values, that number goes up to 91.6%\n",
    "* with 3 singular values, we have 98.7%\n",
    "\n",
    "To mix things up a bit, let's try visualizing the countries and languages in 3D.\n",
    "\n",
    "For countries, we will take the first 3 columns of $U$ as the $x$-, $y$- and $z$-coordinates.\n",
    "\n",
    "But since `numpy.linalg.svd` returns $V^{\\intercal}$ instead of $V$, we will take the first 3 _rows_ of $V^{\\intercal}$ for the $x$-, $y$- and $z$-coordinates for the languages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_x = U_lambda[:, 0]\n",
    "country_y = U_lambda[:, 1]\n",
    "country_z = U_lambda[:, 2]\n",
    "\n",
    "lang_x = Vt_lambda[0, :]\n",
    "lang_y = Vt_lambda[1, :]\n",
    "lang_z = Vt_lambda[2, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing the relation between countries and languages\n",
    "\n",
    "That was a bit of work moving from raw data to contingency table, to correspondence matrix, to the inertia matrix, and then finally to singular value decomposition, but we are now ready to see how the categories of country and language relate to one another in 3 dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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",
       "            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, 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 * 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 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",
       "    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",
       "        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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\" width=\"750\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab\n",
    "from mpl_toolkits.mplot3d import Axes3D, proj3d\n",
    "\n",
    "fig = pylab.figure(figsize=(7.5,5.5))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.scatter(country_x, country_y, country_z, marker='s', s=50, c='#2171b5')\n",
    "cntry_labels = []\n",
    "for i,(x,y,z) in enumerate(zip(country_x, country_y, country_z)):\n",
    "    x2, y2, _ = proj3d.proj_transform(x,y,z, ax.get_proj())\n",
    "    \n",
    "    label = pylab.annotate(Lambda.index[i],\n",
    "                          xy=(x2,y2),\n",
    "                          xytext=(-2,2),\n",
    "                          textcoords='offset points',\n",
    "                          ha='right',\n",
    "                          va='bottom',\n",
    "                          color='#2171b5',\n",
    "                          alpha=0.9)\n",
    "    cntry_labels.append(label)\n",
    "\n",
    "ax.scatter(lang_x, lang_y, lang_z, marker='o', s=50, c='#fc4e2a')\n",
    "lang_labels = []\n",
    "for i,(x,y,z) in enumerate(zip(lang_x, lang_y, lang_z)):\n",
    "    x2, y2, _ = proj3d.proj_transform(x,y,z, ax.get_proj())\n",
    "    \n",
    "    label = pylab.annotate(Lambda.columns[i],\n",
    "                          xy=(x2,y2),\n",
    "                          xytext=(-2,2),\n",
    "                          textcoords='offset points',\n",
    "                          ha='right',\n",
    "                          va='bottom',\n",
    "                          color='#fc4e2a',\n",
    "                          alpha=0.4)\n",
    "    lang_labels.append(label)\n",
    "\n",
    "def update_position(e):\n",
    "    for i,(x,y,z) in enumerate(zip(country_x, country_y, country_z)):\n",
    "        x2, y2, _ = proj3d.proj_transform(x,y,z, ax.get_proj())\n",
    "        cntry_labels[i].xy = x2, y2\n",
    "    for i,(x,y,z) in enumerate(zip(lang_x, lang_y, lang_z)):\n",
    "        x2, y2, _ = proj3d.proj_transform(x,y,z, ax.get_proj())\n",
    "        lang_labels[i].xy = x2, y2\n",
    "    fig.canvas.draw()\n",
    "fig.canvas.mpl_connect('button_release_event', update_position)\n",
    "\n",
    "ax.set_xlabel(r'singular value $\\sigma_{1}$')\n",
    "ax.set_xticks([-0.5, 0.0, 0.5])\n",
    "ax.set_ylabel(r'singular value $\\sigma_{2}$')\n",
    "ax.set_yticks([-0.5, 0.0, 0.4])\n",
    "ax.set_zlabel(r'singular value $\\sigma_{3}$')\n",
    "ax.set_zticks([-0.5, 0.0, 0.5])\n",
    "ax.set_title('3D plot of Countries/Languages dataset',\n",
    "             fontsize=16,\n",
    "             y=1.1)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And there we are!\n",
    "\n",
    "You can see how the anglophone countries Canada, USA and England are in close proximity of English and with each other, with Spanish being close to the USA while French is closer to Canada. German is close to Switzerland, with French somewhat in proximity. Italian, however, is out close to Italy, both being located largely in isolation from the other countries and languages.\n",
    "\n",
    "This should match up with your intuition from contingency table $F$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helpful Resources\n",
    "\n",
    "* [Making sense of principal component analysis, eigenvectors & eigenvalues](https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues/140579#140579)\n",
    "* [Correspondence analysis is a useful tool to uncover the relationships among categorical variables](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3718710/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix A\n",
    "\n",
    "## PCA and SVD\n",
    "\n",
    "Principal components analysis (PCA) and singular value decomposition are closely related, and you may often hear both these terms used in the same breath.  \n",
    "\n",
    "Here is a quick mathematical treatment explaining how PCA and SVD are related.\n",
    "\n",
    "Consider data matrix $X \\in \\mathbb{R}^{m \\times n}$ where $m > n$, and all $x_{ij}$ are centered about the column means. With principal components analysis, we have\n",
    "\n",
    "\\begin{align}\n",
    "  \\text{covariance matrix } C &= \\frac{1}{m} \\, X^{\\intercal} \\, X & \\text{from PCA} \\\\\n",
    "  &= \\frac{1}{m} \\, V \\, \\Gamma \\, V^{\\intercal} & \\text{by eigendecomposition of } X^{\\intercal} \\, X \\\\\n",
    "  \\\\\n",
    "  \\text{ but } X &= U \\, \\Sigma V^{\\intercal} & \\text{from SVD} \\\\\n",
    "  \\\\\n",
    "  \\Rightarrow C &= \\frac{1}{m} \\, V \\, \\Sigma \\, U^{\\intercal} \\, U \\, \\Sigma V^{\\intercal} \\\\\n",
    "  &= \\frac{1}{m} \\, V \\, \\Sigma^{2} \\, V^{\\intercal} \\\\\n",
    "\\end{align}\n",
    "\n",
    "So we see that:\n",
    "\n",
    "1. the singular values in $\\Sigma$ obtained via SVD are really just the square roots of the eigenvalues in $\\Gamma$ of the covariance matrix in PCA.\n",
    "1. if you mean-center your raw data matrix $X$ and then calculate SVD, you are doing the same thing as PCA.\n",
    "1. the above example shows covariance of $X$ with respect to its columns ($X^{\\intercal} \\, X$); it also applies for covariance of $X$ with respect to rows ($X \\, X^{\\intercal}$).\n",
    "\n",
    "#### Iris dataset: PCA & SVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA()\n",
    "pca.fit(iris.data)\n",
    "\n",
    "# don't forget to mean-center the data before SVD\n",
    "X = iris.data - np.mean(iris.data, axis=0)\n",
    "U, S, Vt = np.linalg.svd(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare the eigenvalues of $\\Gamma$ derived from PCA with the singular values from $\\Sigma$ derived with SVD: $\\Gamma = \\Sigma^{2}$?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eigenvalues from PCA:\n",
      "[ 633.72611525   36.33653574   11.77858621    3.55245407]\n",
      "\n",
      "squared singular values from SVD:\n",
      "[ 629.50127448   36.09429217   11.70006231    3.52877104]\n"
     ]
    }
   ],
   "source": [
    "Cov_pca = pca.get_covariance()\n",
    "\n",
    "print('eigenvalues from PCA:\\n{}\\n'.format(np.linalg.eigvals(Cov_pca * X.shape[0])))\n",
    "\n",
    "print('squared singular values from SVD:\\n{}'.format(np.square(S)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Can we obtain the covariance matrix $C$ derived with PCA, but using  $\\frac{1}{m} \\, V \\, \\Sigma^{2} \\, V^{\\intercal}$ from SVD?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "covariance matrix C derived from PCA:\n",
      "[[ 0.68569351 -0.03926846  1.27368233  0.5169038 ]\n",
      " [-0.03926846  0.18800403 -0.32171275 -0.11798121]\n",
      " [ 1.27368233 -0.32171275  3.11317942  1.29638747]\n",
      " [ 0.5169038  -0.11798121  1.29638747  0.58241432]]\n",
      "\n",
      "covariance matrix using S and Vt from SVD:\n",
      "[[ 0.68112222 -0.03900667  1.26519111  0.51345778]\n",
      " [-0.03900667  0.18675067 -0.319568   -0.11719467]\n",
      " [ 1.26519111 -0.319568    3.09242489  1.28774489]\n",
      " [ 0.51345778 -0.11719467  1.28774489  0.57853156]]\n",
      "\n",
      "Are these matrices equivalent (element-wise closeness comparison)?\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print('covariance matrix C derived from PCA:\\n{}\\n'.format(Cov_pca))\n",
    "\n",
    "Cov_svd = (1. / X.shape[0]) * Vt.T.dot(np.diag(np.square(S))).dot(Vt) \n",
    "print('covariance matrix using S and Vt from SVD:\\n{}\\n'.format(Cov_svd))\n",
    "\n",
    "allclose = np.allclose(Cov_pca, Cov_svd, atol=1e-1)\n",
    "print('Are these matrices equivalent (element-wise closeness comparison)?\\n{}'.format(allclose))"
   ]
  },
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   "source": []
  }
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