{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6a764d92",
   "metadata": {},
   "source": [
    "# Mixture Models\n",
    "\n",
    "One common objective is to find similarities in data and cluster it. There are several heuristic methods to cluster them. We are going to focus on a few strongly theoretically motivated methods. Other methods use heuristics to identify similarities in data and are mentioned in the end.\n",
    "\n",
    "For the purposes of this example, we will generate a fake dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fce4d8e8",
   "metadata": {},
   "source": [
    "We start by loading the necessary Python modules. If you have not yet installed them, run the following cell to install them with pip:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "44ca341e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in /home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages (1.19.2)\n",
      "Requirement already satisfied: scikit-learn in /home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages (0.24.2)\n",
      "Requirement already satisfied: pandas in /home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages (1.1.5)\n",
      "Requirement already satisfied: matplotlib in /home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages (3.3.4)\n",
      "Requirement already satisfied: scipy>=0.19.1 in /home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages (from scikit-learn) (1.5.2)\n",
      "Requirement already satisfied: joblib>=0.11 in /home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages (from scikit-learn) (1.0.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages (from scikit-learn) (2.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.2 in /home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages (from pandas) (2021.3)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages (from matplotlib) (3.0.4)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages (from matplotlib) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages (from matplotlib) (8.3.1)\n",
      "Requirement already satisfied: six>=1.5 in /home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy scikit-learn pandas matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "300cf8d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.mixture import GaussianMixture, BayesianGaussianMixture\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ecd6a69",
   "metadata": {},
   "source": [
    "Let's generate the fake data now to have something to cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4959a292",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_clusters(mu: np.ndarray, sigma: np.ndarray, N: int) ->np.ndarray:\n",
    "    assert len(mu) == len(sigma)\n",
    "    assert N > 1\n",
    "    D = len(mu[0].shape)\n",
    "    data = np.concatenate([np.random.default_rng().multivariate_normal(mean=mu_k, cov=sigma_k, size=N)\n",
    "                           for mu_k, sigma_k in zip(mu, sigma)], axis=0)\n",
    "    source = np.concatenate([k*np.ones([N, 1]) for k in range(len(mu))], axis=0)\n",
    "    return np.concatenate([data, source], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "82929490",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/daniloefl/miniconda3/envs/mltut/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: covariance is not positive-semidefinite.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "data = generate_clusters(mu=[np.array([5.0, -2.0]),\n",
    "                             np.array([1.0, 5.0]),\n",
    "                             np.array([-5.0, -1.0])],\n",
    "                         sigma=[np.array([[0.1, 0.2],\n",
    "                                          [0.2, 0.1]]),\n",
    "                                np.array([[1.0, 0.5],\n",
    "                                          [0.5, 1.0]]),\n",
    "                                np.array([[2.0, 0.0],\n",
    "                                          [0.0, 5.0]])],\n",
    "                         N=1000)\n",
    "data = pd.DataFrame(data, columns=[\"x\", \"y\", \"source\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8295e8a",
   "metadata": {},
   "source": [
    "Let's print out the dataset read first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "024fb65a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.653241</td>\n",
       "      <td>-1.382435</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.623728</td>\n",
       "      <td>-2.254146</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.313702</td>\n",
       "      <td>-1.596076</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.830723</td>\n",
       "      <td>-1.521429</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.716383</td>\n",
       "      <td>-1.686052</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>-5.327421</td>\n",
       "      <td>-0.196162</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>-6.919531</td>\n",
       "      <td>-1.834852</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>-6.075992</td>\n",
       "      <td>-0.312784</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>-3.835365</td>\n",
       "      <td>5.404520</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>-3.402078</td>\n",
       "      <td>-3.187637</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             x         y  source\n",
       "0     5.653241 -1.382435     0.0\n",
       "1     4.623728 -2.254146     0.0\n",
       "2     5.313702 -1.596076     0.0\n",
       "3     4.830723 -1.521429     0.0\n",
       "4     4.716383 -1.686052     0.0\n",
       "...        ...       ...     ...\n",
       "2995 -5.327421 -0.196162     2.0\n",
       "2996 -6.919531 -1.834852     2.0\n",
       "2997 -6.075992 -0.312784     2.0\n",
       "2998 -3.835365  5.404520     2.0\n",
       "2999 -3.402078 -3.187637     2.0\n",
       "\n",
       "[3000 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c178424",
   "metadata": {},
   "source": [
    "We can plot this fairly easily using Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e63b38c5",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\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",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\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.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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\", \"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.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = 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, ax = plt.subplots(figsize=(8, 8))\n",
    "data.plot.scatter(x=\"x\", y=\"y\", c=\"source\", colormap='viridis', ax=ax)\n",
    "ax.set(xlabel=\"x\", ylabel=r\"y\", title=\"\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a376636d",
   "metadata": {},
   "source": [
    "There are several ways of finding similarities in the data. The Gaussian Mixture Model assumes that the data has been produced exactly as in this example. For each sample, the procedure assumed to be used for the generation of each sample is the following:\n",
    "  * a random integer is chosen according to a discrete probability distribution, which identifies to which cluster the sample belongs to (this is the `source` variable in the generation procedure above);\n",
    "  * that random integer is assumed to be used to choose the mean and covariance matrix for a Gaussian random variable, which is then used to produce the observed sample.\n",
    " \n",
    "Out objective here is then to find out the probability of producing a sample for each cluster (the probabilities with which a sample belongs to one cluster instead of the others), the means and covariance matrices for the model. Using Bayes' theorem, one may write down the posterior probability for the true means, covariances and cluster probabilities ($\\theta$) given the data ($\\x$), but it is very hard to calculate the parameters from it. Let us call the cluster that a specific sample belongs to, $z$.\n",
    "\n",
    "The true posterior would look like this:\n",
    "\n",
    "$p(\\theta|x) = \\frac{p(x|\\theta) p(\\theta)}{p(x)}$\n",
    "\n",
    "We assume $p(\\theta)$ is constant for all $\\theta$ and we know $p(x)$ is independent of $\\theta$ (because it is just the probability we obtained the existing data). We also know that $p(x|\\theta) = \\sum_z p(x,z|\\theta)$, since summing over all $z$, we obtain a total probability 1 of getting any $z$.\n",
    "\n",
    "$p(\\theta|x) \\propto \\sum_z p(x,z|\\theta) = \\sum_z p(x|\\theta,z) p(z|\\theta)$\n",
    "\n",
    "Where we have expanded it on $z$ using $p(a,b) = p(a|b)p(b)$.\n",
    "\n",
    "Now if we want to find the most probable $\\theta$ from this posterior (the \"maximum a posteriori\", or \"MAP\"), we just need to maximize the right-hand side of the last equation. This is very hard to do, but we can use the Expectation-Maximization (EM) algorithm, on which we iteratively improve our probability estimates. This method works as follows.\n",
    "\n",
    "First we define the log-likelihood $LL(\\theta|z) = \\log L(\\theta|z) = \\log p(x|\\theta,z) = \\sum_{k=\\text{sample}} \\log p(x_k|\\theta,z_k)$. Since we are assuming that the probability for each data sample $x_k$ is a Gaussian distribution, each of the terms in the sum is a Gaussian probability, so if we know $\\theta$ and $z_k$, this full sum can be calculated. The issue is that we do not know $z_k$ (that is the whole point!).\n",
    "\n",
    "We start by assuming $\\theta=\\theta_0$ for some random initial value of the parameters. The EM method iterates on two steps:\n",
    "\n",
    "  1. Calculate the *average* value of $LL(\\theta|z)$ over $z$ assuming the current $\\theta_i$ to calculate $p(z|\\theta)$, that is: $Q(\\theta) = \\sum_z LL(\\theta|z) p(z|\\theta_i)$. This means we calculate the weighted sum of the log-likelihood a point belongs to each Gaussian, weighted by the probability that that Gaussian was the reason that sample was generated. This avoids needing to know the correct $z_k$, since we just sum over all possibilities. The weights of this weighted sum are simply one of the parameters $\\theta$, namely, the cluster probabilities.\n",
    "  \n",
    "  2. Find the $\\theta$ which maximizes $Q(\\theta)$ and use this as the next $\\theta_{i+1}$.\n",
    "\n",
    "These two steps are iterated to improve the $\\theta$ for several iterations. It can be shown that an improvement on $Q$ following this procedure leads to a new $\\theta$ that improves the posterior probability $p(\\theta|x)$ above (see https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm#Proof_of_correctness ).\n",
    "\n",
    "We will not write all of this from scratch. Instead, we use the `GaussianMixture` function in `scikit-learn`, which has this ready for us. It is nevertheless important to understand the assumptions made here: the underlying samples are assumed to come from a discrete combination of Gaussians. Other mixture models are possible using other underlying distributions, but note that this method will not work if the samples do not follow this generative pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0837b3ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmm = GaussianMixture(n_components=3, covariance_type=\"full\", max_iter=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8798f857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianMixture(max_iter=20, n_components=3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmm.fit(data.loc[:, [\"x\", \"y\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e928f498",
   "metadata": {},
   "source": [
    "Now that we have model fit, we can even check the fit parameters $\\theta$, which include the means, the covariance matrices and the probabilities given to each Gaussian in the mixture:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb5796e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-5.00533402, -1.00291721],\n",
       "       [ 4.98736078, -1.99324473],\n",
       "       [ 0.99263505,  4.95370197]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmm.means_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "182904d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1.97696177, -0.14298004],\n",
       "        [-0.14298004,  5.08432238]],\n",
       "\n",
       "       [[ 0.19127883,  0.09340703],\n",
       "        [ 0.09340703,  0.18650429]],\n",
       "\n",
       "       [[ 1.00348273,  0.47652348],\n",
       "        [ 0.47652348,  0.98154818]]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmm.covariances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2faa1f72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.33306256, 0.33333333, 0.33360411])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmm.weights_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b54001fc",
   "metadata": {},
   "source": [
    "We can predict to which cluster each sample belongs now by selecting the cluster for each sample that maximizes the probability $p(z|\\theta,x)$, now that we know $\\theta$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cc8fc1f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "guess = gmm.predict(data.loc[:, [\"x\", \"y\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68560fb4",
   "metadata": {},
   "source": [
    "Let's plot it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "88982b21",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[:, \"guess\"] = guess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "333581b5",
   "metadata": {},
   "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",
       "    // 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",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\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.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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\", \"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.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = 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=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10), ncols=2)\n",
    "data.plot.scatter(x=\"x\", y=\"y\", c=\"guess\", colormap='viridis', ax=ax[0])\n",
    "data.plot.scatter(x=\"x\", y=\"y\", c=\"source\", colormap='viridis', ax=ax[1])\n",
    "ax[0].set(xlabel=\"x\", ylabel=r\"y\", title=\"Guessed source\")\n",
    "ax[1].set(xlabel=\"x\", ylabel=r\"y\", title=\"True association\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b1363ec",
   "metadata": {},
   "source": [
    "Note that if the sample clusters were not \"blobs\" of data, but were in concentric circles, the assumption of this method would be false and the method would simply not work well. This is why it is important to understand the underlying assumptions made in the method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7076f779",
   "metadata": {},
   "source": [
    "## K-Means\n",
    "\n",
    "Another common method used for clustering is the K-Means, on which one simply tries to find the cluster centers which minimize in-cluster distances (in an Euclidean sense) while maximizing distances between the centers. It can be shown that this method is a special case of the Gaussian Mixture Model when the covariance matrices are diagonal, which would mean that within each blob, there is no correlation between the variables (see https://en.wikipedia.org/wiki/K-means_clustering#Gaussian_mixture_model and references).\n",
    "\n",
    "While this is an approximation of the GMM model, it is still a very useful approach, since there are faster algorithms to achieve the clustering than for GMMs.\n",
    "\n",
    "The scikit-learn module also provides an easy-to-use implementation of this algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f280e1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3)\n",
    "kmeans.fit(data.loc[:, [\"x\", \"y\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "950a7eec",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[:, \"guess_kmeans\"] = kmeans.predict(data.loc[:, [\"x\", \"y\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "16a56489",
   "metadata": {},
   "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",
       "    // 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",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\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.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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\", \"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.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = 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=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10), ncols=2)\n",
    "data.plot.scatter(x=\"x\", y=\"y\", c=\"guess_kmeans\", colormap='viridis', ax=ax[0])\n",
    "data.plot.scatter(x=\"x\", y=\"y\", c=\"source\", colormap='viridis', ax=ax[1])\n",
    "ax[0].set(xlabel=\"x\", ylabel=r\"y\", title=\"Guessed source using K-Means\")\n",
    "ax[1].set(xlabel=\"x\", ylabel=r\"y\", title=\"True association\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fe3cad2",
   "metadata": {},
   "source": [
    "## Variational Inference on Gaussian Mixture Models\n",
    "\n",
    "While the Gaussian Mixture Model presented beforehand had a very strong theoretical background, it still assumes that there is a single correct value for the Gaussian parameters. This may not be the case if the Gaussian model is only approximate (as is very often the case!).\n",
    "\n",
    "One improvement to the Gaussian Mixture Models to allow for some uncertainty on the means and covariances would be to assume that also those quantities are random variables which came from some other probability distribution (as with the cluster identification, $z$ previously). In this case, we would obtain an uncertainty for our Gaussian parameters themselves!\n",
    "\n",
    "Optimizing this model becomes even more complicated as the derivation shown before and if we wanted to be fully general, we would need to use very slow algorithms, such as Monte-Carlo sampling to obtain uncertainties with the least amount of extra assumptions. This is often undesirable, since we need fast clustering and often Monte-Carlo sampling is very slow with even more data! An alternative is to assume some underlying prior probability for the means and covariances and find those parameters as well in an optimization algorithm. This is what is done in Variational Inference. Further details can be seen in Bishop (2006), or in a more practical approach, here: https://scikit-learn.org/stable/modules/mixture.html#bgmm\n",
    "\n",
    "We can also easily use this method in our toy data, taking the code from scikit-learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0a8642bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgmm = BayesianGaussianMixture(n_components=3)\n",
    "bgmm.fit(data.loc[:, [\"x\", \"y\"]])\n",
    "data.loc[:, \"guess_bgmm\"] = bgmm.predict(data.loc[:, [\"x\", \"y\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ccbf4019",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
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       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
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       "mpl.figure.prototype._init_canvas = function () {\n",
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       "    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",
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       "        'keydown',\n",
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       "    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",
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       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
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       "            canvas.setAttribute(\n",
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       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
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       "\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",
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       "\n",
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       "\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",
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       "\n",
       "    // Disable right mouse context menu.\n",
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       "        return false;\n",
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       "\n",
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       "        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",
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       "            return fig.toolbar_button_onclick(name);\n",
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       "\n",
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       "\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",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\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.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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\", \"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.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = 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": {
      "text/html": [
       "<img 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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10), ncols=2)\n",
    "data.plot.scatter(x=\"x\", y=\"y\", c=\"guess_bgmm\", colormap='viridis', ax=ax[0])\n",
    "data.plot.scatter(x=\"x\", y=\"y\", c=\"source\", colormap='viridis', ax=ax[1])\n",
    "ax[0].set(xlabel=\"x\", ylabel=r\"y\", title=\"Guessed source using Variation Inference GMM\")\n",
    "ax[1].set(xlabel=\"x\", ylabel=r\"y\", title=\"True association\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72508ead",
   "metadata": {},
   "source": [
    "## Other heuristic approaches\n",
    "\n",
    "Many other heuristic approaches for clustering have been developed. Some of them rely on a strong foundation in statistics, others take a more practical approach and make assumptions on how the data samples are expected to be distributed in the $xy$ scatter plot. The following methods deserve a look if you are not familiar with them already:\n",
    "\n",
    "  * DBSCAN (see https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html ). Uses the euclidean distances between the samples to define if a sample belongs in a cluster or another. The assumption is related to GMMs. While the GMM requires fixing the number of clusters, DBSCAN relies on the maximum distance parameter do define which samples belong to a cluster.\n",
    "  * Agglomerative clustering (see https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html#sklearn.cluster.AgglomerativeClustering). It uses the nearest neighbour to each sample to define which samples belong together in the same cluster. Since the cluster association is topological, it does not assume the samples make a \"blob\" of some sort, as in the case of GMM. One could for example have samples close to one another making a circle in the $xy$-scatter plane and those samples would belong to the same cluster (depending on the definition of closeness and other parameters of the algorithm).\n",
    "  * Spectral clustering (see https://scikit-learn.org/stable/modules/clustering.html#spectral-clustering). Uses K-means on another representation of the data. This representation of the data transforms the data points from the $xy$ space shown before into a space where connectedness of nearest neighbours is the criteria for similarity. For the mathematical foundation, this document may be useful: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.165.9323&rep=rep1&type=pdf\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "488df5eb",
   "metadata": {},
   "source": [
    "### Contact us at the EuXFEL Data Analysis group at any time if you need help analysing your data!\n",
    "\n",
    "#### Data Analysis group: da@xfel.eu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "941c7cb8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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