day1/DimensionalityReduction.ipynb

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    "# Introduction to Dimensionality Reduction\n",
    "\n",
    "#### Bryan Scott, CIERA/Northwestern\n",
    "version 0.1, August 2023\n",
    "\n",
    "Presented at LSSTC Data Science Fellowship Program Session 19: Machine Learning\n",
    "\n",
    "<img src=\"images/three_body.png\" alt=\"drawing\" width=\"400\"/>"
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    "### Learning Outcomes \n",
    "\n",
    "At the end of this lecture & tutorial, you should be able to \n",
    "\n",
    "1. Relate dimensionality reduction in the landscape of other learning techniques\n",
    "2. Solve problems with dimensionality reduction (the \"why\" of dimensionality reduction)\n",
    "3. Implement PCA on SDSS images and use t-SNE to produce a lower dimension representation of COSMOS photometry. "
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    "### Further Reading\n",
    "\n",
    "- $\\textit{Introduction to Statistical Learning}$, by James, Witten, Hastie & Tibshirani\n",
    "- $\\textit{Mapping the Galaxy-Color Redshift Relation: Optimal Photometric Redshift Calibration Strategies for Cosmology Surveys}$, Masters, et al., https://arxiv.org/pdf/1704.06665.pdf\n",
    "- $\\textit{Visualizing Data using t-SNE}$, van der Maaten & Hinton https://jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf"
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    "## Perspective I: Dimensionality Reduction as Learning\n",
    "\n",
    "One way of thinking about the learning problem is to infer the probability distribution over some features:\n",
    "\n",
    "$$\n",
    "Pr(X)\n",
    "$$\n",
    "\n",
    "where X consists of d $\\textit{features}$ over N observations - $\\{\\vec{x}_1, \\vec{x}_2, ..., \\vec{x}_N \\}$. Here the subscript indicates the observation, with $\\vec{x}$ a vector in $\\mathbb{R}^d$.\n",
    "\n",
    "This is a problem of characterizing a probability distribution in a potentially very high number of dimensions. We will also study the supervised learning problem this week, which is generally simpler because it typically involves making a point estimate $\\mu(x)$ of y for some set of data labelled by y. In the unsupervised case, we are trying to fully characterize the distribution Pr(X).\n",
    "\n",
    "In this interpretation, the task of dimensionality reduction is to find a lower dimensional, or $\\textit{compressed}$, representation of Pr(X) that maintains the (some of the) structure of the full Pr(X) in the higher dimension space. "
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    "## Perspective II: Dimensionality Reduction as Visualization\n",
    "\n"
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   "source": [
    "%matplotlib nbagg"
   ]
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    "import numpy as np\n",
    "from matplotlib.animation import FuncAnimation\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "X = []\n",
    "Y = []\n",
    "Z = []\n",
    "\n",
    "\n",
    "def update(t):\n",
    "    ax.cla()\n",
    "\n",
    "    x = np.cos(t/10)\n",
    "    y = np.sin(t/10)\n",
    "    z = t/10\n",
    "\n",
    "    X.append(x)\n",
    "    Y.append(y)\n",
    "    Z.append(z)\n",
    "\n",
    "    ax.scatter(x, y, z, s = 100, marker = 'o')\n",
    "    ax.plot(X, Y, Z)\n",
    "\n",
    "    ax.set_xlim(-2, 2)\n",
    "    ax.set_ylim(-2, 2)\n",
    "    ax.set_zlim(-1, 10)"
   ]
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       "        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",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://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",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // 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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "\n",
    "ani = FuncAnimation(fig = fig, func = update, frames = 100, interval = 100, repeat = False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70ed79d8",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Why do we need (or want) to do this?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e2ecfdc",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "($\\textit{Discuss with your neighbor.}$)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce049ab5",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "There are a few reasons you might (want to) do this:\n",
    "\n",
    "1. High dimension data is computationally challenging. \n",
    "2. Many algorithms have poor scaling behavior in higher dimensions - generalize poorly when applied to high dimensional data. \n",
    "3. To enable interpretation through visualization. I can see relationships in 2D, 3D, or 4D, but what about 5D, 10D or 20D?\n",
    "4. Many datasets are sparse in high dimensions or some dimensions may 'matter' more than others (more on this in a minute).\n",
    "\n",
    "The first two reasons lead to a third interpretation of dimensionality reduction as a sort of $\\textit{feature selection}$ or $\\textit{feature engineering}$ problem."
   ]
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    "In other words. Worry about the data. \n",
    "\n",
    "And worry about your features. \n",
    "\n",
    "Choosing the right features can matter a lot for simplifying the learning task and making it tractable.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28283beb",
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   "source": [
    "## Why does this work (at all)?\n",
    "\n",
    "The key intuitive insight that makes dimensionality reduction possible is the idea of $\\textit{sparsity}$. Basically as data becomes increasingly high dimensional, the data samples smaller and smaller regions of the high dimensional space it lives in. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df872bc8",
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   "source": [
    "## Dimensionality Reduction Techniques\n",
    "\n",
    "Because there is no notion of 'ground truth' in the unsupervised learning context - we only have a sampling from from some distribution Pr(X) - it is hard to quantify the success or failure of a technique. This has led to a large proliferation of dimensionality reduction schemes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f551443",
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   "source": [
    "I think of this as a feature and not a bug. We have lots of options for solving the dimensionality reduction problem and some should work better than others depending on the goals we have in characterizing the higher dimensional dataset. We also should have different visualizations with which to compare."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17ed40d9",
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   "source": [
    "## Techniques we will cover today:\n",
    "\n",
    "1. Principal Component Analysis \n",
    "2. t-distributed Stochastic Neighbor Embedding (t-SNE)\n",
    "3. Self-Organizing Maps"
   ]
  },
  {
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   "id": "2ab235d1",
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   "source": [
    "## First Technique: Principal Component Analysis (PCA) \n",
    "\n",
    "The principal components of a set of data $\\{\\vec{x}_1, \\vec{x}_2, ..., \\vec{x}_N \\}$ in $\\mathbb{R}^p$ are the sequence of best linear approximations of rank $q \\le p$. "
   ]
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  {
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   "id": "f24362cd",
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   "source": [
    "By linear approximation, we mean the following:\n",
    "\n",
    "$$\n",
    "f(\\lambda) = \\mu + V_q \\lambda\n",
    "$$\n",
    "\n",
    "where $\\lambda$ is a length q vector of parameters and $V_q$ is a $p \\times q$ matrix with q orthogonal unit vectors as columns. $\\mu$ is a location in $\\mathbb{R}^p$."
   ]
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  {
   "cell_type": "markdown",
   "id": "5a00eb35",
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   "source": [
    "It turns out the $\\lambda$ can be written in terms of V_q. This allows us to define the notion of \"best\" as an optimization of the $V_q$. \n",
    "\n",
    "$$\n",
    "min_{V_q} \\Sigma_{i=1}^N || (x_i - \\bar{x}) - V_q V_q^T (x_i - \\bar{x}) ||^2\n",
    "$$\n",
    "\n",
    "The notation $H_q = V_q V_q^T$ is sometimes used. $H_q$ is a projection of the data onto a basis spanned by the columns of this matrix $H_q$. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a20a4ca0",
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   "source": [
    "The proceeding optimization problem can be solved to decompose $X = (x_i - \\bar{x})$ in terms of the $V_q$ matrices. A formal proof can be found in many linear algebra and ML texts (e.g. $\\textit{Introduction to Statistical Learning}$). \n",
    "\n",
    "The result is that if we take the data and stack it into the rows of an $N \\times p$ matrix, called X, then the $\\textit{singular value decomposition}$ of X: \n",
    "\n",
    "$$\n",
    "X = U D V^T\n",
    "$$"
   ]
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  {
   "cell_type": "markdown",
   "id": "713bee64",
   "metadata": {
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   "source": [
    "gives the principal components. U is a $N \\times p$ matrix that spans the column space. $V^T$ is a $p \\times p$ matrix that spans the row space. D is a important diagonal object whose values are called the singular values. They are related to the variance of the data. In fact, the covariance of X is given by\n",
    "\n",
    "$$\n",
    "Cov(X) = V D^2 V^T\n",
    "$$\n",
    "\n",
    "and \n",
    "\n",
    "$$\n",
    "Var(X v_i) = d_i^2/N\n",
    "$$\n",
    "\n",
    "Where the $d_i$ are the diagonal entries of D and $v_i$ is the first principal component (column of V). We'll come back to this in a second. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d2dad11",
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   "source": [
    "We started this process with the goal of finding a matrix that projects some high dimension data into small number of dimensions. We found this matrix in terms of the singular value decomposition of the data, X. This gives the first interpretation of PCA as primarily about dimensionality reduction.\n",
    "\n",
    "What we noticed on the last step is that the PCA procedure also organizes the principal components by the variance of the data. In fact, PCA picks out the \"principal components\" as exactly those vectors that maximize the variance in the data. This makes it very useful for constructing models of the data as weighted sums of these special vectors. \n",
    "\n",
    "We will explore this more with SDSS images in the tutorial."
   ]
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   "id": "6e77ec8d",
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   "source": [
    "## Second Technique: t-distributed Stochastic Neighbor Embedding (t-SNE)"
   ]
  },
  {
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   "id": "1b82cde5",
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   "source": [
    "### Stochastic Neighbor Embedding\n",
    "\n",
    "$\\textit{This section is based on the remarkably readable original t-SNE paper.}$\n",
    "\n",
    "The goal of dimensionality reduction is often for visualizing data so that we can interpret structures or patterns. We saw this in the PCA example where we can read off the structure of the data by visualizing its variance maximizing components."
   ]
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  {
   "cell_type": "markdown",
   "id": "1f415bd1",
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   "source": [
    "The key difference between Stochastic Neighbor Embedding and PCA is that PCA focuses only on keeping dissimilar points in the high dimensional space far apart without also ensuring that similar points are well represented in the lower dimensional space. This makes interpretation hard - we want to know if points are the same as much as we want to know if they're different! \n",
    "\n",
    "Wouldn't it be nice if the dimensionality reduction technique could come with some additional structure or machinary for interpreting the data?"
   ]
  },
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   "id": "c0099dbc",
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   "source": [
    "One technique that aims at this is called Stochastic Neighbor Embedding. Here we take a datset with high dimensional components $\\mathscr{X} = \\{x_1, x_2, ..., x_n\\}$ and convert it to some two dimensional data $\\mathscr{Y} = \\{y_1, y_2, ..., y_n\\}$. We call $\\mathscr{Y}$ a map and its components - map points. "
   ]
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   "source": [
    "<img src=\"images/MNIST.png\" alt=\"drawing\" width=\"500\"/> <img src=\"images/tSNE.png\" alt=\"drawing\" width=\"500\"/>"
   ]
  },
  {
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   "source": [
    "SNE's key insight is to treat distances in the lower and higher dimensional spaces probabilistically. What that means is that rather than, as we'll see shortly, looking for a mapping that minimizes distances, we'll instead compare the distributions of data points. The most common way of doing this - and shared between variants of SNE techniques - is to consider the distributions of the similarity of points in the original high dimensional dataset $\\mathscr{X}$ to be given by $p(j|i)$ and the distibution of similiarity of points in the lower dimensional map $\\mathscr{Y}$ as $q(j|i)$. "
   ]
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   "source": [
    "These are conditional probabilities for the jth point given a fixed ith. \n",
    "\n",
    "$$\n",
    "KL_{ij} = \\Sigma_i \\Sigma_j p(j|i) log \\frac{p(j|i)}{q(j|i)}\n",
    "$$\n",
    "\n",
    "which is called the Kullback-Leibler divergence. This is the \"most natural\" distance between two probability distributions. We will return to the KL divergence in future sessions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7931d890",
   "metadata": {
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    }
   },
   "source": [
    "The two conditional probabilities $p(j|i)$, $q(j|i)$ represent the similarity of nearby datapoints to one another. This is typically a gaussian in both the higher and lower dimensional space, but could be any function (we'll come back to this in a second!), in the Gaussian case, we have:\n",
    "\n",
    "$$\n",
    "p_{j|i} = \\frac{e^\\frac{-|| x_i - x_j ||^2}{2 \\sigma^2}}{\\Sigma_ke^\\frac{-|| x_i - x_k ||^2}{2 \\sigma^2}}\n",
    "$$\n",
    "\n",
    "In any case, this means that the parameters of the distributions used to represent how close two particles are has at least one free parameter - the variance $\\sigma$ - and will also make use of the distances $x_i - x_j$ and $y_i - y_j$ in the dataset and in the map. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22af0899",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Some brief technical comments: \n",
    "\n",
    "- different techniques exist for picking the variance of these gaussians. This is controlled in sklearn tSNE by the $\\textit{perplexity}$ parameter.\n",
    "\n",
    "- Training proceeds by explicitly calculating derivatives of the KL divergence as the map is varied. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f283a6be",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Putting the t in tSNE\n",
    "\n",
    "t-SNE proceeds in roughly the same way as all other SNE based techniques. The main difference is that rather than using a Gaussian assumption for the similarities in the map (lower dimensional representation) it instead uses a Student's t-distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6fa94b7d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import t, norm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = 2.74"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b44c1eb",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
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       "        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",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://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",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // 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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "x = np.linspace(t.ppf(0.01, df), t.ppf(0.99, df), 100)\n",
    "ax.plot(x, t.pdf(x, df), lw=5, alpha=0.6, label='t pdf')\n",
    "\n",
    "x2 = np.linspace(norm.ppf(0.000001), norm.ppf(0.999999), 100)\n",
    "ax.plot(x2, norm.pdf(x2),'r-', lw=5, alpha=0.6, label='norm pdf')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c33daa",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "What is the difference between the Student's t-distribution and the Gaussian, why does it matter here? ($\\textit{Discuss with your neighbor.}$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7164628c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://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",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // 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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcf39579e80>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "ax.plot(x2, t.pdf(x, df)/norm.pdf(x2),'r-', lw=5, alpha=0.6, label='norm pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8a1267d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Using the t-distribution allows you to \"trade\" extra probability in the 'fat tail' of the lower dimensional space for the larger distances in the higher dimensional space that would normally be assigned zero similarity at the tails. By better mapping large distances in high-d to higher probabilities in lower-d spaces, t-SNE solves the 'crowding problem' where large distances are incorrectly treated in other SNE techniques. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30c3458e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Third Technique: Self-Organizing Maps (SOM) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5063bde6",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "A self-organizing map is a projection of data in some high dimensional space to a 2D plane, where the mapping seeks to preserve local relationships. \n",
    "\n",
    "Unlike the stochastic neighbor embedding techniques (like tSNE), which have an explicit notion of 'similarity', a SOM is an example of 'prototype learning'. In this kind of learning, we start with a grid of examples and adjust all of the examples until they (in some sense) best represent the data. The prototypes get many names in the SOM literature - $\\textit{neurons}$ and $\\textit{best matching unit}$ are common. I will use the term $\\textit{prototype point}$. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e2f48fb",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's look at an example of a self-organizing map before getting into the mathematical details."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1a4ca33",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "arxiv: 1509.03318\n",
    "\n",
    "<img src=\"images/SOM_masters.png\" alt=\"drawing\" width=\"900\"/> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39188816",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Define a self organizing map as a 2D grid of K prototype points $m_j \\in \\mathbb{R}^p$ parameterized by an ordered pair $\\textit{l}_j \\in Q_1 \\times Q_2$ s.t. $\\{1, 2 ..., q_l\\} = Q_1, Q_2$ and $K = q_1 \\times q_2$. The $m_j$ are (often but not always) initialized in a 2D principle component plane. The ordered pair can be thought of as labelling the location of the prototype points. \n",
    "\n",
    "After initialization, compute all of the Euclidean distances in the high dimensional $\\mathbb{R}^p$ space and find the closest prototype point. Then for all the neighbor prototype points $m_k$ (defined by some threshold), move them closer to the data via:\n",
    "\n",
    "$$\n",
    "m_k \\leftarrow m_k + \\alpha(x_j - m_k)\n",
    "$$\n",
    "\n",
    "The 'big idea' of the SOM procedure is that this bends the 2D plane so that the prototype points approximate the data as well as possible. You can think of this as combining k-means clustering (from Adam's lecture) with the intuition from principal component analysis. This is a type of $\\textit{competitive learning}$. These are interesting because the topology of the high dimensional space is maintained in the lower dimensional representation of the SOM."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87267635",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Pseudo-code for implementing a SOM is given as a challenge problem in the tutorial. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2cd2f48",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Important Caveats about Each Technique\n",
    "\n",
    "Principal Component Analysis: \n",
    "\n",
    "- Be careful about interpreting the principal components. They are not always physically meaningful.\n",
    "- PCA decomposes data into variance ordered components, but it doesn't have any way of decomposing the variance. In other words - PCA has no notion of noise.\n",
    "- As an extreme example, PCA is unable to handle missing data. This is a problem for spectroscopic or time-series data that may not cover the same range. \n",
    "- Extensions of PCA attempt to address these issues (\"Expectation Maximization PCA\")."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80afd471",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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    "tSNE: \n",
    "- tSNE relies on matching probability distributions. This makes it (also) susceptible to the presence of noisy dimensions. \n",
    "- (lack of) structure in tSNE plots is hard to interpret. Categorical data that is well separated will often be easily separated by other techniques, but data that isn't seperated in the tSNE plane isn't necessarily difficult to classify with other techniques. "
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    "SOM: \n",
    "- the choice of SOM updating scheme impacts the map. There is no 'ground truth' or 'correct' SOM. \n",
    "- cells in the SOM can often be supported by a single or very few data points. \n",
    "- SOMs preserve local structure but may not preserve the global structure of the map."
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    "## What's next? The tutorial notebook. Here's an overview: \n",
    "\n",
    "The tutorial notebook starts by asking you to implement Principal Component Analysis and apply it to a set of images from SDSS. After using your own implementation, you'll work through the PCA implementation in sklearn and compare the two. \n",
    "\n",
    "Once you've done that, you'll get a chance to work with tSNE. We'll transition to the problem of photometric redshift estimation. I've given you 28 band photometry from Cosmos2020 and binned (categorical) redshift data. Generate a tSNE map of the 28 band photometry. \n",
    "\n",
    "Finally, we'll continue with the theme of photo-zs by interpreting a SOM produced using the RAIL package within the LSST Dark Energy Science Collaboration. The exercise will walk you through interpreting a self-organizing map and point out several important caveats to their use. "
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