Mixture Models.ipynb

       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"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([[ 4.98067655, -2.00779217],\n",
       "       [-4.96087396, -1.06118412],\n",
       "       [ 1.02445445,  5.03277158]])"
      ]
     },
     "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([[[ 0.19237869,  0.09361426],\n",
       "        [ 0.09361426,  0.20047796]],\n",
       "\n",
       "       [[ 1.95114689, -0.01346582],\n",
       "        [-0.01346582,  5.10415062]],\n",
       "\n",
       "       [[ 0.91835643,  0.48388716],\n",
       "        [ 0.48388716,  1.03723078]]])"
      ]
     },
     "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([0.33333333, 0.33282526, 0.33384141])"
      ]
     },
     "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": {
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       "    canvas_div.setAttribute(\n",
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       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
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       "    canvas.classList.add('mpl-canvas');\n",
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       "\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",
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       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
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       "\n",
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       "            canvas_div.style.width = width + 'px';\n",
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       "        canvas_div.focus();\n",
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       "\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",
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       "\n",
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       "        };\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",