session-4-kmeans-pca/PCA Tutorial.ipynb

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    "### Curse of dimensionality\n",
    "\n",
    "#### In machine learning, a feature of an object can be an attribute or a characteristic that defines it. Each feature represents a dimension and group of dimensions creates a data point. This represents a feature vector that defines the data point to be used by a machine learning algorithm(s).\n",
    "\n",
    "#### In theory, an increase in the dimensions indicates addition of more information to data thereby improving the quality of data,but practically more dimensions increase noise and redundancy during its analysis. You can form an intuition for the same with the following fact- if we pick 2 points randomly in a unit square, the distance between these 2 points on average be 0.52 . Similarly, average distance between 2 points will be 0.66 if 2 points are chosen randomly in a unit 3D cube. On the similar lines, this average distance becomes 408.25 if we are dealing with 1,000,000-dimensional hypercube. It's a little difficult to believe these numbers given the fact that we are picking these numbers from the same unit hypercube !! But, we shouldn't ignore the fact that there's just plenty of space in higher dimensions. \n",
    "\n",
    "#### As a result, high dimensional datasets are at a high risk of being sparse. This also means that any new instance is likely to be very far from any training instance, making predictions much less reliable than in lower dimensions."
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   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import manifold, datasets\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### PCA uses projection to reduce dimensionality of data with the goal of choosing the hyperplane that preserves variance the most.\n",
    "\n",
    "### Let's begin with calculation of eigen values/vectors for a matrix."
   ]
  },
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   "source": [
    "# Given an input matrix A\n",
    "A = np.array([[3, 4], [6, 5], [3, 6]])"
   ]
  },
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       "array([4., 5.])"
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     "execution_count": 114,
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   "source": [
    "# Compute the mean of the columns of input matrix A\n",
    "M = np.mean(A.T, axis=1)\n",
    "M"
   ]
  },
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   "execution_count": 115,
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   "outputs": [
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     "data": {
      "text/plain": [
       "array([[-1., -1.],\n",
       "       [ 2.,  0.],\n",
       "       [-1.,  1.]])"
      ]
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     "execution_count": 115,
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   "source": [
    "# Center the matrix by subtracting the mean values of each column from each column entry\n",
    "C = A - M\n",
    "C"
   ]
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3., 0.],\n",
       "       [0., 1.]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the covariance V of the centered matrix C\n",
    "V = np.cov(C.T)\n",
    "V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
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     "text": [
      "Eigen Values:  [3. 1.]\n",
      "Eigen Vectors:  [[1. 0.]\n",
      " [0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "# Perform eigen decompostion of the covariance matrix V\n",
    "eigen_values, eigen_vectors = np.linalg.eig(V)\n",
    "print(\"Eigen Values: \", eigen_values)\n",
    "print(\"Eigen Vectors: \", eigen_vectors)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 118,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3., 4.],\n",
       "       [6., 5.],\n",
       "       [3., 6.]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Projected matrix P\n",
    "P = eigen_vectors.T.dot(A.T)\n",
    "P.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3., 6., 3.])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Project the matrix A onto the first principal component\n",
    "P_with_first_eigen_vector = eigen_vectors[0].T.dot(A.T)\n",
    "P_with_first_eigen_vector.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1ErWExK6rhkg"
   },
   "source": [
    "### Data Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
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     "base_uri": "https://localhost:8080/"
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    "id": "bAzP7uX1h9JO",
    "outputId": "45153f33-be5c-484e-82e5-34fa20a2e28a"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.70710678, -1.22474487],\n",
       "       [ 1.41421356,  0.        ],\n",
       "       [-0.70710678,  1.22474487]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = np.array([[3, 4], [6, 5], [3, 6]])\n",
    "X_scaled = StandardScaler().fit_transform(X)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "WQ13hnLmsMjv",
    "outputId": "73c3913a-1e47-4820-8c7c-da52f0ff2c10"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.5, 0. ],\n",
       "       [0. , 1.5]])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = X_scaled.T\n",
    "cov_matrix = np.cov(features)\n",
    "cov_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "pWF1FHKwsVsu",
    "outputId": "b435d082-ffe5-4156-ceed-bc0ad6a1ab6c"
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     "name": "stdout",
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     "text": [
      "Eigen Values:  [1.5 1.5]\n",
      "Eigen Vectors:  [[1. 0.]\n",
      " [0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "eigen_values, eigen_vectors = np.linalg.eig(cov_matrix)\n",
    "print(\"Eigen Values: \", eigen_values)\n",
    "print(\"Eigen Vectors: \", eigen_vectors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2ZwWhWp7s7zO"
   },
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "id": "cq0TN3nN78MW"
   },
   "outputs": [],
   "source": [
    "#import corresponding libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
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       "      <th>PC1</th>\n",
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       "        PC1       PC2\n",
       "0 -0.707107 -1.224745\n",
       "1  1.414214  0.000000\n",
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    "projected_1 = X_scaled.dot(eigen_vectors.T[0])\n",
    "projected_2 = X_scaled.dot(eigen_vectors.T[1])\n",
    "res = pd.DataFrame(projected_1, columns=['PC1'])\n",
    "res['PC2'] = projected_2\n",
    "res.head()"
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       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"400\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PC1', ylabel='PC2'>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 2))\n",
    "sns.scatterplot(data = res , x = 'PC1', y = 'PC2', palette= 'icefire')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L7k0gSjjuJ6e"
   },
   "source": [
    "### PCA for Iris Dataset\n",
    "#### Let us perform the same steps as above for a slightly complex dataset \n",
    "#### Reference: https://towardsdatascience.com/principal-component-analysis-pca-from-scratch-in-python-7f3e2a540c51 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "id": "VpyINUuwt4Qv",
    "outputId": "3255da51-d16a-4cc0-9a20-fac2e1dd9007"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df = pd.read_csv('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv')\n",
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-44wiZpzwmIN",
    "outputId": "836fc420-26fb-4392-85e1-3ce778bf5224"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]]\n",
      "0    setosa\n",
      "1    setosa\n",
      "2    setosa\n",
      "3    setosa\n",
      "4    setosa\n",
      "Name: species, dtype: object\n"
     ]
    }
   ],
   "source": [
    "iris_data_df = iris_df[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]\n",
    "X = iris_data_df.to_numpy()\n",
    "y = iris_df['species']\n",
    "print(X[:5])\n",
    "print(y[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5eelDnnDuj9o",
    "outputId": "aeff2273-2c80-4f93-f9ba-88abe22e0c96"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999999 \n",
      " [0.7277045209380132, 0.2303052326768065, 0.03683831957627386, 0.005151926808906394]\n"
     ]
    }
   ],
   "source": [
    "# All the following steps are same as before\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X_scaled = StandardScaler().fit_transform(X)\n",
    "features = X_scaled.T\n",
    "cov_matrix = np.cov(features)\n",
    "values, vectors = np.linalg.eig(cov_matrix)\n",
    "explained_variances = []\n",
    "for i in range(len(values)):\n",
    "    explained_variances.append(values[i] / np.sum(values))\n",
    " \n",
    "print(np.sum(explained_variances), '\\n', explained_variances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "id": "QK0x2HINvLUl",
    "outputId": "d525ce1d-9ed3-4d87-9c1f-d2e5b2e27977"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.264542</td>\n",
       "      <td>-0.505704</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.086426</td>\n",
       "      <td>0.655405</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.367950</td>\n",
       "      <td>0.318477</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.304197</td>\n",
       "      <td>0.575368</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.388777</td>\n",
       "      <td>-0.674767</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       Y\n",
       "0 -2.264542 -0.505704  setosa\n",
       "1 -2.086426  0.655405  setosa\n",
       "2 -2.367950  0.318477  setosa\n",
       "3 -2.304197  0.575368  setosa\n",
       "4 -2.388777 -0.674767  setosa"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "projected_1 = X_scaled.dot(vectors.T[0])\n",
    "projected_2 = X_scaled.dot(vectors.T[1])\n",
    "res = pd.DataFrame(projected_1, columns=['PC1'])\n",
    "res['PC2'] = projected_2\n",
    "res['Y'] = y\n",
    "res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 623
    },
    "id": "cJ7nkBv4vSCp",
    "outputId": "3ce4ce30-f5f8-4eef-e0ae-83b92fb1218b"
   },
   "outputs": [
    {
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       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            '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",
       "\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",
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       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
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       "\n",
       "    this.root = document.createElement('div');\n",
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       "\n",
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       "\n",
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       "\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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
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       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
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       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
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       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
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       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
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       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PC1', ylabel='PC2'>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.scatterplot(data = res , x = 'PC1',y = 'PC2' , hue = 'Y' , s = 200 , palette= 'icefire')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4WRKrBdlWLyC"
   },
   "source": [
    "#We now know how to compute PCA, but why PCA? \n",
    "The idea behind PCA is simply to find a low-dimension set of axes that **summarize** data. \n",
    "## Why should data be summarized?\n",
    "Consider this example: We have a dataset composed by a set of properties from cars. These properties describe each car by its size, color, circularity, compactness, radius, number of seats, number of doors, size of trunk and so on. However, many of these features will measure related properties and so will be redundant. Therefore, we should remove these redundancy and describe each car with less properties. This is exactly what PCA aims to do."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zr3nvbfWW4kq"
   },
   "source": [
    "For example, think about the number of wheel as a feature of cars and buses, almost every example from both classes have four wheels, hence we can tell that this feature has a low variance (from four up to six wheels or more in some of rare buses), so this feature will make bus and cars look the same, but they are actually pretty different from each other. Now, consider the height as a feature, cars and buses have different values for it, the variance has a great range from the lowest car up to the highest bus. Clearly, the height of these vehicle is a good property to separate them. PCA looks at the variance of each feature because it reasonably assumes that features that present high variance are more likely to have a good split between classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ez48zRxOW-3c"
   },
   "source": [
    "Let us go ahead and achieve what we just discussed!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BzyKPmK7Xjv1"
   },
   "source": [
    "**NOTE:** *Often, people end up making a mistake in thinking that PCA selects some features out of the dataset and discards others. The algorithm actually constructs new set of properties based on combination of the old ones.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_S7DRGBeZjwm"
   },
   "source": [
    "### Principal Component Analysis using sklearn\n",
    "### Reference: https://machinelearningmastery.com/principal-components-analysis-for-dimensionality-reduction-in-python/ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TT3M1k-EZrkB"
   },
   "source": [
    "Let us first use the *make_classification()* function to create a synthetic binary classification problem with 1,000 examples and 20 input features, 15 inputs of which are meaningful.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "id": "9qzxHXXubFwi"
   },
   "outputs": [],
   "source": [
    "#import necessary functions\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SsFKji7OZ0x2",
    "outputId": "cd25c046-92b0-4c78-ad73-b10678183653"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 20) (1000,)\n"
     ]
    }
   ],
   "source": [
    "# test classification dataset\n",
    "# define dataset\n",
    "X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7)\n",
    "# summarize the dataset\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "id": "0wPI3qgWdbKW"
   },
   "outputs": [],
   "source": [
    "# split the data into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "id": "g0sLJObBaAth"
   },
   "outputs": [],
   "source": [
    "# Standardizing the features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing right number of dimensions - Instead of arbitrarily choosing the number of dimensions to reduce down to, it's better to choose the number of dimensions that add up to sufficiently large portion of variance (eg- ~95%)\n",
    "\n",
    "### explained_variance_ratio : this ratio indicates the proportion of the dataset's variance that lies along each principal component."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "id": "xz7FHEgFbYz6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.16304994 0.14974673 0.10901591 0.09869166 0.08600576 0.0660893\n",
      " 0.0572263  0.05456994 0.04447539 0.04083015]\n",
      "0.869701068056919\n"
     ]
    }
   ],
   "source": [
    "# performing PCA\n",
    "pca = PCA(n_components=10)\n",
    "principalComponents = pca.fit_transform(X_scaled)\n",
    "print(pca.explained_variance_ratio_)\n",
    "print(np.sum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's try to plot explained variance as a function of number of principal components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
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       "    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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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ceNMWdGOGEV3P1CftGJU8jLjnkEcwoMXOLgfyadT8eCEexk6wYERNbxDDpYwfqgbzLfr4R0Pb+jb8ODw2WefmXLj4RH9TVr3G/okxFNXH4mRfFzLfSQOo1SuEUDERfS3MG84MLoFLaIvgIbwcAeDBI2jLIjl4Ipy48ER/VZKJi61GIAHdpgu1wMGrglvgMEPT01c8jYDVzy0oF/CSCJmosDRfcaOJi4F1Xj6TpzrPDzBwYC5Pz3gpsETDIwbhmUxaodhc9eBwIz34lyCxM8xaoVRiuTTc5gSgeAQtPFUC/OGa0JwePJBQEEH5S8Th84HhgijSXg6w5M7hJmeicMNhaeQlStXGnOK0S2MeKDcONIaKYThwR/c7GCLMrgMsnuTISDgKQqdmOvA0yPMADpabzgjoKHDRyeDQIYDw/BoSxzJ35FJ66MKPDGhg3I35HiaRIeLIJXcxGFUA0/grgOGGYEBnTKG/1Eel77whIvghNEOT7WRXOYpmTj3F2PT+prZk3fi3NkgQMIMuo9uQQvolNFG7gdGaxCIXfyTt3XyL5FTmk7Fg4LrcE3FYwQGX5ldddVVZlQYnSBGGVFOV8BHGtyzmCJyTcenFlQ85Z7WPeLvfsPTd+Jc9yH6Kkzpuz+ogR9YYQQj+eHJvYUghn4Lxtr1GkV696y7vhCkcT/DCMHAob9EMEbgQlumNJ3q/qW6uz7SKi9GajFFhRE41wEWyD8lEwdWuC/RX2MEBqYfBgN1xPVhHNDvo/9wMcRDEg7EDZgW1+sSrp+5plO96TeR1p0XNI37Bw957gdG49KKUa62caVBHWDg8KCKfgf5uUxnchOHemD0yH20FnEKfR3iHuqL/g0jcDhQP9yL6APwBw9RMEzgiAdwxJfkX4LCqMEIu/eR0AWYups4xBnXtDJiIsyT67q4Nh4MUSfEITz8YzQOpg1GFCNn0Av+xsMHHvJSMnGu12CQn/s9hmuh73GfTUM/Dp16auLQVyJuQEvgDJ0hXmJUFzMEmH35+eefzRQq+lTEPpq4FHppb00cbho85WCeGw2W/EipM4UhwbAvGseTAw4cHeIvv/xinDnEh6k2PMW4zKGticOTHowgpvFwQEB4knItT+IqH244dD4YosZICTpW/BvDzzjQ4SIA4KbATY2bBCYTUw2YPsB1MIrkGgVIy8S5RgDQGaJjRXBP6XA91aNjxzVw4AkNbQjj6Q1n1AV8MT2KDhpPVDBLNiYOHRPMdnKT4qpDcvOBdwzdnzLR1ggg6Y3EIT9PtJGcXUZMHNoT2nBfpiItg4vRSGjdZd7T0rtLewjYmNZzP9Ae0BQ6NteB9sU0UVpLtSCYwBRiJBNTOngVAgdGRKFzdIw2hyfc0zJx/u438NCFIOpuatJ6/xXBGvcNRrKSB/WU+KR3b2GkClNC6Kfw0OcaSU/vnnUvI0Y3kA5TYigb3leDqXSNGntj4tIqL+59b0biwAMPIejfYD7wAISpaRy4Z/FAiL4SIz/oJzES5ZrFSSm+uP/Mm34T13PnBU3joST5SByMZFoxKi39YxQWZcKDNB7i0xuJw7Uwmp7aSBxMCNoNo1/uBx6u8OCOe9k1Te36PeKTJyNx7g+iyUfi0G9gMAQjcRhdRD+2f/9+E5NgLDGVjnvm+++/NzEKRtMbE5fRkTjUEdPS6Ccx8gbOuCcRl93fcQNf/AxTtoj9qBf0w69T3dTkrYlzPYXgaRM3Mp54IFB0Cq6n/eQjCDAmcPxoeEz9IWhhnh7OOqUDgnO9j+d65wBPCri2650sWxOH98Dw4iuCJG4+PFHBHCJQ4/0EGCpME0BQMCWukS48GUBsGHpGZ4aOEE9LeMcI5cJ7D6g/giSmgjG0jWlRT0wcRjoQYHE+ngBdQ/HJ2bimqvF7XBt1QHDENAeG573hjClNtBluZnTCGI1BXWxMHKan8DQKXnhKhwHGUxpMPv6k9E4cyg/2uCamfzDUDy3h/TgECowmuT6aAVfXtK4n2vClicOIAbQGxq6R5LRMHEYj0PmjnHjSRucEzhilxM+TH+jIMVWGIAjNud6JQ2cFfeJdPTxdQ6NodwSstEwcprURhKBlGD7XBxXQCvJBkMWTOdiiI8UIDDp710dLqa2l5wn39Ear0d7+6jdwz+EegIlDfZIHfBd396AMHnh/CMYJD2QwSwh6yT8oQtq07i30Dbj/EbBh4vBBAu4rTK+ld8+6mxL0p9AbNIB3ljCyjwcCGxOXVnnR1+E9X9x7mLbCyNGzzz6b6jtxqD+CPaZFoUuMJLleQEdwxQMlyomHEvStrverkC49E+dNv5m8TfGwC/MCTeNvPGzjD+6ztLSW/B5En4/3rWB2MNqIh1nMqGB0Cw+4GC1yvS8L04E6/vjjj+ZBCaNJePcU7/+63onD/YwHKHC57bbbDGvEM6TF6BgMEx7EMQKG/h6xzf3ALAbep8T9jlFBDGSgn0AZ3Efi3I0M+l58wIAHANQD6WDWXPEHhg0xCXVCfojZ+DdG8lwDI96YODxgIJaj3cED+kB/5+k7cXi4xAMA+iuYf9yDKD/iM/pX9P+IJXiIwXt9+DeuiRk9cMU7cuDoz8PxHzYkXyfO/WaE6YCJwagDDBnMB4Zo8XViSiNxcNN4TwgvduJGh5DxfpvrBeyUzAo6UtwIMEiuz5Px4q3rqcXWxOFaEDhuPHTcMBMYBnaZOAgdnQOMCL4IwtO168kA5cUfBHN0eqiTa1oAAREjhrg5MHWEzhGdsScmDmVCvfA0Dz7JR2VcfFxsXV+64QZEEHC9f+MNZzyVod7orNB+YIJpURsTh/LBtGGEDSOVCEDoCNFeMBMpTQO6vk5F0ANHdFI40LmhA0bbIx88EGCkzzXlAAbpacOXJg4dB4I9OncYbfyd3lQzOk/cC2CCEVt09tANmCQ/YFhdX0hiehMdPkZIcaAjg84wbQJtofN1fUGY1nIeeNhAh4xpJdf7XsgPHTymjTBqCu3gXUSYQ5geBBSwxnVSOjzhnp6J83e/gaAA8wTmmIrCPZ58sV93EwetgS9GJ1xP+BhFwsvUyY/U7i1oE4YGD354pwwHPsrB+4ow73hXzv3r1OT3rLuJw6ggAiGCPfoAGGeYaxsTl15fgDJiigr6S+vrVBcHaBOGBg8aeOByvYeJ+wG8oCn0lyg/pug9HYnzpt9MyZjDPODBGWVCXMEMCGJLWlpL3rbot3CvQQ8wDdCAK86gf0c7wDDhAQp1xSLziBu4v9DXw3zjdRCXaYUxw0v7iAXo1xD3MDKIeIn2xs9xX2IkCu+VpTQSDHODmAr9Ql+Iv3gFCZpOaUoR7Y0BD5gqPEhiNBjlci2oi7R4Jxb9vGu6E0YRDxyu97a9MXGoK/opfEzl+goXGnE9wCZnnDxv3KPoa2DAwQb9JR480A7oX8EZRhl9Hx604DcwOokDfRgeemF2cb97OsPnreFzjInztmI83z8E8ESH0R50SjxIIDMJoPPFKBYMuCdTi5lZNqdfK613SZ1eN5b/SgJpfbSREV54VQAmGaNVgXpghB/GDOYzGA6auGBoxUyqA54AMR2JpzL3F4Az6fK8DAmQgJ8I0MT5CWyAZusrEwfdwLRh9BOzEhhdxPt6nqzrmFloMLKP6XBMb2KEEKNy+Cgo0LfT8pQPTZynpJSfhylUDHHj3ZLUPgxQjojVJwHHEqCJc2zTWRXcVyYO06KYJsY0P6YTMe3vem/VqmB+SISpYbwyg6laTM3iS9uU1tr0w6UzJUuauEzBzIuQAAmQAAmQAAmQgG8J0MT5lidzIwESIAESIAESIIFMIUATlymYeRESIAESIAESIAES8C0Bmjjf8mRuJEACJEACJEACJJApBGjiMgUzL0ICJEACJEACJEACviVAE+dbnsyNBEiABEiABEiABDKFAE2cBWbs/oBVmvGZtmtjX4tsmIQESIAESIAESCCDBLBTBXZbwLZX2BFD00ETZ9Ha2KoE+2jyIAESIAESIAESCAwC2A7LtQ9zYJTI/6WgibNgvHjxYrNpNASDzXl5kAAJkAAJkAAJZA0B7CmOgRXsA449XDUdNHEWrY3NorEBODZlD6TtRSyqwiQkQAIkQAIk4GgCmmMyTZyFdDULxgIXk5AACZAACZCA3whojsk0cRay0iwYC1xMQgIkQAIkQAJ+I6A5JtPEWchKs2AscDEJCZAACZAACfiNgOaYTBNnISvNgrHAxSQkQAIkQAIk4DcCmmMyTZyFrDQLxgIXk5AACZAACZCA3whojsk0cRay0iwYC1xMQgIkQAIkQAJ+I6A5JtPEWchKs2AscDEJCZAACZAACfiNgOaYTBNnISvNgrHAxSQkQAIkQAIk4DcCmmMyTZyFrDQLxgIXk5AACZAACZCA3whojsk0cRay0iwYC1xMQgIkQAIkQAJ+I6A5JtPEWchKs2AscDEJCZAACZAACfiNgOaYTBNnISvNgrHAxSQkQAIkQAIk4DcCmmMyTZyFrDQLxgIXk5AACZAACZCA3whojskBb+LOnj0rb7/9tqxcuVJWrFghBw8elO7du8uECRM8FsT69evl6aeflkWLFpk0TZs2lWHDhkn16tU9zsP9RM2CsQLGRCRAAiRAAiTgJwKaY3LAm7hdu3ZJuXLlpHjx4lK3bl2ZPXu2VyZu69atUr9+fSlQoID079/fSOiDDz6QkydPyrJly+Tqq6/2WlaaBeM1LCYgARIgARIgAT8S0ByTA97EXbhwQY4ePSolS5aU+Ph4iYiI8MrE3XnnnTJv3jzZtGmTlC5d2sho7969UrVqVWnbtq1Mnz7da2lpFozXsJiABEiABEiABPxIQHNMDngT597u3po4TMUWLFhQ7rnnHpk4ceJlEsKU7FdffSXHjh2TXLlyeSUvzYLxChRPJgESIAESIAE/E9Ack4PaxC1dulQaN24so0ePlj59+lwmI/ysX79+gnMaNWrklcQ0C8YrUDyZBEiABLKYwN7j0bJ421GJiUvI4pLw8iBQKE82ubVmCZ/C0ByTg9rEffPNN4Lp1JkzZ0r79u0vEw1+1rFjR/n666/ljjvuSFVQBw4cEPxxPzA1261bN/OxRZ06dXwqRmZGAiRAAiSQMQJ7jkXLD+sPyA/rDsjaf09lLDOm9imBWqXzyYxHmvg0T5o4n+L0X2beTqd+/vnncv/998uPP/4oN99882UF++mnn6R169aCc2DIUjsGDx4sr7zySoq/ponzX1szZxIgARLwhsDuY+dkzrqLxm39vtPeJOW5mUiAJs63sDkSx5E43yqKuZEACZBAJhHYdfR/xm3D/iuNW97s4XLzNcWkXY1iUiJfjkwqFS+TFoHs4WFStpB376GnR5QjcekRCpDfezsSx3fiAqThWAwSIAES8BGBHUfOmtG2OesOyqYDVxq3qBwR0vqaotKuRnFpXKGQRIaH+ujKzCZQCdDEBWrLJCuXtyYuva9Tp06dar5OzZ07t1cENAvGK1A8mQRIgAR8QGA7jNtaGLcDsvngmStyzJczQlpXKybtasK4FZSIMBo3H2B3TBaaY3LQTKfGxcXJ9u3bJSoqyiwM7Drw0QLeidu8ebOUKlXK/Ni1ThzeicPHD94emgXjLSueTwIkQAI2BLYdPiNz1h40o25bDl1p3PLDuJmp0uJyHY2bDeKgSaM5JjvCxI0cOdLssJCYmCgvv/yy1K5dWzp16mQE2KFDB6lZs6a4dnZIviXXli1bpEGDBma9uMcee8ykwY4NGIHDjg2VK1f2WsiaBeM1LCYgARIgAQ8JbD105tLHCf8cOntFqgK5Iv8zbsWkUXmOuHmINehP0xyTHWHiypYtK7t3705RiOPHj5cePXqkauKQaO3atfLMM89ctnfqW2+9ZcyfzaFZMDa8mIYESIAEUiOAqdKZq/ebEbeth680bgVh3KoXk1tqFJeG5QpIOKdKKaZkBDTHZEeYuEBTrGbBBFpbsDwkQALOI4CFd+etPyhfLtsjy3Yev6IChXJfHHGDcWtA4+a8Bs7kEmuOyTRxFmLTLBgLXExCAiRAAoYApkvN26RYAAAgAElEQVSnLNsr3676V05Gx11GpVDubNKm+sWvShuWKyhhoSGkRgIeEdAck2niPJLI5SdpFowFLiYhARJQTACjbpgqnbJsjyzfdeIyEhFhIdKmenG5p35p844bjZtioWSg6ppjMk2chXA0C8YCF5OQAAkoJPCPGXXbI9/+vU9Onb981K18oVxyb4My0qlOSSmYO5tCOqyyLwlojsk0cRZK0iwYC1xMQgIkoIQARt3mrL046rZi9+WjbpFhodK2RjFj3vCBQkgIp0uVyMLv1dQck2niLOSlWTAWuJiEBEggyAlsOegadftXTsfEX1bbCoVdo26lBEuE8CABXxPQHJNp4izUpFkwFriYhARIIAgJnI9NkNlr95tRt7/3nLyshtjqCl+WYtStftn8HHULwvYPpCppjsk0cRZK1CwYC1xMQgIkEEQEsF8pjNt3q/bJmWSjbhWL5L74rlvtkpKfo25B1OqBXRXNMZkmzkKbmgVjgYtJSIAEHE4gOjZeZq89IF/+tUdW77181C2ba9StYRmpdxVH3Rze1I4svuaYTBNnIVnNgrHAxSQkQAIOJXDg1Hn5aP52+R6jbhcuf9etUtGLo2631y4p+XLyXTeHNnFQFFtzTKaJs5CwZsFY4GISEiABhxGIjU+UzxbvlA9+3SrRsQmXSo9Rt1trlpAuDUtLnTIcdXNYswZtcTXHZJo4C1lrFowFLiYhARJwEIHF247KSzPWy/Yj5y6VGqNuXRteJbddW1KickY4qDYsqgYCmmMyTZyFwjULxgIXk5AACTiAwP6T5+X1OZtkzroDl0qLzeefbVtF7qhTSkK5DZYDWlFnETXHZJo4C81rFowFLiYhARIIYAKYOv100cWp0/NxF6dO4dfua3SVPHFTZY68BXDbsWgXCWiOyTRxFneBZsFY4GISEiCBACXwx9Yj8vLMDbLDbeq07lX55dWO18g1JaICtNQsFglcTkBzTKaJs7gbNAvGAheTkAAJBBiBfSfPy5DZG2Xu+oOXSlYoN6ZOq5o13jh1GmANxuKkSUBzTKaJs7g5NAvGAheTkAAJBAiBC/EJMu6PnTLyt22XTZ3ef11Z+b+bKklUDn60ECBNxWJ4QUBzTKaJ80IorlM1C8YCF5OQAAkEAIEF/xyRwTM3yM6j//vqFIvzvtqxulQrkTcASsgikIAdAc0xmSbOQjOaBWOBi0lIgASykACmTl+btVHmbXCfOs0mg9pWkU51SnJf0yxsG17aNwQ0x2SaOAsNaRaMBS4mIQESyAICmDodu3CHjJy/TWLiEk0J8NVp98YXp07zZufUaRY0Cy/pBwKaYzJNnIWgNAvGAheTkAAJZDKB37ccNlOnu45FX7py/bIXp06rFufUaSY3By/nZwKaYzJNnIW4NAvGAheTkAAJZBKBvcej5bXZG+WnjYcuXbFQ7mzy/C1VzG4LISEhmVQSXoYEMo+A5phME2ehM82CscDFJCRAAn4mEBOXIJ8s3CGj5m+TC/EXp07DQkOk+3Vl5fGbrubUqZ/5M/usJaA5JtPEWWhPs2AscDEJCZCAHwnM33xYBs/aILvdpk4blCtgFuytUoxTp35Ez6wDhIDmmEwTZyFCzYKxwMUkJEACfiBw4NR5eXnGhsumTgvnySbPt6sqHa8twalTPzBnloFJQHNMpomz0KRmwVjgYhISIAEfEkhMTJLJf+2WYfO2yNkL8ZemTns0LiuP33i15OFXpz6kzaycQEBzTKaJs1CoZsFY4GISEiABHxHYcvCMDPp2rfy95+SlHPHV6ZDbakjlYnl8dBVmQwLOIqA5JtPEWWhVs2AscDEJCZBABgngwwV8tPDxgu0Sl5BkcsuTLVyebVdF7q1fhnudZpAvkzubgOaYTBNnoV3NgrHAxSQkQAIZIPDnjmPy3LfrZIfbdlltqxeTwR2ukaJ5s2cgZyYlgeAgoDkm08RZaFizYCxwMQkJkIAFgVPRcTJ07iaZunzvpdTF8mY3X53efE0xixyZhASCk4DmmEwTZ6FpzYKxwMUkJEACXhBISkqSOesOyOCZG+Xo2QsmJdbovb/RVfJU68r8cMELljxVBwHNMZkmzkLjmgVjgYtJSIAEPCSAzepf+n69/Lr58KUUlYrmlqGdakrdq/J7mAtPIwFdBDTHZJo4C61rFowFLiYhARJIh0BCYpJMWrpL3v5xi5yLTTBnR4aHymOtKsrDzSuYf/MgARJImYDmmEwTZ3FXaBaMBS4mIQESSIPApgOn5dlv18mavf9bNqRhuQIytFMNKV84N9mRAAmkQ0BzTKaJs7g9NAvGAheTkAAJpEAAy4Z88OtWs+dpfOLFZUPyZg+X52+pKp3rleaOC1QNCXhIQHNMponzUCTup2kWjAUuJiEBEkhGYMn2o2bZkF1u+53eWrO4vNS+mhTJw2VDKBgS8IaA5phME+eNUv47V7NgLHAxCQmQwH8ETkbHyutzNsn0lf9eYlIiKrsMub26tKpSlJxIgAQsCGiOyTRxFIwFASYhARLwhgCWDZm19oC8OmuDHD0ba5Ji2RDsd/rUzZUlV7Zwb7LjuSRAAm4EaOIoB68IaBaMV6B4MgmQgPx7Ilpe+H69/L7lyCUaVYrlkTfvqCnXls5HQiRAAhkkoDkmcyTOQjyaBWOBi0lIQCUBLBsyfvFOeeenf+R83MVlQ7KFh8rjN1aSXs3KSUQYlw1RKQxW2ucENMdkmjgLOWkWjAUuJiEBdQROx8TJgCmrZL7b6FuTigXl9dtqSNlCudTxYIVJwJ8ENMdkmjgLZWkWjAUuJiEBVQR2Hj0nvSYul+1Hzpl658sZIS/cUk3uqFOSy4aoUgIrm1kENMdkmjgLlWkWjAUuJiEBNQT+2HpEHvnibzkdE2/qXKNklIy5r66UyJdDDQNWlAQym4DmmEwTZ6E2zYKxwMUkJBD0BPD16fjFu2TInI3y37q90r5WCRl2R03JERkW9PVnBUkgKwlojsk0cRbK0ywYC1xMQgJBTeBCfIK8+P16mbbi4tpvWDoEy4b0a1mB06dB3fKsXKAQ0ByTaeIsVKhZMBa4mIQEgpbAkTMXpM/klbJy9wlTx1yRYfL+PbXlpmpcuDdoG50VCzgCmmMyTZyFHDULxgIXk5BAUBJYv++UPDRphRw4FWPqV6ZAThnXvZ5UKponKOvLSpFAoBLQHJNp4ixUqVkwFriYhASCjsCsNftl4NdrJCYu0dStcYWCMqpLHcmfKzLo6soKkUCgE9Ack2niLNSpWTAWuJiEBIKGQGJikrz78z8ycv62S3XC1lnP31KVi/cGTSuzIk4joDkm08RZqFWzYCxwMQkJBAWBsxfi5f++Wi0/bzxk6hMRFiKvdqwu9zYoExT1YyVIwKkENMdkmjgL1WoWjAUuJiEBxxPYcyxaek1aLv8cOmvqUiBXpHzcra40KFfA8XVjBUjA6QQ0x2SaOAv1ahaMBS4mIQFHE1iy/aj0++JvORkdZ+qBzevxAUOp/DkdXS8WngSChYDmmEwTZ6FizYKxwMUkJOBIAljAd/Kfu2XwrI2CzexxtK1eTN7pXEtyRoY7sk4sNAkEIwHNMZkmzkLRmgVjgYtJSMBxBGLjE2XwrA3y5V97LpX9/26sJP1bVZTQ0BDH1YcFJoFgJqA5JtPEWShbs2AscDEJCTiKwLGzF6TvF3/Lsp3HTblzRITJe3fXkjbVizuqHiwsCWghoDkm08RZqFyzYCxwMQkJOIbApgOnpdfEFbLv5HlT5pL5csjY++tJtRJ5HVMHFpQEtBHQHJNp4izUrlkwFriYhAQcQWDe+gPyxLQ1Eh2bYMqLL09Hd60jBXNnc0T5WUgS0EpAc0ymibNQvWbBWOBiEhIIaAJYwPeD37bK+79svVTOLg3LyOD210hkeGhAl52FIwESENEck2niLO4AzYKxwMUkJBCwBKJj4+XJaWtk7vqDpoxhoSEyuMM1cl+jqwK2zCwYCZDA5QQ0x2SaOIu7QbNgLHAxCQkEJIF/T0TLQ5NWCt6Dw5EvZ4R81LWONK5QKCDLy0KRAAmkTEBzTKaJs7grNAvGAheTkEDAEViz96Q8OHG5HD0ba8pWuWge8wFDmYJcwDfgGosFIoF0CGiOyTRxFreHZsFY4GISEggoAtj79LEpq+R83MUPGG6sWlTev+dayZ2NC/gGVEOxMCTgIQHNMZkmzkORuJ+mWTAWuJiEBAKGwKSlu2TwzA3y3wYM8lCzcjKobVUu4BswLcSCkID3BDTHZJo47/Wi+ksYC1xMQgJZTgBfoA6du0nG/rHTlAWbLrzc/hrp3rhslpeNBSABEsgYAZq4jPFTl1qzYNQ1NivseAIxcQnyxLTV8sO6i1+gZo8IlQ/vrSM3VSvq+LqxAiRAAlxi5OLOzjw8JkAT5zEqnkgCWUrg+LlYeWjSClm5+4QpR6HckTKue325tnS+LC0XL04CJOA7AppjMqdTLXSkWTAWuJiEBLKEwK6j56TnhOWy8+g5c/3yhXPJxJ4NpHQBfoGaJQ3Ci5KAnwhojsk0cRai0iwYC1xMQgKZTgAjbxiBw0gcDmyh9cl9dSVfzshMLwsvSAIk4F8CmmMyTZyFtjQLxgIXk5BAphLAHqgDpq6WC/GJ5rrta5WQt++qKdnCwzK1HLwYCZBA5hDQHJNp4iw0plkwFriYhAQyjcCni3bKkDkbJem/N337tqwgA2+uzCVEMq0FeCESyHwCmmMyTZyF3jQLxgIXk5CA3wkkJCbJa7M3yoQlu8y1sITIa7dVl64NuQeq3+HzAiSQxQQ0x2SaOAvxaRaMBS4mIQG/EjgfmyADpq6SnzYeMtfJGRkmo7rUkeurFPHrdZk5CZBAYBDQHJNp4iw0qFkwFriYhAT8RuDo2Qvy4MQVgr1QcRTOk03G96gv1UtG+e2azJgESCCwCGiOyTRxFlrULBgLXExCAn4hsP3IWek5frnsOR5t8r+6SG4Z37O+lMrPJUT8ApyZkkCAEtAck2niLESpWTAWuJiEBHxOYPmu42YJkZPRcSbv68oXlI/vqytROSJ8fi1mSAIkENgENMdkmjgLbWoWjAUuJiEBnxKYtWa/PDl9jcT+t4RIp9ol5c07akpkeKhPr8PMSIAEnEFAc0ymibPQqGbBWOBiEhLwCYGkpCQZs3CHvDl386X8HmtVUf7vpkoSEhLik2swExIgAecR0ByTaeIs9KpZMBa4mIQEMkwgPiFRBs/aIJP/3GPyCg8NkTduryGd65fOcN7MgARIwNkENMdkmjgL7WoWjAUuJiGBDBE4dyFe+k9ZJb9tPmzyyZ0tXD7qWkeaVyqcoXyZmARIIDgIaI7JNHEWGtYsGAtcTEIC1gQOn4mRByYsl/X7Tps8iuXNLp/1qC/VSuS1zpMJSYAEgouA5phME2ehZc2CscDFJCRgRWDroTPSY/xy2XfyvElfpVges4RI8agcVvkxEQmQQHAS0ByTaeIsNK1ZMBa4mIQEvCawYtdx6TlhuZyJiTdpm11dyEyh5snOJUS8hskEJBDkBDTHZJo4C3FrFowFLiYhAa8ILNp61KwBdz4uwaS7q24peaNTDYkI4xIiXoHkySSghIDmmEwTZyFyzYKxwMUkJOAxgZ82HJRHv1wlsQmJJk3/VhXlCS4h4jE/nkgCGglojsk0cRaK1ywYC1xMQgIeEZixep88MW2NJCQmmfMHta0ivVtU8CgtTyIBEtBLQHNMpomz0L1mwVjgYhISSJfA1GV7ZNB36yTpon+T126rLvc1uirddDyBBEiABDTHZJo4C/1rFowFLiYhgTQJfLpop7w2e6M5JzREZPidteSOuqVIjQRIgAQ8IqA5JtPEeSSRy0/SLBgLXExCAikSwDZaI3/bJu/8/I/5fURYiHxwT21pW6M4iZEACZCAxwQ0x2SaOI9l8r8TNQvGAheTkMAVBGDg3py3WcYs2GF+ly08VD6+r65cX7kIaZEACZCAVwQ0x2SaOK+kcvFkzYKxwMUkJHAZgcTEJHlp5vpL+6DmigyTT3vUl0blC5IUCZAACXhNQHNMponzWi40cRbImIQEDAFsZP/0N2vl27/3mf9H5YiQCT3rS+0y+UmIBEiABKwI0MRZYcu8RAkJCTJ8+HAZN26c7N27V0qXLi29evWSgQMHSlhYWLoFmTp1qowYMUI2b94smMapWLGiPPzwwyaP0FDvFxDVLJh0YfMEEkiFQGx8ogyYukrmrj9oziiUO1I+f7ChVC3OfVApGhIgAXsCmmOyI0bi+vXrJ6NHj5aePXtK48aNZfHixTJhwgTBz0eNGpVmy7/55psyaNAgad26tXTo0MGYuG+++Ubmz58vTz75pLz99tteK0ezYLyGxQQkICIxcQnSZ/JK+X3LEcOjeFR2mdyroVQonJt8SIAESCBDBDTH5IA3cevWrZNatWpJ//79zWia6xgwYIB8+OGHsmbNGqlRo0aqAihatKiUKVNGli1bJiEhIea8xMREqVOnjuzatUtOnjzptXg0C8ZrWEygnsDZC/Hy4ITl8tfO44ZFmQI55YteDaV0gZzq2RAACZBAxglojskBb+Kef/55eeONN2THjh1Srly5S629c+dOKV++vOD3Q4YMSVUFOXLkkBtuuEFmz5592TkYmYNB3L9/v9cK0iwYr2ExgWoCJ6Njpfv45bJm78WHpauL5DYjcEXzZlfNhZUnARLwHQHNMTngTRzMFkbbDh68+B6N+4FRttq1a8u8efNSVUO7du3kxx9/NNOmHTt2NNOp06dPN+Zv5MiR0rdvX6+VpFkwXsNiArUEjpy5IPd9+pdsPnjGMLimRF7zDlyBXJFqmbDiJEACviegOSYHvInDVGlkZKSsXLnyipbHlGhcXJwZUUvtwEhbt27dzDtwriN79uwyduxY8/P0jgMHDgj+uB+bNm0yaVEmlIEHCZDA5QT2nzwv3cb9JTuOnjO/qHtVfvmsR33zNSoPEiABEvAlAZo4X9L0cV4VKlQQjLgtWbLkipzxkcPhw4dl27ZtqV711KlT5sOG6OhowagcTN+kSZPkt99+kylTpsidd96ZZokHDx4sr7zySorn0MT5uLGZXVAQ2H3snHQZ+5fsO3ne1KdJxYIy9v56kjMyPCjqx0qQAAkEFgGauMBqj8tKk5GROHzA0LBhQ/Mu3bRp0y7liynVpk2bypYtW8ySJXhvLrWDI3EBLA4WLeAIbD10RrqO+0sOn7lgynZj1SIysksdyR6R/lJAAVcZFogESMARBGjiAriZMvJO3IIFC6Rly5by1VdfSefOnS+r5TvvvCNPPfWULF++XOrVq+cVAc2C8QoUT1ZFYP2+U+YduBPRcabe7WuVkHc715KIMO/XYlQFjpUlARLIEAHNMTng34l77rnnZOjQoal+nYrfv/766ykKANOlXbp0kS+//FLuvffey85566235Nlnn5WlS5dKo0aNvBKQZsF4BYonqyGwYtdx6Tl+uZy5EG/qfHe90vJGpxoSFnpxWR8eJEACJOAvAppjcsCbOHyZii9QU1snbvXq1VKzZk3zrtv27dslKipKihcvbrSyatUq8+FBmzZtZO7cuZf0Ex8fb0bfMJ165MgRyZ3buwVHNQvGXzch83UugUVbj8pDk1bI+bgEU4meTcrKi7dUk1AaOOc2KktOAg4ioDkmB7yJg4769OkjY8aMMTs2NGnSxOzYMH78eOndu7d8/PHHRmpYuBfvvnXv3t3s5uA6br31VpkzZ460aNFCOnXqJDBwkydPNgbv5ZdfFny44O2hWTDesuL5wU3g542H5JEv/pbYhERT0f6tKsoTN1W6tLB2cNeetSMBEggEAppjsiNMHIzXsGHDzN6p+/btk5IlS5p9T59++mkJD7/4xVtqJu7ChQvy0UcfmS9SsWBwbGysVKtWzawPhzxsDs2CseHFNMFJYOaa/fJ/X62WhMQkU8Fn2lSRvi0rBGdlWSsSIIGAJaA5JjvCxAWacjQLJtDaguXJGgLTV+yVp79ZK0kX/Zu82vEauf+6sllTGF6VBEhANQHNMZkmzkL6mgVjgYtJgozA71sOy4MTV5gROLz2NuzOWnJn3VJBVktWhwRIwCkENMdkmjgLlWoWjAUuJgkiApsOnJa7Pl4q2NQ+JERkxD21pUOtEkFUQ1aFBEjAaQQ0x2SaOAu1ahaMBS4mCRICh0/HyG2jFsv+UzGmRs+1qyIPN+c7cEHSvKwGCTiWgOaYTBNnIVvNgrHAxSRBQCA6Nl7uHvOnrNt3ytTm3gZl5I3bq/Mr1CBoW1aBBJxOQHNMpomzUK9mwVjgYhKHE8C7b30mrxQsJ4Kj2dWFzGb23InB4Q3L4pNAkBDQHJNp4ixErFkwFriYxOEEhszeKOMW7TS1qFw0j0zve53kzR7h8Fqx+CRAAsFCQHNMpomzULFmwVjgYhIHE/h86S55ccYGU4NCubPJ9480llL5czq4Riw6CZBAsBHQHJNp4izUrFkwFriYxKEE5mMpkQnLBWv5Zo8Ila8evk5qlc7n0Nqw2CRAAsFKQHNMpomzULVmwVjgYhIHEti4H0uJLJFzsQlmKZHRXetKm+rFHFgTFpkESCDYCWiOyTRxFurWLBgLXEziMAKH/ltK5MB/S4k8366qPNS8vMNqweKSAAloIaA5JtPEWahcs2AscDGJgwhgKZHOY5bK+n2nTam7NCwjr9/GpUQc1IQsKgmoI6A5JtPEWchds2AscDGJQwhgKZHen6+UXzZdXEqkeaXC8ln3ehIeFuqQGrCYJEACGglojsk0cRaK1ywYC1xM4hACr83eKJ+6LSXydd/rJA+XEnFI67GYJKCXgOaYTBNnoXvNgrHAxSQOIOC+lEjhPFhKpImUzJfDASVnEUmABLQT0ByTaeIs1K9ZMBa4mCTACczffFgenPi/pUSm9b5OapbiUiIB3mwsHgmQwH8ENMdkmjiL20CzYCxwMUkAE+BSIgHcOCwaCZCARwQ0x2SaOI8kcvlJmgVjgYtJApQAlxIJ0IZhsUiABLwioDkm08R5JZWLJ2sWjAUuJglAAucuXFxKZMP+i0uJdG1YRoZwKZEAbCkWiQRIID0CmmMyTVx66kjh95oFY4GLSQKMwMWlRFbIL5sOm5JxKZEAayAWhwRIwCsCmmMyTZxXUuFInAUuJgkwAq/O2iifLd5pSlWlWB6Z3odLiQRYE7E4JEACXhCgifMCFk/ldCo14FwCk5bukpdmbDAV4FIizm1HlpwESOB/BGjiqAavCGgWjFegeHJAEeBSIgHVHCwMCZCAjwhojsmcTrUQkWbBWOBikgAgkHwpkY+71ZXW1xQLgJKxCCRAAiSQMQKaYzJNnIV2NAvGAheTZDGBg6di5LZRi+Xg6RhTkhduqSq9mpXP4lLx8iRAAiTgGwKaYzJNnIWGNAvGAheTZCGB5EuJdGtURl7rWF1CQkKysFS8NAmQAAn4joDmmEwTZ6EjzYKxwMUkWUQg+VIiLSoVlk+715PwsNAsKhEvSwIkQAK+J6A5JtPEWehJs2AscDFJFhF4ZdYGGb94l7k6lxLJokbgZUmABPxOQHNMpomzkJdmwVjgYpIsIDBxyS55eSaXEskC9LwkCZBAJhPQHJNp4izEplkwFriYJJMJ/L7lsDwwYbkkJonkiAiTab2vkxqlojK5FLwcCZAACWQOAc0xmSbOQmOaBWOBi0kykcD2I2fNl6hnYuIF3y6M6VZXbuZSIpnYArwUCZBAZhPQHJNp4izUplkwFriYJJMInDofJ7ePWiw7jp4zV3ymTRXp27JCJl2dlyEBEiCBrCGgOSbTxFloTrNgLHAxSSYQwJeomEJd8M8Rc7WO15aQ9+++lkuJZAJ7XoIESCBrCWiOyTRxFtrTLBgLXEySCQSG/rBJxizcYa5Uo2SU2dQ+e0RYJlyZlyABEiCBrCWgOSbTxFloT7NgLHAxiZ8JfPv3v/LEtDXmKoVyZ5NZ/ZtI8agcfr4qsycBEiCBwCCgOSbTxFloULNgLHAxiR8JrN57UjqPWSqx8YkSGRYqUx5uJHWvyu/HKzJrEiABEggsAppjMk2chRY1C8YCF5P4icCh0zHSYeQiOXT6grnC8Dtryl31SvvpasyWBEiABAKTgOaYTBNnoUnNgrHAxSR+IBATlyB3f/KnrNl70uT+QJNy8lL7an64ErMkARIggcAmoDkm08RZaFOzYCxwMYmPCSQlJcmT09bIt6v2mZybXV1Ixveozz1RfcyZ2ZEACTiDgOaYTBNnoVHNgrHAxSQ+JjB24Q55/YdNJteyBXPKjEeaSlTOCB9fhdmRAAmQgDMIaI7JNHEWGtUsGAtcTOJDAlgHruf4ZWZLrdzZwuX7RxpLxSJ5fHgFZkUCJEACziKgOSbTxFloVbNgLHAxiY8I7DhyVjq6bak17v56ckPVoj7KndmQAAmQgDMJaI7JNHEWmtUsGAtcTOIDAqdj4syeqDuOXNxS6+k2laVfy4o+yJlZkAAJkICzCWiOyTRxFtrVLBgLXEySQQLYUuvBicvl9y0Xt9TqUKuEjLiHW2plECuTkwAJBAkBzTGZJs5CxJoFY4GLSTJIYOjcTTJmwf+21JrW+zrJEckttTKIlclJgASChIDmmEwTZyFizYKxwMUkGSDw3ap/5f++4pZaGUDIpCRAAkFOQHNMpomzELdmwVjgYhJLAljI9y5uqWVJj8lIgAS0ENAck2niLFSuWTAWuJjEgsDh0zHS3m1LrWF31pTO3FLLgiSTkAAJBDsBzTGZJs5C3ZoFY4GLSbwkgC217vnkT8Hm9jh6NikrL7e/xstceDoJkAAJ6CCgOSbTxFloXLNgLHAxiRcEsKXWU9PXyjd//2tSNa1YSCb05JZaXiDkqSRAAsoIaI7JNHEWYtcsGAtcTOIFgXF/7JAhcy5uqXWV2VKrieTLGelFDjyVBEiABHQR0ByTaeIstK5ZMBa4mMRDAsm31PquX3BmJbMAACAASURBVGO5uii31PIQH08jARJQSkBzTKaJsxC9ZsFY4GISDwhgSy3syHA6Jl5CQkTG3ldPbqzGLbU8QMdTSIAElBPQHJNp4izEr1kwFriYJB0CybfUGti6sjxyPbfUonBIgARIwBMCmmMyTZwnCkl2jmbBWOBikjQIYEutXhOXy/z/ttRqX6uEfMAttagZEiABEvCYgOaYTBPnsUz+d6JmwVjgYpI0CLhvqVW9ZF6Z3rsxt9SiYkiABEjACwKaYzJNnBdCcZ2qWTAWuJgkFQLfr9onj3+12vy2UO5sMvPRJlIiXw7yIgESIAES8IKA5phME+eFUGjiLGAxSYoEsKVW5zFL5UJ8okSEhciUhxpJvbIFSIsESIAESMBLAjRxXgLTfrpmwWhve1/U/4otte6oKZ3rl/ZF1syDBEiABNQR0ByTORJnIXfNgrHAxSRuBOITEuXesX/K8l0nzE97NC4rgztwSy2KhARIgARsCWiOyTRxFqrRLBgLXEziRuDDX7fKOz//Y37SpGJBmdizgYSHhZIRCZAACZCAJQHNMZkmzkI0mgVjgYtJ/iOADe3vGL1EsKxIwVyRMvfxZlIkT3byIQESIAESyAABzTGZJs5COJoFY4GLSUTk3IV4ueWDP2TXsWjD49Pu9eSGqtyRgeIgARIggYwS0ByTaeIs1KNZMBa4mEREnv1mrUxdvtew6NqwjLx+ew1yIQESIAES8AEBzTGZJs5CQJoFY4FLfZJ56w9Kn8krDYfyhXPJnP7NuKCvelUQAAmQgK8IaI7JNHEWKtIsGAtcqpMcOh0jrd9fKCej48x6cN/1ayLVS0apZsLKkwAJkIAvCWiOyTRxFkrSLBgLXGqTJCYmSffxy+SPrUcNg2faVJG+LSuo5cGKkwAJkIA/CGiOyTRxForSLBgLXGqTjPtjhwyZs8nUv2G5AvLlQ40kLDRELQ9WnARIgAT8QUBzTKaJs1CUZsFY4FKZZNOB09Jx5GKJTUiUvNnDZd7jzbkvqkolsNIkQAL+JqA5JtPEWahLs2AscKlLEhOXYAzclkNnTN0/vLe2tK9VQh0HVpgESIAEMoOA5phME2ehMM2CscClLskrszbI+MW7TL071S4p7959rToGrDAJkAAJZBYBzTGZJs5CZZoFY4FLVZIF/xyR7p8tM3UuXSCH/PBYM8mTPUIVA1aWBEiABDKTgOaY7FMTd/r0aVm8eLEcPnxYWrduLcWKFTPtmJiYKKGhwbM/pGbBZOaN6bRrHTt7QdqM+EOOnLkg+H5hWu/rpF7ZAk6rBstLAiRAAo4ioDkm+8zEvf322zJ48GCJjo6WkJAQ+fnnn6VVq1Zy9OhRKV26tLz33nvSp08fRwkjtcJqFkxQNKAfKpGUlCQPf75Sft54yOT+WKuK8sTNlf1wJWZJAiRAAiTgTkBzTPaJiZs0aZL06NFD7rjjDmnXrp08+OCD8ssvvxgTh+Ouu+4SjNL9+OOPQaE8zYIJigb0QyWmLNsjg75dZ3KuVTqffN3nOokIC57RZz8gY5YkQAIk4BMCmmOyT0xc7dq1zdTp3Llz5dixY1K4cOHLTNzQoUPlo48+kr17L+4d6fRDs2Cc3nb+KP+OI2fllg8Wyfm4BMkZGWbegytbKJc/LsU8SYAESIAEkhHQHJN9YuJy5Mgh7777rvTt2zdFE/fpp5/KI488IjExMUEhPs2CCYoG9GEl4hIS5Y7RS2Ttv6dMrsPuqCmd65f24RWYFQmQAAmQQFoENMdkn5i4AgUKyPPPPy9PPvlkiibupZdekrFjx8qBAweCQomaBRMUDejDSgz/cbOMmr/d5NjmmmIyulsd804oDxIgARIggcwhoDkm+8TE4T24EydOyNKlS68wcefOnZOqVatKo0aNZNq0aZnTon6+imbB+Bmto7L/a8cxuWfsn5KUJFI0bzaZN6C55M8V6ag6sLAkQAIk4HQCmmOyT0zckiVLpGXLluajBnzg0KlTJxkzZoxERUXJkCFD5J9//jEGD+/OBcOhWTDB0H6+qMOp83HSbsQfsu/keZPd5AcbStOrC/kia+ZBAiRAAiTgBQHNMdknJg6sv//+e3n44YfNSByWW8CUEv4uWLCg4J24Dh06eNEkgX2qZsEEdstkXukGTF0lM1bvNxfs1bScvHBrtcy7OK9EAiRAAiRwiYDmmOwzEwea+HAB68Nt3rzZGLiKFSuaRX9z5QquL/U0C4b9hsiM1ftkwNTVBkWVYnlkxqNNJFt4GNGQAAmQAAlkAQHNMdmnJi4L2i5LLqlZMFkCPIAuuvd4tJlGPXMhXrKFh8qs/k2lUtE8AVRCFoUESIAEdBHQHJN9YuI2bdokK1eulG7duqWonMmTJ0u9evWkSpUqQaEszYIJiga0rERCYpLc+8mfsmzXcZPD4PbVpEeTcpa5MRkJkAAJkIAvCGiOyT4xcfiQAdttzZs3L8X2wAcPmFKdPn26L9ory/PQLJgsh5+FBRg1f5sM/3GLKUGLSoVlQs/6XE4kC9uDlyYBEiABENAck31i4kqUKCEDBgyQZ555JkVFYV/V999/X/7999+gUJxmwQRFA1pUYu2/J6XTR0skPjFJCuSKlHmPN5MiebJb5MQkJEACJEACviSgOSb7xMRlz55dRowYIb17906xXbDcCEwed2zwpWyZV2YRiI6NN9tq7Tx6zlxy3P315MZqRTPr8rwOCZAACZBAGgRo4jIoj/Lly8tNN91k1oZL6XjooYfkp59+kt27d2fwSoGRXLNgAqMFMrcUg75dK1OWXdz3t0vDMvLG7TUytwC8GgmQAAmQQKoENMdkn4zEYZRt9OjRMmPGDGnbtu1loOfMmSO33367WUNu5MiRQSFDzYIJigb0ohI/bjgovT9faVKUL5xLZvdvKjkjw73IgaeSAAmQAAn4k4DmmOwTE4cFfuvXr29G2po2bSo1a9Y07bVmzRpZvHixlClTRpYtWyaFCxf2ZztmWt6aBZNpkAPgQodPx0jr9xfKieg4CQ8Nke/6NZEapaICoGQsAgmQAAmQgIuA5pjsExMHkEePHpVnn31WvvnmGzl16pRhi2237rzzTnnjjTeCxsChXpoFo6XbSExMku7jl8kfW4+aKj/dprL0a1lRS/VZTxIgARJwDAHNMdlnJs7V2tip4ciRI+a/GHnD9lvBdmgWTLC1ZWr1+WzRTnl19kbz64blCsiXDzWSsNDg07KW9mQ9SYAEgpeA5pjscxPna5kkJCTI8OHDZdy4cbJ3714pXbq09OrVSwYOHChhYZ5tdTRt2jT58MMPzfRuYmKi4EOMBx980Hwxa3NoFowNL6el2XzwtHQYuVhi4xMlT/Zwmfd4cymZL4fTqsHykgAJkIAKAppjsk9NHPZM3b59uxw/ftzsnZr8uP/++70WVL9+/cxHEz179pTGjRubd+wmTJgg+PmoUaPSze/JJ580a9RhWrdly5ZmZBBlhDl89913002f0gmaBWMFzEGJLsQnSMeRi2XzwTOm1B/cW1s61CrhoBqwqCRAAiSgi4DmmOwTE7dnzx657777ZNGiRSmaN8gJ5gnGyZtj3bp1UqtWLenfv79Zh851YATNNbJWo0bqyz3Mnj1b2rdvL5MmTTLl89WhWTC+Yhio+YxZsF2Gzt1sind77ZLy3t3XBmpRWS4SIAESIAHl76n7xMRhW60FCxaYDxhatGgh+fPnT1FYV111lVeCe/75502eO3bskHLl/rdH5c6dO82UKH4/ZMiQVPNs3ry5nDt3zuzriuPMmTOSJ0/GNyunifOqGR1z8snoWGk+bL6cjok3uzL8PrCl5M0e4Zjys6AkQAIkoJGA5pjsExOHfVExbfnqq6/6VD+tW7c277EdPHjwinyLFi0qtWvXTnW/1rNnz5qvY/v27SvFihWT9957z0zzwmDifTiYw4gIuwCtWTA+beAAy+yNHzbJJwt3mFK90uEa6d64bICVkMUhARIgARJITkBzTPaJiYOhevnll817ar48MFUaGRl5aSTNPe86depIXFycYMo1pWP16tXG5OELWZz3wgsvSNmyZWXmzJlmerVr164yefLkdIt74MABwR/3Y9OmTdKtWzdTLpSDh/MJ/HsiWlq9vUBiExLlqoI55ef/ayGR4aHOrxhrQAIkQAJBToAmLoMN/Nhjjwk+asDWWr48KlSoIDCIS5YsuSJbfORw+PBh2bZtW4qXxPt5zZo1M7/77bff5Prrr790HgzYF198IRs2bJBq1aqlWeTBgwfLK6+8kuI5NHG+bO2szeuJr1bLt6v2mUKM7FJbbq3JjxmytkV4dRIgARLwjABNnGecUj0Lo2EPPPCAlCpVyozGYcQrpeU/8B6bN0dGRuJgsOrVq2fKhKVJ3I+ff/5Zbr75Zvnoo4/MdGtaB0fivGkxZ567cf9pueXDPwQfVNcqFSXfP9IkKNc3dGbrsNQkQAIkkDYBmrgMKiQ0NNQEPSwrktbivt5+nZqRd+LwHl3x4sWlbt26smLFistquH79eoFBfP311+W5557zuvaaBeM1LAckuP+zZbLwn4sLVE99uJE0Kl/QAaVmEUmABEiABEBAc0z2yTtxmHL0ZGcGvDfnzQGDNXTo0FS/TsXvYcRSO7AwcHx8/BXvtM2dO1fwRe3YsWPNwsHeHpoF4y2rQD9/0daj0u3Tv0wxW1UpIp/1qB/oRWb5SIAESIAE3Ahojsk+MXH+UhO+TMXHCamtE4ePF2rWrGk+XMACvvgaFaNvruOZZ56RYcOGyXfffSe33Xab+TFGCzt27Cg//PCDeZ8OU7/eHpoF4y2rQD4f+6O2H7lINuw/LdhRa+6A5lK5WMaXoAnkOrNsJEACJBBsBDTH5IA2cRBanz59ZMyYMWbHhiZNmpgdG8aPHy+9e/eWjz/+2Ghx165dZh257t27m90cXMeJEyfMe3H79+83RhCGbdasWWZZkmeffdaM8tkcmgVjwytQ08xYvU8GTF1tite5XikZdmetQC0qy0UCJEACJJAKAc0x2acmDlOX+Er15MmTZo/S5AcW3/X2QJ4YTcPeqfv27ZOSJUuaKdCnn35awsPD0zRx+CU+TBg0aJAZeTt16pRUrFhRHn300XQ/aEirnJoF4237Ber52F7rhncWyL8nzku28FCzsG/xKO6PGqjtxXKRAAmQQGoENMdkn5k4vBeHBXWxyG5qh7cfNgSqZDULJlDbxNtyjftjhwyZs8kk69uygjzTpoq3WfB8EiABEiCBACCgOSb7xMTBvGHHhh49ekirVq0EG92/9dZb5h017HGaPXt2efPNN+WGG24IgObOeBE0Cybj9LI+h1Pn46TF8PlyMjpO8ueMkAVPX8/ttbK+WVgCEiABErAioDkm+8TEYcHcSpUqyffffy/Hjh0zuyT88ssvxtDFxMSYZT66dOli9joNhkOzYIKh/d6at1lG/77dVOXFW6vJg03/ty9vMNSPdSABEiABTQQ0x2SfmLgcOXLIO++8Yxb6xftwBQoUMO+gtWnTxugIo3KffPKJ+YI0GA7NgnF6++0/eV6uf/t3uRCfKKXy55Bfn2wh2cLDnF4tlp8ESIAE1BLQHJN9YuIw8oY14PDBAN57w/QpvhzFRvM4Ro8ebaZbo6Ojg0JkmgXj9AYcOH2NTF/5r6nGiHuulY7XlnR6lVh+EiABElBNQHNM9omJw9IfWM9t5MiRRkjYFD5PnjxmShVfl+JduOPHj5svV4Ph0CwYJ7ff5oOnpd2IPyQxSaRGySiZ8UgTCcUCcTxIgARIgAQcS0BzTPaJiRsyZIi8//77ZgmQbNmyyeTJk83HDZhmxU4O58+flw8++EAeeeQRx4rEveCaBePkBuw5fpnM33Jxe60vejWUJhULObk6LDsJkAAJkAC33cK23xk7sAtCbGysMXCuY9q0aTJ16lQJCwszuyV07do1YxcJoNQ0cQHUGB4WZen2Y3Lv2D/N2S0qFZaJDzTwMCVPIwESIAESCGQCmmOyT0biArlx/VE2zYLxB09/54mHjNtGLZY1/56SkBCRHx5rJlWL5/X3ZZk/CZAACZBAJhDQHJNp4iwEplkwFriyPMnstfvl0S9XmXJ0qlNS3u18bZaXiQUgARIgARLwDQHNMdnKxD3wwAPmXTcsG4LpUvw/vQPnf/rpp+md5ojfaxaMIxrIrZCx8Yly47sLZM/xaIkMD5X5T7WUkvm4vZbT2pHlJQESIIHUCGiOyVYmLjQ09NIHC5GRkYL/p3fAxHHbrfQo8fe+JjBh8U4ZPGujybZ38/IyqF1VX1+C+ZEACZAACWQhAZq4LITvxEtrFoyT2utMDLbX+l2On4uVqBwRsnDg9RKVM8JJVWBZSYAESIAE0iGgOSZbjcS587xw4YL8+OOPUrZsWalZs6YKsWkWjJMa+O0ft8jI+dtMkZ9rV0Uebl7BScVnWUmABEiABDwgoDkmZ9jE4cs/LC0yYsQI6du3rwe4nX+KZsE4pfUOnY4xm9zHxCWad+CwvVb2CG6v5ZT2YzlJgARIwFMCmmNyhk0cIF999dXSq1cveeaZZzxl7ujzNAvGKQ036Nu1MmXZXlPcdzvXkk51Sjml6CwnCZAACZCAFwQ0x2SfmLj33nvP7I+6fPlyiYqK8gK9M0/VLBgntNjWQ2ek9fsLzfZaWA9uTv+m3F7LCQ3HMpIACZCABQHNMdknJg5Lh2A69eDBg3LfffdJ+fLlzZZbyQ9PliKxaL9MT6JZMJkO2+KCvSaukF82HTIpJz3QQJpXKmyRC5OQAAmQAAk4gYDmmOwTE8clRpwgcx1lXL7ruNz18VJT2aYVC8nkXg11VJy1JAESIAGlBGjiMtjwCxYs8CiHFi1aeHReoJ+kWTCB3Db4yKbT6CWyas9JU8zZ/ZtK9ZLBP70fyG3CspEACZCAvwlojsk+GYnzdwMFWv6aBRNobeFenrnrDkjfL/42P+p4bQkZcU/tQC4uy0YCJEACJOADAppjMk2chYA0C8YCV6YkiUtIlJvfWyg7j56TyLBQs6RI6QI5M+XavAgJkAAJkEDWEdAck31q4v766y/zherJkyclMTHxshbFtlsvvvhi1rWyD6+sWTA+xOjTrD7/c7e8+P16k+eDTcvJi7dW82n+zIwESIAESCAwCWiOyT4xcWfOnJEOHTrIwoULBe8lwbDhbxyuf3Pv1MAUfzCU6uyFeGk5fL4cPRsrebKHm+218ueKDIaqsQ4kQAIkQALpEKCJy6BE+vfvL2PHjpVPPvlEmjVrJhUqVLi0FdewYcNk9erVMm/ePClYsGAGrxQYyTULJjBa4PJSvPfzPzLi163mh8+0qSJ9W3J7rUBsJ5aJBEiABPxBQHNM9slIXJkyZaR9+/YyatQoOXbsmBQuXFh++eUXadWqlWmvtm3bSokSJQTryQXDoVkwgdZ+h8/ESMvhv0t0bIIUj8ou859qye21Aq2RWB4SIAES8CMBzTHZJyYOe6fCwGHrrdOnT0u+fPlk5syZcuutt5pmw+9effVVOXTo4gKsTj80CybQ2u6F79fJ5D/3mGINu7OmdK5XOtCKyPKQAAmQAAn4kYDmmOwTE1eqVCl5/PHH5amnnjLvwuXKlUveeOMN8zMcw4cPNyYO784Fw6FZMIHUftuPnDVfpCYkJknlonnkhwHNJCw0JJCKyLKQAAmQAAn4mYDmmOwTE3fLLbdI3rx5ZcqUKaapbrrpJtm2bZt8/vnnkpCQYLbiKlmypCxdenElfacfmgUTSG3X5/OVMm/DQVOk8T3qy/VVigRS8VgWEiABEiCBTCCgOSZbm7gnnnhCevbsKTVq1JCpU6fKxx9/bD5eyJ49uyxbtsy8D3f+/HnTfJhunTNnjlx//fWZ0Jz+v4RmwfifrmdXWLn7hNwxeok5uVH5AjLloUbmS2geJEACJEACughojsnWJg77pSJo1qxZU7p37y5du3Y1HzS4jt27d8uMGTMkLCxM2rRpY75YDZZDs2ACoQ0xZY/9UVfsPmGKM+ORJlKrdL5AKBrLQAIkQAIkkMkENMdkaxOHhX0nTpwo06ZNk+PHj0tERIQxazB0+FIV/w/WQ7NgAqFNf9pwUB7+fKUpyq01i8vILnUCoVgsAwmQAAmQQBYQ0ByTrU2cq53i4uLMl6iTJk0y06nx8fGSP39+ueeee4yhq1+/fhY0qX8vqVkw/iWbfu7xCYnS+v2Fsv3IOYkIC5FfnmghVxXMlX5CnkECJEACJBCUBDTH5AybOHdFYI24L774whg6QMV0a6VKlaRHjx7SrVs383FDMByaBZPV7Tdl2R4Z9O06U4wejcvK4A7XZHWReH0SIAESIIEsJKA5JvvUxLm34caNG42Zg6nbv3+/eTcuNjY2C5vZd5fWLBjfUfQ+p9j4RLO91v5TMZI7W7gsGNhSCubO5n1GTEECJEACJBA0BDTHZL+ZuFOnTslXX30l48ePF7w/x71Tg+Z+ybKKTF+xVwZ+vdZc/7FWFeWJmytnWVl4YRIgARIggcAgQBPno3bAmnBz5841I3CzZ8+WCxcumN0b7r77bvN+XMOGDX10pazNRrNgsoo8FvS96b0FsuPIOckRESaLn20lBbjJfVY1B69LAiRAAgFDQHNM9slIHADCuGG9uCNHjpip09atWxvj1qFDB4mMjAyYxvZFQTQLxhf8bPKYu+6A9P3ib5P0gSbl5KX21WyyYRoSIAESIIEgI6A5JlubOLznNnnyZLMrA95/w9pd7mvGFSkSvKvnaxZMVtz70FaHkYtl3b5T5ovUhU9fL8WjcmRFUXhNEiABEiCBACOgOSZbm7jw8HBJTEwUmLUuXbqYUbdatWoFWNP6pziaBeMfomnnuvCfI3L/Z8vMSXfXKy1v3VkzK4rBa5IACZAACQQgAc0x2drE3XXXXca4tW3b1kyfajo0CyYr2vmeT5bKnzuOC3bV+vWJFlK+cO6sKAavSQIkQAIkEIAENMdkaxMXgO2YaUXSLJhMg/zfhdz3SL2lRnEZ1ZW7M2R2G/B6JEACJBDIBDTHZJo4C2VqFowFrgwl6TVxufyy6bDJY3b/plK9ZFSG8mNiEiABEiCB4CKgOSbTxFloWbNgLHBZJ9l88LS0ef8Pk75FpcIy8YEG1nkxIQmQAAmQQHAS0ByTaeIsNK1ZMBa4rJMMmLpKZqzeb9JP632dNChXwDovJiQBEiABEghOAppjMk2chaY1C8YCl1WSPceipeXb8yUxSaTeVfnl676NrfJhIhIgARIggeAmoDkm08RZaFuzYCxwWSV5/rt18sVfe0zaz3rUk1ZVilrlw0QkQAIkQALBTUBzTKaJs9C2ZsFY4PI6yeHTMdL0rfkSm5AoVYrlkbkDmpm9d3mQAAmQAAmQQHICmmMyTZzF/aBZMBa4vE4y9IdNMmbhDpPug3trS4daJbzOgwlIgARIgAR0ENAck2niLDSuWTAWuLxKcio6Thq/+auci02QsgVzyq9PtpSwUI7CeQWRJ5MACZCAIgKaYzJNnIXQNQvGApdXST74dau8+/M/Js3QTjXk3gZlvErPk0mABEiABHQR0ByTaeIstK5ZMBa4PE4SHRsvTd78TU5Ex0nRvNnMRvfZwnVt6eYxLJ5IAiRAAiRgCGiOyTRxFjeBZsFY4PI4yaeLdsprszea81+4par0albe47Q8kQRIgARIQCcBzTGZJs5C85oFY4HLoySx8YnSYvh8OXAqRvLljJDFz7SSXNnCPUrLk0iABEiABPQS0ByTaeIsdK9ZMBa4PEoybfleefqbtebcx2+8Wh6/sZJH6XgSCZAACZCAbgKaYzJNnIX2NQvGAle6SRISk+TGdxfIzqPnJGdkmCx5tpXkyxmZbjqeQAIkQAIkQAKaYzJNnIX+NQvGAle6SeasPSCPfPm3Oa9X03Lywq3V0k3DE0iABEiABEgABDTHZJo4i3tAs2AscKWZJCkpSW75YJFsPHBaIsNCzRepxaKy+/oyzI8ESIAESCBICWiOyTRxFqLWLBgLXGkm+X3LYekxfrk5594GpWVop5q+vgTzIwESIAESCGICmmMyTZyFsDULxgJXmkk6j1kqy3YeF2zK8NuTLaVsoVy+vgTzIwESIAESCGICmmMyTZyFsDULxgJXqklW7Doud3681Py+fa0S8uG9tX2ZPfMiARIgARJQQEBzTKaJsxC4ZsFY4Eo1yQMTlstvmw+b3//wWDOpViKvL7NnXiRAAiRAAgoIaI7JNHEWAtcsGAtcKSbZuP+0tPvgD/O7VlWKyGc96vsqa+ZDAiRAAiSgiIDmmEwTZyF0zYKxwJVikv5TVsmsNfvN777uc53UK1vAV1kzHxIgARIgAUUENMdkmjgLoWsWjAWuK5LsOnpOWr3zuyQmiTQoV0Cm9b7OF9kyDxIgARIgAYUENMdkmjgLwWsWjAWuK5IM+natTFm21/x8Qs/60rJyEV9kyzxIgARIgAQUEtAck2niLASvWTAWuC5LcvBUjDQfNl9iExLlmhJ5ZXb/phISEpLRbJmeBEiABEhAKQHNMZkmzkL0mgVjgeuyJENmb5Rxi3aan43qUkduqVk8o1kyPQmQAAmQgGICmmMyTZyF8DULxgLXpSQnzsVKk7d+k+jYBClfKJf8/EQLCcMqvzxIgARIgARIwJKA5phME2chGs2CscB1Kcn7v/wj7/+y1fz/rTtqyN31y2QkO6YlARIgARIgAdEck2niLG4AzYKxwGWSnLsQL43f/E1OnY+T4lHZZcHA6yUyPNQ2O6YjARIgARIgAUNAc0ymibO4CTQLxgKXSTLujx0yZM4m8+8Xb60mDzYtZ5sV05EACZAACZDAJQKaYzJNnMWNoFkwFrjkQnyC+SL10OkLkj9nhCx+tpXkjAy3yYppSIAESIAESOAyAppjMk2cxc2gWTAWuGTKsj0y6Nt1JukTN1WSx2642iYbpiEBEiABEiCBKwhojsk0cRY30gW3MAAAIABJREFUhGbBeIsrPiFRbnh3gew+Fi25IsNkybM3SFTOCG+z4fkkQAIkQAIkkCIBzTGZJs7iptAsGG9xzVyzXx6bssok6928vAxqV9XbLHg+CZAACZAACaRKQHNMpomzuDE0C8YbXElJSdJ2xB+y+eAZ8yXqoqevlyJ5s3uTBc8lARIgARIggTQJaI7JNHEWN4dmwXiD67fNh+SBCStMkq4Ny8jrt9fwJjnPJQESIAESIIF0CWiOyTRx6crjyhM0C8YbXHeOXiIrdp8QbMrw+1PXS5mCOb1JznNJgARIgARIIF0CmmMyTVy68qCJs0Aky3Yel85jlpqkHa8tISPuqW2TDdOQAAmQAAmQQJoEaOIoEK8IaBaMp6C6f7ZMFvxzxJw+7/FmUqVYXk+T8jwSIAESIAES8JiA5pjMkTiPZfK/EzULxhNc6/edkls/XGROvbFqERnXvb4nyXgOCZAACZAACXhNQHNMponzWi6692nzBNcjX/wtc9YdMKd+26+x1CmT35NkPIcESIAESIAEvCZAE+c1Mt0JNAsmvZbfceSsWdw3KUmkUfkCMvXh69JLwt+TAAmQAAmQgDUBzTGZI3EWstEsmPRwPfP1WvlqxV5z2qQHGkjzSoXTS8LfkwAJkAAJkIA1Ac0xmSbOQjaaBZMWroOnYqTZsN8kLiFJapSMkpmPNpGQkBALwkxCAiRAAiRAAp4R0ByTaeI808hlZ2kWTFq43v5xi4ycv82c8lHXOtKuRnELukxCAiRAAiRAAp4T0ByTaeI818mlMzULJjVcMXEJ0vjN3+T4uVgpmS+HLBjYUsLDQi3oMgkJkAAJkAAJeE5Ac0ymifNcJzRxabCavmKvDPx6rTljUNsq0rtFBQuyTEICJEACJEAC3hGgifOOl/qzNQsmpcbHRvdYF27D/tOSPSJU/hx0g+TLGaleJwRAAiRAAiTgfwKaYzJH4iz0pVkwKeFavuu43PXxxS227m1QRoZ24kb3FrJiEhIgARIgAQsCmmMyTRwFY0Hg8iTui/v++HhzqVwsT4bzZAYkQAIkQAIk4AkBmjhPKGXhOQkJCTJ8+HAZN26c7N27V0qXLi29evWSgQMHSlhYmFcla9GihSxcuFC6du0qkydP9iqt62TNgkkObP/J89Js2HxJSEyS68oXlCkPN7JiykQkQAIkQAIkYENAc0x2xEhcv379ZPTo0dKzZ09p3LixLF68WCZMmCD4+ahRozxu888//1z69u0r586do4nzmFraJw6bt1k++n27OWnMfXWl9TXFfJQzsyEBEiABEiCB9AnQxKXPKMvOWLdundSqVUv69+8vI0aMuFSOAQMGyIcffihr1qyRGjXSfwfr5MmTUqVKFXn88cdl0KBBNHE+aFEsK3Ld0F/lRHScWVZk4dPXS1goF/f1AVpmQQIkQAIk4CEBmjgPQWXFac8//7y88cYbsmPHDilXrtylIuzcuVPKly8v+P2QIUPSLdqjjz4qP/30k6xfv16yZctGE5cusfRPmLZirzz937Iiz7WrIg8357Ii6VPjGSRAAiRAAr4kQBPnS5o+zqt169ZmtO3gwYNX5Fy0aFGpXbu2zJs3L82rooHr168vs2bNknbt2pmtoPhOXMYaCsuK3PLBItl44LTkiAgzy4pE5YzIWKZMTQIkQAIkQAJeEqCJ8xJYZp6OqdLIyEhZuXLlFZetU6eOxMXFCaZcUzsSExPNe3RFihSRmTNnmtO8MXEHDhwQ/HE/Nm3aJN26dTNlQhk0Hst2HpfOYy4uK9KlYRl54/b0p7Q1cmKdSYAESIAE/EuAJs6/fDOUe4UKFQQjbkuWLLkiH5izw4cPy7ZtF/frTOn45JNP5LHHHpMNGzYI8vr/9s4Duqoq+/+bEKo0kSKgAQyKSjMgggRJEBGZAZRRxxIGpChlbBSpwyADAaTIKKggKEX4j4withEFAoK0ECkaxoBSAoIBjDJIh0D+63vw5vcIKe+d186993vWcslK7rnv7M/e9+1v9inXVxH34osvyujRo/O8t5tFXL+Fm+Wz1EvV0WX9W8lNVXmsiF+Bzs4kQAIkQAJaBCjitLCFppM/lbjMzEypW7eu2sU6ZsyYnAGzEuef7w7+77S0+v1Ykdg618jCXjxWxD+i7E0CJEACJKBLgCJOl1wI+vmzJg47WHEW3FdffSWlS5fOGS02SDzwwAMydepUqVixopQrV84nS9wcMAD10uc75I3fjxWZ1fV2aXtrVZ/48WISIAESIAESCBQBN+dk48+JGz58uIwfPz7f3an4fWJiYp6xAKH20UcfFRgnOER40KBBPsWSmwMGx4o0H58k/zt1Xq67upSsfoHHivgUPLyYBEiABEggoATcnJONF3HYmYodqPmdE7dt2zZp2LCh2uCwe/duKV++vFSrVk0FSHJy8hWbEvDzzp07C97cgDPj6tevL3Xq1PEpoNwcMItS9suQxZc2koz4wy3yZKsbfGLHi0mABEiABEggkATcnJONF3FwdJ8+fWTmzJnqjQ2xsbHqjQ1z5syR3r17y4wZM1QspKenq3PkunXrpt7mUFDzZU1cXvdxa8DgWJE/vLpW0nisSCC/f3gvEiABEiABPwi4NScDmS1EXFZWlkycOFG9O/XgwYNSo0YN9e7UwYMHS2RkJEWcH8HvS9fkPb/II29uVF0SmkVJIo8V8QUfryUBEiABEggCAYq4IEB18i3dGjB9F2yWpdsvHSuyvH8ruZHHijg5zGkbCZAACdiCgFtzsm0qcaZFkRsDBseK3PXSSrmYLdKyTiVZ0KuZaW7heEiABEiABFxIwI052XKzLaZTTYtJNwbMhKU7ZMbq3coVs7veLvfwWBHTwpLjIQESIAFXEnBjTqaI8yPU3RYwp89dkDsnXDpWJKpiaVk1KF6KRhTxgyC7kgAJkAAJkEBgCLgtJ3tSYyVOI4bcFjDvbtovQz+4dKzI3/54i/S6i8eKaIQNu5AACZAACQSBgNtyMkWcn0HkpoDBsSLtX/lKdhw6LqWKFZWNw9tI+VLF/CTI7iRAAiRAAiQQGAJuysm5ibESpxFDbgqYDbt/kcdmXTpWpEvzKBn7QAMNYuxCAiRAAiRAAsEh4KacTBEXgBhyU8D0eWezfP5fHisSgLDhLUiABEiABIJAwE05mSIuAAHkloA5cPSUtJq4Sh0rcteNleSdnjxWJADhw1uQAAmQAAkEkIBbcnJeyDidqhFIbgmY8UvTZObqPYrQW91ulza3VNWgxS4kQAIkQAIkEDwCbsnJFHEBiiE3BAyOFWk+PkmOnT4vNa8pLasGxksEjxUJUATxNiRAAiRAAoEi4IacnB8rVuI0osgNAfOvTftl2O/HiozscKv0bFlbgxS7kAAJkAAJkEBwCbghJ1PEBTCGnB4wOFbkvn9+JTsPH5fSxS8dK1KuJI8VCWAI8VYkQAIkQAIBIuD0nFwQJlbiNILI6QGzfnemPD4rWZHpemdN+cf99TUosQsJkAAJkAAJBJ+A03MyRVyAY8jpAfPU/K9l2XeHFbUVA+KkTpUyASbI25EACZAACZBAYAg4PSdTxAUmTnLu4uSA+fHXUxI3iceKBDhkeDsSIAESIIEgEXByTi4MGadTCyOUx++dHDDjP0uTmWsuHSvy9hO3y90381gRjRBhFxIgARIggRARcHJOLgwhRVxhhFwk4k6dy5Lm45LktzNZPFZEIy7YhQRIgARIIPQEKOJCz9zWn+jUgPl/yftl+JJU5Zu/d7hVevBYEVvHKQdPAiRAAm4g4NSc7I3vWInzhlKua5wYMDhWpN0/18j3h0/IVcWLygYeK6IRGexCAiRAAiQQagJOzMneMqSI85aUx3VODJj1uzLl8dmXjhXpdmdNGc1jRTQig11IgARIgARCTcCJOdlbhhRx3pJyuIh7cv7Xsvz3Y0WSBsZJdGUeK6IRGuxCAiRAAiQQYgIUcSEGbvePc1rA4FiRVpNWSXa2SNxNlWVejzvs7iKOnwRIgARIwCUEnJaTfXEbK3G+0Pr9WqcFTOJ/vpNZX+1V1s3p3lRa162iQYVdSIAESIAESCD0BJyWk30hSBHnCy0HijjPY0VqV7pKkgbESUREEQ0q7EICJEACJEACoSdAERd65rb+RCcFzMLkfTJiyXblj1Edb5XusbVt7RsOngRIgARIwF0EnJSTffUcK3G+EhMRpwQMjhW5d+oa+eHIpWNFNg5vI2VLFtMgwi4kQAIkQAIkEB4CTsnJOvQo4jSoOSVg1u3KlITfjxV5okUtebFTPQ0a7EICJEACJEAC4SPglJysQ5AiToOaUwKm17yvZUXaYUVg5cA4uYHHimhEA7uQAAmQAAmEk4BTcrIOQ4o4DWpOCJj9v5ySuMmXjhWJr1tZ5nbnsSIaocAuJEACJEACYSbghJysi5AiToOcEwJm7Kffyey1l44Vmdu9qcTzWBGNSGAXEiABEiCBcBNwQk7WZUgRp0HO7gFz8myWNB+fJMfPZMkNla6SFTxWRCMK2IUESIAESMAEAnbPyf4wpIjToGf3gHln4z4Z+eGlY0VGd6on3VrU0qDALiRAAiRAAiQQfgJ2z8n+EKSI06Bn54DBsSJtp66RXUdOSJkSkepYEfyfjQRIgARIgATsSMDOOdlf3hRxGgTtHDBrf8iULm8lK6t5rIiG89mFBEiABEjAKAJ2zsn+gqSI0yBo54DpNS9FVqQdUVavGhQveNUWGwmQAAmQAAnYlYCdc7K/zCniNAjaNWD2/XJS4id/qY4VaV23sszhsSIa3mcXEiABEiABkwjYNScHgiFFnAZFuwZM4n++k1lfXTpWZF6POyTupsoa1rMLCZAACZAACZhDwK45ORAEKeI0KNoxYLChocWElZJx7IzUvKa0rBoYLxERRTSsZxcSIAESIAESMIeAHXNyoOhRxGmQtGPApB44Jh2nr1XW9omLlqHtb9awnF1IgARIgARIwCwCdszJgSJIEadB0o4BM2XZTpm2cpey9oN+LaRx1NUalrMLCZAACZAACZhFwI45OVAEKeI0SNoxYNpNXSM7Dx+XymVLSPKwNpxK1fA7u5AACZAACZhHwI45OVAUKeI0SNotYNIzL+1KRXu8WZSM69xAw2p2IQESIAESIAHzCNgtJweSIEWcBk27BcysNXsk8bM0ZSlfdq/hcHYhARIgARIwloDdcnIgQVLEadC0W8A8PGO9pKQfVa/X2jzyHikRWVTDanYhARIgARIgAfMI2C0nB5IgRZwGTTsFTOaJs9I0cYU64LdDw2oy/fHGGhazCwmQAAmQAAmYScBOOTnQBCniNIjaKWAWpeyXIYtTlZWvPhYjnRpV17CYXUiABEiABEjATAJ2ysmBJkgRp0HUTgHTc26KJO04IsWKFpHNI9tKuZLFNCxmFxIgARIgARIwk4CdcnKgCVLEaRC1S8CcPJslMWOWy7msi9Lqpsoyv8cdGtayCwmQAAmQAAmYS8AuOTkYBCniNKjaJWCWpmZI34VblIVjH6gvXZrX1LCWXUiABEiABEjAXAJ2ycnBIEgRp0HVLgHTf9E2WbL1oLJw0/A2UqVcSQ1r2YUESIAESIAEzCVgl5wcDIIUcRpU7RAw5y9clCZjlstvZ7IkJqqCLOkXq2Epu5AACZAACZCA2QTskJODRZAiToOsHQJm3a5MSZidrKwbct/N0jc+WsNSdiEBEiABEiABswnYIScHiyBFnAZZOwTMqI+2y7wN+5R1SQPjJLpyGQ1L2YUESIAESIAEzCZgh5wcLIIUcRpkTQ+Y7OxsaTFhpWQcOyPRla+SpIHxGlayCwmQAAmQAAmYT8D0nBxMghRxGnRND5jUA8ek4/S1yjJMo2I6lY0ESIAESIAEnEjA9JwcTOYUcRp0TQ+YKct2yrSVu5RlS/q1kJioqzWsZBcSIAESIAESMJ+A6Tk5mAQp4jTomh4w7aaukZ2Hj0uVsiVk47A2EhFRRMNKdiEBEiABEiAB8wmYnpODSZAiToOuyQGTnnlS4id/qaxKaBYliZ0baFjILiRAAiRAAiRgDwIm5+RgE6SI0yBscsDMWrNHEj9LU1bN63GHxN1UWcNCdiEBEiABEiABexAwOScHmyBFnAZhkwPm4RnrJSX9qJQtEaleeF88MkLDQnYhARIgARIgAXsQMDknB5sgRZwGYVMDJvPEWWmauEKys0U6Nqou0x6L0bCOXUiABEiABEjAPgRMzcmhIEgRp0HZ1IBZlLJfhixOVRZBwEHIsZEACZAACZCAkwmYmpNDwZwiToOyqQHTc26KJO04IsWKFpEtI9tK2ZLFNKxjFxIgARIgARKwDwFTc3IoCFLEaVA2MWBOns2SmDHL5VzWRbWZAZsa2EiABEiABEjA6QRMzMmhYk4Rp0HaxIBZmpohfRduUdYkdq4vCc1qaljGLiRAAiRAAiRgLwIm5uRQEaSI0yBtYsD0X7RNlmw9qKzZNLyNVClXUsMydiEBEiABEiABexEwMSeHiiBFnAZp0wLm/IWL0mTMcvntTJbERFWQJf1iNaxiFxIgARIgARKwHwHTcnIoCVLEadA2LWDW7cqUhNnJyhK87B4vvWcjARIgARIgATcQMC0nh5I5RZwGbdMCZtRH22Xehn3KkqSBcRJduYyGVexCAiRAAiRAAvYjYFpODiVBijgN2iYFTHZ2trSYsFIyjp2R6MpXSdLAeA2L2IUESIAESIAE7EnApJwcaoIUcRrETQqY1APHpOP0tcoKTKNiOpWNBEiABEiABNxCwKScHGrmFHEaxE0KmCnLdsq0lbuUFUv6tZCYqKs1LGIXEiABEiABErAnAZNycqgJUsRpEDcpYNpNXSM7Dx+XKmVLyMZhbSQiooiGRexCAiRAAiRAAvYkYFJODjVBijgN4qYETHrmSYmf/KWyIKFZlCR2bqBhDbuQAAmQAAmQgH0JmJKTw0GQIk6DuikBM2vNHkn8LE1ZgNds4XVbbCRAAiRAAiTgJgKm5ORwMKeI06BuSsA8PGO9pKQflbIlImXzyLZSPDJCwxp2IQESIAESIAH7EjAlJ4eDIEWcBnUTAibzxFlpmrhCsrNFOjaqLtMei9GwhF1IgARIgARIwN4ETMjJ4SJIEadB3oSAWZSyX4YsTlWjh4CDkGMjARIgARIgAbcRMCEnh4s5RZwGeRMCpufcFEnacUSKFS0iW0a2lbIli2lYwi4kQAIkQAIkYG8CJuTkcBGkiNMgH+6AOXk2S2LGLJdzWRfVZgZsamAjARIgARIgATcSCHdODidzijgN+uEOmKWpGdJ34RY18sTO9SWhWU0NK9iFBEiABEiABOxPINw5OZwEKeI06Ic7YPov2iZLth5UI980vI1UKVdSwwp2IQESIAESIAH7Ewh3Tg4nQYo4DfrhDJjzFy5KkzHL5bczWRITVUGW9IvVsIBdSIAESIAESMAZBMKZk8NNkCJOwwPhDJh1uzIlYXayGjVedo+X3rORAAmQAAmQgFsJhDMnh5s5RZyGB8IZMKM+2i7zNuxTo04aGCfRlctoWMAuJEACJEACJOAMAuHMyeEmSBGn4YFwBUx2dra0mLBSMo6dkejKV0nSwHiN0bMLCZAACZAACTiHQLhysgkEKeI0vBCugEk9cEw6Tl+rRoxpVEynspEACZAACZCAmwmEKyebwJwiTsML4QqYKct2yrSVu9SIl/RrITFRV2uMnl1IgARIgARIwDkEwpWTTSBIEafhhXAFTLupa2Tn4eNSpWwJ2TisjUREFNEYPbuQAAmQAAmQgHMIhCsnm0CQIk7DC+EImPTMkxI/+Us12oRmUZLYuYHGyNmFBEiABEiABJxFIBw52RSCthBxFy5ckEmTJsns2bPlxx9/lOuvv1569eolL7zwghQtWjRflqdOnZL58+fLxx9/LN9++638+uuvUqtWLenQoYMMHz5cKlSooOWHcATMrDV7JPGzNDVevGYLr9tiIwESIAESIAG3EwhHTjaFuS1EXL9+/eSNN96Q7t27S4sWLWTdunUyd+5cwc9fe+21fFlu375dGjZsKHfddZe0a9dOqlSpIps3b1ZiEGIO/y5XrpzPvghHwDw8Y72kpB+VsiUiZfPItlI8MsLncbMDCZAACZAACTiNQDhysikMjRdxqamp0qhRI3nmmWfklVdeyeH23HPPybRp0+Sbb76RBg3ynlrMzMyUn376SQk5z/b2229Lz549ZcqUKTJgwACffRHqgMk8cVaaJq6Q7GyRjo2qy7THYnweMzuQAAmQAAmQgBMJhDonm8TQeBE3YsQIGTdunOzZs0dq166dw27v3r1yww03CH4/duxYn5geP35cVeAg5FCV87WFOmAWpeyXIYtT1TAh4CDk2EiABEiABEiABERCnZNNYm68iMM0KKpthw4duoJb1apVJSYmRj7//HOfmO7cuVNuvvlmGTp0qIwfP96nvrg41AHTc26KJO04IsWKFpEtI9tK2ZLFfB4zO5AACZAACZCAEwmEOiebxNB4EYep0uLFi6v1a7lb48aN5fz584IpV19a165dZcGCBbJ161Y1VVtQy8jIEPzn2dLS0qRLly5qTBhDMNvJs1kSM2a5nMu6qDYzYFMDGwmQAAmQAAmQwCUCFHEGR0J0dLSg4rZ+/forRolNDkeOHJFduy4dgOtNe/PNN6V3797y/PPPy9SpUwvt8uKLL8ro0aPzvC4UIm5paob0XbhFfX5i5/qS0KxmoWPmBSRAAiRAAiTgFgIUcQZ7OpCVuA8//FAeeughue+++wT/joyMLNTycFfi+i/aJku2HlTj3DS8jVQpV7LQMfMCEiABEiABEnALAYo4gz0dqDVxy5Ytk06dOkmzZs3UGrpSpUppWx2qgDl/4aI0GbNcfjuTJTFRFWRJv1jtMbMjCZAACZAACTiRQKhysonsjF8Th0N5sfkgv92p+H1iYmKBbFevXi3t27eXevXqSVJSktbZcJ4fEKqAWbcrUxJmJ6uPxsvu8dJ7NhIgARIgARIggf8jEKqcbCJz40UcdqZiB2p+58Rt27ZNnQOHDQ67d++W8uXLS7Vq1XJYJycnyz333KMO94WYq1ixot9+CFXAjPpou8zbsE+NN2lgnERXLuP32HkDEiABEiABEnASgVDlZBOZGS/iAK1Pnz4yc+ZM9caG2NhY9caGOXPmqA0KM2bMUFzT09PVOXLdunVTb3NA27dvnxKAOBcO1bxrr732Mh9gw0Tbtm199ksoAiY7O1taTFgpGcfOSHTlqyRpYLzP42QHEiABEiABEnA6gVDkZFMZ2kLEZWVlycSJE9XBvAcPHpQaNWqod6cOHjw4Z3NCXiLuyy+/lNatW+fLPi4uTnCNry0UAZN64Jh0nL5WDQ3TqJhOZSMBEiABEiABEricQChysqnMbSHiTIMXioCZsmynTFt56eiUJf1aSEzU1aZh4HhIgARIgARIIOwEQpGTw25kPgOgiNPwTCgCpt3UNbLz8HGpUraEbBzWRiIiimiMlF1IgARIgARIwNkEQpGTTSVIEafhmWAHTHrmSYmffGmaN6FZlCR2bqAxSnYhARIgARIgAecTCHZONpkgRZyGd4IdMLPW7JHEz9LUyPCaLbxui40ESIAESIAESOBKAsHOySYzp4jT8E6wA+bhGeslJf2olC0RKZtHtpXikREao2QXEiABEiABEnA+gWDnZJMJUsRpeCeYAZN54qw0TVwh2dkiHRtVl2mPxWiMkF1IgARIgARIwB0EgpmTTSdIEafhoWAGzKKU/TJkcaoaFQQchBwbCZAACZAACZBA3gSCmZNNZ04Rp+GhYAZMz7kpkrTjiBQrWkS2jGwrZUsW0xghu5AACZAACZCAOwgEMyebTpAiTsNDwQqYk2ezJGbMcjmXdVFtZsCmBjYSIAESIAESIIH8CQQrJ9uBOUWchpeCFTBLUzOk78ItakSJnetLQrOaGqNjFxIgARIgARJwD4Fg5WQ7EKSI0/BSsAKm/6JtsmTrQTWiTcPbSJVyJTVGxy4kQAIkQAIk4B4CwcrJdiBIEafhpWAEzPkLF6XJmOXy25ksiYmqIEv6xWqMjF1IgARIgARIwF0EgpGT7UKQIk7DU8EImHW7MiVhdrIaDV52j5fes5EACZAACZAACRRMIBg52S7MKeI0PBWMgNmbeVLe3bRfvvjvIXnriaYSXbmMxsjYhQRIgARIgATcRSAYOdkuBCniNDwVzIDJzs6WIkX4snsNt7ALCZAACZCACwkEMyebjpMiTsNDbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFWbg4YDVzsQgIkQAIkQAJBI+DmnEwRpxFW69atk5YtW8qCBQvklltu0bgDu5AACZAACZAACQSCQFpamnTp0kXWrl0rsbGxgbilbe5BEafhqoULF6qAYSMBEiABEiABEjCDAAorCQkJZgwmRKOgiNMAnZmZKV988YVKYxfmAAAX80lEQVTUqlVLSpUqpXEH+3Sx/sJxU9WRNrujukw/u8PP+Lalr53t69OnT0t6erq0a9dOKlWqZJ8EG4CRUsQFAKKTb+HGtQa0ubGTQzrHNvrZHX6Gw+lr9/jaFV9eHkZSxLnN4z7ayy8/d3z50c/0s49fDba6nPHtjvi2VVAGaLAUcQEC6dTb8MvPHV9+9DP97NTvMFbiNkvjxu6IbyfHcH62UcS50es+2Mzk7o4vP/qZfvbha8F2lzK+3RHftgvMAAyYIi4AEJ18i4yMDJk5c6b07t1bqlWr5mRTc2yjzfSzUwPdjbENX7rRbjfa7NTntiC7KOLc6HXaTAIkQAIkQAIkYHsCFHG2dyENIAESIAESIAEScCMBijg3ep02kwAJkAAJkAAJ2J4ARZztXUgDSIAESIAESIAE3EiAIs6NXqfNJEACJEACJEACtidAEWd7F9IAEiABEiABEiABNxKgiHOZ17/++mvBe1BXrlwpe/fulauuukrq1asnw4YNk3vuuadQGk888YTMmzcvz+t+/PFHue666wq9RzguwHv1ateunedH9+zZU2bPnl3osLZv3y6DBw+WtWvXqmtbtmwpEydOlPr16xfaNxwXFOQrjGfs2LEyYsSIfIdmuq9PnDghkydPls2bNwvi+tChQ9KtWzeZO3fuFTZduHBBJk2apPyMOL3++uulV69e8sILL0jRokW9co8J/vfWZn+fcwAxxf/e2hyIZxx228nP/j7jJvnZq4eQF11BgCLOZUHx0EMPyerVq+XBBx9Up3jjC3LOnDnqi+v111+Xvn37FkjE+tKAkIuIiLjs2j/96U9SunRpI4laX/D333+/gIFnq1OnjjRv3rzAcf/www/StGlTqVixojzzzDPq2ldffVX+97//yaZNm+TGG280zu4NGzbI7t27rxjXK6+8okQPxE9BJ7mb7mvLpzi/sEmTJvLpp5/mK+L69esnb7zxhnTv3l1atGgh69atU2IPP3/ttdcK9Z0p/vfWZn+fc8/kHu5n3Vub/X3GYbPd/OzvM26Snwt9CHlBngQo4lwWGEhet99+u5QoUSLH8tOnT8ttt90mP//8sxw5ckQiIyMLrc6cP3++wOtMw2p9waPyhAqUrw1J8fPPP5e0tDRVxUFDReeWW26R9u3by3vvvefrLcNy/alTp+Taa6+VWrVqybfffuuVYDfV12fPnpXMzEypUaOGZGVlSbFixfIUcampqdKoUSMlviFgrfbcc8/JtGnT5JtvvpEGDRoUyMIU/3trs7/PuWdyD7f/vbXZ32ccNtvNz3kFrS/PuEl+DssXogM+lCLOAU4MhAkDBw6Ul19+WQmTgqZErerMuXPnBOKvTJkyV1TkAjGeQN/D8wvemkIsVaqUVx+DauU111wjjz766BVTyZi+W7Rokfzyyy9qatr0tnDhQunSpYuahoTPC2p28nVBIg7+HjdunOzZs+eyKXUsJ7jhhhvUlHJBwt5U/xdkc35+9fY590zuJj3rBdnszzMOe53iZ1+ecVP9bPr3qEnjo4gzyRthHMtjjz0m77//vhw9elQJs/yaldjLli0rx48fFwghVKKwNiw6OjqMFhT80dYXPGzDlzUaplH79++vptQKapiywBQcpuP69Olz2aX4GfrjmsKmZE2Ac++998qqVavkwIEDUrVqVa9EnB18XVByb9eunaq2Yc1c7gYGMTExqsqaXzPV/zoiztvn3DO5m+R/b0SczjMOe53iZ1+ecVP9bMJ3pV3GQBFnF08FcZyYIsR0aocOHWTx4sUFftLQoUPV1BXWIBUvXlzWr18v06dPl3Llyql1VjVr1gziSPVvvX//funRo4d07txZoqKi5KeffpI333xT8GLsQYMGqUXv+TUwwTTLxx9/LB07drzsMvwM6+wggLHO0OR28OBBZTtEN9aPFdbs5OuCkjumShGrWAOYu2FNIKYLMeVqN//7KuJ8ec7BwkT/F2SzP8847DX1OffFz74+46b6ubDvJv7+/whQxLk8Go4dOyZ33nmnEjWoVuiIMFQxIAzy2xloKmLsWIyLi1N/gX///ff5VhLfeecd6dq1q3zxxReCv3I927JlywSVHlyDaUqT24QJE9QuZKzfy725w9txm+rrghIdKsSouOEPjtwNFVasA921a1e+CEz1vy/JPRDPOQCF2/++2IzxevuM41on+DkQz7gJfvb2+4jXiVDEuTgKsKYNAgS7K5cuXSqtW7fWpoHKXEZGhhKDdmqffPKJdOrUSWbOnClPPfVUnkM39S90XznfeuutcvjwYeUjz40tvt7HRF+zEnflsSqWXwP5nOOe4fS/ryIO4/XmGXdKJS5Qz3i4/ezrd5Kbr6eIc6n3sVgZ4iUpKUlNBWJK0J+G40UwRYf72qlhhyZ2LiYmJsrw4cPzHLqpa2V84ZySkiJ33HGH10dqFHRvE33NNXF5i7hAP+eIi3D6X0fEefOMwy5Tn3NvbQ7kMx5uP/vy3eb2ayniXBgB+FLAdBr+Qp0/f74kJCT4TaFhw4ZqUwR2t9qpffDBB2otGw6BxaG/ebXCdq29++67andqQRtCws3k6aefVuehJScnKzHnTzPR1wUlOojz8ePH57s7Fb+HiM+vmer/wpJ7MJ5zMAqn/wuzOS8fevOMo59d/WzZHMhnPNx+9uf7yW19KeJc5vGLFy/K448/ro7FmDFjhvTu3TtPAljsjYNiy5cvLzhMFe3kyZPq/7mP0sC9cPwG7oV7mtiw7qlKlSqXDQ3TTLGxseqgY9iK89/yshudIPSwJm7Hjh05R7BY58RhSrqwDSHhZIJqDHyIdWHffffdFUNxgq8LSu5Y64kdqPmdE7dt2zYlTNDs5P+CbPb2Oc/PZlOf9YJs9vYZd5KfrYe5sGfcbn4O5/el3T6bIs5uHvNzvAMGDJCpU6dKq1at5Mknn7zibm3btlXJ3jqSw3OzApLd3XffLY888ojUrVtX7fjDFATOJcKuR1R5Cju2ws/ha3fHrlRUyzB+nIOHdWE4iR5nh6FKg514aHnZjZ/v3LlTVbBwXtyzzz6rrsUbG3BPrCkED1ObVYnAouchQ4ZcMUw7+xo7o/HWDIiWUaNGKbGG6T40LBewxBmOhsG6R7yxAcIdh+HiTSW5//Cwg/+9sdnb5zy/mDftWffGZm+fcbs8597YbD3MhT3jdvGzqd+hJo+LIs5k7wRhbPHx8eq1W/k1nCGGa/JKZjhnC4eFYu0FRBCqFqhe4dgNHJhaqVKlIIw4MLd86623lGiDGPv111/V1CeOl8Cp/Uj2VssvieP3WFsDEeT57tSXXnopRygEZqSBvwvWO2K9IiqH1atX90rE2cXXePPEvn378oQGkYZzDdFQwcFZhpg2xzEMeMsD3p2Kd+F6vqHEDv73xmZvn/P8krtp/vfGZm+f8YJEnEnPuTc2W4Ff2DNuFz8H/tvP+XekiHO+j2khCZAACZAACZCAAwlQxDnQqTSJBEiABEiABEjA+QQo4pzvY1pIAiRAAiRAAiTgQAIUcQ50Kk0iARIgARIgARJwPgGKOOf7mBaSAAmQAAmQAAk4kABFnAOdSpNIgARIgARIgAScT4Aizvk+poUkQAIkQAIkQAIOJEAR50Cn0iQSIAESIAESIAHnE6CIc76PaSEJkAAJkAAJkIADCVDEOdCpNIkESIAESIAESMD5BCjinO9jWkgCJEACJEACJOBAAhRxDnQqTSIBHQJffvmltG7dWmbNmqXeK2qn9q9//UvGjh0ru3btknPnzsnRo0elQoUKdjKBYyUBEiABnwlQxPmMjB1IwHcClkBCz48++kg6dep02U1WrFghbdu2Fc+Xtvv+Kf71sKuI27lzp9SrV0/i4uIkISFBihcvLo888ogUK1bMPyDs7TeBixcvyj/+8Q+57bbb5IEHHvD7frwBCZDA5QQo4hgRJBACAp4irmHDhrJt2zYpUqRIzidTxOk7YcaMGdK3b1/ZunWrEgts5hDIyspSYrpbt24yd+5ccwbGkZCAQwhQxDnEkTTDbAKWiGvSpIls3rxZFi5cKI8//rhrRRwqNJj2LFmypN+OQ6Vn1KhRsnfvXqlVq5bf9+MNAkeAIi5wLHknEsiLAEUc44IEQkDAEnGvv/66TJo0SYoWLSppaWkSGRmpPj2vShwqF927d89TnKCKB+Hy4osvqv6eU6EnT56UV199VX766Sdp3LixvPHGG4Lq37vvvpuzbiw6OlqmTZsmd999d4711j3efPNN+e2332T69OmSkZEht956q4wfP17atWt3Bal58+bJa6+9Jv/9738lIiJCmjdvrqbP7rzzzjzve/z4cQGD9PR0ef/99wucYvvhhx9kxIgRsnLlSjlx4oTceOON0qdPH/nrX/+ac2/Paqb1Q2+qPpi2BpfvvvtOjbtu3brq3j179sy5tzefj4vj4+PVWrw1a9bIs88+K6tXr5bSpUtL7969ZfTo0fLLL7/Ic889J0uXLhWIGkz1gm2JEiVyPsu6B3zw9NNPy9q1a5XAffjhh1W8lClT5jL2X3/9tfz973+XdevWKTFcv359GTRokLq3Z4Oove6665SPMIZNmzZJuXLl1JpH+Am2e7b169erGNmwYYOcPn1abr75Zunfv7+qpHk2cMfUdY8ePWTIkCGSmpoqVatWVWN45pln1KXwce3ata+IGUx7w84LFy7IlClTVIUO1+JZuP766+Whhx5S3NhIgAQKJ0ARVzgjXkECfhPwFFlYs4WkOHPmTHnqqacCKuJiYmJUUof4O3XqlLz00ktSvnx5SUxMVIkRwgLJEj8/f/687Nu3T/3eUwhiShIbA3AtkjVE3Y8//qiEJhKw1ZCwX375ZXnwwQeVkMHnvf3227Jnzx5JSkqSli1bXnZfCA0IAwglCInY2Nh8pz93794td9xxhxojRE21atVkyZIlsmrVKhkwYIBK/mgLFiyQDz74QP1u6tSpUqlSJYFA9RSRuZ2H+0HUQHBinRYE0jfffCNHjhyRDz/8UF3u7edbIg4iBhsp7r33XiWYMSbwggBD1RW2t2jRQnFZvHjxZQLc8x4YC7iBTXJyssyfP1+tlVy2bFmOGRBYEN/4PAhPsMRnoMIL8W6JKHSAiMMfDBD2jz76qBJlEJMff/yxzJ49+zLRCtshGiH8IaQgRHEdPhvxMnjw4JwxIC5g06FDh1QM16hRQxYtWqSELOxu06aN+kzYili/6667cmIdYg824Q8QxOQTTzyh/IW4/f7775UvIITZSIAECidAEVc4I15BAn4T8BRxqF4gAaIqhWoPKi6BqsSh6oIKn1W5eeWVV+T5559XQg0bAJBA0d577z3585//fJmQtMaIvri2evXq6trDhw/LTTfdJHXq1FFCAS0lJUWJLIgUiDmroWIG2yC6IDY8xeE111yjknTFihUL5YmKEsaIyhDEFhqmYDt27KhECCpoECRolhjwZjoVFS4ICtiOHa2elajs7OycdYq+fD4ELETHP//5T1XtQoMgQVXp559/lhdeeEGJIKuBz6+//qoqpVaz7uEpUPE7VLkmTpyoxBRsR2vWrJmqfIGBNX185swZJYTA98CBA3L11Vera/F7CHVUPSG2rQahjj8mUJlDg7iOiopSrPFZnhVOCLrPPvtMjdfa8Yvf4z8IzaZNm6p7nD17Vt0DQv/f//63+llB06n4gwMx9p///KfQeOAFJEACeROgiGNkkEAICOTe+YkKBZIjKkpI3IESccOGDZNx48blWIRpNyTZrl27CqY+rYZED5GB6oolMKwxPvnkk6r65tn69eunph8PHjyoEi/GjClBVKxKlSp12bVDhw5VFTlMyUIQWveFwIHQKaxhmg2iE2IFlSvPhkoPRIJnZcgXEYfpTkwjY/oTFbu8mq+fDwH21VdfqcqT5xq/Dh06KIGCyqTntCKmMt966y1VubTYWSLO4muNC9VBCG/LJxDU1157rZrGxD08G6p2qHqhIgaRigYRB3GFaXHPBl+giompXjQIt/vvv19Vzlq1anXZtZ988on6vE8//VT++Mc/qt9BwEHwWULd6oB7ILYssV+QiMNxNmADgYjdxWwkQAK+E6CI850Ze5CAzwTyOr7j9ttvl/3796tEtnHjxiuOGNFZE4edmpgGtRrECtaS/e1vf5MxY8bk/BwVs7Jly6q1UTgXDs0aoyUsPY20KnqoZGGq7w9/+IOqiBXULPFi3Tf3VF9+fTFFh0oepj0huDxbZmamVK5cWU0jQlSi+SLi2rdvrwQX7A/U50OAoXKZWyh16dJFTXNCyGBK02qoXIIxxA6mIdFwjy1btijhm7uhqoZYWb58uYoTVNwmT54sAwcOvOxSS7BPmDBBVfDQIOI8q6JWB4sZqo9oqPZZffLjAmGOaXo0iDhMz6Ka6dkwNYqqJKqiaAWJOAhAiD5UK1HlhajD9DZii40ESMA7AhRx3nHiVSTgF4G8RNwXX3wh9913X85GgNznxKFyhqSYe5oQlSKsa8tvY4PnQb2WiPO8FoZYIg7r07A2Cs0XEQcxhKoYzrzLr0HsodLk6/lzdhRx4AxR5tksEWcJJet3lojDOkNMf6MFU8ThMyC+PVtuEYfKJiqoEMYQVHk1bHCxptitjQ2o5uUWcfA3NioUJuLweywpwB8DqLhCpCLWEVuo+uXedOHXA8jOJOBQAhRxDnUszTKLQH5CBlODWMiN6Uusw/I87Nea4kKFBuuHrIZ1dFijFiwR5810KhbPW7tXMb1XUPNVxBU0nYkqGqb7UDnCWjM0Xypx1nQqGOYnVnz9fGtnqb8iDhUsf6ZT33nnHTVtjvVo2KCAZu1OLUzEWdP7qKyhwlZY81bEWX9weLNjGGIXPkWVEqLOc+d0YePh70nArQQo4tzqedodUgL5CRkcEYHdiNb5cZ4iDhsUUP3IPXWGIzZwTEewRFx+GxuwhgyCEg1TYdhtiek1rM3KfdQH1nJVqVJFXeuriEMfCAmIEUwfYgMFGjY2YPoN68yCvbHBl88PpIjLb2OD51s+sBbN2thQs2ZNxQbr3uCPHTt2XLGxwZtKHCqzEHwQ5GCe+0gTTHli56/lZ29FHMaGdYI4niZ31RZT47inZ0Nl7y9/+csVGzFC+rDyw0jARgQo4mzkLA7VvgQKEjJYLI7F3Wi5X7sFgYAdgDirC8kYxz1glyB2hwZLxFlHjGDdGZI1jkLB2j18tmd1xNo5iQ0IEFdIyJgihK2oqqBqpivisGECGzKwpgpVP4gLiABUaHILHV8qcRgP3u6AtYNYW4Y1WFgbCFGENW04qgTNl88PlIj79ttv1ViwexaCzDpiBMd1YKrRElDWESNYKwdb0Adr77AmLq8jRrwRcbAZfFHBw0YKTONDIEKM400YqApj44Z1rqEvIg5/pOAeOE4EY4G4RxxhbSOm3OFnrNvDFCz+OMEbHvAHDN99a9/vO448dAQo4kLHmp/kYgIFiTi8ggtnc0H45BZxEEXYGYoDb3EkBI6ZsM5DC5aI8zzsF4IR1UDseMVapdwNx4DgzDUkaRyrgWSMpAwRYF2vU4nD5+C4jPwO+/Ws/Pkq4sAZNkKcQizg0F1MT0MQWQv3ffn8QIk4rKvDOXgQrZj+xLiwgxmVWAg1zwYRbx32i7P0sLsTa+1yT4V6O51q3Ru7SnGwMwQ4zgqE0IL/IXYRhzqVOAhkVI9xb+zItQ77xedg7Rs2hWBtHGIH60Lhc755w8VfljTdJwIUcT7h4sUkQAIkEHgC+QnBwH8S70gCJOAkAhRxTvImbSEBErAlAYo4W7qNgyaBsBOgiAu7CzgAEiABtxOgiHN7BNB+EtAjQBGnx429SIAESCBgBCjiAoaSNyIBVxGgiHOVu2ksCZAACZAACZCAUwhQxDnFk7SDBEiABEiABEjAVQQo4lzlbhpLAiRAAiRAAiTgFAIUcU7xJO0gARIgARIgARJwFQGKOFe5m8aSAAmQAAmQAAk4hQBFnFM8STtIgARIgARIgARcRYAizlXuprEkQAIkQAIkQAJOIUAR5xRP0g4SIAESIAESIAFXEaCIc5W7aSwJkAAJkAAJkIBTCFDEOcWTtIMESIAESIAESMBVBCjiXOVuGksCJEACJEACJOAUAhRxTvEk7SABEiABEiABEnAVAYo4V7mbxpIACZAACZAACTiFAEWcUzxJO0iABEiABEiABFxF4P8DzGXFA1ifPvYAAAAASUVORK5CYII=\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "candidate_components=np.arange(1,20,1)\n",
    "variances=[]\n",
    "for candidate in candidate_components:\n",
    "    pca = PCA(n_components=candidate)\n",
    "    principalComponents = pca.fit_transform(X_scaled)\n",
    "    variances.append(np.sum(pca.explained_variance_ratio_))\n",
    "    \n",
    "plt.figure(figsize = (5, 5))\n",
    "plt.plot(candidate_components,variances)\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('Variance')\n",
    "plt.title('There will usually be an elbow in the curve, where the explained variance stops growing fast',fontsize=7);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In the above case, elbow seems to be present somewhere near 12."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.16304994 0.14974673 0.10901591 0.09869166 0.08600576 0.0660893\n",
      " 0.0572263  0.05456994 0.04447539 0.04083015 0.03836937 0.02972756]\n",
      "0.9377980058090926\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=12)\n",
    "principalComponents = pca.fit_transform(X_scaled)\n",
    "print(pca.explained_variance_ratio_)\n",
    "print(np.sum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ELptmL4eblR4",
    "outputId": "0f370e76-5421-4cb3-b762-292eba86a5d9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(principalComponents, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "id": "xwsCqXfurnWQ"
   },
   "outputs": [],
   "source": [
    "# perform the same transforms on test data\n",
    "X_scaled_test = scaler.transform(X_test)\n",
    "test_principalComponents = pca.transform(X_scaled_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eewzLKRJsQLt"
   },
   "source": [
    "### Question: What will happen if you do not perform the above? What is the difference between .fit_transform() and .transform() ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PzHSzBopshdW",
    "outputId": "fbcb4cad-d412-4764-b689-a4c8a83bdc3b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.8233333333333334\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "yhat_test = lr.predict(test_principalComponents)\n",
    "print(\"Accuracy: \", accuracy_score(y_test, yhat_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vvvNTir1tZxZ"
   },
   "source": [
    "### Question: How does changing the PCA *n_components* affect the above accuracy?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JsnMP1RhxtmD"
   },
   "source": [
    "#### Principal Component Analysis on the vehicles dataset\n",
    "#### Download dataset from: https://github.com/Meigarom/machine_learning/tree/master/dataset \n",
    "#### Let us improve classification outcome of a real dataset!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "id": "YGqjWpTOxT0c"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"vehicle.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 223
    },
    "id": "q2X713X0wVrN",
    "outputId": "e61c9609-a8f6-4803-a93b-65075094d6a4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95</td>\n",
       "      <td>48</td>\n",
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       "      <td>72</td>\n",
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       "      <td>42</td>\n",
       "      <td>20</td>\n",
       "      <td>159</td>\n",
       "      <td>176</td>\n",
       "      <td>379</td>\n",
       "      <td>184</td>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>187</td>\n",
       "      <td>197</td>\n",
       "      <td>van</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>41</td>\n",
       "      <td>84</td>\n",
       "      <td>141</td>\n",
       "      <td>57</td>\n",
       "      <td>9</td>\n",
       "      <td>149</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>143</td>\n",
       "      <td>170</td>\n",
       "      <td>330</td>\n",
       "      <td>158</td>\n",
       "      <td>72</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>189</td>\n",
       "      <td>199</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104</td>\n",
       "      <td>50</td>\n",
       "      <td>106</td>\n",
       "      <td>209</td>\n",
       "      <td>66</td>\n",
       "      <td>10</td>\n",
       "      <td>207</td>\n",
       "      <td>32</td>\n",
       "      <td>23</td>\n",
       "      <td>158</td>\n",
       "      <td>223</td>\n",
       "      <td>635</td>\n",
       "      <td>220</td>\n",
       "      <td>73</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>188</td>\n",
       "      <td>196</td>\n",
       "      <td>saab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93</td>\n",
       "      <td>41</td>\n",
       "      <td>82</td>\n",
       "      <td>159</td>\n",
       "      <td>63</td>\n",
       "      <td>9</td>\n",
       "      <td>144</td>\n",
       "      <td>46</td>\n",
       "      <td>19</td>\n",
       "      <td>143</td>\n",
       "      <td>160</td>\n",
       "      <td>309</td>\n",
       "      <td>127</td>\n",
       "      <td>63</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>199</td>\n",
       "      <td>207</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85</td>\n",
       "      <td>44</td>\n",
       "      <td>70</td>\n",
       "      <td>205</td>\n",
       "      <td>103</td>\n",
       "      <td>52</td>\n",
       "      <td>149</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>144</td>\n",
       "      <td>241</td>\n",
       "      <td>325</td>\n",
       "      <td>188</td>\n",
       "      <td>127</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>180</td>\n",
       "      <td>183</td>\n",
       "      <td>bus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "0           95           48                    83           178   \n",
       "1           91           41                    84           141   \n",
       "2          104           50                   106           209   \n",
       "3           93           41                    82           159   \n",
       "4           85           44                    70           205   \n",
       "\n",
       "   pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "0                    72                       10            162   \n",
       "1                    57                        9            149   \n",
       "2                    66                       10            207   \n",
       "3                    63                        9            144   \n",
       "4                   103                       52            149   \n",
       "\n",
       "   elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "0             42                      20                        159   \n",
       "1             45                      19                        143   \n",
       "2             32                      23                        158   \n",
       "3             46                      19                        143   \n",
       "4             45                      19                        144   \n",
       "\n",
       "   scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "0              176                379                        184   \n",
       "1              170                330                        158   \n",
       "2              223                635                        220   \n",
       "3              160                309                        127   \n",
       "4              241                325                        188   \n",
       "\n",
       "   scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "0                           70               6                16   \n",
       "1                           72               9                14   \n",
       "2                           73              14                 9   \n",
       "3                           63               6                10   \n",
       "4                          127               9                11   \n",
       "\n",
       "   skewness_about.2  hollows_ratio class  \n",
       "0               187            197   van  \n",
       "1               189            199   van  \n",
       "2               188            196  saab  \n",
       "3               199            207   van  \n",
       "4               180            183   bus  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315
    },
    "id": "_wlyY3QbwWvW",
    "outputId": "af84adb3-6b3a-46cf-ee71-e2e8ac3d3a85"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
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       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
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       "  </thead>\n",
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       "      <th>count</th>\n",
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       "      <td>846.000000</td>\n",
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       "      <td>846.000000</td>\n",
       "      <td>846.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>93.678487</td>\n",
       "      <td>44.861702</td>\n",
       "      <td>82.088652</td>\n",
       "      <td>168.940898</td>\n",
       "      <td>61.693853</td>\n",
       "      <td>8.567376</td>\n",
       "      <td>168.839243</td>\n",
       "      <td>40.933806</td>\n",
       "      <td>20.582742</td>\n",
       "      <td>147.998818</td>\n",
       "      <td>188.625296</td>\n",
       "      <td>439.911348</td>\n",
       "      <td>174.70331</td>\n",
       "      <td>72.462175</td>\n",
       "      <td>6.377069</td>\n",
       "      <td>12.599291</td>\n",
       "      <td>188.932624</td>\n",
       "      <td>195.632388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.234474</td>\n",
       "      <td>6.169866</td>\n",
       "      <td>15.771533</td>\n",
       "      <td>33.472183</td>\n",
       "      <td>7.888251</td>\n",
       "      <td>4.601217</td>\n",
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       "      <td>7.811560</td>\n",
       "      <td>2.592138</td>\n",
       "      <td>14.515652</td>\n",
       "      <td>31.394837</td>\n",
       "      <td>176.692614</td>\n",
       "      <td>32.54649</td>\n",
       "      <td>7.486974</td>\n",
       "      <td>4.918353</td>\n",
       "      <td>8.931240</td>\n",
       "      <td>6.163949</td>\n",
       "      <td>7.438797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>109.00000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>181.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>146.250000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>318.250000</td>\n",
       "      <td>149.00000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>190.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>93.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>178.500000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>173.00000</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>197.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>587.000000</td>\n",
       "      <td>198.00000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>201.000000</td>\n",
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       "      <th>max</th>\n",
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       "      <td>112.000000</td>\n",
       "      <td>333.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>1018.000000</td>\n",
       "      <td>268.00000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>211.000000</td>\n",
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      ],
      "text/plain": [
       "       compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "count   846.000000   846.000000            846.000000    846.000000   \n",
       "mean     93.678487    44.861702             82.088652    168.940898   \n",
       "std       8.234474     6.169866             15.771533     33.472183   \n",
       "min      73.000000    33.000000             40.000000    104.000000   \n",
       "25%      87.000000    40.000000             70.000000    141.000000   \n",
       "50%      93.000000    44.000000             80.000000    167.000000   \n",
       "75%     100.000000    49.000000             98.000000    195.000000   \n",
       "max     119.000000    59.000000            112.000000    333.000000   \n",
       "\n",
       "       pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "count            846.000000               846.000000     846.000000   \n",
       "mean              61.693853                 8.567376     168.839243   \n",
       "std                7.888251                 4.601217      33.244978   \n",
       "min               47.000000                 2.000000     112.000000   \n",
       "25%               57.000000                 7.000000     146.250000   \n",
       "50%               61.000000                 8.000000     157.000000   \n",
       "75%               65.000000                10.000000     198.000000   \n",
       "max              138.000000                55.000000     265.000000   \n",
       "\n",
       "       elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "count     846.000000              846.000000                 846.000000   \n",
       "mean       40.933806               20.582742                 147.998818   \n",
       "std         7.811560                2.592138                  14.515652   \n",
       "min        26.000000               17.000000                 118.000000   \n",
       "25%        33.000000               19.000000                 137.000000   \n",
       "50%        43.000000               20.000000                 146.000000   \n",
       "75%        46.000000               23.000000                 159.000000   \n",
       "max        61.000000               29.000000                 188.000000   \n",
       "\n",
       "       scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "count       846.000000         846.000000                  846.00000   \n",
       "mean        188.625296         439.911348                  174.70331   \n",
       "std          31.394837         176.692614                   32.54649   \n",
       "min         130.000000         184.000000                  109.00000   \n",
       "25%         167.000000         318.250000                  149.00000   \n",
       "50%         178.500000         364.000000                  173.00000   \n",
       "75%         217.000000         587.000000                  198.00000   \n",
       "max         320.000000        1018.000000                  268.00000   \n",
       "\n",
       "       scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "count                   846.000000      846.000000        846.000000   \n",
       "mean                     72.462175        6.377069         12.599291   \n",
       "std                       7.486974        4.918353          8.931240   \n",
       "min                      59.000000        0.000000          0.000000   \n",
       "25%                      67.000000        2.000000          5.000000   \n",
       "50%                      71.500000        6.000000         11.000000   \n",
       "75%                      75.000000        9.000000         19.000000   \n",
       "max                     135.000000       22.000000         41.000000   \n",
       "\n",
       "       skewness_about.2  hollows_ratio  \n",
       "count        846.000000     846.000000  \n",
       "mean         188.932624     195.632388  \n",
       "std            6.163949       7.438797  \n",
       "min          176.000000     181.000000  \n",
       "25%          184.000000     190.250000  \n",
       "50%          188.000000     197.000000  \n",
       "75%          193.000000     201.000000  \n",
       "max          206.000000     211.000000  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "If7vxVbSwava",
    "outputId": "980bb0fa-53b5-4a89-d9c7-c2fef70dba00"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "compactness                    0\n",
       "circularity                    0\n",
       "distance_circularity           0\n",
       "radius_ratio                   0\n",
       "pr.axis_aspect_ratio           0\n",
       "max.length_aspect_ratio        0\n",
       "scatter_ratio                  0\n",
       "elongatedness                  0\n",
       "pr.axis_rectangularity         0\n",
       "max.length_rectangularity      0\n",
       "scaled_variance                0\n",
       "scaled_variance.1              0\n",
       "scaled_radius_of_gyration      0\n",
       "scaled_radius_of_gyration.1    0\n",
       "skewness_about                 0\n",
       "skewness_about.1               0\n",
       "skewness_about.2               0\n",
       "hollows_ratio                  0\n",
       "class                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aONbEmAPxmXY"
   },
   "source": [
    "Seems like we are good to go!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BAIdraVcxlEh",
    "outputId": "81195b10-77a6-4ee7-8119-e466a8767cde"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bus     218\n",
       "saab    217\n",
       "opel    212\n",
       "van     199\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check how many classes are present in the dataset\n",
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "066n4a0v1Zln"
   },
   "source": [
    "### Let us make a binary classification dataset for ease of use!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315
    },
    "id": "A6f1Cn79xbp2",
    "outputId": "b54d9d24-14f2-4dad-d59f-ce1ed92fcb74"
   },
   "outputs": [
    {
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       "      <td>218.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>218.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>91.591743</td>\n",
       "      <td>45.068807</td>\n",
       "      <td>76.720183</td>\n",
       "      <td>166.004587</td>\n",
       "      <td>63.431193</td>\n",
       "      <td>7.013761</td>\n",
       "      <td>170.022936</td>\n",
       "      <td>40.114679</td>\n",
       "      <td>20.573394</td>\n",
       "      <td>146.701835</td>\n",
       "      <td>192.889908</td>\n",
       "      <td>448.894495</td>\n",
       "      <td>180.949541</td>\n",
       "      <td>77.123853</td>\n",
       "      <td>4.844037</td>\n",
       "      <td>10.211009</td>\n",
       "      <td>187.811927</td>\n",
       "      <td>191.325688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.618024</td>\n",
       "      <td>5.030766</td>\n",
       "      <td>12.092564</td>\n",
       "      <td>30.574152</td>\n",
       "      <td>8.802877</td>\n",
       "      <td>4.757220</td>\n",
       "      <td>33.356812</td>\n",
       "      <td>6.501376</td>\n",
       "      <td>2.728351</td>\n",
       "      <td>10.492398</td>\n",
       "      <td>33.996568</td>\n",
       "      <td>193.138314</td>\n",
       "      <td>31.247374</td>\n",
       "      <td>7.684941</td>\n",
       "      <td>3.220540</td>\n",
       "      <td>6.872872</td>\n",
       "      <td>7.321230</td>\n",
       "      <td>7.918326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>181.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>85.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>68.250000</td>\n",
       "      <td>139.250000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>327.000000</td>\n",
       "      <td>163.250000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>184.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>168.500000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>189.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>98.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>80.750000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>177.750000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>149.750000</td>\n",
       "      <td>203.750000</td>\n",
       "      <td>486.500000</td>\n",
       "      <td>190.750000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>198.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>114.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>1018.000000</td>\n",
       "      <td>268.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>209.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "count   218.000000   218.000000            218.000000    218.000000   \n",
       "mean     91.591743    45.068807             76.720183    166.004587   \n",
       "std       8.618024     5.030766             12.092564     30.574152   \n",
       "min      78.000000    35.000000             59.000000    113.000000   \n",
       "25%      85.000000    42.000000             68.250000    139.250000   \n",
       "50%      89.000000    44.000000             72.000000    168.500000   \n",
       "75%      98.000000    47.000000             80.750000    188.000000   \n",
       "max     114.000000    58.000000            109.000000    252.000000   \n",
       "\n",
       "       pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "count            218.000000               218.000000     218.000000   \n",
       "mean              63.431193                 7.013761     170.022936   \n",
       "std                8.802877                 4.757220      33.356812   \n",
       "min               47.000000                 4.000000     141.000000   \n",
       "25%               56.000000                 6.000000     149.000000   \n",
       "50%               64.000000                 6.000000     152.000000   \n",
       "75%               69.000000                 7.000000     177.750000   \n",
       "max              126.000000                52.000000     265.000000   \n",
       "\n",
       "       elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "count     218.000000              218.000000                 218.000000   \n",
       "mean       40.114679               20.573394                 146.701835   \n",
       "std         6.501376                2.728351                  10.492398   \n",
       "min        26.000000               18.000000                 124.000000   \n",
       "25%        36.000000               19.000000                 142.000000   \n",
       "50%        44.000000               19.000000                 145.000000   \n",
       "75%        45.000000               21.000000                 149.750000   \n",
       "max        47.000000               29.000000                 175.000000   \n",
       "\n",
       "       scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "count       218.000000         218.000000                 218.000000   \n",
       "mean        192.889908         448.894495                 180.949541   \n",
       "std          33.996568         193.138314                  31.247374   \n",
       "min         163.000000         299.000000                 117.000000   \n",
       "25%         170.000000         327.000000                 163.250000   \n",
       "50%         177.000000         344.000000                 176.000000   \n",
       "75%         203.750000         486.500000                 190.750000   \n",
       "max         288.000000        1018.000000                 268.000000   \n",
       "\n",
       "       scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "count                   218.000000      218.000000        218.000000   \n",
       "mean                     77.123853        4.844037         10.211009   \n",
       "std                       7.684941        3.220540          6.872872   \n",
       "min                      65.000000        0.000000          0.000000   \n",
       "25%                      71.000000        2.000000          5.000000   \n",
       "50%                      76.000000        5.000000         10.000000   \n",
       "75%                      82.000000        7.000000         14.000000   \n",
       "max                     127.000000       17.000000         31.000000   \n",
       "\n",
       "       skewness_about.2  hollows_ratio  \n",
       "count        218.000000     218.000000  \n",
       "mean         187.811927     191.325688  \n",
       "std            7.321230       7.918326  \n",
       "min          178.000000     181.000000  \n",
       "25%          181.000000     184.000000  \n",
       "50%          186.000000     189.000000  \n",
       "75%          195.000000     198.750000  \n",
       "max          202.000000     209.000000  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_class1_df =  df.loc[df['class'] == 'bus']\n",
    "binary_class1_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315
    },
    "id": "r6r8Yi1G0eUL",
    "outputId": "3b39cca0-06ea-4d45-e9cf-820fddf01cc9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>97.281106</td>\n",
       "      <td>45.534562</td>\n",
       "      <td>88.672811</td>\n",
       "      <td>180.801843</td>\n",
       "      <td>61.142857</td>\n",
       "      <td>8.792627</td>\n",
       "      <td>179.668203</td>\n",
       "      <td>38.317972</td>\n",
       "      <td>21.447005</td>\n",
       "      <td>148.691244</td>\n",
       "      <td>197.152074</td>\n",
       "      <td>493.797235</td>\n",
       "      <td>179.456221</td>\n",
       "      <td>69.755760</td>\n",
       "      <td>7.658986</td>\n",
       "      <td>15.299539</td>\n",
       "      <td>189.714286</td>\n",
       "      <td>198.041475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.081384</td>\n",
       "      <td>6.819088</td>\n",
       "      <td>17.005142</td>\n",
       "      <td>30.804828</td>\n",
       "      <td>4.321871</td>\n",
       "      <td>2.151300</td>\n",
       "      <td>31.523809</td>\n",
       "      <td>7.486735</td>\n",
       "      <td>2.452928</td>\n",
       "      <td>16.134606</td>\n",
       "      <td>27.920023</td>\n",
       "      <td>163.156240</td>\n",
       "      <td>33.776076</td>\n",
       "      <td>5.291527</td>\n",
       "      <td>5.816947</td>\n",
       "      <td>10.066798</td>\n",
       "      <td>4.991792</td>\n",
       "      <td>6.610397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>182.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>195.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>97.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>505.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>199.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>105.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>659.000000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>119.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>211.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "count   217.000000   217.000000            217.000000    217.000000   \n",
       "mean     97.281106    45.534562             88.672811    180.801843   \n",
       "std       9.081384     6.819088             17.005142     30.804828   \n",
       "min      78.000000    33.000000             40.000000    104.000000   \n",
       "25%      90.000000    39.000000             77.000000    161.000000   \n",
       "50%      97.000000    46.000000             93.000000    188.000000   \n",
       "75%     105.000000    52.000000            104.000000    203.000000   \n",
       "max     119.000000    56.000000            112.000000    234.000000   \n",
       "\n",
       "       pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "count            217.000000               217.000000     217.000000   \n",
       "mean              61.142857                 8.792627     179.668203   \n",
       "std                4.321871                 2.151300      31.523809   \n",
       "min               51.000000                 3.000000     112.000000   \n",
       "25%               58.000000                 7.000000     152.000000   \n",
       "50%               61.000000                 9.000000     183.000000   \n",
       "75%               64.000000                11.000000     211.000000   \n",
       "max               73.000000                12.000000     222.000000   \n",
       "\n",
       "       elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "count     217.000000              217.000000                 217.000000   \n",
       "mean       38.317972               21.447005                 148.691244   \n",
       "std         7.486735                2.452928                  16.134606   \n",
       "min        30.000000               17.000000                 118.000000   \n",
       "25%        32.000000               19.000000                 133.000000   \n",
       "50%        36.000000               22.000000                 150.000000   \n",
       "75%        43.000000               24.000000                 163.000000   \n",
       "max        61.000000               25.000000                 177.000000   \n",
       "\n",
       "       scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "count       217.000000         217.000000                 217.000000   \n",
       "mean        197.152074         493.797235                 179.456221   \n",
       "std          27.920023         163.156240                  33.776076   \n",
       "min         130.000000         184.000000                 115.000000   \n",
       "25%         174.000000         349.000000                 149.000000   \n",
       "50%         202.000000         505.000000                 179.000000   \n",
       "75%         223.000000         659.000000                 213.000000   \n",
       "max         235.000000         721.000000                 240.000000   \n",
       "\n",
       "       scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "count                   217.000000      217.000000        217.000000   \n",
       "mean                     69.755760        7.658986         15.299539   \n",
       "std                       5.291527        5.816947         10.066798   \n",
       "min                      59.000000        0.000000          0.000000   \n",
       "25%                      66.000000        3.000000          7.000000   \n",
       "50%                      70.000000        7.000000         14.000000   \n",
       "75%                      72.000000       12.000000         22.000000   \n",
       "max                      86.000000       22.000000         41.000000   \n",
       "\n",
       "       skewness_about.2  hollows_ratio  \n",
       "count        217.000000     217.000000  \n",
       "mean         189.714286     198.041475  \n",
       "std            4.991792       6.610397  \n",
       "min          179.000000     182.000000  \n",
       "25%          187.000000     195.000000  \n",
       "50%          189.000000     199.000000  \n",
       "75%          192.000000     202.000000  \n",
       "max          203.000000     211.000000  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_class2_df =  df.loc[df['class'] == 'saab']\n",
    "binary_class2_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "id": "mVRIM5bB0q9M"
   },
   "outputs": [],
   "source": [
    "new_df = pd.concat([binary_class1_df, binary_class2_df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315
    },
    "id": "c_rmOAT81vcE",
    "outputId": "cbe7bed1-9c24-4bab-da15-075f1a1f532a"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>435.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94.429885</td>\n",
       "      <td>45.301149</td>\n",
       "      <td>82.682759</td>\n",
       "      <td>173.386207</td>\n",
       "      <td>62.289655</td>\n",
       "      <td>7.901149</td>\n",
       "      <td>174.834483</td>\n",
       "      <td>39.218391</td>\n",
       "      <td>21.009195</td>\n",
       "      <td>147.694253</td>\n",
       "      <td>195.016092</td>\n",
       "      <td>471.294253</td>\n",
       "      <td>180.204598</td>\n",
       "      <td>73.448276</td>\n",
       "      <td>6.248276</td>\n",
       "      <td>12.749425</td>\n",
       "      <td>188.760920</td>\n",
       "      <td>194.675862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.289334</td>\n",
       "      <td>5.987612</td>\n",
       "      <td>15.900774</td>\n",
       "      <td>31.536283</td>\n",
       "      <td>7.025218</td>\n",
       "      <td>3.796296</td>\n",
       "      <td>32.775528</td>\n",
       "      <td>7.059695</td>\n",
       "      <td>2.628260</td>\n",
       "      <td>13.623504</td>\n",
       "      <td>31.151292</td>\n",
       "      <td>180.014082</td>\n",
       "      <td>32.504477</td>\n",
       "      <td>7.554348</td>\n",
       "      <td>4.900199</td>\n",
       "      <td>8.974578</td>\n",
       "      <td>6.333136</td>\n",
       "      <td>8.024917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>181.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>86.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>156.500000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>187.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>93.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>72.000000</td>\n",
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       "      <td>11.000000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>196.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>102.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>203.500000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>220.000000</td>\n",
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       "      <td>202.000000</td>\n",
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       "      <td>9.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>201.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>119.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>1018.000000</td>\n",
       "      <td>268.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>211.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "count   435.000000   435.000000            435.000000    435.000000   \n",
       "mean     94.429885    45.301149             82.682759    173.386207   \n",
       "std       9.289334     5.987612             15.900774     31.536283   \n",
       "min      78.000000    33.000000             40.000000    104.000000   \n",
       "25%      86.000000    41.000000             70.000000    148.500000   \n",
       "50%      93.000000    45.000000             77.000000    178.000000   \n",
       "75%     102.000000    51.000000            100.000000    199.000000   \n",
       "max     119.000000    58.000000            112.000000    252.000000   \n",
       "\n",
       "       pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "count            435.000000               435.000000     435.000000   \n",
       "mean              62.289655                 7.901149     174.834483   \n",
       "std                7.025218                 3.796296      32.775528   \n",
       "min               47.000000                 3.000000     112.000000   \n",
       "25%               57.000000                 6.000000     150.000000   \n",
       "50%               62.000000                 7.000000     162.000000   \n",
       "75%               66.000000                 9.000000     203.500000   \n",
       "max              126.000000                52.000000     265.000000   \n",
       "\n",
       "       elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "count     435.000000              435.000000                 435.000000   \n",
       "mean       39.218391               21.009195                 147.694253   \n",
       "std         7.059695                2.628260                  13.623504   \n",
       "min        26.000000               17.000000                 118.000000   \n",
       "25%        32.000000               19.000000                 138.000000   \n",
       "50%        40.000000               20.000000                 146.000000   \n",
       "75%        45.000000               23.000000                 159.000000   \n",
       "max        61.000000               29.000000                 177.000000   \n",
       "\n",
       "       scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "count       435.000000         435.000000                 435.000000   \n",
       "mean        195.016092         471.294253                 180.204598   \n",
       "std          31.151292         180.014082                  32.504477   \n",
       "min         130.000000         184.000000                 115.000000   \n",
       "25%         171.000000         330.000000                 156.500000   \n",
       "50%         185.000000         399.000000                 177.000000   \n",
       "75%         220.000000         624.000000                 202.000000   \n",
       "max         288.000000        1018.000000                 268.000000   \n",
       "\n",
       "       scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "count                   435.000000      435.000000        435.000000   \n",
       "mean                     73.448276        6.248276         12.749425   \n",
       "std                       7.554348        4.900199          8.974578   \n",
       "min                      59.000000        0.000000          0.000000   \n",
       "25%                      68.000000        2.000000          6.000000   \n",
       "50%                      72.000000        5.000000         11.000000   \n",
       "75%                      78.500000        9.000000         19.000000   \n",
       "max                     127.000000       22.000000         41.000000   \n",
       "\n",
       "       skewness_about.2  hollows_ratio  \n",
       "count        435.000000     435.000000  \n",
       "mean         188.760920     194.675862  \n",
       "std            6.333136       8.024917  \n",
       "min          178.000000     181.000000  \n",
       "25%          183.000000     187.000000  \n",
       "50%          189.000000     196.000000  \n",
       "75%          193.000000     201.000000  \n",
       "max          203.000000     211.000000  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z9q4tv6R1yM3",
    "outputId": "0cdeff48-09f9-4f89-a8bc-b59e51b314f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bus     218\n",
       "saab    217\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "id": "ePSYueuL13Ug"
   },
   "outputs": [],
   "source": [
    "# Encoding bus as 0 and saab as 1\n",
    "new_df['class'].replace('bus', 0, inplace=True)\n",
    "new_df['class'].replace('saab', 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "id": "B2G2hKwV2h49"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, shuffle=True, random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lvRZUnmy4t72",
    "outputId": "be29a72c-3c2d-434d-c41b-e2bf859dbb65"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 20)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "id": "vjgL3ZMY25h3"
   },
   "outputs": [],
   "source": [
    "# Standardizing the features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "id": "6sxU4fiL3DIN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.16750341 0.1505305  0.10637706 0.09805535 0.08735434 0.06530053\n",
      " 0.0568136  0.05300465 0.04473913 0.04026384]\n"
     ]
    }
   ],
   "source": [
    "# performing PCA\n",
    "pca = PCA(n_components=10)\n",
    "principalComponents = pca.fit_transform(X_scaled)\n",
    "print(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7FXm0ARF3FK3",
    "outputId": "6d754eca-2463-436c-b3d3-3485eb1f5cad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train one LR with PCA\n",
    "lr1 = LogisticRegression()\n",
    "lr1.fit(principalComponents, y_train)\n",
    "\n",
    "#Train one LR without PCA (for comparison)\n",
    "lr2 = LogisticRegression()\n",
    "lr2.fit(X_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "id": "2j8GabVr3WaA"
   },
   "outputs": [],
   "source": [
    "# perform the same transforms on test data\n",
    "X_scaled_test = scaler.transform(X_test)\n",
    "test_principalComponents = pca.transform(X_scaled_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Eo7O4RuQ3cT0",
    "outputId": "ce428b00-a799-406a-e02a-3eda1b192f3d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with PCA:  0.8\n",
      "Accuracy without PCA:  0.855\n"
     ]
    }
   ],
   "source": [
    "yhat_test_pca = lr1.predict(test_principalComponents)\n",
    "yhat_test = lr2.predict(X_scaled_test)\n",
    "print(\"Accuracy with PCA: \", accuracy_score(y_test, yhat_test_pca))\n",
    "print(\"Accuracy without PCA: \", accuracy_score(y_test, yhat_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fQI85BeZ5_3L"
   },
   "source": [
    "Notice that out of 18 dimensions (columns), 15 dimensions can get an accuraccy as good as using the initial columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is projection an only method for dimensionality reduction? Let's consider this case of swiss roll dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "sr_points, sr_color = datasets.make_swiss_roll(n_samples=1500, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "        alert(\n",
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       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
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       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
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       "                'Performance may be slow.';\n",
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       "    }\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.add_subplot(111, projection=\"3d\")\n",
    "fig.add_axes(ax)\n",
    "ax.scatter(\n",
    "    sr_points[:, 0], sr_points[:, 1], sr_points[:, 2], c=sr_color, s=50, alpha=0.8\n",
    ")\n",
    "ax.set_title(\"Swiss Roll in Ambient Space\")\n",
    "ax.view_init(azim=-66, elev=12)\n",
    "_ = ax.text2D(0.8, 0.05, s=\"n_samples=1500\", transform=ax.transAxes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Can we transform the data in a way such that classification of points in different colors becomes easier? Surely projection won't work.\n",
    "### (Hint: What if this swiss roll can be unrolled in 2D space !? One can easily see the decision boundaries in the unrolled version)\n",
    "\n",
    "### Let's see another complex distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "vertical_swiss_data=pd.read_csv('vertical_swiss_roll_dataset.txt',sep='\\s+',header=None).to_numpy()\n",
    "vertical_swiss_color =[0]*1600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in np.argwhere(vertical_swiss_data[:,2]>0):\n",
    "    vertical_swiss_color[i[0]]=1\n",
    "\n",
    "for i in np.argwhere(vertical_swiss_data[:,2]<=0):\n",
    "    vertical_swiss_color[i[0]]=2\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.add_subplot(111, projection=\"3d\")\n",
    "fig.add_axes(ax)\n",
    "ax.scatter(\n",
    "    vertical_swiss_data[:, 0], vertical_swiss_data[:, 1], vertical_swiss_data[:, 2], c=vertical_swiss_color, s=50, alpha=0.8\n",
    ")\n",
    "ax.set_title(\"Decision boundary may not always be simpler with lower dimensions\")\n",
    "ax.view_init(azim=-66, elev=12)\n",
    "_ = ax.text2D(0.8, 0.05, s=\"n_samples=1600\", transform=ax.transAxes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "PCA.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}